From 7b74176a0bb63a2b1d144c1c76535e62c465f245 Mon Sep 17 00:00:00 2001
From: sakeeb hasan <100307524+Sakeebhasan123456@users.noreply.github.com>
Date: Fri, 4 Oct 2024 17:31:07 +0530
Subject: [PATCH 1/3] Delete Deep_Learning/Spam Vs Ham Mail Classification
 [With Streamlit GUI]/Model/model1.ipynb

---
 .../Model/model1.ipynb                        | 1461 -----------------
 1 file changed, 1461 deletions(-)
 delete mode 100644 Deep_Learning/Spam Vs Ham Mail Classification [With Streamlit GUI]/Model/model1.ipynb

diff --git a/Deep_Learning/Spam Vs Ham Mail Classification [With Streamlit GUI]/Model/model1.ipynb b/Deep_Learning/Spam Vs Ham Mail Classification [With Streamlit GUI]/Model/model1.ipynb
deleted file mode 100644
index 5d41673919..0000000000
--- a/Deep_Learning/Spam Vs Ham Mail Classification [With Streamlit GUI]/Model/model1.ipynb	
+++ /dev/null
@@ -1,1461 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "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>Label</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
-       "      <td>ham</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Ok lar... Joking wif u oni...</td>\n",
-       "      <td>ham</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
-       "      <td>spam</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>U dun say so early hor... U c already then say...</td>\n",
-       "      <td>ham</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
-       "      <td>ham</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                Text Label\n",
-       "0  Go until jurong point, crazy.. Available only ...   ham\n",
-       "1                      Ok lar... Joking wif u oni...   ham\n",
-       "2  Free entry in 2 a wkly comp to win FA Cup fina...  spam\n",
-       "3  U dun say so early hor... U c already then say...   ham\n",
-       "4  Nah I don't think he goes to usf, he lives aro...   ham"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df=pd.read_csv('./../Dataset/spam-vs-ham-dataset.csv',encoding=\"ISO-8859-1\")\n",
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text     0\n",
-       "Label    0\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "403"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.duplicated().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df.drop_duplicates(keep='first',inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(5171, 2)"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "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>Label</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Ok lar... Joking wif u oni...</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>U dun say so early hor... U c already then say...</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                Text  Label\n",
-       "0  Go until jurong point, crazy.. Available only ...      0\n",
-       "1                      Ok lar... Joking wif u oni...      0\n",
-       "2  Free entry in 2 a wkly comp to win FA Cup fina...      1\n",
-       "3  U dun say so early hor... U c already then say...      0\n",
-       "4  Nah I don't think he goes to usf, he lives aro...      0"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from sklearn.preprocessing import LabelEncoder\n",
-    "\n",
-    "le=LabelEncoder()\n",
-    "df['Label']=le.fit_transform(df['Label'])\n",
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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J94eLcOeyNNExiEiFksMD8L+HR8Cg14mO4lFecyX1f7z4OCIRtU1uhQVrDxWKjuFxXlEQOeV12HqiTHQMIlKx97ecFB3B47yiID7ZmQvvOJBGRO6yL+800otrRMfwKM0XhNXuxMrdnNpKRG33uZdNk9d8QXx3uAjlddwQiIja7ou9p+DwoittNV8Qn3AjciJykZKaRmzOKBUdw2M0XRBFVQ3YfpIbfxCR63jTagyaLojvDhfx5DQRudT6I8WobrCJjuERmi6IdYeKREcgIo2x2p34zkveWzRbEBV1VuzMrhAdg4g0aPUB77hoTrMFsf5IkVfNNiAiz9maWYZKL5gdqdmC4OElInIXu1PCWi94j9FkQdQ02LD1BGcvEZH7rD4gdn8bT9BkQfx4rARWu2t2oCMikrM9q1zzu81psiB4eImI3M0pAds0fqRCcwXRYHNgU7r3XOlIROJs0/gq0ZoriE3ppbB46faARORZP2WyIFTFWy5gISLx8irqkVdhER3DbYyiA7ja5gx1NLrkdKDqp09Qe2QjnHWVMJjDEdhzNEKuuhU6XdO2hjkvjpe9b+iI2Qi54veyX6vZuwY1e9fAXlUMAPCJTEboVdPg33EgAMBeVYxT79wue9/IiY8hsOuwtr40Iq/yU2YZpg1OFh3DLTRVENlldShTyayC6h2fo2bfWkT87kGYIpPRWJiB8rWvQe8biOCBEwAAifcua3af+qzdKF/7OgK6DL3g4xqCIhB2zUwYw+IBALWHNqBk1bOIm/UaTFHtYAiKPO9xa/avQ/XOVfDvMMDFr5JI+7ayINQhLadSdIQWazx1FP6pVyCg4yAAgDEkBpajm2EtTD93G4M5rNl9LJk74NeuF3xCYy/4uAGpVzT7OGz4H1G7dw0aC47DFNUOOr3h/MdN/xkBXYZBb/Jv68si8jo/nyiHJEnnRv5aoqlzELtVVBC+Cd3QkLMftopTAABrSRYa8o/A7wK/xTvqKlF/YhfMvce2+DkkpwN1RzbBaWuAb0JX2ds0FmXCVpLVqsclol+U11lxtFCbW5FqbAShnsX5godMgbPRgoLFcwC9HnA6ETp8Bsw9RsrevvbQBuhN/gjofNUlH9tamo2iZQ9DsluhM/kjevKTMEXKD4FrD3wPn4gk+CV2a9PrIfJm206UoXt8sOgYLqeZgqiqtyGjpFZ0jBazHN2CuiMbEXnjw/CJagdrcRYqNyyGwRwBc6/R592+9sAPCOw+Ajqj6ZKP7ROegLjZr8PZaIHl+E8o+/YfiJn+wnkl4bQ1ou7IJoReNdVlr4vIG23NLMOfru4gOobLaeYQ057cSlVtDlS58UOEDJmCwO7XwBSVAnPPUQgaNBFV2z8777YNeYdgr8iHuU/LDgPpDD7wCYuHb2wqwq6ZBVN0e9Ts/vq821mOb4Vka0Rgz/MLiYhabufJCtgc2lveRzMFkZatnvMPACDZGgFd8//8Op0ekM7/Ias9sB6m2FSYoi/vNxRJkiA5zt8Bq/bA9whIHQxDQMhlPS4RNamzOrA/77ToGC6nnYJQ0QlqAPBPHYyqbf+F5cQu2KuKYUnfhupdXyKg85XNbnf2MNGFTiIXL38C1WnfnPu4ctOSphFHVTGspdmo3LQEjbkHEdh9RLP72SoL0Jh3GOY+17n8tRF5o4OnqkRHcDlNnIOwO5zYp7L2Dh9zF05v+QgV3/8LTksVDOZwmPuOQ+jQW5vdru7oZkACArtfI/s4tsoi+NZXn/vYUVeFstWvwlFXAb1vIExRKYi+5Rn4t+/X7H61B9bDEBQJv998noguT3qx9mYy6SRJTUfu5R3IP40Jb24VHYOIvFi/5FB8cc+FL2JVI00cYlLb4SUi0p6MYvXMomwpTRSEFk8OEZG61DbakV+prYX7NFEQJ8vqREcgIsLxIm2dh9BEQWSXa6u1iUidjmvsRLXqC+K0xYqq+vPn+BMReVo6RxDKwtEDESnFMRaEsuSU8/wDESlDVlkd7BpackP1BZFdxhEEESmD1e5EtoZ+aVV9QXAEQURKcrxIO9dDqL4gtNTWRKR+J8tYEIqRw5PURKQgZbVW0RFcRtUFUdNgQ3mddr4ZRKR+ZbWNoiO4jKoLgqMHIlKaco4glIEFQURKU17HEYQiFFc3iI5ARNQMRxAKcZpLbBCRwlRarHA6Vb/NDgCVF0Q1C4KIFMYpARUWbYwiVF0QpzXyTSAibdHKYSZVFwRXcSUiJSrXyFRXVRcEz0EQkRKVsiDEq2mwi45ARHQeHmJSgHqrQ3QEIqLzVGhkhQd1F4SNBUFEymPRyC+v6i4IjXwTiEhbnBKvgxCuwc6CICLlsTu1saucaguiweaARkqaiDTGwSupxWq0aaOhiUh7WBCCGQw60RGIiGTZNVIQRtEBLpevUbXdRgrkb3BgbfuViKk/IToKaYDVPBZAX9Ex2ky1BeFj0MOg12lmKEdi1TsMuKNwAlYHvwjfynTRcUjl/JP6io7gEqr+NZyjCHKljDp/3Fj9KKyhqaKjkNrpDaITuISq32FZEORq6XX+mFj7GKyhHURHITUz+IhO4BKqfof1NWqjpUlZjtYG4Pd1j8MWkiI6CqmVXrVH75tRdUH4+ag6PinYwZpA3Fz/BOzByaKjkBqxIMTjCILcaV+1Gbc0Pgl7UKLoKKQ2PAchni9HEORme6qCMN02H/agBNFRSE0MvqITuISq32F5kpo8YefpYMywz4fDHCc6CqlFYJToBC6h6ndYPx9tDONI+X6uDMFs51NwBMaIjkJqEKSNnxNVFwRHEORJmytCcbv0NJwB2vjtkNwoSBujTVW/w5p9tTFTgNRjY0UY7tAtgNM/UnQUUjIzRxDCxYX6i45AXmhDeTjuMS6A0z9CdBRSKo4gxItnQZAg60ojcJ9xAZx+YaKjkNL4hwNGk+gULqHqgkhkQZBAa0oj8YBpIZx+oaKjkJIExYpO4DKqLgiOIEi0b0qi8LDvQki+waKjkFKwIJQhIYwFQeKtKo7GPP+FkHyDREchJTCzIBTB7GtEsB9nMpF4nxXF4vGAhZBMZtFRSDSOIJSDh5lIKZYXxuFp8wJIPoGio5BILAjlSGBBkIIsK0jAoqAFkHwCREchUVgQysERBCnNkoJEPBeyAJKRP5teiecglIMnqkmJ3s9PwkthT0My+omOQp7GEYRycARBSvV2Xju8Gv4UJI0s/UwtYPAFguNFp3AZ1RcEz0GQkr2R2x6vRzwFyaCNK2vpEmJ6aGY/akADBdEpxgydTnQKogv7R24HvBU5H5JeO28cdAHx/UQncCnVF0Swnw/aR3JaISnb33NS8W7Uk5A0slcxXUB8X9EJXEr1BQEAfRJDRUcguqQXcjrjg+gnIem40ZVmcQShPL0TQ0RHIGqRZ7O7YGnsEywJLTL6AVHdRKdwKY0URKjoCEQttvBkN3wc9xgknSb+96OzYnoCBm0dQtTEq+kRHwyjXge7UxIdhahF5mf1gLHDo5ha8AJ0EP9zuznHjpe3WZFW4EBhrYQvpvpjUtemk+o2h4T5PzZiTaYdWZVOhPjqMKaDES+M8UV80MVL7lS1E4/+0Ii1mXZYbBJSw/X4cKI/BsY3jaAWbmzA8kN25FU7YTIAA+IMeG6UL65IVOFbk8bOPwAaGUH4+RjQOYYraZK6PJbVC6sSHoEE8dPw6qwS+sTo8dYN51/YZ7EBe4oceGq4L/bcGYhVU/1xvNyBCZ9aLvqYlfUShv67Dj4GYO0fAnDkHjNeGeuHML9fXm/nCAPevMEPB+8246fZgUgJ1WPsRxaU1jld/hrdTmPnHwCNjCAAoE9SCI4UVouOQdQqD53oC0PqQ5iY/4rQkcS4Tj4Y1+nsNNz6Zl8L8dNh/YzmMwXfHOePwe/XIbfKieQQ+d8zX9zaiKSQphHDWe3Dmt92eq/mU39fvc4PH+y14UCxE6M7qOz317i+ohO4nMq+AxfWKyFUdASiy/KXzP74NvFB0TFapaqxadwT6nfh0c/Xx+0YGGfAzZ9ZEP1yDfq9W4vFadYL3t7qkPBemhUhvkCfWJW9NRn9gWhtnaAGNDSC4EwmUrP7MgfCmPoArs9/TXSUS2qwS3j0hwZM62VEsO+FCyKr0om3d1sx90oTnhjmi10FDty/rgEmAzCz7y9Xlq9Ot+HWlfWw2IC4oKbRSmSAygoitieg197MNJV9Fy6sa2wQfI2aeTnkheZkXoH1ifeLjnFRNoeEWz6rhyQBb//u4svcOCWgf5wBz4/2Q784A+4cYMId/U14J83W7HYjU4zYN8eMbbcH4PqORtyy0oIStZ2D0OD5B0BDBWE06NE9nvsCk7rdkTkEPybdKzqGLJtDwi0r65FT5cT6GQEXHT0ATaOB7lHN32K6ReqRW9X8zT/QpENquB5DEo34YKI/jHodPtjTvEQUT4PnHwANFQQADEgOEx2BqM1uyxiKzUl3i47RzNlyyCh34ocZAYhowSGgoUkGHC9vXgbp5U60u8BJ7bOckoRGh/ipv62SOFB0ArfQVEEM7xwlOgKRS/wx42psS7rTY89Xa5Wwr8iBfUUOAMDJSif2FTmQW+WEzSFhymf12F3gwMc3+cMhAUW1ThTVOmH91Rv56P/U4c2dv5yEfnCIL7bnO/D8lkZkVjjxyUEb3ttjxb2Dms4/1FklPLGhAdvz7cg57URagQO3fVWPU9USbu6uooUNw1KAqC6iU7iFZk5SA8AVHcLh72NAvc0hOgpRm03PGIEVnRwYnPeB259rd4EDI5f+cl3D3O8bATRiZh8fLBzhi6+P2wEAfd+ta3a//80MwIiUpreRExVOlFl+GTEMSjDgi6n+eHxDI57Z1Ij2YXr88zo//KF305u/QQ8cK3Ni6f56lFkkRPjrMCjBgC2zA9EjWkUnfLvcIDqB2+gkSVLZWO7iZn+4E/87Xio6BpHLrOr8PfrnLhEdgy5k5jdA++GiU7iFpg4xAcA1PMxEGnNT+ljsT54hOgbJ8QsFkq8SncJttFcQXaJFRyByuYnp43Ao6Q+iY9BvdRqruQX6fk1zBdE+MpAbCJEmjc/4HY4m3So6Bv1al3GiE7iV5goCAK7tHiM6ApFbjMuYgONJt4iOQQBgMAGdrhWdwq00WRBjWRCkYddnTkRm0u9Fx6CUYYCvtleR1mRB9E8OQ6TZV3QMIreQJB2uzbwJJ5MmiY7i3TQ8vfUsTRaEXq/DmG48WU3aJUk6jMmcgpzECaKjeC8WhHqN7cHDTKRtDkmPUSduQV7ieNFRvE9sbyAkQXQKt9NsQQxNjUSQn3annxEBZ0oiaxpOJWh7No3idP2d6AQeodmC8DUaMKmv9hueyObUYdTJP6AoYazoKN5D49Nbz9JsQQDArYOTREcg8ohGpx4js2egOH6M6CjaF5oMxPURncIjNF0QPeJDuNMceY16hwEjcmaiNH6U6Cja1u+PohN4jKYLAgBuHZQsOgKRxzSVxG0oj7tGdBRt0huB/t6zLpbmC2JC33gEmFS0dDBRG9U59Bie9ydUxA4THUV7Ol8PBMWKTuExmi8Is68RN/aOFx2DyKPq7AZck38nTsdqd6VRIQbMEp3AozRfEABPVpN3qrEbcc2pOaiKGSI6ijaEJAMdR4tO4VFeURD9ksPQNVbba6YQyamyGTGq8G5UxwwWHUX9+v8R0HvFW+Y5XvNqpw7iKIK8U7nVB6ML70VN9EDRUdRL7+NVJ6fP8pqCuKlfInyNXvNyiZoptfpgdPGfURvVT3QUdeox2atOTp/lNe+YIQE+GNfT+77BRGeVNPrg2tIHYIn0jou8XOrKe0QnEMJrCgIA/nhViugIREIVNphwbdlfUB/ZU3QU9Ui+Eohv28hr5cqV6NWrF/z9/REREYExY8agrq4Os2bNwqRJk7Bo0SJERUUhODgYc+bMgdVqPXffdevWYdiwYQgNDUVERATGjx+PEydOnPt6dnY2dDodVqxYgauvvhr+/v4YNGgQ0tPTsWvXLgwcOBBmsxnjxo1DaWlpq3J7VUH0Tw7D1Z0iRccgEupUgy+uK5+L+ogeoqOow5C723T3wsJCTJs2DbfddhuOHj2KjRs34qabboIkSQCADRs2nPv8p59+ilWrVmHRokXn7l9XV4e5c+di9+7d2LBhA/R6PSZPngyn09nseRYsWID58+djz549MBqNmD59OubNm4fXXnsNW7ZsQWZmJp5++ulWZddJZ1N6ibScCvz+7Z9FxyASLsW/AevCXoZfxVHRUZQrNBm4fx+gv/yLbffs2YMBAwYgOzsb7dq1a/a1WbNm4ZtvvkFeXh4CAgIAAO+88w4eeeQRVFVVQS8za6qsrAxRUVE4ePAgevbsiezsbLRv3x7vv/8+br/9dgDA8uXLMW3aNGzYsAGjRjUtvfLCCy9gyZIlOHbsWIuze9UIAgAGtAvHsFSOIoiy6/3wu9MPozGsi+goyjX4zjaVAwD06dMHo0ePRq9evXDzzTdj8eLFqKysbPb1s+UAAFdeeSVqa2uRl5cHAMjIyMC0adPQoUMHBAcHIyUlBQCQm5vb7Hl69+597t8xMU374fTq1avZ50pKSlqV3esKAgAeGNNJdAQiRThh8cf46nmwhvH/ifMERrnkymmDwYD169dj7dq16N69O9544w106dIFJ0+ebNH9b7zxRlRUVGDx4sXYsWMHduzYAQDNzlMAgI+Pz7l/63Q62c/99rDUpXhlQQxKCcdVHSNExyBShIw6f0yoeRTW0I6ioyjL8HmAr2susNXpdBg6dCgWLVqEvXv3wmQy4YsvvgAA7N+/H/X19eduu337dpjNZiQlJaG8vBzHjx/H/PnzMXr0aHTr1q3Z6MPdvLIgAOCB0fyNieisY7UBmFz3GGwh7UVHUYbwDsDA2S55qB07duD555/H7t27kZubi1WrVqG0tBTdunUD0DQSuP3223HkyBGsWbMGCxYswH333Qe9Xo+wsDBERETgvffeQ2ZmJn788UfMnTvXJblawmsL4ooOERjSIVx0DCLFOFwTiN/XPw5bcLtL31jrRs0HDD6Xvl0LBAcHY/PmzbjhhhvQuXNnzJ8/H6+88grGjWvalW706NHo1KkThg8fjqlTp2LChAlYuHAhAECv12P58uVIS0tDz5498eCDD+Lll192Sa6W8LpZTL+2Pasct763XXQMIkXpH1KLFb5/hbE6T3QUMeL7AXf8DzhzHN+dZs2ahdOnT+PLL790+3NdDq8dQQDAkA4RuKI9RxFEv7anyozp1idhD/LSPd2vfcYj5aAGXl0QAGc0EcnZeToYM+zz4TB72V4qqWOA9sNFp1AMrz7EdNat7/2M7VkVomMQKc6w8Cos1S2Coa5IdBT30+mBu7YAsVyG5CyvH0EAwMIJPWDUc0hJ9Fs/VYTgNjwFR2C06Cju1+sWlsNvsCAAdI0Nxkwu5Ecka1N5GO7AAjgDNLwCgcEXGPWk6BSKw4I448FrOyM22E90DCJF+rE8DHfpF8Lpr9GSGHxH07pL1AwL4gyzrxHzx3cTHYNIsdaXheNe49Nw+mts5p9fCHD1Q6JTKBIL4lfG947ncuBEF7G2NBL3GxfC6RcqOorrDP0LEKCx0nMRFsRvPDOxJ0zcmpToglaXRmKu70JIviGio7RddHfgyvtEp1AsvhP+RvvIQMwZ3kF0DCJF+7I4Go/4L4TkosXshND7AJPeBowm0UkUiwUh456RqUgOD7j0DYm82MqiGDwesBCSySw6yuUZ/jAQ31d0CkVjQcjw8zFg4YTuomMQKd7ywjjMD1wIyRQoOkrrxPUBrn5YdArFY0FcwKiuMRjbPUZ0DCLF+7gwHgvNCyH5qGTUbTABk94BDEbRSRSPBXERCyf0QJAff4iILmVpQQKeDV4AyegvOsqljXgciOERgpZgQVxEfKg//jqRl94TtcQHp5LwQugCSEYFX3CaOAgY+oDoFKrBgriESf0SMLGvl61oSXSZ3s1Pxt/DnoZk8BUd5XxG/6ZDS3qD6CSqwYJogb9O6onEMBUMnYkU4K28FPwz4ilIBoVNHx39NBCZKjqFqrAgWiDYzwf/mNoXBq74StQir+V2wJuRT0HSu2bbzjZrNwwYcrfoFKrDgmihQSnhuHdER9ExiFTjlZyOeDtqPiS94IkeJjMw6S3uEncZWBCt8MCYzhjSgWu2ELXUSzmd8H70k2JL4tpngLAUcc+vYiyIVjDodXh9Wj9EmhV4Ao5IoZ7L7oIlMY9D0gk4OdzjJmDQ7Z5/Xo1gQbRSdJAfXr+1L3g6gqjlFp3sho9iH4Ok8+BbTnx/YNK/PPd8GsSCuAxXpUbigdGdRccgUpWnTvbAp3GPeqYkguKAWz8BfDj7sC1YEJfpz6NSuXcEUSs9kdULn8U/AgluHIIb/ZvKITjOfc/hJVgQl0mv1+GtP/RHlxgVL3dMJMC8E33wRcLDbioJXdNhpYT+bnhs78OCaINgPx98OHsQYoJ50pqoNeae6IdvEue6/oGveRToeZPrH9dLsSDaKD7UH/+eNQhmXy7qR9Qa92cOwJrEB133gN0nASMec93jEQvCFXrEh+CtP/SHkVObiFrlnsxB+C7RBYvnxfUFJr/Di+FcjAXhItd0jsJzk7nyK1Fr3ZV5BTYk/fnyHyAoDpj2KWcsuQELwoWmDkrGfSO5GBhRa92ecSU2JV3GWklGf+DWj4FgrrjsDiwIF3v4ui64qV+C6BhEqjMz42psS7qzdXea+CaQMMA9gYgF4Q4vTumNqzpGiI5BpDrTM0ZgR9KfWnbj0U8Dvaa4N5CXY0G4gY9Bj3dmDEDnGLPoKESqMzVjFHYn33bxG13zGHD1Q54J5MVYEG4S7OeDpbcNRvvIQNFRiFRnSvoY7E2eKf/Fqx8GRj7u2UBeigXhRnEh/vjvnUPQKZojCaLWmpx+HQ4m/1/zTw79CzD6KSF5vJFOkiRJdAitK69txIwPduJIYbXoKESqs6bTN+ie9ylw5X3Adc+JjuNVWBAeUmWx4Y8f7sT+vNOioxCpzg/jqpB6zXTRMbwODzF5SEiADz66fTAGpYSJjkKkKveM6MhyEIQjCA+zWO244z+7sTWzXHQUIsV75LouuJcXnwrDghCgwebA3R+l4X/HS0VHIVKsp8d3x23D2ouO4dVYEIJY7U7c/+lerDtcJDoKkaLodcCzk3ph+hXJoqN4PRaEQHaHE3NX7MfX+wtERyFShECTAf+8tR+u7R4jOgqBBSGc0ynh798fx782nhAdhUioxDB/vD9zILrGBouOQmewIBRi9YECPPLZAdTbHKKjEHnc4JRwvDNjAMIDTaKj0K+wIBTkSEE17ly2G/mV9aKjEHnM1IFJeHZyT/gYOOteaVgQClNRZ8U9H6dhe1aF6ChEbmXQ6/DEDd1wO2cqKRYLQoHsDif+uvoIlv6cIzoKkVsE+xnx5vT+GN45SnQUuggWhIKt2JWH+V8egtXhFB2FyGXaRwbi/ZkD0TGKi1gqHQtC4fbkVmLOsjSU1DSKjkLUZsNSI/HW9P4ICfARHYVagAWhAsXVDbhrWRr2caE/UimjXof7R3fCvSNTYdDrRMehFmJBqITV7sQr649j8eYsOPkdIxVJjTbjH7f0Ra/EENFRqJVYECqzI6scc1fsx6nTnApLyqbTATOvTMFj47rCz8cgOg5dBhaECtU02LDg68NYteeU6ChEsuJC/PDylD4Y1ilSdBRqAxaEiq05WIj5Xx5CRZ1VdBSicyb1jceiiT0R4s8T0WrHglC58tpGLPj6MFYfKBQdhbxcaIAPnp3UE+N7x4uOQi7CgtCIdYeK8NRXh1DK6bAkwPDOUXh5Sm/EBPuJjkIuxILQkCqLDYtW89wEeU6k2RfzruuCWwYliY5CbsCC0KBtmWV49tujOFJYLToKaZTJqMdtQ9vjvlGpMPsaRcchN2FBaJTTKeHzPfn4+/fHUVzNw07kOtf3iMUTN3RDckSA6CjkZiwIjau3OvDe5iy8u/kELFbuNUGXr1tcMJ4e3x1XdowQHYU8hAXhJUqqG/DK9+n4LC2PV2JTq0SaTXhobBdMHZgEPZfJ8CosCC9zrKgaz317FFsyykRHIYUzGfSYPTQF941KRZAfr2nwRiwIL7XxeAmeX3MU6cW1oqOQwuh1wLiecXjkui5IiQwUHYcEYkF4MYdTwqo9+Vi8JYtFQTDqdZjQNx73jEhFajT3aiAWBJ2x8XgJ3t9yEj9l8tCTtzEZ9ZgyIBF3X9MRSeGcmUS/YEFQM0cKqvH+lix8c6AANgd/NLQsyM+IaYOTcdvQ9ogN4RXQdD4WBMkqqmrAkm3Z+GRHDqob7KLjkAslhftj9lXtMXVQEgJ5kRtdBAuCLqqu0Y7/7srDv7eeRH4l96BQswHtwvCnYe0xtkcsd3WjFmFBUIs4nBLWHSrC8l252HaiHA5eTKEKCaH+mNg3Hjf1T0BqdJDoOKQyLAhqtbLaRqzeX4Cv9hdgb+5p0XHoN4J8jRjXKxaT+yViSIdw6HQcLdDlYUFQm+RVWPDVvlP4al8BMko4VVYUo16HazpHYVK/BFzbPYZbfJJLsCDIZY4UVOOr/aewen8h98z2kD6JIZjcLwE39olHhNlXdBzSGBYEuZwkSdidU4mv9p3Cj0dLUFDVIDqSZvj7GDC4fTiu7hSJkV2j0TGKF7SR+7AgyO0yS2qxJaMUWzLKsD2rnKvKtoJeB/RKCMGwTpEYlhqFAe3CYDLqRcciL8GCII+y2p1Iy6nEz1nl2HmyHPvyTqPB5hQdS1GSwwPOFEIkruoYgdAAk+hI5KVYECSU1e7EgfzT2HGyAjtPVmBf3mlU1dtEx/IYX6MenWLM6BITjP7tQnF1ahQ34iHFYEGQ4pRUNyC9uBbpxTXIKKlBxpl/q/2K7oRQf3SLC0LX2GB0jQtC19ggtI8086I1UiwWBKlGcXUD0otrkF5ci4zimjMFUosaBRWHyahHTLAv4kL80SnajK5xwegWG4QusUHcU4FUhwVBqtdgc6Cizir7p7zOisqzH1ua/q5tsAM6QAdApwP0Ot2Zf+ugO/d5HfS6M58DEOBrQHiACWGBJoQFNP0JD/RBhNkXscF+iAn2Q2yIH8IDeb6AtIMFQUREsjhfjoiIZLEgiIhIFguCiIhksSCIiEgWC4KIiGSxIIiISBYLgoiIZLEgiIhIFguCiIhksSCIiEgWC4KIiGSxIIiISBYLgoiIZLEgiIhIFguCiIhksSCIiEgWC4KIiGSxIIiISBYLgoiIZLEgiIhIFguCiIhksSCIiEgWC4KIiGSxIIiISBYLgoiIZLEgiIhIFguCiIhksSCIiEgWC4KIiGSxIIiISBYLgoiIZLEgiIhIFguCiIhksSCIiEgWC4KIiGSxIIiISBYLgoiIZLEgiIhIFguCiIhksSCIiEgWC4KIiGSxIIiISBYLgoiIZLEgiIhIFguCiIhksSCIiEjW/wMFknVq/9IZkgAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.pie(df['Label'].value_counts(),labels=['ham','spam'],autopct=\"%.02f\")\n",
-    "plt.savefig('./../Image/Spam-vs-ham-piechart.jpg')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[nltk_data] Downloading package punkt to\n",
-      "[nltk_data]     C:\\Users\\Hp\\AppData\\Roaming\\nltk_data...\n",
-      "[nltk_data]   Package punkt is already up-to-date!\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import nltk\n",
-    "nltk.download('punkt')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df['num_chr']=df['Text'].apply(len)\n",
-    "df['num_words']=df['Text'].apply(lambda x:len(nltk.word_tokenize(x)))\n",
-    "df['num_sent']=df['Text'].apply(lambda x:len(nltk.sent_tokenize(x)))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "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>Label</th>\n",
-       "      <th>num_chr</th>\n",
-       "      <th>num_words</th>\n",
-       "      <th>num_sent</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>4755</th>\n",
-       "      <td>Ok lor... Or u wan me go look 4 u?</td>\n",
-       "      <td>0</td>\n",
-       "      <td>34</td>\n",
-       "      <td>12</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5435</th>\n",
-       "      <td>You're gonna have to be way more specific than...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>51</td>\n",
-       "      <td>12</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1299</th>\n",
-       "      <td>Your daily text from me – a favour this time</td>\n",
-       "      <td>0</td>\n",
-       "      <td>46</td>\n",
-       "      <td>10</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2254</th>\n",
-       "      <td>Lol enjoy role playing much?</td>\n",
-       "      <td>0</td>\n",
-       "      <td>28</td>\n",
-       "      <td>6</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>998</th>\n",
-       "      <td>Not a lot has happened here. Feels very quiet....</td>\n",
-       "      <td>0</td>\n",
-       "      <td>148</td>\n",
-       "      <td>36</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                   Text  Label  num_chr  \\\n",
-       "4755                 Ok lor... Or u wan me go look 4 u?      0       34   \n",
-       "5435  You're gonna have to be way more specific than...      0       51   \n",
-       "1299     Your daily text from me – a favour this time      0       46   \n",
-       "2254                       Lol enjoy role playing much?      0       28   \n",
-       "998   Not a lot has happened here. Feels very quiet....      0      148   \n",
-       "\n",
-       "      num_words  num_sent  \n",
-       "4755         12         1  \n",
-       "5435         12         1  \n",
-       "1299         10         1  \n",
-       "2254          6         1  \n",
-       "998          36         5  "
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.sample(5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "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>Label</th>\n",
-       "      <th>num_chr</th>\n",
-       "      <th>num_words</th>\n",
-       "      <th>num_sent</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>count</th>\n",
-       "      <td>5171.000000</td>\n",
-       "      <td>5171.000000</td>\n",
-       "      <td>5171.000000</td>\n",
-       "      <td>5171.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mean</th>\n",
-       "      <td>0.126281</td>\n",
-       "      <td>79.457552</td>\n",
-       "      <td>18.590988</td>\n",
-       "      <td>1.973893</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>std</th>\n",
-       "      <td>0.332198</td>\n",
-       "      <td>58.401504</td>\n",
-       "      <td>13.383728</td>\n",
-       "      <td>1.458880</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>min</th>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>2.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25%</th>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>36.000000</td>\n",
-       "      <td>9.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50%</th>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>61.000000</td>\n",
-       "      <td>15.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75%</th>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>119.000000</td>\n",
-       "      <td>26.000000</td>\n",
-       "      <td>2.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>max</th>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>910.000000</td>\n",
-       "      <td>220.000000</td>\n",
-       "      <td>38.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "             Label      num_chr    num_words     num_sent\n",
-       "count  5171.000000  5171.000000  5171.000000  5171.000000\n",
-       "mean      0.126281    79.457552    18.590988     1.973893\n",
-       "std       0.332198    58.401504    13.383728     1.458880\n",
-       "min       0.000000     2.000000     1.000000     1.000000\n",
-       "25%       0.000000    36.000000     9.000000     1.000000\n",
-       "50%       0.000000    61.000000    15.000000     1.000000\n",
-       "75%       0.000000   119.000000    26.000000     2.000000\n",
-       "max       1.000000   910.000000   220.000000    38.000000"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>num_chr</th>\n",
-       "      <th>num_words</th>\n",
-       "      <th>num_sent</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>count</th>\n",
-       "      <td>5171.000000</td>\n",
-       "      <td>5171.000000</td>\n",
-       "      <td>5171.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mean</th>\n",
-       "      <td>79.457552</td>\n",
-       "      <td>18.590988</td>\n",
-       "      <td>1.973893</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>std</th>\n",
-       "      <td>58.401504</td>\n",
-       "      <td>13.383728</td>\n",
-       "      <td>1.458880</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>min</th>\n",
-       "      <td>2.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25%</th>\n",
-       "      <td>36.000000</td>\n",
-       "      <td>9.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50%</th>\n",
-       "      <td>61.000000</td>\n",
-       "      <td>15.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75%</th>\n",
-       "      <td>119.000000</td>\n",
-       "      <td>26.000000</td>\n",
-       "      <td>2.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>max</th>\n",
-       "      <td>910.000000</td>\n",
-       "      <td>220.000000</td>\n",
-       "      <td>38.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "           num_chr    num_words     num_sent\n",
-       "count  5171.000000  5171.000000  5171.000000\n",
-       "mean     79.457552    18.590988     1.973893\n",
-       "std      58.401504    13.383728     1.458880\n",
-       "min       2.000000     1.000000     1.000000\n",
-       "25%      36.000000     9.000000     1.000000\n",
-       "50%      61.000000    15.000000     1.000000\n",
-       "75%     119.000000    26.000000     2.000000\n",
-       "max     910.000000   220.000000    38.000000"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df[['num_chr','num_words','num_sent']].describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>num_chr</th>\n",
-       "      <th>num_words</th>\n",
-       "      <th>num_sent</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>count</th>\n",
-       "      <td>4518.000000</td>\n",
-       "      <td>4518.000000</td>\n",
-       "      <td>4518.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mean</th>\n",
-       "      <td>70.976538</td>\n",
-       "      <td>17.265162</td>\n",
-       "      <td>1.827579</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>std</th>\n",
-       "      <td>56.610538</td>\n",
-       "      <td>13.566409</td>\n",
-       "      <td>1.394245</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>min</th>\n",
-       "      <td>2.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25%</th>\n",
-       "      <td>34.000000</td>\n",
-       "      <td>8.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50%</th>\n",
-       "      <td>53.000000</td>\n",
-       "      <td>13.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75%</th>\n",
-       "      <td>91.000000</td>\n",
-       "      <td>22.000000</td>\n",
-       "      <td>2.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>max</th>\n",
-       "      <td>910.000000</td>\n",
-       "      <td>220.000000</td>\n",
-       "      <td>38.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "           num_chr    num_words     num_sent\n",
-       "count  4518.000000  4518.000000  4518.000000\n",
-       "mean     70.976538    17.265162     1.827579\n",
-       "std      56.610538    13.566409     1.394245\n",
-       "min       2.000000     1.000000     1.000000\n",
-       "25%      34.000000     8.000000     1.000000\n",
-       "50%      53.000000    13.000000     1.000000\n",
-       "75%      91.000000    22.000000     2.000000\n",
-       "max     910.000000   220.000000    38.000000"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df[df['Label']==0][['num_chr','num_words','num_sent']].describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>num_chr</th>\n",
-       "      <th>num_words</th>\n",
-       "      <th>num_sent</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>count</th>\n",
-       "      <td>653.000000</td>\n",
-       "      <td>653.000000</td>\n",
-       "      <td>653.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mean</th>\n",
-       "      <td>138.136294</td>\n",
-       "      <td>27.764165</td>\n",
-       "      <td>2.986217</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>std</th>\n",
-       "      <td>29.934972</td>\n",
-       "      <td>6.988123</td>\n",
-       "      <td>1.494815</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>min</th>\n",
-       "      <td>13.000000</td>\n",
-       "      <td>2.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25%</th>\n",
-       "      <td>132.000000</td>\n",
-       "      <td>25.000000</td>\n",
-       "      <td>2.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50%</th>\n",
-       "      <td>149.000000</td>\n",
-       "      <td>29.000000</td>\n",
-       "      <td>3.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75%</th>\n",
-       "      <td>157.000000</td>\n",
-       "      <td>32.000000</td>\n",
-       "      <td>4.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>max</th>\n",
-       "      <td>224.000000</td>\n",
-       "      <td>46.000000</td>\n",
-       "      <td>9.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "          num_chr   num_words    num_sent\n",
-       "count  653.000000  653.000000  653.000000\n",
-       "mean   138.136294   27.764165    2.986217\n",
-       "std     29.934972    6.988123    1.494815\n",
-       "min     13.000000    2.000000    1.000000\n",
-       "25%    132.000000   25.000000    2.000000\n",
-       "50%    149.000000   29.000000    3.000000\n",
-       "75%    157.000000   32.000000    4.000000\n",
-       "max    224.000000   46.000000    9.000000"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df[df['Label']==1][['num_chr','num_words','num_sent']].describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(12,6))\n",
-    "\n",
-    "sns.histplot(df[df['Label']==0]['num_chr'])\n",
-    "sns.histplot(df[df['Label']==1]['num_chr'],color='red')\n",
-    "plt.savefig('./../Image/spam-ham-num_chr.jpg')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(12,6))\n",
-    "\n",
-    "sns.histplot(df[df['Label']==0]['num_words'])\n",
-    "sns.histplot(df[df['Label']==1]['num_words'],color='red')\n",
-    "plt.savefig('./../Image/spam-ham-num_word.jpg')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(12,6))\n",
-    "\n",
-    "sns.histplot(df[df['Label']==0]['num_sent'])\n",
-    "sns.histplot(df[df['Label']==1]['num_sent'],color='red')\n",
-    "plt.savefig('./../Image/spam-ham-num_sent.jpg')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 808.875x750 with 12 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "sns.pairplot(df,hue='Label')\n",
-    "plt.savefig('./../Image/PairPlot_withHue.png',dpi=300)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[nltk_data] Downloading package wordnet to\n",
-      "[nltk_data]     C:\\Users\\Hp\\AppData\\Roaming\\nltk_data...\n",
-      "[nltk_data]   Package wordnet is already up-to-date!\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "nltk.download('wordnet')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from nltk.corpus import stopwords\n",
-    "import string\n",
-    "\n",
-    "from nltk.stem.porter import PorterStemmer\n",
-    "ps = PorterStemmer()\n",
-    "\n",
-    "from nltk.stem import WordNetLemmatizer\n",
-    " \n",
-    "lemmatizer = WordNetLemmatizer()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def transform_text(text):\n",
-    "    text = text.lower()\n",
-    "    text = nltk.word_tokenize(text)\n",
-    "    \n",
-    "    y = []\n",
-    "    for i in text:\n",
-    "        if i.isalnum():\n",
-    "            y.append(i)\n",
-    "    \n",
-    "    text = y[:]\n",
-    "    y.clear()\n",
-    "    \n",
-    "    for i in text:\n",
-    "        if i not in stopwords.words('english') and i not in string.punctuation:\n",
-    "            y.append(i)\n",
-    "            \n",
-    "    text = y[:]\n",
-    "    y.clear()\n",
-    "    \n",
-    "    for i in text:\n",
-    "        y.append(lemmatizer.lemmatize(i))\n",
-    "    \n",
-    "            \n",
-    "    return \" \".join(y)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "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>Label</th>\n",
-       "      <th>num_chr</th>\n",
-       "      <th>num_words</th>\n",
-       "      <th>num_sent</th>\n",
-       "      <th>Transformer_text</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>111</td>\n",
-       "      <td>24</td>\n",
-       "      <td>2</td>\n",
-       "      <td>go jurong point crazy available bugis n great ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Ok lar... Joking wif u oni...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>29</td>\n",
-       "      <td>8</td>\n",
-       "      <td>2</td>\n",
-       "      <td>ok lar joking wif u oni</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
-       "      <td>1</td>\n",
-       "      <td>155</td>\n",
-       "      <td>37</td>\n",
-       "      <td>2</td>\n",
-       "      <td>free entry 2 wkly comp win fa cup final tkts 2...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>U dun say so early hor... U c already then say...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>49</td>\n",
-       "      <td>13</td>\n",
-       "      <td>1</td>\n",
-       "      <td>u dun say early hor u c already say</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>61</td>\n",
-       "      <td>15</td>\n",
-       "      <td>1</td>\n",
-       "      <td>nah think go usf life around though</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                Text  Label  num_chr  \\\n",
-       "0  Go until jurong point, crazy.. Available only ...      0      111   \n",
-       "1                      Ok lar... Joking wif u oni...      0       29   \n",
-       "2  Free entry in 2 a wkly comp to win FA Cup fina...      1      155   \n",
-       "3  U dun say so early hor... U c already then say...      0       49   \n",
-       "4  Nah I don't think he goes to usf, he lives aro...      0       61   \n",
-       "\n",
-       "   num_words  num_sent                                   Transformer_text  \n",
-       "0         24         2  go jurong point crazy available bugis n great ...  \n",
-       "1          8         2                            ok lar joking wif u oni  \n",
-       "2         37         2  free entry 2 wkly comp win fa cup final tkts 2...  \n",
-       "3         13         1                u dun say early hor u c already say  \n",
-       "4         15         1                nah think go usf life around though  "
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['Transformer_text']=df['Text'].apply(transform_text)\n",
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df.to_csv('./../Dataset/newData.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.feature_extraction.text import CountVectorizer,TfidfVectorizer\n",
-    "\n",
-    "cv=CountVectorizer()\n",
-    "tfidf=TfidfVectorizer(max_features=3000)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "X=tfidf.fit_transform(df['Transformer_text']).toarray()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(5171, 3000)"
-      ]
-     },
-     "execution_count": 37,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "X.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "y=df['Label'].values"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.model_selection import train_test_split\n",
-    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.naive_bayes import GaussianNB,MultinomialNB,BernoulliNB\n",
-    "from sklearn.metrics import accuracy_score,confusion_matrix,precision_score\n",
-    "from sklearn.svm import SVC\n",
-    "from sklearn.naive_bayes import MultinomialNB\n",
-    "from sklearn.tree import DecisionTreeClassifier\n",
-    "from sklearn.neighbors import KNeighborsClassifier\n",
-    "from sklearn.ensemble import RandomForestClassifier"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "gnb=GaussianNB()\n",
-    "mnb=MultinomialNB()\n",
-    "bnb=BernoulliNB()\n",
-    "svm=SVC()\n",
-    "knn=KNeighborsClassifier()\n",
-    "rfc=RandomForestClassifier(n_estimators=50, random_state=2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.885024154589372\n",
-      "[[796  91]\n",
-      " [ 28 120]]\n",
-      "0.5687203791469194\n"
-     ]
-    }
-   ],
-   "source": [
-    "gnb.fit(X_train,y_train)\n",
-    "y_pred=gnb.predict(X_test)\n",
-    "print(accuracy_score(y_test,y_pred))\n",
-    "print(confusion_matrix(y_test,y_pred))\n",
-    "print(precision_score(y_test,y_pred))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.9623188405797102\n",
-      "[[886   1]\n",
-      " [ 38 110]]\n",
-      "0.990990990990991\n"
-     ]
-    }
-   ],
-   "source": [
-    "mnb.fit(X_train,y_train)\n",
-    "y_pred=mnb.predict(X_test)\n",
-    "print(accuracy_score(y_test,y_pred))\n",
-    "print(confusion_matrix(y_test,y_pred))\n",
-    "print(precision_score(y_test,y_pred))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.9739130434782609\n",
-      "[[886   1]\n",
-      " [ 26 122]]\n",
-      "0.991869918699187\n"
-     ]
-    }
-   ],
-   "source": [
-    "bnb.fit(X_train,y_train)\n",
-    "y_pred=bnb.predict(X_test)\n",
-    "print(accuracy_score(y_test,y_pred))\n",
-    "print(confusion_matrix(y_test,y_pred))\n",
-    "print(precision_score(y_test,y_pred))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.9632850241545894\n",
-      "[[886   1]\n",
-      " [ 37 111]]\n",
-      "0.9910714285714286\n"
-     ]
-    }
-   ],
-   "source": [
-    "svm.fit(X_train,y_train)\n",
-    "y_pred=svm.predict(X_test)\n",
-    "print(accuracy_score(y_test,y_pred))\n",
-    "print(confusion_matrix(y_test,y_pred))\n",
-    "print(precision_score(y_test,y_pred))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.9062801932367149\n",
-      "[[887   0]\n",
-      " [ 97  51]]\n",
-      "1.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "knn.fit(X_train,y_train)\n",
-    "y_pred=knn.predict(X_test)\n",
-    "print(accuracy_score(y_test,y_pred))\n",
-    "print(confusion_matrix(y_test,y_pred))\n",
-    "print(precision_score(y_test,y_pred))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Here we can see that the BernoulliNB Algorithm has the highest accuracy and give correct decision\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**STREAMLIT GUI**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2024-02-11 19:05:31.396 \n",
-      "  \u001b[33m\u001b[1mWarning:\u001b[0m to view this Streamlit app on a browser, run it with the following\n",
-      "  command:\n",
-      "\n",
-      "    streamlit run C:\\Users\\Hp\\AppData\\Roaming\\Python\\Python311\\site-packages\\ipykernel_launcher.py [ARGUMENTS]\n"
-     ]
-    }
-   ],
-   "source": [
-    "import streamlit as st\n",
-    "from nltk.stem import WordNetLemmatizer\n",
-    "import pickle\n",
-    "import nltk\n",
-    "import string\n",
-    "from nltk.corpus import stopwords\n",
-    "\n",
-    " \n",
-    "lemmatizer = WordNetLemmatizer()\n",
-    "\n",
-    "def transform_text(text):\n",
-    "    text = text.lower()\n",
-    "    text = nltk.word_tokenize(text)\n",
-    "    \n",
-    "    y = []\n",
-    "    for i in text:\n",
-    "        if i.isalnum():\n",
-    "            y.append(i)\n",
-    "    \n",
-    "    text = y[:]\n",
-    "    y.clear()\n",
-    "    \n",
-    "    for i in text:\n",
-    "        if i not in stopwords.words('english') and i not in string.punctuation:\n",
-    "            y.append(i)\n",
-    "            \n",
-    "    text = y[:]\n",
-    "    y.clear()\n",
-    "    \n",
-    "    for i in text:\n",
-    "        y.append(lemmatizer.lemmatize(i))\n",
-    "    \n",
-    "            \n",
-    "    return \" \".join(y)\n",
-    "\n",
-    "\n",
-    "# Store the model in your file\n",
-    "# tfidf=pickle.load(open('vectorizer.pkl','rb'))\n",
-    "# model=pickle.load(open('bnb.pkl','rb'))\n",
-    "\n",
-    "st.title('SMS Spam Classification')\n",
-    "\n",
-    "sms_input=st.text_area(\"Enter the text\")\n",
-    "\n",
-    "if st.button('Predict'):\n",
-    "    transform_sms=transform_text(sms_input)\n",
-    "\n",
-    "    vector_input=tfidf.transform([transform_sms])\n",
-    "\n",
-    "    result=bnb.predict(vector_input)[0]\n",
-    "\n",
