pre-owned_cars_price_estimation_and_analysis_modelevaluation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/kaggle/input/vehiclesmodelling/vehiclesModelling.csv\n"
     ]
    }
   ],
   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load in \n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "# Input data files are available in the \"../input/\" directory.\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
    "\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))\n",
    "\n",
    "# Any results you write to the current directory are saved as output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a"
   },
   "outputs": [],
   "source": [
    "# Final - preprocessing libraries for modelling\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import ensemble\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from math import sqrt\n",
    "\n",
    "\n",
    "# visulizations\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Modelling, stats\n",
    "from sklearn.preprocessing import RobustScaler\n",
    "from sklearn.linear_model import LinearRegression\n",
    "import sklearn.model_selection as ms\n",
    "import math\n",
    "import sklearn.metrics as sklm\n",
    "import statsmodels.api as sm\n",
    "\n",
    "# Model saving\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "modelling=pd.read_csv(\"../input/vehiclesmodelling/vehiclesModelling.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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",
       "    }\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>price</th>\n",
       "      <th>year</th>\n",
       "      <th>manufacturer</th>\n",
       "      <th>condition</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>fuel</th>\n",
       "      <th>odometer</th>\n",
       "      <th>title_status</th>\n",
       "      <th>transmission</th>\n",
       "      <th>drive</th>\n",
       "      <th>type</th>\n",
       "      <th>paint_color</th>\n",
       "      <th>state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17899</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>excellent</td>\n",
       "      <td>4</td>\n",
       "      <td>gas</td>\n",
       "      <td>63500.0</td>\n",
       "      <td>clean</td>\n",
       "      <td>manual</td>\n",
       "      <td>4wd</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>black</td>\n",
       "      <td>west</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13999</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>ram</td>\n",
       "      <td>good</td>\n",
       "      <td>6</td>\n",
       "      <td>gas</td>\n",
       "      <td>120057.0</td>\n",
       "      <td>clean</td>\n",
       "      <td>automatic</td>\n",
       "      <td>4wd</td>\n",
       "      <td>pickup</td>\n",
       "      <td>silver</td>\n",
       "      <td>west</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>34500</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>ford</td>\n",
       "      <td>excellent</td>\n",
       "      <td>6</td>\n",
       "      <td>diesel</td>\n",
       "      <td>95484.0</td>\n",
       "      <td>clean</td>\n",
       "      <td>automatic</td>\n",
       "      <td>4wd</td>\n",
       "      <td>pickup</td>\n",
       "      <td>white</td>\n",
       "      <td>west</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14500</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>gmc</td>\n",
       "      <td>excellent</td>\n",
       "      <td>6</td>\n",
       "      <td>gas</td>\n",
       "      <td>89340.0</td>\n",
       "      <td>clean</td>\n",
       "      <td>automatic</td>\n",
       "      <td>4wd</td>\n",
       "      <td>pickup</td>\n",
       "      <td>white</td>\n",
       "      <td>west</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12500</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>ford</td>\n",
       "      <td>salvage</td>\n",
       "      <td>6</td>\n",
       "      <td>gas</td>\n",
       "      <td>226031.0</td>\n",
       "      <td>clean</td>\n",
       "      <td>automatic</td>\n",
       "      <td>4wd</td>\n",
       "      <td>pickup</td>\n",
       "      <td>white</td>\n",
       "      <td>west</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413642</th>\n",
       "      <td>15476</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>nissan</td>\n",
       "      <td>excellent</td>\n",
       "      <td>6</td>\n",
       "      <td>gas</td>\n",
       "      <td>103135.0</td>\n",
       "      <td>clean</td>\n",
       "      <td>automatic</td>\n",
       "      <td>4wd</td>\n",
       "      <td>SUV</td>\n",
       "      <td>white</td>\n",
       "      <td>west</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413643</th>\n",
       "      <td>9881</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>bmw</td>\n",
       "      <td>good</td>\n",
       "      <td>6</td>\n",
       "      <td>gas</td>\n",
       "      <td>108277.0</td>\n",
       "      <td>clean</td>\n",
       "      <td>automatic</td>\n",
       "      <td>2wd</td>\n",
       "      <td>convertible</td>\n",
       "      <td>white</td>\n",
       "      <td>west</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413644</th>\n",
       "      <td>24895</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>jeep</td>\n",
       "      <td>like new</td>\n",
       "      <td>4</td>\n",
       "      <td>gas</td>\n",
       "      <td>6973.0</td>\n",
       "      <td>clean</td>\n",
       "      <td>automatic</td>\n",
       "      <td>4wd</td>\n",
       "      <td>SUV</td>\n",
       "      <td>white</td>\n",
       "      <td>west</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413645</th>\n",
       "      <td>32500</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>excellent</td>\n",
       "      <td>6</td>\n",
       "      <td>gas</td>\n",
       "      <td>37520.0</td>\n",
       "      <td>clean</td>\n",
       "      <td>automatic</td>\n",
       "      <td>4wd</td>\n",
       "      <td>sedan</td>\n",
       "      <td>red</td>\n",
       "      <td>west</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413646</th>\n",
       "      <td>12900</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>ram</td>\n",
       "      <td>fair</td>\n",
       "      <td>6</td>\n",
       "      <td>gas</td>\n",
       "      <td>152296.0</td>\n",
       "      <td>clean</td>\n",
       "      <td>automatic</td>\n",
       "      <td>4wd</td>\n",
       "      <td>pickup</td>\n",
       "      <td>black</td>\n",
       "      <td>west</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>413647 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        price    year manufacturer  condition  cylinders    fuel  odometer  \\\n",
       "0       17899  2012.0   volkswagen  excellent          4     gas   63500.0   \n",
       "1       13999  2009.0          ram       good          6     gas  120057.0   \n",
       "2       34500  2017.0         ford  excellent          6  diesel   95484.0   \n",
       "3       14500  2007.0          gmc  excellent          6     gas   89340.0   \n",
       "4       12500  2013.0         ford    salvage          6     gas  226031.0   \n",
       "...       ...     ...          ...        ...        ...     ...       ...   \n",
       "413642  15476  2012.0       nissan  excellent          6     gas  103135.0   \n",
       "413643   9881  2008.0          bmw       good          6     gas  108277.0   \n",
       "413644  24895  2019.0         jeep   like new          4     gas    6973.0   \n",
       "413645  32500  2017.0      unknown  excellent          6     gas   37520.0   \n",
       "413646  12900  2012.0          ram       fair          6     gas  152296.0   \n",
       "\n",
       "       title_status transmission drive         type paint_color state  \n",
       "0             clean       manual   4wd    hatchback       black  west  \n",
       "1             clean    automatic   4wd       pickup      silver  west  \n",
       "2             clean    automatic   4wd       pickup       white  west  \n",
       "3             clean    automatic   4wd       pickup       white  west  \n",
       "4             clean    automatic   4wd       pickup       white  west  \n",
       "...             ...          ...   ...          ...         ...   ...  \n",
       "413642        clean    automatic   4wd          SUV       white  west  \n",
       "413643        clean    automatic   2wd  convertible       white  west  \n",
       "413644        clean    automatic   4wd          SUV       white  west  \n",
       "413645        clean    automatic   4wd        sedan         red  west  \n",
       "413646        clean    automatic   4wd       pickup       black  west  \n",
       "\n",
       "[413647 rows x 13 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modelling.drop([\"age\"],axis=1,inplace=True)\n",
    "modelling.drop(['state'],axis=1,inplace=True)\n",
    "modelling.rename(columns={'state_trans':'state'},inplace=True)\n",
    "modelling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Log Transformation of Price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "modelling[\"price\"]=np.log1p(modelling[\"price\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fbb1921d990>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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O/voLTwr6ZiBPRHKBCmAZcGvvBsaY3FO3ReTvwCtazNVwWn+oFke34ZJJqW6fz4yP5N5l53s5lert7iWTeWtfFfe8tl//LSwyYEE3xjhEZCU9s1fswKPGmD0issL1/FnHzZU6VydbO9lw5ARTM+NIiR3a7oZq5GUnRvGVi8Zx37tFfPaCsW7bdHU7eXPPcd7eV8WWknpiIkJJiQljVEQos8YmnLHFghocj+ahG2NWA6v7POa2kBtj7jj3WEr926Prj9DpcHJpP71z5Tu+esl4nttSzm1/3cQlk1JYML5nGmNNcwdbjtaxpbSe9i4nsREhzMtNpMPhpLimheLaFgpL6lk6M0OHa86BrhRVPq3kRAsPrS1mWmYc6XERVsdRA4gKC+HZry7gZy/t4bXdx3n/QA0djm6cBmzSs6HXD66ezLxxiYTae+ZkGGP4/rM7Wb2rkgfeP8wXFuaecZ5EeUYLurJMY3sXb+2pwm4XokLtTMmMO20lpTGGH7+wm1C7ze93JgwmGfGRPHJ7AT95YRd7K5uIiwwhMTqMiWmxxEaEnrH4SESYNSaBiWmx/GVtMY9vOMqXLxynPfUh0IKuLLN6VyW7KxqICLXT3tXNuqJarp+e8fEVbV7YVsH6olp+sXQKdpteLdHf5GfEkZ/hfhWvOzHhIXxhYQ4PrS3m7x8cZcXF40cwXWDSgm4Rd7v5BdOluTYdqWNneQOXTU7livPSaO108PTmMl7cXkFtcwcGw/bSk5w/Jp7PzhvLqs1lA7+p8nvxUWHcsSCHh9YeZtXmUpZfPO7joRk1MC3oyuu6nYb/fHkPcZGhXJTXs8AsKiyE2xfk8M6+ag7XNJMUE8alk1P53lWTsA1y2fxgtr5VvidtVAQ3nJ/FU5tK+d83D3L3Et3F0VNa0JXXPVNYxt7KRpbNyT7tIhI2Ea7MT+Nv8+ZYmE75gmmZcRTlJPDgmsMsnJDEhXm6stwT+ruM8rpH1x9hRnY80/rZJVEpgGunZZCXGsO3n95BbXOH1XH8ghZ05VWHqpo4VN3MTedwMQkVHMJCbNx36/k0tXfx3Wd24HT23UJK9aUFXXnVq7sqEYHFU9OtjqL8wOT0UfzkunzWHKzhr7rx14C0oCuvWr2rkjk5iaTG6iIh5ZnPzRvD1VPS+M0b+9lVrhekPhs9Kaq8pqi6iYNVzfzXJ6ZYHeVjOiPG94kI99w0ncX3ruNbT2/jlbsuJDLMbnUsn6Q9dDVkFSfb6HQ4PW7/6s7jOtyihiQ+Koz//dQMDte08KvX9lkdx2dpD10Nyepdldz/XhGj4yL4/AVjiY8a+KpAq3dVUjA2gbRROtyiBm/hhGTuXJTLX9cfQRAmpcee9nwwLczrj/bQ1aAdqW3hB8/uJDU2nLqWTh5Yc5iK+razvuZgVRMHqpq4VvdkUefg+1dPIjU2nJd3VNDV7flvh8FCe+hqUNq7uvnqE1sIsQt3LMih3eHksQ+P8tiGo3zvqkn9LtN+bks5ITbh+hkZA36Gjmur/kSE2rluegaPfnCED4pq+73gSbDSHroalL+uP8L+4038/tMziY8KI31UBDcXZNHU7mDTkTq3r3F0O3lhWwWXTEolKUYvUKHOzYTUGPJHj+L9AzU0tHVZHcenaA9dnaa/3vGp8cmXtlcwNyeRSyelftx2XHIM41Kief9gDXNyEs947fqiWqqbOrh5dubIBVeWGsxvVcPxG9g100Zz79sHeWPPcT5VkD3wC4KE9tCVxw5W9Uw7vHb6mePgV0xOo6XDwcYjJ8547tkt5cRHhXLpZP31WA2PxOgwFk5IZnvZSWqbdFuAU7SgK4+9urNnleeSaWdOO8xJjiYvNYY1B2toav/3r8ENbV28ubeKpTMyCA/RucNq+CwYn4TdJnxYfGYnIlhpQVceMcbwys5jzMvtf5XnlflptHd1842ntuHodmKM4S9ri+l0OLlpdpaXE6tAFxsRyoyseLaW1NPW2W11HJ+gBV155EBVE4drWrh2ev+zVLISovjEjEzeO1DDL17Zy89f2cuf3ivi+hkZurOiGhELxifR2e2ksMT9CflgoydF/dRAJy+H26s7K7EJLBlglefc3ESSY8J4xLWR0hcX5vKTa8/TnRXViMiIjyQ3OZqPik/g6HYSEuRXN9KCrjzy6q5KLhiXRLIH0w5zkqNZOD6JpJhwJqTGsGpzma7iU0PiyYyYheOTeGJjKW/vqw76bSU8+u9MRBaLyAERKRKRu908v1REdorIdhEpFJFFwx9VWaWmqYPimhaPf1hsIlw7PYMLxiWNcDKlYPLoUcRGhPD81nKro1huwIIuInbgfmAJkA98RkTy+zR7B5hhjJkJfBF4ZLiDKuvsP94IwGU67VD5IJsI0zPjehYatQb3QiNPeuhzgSJjTLExphNYBSzt3cAY02yMOXU5kWhALy0SQPZVNjE5PZashCiroyjl1ozseDq7nby2u9LqKJbypKBnAmW97pe7HjuNiNwgIvuBV+nppZ9BRJa7hmQKa2pqhpJXnQNjDB2OwU3vau10UFrXwhXnpY1QKqXOXWZ8JOOSo3lxe4XVUSzlyUlRd9MTzuiBG2NeAF4QkYuAXwBXuGnzMPAwQEFBgfbivaTiZBvrDtVQcqKVxrYubpufc8bWo/05WNWE08AV+VrQle8SEXJTonl3XzUPvH+YuMhQIPi21PWkh14O9N4sIQs41l9jY8xaYLyIJJ9jNjUMup2GVZtKOVTVzJjEKBKiwwa19ei+yiZiw0OYrvPIlY+bmRWPAXaUnbQ6imU8KeibgTwRyRWRMGAZ8HLvBiIyQVwTjUVkFhAG6HpcH7B6VyUnWjq54fxMPjN3DDecn0l9axdrDw485OVwOjlY1cSk9FhsNp1HrnxbUkw42QmR7CjXgt4vY4wDWAm8AewDnjHG7BGRFSKywtXsJmC3iGynZ0bMp3udJFUWMcZw/3tFpMSEk58xCoDxKTFMz4pjzcEaSk60nPX1xTUtdDicTE4f5Y24Sp2zGdnxVDa0U9XYbnUUS3g0D90Ys9oYM9EYM94Y80vXYw8aYx503b7HGDPFGDPTGDPfGLN+JEMrz7y7v5r9x5u4eFIKtl4rNZdMHY3NJvz6tf1nff3mo3VEhdmZmBYz0lGVGhbTMuOwSfAOuwT3OtkAdqp3npUQyYys+NOei4sMZcH4JF7fc5yi6ma3r29q72JfZSOzxiQE/XJq5T9iI0IZnxLDjvKTBOMggf6kBqg9xxrZWnqSLy3Kxe5m/HvB+GTCQ2w8uOaw29dvLanHaXB7wQqlfNnM7HjqW7sorWu1OorXaUEPUM9uKSfMbuOG891vWxsTHsKyOWN4cVsFFSdPv8Cz0xg2l9STmxxNSqxeMk75l/zRowi1C9uDcNhFC3oA6nQ4eWl7BVdOSSMuKrTfdssvGgfAX9YWn/Z4cU0LdS2d2jtXfik81M7k9FHsqmjweHpuoNDdFofI3S5wvrKI4d391dS3dnHzrLNfVCIjPpIbzs/kyU2lXHFeGovykmntcPD67koiQ+1MydDZLco/nZ8dz66KBt4Jsh0YtYcegJ7bWk5KbDgX5g28tusHiyczLjmaL/x9E09sKOHhdcVUN3VwS0EWoXoyVPmpvLRYRkWE8OSmc78gtT/Rn9gAU9vcwXv7q7nh/EyPZqekxIbz9PL5zMiK5ycv7uZkWxd3LMjRuefKr9ltQkFOIusO1VAWRCdHtaAHmBe2VuBwGm4aYLilt7ioUB6/cx53XTaBLy8ax7gUnXeu/N+cnEQEeCqIeula0ANIt9Pw9w+PMjc30ePNt06JDLPz3asmkZkQOULplPKuuMhQLpucyjOF5UFzclQLegDZW9lIxck27lyUa3UUpXzCrfPGUNvcwVt7q6yO4hVa0APIB0W1jEmM0r3LlXK5eGIqWQmRPLTmcFCsHNWCHiDK6loprWvlCwtz3K4MVSoY2W3CXZdNYEd5zxTGQKfz0APE+qJaIkJt3FKQPXBjpYLEkxtL6XYaEqPD+OlLuzne2I5NxGfWjAw37aEHgNrmDnZXNDA3J5GYcP0/Wqne7Dbh8smpVDa0s+dYo9VxRpQW9ACw5kANdpuwcIJeJEopd2Zkx5MSE87b+6rodgbuWLp25/xcfUsn28rqmTcuidiI/vdtUb7L3TYSanjZRLh6SjpPbCxhfVEtn58/1upII0J76H5uzaEaRISL8lKsjqKUT8vPGEX+6FG8s6+Ko7Vnv1qXv9KC7sca2rrYUlLP7DEJH1/lXCnVv+tnZGC3CT9+cVdATmPUIRc/tu5QDcYYLpo48r1zHRZQgSAuMpSrp6Tz8o5jfPeZHRT02iI6EGa+aEH3U03tXWw+WsfM7AQSo8OsjqOU35ibm8iuigZe2VXJuJSYs/789NeR8dXir0MuPsBpDJ2Owe018UHRCRzdhku80DtXKpDYRLh5VhZCz1bTzgAaetGCbiFjDPuPN/L7tw7yu7cOcLyh3aPX1bd0suHICaZlxZGsl4hTatASosO4bvpojtS28GFRrdVxho1HBV1EFovIAREpEpG73Tz/WRHZ6fr6UERmDH/UwOI0hic3lfLYRyWICO0OJ195vJD2ru4BX/u3D4/S6XByyaRULyRVKjDNGpPA5PRY3txbxYnmDqvjDIsBC7qI2IH7gSVAPvAZEcnv0+wIcLExZjrwC+Dh4Q4aaIqqm9lzrJGLJ6bwjcsn8KnZ2ewob+BHz5/97Htjexd/++AIUzJGkT4qwouJlQosIsLSmZnYbcIL2ysCYtaLJz30uUCRMabYGNMJrAKW9m5gjPnQGFPvursB8PzqCkHqw8O1xIaHcPl5qYTYbORnjOI7V07khW0VvLi9ot/XPf5RCU3tDu2dKzUM4iJDWTw1neKaFv5VWG51nHPmSUHPBMp63S93PdafO4HXziVUoCuuaeZgVTNzxyUSYvv3P8HKSycwIyuOX7+2n5YOxxmva+lw8Mi6Yi6dlEJmvF6IQqnhMCcnkZykKP7vq3up9fOhF08Kuru9WN3+biIil9JT0H/Yz/PLRaRQRApramo8TxlgHvuoBLtNmNtrDiyAzSb87Pp8qho7eHDN4TNe9+TGUupbu1h5WZ63oioV8GwifHJmJi2d3fzxnUNWxzknnhT0cqD3nqxZwLG+jURkOvAIsNQYc8LdGxljHjbGFBhjClJSgnO6XWN7F/8qLGN6ZpzbvVdmj01k6cwMHl5bTHn9vy9uW9nQxkNrD7NwQhKzxyZ4M7JSAS91VATL5mTz5MZSimuarY4zZJ4U9M1AnojkikgYsAx4uXcDERkDPA983hhzcPhjBo4Xt1XQ0tnN/PFJ/bb54eLJiMBXn9hKUXUT1Y3t3PqXjXR0OfnxNX3PRyulhsM3r8gjLMTGb984YHWUIRtwpagxxiEiK4E3ADvwqDFmj4iscD3/IPAzIAn4s4gAOIwxBSMX23+9sK2CyemxZCVE9dsmIz6SPyw7nx8+t5Nr/rielJhw6ls7efzOueRnjPJiWuUJ3RYhMKTGRvCVi8bz+7cPsqWkjtljEwd+kY/xaB66MWa1MWaiMWa8MeaXrscedBVzjDFfMsYkGGNmur60mLtRcqKFbaUnueH8s51T7nH1lHTe+vbFXD45lZOtnTx6xxy//AZTyp986cJckmPC+OM7RVZHGRLdy8WLXtx2DBH4xMwM3ts/8EnhlNhwHvjcbBzdTkLsuqhXqZEWHR7C5y/I4fdvH6So2v/G0rVKeIkxhhe3V3BBbhKj4wY35VCLuVLe89kLxhBmt/GPD49aHWXQtIfuJTvLGzhS28KKi8dZHUUp5UbvcyFTM+NYtbmUuxefR2SY3cJUg6NdPy95YVsFYSE2Fk8dbXUUpdQAFoxPoqvbUFhSZ3WUQdGC7gV1LZ08u6Wcq/LT9MpCSvmBjPhIcpOj+ejwCb/aXlcLuhfc/14RrZ0Ovnm5rvBUyl/MyUnkZFsXpSdaB27sI7Sgj7Dy+lYe/6iEm2ZlkZcWa3UcpZSHzkuPJcQm7DrWYHUUj+lJ0RH2+7cOgcC3r5zolc/TRS5KDY/wUDsT02LZU9HAtdNGYxN321r5Fu2hDwOnMRxvbOepTaW8vbeKhrYuyupa+Y+XdvP8tnJunz+WDN0dUSblK8wAAA5SSURBVCm/MzUzjsZ2h98Mu2gP/RwVVTfz9OZSWjr/faUhkZ4d3GwCt8zO4hs6dq6UX/p42KWigZzkaKvjDEgL+jlo7XDwry1lRIaFsHjqaFZeNoHjDe1sPNJzAedb543RnrlSfuzUsMvuYw1cO933h120oJ+Dl3Yco7Wjm9vn53w8zSk3OfqsOykGKx3bV/5qWmYceysbKTnRSq6P99K1oA/RjvKT7Kpo4Kr8tAF74VrMlPJfk9JjsQkcqmry+YKuJ0WHwNHt5PXdx8lKiOTCvOC8UIdSwSIi1E52QhRFfnDhCy3oQ/DO/moa2rq4ZGIKdptvj6kppc7d+NQYKurbaOs1+cEXaUEfgic2lBAXGcqkdL3YhFLBYEJKDAY47OO9dC3og1Rc08y6Q7XMyUnU3rlSQSI7MYqwEJvPF3Q9KTpI/9xYSqhdmJPj/xdq1pO1SnnGbhNyk6J9/qIXQVnQ+ytkt84bc9bXtXV286/CMhZPHU1shO6aqFQwmZAaw4GqJupbO62O0i8dchmEV3Yeo7HdwWcHKPxKqcAzPjUGgMM+3EvXgj4IT28uY1xKNPNy9WLNSgWbtNhwYsNDOKQF3f8dqmqisKSeZXOyER9f/quUGn4iQm5KNEdrWzA+etGLoBxDH4pVm8sItQs3zsry+mfryUulfENucjQ7yxs46qPbAHjUQxeRxSJyQESKRORuN89PFpGPRKRDRL43/DGt1eHo5vmt5VyZn0ZyTLjVcZRSFslN6inim46csDiJewMWdBGxA/cDS4B84DMikt+nWR3wDeB/hj2hD3hjTxX1rV0sm6MnQ5UKZimx4USH2dlY7JsXj/ZkyGUuUGSMKQYQkVXAUmDvqQbGmGqgWkSuHZGUFjLG8Mi6YsYkRrFoQrLVcVQfOhylvElEyEmOZuMR3yzongy5ZAJlve6Xux4bNBFZLiKFIlJYU1MzlLfwuvcP1LCzvIGvXzoem64MVSro5SZHU3GyjfJ637uKkScF3V0VG9IpXmPMw8aYAmNMQUqK7+9SaIzh3rcPkpUQacnJUKWU78lxjaNvPup7vXRPCno5kN3rfhZwbGTi+Jb3D9awo7yBr186gVC7zvBUSkF6XASxESE+OY7uyRj6ZiBPRHKBCmAZcOuIpvIB3U7DvW8fIjM+kpu81DvX8WClfJ9NhLk5iWzywXH0AbudxhgHsBJ4A9gHPGOM2SMiK0RkBYCIpItIOfAd4CciUi4ifr237H3vHmJH2Um+c+VEwkK0d66U+re5uYkU17ZQ3dRudZTTeLSwyBizGljd57EHe90+Ts9QTEBYd6iGP7xziBtnZXLjrCGd/1VKBbC5ru0/Nh2p47rpGRan+TddKdrLkxtLOdHcwQNrDpMSE870zHhd5q+UOsPUzDiiwuxa0H3ZvspG/rWlZ4bmrfPG6FCLUsqtULuN2WMTfG4cXSsW0N7Vzas7j/H4hhISo8NYeWkeqbERVsdSSvmwuTmJ7D/exEkf2h89qHvoxhgKj9bz5t7jtHZ2My83kWumjdYpikqpAfUeR79qSrrFaXoEbUHvdDh5dms5uysayEmK4trpGWTGR1odSynlJ2ZkxxMWYtOCbrWTrZ08vqGE4w3tLJmazqIJyXryUyk1KBGhdmZmx7PJh1aMBt3YQoejmyc2lFDX0slt83O4MC9Fi7lSakjm5Sayu6KB5g6H1VGAIOyh//q1/RxraOfzF4xlUnrssL63rvRUKrjMzU3kvneh8Ggdl0xKtTpOcPXQ395bxd8+OMr88UmcN9qvF7IqpXzA7LEJhNiEj4p944IXQdNDb2zv4gfP7SR/9CiWDOIEhva6lVL9iQoLYW5uIu/sq+ZHS86zOk7w9NAffP8wdS2d3HPTdEJ0WqJSaphclZ9GUXUzh2uarY4SHAX9eEM7j35whE/MyGBaVpzVcZRSAeRK12/8b+6psjhJkBT0e98+SLfT8P2rJ1kdRSkVYDLjI5mWGcebe49bHSXwC/qhqiaeKSzjcxeMJTsxyuo4SqkAdFV+GttKT1LdaO12ugFf0O95/QBRYSHcdVme1VGUUgHq6qmuYZe91g67BHRB33y0jrf3VbHi4nEkRodZHUcpFaDyUmPISYrSgj5SjDH8avU+UmPD+eKiXKvjKKUCmIiwZNpoPiiqpayu1bIcAVvQ39hTxdbSk3z7yolEhQXNdHullEVun5+DTeChtYctyxCQBb25w8EvV+9lfEo0t8wOmCvjKaV8WHpcBDfPzuKZwnLLTo4GZEH/5at7Ka9v49e6iEgp5UUrLh6Po9vJX9YVW/L5AVft3ttfzVObylh+4Tjm5CRaHUcpFUTGJkVz/YwM/rmxlLoW71/JKKAKelldKz98bieT0mL59pUTrY6jlApCX790Ah0OJ994ahudDqdXPztgCvreY43c+MCHtHd1c++ymUSE2q2OpJQKQhPTYvn1jdNYX1TLD5/bidNpvPbZHhV0EVksIgdEpEhE7nbzvIjIH13P7xSRWcMf1b1Oh5Nnt5Tz6Yc+IsQmPPvVBbo1rlLKUrcUZPO9qybywrYKvvfsDiob2rzyuQPO5xMRO3A/cCVQDmwWkZeNMXt7NVsC5Lm+5gEPuP4cdu1d3R/vbLavsonnt5ZT3dTBlIxR/OW2AjL0uqBKKR/w9Usn0NbVzUNrinllRyWfPD+DgrGJTEiLIS81htiI0GH/TE8maM8FiowxxQAisgpYCvQu6EuBx4wxBtggIvEiMtoYUzncgV/ffZxvPb0dAJvAorwUfntLLhdOSMZm00vJKaV8g4jw/asns2zOGB5Yc5jnt5bzTGE5AHcuyuWn1+UP+2d6UtAzgbJe98s5s/ftrk0mcFpBF5HlwHLX3WYROTCotG4cAR4//aFkoPZc39cC/pjbHzODf+b2x8zgn7kHzPzZc/yAn90DPxv6y8f294QnBd1dt7fvKL8nbTDGPAw87MFnDpmIFBpjCkbyM0aCP+b2x8zgn7n9MTP4Z25/zHyKJydFy4HsXvezgGNDaKOUUmoEeVLQNwN5IpIrImHAMuDlPm1eBm5zzXa5AGgYifFzpZRS/RtwyMUY4xCRlcAbgB141BizR0RWuJ5/EFgNXAMUAa3AF0Yu8oBGdEhnBPljbn/MDP6Z2x8zg3/m9sfMAEjPxBSllFL+LmBWiiqlVLDTgq6UUgHCLwu6iEwSke29vhpF5Ft92lwiIg292pzDtM/hISLfFpE9IrJbRJ4SkYg+z1u2hcLZeJDbF4/1N1159/T93nA976vHeqDcPnGsReRREakWkd29HksUkbdE5JDrz4R+XnvWrUR8NPNREdnlOuaF3so8aMYYv/6i50TtcWBsn8cvAV6xOl+vPJn0rIOKdN1/BrijT5trgNfomdd/AbDRT3L72rGeCuwGoug58f82kOcHx9qT3D5xrIGLgFnA7l6P/Qa423X7buAeN6+zA4eBcUAYsAPI9+XMrueOAslWH/eBvvyyh97H5cBhY0yJ1UE8EAJEikgIPT+0fefqf7yFgjFmAxAvIqO9HdKNgXL7mvOADcaYVmOMA1gD3NCnjS8ea09y+wRjzFqgrs/DS4F/uG7/A/ikm5d+vJWIMaYTOLWVyIg7h8x+IxAK+jLgqX6emy8iO0TkNRGZ4s1QfRljKoD/AUrp2RKhwRjzZp9m/W2hYBkPc4MPHWt6erkXiUiSiETR0xvP7tPG5441nuUG3zrWvaUZ1/oT15+pbtr42nH3JDP0rHx/U0S2uLYw8Ul+XdBdC50+AfzLzdNb6RmGmQHcB7zozWx9ucbmlgK5QAYQLSKf69vMzUstnVfqYW6fOtbGmH3APcBbwOv0/Frv6NPM5461h7l96lgPgc8ddw8tNMbMomdn2a+LyEVWB3LHrws6PQd3qzGmqu8TxphGY0yz6/ZqIFREkr0dsJcrgCPGmBpjTBfwPLCgTxtf3EJhwNw+eKwxxvzVGDPLGHMRPb9mH+rTxBeP9YC5ffFY91J1atjK9We1mza+dtw9yYwx5pjrz2rgBXqGjnyOvxf0z9DPcIuIpIuIuG7PpefvesKL2foqBS4QkShXrsuBfX3a+OIWCgPm9sFjjYikuv4cA9zImd8nvnisB8zti8e6l5eB2123bwdectPGk61EvGnAzCISLSKxp24DV9EzPOZ7rD4rO9Qvek7OnQDiej22Aljhur0S2EPPr60bgAU+kPm/gP30fDM8DoT3ySz0XEzkMLALKLA6s4e5ffFYr6Nnz/4dwOVuvj989VgPlNsnjjU9/9FUAl309LrvBJKAd+j5reIdINHVNgNY3eu11wAHXcf+x76emZ4ZOTtcX3u8mXmwX7r0XymlAoS/D7kopZRy0YKulFIBQgu6UkoFCC3oSikVILSgK6VUgNCCrlQvIvJzEbnC6hxKDYVOW1TKRUTsxphuq3MoNVTaQ1dBQURyRGS/iPzDtf/5s67Vr0dF5Gcish64RUT+LiI3u14zR0Q+dG2EtUlEYkXELiK/FZHNrvf5isV/NaU+pgVdBZNJwMPGmOlAI/A11+PtxphFxphVpxq6lqU/DXzT9GyEdQXQRs/qwgZjzBxgDvBlEcn15l9Cqf5oQVfBpMwY84Hr9hPAItftp920nQRUGmM2w8ebYjno2cfjNhHZDmykZ+l43sjGVsozIVYHUMqL+p4wOnW/xU1bcdP+1ON3GWPeGM5gSg0H7aGrYDJGROa7bn8GWH+WtvuBDBGZA+AaPw8B3gC+KiKhrscnunbgU8pyWtBVMNkH3C4iO4FE4IH+Gpqey6N9GrhPRHbQc9GJCOARenZD3Oq62PBD6G+6ykfotEUVFEQkh56LK0+1OIpSI0Z76EopFSC0h66UUgFCe+hKKRUgtKArpVSA0IKulFIBQgu6UkoFCC3oSikVIP4/N1J7REyu2DEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(modelling.price)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 80-20 Splitting "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "y=modelling[\"price\"]\n",
    "x=modelling.drop('price',axis=1)\n",
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2,random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"\n",
      "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  if __name__ == '__main__':\n",
      "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:10: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  # Remove the CWD from sys.path while we load stuff.\n"
     ]
    }
   ],
   "source": [
    "x_odomin=x_train[\"odometer\"].min()\n",
    "x_odomax=x_train[\"odometer\"].max()\n",
    "x_train[\"odometer\"]=((x_train[\"odometer\"]-x_odomin)/(x_odomax-x_odomin))\n",
    "\n",
    "x_test[\"odometer\"]=((x_test[\"odometer\"]-x_odomin)/(x_odomax-x_odomin))\n",
    "\n",
    "x_yearmin=x_train[\"year\"].min()\n",
    "x_yearmax=x_train[\"year\"].max()\n",
    "x_train[\"year\"]=((x_train[\"year\"]-x_yearmin)/(x_yearmax-x_yearmin))\n",
    "x_test[\"year\"]=((x_test[\"year\"]-x_yearmin)/(x_yearmax-x_yearmin))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ordinal Encoding -Condition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n",
      "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    }
   ],
   "source": [
    "condition_dict={\"salvage\":0,\"fair\":1,\"good\":2,\"excellent\":3,\"like new\":4}\n",
    "x_train[\"condition\"]=x_train.condition.map(condition_dict)\n",
    "x_test[\"condition\"]=x_test.condition.map(condition_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6     201107\n",
       "4      64043\n",
       "8      62270\n",
       "5       1866\n",
       "10      1202\n",
       "3        359\n",
       "12        70\n",
       "Name: cylinders, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train[\"cylinders\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n",
      "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    }
   ],
   "source": [
    "cylinder_dict={3:0,4:1,5:2,6:3,8:4,10:5,12:6}\n",
    "x_train[\"cylinders\"]=x_train.cylinders.map(cylinder_dict)\n",
    "x_test[\"cylinders\"]=x_test.cylinders.map(cylinder_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    201107\n",
       "1     64043\n",
       "4     62270\n",
       "2      1866\n",
       "5      1202\n",
       "0       359\n",
       "6        70\n",
       "Name: cylinders, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train[\"cylinders\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  after removing the cwd from sys.path.\n",
      "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n",
      "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  if __name__ == '__main__':\n"
     ]
    }
   ],
   "source": [
    "x_cylmin=x_train[\"cylinders\"].min()\n",
    "x_cylmax=x_train[\"cylinders\"].max()\n",
    "x_train[\"cylinders\"]=((x_train[\"cylinders\"]-x_cylmin)/(x_cylmax-x_cylmin))\n",
    "x_test[\"cylinders\"]=((x_test[\"cylinders\"]-x_cylmin)/(x_cylmax-x_cylmin))\n",
    "\n",
    "x_conmin=x_train[\"condition\"].min()\n",
    "x_conmax=x_train[\"condition\"].max()\n",
    "x_train[\"condition\"]=((x_train[\"condition\"]-x_conmin)/(x_conmax-x_conmin))\n",
    "x_test[\"condition\"]=((x_test[\"condition\"]-x_conmin)/(x_conmax-x_conmin))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>manufacturer</th>\n",
       "      <th>condition</th>\n",
       "      <th>cylinders</th>\n",
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       "      <th>odometer</th>\n",
       "      <th>title_status</th>\n",
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       "      <th>301757</th>\n",
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       "      <td>0.478862</td>\n",
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       "      <th>344251</th>\n",
       "      <td>0.716667</td>\n",
       "      <td>ford</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>diesel</td>\n",
       "      <td>0.592654</td>\n",
       "      <td>clean</td>\n",
       "      <td>automatic</td>\n",
       "      <td>4wd</td>\n",
       "      <td>pickup</td>\n",
       "      <td>blue</td>\n",
       "      <td>west</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>403805</th>\n",
       "      <td>0.850000</td>\n",
       "      <td>honda</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>gas</td>\n",
       "      <td>0.310706</td>\n",
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       "      <td>south</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.866667</td>\n",
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       "      <td>automatic</td>\n",
       "      <td>2wd</td>\n",
       "      <td>coupe</td>\n",
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       "    <tr>\n",
       "      <th>365838</th>\n",
       "      <td>0.783333</td>\n",
       "      <td>mercedes-benz</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>gas</td>\n",
       "      <td>0.492276</td>\n",
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       "      <td>automatic</td>\n",
       "      <td>4wd</td>\n",
       "      <td>sedan</td>\n",
       "      <td>black</td>\n",
       "      <td>northeast</td>\n",
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       "    <tr>\n",
       "      <th>131932</th>\n",
       "      <td>0.766667</td>\n",
       "      <td>chevrolet</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>gas</td>\n",
       "      <td>0.547425</td>\n",
       "      <td>clean</td>\n",
       "      <td>automatic</td>\n",
       "      <td>4wd</td>\n",
       "      <td>truck</td>\n",
       "      <td>grey</td>\n",
       "      <td>south</td>\n",
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       "      <th>146867</th>\n",
       "      <td>0.783333</td>\n",
       "      <td>chevrolet</td>\n",
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       "      <td>diesel</td>\n",
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       "      <td>red</td>\n",
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       "    <tr>\n",
       "      <th>121958</th>\n",
       "      <td>0.883333</td>\n",
       "      <td>nissan</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>gas</td>\n",
       "      <td>0.163893</td>\n",
       "      <td>clean</td>\n",
       "      <td>automatic</td>\n",
       "      <td>4wd</td>\n",
       "      <td>pickup</td>\n",
