Linear_Regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python 3.9.16\n"
     ]
    }
   ],
   "source": [
    "!python -V"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction import DictVectorizer\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.linear_model import Lasso\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import classification_report\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_parquet('/home/brandon/MLOps Zoomcamp/yellow_tripdata_2022-01.parquet')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VendorID</th>\n",
       "      <th>tpep_pickup_datetime</th>\n",
       "      <th>tpep_dropoff_datetime</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>RatecodeID</th>\n",
       "      <th>store_and_fwd_flag</th>\n",
       "      <th>PULocationID</th>\n",
       "      <th>DOLocationID</th>\n",
       "      <th>payment_type</th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>extra</th>\n",
       "      <th>mta_tax</th>\n",
       "      <th>tip_amount</th>\n",
       "      <th>tolls_amount</th>\n",
       "      <th>improvement_surcharge</th>\n",
       "      <th>total_amount</th>\n",
       "      <th>congestion_surcharge</th>\n",
       "      <th>airport_fee</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2022-01-01 00:35:40</td>\n",
       "      <td>2022-01-01 00:53:29</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.80</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>142</td>\n",
       "      <td>236</td>\n",
       "      <td>1</td>\n",
       "      <td>14.50</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>3.65</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>21.95</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2022-01-01 00:33:43</td>\n",
       "      <td>2022-01-01 00:42:07</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.10</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>236</td>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
       "      <td>8.00</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>4.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>13.30</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-01-01 00:53:21</td>\n",
       "      <td>2022-01-01 01:02:19</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.97</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>166</td>\n",
       "      <td>166</td>\n",
       "      <td>1</td>\n",
       "      <td>7.50</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.76</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>10.56</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-01-01 00:25:21</td>\n",
       "      <td>2022-01-01 00:35:23</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.09</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>114</td>\n",
       "      <td>68</td>\n",
       "      <td>2</td>\n",
       "      <td>8.00</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>11.80</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-01-01 00:36:48</td>\n",
       "      <td>2022-01-01 01:14:20</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.30</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>68</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>23.50</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>30.30</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2463926</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-01-31 23:36:53</td>\n",
       "      <td>2022-01-31 23:42:51</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.32</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>90</td>\n",
       "      <td>170</td>\n",
       "      <td>0</td>\n",
       "      <td>8.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.39</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>13.69</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2463927</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-01-31 23:44:22</td>\n",
       "      <td>2022-01-31 23:55:01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>107</td>\n",
       "      <td>75</td>\n",
       "      <td>0</td>\n",
       "      <td>16.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>4.35</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>24.45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2463928</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-01-31 23:39:00</td>\n",
       "      <td>2022-01-31 23:50:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>113</td>\n",
       "      <td>246</td>\n",
       "      <td>0</td>\n",
       "      <td>11.22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>16.52</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2463929</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-01-31 23:36:42</td>\n",
       "      <td>2022-01-31 23:48:45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.92</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>148</td>\n",
       "      <td>164</td>\n",
       "      <td>0</td>\n",
       "      <td>12.40</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>15.70</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2463930</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-01-31 23:46:00</td>\n",
       "      <td>2022-02-01 00:13:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.94</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>186</td>\n",
       "      <td>181</td>\n",
       "      <td>0</td>\n",
       "      <td>25.48</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>6.28</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>35.06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2463931 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         VendorID tpep_pickup_datetime tpep_dropoff_datetime  passenger_count  \\\n",
       "0               1  2022-01-01 00:35:40   2022-01-01 00:53:29              2.0   \n",
       "1               1  2022-01-01 00:33:43   2022-01-01 00:42:07              1.0   \n",
       "2               2  2022-01-01 00:53:21   2022-01-01 01:02:19              1.0   \n",
       "3               2  2022-01-01 00:25:21   2022-01-01 00:35:23              1.0   \n",
       "4               2  2022-01-01 00:36:48   2022-01-01 01:14:20              1.0   \n",
       "...           ...                  ...                   ...              ...   \n",
       "2463926         2  2022-01-31 23:36:53   2022-01-31 23:42:51              NaN   \n",
       "2463927         2  2022-01-31 23:44:22   2022-01-31 23:55:01              NaN   \n",
       "2463928         2  2022-01-31 23:39:00   2022-01-31 23:50:00              NaN   \n",
       "2463929         2  2022-01-31 23:36:42   2022-01-31 23:48:45              NaN   \n",
       "2463930         2  2022-01-31 23:46:00   2022-02-01 00:13:00              NaN   \n",
       "\n",
       "         trip_distance  RatecodeID store_and_fwd_flag  PULocationID  \\\n",
       "0                 3.80         1.0                  N           142   \n",
       "1                 2.10         1.0                  N           236   \n",
       "2                 0.97         1.0                  N           166   \n",
       "3                 1.09         1.0                  N           114   \n",
       "4                 4.30         1.0                  N            68   \n",
       "...                ...         ...                ...           ...   \n",
       "2463926           1.32         NaN               None            90   \n",
       "2463927           4.19         NaN               None           107   \n",
       "2463928           2.10         NaN               None           113   \n",
       "2463929           2.92         NaN               None           148   \n",
       "2463930           8.94         NaN               None           186   \n",
       "\n",
       "         DOLocationID  payment_type  fare_amount  extra  mta_tax  tip_amount  \\\n",
       "0                 236             1        14.50    3.0      0.5        3.65   \n",
       "1                  42             1         8.00    0.5      0.5        4.00   \n",
       "2                 166             1         7.50    0.5      0.5        1.76   \n",
       "3                  68             2         8.00    0.5      0.5        0.00   \n",
       "4                 163             1        23.50    0.5      0.5        3.00   \n",
       "...               ...           ...          ...    ...      ...         ...   \n",
       "2463926           170             0         8.00    0.0      0.5        2.39   \n",
       "2463927            75             0        16.80    0.0      0.5        4.35   \n",
       "2463928           246             0        11.22    0.0      0.5        2.00   \n",
       "2463929           164             0        12.40    0.0      0.5        0.00   \n",
       "2463930           181             0        25.48    0.0      0.5        6.28   \n",
       "\n",
       "         tolls_amount  improvement_surcharge  total_amount  \\\n",
       "0                 0.0                    0.3         21.95   \n",
       "1                 0.0                    0.3         13.30   \n",
       "2                 0.0                    0.3         10.56   \n",
       "3                 0.0                    0.3         11.80   \n",
       "4                 0.0                    0.3         30.30   \n",
       "...               ...                    ...           ...   \n",
       "2463926           0.0                    0.3         13.69   \n",
       "2463927           0.0                    0.3         24.45   \n",
       "2463928           0.0                    0.3         16.52   \n",
       "2463929           0.0                    0.3         15.70   \n",
       "2463930           0.0                    0.3         35.06   \n",
       "\n",
       "         congestion_surcharge  airport_fee  \n",
       "0                         2.5          0.0  \n",
       "1                         0.0          0.0  \n",
       "2                         0.0          0.0  \n",
       "3                         2.5          0.0  \n",
       "4                         2.5          0.0  \n",
       "...                       ...          ...  \n",
       "2463926                   NaN          NaN  \n",
       "2463927                   NaN          NaN  \n",
       "2463928                   NaN          NaN  \n",
       "2463929                   NaN          NaN  \n",
       "2463930                   NaN          NaN  \n",
       "\n",
       "[2463931 rows x 19 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VendorID                 1\n",
       "fare_amount              1\n",
       "congestion_surcharge     1\n",
       "total_amount             1\n",
       "improvement_surcharge    1\n",
       "tolls_amount             1\n",
       "tip_amount               1\n",
       "mta_tax                  1\n",
       "extra                    1\n",
       "payment_type             1\n",
       "tpep_pickup_datetime     1\n",
       "DOLocationID             1\n",
       "PULocationID             1\n",
       "store_and_fwd_flag       1\n",
       "RatecodeID               1\n",
       "trip_distance            1\n",
       "passenger_count          1\n",
       "tpep_dropoff_datetime    1\n",
       "airport_fee              1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.tpep_pickup_datetime = pd.to_datetime(df['tpep_pickup_datetime'])\n",
    "df.tpep_dropoff_datetime = pd.to_datetime(df['tpep_dropoff_datetime'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['duration'] = df.tpep_dropoff_datetime -df.tpep_pickup_datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "td = df.duration.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17.816666666666666"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "td.total_seconds() / 60"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          17.816667\n",
       "1           8.400000\n",
       "2           8.966667\n",
       "3          10.033333\n",
       "4          37.533333\n",
       "             ...    \n",
       "2463926     5.966667\n",
       "2463927    10.650000\n",
       "2463928    11.000000\n",
       "2463929    12.050000\n",
       "2463930    27.000000\n",
       "Name: duration, Length: 2463931, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duration.apply(lambda td: td.total_seconds() / 60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VendorID</th>\n",
       "      <th>tpep_pickup_datetime</th>\n",
       "      <th>tpep_dropoff_datetime</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>RatecodeID</th>\n",
       "      <th>store_and_fwd_flag</th>\n",
       "      <th>PULocationID</th>\n",
       "      <th>DOLocationID</th>\n",
       "      <th>payment_type</th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>extra</th>\n",
       "      <th>mta_tax</th>\n",
       "      <th>tip_amount</th>\n",
       "      <th>tolls_amount</th>\n",
       "      <th>improvement_surcharge</th>\n",
       "      <th>total_amount</th>\n",
       "      <th>congestion_surcharge</th>\n",
       "      <th>airport_fee</th>\n",
       "      <th>duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2022-01-01 00:35:40</td>\n",
       "      <td>2022-01-01 00:53:29</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.80</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>142</td>\n",
       "      <td>236</td>\n",
       "      <td>1</td>\n",
       "      <td>14.50</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>3.65</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>21.95</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0 days 00:17:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2022-01-01 00:33:43</td>\n",
       "      <td>2022-01-01 00:42:07</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.10</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>236</td>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
       "      <td>8.00</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>4.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>13.30</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0 days 00:08:24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-01-01 00:53:21</td>\n",
       "      <td>2022-01-01 01:02:19</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.97</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>166</td>\n",
       "      <td>166</td>\n",
       "      <td>1</td>\n",
       "      <td>7.50</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.76</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>10.56</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0 days 00:08:58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-01-01 00:25:21</td>\n",
       "      <td>2022-01-01 00:35:23</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.09</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>114</td>\n",
       "      <td>68</td>\n",
       "      <td>2</td>\n",
       "      <td>8.00</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>11.80</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0 days 00:10:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-01-01 00:36:48</td>\n",
       "      <td>2022-01-01 01:14:20</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.30</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>68</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>23.50</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>30.30</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0 days 00:37:32</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",
       "      <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>2463926</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-01-31 23:36:53</td>\n",
       "      <td>2022-01-31 23:42:51</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.32</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>90</td>\n",
       "      <td>170</td>\n",
       "      <td>0</td>\n",
       "      <td>8.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.39</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>13.69</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0 days 00:05:58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2463927</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-01-31 23:44:22</td>\n",
       "      <td>2022-01-31 23:55:01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>107</td>\n",
       "      <td>75</td>\n",
       "      <td>0</td>\n",
       "      <td>16.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>4.35</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>24.45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0 days 00:10:39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2463928</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-01-31 23:39:00</td>\n",
       "      <td>2022-01-31 23:50:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>113</td>\n",
       "      <td>246</td>\n",
       "      <td>0</td>\n",
       "      <td>11.22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>16.52</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0 days 00:11:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2463929</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-01-31 23:36:42</td>\n",
       "      <td>2022-01-31 23:48:45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.92</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>148</td>\n",
       "      <td>164</td>\n",
       "      <td>0</td>\n",
       "      <td>12.40</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>15.70</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0 days 00:12:03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2463930</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-01-31 23:46:00</td>\n",
       "      <td>2022-02-01 00:13:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.94</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>186</td>\n",
       "      <td>181</td>\n",
       "      <td>0</td>\n",
       "      <td>25.48</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>6.28</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>35.06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0 days 00:27:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2463931 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         VendorID tpep_pickup_datetime tpep_dropoff_datetime  passenger_count  \\\n",
       "0               1  2022-01-01 00:35:40   2022-01-01 00:53:29              2.0   \n",
       "1               1  2022-01-01 00:33:43   2022-01-01 00:42:07              1.0   \n",
       "2               2  2022-01-01 00:53:21   2022-01-01 01:02:19              1.0   \n",
       "3               2  2022-01-01 00:25:21   2022-01-01 00:35:23              1.0   \n",
       "4               2  2022-01-01 00:36:48   2022-01-01 01:14:20              1.0   \n",
       "...           ...                  ...                   ...              ...   \n",
       "2463926         2  2022-01-31 23:36:53   2022-01-31 23:42:51              NaN   \n",
       "2463927         2  2022-01-31 23:44:22   2022-01-31 23:55:01              NaN   \n",
       "2463928         2  2022-01-31 23:39:00   2022-01-31 23:50:00              NaN   \n",
       "2463929         2  2022-01-31 23:36:42   2022-01-31 23:48:45              NaN   \n",
       "2463930         2  2022-01-31 23:46:00   2022-02-01 00:13:00              NaN   \n",
       "\n",
       "         trip_distance  RatecodeID store_and_fwd_flag  PULocationID  \\\n",
       "0                 3.80         1.0                  N           142   \n",
       "1                 2.10         1.0                  N           236   \n",
       "2                 0.97         1.0                  N           166   \n",
       "3                 1.09         1.0                  N           114   \n",
       "4                 4.30         1.0                  N            68   \n",
       "...                ...         ...                ...           ...   \n",
       "2463926           1.32         NaN               None            90   \n",
       "2463927           4.19         NaN               None           107   \n",
       "2463928           2.10         NaN               None           113   \n",
       "2463929           2.92         NaN               None           148   \n",
       "2463930           8.94         NaN               None           186   \n",
       "\n",
       "         DOLocationID  payment_type  fare_amount  extra  mta_tax  tip_amount  \\\n",
       "0                 236             1        14.50    3.0      0.5        3.65   \n",
       "1                  42             1         8.00    0.5      0.5        4.00   \n",
       "2                 166             1         7.50    0.5      0.5        1.76   \n",
       "3                  68             2         8.00    0.5      0.5        0.00   \n",
       "4                 163             1        23.50    0.5      0.5        3.00   \n",
       "...               ...           ...          ...    ...      ...         ...   \n",
       "2463926           170             0         8.00    0.0      0.5        2.39   \n",
       "2463927            75             0        16.80    0.0      0.5        4.35   \n",
       "2463928           246             0        11.22    0.0      0.5        2.00   \n",
       "2463929           164             0        12.40    0.0      0.5        0.00   \n",
       "2463930           181             0        25.48    0.0      0.5        6.28   \n",
       "\n",
       "         tolls_amount  improvement_surcharge  total_amount  \\\n",
       "0                 0.0                    0.3         21.95   \n",
       "1                 0.0                    0.3         13.30   \n",
       "2                 0.0                    0.3         10.56   \n",
       "3                 0.0                    0.3         11.80   \n",
       "4                 0.0                    0.3         30.30   \n",
       "...               ...                    ...           ...   \n",
       "2463926           0.0                    0.3         13.69   \n",
       "2463927           0.0                    0.3         24.45   \n",
       "2463928           0.0                    0.3         16.52   \n",
       "2463929           0.0                    0.3         15.70   \n",
       "2463930           0.0                    0.3         35.06   \n",
       "\n",
       "         congestion_surcharge  airport_fee        duration  \n",
       "0                         2.5          0.0 0 days 00:17:49  \n",
       "1                         0.0          0.0 0 days 00:08:24  \n",
       "2                         0.0          0.0 0 days 00:08:58  \n",
       "3                         2.5          0.0 0 days 00:10:02  \n",
       "4                         2.5          0.0 0 days 00:37:32  \n",
       "...                       ...          ...             ...  \n",
       "2463926                   NaN          NaN 0 days 00:05:58  \n",
       "2463927                   NaN          NaN 0 days 00:10:39  \n",
       "2463928                   NaN          NaN 0 days 00:11:00  \n",
       "2463929                   NaN          NaN 0 days 00:12:03  \n",
       "2463930                   NaN          NaN 0 days 00:27:00  \n",
       "\n",
       "[2463931 rows x 20 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_6089/3426635498.py:1: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(df.duration)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='duration', ylabel='Density'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df.duration)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count                      2463931\n",
       "mean     0 days 00:14:12.732175129\n",
       "std      0 days 00:46:26.718308266\n",
       "min              -3 days +14:37:36\n",
       "50%                0 days 00:10:11\n",
       "95%                0 days 00:31:56\n",
       "98%                0 days 00:42:09\n",
       "99%                0 days 00:50:51\n",
       "max                5 days 21:53:11\n",
       "Name: duration, dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duration.describe(percentiles=[0.95, 0.98, 0.99])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[(df.duration >= '0 days 01:00:00') & (df.duration <= '0 days 60:00:00')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VendorID</th>\n",
       "      <th>tpep_pickup_datetime</th>\n",
       "      <th>tpep_dropoff_datetime</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>RatecodeID</th>\n",
       "      <th>store_and_fwd_flag</th>\n",
       "      <th>PULocationID</th>\n",
       "      <th>DOLocationID</th>\n",
       "      <th>payment_type</th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>extra</th>\n",
       "      <th>mta_tax</th>\n",
       "      <th>tip_amount</th>\n",
       "      <th>tolls_amount</th>\n",
       "      <th>improvement_surcharge</th>\n",
       "      <th>total_amount</th>\n",
       "      <th>congestion_surcharge</th>\n",
       "      <th>airport_fee</th>\n",
       "      <th>duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-01-01 00:31:12</td>\n",
       "      <td>2022-01-02 00:00:58</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.03</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>79</td>\n",
       "      <td>261</td>\n",
       "      <td>2</td>\n",
       "      <td>15.00</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.3</td>\n",
       "      <td>18.80</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0 days 23:29:46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>481</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-01-01 00:15:34</td>\n",
       "      <td>2022-01-01 01:29:42</td>\n",
       "      <td>6.0</td>\n",
       "      <td>16.21</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>161</td>\n",
       "      <td>134</td>\n",
       "      <td>1</td>\n",
       "      <td>60.50</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>14.17</td>\n",
       "      <td>6.55</td>\n",
       "      <td>0.3</td>\n",
       "      <td>85.02</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0 days 01:14:08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>575</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-01-01 00:17:11</td>\n",
       "      <td>2022-01-01 23:52:41</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.48</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>142</td>\n",
       "      <td>48</td>\n",
       "      <td>1</td>\n",
       "      <td>10.50</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.3</td>\n",
       "      <td>14.30</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0 days 23:35:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1050</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-01-01 00:35:28</td>\n",
       "      <td>2022-01-02 00:03:07</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.73</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>142</td>\n",
       "      <td>125</td>\n",
       "      <td>2</td>\n",
       "      <td>19.00</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.3</td>\n",
       "      <td>22.80</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0 days 23:27:39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1368</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-01-01 00:53:25</td>\n",
       "      <td>2022-01-02 00:50:13</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.02</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>68</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>13.00</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>4.20</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.3</td>\n",
       "      <td>21.00</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0 days 23:56:48</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",
       "      <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>2463360</th>\n",
       "      <td>6</td>\n",
       "      <td>2022-01-31 19:01:10</td>\n",
       "      <td>2022-01-31 20:01:37</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.71</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>265</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>16.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.3</td>\n",
       "      <td>17.55</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0 days 01:00:27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2463361</th>\n",
       "      <td>6</td>\n",
       "      <td>2022-01-31 19:01:34</td>\n",
       "      <td>2022-01-31 20:01:41</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.60</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>265</td>\n",
       "      <td>152</td>\n",
       "      <td>0</td>\n",
       "      <td>24.43</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.3</td>\n",
       "      <td>25.23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0 days 01:00:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2463532</th>\n",
       "      <td>6</td>\n",
       "      <td>2022-01-31 20:01:41</td>\n",
       "      <td>2022-01-31 21:01:55</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>265</td>\n",
       "      <td>69</td>\n",
       "      <td>0</td>\n",
       "      <td>43.20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.3</td>\n",
       "      <td>44.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0 days 01:00:14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2463557</th>\n",
       "      <td>6</td>\n",
       "      <td>2022-01-31 20:01:03</td>\n",
       "      <td>2022-01-31 21:01:59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.63</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>265</td>\n",
       "      <td>76</td>\n",
       "      <td>0</td>\n",
       "      <td>45.20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.3</td>\n",
       "      <td>46.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0 days 01:00:56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2463792</th>\n",
       "      <td>6</td>\n",
       "      <td>2022-01-31 22:01:36</td>\n",
       "      <td>2022-02-01 00:02:09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.97</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>265</td>\n",
       "      <td>141</td>\n",
       "      <td>0</td>\n",
       "      <td>38.64</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.3</td>\n",
       "      <td>39.44</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0 days 02:00:33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12564 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         VendorID tpep_pickup_datetime tpep_dropoff_datetime  passenger_count  \\\n",