-    "    if result==1:\n",
-    "        st.title(\"SMS is Spam\")\n",
-    "\n",
-    "    else:\n",
-    "        st.title(\"SMS is not Spam\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "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": "3.11.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

From 35012fb3b3d31dc369b81d585399e93d823bfe55 Mon Sep 17 00:00:00 2001
From: sakeeb hasan <100307524+Sakeebhasan123456@users.noreply.github.com>
Date: Fri, 4 Oct 2024 17:31:38 +0530
Subject: [PATCH 2/3] Add files via upload

---
 .../Model/model1_(1) (1).ipynb                | 4432 +++++++++++++++++
 1 file changed, 4432 insertions(+)
 create mode 100644 Deep_Learning/Spam Vs Ham Mail Classification [With Streamlit GUI]/Model/model1_(1) (1).ipynb

diff --git a/Deep_Learning/Spam Vs Ham Mail Classification [With Streamlit GUI]/Model/model1_(1) (1).ipynb b/Deep_Learning/Spam Vs Ham Mail Classification [With Streamlit GUI]/Model/model1_(1) (1).ipynb
new file mode 100644
index 0000000000..79f6f853f4
--- /dev/null
+++ b/Deep_Learning/Spam Vs Ham Mail Classification [With Streamlit GUI]/Model/model1_(1) (1).ipynb	
@@ -0,0 +1,4432 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 61,
+      "metadata": {
+        "id": "mGHDwLdcKKXi"
+      },
+      "outputs": [],
+      "source": [
+        "import optuna\n",
+        "import pickle\n",
+        "import nltk\n",
+        "import string\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt\n",
+        "import seaborn as sns\n",
+        "from nltk.corpus import stopwords\n",
+        "from gensim.models import Word2Vec\n",
+        "from sklearn.model_selection import cross_val_score, train_test_split\n",
+        "from sklearn.naive_bayes import BernoulliNB, MultinomialNB\n",
+        "\n",
+        "\n",
+        "from sklearn.pipeline import make_pipeline\n",
+        "from nltk.corpus import stopwords\n",
+        "from nltk.stem import WordNetLemmatizer\n",
+        "from sklearn.metrics import accuracy_score\n",
+        "\n",
+        "import optuna\n",
+        "\n",
+        "from gensim.models import Word2Vec\n",
+        "\n",
+        "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, AdaBoostClassifier\n",
+        "from sklearn.linear_model import LogisticRegression\n",
+        "from sklearn.svm import SVC\n",
+        "from sklearn.metrics import accuracy_score\n",
+        "\n",
+        "from sklearn.neighbors import KNeighborsClassifier\n",
+        "import xgboost as xgb  # Make sure to install xgboost if not already installed"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!pip install optuna\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "lpOuiv5YNcCy",
+        "outputId": "665f49d0-cd10-411f-bb55-d32c98907dbb"
+      },
+      "execution_count": 50,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Collecting optuna\n",
+            "  Downloading optuna-4.0.0-py3-none-any.whl.metadata (16 kB)\n",
+            "Collecting alembic>=1.5.0 (from optuna)\n",
+            "  Downloading alembic-1.13.3-py3-none-any.whl.metadata (7.4 kB)\n",
+            "Collecting colorlog (from optuna)\n",
+            "  Downloading colorlog-6.8.2-py3-none-any.whl.metadata (10 kB)\n",
+            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from optuna) (1.26.4)\n",
+            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from optuna) (24.1)\n",
+            "Requirement already satisfied: sqlalchemy>=1.3.0 in /usr/local/lib/python3.10/dist-packages (from optuna) (2.0.35)\n",
+            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from optuna) (4.66.5)\n",
+            "Requirement already satisfied: PyYAML in /usr/local/lib/python3.10/dist-packages (from optuna) (6.0.2)\n",
+            "Collecting Mako (from alembic>=1.5.0->optuna)\n",
+            "  Downloading Mako-1.3.5-py3-none-any.whl.metadata (2.9 kB)\n",
+            "Requirement already satisfied: typing-extensions>=4 in /usr/local/lib/python3.10/dist-packages (from alembic>=1.5.0->optuna) (4.12.2)\n",
+            "Requirement already satisfied: greenlet!=0.4.17 in /usr/local/lib/python3.10/dist-packages (from sqlalchemy>=1.3.0->optuna) (3.1.1)\n",
+            "Requirement already satisfied: MarkupSafe>=0.9.2 in /usr/local/lib/python3.10/dist-packages (from Mako->alembic>=1.5.0->optuna) (2.1.5)\n",
+            "Downloading optuna-4.0.0-py3-none-any.whl (362 kB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m362.8/362.8 kB\u001b[0m \u001b[31m11.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading alembic-1.13.3-py3-none-any.whl (233 kB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m233.2/233.2 kB\u001b[0m \u001b[31m15.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading colorlog-6.8.2-py3-none-any.whl (11 kB)\n",
+            "Downloading Mako-1.3.5-py3-none-any.whl (78 kB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m78.6/78.6 kB\u001b[0m \u001b[31m5.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hInstalling collected packages: Mako, colorlog, alembic, optuna\n",
+            "Successfully installed Mako-1.3.5 alembic-1.13.3 colorlog-6.8.2 optuna-4.0.0\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "RV7U1BvlKKXj",
+        "outputId": "496e43b0-7fe9-4b49-baee-29c5be226649"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                                                Text Label\n",
+              "0  Go until jurong point, crazy.. Available only ...   ham\n",
+              "1                      Ok lar... Joking wif u oni...   ham\n",
+              "2  Free entry in 2 a wkly comp to win FA Cup fina...  spam\n",
+              "3  U dun say so early hor... U c already then say...   ham\n",
+              "4  Nah I don't think he goes to usf, he lives aro...   ham"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-fb71f7c2-8d9f-4b13-9b0f-ed82d43ca00a\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
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+              "        vertical-align: top;\n",
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+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
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+              "      <th></th>\n",
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+              "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
+              "      <td>ham</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Ok lar... Joking wif u oni...</td>\n",
+              "      <td>ham</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
+              "      <td>spam</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>U dun say so early hor... U c already then say...</td>\n",
+              "      <td>ham</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
+              "      <td>ham</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fb71f7c2-8d9f-4b13-9b0f-ed82d43ca00a')\"\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-fb71f7c2-8d9f-4b13-9b0f-ed82d43ca00a button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-fb71f7c2-8d9f-4b13-9b0f-ed82d43ca00a');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-100e35f2-9558-4139-bdf3-25779ec92840\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-100e35f2-9558-4139-bdf3-25779ec92840')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-100e35f2-9558-4139-bdf3-25779ec92840 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df",
+              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 5574,\n  \"fields\": [\n    {\n      \"column\": \"Text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5171,\n        \"samples\": [\n          \"K, makes sense, btw carlos is being difficult so you guys are gonna smoke while I go pick up the second batch and get gas\",\n          \"URGENT! Your mobile No *********** WON a \\u00c2\\u00a32,000 Bonus Caller Prize on 02/06/03! This is the 2nd attempt to reach YOU! Call 09066362220 ASAP! BOX97N7QP, 150ppm\",\n          \"If you still havent collected the dough pls let me know so i can go to the place i sent it to get the control number\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Label\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"spam\",\n          \"ham\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 8
+        }
+      ],
+      "source": [
+        "df=pd.read_csv('/content/spam-vs-ham-dataset .csv',encoding=\"ISO-8859-1\")\n",
+        "df.head()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 147
+        },
+        "id": "OLAP-WNMKKXk",
+        "outputId": "6039d80a-b278-4f7d-b4a3-e58b50680cea"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Text     0\n",
+              "Label    0\n",
+              "dtype: int64"
+            ],
+            "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>0</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>Text</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Label</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> int64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 9
+        }
+      ],
+      "source": [
+        "df.isnull().sum()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "9aLELnWeKKXk",
+        "outputId": "15c00c60-ee8a-4be7-bea5-38dd1c053dcc"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "403"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 10
+        }
+      ],
+      "source": [
+        "df.duplicated().sum()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {
+        "id": "WYJvZzCAKKXk"
+      },
+      "outputs": [],
+      "source": [
+        "df.drop_duplicates(keep='first',inplace=True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "kSBwbgkEKKXk",
+        "outputId": "56b3fabe-2699-4c6e-d158-17d528cd559f"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(5171, 2)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 12
+        }
+      ],
+      "source": [
+        "df.shape"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "2xzMdugsKKXl",
+        "outputId": "1bc3bf87-e2bd-413f-da3f-3635e9814082"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                                                Text  Label\n",
+              "0  Go until jurong point, crazy.. Available only ...      0\n",
+              "1                      Ok lar... Joking wif u oni...      0\n",
+              "2  Free entry in 2 a wkly comp to win FA Cup fina...      1\n",
+              "3  U dun say so early hor... U c already then say...      0\n",
+              "4  Nah I don't think he goes to usf, he lives aro...      0"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-c294ccfc-84d2-4b60-b1ff-986c3cb4f0e7\" class=\"colab-df-container\">\n",
+              "    <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>Label</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Ok lar... Joking wif u oni...</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>U dun say so early hor... U c already then say...</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c294ccfc-84d2-4b60-b1ff-986c3cb4f0e7')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-c294ccfc-84d2-4b60-b1ff-986c3cb4f0e7 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-c294ccfc-84d2-4b60-b1ff-986c3cb4f0e7');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-1d8c77b3-5c48-4f6f-90c2-c31cef632b18\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-1d8c77b3-5c48-4f6f-90c2-c31cef632b18')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-1d8c77b3-5c48-4f6f-90c2-c31cef632b18 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df",
+              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 5171,\n  \"fields\": [\n    {\n      \"column\": \"Text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5171,\n        \"samples\": [\n          \"K, makes sense, btw carlos is being difficult so you guys are gonna smoke while I go pick up the second batch and get gas\",\n          \"URGENT! Your mobile No *********** WON a \\u00c2\\u00a32,000 Bonus Caller Prize on 02/06/03! This is the 2nd attempt to reach YOU! Call 09066362220 ASAP! BOX97N7QP, 150ppm\",\n          \"If you still havent collected the dough pls let me know so i can go to the place i sent it to get the control number\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Label\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 13
+        }
+      ],
+      "source": [
+        "from sklearn.preprocessing import LabelEncoder\n",
+        "\n",
+        "le=LabelEncoder()\n",
+        "df['Label']=le.fit_transform(df['Label'])\n",
+        "df.head()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 30,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 510
+        },
+        "id": "IWXdtiROKKXl",
+        "outputId": "09ccff3f-ee90-49ee-96c8-71b45dd4ee54"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "([<matplotlib.patches.Wedge at 0x7f56fbeba320>,\n",
+              "  <matplotlib.patches.Wedge at 0x7f56fbeba230>],\n",
+              " [Text(-1.0145649212950327, 0.4250388458454174, 'ham'),\n",
+              "  Text(1.014564901397527, -0.4250388933406287, 'spam')],\n",
+              " [Text(-0.5533990479791087, 0.23183937046113673, '87.37'),\n",
+              "  Text(0.5533990371259238, -0.23183939636761564, '12.63')])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 30
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "plt.pie(df['Label'].value_counts(),labels=['ham','spam'],autopct=\"%.02f\")\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 31,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "1j0Cmr83KKXl",
+        "outputId": "659480ab-2997-4de5-c415-1abdf5223229"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
+            "[nltk_data]   Package punkt is already up-to-date!\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "True"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 31
+        }
+      ],
+      "source": [
+        "import nltk\n",
+        "nltk.download('punkt')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 32,
+      "metadata": {
+        "id": "Z_UL0LYrKKXl"
+      },
+      "outputs": [],
+      "source": [
+        "df['num_chr']=df['Text'].apply(len)\n",
+        "df['num_words']=df['Text'].apply(lambda x:len(nltk.word_tokenize(x)))\n",
+        "df['num_sent']=df['Text'].apply(lambda x:len(nltk.sent_tokenize(x)))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 33,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "VB1OaIlbKKXl",
+        "outputId": "920c5cd5-e612-4422-fadf-a9e86fe482ac"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                                                   Text  Label  num_chr  \\\n",
+              "5557  Yeh. Indians was nice. Tho it did kane me off ...      0      153   \n",
+              "2800  I've told him that i've returned it. That shou...      0       63   \n",
+              "4223  Yo you around? A friend of mine's lookin to pi...      0       65   \n",
+              "505   +123 Congratulations - in this week's competit...      1      170   \n",
+              "2501               Remember to ask alex about his pizza      0       36   \n",
+              "\n",
+              "      num_words  num_sent  \n",
+              "5557         43         6  \n",
+              "2800         17         2  \n",
+              "4223         15         2  \n",
+              "505          32         3  \n",
+              "2501          7         1  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-7b885f81-fc82-44ec-9dc7-c4732f3b0ece\" class=\"colab-df-container\">\n",
+              "    <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>Label</th>\n",
+              "      <th>num_chr</th>\n",
+              "      <th>num_words</th>\n",
+              "      <th>num_sent</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>5557</th>\n",
+              "      <td>Yeh. Indians was nice. Tho it did kane me off ...</td>\n",
+              "      <td>0</td>\n",
+              "      <td>153</td>\n",
+              "      <td>43</td>\n",
+              "      <td>6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2800</th>\n",
+              "      <td>I've told him that i've returned it. That shou...</td>\n",
+              "      <td>0</td>\n",
+              "      <td>63</td>\n",
+              "      <td>17</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4223</th>\n",
+              "      <td>Yo you around? A friend of mine's lookin to pi...</td>\n",
+              "      <td>0</td>\n",
+              "      <td>65</td>\n",
+              "      <td>15</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>505</th>\n",
+              "      <td>+123 Congratulations - in this week's competit...</td>\n",
+              "      <td>1</td>\n",
+              "      <td>170</td>\n",
+              "      <td>32</td>\n",
+              "      <td>3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2501</th>\n",
+              "      <td>Remember to ask alex about his pizza</td>\n",
+              "      <td>0</td>\n",
+              "      <td>36</td>\n",
+              "      <td>7</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7b885f81-fc82-44ec-9dc7-c4732f3b0ece')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-7b885f81-fc82-44ec-9dc7-c4732f3b0ece button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-7b885f81-fc82-44ec-9dc7-c4732f3b0ece');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-898d903f-558c-4e4a-b7bd-162fd071d17f\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-898d903f-558c-4e4a-b7bd-162fd071d17f')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-898d903f-558c-4e4a-b7bd-162fd071d17f button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"I've told him that i've returned it. That should i re order it.\",\n          \"Remember to ask alex about his pizza\",\n          \"Yo you around? A friend of mine's lookin to pick up later tonight\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Label\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"num_chr\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 59,\n        \"min\": 36,\n        \"max\": 170,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          63,\n          36\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"num_words\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14,\n        \"min\": 7,\n        \"max\": 43,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          17,\n          7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"num_sent\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 6,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 33
+        }
+      ],
+      "source": [
+        "df.sample(5)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 34,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 300
+        },
+        "id": "WUjGhZp9KKXl",
+        "outputId": "b46a6289-7165-4285-cce3-308f00deaefa"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "             Label      num_chr    num_words     num_sent\n",
+              "count  5171.000000  5171.000000  5171.000000  5171.000000\n",
+              "mean      0.126281    79.457552    18.590988     1.973893\n",
+              "std       0.332198    58.401504    13.383728     1.458880\n",
+              "min       0.000000     2.000000     1.000000     1.000000\n",
+              "25%       0.000000    36.000000     9.000000     1.000000\n",
+              "50%       0.000000    61.000000    15.000000     1.000000\n",
+              "75%       0.000000   119.000000    26.000000     2.000000\n",
+              "max       1.000000   910.000000   220.000000    38.000000"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-58f33512-5643-4c0d-83cb-141ead4a4668\" class=\"colab-df-container\">\n",
+              "    <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>Label</th>\n",
+              "      <th>num_chr</th>\n",
+              "      <th>num_words</th>\n",
+              "      <th>num_sent</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>5171.000000</td>\n",
+              "      <td>5171.000000</td>\n",
+              "      <td>5171.000000</td>\n",
+              "      <td>5171.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>0.126281</td>\n",
+              "      <td>79.457552</td>\n",
+              "      <td>18.590988</td>\n",
+              "      <td>1.973893</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>0.332198</td>\n",
+              "      <td>58.401504</td>\n",
+              "      <td>13.383728</td>\n",
+              "      <td>1.458880</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>2.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>36.000000</td>\n",
+              "      <td>9.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>61.000000</td>\n",
+              "      <td>15.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>119.000000</td>\n",
+              "      <td>26.000000</td>\n",
+              "      <td>2.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>910.000000</td>\n",
+              "      <td>220.000000</td>\n",
+              "      <td>38.000000</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-58f33512-5643-4c0d-83cb-141ead4a4668')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-58f33512-5643-4c0d-83cb-141ead4a4668 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-58f33512-5643-4c0d-83cb-141ead4a4668');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-2ff50661-7236-4d8b-8980-bc743deff0da\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-2ff50661-7236-4d8b-8980-bc743deff0da')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-2ff50661-7236-4d8b-8980-bc743deff0da button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Label\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1828.15095056236,\n        \"min\": 0.0,\n        \"max\": 5171.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.12628118352349643,\n          1.0,\n          0.332198114772276\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"num_chr\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1789.5371678264207,\n        \"min\": 2.0,\n        \"max\": 5171.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          79.45755173080641,\n          61.0,\n          5171.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"num_words\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1814.3711563221152,\n        \"min\": 1.0,\n        \"max\": 5171.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          18.590988203442276,\n          15.0,\n          5171.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"num_sent\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1825.9243229401598,\n        \"min\": 1.0,\n        \"max\": 5171.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          5171.0,\n          1.9738928640495068,\n          38.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 34
+        }
+      ],
+      "source": [
+        "df.describe()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 35,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 300
+        },
+        "id": "vJ5osXm4KKXm",
+        "outputId": "81940468-69f8-442d-b347-c93b11bad67d"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "           num_chr    num_words     num_sent\n",
+              "count  5171.000000  5171.000000  5171.000000\n",
+              "mean     79.457552    18.590988     1.973893\n",
+              "std      58.401504    13.383728     1.458880\n",
+              "min       2.000000     1.000000     1.000000\n",
+              "25%      36.000000     9.000000     1.000000\n",
+              "50%      61.000000    15.000000     1.000000\n",
+              "75%     119.000000    26.000000     2.000000\n",
+              "max     910.000000   220.000000    38.000000"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-35f11b06-d378-48c5-9fcf-c8bfcd87b245\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>num_chr</th>\n",
+              "      <th>num_words</th>\n",
+              "      <th>num_sent</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>5171.000000</td>\n",
+              "      <td>5171.000000</td>\n",
+              "      <td>5171.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>79.457552</td>\n",
+              "      <td>18.590988</td>\n",
+              "      <td>1.973893</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>58.401504</td>\n",
+              "      <td>13.383728</td>\n",
+              "      <td>1.458880</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>2.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>36.000000</td>\n",
+              "      <td>9.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>61.000000</td>\n",
+              "      <td>15.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>119.000000</td>\n",
+              "      <td>26.000000</td>\n",
+              "      <td>2.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>910.000000</td>\n",
+              "      <td>220.000000</td>\n",
+              "      <td>38.000000</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-35f11b06-d378-48c5-9fcf-c8bfcd87b245')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-35f11b06-d378-48c5-9fcf-c8bfcd87b245 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-35f11b06-d378-48c5-9fcf-c8bfcd87b245');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-0f727ead-5349-4a20-ba5e-ce8370ee20e3\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0f727ead-5349-4a20-ba5e-ce8370ee20e3')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-0f727ead-5349-4a20-ba5e-ce8370ee20e3 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"df[['num_chr','num_words','num_sent']]\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"num_chr\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1789.5371678264207,\n        \"min\": 2.0,\n        \"max\": 5171.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          79.45755173080641,\n          61.0,\n          5171.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"num_words\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1814.3711563221152,\n        \"min\": 1.0,\n        \"max\": 5171.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          18.590988203442276,\n          15.0,\n          5171.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"num_sent\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1825.9243229401598,\n        \"min\": 1.0,\n        \"max\": 5171.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          5171.0,\n          1.9738928640495068,\n          38.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 35
+        }
+      ],
+      "source": [
+        "df[['num_chr','num_words','num_sent']].describe()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 36,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 300
+        },
+        "id": "MsxsbXhAKKXm",
+        "outputId": "9633dd45-7f27-4449-801d-a3d966350108"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "           num_chr    num_words     num_sent\n",
+              "count  4518.000000  4518.000000  4518.000000\n",
+              "mean     70.976538    17.265162     1.827579\n",
+              "std      56.610538    13.566409     1.394245\n",
+              "min       2.000000     1.000000     1.000000\n",
+              "25%      34.000000     8.000000     1.000000\n",
+              "50%      53.000000    13.000000     1.000000\n",
+              "75%      91.000000    22.000000     2.000000\n",
+              "max     910.000000   220.000000    38.000000"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-c70d7bf1-d292-42bb-a33e-d2c97bd5c77a\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>num_chr</th>\n",
+              "      <th>num_words</th>\n",
+              "      <th>num_sent</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>4518.000000</td>\n",
+              "      <td>4518.000000</td>\n",
+              "      <td>4518.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>70.976538</td>\n",
+              "      <td>17.265162</td>\n",
+              "      <td>1.827579</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>56.610538</td>\n",
+              "      <td>13.566409</td>\n",
+              "      <td>1.394245</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>2.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>34.000000</td>\n",
+              "      <td>8.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>53.000000</td>\n",
+              "      <td>13.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>91.000000</td>\n",
+              "      <td>22.000000</td>\n",
+              "      <td>2.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>910.000000</td>\n",
+              "      <td>220.000000</td>\n",
+              "      <td>38.000000</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c70d7bf1-d292-42bb-a33e-d2c97bd5c77a')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-c70d7bf1-d292-42bb-a33e-d2c97bd5c77a button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-c70d7bf1-d292-42bb-a33e-d2c97bd5c77a');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-79c4dd09-3a39-4d9f-93ed-0e6078864003\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-79c4dd09-3a39-4d9f-93ed-0e6078864003')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-79c4dd09-3a39-4d9f-93ed-0e6078864003 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"df[df['Label']==0][['num_chr','num_words','num_sent']]\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"num_chr\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1565.1926642194865,\n        \"min\": 2.0,\n        \"max\": 4518.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          70.97653829127933,\n          53.0,\n          4518.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"num_words\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1584.14040086219,\n        \"min\": 1.0,\n        \"max\": 4518.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          17.265161575918547,\n          13.0,\n          4518.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"num_sent\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1595.0712109951553,\n        \"min\": 1.0,\n        \"max\": 4518.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          4518.0,\n          1.8275785745905269,\n          38.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 36
+        }
+      ],
+      "source": [
+        "df[df['Label']==0][['num_chr','num_words','num_sent']].describe()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 37,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 300
+        },
+        "id": "_VHEYgX4KKXm",
+        "outputId": "387d637b-9e3b-46c2-e7ff-03450f603e0d"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "          num_chr   num_words    num_sent\n",
+              "count  653.000000  653.000000  653.000000\n",
+              "mean   138.136294   27.764165    2.986217\n",
+              "std     29.934972    6.988123    1.494815\n",
+              "min     13.000000    2.000000    1.000000\n",
+              "25%    132.000000   25.000000    2.000000\n",
+              "50%    149.000000   29.000000    3.000000\n",
+              "75%    157.000000   32.000000    4.000000\n",
+              "max    224.000000   46.000000    9.000000"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-cb9efe32-0c07-4c1d-a334-c877e54dba8d\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>num_chr</th>\n",
+              "      <th>num_words</th>\n",
+              "      <th>num_sent</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>653.000000</td>\n",
+              "      <td>653.000000</td>\n",
+              "      <td>653.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>138.136294</td>\n",
+              "      <td>27.764165</td>\n",
+              "      <td>2.986217</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>29.934972</td>\n",
+              "      <td>6.988123</td>\n",
+              "      <td>1.494815</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>13.000000</td>\n",
+              "      <td>2.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>132.000000</td>\n",
+              "      <td>25.000000</td>\n",
+              "      <td>2.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>149.000000</td>\n",
+              "      <td>29.000000</td>\n",
+              "      <td>3.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>157.000000</td>\n",
+              "      <td>32.000000</td>\n",
+              "      <td>4.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>224.000000</td>\n",
+              "      <td>46.000000</td>\n",
+              "      <td>9.000000</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cb9efe32-0c07-4c1d-a334-c877e54dba8d')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-cb9efe32-0c07-4c1d-a334-c877e54dba8d button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-cb9efe32-0c07-4c1d-a334-c877e54dba8d');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-516a4640-3c15-4437-b1bb-8c9fa1425286\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-516a4640-3c15-4437-b1bb-8c9fa1425286')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-516a4640-3c15-4437-b1bb-8c9fa1425286 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"df[df['Label']==1][['num_chr','num_words','num_sent']]\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"num_chr\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 200.43776521896672,\n        \"min\": 13.0,\n        \"max\": 653.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          138.13629402756507,\n          149.0,\n          653.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"num_words\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 222.784033715766,\n        \"min\": 2.0,\n        \"max\": 653.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          27.76416539050536,\n          29.0,\n          653.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"num_sent\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 229.69788497458427,\n        \"min\": 1.0,\n        \"max\": 653.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          2.986217457886677,\n          3.0,\n          653.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 37
+        }
+      ],
+      "source": [
+        "df[df['Label']==1][['num_chr','num_words','num_sent']].describe()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 39,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 559
+        },
+        "id": "XTIUjhmzKKXm",
+        "outputId": "53773b27-69dc-4b5b-9b6b-58263d56f515"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<Axes: xlabel='num_chr', ylabel='Count'>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 39
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1200x600 with 1 Axes>"
+            ],