       "      <td>black</td>\n",
       "      <td>northeast</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>330917 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            year   manufacturer  condition  cylinders    fuel  odometer  \\\n",
       "301757  0.900000      chevrolet       0.75   0.166667     gas  0.208972   \n",
       "1334    0.783333          volvo       0.50   0.500000     gas  0.478862   \n",
       "276948  0.933333      chevrolet       0.25   0.500000     gas  0.489253   \n",
       "344251  0.716667           ford       0.00   0.500000  diesel  0.592654   \n",
       "403805  0.850000          honda       0.75   0.500000     gas  0.310706   \n",
       "...          ...            ...        ...        ...     ...       ...   \n",
       "259178  0.866667      chevrolet       1.00   0.500000     gas  0.086893   \n",
       "365838  0.783333  mercedes-benz       0.75   0.500000     gas  0.492276   \n",
       "131932  0.766667      chevrolet       0.75   0.666667     gas  0.547425   \n",
       "146867  0.783333      chevrolet       0.75   0.666667  diesel  0.873332   \n",
       "121958  0.883333         nissan       0.50   0.500000     gas  0.163893   \n",
       "\n",
       "       title_status transmission drive    type paint_color      state  \n",
       "301757        clean    automatic   2wd   sedan       brown  northeast  \n",
       "1334          clean    automatic   2wd   sedan      silver  northeast  \n",
       "276948        clean    automatic   4wd   truck       white      south  \n",
       "344251        clean    automatic   4wd  pickup        blue       west  \n",
       "403805        clean    automatic   4wd   sedan       white      south  \n",
       "...             ...          ...   ...     ...         ...        ...  \n",
       "259178        clean    automatic   2wd   coupe      orange       west  \n",
       "365838        clean    automatic   4wd   sedan       black  northeast  \n",
       "131932        clean    automatic   4wd   truck        grey      south  \n",
       "146867        clean    automatic   2wd  pickup         red      south  \n",
       "121958        clean    automatic   4wd  pickup       black  northeast  \n",
       "\n",
       "[330917 rows x 12 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### One Hot Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train=pd.get_dummies(x_train)\n",
    "x_test=pd.get_dummies(x_test)\n",
    "\n",
    "missing_cols=set(x_train.columns)-set(x_test.columns)\n",
    "for val in missing_cols:\n",
    "    x_test[val]=0\n",
    "    \n",
    "x_test=x_test[x_train.columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
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       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121958</th>\n",
       "      <td>0.883333</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.163893</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>330917 rows × 93 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            year  condition  cylinders  odometer  manufacturer_acura  \\\n",
       "301757  0.900000       0.75   0.166667  0.208972                   0   \n",
       "1334    0.783333       0.50   0.500000  0.478862                   0   \n",
       "276948  0.933333       0.25   0.500000  0.489253                   0   \n",
       "344251  0.716667       0.00   0.500000  0.592654                   0   \n",
       "403805  0.850000       0.75   0.500000  0.310706                   0   \n",
       "...          ...        ...        ...       ...                 ...   \n",
       "259178  0.866667       1.00   0.500000  0.086893                   0   \n",
       "365838  0.783333       0.75   0.500000  0.492276                   0   \n",
       "131932  0.766667       0.75   0.666667  0.547425                   0   \n",
       "146867  0.783333       0.75   0.666667  0.873332                   0   \n",
       "121958  0.883333       0.50   0.500000  0.163893                   0   \n",
       "\n",
       "        manufacturer_alfa-romeo  manufacturer_aston-martin  manufacturer_audi  \\\n",
       "301757                        0                          0                  0   \n",
       "1334                          0                          0                  0   \n",
       "276948                        0                          0                  0   \n",
       "344251                        0                          0                  0   \n",
       "403805                        0                          0                  0   \n",
       "...                         ...                        ...                ...   \n",
       "259178                        0                          0                  0   \n",
       "365838                        0                          0                  0   \n",
       "131932                        0                          0                  0   \n",
       "146867                        0                          0                  0   \n",
       "121958                        0                          0                  0   \n",
       "\n",
       "        manufacturer_bmw  manufacturer_buick  ...  paint_color_orange  \\\n",
       "301757                 0                   0  ...                   0   \n",
       "1334                   0                   0  ...                   0   \n",
       "276948                 0                   0  ...                   0   \n",
       "344251                 0                   0  ...                   0   \n",
       "403805                 0                   0  ...                   0   \n",
       "...                  ...                 ...  ...                 ...   \n",
       "259178                 0                   0  ...                   1   \n",
       "365838                 0                   0  ...                   0   \n",
       "131932                 0                   0  ...                   0   \n",
       "146867                 0                   0  ...                   0   \n",
       "121958                 0                   0  ...                   0   \n",
       "\n",
       "        paint_color_purple  paint_color_red  paint_color_silver  \\\n",
       "301757                   0                0                   0   \n",
       "1334                     0                0                   1   \n",
       "276948                   0                0                   0   \n",
       "344251                   0                0                   0   \n",
       "403805                   0                0                   0   \n",
       "...                    ...              ...                 ...   \n",
       "259178                   0                0                   0   \n",
       "365838                   0                0                   0   \n",
       "131932                   0                0                   0   \n",
       "146867                   0                1                   0   \n",
       "121958                   0                0                   0   \n",
       "\n",
       "        paint_color_white  paint_color_yellow  state_midwest  state_northeast  \\\n",
       "301757                  0                   0              0                1   \n",
       "1334                    0                   0              0                1   \n",
       "276948                  1                   0              0                0   \n",
       "344251                  0                   0              0                0   \n",
       "403805                  1                   0              0                0   \n",
       "...                   ...                 ...            ...              ...   \n",
       "259178                  0                   0              0                0   \n",
       "365838                  0                   0              0                1   \n",
       "131932                  0                   0              0                0   \n",
       "146867                  0                   0              0                0   \n",
       "121958                  0                   0              0                1   \n",
       "\n",
       "        state_south  state_west  \n",
       "301757            0           0  \n",
       "1334              0           0  \n",
       "276948            1           0  \n",
       "344251            0           1  \n",
       "403805            1           0  \n",
       "...             ...         ...  \n",
       "259178            0           1  \n",
       "365838            0           0  \n",
       "131932            1           0  \n",
       "146867            1           0  \n",
       "121958            0           0  \n",
       "\n",
       "[330917 rows x 93 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#x_train.to_csv(\"x_train.csv\",index=False)\n",
    "#x_test.to_csv(\"x_test.csv\",index=False)\n",
    "#y_train.to_csv(\"y_train.csv\",index=False)\n",
    "#y_test.to_csv(\"y_test.csv\",index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Metrics Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "# storing the model metrics (r2, rmse) for further anaysis and comparing the model\n",
    "acc_train_r2 = []\n",
    "acc_test_r2 = []\n",
    "acc_train_rmse = []\n",
    "acc_test_rmse = []"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modelling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gradient Boosting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_GB(est,max_depth,lr):\n",
    "    params={'n_estimators': est, 'max_depth': max_depth,'learning_rate': lr}\n",
    "    gb=ensemble.GradientBoostingRegressor(**params)\n",
    "    gb_model=gb.fit(x_train,y_train)\n",
    "    y_pred=gb_model.predict(x_test)\n",
    "    ybar=np.sum(y_test)/len(y_test)\n",
    "    sse = np.sum((y_test-y_pred)**2)  \n",
    "    sst = np.sum((y_test - ybar)**2) \n",
    "    rsq=1-(sse/sst)\n",
    "    print(\"{}---> NEstimators{}-MaxDepth{}-LearningRate{}\".format(rsq,est,max_depth,lr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8092529322840317---> NEstimators450-MaxDepth7-LearningRate0.05\n",
      "0.8207360990590165---> NEstimators450-MaxDepth7-LearningRate0.1\n",
      "0.816711536361173---> NEstimators450-MaxDepth8-LearningRate0.05\n",
      "0.8273520458807018---> NEstimators450-MaxDepth8-LearningRate0.1\n",
      "0.8108780709352013---> NEstimators500-MaxDepth7-LearningRate0.05\n",
      "0.8221776016721983---> NEstimators500-MaxDepth7-LearningRate0.1\n",
      "0.8185114459677675---> NEstimators500-MaxDepth8-LearningRate0.05\n",
      "0.8288798612708208---> NEstimators500-MaxDepth8-LearningRate0.1\n"
     ]
    }
   ],
   "source": [
    "for n_est in range(450,550,50):\n",
    "    for max_depth in range(7,9,1):\n",
    "        for lr in [0.05,0.1]:\n",
    "            train_GB(n_est,max_depth,lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "params={'n_estimators': 500, 'max_depth': 8,'learning_rate': 0.1}\n",
    "gb=ensemble.GradientBoostingRegressor(**params)\n",
    "gb_model=gb.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature Importances\n",
      "manufacturer>>0.0012660307000198529\n",
      "fuel>>0.016003921848824106\n",
      "title_status>>0.0021027656267992765\n",
      "transmission>>0.0017003056076721687\n",
      "drive>>0.004369806081954241\n",
      "type>>0.00605464144306396\n",
      "paint_color>>0.0004606464668676538\n",
      "state>>0.0027901451614326367\n",
      "Year>>0.5309603546775253\n",
      "Condition>>0.014666056723569042\n",
      "Cylinders>>0.08563466218260139\n",
      "Odometer>>0.10125326986821646\n"
     ]
    }
   ],
   "source": [
    "features=pd.DataFrame(gb_model.feature_importances_,index=x_train.columns)\n",
    "features[\"columns\"]=features.index\n",
    "features.index=range(0,len(features))\n",
    "features.columns=[\"value\",\"columns\"]\n",
    "\n",
    "print(\"Feature Importances\")\n",
    "importances = []\n",
    "feature_list=[]\n",
    "categorical_columns=['manufacturer','fuel','title_status','transmission','drive','type','paint_color','state']\n",
    "for val in categorical_columns:\n",
    "    p=(features[features[\"columns\"].str.startswith(val)][\"value\"].sum())/len(features[features[\"columns\"].str.startswith(val)][\"value\"])\n",
    "    print(\"{}>>{}\".format(val,p))\n",
    "    importances.append(p)\n",
    "    feature_list.append(val)\n",
    "\n",
    "for index,row in features.iterrows():\n",
    "    if(row[\"columns\"]==\"odometer\"):\n",
    "        print(\"Odometer>>{}\".format(row[\"value\"]))\n",
    "        importances.append(row[\"value\"])\n",
    "        feature_list.append(\"odometer\")\n",
    "    if(row[\"columns\"]==\"year\"):\n",
    "        print(\"Year>>{}\".format(row[\"value\"]))\n",
    "        importances.append(row[\"value\"])\n",
    "        feature_list.append(\"year\")\n",
    "    if(row[\"columns\"]==\"cylinders\"):\n",
    "        print(\"Cylinders>>{}\".format(row[\"value\"]))\n",
    "        importances.append(row[\"value\"])\n",
    "        feature_list.append(\"cylinders\")\n",
    "    if(row[\"columns\"]==\"condition\"):\n",
    "        print(\"Condition>>{}\".format(row[\"value\"]))\n",
    "        importances.append(row[\"value\"])\n",
    "        feature_list.append(\"condition\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "# Set the style\n",
    "plt.style.use('fivethirtyeight')\n",
    "# list of x locations for plotting\n",
    "x_values = list(range(len(importances)))\n",
    "# Make a bar chart\n",
    "plt.bar(x_values, importances, orientation = 'vertical')\n",
    "# Tick labels for x axis\n",
    "plt.xticks(x_values, feature_list, rotation='vertical')\n",
    "# Axis labels and title\n",
    "plt.ylabel('Importance'); plt.xlabel('Variable'); plt.title('Variable Importances');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rmse (Test)=0.30273570188345894\n",
      "r2 (Test)=0.8288649943613691\n"
     ]
    }
   ],
   "source": [
    "y_pred=gb_model.predict(x_test)\n",
    "mse = mean_squared_error(y_test,y_pred)\n",
    "rmse=sqrt(mse)\n",
    "rmse\n",
    "print(\"rmse (Test)={}\".format(rmse))\n",
    "\n",
    "acc_test_rmse.insert(0,rmse)\n",
    "ybar=np.sum(y_test)/len(y_test)\n",
    "sse = np.sum((y_test-y_pred)**2)  \n",
    "sst = np.sum((y_test - ybar)**2) \n",
    "rsq=1-(sse/sst)\n",
    "print(\"r2 (Test)={}\".format(rsq))\n",
    "acc_test_r2.insert(0,rsq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rmse (Train)=0.2754188598023748\n",
      "r2 (Train)=0.8585248593271049\n"
     ]
    }
   ],
   "source": [
    "y_predtr=gb_model.predict(x_train)\n",
    "\n",
    "mse = mean_squared_error(y_train,y_predtr)\n",
    "rmse=sqrt(mse)\n",
    "rmse\n",
    "print(\"rmse (Train)={}\".format(rmse))\n",
    "acc_train_rmse.insert(0,rmse)\n",
    "ybar=np.sum(y_train)/len(y_train)\n",
    "sse = np.sum((y_train-y_predtr)**2)  \n",
    "sst = np.sum((y_train - ybar)**2) \n",
    "rsq=1-(sse/sst)\n",
    "print(\"r2 (Train)={}\".format(rsq))\n",
    "\n",
    "acc_train_r2.insert(0,rsq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pickle.dump(gb_model, open('GradBReg.pkl','wb'))\n",
    "#gb_model=pickle.load(open('GradBReg.pkl', 'rb'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8500994886849162>>>>>N Estimators-350\n",
      "0.8501604547156063>>>>>N Estimators-400\n",
      "0.8501995623326741>>>>>N Estimators-450\n",
      "0.8502348127281153>>>>>N Estimators-500\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "def train_RF(est):\n",
    "    regressor = RandomForestRegressor(n_estimators=est,random_state=0,n_jobs=-1)\n",
    "    regressor.fit(x_train,y_train)\n",
    "    print('{}>>>>>N Estimators-{}'.format(regressor.score(x_test, y_test),est))\n",
    "    \n",
    "for est in range(350,550,50):\n",
    "    train_RF(est)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n",
       "                      max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "                      max_samples=None, min_impurity_decrease=0.0,\n",
       "                      min_impurity_split=None, min_samples_leaf=1,\n",
       "                      min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                      n_estimators=500, n_jobs=-1, oob_score=False,\n",
       "                      random_state=0, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor = RandomForestRegressor(n_estimators=500, random_state=0,n_jobs=-1)\n",
    "regressor.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature Importances\n",
      "manufacturer>>0.0018091284361750574\n",
      "fuel>>0.013667627585434623\n",
      "title_status>>0.002291018228181505\n",
      "transmission>>0.002570892847682676\n",
      "drive>>0.003574416695188176\n",
      "type>>0.005710921737854474\n",
      "paint_color>>0.0021615295256521908\n",
      "state>>0.005828211836896983\n",
      "Year>>0.47216448921659326\n",
      "Condition>>0.019108417832917064\n",
      "Cylinders>>0.07728242357750649\n",
      "Odometer>>0.12578859104403878\n"
     ]
    }
   ],
   "source": [
    "features=pd.DataFrame(regressor.feature_importances_,index=x_train.columns)\n",
    "features[\"columns\"]=features.index\n",
    "features.index=range(0,len(features))\n",
    "features.columns=[\"value\",\"columns\"]\n",
    "\n",
    "print(\"Feature Importances\")\n",
    "importances = []\n",
    "feature_list=[]\n",
    "categorical_columns=['manufacturer','fuel','title_status','transmission','drive','type','paint_color','state']\n",
    "for val in categorical_columns:\n",
    "    p=(features[features[\"columns\"].str.startswith(val)][\"value\"].sum())/len(features[features[\"columns\"].str.startswith(val)][\"value\"])\n",
    "    print(\"{}>>{}\".format(val,p))\n",
    "    importances.append(p)\n",
    "    feature_list.append(val)\n",
    "\n",
    "for index,row in features.iterrows():\n",
    "    if(row[\"columns\"]==\"odometer\"):\n",
    "        print(\"Odometer>>{}\".format(row[\"value\"]))\n",
    "        importances.append(row[\"value\"])\n",
    "        feature_list.append(\"odometer\")\n",
    "    if(row[\"columns\"]==\"year\"):\n",
    "        print(\"Year>>{}\".format(row[\"value\"]))\n",
    "        importances.append(row[\"value\"])\n",
    "        feature_list.append(\"year\")\n",
    "    if(row[\"columns\"]==\"cylinders\"):\n",
    "        print(\"Cylinders>>{}\".format(row[\"value\"]))\n",
    "        importances.append(row[\"value\"])\n",
    "        feature_list.append(\"cylinders\")\n",
    "    if(row[\"columns\"]==\"condition\"):\n",
    "        print(\"Condition>>{}\".format(row[\"value\"]))\n",
    "        importances.append(row[\"value\"])\n",
    "        feature_list.append(\"condition\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "# Set the style\n",
    "plt.style.use('fivethirtyeight')\n",
    "# list of x locations for plotting\n",
    "x_values = list(range(len(importances)))\n",
    "# Make a bar chart\n",
    "plt.bar(x_values, importances, orientation = 'vertical')\n",
    "# Tick labels for x axis\n",
    "plt.xticks(x_values, feature_list, rotation='vertical')\n",
    "# Axis labels and title\n",
    "plt.ylabel('Importance'); plt.xlabel('Variable'); plt.title('Variable Importances');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rmse (Test): 0.2832041746470592\n",
      "r2 (Test):0.8502348127281153\n",
      "rmse (Train): 0.1371690630102667\n",
      "r2 (Train):0.9649082419438797\n"
     ]
    }
   ],
   "source": [
    "y_pred_test = regressor.predict(x_test)\n",
    "y_pred_train = regressor.predict(x_train)\n",
    "\n",
    "# Metrics\n",
    "from sklearn import metrics\n",
    "\n",
    "print('rmse (Test):', np.sqrt(metrics.mean_squared_error(y_test, y_pred_test)))\n",
    "print(\"r2 (Test):{}\".format(regressor.score(x_test, y_test)))\n",
    "\n",
    "print('rmse (Train):', np.sqrt(metrics.mean_squared_error(y_train, y_pred_train)))\n",
    "print(\"r2 (Train):{}\".format(regressor.score(x_train, y_train)))\n",
    "\n",
    "acc_train_rmse.insert(1, np.sqrt(metrics.mean_squared_error(y_train, y_pred_train)))\n",
    "acc_test_rmse.insert(1, np.sqrt(metrics.mean_squared_error(y_test, y_pred_test)))\n",
    "acc_test_r2.insert(1,regressor.score(x_test, y_test))\n",
    "acc_train_r2.insert(1,regressor.score(x_train, y_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pickle.dump(regressor, open('RandomFReg.pkl','wb'))\n",
    "#regressor=pickle.load(open('RandomFReg.pkl', 'rb'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ridge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The best value of Alpha is:  {'alpha': 1}\n"
     ]
    }
   ],
   "source": [
    "import sklearn.model_selection as GridSearchCV\n",
    "from sklearn.linear_model import Ridge\n",
    "\n",
    "ridge=Ridge()\n",
    "parameters= {'alpha':[x for x in range(1,101)]}\n",
    "\n",
    "ridge_reg=ms.GridSearchCV(ridge, param_grid=parameters, scoring='neg_mean_squared_error', cv=15)\n",
    "ridge_reg.fit(x_train,y_train)\n",
    "print(\"The best value of Alpha is: \",ridge_reg.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ridge(alpha=1, copy_X=True, fit_intercept=True, max_iter=None, normalize=False,\n",
       "      random_state=None, solver='auto', tol=0.001)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import Ridge\n",
    "ridge_mod=Ridge(alpha=1)\n",
    "ridge_mod.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rmse (Test) = 0.4335325696890911\n",
      "r2 (Test):0.6490420925029677\n",
      "rmse (Train) = 0.4305915513560154\n",
      "r2 (Train):0.6542009539081212\n"
     ]
    }
   ],
   "source": [
    "y_pred_train_ridge=ridge_mod.predict(x_train)\n",
    "y_pred_test_ridge=ridge_mod.predict(x_test)\n",
    "\n",
    "print('rmse (Test) = ' + str(math.sqrt(sklm.mean_squared_error(y_test, y_pred_test_ridge)))) \n",
    "print(\"r2 (Test):{}\".format(ridge_mod.score(x_test, y_test)))\n",
    "print('rmse (Train) = ' + str(math.sqrt(sklm.mean_squared_error(y_train, y_pred_train_ridge))))\n",
    "print(\"r2 (Train):{}\".format(ridge_mod.score(x_train, y_train)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "acc_train_rmse.insert(2, math.sqrt(sklm.mean_squared_error(y_train, y_pred_train_ridge)))\n",
    "acc_test_rmse.insert(2, math.sqrt(sklm.mean_squared_error(y_test, y_pred_test_ridge)))\n",
    "acc_test_r2.insert(2,ridge_mod.score(x_test, y_test))\n",
    "acc_train_r2.insert(2,ridge_mod.score(x_train, y_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pickle.dump(ridge_mod, open('RidgeReg.pkl','wb'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lasso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6639.003702260219, tolerance: 16.565548711343745\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6637.244678750161, tolerance: 16.569791276616424\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6625.531320993701, tolerance: 16.55823379906485\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6612.476895029737, tolerance: 16.56670949673165\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6623.073695118761, tolerance: 16.560585021554598\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6606.031652677706, tolerance: 16.538092072736887\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6616.513513106576, tolerance: 16.557882946182964\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6630.910994267444, tolerance: 16.564845205853885\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6630.634563281397, tolerance: 16.554316670773602\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6596.107073518069, tolerance: 16.56046668323337\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6616.078281964772, tolerance: 16.555615886789546\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6603.134885146803, tolerance: 16.567455894951117\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6604.538894087564, tolerance: 16.56487809745034\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6623.983910764175, tolerance: 16.55464785242183\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6622.065262062562, tolerance: 16.562432262594907\n",
      "  positive)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The best value of Alpha is:  {'alpha': 1e-08}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7092.086698821655, tolerance: 17.74296909614564\n",
      "  positive)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import Lasso\n",
    "parameters= {'alpha':[1e-8,0.0001,0.0009,0.001,0.002,0.003,0.01,0.1,1,10,100]}\n",
    "lasso=Lasso()\n",
    "lasso_reg=ms.GridSearchCV(lasso, param_grid=parameters, scoring='neg_mean_squared_error', cv=15)\n",
    "lasso_reg.fit(x_train,y_train)\n",
    "print('The best value of Alpha is: ',lasso_reg.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7092.086698821655, tolerance: 17.74296909614564\n",
      "  positive)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Lasso(alpha=1e-08, copy_X=True, fit_intercept=True, max_iter=1000,\n",
       "      normalize=False, positive=False, precompute=False, random_state=None,\n",
       "      selection='cyclic', tol=0.0001, warm_start=False)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import Lasso\n",
    "lasso_mod=Lasso(alpha=1e-8)\n",
    "lasso_mod.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rmse (Test): 0.4335341223548044\n",
      "r2 (Test):0.6490395786374653\n",
      "rmse (Train): 0.4305865700995712\n",
      "r2 (Train):0.6542089545470466\n"
     ]
    }
   ],
   "source": [
    "y_lasso_train=lasso_mod.predict(x_train)\n",
    "y_lasso_test=lasso_mod.predict(x_test)\n",
    "\n",
    "print('rmse (Test): ' + str(math.sqrt(sklm.mean_squared_error(y_test, y_lasso_test))))\n",
    "print(\"r2 (Test):{}\".format(lasso_mod.score(x_test, y_test)))\n",
    "print('rmse (Train): ' + str(math.sqrt(sklm.mean_squared_error(y_train, y_lasso_train))))\n",
    "print(\"r2 (Train):{}\".format(lasso_mod.score(x_train, y_train)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "acc_train_rmse.insert(3, math.sqrt(sklm.mean_squared_error(y_train, y_lasso_train)))\n",
    "acc_test_rmse.insert(3, math.sqrt(sklm.mean_squared_error(y_test, y_lasso_test)))\n",
    "acc_test_r2.insert(3,lasso_mod.score(x_test, y_test))\n",
    "acc_train_r2.insert(3,lasso_mod.score(x_train, y_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pickle.dump(lasso_mod, open('LassoReg.pkl','wb'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bagging Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import BaggingRegressor\n",
    "# train the bagging regressor model\n",
    "bagging = BaggingRegressor()\n",
    "bag_mod = bagging.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rmse (Test):0.2940810046003759\n",
      "r2 (Test):0.8385100430842275\n",
      "rmse (Train): 0.15144645079966337\n",