       "70              2  2022-01-01 00:31:12   2022-01-02 00:00:58              1.0   \n",
       "481             2  2022-01-01 00:15:34   2022-01-01 01:29:42              6.0   \n",
       "575             2  2022-01-01 00:17:11   2022-01-01 23:52:41              1.0   \n",
       "1050            2  2022-01-01 00:35:28   2022-01-02 00:03:07              1.0   \n",
       "1368            2  2022-01-01 00:53:25   2022-01-02 00:50:13              1.0   \n",
       "...           ...                  ...                   ...              ...   \n",
       "2463360         6  2022-01-31 19:01:10   2022-01-31 20:01:37              NaN   \n",
       "2463361         6  2022-01-31 19:01:34   2022-01-31 20:01:41              NaN   \n",
       "2463532         6  2022-01-31 20:01:41   2022-01-31 21:01:55              NaN   \n",
       "2463557         6  2022-01-31 20:01:03   2022-01-31 21:01:59              NaN   \n",
       "2463792         6  2022-01-31 22:01:36   2022-02-01 00:02:09              NaN   \n",
       "\n",
       "         trip_distance  RatecodeID store_and_fwd_flag  PULocationID  \\\n",
       "70                3.03         1.0                  N            79   \n",
       "481              16.21         1.0                  N           161   \n",
       "575               1.48         1.0                  N           142   \n",
       "1050              4.73         1.0                  N           142   \n",
       "1368              3.02         1.0                  N            68   \n",
       "...                ...         ...                ...           ...   \n",
       "2463360           4.71         NaN               None           265   \n",
       "2463361           7.60         NaN               None           265   \n",
       "2463532          11.08         NaN               None           265   \n",
       "2463557           7.63         NaN               None           265   \n",
       "2463792          10.97         NaN               None           265   \n",
       "\n",
       "         DOLocationID  payment_type  fare_amount  extra  mta_tax  tip_amount  \\\n",
       "70                261             2        15.00    0.5      0.5        0.00   \n",
       "481               134             1        60.50    0.5      0.5       14.17   \n",
       "575                48             1        10.50    0.5      0.5        0.00   \n",
       "1050              125             2        19.00    0.5      0.5        0.00   \n",
       "1368               45             1        13.00    0.5      0.5        4.20   \n",
       "...               ...           ...          ...    ...      ...         ...   \n",
       "2463360            42             0        16.75    0.0      0.5        0.00   \n",
       "2463361           152             0        24.43    0.0      0.5        0.00   \n",
       "2463532            69             0        43.20    0.0      0.5        0.00   \n",
       "2463557            76             0        45.20    0.0      0.5        0.00   \n",
       "2463792           141             0        38.64    0.0      0.5        0.00   \n",
       "\n",
       "         tolls_amount  improvement_surcharge  total_amount  \\\n",
       "70               0.00                    0.3         18.80   \n",
       "481              6.55                    0.3         85.02   \n",
       "575              0.00                    0.3         14.30   \n",
       "1050             0.00                    0.3         22.80   \n",
       "1368             0.00                    0.3         21.00   \n",
       "...               ...                    ...           ...   \n",
       "2463360          0.00                    0.3         17.55   \n",
       "2463361          0.00                    0.3         25.23   \n",
       "2463532          0.00                    0.3         44.00   \n",
       "2463557          0.00                    0.3         46.00   \n",
       "2463792          0.00                    0.3         39.44   \n",
       "\n",
       "         congestion_surcharge  airport_fee        duration  \n",
       "70                        2.5          0.0 0 days 23:29:46  \n",
       "481                       2.5          0.0 0 days 01:14:08  \n",
       "575                       2.5          0.0 0 days 23:35:30  \n",
       "1050                      2.5          0.0 0 days 23:27:39  \n",
       "1368                      2.5          0.0 0 days 23:56:48  \n",
       "...                       ...          ...             ...  \n",
       "2463360                   NaN          NaN 0 days 01:00:27  \n",
       "2463361                   NaN          NaN 0 days 01:00:07  \n",
       "2463532                   NaN          NaN 0 days 01:00:14  \n",
       "2463557                   NaN          NaN 0 days 01:00:56  \n",
       "2463792                   NaN          NaN 0 days 02:00:33  \n",
       "\n",
       "[12564 rows x 20 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical = ['PULocationID', 'DOLocationID']\n",
    "numerical = ['trip_distance']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_6089/790595652.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df[categorical] = df[categorical].astype('str')\n"
     ]
    }
   ],
   "source": [
    "df[categorical] = df[categorical].astype('str')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PULocationID    object\n",
       "DOLocationID    object\n",
       "dtype: object"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[categorical].dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dicts = df[categorical + numerical].to_dict(orient='records')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "dv = DictVectorizer()\n",
    "x_train = dv.fit_transform(train_dicts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<12564x477 sparse matrix of type '<class 'numpy.float64'>'\n",
       "\twith 37692 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "target = 'duration'\n",
    "y_train = df[target].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([84586000000000,  4448000000000, 84930000000000, ...,\n",
       "        3614000000000,  3656000000000,  7233000000000],\n",
       "      dtype='timedelta64[ns]')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr = LinearRegression()\n",
    "lr.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = lr.predict(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_6089/1672306066.py:1: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(y_pred, label='prediction')\n",
      "/tmp/ipykernel_6089/1672306066.py:2: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(y_train, label='actual')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fcb202e5610>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(y_pred, label='prediction')\n",
    "sns.distplot(y_train, label='actual')\n",
    "\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "# Convert timedelta values to numerical representations (seconds)\n",
    "y_train_seconds = pd.to_timedelta(y_train).total_seconds()\n",
    "y_pred_seconds = pd.to_timedelta(y_pred).total_seconds()\n",
    "\n",
    "# Calculate mean squared error\n",
    "mse = mean_squared_error(y_train_seconds, y_pred_seconds, squared=False)\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24680.614363581433\n"
     ]
    }
   ],
   "source": [
    "#Print MSE\n",
    "print(mse)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/brandon/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:592: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.6240282873365766e+30, tolerance: 1.259629306361064e+27\n",
      "  model = cd_fast.sparse_enet_coordinate_descent(\n"
     ]
    }
   ],
   "source": [
    "lasso = Lasso(alpha=1.0)\n",
    "lasso.fit(x_train, y_train)\n",
    "\n",
    "y_pred = lasso.predict(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Put all previous code into a function\n",
    "def read_dataframe(filename):\n",
    "    df = pd.read_parquet(filename)\n",
    "    \n",
    "    df.tpep_dropoff_datetime = pd.to_datetime(df['tpep_dropoff_datetime'])\n",
    "    df.tpep_pickup_datetime = pd.to_datetime(df['tpep_pickup_datetime'])\n",
    "    \n",
    "    df['duration'] = df.tpep_dropoff_datetime - df.tpep_pickup_datetime\n",
    "    df.duration = df.duration.apply(lambda td: td.total_seconds() / 60)\n",
    "    \n",
    "    df = df[(df.duration >= 0) & (df.duration <= 60)]\n",
    "    \n",
    "    categorical = ['PULocationID', 'DOLocationID']\n",
    "    df[categorical] = df[categorical].astype('str')\n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = read_dataframe('/home/brandon/MLOps Zoomcamp/yellow_tripdata_2022-01.parquet')\n",
    "df_val = read_dataframe('/home/brandon/MLOps Zoomcamp/yellow_tripdata_2022-02.parquet')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2450090, 2950274)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_train), len(df_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical = ['PULocationID', 'DOLocationID']\n",
    "numerical = ['trip_distance']\n",
    "\n",
    "dv = DictVectorizer()\n",
    "\n",
    "train_dicts = df_train[categorical + numerical].to_dict(orient='records')\n",
    "x_train = dv.fit_transform(train_dicts)\n",
    "\n",
    "val_dicts = df_val[categorical + numerical].to_dict(orient='records')\n",
    "x_val = dv.transform(val_dicts)\n",
    "\n",
    "target = 'duration'\n",
    "y_train = df_train[target].values\n",
    "y_val = df_val[target].values\n",
    "\n",
    "lr = LinearRegression()\n",
    "lr.fit(x_train, y_train)\n",
    "\n",
    "y_pred = lr.predict(x_val)\n",
    "\n",
    "mse = mean_squared_error(y_val, y_pred, squared=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8.078103552665414\n"
     ]
    }
   ],
   "source": [
    "#Print MSE\n",
    "\n",
    "print(mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "lasso = Lasso(alpha=1.0)\n",
    "lasso.fit(x_train, y_train)\n",
    "\n",
    "y_pred = lasso.predict(x_val)\n",
    "\n",
    "mse = mean_squared_error(y_val, y_pred, squared=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9.756305201183736\n"
     ]
    }
   ],
   "source": [
    "#Print MSE\n",
    "\n",
    "print(mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "ridge = Ridge(alpha=1.0)\n",
    "ridge.fit(x_train, y_train)\n",
    "\n",
    "y_pred = ridge.predict(x_val)\n",
    "\n",
    "mse = mean_squared_error(y_val, y_pred, squared=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8.068629409270882\n"
     ]
    }
   ],
   "source": [
    "#Print MSE\n",
    "\n",
    "print(mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "#try pick up and drop off location as a feature\n",
    "\n",
    "df_train['PU_DO'] = df_train.PULocationID + '_' + df_train.DOLocationID\n",
    "df_val['PU_DO'] = df_val.PULocationID + '_' + df_val.DOLocationID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VendorID</th>\n",
       "      <th>tpep_pickup_datetime</th>\n",
       "      <th>tpep_dropoff_datetime</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>RatecodeID</th>\n",
       "      <th>store_and_fwd_flag</th>\n",
       "      <th>PULocationID</th>\n",
       "      <th>DOLocationID</th>\n",
       "      <th>payment_type</th>\n",
       "      <th>...</th>\n",
       "      <th>extra</th>\n",
       "      <th>mta_tax</th>\n",
       "      <th>tip_amount</th>\n",
       "      <th>tolls_amount</th>\n",
       "      <th>improvement_surcharge</th>\n",
       "      <th>total_amount</th>\n",
       "      <th>congestion_surcharge</th>\n",
       "      <th>airport_fee</th>\n",
       "      <th>duration</th>\n",
       "      <th>PU_DO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2022-02-01 00:06:58</td>\n",
       "      <td>2022-02-01 00:19:24</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.40</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>138</td>\n",
       "      <td>252</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1.75</td>\n",
       "      <td>0.5</td>\n",
       "      <td>3.90</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.3</td>\n",
       "      <td>23.45</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.25</td>\n",
       "      <td>12.433333</td>\n",
       "      <td>138_252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2022-02-01 00:38:22</td>\n",
       "      <td>2022-02-01 00:55:55</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.40</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>138</td>\n",
       "      <td>41</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1.75</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.55</td>\n",
       "      <td>0.3</td>\n",
       "      <td>30.10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.25</td>\n",
       "      <td>17.550000</td>\n",
       "      <td>138_41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2022-02-01 00:03:20</td>\n",
       "      <td>2022-02-01 00:26:59</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.50</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>138</td>\n",
       "      <td>200</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1.75</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.55</td>\n",
       "      <td>0.3</td>\n",
       "      <td>44.60</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.25</td>\n",
       "      <td>23.650000</td>\n",
       "      <td>138_200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-02-01 00:08:00</td>\n",
       "      <td>2022-02-01 00:28:05</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.88</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>239</td>\n",
       "      <td>200</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.3</td>\n",
       "      <td>34.80</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>20.083333</td>\n",
       "      <td>239_200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-02-01 00:06:48</td>\n",
       "      <td>2022-02-01 00:33:07</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.16</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>138</td>\n",
       "      <td>125</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.5</td>\n",
       "      <td>8.11</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.3</td>\n",
       "      <td>48.66</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1.25</td>\n",
       "      <td>26.316667</td>\n",
       "      <td>138_125</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",
       "      <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>2979426</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-02-28 23:50:00</td>\n",
       "      <td>2022-03-01 00:06:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.40</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>163</td>\n",
       "      <td>193</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.3</td>\n",
       "      <td>17.67</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>163_193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2979427</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-02-28 23:06:57</td>\n",
       "      <td>2022-02-28 23:19:12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.48</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>141</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.3</td>\n",
       "      <td>19.81</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.250000</td>\n",
       "      <td>141_4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2979428</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-02-28 23:48:13</td>\n",
       "      <td>2022-03-01 00:03:33</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>161</td>\n",
       "      <td>151</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.5</td>\n",
       "      <td>3.81</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.3</td>\n",
       "      <td>21.49</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.333333</td>\n",
       "      <td>161_151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2979429</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-02-28 23:56:41</td>\n",
       "      <td>2022-03-01 00:04:57</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.62</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>141</td>\n",
       "      <td>226</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.71</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.3</td>\n",
       "      <td>17.54</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.266667</td>\n",
       "      <td>141_226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2979430</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-02-28 23:04:00</td>\n",
       "      <td>2022-02-28 23:10:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.41</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>237</td>\n",
       "      <td>142</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.57</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.3</td>\n",
       "      <td>14.67</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>237_142</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2950274 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         VendorID tpep_pickup_datetime tpep_dropoff_datetime  passenger_count  \\\n",
       "0               1  2022-02-01 00:06:58   2022-02-01 00:19:24              1.0   \n",
       "1               1  2022-02-01 00:38:22   2022-02-01 00:55:55              1.0   \n",
       "2               1  2022-02-01 00:03:20   2022-02-01 00:26:59              1.0   \n",
       "3               2  2022-02-01 00:08:00   2022-02-01 00:28:05              1.0   \n",
       "4               2  2022-02-01 00:06:48   2022-02-01 00:33:07              1.0   \n",
       "...           ...                  ...                   ...              ...   \n",
       "2979426         2  2022-02-28 23:50:00   2022-03-01 00:06:00              NaN   \n",
       "2979427         2  2022-02-28 23:06:57   2022-02-28 23:19:12              NaN   \n",
       "2979428         2  2022-02-28 23:48:13   2022-03-01 00:03:33              NaN   \n",
       "2979429         2  2022-02-28 23:56:41   2022-03-01 00:04:57              NaN   \n",
       "2979430         2  2022-02-28 23:04:00   2022-02-28 23:10:00              NaN   \n",
       "\n",
       "         trip_distance  RatecodeID store_and_fwd_flag PULocationID  \\\n",
       "0                 5.40         1.0                  N          138   \n",
       "1                 6.40         1.0                  N          138   \n",
       "2                12.50         1.0                  N          138   \n",
       "3                 9.88         1.0                  N          239   \n",
       "4                12.16         1.0                  N          138   \n",
       "...                ...         ...                ...          ...   \n",
       "2979426           3.40         NaN               None          163   \n",
       "2979427           3.48         NaN               None          141   \n",
       "2979428           3.05         NaN               None          161   \n",
       "2979429           2.62         NaN               None          141   \n",
       "2979430           1.41         NaN               None          237   \n",
       "\n",
       "        DOLocationID  payment_type  ...  extra  mta_tax  tip_amount  \\\n",
       "0                252             1  ...   1.75      0.5        3.90   \n",
       "1                 41             2  ...   1.75      0.5        0.00   \n",
       "2                200             2  ...   1.75      0.5        0.00   \n",
       "3                200             2  ...   0.50      0.5        0.00   \n",
       "4                125             1  ...   0.50      0.5        8.11   \n",
       "...              ...           ...  ...    ...      ...         ...   \n",
       "2979426          193             0  ...   0.00      0.5        0.00   \n",
       "2979427            4             0  ...   0.00      0.5        2.00   \n",
       "2979428          151             0  ...   0.00      0.5        3.81   \n",
       "2979429          226             0  ...   0.00      0.5        1.71   \n",
       "2979430          142             0  ...   0.00      0.5        2.57   \n",
       "\n",
       "         tolls_amount  improvement_surcharge  total_amount  \\\n",
       "0                0.00                    0.3         23.45   \n",
       "1                6.55                    0.3         30.10   \n",
       "2                6.55                    0.3         44.60   \n",
       "3                3.00                    0.3         34.80   \n",
       "4                0.00                    0.3         48.66   \n",
       "...               ...                    ...           ...   \n",
       "2979426          0.00                    0.3         17.67   \n",
       "2979427          0.00                    0.3         19.81   \n",
       "2979428          0.00                    0.3         21.49   \n",
       "2979429          0.00                    0.3         17.54   \n",
       "2979430          0.00                    0.3         14.67   \n",
       "\n",
       "         congestion_surcharge  airport_fee   duration    PU_DO  \n",
       "0                         0.0         1.25  12.433333  138_252  \n",
       "1                         0.0         1.25  17.550000   138_41  \n",
       "2                         0.0         1.25  23.650000  138_200  \n",
       "3                         2.5         0.00  20.083333  239_200  \n",
       "4                         2.5         1.25  26.316667  138_125  \n",
       "...                       ...          ...        ...      ...  \n",
       "2979426                   NaN          NaN  16.000000  163_193  \n",
       "2979427                   NaN          NaN  12.250000    141_4  \n",
       "2979428                   NaN          NaN  15.333333  161_151  \n",
       "2979429                   NaN          NaN   8.266667  141_226  \n",
       "2979430                   NaN          NaN   6.000000  237_142  \n",
       "\n",
       "[2950274 rows x 21 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical = ['PU_DO']\n",
    "numerical = ['trip_distance']\n",
    "\n",
    "dv = DictVectorizer()\n",
    "\n",
    "train_dicts = df_train[categorical + numerical].to_dict(orient='records')\n",
    "x_train = dv.fit_transform(train_dicts)\n",
    "\n",
    "val_dicts = df_val[categorical + numerical].to_dict(orient='records')\n",
    "x_val = dv.transform(val_dicts)\n",
    "\n",
    "target = 'duration'\n",
    "y_train = df_train[target].values\n",
    "y_val = df_val[target].values\n",
    "\n",
    "lr = LinearRegression()\n",
    "lr.fit(x_train, y_train)\n",
    "\n",
    "y_pred = lr.predict(x_val)\n",
    "\n",
    "mse = mean_squared_error(y_val, y_pred, squared=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.642012599379987\n"
     ]
    }
   ],
   "source": [
    "#Print MSE\n",
    "\n",
    "print(mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "lasso = Lasso(alpha=1.0)\n",
    "lasso.fit(x_train, y_train)\n",
    "\n",
    "y_pred = lasso.predict(x_val)\n",
    "\n",
    "mse = mean_squared_error(y_val, y_pred, squared=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9.756305201183736\n"
     ]
    }
   ],
   "source": [
    "#Print MSE\n",
    "\n",
    "print(mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "ridge = Ridge(alpha=1.0)\n",
    "ridge.fit(x_train, y_train)\n",
    "\n",
    "y_pred = ridge.predict(x_val)\n",
    "\n",
    "mse = mean_squared_error(y_val, y_pred, squared=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.545700226794958\n"
     ]
    }
   ],
   "source": [
    "#Print MSE\n",
    "\n",
    "print(mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "#save model\n",
    "with open('model1.bin', 'wb') as f_out:\n",
    "    pickle.dump((dv, lr), f_out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = df_train.sample(n=20_000, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Experiment with PyCaret\n",
    "from pycaret.regression import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_791da_row12_col1 {\n",
       "  background-color: lightgreen;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_791da\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_791da_level0_col0\" class=\"col_heading level0 col0\" >Description</th>\n",
       "      <th id=\"T_791da_level0_col1\" class=\"col_heading level0 col1\" >Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_791da_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_791da_row0_col0\" class=\"data row0 col0\" >Session id</td>\n",
       "      <td id=\"T_791da_row0_col1\" class=\"data row0 col1\" >42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_791da_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_791da_row1_col0\" class=\"data row1 col0\" >Target</td>\n",
       "      <td id=\"T_791da_row1_col1\" class=\"data row1 col1\" >duration</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_791da_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_791da_row2_col0\" class=\"data row2 col0\" >Target type</td>\n",
       "      <td id=\"T_791da_row2_col1\" class=\"data row2 col1\" >Regression</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_791da_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_791da_row3_col0\" class=\"data row3 col0\" >Original data shape</td>\n",
       "      <td id=\"T_791da_row3_col1\" class=\"data row3 col1\" >(20000, 21)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_791da_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_791da_row4_col0\" class=\"data row4 col0\" >Transformed data shape</td>\n",
       "      <td id=\"T_791da_row4_col1\" class=\"data row4 col1\" >(20000, 25)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_791da_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_791da_row5_col0\" class=\"data row5 col0\" >Transformed train set shape</td>\n",
       "      <td id=\"T_791da_row5_col1\" class=\"data row5 col1\" >(14000, 25)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_791da_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_791da_row6_col0\" class=\"data row6 col0\" >Transformed test set shape</td>\n",
       "      <td id=\"T_791da_row6_col1\" class=\"data row6 col1\" >(6000, 25)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_791da_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_791da_row7_col0\" class=\"data row7 col0\" >Ordinal features</td>\n",
       "      <td id=\"T_791da_row7_col1\" class=\"data row7 col1\" >1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_791da_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_791da_row8_col0\" class=\"data row8 col0\" >Numeric features</td>\n",
       "      <td id=\"T_791da_row8_col1\" class=\"data row8 col1\" >14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_791da_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_791da_row9_col0\" class=\"data row9 col0\" >Date features</td>\n",
       "      <td id=\"T_791da_row9_col1\" class=\"data row9 col1\" >2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_791da_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "      <td id=\"T_791da_row10_col0\" class=\"data row10 col0\" >Categorical features</td>\n",
       "      <td id=\"T_791da_row10_col1\" class=\"data row10 col1\" >4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_791da_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "      <td id=\"T_791da_row11_col0\" class=\"data row11 col0\" >Rows with missing values</td>\n",
       "      <td id=\"T_791da_row11_col1\" class=\"data row11 col1\" >2.9%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_791da_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "      <td id=\"T_791da_row12_col0\" class=\"data row12 col0\" >Preprocess</td>\n",
       "      <td id=\"T_791da_row12_col1\" class=\"data row12 col1\" >True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_791da_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "      <td id=\"T_791da_row13_col0\" class=\"data row13 col0\" >Imputation type</td>\n",
       "      <td id=\"T_791da_row13_col1\" class=\"data row13 col1\" >simple</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_791da_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "      <td id=\"T_791da_row14_col0\" class=\"data row14 col0\" >Numeric imputation</td>\n",
       "      <td id=\"T_791da_row14_col1\" class=\"data row14 col1\" >mean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_791da_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "      <td id=\"T_791da_row15_col0\" class=\"data row15 col0\" >Categorical imputation</td>\n",
       "      <td id=\"T_791da_row15_col1\" class=\"data row15 col1\" >mode</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_791da_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "      <td id=\"T_791da_row16_col0\" class=\"data row16 col0\" >Maximum one-hot encoding</td>\n",
       "      <td id=\"T_791da_row16_col1\" class=\"data row16 col1\" >25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_791da_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "      <td id=\"T_791da_row17_col0\" class=\"data row17 col0\" >Encoding method</td>\n",
       "      <td id=\"T_791da_row17_col1\" class=\"data row17 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_791da_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "      <td id=\"T_791da_row18_col0\" class=\"data row18 col0\" >Fold Generator</td>\n",
       "      <td id=\"T_791da_row18_col1\" class=\"data row18 col1\" >KFold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_791da_level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "      <td id=\"T_791da_row19_col0\" class=\"data row19 col0\" >Fold Number</td>\n",
       "      <td id=\"T_791da_row19_col1\" class=\"data row19 col1\" >10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_791da_level0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
       "      <td id=\"T_791da_row20_col0\" class=\"data row20 col0\" >CPU Jobs</td>\n",
       "      <td id=\"T_791da_row20_col1\" class=\"data row20 col1\" >-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_791da_level0_row21\" class=\"row_heading level0 row21\" >21</th>\n",
       "      <td id=\"T_791da_row21_col0\" class=\"data row21 col0\" >Use GPU</td>\n",
       "      <td id=\"T_791da_row21_col1\" class=\"data row21 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_791da_level0_row22\" class=\"row_heading level0 row22\" >22</th>\n",