+            "image/png": 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cckvmzp2bxYsX50c/+lHuu+++dp+tdfPNN+eoo45KbW1tamtr09DQkJ/+9Kfl/vXr12fatGnZZ599stdee2XixIlZuXJlu300NjZm/Pjx2XPPPTN48ODMmDEjGzdu3J7TAgAAgELZrtC+YcOGfPKTn/zIB99vv/1y9dVXZ8mSJXnyySdzyimn5Atf+EKWLVuWJDn//PNz//335957782CBQuyYsWKTJgwobz9pk2bMn78+GzYsCELFy7M7bffnrlz5+aSSy75yLUBAABApW1XaP/KV76Su+666yMf/POf/3w+97nP5eCDD87HP/7xXHXVVdlrr73y+OOPZ926dbn11ltz7bXX5pRTTsmoUaNy2223ZeHChXn88ceTJA8//HCeffbZ/OAHP8gxxxyTcePG5corr8ycOXOyYcOGj1wfAAAAVNJ2PdO+fv36/PM//3MeeeSRHHXUUenZs2e7/muvvXab97lp06bce++9efvtt9PQ0JAlS5aktbU1o0ePLo859NBDM3z48CxatCgnnnhiFi1alCOPPDJ1dXXlMWPHjs3UqVOzbNmyHHvssR0eq6WlJS0tLeXvzc3N21wvAAAA7GjbFdp//etf55hjjkmSPPPMM+36tnVRuqeffjoNDQ1Zv3599tprr9x3330ZOXJkli5dml69eqV///7txtfV1aWpqSlJ0tTU1C6wb+7f3NeZWbNm5fLLL9+mOgEAAKC7bVdo//nPf95lBRxyyCFZunRp1q1bl//9v/93Jk+enAULFnTZ/jsyc+bMTJ8+vfy9ubk5+++//w49JgAAAGyr7X5Pe1fp1atX/uiP/ihJMmrUqDzxxBP53ve+ly996UvZsGFD1q5d2+5u+8qVK1NfX58kqa+vz69+9at2+9u8uvzmMR2pqalJTU1NF58JAAAAdK3tCu2f+cxnPnAa/KOPPrrdBbW1taWlpSWjRo1Kz549M3/+/EycODFJ8sILL6SxsTENDQ1JkoaGhlx11VVZtWpVBg8enCSZN29eamtrM3LkyO2uAQAAAIpgu0L75ufZN2ttbc3SpUvzzDPPZPLkyVu9n5kzZ2bcuHEZPnx43nzzzdx111157LHH8rOf/Sz9+vXLlClTMn369AwcODC1tbU555xz0tDQkBNPPDFJMmbMmIwcOTJnnnlmZs+enaamplx00UWZNm2aO+kAAADs9LYrtH/3u9/tsP2yyy7LW2+9tdX7WbVqVb785S/n9ddfT79+/XLUUUflZz/7Wf70T/+0fJwePXpk4sSJaWlpydixY3PTTTeVt99jjz3ywAMPZOrUqWloaEjfvn0zefLkXHHFFdtzWgAAAFAoXfpM+1/8xV/k+OOPzz/8wz9s1fhbb731A/t79+6dOXPmZM6cOZ2OOeCAA/Lggw9uU50AAACwM+jRlTtbtGhRevfu3ZW7BAAAgN3Wdt1pnzBhQrvvpVIpr7/+ep588slcfPHFXVIYu44Xn38uJ4+bsEX70EEDcs8dHzzbAgAAYHe2XaG9X79+7b736NEjhxxySK644oqMGTOmSwpj19Fa6pFhp83Yon35fddUoBoAAICdx3aF9ttuu62r6wAAAADe5yMtRLdkyZI899xzSZLDDz88xx57bJcUBQAAAGxnaF+1alVOP/30PPbYY+nfv3+SZO3atfnMZz6Tu+++O4MGDerKGgEAAGC3tF2rx59zzjl58803s2zZsqxevTqrV6/OM888k+bm5nzta1/r6hoBAABgt7Rdd9ofeuihPPLIIznssMPKbSNHjsycOXMsRAcAAABdZLvutLe1taVnz55btPfs2TNtbW0fuSgAAABgO0P7KaecknPPPTcrVqwoty1fvjznn39+Tj311C4rDgAAAHZn2xXab7zxxjQ3N+fAAw/Mxz72sXzsYx/LiBEj0tzcnBtuuKGrawQAAIDd0nY9077//vvnqaeeyiOPPJLnn38+SXLYYYdl9OjRXVocAAAA7M626U77o48+mpEjR6a5uTlVVVX50z/905xzzjk555xz8olPfCKHH354/s//+T87qlYAAADYrWxTaL/uuuvy1a9+NbW1tVv09evXL//jf/yPXHvttV1WHAAAAOzOtim0//u//3s++9nPdto/ZsyYLFmy5CMXBQAAAGxjaF+5cmWHr3rbrLq6Om+88cZHLgoAAADYxtA+bNiwPPPMM532//rXv86QIUM+clEAAADANob2z33uc7n44ouzfv36LfrefffdXHrppfmzP/uzLisOAAAAdmfb9Mq3iy66KD/84Q/z8Y9/PGeffXYOOeSQJMnzzz+fOXPmZNOmTfnWt761QwoFAACA3c02hfa6urosXLgwU6dOzcyZM1MqlZIkVVVVGTt2bObMmZO6urodUigAAADsbrYptCfJAQcckAcffDBr1qzJyy+/nFKplIMPPjgDBgzYEfUBAADAbmubQ/tmAwYMyCc+8YmurAUAAAB4j21aiA4AAADoPkI7AAAAFJTQDgAAAAUltAMAAEBBCe0AAABQUEI7AAAAFJTQDgAAAAUltAMAAEBBCe0AAABQUEI7AAAAFJTQDgAAAAUltAMAAEBBCe0AAABQUEI7AAAAFJTQDgAAAAUltAMAAEBBCe0AAABQUEI7AAAAFJTQDgAAAAUltAMAAEBBCe0AAABQUEI7AAAAFJTQDgAAAAUltAMAAEBBCe0AAABQUEI7AAAAFJTQDgAAAAUltAMAAEBBCe0AAABQUEI7AAAAFJTQDgAAAAUltAMAAEBBCe0AAABQUEI7AAAAFJTQDgAAAAUltAMAAEBBCe0AAABQUEI7AAAAFFR1pQuAjnzpy1Oy4o01W7QPHTQg99xxawUqAgAA6H5CO4W04o01GXbajC3al993TQWqAQAAqAzT4wEAAKCghHYAAAAoKKEdAAAACkpoBwAAgIIS2gEAAKCgrB5Pxbz4/HM5edyEDvteevnlDNuGbbwKDgAA2BVVNLTPmjUrP/zhD/P888+nT58++eQnP5nvfOc7OeSQQ8pj1q9fn69//eu5++6709LSkrFjx+amm25KXV1deUxjY2OmTp2an//859lrr70yefLkzJo1K9XVfidRZK2lHh2+1i1Jln37K9u0jVfBAQAAu6KKTo9fsGBBpk2blscffzzz5s1La2trxowZk7fffrs85vzzz8/999+fe++9NwsWLMiKFSsyYcJ/3mndtGlTxo8fnw0bNmThwoW5/fbbM3fu3FxyySWVOCUAAADoMhW9Ff3QQw+1+z537twMHjw4S5YsyX/5L/8l69aty6233pq77rorp5xySpLktttuy2GHHZbHH388J554Yh5++OE8++yzeeSRR1JXV5djjjkmV155ZS688MJcdtll6dWrVyVODQAAAD6yQi1Et27duiTJwIEDkyRLlixJa2trRo8eXR5z6KGHZvjw4Vm0aFGSZNGiRTnyyCPbTZcfO3Zsmpubs2zZsg6P09LSkubm5nYfAAAAKJrChPa2tracd955Oemkk3LEEUckSZqamtKrV6/079+/3di6uro0NTWVx7w3sG/u39zXkVmzZqVfv37lz/7779/FZwMAAAAfXWFC+7Rp0/LMM8/k7rvv3uHHmjlzZtatW1f+vPbaazv8mAAAALCtCrG8+tlnn50HHnggv/jFL7LffvuV2+vr67Nhw4asXbu23d32lStXpr6+vjzmV7/6Vbv9rVy5stzXkZqamtTU1HTxWQAAAEDXquid9lKplLPPPjv33XdfHn300YwYMaJd/6hRo9KzZ8/Mnz+/3PbCCy+ksbExDQ0NSZKGhoY8/fTTWbVqVXnMvHnzUltbm5EjR3bPiQAAAMAOUNE77dOmTctdd92Vf/u3f8vee+9dfga9X79+6dOnT/r165cpU6Zk+vTpGThwYGpra3POOeekoaEhJ554YpJkzJgxGTlyZM4888zMnj07TU1NueiiizJt2jR30wEAANipVTS033zzzUmST3/60+3ab7vttvzlX/5lkuS73/1uevTokYkTJ6alpSVjx47NTTfdVB67xx575IEHHsjUqVPT0NCQvn37ZvLkybniiiu66zQAAABgh6hoaC+VSh86pnfv3pkzZ07mzJnT6ZgDDjggDz74YFeWBgAAABVXmNXjAQAAgPaEdgAAACgooR0AAAAKSmgHAACAghLaAQAAoKCEdgAAACgooR0AAAAKSmgHAACAghLaAQAAoKCEdgAAACgooR0AAAAKSmgHAACAghLaAQAAoKCEdgAAACgooR0AAAAKSmgHAACAghLaAQAAoKCEdgAAACio6koXAHSPy6dOzbuNje3a+gwfnktvvrlCFQEAAB9GaGeX8OLzz+XkcRM67Bs6aEDuuePWbq6oeN5tbMzV77zTru2b7wvxAABAsQjt7BJaSz0y7LQZHfYtv++abq4GAACgawjtu4EvfXlKVryxZov2l15+OcMqUA8AAABbR2jfDax4Y02Hd6GXffsrFagGAACArWX1eAAAACgooR0AAAAKSmgHAACAghLaAQAAoKCEdgAAACgooR0AAAAKSmgHAACAghLaAQAAoKCEdgAAACgooR0AAAAKSmgHAACAgqqudAGwo734/HM5edyELdqHDhqQe+64tQIVAQAAbB2hnV1ea6lHhp02Y4v25fddU4FqAAAAtp7p8QAAAFBQQjsAAAAUlNAOAAAABSW0AwAAQEEJ7QAAAFBQQjsAAAAUlNAOAAAABSW0AwAAQEEJ7QAAAFBQQjsAAAAUlNAOAAAABVVd6QKAHetLX56SFW+sSY8nl+Sptk3l9pqe1cmoP65gZQAAwIcR2mEXt+KNNRl22oys+d3XU7Ohpdze8h+NFawKAADYGqbHAwAAQEEJ7QAAAFBQQjsAAAAUlNAOAAAABSW0AwAAQEEJ7QAAAFBQQjsAAAAUlNAOAAAABSW0AwAAQEEJ7QAAAFBQQjsAAAAUlNAOAAAABSW0AwAAQEEJ7QAAAFBQ1ZUuACrlxeefy8njJnTYN3TQgNxzx63dXBEAAEB7Qju7rdZSjww7bUaHfcvvu6abqwEAANiS6fEAAABQUEI7AAAAFJTQDgAAAAVV0dD+i1/8Ip///OczdOjQVFVV5Uc/+lG7/lKplEsuuSRDhgxJnz59Mnr06Lz00kvtxqxevTqTJk1KbW1t+vfvnylTpuStt97qxrMAAACAHaOiof3tt9/O0UcfnTlz5nTYP3v27Fx//fW55ZZbsnjx4vTt2zdjx47N+vXry2MmTZqUZcuWZd68eXnggQfyi1/8ImeddVZ3nQIAAADsMBVdPX7cuHEZN25ch32lUinXXXddLrroonzhC19Iktxxxx2pq6vLj370o5x++ul57rnn8tBDD+WJJ57IcccdlyS54YYb8rnPfS7/8A//kKFDh3bbuQAAAEBXK+wr31599dU0NTVl9OjR5bZ+/frlhBNOyKJFi3L66adn0aJF6d+/fzmwJ8no0aPTo0ePLF68OKeddlqH+25paUlLS0v5e3Nz8447EaiAy6dOzbuNjUmSHk8uyZrffT3vLn8lGbRfhSsDAAC2RWFDe1NTU5Kkrq6uXXtdXV25r6mpKYMHD27XX11dnYEDB5bHdGTWrFm5/PLLu7hiKI53Gxtz9TvvJEmeatuUmg0tOad1Q4WrAgAAttVuuXr8zJkzs27duvLntddeq3RJAAAAsIXC3mmvr69PkqxcuTJDhgwpt69cuTLHHHNMecyqVavabbdx48asXr26vH1HampqUlNT0/VFs8v70penZMUba7ZoHzpoQO6549YKVAQAAOzKChvaR4wYkfr6+syfP78c0pubm7N48eJMnTo1SdLQ0JC1a9dmyZIlGTVqVJLk0UcfTVtbW0444YRKlc4ubMUbazLstBlbtC+/75oKVAMAAOzqKhra33rrrbz88svl76+++mqWLl2agQMHZvjw4TnvvPPy93//9zn44IMzYsSIXHzxxRk6dGi++MUvJkkOO+ywfPazn81Xv/rV3HLLLWltbc3ZZ5+d008/3crx7FbePwOgx5NL8lTbpiTJu+++G/NKAABg51TR0P7kk0/mM5/5TPn79OnTkySTJ0/O3Llzc8EFF+Ttt9/OWWedlbVr1+ZTn/pUHnroofTu3bu8zZ133pmzzz47p556anr06JGJEyfm+uuv7/Zzgc50x5T6988AWPO7r6dmwx/ekPD2/3u+S44BAAB0v4qG9k9/+tMplUqd9ldVVeWKK67IFVdc0emYgQMH5q677toR5UGXMKUeAADYXrvl6vEAAACwMyjsQnRQSS8+/1xOHjdhi/aXXn45wypQDwAAsHsS2qEDraUeHU5pX/btr1SgGgAAYHcltEOFdHY3P/HedwAA4A+EdqiQzu7mJxapAwAA/sBCdAAAAFBQ7rRDAXU2dd60eQAA2L0I7VBAnU2dN20eAAB2L6bHAwAAQEEJ7QAAAFBQQjsAAAAUlNAOAAAABSW0AwAAQEEJ7QAAAFBQXvkGO5HO3t/+0ssvZ1gF6gEAAHYsoR12Ip29v33Zt79SgWoAAIAdTWiHLtDZHfDEXXAAAGD7Ce3QBTq7A564Cw4AAGw/C9EBAABAQQntAAAAUFCmx8NO7Hd3zk7f1Stz4MrGrLnh6+X2d5e/kgzar4KVAQAAXUFoh51Y39Urc+2GlqwutWXghpZy+zmtGypYFQAA0FVMjwcAAICCEtoBAACgoIR2AAAAKCihHQAAAApKaAcAAICCEtoBAACgoIR2AAAAKCihHQAAAAqqutIF0DW+9OUpWfHGmg77Xnr55Qzr5nroer+7c3YOXNmYNTd8vdz27vJXkkH7VbAqAABgRxLadxEr3liTYafN6LBv2be/0s3V8FHt/fvX24XzJOmx/JV8r9SWgRtaym3ntG7o7tIAAIBuJLRDAe2zsTXXviecJwI6AADsjjzTDgAAAAUltAMAAEBBCe0AAABQUEI7AAAAFJTQDgAAAAUltAMAAEBBCe0AAABQUEI7AAAAFJTQDgAAAAVVXekCgMp49513suhXS3LyuAlb9A0dNCD33HFrBaoCAADeS2iH3VRbKeldf1AGnDZji77l911TgYoAAID3Mz0eAAAACkpoBwAAgIIS2gEAAKCghHYAAAAoKKEdAAAACsrq8bAbW738N8kNX9+i/a11r1egGgAA4P2EdtiN7d3akms3tGzR/rfvvluBagAAgPczPR4AAAAKyp32ncyXvjwlK95Ys0X7Sy+/nGEVqAcAAIAdR2jfyax4Y02GnTZji/Zl3/5KBaoBAABgRzI9HgAAAArKnXaooN/dOTt9V6/cor26dX0FqgEAAIpGaIcK6rt6ZYert5/VVqpANQAAQNEI7dBN3n9X/cCVjXm3ec9k0H4VrAoAACgyoR0+gs1B/MCVjVlzw9fL7W8PrMt+ky5oN/b9d9VXl9pyaeuGbqu1K3T29oKhgwbknjturUBFAACwaxPa4SPYHMRXl9oy8D2BfHoHz6nvTN56662cPG7CFu0vvfxyPj3jn7ZoX37fNd1RFgAA7HaEdtgBVi//TfL/33nffBf+3eWv7DRT4Uup8mpBAAAoAKEdOrD3719vN909SVb+/vUcuOY/2rV3FsT3bm0pT4XffBf+nJ1sKjwAAFB5Qjt0YJ+NrVus6n7Om2tz+fumwQviH85z8AAAsP2EdnYav7tz9hYLviV/WPSN4lrxxpoOp9p/0HPwgj4AAPyB0E4hdTQ9vcfyV/K9993pTnb+Rd92BS8+/1yHC9clf1i8bth2bGPBOwAAENopqA6np5uKXlitpR4d3k1POl+8bnu2AQCA3Y3Qzg6x+f3lyX+unt7Ru8u7wurlv8mB7779oe9Jh4+qs2n7ian7AADsGEI7O8Tm95cn/7l6+o6axr53a8sWC8Rty7He+wuGzapb13dZfTujDa3rd/m1Az4ogDe++kqGj/jYFu2dTdtPunbqvmf6AQDYbJcJ7XPmzMk111yTpqamHH300bnhhhty/PHHV7qs3UJHC8RV+p3kHb0nfeXvX0/dPkPKYza391j+Sq59X61ntZW6td6i6ddW2uLxhL98etEWMxo2/52+9+dfiVkO23MHvLMF8pI/TM+v5HvqO6vt57P+utN1ADr7RUNn7d31CwC/gAAA+Gh2idB+zz33ZPr06bnllltywgkn5LrrrsvYsWPzwgsvZPDgwZUub6e0te8pTzpeIG5bnj/fEXe6O3xP+ptrc+3eA8tjvD9923Q0o2Hz3+nq97TvyIUBO1u87oPugHcWdDtbIK+ra+vKcPph6wB09ouGbf0FwPbU3Fk4t6ggAMBHs0uE9muvvTZf/epX81d/9VdJkltuuSU/+clP8v3vfz/f/OY3K1xdZWzLM+Udhea6d9/cqveUJx99gbj3TqXfbHe/003HOgutH3QHfHu26craOgvHlb7T/EG/ANieQN3Z7ICiLiq4PY9HdNaeVP7n2ZW2Z3aEGRUAsOPs9KF9w4YNWbJkSWbOnFlu69GjR0aPHp1FixZ1uE1LS0taWv4zJK5bty5J0tzcvGOL7QIbN7am9d23t2gvtW1q117zxvJcseEPYXpN26YMePed/N0by9uN2bzNe8du9rVNbXlr06b2xy6V8naplF5b0b6xVCpvv7lv46aNHR5/46aNHR6ry4//vn121r4tx988bnuP39Fxdvbjd/Zzfr9S26b89var0nfNqnbtbw8Y/IHbfFj78n+9rt0+92/6f3njunPz9oDBGfbn533ovrb2OFvbt2FTKYM/+7dbtDc+8L1O/52ztf+cf5SaP2hfzz/7TBr+9PNbtP/ut69mvwNHdLjNK7/5TQZvw3E6O0aSDNmnf+b+85wO+7bVX541La//fu0W7a/85jc5+Wvf63CbZ/7x7A5/Zp21Jx/889xWndX8QX//nf2ddbavD/o7bnx9VYb+2blbtn/AOW7PNp3ZnprZ8Tr7uSTb97Ppyp+za4au4lra+ezsP7PN/40slT74hmVV6cNGFNyKFSsybNiwLFy4MA0NDeX2Cy64IAsWLMjixYu32Oayyy7L5Zdf3p1lAgAAwBZee+217Ldf5+uB7fR32rfHzJkzM3369PL3tra2rF69Ovvss0+qqqoqWFnHmpubs//+++e1115LbW1tpcuBHcJ1zu7Ctc7uwHXO7sB1zkdVKpXy5ptvZujQoR84bqcP7fvuu2/22GOPrFzZ/pnslStXpr6+vsNtampqUlNT066tf//+O6rELlNbW+tfCOzyXOfsLlzr7A5c5+wOXOd8FP369fvQMT26oY4dqlevXhk1alTmz59fbmtra8v8+fPbTZcHAACAnc1Of6c9SaZPn57JkyfnuOOOy/HHH5/rrrsub7/9dnk1eQAAANgZ7RKh/Utf+lLeeOONXHLJJWlqasoxxxyThx56KHV1dZUurUvU1NTk0ksv3WJKP+xKXOfsLlzr7A5c5+wOXOd0l51+9XgAAADYVe30z7QDAADArkpoBwAAgIIS2gEAAKCghHYAAAAoKKF9JzBnzpwceOCB6d27d0444YT86le/qnRJsFVmzZqVT3ziE9l7770zePDgfPGLX8wLL7zQbsz69eszbdq07LPPPtlrr70yceLErFy5st2YxsbGjB8/PnvuuWcGDx6cGTNmZOPGjd15KrDVrr766lRVVeW8884rt7nO2VUsX748f/EXf5F99tknffr0yZFHHpknn3yy3F8qlXLJJZdkyJAh6dOnT0aPHp2XXnqp3T5Wr16dSZMmpba2Nv3798+UKVPy1ltvdfepQIc2bdqUiy++OCNGjEifPn3ysY99LFdeeWXeu3a365zuJrQX3D333JPp06fn0ksvzVNPPZWjjz46Y8eOzapVqypdGnyoBQsWZNq0aXn88cczb968tLa2ZsyYMXn77bfLY84///zcf//9uffee7NgwYKsWLEiEyZMKPdv2rQp48ePz4YNG7Jw4cLcfvvtmTt3bi655JJKnBJ8oCeeeCL/9E//lKOOOqpdu+ucXcGaNWty0kknpWfPnvnpT3+aZ599Nv/4j/+YAQMGlMfMnj07119/fW655ZYsXrw4ffv2zdixY7N+/frymEmTJmXZsmWZN29eHnjggfziF7/IWWedVYlTgi185zvfyc0335wbb7wxzz33XL7zne9k9uzZueGGG8pjXOd0uxKFdvzxx5emTZtW/r5p06bS0KFDS7NmzapgVbB9Vq1aVUpSWrBgQalUKpXWrl1b6tmzZ+nee+8tj3nuuedKSUqLFi0qlUql0oMPPljq0aNHqampqTzm5ptvLtXW1pZaWlq69wTgA7z55pulgw8+uDRv3rzSn/zJn5TOPffcUqnkOmfXceGFF5Y+9alPddrf1tZWqq+vL11zzTXltrVr15ZqampK/+t//a9SqVQqPfvss6UkpSeeeKI85qc//WmpqqqqtHz58h1XPGyl8ePHl/76r/+6XduECRNKkyZNKpVKrnMqw532AtuwYUOWLFmS0aNHl9t69OiR0aNHZ9GiRRWsDLbPunXrkiQDBw5MkixZsiStra3trvFDDz00w4cPL1/jixYtypFHHpm6urrymLFjx6a5uTnLli3rxurhg02bNi3jx49vdz0nrnN2HT/+8Y9z3HHH5b/9t/+WwYMH59hjj82//Mu/lPtfffXVNDU1tbvW+/XrlxNOOKHdtd6/f/8cd9xx5TGjR49Ojx49snjx4u47GejEJz/5ycyfPz8vvvhikuTf//3f88tf/jLjxo1L4jqnMqorXQCd+4//+I9s2rSp3f/EJUldXV2ef/75ClUF26etrS3nnXdeTjrppBxxxBFJkqampvTq1Sv9+/dvN7auri5NTU3lMR39M7C5D4rg7rvvzlNPPZUnnnhiiz7XObuK3/zmN7n55pszffr0/N3f/V2eeOKJfO1rX0uvXr0yefLk8rXa0bX83mt98ODB7fqrq6szcOBA1zqF8M1vfjPNzc059NBDs8cee2TTpk256qqrMmnSpCRxnVMRQjvQLaZNm5Znnnkmv/zlLytdCnSp1157Leeee27mzZuX3r17V7oc2GHa2tpy3HHH5dvf/naS5Nhjj80zzzyTW265JZMnT65wddA1/vVf/zV33nln7rrrrhx++OFZunRpzjvvvAwdOtR1TsWYHl9g++67b/bYY48tVhheuXJl6uvrK1QVbLuzzz47DzzwQH7+859nv/32K7fX19dnw4YNWbt2bbvx773G6+vrO/xnYHMfVNqSJUuyatWq/PEf/3Gqq6tTXV2dBQsW5Prrr091dXXq6upc5+wShgwZkpEjR7ZrO+yww9LY2JjkP6/VD/r/lvr6+i0W0924cWNWr17tWqcQZsyYkW9+85s5/fTTc+SRR+bMM8/M+eefn1mzZiVxnVMZQnuB9erVK6NGjcr8+fPLbW1tbZk/f34aGhoqWBlsnVKplLPPPjv33XdfHn300YwYMaJd/6hRo9KzZ8921/gLL7yQxsbG8jXe0NCQp59+ut1//ObNm5fa2tot/ucRKuHUU0/N008/naVLl5Y/xx13XCZNmlT+s+ucXcFJJ520xWs7X3zxxRxwwAFJkhEjRqS+vr7dtd7c3JzFixe3u9bXrl2bJUuWlMc8+uijaWtrywknnNANZwEf7J133kmPHu0j0h577JG2trYkrnMqpNIr4fHB7r777lJNTU1p7ty5pWeffbZ01llnlfr3799uhWEoqqlTp5b69etXeuyxx0qvv/56+fPOO++Ux/zN3/xNafjw4aVHH3209OSTT5YaGhpKDQ0N5f6NGzeWjjjiiNKYMWNKS5cuLT300EOlQYMGlWbOnFmJU4Kt8t7V40sl1zm7hl/96lel6urq0lVXXVV66aWXSnfeeWdpzz33LP3gBz8oj7n66qtL/fv3L/3bv/1b6de//nXpC1/4QmnEiBGld999tzzms5/9bOnYY48tLV68uPTLX/6ydPDBB5fOOOOMSpwSbGHy5MmlYcOGlR544IHSq6++WvrhD39Y2nfffUsXXHBBeYzrnO4mtO8EbrjhhtLw4cNLvXr1Kh1//PGlxx9/vNIlwVZJ0uHntttuK4959913S3/7t39bGjBgQGnPPfcsnXbaaaXXX3+93X5++9vflsaNG1fq06dPad999y19/etfL7W2tnbz2cDWe39od52zq7j//vtLRxxxRKmmpqZ06KGHlv75n/+5XX9bW1vp4osvLtXV1ZVqampKp556aumFF15oN+b3v/996Ywzzijttddepdra2tJf/dVfld58883uPA3oVHNzc+ncc88tDR8+vNS7d+/SQQcdVPrWt77V7vWbrnO6W1WpVCpV8k4/AAAA0DHPtAMAAEBBCe0AAABQUEI7AAAAFJTQDgAAAAUltAMAAEBBCe0AAABQUEI7AAAAFJTQDgBUzG9/+9tUVVVl6dKllS4FAApJaAcAAICCEtoBgJ3Kpk2b0tbWVukyAKBbCO0AsAv59Kc/na997Wu54IILMnDgwNTX1+eyyy5L0vFU9LVr16aqqiqPPfZYkuSxxx5LVVVVfvazn+XYY49Nnz59csopp2TVqlX56U9/msMOOyy1tbX57//9v+edd97Zqpra2toye/bs/NEf/VFqamoyfPjwXHXVVe3G/OY3v8lnPvOZ7Lnnnjn66KOzaNGict/cuXPTv3///PjHP87IkSNTU1OTxsbGj/T3BAA7C6EdAHYxt99+e/r27ZvFixdn9uzZueKKKzJv3rxt2sdll12WG2+8MQsXLsxrr72WP//zP891112Xu+66Kz/5yU/y8MMP54Ybbtiqfc2cOTNXX311Lr744jz77LO56667UldX127Mt771rXzjG9/I0qVL8/GPfzxnnHFGNm7cWO5/55138p3vfCf/83/+zyxbtiyDBw/epvMBgJ1VdaULAAC61lFHHZVLL700SXLwwQfnxhtvzPz583PwwQdv9T7+/u//PieddFKSZMqUKZk5c2ZeeeWVHHTQQUmS//pf/2t+/vOf58ILL/zA/bz55pv53ve+lxtvvDGTJ09OknzsYx/Lpz71qXbjvvGNb2T8+PFJkssvvzyHH354Xn755Rx66KFJktbW1tx00005+uijt/ocAGBX4E47AOxijjrqqHbfhwwZklWrVm33Purq6rLnnnuWA/vmtq3Z53PPPZeWlpaceuqpW328IUOGJEm7/ffq1WuL8wKA3YHQDgC7mJ49e7b7XlVVlba2tvTo8Yf/7JdKpXJfa2vrh+6jqqqq031+mD59+mxzzVVVVUnSbv99+vQptwPA7kRoB4DdxKBBg5Ikr7/+erltR78f/eCDD06fPn0yf/78HXocANhVeaYdAHYTffr0yYknnpirr746I0aMyKpVq3LRRRft0GP27t07F154YS644IL06tUrJ510Ut54440sW7YsU6ZM2aHHBoBdgdAOALuR73//+5kyZUpGjRqVQw45JLNnz86YMWN26DEvvvjiVFdX55JLLsmKFSsyZMiQ/M3f/M0OPSYA7CqqSu99sA0AAAAoDM+0AwAAQEEJ7QDAdmtsbMxee+3V6aexsbHSJQLATs30eABgu23cuDG//e1vO+0/8MADU11tCR0A2F5COwAAABSU6fEAAABQUEI7AAAAFJTQDgAAAAUltAMAAEBBCe0AAABQUEI7AAAAFJTQDgAAAAUltAMAAEBB/X+x7bh9+KXQWQAAAABJRU5ErkJggg==\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "plt.figure(figsize=(12,6))\n",
+        "\n",
+        "sns.histplot(df[df['Label']==0]['num_chr'])\n",
+        "sns.histplot(df[df['Label']==1]['num_chr'],color='red')\n",
+        "plt.savefig('./../Image/spam-ham-num_chr.jpg')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 40,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 559
+        },
+        "id": "baQpBdLkKKXm",
+        "outputId": "a889b3e5-8047-431d-d693-9b9bf209c854"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<Axes: xlabel='num_words', ylabel='Count'>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 40
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1200x600 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "plt.figure(figsize=(12,6))\n",
+        "\n",
+        "sns.histplot(df[df['Label']==0]['num_words'])\n",
+        "sns.histplot(df[df['Label']==1]['num_words'],color='red')\n",
+        "plt.savefig('./../Image/spam-ham-num_word.jpg')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 41,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 559
+        },
+        "id": "kKL_XLITKKXm",
+        "outputId": "0082e901-fb55-4ced-ca13-8a987bb2e13f"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<Axes: xlabel='num_sent', ylabel='Count'>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 41
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1200x600 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "plt.figure(figsize=(12,6))\n",
+        "\n",
+        "sns.histplot(df[df['Label']==0]['num_sent'])\n",
+        "sns.histplot(df[df['Label']==1]['num_sent'],color='red')\n",
+        "plt.savefig('./../Image/spam-ham-num_sent.jpg')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Z3VsJOw5KKXm",
+        "outputId": "3f02acfa-e6c6-41ba-c3e6-4d9d97a8748f"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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HyOMPyI2rZlbu8DJraW6j+z9ekd+CoxHHI7cvgEsKFbUameNuBr5A8JCpXLVu+eARQohjFQiGCB1i7XJbucivcHjYVlzLe4t2U+3ycXafVE7tlkRajKG1h3bCCYZCuHyNX3TWuH0tOBpxPCmtcbOxwM5/F+/B6w9y6aB0hmTFk2LTt/bQTioSmDQDbyCIpoHAxCCpXEII0WQseg2dEk3sLHU0uH9AZmwLj+jIVTg8PDt3O/9bsieybdHOctrFGPjwxmFkxBlbcXQnHptBw+nZScxeV9jg/vP7t2vhEYnjQWmNm799vp65m0si237fUUZ2ioV3rhlMqk1uErSUVk/l2rt3L1deeSXx8fEYDAZ69+7NihUrIvtDoRAPPvggqampGAwGxo0bx/bt26Oeo6KigiuuuAKr1UpMTAzXXXcdtbW1Lf1WIrz+UINrTCSVSwghmk6iRccj5/eigcxZLujfjmSrruUHdYT2VrqigpLI9ioXry3YiccvKSVNyahVc8e4rpE1nwfq3c5K91RrK4xKtLbNhTVRQUmdLUU1fLO2QMpKt6BWDUwqKysZOXIkGo2G77//nk2bNvHMM88QG7v/LtdTTz3FCy+8wIwZM1i6dCkmk4nx48fjdrsjx1xxxRVs3LiRuXPnMnv2bBYsWMCNN97YGm8JCKdyNbTGRKVUoFMrJZVLCCGaSL+MGL6aPpLRXRIwaVV0iDfy5EW9+fvE7DZRXenLNXsb3ffZyr1UOGQBf1PLjDfyzW2jOK9fGmadmiSLjrvO6Mob0waRbJW0nZONxx/g/SW7G90/a2ku5XIetphWTeV68sknycjI4J133olsy8rKivw7FArx/PPP88ADD3DeeecB8N///pfk5GS+/PJLJk+ezObNm5kzZw7Lly9n0KBBALz44otMnDiRp59+mrS0tJZ9U9RV5WrgFh7hdC5J5RJCiKPn8QVQKBRo1UoMWjW902N4ecoAHF4/aqWCREvbubj0+BtvuOsPSjPe5qBWKemUaObxC3pTfZYPpUJBglnX4A3FlhIIBPEGg+jVKhSK1hvHySgUCuHzNz4j4g+GCCEzJi2lVWdMvv76awYNGsQll1xCUlIS/fv354033ojsz8nJoaioiHHjxkW22Ww2hg4dyuLFiwFYvHgxMTExkaAEYNy4cSiVSpYuXdrg63o8Hux2e9RXUwrPmDT8ozVqVNRKYCJOUs197okTW5HdzffrC/nT/1Zy6wer+G17KWU14VLBVoOGVJuhTQUlAOf2bfzm2fieKU3a80rOv2hGnZpUm4Fkq77VghKnx8/Wohoemr2JG/+7kpfn72BPuYOApA61GL1GzSWDMhrdf17fNGm82YJaNTDZtWsXr776Kl26dOGHH37g5ptv5s9//jPvvfceAEVFRQAkJydHPS45OTmyr6ioiKSkpKj9arWauLi4yDEHe/zxx7HZbJGvjIzGfyGPhtff8OJ3CFfmkjUm4mTV3OeeOHEVVbu4/t3l3DxzFb9sLeXHjcVMfWsZD3y5ntIazx8/wXEqK8HMqd0S62236tXceUZXjE3YIFLOv+OLxxdg3pYSJvzfAv67eA+/bS/j6R+3MfH/fmNL4ckdNLa0gZmx9Eqrv74oyaLj8qHt0ahafUn2SaNVf9LBYJABAwbw2GOP0b9/f2688UZuuOEGZsyY0ayve//991NdXR35ysvLa9Ln9/gllUuIhjT3uSdOTKFQiG/XF7GhoP7F2pyNxWwparsXcYkWHU9d1If/XNyH7BQL6bEGrh6Ryde3jaJDvKlJX0vOv+NLSY2Hv36ytl7Ja4c3wF2frKW8tu0G3G1Nik3Pm1cN4sFzetAp0Uz7OCPTT+vM57eMID1WKuO1pFZdY5KamkqPHj2itnXv3p3PPvsMgJSUFACKi4tJTU2NHFNcXEy/fv0ix5SURFdS8Pv9VFRURB5/MJ1Oh07XfNVafIEgek3D0+96jYoaWfwuTlLNfe6JE1NZrYdZS+tXrqrz38W7GdIhDp2mfqWltiDJqueSQRmcnp1EIBgixqhFq276+4Zy/h1fdpXVNrrGaEtRDZVOH/Fm+f/VUlJsBq4Z0YFz+6YRDIWIM2pRy0xJi2vVn/jIkSPZunVr1LZt27aRmZkJhBfCp6SkMG/evMh+u93O0qVLGT58OADDhw+nqqqKlStXRo75+eefCQaDDB06tAXeRX3eQKjRGZNwYCINnIQQ4nCFOPQicY8vyImQkh9v1pFk1TdLUCKOP95D/E4Dss6kFSj2FUJIsuglKGklR/xTD4VC5ObmRpXrPVp33HEHS5Ys4bHHHmPHjh3MmjWL119/nenTpwPhX5Dbb7+dRx55hK+//pr169czbdo00tLSOP/884HwDMuECRO44YYbWLZsGQsXLuTWW29l8uTJrVKRC8Dnb7hcMIR7mTg8UpdeCCEOV6xRw9l9Uhvdf8mgjEgDWyHaii5JlgZ78AC0izEQY2y6wgdCtBVHFZh07ty5SXJTBw8ezBdffMEHH3xAr169ePjhh3n++ee54oorIsfcc8893Hbbbdx4440MHjyY2tpa5syZg16/v/LKzJkzyc7OZuzYsUycOJFRo0bx+uuvH/P4jpYvEGx0oZRRK1W5hBDiSGhUKq4clkliA2kt2SkWBneIa4VRCXFsEsxabju9S73tCgU8dkEv6akiTkqKUOjgZVd/rGfPnrz11lsMGzasOcbU4ux2OzabjerqaqzWY+/6OvrJnxmQGcvkwe3r7fti9V5+2lTEqgfPPObXEaKta+pzT5zY8iucvL9kD9+sLUCjVjJlSHvO7ZdGqs3Q2kNrk+T8a32VDi+rcit58ecd7K1y0Sfdxu3jutIp0dSkFdmEaCuO6rf+iSee4O677+bVV1+lV69eTT2mNs97qAaLGhUOr6RyCSHEkUqPM/LX8V25dlQWSgXEm3QoW7EpnhDHKtakZWz3ZAZmxuLxBzFp1Zj1EpCIk9dR/fZPmzYNp9NJ37590Wq1GAzRd6sqKiqaZHBtlc8fRN1Ig0WDVoXHH8TrD8oCRyGEOEIalYpkq6wnESeWGGngJwRwlIHJ888/38TDOLH4AiHUjTRYNO1boGl3+0iQMoBCCCGEEEIARxmYXHXVVU09jhOKL9D4jIlJF/6RV7skMBFCCCGEEKLOUScyBoNBduzYQUlJCcFgdC3uMWPGHPPA2rJDzZgY62ZMXNLLRAghhBBCiDpHFZgsWbKEKVOmsGfPHg4u6qVQKAgETt7F3YFgiECo8QaL5gNmTIQQQgghhBBhRxWY3HTTTQwaNIhvv/2W1NRUFAqpilLHFwjPHjXWMdQkgYkQQgghhBD1HFVgsn37dj799FM6d+7c1ONp87x1gUkjMyY6tRKlAuxuabIohBBCCCFEnaOqVzt06FB27NjR1GM5Ifj8dTMmDQcmCoUCs04ta0yEEEIIIYQ4wGHPmKxbty7y79tuu4277rqLoqIievfujUajiTq2T58+TTfCNsYXCK+5aWzGBMLrTCSVSwghhBBCiP0OOzDp168fCoUiarH7tddeG/l33b6TffF7ZI1JI+WCIbzORGZMhBBCCCGE2O+wA5OcnJzmHMcJI7LGpJFULgh3f5cZEyGEEEIIIfY77MAkMzOzOcdxwjisGROtmiqnBCZCCCGEEELUOarF748//jhvv/12ve1vv/02Tz755DEPqi3z+fetMTnEjIlJp8LulsBECCGEEEKIOkcVmLz22mtkZ2fX296zZ09mzJhxzINqy/6oXDCAUSuL34UQQgghhDjQUfUxKSoqIjU1td72xMRECgsLj3lQbZksfhdCnGicHj9Fdjc/bymhrNbDqV2T6JhoIsmqb+2hiRNQrdtHsd3D3E3FVLt8nJadSFaCiUSL/L4JcaI7qsAkIyODhQsXkpWVFbV94cKFpKWlNcnA2irfYSx+N+vU1Lj9BIIhVIeYWRFCiNbm9Pj5bkMRd3+6lrqijDN+3UWvNCtvXjWIFJuhdQcoTig1bh+fr9rLP7/eGNn26q87GZQZy0tTBpBik+BEiBPZUaVy3XDDDdx+++2888477Nmzhz179vD2229zxx13cMMNNzT1GNsU32Gkcln0akJAldPbQqMSQoijU2R3RwUldTYU2Hnr9xy8/pO3PLxoeoXV7qigpM6KPZV8tDyXwL7PWCHEiemoZkzuvvtuysvLueWWW/B6wxfXer2ee++9l/vvv79JB9jWeOsWvx8ilcuqDzekrHB4iTfrWmRcQghxNH7aXFwvKKkzc2ku147MIjVGZk1E0/h6zd5G9723eA+XDW4vsyZCnMCOKjBRKBQ8+eST/OMf/2Dz5s0YDAa6dOmCThd9kZ2fn09aWhrKQ1ykn2gOJ5XLqg//2MsdXrq0yKiEECJarduP3e1DAcSbdWjVDf+dLqvxNPocTm+AQGNRixANCARDlNV4CIRCGDQqYk3aqP0lh/h9q3b5opo8CyFOPEcVmNQxm80MHjy40f09evRgzZo1dOzY8Vhepk3x+g8jlcsQnjEpr5VULiFEy/IHguSUOfjPD1uZt6UErUrJxYPSuXpEB2pcPuLMOhLMWoza8MfDqd2SeP23hhvs9m8fg0l7TB8j4iRSYnfz+ap83vgthwqnl34ZMfx9Yne6p1ox6cK/R2f2TOHjFfkNPn5Ep3iMOvl9E+JE1qxTGSfjnY26GZNDLWo3aVWolAoqHI3fGRJCiOaQW+Hk3JcW8uOmYgLBEC5fgPcX7+Gad5aTX+Xi9Kd/YcavuyJ/nzonmemZZq33PEoF/GNSj3p3vIVoSIXDw9++WM8Tc7ZS7vASCsHq3CoueW0xq3IrI8f1TLXSKdFU7/FqpYJ7JnTDtu/GnhDixHTy5Fi1EF8giFqpQKFoPDBRKBRY9WrKHTJjIoRoOU6vn5d+3oHLV3/Bem6FkxK7h8x4Ey/M285v28sASLLqefOqQVw7sgMGjQqAfhk2Pr1pBJ2TzASCJ98NKHHkCqvd/LS5pN72UAj++dVGSmrcAASCQf5xdg8uGZiObl964cDMWF69cgAOTwCvP4jb52/RsQshWo7MiTYxbyB0yPUldax6DRUSmAghWpDd5eOXbaWN7l+0s4x+GTZ2ltby/E/bGdEpnkSLHr1axeVD2nN2nzSsBjU6tYpv1xXw7Nxt9EizctngDDJiDWjVqhZ8N6ItWbmnstF9u8oc1Lr9JFngp80lPPrdZib0TOE/F/dBrVKytchOsd2D0xvgzx+swu0LcuHAdIZ0iGv2hfAur5/SWg+VDh96jYp4s5YEKVojRLORwKSJhWdM/ngiyiIzJkKIFqZSKjDr1I3eFDHrNJHZlD3lDgLBEHkVTu74eA0rdocvLJUKOLtPGmf0SGbxrm38vqOMt3/P4d1rhjCiUzxK6c0kGnCoFCylYn/BmI0FdnyBEN+sK+SbdeGGzfeflc1v28v4YWNR5DG/bCula7KZ964dQmoz9dIpr/Xw5u85vPVbDt59adrdki28csUAOiWZm+U1hTjZNWsq16HSmU5UPn/wsGZMLHqNLH4XQrSoBLOOa0d2aHT/GT2SWbAtnMLVLyOGQDDEje+viAQlAMEQfL22gAXbS7mgfzsA/MEQf/lwNcV2d7OOX7Rd/TNiGy0Kc2aPFOL3rVUakBkbtS/WqCHZqo8KSupsK67l0xX5zdLbJBAM8cXqvbz6y85IUAKwtbiGy99YQmGVq8lfUwghi9+bXN0akz9i0aspr5XF70KIlqNQKDirdyrDO8bV2zd1WCabCu10SDDy8pQBTD+1MwVVbjYX1jT4XF+tLuCMHsmR78sdXspkFlg0Ismq44XL+3Pwx2N6rIF7JnRjdW4VX67ZS590G/eM74Zm3w2+0V0SmbupuNHn/WBZbrP83pXY3bw8f0fD+2o8bCtu+LwQQhybZk3l2rRpE2lpac35EscdbyB0WKlcVoOGCun8LoRoYclWPS9c3p9dZQ6+XVeIXqNiWMd4luwq5/ftpdx2Whfu/nQtWrWSv4zt2ujzeAPBegvfA0Hpyi0apteoOK1bIvPuOoU5G4rJr3QyuksiHRNMXPvucnaXOyPHntkjmZenDOBP/1uJSqnA3UCxhjq+QKjRBqDHwu0PUun0Nbp/S1ENp3RLavoXFuIkd1SBidvt5sUXX2T+/PmUlJQQPOjDaNWqVQBkZGQc+wjbGF/g8FK5rHoNlQ4vgWDokKWFhRCiqSVa9CRa9AzNigfCd4fTYvRcMjCd815eiNMbQKtSkmhpfJGvWqmImh02alWyKFgckkGrJivBzM2nhtdn7K10cvozv+LxR19D/LipmO6pVubfdQpuX5Dd5Q5+bGTWZGLvFOKaoWS1Tq3EZtBQ7Wo4OOkia0yEaBZHFZhcd911/Pjjj1x88cUMGTLkpFxL0pjDTeWKNWkIhsKL65KszVtVRAghDiXJqifJquf79YU4veG7095AkLJaD50SzewsrQXCwcd5/dIYmBlLnFFLTpkDhSJc8vXBc3qQdIhARoiD/b6jrF5QUuedRTlMHpJBhwQzsSYtfdpZWbfXHnVMnEnLdaOy0KqPLivd4fFjd/tQoiDBrEWl2v88yRYdfxrTkad+2FrvcfEmLdmp9Xv7CCGO3VEFJrNnz+a7775j5MiRTT2eNu9wZ0xijeE7PCU1EpgIIY4P+ZXRC3qf/2kbz17ajyfnbEGhgHvGZzNrWS7//GojBq2Kc/umMev6oWjUSromWaRcsDgiuRX7f98sOjUGrYryfZkEdpcffyCco5Vs1fP6tEF8t76I95fswe0LMLF3KleN6ED7OOMRv64/EGR3uZPn5m5j/tYSTFo1Vwxrz2WDMyIVvlQqJZcOSqfQ7mbW0txI2mJmvJHXpw4iLaZ5KoEJcbI7qsCkXbt2WCyWph7LCcHrD6E6jDUmdYFJsd1Nr3a25h6WEOIEUO3yUbZv4a1ZryYrwUSyRY/mKO8YH6xPevTforJaL3d8tIbpp3VmWMc4LpmxGMe+GRWHN8DbC3ezaEc57147BKt05BZHaFCHWPqm27hxTCfc/gB2l4/MeBOrciuZs6EIvWZ/oJtiM3DNyA6c0zeNYChErFFz1IHw7nIn57z4e6Q0ttMb4PmftjN3UzFvXz2Y5H03CxMseu4d343rR2VRVuvFoFWRYNLKzUQhmtFRBSbPPPMM9957LzNmzCAzM7Opx9SmHW4ql82gQUF4xkQIIf5IWa2HJ7/fwicr8yPbTFoVr00dyJCsuCaZreiQYIpK3YJwta2txTWszquMBCUH2lJcw/q91c3e6E6ceHqmWrj51E7c8+k67O793dzP7JHMS5f3r7fGSaFQHHLd0+FwePw8N3dbJCg50MYCOxsL7JHABMCs12DWa8iMNx3T6wohDs9R3WYbNGgQbrebjh07YrFYiIuLi/o6Wk888QQKhYLbb789ss3tdjN9+nTi4+Mxm81cdNFFFBdHL4LLzc1l0qRJGI1GkpKSuPvuu/H7/bSGww1MVEoFNqOGErsEJkKIQwuFQsxeVxAVlEB41uKad5dTUN00/UOSrXr+e+1gzuyRHCnrGmfSctngDH7Z0njH+C9X5xMMnnzl4cWx8fhD/OXDNVFBCYQXv/+4qbhZ+pPY3T5+3lLS6P6v1+xt8tcUQhy+o5oxufzyy9m7dy+PPfYYycnJTbL4ffny5bz22mv06dMnavsdd9zBt99+yyeffILNZuPWW2/lwgsvZOHChQAEAgEmTZpESkoKixYtorCwkGnTpqHRaHjssceOeVxHyhcIHlYqF4TTuYprpCGZEOLQSmo8vDJ/Z4P7fIEQ87eUcM3IrCZ5rXaxRp69tB/lDg8efxCzTo1SocCoU1HjafiGj0WvkY7v4ogt2VXe6OL3t37P4ZJB6U3e1V2hUGDSqRqcMYFwxUwhROs5qsBk0aJFLF68mL59+zbJIGpra7niiit44403eOSRRyLbq6ureeutt5g1axann346AO+88w7du3dnyZIlDBs2jB9//JFNmzbx008/kZycTL9+/Xj44Ye59957+de//oVW2/RlBA/Fe5id3wFiDBpKpFOyEOIPBIKhQ6Z97iypbXTf0TDr1Zj1+z8eAsEQVw7N5Jm52xo8fsqQ9k36+uLksLvM2ei+apcvsvi9KSWYtEwZ0p4Xfm64eeIlg9Kb/DWFEIfvqFK5srOzcblcf3zgYZo+fTqTJk1i3LhxUdtXrlyJz+eL2p6dnU379u1ZvHgxAIsXL6Z3794kJ+/vQDx+/HjsdjsbN25s8PU8Hg92uz3qq6mEGyweZmBi1FAkqVziJNKc596JTKdW0j218YIjwzrF19sWCIaodfvxHnBH2unxk1vuYEuRnfwKZ9S+Q1EpFVwyKJ3e7eqXSL1hdBYZR1EZSbS84+38G9ghptF9mfFGdJr6lygunx+Hp/HGh39ErVJy+dD29GjgfLr5lI7yuyxEKzuqGZMnnniCu+66i0cffZTevXuj0URPfVqth1/f+8MPP2TVqlUsX7683r6ioiK0Wi0xMTFR25OTkykqKoocc2BQUre/bl9DHn/8cR566KHDHuOR8AWCmLSHtwg11qhlY4FcmImTR3OeeyeyeLOO+8/qzrS3l9Xbl2jRMaB9bOT7YDBEfpWTL1btZVVuFZN6p9AvIxaFAnaU1vLU91vIKXdi0Ki4dlQHrhnRgQTLHy9cT7EZePOqwWwqsPPF6nyseg2XDs4gI84YqTIojm/H2/nXPdVKVoKJwR3iOC07kVAonLY4c8ke7jijK0kH/F6W1XjYUFDNe4t34/YGuXBAO0Z1TiD1KMr2ptoMvH31EDYWVPPVmr3YDFouHZRORqyRGPldFqJVHVVgMmHCBADGjh0btT0UCqFQKAgEGs7dPFheXh5/+ctfmDt3Lnp9y1V0uf/++7nzzjsj39vt9ibrUu/1B7EdZtnMWJOW8lov/kAQtappyn0KcTxrznPvRNcvI4YXJvfj4dmbKa0Nz7QO7hDLkxf1ieqpsL2klotfXYTNqOGxC3rz7qLd3Pf5eoIh6JFq5e4J2XyyIp/5W0t4ef5O/IEQd5zRNao0a2OSrXqSrXpO7ZYojXXboOPt/FMp4Y1pA3lh3nb+/MFqfIEQWQkm7hnfjQ4J+2cuymo8/OOrDXy/Yf/NxsW7yumYYOJ/1w+t11OkuNpNpcuLgvANwIbK+6bY9KTY9JyenSS/y0IcR44qMJk/f36TvPjKlSspKSlhwIABkW2BQIAFCxbw0ksv8cMPP+D1eqmqqoqaNSkuLiYlJQWAlJQUli2LvotYV7Wr7piD6XQ6dLrm6VB8uFW5IFztJhAKUVbrlVKb4qTQnOfeic5q0HB2nzQGZ8Vhd/nQqJTEGjWYdPtvhFQ6vdz32TpqPH6evrQvd3+6luID0kU3Fdq57YPVvD51IKtyK6l2+Xh30W6uHJZ5RCksciHXNh1v519ZrY8/f7CaHQeskcopc3DzzFW8MLkfSRY98WYd20tq+H5DETFGDaM7J6BWKVm5p5JdZQ4+Xp7Hbad3RqVS4vYFWLmnkns+XcfeqnC6eUacgWcu6Uu/jJgGS2rL77IQx5ejCkxOOeWUJnnxsWPHsn79+qht11xzDdnZ2dx7771kZGSg0WiYN28eF110EQBbt24lNzeX4cOHAzB8+HAeffRRSkpKSEpKAmDu3LlYrVZ69OjRJOM8EkcSmMSbwlPGhdUuCUyEEH9IqVSQajNg0qrJq3Ty5g85FNe4OaNHMqM7JxAIhlidV0XXZDO7yxxRQUmdQDDEe4t2c9eZXXl5/g6K7R7s7qPP2RfiaO2tdEUFJQd6af4OBmTG4g8Gmbl0D/dO6Ea7GANzNxXj8Pi5YXRHTDoVr/6yk8uHtifZqmdPuZNpby+LdGkHyKtwccWbS5nzlzF0SjK31FsTQhylowpMFixYcMj9Y8aMOaznsVgs9OrVK2qbyWQiPj4+sv26667jzjvvJC4uDqvVym233cbw4cMZNmwYAGeeeSY9evRg6tSpPPXUUxQVFfHAAw8wffr0Vrkz5D2CtKx4U3h8RU3Ug0AIceKrdfv4dGU+/569KbJt3uYSkiw6Zl4/FJNWRXaKldV5VY0+x6rcKq4fncWdZ3RDrVRg1h3VR4EQx2RVbmWj+7YV11JU7WbprgrO7JHC3E3FPDlna2T/j5uK6Z5q4f6J2RACl9fPa7/ujApK6vgCId5bvJsHJnVvkkakQojmc1SfRqeeemq9bQdOhx7uGpPD8dxzz6FUKrnooovweDyMHz+eV155JbJfpVIxe/Zsbr75ZoYPH47JZOKqq67i3//+d5ON4Uj4jqAql0mnQqdWUiiBiRDiMJXUeKKCkgO3PzN3KzeO6cjqvCraHWJRcLxZy/aSWh6evZnOSWbeyRrcnEMWokF1WQMNMWlV7K1y8eZvu7jvrGy+WVdY75jNhTWszatiZKcEaj1+1u2tbvT51uRV4fQGJDAR4jh3VCuuKysro75KSkqYM2cOgwcP5scffzymAf3yyy88//zzke/1ej0vv/wyFRUVOBwOPv/883prRzIzM/nuu+9wOp2Ulpby9NNPo1a3zh1Arz+I6jADE4VCQbxJS5H0MhFCHKbfd5Q1um/uphLG9UhmeU4Fp2UnNXrcJQMz+GpNAQA7SmqZ8ctO3I00nBOiuQzMjG30Rt4FA9L5dl0hnZPMfLYqv9Hn+GzVXqpdPvQaFe0PsU4qM950WAUehBCt66gCE5vNFvWVkJDAGWecwZNPPsk999zT1GNsU3xHWGEr1qSVGRMhxGFzehsPIALBEB5/kFeuGEB+pZP7z8rm4Ou+sd2TSLHpWJe//+7yp6vyKXd4m2vIQjRIpVTw6AW90RzUlHhA+1hGd0lg3pYSNGolLl/j/XbcvgDBEJh0am45tVOjx904OksCEyHagCadVkhOTmbr1q1/fOAJzBcI1vsjeyhxRi2FVU3XrFIIcWIb3TmBJxrZ1799DMkWHW/8uguTXs3p2Ul8evMI1uRW4fIF6JpsZktRDfd9Fl10xOMPEmwgN1+I5pRi1fPx8jxmXDmQnDIHtR4//TJiyClz8JcPVxMIhlixu5IbRndk7qbiBp9jQs8UYk3hynRdky08cn4v/v3NJryBcDCjUyt59ILedEgwtdj7EkIcvaMKTNatWxf1fSgUorCwkCeeeIJ+/fo1xbjaLP8RrDEBiDNrWb67ohlHJIQ4kaTG6JnYO4Xv1kc3kNWoFDx0bk/axRp58JweFNvdbC2uAaBTkgmzTs2UN5biaaDbe+92tsNuDCvEsXB4/JQ7vHj9AUxaNbeP68LLv+xk8Y5ykm06OsQbeeib/WuociucmPVquqda2FxYE/VcNoOGG8Z0RLdv3YjVoOGigemc0jWRPeUOFAoF7eONJJp1MlsiRBtxVIFJv379UCgUhELRd9iGDRvG22+/3SQDa4uCwRD+YAi18vBTueJMWkrsHoLBEMojCGiEECenOFO4C/zAzDhmLd1DhcPLkKw4pp/WOdKLJDXGQIpNT6rNgDcQRKNWolKEA5AVe6IrISkV8M9zehBnPn76W4gTU0GVi8e/28x3G4oIBEMkmLXcMyGbW0/rzA2jOxIIhlAooGuymW3F+8sI//OrDTxzSV+2FNXw4fI83L4AZ/VK4YYxHWkfZ6TCES6LHWPQYtCoyIgzHlFfHiHE8eOoApOcnJyo75VKJYmJiS3avf145AuG70SqjyCVK96kwx8MUVbrabA7rRDi5OL0+Cmr9eDyBdCqlKiUCvzBEDFGLXEmLcV2Nze9v4K7J2Tz7/N6UVbrIafMwS9bSjivfzs0KgUGjRqVUkGyTY/XH8QbCGDUqHl5ygDeX7KH/y7ejd3tZ2BmLP+Y1J1uqdbWftviBFda4+b695aTW+HinD5pWA1qthfXcs+n63j20r5c0L9dpLrn/64bQmG1G6VSiS8QRKdSYtWrGNM1kcsGZxAMQaxJQ6XDx6ylucxalos/EOKige04u09avU7wQoi246gCk8zMTObNm8e8efMoKSkhGIxODThZZ018gfAM0hGlckWaLLolMBHiJFdY7eKpOVv4Zm0h/mAIo1bF5UPak5Vg4uMVefzn4r44vT6uGpnFC/N2sK24hmEd4xjTNZHMeBPPzd1OYbWLwR3iuHBAO7z+IDN+3UmR3c3oLomc0zeNv4ztzJXD2hMIgSIUwh+C8loPCZLuIppRboWL4Z0SuG1sLLPXFrKtuIZ+GTH86ZSOzPh1ByM6xZNiCwcUlU4fD32zOdLnJMmi428TuzOqszryOVlUHQ50NhTYI6/x2HdbmLk0lw9uGHbcBif+QJCSGg8efxC9RkmyRS/ZEkIc4KgCk4ceeoh///vfDBo0iNTU1KgeJicz377cbdURpHLFm/d3f++bEdMcwxJCHKcc+2ZHHB4/Zr2G+z5bx6Kd5ZH9Tm+At37P4dqRHciINXLpa4v53/VD8AeCnNI1getHZ/Hr1hJW51Zi0KgYkhXHw7M3sTSngrd+z+GFy/uzYk8l+ZUuluyq4LVfd/LZzSNItupZmlPBw7M3kVvhRKdWctGAdG4b25lU2/F5QSeOb7UeH2U1XpzeAGa9mkSzFoN2/yVGldOLUavi5v+timxbsquCWctyeWFy/0i56pyyWqa8sTSqSlxJjYfbP1rDe9cM5pRu+n2PLY8KSursKXfyzdoCbhjd8ZAX/MV2Nxv3VvP12gJsBg0XD0wnI85IjLHx3irHqrTGzQfL8njzt13Y3X7iTVpuG9uZc/qkES+plEIARxmYzJgxg3fffZepU6c29XjatLoqIEeSymXRqdGqpMmiECebomo3T87ZzNdrw43jXp86MCooOdCsZbk8e2k/vl1fyPKcSrISjazYU8lzP22PHPPF6gIGtI/hPxf34dYPVuPyBXjmx61MG96Bx77bDIDd7eeNBbsY2z2JPx1wgejxB5m1LJeNBdW8cdUgkiwyeysOX2G1i0e/3cx36wsJhkCrUnL50Aymn9Y58ruUYtPz0n931Hus3eVnxq87efrivkA44GisdPXTP26jc5IFm1HNR8vzGh3PJyvzuXhgeqMX+4XVLq57dzmbDlhM/97iPdxyaiduHNOxWYKTGrePZ37cxocHjLvc4eVfX2+i0uHjltM6RRbxC3EyO6o+Jl6vlxEjRjT1WNo8z75a69oj6GOiUCiIN0svEyFOJtUuLw9+tYEvVhcQCIaw6tUUHeJvgPuAPg5Lc8qJNWr5fNXeesetyq1iR2kt/ffNvm4pqiEzPnoRcO/0GB7ZF6gcbG1+NXkVUr5cHL5Kh5e7P1nH7HXhoATCN+neW7SHF+ftwOn1A7A+v5pQIxWpl+yqwL/vwct3VzZ8ELCxoBpfIIgCBYdK1FAdYmcgEOTDZXlRQUmdV37ZSX5l8/z+l9d6+WhFw8HUjF93UmL3NMvrCtHWHFVgcv311zNr1qymHkub5w2Ep6KPZMYEwutMJDAR4uRRXuvlxwP6Mji9gUPepVUoQLPvhkdGrJGF2xvv/v7VmgLG90qJfB886Gow3qw9ZPCxOrfxC0MhDlZW6+H3HQ3/Pn64PJeymvAFty/QeJNECAczRXbXIbu316UZmnRqpg7LbPS4y4dkRNZvHqzU4WXm0j2NPvaTRoKHY1VU7W40MPP4g1Q5fc3yukK0NUeVyuV2u3n99df56aef6NOnDxqNJmr/s88+2ySDa2vq+gNojmDGBMJNFgukyaIQJ42Kg1JVPP4gDq+fdjEG9jbwt+DUrkks2VWOUgETe6dSUuPmlSsG4PUH+WZdAfO3lETuVjs8/sgi9vZxRipqo19Lr1GiVSkjqacHS7JIrrs4fMX2xm+q+QIh7O7wjEm/Q6yh7N3Oxk+binl67lZm3zqKl37eEZlBOdB1o7JI2ff7OSAzlqFZcSzNie4Dlp1iYXzPlMbXvoZCODyBRsdS7WqeAMGsP/Tlll5zVPeJhTjhHNWZsG7dOvr164dSqWTDhg2sXr068rVmzZomHmLbcdSBiVkCEyFOJla9pt62F3/ezmMX9CLloOp8fdJt3H9WNmk2Pa9cMQCn188j327mlpmr+MeXG2gfZ+SFyf3R7JupHdM1kVV7KlEpFdx1ZlfeW7w78lxalZKMWCMXDmjX4Lh0aiX92sc02fsUJ77YRmYmIDzTZ9SqqHH7CIZCXDowvd4xSWYdj5zfi+6pVl66fAAef4BXrxyI4aAKcRcNaMcZPZLR68IX+MlWPS9c3p+XpwxgWMc4BmXG8vTFfXjnmsGkHqIil0Wv4bTsxEb3n9uv4XPjWCWadbRrZFy92lkjhXCEONkd1YzJ/Pnzm3ocJwRvXWByhKX/4k1aSms8BIIhVFI2UIgTXpxZS880KxsPqCqUV+HiH19t5N/n9cSqV5Nf5cZmUJNf6eLiGYvIiDPy6Pm9uPez9ZE8+BqPn3cW7mZ3mZNbT+vMW7/ncOGAdny0LI+vp4/E4fVj0qpJjzUwNCuOm07pRGaCkb+M7cLGgmrW793/+jq1kjevGlQvMBLiUBItOjolmtlZWltv36ldE1mdW8UHy3IZmBnLlcMzObVbEm/8tovSWg9ndE/mssEZPPj1Rpbtm/mIMWp4+NwefHXrSHaXOahx+8lOtbCn3ElZrYdatw+XP0iSRUeiWcekPqmc0jWRECEsDQT8BzPp1Nx5RjfmbynF5YueOemRaqFnM/X0SbbpefOqQUx5YwmVB6Rtpdr0vHh5f+JMMlMpBIAidHD79pOQ3W7HZrNRXV2N1Xr0f5QWbCtl2tvLeGFyfxKPIB1i5Z5Knv5xK8v+NlZ6mYiTSlOde23RnnIH17+3gu0l+y/oJvRK4f4J2ZTUuLntgzVUOr2RmVgAq0HN85f159p3l9d7vg9uGIpBoyLerCXepMO4785yldOLLxDErFdj0Oy/F1Va42ZPhZNVeypJserp3z6WZKsOrVQGOmk01fm3u8zBNe8uJ6fMEdnWPyOGP4/twq2zVuHwhgMAo1bFZzePQK1UEAyBRqXgyjeXUtDAGsvnL+tHWa2H0ho3mXEmTHo136wtoGuyhVd+2Yleo+T+s7pzXr+0I66i5Q8E2V3u5Lm52/h5SwkmnYorhmYyeUhGs5bLDoVCFFS52VxkZ2dJLd1TrXRJNkuJbiEOcFQzJqJhkRmTI1z8XjeFWyBNFoU4odR6fFQ4vHj9Qcw6NclWfST3PTPexP+uH0pJjZsSu4eMWAPVLj/bS2p447ccihrI3be7/KzLr6Jvuo21+dVR+/aUO3l3UQ5vXz0kEpQAjV60JVr0JFr0DMqMa8J3LE4GXn+A0hoPTm8Ag1ZFWoyej24cRpHdTWmNhySLnqW7yvnzB6sjQYlWpeSpi/rw1Zq9fLg8D71axV1ndm0wKAF4du42rhuVxWsLcgDonGTmbxO74/SE16y4fUH++fVGOiWaGNWl8dSs6HEHUSpArVLSOcnMUxf3we72oVAoSDBpUR9hGvaRUigUtIs10C7WwLjuyc36WkK0VRKYNKGjXmOyL0e3qNoF0mRRiBNCfqWTR2Zv5sdNRQRDkGLV88DZ3RnTJRGH18+yXRV8tbaAOJOGG0d3ZEVuJalWA+UOH5saaBxXZ3OhnQ4JpnqBiVmv5sweKSRIrrpoRqU1bt5btJu3F+7G6Q1g1Kq4ekQHrhnZgT7pMQD8tKmoXknqO8/sygfLc1m4I9yrZ0iWpcEGiXVyK5xRmQc7Smr5ZWsJ5/dL45SuiazLr6LSGe4N0qud7ZCzJsV2N2vyqvhkRR56jYorh2bSOdlMglmHSSeXQUIcT+SMbEJ15YKPNDCpa7JYUCUlg4Voq2rdfsodHty+IHqNkqvfiU5tKbK7uXXWal6bOpAPl+cxf0tJZN+YLol8t76QXmk2shJM/H1Sd7RqJXvKnXy8Ii+qnHiqzVAvn9+qV5No1jF4WBxatYqCKhdr86tYuL2MjDgjZ/ZMIdWmj1TrEuJoODx+np+7nZnLciPbnN4Ar/yykyqnj79NzMas1+ALhNCplVwyMJ3RXRMJBkMkmHU88f2WyOPKa72k2hrPEIgxanB5968BUSqgwuHBoFVz/agstGolO0treXHe9kjX+PDzesitcDJ7XQEKhYIpQ9rzlw9XR62nmr2ukPP6pvGPc3qQIB3XhTiuSGDShOpSuY60j0ldk8WGUjeEEMe/vVUu/v3NRuZuKibOpOX+s7pHBSUHeuL7LUwbnhkVmKiUSnz+EEOz4tlT4eT9xbvJr3TRPdXK3yZ2Z+GOMj5cnodSAWO7J/H+kv19GAwaFW9MG0SXZBNxJj17yh1c/vqSqBSZp37YyowrBzKmSwI6CU7EUSqr9fDB8twG9324PJcbT+mIWa+ha4qZV64YwKcr87ll5io6JZqY2Ds16vidpbV0TjKj1yijGojWmTw4gy9Wh5uIxho1PHNpP37dVsoVby7F7QtwardErhrRgecn9490TC+t8fDQNxuZva4QgMEdYtFrVFFBSZ2v1hYweUiGBCZCHGckMGlCHn8QtVKB8lAtaRsRZ5KSwUK0RaU1bq57dzlbisKdpDPjTWwqbDxFJafMQfJBa8k0KgW92ln5bXspby/cHdm+Jq+K2z5Yzb/P68mwrFimjsgi2aLj9akDWZZTQZJVR3aKldIaD+1iDWhUPh76emO9vP1AMMT0mauYd9cpZByigZ0Qh1Lp9NJAexEAgiGocngh3kQwCC/9vIPVeVUAuHwBLPv6eMQYNZzZIwWbQcOiHWU8e2k/7vt8HXaXP/JcE3ql0DXZwmsLdgHw0Lk9efTbzVEzhd+tL+K3bWXMvGEoIcKDWrG7IhKUAIzrnswHyxoOpAD+u3gPAzPj0Kqlh4gQxwsJTJqQ1x884jSuOtJkUYi2KbfCFQlKINygLfEQd2HNOnVkdrXO7nIHp2cnc+VbSxt8zAvztvPRjcP5aFkuVU4vC7aVck7fNDQqJS/9vJ3e6TFYDRqyEozM31ba4HN4A0E2FlRLYCKO2sG9Rert14YvKapdvkhQEmPUoFIo6JhgYvqpneieauWrtQVsL66hf/sYkiw63pg6CF8giMMbIDPOyOJd5dz96TpCIeiYYKLc4W2wHHGNx887v+/m7gndqHb5ePP3nKj9GpWy3rl2IJcvQFAKkwpxXJHApAl5/MEjrshVJ96sZXtO/T+8Qojj2+aDZkd2ldYyrGMcb04bhMcfRKtWsjq3kv8u3kOtx8+Uoe1pF6Onf0ZM5OJtzoZCMuNNNHaNVFbrZU+Fkx83F3OZOYMfNhbzw8Zi+mXE8MCk7jz67Wb6ptvwB2n0OaD5ulqLk0O8WUf3VAubC2vq7euWbMGkVZFf6UStVDChZzJn90nD7Q9QYvegVas4pWsiK3MruXRQBl5/ELVKwQfLcrlqRAcSdFo6JVoAMGhVlNd6+XRlPkOz4li4o6zRMS3YXspfxnVGp1ZS447+/V6aU8Gp3ZKY1cisycUD02XdlRDHGZm/bEKeY5gxiTfrKLG78Qcav7sjhDj+tIvd34NAqYA3pg6ixu3HFwjfqFi+u4LtJTW8NKU/Z3RPol9GDNe+u5zrR3dkQPtYALYW1/7h3Wh/IMiITgmsydtfjWtNXhU5ZQ5qPH6yU6wYtSrSYxvvidB3X9UkIY5GglnHK1cMqPc71i7GwH8u6cP1769g1JPz+fOHazizZwp7Kpz89ZN1PPXDVu78eA0hQImCYCiEQgEub4A+GTZySh1R/XMy403cMCaLD24Yyl/GdsF8iMaJRp0KnUaF1aDhjINK8P60uZgJvVIilS8P1C3FzKDM2GP7gQghmpzMmDShY0nlSjBrCYaguMZDuxhptiREW9E1yYLVoMbu8vPweb2odPl4+oetFNndKBQwqnMCN4zuyNu/7+KmUzpz1TvL8PiD/PWTtbxzzSCW51QwsEMcaTYDJq0q0vfhQD1SreRWODm7TypT314Wte/jFXncfEonEsxabEYtD53bk+veW1HvOc7qmSJ9ksQxy0ow8+lNI9hd7mBXaS1ZCeGZvpveXxlZ25Rb4eTOj9dyxxldOa1bEvO3lvD3Sd3xB0PklDuodHmx6DXkVzrpmGDGEqvGcNA6D5tBi80QDiimDGnPl/sWwh/syqGZJFv0KJUKLhvSnpnLcqna11k9EAzx4FcbePHy/szdVMy36wvRqZVMGdKeCwa0I0UaGwpx3JEZkybkPZZULlM4J13WmQjRtqTa9My8bih9020kWXX89ZO1kQp7oRD8tr2M+z9fz8UDM/AHgyTt681wfv80giFYnVfN37/YwEfLc3l+cj/Uyui/IVaDmgfO7k7/jBh2ldYSOGj1cSAIIzrHY9vXx2Fox3g+vHEYvdvZgPBNj/vPyubf5/ds8M6xEEei1uOn2O7mg6W5fLe+EI1KyZVvNdy9/X9L9jD9tE7ccmonOiaYyS130L99LCv3VPLpynw8/iDdUizUuP0UVbu559O1/LCxiPwKZ9TzdE4yM3VY+3rP3y/Dxnn90lDuO2cyYg18ccsIzuuXhkalQKtSMjAzlvRYA3+bmM3s20bx+c0j+NMpnaTbuhDHKZkxaULeQOCoO8fWlSyUwESItkWpVNAzzcb/Te7Pn95f2eAxe6tcVLt86NRKXr1iADajhvlbS5nyxv7F7q/+uotTuyXy+S0j+H59IbkVLnqkWZnQM4XZ6/by+oIc/nNJ33rPPXlIRtRFllmnZljHeN67dghuXwCVQkGiRRe5eBPiaPkDQeZvKeG2D1YDYNSq2FPubLBS1+VDMjg9O4mZS/dg1KoIBoOs21vNB8vyIsfklDn4dl0hr14xAKNWTbcUC3M2FPHB0j08fmFvnN4g/mAIo1bJHWd05eKBGXy0Ig+nx88F/dvRPdUaNQuoUCjISjDz+AW9+ec5PVAAeq0KgyZ8qZNslfUkQhzvJDBpQh7f0c+YGLQqzDo1+ZUSmAjRFlU6vWwtrr8ouM7GAjtd+qfx3E/buGdCdlSzuTq/bC1lXV4VL1zenz3LwnekzTo1n6zcSzAEvoPWoGWnWBjTJaHB15PZEdHUims8/P2L9ZHvvf4gBm39i/3xPVPC60T+Gw7Ux/dIIhAiKiip4/EH+b9523n20r689VsOFw5sx62ndeafX29i3pYSAsEQXZLMPHB2d3qlWnnsgt6HHGNpjYedJbV8vCIPpRIuHZRBx0Sz9CsRoo2QwKQJeQNHv8YEwikXMmMiRNtT6fRSUOUm1qih0tlw5auMOAMxBi0XDcygwuHD2cBaEoAKp48aj5/v1hcBsKvMQbsYA2f0SMagUZGdYkGhgMuHtOeMHsmSJy9aTHmtB7t7f78RfzCExx8kwaylrNYb2X7poHRu+t/+2UObQcvafRXoGrI2vxp/MMQDZ/ega7KZx77bzLwDGpBuL6nl6neW89GNw4m3NL5OqqTGzT2fruOXrftLZn+6ci9n9Uzh3+f3JPEQjxVCHB9kjUkT8u5rsHi04k06CUyEaCtqS6BkMxSsQeGtZeGOEi4fUj8PHsINFId3jOeiVxcxfdaqPyzbq2D/35EO8UYsejU3ju7IGT2SmXXDUP533VCmDsuUPHnRog78dFMpFdgMGt5YsJNHL+gdqSqXYNZSUuPBF9if37WztPYPb9qVO7zcMnMV095axunZSVw5NPpcCoXgPz9soaDS2cgzwJKdFVFBSZ3vNxaxNr+6gUcIIY43MmPShDz+4FGvMQFIsOjYUdJ4KogQ4jgQCkHpFvjk6vB/jXEEJrzGBf0Hoteo2Vlayw8biyOH6zVKnru0H8/M3UaNJ3y32R8IYtWro+4+10m06HDsO86qVzOiUzyTeqdGcunjTJKSIlpHgllHh3gj04Z3ICPOQGmNl0SLFpc3wGtTB5Jb4SC/woVJF31psSqviofP74VC0XCfnaFZcSzeWQ5AQbWbv3+5gf9c3IfFuyqiGiuuyq3CG2i4UU+V08s7C3Ma3Afw9u85DO8Yh0nXeOlhIUTrk8CkCXn8gaNeYwKQbNUxf0sJwWBIFqoKcbyqzoN3zgJXJRhiyTv/S/70vZ1NhUtQKxXcenpnrhvVkV2ltaiUCnq1s/HI7E0s3HfhBfDe4t3cP7E7f/9ifdTCYbVSwd8mduet33fRLsbA69MG0jXZgkIhfw9E67MoPbx+ZX9u/3g9mw5oLNo91cLzl/YBH8zbXMO5/dJQKoj8bodC8PXaAu4Y15Vn526Lfk6dmptP7cRfP1kbtX3GrzuZMrQ9D8/eHNkWb9ISCoVYm1dFgllLklWHRhWeqQkEQ42mR0K4Z8ohmsALIY4TEpg0Ia8/iF599FU/kq16vIEgRXY3adLLRIjjU+6ScFACVAz/G3+ea49cpPmDIZ7/aTuVtV7MejVdUyzsKnVEBSUAy3dXkmjR8eZVg5mzoZAdJQ66pZi5YmgmEOLJi/qQYNKRbJOceHH8cHu93PfFtqigBGBzYQ33fb6BVy/uwpaiGiocXq4b1ZE3ftsVOWbGr7u4Z3xXPrxxGO8t2k2x3cPQrFiGdYznyTlbo9aoAOwsddRLVZw6LJN5W4p55NstmLQq/nF2D87qnYLNoCXGoOGs3imNFqA4p28aVr1c8ghxvJOztAm5fUHMxzBNnLIvVWN3uUMCEyGOUwFnNcXnf4JbbcNjy2L1d8vRqpSc0zeV07PDnacVCrC7fIRCIeJMDf9N+G59EfM2l/Dxn4ajUipQKxXYDGpSY4zh1wmGKKlxEwqFK2wdS2ENIZpChUfJqtyG12qszqvG7lcRCIZQq5QYtSqeuaQvH63Io7DaRa80Gz3SbHywbA9/m5iN1x9kV6mDq99d3mB6l0WnxnPAFMeYrgmc0zeNU5/+BQCHN8B9n6+nfbyREZ0SUKmUXDwgnZlLcimt9UQ9V6pNz/heKTLzKEQb0KqfdI8//jiDBw/GYrGQlJTE+eefz9atW6OOcbvdTJ8+nfj4eMxmMxdddBHFxcVRx+Tm5jJp0iSMRiNJSUncfffd+P31c7eb27E0WIRwbrlSAbnljS/uE0K0nrJaD+96xjDpazj3k0pKXQpiDGo+vmkYlw3OQKkArVrJxr3VaNVKUmMMJJh19MuIafD5Um16ql0+Xp6/gwn/9xtzNhbj9PgprHLx6i87uOjVRVzw8kKe+XEr+YdY9CtES3D4Gl7fYdCouH50FkGlhqcu7kOCWUswFOLZH7fRLyOGK4ZkYtFruGXmKuJNOv7zw1bGPruARKuu0YIxU4dnYtGruGd8Nz6/eTj3TcjmvJd/q3fcs3O3UeUMz7akxxn57JYRTB3WHqtBTYxRw7UjO/DJTcNpJzf7hGgTWnXG5Ndff2X69OkMHjwYv9/P3/72N84880w2bdqEyWQC4I477uDbb7/lk08+wWazceutt3LhhReycOFCAAKBAJMmTSIlJYVFixZRWFjItGnT0Gg0PPbYYy36fo61XLBGpSTBrGO3BCZCHHe8/iA/bCjEhIuvLk0gPlRBkUXF+9cPY8YvO/h+QxHBECgVMKFXCr3TY9haZGfephLuPyubuz9dR+4BHa2TLDr+c0lfiu1u5mwMlwZ+5NvNjOmSyI3vr4xa9Dvj1118sXovn908gvRYY4u/dyEALAZdvQXsRq2KFy7vz6yluZz1wu+EQuG1UhcPTOfRC3tx3XsrCARDaFQKLhqQTo80K3d/ug6tSonHF+Spi/ty32fromZHRnSKZ3SXBPpm2DilSxL3fLqWL9YUNDimnSW1uH3715a0jzPywNk9mH5aZyA826g9hhRrIUTLatXAZM6cOVHfv/vuuyQlJbFy5UrGjBlDdXU1b731FrNmzeL0008H4J133qF79+4sWbKEYcOG8eOPP7Jp0yZ++uknkpOT6devHw8//DD33nsv//rXv9BqW67J2LHOmEB4ncmeckcTjUgIcaz8gSAVTi+r9lRyRkaQhLkPoJz/G2jN6G9Yz1Pfb+eHTftncYOhcJqWLxCiV5qVaSM6UOXy8Z+L+6BRKyEUwucPYjFo+HpNAa/9tityoRcIhliTV4XTW3/Gt9ju4dOV+dx2WmdUktYlWoFGreLMHsn8sLGYHqlWUmP0nNE9mdcX7GJZTkXkOH8wxIfLw80U594xhnKHF4tOzVsLc7jn03WEQtCrnZWft5SQX+nks5uH4/YF8fiDxBo17C5zcs27y/nx9jG0jzeREWdqdExZCSb0mujAQ6dWSX8fIdqo42qNSXV1OHc1Li4OgJUrV+Lz+Rg3blzkmOzsbNq3b8/ixYsZNmwYixcvpnfv3iQnJ0eOGT9+PDfffDMbN26kf//+LTb+cFWuY7tgSLXp2XaI7tFCiJZRbHeztcjO9xuK0GtUTOyVjDZ3Lsrd4XSS2n7XUugI8cOmYmKMGtrHGal2+dizb8bzp83F3HJqJ7YV1/Dot5sjpYHTbHoePKcnz/y0Pepiro7D60fVSHrL12sKuHJoJgkWKRksWl5ZjYepwzK5blQWy3Iq2VVWi8sX4E9jOuLyBli/N3r9yacr85k2PJP3F+1hSJaNT1bkR/YpFApsRg0jOmVw+0dr2VESniHUqZVcPaIDt4/tii8Y4u+fr+fK4ZnM+HUn3kD9slp3ntGNGGPL3YAUQjSv4yYwCQaD3H777YwcOZJevXoBUFRUhFarJSYmJurY5ORkioqKIsccGJTU7a/b1xCPx4PHs39xnN1ub/C4I+U9xj4mAJnxJn7aXIzLG8Cg/YPp5x3z4OdHYPIssKYe0+sK0RKa69xraoXVLq57dzmbCvffJHhn4W4eGNubSwdOx7rqVRw9LkenUvLVnwZR6Q6yItdOrElDZryJ1xfsYneZgxq3n3s/Wx/13AXVbv7y4WpenzqQ5bsrotJiFArommRhbyONVrVqJUqZLBFH6VjPP6NWRaXTx5/eX4ljX2nez1ftxaRV8fzkfjzx/RZ2loZn/PtlxDD9tE5sLa4hI85AWoyJZy7py/+W7ObsrgaSjAp6dE7ikteXUuHYX5HL4w/y2oJd/OPsHhg1SoKESwe/cHk//vHlxsjCdoNGxd8mZtM73XaMPxUhxPHkuAlMpk+fzoYNG/j999+b/bUef/xxHnrooSZ/3vAak2NL5cqMNxIMwZYiO/3bxx764N+fh4JVsOlLGHbzMb2uEC2huc69puQLBHhv0e6ooKTOI/MKGH3D9VR2n4pJqydW4ebaT7ayrWR/+qVOreTpS/pS4fDw8vwdDb6Gxx/ktx1lDOsYH2ksB3DbaZ3RqZUNVimC8IJgabAojtaxnn96rYp/fr0xEpTUcXgD/POrjbw+pTe+mhLKvWpC+hj+8uGaqN4iHeKNvHtFDzosexi8Wr7PuykqKDnQGwt2MapzPLVuHxN6pZJk0fPq1AHoVEoCoRAJZh2JZh06jawfEeJEclzce7v11luZPXs28+fPJz09PbI9JSUFr9dLVVVV1PHFxcWkpKREjjm4Slfd93XHHOz++++nuro68pWXl9ck7yO8xuTYfqQZsUaUCurVia8nFIKideF/F6w5ptcUoqU017nXlMprvcxcktvo/m92eIlJSMYV0vL4zwVRQQmEg457Pl3H4A5xbC+pbeRZYEdJLZN6pdAxwcTwjvHMuHIAU4a2Jz3OwNjspHrH98uIYWx2cgPPJMThOdbzr9rla3Q2r6DajdJVTv9PR9LdaOeuT9bWa3i4u9zJv+bsZlG3e/gg5kbWljRePbPI7qa81ss36wqZPmsV932+DpNWTaJFR7+MWNJjjRKUCHECatUZk1AoxG233cYXX3zBL7/8QlZWVtT+gQMHotFomDdvHhdddBEAW7duJTc3l+HDhwMwfPhwHn30UUpKSkhKCn+Yz507F6vVSo8ePRp8XZ1Oh07XtHcd/YEgwRDHPGOiVStJjzWyseAPApPaEnBXgcYIFTuP6TWFaCnNce41KU8NQZ8fRwOLz+tUOdzsqAqiwMBFA9O5ZmQWbl8As15NrdtPboWTj1fk4/EHyYwzNnpHuFOiiVqPn4sGplPp9PLPrzfy9tWD6Zlm44mLerOlsIb3l+7BHwgxeXAGfTNiSLZKw0Vx9I71/PP6G5nK20ep1kD3cykO2CBUyW2nd6ZvRkykMMyPG4v5bHU+U4ZmMmPRTq4a3qHR50o061ArgvxrbDK/5Pr4dUcFn6/K5/Ih7dlYUE2sUUuSRXfM6dNCiONLqwYm06dPZ9asWXz11VdYLJbImhCbzYbBYMBms3Hddddx5513EhcXh9Vq5bbbbmP48OEMGzYMgDPPPJMePXowdepUnnrqKYqKinjggQeYPn16i14A1ZU6VDdBAnhWgokVu+svio1SF4yk9YfyhtNFhBBHwF4IPz6A2daVkR1H89vOysiuRIuOZIuORIuW20/JQKFQUOhSsXRTMXa3j4GZsahrlSgUsD6/mgk9U1AqFPx5bBeueXd5vZdSKxWM6ZLItftKqdb5YvVeeqbZSLToSbToGdoxnhAhdFLuVBwHEsxadGplVGlfgAHtY7nzjC4sL63lA9099HEb+eSmEcz4dQcv/hz+fNKoFFw8IJ0nLuxD+xgt/70gEa/RhlWvjhSGONAtY9ozKO8thlRu4cKMbEpGn8ftP5TTNdnC3Z+uw2bQ8O/zejI2OwnNvvTHg6tzCSHanlYNTF599VUATj311Kjt77zzDldffTUAzz33HEqlkosuugiPx8P48eN55ZVXIseqVCpmz57NzTffzPDhwzGZTFx11VX8+9//bqm3AYBrXx11nebYA5O+6TH8uq2UvVWuxptCVe4O/zepB+QuhmAAlPJHWYhGVedDyRYo2wpJ3SGhG9jagc8FtaWw6QvodhbWxGze6qWgtkZJ0JRIwB/AFKrBZNBD3lLYvhIyR2LRGLilV5AN1Qbu+HorCWYdd53ZlcuHtOf2j1Zjd/vonGTm7jO78sLPOyIXc7FGDQ+e05O3F+6OCkog3C3+QFq13A0Wxw+LJsSfT++M0mNnQpYKbaCWoCGBvSRw9TvLcfv2BSxLcok1aphx5UA6xJvQqVXsLKuloKKG2wfrSShfiqpsM6G0Afw+vTer8+x0MrpRBb1UKyy4dAl0tXhxOc4klHEaGlMsyRV5vHxWGuoYC1v/2oMd1SpqgiqW7ypl5vJ8/AGYPCiV/hkxJMdaWvcHJYQ4aq2eyvVH9Ho9L7/8Mi+//HKjx2RmZvLdd9815dCOmGtfLq2+Ce5s9km3oVTAz1tKmDoss+GDqvLAEAuWVAgFw6ldUplLiIaVbIH3zgZH6f5tyX3gio8JeewoXJXgqoLMUfDVdLSFa4iLzYKJT0FVPiG/C8WPf4/qLKfpfSmKkXcwfNeL/HzT/Xy7y0dZrYecMgdvXzOEaW8t49/n9eSHjUW8euUA9GoV/mCIYCjEc3O3sTa/ut4wz+6T1gI/DCGOjsvj5/LuamJW/I+K8n54re0JGuJ4+qtt+4OSfaYOz6TI7mbRznKK7G7O653EI/386N4fE74ZACgAS2o/Thn3L/jgMvB7SFXrCI34M4r4TrhyV+NXm9AXLcOZdSaJvbqid+9CWVtEYvwg/vPNLn7Ztr94xC/bSumXYePpi/vyzqLdKBRw4YB0MuOMxJuP4xRSIUTEcVOVq62rW+TXFHc4TTo13VOtzF5b0HhgUp0HpkTQ7yuV6CyTwESIhtQUw0dTooOSzJEw8WlCRetQbPoatn4Lkz+ELbOh+7kw+DqIzYJlbxEa9WcUb5xW72kV6z9G1fFUdg97mC+X57Mwp5oki46rR3TA4wtwz/iuGDQqFu8s54eNxbw8pT/frS/k0kEZbG2gV1Gfdla6pcidXnH8MilcKArX8WPy9by0pJIatxeLfiuTh7RndOcEnvtpOwCXDErH5Q3ylw/XRB47ITOE7qPJkaCkjqJwDayZCb0ugjWzIOCjQpfOJtUw3i7rRK3Hz6TO53BGsp+4ks2w+XPIW8zakTOjgpI6a/KqmbOxmPV7q1mXX83/luRyXt9U/nF2z8Pu/xMMhlA20ktICNG8JE+giURSuZoo9eLMHikszalg5Z5G1prYC8CYsD8wcZQ1yesKccJxlEL5AQUi4jvDiD/DFzeh8HshsStMegZWvUuoag+h2PZga0/IEEtgyHWw4p1Gn1q5+CVWbs3hhV92s3JPJd9vKOKy15cwb3MJnRLNBIMhLh4YrjS4cEc5Z/VKRadR8slNIzirVwoGjYpEs447z+jK69MGyeJ2cVwzqvx868xGrwzw2ml+Phm+h9dP85OhqUajUjKxd7gS5sReqbz1+67I49JseuLtm8DnbPiJN30FXScAUHHq4zyxJ5up/9vE/K2lLN9dyb9+KuTiL2so17fHM/BGnJ0n8d7aRp4L+HZ9AacfUNnuq7WFrM//g3WbQGGVi9nrCpg+axX/+HIDG/dWU+1quHiFEKJ5yIxJE3Huq+LTVIvvBnWIJSPWwENfb+KTm4fXX/xqz4f4rgfMmNS/cySEAHzR5XwZcRt4a2DCo+Aoh/zlMPdBIJxaQkwmKJQofn8WVb8pUNNwo1YAHKVYNfVTUp/+cSundkuktNbD0I7xvL1wN8lWHa/+upONBXYm9Ezm2lFZPHh2DxQKBYlmLSqpLiSOcxVBM2NS7LT/dhpU5kS2p8VmkTXpf/RMy2D57kpyK5wcuHzKotegdhQ38Iz7BLzhNEmdhbz4kXzyff1y3YXVbmasrOH+01IJKTX4GugCX8frD6I6aMbjzYV7GNIxAZNO0+Bj9lY6mfLmUvaU7w943l+yh3vGd+PKYZlYDQ0/TgjRtOSTsIm4mjCVC0CpUPCnUzqxucjOA19sIHjQIlnshWCKB7UeVFpw/vHdICFOSqZEUOw7L5VqyBgKC/8Pdv4KQR9U74Wu4yGhC5gSILkH/PZ0eO1WySbIGNLoU/vaDWdlUf2KQsEQrNtbzddrCwgEQ2hUCvqkx7CxwI5CARcNTCfWqCE1xkCKTS9BiWgT9EEn7X+6OSooAaAyh/Y/3UQnk5tQKIT6oKAgv9KJO6lf409sSw+Xv28/jC+2eRo97PP1FVT71Zj3zOPi7MZnF0/PTmZZTvRnosMTwO+Jfu5Kp5e8CidF1U5emr+DQDDEPeO78coVA3jx8v6c2zeN537aRrHd3fjYhRBNSj4Nm0hTp3IBdEo0c8Pojny6Mp+/fbF+fwUfTy147GCMB4UCdBZwVR76yYQ4WRkTYeA14X9nnw3L34b+02DQtYQsadD1zHBa5MCrw+tM1n6w/7F7V0Fq33DAcjCVhtJBtzNrTcM3BfyBEB5/kAqHl9enDuKt33NItOh4YXJ/jFoVaTHGpn+vQjQjS6Bqf2PfgxWtJ15hp6zWS/s4Y1Rw4vAGWFtjxZ86sOHHjrwdVr4HCiW+xidCCARDKKrzoXgDo+Jr6JxkrndMqk3PiE7x/L4jOr15Yvc43CEle6ucuLx+1uZVce27yxn91HxW7KmixuXj/rO68+36Qm6ZuYq/frIWrVrJa1MH8cvWkj/4yQghmoqkcjWRusXvTd1vYHSXRIIheH3BTorsbu6dkM2azVv52PMQw3YnckdmCK3WAi6ZMRGiQXoLnHofWJIhvguYksBRAkVrUXw8LZxGUmfsP0EfCz3OB/vecJrXD3+DC16HxS/BrvnhlJOUPgQmPMnrqxTUehpuxpgZb+SqER3olGBCoYArhrXH6QnQM81KWoxBei6INkfpP2DheruBYMsIpxXnrwBA7XfyxrSBZJk8PDwhg/u/25+Sdc+cIrKmvEK3La+gXP8h+D3hmZKx/4SKXVCwCgyxnD9Bx8wVDb/+pF5J2Db/D4DU767i/XM/4Iu9SXy0qohAMMQ5fVMZ0SmBez5dd2ABPZIsOgZ0TOa0ZxagVSu5aEA6Fw5oR8G+LvYGjYpJfdO4ZeaqyOM8/iCfrsxnfX41D53bs+l+iEKIQ5LApIm4vAHUKkW9vNamcErXRGwGDa/+soOz/u83FEAPhZrXd9iIT/Byvc4sMyZCHIo5CUbdFe5Xsue3cErXFzdFBSWBtIHszTiXef7xrCp006e9gjNHK2n3+99Qf3ot9L+S0Ji7CWht2INa3lrrZni3RGatLMZ7UL77VcMziTdpMSp8rM2vRqNWcsvMVQB8++dREpSINimgj4H0Yewd/QQ/5YZYXRqiX2b4PEn7/T6Chji81SH8qDnb9wO9p03grTVO8qr9DO1gw6AKELKkhgN9hSKcgrzxS4L9rqRm2jwcTifxsUmc2tXJL9uiZzxsBg1/HmzC+OG+GU1XFdZd39EtbSqXD2mPSqlgR3ENhGBk53hmrytEqVBwXt80JvZJ5faP1uD0BnB6A7z1ew7zNhfz+rRBnPfSQgBm/LKThjoYbC2uiWRECCGanwQmTcTlCzRJD5PG9MuI4f8m92dnaS3JlatIWP4/no17kNk7fVxvM4V7MAghGqdSg98JC5+HUXeGc9rrxHdm46iXmfz2jsjs5zfA02olMyc/w8AF16FY/ib+/lfxe0UMW4rsFNQEyHJ7+XL6CF5fsIs1eVUkWnT86ZROdEsyYtKoefbnPMZ0ScC978LGolMTI4toRRtVoYijaMxrTHl/S+RifTbwtEbJrCtnsG2bi/tmr6Jzkpn7Tj+fQYXf8KRuG57s7ujb9UQz8+Lw2q2DKLd9T95Fv3LOzGI0ylU8dXEfzu2byjuLcnF4/IzonMCk3imorGqKznoH/C588d0I6OOYNTefn7eWEArB8I5xJJvVPDTQw109rQQtafyyV8F1766od/Ngd7mTzQV2hnWMwx8MNdhXqM7CHWWcdkCVLyFE85HApIk4vQH0TdD1/VD0GhU902xQXgpaE4NTNbyyxktRTCIp7tI/fgIhTmbuakLBAIrqvYT8Hg6c2ywe9gC3fFMYCUrqePxBbv5yL19f8CQKpZrnfq1lYJaBr9YUkBFn5H9L8thcuJGJvVO5ekQHTu2WSMhZScqmj7inYDTjuqfw+/YyOu7Lhb9rfFeSpCSwaKNc6LjlszX1ZhDcviC3fLaTGVcO4OUpMZTVenj21zyuHHQ2l6pexLJ7Dn69tsGgBIBQkFi9glGdExiSFYdZpyYjxsClg9Kxu/2syaviyreWcd3ILBLMXVm3t5qBPiPJ1hAD2sdw/8RsdGoVVoMam0ELwcGYnOXkuvW8+POyekFJne83FHF+v3Y4vQHMOnWjaZlJVmnOKERLkcXvTcTl9TdZRa4/5CgHnZUBKSoUwAJ3J0nlEuIPBFx2FAoFdJsIaQPCqST7VJg6kV/pavBxpbUeCk3dufhrFx+tLMSiU1Ph8DJ3UzFr8qrw+IN8uWYvFoOGYDBAyrJHCXY7i2tHdeS1X3cSa9KydFcZL03pz/n92qGRClyijap1+ymsbrhCVZHdTVmtl+mzVvHm77u4bHAGlR7YNeRf7DrtFVypjVe3I74zJmsM6bEG5m4q5vr/ruDeLzZT6fTxnx+2MndTMYFgiI9W5NEt1YJZp+Z/S3K5ZeZqqlw+4gwaMuKM4aAEQKkEcyIKpQqTrvFMBpNWRSAY4rv1hVzQv12DxygUMK578mH/jIQQx0Y+IZuIyxdo8oXvjXKWgd6CSaMgxaRgqzcR3I1PQwtx0guFUNYUAApCSdkoVr0HfadEdh+qEhCA0xci3hS+6Pl9RxkvX9Gf3u1sxJu0jOmSwOtTB0EoSNrGN9EMuJKPt8HFry7CZtRw8cB0Hjq3F2f3SSPGqG3GNylE82ps5uHg/XkVLv759Ub8IVCEgsSXr6BCmYC345n1H6RQUDrmUYoCNj5Ylse6fSlVa/Kq6lXdcnr9VDt9fLg8j52ltbSPMzK6SyLuRsaVYtVy6aCMRsd7yeAMPlyey89bShjTNYF+GbaDh8azl/YjRWY5hWgxksrVRJzeQJOWCj6k2lIwxgGQblGy1R0DAXvLvLYQbZG9AEXAB1V7UMx9MHzFMek5fN3OwV24hVijFqNWVS+VC0CrUuILhpgyNJO1+euwGTR4fEHO7ZuKTqMir8LJxr1VXNzTylr1mdgMGZS5SvjwxmFkxBlJMEsaiDgxWPVq9Bol7gYieZ1aWa9/yRsLdjGp13A2qHtiVVgo6vcQ2WlDsK1+DZxlBFMHUjriH/xclUwXq7/e8x3cv2tMl0RUKgUD2sdyardEslMsPPfTNv7vsv4NjlejVnNW7xR+2FjMqtzorIILB7QjK97I6dnJnN+/HZ2SzLx6xUDyq1z8tq2UeLOOU7omkmjRYdTJpZIQLUXOtibi8gZaLpXLWQpxWQCkWxT8XmEGpQv8XlDLHVkhovhcBP0eaq0dscy+CQXgyzyFgozzKXWFqEjuT6pWz93jdTz0zaZ6D79uVBafrshnYu9UAIZkxWHRaTilaxIut5seKWY+WJ7PmJd2EArBwns7c8cZXVv4TQrR/CxaBbeP68oT32+pt+8vY7sQb9Ly0pT+GLVqVuyu4K3fcyit9fHX7wp4YJKNQNDGq7mjueq08dh0StaXePlpqY/rRsVTWhvd/HBSn1Tmbt7fLV6nVnL1yA4kG5UMyIxh3uZinp27jTvO6HrIdK32cSaeu6wvmwvtfL+hCJ1ayXl925GZYCQ91ljvXE2NMTC4Q9wx/qSEEEdLApMm4mqpGZOAB9x2MMQCkGFRUurVUqkzE+uxg7qBRnBCnKycFVS5vMzfFcJKOWMJUXHlPCqt3XhiTjh3vc4/JnXn9akDef6n7ewsrQ33IRnegbJaD99tKOTcfmncd1Y2OWUOqk0+NCoFt8xcHfVyp3ZNxCx3V8UJKhQKMigzhscv7M17i3aTU+YgK8HEn8d2pqzGy0UzFkeOPT07kXeuHkx3cy2fXZGJWhvkP7+VccWwLHZVOCgr9dI9NZazzAF+2FiMx79/trJTopmrR3TgpvdXotcoGdU5ganDMnl+7naeOL8bb/4W7jw/uEMsozrFo1Qcukx/ZryJdjEGhndKQAFY9OrwejMhxHFHPkGbiMPrb5k1Jo59td314VzYdGs4GNoaSmeYq6rhDtVCnKRC9r24fSb6phqwmhLYmPw2NT4Fs+btiApKAB7+djOndU3k5Sn9cXgDbCqw89qCnewsdXBKt0Q6J5rx+AIkmHWkxxoY9+yCqMfr1Erum5iNVcoBixOUw6/gxvdXYdWruXRQBqkxBpItOt5dvJsfN0afTz9vKcWoVXPnmBSKy6tJsgS4b7iJmevy+GxjDVa9hpnL9tAx3sR/LunLnPVFTBmSwWndkvAHQ7z6y05uPb0zeo2K1blVTJ+1mow4A0oCXDoog5Gd4wkGocbtiwpqGqNWKbEZZFmtEMc7CUyaiMsbwKpvgQuS2pLwf/UxAKSaFCgIkRNMZZhHFsALAYQD+JxfUfz2DCn2AkLpQ8k94zUufXMlH980nG/XFzb4sPnbSrm51stdH6/hrjO7kR5rxOMP8s+zexAKhciIMxBv0rGnwsktp3Xi81V7qXb5GN0lgdvHdaVDvLGF36gQLcfu9lPh8FLh8PLUD1tRKuD1aYPqBSV1vltfyLThHbj8ozwAzsyO498T2jOwcyYrcivpnGShtNbDZa8t5qtbR/Hg1xvomqDnnBlLsbv8fL+hKOr57h2byeZSH6k2Pe59BWe+XFPAI1mSeiXEiUICkybSYovfa0sARWTGRK1UkKCHXF+SVOYSAsBVDb89C0teDn+vMVI59il+2FiEwxvA5Q0QCDbQ4nmfEnu4HOrdn67l85tHYNarKaxy4/IFSLUZ8PoD1Hr8DM2KY3SXBCw6Nak2AxaZKREnOAXR6U8GjYpKh7fR44Mhoh7x45YK4owa7j8jk3/NLmRv1S6c3gCTeqeiIMT8LaV0jNHw0TX9uP2zrWwtrgHAalDzwLhM4k0avlhajFal5Ks1e8mvdPHNbaMw6+TcE+JEIYFJE3H5Aug0LZDKVVsMBlu4i/U+SUYFuVUSmAgBgKMUlr4S+bbqtCdYVa5leV64CWld9SB/I8FJaoyewmo3/mCI7zcUcdWITDYV2hmSFYdaCZ5AiL2VLmpcfgZ1iKV9nBG19CYRJ4E4kwaLTk3NvkaETl+AWFPjBVdUSgUWvZoJvVLYVVrLtuJaPltbws2nZLG9pBaALklmbjmtE759JX/fWlJApcvPK5d2IxAM4fb5sRr06FQh7pu9iwXbywiFoHuqhQ9vHEZWgqn537gQosVIYNJEWqwqV21xZOF7nSSTij2VyeCqav7XF+I4FyregCK0P+io7ng27YJqRnawMnVYJgatipen9Oc/P25jx76Lozo906yU1XgiQcv2klpK7B7W5lURb9JS4fDy5JytJFl1OL0Bvpw+UoIScdIIheD+idnMWprL9NM6k2jRoVEpObdvGl+vLah3/Dl9Utld7kCrUnLhgHQ6JZp47LstuH0hrh+dRZ/0GDomGDEcVIL487UlfLuxjG//PIpbZi2nqNqNSafmtakD+ec5PVEoFFgNauJNUopbiBONBCZNxOULoG+JwKSmsIHARMmqUDK4638wCHHSUe2/g2sfeAurClwkmHXYzEYe/GoDeyqcdEww8adTOpFf4eSFn3cAMCgzlgfP6cG0t5dFHt8n3UZBlZNrR3ZAp1Fx72fr8AaC5Fe6GN4xDptB/oSKk0dZrZehWXGYdGr+88NWcsoddIg3cfu4LozsFM+9n68Hwm2CzuqVwoReKfz5gzV4A0G+XltAvEnLc5f1xaTXsGpPJZ+tzKfS6ePpS/qQnWyJeq3z+6WxvbiG/EoXek24VLBFp8aoUZESY4gcFwiGKHd4CIUgzqRFIzcKhGjT5FO1CQSCITz+YMtU5aophPQhUZuSjAqqMVFdU4utkYcJcVJwVoYr06l1YEpiQ+bVdE+0MGdjEVuKajmnbxqVTh/frS/knk/Xcd+EbD68YRgqlQKPN8C8zcVUOX1AOH++b3oMiRYdCkK8PH8Hk/qksX6vHaNWxb/O7YXNIH2DxMkjLUbHF6sLePrHbZFtOWUO/vLhGu6fkM1Pd46h2uUn1qDhs1X5kaCkTrnDy//9tIPnJ/dlVW5VZPvzP21nxpUDI993TjIzsXcqqTY9L08ZgFql4PNV+ewsqeWhc3pGjiuocvH5qnw+XJ5HIBjivH5pXDksk/RYKUIhRFslgUkTqHGHL2RMzd2/wOsI9zAxxkdtTjKGlxfmVXslMBEnNaerFqcijphzX6VC355Ycwo6tZIB7eMorPYwb3MJSRYd/zqnB1uKavi/edt5fnI//v3NJq4fnUWvdjFoVUpSY/T8fWJ3npu7jRtP6ciT328hv9LFuf3acf2oLK4clklGnFz8iJOL2xfixX0zjAeaPDiDDokmnvlxG3vKnfRIszKpdypbimuYt7kk6tiVuZU4vdHlffMrXZHnGZgZS5xJi9sXwBcIMX3WqshxOrWSv0/sDkBhlYupby1lZ6kjsn/Gr7v4cnUBn948XIITIdooCUyaQN0dVvMhus82iZp9qVr1ApPw1HWuPUSv5h2BEMcNp9dPpcNLIARmnRolIT5c5yI11ki/1LGEVGqcDi8Ob4Ab/rsCjz9853ZTIfyyrZSbT+nE2X1SCQRD1Hr86DUq1u+t4v3rhrCtuJbHv99CTpmDLYV2DFoVHRJM9EyzcUrXRLQtMTsqxHGm3OGNnEd1Tu2WSOckM396f2Vk26ZCO1+s3sszl/SlsMrNpkJ71GN8gejCExpV+OZaQZWLn7eUUFLj4frRWXRKNEcdp1IqImW+ftteFhWU1Cmyu/lsZT63ntYZlaR1CdHmyFnbBKpcLTRjYt8XmBzURNGiBaPCy55auVgSJ4e8CicPfLGBU5/+hTFPzee+z9exMreKnHIXnRItBFVq1uVXs2J3JQ9+tbHexRTAawt2MrF3KhqVgtFdEliVW4nDE+CZudv4x1cbyCkLX/RkJZjZW+XirjO7khZjkKBEnLS0qvrd0i8f0p6nf9xab3sgGOKJ77dw1YgOUdtTrPp66zEn9U5l7qZiFmwvo6TGA8DHy/PqVc67aEA74kxaat1+PluV3+g4v15bQOW+z2UhRNsigUkTqHKG67ibmzswqd4LWjNoDFGbFQoFyRoXuS7JdxcnvoIqF5e/sYTPV++N3HkdlBlLfqWTG8d0JMaowesP8tScLXRIMNW7W1snGILdZQ5cnnAn6W/WFjAkK451+VWRYzolmvAHgtxySieGdYxv8HmEOFnYjBra7Vt4btCoyIo34fEFoipqHajI7sZ2UH+fv4zrEtXcpHuKhatHdGDGrzujjrO7/SiV+w9Ms+n50ymd0KlVKJUcsgqmVq1EqagfRAkhjn+SytUEqvfdmTHrmzswyQNzYoO7krRednvMDe4T4kSyKrcykpMOcPvYzpzZI4XvNxTi9QVRq5TUuP1cP6YjyZZDlxO16NW0TzDxwJfrGdc9mV2ltbh9QRQKOLVrIg9M6oFeoyTerEPfEn2KhDiO6dVKXprSjx0lDgxaFcXVnnqBx8HUynCqVq80G9eNzmJvpQtCcPeZXenXPpZKh5dp7yyrN6vZMcGESqFgWMc4zu6TxunZSaTtC4qMWjVXjejAb9vLGnzNacM7EHeI/ipCiOOXBCZNoMrpQ6NSoG3ufNbqXDAlNbgrWe9jhSO2wX1CnChCoRBzNhRFvr99bBfO65fGHR+u4a7xXTHp1fzz643M3xpupnjfhGx6pFobnDVRKqBrioUnvt/Cn8Z0onuqhV2lDt65ejAZcQaSrXoseukoLUQdjz+ESqnklV92RlIdX5s6EP1BfUjqpFj1xJq0/OfivuSUOZi5ZA9/n9QDs05FgkVLrFHD/Z+vx+7y13vsTad0wqhV8dZVgzA10Nm9b7qNsdlJzNsSvbh+QPsYTu/W8OekEOL4J4FJE6hy+rDoNCiac+o4GAinciU1vLw9SR+kKBSD1+tHq5X/reLEpFAoSLXpAeicaOLyoRnkV7q4f1I2To+f1XmVXDQwnUsGZbA2r4qv1xZwxxlduHXW6np3ZO84oytWvZqz+6TQq52VRTvKSbDoKKlx0yfdJkGJEAfxBoLc//n6SFAC8MGyXP56ZjfmbirmwgHpWPRqymu9fLIyjzvP6EogGCKnzEGXZDPDO8XjDQTxBUPc+9kG/nfdUB45vxdvL8zh122lhEKQbNUx/bTOWPRqEi26RhuYJlr0PHFRb7YU1fD+kj34AyEuH9KePuk2kq36lvqRCCGamFzBNoEqlxeTvgUqcgV8YElpcHeySUkQJfnFxXTMaNe8YxGiFV08MJ03fsvh6Uv6Ru7SFle76Z5m46MftzJ3UzH+YIgRneK5e3w3fthYyBvTBvHTpmJW7KkkxaZjytBM1uZV8eOmYrISTLz9Ww7DOyfwlw9X4/YFmXtHLPFm6SotxIE8/iAbC6JnH3/dVso1IzsQCIb4zw9bKKv1kh5r4C9ju+DxBegQb2RgZix2l4+X5+9gS1ENH1w/FICX5+9g6rD2dE2ycPmQ9vgDIRxeP9uK7IzvmdJoUFIn0aIn0aJnaFZ4/deh1p0IIdoGCUyaQLXT1/wL3ytywv9tJDBJsYRfP6ewTAITcUJLizEw44oBWPRqiu1u7v1sHW9OG8yUN5dQbPdEjlu0s5xVuZW8PnUQN/9vJY9d2JvLh2TgC4S4+t3lVDi8vHrFAIqr3awvsBNr1kYCnYOrAQkhwO0L1Ns2dVgm36wt5NOV+6tk5Ve6uPvTddw+rgtdkixMe3tZ1GNc+2YvF+8qxxcI8uexXfD4Azi9fnq3szGue/IRrRGRgESIE4eczU2gyunF1NzpUxU7wRADWlODu+NNeoy42VpQ0bzjEKKVWfQaOiYacfmCKBUKnriwN79tL4sKSuq4fUE+W5XP+F4pGDQqVEoFf/tiPRUOL2admq7JFh74aiNTh2fy6YrwhVW8SUvMHyzoFeJkZNNrUB6UsXxat6RGS/e+sWAX/lD9IP/AcsHr91Zj0Kg4o0cKlwzKoHuqVRauC3ESk8CkCVS6fM3fw6RsO1jSGt2t0JnIUJSwtcTZvOMQopW5fQHe/i0HvUaJVqXAqFMzb0txo8cv3VXB0Kw4MuNNaFQKNhTYMWhUvDFtIDtLa7h7fDc2F9opqHYD8K9ze0qOuhANMGiVnN9v/4y8UauiwuGlgdgDAIc3QK07emH70Kw41EoFGXEGzu2bxmc3j6BLslSUFEKESSpXE6h0eEmzGf74wKMVDEDZVsg6pfFjNCbSFWVsLms8eBHiRFDrdHH16I78vLmEFJueeVtKDjnDEWPU0LudDa8/QFmtn0fO68nwzgkQCpEWY2D+1lK+WL2X/u1juHdCNj3TrFH9E4QQYR5fkClD22PQqvh0ZT5efxCT7tDrKz3+/elfSRYdt4/rAsB/rx2CSacmySI3AYQQ+50wMyYvv/wyHTp0QK/XM3ToUJYtW/bHD2oi1a5mXmNSsQN8bojNavwYpZIMdRW7alT4Ag03uxLiRKBVhthZUkuiVcfDszczf0sJE3qlNnr8tSOzsBo0+PxB8iqcjOycQEGVE6c3QGa8ibvO7Mb8v57K21cNZljHeKnGJUQjgiGY+tZSatx+nrusH/83uT/psUYSzA2nXvVMswJw2eAMHj6vJw+d15MF20pRKSArwSxBiRCinhMiMPnoo4+48847+ec//8mqVavo27cv48ePp6Sk5I8ffIx8gSDVLh+W5myuuHcVqPVgPfSi9gydE39Iwc7S2uYbixCtzO5X8s7C3ejVKkprPdjdfjYV2rlmZId6x47NTmJYpziW7ipnwY4yeqVZWZlbxbzNxZFmbQlmHak2A7GS1y7EIdndPly+IF+vLeCWmauYPmsVd368hscu6I1JGz1zkmDW8vQlfemWYqa42s2bv+fwxaq9jOqSgLK5e34JIdqsEyKV69lnn+WGG27gmmuuAWDGjBl8++23vP3229x3333N+tqFVW6CIUj8gw7Tx2T375DQFVSH/t/V0ehBXRNkeU4F2SnW5huPEK3I7QtS6fShOiDd6uX5O7hmZAfeumoQy3Iq8AWCnNkzBbNOzfNzt2E1aJnYO4VPVuUzslMCt57WVcoBC3GEGqp+ta24lpfm7+D5yf2pdHrZXlxDr3Y2uqdaserVVDq8TBnaHoteze87yskpczBsX3lfIYQ4WJsPTLxeLytXruT++++PbFMqlYwbN47Fixc3+BiPx4PHs7+Cj91evyv04cqtCC82b7bFsiWboHI3DLzqDw/VGwx00ZaxYHsZU4d3aJ7xCHEMjvXcCwaDKBUwqks8m4tq6J8Rw+q8KgDeWbib/y3ZQ+92Nsw6NZcN1hBjUHPHuK6Y9Spc3iB3ndEVq0FmRsTJ6VjPP6teTbsYA3urXFHb1+VX88+vNvDJTcMZ1jGOUCiEWqXC5Q3w6LdbWLizHJtBw93juzGuezIqmTERQjSizf91KCsrIxAIkJycHLU9OTmZoqKiBh/z+OOPY7PZIl8ZGRlH/fp7KhwoFRDfSI7tMQn4YNnrYEkNz5j8EUMMvRS7WLyzHL+sMxHHoWM995RKJTmlDq4cmslnK/K4fVwX4g9IwfIFQqzfW801IzugUsB/fthGrElLrElPWqxRghJxUjvW80+nUvL0JX0wHpS2ZdSqePqSvmhVChSA3emlsNKFUafi4fN78f1fRvP1rSO5eGC6pEwKIQ5JEQo1VuivbSgoKKBdu3YsWrSI4cOHR7bfc889/PrrryxdurTeYxq6a5SRkUF1dTVW65GlQD3+/Wa+XL2X5y/rf/RvoiEBD/zyJOxdCUNuhJjD+ADJX8HOdYt4IHA971w9mNOyk5p2TEIco6Y493LKalGEwmW631+8h/G9UsitcLK50E7nJDOndUtCq1Iw+fWlfHLTcDokNNz7R4iTzbGef4VVTkrsHhRKBb9tL2NnSS2dksyM7pIAQUiwavH6Q2hUCtrFGpvzrQghTlBtPpUrISEBlUpFcXF0H4Pi4mJSUhrukq7T6dDpmia/PK/C2fTrSwIemPsvKN0M/a84vKAEwBhPR0UhneI0vPrLTglMxHGnKc49k0ZFlduHVa/h9OwkQqEQgzPjGNIhjq1FdspqPdS4fHw5fSTtYpuxjLcQbcyxnn+JFj0ef4h3F+Vwbt92nNkjmRqPn89X5XP1iCySLHrUkqYlhDgGbf4viFarZeDAgcybNy+yLRgMMm/evKgZlOayu8zZtCUPQ0FY8AyUboGB10Bi9uE/1pSAQgHntXezbHcFX67e23TjEuI4kWQzYDOo0agV9Eq30inRTJXLi9cfZFjHeAZlxjCxT5oEJUI0MbVKSYJJy7WjstCqleRXutCqlFw3Kot4k1aCEiHEMWvzMyYAd955J1dddRWDBg1iyJAhPP/88zgcjkiVrubiDwTZU+6gb7qt6Z501fuwZxH0vxLiDtG3pCFaM2hNDFLvZkzXodz96VpcvgCTB2egUEjDOHHiSLaG00TsrnB1ro6J5qgqXUKI5mE2aDAbNHh8frISjKiVCnSaE+JSQghxHDgh/ppcdtlllJaW8uCDD1JUVES/fv2YM2dOvQXxTW1tfjUOb4Ds1KMozRvwwMYvoXgTmJMgsSsUbYQdc6HbREjuceTPqVCArR2Ksi3ccPrlaJRK7v98PXM2FPHoBb1Il5xfcYKxHqLjuxCi+eg0aqTgthCiqZ0QgQnArbfeyq233tqir/nrtlLMOjWdEs1H9kBnGcz7N1TlQlwn2JsLW78Lz3j0vAAyhhz9oGI7wq5fUOPn+tEd6d8+lncW5nDGswu468yuXDksE71G9cfPI4QQQgghRAs6YQKTlhYKhfh5czG92lkPnULidcDOn6FgJfg8oDVB0TpQaWDITWBLCx8X8IFSBYpjzNFN6gnb5sDuhdDpNAZmxtI91cKHy/N47LvNvDR/B/3SYwiEQhRVu3F4/cQZtQzMjGVSnzQGd4iNSvty+wLkVThxeAPEGDSkxxokj1gIIYQQQjQ5CUyO0nfri9hQYOevZ3ZrYG8IKnbBjp9hx4/gd4dnRtQGcJVDu4GQNSYcpNRRNVFKijkBErvDynchuSeYkzBq1Vw7MosJPVP4bXspeZWu/2fvvsOjKrMHjn+nl5RJJr2T0GvoXcAKFhSxoSgW7KCrrrqrP/vqou7aRbFXsK6CFQUEpEvvhJZAek8mbfr8/hgyMGRCCYGEcD7Pk8fNvXfuvJPlzsy573vOQaVQkBoZhFGrprzWzk+b8/lkxT7iTHr6tzOjUSrYWVjFjoIqnO6DFaUNGhX924XTP8VMcoQBBQqqbE7weEgIN5CeGHbEjtoej0fyXYQQQgghRAOnfR+T5lBZWUlYWBjZ2dlHrOWeU2HloxXZbM6rYkdhDVqlh3sTdqFwO1E4a1DUlaOoKfZ7jMdgxhXRGTSnML/DUYs650DXe7Ued1CU9/mVGnC7vLMzeEClwaPWgVKDW6llh64nawscFFi8de5D9WpSIwwkhukxaFRU2ZzsK6sjo6iGo/2rCTeoCTVoqLO7KKq2N3qMOUiLVq3E5fbgOhAAKRTg8YDN6abO4aLO4cZxoGGkTq0kSKsiRK8mWKdGp1aiUyvRqhToNSr0aiUalQKFQoFCAQpoNBDyeDx48D6X0+3B4XJjdbixO904PR7fa3R7PLjdHtwe8OA9p1qlQKdSotco0WlUaFUKlMfwnMfj0PG53B7sLjc2p3d8bg+olQq0B16/5jifv/6yrz+/w+XG4fJgdR74W3sOvMYD51crvX/TUR3NDE4NP6bxh4SEHHUcx3rtCSGO3bFceyDXnxAnw7FefyIwCUyAnJycY+qAG3nZPwnqMvwUjEiI1snjdrH/v5d7y1ofxbE0bTvWa08IceyOtWGiXH9CNL+mNOsWB0lggrfvSV5eXpOi3PrOuaf7Hae28DrawmuAtvM6juV6kmuvdZC/ZfNoLX/HY72ejuf6ay2v7UTIa2g92sLraOw1yIzJiZEcE0CpVJKYmHhC5wgNDT1tL65DtYXX0RZeA7Sd13Ekcu21LvK3bB6ny9+xKdff6fLajkReQ+vRFl5HW3gNrYmUVxJCCCGEEEK0OAlMhBBCCCGEEC1OApMTpNPpePLJJ9HpTu8euG3hdbSF1wBt53WcbPJ3aj7yt2webfnv2BZem7yG1qMtvI628BpaI0l+F0IIIYQQQrQ4mTERQgghhBBCtDgJTIQQQgghhBAtTgITIYQQQgghRIuTwEQIIYQQQgjR4iQwATweDxaLBakDIMSpJdeeEC1Hrj8hRGsjgQlQVVWFyWSiqqqqpYcixBlFrj0hWo5cf0KI1kYCEyGEEEIIIUSLk8BECCGEEEII0eIkMBFCCCGEEEK0OAlMhBBCCCGEEC1O3dIDEKI1q7E5Kam2YXO4MepUxITq0agknhdCCCHamrIaG+U1DtweDyaDhuhQfUsP6YwjgYkQjcivqOPfv+7gl835uNweQnRq7j67PVf3TyIiWNfSwxNCCCFEM3C5PewsrOLBbzayNc8CQLLZyPPje9InJRyDRtXCIzxzyK1fIQIoqbYx9Yv1/LgxD5fbW+O/yubkhbkZfLMmB4fL3cIjFEIIIURzyK2o5aoZK3xBCcD+slpu+PAvMotrWnBkZx4JTIQIoLDSytp95QH3TV+4myKL7RSPSAghhBDNze328MOGPKptzgb7XG4Pry/YRU2AfeLkkMBEiAD2FFc3uq/K5gz4BiZaF4/H45vtEkIIIQKxOd2s2Fva6P4N2RXymX8KSWAiRAAxR0h4UykVGDRy6bR217yzknNfWtTSwxBCCNGKaVQKUsxBje6PNenRqeUz/1SRv7QQASSbjUQ1kuB+Uc84SX4/DfyVVUZWaS1Wh6ulhyKEEKKVUquUTBqS0uj+e87pQJhRewpHdGaTwESIAGJNej6dPLBBcNIvJZxHL+xCkE4K2p0uVmWWtfQQhBBCtGKJZiMvXZWORqXwbVMoYMqoDvRJDmu5gZ2B5NuVEAEoFAq6xIYwZ+owsstqKayy0T4yiBiTnkiZLWn1PB4PWpUSu8vNwh1FjOwU1dJDEkII0UoF69Rc1CuOgalmMgqrsDvcdIsPJSJYS4he09LDO6NIYCJEIxQKBfFhBuLDDC09FHGcqmxO7AdKOmeWSKlHIYQQR2bQqEgyG0kyG1t6KGc0WcolhGhz6ss5x5n0lNXYW3g0QgghhDgWEpgIIdqc4ipvYJIUbqS8VgITIYQQ4nQggYkQos0prvYGJolmA+UyYyKEEEKcFiQwEUK0OcVVNnRqJVHBOmrsLuxOd0sPSQghhBBHIYGJEKLNKa6yEWbUEnqgmkqFLOcSQgghWj0JTIQQbU5xlQ2TQU2w3lt4sEwCEyGEEKLVk8BECNHmVFkdGLVqQuoDE8kzEUIIIVo9CUyEEG2O1eFCq1b6GmNV1DpaeERCCCGEOJoWDUymTZvGgAEDCAkJITo6mnHjxpGRkeF3jNVqZcqUKURERBAcHMwVV1xBYWGh3zH79+/n4osvxmg0Eh0dzUMPPYTT6TyVL0UI0YrUOVzoVEqMWhVKhcyYCCGEEKeDFg1MFi9ezJQpU1i5ciXz5s3D4XBwwQUXUFNzsFPz/fffz48//sg333zD4sWLycvLY/z48b79LpeLiy++GLvdzvLly/nkk0/4+OOPeeKJJ1riJQkhWoFau3fGRKlQEKLXSMlgIYQQ4jSgbsknnzt3rt/vH3/8MdHR0axdu5YRI0ZQWVnJBx98wKxZszjnnHMA+Oijj+jatSsrV65k8ODB/P7772zbto358+cTExND7969+de//sU//vEPnnrqKbRabUu8NCFEC7I6XOjU3vsuIXo15bKUSwghhGj1WlWOSWVlJQBmsxmAtWvX4nA4OO+883zHdOnSheTkZFasWAHAihUr6NmzJzExMb5jRo8ejcViYevWradw9EKI1qLO4UKrVgEQrFNL93chhBDiNNCiMyaHcrvd3HfffQwbNowePXoAUFBQgFarJSwszO/YmJgYCgoKfMccGpTU76/fF4jNZsNms/l+t1gszfUyhDhpKmvtFFfb2ZxbSbBORdfYUKJCdOg0qpYe2jE7Vdee1eFGe2DGxKBVUWWVnDMhztTPPkudg5JqG5tzK9GplXSL8753GrSt5iuQEOKAVnNVTpkyhS1btrB06dKT/lzTpk3j6aefPunPI0RzKa6y8d/fMvhqTbZvm06t5PVr+zCiY+Rp8wF7qq69Q5dyGTUqqqyylEuIM/Gzr6zGztuLdvPekkzfNrVSwbTxPbmwZxzButPjvVOIM0WrWMo1depUfvrpJxYuXEhiYqJve2xsLHa7nYqKCr/jCwsLiY2N9R1zeJWu+t/rjzncI488QmVlpe8nOzs74HFCtBYLthf6BSUANqebuz5fS16ltYVGdfxOxbXn8Xh85YJBZkyEqHcmfvatzirzC0oAnG4PD327ieyy2hYalRCiMS0amHg8HqZOncr333/PH3/8QWpqqt/+fv36odFoWLBggW9bRkYG+/fvZ8iQIQAMGTKEzZs3U1RU5Dtm3rx5hIaG0q1bt4DPq9PpCA0N9fsRorUqqrIyfdHugPvcHvhhQ94pHlHTnYprz+5y4/ZwcMZEq6bKJjMmQpxpn31lNTbe/CPweyfA5yv34XS5T+GIhBBH06JzmFOmTGHWrFnMmTOHkJAQX06IyWTCYDBgMpmYPHkyDzzwAGazmdDQUO655x6GDBnC4MGDAbjgggvo1q0bN9xwAy+++CIFBQU89thjTJkyBZ1O15IvT4hm4XJ5KLLYGt2fWVJ9CkfT+lnt3i8avhkTjYpqmTER4ozjcHkotDQ+o5xdVovd5UatahWLR4QQtPCMydtvv01lZSWjRo0iLi7O9/PVV1/5jnnllVe45JJLuOKKKxgxYgSxsbF89913vv0qlYqffvoJlUrFkCFDuP7665k0aRLPPPNMS7wkIZqdUauiZ6Kp0f0jOkWfwtG0flanCzh0xkRFtU0CEyHONEFaFX2SwxvdP6xDJIbTqHiIEGeCFp0x8Xg8Rz1Gr9czffp0pk+f3ugxKSkp/PLLL805NCFaDZNRyz/HdOHKGSsa7IsM1jIkzdwCo2q96uzewESrOphj4nB580708iVEiDNGsF7D387tyPzthbjc/t83QvVqLuoZh0KhaKHRCSECkflLIU4DXWND+eimASSGG3zbBqWa+eqOISSEG1twZK1PneNAYHKgj4lB6/2vzJoIceZJiwpi5q2DSI0M8m3rkxzG13cOISHMcIRHCiFagtTJE+I0EKRXc3aXaP4XPxRLnQO1SkG4UUuYUdvSQ2t16gOTQ8sFA1RZnUQGS96ZEGcSvUbF4LQIvrpjMJY6B0qFgjCjFnOQvHcK0RpJYCLEaSQmVE9MqL6lh9GqWeuXcvnKBXvf5iQBXogzV3SInugQee8UorWTpVxCiDalwYyJtn7GREoGCyGEEK2ZBCZCiDbF6mhYLhigSnJMhBBCiFZNAhMhRJtyMPn9YFUukKVcQgghRGsngYkQok2pc7hQKRWold63N41KiUalkKVcQgghRCsngYkQok2x2l2+/JJ6QVq1lAsWQgghWjkJTIQQbUqdo2FgYtCqqJKlXEIIIUSrJoGJEKJNsTpc6NT+Hd6NWhUWCUyEEEKIVk0CEyFEm1LncPkS3+vpNSpqZCmXEEII0apJYCKEaFOsDhdatcJvmwQmQgghROsnnd+FEACUVNnIr6xjd3E1caEGkiOMxJn0KBSKoz+4FbE63GhUDWdMLFKVS4hmUWixklNex77SGlIigkgMNxATKl3VhRAnTgITIQR5FXXcPXMtG7Irfdsig7V8NnkQXWJDTqvgxO50+0oF1zNoVJTX2FtoREK0HftKa5j04V/sK631bUs2G/n0loG0iwxqwZEJIdoCWcolxBmu2ubguZ+3+wUlACXVdm74YBX5ldYWGlnT2F1u1Er/QMqgUUq5YCFOUGm1jbtnrvMLSgD2l9Vy98x1lFTbWmhkQoi2QgITIc5wpdV2ft2SH3BfSbWd7PLagPtaK7uz4VIug/QxEeKEldbY2ZpnCbhvW76FsmqZlRRCnBgJTIQ4w1kdLtyexvcXW06vu6A2pxu16vAZE0l+F+JE1dqPfA0dbb8QQhyNBCZCnOGCdGpCdI2nm6VGnV7rxu1OV8OlXFolNqcbh8vdQqMS4vQXZtSibCTdTKnw7hdCiBMhgYkQZ7iYUD1TzukQcN+AduHEnmbVduwuT8OlXBpv4CWzJkI0XWSwliv7JQbcN75vIhHBEpgIIU6MVOUS4gynUSm5ql8iSuDNhbuxWJ2olQrGpsfx8JguRATrWnqIx8XudKFuUC7Y+3uV1Sl3dYVoomCdhodGdybUoOHzlfuwOtzoNUquH5TC7SPTCNFrWnqIQojTnAQmQggignXcMjyVi9PjqLG50GuURAbpMB5hiVdr5U1+919vYtSqAKiRNfBCnJCoED0Pj+7CjUPaUedwYdCoiA7VoVOrWnpoQog24PT71iGEOCnUKiUJYcaWHsYJC1QuWK/xfmmqtkpgIsSJ0qqVJJlP//cKIUTrIzkmQog2xe50N1jKZagPTCTHRAghhGi1JDARQrQpDpcHTYOqXBKYCCGEEK2dBCZCHKcqq0O+4LZigWZM9AfWv0tVLiFEPY/Hg6XOIe8LQrQikmMixDHKr6hj8a5ivl2Tg0al5IYhKfRLCSfmNCun29Y5XA0bLCqVCvQaJVWSYyKEAHIr6vhtawE/bczHqFUxeXgqPRNMRIacXlUIhWhrJDAR4hjkV9Rx/Qd/sae42rdtxd5ShrWP4JVrehMtwUmr4HZ7cLo9aJQNJ4ONGjU1NlcLjEoI0Zpkl9Vy9TsryK+0+rYt3V3C2F5xPHlpdyJPsxLpQrQlspRLiKNwuz38sDHPLyipt2xPKRtzKltgVCIQ+4HO7ofPmADotUqqrI5TPSQhRCtic7j4YOlev6Ck3o+b8tlXWtMCoxJC1JPARIijKK2x8dXq7Eb3z1y5jzqHLBFqDeoDk8M7v4O3Mpf0MRHizFZea+e79bmN7v96Tc4pHI0Q4nASmAhxVArcHk+je10eD3ga3qEXp57deWDGRNnw/w+jVi05JkKc4TyAx934fqfrCDuFECedBCZCHIXZqGF838RG9187MNlXjla0LF9gEmDGRJLfhRDhRi2X9IprdP9V/ZJO4WiEEIeTwESIAypq7ewuquaHjXksyigip7wWu9OFSqXkir6JJIYbGjymd5KJfinhLTBaEYjDt5Sr4YyJQaOSHBMhznB6jYq7RrUnIkjbYN+ozlG0jw4+5WPKr6hj1d5S5mzIZXNOBSVVtlM+BiFaC6nKJQRQXGVj2i/b/dYeGzQqZlzfl8FpESSEG/jqjiH8tDGP79blolYpuGFwCqM6R0u54FakfsYkUI6JUasOmPAqhDizJEcEMXvKML5Zk80vWwoI0qq4ZVgqQ9pHEHWKywVnFFi4/oO/KD4kGElPNPH29f2ID2t4M0yItk4CE3HG83g8/LQpr0FCZJ3DxeRP1jD/gZG0iwwiIczAbWelcVX/RBQoCA9wx020LNuBwEQVIMfEoFVRJY3UhBBAktnIved25Mah7VApFYQZT/37eX5lHZM+9A9KADbmVPL0j1t56areBOvla5o4s8hSLnHGK6qy8faiPQH3Od0e5m8v9P2uVCowB+kkKGmljlqVS3JMhBAHqFVKIoJ1LRKUAOSW11FoCbxsa962QkprZEmXOPO0aGDy559/MnbsWOLj41EoFMyePdtv/0033YRCofD7GTNmjN8xZWVlTJw4kdDQUMLCwpg8eTLV1Q37TQjRGJfbQ9ER1vTuLZa69qcL31KugFW5VFTbnHiOUGFNCCFOlZJqe6P73B7vrL0QZ5oWDUxqampIT09n+vTpjR4zZswY8vPzfT9ffPGF3/6JEyeydetW5s2bx08//cSff/7J7bfffrKHLtoQnUZJ9/jQRvcP7RBxCkcjToTD1XhVLoNWhQeoscuHvRCi5aVEGBvdZ9SqCNbJMi5x5mnRf/UXXnghF1544RGP0el0xMbGBty3fft25s6dy+rVq+nfvz8Ab7zxBhdddBH//e9/iY+Pb/Yxi7YnIkjHoxd1ZeL7qxrsiw7R0TdZqm6dLg6WCw5clQug2uqUD3whRIuLCdEzOM3Myr1lDfbdObI90ac4EV+I1qDV55gsWrSI6OhoOnfuzF133UVpaalv34oVKwgLC/MFJQDnnXceSqWSVasafsmsZ7PZsFgsfj/izNYr0cTb1/clJvTgB8HgNDNf3TFYKqM0o5N97R1cyhW4KhdAtU1KBoszk3z2tS7mYC2vXNOb8X0TfE1hg3VqHhrdiYmDktGqpT+WOPM06bbhunXr0Gg09OzZE4A5c+bw0Ucf0a1bN5566im02uZJJBszZgzjx48nNTWVPXv28Oijj3LhhReyYsUKVCoVBQUFREdH+z1GrVZjNpspKCho9LzTpk3j6aefbpYxirYhRK9hTPdY+iSFYbE60aiUhBs1LZYU2Vad7GvP7jrCjMmBJpgWSYAXZyj57Gt94kwGnr2sB/ed25E6h5sgnYqYUH3AAh5CnAma9C//jjvuYOfOnQDs3buXCRMmYDQa+eabb3j44YebbXATJkzg0ksvpWfPnowbN46ffvqJ1atXs2jRohM67yOPPEJlZaXvJzs7u3kGLE5rCoWCWJOBTjEhpEYGSVByEpzsa+9IfUwOXcolxJlIPvtaJ6NOTXJEEJ1jQ0gMN0pQIs5oTfrXv3PnTnr37g3AN998w4gRI5g1axYff/wx//vf/5pzfH7S0tKIjIxk9+7dAMTGxlJUVOR3jNPppKysrNG8FPDmrYSGhvr9CCFOvpN97dldbhRAgKJcGA/MmFRLLxNxhpLPPiFEa9ekwMTj8eB2e+9Mzp8/n4suugiApKQkSkpKmm90h8nJyaG0tJS4uDgAhgwZQkVFBWvXrvUd88cff+B2uxk0aNBJG4cQonWyO91oVEoUisaT36uskmMihBBCtEZNyjHp378/zz77LOeddx6LFy/m7bffBiAzM5OYmJhjPk91dbVv9qP+8Rs2bMBsNmM2m3n66ae54ooriI2NZc+ePTz88MN06NCB0aNHA9C1a1fGjBnDbbfdxowZM3A4HEydOpUJEyZIRS4hzkDewCTAdAne5ph6jZIqWcolhBBCtEpNmjF59dVXWbduHVOnTuX//u//6NChAwDffvstQ4cOPebzrFmzhj59+tCnTx8AHnjgAfr06cMTTzyBSqVi06ZNXHrppXTq1InJkyfTr18/lixZgk53sHLSzJkz6dKlC+eeey4XXXQRw4cP5913323KyxJCnObqZ0waY9SqZSmXEEII0Uo1acakV69ebN68ucH2//znP6hUx17ebtSoUUfswvzbb78d9Rxms5lZs2Yd83MKIdouh8sdsCJXPaNWhaVOAhMhhBCiNWrWLmN6vb45TyeEEMfF5nKjDtDDpJ5Rq8IiOSZCCCFEq3TMgUl4eHjAhNJAysoadjEVQoiT7Ug5JgBBWjWWOglMhBBCiNbomAOTV1991fe/S0tLefbZZxk9ejRDhgwBvF3Yf/vtNx5//PFmH6QQQhwL71KuI8+YVEhgIoQQQrRKxxyY3Hjjjb7/fcUVV/DMM88wdepU37Z7772XN998k/nz53P//fc37yiFEOIY2J1u1IGamBxg1KnJLqs9hSMSQgghxLFqUlWu3377jTFjxjTYPmbMGObPn3/CgxJCiKZwuDxHTH4P0qqplBkTIYQQolVqUmASERHBnDlzGmyfM2cOERERJzwoIYRoCvtRkt+DdCrpYyKEEEK0Uk2qyvX0009z6623smjRIl+H9VWrVjF37lzee++9Zh2gEEIcK8dRlnIFHehj4nJ7UB3hOCGEEEKcek0KTG666Sa6du3K66+/znfffQd4u7AvXbrUF6gIIcSp5nC5jxhwGHXePktVVgdhRu2pGpYQQgghjsFxByYOh4M77riDxx9/nJkzZ56MMQkhRJPYj9JgMUjrfcurrJPARAghhGhtjjvHRKPR8L///e9kjEUIIU6ItyrXkXJMvIGJdH8XQgghWp8mJb+PGzeO2bNnN/NQhBDixBytXHCQ1ruUSypzCSGEEK1Pk3JMOnbsyDPPPMOyZcvo168fQUFBfvvvvffeZhmcEEIcD2+54CM0WKyfMbFKYCKEEEK0Nk0KTD744APCwsJYu3Yta9eu9dunUCgkMBFCtAjHUXJMjFoVCmTGRAghhGiNmhSYZGZmNvc4hBDihHn7mDQemCgVCoxaFRYJTIQQQohWp0k5JofyeDx4PJ7mGIsQQpwQx1EaLII3Ab5CAhMhhBCi1WlyYPLpp5/Ss2dPDAYDBoOBXr168dlnnzXn2IQQ4rh4c0yO3DgxRK+motZ+ikYkhBBCiGPVpKVcL7/8Mo8//jhTp05l2LBhACxdupQ777yTkpIS7r///mYdpBBCHIujdX4HCNapKa+RGRMhhBCitWlSYPLGG2/w9ttvM2nSJN+2Sy+9lO7du/PUU09JYCKEaBF2lxvNEapywYHARGZMhBBCiFanSYFJfn4+Q4cObbB96NCh5Ofnn/CghKhXWWen1u5CrVQSFaJr6eGIVs5xlOR3gGC9hr3F1adoREKI5uJwuSmrtuPGg8mgwaht0lcYIUQr1qSrukOHDnz99dc8+uijftu/+uorOnbs2CwDE2e2WruTnQVVPD93BxuyK4gO0XPXqPac1zVGAhQRkMfjweHyoDpKjonMmAhx+smrqGPmyv18uXo/VoeLC7rHcu85HUiJCEJ5lJsRQojTR5MCk6effpprrrmGP//805djsmzZMhYsWMDXX3/drAMUbZ/D5aLIYqPW7sKgVREdomftvnImffgX9QXf9pfV8sh3m1ndp4wnxnYjzKht2UGLVsfp9v5jOVpVrlC9mopaBx6PB4VCvtAI0doVVFq54YO/2HPITOf363OZv62QH+4ZTmpk0BEe3bgiixWL1YlaqSA8SIvJoGmuIQshmqhJgckVV1zBqlWreOWVV5g9ezYAXbt25a+//qJPnz7NOT7RxpVU2fh0RRYfLM2kxu7CoFFx/eBk+qaEo1QocB1Wivq79bncOaq9BCaiAYfLDXAMS7nUON0eauwugnWyFESI1m7NvjK/oKRelc3JjEW7efqyHug1qmM+X63NyZp95Tw2ewv7y2oBGN4hkmcu605aVHCzjVsIcfya/Kncr18/Pv/88+YcizjD1NqcvLFwF58s3+fbVudw8d6STMZWWpk0JIVv1+Rg1Kkorbb77ohvzK4gMdxASZWNoiobeo2KyGAdMaE6uQN+BnM4D8yYHMNSLoDyGrsEJkK0Qh6PhwKLlZJqOzaHi8hgHZf1jmfOhrwGx87fXsT95zuINR17YJJRWMWNHx2ckQdYuruEa95ZyewpQ0kINzbHyxBCNEGTPpUnTZrE2WefzciRI0lLS2vuMYkzREm1jZkr9wfc99PmfH6cOpyeCSYq6xwkm41szq1k+sLddI4N4e1Fe3h70R5fsBITquOdG/rTM8GEStYbn5FsLhcAmqMs5QrRe5drlNfaSTLLFxAhWhOny83GnEru/HwtxVU2ADQqBTcObcfDozvz4m8ZfscbtCqO5y2/otbO879uJ1Bf6OJqGyv2lnFlv4PvC2U1doqrbOwuqsIcpCU5IojYUL18zojT3scff8x9991HRUXFCZ1HoVDw/fffM27cuGYZV5MCE61Wy7Rp05g8eTIJCQmMHDmSUaNGMXLkSEl+F8esvNbhCywO5/F480oe/GYj9YeM6hzFe5P6k1FQxRt/7PY7vtBi47r3VvLbfSPky+YZyuE6thmTEP2BGZNa6WUiRGuTV1HHxPdXYnW4fdscLg/vL8nkqUu70ykmmJ2FB5d1TRqSQmTwsRdEqXO42JBd2ej+RRlFXNkvEYBCi5VHv9/Mgu1Fvv0mg4ZPbh5Az8QwCU5Ei7vpppuoqKjwpVW0BU3q/P7++++zc+dOsrOzefHFFwkODuall16iS5cuJCYmNvcYRRtl1B556l2rVnJo3LIoo5hVe8tYk1UW8Phau4ulu0uac4jiNOJwHmOOyYHlW9L9XYjW54+MYr+g5FCfLs/i6v5Jvt97J4VxaXrCcVXlUikVRIc2HsjUJ9LbnS4+XJrpF5QAVNY5mPj+KvIr6o75OYUQx65JgUm98PBwIiIiCA8PJywsDLVaTVRUVHONTbRxEcFaeiaYAu5rHxVM9oGkxEN9tnIfQ9pHNnrObXmN3wkTbZsv+f0oDRZ1aiUalYLyGglMhGhttuRWNLovq7SGHgkmxvWO55NbBvDODf2INemP6/zRIXruGtk+4D6lAi7rnQB4l3V9tnJfwONq7C4258pnjWjdXn75ZXr27ElQUBBJSUncfffdVFc3LCIxe/ZsOnbsiF6vZ/To0WRnZ/vtnzNnDn379kWv15OWlsbTTz+N0+k8aeNuUmDy6KOPMnToUCIiIvjnP/+J1Wrln//8JwUFBaxfv765xyjaKHOQjjev60PyYUuv4k16nrikKzMW72nwmGqbkyBd4zMt6UnhzT5OcXqwHwhMjra8QqFQYDJoKJPARIhWo6TKRmmVjd5HeA/vEB1Mh+hgXrmmNyM7RRMTenxBSb3zu8VwZV//1R0alYI3ru1LfJj3nHanh1q7q9Fz7A9w40yI1kSpVPL666+zdetWPvnkE/744w8efvhhv2Nqa2t57rnn+PTTT1m2bBkVFRVMmDDBt3/JkiVMmjSJv/3tb2zbto133nmHjz/+mOeee+6kjbtJOSbPP/88UVFRPPnkk4wfP55OnTo197jEGSIlIohv7hzC/rJa9hRVkxhuIMyo4Y7P1lFosTU4Pt6kb/TDKFSvZnCa+WQPWbRS9TkmmqPMmACE6jWUSGAiRIsrrrLyx44i3luSSWWtg+kT+xCsU1Nta3hH9sELOh9XPkljokL0PHZJV+4YmcaGnApCdGq6xZuIDtH5yg4bNCpiQ/UUWKwBz9ErMfBsvxCtxX333ef73+3atePZZ5/lzjvv5K233vJtdzgcvPnmmwwaNAiATz75xNf+Y+DAgTz99NP885//5MYbbwQgLS2Nf/3rXzz88MM8+eSTJ2XcTQpM1q9fz+LFi1m0aBEvvfQSWq3WlwA/atQoCVTEcYkJ9QYbBo2KGz5YxeOXdGv0rvc/xnShXUQQj13clZd+30mdw3tHKzUyiLcm9iUhzHAqhy5akWPtYwIQYlBTWt0w8BVCnDql1TYe/X4z87YdzON4+sdtvDahN0//uM03K2HUqnhodGcGpjbfjacwo5Ywo5aOMSEB98eE6nhodGf+/s3GBvtSI4Ok34lo9ebPn8+0adPYsWMHFosFp9OJ1WqltrYWo9G7UkWtVjNgwADfY7p06UJYWBjbt29n4MCBbNy4kWXLlvnNkLhcrgbnaU5NCkzS09NJT0/n3nvvBWDjxo288sorTJkyBbfbjcvV+PSnEIGUVtt46NuNlNc6mPbLDl66Op13/9zrS2YPM2p4eHQXRnSOItSgYdKQFMb0iKWsxo5WpcQcrCU6pGnT+qJtsB9j8jscmDGplhkTIVpSTnmdX1ACsDXPwjM/beMfYzqTGhmE0+3BHKQlOkSHVn3svUpOlEKh4Jwu0Tw7rgf//T2DiloHCgWM7BjFv8b1aPIyMiFOhaysLC655BLuuusunnvuOcxmM0uXLmXy5MnY7fZjDiiqq6t5+umnGT9+fIN9ev3JuQaaFJh4PB7Wr1/PokWLWLRoEUuXLsVisdCrVy9GjhzZ3GMUZwCL1cH2/CrAm3Q4ddY6JgxMZtKQFBwuDwlhBnomHuxRolWrSAw3kiiNsMQB9mNMfgdvyU9ZIy5Ey/pjR1HA7ftKa5kyaz1LHz6bxBYs/x4epOXaAUmc0yWaKqsDnVqFOUhLqEHTYmMS4lisXbsWt9vNSy+9hPJAb6+vv/66wXFOp5M1a9YwcOBAADIyMqioqKBr164A9O3bl4yMDDp06HDKxt6kwMRsNlNdXU16ejojR47ktttu46yzziIsLKyZhyfOFMrDOrZbrE7e/XMv7x74/cUre4HCQ63NRZLZSGSIDoPm1N09E63fsZYLBu+MSZnMmAjRoo70Hq5WKqh1uFiys5jkCCMRQVqC9ac+IFCplMSHGQBZJixap8rKSjZs2OC3LTIyEofDwRtvvMHYsWNZtmwZM2bMaPBYjUbDPffcw+uvv45arWbq1KkMHjzYF6g88cQTXHLJJSQnJ3PllVeiVCrZuHEjW7Zs4dlnnz0pr6dJgcnnn3/OWWedRWho6BGPy8nJIT4+3hetCdGYcKOWszpGsmRXwz4kSgVEh+gYN305AFqVkv+7uCvj+iRgkjtX4oBjbbAIEGrQUGVzYnO60J3C5SFCiIPO6RrN83N3BNx3QfcYPliayVers1EqYPLwNO4cmUZEMyS/C9GWLFq0iD59+vhtmzx5Mi+//DIvvPACjzzyCCNGjGDatGlMmjTJ7zij0cg//vEPrrvuOnJzcznrrLP44IMPfPtHjx7NTz/9xDPPPMMLL7yARqOhS5cu3HrrrSft9Sg8Hk/g1tvNIDQ0lA0bNpCWlhZw/59//sl//vMf1q5dS35+foOW9h6PhyeffJL33nuPiooKhg0bxttvv+3XXb6srIx77rmHH3/8EaVSyRVXXMFrr71GcPCxJ6ZZLBZMJhOVlZVHDbZEQ26357gaXDVmT3E1V81Y0aCM64MXdGZnYRU/bMzz2/71HYMZmBpxws8rWk5zXnuz1+dy31cb+OTmgWjVR74ZsiG7ghfm7mDFI+cQZ5I7oeLM1NKffZY6Bx8vz+LleTv9tseG6nnxyl7c8dlaX4ETgJeuSueKfm2jiXNzfW4K0dY0acbkWB0t5qmpqSE9PZ1bbrklYGLNiy++yOuvv84nn3xCamoqjz/+OKNHj2bbtm2+pJuJEyeSn5/PvHnzcDgc3Hzzzdx+++3MmjXrpLwm4eVwusmtqGPulgI25lTQOymMMT1iSQgzHNMa/0DaRwXzw9RhzN9exKIdRcSFGbikVxy/bM7np015jOoURVpUEGU1DuZtK+DNP3YzfWIoIS0wvS9aH/txVOWqn2krrbZLYCLEKWZzurA73QTp1EwaksKoTlF8unIfJdU2RnWKIj7MwCPfbfYLSgBe/2MXZ3WKPG0LndidLvIqrPy0KY+teRb6tzNzftdoEsKNR+2/JMSZ4qQGJkdz4YUXcuGFFwbc5/F4ePXVV3nssce47LLLAPj000+JiYlh9uzZTJgwge3btzN37lxWr15N//79AXjjjTe46KKL+O9//0t8fPwpey1nEo/Hw8acCia+vwrbgXX9v24p4JX5O/nitsH0SW56k8PEcCM3DW3HtQOScLjc3PbZWursLj6+eSALM4rYlFNJrEnPy9f0ZnNOJVaHSwITAXjLBSsVHNNdyFC9962vWEoGC3HKVNY52FtczQdLMymotDKiUxTj+iTQI8HE81f0xOXy8PaiPTz147aAj88uq8XlOmmLPE4ql9vD2n0V3PjhX76bKL9uKeDl3zP48vYh9JS+KEIALRyYHElmZiYFBQWcd955vm0mk4lBgwaxYsUKJkyYwIoVKwgLC/MFJQDnnXceSqWSVatWcfnllwc8t81mw2Y7+IXEYrGcvBfSBhVYrNw1c50vKKlndbiZMnMd3989jBiTHrfbQ3G1DbfHQ4heTbDu2AMInUaFUqlgcKqZ7gkm7vx8rV8X3p825fPoRV3QyF2m08rJvPbsTvcxNVcEfFV1SiUBXpxBWvKzr8rq4ItV+3h+boZv25p95by/ZC/f3jWUTjEhqJUcSDSHoe0juLBHHHqNkg3ZFcxen0tKRNBRl2m2VoUWK3fPXOsLSurV2F3c88U6vr5zyGk7EyREc2q1V3hBQQEAMTExfttjYmJ8+woKCoiOjvbbr1arMZvNvmMCmTZtGiaTyfeTlJTUzKNv20qr7RRXBb7TnFdppazWRqHFyntL9jJu+jLOe2kxD3y1kYyCKgoq68guq6WoKnA33UNpVEou653Ay7/v9AtK6r04NwOLtWF3YNF6ncxrz+FyH9MyLvD+2wrSqiiRGRNxBmnJz76SKhsv/JbRYLvF6uSJOVvILa8lu6yWwWlm3pvUj54JJt74YxdP/bCV4iobb1/fj6cv7XbaJr8XVdkor3UE3JdVWitVAoU44KQGJgpF67yb/cgjj1BZWen7yc7ObukhnVYch93xOZxGpeS+L9cz7dcd5FdaqbG7+H1bIWPfWMqOgioum76Mq2esYO6WfCrrAr9R13O63WzLD3xXz+n2+HqfiNPDybz2HC7PceU3hRm1jQbYQrRFLfnZ91dWOY2lna7cW8bWPAtnvbiQXUXVTF+4m3f+3EtRlc33+XH7Z2tO6/4hNueRG0873afnEjUhmttJDUxOpOBXbGwsAIWFhX7bCwsLfftiY2MpKvJv0OR0OikrK/MdE4hOpyM0NNTvRxy7yBAdukam0+NMekqq7azYW9Zgn93l5p0/93JN/ySySmu58/N1zNtWiOuIb8hHDm6d7iMHSaJ1OZnXnncp17HfDAkzaiiSwEScQVrys895lBtaLo+HqBAd5bV2NmRXNthvdbh56fedVNuOfDOrtYozGRp9fwrVqwkP0p7iEQnROp3UwGTbtm2kpKQ06bGpqanExsayYMEC3zaLxcKqVasYMmQIAEOGDKGiooK1a9f6jvnjjz9wu90MGjToxAYvGhUdrOOh0Z0D7nt6bPdGu/kaNCr2FFX7JflN+2U7RZbGl3WZDBrSIoMC7lMooHu8JAwKL+9SrmN/SzMZNEf8tyeEaD6D0syN7uuVaCK7tI4RHSP5K7PhTa16CzOKqKo7PZfvRgZruffcjgH3PX5JN2JCTs8lakI0tyYlv1utVt544w0WLlxIUVER7sPuWq9btw7gqOtXq6ur2b17t+/3zMxMNmzYgNlsJjk5mfvuu49nn32Wjh07+soFx8fH+3qddO3alTFjxnDbbbcxY8YMHA4HU6dOZcKECVKR6yTSaVRc2S+R1MggXvp9J5klNaRFBfHQ6M70Swkno9B/eVWy2cjfzu2IVq2ktNpGdIiO/7u4K6/N30VpjR2L1UFcI111o0J0vHBlL659d2WDqe57z+lIRLDcZRJeDpf7uEpuhhk0bC+QpYBCnApRIXpuGdaOD5dl+W3vlxzGvy7vya7CKoL14XSIDiEqRM87i/c0eM83aFW00hXiR2XUqpk4KIWO0SG8On8n+0pr6RgTzMNjOtMzwdTkMvtCtDVNCkwmT57M77//zpVXXsnAgQObnEuyZs0azj77bN/vDzzwAAA33ngjH3/8MQ8//DA1NTXcfvvtVFRUMHz4cObOnevrYQIwc+ZMpk6dyrnnnutrsPj66683aTzi2IUZtQxOM/PWxD5U1DqotbuICdXhcnsY0yOWlw40zEo2G3nmsu7883+bKTjk7nSfpDBem9CbKbPWoT1K5+1eCSZ++dtZvLVoN+v3VxBn0jNlVAd6JJqkVLDwsTvdx9T1vZ7kmAhx6pgMGqac04FhHSJ5a9EeSqptXNE3kX4p4Ux4Z4VfIZMLusXwn6vSeeDrDX55KdcNSD6tb0aZg7SM6RHLgHbh2F1u9GqVLOES4jBN6vxuMpn45ZdfGDZs2MkY0ynX0t1vWyv7gXLAgcozWuocfLk6m3//st1v+5V9E3lwdCe+X5/LC3MzeOmqdP77ewb5lQ2XzIzuHsOAdmau6p/ka3h3JHUOJ9VWJzq16rROghQHNee198h3m1idVca/Lut5TMcv2VXMW4v2sP2ZMRi0Rw6OhWiLWuqzr7LWjt3lpsrq5KLXl2B1NMw/uWNEGtvyLSzZVQJAh+hgPrl5ADGhetyewJ9Lh6qzewMdg7bVdkUQQgTQpCs2ISGBkJCQ5h6LaCUKLVa25Fby5epslAqYOCiFLnEhfjXWs0prGgQlAN+uy2FU5yiuG5TMWZ2iKLJYAwYlAPO2FfLgBZ2PKSgBMGjUGDTyISMCszmPL8ckzOi9U1lcZSM5wniyhiWEOIzpwLW3MCM7YFAC8OXqbN68rg+heg3D2kfQJT6UjMJqpv26g1q7iyv7JdI3OYxYk/8y4OyyWrbkVvLd+lyUCriyXyJd40JJDJdrXIiTafr06fznP/+hoKCA9PR03njjDQYOHHjc52nSt7yXXnqJf/zjH8yYMaPJye2idSq0WLln1nr+yjqYgPjb1kJGdY7ixSt7ER2ix+Fy88mKfY2e4+3FexjaIZI6u4Nqa+MlEt0eGjRpFKKpjnsp14GAuLjaKoGJEC0gs7i60X2VdQ4UeIucOD0ePliSyc+b8337/9hRRJfYED66eQBxB4KT7LJaHvhqA6v3lfuO+21rIcM7RDBtfC+SzHKdizODy+3hr8wyiqqsRIfoGZhqPq4czOP11Vdf8cADDzBjxgwGDRrEq6++yujRo8nIyGjQb/BompRt1b9/f6xWK2lpaYSEhGA2m/1+xOlr6a4Sv6Ck3qKMYjbsrwDA6fJQ2MgsCHgbMNbZneRX2AgPanw2xKhVHaUYsBDHzu50ozmuGRPvv80ii+SZCNESjlRVMSXCyO7iGpbtLsFk0PgFJfV2FFTx/bpcX8n5pbuL/YKSekt3l7J+f8PtQrRFc7fkM/yFP7j2vZX87csNXPveSoa/8AdztzS8hprLyy+/zG233cbNN99Mt27dmDFjBkajkQ8//PC4z9WkGZNrr72W3Nxc/v3vfxMTE9NqGymK41Nea+fj5VmN7v9oeRZDO0QSpFVxdpdoNuVWcFW/JPq3CwdgZ2E1X6zaz6A0M3q1ihC9BrfHQ9/kcNYF+FC4blAyc7fkkxxhlCR2ccKOd8YkWKdGrVJQKCWDhThlbE4XJVU2HC4PHWOCiTfpyQtwo+u2s9L4fOU+hneIZN62wgBn8pq5aj9X9EsEj4cv/mq8YeQXf2UzrEPkads5XohjMXdLPnd9vo7Dk8cLKq3c9fk63r6+L2N6xDXrc9rtdtauXcsjjzzi26ZUKjnvvPNYsWLFcZ+vSYHJ8uXLWbFiBenp6U15uGilPB6PL+E9EJvDjdvtQaFQcHHPWDrFBPP+kkw+XJaJxwO9k8L417judIgKJsyoQa9RsWZfKX+/oBOfrdzna6Zo1KqYOCiFZLOR79blcOuI9qfwVYq2yuZ0H1fJTYVCgdmopVAqcwlxSuRX1vHWwj18vSYbm9NNv5Qw3p3Un2d/3sbKA015w40appzdgeyyWnYUVNE1LpQaW+O9S2xOFx6Pd+nKET+/nK6jNPMV4vTmcnt4+sdtDYISAA/edtVP/7iN87vFNuuyrpKSElwuFzExMX7bY2Ji2LFjx3Gfr0mBSZcuXairq2vKQ0UrZjJouTQ9jv/8Hri3w/i+Cb5qWE63h799uYGyGrtv/4bsCqbMXM9P9w5HpVISZ9KzNdeC3eEhMljH2xP74nB5L5kfNubx3pK9PHNZd0L1ktAuTpzd5SboOKtrhRu1MmMixClQUmVj6qz1rD1kqdXafRVMfH8VMyb25e5RHqqsTsBDvEnP+0uzUCkVrNlXxpRRHfi9kVmTC3vEYg7SolDA6O6x7GikN9FFPeOIDtUH3CdEW/BXZlmjxYbAG5zkV1r5K7OMIe0jTt3AjlOTckyef/55/v73v7No0SJKS0uxWCx+P+L0pFIqGNcnkThTwzfvlAgj53TxJjB5PB5+31ZIWY2deJOeZLPRF33bXW7eWrSbWpuTlAgj4/sm0iclnD+2F3L7Z2uZMmsdU2at47etBSSbjZzXVZYCiuZhc7rRHGeTsjCjhoIjvJELIZpHTkWtX1BSr7LOwYPfbqKg0srUL9bx6YosNCol/768B4seHMVntwxiWIdIesQ3LGccZtRw61lpaNVKNColl/dJID7A51ey2cj53WIabBeiLSmqOrbPsmM97lhFRkaiUqkoLPS/eVBYWEhsbOxxn69Jt6rHjBkDwLnnnuu33ePxLvNxuRqvxCRat4RwA9/cOYSZK/cze0MuSoWCq/olcvWAJOLDvJVPrA43lTV2PrxpAPtKa7A53XSOCWHp7hI+XJbJyj1lVNmcGHVqhnWIpMBSx/s3DuDHjXnM3pCLAm8JxwkDk33nFOJE2Z2u4+6ebA7SkiHd34U46eqLpwSSW1FH7+QwVj96HlqVgtAD5YTrywoDBz5DclmdVYbbA6mRQUwclIJaCT9syMPlcTOgnZmZtw1m5sp9/Lw5H6VCwaXpcUwYmExKRJDfc1bU2imqsrEpp4IgrZoeCSaiQnToNdLTSJyeDm3p0BzHHSutVku/fv1YsGAB48aNA8DtdrNgwQKmTp163OdrUmCycOHCpjxMnCYSw408cEEnbhrWDgUQEaRFpVJSVmMjt7yOWruLsCAtt326xm/N7tX9k3js4q58ty4Xu9NFrc1JUZWNJTtLKK22M6pzFNcOTEKvUWEO0h73l0ghjsTudKM5juR38C7lKpIcEyFOuiMto9KoFNTanfy4MY/hHSJJjQpq8OXJqPMWXXF5PNTa3JzTJYpCi5VJH67C5jz4OXTPqA7cc04HJg3xtjKIDdWjPSzYKK6y8fyv2/nfuly/Mbx6TW/O7hKNUZoyitPQwFQzcSY9BZXWgHkmCiDW5C0d3NweeOABbrzxRvr378/AgQN59dVXqamp4eabbz7uczXp6hs5cmRTHiZOIxqVkhCdGhSgUikprrIyY/Ee3B64vHc8HyzNbJBI+PWabP51WQ9uGZ5KXoWVVZnlPPTtRjwHDntj4W76Jofx1sR+EpSIZmd3HV+DRfDOmFTbnNTYnATp5MuIECdLj4RQdGplwN5VF/aIY+aq/XyzJoc3/thNz4RQ3r+xPyaD5kBeooeZK/bzwm8Zvse8/scuRnaK4t/je/H3rzf6tr+xaDcD0syM6BTV6FjmbSvwC0oAHC4PU79Yz/z7R9I+OvjEX7AQp5hKqeDJsd246/N1KMAvOKm/Zffk2G4npZ/JNddcQ3FxMU888QQFBQX07t2buXPnNkiIPxZN+iT+888/j7h/xIgRTTmtaCUKLFZWZ5bx5er9KBUKbh7WjtTIINpHBfPTpnxemreLu0d1ADw89cM27K6DHzRfrt7P4xd3w+n2+AUl9dbtr+CzlVncd14nXz5AWY0Nt9u7XlgCFtFUTZoxCfIuFSmwWGkfJV9GhDhZ1EoFb17Xh6mz1vsFJ93iQrhpaAoLdxTz0OjObM2rpHdSGBuzK/l6TTbVNidjusfSISaY2FA9BYcUq1i8s5hBqWZ6JpjYnFvp2z594W56J4X5irUcqqjKytuL9wQco8cDszfk8vcLOjfjKxfi1BnTI463r+/L0z9u80uEjzXpeXJst2YvFXyoqVOnNmnp1uGaFJiMGjWqwbZDE5glx+T0lV1Wy5bcSnYWWGgXEUSvRBPtIoK487O1ZBQe7NK7eGcxwzpE8MTYbjw2e4tve5HFRoHFys7CqgZBSb1Pl+/jhsEpKBQKluws5qPlWVgdLi7sEceEgUkkhkt3XnH87MdZLhi8pUkBCiUwEeKkyi6rpajKxqe3DMSDB4/Hu3xKq1aRVVLD0t0lVNQ5GNo+gn4pZp6Ys4Wted5iOiv3lhFv0vPCFT259dM1vuqOAN+uzeGaAUl+gUmBxYrN6QIaBiYut+eITVUzS2qa70UL0QLG9Ijj/G6xp7Tze3NqUmBSXu5fWcPhcLB+/Xoef/xxnnvuuWYZmDi1ympsLNtdymsLdpFfUUe3uFDuGtWePcXVfLcu1y8oqbdsdymXpicQZ9L7IvNeSSZKq21U1jkafa4qmxO7080j321m2Z5S3/Y3F+7my9X7+e7uYSSbJTgRx8fuOv4ZE/OBGRMpGSzEyWXQqlErlazdV85Xa7IpqbLx5NjuLNtTwpwNeb7jMktqmL0+l+kT+3LfVxuoqPV+luRVWvlpUz5jusfy46aDHawr6hwNlmEOaGcmuJGlmQaNit5JJlZlBu4EP6pz9Im+VCFanEqpaNUlgY+kSetmTCaT309kZCTnn38+L7zwAg8//HBzj1GcZFVWB+8s3ss9X6xnd1E1NXYXq/eVc8sna+iVGMYvm/MbfezcLQW+tbwqpYKJg1L4cvV+Rh5hfe+5XaPIKq31C0rqlVTbee/PPUdslCVEIA6n57hzTHRqFSF6NXkVEpgIcTLV2l38saOIF3/LYF9pLS6PhzCjxi8oqVdjd/HB0kyu7Jfot/3nzfmc3cU/cBiSZiaj4GCbAq1KyZ0j0zA0ksAeZtTyjzFdCVSlPipYx5C05k8MFkIcu2Zd0B8TE0NGRsbRDxStSmm1nXeX7A24b0dBFc4jdMt1ud2oFAraRwXz2jW9mb+9kL+d1wmVUkGnAAmESgX830Xd+HZtdqPn/HFTvl/jRiGOxuX24PJ4UB/njAlAZLCOvAppGCvEyaRRKvhta4Hv9+7xJv7KKmv0+KW7S+ibHO63zeX2+C1H0amV3Dg01VeVq3NMCF/ePvioM+5dYkP45OaBfscN6xDBV3cMJkGWEgvRopq0lGvTpk1+v3s8HvLz83n++efp3bt3c4xLnEK7i6obzQdZmFHE2PQ4pi/cQ6hejUalpKzW7jv+yn6J9EgwccvwduRXWmkfFcT363O4c0R7Prx5AO8s2sPXa3OwOd30TDDx1KXdSAjTH/HOtkpxeD0JIY6sfobteBssApiNWvIlMBHipDo0BwS83xtUh0xbBOvUGDQqSmtsuD2gVChQKRREBmsprfF+5ozpHktSuIG3JvYlVK9Gr1ExY/EeHr2oK3eOTMNk0BARrDvqWIw6NSM6RfHtnUOwWB2olUrCg7SYAiTLCyFOrSYFJr1790ahUOA57Nvs4MGD+fDDD5tlYOLUMWgbNpRSKxWkRQWRX1HHk5d0o19yOGW1dursbhLDDSzYUURWSTUdo4PZWViN3ekmyWzgvK4xXN47AfOBD4fHxnbjzrM74HZ7CNKpMAd5t08YmMR363MbPC94g52IYG3AfUIE4gtMmpDcFxGsJatUEl6FOJmC9f5f+rfkWrj77A4s31PKHSPTcLg8WOocJJmNrMkqI6ukGpNRw73ndiQp3MiqrFLO6hDF43O24nJ7yK2o4/YRaaQnhhGsUx+xT0pjokP1TXqcEOLkaVJgkpmZ6fe7UqkkKioKvV4u8NakuMrG/rJa1u8vJ9akJz0xjJhQHVq1fyCSEmEkKdxAWlQwdQ4X6YkmhrSPZHNOBWmRQWQUVnP/Vxuoc3irrSkUMGlwCs9d3oNbPl7DnuKDX+o6x4TwwY39qV+lq1OrSAjQ3T0tKphLesXx0yb//JXEcAOThqSgUUn3XXHsbAcqATal3HREsI4VAfKdhBCB5VfUsbOoip0F1XSKCaZjTAjxAd7nD9UnKQylAupXBttdbnLKanng/E787av1WOqcvmNHd4/l7xd05oq3l1Nl9W6/dmASkcE6Lu4Zh9XpomdCGFtyK+kRH0rkMcySCCFOD00KTFJSUliwYAELFiygqKgIt9s/UVlmTVpefkUdd3y2hk25B5MCdWolH900gP7twv2CE7VSwVOXdmfetkIu6BbDst2lzFy1j6QwA117xTH61SV+zRQ9HvhkxT46RAejVioY0TGSGJOevIo6lu8p5b6vN/DuDf19FY8CiQzW8dTY7lzZL5GPlmdRa3MyrncCZ3eJPuoHnBCHO7iUqwkzJkFaqmxOqqwOQvSylEOII9lTVM21762kqOpgyd1zu0TxyEVdWbm3jJ2FVfRNDqdfSjhKJfy+tZDMkhrO7xrDs+N68Oj3B8vLJ5qN3DVzLVaH/3eI37YW0DUuhNvOSmN/WS0bsiv44q9sOsUEU2N3UlBpZc6GPLrFhdI5JhiL1UGYsfHPm5IqG1mlNfyyOR+9RsXFveJICDMc8TFCiJbRpMDk6aef5plnnqF///7ExcX59TARLa/O7uS1Bbv8ghIAm9PNzR+vZv4DI0k6kPRXUGnl1k/WsCXPQpxJz0U94hjWMZK6bS6yymr5ZXMB70/qz39+y2Bbvv/5tuRW8sIV6czekEtWaQ1dYkO5/aw03lq0h7Ia+xEDE4DIEB2jOkczKDUCp9stXwpFk9UHJk2ZMam/25pfaZV/g0IcQXGVjTs+X+sXlHSLC+WKfklc/PpSX+PET1fsI8yo4dVrevPun3vJr7Ty6Yp93DgkhR+mDuO7A13X8yvqGgQlAH87tyOdYkL4fWsBFquT6wYm0TclnLwKK2v3VWB1uJg8PJUQvYaSajtOV+NVHIssVh76dhOLdxb7tr21aA+3nZXK3aM6+JqsCiFahyYFJjNmzODjjz/mhhtuaO7xiGZQWmP3vfEfzuZ0szGngiSzEYfLzW9b8hnTI44r+iWSGhlEbmUtj36/xZfcviijmBCdmteu7cOj3232dd3tmWBiWIdIrnpnua/Z1aKMYj5fuY9XrumN8ziabHpzXGTplmi6+n+DTc0xAcitqKNTTEizjkuItqSsxsbuIv+eVrePSOOx2Vv8urkDVNQ6eP7XHdw8LJV//7IdAJNRQ2WtnREdIw+U6a4jSKuixn7w8+LuUe0pqrLy2oJdvm0D2oXz/pJMv6W/87cX0SU2hEcv6ord1XixlAU7ivyCknrvLclkTI84+klgIkSr0qRywXa7naFDhzb3WEQzsTvd2I94B8l7t6uw0orV6eaHjbnMWLwHg0bFUz9sa1Chq8rm5KXfM5g0JMW3bfLwVJ76cZtfB17wBj7/+mkbOo0EGuLUOZEZk3CjFpVSQU65VOYS4kjq8wzrqZUKtGplo+XddxRUkRrpnZ2/YVAy/VPM7Cmp5aV5O5kyaz2/bStk+sS+DO8QCXibH6YnhfHFXwfLyYcZNcSHGRrkI9aff/mekkaXcJZU2/hgaWbAfQCfrsjCcYTPSiHEsfnzzz8ZO3Ys8fHxKBQKZs+e3eRzNSkwufXWW5k1a1aTn1ScXEE6NYnhjedp9E4Ko7TaxtM/bmXarzvYWViNzekms7SmwV2velvzLLQ/pC+JXqNq9MMov9JKje3YZ0yEOFH2AzN0TckxUSkVRAXr2C+VuYQ4onCjFvUhs5IalZI6+5Hf6x0uD0oFXNU/ie/W5fLUD1vZmmehqMrGL5sLuO3TNdwwJIXEcAMDU838edjsxlkdIpm3rbDR8/+4Mb/RG3Eut4cqq6PRx1bUOnC5JTARbZDbBZlLYPO33v+6T+53spqaGtLT05k+ffoJn6tJS7msVivvvvsu8+fPp1evXmg0/uuyX3755RMemGi6mFA9/3dRV+6aua7BvvREE8lmI9lltczbXuTbrlEqsQVY63uo+gT42FAdofoj/9NxN9YYRYiTwHYCfUwAokN17C+rbc4hCdHmRAXrmDQkhQ+XZQHeGRRzkNav2tahQnRqPB7oGB2C3eVm9oaGS4wdLg+vL9jF9YNSWL2vrMHNMbVKSbXN2eBx9WxOFypF4OveZNAwqlMUX63JCbj/4p5x6DVN+hokROu17QeY+w+w5B3cFhoPY16AbpeelKe88MILufDCC5vlXE1usFjfSHHLli1++yQRvnUY2iGCGdf35dmft5NbUUeYQcOF3WO559yORIbo+Gh5JqM6R3F1/yQUCm9Tw1iTHoWCgM0WE8IMJJuNvH19XxRArEmPXqMMmLgYolMfU5MrIZqLbylXE3JMAKJDdGSVSmAixJEYdWruPrsDUSE6ZizeS2WdgyW7i7l+cAqfrtjX4Pg7R7Xn6zXZJIQbWL+/gshgLZOHp9IxJgS7041GpeCXzQV8vz6Xl65K59xu0RRUWvl27cFAYu2+cm4fkdborMmFPWIJDwpctEKvUXHnqA78uCmf2sNmdhLCDAzvGHkCfw0hWqFtP8DXk2jQpNqS791+9acnLThpLk0KTBYuXNjc4xDNzGTQMqZHHD0STFTUOiitthEdqkej9t5Z6pMUjkGj5qFvNvoSD+85uwOTh6fy/hL/NbkKBTxwfiee/nErPRNMXNQzjryKOu4e1YGX5+1s8NxPXtqd6BAJTMSpcyKd38E7y7h8Tykej0durghxBJHBOm4/K43LeidgdbjQa1R4PB6Swo18uCyT/Eor7aOCuHtUB3olmvh8xT6MOhWXpsfx8tW9efG3HWyZmwGAVqVkwsAknrmsOyi8+Y/J4UYGtjPzV1YZAPvLajFqVXSNC2F7fpXfWMKMGm49Kw2duvGcxmSzkR+mDuPFuRnM316IRqXkir6J3DWqvZSmF22L2+WdKTk8KIED2xQw95/Q5WJQtt48YJnDbCOqrA5Kqm1szKlErVSQnmgCFEz9Yh0bsyt9x/VOMvHmdX2xu9z89/cMv3O8sXA3j13clTeu7cOMxXvIr7TSPT6Uu0a2R6GACQOSWbyzCJ1axXXvreKmYe14+ep0Pl+5n6zSGtpHBfHA+Z3oHm9q8hdEIZqifo25ugk5JgDRIXpq7S5Ka+zSrE2Io1CplL4v9aXVNm7/bA1Ol4cpZ3cg3Kglv7KOtxbtoU+yiVm3DaLa7iRYq+b6D/4it+JgkQm7y82nK/Zx77kdWb+vnEe+34JBq+TjmwayNc/CpyuyqLY52ZBdzozr+/Hr5gI+X7UPm8PNmJ6x3Do8leQDpe8bHatSQYfoEF6+ujcWqwOFwpsro5cCLaKt2bfcf/lWAx6w5HqPSz3rlA3reElg0gaU1dj5YEkmby3e7VuG9eAFnViUUewXlABsyK5k5Z5S3lsSuFLJsz9v55Wr03l+fE80aiWVdQ7eXriHwior53eN4f7zOzFl5nrsLjfv/rmXOJOeK/olMr5vAnkVdaiVSkIN0gtCnFonPmPiDUb2l9VKYCLEcSiptrF2XwUAG3P8P2+ySmsY3T2Of/+8jSfGdvcLSg71yfIsnh3XA4A6u5tr3l3Jq9ek8/UdQ3B7wGRQY9CquX1EGuP7JuDxQHiQFq362K/3YL2a4KPkRgpxWqtuvEhEk45rIXKVtgGbciqYvmi337aucaH89/eGy6wUCm9jw93F1Q321Vuyq4RftxTw+7ZC2kUYuaB7LD0TTazdX86YHrF+jRbzK628+cfB544K0TEg1dwMr0qIY3eiOSYxoXoA9hbX0Dc5vNnGJURbV3xIs8XDaVVKwo0abh+Rxs7CqkaPq6xzoDxsCeW/ftrOj/cM91tupVQqiD5wrQohDhMc07zHtRAJTE5zlXUOv8CgXqCk9O7xofzfRV0xalWkmI3sLQlcHrVbfCg/bvROB2aV1vLun3t9+0qr7QTr1I1WSUmLCmrKyxDihNhdbtQqRZPzQ/QaFdEhOnYd4cuTEKKhxgqdXDMgifO6xvDFX9lY6uyM65PQ6DmCdWqiQrQ8eEFntuRVMn9bIWFGLXanm5JqGyaD5rhmQ91uDxV1dkBBuFEjeWPizJAy1Ft9y5JP4DwThXd/SvP3Iayurmb37oPfRTMzM9mwYQNms5nk5OTjOpckApzm7E43RQHuWHm7qR8UHaLj2XE9sFgd/G9dDjcPaxfwfDq1krM7RzXo7ltvye7iRh8bGaylY7R0zhannt3pRqM8sbezhHDDEe/qCiEaigjS0jXO/31/dPcY2kcFc9una/jfuhzmbS/C7fGWGw7khiHJzN9exPfrcwgzaJgzdRj3ntuev325nmveWcnLv+9kf1ktnmMoQ59XUceHyzK59t1VTHx/JZ+s2Ed+pTRPFWcApcpbEhiAw4PxA7+Pef6kJL6vWbOGPn360KdPHwAeeOAB+vTpwxNPPHHc55LA5DSnVEDf5DDf7woFjOoURahezeeTB/LIhV1IjQzinxd2Yd3+cu78fB2frdxPtc3JrWel+jWkiwzW8sa1ffjfulxmXN+P/ikNl7RolEpuGJziKzNcL9lsZNZtg6XKiWgRtgOlR09EYpiBHQUSmAhxPJxuN49c2JXu8aG+bVf3T+I/v+1gWIcI/nNlL6Zf1xerw8kr1/T2S1ZXKODyPgkMTo2gW1woD5zfmYRwA3/7Yj2xoQbuHJnGA+d3QqNWcMvHf7HvKCW98yrqmPj+Kp79eTsZhVVsz6/iqR+2ctNHf0lwIs4M3S71lgQOjfPfHhp/UksFjxo1Co/H0+Dn448/Pu5zKTzHcguijbNYLJhMJiorKwkNDT36A1qBOoeL3YVV7CioomtcKJe/tQwFCl6+Jp0N2RV8vSYbS52TTjHB3DmyPb0STYx9Yxl1joO13Mf1TmBsehxWh4tYkwGTQU1mSTW3fboOnVrJjOv7cf/XG6io9XbONWpV/Pq3s0iJCKLK6qC02k5xtY0grZrIYK2s/RXHrbmuvVfm7eTzlft487q+TT7Hkl3FvLVoD5ufuoAQvRRwEG1fc1x/q7PKuP3TNdw1qgPto4JwuNwogBq7i91F1Xzx137Kax20jwri/vM6YTJoMBk1VNucRARpqbW7+G5dDv9bl0ut3UWvRBN3jmzPzgILdQ437/y5l34p4dw5sj27Ci3cNCwVo7bhKnSPx8OHy7L410/bAo7zP1f24qr+SU16jUKcdtwub/Wt6kJvTknK0FZdIvhQkmNymtpbVE2BxcYnK7J49KKuvH5tH0qrbXy1Opslu0p8x+0srOaBrzfy1sS+pEUFsTXvYOL67A25zN6Qi0qp4M3r+lBUBd1ivR9OGpWSnPJa3p/Un6IqG0atimSzkcRw792uEL2GEL2GdpGSUyJant3lPuES1fX/tncWVtMvwGyhEMKrosZGSY2daquTYJ2aC3vE8Z/fduBweYgP0/PGtX347s+9/Lb1YPWfPcU1TP1iPU+O7cbgNDMrdpcwpEMk037Zwebcg9W8NuVUMmXWOt68ti9GrYp3/tzL2n3l3PX5Wt6d1I/iKhu19lrCjBpiQvQoDxS8KK+187+1gTu8A3y5OpvR3WOlaqQ4MyhVrbok8JG0+qVcTz31FAqFwu+nS5cuvv1Wq5UpU6YQERFBcHAwV1xxBYWFrbsUWlNZ6hxkltSwLqsMhULB1Fnr2JJrobjKxvt/7qFTTIhfUHKof/+ynYmDAicgudweFCj4eFkWVTYH3eJCefO6PszbVsiVM1Zw98x13Pn5Wr5bl0t5rf1kvkQhmsTqcKFRn+BSrnADGpWCjdkVzTMoIdqgnPJanvl5O2NeXcK4t5Yz9o2lVNucvDahD1qVEo8b9GqVX1ByqLcW7kGjUhIfpqfO7vILSup5PPD24t1+Hd2dbg8fLM3E4XRz4WtLuPSNZczbXugrxKJAwZHSzFRKBZIDL0Tr1+oDE4Du3buTn5/v+1m6dKlv3/3338+PP/7IN998w+LFi8nLy2P8+PEtONqTo8hi5fE5WzjnpUV8tDyLD5dlYnO6MWhULN5ZzJSzO7Iqs6zRx+eU1xFu1AbcFxuqo0N0EDcObUdehZWXr0ln2e4S/jwkyLE63Ly5cDczV+3H4XIFPI8QLcXqcKE9wRkTjUpJWlQwa/Y1fh0JcSYrqbLy/C/b+W5dLk63dxW40+3hh415zF6fy6ShKYQa1GQ0UkSiY3Qw/3dxVyrrHHSMCWVzTsOgpN6WXAu2w6pLrtxbhuvA6vPiaht3fLaWnQfywsKDtFw7sPHqP9cPTpElmkKcBk6LwEStVhMbG+v7iYyMBKCyspIPPviAl19+mXPOOYd+/frx0UcfsXz5clauXNnCo24+dQ4nry3YxZwNeXg80CEmhG35Fv7v4q68ck1vEsMNVNQ5SD3Csiqlwlt16PA7RjcMTua9Gwcwf1shu4uqUauUfLMmmxC9hlsCVN9678+9FFkar1svREuwOtzH1WytMR2jg1mTVX5M1X+EONOU1Tr4eUtBwH3zthdydudolAoFpgDLpbrEhvCvcd0JNajZkmth6e4S+qaE8+y4HgTrGq4q12uUlFT7f9aYDBpyyv2T2P/7ewaVdd48yHO7RNMjvmGuTL+UcAZJfy0hTgunRY7Jrl27iI+PR6/XM2TIEKZNm0ZycjJr167F4XBw3nnn+Y7t0qULycnJrFixgsGDBwc8n81mw2Y7+IZnsVgCHtdaFFfZ+XlTPmN6xBKiU5EWYeSF8T2Z9ssOlu8t9R33wY390amV2JwNe5ic3SWarXmVzJkyjA+WZpJdVstNQ9uxq6iasW8s9Tt24qBkwo0K+qaE8936XF/yO0C1zUmNTWZMRNOcrGuvOWZMADrHhPDTpnxyK+p8OSdCtBUnev1V1jloLGb3eLyzJ+P6JBARrCVIq6LGfvCz4rGLu1Jabefv32z067PVIyGU1yb05u6Z6/w+uy5Lj+fXw4Kg6wcl88mKLL9t2/Mt1NmdmAwaYk0G3r+xPyv2lvLlX9koFQomDk5mQDuzr4nqsbA73aiUClRNbNgqhGi6Vj9jMmjQID7++GPmzp3L22+/TWZmJmeddRZVVVUUFBSg1WoJCwvze0xMTAwFBYHv6gBMmzYNk8nk+0lKat2VOhxOFy9c2Yv+KeFc2T+JlAgjC3cUkWg2ctfI9ozpEYtGpeCDpZn8e3zPBt2vk81G7jmnI58u38fijCKu6Z/E8I6RhOg1vBGgOePMVfuJNelYsL2IS3r6l5zTqBTotadHZQfR+pysa6/O7mqWGZMusaEoFbB4Z3EzjEqI1uVEr7+go7z3K4Bpv+7gmR+38ca1fdAduCajQnSYDBoe/GZTg+a/W3It/LAxj7Hp8b5tXWJDGN0jjt+2Hvwc75scRq/EsAZ5lPFhBrTqg+OKNRm4vE8ib17Xhzeu7cMlveKPOSjJq6jju3U53DVzLY98t4lNORVUSF6lEKfUaVcuuKKigpSUFF5++WUMBgM333yz3x0ggIEDB3L22WfzwgsvBDxHoLtGSUlJrbJcsKXOwUfLsliZWcr4A51zB6SEsaOwhu/X51BldXJFv0S6xISwbn85a7LKuWV4Kst2l1BWa2dAipmEMAN/7iwiWK+hQ3Qwm3IqOL9bLNN+3c787UUBn3dI+wjO6hCJzenmtQW7fNuv6Z/IU5d1x6A5LSbbRCtzsq69a95ZgVqpYOo5HU94jM/9vI3wIC2fTR50wucSojU50esvq6Sa+7/aSHZ5LZf3SSQ+TE9ehZXv1+fQOzGMJ8d281XIK6yso9ruZmdhFUatiiCdmge+3hjwvFqVkq/uGMxPG/Po3y6cYJ0G1YFCFEVVNs7tEk1WaS0/b8pnZKco1CoFq7PKmb+9kBnX9+X8brG+c+VX1rFkV8mBGRO4blAyQ9tHEms6cnCSU17LhHdXNlgqdt+5Hbl5eDtMhsA5mkKI5nXafbsMCwujU6dO7N69m/PPPx+73U5FRYXfrElhYSGxsbGNnkOn06HTBe5A29oUWKy8Mn8n3901lCfmbGH61V1Yu7+Sgiord4/qQJBORXmNna/WZLOrsIqbh6WiVSu4tHc8e4qrmf7HHnIr6hjZKYoxPcL4bl0uk89qR25FXcCO8fVKq20khhv8ApeRnSK5//zOEpSIJjtZ157V4cIc1DxfHAa0M/PZyn1U1jowGSVZVrQdJ3r9OZ0uXrumJ9sKavlkeRY/bMwlNTKI/16VTrhRw/1fbySnvI5OMcHcc05HooLVhAdFYDZq+H1b49Uy7S43Rq2K24bGoawuxo2HTHsYl/SKw+ny4HC6iQrREqJTo1YpUCoUhHXTcM857Uk6ZMllQWUdt3y8mu35B5Pv1+wrJz3RxDs39G80OLE6XLyxYFeDoATg1QW7uKhnnAQmQpwip903zOrqavbs2cMNN9xAv3790Gg0LFiwgCuuuAKAjIwM9u/fz5AhQ1p4pM1j3tYCrumfiEbh4rOrkqlQGVCrvTkn//0tg/gwA3eObM+1A5Jxedzc+fk6xvVJoKTKSrhRx41D2/HA1xuY9dd+5m4t4NVrerMlz8KuwioGpJjZ1EhVlD7J4XSNC0WnUTKkfQQxoTo6Rocc9a6TEC2hzuHyW85xIgakmvl05T6+X5/DTcNSm+WcQrQFUVoHv+yq5pHvt/q2FVpsrNxbxqMXdcWoVVFgsVJUZeXCHnFEBGtwub1VJbsFSEqvF2/Sszm3kn/8bzMjO0bx2CVdWbo9m89nbaLG5mREx0juP78T+ZVWpi/cjc3pZmTnKBLCDFTbXFistXjwznoUB7jhtjGnkpV7SxjXJzHg85fV2Pl+fV6j45u7pYBOsSHH/ocSQjRZqw9MHnzwQcaOHUtKSgp5eXk8+eSTqFQqrr32WkwmE5MnT+aBBx7AbDYTGhrKPffcw5AhQxpNfD/d1DlcPDoiguBtH1GTNIq1FUoe/GaTb39OeR2Pzd7C9YOSuWFICp9NHsiVby/nxSvTmfzJGganmfnHmC4889M2ymrs/LQpn9RII6U1di5LT+CrNdm+OvD1DBoVNw5J4blftrN8dykGrYoqq4OZtw4mySwJwaL1sTrcaFXNk6gabtQyoF04H6/IYtKQdr4GbkKc6cpcWp79eUfAfa/N38l/r07nz10l3DGyPWlRRnIrrITqNdhdboJ1am4fkYrD5WFo+wgcLg8Ol5uv13gbH366Yh8ut4cLe8YyddZ6tuUfTMz/I6OYZXtKeeeGfiiU3hmWvcU1OFwe/m/2ZhbvLMbjgYGpZl68Mp3XF+xiw2H9iGau2s+5XWMClgz2eMDhblg0pl613dnoPiFE82r1gUlOTg7XXnstpaWlREVFMXz4cFauXElUVBQAr7zyCkqlkiuuuAKbzcbo0aN56623WnjUJ66k2kZZjZ3r0k2ELP83RZ0nUGHsxL9n/RXw+Jl/7Wfi4BS+WZPNJ7cMYl9ZDQaNipV7y5g0pB3BOjXVNicLdxRx9rju7C6q5oW5O3hvUj9embeTv7LKAeiXHM4/L+zCW4v2sCjDmwBsr/O+YbtPr3QkcQaxOZsn+b3ehT3iePKHrczbXsjo7o0vCxXiTFJa4/CrtGXQqKhzuAjSqrhxaDsSw43MuL4vaZHBuD0ePl+1n1+3FKBWKogM0vH29X15/Y/d3PHZWtweCDNquPecjsSG6tmUU0lMqA4P+AUl9WxON5+u2McnNw8kv9JKamQQ17630q9q5F+ZZWzOqeSdG/px6ydrsLsOBhseD41WFNOqFAxtH8Gy3aUB95/TJbppfzAhxHFr9YHJl19+ecT9er2e6dOnM3369FM0opNvT1E193zhvWO06JYkirreQGVoZ2xOD2U1gSuEeDywKaeCiGAdizOKGNU52hdIbMiuoGN0MOuzK9CoFEQF61ixp5S8Sis/b8rnmoFJ3DoijcQwA8t2lzBl1roG+Sc6tZJkmS0RrZS3KlfzVYvrFBNC9/hQXp2/kwu6xaCQltFCoFEqCNWrufvsDnSJDcFqdxGkU2MO0vLCbzt4a9EewNs36/pBydx2VhqXpsdjsTrpEBXMX1ll/LmzmAO9GamodfDMT9t45MIu9E8JJ8yoYdXewMEBwJ87ixnXO54vVmfTLyXcLyipV+dw8cPGPEZ3j+HHTfm+7dcOTCY0QH8VAKfbzW1npbEmq7xBuf0h7SOwOaREvhCnSqsvF3ymyauoY8J7K9mWbyHcqEEVEs0aWyKzVudQaLEe8bEJYXo+Wb6PyBA9JdU2X6f3yCANVqf3jfXS3vFU1jnIq/Sea2w3E0PbmeiZYKJjdDDd4k2UBgh+nhzbjaiQ06NggDjz2JzuZuljcqjxfRPZnl91xKRdIc4k0UZ4b1J/UiKMVFmdZJXW4vJ4yCmvI6PgYMJ5bKie87rF8rcvN3Dbp2v5+9cbuWz6MpbsKuHVCb3RHLbs8v2lmdw2Io2kcGPAZov1gnRqbE433eNCWLGn8QBmTVYZXeIO5rR0iwuhX0o4czbk8uvmfPaX1lB3yPIsDwpmLNrNe5P6c1HPWMKNGtpFGPn7BZ2YMCCJ7QWBO9kLIZpfq58xOdNszav0Je9d1DOW2VstGPVqPlmeRUyIjh4JoWzJbTjNHaxTk2x08Ob4NB76MZOHRnemXaSRwiorZ3UwM21uBh2ig7msdwLj314GwPieZjoUzSNC3w1SvMUC+qeE88u9Z/Hun3vYnFtJstnI3aM60DE6GL1G+peI1sft9ngDk2ZcygXQLS6U7vGhvDJvJ+d3jZFcE3HG0+EClEz7ZQf7y2p925PMBv5zZTp//3ojxdU27j+/E49+v7lBlatFGcWYg7SM653AN2tzADi/WwzXDUwmq7QGjVrJxT1i+GTFvoDPf+2ARH7ZXMCIjhHsD1BBq545SIteraRngokJA5LoHBvCpW8uxWL1BiNqpYLnLu9J7yQTv28tpGtcCJ1iQ7njs7Vc1juev1/QmVq7i5835bExp5L5D4w8wb+cEOJYSWDSipTV2Nh8SNAxrnc8W/Or+HBpFgBbcit55tIeTP5kNeW1DsxBWsZ0CcekgyFdUlA6yumoLKW02oo5SEt5jYPnL+2Ew1rNf69Mp1tcCG8s2MXIjlHc2ENHp9q1RCx4CC5+xReY6DQqOseG8NzlPamxOdFrvPXnhWit6pde6Jo5MAHvrMm/ftrGop1FnNMlptnPL8TppMKj5+kfN/gFJQDZZXVM+3U7t41I45V5OzFoVAFL7wIs3FHEJzf2IULjQK0PIi7MyANfb+CCLmbCdRCptvPQyFj+s9i/SXL3uGCuTw/hb3PKKK1xML5PAr9vDTybed3AZIZ3imR830R2FFi4csYKv/1Ot4d//G8T703qz5sLd+P2eHj3hv6s2FPKl6uzYXW279inLu1GTKisFhDiVJFvnK2Ay+2hqs7Oxv0VJJsNvu16FUQG6cgu934IjOwczXM/b+O/V6WToq8jsi6T0E2voSyvxF1+Cc7Uc1DvWMkdQ88nWK/mmcu60VFTQpkmmCHKQqJXPstzY55i4d46Bma9iGrjLO8TmRuWRNVrVDJDIk4L1gPrv5t7KRdA19gQOsYEM2PRXglMxBmvxuYKmJgOsD2/ir9fEESYUUNhVeBlx/cOi2JCewexax6lV1U+jtRzqEm8mPOvMWHe8CbqilLce87mhp5jODetPbN31FBm83BxmpYuyhxi5lzFS6M/Zr/FBRolNw9rx0fLsvye47Le8Ri0KoK1GuwuF//+JXAVMYDftxZwdpdo5m4p4P6vN/Dk2O4EaVXM215IdLCOy3onEBumD1jJSwhxckhg0sJyy2uZsyGPiGAtHg9Eh+owGTTYnS6CDTrysyx0jA5mZ2E1wTo1a/dX0M1YSeyG11Cs/9x3HmXmYrSmJJj4LSNqDCjwYKKGGgy02/Ay6pVvAFDS5XZ+2OLkogg9KoCQOIho3zIvXohmUFcfmJyEGROFQsElPeN5Zf5OtuRW0iPB1OzPIcTpwupsvKQugN3ppqzGTnyYocG+Sf0imBy0HNPXT/q2abKWELbiNbjifdj3J9SWosz8k9Dgtwm9/B3+WfMuKIA/N4IlD5QqktVltFvxAI6u40jsfQNndx7I6qwyXG4PfZLD2ZBdQazBTbBeTaHFecTczEKLlYRw71grah3c/9UGHrygE/+5Mr1pfyAhxAmT5PcWlFtey1UzVvDibxm4Pd47vq/P38XMWwfy49Th7CurZVBqOPef3wmA7LJaxvWOJ9pd6BeU+FRm41n5NslmI1a7i9CQYBx11aj/etu7PziagjoVwxLV6Eu2gjkNJs2B0IRT+KqFaF7WkxiYAPRLCScyWMunK7JOyvmFOF2EGzU0lmqlVHhn2m1ONyVVdjrHHGxIqFDALelGTEueavhAawUsew36Tjq4rboIVn8AIbGQ8as3KAGI600deijajmbxNNK+PpdERTGLMopZsquEB77eQLLBSqe8H6AskyBPDX2Swxp9PT0STOwprvHbtrNQEt2FaEkSmLQQh8vNrL+yfdWxtudbSIkwcu2gZIK0aoqrbewqrKHAYqNdhJHv7x5KTnktD41KQLX1+0bPq9j8NTqnBYPKjdWtJKp4Oai809D2kf/HBxvrGJOeDBe/BDf/ClGdT8nrFeJksTq8d3FPVmCiUio4t0sMczbkUVEbuFy3EGcCvVrJVf0Cd08f3zeRdhEGvr97KANSw3l1QjpD0iIASI0IIrhwdeONRLKWQEI//207f4X2Z/ttKhr8GNW1h+SuVOWTsmcWr4zvzJMjTfx6tYlx2S8Qsul92PodwTP6ct/QSFQBoqlQvZqBqWb+yizz2y59i4RoWbKUq4WU1diZvT7X97tKAcVVVgammimtslFR66DAYuWV+TuptbsINaiZenYHIoxK4AiNDt0O3B4F572+ErVSwcXd0/nHNb8RVLSeyrjz+EdiKInRIaCIPPkvUohToO4k5pjUG9U5im/X5fD9+lxuHtYwJ0uIM4HT5eHq/kmc3T6YVH0NWlctdpWRLGsQplATl765nGqbE5NBw21npTL1nA48cUkX1DWFhJftPvLJPYctE3O78K7jAkxJlIx4lt/Lorms9lu/w1RbvyGh13V0mD3ywGOAgbdBzmqoqyB15WPMvPb/eOS3QjJLvLMjfZLCuOfcjjz/q3/+SWK4gb7J4U398wghmoEEJi2g1u6kxubkibHdUCoUGLUqYkJ1qJQKsstqcbk8zNtWwKKdJbgOdKJyuTwMj7ajmf84dDwH1nwY8NyezhdTavf+3+p0e5izuZgdRcH8/YLRRLr09E0MDfg4IU5XtpO1lMtlh7Ufg1pHWJ/r6Z8SzsxV+7lpaDtpuCjOSE6PB6oLODfzTTRbvgK3E5Rq2ve4hg0dp+LxeIgI0lJZ5+C/v+9k8vBUbugfS7v5N8MlrzZ+4rjeULrHb5MnbRTOqO5kXrmA7FoNb6ys5l+XxhMy52f/xyqUKBy1B4MSoxk6XQgzrwRAv+tHBpfv4OvxH1IZ3A+lQkGoXkNBZZ2vkp9GpeCSXvH8/YJOxAXIjxFCnDoSmJxipdU23v1zLx8szcTp9jBtfE+UCg9qpYLiKhtpUUHoNSouSU+gb4qZMKMGrUqJwWWh07L7UGUvg4R0SBsFexf5n1xvQjHiQfYW+FdNySisps7hJi5Mf8pepxCnyklLft/6PWz7AfCA3sS5Xc/m379sZ+2+cvq3MzfvcwlxGtC7a0ne8iKaHd+DMcKbA1JVgHrTTPo465h967OsynMSE6pjX2kt7/y5h+sHJlI97hNQa1H1vgXDhsNuqqm0MPIh+PWfB7dpjFjPfoZf9rp59MdihnWI5P7zOvLF6mySul1P2J9P+A619ZiAJmsRaIzQ5WLofR38+rD/DEzJLqIW/I2o678Dg3dGJDJEx0c3D6Ta5kSlUBAepMGola9EQrQ0uQpPIbfbw4+b8pi5aj8DUs30Tw6jU3QwO4uqWbijiCHtIwjSqXlyzlYW7Szm6v5JXNQzlh35VQRrVcQNf5GozNmY5j0BY1+D9ufCpq/AVoWn42gYeCuePQuZuatPg+fOLKnh4p5xLfCqhTi56nNMmrePice7xj2xv/cLztbv6X7FWGJCdcxatV8CE3FGilRUoi1cT/7479jjiGRnmYOOZg0dNKXELbwfVV0pj8329gDplWjiP1emU2d38v4OD2d1CGZ/5E0Mu3g40RumQ00xtvjBuIf9DXt5LiZjBCgU1CWPoqTX7Ty3sJY7RiXz/BVGNmRXMHXWemodLu65Np2w+gGFp0Lfm3CrlKh6jofN38AX14IzQCUufbg3CDqEOUiLOUjb8FghRIuRwOQUKqyy4vHAC1f04q+sUlwecHugXYSRpHADXeJCeHXeLhbtLOa2s9LQqBTc/PFqv3zBWwZcwNRR8Zi/vxNie0K3y0BjwNP+PJTf3wGXv8uy37IaPHdSuBH1SVyDL0RLOSl9TCqyoboYOl/i/TKTuxZlSQZnd47m+/W5PDm2Oyaj9DYQZxa1s5Y9F33BxG/yKbDk+LbHhOqYdeUsgjzVvm2bcir5ZHkW/7qsB4lhBqqsLrSh0UxdXssF7f9DpF7B2gIX38zIJjHcyBcTv2BpRj4r8lzM/igHu8vNdYPTuP+rDX7Po1QqIGkgdV2vwtn+AsqIxKTTEGbUQnSPwEEJwFkP4lQZULjdqJTyWShEayWBySnkcLpZsquEP3YUATAw1czAVDOrM8vYUVhF19hQzu8WQ4HFSu+kMKbMWtfgHB+uLmbY+H6cG5YMBZu9PwolinbDYei9sPwNLuk+ic/WFPkeo1MrGZAqd3hF21TncKFUELDyTpMVbgGFEsLbgVIN2iDIXcfIzlfz9Zps5mzMZdKQds33fEKcBsq1cdz51Q4KDusNUmixccdPJXwwoRNQAsAlveIY3zeRj5Zlsq+slu7xJvqlhPHw6E7c+tlaKmodvsfnltdR6Azi/l8OdnsPN2qIOizd46ah7fDEhLNl1Ac4NSGoHAoemrWGd28c4A1MkgZArwmw6cuDD9IGU3jNz2yujeGbWevRa5RMHJRM+6hgIoKlo7sQrY0EJqfQuv0VvqCkU0wwNw1txx2frcV2oGnVgu1FaFVKZt42kA+XZjV6nrfW1tC/+w2Ylj3n3dD5Ijx1lShWvoXC7SQh+eAXNJ1ayYc3DSDWJG/Aom2yOlzo1KrmTUgv2eVdP68+sMwjvB0Ubiasz/X0Swnni7/2S2Aizjhldg27iqoD7ttdVE2J3TuLOLJTFINSI5j8ycEZ//nbiwg1qHl7Yj9uPyuNF3/L8D32ir4JWOocfue7fWAEasfBfMlzu0RzaWcj1qoK1hQo+XnzdlZnldM3ORxV/aUfHA2j/w2D74LtP4BCRUGvO7njy+1szDlYBXPOhjwu75PA/13clUgJToRoVSQwOYmqrA6qrU4UCgVqJXywNNO3b/LwNJ75cZsvKKlnd7lZnFFMcZWt0fOWVNuw67314UkeDCMegry1YAjDE5bChT3jCDIaiTKH0z3BREyIDq1KdVJeoxAtzeZ0N3/ie9keCDkkJyssBfYsALeLEZ2ieOn3nWQUVNE5NqTxcwjRxtTanUfcX1ztDS6uH5zC1FnrGrQtsdQ5eWHuDt6+uiv9zclsKLCzvULF5X0TeGvhwapcV/WK4MqEChxaM49d1JmB7cKIsu4n7qP+rLnkd576cZ/v2FuHpxKqP2RZZVCE9ye+Nx6Ph5+XZbExp7LBWL9fn8s1/ZMkMBGilZHA5CSwu1zsLarhxbk7WLq7lESzgenX9cFiPXhHKNyo8U2Hq5UKbhiSwqXp8aiVCvQaFWqlkjX7ygOef1C7MEKS0+G6r6F4B3w2DsWNP8Ly6SjOfZKUJf/HDYPuguRup+T1CtGSqm1O9JpmDEw8LqjYDx27HtxmSgSnDSr20zsxhRC9mtkbcvnHmC7N97xCtHLhQd6y9vVl7A+lUipIDDfyzg39iAjW0j0+lHX7KxoctymnEmtZDgPnnMOAxIHYzn6KEr2L24YlcV03DakmFRH7fsJjbY82qA+35t4Ny5aBzYI7aTAbCr037YxaFfec04H20UGEGALne5VU2/l85b6A+wA+XZFF35Twk9acVQhx/CQwOQn2FNVw2ZvLsLu8syEXdIvh1y0FjOoUhdPtYUSnKCKDdbw2oTc/b8rnjpFpzNmQx3XvraLO4SItMojpE/vy4bJMLFb/O1Q6tZI7euvRL/oH9LoG5h0om1ixHy76r3c9fOFWCEs+1S9biBZRZXU0b5nP6kJwOSAo+uC20AOzJ2V7UJtTGdjOzJwNuTw8urP0NBFnDLVaxbUDk/h85f4G+67pn8R363L4cFkWMaE6bh6WyvndYnhhbkaDY11OB7idKPYvR//paAzjv6EjHiIW3g4oKB/yT6pjB5FkyYCMX70PUiiwn/M0HexJvDUxHq1aicmgITHc2Oh43R6PrzhGILV2F26PG5DARIjWQq7GZlZZ5+Dfv2z3BSUAvZPC2FlQxc3DUimvtXP3zHVcOWM5z/y0lZuGtmPaLzv4dMU+Xz+GvSU13P/VBj69ZSCD0w4mrfdKNPHtdSkkL3kQMhcDbgiNB0DhdsGi56C2DK775uAXKSHauCqrE4OmGZcqVhyoNhR8SGCi1kNQJJR5l2MOSosgr8LK1jxLgBMI0TZV29zcOKQd95zTgVC992ZAqF7NlFHt6ZVk4qPlWYA3Gf75X3dQbXNxQbcYv1LeieEGTHUHK3rh8RCx+FGMMR3Ye/FXbLrkRzLjx2Kq2gm753uPieyI7drv2OlJJjZUR1pUED3iTfRPCSdI1/hNiXCjhjE9YhvdP75fInqN3J8VojWRK7KZ1dicLN1dQlSIjilndyAhzEB8mJ77z+/EfV+tx+WGl69OR6tW4nZ7UCkVAZds7Sio4vEftvDG1T2hZCcejwdTyXrMc18Hy4EkvswlEN8HqotArYPaMjyGMBTmdqf2RQvRgqrqHBi0zRiYWHJApQN9qP/2kFhfYNI1LoQgrYrftxXSI8HUfM8tRCum16h4Zf5OLHVOnrmsBxqVktRII//9fSdvLd7TIKfko2WZfHvXUC5JjyNUr+GvzDIGxbiJWfm4/4ElO1F5HGBKItZtIXr149QNuBtb+iRsna+k1KlnS6WWjMIqrh8UTqzp2Lqza9Uqbh6Wyuz1uZTX+ifXd4wJZkC78BP5cwghTgIJTJqZQgEpZiPPXNaDf/20DZUC3r6+H8XVNoxaNdcOTOaNP3YRrNMQalDT8whfajbnWCitcdBv0d+gaFvDA3QhUFMCIx6ETd/AxS+j0IWdvBcnRCtksToxNueMiSXXmzx7+BKt4FjI/gsAtVJJ7+Qwft9awAPnd2q+5xaiFbM6Xfy4MQ+3BzIKLLSLDGLioGRftcnD1dpdZJXUcO8XGwDvsuYb+qVC2V7/AxVK3KhImzUUd+ooqoc8yH3zLPztAhNj39ziO+z1Cb0xBx1fsnpSuIE5U4bx3pK9/LK5AK1aybUDkrmqfyJxxxjgCCFOHQlMmllEkJZnx/Xg8TlbMRvV/Oeq3vxvbQ4pkUH888LOOJweHhrdmVWZZbSLCKJngol52wrZUVDV4FwalQKdRgX2wOUZPZ0vRuGoAVsVJA/BowtFoZE3WnFmsVgdzdu9uTIXjBENtwfHgM0CdeVgCKdPUjhvLtxNkcVKdKi++Z5fiFaqqs5Ju4ggHhzdmbIaO5klNbg98NbEvrz0ewYmg4aoED3ZZbVsy/cuc9Qc0vj0922FRARpeGLMyxh+vNO33dFhDDXqMJadPZuVuQ5yFtm4ZUQn7+ffIaYv3MPQDpG+SlqVdXaKLDb+3FmMw+1hZKcoYkP1hB/yfqBQKEiOCOKxS7ox5ewOKBQKIoO0qKThsBCtkgQmzUyrVhETqufNa3vj9sDHy7Pok2SiR0IYq/aWsnpfOYnhRoa1j+S9JXt5ce4OXp3Qm+d+3s6e4hq/c43pHovZoAJdaMMnOutBFOb2sHMuxPaAH6aiuGYmuO2n6JUK0TpUWZ3Nm/xemQtx6Q23B8d4/1uxDwzhvtnOJbtKuKJfYvM9vxCtVJhRzRNju/HA1xspqzn4WXNe12jevK4vv2zOZ39ZLRf3iuOBCzoxZ0MuGYfddPt2XS53DLuAdvUbQhMoH/4kToWOyd8d7DXSISaYK/sk+D12V1EVjgMl9str7Ly/JJPpi3b79j//6w6u7p/IP8Z0adA8UadWHfMSMCFEy5HApBmV19jJKa8jWKdiwa5Snp+7g3iTnvF9E7hixnIsdQcrbGlVSl66Op33l+zl0e+28PcLOvHQt5t8+zvFBHPvuR2JnDcVhtyNR6mBPQu9MyIdzvVW4aougGUvw/n/gjHPw3e3QffLYfj9oJS+JeLMUG11YmyuHBNnHdSVeRPdD2eMAJUGyvdBXG9CDRrSIoNYsqtYAhNxRtCpVTzz4za/oKRngonLeif4VaIEb1L8p5MH8fjszX7ncLg8VDuVOHtcQ3nCKErMfXCpY9HZ/G+qfbIii8sPC0ySzUbUB2Y6dhdX+wUl9b5ek8N5XWO4oHvjSe9CiNZL5jKbSWWdgy9X76fW7qTW4eL5uTsAuGV4Kg9/u8kvKAFvI8XHZm/h1rPSKLBYSY0M4oXxPZl6dgc+uLE/r03oQ4izHO2O2TD7bhTaIBRKBZRnweIXIW0kLH4Brv4MgqNg0TQo3AKr3vYmwwtxBnC7PVTbnM2X/G7J9/430FIupdJbqas8y7epR4KJJbtK8Bye9StEG1RpdbC3xH9m/7azUnl8zha/oAS8uV8PfbORcYcFF3qNEr1Gxex2T7DBdC7ZzjDCdUpeW+jfb8RS58Rx2DnvO68TUSE6rA4XHyw5LE/lEDMW76GiVlYPCHE6khmTZpJbXodGpSTOpPfVeI8K0TEo1cyzP28P+JjKOgcKhXf2xGJ18OfOIkIMWt5cuJsLu0YwueQ/3mpb5z0DoYmQMBDSO3rv5lbmwMDbYOG/Ye/CgyetLQOPO+DzCdHW1NideKD5ZkwqD5QxDYoKvD84BiqyfL/2SDDxw8Y8Mgqr6BIbYMmlEG1Ind2/J4hGpUClVFJxWMWreruKqkk2+/cZuaZ/EgaNikiDh+gQFUoUbMit4ZfN+X7HpUYGUVnnPa9aqeDOke0Z0cl7XTpcboqrGw88ymrsDYIaIcTpQQKTZrIoo4iLe8axvaCKiCANc+4eik6roqTKdsTHWR0utGoFJoOGn7cUolMruXVYEpf3MFNdeiOGUY+iddXhcVrxdLoApUIDRZvhm5u8ibiHSx4K2sYbTgnRllTbvDORzRaYWHJBG9T4NRQcA3u3eYN/hZLOMSFoVAqW7S6VwES0eeFGLRqVAofLO0OoViqxOhtvYAj4jtWoFIzvm8jY9Hgcbg9RWidapYeft1fw+h+7ObyZ/N/P70REsJavbh9MnElPZLAO44GeJUFaNed1iWZtgFL7AGd1jMRkaMaCGEKIU0YCk2ZgczjpnRTGvG0FhOjVnNctBqVCgd3lJjpUj8mg8d35OZRSAZHBOsamx2M2qJh9xwBCDRpMWjeFVjVfZ4Tzz/42PO46POGpKIMPrHv3dPbOmhwemCiUcMEzYJDa7OLMUGWtD0ya6a2sMqfx2RLwlgx2WL35XSHe7tOdY0JYtruEycNTm2cMQrRSBq2Km4a0o9Lq5Lyu0TjdHtpFGFEpFbgOjyyAUIOa1Mgg3prYF51aid3pYmN2BYPbR9D9q0FgTmXcuNms3x/B4l0leDzelQYPXtCJLrHBtI8KoX2Ay1GpVHBJejzvLtnboD+JQaPiluFpaNWyUl2I05Fcuc3A5nDh9nioc7oZkBpBtdXFN2tzuPyt5Tz5w1amntMh4OMmDkrBandx6/A09pRYWZdTRWjhKsIWPExVlYUbB8aiNwahSBpwMCgBCE2ASXMg/VpvMi54qwjd/CtEdzsFr1iI1qHK6v1S0myd3yv2HTkwCYnz/vdAo0WA7gkmVu4txe6UpSOibbM5XFzWJx63x83dM9dx98x1fLpiHzcOSQG8syLhRg0qpbcH0D9Gd0GnUuDxeJsPf7Umh09W7CNC66Zq0gKKL/uCrzeV0yEmhLl/G8F3dw3lo5v60zcpjLiwI8/8J4Yb+N9dQzmva7Sv5dCQNDPfTxlKUrhU3xLidCUzJs3Ag4Ilu0oY0z0Gl9uDxeYgIczAaxP6kFlSQ0mVjZeuSufDZZlkFFSRZDZy58g0BrQzo1Mr+WDpXm7uH8nZ6/8P1a5fwRBOj5GPo1fXQXhK4CcNS4ZLXoaz/w/cTm+zxUCVhIRowyzWZlzK5XF5Z0yiuzd+jC4YtMFQngkpwwBITwzjq9XZrNtfzuC0AEnzQrQRTreH6Qv38OuWAt+2L1dn8+y4HvzvrqGU19gptFhJCDdg0mvYX1aLywNTZq0DQKVU8M71fQlT1tHlrf2+cygUMGFAMhHBWoK0aiJDjt5EUaFQkBYVzCvX9PbluIQa1LKES4jTnAQmJ6i4yorHA2d3iSZIr+G2T9eQVVrr2983OYx7zunItF+3c2l6Av+9Kp0Ve0pYuruULrEh5NXZuStuJ1HvjQD3gbW6+jCClE4ITT7yk2uMcJS7SkK0Zc26lMuSDy4HhByhzKhCAaFxfp2rUyKMmAwaluwqlsBEtGkeYO7WAr9t0SE6ks1GHvxmI5mHVOzqnRTGfed1BAXEmfT0SDBx7cBk/rc2m87n+y97HNExiphQHSF6zXGPKUSvadLjhBCtkyzlOgF5FXXc+OFqwINOreSOz9b6BSUA6/ZX8MmKLEZ0iuK/v2ewKaeCT1fsY/LwdkQG6xi+7Baifrr5YFACMPhOiGgParnzI8SRVFkdKACdphneysr2eP9bv1yrMSFxULrH96tSoaBHfCgLdxSf+BiEaMWqrU4Or4w99ZwOPP3jNr+gBGBDdgUfLs0ED0wenkp0iI57v1jPz5sLqHIofMdFBmt5cmw3CS6EEIAEJk1WY3Py/K87uHNkGhV1TuocrgZvzPUW7yxmUKr3TmpiuIEPbuxPpFFL1J7ZsG+p/8Fpo6DbOHyLZoUQjbLUOTHqVCib43op2QUG89Gr2oXGQ00JWCt9m/qmhLMt30JuRd2Jj0OIVurwJZPKA7Mhe4qrAx7/564S3B4Pz/68nZmr9lNtc6JWKtCq1ZzfLYZ/X96DOVOGkRYVfCqGL4Q4DchSriYqq7FzVb9EokJ0TP54NVPP6djosR6Pt+56YriBOJMBnVpBlc1FcehQet26EOXOX8BRh6LrWDCneZu4CSGOqtBiJdzYTDOLRdvAlHT040IONIwr3Q0J/QDvshW1UsG8rQXcNEyqc4m2yaBV0ScpjPXZFd7fNaqAFScPVb/cst4lveLQqpW8NbEvGpXcGxVC+JN3hSZyut2EBWl4bPYWcivqiAxu/MuRVqXEoFHxwY39cbvdOFwevvxrPwU1HlRBESiGP4Bi9HOQPFiCEiGOQ7MFJo5ab6BhPoagIsgMGv3BpV94c1y6xYf6JQUL8sHckgAAmQdJREFU0dY4XR4eHN2ZzjEhANQ6XJiDGk9U16gUWA4JXAanmblpaDu0KqUEJUKIgNrMO8P06dNp164der2eQYMG8ddff53U59tZUIVaqWTNvnLcHtieX8WgVHPAYycMTCItKohteRZW76tge76F2FA9gzvEeqtuSUNEIZokv9JKuLEZ1qbnb/DmeUUELu3tR6H0zpqU7PLbPDgtgr8yyyiotJ74eIRohWrsLu75Yj3XDUrmnRv68a/LehARpGVY+8BFHyYMSCIhzMDz43vy4U39uWFwCuFGLQ63lNYWQgTWJgKTr776igceeIAnn3ySdevWkZ6ezujRoykqKjopz1dRa2P2+jycroNvrtMX7ubWs9IY3T2WAyXc0amV3DKsHdcPTsFS52Tt/nKSzUZSIoK4rHc84SFSa12IE1FgsWIOaoYZk6xl3q7uQcdYVcuUAMUZfpsGpZpRqxT8sDH3xMcjRCvkcrspq7F7+3PNWsfL83Zy/QcruWlYO8b0OPjZp1UpuWlICjcNTSVIp6ZTTDAJYQaiQ3Us3l1EvEk++4QQgbWJwOTll1/mtttu4+abb6Zbt27MmDEDo9HIhx9+eFKer8bmJMako8rmJNnsne2oc7iYOmsdMaE63rmhH9Ov68tP9wxnTPdYps5aR3Z5Led0icZkUHPxG0v5cVM+7gCdcoUQx8bt9lBSZSP8RAMTmwX2LYP43sf+GFMS1Jby/+zddXhUZ/bA8e/c8UkmE3chENwdSinUvdTdfbeyW1npb7vb3a60K7Wtu3u3LpSWenF3DyQQ94zLvb8/JhkyZAIEEhLgfJ6Hp+XemTt3Qt6Ze+77nnNw10Q22UwGxuQn8e6i7Wi7li4S4hCQZDNFmpkGQhp1Lj/N3hA3v7GUbIeFmb8+indvmMyHNx3BaSOzqXP5qHX6OPvJuVzy3Hw2VDo5bXgOelnGJYTowEH/6eD3+1m8eDHHHXdcZJuiKBx33HHMnTu3W17ToNdzzKB0tlQ5ue34AZECWr6gyitzt3HdK4v5eVM1y7c38OnKcp68dCzDshMYnGnnV28tR9PgsxXlNHl3nzQohOhYjctHUNVI3t8ck/Wfh/+bM27vn5PY0mOoal3U5mMGpbOxysmSkob9OycheqFgSOX3Jw9qt90XVMlLtqFpGtVOH1trXbh8QdDpSE+wMPuOaXxyy5FcND6/a2Y4hRCHrIO+KldNTQ2hUIiMjIyo7RkZGaxbty7mc3w+Hz6fL/L3pqamTr2mUYEshxVFp+P1+SU8c9k4XppTzModjWTYLdw4rR9jCxJp9gbRVC08va1pXP7CokhZRZvZgEHuGonDzP6OvbYqG8PH2a8ZE38zrHo/XF3L3ImSpZaEcGnhqrXQ58jI5mE5DjISzLw2bxtjC5L2/byE6Ab7O/5MBoVap4+nLx3Lk99vZku1k8LUOH4xvR9ry5sxGxT0ig6vXyXBYSQv2UpqvKWr34YQ4hB2WF4Z33fffTgcjsifvLy9KBHahqIoGPQ68pJs9EmN47fvLWdwVgJ3nTyYs8fk8PbCEgIhjZCqkpNkZc7mGo5+8Ac2Vu2s9X7tkYXEmw/6uFCITtnfsddWRVM4yXy/7sAufgXUIPQ7pvPPTe4DFcujNik6HccPzuST5WWSBC96nf0df4k2A6Pzk/j752s4akAqd50ymOkD07nv83WMzHOQaDMwMNPOpL4pjM5PkqBECNFpB31gkpqail6vp7KyMmp7ZWUlmZmZMZ9z11130djYGPlTWlraqddMtJkw63WsKW/k4gl5PHXpWCoavXy8vAwNuP+cEfy8sQqDomNdRTN/+GA1oTb5JKeNyGJkbmJn36oQB739HXttVTR6UHTg2KeO0Rqs/yy8jGvASWC2d/4Qyf2gbmtUo0WAowelYTIovPBz8T6clxDdZ/+/+ywMzIjn6cvG0egO8vGyMhrcAZ66bCyDMuzYDAp9UuLIcEhAIoTYNwf9LXuTycTYsWOZPXs2Z555JgCqqjJ79mxuvvnmmM8xm82YzR3XXt8b2UlxhLRw86h6l5c7TxiAXlFwmBV0Oo1LJhdi1CtkJtoYnpPI/5ZsR9HBOWPz6JNiIyV+/15fiINRV4y9Vqt2NJGXbENROtH13VkBK9+D7QvD3dvzj4C8ift2Aqn9AS18rKKdOW42k4ETh2by8pytXHFEH3ISpQKR6B26YvzFWxQ8AYVfTCvEH1Ix6RVcfpV4ix6TSfJHhBD756APTABuv/12rrjiCsaNG8eECRN4+OGHcblcXHXVVd36unnJcQAMyXZ0+JjUeDOp8WbGd9DjRAixb5aW1lOU1om8kLKl8N0/QDFA5nAYevbeNVTsiNkOSX2g+MeowATg9BHZfLuuins+WsWzl49Dp+tE8CREL5ZgtZAgsbYQopscEoHJBRdcQHV1NX/605+oqKhg1KhRzJw5s11CvBDi0NDsDbCx0sn0gel7fnDQCxtmwaLnIaUvjLgo3Lm9K2SPhjUfQXM52LMim60mPddMLeSBWRu4f+Y6fnfioM7N7AghhBCHoUMiMAG4+eabO1y6JYQ4tKzc3ogG9E/vYMZEDcK6z2HTLKjfBpoK+ZNh0Kmg6LvuRLJHwaavYfGLMP0uYGfwMa4gmUsm5vPM91v4aWMNZ43OYXR+EsNyEjAbuvAchBBCiEPEIROYCCEOH+8v3U6SzUh2rPyNxlL4/t9QXxxesjX4DEgp2vuu7p2hN8Gg02D5m7DkVRh9Keh21hQ5bUQ2fVPj+HRFOf+auR5/SMViUDhqQBqnjcxm+sA0EmIk72uaxpryJhYW1+EOhBiYYWdKUSoWowQ0QgghDl0SmAghDirrK5r5YEkZF0/MR2nN3dBUaNoOm2aHl1ZZk2DSL8GR0/0nlDUCvPWw4m0oXw7Dzw0v8TKEl4sNyXYwJNtBMKRSUudmVVkTC7fWceubSzEoOoZkJTAiz0FRWjzxFiMltS6+WFXBxionRr2uJbk4hNWoZ2r/VAZk2FF00OQNEgip2C1G+qbFMTzHQf/0+Eh/JFXVqHH6qHcH0CvhfDeH1Sj5LkIIIXotnaZp2p4fdmhrbGwkMTGR0tJSEhISevp0hDgk2O32PV4E7+3Y8wZC/POrLfxvWUVk2/mGHzCpMXqFKHrUpEI03YG976Lz1KK4qtvv0BvRFCNq5ghCKQMim2tdAVaVNVPZ7I95vCGZ8QzOjEOv6Khs8rO4tBGnL9Rdp7/fDIoOq0mPUdGhV3ThRW1dEQNpoGoaIVUjoGp4AypBdc9fW2aDgtmgYFB0tKb3qBoEQireoEogtPMYeh1YjHqMeh0GRYdOF+P8W85D1SCkafgC4eO00gFmY9vX3MPPQAMNIu/LFwjh3+WczEY9Zn3459knxcafT+lPfvKeM8/3ZuyBfPcJ0R32dvyJ2CQwAbZv375fjd6EEO01Njbu8WJnb8eeITGTnBuei9oWj7vd43rDh1lHX0eqBs7YMcjO5+oNaKFg51/TaEGnHPRtqcQe1H75OM5lX+zxcXsz9kC++4ToDns7/kRsEpgQ7ntSVla2T1FuU1MTeXl5B/0dp0PhfRwK7wEOnfexN+NJxl7vID/LrtFbfo57O546M/56y3vbH/Ieeo9D4X109B5kxmT/SI4JoCgKubm5+3WMhISEg3ZwtXUovI9D4T3AofM+dkfGXu8iP8uucbD8HPdl/B0s72135D30HofC+zgU3kNvInP/QgghhBBCiB4ngYkQQgghhBCix0lgsp/MZjP33HMPZrO5p09lvxwK7+NQeA9w6LyP7iY/p64jP8uucSj/HA+F9ybvofc4FN7HofAeeiNJfhdCCCGEEEL0OJkxEUIIIYQQQvQ4CUyEEEIIIYQQPU4CEyGEEEIIIUSPk8BECCGEEEII0eMkMAE0TaOpqQmpAyDEgSVjT4ieI+NPCNHbSGACNDc343A4aG5u7ulTEeKwImNPiJ4j408I0dtIYCKEEEIIIYTocRKYCCGEEEIIIXqcBCZCCCGEEEKIHieBiRBCCCGEEKLHGXr6BITozaqbfdQ6fTR5A6TGm0mNN5NgNfb0aQkhDiGqqlHZ7KWm2Y83GCLDHv6ssZnlK1oIcXiRTz0hOlBc4+S6VxazqcoZ2XbKsEzuOWMoGQmWHjwzIcShIhhSWb69kRtfXUy10weAQdHxy6OLuPKIPiTHmXr4DIUQ4sCRpVxCxFDZ6OXyFxZEBSUAn6+q4NHZG/H4gz10ZkKIQ0lZg4dLnpsXCUoAgqrGf2dv5IcN1T14ZkIIceBJYCJEDNsbPJTWeWLue2fRdqqd/gN8RkKIQ9G366vxBtSY+x76egPVzb6Y+4QQ4lAkgYkQMeyod3e4zx9S8fhDB/BshBCHqtVljR3uK6lzEwzFDlp6gwa3n0Z3oKdPQwhxCJHARIgY8lNsHe6zGBXiTPoDeDZCiEPVmPykDvf1S4vHaOi9X9N3f7iKP3+yuqdPQwhxCOm9n3hC9KCcRCtF6fEx910xuQ/pkvwuhOgCU4pSsXdQfeu3Jw4kNd58gM9o7zW4AzS4ZVmrEKLrSGAiRAxpdgsvXjmecX123s00KDoum1TAtVMLMfXiu5hCiINHTqKVt26YRGFqXGRbnEnPn88YwoTC5B48sz0LhFQCIa2nT0MIcQiRcsFCdCAv2cazl4+j1unH7Q/isBpJk94CQogupCg6hmY7ePuGSdS5/ASCKslxJtLsZkyG3r1kNKRqQO/NgRFCHHzkCkuI3UiymUiySR8BIUT3SrdbSLcfXEtEA6qKJgsvhBBdSAITIYQQQnRaMKShaTJjIoToOhKYCCGEEKLTAiEVTZMZEyFE15HARAghhBCdFlJlxkQI0bUkMBFCCCFEpwVCGnqZMBFCdCEJTIQQQgjRaUFVRdN0PX0aQohDiAQmQgghhOi0kKqhSlwihOhCEpgIIYQQotMCIQ1FJw0WhRBdRwITIYQQQnRaMKSi6GTKRAjRdSQwEUIIIUSnBVUNReISIUQXksBECCGEEJ0WUjVUZCmXEKLrSGAihBBCiE4LhjRkJZcQoitJYCKEEEKITtE0jZCmgRb+f51EKEKILiCtkYQQQgjRKYHQziVcQVWWcwkhuoYEJkIIIYTolFCbYCQYksBECNE1JDARQgghRKcEVDXm/wshxP6QwEQIIYQQndJ2lkRmTIQQXUUCEyGEEEJ0SrDNLEkwJDMmQoiuIYGJEEIIITql7SxJQJLfhRBdRAITIYQQQnRK9FIumTERQnQNCUyEEEII0SlRye+SYyKE6CISmAghhBCiU6LKBUtVLiFEF5HARAghhBCdEgi1TX6XGRMhRNeQwEQIIYQQnRKV/C45JkKILiKBiRBCCCE6JRi1lEtmTIQQXUMCEyGEEEJ0SttKXDJjIoToKhKYCCGEEKJTomZMJMdECNFFJDARQgghRKdEJb9LVS4hRBeRwEQIIYQQndK2XLD0MRFCdBUJTIQQQgjRKYGQLOUSQnQ9CUyEEEII0Sltl2/JUi4hRFeRwEQIIYQQnRLdx0RmTIQQXUMCEyGEEEJ0SnRVLpkxEUJ0DQlMhBBCCNEprcGIXtERkAaLQogu0usDkyeffJIRI0aQkJBAQkICkydP5osvvojs93q93HTTTaSkpBAfH88555xDZWVlD56xEEIIcWgLqBqKDgyKTmZMhBBdptcHJrm5udx///0sXryYRYsWccwxxzBjxgxWr14NwG233cYnn3zCu+++y/fff09ZWRlnn312D5+1EEIIcegKhVQMioJe0UlVLiFElzH09Ansyemnnx7197///e88+eSTzJs3j9zcXJ5//nneeOMNjjnmGABefPFFBg8ezLx585g0aVJPnLIQQghxSAuqGnpFh0HREZCqXEKILtLrA5O2QqEQ7777Li6Xi8mTJ7N48WICgQDHHXdc5DGDBg0iPz+fuXPndhiY+Hw+fD5f5O9NTU3dfu5CCBl7QvSkrhx/gZCGQa+TGRMhRJfq9Uu5AFauXEl8fDxms5kbb7yRDz74gCFDhlBRUYHJZCIxMTHq8RkZGVRUVHR4vPvuuw+HwxH5k5eX183vQAgBMvaE6EldOf6CIRW9rjUwkRkTIUTXOCgCk4EDB7Js2TLmz5/PL37xC6644grWrFmzz8e76667aGxsjPwpLS3twrMVQnRExp4QPacrx1/rUi6pyiWE6EoHxVIuk8lEUVERAGPHjmXhwoU88sgjXHDBBfj9fhoaGqJmTSorK8nMzOzweGazGbPZ3N2nLYTYhYw9IXpOV46/oKpGAhOZMRFCdJWDYsZkV6qq4vP5GDt2LEajkdmzZ0f2rV+/npKSEiZPntyDZyiEEEIcuoIhDUPrjInkmAghukivnzG56667OPnkk8nPz6e5uZk33niD7777ji+//BKHw8E111zD7bffTnJyMgkJCdxyyy1MnjxZKnIJIYQQ3SSoaiiKDoOiEJSqXEKILtLrA5Oqqiouv/xyysvLcTgcjBgxgi+//JLjjz8egIceeghFUTjnnHPw+XyceOKJPPHEEz181kIIIcShKxhqXcqFVOUSQnSZXh+YPP/887vdb7FYePzxx3n88ccP0BkJIYQQh7eA2rKUS6fIUi4hRJc5KHNMhBBCCNFzds6Y6GQplxCiy0hgIoQQQohOCaoaik6HooOQlAsWQnQRCUyEEEII0SnBULiPiaLToWoSmAghuoYEJkIIIYTolJCqodfp0CkyYyKE6DoSmAghhBCiU0Kqhk4BBR3SX1EI0VUkMBFCCCFEp4Q0DQUdiiJLuYQQXUcCEyGEEEJ0iqZp6CT5XQjRxSQwEUIIIUSnhFQNRQeKTieBiRCiy0hgIoQQQohOCakailTlEkJ0MQlMhBBCCNEpIS08Y6KTpVxCiC4kgYkQQgghOkVVackxkRkTIUTXkcBECCGEEJ0SnjGR5HchRNeSwEQIIYQQnaK2TX6XGRMhRBeRwEQIIYQQnRKZMVGkwaIQoutIYCKEEEKITtk5YxL+fyGE6AoSmAghhBCiU0IaLTkm0sdECNF1JDARQgghRKeoqiZVuYQQXU4CEyGEEEJ0SqjNUi6ZMRFCdBUJTIQQQgjRKWqb5HeZMRFCdJVuC0zuvfde3G53u+0ej4d77723u15WCCGEEN1M1aRcsBCi63VbYPKXv/wFp9PZbrvb7eYvf/lLd72sEEIIIbpZSNVQFB06XbgLvBBCdIVuC0w0LZwYt6vly5eTnJzcXS8rhBBCiG62s/O7zJgIIbqOoasPmJSUhE6nQ6fTMWDAgKjgJBQK4XQ6ufHGG7v6ZYUQQghxgGga6KSPiRCii3V5YPLwww+jaRpXX301f/nLX3A4HJF9JpOJPn36MHny5K5+WSGEEEIcIOGqXDJjIoToWl0emFxxxRUAFBYWcsQRR2A0Grv6JYQQQgjRg9omv8uMiRCiq3R5YNJq2rRpqKrKhg0bqKqqQt0lO+6oo47qrpcWQgghRDfaOWMifUyEEF2n2wKTefPmcfHFF7Nt2za0XaZ5dTodoVCou15aCCGEEN1I1WjTx6Snz0YIcajotsDkxhtvZNy4cXz22WdkZWXFrNAlhBBCiIOPqmotye86mTERQnSZbgtMNm7cyHvvvUdRUVF3vYQQQgghesDOcsG0WxUhhBD7qtv6mEycOJFNmzZ11+GFEEII0UNUqcolhOgG3TZjcsstt3DHHXdQUVHB8OHD21XnGjFiRHe9tBBCCCG6kaqBooRzRqXzuxCiq3RbYHLOOecAcPXVV0e26XS6SEd4SX4XQgghDk6qpqGjpSqXzJgIIbpItwUmxcXF3XVoIYQQQvSgkKqhKOHkd2hZ2qVIkRshxP7ptsCkoKCguw4thBBCiB6ktia/t2SqhjQNBQlMhBD7p9uS3wFeffVVpkyZQnZ2Ntu2bQPg4Ycf5qOPPurOlxVCCCFEN4r0MWmZMZGSwUKIrtBtgcmTTz7J7bffzimnnEJDQ0MkpyQxMZGHH364u15WCCGEEN1IbQlCWvuYQHgGRQgh9le3BSaPPvoozz77LH/4wx/Q6/WR7ePGjWPlypXd9bJCCCGE6Eatye6tfUxAZkyEEF2j2wKT4uJiRo8e3W672WzG5XJ118sKIYQQohu1BiFK2xkTKRkshOgC3RaYFBYWsmzZsnbbZ86cyeDBg7vrZYUQQgjRjVpXbbXNMZGlXEKIrtBtVbluv/12brrpJrxeL5qmsWDBAt58803uu+8+nnvuue56WSGEEEJ0o51LuUCnRG8TQoj90W2BybXXXovVauXuu+/G7XZz8cUXk52dzSOPPMKFF17YXS8rhBBCiG60cymXLqqPiRBC7K9uC0wALrnkEi655BLcbjdOp5P09PTufDkhhBBCdDM1RmAiMyZCiK7QbTkmHo8Ht9sNgM1mw+Px8PDDDzNr1qzuekkhhBBCdLPWfBKdglTlEkJ0qW4LTGbMmMErr7wCQENDAxMmTOCBBx5gxowZPPnkk3t9nPvuu4/x48djt9tJT0/nzDPPZP369VGP8Xq93HTTTaSkpBAfH88555xDZWVll74fIYQQQrTJMUEnVbmEEF2q2wKTJUuWMHXqVADee+89MjMz2bZtG6+88gr//e9/9/o433//PTfddBPz5s3jq6++IhAIcMIJJ0SVHL7tttv45JNPePfdd/n+++8pKyvj7LPP7vL3JIQQQhzuWoMQpe2MiSzlEkJ0gW7LMXG73djtdgBmzZrF2WefjaIoTJo0iW3btu31cWbOnBn195deeon09HQWL17MUUcdRWNjI88//zxvvPEGxxxzDAAvvvgigwcPZt68eUyaNKnr3pQQQghxmItusNiSYyJLuYQQXaDbZkyKior48MMPKS0t5csvv+SEE04AoKqqioSEhH0+bmNjIwDJyckALF68mEAgwHHHHRd5zKBBg8jPz2fu3Lkxj+Hz+Whqaor6I4TofjL2hOg5XTX+opLfFeljIoToOt0WmPzpT3/izjvvpE+fPkycOJHJkycD4dmTWB3h94aqqvz6179mypQpDBs2DICKigpMJhOJiYlRj83IyKCioiLmce677z4cDkfkT15e3j6djxCic2TsCdFzumr8RZLfdZL8LoToWt0WmJx77rmUlJSwaNGiqOVYxx57LA899FDk79u3b0fdy6y5m266iVWrVvHWW2/t17ndddddNDY2Rv6Ulpbu1/GEEHtHxp4QPaerxl/bPiY66fwuhOhC3drHJDMzk8zMzKhtEyZMiPr7kCFDWLZsGX379t3tsW6++WY+/fRTfvjhB3Jzc6New+/309DQEDVrUllZ2e61W5nNZsxmcyffjRBif8nYE6LndNX4U2PkmEhVLiFEV+i2GZO9pe3hLoumadx888188MEHfPPNNxQWFkbtHzt2LEajkdmzZ0e2rV+/npKSksjyMSGEEEJ0jdZVW4pOqnIJIbpWt86YdIWbbrqJN954g48++gi73R7JG3E4HFitVhwOB9dccw233347ycnJJCQkcMsttzB58mSpyCWEEEJ0sdalXDqpyiWE6GK9PjBpbcY4ffr0qO0vvvgiV155JQAPPfQQiqJwzjnn4PP5OPHEE3niiScO8JkKIYQQh76dOSZIVS4hRJfq9YHJnpZ6AVgsFh5//HEef/zxA3BG4lAVUjWqmrw0eAKYDApJNhPJcaaePi0hRBuVjV7qPX70Oh1JcSZS4yVn6UCL5JgoOqnKJYToUj0emLRW9BCiJzV5Anyzroq/frqGWpcfgFF5ifznvJEUpcf38NkJIdz+IAuL67nr/RWUNXoB6J8ez4Pnj2RwVgIGfY+nTB42duaYtE1+l8BECLH/evyTfG9mRITobktLG/j128siQQnAstIGLnxmLjvqPT14ZkIIgOJqF1e9tCASlABsrHJy/tPz2NEgY/RAiuSYIMnvQoiu1eOByZo1aygoKOjp0xCHsVqnj/s+XxtzX43Tz6JtdQf4jIQQbTl9AR6ZvZFYN+U9gRAfLNkhd+wPoOilXJL8LoToOt22lMvr9fLoo4/y7bffUlVV1a6J4pIlSwCk87Pocb6gyrqK5g73z9tSx4xROQfwjIQQbbl8IVbuaOxw/4KtdXgCIeLMPb46+bDQNvldGiwKIbpSt32KX3PNNcyaNYtzzz2XCRMmSC7JIcrpC+Dxh7CZDAftRYFe0ZHlsFDeZolIW/0lx0QconzBEE2eACaDHofV2NOn0yGzQSHLYe1wjBamxmEy9PgCgMNGdIPFlm3SYFEI0QW67Ury008/5fPPP2fKlCnd9RKiBzm9ATZVu3h09kY2VzsZkGHn5mOK6JcWf9AFKOl2MzcdXcTdH65qt8+kVzh2cHoPnJUQ3ScYUimtc/Pcj8XM2VJLcpyJX0zvx5j8RJLjel+Vq0SbiV8dW8QVLy5st0+ng8smF2CU5PcDpjUIaVsuWHJMhBBdods+yXNycrDb7d11eNGD/KEQs9dWcebjPzN7XRVba93MWlPJGY/9zHfrqwiGDq5bZzqdjpOGZnLxxPyo7XazgZeuGk+Ww9JDZyZE99hQ6eTk//7I6wtKKK5xsXhbPde+vIhHvt5Ig9u/5wP0gBG5idxx/AD0ys7Zd7NB4eELRpGXZOvBMzv8hKJmTKQqlxCi63Tbre0HHniA3/3udzz11FOS3H6IqW7y8YcYswsAd32wktH5SWQnWg/wWe2fVLuZ3580iOumFrKpykW82UB+so2MBLOUIRWHlAa3n7s/Wok30P4Gwstzt3HJpAISbb2vf09SnImrjyzkjFHZbK5yYtArFKbFkR5vxmzU9/TpHVbUqM7v4W2twcoVLyzg4on5nDg0s6dOTwhxEOu2wGTcuHF4vV769u2LzWbDaIxev1xXJ5WODlY1Tj9OXzDmviZPkFqn76ALTAASrEYSrEYKUyWnRBy6Gj0Blmxr6HD/nM01DMjonbPdceZwLltBSlxPn8phbWeOCe2qci3cWseIXIcEJkKIfdJtgclFF13Ejh07+Mc//kFGRoYkvx9C9vRPKf/WQhy8DIrMEIrd21mVq81SrpZgxRdUafbGvnElhBB70m2ByZw5c5g7dy4jR47srpcQPSQ13ozDaqTRE2i3LyXOREpc71sGIoQIS7SZmFKUws+bamPun9wv5QCfkTjYRFXlaoljQyoEQiohVaPJ2/67QQgh9ka33RobNGgQHo904z0UpdvNPHj+SJRdJkb0io4Hzx9JRoIkiwvRWzmsRv58+lASrO3vS/36uP6kxfe+qlyid2mtb6IooLAz+d0bCAHIjIkQYp9124zJ/fffzx133MHf//53hg8f3i7HJCEhobteWnQzg17hiKJUZv76KF76eSvrKpoZmmXn8iP6kJ9si5SPFEL0TkXp8Xx2y1Q+WLqD7zdUk243c+3UQvqlxZPQi/uZiN6h7YyJrk3yuy8YjliaYsymCyHE3ui2wOSkk04C4Nhjj43armkaOp2OUCjUXS8tDgCrUc+ADDt/PmMo3kAIq0nBqJfKOEIcDHQ6HXnJNm46uogrj+iDyaBgkcpWYi+1BiY63c7KXKE2MyaylEsIsa+6LTD59ttvu+vQohcxGRTpuCzEQUqv6GSGRHRa2+T31v9qmhYpQS1LuYQQ+6rbApNp06Z116GFEEII0UNiBSYhVcMXlBwTIcT+6bbA5Icfftjt/qOOOqq7XloIIYQQ3aRlJVekAIqiQEgjMmPi9AYjy7aFEKIzui0wmT59erttbT+kJMdECCGEOPiEtJ2d3yE8Y6KqGr6WHJOQpuEJhLCZuu0SQwhxiOq25ID6+vqoP1VVVcycOZPx48cza9as7npZIYQQQnSjkKpFlYtXdDpCmoY3uPOGoyznEkLsi267neFwONptO/744zGZTNx+++0sXry4u15aCCGEEN1E1bSosvCtVbl8LUu5AJq9AelpJYTotANeTikjI4P169cf6JcVQgghRBdQVQ19m6XZihJeytV2xqRJZkyEEPug22ZMVqxYEfV3TdMoLy/n/vvvZ9SoUd31skIIIYToRiENdLGWckXNmEhgIoTovG4LTEaNGoWupbZ5W5MmTeKFF17orpcVQgghRDdSVS1SKhjCS7naJr9DeCmXEEJ0VrcFJsXFxVF/VxSFtLQ0LBZZcyqEEEIcrELaroFJa/K7SpxJj9sfkhkTIcQ+6bbApKCggNmzZzN79myqqqpQVTVqv8yaCCGEEAcfVWtflUvVwBsIYTbow+WC/dISQAjRed0WmPzlL3/h3nvvZdy4cWRlZUmjJSGEEOIQ0OFSrqCK0aDDEFIIhNTdHEEIIWLrtsDkqaee4qWXXuKyyy7rrpcQh7CQqlHZ5KWkzk29y09Rejyp8WaS4kw9fWpCHHYqGr3saPBQ1eSlICWO9AQzqfHmnj4t0UNCKlGBiU6nI6Rq+EMqJr2CT69KYCKE2CfdFpj4/X6OOOKI7jq8OIQFQyordzRy5YsLafTsTKA8eVgmfzljKOlSG1+IA2ZTVTOXP7+AskZvZNvo/EQev3gM2YnWHjwz0VNCmobSptmAohCpymUyKBgCOvwhreMDCCFEB7qtj8m1117LG2+80V2HF4ewiiYvlzw3PyooAfhiVQWvzN0md+KEOEAqGj1c8cLCqKAEYGlJA3/9dA1OSXA+LGkxkt/DS7lCGPQKBkWWcgkh9k23zZh4vV6eeeYZvv76a0aMGIHRaIza/+CDD3bXS4uD3NKSBtwdJE6+NGcrF0/Mlzu1QhwA5S1LuGL5cnUFvztpEPGWbvsaEb1USNVi9jHxBcJLuQx6HYGgBCZCiM7r1gaLrY0UV61aFbVPEuHF7pTUujvc5/QF8cudOCEOiOpmX4f7VA08Aam8dDjatVywXgkHK95ACKNewaDoZMZECLFPui0w+fbbb7vr0IclbyBErctPSA2XaQyXatSRbjdj0HfbirweMTLP0eG+bIcFi+HQer/i4FfV7MXrD2E0KIRUDVUDu9lw0BdryE+2dbjPYlSIN8tsyeFo16pcBkXBH9TwBkMY9ToMeskxEULsG/lWOQiUNXh47JtN/G/JdnxBlcFZdm6aXsR3G6rIS7Jx8cR80uyHTkJ4UbqdvGQrpXXtl5D85qRBZDpkGZfoHZo8AeYX1/H095u49dgBvLWwhFmrKwmqGqPzE/nz6UMZlGnHbNT39Knuk3S7hQmFySwormu379oj+5IhhSgOS6pGVB8Tg16HLxjCG1CxmfToJcdECLGP5NZzL1fZ5OXqlxbyxoISfC1rdteWN3PLW0uZPjCddxZt595P1tDg9vfwmXadTIeF16+dxNT+qZFtDquRv505jOkD0nrwzISI9tOmGq57ZRFXH9mX//tgJZ+vrCCohu8ULy1p4Jwn57ClxtXDZ7nvkuNNPHLhKE4bkRW5ELUa9dxyTBFXHtEHk8xeHpbCOSY7IxOjouALqngDoXCOiSzlEkLsI5kx6eU2VTlZV9HcbrumwVPfb+biifn8+8v1/Oq4ASTaDu5lI23lJ9t47OIx1Ln8+AIhEqxGMuxm9IfYsjVx8Kps9PLXT9fQLy2OyiYv2+vbz/AFVY3/zFrPwxeMwm4xxjhK75flsHL/2cP5zYkD8QRCxJkMZCSYMRkOzlkgsf927fxuNCj4AiF8ARWjQQITIcS+k8Ckl/txY3WH+1btaOIX04oA2FLtpCg9/kCd1gHhsBpxWA/Oizlx6HP6g5Q3ejltRBYLt7Zf6tRq7uZanL7gQRuYAMRbjMQfxOcvulZolxwTo75lxiQYnjHRt+ScCCFEZ8nt514ufTe5I3EmfeSulMPWMxcNwZDaJWUhNS1c0UVVe/+XmT8oXY0Pd75ACKOiQ6/ocPqCJO1mtjLJZkLfjZUIu2oMdoY/qBLshWPAFwgROgg+Qw52qkZUg0WTXrdzKZehpVxwL/z9EEL0fjJj0ssdPSidv322hljftWeNyeXzleUk2ozkJXVcPac71Dh9bKp08uq8rfiCKueOzWNUXiKZjs4lwwZCKjvqPXywdAfLtzcwJCuBc8fmkptk7XVLRSqbvKwobeCdxdsx6XVcMrGAARl2Uu3mnj41cYDsqPfww8ZqvlxdwanDMjlxaAaz11bxxCVjeH1+ScznXDu1kLRu+B2pdfrY2AVjsDPKGz0s3lrPB8t2EGcycOmkAorS40iO69kxsL3ezddrKvluQzW5iVYunlRAXpL1oJ6l6s1UVUPHrjMmoZ19TBQdfuljIoTYBxKY9HIOq4F/njOC3/1vRVRwMjLXwbQBadz29jJeuXrCAa2OU+P08ddP1vDR8rLItq/XVjEsO4Hnrhi/1xdGmqaxrLSBS5+bH0ns/259Nc/+uIWXrprApL4p6JXe0fOmotHDja8tYVlpQ2TbZysrOHlYJvfOGNYtF56id9lW6+K8p+ZS1dLbY0FxHU9cMob1Fc3MXlvF7ccP4MGvNkQ9Z/rANE4dkdXlvZs6GoNDsxN4vhNjsDPKGjxc/sICNlU5I9s+Xl7GRRPy+M2JA3ssONlU5eS8p+ZQ7w5Etr02v4T7zh7OjFHZ2EzyNdfVgqoW9dls0Cv4AiqeQAhzS45JR01yhRBid+QTu5ercfr5em0lz18xntVljXgDKhMKk4HwhcLnvzqSbIf1gF7Ab6xsjrogarWqrIlPVpRxzZRClL04n8omH7e8sTQSlLQKhDRueXMpn95yZK/o8K5pGrPWVEYFJa2+WFXBRRPySbNLtbBDmcsX5F8z10eCEgC3P8Rtby/jD6cOJi3eTLM3yMc3T2Hh1jrcvhBHDUgjN8lKSnzXX7B3NAZXlzXx8fIdXHtk370ag3srGFJ5a0FJVFDS6s0FpZw/Lq9HApNGj58/frgyKihpdfeHqziiXwoFKfI119WCqhr1nWPShwORoKqFk9/1Cv5Q+38TIYTYE/nE7uVmra7gy9WVfLm6kkGZdmwmA1+sKifRZqLR42fqgLQD2mDRH1R5Ze62Dve/Nm8bZ47K3qu+KnUuHxVN3g72+alx+npFYFLr8vPqbt7zy3O3MqEwGctB2qtC7Fm928/M1RUxtge4890V/P2soVwysQ8AI3ITu/Vc/EF1t7+Pr88v4azROV3a26jW5efNBaUd7n9zfgmj8hK7fGZoTxrcAeZuiV14IKRqLC1poCAl7oCe0+EgGNKi8qaMeoUmbzgQMUtVLiHEfpDApJfzBMIf7noFtta68AZaP+xbeiNoBzbRU0Nrcw7t+QJqzHyYWPaUpBrsJUmsmka7WZ22vIEQ6gH+dxAHlqa1/3016RX8LRdfDa7ggTsXtMjnQiy+gNrlHwuapuELdbw0xx0IoWlwgOOSPX6GeAKynKg7BFU1akbOqFciS7cifUykKpcQYh/0+sDkhx9+4N///jeLFy+mvLycDz74gDPPPDOyX9M07rnnHp599lkaGhqYMmUKTz75JP379++5k+5CZ43O4uRhmXj8IdyBIGnxFuZtqeWvn63hyKLUbkvu9AVCVDt9VDR6UXQ6Mhxm0u0WzAY954zN4dv1VTGfd+qITJL2skJYcrwZu9lAs6/9RZ3FqJDeS/I2Em1GTh2exZPfb465/9yxuQdkHbvbH6TG6aei0YPZoCfdbiY9wdJr8nAOZp6Wn215owdT68/Wbo7MRtotBsb1SWL1jiZumNaXkbmJNHoCxFsMbKt1cUS/1D28wt7TNI3KJi/VzT7cgRCZCRZS483EmcO/Y3szBhO7uEqfw2rkxCGZvLt4e8z954zN7dKlY3srwWpkQEY8GyrbLzEDGFeQ1G2vXev0UeP00eAOkBJvIiXOTFLcodNLaneCoegcE2ObWXtTZCmXzJgIITqv1wcmLpeLkSNHcvXVV3P22We32/+vf/2L//73v7z88ssUFhbyxz/+kRNPPJE1a9ZgsRy4hPDu4PYFaHAHufmNpZElT4oOLpmYzytXTSDNbiahG/p8NHkDzFxVwT0frY7ccYw3G/jPeSM4akAa4wqSGJRpb9f4MTnOxBVHFO51Na10u5l7zhjKne8ub7fvrpPD6/Z7A6Ne4ZKJ+by3eDvVTl/Uvn5p8Uzqm9Lt51Dn8vPavG08+s1GAqHwnciUOBNPXDqG0fmJmPSyjGxfNbj9vL2wlAdmbYhcTCXajDx20RgmFCZhMuhJtJn4y+lDqWjy8sLPxTz89cbI84dmJ3DsoIwuOZeQqrG6rJHrXllEZVP4d02v6LjyiD78Yno/UlvGRFeNwb1lNRm46egivlxTQZMn+kbCqLxEhmYldOnr7a3UeDN/P2s4Fz4zr93syYXj87qtKMX2Ojc3vr6YVTuaItumFKXwn3NHktULlp92t0AoOsfEaGiTbyJLuYQQ+0GnaQfPGhSdThc1Y6JpGtnZ2dxxxx3ceeedADQ2NpKRkcFLL73EhRdeuFfHbWpqwuFw0NjYSEJCz3zBxrKpqpmzHp8Tc0bhj6cN5vJJBRi7oaTukpJ6zn5iTrvtOh18cetUBmUlUN7o4cMlO3h9QQn+oMopw7O4+shC8pM7V7a42RNgTUUTD8xaz+YqFwWpNm4/fiDDcxy9rrliaZ2bl+Zs5ZPlZRj1ChdNyOOcsblkObr/QuTzleX88vUl7babDQpf/voo+qQenOvoe8PY+2ZtFVe/vLDddqNex6zbjqIwNdy4tMHt5/fvr2Dmqsp2jx2Uaee1aybud+no7XVuTnrkR5wxxvxfZwzl0kkFkTyOtmPQF1Q5dR/H4N7SNI1tdW6e+2ELX66uxGrSc9nkAk4fkd2tJYr3xBsIUVzj4qGvNrB4Wz1pdjO/nF7ElKKUbik8UOP0ccULC1hd1tRu3/FD0nngvFHdcsOoO+zr+LvomXmgg1uPCa9M+GFDdWRG+R9nDWdpST3frq9i0d3Hd8t5CyEOXb1+xmR3iouLqaio4LjjjotsczgcTJw4kblz53YYmPh8Pny+nXe+m5raf8H0NH9QZd6W2phBCcBT323hpKGZ5HRx/xKnL8Bj32yKuU/T4Pmfivn7WcPJcli5flo/zhmbiwYk2Yz7dJfWbjUysTCFZy8bhyegYjEqJO6mWV1Pyku28buTBnL9UX3RASnx5gOyjKrG6WtXhraVL6jy5eoKbpjWr9vPoyv0trFX5/Lz4NfrY+4LhDQ+XFrGbccPAMKJ1l+ubh+UAKyraKba6dvvwGTB1rqYQQnAf7/ZxAlDMsloCQLaj0ETJkP3FcLQ6XT0SYnjj6cP4eZj+qNXICXO3CNLuNqyGPUMzkrgwQtG4vSGMOp13RKQtKp1+mIGJRAu2Vzr9PXawKSrxl9QVbG0+byPWsqlDy/lap3ZFUKIzjioO79XVISr5GRkRC+jyMjIiOyL5b777sPhcET+5OXldet57otASGVzlavD/dVOX7c0sPL6VbbWdPy6m6udkeVdekVHeoKFjATLfi8dcdhMZDosvTYoaWUy6MlIsBzQ3I5AUKW0zt3h/tVljQfkPLpCbxt7/mCIkt38bNeUN0WWpLh8wd0mldc0+zreuZfW77I0q63qZl+75THRY/DAfJybDXoyHRbS7JYeD0raijcbyXRYujUogXAw2xFNA6f/wBVC6KyuGn+BXXNMZCmXEKKLHNSByb666667aGxsjPwpLe24DGZPMRl0DM7ueGr9uEFpKDooq22iztX5C6Jmb4DyBg8VjR4Cbart2Ex6BmXaO3ze0BwHVmPnf21qnT7KGzxUdVAeWHTMbFQoSo/vcP/YguQDeDb7p7eNPYtRT//0jn/fx+QlYvQ3QeMO4k3sNhjNT7FR0eSlrMFDfQcXrzXNLeOgOfY4GJHr6PD4uUnWTgUfFY0ettW6KKlzE5Qu3F0mdTeBj6IDu7l3zpZA142/YGjXPia7Jr9LYCKE2DcH9VKuzMxMACorK8nKyopsr6ysZNSoUR0+z2w2Yzb3jsTqjhj1esYVJJESZ6K2zUWOooO/nTkcbyDIhc/Op6LJy+DMBO46aQCj8pOx72EJQSCosqXGxT9nruX7DTVYjXounpjPlUf0ITvRitWk54Zp/fhidUW7u8MGRccVk/t0anakyRNgaUk9932xjvWVzWQ7rNx8TBEnDMno9jubh4rkODO/OXEgV77YPg8i3mzg6EHpPXBW+6a3jb1Em4nfnDiQC5+Z126f1ajnlMGJ8MoZULGC1Mm/4ZxRJ/POkvKoxxn1Op66dCxfrq7g2R+LqXH6GJWXyP+dMpghWQnEmQ00uP0sKK7jnzPXs7naSV6ylduOG8D0gekkt6nkNCovieQ4U8y78necMID0hL3rD7S2vJl/zVzHih2NpMabuWpKH2aMzO7ypZ+Ho5R4M5P6JjMvRv+UGaOySbP33lnfrhp/u3Z+b7uUK9zHJLyUS9O0A97bRghxcDuoZ0wKCwvJzMxk9uzZkW1NTU3Mnz+fyZMn9+CZdY3cJCsvXz2BgRk77+heP7Uvy0rruffTtZQ3etG08HKTy15cxLfrKlH3UNe/uNbFGY/9xDfrqgmpGk5fkGd+2MKVLy6gotHL9noPr83fxv1njyClzQVTRoKZF68aT17S3id6h1SN2esqueLFhayraEbTYEeDh7veX8kjszfS7JXOwHtrdH4S9589HLt5572EPik23rp+EjmHQRWg7jQ0K4EHzhtJgnXnzzYv2cqbV40k55OLoXw5aBpx8x7kziENnDMqg7YTJ/edPZy3F5byj8/XUd3sQ9NgaUkD5z01l0Xb6gmEQny8rIzrX13M5upwWdvSOg+3v7OcF34qxt0mpyQnycrb109iQMbOGTKrUc9dJw9i+oC9C0AXba3nkufms3x7I5oWXgL2r5nr+csna6ho9OznT0skx5l46PxRHDMwLbJN0cGZo7P5/cmDievFMyZdZbflglv6mEDv6UUlhDh49PoZE6fTyaZNO5Oxi4uLWbZsGcnJyeTn5/PrX/+av/3tb/Tv3z9SLjg7Ozuq18nBymTQMzQ7geeuGEejJ4AvECLeoufEh3+K+fh7P1vL+L4pHVaJcvkCPDBrQ8xmgRsqnazc0UBxtZt3F21nU6WTu08bgsWooENHszdAZZMn6gtoT6qavNz7yZqY+16dt42rphR2Wx+WQ43DauScsblMHZBGndOP0aAjOc5Eehd29z5c2a1GZozKZlK/FOpcfgyKjhSzSvqHF8GO+TsfqAZJf/88/jL2Rm759R00BRTizQY8gRB3vrsi5rHv+WgVL189gX/OXBdz/1Pfb+b88Xnktwk4+2fYeeO6SdQ5/fiCIZLiTKTbzXs1U1la5+beT2OPuVlrKrn12P5kHoAqcoe6rEQrD184mhqnD6cvSILVSGq8ifjDICgBCKi7lAvWh//foOhQFB2Glr8HQmqnvjOEEKLXByaLFi3i6KOPjvz99ttvB+CKK67gpZde4re//S0ul4vrr7+ehoYGjjzySGbOnHnQ9zBppdPpyEu2kQdQtZYvN3eco1Hj9NPkCXQYmDR5g/ywobrD53+yvCyyfnppaQNL314WtX90XiLHD8na6zK+DZ4A9e7YsyKaBluqnRQepGVue4JRr5CTaJUZkm5g2PVnW7UWStqXzEYNEr/wMeLzx8DwcwB4Y35Jh8fdWuvG5Qvi8sfuQB5UNSoaPe1K/KbGm3eby9CRZm+Q7fUdz4os3lbPsJyO81jE3kuwGntt9a3uFgxpkVkR2DljYm7JgTIo4f8Gghr03pVtQoheqNcHJtOnT2d3rVZ0Oh333nsv99577wE8qx6iGIk377zQ1+lgTH44D6W4xsXGKudu704pOh3xFkOkqtaukm0mlN2sB7ZbDRg7UYVnT3fK4sy9/tdPHK70HVxN2VIgdzwk941sSrObOG5wOv6gyvziuqgZSUW353FgMXZdLyKTQYdOR4fVw3pbbyBxcAq2mzEJ/46bIoFJeJ90fxdCdJZcGR4gmqaxo8HDTxtrWLS1nkFZdo4fkkF2onXvp7ptSeQnqcSZ9Izvk8wVR/RhQXEd5Y1ezhuXx+BMOynxHd+eSo03c9mkgg57Ypw3Po+SWjfP/VQcc/81RxZi2yWYqHP52F7v4YMlO/AGQ8wYlUO/tDjS7BaS44wMz3Gwckf7crZ2s4G8bmoEJ8SugiGVsgYP36yvZuX2BkblJTJtYDo5idbYlbZsyVAwBbb9HP67wQIn/A0sCbDlO5qqS6ggj1pvOOB3WI1YTQYum1zA/OI6nvsxPIaOGZSO3WKgb2ocW2KU4U6LN+9zd/J6l5/t9W4+WLoDTyDEGSOz6ZMSx9SiVH7YWNPu8Sa9wvDdVP0SYm8FQlrUTazWpVzmluWGbZdyCSFEZ0hgcoCsq2jmgqfn0uTdmej67y/X88rVExjXJ3mvemIETElUo/L4xWOodfm57pVFkeTCD5ftICXOxDvXjcdhjR2c6BUd54/L45t1VSwrbYjad9tx/clNspFuN3Py0Ey+WB3dB+bcMTkMzY6+qKl1+Xhw1gZeb7OU5c0FpRxZlMqD548kPcHCg+eP5IJn5kVVGTLpFZ6+bCwZ+9mMToi9oWkaK7Y3cvFz8/AGwhdK/1uygzjTOt66fnLsi3VrEpz+CLx0Cjir4IxHYfFLsO1nmsfcwLs1+eQagzz6zUZW7djZpO61edu4akofrj+qL58uL+OPpw0h02HlsYvHcOEz0ePfYlR46rIxZOxDnlCdy89/Z2/gpTnbItveXFDKqSMy+eNpQ7j8hQWUN+5c9qlXdDx4/kgZc6JLBENq1FKu1pmS1n4mrfskMBFCdJYEJgdATbOPW99cGnVRAuGu3Te8tpjPb51K9l7kDVQ1e7n8pSU8ddlY/vjRqnYVT2pdfn77/mqev2IsiXGxL3YyHRaeuWwsG6ucfLaiHIfNyIyR2WQlWlqWeRj561nDuO6ovny0vAy9DmaMyiEv2UpyXPRFzaZKZ1RQ0uqnTTV8s76KC8fn0z/Dzic3T2HB1jrmbaljYIadYwank+2wYJCkSHEAVDZ5ufG1xZGgpJXLH+KXbyzmvRuPICNWGd7U/nDtN1CzIZxzsu1nUAxUDLycb390McRfHxWUtHrx5628e+NkrjqiD1kt43pQpp3Pbp3KnM01LClpYGh2AtMHpJOduG9NCrdUO6OCklafrajg1GFZvHn9JJZsq2fellqyE62cMiyLTIeFeCk2IbpASNXQKzs/v1tzSiI5Ji2f7RKYCCE6SwKTA6DO5WdjlTPmvgZ3gIom714FJtvq3CgKlDd4cHeQTLu4pIG6Zm+HgQlAekvn8ilFqTH3tybejilI6vAY/qDKS3O2drj/hZ+KOX5wuFdJTpKNs5JsnDU6t8PHC9Fdapx+qjroyl5a56HO5Y8dmAAktnTG/vL/wv/NHccXm7ycNCyTR2Zv7PA1P1lWxr1nDov8XVHCRSwuSM7ngvH5+/Q+WgVCKq/M3drh/v/M2sA7N07m7DG5nD1Gxpzoeu37mOha/hsOSFr3+YNSLlgI0Tlyy/oA2NNdI28HQcauXL4gZoO+wwo/e/t6XSGoqjTtpg+JyxcitJuiBUIcKP49dD3f43hRg+BvubFgsNLgCy/DcvmCHT6l3u3fbdGO/RFSNZo8Hb+20xckJP0jRDfatY+JTqfDqNe1S36XGRMhRGfJjMkBkBhnIsFiaLeUC8J3lnLbNC2sdfqoc/nR6UCvU1oqaGmYjXrykmzUuXwMSI9vd5xW6XYzDlt0jkkgpFLV7KPO6UOvKKTEmzq+Q0w4ob262UezN4jdYsBm0hMIaTR7gyTajKTFm7GZDZw+IpufN9XGPMZxg9NJtJqoavZS5/LjC6gkWA2EVA1vQA2fg333y1jc/iA1zT7q3QFsJj0p8aZ2y8mE2JNUuwmTXolZIchq1Ed1XgdocPupc/lp9gbR6TTyEtKwXvAuihaAoJ/bzUl8vi3AlKJUvlpTGfM1Tx+ZjU6no8bpo9bpxxsIkRxnIs1u3mMVrnqXn1qXnyZvgDiTIdLM0ahX0Cs6XL4gtx7Xn1uP60+zJ4DTF8Jk0PHJ8nI+Xl7G8YMzsBr1lNS6um3sNHkC1Dp9NHmDJFiMpMSbDtvSuYcbTdMIaVq7vEiTXsGkl6VcQoj9I4HJAZBhN/N/pwzm9++vbLfv+qmFpLT0K9hc7eQ37y7nV8cNwGJQePSbTZiNCqeNyOYfn63lr2cO477TiijUV3LRmFTeXNK+8s49pw4koO78wmjyBJi1uoK/fLKG5pY7vDmJVh69aDQjch3t8jy21bq4453lLNpWD4QDpzNHZXP1kYU8+u1GvltXzaWT8rnp6P4cNSCN3CRru74JCRYD1x/Vly3VTn75+pJINSKrUc81Uwsx6hVem7uN/5w3kol9k2NeqNU0e3n8u828OndbJJdmRK6DRy8aTUGK9D4Rey8t3sytxxbxn1ntq9H95sSBpLdJCN9e72ZztYs/frgKXzDEuzdMxuoqxfLRtVC5GgCT3sTpk25n0NE38OPG6na5K/3T4ylMjaO0zs0vX1/MypY8FLNB4ZdHF3HppHxSOggSdtS7uf2d5cwvrgPC5YZPG5HNScMyaXD52d7gZkpRGgZFxz0fr2ZdRXPk2JdOKuDeGUOZPiCNh7/ewKvzthEI7Rw7/71wNH26oG9QRaOHez5ew6w1FZGyxMcPSeevM4ZJ88bDQOvv1K6BiVGvRJqASrlgIcS+kqVcB4BBr3Dy8Cxeumo8g7Ps6BUdBSk2Hjx/JNcd1Zc4s4HyBg8XPTOP6QPTqWz08u8v17Nwax2XTSrgzneXkxRnpN7l59RcD1mvTOWOwu3cd1IOuUnhcqfDcxy8efVYjiiwcsbjP7OjJVhYXd7Ene+tiAQlADsaPFz07Dx2NEQHFFXNXq59eVEkKIHwspH/LdnBG/NLuOXoIvSKjpfmbOPVuVtJjTfx9vWTuPKIAuLNBswGhRmjsvno5imoGpz/zNyoEqmeQIjHvtlEotVIqt3E1S8vpKTO3e7nFQiFeHVeCS/+vDUqwX/F9kYue34BlU0dN5kUYldWk4FLJhbw+MWj6ZcWh17R0T89nmcuG8vZY3IiF1OVTV6Ka1z88rXFlNS5+c+5I0kIVGF5Y0YkKAEg5Mf68/30q/mWl6+awDGD0jHqdSRYDVw7tZA/nzGUW99aysaqZqb2T4s8zRdUeeirDcxaVRlzmVdNs4/rX10cCUoAVA0+Xl7Gt+uqWFrawKi8JO79ZDVVzb5IUNJ67Od/KkZVNeZtqeWFn7dGLiAhPHYuf2EBFfs5dho9Af740Sq+XF0R1SvlqzVV3PX+Shrd/o6fLA4JQTUcbBh2DUwMbWZMIku5ZEmhEKJzZMbkAHFYjUwfmM7wHAf+oIpBr0T1L9hQ2UxVsy/SlXnRtnpOHZ7FpyvKCakaF47Pp6y2HuO2x0ALkfrZ1VyUPYZjp91IKC4dc+0KkjZ/RijxKtLsZpaV1mO36Hlg1vqY5+MLqnyyvIybj+kf2Vbe4O0wSf+9xds5bUQWxw/J4NMV5Tz/UzEXjM8jJ8nG/50yhBun9UPTwu/TZjbwwdLtHa6Df/6nYq4+spA/f7yaF34q5t4ZwyJrkwGqmvw830EvlZI6N9tq3btdiibErpLiTJw6IpuJhSkEQipGg9Kus3qt08fSkoZIDldmghlT7SJoroh1SOK+/i19LvqaX0zryw1H9aWs0cP8LXVsqnKytryZJk+QiYXJPPHd5qjnPfT1Bo4elE6mI/p3uLLZy+qy9lW+IFwO/LGLx/DWwlLGF6bgD6kk2YzUu6PzvB77dhN3nTw45jFK6tyU1LrI3I+xU+v08dWaqpj7vl1fTY3L324pqTi0tN4sajdjoujaV+XaQ36XEELsSgKTAywlPvYSjnUVzRj1Oly+AK2f5TlJVha03D3NdFjQeWox1a7Z+aSyJaSXXb/z71kj0Q07m6HZCazc0cjkvqlsrIwdaAAsLW3AH1QjQUGs2YtWvqCK2x+KVA9z+UORymAmg9JuCcfy0vZNFVuV1Lkjy2dWlzXh9gcxGXZezHgCQZy7SSzeUu1kQmFyh/uF6Ejqbvp4NHgCbGoJzO0WA0FVQ1e1usPH46qhpqGR815bE7X5iUvGAOHf8yRbYrunVTX7Yi5xKWvoeDYjENIIqRqbqpxM6JPMtloXGQmWdoFJjdOP1dRxDsvmahcTClM6fk97ECtPrq1GT8cFMcShIdjBUq5LJxWQ3tKTR5LfhRD7SgKTHlLr9BEIaRj1OlRNpW9aHIGQRrzZQGGikU9uHIvJoOfoAalsrHaRFm/Gn5RMIGkgRmcljSOvw9PvZPRxySSEGmHbHEyVy3DaCpja30+63UxAVSlIsbFie/sg4VfHFnHaiGx21LuxWwyk2i3k7KZksVGvIyPBwokt3ep/3FCF1aQnFFIpb/TgD2lYjfpI34bBWfYOj5XtsEQaLvbPiMe6S46JxajHatS3JP6315pj4vYFafIG0Cs6UuPN6HSd7wchDk/+YIg6VwCdDpLjjDS6A8SbDAzNTmBYskpRkp5Ms49g3+OpiOuHEnSRtu51AvG5NBSdic2RjC3QQLLVxpCsBCb2TaZ/ejyBoIrackc5J9EKOg2TPrzEccaobFLiTWjo0Mf4Vc1I6Dho0is6EiwG+qTYaPT4mT4wnfxkGxWNPj5Yup3N1eElk4k2I74Oxg1AnxQbANXNPkKqit1iJM5sQFU1alw+VFXDYTNiNbb/atA0jQSLgX+cNYwV2xv5eHlZu7LlCRb5SjnUBVuCjV0Dk9H5O8vLt3Z+lxwTIURnybfIAVbr9PHzphoe/3Yzlc1eRuc6+O3Jg8lIsHDswFT6p5h5b1EJszY08PuTB/HTxho+Xh5eznXysExGH/swtbXV/OfHGtbMqyQnsYlbJyUxJnkYCQNOIOSqYc1WN5vj4klPsHDbcQO46qWFkdd3WPV8+Msj+WZ9Fde/upgGt58Jhcn86tj+ZDosFKTY2FbbfubkjJHZfLq8DIfNSG6SjUFZCaiaxjfrq3n8201srXXRNzWeXx1bxOCsBCYWphBnil3a+Ioj+vDOolJ0Orhual/MuwQm6XYzl08u4OkftrR7bmaCBZtRz/qKZp74bhM/bKjGbjFy9ZQ+nDw8S5Z4iT0qrXPzws/FfLq8PFzcYXQORxalUBAf4srsUozf30cgMJKtef/HU3NVvt8Yz6DMLP7v5NcpKa9mvLkC2w9/RalaQ9LwS/nnjFt48NttfLB0B2nx4d/df507giSbia01Ll6/diIfLN3Br99ehkmvcPrIbKYOSGVzlZMReYkktix9ynRYKEqPj8zatHXaiCwaPUHuPnUIVc1eHvtmIyvLmshyWLjiiD74gip//2wtNx7Vr8OAPjPBQmaChdfmbeP5n4ppcPuZ3C+FW4/pz4bKZv49az0uX4jpA9K4+Zgi8pNtkSU5VU1evlxdwfM/FdPoCTCxbwpPXTqWR2ZvZHFLTtrEwuQOk/rFoSPQupRrNzeCWhsu+oMqNU4f4/72Nf/7xRGM3U1vLCGEANBp3VVs/yDS1NSEw+GgsbGRhISEbnudRk+A/3y5nlfn7ezYfOuxRWysdFJc4+L5S4Zz+Ssr2Fzt5MlLx/DPL9axtU2QMDLXwSUTC/jt/1a0O/ZNR2TwC8tMDCMvxFmxmcu+s+INqDx16Ri+WlPFo99sxBdU+eLWqdw/cy3fb4iu6GXSK3zwy8nYTAZufG0J6yt3JtaeMCSDGaNy+NVbSwmqGtMGpHHjtL6s2N7IfV+sa3cufztzGClxRgx6hbs/XEVlU7i5nUHRcemkAvKSrTz81Ub+de4Ipg1Iw2ZuHx9XNXn558x1vL90RyTJtl9aHPeeMRQVuO6VRe2qIR3RL5lHLhxNml2Ck4PFgRp7rUrr3Jz9xByqndENF+8+qYjL4+Zj+uwWcOSx5vwfOOepBXgCIfSKjhevHM8rc7bwr8HFJH9xQ/hJKf2YP+01Ln5ra7u+IeeNzeXWY4sob/Rx3SuL2i1xGpRp58Zp/ahs9nL55D5YjXpcviCbqpzc9f5K1pTvzDU5ZlA6d5wwgD99uApfUOWqKYXc8e7yqONdPCGfcX2SWpLtNe7/ov3YeeD8kbwyZxvvL90R9VyTXuGJS8dw1/9WRn4uNpOej28+kqL0eGqafdz+zjJ+2Bj9mWE2KDx16Vhuf2cZ/dPjeejC0buddRW9z76Mv221Lqb9+zv+cMrgSE5kLJc9P597Th/C0BwHZz8xh5OHZfLkpWO76tSFEIcomTE5gKqbfVFBiUmvMDwnkf/O3sSt0/JZsLmSzdVOhmYnUFztigpKAC6bXMA/Z8ZOZn9ybiXnX3k6KetnEmdN4o7Jw7j2/e0s3FqH2x/k01uOxBsI0OwLtgtKIDzl/udP1vDoRaN59ZoJVDf7KK13YzbomV9cy6/fXhpJevx+QzV3nzqYB79qX34V4J9frOOt6yfxq7eXcccJA0m0GtGAvCQrvqCKpsHM244i3W6OdAreVXqChT+fMZQrj+jD1lo3FqOesgYP84vrWL69sV1QAjBncx3FNS4JTERMgZDKWwtK2wUlAOcMMGJ6OdzdvfbY//CfWZsjMw/HDU5n5qoKrhtlJXnWXZHnVI+9nf/7qjpmM8N3F2/n6imFfLJ8R8y8i3UVzTR5A/xv8XZOGZZFXrKNGqePa19eyPXT+nF76gA8/hBxZgOLttZxwyuL+fXx/bnz3RX4gmq7Mt1vLizhmiMLIwU1bj6miFNHZOPyBbEY9VQ1eal3+9sFJRAe+09/v5kLJuTx2DebAHD7Qzwwaz3/Pm8E2+rc7YISCOedvfDTFj745RQSrAbpMXSYaK20tWtVrl1ZjOEZc2dLXlJHhR2EEKItCUwOoBXbG6L+Xpgax+qycP7Hcf0d/Pv7cgDG90nm+w3V7Z5vMxmobm5/UQXhsqKbGzUKtn6Nc9qf6dMczuH4dEU5Rw9M5+u1lfxiehH/mtl+hqPVwq31OH1BitLtNHgC/PL1JcRqIK0oUOP04eug4kqzL5y8vqnKyW/fC8/uKLrwOSoK/PibY/b6zupfP1vLoq11kfN47KLRPPbtpg4f/8XKiv1K7hWHrga3n89WlsXcZ/bXgS88S9icOorvNy6J7BvfJ5kXf97K9QNs4N5ZyrfZMYDN1e3HaavFJfWsLWvucP/3G6oZnuOgoslLXrKNLdVOqp1+/v7ZWiC8hr9t0BPXMrP448ZqxuQnRQUmmgbrK5rp19J8dc6mWv7w4arIuBtbkMSovMQOz2Xh1nquPrIwatvXayvxBVS+WhO7KhnAj5tqMep1EpQcRlrLBe+aY7Iri1HB7Q/R0BKYl9S58QZCe2wwKoQ4vElgcgAVJNv47YkD2VjVTHm9h0lFqeQn27AYFewWA1bjzkomrR/eBSk2MhIs7Kj37PmLwKCA3oCmaRit8UztrzImP5HjB6fT0FK9x2LUR45Z1uCJurgxKDqUlnXDig4UnQ5V21mBZWh2Aka9wvqK5naNGXdl2CW7V9XCy0kyEizs4alRx7AYlKjgKKhqmAxKzBkTgDizfOmJ2BSdDotRz4lDMihItWEzGihv9LCqrBGDwQimeMgYiqI3MDQrAbvVgNkQ7gxvtxgwGo2QMwYUI1SuipnA3pbNpEfX8rseZ9IzKCuB5Dgjzd4g5Y1eLAZ9SwGM8INa+6m02nUmpnVNf5LNRFKckaHZCawpb4os17Kadg4sW0tlLlULj90+KTb6pcUTbzbErHhnUHSouwyp4TkO9IoOm6njrwmzQZGiE4eZjqpy7cps0OP2BaN626yvaGbkbgJkIYSQwOQAqHf5qW72olPCF/o5iTYuGJ9PMKTiCYR4/xdHsKG2mcvHpDFrTTXfrKviT6cP5pKJ+ZTWuymt8zBjVDYD0m0MzLBH5X+0shr15KfE0TzkYjyWDH4s9jMsW8eI3ESc/hA/ba6h2RfklOGZxJsNlNS5OX1EuErQv2aGc1lOGpqJwxr+lbBbjBw/JIMvVlVw/rg8Th6WyaJt9XgDIa6eUkhKnImUOBO1rvYN1bIdlkiAAzAmP4lfHt2PzVVOapw+lm9vpN4dIMlmxOUL8c36KpzeINMHppEWb2ZbnZu5m2vJSbRy1ymD4fO1kaUks9dWctqIbN5bvD3mz/rUEdld8U8mDjGaphFUVf570Wjmba6lvNHL0JwEThuZzdryRnwGH6ZL36fa1pe0UAPPnRxHtS6Fb7a42FHn5uWLBpCmVUPfYyDogck3k2gMMrlvEnO31Ld7Pb2iY2RuIhWNHo4dnEG/1DiWb2/EbFCY1DeFZm+ADIeFB2atxx8M8eb8Eib2Te6wGt3ADDs5iVZeunI8ekXHt+urOHZwOneeMJAPlu7gy9UV9EmNjzx+dEESig7OG5fHSUPDY7eswc0/zxlBeaOHf81cH1Ux6YShGZFZ2ukD07hich82VTt54adijhuS0eGyzTNHZZMcJ31LDicd9THZlcWo4PKHIjfFAJq8Uk5aCLF7Eph0sxqnjy9XVdDgCfDvL3fmhzz27Sb+eNpg3L4Q172yGJ0Ovrp5PGcMTWF1lY/MBCuXPb8gcndT0cGrV0/g7lMH84vXl0Td9VR08MjpuaSXzkQ3+HQmPLwcT+uMwvfhJWP/OGsY3oDKWY/PieoCnxJn4sHzR/LAVxu47fgBbKl24rCayEiwcPvxAyhMicNg0EVV9nqeYi6dmM/DF4zi6pcXRnX3NRsUHjh/VKQnyvAcB9dOLeTmN5ZEZjme/bGYbIeFJy8dyxUvLoh8cT0yeyNT+qVw/vi8yHItg6LjoQtGEdI0ft5Uy8zVFTx7+TgWFNe167ty09FFkT4rQrS1rdbFuopmbn5jZ64UQL+0eN67tC9xS56jZPRt2MqX4W6u4KGS/ry5bCUAd05Nx7b8fZQ59+084NzHcQw7m7+dch/nvbgyUv661V9nDCVOc3Hs4Azu/2Id9+9SJOLGaX1RdDp+Mb2IF38q5vNVFRzVP5UHzh/JzW9EL6GMNxv4zUkDuf7VxZw3Lpd6d4CX52wFQKeDu04ezLljc3j2h83cfEx/shOtBEMaz1w2lpU7mqLGLsAR/VL493kj+NVbywDIcli4YFweN762hGkD0jhleBbXvbIo8nNq9Aa5/qi+PLNLlbzcJCs3H9NfluYcZjoqF7wri1GP2x+kwRMg0WqkwROI5JsIIURHJDDpZktL6sl0WPjDh6uitttMevKSbFz/6mIgvEb8vOeX8caVIzCYrFz6wsKo4OOoAWnMXldFcY2LT24YxderdjCvPERRosJ5g8zkLH0A07r3aUzqT7zFiCewMxeluMbFy3O20jc1PiooAah1+fnH5+t44tIxbKxo5r/fbOLJS8dQkBJHYWocJw/P5PTHfm73vl6bX8KEwmQ+uflIPlxWxsaqZgZn2ZkxMof8ZCuegJ13bphESNW44bXF7ZZelTV6+cfna7lkYgGPt8kZ+XlzLeMLkxmTn8SSknqCqsYd7yznk1uO5NFvNhIIhftCvHbNBJaWNvDpinKSbEYumVhAQYoNh9W47/9Y4pBU2eTFG1S59c1lUUEJgN2ix7r5c2qHXIKvqZr87/6PbyY9z5vLSoHwEqyzCgPY37mv/YFXvU/fohP53/Un8v2men7aVEua3cyxgzP4ek0lR/VzsGBDLd+ub5+H8tT3W3jq0rH89t0V/OLofny+qoIfNtaQaDPyxa+m8s6i7WytdTEoM4FxBUn8Z9Z6djR4ePjrjTx7+VjeWlASKSTxj8/X8vb1k3h70XY0DW47fgA3vLaIe88YxiOzN7Z77TmbazmyKJXrpxaSEm9mcr8UdIRLEp8zNpfLnp8f9XN6ec5Wrj+qL69cPYHPVpZT6/Rx6vAsJvZNkRsBh6HAXi7lMhlackzcAVLtZho8gXbfP0IIsSsJTLpRoyfAnE21xMqGmNo/jS9XRyeV1rsDnPrkYt66fnKkxG6r00dk85dPV3PNuBQKv7yK69QgVyT1x9hUju7t70ENf+AnLH2as4fexdPzKqOeP2tNJU9ckhvzPNdXNrOl2snTP2zh6EHpbKl2UZASh0Gv8L8OlkwB/OvL9Vx3ZCFDsuzcfHRf4i07l3QE1CAvzSnm9BE5NHlifxnNL67j2qmF7ba/t3g71xxZyJKS8BIZf0hlY1UzD184CjQi+S35KXGcMjwLRafb45ekOHy5fUFWlDbGbPZ25cg4LIueYnvB2dir59Lc/3SeWeqK7D96YAppa1/o8Ni6nx9k0bAB2GxpDGhZZvnL15bgD6mcPzaXl+Zu6/C5s9ZUMCjLTqLViE4Xvjnx8fJyrj+qHw1uH4lWI/O21EYF7gBfr61iav9Uvl5bFdn2zboqxuQn8v7SHVwztZCC5Dg+WR470R/gjQUl/On0ITzw5Qbu+2Ids+84ivvPGcFHy3ZEzYC2euaHLby9sISvbptGos2EybCXiWLikNOa/L7HqlyGcAlsVdNwWIyYDQrNMmMihNgDCUy6UTCkoqJFrbFtFW/WR6qVtGXUK7j97T+8LUY9TZ4giWYdeOqhag2m0vntHqdz15EYoyiVqrVPpm3LH9Sod/uJM+mpb0lWVFWNyg6qgAE0uANYzQbeWbSd44ZkRO0LBFWaPEGqdvP81teNddy4XXqb1Dr9kaZdbXVUbliIVpoGta7Yv4eOlvEUDKkonloCpsSo8eow6zB5O668hbsOu0nHwspmlpbUs6SkIbIrqMUe+63qXQFS4k14AiEMii4SEIRUja/WVNHUwUVcgzvQLiG93h0gzmTAF1QJhDTizYaYny9tj9HgDkTy1bwBFb2io9bZPmesVaMniMsfIj1BxtzhbGfy++5/DyxGhTq3H19QJc6sJ85kkKVcQog9ksCkG1mMCilxJorS7Hy0LHz3UtHBjdP6ccrwLAIhlTuOH4DVqCekaVgNOrzB8IXJCUMyOGt0Dul2M0k2AxnU8/NVGSjmeALBYzC6a2HC9ZA+CNQQBL2w6EV8+VOZt639BUlOopXmXRIPxxYkce2R4eUccWY9z14xDh1gVBRK61zoCDdX/GJV7HKhEwqTSbeb+fVx/dla68ZqUNA0DbPJQGqciVF5SeQl2zr8+STZjJG7b7sed9ea91OKUthe56bZF8Rq1JMSb8JukWVbYvfqXH4Cqsr4PsmRbYOz7PztjCFkOUyYVD+eK7+ib7AS3aCpuIM6TlGDXDG5oKUqlUJgx2kYq9fDxBsgsQA0FXxNsOA5fIlFzCsLcuyIXAZn2nH6gqTEmblwQh4Wo55HLx7Nwq31vDJna7tCEZP7JuMOhOifHs8jF44mwWLAqFfQ6eDJS8eyoLiOV+dta5e/MqEwiTcXlEZtG98nie31bq6c0odASOXcsbmU1LmZ2cHYnViYTH6SjScuGYNRryOhZSyN79NxZ+7C1Dipeif2Ovk9XJUrhKoFybDbsZr0OH2S/C6E2D0JTLpJeYOHb9ZVMrYgmQa3nz4pNkrrPbx45Xhmrang7Cfm4A+F71KeOjyTO08cyPyt9eFE2XNGMCQ7AaNBobSikkLdMuJ+uJs4Vw0oerj6Kyg6Fr75a/gPQFwqHPVb/AXH8cPs9r1Kbj9+ADsadpYGvnRiPoOzE7j7w1WRC6bhOQ5+e9JAQmq4hOnWGiej85PaNXMDMOp13Hpsf+75aBXLt4d7sWQ5LNx5wkDmbq5hRG4i54/P5YMlOzhxaGa7ZWsAv5xexFu7XGAZ9Toum1TArW8ujWx7/OLRfLq8nKd/2IInEEKng2MGpnPvjGHkJMkadxHbxspm7nx3Ocu3N/Kvc4czuW8SdouJu44vxBnUoXNW4/j5Lxg3fBIONgwWTGOu5eqJN/FTuY5fvrGUkjo3X183haJTs+Gre6BqTfjg9iw45o+4U8ey6tM6BtS5eeK7zdxz+hB8QZW7P1wVmS0ZnZfIQxeM4g8frqS0LjyO8pKsTBuYzpPfb+aMx34Ol8HWK8wYnc3UolRuf2c5I/MSefiCUdz1/srI2M1NspLlsLKpyhl5n/3S4hlXkMTM1RX895tNaFr4psir10zscOzedEwRd7yzjOKacAGJgRl2HrpgFPnJNsb3SWLh1vaVxu45fQjp0rz0sLf3ye8KLn8Qb0AlzmzAalJilqoWQoi2ZE6+G/iDIRZureMPH67m75+tJclm5F/njuRfZw/n85XlvDavJLLePaSGq01tqnJy+zvLGZyVwNKSejQNPl1exhHWUpK/uBFcLZ2X1RB4G+D9a6FsZxM4XDXwxW+xNW7mdycOwN6yFKpfWhwPnT+SpSX1ZDks/O6kQYzITWBcn2T+8MGqqLu4K3c0cuubSwmGNAIhlfxkG1+truThC0Zx+ogsjC2NG8YVJPHODZN5+KsNkaAEoLzRy53vLefEYVn856v1LN5Wz9ljcrl4Yh43TusbSUzPT7bxr3OHc8ygdLISLZHjji9I4s3rJ/HpyjKafUHizQb+dc4ItlS7eHj2xkgZVU2D2euquP7VRR02nBSHtx31Hi58Zl7k9/OoLJVHjrFw5wn9qXYF8TXXkvbVLRjXfxQOSgCCXqwLHsO68Am+W709UvXNRADevWpnUALQXA4f34Qp0Mgtxxbx98/Wsr3ew42vLQkvqWpT7GFpaQN3vLuc/ztlMAZFxynDM3nqsrE8MGs9Hy7dEbkD7Q+pvLtoO7PXVXHhhHwWb6vnt++t4K6TB2HU6zh9RBbPXT6OdxaFg3mzQeGcMTk8c/lY/vzxamavrYr0NPEGVG56fQmPXTyGGSOzo8bYW9dN4j9fro8EJRDOMzv/6bk4/SEevWgM1x/Vl7iWXigDMuJ57ZoJjCvoeDZFHD4CrTMme+hfYzaGZ0yaPAHiLQasRkOHyxOFEKKVzJh0g/JGL//9Jpywuqa8iV+8vpRLJ+Vz9uhcfvv+ynaPP3dsLs/+UAzA2WNy+ONHq3j4gtGk6Z2kz/t79IPTBoYvkNy1MV9b/+29XHnG0+SnjCA1zkBBcCsuqw6TIZUmb4Ad9W7+fe5I7nh3eczn17sDbKp2Uu/yk51opbTBTa3Lh8Wo56ELRqHodNQ6fbh8Qb6L0Z1e0+D1+ds4a3Qu/565ng9+OYUx+UkMzLRzwfi8SMO3BLOB5Hgzfz9rGHccPwBVA7vFQKLNRJ/kOH597IBI5+uTH/kx5rmuLmuivNFDml26TotoC7fWRYLuvCQriZ5S1LWf0jD6TlxB6K9vwlDavtocgH7RM1x61vm8tRQmFibh2PIp+J3tH6hpWH66n2Upf8DlDwfNIVXjvcXbOXVEVlSvnepmH76Ayuw7jgJ0+AIqs9ZUtj8m8MnyMp65fByvzdtGRZMXvaLjresm8daiUm58bTHnjM3l/HF5qKrGrDWVvDJ3K1Zz+4/yqmYfV724gI9vPpLbjg+PJ5Nex72frGHO5vafH05fkJmrwsn3d54wgCuP6ENI1bAa9aTKGBMt9nrGxKBE8pzizQasRr3kmAgh9kgCk24QDGlRSy0aPQE+XVHOsYMyYiagF6TE8dbC8F1Qo14hENSod/kZmGqCn3YJZJL7QuWqdseIqFyF0d/Ea/Oa8QVVXuz3PZtST+Wmd3YumZoxOoe15e2bNLZaW95EotWIRrgZZEiFdxdv592WCy2zQeGJS8bs9vnTBqRR1ujFGwyR4bB0mA9iNRrISYr+NWx7EbShsnm30/+bq52MyE3scL84PC0orov8//BcB7qKebiSBqMoCkE1hN5V2vGTgz7itPBsQv8UM47K9kUmWukrVzCof3TexbqKJs4cndPusfOLa3FYjfzhg5X869yRHR5T1cAf3DnjsqSknjqXn/8t2QHAA7Oimx0Oz3EwpShGxQvCNxpc/iCDMhMAqGj08POW2Dc1AOZtqePKIwoxGfRSCljE1Jr8vqeqXOY2/W0SLEasJn27PEchhNiVBCZdSNM0apw+bGY9P/1uOnp0+EIaJkP4A9wXVHnogpH0T7NjMSr4QyouX5C8BD3TfjEaVA2/YkZDI8FqoLIhAI48qN+680Vc1ZAxtOOTSMjFb0nm6IFGqhubsWheKpw7O0mbDQpmg0J2oiWy3n1XOYnW8IWRFp4dMeoTovZnJ1rZTYEvchKt1Dj9kWTe/WEx6jEounb9JyLn4pCLJ7FTg9tPSFO5+Zh+3DCtL1YlSDxePPorcPt1WPU6Rtlt+LzTqLh6Mamb38Pwwz8BjWDfE6jPPw696sORnE5ukpsKVwhPcj+sfB37BR15xMXZojq2ZzmsMatbZSdaSYk38cD5I4jfQ+GGtuV4c5NsuHYTnOclx349CN/Vtra5QDQZFHISrR1WC+uXFh9Z9iVELAFVRQcoe1EuuFWa3YzVpKesIfZ3jhBCtJIcky5S3uBh5qoKKhq9rC9vYtHWem58fQlXvbSQJ7/bjD+o4vGHSLdbmF9cy6/fWcbNbyxl9roqSptVfvPRZv74+RYaGhv59pejaPYG+XSzSt3YX0W/0PZFqH2PBX3sCxv/lDuYW5/Il6vLGZmXQs2gS3luSXh25Ih+KTxz2VgWb6vnl9OLYj7fqNcxoTCZUXmJFKRYcfuD/LixJuoxNxzVlwEZ8XQUc1w4IZ8Pl+7g6iMLSd/PJSCp8aaYd58B0u1m8ndT9UscPmqdPhZvraPZG6C8wcfj32ziihcW8JfPN1PiMfHkj9tYsLWOJk+Aj5aVc8Obq7jk3e085JtByS82U3blAv6b+FsuXDSAy1aO4rOtOp64ZAxT+qVSM+Ai0MX+Za8ddxuziv08cckYjhmUDsCF4/Pa9RAxGxRG5iZSWudh5Y5m0DSGZCXEOiTj+ySxakc4N8ZiVOiXHscZo2KPAYDLJ/dhaWn7ZHWA04ZnkRq/cwwmx5m59dj+MR+r6OCC8bno9pA7IA5vwZCGfi+CV4tx55hJiTdhM+qlwaIQYo8kMOkC5Y0e7vloFTaznjmba3l74XZ+/Xa4GlBxjYvBWQmUN3pZVtrAY99s4q+frWXVjia21Lh46vst3PDqYm6c1o+fNtdz1vMrmbulliOzVC6ZmM8y60Rco66B1ouFzOGs9qdTPeMNMNt3noROR2DMNay2H8n7S3YwbWA6N721nAfmOzluSAb5yTaumNyHm95YQr+0eBKtRi6dlE/ba5B4s4F/nzuSmmYfZqNCht3CmaNzI8m2Oh1cNCEPTyDEw19v4NWrJ2Juc2dXr+i4bmpfyhs8jMlP5JKJ+ZFmiPvKZjJw5wkD2i1VyUyw8Oo1E8mS5SaHvXqXnwdnrSM13kxJnYdzn5rD6wtKKW/0csH4PC58biEhdGQkmPnjR6v522drWV3WxOZqF49/t4VznllCSTCFJ+ZWsbnaxeqyJv748Rru/WQNhWnxLG2yU3vKs2Bs87umU2iedAczG/N5ZW4J176yiDNHZXPvjKH0S4+PKiqRYDHw8AWjiDMp3PfFWv7x+Voe/GoD/z5vBP3S4qPey5CsBG4+uogXfi7GYTXy3wtHYzXoWbS1jrtPHRw1m2HSK/z+pEEs3VbHQxeMJtsRXTFrUt9k7jplULueQBP6JHHT0UW0veFtNep58tKx5Mh4EnsQVLU9Jr5D9FIug6JgNelxSY6JEGIPdJqm7WZRzuGhqakJh8NBY2MjCQmx72Luzv8Wb8cbCPH6/G3cemx/bnxtZ7WsJJuRZy4by0tztnHM4HTueCd20vnZY3IIqRofLSsjzqTny6v74TIl4w4qpBl9JNGIqW4DtUmjOOvlDeQ6jPzf1CQy1Qp0PideRz8+3OhjeL98rnl5EU9fNpZfv7UMTyDEB7+YjEmvcNu7yxmRm4g/qPLJijJ+c8JAjh6UzrZaF2aDnowEMyaDQpM3wHM/FhMKadw4vR/FNS48/hCj8xN5c0Epr84Ld7P+6KYpWI0KFU0+fMEQ+ck2nN4QDpuR1HgTiTZTzPe6L+pcPqqb/ZTUuUiJM5OVaCFLlnEd9PZ37EE4p2lDZTMWo8JzPxZHSt2eOzYXf1Dl85XlvHrtBErrPPz2vRUxj3HJxHzq3X4+Xxld1vrf545gWHYCSeYQ6YFy1OZKahqbcdn78upKNy8t2jmbmJ9s40+nDWFNeROnDM9kY5UTi0FPeoKZ0joPf/tsTVTp3ocuGElI1Ui0mvAFQ2QnWnFYjWysasak15NuN7Otzs3fPl1DWaOXO08YwCnDs9ha40IjXJ5bVTV0ig5Hy9KwaqeP6mYf+ck20hPMJMfFnrF0+YLUOH1sqnJiMeopSLGRbjdjMkifksPJvoy/p7/fzH9nb+S5K8bv9nFbqp384cNVxJn0PHfFeGavq+T5H4vZ/I9T9rgMTAhx+JIck/3U7A3wzqJSbjq6iESbKSrpFsJNDIOqhqpp/BCjilWrr9dUcvdpQ/hoWRkuf4gadxCDt5pr3y3lg3MdGBJsGD+/nYZT3qW80Ut5o5ezXm8mzqTHZIijwVOKpsG/MzOxmw0sKK5jWE4CC7fW89XaKqYPSGNDpZMbpvXjjx+uQtPgX1+u59+z1pPlsJBgMeLyBZk2MI2qJh+z1lSi6OD0Udn88cNVmAwKd582hPeX7Kw0NHdzLTdO78eAzG778UYkx4UvsgZm2vf8YHFY+XZdFUcPTGdHgyeq/8YR/VL4wwerKEyNo9kT5Lv1VR0eY+aqCu44YUC7wOTb9VUkx5mwWZtRPr2S7yc9z22fB2lwl7Q7Rkmdm4Cq8tmKcgpSbGTYzbw8ZyvfrK/GF2zfSPSLlRXEWwx8urycZy4fywNfbWBwZrgZq1Gvo8bpj3reA19t4KzRORwzOKPD95G7l0sb48wG4swGClLi9urxQrQKqtoeK3JBOD8QIKGlTLzNqEcD3IEQ8TGqyAkhBEhgst90uvASJp0uXCrXqFdIjTeRmWAhO9HC8BxHJAE83mxgQEY8tU5/uy7QiqIj0WpgRI6DTdVOFMCgV0iJN2Mwttz1dFWhI3qCy+UPRUqVth5H1cJT7S1VHTHqFbSW52maFlVNRdOgrMFLGV4gXA0opO3s7KtpO19D0emikt7NRlkJKHqeQa8DXXjdu04XnrlIjTOTm2hhSlEKBr0OgwJxRj0DMuJpcAeoaul/k2AxkOmwoGszXtrS63TEm/SYzVZIG4ROMXSYOA6gQxe5aAuoWrvgoi2lpRy2P6SiaWDQ6TDqlci55SRasZn0lNa78QZUFMn9EL1AIKRiUPb82d+6zDehZTbP2tIXx+kNSmAihOiQfDrsp3izkV9O70e/VBsPnz+cZp/K6PxEyhq8FKbGkRpvwmE1ce2RhTT7gqTZzWS3XHA89NVGNlc7GZOfxP+dMogdDR7OGJXNkKwEkuywvsbHLccUUWO2UhFQyT7vE5J82ylIiWdbrbvduZgNCv3S4vAFVSb0TealOVsBmDYgDbNB4Z7Th/D9hmpOGpYVyRvZ1ekjswmpGiFVI95s4IeN4Vkei1HBZgpXHooz6fntSYMYmp3AK3O3kplgYUh2ApkJlv3OKRGis04dnoWmQXyagbevn0RxjQtfQMWg13PjtH7YTHqsJj1njsklJzm8ZCnNbkbVwBsIUVzjCudd2Yz0T49nY5tS3+eOy8Vi0rO4GvpOfZR0dBj1WwmE2q+ALUqPp7zBw8nDM0mymchyWBjbJ4mF22Inph8/JIP7P1/HkKwE9IqOGaOzGZ7toLTOzYzROZTWuWnyBBiUlcDWGhdry5tIiuu65ZFC7Iu9TX5vDURG5DqAcL4ghMvnZ+6SDyWEEK0kMNlPLl+A7EQLiqJQ2ezjyhcXUtdmNqRPio2nLxvHPZ+sZtWOpsj25DgTD5w3kv8t2c6pw7O47PkFkXKjAGPyE7n56CLueGcJJoPCf84byROLLfxlehYPnhLi4jeL292J/f3Jg/h+XTWvXjOB79ZX4w+pXD65gE9WlPHiz1s5d2wuRxalkuWw8tPGasoavVHPP2V4Jiu2N/Lw1xu4d8YwBmXYuei5eQD87qRBvDG/BJNe4ZGLRvPY7I3c8/HOru82k56XrprAmPxECU7EAbO9zk2zN4BOp2Pellru/XRN1Kze2WNyuPbIQm56fSlryneOv3hzOCH92R+3RMZlks3IA+eP4u+frWFztYtjBqWzqcrJ/xbv4DcnDuT8Z+bx8Pmj+ONpQ/jTR6ujzsNiVLj71MGY9QoqEFLDMxwnD8vky9WVFNe4oh4/fUAazd4gTl+Qf5w9jLveX8mOBg9njc7h9JHZ3PT6kqjxPblfCv88Z3jk4k6InhJQ1b1KfreZDDx0/ijSE8Iz/q0zJ7UuHyBLcoUQsUnyO/uXgFtc7cRu1lPnDnL5CwuoaPK2e8yEwmQmFibzaEs3+FZZDguvXD2BMx77OSooaXXu2FyCIY0Pl+0IV8w6bwRPf7+FZ0+Op96az8vztrOuopmcRCunj8zmy9UVvLd4O384dTD1Tg/TC8w0hsxc9/rOhN//nDeCQEglPzmOFdsb+GZdFXEmA6eOyKLJG+Dvn61F1cKlQ9+8bhJvLSjl8iMK2FHv4aU5Wzl5WCbrK5t5Z9H2dudrNeqZddtR5EkJX7GX9mfs1TR7WVbaSLxZwWQ0cPYTc2I+7v5zhvP091vaBQcJFgP3nzOCX76+s1hFut3MfWcPp9oZ7tT+10/XEFQ1ThqWSUqciTcXlDDrtqMornHx8bIyyhq9DMlK4PghGfx39kaO7J/KGSOyeWNhCT9uqOHZy8eyrdZNSb2bj5aVYTYonD06B6NBYeX2BqYUpfHQVxtYWtoAwItXjue6VxbF7Ntz6zH9ufXYIgn8RZfZl/H310/XMHNVBf85r+MmobF4/CGufnkhj140mtNHZu/L6QohDgPyDbefSuvcNHpDNHj8MYMSCHehjtWdvLzRS43TRyDW4nbgk+VlHD8knOjq9AVpcAfYUe9hqy6Xj1dWUtnkZVRe+Li3vrmU91o6sz/34xYu6+dhwmcnkaOPXkby9PdbKEyNZ8X2Bib1TebI/qlkJ1r595fr+eunayN3m1UNFm+r5+9nDWNAhp2Th2fx4lXjOX5oBh8uje7R0MoTCEXdlRaiO9W7AyTajFiNBt6c3z4ZvdVr87YxY1T7C6GmlhmL5DbLo6qafSg6HU9+u5l7Pl4dCRC+WlPJ1P5pqBp8uqKcF3/ail5RGJWXyI4GD1e9tJBF2+p5ac5Wlm9v4Lkfi1lf2UyjJ8BNby4hM8HCSUMzuXB8XjgPJsnG+konV764IBKUDM1OYMWOxg6bib40p5gap28/fmJC7L9gSN2r5PddWYwKJr1CjdNHcY2Lm15fwuPfbtrzE4UQhxVZF7CfAqqGXmG3CbFAh8FHvTuAxajHGaPxlC+oRvUZqXX6sVsMOH0httd5+Xpt7CpDNU4/+N3gqsYQ8u2yz8fmKif/nLmexy8ew8Nfb6SjObPt9R5sbZIU7RYj1c0+/B28Fwj3dBHiQPAFQwRbcj3KG2PfFACoafbjsMZuSFrr9GG3GKKWX5Y3emj0Ro/n1rwrgPIGL2ajng+X7Wh3vAZ3IKp/g9sfwm42sqXGxd8/WwuEi0o8csEovlsfXaUvoWV8daTJG+wwaBHiQGn2BrGZOl9WWqfT4bAZqXX6mbW6gs9WlvPZynKuOKKPJMMLISLk02A/qKrGkEw7BoNut2U3LUaFjpbk9kmxxVzGBTA8O4GUOBNPXToWDY2cRCsBn4fJKS4mHWXmj0cNZqvPzmerqnh30fZIV91RuYn4EnIpueBrgpYMrMbayGuMyU9iar8k5vx6LCVOHSNzE1nWcse2LZNe4ZwxuZTWufEFVeJMetITLMSZDGQ5LB1eCMaaGRIHh5CqUdnkxe0PYTaEq8tZe3FOQ26ihSZvCKOiY2r/VH7aVBPzcSPzEtlQ2RxzX2FqPBUtv8s6HRw7KJ2ReYn8+9wRpMcZcGiNeF1NrK7bOYbPGJlNsy/AuWNz0dD4clUFn6+qIKRqDM1OIMth4cShGcxeV0VKvIm/zhhKcryJ+88ZzhvzS1ixvRGb2RCp5Ndqc7WTM0d33OF9YIY9UoJV7J9mb4A6l59ASCPBYiA9QZKx91aD27/PuU4Oq4Fal4/KNqsLtta4GJbj6KrTE0Ic5HrvVUcvV93soc4VwGRQ+GFtDU2eAMcPyeCrNZXtHnvNlEI+XtZ++dMJQzLYWuvmmcvGcsOri6Puhg7NTuAvZw7jr5+siSz1GJxl5x+nFKDMvBPT5llYrUkYx/+K4wpOZvrAMfzmveVUNfu4/YQBnP/qMmpdfo4brOOpS8fw67eX0eQNcsmkAk57fC7Dsu3cc8ZQbjq6H9e9sjjqvKxGPU9dOoZ3FpfywZId+EMqyXEmbj9+AKcMy+T3Jw/iV28ta/d+huckkJckTQ8PRnUuH58uL+ehrzdQ7w60BKY5/Oq4Ab2ygk5Fg5sdjV621bhp8AQYnGXHYTXS6Ime6TDqddxwVF8ufX5+u2OM65NEmt2EPxSembz/7BEU1zi54Ol5OH3hu8JXjE3lqoJqzln3H2qP/icvXDaaZdsbePr7zTR5g1iMCmeNzuGRC0Zx2zvLuOWYIn733gqO7J/KK1dNYM6mGv78STghP8th4aajixiVl8gPG6o5Y2Q2H7X5XKhq9mEyKPRJsbE1RtW93588iASLfGTvr221Lv788Wq+21CNpkFespV7zxjG+MJkuXO/Fxo8AeL28ecUnnX3U+P0MTovkaWlDWyrdUtgIoSIkByTfaCqGk5viKCq8fWaSu75eDX//WYjM0Zmc8XkAqwtdzUTbUZ+fVx/pg1MZ0CGnURbSz13o55LJxVw+shsfvXWUj5YuoN/njMCY0sJxqwEC/efM4LLn9+5/hxgbXkzF7y0hpJJ94LBAp56En74M8MrP+DrVaX87cxhvHzVeN5bXEpVs4+QqvHl6kr++tla/nbmcP574WhenbuNJm+QOVvqufjZBSTZTDx4/kj6pIQT1hVduNv1499t5u2FpZFlW3UuP3d/uIqPV5QxfWAaj1wwisyWu4xGvY5zx+by9GXjSLP3votYsXvBkMoHS8v408erqW9ZkugPqby5sJQ73llGbS/La9hW7aTRG+Tp77dQ2ezl3k/XcO+na3nykjFM6pscedzgLDuPXjSGjZXN3HHCANLs4epAZkM46LrhqH58uHQH543N4+IJeazY3sBT32+JLKt0+0M8ObeSf69NpjnnSFLePp0xjiYe/GoDTd7wY7wBlTcXlPLRsh28ff0kttW62VDl5IWft/LJijLWVzojeVvljV7u/nAVQ7MdzNtSy3GD0/nF9L7EmVob0Rmod/l48arxnDAkI7KOPy/Zyr/OHcGs1RWU1LUPWMTeK2vwcOEz8/h2fXVktqq0LpwjtKascfdPFkC43O++BnAJFiPVTi+bq5wMyLQTbzawtda15ycKIQ4bcntoH9S5w/kdU4pSeOK7zQAEQhq/ensZr187kYmFyWjo8AVDvL9kBw9/vZEji1L502lD6JcWT2m9m89WlPPrt5cRUjU+XVHOL6f345ELR6NpYDUpfLxsR8y8E39I5alFTfx1yPlYV7wCgH3ho5xz+qmUB4OMa5rF696hUc/ZVOXEbFT47+yNrNi+88u31uVn3pY6Vpc1ctWUQlLjzRj0OtLt5nYd7Fs99NVGThiSyRmjspnYNwWXP4hJr5BqN2E1yq/Twaiy2csjX2+Iue/nzbVUNftIiTcf4LPqmNmo8MT3mzljZDZ3vb8SCP+O72jwcFT/NK48ohBV0yipc/O3z9awvd7D2IIknrt8HOWNHkIqfL22kpteX4KqaXx00xTQwYzHfo75eu+tqOGmK8/CvuBhjEtf4oRBZ/LFmtqox3y1toqrphSSk7hzxvCdRdt55rKxvL5LYv5T32/mucvHYTPrOWFIJpdOLMAbVLEa9aTGm3j2hy2k2808dvFoVBXq3X5e/LmYteXNBFSNv84Y2quX2PVmy0obOlyG+rfP1vLSVROiiiGI9vZnxsRhNTK/uBZvQCUn0UpmgpmtNRKYCCF2km+3fRAIauEEWU8gcocZwmv0a51+bnpjabvn/LSphp821XDvjKH8d/bGcIJ6G5VNPm57exm+oMrVU/pEBRC7WrjdjXPSKKyEAxP8Luw6Nx9sDHKSZR2jM0bw5dro5yworovZOXrF9gZsJgP3fBzuy5CTaOWGaX07fO1GTwCnL4hOp+uVS3xE5zm9wcgMQCybq50MzupcKd/uoqoqroDKom0NHDs4I5JXBeG+Cf/6ckXM5y3eVs8Xq8qZu7mW5buMrW0tsxAdJZZrGtT6DfTRKcSVzWVUwbl8sab948oaPcSbdybZh1QNb6B9oYjiGhcakOUIBzE5STvLazd7A3y3oZqFW+t5LUalsYVb63D6QhKY7KMfN8bOQwJYuaMRbwf5fiJM0zSaPAHiOvP7V70OFr0IiXk4bDMiYyIn0UpGgqVdGW8hxOFNlnLtA72iw2xQsJkM7commgy6DhPdAZJsJly+9l9+doshsmyqwROINKWKJS3ejMkTXdEnpLeQ59AT0hmpdLe/wEqzm2lw+2Nv9+zc3uwLRJZoxaLowkthxKHDYtSzu+qfqXG9Z7ZEURRMLbN6Oh1RY03VtMgyyljCv+vtq+cZ9QqmPfxO2wyAphKKy6Sqg9VUdouRXQ8T67jxZgOGDn7gJoMSCVhiSYs37/FcRccKdtNjKTXOjLIPZXAPJ96ASiCkEb+7XKeABzwtM+71W+GrP4K7FjZ9TVxZuNdQZoKZzAQLGQ4LxbKUSwjRxiHzDff444/Tp08fLBYLEydOZMGCBd32WglWIycOzSTerOfEoZlR+xYU1zO1KDXm89LizQRCarsqXHnJVly+YGTN81erKzltRMcNqG4aH49j9auRv4cKj+brbSrH5kJl7nF8sCq6d4nZoFCYGhczofaYQen83KaakdMbpCg9PpIPs6uThmWSEi9LHQ4lKXHmSL+cXSXHmchP6V0NMw2KwrVTC5m3pY5jBqZHtn+xqpwzR8ceN2aDQn5yHNt2GQNDsxPYXOVkU6WTYTmxZ4X6pcWR2rgKgIYxv+D9Ve2XOeYkWlGAuVt27huUaWdbjIuuiyfkk+GIHeyZDXquObIw5j6Am44u6rD0sdizE4dldtiD4/qj+pLei5Ys9katN7HiOioXXPwDvHMpvH0ZfHobzPw9WBJh4vUw4CRyy78C4MZpRSiKjtxEG7VOf6Rcd73Lv9uS2UKIQ98hEZi8/fbb3H777dxzzz0sWbKEkSNHcuKJJ1JVFbvPx/6yGA2k2c1sq3Nx23H9Gd7mgubNBSVcO7UvAzPio56TZDPy/JXj+GBJdMf0NLuZZy4dx3tttjf7giwrqef3Jw1qdyf7hknpjHLPg8aWx6cNZOsR/2BwtoN07xYWNKdF9VQJV9gay8pdlq8YFB1/nTGUT1eUE2jpBWHU63j84jFkJph59eoJJFij74oNyU7g7lOHRC1XEQe/eIuBP502lCFZ9qjtDquRl6+aQFYvW7KnodE/PZ54k55fTO/HoIzwec9aXcn0gemM75MU9XirUc8zl4/jvUWlUdtzk6z87qRBPPfTFl6Zu5V/njOC3F2qymUmWHjm9DTS5txL85S78CUNINEWHZinxZu5/5zh2K0G3loQXn6Vk2jlofNH8vKcrVGPPbIohcsnF+w2H6tPqo0/nja4/dg/qi+j8qR60f7IdFh46pIxmPTRX30nD83kzNHZMmOyB61V7yI5Jp4G2PYz1G2B9Z/BD/+G1IEw4gIwmCFjKIy7JlysJW8S/VLNvGp/koHBtdC4nYKU8HhbW97Et+urmPqvb7ksRgU9IcThQ6dpHbXXO3hMnDiR8ePH89hjjwHhdeh5eXnccsst/P73v9/j85uamnA4HDQ2NpKQsPdr6Ssb3AQ0jWBIo8bpZ11FMynxJvKTbcSZ9FQ2+Vhf2UyG3UJOkpUUmxFvSGVHvYcNVU76psZRkGJjR72HlHgz3kCIVTsaSY4zUZgah92ip9kbYlVZI5oGI3MTcejcpDStRmnYhi95EC5rFgF9HEkGD1VePXqrg3p3kCUlDWQkWOiXHs+mqmayHFYsRj3LS+qI04cYkWPHajZS59OzbHsDCRYjw3IcpNvNWIx6VDWcR7OpyklZo5fBmXZykqxSdesQVt3sY3u9m3XlzWQnWShKt5PtsKDb3drE/bTPY6/JjT+g4Q+pBFVo8gZYX95MpsNMn9Q4mr1BVpc1kWQzkptkw2zQYTbqKal1s7XWTVF6PBkJZpaXNmDQK/TPiMek6FAUHZWNHoqrmihMi6ePzUdK/XI8iQMoDyVgtiWg00FFky/8c0q00CfFRrxRY0eDl7WVbvqkxlGQZMGieWnGysZKJzVOHyNyE0mzm8lO3HNJbZcvSHWzj2WlDQRDKmMKkkiNN5MgsyX7zRcIUdXsY9WORhq9AUbnJZJut5B0GCa9d3b8zd9SywXPzOOB80aSrVXAF78Hv3PnA/InweDTQdfBPU9vMyx9GRrDzUnVlAFcXX0hvzlpEB8vL2NbrZtGT4CvbjuK/hn22McQQhzSDvrAxO/3Y7PZeO+99zjzzDMj26+44goaGhr46KOP2j3H5/Ph8+2cLm5qaiIvL6/TF0dCiM6RsSdEz9nf8ffl6gpueHUxT108CsfXd0DQB2MuD8+cGC1gz9zjMdBUcNeBuwbWfsIfneeSkt2H+aUebpzWl1fmbuO6qX257fgB+/FOhRAHq4N+KVdNTQ2hUIiMjOg18hkZGVRUVMR8zn333YfD4Yj8ycvLOxCnKsRhT8aeED1nf8dfZCnX9u/Die3DzgZrIiT32bugBMKzKXGpkDYIxl9Pvr6W+aUeTLoQE6rfZ1y6xkfLd9B6z3RJST1VzbFLPAshDj0HfWCyL+666y4aGxsjf0pLS/f8JCHEfpOxJ0TP2d/x1+QJYDUqGJa/AZkjwZG7fydkdTBt9FAm2Wv4hWMe1vJ5TCl/la01blaV1PL7/63g7CfmcPGz82n2tq+oJ4Q49Bz0xfBTU1PR6/VUVlZGba+srCQzM/YdHLPZjNks1VeEONBk7AnRc/Z3/DV6AsTpfOCth7FXdMk5DUi3MiA9H8gH7TiGbl+MY5mTXz33BVsCyZwzOpsvVldy94ereOTC0dS5/MxeW8mEwmQKUuK65ByEEL3HQR+YmEwmxo4dy+zZsyM5JqqqMnv2bG6++eaePTkhhBDiEBGs2ojB3wyDpoWXY3U1nQ593jgu91Ty4SY4RZnHuWu/I906jSeXTUat2cTX5VY8IR16HfzmmHyuPWYoBv1hufhDiEPSQR+YANx+++1cccUVjBs3jgkTJvDwww/jcrm46qqr9ur5rWtZm5qauvM0hTis2O32PVb0krEnRNfbm7EHez/+jKvexjrrTghcQAUzeKo0F0rXdcm5dqSvBdyBRJ72nYjmC7+XT7aHeyoN121mpdaP+2eXcP/skm49DyEAZozI4C+n9kfZi3G1t+NPxHZIBCYXXHAB1dXV/OlPf6KiooJRo0Yxc+bMdgnxHWlubgaQRFwhutDeVPqRsSdE19vbKlt7O/5+NdHEwydZmKSs5SvvYHY0dMVZ7g0FCC/XKmIDoEP5//buPSiq8/wD+PcgLC7VZUXk1pSLIgIKTESDK42YQjXotDGXhlHT3DTWlozaOF7SJsZLMtLcGnNp/tAGOpO0jI1iMlajBAHFQSLIqhCCghiSBsUaUVCDwD6/PxzOL0dRV27nLH4/Mzvjvu+7Z5/nnPc5u6+7nFWANgWIGXQcXyEUbeAltKnvbf6sEG//1gY42m85lleZ7BmXv1xwb3A4HPjuu++6tcrtvNziN99849ITcSDkMRByAAZOHs7UE2vPGLgve4dR9qOz9XQ79WeU3HqCORjHQMjjRjnwE5OeGRCfmPSUm5sb7rqrZ1cXsVgsLltcPzYQ8hgIOQADJ4+bYe0ZC/dl73CV/did+nOV3G6GORjHQMhjIORgJPyLMSIiIiIi0h0XJkREREREpDsuTHrI09MTL730ksv/NsNAyGMg5AAMnDz6GvdT7+G+7B0DeT8OhNyYg3EMhDwGQg5GxD9+JyIiIiIi3fETEyIiIiIi0h0XJkREREREpDsuTIiIiIiISHdcmPTQe++9h9DQUAwePBgJCQn44osv9A5JtX79ekycOBFDhw6Fn58fZs2aherqas2YqVOnQlEUzW3hwoWaMfX19Zg5cya8vLzg5+eHZcuWob391r9+2htWr159XXyRkZFq/w8//ID09HQMHz4cQ4YMwcMPP4zTp08bJv5OoaGh1+WhKArS09MBGP84GJGRa88I9u7di1/96lcICgqCoijYtm2bpl9EsGrVKgQGBsJsNiMlJQXHjx/XjPn+++8xd+5cWCwWWK1WzJs3Dy0tLf2Yhf6cOY+6ynmou1yt1npj7uutt+adnt5//33Exsaqv/Nhs9mwc+dOtd/o8XclIyMDiqJgyZIlapsr5mFoQt2WnZ0tJpNJPvjgA6msrJRnnnlGrFarnD59Wu/QRERk+vTpkpmZKRUVFWK322XGjBkSHBwsLS0t6pikpCR55plnpKGhQb2dP39e7W9vb5dx48ZJSkqKlJeXy44dO8TX11eef/75fsnhpZdekrFjx2riO3PmjNq/cOFC+dnPfiZ5eXlSWloqkyZNksmTJxsm/k6NjY2aHHJzcwWA5Ofni4jxj4PRGL32jGDHjh3y5z//WbZu3SoAJCcnR9OfkZEh3t7esm3bNjl8+LD8+te/lrCwMLl8+bI65v7775e4uDg5cOCA7Nu3T8LDw2X27Nn9nIm+nDmPusp5qDtcsdZ6Y+7rrTfmnd4+/fRT+c9//iPHjh2T6upq+dOf/iQeHh5SUVEhIsaP/1pffPGFhIaGSmxsrCxevFhtd7U8jI4Lkx645557JD09Xb3f0dEhQUFBsn79eh2jurHGxkYBIIWFhWpbUlKSpsCutWPHDnFzc5NTp06pbe+//75YLBZpbW3ty3BF5OrCJC4ursu+pqYm8fDwkH//+99qW1VVlQCQ4uJiEdE//htZvHixjBo1ShwOh4gY/zgYjavVnt6ufXPmcDgkICBAXnvtNbWtqalJPD095V//+peIiHz55ZcCQA4ePKiO2blzpyiKIv/973/7LXajufY86srnIWe4eq11Z+4bUXfmnRENGzZMNm3a5HLxNzc3y+jRoyU3N1fzeu1qebgCfpWrm65cuYKysjKkpKSobW5ubkhJSUFxcbGOkd3Y+fPnAQA+Pj6a9o8++gi+vr4YN24cnn/+eVy6dEntKy4uRkxMDPz9/dW26dOn48KFC6isrOyXuI8fP46goCCMHDkSc+fORX19PQCgrKwMbW1tmmMQGRmJ4OBg9RgYIf5rXblyBR9++CGefvppKIqithv9OBiFK9ae0dTV1eHUqVOafejt7Y2EhARN7VitVkyYMEEdk5KSAjc3N5SUlPR7zEZx7XnUVc9DzhiItebM3Dei7sw7I+no6EB2djYuXrwIm83mcvGnp6dj5syZmngB1zsOrsBd7wBc1f/+9z90dHRoXmgAwN/fH1999ZVOUd2Yw+HAkiVLkJiYiHHjxqntc+bMQUhICIKCgnDkyBGsWLEC1dXV2Lp1KwDg1KlTXebY2dfXEhISkJWVhTFjxqChoQFr1qzBvffei4qKCpw6dQomkwlWq/W6+Dpj0zv+rmzbtg1NTU148skn1TajHwcjcbXaM6LOOdPVPvxx7fj5+Wn63d3d4ePjc8fNuU5dnUdd9TzkjIFYa87MfaPp7rwzgqNHj8Jms+GHH37AkCFDkJOTg+joaNjtdpeIHwCys7Nx6NAhHDx48Lo+VzkOroQLkztEeno6KioqUFRUpGlfsGCB+u+YmBgEBgYiOTkZtbW1GDVqVH+HeZ3U1FT137GxsUhISEBISAg2b94Ms9msY2Td9/e//x2pqakICgpS24x+HIjoxudRor7kyvNuzJgxsNvtOH/+PD7++GM88cQTKCws1Dssp33zzTdYvHgxcnNzMXjwYL3DuSPwq1zd5Ovri0GDBl135YXTp08jICBAp6i69uyzz2L79u3Iz8/HXXfdddOxCQkJAICamhoAQEBAQJc5dvb1N6vVioiICNTU1CAgIABXrlxBU1PTdfF1xma0+L/++mt8/vnnmD9//k3HGf046MmVas+oOvfTzfZhQEAAGhsbNf3t7e34/vvv78j9fKPzqCueh5w1EGvNmblvJD2Zd0ZgMpkQHh6O+Ph4rF+/HnFxcdiwYYPLxF9WVobGxkaMHz8e7u7ucHd3R2FhId5++224u7vD39/fJfJwJVyYdJPJZEJ8fDzy8vLUNofDgby8PNhsNh0j+38igmeffRY5OTnYs2cPwsLCbvkYu90OAAgMDAQA2Gw2HD16VPMGJTc3FxaLBdHR0X0S9820tLSgtrYWgYGBiI+Ph4eHh+YYVFdXo76+Xj0GRos/MzMTfn5+mDlz5k3HGf046MkVas/owsLCEBAQoNmHFy5cQElJiaZ2mpqaUFZWpo7Zs2cPHA6HunC+E9zqPOqK5yFnDcRac2buG0FvzDsjcjgcaG1tdZn4k5OTcfToUdjtdvU2YcIEzJ07V/23K+ThUnT+43uXlp2dLZ6enpKVlSVffvmlLFiwQKxWq+bKK3r6/e9/L97e3lJQUKC5DO2lS5dERKSmpkbWrl0rpaWlUldXJ5988omMHDlSpkyZom6j8zKX06ZNE7vdLp999pmMGDGi3y5zuXTpUikoKJC6ujrZv3+/pKSkiK+vrzQ2NorI1cv0BQcHy549e6S0tFRsNpvYbDbDxP9jHR0dEhwcLCtWrNC0u8JxMBqj154RNDc3S3l5uZSXlwsAefPNN6W8vFy+/vprEbl6yVSr1SqffPKJHDlyRB544IEuLxd89913S0lJiRQVFcno0aPvuMsF3+o8KuJa56Hb5Yq11htzX2+9Me/0tnLlSiksLJS6ujo5cuSIrFy5UhRFkd27d4uI8eO/kWuvoumqeRgVFyY99M4770hwcLCYTCa555575MCBA3qHpALQ5S0zM1NEROrr62XKlCni4+Mjnp6eEh4eLsuWLdP8foaIyMmTJyU1NVXMZrP4+vrK0qVLpa2trV9ySEtLk8DAQDGZTPLTn/5U0tLSpKamRu2/fPmy/OEPf5Bhw4aJl5eXPPjgg9LQ0GCY+H9s165dAkCqq6s17a5wHIzIyLVnBPn5+V3W/xNPPCEiVy+b+uKLL4q/v794enpKcnLydXPz7NmzMnv2bBkyZIhYLBZ56qmnpLm5WYds9HOr86iIa52HusPVaq035r7eemve6enpp5+WkJAQMZlMMmLECElOTlYXJSLGj/9Grl2YuGoeRqWIiPTDBzNEREREREQ3xL8xISIiIiIi3XFhQkREREREuuPChIiIiIiIdMeFCRERERER6Y4LEyIiIiIi0h0XJkREREREpDsuTIiIiIiISHdcmBARERERke64MKE7xsmTJ6EoCux2u96hEJETsrKyYLVa9Q6DiIj6CRcmRERERKTifwqQXrgwIbqJjo4OOBwOvcMgGtCuXLmidwhERGQAXJhQt02dOhWLFi3C8uXL4ePjg4CAAKxevRpA11+bampqgqIoKCgoAAAUFBRAURTs2rULd999N8xmM37xi1+gsbERO3fuRFRUFCwWC+bMmYNLly45FZPD4cCrr76K8PBweHp6Ijg4GK+88opmzIkTJ3DffffBy8sLcXFxKC4uVvs6/5fo008/RXR0NDw9PVFfX9+j/UR0O4xWV9u3b4fVakVHRwcAwG63Q1EUrFy5Uh0zf/58PPbYY+r9LVu2YOzYsfD09ERoaCjeeOMNzTZDQ0Oxbt06PP7447BYLFiwYAGAq/UXHBwMLy8vPPjggzh79qzmcYcPH8Z9992HoUOHwmKxID4+HqWlpU7vW6LuMFpNAsDHH3+MmJgYmM1mDB8+HCkpKbh48aLav2nTJkRFRWHw4MGIjIzE3/72N7WvM+atW7d2+VpYUFCAp556CufPn4eiKFAURc2XqM8JUTclJSWJxWKR1atXy7Fjx+Qf//iHKIoiu3fvlrq6OgEg5eXl6vhz584JAMnPzxcRkfz8fAEgkyZNkqKiIjl06JCEh4dLUlKSTJs2TQ4dOiR79+6V4cOHS0ZGhlMxLV++XIYNGyZZWVlSU1Mj+/btk40bN4qIqDFFRkbK9u3bpbq6Wh555BEJCQmRtrY2ERHJzMwUDw8PmTx5suzfv1+++uoruXjxYq/uN6KbMVpdNTU1iZubmxw8eFBERN566y3x9fWVhIQEdUx4eLhaZ6WlpeLm5iZr166V6upqyczMFLPZLJmZmer4kJAQsVgs8vrrr0tNTY3U1NTIgQMHxM3NTf7yl79IdXW1bNiwQaxWq3h7e6uPGzt2rDz22GNSVVUlx44dk82bN4vdbu/+ziZygtFq8rvvvhN3d3d58803pa6uTo4cOSLvvfeeNDc3i4jIhx9+KIGBgbJlyxY5ceKEbNmyRXx8fCQrK0tEbv1a2NraKm+99ZZYLBZpaGiQhoYGddtEfY0LE+q2pKQk+fnPf65pmzhxoqxYseK2Ttaff/65Omb9+vUCQGpra9W23/3udzJ9+vRbxnPhwgXx9PRU3yBdqzOmTZs2qW2VlZUCQKqqqkTk6sIEAN/skG6MVlciIuPHj5fXXntNRERmzZolr7zyiphMJmlubpZvv/1WAMixY8dERGTOnDnyy1/+UvP4ZcuWSXR0tHo/JCREZs2apRkze/ZsmTFjhqYtLS1NszAZOnSo+uaKqL8YrSbLysoEgJw8ebLL/lGjRsk///lPTdu6devEZrOJiPOvhT+uPaL+wq9yUY/ExsZq7gcGBqKxsbHb2/D394eXlxdGjhypaXNmm1VVVWhtbUVycrLTzxcYGAgAmu2bTKbr8iLqT0aqKwBISkpCQUEBRAT79u3DQw89hKioKBQVFaGwsBBBQUEYPXo0gKt1mJiYqHl8YmIijh8/rn4dDAAmTJigGVNVVYWEhARNm81m09x/7rnnMH/+fKSkpCAjIwO1tbVOxU/UU0aqybi4OCQnJyMmJga/+c1vsHHjRpw7dw4AcPHiRdTW1mLevHkYMmSIenv55Zevq5dbvRYS6YELE+oRDw8PzX1FUeBwOODmdnVqiYja19bWdsttKIpyw23eitlsvu2YFUUBAM32zWaz2k6kByPVFXD1O/ZFRUU4fPgwPDw8EBkZialTp6KgoACFhYVISkpyajs/9pOf/OS2H7N69WpUVlZi5syZ2LNnD6Kjo5GTk3Pb2yG6XUaqyUGDBiE3Nxc7d+5EdHQ03nnnHYwZMwZ1dXVoaWkBAGzcuBF2u129VVRU4MCBAzeNBwAv9kK648KE+sSIESMAAA0NDWpbX/9+yOjRo2E2m5GXl9enz0OkFz3qCgDuvfdeNDc3469//au6COlcmBQUFGDq1Knq2KioKOzfv1/z+P379yMiIgKDBg264XNERUWhpKRE03btGykAiIiIwB//+Efs3r0bDz30EDIzM3uQGVHP6FWTiqIgMTERa9asQXl5OUwmE3JycuDv74+goCCcOHEC4eHhmltYWJjT2zeZTJpPOIn6i7veAdDAZDabMWnSJGRkZCAsLAyNjY144YUX+vQ5Bw8ejBUrVmD58uUwmUxITEzEmTNnUFlZiXnz5vXpcxP1Bz3qCgCGDRuG2NhYfPTRR3j33XcBAFOmTMGjjz6KtrY2zScmS5cuxcSJE7Fu3TqkpaWhuLgY7777ruaqQF1ZtGgREhMT8frrr+OBBx7Arl278Nlnn6n9ly9fxrJly/DII48gLCwM3377LQ4ePIiHH364b5ImcoIeNVlSUoK8vDxMmzYNfn5+KCkpwZkzZxAVFQUAWLNmDRYtWgRvb2/cf//9aG1tRWlpKc6dO4fnnnvOqecIDQ1FS0sL8vLyEBcXBy8vL3h5efVlWkQA+IkJ9aEPPvgA7e3tiI+Px5IlS/Dyyy/3+XO++OKLWLp0KVatWoWoqCikpaXxO7M0oOhRV8DVvzPp6OhQPx3x8fFBdHQ0AgICMGbMGHXc+PHjsXnzZmRnZ2PcuHFYtWoV1q5diyeffPKm2580aRI2btyIDRs2IC4uDrt379a8wRs0aBDOnj2Lxx9/HBEREXj00UeRmpqKNWvW9EW6RE7r75q0WCzYu3cvZsyYgYiICLzwwgt44403kJqaCuDq5bs3bdqEzMxMxMTEICkpCVlZWbf1icnkyZOxcOFCpKWlYcSIEXj11Vf7Kh0iDUV+/MVIIiIiIiIiHfATEyIiIiIi0h0XJuQy6uvrNZc/vPbGX2gnun2sKyJjYU3SnYxf5SKX0d7ejpMnT96wPzQ0FO7uvJ4D0e1gXREZC2uS7mRcmBARERERke74VS4iIiIiItIdFyZERERERKQ7LkyIiIiIiEh3XJgQEREREZHuuDAhIiIiIiLdcWFCRERERES648KEiIiIiIh0x4UJERERERHp7v8AbYdpdvnxm9gAAAAASUVORK5CYII=",