      "r2 (Train):0.9572229351367784\n"
     ]
    }
   ],
   "source": [
    "bag_test = bag_mod.predict(x_test)\n",
    "bag_train = bag_mod.predict(x_train)\n",
    "\n",
    "print('rmse (Test):' + str(math.sqrt(sklm.mean_squared_error(y_test, bag_test))))\n",
    "print(\"r2 (Test):{}\".format(bag_mod.score(x_test, y_test)))\n",
    "print('rmse (Train): ' + str(math.sqrt(sklm.mean_squared_error(y_train, bag_train))))\n",
    "print(\"r2 (Train):{}\".format(bag_mod.score(x_train, y_train)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "acc_train_rmse.insert(4, math.sqrt(sklm.mean_squared_error(y_train, bag_train)))\n",
    "acc_test_rmse.insert(4, math.sqrt(sklm.mean_squared_error(y_test, bag_test)))\n",
    "acc_test_r2.insert(4,bag_mod.score(x_test, y_test))\n",
    "acc_train_r2.insert(4,bag_mod.score(x_train, y_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pickle.dump(bag_mod, open('BagReg.pkl','wb'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Averaging the Regressors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Averaging the regressor models\n",
    "\n",
    "#GradientBoosting Regressor \n",
    "gb_test = gb_model.predict(x_test)\n",
    "gb_train = gb_model.predict(x_train)\n",
    "\n",
    "#RandomForest Regressor \n",
    "rf_test = regressor.predict(x_test)\n",
    "rf_train = regressor.predict(x_train)\n",
    "\n",
    "#Bagging Regressor \n",
    "bag_test = bag_mod.predict(x_test)\n",
    "bag_train = bag_mod.predict(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rmse (Test):0.27892300351594695\n",
      "r2 (Test)=0.8547285610311268\n",
      "rmse (Train):0.16850743780029537\n",
      "r2 (Train):0.9470420783753455\n"
     ]
    }
   ],
   "source": [
    "final_test=(0.4*rf_test+0.3*gb_test+0.3*bag_test )\n",
    "final_train = (0.4*rf_train+0.3*gb_train +0.3*bag_train)\n",
    "print('rmse (Test):' + str(math.sqrt(sklm.mean_squared_error(y_test, final_test))))\n",
    "acc_test_rmse.insert(5,math.sqrt(sklm.mean_squared_error(y_test, final_test)))\n",
    "ybar=np.sum(y_test)/len(y_test)\n",
    "sse = np.sum((y_test-final_test)**2)  \n",
    "sst = np.sum((y_test - ybar)**2) \n",
    "rsq_test=1-(sse/sst)\n",
    "print(\"r2 (Test)={}\".format(rsq_test))\n",
    "acc_test_r2.insert(5,rsq_test)\n",
    "\n",
    "print('rmse (Train):' + str(math.sqrt(sklm.mean_squared_error(y_train, final_train))))\n",
    "acc_train_rmse.insert(5,math.sqrt(sklm.mean_squared_error(y_train, final_train)))\n",
    "ybar=np.sum(y_train)/len(y_train)\n",
    "sse = np.sum((y_train-final_train)**2)  \n",
    "sst = np.sum((y_train - ybar)**2) \n",
    "rsq_train=1-(sse/sst)\n",
    "print(\"r2 (Train):{}\".format(rsq_train))\n",
    "acc_train_r2.insert(5,rsq_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "acc_test_rmse =[0.30273570188345894,\n",
    "0.2832041746470592,\n",
    "0.4335325696890911,\n",
    "0.4335341223548044,\n",
    "0.2940810046003759,\n",
    "0.27892300351594695]\n",
    "\n",
    "acc_test_r2 =[0.8288649943613691,\n",
    "0.8502348127281153,\n",
    "0.6490420925029677,\n",
    "0.6490395786374653,\n",
    "0.8385100430842275,\n",
    "0.8547285610311268]\n",
    "\n",
    "acc_train_rmse =[0.2754188598023748,\n",
    "0.1371690630102667,\n",
    "0.4305915513560154,\n",
    "0.4305865700995712,\n",
    "0.15144645079966337,\n",
    "0.16850743780029537]\n",
    "\n",
    "acc_train_r2 =[0.8585248593271049,\n",
    "0.9649082419438797,\n",
    "0.6542009539081212,\n",
    "0.6542089545470466,\n",
    "0.9572229351367784,\n",
    "0.9470420783753455]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "models = pd.DataFrame({\n",
    "    'Model': ['Gradient Boosting', 'Random Forest', \n",
    "              'Ridge','Lasso', 'BaggingRegressor', \n",
    "              'Averaging the Regressors'],\n",
    "    \n",
    "    'r2_train': acc_train_r2,\n",
    "    'r2_test': acc_test_r2,\n",
    "    'rmse_train': acc_train_rmse,\n",
    "    'rmse_test': acc_test_rmse\n",
    "                     })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.options.display.float_format = '{:,.2f}'.format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[25,6])\n",
    "xx = models['Model']\n",
    "plt.tick_params(labelsize=14)\n",
    "plt.plot(xx, models['r2_train'], label = 'r2_train')\n",
    "plt.plot(xx, models['r2_test'], label = 'r2_test')\n",
    "plt.legend()\n",
    "plt.title('R2-criterion')\n",
    "plt.xlabel('Models')\n",
    "plt.ylabel('R2-criterion, %')\n",
    "plt.xticks(xx, rotation='vertical')\n",
    "plt.savefig('graph.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[25,6])\n",
    "xx = models['Model']\n",
    "plt.tick_params(labelsize=14)\n",
    "plt.plot(xx, models['rmse_train'], label = 'rmse_train')\n",
    "plt.plot(xx, models['rmse_test'], label = 'rmse_test')\n",
    "plt.legend()\n",
    "plt.title('RMSE-criterion')\n",
    "plt.xlabel('Models')\n",
    "plt.ylabel('RMSE-criterion, %')\n",
    "plt.xticks(xx, rotation='vertical')\n",
    "plt.savefig('graph.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "rmse_test_scores = models[['Model','rmse_test']].set_index('Model')['rmse_test'].to_dict()\n",
    "rmse_train_scores = models[['Model','rmse_train']].set_index('Model')['rmse_train'].to_dict()\n",
    "\n",
    "r2_train_scores = models[['Model','r2_train']].set_index('Model')['r2_train'].to_dict()\n",
    "r2_test_scores = models[['Model','r2_test']].set_index('Model')['r2_test'].to_dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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de++9mjFjRq0bpPfff788PT21fft25eTkqKqqSuHh4erTp48mTJigcePGXXTuwOX2wb5CTfz8mBq0ioLBtR7dtbeWzXlBtxisOXEpmvK1DgAA4M5YdLseLLoNAADgfqpr7Br6zyP67ljZJfd1VaCXtvzmakUH8PwO4G6+PVqqhH8eUbXt/G3P57/7huu1QW3O3xAAAACmcXb/3eOXjQEAAAB3ND89r1GKFZL0c7FV/73hZKP0BaDxWG12PfDl8UYpVkjSX9JytfXkeaaPAwAAgNugYAEAAAC3V2Oz6/UdeY3a54cHCpVdarBgNwCX+ORgsQ4VVTdqn4392QEAAIDLhzHwAAAAcHufHylp9JuYVpv0Znqu/qdfRKP2C/MxyW3zcTmKC8sPFGnujW0U4cfPXwAAAHfHNzYAAAC4vQU78y9Lvy9vydXLW3IvS98A3ENVjV2L9xTovylOAgAAuD2mhAIAAIBbs9ntWvdzqavTANCEfZXFZwgAAEBTQMECAAAAbq2o0qZyK3P+ALh42WWsVwMAANAUULAAAACAWyuutrk6BQBNXFElnyMAAABNAQULAAAAuLUAb76yArg0gT58jgAAADQFfGsDAACAWwv28ZCPp8XVaQBowqL8PV2dAgAAABqAggUAAADcmqeHRYOu8Hd1GgCasJuuDHB1CgAAAGgAL1cnAAAAAJzP5J6hWvdzaaP3+0iPEM3oG97o/cJ8jMFpHu779Ki2n6ps1D49LdKkHiGN2icAAAAuDwoWAAAAcHt3XxOoaH8vZZdZG61Pi6SZv4pQx2CfRusTwKWZ0TdCEz4/1qh93n1NoK5s7d2ofQIAAODyYEooAAAAuD1vT4umxIU2ap93Xh1IsQJwM6M7nSlONqbpvcMatT8AAABcPhQsAAAA0CT8V99w9Yxo1Sh9hfl6au5NbRqlLwCNp5WXhxYOadtoU3w92D1EN7Vj/QoAAICmgoIFAAAAmgR/bw99eld7tWt9aU9f+3patObOq3Q1oysAt3TnNYH6eyMUFBPaByh5cNtGyAgAAABmoWABAACAJqNdoLdS77rqop++Dvf11LpRHTTwCv9GzQtA4/p9n3AtHNJWnpcw1GLujW3kcykdAG4T8gEAACAASURBVAAAwHQULAAAANCkrPu5TPYL3MfTIo3pHKSt42J0PcUKoEl4pGeo1o/uqOvb+l3U/gt25TdyRgAAALjcKFgAAACgybDZ7UpKz2tw+7YBXnpuQISOTOqsFcPbscg20MTccKW/Nt0Xox3/52pN7hmqAO+Gj5hYvKdAeRU1lzE7AAAANLZLmwAYAAAAMNFXR0r1n8Jqw/jvugZrQtdg+Xt5KNLfS1cHe8vDwpQwQFPXK9JXC4a01es3RyuzoEp5FTWqttn1xeES/Tktt959yqx2LdiZp6f7R5qcLQAAAC4WBQsAAAA0GfN3Go+u8LBIL/w6Su2DvE3MCICZfDwt6hbeyvG6f7SfFu3OV36lrd72b+zI14z4cLXyYnIBAACApoBvbQAAAGgSDhdWKfVgiWF8ZEwgxQqghQnw9lBiXKhhPLvMqv+7v8jEjAAAAHApKFgAAACgSVi4K9/pYttTexnftATQfD3aK0zeTn7Zzt2eK7vd2acHAAAA3AUFCwAAALi9CqtNb+8pMIx3DvHR0PYBJmYEwF1c0dpb42ODDeO7TldqbVapiRkBAADgYlGwAAAAgNv76KcinS6vMYxPiQtlcW2gBXsyPtxpfM62+hfmBgAAgHuhYAEAAAC3l5Sebxjz87Lod91CTMwGgLuJi/RVgpNRVl8cKdXu0xUmZgQAAICLQcECAAAAbm1bTrl+yC43jP+fa4MV6utpYkYA3NGM84yymLstz6RMAAAAcLEoWAAAAMCtzXcyukKSpsax2DYA6dYOAeoR3sow/sH+QmWXWk3MCAAAABeKggUAAADcVn5FjZbtKzSM/7qtn/pE+ZmYEQB3ZbFYnK5lUVVj15vpjLIAAABwZxQsAAAA4LYW7y1QRY3dMD6tV5iJ2QBwd+NjgxTt72UYT96Zr9Jqm4kZAQAA4EJQsAAAAIBbstntSt5pPB1UpJ+nRncKNDEjAO6ulZeHHu1tPE1cXkWNluwtMDEjAAAAXAgKFgAAAHBLXx0pVWZBlWH8oR4hauXF11kAtSX2DJWfl8Uw/rfteaqxGY/cAgAAgOvwCw8AAABuaf5O47nmPSzS5J4stg2grnA/Lz3QLcQwnllQpU8OFpuYEQAAABqKggUAAADczuHCKqUeLDGMj4hprQ5BPiZmBKApeSI+XMZjLKQ523JNywUAAAANR8ECAAAAbmfhrnw5m7CFxbYBONMpxEd3XWO8xs3G4+Xakl1uYkYAAABoCAoWAAAAcCsVVpve3mO8KG6nEB8NbR9gYkYAmqIZ8eFO43PSGGUBAADgbihYAAAAwK2s/KlIp8trDONT40LlYXE22QsASAOv8FP/Nr6G8ZWZRTpcWGViRgAAADgfChYAAABwK/PT8w1jfl4W/c7JYroAcJbFYtGMvsajLGx2ad6OPBMzAgAAwPlQsAAAAIDb2JZTrh+czCs/PjZYob6eJmYEoCm7t1OQOgR6G8bf3l2gggrjEV0AAAAwFwULAAAAuI0kJ6MrJGlar1CTMgHQHHh5WPRYnzDDeEm1TW/tdv65AwAAAPNQsAAAAIBbyK+o0bL9hYbxX7f1U58oPxMzAtAcTOoeoiAf45++r+/IU3WN3cSMAAAAYISCBQAAANzCe3sLVG41vmk4NY7RFQAuXFArTz3S0/jz42iJVSt+KjIxIwAAABihYAEAAACXs9ntStppPC1LpJ+nxnQOMjEjAM3J73uHycvJr985abmy2xllAQAA4GoULAAAAOByXx0pVWZBlWH8oR4hauXsbiMAOHFVoLfGOil6bj9VoW+PlpmYEQAAAOrDrz4AAAC4XNLOPMOYh0Wa7GQ6FwBoiBl9w53G52zLNSkTAAAAGKFgAQAAAJc6UlSl1EMlhvERMa3VIcjHxIwANEfxUX66uZ2/YTz1UIn25VWamBEAAAB+iYIFAAAAXGrBznzZnEwdPzUuzLxkADRrM+Kdj7L4G6MsAAAAXIqCBQAAAFym0mrT23sKDOOdQnyU0CHAxIwANGfDYlorNtR4xNY/Mgp1qsxqYkYAAAA4FwULAAAAuMxHPxXpdHmNYXxqXKg8LBYTMwLQnHlYLHrSySiLihq7knbmm5gRAAAAzkXBAgAAAC7j7Magn5dFv+sWYmI2AFqCiV2DFennaRifn56ncqvNxIwAAABwFgULAAAAuMT2nHL9+0S5YXx8bLBCfY1vKgLAxfDz8tDUuFDD+KnyGi3NKDQxIwAAAJxFwQIAAAAuMT/d+bQrU3sZ31AEgEsxtVeYWnkaTzc3d1uubHa7iRkBAABAomABAAAAF8ivqNGy/cZPMF8X7af4KD8TMwLQkkT5e+m3XYMN4/vyq/TZ4RITMwIAAIBEwQIAAAAu8N7eApVbjZ9ensboCgCXmbPFtyVpTlquSZkAAADgLAoWAAAAMJXNbne62HaEn6dGdw4yMSMALdG1Ya00PKa1Yfybo2XanmO8zg4AAAAaHwULAAAAmGptVqkyC6oM4w91D5GvF19TAVx+M843ymJbnkmZAAAAQKJgAQAAAJPNTze+AWiRlBjHdFAAzHFzO3/1ifQ1jC8/UKijxdUmZgQAANCyUbAAAACAaY4UVSn1kPFCtiOubq0OQT4mZgSgJbNYLJrR13iUhdUmvb6DURYAAABmaRIFi+rqas2ePVv9+/dX//799eKLL8pqtTrdp6KiQgkJCerXr59jW1VVlf74xz9q8ODB6tOnj26//XatXLnycqcPAACA/2/hrnzZjNfa1rS4MPOSAQBJYzsH6crWXobxRbvyVVxVY2JGAAAALVeTKFgkJycrLS1NqampSk1N1datW7VgwQKn+8ybN0/R0dG1tlmtVkVGRuq9997Ttm3b9Oqrr+rPf/6zNm7ceDnTBwAAgKRKq01v7y4wjHcK8VFChwATMwIAydvTot/3Ni6WFlbZ9I6Tzy4AAAA0niZRsEhJSdGUKVMUFRWlqKgoJSYmKiUlxbD9nj179N133+mRRx6ptd3f31+PPfaY2rdvL4vFot69e2vAgAFKS0u73KcAAADQ4q3MLNapcuOnlKfEhcrDYjExIwA445EeoWrtbfzz+O/b82R1NjwMAAAAjcLtCxaFhYXKzs5W165dHdu6du2q48ePq7i4uE57q9WqZ599VrNmzZKPj/P5jysrK7Vz507FxsY2et4AAACozdli235eFj3QLcTEbADgf4X4empSd+PPoCPF1fpnZpGJGQEAALRMbl+wKCsrkyQFBgY6tgUFBUmSSktL67R/9913FRsbqwEDBjjt126365lnnlGHDh106623NmLGAAAA+KXtOeX694lyw/j42GCF+nqamBEA1PZYnzB5OBnkNSctV3Y7oywAAAAuJ7cvWPj7+0uSSkpKHNvOjqwICKg9x3FWVpaWLVumP/zhD077tNvteu6553To0CElJSXJw8Pt3wYAAIAmLWlnvtP41F6hJmUCAPWLCfbRqE5BhvEtJyv0vZPCKwAAAC6d29+pDw4OVnR0tDIyMhzbMjIy1LZt21qjLiRp69atysvL04gRIzRw4EBNnz5dJSUlGjhwoHbu3CnpTLHihRde0K5du/Tuu+/W6QMAAACNK7+iRh/sKzSMXxftp/goPxMzAoD6zYg3XnxbOjPKAgAAAJePl6sTaIh7771XCxYsUHx8vCRp4cKFGj16dJ12w4YN06BBgxyvt2/frpkzZ2r16tUKCTkzH+ns2bO1bds2LVmyRMHBweacAAAAQAv23t4ClVuNp1FhdAUAdzGgrb8GXuGnTcfrH0mx+j/FyiyoUqcQ5+slAgAA4OI0iYLF1KlTVVBQoGHDhkmSRo4cqcTEREnSrFmzJJ0pRPj6+srX19exX3BwsCwWiyIjIyVJx44d07Jly+Tj46PBgwc72o0cOVKzZ88263QAAABaDJvdrmQn00FF+HlqTGfjKVgAwGwz4sO16fjRemN2SX/fnqs3b2lrblIAAAAthMXOqmF1xMbGOv7ev3+/CzMBAABo2r48UqLbVmUZxp/qF64/3dDGxIwAwLkam12xSzL1n8LqeuP+Xhb9/FAXhfl6mpwZAABA8+Ds/rvbr2EBAACApisp3Xh0hUXS5J5MBwXAvXh6WPREfLhhvMxq14KdeSZmBAAA0HJQsAAAAMBlkVVUrU8OFRvGR1zdWh2DmQcegPv5XbcQhbYy/rn8xo58VVptJmYEAADQMlCwAAAAwGWxYFeebE4mH50WF2ZeMgBwAQK8PTTFyWdUdplV/3d/kYkZAQAAtAwULAAAANDoKq02vb27wDDeKcRHCR0CTMwIAC7Mo71D5eNpMYzP3Z4rloQEAABoXBQsAAAA0OhWZhbrVHmNYXxKXKg8LMY3AgHA1doGeGt8bJBhfNfpSn2VVWpiRgAAAM0fBQsAAAA0uvnpxgvS+nlZ9EC3EBOzAYCL86STxbclaU5arkmZAAAAtAwULAAAANCotueU698nyg3j42KDFerraWJGAHBxekb46tb2xtPXfZlVql2nK0zMCAAAoHmjYAEAAIBGlbQz32l8Wq9QkzIBgEs3o6/zURZztzHKAgAAoLFQsAAAAECjya+o0Qf7Cg3jA6L9FB/lZ2JGAHBpEtoHqGdEK8P4B/sKdaK02sSMAAAAmi8KFgAAAGg0S/YWqNxqN4wzugJAU2OxWPRkH+NRFtU26c0dzkeWAQAAoGEoWAAAAKBR2Ox2p9NBRfh5akznIBMzAoDGMS42SNH+Xobx5J15Kq22mZgRAABA80TBAgAAAI1iXVapfiqoMoxP6h4iXy++fgJoelp5eejR3sYjxPIrbXpvb4GJGQEAADRP/GIEAABAo5ifbjy6wiIpsSfTQQFouhJ7hsrPy2IY/9u2XNXYjKfEAwAAwPlRsAAAAMAlyyqq1ieHig3jI65urY7BPiZmBACNK9zPSw90CzGM/6ewWp8cNP4cBAAAwPlRsAAAAMAlW7grX84eLJ4aF2ZeMgBwmTwRHy7jMRbSnG25puUCAADQHFGwAAAAwCWptNr01m7j6aCuCfbWrR0CTMwIAC6PTiE+uuuaQMP4xuPl2pJdbmJGAAAAzQsFCwAAAFySlZnFOlVeYxifEhcmD4uzZ5IBoOmYER/uND4njVEWAAAAF4uCBQAAAC5JUnqeYczX06IHuhvP+Q4ATc3AK/zUv42vYXxlZpEOF1aZmBEAAEDzQcECAAAAF21HToW+P2E8/cn4a4MV5utpYkYAcHlZLBbN6Gs8ysJml+btMC7kAgAAwBgFCwAAAFy0+Tud35SbGhdqUiYAYJ57OwWpQ6C3Yfzt3QUqqDCeKg8AAAD1o2ABAACAi1JQUaMP9hUaxgdE+6lvGz8TMwIAc3h5WPR4nzDDeEm1TW/tzjcxIwAAgOaBggUAAAAuynt7C1RutRvGp/VidAWA5mtSjxAF+xj/pH59R56qa4w/IwEAAFAXBQsAAABcMJvdrqSdxk8PR/h5akznIBMzAgBzBfp46pGexoXZoyVWrfipyMSMAAAAmj4KFgAAALhg67JK9VNBlWF8UvcQ+XrxVRNA8/b73mFy9lE3Jy1XdjujLAAAABqKX5EAAAC4YPPTjUdXWCQlOnnqGACai3aB3rqvS7BhfPupCq0/WmZiRgAAAE0bBQsAAABckKyian1yqNgwPjymtToG+5iYEQC4zox448W3JWnOtlyTMgEAAGj6KFgAAADggizclS+bkxlOpvVyfvMOAJqTPlF+uqWdv2H8X4dKlJFXaWJGAAAATRcFCwAAADRYpdWmt3YbTwd1TbC3bu0QYGJGAOB6M/qGO43/jVEWAAAADULBAgAAAA2WklmsU+U1hvEpcWHysFhMzAgAXO+Ojq11bajxVHj/yChUTpnVxIwAAACaJgoWAAAAaLD56XmGMV9Pix7oHmJiNgDgHjwsFj0RbzzKorLGriQnn58AAAA4g4IFAAAAGmRHToW+P1FuGB8XG6wwX08TMwIA9zGxa7Ai/Yw/A+fvzFe51WZiRgAAAE0PBQsAAAA0SNJO508HT+sValImAOB+/Lw8NDXO+HPwdHmN3s8oNDEjAACApoeCBQAAAM6roKJGH+wzvtE2INpPfdv4mZgRALifqb3C1MrTeB2fudtyZbPbTcwIAACgaaFgAQAAgPNaklGgMqvxTTZnTxUDQEsR5e+l33YNNozvz6/SZ4dLTMwIAACgaaFgAQAAAKdsdruSduYbxiP8PDW2S5CJGQGA+3rSyeLbkjQnLdekTAAAAJoeChYAAABwal1WqQ7kVxnGJ3UPka8XXysBQJKuDWul4TGtDePfHC3T9pxyEzMCAABoOvhlCQAAAKecja6wSJrck+mgAOBcM843ymJbnkmZAAAANC0ULAAAAGAoq6haaw4WG8aHx7RWTLCPiRkBgPu7uZ2/+kT6GsaXHyjU0eJqEzMCAABoGihYAAAAwNDCXfmyGa+1zWLbAFAPi8WiGX2NR1lYbdLrOxhlAQAA8EsULAAAAFCvSqtNb+8xng7qmmBv3dbReJ52AGjJxnYOUrvWXobxRbvyVVxVY2JGAAAA7o+CBQAAAOqVklmsnDLjm2lT4sLkYbGYmBEANB3enhb9vneYYbywyqZ3dheYmBEAAID7o2ABAACAeiXtNJ6uxNfToge6h5iYDQA0PQ/3CFVrb+Of3X/fniers3n3AAAAWhgKFgAAAKgj/VSFNh0vN4yPiw1WmK+niRkBQNMT4uuph3oYF3ePFFfrn5lFJmYEAADg3ihYAAAAoI756c4Xg53Wi8W2AaAhHusdJg8ns+fNScuV3c4oCwAAAImCBQAAAH6hoKJGH+wrNIz3b+Orvm38TMwIAJqujsE+Gt0pyDC+5aTzEW0AAAAtCQULAAAA1LIko0BlVuOnfaf1Ml5EFgBQ14y+4U7jc7blmpQJAACAe6NgAQAAAAeb3a6knfmG8XBfT43tYvykMACgrv7RfrrhCuORaR//p1g/5VeamBEAAIB7omABAAAAh69/LtWB/CrD+KTuIfL14iskAFyoGfHGoyzskv6+3fnaQQAAAC0BvzYBAADgMD/deHSFRVJiHIttA8DFGHl1oDqF+BjGF+8tUG651cSMAAAA3A8FCwAAAEiSsoqqteZgsWF8WExrxQQb32wDABjz9LDo8T7GawCVW+1asMu4aAwAANASULAAAACAJGnR7nzZjNfa1jRGVwDAJfldtxCFtjL+Gf7mjnxVWm0mZgQAAOBeKFgAAABAlVab3tpt/GTv1cHeuq1jaxMzAoDmJ8DbQ1PijEdZZJdZtWx/kYkZAQAAuBcKFgAAANA/M4uVU1ZjGJ8SFyoPi8XEjACgeXq0d6h8PI0/T+duy5Xd7mS4GwAAQDNGwQIAAACavzPPMObradGD3ZkOCgAaQ9sAb42PDTKM786t1FdZpSZmBAAA4D4oWAAAALRw6acqtOl4uWF8XGywwnw9TcwIAJq3J+PDncbnpOWalAkAAIB7oWABAADQwiWlG4+ukKSpvRhdAQCNqWeEr25tH2AY/zKrVLtOV5iYEQAAgHugYAEAANCCFVTUaOm+QsN4/za+6tfGz8SMAKBlmNHX+SiLudsYZQEAAFoeChYAAAAt2JKMApVZjRd3ndorzMRsAKDlSGgfoJ4RrQzjH+wr1InSahMzAgAAcD0KFgAAAC2U3W5X0s58w3i4r6fu62K8MCwA4OJZLBY92cd4lEW1TXpzh/FnNAAAQHNEwQIAAKCFWvdzqQ7kVxnGJ3UPka8XXxcB4HIZFxukaH8vw3jyzjyVVttMzAgAAMC1+AUKAADQQiWlGz+5a5GUGMdi2wBwObXy8tD03saftfmVNr23t8DEjAAAAFyLggUAAEAL9HNxtT4+WGwYHxbTWjHBPiZmBAAtU2JcmPy9LIbxv23LVY3NeK0hAACA5oSCBQAAQAu0cFe+nN3/msboCgAwRZivpx7oHmIY/09htdY4KTADAAA0JxQsAAAAWpiqGrve2m08HdTVwd66rWNrEzMCgJbt8T7hMh5jIc3ZlmtaLgAAAK5EwQIAAKCFSfmpSDllNYbxKXGh8rA4u3UGAGhMnUJ8dPc1gYbxTcfLtflEmYkZAQAAuAYFCwAAgBZm/s48w5ivp0UPdDOemgQAcHnM6BvuND5nm/FnNwAAQHNBwQIAAKAFST9VoU3Hyw3jv4kNUrifl4kZAQAk6fq2fhoQ7WcYT8ks0qHCKhMzAgAAMB8FCwAAgBYkKd35E7rTeoWZlAkA4FwWi0Uz4o1HWdjs0rztjLIAAADNGwULAACAFqKgokZL9xUaxn/Vxlf92hg/3QsAuLzu6RSoDoHehvF39hSooMJ4DSIAAICmjoIFAABAC/GPjAKVWe2GcUZXAIBreXlY9Hgf48/ikmqbFu3ONzEjAAAAczWJgkV1dbVmz56t/v37q3///nrxxRdltVqd7lNRUaGEhAT169fvkvsCAABo6ux2u5J2Gt/kCvP11H1dgkzMCABQn0k9QhTsY/xT/fUdeaqqMS4+AwAANGVNomCRnJystLQ0paamKjU1VVu3btWCBQuc7jNv3jxFR0c3Sl8AAABN3dc/l2p/vvFirZO6h8jXq0l8NQSAZi3Qx1OP9Aw1jB8rsWrFAePp/QAAAJqyJvGrNCUlRVOmTFFUVJSioqKUmJiolJQUw/Z79uzRd999p0ceeeSS+wIAAGgO5qcbj66wSJoSZ3xzDABgrt/3DpOzGvLcbXmy2xllAQAAmh+3L1gUFhYqOztbXbt2dWzr2rWrjh8/ruLi4jrtrVarnn32Wc2aNUs+Pj6X1BcAAEBz8HNxtT4+aPxdZ1hMa8UE+xjGAQDmahforfu6BBvGt5+q0PqjZSZmBAAAYA63L1iUlZ35EhYYGOjYFhR0Zn7l0tLSOu3fffddxcbGasCAAZfcFwAAQHOwaFe+bE4exJ3K6AoAcDsz4o0X35akOdtyTcoEAADAPG5fsPD395cklZSUOLadHQ0REBBQq21WVpaWLVumP/zhD5fcFwAAQHNQVWPXW7uNp4OKCfLW7R1bm5gRAKAh+kT56ZZ2/obxfx0qUUZepYkZAQAAXH5uX7AIDg5WdHS0MjIyHNsyMjLUtm3bWiMlJGnr1q3Ky8vTiBEjNHDgQE2fPl0lJSUaOHCgdu7ceUF9AQAANAcpPxXpZFmNYXxKXKg8LBYTMwIANNSMvuFO439jlAUAAGhmvFydQEPce++9WrBggeLj4yVJCxcu1OjRo+u0GzZsmAYNGuR4vX37ds2cOVOrV69WSEjIBfUFAADQHCTtzDOM+Xpa9GD3EBOzAQBciDs6tta1oT7al19Vb/wfGYV66fooRfk3iZ/2AAAA59UkvtVMnTpVBQUFGjZsmCRp5MiRSkxMlCTNmjVLkjR79mz5+vrK19fXsV9wcLAsFosiIyMb1BcAAEBzsvNUhTYeLzeM/yY2SOF+TeLrIAC0SB4Wi56MD9cj607UG6+ssSspPU/P/zrK5MwAAAAuD4vdbneyBGPLFBsb6/h7//79LswEAADg4k1ee1yLdhcYxrf8Jka/ivYzMSMAwIWqsNrU/p2fdKq8/un9Ivw8lTWps/y83H7GZwAAAEnO77/zjQYAAKAZKqys0dJ9hYbxX7XxpVgBAE2Ar5eHpvUKM4yfLq/R+xnGn/cAAABNCQULAMD/Y+/Oo6Os7/7/v2ay75kJOyibEDCQQFCQqsVbQNFqK1ZUXGhBqkDUqtyLvfu9Pa1rb/vDpWVXqwLqXSuIFdRasK1rVQgkLEmQxSUJIGSdJCQzyVy/PzympeQzYWByZSZ5Ps7pOcm8L8ZX/unMdb0/788HQBf0/O5qNTSbB2kDPfwCAISXBdkuxUc5jPXH8ivkZ/MEAADQBdCwAAAA6GIsy9LSwipj3R0fpWuHp9qYCABwOnomRmvWyDRjvaTKqzcO1NmYCAAAoGPQsAAAAOhi3vmqXiVVXmP9lqx09joHgAhzd25GwPqi/AqbkgAAAHQc7lQBAAC6mCUF5ukKh6R52S77wgAAQmKEO05XDE421v9a2qD8r4/ZmAgAACD0aFgAAAB0IaUen17b7zHWLxuUrCFpsTYmAgCEysL2piy2MmUBAAAiGw0LAACALmTFjir5A5y7mpfDdAUARKpJAxKV2yveWP/9nlp95fHZmAgAACC0aFgAAAB0Ed4WS0/tNG8HNTg1RpcONG8nAgAIbw6HI+CURYsl/WZ7pY2JAAAAQouGBQAAQBexbm+tDje0GOvzs12KcjpsTAQACLUZw1I1IDnaWF+5o0q1TebPAgAAgHBGwwIAAKCLWFJgXlUbF+XQnKx0G9MAADpCTJRDd45xG+u1Xr+e2VVtYyIAAIDQoWEBAADQBRQeadT75ceM9euHpyojwbwiFwAQOX4yyqXkGPPt/JPbKtUc6EAjAACAMEXDAgAAoAtYWmg+u0KS8nLMq3EBAJElPT5Kc0eZp+a+8Pi0bm+tjYkAAABCg4YFAABAhKtpatGaYvP2H+f2jte5fRJsTAQA6Gg/HeNWoGOJFm2tkGUxZQEAACILDQsAAIAIt6qoRvU+80OpBdlMVwBAVzMoLVbXnJVqrH9yuFEfBNgqEAAAIBzRsAAAAIhglmVpaYDDtt3xUbou0/xACwAQuRaOywhYX5RfYVMSAACA0KBhAQAAEMHe+apexVVeY31OVroSovnKBwBd0fg+Cbqgn3nLv9f2efRZVZONiQAAAE4Pd68AAAARLNBh2w5J87Nd9oUBANhuYa55ysKS9MQ28xQeAABAuKFhAQAAEKFKPT69ts9jrF82KFlD0mJtTAQAsNuVQ1J0Vrr5/+uf3V2timPNNiYCAAA4dTQsAAAAItSKHVVqMZ+1rQU5TFcAQFcX5XTo7rFuY/1Ys6XlO8zTeAAAAOGEhgUAAEAE8rZYemqn+QHU4NQYTRuYbGMiAEBn+fHZ6XLHRxnri7dXqanZb2MiAACAU0PDAgAAIAKtl/ZIQwAAIABJREFU21urww0txvr8bJeinA4bEwEAOktijDPgmUWHGpr1YkmtjYkAAABODQ0LAACACBTosO24KIdmZ6XbmAYA0Nluz3ErNsrcqH4sv0KWFWAfQQAAgDBAwwIAACDC7DjaqPfKGoz164enqkdCtI2JAACdrU9StG7MTDPWd1Y06e0v6m1MBAAAEDwaFgAAABFmSUHgw1PzcsyHrwIAuq57cgP///+i/AqbkgAAAJwaGhYAAAARpKapRWuKq431c3rH69w+CTYmAgCEi1E94nXpwCRj/c9f1qvwSKONiQAAAIJDwwIAACCCrCqqUb3PvAd5XjbTFQDQnS3MzQhYf4wpCwAAEMZoWAAAAEQIy7K0tKDSWHfHR+m6zFQbEwEAws2UM5OU3SPOWH+xpEbldT4bEwEAAJw8GhYAAAAR4i+lDSqu8hrrc7LSlRDN1zsA6M4cDofuCTBl4fNLiwM0vwEAADoTd7QAAAARYkmAB0wOSfNGu+wLAwAIW9cPT1WfxGhjfXlhlep9fhsTAQAAnBwaFgAAABGg1OPTa/s8xvq0Qckamh5rYyIAQLiKi3bqjjHmJnZVk1/P7qq2MREAAMDJoWEBAAAQAVburFKL+axt5eUwXQEA+Id52W4lRjuM9Se2VajFH+CDBQAAoBPQsAAAAAhz3hZLK3dUGeuDU2M0bWCyjYkAAOHOHR+l2Vnpxvq+Gp/+uN88uQcAANAZaFgAAACEuVf31upwQ4uxPi/bpSineRUtAKB7umtshgJ9OizKr7AtCwAAwMmgYQEAABDmlhSapyviohyaE2AFLQCg+zorPVZXDU0x1j8oP6aPDzbYmAgAACAwGhYAAABhbMfRRr1XZn6YdP3wVPVIiLYxEQAgkiwclxGwvii/0qYkAAAA7aNhAQAAEMaWFpinKyRpQY7bpiQAgEj0nb4JmtAnwVhfu7dWB2q8NiYCAAAwo2EBAAAQpmqaWrS6uNpYP6d3vMYHeAgFAIDD4dDCXPOUhd+SntzGlAUAAAgPNCwAAADC1KqiGtX7LGN9QTbTFQCA9k0/K0WDUmOM9Wd2Vau6scXGRAAAAG2jYQEAABCGLMvS0gLzild3fJSuz0y1MREAIFJFOx26a6y5yV3n82vlzsBbEAIAANiBhgUAAEAY+ktpg4qrzHuKz8lKV0I0X+UAACdnTla60mLNnxu/2V4pb4t5qg8AAMAO3OUCAACEoUDTFQ5J80a77AsDAIh4KbFRui3AZ0dZXbNe3lNjYyIAAIAT0bAAAAAIM6Uen9bv8xjr0wYla2h6rI2JAABdwR1j3Ao0nLcov1KWxZQFAADoPDQsAAAAwszKnVUKtCtHXg7TFQCA4A1IidH1w9OM9e1HGvWX0gYbEwEAAByPhgUAAEAY8bZYempHtbE+KDVG0wYm25gIANCVLMzNCFhftLXCpiQAAAAnomEBAAAQRl7dW6tDDc3G+vxsl6KcDhsTAQC6kjG94nXxGYnG+huf16mossnGRAAAAP9AwwIAACCMLCmsMtbiohyak5VuYxoAQFfU3pTFY/lMWQAAgM5BwwIAACBM7DjaqPfKzHuHXzc8VT0Som1MBADoiqYNStZId6yxvrqoRofrzdN+AAAAHYWGBQAAQJhYWmCerpCkvBy3TUkAAF2Z0+HQ3WPNUxZNLZaWFlbamAgAAOAbNCwAAADCQE1Ti1YXmw/bHtcrXuf2jrcxEQCgK7t5ZJp6JkQZ60sLq3Ss2W9jIgAAABoWAAAAYWF1UY3qfZaxnpfjlsPBYdsAgNCIj3YGnNw7eqxFq3bX2JgIAACAhgUAAECns6zAW2+44py6PjPVxkQAgO5gQbZL8VHmZvjj2yrkt8zNdAAAgFCjYQEAANDJ/lraoKJKr7E+JytdCdF8bQMAhFbPxGjNGplmrJdUefXGgTobEwEAgO6OO18AAIBOtqTAPF3hkDQ/m8O2AQAd4+5c8+HbkrQov8KmJAAAADQsAAAAOlWpx6f1+zzG+rRByRqaHmtjIgBAdzLCHacrBicb638tbVD+18dsTAQAALozGhYAAACd6KmdVWoJsD34gmyXfWEAAN3SwvamLLYyZQEAAOxBwwIAAKCTeFssrdxRbawPSo3RZYPMq14BAAiFSQMSldsr3lj//Z5afeXx2ZgIAAB0VzQsAAAAOsmre2t1qKHZWJ832qUop8PGRACA7sjhcAScsmixpN9sN5+3BAAAECo0LAAAADrJ0sIqYy0uyqFbRqXbmAYA0J3NGJaqAcnRxvrKHVWqbWqxMREAAOiOaFgAAAB0gh1HG/VuWYOxft3wVPVIMD84AgAglGKiHPrpWLexXuv165ld5m0MAQAAQoGGBQAAQCdYWmCerpA4bBsAYL+fjHIpJdb8mODJbZVq9ls2JgIAAN0NDQsAAACb1Ta1aHWxeZXquF7xGt8nwcZEAABIaXFRmptl3o7wC49Paz+rtTERAADobmhYAAAA2GxVUY3qfeYVqnk5bjkcHLYNALDfT8e6FRXgI2hRfoUsiykLAADQMWhYAAAA2MiyLC0trDTWXXFOXTc81cZEAAD8w8DUWF0zzPw59OnhRr1fbj6DCQAA4HTQsAAAALDRX0sbVFTpNdbnZKUrMYavaACAzrMwNyNgfdFWc+MdAADgdHA3DAAAYKMlBYEf8szPdtuUBACAtp3bJ0EX9k801v+436PPqppsTAQAALoLGhYAAAA2Kavzaf0+j7E+bWCShqbH2pgIAIC2BZqysCQ9vo0pCwAAEHo0LAAAAGyyckeVWgKcU5qXw3QFACA8XDkkWWcFaKI/t7taFceabUwEAAC6AxoWAAAANvC2WFq5o9pYH5gSo8sGJduYCAAAM6fDobvHmhvpx5otLSussjERAADoDmhYAAAA2GD9vlodajCvRJ2f7VKU02FjIgAAAvvx2elyx0cZ64sLKtXY7LcxEQAA6OpoWAAAANhgSYF5FWpclEO3jEq3MQ0AAO1LjHFqfrbLWD/c0KKXSmptTAQAALq6iGhY+Hw+3X///Ro/frzGjx+vBx54QM3Nba9QfOCBBzRp0iTl5ubqwgsv1EMPPSSv19taP3z4sBYsWKAJEyZowoQJuvPOO3X06FG7/hQAANAN7TzaqHfLGoz1a4enqkdCtI2JAAA4ObfnuBUbZZ4AfCy/QpYV4IAmAACAIEREw2LZsmXaunWrNmzYoA0bNmjLli1avnx5m9fecMMNevPNN5Wfn6/XXntNxcXFevrpp1vrv/jFLyRJ77zzjjZv3iyv16uHHnrIjj8DAAB0U0vb2eM7L8DqVQAAOlOfpGjdmJlmrO+saNLbX9TbmAgAAHRlEdGwWLt2rebPn69evXqpV69emjdvntauXdvmtUOHDlViYmLr706nU1988UXr76WlpbrsssuUlJSk5ORkXX755frss886/G8AAADdU21Ti1YX1Rjr43rFa3yfBBsTAQAQnHtyzYdvS9Ki/AqbkgAAgK4u7BsWNTU1OnTokEaOHNn62siRI1VeXi6Px9Pmv1m5cqXGjh2riRMnqri4WDfddFNrbfbs2Xrrrbfk8XhUW1urjRs3atKkSR3+dwAAgO5pdXGN6nzmA0kX5LjkcHDYNgAgfI3qEa9LByYZ63/+sl6FRxptTAQAALqqsG9YNDR8s99zSkpK62upqamSpPr6tsdOb731Vm3btk1vvPGGrr/+evXs2bO1lpubq4qKCp177rkaP368ampqNH/+/A78CwAAQHdlWZaWFFQa6644p64fbt5mAwCAcLEwNyNg/TGmLAAAQAiEfcPi2+2d6urqWl/7drIiKcm8wkP6ZnuoESNG6N5775Uk+f1+zZkzR7m5udq2bZu2bdum3NxczZ07t4PSAwCA7uyvpQ0qqvQa67Oz0pUYE/ZfxwAA0JQzk5TdI85Yf7GkRuV1PhsTAQCArijs75DT0tLUp08fFRUVtb5WVFSkvn37Hjd1YdLc3Nx6hkV1dbXKyso0a9YsJSQkKCEhQTfffLO2bdumykrz6kcAAIBTsbQw8PeL+dmB9wQHACBcOBwO3RNgysLnlxYHmCoEAAA4GWHfsJCkq6++WsuXL9eRI0d05MgRrVixQtdcc80J19XX12vt2rWqra2VZVkqKSnRsmXLdMEFF0iS3G63Bg4cqBdeeEFNTU1qamrSCy+8oD59+sjt5oEBAAAInbI6n17d2/Z5W5I0bWCSzkqPtTERAACnZ2ZmmvomRRvrywurVB/g3CYAAID2mL9phJEFCxaourpal19+uSTpyiuv1Lx58yRJ9913nyTp/vvvl8Ph0IYNG/Too4/K6/XK7Xbrkksu0Z133tn6XkuXLtUjjzyi7373u/L7/Ro5cqSWLVtm/x8FAAC6tJU7qtRimesLclgsAQCILLFRDt2R49Z/f/h1m/WqJr+e3VWt28fwGQcAAE6Nw7KsALfS3VNmZmbrzyUlJZ2YBAAARCJfi6Uzn/lMhxqa26wPTInRvtlnKcrpsDkZAACnp7KxRWc8vUcNzW0/ShiSFqM9P+IzDgAAmAV6/h4RW0IBAABEklf31RqbFZI0P9vFgxwAQERyx0dpTla6sb6/xqfX9pu3RAQAAAjklLaE2rdvnw4ePKiqqirFx8fL7XYrMzNTycnJoc4HAAAQcZYWVBlrsVGOgA96AAAId3eNzdCSgiqZtmtYtLVCV5+VamsmAADQNZx0w+Kjjz7SK6+8oo8++khVVSfehDudTo0cOVKXXnqpfvjDH3KINQAA6JZ2Hm3U38oajPXrhqeqZ2JEHCMGAECbhqbHavpZKVq3t+1Jig8PHtPfDzbovL6JNicDAACRrt275bfffluPP/64Pv/8c1mWpd69e2vy5Mnq0aOH0tLS1NTUpOrqau3fv19FRUXauXOnfvvb3+qqq67SnXfeqR49etjxdwAAAISFpYXm6QpJyst22ZQEAICOszA3w9iwkKRF+RX6w/doWAAAgOAEbFjceOON2rp1q4YOHap77rlH3/ve99SvXz/j9V6vVx9//LHWr1+vP/7xj9q4caMeffRRTZ48OeTBAQAAwk1tU4tWF9UY67m94jW+T4KNiQAA6Bjf6Zeo8/ok6O+HjrVZX7fXowM1Xg1Oi7U5GQAAiGQBGxb19fVasmTJSTccYmNjdeGFF+rCCy9URUWFli9frgMHDoQkKAAAQLhbXVyjOp/fWM/Lccnh4LBtAEDXsHBchmZsLG2z5rekJ7ZV6smL+ticCgAARDKHZVmmc7K6rczMzNafS0pKOjEJAACIFJZlKWv1PhVVetusu+KcKp07XIkxTpuTAQDQMZr9loY9t1ef1/rarCfFOPTVLcPlio+yORkAAAhngZ6/c8cMAAAQAn8rbTA2KyRpdlY6zQoAQJcS7XTorrFuY73eZ2nljsBnOwEAAPyzoO6aJ0+erFWrVgW85oUXXuDMCgAA0O0sKawMWJ+fbX6gAwBApJqTla60WPOjhd9sr5S3hY0dAADAyQmqYVFWVqba2tqA19TW1qq8vPy0QgEAAESSsjqfXt3rMdYvHZiks9I5dBQA0PWkxEbpttEuY728vlkv76mxMREAAIhkId+XoKGhQTExMaF+WwAAgLD11I4qBVo8mpfDdAUAoOu6Y4xb0QGeLizKrxTHZwIAgJMR3d4F/zot4fF42pygaGlp0cGDB/WnP/1JZ5xxRugSAgAAhDFfi6WVO6uN9YEpMbp8ULKNiQAAsNeAlBhdPzxNa4rbnqTYfqRRfylt0MVnJNmcDAAARJp2GxYXX3yxHA5H6++rVq0KeI6FZVm69957Q5MOAAAgzK3f59HB+mZjfV62S1FOh7EOAEBXsDA3w9iwkKRFWytoWAAAgHa127C46qqr5HA4ZFmW1q9fr8zMTI0cOfKE65xOp9LT0zVx4kRdcMEFHRIWAAAg3CwpMB+2HRvl0C1Z6TamAQCgc4zpFa+Lz0jUO181tFl/4/M6FVU2aaQ7zuZkAAAgkrTbsPjVr37V+vP69es1ZcoU3X777R0aCgAAIBLsPNqov5W1/WBGkq4bnqqeie1+3QIAoEtYmJthbFhI0mP5FXpqSj8bEwEAgEgT1B10cXFxR+UAAACIOMsKqwLWF2S7bEoCAEDnmzYoWSPdsSqq9LZZX11Uowcn9lLvJJr5AACgbc5QvMm+ffv03HPP6f/+7//k8XhC8ZYAAABhrbapRauKzHt15/aK14Q+CTYmAgCgczkdDt2Tm2GsN7VYWlpo3koRAAAgqIbF4sWLdcEFF6i6urr1tQ8//FDTp0/X//7v/+qXv/ylpk+frqqqwKsNAQAAIt3q4hrV+fzG+oJslxwODtsGAHQvN41IU6/EKGN9aWGVjjWbPz8BAED3FlTD4r333tPgwYOVnv6PwyMXLVokh8OhO+64QzNnzlRpaalWrVoV8qAAAADhwrIsLS0wL9BwxTk1MzPNxkQAAISH+Gin8rLdxvrRYy1atds8oQgAALq3oBoWZWVlGjp0aOvvhw8f1q5du3TDDTdowYIFuu+++3Teeedp06ZNIQ8KAAAQLv5W2qDdlU3G+uysdCXGhGTnTQAAIs78bJfio8xTho9tq5DfsmxMBAAAIkVQd9I1NTVKS/vHasGtW7fK4XDooosuan0tKytLBw8eDFlAAACAcLO0ncO2543msG0AQPfVMzFaPzo73VjfU+XVxgN1NiYCAACRIqiGhdvt1tdff936+8cff6zo6Gjl5OS0vubz+eT3sx8lAADomsrrfHp1X62xfunAJA1zxdmYCACA8HP3WPO2UJK0aGuFTUkAAEAkCaphMXLkSL3zzjvas2ePvvjiC7355psaN26c4uPjW68pKytTz549Qx4UAAAgHKzcUaVAZ4Xm5QR+QAMAQHeQ6Y7TlUOSjfW/lTVo6+FjNiYCAACRIKiGxdy5c+XxePSDH/xA06ZNk8fj0ezZs1vrTU1N+uSTTzRq1KiQBwUAAOhsvhZLK3dWG+sDU2J0+SDzwxkAALqThbkZAeuL8pmyAAAAxwuqYXHOOedo+fLlmjJliqZOnarf/OY3mjRpUms9Pz9f/fv319SpU0MeFAAAoLOt3+fRwfpmY31etktRTvMhowAAdCff7Z+ocb3ijfWX99Tqy1qfjYkAAEC4c1iWZXV2iHCTmZnZ+nNJSUknJgEAAOHkoj98rr+VNbRZi41yqPSWYeqZGG1zKgAAwtdLxTW64a0yY31hrlv/33f72JgIAAB0tkDP34OasAAAAOiudlU0GpsVknTtsFSaFQAA/ItrhqVqQLL58/GpndWqbWqxMREAAAhnQTcs/H6/Vq9erWuvvVbjxo3T2Wef3VrbvXu3fvGLX+jAgQMhDQkAANDZlhZUBazn5bhsSgIAQOSIiXLop2Pdxnqt16+nd5nPhwIAAN1LUA0Lr9er2bNn6+GHH9aXX36ppKQk/fOOUgMGDNDatWv1+uuvhzwoAABAZ6ltatGqohpjfWzPeE3ok2BjIgAAIsdPRrmUEmt+/PDktko1+9mtGgAABNmweOaZZ/Txxx8rLy9PH374oWbMmHFcPTU1Veeee67ef//9kIYEAADoTGuKa1Tn8xvreTkuORwctg0AQFvS4qI0NyvdWP/S49Paz2ptTAQAAMJVUA2L119/Xbm5ubr99tvldDrbvDEfMGCAysvLQxYQAACgM1mWpSUBtoNKj3NqZmaajYkAAIg8Px3rVlSA3v6i/IrjdnAAAADdU1ANi9LSUuXk5AS8Ji0tTTU15i0TAAAAIsm7ZQ3aXdlkrM8+O12JMUEfCwYAQLcyMDVW1wxLNdY/Pdyo98sbbEwEAADCUVB31/Hx8fJ4PAGvKS8vV2qq+UsIAABAJAk0XSFJ87M5bBsAgJOxMDcjYH3R1kqbkgAAgHAVVMNixIgR+uCDD+T1etusezwevf/++xo9enRIwgEAAHSm8jqfXt1n3lP70oFJGuaKszERAACR69w+Cbqwf6Kx/sf9Hn1WZZ5qBAAAXV9QDYtrr71WBw8e1L//+7+rrq7uuFptba3uvfde1dbWaubMmSENCQAA0Bme2lmtZvNZ21qQ7bYvDAAAXUCgKQtL0uPbmLIAAKA7iw7m4iuuuEIffvih1q1bp3feeUdpad8cMHn11Vdr79698nq9uvHGGzVp0qQOCQsAAGAXX4ulFTvM20GdmRKj7w1OtjERAACR78ohyRqWHqvPqtveueG53dV6YGJPZSQE9bgCAAB0EUGfEPnwww/r4Ycf1llnnaXKykpZlqXdu3dr4MCBeuihh/Q///M/HZETAADAVuv3eXSwvtlYnzfapSinw8ZEAABEPqfDobvHmicUjzVbWlYY+PwoAADQdTksy7JO9R83NjaqpqZGKSkpSkw070MZaTIzM1t/Likp6cQkAACgs/zbK5/rr6UNbdZioxwqvWWYeiay+hMAgGA1+Pw685nPVNHY0ma9d2KUPp8zTPHRQa+xBAAAESDQ8/egPv0//fRTlZeXt/4eHx+v3r17H9esOHjwoD799NNTzQoAANDpdlU0GpsVknTtsFSaFQAAnKLEGKfmZ7uM9cMNLXqxpMbGRAAAIFwE1bCYNWuW1q1bF/Ca9evXa9asWacVCgAAoDMtLQi8FcWCAA9ZAABA+/Jy3IqNMm+t+Fj+N1tQAwCA7iWohsXJfFmwLEsOB/s5AwCAyOTxtmhVkXlV59ie8Tqvb4KNiQAA6Hr6JEXrphFpxvquiib96Yt6GxMBAIBwEPINIQ8ePKikpKRQvy0AAIAtVhfVqM7nN9bzclwszgAAIATuyTUfvi1Ji/IrbEoCAADCRbubLy9evPi43z/55JMTXpMkv9+vgwcPauPGjRo3blzoEgIAANjEsiwtLTRvB5Ue59TMTPNqUAAAcPKyMuI1bWCS3jJMUmz6sl4FRxqV0zPe5mQAAKCzBNWwcDgc+uSTT/TJJ58Yr+/du7cWLlwYmnQAAAA2eresQbsqmoz12WenKzEm5AOqAAB0WwvHZRgbFpL0WH6Fnr+0v42JAABAZ2q3YbFq1SpJ36w4/NGPfqTp06dr+vTpJ1zndDrlcrk0ePBgOZ3cyAMAgMizpJ3Dtudz2DYAACE1+YwkZfeIU+HRthcMvFRSo0fO76V+yTE2JwMAAJ2h3YbF+PHjW3+ePn26pkyZctxrAAAAXUF5nU+v7qs11i85M0nDXHE2JgIAoOtzOBy6JzdDP367vM26zy/9dnulHrmgt83JAABAZwhqFOKRRx7R5MmTOyoLAABAp3lqZ7WazWdtKy8n8MGgAADg1MzMTFPfJPN6yuU7qlTnDfAhDQAAuozT2rvp+eefp4EBAAAinq/F0ood5u2gzkyJ0fcGJ9uYCACA7iM2yqE7AiwMqG7y67nd1TYmAgAAneW0GhYej0fl5W2PbQIAAESK1/Z7