       "      <td id=\"T_791da_row22_col0\" class=\"data row22 col0\" >Log Experiment</td>\n",
       "      <td id=\"T_791da_row22_col1\" class=\"data row22 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_791da_level0_row23\" class=\"row_heading level0 row23\" >23</th>\n",
       "      <td id=\"T_791da_row23_col0\" class=\"data row23 col0\" >Experiment Name</td>\n",
       "      <td id=\"T_791da_row23_col1\" class=\"data row23 col1\" >reg-default-name</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_791da_level0_row24\" class=\"row_heading level0 row24\" >24</th>\n",
       "      <td id=\"T_791da_row24_col0\" class=\"data row24 col0\" >USI</td>\n",
       "      <td id=\"T_791da_row24_col1\" class=\"data row24 col1\" >4f45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fca8402df40>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<pycaret.regression.oop.RegressionExperiment at 0x7fc9d5c40fd0>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "setup(data=df_train, target='duration', session_id=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Initiated</th>\n",
       "      <td>. . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>08:24:45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Status</th>\n",
       "      <td>. . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>Loading Dependencies</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Estimator</th>\n",
       "      <td>. . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>Compiling Library</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                    \n",
       "                                                                    \n",
       "Initiated  . . . . . . . . . . . . . . . . . .              08:24:45\n",
       "Status     . . . . . . . . . . . . . . . . . .  Loading Dependencies\n",
       "Estimator  . . . . . . . . . . . . . . . . . .     Compiling Library"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "best = compare_models()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Initiated</th>\n",
       "      <td>. . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>08:30:14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Status</th>\n",
       "      <td>. . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>Loading Dependencies</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Estimator</th>\n",
       "      <td>. . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>Compiling Library</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                    \n",
       "                                                                    \n",
       "Initiated  . . . . . . . . . . . . . . . . . .              08:30:14\n",
       "Status     . . . . . . . . . . . . . . . . . .  Loading Dependencies\n",
       "Estimator  . . . . . . . . . . . . . . . . . .     Compiling Library"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_24192_row10_col0, #T_24192_row10_col1, #T_24192_row10_col2, #T_24192_row10_col3, #T_24192_row10_col4, #T_24192_row10_col5 {\n",
       "  background: yellow;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_24192\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_24192_level0_col0\" class=\"col_heading level0 col0\" >MAE</th>\n",
       "      <th id=\"T_24192_level0_col1\" class=\"col_heading level0 col1\" >MSE</th>\n",
       "      <th id=\"T_24192_level0_col2\" class=\"col_heading level0 col2\" >RMSE</th>\n",
       "      <th id=\"T_24192_level0_col3\" class=\"col_heading level0 col3\" >R2</th>\n",
       "      <th id=\"T_24192_level0_col4\" class=\"col_heading level0 col4\" >RMSLE</th>\n",
       "      <th id=\"T_24192_level0_col5\" class=\"col_heading level0 col5\" >MAPE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >Fold</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_24192_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_24192_row0_col0\" class=\"data row0 col0\" >3.0759</td>\n",
       "      <td id=\"T_24192_row0_col1\" class=\"data row0 col1\" >23.2644</td>\n",
       "      <td id=\"T_24192_row0_col2\" class=\"data row0 col2\" >4.8233</td>\n",
       "      <td id=\"T_24192_row0_col3\" class=\"data row0 col3\" >0.6965</td>\n",
       "      <td id=\"T_24192_row0_col4\" class=\"data row0 col4\" >0.3901</td>\n",
       "      <td id=\"T_24192_row0_col5\" class=\"data row0 col5\" >1.6464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_24192_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_24192_row1_col0\" class=\"data row1 col0\" >3.1699</td>\n",
       "      <td id=\"T_24192_row1_col1\" class=\"data row1 col1\" >24.2564</td>\n",
       "      <td id=\"T_24192_row1_col2\" class=\"data row1 col2\" >4.9251</td>\n",
       "      <td id=\"T_24192_row1_col3\" class=\"data row1 col3\" >0.7032</td>\n",
       "      <td id=\"T_24192_row1_col4\" class=\"data row1 col4\" >0.4033</td>\n",
       "      <td id=\"T_24192_row1_col5\" class=\"data row1 col5\" >1.2614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_24192_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_24192_row2_col0\" class=\"data row2 col0\" >2.9807</td>\n",
       "      <td id=\"T_24192_row2_col1\" class=\"data row2 col1\" >22.5144</td>\n",
       "      <td id=\"T_24192_row2_col2\" class=\"data row2 col2\" >4.7449</td>\n",
       "      <td id=\"T_24192_row2_col3\" class=\"data row2 col3\" >0.7216</td>\n",
       "      <td id=\"T_24192_row2_col4\" class=\"data row2 col4\" >0.3769</td>\n",
       "      <td id=\"T_24192_row2_col5\" class=\"data row2 col5\" >1.9729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_24192_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_24192_row3_col0\" class=\"data row3 col0\" >3.0100</td>\n",
       "      <td id=\"T_24192_row3_col1\" class=\"data row3 col1\" >21.1959</td>\n",
       "      <td id=\"T_24192_row3_col2\" class=\"data row3 col2\" >4.6039</td>\n",
       "      <td id=\"T_24192_row3_col3\" class=\"data row3 col3\" >0.7358</td>\n",
       "      <td id=\"T_24192_row3_col4\" class=\"data row3 col4\" >0.3885</td>\n",
       "      <td id=\"T_24192_row3_col5\" class=\"data row3 col5\" >1.8127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_24192_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_24192_row4_col0\" class=\"data row4 col0\" >2.9411</td>\n",
       "      <td id=\"T_24192_row4_col1\" class=\"data row4 col1\" >20.4473</td>\n",
       "      <td id=\"T_24192_row4_col2\" class=\"data row4 col2\" >4.5219</td>\n",
       "      <td id=\"T_24192_row4_col3\" class=\"data row4 col3\" >0.7601</td>\n",
       "      <td id=\"T_24192_row4_col4\" class=\"data row4 col4\" >0.3320</td>\n",
       "      <td id=\"T_24192_row4_col5\" class=\"data row4 col5\" >0.8460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_24192_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_24192_row5_col0\" class=\"data row5 col0\" >3.1758</td>\n",
       "      <td id=\"T_24192_row5_col1\" class=\"data row5 col1\" >32.4231</td>\n",
       "      <td id=\"T_24192_row5_col2\" class=\"data row5 col2\" >5.6941</td>\n",
       "      <td id=\"T_24192_row5_col3\" class=\"data row5 col3\" >0.6158</td>\n",
       "      <td id=\"T_24192_row5_col4\" class=\"data row5 col4\" >0.4103</td>\n",
       "      <td id=\"T_24192_row5_col5\" class=\"data row5 col5\" >1.7604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_24192_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_24192_row6_col0\" class=\"data row6 col0\" >2.9346</td>\n",
       "      <td id=\"T_24192_row6_col1\" class=\"data row6 col1\" >18.9211</td>\n",
       "      <td id=\"T_24192_row6_col2\" class=\"data row6 col2\" >4.3498</td>\n",
       "      <td id=\"T_24192_row6_col3\" class=\"data row6 col3\" >0.7603</td>\n",
       "      <td id=\"T_24192_row6_col4\" class=\"data row6 col4\" >0.3633</td>\n",
       "      <td id=\"T_24192_row6_col5\" class=\"data row6 col5\" >0.9712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_24192_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_24192_row7_col0\" class=\"data row7 col0\" >3.0104</td>\n",
       "      <td id=\"T_24192_row7_col1\" class=\"data row7 col1\" >23.3436</td>\n",
       "      <td id=\"T_24192_row7_col2\" class=\"data row7 col2\" >4.8315</td>\n",
       "      <td id=\"T_24192_row7_col3\" class=\"data row7 col3\" >0.7224</td>\n",
       "      <td id=\"T_24192_row7_col4\" class=\"data row7 col4\" >0.3794</td>\n",
       "      <td id=\"T_24192_row7_col5\" class=\"data row7 col5\" >0.8473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_24192_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_24192_row8_col0\" class=\"data row8 col0\" >2.8202</td>\n",
       "      <td id=\"T_24192_row8_col1\" class=\"data row8 col1\" >17.8548</td>\n",
       "      <td id=\"T_24192_row8_col2\" class=\"data row8 col2\" >4.2255</td>\n",
       "      <td id=\"T_24192_row8_col3\" class=\"data row8 col3\" >0.7626</td>\n",
       "      <td id=\"T_24192_row8_col4\" class=\"data row8 col4\" >0.3672</td>\n",
       "      <td id=\"T_24192_row8_col5\" class=\"data row8 col5\" >1.0068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_24192_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_24192_row9_col0\" class=\"data row9 col0\" >3.0480</td>\n",
       "      <td id=\"T_24192_row9_col1\" class=\"data row9 col1\" >21.7162</td>\n",
       "      <td id=\"T_24192_row9_col2\" class=\"data row9 col2\" >4.6601</td>\n",
       "      <td id=\"T_24192_row9_col3\" class=\"data row9 col3\" >0.7486</td>\n",
       "      <td id=\"T_24192_row9_col4\" class=\"data row9 col4\" >0.3799</td>\n",
       "      <td id=\"T_24192_row9_col5\" class=\"data row9 col5\" >0.8232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_24192_level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
       "      <td id=\"T_24192_row10_col0\" class=\"data row10 col0\" >3.0167</td>\n",
       "      <td id=\"T_24192_row10_col1\" class=\"data row10 col1\" >22.5937</td>\n",
       "      <td id=\"T_24192_row10_col2\" class=\"data row10 col2\" >4.7380</td>\n",
       "      <td id=\"T_24192_row10_col3\" class=\"data row10 col3\" >0.7227</td>\n",
       "      <td id=\"T_24192_row10_col4\" class=\"data row10 col4\" >0.3791</td>\n",
       "      <td id=\"T_24192_row10_col5\" class=\"data row10 col5\" >1.2948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_24192_level0_row11\" class=\"row_heading level0 row11\" >Std</th>\n",
       "      <td id=\"T_24192_row11_col0\" class=\"data row11 col0\" >0.1028</td>\n",
       "      <td id=\"T_24192_row11_col1\" class=\"data row11 col1\" >3.7905</td>\n",
       "      <td id=\"T_24192_row11_col2\" class=\"data row11 col2\" >0.3807</td>\n",
       "      <td id=\"T_24192_row11_col3\" class=\"data row11 col3\" >0.0421</td>\n",
       "      <td id=\"T_24192_row11_col4\" class=\"data row11 col4\" >0.0209</td>\n",
       "      <td id=\"T_24192_row11_col5\" class=\"data row11 col5\" >0.4337</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fc9cc35d490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "60fcee57583d408bae3bd42a7f7c2a26",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Processing:   0%|          | 0/4 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lr = create_model('lr')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Initiated</th>\n",
       "      <td>. . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>08:30:18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Status</th>\n",
       "      <td>. . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>Loading Dependencies</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Estimator</th>\n",
       "      <td>. . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>Compiling Library</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                    \n",
       "                                                                    \n",
       "Initiated  . . . . . . . . . . . . . . . . . .              08:30:18\n",
       "Status     . . . . . . . . . . . . . . . . . .  Loading Dependencies\n",
       "Estimator  . . . . . . . . . . . . . . . . . .     Compiling Library"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_17c53_row10_col0, #T_17c53_row10_col1, #T_17c53_row10_col2, #T_17c53_row10_col3, #T_17c53_row10_col4, #T_17c53_row10_col5 {\n",
       "  background: yellow;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_17c53\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_17c53_level0_col0\" class=\"col_heading level0 col0\" >MAE</th>\n",
       "      <th id=\"T_17c53_level0_col1\" class=\"col_heading level0 col1\" >MSE</th>\n",
       "      <th id=\"T_17c53_level0_col2\" class=\"col_heading level0 col2\" >RMSE</th>\n",
       "      <th id=\"T_17c53_level0_col3\" class=\"col_heading level0 col3\" >R2</th>\n",
       "      <th id=\"T_17c53_level0_col4\" class=\"col_heading level0 col4\" >RMSLE</th>\n",
       "      <th id=\"T_17c53_level0_col5\" class=\"col_heading level0 col5\" >MAPE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >Fold</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_17c53_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_17c53_row0_col0\" class=\"data row0 col0\" >3.1062</td>\n",
       "      <td id=\"T_17c53_row0_col1\" class=\"data row0 col1\" >23.5879</td>\n",
       "      <td id=\"T_17c53_row0_col2\" class=\"data row0 col2\" >4.8567</td>\n",
       "      <td id=\"T_17c53_row0_col3\" class=\"data row0 col3\" >0.6923</td>\n",
       "      <td id=\"T_17c53_row0_col4\" class=\"data row0 col4\" >0.3933</td>\n",
       "      <td id=\"T_17c53_row0_col5\" class=\"data row0 col5\" >1.6697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_17c53_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_17c53_row1_col0\" class=\"data row1 col0\" >3.1796</td>\n",
       "      <td id=\"T_17c53_row1_col1\" class=\"data row1 col1\" >24.0512</td>\n",
       "      <td id=\"T_17c53_row1_col2\" class=\"data row1 col2\" >4.9042</td>\n",
       "      <td id=\"T_17c53_row1_col3\" class=\"data row1 col3\" >0.7057</td>\n",
       "      <td id=\"T_17c53_row1_col4\" class=\"data row1 col4\" >0.4038</td>\n",
       "      <td id=\"T_17c53_row1_col5\" class=\"data row1 col5\" >1.2690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_17c53_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_17c53_row2_col0\" class=\"data row2 col0\" >3.0032</td>\n",
       "      <td id=\"T_17c53_row2_col1\" class=\"data row2 col1\" >22.6916</td>\n",
       "      <td id=\"T_17c53_row2_col2\" class=\"data row2 col2\" >4.7636</td>\n",
       "      <td id=\"T_17c53_row2_col3\" class=\"data row2 col3\" >0.7194</td>\n",
       "      <td id=\"T_17c53_row2_col4\" class=\"data row2 col4\" >0.3827</td>\n",
       "      <td id=\"T_17c53_row2_col5\" class=\"data row2 col5\" >1.9989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_17c53_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_17c53_row3_col0\" class=\"data row3 col0\" >3.0309</td>\n",
       "      <td id=\"T_17c53_row3_col1\" class=\"data row3 col1\" >21.3009</td>\n",
       "      <td id=\"T_17c53_row3_col2\" class=\"data row3 col2\" >4.6153</td>\n",
       "      <td id=\"T_17c53_row3_col3\" class=\"data row3 col3\" >0.7345</td>\n",
       "      <td id=\"T_17c53_row3_col4\" class=\"data row3 col4\" >0.3886</td>\n",
       "      <td id=\"T_17c53_row3_col5\" class=\"data row3 col5\" >1.7958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_17c53_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_17c53_row4_col0\" class=\"data row4 col0\" >2.9612</td>\n",
       "      <td id=\"T_17c53_row4_col1\" class=\"data row4 col1\" >20.3718</td>\n",
       "      <td id=\"T_17c53_row4_col2\" class=\"data row4 col2\" >4.5135</td>\n",
       "      <td id=\"T_17c53_row4_col3\" class=\"data row4 col3\" >0.7609</td>\n",
       "      <td id=\"T_17c53_row4_col4\" class=\"data row4 col4\" >0.3336</td>\n",
       "      <td id=\"T_17c53_row4_col5\" class=\"data row4 col5\" >0.8329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_17c53_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_17c53_row5_col0\" class=\"data row5 col0\" >3.1992</td>\n",
       "      <td id=\"T_17c53_row5_col1\" class=\"data row5 col1\" >31.4278</td>\n",
       "      <td id=\"T_17c53_row5_col2\" class=\"data row5 col2\" >5.6061</td>\n",
       "      <td id=\"T_17c53_row5_col3\" class=\"data row5 col3\" >0.6276</td>\n",
       "      <td id=\"T_17c53_row5_col4\" class=\"data row5 col4\" >0.4116</td>\n",
       "      <td id=\"T_17c53_row5_col5\" class=\"data row5 col5\" >1.6557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_17c53_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_17c53_row6_col0\" class=\"data row6 col0\" >2.9664</td>\n",
       "      <td id=\"T_17c53_row6_col1\" class=\"data row6 col1\" >19.1734</td>\n",
       "      <td id=\"T_17c53_row6_col2\" class=\"data row6 col2\" >4.3787</td>\n",
       "      <td id=\"T_17c53_row6_col3\" class=\"data row6 col3\" >0.7571</td>\n",
       "      <td id=\"T_17c53_row6_col4\" class=\"data row6 col4\" >0.3669</td>\n",
       "      <td id=\"T_17c53_row6_col5\" class=\"data row6 col5\" >1.0222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_17c53_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_17c53_row7_col0\" class=\"data row7 col0\" >3.0353</td>\n",
       "      <td id=\"T_17c53_row7_col1\" class=\"data row7 col1\" >23.6407</td>\n",
       "      <td id=\"T_17c53_row7_col2\" class=\"data row7 col2\" >4.8622</td>\n",
       "      <td id=\"T_17c53_row7_col3\" class=\"data row7 col3\" >0.7189</td>\n",
       "      <td id=\"T_17c53_row7_col4\" class=\"data row7 col4\" >0.3820</td>\n",
       "      <td id=\"T_17c53_row7_col5\" class=\"data row7 col5\" >0.8548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_17c53_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_17c53_row8_col0\" class=\"data row8 col0\" >2.8488</td>\n",
       "      <td id=\"T_17c53_row8_col1\" class=\"data row8 col1\" >18.2076</td>\n",
       "      <td id=\"T_17c53_row8_col2\" class=\"data row8 col2\" >4.2670</td>\n",
       "      <td id=\"T_17c53_row8_col3\" class=\"data row8 col3\" >0.7579</td>\n",
       "      <td id=\"T_17c53_row8_col4\" class=\"data row8 col4\" >0.3665</td>\n",
       "      <td id=\"T_17c53_row8_col5\" class=\"data row8 col5\" >0.9872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_17c53_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_17c53_row9_col0\" class=\"data row9 col0\" >3.0738</td>\n",
       "      <td id=\"T_17c53_row9_col1\" class=\"data row9 col1\" >21.8613</td>\n",
       "      <td id=\"T_17c53_row9_col2\" class=\"data row9 col2\" >4.6756</td>\n",
       "      <td id=\"T_17c53_row9_col3\" class=\"data row9 col3\" >0.7469</td>\n",
       "      <td id=\"T_17c53_row9_col4\" class=\"data row9 col4\" >0.3772</td>\n",
       "      <td id=\"T_17c53_row9_col5\" class=\"data row9 col5\" >0.7762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_17c53_level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
       "      <td id=\"T_17c53_row10_col0\" class=\"data row10 col0\" >3.0405</td>\n",
       "      <td id=\"T_17c53_row10_col1\" class=\"data row10 col1\" >22.6314</td>\n",
       "      <td id=\"T_17c53_row10_col2\" class=\"data row10 col2\" >4.7443</td>\n",
       "      <td id=\"T_17c53_row10_col3\" class=\"data row10 col3\" >0.7221</td>\n",
       "      <td id=\"T_17c53_row10_col4\" class=\"data row10 col4\" >0.3806</td>\n",
       "      <td id=\"T_17c53_row10_col5\" class=\"data row10 col5\" >1.2862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_17c53_level0_row11\" class=\"row_heading level0 row11\" >Std</th>\n",
       "      <td id=\"T_17c53_row11_col0\" class=\"data row11 col0\" >0.1000</td>\n",
       "      <td id=\"T_17c53_row11_col1\" class=\"data row11 col1\" >3.4688</td>\n",
       "      <td id=\"T_17c53_row11_col2\" class=\"data row11 col2\" >0.3509</td>\n",
       "      <td id=\"T_17c53_row11_col3\" class=\"data row11 col3\" >0.0386</td>\n",
       "      <td id=\"T_17c53_row11_col4\" class=\"data row11 col4\" >0.0208</td>\n",
       "      <td id=\"T_17c53_row11_col5\" class=\"data row11 col5\" >0.4315</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fca3bb41220>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5095091e6ba046e990906feb2a512069",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Processing:   0%|          | 0/4 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ridge = create_model('ridge')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Initiated</th>\n",
       "      <td>. . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>08:30:20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Status</th>\n",
       "      <td>. . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>Loading Dependencies</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Estimator</th>\n",
       "      <td>. . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>Compiling Library</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                    \n",
       "                                                                    \n",
       "Initiated  . . . . . . . . . . . . . . . . . .              08:30:20\n",
       "Status     . . . . . . . . . . . . . . . . . .  Loading Dependencies\n",
       "Estimator  . . . . . . . . . . . . . . . . . .     Compiling Library"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_e5989_row10_col0, #T_e5989_row10_col1, #T_e5989_row10_col2, #T_e5989_row10_col3, #T_e5989_row10_col4, #T_e5989_row10_col5 {\n",
       "  background: yellow;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_e5989\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_e5989_level0_col0\" class=\"col_heading level0 col0\" >MAE</th>\n",
       "      <th id=\"T_e5989_level0_col1\" class=\"col_heading level0 col1\" >MSE</th>\n",
       "      <th id=\"T_e5989_level0_col2\" class=\"col_heading level0 col2\" >RMSE</th>\n",
       "      <th id=\"T_e5989_level0_col3\" class=\"col_heading level0 col3\" >R2</th>\n",
       "      <th id=\"T_e5989_level0_col4\" class=\"col_heading level0 col4\" >RMSLE</th>\n",
       "      <th id=\"T_e5989_level0_col5\" class=\"col_heading level0 col5\" >MAPE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >Fold</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_e5989_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_e5989_row0_col0\" class=\"data row0 col0\" >3.3200</td>\n",
       "      <td id=\"T_e5989_row0_col1\" class=\"data row0 col1\" >26.1077</td>\n",
       "      <td id=\"T_e5989_row0_col2\" class=\"data row0 col2\" >5.1096</td>\n",
       "      <td id=\"T_e5989_row0_col3\" class=\"data row0 col3\" >0.6594</td>\n",
       "      <td id=\"T_e5989_row0_col4\" class=\"data row0 col4\" >0.4281</td>\n",
       "      <td id=\"T_e5989_row0_col5\" class=\"data row0 col5\" >2.0904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e5989_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_e5989_row1_col0\" class=\"data row1 col0\" >3.3939</td>\n",
       "      <td id=\"T_e5989_row1_col1\" class=\"data row1 col1\" >26.8617</td>\n",
       "      <td id=\"T_e5989_row1_col2\" class=\"data row1 col2\" >5.1828</td>\n",
       "      <td id=\"T_e5989_row1_col3\" class=\"data row1 col3\" >0.6713</td>\n",
       "      <td id=\"T_e5989_row1_col4\" class=\"data row1 col4\" >0.4324</td>\n",
       "      <td id=\"T_e5989_row1_col5\" class=\"data row1 col5\" >1.6418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e5989_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_e5989_row2_col0\" class=\"data row2 col0\" >3.2400</td>\n",
       "      <td id=\"T_e5989_row2_col1\" class=\"data row2 col1\" >25.6872</td>\n",
       "      <td id=\"T_e5989_row2_col2\" class=\"data row2 col2\" >5.0683</td>\n",
       "      <td id=\"T_e5989_row2_col3\" class=\"data row2 col3\" >0.6824</td>\n",
       "      <td id=\"T_e5989_row2_col4\" class=\"data row2 col4\" >0.4111</td>\n",
       "      <td id=\"T_e5989_row2_col5\" class=\"data row2 col5\" >2.4416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e5989_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_e5989_row3_col0\" class=\"data row3 col0\" >3.2354</td>\n",
       "      <td id=\"T_e5989_row3_col1\" class=\"data row3 col1\" >23.8022</td>\n",
       "      <td id=\"T_e5989_row3_col2\" class=\"data row3 col2\" >4.8788</td>\n",
       "      <td id=\"T_e5989_row3_col3\" class=\"data row3 col3\" >0.7033</td>\n",
       "      <td id=\"T_e5989_row3_col4\" class=\"data row3 col4\" >0.4205</td>\n",
       "      <td id=\"T_e5989_row3_col5\" class=\"data row3 col5\" >2.1023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e5989_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_e5989_row4_col0\" class=\"data row4 col0\" >3.1607</td>\n",
       "      <td id=\"T_e5989_row4_col1\" class=\"data row4 col1\" >22.7044</td>\n",
       "      <td id=\"T_e5989_row4_col2\" class=\"data row4 col2\" >4.7649</td>\n",
       "      <td id=\"T_e5989_row4_col3\" class=\"data row4 col3\" >0.7336</td>\n",
       "      <td id=\"T_e5989_row4_col4\" class=\"data row4 col4\" >0.3644</td>\n",
       "      <td id=\"T_e5989_row4_col5\" class=\"data row4 col5\" >1.0734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e5989_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_e5989_row5_col0\" class=\"data row5 col0\" >3.3693</td>\n",
       "      <td id=\"T_e5989_row5_col1\" class=\"data row5 col1\" >30.7953</td>\n",
       "      <td id=\"T_e5989_row5_col2\" class=\"data row5 col2\" >5.5494</td>\n",
       "      <td id=\"T_e5989_row5_col3\" class=\"data row5 col3\" >0.6351</td>\n",
       "      <td id=\"T_e5989_row5_col4\" class=\"data row5 col4\" >0.4305</td>\n",
       "      <td id=\"T_e5989_row5_col5\" class=\"data row5 col5\" >1.8339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e5989_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_e5989_row6_col0\" class=\"data row6 col0\" >3.1739</td>\n",
       "      <td id=\"T_e5989_row6_col1\" class=\"data row6 col1\" >22.6985</td>\n",
       "      <td id=\"T_e5989_row6_col2\" class=\"data row6 col2\" >4.7643</td>\n",
       "      <td id=\"T_e5989_row6_col3\" class=\"data row6 col3\" >0.7124</td>\n",
       "      <td id=\"T_e5989_row6_col4\" class=\"data row6 col4\" >0.4091</td>\n",
       "      <td id=\"T_e5989_row6_col5\" class=\"data row6 col5\" >1.8584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e5989_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_e5989_row7_col0\" class=\"data row7 col0\" >3.2387</td>\n",
       "      <td id=\"T_e5989_row7_col1\" class=\"data row7 col1\" >26.7659</td>\n",
       "      <td id=\"T_e5989_row7_col2\" class=\"data row7 col2\" >5.1736</td>\n",
       "      <td id=\"T_e5989_row7_col3\" class=\"data row7 col3\" >0.6818</td>\n",
       "      <td id=\"T_e5989_row7_col4\" class=\"data row7 col4\" >0.4177</td>\n",
       "      <td id=\"T_e5989_row7_col5\" class=\"data row7 col5\" >1.0228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e5989_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_e5989_row8_col0\" class=\"data row8 col0\" >3.0606</td>\n",
       "      <td id=\"T_e5989_row8_col1\" class=\"data row8 col1\" >20.6095</td>\n",
       "      <td id=\"T_e5989_row8_col2\" class=\"data row8 col2\" >4.5398</td>\n",
       "      <td id=\"T_e5989_row8_col3\" class=\"data row8 col3\" >0.7259</td>\n",
       "      <td id=\"T_e5989_row8_col4\" class=\"data row8 col4\" >0.3865</td>\n",
       "      <td id=\"T_e5989_row8_col5\" class=\"data row8 col5\" >0.9532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e5989_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_e5989_row9_col0\" class=\"data row9 col0\" >3.2645</td>\n",
       "      <td id=\"T_e5989_row9_col1\" class=\"data row9 col1\" >23.9860</td>\n",
       "      <td id=\"T_e5989_row9_col2\" class=\"data row9 col2\" >4.8976</td>\n",
       "      <td id=\"T_e5989_row9_col3\" class=\"data row9 col3\" >0.7223</td>\n",
       "      <td id=\"T_e5989_row9_col4\" class=\"data row9 col4\" >0.4062</td>\n",
       "      <td id=\"T_e5989_row9_col5\" class=\"data row9 col5\" >0.9672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e5989_level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
       "      <td id=\"T_e5989_row10_col0\" class=\"data row10 col0\" >3.2457</td>\n",
       "      <td id=\"T_e5989_row10_col1\" class=\"data row10 col1\" >25.0018</td>\n",
       "      <td id=\"T_e5989_row10_col2\" class=\"data row10 col2\" >4.9929</td>\n",
       "      <td id=\"T_e5989_row10_col3\" class=\"data row10 col3\" >0.6928</td>\n",
       "      <td id=\"T_e5989_row10_col4\" class=\"data row10 col4\" >0.4106</td>\n",
       "      <td id=\"T_e5989_row10_col5\" class=\"data row10 col5\" >1.5985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e5989_level0_row11\" class=\"row_heading level0 row11\" >Std</th>\n",
       "      <td id=\"T_e5989_row11_col0\" class=\"data row11 col0\" >0.0948</td>\n",
       "      <td id=\"T_e5989_row11_col1\" class=\"data row11 col1\" >2.7235</td>\n",
       "      <td id=\"T_e5989_row11_col2\" class=\"data row11 col2\" >0.2701</td>\n",
       "      <td id=\"T_e5989_row11_col3\" class=\"data row11 col3\" >0.0304</td>\n",
       "      <td id=\"T_e5989_row11_col4\" class=\"data row11 col4\" >0.0202</td>\n",