+            "text/plain": [
+              "<Figure size 808.875x750 with 12 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "sns.pairplot(df,hue='Label')\n",
+        "plt.savefig('./../Image/PairPlot_withHue.png',dpi=300)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 42,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "6ApBTj0lKKXn",
+        "outputId": "bb618353-985f-44dd-d0a5-09a7cbe52f06"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
+            "[nltk_data]   Package wordnet is already up-to-date!\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "True"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 42
+        }
+      ],
+      "source": [
+        "nltk.download('wordnet')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 43,
+      "metadata": {
+        "id": "QsNoEX6RKKXn"
+      },
+      "outputs": [],
+      "source": [
+        "from nltk.corpus import stopwords\n",
+        "import string\n",
+        "\n",
+        "from nltk.stem.porter import PorterStemmer\n",
+        "ps = PorterStemmer()\n",
+        "\n",
+        "from nltk.stem import WordNetLemmatizer\n",
+        "\n",
+        "lemmatizer = WordNetLemmatizer()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 53,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "wfzHLaqhKKXn",
+        "outputId": "799a6081-4069-4627-ba0d-bf5db7938def"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
+            "[nltk_data]   Package punkt is already up-to-date!\n",
+            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
+            "[nltk_data]   Unzipping corpora/stopwords.zip.\n",
+            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
+            "[nltk_data]   Package wordnet is already up-to-date!\n"
+          ]
+        }
+      ],
+      "source": [
+        "import nltk\n",
+        "from nltk.corpus import stopwords\n",
+        "from nltk.stem import WordNetLemmatizer\n",
+        "import string\n",
+        "\n",
+        "# Make sure the necessary resources are downloaded\n",
+        "nltk.download('punkt')\n",
+        "nltk.download('stopwords')\n",
+        "nltk.download('wordnet')\n",
+        "\n",
+        "# Initialize lemmatizer and stopwords once globally to avoid doing it in every function call\n",
+        "lemmatizer = WordNetLemmatizer()\n",
+        "stop_words = set(stopwords.words('english'))\n",
+        "punctuations = set(string.punctuation)\n",
+        "\n",
+        "def transform_text(text):\n",
+        "    # Lowercase and tokenize\n",
+        "    text = nltk.word_tokenize(text.lower())\n",
+        "\n",
+        "    # Filter tokens that are alphanumeric, not in stopwords, and not punctuation, while lemmatizing them\n",
+        "    transformed_text = [\n",
+        "        lemmatizer.lemmatize(word)\n",
+        "        for word in text\n",
+        "        if word.isalnum() and word not in stop_words and word not in punctuations\n",
+        "    ]\n",
+        "\n",
+        "    return \" \".join(transformed_text)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 54,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "R-3EuL2jKKXn",
+        "outputId": "3a78eaa3-3789-4479-b739-27c2df982fe7"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                                                Text  Label  num_chr  \\\n",
+              "0  Go until jurong point, crazy.. Available only ...      0      111   \n",
+              "1                      Ok lar... Joking wif u oni...      0       29   \n",
+              "2  Free entry in 2 a wkly comp to win FA Cup fina...      1      155   \n",
+              "3  U dun say so early hor... U c already then say...      0       49   \n",
+              "4  Nah I don't think he goes to usf, he lives aro...      0       61   \n",
+              "\n",
+              "   num_words  num_sent                                   Transformer_text  \n",
+              "0         24         2  go jurong point crazy available bugis n great ...  \n",
+              "1          8         2                            ok lar joking wif u oni  \n",
+              "2         37         2  free entry 2 wkly comp win fa cup final tkts 2...  \n",
+              "3         13         1                u dun say early hor u c already say  \n",
+              "4         15         1                nah think go usf life around though  "
+            ],
+            "text/html": [
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+              "    <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",
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+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
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+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Text</th>\n",
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+              "      <th>2</th>\n",
+              "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
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+              "      <td>155</td>\n",
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+              "      <td>2</td>\n",
+              "      <td>free entry 2 wkly comp win fa cup final tkts 2...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>U dun say so early hor... U c already then say...</td>\n",
+              "      <td>0</td>\n",
+              "      <td>49</td>\n",
+              "      <td>13</td>\n",
+              "      <td>1</td>\n",
+              "      <td>u dun say early hor u c already say</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
+              "      <td>0</td>\n",
+              "      <td>61</td>\n",
+              "      <td>15</td>\n",
+              "      <td>1</td>\n",
+              "      <td>nah think go usf life around though</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bfd3cef4-faf9-4d86-8d6d-d1f7429b014c')\"\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
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+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-bfd3cef4-faf9-4d86-8d6d-d1f7429b014c button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-bfd3cef4-faf9-4d86-8d6d-d1f7429b014c');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
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+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
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+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
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+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-16cc1674-2f73-47d2-8243-2bdc98e16063 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df",
+              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 5171,\n  \"fields\": [\n    {\n      \"column\": \"Text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5171,\n        \"samples\": [\n          \"K, makes sense, btw carlos is being difficult so you guys are gonna smoke while I go pick up the second batch and get gas\",\n          \"URGENT! Your mobile No *********** WON a \\u00c2\\u00a32,000 Bonus Caller Prize on 02/06/03! This is the 2nd attempt to reach YOU! Call 09066362220 ASAP! BOX97N7QP, 150ppm\",\n          \"If you still havent collected the dough pls let me know so i can go to the place i sent it to get the control number\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Label\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"num_chr\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 58,\n        \"min\": 2,\n        \"max\": 910,\n        \"num_unique_values\": 273,\n        \"samples\": [\n          84,\n          83\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"num_words\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13,\n        \"min\": 1,\n        \"max\": 220,\n        \"num_unique_values\": 92,\n        \"samples\": [\n          21,\n          48\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"num_sent\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 38,\n        \"num_unique_values\": 16,\n        \"samples\": [\n          2,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Transformer_text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5100,\n        \"samples\": [\n          \"ca right second got ta hit people first\",\n          \"jus finish lunch way home lor tot u dun wan 2 stay sch today\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 54
+        }
+      ],
+      "source": [
+        "\n",
+        "\n",
+        "df['Transformer_text']=df['Text'].apply(transform_text)\n",
+        "df.head()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "preprocessed_text = df['Transformer_text']"
+      ],
+      "metadata": {
+        "id": "9VJir2z-OuQ9"
+      },
+      "execution_count": 62,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "## Train a Word2Vec model on the preprocessed data\n",
+        "sentences = [sentence.split() if isinstance(sentence, str) else [] for sentence in preprocessed_text]  # Tokenize the text, handle non-string values\n",
+        "word2vec_model = Word2Vec(sentences=sentences, vector_size=100, window=5, min_count=1, workers=4, seed=42)"
+      ],
+      "metadata": {
+        "id": "_rVE7IKQOuVI"
+      },
+      "execution_count": 63,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Convert text to Word2Vec embeddings (average of word vectors for each sentence)\n",
+        "def vectorize_text(text, model):\n",
+        "    if isinstance(text, str):  # Check if the text is a string\n",
+        "        words = text.split()\n",
+        "        word_vectors = [model.wv[word] for word in words if word in model.wv]\n",
+        "        if len(word_vectors) > 0:\n",
+        "            return np.mean(word_vectors, axis=0)\n",
+        "        else:\n",
+        "            return np.zeros(model.vector_size)  # Return a zero vector if no word is in the vocabulary\n",
+        "    else:\n",
+        "        return np.zeros(model.vector_size)  # Return a zero vector if text is not a string\n",
+        "\n",
+        "# Convert entire dataset to vectors\n",
+        "X = np.array([vectorize_text(text, word2vec_model) for text in preprocessed_text])\n",
+        "y = df['Label'].values  # Target variable\n"
+      ],
+      "metadata": {
+        "id": "mXPOx2SlOuYF"
+      },
+      "execution_count": 65,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Split data for training and testing\n",
+        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
+      ],
+      "metadata": {
+        "id": "9dZ5fZhrOuaM"
+      },
+      "execution_count": 66,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def objective(trial):\n",
+        "    model_type = trial.suggest_categorical('model_type', [\n",
+        "        'BernoulliNB', 'LogisticRegression', 'RandomForest', 'SVC',\n",
+        "        'KNeighborsClassifier', 'GradientBoostingClassifier', 'AdaBoostClassifier', 'XGBoost'\n",
+        "    ])\n",
+        "\n",
+        "    # Define models and hyperparameters to tune\n",
+        "    if model_type == 'BernoulliNB':\n",
+        "        alpha = trial.suggest_float('alpha', 1e-5, 1.0, log=True)\n",
+        "        model = BernoulliNB(alpha=alpha)\n",
+        "\n",
+        "    elif model_type == 'LogisticRegression':\n",
+        "        C = trial.suggest_float('C', 1e-5, 1.0, log=True)\n",
+        "        model = LogisticRegression(C=C, max_iter=1000)\n",
+        "\n",
+        "    elif model_type == 'RandomForest':\n",
+        "        n_estimators = trial.suggest_int('n_estimators', 10, 200)\n",
+        "        max_depth = trial.suggest_int('max_depth', 2, 32, log=True)\n",
+        "        model = RandomForestClassifier(n_estimators=n_estimators, max_depth=max_depth)\n",
+        "\n",
+        "    elif model_type == 'SVC':\n",
+        "        C = trial.suggest_float('C', 1e-5, 1.0, log=True)\n",
+        "        kernel = trial.suggest_categorical('kernel', ['linear', 'rbf'])\n",
+        "        model = SVC(C=C, kernel=kernel)\n",
+        "\n",
+        "    elif model_type == 'KNeighborsClassifier':\n",
+        "        n_neighbors = trial.suggest_int('n_neighbors', 1, 20)\n",
+        "        model = KNeighborsClassifier(n_neighbors=n_neighbors)\n",
+        "\n",
+        "    elif model_type == 'GradientBoostingClassifier':\n",
+        "        n_estimators = trial.suggest_int('n_estimators', 50, 200)\n",
+        "        learning_rate = trial.suggest_float('learning_rate', 0.01, 1.0, log=True)\n",
+        "        model = GradientBoostingClassifier(n_estimators=n_estimators, learning_rate=learning_rate)\n",
+        "\n",
+        "    elif model_type == 'AdaBoostClassifier':\n",
+        "        n_estimators = trial.suggest_int('n_estimators', 50, 200)\n",
+        "        learning_rate = trial.suggest_float('learning_rate', 0.01, 1.0, log=True)\n",
+        "        model = AdaBoostClassifier(n_estimators=n_estimators, learning_rate=learning_rate)\n",
+        "\n",
+        "    elif model_type == 'XGBoost':\n",
+        "        n_estimators = trial.suggest_int('n_estimators', 50, 200)\n",
+        "        learning_rate = trial.suggest_float('learning_rate', 0.01, 1.0, log=True)\n",
+        "        max_depth = trial.suggest_int('max_depth', 2, 32, log=True)\n",
+        "        model = xgb.XGBClassifier(n_estimators=n_estimators, learning_rate=learning_rate, max_depth=max_depth, use_label_encoder=False, eval_metric='mlogloss')\n",
+        "\n",
+        "    # Use cross-validation to evaluate model performance\n",
+        "    scores = cross_val_score(model, X_train, y_train, cv=3, scoring='accuracy')\n",
+        "\n",
+        "    return 1 - scores.mean()  # Minimizing the error rate\n",
+        "\n",
+        "# Optuna optimization process\n",
+        "study = optuna.create_study(direction='minimize')\n",
+        "study.optimize(objective, n_trials=50)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "YNmtAwAdOucl",
+        "outputId": "f5b5eebc-6731-4242-b35d-62c127f3857a"
+      },
+      "execution_count": 67,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "[I 2024-10-04 11:46:42,871] A new study created in memory with name: no-name-59ba621e-6ee7-4258-91d5-81bfea60ad05\n",
+            "[I 2024-10-04 11:46:42,925] Trial 0 finished with value: 0.12475823509249429 and parameters: {'model_type': 'BernoulliNB', 'alpha': 2.737305409820249e-05}. Best is trial 0 with value: 0.12475823509249429.\n",
+            "[I 2024-10-04 11:46:43,057] Trial 1 finished with value: 0.12475823509249429 and parameters: {'model_type': 'LogisticRegression', 'C': 0.5855544341610596}. Best is trial 0 with value: 0.12475823509249429.\n",
+            "[I 2024-10-04 11:46:43,344] Trial 2 finished with value: 0.08124090257027705 and parameters: {'model_type': 'KNeighborsClassifier', 'n_neighbors': 4}. Best is trial 2 with value: 0.08124090257027705.\n",
+            "[I 2024-10-04 11:46:55,892] Trial 3 finished with value: 0.06818217698401596 and parameters: {'model_type': 'RandomForest', 'n_estimators': 171, 'max_depth': 23}. Best is trial 3 with value: 0.06818217698401596.\n",
+            "/usr/local/lib/python3.10/dist-packages/xgboost/core.py:158: UserWarning: [11:46:56] WARNING: /workspace/src/learner.cc:740: \n",
+            "Parameters: { \"use_label_encoder\" } are not used.\n",
+            "\n",
+            "  warnings.warn(smsg, UserWarning)\n",
+            "/usr/local/lib/python3.10/dist-packages/xgboost/core.py:158: UserWarning: [11:47:02] WARNING: /workspace/src/learner.cc:740: \n",
+            "Parameters: { \"use_label_encoder\" } are not used.\n",
+            "\n",
+            "  warnings.warn(smsg, UserWarning)\n",
+            "/usr/local/lib/python3.10/dist-packages/xgboost/core.py:158: UserWarning: [11:47:12] WARNING: /workspace/src/learner.cc:740: \n",
+            "Parameters: { \"use_label_encoder\" } are not used.\n",
+            "\n",
+            "  warnings.warn(smsg, UserWarning)\n",
+            "[I 2024-10-04 11:47:21,854] Trial 4 finished with value: 0.06528047185072372 and parameters: {'model_type': 'XGBoost', 'n_estimators': 193, 'learning_rate': 0.02774571843947768, 'max_depth': 32}. Best is trial 4 with value: 0.06528047185072372.\n",
+            "[I 2024-10-04 11:47:21,906] Trial 5 finished with value: 0.12475823509249429 and parameters: {'model_type': 'LogisticRegression', 'C': 0.0007450972951236175}. Best is trial 4 with value: 0.06528047185072372.\n",
+            "[I 2024-10-04 11:48:26,162] Trial 6 finished with value: 0.06334722966271666 and parameters: {'model_type': 'GradientBoostingClassifier', 'n_estimators': 163, 'learning_rate': 0.5512854613697848}. Best is trial 6 with value: 0.06334722966271666.\n",
+            "[I 2024-10-04 11:48:39,031] Trial 7 finished with value: 0.06842337179469637 and parameters: {'model_type': 'RandomForest', 'n_estimators': 181, 'max_depth': 17}. Best is trial 6 with value: 0.06334722966271666.\n",
+            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
+            "  warnings.warn(\n",
+            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
+            "  warnings.warn(\n",
+            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
+            "  warnings.warn(\n",
+            "[I 2024-10-04 11:49:11,173] Trial 8 finished with value: 0.0730167734764996 and parameters: {'model_type': 'AdaBoostClassifier', 'n_estimators': 199, 'learning_rate': 0.8132472716046877}. Best is trial 6 with value: 0.06334722966271666.\n",
+            "[I 2024-10-04 11:49:11,658] Trial 9 finished with value: 0.12475823509249429 and parameters: {'model_type': 'SVC', 'C': 6.607125725928423e-05, 'kernel': 'linear'}. Best is trial 6 with value: 0.06334722966271666.\n",
+            "[I 2024-10-04 11:49:45,578] Trial 10 finished with value: 0.06697392254331236 and parameters: {'model_type': 'GradientBoostingClassifier', 'n_estimators': 87, 'learning_rate': 0.62687401966897}. Best is trial 6 with value: 0.06334722966271666.\n",
+            "/usr/local/lib/python3.10/dist-packages/xgboost/core.py:158: UserWarning: [11:49:45] WARNING: /workspace/src/learner.cc:740: \n",
+            "Parameters: { \"use_label_encoder\" } are not used.\n",
+            "\n",
+            "  warnings.warn(smsg, UserWarning)\n",
+            "/usr/local/lib/python3.10/dist-packages/xgboost/core.py:158: UserWarning: [11:49:46] WARNING: /workspace/src/learner.cc:740: \n",
+            "Parameters: { \"use_label_encoder\" } are not used.\n",
+            "\n",
+            "  warnings.warn(smsg, UserWarning)\n",
+            "/usr/local/lib/python3.10/dist-packages/xgboost/core.py:158: UserWarning: [11:49:46] WARNING: /workspace/src/learner.cc:740: \n",
+            "Parameters: { \"use_label_encoder\" } are not used.\n",
+            "\n",
+            "  warnings.warn(smsg, UserWarning)\n",
+            "[I 2024-10-04 11:49:47,017] Trial 11 finished with value: 0.12475823509249429 and parameters: {'model_type': 'XGBoost', 'n_estimators': 133, 'learning_rate': 0.016074842568666505, 'max_depth': 2}. Best is trial 6 with value: 0.06334722966271666.\n",
+            "[I 2024-10-04 11:50:45,652] Trial 12 finished with value: 0.0933259027790202 and parameters: {'model_type': 'GradientBoostingClassifier', 'n_estimators': 149, 'learning_rate': 0.029065949116498416}. Best is trial 6 with value: 0.06334722966271666.\n",
+            "/usr/local/lib/python3.10/dist-packages/xgboost/core.py:158: UserWarning: [11:50:45] WARNING: /workspace/src/learner.cc:740: \n",
+            "Parameters: { \"use_label_encoder\" } are not used.\n",
+            "\n",
+            "  warnings.warn(smsg, UserWarning)\n",
+            "/usr/local/lib/python3.10/dist-packages/xgboost/core.py:158: UserWarning: [11:50:47] WARNING: /workspace/src/learner.cc:740: \n",
+            "Parameters: { \"use_label_encoder\" } are not used.\n",
+            "\n",
+            "  warnings.warn(smsg, UserWarning)\n",
+            "/usr/local/lib/python3.10/dist-packages/xgboost/core.py:158: UserWarning: [11:50:48] WARNING: /workspace/src/learner.cc:740: \n",
+            "Parameters: { \"use_label_encoder\" } are not used.\n",
+            "\n",
+            "  warnings.warn(smsg, UserWarning)\n",
+            "[I 2024-10-04 11:50:50,450] Trial 13 finished with value: 0.058752600080059114 and parameters: {'model_type': 'XGBoost', 'n_estimators': 160, 'learning_rate': 0.10911898478706751, 'max_depth': 5}. Best is trial 13 with value: 0.058752600080059114.\n",
+            "[I 2024-10-04 11:51:29,032] Trial 14 finished with value: 0.06963197706421542 and parameters: {'model_type': 'GradientBoostingClassifier', 'n_estimators': 98, 'learning_rate': 0.15271302722710225}. Best is trial 13 with value: 0.058752600080059114.\n",
+            "/usr/local/lib/python3.10/dist-packages/xgboost/core.py:158: UserWarning: [11:51:29] WARNING: /workspace/src/learner.cc:740: \n",
+            "Parameters: { \"use_label_encoder\" } are not used.\n",
+            "\n",
+            "  warnings.warn(smsg, UserWarning)\n",
+            "/usr/local/lib/python3.10/dist-packages/xgboost/core.py:158: UserWarning: [11:51:30] WARNING: /workspace/src/learner.cc:740: \n",
+            "Parameters: { \"use_label_encoder\" } are not used.\n",
+            "\n",
+            "  warnings.warn(smsg, UserWarning)\n",
+            "/usr/local/lib/python3.10/dist-packages/xgboost/core.py:158: UserWarning: [11:51:31] WARNING: /workspace/src/learner.cc:740: \n",
+            "Parameters: { \"use_label_encoder\" } are not used.\n",
+            "\n",
+            "  warnings.warn(smsg, UserWarning)\n",
+            "[I 2024-10-04 11:51:32,367] Trial 15 finished with value: 0.05633486329779791 and parameters: {'model_type': 'XGBoost', 'n_estimators': 154, 'learning_rate': 0.15780012721327144, 'max_depth': 4}. Best is trial 15 with value: 0.05633486329779791.\n",
+            "/usr/local/lib/python3.10/dist-packages/xgboost/core.py:158: UserWarning: [11:51:32] WARNING: /workspace/src/learner.cc:740: \n",
+            "Parameters: { \"use_label_encoder\" } are not used.\n",
+            "\n",
+            "  warnings.warn(smsg, UserWarning)\n",
+            "/usr/local/lib/python3.10/dist-packages/xgboost/core.py:158: UserWarning: [11:51:33] WARNING: /workspace/src/learner.cc:740: \n",
+            "Parameters: { \"use_label_encoder\" } are not used.\n",
+            "\n",
+            "  warnings.warn(smsg, UserWarning)\n",
+            "/usr/local/lib/python3.10/dist-packages/xgboost/core.py:158: UserWarning: [11:51:34] WARNING: /workspace/src/learner.cc:740: \n",
+            "Parameters: { \"use_label_encoder\" } are not used.\n",
+            "\n",
+            "  warnings.warn(smsg, UserWarning)\n",
+            "[I 2024-10-04 11:51:35,136] Trial 16 finished with value: 0.06382997011289326 and parameters: {'model_type': 'XGBoost', 'n_estimators': 117, 'learning_rate': 0.12484462052534329, 'max_depth': 4}. Best is trial 15 with value: 0.05633486329779791.\n",
+            "/usr/local/lib/python3.10/dist-packages/xgboost/core.py:158: UserWarning: [11:51:35] WARNING: /workspace/src/learner.cc:740: \n",
+            "Parameters: { \"use_label_encoder\" } are not used.\n",
+            "\n",
+            "  warnings.warn(smsg, UserWarning)\n",
+            "/usr/local/lib/python3.10/dist-packages/xgboost/core.py:158: UserWarning: [11:51:36] WARNING: /workspace/src/learner.cc:740: \n",
+            "Parameters: { \"use_label_encoder\" } are not used.\n",
+            "\n",
+            "  warnings.warn(smsg, UserWarning)\n",
+            "/usr/local/lib/python3.10/dist-packages/xgboost/core.py:158: UserWarning: [11:51:40] WARNING: /workspace/src/learner.cc:740: \n",
+            "Parameters: { \"use_label_encoder\" } are not used.\n",
+            "\n",
+            "  warnings.warn(smsg, UserWarning)\n",
+            "[I 2024-10-04 11:51:42,085] Trial 17 finished with value: 0.05633486329779791 and parameters: {'model_type': 'XGBoost', 'n_estimators': 140, 'learning_rate': 0.2443674061045938, 'max_depth': 6}. Best is trial 15 with value: 0.05633486329779791.\n",
+            "/usr/local/lib/python3.10/dist-packages/xgboost/core.py:158: UserWarning: [11:51:42] WARNING: /workspace/src/learner.cc:740: \n",
+            "Parameters: { \"use_label_encoder\" } are not used.\n",
+            "\n",
+            "  warnings.warn(smsg, UserWarning)\n",
+            "/usr/local/lib/python3.10/dist-packages/xgboost/core.py:158: UserWarning: [11:51:43] WARNING: /workspace/src/learner.cc:740: \n",
+            "Parameters: { \"use_label_encoder\" } are not used.\n",
+            "\n",
+            "  warnings.warn(smsg, UserWarning)\n",
+            "/usr/local/lib/python3.10/dist-packages/xgboost/core.py:158: UserWarning: [11:51:46] WARNING: /workspace/src/learner.cc:740: \n",
+            "Parameters: { \"use_label_encoder\" } are not used.\n",
+            "\n",
+            "  warnings.warn(smsg, UserWarning)\n",
+            "[I 2024-10-04 11:51:49,307] Trial 18 finished with value: 0.05754329315290907 and parameters: {'model_type': 'XGBoost', 'n_estimators': 130, 'learning_rate': 0.27292409528118594, 'max_depth': 9}. Best is trial 15 with value: 0.05633486329779791.\n",
+            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
+            "  warnings.warn(\n",
+            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
+            "  warnings.warn(\n",
+            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
+            "  warnings.warn(\n",
+            "[I 2024-10-04 11:51:58,694] Trial 19 finished with value: 0.1032392726196002 and parameters: {'model_type': 'AdaBoostClassifier', 'n_estimators': 57, 'learning_rate': 0.3154948519576276}. Best is trial 15 with value: 0.05633486329779791.\n",
+            "[I 2024-10-04 11:51:59,466] Trial 20 finished with value: 0.12475823509249429 and parameters: {'model_type': 'SVC', 'C': 0.45781666475678773, 'kernel': 'rbf'}. Best is trial 15 with value: 0.05633486329779791.\n",
+            "/usr/local/lib/python3.10/dist-packages/xgboost/core.py:158: UserWarning: [11:51:59] WARNING: /workspace/src/learner.cc:740: \n",
+            "Parameters: { \"use_label_encoder\" } are not used.\n",
+            "\n",
+            "  warnings.warn(smsg, UserWarning)\n",
+            "/usr/local/lib/python3.10/dist-packages/xgboost/core.py:158: UserWarning: [11:52:01] WARNING: /workspace/src/learner.cc:740: \n",
+            "Parameters: { \"use_label_encoder\" } are not used.\n",
+            "\n",
+            "  warnings.warn(smsg, UserWarning)\n",
+            "/usr/local/lib/python3.10/dist-packages/xgboost/core.py:158: UserWarning: [11:52:03] WARNING: /workspace/src/learner.cc:740: \n",
+            "Parameters: { \"use_label_encoder\" } are not used.\n",
+            "\n",
+            "  warnings.warn(smsg, UserWarning)\n",
+            "[I 2024-10-04 11:52:06,952] Trial 21 finished with value: 0.05730157209900533 and parameters: {'model_type': 'XGBoost', 'n_estimators': 133, 'learning_rate': 0.2383782473849926, 'max_depth': 9}. Best is trial 15 with value: 0.05633486329779791.\n",
+            "/usr/local/lib/python3.10/dist-packages/xgboost/core.py:158: UserWarning: [11:52:07] WARNING: /workspace/src/learner.cc:740: \n",
+            "Parameters: { \"use_label_encoder\" } are not used.\n",
+            "\n",
+            "  warnings.warn(smsg, UserWarning)\n",
+            "/usr/local/lib/python3.10/dist-packages/xgboost/core.py:158: UserWarning: [11:52:08] WARNING: /workspace/src/learner.cc:740: \n",
+            "Parameters: { \"use_label_encoder\" } are not used.\n",
+            "\n",
+            "  warnings.warn(smsg, UserWarning)\n",
+            "/usr/local/lib/python3.10/dist-packages/xgboost/core.py:158: UserWarning: [11:52:10] WARNING: /workspace/src/learner.cc:740: \n",
+            "Parameters: { \"use_label_encoder\" } are not used.\n",
+            "\n",
+            "  warnings.warn(smsg, UserWarning)\n",
+            "[I 2024-10-04 11:52:12,230] Trial 22 finished with value: 0.05705985104510136 and parameters: {'model_type': 'XGBoost', 'n_estimators': 143, 'learning_rate': 0.2459054688443792, 'max_depth': 9}. Best is trial 15 with value: 0.05633486329779791.\n",
+            "/usr/local/lib/python3.10/dist-packages/xgboost/core.py:158: UserWarning: [11:52:12] WARNING: /workspace/src/learner.cc:740: \n",
+            "Parameters: { \"use_label_encoder\" } are not used.\n",
+            "\n",
+            "  warnings.warn(smsg, UserWarning)\n",
+            "/usr/local/lib/python3.10/dist-packages/xgboost/core.py:158: UserWarning: [11:52:13] WARNING: /workspace/src/learner.cc:740: \n",
+            "Parameters: { \"use_label_encoder\" } are not used.\n",
+            "\n",
+            "  warnings.warn(smsg, UserWarning)\n",
+            "/usr/local/lib/python3.10/dist-packages/xgboost/core.py:158: UserWarning: [11:52:13] WARNING: /workspace/src/learner.cc:740: \n",
+            "Parameters: { \"use_label_encoder\" } are not used.\n",
+            "\n",
+            "  warnings.warn(smsg, UserWarning)\n",
+            "[I 2024-10-04 11:52:14,627] Trial 23 finished with value: 0.07591812778097617 and parameters: {'model_type': 'XGBoost', 'n_estimators': 147, 'learning_rate': 0.07093590503430376, 'max_depth': 3}. Best is trial 15 with value: 0.05633486329779791.\n",
+            "[I 2024-10-04 11:52:14,680] Trial 24 finished with value: 0.12475823509249429 and parameters: {'model_type': 'BernoulliNB', 'alpha': 0.7599815704318412}. Best is trial 15 with value: 0.05633486329779791.\n",
+            "[I 2024-10-04 11:52:14,907] Trial 25 finished with value: 0.09840432529830101 and parameters: {'model_type': 'KNeighborsClassifier', 'n_neighbors': 20}. Best is trial 15 with value: 0.05633486329779791.\n",
+            "/usr/local/lib/python3.10/dist-packages/xgboost/core.py:158: UserWarning: [11:52:14] WARNING: /workspace/src/learner.cc:740: \n",
+            "Parameters: { \"use_label_encoder\" } are not used.\n",
+            "\n",
+            "  warnings.warn(smsg, UserWarning)\n",
+            "/usr/local/lib/python3.10/dist-packages/xgboost/core.py:158: UserWarning: [11:52:17] WARNING: /workspace/src/learner.cc:740: \n",
+            "Parameters: { \"use_label_encoder\" } are not used.\n",
+            "\n",
+            "  warnings.warn(smsg, UserWarning)\n",
+            "/usr/local/lib/python3.10/dist-packages/xgboost/core.py:158: UserWarning: [11:52:21] WARNING: /workspace/src/learner.cc:740: \n",
+            "Parameters: { \"use_label_encoder\" } are not used.\n",
+            "\n",
+            "  warnings.warn(smsg, UserWarning)\n",
+            "[I 2024-10-04 11:52:23,255] Trial 26 finished with value: 0.06673114900296218 and parameters: {'model_type': 'XGBoost', 'n_estimators': 113, 'learning_rate': 0.07174572543553345, 'max_depth': 7}. Best is trial 15 with value: 0.05633486329779791.\n",
+            "/usr/local/lib/python3.10/dist-packages/xgboost/core.py:158: UserWarning: [11:52:23] WARNING: /workspace/src/learner.cc:740: \n",
+            "Parameters: { \"use_label_encoder\" } are not used.\n",
+            "\n",
+            "  warnings.warn(smsg, UserWarning)\n",
+            "/usr/local/lib/python3.10/dist-packages/xgboost/core.py:158: UserWarning: [11:52:24] WARNING: /workspace/src/learner.cc:740: \n",
+            "Parameters: { \"use_label_encoder\" } are not used.\n",
+            "\n",
+            "  warnings.warn(smsg, UserWarning)\n",
+            "/usr/local/lib/python3.10/dist-packages/xgboost/core.py:158: UserWarning: [11:52:26] WARNING: /workspace/src/learner.cc:740: \n",
+            "Parameters: { \"use_label_encoder\" } are not used.\n",
+            "\n",
+            "  warnings.warn(smsg, UserWarning)\n",
+            "[I 2024-10-04 11:52:27,792] Trial 27 finished with value: 0.057785014206812924 and parameters: {'model_type': 'XGBoost', 'n_estimators': 149, 'learning_rate': 0.38213643994471486, 'max_depth': 11}. Best is trial 15 with value: 0.05633486329779791.\n",
+            "/usr/local/lib/python3.10/dist-packages/xgboost/core.py:158: UserWarning: [11:52:27] WARNING: /workspace/src/learner.cc:740: \n",
+            "Parameters: { \"use_label_encoder\" } are not used.\n",
+            "\n",
+            "  warnings.warn(smsg, UserWarning)\n",
+            "/usr/local/lib/python3.10/dist-packages/xgboost/core.py:158: UserWarning: [11:52:29] WARNING: /workspace/src/learner.cc:740: \n",
+            "Parameters: { \"use_label_encoder\" } are not used.\n",
+            "\n",
+            "  warnings.warn(smsg, UserWarning)\n",
+            "/usr/local/lib/python3.10/dist-packages/xgboost/core.py:158: UserWarning: [11:52:30] WARNING: /workspace/src/learner.cc:740: \n",
+            "Parameters: { \"use_label_encoder\" } are not used.\n",
+            "\n",
+            "  warnings.warn(smsg, UserWarning)\n",
+            "[I 2024-10-04 11:52:34,039] Trial 28 finished with value: 0.05706002645950925 and parameters: {'model_type': 'XGBoost', 'n_estimators': 146, 'learning_rate': 0.23319601364552314, 'max_depth': 5}. Best is trial 15 with value: 0.05633486329779791.\n",
+            "[I 2024-10-04 11:52:34,095] Trial 29 finished with value: 0.12475823509249429 and parameters: {'model_type': 'BernoulliNB', 'alpha': 0.017306697812539056}. Best is trial 15 with value: 0.05633486329779791.\n",
+            "/usr/local/lib/python3.10/dist-packages/xgboost/core.py:158: UserWarning: [11:52:34] WARNING: /workspace/src/learner.cc:740: \n",
+            "Parameters: { \"use_label_encoder\" } are not used.\n",
+            "\n",
+            "  warnings.warn(smsg, UserWarning)\n",
+            "/usr/local/lib/python3.10/dist-packages/xgboost/core.py:158: UserWarning: [11:52:35] WARNING: /workspace/src/learner.cc:740: \n",
+            "Parameters: { \"use_label_encoder\" } are not used.\n",
+            "\n",
+            "  warnings.warn(smsg, UserWarning)\n",
+            "/usr/local/lib/python3.10/dist-packages/xgboost/core.py:158: UserWarning: [11:52:35] WARNING: /workspace/src/learner.cc:740: \n",
+            "Parameters: { \"use_label_encoder\" } are not used.\n",
+            "\n",
+            "  warnings.warn(smsg, UserWarning)\n",
+            "[I 2024-10-04 11:52:36,710] Trial 30 finished with value: 0.05826880714343596 and parameters: {'model_type': 'XGBoost', 'n_estimators': 174, 'learning_rate': 0.17141820915284878, 'max_depth': 3}. Best is trial 15 with value: 0.05633486329779791.\n",
+            "/usr/local/lib/python3.10/dist-packages/xgboost/core.py:158: UserWarning: [11:52:36] WARNING: /workspace/src/learner.cc:740: \n",
+            "Parameters: { \"use_label_encoder\" } are not used.\n",
+            "\n",
+            "  warnings.warn(smsg, UserWarning)\n",
+            "/usr/local/lib/python3.10/dist-packages/xgboost/core.py:158: UserWarning: [11:52:38] WARNING: /workspace/src/learner.cc:740: \n",
+            "Parameters: { \"use_label_encoder\" } are not used.\n",
+            "\n",
+            "  warnings.warn(smsg, UserWarning)\n",
+            "/usr/local/lib/python3.10/dist-packages/xgboost/core.py:158: UserWarning: [11:52:39] WARNING: /workspace/src/learner.cc:740: \n",
+            "Parameters: { \"use_label_encoder\" } are not used.\n",
+            "\n",
+            "  warnings.warn(smsg, UserWarning)\n",
+            "[I 2024-10-04 11:52:40,689] Trial 31 finished with value: 0.05633433705457458 and parameters: {'model_type': 'XGBoost', 'n_estimators': 144, 'learning_rate': 0.18300574956434856, 'max_depth': 5}. Best is trial 31 with value: 0.05633433705457458.\n",