dLC+2VifN9qlKKfDxkQAAHQvt2W7lBht/qx9fFuFWvyWjYkAAEBn4HRsAADQ7S0pqDTWYqMcumVUuo1pAADoftzxUZqTZf683V/j02v7PTYmAgAAnYGGBQAA6NZ2VzTpr6UNxvqMYanqlWjeVxsAAITGXWMzFGiecdHWCtuyAACAzkHDAgAAdGtLC83TFZKUl+2yKQkAAN3b0PRYTT8rxVj/8OAx/f2geZEBAACIfKfVsJgyZYoefvjhUGUBAACwlcfbolVFNcb62J7xOq9vgo2JAADo3hbmZgSsL8pnygIAgK7stBoWI0aM0PTp00OVBQAAwFZrimrk8fqN9QU5LjkcHLYNAIBdvtMvUef1MS8WWLfXowM1XhsTAQAAO7XbsPj0009VXl5+0m9YXFys9evXn1YoAACAjmZZlpYUVhnrabFO3ZCZZmMiAAAgSQvHmacs/Jb0xLbA2zkCAIDI1W7DYtasWVq3bt1xr61cuVITJkxo8/pNmzbpZz/7WWjSAQAAdJB3yxq0q6LJWJ+dla7EGI77AgDAbtOHpmhwaoyx/syuKlU1ttiYCAAA2KXdu3DLsk54zev1qra2tkMCAQAA2GFpgXm6QpIWcNg2AACdIsrp0F1j3cZ6vc/Syh2BP8cBAEBkYtkgAADodg7W+7Run3nxxSVnJmmYK87GRAAA4J/NyXIpPc78yOI32yvlbTlxgSUAAIhsNCwAAEC3s3JHtZrNZ21rQY55VScAAOh4ybFO3TbaPO1YXt+s3++psTERAACwAw0LAADQrfhaAm8jcWZKjK4YnGxjIgAA0JY7xrgVHeCpxaKtFW1uYw0AACIXDQsAANCtvLbfo/L6ZmN93miXopwOGxMBAIC29E+O0czMNGO94GiT3vmq3sZEAACgo51Uw8Lh4KYdAAB0DUsLKo212CiHbhmVbmMaAAAQyMLcjID1Rfnmz3UAABB5ok/mosWLF2vx4sUnvD5y5MiQBwIAAOgouyua9JfSBmN9xrBU9Uo8qa9HAADABjk94zX5jCRtNkxSvPl5nXZXNOnsjDibkwEAgI5wUhMWlmUF9T8AAIBwtLQw8CrMvGzz4Z4AAKBzLMx1B6w/ll9hUxIAANDR2l1CWFxcbEcOAACADuXxtmhVUY2xPqZnvM7rm2BjIgAAcDKmDUrW2e447a5sarO+urhGD32nl3onMSUJAECk49BtAADQLawpqpHH6zfW83JcnNsFAEAYcjgcujvAlIW3xdKSdqYoAQBAZKBhAQAAujzLsrSksMpYT4t1amZmmo2JAABAMG4akaZeiVHG+tKCKjX4zAsTAABAZDiphoXf3/aHvsfj0SOPPKIf/OAH+v73v68HH3xQlZWsagAAAOHlvbIG7apoexsJSZqdla6kGNZxAAAQruKjncrLNk9ZVDS2aHWArR8BAEBkaPfOfPXq1crKytIHH3xw3Oter1c333yzVq1apZKSEu3Zs0cvvPCCbrjhBjU0NHRYYAAAgGAtKTBPV0jSfA7bBgAg7M3Pdik+yrx942PbKuS3LBsTAQCAUGtFahN6AAAgAElEQVS3YbFlyxa53W6df/75x73+yiuvqLi4WEOGDNGzzz6rl19+WVOmTNEXX3yhF198scMCAwAABONgvU/r9tUa61PPTNJwV5yNiQAAwKnomRitH52dbqzvqfJq44E6GxMBAIBQa7dhUVxcrJycnBNef/PNN+VwOPSrX/1KEydOVHZ2th5//HFlZGRo8+bNIQ3p8/l0//33a/z48Ro/frweeOABNTc3t3ntAw88oEmTJik3N1cXXnihHnroIXm93uOu2bx5s37wgx9ozJgxuuCCC/TSSy+FNC8AAAgfT+2oVnOALa3zcszbSwAAgPBy99jAn9uLtlbYlAQAAHSEdhsWVVVVGjBgwHGv+f1+7dixQ/369dPo0aNbX4+OjtaFF16o/fv3hzTksmXLtHXrVm3YsEEbNmzQli1btHz58javveGGG/Tmm28qPz9fr732moqLi/X000+31t9991398pe/1H//939r69at2rhxo8aPHx/SvAAAIDz4Wiyt2GHeDurMlBhdMTjZxkQAAOB0ZLrjdOUQ82f338oatPXwMRsTAQCAUGq3YdHQ0KDo6OjjXtu/f78aGxs1ZsyYE67v1auX6uvrQ5dQ0tq1azV//nz16tVLvXr10rx587R27do2rx06dKgSExNbf3c6nfriiy9af3/yySeVl5enCRMmKCoqSmlpaRo6dGhI8wIAgPDwx/0elde3PZUpSbeNTleU07wXNgAACD8LczMC1hflM2UBAECkardh4XK5dODAgeNeKygokCRlZWWdcH1TU5NSUlJCFE+qqanRoUOHNHLkyNbXRo4cqfLycnk8njb/zcqVKzV27FhNnDhRxcXFuummmyR903zZtWuX6urqNG3aNJ1//vm66667dOTIkZDlBQAA4WNJQaWxFhvl0NxRHLYNAECk+W7/RI3rFW+sv7ynVl/W+mxMBAAAQqXdhkV2drbee+897d27V5JkWZZeffVVORwOTZgw4YTr9+7dq169eoUsYENDgyQd1wRJTU2VJOMkx6233qpt27bpjTfe0PXXX6+ePXtKkmpra2VZll577TU988wzevvttxUdHa3//M//DFleAAAQHnZXNOkvpQ3G+oxhqeqVGG2sAwCA8ORwOAJOWbRY0m+2M2UBAEAkardhccMNN6i5uVkzZ87U7bffrquuukpbt27VyJEjT5iwaGpq0tatW9ucvDhV327vVFdX1/rat5MVSUlJAf/t0KFDNWLECN17773HvdfNN9+s/v37KykpSXfeeac++uij1sYIAADoGpYVmqcrJGlBNtMVAABEqmuGpeqMFPPCg6d2Vqu2qcXGRAAAIBTabVh8u21SQ0ODNm3apJKSEvXt21e/+tWvTrj2jTfe0LFjx3TBBReELGBaWpr69OmjoqKi1teKiorUt2/fk9p6qrm5ufUMi9TUVPXr108Ox4l7VVuWFbLMAACgc3m8LXq+qMZYH9MzXhP7JtiYCAAAhFJMlEM/HWOesqj1+vX0rmobEwEAgFBot2EhSfPmzdOf//xnPf7443r22We1ceNGDR8+/ITrhg4dqsWLF+uiiy4Kacirr75ay5cv15EjR3TkyBGtWLFC11xzzQnX1dfXa+3ata1bP5WUlGjZsmXHNVCuvfZarV69WocPH1ZjY6OWLFmiiRMntjutAQAAIseaohp5vH5jfUG2q80FDAAAIHLMHZWulFjzY40nt1Wq2c/iRAAAIslJb9zcr18/9evXL+A12dnZpx2oLQsWLFB1dbUuv/xySdKVV16pefPmSZLuu+8+SdL9998vh8OhDRs26NFHH5XX65Xb7dYll1yiO++8s/W9br31VtXU1Oj73/++JGnChAl69NFHOyQ3AACwn2VZWlpYZaynxTp1w4g0GxMBAICOkBYXpZ+MStdj+W1vA/mlx6dXPqvV9Zl87gMAECkcFnshnSAzM7P155KSkk5MAgAAgvVuab0mvfKFsX7XWLcen9THxkQAAKCjfFnr05BnP1OL4cnGOb3j9cn1g5msBAAgjAR6/t7uhMWnn356Sv/Rc88995T+HQAAwOkINF0hSfM5bBsAgC7jzNQYzRiWqv/bU9tmfcvhRr1X1qDvDmAbaAAAIkG7DYubb775lFYi/PMh2QAAAHY4WO/T2r1tP7CQpKlnJmm4K87GRAAAoKMtHJdhbFhI0qL8ChoWAABEiJM6wyIqKkrnn3++UlNTOzoPAADAKXtqR7WazWdtKy/HbV8YAABgi3N6J+i7/RP1bllDm/XX99dpT1UTixYAAIgA7TYsBgwYoNLSUv3973/XJZdcomuvvZbtngAAQNjxtVhascO8HdQZKdH63uBkGxMBAAC7LMzNMDYsLEmP51dq2eS+9oYCAABBc7Z3waZNm/Tss8/q3/7t3/TWW29p1qxZuuyyy/S73/1OlZWVdmQEAABo1x/3e1Re32yszxvtUrSTAzcBAOiKrhiSrGHpscb6c7urdfSY+XsCAAAID+02LCRp4sSJeuKJJ/Tuu+/qP/7jP+RwOPToo49q0qRJuuuuu/Thhx92dE4AAICAlhSYF1LEOKVbsjhsGwCArsrpcOjuseatHxtbLC0rNE9iAgCA8HBSDYtvuVwuzZkzR2+88YbWrFmjadOm6S9/+YtuueUWTZ06VcXFxR2VEwAAwKioskl/KW17GwhJmjEsVb2TTuroLgAAEKF+dHa6MuKjjPXFBZVqDHTYFQAA6HRBNSz+2TnnnKNf//rXev7559W7d2+VlpaqrKwslNkAAABOytIA0xUSh20DANAdJMY4NT/bPFH5dUOLXiypsTERAAAI1ik1LLxer1577TXddNNNmjlzpg4dOqRx48ZpyJAhoc4HAAAQkMfboueLzA8fcnrEaWLfBBsTAQCAzpKX41ZslPnMqsfyK2VZlo2JAABAMILaG6G4uFivvPKKXn/9ddXU1MjlcunHP/6xZsyYQbMCAAB0iheKa+Txmrd3yMtxy+HgsG0AALqDPknRumlEmn63q7rN+q6KJv3pi3pNG5RsczIAAHAy2m1YNDQ0aOPGjXr55Ze1c+dOSdJ5552nGTNmaOrUqYqJienwkAAAAG2xLEtLCswHaKbFOnXDiDQbEwEAgM52T67b2LCQpEX5FTQsAAAIU+02LM4//3w1NjaqR48euvXWWzVjxgwNGDDAjmwAAAABvV/eoJ0VTcb6j89OV1LMKR/ZBQAAIlBWRrymDUzSW1/Ut1nf9GW9Co40KqdnvM3JAABAe9ptWBw7dkzR0dHq37+/tmzZoi1btrT7pg6HQ2vWrAlJQAAAAJNA0xWStCDHfPAmAADouhaOyzA2LCTpsfwKPX9pfxsTAQCAk3FSZ1g0Nzdr+/btJ/2m7BMNAAA62sF6n9burTXWp56ZpOGuOBsTAQCAcDH5jCRl94hT4dG2JzFfKqnRI+f3Ur9ktrkGACCctNuw2Lx5sx05AAAAgvL0zmo1m8/a1oJspisAAOiuHA6HFuZm6Edvl7dZ9/ml326v1CMX9LY5GQAACKTdhkX//oxIAgCA8NLst7Rih3k7qDNSonXFkBQbEwEAgHBzfWaafvbB1yqvb26zvnxHlX4+vqeSYznvCgCAcNEhn8qVlZUd8bYAAACSpNf2eVRW1/bDB0m6bbRL0U62qAQAoDuLjXLojjFuY726ya9nd1fbmAgAALQnpA0Lj8ejxx57TFOmTAnl2wIAABxnaaF5cUSMU5qbxXZQAADgm0UMSTHmRQxPbKtQi9+yMREAAAjkpBsWZWVlevvtt/XOO+/o6NGjx9Wampq0YsUKTZkyRStXrpRl8WEPAAA6RlFlk975qsFYnzEsVb2T2t31EgAAdAOu+CjNCbCQYX+NT+v3eWxMBAAAAjmpu/kHH3xQL774YmsjIiYmRv/1X/+lG2+8UR9//LHuvfdeHTp0SLGxsZo1a5Zuu+22Dg0NAAC6r2WF5rMrJGlBjnnrBwAA0P3cNdatJQWVMg1SLMqv0A+HpdobCgAAtKndhsWrr76qNWvWyOl0aujQobIsSwcOHNBDDz2kxMRE3XffffL7/bruuus0f/589e7d247cAACgG6rz+vV8gL2mc3rE6Tt9E2xMBAAAwt2QtFhNH5qitXvbnqT46OAxfVTeoIn9Em1OBgAA/lW7DYt169YpJiZGq1at0tixYyVJn376qWbPnq2f//zn6tOnj5YtW6bMzMwODwsAALq3NcXVqvX6jfW8HLccDg7bBgAAx1uYm2FsWEjfTFm8QsMCAIBO1+4ZFnv27NHUqVNbmxWSdO6552rKlCmyLEsPPfQQzQoAANDhLMvS0gDbQaXFOnXDiDQbEwEAgEgxsV+iJgaYwnx1n0f7a7w2JgIAAG1pt2Hh8Xh05plnnvD6wIEDJem4RgYAAEBHeb+8QTuONhnrPz47XUkx7X61AQAA3dTC3AxjzW9JT2yrtDENAABoS7t39X6/X9HRJ+4cFRMTI0mKj48PfSoAAIB/saQg8GHb87NdNiUBAACR6KqhKRqcGmOs/25XlaoaW2xMBAAA/tVJLUNkL2gAANCZDtU3a+3eWmN9yplJynTH2ZgIAABEmiinQ3eNdRvr9T5LK3cEXiABAAA6lsOyLCvQBSNGjAi6YeFwOLR79+7TCtaZ/vlMjpKSkk5MAgAAJOmBj4/ovo+OGOuvXjFAV52VamMiAAAQieq8fp3xzB5VN/nbrPdLitaBOcMUG8XCTQAAOkqg5+8nNWFhWVZQ//P72/7gBwAACFaz39KKAKsdz0iJ1hVDUmxMBAAAIlVyrFO3jTZvI1le36zf76mxMREAAPhnJx5O8S+Ki4vtyAEAANCmP+73qKyu2Vi/bbRL0U5WQQIAgJNzxxi3FuVXqNmw1nLR1grdNCKN7bEBAOgEJzVhAQAA0FmWFFQaazFOaW4Wh20DAICT1z85RjMz04z1gqNNeuerehsTAQCAb9GwAAAAYauosknvfNVgrF8zLFW9k9odGAUAADjOwtyMgPVF+eYFEwAAoOMEbFgcPnz4tP8DX3/99Wm/BwAA6J6WFZrPrpCkvBy3TUkAAEBXktMzXpPPSDLW3/y8TrsrmmxMBAAApHYaFlOnTtXDDz8cdOPCsixt2rRJV111lV5++eXTCggAALqnOq9fz++uNtZzesTpO30TbEwEAAC6koW5gRc+PJZfYVMSAADwrYANi1tuuUUvv/yyLr74Ys2dO1dr167V559/3ua19fX1+uijj/TrX/9akyZN0h133KG4uDhdcsklHZEbAAB0cS8U16jWazgNU9KCHDeHYQIAgFM2bVCyznbHGeuri2t0uL7ZxkQAAMBhWZYV6IJDhw5pyZIl2rBhgxobGyVJSUlJ6tGjh9LS0tTU1KTq6modOXJEfr9flmXp7LPP1i233KLvfe97tvwRoZaZmdn6c0lJSScmAQCge7IsSzkv7NeOo21vxZAa61T5T4YrKYbjuAAAwKl7ZmeV5m46aKz/z4Qeun9iLxsTAQDQ9QV6/t5uw+JbdXV1ev311/XRRx8pPz9fR48eba3FxMRo+PDhGj9+vC699FKNGTMmRNE7Bw0LAAA613tl9fruH74w1u8c49aTF/WxMREAAOiKGpv9Gvi7z/R1Q0ub9Yz4KH15yzAlskgCAICQCfT8Pfpk3yQ5OVkzZ87UzJkzJUk+n0/V1dWKj49XSkpKiKICAABISwsCH7a9INtlUxIAANCVxUc7dXuOW/d9dKTNekVji1YVVWteduDzLgAAQGic8hKBmJgY9ezZk2YFAAAIqUP1zVq7t9ZYn3JmkjID7DcNAAAQjPnZLiVEm8/FenxbpfwntzkFAAA4Tcw0AgCAsPLUzir5zGdtM10BAABCqkdCtH40Mt1Y31Pl1Yb9dTYmAgCg+6JhAQAAwkaz39KKHebtoM5IidaVQ5juBAAAoXV3rlvmGQtpUX6FbVkAAOjOaFgAAICw8cf9HpXVNRvrt412KdoZ6HECAABA8Ia74nTlkGRj/d2yBm05fMzGRAAAdE80LAAAQNgIdNh2jFOam8V2UAAAoGMszM0IWF+0lSkLAAA6Gg0LAAAQFoorm7T5q3pj/ZphqeqdFG1jIgAA0J1c2D9R5/SON9b/8Fmtvqz12ZgIAIDuh4YFAAAIC0sLzdMVkpSX47YpCQAA6I4cDkfAKYsWS3pyO1MWAAB0JBoWAACg09V5/Xp+d7Wxnt0jTt/pm2BjIgAA0B398KxUnZFinuh8ame1appabEwEAED3ckoNi8rKSr300kt68MEH9fOf//y41wsLC9XY2BiygAAAoOt7obhGtV6/sZ6X45bDwWHbAACgY8VEOfTTMeYpC4/Xr2d2mRdZAACA0xN0w+IPf/iDLr74Yt1///1as2aN1q1b11o7evSorrvuOr3++ushDQkAALouy7K0pLDSWE+NdeqGzDQbEwEAgO5s7qh0pcSaH5c8ua1SzX7LxkQAAHQfQTUsPvjgA913330aNGiQFi9erJkzZx5XHz58uM466yxt3rw5pCEBAEDX9UH5Me042mSs//jsdCUHeGgAAAAQSmlxUfrJqHRj/UuPT698VmtjIgAAuo+g7v6feuop9ezZU2vWrNHkyZOVkXHimGRmZqb27t0bsoAAAKBrW1Jgnq6QpAXZLpuSAAAAfOOnYzIUFWA3ykX5FbIspiwAAAi1oBoWO3fu1EUXXaTk5GTjNX369NHRo0dPOxgAAOj6DtU3a+1e8wrFyWckKdMdZ2MiAAAA6czUGM0YlmqsbzncqPfKGmxMBABA9xBUw8Ln8ykxMTHgNbW1tXI62bYBAAC07+mdVfKZz9pWXg7TFQAAoHMsHGc+fFv6ZsoCAACEVlCdhf79+2vXrl0BryksLNTgwYNPKxQAAOj6mv2WVuyoMtYHJEfryiEpNiYCAAD4h3N6J+i7/c2LNl/fX6c9VeZzuAAAQPCCalhMnjxZW7Zs0Ztvvtlmfe3atSopKdGll14aknAAAKDren2/R6V1zcb6baNdinYG2DwaAACggy3MNU9ZWJIezw98FhcAAAhOdDAXz507Vxs3btTChQv1pz/9SR6PR5K0Zs0abdmyRX/+8581cOBA3XTTTR0SFgAAdB1LCszTFTFOae4otoMCAACd64ohyRruitWeKm+b9ed2V+uB7/RUj4SgHq8AAACDoCYs0tLStGbNGo0bN05vvfWWPvjgA1mWpQcffFBvvfWWxo4dq+eff77dcy4AAED3VlzZpM1f1RvrPzwrVX2SuPEHAACdy+lw6O6xbmO9scXSskLzIgwAABCcoJ8E9OvXT6tXr1ZxcbG2b9+u6upqpaSkKCcnR6NGjeqIjAAAoItp78Y+L8f8YAAAAMBOs0am6/99eEQVjS1t1hcXVOo/xmUoPjqoNaEAAKANQTUsPv30UyUnJ2vkyJEaMWKERowY0VG5AABAF1Xn9eu53dXGenaPOJ3fL8HGRAAAAGaJMU4tyHHpgY+Ptln/uqFFLxTX6Ba2swQA4LQF1f6fNWuWfv/733dUFgAA0A28UFyjWq/fWF+Q7ZbDwWHbAAAgfORluxUXZf5+8lh+pSzLsjERAABdU1ANC5fLpfj4+I7KAgAAujjLsrS0sNJYT4116sYRaTYmAgAAaF/vpGjdFOA7yu7KJr31eZ2NiQAA6JqCaliMHz9e27Zt66gsAACgi/ug/JgKjzYZ6z8+O13Jsez/DAAAws89uRkB64vyzYsyAADAyQnqicBdd92lAwcO6IknnpDP5+uoTAAAoIsKNF0hSfOz2fsZAACEp7Mz4nTZoGRjffNX9dr+daONiQAA6HqCOnR7xYoVGjZsmFasWKFXXnlFI0aMUM+ePU+4zuFw6OGHHw5ZSAAAEPkO1zfrlc9qjfXJZyRphDvOxkQAAADBWZjr1psBtn56bFuFVl3a38ZEAAB0LUE1LF599dXWn48ePar333+/zetoWAAAgH/11M4q+cxnbSsvh+kKAAAQ3i4+I0k5PeJUYNji8qWSGj1yfi/1T46xORkAAF1DUA2LzZs3d1QOAADQhTX7La3YUWWsD0iO1pVDUmxMBAAAEDyHw6F7cjP0o7fL26w3+6Xfbq/Ury7obXMyAAC6hqAaFv37M9YIAACC9/p+j0rrmo3120a7FO102JgIAADg1FyfmaafffC1yuvb/m6zYkeV/t/4nkqODerYUAAAoCAP3QYAADgVSwrM0xUxTmnuKLaDAgAAkSE2yqE7xriN9eomv57dXW1jIgAAuo6gJiy+tX37dv3hD39QUVGRamtrlZKSoqysLF199dXKzc0NdUYAABDBiiubtPmremP9h2elqk/SKX0lAQAA6BS3jXbpwU+OqN5ntVl/YluFFmS7FMUEKQAAQQn66cDjjz+ulStXyrKO/1AuKirS2rVr9ZOf/ET33HNPyAICAIDItqzQPF0hSXk55hWKAAAA4cgVH6U5WS79dntlm/X9NT6t3+fRD4el2pwMAIDIFtSWUG+++aZWrFihvn376sEHH9SmTZtUWFioTZs26cEHH1Tfvn311FNP6Y033uiovAAAIILUef16LsCWCKN7xOn8fgk2JgIAAAiNu8a6FWiAYlF+hX1hAADoIoJqWKxZs0Y9evTQK6+8omuuuUYDBgxQbGysBgwYoGuuuUb/P3v3HV9Vne3//33Sezkh9CIECQEhJKjICGJF5YoI4qCCWAklyqjcmQtTUOGrOPxE5eFAgEHQGUW8igVib4iOnSAlkIBUKYH03s/+/cHlQEz2oSU75ySv5+Ph43Fy1s7e62QG2Nnrsz7rzTfflN1u16pVq5oqXwAA4EFWZRaqqMphGk/ub5fNxlYJAADA8/QI99PomFDT+LdHyvXt4TILMwIAwPOdVcEiIyND119/vez2hrdusNvtuuGGG7Rjx45GSe6E6upqzZkzR5deeqkuvfRSzZ07VzU1NQ0eO3fuXA0bNkyJiYkaOnSonnzySVVVVdU7rqKiQtddd50uvvjiRs0VAAAcZxiGFm1ueJsESQrz89L43uEWZgQAANC4ZiRGuYzTZQEAwNk5q4JFbW2tAgICXB4TEBCg2tra80rqt1JSUrRx40alpqYqNTVVP/30k5YsWdLgsXfeeac++OADpaWl6d1331VGRoaWL19e77iFCxeqffv2jZonAAA46Zsj5dqSU2kav7tPhEL8zupWBAAAwK0M7hikwR3Mt7d8e3ex9hTWX0QJAAAadlZPCbp27ar169fL4Wh4aweHw6ENGzaoS5cujZLcCWvWrNHUqVPVtm1btW3bVlOmTNGaNWsaPDYmJkZBQUHOr728vLR///46x6Snp2vDhg1KSkpq1DwBAMBJrrorJGla/0iLMgEAAGg6rrosHIb0/CbX90QAAOCksypY3HTTTdq9e7emTZumffv21YkdOHBA06dP1y+//KKRI0c2WoKFhYXKyspSXFyc8724uDgdPnxYxcXFDX7PsmXLlJCQoMGDBysjI0MTJkxwxmpqavS3v/1Ns2fPlp+fX6PlCQAATjpaWqM3dxWZxq/pEqzedn8LMwIAAGgat8SEqke4r2l8RXq+8isadycKAABaKp+zOfiee+7RV199pfXr12vDhg1q27atoqOjlZOTo6NHj8rhcGjgwIG65557Gi3BsrLjA6pCQ08OsgoLC5MklZaW1nn/hKSkJCUlJWn37t1au3atoqOjnbEVK1YoNjZWgwYN0vfff99oeQIAgJOWp+er2nzWtqbF010BAABaBm8vmx5OiNL09VkNxkurDS3dmq+Zl7SxODMAADzPWXVY+Pn5acWKFXrkkUfUuXNnZWVlaevWrTpy5Ig6d+6sRx55RC+99FKjdi6c2N6ppKTE+d6Jzorg4GCX3xsTE6PevXtr5syZko53gaxatUp/+tOfGi0/AABQV43D0JIt+abxTiE+urlH/QUHAAAAnurePhGK8Dd/xPLCz3mqqjUszAgAAM90Vh0WkuTr66vJkydr8uTJKi0tVUlJiUJCQk5bPDhX4eHhat++vXbs2KGuXbtKknbs2KEOHTo02F3xWzU1Nc4ZFj/99JPy8vJ00003SZKqq6tVUlKiyy+/XCkpKerfv3+TfAYAAFqTdXuKdbCkxjQ+uV+kfLxsFmYEAADQtEL8vDSlX6Se/im3wfjh0hqtzizUxD4RFmcGAIBnOeuCxamCg4ObrFBxqjFjxmjJkiVKTEyUJC1dulRjx46td1xpaak+/PBDXXfddQoNDdXOnTuVkpKiIUOGSJJGjBihoUOHOo/ftGmTZs2apXfeeUcREdw0AADQGBa76K7w9ZImXcR2UAAAoOV5aIBdC9JyTbfFXJCWq7viwmWzsXADAAAzZ1Ww2LZtm9avX6/bb79dbdrU33sxOztbr7/+uq655po6Q7LP17Rp01RQUKARI0ZIkkaOHKkpU6ZIkmbPni1JmjNnjmw2m1JTUzV//nxVVVXJbrdr+PDhmj59uiQpICBAAQEBzvOGhx+/UTh1xgUAADh3mXmV+vRAqWn81p5hah98XuslAAAA3FLHEF/dERuuf+0obDC+JadSn/1aqmu7hlicGQAAnsNmGMYZb6I4Y8YMbdy4UV988UWDKwIMw9DVV1+tSy65RPPnz2/URK0UGxvrfJ2ZmdmMmQAA4FkeXp+lhT/nmcY33NZNQzs1fXcmAABAc9icXaEBr+4xjd/QLVgfjO5mYUYAALgfV8/fz2ro9qZNmzRo0CDT9kWbzabLLrtMaWlp55AmAADwZKXVDr20vcA03q+Nv4Z0DLIwIwAAAGvFRwfo2q7mizM+3F+q9NwKCzMCAMCznFXBIicnR+3bt3d5TNu2bZWdnX1eSQEAAM/zakahCqtMNm2WlNzfzp7NAACgxZuRGOUy/myaeTcqAACt3VkVLAIDA5WX5/of1ry8PPn5+Z1XUgAAwLMYhqFFm83vEcL8vDS+d7iFGQEAADSP67sFq5akJ7MAACAASURBVI/d3zT+SkahskprLMwIAADPcVYFi969e+uzzz5TaWnDwzRLSkr02WefqXfv3o2SHAAA8AzfHCnXlpxK0/jdfSIU4ndWtx0AAAAeyWaz6dFEu2m8qtbQ4i10WQAA0JCzenIwbtw45eXl6b777lNGRkadWEZGhu677z7l5+dr3LhxjZokAABwb666KyRpav9IizIBAABofuN7h6ttkLdpfPHmfJVVm2+lCQBAa+VzNgePGDFCGzZs0DvvvKPRo0crKipK7dq109GjR5WbmyvDMDR69GjddNNNTZUvAABwM0dLa/TmriLT+NVdghTnYlsEAACAlibAx0sPxts1+9uGZ3zmVtTqXzsKNKW/eScGAACt0VnvzfD000/riSeeUM+ePZWTk6P09HTl5OSoZ8+emjt3rubNm9cUeQIAADe1PD1frhYIJsfzizgAAGh9pvaPVKCPzTT+3KY8OQzDwowAAHB/Z9VhccK4ceM0btw4lZeXq6ioSGFhYQoMDGzs3AAAgJurcRhasiXfNN4pxEc39wi1MCMAAAD30CbQR3fHRWjJ1obvlXbmVyl1T4lujuFeCQCAE85r+mVgYKDatWtHsQIAgFYqdU+xDpbUmMYn94uUj5f5ykIAAICW7JFEu1zdCS1Iy7UsFwAAPME5dVic6rPPPtN3330nwzB0ySWX6Prrr2+MvAAAgAdY5KK7wsdLmnQRw7YBAEDr1SvSXyN7hGjtnpIG4xsOlemno+W6uB0LQQEAkM6gw+Lzzz/X+PHj9cMPP9SLzZo1Sw8++KD+/e9/65VXXtHDDz+shx56qEkSBQAA7iUzr1KfHig1jd/aM0ztg897bQQAAIBHm5EY5TK+YCNdFgAAnHBGBYvt27crPj6+zvtffPGF3n77bQUEBGjq1Kn67//+b3Xp0kWffvqpUlNTmyxhAADgHlJcdFdIUnI83RUAAABDOwXp4nYBpvE3dhXpQFG1hRkBAOC+Tluw2LJliwYMGCB/f/86769Zs0Y2m03z5s3TH/7wBz3wwAN69dVX5e/vr3Xr1jVZwgAAoPmVVjv00vYC03i/Nv4a0jHIwowAAADck81mc9llUWtIC3+mywIAAOkMChY5OTnq0qVLvfd//PFHhYWF1ZlZER0drWHDhmn79u2NmyUAAHArqzIKVVjlMI1P6x8pm41h2wAAAJI09sIwdQ31NY3/c1uBCitrLcwIAAD3dNqCRVFRkSIiIuq8d/jwYRUWFioxMbHew4jOnTuroMB8xSUAAPBshmFo0eY803ion5cm9I4wjQMAALQ2Pl42/SHBbhovrnJo+TaepQAAcNqCRXBwsLKysuq8l56eLknq06dPg9/z2+2jAABAy/HNkXJtzqk0jd8dF64Qv9PeYgAAALQqD/SNUJiLe6SFP+equtawMCMAANzPaZ8m9OrVS19++aVKS0ud733yySey2WwaOHBgveMPHjyo6Ojoxs0SAAC4jcUuuiskaVq8+epBAACA1irM31uTLjLvQv21uEZv7iqyMCMAANzPaQsWI0eOVGFhoe666y7961//0pw5c7Ru3Tq1adNGgwYNqnOsYRjauHGjevbs2WQJAwCA5nO0tEZvuPhF+uouQYqz02kJAADQkOkDouTtYszXgrRcGQZdFgCA1svndAeMHTtWH3/8sb7++mvt2LFDhmHIx8dHf/nLX+Tt7V3n2G+//VY5OTkaPHhwkyUMAACaz/L0fFWbz9rWtP50VwAAAJjpGuar3/cK02uZDS8A2XisQhsOlWlY52CLMwMAwD2ctmDh5eWlZcuWKTU1VZs2bVJERISGDx+uuLi4esfm5+dr4sSJuuaaa5okWQAA0HxqHIaWbs03jXcK8dGomFALMwIAAPA8MxKjTAsW0vEuCwoWAIDWymbQa1hPbGys83VmZmYzZgIAgPt455cijU49aBqfMzhafxvEHCsAAIDTufKNffryUJlpPGNijGLZZhMA0EK5ev5+2hkWAAAAkrR4i3l3hY+X9ICLIZIAAAA4acbAKJfx5zblWZQJAADuhYIFAAA4rZ35lfrkQKlp/NaeYeoQ7GthRgAAAJ7rv7qHqFekn2n85e0Fyi6rsTAjAADcAwULAABwWos3m3dXSFJyfKRFmQAAAHg+L5tNjyTYTeMVtYaWuJgdBgBAS0XBAgAAuFRa7dBL2wtM4xdF+WtIxyALMwIAAPB8E+MiFBXgbRr/x+Y8VdQ4LMwIAIDmR8ECAAC4tCqjUIVV5r8sJ8dHymazWZgRAACA5wvy9dI0F12qx8pq9WpGoYUZAQDQ/ChYAAAAU4ZhaNFm86GPoX5eGt873MKMAAAAWo7k/nb5e5sv/Hg2LU+GYViYEQAAzYuCBQAAMPXtkXJtzqk0jd8dF65QP/OtDAAAAGCuXbCPJrhY/LE9r1If7iuxMCMAAJoXBQsAAGDKVXeFJE2LNx8WCQAAgNN7NDHKZXxBmuv7MQAAWhIKFgAAoEHHymr0xq4i0/hVnYMUZ/e3MCMAAICWp0+Uv268IMQ0/tmvpfr5WIWFGQEA0HwoWAAAgAYt35avavNZ20qmuwIAAKBRzEh0fV/17KZcizIBAKB5UbAAAAD11DgMLdmabxrvGOyjUTGhFmYEAADQcl3dJVjxbcw7V1/LLNShkmoLMwIAoHlQsAAAAPW8t7dEvxbXmMYn94uUj5fNwowAAABaLpvNphkDzWdZ1DikF35mlgUAoOWjYAEAAOpxNWzbx0ua1C/CwmwAAABavnG9wtUx2Mc0vnRrvkqqXOzXCQBAC0DBAgAA1LEzv1KfHCg1jY+JCVOHYF8LMwIAAGj5/Lxtmj7AfJZFQaVDK9LNt+wEAKAloGABAADqSNni+hfh5PhIizIBAABoXZL6RSrY13zbzec35anWYViYEQAA1qJgAQAAnEqrHVqZXmAavyjKX0M7BVmYEQAAQOsRGeCt+/uaLw7ZW1Stt3cXW5gRAADWomABAACcVmUUqtDF3sjT+kfKZmPYNgAAQFN5OMEuLxe3Wws25lqXDAAAFqNgAQAAJEmGYWixi+2gQv28NCEu3MKMAAAAWp/u4X4a0zPUNP5dVrm+OVxmYUYAAFiHggUAAJAkfXukXD9nV5jG744LV6ift4UZAQAAtE4zEqNcxhek0WUBAGiZKFgAAABJctldIUlT+9stygQAAKB1u6xDkH7XIdA0/vYvxdpdUGVhRgAAWIOCBQAA0LGyGr2xq8g0flXnIPWJ8rcwIwAAgNZtxkDzLgtD0vOb6LIAALQ8FCwAAICWb8tXVa1hGk+Op7sCAADASqN6hKpHuK9pfEV6gfIrai3MCACApkfBAgCAVq7WYWjJVvPtoDoG++jmHuaDHwEAAND4vL1sejjBvMuirMbQUhf3cAAAeCIKFgAAtHKpe0v0a3GNaXxyv0j5etsszAgAAACSdG+fCEX4mz+6eeHnPJddsgAAeBoKFgAAtHKLNueZxny8pEn9IizMBgAAACeE+HlpSr9I0/jh0hqtziy0MCMAAJoWBQsAAFqxnfmV+uRAqWl8TEyYOgSb750MAACApvXQALt8XTy9WZCWK8OgywIA0DJQsAAAoBVL2eJ63+PkePMVfQAAAGh6HUN8dUdsuGl8S06lPvvVfAEKAACehIIFAACtVGm1QyvTC0zjfaP8NbRTkIUZAQAAoCGPJpoP35akBRtzLcoEAICmRcECAIBW6rXMQhVWOUzjyf0jZbMxbBsAAKC5xUcH6NquwabxD/eXKj23wsKMAABoGhQsAABohQzD0KLN5ttBhfp5aUKc+dYDAAAAsNaM03RZPJuWZ1EmAAA0HQoWAAC0Qt8dKdfP2ear8CbGhSvUz9vCjAAAAODK9d2C1TfK3zT+SkahskprLMwIAIDGR8ECAIBWaNFphm1P62+3KBMAAACcCZvNpkcTze/RqmoNLdpMlwUAwLNRsAAAoJU5VlajN3YVmcav6hykPi5W7wEAAKB5jI8NV7sg8y7YlC35Kqs2n1EGAIC7o2ABAEAr8+K2AlXVGqbxafF0VwAAALgjfx8vPejiXi23olYvby+wMCMAABoXBQsAAFqRWoehJVvNt4PqGOyjUT1CLcwIAAAAZ2Nq/0gF+thM489typPDMF+cAgCAO6NgAQBAK5K6t0QHiqtN40n9IuXrbf4LMAAAAJpXVKCP7ukTYRrfVVCldXtKLMwIAIDGQ8ECAIBWZLGLQYw+XlJSP/NffgEAAOAeHkmwy9USkwVpuZblAgBAY6JgAQBAK7Ezv1IfHyg1jY+JCVOHYF8LMwIAAMC5uDDSXze72Mbzq0Nl+jGr3MKMAABoHBQsAABoJZZsMZ9dIUnT4iMtygQAAADna8ZA8+HbEl0WAADPRMECAIBWoKzaoZXbC0zjfaP8dUWnIAszAgAAwPkY0jFIl7QLMI2/uatI+4uqLMwIAIDzR8ECAIBWYFVmoQoqHabx5P6RstkYtg0AAOApbDabHk2MMo3XGtLCTebzywAAcEcULAAAaOEMw9CizebbQYX6eWlCXLiFGQEAAKAxjL0wTF1DzWeQLU8vUGFlrYUZAQBwfihYAADQwn13pFw/Z1eYxifGhSvUz9vCjAAAANAYfLxs+kOC+SyL4iqHlm8z3xYUAAB3Q8ECAIAWbtFphm1P7c+wbQAAAE/1QN8IhfmZP95Z+HOuqmsNCzMCAODcUbAAAKAFO1ZWozd2FZnGr+wcpL5R5sMaAQAA4N7C/L016aII0/ivxTV608X9IAAA7oSCBQAALdiL2wpU5WJFXXK8+RYCAAAA8AzTB0TJ22YeX5CWK8OgywIA4P4oWAAA0ELVOgwt2Wq+HVTHYB+N6hFqYUYAAABoCl3DfPX7XmGm8Y3HKrThUJmFGQEAcG4oWAAA0EK9t7dEB4qrTeNJ/SLl62opHgAAADzGjMQol/EFabkWZQIAwLmjYAEAQAu1aHOeaczHSy73OgYAAIBnGdguUMM6BZnG1+0pUWZepYUZAQBw9jyiYFFdXa05c+bo0ksv1aWXXqq5c+eqpqamwWPnzp2rYcOGKTExUUOHDtWTTz6pqqoqSVJVVZX++te/6uqrr1ZCQoJuuOEGvfnmm1Z+FAAALLErv1IfHyg1jY+OCVPHEF8LMwIAAEBTmzHQdZfFc5vMF7QAAOAOPKJgkZKSoo0bNyo1NVWpqan66aeftGTJkgaPvfPOO/XBBx8oLS1N7777rjIyMrR8+XJJUk1NjaKjo/XSSy8pLS1NTz/9tP7+97/r66+/tvLjAADQ5FK2mM+ukKTk+EiLMgEAAIBV/qt7iGIj/UzjL28vUHZZwwtAAQBwBx5RsFizZo2mTp2qtm3bqm3btpoyZYrWrFnT4LExMTEKCjrZAunl5aX9+/dLkoKCgvSHP/xBXbt2lc1m04ABAzRo0CBt3LjRks8BAIAVyqodWrm9wDTeN8pfV7jYLgAAAACeyctm0yMJ5l0WFbXGaRe2AADQnNy+YFFYWKisrCzFxcU534uLi9Phw4dVXFzc4PcsW7ZMCQkJGjx4sDIyMjRhwoQGj6usrNSWLVsUGxvbJLkDANAcXsssVEGlwzQ+rX+kbDaGbQMAALREE/uEq02gt2l80ZY8VdSY3ysCANCc3L5gUVZWJkkKDQ11vhcWFiZJKi1teG/upKQkbdq0Se+//75uv/12RUdH1zvGMAz95S9/Ubdu3TR8+PAmyBwAAOsZhqFFm81XzYX4eumuuHALMwIAAICVAn28NK2/+fafx8pq9UpGoYUZAQBw5ty+YHFie6eSkhLneyc6K4KDg11+b0xMjHr37q2ZM2fWed8wDD322GPau3evFi9eLC8vt/8xAABwRr47Uq5N2RWm8Ylx4Qr1M19xBwAAAM+XHG+Xv7d5R+2zablyGIaFGQEAcGbc/kl9eHi42rdvrx07djjf27Fjhzp06FCn68JMTU2Nc4aFdLxY8cQTT2jr1q1asWLFGZ0DAABPsfg0exJPY9g2AABAi9c2yMdlV+2OvCp9uK/ENA4AQHNx+4KFJI0ZM0ZLlixRdna2srOztXTpUo0dO7becaWlpVqzZo2KiopkGIYyMzOVkpKiIUOGOI+ZM2eO0tLStGLFCoWHsyUGAKDlOFZWo//dVWQav7JzkPpGBViYEQAAAJrLo4nmw7claUFarkWZAABw5nyaO4EzMW3aNBUUFGjEiBGSpJEjR2rKlCmSpNmzZ0s6Xoiw2WxKTU3V/PnzVVVVJbvdruHDh2v69OmSpEOHDmnVqlXy8/PT1Vdf7Tz/yJEjNWfOHIs/FQAAjevFbQWqqjVv7Z/W325hNgAAAGhOcXZ/jbggRO+bdFJ8/muZfj5WoQFtWdACAHAfNsNg08Lfio2Ndb7OzMxsxkwAADgztQ5DPVb+ogPF1Q3GOwb7aN99F8rXxV7GAAAAaFk+/7VU16zZbxqf0Dtc/76hk4UZAQDg+vm7R2wJBQAAXHtvb4lpsUKSkvpFUqwAAABoZa7qHKQB0eYdFKt3Fuqgi3tIAACsRsECAIAWYPGWPNOYj5c06aIIC7MBAACAO7DZbHo00Xxb0BqH9I/N5veRAABYjYIFAAAebld+pT7aX2oaHx0Tpo4hvhZmBAAAAHcxrle4OgabjzBdujVfJVUOCzMCAMAcBQsAADxcypZ8l/Hk+EiLMgEAAIC78fO2afoA8y6LgkqHVqS7vp8EAMAqFCwAAPBgZdUOrdxeYBrvG+WvKzoFWZgRAAAA3E1Sv0gF+5rPM3t+U55qHYaFGQEA0DAKFgAAeLDXMgtVUGnewj+tf6RsNoZtAwAAtGaRAd66v6951+3eomq9vbvYwowAAGgYBQsAADyUYRhatNm8fT/E10sTeodbmBEAAADc1cMJdnm5WMeyYGOudckAAGCCggUAAB7q+6xybcquMI1PjAtXmL+3hRkBAADAXXUP99OYnqGm8e+yyvXN4TILMwIAoD4KFgAAeChX3RWSNI1h2wAAADjFjMQol/EFaXRZAACaFwULAAA8UHZZjf53V5FpfFinIPWNCrAwIwAAALi7yzoE6XcdAk3jb/9SrN0FVRZmBABAXRQsAADwQC+mF6iq1jCNJ8fbLcwGAAAAnmLGQPMuC0PS85vosgAANB8KFgAAeJhah6ElW8y3g+oQ7KNbYsz3JwYAAEDrNapHqGLCfU3jK9ILlFdRa2FGAACcRMECAAAP8/6+Eu0vrjaNJ10UIV9vm4UZAQAAwFN4e9n0cIJ5l0VZjaGlLhbHAADQlChYAADgYRZtzjONedukpH4M2wYAAIC5e/tGKNLf/JHQC5vzXG4/CgBAU6FgAQCAB9mVX6mP9peaxkf3DFXHEPMWfwAAACDY10tT+psvcjlSWqPXMgstzAgAgOMoWAAA4EGWbHXdnp/cn2HbAAAAOL0H4+3ydfFUaEFargyDLgsAgLUoWAAA4CHKqh1akV5gGu9j99ewzkEWZgQAAABP1THEV3fGhpvGt+ZU6tMD5p29AAA0BQoWAAB4iNcyC1VQ6TCNT4uPlM3GsG0AAACcmUcTzYdvS8e7LAAAsBIFCwAAPIBhGFq02Xw7qBBfL93V23yFHAAAAPBb/aMDdF3XYNP4R/tLtS2nwsKMAACtHQULAAA8wPdZ5dqUbf7L4sS4cIX5e1uYEQAAAFqCGafpsng2Lc+iTAAAoGABAIBHWLzF9bDtafGRFmUCAACAlmR4t2D1jfI3jb+aWais0hoLMwIAtGYULAAAcHPZZTV6fWeRaXxYpyD1jQqwMCMAAAC0FDabTY8m2k3jVbWGFm2mywIAYA0KFgAAuLkX0wtUVWuYxpPjzX/BBAAAAE5nfGy42gWZby+asiVfZdUOCzMCALRWFCwAAHBjtQ5DS1xsB9Uh2Ee3xIRamBEAAABaGn8fLz3oYhFMbkWtXt5eYGFGAIDWioIFAABu7P19JdpfXG0aT7ooQr7eNgszAgAAQEs0tX+kAn3M7yuf25Qnh2He9QsAQGOgYAEAgBtztV+wt02a1I9h2wAAADh/UYE+uqdPhGl8V0GV1u0psTAjAEBrRMECAAA39UtBlT7aX2oaH90zVJ1CfC3MCAAAAC3ZIwl2uerdXZCWa1kuAIDWiYIFAABuKmWLeXeFJCX3Z9g2AAAAGs+Fkf66uYf5fLSvDpXpx6xyCzMCALQ2FCwAAHBDZdUOrUg3H2zYx+6vYZ2DLMwIAAAArcGMga4XxdBlAQBoShQsAABwQ6t3Fqqg0mEanxYfKZuNYdsAAABoXEM6BumSdgGm8Td3FWl/UZWFGQEAWhMKFgAAuBnDMLRoc75pPMTXS3f1DrcwIwAAALQWNptNMxKjTOO1hrRwk+utSwEAOFcULAAAcDM/ZJUr7ViFafyuuHCF+XtbmBEAAABak1svDFO3UF/T+PL0AhVW1lqYEQCgtaBgAQCAm1m0xby7QpKS4yMtygQAAACtkY+XTX9IMJ9lUVzl0D+3ub5nBQDgXFCwAADAjWSX1ej1nUWm8WGdgtQ3ynxPYQAAAKAx3N83QmF+5o+NFm7KU3WtYWFGAIDWgIIFAABuZEV6gapc/OI3je4KAAAAWCDM31tJ/czvPQ+W1OiNXeYLbQAAOBcULAAAcBO1DkMpLraDah/ko9ExYRZmBAAAgNZs+gC7fFw8OVqQlivDoMsCANB4KFgAAOAm3t9Xov3F1abxpH4R8vW2WZgRAAAAWrMuob76/YXmC2bSjlXoy4NlFmYEAGjpKFgAAOAmFm/OM4152+SyJR8AAABoCjMGRrmML0jLtSgTAEBrQMECAAA38EtBlT7cX2oaH90zVJ1CfC3MCAAAAJAS2wbqys5BpvHUvSXKyKu0MCMAQEtGwQIAADewZIt5d4UkTetvtygTAAAAoK4Zia67LJ6jywIA0EgoWAAA0MzKqh1akV5gGu9j93e5qg0AAABoSiO6hyg20s80/q8dhcouq7EwIwBAS0XBAgCAZrZ6Z6HyKx2m8WnxkbLZGLYNAACA5uFls+mRBPMui4paQylb8i3MCADQUlGwABpQXV2tZ555Rv369VNgYKDsdrtuvPFGffXVV2f0/Xl5eZoyZYoSEhLUpk0b+fj4KCwsTBdffLGeeuoplZeXn9c1165dqwceeEAXXXSRoqKi5Ofnp65du+q+++7T/v376xz70ksvyWazufzvggsuOK/cJengwYNKTk5WTEyMAgICFBkZqYSEBM2aNeuMfmZAa2UYhhZtNv/lLsTXS3f1DrcwIwAAAKC+iX3C1SbQ2zS+aEueKmrMF+EAAHAmbIZhGM2dhLuJjY11vs7MzGzGTNAcHA6HRo4cqffff79ezMvLS6tWrdK4ceNcniMjI0NxcXGm8euuu04ff/zxOV+zd+/epv/fjIqK0nfffaeePXtKOl6wuPfee13m27NnT+3ateuccpek7777TjfeeKMKCupvaRMeHt7g+wCO+/5ImS57fZ9pfGr/SC2+uoN1CQEAAAAmHvv2mOZ8n2Ma/+e1HfTARZEWZgQA8ESunr/TYQH8xtKlS52Fg7i4OL355puaP3++vL295XA4NHnyZGVnZ7s8h7+/v37/+99r2bJl+vDDD/Xee+/prrvucsY/+eSTOn8Yz+Wa/fr10/PPP69PPvlEzz//vMLCwiRJubm5mjNnjvO4ESNG6Kuvvqr33w033OA8ZvTo0eece2FhoW699VYVFBTI29tbkyZN0po1a/TRRx9pyZIlGjt27Ol/6EArtug0rfPT+vMLHwAAANxDcrxd/t7mW5U+m5YrB+tiAQDngQ6LBtBh0br169dP27Ztk3S8c2DQoEGSpEmTJmn58uWSpGeffVaPPPLIWZ87IiJChYWFkqQff/xRF1988Tld8+OPP9Z1111XZ0/7hQsX6uGHH5Z0vOixfft20zyKi4vVqVMnFRcXy8vLS7t3766zLdTZ5P7MM8/oj3/8oyRpzpw5+tvf/nbmPxCglcspr1Gn5btUVdvwP8VXdArSl7ddYG1SAAAAgAuTPj2s5dvMu+jfG9VFI7qHWpgRAMDT0GEBnKH8/Hxn4cDX19f5UF6SLr/8cufrDRs2nNV5CwoKtGzZMucD/7Zt26pv377nfM3hw4fXG8B74YUXOl8HBwe7zOfll19WcXGxJOmmm25yWaxwlbskrVu3rs7xAwYMUFBQkLp06aJHHnnEeR0A9b24rcC0WCFJyfF0VwAAAMC9PJpoPnxbkhak5VqUCQCgJaJgAZxi3759ztdRUVHy9j45UKx9+/bO13v37j2j8z344IOy2WyKjIzU5MmTJUkJCQlat26dAgMDG/Wab775pvP1jTfe6PLYxYsX18nxXHOXpPT0dOfr2bNna/PmzSovL9fBgwf1/PPP69prr1VVVZXLfIDWqNZhKMXFdlDtg3x0S0yYhRkBAAAApxdn99eIC0JM45//Wqafj1VYmBEAoCWhYAGcorS01Pnaz8+vTuzUr0tKSs75Gl5eXqqtrW3Ua65YsUIrV66UJMXExGjGjBmmx3766afasWOHpOPDu6+99tpzzl2Ss/NCkiIjI/Xyyy9r1apVatu2rSTphx9+0EsvvXTG1wBaiw/2lWh/cbVpPKlfhPxc7A8MAAAANJcZA+myAAA0DQoWwClO3Urpt10Bp34dEmK+muRUjz76qDZs2KC33npLd955pyRp48aNuu6665SVldUo11y0aJEeeOABSVKHDh30wQcfKDw83DSnf/zjH87XycnJ9baWOpvcpeNDuk+YOnWqJk6cqDvuuEOPPvqo8/1PPvnENB+gtVq0Oc805m2TkvqxHRQAAADc01WdgzQgOsA0vnpnoQ66WJwDAIAZChbAKU6d5ZCbm1unm+DUh/Tdu3c/o/P16NFDQ4cO1ejRo/Xqq69q6NChko53Vbz77rvnfc158+bpwQcflGEY6tKli9avX19nlsVvHThwQKmpqZKk0NBQ3X333eeVuyR17drV+bpbntbDigAAIABJREFUt27O16d+rqKiItPrAK3R7oIqfbi/1DR+S0yoOoX4WpgRAAAAcOZsNptmJNpN4zUO6YWfzRfoAABghoIFWq2SKof+uTVfd7x/UNes2adBr+3R2M8LFd6tlySpurpaP/74o/P4//znP87XV1xxhctzl5eXN/i+l9fJP3IntlKKjIx0DrE+m2vOnDlTf/7znyUdH7j99ddfq1evXi7zSklJcRZE7r77boWGhp5X7lLdweAHDhxwvt6/f7/zdZcuXVzmBbQ2KVtc//KWHG/+yx8AAADgDsb1ClenEB/T+NKt+SquqjWNAwDQEPN/WYAWKiOvUos25+nlHYUqrnLUP+Di26T9T0qS/uv2iXriiTmqyD7onBERFhamCRMmSJIef/xxPfHEE5Kkxx57TI8//rgk6a677lJ1dbVuvPFGde/eXZWVlVqzZo2+/PLLk5e5+GLn66lTpzqHX993332aO3eu9u7d2+A1Jenhhx/WwoULJUkRERGaN2+eDhw4UKdgMGTIkDofq7KyUsuXL5d0fDVMcnJygz+fs809KSlJL774ogzDUEpKimJjY+Xr66tnn33Wecxtt93W4LWA1qis2qEV6QWm8Ti7n67sHGRhRgAAAMDZ8/W2afoAu/7n62MNxgurjt/3/iHB9bwLAABORcECrYZhGHr8u2z9vx9y5DBcHPi7cVL6einjP8rbv0sP3XOHM+Tl5aWUlBRFR0e7vFZNTY3Wrl2rtWvXNhi/4447dPXVVzu/njJlitatW6ePPvpIO3bs0NixY11e85133nG+LigoqHP8qZ/3VKtXr1ZOTo4k6dprr1Xv3r0bJfdLLrlEs2bN0lNPPaW8vDxNnDixzvH33Xefrr/++gbPBbRGr+8sUn5lA8XS/zOtv910tgwAAADgTpIuitTc73NUUt3w/e3zm/KUHG+Xjxf3twCAM8OWUGgVah2G7v74sOZ8f5pihSR5eUv3/0O66VGpfU/J118KDFPngcP06WefOQdQu3L33XdrzJgx6t69u4KDg+Xr66v27dvrxhtv1KuvvqpXX321zvHe3t5au3at/v73v6tv374KCAhQRESEhg8frs8///yMrnk6ixYtcr4+0c3RGLlL0pNPPqlXX31Vl112mYKDgxUYGKiBAwdq6dKlzq4OAMcLia6GbQf72jQxLtzCjAAAAIBzFxHgrfv7RpjG9xVV6+1fii3MCADg6WzGb5dhQ7Gxsc7XmZmZzZgJGoNhGHpofZYWbc4/73P9z8VRenpIu0bICkBr9P2RMl32+j7T+JR+kUq5poN1CQEAAADnaW9hlXq+9Ivp4sBB7QP17bgL6CIGADi5ev5OhwVavNWZRY1SrJCkv/+Uq3d3szoEwLlZvMX130XJ8ZEWZQIAAAA0ju7hfrq1Z5hp/Puscn1zpNzCjAAAnoyCBVo0wzA094fsRj3n/2vk8wFoHXLKa/T6ziLT+BWdgnRRmwALMwIAAAAax4xEu8v4go25FmUCAPB0FCzQon15sEw78qoa9Zw/Ha3Qj1msDgFwdl7cVqDKWvNdGKf1p7sCAAAAnmlQhyBd3jHQNP7O7mL9UtC4v5sDAFomChZo0U63/cq5n9d8aC4A/Fatw9CSreZ/H7UP8tFoF230AAAAgLubkRhlGjMkLdxElwUA4PQoWKDFKq9x6J3d5tuvnI83dhWp2sVKaQA41Qf7SrSvqNo0ntQvQn7eDCEEAACA57q5R6hiwn1N4yvSC5RXUWthRgAAT0TBAi3W/qJqVTua5tyl1Yayymqa5uQAWhxX3V7eNimpH9tBAQAAwLN5e9n0cIJ5l0VZjaGlTbQLAgCg5aBggRYrv4lXbmTmVTbp+QG0DLsLqvThvhLT+C0xoeoUYr4SDQAAAPAU9/aNUKS/+aOmFzbnqYrdCgAALlCwQIvl1cS7q1z39gFdtnqvnv4xRxkULwCYSNmSJ1e/kk2Lt1uWCwAAANCUgn29NKW/effwkdIavZZZaGFGAABPQ8ECLZY9wLvJr/F9Vrlm/eeY4v61W7Ev/6L/+eqovj1cJofBihEAx2fprEgvMI3H2f10VecgCzMCAAAAmtaD8Xb5unjatCAtVwa/MwMATFCwQIt1QZifgn2tG2K7M79K8zfm6nf/u08d/7lTkz89rPf3FquipokGaQBwe6szi5Rfaf53wLT+dtlsDNsGAABAy9ExxFd3xoabxrfmVOrTA6UWZgQA8CQULNBi+XrbdHsv85ukpnS0rFbLthXov979VdFLd+r37x3UqoxCFTTxXA0A7sMwDC3anGcaD/a16a645vk7CgAAAGhKjyaaD9+WjndZAADQEAoWaNGmxZvvnWmVkmqH3thVpPEfHlL0skwNf2u/Fm/O08Hi6uZODUAT+vFohTYeqzCN39U7QuH+Tb91HQAAAGC1/tEBuq5rsGn8o/2l2pZjfq8MAGi9KFigRUtsG6hB7QObOw2nGof0yYFSJX+RpS4v7tIlr+3Rkz9kKz23gj08gRbGVXeF5B4FVQAAAKCpzDhNl8Wzaa7vlwEArRMFC7R4T/4uulHP1z3Mt9HO9dPRCv31m2xd9O896vXybv3xq6P6+lCZah0ULwBPllNeo9d3FpnGh3YKUr82ARZmBAAAAFhreLdgXRTlbxp/NbNQWaU1FmYEAPAEHlGwqK6u1pw5c3TppZfq0ksv1dy5c1VT0/A/anPnztWwYcOUmJiooUOH6sknn1RVVdU5nQstwzVdQ/TM0HaNcq6Uq9trz30Xatc9PfXM0HYa0jFQjTUu95eCKj2zMVdD39inDv/cqQc+OazUPcUqZ2g34HFWpBeosta88Jjcn+4KAAAAtGw2m83lLIuqWkP/OE1XMgCg9fGIgkVKSoo2btyo1NRUpaam6qefftKSJUsaPPbOO+/UBx98oLS0NL377rvKyMjQ8uXLz+lcaDkeTbTricvOr9Pi/xvaVlP62yVJPSP8NGNglL76fXdlJfXS8ms76KbuIfL3bpzyRXZ5rV5ML9DItb8qemmmxqb+qld2FCifod2A26t1GErZkm8abx/ko9E9wyzMCAAAAGged8aGqX2Qj2k8ZUu+SqtZpAcAOMkjChZr1qzR1KlT1bZtW7Vt21ZTpkzRmjVrGjw2JiZGQUFBzq+9vLy0f//+czoXWg6bzabZl0Xr5eEdFeZ3dv+3j/T30v+O6Kz/HtimwXjbIB/df1Gk1o3qqpzJsVpzU2fdFReuSP/G+eNVWm1ozS/Fuuujw4pemqlr1uzTCz/n6UARQ7sBd/Th/hLtc/Hnc1K/CPk1UnETAAAAcGf+Pl56cIB5d3FeRa1e3l5gYUYAAHfn9gWLwsJCZWVlKS4uzvleXFycDh8+rOLi4ga/Z9myZUpISNDgwYOVkZGhCRMmnPO50LJM7BOh/fddqIXD2ik20s/lsX2j/LXoqvbad9+Fuq3Xma2GDvHz0pieYfrX9Z10NClWn93aTQ8NsKtLqPmKkrNRa0if/1qm6euz1G3FLg1ctUdzv8/W1hyGdgPuYtFm8+4Kb5uUdBHbQQEAAKD1mNIvUoE+5gt2ntuUxxxHAIBT4zxFbUJlZWWSpNDQUOd7YWHHHx6XlpbWef+EpKQkJSUlaffu3Vq7dq2io6PP+VxoeSICvDU9IUoPDbDry4Nl+uJgqY6W1aq4qlZhft5qF+Sta7oGa0jHINls574K2tfbpqu7BOvqLsFaOKydfs6u0Du7i/XO7mJtyalslM+SdqxCaccqNPvbbHUP89UtMaG6JSZUl3cMkrcXK7gBq+0uqNKH+0pM46NiQtU51NfCjAAAAIDmFRXoo3v7RGixybapvxRUad2eYt3CtqkAAHlAweLE9k4lJSWy24/PDzjRDREcHOzye2NiYtS7d2/NnDlTL7300nmdCy2PzWbTlV2CdWWXpv/f3mazKaFtoBLaBuqJwW21p7BK7/5f8eLrw2VqjMUke4uq9dymPD23KU9tAr01svvx4sV13YIV6OP2zVRAi7Bka75c/XFOjrdblgsAAADgLh5JjFLKFvN75QVpuRQsAACSPGBLqPDwcLVv3147duxwvrdjxw516NDhjDoiampqnDMszvdcQGPpEe6nRxKj9OVtF+hoUi+tvK6jRvUIVUAj7WufU16rldsLNGrdr2qzJFOj1/2ql7cXKLe8plHOD6C+8hqHVqSb77/bO9JPV3UOMo0DAAAALVXPCD+NijF/7vL14XL9kFVuYUYAAHfl9gULSRozZoyWLFmi7OxsZWdna+nSpRo7dmy940pLS7VmzRoVFRXJMAxlZmYqJSVFQ4YMOetzAVZpE+ije/pG6J2buyhnSqzevqmz7o4Llz3Au1HOX1Zj6J3dxbrn48Nqt2ynrnpznxZuytW+wqpGOT+A41ZnFimvotY0Pi3efl7bzAEAAACebEZilMv4go25LuPV1dV65pln1K9fPwUGBsput+vGG2/UV199dUbXP3LkiObNm6drrrlG3bp1U0BAgEJDQzV48GCtXLmywbmQ+/bt0/3336/OnTvLz89P7du31/jx47Vr1y6X16qqqlLfvn1ls9mc/xUU1F/ctGXLFo0dO1bR0dEKCAhQr1699Le//U2lpaX1jj106JCSk5N14YUXKjAwUH5+furatavGjx+v9PT0etd/4okndMMNNygiIsKZw4ABA87oZwUAzclmeMCk3urqaj311FNKTU2VJI0cOVJ//vOf5ePjo9mzZ0uS5syZo7KyMiUnJ2v79u2qqqqS3W7X8OHDNX36dAUGBp72XCfExsY6X2dmZlr1MYE6ahyGvj5Upnf3HN86al9RdaNfI76N///NvQhTfLQ/D1OB83DJa3v009GKBmPBvjYdeqCXwv0bpxAJAAAAeBrDMHTZ6r36weSe2csm7b6npy4I96sXczgcGjlypN5///363+flpVWrVmncuHEur7969WrdcccdpvHp06dr4cKFzq+3bdumK664Qvn59WdvhIeH64svvlBCQkKD55o9e7bmzp1b5738/HxFREQ4v16/fr1uvPFGVVTU/3lceuml+uKLL5xbm+fl5al///46dOhQg9cLDg7Wjz/+qLi4OElSQUGBIiMj6x0XHx+vn3/+ucFzAICVXD1/94iChdUoWMDdGIahLTmVzqHdP2c3fIN3PrqFnhzaPaRTkHwY2g2csR+yyjVo9V7T+JR+kUq5poOFGQEAAADu5/XMQt3+QcMP3SXpkQS7nh3Wvt77KSkpmjZtmiQpLi5Oc+fO1Z49ezRr1izV1tYqPDxcu3btUnR0tOm5V69erXvvvVfjx4/XiBEj5Ofnp5SUFGcRxGazadeuXYqJiZEkXXnllfryyy8lSXfccYfuvvturV+/Xk8//bQk84f/W7du1cCBA+Xt7V2nGHFqwaKyslK9evXSgQMHJEkzZ87UZZddpvnz5+ubb76RJM2aNUtPPfWUJGnZsmWaPHmyJKldu3Z6/vnnFRQUpD/96U/O51Z//OMfNX/+fElSUVGRhg8frsGDByswMFDz5s1zmTMAWM3V83eP2BIKaO1sNpviowP02GXR2jS+h/be21MLh7XTVZ2D1EhjL7S/uFoLf87TVWv2q92ynbr7o0N6+5cilVY7GucCQAu2eHOey/i0+PqrmwAAAIDW5tYLw9Qt1Nc0vjy9QIWV9bdZXbx4sfP1ypUrdeutt+qPf/yj7r33XklSYWGhXnnlFZfXjo+P186dO7V8+XKNGTNGN910k9566y21a9dO0vGFghs3bpQklZSUaMOGDZKOd3AsX75c119/vebNm6devXpJkjZv3qxvv/22zjVqa2t1//33q7q6Wo8//rhpLqmpqc5ixQ033KB58+Zp1KhRev311507Hyxbtkw1NTXOz3fCyJEjdfvtt+vmm2/WhAkTnO9XVZ3c9jksLEzfffednnvuOQ0fPtzlzwUA3A0FC8ADXRDup+kJUfp87PGh3S8P76jRMaEK9Gmc6kVeRa3+taNQY1IPqs2STI1ae0Ar0wuUXcbQbuC3csprtHpnkWl8aKcg9WsTYGFGAAAAgHvy8bLpDwl203hxlUP/3FZ3C6b8/Hxt27ZNkuTr66uLL77YGbv88sudr08UGMzExcWpS5cudd7z9/dX165dnV8HBwdLknM2qiR5e3srIODk/XxISIjz9W/nZzz33HP68ccflZiYqBkzZpjmcmqugwcPdr7u3LmzM5/c3FznbIorr7zSecy6dev0+uuva+3atc4ijZeXl2677TYXnx4APAcFC8DDRQX6aGKfCL01sotyJsfq3ZFddF/fCLUJbJy98itqDa3dU6L7Pjms9v/cqSve2Kdn03K1h6HdgCRpRXqBKmvNd1dM7k93BQAAAHDC/X0jFOZn/jhq4aY8VZ9yf71v3z7n66ioKHl7n/xdt337k9tH7d1rvkWrmd27d2vTpk2Sjhcihg4dKun4tksntm+qrq7WCy+8oLKyMn3wwQd1tlT69ddf65xr9uzZ8vX11YoVK+rMSv2tUz/TiQ4PV5/pkksu0bJly2S323X06FHdfvvtGjVqlDIzM9W3b199/PHHdYo3AODJKFgALUiQr5dujgnVi9d1VNakXtpwWzc9mmhXj3Dzltuz4TCkrw6VacaGo4pZ+Yv6v7Jbs789prRj5WIcDlqjWoehJVvqD+E7oV2Qt0b3DLMwIwAAAMC9hfl7K6mf+aKegyU1emPXyQ7m0tJS52s/v7oDuU/9uqSk5KzyyM/P16233urcdunpp59WWNjxe3dvb+86HRIPP/ywgoODNWLECDkcJ7dNPjGjwjAMTZo0SeXl5Zo5c6bi4+NdXvtcPlOHDh3UsWPHeufKyMjQ6tWr62wJBQCejIIF0EJ5e9k0tFOwFlzRXr/c01NbJ/TQ3MHRGti28bam2ZpTqbnf52jgqr3qtmKXHvriiD47UFJnNQzQkn24v0R7i6pN40n9IuXXWINmAAAAgBZi+gC7fFw8kVqQlutcFHdimyZJ9R7Kn/r1qVs1nc7Ro0c1bNgwbd68WZI0Y8YMJScn1znmL3/5i5544ok614+OjtZVV13l/PpEF8abb76pL774Qn369NFf//rX017/bD/T559/rlGjRmnbtm265JJLtHfvXuXm5mrcuHGqra3V8uXL9dhjj53pxwcAt0bBAmgFbDabLmoToL8OitZPd/bQ/vsu1AtXttc1XYIbbWj3r8U1+sfmfF371gG1XZapuz48pDW7ilRSxdButFyLNpt3V3jbpKSL2A4KAAAA+K0uob76/YXmnchpxyr09i/F2plfKW/7ya6C3Nxc1daeHMqdlZXlfN29e/czuvaBAwc0dOhQbd26VZI0c+ZMPfPMM/WOs9lsmj17tnJycrRp0yalp6fr0KFDuuCCC5zH9O3bV5J06NAhSdL27dvl7+8vm83mHJ59QmRkpHMWxannOHr0aJ3jGvpML774orOz48EHH9QFF1wgu92uP//5z85j33rrrTP6/ADg7ihYAK1Q1zBfPTjArk9v7absybH69/UdNfbCUAX7Nk71oqDSoVcyCjX2vYNqszRTN717QMu35etoKUO70XLsLqjSh/vM285HxYSqc2jjbMcGAAAAtDQzBka5jN/63kHFvrxb8W/nSu17Sjo+T+Kzr79zHvOf//zH+fqKK6447TV37typIUOGaNeuXZKkp556SvPmzXP5PQEBARowYID69OmjI0eO6PXXX5d0fAD4DTfccNprNuTErAxJ+uabb5yvDx48qAMHDkg6Pq+jT58+ko4Xak4oLi4+7WsA8GTmE4AAtAqRAd6aEBehCXERqqhx6LNfS/XO7mKt3VOsY2W1pz/BaVTWGnpvb4ne21sim47odx0DdUtMqG6JCVPPCL/TnwBwU0u25svV5mfJ8XbLcgEAAAA8TWLbQF3ZOUjrD5ad/uDLx0lrnpQk3XD7RF01aaZ+55+nlStXSpLCwsI0YcIESdLjjz+uJ554QpL02GOP6fHHH5ckZWZm6oorrtCxY8ckSePHj9fQoUP19ddfOy/TtWtXde3aVZL03nvv6cUXX9SoUaPUsWNH7dy5U0899ZTKyo7nm5SU5ByQfdVVV+mFF16ol/ZDDz3kfD1//nz17Hm88DJy5Eh17txZBw8e1EcffaRZs2bpsssu0/z5851bYU2aNEm+vscXQPXp00cfffSRJGnevHmKiIhQWFiY83NK0oABA+pc+5133pEkbdu2zfleYWGh8/2uXbsqMTHR9c8dAJqBzWBSbj2xsbHO15mZmc2YCdB8ah2Gvssq17u7i/X27mL9UtD4A7z6RvnrlphQjeoRqovbBdRrmQXcVXmNQ52X71Jexf/P3p2HRVX9fwB/zwADM+wgsgmIKEgJCiYqKi64r7hkuZY7ZpvZL5fKrUXLvpZlmqmpWW5loLllagouuJX7hggKyBL7MsM2c39/jF4hWUQHBvT9eh6fZ+6cO+ecGWG4937u53zKD+o1t5bhylgP/kwTEREREVVie3QOhu1OqHpHjRpYPRW4duyhJqlUio0bN2LkyJEAKg5YrF+/HuPGjat0mNL7h4eHY/DgweXu161bN/z+++9QKBSV9lf6fCAzM1OseQEABw8eRL9+/VBYWPjQ61q3bo0jR46ItS7u3LmD1q1bIy0trdxx5HI5jhw5gjZt2pQ7dnleeeUVrF+/vtJ9iIhqSmXX37kkFBGVy0AqQQcnBT7vZI8br3jg8hgPfBJohzb2uivafTm9EJ+cSkPAlli4rI3GtENJ+PN2HopYtJvquK3XcyoMVgDAay1tGKwgIiIiIqpEYYkGy8+lV70jAEgNgAnLgf7vaJeHMjIG5BaQNu+ApVt2i8EKXfL29sawYcPg5uYGExMTmJmZoW3btvj222/xxx9/VBmsqEpwcDCioqIwePBg2NraQiaToWnTppgzZw4OHz5cpjC3q6srzp49i6lTp6Jp06YwNjaGoaEhGjVqhFGjRuHUqVNlghVERPUZMyzKwQwLosol5BZj561c7IjJxaGEfJTouK62pUyKvu5mCPEwR283M1gYG+h2AKIn1GbzLZxJKSi3zdRIgsSJnrDkzy0RERERUbkEQcCofYnYfD3nifuyMpbixEvuaG5jrIOZERFRbajs+jsDFuVgwILo0WUVqLE3Lg/hMbnYE5eHvGLdRi9kBhIEu5gixMMcA5uYw8GUpXdIv04nqxCwJbbC9ik+1vgu2LEWZ0REREREVL+E38zB4F2PsBTUIwpyVuDIi4111h8REdWsyq6/88ofET0RKxMDjGhuiRHNLVFYosGh+HzsuJWHHTG5SFaWPHH/RWoBe+PysDcuD6EHk9DOUS7WvfDiHTSkB9+ez6i0/TVf61qaCRERERFR/fTVP5UfU1dXRKIS/6Sq4NdQrtN+iYio9jHDohzMsCB6chpBwKlkFcLvFe2+kan7ot3NrWUI8TBHiIc52jjIIWXNAKphaaoSNFoTjcIK6qx0dJIjcrh7Lc+KiIiIiKj+uJxegBYbb+m830ktrPB9dyed90tERLrHottEVOukEgnaOSqwuKM9rr/SFFfHemBRh4Zo56C7O16uZRZh8Zl0tNsah0ZrohF6MAn74vJQqOuiGkT3rLucVWGwAgCmtbSpxdkQEREREdU/313IrJF+f76WjexCdY30TUREtYcZFuVghgVRzbqbV4zfb+UhPCYHB+PzoeOyFzCXSdG3sbZod5/GZix+TDqh1ghotv4mYnOKy223VxjgzgRPyAyY6UNEREREVJHmG27ieg1k4APAnkEu6ONuXiN9ExGR7rCGBRHVKU5mRpjia40pvtbIKdQW7d5xKxe7Y/OQU/Tk0YvcIg223sjB1hs5MJIC3UoV7XYyM9LBO6Bn0b7beRUGKwBgUgtrBiuIiIiIiKqQlP/ktQ4r8sftfLR3VMDKhDetERHVV8ywKAczLIj0o0gt4HBCPsJjcrEjJhd3a+BANsDeBCEeFghpao7m1jJIWPeCHlG/8DvYE5dXbpuBBIgb3wyNzBkQIyIiIiKqjPSrK6jJC1ESAC3tTBDkrECQswKdnBVoqOD9ukREdUll198ZsCgHAxZE+qcRBJxJKUB4TA52xOThSkahzsfwtJYhpIk5BnmYo50ji3ZTxW5lF6HpupsVnlgNaWqO7f1danVORERERET1kfm315Cn63WBq9DcWqYNYDQyRZCzAi680YiISK8YsKgmBiyI6p4bmYXYEZOL8JhcnEhS6fyOHHuFAQY2MUeIhzm6uZjCxFCq4xGoPnsvMgVLzqZX2H5giCuCXc1qcUZERERERPWTx7po3MqueKnV2tDYwqhMBkYzK2bfExHVJgYsqokBC6K6LTm/BL/fysWOW7n4804+itS6/RozM5Kiz72i3X0bm3H902ecqkSDRmuikVGgLre9ubUMV8Z68ASHiIiIiOgRjN9/F+uuZOl7GmXYKwwQ5GwqBjFaNDBmBj4RUQ1iwKKaGLAgqj9yi9T447a27sWuW7nI1kHR7tIMpUCXRtqi3YOamLNGwTNo/eUsjPvzboXtX3dxwButbGpxRkRERERE9dfJJCXabY3T9zQqZW0sRcd7wYsgZ1P42ZnAyIABDCIiXWHAopoYsCCqn4rVAo4kaoMX4TG5SMzTfdHuF+xNxODF87bGvKv+GRCw+RZOpxSU22ZqJEHiRE9YGjMLh4iIiIjoUQiCgBc2x+Lv1PKPsesiUyMJAh0VYgZGgIOcywgTET0BBiyqiQELovpPEAT8nVogBi8upeu+aLeHpRFCPMwR4mGB9o5yGEgZvHjanE5WIWBLbIXtU3ys8V2wYy3OiIiIiIio/qsqi7m6rI2lsDQ2QFxO7dTGkBlI0NZBLgYw2jvKYS7jTUxERI+KAYtqYsCC6OlzM6sIO2K0dS+OJip1XrTbTv6gaHewqynkvNvmqfDqH4nYcDW7wvbzo5rA186kFmdERERERFT/qTWJVpbxAAAgAElEQVQCeobdxqF45RP3ZWwgwZFhbmjrqMCdnGJE3lUiIiEfEYlKXMss0sFsq2YgAfwbmohLSHV0VsCGtRCJiCrEgEU1MWBB9HRLVZZgV6w282L/7XwU6rhot8JQgt73inb3czfngWo9la4qgfOa6Ap/Pjo6yRE53L2WZ0VERERE9HTIKlAj6Nc4XEx7/Gx4CYCtfRvhRU+LcttT8ktw9K4SEYnaf+f/LdD5zWsV8WlgLGZgdHJWwNGU9RCJiO5jwKKaGLAgenbkFWmw/06eWLQ7s1C3RbsNJEDnRgqEeFhgUBNzuFrwILW+WHImDe8dTa2wfXMfZ7zsZVmLMyIiIiIierqkq0ow6Pd4HLurqvZrTQwk2NTHGYOblh+sKE9WgRrHkpSISNAGMM6kqlCi21PACjWzkokBjCBnBdwsjFgTkYieWQxYVBMDFkTPpmK1gKN3lWLdizu5ul//1M/O5F7dC3P4NGDR7rpKrRHQbP1NxFawBq69wgB3JnhCZsD/PyIiIiKiJ1FQosGnp9Ow/FzGI99A1t3VFF90skfLJ1yeNb9Yg6ikBxkYUUkqFOg4A78iLuaGCHI2FQMYXtYynh8S0TODAYtqYsCCiARBwLl/HxTtvvAEacoVcbcwEoMXgU4KGLJod52xJzYX/XbEV9j+QUADfBTYsBZnRERERET0dFMWa7D1Rg5WXMjAmZSCh9qtjKV49TkrhPpYw8vGuEbmUFiiwZnUgnsZGPk4lqRCblHtpGDYyQ3QqVQGhm8DExjwHJGInlIMWFQTAxZE9F+x2dqi3eExuYi8q4RGx9+ctiYGGNDEDCEeFujhagqFEYt261O/8DvYE5dXbpuBBIgb3wyNzLm8FxERERFRTUjKL0ZCbgmyCtUwMZTA1sQQzaxkMKrlDOcSjYDz/xaIGRiRiUqkF6hrZWwLmRQdnR4EMFrby5nhTURPDQYsqokBCyKqTJqqBLtuaete7L+TB1WJbr9G5YYS9HLTFu3u724GW7mhTvunyt3KLkLTdTcrLMY32MMcvw1wqdU5ERERERGR/mkEAVczChGRoETkXSWOJChxN7+kVsaWG0rQ3lEuLiPV1kHOG92IqN5iwKKaGLAgokelLNbgzzv5CI/Jwe+38nR+t41UAnRyUiDEwxyDPMzhbinTaf/0sPciU7DkbHqF7QeGuCLY1awWZ0RERERERHWRIAiIzSkWi3hHJOYjJlv3tRDLYyQF2tjLxQyMQCcFLI0NamVsIqInxYBFNTFgQUSPo0Qj4Fipot1xFRRsfhItGxjfq3thgZZ2LNqta6oSDRqtiUZGBYEnL2sZro714OdORERERETlSswrRmTig0Lel9N1Xw+xPFIJ0MrORAxgdHRSwE7BbH0iqpsYsKgmBiyI6EkJgoCLaYVi8OKffx8uGvek3MyNMOhe0e5OzizarQsbrmTh1f13K2xf1tkeb/rZ1uKMiIiIiIioPktTleBoqQDGP/8W6LwmYkWeszEWAxidnBWsw0dEdQYDFtXEgAUR6drtnAdFuyMSlVDr+JvXxsQA/d21dS96upnBlGuZPpaAzbdwOqX84JLCUIK7kzyZZk1ERERERI8tp1CN40kqRCTmIyJRiVPJKhRramfsJpZG9wIY2joYTSyNmD1ORHrBgEU1MWBBRDUpo0CN3bHa4MW+uDwodVy028RAgh5upgjxMMcAd3OmAT+i08kqBGyJrbB9cgsrrOruVIszIiIiIiKip52qRIOTSSoxA+N4khIqHZ8jVsTJ1FDMvghyVuA5W2NIGcAgolrAgEU1MWBBRLVFVaLBgTv5CI/Jxc5buUhT6b5od4f7RbubmMPDikW7KzJufyLWX8musP38qCbwtTOpxRkREREREdGzpkgt4O/UBwGMo4lKZBfVTgqGjYkBOjkpENRIG8BoZWfCpYeJqEYwYFFNDFgQkT6oNQJOJKkQHpODsJhc3MrWfdFunwbGGNREW/fCv6EJ03/vSVeVoNGaaBRUsFZXRyc5Ioe71/KsiIiIiIjoWafWaOsj3l9CKiJRiX91fKNbRcyMpOjgJBeXkWpjbwJjQy4/TERPjgGLamLAgoj0TRAEXE5/ULT7bKrui3a7mBuKwYsgZ1MYGTy7wYslZ9Lw3tHUCts39XbGiOaWtTgjIiIiIiKihwmCgOuZRWLwIiIxH/G5JbUytrGBBO0c5GIGRntHBesnEtFjYcCimhiwIKK6Jj63GDtvaYMXhxPyUaLjjGArYyn6uWuDF73dzGAme3YOOjWCgKbrbiI2p/yMFnuFAe5M8ITsGQ7oEBERERFR3XU7p3QAQ4kbmUW1Mq6hFGjd8H4GhgIdnBSwNjGolbGJqH6r7Pr7s3NFioioHnMxN8K0ljb4c4gbUid74afezhjWzBymRrq5iJ5VqMHP17Lx4u4ENFh1Hf133MGaS5lIya+dO3X0pbi4GJPe/xSxH/QD3vMH3m8PrJoCxJwV95nUwrrCYEVSUhIWLVqE4OBguLm5wcTEBObm5mjfvj3WrVuH8u4JOHXqFIYNGwZnZ2cYGRlBoVDA29sb7777LtLT08vsu2XLFvTr1w9ubm4wNTWFTCaDi4sLXnzxRURFRZXZt6ioCN999x2GDh2KZs2awdzcHHK5HM899xzmzp2L/Pz8ct9DWFgYOnfuDAsLC5iZmSEgIAA//PBDuXMvbeLEiZBIJOK/8PDwSvcnIiIiIqKa4WYhwxhvK6zu7oTrrzRF0iRPbOvbCK+3tIZvA+MaG7dEA5xMVmHJ2XQM2BkP2++uo9VPMXjzcDJ+jc556s8niahmMMOiHMywIKL6oqBEg0Px2qLdO27lIlWp27VMJQACneTi0lHNrGvuYLe2aTQaDBgwAHv27Hm4USIFxnwOqX8fxI1vBhdzo3L72LJlC0aMGFHhGG+++SaWLVsmbh89ehTdunVDcXH52Rze3t74+++/YWKiLe798ssvY+vWreXua2hoiAMHDqBz584AgOTkZDg6OlY4lzZt2uDo0aOQyR4UXl+8eDFmz55d7v7Tpk3D8uXLy207dOgQgoODyzwXFhaGkJCQCscnIiIiIiL9yChQ49jdexkYCfk4m1qACsr36ZyXtUzMwAhyNoWrRfnnVkT0bOGSUNXEgAUR1UdqjYCTySqEx+QiLCYXN7N0nwb8nI0xQjy0wYvW9iaQ1uOi3StXrsRrr72m3bBvAvR5A0hPAHZ/BWjUgIk5+q6OwO7RrSrsY8uWLRg3bhxGjRqFvn37QiaTYeXKlWIQRCKRIDo6Gh4eHgCAkSNHYvPmzQCAdu3aYe7cuUhKSsIbb7wBpVIJANi9ezf69u0LAFiwYAFKSkrg6+sLW1tbxMTEYP78+bh79y4AbUDjfn/3AxZ9+vTBSy+9BEdHR+zfvx//+9//xPmuXbsW48ePBwBcunQJrVq1glqthqmpKb788kuYmprinXfeQUpKCgDgjz/+QM+ePcu8Z6VSCR8fH9y6dQsmJiYoKNDWV2HAgoiIiIiofsgr0uBE0oMlpE4mq1BYSxEMN3MjsQZGkLMCzaxkkNTj80oiejyVXX83rO3JEBFRzTCQShDopECgkwKfdWyIqxlFCI/JwY6YXJxK0U3R7isZhbiSUYhPT6fB2UxbtHuQhzm6NDKtdzUeVqxY8WBjxCeAm6/28b9xQNR2oCAXrjf2Aag4YNGyZUvcuHEDLi4u4nM9evSAm5sbUlJSIAgCzp49KwYssrOzxf1ef/119OnTBwCwadMmHDx4EIB2aaf75s2bV2a8bt26IT8/H9OnTwcA5Obmim2mpqY4duwYAgMDxed69uyJmJgYcbmmM2fOiAGLVatWQa3WZuR8+OGHmDRpEgBtEb/Ro0cDAL799tuHAhYffPABbt26hd69e0OlUuHIkSMVfj5ERERERFT3mMmk6OFmhh5uZgC0mfunU1T3MjCUOJakRH5xzQQwbucWY+PVbGy8qj03slcYIMjZFEHOCnRyVsCngXG9vjGOiJ4cAxZERE8hiUSC52yN8ZytHeYE2CExrxg7Y7RFuw/pqGh3Yl4JVlzIxIoLmbCQSdHP3Uws2m1hXLcLrWVmZuLSpUvaDQNDwOX5B43uftqABYDkiycr7cfb2/uh54yNjeHq6ipmKZiamoptXbp0EbMvvv32W9jY2CA5ORknTpwAADRs2BBdu3Ytd6zi4mJER0eXWSKq9L7m5uZlghX3NWvWTHxcei4RERHi4/bt24uPO3ToID6OjIws09epU6ewbNkymJubY9WqVRg7dmy5cyUiIiIiovrDxFCKTs6m6ORsivcDgBKNgH9SC+5lYOQjMlGJzEIdnESWI0Wpxi/ROfglOgcAYGUsRUenBxkY/g3lMKpnN8cR0ZNhwIKI6BngbGaEqS1tMLWlDbIK1Ngbl4cdt3KxJy4PuUVPfuCZU6TB5us52Hw9BzIDCYJdTDGoiTkGepjB0bTurVEaFxf3YENhBUhLBVjMG4gPY2Njq913TEwM/vnnHwCAmZkZOnXqJLa9/fbbuH37Nr7//nucOHFCXPoJAPr164dly5bB0tKyTH9paWmws7Mr85ypqSlCQ0Px1ltvVTqX4uJi7Ny5U9y+n9EBlP0M7O3txccODg7i48zMTGRlZcHKygrFxcWYMGECNBoNPvvsM7i6ulY6NhERERER1U+GUgnaOMjRxkGOGa1toREEXE4vFDMwIhKVSFbWTEHtrEINdsXmYVdsHgBAYahdSeB+ACPAQQ65obRGxiaiuoEBCyKiZ4yViQFGNLfEiOaWKCzR4K8EpbZod0yuTg46i9QC9sblYW9cHkIPAe0c5GLdCy+bulG0Oz8//8GG4X8CKqW28/LyqtVvZmYmhg4dipIS7ee4ePFiWFhYPOja0BCNGzeGlZUV/v333zKvjYyMxN69e/H6669XOY5EIoEgCKisDJVGo8HkyZPFtSCHDRuGbt26ie2lP4PShbhLPwa0n4GVlRU++eQTXLp0CUFBQQgNDa1yjkRERERE9HSQSiTwaWACnwYmmNbSBoIg4GZWkVgDIyJRibic4hoZW1ki4MCdfBy4oz1/kRlIEGBvIi4jFegkh7msbmf4E1H1MGBBRPQMMzaUondjM/RubIYV3Rxw6l7R7h0xubiWqZui3VHJKkQlqzDrWCqaW8vE4EUbB7ne1iaNU5U6oC35z4F1qW0zM7NH7jMlJQU9evTAxYsXAQAzZszAtGnTyuyzcOFCzJ8/HwAwceJELFmyBGlpaRg0aBCuXLmCN954A82aNUOvXr3E11hZWSEyMhIFBQW4evUqPv/8cyQkJGDp0qUAUKaotvgWSkrwyiuvYNOmTQCATp06YcOGDWX2MTU1RU6ONu26dN2M0o/vfwaJiYlYtGgR5HI51qxZw6J4RERERETPMIlEgmbWxmhmbYwJLawBAHdyihF5V4mIhHxEJCp1dj75X0VqAUfvqnD0rgqfngakEsC/ocm9DAxTdHSSw1bOy51E9ZlEqOz2zGdUZVXKiYieFdcyCrHjXt2LqGSVzvt3NDXEwCba4EXXRgoY12Ja78jtl7F5WAvthoEh8PnfD5aFOhUGbP4AABASEoKwsLAq+7tz5w66d++O6OhoAMCsWbOwaNGih/ZzdnbG3bt3AQC3b98Wl1X6+uuvxeWdJk+ejFWrVlU41sGDB9G9e3cA2uWbkpKSyrQXFhZi+PDh4lJQwcHB2LFjR5n6FYC2YPiFCxcAAEeOHEFQUBAA7VJR7u7uALTBkszMTJw7dw5+fn5Vfg4AKs36ICIiIiKiZ0OqsgSRiUpE3svAOPdvAWrrTKGFrbG4hFQnZwWczOreMsVEz7rKrr8z5EhEROVqbmOM5jbGmNmmAZLyi/H7rTyEx+TiYHw+itRPfqiZlF+CVRczsepiJsxlUvRtbIZBTczR190Mljos2p1VoEZYTC4upxcgq1ADVYkG2xIkgENTIPkmoC4B7lwCGrcEANinXETKvdfev4hfmRs3bqB79+6Ij48HAHz66aeYPXt2ufump6eLj3Nzcyt9XFRUBENDQ0ilZQM5BgYPPpvs7Owybfn5+Rg0aBAOHjwIABg4cCC2bdsGY+OHl+Lq1KmTGLA4fvy4+F6PHTsm7vMo75+IiIiIiOi/GioMMbSZBYY20y6Rm1WgxvGkB0tInU5RoaRm6njjUnohLqUXYsWFTABAUyuZGMAIclagsYURs8aJ6jBmWJSDGRZERBXLKVRj321t8GJ3bB5ydFC0uzQjKdC1kSlCPMwx0MMczo95N8w/qSqsuJCJn69lQ1VSzp+6o5uA7Z9oH9s3Afq8AWQkQrr7S2jUalhYWODmzZuws7PD/PnzsWDBAgDAvHnzxGWdrl+/jqCgIKSmpgIARo0a9VB9B1dXVzGTwt/fXyzI3aFDB8yePRtpaWl49913kZaWBgD47LPP8N577+HcuXMYNGgQxowZA19fX9ja2uL69etYvHixGBzp1KkTIiIiAAAqlQrdunVDVFQUAMDX1xfLli2DoeGDexMsLS3h4+MDALhw4QL8/Pyg0WhgZmaGpUuXwtTUFO+88w5SUrQhm3379qFXr15IS0vDli1bHvoIly1bhps3bwIAJk2aBF9f30eqwUFERERERM+2/GINTiarxCWkTiSpUKCDG+MeRSMzQ3EJqaBGCjS3ljGAQVTLKrv+zoBFORiwICJ6NEVqAYcT8sW6F3fzn7xo938F2JsgxMMCIU3NH+lAMrtQjQl/3sX2m7mV7geNGlg9Fbh27KEmqVSKjRs3YuTIkQBQYcBi/fr1GDduXKXDlN5/165dGDRoEDSa8oM8Hh4eOHv2LCwtLatchsnCwgIHDx7ECy+8AKDsUk4V6dy5Mw4fPixuf/zxx/jwww/L3Tc0NBQrV66stL8uXbrgyJEjAICwsDCEhIRUuj8REREREVF5Cks0OJtaIGZgHL2rRK6Ob46rSAO5QZkMDN8GJjCQMoBBVJO4JBQREdUImYEEPd3M0NPNDMu7OuBsSgHC79W9uJJRqJMxTqUU4FRKAeYcT0UzqwdFu9s5Ply0+25eMXqH38HFtEcYW2oATFgOHNkInNkJpMdDYmiMDu0D8PHcD9C5c2edzL+0/v37IyIiAl9++SVOnDiB1NRUGBoaonHjxujXrx9mz54NS0tLAICLiwveeecdREZGIi4uDpmZmTA2Noa7uzu6d++Ot99+G25ubk80nw8++ADe3t746quvcO7cOWg0Gnh7eyM0NBQTJkzQxVsmIiIiIiKqkrGhFIFOCgQ6KTCrDVCiEXAhrQARCQ+WkUovUNfI2GkqNX67mYvf7t30ZiGTooPTgwDGC/ZyyAwYwCCqLcywKAczLIiIntyNTG3R7h23cnH8rkrnBdbsFQZi0e5uLqYoVAvouC0Ol9KfLFDibmGEEy+5w96UMX0iIiIiIqK6QCMIuJZRhIhE7RJSRxKUNZLhXx65oQTtHOTaAEYjU7RzkENhJK36hURUIS4JVU0MWBAR6VZKfgl+j9VmXhy4k49CHa9NamokgaXMQGcHrJ2cFTg8zO2hDA4iIiIiIiLSP0EQEJtTXCoDIx8x2cW1MraRFHjBXi5mYHRwUsDS2KBWxiZ6WjBgUU0MWBAR1ZzcIjX+uJ1/r2h3LrIKa2dd0uraNcgF/dzN9T0NIiIiIiIiegSJecWITHywhNTlJ8y+f1RSCdCygcm9DAwFOjkpYKdgxj5RZRiwqCYGLIiIakexWkBEYr5Y9yIhr3ZSeh9F38Zm2B3iqu9pEBERERER0WNIU5XgaKkAxj//FkBTS1dBvW1kCHI2FbMwGpkb1c7ARPVEZdffueAaERHpjZGBBMGuZvimqyPuTGiGMyPc8UFAA/g0MNb31LA3Lg+3sov0PQ0iIiIiIiJ6DA3khghpaoGlnR1wZmQTZIZ6YV+IK+a0aYCOTjVbSPtqRhFWXczEqH2JcFkbjSY/ROPVPxLxw+VM3MwqAu8fr1hxcTG++OIL+Pj4QC6Xw8bGBn369EFkZOQjvT4pKQmLFi1CcHAw3NzcYGJiAnNzc7Rv3x7r1q176LNv3LgxJBJJpf/Wr19f5jWbNm1C165dYWtrC0NDQ1hYWKBt27ZYtmwZ1OqHi8OvW7cO7dq1g5mZGRQKBfz9/bFy5UpoNGVXnKju3M+ePYuRI0fCy8sLlpaWMDIygp2dHYKDg/Hzzz8/0udVFzHDohzMsCAi0r+YrCLsuKXNvDiaqNR50e5HsbC9HT5sa6eHkYmIiIiIiKgmqUo0OJWsEjMwjt9VQllSO2eejqaGYvZFkLMCz9kas4YiAI1GgwEDBmDPnj0PtUmlUmzatAkvvfRSpX1s2bIFI0aMqLD9zTffxLJly8Ttxo0b4/bt25X2+dNPP2HUqFEAgM8//xwzZ86scN9x48bhhx9+ELcnTZqENWvWlLvv2LFjsWHDhsee+3fffYepU6dWuP8nn3yCOXPmVNiuT1wSqpoYsCAiqltSlSXYda9o95+381Gg46LdFXmxmQW29WtUK2MRERERERGR/hSrBfyd+iCAEZmoRHZR7dRctDExQCenBzUw/BqawFD67AUwVq5ciddeew0A4O3tjY8++gi3bt3C7NmzoVarYWlpiejoaNjZVXxj4ZYtWzBu3DiMGjUKffv2hUwmw8qVK8UgiEQiQXR0NDw8PAAAZ86cQUFBQZk+kpKSMHz4cACAiYkJEhISYGtrCwBwcnJCUlISAGD8+PEYMWIEDh06hEWLFgHQBlaysrJgbm6Ow4cPo2vXrgAAGxsbfPnll7CwsMDMmTNx48YNAMBvv/2GwYMHP9bct2/fjoMHD6JDhw5wcHBARkYGli5diqioKACAg4ODONe6hgGLamLAgoio7sov1mD/7TyEx+Ti91u5yKzBot3dXU3x5xC3GuufiIiIiIiI6ia1RsCl9MJ7AYx8RCQqkap8eLmfmmBmJEWgoxxBjbQZGG3s5TAxfPpX9vfx8cGlS5cAAFFRUWjbti2AslkKS5cuxfTp0yvs4+rVqzAzM4OLi4v4XGFhIdzc3JCSkgIA2Lp1qxiQKM+8efOwcOFCAA9nTJiamkKpVAIAEhIS4OzsDEEQYGxsjOLiYgBAZmYmrKys8H//93/44osvAACzZs0SgxqbN2/GyJEjAQA9e/bEH3/8obO5nzt3Dn5+fuJc8/LyKtxXnyq7/s6S9UREVK+YGkkxuKkFBje1QIlGQPftt3EkUVkjY6lrqyIbERERERER1SkGUgla2pmgpZ0J3mhlA0EQcCOzSMzAOJKYj/jckhoZO69Yg/138rH/Tj4AwNhAgrYOcnEJqfaOCpjJnq4ARmZmphisMDIywgsvvCC2dejQQQxYREREVBqw8Pb2fug5Y2NjuLq6ihf9TU1NK3x9cXExVq9eLW6//vrrZdq7dOkiZjzMnTsXI0eOxMGDB8VgRa9evWBlZQUAyM7OFl9XekwzMzPx8fHjx6HRaCCVSp9o7hqNBklJSVi5cqX43P3sjvqGAQsiIqq3DKUSeFnLaixgYW1iUCP9EhERERERUf0ikUjgZWMMLxtjTPKxBgDcznkQwIhIVOJGZlGNjF2oFsQxAMBAArRuaIKgRqYIclago5Oi3p+/xsXFiY9tbW1hYPDg/Tg4OIiPY2Njq913TEwM/vnnHwDaYEGnTp0q3Hf79u3iMkqBgYHw9/cv07527VqMHTsWf/75J3744Qcx+0IqleLNN9/Exx9/LO7bvHlz8fHmzZsxatQoWFhYlAkq5OXlITMzU1xy6nHm3qJFC1y+fFnclkql6N+/f5nAS33CgAUREdVrzaxlNdZ3U8ua65uIiIiIiIjqNzcLGcZYyDDGW3tHfXJ+CSJLLSF1Ma0QNZG3rxaAUykFOJVSgC/OpkMCwKeB8b0MDFN0clbAwbR+XfbNz88XH8tkZc/FS29Xd4mjzMxMDB06FCUl2myYxYsXw8LCosL9ly9fLj5+4403HmpXKBRo0qQJZDIZiooeBKg0Gg3Cw8MxZMgQMajwyiuv4LPPPkNqaiquXLmCJk2alDvmf2toPO7cS5NIJNBoaqcGi649XblDRET0zBnW1AI1VYrsZa9HOxAgIiIiIiIicjA1xIueFvimqyPOj/ZAWqgXdg50wbutbRFgbwKDGjp5FQBcSCvE8vOZGL4nAY6rb8Brw01MOnAXG69m4XZOzWR+6FLppY5KBwL+u116OaWqpKSkoHPnzjh//jwAYMaMGZg2bVqF+58/fx7Hjh0DADg6OmLo0KEP7TNo0CCsWrUKRUVFWL16NfLy8vDXX39BLpcjLi4O/fr1Q1paGgBtpsihQ4cQEBBQpo+BAwfCxMRE3L6/hNTjzn3jxo3466+/8OOPP6Jdu3bQaDTYsWMH+vfvj/pYvrp+hdqIiIj+o7GlDP3czbArVreFpNo7yuHXUK7TPomIiIiIiOjZYWNigAFNzDGgiTkAIK9IgxNJD5aQOpmsQqG6Zi4o38gswo3MIqy5lAUAcDU3EmtgBDkr4Gktg0RSU7f/VV/jxo3Fx+np6VCr1eKyUMnJyWKbu7v7I/V3584ddO/eHdHR0QDKFr2uSOnsiilTpsDIyKhMe3R0NA4fPizOY+LEiQC0dS169+6NsLAw5Obm4s8//8SIESMAAM8//zxOnjyJ+Ph4JCcno3HjxlCpVHBzcwMAuLm5PVSXorpzv19kGwCGDBmCBg0aoKCgAGfPnsWNGzfKFLiuDxiwICKieu81X2udByxe87XWaX9ERERERET0bDOTSdHDzQw93LRZAgUlGpxOUd1bRkqJY3dVyCuumWV87uQW46dr2fjpmrYQdEOFgbiEVJCzAi1sjWEgrb0AxsW0AkQlqZBeoEahWoClTAqXZt6Ij76K4uJinD59Gu3atQMAMesBAIKCgqrs+8aNG+jevTvi4+MBAJ9++ilmz55d6WsyMzOxadMmANqi31OmTHlon/T0dPFxfn6+WCwbAFMhLj0AACAASURBVHJzc8W20o/vc3FxgYuLC4CyhbwHDBjw2HNXqVSQy8veaCmRSMoEokoX/q4vGLAgIqJ6r1djM7SxN8HplPLXfawuT2sZXmzG5aCIiIiIiIio5pgYStHJ2RSdnE0xB0CJRsC5fwu0GRgJSkTeVSKjQF0jY6cq1fg1Ohe/RmsvrlsZS9HR6UEGhn9DOYx0vIaVqkSDbTdysOJ8Bk6Vd/7uOwSI/gQAMPKVcVjy6ceIjY3FunXrAAAWFhYYPXo0AGD+/PlYsGABAGDevHmYP38+AOD69esICgpCamoqAGDUqFHo1KkTjh49Kg7j6uoKV1fXMkP/8MMPUCq1Rc2HDRtWptD3fZ6enjA0NERJSQlSU1MRGhqKYcOG4cyZMzh48KC4X6tWrcTHISEh8PPzg7+/P0pKShAWFoaNGzcC0C5vNWPGDHHf6s7dy8sLffr0QWBgIBo1aoSUlBR8++23UKlUALT1Np577rly/ifqNolQHxeyqmGl02SuX7+ux5kQEdGjissuQvutcUhWljxRP5YyKY691BjP25pUvTMRERERERFRDdEIAq6kF4pLSB1JUD7xOe+jUhhK0N7xQQCjraMccsPHL4e84UoWZkSkIL2yAIxGDayeClw79lCTVCrFxo0bMXLkSAAVByzWr1+PcePGVTqX0vsDgCAIaNasGWJiYgBoMzoCAwPLfe3MmTPx+eefV9j30KFD8euvv4rbrVq1EutQlGZsbIytW7di0KBB4nPVnbuVlVWFGRQSiQQrV64sN1OkLqjs+jszLIiI6KnQ2FKGvSGu6BV+G6nKx7sDxVImxe+DXBisICIiIiIiIr2TSiRo0cAELRqY4LWWNhAEATHZxYhIzEdEgjaIEZtTXCNjK0sEHIzPx8H4fACAkRQIcJCLy0gFOsphYWxQZT+CIGDW0VR8fja9yn0hNQAmLAeObATO7ATS4yExMkbbgAAsnv8BOnfu/KRvq1x79+4VgxV+fn4VBisAYPHixWjRogV++OEHXLhwAdnZ2VAoFPD29sbIkSMfKow9evRoyGQyxMTEIDc3Fw0bNkRwcDBmzZoFb2/vJ5r3nDlzsH//fly7dk0s9O3k5ITAwEC89tprlb6PuqxeZFgUFxdj0aJF2LVrFwDt2l6zZ8+GoWHZeEtRUREWLlyI48ePIzMzE/b29pg4cSKGDRsm7pOSkoIFCxbg7NmzAIC2bdti7ty5aNCggbgPMyyIiOqv2OwiDPo9HhfTCqv1Og9LI4QPcEGLBgxWEBERERERUf0Qn1ss1sCISMzH1YyiWhlXKgH87EzEDIyOzgo0kD98b/ycYylYdPoRghWVsDaW4vhL7mhuY/xE/VDdUdn193oRsPj6669x8OBBrF69GgAwadIk9OjRo0yBEgBQKpVYvXo1Bg8eDBcXF5w/fx6TJk3Cl19+iY4dOwIApk6dColEgiVLlkAQBLz77ruQy+X48ssvxX4YsCAiqt+K1QK2RWvXxTyepKp0Xz87E0xraY2RzS2fKL2ViIiIiIiISN9SlSU4elcpZmCc+7cAtXXx93lbYzGAEeSswF8JSozel6iTvt0tjHB+dBOYy6rO6qC6r94HLDp37ozZs2ejd+/eALRpOp9//jn++uuvKl/7+uuvo1mzZnjrrbcAaLMzJk+eLFZg37lzJ77//nsxewNgwIKI6GlyLrUAP13LwpWMImQWqCFAe3eGp7UxRnpZIMBBDolEt4XEiIiIiIiIiOqCrAI1jicpxToYp1NUKNHUztgGEkCtwyvPnwTaYU6Ane46JL2p1zUssrOzkZycXGZNL29vb9y9exe5ubkwNzev8LWFhYW4cOEC+vfvLz43btw47Nu3D126dIEgCNi9e3eNrX9GRET616qhCVo1dND3NIiIiIiIiIhqnZWJAfq6m6Ovu/YaqrJYg6hkFSIS8hGRqMSJJBUKdBlVKEXX3X53MRMzX2gAAylvOnya1fmAhVKpBIAygQkLCwsAQH5+foUBC0EQ8P7778PNzQ09e/YUn/f398e2bdvQpk0bANpK7VOnTq2p6RMRERERERERERHVCQojKbq5mKKbiykAoEgt4EyKChGJSkQmKnH0rhI5RbWUglFN8bkl2B2bh4EeFd/ATvVfnV+sW6FQAADy8vLE53JzcwEApqam5b5GEATMmzcPsbGxWLFiBaRS7dvUaDQYP348/P398c8//+Cff/6Bv78/Jk6cWMPvgoiIiIiIiIiIiKhukRlIEOikwKw2DbA7xBUZoV74e6Q7vupsjyFNzdFAXrdqRqy9nKnvKVANq/MZFpaWlnBwcMDVq1fh6uoKALh69SocHR3Lza4QBAELFizAxYsXsX79+jL7ZGVlITExEWPHjoVcLgcAjBkzBmvXrkVGRgZsbGxq500RERERERERERER1TEGUgn8Gsrh11COt/xsIQgCrmUW3SvinY8jiUok5pXobX5nUwv0NjbVjjofsACAIUOG4LvvvoO/vz8AYNWqVRg2bFi5+y5cuBB///03NmzYAEtLyzJtNjY2cHNzw88//4zXX38dAPDzzz/DwcGBwQoiIiIiIiIiIiKiUiQSCbxtjOFtY4wpvtYQBAFxOcViEe+IRCVuZhXV2nwyCtS1NhbpR70IWLz22mvIyspC3759AQADBgxAaGgoAGDu3LkAtIGKxMREbNq0CTKZDN26dRNfP2DAACxcuBAAsGLFCixatAhBQUHQaDTw9vbGypUra/kdEREREREREREREdUvEokE7pYyuFvK8MpzVgCAu3nFiCwVwLiUXlhj4xfVUIFwqjskgiDwf/k/vLy8xMfXr1/X40yIiIiIiIiIiIiI6o8V5zIw7XByjfRta2KAtFCvqnekOq2y6+91vug2EREREREREREREdUPzzcwrrG+3SyMaqxvqhsYsCAiIiIiIiIiIiIinWjvqEBDhUGN9B3iYV4j/VLdwYAFEREREREREREREemEzECCSS2sdd6voRQ10i/VLQxYEBEREREREREREZHOTG5hDalEt30ObWoBB1ND3XZKdQ4DFkRERERERERERESkM64WRhjV3FJn/UkAvNvaVmf9Ud3FgAURERERERERERER6dSyzg7wtpHppK/POjbEC/ZynfRFdRsDFkRERERERERERESkU9YmBtgb4orGFkZP1M8MfxtmVzxDGLAgIiIiIiIiIiIiIp1zs5Ah6iV3BNibVPu1BhLgf0H2WNLJHhKJjgtiUJ3FgAURERERERERERER1Qh7U0Mce8kda3s4wr9h1YELEwMJxj9vhX9GNcE7/rYMVjxjWFadiIiIiIiIiIiIiGqMoVSC8c9bY9xzVjiVrMLay1k4maxCeoEaqhIBVsZSuJgZYaCHOV59zgo2Jgb6njLpCQMWRERERERERERERFTjJBIJ2joq0NZRoe+pUB3FJaGIiIiIiIiIiIiIiEjvGLAgIiIiIiIiIiIiIiK9Y8CCiIiIiIiIiIiIiIj0jgELIiIiIiIiIiIiIiLSOwYsiIiIiIiIiIiIiIhI7xiwICIiIiIiIiIiIiIivWPAgoiIiIiIiIiIiIiI9I4BCyIiIiIiIiIiIiIi0jsGLIiIiIiIiIiIiIiISO8YsCAiIiIiIiIiIiIiIr1jwIKIiIiIiIiIiIiIiPSOAQsiIiIiIiIiIiIiItI7BiyIiIiIiIiIiIiIiEjvGLAgIiIiIiIiIiIiIiK9Y8CCiIiIiIiIiIiIiIj0jgELIiIiIiIiIiIiIiLSOwYsiIiIiIiIiIiIiIhI7xiwICIiIiIiIiIiIiIivWPAgoiIiIiIiIiIiIiI9I4BCyIiIiIiIiIiIiIi0jsGLIiIiIiIiIiIiIiISO8YsCAiIiIiIiIiIiIiIr1jwIKIiIiIiIiIiIiIiPSOAQsiIiIiIiIiIiIiItI7BiyIiIiIiIiIiIiIiEjvGLAgIiIiIiIiIiIiIiK9Y8CCiIiIiIiIiIiIiIj0zlDfE6jrvLy89D0FIiIiIiIiIiIiIqKnHjMsiIiIiIiIiIiIiIhI7xiwICIiIiIiIiIiIiIivZMIgiDoexJERERERERERERERPRsY4YFERERERERERERERHpHQMWVKdMnz4ds2bNEre7deuGzZs363FGdcvJkyfh5eWFwsJCfU+FqFZ98cUXGDNmjL6nQUR1wKP8Lfzv8QQRERHpFs/VieqnWbNmYfr06XoZ28vLCxEREXoZm+oXBiyoUklJSfjwww/RpUsXtGjRAl26dMHkyZNx+PDhWhn/119/xeDBg3XWX0xMDLy8vJCQkFDpfr/99hu8vLzEf61bt8bYsWNx8eJFnc2lKuVdbPHz88PRo0dhbGxca/Mgum/WrFni70SLFi3Qs2dPrFixAk/zyoKlvwfu/3v//ff1Np9H/Q4jqs+q+q7h30Kip4M+L5gQ1Wel/042b94cgYGBmDFjBtLT02t1Hro+VweAMWPGiO/N19cX/fr1w5YtW3Q6BpGu7N27F97e3vj000/1PZVqef/997Fw4cIaHWPz5s3o1q1bjY5x37N4neJZYKjvCVDdFRcXhxEjRsDd3R3z58+Hu7s7cnJycPXqVfzvf/9D+/bty71YUFxcDCMjI53MwcbGRif9PO7YO3fuBABkZ2dj5cqVmDJlCiIjI2FgYKCXOclkMtjZ2ellbCIACA4OxoIFC1BSUoKLFy9i1qxZsLS0xKhRo/Q9tRrzv//9D23bthW35XL5Y/UjCALUajUMDfmnl6gqlX3X8G8hERE96+7/nRQEAYmJiViwYAEWLFiAr7/+utbmUFPn6qNHj0ZoaCiKiopw/PhxzJ8/H46OjujcuXONjAfo9hqGLvC8oX4ICwvDuHHjEBYWhv/7v/+r0Z8hXf5MmJub62BGdYs+rlOo1WpIJBJIpXUnF6CufZc9ibrzqVKdM3/+fDg7O+Onn35Cly5d4ObmBh8fHwwfPhw7d+4UgxW//fYbOnTogD179qBHjx7w9fWFIAj47bffMHjwYPj5+SEoKAjz589Hfn5+mTG++eYbtG3bFm3atMGSJUseioD+N800ISEB06ZNg7+/PwIDAzFr1ixkZ2eL7WPGjMFnn32Gjz/+GK1bt0ZQUBDWr18vtvft2xeA9svMy8ur0uUipFIp7OzsYGdnh6ZNm2LKlClIT09HamqquM/ly5cxcuRI+Pj4oEOHDvjiiy+gVqvF9oyMDEyfPh3+/v7w8/PDW2+9VebOlxMnTmDIkCFo2bIlAgICMGbMGOTl5eGbb77Bnj17EBYWJkaKExISHloG45tvvsHw4cOxbds2dO7cGW3atMH777+PoqIicYzk5GSMGzcOPj4+6NOnD44cOQIvLy+cPHmy4v98ogoYGxvDzs4Ojo6O6NmzJzp06IATJ06I7RkZGXjrrbfQsWNHtGrVCkOGDCnTDmizFrZv345JkyahZcuW6NevH6Kiosrss3nzZnTs2BF+fn6YM2dOmZ9pAFCpVJg7dy7atm2Lli1bYsKECbh9+7bYfv9346effkLHjh3xwgsvYPny5SgqKsKCBQvQunVrBAcHIzIyssr3bGFhIX4X2NnZwczMTGzbtWsXevXqhRYtWqBXr17YsWOH2JaQkAAvLy/s2bMHw4cPh4+PD86cOQMA2LdvHwYMGAAfHx/07t0b27ZtE19XVFSEefPmITAwED4+PujevTt++eUXANX7DiOqzyr7rilvSaiqjice5W/htWvX8Oqrr6Jly5YICgrCokWLuAQjkR5UdSwhCAK++uorBAUFiRng3377bZVtABAfHy8efwQEBODDDz+EUqms9fdI9KTu/51s2LAh/Pz8MGzYMFy5cgUAEBsbiylTpqB9+/bw9/fH6NGjxbb7Tp8+jX79+sHX1xdjxozBtm3b4OXlVWafr776Svzb+sUXX1S6fPP9494DBw7g5ZdfRsuWLTF06FBcu3atWn0C2puD7Ozs4OzsjBdffBFeXl44fvx4mX1OnjyJ4cOHw9fXF8HBwVi5cmWZ8/AbN27gxRdfhI+PD4YMGYI9e/aUyVKu6BqGRqPB8uXL0aVLF7Rq1QrDhw/H2bNnxX4TEhIwefJktG7dGn5+fhg0aBDOnz9fZRvw+OcNVDf9+++/OH36NKZOnYqGDRviyJEjYtuNGzfQvHlzJCUllXnNrFmz8NZbb4nblZ0TVvQzce7cOYwdOxYBAQEICAjA5MmTER8fX2acP/74A926dUPLli0xbdo0fP/992WyHf6b4ditWzesWbMGM2bMgJ+fH4KDg7Fnz55q9VnayZMnMX/+fCQmJorXs3777bcyn11l1wKq+v0uT1XXKYCqj/WrOl+4fw5y5MgR8f8tMTERALBp0yb07NkTvr6+GDhwIA4cOCD2m5WVhenTp4vXLvr06YNDhw5V2QYAx44dQ0hICFq0aIGuXbvihx9+KPOevLy8sHXrVowfPx4+Pj7YuXNnld9F9QUDFlSujIwMREVFYfz48eVGCyUSSZntnJwcbN68GUuXLkV4eDgAbbRxxowZ2LlzJ5YsWYKTJ0/iq6++El+zY8cOrF27Fh988AG2bNmCzMzMMl/y/1VcXIwJEybA3t4ev/zyC9auXYukpKSHDnC2bduGhg0bYvv27ZgyZQoWLVqEq1evAgB+/vlnAMAvv/yCo0ePPvLSLkVFRfj999/h4OAg3tWZn5+PiRMnwtnZGWFhYfjoo4/w66+/4vvvvxdf99577yEuLg7r1q3Dhg0bkJCQgHfffRcAUFJSgtdffx0DBw7E7t27sXHjRvTu3RsAMH78eAQHB6NPnz44evQojh49CkdHx3LndvPmTURFRWHt2rVYtmwZ9u/fXybI895770GlUmHr1q34+OOPa/WuG3q6Xbt2DX///XeZuzwKCgrg5+eHNWvWYMeOHejatSumTp2KlJSUMq9dvnw5hg4dih07dsDX1xfvvvuuGJQ4c+YMPvroI4SGhmL79u2ws7MTL9jft3jxYhw9ehRfffUVtm7dCqlUitDQ0DIHMtHR0bhw4QI2bNiAOXPm4JtvvkFoaCjc3Nywfft2BAUFYebMmY99QfL8+fP4v//7PwwfPhy///47RowYgdmzZz90crFs2TJMmzZNPFE6deoU5s6diylTpmDPnj145513sGTJEvz5558AgB9//BHnz5/Hd999h7179+Kjjz4S72B73O8wovqsvO+a0h7leKKqv4VZWVl49dVXERAQgPDwcCxbtgxRUVFYsmRJjb0vIipfVccS+/btQ3h4OJYuXYo//vgDX3zxBVxcXKps02g0mDp1KtRqNbZu3YpvvvkGJ06cqHdLeRD9V0ZGBg4dOgQfHx8AgFKpRPfu3fHjjz/il19+QZMmTTBlyhSoVCoAQG5uLl577TW0bt0aYWFhePnllx/6uxgeHo4NGzZg7ty52LJlC3Jycio9V7/v/nFvWFgYrK2ty5yrV7dPQRBw8uRJxMTElDkGiIuLQ2hoKAYPHoxdu3Zh4cKF2LZtGzZu3AhAex3ijTfegJ2dHbZv3463334by5Yte6j/8q5hrFixAvv378fixYuxc+dO9OjRAxMnTkRycjIAYOHChbC0tMS2bdsQFhaG0NBQ8W7mytoe97yB6q4dO3YgMDAQ5ubm6N+/P8LCwsQ2T09PNG3atMxF/6KiIhw4cAD9+/cHgCrPCe/778+EUqnE8OHDsXXrVvz4448wNDTEm2++Kd6sEx8fj3feeQeDBw9GeHg4AgMDsWbNmirfz5o1a9C2bVuEh4ejf//+mDVrFtLS0h6rTz8/P7z77rtwcHAQr2fdv/EOqPxaQFW/34+ivHOHRznWf9RrZ19//TXmzZuHXbt2wdbWFmFhYVi9ejVmz56N3bt345VXXsH06dNx6dIlANr/w+zsbGzYsAG7du3Ce++9B4VCUWXb3bt3ERoaivbt22Pnzp146623sGzZMnElmPu++eYbMTDbsWPHSr+L6hWBqBznzp0TPD09hStXrojP3bx5U2jVqpX479ixY4IgCML27dsFT09PITY2ttI+9+zZI3Ts2FHcfvHFF4XFixeL24WFhUKHDh2EmTNnis917dpV2LRpkyAIghAWFiYMHDiwTJ9xcXGCp6en8O+//wqCIAijR48WXn311TL79OzZU1i3bp34Hjw9PYX4+PhK57p9+3bBy8tLfK9eXl5CQECAcObMGXGfzZs3C+3btxcKCwvF53766SchICBAEARBiImJeegzjI6OFjw9PYWrV68KmZmZgqenp5CQkFDuHN5+++0yn4UgCEJUVJTg6ekpFBQUCIIgCF9//bXg7+8vKJVKcZ8PPvhAmDp1apn3Gx0dLbYfPnxY8PT0FKKioir9DIj+a+bMmcJzzz0ntGrVSmjRooXg6ekpPP/888Lp06crfV3fvn2FrVu3ituenp7CihUrxO3k5OQyvytvv/228MYbb5TpIyQkRBg9erQgCIKQl5cnPP/888Kff/4ptmdkZAi+vr7CgQMHBEHQ/m60bt1aUKlU4j69evUSxo8fL26npqYKnp6ewuXLlyucu6enp+Dj41Pmu+/atWuCIAjC9OnTxd+1+958801h2rRpgiAIQnx8vODp6VnmvQuCIIwdO1b8Trpv6dKlwoQJEwRBEISPPvpImDVrVrnzedTvMKL6rKrvmv/+LazqeOJR/hYuX778od/nY8eOCb6+voJGo6nR90v0rJo5c6bw9ttvP9K+/9/evcfVmO1/AP+giBS6GKKrTjRJurjH4ThupRu6jEqNy7idMfyQUTNyacyrIk0hzcwxzCCXSmdS4zJhUpphykwRSbvU5ETJnUbl+f3h1XPatWsnUszn/Xrtl9fea+3nWU/2s5611vd51qrdlti5c6cwa9YsmedmY2nJycmCiYmJUFZWJn52+vRpwdjYWLh7924zj4Lo9at9nTQzMxOMjIwEOzs7qd92bdXV1YK5ubmQkpIiCIIg7N27V7C2thYqKyvFPIGBgYKRkZH4fsaMGUJgYKD4vrKyUrC2tm6wr17T7v3Pf/4jpqenpwtGRkbCvXv3mrxNDw8PwcTERBg8eLBgYmIiGBkZCVZWVoJEIhHzrF69WggICJA6xoMHDwqTJ08WBOH5eW1qaircuXNHTN+zZ49UG1rWGEZFRYVgZmYm1X8XBEFwc3MTIiMjBUEQhKlTpwqxsbEy/sqNpzW330Btl62trZCQkCAIgiDcuHFDGDhwoHD79m0xffv27YKTk5P4/sSJE4KFhYU4fiSvT9jU38Tt27elfstBQUHC9OnTpfIsWbJEGDdunPi+7vV33Lhxgp+fn/i+srJSGDRokNjfbso269q3b5/MdHljAfLOb1maMk4hr63flP5CTR+kpi6tMX78eKmxCUEQhOXLlwtr1qwRBEEQ5s+fL4SHh8sse2NpmzZtqjcGGhgYKDg6OorvjYyMhJCQEKk8jdVFbxJOiEcyCTIWp9HW1hbvPJg4cSKqqqrENGVlZejp6Unlv3jxIsLCwpCTk4P79++juroaT58+RVVVFRQUFCCRSODt7S3m79ixI4yNjRssU05ODq5duwZzc/N6aYWFhdDQ0ACAenciaGpqory8XO4x19W9e3ccOHAAwPO7VI4ePYrFixfj0KFD0NbWhkQigYmJCTp27Ch+x9LSEnfv3sXt27chkUigpKQkdUyGhoZQVVWFRCLBgAEDYG9vDzs7O1hbW8Pa2hqTJk1Ct27dXqicurq6UnPqa2pqIjc3F8Dzx4E7d+4MQ0NDMX3gwIEv/LcgqmFtbQ1fX188fPgQ27dvh6GhIaysrMT06upqRERE4OjRo7h58yaqqqpQUVGBGzduSG2n9nnas2dPABCnS5NIJOKdJzXMzMyQl5cH4PkdHpWVlVJ1QY8ePaCvrw+JRILx48cDAHR0dKCkpCTm0dDQwN/+9jep9wDk1g/+/v5Sx1jztJNEIoGtra1UXgsLC7HeqGFiYiL1PicnBxcuXJC606uqqgpaWloAAAcHB8yePRvZ2dkYNWoU/vnPf8LCwqLRMhK9beTVNbXJa0805VqYk5OD5ORkqXpFEARUVFTg1q1beOedd17RkRGRPPLaEpMmTcI333yDyZMnY/To0Rg7diysra3lpkkkEujo6EBdXV3cl4WFBaqrq3H9+nUMGjTo9R8sUTPVXCeB523oL7/8EgsWLMD+/ftRUVGBsLAw/PTTTygtLcWzZ8/w5MkT8RzKz8+HsbGx1N3Hda+L+fn5mD17tvheQUEBAwYMkFuu2m38mpkJbt++DVVV1SZvc9q0aZgzZw7Ky8sRHBwMR0dH6Ovri+k5OTm4evUqoqOjxc+qq6vx7Nkzseza2tro3r27mF7z9EltdccwCgsL8eTJE8ycOVMq39OnT8U2hIeHBz799FPx7vrJkydDR0dHblpz+w3UNmVmZqK4uBjjxo0D8Lx/aGpqivj4eHh5eQEAbG1tERoaiuvXr0NXVxeJiYmYOHGiOH4kr09Yo+5voqysDFu2bMH58+dRVlYmjt3997//hZ6eHvLz8+v93gcOHIisrKxGj6n2uaugoAA1NTWxf97cbTZlX3XHAuSd3w2R13eQ19Z/kbGz2v8njx8/RlFREVasWCE1E01lZSWGDh0KAHB1dcXSpUuRkpKCkSNHYuLEiWLd11iaRCKpN/5pYWGBffv2NVgeoPG66E3CgAXJpKOjg3bt2omNGeD5AICurq7M/HUXoX38+DHmzJmD8ePHY8GCBejRowfS09Ph5+cntVBQ3amlZAVKam/T3Nwcn332Wb202gMJdaeMaNeundzKTZYOHTpIHa+xsTGOHz+O6Ohoqfn+aqtb/rrHVzdPcHAwsrKykJycjP3792PLli04ePCg+Oh6U7yq4yVqii5duojnRUhICCZOnIghQ4aIgwH//ve/ERUVBT8/PxgYGKBz585YsmSJVIATkP7d1pwntX+3L1I3NJRH1rkhb7+yaGpqVpTYMgAAFAVJREFUNlj3ySsDAKmgCfC8Llu+fDnGjh0rs7ympqZISkrCTz/9hLNnz2LOnDlwc3PDqlWrmlQGoreBvLqmrubUGbU9fvwYkyZNwpIlS+ql1R7cJKKWJ68toaWlhWPHjiElJQWpqalYvnw5hg0bhrCwsEbTiN4mta+Turq6CAgIwKhRo/Dzzz/j+PHj+OWXX7By5Upoa2ujU6dOcHV1rdcebwnNaWvXpaqqCl1dXejq6uKLL76Ara0tBg8eDCMjIwDPr9menp5wdXV9qbLKGsMAgG+++QY9evSQSqtZw87V1RWjRo3C6dOncebMGYSHhyMkJAQTJkxoNE2WpvQbqG06fPgwHj9+DEtLS/GzZ8+e4fHjx2LAQkdHB6ampkhISMDs2bNx6tQphIeHi/nl9Qlr1P1NfPzxx3j48CE+/fRT8UY6W1tbVFZWvtQxvc5xpcbqieae3/L6Dq+yrV+77qipNwIDA+sFYGv+78aNG4ekpCScOnUKqampcHZ2ho+PDzw9PRtNa6q6v5EXrYvaKq5hQTKpqalh6NCh2LlzZ7MqKYlEgrt372LFihWwsLCAvr4+SktLpfIYGBggMzNTfF9ZWVlvUa7ajI2NkZ+fj549e4oNmJpXUy/sNdHs5la8HTp0QEVFhVj+S5cuSS0GnJGRge7du0NdXR0GBgZ48uSJuH4G8Hy9iQcPHqBfv37iZ6ampli8eDFiYmLQtWtXcXEeRUVFuQsLyaOvr48nT56Id6YDEOfRI3pZnTp1gre3NwIDA8UGd0ZGBiZNmgQbGxsMGDAAmpqa9Z6ukKdu3QBA6u4NbW1tKCoq4sKFC+Jnd+7cQUFBgdS51dIMDAykygAAFy5ckFsGY2NjFBYW1qvH+vTpI+ZRVVWFnZ0dPv/8c/j7+4t3X71sHUb0JpJV19Qmrz3RlGuhsbExcnNzoa2tXe/cbGjtDCJqGU1pS3Tu3BkTJkzA2rVrsXXrVhw7dgx3795tNM3AwACFhYXiXZw1+6p7kxLRm6hm3cmKigpkZGTA2dkZ48ePh5GRETp16iSeH8Dz6+Lly5elAhiXLl2S2p6+vr7UtbKqqqrRvnpTNGebmpqasLe3x+bNm8XPjI2NIZFI6l2va85jfX19FBUV4d69e+J3mtIH7tevHxQVFXHz5s162609oNm3b194eHggMjISU6dORUxMjNy05vYbqO15+vQpEhMTsX79esTFxYmvgwcPIjc3V+o3bWtri8TERJw8eRJKSkoYMWKEmNaUPqEs6enp8Pb2xujRo2FoaCj1Owfqn2dA/fP7RTVnm80dz5J3fjeFrL6DvLZ+c8fONDQ0oKmpieLi4nrbrX1jtYaGBpydnREaGor58+dLLbDeUJqseiMjIwMGBgZyy9VYPfWmYMCCGuTv74+ioiJ4eHjg9OnTKCwsRE5Ojri4TocOHRr8bu/evaGoqIg9e/agqKgIR44cwf79+6XyzJw5E/v370dCQgLy8vKwbt06PHr0qMFt2tnZQUlJCUuWLMFvv/2GwsJCJCcnY82aNU0+Jk1NTSgqKiI5ORnl5eWN7u/Zs2coLS1FaWkprl+/jsjISFy7dk2MgNvZ2UEQBHzyySfIy8vDyZMnER4eLk5LYWBggDFjxsDX1xeZmZnIzMzEqlWrMHLkSPTv3x9FRUUICQnBb7/9huLiYiQlJeHWrVti5dOnTx9cunQJRUVFKC8vb9YAZb9+/TBs2DB88sknyM7ORkZGBrZu3QpA9tMfRC9qxowZ+OOPP3D8+HEAz4MJZ86cQWZmJnJycuDj4/PCDZWZM2ciKSkJe/fuRX5+PkJDQ5Gfny+mKysrY8aMGdi4cSN+/vlncT9aWlr4+9///kqPrzFeXl44deoUdu7cifz8fOzatQsnTpwQ76ppyMKFC3HgwAF89dVXyMvLQ05ODg4dOoSoqCgAwK5du5CYmIj8/Hzk5eUhKSlJrBdepA4jepvUrWtqk9eeaMq10N3dHSUlJVi9ejWys7NRUFCAH3/8EYGBga/nAIn+ou7fv4/Lly9LvXr37t1oW+Lw4cOIiYlBbm4url+/joSEBGhoaEBVVbXRtFGjRkFPTw8+Pj64cuUKzp07hw0bNmDatGkvPCUrUWv7888/xb5qTk4O1q9fj+7du8Pc3Bza2to4duwYcnJykJWVhZUrV6JTp07id+3s7FBRUYGAgABIJBIkJibWW8TV3d0d+/btQ2JiIiQSCQICAvDo0aOX6kM2d5uenp5ITk4WBw/nzp2L1NRUBAUF4erVq8jLy8ORI0ewY8cOAM+nhunduzf8/PyQm5uLM2fOYPfu3QAa7wN37doVs2bNwoYNG5CQkICioiJkZmZi+/btOHfuHABg48aNSE1NRVFREX7//XdcuHBBbKc3ltbcfgO1PUlJSXj69CkcHR1hZGQkvgYNGgQrKyvExsaKeW1sbJCXl4eIiAhMmTJFagxNXp+wITo6OoiLi0N+fj7Onz9fr63q6uqK7OxsbNu2DQUFBYiKikJaWtpLHXNzttmnTx+UlZXh999/R3l5udSNvo2Rd343Vd2+g7y2/suMnS1cuBBbt27FgQMHcP36dWRnZ2P37t344YcfADxfpPvkyZMoLCzE5cuXcfbsWbFuaCztvffeg0QiQVBQECQSCeLi4rB3716pqXBlaawuepMwYEEN6tevH2JiYqCnpwd/f39MmTIFnp6eSEtLQ2hoKEaNGtXgd9XV1REQEIDDhw/D1tYW0dHRWLp0qVQeBwcHeHl5Yd26dXBzc4OKikqjg43KysrYu3cvVFRU8MEHH4h3H9eem1IeJSUlrF69GpGRkRg5ciQ2bNjQYN7y8nJxbQkHBwf88MMP2LRpkxgVV1ZWxtdff42ioiI4Ojrik08+wYwZM/DBBx+I2wgMDISenh68vb3h5eWFvn37YtOmTQCe3/117do1LF68GJMnT0ZQUBD+7//+T/wbvPfee1BXV4ednR1GjBjxwnep1wgKCoKSkhJcXFzg6+uLefPmAYBUo5WouVRUVODk5ISIiAgIgoCFCxeiX79+8PLywty5czFkyJAXnot1yJAh8PX1RUREBKZNm4aSkhI4OztL5Vm1ahWsra3x0UcfwdnZGc+ePcOOHTsaDaS+amZmZggKCsKBAwdgZ2eHqKgofP755w3Os19j7NixCAsLw7Fjx+Do6IhZs2bh+++/F++m6dy5M3bs2AEnJye4ubmhsrISISEhAF6sDiN6m9Sta2prSntC3rWwV69e2LdvH+7duwcPDw84OTkhPDyca1cQtbCUlBQ4OjpKvfr27dtoW0JFRQVRUVFwdXWFk5MT8vLyEBkZifbt2zea1r59e0RERKB9+/ZwdXXFv/71L4wYMUJcB4DoTZKUlCT2VT09PXHv3j189dVXUFNTw+rVq9GxY0c4Oztj2bJlcHNzk3pCQEVFBdu2bcO5c+fg4OCAqKgozJs3T6p/WHNtXbt2LVxdXaGiooKhQ4dKrd/4opq7TV1dXYwZMwbbtm0DALz77rvYvXs3srKy4OzsDBcXF3z77bfi3P8dOnRAeHg4SkpK4OTkhC1btmDBggUA5PeBV6xYAW9vb4SGhmLKlClYtGgRrly5Is6zX1VVhTVr1sDGxgaLFy/G8OHDxSlmGktrbr+B2p64uDiMHj1a5m9pwoQJiI+PF6dneuedd2BlZYWrV6/CxsZGKq+8PmFDPvvsM9y8eRP29vZYt25dvenKdXR0sHnzZsTExMDe3h4pKSlwd3d/qfGf5mxz+PDhsLe3x7x58zBixAgcOXKkSfuSd343Vd2+Q1Pa+s0dO3N3d4efnx++++472NraYs6cOUhJSRH/LxUUFBAUFISpU6fi/fffR+/eveHv7y83TUtLCzt27MDZs2dhb2+P0NBQfPTRR7C3t2+0PI3VRW+SdsKLTvJLRG+0tLQ0eHt7Iy0tDWpqaq1dHCIioteO10IiIqL/+eKLL3DixIkGBxWrq6sxYcIEuLu7Y86cOa9kny2xzYZER0dj48aN+PXXX8Xps4j+Knx8fHDv3j1ERka26W22NewvtC5Oykv0lktJSUFVVRX69euHP/74A+vXr8eYMWNY4RIR0V8Gr4VERET/ExMTAwMDA2hoaCA9PR27d+/GwoULxfQHDx4gLi4OI0eORHV1Nb799luUlZVh8uTJzd5nS2yzIUePHkW3bt3Qt29f5OTkIDQ0FPb29gxW0F/Cnj17MHjwYKioqCA5ORkJCQkIDg5uc9tsa9hfaFsYsCB6yz19+hTBwcEoLi5Gt27dYG1tjdWrV7d2sYiIiF4bXguJiIj+p6SkBGFhYSgvL0fv3r0xf/58qacc2rVrh+PHjyMsLAzV1dUwMjLCrl275E5X05iW2GZDHjx4gODgYNy6dQuampqws7OrN0U10dsqNzcXERERePDgAXR0dLB27dp6U1K1hW22NewvtC2cEoqIiIiIiIiIiIiIiFodn4cjIiIiIiIiIiIiIqJWx4AFERERERERERERERG1OgYsiIiIiIiIiIiIiIio1TFgQURERERERERERERErY4BCyIiIiIiIiIiIiIianUMWBARERERERERERERUatjwIKIiIiIiIiIiIiIiFodAxZERERERERERERERNTqGLAgIiIiIiIiIiIiIqJWx4AFERERERERERERERG1OgYsiIiIiIiIiIiIiIio1TFgQUREREREb71ffvkF/fv3R3h4+EttJzY2Fv3790dsbOwrKhkREREREdVgwIKIiIiIiF65/v37o3///hgwYAAKCwsbzOfp6SnmZRCAiIiIiOivjQELIiIiIiJqEQoKChAEAdHR0TLTCwoKcP78eSgoKLzmkhERERERUVvEgAUREREREbUIdXV1DBw4ELGxsaiqqqqXfujQIQiCgLFjx77+whERERERUZvDgAUREREREbUYFxcXlJaW4vTp01KfV1ZWIi4uDubm5jA0NGzw+wUFBfDx8cHo0aMxcOBAWFtbw8fHBwUFBTLzl5WVwdfXFyNHjsSgQYPg4OCAw4cPN1rGu3fvYvPmzZgyZQoGDRoES0tLeHl5ISUl5UUPl4iIiIiIXgIDFkRERERE1GJsbW3RpUsXHDp0SOrzkydPoqysDC4uLg1+NzMzE9OnT8f3338PU1NTzJ49G4MHD0Z8fDymT5+OrKwsqfx37tyBm5sbYmJioKenBy8vLxgbG8Pf3x+7du2SuY/i4mJMmzYNX375JdTU1ODm5gYbGxvk5eVh7ty5OHjw4Ev/DYiIiIiIqGk4WSwREREREbWYrl27wsbGBocPH0ZJSQl69eoFADh48CC6du2KKVOmYMeOHfW+JwgCVq1ahYcPHyI4OBj29vZiWmJiIpYtW4aVK1ciMTER7ds/vw8rJCQERUVF8PLygq+vr5jf3d0dbm5uMsv38ccf48aNGwgJCYGtra34+f379+Hp6YmAgAD84x//gIaGxiv5exARERERUcP4hAUREREREbUoFxcXVFdXi4tvFxcX4+zZs7Czs0Pnzp1lficjIwMSiQTm5uZSwQoAsLGxgaWlJfLz85Geng7g+RRT8fHxUFZWxocffiiV39TUFHZ2dvX2ceXKFZw7dw4TJ06UClYAgKqqKj788EP8+eefOHbsWLOPnYiIiIiImo5PWBARERERUYsyMzODkZERYmNjsWjRIhw6dAjPnj1rdDqo7OxsAMCwYcNkpg8fPhzp6enIzs7GkCFDIJFI8OTJE1hZWUFFRaVe/qFDh9Zby+LChQsAgIcPHyI8PLzed8rLywEAEomkaQdKREREREQvhQELIiIiIiJqcS4uLggICMCZM2cQGxsLExMTvPvuuw3mf/DgAQCgZ8+eMtM1NTWl8tX8q66uLjO/rCmd7t69CwBITU1Fampqg2V5/Phxg2lERERERPTqMGBBREREREQtzsHBAZs2bcKaNWtw8+ZNLF68uNH8NU9JlJaWykyv+bxr165S+W/fvi0zf1lZWYP78PPzw6xZs5pwFERERERE1JK4hgUREREREbU4VVVVTJo0CSUlJejSpUu9NSPqMjY2BgCcO3dOZnrN5yYmJgAAAwMDdO7cGZcvXxaftpCVvzYzMzMAwK+//tr0AyEiIiIiohbDgAUREREREb0WS5cuxbZt2/D111+LT0Y0xNLSEvr6+khPT8fRo0el0o4ePYrz589DT08PlpaWAABFRUXY2dnh0aNH9dajyMrKQnx8fL19mJqawsrKCidOnBAXBK8rJyenwac2iIiIiIjo1eKUUERERERE9FpoaWlBS0urSXnbtWuHwMBAvP/++1i2bBmOHDkCAwMD5Ofn48cff4SysjKCgoLQvv3/7sFatmwZ0tLSsHv3bly8eBGWlpYoLS1FYmIixowZg5MnT9bbz+bNm+Hl5QU/Pz989913MDMzg4qKCkpKSnD16lVcvXoVBw4caHBtDCIiIiIienUYsCAiIiIiojbJzMwM0dHRiIiIQFpaGk6dOoUePXrA1tYWixYtgoGBgVR+NTU1REVFISQkBKdOncLFixehr6+PtWvXok+fPjIDFr169UJMTAz27NmD48ePIz4+HtXV1dDQ0IChoSE8PDxgZGT0ug6ZiIiIiOgvrZ0gCEJrF4KIiIiIiIiIiIiIiP7auIYFERERERERERERERG1OgYsiIiIiIiIiIiIiIio1TFgQURERERERERERERErY4BCyIiIiIiIiIiIiIianUMWBARERERERERERERUatjwIKIiIiIiIiIiIiIiFodAxZERERERERERERERNTqGLAgIiIiIiIiIiIiIqJWx4AFERERERERERERERG1OgYsiIiIiIiIiIiIiIio1f0/N3ZBxSddJSQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1728x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for a,b in zip(rmse_test_scores.keys(),rmse_test_scores.values()):\n",
    "    rmse_test_scores[a] = (b,0)\n",
    "\n",
    "# Plot the predictions for each model\n",
    "sns.set_style(\"white\")\n",
    "fig = plt.figure(figsize=(24, 12))\n",
    "\n",
    "ax = sns.pointplot(x=list(rmse_test_scores.keys()), y=[score for score, _ in rmse_test_scores.values()], markers=['o'], linestyles=['-'])\n",
    "for i, score in enumerate(rmse_test_scores.values()):\n",
    "    ax.text(i, score[0] + 0.002, '{:.6f}'.format(score[0]), horizontalalignment='left', size='large', color='black', weight='semibold')\n",
    "\n",
    "plt.ylabel('Score (RMSE-test)', size=20, labelpad=12.5)\n",
    "plt.xlabel('Model', size=20, labelpad=12.5)\n",
    "plt.tick_params(axis='x', labelsize=13.5)\n",
    "plt.tick_params(axis='y', labelsize=12.5)\n",
    "\n",
    "plt.title('Scores of Models', size=20)\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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pw+rVq4mIiKBPnz50796dgoIC1qxZQ/PmzTl9+nSxa4wcOZLDhw+zcOFCNmzYQPv27alevTrJycmcOnWKXbt2MXbsWO64445Sx/nzzz/z7rvvEhYWxm233Ya/vz9xcXGsX78eFxcXRowYcXlvlIiIiIjIVaLAQkRERETECYYPH079+vX5+eefOXLkCFu3biUvLw8/Pz/uuusu+vTpQ3h4uENNCZPJxP/+7//SoUMHFi9ezA8//EBeXh6BgYHceeeddOvWzWjr4eHB3LlzmTt3LqtWrWL+/Pm4urrSuHFjJkyYQJ8+fS57zMHBwSxdupT58+ezbt06Vq5cidVqJSAggDvuuIMhQ4bQsGFDAGrVqsXo0aPZsWMHv/76K6mpqfj5+RESEsIrr7zCgw8+WK5r1qlTh6VLlzJjxgw2b97Mjh078PHxoWPHjrzwwgu0aNHisu+jovj5+bFo0SI+++wzfvzxR+bOnYvZbKZFixaMGDHCKKx+gclk4sMPP2TWrFksW7aM+fPnExQURP/+/XnppZdo3rx5sWu4u7szY8YMli9fzrJly9i4cSPZ2dlUrVqV2rVr8z//8z+Eh4eXOc6OHTsaNUXWr19PZmYmQUFBdOjQgaeeeorWrVtf1fdFRERERKS8TPYLi+KKiIiIiIiIiIiIiIg4iWpYiIiIiIiIiIiIiIiI0ymwEBERERERERERERERp1NgISIiIiIiIiIiIiIiTqfAQkREREREREREREREnM7N2QO4HjVq1MjZQxARERERERERERERuakdOXLE4bVmWIiIiIiIiIiIiIiIiNMpsBAREREREREREREREafTklCX8OcpKSIiIiIiIiIiIiIi8teUVZJBMyxERERERERERERERMTpFFiIiIiIiIiIiIiIiIjTKbAQERERERERERERERGnU2AhIiIiIiIiIiIiIiJOp8BCREREREREREREREScToGFiIiIiIiIiIiIiIg4nQILERERERERERERERFxOgUWIiIiIiIiIiIiIiLidAosRERERERERERERETE6RRYiIiIiIiIiIiIiIiI0ymwEBERERERERERERERp1NgISIiIiIiIiIiIiIiTqfAQkREREREREREREREnE6BhYiIiIiIiIiIiIiIOJ0CCxERERERERERERERcToFFiIiIiIiIiIiIiIi4nQKLERERERERERERERExOkUWIiIiIiIiIiIiIiIiNMpsBAREREREREREREREadTYCEiIiIiIiIiIiIiTpefn897771H8+bN8fLyolq1ajzwwANs2bKl3H3Exsby0ksv0aBBA7y8vPDw8KBu3bo88cQTHDhwwKHtxo0bMZlMpf489dRTpV4nLy+Ppk2bOrRPS0szjn/55Zdl9m0ymahfv77R/uzZs0ydOpXu3btTr149zGYzlStX5u6772bu3LnY7fZiYzh58iQjRoygdu3aeHh4EBwczBNPPMHRo0fL/X5db9ycPQARERERERERERERubXZbDYefvhhVq9ebeyzWCysWbOGdevWsWDBAh5//PEy+0hJSaFdu3bExsY67I+OjmbBggUsX76cnTt3EhoaesXjfeeddzh48OAV9eHu7m5sb9q0iQkTJjgcz83NZfv27Wzfvp3ff/+dDz/80Di2f/9+OnXqRGpqqrEvPj6eBQsW8P3337NhwwbCwsKuaHzOoBkWIiIiIiIiIiIiIuJUn332mRFWhIaGsmTJEqZNm4arqys2m43nn3+exMTEMvtYsmSJEVZUr16d//73vyxfvpxGjRoBkJWVxdy5c0s896OPPmLLli0OPxMnTiyx7b59+3j33Xcxm82ljqV3797F+tuyZQv333+/0aZfv34O55jNZkaMGMHSpUtZuXIlvXv3No5Nnz6d48ePG69HjRplhBWDBg1izZo1jBs3DoD09HSefvrpUsd2PdMMCxERERERERERERFxqhkzZhjbc+fOpV27dgBERUUxe/Zs0tPTmT9/PmPHji21j/T0dGM7PDycgQMHArB3715ef/11oHApp5I0b96ce++995LjtFqtjBgxgvz8fN5++20jJPizoKAggoKCHPZlZGSwbds2AFxcXBg5cqRxrGXLlkRFRVGnTh1jX8+ePalXrx7x8fHY7XYiIyO5/fbbyczMZPPmzUY/s2fPxtvbm/vuu49vv/2WqKgo9uzZwy+//MLdd999yXu6nmiGhYiIiIiIiIiIiIg4TWpqKvv37wcKl0lq06aNcaxDhw7G9oWH9KXp0qWLsb1y5UoWLVrEihUrmD9/PlD4cH/AgAElnvvEE0/g6elJlSpV6Ny5M0uWLCmx3b///W927txJ69ateeWVV8p1fxd89dVXZGRkANCnTx+HGhahoaEOYQWAp6cndevWNV77+PgAcO7cOaOmhaurq8NMj0qVKhnbl1P743qhwEJEREREREREREREnObkyZPGtr+/P66ursbr4OBgY/vEiRNl9tO2bVtmzZpFtWrViI+PZ+DAgfTt25cjR47QtGlT1q1b5xCAXOzMmTPk5eVx7tw5Nm/ezIABA5g8ebJDm+PHj/PGG2/g7u7OF198gZvb5S1gdPEsklGjRl2y/fHjx9m9ezdQGER07NgRKFzuys/PDygsVD59+nSys7P54Ycf+P33343zo6OjL2t81wMFFiIiIiIiIiIiIiJyTaVZrBxPy+Ngci4nE4uWcvLw8HBod/HrzMzMS/Zbo0YNatasWWz/4cOHWbhwocOSUC4uLnTu3JkPPviA1atXs2TJErp3724cf/vtt40wxW638+yzz5KTk8O4ceNo2bJlue8V4KeffuLQoUMANG7cmB49epTZPjU1lf79+1NQUADAu+++i6+vL1A4q+Li2R1jxozBx8eH3r17Y7PZjP0Wi+Wyxng9UA0LEREREREREREREalwSTkFzD2QxuwDaUSlXlRLIibZ2MzIycVmt+NiMgGONScuXu6oJBEREfTt2xebzUbbtm1ZvHgxvr6+jBw5kkWLFjF79mwCAgKYOnUqAJ06dWLjxo0OfYSHh9OkSROOHz+O1Wpl3bp1PPfccyxZsoQNGzbQpEkT/vGPf1z2vX/88cfG9ksvvYTp/P2VJD4+np49e7Jv3z4AXnnlFV566SWHNhMnTsRmszFt2jSysrIACAwMpFmzZmzYsAHAmIVxI9EMCxERERERERERERGpMKfP5fPU2lhqzz7Ka1sTHMMKgGpFMyJSU5JpODeKz/amYrPbiYuLM46FhISUeZ05c+YYMwxGjRpF/fr1qVatGhMmTDDafPvtt2X24eHhQVhYmPE6KSkJgNjYWAAOHjyIp6cnJpOpWOhQtWpVhzoaF5w+fZpVq1YBULlyZYYNG1bq9U+fPk3Hjh2NsGLcuHG89957xdqZTCbeeOMNkpKS2L17NwcOHCA2NtahLkbTpk3LvNfrkWZYiIiIiIiIiIiIiEiF2BqbTd+V0aRYrKU38q4CwXdA3DGwFnB87y5eONeStacyqbJlq9GsU6dOZV4rOfmimRrni1uXtf3bb79x5513OgQPeXl5Rt0IKKwXcaVmzpyJ1Vp4/8OGDaNy5coltouKiqJHjx5G7YkpU6Ywfvz4Mvs2m820atUKKAw7Fi1aBBQWL7///vuveOzXmgILEREREREREREREbnqIqKz6P3daXKt9ks37vA4LP1n4fbCf8ADo1m2IRZWfQmAr68vQ4YMAWDSpElGQew333yTSZMmAdCkSRPWrl0LwNSpU/Hz88PX19ehePaFh/sAr776KsnJyTz55JO0atWKc+fOMXPmTI4fPw6Ap6en8dC/a9euTJ8+vdiwR48ebWxPmzaNO+64w+F4bm4us2fPBgpnRfx5aacLjhw5QqdOnUhISADgiSeeoGPHjmzdWhTY1K1bl7p16wLw/fffM2fOHPr27UvNmjWJiopiypQpZGdnA/Dcc885FCy/USiwEBEREREREREREZGr6mByLv1WRpcvrAC453E4sBEOb4P4P+DLscYhk4sLM2bMIDAwsMwuxowZw7x580hKSiI2NtYIOC7w8vJyCC8A9u/fz2uvvVasL5PJxPvvv0+tWrUAaNmyZYmFti8OLJ599tlidSMWLlxoLCvVo0cPGjduXOLYf/nlFyOsAPj666/5+uuvHdpcHM7k5+ezbNkyli1bVqyvbt26MW3atBKvc71TDQsRERERERERERERuaqeX3+Gc3m28p/g4gojPoY+LxcuD+XuCV6+0Oge7C9+gVe78Et2UbduXSIjI3nxxRe544478PT0xM3Njdq1a/PEE0+wY8cO2rZta7R/7733ePnll2nVqhUBAQG4ublRvXp1Hn74YTZu3MjIkSP/yq07+OSTT4ztUaNGXXF/F4SGhvLoo49Sr149zGYzlSpVol27dnzyySesXbsWb2/vq3ata8lkt9vLGXHdOho1amRsHzlyxIkjERGRipSfn8+HH37IV199xbFjx/Dy8qJdu3ZMmDCBjh07lquP2NhYpkyZwrp164iJicFqtRIcHEzHjh2ZMGGCQ4GrjRs30rVr11L7GjZsGF9++aXDvqSkJCZPnszKlSs5e/Ys/v7+9OrVi8mTJ1OvXj2jXV5eHl988QU//vgje/fuJS4ujoKCAkJCQnj00Uf5+9//jo+Pj9E+JSWFCRMm8OuvvxIdHU1aWhre3t40bNiQRx55hLFjx+Ll5XVFYxcREREREZFb02/xObT974mr2menWt5sGlD/qvYpzlHW83cFFiVQYCEicvOz2WyEh4ezevXqYsdcXFxYsGABjz/+eJl9pKSk0KJFC2JjY0s87uPjw86dOwkNDQUu/6H/2bNn6dChAydOFP8lLyAggC1bthhTSePi4qhRo0apfbdt25atW7fi4eEBwOHDh41xlaRnz56sW7fOeK3AQkRERERERMprxI9n+OJA2lXvd9+Q22gWYL7q/cq1Vdbzdy0JJSIit6TPPvvMCCtCQ0NZsmQJ06ZNw9XVFZvNxvPPP09iYmKZfSxZssQIK6pXr85///tfli9fbvyPNysri7lz55Z47kcffcSWLVscfiZOnOjQZsyYMUZY0bdvX1asWMHw4cOBwpkXF7Yv9sADD/Dll1+ydu1aXnnlFWP/zp07mT9/vvHa09OTxx57jFmzZrFmzRq+//57hg4dahz/8ccfSw3tyzN2ERERERERuTWlWawsOJxeIX1/ui+1QvqV64eKbouIyC1pxowZxvbcuXNp164dAFFRUcyePZv09HTmz5/P2LFjS+uC9PSiX8DCw8MZOHAgAHv37uX1118HCpdqKknz5s259957S+07Pj6epUuXAuDn58fChQsxm8306dOHrVu3EhUVxS+//MLu3bsJCwvDx8eHbdu2cc899xh99OrVi+PHj/Pdd98B8NtvvxkhR0hICIsWLXK4Zu/evVmxYoVxXxkZGX9p7CIiIiIiInLr+vlsNpbyFtq+TD+ezqqQfuX6oRkWIiJyy0lNTWX//v0AuLu706ZNG+NYhw4djO3NmzeX2U+XLl2M7ZUrV7Jo0SJWrFhhzGRwcXFhwIABJZ77xBNP4OnpSZUqVejcuTNLlixxOP7zzz9jtVoBCAsLw2wunPJqMpm4++67i42xcuXKDmHFBQ0aNDC2L65h8WdpaWnMmjXLCCuCgoIc6m9czthFRERERETk1pGea2VTTBb/3pXM0DWxPPvT2Qq7VnxWQYX1LdcHzbAQEZFbzsmTJ41tf39/XF1djdfBwcHGdkm1Iy7Wtm1bZs2axbhx44iPjzdmWAA0bdqUDz/80CEAudiZM2eAwhkYmzdvZvPmzUyaNIk333yz2BirV6/ucG55x5ifn8+KFSuM1w888ECxNqNGjeKTTz5x2BcWFsann37qUHT7csYuIiIiIiIiN6fE7AJ2J1rYlXDhJ4fj6fnX7Prn8mzX7FriHAosRETklpOVVTSF9EIR6pJeZ2ZmXrKvGjVqULNmTVJSUhz2Hz58mIULF9KxY0ejTxcXFzp37ky/fv1o2LAh2dnZzJw5k/Xr1wPw9ttvM2zYMOrXr3/FY7TZbDz33HNGHYpHH32Ubt26XfJ+LozzwuyOi/eVd+wiIiIiIiJyY7Pb7ZzJKnAIJnYnWojOcJbVc60AACAASURBVO4Mh0oeWjDoZqfAQkREbmrJOQUsPZbBgeRcUnOt2O2QfzrbOP7nGhMXv65UqVKZfUdERNC3b19sNhtt27Zl8eLF+Pr6MnLkSBYtWsTs2bMJCAhg6tSpAHTq1ImNGzc69BEeHk6TJk04fvw4VquVdevW8dxzzzks33S5YywoKGDYsGEsWLAAgI4dO/LVV1+VeA8vv/wyjz/+OElJSSxZsoQFCxYQGRlJz549OXbsmDGb43LGLiIiIiIiIjcOu93OiXP5RcFEgoVdiRYSsq2XPvkaC/JyvXQjuaEpsBARkZuO3W5nZ7yFGXtSWBh1jtw/F/vKrmxsJiQls+HUObrUrYzJZCIuLs44FhISUuZ15syZg81WOB111KhRxuyCCRMmGAWtv/32WyOwKImHhwdhYWEcP34cgKSkJACHmQrx8fEO55Q1xtzcXB577DFjKaju3buzfPlyvL29S7z+bbfdxm233QZAv379iI6OZsuWLWRlZbF8+XKef/75yx67iIiIiIiIXJ+sNjtRaXnsSrCwOyGn8M9EC2m5N8ZSS51rl16bUW4OCixEROSmkmqx8tS6WFb8UcZyTt5VIPgOiDuGrSCfbv/+nq733s2CB2qzbds2o1mnTp3KvFZycrKxnZGRccnt3377jTvvvBOTyWTsy8vLY/fu3cbrC/Uq7rnnHlxdXbFarezatQuLxYLZbMZut/Pzzz+XOMasrCz69u1rLNP00EMPsXjxYjw9PYuNPScnp8QaFS4uRdNrLxTgvtyxi4iIiIiIiPPlW+0cTMk1Zk7sSrDwe6KF7AL7pU++Tr3QvKqzhyAVTIGFiIjcNGIy8rlv2WkOpuReunGHx2HpPwu3F/6DDemjCV0cR8a3cwHw9fVlyJAhAEyaNInJkycD8OabbzJp0iQAmjRpwtq1awGYOnUqfn5++Pr6Gm0BWrVqZWy/+uqrJCcn8+STT9KqVSvOnTvHzJkzjRkKnp6e3H///UDhw/9+/fqxZMkS0tPTGThwICNGjOC7777j6NGjALRv356wsDCgMIDo0aMH27dvB6BFixaMHTuWnTt3GtevUqUKzZs3B2Do0KHk5+fzwAMPEBISQm5uLkuXLmXTpk1G+zZt2vylsYuIiIiIiMi1lVNgY19SrhFM7EqwsC85l7w/rzhwA2tT3Uzb4OJfvJObiwILERG5KaRarPRadopDKXmXbgxwz+NwYCMc3gbxf8CXY0k7f8jFxYWZM2cSGBhYZhdjxoxh3rx5JCUlERsbawQcF3h5eTmEFwD79+/ntddeK9aXyWTi/fffp1atWsa+Dz74gJ07d3Lq1CmWL1/O8uXLjWPVqlVjzpw5xuv4+HgjrADYu3cvXbt2dbhG586djToUBQUFrFixwlg66s8GDRpUrEj35YxdREREREREKkZGnpXfEy1GMLE70cLB5FycnU3U9HGjdZCZ1kFmwgLNTPw5sXxfKCyn0S2rXbW+5PqlwEJERG54drudoWtjyx9WALi4woiPYdM8+G0FJEeDmyfUbUbjx0czcNDjl+yibt26REZG8u677/Ljjz8SHR2N1WolODiYzp07M27cOJo1a2a0f++99/jvf/9LREQEMTExpKWl4e/vz913383YsWOLLUFVq1YtduzYwVtvvcWKFSuIi4vD39+fXr16MWnSpEvW2CjLsGHDcHV1Zffu3SQkJJCXl4e/vz9hYWEMGTKEQYMGObS/3LGLiIiIiIjIlUvOKWD3+XDiQjHso6l5OHveRIivuxFOtA7yIizQTHUfx0fNtSq50+mbk1iuQpLSs64PQ0KrXHE/cv0z2e12Z//9vu40atTI2D5y5IgTRyIiIuXxy5ls7ll88qr2ueKhOoTfVvnSDUVERERERESugrNZ+UXBxPmfUxn5Th2TCWhU1aMomDg/e6Kq2bVc53977BwDvo/BdgVPoFsGeLJ5QH18Pct3Tbn+lfX8XTMsRETkhjdjb+rV73NPigILERERERERuersdjunzuUbMycu/MRlFzh1XG4u0LSapxFMtA4y0zLATCUPl7/c5yN3+LKsTx0G/hBDzl8o9t25ljfLwusorLiFKLAQEZEbWmJ2AYuPnrvq/a45lcXxtDxu9/O46n2LiIiIiIjIrcFmt3MsLc8hmNiVkENqrs2p4/J0NdEywJOwIC9jaadm/p6Y3f56OFGah26vzI6BIfxtSzxrTmWV6xx/syv/r1U1/t7GH88KGJNcv26IwCI/P5+pU6eyatUqAMLDwxk/fjxubsWHHx8fz+TJk4mMjASgXbt2vPHGGwQEBAAwbtw4Vq1ahbu7u3HOF198QVhY2DW4ExERudq+OXqOvAqqLDb/cDpvti+78LaIiIiIiIgIQIHNzqGUXIdg4vfEXDLznRtOVHJ3oVVg4cyJC+FE46qeuLuartkYmgWY+aFfPY6l5fHZvlS+OJBGisVarF37YC9GtqzKgAa+FRKeyPXvhggsZs6cSWRkpBFYPPvss3z66aeMGjWqWNtJkyZhMpmIiIjAbrfz6quv8s9//pN///vfRptBgwYxceLEazZ+ERGpOPuTcyuwb0uF9S0iIiIiIiI3LkuBjf3JReHE7oQc9iblXpUC01eiqqeLQzDROsjMHX4euJiuXThRljv8PPi/jtV5t0MQx9PzSM6xkmO14+fpQp1K7gR63xCPq6UC3RB/A5YuXcr48eMJCgoC4IUXXmDatGklBhYxMTE899xz+Pj4ANC7d29mzZp1TccrIiIVy2a3sy8pl4joLJYfz6iw66RanPstGBEREREREXG+zDwbe5IsFxXEzuFASi4FTv7IWN3blTsvCifCAs3U83XHdJ2EE2VxdTHRsKonVHX2SOR6c90HFunp6cTFxREaGmrsCw0N5cyZM2RkZFC5smNB1Keffpo1a9bQpUsX7HY733//PZ07d3Zos3z5cpYvX05gYCD9+/fnqaeewsVFU4xERK5Xdrudw6l5bIjOIiI6i40x2SSXMHX0arPZnfvNGBEREREREbm2Ui1WdicWBRO7EiwcSc3D2Z8O61V2Lwomzv9Zw8f90ieK3GCu+8AiOzsbwCGY8PX1BSArK6tYYNG6dWsWL15M27ZtAWjVqhUvvviicXzo0KG89tprVKlShX379jFmzBhcXFx46qmnKvhORESkvOx2OyfO5RMRnXU+pMgmLrvgmo+jqtn1ml9TREREREREro34rAJ2JxYFE7sSLJw4l+/sYdGwqgetA4uCibBAM/5e1/1jXJGr4rr/m+7t7Q1AZmYm1apVAyAjo3D5jwvLPl1gs9kYPnw4999/P3PnzgVg+vTpPPPMMyxcuBCApk2bGu1btWrFs88+y/LlyxVYiIg4WUxGPhtiss6HFNmcynD+L4kN/DycPQQRERERERG5Qna7nZjMAqMQ9oVw4kzWtf9i3MVcTRBazdOh3kTLADO+nvrynNy6rvvAokqVKgQHB3Po0CHq1q0LwKFDh6hRo0ax2RVpaWnExsby5JNP4uXlBRTOqJgzZw4pKSlG4HExLQUlIuIcCdkFbIwpnD0REZ3F0bQ8Zw+pmEGNqjh7CCIiIiIiInIZbHY7f6TnOwQTuxIs12RZ4bJ4uJpo7n9xOOFF8wBPvNz0bFLkYtd9YAHwyCOP8Omnn9K6dWsAPvvsMx599NFi7apVq0a9evX4+uuvjYLcX3/9NcHBwUZYsXr1ajp16oSPjw/79+/n888/Z/DgwdfuZkREblGpFiubYrLYEFMYUOxPznX2kMrUoaYXLQPNzh6GiIiIiIiIlKLAZudIaq5DMPF7ooVzec6thu3tZqJVYFEw0TrITGg1Tzxcr/9i2CLOdkMEFiNHjiQtLY3evXsDEB4ezgsvvADAG2+8AcBbb70FwIwZM5g6dSqdOnXCZrMRGhrKzJkzjb6+/vpr3njjDaxWK0FBQQwaNIjhw4df4zsSEbn5ZeRZ2RqbTURMNhuis9iVYHF6kbLLMbJF8Vl5IiIiIiIi4hy5BTYOpBSFE7sTLOxJspBT4NxPmlU8XBxmTYQFmWno54Gri8IJkb/CZLfbb6TnR9dEo0aNjO0jR444cSQiIjeOnAIbv5zNISK6sA7FzvgcCir4Sy1mVxMdanrTpbY3C4+kcyDl6iwrFVrNg9+fuF3ffhEREREREXGC7Hwbe5MsDjMn9idbyHfuxAkCvVy5M+hCMezCmRMhvu6YTPrsKHI5ynr+fkPMsBARketPntXOzviigOKXsznkWis2A3dzgfbBXnSr40PX2j60r+GF+fx6n8Oa+NF+4YkrLppW1dOFb/vUUVghIiIiIiJyDaTnWvk90TGcOJyai83JX7GuU9mNsMCiYKJ1kJmaPm4KJ0QqmAILEREpF6vNzq4ECxtiCgOKLbHZZFfw1FsXE9wZZKZbHR+61fGhQ01vfNxLLkhWp7I7a/rVpde3p4nL/muhhZerie/71qVxNc8rGbaIiIiIiIiUIDG7gN0O4UQOx9PznT0sbq/i7hBMhAWaCfTWY1MRZ9C/eSIiUiKb3c7+pFwiorPYEJPFpphs0q9B4bKWAZ50PR9QdKrlTRVP13Kf2zzAzPaB9em3MobdiZbLvnawjxvta3hd9nkiIiIiIiJSxG63cyarwCGY2J1oITrjymbEXykXEzSu6nlRzQkzrQLNl/W5U0QqlgILEREBCn+hjErNK1ziKSaLjTHZJOVYK/y6jat6FC7xVMeHLrW9CfC6sv811fP1YMegEJYdy2DG3hQ2xmSX+9wT5/LZEJNNtzo+VzQGERERERGRW4XdbufEufyiYCLBwq5ECwnZFf95sizuLtDM3+wQTrQIMONdyqx9Ebk+KLAQEbmFnUjPO7/EUzYR0VmcvcL6D+UR4utuBBRda3tTs5L7Vb+Gm4uJAQ19GdDQl4PJucw/nMaB5FxSc23k5Fv5LSG31HNn7ElRYCEiIiIiIlICq81OVFoeuxIs7E7IKfwz0UJarnOrYXu5mWgZUBRMhAWZaVrNE083hRMiNxoFFiIit5DYzHw2RGexIaYwoDh5ruLXCq3p42bUoOha25v6VTwq/JoXa+LvyZQO1R32PfjdaVafzCyx/XfHM4jNzKdWBQQpIiIiIiIiN4p8q52DKbnGzIldCRZ+T7RUeC3DS/H1cDlfDLswmGgdZKZRVU/cXFQMW+RmoMBCROQmlphdwMbz4cSGmCyOpOZV+DUDvFzpWtuHbnW86VbHhwZ+HphM19cvjiNbVi01sLDa4fN9qUy6O+gaj0pERERERMQ5cgps7EvKNYKJXQkW9iXnkmd1bjjhb3Z1WNKpdZAXt1Vxx+U6+4wpIlePAgsRkZtImsXK5thsow7FvqTSlz66Wqp4uNCltg9dzwcUTf09r/tfHu+vV4n6vu6lzjCZtT+NiXcF4u56fd+HiIiIiIjI5crIs/J7ouWigtgWDqXk4uRsgpo+bg7BROsgM7UruV13X4ATkYqlwEJE5AaWmWdj65lsNpwPKHYlWLBV8C+ZPu4mOtb0NupQhAWacb3Bpt66uph4oXlVxm1LKPH42awClv+RwaMNfK/xyERERERERK6e5JwCdl8UTuxOtBB1DWbeX0qIr7tDOBEWaKa6jx5TiogCCxGRG4qlwMYvZ3OMJZ5+jcuhoIJrm3m6mrinhpdRh6Jtda+bYubB8KZ+vLE9sdQpzjP2pCiwEBERERGRG8bZrPyiYOL8n6cyKr5uYVlMQKOqHkXBRJCZsEAzVc2uTh2XiFy/FFiIiFzH8q12dsYXBhQR0Vn8fDaH3Aqep+vmAu2Cvc7XofDh7hpemN1cKvSazhDo7cZjDXyZfzi9xOMbYrI5lJJLaDXPazwyERERERH5s/z8fD788EO++uorjh07hpeXF+3atWPChAl07NixXH3ExsYyZcoU1q1bR0xMDFarleDgYDp27MiECRNo2rSp0TYyMpJ//etfREZGEhcXR3Z2Nn5+frRo0YLhw4fzxBNPFOt/wYIFfP755+zdu5f09HS8vb0JDQ1l8ODBjBo1ClfXoof0Xbp0YdOmTaWO9cSJE9SvX994/eWXX7J69Wp+/vlnYmNjjf2vroniYI4nuxIsxGUXFHVgzYfN82Hnckg6De6eULcF9HgObr+zXO/X5XJzgabVPI1gonWQmZYBZip53HyfJ0Wk4pjsdruTV6i7/jRq1MjYPnLkiBNHIiK3GqvNzu+JFiOg2HImm6z8iv3PtIsJWgeZ6Va7cImne2t63zK/UP5yJpt7Fp8s9fjoVtX4qEvwtRuQiIiIiIgUY7PZCA8PZ/Xq1cWOubi4sGDBAh5//PEy+0hJSaFFixYOD/sv5uPjw86dOwkNDQXg008/5cUXXyy1v3/+859MmDDBeD1t2jT+/ve/l9r+6aef5osvvjBelzewsNntHE3N474ObTh1eH/xhlN+Aa8/zQy32WD2S3Boc/H2JhcYOg3CHij12uXh6WqiZYAnYedrTbQOMtPM3/Om/LKbiFx9ZT1/V2BRAgUWInKt2Ox2DiTnFtagiM5mU2wWabkVvMYT0DzAk27nZ1B0quWN3y06Hddut9N6wQl+T7SUeNzXw4XYZxreMgGOiIiIiMj1aObMmYwcORKA0NBQ3n77bf744w/Gjx+P1WqlSpUqHD16lMDAwFL7mDVrFs8//zwA1atX54MPPsDb25vXXnvNePbzt7/9jWnTpgGwdOlS1q9fT4cOHQgODiYlJYX333+f7du3AxAcHMzZs2eN/mvWrGm8Hj58OIMGDSIiIoKpU6cChcFKWloalStXBooCi1atWjF9+nQACmx2Tp3LIyo1j7TqTdibZuP3xFwy823w1Svg6Q31WsDiSUU3VlJgsW0hLHm7cLv6bfDAaEiOge8/AJsVzJVh4mqoVK1c738ldxdaBXoahbBbB5lpXNXzplgqWESco6zn71oSSkTkGrLb7RxNyyusQRGdzYaYLBJzrBV+3UZVPYwlnrrU9ibQW//5BzCZTIxsUZXn1p8t8fi5PBsLjqTzXPOq13hkIiIiIiJywYwZM4ztuXPn0q5dOwCioqKYPXs26enpzJ8/n7Fjx5baR3p60VKw4eHhDBw4EIC9e/fy+uuvA5CXV1SMun///vTv39+hjwYNGhAWFgZARkZGqf2/9dZb1KpVi+7du/Pee++Rn5+PzWbDai367Gc7//XhbDcf5ufezq6EHPYl5WK5sARwcpbjDQz7V9H2xYFFSbYtLNoe9M/CkAMg8SRsXwqWDPhtJXQZVuzUqp4uDsFE6yAzd/h54GJSOCEi14aeWImIVLBT5/KIiM42CmXHZhZc+qQrVK+yO93r+tC1tjdd6/hQq5J7hV/zRjW4cRVe3RLPubySZ7Z8sieFZ5v5YdIv6CIiIiIi11xqair79xcuheTu7k6bNm2MYx06dGD27NkAbN68uczAokuXLsb2ypUrWbRoEV5eXsyfPx8onAExYMCAEs+12WycPXuWmTNnGvu6du1arP8LS1a98cYbDB48mPXr15OfX1j0+q4uPZh30sbuhDPsSshhz5lsAKL27SaqbxOwZEGVIGjSCXo+D76lzxYpU3Y6nD1auO3qBnWK6nIQElYYWAD8EUn13sO586JwIizQTD1fd332ERGnUmAhInKVnc3KZ8P5gCIiOosT5/Ir/Jo1fNzoVseHbnW86Vrbh5AqHhV+zZuFj7sLTzXx46PfU0o8vjcpl1/O5nBPTe9rPDIRERERETl58qSx7e/v71C4Oji4qN7ciRMnyuynbdu2zJo1i3HjxhEfH2/MsABo2rQpH374IR06dCh2XrNmzThw4IDx2sXFhT59+vD55587tJszZw5PPvkkP/74I1988UVRvQqTC3R8gh33/z92bIwrOuHCAu252YU/ACmxsPW/sC8CxvwX/KqXeU8lSjlTtO3tBy6F71e9yu7UaliXn88fakIiB55rVPx8EREnU2AhInKFknIK2BiTfb4ORRaHU/MufdIVCvBypUttb6MORcOqHvoWzBV4sUXVUgMLgBl7UxVYiIiIiIhcAwU2O7sTLJzOyCc9z8qpg/HGMQ8Pxy9mXfw6MzPzkn3XqFGDmjVrkpLi+Lv/4cOHWbhwIR07dix2jZKYTCZsNhvxWQXsTrSwKyGHX0+msD3PH1zdwXrRl9bsNtgfAS16wu13Fu2v7A8dn4D6LcGnKkQfgJ8+h9wsSI+HNZ/AwLcuOZZi8rKNTT8fM988UpewQDP+Xm5ERETT/fyx3Oysks8XEXEyBRYiIpcpPdfK5tjzSzxFZ7EnKbfCr+nr4UKX2t5GHYpmAZ5aQ/QqalzNk251vImIzi7x+DdHz/F+p+oEqfaHiIiIiEiFOJuVz+f70pi1P9VxGd2YogfrCRk5RERn0bW2NyaTyaHmRKVKlcrsPyIigr59+2Kz2Wjbti2LFy/G19eXkSNHsmjRImbPnk1AQIBRJPuCefPmkZaWxp6jJ/l05kyO/L6T5cuXs/r3Y+T/zyK48Lnsk6fg2M7C7ccnQ1hviN4Ps14snDnx+Yvwj7VQ6Xx9vItrUgA0uqcwxFhYWE+DQ5vL9b6FVvWkbUgVY1knYrLpVFjDGzMF9Khb9L5czvslIuIsevIiInIJWfk2tp0pWuIpMsFiFEirKN5uJjrW8qZbHR+61vYhLMiMm4sCioo0skW1UgOLPKudLw6kMa5twDUelYiIiIjIzS3FYuXVzfHMO5xGQUll5arVNDYt51Lp/s0fhAZ48WHnYOLiipZYCgkJKfM6c+bMwWYrvMCoUaOoX78+ABMmTGDRokUAfPvtt/xzyhT+SM9nV0IOuxIs7Eqoxq4Eb5It1WFgczhwL+Tnkn/qQGER66AQSDxVFFZUqw3tHy3cvuMuaHwv7PupcOZE1M/Q+sHSB1m3RdF2VqrDIQ9XE839PWkdZObixah+HhiCn5+f8TrV+3ZjOzk5GavVaiyjdTnvl4iIsyiwEBH5E0uBje1nc9gQUxhQ/BqXQ37J9ZivGk9XE3fX8Dpfh8KHttW98HBVQHEtPXRbZWr6uHEmq+Si6J/uTeVvd/rjquBIREREROSqOJGexwPfneZIWcvqeleB4Dsg7hhYC+D0fg65tOT+707TflOE0axTp05lXis5OdnYzsjIAAqXn9oTk2TsP5mYRtWZRziXZ4M8C3iYHTsxmYCLPg/kFPZDVlrRvrwcsNnAxaXwde5FSy9Zzm+nJ4DdXrxGxak9xqZHlUCea1mV1ueLYodW8zQ+IzpWz3BUtWpVmjZtyoEDB8jPz2fnzp20b98egG3bthntLvV+iYg4iwILEbnl5Vvt/BafQ0RMFhuis9l2JhuLtWKnULia4K7gooDi7hpeeLm5VOg1pWzuriaea16VSdsTSzx+KiOfH05m0ue2ytd4ZCIiIiIiN5/YzHw6LzlJdEbJXxhy0OFxWPrPwu2F/4AHRmNLieXnVQsA8PX1ZciQIQBMmjSJyZMnA/Dmm28yadIkABo1DmXt2rUA/H3SP/ngUB6n8z3JWz3DuExecCPy8s5/W23qgxDaEeq3Ar9gyEgqLIidbyk87uEFwednMwTWAxc3sBVAZjJ8Mxla9iqsS3F0e9F91Gpc+GfiSZj1ArS6H6/mnWhYK4jK8Yf4bdV0zvfOc4P7M71rDePUrVu3kpRUFK5c8P333+Pj44O3tze9evUC4MUXX2TUqFEADB8+nLfffpsTJ04wd+7cYu+XiMj1xmS32yt4YZMbT6NGjYztI0eOOHEkIlIRrDY7e5Is55d4ymZLbDaZFTyFwgS0DjLTtY4P3Wp7c28tbyp7uFboNeXyncnMp+6co5SWVz1QvxKrH657bQclIiIiInKTyS2w0X7RSX5PtFy6MYDNWlgD4vC24sdMLrz278/53/8ZDjgGFr2ff40a/f+HXQkW9h39g4L3Hiu21JLB3QyjvoS6zQtfj28PloyS25pM8OgbcM9jRftW/gsivij9Hlr0JHDkdO4MMlMtJpIF/69/qU1DQ0PZunUr1apVM/Z16dKFTZs2lXpOvXr1OHnyJABWq5UHH3zQCGgu5uLiwrx58xg8eHDpYxURqWBlPX/XDAsRuenZ7XYOJOeyIaawDsXGmCzScit4jSegmb/n+RoU3nSu7UNVswKK613NSu70u6MyS46W/MFkzclMjqflcbufxzUemYiIiIjIzeOzfanlDysAXFxhxMewaR78tgKSo8HNE+o2gx7PMd+7PdUjk9idmMuaPSnGaatPZsKB88s1+dWEVxbD+tlw5BdIiytcusk3AG5vA92fgRoNiq7Z41k48jMknCgKOXyDIKQVdBgIIWGOY+zzMgQ3gB3fwpkosGTi5ulF9ZCGPPDIY/zjlf9H3SpmTCYTmZn+dDHPYvny5Rw8eNCoLdGgQQMeeeQRXnnllSsqiu3q6sqKFSv44IMP+M9//sPx48cxm83cddddTJgwgc6dO//lvkVEKppmWJRAMyxEbmx2u51jaXlERGezISaLDTFZJGRbK/y6Dfw8zi/x5E2X2v+fvTsPj7I+9z/+eWYm22SZZAKEfZMtIIGgghsoWpeiVq2iWJe6VSByLHiW9qc959ej59Tfae0Bt4ACdUe0UDe0FuuGWK2ogbDvsq/Z92Vmfn+EBiP5PhBInmRm3q/r8vLJ3E+euZMLmHnm/t7fO1FdvNSEw9FHuyp00eIdxvi/npGu347NMMYBAAAAmIVCIQ15Yas22c2tCDOn+WIaZ02M6hKv7M7x6sz9IAAY0WEBIOLtLK3Th7sqGgdl7y4/gX1QT1Hv5Bhd1Mt7pIsiUT2TY9r8OdH2Luzp1ZC0WG0w3EDNX1us/zynMzNHAAAAgJPw4a6KsC1WuCxpSFpcY2FiVJd4jewcL18c3fQA0FooWAAIS/sr6vXRroojg7IrtLWkrs2fs6vXo4t6eRvmUPRKVL