       "      <td id=\"T_e5989_row11_col5\" class=\"data row11 col5\" >0.5247</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fc9d59767f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cacc0e9d2b5f4e018c25f4257026f7d1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Processing:   0%|          | 0/4 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lasso = create_model('lasso')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0YAAAHUCAYAAAAeFTh5AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjYuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/P9b71AAAACXBIWXMAAA9hAAAPYQGoP6dpAACOAElEQVR4nOzdeXxM1//H8ddkDwmK0hB72ohKiC1SBKGUoval9q0qbdXSoi1V1FrL19qqUpRaa0kpVUttrbZoKyp2QYhErSEj6/z+8DA/KZFJZETM+/l4eMjce+49n/uZ4XE/OeeeMZhMJhMiIiIiIiI2zC67AxAREREREcluKoxERERERMTmqTASERERERGbp8JIRERERERsngojERERERGxeSqMRERERETE5qkwEhERERERm6fCSEREREREbJ4KIxERERERsXkqjEREJEcZOnQo3t7eaf6ZPXt2dodoFb/99hve3t7s3Lkzu0N5KkVGRt73WSpbtixBQUGEhIRw5MgRc1u9FyJPJ4fsDkBERCSj8ufPT2ho6AP35c6dO8v7GzJkCJ6enrzzzjtZfu6n0XfffcfatWv55ptvsjuUDHvvvfdo3rw5AMnJyURGRjJr1izatm3L8uXL8fHxyfS59TkSebJpxEhERHIcOzs7nn322Qf+yZUrV5b39+effz7yOZKSkjCZTFkQzZMrISEByJp8ZRc3NzfzZ+m5556jSpUqzJgxA3t7exYvXvxI587JeRGxBSqMRETkqfXDDz/QvHlzfH19qVatGgMGDCA6OjpVm/Xr19OyZUsqVapE5cqV6dChA7///rt5v7e3N2fOnGHmzJl4e3sTGRnJjBkz8Pb2Jj4+PtW5vL29mTRpEvD/U7NWrlxJ+/bt8fPzIzY2FoB//vmHnj174u/vT4UKFejUqRMHDhzI0LUlJibi7e3N4sWLGTVqFJUrV6ZKlSqMGzeOhIQEPv30U6pVq0a1atUYNmwYiYmJAERERODt7c26dev46KOPqFq1KhUrViQkJITLly+bz28ymfjqq69o2LAh5cuXJyAggH79+nHu3DlzmxkzZlClShW2bt1KzZo1GTBgAJ07d2blypX8/vvveHt7s3r1agDCwsLo2bMnAQEBVKhQgcaNG7Ns2bJU11SrVi3GjBnD0qVLCQ4OpkKFCrRo0eK+3Gzbto2WLVvi6+tLrVq1GD16NLdu3TLvv3z5Mh988AGBgYGUL1+eV199lVWrVmUov/dyc3PD09OTqKioNNv8+eefdO3aFX9/f/z8/GjRogU//PCDef+DPkci8mRRYSQiIk+l9evXM2DAAKpVq8batWuZNWsWJ06coFu3buaRjX379jFo0CBq1KjB2rVrWblyJcWKFaNPnz7mAmrbtm0A9OjRg927d+Ph4ZGhOL7++mtat27Njz/+iJubG2fOnKFTp04kJSWxaNEiVqxYQcGCBenevTunTp2y+LyOjo4ALF68mBIlSrB69Wratm3LggUL6N69O0WKFGHlypX06dOHlStXsmHDBgDs7e0BmDZtGmXLlmX16tVMmjSJP/74gw8++MB8/unTp/O///2PDh068P333zN9+nROnz5Nly5diIuLM7dLTk5m8eLFfPHFF3zyySfMmDGDF198EX9/f3bv3k3jxo25desW3bt3x87OjkWLFrFhwwbatWvHiBEjzPm9e0179uxh//79zJkzh2XLlpGYmMj7779vbvPrr7/y1ltvERQURGhoKBMnTmTz5s3m2BMSEujWrRt79+5l/PjxfP/99zRt2pSPPvqItWvXZui9uyshIYHo6GgKFy78wP0nTpyga9euuLq6snDhQr777jsqV67MgAEDzNf3qJ8jEbE+FUYiIvJU+vzzz/H39+fDDz+kTJkyVK1alQkTJnDq1Cm2bNkCQPny5dmyZQvvvvsuxYsXp3Tp0vTp04e4uDjzKEXBggUByJUrF88++6y5sLCUl5cXrVu3plixYtjZ2bFgwQLgTmHi6+uLt7c3EyZMwM3NjYULF2b4OosWLUrXrl0pUaIEvXr1AsDJyYkePXpQokQJevToQa5cuTh8+DAABoMBgHLlytG5c2eKFStG/fr16dq1Kzt37uTq1askJCSwcOFCmjZtSrdu3ShVqhQBAQGMHj2aCxcusHnzZnP/cXFxdOnShfLly/Pss8+SL18+HBwccHR05Nlnn8XFxQUXFxe+//57pk6dire3N56ennTt2pUCBQqwa9euVNcTGxvL2LFjef755/Hx8aFly5ZERkZy5coVAObNm4evry/9+/enVKlSBAYGMmzYMNzc3EhMTGTr1q0cO3aM0aNHU7t2bUqVKsWbb75JcHAwc+bMyXB+//33X0aMGMGtW7fo0KHDA9ssWrQIBwcHJk+ejJ+fH88//zzDhg2jdOnS5vf0UT9HImJ9WnxBRERynMuXL+Pv7//AfdOmTaNSpUqcOHGCN998M9W+cuXKkS9fPg4cOEDjxo1xdnZmy5YthIaGcv78eRITE83PAV27di1LYi1fvnyq13///Tfe3t7ky5fPvM3Z2Rl/f/8MT6eDO1O07nrmmWcAUi0QYDAYyJcvn3ka312VKlVK9bpcuXKYTCZOnz5Nrly5uHXrFtWqVUvVxs/PD3t7+1QrtD3oGv/L3t6eo0ePMn/+fE6cOIHRaATAaDTel+dy5crh5ORkfp03b17gzvuRP39+wsLCaNy4capjGjZsSMOGDYE7+TUYDPfFHhgYyLZt27h27Vqq3P/X2LFjmThxIgApKSnEx8fj7e3NvHnz8PPze+AxYWFhvPjii/ct/FGxYsVUI2Ii8mRTYSQiIjlOvnz5WL58+QP3FSpUiOvXrwN3RhcWLVqUar/RaOTSpUvAnWlo48ePp2fPnjRq1Ig8efIQHR1N586dsyxWd3f3VK9jY2M5f/78fYVdQkICefLkyfD5XV1dzT/fHQ26d9vd7f9d+OG/cd095vbt26SkpAD/X5TcZWdnh5ubGzdv3ky1Pb24Dx8+zFtvvUXNmjWZNm0aBQsWxM7O7oF5/u/iGXev6W78sbGxuLi4pNlXbGwsJpOJgICAVNuTkpKAOyNADyuM3nzzTZo0aWLu+9lnn70vn/918+ZNPD0979ueJ0+e+3IlIk8uFUYiIpLj2NvbU6JEiTT3372x79q1K23btr1v/92b7x9++IGKFSsyePBg8767RdXD3L1Zv9ft27fTPQ7u3Cw/99xzfPrpp/fts7N7fDPc/3uddxcvyJ07t7nw+O9oTnJyMrGxsfcVVen58ccfMRgMTJkyxTyqkpKSkqmiwd3d/aGjeXny5MHZ2TnN54nSe7Ynf/78D/1sZSSma9euZThXIpJ99IyRiIg8dXLnzs0LL7zAmTNnKFGiRKo/CQkJFChQALjzm/6708/uWrNmDcB9Iyz3vr47QnLv9LTw8HCLYqtYsSKnT5/Gw8MjVVwmk4lChQpl/GIz6b9LR4eHh+Pg4EDp0qUpXbo07u7uqVbnA9i/fz8pKSn4+vqme/5783Xr1i2cnJxSTTX78ccfuXXrVoaXMPf19b1vyuFPP/1Ex44duXXrFhUrViQ+Ph6j0Zgqvy4uLuTJkyfVNL2sUqFCBQ4dOpRqZTyTycT+/fvvy9XTvmS7SE6mwkhERJ5Kffr0YcuWLcycOZOTJ09y4sQJJkyYQPPmzTl27BgA/v7+/Pbbb/zyyy+cPn2ayZMnk5ycjIODAwcPHuTKlSs4OTnh4uLCX3/9xZEjR7hx44b5WZPly5eTnJzMuXPnmDlzpkXfodSlSxdu3brFe++9xz///MO5c+dYsWIFzZs3f6QlpTPq77//Zt68eZw+fZotW7awaNEi6tWrh7u7O46OjvTo0YPvv/+eBQsWEBERwS+//MLw4cMpXbo09evXf+i58+TJQ0REBGFhYURFReHv78+tW7dYsGAB586d47vvvmPJkiX4+/tz/PjxDC1d3bNnT86cOcOnn37KyZMn2bt3L+PGjaNAgQLkzp2bunXr8sILL/D+++/z66+/cv78eXbs2EGnTp345JNPHjFrD9alSxdSUlIYNGgQ//zzD0eOHGH48OGcP3+enj17AjzwcyQiTxYVRiIi8lRq0qQJU6ZMYevWrTRv3px27dpx6NAh5s+fT9myZQHo378/gYGBvP3223Ts2JHExESGDRtGly5d+PHHH/nf//6HwWAgJCSEP//8kx49enDq1CkqVqzIoEGDWLFiBZUqVaJ///68/fbb5MqVi+Tk5IfGVaJECb755htu3bpFp06daNKkCYsXL2bo0KF07NjxcaQGuPMszfHjx2nTpg3vv/8+gYGBjB492ry/b9++DBo0iG+//ZZXX32VgQMH4ufnx8KFC9MddenevTsmk4nu3buzefNmGjVqRPfu3ZkzZw6vvfYaW7duZcqUKXTr1o3o6Gj69OljcdyBgYHMnDmTffv20bx5c95//33q1q3L2LFjgTsFyIIFCyhfvjwDBw6kYcOGjBw5kvr16zNhwoTMJSsdpUqVYtGiRcTFxdGxY0fatm3L8ePH+eKLL6hevTrAAz9HIvJkMZg0pisiImIzIiMjqVevHuPGjaNly5bZHY6IyBNDI0YiIiIiImLzVBiJiIiIiIjN01Q6ERERERGxeRoxEhERERERm6fCSEREREREbJ4KIxERERERsXkO2R2ASE72559/YjKZcHR0zO5QREREROQBEhMTMRgM+Pv7P7SdRoxEHoHJZOJxrl9iMplISEh4rH3aAuXVepRb61FurUN5tR7l1jqU1/RZer+mESORR3B3pMjX1/ex9BcXF0d4eDheXl7kypXrsfRpC5RX61FurUe5tQ7l1XqUW+tQXtMXFhZmUTuNGImIiIiIiM1TYSQiIiIiIjZPhZGIiIiIiNg8FUYiIiIiImLzVBiJiIiIiIjNU2EkIiIiIiI2T4WRiIiIiIjYPBVGIiIiIiJi81QYiYiIiIiIzVNhJCIiIiIiNu+pKIzOnz+Pr68vp0+fzu5Q5BH89ttveHt7Ex8fn92hiIiIiIgVmEwmdp6MZtmfp9l5MhqTyZTdIZk5ZHcAWaFo0aKEhYVldxg5ytdff03nzp1xcHgqPgIiIiIi8oRbE3aWId8f4OTlWPO2MgXcmdC0Ei18i2djZHc8FSNGj0NSUlJ2h5Blrly5woQJE0hOTn6s/T7O/p6m90tEREQkp1sTdpa2C3emKooATl6Ope3CnawJO5tNkf2/p2K4IDIyknr16vHDDz/QvXt3+vbtyw8//MDff/9NuXLlmDp1KpMmTWLbtm0899xzTJkyBR8fH1asWMGcOXN48803mTZtGomJibRo0YLBgwdjZ2fH0KFDcXBw4OzZs8TExLBp0yauX7/OmDFj+OWXX0hOTqZq1ap8/PHH5M+fn6CgIAYNGkSLFi3MsfXt25dnn32WUaNGcfToUUaPHk1YWBgFChSgSZMmvPPOOzg6OrJy5UoWLVpEp06d+N///kdycjL9+/fH09OT0aNHc/nyZVq2bMmwYcMASEhIYPz48WzYsAGTyYSfnx8ff/wxxYsXJykpiRdffJEZM2Ywf/58jhw5QokSJZg4cSIFChSgTp06mEwmqlSpwsiRI2nZsuVD8/v3338zZswYjh8/jpOTE/Xr12f48OG4uLgQHBxM79696dChAwA7d+6kd+/eHD16FABvb28+/PBD5s6dS8eOHenbty+7du1i/PjxREZGUrJkSYYOHUpgYKC5vwMHDjB69GiioqKoWrUqU6ZMwc3NjZSUFKZOncratWuJjY2lVKlSfPjhh1StWhWAzp07U6FCBXbt2kXBggWZN28ef/31FyNGjODs2bMEBARQq1YtvvjiC3bt2gXA77//zvjx4zl58iTPPfccHTp0oGvXrhgMhqz7gGax6zhx0ZiMiykxu0N5aty+nay8Wolyaz3KrXUor9aj3FpHTsiryWRiUOh+UtKYNpdiMjF0/QGaly+WrfdgT0VhdC8HBwe+/fZbZs6ciZubG6+99hqdO3dm9OjRjB07ljfeeIPPP/+c6dOn4+DgwKVLlzh69Cjbtm3j+PHjdO7cmRdeeMFcLGzdupUJEyZQs2ZNAIYNG0ZcXByhoaE4ODgwbNgw3nrrLZYvX06DBg3YsmWLuTCKi4tjz549zJ07l8TERPr27UvHjh2ZP38+0dHRhISE4ObmxhtvvIG9vT3nz5/n0qVL7Nixg6+++orPPvuMhg0bsnbtWv766y969OhB27ZteeGFF5g1axbHjh0jNDSUvHnzMn36dEJCQsxxAcyfP58JEyZQuHBhevfuzbRp05g9ezbz5s2jS5cu7Nu3D2dn53RzOnjwYHr16kWrVq24fPmy+Xq7du1q0XuydetWQkNDyZcvH1euXKFfv358+umnNGjQgO+//56QkBC2bdtmbr9hwwaWL1/OjRs3aNWqFatWraJbt26EhoayYsUKVq5cSdGiRfniiy/o168fu3fvxt7eHoAffviBGTNm4OPjQ0JCAn369KF58+asWLGC/fv3M3jwYJycnAC4evUqISEhfPLJJzRs2JBTp07Rq1cvChUqROPGjS38xN35xx4XF2dx+0dhNBr53c6D38/GA3oWK0spr9aj3FqPcmsdyqv1KLfW8YTn9XT0Fc5cufnQNif+jWVL+DlqlCyY5f2bTCaLCq6nrjACqFu3LiVKlADA19eXuLg484jESy+9xA8//GBuGx8fT0hICE5OTrz44ovUqVOHXbt2mQsjDw8PgoKCALh27Ro//fQTy5YtI3/+/AC89dZbNGvWjMjISBo1akTv3r25ffs2Li4u7Nq1izx58lC1alW2b99OUlISPXv2BKBYsWL07NmTuXPn8sYbbwBw+/ZtevXqhZOTE3Xq1GHatGm0bt2a3LlzU6NGDRwdHTl79iwvvPACy5YtY8qUKRQuXBiA/v37s3jxYg4dOoSfnx8ATZs2NeehXr16LF++PFP5vHz5Mrly5cLOzo5nn32WZcuWYWdn+SzMBg0amPO1fv16ihUrxquvvgpAy5YtcXZ2TvXgXffu3XF3d8fd3Z3KlSubF9Vo2rQp9erVw93dHYDGjRszffp0Lly4QLFixQAoX748L774InBnpOvatWu8+eabODs789JLL1G9enUOHDgAwPfff4+XlxdNmjQB7oxutW/fnrVr12aoMEpMTCQ8PNzi9o/MrsTj60tERETkEcUab1vUbv+RE+Q3XrJKDHd/Mf4wT2VhdLdYAHBxcTHfSAM4OzunWvUsT5485pt2uFMI/fXXX+bXRYsWNf984cIFTCaTudgAzDfkkZGRVKtWDTc3N3bv3k39+vX56aefeOWVV7Czs+PcuXPExMTg6+trPtZkMqUascmbNy8uLi7mOB90LfHx8Vy/fp1r167Rp0+fVNVvSkoKUVFR5sLI09MzzevOiGHDhpmnwwUFBfHaa69RpkwZi48vUqSI+eezZ8+myilgLpLuune/k5OTOe7r168zbtw49u7dy7Vr18zFVEJCwgP7unjxIm5ubjzzzDPmbS+++KK5MDp79ix///33fe9J6dKlLb42AEdHR7y8vDJ0TGYZjUaqRVzAw8PDotE+sUx8fDxRUVHKqxUot9aj3FqH8mo9yq115IS8lsSd5bvTb1e5rBc+VhgxOnHihEXtnsrC6L+jGQ8b3fjvEoH/LVYsXbXNYDBgZ2dHw4YN2bp1K7Vr1+bnn3/myy+/NO/38vJi/fr1Fsd997i02i1dujTVTb0lx2ZG8+bNqV27Ntu3b+fnn3+mRYsWTJ06lXr16t3X9kFLLv43h+kty5hW3BMnTuTIkSMsWbKE4sWLc+7cOerXr59mXw8aNr33tZ2dHUFBQcyZM+eh8aTHYDCQK1euRzpHRuQlgRL5cj3WPp92cXH2xEUpr9ag3FqPcmsdyqv1KLfWkRPyWiq/Gx9v/Pu+hRfu5VXQnfo+1nnGyNJz2vyqdLGxsVy9etX8OioqKtUozb08PT2xs7MjIiLCvO3MmTMAFC9+Z4nBV155hR07drB3717c3d3x9/c374+MjOTWrVvmY69evcrNmw+fb/kg7u7u5MuXj2PHjqXaHhkZmeFzWeLq1as888wztGzZkunTp/PGG2+watUq4M5IVGLi/z/od/HixYeeq1ixYqnyB7B48WLOnTuXbhxhYWG0atXKnOsjR448tP2zzz7LzZs3U+X48OHD5p+LFy/O8ePHUxVqly5dSjUCJSIiIiKPxmAwMKFpJezSKFDsDAbGN6mU7Ytf2Xxh5OjoyKxZs4iLi+Off/5hx44dDxwJgTvT7ho0aMCMGTO4evUqV69eZdq0aQQEBODh4QFA5cqVsbOz48svv+SVV14xv8E1a9Ykf/78fPbZZ9y6dYtLly7x7rvvMnny5EzF3b59e+bMmcPJkydJTExkwYIFtG7dGqPRmO6xd6frnTp1Kt3CLCoqiuDgYHbv3k1KSgo3b97kxIkT5imEJUuWZN++fQDcuHGDdevWPfR8TZo04eLFi6xatYrExEQ2bNjA5MmTyZ07d7pxP/fccxw4cIDExEQOHTpkLs5iYmIe2N7Pzw9XV1fmzp1LQkICv/zyC3/88Yd5/6uvvsq1a9f44osviI+P59y5c/To0YNFixalG4uIiIiIWK6Fb3FWdA3Cq6B7qu1eBd1Z0TVI32P0JMibNy+lS5emQYMG9OzZk44dO6ZZGAGMGDECNzc3goODefXVV3Fzc2PatGnm/Xen0/3++++pnp1xdHRk9uzZnDhxgpdeeokmTZpQokQJBg8enKm4Q0JCqFGjBq+//jpVq1Zl06ZNzJ07F1dX13SP9fHxwd/fnw4dOpiLi7R4eHgwZswYJkyYQKVKlXj55ZdxcXGhX79+ALz77rtERERQt25d+vTpY162O63vESpQoADTpk3j66+/pnLlysyZM4fZs2enes4rLYMGDeLUqVNUrVqVSZMmMWrUKOrXr0+/fv1SjQTdlTt3bqZNm8amTZsICAjgu+++o1OnTuZi9ZlnnmH27Nn89NNPVKlShQ4dOlCnTh26d++ebiwiIiIikjEtfItzZOhrbA9pwLedavHzWw04MvS1J6IoAjCY0nvg4ym2evVqJk+ezJ49e7I7FLGSu18qe3c57+nTp7N3716+/fbbLDl/WFgYwEOf9cpKcXFxhIeH4+Pj88TOI86JlFfrUW6tR7m1DuXVepRb61Be02fp/ZrNjxjJ08tkMtGoUSOmTp1KYmIiERERrF27ljp16mR3aCIiIiLyhHkqV6UTy40ePZoVK1akub9v376EhIQ8xoiyjsFgYPLkyXz66adUq1YNd3d3GjduTLdu3bI7NBERERF5wth0YdSyZUvzF7naquHDhzN8+PDsDsNqfH19M/3FtiIiIiJiOzSVTkREREREbJ4KIxERERERsXkqjERERERExOapMBIREREREZunwkhERERERGyeCiMREREREbF5KoxERERERMTmqTASERERERGbp8JIRERERERsngojERERERGxeSqMRERERETE5qkwEhERERERm6fCSEREREREbJ4KIxERERERsXkqjERERERExOapMBIREREREZunwkhERERERGyeCiMREREREbF5KoxERERERMTmqTASERERERGbp8JIRERERERsngojERERERGxeSqMRERERETE5qkwEhERERERm+eQ3QGIiIhI9jGZTOw6FcOFG3EUyZOLWqULYTAYsjssEZHHToWRZLvk5GQWLVpE9+7ds+R8mzdvxtvbmxIlSmTJ+UREnlZrws4y5PsDnLwca95WpoA7E5pWooVv8WyMTETk8dNUOsl2hw8f5quvvsqy802fPp0zZ85k2flERJ5Ga8LO0nbhzlRFEcDJy7G0XbiTNWFnsykyEZHsoREjsYqkpCRefPFFpkyZwldffcXJkycJDAxk1KhRfPjhh+zfvx8vLy+GDBlCt27dSEpKwtfXl7lz51KtWjWmTp3K2rVriY2NpVSpUnz44YdUrVo13X6bNWvG8ePHCQkJoWnTpowbN47du3czefJkIiIicHNzo02bNvTr1w+TycTrr79OxYoVGTJkCADLli3j888/Z8OGDbi5uVk7TZlyHScuGpNxMSVmdyhPjdu3k5VXK1FuredRcmsymRgUup8Uk+mB+1NMJoauP0Dz8sU0rU5EbIbBZErjf0WRR+Tt7W0ucm7dukWTJk0oVaoUEydOpGjRorRr147atWvz/PPPM3nyZPbs2QPA2rVrGTduHCtXrqRo0aJ88cUXLF68mN27d2Nvb29Rv3PnziUoKIi4uDhq1KjBwIED6dixIydOnKBdu3ZMnjyZ4OBgTpw4QZs2bfjuu+8oWLAgDRo0YPz48dSpU8eiawwLC8NkMuHl5fUoqbKY0Whk7ln9kxWRR3M6+gpf/fRbuu029axNjZIFH0NETw6j0UhERAQlS5bE1dU1u8N5qii31qG8pu/EiRMYDAZ8fX0f2k4jRmJVTZo0oWDBghQsWJBSpUpRvnx5ypYtC0CVKlU4c+YMzz//fKpjmjZtSr169XB3dwegcePGTJ8+nQsXLlCsWLEM9Z8rVy527txJrly5sLOz44UXXsDb25tDhw4RHByMl5cXPXr0YOzYsZQsWZIaNWpYXBTdlZiYSHh4eIaOeSR2enZKRB5NrPG2Re32HzlBfuMlK0fzZIqIiMjuEJ5ayq11KK8P5+TklG4bFUZiVc8995z5ZxcXFwoXLmx+7ezsTHx8/H3HXL9+nXHjxrF3716uXbvG3UHNhISETMWwbt06lixZQlRUFMnJySQmJlKlShXz/j59+vDaa68RFhbGxo0bM3x+R0fHxzpiVC3iAh4eHjg7Oz+WPm1BfHw8UVFRyqsVKLfW8yi5LYk7y3en365yWS98NGIkWUS5tQ7lNX0nTpywqJ0KI7Gq/85Nt7NLf72PiRMncuTIEZYsWULx4sU5d+4c9evXz1T/v/32G2PHjmXKlCnUr18fBwcHOnbsmKrNzZs3uX79OsnJycTExJA/f/4M9WEwGMiVK1em4suMvCRQIl+ux9rn0y4uzp64KOXVGpRb63mU3JbK78bHG/++b+GFe3kVdKe+j+0+Y+Tq6qrPrJUot9ahvKbN0v/HtCqdPHHCwsJo1aoVxYvfWSr2yJEjj3Su559/nldeeQUHBwcSEhI4depUqjbjx4+nXr169O3bl48++ojk5ORHil9E5ElnMBiY0LQSdmncLNgZDIxvUslmiyIRsU0qjCTbubi4EBsbS3R0NEajkeeee44DBw6QmJjIoUOHWLVqFQAxMTEWnc/Z2ZkzZ85w48YNPDw8iIyM5Pz589y4cYMPPviAZ599lujoaAB++eUXdu7cycCBA+nSpQu3b99mwYIF1rpUEZEnRgvf4qzoGoRXQfdU270KurOia5C+x0hEbI6m0km2q169Op6enjRq1IgxY8YwaNAgBg8eTNWqValYsSITJkxg1KhR9OvXj4ULF1KuXLmHnq99+/ZMmjSJP/74g8mTJ7NlyxaaNGlCgQIFeO+996hXrx4fffQRY8eOZdu2bQwcOJBnnnkGgOHDh/Pmm2/y8ssvm0esRESeVi18i9O8fDF2nYoh6oaRInldqVmqkEaKRMQmablukUcQFhYGkO7yj1klLi6O8PBwfHx8NI84Cymv1qPcWo9yax3Kq/Uot9ahvKbP0vs1TaUTERERERGbp6l0kqNUqVLlgUt837Vp0yaKFi36GCMSERERkaeBCiPJUfbt25fdIYiIiIjIU0hT6URERERExOapMBIREREREZunwkhERERERGyeCiMREREREbF5KoxERERERMTmqTASERERERGbp8JIRERERERsngojERERERGxeSqMRERERETE5qkwEhERERERm6fCSEREREREbJ4KIxERERERsXkqjERERERExOapMBIREREREZunwkhERERERGyeCiMREREREbF5KoxERERERMTmqTASERERERGbp8JIRERERERsngojERERERGxeSqMRERERETE5qkwEhERERERm6fCSEREREREbJ4KIxERERERsXkqjCRD1q5dS3BwcHaHkab4+Hi6du1KhQoV2Lx5c3aH81iZTCZ2noxm2Z+n2XkyGpPJlN0hiYiIiOQYDtkdgOQszZs3p3nz5o+1z6+//prOnTvj4JD+x3XPnj3s37+fHTt2kD9//scQ3ZNhTdhZhnx/gJOXY83byhRwZ0LTSrTwLZ6NkYmIiIjkDBoxkiyTnJyc5ee7cuUKEyZMsPjc169fJ2/evBQoUACDwZCl8Typ1oSdpe3CnamKIoCTl2Npu3Ana8LOZlNkIiIiIjmHCiN5oEOHDtGhQwcqVarESy+9xIgRI0hMTGT16tXUqFEDgMjISLy9vVm2bBnVqlVj/fr1TJ06lS5dujB79mwCAgKoWbMmc+fONZ83Pj6eTz/9lDp16lCtWjV69OhBRESEeb+3tzcLFy6kZs2afP755wQFBWEymahSpQqrV69+aMwrV65k2LBh/Pvvv/j6+rJx40ZMJhMzZsygZs2aVK5cmddff51Dhw6Zj7lx4waDBg2iSpUqBAYG0r9/fy5fvpy1ycxi13HiojGZC7cSOX8zgUGh+0lJY9pcisnE0PUHNK1OREREJB2aSicP1L9/f15++WW++eYbLl26RPv27fHy8iJ37tz3tf3999/Zvn07rq6uzJo1i0OHDvHSSy+xZ88efvvtN3r16oWfnx8BAQFMnTqVffv2sXjxYvLnz8+UKVPo3r07mzdvxtHREYCtW7cSGhpKvnz5qFq1Kl26dGHfvn04Ozs/NOY2bdpgb2/P5MmT2bNnDwArVqxg48aNLF68mCJFirB8+XJ69erFtm3byJUrFyNHjiQpKYmtW7dib2/PiBEjGDJkCF999ZXFuTKZTMTFxWUgu5lnNBr53c6D38/GA/Gcjr7CmSs3H3rMiX9j2RJ+jholCz6WGHMio9GY6m/JOsqt9Si31qG8Wo9yax3Ka/pMJpNFM4lUGMkDrVu3DkdHRxwcHPDw8KBKlSocOnSIgICA+9o2bdo0VcFkZ2dH7969sbe3p0aNGvj5+bFr1y4CAgJYtWoVI0eOxNPTE4B+/fqxZMkSDhw4YD53gwYNsuz5oKVLl9KtWzdKliwJQOfOnVm4cCE7duygRo0a/PDDD2zcuJG8efMC8N5771GnTh2uXLlicQyJiYmEh4dnSbwWsSth/jHWeNuiQ/YfOUF+4yVrRfTUuHf0UrKWcms9yq11KK/Wo9xah/L6cE5OTum2UWEkD7Rjxw7mzJnD2bNnSUpKIikpiVdeeeWBbYsUKZLqtaenJ/b29ubXHh4exMTEcP36dWJjYylR4v9v7PPkyUPevHmJjIw0F0b/Pd+jOHv2LKNGjWL06NHmbSkpKURFRREZGUlKSgpNmzZNdYy9vT3nz5+3uDBydHTEy8sry2J+GKPRSLWIC3h4eODs7ExJ3Fm+O/3jKpf1wkcjRmkyGo1ERERQsmRJXF1dszucp4pyaz3KrXUor9aj3FqH8pq+EydOWNROhZHc58yZM7z//vsMGTKE9u3b4+TkxODBg0lMTHxg+/+uFvff51lMJlO60+DuHd60ZPU5S9nZ2TFp0qQHFnVHjhwBeOQV7AwGA7ly5cr08RmVlwRK5MtFrly5KJXfjY83/n3fwgv38iroTn2fYjazGMWjcHV1fazvpS1Rbq1HubUO5dV6lFvrUF7TZuk9kBZfkPscPnwYV1dXunTpgpOTEyaTiaNHj1p8fFRUFElJSaleFy5cmLx585I3b95UQ73Xrl3j2rVrFC9unSWlixUrxrFjx1Jti4yMBKBo0aLY2dml2p+YmEh0dLRVYrEGg8HAhKaVsEvjH7ydwcD4JpVUFImIiIikQ4WR3MfDw4Nbt27xzz//YDQaGTNmDPb29sTExFi0ull8fDzz5s0jPj6e3bt3ExYWRr169QBo3bo1c+fOJSoqips3bzJlyhSKFy+Ov7//A8/l4uICwKlTp7h58+GLDDxI+/btWbp0KX/99RfJycn88MMPvPrqq1y8eBF3d3caN27M1KlTuXjxIrdv32bKlCn07NkzR63i1sK3OCu6BuFV0D3Vdq+C7qzoGqTvMRIRERGxgKbSyX0qVqxIx44d6dKlC7lz56ZPnz688sor9O3bl2nTpqV7vLe3N0ajkVq1auHs7MzQoUPx8fEB7iy2cOPGDZo1a4adnR3+/v58/fXXqZ5JupePjw/+/v506NCB/v37061btwxdS+vWrYmKiuLtt9/mxo0blC5dmpkzZ/Lcc88BMHz4cEaPHs2rr76KyWTC39+f2bNn57gRlha+xWlevhi7TsUQdcNIkbyu1CxVKMddh4iIiEh2MZhy0q/G5Yk3Y8YMdu3axYoVK7I7lMciLCwMAF9f38fSX1xcHOHh4fj4+GgecRZSXq1HubUe5dY6lFfrUW6tQ3lNn6X3a5pKJyIiIiIiNk9T6STHGD169ENHovr27UtISMhjjEhEREREnhYqjCRLvfPOO7zzzjtWOffw4cMZPny4Vc4tIiIiIrZNU+lERERERMTmqTASERERERGbp8JIRERERERsngojERERERGxeSqMRERERETE5qkwEhERERERm6fCSEREREREbJ4KIxERERERsXkqjERERERExOapMBIREREREZunwkhERERERGyeCiMREREREbF5KoxERERERMTmqTASERERERGbp8JIRERERERsngojERERERGxeSqMRERERETE5qkwEhERERERm6fCSEREREREbJ4KIxERERERsXkqjERERERExOapMBIREREREZunwkhERERERGyeCiMREREREbF5KowyoGHDhqxcuTLddp07d2bSpEmPIaL01ahRg9WrV1u9n2HDhjF48GCr92OJAQMGMHTo0OwO47EzmUzsPBnNsj9Ps/NkNCaTKbtDEhEREckxHLI7gLs2b96Mt7c3JUqUyO5Q0vTjjz9mdwhWlZH3IDk5mUWLFtG9e3cAPv30U2uHJw+xJuwsQ74/wMnLseZtZQq4M6FpJVr4Fs/GyERERERyhidmxGj69OmcOXMmu8OwaRl5Dw4fPsxXX31l5YjEEmvCztJ24c5URRHAycuxtF24kzVhZ7MpMhEREZGc44kojJo1a8bx48cJCQkhODgYHx8ftm7dSr169ahSpQpDhgzh9u3b5vZLliwhODgYf39/WrZsyZ49e8z7atWqxbx58+jWrRsVK1akRYsWHD582KI4pk6dSpcuXZg9ezYBAQHUrFmTuXPnmvcHBwezdOlS4M6IycSJE6levTpVqlTh3Xff5fr16/ed02Qy0b9/f7p3705iYuJ90+xOnjyJt7c3kZGRJCYm4u3tzcqVK2ndujX+/v507NiRyMhIi+JPSkpi9OjRBAQEUKtWrfum/V29epV+/foREBBAlSpV6N27N1FRUUDq9+CDDz4A4OjRo3Tq1IkKFSoQHBzMlClTSExM5ODBg7Rv355///0XX19f9u7dy9ChQxkwYAAAK1eupGnTpixfvpzAwECqVavGt99+y86dO3n55ZepVKlSqhGmhIQERo0aRUBAANWqVaNXr16cPWv5zfyKFSsIDg6mcuXKjBw5kpSUFPO+lJQUJk+eTK1atcyfhz/++AOAWbNm0bJly1Tn2rdvH35+fty8edPi/h+36zhx0ZjMhVuJnL+ZwKDQ/aSkMW0uxWRi6PoDmlYnIiIiko4nYipdaGgo3t7ezJ49G2dnZ7p06cKmTZtYt24dV69epXPnzsydO5d33nmHPXv2MHv2bObMmUPZsmXZtm0bISEhbNq0CQ8PDxwdHVm8eDGff/45JUuW5NNPP+Wdd95hy5YtGAyGh8bh4ODAoUOHeOmll9izZw+//fYbvXr1ws/Pj4CAgFRtly1bxtatW1m5ciX58+dnwIABjBw5kilTpqRqN2vWLE6ePMnSpUtxdHR8aP939y9evJhZs2aRL18++vfvzwcffMA333yTbh6/++47Nm3axLfffkvhwoWZOHFiqmJt4sSJXL16lS1btmBvb0///v0ZO3YsM2bMSPUeBAUFkZiYSN++fenYsSPz588nOjqakJAQ3NzceOONNxg9ejSTJ082F6Vr164192Nvb8/58+e5dOkSO3bs4KuvvuKzzz6jYcOGrF27lr/++osePXrQtm1bXnjhBWbNmsWxY8cIDQ0lb968TJ8+nZCQEEJDQ7Gze3jtfurUKT7++GNmzpxJUFAQ69atY8yYMbzyyivAnc/WihUrWLlyJUWLFuWLL76gX79+7N69m2bNmjFjxgxOnjxJmTJlgDvTJevWrYubm1u6+b7LZDIRFxdncftHYTQa+d3Og9/PxgPxnI6+wpkrDy/iTvwby5bwc9QoWfCxxJgTGY3GVH9L1lFurUe5tQ7l1XqUW+tQXtNnMpnSrQPgCSmMHqRLly64ubnh5uZG06ZN2bVrF++88w5Lly6ldevWlC9fHoAGDRqwZMkSNmzYQK9evQCoW7cuZcuWBaBHjx6sXLmSU6dOmW9+H8bOzo7evXtjb29PjRo18PPzY9euXfcVRqtWraJDhw4UK1YMgOHDh3Py5MlUbTZt2sTy5ctZsWJFhm60mzVrhqenJwBdu3bljTfewGg04urq+tDjfvrpJxo3bmy+znfffZfly5eb948cOZKkpCRy5coFQL169fjiiy8eeK6dO3eSlJREz549AShWrBg9e/Zk7ty5vPHGG+lew+3bt+nVqxdOTk7UqVOHadOm0bp1a3Lnzk2NGjVwdHTk7NmzvPDCCyxbtowpU6ZQuHBhAPr378/ixYs5dOgQfn5+D+1ny5YteHt7U79+fQDatGnD119/bd7ftGlT6tWrh7u7OwCNGzdm+vTpXLhwgWLFilG5cmW+//57+vfvbz7fRx99lO713SsxMZHw8PAMHfNI7P7/GbBY4+2HNPx/+4+cIL/xkrUiempERERkdwhPLeXWepRb61BerUe5tQ7l9eGcnJzSbfPEFkYlS5Y0/+zh4UFMTAwAZ8+eZfv27cyfP9+832Qy8fzzz5tf37t4QJEiRQCIiYmxqDDy9PTE3t7+gX3f6+zZsxQtWtT8ulixYuYiCSA8PJzFixfz0Ucf4eHhkW6/9/pv/CkpKfz777+pzv8g0dHR1KxZ0/y6QIEC5M6d2/z6xIkTTJw4kfDwcOLi4khJSSFfvnwPPNe5c+eIiYnB19fXvM1kMuHs7GzRNeTNmxcXFxcA8zF3Cx8AFxcX4uPjuX79OteuXaNPnz6pKvmUlBSioqLSLYyio6PN7/FdxYv//2ID169fZ9y4cezdu5dr166Zp5QlJCQA0Lx5c+bMmUP//v05ePAgt27dIigoyKJrvMvR0REvL68MHZNZRqORahEX8PDwwNnZmZK4s3x3+sdVLuuFj0aM0mQ0GomIiKBkyZLp/gJCMka5tR7l1jqUV+tRbq1DeU3fiRMnLGr3xBZG9z4Tce8NuZ2dHQMHDjSPZFhyLGDxDf1/n8V4WDFw77Ms/7V//37q1q3L559/TqNGjTI0YvSgeCypcu/e7KcV49tvv42/vz8//fQTefLkYdWqVUydOvWB5zIYDHh5ebF+/fpMxf2gKXAPGsK8227p0qWpijBLpXfNEydO5MiRIyxZsoTixYtz7tw58+gSwCuvvMLo0aP5888/2bZtG6+88opFub6XwWAwj8I9DnlJoES+XOTKlYtS+d34eOPf9y28cC+vgu7U9ylm0RCyrXN1dX2s76UtUW6tR7m1DuXVepRb61Be02bpPdATsfjCg5w7d878c1RUFIUKFQLujAYcO3YsVdsLFy6kKmjuXazgwoULQOrRioeJiooiKSkp1esHHVusWLFUK7idOXOGJUuWmF936NCBSZMm8eyzzzJ27FjzdmdnZxITE1Od/7/uvfYLFy7g4OBAgQIF0o29UKFCqc4XHR1tnm965coVzp8/T69evciTJw8AR44cSfNcxYsXJzIyklu3bpm3Xb16NcsXJXB3dydfvnz3vaeWLjhRqFAhLl68mGrbve9LWFgYrVq1Mo8i/fea3d3dqVevHps3b2bz5s00a9YsM5eRbQwGAxOaVsIujX/wdgYD45tUUlEkIiIiko4npjBydnbmzJkzxMbe+c33/PnzuXHjBufOnWP9+vXUq1cPgHbt2rFx40Z27NhBcnIye/fupUmTJoSFhZnPtWXLFg4dOsTt27eZP38+ZcuWTTXt7WHi4+OZN28e8fHx7N69m7CwMHPf92rdujXLli3j9OnT3Lp1i88++4x9+/aZ99vb22Nvb8/48ePZsGED27dvB+5MEfzzzz9JTk4mISGBFStW3HfuNWvWcObMGW7evMmiRYuoXbs2Dg7pD+7VqlWLDRs2cPr0aW7evMm0adPMo1158uQhV65c/P777yQnJ7N27VrCwsK4efOmufi5+x7cuHGDmjVrkj9/fj777DNu3brFpUuXePfdd5k8eTJwZypcbGxsquIrs9q3b8+cOXM4efIkiYmJLFiwgNatW1t03qCgIMLDw9m+fTsJCQl8++23qaY+Pvfccxw4cIDExEQOHTrEqlWrAFK1ad68OStXriQhIYHKlSs/0rVkhxa+xVnRNQivgu6ptnsVdGdF1yB9j5GIiIiIBZ6YqXTt27dn0qRJ5udS6tSpQ/Pmzblx4wYNGjTg9ddfB6BGjRoMHjyYkSNHcunSJYoWLcqIESNSPYvSsmVLJk6cyN9//02ZMmVSLY+dHm9vb4xGI7Vq1cLZ2ZmhQ4fi4+NzX7sOHTpw/vx52rdvT3JyMjVq1ODjjz++r12pUqUYOHAgw4cPJzQ0lB49ejBo0CDq1KlDoUKFeOutt/jxxx9JTk5OFf+AAQM4ffo05cuXZ8yYMRbF3q1bN86dO0e7du1wcnKiX79+7N+/n6SkJBwcHPjkk0/47LPPmDFjBo0bN2bmzJl07NiRxo0bs2PHDvN78McffzB9+nRmz57Np59+yksvvYSLiwsNGjRg8ODBAFSvXh1PT08aNWpkcXxpCQkJ4caNG7z++uvEx8dTtmxZ5s6da9E82QoVKjBs2DBGjhzJjRs3aNq0KY0bNzYv7z5o0CAGDx5M1apVqVixIhMmTGDUqFH069ePhQsXUq5cOWrWrImzszNNmjTJsSMrLXyL07x8MXadiiHqhpEieV2pWapQjr0eERERkcfNYHrCvuDkt99+o0uXLhw8eNDi54LuFRwcTO/evenQoUOGj50xYwa7du164CjO4+Lt7c3cuXMzvACAZN7NmzepU6cOK1eupFSpUhk69u5IZWaej8qMuLg4wsPD8fHx0TziLKS8Wo9yaz3KrXUor9aj3FqH8po+S+/XnpipdCLZIT4+nlGjRhEYGJjhokhEREREnh5PzFQ6a9u4caN5GtiDVK1aFX9//8cYUcbMmzeP//3vf2nuf+211/j0008fX0CPwcGDB+nYsWOa+4sUKcKPP/6Y6fPv37+fHj164Ofnx7Rp0zJ9HhERERHJ+Z64wiggIICjR49m+vht27Y9cHujRo1o1KhRuse/8847me47K6R17T179nzoEuVPIz8/v1SLamS1ypUr8/fff1vt/CIiIiKSc2gqnYiIiIiI2DwVRiIiIiIiYvNUGImIiIiIiM1TYSQiIiIiIjZPhZGIiIiIiNg8FUYiIiIiImLzVBiJiIiIiIjNU2EkIiIiIiI2T4WRiIiIiIjYPBVGIiIiIiJi81QYiYiIiIiIzVNhJCIiIiIiNk+FkYiIiIiI2DwVRiIiIiIiYvNUGImIiIiIiM1TYSQiIiIiIjZPhZGIiIiIiNg8FUYiIiIiImLzVBiJiIiIiIjNU2EkIiIiIiI2T4WRiIiIiIjYPBVGIiIiIiJi8zJVGCUlJbFixQrz6507d/L2228zadIkEhISsiw4ERERERGRxyFThdGkSZNYsGABABcuXKBfv37kyZOHffv2MWnSpKyMT0RERERExOoyVRht3LiRzz//HIDvv/8ePz8/xo4dy/Tp09m8eXOWBpgd2rZty4wZM7Kl73///ZeWLVtSoUIFwsLC+OOPP6hduzYBAQGZOt+AAQMYOnRoFkd5v7Vr1xIcHGz1fiyxdOnSJyaWx8VkMrHzZDTL/jzNzpPRmEym7A5JREREJEdxyMxBN27coESJEgDs2bPHfBNaqFAhrl27ZvF5Nm/ejLe3t/lccqfovHTpEnv37sXFxYV+/frh6+vL9OnTrd73P//8w/Xr13nppZcsar9q1SqCg4PJnz8/zZs3p3nz5tYNUB5oTdhZhnx/gJOXY83byhRwZ0LTSrTwLZ6NkYmIiIjkHJkaMXrmmWeIjIwkJiaGAwcOUKtWLQCioqJwc3Oz+DzTp0/nzJkzmQkhWyQlJVm9j+vXr+Ph4YGrqysGg4Fr165RsmRJ7Oysv07Gd999xy+//GJR2+TkZMaPH8/Vq1etHJU8zJqws7RduDNVUQRw8nIsbRfuZE3Y2WyKTERERCRnydTddvPmzWnfvj2tW7ematWqlClThlu3bjFkyBBq165t0TmaNWvG8ePHCQkJITg4GB8fH7Zu3Uq9evWoUqUKQ4YM4fbt2+b2S5YsITg4GH9/f1q2bMmePXvM+2rVqsW8efPo1q0bFStWpEWLFhw+fNji65k1axY1a9YkICCAWbNmpdo3dOhQhg0bRpcuXWjSpAlwp3gZPHgwNWvWJDAwkH79+vHvv/8Cd0bQ0ruWZcuW0ahRI/z9/WnRogU7d+4E4H//+x+zZ8/m4MGD+Pr64ufnxx9//MH8+fOpUqWKRdeyYsUKgoODqVy5MiNHjiQlJcW8LyUlhcmTJ1OrVi1znv744w8ARo8ezbfffsv8+fN5+eWXgTsjg4MGDaJKlSoEBgbSv39/Ll++DEC1atWIjY3ltddeY+bMmaxevZoaNWoAcObMGby9vfn5559p2LAhFSpU4IMPPuDcuXO0a9eOihUr0q1bN2Jj//9m/mHvb3r+/vtvmjVrRsWKFenevbs5xrtCQ0Np1KgRFStWJDg4mG+//RaAffv2Ub58+VTF3e3bt/H392f37t0W9/+4XceJi8Zkzt9MYFDoflLSmDaXYjIxdP0BTasTERERsUCmptL169cPLy8vLl26RKtWrQBwdHSkePHiFj/PEhoaire3N7Nnz8bZ2ZkuXbqwadMm1q1bx9WrV+ncuTNz587lnXfeYc+ePcyePZs5c+ZQtmxZtm3bRkhICJs2bcLDwwNHR0cWL17M559/TsmSJfn0009555132LJlCwaD4aFx7N69my+//JKvv/6acuXK8eWXX3Ls2DHzKBjA1q1bmTBhAjVr1gRg2LBhxMXFERoaioODA8OGDeOtt95i+fLlODg4kJKSkua1bNu2jUmTJvHFF1/g5+fH5s2b6du3L2vXrqV///7Y29uza9cu86p/nTt3pkKFCrz33nvp5vTUqVN8/PHHzJw5k6CgINatW8eYMWN45ZVXzDlfsWIFK1eupGjRonzxxRf069eP3bt3M3z4cI4dO5aqr5EjR5KUlMTWrVuxt7dnxIgRDBkyhK+++op169ZRr1491q1bR5kyZVi9erU5Dnt7ewDWrVvHd999R3h4OJ06dSIyMpIpU6bg6OhI06ZNWbduHZ06dUr3/X2Y5ORk+vXrx6uvvsq7777L4cOHeffdd3FwuPPRjoyMZMiQIcycOZPg4GD27t1Ljx49qFSpEpUrV6Zw4cJs2rSJDh06mD8PuXPnJjAwMN1832UymYiLi7O4/aMwGo38bufB72fjOR0dxZkrNx/a/sS/sWwJP0eNkgUfS3w5ldFoTPW3ZB3l1nqUW+tQXq1HubUO5TV9JpMp3ZoAMlkYATRu3Bj4/+llTk5OfPrpp5k9HQBdunTBzc0NNzc3mjZtyq5du3jnnXdYunQprVu3pnz58gA0aNCAJUuWsGHDBnr16gVA3bp1KVu2LAA9evRg5cqVnDp1ijJlyjy0z59++okaNWpQqVIlAPr27cuiRYtStfHw8CAoKAiAa9eu8dNPP7Fs2TLy588PwFtvvUWzZs2IjIxM91pWrVpF48aNzSNATZo0Yf78+WzatInnn3/+kfK3ZcsWvL29qV+/PgBt2rTh66+/Nu9v2rQp9erVw93dHbjzHk6fPp0LFy5QrFixVOe6ceMGP/zwAxs3biRv3rwAvPfee9SpU4crV65YFE+rVq1wc3OjatWquLm5Ub16dYoWLQpA+fLlzdMoLXl/03Lo0CEuXrxInz59cHZ2xt/fn+DgYPMoXNGiRdm7d6/5GgIDAylQoAD//PMPZcuWpVmzZnz//ffmwmjz5s00btzYXNxZIjExkfDwcIvbPzK7O8/kxRpvp9Pwjv1HTpDfeMmaET01IiIisjuEp5Zyaz3KrXUor9aj3FqH8vpwTk5O6bbJVGGUnJzMvHnzWLZsGZcvX+bvv//GaDQyceJEPvjgA4s6fpCSJUuaf/bw8CAmJgaAs2fPsn37dubPn2/ebzKZUhUS9y7gUKRIEQBiYmLSLYyio6Px9PQ0v3Z0dLxvlOLuzTzcWZ7cZDKl6u9uUREZGWmuRtO6lsjIyPumxXl6eqYqqjIrOjrafO13FS/+/w/fX79+nXHjxrF3716uXbtmnmL1oO+eioyMJCUlhaZNm6babm9vz/nz53nmmWfSjee5554z/+zi4kLhwoXNr52dnYmPjwcse3/TcvHiRdzc3MyFD3DfYh4LFixg/fr1xMTEkJKSQkJCgvmamzdvzueff8758+cpVKjQfXFYwtHRES8vrwwdk1lGo5FqERfw8PCgJO4st2DGX+WyXvhoxOihjEYjERERlCxZEldX1+wO56mi3FqPcmsdyqv1KLfWobym78SJExa1y1RhNG/ePJYsWULXrl353//+B0BcXBwHDhxg6tSpDBkyJDOnTfUshMlkwtnZGQA7OzsGDhxIz549LT4WMB//MA8qCu59LgcwT8tKz71DdGldiyXHZlZ61zJx4kSOHDnCkiVLKF68OOfOnTOPLv3X3cUeduzYYR4Zu5clhdx/rymtBSQseX/T8qBrTk5ONv+8Zs0aFi1axKxZs6hWrRp2dnbUqVPHvL9EiRJUrFiR9evXU65cOQoUKICvr2+GYjAYDOTKlSvDsWdWXhIokS8XZT0K8PHGv+9beOFeXgXdqe9TLEs+X7bA1dX1sb6XtkS5tR7l1jqUV+tRbq1DeU2bpfdBmVp8Ye3atcyePZsePXqYOypQoABTp07lhx9+yMwpATh37pz556ioKAoVKgTcGfU4duxYqrZ3R27uuvdG/cKFCwCpRijSUqhQIS5evGh+nZCQYD7+QTw9PbGzs0s1XHl3Sti9ozMPu5b/DnWeOXPmvqlsmfHfa7k3NoCwsDBatWpljvPIkSNpnqto0aLY2dmlyntiYiLR0dGPHOd/WfL+pqVQoULcvHkz1UIO9+Y3LCyMgIAAqlevjp2dHZcvX+bSpdTTyl577TV+/PFHNm/efN8I2ZPMYDAwoWkl7NL4x25nMDC+SSUVRSIiIiIWyFRhFBUVRbly5e7bXqJEiQwt3+zs7MyZM2fMN7Xz58/nxo0bnDt3jvXr11OvXj0A2rVrx8aNG9mxYwfJycns3buXJk2aEBYWZj7Xli1bOHToELdv32b+/PmULVs21RS4tAQFBbF7924OHjzI7du371uV7r/y5MlDgwYNmDFjBlevXuXq1atMmzaNgICAVFPw0rqWNm3asGHDBvbv309CQgLfffcdJ0+e5NVXX7U4bw+7lvDwcLZv305CQgLffvuteQof3JnaduDAARITEzl06BCrVq0CMLdxdnYmMjKSq1ev4u7uTuPGjZk6dSoXL17k9u3bTJkyhZ49e2IymXBxcQHuFCH3FiWZYcn7m5YKFSqQL18+5s6dS0JCAvv27TM/XwR3pjEePXqUa9euERMTw7Bhw/Dw8EhV4DVu3Jjjx4+zcePGHFUYAbTwLc6KrkF4FXRPtd2roDsrugbpe4xERERELJSpqXTPPPMMx44dw9vbO9X2X3/9lYIFLX+WoX379kyaNMl8k12nTh2aN2/OjRs3aNCgAa+//joANWrUYPDgwYwcOZJLly5RtGhRRowYgZ+fn/lcLVu2ZOLEifz999+UKVOGSZMmWRRDo0aNOHr0KG+++SbJycl07tyZSpUqPfQ7i0aMGMEnn3xCcHAwrq6uvPTSS4wbNy5Vm7SupXbt2rz99tsMHDiQGzduUKZMGebPn5/qmaTMqlChAsOGDWPkyJHcuHGDpk2b0rhxY/NS4YMGDWLw4MFUrVqVihUrMmHCBEaNGkW/fv1YuHAhLVu2ZNiwYTRt2pQdO3YwfPhwRo8ezauvvorJZMLf35/Zs2djMBgoWLAgDRs2ZODAgbRr18688EVmWPL+psXFxYVZs2bxySefsHDhQvz9/enRowfz5s0D7nzGfvvtN+rUqYOnpycjRozg4MGDzJw5k0KFCvH666+TN29e6tatS3R0dKpRv5yihW9xmpcvxq5TMUTdMFIkrys1SxXSSJGIiIhIBhhMmfiSk9mzZ7N27Vq6devGuHHjmDRpEocOHWLZsmV0796dkJCQDJ3vt99+o0uXLhw8eNCi54L+Kzg4mN69e5tXFstOj3otkj06d+5M06ZNadu2bYaOuzuqldHnkjIrLi6O8PBwfHx8NI84Cymv1qPcWo9yax3Kq/Uot9ahvKbP0vu1TI0Y9e3bF2dnZ2bPnk1iYiLvvvsuBQsW5M0336R79+6ZOaVItkhJSWH58uWcOXOGZs2aZXc4IiIiIpJNMlUYGQwGevbsSc+ePbl5884XTLq5uWVpYFlh48aNDB48OM39VatWzfDSzNnl4MGDdOzYMc39RYoU4ccff3yMET0eVapUMS/r/SCbNm2y6FmytFSqVIlnn32W6dOnm6d0ioiIiIjtyXBhlJSUREBAAPv27cNgMGRJQRQQEMDRo0czffy2bdseuL1Ro0Y0atQo0+fNjEe9lrT4+flZtBjB02bfvn1WPf9ff/1l1fOLiIiISM6Q4VXpHBwceP75561+wyoiIiIiIvK4ZGoqXc2aNRk8eDDlypWjePHiODo6pto/cODALAlORERERETkcchUYbR69WoMBgPh4eGEh4en2mcwGFQYiYiIiIhIjpKpwiitZ3pERERERERyokwVRgkJCWnuS05OxtXVNdMBiYiIiIiIPG6ZKoz8/PwwGAxp7v/v9DoREREREZEnWaYKo7Fjx6YqjFJSUoiMjOSHH34gJCQky4ITERERERF5HDJVGLVs2fKB25s3b87EiRN57bXXHikoERERERGRxynD32P0MCVKlODUqVNZeUoRERERERGry9LCaN++fcTGxmblKUVERERERKwu01/w+l+JiYncuHGD119//ZGDEhEREREReZwyVRi1b9/+vm3Ozs48//zz1KlT51FjEhEREREReawyVRhVrlyZwMDA+7YbjUZ++OEHGjdu/MiBiYiIiIiIPC6ZesbozTfffOD227dv88knnzxKPCIiIiIiIo9dhkaMVq5cyapVq0hISHjgdLqYmBhcXFyyLDgREREREZHHIUOFUVBQELdv3yYsLIxSpUrdt79cuXL6DiMREREREclxMlQYFS5cmM6dOxMVFcXgwYMf2ObYsWNZEpiIiIiIiMjjkqlnjO4WRSkpKSQkJJj/RERE0Llz5ywNUERERERExNoytSrduXPneP/99zl06BDJycmp9nl5eWVJYCIiIiIiIo9LpkaMRo8eTe7cuRk+fDj29vZ8+umntGvXDn9/f5YsWZLVMYqIiIiIiFhVpgqjv//+m2nTptGuXTvs7e1p1aoVn3zyCQ0bNuSrr77K6hhFRERERESsKlOFUXJyMrly5QLAwcGBhIQEADp06MDq1auzLjoREREREZHHIFOFUenSpfnyyy9JTk6mSJEibNq0CYCoqCiMRmOWBigiIiIiImJtmSqM3nrrLWbOnInRaKRVq1YMHTqU5s2b06pVK2rVqpXVMYqIiIiIiFhVpgqj2rVrs2PHDtzc3OjWrRsTJ06kcuXKhISEMG7cuKyOUaxo9uzZdOrUySrn3rlzJ97e3gD88ccf+Pr6mqddStYxmUzsPBnNsj9Ps/NkNCaTKbtDEhEREclxMrVcN0CBAgUASEpKokmTJjRp0iTLgpKstXnzZry9vSlRosR9+0JCQggJCbF6DFWrViUsLMyitqtWrSI4OJj8+fNbOaqcb03YWYZ8f4CTl2PN28oUcGdC00q08C2ejZGJiIiI5CyZXnzhyy+/JDg4mMqVKwNgNBoZOXKkRgSeQNOnT+fMmTP3bf/vd1A9CZKTkxk/fjxXr17N7lCeeGvCztJ24c5URRHAycuxtF24kzVhZ7MpMhEREZGcJ1OF0bx581iyZAmdOnUyT9uJi4vjwIEDTJ06NUsDlEfTrFkzjh8/TkhICF26dMHb25tly5ZRrVo11q9fz4wZM2jbti0AK1asoF69eqxcuZKaNWsSEBDA+PHjSUlJsaiviIgI2rdvj7+/P23atElVjP322294e3sTHx8PwJdffkndunWpUKECDRs2ZN26dQBUq1aN2NhYXnvtNWbOnAlAaGgojRo1omLFigQHB/Ptt9+azzt16lT69u3L3Llzeemll6hWrRoTJkww7zcajXz44YdUqVKFgIAAPv74Y3PxnpCQwKhRowgICKBatWr06tWLs2ef/GLiOk5ExSUxKHQ/KWlMm0sxmRi6/oCm1YmIiIhYKFNT6dauXcvs2bN58cUXmTZtGnBnat3UqVPp3r07Q4YMydIgJfNCQ0Px9vZm9uzZlC5dmnr16vH777+zfft2XF1dmTVrlrmtg4MDly5d4ujRo2zbto3jx4/TuXNnXnjhBVq2bJluX0OHDqVo0aLMnz+fqKgo+vXr98B2Bw4cYNGiRaxYsQIPDw/27t1LSEgINWvWZN26ddSrV49169ZRpkwZIiMjGTJkCDNnziQ4OJi9e/fSo0cPKlWqRNmyZXFwcODPP//E39+fn3/+mV9//ZU33niDZs2a4ePjw4wZMzh16hSbN28GoFevXsyYMYNBgwYxa9Ysjh07RmhoKHnz5mX69OmEhIQQGhqKnZ3lvzMwmUzExcVZ3P5RGI1GfrfzYPm+i5y5cvOhbU/8G8uW8HPUKFnwscSWk91dTVOramY95dZ6lFvrUF6tR7m1DuU1fSaTCYPBkG67TBVGUVFRlCtX7r7tJUqU0BSoHKBp06bkzp37gfvi4+MJCQnBycmJF198kTp16rBr1650C6NLly7x559/MmLECHLlykWZMmVo3rw5kyZNuq/tlStXsLOzw9XVFYPBQGBgIPv378fOzo7IyMhUbYsWLcrevXvJmzcvAIGBgRQoUIB//vmHsmXLAmBvb0+vXr2ws7Ojdu3auLu7c/r0aXx8fFi1ahVjxowxP680duxYbty4AcCyZcuYMmUKhQsXBqB///4sXryYQ4cO4efnZ3E+ExMTCQ8Pt7j9I7MrQazxtkVN9x85QX7jJSsH9PSIiIjI7hCeWsqt9Si31qG8Wo9yax3K68M5OTml2yZThdEzzzzDsWPHzCuO3fXrr79SsKB+O/2kK1KkSJr78uTJk2rRAw8PD/766690zxkdHX3fuR+02ANAUFAQfn5+1KlTh8DAQOrUqUOzZs3MXxr8XwsWLGD9+vXExMSQkpJCQkJCqmfZPDw8Uo3wODs7c/v2ba5fv87169cpWrSoed/dYur69etcu3aNPn36pPoNQkpKClFRURkqjBwdHfHy8rK4/aMwGo1Ui7hA0aIuLLegfeWyXvhoxChdRqORiIgISpYsiaura3aH81RRbq1HubUO5dV6lFvrUF7Td+LECYvaZaowat26Ne+88w7dunUjJSWFH3/8kUOHDrFs2TK6d++emVPKY+TgkPbb/t9nUkwmE87Ozume80GLbqS1uIOTkxMzZ87kyJEjbN++nSVLlvD111/z3Xff3dd2zZo1LFq0iFmzZlGtWjXs7OyoU6dOqjbpTXt70DNSd49ZunQpvr6+Dz0+PQaDIc2izhrykkDAi6UZXcD9voUX7uVV0J36PsUsGjqWO1xdXR/re2lLlFvrUW6tQ3m1HuXWOpTXtFl6L5SpxRf69u1Lu3btmD17NomJibz77rusWbOGN998kzfffDMzp5QnRGxsbKrpkFFRUeapZg9TqFAhc/u70hrSTUxM5ObNm5QtW5a+ffuydu1a4M6I43+FhYUREBBA9erVsbOz4/Lly1y6ZNnUsLx585InT55Ui0D8888/hIaG4u7uTr58+Th27FiqY/47le9JZTAYmNC0EnZp/EO3MxgY36SSiiIRERERC2WoMBowYABw56asZ8+e7N69m7fffpt9+/axe/duevbsmaGH1uXxcHZ25syZM8TGpj26cJejoyOzZs0iLi6Of/75hx07dlCvXr10j/P09KRMmTLMnz8fo9HI8ePH+f777x/Ydt68efTu3ZuLFy8CcPr0aa5fv46npycuLi7AnaIqNjYWDw8Pjh49yrVr14iJiWHYsGF4eHiYp+6lp3Xr1sybN4+YmBiuXr3K6NGjzcVQ+/btmTNnDidPniQxMZEFCxbQunXrHPPwYgvf4qzoGoRXQfdU270KurOia5C+x0hEREQkAzI0lW7btm33bZs7dy5vv/12lgUkWa99+/ZMmjSJ2rVrp9s2b968lC5dmgYNGpCUlETHjh0tKozgzvclffDBBwQGBlKmTBl69+7N0KFDSUxMTNWuR48eREdH06pVK27dukXhwoUZNGgQPj4+ADRs2JCBAwfSrl073n77bX777Tfq1KmDp6cnI0aM4ODBg8ycOdM8SvUw77zzDteuXaNRo0Y4ODjQoEED82p5ISEh3Lhxg9dff534+HjKli3L3Llzc9T83Ba+xWlevhi7TsUQdcNIkbyu1CxVSCNFIiIiIhlkMGXgi078/Pw4ePBgutskZ1q9ejWTJ09mz5492R1KjhEWFgbwyM8pWSouLo7w8HB8fHw0jzgLKa/Wo9xaj3JrHcqr9Si31qG8ps/S+7UMzXt70G+h9ZtpERERERHJ6TK1Kp3YnmbNmnH69Ok098+fP5+qVas+xohERERERLKOCiMxa9myZZpf5BoaGvqYoxEREREReXwyVBglJiYyaNCgdLdNnjz50SMTERERERF5TDJUGFWuXJmYmJh0t4mIiIiIiOQkGSqMvvnmG2vFISIiIiIikm30bawiIiIiImLzVBiJiIiIiIjNU2EkIiIiIiI2T4WRiIiIiIjYPBVGIiIiIiJi81QYiYiIiIiIzVNhJCIiIiIiNk+FkYiIiIiI2DwVRiIiIiIiYvNUGImIiIiIiM1TYSQiIiIiIjZPhZGIiIiIiNg8FUYiIiIiImLzVBiJiIiIiIjNU2EkIiIiIiI2T4WRiIiIiIjYPBVGIiIiIiJi81QYiYiIiIiIzVNhJCIiIiIiNk+FkYiIiIiI2DwVRiIiIiIiYvNUGOVQa9euJTg4OLvDyFIzZsygbdu22R1GjmMymdh5Mpplf55m58loTCZTdockIiIikuM4ZHcAYrlVq1YRHBxM/vz5ad68Oc2bN8/ukCSbrQk7y5DvD3Dycqx5W5kC7kxoWokWvsWzMTIRERGRnEUjRjlEcnIy48eP5+rVq9kdSqaYTCZSUlIeW3/JycmPra/ssibsLG0X7kxVFAGcvBxL24U7WRN2NpsiExEREcl5bLowOnv2LN26daNChQrUqVOHxYsXA3Dx4kX69u1LQEAAtWrV4qOPPuLmzZsA/PLLL1SuXJldu3bRoEED/P39eeONN8z7ExMTGTVqFNWrV6dOnTps2LCBhg0bsmLFCgASEhIYNWoUAQEBVKtWjV69enH27P/fwH755ZfUrVuXChUq0LBhQ9atWwdAtWrViI2N5bXXXmPmzJmsXr2aGjVqmI87fvw4Xbp0oWrVqgQHBzNx4kQSEhIAWLlyJc2aNWPt2rXUrl2bSpUq8f7771tcPKxevZqGDRtSsWJF6taty/z58wGIjIzE29ubkydPmttOmjSJzp07A/Dbb7/h7+/PN998Q6VKlThw4AAA8+bNo1atWvj7+9OzZ0/Onz+fqr9vv/2WmjVrUrVqVSZMmGDefvXqVfr160dAQABVqlShd+/eREVFmfd7e3uzcOFCatasyZdffgnA8uXLCQ4OpkqVKnz22WcMHz6cQYMGmY9ZsmQJwcHB+Pv707JlS/bs2WNRTrLTdZyIiktiUOh+UtKYNpdiMjF0/QFNqxMRERGxkE1PpRs4cCAVK1bkiy++4PTp03Ts2JESJUowdepUXnjhBbZs2cLt27d55513+Pjjj5kyZQr29vYYjUZ++OEHvvvuO27evEnz5s1ZtWoV3bp145tvvmHjxo0sWrSIIkWKMGzYMGJiYnBwuJPqWbNmcezYMUJDQ8mbNy/Tp08nJCSE0NBQ/vrrLxYtWsSKFSvw8PBg7969hISEULNmTdatW0e9evVYt24dZcqUYfXq1ebrSEhIoEePHrRs2ZI5c+Zw6dIlevfujb29PYMGDcLe3p4LFy5w+PBhfvzxR06fPk2bNm1o2LAh9evXf2iOLl68yKhRo1i+fDne3t4cPnyYHj16UL16dfLkyZNujhMTEzlz5gx79+7FycmJHTt28NVXXzF//nzKlCnDqFGjGDhwIMuXLwfgzJkz3Lx5k23btvHrr7/yxhtv0KxZM3x8fJg4cSJXr15ly5Yt2Nvb079/f8aOHcuMGTPM/W3dupXQ0FDy5ctHWFgYH3/8MdOmTaNevXrMmzePVatWUbduXQD27NnD7NmzmTNnDmXLlmXbtm2EhISwadMmPDw8LP4cmUwm4uLiLG7/KIxGI7/bebB830XOXLn50LYn/o1lS/g5apQs+Fhiy8mMRmOqvyXrKLfWo9xah/JqPcqtdSiv6TOZTBgMhnTb2WxhdOTIEcLCwvjqq69wcXHBx8eHmTNn4urqyj///MOcOXNwd3fH3d2dN954g379+plHYJKTk+nZs6d5f+XKlTl9+jQAP//8M6+++iovvPACAAMGDGDjxo3mfpctW8aUKVMoXLgwAP3792fx4sUcOnSIK1euYGdnh6urKwaDgcDAQPbv34+dnR2RkZFpXsvOnTuJi4vjrbfewsnJieLFi9OpUye+/vpr8+jIrVu3ePfdd83XWqZMGU6dOpVunq5evUpKSgq5c+cGoFy5cvzyyy/pxnRXYmIibdu2xdnZGbgzevXqq6/i4+Njzs/vv/9unmbn6OhI7969MRgM1K5dmzx58nD69Gl8fHwYOXIkSUlJ5MqVC4B69erxxRdfpOqvQYMG5M+fH4AdO3bwwgsv8MorrwDQp08fli1bZm67dOlSWrduTfny5c3HLlmyhA0bNtCrV690r+3eawwPD7e4/SOzK0Gs8bZFTfcfOUF+4yUrB/T0iIiIyO4QnlrKrfUot9ahvFqPcmsdyuvDOTk5pdvGZgujc+fO4ebmRr58+czbXnrpJX766Sdy5crFs88+a95erFgxEhMTiY6ONm8rWrSo+WdnZ2fi4+MBiI6OTrVaXIkSJcxFxfXr17l27Rp9+vRJVbWmpKQQFRVF3bp18fPzo06dOgQGBlKnTh2aNWtmLgTSEhkZSZEiRVK94cWKFePChQvmgiNfvnzmOP4b88P4+PjQunVrXnnlFapUqULt2rVp3rw5zzzzTLrH3lWkSBHzz2fPnqVKlSrm1wUKFKBRo0bm1x4eHqly4+TkxO3bd4qAEydOMHHiRMLDw4mLiyMlJSXV+/ffvqKjo/H09DS/NhgMlC1bNlUs27dvN08NhDu/UXj++ectvja4U8x5eXll6JjMMhqNVIu4QNGiLiy3oH3lsl74aMQoXUajkYiICEqWLImrq2t2h/NUUW6tR7m1DuXVepRb61Be03fixAmL2tlsYQRkeDGAe2/Y7ewe/HjWg57puNv27t9Lly7F19f3gcfPnDmTI0eOsH37dpYsWcLXX3/Nd999l6E4HySteC3x8ccf07VrV7Zt28aPP/7I3LlzWbly5QPbPuj6HR0dU71+WN4fNsz59ttv4+/vz08//USePHlYtWoVU6dOTdXm7pTFu7H893z35sHOzo6BAwfSs2fPNPu0hMFgSLd4zUp5SSDgxdKMLuB+38IL9/Iq6E59n2IWDR3LHa6uro/1vbQlyq31KLfWobxaj3JrHcpr2iy9F7LZxReKFStGXFwcly79/zSjLVu2kC9fPuLi4oiJiTFvP3PmDM7Ozubpbw9TsGBBLl68aH597tw5YmPv3Ly6u7uTL18+jh07luqYu1PSEhMTuXnzJmXLlqVv376sXbsWgF9//TXdazl//rx5qh/cGU719PR8pIII7hQxN27coESJEnTv3p1ly5bx/PPP89NPP+Hi4mKO+657rz2tWO8d6r1y5Qrz589PdY4HuXLlCufPn6dXr17mZ5uOHDny0GP++16YTKZUU96KFy9+33tx4cKFHLFggcFgYELTStil8Q/dzmBgfJNKKopERERELGSzhVHZsmUpX74806ZNIy4ujmPHjvHRRx9hNBrx9fVlypQp3Lx5k6ioKL744gteffXV+0Y+HuSll15iw4YNnDp1ips3bzJt2rRU1Xv79u2ZM2cOJ0+eJDExkQULFtC6dWuMRiPz5s2jd+/e5pv506dPc/36dTw9Pc1FSEREhLnQuqtWrVq4u7sza9Ysbt++zcmTJ1m8eHGWfM/Rhg0baNOmjfl5pKioKC5evIinpyf58+cnT5487N+/H4Bjx47x22+/PfR8rVu3ZuPGjRw8eJCEhARmzZrFpk2b0s1tnjx5yJUrF7///jvJycmsXbuWsLAwbt68ya1btx54zEsvvcThw4fZvn07iYmJzJkzJ1Xx2K5dOzZu3MiOHTtITk5m7969NGnShLCwsIykKNu08C3Oiq5BeBV0T7Xdq6A7K7oG6XuMRERERDLApqfSTZ48mY8//pjAwECeeeYZ3nrrLYKCgihVqhQjRoygRo0a5M2bl5dffpn33nvPonP27NmTiIgI2rZtS/78+fnwww/NixUAhISEcOPGDV5//XXi4+MpW7Ysc+fOxdXVlR49ehAdHU2rVq24desWhQsXZtCgQeaFCho2bMjAgQNp165dqmdlnJycmDVrFqNHj+brr7/m2WefpXnz5rz55puPnKMmTZpw+vRpevTowdWrVylQoABt2rQxr2Y3YsQIJk2axMKFC/Hx8aF9+/YPXfK6bt26vPHGG4SEhHDr1i0qVarElClT0o3DwcGBTz75hM8++4wZM2bQuHFjZs6cSceOHWncuDE7duy475hq1aoxYMAAhg8fTlJSEt26daNq1armUZQaNWowePBgRo4cyaVLlyhatCgjRozAz88vk9l6/Fr4Fqd5+WLsOhVD1A0jRfK6UrNUIY0UiYiIiGSQwZQT5g3lMAkJCeaFEBITE6lYsSJfffUVgYGB2RyZ7bn3vQDo1KkTVapUoX///lly/rujS2k9M5bV4uLiCA8Px8fHR/OIs5Dyaj3KrfUot9ahvFqPcmsdymv6LL1fs9mpdNaydu1a6taty+nTp0lISGDOnDnkyZPnsd04y/+LjIykYsWKbNu2jZSUFHbt2sWBAweoU6dOdocmIiIiIk8Ym55KZw3NmjXj+PHjdOrUibi4OF544QVmz56Nm5tbdof2QM2aNTN/B9ODzJ8/n6pVqz7GiLKOp6cnY8eOZcKECQwYMIDnnnuOUaNGUbFixewOTURERESeMCqMspidnR3vv/8+77//fnaHYpHQ0NDsDsGqmjdvniWLUIiIiIjI001T6URERERExOapMBIREREREZunwkhERERERGyeCiMREREREbF5KoxERERERMTmqTASERERERGbp8JIRERERERsngojERERERGxeSqMRERERETE5qkwEhERERERm6fCSEREREREbJ4KIxERERERsXkqjERERERExOapMBIREREREZunwkhERERERGyeCiMREREREbF5KoxERERERMTmqTASERERERGbp8JIRERERERsngojERERERGxeSqMRERERETE5qkwEhERERERm6fCSEREREREbJ5DdgcgIo/GZDKx61QMF27EUSRPLmqVLoTBYMjusERERERyFBVGIjnYmrCzDPn+ACcvx5q3lSngzoSmlWjhWzwbIxMRERHJWTSVzkb88ccf1K5dm4CAgOwORbLImrCztF24M1VRBHDycixtF+5kTdjZbIpMREREJOdRYWQjFi1ahK+vL7/++mt2h/LESk5O5uuvv87uMNJ1HSei4pIYFLqfFJPpgW1STCaGrj+AKY39IiIiIpKaptLZiGvXrlGhQgXs7CyvhU0mEyaTKUPH5GSHDx/mq6++onv37hk6zmQyERcXZ6WoUjMajfxu58HyfRc5c+XmQ9ue+DeWLeHnqFGy4GOJLSczGo2p/paso9xaj3JrHcqr9Si31qG8ps9kMln0/LXBpF8pP/U6derEvn37sLOzI1euXCxYsIAxY8Zw9OhRXFxcePnllxk2bBiOjo789ttvvPnmmwwcOJApU6Ywd+5cqlSpwpIlS5g3bx5Xr16lVKlSDBo0iBo1aljU/6FDh9Ls75dffuHtt9/ms88+Y/To0Vy/fp2uXbvy8ssvM3ToUCIjIwkKCmLy5Mk4ODiQkpLC559/ztq1a7ly5QrPP/88H374IX5+fgB4e3szd+5cgoKCAFi6dClz585l27ZtnDlzhgYNGrBgwQLGjh3LuXPnKFeuHFOmTCEmJoYOHTqQlJSEk5MTc+fOpXr16uleW1hYGAkJCZl/czLhJ7sSHIy4wPLdf6fbdkyNorxcIu9jiEpERETkyeXk5ISvr+9D22jEyAYsXryYzp07U6FCBd577z3q16/Pyy+/zDfffMOlS5do3749Xl5edO7cGYDExETOnDnD3r17cXJyYs+ePcyePZs5c+ZQtmxZtm3bRkhICJs2bcLDwyPd/vv3759mf/b29hiNRn799Vc2bdrEpk2bGDp0KCdPnmThwoVcuXKF1157jV27dlG3bl2WLFnCqlWr+OKLLyhZsiTffPMN3bp1Y8uWLeTPn/+hcdjb2wOwcOFC5s+fj7OzM23atGHevHl89NFHjB49msmTJ7Nnz54M5dfR0REvL68MHZNZRqORahEXKFrUheUWtK9c1gsfjRily2g0EhERQcmSJXF1dc3ucJ4qyq31KLfWobxaj3JrHcpr+k6cOGFROxVGNmjdunU4Ojri4OCAh4cHVapU4dChQ+b9iYmJtG3bFmdnZ+DOqEvr1q0pX748AA0aNGDJkiVs2LCBXr16PXJ/KSkpdOzYERcXF+rWrYvJZKJevXrkz5+f/Pnz4+npyZkzZwBYtWoVHTp0wNvbG4AePXrw1Vdf8fPPP9OyZUuLrr9Dhw48++yzAAQFBXH69GmLjkuLwWAgV65cj3SOjMhLAgEvlmZ0Aff7Fl64l1dBd+r7FNPS3Rng6ur6WN9LW6LcWo9yax3Kq/Uot9ahvKbN0nshFUY2aMeOHcyZM4ezZ8+SlJREUlISr7zySqo2RYoUMf989uxZtm/fzvz5883bTCYTzz//fJb199xzzwHg4uICQOHChc37XFxczNPVIiMjKVGihHmfnZ0dRYsWJTIy0qJYAIoWLWr+2dnZmfj4eIuPfVIYDAYmNK1E24U7H7gAg53BwPgmlVQUiYiIiFjINp6qF7MzZ87w/vvv06pVK3777TfCwsJo2rTpfe0cHR3NP9vZ2TFw4EDCwsLMfw4dOsSwYcOyrL//LvCQ0QUf0ioAHvQI3dNSLLTwLc6KrkF4FXRPtd2roDsrugbpe4xEREREMkAjRjbm8OHDuLq60qVLF+BO4XD06FFKly6d5jHFixfn2LFjqbZduHABDw+PdIuMzPT3MMWLFyciIsL8OikpicjISIoVKwbcGQFKTEw077948WKm+skpWvgWp3n5Yuw6FUPUDSNF8rpSs1Shp6b4ExEREXlcNGJkYzw8PLh16xb//PMPRqORMWPGYG9vT0xMTJrfedOuXTs2btzIjh07SE5OZu/evTRp0oSwsDCr9PcwrVu3ZunSpRw/fpzbt28zZ84cTCYTwcHBAJQsWZJ9+/YBd4qin376yeJzu7i4EBsbS3R0dI5a8tJgMBBUpjDt/EtSq3RhFUUiIiIimaDCyMZUrFiRjh070qVLFxo2bEipUqX48MMPOXbsGO+///4Dj6lRowaDBw9m5MiRVKxYkU8++YQRI0aYl8jO6v4epn379jRp0oSuXbtSo0YN9u7dy6JFi8iTJw8AH3zwAVu3bqVevXp89NFHdOrUiaSkJIvOXb16dTw9PWnUqBE///xzhmMTERERkZxL32Mk8gjujpqlty5+VomLiyM8PBwfHx+tPJOFlFfrUW6tR7m1DuXVepRb61Be02fp/ZpGjERERERExOZp8QV5JFWqVHnoctebNm1KtTy2iIiIiMiTSIWRPJK7Cx2IiIiIiORkmkonIiIiIiI2T4WRiIiIiIjYPBVGIiIiIiJi81QYiYiIiIiIzVNhJCIiIiIiNk+FkYiIiIiI2DwVRiIiIiIiYvNUGImIiIiIiM1TYSQiIiIiIjZPhZGIiIiIiNg8FUYiIiIiImLzVBiJiIiIiIjNU2EkIiIiIiI2T4WRiIiIiIjYPBVGIiIiIiJi81QYiYiIiIiIzVNhJCIiIiIiNk+FkYiIiIiI2DwVRiIiIiIiYvNUGImIiIiIiM1TYSQiIiIiIjZPhZGIiIiIiNg8FUbyUCdPnsTb25vIyEiL2i9YsIDKlSvzzjvvPFK/AwYMYOjQoRa1/ffff2nZsiUVKlQgLCyM4OBgli5d+kj9P+lMJhM7T0az7M/T7DwZjclkyu6QRERERHI0h+wO4Gm0atUqgoODyZ8/f3aH8th9/vnnvPPOO3Tt2vWx9blx40YuXbrE3r17cXFxeWz9Zpft527QftOPnLpyy7ytTAF3JjStRAvf4tkYmYiIiEjOpRGjLJacnMz48eO5evVqho9NSkqyQkSP17Vr1yhdujQGg+Gx9Xn9+nU8PDxwdXV9rP1mh9DD5/lgd2Sqogjg5OVY2i7cyZqws9kUmYiIiEjOpsLoEXz55ZfUrVuXChUq0LBhQ9atW0e1atWIjY3ltddeY+bMmQDs27ePtm3bUqlSJRo0aMBXX31lnvo0Y8YM+vTpw8CBA6lcuTIACQkJjBo1ioCAAKpVq0avXr04e9byG97Q0FAaNWpExYoVCQ4O5ttvvzXvmzp1Kn379mXu3Lm89NJLVKtWjQkTJpj3X758mV69euHv78+rr77KwYMHLeozISEBX19fAEJCQnjzzTfx8fEhNjYWgNu3b1O+fHk+++wz8zHTpk3j7bffBmDFihUEBwdTuXJlRo4cSUpKikX9/u9//2P27NkcPHgQX1/f++KNj49n+PDhBAYG4u/vz+uvv86xY8fM+0+fPk379u3x9/enY8eO/PDDD3h7e5OYmGhR/4+TyWRi+I9hpKQxay7FZGLo+gOaViciIiKSCZpKl0kHDhxg0aJFrFixAg8PD/bu3UtISAgLFy6kTZs2rFu3jjJlyvDvv//Ss2dPhg4dSqtWrTh+/Di9evXCzc2N9u3bA/DXX38xaNAgJk6cCMCsWbM4duwYoaGh5M2bl+nTpxMSEkJoaCh2dg+vZSMjIxkyZAgzZ84kODiYvXv30qNHDypVqkTZsmVxcHDgzz//xN/fn59//plff/2VN954g2bNmuHj48PYsWOJj4/n559/xmg0MmjQIIvy4eTkRFhYGN7e3syePZugoCDq1q3LX3/9Ra1atfjrr78oXrw4+/fvNx+zb98+XnnlFU6dOsXHH3/MzJkzCQoKYt26dYwZM4ZXXnkl3X779++Pvb09u3btYsWKFfftnzt3Lr///jvff/89efLkYfTo0QwdOpTVq1cD0K9fP0qXLs28efM4e/Ys/fv3B8DR0dGi64Y7BUtcXJzF7TNrd8Sl+0aK/uvEv7FsCT9HjZIFrR7P08RoNKb6W7KOcms9yq11KK/Wo9xah/KaPpPJZNGsIhVGmXTlyhXs7OzM07cCAwPZv38/Fy5cSNVu/fr1eHh40KFDBwBefPFFXnvtNTZs2GAujOzs7GjTpo35DVu2bBlTpkyhcOHCwJ2b/8WLF3Po0CH8/PweGlfRokXZu3cvefPmBSAwMJACBQrwzz//ULZsWQDs7e3p1asXdnZ21K5dG3d3d06fPo2Pjw9btmxh8uTJ5M2bl7x589KxY0f27duXqRwFBATw559/UqtWLfbt20fTpk355ptvSEhIwGAwcPDgQUaOHMmWLVvw9vamfv36ALRp04avv/46U33+V58+fejWrRtubm4ANGzYkO+++46kpCRiYmI4duwY48ePJ3fu3Pj4+PDKK6/wxRdfZKiPxMREwsPDsyTeh9kfcd2ydkdOkN94ycrRPJ0iIiKyO4SnlnJrPcqtdSiv1qPcWofy+nBOTk7ptlFhlElBQUH4+flRp04dAgMDqVOnDs2aNbuvXWRkJCVLlky1rVixYvz444/m1x4eHuai6Pr161y7do0+ffqkqmxTUlKIiopKtzCCOyvDrV+/npiYGFJSUkhISCAhISFVf/eOPDk7O3P79m2uXr3K7du3KVKkiHlfiRIl0k9GGgICAggNDQXgjz/+4K233mLfvn0cPHgQe3t78uXLR+nSpYmOjk7VJ0Dx4lmziMDFixcZN24cf//9N9ev3ykskpOTSU5O5uLFiwB4enqa27/44osZ7sPR0REvL68sifdhLrtegl/Op9uuclkvfDRilCFGo5GIiAhKliyJq6trdofzVFFurUe5tQ7l1XqUW+tQXtN34sQJi9qpMMokJycnZs6cyZEjR9i+fTtLlizh66+/Ztq0aRYdf2/R4+Dw/2/D3YJl6dKl5md2MmLNmjUsWrSIWbNmUa1aNezs7KhTp06qNmlNx7u3eLorOTk5wzHcVb16dUaPHk18fDyHDx/Gz8+PihUrsn//fuzs7AgMDEyzX0ufMUrPhx9+iJ2dHWvXruXZZ5/l119/pVu3bgDmZ3HufS/Sm6r4IAaDgVy5cmVJvA/zsk9xSufP/dDpdF4F3anvU+ypX4TCWlxdXR/Le2mLlFvrUW6tQ3m1HuXWOpTXtFl6X6TFFzIpMTGRmzdvUrZsWfr27cvatWsB2Lt3b6p2xYsX5/Tp06m2RUREUKxYsQee193dnXz58qVaIACw+HuEwsLCCAgIoHr16tjZ2XH58mUuXbJsWlX+/PlxdHQkKioqVayZ5eHhQYECBVi9ejVlypTBxcWFihUrcuDAAfbv328ujAoVKmQevbnrzJkzme73XmFhYXTu3Jlnn30WgKNHj5r33d127/X+888/WdKvNRgMBkY39MUujX/bdgYD45tUUlEkIiIikgkqjDJp3rx59O7d23xDf/r0aa5fv46/vz9wp6CIjY2lSZMmREdHs3TpUhISEvjrr78IDQ2lRYsWaZ67ffv2zJkzh5MnT5KYmMiCBQto3bq1RQ/VeXh4cPToUa5du0ZMTAzDhg3Dw8OD6OjodI91dHSkevXqfPPNN8TGxnLhwgWWLVtmYUYerHr16ixatIhKlSoBUKFCBQ4ePEhYWJi5MAoKCiI8PJzt27eTkJDAt99+S0xMzCP1e9dzzz3HH3/8QXJyMrt372br1q0AREdHU7x4cTw9PZk/fz5xcXEcPnyYzZs3Z0m/1tKsXFHG1fSkTP7cqbZ7FXRnRdcgfY+RiIiISCZpKl0m9ejRg+joaFq1asWtW7coXLgwgwYNMi/dPXDgQNq1a8eHH37IzJkzmTBhAuPGjcPDw4N+/frRvHnzNM8dEhLCjRs3eP3114mPj6ds2bLMnTvXonmj7du357fffqNOnTp4enoyYsQIDh48yMyZMylUqFC6x48ZM4YhQ4YQFBSEh4cH/fv3Z//+/Zn+jqWAgABWrFjBe++9B0CePHnMX3x7N54KFSowbNgwRo4cyY0bN2jatCmNGzfm9u3bmerzXsOHD+fjjz9mxYoV1KpVi2nTptGnTx/atGnDhg0bmDZtGh999BEvvfQSlStXplevXnzwwQeP3K811S2Wh74vV2P/xZtE3TBSJK8rNUsV0kiRiIiIyCMwmPSlJ2LDTCYTSUlJ5uW5v/vuO6ZPn86OHTssOj4sLAwgU8+DZUZcXBzh4eH4+PhoHnEWUl6tR7m1HuXWOpRX61FurUN5TZ+l92uaSic2rVu3bnzwwQcYjUZiYmJYsmTJfYtViIiIiMjTT1PpcpCDBw/SsWPHNPcXKVIk1TLgT0Pfb775Jnv27Elz/+jRox86LTE9o0eP5uOPP6ZGjRq4uLhQu3Zt87Q/EREREbEdKoxyED8/P/NQoK30ndEvW82o4sWLs2DBAqv2ISIiIiJPPk2lExERERERm6fCSEREREREbJ4KIxERERERsXkqjERERERExOapMBIREREREZunwkhERERERGyeCiMREREREbF5KoxERERERMTmqTASERERERGbp8JIRERERERsngojERERERGxeSqMRERERETE5qkwEhERERERm6fCSEREREREbJ4KIxERERERsXkqjERERERExOapMBIREREREZunwkhERERERGyeCiMREREREbF5KoxERERERMTmqTASERERERGbp8JIRERERERsngojERERERGxeSqMRERERETE5jlkdwAiknEmk4mdJ6O5cCOOInlyUat0IQwGQ3aHJSIiIpJjqTASyWG2n7tB+00/curKLfO2MgXcmdC0Ei18i2djZCIiIiI5lwojsarg4GCio6Oxs7sza9PJyYnnn3+eAQMGEBAQwNChQ4mPj2fq1KmpjouPj8fPz49FixYREBCQbj+dO3dm//792NvbA5AnTx4qVapEr169qFChQqq2p06dYtasWezdu5ebN2+SP39+6tSpw1tvvUXBggWz6MqtI/TweT7YHUmKKfX2k5djabtwJyu6Bqk4EhEREckEPWMkVjds2DDCwsIICwtjz549NGrUiDfeeIMzZ85kaT89evQw97NmzRp8fX3p2rUrGzZsMLcJDw+nTZs2FCpUiDVr1vD333+zZMkSbt++TYsWLYiOjs7SmLKSyWRi+I9h9xVFd6WYTAxdfwCTKY0GIiIiIpImjRjJY+Xi4kLXrl1ZsmQJu3fvtlo/hQoV4o033sDOzo4RI0YQFBSEu7s7o0aNombNmgwZMsTctkiRIowbN44OHTowadIkPvvsswz1ZTKZiIuLy+pLuM/uiEupps89yIl/Y9kSfo4aJZ/ska8njdFoTPW3ZB3l1nqUW+tQXq1HubUO5TV9JpPJomexVRhJtkhOTsbBwfofv65du/K///2P3bt3U7VqVQ4cOMCSJUse2LZz5858+OGHJCUlZSi2xMREwsPDsyrkNO2PuG5ZuyMnyG+8ZOVonk4RERHZHcJTS7m1HuXWOpRX61FurUN5fTgnJ6d026gwksfq1q1bLFu2jKtXrxIUFMSff/5p1f4cHR0pVqwY586dw8PDA4CSJUs+sG2pUqUwGo1cvnyZwoULZ6gPLy+vrAj3oS67XoJfzqfbrnJZL3w0YpQhRqORiIgISpYsiaura3aH81RRbq1HubUO5dV6lFvrUF7Td+LECYvaqTASq/v0008ZO3YsAM7Ozvj4+DBv3jxzoWJtBoMBJycn8yhQcnLyA9ulpKRk+vy5cuXKdHyWetmnOKXz537odDqvgu7U9ymmpbszydXV9bG8l7ZIubUe5dY6lFfrUW6tQ3lNm6X3RSqMxOqGDRtGhw4dHrjPycmJ69fvnyJ269adm/9H/c3HzZs3OXfuHKVKlcLT0xN7e3tOnTr1wBGhiIgI3NzcntiV6QwGA6Mb+tJ52d4HLsBgZzAwvkklFUUiIiIimaBV6SRbeXl5cfTo0ftGcQ4cOICzszOlS5d+pPN/++235MmTh8DAQPLly0dgYCALFix4YNtvvvmGxo0bm5f8fhI1K1eUcTU9KZM/d6rtXgXdtVS3iIiIyCNQYSTZ6rXXXiM5OZlRo0bx77//kpCQwN69exkzZgzdu3fHzc0tU+e9cuUKX3zxBbNmzWLMmDHmB+4++ugj/vzzT0aNGmVemvvChQt8+OGHxMTEMGDAgCy7NmupWywPf/ZvyPaQBnzbqRY/v9WAI0NfU1EkIiIi8gg0lU6yVd68eVmyZAmTJ0/m1Vdf5fbt2xQrVoyePXvSsWPHDJ1r/vz5LFy4EJPJRO7cufH392fhwoVUrFjR3KZ06dJ89913zJw5k9atW3Pjxg0KFChAcHAwq1atIn/+/Fl8hdZhMBgIKmP5AhEiIiIi8nAqjMSqtm3blm4bT09Ppk6d+kj9fPPNNxa3LVasGBMmTHik/kRERETk6aKpdCIiIiIiYvM0YiRPvGbNmnH69Ok098+fP5+qVas+xohERERE5GmjwkieeKGhodkdgoiIiIg85TSVTkREREREbJ4KIxERERERsXkqjERERERExOapMBIREREREZunwkhERERERGyeCiMREREREbF5BpPJZMruIERyqgMHDmAymXBycnos/ZlMJhITE3F0dMRgMDyWPm2B8mo9yq31KLfWobxaj3JrHcpr+hISEjAYDFSqVOmh7fQ9RiKP4HH/B2QwGB5bEWZLlFfrUW6tR7m1DuXVepRb61Be02cwGCy6Z9OIkYiIiIiI2Dw9YyQiIiIiIjZPhZGIiIiIiNg8FUYiIiIiImLzVBiJiIiIiIjNU2EkIiIiIiI2T4WRiIiIiIjYPBVGIiIiIiJi81QYiYiIiIiIzVNhJCIiIiIiNk+FkUgOERkZSc+ePalYsSKBgYF89tlnpKSkZHdYOdKuXbt46aWXGDBgwH37NmzYQMOGDfH19aVJkybs2bMnGyLMmSIjI+nbty/VqlUjMDCQwYMHc/36dQDCw8Np3749fn5+BAUF8fXXX2dztDnHkSNH6NatG1WqVKF69eq8++67xMTEAPDrr7/SrFkzfH19efnllwkNDc3maHOusWPH4u3tbX6t3Gaet7c35cuXx9fX1/xn9OjRgPKaFWbPnk3NmjXx9/enW7dunDt3DlBus4RJRJ54KSkpptdee800aNAg07Vr10wnTpww1a1b1zR//vzsDi3H+fLLL00NGjQwtW/f3tS/f/9U+8LCwkwvvviiacOGDSaj0WhasWKFqUKFCqaoqKhsijZnadKkiWno0KGmmzdvmqKjo00tW7Y0ffjhh6a4uDhTjRo1TBMmTDDdvHnT9Oeff5qqVKli+vHHH7M75CdefHy8KTAw0DRz5kxTfHy86fLly6ZOnTqZ+vbta7p48aKpQoUKpoULF5ri4uJMW7duNfn6+pr+/vvv7A47xzl8+LCpWrVqphdeeMFkMpmU20f0wgsvmM6dO3ffduX10S1ZssTUqlUrU2RkpOnatWumoUOHmkaOHKncZhGNGInkAGFhYRw9epRhw4aRN29eypQpQ+/evVm2bFl2h5bjODs7s2rVKkqUKHHfvu+++46goCAaN26Mi4sLbdq04YUXXmDdunXZEGnOEhsbS/ny5XnvvffInTs3hQoVomXLlvzxxx/8/PPPJCYmMmjQIHLnzk3FihVp164dy5cvz+6wn3hGo5EBAwbQp08fnJycyJ8/Pw0bNuTEiRN8//33lChRgi5duuDq6kpwcDD16tVj1apV2R12jpKSksKIESPo1q2beZtyax3K66ObN28ew4cPp2jRouTNm5dx48bx8ccfK7dZRIWRSA5w+PBhihYtSr58+czbXnzxRSIiIrh582b2BZYDdenSBXd39wfuO3z4MC+++GKqbeXKlePQoUOPI7Qczf3/2rn3oKirPo7jbxBQA0Ugxwtewxm0WgmDoVFSE3OQUSELEoHMTDMlckwdxrsjBSook2lCWZo3TGVEmQYNG8eMDNRK8YYKqYiU5JLhDRCeP3zYaVNrFZ7hwf28/lrO+XH2u5/5zSxfztlt1Yr4+Hjc3NxMYyUlJbi6unL8+HF69uxJs2bNTHPK1TLOzs6EhoZiZ2dHbW0thYWFpKenM3ToUN2vDSQtLY0WLVowfPhw05iyrb+kpCT8/f3x9/dnzpw5XLt2TbnW06+//kppaSnnzp1jyJAh+Pn5MWXKFIxGo7JtIGqMRJoAo9GIs7Oz2Vjdz0ajsTFKeiQZjUaz5hPu5HzlypXGKagJO3r0KOvWrePtt9++5/3bpk0bysvL9Tk5C128eJGnn36aoKAgDAYD77777n1z1f1qubKyMlasWMH8+fPNxpVt/dR9FjYrK4u1a9fy008/MX/+fOVaT6WlpdjY2JCdnc3mzZvZvn07Fy9eZM6cOcq2gagxEmkCbGxsGrsEq3C/nJX/gzl06BDjxo3jvffeY8CAAcqvAbi7u5Ofn09WVhaFhYVMnz5d92sDiI+PJywsjCeeeMJsXNnWz+bNmwkLC8PJyQkPDw+mTZtGZmYm1dXV97xeuVqmqqqKqqoqpk+fjouLCx06dCAmJobs7Gzdsw1EjZFIE+Dq6kp5ebnZWN1OkaurayNU9GhycXG5awfOaDQq4wfwzTffMGHCBGbNmsWYMWOA+9+/Li4u2NrqbchSNjY2dOvWjRkzZpCZmYmdnd09c9X9apnvv/+e/Px8Jk6ceNeci4uLsm1AnTp1oqamBltbW+VaD3UnGpycnExj7u7u1NbWUlVVpWwbgN6RRJoAg8FASUmJ2R/tR44coUePHjg6OjZiZY8Wg8HAsWPHzMaOHj1K7969G6mipuXw4cPExsby4YcfEhwcbBo3GAycOnXK7L/FR44cUa4WyM3NZfDgwWbZ1R0/7Nu37133q3K13I4dOygtLaV///74+fkxcuRIAPz8/PD09FS2D+nEiRMsXrzYbKyoqAgHBwcGDhyoXOuha9euODk5mWV48eJF7OzslG0DUWMk0gT06tWL3r17ExcXx9WrVzl16hSpqalEREQ0dmmPlNDQUL777ju++uorbt68ybp16zh//jwhISGNXdr/verqambPns2MGTPo16+f2Vz//v1xdHQkKSmJa9eukZuby5dffqn71wJPPvkkN27cICkpiRs3bnDlyhWWL1+Oj48Pw4cP5+LFi6xZs4YbN26QlZXFvn37ePXVVxu77CYhNjaWXbt2kZGRQUZGBqmpqQBkZGQwbNgwZfuQ3Nzc2LRpE2vWrKGqqoqioiKSk5MJDw9nxIgRyrUe7O3tCQ0NJTExkdLSUi5fvsyKFSsIDg4mJCRE2TYAm9ra2trGLkJE/l1paSlz587lhx9+wNHRkdGjRxMdHd3YZTU5BoMBwPQfeDs7O+DOzhDA7t27SUpKoqSkBA8PD2bPno2Pj0/jFNuEHDx4kIiICBwcHO6ay8rK4vr168ydO5djx47h5ubGhAkTCA8Pb4RKm54TJ06waNEi8vPzsbOzw8/Pj5kzZ9KuXTsOHjzIwoULKSwspGPHjkybNo0XX3yxsUtukoqLiwkICODUqVMAyrYe8vLySExMpKCgABcXF4KCgoiJicHBwUG51lNlZSUJCQlkZmZia2vLoEGDmDlzJk5OTsq2AagxEhERERERq6ejdCIiIiIiYvXUGImIiIiIiNVTYyQiIiIiIlZPjZGIiIiIiFg9NUYiIiIiImL11BiJiIiIiIjVU2MkIiIiIiJWT42RiIiIiIhYPTVGIiIi/0Pbt2/HYDBQWVlp0fXLly9n0KBB950vLi7G09OTffv2NVSJIiKCGiMRERHGjRtHeHj4fefnzp3LCy+8wO3btx947ZCQEI4ePYqDg0N9SmxQsbGxhIWFNXYZ95SRkcG5c+cauwwRsUJqjERExOpFRkZy+PBhTp48eddcRUUFO3fuJDw8nGbNmj3QutXV1Q1V4iOvqqqK2tpa4uPj1RiJSKNQYyQiIlZvwIABdOnShY0bN941l5GRQU1NDWFhYfzyyy+89dZb+Pn54e3tzciRI9m/f7/p2uXLlxMcHMxHH32Et7c3u3btIj09HU9PT27dugXwr2vU2b59OwEBAXh7exMVFcX58+fvWXtNTQ0rV65k0KBBeHl5ERgYSEpKygM1ZeHh4cTFxZGQkICvry9+fn6kpqZSUFDAK6+8gpeXFyEhIZw+fdr0Gjw9PcnMzCQ8PJxnnnmGIUOGsG3bNtOa1dXVfPzxxwQGBuLt7U1QUBCrV6+mpqYGgPT0dHx8fNiyZQu+vr6kpKRgMBgwGo1MmjSJyMhIi/JatmwZL730Env27GHo0KF4eXkRHBzMjz/+aLqmrKyMKVOm4OPjg4+PDxMnTuTChQum+by8PEaNGoWXlxf+/v7ExMRQUlJicX4i8mhQYyQiIlbP1taWiIgIdu7cSUVFhdlcWloaw4YNo02bNsTExGBjY0N2dja5ubk8//zzvPPOOxiNRtP1paWl/PHHHxw4cIChQ4fe9VyWrPH7779z4MABtm3bxp49ewCYNGnSPWv//PPPSU9PZ8WKFRw+fJglS5awYcMGVq9ebfHrt7OzIzMzk6eeeoqcnBzGjBnDsmXLSExMZPny5aZGJDk52XQ9QEpKCnFxceTm5hIREcHMmTM5cuQIAKtWrWLDhg3Ex8eTm5vLggULWLVqFampqabnrays5NChQ+zdu5dJkyaRlZUFwMqVK1m/fr1FednZ2VFcXEx2djZpaWkcOHCAxx9/nHnz5pmeZ/r06dy4cYOvv/6avXv34uDgwMSJE6mtreXSpUuMHz+e4OBg8vLy2LFjB/b29owfP/6hjk6KSNOlxkhERAR4+eWXgTs7NXXy8vIoKCggKioKgM2bN7Ns2TJatWqFvb09ISEhXL9+nYKCAtPvlJeXM3nyZJo3b46t7d1vs5ascevWLaZPn06bNm1wdXXlzTff5PTp0/c8YvbZZ58xduxYevXqRbNmzTAYDIwZM4a0tLQHev2dOnVi+PDh2NvbM2TIEGpqaggMDKRDhw60atWKvn37UlhYaPY7I0eOxMPDAwcHB6KionBzczM1cuvWrWP06NF4e3tjb2+Pr68vwcHBZrtKt27d4rXXXsPR0fGeWVma159//klsbCzOzs60bNmSgIAAzp49S01NDWfPniUnJ4fo6GhcXFxwcnJi1qxZTJ48mcrKSjZs2ICHhwfh4eE4ODjg6urKrFmzOHPmDHl5eQ+UoYg0bXaNXYCIiMj/g1atWhESEkJaWprpGNemTZvw9fWlZ8+eAOTm5rJq1SqKiorMdpbqjskBODs706ZNm/s+j6VruLm5mX7u1KkTAJcuXTI9hjsNQVlZGfHx8SQkJJjGa2trgTs7MpZ+6YO7u7vpcYsWLQDo0KGDaaxly5ZmNQJ4eHiYHtva2tKxY0cuXbrE1atXKS8vN5sH6N69O+vXrzcdpwPo0qXLP9ZlSV4uLi44Ozub1V9dXU1VVRVFRUUAdO7c2TTfrl07goKCACgqKuL48eMYDAaz563biRIR66HGSERE5L8iIyPZuHEjubm5eHh4sHv3bpKSkoA7X5MdHR1NWFgYKSkptG7dmgsXLjB48GCzNezt7e+7vqVr/H33pK6RaN68udm4jY0NAIsXLzb9of+w7rVjc79dnDp1Ddhff27RooWprr/P/7UhqvO/yOte7vXccCdDf39/Pvnkk39dQ0QebTpKJyIi8l8eHh7069ePrVu3smPHDtq2bWv6Izw/P5/KykpiYmJo3bo1AEePHn2g9S1dw2g0mn3mqO6LF/66gwPg5ORE27ZtOXbsmNl4WVkZ169ff6DaHkbdbgzcaTxKSkpMR+/c3Nw4c+aM2fVnz56la9euFjUy0DCZd+vWDcDsGODly5dZvXo1FRUVdO/enVOnTpl9WcXt27e1WyRihdQYiYiI/EVkZCTZ2dmkp6ebfUV33VGz/fv3U1NTQ05OjunzSJcuXbJobUvXaN68OYmJiVy9epUrV67w6aef0qdPH9q3b3/Xmq+//jppaWl8++23VFdXU1hYyBtvvMGiRYseNgKLbdmyhZMnT1JZWcnatWsxGo2mL5yIiopi06ZN/Pzzz1RVVZGTk8POnTsZNWrUfdd77LHHgDtNTHl5eYNk3qNHD5577jmSk5O5fPky165dIykpia1bt+Lo6Eh4eDjl5eUkJiZSUVFBRUUFiYmJhIaGcu3atXqkIyJNjY7SiYiI/MXAgQNxc3Pj3LlzhIaGmsYNBgPR0dEsXLiQuXPn0rdvXxISEvjggw9ISEgwHR/7J5asUV1djbu7O3369CE4OJjy8nK8vLx4//3377nm2LFjuXnzJvPmzeO3337DxcWFoKAgpk6d2mCZ3E9UVBQLFizg+PHjtG/fnqVLl5p2aMaPH8/169eJjo7m6tWrdO7cmalTpzJ69Oj7rufq6sqIESNYunQpO3bsID09vd6ZAyxZsoS4uDgCAwOxsbHh2WefJSUlBRsbGzp27EhqairJycn069cPe3t7vLy8WLNmDY6Ojg0Rk4g0ETa1fz8ALCIiIvIPiouLCQgI4IsvvsDPz6+xyxERaRA6SiciIiIiIlZPjZGIiIiIiFg9HaUTERERERGrpx0jERERERGxemqMRERERETE6qkxEhERERERq6fGSERERERErJ4aIxERERERsXpqjERERERExOqpMRIREREREaunxkhERERERKzefwAE45TuNMZY2wAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Metrics for Linear Regression\n",
    "plot_model(lr, plot='feature')\n",
    "plot_model(lr, plot='error')\n",
    "plot_model(lr, plot='learning')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0YAAAHUCAYAAAAeFTh5AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjYuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/P9b71AAAACXBIWXMAAA9hAAAPYQGoP6dpAACNRklEQVR4nOzdd3xO9///8ceVHRIUpbFH2kgrIVaaIiXUql2z9irSUqNFW6NoFTVqtqoUpQhtSa2qUetTWqNExRaESNRKIpfM6/eHn+srFTKE5Or1vN9ubpJz3ue8X+e8rvZ2Xt7v874MJpPJhIiIiIiIiBWzyekAREREREREcpoKIxERERERsXoqjERERERExOqpMBIREREREaunwkhERERERKyeCiMREREREbF6KoxERERERMTqqTASERERERGrp8JIRERERESsngojERGxKCNGjMDDw+Ohf+bOnZvTIT4R+/btw8PDg507d+Z0KP9J4eHhD3yWKlSogL+/P4GBgRw/ftzcVrkQ+W+yy+kAREREMqtgwYIEBwenuS9v3rzZ3t/w4cMpUaIEAwYMyPZz/xf98MMPrFmzhu+++y6nQ8m09957j5YtWwKQnJxMeHg4c+bMoV27dqxcuRJPT88sn1ufI5HcTSNGIiJicWxsbHj22WfT/JMnT55s7+/QoUOPfY6kpCRMJlM2RJN7JSQkANlzv3KKi4uL+bP03HPPUa1aNWbNmoWtrS1Lly59rHNb8n0RsQYqjERE5D9rw4YNtGzZEi8vL2rUqMHgwYOJjIxM1WbdunW0bt2aKlWqULVqVTp27Mgff/xh3u/h4cH58+eZPXs2Hh4ehIeHM2vWLDw8PIiPj091Lg8PD6ZMmQL839SsVatW0aFDB7y9vYmJiQHg77//plevXvj4+FCpUiU6d+7MwYMHM3VtiYmJeHh4sHTpUsaNG0fVqlWpVq0an332GQkJCXzyySfUqFGDGjVqMHLkSBITEwEICwvDw8ODtWvX8tFHH1G9enUqV65MYGAg165dM5/fZDLxzTff0LBhQypWrIivry8DBw7k4sWL5jazZs2iWrVqbN26lVq1ajF48GC6dOnCqlWr+OOPP/Dw8ODHH38EICQkhF69euHr60ulSpVo0qQJK1asSHVNtWvX5tNPP2X58uUEBARQqVIlWrVq9cC92bZtG61bt8bLy4vatWszfvx4bt++bd5/7do1PvjgA/z8/KhYsSKvv/46q1evztT9vZ+LiwslSpQgIiLioW0OHTpEt27d8PHxwdvbm1atWrFhwwbz/rQ+RyKSu6gwEhGR/6R169YxePBgatSowZo1a5gzZw6nT5+me/fu5pGN/fv3M3ToUGrWrMmaNWtYtWoVJUuWpG/fvuYCatu2bQD07NmT3bt34+bmlqk4vv32W9q0acMvv/yCi4sL58+fp3PnziQlJbFkyRKCgoIoXLgwPXr04OzZsxk+r729PQBLly6ldOnS/Pjjj7Rr145FixbRo0cPihUrxqpVq+jbty+rVq1i/fr1ANja2gIwY8YMKlSowI8//siUKVP4888/+eCDD8znnzlzJl988QUdO3bk559/ZubMmZw7d46uXbsSFxdnbpecnMzSpUv56quv+Pjjj5k1axYvvfQSPj4+7N69myZNmnD79m169OiBjY0NS5YsYf369bRv354xY8aY7++9a9qzZw8HDhxg3rx5rFixgsTERN5//31zm99//523334bf39/goODmTx5Mps3bzbHnpCQQPfu3dm7dy8TJ07k559/plmzZnz00UesWbMmU7m7JyEhgcjISIoWLZrm/tOnT9OtWzecnZ1ZvHgxP/zwA1WrVmXw4MHm63vcz5GIPHkqjERE5D/pyy+/xMfHhw8//JDy5ctTvXp1Jk2axNmzZ9myZQsAFStWZMuWLbz77ruUKlWKcuXK0bdvX+Li4syjFIULFwYgT548PPvss+bCIqPc3d1p06YNJUuWxMbGhkWLFgF3CxMvLy88PDyYNGkSLi4uLF68ONPXWbx4cbp160bp0qXp3bs3AA4ODvTs2ZPSpUvTs2dP8uTJw7FjxwAwGAwAvPjii3Tp0oWSJUtSv359unXrxs6dO7lx4wYJCQksXryYZs2a0b17d8qWLYuvry/jx4/n8uXLbN682dx/XFwcXbt2pWLFijz77LMUKFAAOzs77O3tefbZZ3FycsLJyYmff/6Z6dOn4+HhQYkSJejWrRuFChVi165dqa4nJiaGCRMm8Pzzz+Pp6Unr1q0JDw/n+vXrACxYsAAvLy8GDRpE2bJl8fPzY+TIkbi4uJCYmMjWrVs5efIk48eP59VXX6Vs2bL069ePgIAA5s2bl+n7+88//zBmzBhu375Nx44d02yzZMkS7OzsmDp1Kt7e3jz//POMHDmScuXKmXP6uJ8jEXnytPiCiIhYnGvXruHj45PmvhkzZlClShVOnz5Nv379Uu178cUXKVCgAAcPHqRJkyY4OjqyZcsWgoODuXTpEomJieb3gG7evJktsVasWDHV74cPH8bDw4MCBQqYtzk6OuLj45Pp6XRwd4rWPc888wxAqgUCDAYDBQoUME/ju6dKlSqpfn/xxRcxmUycO3eOPHnycPv2bWrUqJGqjbe3N7a2tqlWaEvrGv/N1taWEydOsHDhQk6fPo3RaATAaDQ+cJ9ffPFFHBwczL/nz58fuJuPggULEhISQpMmTVId07BhQxo2bAjcvb8Gg+GB2P38/Ni2bRs3b95Mde//bcKECUyePBmAlJQU4uPj8fDwYMGCBXh7e6d5TEhICC+99NIDC39Urlw51YiYiORuKoxERMTiFChQgJUrV6a5r0iRIty6dQu4O7qwZMmSVPuNRiNXr14F7k5DmzhxIr169aJx48bky5ePyMhIunTpkm2xurq6pvo9JiaGS5cuPVDYJSQkkC9fvkyf39nZ2fzzvdGg+7fd2/7vhR/+Hde9Y+7cuUNKSgrwf0XJPTY2Nri4uBAbG5tqe3pxHzt2jLfffptatWoxY8YMChcujI2NTZr3+d+LZ9y7pnvxx8TE4OTk9NC+YmJiMJlM+Pr6ptqelJQE3B0BelRh1K9fP5o2bWru+9lnn33gfv5bbGwsJUqUeGB7vnz5HrhXIpJ7qTASERGLY2trS+nSpR+6/96Dfbdu3WjXrt0D++89fG/YsIHKlSszbNgw8757RdWj3HtYv9+dO3fSPQ7uPiw/99xzfPLJJw/ss7F5ejPc/32d9xYvyJs3r7nw+PdoTnJyMjExMQ8UVen55ZdfMBgMTJs2zTyqkpKSkqWiwdXV9ZGjefny5cPR0fGh7xOl925PwYIFH/nZykxMN2/ezPS9EpGco3eMRETkPydv3ry88MILnD9/ntKlS6f6k5CQQKFChYC7/9J/b/rZPT/99BPAAyMs9/9+b4Tk/ulpoaGhGYqtcuXKnDt3Djc3t1RxmUwmihQpkvmLzaJ/Lx0dGhqKnZ0d5cqVo1y5cri6uqZanQ/gwIEDpKSk4OXlle75779ft2/fxsHBIdVUs19++YXbt29neglzLy+vB6Yc/vrrr3Tq1Inbt29TuXJl4uPjMRqNqe6vk5MT+fLlSzVNL7tUqlSJo0ePploZz2QyceDAgQfu1X99yXYRS6bCSERE/pP69u3Lli1bmD17NmfOnOH06dNMmjSJli1bcvLkSQB8fHzYt28f//vf/zh37hxTp04lOTkZOzs7jhw5wvXr13FwcMDJyYm//vqL48ePEx0dbX7XZOXKlSQnJ3Px4kVmz56doe9Q6tq1K7dv3+a9997j77//5uLFiwQFBdGyZcvHWlI6sw4fPsyCBQs4d+4cW7ZsYcmSJdSrVw9XV1fs7e3p2bMnP//8M4sWLSIsLIz//e9/jBo1inLlylG/fv1HnjtfvnyEhYUREhJCREQEPj4+3L59m0WLFnHx4kV++OEHli1bho+PD6dOncrU0tW9evXi/PnzfPLJJ5w5c4a9e/fy2WefUahQIfLmzUvdunV54YUXeP/99/n999+5dOkSO3bsoHPnznz88cePedfS1rVrV1JSUhg6dCh///03x48fZ9SoUVy6dIlevXoBpPk5EpHcRYWRiIj8JzVt2pRp06axdetWWrZsSfv27Tl69CgLFy6kQoUKAAwaNAg/Pz/eeecdOnXqRGJiIiNHjqRr16788ssvfPHFFxgMBgIDAzl06BA9e/bk7NmzVK5cmaFDhxIUFESVKlUYNGgQ77zzDnny5CE5OfmRcZUuXZrvvvuO27dv07lzZ5o2bcrSpUsZMWIEnTp1ehq3Brj7Ls2pU6do27Yt77//Pn5+fowfP968v3///gwdOpTvv/+e119/nSFDhuDt7c3ixYvTHXXp0aMHJpOJHj16sHnzZho3bkyPHj2YN28eLVq0YOvWrUybNo3u3bsTGRlJ3759Mxy3n58fs2fPZv/+/bRs2ZL333+funXrMmHCBOBuAbJo0SIqVqzIkCFDaNiwIWPHjqV+/fpMmjQpazcrHWXLlmXJkiXExcXRqVMn2rVrx6lTp/jqq694+eWXAdL8HIlI7mIwaUxXRETEaoSHh1OvXj0+++wzWrdundPhiIjkGhoxEhERERERq6fCSERERERErJ6m0omIiIiIiNXTiJGIiIiIiFg9FUYiIiIiImL1VBiJiIiIiIjVs8vpAEQs2aFDhzCZTNjb2+d0KCIiIiKShsTERAwGAz4+Po9spxEjkcdgMpl4muuXmEwmEhISnmqf8viUN8ul3Fkm5c1yKXeWKbfnLaPPaxoxEnkM90aKvLy8nkp/cXFxhIaG4u7uTp48eZ5Kn/L4lDfLpdxZJuXNcil3lim35y0kJCRD7TRiJCIiIiIiVk+FkYiIiIiIWD0VRiIiIiIiYvVUGImIiIiIiNVTYSQiIiIiIlZPhZGIiIiIiFg9FUYiIiIiImL1VBiJiIiIiIjVU2EkIiIiIiJWT4WRiIiIiIhYvf9EYXTp0iW8vLw4d+5cTocij2Hfvn14eHgQHx+f06GIiIiIyBNgMpnYeSaSFYfOsfNMJCaTKadDMrPL6QCyQ/HixQkJCcnpMCzKt99+S5cuXbCz+098BEREREQkl/sp5ALDfz7ImWsx5m3lC7kyqVkVWnmVysHI7vpPjBg9DUlJSTkdQra5fv06kyZNIjk5+an2+zT7+y/lS0RERMTS/RRygXaLd6YqigDOXIuh3eKd/BRyIYci+z//ieGC8PBw6tWrx4YNG+jRowf9+/dnw4YNHD58mBdffJHp06czZcoUtm3bxnPPPce0adPw9PQkKCiIefPm0a9fP2bMmEFiYiKtWrVi2LBh2NjYMGLECOzs7Lhw4QJRUVFs2rSJW7du8emnn/K///2P5ORkqlevzujRoylYsCD+/v4MHTqUVq1amWPr378/zz77LOPGjePEiROMHz+ekJAQChUqRNOmTRkwYAD29vasWrWKJUuW0LlzZ7744guSk5MZNGgQJUqUYPz48Vy7do3WrVszcuRIABISEpg4cSLr16/HZDLh7e3N6NGjKVWqFElJSbz00kvMmjWLhQsXcvz4cUqXLs3kyZMpVKgQderUwWQyUa1aNcaOHUvr1q0feX8PHz7Mp59+yqlTp3BwcKB+/fqMGjUKJycnAgIC6NOnDx07dgRg586d9OnThxMnTgDg4eHBhx9+yPz58+nUqRP9+/dn165dTJw4kfDwcMqUKcOIESPw8/Mz93fw4EHGjx9PREQE1atXZ9q0abi4uJCSksL06dNZs2YNMTExlC1blg8//JDq1asD0KVLFypVqsSuXbsoXLgwCxYs4K+//mLMmDFcuHABX19fateuzVdffcWuXbsA+OOPP5g4cSJnzpzhueeeo2PHjnTr1g2DwZB9H9BsdgsHrhiTcTIl5nQokkF37iQrbxZKubNMypvlUu4sU3p5M5lMDA0+QMpDps2lmEyMWHeQlhVL5ugz2H+iMLqfnZ0d33//PbNnz8bFxYUWLVrQpUsXxo8fz4QJE3jrrbf48ssvmTlzJnZ2dly9epUTJ06wbds2Tp06RZcuXXjhhRfMxcLWrVuZNGkStWrVAmDkyJHExcURHByMnZ0dI0eO5O2332blypU0aNCALVu2mAujuLg49uzZw/z580lMTKR///506tSJhQsXEhkZSWBgIC4uLrz11lvY2tpy6dIlrl69yo4dO/jmm2/4/PPPadiwIWvWrOGvv/6iZ8+etGvXjhdeeIE5c+Zw8uRJgoODyZ8/PzNnziQwMNAcF8DChQuZNGkSRYsWpU+fPsyYMYO5c+eyYMECunbtyv79+3F0dEz3ng4bNozevXvzxhtvcO3aNfP1duvWLUM52bp1K8HBwRQoUIDr168zcOBAPvnkExo0aMDPP/9MYGAg27ZtM7dfv349K1euJDo6mjfeeIPVq1fTvXt3goODCQoKYtWqVRQvXpyvvvqKgQMHsnv3bmxtbQHYsGEDs2bNwtPTk4SEBPr27UvLli0JCgriwIEDDBs2DAcHBwBu3LhBYGAgH3/8MQ0bNuTs2bP07t2bIkWK0KRJkwx+4u7+xx4XF5fh9o/DaDTyh40bf1yIB/QulkVR3iyXcmeZlDfLpdxZpkfk7Vzkdc5fj33k4af/iWFL6EVqlimc7aGZTKYMFVz/ucIIoG7dupQuXRoALy8v4uLizCMSr7zyChs2bDC3jY+PJzAwEAcHB1566SXq1KnDrl27zIWRm5sb/v7+ANy8eZNff/2VFStWULBgQQDefvttmjdvTnh4OI0bN6ZPnz7cuXMHJycndu3aRb58+ahevTrbt28nKSmJXr16AVCyZEl69erF/PnzeeuttwC4c+cOvXv3xsHBgTp16jBjxgzatGlD3rx5qVmzJvb29ly4cIEXXniBFStWMG3aNIoWLQrAoEGDWLp0KUePHsXb2xuAZs2ame9DvXr1WLlyZZbu57Vr18iTJw82NjY8++yzrFixAhubjM/CbNCggfl+rVu3jpIlS/L6668D0Lp1axwdHVO9eNejRw9cXV1xdXWlatWq5kU1mjVrRr169XB1dQWgSZMmzJw5k8uXL1OyZEkAKlasyEsvvQTcHem6efMm/fr1w9HRkVdeeYWXX36ZgwcPAvDzzz/j7u5O06ZNgbujWx06dGDNmjWZKowSExMJDQ3NcPvHZlP66fUlIiIi8phijHcy1O7A8dMUNF59IjHc+4fxR/lPFkb3igUAJycn84M0gKOjY6pVz/Lly2d+aIe7hdBff/1l/r148eLmny9fvozJZDIXG4D5gTw8PJwaNWrg4uLC7t27qV+/Pr/++iuNGjXCxsaGixcvEhUVhZeXl/lYk8mUasQmf/78ODk5meNM61ri4+O5desWN2/epG/fvqmq35SUFCIiIsyFUYkSJR563ZkxcuRI83Q4f39/WrRoQfny5TN8fLFixcw/X7hwIdU9BcxF0j3373dwcDDHfevWLT777DP27t3LzZs3zcVUQkJCmn1duXIFFxcXnnnmGfO2l156yVwYXbhwgcOHDz+Qk3LlymX42gDs7e1xd3fP1DFZZTQaqRF2GTc3twyN9knuEB8fT0REhPJmgZQ7y6S8WS7lzjKll7cyuLJyd/rnqVrBHc8nMGJ0+vTpDLX7TxZG/x7NeNToxr+XCPx3sZLRVdsMBgM2NjY0bNiQrVu38uqrr/Lbb7/x9ddfm/e7u7uzbt26DMd977iHtVu+fHmqh/qMHJsVLVu25NVXX2X79u389ttvtGrViunTp1OvXr0H2qa15OK/72F6yzI+LO7Jkydz/Phxli1bRqlSpbh48SL169d/aF9pDZve/7uNjQ3+/v7MmzfvkfGkx2AwkCdPnsc6R2bkJ4HSBfI81T7l8cTF2RIXobxZIuXOMilvlku5s0zp5a1sQRdGbzz8wMIL93Mv7Ep9zyfzjlFGz2n1q9LFxMRw48YN8+8RERGpRmnuV6JECWxsbAgLCzNvO3/+PAClSt1dYrBRo0bs2LGDvXv34urqio+Pj3l/eHg4t2/fNh9748YNYmMfPd8yLa6urhQoUICTJ0+m2h4eHp7pc2XEjRs3eOaZZ2jdujUzZ87krbfeYvXq1cDdkajExP97ye7KlSuPPFfJkiVT3T+ApUuXcvHixXTjCAkJ4Y033jDf6+PHjz+y/bPPPktsbGyqe3zs2DHzz6VKleLUqVOpCrWrV6+mGoESERERkcdjMBiY1KwKNg8pUGwMBiY2rZLji19ZfWFkb2/PnDlziIuL4++//2bHjh1pjoTA3Wl3DRo0YNasWdy4cYMbN24wY8YMfH19cXNzA6Bq1arY2Njw9ddf06hRI3OCa9WqRcGCBfn888+5ffs2V69e5d1332Xq1KlZirtDhw7MmzePM2fOkJiYyKJFi2jTpg1GozHdY+9N1zt79my6hVlERAQBAQHs3r2blJQUYmNjOX36tHkKYZkyZdi/fz8A0dHRrF279pHna9q0KVeuXGH16tUkJiayfv16pk6dSt68edON+7nnnuPgwYMkJiZy9OhRc3EWFRWVZntvb2+cnZ2ZP38+CQkJ/O9//+PPP/8073/99de5efMmX331FfHx8Vy8eJGePXuyZMmSdGMRERERkYxr5VWKoG7+uBd2TbXdvbArQd389T1GuUH+/PkpV64cDRo0oFevXnTq1OmhhRHAmDFjcHFxISAggNdffx0XFxdmzJhh3n9vOt0ff/yR6t0Ze3t75s6dy+nTp3nllVdo2rQppUuXZtiwYVmKOzAwkJo1a/Lmm29SvXp1Nm3axPz583F2dk73WE9PT3x8fOjYsaO5uHgYNzc3Pv30UyZNmkSVKlV47bXXcHJyYuDAgQC8++67hIWFUbduXfr27Wtetvth3yNUqFAhZsyYwbfffkvVqlWZN28ec+fOTfWe18MMHTqUs2fPUr16daZMmcK4ceOoX78+AwcOTDUSdE/evHmZMWMGmzZtwtfXlx9++IHOnTubi9VnnnmGuXPn8uuvv1KtWjU6duxInTp16NGjR7qxiIiIiEjmtPIqxfERLdge2IDvO9fmt7cbcHxEi1xRFAEYTOm98PEf9uOPPzJ16lT27NmT06HIE3LvS2XvLec9c+ZM9u7dy/fff58t5w8JCQF45Lte2SkuLo7Q0FA8PT0199qCKG+WS7mzTMqb5VLuLFNuz1tGn9esfsRI/rtMJhONGzdm+vTpJCYmEhYWxpo1a6hTp05OhyYiIiIiucx/clU6ybjx48cTFBT00P39+/cnMDDwKUaUfQwGA1OnTuWTTz6hRo0auLq60qRJE7p3757ToYmIiIhILmPVhVHr1q3NX+RqrUaNGsWoUaNyOownxsvLK8tfbCsiIiIi1kNT6URERERExOqpMBIREREREaunwkhERERERKyeCiMREREREbF6KoxERERERMTqqTASERERERGrp8JIRERERESsngojERERERGxeiqMRERERETE6qkwEhERERERq6fCSERERERErJ4KIxERERERsXoqjERERERExOqpMBIREREREaunwkhERERERKyeCiMREREREbF6KoxERERERMTqqTASERERERGrp8JIRERERESsngojERERERGxeiqMRERERETE6qkwEhERERERq6fCSERERERErJ4KIxERERERsXp2OR2AiIiI5C4mk4ldZ6O4HB1HsXx5qF2uCAaDIafDEhF5olQYSY5LTk5myZIl9OjRI1vOt3nzZjw8PChdunS2nE9ExJr8FHKB4T8f5My1GPO28oVcmdSsCq28SuVgZCIiT5am0kmOO3bsGN988022nW/mzJmcP38+284nImItfgq5QLvFO1MVRQBnrsXQbvFOfgq5kEORiYg8eRoxkiciKSmJl156iWnTpvHNN99w5swZ/Pz8GDduHB9++CEHDhzA3d2d4cOH0717d5KSkvDy8mL+/PnUqFGD6dOns2bNGmJiYihbtiwffvgh1atXT7ff5s2bc+rUKQIDA2nWrBmfffYZu3fvZurUqYSFheHi4kLbtm0ZOHAgJpOJN998k8qVKzN8+HAAVqxYwZdffsn69etxcXF50rcpS27hwBVjMk6mxJwORTLozp1k5c1CWVPuTCYTQ4MPkGIypbk/xWRixLqDtKxYUtPqROQ/SYWRPBF2dnc/WitWrGD+/Pncvn2bpk2b0qdPHyZPnkzx4sVp374927ZtY/z48UydOpU9e/YAsGbNGoKCgli1ahXFixfnq6++YuDAgezevRtbW9tH9hscHIyHhwdz587F39+fuLg4BgwYwJAhQ+jUqROnT5+mffv2VKxYkYCAAMaPH0/btm1p27YthQsX5osvvmDixImZKopMJhNxcXFZv1mZYDQa+cPGjT8uxAPxT6VPySbKm+Wyktydi7zO+euxj2xz+p8YtoRepGaZwk8pqqwxGo2p/hbLodxZptyeN5PJlKF/0FFhJE9U06ZNKVy4MIULF6Zs2bJUrFiRChUqAFCtWjXOnz/P888/n+qYZs2aUa9ePVxdXQFo0qQJM2fO5PLly5QsWTJT/efJk4edO3eSJ08ebGxseOGFF/Dw8ODo0aMEBATg7u5Oz549mTBhAmXKlKFmzZrUqVMnU30kJiYSGhqaqWMei43enRKR7BdjvJOhdgeOn6ag8eoTjiZ7hIWF5XQIkkXKnWXKzXlzcHBIt40KI3minnvuOfPPTk5OFC1a1Py7o6Mj8fEP/gvsrVu3+Oyzz9i7dy83b97E9P+ndSQkJGQphrVr17Js2TIiIiJITk4mMTGRatWqmff37duXFi1aEBISwsaNGzN9fnt7e9zd3bMUW2YZjUZqhF3Gzc0NR0fHp9KnPL74+HgiIiKUNwtkTbkrgysrd6ffrmoFdzwtYMQoLCyMMmXK4OzsnNPhSCYod5Ypt+ft9OnTGWqnwkieqH8PW9rYpL/ex+TJkzl+/DjLli2jVKlSXLx4kfr162ep/3379jFhwgSmTZtG/fr1sbOzo1OnTqnaxMbGcuvWLZKTk4mKiqJgwYKZ6sNgMJAnT54sxZcV+UmgdIE8T7VPeTxxcbbERShvlsiacle2oAujNx5+YOGF+7kXdqW+p+W8Y+Ts7Pyfz9t/lXJnmXJr3jL6/yytSie5TkhICG+88QalSt1dFvb48eOPda7nn3+eRo0aYWdnR0JCAmfPnk3VZuLEidSrV4/+/fvz0UcfkZyc/Fjxi4hYIoPBwKRmVbB5yAOEjcHAxKZVLKYoEhHJLBVGkuOcnJyIiYkhMjISo9HIc889x8GDB0lMTOTo0aOsXr0agKioqAydz9HRkfPnzxMdHY2bmxvh4eFcunSJ6OhoPvjgA5599lkiIyMB+N///sfOnTsZMmQIXbt25c6dOyxatOhJXaqISK7WyqsUQd38cS/smmq7e2FXgrr563uMROQ/TVPpJMe9/PLLlChRgsaNG/Ppp58ydOhQhg0bRvXq1alcuTKTJk1i3LhxDBw4kMWLF/Piiy8+8nwdOnRgypQp/Pnnn0ydOpUtW7bQtGlTChUqxHvvvUe9evX46KOPmDBhAtu2bWPIkCE888wzAIwaNYp+/frx2muvmUesRESsSSuvUrSsWJJdZ6OIiDZSLL8ztcoW0UiRiPznGUymh3xhgYikKyQkBAAvL6+n0l9cXByhoaF4enrmyjm8kjblzXIpd5ZJebNcyp1lyu15y+jzmqbSiYiIiIiI1dNUOrEo1apVS3OJ73s2bdpE8eLFn2JEIiIiIvJfoMJILMr+/ftzOgQRERER+Q/SVDoREREREbF6KoxERERERMTqqTASERERERGrp8JIRERERESsngojERERERGxeiqMRERERETE6qkwEhERERERq6fCSERERERErJ4KIxERERERsXoqjERERERExOqpMBIREREREaunwkhERERERKyeCiMREREREbF6KoxERERERMTqqTASERERERGrp8JIRERERESsngojERERERGxeiqMRERERETE6qkwEhERERERq6fCSERERERErJ4KIxERERERsXoqjERERERExOqpMBIREREREaunwkhERERERKyeCqNMaNiwIatWrUq3XZcuXZgyZcpTiCh9NWvW5Mcff3zi/YwcOZJhw4Y98X4yYvDgwYwYMSKnw8gxJpOJnWciWXHoHDvPRGIymXI6JBEREZFczy6nA7hn8+bNeHh4ULp06ZwO5aF++eWXnA7hicpMDpKTk1myZAk9evQA4JNPPnnS4UkG/BRygeE/H+TMtRjztvKFXJnUrAqtvErlYGQiIiIiuVuuGTGaOXMm58+fz+kwrFpmcnDs2DG++eabJxyRZMZPIRdot3hnqqII4My1GNot3slPIRdyKDIRERGR3C9XFEbNmzfn1KlTBAYGEhAQgKenJ1u3bqVevXpUq1aN4cOHc+fOHXP7ZcuWERAQgI+PD61bt2bPnj3mfbVr12bBggV0796dypUr06pVK44dO5ahOKZPn07Xrl2ZO3cuvr6+1KpVi/nz55v3BwQEsHz5cuDuiMnkyZN5+eWXqVatGu+++y63bt164Jwmk4lBgwbRo0cPEhMTH5hmd+bMGTw8PAgPDycxMREPDw9WrVpFmzZt8PHxoVOnToSHh2co/qSkJMaPH4+vry+1a9d+YNrfjRs3GDhwIL6+vlSrVo0+ffoQEREBpM7BBx98AMCJEyfo3LkzlSpVIiAggGnTppGYmMiRI0fo0KED//zzD15eXuzdu5cRI0YwePBgAFatWkWzZs1YuXIlfn5+1KhRg++//56dO3fy2muvUaVKlVQjTAkJCYwbNw5fX19q1KhB7969uXAh4w/xQUFBBAQEULVqVcaOHUtKSop5X0pKClOnTqV27drmz8Off/4JwJw5c2jdunWqc+3fvx9vb29iY2Mz3P/TdgsHrhiTuXw70fznUmwCQ4MPkPKQaXMpJhMj1h3UtDoRERGRh8gVU+mCg4Px8PBg7ty5ODo60rVrVzZt2sTatWu5ceMGXbp0Yf78+QwYMIA9e/Ywd+5c5s2bR4UKFdi2bRuBgYFs2rQJNzc37O3tWbp0KV9++SVlypThk08+YcCAAWzZsgWDwfDIOOzs7Dh69CivvPIKe/bsYd++ffTu3Rtvb298fX1TtV2xYgVbt25l1apVFCxYkMGDBzN27FimTZuWqt2cOXM4c+YMy5cvx97e/pH939u/dOlS5syZQ4ECBRg0aBAffPAB3333Xbr38YcffmDTpk18//33FC1alMmTJ6cq1iZPnsyNGzfYsmULtra2DBo0iAkTJjBr1qxUOfD39ycxMZH+/fvTqVMnFi5cSGRkJIGBgbi4uPDWW28xfvx4pk6dai5K16xZY+7H1taWS5cucfXqVXbs2ME333zD559/TsOGDVmzZg1//fUXPXv2pF27drzwwgvMmTOHkydPEhwcTP78+Zk5cyaBgYEEBwdjY/Po2v3s2bOMHj2a2bNn4+/vz9q1a/n0009p1KgRcPezFRQUxKpVqyhevDhfffUVAwcOZPfu3TRv3pxZs2Zx5swZypcvD9ydLlm3bl1cXFzSvd/3mEwm4uLiMtz+cRiNRv6wceOPC/FAvHn7ucjrnL/+6GLu9D8xbAm9SM0yhZ9wlPJvRqMx1d9iOZQ7y6S8WS7lzjLl9ryZTKZ06wDIJYVRWrp27YqLiwsuLi40a9aMXbt2MWDAAJYvX06bNm2oWLEiAA0aNGDZsmWsX7+e3r17A1C3bl0qVKgAQM+ePVm1ahVnz541