+            "[I 2024-10-04 11:52:40,739] Trial 32 finished with value: 0.12475823509249429 and parameters: {'model_type': 'LogisticRegression', 'C': 0.014937136486002044}. Best is trial 31 with value: 0.05633433705457458.\n",
+            "/usr/local/lib/python3.10/dist-packages/xgboost/core.py:158: UserWarning: [11:52:40] WARNING: /workspace/src/learner.cc:740: \n",
+            "Parameters: { \"use_label_encoder\" } are not used.\n",
+            "\n",
+            "  warnings.warn(smsg, UserWarning)\n",
+            "/usr/local/lib/python3.10/dist-packages/xgboost/core.py:158: UserWarning: [11:52:42] WARNING: /workspace/src/learner.cc:740: \n",
+            "Parameters: { \"use_label_encoder\" } are not used.\n",
+            "\n",
+            "  warnings.warn(smsg, UserWarning)\n",
+            "/usr/local/lib/python3.10/dist-packages/xgboost/core.py:158: UserWarning: [11:52:47] WARNING: /workspace/src/learner.cc:740: \n",
+            "Parameters: { \"use_label_encoder\" } are not used.\n",
+            "\n",
+            "  warnings.warn(smsg, UserWarning)\n",
+            "[I 2024-10-04 11:52:49,100] Trial 33 finished with value: 0.06455495786019672 and parameters: {'model_type': 'XGBoost', 'n_estimators': 139, 'learning_rate': 0.06981584259611646, 'max_depth': 6}. Best is trial 31 with value: 0.05633433705457458.\n",
+            "[I 2024-10-04 11:52:49,282] Trial 34 finished with value: 0.08921787276351012 and parameters: {'model_type': 'KNeighborsClassifier', 'n_neighbors': 16}. Best is trial 31 with value: 0.05633433705457458.\n",
+            "[I 2024-10-04 11:52:49,850] Trial 35 finished with value: 0.12475823509249429 and parameters: {'model_type': 'RandomForest', 'n_estimators': 14, 'max_depth': 4}. Best is trial 31 with value: 0.05633433705457458.\n",
+            "/usr/local/lib/python3.10/dist-packages/xgboost/core.py:158: UserWarning: [11:52:49] WARNING: /workspace/src/learner.cc:740: \n",
+            "Parameters: { \"use_label_encoder\" } are not used.\n",
+            "\n",
+            "  warnings.warn(smsg, UserWarning)\n",
+            "/usr/local/lib/python3.10/dist-packages/xgboost/core.py:158: UserWarning: [11:52:51] WARNING: /workspace/src/learner.cc:740: \n",
+            "Parameters: { \"use_label_encoder\" } are not used.\n",
+            "\n",
+            "  warnings.warn(smsg, UserWarning)\n",
+            "/usr/local/lib/python3.10/dist-packages/xgboost/core.py:158: UserWarning: [11:52:52] WARNING: /workspace/src/learner.cc:740: \n",
+            "Parameters: { \"use_label_encoder\" } are not used.\n",
+            "\n",
+            "  warnings.warn(smsg, UserWarning)\n",
+            "[I 2024-10-04 11:52:54,438] Trial 36 finished with value: 0.06020257557466635 and parameters: {'model_type': 'XGBoost', 'n_estimators': 161, 'learning_rate': 0.4066289706140343, 'max_depth': 13}. Best is trial 31 with value: 0.05633433705457458.\n",
+            "[I 2024-10-04 11:52:54,505] Trial 37 finished with value: 0.12475823509249429 and parameters: {'model_type': 'LogisticRegression', 'C': 1.2605782078690727e-05}. Best is trial 31 with value: 0.05633433705457458.\n",
+            "/usr/local/lib/python3.10/dist-packages/xgboost/core.py:158: UserWarning: [11:52:54] WARNING: /workspace/src/learner.cc:740: \n",
+            "Parameters: { \"use_label_encoder\" } are not used.\n",
+            "\n",
+            "  warnings.warn(smsg, UserWarning)\n",
+            "/usr/local/lib/python3.10/dist-packages/xgboost/core.py:158: UserWarning: [11:52:56] WARNING: /workspace/src/learner.cc:740: \n",
+            "Parameters: { \"use_label_encoder\" } are not used.\n",
+            "\n",
+            "  warnings.warn(smsg, UserWarning)\n",
+            "/usr/local/lib/python3.10/dist-packages/xgboost/core.py:158: UserWarning: [11:53:00] WARNING: /workspace/src/learner.cc:740: \n",
+            "Parameters: { \"use_label_encoder\" } are not used.\n",
+            "\n",
+            "  warnings.warn(smsg, UserWarning)\n",
+            "[I 2024-10-04 11:53:01,822] Trial 38 finished with value: 0.058994496548370634 and parameters: {'model_type': 'XGBoost', 'n_estimators': 116, 'learning_rate': 0.1762153119112291, 'max_depth': 7}. Best is trial 31 with value: 0.05633433705457458.\n",
+            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
+            "  warnings.warn(\n",
+            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
+            "  warnings.warn(\n",
+            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
+            "  warnings.warn(\n",
+            "[I 2024-10-04 11:53:26,344] Trial 39 finished with value: 0.09187610269882074 and parameters: {'model_type': 'AdaBoostClassifier', 'n_estimators': 156, 'learning_rate': 0.20423990139994846}. Best is trial 31 with value: 0.05633433705457458.\n",
+            "[I 2024-10-04 11:53:27,561] Trial 40 finished with value: 0.12475823509249429 and parameters: {'model_type': 'SVC', 'C': 0.016238972493323928, 'kernel': 'rbf'}. Best is trial 31 with value: 0.05633433705457458.\n",
+            "/usr/local/lib/python3.10/dist-packages/xgboost/core.py:158: UserWarning: [11:53:27] WARNING: /workspace/src/learner.cc:740: \n",
+            "Parameters: { \"use_label_encoder\" } are not used.\n",
+            "\n",
+            "  warnings.warn(smsg, UserWarning)\n",
+            "/usr/local/lib/python3.10/dist-packages/xgboost/core.py:158: UserWarning: [11:53:28] WARNING: /workspace/src/learner.cc:740: \n",
+            "Parameters: { \"use_label_encoder\" } are not used.\n",
+            "\n",
+            "  warnings.warn(smsg, UserWarning)\n",
+            "/usr/local/lib/python3.10/dist-packages/xgboost/core.py:158: UserWarning: [11:53:30] WARNING: /workspace/src/learner.cc:740: \n",
+            "Parameters: { \"use_label_encoder\" } are not used.\n",
+            "\n",
+            "  warnings.warn(smsg, UserWarning)\n",
+            "[I 2024-10-04 11:53:31,152] Trial 41 finished with value: 0.05391695110112893 and parameters: {'model_type': 'XGBoost', 'n_estimators': 142, 'learning_rate': 0.24928047667141826, 'max_depth': 5}. Best is trial 41 with value: 0.05391695110112893.\n",
+            "/usr/local/lib/python3.10/dist-packages/xgboost/core.py:158: UserWarning: [11:53:31] WARNING: /workspace/src/learner.cc:740: \n",
+            "Parameters: { \"use_label_encoder\" } are not used.\n",
+            "\n",
+            "  warnings.warn(smsg, UserWarning)\n",
+            "/usr/local/lib/python3.10/dist-packages/xgboost/core.py:158: UserWarning: [11:53:32] WARNING: /workspace/src/learner.cc:740: \n",
+            "Parameters: { \"use_label_encoder\" } are not used.\n",
+            "\n",
+            "  warnings.warn(smsg, UserWarning)\n",
+            "/usr/local/lib/python3.10/dist-packages/xgboost/core.py:158: UserWarning: [11:53:32] WARNING: /workspace/src/learner.cc:740: \n",
+            "Parameters: { \"use_label_encoder\" } are not used.\n",
+            "\n",
+            "  warnings.warn(smsg, UserWarning)\n",
+            "[I 2024-10-04 11:53:33,831] Trial 42 finished with value: 0.05633363539694347 and parameters: {'model_type': 'XGBoost', 'n_estimators': 140, 'learning_rate': 0.36369763235514757, 'max_depth': 4}. Best is trial 41 with value: 0.05391695110112893.\n",
+            "/usr/local/lib/python3.10/dist-packages/xgboost/core.py:158: UserWarning: [11:53:33] WARNING: /workspace/src/learner.cc:740: \n",
+            "Parameters: { \"use_label_encoder\" } are not used.\n",
+            "\n",
+            "  warnings.warn(smsg, UserWarning)\n",
+            "/usr/local/lib/python3.10/dist-packages/xgboost/core.py:158: UserWarning: [11:53:34] WARNING: /workspace/src/learner.cc:740: \n",
+            "Parameters: { \"use_label_encoder\" } are not used.\n",
+            "\n",
+            "  warnings.warn(smsg, UserWarning)\n",
+            "/usr/local/lib/python3.10/dist-packages/xgboost/core.py:158: UserWarning: [11:53:35] WARNING: /workspace/src/learner.cc:740: \n",
+            "Parameters: { \"use_label_encoder\" } are not used.\n",
+            "\n",
+            "  warnings.warn(smsg, UserWarning)\n",
+            "[I 2024-10-04 11:53:35,678] Trial 43 finished with value: 0.05512538095624009 and parameters: {'model_type': 'XGBoost', 'n_estimators': 125, 'learning_rate': 0.4344082122450397, 'max_depth': 3}. Best is trial 41 with value: 0.05391695110112893.\n",
+            "[I 2024-10-04 11:53:40,212] Trial 44 finished with value: 0.12475823509249429 and parameters: {'model_type': 'RandomForest', 'n_estimators': 124, 'max_depth': 3}. Best is trial 41 with value: 0.05391695110112893.\n",
+            "/usr/local/lib/python3.10/dist-packages/xgboost/core.py:158: UserWarning: [11:53:40] WARNING: /workspace/src/learner.cc:740: \n",
+            "Parameters: { \"use_label_encoder\" } are not used.\n",
+            "\n",
+            "  warnings.warn(smsg, UserWarning)\n",
+            "/usr/local/lib/python3.10/dist-packages/xgboost/core.py:158: UserWarning: [11:53:40] WARNING: /workspace/src/learner.cc:740: \n",
+            "Parameters: { \"use_label_encoder\" } are not used.\n",
+            "\n",
+            "  warnings.warn(smsg, UserWarning)\n",
+            "/usr/local/lib/python3.10/dist-packages/xgboost/core.py:158: UserWarning: [11:53:41] WARNING: /workspace/src/learner.cc:740: \n",
+            "Parameters: { \"use_label_encoder\" } are not used.\n",
+            "\n",
+            "  warnings.warn(smsg, UserWarning)\n",
+            "[I 2024-10-04 11:53:41,542] Trial 45 finished with value: 0.06382997011289326 and parameters: {'model_type': 'XGBoost', 'n_estimators': 106, 'learning_rate': 0.43992773168709765, 'max_depth': 2}. Best is trial 41 with value: 0.05391695110112893.\n",
+            "[I 2024-10-04 11:53:41,594] Trial 46 finished with value: 0.12475823509249429 and parameters: {'model_type': 'BernoulliNB', 'alpha': 1.748968046094416e-05}. Best is trial 41 with value: 0.05391695110112893.\n",
+            "/usr/local/lib/python3.10/dist-packages/xgboost/core.py:158: UserWarning: [11:53:41] WARNING: /workspace/src/learner.cc:740: \n",
+            "Parameters: { \"use_label_encoder\" } are not used.\n",
+            "\n",
+            "  warnings.warn(smsg, UserWarning)\n",
+            "/usr/local/lib/python3.10/dist-packages/xgboost/core.py:158: UserWarning: [11:53:42] WARNING: /workspace/src/learner.cc:740: \n",
+            "Parameters: { \"use_label_encoder\" } are not used.\n",
+            "\n",
+            "  warnings.warn(smsg, UserWarning)\n",
+            "/usr/local/lib/python3.10/dist-packages/xgboost/core.py:158: UserWarning: [11:53:43] WARNING: /workspace/src/learner.cc:740: \n",
+            "Parameters: { \"use_label_encoder\" } are not used.\n",
+            "\n",
+            "  warnings.warn(smsg, UserWarning)\n",
+            "[I 2024-10-04 11:53:43,925] Trial 47 finished with value: 0.055124503884201204 and parameters: {'model_type': 'XGBoost', 'n_estimators': 124, 'learning_rate': 0.8356649891685661, 'max_depth': 4}. Best is trial 41 with value: 0.05391695110112893.\n",
+            "[I 2024-10-04 11:53:44,084] Trial 48 finished with value: 0.12040725612222591 and parameters: {'model_type': 'KNeighborsClassifier', 'n_neighbors': 1}. Best is trial 41 with value: 0.05391695110112893.\n",
+            "/usr/local/lib/python3.10/dist-packages/xgboost/core.py:158: UserWarning: [11:53:44] WARNING: /workspace/src/learner.cc:740: \n",
+            "Parameters: { \"use_label_encoder\" } are not used.\n",
+            "\n",
+            "  warnings.warn(smsg, UserWarning)\n",
+            "/usr/local/lib/python3.10/dist-packages/xgboost/core.py:158: UserWarning: [11:53:44] WARNING: /workspace/src/learner.cc:740: \n",
+            "Parameters: { \"use_label_encoder\" } are not used.\n",
+            "\n",
+            "  warnings.warn(smsg, UserWarning)\n",
+            "/usr/local/lib/python3.10/dist-packages/xgboost/core.py:158: UserWarning: [11:53:45] WARNING: /workspace/src/learner.cc:740: \n",
+            "Parameters: { \"use_label_encoder\" } are not used.\n",
+            "\n",
+            "  warnings.warn(smsg, UserWarning)\n",
+            "[I 2024-10-04 11:53:46,040] Trial 49 finished with value: 0.05996173159280149 and parameters: {'model_type': 'XGBoost', 'n_estimators': 122, 'learning_rate': 0.8811529800090732, 'max_depth': 3}. Best is trial 41 with value: 0.05391695110112893.\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Retrieve the best model from Optuna study\n",
+        "best_model_params = study.best_trial.params\n",
+        "best_model_type = best_model_params['model_type']\n",
+        "\n",
+        "# Train the best model using the entire training data\n",
+        "if best_model_type == 'BernoulliNB':\n",
+        "    best_model = BernoulliNB(alpha=best_model_params['alpha'])\n",
+        "elif best_model_type == 'LogisticRegression':\n",
+        "    best_model = LogisticRegression(C=best_model_params['C'], max_iter=1000)\n",
+        "elif best_model_type == 'RandomForest':\n",
+        "    best_model = RandomForestClassifier(n_estimators=best_model_params['n_estimators'], max_depth=best_model_params['max_depth'])\n",
+        "elif best_model_type == 'SVC':\n",
+        "    best_model = SVC(C=best_model_params['C'], kernel=best_model_params['kernel'])\n",
+        "elif best_model_type == 'KNeighborsClassifier':\n",
+        "    best_model = KNeighborsClassifier(n_neighbors=best_model_params['n_neighbors'])\n",
+        "elif best_model_type == 'GradientBoostingClassifier':\n",
+        "    best_model = GradientBoostingClassifier(n_estimators=best_model_params['n_estimators'], learning_rate=best_model_params['learning_rate'])\n",
+        "elif best_model_type == 'AdaBoostClassifier':\n",
+        "    best_model = AdaBoostClassifier(n_estimators=best_model_params['n_estimators'], learning_rate=best_model_params['learning_rate'])\n",
+        "elif best_model_type == 'XGBoost':\n",
+        "    best_model = xgb.XGBClassifier(n_estimators=best_model_params['n_estimators'], learning_rate=best_model_params['learning_rate'], max_depth=best_model_params['max_depth'], use_label_encoder=False, eval_metric='mlogloss')\n",
+        "\n",
+        "# Train the best model\n",
+        "best_model.fit(X_train, y_train)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 326
+        },
+        "id": "KGQ4K0eKOuf8",
+        "outputId": "b9e1e140-7af0-4a60-fad5-56df0ce54a16"
+      },
+      "execution_count": 68,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/xgboost/core.py:158: UserWarning: [11:53:59] WARNING: /workspace/src/learner.cc:740: \n",
+            "Parameters: { \"use_label_encoder\" } are not used.\n",
+            "\n",
+            "  warnings.warn(smsg, UserWarning)\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
+              "              colsample_bylevel=None, colsample_bynode=None,\n",
+              "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
+              "              enable_categorical=False, eval_metric='mlogloss',\n",
+              "              feature_types=None, gamma=None, grow_policy=None,\n",
+              "              importance_type=None, interaction_constraints=None,\n",
+              "              learning_rate=0.24928047667141826, max_bin=None,\n",
+              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
+              "              max_delta_step=None, max_depth=5, max_leaves=None,\n",
+              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
+              "              multi_strategy=None, n_estimators=142, n_jobs=None,\n",
+              "              num_parallel_tree=None, random_state=None, ...)"
+            ],
+            "text/html": [
+              "<style>#sk-container-id-1 {\n",
+              "  /* Definition of color scheme common for light and dark mode */\n",
+              "  --sklearn-color-text: black;\n",
+              "  --sklearn-color-line: gray;\n",
+              "  /* Definition of color scheme for unfitted estimators */\n",
+              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+              "  --sklearn-color-unfitted-level-3: chocolate;\n",
+              "  /* Definition of color scheme for fitted estimators */\n",
+              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+              "\n",
+              "  /* Specific color for light theme */\n",
+              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-icon: #696969;\n",
+              "\n",
+              "  @media (prefers-color-scheme: dark) {\n",
+              "    /* Redefinition of color scheme for dark theme */\n",
+              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-icon: #878787;\n",
+              "  }\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 pre {\n",
+              "  padding: 0;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 input.sk-hidden--visually {\n",
+              "  border: 0;\n",
+              "  clip: rect(1px 1px 1px 1px);\n",
+              "  clip: rect(1px, 1px, 1px, 1px);\n",
+              "  height: 1px;\n",
+              "  margin: -1px;\n",
+              "  overflow: hidden;\n",
+              "  padding: 0;\n",
+              "  position: absolute;\n",
+              "  width: 1px;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+              "  border: 1px dashed var(--sklearn-color-line);\n",
+              "  margin: 0 0.4em 0.5em 0.4em;\n",
+              "  box-sizing: border-box;\n",
+              "  padding-bottom: 0.4em;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-container {\n",
+              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+              "     so we also need the `!important` here to be able to override the\n",
+              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+              "  display: inline-block !important;\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+              "  display: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-parallel-item,\n",
+              "div.sk-serial,\n",
+              "div.sk-item {\n",
+              "  /* draw centered vertical line to link estimators */\n",
+              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+              "  background-size: 2px 100%;\n",
+              "  background-repeat: no-repeat;\n",
+              "  background-position: center center;\n",
+              "}\n",
+              "\n",
+              "/* Parallel-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item::after {\n",
+              "  content: \"\";\n",
+              "  width: 100%;\n",
+              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+              "  flex-grow: 1;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel {\n",
+              "  display: flex;\n",
+              "  align-items: stretch;\n",
+              "  justify-content: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+              "  align-self: flex-end;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+              "  align-self: flex-start;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+              "  width: 0;\n",
+              "}\n",
+              "\n",
+              "/* Serial-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-serial {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "  align-items: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  padding-right: 1em;\n",
+              "  padding-left: 1em;\n",
+              "}\n",
+              "\n",
+              "\n",
+              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+              "clickable and can be expanded/collapsed.\n",
+              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+              "*/\n",
+              "\n",
+              "/* Pipeline and ColumnTransformer style (default) */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable {\n",
+              "  /* Default theme specific background. It is overwritten whether we have a\n",
+              "  specific estimator or a Pipeline/ColumnTransformer */\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable label */\n",
+              "#sk-container-id-1 label.sk-toggleable__label {\n",
+              "  cursor: pointer;\n",
+              "  display: block;\n",
+              "  width: 100%;\n",
+              "  margin-bottom: 0;\n",
+              "  padding: 0.5em;\n",
+              "  box-sizing: border-box;\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+              "  /* Arrow on the left of the label */\n",
+              "  content: \"▸\";\n",
+              "  float: left;\n",
+              "  margin-right: 0.25em;\n",
+              "  color: var(--sklearn-color-icon);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable content - dropdown */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable__content {\n",
+              "  max-height: 0;\n",
+              "  max-width: 0;\n",
+              "  overflow: hidden;\n",
+              "  text-align: left;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+              "  margin: 0.2em;\n",
+              "  border-radius: 0.25em;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+              "  /* Expand drop-down */\n",
+              "  max-height: 200px;\n",
+              "  max-width: 100%;\n",
+              "  overflow: auto;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+              "  content: \"▾\";\n",
+              "}\n",
+              "\n",
+              "/* Pipeline/ColumnTransformer-specific style */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific style */\n",
+              "\n",
+              "/* Colorize estimator box */\n",
+              "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+              "#sk-container-id-1 div.sk-label label {\n",
+              "  /* The background is the default theme color */\n",
+              "  color: var(--sklearn-color-text-on-default-background);\n",
+              "}\n",
+              "\n",
+              "/* On hover, darken the color of the background */\n",
+              "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Label box, darken color on hover, fitted */\n",
+              "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator label */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label label {\n",
+              "  font-family: monospace;\n",
+              "  font-weight: bold;\n",
+              "  display: inline-block;\n",
+              "  line-height: 1.2em;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label-container {\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific */\n",
+              "#sk-container-id-1 div.sk-estimator {\n",
+              "  font-family: monospace;\n",
+              "  border: 1px dotted var(--sklearn-color-border-box);\n",
+              "  border-radius: 0.25em;\n",
+              "  box-sizing: border-box;\n",
+              "  margin-bottom: 0.5em;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-estimator.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "/* on hover */\n",
+              "#sk-container-id-1 div.sk-estimator:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+              "\n",
+              "/* Common style for \"i\" and \"?\" */\n",
+              "\n",
+              ".sk-estimator-doc-link,\n",
+              "a:link.sk-estimator-doc-link,\n",
+              "a:visited.sk-estimator-doc-link {\n",
+              "  float: right;\n",
+              "  font-size: smaller;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1em;\n",
+              "  height: 1em;\n",
+              "  width: 1em;\n",
+              "  text-decoration: none !important;\n",
+              "  margin-left: 1ex;\n",
+              "  /* unfitted */\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted,\n",
+              "a:link.sk-estimator-doc-link.fitted,\n",
+              "a:visited.sk-estimator-doc-link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "/* Span, style for the box shown on hovering the info icon */\n",
+              ".sk-estimator-doc-link span {\n",
+              "  display: none;\n",
+              "  z-index: 9999;\n",
+              "  position: relative;\n",
+              "  font-weight: normal;\n",
+              "  right: .2ex;\n",
+              "  padding: .5ex;\n",
+              "  margin: .5ex;\n",
+              "  width: min-content;\n",
+              "  min-width: 20ex;\n",
+              "  max-width: 50ex;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  box-shadow: 2pt 2pt 4pt #999;\n",
+              "  /* unfitted */\n",
+              "  background: var(--sklearn-color-unfitted-level-0);\n",
+              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted span {\n",
+              "  /* fitted */\n",
+              "  background: var(--sklearn-color-fitted-level-0);\n",
+              "  border: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link:hover span {\n",
+              "  display: block;\n",
+              "}\n",
+              "\n",
+              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+              "\n",
+              "#sk-container-id-1 a.estimator_doc_link {\n",
+              "  float: right;\n",
+              "  font-size: 1rem;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1rem;\n",
+              "  height: 1rem;\n",
+              "  width: 1rem;\n",
+              "  text-decoration: none;\n",
+              "  /* unfitted */\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
+              "              colsample_bylevel=None, colsample_bynode=None,\n",
+              "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
+              "              enable_categorical=False, eval_metric=&#x27;mlogloss&#x27;,\n",
+              "              feature_types=None, gamma=None, grow_policy=None,\n",
+              "              importance_type=None, interaction_constraints=None,\n",
+              "              learning_rate=0.24928047667141826, max_bin=None,\n",
+              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
+              "              max_delta_step=None, max_depth=5, max_leaves=None,\n",
+              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
+              "              multi_strategy=None, n_estimators=142, n_jobs=None,\n",
+              "              num_parallel_tree=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;XGBClassifier<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
+              "              colsample_bylevel=None, colsample_bynode=None,\n",
+              "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
+              "              enable_categorical=False, eval_metric=&#x27;mlogloss&#x27;,\n",
+              "              feature_types=None, gamma=None, grow_policy=None,\n",
+              "              importance_type=None, interaction_constraints=None,\n",
+              "              learning_rate=0.24928047667141826, max_bin=None,\n",
+              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
+              "              max_delta_step=None, max_depth=5, max_leaves=None,\n",
+              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
+              "              multi_strategy=None, n_estimators=142, n_jobs=None,\n",
+              "              num_parallel_tree=None, random_state=None, ...)</pre></div> </div></div></div></div>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 68
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Evaluate the model on the test data\n",
+        "y_pred = best_model.predict(X_test)\n",
+        "accuracy = accuracy_score(y_test, y_pred)\n",
+        "print(f\"Test Accuracy: {accuracy}\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Degd_vUZWrAD",
+        "outputId": "fde8bc0b-f122-4f1b-d534-2a018ec4c572"
+      },
+      "execution_count": 69,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Test Accuracy: 0.9400966183574879\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "# Save the best model as a .pkl file\n",
+        "model_filename = 'best_model.pkl'\n",
+        "with open(model_filename, 'wb') as f:\n",
+        "    pickle.dump(best_model, f)\n",
+        "\n",
+        "print(f\"Best model saved as {model_filename}\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Ud0SIs3WWrDa",
+        "outputId": "5f3038bc-04da-4a74-e263-a99108306a5e"
+      },
+      "execution_count": 70,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Best model saved as best_model.pkl\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "\n",
+        "from sklearn.metrics import roc_curve, auc\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "# Assuming y_test and y_pred are already defined from your model evaluation\n",
+        "y_pred_proba = best_model.predict_proba(X_test)[:, 1]  # Probability of being class 1 (spam)\n",
+        "\n",
+        "fpr, tpr, thresholds = roc_curve(y_test, y_pred_proba)\n",
+        "roc_auc = auc(fpr, tpr)\n",
+        "\n",
+        "plt.figure()\n",
+        "plt.plot(fpr, tpr, color='darkorange', lw=2, label='ROC curve (area = %0.2f)' % roc_auc)\n",
+        "plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
+        "plt.xlim([0.0, 1.0])\n",
+        "plt.ylim([0.0, 1.05])\n",
+        "plt.xlabel('False Positive Rate')\n",
+        "plt.ylabel('True Positive Rate')\n",
+        "plt.title('Receiver Operating Characteristic (ROC)')\n",
+        "plt.legend(loc=\"lower right\")\n",
+        "plt.show()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 472
+        },
+        "id": "Y1K69zh3YcGX",
+        "outputId": "5a006ed8-b2e6-440c-84aa-6e8a1c8a2f8c"
+      },
+      "execution_count": 74,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "\n",
+        "from sklearn.metrics import confusion_matrix, classification_report, f1_score\n",
+        "\n",
+        "# Generate the confusion matrix\n",
+        "cm = confusion_matrix(y_test, y_pred)\n",
+        "print(\"Confusion Matrix:\")\n",
+        "print(cm)\n",
+        "\n",
+        "\n",
+        "report = classification_report(y_test, y_pred)\n",
+        "print(\"\\nClassification Report:\")\n",
+        "print(report)\n",
+        "\n",
+        "\n",
+        "f1 = f1_score(y_test, y_pred)\n",
+        "print(f\"\\nF1-Score: {f1}\")\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "meMcoimrZRgI",
+        "outputId": "179de4da-6625-4d97-bd95-a5e76f0439c9"
+      },
+      "execution_count": 76,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Confusion Matrix:\n",
+            "[[885  13]\n",
+            " [ 49  88]]\n",
+            "\n",
+            "Classification Report:\n",
+            "              precision    recall  f1-score   support\n",
+            "\n",
+            "           0       0.95      0.99      0.97       898\n",
+            "           1       0.87      0.64      0.74       137\n",
+            "\n",
+            "    accuracy                           0.94      1035\n",
+            "   macro avg       0.91      0.81      0.85      1035\n",
+            "weighted avg       0.94      0.94      0.94      1035\n",
+            "\n",
+            "\n",
+            "F1-Score: 0.7394957983193278\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import streamlit as st\n",
+        "from nltk.stem import WordNetLemmatizer\n",
+        "import pickle\n",
+        "import nltk\n",
+        "import string\n",
+        "from nltk.corpus import stopwords\n",
+        "\n",
+        "\n",
+        "lemmatizer = WordNetLemmatizer()\n",
+        "\n",
+        "def transform_text(text):\n",
+        "    text = text.lower()\n",
+        "    text = nltk.word_tokenize(text)\n",
+        "\n",
+        "    y = []\n",
+        "    for i in text:\n",
+        "        if i.isalnum():\n",
+        "            y.append(i)\n",
+        "\n",
+        "    text = y[:]\n",
+        "    y.clear()\n",
+        "\n",
+        "    for i in text:\n",
+        "        if i not in stopwords.words('english') and i not in string.punctuation:\n",
+        "            y.append(i)\n",
+        "\n",
+        "    text = y[:]\n",
+        "    y.clear()\n",
+        "\n",
+        "    for i in text:\n",
+        "        y.append(lemmatizer.lemmatize(i))\n",
+        "\n",
+        "\n",
+        "    return \" \".join(y)\n",
+        "\n",
+        "\n",
+        "# Store the model in your file\n",
+        "# tfidf=pickle.load(open('vectorizer.pkl','rb'))\n",
+        "# model=pickle.load(open('bnb.pkl','rb'))\n",
+        "\n",
+        "st.title('SMS Spam Classification')\n",
+        "\n",
+        "sms_input=st.text_area(\"Enter the text\")\n",
+        "\n",
+        "if st.button('Predict'):\n",
+        "    transform_sms=transform_text(sms_input)\n",
+        "\n",
+        "    vector_input=tfidf.transform([transform_sms])\n",
+        "\n",
+        "    result=bnb.predict(vector_input)[0]\n",
+        "\n",
+        "    if result==1:\n",
+        "        st.title(\"SMS is Spam\")\n",
+        "\n",
+        "    else:\n",
+        "        st.title(\"SMS is not Spam\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "4w0YvhO_WrF3",
+        "outputId": "a12e84e8-00b2-4dee-f0b2-53223bcdb9cf"
+      },
+      "execution_count": 73,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "2024-10-04 11:55:11.061 WARNING streamlit.runtime.scriptrunner_utils.script_run_context: Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
+            "2024-10-04 11:55:11.194 \n",
+            "  \u001b[33m\u001b[1mWarning:\u001b[0m to view this Streamlit app on a browser, run it with the following\n",
+            "  command:\n",
+            "\n",
+            "    streamlit run /usr/local/lib/python3.10/dist-packages/colab_kernel_launcher.py [ARGUMENTS]\n",
+            "2024-10-04 11:55:11.196 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
+            "2024-10-04 11:55:11.199 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
+            "2024-10-04 11:55:11.202 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
+            "2024-10-04 11:55:11.204 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
+            "2024-10-04 11:55:11.206 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
+            "2024-10-04 11:55:11.207 Session state does not function when running a script without `streamlit run`\n",
+            "2024-10-04 11:55:11.209 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
+            "2024-10-04 11:55:11.210 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
+            "2024-10-04 11:55:11.212 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
+            "2024-10-04 11:55:11.213 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
+            "2024-10-04 11:55:11.215 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
+            "2024-10-04 11:55:11.216 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
+            "2024-10-04 11:55:11.218 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "Gk9b3FkiWrI9"
+      },
+      "execution_count": 76,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "6h4PHGmoZHc4"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "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": "3.11.3"
+    },
+    "colab": {
+      "provenance": []
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file

From f6ddb3e75586d2c8cde69eae455018dd5bbbbe2e Mon Sep 17 00:00:00 2001
From: sakeeb hasan <100307524+Sakeebhasan123456@users.noreply.github.com>
Date: Fri, 4 Oct 2024 17:37:45 +0530
Subject: [PATCH 3/3] Update app1.py

---
 .../Model/app1.py                                             | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/Deep_Learning/Spam Vs Ham Mail Classification [With Streamlit GUI]/Model/app1.py b/Deep_Learning/Spam Vs Ham Mail Classification [With Streamlit GUI]/Model/app1.py
index fdaa5846c1..e8ad13ac84 100644
--- a/Deep_Learning/Spam Vs Ham Mail Classification [With Streamlit GUI]/Model/app1.py	
+++ b/Deep_Learning/Spam Vs Ham Mail Classification [With Streamlit GUI]/Model/app1.py	
@@ -41,7 +41,7 @@ def transform_text(text):
 # Store the model in your file
 # here we can store the tfidf and model pkl file in a specfic folder and use it.
 tfidf=pickle.load(open('vectorizer.pkl','rb'))
-model=pickle.load(open('bnb.pkl','rb'))
+model=pickle.load(open('best_model.pkl','rb'))
 
 st.title('SMS Spam Classification')
 
@@ -58,4 +58,4 @@ def transform_text(text):
         st.title("SMS is Spam")
 
     else:
-        st.title("SMS is not Spam")
\ No newline at end of file
+        st.title("SMS is not Spam")