+UGFmW1ebPC2dZlqWcEX7d9/H+ZuOF1QH9cVOpbhua6nBmAAAAUT3KkQAAIABJREFUQPibu6a4vVM4ITEu6fT0+CbFiaxO8fLGsHAJANoSBQsAYaGgql6f7Kk8Mii7QusL235Fjj/erfE9j3RQ9ErUkLRYChRR4rZMn365/IAq65vfNTE3v4iCBQAAAHASPt9X1d4pHCPBY2lEpyPbOR0pTgzzxymOrmoAcBwFCwAdUmlNQMv2VB7Z4qlSqw5Vq60H7qTEujSuh/fIHIpEDe8UJxcFiqjki3PrliE+PWNY/fX3/VX6+kCVzshIcDgzAAAAILwdrmr77XvtJMe6lN25aefE4LQ4eVzc+wFAR0DBAkCHUFkX1Gd7K4/MoajUVweqFGjjCkWCx9LY7ke3eBrVJZ43qWg0NctvLFhI0uz8Is27hIIFAAAA0BLVbX2j9x3p8e4mhYlRXRLU3xfDwjQA6MAoWABoFzX1Qf19f1XDFk+7K/XFvkrVBdv2OWPdls7pmnBkiyevRmck0OILo5Fd4nVutwT9zdCyvmBjiX43NkNp8QzYAwAAAE5USqxLxTVtc/M3olOcrj4tWaO6JGhUl3j1TPKwrS8AhBkKFgAcUR8M6asDVfpoV6U+3F2hz/ZWqsowH6C1uC3prIyEI1s8eXVONy8D0tAiOSP8+tu+Pc3GqupDen5dsaaPSnc4KwAAACB89UyKUXFNTZtc+9FxGfpB76Q2uTYAwBkULAC0iWAopFWHqvXhroY5FMv2VKqstm1bKCxJ2V3iGwdlj+3hVXIsq99x8q4fkKzpCW4drgo0G8/NL9J92X5aygEAAIATNKFvktYUtH7Bwhfr0rndvK1+XQCAsyhYAGgVoVBI6wtrG7Z42lWhT/ZUqrC6+Q95W9Ow9LiGLZ56enVBz0T52Z4HrSjO49Ldw1L1/74qaDa+ubjhzzyruAAAAIATMzkrTb/7ukCt3W9/x7BUOuoBIAJQsABwUkKhkLaW1OmjIwWKj3ZX6EBl2xcoBqTG6qIjHRQX9kxURiL/jKFtTR6epv/5ynxDlbuqiIIFAAAAcIL6+2L1w75Jevfb8la97pThaa16PQBA++CTPgAnbFdZXUNxYleFPtxdoV1l9W3+nL2SPbqoZ+KRQdmJ6pUc0+bPCXxXX1+sruiXpCXbm7+henNbmXaX1aknfzYBAACAE/LPZ6S3asHiqv5JGuyPa7XrAQDaDwULAEYHKur10e5/dFBUaktxbZs/Z4bXrfFHChQX9UpUf1+MLOYDoJ3lZKUZCxbBkPTMmiI9dE4Xh7MCAAAAwtNFvRI1PduvWXmFp3ytHkkezb6oWytkBQDoCChYAGhUWB3QJ0cKFB/uqtS6wtYfhPZ9aXEuje+V2FikyPTHUqBAh3NZ3yT1S4nR9tK6ZuNzVxfrV6M7K9bNn10AAADgRDw6NkN7y+v02uayk76GP96td6/urR5JdDsDQKSgYAFEsdKagD7dW3lkDkWlVh6qbvXBZ9+XHOvSuB7exkHZIzrHy0WBAh2cy7I0NStN/7b8YLPx/ZX1emNrqW4Y5HM4MwAAACA8uV2WJg9PO+mCRX9fjP58TW8NSmMrKACIJBQsgChSWRfU3/ZVHplDUakVB6oUaOMKRYLH0nndvUfmUHh1RkaCPC4KFAg/dwxL1b9/fkg1hr80uauKKFgAAAAALfD4yqIWf0/3RI/uGZ6m+0f5lRzrboOsAADtiYIF0Iy6ujo99thjev7557VlyxYlJCRozJgxeuCBBzR27NgTusaePXv0m9/8RkuXLtXu3bsVCATUtWtXjR07Vg888ICGDRvWeG5eXp5eeeUVffzxx9q1a5cKCgrk8/l09tln69/+7d+afc4FCxZo7ty5ys/PV0lJibxerzIzM/WTn/xE06ZNk9vtVm0gpL/vr9RfthTq5TmPa8fydxQ6tEOqq5ESUqReQ6XzfyINu/DYH6C2Slr2kpT3Z+nwzobHUjpL/UdJV94vJac3PLblS+mpO4y/h+tuvU0vvvD8Cf3OgI6sU4JHNw5K0QvrS5qNf7KnUmsLqjUsPd7hzAAAAIDws7W4Vm9tM3dXWFLjDgBpcS6N6Zqgu09P04/6JyuGrVgBIGJZoVCorXeACTuDBw9uPN64cWM7ZoL2EAwGddVVV+ndd989JuZyubRgwQLdeOONttcoLCxUVlaW9uzZ02w8MTFRK1asUGZmpiRpypQpevrpp5s91+Vy6bXXXtN1113X+Nhvf/tb/eIXvzA+/xlX3ST/7Y9o+d5KVdWHpGemSOs/NSd8039Lo685+nVZgTT7bmnfpubP/5dFUo+G3K0tXypkU7D46U9/queee8783EAY+fu+Sp396rfG+L0j0vTkeAb+AQAAAMfz84/36/GV5qHbb17VUz/onaQ4tyU3XfoAEFHsPn93OZ0M0NE9/fTTjcWKzMxMLVq0SL/97W/ldrsVDAY1efJkHTp0yPYaixYtaixWZGRk6JVXXtGbb77Z+JexoqJCzz77bJPvycjI0IMPPqg///nPWrBgQeO5wWBQ999/f5NzZ82a1Xh89U0/1eTcxRpw3b2Nj3295FW9v+lgQ7Hi4LdHixWWS7r6X6Upc5t2VXz2StMf4JVfHS1WDDxbuuV/pKnzG/5/7o3K7NrQfrvk6l56+0e9Gr/t8ccf16efftrkvwcffND2dwWEk9FdEzSqi7mD4oX1JSqrDTiYEQAAABB+SmoC+sPaYmN8QGqsruyfLG+Mi2IFAEQZtoQCvic3N7fx+Nlnn9WYMWMkSZs2bdK8efNUUlKil156STNmzDBeo6Tk6JYxV111lSZNmiRJys/P17//+79LkmpraxvPufnmm/W///u/8nq9jY8NHTpUI0eOlCTt3LlTBw8eVOfOnbWhqFYFRUev/+agn0o1GdJ5g6U3npYC9VIoKIWOfGha/Z0W2/Se0oW3NxzHxElrP244rq87es6utdL6ZQ3HA8+Wps7T0PR4je/VMCj7gh5epScc/afj4x1H9wwdPny4zj//fOPvBQh3lmUpJytNd/91X7PxstqgXt5QoilZfoczAwAAAMLH/LXFKq8LGuM/H+mXy6JQAQDRiA4L4DuKioq0Zs0aSVJMTIzOPPPMxth5553XeLxs2TLb61x44YWNx2+//bZeffVVvfXWW3rppZckNWzzNHHixMZzxo4d26RYIUkDBw5s8vW9y4vUfe5mDX1hq2r7Hc1L7z0pbfpCevexhmKFJA05r2FGhSR1HSAlHfnwtGC39PFz0sa/SR/+4eg1sn949HjNR42HF48aqvPemKY9952hBdcN0yv/5y4V7fnW+HPffPPNiouLk8/n0wUXXKBFixYZzwXC1U2DffLFml8+c/OLxG6LAAAAQPPqgyE9nmfeCsoX69LtQ1MdzAgA0JFQsAC+49tvv208Tk9Pl9t9tHuga9eujcfbt2+3vc5ZZ52lZ555Rn6/XwcOHNCkSZN09dVXa+PGjRo2bJiWLl3apADyXbvL6vTi+mJd9n/nHn2w/xlatDOg/ZVHChKTHpIGndtw/Pc/SbPvkv46t2HLp3G3Srcf3TJKsQnSXU9Infs2dF68+Ttpzs8auiu8KdJ1v1LPa6bqtkyfnru0u34Yv7/xWz949Q/6bNnHKikpUVFRkRYtWqQxY8Zo69atzea+d+9e1dbWqrS0VMuWLdPEiRP1n//5n7a/KyDceGNcumOY+QZq9eEafba3ysGMAAAAgPDx5tYy7SirM8Z/NjxNSTYLhAAAkY1XAES1irqglu2u0KLNpXpubbGWbDjYGIuNjW1y7ne/Li8vP+61u3Xrpu7dux/z+IYNG7Rw4cLGLaEOVtbr1Y0lmvLBPg16bot6zd+s2+Z/oOVPNmwdJU+sdM33BmzHJjRs7+SOafp4KCit+VDavb7p4wnJUqdeOkZlqc7c+7E+v9yr5y/roZ8OTVVdZVmTUx5++GEtWbKkcWuswsLCxm2tpIZukQsuuECzZs3Su+++q0WLFuniiy9u8v3fLQQBkWDK8DTbeG6+ecUYAAAAEM1m5hUYY25L+qcRbK8KANGMGRaISmsOV2t2fpFeWF/SdN/M3UcLEYfLq7WhsEZD/HGSms6cSEpKsr3+hx9+qKuvvlrBYFBnnXWWXnvtNaWkpCgnJ0evvvqq5s2bp7zKeNVcPl1rCmqafvP2PGluTsPsCZdHuvV3Uq9hTc+ZP03asqLh+Mb/lLInSLvWSM9MlQr3SHOnSr/6i5SUJlUUy3riNoUqipWQ7NPc197Q1eefqblz5+r+++/XV58v18SJE/X5559LkuLi4hqf5txzz9WvfvUrSVKnTp109tlnS5Lef//9xnPGjRunjz/+uEl6V111lYYOHaqtW7cqEAho6dKluueee2x/Z0A4GeyP0w96J+qvOyuajS/aXKqZ4+qVkcjLLAAAAPAPK/ZX2XYjXzcgRb1TYoxxAEDko8MCUWVDYY0uXvythr+0Tbn5RccO+fIf7YioLClU5nObdPnrO7S1uFb79x/dKqlfv362zzN//nwFgw3XvmtKjtaH0vXI2jqtGnlb4zlfv7/k2GLFxs8btmuqKm3orLh9ppT1g6bnHNpxtFjh7ymdfb0U55UGjJaGHBl4XVOhkUXf6NGxGXowPk+himJJ0qTrf6ybL79QSUlJmjFjhtLSGlaJf/HFF9q3r2GIcO/evRufqk+fPo3Hffv2bTwuLS21/fljY2OVnZ3d+PXhw4dtzwfCUU6WucuiLijNX1vkYDYAAABAxzfLprtCkqZn010BANGOggWixoe7KjRm4XZ9uKvSfJLX1zCkWmoYYL1zjf6yo0KjF27Xn97/pPG0cePGGS9RVR/Upj1Ht5aa+u4WTXhzlx79ukAb9n5nm5ia763MXv2BNC9Hqq1q2PLp7lxp+EXHPsGR4oOkhnODQcW7LV3cK1GnJRztApk6OE7/fEa6kuqObvFUVnb0uLa2VjU1NcfEvjtbY+fOnY3HO3bsaDzu1evo9lJfffXVMQOGa2trlZeX1/h1RkbGsT8HEOau6p+sHknmDoo5q4sUCDJ8GwAAAJCkPeV1em2zefHbmK4JOqe718GMAAAdEQULRIVPdlfoh2/sVGlt8Pgnn3fj0eOFv5JWLVXhn+fp7YUvSpJSUlJ0yy23SJJ+/etfy7IsWZal8Xf/q8Yv+lapszfqKx3t1Aj9dZ709RJpzUfS6//v6LV7DDl6vPIv0nP3S/W1kmVJl+VIMbHStq+P/ld1pNjQuU/DVlGSVF6gK7/+vf44YIcu2viytq34tPGSI0eOlCQNHTq08bE33nhDv//977V06VLdeeedqqxsKN74fL7GrpFrr722scDw2Wef6Te/+Y3effddTZ8+vfE6EydObDz+l3/5F2VlZel3v/ud3n//fS1evFgTJkxoHMwdFxenyy+//Pi/dyDMeFyWJtvMsthVVq93th9/3g0AAAAQDZ5aVah6m1tyuisAAJJkhb6/NBoaPHhw4/HGjRvbMRO0hm0ltTpjwTYV15xAsUKSgoGGGRAbPjs2Zrk065lnde6VE/Xhrgo98+h/adtrjzXELsuRLr+34bhor/T7G6QKw5YwMfHStOek3sMbvl7wgLTiTdu0hvzqZV1z2cUa3ytR7z3xkGb+/nfGc6+77jotWrRIkhQIBDR27NjGGRXNeeKJJzRt2rTGr9955x1de+21qqurO+bcESNG6NNPP1VycrIk6cILL9Qnn3xyzHmSZFmWnnzySeXk5Nj+bEC42ldRp97zNxtvvC7rk6j3ru3TfBAAAACIEpV1QfWav1mF1YFm4z2TPNp2x0DFuC2HMwMAtAe7z9/psEDE+z/LD554sUKSXG7prielK+9v2B4qJk5KSJEGnyvl/EH/WnmmRi/crl9+dlDbSo79QF+SlNZd+ufXGro1OvVumEfh8kipXaUzrpRmLDxarDhBsy/urkfOz9ClfZL0+9/9j1544QVdeOGF8vv9crvdSk5O1ujRozVr1iwtXLiw8fvcbrfef/99Pfzww8rOzlZiYqLcbrfS09N12WWX6a233mpSrJCkK664QsuXL9dVV10lv9+vmJgYDRgwQL/85S+1fPnyxmKFJD366KO6//77NXLkSHXq1Ekej0cZGRm65ppr9PHHH1OsQETrlhijH5+WYoz/ZUeFthTXGuMAAABANHhxfYmxWCFJ00b4KVYAACTRYdEsOiwix97yhtXPgTD4Uz4kLVYX9UrURb0SdUFPrzolmPfGB9BxfLK7Qhcu2mGM//Movx4d19XBjAAAAICOIxgKadgLW7WhqPmFPF6PpV13D5I/3u1wZgCA9mL3+TufiCKizV1T3GGLFf19MRrfs6FAMb6XV90SY9o7JQAnYVwPr4b647SusKbZ+B/WFuvhc7sowUNTIwAAAKLP0h0VxmKFJP10aCrFCgBAIwoWiFihUEhz1xhmSLSDHkmehuJEz0SN7+lVX19se6cEoBVYlqWcEWma9tH+ZuNFNUG9urFUtw9LdTgzAAAAoP3N/KbANv7zkQzbBgAcRcECEWtXWb32lNe32/N3TnA36aAYmBory2JPTiAS3TrEp18sP6CKuuZbunLzCylYAAAAIOqsLajW0p0VxvgV/ZI02B/nYEYAgI6OggUi1uFqZ4sVqXEuXdAjURf18uqiXokalh5HgQKIEilxbt06JFVzVjff1bXiQLVW7K/SWV0THM4MAAAAaD+P5RXaxqdn010BAGiKggUiVlV92w6vSPBYuvDI9k4X9UrUyM7xcrsoUADRampWmrFgIUmz84soWAAAACBqHK6q14vrS4zx09PjdHGvRAczAgCEAwoWiFgpsW074Dbv5v4anEbrKoAGWZ3jdX73BC3fW9Vs/JWNJXp0XAYDBQEAABAVnl5dpOqAeSHh9Gw/uxIAAI7Rtp/oAu2oR1JMm13bbUk92/D6AMJTTpa5pb06ENJz64odzAYAAABoH7WBkJ5aZe4+7pTg1s1DfA5mBAAIFxQsELH88W6d3Ubbr1zYM1GJMfz1AdDUjwckq4vX3EExO79IwVDbblcHAAAAtLfXNpVoX4V5ruTUrDTFe7inBgAci1cHRLScEWlhdV0A4S3O49Ldw8z/PmwprtVfd1Y4mBEAAADgrFAopJk2w7ZjXPadyQCA6EbBAhFt4sAUpbfyfvHdEz36Uf/kVr0mgMhxz/BUuWy24s21aY0HAAAAwt3yvZX65mC1MX7TYJ+6JjJSFQDQPAoWiGjxHpemtXI3xPRsvzx2n0YCiGp9UmJ1Zb8kY/zt7WXaWVrnYEYAAACAc2Z+Y+6ukBruqQEAMKFggYj3y7M66ZxurTPLYnxPr36end4q1wIQuexa3IMh6Zk1dFkAAAAg8mwrqdUbW8uM8Qt6eJXdpW1mTQIAIgMFC0S8eI9Lb/2olzL9sad0nRGd4vSnK3sp1k13BQB7l/RJ1Gm+GGN87poi1QYYvg0AAIDI8sTKQtm9y50xigWAAAB7FCwQFToleLT8hn4a18N7Ut9/Se9ELZvYV6mtPA8DQGRyWZam2nRZHKwM6E9bSh3MCAAAAGhbpTUBzV9bbIyf5oux3ToVAACJggWiiD/erQ+u66Pc8V01JO3Eui1OT4/T3B9005+v6a2UOIoVAE7c7UN9irfpyMrNZ1soAAAARI4/rC1WWW3QGL9vpF9u5kECAI7D094JAE7yuCxNHeHXlKw0fby7UnPyi/TR7godqgpIkixJnb1uXdwrUVOz0nR+d68sizdUAFouPcGjSYNT9Ny6kmbjn+6p1OrD1RreKd7hzAAAAIDWFQiG9PhK87DtlFiX7hiW6mBGAIBwRcECUcmyLI3vlajxvRIlNby5qqwPKjHGJRcFCgCtJCfLbyxYSNLs/CLlXtTNwYwAAACA1vfWtjJtL60zxu8+PVXJsexaAAA4PraEAiS5XZaSY90UKwC0qrO6JujMDHMHxYvrS1RaE3AwIwAAAKD1zcwzd1e4LOmfRpjnuwEA8F0ULAAAaEM5NsO3y+uCemmDuQMDAAAA6Oi+PlClT/dUGuPXnpasvr4TmyMJAAAFCwAA2tCNg1KUFmd+uc3NL1IoFHIwIwAAAKD1zLLprpCkGaPSHcoEABAJKFgAANCGvDH2AwbXFtTYrkgDAAAAOqq95XV6dZO5Y/jMjHid2y3BwYwAAOGOggUAAG1syvA023hufpFDmQAAAACtJze/SHVBc3xGdrosZkUCAFqAggUAAG1sYFqcLu2daIwv3lKq/RX1DmYEAAAAnJqq+qDm2Cy86Z7o0cSBKQ5mBACIBBQsAABwQM4I8/Dt+qA0bw1dFgAAAAgfL60vUUF1wBifNsKvGDfdFQCAlqFgAQCAA67ol6ReyR5j/OnVRaoPMnwbAAAAHV8oFLIdtp3gsXTPcPMcNwAATChYAADgAI/L0mSbWRa7y+u1ZFuZgxkBAAAAJ+f9nRVaV1hjjN+Wmar0BPNiHQAATChYAADgkLuGpSnG5pWX4dsAAAAIBzO/KbCN/zzbvB0qAAB2KFgAAOCQrokeXTfAPHjw/Z0V2lRkXqkGAAAAtLf1hTV6b0eFMX55n0Rl+uMczAgAEEkoWAAA4KCcEeZtoSRpDl0WAAAA6MAes5ldIUkzRqU7lAkAIBJRsAAAwEHnd/fq9HTzirNn1xWrsi7oYEYAAADAiSmoqtcL64uN8aH+OF3SO9HBjAAAkYaCBQAADrIsSzlZ5i6L4pqgFm4qcTAjAAAA4MQ8s7pYVfUhY3x6tl+WZTmYEQAg0lCwAADAYbdk+pRkM337qVVFCoXMN4IAAACA0+oCIT25yrwdVHq8W7dk+hzMCAAQiShYAADgsORYt26zuZn75mC1VhyodjAjAAAAwN4fN5dqb0W9MT4lK00JHj5mAgCcGl5JAABoB1NttoWSpFyb1WsAAACAk0KhkGbmFRjjMS7ZbnsKAMCJomABAEA7OL1TvMb18BrjCzeVqqDKvIINAAAAcMrf9lXpK5sO4BsH+dQ9KcbBjAAAkYqCBQAA7cRuFVpNIKRn1xU7mA0AAADQvJnfmLsrpIZh2wAAtAYKFgAAtJNrB6Qow+s2xmfnFynI8G0AAAC0o29LavX61jJjfGwPr87ISHAwIwBAJKNgAQBAO4l1W/rZ6eYui20ldVq6o8LBjAAAAICmnlhVqKDNGpoZdFcAAFoRBQsAANrRPcPT5LLM8dx8hm8DAACgfZTVBjRvjXmb0n4pMfpR/2QHMwIARDoKFgAAtKNeyfY3eUu2levbkloHMwIAAAAaPLu2WKW1QWP8vpF+ue1W3wAA0EIULAAAaGd2w7dDkp5ZU+RcMgAAAICkQDCkx1eau32TY126c1iqgxkBAKIBBQsAANrZxb0TNTA11hift6ZYNfXmlW0AAABAa1uyvVxbS+qM8buGpSolzu1gRgCAaEDBAgCAduayLE216bI4VBXQ4i1lDmYEAACAaDfzmwJjzGU1bAcFAEBro2ABAEAHcPvQVCV4zPv/MnwbAAAATsk7WKVP9lQa41f3T1Y/n7lDGACAk0XBAgCADiAt3q2bBvuM8c/2VmnVoWoHMwIAAEC0mpVnv1hmxii6KwAAbYOCBQAAHYTd8G1Jmp3P8G0AAAC0rf0V9XplY4kxPqpLvM7v7nUwIwBANKFgAQBAB3FGRoJGZ8Qb4y9tKFZJTcDBjAAAABBtcvMLVRc0x2dk+2VZ5q1MAQA4FRQsAADoQHJGmNvrK+pCenG9ebUbAAAAcCqq64O2Xb3dEj26YZB5G1MAAE4VBQsAADqQGwalyB/vNsZz8wsVCoUczAgAAADR4uUNJTpcZe7ovXdEmmLddFcAANoOBQsAADqQBI9Ldw5LNcbXF9bqk92VDmYEAACAaBAKhWyHbce7LU0ebj9zDQCAU0XBAgCADmbKcW4Ecxm+DQAAgFb2wa4KrSmoMcZvzfSpU4LHwYwAANGIggUAAB3MaamxurxPojH++tZS7S2vczAjAAAARLqZ35i7KyRpena6Q5kAAKIZBQsAADogu+Hb9UFp3ppiB7MBAABAJNtYWKN3vy03xi/tnaih6XEOZgQAiFYULAAA6IAm9E1S7+QYY/zp1UWqCzB8GwAAAKfusZX23RUzRtFdAQBwBgULAAA6ILfLsp1lsbeiXm9vL3MwIwAAAESiwuqAnl9n7t4dkharS222KwUAoDVRsAAAoIO66/RUxdi8UueuYvg2AAAATs3c1UWqrDd37k7PTpfLshzMCAAQzShYAADQQXXxejRxYIox/sGuCm0orHEwIwAAAESSukBIT64ybwflj3fr1kyfgxkBAKIdBQsAADowu+HbkjQnny4LAAAAnJzFW0q1u7zeGJ88PFVeu5ZfAABaGa86AAB0YOd2S1BWpzhj/Ll1xaqoCzqYEQAAACLFrDxzd4XHJd17nMUzAAC0NgoWAAB0YJZlKSfLfKNYUhvUwo0lDmYEAACASPD53kr9fX+VMX7DwBT1SIpxMCMAAChYAADQ4d08xKfkWPNL9lOrihQKmQclAgAAAN8306a7QpJmjEp3KBMAAI6iYAEAQAeXFOvST22GHeYdqtaXNqvjAAAAgO/aUVqrxVtKjfHzuifozIwEBzMCAKABBQsAAMLAVJttoSQpl+HbAAAAOEFPripS0KZBd0Y23RUAgPZBwQIAgDAwND1OF/b0GuOvbirV4ap6BzMCAABAOCqvDWruavNilz7JMbr6tGQHMwIA4CgKFgAAhAm74ds1gZCeXVvsYDYAAAAIR8+tK1ZJbdAYvy/bL4/LcjAjAACOomABAECYuOa0ZHX1eozx2flFCjJ8GwAAAAbBUEiPrTQP206KcemuYakOZgQAQFMULAAACBMxbkv3DDffQG4vrdNfvi13MCMAAACEk3e2l2tLca0xfuewVPni3A5mBABAUxQsAAAIIz87PU1umw59hm8DAADAZFZegTFmSbpvpHkLUgAAnEDBAgCAMNLzOEMQ39lerm9LzKvmAAAAEJ1WHarWh7sqjfEAHn+KAAAgAElEQVQf9U/WaamxDmYEAMCxKFgAABBmcrLSjLGQpKdX02UBAACApuy6KyRpxii6KwAA7Y+CBQAAYeaiXokanGZe/TZvbbFq6oMOZgQAAICO7EBFvRZsLDXGR3aO17geXgczAgCgeRQsAAAIM5ZlaapNl8XhqoAWbSlzMCMAAAB0ZLPzC1UbCBnjM7L9siybQWkAADiEggUAAGHop5mpSvCYbypzVxU6mA0AAAA6qur6oGbbbBma4XXrxkEpDmYEAIAZBQsAAMJQarxbNw/xGeN/21ellQerHcwIAAAAHdErG0t1sDJgjN87wq84Dx8PAQA6Bl6RAAAIU3bbQkkNrf8AAACIXqFQyHbYdpzb0uTh9u8pAQBwEgULAADC1KguCTq7a4Ix/tKGEpXUmFfTAQAAILJ9tLtS+YdrjPFbhvjUxetxMCMAAOxRsAAAIIzljDCviKusD+mF9SUOZgMAAICOxK67QpJ+nu13KBMAAE4MBQsAAMLYxIEpSo93G+O5qwoVCoUczAgAAAAdweaiGi3ZVm6M/6B3ooZ3incwIwAAjo+CBQAAYSze49Jdw1KN8Q1Ftfp4d6WDGQEAAKAjeGxloeyWrcyguwIA0AFRsAAAIMxNzkqTZRPPZfg2AABAVCmqDujZtcXG+OC0WF3eN8nBjAAAODEULAAACHP9fbH6oc0N5+tbyrS3vM7BjAAAANCe5q0pUmW9ub/i5yP9cll2S14AAGgfFCwAAIgAdsO3AyFp7hrzCjsAAABEjvpgSE+sMnfYpsW5dNtQ85aiAAC0JwoWAABEgMv7JKlvSowx/szqItUFGL4NAAAQ6f60pVS7yuqN8XuGpykxho+DAAAdE69QAABEALfL0pTh5i6LvRX1emtbmYMZAQAAoD3MyjN3V7gt6d4RDNsGAHRcFCwAAIgQdw5LVazbvBcxw7cBAAAi29/3VerzfVXG+MSBKeqVbO7KBQCgvVGwAAAgQnT2enTDwBRj/MNdlVpfWONgRgAAAHCSXXeFJE3PprsCANCxUbAAACCC5GSZt4WSpDn5RQ5lAgAAACftKqvTHzeXGuPndEvQmG5eBzMCAKDlwqJgUVdXp4ceekijR4/W6NGj9fDDD6u+vvkBUgcOHFBOTo7GjBmjMWPG6L777tPhw4dP6loAAISbs7slaGTneGP8uXXFqqgLOpgRAAAAnPDkykIFQub4jOx055IBAOAkhUXBYvbs2fr666+1ZMkSLVmyRF999ZXmzJnT7Lm//vWvJUkffvihPvjgA9XW1uq///u/T+paAACEG8uybLssSmuDWrChxMGMAAAA0NYq6oJ6Zo25k7Z3coyuHZDsYEYAAJycsChYLF68WFOnTlWXLl3UpUsXTZkyRYsXL2723N27d+uHP/yhEhMTlZSUpAkTJmjz5s0ndS0AAMLRT4b4lBJrfonPzS9SKGSz/A4AAABh5fl1xSquMXfR/tPINHlcloMZAQBwcjp8waKkpET79+9XZmZm42OZmZnau3evysrKjjn/jjvu0HvvvaeysjKVlpbqnXfe0QUXXHBS1wIAIBwlxrh0+9BUY3zloWp9sa/KwYwAAADQVoKhkB5baR62nRhj6e5h9nPOAADoKDp8waKyslKSlJx8tHUxJSVFklRRUXHM+aNGjVJBQYHOOussjR49WiUlJZo6depJXQsAgHA19TjDt3MZvg0AABAR/vxtuTYV1RrjdwxNVWq828GMAAA4eR2+YOH1eiVJ5eXljY/9oxsiMTGxybnBYFB33nmnRo0apby8POXl5WnUqFG6++67W3wtAADC2RB/nC7q5TXGX9tcqkOV9Q5mBAAAgLYwK8/cXWFJum+k37lkAAA4RR2+YOHz+dS1a1etX7++8bH169erW7duTTolJKm4uFh79uzRbbfdpoSEBCUkJOjWW29VXl6eCgsLW3QtAADCXU6W+ea0NhDSH9YWO5gNAAAAWtvqw9X6607zjhFX9k/SwLQ4BzMCAODUdPiChST9+Mc/1pw5c3To0CEdOnRITz/9tK6//vpjzvP7/erTp49efvll1dTUqKamRi+//LK6du0qv9/fomsBABDuftQ/Wd0TPcb4nNVFCgQZvg0AABCuHrPprpCk6dnpDmUCAEDrMH+K0YHk5OSouLhYEyZMkCRdddVVmjJliiTpP/7jPyRJDz30kCQpNzdXjzzyiMaNG6dgMKjMzEzNnj37hK4FAEAkiXFbumd4mn79xaFm49+W1um9HeW6oh9dhgAAAOHmYGW9XtpQYoxndYrT+J7mLUIBAOiIrFAoxNLK7xk8eHDj8caNG9sxEwAATs3e8jr1nr9ZAcOr/YS+SXrnmt7OJgUAAIBT9tAXh/R/DQtTJOnZS7rr9mGpDmYEAMCJsfv8PSy2hAIAACene1KMrh1g7qD487fl2lZS62BGAAAAOFU19UHl5pu3g+ridWvS4BQHMwIAoHVQsAAAIMLZDd8OSXo6v8i5ZAAAAHDKFm4q1YHKgDGek+VXvIePfAAA4YdXLwAAItyFPb0akhZrjM9fW6zq+qCDGQEAAOBkhUIhzbIZth3rtjRleJqDGQEA0HooWAAAEOEsy1LOCHOXRUF1QH/cXOpgRgAAADhZn+yu1MpD1cb4zYN9ykj0OJgRAACth4IFAABR4LZMn7weyxjPXcW2UAAAAOHArrtCkn6ebV6oAgBAR0fBAgCAKOCLc+uWIT5j/Iv9VfrmYJWDGQEAAKClthTX6q1tZcb4Rb28GtE53sGMAABoXRQsAACIElNthm9L0myGbwMAAHRoT6wsVMgmPj073bFcAABoCxQsAACIEiO7xOvcbgnG+MsbSlRcHXAwIwAAAJyokpqA/rC22BgfmBqrK/olOZgRAACtj4IFAABRxG74dlV9SM+vN98EAwAAoP3MW1Os8rqgMf7zbL9clnlmGQAA4YCCBQAAUeT6AcnqlOA2xnPzixQK2W00AAAAAKfVB0N6YqV52HZqnEs/zUx1MCMAANoGBQsAAKJInMelu4eZb2Y3FdXqw10VDmYEAACA43lja5l2lNUZ4z87PU1JsXzEAwAIf7yaAQAQZSYPT5PdZgG5DN8GAADoUGblFRhjbkuaZrPtJwAA4YSCBQAAUaavL1YTbAYyvrm1TLttVvABAADAOSv2V+mzvVXG+HUDUtQ7JcbBjAAAaDsULAAAiEI5WWnGWCAkzV1DlwUAAEBHYNddIUnTs+muAABEDgoWAABEocv6JKmfzUq8Z1YXqy7A8G0AAID2tKe8Tq9tLjXGx3RN0DndvQ5mBABA26JgAQBAFHK7LE2x6bLYX1mvN7aWOZgRAAAAvu+pVYWqD5rjdFcAACINBQsAAKLUncNSFec2j9/OzS90MBsAAAB8V2VdUE+vLjbGeyZ5dN2AFAczAgCg7VGwAAAgSnVK8OiGQeab3I93V2pdQY2DGQEAAOAfXlhfrMLqgDH+TyP9irFZfAIAQDiiYAEAQBSzG74tSbPpsgAAAHBcMBTSY3nm92Fej6WfnW7/Pg4AgHBEwQIAgCg2pmuCsjvHG+PPry9Rea3NxskAAABodX/5tlwbimqN8duHpiot3u1gRgAAOIOCBQAAUcyyLOWMMK/OK6sN6uUNJQ5mBAAAgFk23RWSdN9Ihm0DACITBQsAAKLcTYN98sWa3xLk5hcqFAo5mBEAAED0WltQraU7K4zxK/olabA/zsGMAABwDgULAACiXGKMS7cPTTXG8w/X6G/7qhzMCAAAIHrZza6QpOnZdFcAACIXBQsAAKCpxxm+nbuK4dsAAABt7XBVvV5cb96O8/T0OF3cK9HBjAAAcBYFCwAAoMF++5vfP24u1cHKegczAgAAiD5Pry5SdcC8Fef0bL8sy3IwIwAAnEXBAgAASJLt8O26oDR/TbGD2QAAAESX2kBIT60qMsY7J7h18xCfgxkBAOA8ChYAAECS9KP+yeqe6DHG56wuUiDI8G0AAIC28OqmEu2rMHe0Ts1KU7yHj3EAAJGNVzoAACBJ8rgsTR5u7rLYWVand78tdzAjAACA6BAKhTTzG/PMsFi3palZDNsGAEQ+ChYAAKDR3aenym7hHsO3AQAAWt+neyqVd6jaGL9pUIq62nTCAgAQKShYAACARt2TYnTtaSnG+Hs7KrS1uNbBjAAAACLfrDz7RSHTs9MdygQAgPZFwQIAADSRk2XeFkpqmGUBAACA1rGtpFZvbC0zxi/s6dXILvEOZgQAQPuhYAEAAJq4oKdXmf5YY/wPa4tVVR90MCMAAIDI9cTKQoVs4nRXAACiCQULAADQhGVZyrEZ6lhYHdBrm0odzAgAACAyldYENH9tsTF+mi9GV/ZLcjAjAADaFwULAABwjFszfUqMsYxxhm8DAACcuj+sLVZZrblz9b6Rfrld5vdkAABEGgoWAADgGL44t24ZkmqMf3mgWl8dqHIwIwAAgMgSCIb0+ErzIpCUWJfuGGZ+PwYAQCSiYAEAAJo19TjDt2fnM3wbAADgZL25rUzbS+uM8Z+dnqrkWLeDGQEA0P4oWAAAgGaN6Byv87onGOMLNpSoqDrgYEYAAACRY1aeubvCZUnTRphnigEAEKkoWAAAACO74dvVgZCeW2ceEgkAAIDmfX2gSp/uqTTGfzwgWX19sQ5mBABAx0DBAgAAGF03IFmdE8xbEczOL1IwFHIwIwAAgPBn110hSdOz0x3KBACAjoWCBQAAMIrzuHT36eZhj5uLa/XBzgoHMwIAAAhve8vrtHBTiTF+Vka8zu1m3pYTAIBIRsECAADYmjw8TZZNPJfh2wAAACcsN79I9UFzfHp2uizL7t0XAACRi4IFAACw1SclVlf2TzLG39pWpl1ldQ5mBAAAEJ6q6oOaY7PYo3uiRxMHpjiYEQAAHQsFCwAAcFx2w7eDIemZ1XRZAAAAHM9L60tUUB0wxqeN8CvGTXcFACB6UbAAAADHdWmfRPX3xRjjc9cUqTbA8G0AAACTUChkO2w7wWPpnuHm2WEAAEQDChYAAOC4XJalqVlpxviByoBe31LqYEYAAADhZemOCq0rrDHGb8tMVXqCx8GMAADoeChYAACAE3LH0FTF2WxRwPBtAAAAs1l5Bbbx6dnmLTgBAIgWFCwAAMAJSU/waNIg8xDIZXsqteZwtYMZAQAAhIf1hTV6b0eFMf7Dvkka4o9zMCMAADomChYAAOCE5YywX/k3my4LAACAYzxmM7tCorsCAIB/oGABAABO2FkZ8TqjS7wx/sL6EpXVBhzMCAAAoGMrqKrXC+uLjfGh/jhd0jvRwYwAAOi4KFgAAIATZlmWckaYh2+X1wX10voSBzMCAADo2J5ZXayq+pAxPj3bL8syzwkDACCaULAAAAAtMmmQT6lx5rcQuflFCoXMN+UAAADRojYQ0pOrzNtBpce7dUumz8GMAADo2ChYAACAFvHGuHTH0FRjfE1BjZbvrXQwIwAAgI5p0eZS7a2oN8anZKUpwcNHMwAA/AOvigAAoMWmZJm3hZKk3FUM3wYAANEtFAppZl6BMR7jknKO854KAIBoQ8ECAAC02KA0++GQi7eUar/NakIAAIBI99neKn11oNoYv3GQT92TYhzMCACAjo+CBQAAOCl2KwLrgtL8tXRZAACA6DXLprtCkmaM8juUCQAA4YOCBQAAOClX9k9WzySPMf706iLVBxm+DQAAos/2klq9vrXMGB/Xw6tRXRIczAgAgPBAwQIAAJwUj8vS5OHmLotdZfV6Z3u5gxkBAAB0DE+uKpTduo3p2XRXAADQHAoWAADgpN19epo8Nu8mclcVOpcMAABAB1BWG9C8NcXGeL+UGP2of7KDGQEAED4oWAAAgJPWNdGj6wakGONLd1Zoc1GNgxkBAAC0r2fXFqu0NmiM3zfSL7fLcjAjAADCBwULAABwSuyGb0vSnNUM3wYAANEhEAzpsZXmDtPkWJfuHJbqYEYAAIQXChYAAOCUjO3h1bD0OGP82bXFqqwzrzIEAACIFEu2l2tbSZ0xftewVKXEuR3MCACA8ELBAgAAnBLLsmy7LIpqgnp1U6mDGQEAALSPmd8UGGMuq2E7KAAAYEbBAgAAnLJbhviUFGN+W5Gbz/BtAAAQ2fIOVumTPZXG+NX9k9XPF+tgRgAAhB8KFgAA4JSlxLl1a6bPGP/qQLVW7K9yMCMAAABnzcqzX6AxYxTdFQAAHI+nJScfOnRIX3zxhVasWKF9+/apqKhI8fHx8vv9yszM1JgxYzRy5Ei5XNRBAACINlOz0jQ73zxgOze/UM927eFgRgAAAM7YV1GnVzaWGONndInX+d29DmYEAEB4OqGCxbJly7RgwQJ9+umnCgaDCoVCx5yzdOlSWZalLl266IYbbtCkSZOUnp7e6gkDAICOaXineI3t4dWnhq0QFm4s1aNjM5Se0KL1EgAAAB3e7Pwi1QXN8enZflmW5VxCAACEKdtPDNasWaNHHnlEX3/9teLi4jR+/HiNGjVKw4cPV6dOneTz+VRTU6Pi4mJt27ZNeXl5+tvf/qYnnnhC8+fP1+TJk3XHHXcoNpY9GgEAiAY5WWnGgkV1IKTn1pXon89gQQMAAIgcVfVB2y7Tboke3TDIvHUmAAA4yrZgMXHiRPXu3VsPPfSQJkyYoKSkpGbP69atmzIzM3XFFVdIkvLz87Vw4UI98cQTCgQCysnJaf3MAQBAh/PjASnq4t2vg5WBZuOz8ws1Y5RfLlYYAgCACLFgQ4kOVzX/3keS7h2Rplg3730AADgRtgWLhx9+WNdee63cbneLLpqVlaWsrCxNmTJF+/btO6UEAQBA+Ih1W/rZ6Wn67y8PNxvfWlKn93dU6LK+zS+CAAAACCehUEgzbYZtx7stTR6e5mBGAACEN9vp2Ndff32LixXf1bt3b40ZM+akvx8AAISfe05Pk8tmEWFuvvmmHgAAIJx8sKtCawtqjPFbM33qxPwuAABOmG3B4vs++OADbd++3facHTt26IMPPjilpAAAQPjqnRKjq/olG+NLtpdrR2mtgxkBAAC0jZnf2C/EmJ7N7C4AAFqiRQWLadOm6Z133rE9591339W0adNOKSkAABDeckaYtz4IhqRnVhc7mA0AAEDr21BYo3e/LTfGL+2dqKHpcQ5mBABA+GtRwSIUCh33nGAwKItBmgAARLUf9E7UgNRYY3ze2iLV1AcdzAgAAKB1Pb7Svrtixii6KwAAaKkWFSxOxO7du5WcbN4GAgAARD6XZWlqlrnL4mBlQH/aUuZgRgAAAK2nsDqg59eZO0Yz/bG6rE+igxkBABAZjjv56ZFHHmny9fLly1VWduwHDIFAQPv27dMnn3yic845p/UyBAAAYen2oal68LODqg4036GZm1+om4b4HM4KAADg1M1dXaTKevMuFD8fmc7uEwAAnITjFiyef/75xmPLsrRy5UqtXLnSeH7//v31i1/8onWyAwAAYcsf79ZNg3161rD6cPneKuUfqlZW53iHMwMAADh5dYGQnlhl3g7KH+/WrZksygAA4GQct2DxxhtvSGqYX3Httddq0qRJmjRp0jHnuVwupaamqkuXLq2fJQAACEs5I9KMBQtJmp1fpNkXd3MwIwAAgFOzeEup9pTXG+OTh6fKG9PqO3ADABAVjluwGDJkSOPxbbfdpnHjxjV5DAAAwOTMjASdlRGvFQeqm42/uKFY/3N+F6XEuR3ODAAAoOVCoZBmflNgjHtc0r0j/A5mBABAZGlRyf+BBx7Q+eef31a5AACACJSTZb5pr6gL6cUNJQ5mAwAAcPK+2FelLw0LMSTphoEp6pEU42BGAABElhYVLLZu3aq3335b5eXljY/V1tbqv/7rvzR+/HhNmDBBr7/+eqsnCQAAwteNg1OUFmd+y5G7qkihkHloJQAAQEcxM888u0KSZoxKdygTAAAiU4sKFnPmzNEjjzwir9fb+Nhjjz2ml156SQcPHtS2bdv0wAMPaMWKFa2eKAAACE8JHpfuHJZqjK8rrNGyPZUOZgQAANByO0prtXhLqTF+XvcEnZmR4GBGAABEnhYVLPLz83X22WfL5Wr4tkAgoEWLFmnw4MH6/PPP9d577yklJUXPP/98myQLAADC0xSbbaGkhi4LAACAjuzJVUUK2jSFzsimuwIAgFPVooLF4cOH1a1bt8av165dq5KSEv3kJz9RSkqK+vbtq4svvljr1q1r9UQBAED4GpAaq8v6JBrjf9paqn0VdQ5mBAAAcOLKa4Oau9q8wKJvSoyuOS3ZwYwAAIhMLSpYBINBBYPBxq+/+eYbWZal0aNHNz7WuXNnHT58uPUyBAAAEcFu+HZ9UJq3ptjBbAAAAE7cc+uKVVIbNMb/aaRfbpflYEYAAESmFhUsunXrprVr1zZ+/dFHH6lTp07q169f42OHDh1ScjKrCgAAQFNX9EtS7+QYY/zp1UWqt9tnAQAAoB0EQyE9ttI8bDspxqW7bOZ1AQCAE9eigsUll1yiFStW6MEHH9RDDz2kL7/8UpdcckmTc7Zu3aqePXu2apIAACD8uV2WJg8338zvKa/X29vKHMwIAADg+N7ZXq4txbXG+J3DUuWLczuYEQAAkatFBYs777xTAwcO1OLFi7VgwQL17t1b06ZNa4zv2LFDq1at0llnndXqiQIAgPB317A0xdi8+8jNZ/g2AADoWGZ+U2CMWZLuG2ne9hIAALSMpyUn+3w+vf7668rLy5MkZWVlKTY2tjEeDAb1yCOP6Mwzz2zdLAEAQETISPTo+oEpemVjabPxv+6s0MbCGg32xzmcGQAAwLFWHarWR7srjfEf9U/WaamxxjgAAGiZFhUsJMntdhsLEv369WsyzwIAAOD7crL8xoKFJM1ZXaSZF3R1MCMAAIDmzcozd1dI0oxRdFcAANCaWrQl1D+EQiGtXLlSixcv1osvvtjk8dpa876OAAAA53VP0PBO5g6KZ9cWq6Iu6GBGAAAAx9pfUa8FNossRnaO17geXgczAgAg8rW4YPH111/r8ssv10033aQHH3xQv/nNb5rERowYoffee69VkwQAAJHDsizlZKUZ4yW1QS3cWOJgRgAAAMeak1+o2kDIGJ+R7ZdlWQ5mBABA5GtRwWLjxo266667VFxcrHvvvVeXXnppk/iZZ56pbt266f3332/VJAEAQGS5eYhPybHmtyFPrSpSKGT+gAAAAKAtVdcHlZtfZIx39Xp046AUBzMCACA6tKhg8dRTT8nlcmnx4sWaNm2aBg0adMw5WVlZWrt2baslCAAAIk9yrFu3ZfqM8bxD1fpyf5WDGQEAABz1ysZSHaoKGOM5I9IU5zmpXbYBAICNFr26rlixQpdeeql69uxpPKdbt246dOjQKScGAAAi21SbbaEk2a5qBAAAaCuhUEgzbYZtx7ktTRlu/z4GAACcnBYVLMrLy5Wenm57Tl1dnQIB8yoEAAAASRqWHq8LbAZVvrqpVIer6h3MCAAAQPpod6VWH64xxm8Z4lNnr8fBjAAAiB4tKlh07txZ27Ztsz1n48aN6t69+yklBQAAokPOCPPqxJpASM+uLXYwGwAAAGnmN+buCkmanu13KBMAAKJPiwoW5557rpYtW6Z169Y1G//yyy+1YsUKXXDBBa2SHAAAiGzXnJairjYrFGfnFynI8G0AAOCQzf+fvTsPk6o+8/7/OV3V+97N0ggie7P24oJxnoxxXBOVjCiGECMqgQAdFZjJb4zj/JzrZ4yZzHPNgAk2oAZRUWMENQ8oMWJijBmToDQ0S9PsyiLQTVf1vlad3x9e8oj09zQNVadreb+ui2uYug89H+cP6nDuc39vX7vWH2gy1q8dmq6J/VJcTAQAQHzpVcNi7ty5SklJ0Z133qkVK1bo0KFDkqSPPvpIK1as0A9+8APl5OTonnvuCUtYAAAQW5I8luZMyjHWDzR06q2D5ocGAAAAofT4ljrH+iKmKwAACCvLtnv32uKHH36ohQsXqra2VpZlybbtU/+zX79+euKJJ1RcXByuvK4oLCw89fvq6uo+TAIAQOw73NipYSv3KGC4I7l5eIbW/eNQd0MBAIC442sLaMjTu9XS1f1NSWFuknbOHKkEy3I5GQAAscXp+Xuvt0Rdeuml2rhxozZs2KAtW7bI7/crMzNTxcXFuvnmm5Wamnr+iQEAQNwYkpmob47I1Gv7Grutv3GgSQfrOzQsO8nlZAAAIJ48vd1nbFZI0oKSPJoVAACEWY8Ni82bN+uCCy5QQUHBqc9SUlI0depUTZ06NazhAABAfCgrzjU2LGxJK7b59NOvDnQ3FAAAiBtdQVu/2Go+Dio3OUEzx5uPsQQAAKHR4w6LO+64Q2vWrHEjCwAAiFNXX5iuMbnmCYqnd/jV3hV0MREAAIgnr+5t0KHGLmP9+5NylZ7YqzWgAADgHPT4bdvLFRcAAAC9lmBZmj8p11ivbQ1ozd7uJzAAAADO1+LN5ukKjyXdW8yybQAA3MDrAQAAICLcNT5HqV7zudDlDsc0AAAAnKu/fNqivxxrNdZvH52lIZmJLiYCACB+0bAAAAARITfFo+8UZhvr//Npq7acaHMxEQAAiAdLKpxfilh0cb5LSQAAwFk1LCzL/LYjAABAqJQVm4+FkqRllUxZAACA0DnU2Kk1exqM9SsGpWpyQaqLiQAAiG/es7noiSee0LJly3r1g7dv335OgQAAQPy6eECqLi9I1V8NxzKs3lWv//z7gcpO9ricDAAAxKKlW+oUcFjduaiU6QoAANx0VhMWwWBQXV1dvfoFAABwLsqKzFMWLV22nquqdzENAACIVU0dQT253WesD81M1NRRmS4mAgAAZzVh8YMf/EBlZWXhzgIAAKBvjcnSoveOq64t0G29fGud7i3O5chKAABwXp6r8svfHjTW7yvJlTeB+w0AANx01jssPB5Pr34BAACcixRvgr43IcdY3+Xr0LuHW1xMBAAAYk3QtuS5+EkAACAASURBVB2XbacnWpo9wXm3FgAACL2zalgAAAC4ae6kXDm9z1jO8m0AAHAeNhxs0h5/h7F+z/gc5aTwMiYAAG6jYQEAACLOyJwkfX1YhrH+2t5GHW3qdDERAACIJYs3m19+sCTdX5LnXhgAAHAKDQsAABCRnJZvB2zpqe1+F9MAAIBYsa22Te8cajbWbx6RodG5yS4mAgAAn+uxYfG73/1Od955pxtZAAAATvnGsAxdlJlorD+5zafOgO1iIgAAEAucdldI0qLSfJeSAACAL+uxYTF06FBlZ2d3W3v22Wd1zTXXhDwUAACAJ8HSPIcpi6PNXfo/+xtdTAQAAKLdiZYuvbCr3lgv6pesq4akuZgIAAB80XkdCdXY2KijR4+GKgsAAMBpZk3IUZLHvH6b5dsAAKA3llf61O4wobmoNF+WZb73AAAA4cUOCwAAELEGpHl1++gsY/33h1pUVdfuYiIAABCt2ruCji87DEjz6NuF5vsOAAAQfjQsAABARHNavi199qYkAABAT361u0HHWwLGellRnlK8PCYBAKAv8U0MAAAi2hWDUlXcL9lYX7XTr+bOoIuJAABAtLFtW4s3nzTWkzyW5k1yfkkCAACE33k1LK699lo99thjocoCAABwBsuyVFacZ6w3dAT1osPyTAAAgD8ebtHWWvMxkncUZmtgutfFRAAAoDvn1bAYO3aspk6dGqosAAAA3fpOYbayksy3LeWVPtm2eYEmAACIb4srzNMVkrSw1PxyBAAAcA9HQgEAgIiXkZSgu8bnGOtbatr0l09bXUwEAACixV5/h9btbzLWr74wTUX9U1xMBAAATM66YbFz506tWrVKzz//vPbv32+8buPGjXrwwQdDEg4AAOBz83tYvl3O8m0AANCNn2+pk9Mc5qLSfNeyAAAAZ2fVsPjZz36m2267TT/72c/02GOP6eabb9ajjz6qzs7OM67dtWuXXn/99ZAHBQAA8W1cXrL+YUiasf7rPQ2qaelyMREAAIh0/raAVu4wv9QwOidJNw7PcDERAABw0mPD4u2339Yzzzyj9PR03X777ZoxY4by8vL0wgsvaNasWWpt5fgFAADgDqfl2x0BWyt3+F1MAwAAIt0vd/jV3Gmer1hQmqcEy3IxEQAAcNJjw+Kll15ScnKyfv3rX+uRRx7Rww8/rN/+9re6/vrrtWnTJs2dO1ft7e1uZAUAAHHuH0dkalC611hfvs2nQJDl2wAAQOoK2vr5ljpjPSc5QXeNM+/IAgAA7uuxYbFjxw5dd911GjFixKnPMjIy9Pjjj+uuu+7S3/72N82bN08dHR1hDQoAAJDosfT9ieYHCwcbOvXbj81LNQEAQPx4fV+jPmk88yjrz82ZmKuMpLNe7QkAAFzQ4zdzS0uLBg8e3G3twQcf1D333KMPPvhAZWVlNC0AAEDYzZmUK4/DyQ3lW1m+DQAApMWbTxprHku61+GoSQAA0Dd6bFj0799fNTU1xvoDDzygmTNn6v3339f999/f7SJuAACAUBmckahbRmYa6xsONml/PS9RAAAQz/52rFX/86l55+Zto7I0NCvRxUQAAOBs9NiwGDlypDZt2uR4zb/+679qxowZevfdd/Xcc8+FLBwAAEB3nJZv25JWVDJlAQBAPFtSYZ6ukKRFFzNdAQBAJOqxYXHllVfq0KFD+vDDDx2v+/d//3dNmzZNra3mNxgAAABC4R+GpKkwN8lY/+UOv9q6gi4mAgAAkeJwY6de2dNgrF9ekKqvDEpzMREAADhb3p4uuP7663XixAn5/f4ef9ijjz6qgoICHTlyJCThAAAAumNZlsqKcrXgj8e7rZ9sC+iVPQ26c5x5QTcAAIhNT2ytk9N7C4tKma4AACBSWbZt230dItIUFhae+n11dXUfJgEAACb+toAGP71bLV3d38p8pSBVH3x7uMupAABAX2ruDOrCp3fL1959x2JIhlf77xmtRI/lcjIAAPA5p+fvPR4JdS7q6urC8WMBAABOyUnx6I6x2cb6X461avMJjqoEACCePF/lNzYrJOm+kjyaFQAARLCQNiwaGxv13//937r22mtD+WMBAAC6VVbkfKTDMpZvAwAQN4K2rSUV5hco07yW5kzMdTERAADorR53WHzuyJEj2rFjh7xer4qKitSvX79Ttfb2dq1atUorV65UfX29UlNTwxIWAADgi0oGpOiKQan64NPuJyle2FWv//3VgcpJ8bicDAAAuO2tg02q9nUY63ePz1Eu9wQAAES0s2pYPProo3rxxRf1+bqLxMREPfDAA7rjjjv017/+VT/60Y907NgxJSUlaebMmZo7d25YQwMAAHyurCjX2LBo7bL1bJVfC0rzXU4FAADctthhukKS7i9h2TYAAJGux4bFa6+9ptWrVyshIUEjR46Ubds6cOCAfvKTnygtLU0PP/ywgsGgpk+frvnz52vgwIFu5AYAAJAkTRudpUXvHVdta6DbenmlT/eX5MmyOK8aAIBYteNkm97+pNlYv2l4hgrzkl1MBAAAzkWPDYtXX31ViYmJeu6551RaWipJ2rRpk+655x499NBDKigo0LJly07b7A0AAOCWFG+CvjchRz/78GS39d2+Dv3+ULOuGZrhcjIAAOAWp90VkrSolOkKAACiQY9Lt3fv3q3rrrvuVLNCki677DJde+21sm1bP/nJT2hWAACAPjV3Uq6c5ifKWb4NAEDMqmnp0vNV9cb6pH7JuvrCdBcTAQCAc9Vjw6KxsVFDhw494/OLLrpIkk5rZAAAAPSF4dlJunG4eYLiN/sadbix08VEAADALSu2+dQesI31haUcDQkAQLTo8UioYDAor/fMyxITEyVJKSkpoU/1JZ2dnfrpT3+q9evXS5KmTJmiBx98sNtcX26gdHR0aMSIEVq3bp0k6Uc/+pHWr19/Kr8krVy5ksYLAABRrqwoV28caOq2FrClp7b79P9dMcDlVAAAIJzau4J6Yqt5krJ/qkffKcx2MREAADgfPTYsJPX5mwjLli3TRx99dKphMWfOHC1fvlz33nvvGddWVFSc9r9PmTJFN91002mfzZgxQw899FD4AgMAANfdcFGGhmcl6kBD95MUT27z698m91eihzcsAQCIFb/e06BjLV3G+vyiXKV4ezxcAgAARIizalgsXbpUS5cu7bY2bty4Mz6zLEs7d+48v2RfsHbtWj344IMaMOCztyLnzZun//zP/+y2YfFFlZWV2rdvn6ZOnRqyLAAAIDJ5EizNK8rVA++f6LZ+rKVLr+9r1O1jslxOBgAAwsG2bS3ebF62neSxNL+IZdsAAESTs3rNwLbtXv0KBoMhC1hfX69jx46d1hgZN26cjh49qsbGRsc/u2bNGl155ZUaOHDgaZ//5je/0eTJk3XTTTdp5cqVIc0LAAD6zqwJOUp2mKAorzQ/1AAAANHlT0daVFHTZqzPGJOlgvSzek8TAABEiB6/uXft2uVGDqOWlhZJUmZm5qnPsrI+ezOyubn5tM+/qLW1VW+88YZ+9rOfnfb5nXfeqX/5l39Rdna2tm3bpoULFyohIUF33313eP4DAACAa/qlevWtMVl6vqq+2/q7h1u082S7xucnu5wMAACE2uIK5xcRFpbmu5QEAACESsQf5JiWliZJamr6v0s0P5+sSE9PN/65DRs2KDU1VVddddVpn0+YMEF5eXnyeDwqKSnRnDlz9Oabb4Y+OAAA6BNlRbmO9WVMWQAAEPX213foN/vMpy5cNSRNJQNSXEwEAABCIeIbFtnZ2SooKFBVVdWpz6qqqjRo0CDjdIUkvfLKK7rlllvk9ToPkSQkRPz/CwAAQC9cXpCq0v7mBxTPVtWrqYPjIAEAiGY/r6iT7VBfxHQFAABRyfFp/fHjx8/7/8CJE90vvuyNW2+9VcuXL1dNTY1qamq0YsUKTZs2zXj9/v37VVFRodtuu+2M2ptvvqmmpibZtq1t27bpqaee0vXXX3/eGQEAQGSwLEtlxeYpi8aOoF7Y1f2RUQAAIPI1tAe0cqffWB+Znaibhme4mAgAAISKY8Piuuuu02OPPdbrxoVt29q4caNuueUW/frXvz6vgJJUVlamkpIS3XjjjbrxxhtVWlqqefPmSZIefvhhPfzww6ddv2bNGl166aUaPnz4GT/rhRde0FVXXaWLL75YP/zhDzVjxgzNmjXrvDMCAIDIMaMwW9lJ5tuc8so62bbTe5kAACBS/XKHX40O05ILSvPlSbBcTAQAAELFsh3+tf7444/rmWeeUWdnp6644gp94xvf0CWXXKJhw4adcW1zc7MqKyv1/vvva926daqpqVFRUZF+/OMfa8yYMeH8bwi5wsLCU7+vrq7uwyQAAOBcLXz3mB7fYt5X8f63hul/XZDmYiIAAHC+AkFbo1bt1cGGzm7rWUkJOjx7tDKTPC4nAwAAZ8vp+bvjgocFCxZo+vTpeuKJJ7R+/Xr9+c9/lvTZsut+/fopOztb7e3t8vv9qqmpUTAYlG3bGj9+vB544AHddNNNYfjPAQAA6Nn8olzHhkX51joaFgAARJnf7G80Niskac7EHJoVAABEMccJiy9qamrSunXr9MEHH2jz5s2qra09VUtMTNSYMWM0efJk3XDDDSopKQlbYDcwYQEAQGy4du3HeudQc7e1xATp8OwxGpDm+P4GAACIIH//6wN6/2hrt7UES9p39ygNy05yORUAAOiNc56w+KKMjAzNmDFDM2bMkCR1dnbK7/crJSVFmZmZIYoKAAAQOmXFucaGRWdQ+uV2vx6c3M/lVAAA4Fx8eLzV2KyQpFtHZdKsAAAgyjku3XaSmJio/v3706wAAAAR65sjMnVBuvn9jOXbfAoEWb4NAEA0WFJhPupRkhaW5ruUBAAAhMs5NywAAAAinTfB0txJucb6J42devNgk4uJAADAuTja1KmXd9cb65cNTNHfDUp1MREAAAgHGhYAACCmzZ6YI6/DHU/5Vue3NQEAQN97YqtPXUFzfVFpvizLci8QAAAICxoWAAAgpl2QkaipI7OM9d9+3Kx9/g4XEwEAgN5o6QxqxTafsT44w6tpo83f9QAAIHrQsAAAADGvrMh8LJT02S4LAAAQmVbvqtfJtoCxfm9xnhI9TFcAABALaFgAAICY97UhaRqXl2Ssr9zhV6vTORMAAKBP2LatJRUnjfVUr6XvO+yrAgAA0YWGBQAAiHmWZamsKM9Yr2sL6Ne7G1xMBAAAzsbvPm5WVZ356Ma7xuUoL8XjYiIAABBONCwAAEBcuHNcttITzcdFsHwbAIDIs9hhukKSFpSaX0gAAADR55waFnV1dXrppZf06KOP6qGHHjrt88rKSrW1tYUsIAAAQChkJ3v03bE5xvrfjrfpw+OtLiYCAABOdp5s11sfNxvr3xiWobF5yS4mAgAA4dbrhsUrr7yiq6++Wo888ohWr16tV1999VSttrZW06dP17p160IaEgAAIBTm97B8e1kly7cBAIgUj/cwXbGQ6QoAAGJOrxoWf/7zn/Xwww9r2LBhWrp0qWbMmHFafcyYMRo1apTeeeedkIYEAAAIheL+KfpfF6Qa6y/uqpevLeBiIgAA0J2TrV16rqreWB+fl6zrhqa7mAgAALihVw2Lp556Sv3799fq1at1zTXXKD8//4xrCgsLtXfv3pAFBAAACCWn5dttAVurdvpdTAMAALqzYptPbQHbWF9YmifLMu+mAgAA0alXDYvt27frqquuUkZGhvGagoIC1dbWnncwAACAcLhtVKb6p3qM9WWVPgVt8wMSAAAQXh0BW09sNR/T2C/Vo++Oy3YxEQAAcEuvGhadnZ1KS0tzvKahoUEJCee0yxsAACDskr0Jmj3RvHx7j79D73xiXvAJAADC65U9DTra3GWsz5uUq1Qvzx0AAIhFvfqGHzx4sHbs2OF4TWVlpYYPH35eoQAAAMJp7qRcOR0iUc7ybQAA+oRt21q82bxsOzFBKivOdTERAABwU68aFtdcc40+/PBDbdiwodv62rVrVV1drRtuuCEk4QAAAMLhoqwk3TzCfMTl/9nfqEONnS4mAgAAkvTno6366ESbsf7twmwNSk90MREAAHCTtzcXz549W2+88Yb++Z//WW+99ZYaGxslSatXr9aHH36ot99+WxdddJG++93vhiUsAABAqJQV5Wnd/qZua0FbenKbTz/+uwEupwIAIL4trjBPV0ifLdsGAACxy7Lt3m2VPHr0qB544AFt2rTpjNqll16q//qv/9LAgQNDFrAvFBYWnvp9dXV1HyYBAADhErRtjV61V/vru5+kGJjm0SffG6Mkj9PhUQAAIFQO1Hdo1Kq9ChqeUlw5OE1/vH2Yq5kAAEDoOT1/79WEhSRdcMEFev7557Vr1y5t2bJFfr9fmZmZKi4u1sSJE88/LQAAgAsSLEvzi3L1//zpRLf14y0Bvba3QdMLs11OBgBAfPrFljpjs0JiugIAgHjQq4bFpk2blJGRoXHjxmns2LEaO3ZsuHIBAACE3T3jc/Rv/1Oj9kD3T0fKK300LAAAcEFDe0BP7/Ab68OzEvXNEZkuJgIAAH2hV0u3Z86cqZdffjlcWQAAAFyVn+rVt8dkGevvHWnR9lrz4k8AABAaz+z0q7EjaKzfX5InTwLHNAIAEOt61bDIzc1VSkpKuLIAAAC4rqzY+XiJZZU+l5IAABCfAkFbP99SZ6xnJiVo1oQcFxMBAIC+0quGxeTJk1VRURGuLAAAAK67bGCKLhlgfiHjuap6NXYEXEwEAEB8Wbe/UfvrO4312RNylJXscTERAADoK71qWCxcuFAHDhzQkiVL1NlpvpkAAACIFpZlqaw411hv6gxqdVW9i4kAAIgviyvM0xUJlnRfCcu2AQCIF71aur1ixQqNHj1aK1as0Jo1azR27Fj179//jOssy9Jjjz0WspAAAADh9O0x2frn947L39792dnllT7NK8qVZXF2NgAAobT5RKveO9JirN8yMlPDs5NcTAQAAPpSrxoWr7322qnf19bW6v333+/2OhoWAAAgmqQlJuie8TnGNzy3n2zX+0db9PeD011OBgBAbFviMF0hSQtLma4AACCe9Kph8c4774QrBwAAQJ+aV5TreCRF+VYfDQsAAELo0+ZO/arafOziJQNS9NUL0lxMBAAA+lqvGhaDBw8OVw4AAIA+NSY3WdcNTdfbnzR3W1+7t0HHmrtUkN6r2ycAAGBQvtWnzu5PY5T02XQFxzECABBferV0GwAAIJaVFZmXb3cGpV/u8LmYBgCA2NXaFdTybebv1UHpXn1rTLaLiQAAQCQ4p1cEt2zZoldeeUVVVVVqaGhQZmamJkyYoFtvvVUXX3xxqDMCAAC44uYRmRqS4dXhpq5u6yu2+fSjS/vJk8DbngAAnI8XdtWrtjVgrP+gOFdJHr5vAQCIN71uWCxevFhPPvmkbNs+7fOqqiqtXbtWc+bM0T/90z+FLCAAAIBbvAmW5k7K1f/7QU239UONXXrjQJO+OTLT5WQAAMQO27Ydl22neD77PgYAAPGnV0dCbdiwQStWrNCgQYP06KOPauPGjaqsrNTGjRv16KOPatCgQXrqqaf05ptvhisvAABAWM2emCuvwx1SeaX5AQsAAOjZxk+ateNku7E+c1y2+qWyMwoAgHjUq4bF6tWr1a9fP61Zs0bTpk3TkCFDlJSUpCFDhmjatGlas2aN8vLy9OKLL4YrLwAAQFgVpHt126gsY/2tj5u119/hYiIAAGLLYofpCklaUJrvUhIAABBpetWw2LVrl2644Qbl5eV1W8/Ly9PXv/51VVVVhSQcAABAX3Bavi1Jy5myAADgnOyqa9eGg03G+g0XpWt8frKLiQAAQCTpVcMiEAgoJSXF8ZqUlBQFAubFWQAAAJHu7wenaYLDw5KVO/xq7Qq6mAgAgNjweA/TFQuZrgAAIK71qmExdOhQvfvuuwoGu/8HejAY1HvvvacLL7wwJOEAAAD6gmVZjlMWvvagXq5ucDERAADRr64toGer/Mb6uLwk3XBRuouJAABApOlVw+Lmm2/Wvn37VFZWpoMHD55W++STT3T//fdr7969mjJlSigzAgAAuO67Y7OVkWi+VWL5NgAAvfPkNp9au2xjfUFJvizLcjERAACINN7eXHz33XfrT3/6k95991299957GjBggPr376/a2lodP35cwWBQl1xyie6+++4wxQUAAHBHVrJHd47L1rJKX7f1TcfbtOlYqy4rSHU5GQAA0aczYGvpVnOzPy/ls+9dAAAQ33o1YZGUlKSVK1dq0aJFGjJkiI4dO6Zt27bp008/1ZAhQ7Ro0SKtWrVKSUlJ4coLAADgmvk9LN82NTMAAMDp1uxp0JGmLmN97qQcpTlMNgIAgPhg2bZtnsfsQXNzs5qampSRkaH09Ng5Z7KwsPDU76urq/swCQAA6GtXvnJQfzrS0m0txWPpyJwxykvxuJwKAIDoYdu2Lv/VAW063tZt3ZsgHZw1WoMzEl1OBgAA+oLT8/fzen0hPT1dAwcOjKlmBQAAwBc5Ld9uC9hatdO8PBQAAEgffNpqbFZI0vQx2TQrAACApF42LLZv366lS5eqtra223pNTY2WLl2qqqqqkIQDAADoa7eOytKANPMExbJKn4LnPrAKAEDMW1xx0rG+sDTPpSQAACDS9aph8cwzz2jNmjXKz8/vtt6vXz+tXbtWzzzzTEjCAQAA9LUkj6U5E81TFnv9Hdr4SbOLiQAAiB4fN3To1b2NxvpXL0jVpQNTXUwEAAAiWa8aFhUVFbr88stlWVa3dcuy9JWvfEWbN28OSTgAAIBI8P2JuUro/vZHklS+leXbAAB05xdb6hR0GERcWNr9C5EAACA+9aphUVtbq4KCAsdrBgwYoJqamvMKBQAAEEmGZiVqyvBMY33dgUZ90tDpYiIAACJfY0dAT28373oalpWoW0aav18BAED86VXDIjU1VXV1dY7X1NXVKSkp6bxCAQAARJqyYvOxUEFbenI7UxYAAHzRqp31qu8IGuv3leTJ4zTCCAAA4k6vGhZjx47VO++8o+bm7s9pbmpq0jvvvKOxY8eGJBwAAECkuHZoukblmF/KeGq7Tx0Blm8DACBJQdvW4w7LtjMSE/S9CTkuJgIAANGgVw2L6dOnq66uTrNmzdKuXbtOq+3atUuzZs2Sz+fT9OnTQxoSAACgryVYluYXmacsTrQE9OreBhcTAQAQudbvb9K+evNxibMm5Cg72eNiIgAAEA28vbn4xhtv1HvvvafXX39dU6dOVX5+vgYOHKjjx4/r5MmTsm1bU6dO1c033xyuvAAAAH3m7vE5eujPJ9RmmKQor/Tp24XZLqcCACDyLHGYrrAk3V+S514YAAAQNXrVsJCk//iP/1BpaalWr16tPXv2qLa2VpI0evRozZw5U7fffnvIQwIAAESCvBSPZhRm65md3S8Q/dORFm2rbdOkfikuJwMAIHJsOdGmPxxuMdb/cWSmRjocswgAAOJXrxsW0mdHQ02fPl2tra1qaGhQVlaWUlNTQ50NAAAg4pQV5xobFpK0rNKn8qsHuZgIAIDI4jRdIUkLS5muAAAA3evVDosvS01N1cCBA2lWAACAuHHpwFRdNtA8QfF8Vb0a2gMuJgIAIHIca+7SS7vNO51K+6foysFpLiYCAADR5JwmLL7onXfe0V/+8hfZtq3LLrtMN9xwQyhyAQAARKyyojzd8/bRbmtNnUGt3lWvsmLeHgUAxJ9llXXqMOx6kj6brrAsy8VEAAAgmvQ4YfH73/9ed9xxh/72t7+dUXvwwQd177336vnnn9fq1au1cOFC3XfffWEJCgAAECmmF2YpN9l8G1Ve6ZNtmx/WAAAQi9q6glpW6TPWC9K8mj4my8VEAAAg2pxVw2Lnzp0qLi4+7fM//OEPeu2115SSkqL58+frhz/8oS688EJt3LhR69evD1tgAACAvpbqTdCsCTnG+o6T7frTEfOyUQAAYtGL1fWqaTUfi1hWnKtk73mdTA0AAGJcj3cKlZWVKikpUXJy8mmfr127VpZl6ac//akWLFig2bNn64UXXlBycrLWrVsXtsAAAACRYF6R85FP5Q5vmAIAEGts29aSijpjPdljad6kXBcTAQCAaNRjw6K2tlYXXnjhGZ9v2rRJWVlZp+2s6N+/v772ta9p586doU0JAAAQYUblJOmGi9KN9bV7G3SsucvFRAAA9J3fH2rWttp2Y/27Y7PVP+2812gCAIAY12PDoqGhQTk5px95cPToUdXX1+viiy8+Y1nWkCFD5Pf7Q5sSAAAgApU5TFl0BaWntzNlAQCID07TFdJny7YBAAB60mPDIj09XceOHTvtsx07dkiSxo8f3+2f+fLxUQAAALHopuEZGpqZaKyv2OZTV5Dl2wCA2Lbb1671B5qM9euGpmtivxQXEwEAgGjVY8NizJgx+uMf/6jm5uZTn7399tuyLEuXXHLJGdcfPnxY/fv3D21KAACACORJsDR3knn59uGmLq3f3+hiIgAA3Pc40xUAACBEemxYTJkyRfX19brzzjv13HPP6ZFHHtG6devUr18/XX755adda9u2PvroI40aNSpsgQEAACLJ9ybkKtHhjorl2wCAWOZrC2jVTvOx0IW5Sfr6sAwXEwEAgGjW48aradOm6Xe/+53ef/99VVVVybZteb1ePfTQQ/J4PKdd+8EHH6i2tlZXXHFF2AIDAABEkoHpXk0bnaWXqhu6rb/9SbN2+9o1JpcjMwEAseep7T61dJmPP1xQkqeEL+2+BAAAMOmxYZGQkKAnn3xS69evV0VFhXJycnT99ddr3LhxZ1zr8/k0c+ZMXXPNNWEJCwAAEInKivKMDQtJWl7p039/rcDFRAAAhF9nwNYvtpiPg8pNTtDM8eajEwEAAL7Msm2bTZBfUlhYeOr31dXVfZgEAABEA9u2VfzCfm2rbe+2jgCAUAAAIABJREFUnpOcoCOzxyjN6ewoAACizMvV9fr2hiPG+gOX5us/vjrQxUQAACAaOD1/51/NAAAA58myLJUV5Rrr/vagfrW73sVEAACE3xKHZdseS7q3mGXbAACgd2hYAAAAhMAdY7OVmWS+tXpiq08MtgIAYsVfPm3RX461Guu3j87SkMxEFxMBAIBYQMMCAAAgBDKTPJo5LttY33yiTZuOt7mYCACA8HGarpCkRRfnu5QEAADEEhoWAAAAITLf4VgoSSrf6vxwBwCAaPBJQ6fW7Gkw1v9uUKomF6S6mAgAAMQKGhYAAAAhMiE/RV8bnGas/2p3g062drmYCACA0Fu6tU4Bh1MOF5YyXQEAAM4NDQsAAIAQKis2T1m0B2w9s9PvYhoAAEKrqSOop7b7jPWhmYmaOirTxUQAACCW0LAAAAAIoVtGZqkgzWusL6v0KcjybQBAlHq2yi9/e9BYv68kV94Ey8VEAAAgltCwAAAACKEkj6U5k3KM9f31nfrdx80uJgIAIDSCtq3HHZZtpydamj3BeZ8TAACAExoWAAAAIfb9ibnyOLxcWl7J8m0AQPR580CT9vg7jPV7xucoJ8XjYiIAABBraFgAAACE2JDMRH1zhPn87vX7m3Sw3vzABwCASLTEYbrCknR/SZ57YQAAQEyiYQEAABAGTsu3bUlPOiwsBQAg0lTWtOmdQ+YjDW8ekaHRuckuJgIAALGIhgUAAEAYXH1husbkJhnrT2/3q73LvLQUAIBI8vgW5+MMF5Xmu5QEAADEMhoWAAAAYZBgWZo/yTxlUdMa0Nq9jS4mAgDg3Jxo6dILu+qN9aJ+ybpqSJqLiQAAQKyiYQEAABAmd43PUarXvH2b5dsAgGiwrNKn9oBtrC8qzZdlmb/vAAAAzhYNCwAAgDDJTfHoO4XZxvqfj7Zqa02bi4kAAOid9q6gY4N9QJpHMwqzXEwEAABiGQ0LAACAMHJavi199tYqAACR6qXqBp1oCRjrZUV5SvbyaAEAAIQGdxUAAABhdPGAVF1ekGqsr97lV327+UEQAAB9xbZtLak4aawneSzNL3JuzAMAAPQGDQsAAIAwK3N4mNPcaev5KvMiUwAA+sq7h1u0tbbdWL+jMFsD0rwuJgIAALGOhgUAAECYfWtMlvJSPMZ6eWWdbNu8zBQAgL7gNF0hSQtL81xKAgAA4gUNCwAAgDBL8SboexNyjPWqug798XCLi4kAAHC219+hdfubjPWrL0xTUf8UFxMBAIB4QMMCAADABXMn5cpyqJezfBsAEEF+vqVOTrN/i0rzXcsCAADiBw0LAAAAF4zMSdLXh2UY66/ta9DRpk4XEwEA0D1/W0Ard5gb6aNzknTjcPN3GgAAwLmiYQEAAOASp+XbXUHp6e1+F9MAANC9p3f41Nxpnq9YUJqnBMtpbhAAAODc0LAAAABwyTeGZeiizERjfcU2nzoDLN8GAPSdrqCtX2wxT1fkJCfornHmvUwAAADng4YFAACASzwJluY5TFkcbe7SugONLiYCAOB0r+1t1CeN5iMK50zMVUYSjxIAAEB4cJcBAADgolkTcpTkMR+jUb6V5dsAgL6zpOKkseaxpHuL81xMAwAA4g0NCwAAABcNSPPq9tFZxvo7h5q1q67dxUQAAHzmb8da9T+fthrrt43K0tAs89GGAAAA54uGBQAAgMuclm9L0vJKpiwAAO5zmq6QpEUXM10BAADCi4YFAACAy64YlKrifsnG+qqdfjV3Bl1MBACId4cbO/XKngZj/fKCVH1lUJqLiQAAQDyiYQEAAOAyy7JU5nAGeH1HUC9V17uYCAAQ757YWqcuh175olKmKwAAQPjRsAAAAOgD3ynMVlaS+Vbsia0+2bbtYiIAQLxq7gxqxTbzcYRDMry6dZR5/xIAAECo0LAAAADoAxlJCbprfI6xvqWmTX89Zl58CgBAqDy30y9fu3m84r6SPCV6LBcTAQCAeEXDAgAAoI/M72H5djnLtwEAYRa0bT2+pc5YT/NamjPR+fsKAAAgVGhYAAAA9JFxecn6hyHmBaYv725QbWuXi4kAAPHmtwebVO3rMNbvHp+j3BSPi4kAAEA8o2EBAADQh5yWb3cEbK3c4XcxDQAg3iypME9XSNIClm0DAAAX0bAAAADoQ/84IlOD0r3G+vJKnwJBlm8DAEJve22b3v6k2Vi/aXiGxuQmu5gIAADEOxoWAAAAfSjRY+n7E83Ltw80dOqtj5tcTAQAiBdOuyskaRHTFQAAwGU0LAAAAPrYnEm58ljmOsu3AQChVtPSpeer6o31Sf2SdfWF6S4mAgAAoGEBAADQ5wZnJOqWkZnG+psHmnSg3rwQFQCA3lqxzaf2gPnIwYWlebIsh246AABAGNCwAAAAiABOy7dtffZgCQCAUGjvCuqJrebvlf6pHn2nMNvFRAAAAJ+hYQEAABAB/mFImgpzk4z1X+7wq60r6GIiAECsenl3g461dBnr84tyleLlcQEAAHAfdyAAAAARwLIslRXlGuu1rQGt2dPgYiIAQCyybVtLKszLtpM8luYXsWwbAAD0DRoWAAAAEWLmuBylec3nhbN8GwBwvt470qKKmjZjfcaYLBWke11MBAAA8H/RsAAAAIgQOSke3THWfGb4B5+2quJEq4uJAACxxmm6QpIWlua7lAQAAOBMNCwAAAAiSFkPx3AsY8oCAHCO9vk79Jt9jcb6VUPSVDIgxcVEAAAAp6NhAQAAEEFKBqToikGpxvoLu+rlbwu4mAgAECt+saVOtkN9EdMVAACgj9GwAAAAiDBOy7dbumw9V+V3MQ0AIBbUtwf0yx3m74+R2Ym6aXiGi4kAAADORMMCAAAgwkwbnaV+qR5jvbzSJ9t2ekcWAIDTrdzhV1Nn0FhfUJovT4LlYiIAAIAz0bAAAACIMCneBH1vQo6xXu3r0B8Ot7iYCAAQzQJBWz/fYl62nZWUoLvHZ7uYCAAAoHs0LAAAACLQ3Em5cnrPtXyr+cETAABf9Pq+Rh1s6DTW50zMUWaSebIPAADALTQsAAAAItDw7CTd6HCW+Ov7GnWkyfzwCQCAzy2pOGmsJVjSfSV5LqYBAAAwo2EBAAAQoeY7LN8O2NJT23wupgEARKMPj7fq/aOtxvqtozJ1UVaSi4kAAADMaFgAAABEqK9flKFhWYnG+pPb/eoMsHwbAGC2pML5CMFFpfkuJQEAAOgZDQsAAIAI5UmwNG+Secri0+Yu/WZ/o4uJAADR5EhTp17eXW+sXzYwRVcMSnUxEQAAgDMaFgAAABFs1oQcJXnM67dZvg0AMCnf6lNX0FxfVJovyzJ/xwAAALiNhgUAAEAE65/m1bdGZxnrfzjcoqq6dhcTAQCiQUtnUMsddh0NzvBqmsP3CwAAQF+gYQEAABDhyhyWb0vSskqWbwMATrd6V73q2gLG+r3FeUp0mOADAADoCzQsAAAAItxXBqWqpH+Ksf7sTr+aOhzO/AAAxBXbtrWk4qSxnuq19H2HHUkAAAB9hYYFAABAhLMsy3HKoqEjqBerzUtVAQDx5a2Pm1VV12Gs3zUuR3kpHhcTAQAAnB0aFgAAAFHgO2OzlZVkvnV7YmudbNt2MREAIFI5TVdI0oLSPJeSAAAA9A4NCwAAgCiQnpigu8fnGOuVte364NNWFxMBACLRzpPteuvjZmP9G8MyNDYv2cVEAAAAZ4+GBQAAQJSY38Py7XKWbwNA3Hu8h+mKRUxXAACACEbDAgAAIEqMzUvW1RemGeuv7GnQiZYuFxMBACJJbWuXnqsy7zSakJ+sa4emu5gIAACgd2hYAAAARJGyIvObsR0BWyt3+F1MAwCIJE9u86ktYN5ntLA0T5ZluZgIAACgd2hYAAAARJFvjsjUBeleY315pU+BIMu3ASDedARsLd1qPhqwX6pHd4zNdjERAABA79GwAAAAiCKJHkvfn2TeZfFxY6c2HGxyMREAIBK8sqdBnzabjwWcNylXqV4eAQAAgMjG3QoAAECUmTMxRx6HEz1Yvg0A8cW2bS3ebF62nZgglRWbm90AAACRgoYFAABAlLkgI1FTR2Ua67892KR9/g4XEwEA+tL7R1v00Yk2Y/3bhdkalJ7oYiIAAIBzQ8MCAAAgCjkt37YlrdjGlAUAxIslFXWO9YWl5u8MAACASELDAgAAIApdNSRNY3OTjPVf7vCrtSvoYiIAQF84UN+h1/c1GutXDk7TxQNSXUwEAABw7mhYAAAARCHLslRWbH5jtq4toFd2N7iYCADQF36xpU5B21xfxHQFAACIIjQsAAAAotTMcdlK85q3b7N8GwBiW0N7QE/v8BvrI7ITNWWEeecRAABApKFhAQAAEKWykz367thsY/2vx1r10fFWFxMBANz0zE6/GjvMx//dX5InT4K5sQ0AABBpoqJh0dnZqUceeUSTJ0/W5MmT9eMf/1hdXV3dXltaWnrarwkTJmjKlCnn9LMAAAAi3XyH5duStIwpCwCISYGgrccdlm1nJiXonvE5LiYCAAA4f1HRsFi2bJk++ugjrV+/XuvXr9eHH36o5cuXd3ttRUXFab9GjBihm2666Zx+FgAAQKQrGZCivxtkXqb6YnW9fG0BFxMBANywbn+jDjR0GuuzJ+QoK9njYiIAAIDzFxUNi7Vr12r+/PkaMGCABgwYoHnz5mnt2rU9/rnKykrt27dPU6dOPe+fBQAAEKmclm+3dtl6dqf5fHMAQHRa7DBdkWBJ95WwbBsAAESfiG9Y1NfX69ixYxo3btypz8aNG6ejR4+qsbHR8c+uWbNGV155pQYOHHjePwsAACBSTRuVqX6p5rdoyyt9Ctq2i4kAAOG0+USr3jvSYqzfMjJTw7OTXEwEAAAQGhHfsGhp+ewmLDMz89RnWVlZkqTm5mbjn2ttbdUbb7yhadOmnffPAgAAiGTJ3gTNnmA+p3yPv0O/P8S9DgDEiiUO0xWStLCU6QoAABCdIr5hkZaWJklqamo69dnn0xDp6enGP7dhwwalpqbqqquuOu+fBQAAEOnmTsqV5VAv38rybQCIBZ82d+pX1fXG+iUDUvTVC9JcTAQAABA6Ed+wyM7OVkFBgaqqqk59VlVVpUGDBp02KfFlr7zyim655RZ5vd7z/lkAAACRblh2km4anmGs/2Z/ow43mpezAgCiQ/lWnzqD5vqii/NlWU4tbAAAgMgV8Q0LSbr11lu1fPly1dTUqKamRitWrDjtqKcv279/vyoqKnTbbbed988CAACIFmVFucZa0Jae3M6UBQBEs9auoJZvM/9dPijdq9tHZ7mYCAAAILS8PV/S98rKyuT3+3XjjTdKkqZMmaJ58+ZJkh5++GFJ0iOPPHLq+jVr1ujSSy/V8OHDe/WzAAAAotkNwzI0PCtRBxq6n6R4aptf/za5v5I8vHkLANHohV31qm0NGOv3FufydzwAAIhqlm3bdl+HiDSFhYWnfl9dXd2HSQAAAHrnf39Yq395/4Sx/vKNg/WtMdkuJgIAhIJt25q0er92nGzvtp7isXRo9mj1S42K9xIBAEAcc3r+HhVHQgEAAODs3DMhR8kOb9eyfBsAotPGT5qNzQpJmjkum2YFAACIejQsAAAAYki/VK+mjzGfX/7HIy3acbLNxUQAgFBYXFHnWF9Qmu9SEgAAgPChYQEAABBjnJZvS9KySqYsACCaVNW1a8PBJmP9hovSNT4/2cVEAAAA4UHDAgAAIMZMLkjVxQNSjPXnqurV2GFe2goAiCw/72G6YiHTFQAAIEbQsAAAAIgxlmU5Tlk0dgT1wq56FxMBAM7VydYuPVvlN9bH5SXphovSXUwEAAAQPjQsAAAAYtCMwmxlJ5lv9corfbJt28VEAIBz8dR2v1q7zH9fLyzNl2VZLiYCAAAIHxoWAAAAMSgtMUH3TMgx1rfVtuvPR1tdTAQA6K3OgK2lW83HQeWlePTdsdkuJgIAAAgvGhYAAAAxat4k5+Xb5ZXOZ6IDAPrWmj0NOtLUZazPm5SrtET+WQ8AAGIHdzYAAAAxqjAvWdcONZ9rvmZPg443mx+EAQD6jm3bWlxx0lj3Jkhlxc6NaQAAgGhDwwIAACCGOS3f7gxKv9zhczENAOBs/c+nrdp0vM1Ynz4mW4MzEl1MBAAAEH40LAAAAGLYlBGZGpzhNdaXb/MpEGT5NgBEmiUO0xWStLA0z6UkAAAA7qFhAQAAEMO8CZbmOuyyONTYpTcONLmYCADQk4P1HXp1b6Ox/tULUnXpwFQXEwEAALiDhgUAAECMmz0xR16Huz6WbwNAZFm6tU5Ow28LS/PdCwMAAOAiGhYAAAAxblB6om4dmWWsv/Vxs/b6O1xMBAAwaewI6KntfmN9WFaibhmZ6WIiAAAA99CwAAAAiANlxeZjoSRpOVMWABARVu2sV0NH0Fi/vyRPngTLxUQAAADuoWEBAAAQB64cnKbxecnG+sodfrV2mR+QAQDCLxC09bjDsu2MxATNmpDjYiIAAAB30bAAAACIA5ZlOU5Z+NqDerm6wcVEAIAve+NAk/bVdxrr35uQo+xkj4uJAAAA3EXDAgAAIE7cOTZb6YnmY0RYvg0AfWuxw3SFJem+kjz3wgAAAPQBGhYAAABxIivZozvHmo8S2XS8TZuOtbqYCADwuS0n2vTu4RZj/R9HZmpkTpKLiQAAANxHwwIAACCOzC9yXr69rNLnUhIAwBctcZiukKSFpUxXAACA2EfDAgAAII4U9U/RVy9INdZfqq5XXVvAxUQAgGPNXXppt3mPUGn/FF05OM3FRAAAAH2DhgUAAECcKSsyv6XbFrC1aqffxTQAgGWVdeoI2Mb6wtI8WZZ5BxEAAECsoGEBAAAQZ24dlakBaR5jfVmlT0Hb/OAMABA6bV1Bx+P4CtK8+nZhtouJAAAA+g4NCwAAgDiT7E3Q7AnmXRZ7/R3a+Emzi4kAIH69WF2vmlbzUXw/KM5VkofpCgAAEB9oWAAAAMSh70/KUYLD86/yrSzfBoBws21bSyrqjPVkj6W5k8wNZgAAgFhDwwIAACAOXZSVpJuHZxjr6w406pOGThcTAUD8+f2hZm2rbTfW7xyXrf5pXhcTAQAA9C0aFgAAAHHKafl20Jae3M6UBQCE02KH6QpJWlBi/nsaAAAgFtGwAAAAiFPXXZSukdmJxvpT233qCLB8GwDCobquXW8caDLWrxuaron9UlxMBAAA0PdoWAAAAMSpBMvSfIcpixMtAb26t8HFRAAQP36+xXm6YmEp0xUAACD+0LAAAACIY3ePz1aKx7x9u7ySY6EAINTq2gJatdNvrBfmJunrw8x7hgAAAGIVDQsAAIA4lp/q1bcLs4z1Px1p0bbaNhcTAUDse3q7Ty1d5iP3FpTkKcEyN5MBAABiFQ0LAACAOOe0fFuSljFlAQAh0xmw9QuH46BykxM0c3yOi4kAAAAiBw0LAACAOHdZQaouHWhe7Pp8Vb0a2gMuJgKA2PXq3gYdbuoy1udOylV6Iv9UBwAA8Ym7IAAAADhOWTR1BrV6V72LaQAgdi2uME9XeBOkHxSzbBsAAMQvGhYAAADQ9DFZyk023xqWV/pk2+bz1gEAPfvLpy3667FWY/320VkakpnoYiIAAIDIQsMCAAAASktM0D0TzGem7zjZrj8daXExEQDEnsWbzdMVkrSwNN+lJAAAAJGJhgUAAAAkSfMm5TrWy1m+DQDn7JOGTq3d22Cs/92gVE0uSHUxEQAAQOShYQEAAABJ0ujcZF0/NN1YX7u3QceazYtiAQBmS7fWKeBwsh7TFQAAADQsAAAA8AVlDsteu4LS09uZsgCA3mrqCOrJbea/P4dmJmrqqEwXEwEAAEQmGhYAAAA45abhGbow02usr9jmU1eQ5dsA0BvPVvlV3xE01u8ryZU3wXIxEQAAQGSiYQEAAIBTvAmW5jrssjjc1KX1+xtdTAQA0S1o23q8wrxsOz3R0uwJzjuEAAAA4gUNCwAAAJzmexNylehwl8jybQA4e28eaNIef4exPmtCrnJSPC4mAgAAiFw0LAAAAHCagnSvbhuVZay//UmzdvvaXUwEANFrccVJY82SdH+JeXcQAABAvKFhAQAAgDOUFTsfT7KcKQsA6FFlTZt+f6jFWJ8yIkOjcpJcTAQAABDZaFgAAADgDF+9IE0T85ON9Wd2+tXSaV4gCwCQljjsrpCkhaX5LiUBAACIDjQsAAAAcAbLslRWZJ6y8LcH9avd9S4mAoDocry5Sy9Um/+eLO6XrKuGpLmYCAAAIPLRsAAAAEC3vjsuWxkO27ef2OqTbdsuJgKA6LF8m08dAfPfkQtL82VZlouJAAAAIh8NCwAAAHQrM8mjmeOyjfXNJ9q06Xibi4kAIDq0dQVV/v+zd9/hUVRtH8d/m05JgRCK9JaAECCgIEVEUYqAglIVBEEkiFLUh6oUwUJ56FIUVJQuGEAIoqBIt1GlCYQSQGpIgBTS5v2DN/OwJLsJIWwS+H6uK9e1u2f27D1J5uzMueecs9f2dFCF8zqrU4CXAyMCAADIHUhYAAAAwKbedqaFkqQZe+zPzw4AD6LFh6/qQkySzfI3qhWUuwuX4wAAALfjDAkAAAA2VS3koYbFbc+xvvifq7ocm+jAiAAgZzMMQ5N2XbZZ7uZsSTcZDAAA8KAiYQEAAAC77C2+fSPJ0JcHIh0YDQDkbBtPx2jvpRs2y18O8FbhvC4OjAgAACD3IGEBAAAAu9pU8FKRvM42y2fuvaJkFt8GAEmyO7pCkgbULOigSAAAAHIfEhYAAACwy83Zop5VbY+yCItK0I8nox0YEQDkTEeu3NDqsOs2yxuXzKfAQh4OjAgAACB3IWEBAACAdL0eWEBOFtvlM/ay+DYATN0dIXvjzfoHMboCAADAHhIWAAAASFdJT1c9V87TZvnqsOs6ERXvwIgAIGeJjEuyu6ZPRR83PVs2vwMjAgAAyH1IWAAAACBD7C2+bUj67O8rjgsGAHKYOfuvKDrB9viKfkEF5WSxM1QNAAAAJCwAAACQMY1L5VNFHzeb5XP+jtSNxGQHRgQAOUNisqFpu20nbX3cndS1so8DIwIAAMidSFgAAAAgQ5wsFvW2M8riYmySlh+95sCIACBnCDl6TaeuJdgs71m1gPK7cfkNAACQHs6YAAAAkGHdHvZRHhfbU5qw+DaAB9GkXZdtljlbpDers9g2AABARpCwAAAAQIYV8HBWpwBvm+Vbz8Zqz8U4B0YEANnrt39jtP3fWJvlL1bwUikvVwdGBAAAkHuRsAAAAMAdsbf4tiTN3Mvi2wAeHJN32R9ZNqAmoysAAAAyioQFAAAA7kitInlUu4iHzfL5hyIVdSPJgREBQPY4fS1B3x65arP8saJ59FixvA6MCAAAIHcjYQEAAIA79oad+dijEwx9czDKgdEAQPaYvidCSYbt8v5BjK4AAAC4EyQsAAAAcMfa+3upoIezzfIZeyNkGHZ68QAgl4tOSNZn+2xPgVfS00UvVvRyYEQAAAC5HwkLAAAA3LE8Lk7qXsXHZvnBiHj9ejrGgREBgGN9fSBSV24k2yx/s3pBuThZHBgRAABA7kfCAgAAAJkSHGh/8e0ZLL4N4D6VbBiavNv2Ytt5XSzqWdV+GwkAAIDUSFgAAAAgU8r7uKlZ6Xw2y0OOXdXZ6wkOjAgAHOOHE9f1z5V4m+XdHvZRATvT5gEAACBtJCwAAACQafYW305Mlub8HenAaADAMSbtsj26QpL6sdg2AABAppCwAAAAQKY9Wya/Snm62iyfve+KEpJYfBvA/ePvS3FafyraZnmLsvnlX8DdgREBAADcP0hYAAAAINOcnSx217I4G52o749fc2BEAHBvTU5ndMUARlcAAABkGgkLAAAA3JUeVX3kauescsYeFt8GcH+4GJOo+YeibJYHFnLXUyVtr+0DAAAA+0hYAAAA4K4UzuuidhW9bJZvCI/WoYgbDowIAO6NWfuu6Iadae76BxWUxWJxYEQAAAD3FxIWAAAAuGv2Ft+WpFl7GWUBIHe7kZisT/fYng7KL4+zXgrwdmBEAAAA9x8SFgAAALhr9YrlUbVCtheZ/epApKITkh0YEQBkrSX/XNX5mCSb5b2rFZCHC5fYAAAAd4OzKQAAANw1i8WiN6rZHmURFZ+sRYdtz/sOADmZYRiaZGexbTdni3rbaQMBAACQMSQsAAAAkCVeruQtTzfbp5ef7rkiw7A99zsA5FSbzsRo98U4m+Wd/L1UNJ+LAyMCAAC4P5GwAAAAQJbI7+akrpVtz9+++2KcfjsX68CIACBrTNppe3SFJPUP8nVQJAAAAPc3EhYAAADIMulNiTKDxbcB5DLHIuO1KuyazfJGJfKqRmEPB0YEAABw/yJhAQAAgCzzsK+7GpXIa7N8yT9XdSk20YERAcDdmbo7QvYmsxvA6AoAAIAsQ8ICAAAAWcre4tvxSYa+2B/pwGgAIPOibiTZbbPKe7uqZbn8DowIAADg/kbCAgAAAFmqdXlPFc1re/HZWXuvKCmZxbcB5Hxz90fqekKyzfJ+Qb5yslgcGBEAAMD9jYQFAAAAspSrs0WvB/rYLD9+NUHrTl53YEQAcOcSkw1N3WV7sW1vNye9+rDttg4AAAB3joQFAAAAslzPqgXkbOemYxbfBpDTrTx2TSevJdgsf62qj/K7cUkNAACQlTi7AgAAQJYr4emq58t72iwPPX5dx6PiHRgRANyZSbsu2yxzskhv1bC9Xg8AAAAyh4QFAAAA7ok3qhWwWWZImr2PURYAcqY/zsVq69lYm+UvVPBUaS83B0YEAADwYCBhAQAAgHviqZL5FFDAdofe3P2Riku0vZgtAGSXyXZGV0jSgCBfB0UCAADwYCFhAQAAgHvCYrGot51RFpdik7TsyFUHRgQA6TtzPUFL7bRNjxbxUN1ieRwYEQAAwIODhAUAAADuma6VfZTHxfbq2yy+DSCn+XRPhOzzGyOJAAAgAElEQVQN/hoQ5CuLxXa7BgAAgMwjYQEAAIB7xsfDWS9X8rZZvv3fWO26YHueeABwpJiEZM3eF2mzvHh+F7Wt6OXAiAAAAB4sJCwAAABwT9mbFkqSZjLKAkAO8c3BKEXEJdksf7N6Qbk6M7oCAADgXiFhAQAAgHuqZuE8eqyo7fneFxyKUqSdDkIAcIRkw7C72HYeF4teD7SfgAUAAMDdIWEBAACAe+6N6rY7+WISDX190PYULADgCD+ejNahK/E2y7tW9lFBD2cHRgQAAPDgIWEBAACAe65dRS/52unom7H3igzDcGBEAGBt0k7boyskqV9QQQdFAgAA8OAiYQEAAIB7zsPFST2q+NgsP3wlXr+cjnFgRADwP/svx+nHU9E2y5uXya9KBd0dGBEAAMCDiYQFAAAAHKJXtQKyt1TtjD0RDosFAG41ZZf99mcAoysAAAAcgoQFAAAAHKKct5ual8lvs3zFsWs6cz3BgREBgHQpNlHfHIyyWV7F111Pl8rnwIgAAAAeXCQsAAAA4DD2Ft9OMqTP911xYDQAIM3ed0VxSbbX0OkfVFAWi73xYQAAAMgqJCwAAADgMM1K51cZL1eb5Z/9HakEOx2HAJCV4pMMfbrHdqK0UB5nvVzJ24ERAQAAPNhIWAAAAMBhnJ0sCg60Pcri3+hErQy75sCIADzIlv4TpX+jE22WBwcWUB4XLpsBAAAchTMvAAAAOFT3Kj5yc7Y9vQqLbwNwBMMwNMnOYtuuTvansQMAAEDWI2EBAAAAh/LL66L2Fb1slv9yOkYHI244MCIAD6ItZ2O080KczfKOAd4qls/2FHYAAADIeiQsAAAA4HBvVLN/1/LMvSy+DeDemrTT/miu/kEFHRQJAAAAUpCwAAAAgMM9ViyPavh52CyfdyBS1+OTHRgRgAdJWFS8VhyzvV5Ow+J5VbNwHgdGBAAAAImEBQAAALKBxWKxO8rianyyFh6OcmBEAB4k03ZHyLBTPoDRFQAAANmChAUAAACyxUuVvOXlZvt0dObeKzIMe12KAHDnrt5I0tz9kTbLy3m7qlU5TwdGBAAAgBQkLAAAAJAt8rk6qdvDPjbLd1+M045/Yx0YEYAHwRf7I3XNzpRzfWsUlLOTxYERAQAAIAUJCwAAAGSb3uksvj2DxbcBZKGkZENTd9tebNvTzUmv2kmkAgAA4N4iYQEAAIBsU6mgu54qmddm+dIjV3UxJtGBEQG4n60Ku6bjVxNslr9WxUde7s4OjAgAAAC3ImEBAACAbPVGNduL28YnGfrCzlzzAHAnJu2yPbrCySK9VYPFtgEAALITCQsAAABkq+fKeeqhfC42y2ftu6KkZBbfBnB3/jofq81nYmyWty7vqbLebg6MCAAAALcjYQEAAIBs5eps0euBtteyOHE1QT+cvO7AiADcjybbGV0hSQOCfB0UCQAAAGwhYQEAAIBs17Oqj5wttstn7GHxbQCZd/Z6gpb8E2WzvFZhD9V/KI8DIwIAAEBaSFgAAAAg2z2U31VtKnjaLF974rrCouIdGBGA+8mMvVeUkGy7fEBNX1ksdrKmAAAAcAgSFgAAAMgR7C2+bUiavZdRFgDuXGxismbZaT+K5XNRu4peDowIAAAAtpCwAAAAQI7QqEReVSpge8HbufsjFZdo5xZpAEjD/INRuhyXZLP8zeoF5GZvTjoAAAA4DAkLAAAA5AgWi0VvVLc9yuJyXJK+PXLVgREByO0Mw7C72LaHs0WvBxZwYEQAAACwh4QFAAAAcoxXKnsrr4vtO51ZfBvAnfjpVLQORNywWf5KZW8VyuPiwIgAAABgDwkLAAAA5Bje7s7qXMnbZvmOc7HaeSHWgREByM0m7bxst7xfkK+DIgEAAEBGkLAAAABAjtLbzuLbkjSTxbcBZMDBiBv64WS0zfKmpfPpYV93B0YEAACA9JCwAAAAQI5So7CH6hXLY7N8waEoRdpZQBcAJGmKnbUrJGkAoysAAAByHBIWAAAAyHHsLb4dm2ho3sFIB0YDILe5HJuor+20E5ULuqlJ6XwOjAgAAAAZQcICAAAAOU7bCp4qlMfZZvmMvVdkGIYDIwKQm3y2L1KxibbbiP5BvrJYLA6MCAAAABlBwgIAAAA5jruLk16r4mOz/J8r8fo53Pbc9AAeXAlJhqbvsT0dVEEPZ3Wu5O3AiAAAAJBRJCwAAACQI/UKLCB79z/PYPFtAGn49shVnY1OtFkeHFhAeV25FAYAAMiJOEsDAABAjlTG200tyua3Wb7y2DWdvpbgwIgA5HSGYWjSrss2y12cpDeqF3BgRAAAALgTJCwAAACQY71RzXbHYpIhff43oywA/M+2f2P15/k4m+Ud/L1VPL+rAyMCAADAnSBhAQAAgByraZn8Kutlu3Pxs32RSkhi8W0AN03aaXt0hSQNCCrooEgAAACQGSQsAAAAkGM5WSzqbWeUxbmYRK04ds2BEQHIqU5ExSvETnvQ4KE8qlUkjwMjAgAAwJ0iYQEAAIAc7dUqPnJ3tr389oy9ETbLEhISNGHCBAUGBipPnjwqWLCgmjdvrs2bN99RDDExMfr4449Vo0YN5c+fX/nz51fFihX16quv6sKFCzbf99prr8lisZg/K1asSLXN5s2b1bJlS/n6+srd3V3ly5fXwIEDFRUVlWade/fuVdu2beXn5ycPDw/5+/vr/fffV3R0tN19mD9/vlUs/fv3tyrftWuXBg4cqNq1a6tYsWJyc3OTn5+fWrVqdce/L8DREhIS1HXIh0oe21oaWFMaVlea3Us69pe5zYCavunW8yAc61LWtY0AAABZzWIYBmPobxMQEGA+Pnz4cDZGAgAAAEnquu6Mvj6YdqeeJO3vUl4P+7pbvZacnKxWrVopNDQ01fZOTk5auHChOnTokO5nX7hwQU8//bT27duXZvmuXbtUo0aNVK///PPPaty4sdVrISEhat26tfn8m2++Ubdu3ZScnJzq/YGBgdq8ebO8vb3N1zZu3KjmzZsrLi71HP21a9fWL7/8orx586Yqu3jxoh5++GFdunTJfK1fv36aPHmy+Tw4OFizZ89Ocx+dnJy0dOlSvfjii2mWA9kpOTlZzVu01I8/rE1daHGSuoxTmSee09FuFeTsZDv5+aAc61nVNgIAAGSWvf53RlgAAAAgx7O3+LYkzUxjlMXs2bPNDrnKlStr2bJlGjdunJydnZWcnKxevXrp4sWL6X72q6++anZgNm7cWAsWLND69es1f/58BQcHK3/+/KneExMTo549e0qSPDw80qz36tWrevPNN80OzDFjxmjt2rVq1aqVJGnfvn0aPny4uf2NGzfUtWtXswNz8ODBWrFiherVqydJ+v333zVmzJg0P+utt97SpUuXbMaSokiRIho2bJjWrl2rhQsXmhcSycnJevvtt+2+F8gus2fP/l+yokg5qdskqdU7kpOzZCRLS0epe+lku8kK6cE51rOqbQQAALgnDKTi7+9v/gAAACD7JScnGzUXHDM0aX+aP56fHjSu3Uiyek/VqlUNSYYkY8eOHebrr732mvn6xIkT7X7un3/+aW7buHFjIzk5OUPxDhgwwJBkNGvWzHjiiSfMOkJCQsxtVq9ebb7+2GOPma+fPXvWfN3T09OIi4szDMMwli1bZr7erFkzc/vw8HDDYrEYkgxfX18jISHBKpZVq1YZkoyiRYsaffv2Nevo16+f1XabNm0yoqOjrV7bvXu3ub0k4/z58xnaf8CRbj3W1X/R/9qGx140X/9w3AS7dTxIx3pWtI0AAAB3w17/OyMsAAAAkONZLBa7oyyuxSdrwaH/TRl15coV/f3335IkV1dXPfLII2ZZ/fr1zcebNm2y+7mrVq0yH1esWFGNGzeWj4+PChYsqHbt2uno0aOp3vP7779rypQp8vT0tDnFkiSreevz5ctnPr71Lu5r165pz549qWKtW7eu+bhEiRIqVaqUJOny5cvav3+/WXb16lX17t1bkjRjxgwVKGD7d/j444+nmmKmYsWKVs/TmoIGyE63HutydpFKVvlfYdkg8+Ef27bYredBOdazqm0EAAC4V0hYAAAAIFfoFOAtbzfbp68z9kbI+P/l2U6cOGG+7uvrK2dnZ/N50aJFzcfHjx+3+5m3dgjOmjVLv/zyi6KionTlyhUtW7ZMderU0bFjx8xtEhIS1KNHDyUnJ2vs2LFm52JaKlWqZD7eunWrNm/erOjoaE2YMMFqu/Dw8FT7VKRIEattbO3Tu+++qzNnzqh9+/Zq06aN3X1Ny7Jly8zHjz/+eJpT4gDZ6dbjQnl9bk4DlcKzkPmQY12p6r6bthEAAOBeIWEBAACAXCGvq5NereJjs3zvpRva9m+sJCk6Otp83c3NzWq7W59fv37d7mfeeme0JI0ePVqrV69WnTp1JEkRERF6//33zfIPP/xQf//9txo2bKjg4GC7ddesWdNcqDcuLk4NGzZU/vz59cEHH1htlzKP/Z3u08aNGzVnzhz5+vpq2rRpdmNJy65du9S3b19Jkru7uyZNmnTHdQD32u7Tt6xf4+JqXXjLc451ZapuAAAAR3PJ7gAAAACAjAoOLKDJu1IvsJ3irV/+Vctynoo8EW++Fh8fb7XNrc/TGzHg7u5uPq5Xr57ee+89SVKhQoX02GOPSZJ++uknSdKZM2f08ccfK0+ePJozZ44sFvsL/Eo3RzC88cYbWrJkibkgb2BgoBITE3Xw4EFJko/PzSTNrVPJZGSf+vTpI8MwNGXKFBUuXDjdWG61bds2tWjRQlFRUXJxcdGiRYtUq1atO6oDyGqGYeiP83HaeSFWEXFJSkyWlvwd878NEhOs33DLc451ZapuAAAARyNhAQAAgFwjoKC7ni6VT+tPRadZvuviDe26eEOKyWO+dv7iZS05dEUvVPSRq7NF586dM8vKli1r9/NunealdOnS5uMyZcqYj69evSpJunjxotnh5+/vn2Z9KVO1pExd5ePjo4ULF+rTTz/V0aNH5efnp9KlS6t8+fLme6pUqZLqM8+fP29Vb1r7dObMGUlS586d1blz51SxTJkyRVOmTNEvv/yiRo0ama+vX79erVu3VnR0tNzd3bVkyRI9//zzae4P4AhRN5L0zcEozdgboYMR1p3sMv437ZNiIqXkJHNaqFKK0qn/L+JYv3msV69e3Xz98uXLSkpKMqeFupO2EQAA4F5hSigAAADkKvYW3zbl9ZaKVpAkGUkJ6jjrB5X78ohWh13T1q1bzc0aNmxot5pbF6E9deqU+fjkyZPm45IlS2Y0dJsKFCigRx99VGXKlNHy5cvN+eMDAwPNzsvHH3/c3H7btm3m49OnT5ux+fr66uGHH850HCtWrFDLli0VHR2tfPnyafXq1SQrkG0Mw9D4Py+p+Jx/9NbGc6mTFZLVsa6kROnU32ZR2Yj/rUvBsf6/z09JjCQkJOiPP/4wy+6kbQQAALhXGGEBAACAXMXdySKLJCO9Det3kJZ/ePPx4vd0uvlbavX9GTmt+VKS5OXlZd6NPHLkSI0aNUqSNGLECI0cOVLSzbukixQpovPnz2vr1q366KOPVKNGDY0ZM8b8mHbt2kmSSpQokeb88VOmTNHRo0clST179lS1atXMshEjRujSpUt64okn5OnpqW3btlktxDt8+HDzcatWrVSiRAmdPn1a69at05AhQ/TYY49p3Lhx5l3cPXv2lKvrzXn7x48frxs3bljFEhoaqrVr10qSGjVqpBdffFEVK1aUdHPKmk6dOikxMVEWi0UjRoyQh4eHtmzZYr4/MDBQ3t7e6f3mgbsWn2Sox09nNf9QVPob33asq/lbskSc0ZY1CyRxrN9+rPfu3VtvvvmmJKl79+4aPXq0jh8/ri+/TN02AgAAOBoJCwAAAOQam05Hq82a0+knKySpXgdp/0bp0FbpfJj01QBJUrIki5OTZs6cKT8/P7tV5M2bV3PnzlWbNm2UkJCgYcOGWZVXr15dQ4cOlXRzrvuUTsBbLVu2zOzEfPbZZ9W6dWuzLCoqSjNmzNCMGTNSvW/QoEFq27at+dzd3V1fffWVWrRooRs3buiTTz6x2r5WrVrmvPvSzQ7N2126dMnsxKxevbpVvKtXr1ZiYqKkm3e2Dxw4MNX7b58+CrgXDMNQ95/OakFGkhVSmse6ISlJkhPHeqpjPTg4WN9//73WrVungwcPWn12Rn9fAAAA9wpTQgEAACBXCL+WoNbfhys+KUPpipvz2PeYLrV8++aUMa7uUh4vKaCe9MYXeujxjE111KJFC23ZskWtWrVSwYIF5erqqgoVKmjw4MHasmWLPD09M71PzzzzjJ5++mkVKVJErq6u8vX1VfPmzRUaGpqqk1KSGjdurB07dqhNmzby9fWVm5ubKlSooKFDh2rjxo1WC+oCudXHf1zKeLJCsnusBwxboPYdO2WomgflWHd2dtaqVas0duxYValSRR4eHvLx8VGTJk30888/66WXXsp03QAAAHfLYqSMKc3BEhIS9PHHH2v16tWSbg6RHTJkiFxc0h4gsmHDBk2dOlUnT55U/vz51adPH3XqdPMkdfDgwVq9erU5fFaSvvjiCwUFBZnPAwICzMeHDx++F7sEAACAO9Rz/VnN+Tsyy+qrWdhDf3YqK4vFkmV1Arg7l2MTVWLOEcVlNDGZAfObFdfLlZjKDAAAIKew1/+eK6aEmjlzpv766y8zYdGzZ0/NmjUrzWG4mzZt0qhRozR+/Hg98sgjun79ui5dumS1TadOnVIN8QUAAEDOFRmXdGd3XGfAzgtx+v1crOoUy5ul9QLIvC8PRGZpskKSZuyJIGEBAACQS+SKhMXy5cs1ZMgQFS5cWNLNOTfHjRuXZsJiypQp6tOnj+rUqSNJ8vb2ZmFAAACAXG7ewUjFJmb9wOD+v55X9yo+WV4vHI+BMrlfsmFo7J+Xs7zebf/GaveFONUo7JHldQMAACBr5fiERVRUlM6dO6fKlSubr1WuXFlnz57VtWvXrOYRjYmJ0f79+3X9+nU1a9ZM165d06OPPqphw4ZZLRq2cuVKrVy5Un5+fnrxxRfVrVs3OTmxnAcAAEBO9cX+rJsK6lY7zsVqx7nYe1I3gJzjiwORmlq4aHaHAQAAgHTk+F76mJgYSbJKTHh5eUmSoqOjrba9evWqDMPQypUrNXfuXP34449ycXHRwIEDzW26dOmiH374Qdu3b9eHH36or7/+Wl9//bUD9gQAAACZkZBkaO+lG9kdBoBc7K/zJCYBAABygxyfsMib9+acwtevXzdfu3btmiQpX758aW7bpUsXFS9eXPny5VPfvn21fft2M/FRpUoVFSxYUM7OzqpRo4Z69uyp0NBQR+wKAAAAMuHKjaTsDgFALnc5jnYEAAAgN8jxCQtvb28VLVpUBw8eNF87ePCgihUrZjXqQro58uKhhx6SJY0JbA0j7TmPmQoKAAAgZ0tIzvq1KwA8WOKzeCFvAAAA3Bu5orf+hRde0KxZs3Tx4kVdvHhRs2fPVtu2bdPctn379vrmm290/vx5xcXF6dNPP1XdunXN0RihoaG6fv26DMPQvn379Pnnn6tJkyaO3B0AAADcAW835+wOAUAu5+NOOwIAAJAb5PhFtyXpjTfeUGRkpJ599llJUqtWrRQcHCxJGj58uCTpgw8+kCS9/vrrioqK0nPPPSdJqlOnjsaNG2fWtWDBAg0fPlxJSUkqXLiwOnXqpO7duztydwAAAHAH8rla5OvhzJQuADKtjJdrdocAAACADLAYtuZKeoAFBASYjw8fPpyNkQAAAECSeq4/qzl/R2Z5vXlcLKpUwE1S6ilFkXtwQXN/SEw2tP/yjXvy95zX5CG98rDPPagZAAAAd8pe/3uuGGEBAACAB1vvagXuScJiWqOi6lG1QJbXCyBzXll3Rt8cjMrSOn09nNXe3ytL6wQAAMC9kSvWsAAAAMCDrWbhPHqsaJ4srdPbzUmdAryztE4Ad+eNalmfQOxRxUceLlz6AgAA5AactQEAACBXGPxooSytr29QQeV15XQYyEnqFM2jJ4rnzbL68rpY9GaNgllWHwAAAO4trtAAAACQKzxXLr/erJ41d183KpFXw7I4AQLg7lksFs1r+pCK5s2a2YvnPvOQSnqy4DYAAEBuQcICAAAAuYLFYtHkJ4qq413ORV+rsIe+a1lS7kwRA+RIpb3ctLZ1Kfl6ON9VPZOfKKKOTPsGAACQq3CVBgAAgFzD2cmiBc2L691avpl6f8uy+bWxbRkVuMuOUAD3Vo3CHtreoYwq+rjd8Xs9nC1a2Ky4+gVlrp0AAABA9iFhAQAAgFzFyWLR+MeLaFv7Murg76WMDJR4qmRefdeyhFY9V1L53TgFBnKDigXctfvlcprUsIj8C6SfuPByc1LfGgV14JXy6lSJkRUAAAC5kcUwDCO7g8hpAgICzMeHDx/OxkgAAACQnn+jEzT370itO3ldZ6MTdTU+WflcnOSX11mPP5RXPQMLqHJB9+wOE8BdMAxDP4dH68sDUdp1IU4RcUlKSDZUwMNZZb1c1a6il16q5K18riQkAQAAcjp7/e8kLNJAwgIAAAAAAAAAgKxnr/+d208AAAAAAAAAAEC2I2EBAAAAAAAAAACyHQkLAAAAAAAAAACQ7UhYAAAAAAAAAACAbEfCAgAAAAAAAAAAZDsSFgAAAAAAAAAAINuRsAAAAAAAAAAAANmOhAUAAAAAAAAAAMh2JCwAAAAAAAAAAEC2I2EBAAAAAAAAAACyHQkLAAAAAAAAAACQ7UhYAAAAAAAAAACAbEfCAgAAAAAAAAAAZDsSFgAAAAAAAAAAINuRsAAAAAAAAAAAANmOhAUAAAAAAAAAAMh2JCwAAAAAAAAAAEC2I2EBAAAAAAAAAACyHQkLAAAAAAAAAACQ7UhYAAAAAAAAAACAbEfCAgAAAAAAAAAAZDsSFgAAAAAAAAAAINuRsAAAAAAAAAAAANmOhAUAAAAAAAAAAMh2JCwAAAAAAAAAAEC2I2EBAAAAAAAAAACyHQkLAAAAAAAAAACQ7VyyO4CcLiAgILtDAAAAAAAAAADgvscICwAAAAAAAAAAkO1IWAAAAAAAAAAAgGxnMQzDyO4gAAAAAAAAAADAg40RFgAAAAAAAAAAINuRsECOMmDAAA0ePNh8/tRTT2nRokXZGFHO8ttvvykgIEA3btzI7lAAh5owYYK6dOm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      "text/plain": [
       "<Figure size 1728x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for a,b in zip(r2_test_scores.keys(),r2_test_scores.values()):\n",
    "    r2_test_scores[a] = (b,0)\n",
    "\n",
    "# Plot the predictions for each model\n",
    "sns.set_style(\"white\")\n",
    "fig = plt.figure(figsize=(24, 12))\n",
    "\n",
    "ax = sns.pointplot(x=list(r2_test_scores.keys()), y=[score for score, _ in r2_test_scores.values()], markers=['o'], linestyles=['-'])\n",
    "for i, score in enumerate(r2_test_scores.values()):\n",
    "    ax.text(i, score[0] + 0.002, '{:.6f}'.format(score[0]), horizontalalignment='left', size='large', color='black', weight='semibold')\n",
    "\n",
    "plt.ylabel('Score (R2-Test)', size=20, labelpad=12.5)\n",
    "plt.xlabel('Model', size=20, labelpad=12.5)\n",
    "plt.tick_params(axis='x', labelsize=13.5)\n",
    "plt.tick_params(axis='y', labelsize=12.5)\n",
    "\n",
    "plt.title('Scores of Models', size=20)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### End"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}