P/w+io2NDX369MHW1paaNWvi7e3Nrl27HiiMVq9eTceOHSlZsiQAo0aN4syZM6nabNq0iZUrVxIUFJSpB+3mzZtTokQJALp168Zbb72F0WjE2dn5kcf9+uuvNGnSxHyd7777LitXrjTvHzt2LElJSeTJkweAevXq8dVXX6V5rp07d5KUlESvXr0AKFmyJL169WL+/Pm89dZb6V7DnTt36N27Nw4ODtSpU4cZM2bQpk0b8ubNS82aNbG3t+fChQu88MILrFixgmnTplG0aFEABg0axNKlSzl69Cje3t6P7GfLli14eHhQv359ANq2bcu3335r3t+sWTPq1auHq6srAE2aNGHmzJlcvnyZkiVLUrVqVX7++WcGDRpkPt9HH32U7vXdLzExkdDQ0Ewd81hsHnwHLMZ4J42GDzpw/DQFjVezOyLJoLCwsJwOQbJIubNMypvlUu4sU27Om4ODQ7ptcm1hVKZMGfPPbm5uREVFAXDhwgW2b9/OwoULzftNJhPPP/+8+ff7Fw8oVqwYAFFRURkqjEqUKIGtrW2afd/vwoULFC9e3Px7yZIlzUUSQGhoKEuXLuWjjz7Czc0t3X7v9+/4U1JS+Oeff1KdPy2RkZHUqlXL/HuhQoXImzev+ffTp08zefJkQkNDiYuLIyUlhQIFCqR5rosXLxIVFYWXl5d5m8lkwtHRMUPXkD9/fpycnADMx9wrfACcnJyIj4/n1q1b3Lx5k759+6aq5FNSUoiIiEi3MIqMjDTn+J5Spf5vkYFbt27x2WefsXfvXm7evGmeSpaQkABAy5YtmTdvHoMGDeLIkSPcvn0bf3//DF3jPfb29ri7u2fqmKwyGo3UCLuMm5tbqlyUwZWVu9M/vmoFdzw1YvTUGY1GwsLCKFOmTLr/wCG5i3JnmZQ3y6XcWabcnrfTp09nqF2uLYzufxfi/gdyGxsbhgwZYh7JyMixQIYf6P/9DsajioH732X5twMHDlC3bl2+/PJLGjdunKkRo7TiyUiVe+9h/2ExvvPOO/j4+PDrr7+SL18+Vq9ezfTp09M8l8FgwN3dnXXr1mUp7rSmwKU1hHmv3fLly1MVYRmV3jVPnjyZ48ePs2zZMkqVKsXFixfNo0sAjRo1Yvz48Rw6dIht27bRqFGjDN3r+xkMBvMo3NOQnwRKF8iTqs+yBV0YvfHwAwsv3M+9sCv1PUtmaChZngxnZ+en+lmR7KPcWSblzXIpd5Ypt+Yto88+uWLxhbRcvHjR/HNERARFihQB7o4GnDx5MlXby5cvpypo7l+s4PLly0Dq0YpHiYiIICkpKdXvaR1bsmTJVCu4nT9/nmXLlpl/79ixI1OmTOHZZ59lwoQJ5u2Ojo4kJiamOv+/3X/tly9fxs7OjkKFCqUbe5EiRVKdLzIy0jzX8/r161y6dInevXuTL18+AI4fP/7Qc5UqVYrw8HBu375t3nbjxo1sX5TA1dWVAgUKPJDTjC44UaRIEa5cuZJq2/15CQkJ4Y033jCPIv37ml1dXalXrx6bN29m8+bNNG/ePCuXkeMMBgOTmlXB5iH/4dsYDExsWkVFkYiIiMhD5JrCyNHRkfPnzxMTc/dfvBcuXEh0dDQXL15k3bp11KtXD4D27duzceNGduzYQXJyMnv37qVp06aEhISYz7VlyxaOHj3KnTt3WLhwIRUqVEg17e1R4uPjWbBgAfHx8ezevZuQkBBz3/dr06YNK1as4Ny5c9y+fZvPP/+c/fv3m/fb2tpia2vLxIkTWb9+Pdu3bwfuThE8dOgQycnJJCQkEBQU9MC5f/rpJ86fP09sbCxLlizh1Vdfxc4u/cG92rVrs379es6dO0dsbCwzZswwj3bly5ePPHny8Mcff5CcnMyaNWsICQkhNjbWXPzcy0F0dDS1atWiYMGCfP7559y+fZurV6/y7rvvMnXqVODuVLiYmJhUxVdWdejQgXnz5nHmzBkSExNZtGgRbdq0ydB5/f39CQ0NZfv27SQkJPD999+nmvr43HPPcfDgQRITEzl69CirV68GSNWmZcuWrFq1ioSEBKpWrfpY15KTWnmVIqibP+6FXVNtdy/sSlA3f32PkYiIiMgj5JqpdB06dGDKlCnm91Lq1KlDy5YtiY6OpkGDBrz55psA1KxZk2HDhjF27FiuXr1K8eLFGTNmTKp3UVq3bs3kyZM5fPgw5cuXT7U8dno8PDwwGo3Url0bR0dHRowYgaen5wPtOnbsyKVLl+jQoQPJycnUrFmT0aNHP9CubNmyDBkyhFGjRhEcHEzPnj0ZOnQoderUoUiRIrz99tv88ssvJCcnp4p/8ODBnDt3jooVK/Lpp59mKPbu3btz8eJF2rdvj4ODAwMHDuTAgQMkJSVhZ2fHxx9/zOeff86sWbNo0qQJs2fPplOnTjRp0oQdO3aYc/Dnn38yc+ZM5s6dyyeffMIrr7yCk5MTDRo0YNiwYQC8/PLLlChRgsaNG2c4vocJDAwkOjqaN998k/j4eCpUqMD8+fMzNEe1UqVKjBw5krFjxxIdHU2zZs1o0qSJeXn3oUOHMmzYMKpXr07lypWZNGkS48aNY+DAgSxevJgXX3yRWrVq4ejoSNOmTS1+RKWVVylaVizJrrNRREQbKZbfmVpli1j8dYmIiIg8aQZTLvtik3379tG1a1eOHDmS4feC7hcQEECfPn3o2LFjpo+dNWsWu3btSnMU52nx8PBg/vz5mV4AQLIuNjaWOnXqsGrVKsqWLZupY++NVGbl/aisiIuLIzQ0FE9Pz1w5h1fSprxZLuXOMilvlku5s0y5PW8ZfV7LNVPpRHJCfHw848aNw8/PL9NFkYiIiIj8d+SaqXRP2saNG83TwNJSvXp1fHx8nmJEmbNgwQK++OKLh+5v0aIFn3zyydML6Ck4cuQInTp1euj+YsWK8csvv2T5/AcOHKBnz554e3szY8aMLJ9HRERERCxfrptKJ2JJNJVOMkJ5s1zKnWVS3iyXcmeZcnveNJVOREREREQkg1QYiYiIiIiI1VNhJCIiIiIiVk+FkYiIiIiIWD0VRiIiIiIiYvVUGImIiIiIiNVTYSQiIiIiIlZPhZGIiIiIiFg9FUYiIiIiImL1VBiJiIiIiIjVU2EkIiIiIiJWT4WRiIiIiIhYPRVGIiIiIiJi9VQYiYiIiIiI1VNhJCIiIiIiVk+FkYiIiIiIWD0VRiIiIiIiYvVUGImIiIiIiNVTYSQiIiIiIlZPhZGIiIiIiFg9FUYiIiIiImL1VBiJiIiIiIjVU2EkIiIiIiJWT4WRiIiIiIhYPRVGkilr1qwhICAgp8N4qPj4eLp160alSpXYvHlzToeTI0wmEzvPRLLi0Dl2nonEZDLldEgiIiIiuZ5dTgcglqVly5a0bNnyqfb57bff0qVLF+zs0v+47tmzhwMHDrBjxw4KFiz4FKLLXX4KucDwnw9y5lqMeVv5Qq5MalaFVl6lcjAyERERkdxNI0aSbZKTk7P9fNevX2fSpEkZPvetW7fInz8/hQoVwmAwZGs8ud1PIRdot3hnqqII4My1GNot3slPIRdyKDIRERGR3E+FkaTp6NGjdOzYkSpVqvDKK68wZswYEhMT+fHHH6lZsyYA4eHheHh4sGLFCmrUqMG6deuYPn06Xbt2Ze7cufj6+lKrVi3mz59vPm98fDyffPIJderUoUaNGvTs2ZOwsDDzfg8PDxYvXkytWrX48ssv8ff3x2QyUa1aNX788cdHxrxq1SpGjhzJP//8g5eXFxs3bsRkMjFr1ixq1apF1apVefPNNzl69Kj5mOjoaIYOHUq1atXw8/Nj0KBBXLt2LXtvZja7hQNXjMlcvp1o/nMpNoGhwQdIeci0uRSTiRHrDmpanYiIiMhDaCqdpGnQoEG89tprfPfdd1y9epUOHTrg7u5O3rx5H2j7xx9/sH37dpydnZkzZw5Hjx7llVdeYc+ePezbt4/evXvj7e2Nr68v06dPZ//+/SxdupSCBQsybdo0evTowebNm7G3twdg69atBAcHU6BAAapXr07Xrl3Zv38/jo6Oj4y5bdu22NraMnXqVPbs2QNAUFAQGzduZOnSpRQrVoyVK1fSu3dvtm3bRp48eRg7dixJSUls3boVW1tbxowZw/Dhw/nmm28yfK9MJhNxcXGZuLtZZzQa+cPGjT8uxAPx5u3nIq9z/nrsI489/U8MW0IvUrNM4Sccpfyb0WhM9bdYDuXOMilvlku5s0y5PW8mkylDM4lUGEma1q5di729PXZ2dri5uVGtWjWOHj2Kr6/vA22bNWuWqmCysbGhT58+2NraUrNmTby9vdm1axe+vr6sXr2asWPHUqJECQAGDhzIsmXLOHjwoPncDRo0yLb3g5YvX0737t0pU6YMAF26dGHx4sXs2LGDmjVrsmHDBjZu3Ej+/PkBeO+996hTpw7Xr1/PcAyJiYmEhoZmS7wZYlP6gU0xxjsZOvTA8dMUNF7N7ogkg+4fHRXLotxZJuXNcil3lik3583BwSHdNiqMJE07duxg3rx5XLhwgaSkJJKSkmjUqFGabYsVK5bq9xIlSmBra2v+3c3NjaioKG7dukVMTAylS//fg32+fPnInz8/4eHh5sLo3+d7HBcuXGDcuHGMHz/evC0lJYWIiAjCw8NJSUmhWbNmqY6xtbXl0qVLGS6M7O3tcXd3z7aYH8VoNFIj7DJubm6pRtDK4MrK3ekfX7WCO54aMXrqjEYjYWFhlClTBmdn55wORzJBubNMypvlUu4sU27P2+nTpzPUToWRPOD8+fO8//77DB8+nA4dOuDg4MCwYcNITExMs/2/V4v793ssJpMp3Wlw9w9vZmT1uYyysbFhypQpaRZ1x48fB3jsFewMBgN58uTJ8vGZlZ8EShfIk6rPsgVdGL3x8AMLL9zPvbAr9T1LWt2iFLmJs7PzU/2sSPZR7iyT8ma5lDvLlFvzltFnHy2+IA84duwYzs7OdO3aFQcHB0wmEydOnMjw8RERESQlJaX6vWjRouTPn5/8+fOnGma9efMmN2/epFSpJ7OUdMmSJTl58mSqbeHh4QAUL14cGxubVPsTExOJjIx8IrE8SQaDgUnNqmDzkP/wbQwGJjatoqJIRERE5CFUGMkD3NzcuH37Nn///TdGo5FPP/0UW1tboqKiMrSqWXx8PAsWLCA+Pp7du3cTEhJCvXr1AGjTpg3z588nIiKC2NhYpk2bRqlSpfDx8UnzXE5OTgCcPXuW2NhHLy6Qlg4dOrB8+XL++usvkpOT2bBhA6+//jpXrlzB1dWVJk2aMH36dK5cucKdO3eYNm0avXr1ssjV21p5lSKomz/uhV1TbXcv7EpQN399j5GIiIjII2gqnTygcuXKdOrUia5du5I3b1769u1Lo0aN6N+/PzNmzEj3eA8PD4xGI7Vr18bR0ZERI0bg6ekJ3F1sITo6mubNm2NjY4OPjw/ffvttqneS7ufp6YmPjw8dO3Zk0KBBdO/ePVPX0qZNGyIiInjnnXeIjo6mXLlyzJ49m+eeew6AUaNGMX78eF5//XVMJhM+Pj7MnTvXYkdWWnmVomXFkuw6G0VEtJFi+Z2pVbaIxV6PiIiIyNNiMFniP41LrjVr1ix27dpFUFBQTofyVISEhADg5eX1VPqLi4sjNDQUT0/PXDmHV9KmvFku5c4yKW+WS7mzTLk9bxl9XtNUOhERERERsXqaSicWY/z48Y8cierfvz+BgYFPMSIRERER+a9QYSTZasCAAQwYMOCJnHvUqFGMGjXqiZxbRERERKybptKJiIiIiIjVU2EkIiIiIiJWT4WRiIiIiIhYPRVGIiIiIiJi9VQYiYiIiIiI1VNhJCIiIiIiVk+FkYiIiIiIWD0VRiIiIiIiYvVUGImIiIiIiNVTYSQiIiIiIlZPhZGIiIiIiFg9FUYiIiIiImL1VBiJiIiIiIjVU2EkIiIiIiJWT4WRiIiIiIhYPRVGIiIiIiJi9VQYiYiIiIiI1VNhJCIiIiIiVk+FkYiIiIiIWD0VRiIiIiIiYvVUGImIiIiIiNVTYSQiIiIiIlYvS4VRUlISQUFB5t937tzJO++8w5QpU0hISMi24ERERERERJ6GLBVGU6ZMYdGiRQBcvnyZgQMHki9fPvbv38+UKVOyMz4REREREZEnLkuF0caNG/nyyy8B+Pnnn/H29mbChAnMnDmTzZs3Z2uAOaFdu3bMmjUrR/r+559/aN26NZUqVSIkJIQ///yTV199FV9f3yydb/DgwYwYMSKbo3zQmjVrCAgIeOL9ZMTy5ctzTSxPm8lkYueZSFYcOsfOM5GYTKacDklERETEIthl5aDo6GhKly4NwJ49e8wPoUWKFOHmzZsZPs/mzZvx8PAwn0vuFp1Xr15l7969ODk5MXDgQLy8vJg5c+YT7/vvv//m1q1bvPLKKxlqv3r1agICAihYsCAtW7akZcuWTzZAeaSfQi4w/OeDnLkWY95WvpArk5pVoZVXqRyMTERERCT3y9KI0TPPPEN4eDhRUVEcPHiQ2rVrAxAREYGLi0uGzzNz5kzOnz+flRByRFJS0hPv49atW7i5ueHs7IzBYODmzZuUKVMGG5snv07GDz/8wP/+978MtU1OTmbixIncuHHjCUclGfFTyAXaLd6ZqigCOHMthnaLd/JTyIUcikxERETEMmTpabtly5Z06NCBNm3aUL16dcqXL8/t27cZPnw4r776aobO0bx5c06dOkVgYCABAQF4enqydetW6tWrR7Vq1Rg+fDh37twxt1+2bBkBAQH4+PjQunVr9uzZY95Xu3ZtFixYQPfu3alcuTKtWrXi2LFjGb6eOXPmUKtWLXx9fZkzZ06qfSNGjGDkyJF07dqVpk2bAneLl2HDhlGrVi38/PwYOHAg//zzD3B3BC29a1mxYgWNGzfGx8eHVq1asXPnTgC++OIL5s6dy5EjR/Dy8sLb25s///yThQsXUq1atQxdS1BQEAEBAVStWpWxY8eSkpJi3peSksLUqVOpXbu2+T79+eefAIwfP57vv/+ehQsX8tprrwF3RwaHDh1KtWrV8PPzY9CgQVy7dg2AGjVqEBMTQ4sWLZg9ezY//vgjNWvWBOD8+fN4eHjw22+/0bBhQypVqsQHH3zAxYsXad++PZUrV6Z79+7ExPzfQ/yj8puew4cP07x5cypXrkyPHj3MMd4THBxM48aNqVy5MgEBAXz//fcA7N+/n4oVK6Yq7u7cuYOPjw+7d+/OcP9P2y0cuGJM5vLtRC7fTuRSbAJDgw+Q8pBpcykmEyPWHdS0OhEREZFHyNJUuoEDB+Lu7s7Vq1d54403ALC3t6dUqVIZfp8lODgYDw8P5s6di6OjI127dmXTpk2sXbuWGzdu0KVLF+bPn8+AAQPYs2cPc+fOZd68eVSoUIFt27YRGBjIpk2bcHNzw97enqVLl/Lll19SpkwZPvnkEwYMGMCWLVswGAyPjGP37t18/fXXfPvtt7z44ot8/fXXnDx50jwKBrB161YmTZpErVq1ABg5ciRxcXEEBwdjZ2fHyJEjefvtt1m5ciV2dnakpKQ89Fq2bdvGlClT+Oqrr/D29mbz5s3079+fNWvWMGjQIGxtbdm1a5d51b8uXbpQqVIl3nvvvXTv6dmzZxk9ejSzZ8/G39+ftWvX8umnn9KoUSPzPQ8KCmLVqlUUL16cr776ioEDB7J7925GjRrFyZMnU/U1duxYkpKS2Lp1K7a2towZM4bhw4fzzTffsHbtWurVq8fatWspX748P/74ozkOW1tbANauXcsPP/xAaGgonTt3Jjw8nGnTpmFvb0+zZs1Yu3YtnTt3Tje/j5KcnMzAgQN5/fXXeffddzl27BjvvvsudnZ3P9rh4eEMHz6c2bNnExAQwN69e+nZsydVqlShatWqFC1alE2bNtGxY0fz5yFv3rz4+fmle7/vMZlMxMXFZbj94zAajfxh48YfF+KBeADORV7n/PXYRx53+p8YtoRepGaZwk8hSvk3o9GY6m+xHMqdZVLeLJdyZ5lye95MJlO6NQFksTACaNKkCfB/08scHBz45JNPsno6ALp27YqLiwsuLi40a9aMXbt2MWDAAJYvX06bNm2oWLEiAA0aNGDZsmWsX7+e3r17A1C3bl0qVKgAQM+ePVm1ahVnz56lfPnyj+zz119/pWbNmlSpUgWA/v37s2TJklRt3Nzc8Pf3B+DmzZv8+uuvrFixgoIFCwLw9ttv07x5c8LDw9O9ltWrV9OkSRPzCFDTpk1ZuHAhmzZt4vnnn3+s+7dlyxY8PDyoX78+AG3btuXbb78172/WrBn16tXD1dUVuJvDmTNncvnyZUqWLJnqXNHR0WzYsIGNGzeSP39+AN577z3q1KnD9evXMxTPG2+8gYuLC9WrV8fFxYWXX36Z4sWLA1CxYkXzNMqM5Pdhjh49ypUrV+jbty+Ojo74+PgQEBBgHoUrXrw4e/fuNV+Dn58fhQoV4u+//6ZChQo0b96cn3/+2VwYbd68mSZNmpiLu4xITEwkNDQ0w+0fm03qd/JijHce0jC1A8dPU9B49UlEJBkUFhaW0yFIFil3lkl5s1zKnWXKzXlzcHBIt02WCqPk5GQWLFjAihUruHbtGocPH8ZoNDJ58mQ++OCDDHWcljJlyph/dnNzIyoqCoALFy6wfft2Fi5caN5vMplSFRL3L+BQrFgxAKKiotItjCIjIylRooT5d3t7+wdGKe49zMPd5clNJlOq/u4VFeHh4eZq9GHXEh4e/sC0uBIlSqQqqrIqMjLSfO33lCr1fy/d37p1i88++4y9e/dy8+ZN89SqtL57Kjw8nJSUFJo1a5Zqu62tLZcuXeKZZ55JN57nnnvO/LOTkxNFixY1/+7o6Eh8/N0Rj4zk92GuXLmCi4uLufABHljMY9GiRaxbt46oqChSUlJISEgwX3PLli358ssvuXTpEkWKFHkgjoywt7fH3d09U8dkldFopEbYZdzc3HB0dASgDK6szMDMv6oV3PHUiFGOMBqNhIWFUaZMGZydnXM6HMkE5c4yKW+WS7mzTLk9b6dPn85QuywVRgsWLGDZsmV069aNL774AoC4uDgOHjzI9OnTGT58eFZOm+odCJPJZH7ws7GxYciQIfTq1SvDxwLm4x8lraLg/vdyAPO0rPTcP0T3sGvJyLFZld61TJ48mePHj7Ns2TJKlSrFxYsXzaNL/3ZvsYcdO3aYR8bul5FC7t/X9LAFJDKS34dJ65qTk5PNP//0008sWbKEOXPmUKNGDWxsbKhTp455f+nSpalcuTLr1q3jxRdfpFChQnh5eWUqBoPBQJ48eTIde1blJ4HSBfKY+yxb0IXRGw8/sPDC/dwLu1Lfs2S2fM4k65ydnZ/qZ0Wyj3JnmZQ3y6XcWabcmreMPv9kafGFNWvWMHfuXHr27GnuqFChQkyfPp0NGzZk5ZQAXLx40fxzREQERYoUAe6Oepw8eTJV23sjN/fc/6B++fJlgFQjFA9TpEgRrly5Yv49ISHBfHxaSpQogY2NTaqhwntTwu4fnXnUtfx7mPH8+fMPTGXLin9fy/2xAYSEhPDGG2+Y4zx+/PhDz1W8eHFsbGxS3ffExEQiIyMfO85/y0h+H6ZIkSLExsamWsjh/vsbEhKCr68vL7/8MjY2Nly7do2rV1NPJ2vRogW//PILmzdvfmCEzBIYDAYmNauCzUP+o7cxGJjYtIqKIhEREZFHyFJhFBERwYsvvvjA9tKlS2dq+WZHR0fOnz9vfqhduHAh0dHRXLx4kXXr1lGvXj0A2rdvz8aNG9mxYwfJycns3buXpk2bEhISYj7Xli1bOHr0KHfu3GHhwoVUqFAh1RS4h/H392f37t0cOXKEO3fuPLAq3b/ly5ePBg0aMGvWLG7cuMGNGzeYMWMGvr6+qabgPexa2rZty/r16zlw4AAJCQn88MMPnDlzhtdffz3D9+1R1xIaGsr27dtJSEjg+++/N0/hg7tT2w4ePEhiYiJHjx5l9erVAOY2jo6OhIeHc+PGDVxdXWnSpAnTp0/nypUr3Llzh2nTptGrVy9MJhNOTk7A3SLk/qIkKzKS34epVKkSBQoUYP78+SQkJLB//37z+0VwdxrjiRMnuHnzJlFRUYwcORI3N7dUBV6TJk04deoUGzdutMjCCKCVVymCuvnjXtg11Xb3wq4EdfPX9xiJiIiIpCNLU+meeeYZTp48iYeHR6rtv//+O4ULZ/wdhg4dOjBlyhTzQ3adOnVo2bIl0dHRNGjQgDfffBOAmjVrMmzYMMaOHcvVq1cpXrw4Y8aMwdvb23yu1q1bM3nyZA4fPkz58uWZMmVKhmJo3LgxJ06coF+/fiQnJ9OlSxeqVKnyyO8sGjNmDB9//DEBAQE4Ozvzyiuv8Nlnn6Vq87BrefXVV3nnnXcYMmQI0dHRlC9fnoULF6Z6JymrKlWqxMiRIxk7dizR0dE0a9aMJk2amJcKHzp0KMOGDaN69epUrlyZSZMmMW7cOAYOHMjixYtp3bo1I0eOpFmzZuzYsYNRo0Yxfvx4Xn/9dUwmEz4+PsydOxeDwUDhwoVp2LAhQ4YMoX379uaFL7IiI/l9GCcnJ+bMmcPHH3/M4sWL8fHxoWfPnixYsAC4+xnbt28fderUoUSJEowZM4YjR44we/ZsihQpwptvvkn+/PmpW7cukZGRqUb9LE0rr1K0rFiSXWejiIg2Uiy/M7XKFtFIkYiIiEgGGExZ+HKTuXPnsmbNGrp3785nn33GlClTOHr0KCtWrKBHjx4EBgZm6nz79u2ja9euHDlyJEPvBf1bQEAAffr0Ma8slpMe91okZ3Tp0oVmzZrRrl27TB13b1Qrs+8lZVVcXByhoaF4enrmyjm8kjblzXIpd5ZJebNcyp1lyu15y+jzWpZGjPr374+joyNz584lMTGRd999l8KFC9OvXz969OiRlVOK5IiUlBRWrlzJ+fPnad68eU6HIyIiIiI5JEuFkcFgoFevXvTq1YvY2LtfLOni4pKtgWWHjRs3MmzYsIfur169eqaXZs4pR44coVOnTg/dX6xYMX755ZenGNHTUa1aNfOy3mnZtGlTht4le5gqVarw7LPPMnPmTPOUThERERGxPpkujJKSkvD19WX//v0YDIZsKYh8fX05ceJElo/ftm1bmtsbN25M48aNs3zerHjca3kYb2/vDC1G8F+zf//+J3r+v/7664meX0REREQsQ6ZXpbOzs+P5559/4g+sIiIiIiIiT0uWptLVqlWLYcOG8eKLL1KqVCns7e1T7R8yZEi2BCciIiIiIvI0ZKkw+vHHHzEYDISGhhIaGppqn8FgUGEkIiIiIiIWJUuF0cPe6REREREREbFEWSqMEhISHrovOTkZZ2fnLAckIiIiIiLytGWpMPL29sZgMDx0/7+n14mIiIiIiORmWSqMJkyYkKowSklJITw8nA0bNhAYGJhtwYmIiIiIiDwNWSqMWrduneb2li1bMnnyZFq0aPFYQYmIiIiIiDxNmf4eo0cpXbo0Z8+ezc5TioiIiIiIPHHZWhjt37+fmJiY7DyliIiIiIjIE5flL3j9t8TERKKjo3nzzTcfOygREREREZGnKUuFUYcOHR7Y5ujoyPPPP0+dOnUeNyYREREREZGnKkuFUdWqVfHz83tgu9FoZMOGDTRp0uSxAxMREREREXlasvSOUb9+/dLcfufOHT7++OPHiUdEREREROSpy9SI0apVq1i9ejUJCQlpTqeLiorCyckp24ITERERERF5GjJVGPn7+3Pnzh1CQkIoW7bsA/tffPFFfYeRiIiIiIhYnEwVRkWLFqVLly5EREQwbNiwNNucPHkyWwITERERERF5WrL0jtG9oiglJYWEhATzn7CwMLp06ZKtAYqIiIiIiDxpWVqV7uLFi7z//vscPXqU5OTkVPvc3d2zJTAREREREZGnJUsjRuPHjydv3ryMGjUKW1tbPvnkE9q3b4+Pjw/Lli3L7hhFRERERESeqCwVRocPH2bGjBm0b98eW1tb3njjDT7++GMaNmzIN998k90xioiIiIiIPFFZKoySk5PJkycPAHZ2diQkJADQsWNHfvzxx+yLTkRERERE5CnIUmFUrlw5vv76a5KTkylWrBibNm0CICIiAqPRmK0BioiIiIiIPGlZKozefvttZs+ejdFo5I033mDEiBG0bNmSN954g9q1a2d3jCIiIiIiIk9UlgqjV199lR07duDi4kL37t2ZPHkyVatWJTAwkM8++yy7Y5QnaO7cuXTu3PmJnHvnzp14eHgA8Oeff+Ll5WWedilPhslkYueZSFYcOsfOM5GYTKacDklERETEImRpuW6AQoUKAZCUlETTpk1p2rRptgUl2Wvz5s14eHhQunTpB/YFBgYSGBj4xGOoXr06ISEhGWq7evVqAgICKFiw4BOO6r/lp5ALDP/5IGeuxZi3lS/kyqRmVWjlVSoHIxMRERHJ/bK8+MLXX39NQEAAVatWBcBoNDJ27FiNCORCM2fO5Pz58w9s//d3UOUGycnJTJw4kRs3buR0KBblp5ALtFu8M1VRBHDmWgztFu/kp5ALORSZiIiIiGXIUmG0YMECli1bRufOnc1TdeLi4jh48CDTp0/P1gDl8TRv3pxTp04RGBhI165d8fDwYMWKFdSoUYN169Yxa9Ys2rVrB0BQUBD16tVj1apV1KpVC19fXyZOnEhKSkqG+goLC6NDhw74+PjQtm3bVMXYvn378PDwID4+HoCvv/6aunXrUqlSJRo2bMjatWsBqFGjBjExMbRo0YLZs2cDEBwcTOPGjalcuTIBAQF8//335vNOnz6d/v37M3/+fF555RVq1KjBpEmTzPuNRiMffvgh1apVw9fXl9GjR5uL94SEBMaNG4evry81atSgd+/eXLiQ+wuIWzhwxZjM5duJXL6dyKXYBIYGHyDlIdPmUkwmRqw7qGl1IiIiIo+Qpal0a9asYe7cubz00kvMmDEDuDu1bvr06fTo0YPhw4dna5CSdcHBwXh4eDB37lzKlStHvXr1+OOPP9i+fTvOzs7MmTPH3NbOzo6rV69y4sQJtm3bxqlTp+jSpQsvvPACrVu3TrevESNGULx4cRYuXEhERAQDBw5Ms93BgwdZsmQJQUFBuLm5sXfvXgIDA6lVqxZr166lXr16rF27lvLlyxMeHs7w4cOZPXs2AQEB7N27l549e1KlShUqVKiAnZ0dhw4dwsfHh99++43ff/+dt956i+bNm+Pp6cmsWbM4e/YsmzdvBqB3797MmjWLoUOHMmfOHE6ePElwcDD58+dn5syZBAYGEhwcjI1Nxv/NwGQyERcXl+H2j8NoNPKHjRt/XIgH7haZ5yKvc/567COPO/1PDFtCL1KzTOGnEKX8273VOrVqp+VR7iyT8ma5lDvLlNvzZjKZMBgM6bbLUmEUERHBiy+++MD20qVLawqUBWjWrBl58+ZNc198fDyBgYE4ODjw0ksvUadOHXbt2pVuYXT16lUOHTrEmDFjyJMnD+XLl6dly5ZMmTLlgbbXr1/HxsYGZ2dnDAYDfn5+HDhwABsbG8LDw1O1LV68OHv37iV//vwA+Pn5UahQIf7++28qVKgAgK2tLb1798bGxoZXX30VV1dXzp07h6enJ6tXr+bTTz81v680YcIEoqOjAVixYgXTpk2jaNGiAAwaNIilS5dy9OhRvL29M3w/ExMTCQ0NzXD7x2aT+l2xGOOdDB124PhpChqvPomIJIPCwsJyOgTJIuXOMilvlku5s0y5OW8ODg7ptslSYfTMM89w8uRJ84pj9/z+++8ULqx/kc7tihUr9tB9+fLlS7XogZubG3/99Ve654yMjHzg3Gkt9gDg7++Pt7c3derUwc/Pjzp16tC8eXPzlwb/26JFi1i3bh1RUVGkpKSQkJCQ6l02Nze3VCM8jo6O3Llzh1u3bnHr1i2KFy9u3nevmLp16xY3b96kb9++qf4FISUlhYiIiEwVRvb29ri7u2e4/eMwGo3UCLuMm5sbjo6OAJTBlZW70z+2agV3PDVilCOMRiNhYWGUKVMGZ2fnnA5HMkG5s0zKm+VS7ixTbs/b6dOnM9QuS4VRmzZtGDBgAN27dyclJYVffvmFo0ePsmLFCnr06JGVU8pTZGf38LT/+z0Uk8lkfgB/lLQW3XjY4g4ODg7Mnj2b48ePs337dpYtW8a3337LDz/88EDbn376iSVLljBnzhxq1KiBjY0NderUSdUmvWlvab0jde+Y5cuX4+Xl9cjj02MwGB5a1D0J+UmgdIE85j7LFnRh9MbDDyy8cD/3wq7U9yyZoWFkeXKcnZ2f6mdFso9yZ5mUN8ul3Fmm3Jq3jD7/ZGnxhf79+9O+fXvmzp1LYmIi7777Lj/99BP9+vWjX79+WTml5BIxMTGppkNGRESYp5o9SpEiRczt73nYcGpiYiKxsbFUqFCB/v37s2bNGuDuiOO/hYSE4Ovry8svv4yNjQ3Xrl3j6tWMTQfLnz8/+fLlS7UIxN9//01wcDCurq4UKFCAkydPpjrm31P5LIHBYGBSsyrYPOQ/ehuDgYlNq6goEhEREXmETBVGgwcPBu4+iPXq1Yvdu3fzzjvvsH//fnbv3k2vXr0y9dK6PB2Ojo6cP3+emJiHjyjcY29vz5w5c4iLi+Pvv/9mx44d1KtXL93jSpQoQfny5Vm4cCFGo5FTp07x888/p9l2wYIF9OnThytXrgBw7tw5bt26RYkSJXBycgLuFlUxMTG4ublx4sQJbt68SVRUFCNHjsTNzc08dS89bdq0YcGCBURFRXHjxg3Gjx9vLoY6dOjAvHnzOHPmDImJiSxatIg2bdrk2hcHH6WVVymCuvnjXtg11Xb3wq4EdfPX9xiJiIiIpCNTU+m2bdv2wLb58+fzzjvvZFtAkv06dOjAlClTePXVV9Ntmz9/fsqVK0eDBg1ISkqiU6dOGSqM4O73JX3wwQf4+flRvnx5+vTpw4gRI0hMTEzVrmfPnkRGRvLGG29w+/ZtihYtytChQ/H09ASgYcOGDBkyhPbt2/POO++wb98+6tSpQ4kSJRgzZgxHjhxh9uzZ5lGqRxkwYAA3b96kcePG2NnZ0aBBA/NqeYGBgURHR/Pmm28SHx9PhQoVmD9/fq6cG5sRrbxK0bJiSXadjSIi2kix/M7UKltEI0UiIiIiGWAwZeLLTby9vTly5Ei628Qy/fjjj0ydOpU9e/bkdCgWIyQkBOCx31PKqLi4OEJDQ/H09MyVc3glbcqb5VLuLJPyZrmUO8uU2/OW0ee1TM17S+tfnvWv0SIiIiIiYumytCqdWJ/mzZtz7ty5h+5fuHAh1atXf4oRiYiIiIhkHxVGYta6deuHfpFrcHDwU45GREREROTpyVRhlJiYyNChQ9PdNnXq1MePTERERERE5CnJVGFUtWpVoqKi0t0mIiIiIiJiSTJVGH333XdPKg4REREREZEco29jFRERERERq6fCSERERERErJ4KIxERERERsXoqjERERERExOqpMBIREREREaunwkhERERERKyeCiMREREREbF6KoxERERERMTqqTASERERERGrp8JIRERERESsngojERERERGxeiqMRERERETE6qkwEhERERERq6fCSERERERErJ4KIxERERERsXoqjERERERExOqpMBIREREREaunwkhERERERKyeCiMREREREbF6KoxERERERMTqqTASERERERGrZ5fTAYjI4zOZTOw6G8Xl6DiK5ctD7XJFMBgMOR2WiIiIiMVQYWQl/vzzT9577z3u3LnDvn37cjocyUY/hVxg+M8HOXMtxrytfCFXJjWrQiuvUjkYmYiIiIjl0FQ6K7FkyRK8vLz4/fffczqUXCs5OZlvv/02p8PIlJ9CLtBu8c5URRHAmWsxtFu8k59CLuRQZCIiIiKWRYWRlbh58yZlypTBxibjKTeZTKSkpDzBqHKXY8eO8c033+R0GOm6hQNXjMlcik1gaPABUkymNNulmEyMWHcQ00P2i4iIiMj/0VQ6K9C5c2f279/PgQMHWLFiBYsWLeLTTz/lxIkTODk58dprrzFy5Ejs7e3Zt28f/fr1Y8iQIUybNo358+dTrVo1li1bxoIFC7hx4wZly5Zl6NCh1KxZM0P9Hz169KH9/e9//+Odd97h888/Z/z48dy6dYtu3brx2muvMWLECMLDw/H392fq1KnY2dmRkpLCl19+yZo1a7h+/TrPP/88H374Id7e3gB4eHgwf/58/P39AVi+fDnz589n27ZtnD9/ngYNGrBo0SImTJjAxYsXefHFF5k2bRpRUVF07NiRpKQkvLy8mD9/Pi+//HKGrs9kMhEXF5e15GSS0WjkDxs3/rgQz7nICM5fj31k+9P/xLAl9CI1yxR+KvFJ2oxGY6q/xXIod5ZJebNcyp1lyu15M5lMGXr3WoWRFVi6dCldunShUqVKvPfee9SvX5/XXnuN7777jqtXr9KhQwfc3d3p0qULAImJiZw/f569e/fi4ODAnj17mDt3LvPmzaNChQps27aNwMBANm3ahJubW7r9Dxo06KH92draYjQa+f3339m0aRObNm1ixIgRnDlzhsWLF3P9+nVatGjBrl27qFu3LsuWLWP16tV89dVXlClThu+++47u3buzZcsWChYs+Mg4bG1tAVi8eDELFy7E0dGRtm3bsmDBAj766CPGjx/P1KlT2bNnT6bub2JiIqGhoZk65rHYlAYgxngnQ80PHD9NQePVJxmRZFBYWFhOhyBZpNxZJuXNcil3lik3583BwSHdNiqMrNDatWuxt7fHzs4ONzc3qlWrxtGjR837ExMTadeuHY6OjsDdUZc2bdpQsWJFABo0aMCyZctYv349vXv3fuz+UlJS6NSpE05OTtStWxeTyUS9evUoWLAgBQsWpESJEpw/fx6A1atX07FjRzw8PADo2bMn33zzDb/99hutW7fO0PV37NiRZ599FgB/f3/OnTuXoeMext7eHnd398c6R0YZjUZqhF3Gzc2NMriycnf6x1St4I6nRoxylNFoJCwsjDJlyuDs7JzT4UgmKHeWSXmzXMqdZcrteTt9+nSG2qkwskI7duxg3rx5XLhwgaSkJJKSkmjUqFGqNsWKFTP/fOHCBbZv387ChQvN20wmE88//3y29ffcc88B4OTkBEDRokXN+5ycnEhISAAgPDyc0qVLm/fZ2NhQvHhxwsPDMxQLQPHixc0/Ozo6Eh8fn+Fj02IwGMiTJ89jnSMz8pNA6QJ5qOBWiNEbDz+w8ML93Au7Ut+zpJbuziWcnZ2f6mdFso9yZ5mUN8ul3Fmm3Jq3jD4HafEFK3P+/Hnef/993njjDfbt20dISAjNmjV7oJ29vb35ZxsbG4YMGUJISIj5z9GjRxk5cmS29ffvRSEys0gEPPwDn9bCA/+VIsFgMDCpWRVsHnI9NgYDE5tW+c9cr4iIiMiTpMLIyhw7dgxnZ2e6du2Kg4MDJpOJEydOPPKYUqVKcfLkyVTbLl++nKHVzrLSX3qx3D9/NSkpifDwcEqWLAncHQFKTEw0779y5UqW+7IErbxKEdTNH/fCrqm2uxd2Jaibv77HSERERCSDVBhZGTc3N27fvs3ff/+N0Wjk008/xdbWlqioqIcWOu3bt2fjxo3s2LGD5ORk9u7dS9OmTQkJCXki/T1KmzZtWL58OadOneLOnTvMmzcPk8lEQEAAAGXKlGH//v3A3aLo119/zfC5nZyciImJITIyMteuqpKWVl6lOD6iBdsDG/B959r89nYDjo9ooaJIREREJBNUGFmZypUr06lTJ7p27UrDhg0pW7YsH374ISdPnuT9999P85iaNWsybNgwxo4dS+XKlfn4448ZM2aMeYns7O7vUTp06EDTpk3p1q0bNWvWZO/evSxZsoR8+fIB8MEHH7B161bq1avHRx99ROfOnUlKSsrQuV9++WVKlChB48aN+e233zIdW04yGAz4ly9Ke58y1C5XVNPnRERERDLJYNK3P4pk2b1RMy8vr6fSX1xcHKGhoXh6eubKlxslbcqb5VLuLJPyZrmUO8uU2/OW0ec1jRiJiIiIiIjV03Ld8liqVav2yOWuN23alGp5bBERERGR3EiFkTyWewsdiIiIiIhYMk2lExERERERq6fCSERERERErJ4KIxERERERsXoqjERERERExOqpMBIREREREaunwkhERERERKyeCiMREREREbF6KoxERERERMTqqTASERERERGrp8JIRERERESsngojERERERGxeiqMRERERETE6qkwEhERERERq6fCSERERERErJ4KIxERERERsXoqjERERERExOqpMBIREREREaunwkhERERERKyeCiMREREREbF6KoxERERERMTqqTASERERERGrp8JIRERERESsnl1OByAij89kMrHrbBSXo+Moli8PtcsVwWAw5HRYIiIiIhZDhZGFWrNmDTNnzmTbtm05HUq2mTVrFrt27SIoKCinQ7EoP4VcYPjPBzlzLca8rXwhVyY1q0Irr1I5GJmIiIiI5dBUOguyevVqrl+/DkDLli3/U0WRZM1PIRdot3hnqqII4My1GNot3slPIRdyKDIRERERy6LCyEIkJyczceJEbty4kdOhZInJZCIlJeWp9ZecnPzU+nrabuHAFWMyl2ITGBp8gBSTKc12KSYTI9YdxPSQ/SIiIiLyf6x6Kt2FCxcYPXo0hw4d4plnnqF379507tyZK1euMHbsWA4ePIiDgwP+/v588MEHuLi48L///Y8BAwbwxRdfMH78eK5evUr16tWZNm0aLi4uJCYm8tlnn7FhwwacnJx4//33mTlzJr169aJdu3YkJCQwceJE1q9fj8lkwtvbm9GjR1Oq1N0pT19//TXLly/n+vXrPPfccwQGBtKiRQtq1KhBbGwsLVq0oF+/fhQrVoypU6eyZ88eAE6dOsX48eMJDQ3F1dWVRo0aMWjQIBwcHFi1ahXfffcdPXv2ZPr06cTExFCvXj0mTpyIra1tuvfpxx9/ZN68eURGRvLMM8/QpUsXevbsSXh4OPXq1WPDhg2UL18egClTpnD48GG+++479u3bR79+/RgyZAjTpk1j/vz5VKtWjQULFrBo0SJiY2OpUqUK48aNo3jx4ub+vv/+e+bOnUt8fDxt2rRh+PDhANy4cYMxY8awb98+kpOT8fHxYdy4cbi5uQHg4eHBhx9+yPz58+nUqRP9+/dn5cqVzJs3j+joaNq3b090dDRxcXFMnToVgGXLlrFgwQJu3LhB2bJlGTp0KDVr1szU58hkMhEXF5epY7LKaDTyh40bf1yI51xkBOevxz6y/el/YtgSepGaZQo/lfgkbUajMdXfYjmUO8ukvFku5c4y5fa8mUymDL17bdWF0ZAhQ6hcuTJfffUV586do1OnTpQuXZrp06fzwgsvsGXLFu7cucOAAQMYPXo006ZNw9bWFqPRyIYNG/jhhx+IjY2lZcuWrF69mu7du/Pdd9+xceNGlixZQrFixRg5ciRRUVHY2d291XPmzOHkyZMEBweTP39+Zs6cSWBgIMHBwfz1118sWbKEoKAg3Nzc2Lt3L4GBgdSqVYu1a9dSr1491q5dS/ny5fnxxx/N15GQkEDPnj1p3bo18+bN4+rVq/Tp0wdbW1uGDh2Kra0tly9f5tixY/zyyy+cO3eOtm3b0rBhQ+rXr//Ie3TlyhXGjRvHypUr8fDw4NixY/Ts2ZOXX36ZfPnypXuPExMTOX/+PHv37sXBwYEdO3bwzTffsHDhQsqXL8+4ceMYMmQIK1euBOD8+fPExsaybds2fv/9d9566y2aN2+Op6cnkydP5saNG2zZsgVbW1sGDRrEhAkTmDVrlrm/rVu3EhwcTIECBQgJCWH06NHMmDGDevXqsWDBAlavXk3dunUB2LNnD3PnzmXevHlUqFCBbdu2ERgYyKZNm8zFVkYkJiYSGhqa4faPzaY0ADHGOxlqfuD4aQoarz7JiCSDwsLCcjoEySLlzjIpb5ZLubNMuTlvDg4O6bax2sLo+PHjhISE8M033+Dk5ISnpyezZ8/G2dmZv//+m3nz5uHq6oqrqytvvfUWAwcOJCEhAbg7TatXr17m/VWrVuXcuXMA/Pbbb7z++uu88MILAAwePJiNGzea+12xYgXTpk2jaNGiAAwaNIilS5dy9OhRrl+/jo2NDc7OzhgMBvz8/Dhw4AA2NjaEh4c/9Fp27txJXFwcb7/9Ng4ODpQqVYrOnTvz7bffMnToUABu377Nu+++a77W8uXLc/bs2XTv040bN0hJSSFv3rwAvPjii/zvf/9LN6Z7EhMTadeuHY6OjgCsWrWK119/HU9PT/P9+eOPP8zT7Ozt7enTpw8Gg4FXX32VfPnyce7cOTw9PRk7dixJSUnkyZMHgHr16vHVV1+l6q9BgwYULFgQgB07dvDCCy/QqFEjAPr27cuKFSvMbZcvX06bNm2oWLGi+dhly5axfv16evfune613WNvb4+7u3uG2z8Oo9FIjbDLuLm5UQZXVu5O/5iqFdzx1IhRjjIajYSFhVGmTBmcnZ1zOhzJBOXOMilvlku5s0y5PW+nT5/OUDurLYwuXryIi4sLBQoUMG975ZVX+PXXX8mTJw/PPvuseXvJkiVJTEwkMjLSvO3+qV+Ojo7Ex8cDEBkZSUBAgHlf6dKlzUXFrVu3uHnzJn379k01nJeSkkJERAR169bF29ubOnXq4OfnR506dWjevLm5EHiY8PBwihUrlqoSLlmyJJcvXzYXHAUKFDDH8e+YH8XT05M2bdrQqFEjqlWrxquvvkrLli155pln0j32nmLFipl/vnDhAtWqVTP/XqhQIRo3bmz+3c3NLdW9cXBw4M6duyMjp0+fZvLkyYSGhhIXF0dKSkqq/P27r8jISEqUKGH+3WAwUKFChVSxbN++nYULF5q3mUwmnn/++Qxf273zppej7JSfBEoXyEMFt0KM3nj4gYUX7ude2JX6niW1dHcu4ezs/FQ/K5J9lDvLpLxZLuXOMuXWvGX0OchqCyMg04sB3H9TbWzSXrcirRfd77W99/fy5cvx8vJK8/jZs2dz/Phxtm/fzrJly/j222/54YcfMhVnWh4Wb0aMHj2abt26sW3bNn755Rfmz5/PqlWr0myb1vXb29un+v1R9/1RH9x33nkHHx8ffv31V/Lly8fq1auZPn16qjb3pizei+Xf57v/PtjY2DBkyBB69er10D5zM4PBwKRmVWi3eGeaCzDYGAxMbFpFRZGIiIhIBljtqnQlS5YkLi6Oq1f/792LLVu2UKBAAeLi4oiKijJvP3/+PI6Ojubpb49SuHBhrly5Yv794sWLxMTc/Rd9V1dXChQowMmTJ1Mdc29KWmJiIrGxsVSoUIH+/fuzZs0aAH7//fd0r+XSpUvmqX5wd45niRIlHqsggrtFTHR0NKVLl6ZHjx6sWLGC559/nl9//RUnJydz3Pfcf+0Pi/X++afXr19n4cKFqc6RluvXr3Pp0iV69+5tfrfp+PHjjzzm37kwmUyp3gUqVarUA7m4fPmyRa3i1sqrFEHd/HEv7Jpqu3thV4K6+et7jEREREQyyGoLowoVKlCxYkVmzJhBXFwcJ0+e5KOPPsJoNOLl5cW0adOIjY0lIiKCr776itdff/2BkY+0vPLKK6xfv56zZ88SGxvLjBkzUg0pdujQgXnz5nHmzBkSExNZtGgRbdq0wWg0smDBAvr06WN+mD937hy3bt2iRIkS5iIkLCzMXGjdU7t2bVxdXZkzZw537tzhzJkzLF26lJYtWz72fVq/fj1t27Y1v48UERHBlStXKFGiBAULFiRfvnwcOHAAgJMnT7Jv375Hnq9NmzZs3LiRI0eOkJCQwJw5c9i0aVO69zZfvnzkyZOHP/74g+TkZNasWUNISAixsbHcvn07zWNeeeUVjh07xvbt20lMTGTevHmpisf27duzceNGduzYQXJyMnv37qVp06aEhIRk5hbluFZepTg+ogXbAxvwfefa/PZ2A46PaKGiSERERCQTrHoq3dSpUxk9ejR+fn4888wzvP322/j7+1O2bFnGjBlDzZo1yZ8/P6+99hrvvfdehs7Zq1cvwsLCaNeuHQULFuTDDz80L1YAEBgYSHR0NG+++Sbx8fFUqFCB+fPn4+zsTM+ePYmMjOSNN97g9u3bFC1alKFDh5oXKmjYsCFDhgyhffv2qd6VcXBwYM6cOYwfP55vv/2WZ599lpYtW9KvX7/HvkdNmzbl3Llz9OzZkxs3blCoUCHatm1rXs1uzJgxTJkyhcWLF+Pp6UmHDh3MS4inpW7durz11lsEBgZy+/ZtqlSpwrRp09KNw87Ojo8//pjPP/+cWbNm0aRJE2bPnk2nTp1o0qQJO3bseOCYGjVqMHjwYEaNGkVSUhLdu3enevXq5qllNWvWZNiwYYwdO5arV69SvHhxxowZg7e3dxbvVs4xGAz4l09/RFNERERE0mYwWdK8IQuRkJBgXgghMTGRypUr88033+Dn55fDkVmf+3MB0LlzZ6pVq8agQYOy5fz3Rpce9s5YdouLiyM0NBRPT89c+XKjpE15s1zKnWVS3iyXcmeZcnveMvq8ZrVT6Z6UNWvWULduXc6dO0dCQgLz5s0jX758T+3BWf5PeHg4lStXZtu2baSkpLBr1y4OHjxInTp1cjo0EREREcllrHoq3ZPQvHlzTp06RefOnYmLi+OFF15g7ty5uLi45HRoaWrevLn5O5jSsnDhQqpXr/4UI8o+JUqUYMKECUyaNInBgwfz3HPPMW7cOCpXrpzToYmIiIhILqPCKJvZ2Njw/vvv8/777+d0KBkSHByc0yE8US1btsyWRShERERE5L9NU+lERERERMTqqTASERERERGrp8JIRERERESsngojERERERGxeiqMRERERETE6qkwEhERERERq6fCSERERERErJ4KIxERERERsXoqjERERERExOqpMBIREREREaunwkhERERERKyeCiMREREREbF6KoxERERERMTqqTASERERERGrp8JIRERERESsngojERERERGxeiqMRERERETE6qkwEhERERERq6fCSERERERErJ4KIxERERERsXoqjERERERExOqpMBIREREREaunwkhERERERKyeCiN5pDNnzuDh4UF4eHiG2i9atIiqVasyYMCAx+p38ODBjBgxIkNt//nnH1q3bk2lSpUICQkhICCA5cuXP1b/lsBkMrHzTCQrDp1j55lITCZTTockIiIiYrHscjqA/6LVq1cTEBBAwYIFczqUp+7LL79kwIABdOvW7an1uXHjRq5evcrevXtxcnJ6av3mpOBjlxi9+W/OXIsxbytfyJVJzarQyqtUDkYmIiIiYpk0YpTNkpOTmThxIjdu3Mj0sUlJSU8goqfr5s2blCtXDoPB8NT6vHXrFm5ubjg7Oz/VfnPK9ovRdFmxN1VRBHDmWgztFu/kp5ALORSZiIiIiOVSYfQYvv76a+rWrUulSpVo2LAha9eupUaNGsTExNCiRQtmz54NwP79+2nXrh1VqlShQYMGfPPNN+ZpT7NmzaJv374MGTKEqlWrApCQkMC4cePw9fWlRo0a9O7dmwsXMv6wGxwcTOPGjalcuTIBAQF8//335n3Tp0+nf//+zJ8/n1deeYUaNWowadIk8/5r167Ru3dvfHx8eP311zly5EiG+kxISMDLywuAwMBA+vXrh6enJzExdx/e79y5Q8WKFfn888/Nx8yYMYN33nkHgKCgIAICAqhatSpjx44lJSUlQ/1+8cUXzJ07lyNHjuDl5fVAvPHx8YwaNQo/Pz98fHx48803OXnypHn/uXPn6NChAz4+PnTq1IkNGzbg4eFBYmJihvp/2kwmEzMORZHykFlzKSYTI9Yd1LQ6ERERkUzSVLosOnjwIEuWLCEoKAg3Nzf27t1LYGAgixcvpm3btqxdu5by5cvzzz//0KtXL0aMGMEbb7zBqVOn6N27Ny4uLnTo0AGAv/76i6FDhzJ58mQA5syZw8mTJwkODiZ//vzMnDmTwMBAgoODsbF5dC0bHh7O8OHDmT17NgEBAezdu5eePXtSpUoVKlSogJ2dHYcOHcLHx4fffvuN33//nbfeeovmzZvj6enJhAkTiI+P57fffsNoNDJ06NAM3Q8HBwdCQkLw8PBg7ty5+Pv7U7duXf766y9q167NX3/9RalSpThw4ID5mP3799OoUSPOnj3L6NGjmT17Nv7+/qxdu5ZPP/2URo0apdvvoEGDsLW1ZdeuXQQFBT2wf/78+fzxxx/8/PPP5MuXj/HjxzNixAh+/PFHAAYOHEi5cuVYsGABFy5cYNCgQQDY29tn6LrhbrESFxeX4faPY/vJy1yOTXhkm9P/xLAl9CI1yxR+KjFJ+oxGY6q/xXIod5ZJebNcyp1lyu15M5lMGZpVpMIoi65fv46NjY15+pafnx8HDhzg8uXLqdqtW7cONzc3OnbsCMBLL71EixYtWL9+vbkwsrGxoW3btuaErVixgmnTplG0aFHg7sP/0qVLOXr0KN7e3o+Mq3jx4uzdu5f8+fMD4OfnR6FChfj777+pUKECALa2tvTu3RsbGxteffVVXF1dOXfuHJ6enmzZsoWpU6eSP39+8ufPT6dOndi/f3+W7pGvry+HDh2idu3a7N+/n2bNmvHdd9+RkJCAwWDgyJEjjB07li1btuDh4UH9+vUBaNu2Ld9++22W+vy3vn370r17d1xcXABo2LAhP/zwA0lJSURFRXHy5EkmTpxI3rx58fT0pFGjRnz11VeZ6iMxMZHQ0NBsiTc9IWG3MtTuwPHTFDRefcLRSGaFhYXldAiSRcqdZVLeLJdyZ5lyc94cHBzSbaPCKIv8/f3x9vamTp06+Pn5UadOHZo3b/5Au/DwcMqUKZNqW8mSJfnll1/Mv7u5uZmLolu3bnHz5k369u2bqrJNSUkhIiIi3cII7q4Mt27dOqKiokhJSSEhIYGEhP8bZXBzc0s18uTo6MidO3e4ceMGd+7coVixYuZ9pUuXTv9mPISvry/BwcEA/Pnnn7z99tvs37+fI0eOYGtrS4ECBShXrhyRkZGp+gQoVSp7FhC4cuUKn332GYcPH+bWrbtFRXJyMsnJyVy5cgWAEiVKmNu/9NJLme7D3t4ed3f3bIk3PRE2l+B/l9JtV7WCO54aMco1jEYjYWFhlClTBmdn55wORzJBubNMypvlUu4sU27P2+nTpzPUToVRFjk4ODB79myOHz/O9u3bWbZsGd9++y0zZszI0PH3Fz12dv+XhnsFy/Lly83v7GTGTz/9xJIlS5gzZw41atTAxsaGOnXqpGrzsOl49xdP9yQnJ2c6hntefvllxo8fT3x8PMeOHcPb25vKlStz4MABbGxs8PPze2i/GX3HKD0ffvghNjY2rFmzhmeffZbff/+d7t27A5jfw7k/F+lNVUyLwWAgT5482RJveuq+UIwSLvaExz78HSj3wq7U9yxpFQtRWBpnZ+en9lmR7KXcWSblzXIpd5Ypt+Yto89EWnwhixITE4mNjaVChQr079+fNWvWALB3795U7UqVKsW5c+dSbQsLC6NkyZJpntfV1ZUCBQqkWiAAyPD3CIWEhODr68vLL7+MjY0N165d4+rVjE2pKliwIPb29kRERKSKNavc3NwoVKgQP/74I+XLl8fJyYnKlStz8OBBDhw4YC6MihQpYh69uef8+fNZ7vd+ISEhdOnShWeffRaAEydOmPfd23b/9f7999/Z0u+TYjAYGOBTFJuH/PdtYzAwsWkVFUUiIiIimaTCKIsWLFhAnz59zA/0586d49atW/j4+AB3C4qYmBiaNm1KZGQky5cvJyEhgb/++ovg4GBatWr10HN36NCBefPmcebMGRITE1m0aBFt2rTJ0Attbm5unDhxgps3bxIVFcXIkSNxc3MjMjIy3WPt7e15+eWX+e6774iJieHy5cusWLEig3ckbS+//DJLliyhSpUqAFSqVIkjR44QEhJiLoz8/f0JDQ1l+/btJCQk8P333xMVFfVY/d7z3HPP8eeff5KcnMzu3bvZunUrAJGRkZQqVYoSJUqwcOFC4uLiOHbsGJs3b86Wfp+kuiXz8V2Hl3Ev7Jpqu3thV4K6+et7jERERESyQFPpsqhnz55ERkbyxhtvcPv2bYoWLcrQoUPNS3cPGTKE9u3b8+GHHzJ79mwmTZrEZ599hpubGwMHDqRly5YPPXdgYCDR0dG8+eabxMfHU6FCBebPn5+hOZsdOnRg37591KlThxIlSjBmzBiOHDnC7NmzKVKkSLrHf/rppwwfPhx/f3/c3NwYNGgQBw4cyPJ3LPn6+hIUFMR7770HQL58+cxffHsvnkqVKjFy5EjGjh1LdHQ0zZo1o0mTJty5cydLfd5v1KhRjB49mqCgIGrXrs2MGTPo27cvbdu2Zf369cyYMYOPPvqIV155hapVq9K7d28++OCDx+73SWv+YnHaV3Vn19koIqKNFMvvTK2yRTRSJCIiIpJFBpO+8ESsmMlkIikpybw89w8//MDMmTPZsWNHho4PCQkByNL7YFkRFxdHaGgonp6euXIOr6RNebNcyp1lUt4sl3JnmXJ73jL6vKapdGLVunfvzgcffIDRaCQqKoply5Y9sFiFiIiIiPz3aSqdBTly5AidOnV66P5ixYqlWgb8v9B3v3792LNnz0P3jx8//pHTEtMzfvx4Ro8eTc2aNXFycuLVV181T/sTEREREeuhwsiCeHt7m4cCraXvzH7ZamaVKlWKRYsWPdE+RERERCT301Q6ERERERGxeiqMRERERETE6qkwEhERERERq6fCSERERERErJ4KIxERERERsXoqjERERERExOqpMBIREREREaunwkhERERERKyeCiMREREREbF6KoxERERERMTqqTASERERERGrp8JIRERERESsngojERERERGxeiqMRERERETE6qkwEhERERERq6fCSERERERErJ4KIxERERERsXoqjERERERExOqpMBIREREREaunwkhERERERKyeCiMREREREbF6KoxERERERMTqqTASERERERGrp8JIRERERESsnl1OByAimWcymdh5JpLL0XEUy5eH2uWKYDAYcjosEREREYulwkjEwmy/GE2HTb9w9vpt87byhVyZ1KwKrbxK5WBkIiIiIpZLhZE8UQEBAURGRmJjc3fWpoODA88//zyDBw/G19eXESNGEB8fz/Tp01MdFx8fj7e3N0uWLMHX1zfdfrp06cKBAwewtbUFIF++fFSpUoXevXtTqVKlVG3Pnj3LnDlz2Lt3L7GxsRQsWJA6derw9ttvU7hw4Wy68icj+NglPtgdToop9fYz12Jot3gnQd38VRyJiIiIZIHeMZInbuTIkYSEhBASEsKePXto3Lgxb731FufPn8/Wfnr27Gnu56effsLLy4tu3bqxfv16c5vQ0FDatm1LkSJF+Omnnzh8+DDLli3jzp07tGrVisjIyGyNKTuZTCZG/RLyQFF0T4rJxIh1BzGZHtJARERERB5KI0byVDk5OdGtWzeWLVvG7t27n1g/RYoU4a233sLGxoYxY8bg7++Pq6sr48aNo1atWgwfPtzctlixYnz22Wd07NiRKVOm8Pnnn2eqL5PJRFxcXHZfwgN2h11NNX0uLaf/iWFL6EVqlsndI1/Wxmg0pvpbLIdyZ5mUN8ul3Fmm3J43k8mUoXexVRhJjkhOTsbO7sl//Lp168YXX3zB7t27qV69OgcPHmTZsmVptu3SpQsffvghSUlJmYotMTGR0NDQ7Ar5oQ6E3cpYu+OnKWi8+oSjkawICwvL6RAki5Q7y6S8WS7lzjLl5rw5ODik20aFkTxVt2/fZsWKFdy4cQN/f38OHTr0RPuzt7enZMmSXLx4ETc3NwDKlCmTZtuyZctiNBq5du0aRYsWzVQf7u7u2RHuI11zvgr/u5Ruu6oV3PHUiFGuYjQaCQsLo0yZMjg7O+d0OJIJyp1lUt4sl3JnmXJ73k6fPp2hdiqM5In75JNPmDBhAgCOjo54enqyYMECc6HypBkMBhwcHMyjQMnJyWm2S0lJyfL58+TJk+X4Muo1z1KUK5j3kdPp3Au7Ut+zpJbuzqWcnZ2fymdFsp9yZ5mUN8ul3Fmm3Jq3jD4XqTCSJ27kyJF07NgxzX0ODg7cuvXgFLHbt+8+/D/uvzrExsZy8eJFypYtS4kSJbC1teXs2bNpjgiFhYXh4uKSa1emMxgMjG/oRZcVe9NcgMHGYGBi0yoqikRERESyQKvSSY5yd3fnxIkTD4ziHDx4EEdHR8qVK/dY5//+++/Jly8ffn5+FChQAD8/PxYtWpRm2++++44mTZqYl/zOjZq/WJzPapWgfMG8qba7F3bVUt0iIiIij0GFkeSoFi1akJyczLhx4/jnn39ISEhg7969fPrpp/To0QMXF5csnff69et89dVXzJkzh08//dT8wt1HH33EoUOHGDdunHlp7suXL/Phhx8SFRXF4MGDs+3anpS6JfNxaFBDtgc24PvOtfnt7QYcH9FCRZGIiIjIY9BUOslR+fPnZ9myZUydOpXXX3+dO3fuULJkSXr16kWnTp0yda6FCxeyePFiTCYTefPmxcfHh8WLF1O5cmVzm3LlyvHDDz8we/Zs2rRpQ3R0NIUKFSIgIIDVq1dTsGDBbL7CJ8NgMOBfPuMLRIiIiIjIo6kwkidq27Zt6bYpUaIE06dPf6x+vvvuuwy3LVmyJJMmTXqs/kRERETkv0VT6URERERExOppxEhyvebNm3Pu3LmH7l+4cCHVq1d/ihGJiIiIyH+NCiPJ9YKDg3M6BBERERH5j9NUOhERERERsXoqjERERERExOqpMBIREREREaunwkhERERERKyeCiMREREREbF6KoxERERERMTqGUwmkymngxCxVAcPHsRkMuHg4PBU+jOZTCQmJmJvb4/BYHgqfcrjU94sl3JnmZQ3y6XcWabcnreEhAQMBgNVqlR5ZDt9j5HIY3ja//EbDIanVoRJ9lHeLJdyZ5mUN8ul3Fmm3J43g8GQoWc2jRiJiIiIiIjV0ztGIiIiIiJi9VQYiYiIiIiI1VNhJCIiIiIiVk+FkYiIiIiIWD0VRiIiIiIiYvVUGImIiIiIiNVTYSQiIiIiIlZPhZGIiIiIiFg9FUYiIiIiImL1VBiJWIjw8HB69epF5cqV8fPz4/PPPyclJSWnw5I07Nq1i1deeYXBgwc/sG/9+vU0bNgQLy8vmjZtyp49e3IgQklLeHg4/fv3p0aNGvj5+TFs2DBu3boFQGhoKB06dMDb2xt/f3++/fbbHI5W7jl+/Djdu3enWrVqvPzyy7z77rtERUUB8Pvvv9O8eXO8vLx47bXXCA4OzuFoJS0TJkzAw8PD/Lvylrt5eHhQsWJFvLy8zH/Gjx8P/AdyZxKRXC8lJcXUokUL09ChQ003b940nT592lS3bl3TwoULczo0+Zevv/7a1KBBA1OHDh1MgwYNSrUvJCTE9NJLL5nWr19vMhqNpqCgIFOlSpVMERERORSt3K9p06amESNGmGJjY02RkZGm1q1bmz788ENTXFycqWbNmqZJkyaZYmNjTYcOHTJVq1bN9Msvv+R0yFYvPj7e5OfnZ5o9e7YpPj7edO3aNVPnzp1N/fv3N125csVUqVIl0+LFi01xcXGmrVu3mry8vEyHDx/O6bDlPseOHTPVqFHD9MILL5hMJpPyZgFeeOEF08WLFx/Y/l/InUaMRCxASEgIJ06cYOTIkeTPn5/y5cvTp08fVqxYkdOhyb84OjqyevVqSpcu/cC+H374AX9/f5o0aYKTkxNt27blhRdeYO3atTkQqdwvJiaGihUr8t5775E3b16KFClC69at+fPPP/ntt99ITExk6NCh5M2bl8qVK9O+fXtWrlyZ02FbPaPRyODBg+nbty8ODg4ULFiQhg0bcvr0aX7++WdKly5N165dcXZ2JiAggHr16rF69eqcDlv+v5SUFMaMGUP37t3N25Q3y/VfyJ0KIxELcOzYMYoXL06BAgXM21566SXCwsKIjY3NucDkAV27dsXV1TXNff+vnbsPirJ6/zj+BgE1RJ5yfPar4Qw+tBIGQ6OmpuYgo0IWJAKZmWZK1Jg6jJoPIwUpKBNpQlmaT5jKCDINmjaOGRmolSAKKqQiOElChKKA8PvDLzuuQqH5/QHt5/XX7jk35772XHOze+05e+fk5DBw4ECTtgEDBpCdnf3/EZr8BTs7OyIjI3F2dja2FRUV4eTkRE5ODv369aNNmzbGPuWtZbC3t8ff3x8rKyvq6urIz88nKSmJcePG6XprBRITE2nXrh0TJkwwtilvrUNMTAzDhg1j2LBhvPfee1y/fv1fkTsVRiKtQGlpKfb29iZt9c9LS0ubIyR5CKWlpSbFLdzJ47Vr15onIGlUVlYWmzdv5s0332zw+nNwcKCsrEy/82shLl++zJNPPomPjw8Gg4G333670bzpemsZSkpKWLt2LcuWLTNpV95avvrfOqelpbFp0yZ+/vlnli1b9q/InQojkVbAwsKiuUOQR6CxPCq/Lcvx48eZPn067777LiNGjFB+WoHu3buTnZ1NWloa+fn5zJ8/X9dbCxcZGUlAQABPPPGESbvy1vLt2LGDgIAAOnTogIuLC/PmzSM1NZWampoGj29NuVNhJNIKODk5UVZWZtJWv1Lk5OTUDBHJw3B0dLxvha+0tFQ5bEG+/fZbZs6cyaJFi5g6dSrQ+PXn6OiIpaXeRlsKCwsLevfuzYIFC0hNTcXKyqrBvOl6a34//PAD2dnZzJo1674+R0dH5a2V6dGjB7W1tVhaWrb63Ok/ukgrYDAYKCoqMvlQffLkSfr27YutrW0zRiYPwmAwcOrUKZO2rKwsBg0a1EwRyd1OnDhBeHg4H330Eb6+vsZ2g8FAbm6uybehJ0+eVN5agIyMDMaMGWOSm/rtjUOGDLnvelPeWoaUlBSuXLnC8OHD8fLyYtKkSQB4eXnh6uqqvLVgp0+fZuXKlSZtBQUF2NjYMHLkyFafOxVGIq1A//79GTRoEBEREZSXl5Obm0tCQgJBQUHNHZo8AH9/f77//nu+/vprbt68yebNm7l48SJ+fn7NHZrZq6mpYfHixSxYsIChQ4ea9A0fPhxbW1tiYmK4fv06GRkZfPXVV7r+WoABAwZQWVlJTEwMlZWVXLt2jbi4ODw8PJgwYQKXL19m48aNVFZWkpaWxuHDh3n55ZebO2yzFx4ezr59+0hOTiY5OZmEhAQAkpOTGT9+vPLWgjk7O7N9+3Y2btxIdXU1BQUFxMbGEhgYyMSJE1t97izq6urqmjsIEfl7V65cYcmSJfz444/Y2toyZcoUQkNDmzssuYfBYAAwfoNtZWUF3FkZAti/fz8xMTEUFRXh4uLC4sWL8fDwaJ5gxejYsWMEBQVhY2NzX19aWho3btxgyZIlnDp1CmdnZ2bOnElgYGAzRCr3On36NB9++CHZ2dlYWVnh5eXFwoUL6dy5M8eOHWPFihXk5+fTrVs35s2bx/PPP9/cIcs9CgsLGT16NLm5uQDKWwuXmZlJdHQ0eXl5ODo64uPjQ1hYGDY2Nq0+dyqMRERERETE7GkrnYiIiIiImD0VRiIiIiIiYvZUGImIiIiIiNlTYSQiIiIiImZPhZGIiIiIiJg9FUYiIiIiImL2VBiJiIiIiIjZU2EkIiIiIiJmT4WRiIjI/9CePXswGAxUVVU16fi4uDhGjRrVaH9hYSGurq4cPnz4UYUoIiKoMBIREWH69OkEBgY22r9kyRKee+45bt++/cBj+/n5kZWVhY2NzT8J8ZEKDw8nICCgucNoUHJyMhcuXGjuMETEDKkwEhERsxccHMyJEyc4c+bMfX0VFRXs3buXwMBA2rRp80Dj1tTUPKoQ//Wqq6upq6sjMjJShZGINAsVRiIiYvZGjBhBr1692LZt2319ycnJ1NbWEhAQwK+//sobb7yBl5cX7u7uTJo0iSNHjhiPjYuLw9fXl48//hh3d3f27dtHUlISrq6u3Lp1C+Bvx6i3Z88eRo8ejbu7OyEhIVy8eLHB2Gtra1m3bh2jRo3Czc0Nb29v4uPjH6goCwwMJCIigqioKDw9PfHy8iIhIYG8vDxeeukl3Nzc8PPz4+zZs8bX4OrqSmpqKoGBgTz11FOMHTuW3bt3G8esqanhk08+wdvbG3d3d3x8fNiwYQO1tbUAJCUl4eHhwc6dO/H09CQ+Ph6DwUBpaSmzZ88mODi4SfO1Zs0aXnjhBQ4ePMi4ceNwc3PD19eXn376yXhMSUkJ77zzDh4eHnh4eDBr1iwuXbpk7M/MzGTy5Mm4ubkxbNgwwsLCKCoqavL8ici/gwojERExe5aWlgQFBbF3714qKipM+hITExk/fjwODg6EhYVhYWHBgQMHyMjI4Nlnn+Wtt96itLTUePyVK1f4448/OHr0KOPGjbvvXE0Z4/fff+fo0aPs3r2bgwcPAjB79uwGY//iiy9ISkpi7dq1nDhxglWrVrF161Y2bNjQ5NdvZWVFamoqAwcOJD09nalTp7JmzRqio6OJi4szFiKxsbHG4wHi4+OJiIggIyODoKAgFi5cyMmTJwFYv349W7duJTIykoyMDJYvX8769etJSEgwnreqqorjx49z6NAhZs+eTVpaGgDr1q1jy5YtTZovKysrCgsLOXDgAImJiRw9epTHH3+cpUuXGs8zf/58Kisr+eabbzh06BA2NjbMmjWLuro6iouLmTFjBr6+vmRmZpKSkoK1tTUzZsx4qK2TItJ6qTASEREBXnzxReDOSk29zMxM8vLyCAkJAWDHjh2sWbMGOzs7rK2t8fPz48aNG+Tl5Rn/pqysjDlz5tC2bVssLe9/m23KGLdu3WL+/Pk4ODjg5OTE66+/ztmzZxvcYvb5558zbdo0+vfvT5s2bTAYDEydOpXExMQHev09evRgwoQJWFtbM3bsWGpra/H29qZr167Y2dkxZMgQ8vPzTf5m0qRJuLi4YGNjQ0hICM7OzsZCbvPmzUyZMgV3d3esra3x9PTE19fXZFXp1q1bvPLKK9ja2jY4V02drz///JPw8HDs7e1p3749o0eP5vz589TW1nL+/HnS09MJDQ3F0dGRDh06sGjRIubMmUNVVRVbt27FxcWFwMBAbGxscHJyYtGiRZw7d47MzMwHmkMRad2smjsAERGRlsDOzg4/Pz8SExON27i2b9+Op6cn/fr1AyAjI4P169dTUFBgsrJUv00OwN7eHgcHh0bP09QxnJ2djc979OgBQHFxsfEx3CkISkpKiIyMJCoqytheV1cH3FmRaepNH7p372583K5dOwC6du1qbGvfvr1JjAAuLi7Gx5aWlnTr1o3i4mLKy8spKysz6Qfo06cPW7ZsMW6nA+jVq9dfxtWU+XJ0dMTe3t4k/pqaGqqrqykoKACgZ8+exv7OnTvj4+MDQEFBATk5ORgMBpPz1q9EiYj5UGEkIiLyX8HBwWzbto2MjAxcXFzYv38/MTExwJ3bZIeGhhIQEEB8fDwdO3bk0qVLjBkzxmQMa2vrRsdv6hj3rp7UFxJt27Y1abewsABg5cqVxg/6D6uhFZvGVnHq1Rdgdz9v166dMa57++8uiOr9L+arIQ2dG+7M4bBhw/j000//dgwR+XfTVjoREZH/cnFxYejQoezatYuUlBQ6depk/BCenZ1NVVUVYWFhdOzYEYCsrKwHGr+pY5SWlpr85qj+xgt3r+AAdOjQgU6dOnHq1CmT9pKSEm7cuPFAsT2M+tUYuFN4FBUVGbfeOTs7c+7cOZPjz58/z3/+858mFTLwaOa8d+/eACbbAK9evcqGDRuoqKigT58+5Obmmtys4vbt21otEjFDKoxERETuEhwczIEDB0hKSjK5RXf9VrMjR45QW1tLenq68fdIxcXFTRq7qWO0bduW6OhoysvLuXbtGp999hmDBw+mS5cu94356quvkpiYyHfffUdNTQ35+fm89tprfPjhhw87BU22c+dOzpw5Q1VVFZs2baK0tNR4w4mQkBC2b9/OL7/8QnV1Nenp6ezdu5fJkyc3Ot5jjz0G3CliysrKHsmc9+3bl2eeeYbY2FiuXr3K9evXiYmJYdeuXdja2hIYGEhZWRnR0dFUVFRQUVFBdHQ0/v7+XL9+/R/Mjoi0NtpKJyIicpeRI0fi7OzMhQsX8Pf3N7YbDAZCQ0NZsWIFS5YsYciQIURFRfHBBx8QFRVl3D72V5oyRk1NDd27d2fw4MH4+vpSVlaGm5sb77//foNjTps2jZs3b7J06VJ+++03HB0d8fHxYe7cuY9sThoTEhLC8uXLycnJoUuXLqxevdq4QjNjxgxu3LhBaGgo5eXl9OzZk7lz5zJlypRGx3NycmLixImsXr2alJQUkpKS/vGcA6xatYqIiAi8vb2xsLDg6aefJj4+HgsLC7p160ZCQgKxsbEMHToUa2tr3Nzc2LhxI7a2to9imkSklbCou3cDsIiIiMhfKCwsZPTo0Xz55Zd4eXk1dzgiIo+EttKJiIiIiIjZU2EkIiIiIiJmT1vpRERERETE7GnFSEREREREzJ4KIxERERERMXsqjERERERExOypMBIREREREbOnwkhERERERMyeCiMRERERETF7KoxERERERMTsqTASERERERGz938NxsV4pBxEpgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot Metrics for Ridge Regression\n",
    "plot_model(ridge, plot='feature')\n",
    "plot_model(ridge, plot='error')\n",
    "plot_model(ridge, plot='learning')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfAAAAH9CAYAAADlMi44AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjYuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/P9b71AAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOydd5xU1d3/3/feqds7nV16kSJYECWgNMsTEQsqGjXGGDXGEo0lxvboE01MiFET89MklmgsoLFgl1gwiopK6CogS2d7mz5z7/n9cWfuzuzOVrbveb9eCe7MnXvPuVM+53yrIoQQSCQSiUQi6VWo3T0AiUQikUgkbUcKuEQikUgkvRAp4BKJRCKR9EKkgEskEolE0guRAi6RSCQSSS9ECrhEIpFIJL0QKeASiUQikfRCpIBLJBKJRNILkQIukUgkEkkvRAq4pM9x8803M27cuIT/TZs2jVNPPZXHHnuMUCjUadf+7LPPGDduHJ999hkADz30EOPGjSMYDLb7nBdccAFnn312Rw2xWWLjbe5/O3bs6JKxNEcwGOQnP/kJU6dO5bLLLuvw88fex9WrV3f4uSWSjsLW3QOQSDqDnJwcXn31Vevv2tpaPv30U373u9+xceNG7r///i4Zx49+9CPOPfdcnE5nq19zwgkn8Jvf/IYZM2YApqh2Ne+99x4OhyPpczk5OV08msZ88MEHfPjhh9x1113Mmzevu4cjkXQLUsAlfRJVVcnPz7f+zs/PZ9SoUVRWVvKnP/2Jm266iYEDBzZ6XTgcxm63d9g4UlNTSU1NbfXxJSUl7N+/P+GxrKysDhtPa8nLy2vTogOS3zvDMBBCoGlam8fQ3GurqqoAOO6448jLy2vzuWN09PstkXQl0oQu6VeMGzcOwBLJCy64gJ/+9Kc8+OCDTJs2jX/+858A+Hw+/u///o/Zs2czadIkFixYwKOPPkp87x+Px8ONN97IEUccwfTp07nmmmssYYmRzIT+4osvcsoppzB58mTmzp3Lgw8+iK7rfPbZZ8yePRuACy+8kLlz51pjjDehh0Ihli1bxty5c5k0aRLHHXccv/zlL6msrLSOuf766znttNP44osvOP3005kyZQpz585l+fLlHXYvb775Zk477TSee+45jj76aO677z7rHj/66KNcfvnlTJkyhW+//RaAHTt2cPnll3PkkUcyadIkTjnlFOt+x2jqtQ2ve8cddwAwb948LrjgAgDq6uq44447mDVrFpMmTWLOnDn83//9H36/33ptU+93e9m4cSOXXHIJM2bMYOrUqZxyyik899xzCcd8/vnn/OAHP+Coo47i8MMP5/TTT+f111+3nt+3bx/XXnstxx13HJMnT2b+/Pk89NBD6LpuHdOaeyfpf8gduKRfsXv3boCE3ff27dtxOp28+OKL1m7uqquuYtOmTdxxxx0cdthhrFmzhl//+teEQiF+9rOfAXD33Xfz7rvvctdddzF58mQ+++wzfve73zV7/X/961/cfvvt3HTTTcyZM4dt27Zxww034Pf7+fnPf86yZcu4/vrreeihhzjyyCOTnuPWW29l1apV3HrrrRxxxBHs3LmTO++8k0svvZQXXngBRVGw2+1UVlbywAMPcOutt5Kfn8/999/PnXfeycyZMxk2bFhH3E6qq6tZtWoVTz/9dMJO+MUXX+TMM8/kV7/6FQMGDKCiooLzzz+fwsJCHnnkEbKysnjnnXe4++67MQzDEuFkr23Ir371K0aNGsXvf/97VqxYwfDhwwG4/PLL2bVrF3fccQdjx45l06ZN3HnnnZSUlCS4IZK93+3B6/Vy8cUXM23aNP7xj3+QmprKv//9b+644w4KCgqYO3cudXV1XHbZZZx55pncfffdaJrGW2+9xfXXX8+QIUM4/PDDueGGG7DZbPz1r38lKyuLDRs2cNttt+F0OvnJT37Spnsn6V9IAZf0C8LhMGvXruXvf/87c+fOZfDgwdZz+/fvZ/ny5ZapesOGDfznP//hrrvu4pRTTgGgsLCQ7du388QTT/CTn/wEXdd54403OOecczj11FMBKCoqYtu2bTz11FNNjuOvf/0rCxcu5MILL7TOe+ONN7Jr1y4cDgcZGRkAZGZmJvU1l5SU8Oqrr/Kzn/2MM844wzrHDTfcwHXXXceXX35pCX9paSl///vfGTt2LACXXHIJb7zxBps2bWpRwI855pikj8+fPz9hkXLw4EH++te/WteIkZqayk9+8hPr7xdeeIGamhqWLVvG0KFDAbjiiiv44osvePLJJxNEqOFrG5Kenk5aWhpg+uOzsrJYt24dX3zxBb/97W9ZsGCBdV/27dvHsmXL2L9/v/WeN3y/24vL5WLlypUJ47nooot45JFH+Oijj5g7dy47d+7E5/Nx6qmnMmLECAB+8pOfcMwxx1gLj82bN3PllVcyceJEAAYPHszo0aNxu91tvneS/oUUcEmfpKKigmnTpll/B4NBnE4nZ511Ftdee23CsUOHDk34MV+/fj2AFUQWY+bMmTz11FNs27YNTdMIhUJMnjw54Zjp06c3KeAej4fvvvuOM888M+HxpUuXtnpemzZtQgjB0UcfnfD44YcfDsDWrVstAU9JSUkQ1szMTABqampavM6KFSuS+oZTUlIS/nY4HI3EG2DSpEkJf2/cuJFBgwZZAhRj2rRp/Oc//8Hj8Vgi2PC1rWHjxo0AHHXUUQmPx+7L119/bQl4w/e7vWiaxjfffMNjjz3G9u3bLVO93++nuroagDFjxlBUVMTVV1/N0qVLmTlzJpMnT2bKlCnWeRYsWMCf//xnysvLmTVrFkcddRSjR49OmFtr752kfyEFXNInycrK4vnnn7f+drlcFBQUoChKo2Nju94YdXV1AJx++ukJjxuGAUB5ebkVmBYTxabOFY/H47HG0l5i52h43djfseehsdjG5h7vx2+KYcOGtSqIran5Nnzc4/EkFc3YcV6v1xKh5u5hUzR1X2Lnir8v7Tl/MrZs2cKVV17JrFmzeOCBB8jLy0NV1YQdsdvt5rnnnuOxxx7jpZde4v777yc3N5eLL76YH//4xyiKwm9/+1umTp3K66+/zlNPPYXdbue0007jxhtvJD09vU33TtK/kAIu6ZNomkZhYWG7XhsTgSeeeCLpD2d+fj47d+4ESAiQAqydVzJiP7LNHdMS6enpSc8RC57rKHHqaNLT0634g3hi8zhUAYq/L/Hnip2/M+7L22+/jaIo/OEPf7AWdIZhJCwWALKzs7n++uu5/vrr2bt3L//6179YtmwZeXl5nH766WiaxgUXXMAFF1xATU0N7777Lr/73e+IRCLce++9nX7vJL0XGYUukTRg6tSpgLnTLiwstP6XkZGB2+0mJSWFwsJCbDYbX3/9dcJrP//88ybPm5aWxsiRI/niiy8SHn/mmWcaFSNpapc8efJkVFVtdJ3YORua9HsKU6dOZf/+/ezZsyfh8bVr1zJq1Kg2pdo1dX5ofP+/+OILVFW1/MsdidfrxeFwJIz97bffxuv1Wu9fcXEx7733nvX80KFDufrqq5k4cSKbN2+murqaV155xYo4z8zM5KyzzuL0009n06ZN1tw6895Jei9yBy6RNGDSpEnMmjWLu+++GyEE48ePZ//+/fz2t79FVVWWL19OWloa8+bN4+mnn2b8+PFMmDCBTz75xKrA1hSXXHIJt956K3/5y184+eST+e677/jjH/9o+cVju/+PP/6YjIwMJkyYkPD6/Px8zjjjDB577DGGDRvGtGnT2LZtG8uWLWPGjBkJvtVDoby8vMlCLikpKW0WjTPOOIPHH3+cX/ziF9x8881kZGTw6quv8vnnn/Pb3/72kMc7ZcoUZs6cybJly8jIyGDMmDF89dVXPPbYYyxevJiCgoJ2nbe2tpaysrJGj+fl5TFt2jSeeuopnnjiCebNm8fnn3/OSy+9ZL0ne/fuZffu3Vx11VX84he/4IQTTsBms/HFF1+wbds2LrvsMgzD4M477+TTTz/loosuIjMzk+LiYt59912OP/54oPPvnaT3IgVcIknCQw89xP33389dd91FZWUl2dnZHH/88Vx//fWWL/muu+7ijjvu4JZbbgHge9/7HnfccQcXX3wxkUgk6XnPOussDMPg8ccf589//jMFBQX84Ac/4Kc//Slg7qDnzZvHk08+yWuvvcaqVasanePOO+8kNzeXBx54gNLSUrKzs1mwYAHXX399h80/loOejJ/97GdcddVVbTpfTk4OTz31FPfddx8//vGPCQaDjBw5kt/+9rcsXrz4EEdr8qc//Ynf//733HnnnVRVVTFgwAAuuOACK+2vPTR1T9euXcvJJ5/Mxo0beeSRR3jwwQc55phj+MMf/sBXX33FrbfeymWXXcbrr7/Or3/9a/7xj3/w4IMPAjB8+HBuv/12TjzxRAAef/xxHnzwQS666CJ8Ph8DBgzgpJNO4uqrrwa65t5JeieKaE1Ei0QikUgkkh6F9IFLJBKJRNILkQIukUgkEkkvRAq4RCKRSCS9ECngEolEIpH0QqSASyQSiUTSC5ECLpFIJBJJL6Tf5oGvW7cOIUTShg0SiUQikXQH4XAYRVESmjE1Rb8VcCGE9b9kDS56I0IIwuEwdru9z8wJ+ua8euOcqv0h/GG9xePsKuSmunrNvFpDb3y/WqIvzgl677yEEHg8Hux2O5qmteo1/VbA7XY7oVCI0aNHN+ra1Fvx+Xxs3bq1T80J+ua8euOcHli9lZc37kZt5kcxrOscnaNx1+mzes28WkNvfL9aoi/OCXrnvDweD88++ywVFRUcd9xxrW53K33gEomkVSydVoSmNr+jsSsKJxb2zI5oEklPJF68AVS19bIsBVwikbSKgnQ3c0YNIBLti96QiGFw7Ih8ctwyrkQiaQ0NxTszM7NN7WGlgEskklZzy/zJzB87CFVRMKJtFAwhUBWF+WMHceOc8d08Qomkd5BMvM8777w27cD7rQ9cIpG0HU1VuW3hVErr/Dy7rpjaQJhMl52l04vIT3Pj8/m6e4gSSY+nKfGOtRNuLVLAmyEWzajrLUfe9gSCwaD1b2+KvmyJrpyXpmm9Lnq1OyhId3PN7AktHyiRSBqxYcOGQxZvkALeJJFIhKqqKtxud6tD+rsbh8PBiBEjcDgc3T2UDqUr5xUKhaipqSE7OxubTX49JBJJxzNz5kzq6ur47rvv2i3eIAU8KUIIqqqqyMvL61U7sZilwOVy9ZpFR2vo6nmlpqZSXl7e695/iUTSO1AUhYULF+Lz+UhNTW33eWQQWxLC4TBut1v+ePdTFEXB7XYTDoe7eygSiaQP4PF4KCkpSXhMUZRDEm+QAp4UXdf71A5W0nY0Tes1sQ8SiaTnEgtYe+655zh48GCHnlsKuEQikUgknUB8tLnf7+ett95CRNMvOwIp4BKJRCKRdDDJUsVOP/30DnXNSgGXSCQSiaQD6ag875aQAt4POXDgABdccAGnnHIKp556Km+++WZ3D0kikUj6BF0l3iDTyPolmqZxyy23MGHCBMrKyjjjjDOYM2dOr+ncI5FIJD2RrhRvkALeLykoKKCgoACA/Px8srOzqampkQIukUgk7SQSifDcc891mXiDNKH3SS688ELGjRvHuHHjmDRpEieffDIrV65MeuymTZswDINBgwZ1+rj++c9/MnfuXCZPnsySJUvYsGFDi68pKSnhxhtv5IQTTmDatGmceuqpbNy40Xre4/Hw61//mhNOOIEpU6Zw7rnnNjpve64rkUgkbcFmszFt2jSga8QbpID3OYQQbN26lZtuuon//Oc/vPnmm3zve9/jpptuYs+ePQnHVldXc9NNN3HXXXd1+rjeeOMN7r33Xq688kpeeuklxo8fzyWXXGKtVpNRU1PD0qVLsdlsPPTQQ6xcuZKbbrop4Utx66238sknn3DfffexcuVKjjvuOC6++GKraEJ7riuRSCTt4YgjjuB//ud/ukS8QQp4n2P37t14vV6+973vkZ+fz7BhwzjrrLPQdZ2dO3dax4VCIa688kouvfRSpk+f3unjevzxxzn77LM588wzGT16NP/7v/+Ly+XixRdfbPI1f/3rXxk4cCD33HMPkyZNYujQocyaNYvhw4cDEAgEeOedd7jhhhs46qijKCws5KqrrqKwsJBnnnmm3deVSCSS1mAYRqPHJk+e3CXiDdIH3qnEWi7WBEJkuhwsnVZEQbq7U6+5detWMjIyGD16NAAHDx7k/vvvx+FwMG7cOMDcpd98880cc8wxLF68uNXn/n//7//xyCOPNHvM66+/zuDBgxMeC4VCbN68mcsuu8x6TFVVjj32WNatW9fkud577z1mzZrFtddey9q1axkwYADnn38+Z599NmD6nHRdx+l0JrzO6XTy1Vdftfu6EolE0hIej4fly5dz3HHHWb+tXY0U8E5ANwzuWbWRD3eUoBsCVVEwhGDl5j3MGTWAW+ZPRmtD0/a2sHXrVjweD9OnT0fXdYLBIC6Xi//93/9lwIABAHz55Ze88cYbjBs3jlWrVgFw3333tfghPPfcczn55JObPSYWHBdPVVUVuq6Tm5ub8Hhubi7fffddk+fas2cPzz77LBdddBE//OEP+fbbb/m///s/7HY7p59+OmlpaUybNo2HH36YkSNHkpeXx2uvvcZ///tfhg8f3u7rSiQSSXPER5u/8sornHHGGdamqSuRAt4J3LNqI6u+PYBNVVGjVXdURUEIWPXtAQBuWzi1U6799ddfs3TpUi666CJqa2u57777mD59OmeccYZ1zJFHHsnXX3/d5nNnZWWRlZXVgaNtHiEEkyZN4uc//zmBQIDDDz+cHTt28Nxzz3H66acD5sLjlltuYfbs2WiaxsSJE/mf//kfNm/e3GXjlEgk/YeGqWLp6enk5+d3y1ikgHcwpXV+PtxRgq2JHbZNVVm9o5TSOn+nmNO//vprzjnnHAoLCwG44447WLRoEUuWLGHo0KGHdO72mtCzs7PRNK1R4FhFRQV5eXlNnis/P59Ro0YlPDZy5Ejefvtt6+/hw4fz9NNP4/P58Hg8FBQUcO211zJs2LB2X1cikUiS0dV53i0hBbyDeXZdsWU2b4qIYfDsumKumT2hQ6+9Z88e6urqGDNmjPXY6NGjGTZsGK+99hqXX375IZ2/vSZ0h8PBYYcdxpo1a5g/fz5gBn+sWbOGH/zgB02ea/r06QmBdwDFxcUMGTKk0bEpKSmkpKRQU1PDf/7zH2644YZ2X1cikUga0tPEG6SAdzg1gVCz4g2mOb020PG9pjdv3ozNZrN23zFmzpzJu+++e8gCfigm9IsvvpibbrqJSZMmMWXKFJ588kn8fr9l2n/66ad59913efLJJ63XXHTRRSxdupRHHnmEE044gW+//Zbly5cnpL199NFHCCEYMWIEu3fv5r777mPkyJHWeVu6rkQikbRETxRvkALe4WS6HBii+R24IQQZLnuHX3vLli0MHz4ch8OR8Pixxx5r9aIdOHBgh1+3NZxyyilUVlby4IMPUlZWxoQJE/jb3/5mmbKrqqoa5alPmTKFP/3pTyxbtoy//OUvDB06lFtuuYVFixZZx9TV1fGHP/yBgwcPkpWVxcKFC/n5z3+O3W5v1XUlEomkOXqqeAMooiObk/YiNm7cSCgUYsKECY1KiPr9fgDc7rb7qEvr/Jz39Ec0d1dVReGfP5jV4T5wXdcJBAK4XC40TevQc3cn3TGvQ/kMtAafz8fWrVuTfv56M3JevYe+OCfo+Hnt3r2b5cuXE4lEukS8Y5UmJ0+e3OKxspBLB1OQ7mbOqAFEkiT4g+n/nj2qoNPzwSUSiURy6AwfPpwzzzyT3NzcHrPzjiFN6J3ALfPNldPqHaVEDMPKA7epKvPHDrKel0gkEknPZ8SIEVxyySWonVS/o71IAe8ENFXltoVTrUpstYEwmS47S6cXkZ8md94SiUTSU/F4PGzbts1qTBKjp4k3SAHvVArS3R2eKiaRSCSSziE+YM3v93Psscd295CapectKSQSiUQi6WIaRpuvX7+eQCDQzaNqHingEolEIunXNJUq5nK5unlkzSMFXCKRSCT9lp6c590SUsAlEolE0i/pzeINUsAlEolE0g/p7eINUsAlEolE0g9ZuXJlrxZvkALeZ9i7dy8TJ05s1L2rK1i7di1z5sxhxowZ7Nu3j8mTJ3fLOCQSiaS1LFy4kNTU1F4r3iDzwCWtYPPmzdTU1DSZE/mPf/yDyZMn8+CDD6KqqlXLF2DNmjWkpaW1qq6vRCKRdBWx0qg2m61XijfIHbikFbz44ot88sknTT5fXV1NUVFR0kpFTzzxBJs2berM4UkkEkmL+Hw+jAY9KnJzc3uteIMU8D7H5s2bWbRoEdOmTeMnP/kJVVVV1nNvv/02J598MlOnTuX73/8+r776qvXc+vXrOfvss5k2bRozZszgV7/6FYFAgLvvvptnnnmGxx57jAULFjS63g9+8APWrl3LY489xpFHHsnevXsZN24cO3bs4PLLL+eDDz7g//7v/7jooou6ZP4SiUTSEI/Hwz//+U9ee+21RiLem5EC3sd45ZVXePTRR/n3v/9NeXk59913HwA7duzg5ptv5vbbb+err77izjvv5I477mD9+vUA3HjjjSxZsoQvv/yS1157jW3btvH8889z2223cdRRR/GjH/2Id999t9H1nn76aev5L774IuG5//f//h9Dhgzh1ltv5cknn+z8yUskEkkD4qPNt2zZwvvvv9/dQ+owpA+8Dfz5z3/m4YcfbvG4qVOn8swzzyQ8dt5551li2Rw//elPufLKK9s9xrPOOouBAweiaRrnnHMODz30EADLly9n7ty5zJw5E4AjjzySk08+mZdffpmpU6dSUVFBSkoKqqqSn5/Pc8891yOL90skEklrSZYqduSRR3bzqDqOHivga9eu5Uc/+lHCY0IIwuEw33zzDWvWrOHee+9l586dDBw4kKuuuopFixZ16pjq6uo4cOBAi8cNGTKk0WPl5eWtem1dXV27xhZj2LBh1n8PHDiQsrIyDMNg9+7dfPjhh7zzzjvW80IIZs2aBcCtt97KLbfcwl//+ldmz57NaaedxqhRow5pLBKJRNJdeL1eXn755V6fKtYcPVbAjzrqqIRoZoCHH36Yb7/9lpKSEq644gquu+46lixZwpo1a7j22mspKipiypQpnTam9PR0Bg0a1OJxeXl5SR9rzWvT09PbNbYYmqYl/G2321EUBVVVOffcc7n99tuTvm7x4sXMmTOH999/nw8++IDTTz+d+++/n3nz5h3SeCQSiaSr8fv9LF++3NoQ9UXxhh4s4A3Zv38/Tz75JC+99BIrV66ksLCQCy+8EIC5c+cyb948XnjhhU4V8CuvvLLd5u2GJvXOYu/evUydOhUw71lBQQGKojB8+PBGC6KDBw+Sn5+PpmlUVVWRnZ3NGWecwRlnnMGf/vQnXnjhBSngEomkV+H1enn77bdxOBzY7fY+K97Qi4LY7r//fs4880wGDx7Mli1bOOywwxKenzhxokxXAl544QXKysqorKzk+eefZ+7cuYDpG//qq6946aWXCIfDbN26lSVLlvDOO+9w4MAB5s6dy3/+8x8Mw8Dj8bB9+3bLHO90Otm7d29CRHtrcTqd7N69m5qamg6dp0QikTTE4/GwfPlyamtrgb67847RK3bgxcXFrFq1in//+98AVFVVMX78+IRjsrKyqKysbPO5/X5/o8eCwSAOhwNd19s34G4gGAwCcPrpp/PDH/6QkpISZsyYwc9+9jN0XaeoqIjf/e53/OlPf+L2228nLy+PH/7whyxcuBCAu+++m9/85jfs3bsXt9vN7NmzufLKK9F1ncWLF3P77bdz6qmn8t577zUy0wshEEKg67qVomEYBrquc9ZZZ/HQQw+xdu1ann/++XbNTQhh/dtV74lhGIRCIevaHU3sc5fs89ebkfPqPfTFOQWDQes763a7Wbx4MXa7HZ/P180jaz1CCBRFadWxiuisX6gO5Ne//jU1NTVWStSPfvQjxo0bx0033WQds2LFCh5++OFWpwhs3LiRUCjU5PMjRozA6XQe2sAlvZZgMCjLwUokvZBgMMgnn3zCUUcdRVpaWncPp104HI5WVa/sFTvwt99+mzvvvNP6Ozs7m+rq6oRjqqqqyMnJafO5i4qKcLvdCY/FduA9vZl7Q4QQBINBnE5nq1dwvYHumtfIkSM7bRHn9/spLi5O+vnrzch59R764pzAnJfT6ey189q+fXurj+3xAr5t2zZKS0s5+uijrccmT57Mv/71r4TjNmzY0K4ANrfbTUpKSsJjMZFoaCru6cTMy4qi9LqxN0d3zEtVVVwuV6f/ACT7/PUF5Lx6D715Th6Phw8++ID58+c32nD11nm1ZZPS44PYtm7dyqBBgxJMIaeeeir79u3jiSeewO/389Zbb7F69WrOOeecbhypRCKRSLqKWJGWTZs2sXz5cgKBQHcPqcvp8QJeVlZGVlZWwmO5ubk88sgjvPTSSxx99NHcf//9LFu2rFFgm0QikUj6Hg0rrHm9XiuQtz/R403ol1xyCZdcckmjx4888kheeeWVbhiRRCKRSLqLZOVR+3KqWHP0+B24RCKRSCQgxbshUsAlEolE0uOR4t0YKeASiUQi6dFI8U6OFHCJRCKR9Gg+++wzKd5JkALeh9i3bx/HHHMMxcXFjZ5bvXo148aN65TrnnjiiaxYsaJTzi2RSCTHH388Y8eOleLdgB4fhS5pPUOGDOHTTz/t9Apye/bsYfPmzZx00kmAWSmvqeckEonkUNE0jdNOOw2v10tGRkZ3D6fHIHfgkjbzzjvvJIh2a5+TSCSS1uDxeBqVy9Y0TYp3A6SA9yH27dvH9OnT+e677yguLubcc89l2rRpLFmyhF27diUc+8033/CDH/yAqVOnMnfuXP7whz8QDocBszHMokWLePnll5kzZw7Tp0/nhhtuQNd1/v73v/P73/+et956i8mTJ6PrOnPnzuXZZ59t9Nyf/vQnzjjjjITrfvHFF0yZMgWPx9Nl90UikfQeYgFrzzzzTLtaGPcnpID3UW6++WaGDBnCxx9/zG9+8xuee+4567lwOMwVV1zBCSecwNq1a3nyySd5//33efzxxwFzpbt//362bNnC22+/zT//+U/efPNN3n//fS655BJOO+00TjrpJDZu3JhQm7zhc6eddhpbtmxhx44d1jFvv/02J5xwQq/tEiSRSDqP+Gjz2tpa3njjjU5r6dsXkD7wNvD555+zdu3aFo8bOHAgZ555ZsJjL774IgcPHmzxtUcddVRC45b2UFdXx7p167jjjjtISUlh1KhRLF68mN///veAGdAWiUSsCnfDhg3jkksu4a9//Ss/+clPALM04TXXXIPL5WLChAmMGjWK7777rk3jGDZsGEcccQQrV67k2muvBWDVqlX86le/OqT5SSSSvkeyVLHvf//7faqzYkcjBbwNhEIh6urqWjwuPT290WNer7dVr22uR3lriXXvGjx4sPVYYWGh9d979uyhtLQ0od+sECKhdWZWVhapqanW306ns121hhcvXswjjzzCtddey4YNG/B6vcyePbvN55FIJH0XmefdPqSAtwGHw5FUnBsSL3zxj7XmtQ6Ho11jiyfZijUm6rHnR48ezWuvvdbkOVS1Y7wrJ510EnfffTfr1q3jvffe46STTuqQOUokkr6BFO/2IwW8DRx99NHtNm83NKl3JrEP/oEDB6z/js8NHz58OHv37sXr9VqLjaqqKux2e4f7ptPT05k3bx7vvPMO7733Hr/+9a879PwSiaT3IsX70JBBbH2UUaNG8dhjj+H3+9m2bRsrV660nps1axY5OTn87ne/w+v1UlZWxjXXXMOyZctadW6n08mBAweorq4mEom0+NzixYtZsWIFoVCII444ouMmKZFIei3BYFCK9yEiBbyP8uCDD7Jz505mzpzJzTffzKWXXgqYEeh2u52HH36Y7du3c+yxx/L973+fwsJCbrzxxlad+9RTT2Xnzp2cfPLJlJaWtvjcrFmzcDqdMiBFIpFYOBwOqzqkFO/2oYh+GqO/ceNGQqEQEyZMICUlJeE5v98PgNvt7o6htRtd1wkEArhcroT0ru7G4/Fw/PHHs2LFCkaMGNHm13fHvDr7M+Dz+di6dWvSz19vRs6r99AT5iSE4PPPP2f8+PEdJt49YV6HwsaNGwESgoybQvrAJZ1KMBjkrrvuYubMme0Sb0n/o7TOz7PriqkJhMh0OVg6rYiC9N61mJYkRwiRYIVTFIUZM2Z044h6N1LAJZ3Gl19+yY9+9COmTJnCAw880N3DkfRwdMPgnlUb+XBHCbohUBUFQwhWbt7DnFEDuGX+ZLQOyo6QdD0ej4cXXniBefPmMWzYsO4eTp9ACrik0zjiiCNYv359dw9D0ku4Z9VGVn17AJuqokZ3aaqiIASs+vYAALctnNqdQ5S0k/ho8+XLl3POOecwdOjQ7h5Wr0cuZyUSSbdTWufnwx0l2JrYYdtUldU7Simt83fxyCSHSsNUsZSUlFbVxJC0jBRwiUTS7Ty7rhjdaD6eNmIYPLuuuGsGJOkQZJ535yIFPAmapiVULpP0P3Rd71GR/H2dmkDIMps3haoo1AbCXTQiyaEixbvzkQKeBLvdjt/vl11w+ilCCPx+P3a7vbuH0m/IdDkwWvi+GUKQ4ZLvSW9AinfXIIPYkqAoCtnZ2ZSXl+N2u3vNTswwDKvhSEfVMu8JdOW8dF3H7/eTnZ0ti850IUunFbFy8x6a03CbqrJ0WlGXjUnSPqR4dx1951e+g7HZbOTl5fWqxhuhUIidO3d2SEeznkRXzsvhcJCXl4fNJte2XUlBups5owYQMYykz0cMg9mjCmQ+eC9g//79VFZWAlK8Oxv5K9UMiqL0KgGPmfydTmevqyLXHH11XpJEbplvVp5avaOUiGFYeeA2VWX+2EHW85KezdixYzn11FNZvXo1S5culeLdiUgBl0gkPQJNVblt4VSrElttIEymy87S6UXkp8mFW29i4sSJjB07VlqyOhl5dyUSSY+iIN3NNbMndPcwJK3E4/GwZ88eJkxIfM+keHc+8g5LJBKJpF3EAtYqKysJh8NMmTKlu4fUr5BBbBKJRCJpM/HR5kIIPvnkE8JhmafflUgBl0gkEkmbSJYqtnTpUlk7oYuRAi6RSCSSViPzvHsO0gcukXQjZZ4AL3+xS/a+lvQKpHj3LKSASyTdgG4I/raxlG8+LUOgyN7Xkh6PFO+eh/yFkEi6gfs+/JrPDnoxBEl7X9+zamM3j1AiqUcIwb/+9S8p3j0MuQOXSLqY0jo/H+8sw9ZErfX43tfSnC5pK7FCOB3pllEUhXnz5rF8+XJcLpcU7x6CFHCJpIt5dl0xkRY6b8V6X8uCJpLWohsG96zayIc7StAN0eFumSFDhnDOOeeQmpoqxbuHIAVcIuliZO9rSWdwz6qNrPr2ADZVTeqWAbht4dRWny8QCOB0OhO68g0ePLhjBy05JKQPXCLpYmTva0lHU1rn58MdJdia2GHHu2Vag8fj4amnnuK9996zmglJeh5SwCWSLmbptCJsavM7cNn7WtIWnl1XjG60zi3TEvHR5mvXrmXNmjUdNEpJRyMFXCLpYgrS3RxXlN+kH1z2vpa0lY5yyyRLFTvssMM6bJySjkUKuETSDdw4ZzwzBqaigWVON4QZeCR7X0vaSke4ZWSed+9DBrFJJN2Apir8eHIBecNG8PLXJbL3teSQWDqtiJWb99CchjfnlpHi3Tvp8Tvwhx9+mFmzZjFt2jR++MMfsmfPHgDWrFnDokWLmDx5MgsWLODVV1/t5pFKJG0nP83FNbMncNvCKVw9e4IUb0m7KEh3M2fUACKGkfT55twyXq9XincvpUcL+DPPPMN7773H888/zwcffMCgQYN4/PHHKSkp4YorruCss87i888/55e//CW33norGzZs6O4hSyQSSbdwy/zJzB87yMr/hpbdMn6/n+XLl0vx7qX0aBP63//+d/7whz8wZMgQAO69914A/va3v1FYWMiFF14IwNy5c5k3bx4vvPCCbCgvkUj6JZqqctvCqVYltta4ZQzDQNd1QIp3b6THCnhJSQkHDx5k165d3HDDDdTU1DBz5kzuuOMOtmzZ0igycuLEibz55pttvo7f37q8yN5AbC59aU7QN+fVF+cEcl49gTQNLj2yMO4Rgc/na3Sc3+8nNTWVRYsW8eGHH7Jw4ULsdnvSY3sTvem9SoYQIqF4TnP0WAE/ePAgiqKwatUqnn/+eQKBAFdffTW33XYbXq+X8ePHJxyflZVFZWVlm69TXFzcQSPuOfTFOUHfnFdfnBPIefUmKioqmDRpEvv372f//v3dPZwOoze/Vw6Ho1XH9VgBD4fDhMNhbrjhBrKzswG4+uqrufTSSzn22GOTvqa1q5Z4ioqKcLv7RuCQ3++nuLi4T80J+ua8+uKcQM6rp+P1evn000+ZM2cO4XC4T8ypIb39vdq+fXurj+2xAp6VlQVAWlqa9diQIUMQQhAOh6murk44vqqqipycnDZfx+12k5KScihD7XH0xTlB35xXX5wTyHn1RDweDy+//DIVFRUEAgFOPPFEoHfPqTl667zashHtsVHohYWFpKWlsXnzZuuxffv2YbPZOP744xMeB9iwYUOPDWArrfPzwOqt3PXOeh5YvbXV9YglEomkI2iY511eXt5rfcSSenrsDtxut7NkyRJ+//vfM3r0aDRN489//jOnnXYaixcv5uGHH+aJJ57gnHPO4cMPP2T16tUsX768u4edQGe395NIJJKWaKpIi90um+X0dnq0elx33XVMnz6dRYsWceqppzJy5EhuueUWcnNzeeSRR3jppZc4+uijuf/++1m2bFmjwLbuJtbeTwiStve7Z9XGbh6hRCLpy8gKa32bHrsDBzMS7/bbb+f2229v9NyRRx7JK6+80g2jah1tae8nm1ZI+hKV/jAPf7INn2HW6F46rUh+xrsBKd59nx4t4L2ZWHu/5joExdr7XTN7QheOTCLpHHTD4N73t/LOpn04U1Kwa5p0GXUTUrz7B/Lb1El0VHs/iaS3cM+qjby/vQSBdBl1Nx999JEU736AFPBOoiPa+0kkvQXLZaS17DKSdD7z5s1j2LBhUrz7OFLAO4ml04rQ1OZ34M2195NIehMxl1FzxFxGks7H4XCwZMkSzj//fCnefRgp4J3EobT3k0h6G9Jl1L14PB68Xm/CYw6Hg4yMjG4akaQrkEFsnUisfd/qHaVEDMPKA7epapPt/SSS3oh0GXUfsYA1RVFYunQpqamp3T0kSRchBbwTaU97P4mkN7J0WhErN+8hojct4tJl1PE0jDZ/4403WLJkSTePStJVSAHvAgrS3TJVTNKnibmM3t66L+nzEcNg/thB0mXUgSRLFVu4cGE3j0rSlUgfuEQi6RBumT+ZE0YPsFxFYJrNVUWRLqMORuZ5S0DuwCUSSQehqSq/PGECszPDrA+48RuKdBl1AlK8JTGkgEskkg4lx23np9PH9MpWjj0dKd6SeKQJXSKRSHoBfr9firckASngEolE0gtwuVwMGTIEkOItMZEmdIlEIukFKIrCySefTGpqKocffrgUb4kUcIlEIumpCCFQ4ircKYrCnDlzunFEkp6ENKFLJBJJD8Tj8fDMM89QWlra3UOR9FCkgEskEkkPIxZtvmfPHp577jkp4pKkSAGXSCSSHkTDVDG73Y7T6ezmUUl6IlLAJRKJpIcg87wlbUEKuEQikfQApHhL2ooUcIlEIulmpHhL2oMUcIlEIulGpHhL2osUcIlEIulGdu7cKcVb0i5kIReJRCLpRiZPnozf7+fLL7+U4i1pE1LAJRKJpJs5+uijmTp1qkwXk7QJaUKXSCSSLsTj8bBz585Gj0vxlrQVuQOXSPoxpXV+nl1XTE0gRKbLwdJpRRSku7t7WH2WWMBadXU1ixcvZsyYMd09JEkvRgq4RNIP0Q2De1Zt5MMdJeiGQFUUDCFYuXkPc0YN4Jb5k9FUaaDrSBpGm7///vuMHDkSTdO6eWSS3or8hkok/ZB7Vm1k1bcHEALUaLcrVVEQAlZ9e4B7Vm3s5hH2LZKlip1zzjlSvCWHhBRwiaSfUVrn58MdJdia2GHbVJXVO0oprfN38cj6JjLPW9JZSAGXSPoZz64rRjdEs8dEDINn1xV3zYD6MFK8JZ1JvxZwT0jn3ve38MDqrXK3Iek31ARCltm8KVRFoTYQ7qIR9U2keEs6m34dxOaPGHy0sxJFqZDBO5J+Q6bLgSFEsyJuCEGGy96Fo+pbGIbBihUrpHhLOpV+rVSxny8ZvCPpTyydVoSmNr8Dt6kqS6cVdc2AopTW+Xlg9Vbuemd9r7eKqarK7Nmz0TRNirek0+jXO/CGxAfvyFxYSV+lIN3NnFEDWPXtgaSBbBHDYP7YQV32HeirKW2jRo3irLPOIicnR4q3pFPofd+KTkYG70j6A7fMn8z8sYMssQQss/r8sYO4Zf7kLhtLX0lpC4cbxwyMGDFCirek05A78AbI4B1Jf0BTVW5bONWqxFYbCJPpsrN0ehH5aV1nfWpLSltPtorFAtYmT57MMccc093DkfQTpIA3QAbvSPoTBelurpk9oduuH0tpay6gLmYV685xNkd8tPkHH3yA0+lk2rRp3T0sQJbK7etIAW9AdwTvSCT9ld6e0pYsVWzkyJHdPKrWxRVIej9SwOPo6uAdiaS/05tT2npynncsrsCmqknjCgCunyUbqfR2+nUQW6wWVXcF70gk/Z2emtLWEj1ZvFsbV1DmCXTxyCQdTb/egbttKrNH5JGXntrlwTsSSVfTE/2hPS2lrTX0ZPGG1scVrNiwhxOyu3Bgkg6nXwt4mkPj5hMmkJKS0t1DkUg6jZ6eZx2zeq3eUUrEMKzx2VS1x1nFvF4vL7/8co8Vb2hDXEEwTH05K0lvpEcL+Lhx47Db7ShxH8azzz6b2267jTVr1nDvvfeyc+dOBg4cyFVXXcWiRYu6cbQSSc+kNf7Q2xZO7bbx9ZSUttYQCATw+80KcT1RvKENcQVOOxDpuoFJOpweLeAAb731FkOHDk14rKSkhCuuuILrrruOJUuWsGbNGq699lqKioqYMmVKN41UIul59KY86+5OaWsNubm5LF26lDfeeIPFixf3OPEGM65g5eY9iGYaztlUlSVThlG+Z2fXDUzS4fTKILaVK1dSWFjIhRdeiNvtZu7cucybN48XXnihu4cmkfQoZOvQjic/P58LL7ywR4o31McVRAwj6fMRw2D2qALy01xdPDJJR9Pjd+DLli1j7dq1AJxwwgncfPPNbNmyhcMOOyzhuIkTJ/Lmm2+2+fwxc1hfIDaXvjQn6Jvz6qo5ldd5EUKgN7cdAyrqvPh8vkO+Xl97r7xeL//97385/PDDgd4zr2uPHU0oHOGTnWWERX3cg11RmD0in2uPHd3n3qsYvX1eQogEt3Fz9GgBP/zww5k5cyZ33303JSUlXHvttdx5551UVVUxfvz4hGOzsrKorKxs8zWKi4s7aLQ9h744J+ib8+rsOfmrK/F4PC36Q33VKlu3bu2w6/aF98rv9/P2229TW1vL1q1bmTlzZq+a1+KBMDszi7d31eING6TZNU4syiDbBd9+87V1XG+aU1vozfNyOBytOq5HC/jzzz9v/XdaWhq/+MUvuPzyyznyyCOTHt/aVUs8RUVFuN09K1Cmvfj9foqLi/vUnKBvzqsj51TmCbBiwx5qgmEynXaWTBlmmUevGhbgqxWf05wVXQOuWnBkh5hU+8p75fV6Wb58OQ6Hg7y8PEKhEMFgkAkTJvS6eR03PfnjfeW9akhvn9f27dtbfWyPFvCGDB06FMMwUFWV6urqhOeqqqrIyclp8zndbnefSyPri3OCvjmvQ5lTU+lhb20rsdLDClNSOGHMoGbzrI8fO4jCgrZ/d5qjN79XHo+Hl19+mbq6Oux2O5mZmSxevJj9+/f36nk1RV+cE/TeebVlI9pjg9i2bt3Kfffdl/DYzp07cTgcHH/88WzevDnhuQ0bNsgIdEm/orVtOHtS69CeTk8v0iKRxNNjd+C5ubk8++yzFBQUcP7557N3717++Mc/snTpUhYtWsSf/vQnnnjiCc455xw+/PBDVq9ezfLly7t72BJJl9DW9LDekmfdnTQn3h0R4CeRdDQ9VsALCgp49NFH+f3vf88DDzxAdnY2p5xyCldffTUOh4NHHnmEu+++m2XLljF48GCWLVvWKLBNIumrtKcNZ2/Is+4u5M5b0hvpsQIOcNRRRyUEssVz5JFH8sorr3TxiCSSnkFvb8PZ01i1apUUb0mvo0cLuEQiSU5vbsPZE1mwYAGVlZUEg0Ep3pJegxRwiaQX0tpymT2tDWdPIVlntnPOOYdIJCLFW9JrkAIukfRQmmv/2VFtOHtii9HOJJZ6996WXRiKis3uaNSZTSLpLUgBl0h6GK1t/3kobTh7eovRzuKeVRt5e+NOatevRrU7yZ46C9Vmb3VntjJPgJe/2NVvFjySno0UcImkh9Ha9p+H0oazp7cY7QxK6/y8t2UXtetXE/HWAXXUfvMVWYfNAJrvzKYbgr9tLOWbT8sQKP1mwSPp2UgBl0h6EO1p/9nW9LDe1GK0I3nyky2Uf/kBuq8OAM2dStrISQnHNEy9i3Hfh1/z2UEvGWlpaGr/WPBIej5yySiR9CC6ov1nf2wx6vF4WPP2ygTxzpk2B5s7NeG4ZKl3pXV+Pt5Zhq2JiP/4BY9E0pXIHbhE0oPoivzu/pZDHivSIvweBGBrQrwheerds+uKibTQjjW24Fk6rahfBQVKuhcp4BJJD6Ir8rt7Yw55e6Pl4yusTRqUxY66CNlNiDckT71rzYJHURRe3bSHlZv39KugQEn3IgVcIulBdEV+d2/KIT+UaHmv15tQHnVwfi6nTj6B/+yrS3p8U6l3sQVPcxRX1KGqCoXZaf0mKFDS/cgloUTSiZTW+Xlg9Vbuemc9D6ze2qKfNJbfHTGMpM9HDIPZowoOySzbFdfoKFrbcS0ZTqfTajEcK49612kz29yZbem0Imxq0zvwkG5QEwgzsIn7JX3kks5C7sAlkk6gpZ3jtceObvK1h5Lf3Vq64hqHyqFGy9tsNk477TT+/e9/c8wxx1gV1tqaeleQ7ua4onxe3+hJ+vyBWj+ZLjtOm9bkXJqKbpdIDgUp4BJJJ9BSnnUoHGHxwOSvPZT87pZo6Et+YPGRvLvtYKe0GD3UKm/t6bjWEJvNxoknntjo8bam3t04ZzzlZaV866nfsccWPIU5qYgWovr7UlCgpOcgBVwi6WBK6/y8881+SuoC6IZAUxUK0lzWDs2mqnyys4zZmVnNnqcj2392ZeU13RDc+/5W1uyuOKRrtTVa3uPx8Oabb7Jw4cIOr2euqQo/nlxA3rARvPx1ScKC55mvinl5426aG2lPCwqU9A2kgEskHYhuGFz83CdsPlgDQqAoCkIIKrxBstwOCrNTURSFsBC8vauW46Z3zbi6svLa45vL2FgjcNpth3SttkTLx0ebP/PMM53WUSw/zdVoUdWbggI7gvLycjRNIzs7u7uH0u+RQWwSSQdyz6qNbD5YjYKZWkTcv5W+ILuqvIApaN5w8iCyjqYtvuS2Bt01pMwT4KsSHzat5Wu1xNJpRVbVs6awqSqLxuYnRJt3Nb0pKPBQeeWVVzj22GO5+eabu3soEuQOXCLpMGJC6bBpCMKNTKqqolDtDxGM6KgKpNqbDnrqSFrjSw7rOhc/9wnBiH5IZu8VG/agt7LoSUvugdZ0XJs9PItVK1+yxDsWbd7VLUF7Q1DgoVBWVsaNN97IK6+8AsCKFSs4/fTTOemkk7p5ZP0bKeASSQcRE8oBaS4qvAFIomNCCEo9AYZluDmxMKNLxtUaX/Keah+6gFG5iXnMwYjBk2u/44MdJSw6bFiLgWg1wXCHVnlrThhnD89i6MENVFRWAt0n3tC5gYfdiRCCl19+mRtvvDHBwvH973+fadOmdePIJCAFXCLpMGJC6dAUslwOqvyhRrtwRVEI6wbHjsgnp4t+11vyJQcjOlX+EHlpLusxAeyq9FAdCCEEVPmC+EORFnfkmU57i0VP2hLQ1ZQwLhqXz7uvvtQjxDuejgw87G5KS0u54YYbWLlypfVYTk4O9913H6effrrlGpJ0H9IHLpF0EPEVuwpz0sh2O0BRrI24iP5vwsBMbpwzvsvG1ZIvudQTAGBAnIDvqvRQ5Q+BAAVzJ1buDbZYQGXJlGFoLfywtyegKyaMty2cwo+mDzPFu5vN5n0VIQQvvvgixx57bIJ4L1q0iDVr1nDGGWdI8e4hSAGXSDqIeKFUgKKcNCYOyCQvzUWW20F+mospg7J54tzjWgzO6khaCrIK6wbZbgeOaOBZSDeoDiRaDxRFsTqYNReIlp/mYvqAFCJ65wR0ldb5ufOZN/jX51v4bFc5mitFincH88EHH3DppZdSGbVu5Obm8thjj/HEE0+Qn5/fzaOTxCMFXCLpIJIJpUNTGZaZQlFOGgPTXcwbO7BbopFvmT+5yRKiEwZmMiy7vrlHiSfQKCVKCJGw6Giu3ejFh+VzwugBbSpX2hK6YXD3O+s57+mP+CKUQXn6ELbVhnklPITzln/BnW//t11R85LGHH/88cyfPx+AxYsXs2bNGhYvXty9g5IkRfrAJZIOpKdGIzcXZCUEnPf0R5Zo67rRyHdvCEHEEBRXeqzCNE0Fommqwi9PmIBHV3h2XTH7a3xsL69jTF46GS4HFd5gmxcx8XnsmqqSNmoyFelDOFATRq8q5euSGoZnp8ruX+2gtraWlJQU629FUbj//vv58ssvWbRoUTeOTNISUsAlkg6kp0cjNxVkFZ+upWkqgnrftz9ioCpQ7QuapnQh2Ffjo8wbIM1p4/zpI5IKcm6qk9pAiC/3mhXZ1u4JYewub7PI7jxYzrtfbcGekWs9trvKS3VEQQG0aHreoAw3qqLJ7l+tRAjBO++8w6OPPsqjjz7KggULrOeGDBnCkCFDunF0ktYgl6gSSSdQkO5m6bQiMlx2qgMhnvmquEebd+NN7PmpThTF/IEP6gYKwiwDqygEIjr+sE7EEBysDfDQ6q2c9/RH3P3OevQGPvZD6SQWw+PxcOv9f6H8q9WEqsuB5D76WHoeyO5frWH//v1cdNFF/O53v6OmpoZrr72W2tra7h6WpI3IHbhE0sG0pu54T6Oh5eDVTXv4rryW6kCEmOs7ENEJGwLDEGiqGfxW4gmQl+q0dr3XzxoDmBXZDqWTGGCVR62srAQ9Qs03X5F39ALLR28YBmFDIAQoCgTDuvVa2f0rOUIInnnmGX71q18lCPasWbMwmghylPRc5A5cIulgOmLn2V3ETOzvXD6fCYOyzJ04YGDufGNdtwwBuhCEIgabS2rYW+3jw+0llEV3wSs27LGi1puiuUC4+NrmLpuG6kohe8pxKIpCJKITjOj4wjph3bDGUe4LUlzpQUQD5mT3r0T27dvHOeecw1VXXWWJd05ODn//+9955JFHyMrK6t4BStqM3IFL+jWH2vIy2flas/O88PBh7b5GW8bS3rlpqsoxhfnUBSKUeAJUeoOWJSE+Gl1V6+u8CyFYsWEPJ2QfWkW2ePEGOHbccKqMYWguM1K+NhQhpBsJ51dVBYemUekLAjA8O7Vbu3919OfqUBBC8PTTT3PrrbdSV1dnPX7WWWexdOlSZsyY0S3jkhw6UsAl/ZLOaq/Z2h7WMaHrDDpqbpkuBzZVYVhmCsFQhNpAyBLsGGbkuoKqKFQFwrz+9T62p6kcDNsIRHRctqbrvSeryNZQvGNFWgKfFbPq2wPohmh0f4UAu6ZETf1mQNuQzJSEYjExQY1FxI/KS2NIZmqHC2tXtm1tLffccw/Lli2z/h40aBDLli1j9uzZbN26tUvHIulYpIBL+iWd1V6z1T2sg2FotoN0++moucW3yfTruhWZHo+igEMzg9tCusGmA7VUpWpUhxXKfEHcDhvj8zOsXujxNKzI1pR4Z2ZmWnED//xyJwiBTVUI6wJFMcU7dn7dMAjpgoNRwT576nD+uHorL2/aQ1ldgKBuoCnwwXaNTJedVzft5vjRAztMWLuybWtTNNz9n3z6Ev7f//t/eL1eli5dyq9//WuysrLw+XydOg5J5yMFXNLvaEt7zbbuzlrdw9ppByKtGmtbTLEdObf4bmApNhs1SqK5O7bzDelGVEwVwoZBca2OEr1+jS/Eun2VDEx3U5iTZi0AIobB/LGDrDHous7zzz/fZHnUWJCdJxjhpU17iOgGdcEwEUOY5xQCv24QMQQ2BSKG4KWNu7nr7fX4IzrCMNBRQAjCgC5A9xkoitJhwtqZn6vWENv9f7DtAEbUKmIIwUpV4Xs/vIYLZ03mpBNP7PDrSroPGcQm6XfEzNzN0VyAVXO0tof1kinN+8DjK4+9vHE3H24v4eWNu5tM2YrR0XOLpZe57BpqNKAtVvDFrik4NJWIYRVMJ6gLc6euKDg1FYdNJWwIyr0BdlV6mqzIpmkaRx99NIqiNFvbfHBmCkMy3IzISWPKoGwmDcwiL82Fopg54akOGy67DZumUlzpoS4UIawbRAysHu2xhjJhQ1DtD6EbokPSzjrzcxWjuX7tv353A8//8ym2/ulmRMgMJozt/vcNmMRaZWC7ryvpmcgduKTf0WozdzuimJvqYR2M6JR6AoR1gwkDW67b3V5TbEfPrcIbJMPlYMHYQSxfX0wwrBMRYFfNHV4gYiAADQgLUA2DkACHomBTVVw2DZth4LRrKMCCsYO4dOaYpEVtJk+ejKZpDBkypMna5vFmfTBL1RakOqnwBOr97YpCjtvBpoPV1usMAENYPvyYiNsUs5nLkMyUQ04768zPVUu+9ZMGO/n7r66i7rvNAOx/+xmGLbrEen1n7/4l3YMUcEm/o9Vm7nZGMceXUw3rOnuqfWZnLyDb7cAX0rl4xeeMSxXcN65xV7JDMcV21NySCUaq3UZYF+S57Ng1FcMQ1ATCiLCZxiUAQ1EQhkAP69g1gVNTsakq6Q47w7JTMYTgma9Ml0C6XeP8I0clzGHixInNjivZAqnUE0AI04QvgGyXnUp/CEOY5vXm9sQRgTW/Q00768zPVWxBZwizVr2uG2iaSkGKg+ef/gcPvPMMRihoHS8MA2EYlisDZG58X0QKuKTf0XAXl4z2tLyMEV8U5eLnPkEXZpeugjSX1fFLNwSfHfRy34dfc/f/HJHw+mSR7KFo0ZTYD3d+qrPRj3FpnZ+6QIg91V5UVWVA3PXaOrdkFoCi3HR2VXqo9IfIdtkZkZvO+v2VhHUDTa03rSumrZpwtCOZ06ahqgq7q7w8sXYHw7JSEaEgFes+ZMX74zl51tHNBpE1jAP48QyzWEys3rxuRCu5KArZLjuFOWnsqvREBb3+TW74diuKYjVpOZQFW4zO+lyV1vn5YEcJe6t9Vn92BRC15ZS//xTs32Yda8/MY/jiS8kY09g6I3Pj+x5SwCX9jqbM3DEaBlgdCsGIzqjctKTP2RSFT3aWNdpJx5tiBWZv7oQfbqDCG+DVTXv42axxgCm473yzn4O1Aap8IfxhnTJPgBy3o9ngMWgskAvGDExqAYi1SB2sG5R6AkwsyGBXlYeIYRDWsQTbOj4q4nZNJawbVPtDFKS7EaEgles+QPfWUb1lLa9FR9fQJdCS2fgf5x3L8vW7efub/Wwvq2VAuttasGiaikNVzbamZl3YRhH0QggUVaUgzXVIC7YYnfW5enZdMTvL66gOhKMBewZi02r49GWIhKzjnFPmMG7RRWiulKTn6YhFiqRnIQVc0i/pqK5hzUWJtyYn3BeJcMULnzF1SLb1+nhT7K5KD1V+s+537CwKIAzBrioP96zaiCEET3y+g9pA2KxKphtEDINQwMAfiiCAwuxUbNHiLGlOOzet/JJtZbWUeQOUeYLkpjpx2TQMIXj4468JRwyKctOTJro5NJXBGW721wXM3bSACl+QiNF4lysARUBdIIymquTZoXLdB0S8ZkERzZWCKzOX1TtK2XKgine3HbTu5f4aH5/uKmsxDmDptKKEbmoAA9JcVHgDZrqZ0bQpPS/FiaYqh9SjPJ72fK5ayjTYX+OjJhi2msvwxl9g9+b6E6TnwPE/wDlqUpPiDYdmVZL0TKSAS/olbe0a1vBH9uyphfzts23NFuxoLqhJCMF+bxhfbZgST5DqqM925eY9HDE0B01VCEYaN+2IoSgKA9PdvPvNAXZXe6nxh6LCXR8FTrQF6MFaPz88aiS+UIQv9lbwXXkdNcEwvpCObhjYNJUyT4Cs6G49ENYp9wapCYTJTXMlNcWr0Q5gqqJQmGNaGA7WBRrtwh2a2f5TFwbZmoFnw+p68XankjNtDpo7lR3ltZz+xAcMzkhBVRT84QjflNZaY2p4DxrGATTc+To0lSyXg0oRQoQjKDazw5oerZ0OgjSnjTH5GcwZ3XH16dvyuWpt0Zft5XUY0ecVRUEMm1Av4Id9D445HWF3kuq0ETGMTrcqSXoOUsAl/Zqm2mvGaOpH9sGPtqIbgiGZKZR5AuiGsPpkx3aHzQU17a72URvS0VTV+sGN7S4/3VWOTVM5UOu3zObxGEKQk+LEadP4rryOMq+ZMhTbacaOj6WzRQzD8j2bftQwhmGgG2YedEQXBNCp9JkLiUBEJxQxFwNGXYAKb4AsV6KQxsZQ6TNLrBblpDEoI4WvS6vxBiOoqoKmmuf2RyK4RATnrv8SCXjMsblTSZs8iwNBKK+sJBjRyUtxWveq3GuWZq3yh6DSQ1FOvRvCiug3DK544TP+ctaMpDvfYdmppj8eJwVpTsp9IYLhCIGIwbj8dM6YUsj5R4xoc5vX1uTmt/S5guRxBmHdYF9NgMc+286aXWU8ce5xjMpL44PtcZ+CycdD6S6YcCzKENOFogCnTBhChsvR43rRSzoPKeCSXk9n1p1u6ke2yhskoBuUeQI4NdUKiKrwBslyO/hwewkPnn5U0qCmkG5QE/VnKoop+vHYVBVFwKAMF1XRGuOx8yuKQk6Kk8Jssy54TTBEWDfq87OjtmJFqc97FgL2VvsoSHNbO/pwXL6yEi18YghT1F12zWpiEjIMXIpKhS9ITTBMptOOpqkMSndz90lTufrltQTCpqDqhiDb7WCAU+FgQBCI6CgK5Dmgdv0nlAS8uGwaOVlZeIqOZH9tBF2E8Yd1DENQ7gtiq/RQmJ2KbphzBagOhAnpBnZVYVeVl2p/yLoX6/ZWcN7TH1k71stmBhvtfIUg6W64tM5vRcS35nOjG4J739/Kmt0Vze6YW/N5jM80CEZ0Sur8VPrMxZNdVdFUhU+/K+WEy25CCXjIOPwUqgPh6C5chfkXW+cyhHnfh2alcc3sCT22F72k45EC3k/pSc0W2ktn151uKp2r1BMgEDH9zIqi4LAplljqhsGBWj9VviC3vbWeI4bmmDvquHOUeAIYUV+1S1Mp9QYbmal1IShIc6MXCMq9QXTDLB9akObCYdMQQlBc6cETjKDHLxCi/x1bNKjROGwF2FnpqQ+EE1gCad5LgY6ZJ60LrMAzIUwhjhgCEdajJmiBbhis2LAbQwi2lNSYQWLR+x8IR0BRcNltZGsGru/WUhfwAuBXHewfPBVhmIVhwlGTf8NmJJqqJCxcYn5gXzCMoqrWvbLZtEY+8WQ73/jHYkVy2vq5eXxzGRtrBE67LalPPmZtac15n11XTEQ32FPto9ofivZYNwCFiKFjry3F8dEzHDj4HaCQPmAsOUPGJixe4hd0I3LSLP92a3b/kr6BFPB+Rk9sttBeOrvudFNBaMHoj20s7zikG9gQBHSDmAs4rBt8saecmkAIm2buqPXoD255nR9fKIIW3S2XexqbqVVFYXC6i00HqyzzfH5UvAF2VXmp9AVN/7KevCqbgtn2E0BTFCK6UR8Ip5iBcDERVzCLneiGwDB067hYfncsCCwQDjM4I5XCrFQe/3w7YOa2VwfCCCEwhDCrnikghIES9lNdUwsoGHYXyriZeLCTEmviLQQIsDVoRjIqN40KbxCBaTLfX+ONLlQUhKETiuioisKYaFR1WwqVtOdzU+YJ8FWJD3dqatJz2lSVp77YSX6qkxRHcoGPP29NIMSeap/VxS32ecIwsG16H/uXb4AeS/kSRPbvYPC4yQzKcFvWjtiCTlUV5owe0OsW4JJDp3f8UmN21Bk3bpz195o1a1i0aBGTJ09mwYIFvPrqq904ut5Db+5VHU9bip00fF1TpSgb0lQQmj+i15vFhSCi63jDZrlOqO+fXekLYRiCiG5wxLBcFk8ejoIZ6OSymbvIWAlQBFT5Q+yK9rPeWVHHu9sOEo4YVPlDlHsCbC2pobjSQyAcodofMndfzaQFxW/MNUVg01TrMXtcuVchBEaD1xnR/8Wfwxyneb9D0VrkdYEwgzLcTByQSV6ayzT/KuYO2hc2qFBSCIw4AuFKIzzmaHyqA0PU77wVRcEW14wkNp7qQJgst4NAOEJYN8dnCNNcbP5rLjY2Haw2e4DTujKl7f3crNiwB72ZBO9gxIwhqPCHkj7f8Lwq5vutKorlzlCqD+J67Y84P38FJSreamY+Yy65gzELzyI71YnbbmNIZoqZzpeZgstuk/7tfkyv2IFv3bqVV155xfq7pKSEK664guuuu44lS5awZs0arr32WoqKipgyZUo3jrRn093NFjqS1rbtjBU7aY/loakgNLddoyYqgIaIK2BC/b+xneP6/VXkprlYvaOE40cPoLjSQ7rLTsir178w7nXVgTB6pYeaQJhJA1Oxa2o0D9zc4ZqBZmGz4pjbYfpEozvVpnCoCkJRGJGTxrbyWhDmIsKuiaiZvMXbHa2ToqBqCpVRf7iIOt5LPWY62bDMFMIRHW8obO0ow4bAlZ4Dk76HS1HxhSOgmoKdn+Yi2+2wxlR/LQXdEAzKcHOwzg8YGA1S1FQFFBQihqAianYvyklrsVBJWz83MWqijVP21fgwBFbAYmzhUeoJgBBNWkManrc+y19B6DqOje9h/+oNFD0SfUYhfNgcCuYuIW1ALgAzCwu4bOYY6d+WWPR4ATcMgzvuuIMf/vCH/PGPfwRg5cqVFBYWcuGFFwIwd+5c5s2bxwsvvCAFvBna++PVE6kJhIgYghKP36pO1tCPHF95qj1m06YqazltGpoCIV0kiEqyHOi6YBhvKEyxgI0HqkwTuBCEdYEQBi5VtXznYcMUAE/Q3NXG5lKUk5ZQia02EGJkbjqpDhuf7i5vVrxVAMVcdCgIslwOK6/cqakY0drlLWEI0BQzPUsF/KEIdlUxTfhxAXEi6Mc4uBNlwAirSxhgBl4BqqqiR3SyXHaGZpo5y/FjMl9iugxKvUHsmkqW0065P4hhxIS73n8vMHfz1YEwgYjeYqGSfTVe9tX4rDHH3APxn5+GFct0w2Dt7kp21obMVDRhjnFfjY+8FCej8tKtanBaksp31nsRd14dyHY7qaqowPHGw6hlu+rvdUY+ge+dhzZ4NIrdGb3/ZhEW6d+WxHPIAh4KhXA4HB0xlqQ899xzuFwuTj31VEvAt2zZwmGHHZZw3MSJE3nzzTfbfH6//9A6EPUkYnNpak7ldV4zAKmFLVdFnbdH9QpuOC/dEHzyXQmbDlRB1AQtMH3JmS47w7NSrIAqtyrYVVrJ+98eQIGkXbwU4INvD3Dh4cPIj4sIT9Ng5vBc3t9egi3uhzkvxUGZJ0BIb74dqIKCLgR6dMcW0gUGOi6bhtOm4g8ZBMI6RAU86g5GEYLaQJjvKuqsuWgK5LntlHmDGELwXbkHh00hFGl6xwemGTyiC1LtGvtqfPhCOkZ0saAppqDSRGezZPcpdq+FEIQMcCoCVTHvqx4MENryCbbqaoxIGH3IWEBJWGA4VIUwmLvV6HWHZroxhKDKFyRkmDchFIlgGKaVQVMUnKEwQkSd6yQulmK+93KPn1nDMvn9qvVWxPySKeZ7qhuC+z78mpc37KbUEyBkCCtn3a6Z7ozY52dophu3KqzvwL3vb6XM4zPz7K1ysQrCMNhf56c2FCbX7QQhyE+xN9kpLvZ59Pl8pKgwOMOFrmdRY81JITzpBMJHnILmcOKILrB2V3kwDEFlnY9dpZUJn9FDoaXfi95Kb59XLDixNRyygJ900knccMMNnHzyyYd6qkaUl5fz5z//maeeeirh8aqqKsaPT2wCkZWVRWVlZZuvUVxcfChD7JE0NSd/dSUej6fFZgu+apWtW7d20ujaT2xef9tYyr6KOrNhQ4NjKrw64XCYwal2VEVhqsvPQ+9+QW103iHdoCqoW4Fh2U7N/KEUgofe/YKl43MTzndqgaC8TGFdqS8ht9YwjOhOsD5QrCEitvuM/reCKaaR6OucNpWgbiB0cyyKilmwQ1XQdd2ay6AUGwe8YTxhAyMa/BUIRxr5qJvCwIx8B7AppmBl2M0FhVtTCEXMQcaC3po6p4rZtxswK5zpBkKBVEXHU1VB7cZPiHhrzWC06v3oA4rQbC4iEZ2IEJYlw6WpBMJhfF6vdZ/CobB5X3UDp03FiEQQKIT0iLVw0KLpbo0+vgL0SASvX/DT5WvQRb2LZMWX25k+IAVDwNoSL6kqBCMGkbjSquGI2fjDoalUeHUi4TBTXTls3bqVSn+Ydzbto8QTSnSVxL3eE4igGbrZ7CUQwGcISn0hvBHzZqY5VPJcNlw2jakuP1u3bmWqK8wKv58Cl0ra9y9m3yuPEjxmCQwaiVM1g/sC4QgVhpkJkOm08frGnby1qZjpA1K4+LD8FtvWtpa++BsIvXterd0Ut0vAP/vsM2bMmAHA97//fW666SaefvppbrnllkY740Ph3nvv5eyzz2bkyJHs3bvXeryp1UlrVy3xFBUV4Xb3DR+S3++nuLi4yTldNSzAVys+b1JwwGwLedWCIztsld8RxM/Loyt8s6aM3MwMPIbXCgRKOF4Hxe5kwbhBHDd9AqtrtpBaqbO72kdNQK9Pw9EFdRGdTJfK8KxUUrJymDCh3jxZ5gmwYsMe8vLzOTHf3HsKQEVh1fYStpbWUhcMoylKUqtGQgCYouDSVHxhnWA0glhFEDbApsb6VIOiCNOMG/XH+3UoDQo8OgjFTPEyDFBUEK3bOAMQNsxIdENVUFWNkfnp2DWNTQerURSdmIKrirmIaDgbBTPVS9NMn6+IFghJd9pJd9qo+u8nqJEgNk1F1ZxExhyDzeEiIkzRjeWma1Ezc6rLSVqKmwiwp9qHRwe7ppGb6rKsDmFdZ0tJLaqioKoqLtVc8ESiee+xanMxP7Tdbk8aJf5lRZhyX4jh2WmEdR2lOkhsyuZ7Y2YIoJgtUh12O2PHjiU/zcXDn2xDcTjx6iGcNpWwYR7bKLhPs3Hm1EL+tXEv5f6QtbtXFAWvL0KF3+DoIRms/eh9Zn/vexw3fToLa+y8v72ElKIx5Fz1W/bU+M3ubkIQiMSsAxoZcVYlgI01BitLFX55wqGZ0lv6veit9PZ5bd++vdXHtknAKyoquOeee9i9ezcrVqwAsALJfve737FkyRJOO+00fv7zn1NQUNC2UTdgzZo1bNq0iXvuuafRc9nZ2VRXVyc8VlVVRU5OTpuv43a7SUlpun5wb6SpORWmpHDCmEHNNls4fuwgCgvafh+7ArfbzdNf7EIoClo0KEttUNhDRH+I89Ld3HnyNLP+dnoqe6p3WaU/rdSpWKGQqP81Nz2VlJSUJgPeNFVhzqgBpDnt2G0aEwZksrmkhpBuoOhmV6z4lKuGP/JB3QAlmqoVjaYGzOh1VWAYApuiEjYETsWcT8QwKPNFUFASREFFIbHXVvPElji6YbpQynxhhmXaSXPaqY2KRmwhFBt/7N8YDk2zHlMVhR/NGI1DhHntxRfQfaaFIz0jA9+gqQzMyUEIKPMGsUcXKPao/xwgL9XJUUUFpDttPLF2BwPS3Qnd2sAMtMtJcVLuDaAqKqoCbpuGHq1vLgSkOu2MzE1je3kdQ7JSk6ZAVgcjVAfCDBFQ5gvjsmkEiea2i3orSsQQDEx3MSwrhZe/LuGa2RPwGVDpr/eHu+yalToYy323ayoZTjvfVHioC0ei6XqKlSWnKgpq1T42r/wdG0t38+orr/DBBx9w58nTcKzaaFZOUxRG5qbjD0corQtQ5Q+Rm+pMaNASf18+3V2JR1c6JNi0L/4GQu+dV1s2om0S8JNOOolzzz2X++67L+HxYcOG8eCDD/L555/z61//mpNOOolLL72USy65pN3+8VdffZWDBw8ye/ZsoN4cOWPGDC655BJee+21hOM3bNggA9haQUc18egu4lO7FMUs4RkrrRmfG3tMYb71Y75gzEDuent9k64DVVGoCYRZMGYgYAa8vbFlHxX+UEKAnKooCYFwTptGjttBpS+IU7PhDUUaibiZNqZYQqFimsr1JGYQh6bitGkEwhH8EYMUu0bEEER0czkQyzu3RU2nSpKdclOYZxAgFMJ6fbS0w6ahqYpVI1xRzJ02Rn1qmQJkuuzkprnQdTO6/Kypw7n1+HE8++yzLBqdzaYDCjjdHDn3JLaVVrO1VrC90oPLlig+sRKsKQ47X+2tZM6oAoZlpTb53hTmpCGEoC4UMduWxnbjmkJWtHXo7iovmS57QipaPLF7HQsCVCDauCUqxJhdyfJSHVbJ1liwWabLQTiWox1FxXQDxN9bRVHYcqAGFYVUh73+vIaBbf27KF+9iWGY7oft27fzn//8h/nz5yetm+4Jhnj324N9IthU0rm0ScCPPfZY3nvvPU4++WQmTpzY6Pmjjz6al19+mRUrVvDHP/6RFStW8Itf/IJTTjmlzQO7+eabueaaa6y/Dx48yDnnnMMrr7yCYRg88sgjPPHEE5xzzjl8+OGHrF69muXLl7f5Ov2Ntjbx6GkkS+1y2jSGZdWbThu2TXx320EyXfb6dowNEECWy8672w6SneLgybU7qPI3bt8ZK7Syr8Znde+KlTSt9odw2zV80bKgAC6bik1RCOiG5bdVFDP62y/0BJ+q266hRQ9y2W0EI2ajkYhuYIioqGLmbzttmhmIpphiYoj6HXYyQY+fs6KYQh6Llh6Q5qLCE0AIQVA3ort8JeF1Dk1hwoAs7JqCTVWZPaqAq48ZwbPPPktFRQWpDhsLp4zivPPOw263s2nzFm7/ohIEJKsaFrtnEcPgk11lzQqVAozITeeIoTlsLqnhm9IaVEVhQLobm2paYgpzUhHN+IU0VTGDwHxBEBCIGNFoc6wFhhDCKpIT//lZOq2Ihz/+2tpAJB1jdH6+sG5ZMlw2FVG+F+O9f6BW1Lv/jKyBZH3/EtYY+ZxgGGiq2iiy/K53ml5sxpC9vSXQRgF/4IEH+Pjjj7nppptYuXJlwnN1dXVs3LiRDRs2sHGjWQxk//79XHfddTz99NP87//+L2PGjGn1tTIzM8nMzLT+jkTMiN+BA81d0iOPPMLdd9/NsmXLGDx4MMuWLWsU2CZpmt6ajtJUalc8Ddsm1gRCFOWmJ+RTWztkRSE7upOrDYS5ZPkaKrxmg474vO5YoRUqPQxId1HhDTIkM6WRFaDcGyQY0bGrCpMGZfN1WS2+aNUwIcCu1fvLVUAoWMU8NK1eOJ2aSlaKk3Kvn3DEFNx4E3QsjzvUTN5xQ1E3g+nM6w2Ixjg4NJVMlx1VNbub7a/1UxsIoaCQ4bSR5bYzIDOFmYUFCQu95cuXU1FRAZjf1fPOO4/MzEx8Ph+aqnDU8Bw84caWEUfcLlk1VxNNNnyJYQjBkKxU7lt0ZNKF5zNfFfPyxt3JF2dCEAzr+MIR7IaGw6YSNoxo1y5zMRRLS4vVpI///BSku1k8aRiPrtmWdGwCyHbZURRIcWh4ggKhR+CrtxFfvokaDVQQikp4ynzEESehZGU0WymwuSY48fdE9vaWtDmI7bjjjuPFF1+0/v7FL37Bxo0b2b17N0IIcnJymDp1KhdddBFTp04lJyeHP//5z5x55pn84Q9/YP78+e0a6NChQ/nmm2+sv4888siE4i6S/kGy1pHxJGubmOlyIIRZGASgJhAGAVluO4MzU6wodBXBN9GgqWTECq0MzkxhSFYKEb2+dWPMCjA0K5XvKupQiNY8j5pszaAvxTKRx1LDTD93XDWY2LWiu7qpg3OitcbNx2P54ubhApuiEEGgRYPgwnGF0dXoOWKmfCUaqJaX6kzwq47MM3e4X+2tojA7FVVJs9wqs0cVJC1yc+KJJ/LMM8/gCYapLTyCBz4rJtPlYPH4AeY9d9qxa2qCZaQhhhAcV5TP+ztKWr0gS7bwbG5Rt6vKS20wjF1TzXKtmFaMsCGi90rHoalWd7dkn597/mc6n+0qY92eikT3SHTxNyQzhXSXnW9LaxEVu+DfT0LF3vrObdmDCH7vfPT84abVQFWaLZrUmkWqbgg8gRB3vbO+1/YykBw67YpCj/dr79y5k1mzZnH44Ydz+OGHM2zYsEbHP/DAA9x///389re/bbeASyQx2urHP3tqIQ9+tJVqX2KwW3W07GVhdio2VbWCs5rLwxRCcLDOz0+PG4cnGG40Bk1VGJWXTjAcoaQuiBptF2pTBA6bnQy3Da+qEIiYldg0TYt27VIaBY5NGJjJgrGD2bd6K1XRft9Wv21FsUz1dhWGZacS1g08wUi0L7jZDhRFqfeVK5DutDEyN92aT0yw2upWSUtPZ+/AKXy8sxRlZ5U1/1c27mJcquCGk47mrW0lBCP1BWgaFtuxqSqXzhyLAW1akDWkqUVdXSDEwTo/AtNFke60UxcIR69v3suwLshLNQPXVEVJ+vnRVJWVP5zNVc9+wJqyIGFdYLdp5KU6SbHbmD2qgB/PGMPZ//iQCgRUmbtroaiEpy4gfPhC0OymZcWmWTv9pvzYzS1ShRDsrPSgqQqrth3s1b0MJIfOIeeBx+/Gm2PevHk8+uijh3o5iaRFP37DTmv7a7xW9HXYEGb+laLgiJYFNYTgRzNGYwAD0l1WR6xkxHbT508fQUG6u9EY9td4WbOrHKfNxvBsGwMz3GwpqTZzlYUZnTwmP4MtJTVWTrXbbmNMXjqV0aA5W7Rd5xPnHgfA61v2sm5vpSne0QWGEev9rUDIgNK6ADbNNEkrmMFpdkMQEQK7HWyqRprTTmFOajTfu/GCp+HuNlY3viYQwo3OD44ey6AsM8jrnlUb+c++Omyu+h22qpjBcJ8d9PLYFzsRwKaDVWZENomxBEMyU1gwzhTmjgisjD9HWNfZU+3jYJ2fkG76u4Uwq+KlOWw47Vq0cpqZMjdhYBYnjRvc5IKltM7PPz7bjsuuce7U4TjsdgyURoucheMGm/3Wp5+E+G4dwe+dj5FnbmgEZg5+jtthBds158du6p7sqfYhgOHZ9b3ZO7KBj6R30WWlVMeNG8cjjzzSVZeT9AMaCk6yNpGBiM7XJTVm6k9Ej5bBBBSzEpc9atL88YzRLF+/2yzdGY0sT2ZK14VgXEFmgnjHFgrzxwzk6pfXJuyaHJpqlQpVFbPT1qAMN5kuO5WeCEKYvZxTHTZSHebXMWIYzBs70Np1mubtSlKddnwhs4CLGlXEWOxWXcgseBIz0ToUlcGZbk49bChDMlOb7YvdaI4N0uhEKEjFVx/w7L/SOW3xafxk5rjma+orCk9/VWz621EI6NEe14qCMARV/hBFuWmWSHVEYGX8OS5+7hN0YS6MHDZR37FJmPfJpqlWtDnAzMJ8rk4SD6IbBre8vo5XNu0hEI4g9AiDqyOkOh3MGTWAHx9VxBOPP86Pf/xjHA4Ht8yfjCEE/xCLODhlPhGhWkVf7JrKgHS3FcAHzfuxk90TFcE7TVgqoHf1MpB0DF0m4E6n00oJk0g6g2T1zsu8QfwRnXB0J5Zi0+pTfDA7YRmGYPn63ZbvMT6yvGEUdV6qi0fOOiZpP+m/fPwNoYhOUU5aggm+MCcNogF0hmFQ6gkwNNNNJBzGbncwIN3FnhofkYiO3aaxeNKwhF3nwIwUsqJR9AKzGIoQAr3B/AVmXfDYtf1hnTSnPUGcWhO4GH8fRShI5boPCHpqqays5K/Pv8Ly9dPIcptR+A0RQrDXE6LcH8GuqbhsGjbFzGFXbRp5aS4zJQ+FCm8wQWg6KrAyGNEZlZvGnmov5Z6AVX4V6uMYYjvzpkRUNwwWPrKKr/ZWEAtn1HWdujKP2T511zb+8YuLqdhbTE1NDb/85S/RVJU7TjycK44dxx8+2MzfP9uBADLddganuxMC+KBxsGUy4u/JA6u3thidLtPL+hfSWSLpEzTstBaM6Oyp9lJW5yccMVtZmZ23DFw21apHrikKNcEIB2p8lu9RF4KinDQmRFtkZrkd5Ke5GJufzkVHjeTxtduTtmQN62brz11V3oSxKZgNSSYOyCQ/3c2wrFQWHzaUvy0sZMGYAnZU1FHu8eMJRUh3aHy5t4J7Vm20amrXBcOMyE0n2+3ArqrYonnlTRHSDXxhndpAmHe/OdBsy9Tm7qMeDFDx1QeUl1dQ7g3gV+z4cgvZWeG1WpuKuHtdXOlh08EaKnxmbdbYGG2qaom9bpjpWq1p/dkeYg17AArSXEljGYQQ9aVlmxDRW17/inV7K6MFc+KIhKl6fznf/fV2Kvaa4//zn/9sReSDKbq/OfVIrp49nsMGZlKUndZIvCOGwexRBW3aKTfV3jYemV7Wv+jx3cgkktYQ++FWMCOPY7tnf1ivL0gSLdzhbmCCNAyDbeV1QKLvMRZFHR+R/eMZo7ngmY+TmjG1aH/van+IYERvVFjEoakMyXCzaNIwfjR9OPP+/BbbakJWGlNEN9hZ6SXLH8YTCPPprnJmFOaxfl+VGfSGmbdsCFpsYhIxBL5whAO1/jbtyGL3MbbzrqiowB/WUZwpMP4YFGcKSkQnEO1/HQsEFEIgFAVPMGw1O2n449Jw99sZQhMvck6bltQdogB6NNAvWYBcaZ2flzftwapsYz1RDB/+E6oOWg8NGjmO5U/+jdzcxBr60PFFk2R6maQhUsAlfYLYD3dxpcf6wVYUBVU1o7DBNDFHkhT8UBWF0XlmZHZL/tgHVm9tsiXrgDQXFd4Awqjvkd2Q2I7vznc38E2lH81mq883j0aiH6g1A7BcNo0afwhDCL4trQVVscp4tqYCWyTacastQlkTCFniHairNSPk48Qb6tOwYlHxqU6zcUwwusiIjc1IVh8+uvsdkuHuMKGJj0VYv68KfziC227+tCVzhxhCYNOaFtFn1xWb5XGj77GIhGHta6jrV9Wn+6kaKccuYsnlVzbZ/6Gjiya1pwaCpG8jBVzSJ8h0OfCHI1a98xiKYuY+x370DGH6N2OpNkY0iGxwZmLN5Kb8sQ3NmPF9ujXNbO5RGwgnLZUa2/EBrNyyL3F3FyUY0c2WlYaBTcFaCGS6HZR7A9YCpDUCbggo8wZIczb/NY8XwC+/20/JF++jBDx4QxFwuBPEG8wWpA5V4Ama+eymNcAU9Phphw3whcK47bb6+vOYu9+OEJpkNesDEZ1vS2vJdDsozE5NWm7Xrqn86+I5TByYnfS8NYEQdlU1q6+VFMP7/4DqknpTev5wOOFCHEMKyW6FEHeUb789NRAkfRsp4JI+QXzJy3i/p0NTzXKkCKvkaNgQqEp9ac8ROWmtFpOYGVNRlGhlt8SSq0q0bJtNVSxzZ0Oz6Z/+8w2+kJmvrURbXNtVBUVVzXaZ0XNFRH0d79hO8mCd38r/bg26bnCwNnlv92TR5iVfvEdJWbnps3alwMijEsQbzEVRlstBMGxG9Yejvvr4im+xv8MGqLph+cBjBVDa6v9NRrKgRZdNI9PtoCKaChiLNo8V2omJXFPiDeZ7nJfqpMIbROzeBNUl5thVG8qRp8C0BaBq2G1al+92e3svA0nHIgVc0icoSHczNDOVMk9iDrdKrHc1ODXTrO60qeSnOilIc6GqCnNGD2i1mMTMmDsrPFRFu5g1LLkKCrNG5FMdMC0COSlO7j55KhMHZqMbBq9u3kNVIEzEAEU1c8HCOiiKYYl+LPI91vM5tpMckOakpC5AcXVyUW5ImsvBV3urkqYWNRRAQ9NwOl24bBo+1YE28mhwJLaVjZUOBTNNK2IIghE9Ok4lodCMEGbFuFi6XmxeZ04ZfshC0zBoMZ7CqGhX+0OWOb0tIhd7j9McNsqmLMDx3X/BZkeffR72guFmHj2weNKwLt/t9vZeBpKORQq4pM/w/cOGsrOijppgJKHeucuu4bJj1d3OS3UyODMloVRow5zupkpTFqS7OWJoLl/urUBNUn07JsAvbdpr1hiP+uGveOEz5o8dhCFgV6UXu6YmtAcFrCA80+RvdsgqaNCX3e2wM26gi6BucLAu0KwpXVNgXH560tSiZAKo2uxkH/491K1fUpU7kipdTSjCEl83fm+110xlEwK33UZ0nWHuuqPlYxXAblVd08hOsXPmlOH85tQjmhl164gF2yWLRYhF/QciOgPT3Rw+JKdJkYt/31MUg1HhUhadtAAB1IYioNkInHg5ujMdVI1IWMdlU5k+LJd7/mfaIc+jvfTWXgaSjkUKuKTPcP70Eby+ZW9C+U6bplp9pkO6mYO9eNIwhmSmsHR6ETkpzqS9v5srTTkww022y0FNrDFKXJ64oij4wmZb0WBEx2Uz+0dX+oLsKK/DadMYnJlCpS+Ipijosfqp1PelJlr61KmpHKj1o0UbgThtmhkYJgRjCzLxR3Rq/OGkIq4qkOa044oGczUMZGtKAFWbnezJx5AN+MMRagJhvCEdp01N6NddkOai3BvEqSW+3mXTENFWpGb1MYEe7Vw+piCjw0SnNSlVLpvG4UNyuG1h4zbDDd0Hvl1fs+tfjxCuKWPclb9ByxlMjttBtaKgZ+UR1g0Mw6yXP3lQFu9cNl+WLJV0O1LA+wGt3V32duKDfIY1CEoL6QYHav0U5qQyODOFc6eZu7G731nfyI/aUmnKWF52wz7kWW4Hmw5Wo4tYMxLz+PhuZkIIBmemkOmyE9F1lKifO7bDRZjiF62KakVPV3iDZLkdjMhJ47iifFZtO8jUwTms31dJbTBsZTzFUtI0BfJSnEDy1KJYtHnVtv+SOXYaqsPZ6H667TZOHD8EVan3ucbO57bbmDY0h+JKL1W+YIItwmF2aSFimMVmhIDsFAf+kM4Fz3xsLYwqvMF2fy7jU6ri34eGi52mIt1j7gMlEuLAu89T9ulbVqTj1uX/j5zzfklRTpoVpBiJ6OiRMENz0nHabI2K0Egk3YEU8D5Msijdvt74oGGQj6IoFFfUURP1FQpD8PLG3azcvIcjhubwxd7KNpemjIlHwz7kxZUesxIaMRN44g5RCDMwbX+Nj+FZKYTDYfy6uesOGUbUZyxAmLvn+PQygApfkKLcNC6dOdbq4DVxYBabDlZbkeAKitl2VIGC9MbtMWO4RYSKrz5A99UR8daSO21OIxE3hCDL7eCa2ROS+lxzUpzc8vo6/vbptkRLBKCoCnZMny2KwqCMFGth9O43+/lghxnV3d7P5dJpRby6aTfFlR6q/SGrzr0wDPbV+shPdTE6Nz1pkFnMfRDY9Q27Xvp/hCpL6t/zwaOIzD7fyuW3UsniXi+rnUl6ClLA+zDJonT7euODhkE+r27ag6YqTByQaVXDivXFfnHDbgxDMCKuO1dDkv1YLxgzkL98/A3hBh22ahqYqe0NBFxRQBiCmkCYYVkpDE61Y3e5KPOF0aP+8ApfEJuiImJ+cOr9z7luhyXqMUuDQ1PJcTusgDqICW/T7TE9Hg/at5+h+83iNUKPYOgRVBIFvKU2ngC/PfUIQPDiht3RynTgMMIcCAjUaN56tsue0L50X42fSn+ISQOzrMdjn8s3tuyzCti0FIuAolDhCxLWzc5rVjc3XbCvxuxLnpva2LLw5Jqt7F75OOWfvl3/3tjsDF5wLt5x36MmGMEQgq9LahGKsCwpuq5TV1pnllL1N93wRiLpKqSA91Gai9KFvt/4oCDdbUUTD89OS3qMEFATbb8ZLzDxxJemjLdohCK6KZqKYnXYMk8KAjPXuOEu0h6Nho+3N9s1jWGZppl3T40PTVEYPyADRVGS+vF1Q/DsuuIES8OwaIpZTMRzUpxNtsf0eDw8++yzhLy1FGWnsctrkDNtDjZ3YtGZtuQU3/M/00lz2lm9o5RgJMKu8lC0tapqBb3FCEZ0qqPjLPEELFeHACstjzKo8Yewa2qTu/JYeVhNVfCFTatF1ANhpuVpKlW+ELe8/hW/PfVI63UfffQRf776cmpLD1iPpQ4fx/AzLsOVN5g9NT5E9DMhhIEzWjtfYMYeKKqg0hfks93lLd4XiaSzkQLeR2kuSjdGXzcFtnQPNFXBMIwEIWlIvB813qJRlJOGEivZGu2wZW7VTPF2JlkQaKqKXROkO5J/7UIRnay4dpPJxhRbUCRLJ9KiQXACkkZdx8Q7Vrf7f6aNYf+gqXx+0HdIOcXxY/nHZ9+y/EsfjojCwIyURgujUk/AMrfHrA5gindsASJEfSW7pqxFz64rJhA2K7+l2m0JDWpiLgQhBK9s2sv1xx9GQbobXdf5xS9+YYm3YncweMG55B9zEkp0cTAgzUW5J2BG0ivgC0d34NHzRcI6dlWhzh/u0MVvf4lTkXQs/VrAa0M6D3+yjQtnjO1zXxbZ+KDle1CQ5qLCG0wQkobEzMgNLRrJKnypikKKI4I3FEnaRMMQgoHpbs6aWsgXu8qsUqMx0TxsYBbeYPPvR8PArNamEzUU78zMTM477zwyMzNblVPcGoEpSHfz02PHcLCkhM8rdSuFLB492i1NYNaOBzPAsDoQSvD5x1eyS2YtqgmEKPMGLbO9y9b4WoqiENJ1a5GqaRoPPfQQp5xyCinDxzL89Mtx5g5MeI1DU82FnbkWQ1OjO3vqe8EbQmFfja9DFr/9MU5F0nH0awEPRQxe3bqft7aV9Lkvi2x80PI9cNo0Ml32pGILiWbkpmqgxweyGULgsqt8XVJLbZIUsyy3gwuPGskdJx7OrtJKHnr3C1KycshLr+/Xfd7TH3V4revmxBuaXwS0VWDKPAG8IZ29NT5smmbFB8TQVMXKcR8QzXEv8QSsanZAQgGbGA2tRZkuB5GIniQTvx4R9KPo3oRF6tFHH81rr73GO9VO/r29JOnrXDYNTTUNKvHvIQLsNhVXNN5hf03riuk0R3+MU5F0HP1awKHvfllk44PW3YORuekcMSyXr/ZWNmtGbq1F45jCfI4tKuDdbw5woNZvLSAGZbhZMK7+fPlpLpaOz2XChAmkpNSbyttT67ql3fFXX33VpHi3RGsFJib07287QG2dFyEUSuv8VnxAYU4aCvX541lxgW16tPBLDEVRGhWwaWgtWjqtiL98/A2CcFIRF3u/hvefJpCVS9qJMxKeO+aYYzjKMFDUjUlLkma47NQFzC5rYcM0odtUBVUY2G2aWYlNCLZHO9i1l/4epyI5dPq9gMfoa18W2fig+XsQjOgcrPNTmJ3G4MwULjpyJO9uO9ikGbm1Fo0st5NrZk/gsplj21XqsmEaXMQQHKzzI4RgXEEmP54xxjpWNwxueX0dr2zaQyiiY7Np5Kc6G+2OZ82aRW1tLXv27GlWvBsuBBaMGdhqgVn2wWZe3LAb3RAYkQiDs9JQo61VK6N1yYdnp1r54+G4dqiaplomakMIclKcjVqxJnMdnDZpGI9++m1Cx24R8sOal2DLfwDweyoJrVsFJ0xKOF9zJUl///4W/vbZNsuEDqYJXdfrX6+qKmPyms5eaA0yTkVyqEgBj6OvfVlk44MkeeGY+dq1gTAZLjsCrLzw5twoDXfzyYqHuO02FowZyAOrt1oieNnMMUl3zP/4bBvb95YxusqWEIMRE5YDNV4uWb6Gb0pqzR18uhtfKMIFz/yHOaMGcNPcwzj5r+/x1d6KuHKnYWvHG+sXftvCqaiqyimnnILP5yMtrXFEflNm8r98/A2hiG4G7MWJTPzcFeCsJz5gy8Ea06evKEQiEerKPWS7HYwvyKDMG8QwBAvGDuLSmWOs6nex98QMHPMDZnOZWOOWeJJZi+75n2l8sbeCdXsrTXv3nq1mv25PlXXMsMOmcc4Zi5v8fCRzHwzOTCHTaac60MTuHshy2hjUROBja5FxKpJDRQp4HH3ty9IfGx8kMyfftnAqWw5Ucdtb6/nvvkrqQhFG52eQGo0GV1rhRont5t/9Zj+7qnyUe2PR1Cp2VaHMGyQnxcFVL32OIUjqKwYS0tACPh9b6pLHYPzts+14gxGrm1aM2Djf/mY/Ww7WJNRjVwDDgIOV1VRVVlDiCXDm5OFMHJSNqqpJxTs2pmRm8rBumJHhVV6KctIQQrArFnkf9Q37wzq7q73YNNXqOBYT+8q4jmCGEKQ67dbnruHnMsNlo9IbJMXROCajKWuRpqq8c9l8frHiI55/+A8ENn5kPac6XBz/g8t59je3YLe17WcuvmFNdTSWIf4eZ7kdjMhtfQe7ppBxKpJDRQp4HH31y9IfGh80F2wlAITAHzZzkAWwrbw2wTcLiSZhoNFC4Ka5h/HE2h0cqPUBSrQwi0HEMH2k5Z4AiPoWlg19xUCr/Mkt+UZ1Q7BuX6WZax5XKSwY0YkEA9i+/YyQHqZkwkxOf+IDfnDEyCYtC81dS9NUlKgZPBjROVDrp9IXtLqK6YZhVbuLdSWLN32rca912rRGi+P4z2Xs/Wurtei9f/+bd+66lsCB+ns8YsqR/O0vf2LahLFJX9MSscVaWDcYLIgrpQpDc9KxaRpzRrW+g11TyDgVyaEiBTwO+WXpvTS1i9xZ4aHCFyQ3xVkfAR3NC6ryh6DSk7DLDRsGP3zuY0IRo9FCIBDRqfSFSHXazbzj6LlsqoIvmpMcL1gxbKrKu98cAAXsrfAnt+QbLfUEMKKlQ7VoM5FgRCccCGDf/hlK0AOAvnM94dy5zVoWmrpWMKKj6waBiFmvfXeVF28oknBcOJrqZbZrFUQMgaOBGumG4NuyWlKdNrL2OZqMMWmPtWj79u2ce+651g45LS2Nu+66i4suuqjJzILWEu96GZLhRgiBx+PBpWkc30HuJxmnIjlUpIBHkV+W3ktTu8hYfrEW3QmmOW0JP+wKUB0IJ1Ri21PlxRCCkbnpCQuBQFhn3b5KRLSRh0tTMaLX8Id1hBCEDbAb9UVI4jlQ6wcFhmc19u+CKZgldQF++OzHoJgtORsGcsWICa4wDNBUDAGRYFS8A6Z4C4cbo2gqNk1tNkCzoR+2kZkcs057icePpqi4bGp9fXBhVjxzaBoRI4KI3g9b9HSBiE7EEIR0HV3AwTo/5z39UbOxBm2xFo0ePZpLL72URx99lOOPP54HHniAYcOGteq1LdFwQVFR58VfrXLVgiMZXpDTIdcAGaciOTT6vYAbQmBPUnJS0ntoahcZn18soiZ0awceRQhhVWIL6QZV/iDZbid7anzocbXOYxXEIqJemAxhVv4SiGjhD4E/+lxDjGgecUOEEFZDDrNIiFlDvMIbJNvtoDA7tdFuUlPNbmN69PFQwI/t2/qdt3C4CY+Zgd2daqVjNRWg2dAPu6vKm2Amd0bboeqGIGwYiHB9jXMlalFQlfpdeGyKgYhORDeroSiqSpbLbrYaPYSUzZqaGtLT01HjhP+2225j+vTpLFmy5JB33cmILSh8Ph9bt24lr0F626HSH+NUJB1HvxZwh03ltIlDuHDGGPll6cU0Fc0bn1+sKApum4angbgq0eMASur8BMMGFSKAqqhWI5EKbwBhCCJCoBtgGPVCpcSpsgJEDEGtv3EgpBrr9dmA3dU+qv3R8SuKtWCo9AUTgsDiKUhzUekNkuayU1Nbi/rtGkSceEfGzACHm/xUl7WLbypAM94PG6tTrjawUrjtNtIdNg7U+QkbAjsCl8OOoev4whGzdKxNQ6CjEi05akRfjEJeijOhHnp7UjbfeustrrvuOn7+859z6aWXWo+npqZy9tlnt+ocPZn+EKci6Xj6tYBnODSumDqalBQp3r2ZpqJ54/OLhRA47Ro2TbV2mEBCSc8yTwChgKbU7/Bivbw94Qixiquxc8YQJD4eEUajBimDMtw0XGOEdIOagG6NRVEUq3JZlsvsMJbMp66pClOH5hDwegn/90vqAl7TAuB0Ex59NDjcpDvtjIwTzaYCNOP9sPF1yuPnlu2yowtz0aIqZg60Cqiahl1gpay57TZG5aSwvawOmxAoilmwZmhmCnurvQkpd3ZNbVXKZmVlJb/85S9ZsWIFAP/7v//LggULKCoqApouYiNri0v6A/1awCV9g4bRvCHdbFASiuiEdCPa3EKxOnoB9T5eVSU/1UnEENhi9TMboBsGRtPl0q2dOpgdx9RoJ7FYM5KIYbBg3CCAhIClqmC9Sb9h283CnDSINvgoqQswPDs1wTd63ffGcuGvfku1EsKZ6qQ8rBIZMwOHK4XcFCej8jMSFhnNBWjGXEdPfL4DFLMOeKw5iNthIy/VyfaKOrObmpF4i2JNW8K6QV6qRorDjtumQNRsDrC1pCahJGmFN0iW20G1P9T0TQVef/11rr/+ekpLS63HjjvuOJxOZ9Ksg0BE5+GPvyGiG2gq5KWaufk9qba4XFhIOhIp4JJeT32O9gH21fioDoQs37chBJ5gmAynHUe0DGasCUmsEttpk4ZRGwgT1nW+Ka1tdP6wIVDjOn3FC3b8f9tUcNs1EAJdN5oMRlq9o5SwEGbDjqjpvGHbzdg4B+sG6U4bM4sKGvlGbzj7JF5/ZxXbayPsHzyVfX5zp9+wA1hrS7AumVrI3z7bhj9i9jx12DQMQ7C5xCzS4rJpiHAkobkHioLLbiPXrTJ9aC6TB6Sjhvz4FDtl3lCCP9083Py3whfk8yZaclZUVHDzzTfz4osvWo9lZGRw7733cu6556IoCne/s95aDCmKYsYRBEL4wzph3cCuqlR4Q2RF4wgOtVxymSfAs19X4D64xaxd30bhlU1LJJ2BFHBJn+CW+ZP5YEeJ1dYztttz2zTcNg1DCIor6hiRm44RjSS/6KhR1g/nXe+sx223keV2JJjYIdZjWkGJNtkwg83qxVsB7CoMykhhcGYKJXUBxuSnc9L4IY2CkeLbbr4c8HMwKBiUaUam720QOOfQVGyqwonjh1im5tI6f1ylt0yOnzuPX0yeSFp6eqvzqJsSk50VdfhCOi67lugiEIKILgii47bbGJ2XTlUg3KhX+fShuVw/azQfZ0a488uKRv70eDRVxROMNPKDr1y5kl/84heUlZVZj5144on84Q9/YNCgQdY9iM86iLUijQXbqYpCxDDQDSUhjqA9vvfEGu8e0qoMFKWizcIrm5ZIOgMp4JI+QYU3iAJMHJBplfm0Rf2tDptGMKJT7g1y1LBcBmemNFnrPFbGM77aWMyHbrepDEp3U+ELmBHX0VaWtqjZ3BYN5irKSeOJpcc1KRKxtptTXX7u/W81u6oTrQaxwLksV33FL90w+PU761m9syxxB6cqzPEVc8v8ya2OZk4mJhFDUBcyU8EC4Qhue9xPg6KAIqJmchdpTjtpzkR/eryPPcdtJ91pb7LKWMxdoCpYfvDSOj833PcQK//8G+u4rKwsfvOb3zSKMI/POgjpBpX+EKGIWVTGiKa+gWk5cdnUhDiCtpZLjt0rBdotvLJpiaSzkAIu6RPEftTj23vG47RpDMpwMygzhauT/HjH+9Eb9vnGJaj0h4nFnquKCug4NCVh91WQ5mpTPYEctx0DQYUviKbUt+SIBc5V+IIU5aZRkO7mtlc/5aUXXyBr9BSrh3UyIWkpmrkpMYml3LltKkHdwACU6ALGHk0Rs6kqDi35jtqmqiwYM5CHPzFrvAuhkOl2UBeMWPnkZupZvbtAAar9Qe5+Z71ZXjZrDPbMPMI15WROOJIlV9/MmWcd3yg9LJZ1IICvS2vwhiIgqE/nw1xYxQq8CFGfm9+Wcsnx90pPEgTRWuHtKU1LGrpMFo8f0GnXknQNUsAlfYJDbQzRsCpW/EJACEHl/kpCYYMqXxCi5nlfRGBTBQ7VbMLhttuYPaqg1fUEKv1hFAG5Kc6EHX/s39wUJ4qAL7bv5ZUXlmN466ja8AnZU4/DmVP/49uWHVxTYmKl3CkKTs0MQLNpqmXJCIR16kIRkqS4E9Z17DaNq19ea9V4rwwr1AZCZLgcOGLniTO3A+iRCJ/tLscbjGBTVezuVArPuJywp5rsKcfxaVmIe1ZtbLTDjVlLYtXhotlqVnCdGfsAevQBs+yr+d9tKZfcUcLb3U1LmnKZvLJxF+NSBfeNG98p15V0PjJqQtIniP2oN0dLP963zJ/M/LGDrB+42Gt2V/vIdDoYmOFGie5c3XYbqXYbCpCb5uLq2RN45oJZ3LZwaquDkd7eVYuOueOfMCCTvDQXWW4H+WkuJg7IpCgnjVDAz5X3PkjYa/aeVp0uNHfjpiQxIWmJpsQklnIHUX+/ojAsK5WinDSGZqUyKj+DLJcdu6Ym3BtVUbDbNMIRI+pSMM9dkOZEwRQl3RDmeTJTcGgqQgiqNq7h6weuo+Lg/gRrQPqoSeRMnRUtUZtYmz7G0mlFGEJQHQhZEfzWuOOO0w1TvEQ0dgHaVi65o4S3Iz6bh0LMDRD//qjRbIPPDnq578OvO+W6ks5H7sAlfYKOaAyRrCqWiuCduNSvWIpafACXy6Zx3vQRbS4G5Akb1g9qMtO/HgxQ/d8PiXjrUADNnUrOtDnY3I1dBK3dwTWVMz8gzUWF12zGEi94MRRgVF4GDyw+MqFv+vyxA7n6pbXYG0S+O2yaFRAYX6427Klm78rHqN78OQAHXv07o394S5NV1JLtcAvS3aQ57eiGwKGpRKIJ+gqmu94Q5s5EUer94G11bzR3r+JpjfB2Z9OSFv3visInO8uk/72XIgVc0mtp6NM7Ymgun+4qO+TGEPF+5AdWb034AXdoqpXfHX/ueJFpba5vml3FEDrJKp7rwQCV6z4g7K3DbdcIac4mxRtav4NrSkzii8co0Zz5hnOcP3YQEwdlM3FQtvX4A6u3Nmlmjg8IPFDrI333eva+/ji6z2MdY3OlYISDaI7kJUqbWpgcNTyXr0tqqPaH0BSIREvZKpjpfCJa69UQgkyXvc3uDeg44e3OpiWtcQOEheh0/7ukc+jXAl4b0nn4k21cOGOsXH32Ipry6akK2DSzBGr84/GpVG0tpNEWM2pbc31PLMzgq+rqxvOLinfEW4eqKCyaNpYP7CPQXMnFG1q/g2tOTApz0tAr6kx/dHRH3VJjjWT3J6QbVNX4MIRZNa7QEaHmzcfZtWWtdUxeXh7HX3wN2zJGt2uHm+12Mjw7lUEZbko9Acq9QUIRHU01Te9mMRqdIVkp/PTYcZx/RNstJPH3KtkI2yK83dW0pLv975LOpV8LeChi8OrW/by1rUQWU+hF3PL6V7y4YTciKhAFafU1vyO6weSBmRyoC1LtD5GT4uTuk6cyriCzkbj6wxEe/vhrhmam8v3DhnL+9BGNfozbYkZtKdfXEwwzODOVmkAIPRyhtLQKQ8Deaq9VgCVevA0hmDBsID+//BK0z4qTim58QZpn1xW3qsBIc2JyyYzR/HjGGJav39Wqxhrx9yfWyazKG0JRzYUU335O+ccrIOizXrN48WLuu+8+DGcq5z39Ubt2uLHdsaqYrodhWamN3BuD0t0sv2j2IS3OY/fqg28PJPj+2yq83dW0pKPcAJKeSb8WcJDFFHoDsSpYzv2b+WxPFV/uKbcCreJLcw7PSqG4wsMXeyrITXXismn4wxGufmktAghHzLKqCiR0ACvzBNlZUcfrW/ZyxNBcBma4qQuGyXQ5WDBmYKvMqAvGDOTql9cmNZEKIdhb7ePvB7YztiCTkjo/lb4ghmGQk+oCIdhysJpMl51BeIn4vCiKwoRhA/n7HdeRmZnZSHRjc6gNhMlw2RHAyxt3t6rASGvEpLXm1Hgz864qr1VQRVUU+PAZ2PIf69iUzGwefuB+Fi1aZD3WXtNyMktCvHsjYhjMGzvwkC1rsXt14eHDeOjdL0jJyjErsTUhvC1ZeLq6aUlr3AB2RekU/7uk8+n3Ah5DFlPoeTSsglURrqOkLkDEMIXYqdX3pq7wBjhY54/W8IYKXwhntC1nmtNObcDcjRflpPFdpYdST8BKm3JoCjWBMNvL6/hybwXZLgdDslI5WOfnoY+2oqkK2W4nqY7Er0tINzhQ66cwJ5Xb3lpPtS9IbTCS0LTDadOsFp2KorCtvBbdqG8YUuUPkZviZMKATA7W+XFlD+TMM04nu2IHl1z4AzIzM4HGovvq5j2oqsqEgVlWWpbSxsVoR4hJTEjf2LLXqrymx54smmwJeNbkmRSe+iOOOWFBwusPxbQce27VtwfZX+vDMMxWp4MzUjrcLJ2f5mLp+FwmTJhASkpKo+d7aqnUFv3vQnDsiHz5m9dLkQIeR1cUU5C0nvgqWLqAmkA4WjpcIRwValfUdB7UBSHdQIs+b5ZTre8yBmYw1TelNZR6A0C0whqCiG6YFcgiBk6bSoknQKU/aLUUjRgGpXUBbJpCmsNMpQpGdDzBCJkuO4Zu8P72A3iCEWyqWRucqGUgzWHDE4pYaTu+UMSsKR6do6ooVPtDDMpwU5idhqooXHXqHHJTFqBpjcPbCtLd1q4qFiTWkK5ejN4yfzJrdpUhyuoSOrOpRVNwHLGQQeMmkT3xaIwGwVKxxYgA5owqMPO3UdpsWjaEkdASzhDNdJ7pJHpyqdSmFkkaMGNgKjfOkXngvZUeLeBff/01v/nNb9i0aRM2m40ZM2bwq1/9ioKCAtasWcO9997Lzp07GThwIFdddVWCaa41RIRgX42PAelunDZNBnN0Mc2ZGxtWwaoMmGU+URQEwhJxezTdKSbolqUwzuenKObz3pCBJ1q1K/Z0rEyqLkDBgIiI7uJVXLZYb2tBWNdxagrCUDCEQVA3yIvu6HdVeQlEDFAUIobpl44tLMq8QcCsvW6et4EvMhxAVJdSmjqGYVmprVpEtrfASGd1wlIVhfSdX5D5ycdo839IIARup4OBGSk4Tv9RwnHNBftpqsKcUQO4cta4NtUXd2gawxssZrpSNHt6qdSmXCaLxw+gbM/ORimDkt5DjxXwUCjEj370I84//3weffRRPB4P11xzDXfeeSd33HEHV1xxBddddx1LlixhzZo1XHvttRQVFTFlypRWX8MQUO4NUukzuxYNy0qRwRxdQGvMjQ1FSo/WCY/P+wUzzzeGKcZmGc3EjlxKtDIXKIgE8YZ60TeEIGzU75wAgroRXRwoGKjoUfV32TSqg2H+u7+KYESvL4OqmHXFjeg4YmM3hylwxPX1JhyEbZ9DwEPQpkDW1FYtItsaWdyZ5t19+/bx85//nFWrVgEwbPQUcsZMJyU1pdE5WxvsB72rvnhPKZXaEg1dJj6fj7Jmjpf0fHpsyLXf7+fnP/85l112GQ6Hg5ycHE488US2b9/OypUrKSws5MILL8TtdjN37lzmzZvHCy+80ObrxHyRlb4ge2v8MpijC2iqMlTsB/yeVRsbiZQWbWGpYjYPidXVFiLWLSxWazvaXCT6OjM9WCSYdg0BhiEwoo/HNxERQkRLmZrCF9aNaMCc+epQxDDLhUZ0AmGd2kCYYMQgbIjoTt5schLzxSuKea2QruN22Or33+EAyjefQsBjmn1LdmJEIq2KCG5rZa/W3O8YsW5nd72zngdWb21UBS2GEIKnnnqKY4891hJvgLKd31hzb0gs2K+1wtscMdFsjtZWpztUZKqWpLvosTvwzMxMlixZApg/Fjt37uRf//oXJ598Mlu2bOGwww5LOH7ixIm8+eabbb6OiP36Y/7Q+v1+fFrzPww9Fb/fn/BvT6TME+D9mF87SYMIBTNlZ9aIfMK6bu4WdZ0cl426ujBCUXDaNEREN3fiqoIw6ptYKCjY48qCBiI6kYa79Nj/rP9LfM4sBqKYohx7UsT+MU3qsQNjx0N0QWEIDEVYiwZNNcXfadMYm5vG12V1iHAA8fUaCHgRioLiTGHwUccjVBVNCBaPH4DPV5921ZDF4wfwysZdzQqYFj1uV2llq+73+VOG8viXxXxcXEakQb3s44ryuXHOeMvUum/fPm688UY+/PBD6zy29CwcJ/yAukHjKa4NkRWuMxuWRL9bEd3ghNEDeGPzbkIRPUHwfKEwxVW+6HUhxaZx4T9XM3/0QJZMGUZ+WuMiL+V1XmvB1BwVdd5m72Vrae67laJifVabwhACtypaNZYyT4AVG/ZQEwyT6bQ3eQ8Old7we9Eeevu8Yr0QWkOPFfAY+/btY+HChei6zjnnnMM111zDJZdcwvjxiYEXWVlZVFZWtvn8RjQlJ82hkW03eOjdL1g6PreDRt89FBcXd/cQmuTZryuo9Xha/LErLRUEfT5LQO2qQopNoToQRo/qp6aAahiEorqkKqbA+kIRNEXBrkJEb/1iLKYFmmr+oRtGNCgqmv9rmP8dM+cTt3OPvViNirpTA5cqCBiYvnNdx+/349YDeLZ8ghLwmte0O0mZOAOhaNR6PMwYmEr5np2UNzHGSn+Yt3fV4vF4OOANk+uyNXAXmLEdsfO09n5f+NSH+HQDW5LjXt/oobyslEsm5fPmm2/yyCOPJAhR+qRjyZt3NqorFbxhPCGdSl+QSCTCwFQ7NlVlWoGbUwsEj20+QCD6WkPAtuoA/ohhLagEUEWYUk+A0soaVny5nekDUrj4sPwEX62/uhJPK+blq1bZunVrk8e0lWTframuMCviPqvJUBWFqS5/s2PRDcHjm8v4qsSHLuoXUU3dg46iJ/9eHAq9eV4Oh6NVx/V4AR8yZAibNm1i165d3Hbbbdxwww1Nrk5au2qJx6FpFOWkkOo0b1hKVg4TJvTOKHS/309x8f9v783j5Kiuu/3nVlXvPauk0WhDAi0IIYEAARZgFgfwyhI7YEPiLTZx+MWxnRcH2yRO7Nc2ju2XN3ntxIkdJ+B4wQbb8R4M8oIxq8wmgYT2XZqRZp/ea7m/P25VTfdsmhnN1jP3+XxAUnV39a3urnvuOfec79nPsmXLSCSmZ1lIomUb6c6TZwnPa5rLtU2CX+9uxUBFRizTxMPFlWpPWiJxgKhlkDQFoPpDu54yssrhVCY2HbUwDUG+5FDq57kaos94GAKVRQ54flvNgPLX9Z+sg5B+8I7CMCl4IA2BJQTpmMWBzgzx3c+Q9IoUBMhogrpzX81pzfOIGgaXnF7p6ZbjepLPP/oKj+/vwvEkiWQKs5jlUNahJm6wtD6JRNX0lp+n/PO2XZfjmSJO2Cs9RsQ0sV2Xoz0FTmsY2CQl4JXOEh//xCd5+onHw2ORmgYWXf9eas88Lzx2RtylN5en11Uh+jetW8afXng6c30PckX3Lrb1HsUQgi3Husj7yX/l2xwAtoR9WZdzF9SytdvjJ8cFH7uq7778yyUFnnvwmUG7owWYwF9es2FcvNeT3VvXdkf49e5WLHOQUi0/+nDp+cPPK5/99Xa2dksSqYHVBYN9BqdKNcwXY6Har2v37t0jfu60N+CgJsZly5Zx55138kd/9EdcccUVdPWToOzs7KSxsXHU53akZG9HjvqEw5L6JHNqUoPWeVYTiURi2l7D3JoUQrSf1HOaU5Pi/ZedSXTTVn6z8xh7sjZZF+IREzARAgq2ixAC0xA0JqIIvyTLlRLbk9iOS8Q0mJdOcEZjmj1tvfQWbSzfQAZGQ/i14FHLpOS4uJ6HI1U3q+EIvW/6kuek3wwkU7Kx/DK2eakYp9dGOb7zCbqKWRoSUVauWMy8s85j4WnLhhUGCfjUwy/y230nfJlTdWz53NqwFt2yTG44e8mA88ytSYFo51Bnlq5CqWK/vyNfoj4exRAqU3m4RDYvGiNr9v2m1v/Bm3AuvpFIcqDRjxiCxTUp1fu7JslpTX335TsuXsVDu1rJFB3VClT43cQkFUmFBpAtOuRKDjXxKE8d7CDjijAhbWkyyVUrFwwrAnPlqgUsbRr9nDAcQ91bn3j9eUQ3bR20nv0P/Jr04T7f4715njzYTiwy+JRsGsaAz2C8mM7zxalQrdc1Gkd02hrwZ555hrvuuouHHnoIy/I9In8P75JLLuEHP/hBxfO3bNkyqgz0gPIkNqEViSac0TSICMpf3rByHm+577c0RiNELJOmdJzWTIG2QIwFVSN+1vy6UBvb9SQ9RZt0xOKMOTWA2oeOGEKF4GVQL64y1uen4yxtTFOwHdqyRfZ3ZAZkqfcn2P8OJmvHkxigvHYPhCExBfQWbXY//QQ1dob6eBQrkeIzH7iNYnf7kMIg5Zws49owBAc6MvT4Brr/5/2lx7arJiWU7df7FxAcP2t+3bBjMIRg49v/AtnVyvo/fCfbooto6c4x3+8yNtRr+iduBcIiX3zslb7kw2AhRZ/xNgyB50n2d2ZZtyA6aBb3VOmLD8apSqVWSya7ZnoxbQ34mjVryOfz3HPPPXzgAx8gn8/zpS99iQ0bNnDdddfxpS99ifvuu4+3vvWtPProo/z2t7/lgQceGPP7KUNenclr1cRYOjP9es8J5iYs0ulU6MWUHJei64Up6BFDcDxTCHWxQWWCH+3u26t1parH9sIscZUssra5PlRZS0QsTqu3OJ4pqCQ2z8N2JUM545ZQxsbxJKZQiW8CiFkGUdMMS8myzauQ+56lsSZF/frLeWh/N1c1jOwzG2xyl8CBjkzoVSMlX9+8l9/sGajrH2TXD7ayl1JilvX4BpCeR9vmTVipWhrWvir87F7oLBF968d4ScKRriwnMgXaswXq41GVsNbv3ENl1N919True2YP3fnKBUdgvINhBp8rDL4YmCp98eEYq7qdzmTXjIVpa8DT6TRf+9rX+NznPserX/3qUMjlM5/5DHPmzOErX/kKn/rUp7jnnntYuHAh99xzz4DEtpEQhFAb4hEW16f0CncSGK3n1F20w8ktMFxt2SK24/nlYxLHVTX9i+tToSGxDMGZ82vJFh0sw8A01F6rIQRxS2WqNySiFRKpnpQc782r55gCTAMhXIp+olV/HAnC3ysPjGBYZ142HxupevJnXEi6uZFIMk1P0WaAqMsQDDa5H+jIVHrV/mfYv5b6/uf3K31wSaj9HmjICyFoTMaYXxOnPVdkcZ1FseM4B//7K2T2vYyZrCG9bDWRdD2Hu7LMS8VIRiMYAprScdqzxdCLpyPDssbKcPpQjUhMw2D9okZOZPK4UtXyC6kWPkbZ/r/n79UH30v5YmCiRGmmCt10RDMWpq0BBzjrrLO47777Bn1sw4YN/OhHPzql8xvCz26OWpimocKueoU74YzWczIQtGRLmCXoLTk4rkfEMLCFX2uN8iSLjsuBjkxFGP1dFy0nU7R57nAnc1Mx2rPFikXb0kGMTnNtgmM9+TILXKbqRmWcRvb7t1okgF0qgoyGCXEAJGpod03ivrG6/5V2Ei3b1B74MAao/+Recj26CqUK8y99JbPgGjbtbEEAv97dytHeAgvrkhWfi+VrtQfCMotqDFqe/B9aH/kOXqkIgJvrpfuV50ieczkSSEb7jEfMMqlPROnIFZUcbMEOpWxBJW79wTCtNj/1unN5dHcLEoiiJGb7IwQs8xXWgsXAdNUcP1XGq/e4ZnYxrQ34ROP5q//ego3Epi2TpyZu4XpeVU4C1cbJwo3BZL1pdwvdJQ/DKZG13bBO2zQEjtunrGYJONqd5Wi3KsOJmAb/+dRummpi1MSi1MWjzE3HyJdcopaBlHC4O8f8dFwpvPnhewE8f6QTIPSsy4VgyinPYLcMocK+hTyR3c8gUw14K9aHhlcIgeN5HOrK8cs9LoVcjnSnhxDtwxqg/pN7a6YQJqMFCKEMchCh6MwXOdqdQwhOGurOt7eQ+8V9HNv2QngsWj+XxTf+GQ0rzyVqGcSsgfdDoMXelS/heaqVZ3M6hiEEV62YP+we9JoFDZy7uJFnD7VjChHW3Qdj8zzlbdbEoxXbKp96+MVRqbhVi6c+lq0ljWZWG3Dol9SDoDNb5O5NW3Vb0WlAoCAWMU1qIgYdxbDPFbYrsUxBxFSa6KYQ5B0P109pFkDR8TjUneNQd46IIVhQl0R6Hu05FfqNWqqbWVsmT2Myxts3LOeuq9fRni1y7+Y9HOtW4iLDlZIHxhuUhKpRKmLtehoKWYx8BjsWJ3baWf5zJd15m5p4ZFQyov0nd9f1KoywJyWNyRgxy2R/GFpXnumCmgQnMkUKtscxO0930easpjqipoH0PE489RBHH/kO0i6F59vw+jdz8c3vZV5DHbecv4x/fWInj+5uHXDtQgiWNaYpOq7KP6hL8oYzmzk3nuOS88466SL4f257Da//91/xwuEOPxHOo+RfW208wtrmOgwhwm2VIJnP9STHerIDur6Vq7jNScWqzlOfTkl5mupg1hvwouOqMKI/CSajEd1WdIoo95YM4JGdx4j4zUwWpCJkPXCKThgCd1yPVCzC3FScnkKJnqIKwwoBplBZzEHyme2pxjVxywAErudScmGu3ze8MdlXTtVUk+C0+iQHO7PD1hlDv3C6XcTa/XQo0uLFEjBnMaCSwILmK1HToOS4A841nH53+eRuGAJXKllXIVTiXXNNvCK0LqVKqjvWk6fkupQcD8NQSVAvt3RRa/fiPvKfZA/sCN9j6dKlfPGLX+TVr351xXufbH82Zpksqkty/dol3LZh6YiFU6KWxS9vv5ZtxzpVO9Z8iWTE5OzmelKxyIBtlW89t4+9bb10F+yKvfygH/zShhSO3/Gsp1Catt3BhmI6JuVppjez2oBLVAvJkutRG4uEHY10ucbkMti+5sHOLO25Ag2JGIvrEgghmJuM4XmqXCsoA2uIR1hYl+S5w4XwfAJC411eCuZ4kmxJSV5KwHE8jmfyzK9JkohYFRrcBzuzmDBk9vkA7CLRXU9DMauMejRBaflF1KdrkIDteH7inKAtU6AtWyRpSJb3KyEb6rcXTO7HurO8/VuPc7Qrh0DlcHiexyvHexB+6ZUhBEIIio5LT9Em5pd52X7b1KLrqS2J1sPh+W+77TY+/vGPk04PrOse3f7s6Cs51ixo4PvvvvKkz/vptsN0+j3HRdm2hCfhWE+ernyJOakYu493s/1Ez7RodDIWxqNPu2Z2ML1iSFOElJD3E6BAl2tMNuXNNmzX41BXlvZckaIjac8VOdilSsGa0jEs0yBumSQiJvGIiRCqfMzx+hLahhNCkKiQcyBnXnLhcFeOZw93sL8jw7vuf5x3f+dx2rJFTNMY2Q3iG29R9OVRowmKKy5CxpLkSg5SQnNNgrp4tGJ8vSU3vLaAk/32vvb0bmzPo7k2QSxiYhpGeL5cUSWSeVKSjkXCPuRCqO5pyYhJ1DSImQYNc+eRfM2t1MxbwDs//c/c8befGNR4Q18IP/iM++N4Hpcvb5pQY3i8N8/hrlzFd6sW4C4528HxJL1Fm5aeHA9sOcjetl7V52AIJqvRiUYzkcxqDxz8ZhOorNnWTIFF9SksQ+hyjUnieG+eh3ccpbWnQFuuSNFxsYQqzyo5Ho4nsN0CdfUxkqZJfTwalk8Fmdeu59c4+13EBAzwvssZ7N+ZkkO2I4MrJd2FEnnbHfS5AxjEeJdWXASxJAYqdL5ybg3pWIRD3Tkkdt/+tRB0F2yKjkvMzwYfrlwqX7R5cMtBpFS/29p4hN6io8LJgBAGtusyNxkjavaViknPg5cexVh5IdF4CsMQdOdLeKetZ+6ZG3hB1nLrNx8bdm94qvdn739+P3NTMdqyhfBLKToutp/E6H/9uBISEVP9RjqzA0rbAsZjkV4tCXKamcusN+ABQqhkqCPdOZbPqZkx5RrTeZJxPY93f+cJXm7ppmA7OB6AxEbtYQfYruRAT5H6mrQq++rI0J4rYrseRcclV3LDOuJyPfJyIz4SJHCkO6tCzkKctF3lyYx3YFT2d2ZZNa+W+ek47WUGCNQiJBCggYHlUg/vOEpLT4H2XJFsycbxIG4ZxCwlJ1sTixD1SyCFULXwsYgZLmpk5zH49TehdR8c30/pyndgAZYQyggLc0R7w+O1PzvW32N3oUTc6lvASam2RCqCLUJ1qotaZiirW744KudUaqpnaimbpvrQBrwfXXl7wsOBk0E1TDJ3b9rKyy1doeekJmM1I7tSYhh+u1cEWdtjX3uGaMTE9Y11IqIyj2tiqjwrZ6t+2mNpahPgeOqzG/FObiAwU2a8wV88+ProvUWbba1d1MdVKVtXvlT28r6FQnmp0Cd/8QL3PbOHnoJN3nYouX2lbAXHw/UT+HoKNg2JaOhpBtsQlpDI5x6G3/8UXJXcJ3f9HnHO1VhNS9S/AdPfHy/5Eaivb96DAP5s46pB74Gx7s+e6u8xSKQLFnBhKZ0oawErVFJisFCSXuXiqJxTqakOtnxGkyA3nRfSmupFG3D/7g+SoualojOiXGMsk8xYGOvEFJQERS2TklsMJ+OAIMNYSgjMaVuuiCeVMaiJR1i3oKGvxrojQ6HbRQpfDc0QGN7QEqjDEXjOJyUSo7TiIiIHt2IvXhMa7/A8KK0B4TcO78yXqE9EaUj0GfFAGa5/udR//X6vUk5DRSD6D8eWkLNdEpYRiqhETYMlDSnoPMau7/4LHNnT94K6Joyr3kFk3uKy6xQ0peLsH6Ek66kwVqP31Sd38sSBExRLLoe7sjTXJlnWmMaVkhPZor9tonrAGyiFuKhphJ76YFGUU6mpPpEpDKtL3z9BrhoW0prqZVYbcAFEzCDkKrBMwTs2nFH1N9RoJ5mxcKoTU6DvPT8d50hXlsGc5mDuVdnWysNygHg0gpQqU3xhXVJ5Y0AqFiFXcrAMcKXAw8XxRpFJ3u99R0Qkhr18w/BPMQUFX7f9eKbA2uZ6mmtiHO7oxbIivOvCM/izS1aFoeivPrmTjmyRkispOe6Q4y+5HoaAmBC0ZgosTsdof/ynHP/N97FLgZcvSGy4lmWvu4WWnNO3cAAa4hGO9eRGJMl6KpysGctgRu/Tj2zhv36/l85cKdS7LzguR7pzNNcmw2S8UPhFSur9WniApY1pZHsvEV/nfbz27B/ccmhUTUcmayGtmZ3MagMO4dyAaajw220bV031kCoYi4c72klmLJzqxBToe0dNQSJq0ZO3K4x4uQ2NWgYWkqhlYXpeGDZt6S0oHW76JE6jluostnxuDZmCze8Pt1EcWHI9Nuwi1rGdOIvOAnP4W8fyJVWllGSKDoafLCml5KWWLualYtRGTRY11uAC335uP7ect4w5qRj/9exesiUnzJQfCgFhbXmp9RA7vvV18kf3ho+vWLGCC97xIXab83A8D7PgKmNmGDTEIyyoTbD9ePewkqzjUW412k5bd2/ayn3P7KHLLxkLfhhx06DgerT05JmXjqnfgden6R4owwWfzfK5tfy/GzfwyK6WcaupLtflH4ogQW60CxeNZrTMagMuUf3A8SRSGpzWkGZOKjbVwwJOzcMdzSQzFsZjYioXB1k9r5bnj3Qo1TNfTjPwOg0gbpl4rksiYpAtKZMWZCBLJCI030okpeh4FF2PrS1d42q8g4Q1Ucxhn3HBkEY8+FSE8PfBjcre467n0dpboMOA+hqHR3e3ht+tBNoyRRACIQeGzssJHiu5EnniYGi8DcPgL/7iL/joRz9KIpEIF4FHu3P86KVDzPPDzIe6c0NKsgaMhybCaDptBVUJPYWBv2EhBAnL9GVyTdbMT9HSk6e5JhFqupeP++pVC1izoIE1CxrGPPb+1MUiI246oluEaiaa6o4VnyKBEEY6prwRx1VGczpQXhs9mIc73DiDSWY4TiULN5iYAkqukizd35HhUHeOkuudtM5W9ftW1xWzTOan46HASnnI2APytkvKUhnG0q/hDoy37XrYrofjqb9nSzbHM3ke39tKb3Fgg4wxUW68AVHKg6sWP0rXDepiFnVxi5ifIZ6IKINiGAJTGCQjJhHTwPRV4iwj2CNXn6MhBEXH47nD7eo9T2K8QXn5EdMgFbX4+f/5GFdffTVnLF/Bu//h3zAvfTNf3bw/XER98PKz+Nx1F3DD2sUc61HfVUemiFdW2+1JSX0iWpG1PR7lVsFibTjKjV5LT2HYGm5V2w5vPmcp77hwOfGIFZ4/MK4TVdp20zlLwt/tUAQJcrpFqGaimdUeuCEEC2sT4Z4aMC1CWqfq4d50zhIe2tU6YZ2NgokpaJwRJEAFfnDQOKMrXxzyHP31vSUq6jAYJdcjYwsW1Ft05EoUHS9sMGL0RVjD5LPicOLlo2WoUrGo+tw9IGoaCENQE4sgijZIKPiqZ0iJKJvwVVJe0LVMcjxT5LQGtZBqzRSUhrnnYQoDTw6x++25RA9vI77yfEBy4dI5rJ5fz4qb30/b0R5+X4xglHn1Vyyfz0deczaf+9XLPHu4A8+TdBcd8raD43lYhiRuGQPC0DA+LSxHo+T2b0/uPGklQaC0lyk6oy5tO9Vs8Hnp+IibjugWoZqJZlYbcFC1pK2ZQkVHqqkOaZ1q6G00k8xYCCamg53ZPlEVlERnkFTQli3w9MG2Yc8TeEiP7DhGa0ZJoZpCoHzrykzwjCPZ1Z5RzTz8Mq/+qmtB1npAMK4xM0ydd8XTXI9MUfpRCYntqmhA0OjE8zxyrroewxB4rofjgSmUgFBA0KTEEIJUzCRTKGH3uwCj4wjJx76N2XGYkvXnRM9Yy+n1Sc75wk/oypVwpCQRUbXQQVRj085j/GZPK47rYRkGp8+poeR6HO3O0dKbBwm18eigoifj0cJyNJ226uJRP9I0tBGXqM8xMHwjKW0bz2zwkYraTEaLUF2eNruZ1QbckzL0etoyBRoSqt3iVIe0xiP0NpHKWbect4wfbDlAV0ElkBUcV+mTExhNiY1ky+EOPvXwi0gYdHIJxEFyRZtnD7cTMQ1f29rXO0dWZISXHBdhQXlwOUgMA4iYBrbrhVa7bzxjYITGO3gfJeXphNsy5VsBJd9YRy2joi2p48mwAQuomuxgn7w2ZlEXj3C0N6+alrgO0S2PEHvxYYTvmVuPP0BmwSr+c/NeSn7UIVgAWKagPVOgPhFlQW2Cl451cNb8OoKuoFHTCA12Z75Eb9EeIHoyni0sR2P0vvfifjpyQ0dvhBAsrE2OyvCNZzb4SEVtJrJFqC5P08AsN+DBJAoSp+RRcFSW7g1rl0zpuMYj9DaRnY2aahKkYxFcf9856OMcSpd6SoSlNVvkn367nfULG4edXF5s6VIlQULgAbmSE5Y0GcjQk3WlStAyhUQITxk836tV/cENbE+GrntgzNwRFXWXMQrj3Z/AcKeiFnnbDXMFTENpuBccTy04/NC/J2VoOMsFSCzTYEl9iuaaOK9s24bzy69jdhwJ38dtWEDp1X8MhlVhvNWvWeK4UMClM1+iu2jjycFFTQJhlM58idbeAqc1pCZEInU0Ru/aMxdyuCvXl4Xe7/OtjVlcvap5xIZvorLBR+L5T9RCWpenaWCWG3BQe2mgJj/XV266ZmXzlI5pPENvE9XZ6MLT5vBySxdHe3K+qytCT9gwwPOUYS3YbigyMuTk4r8e1H53GB4HQChxFv+zaEhEkQkVpQj23T2pXmd7LoK+79P0S7lMwbA9vftjtewek/EuZ3VTHbvbe8PyONfzcD0RbtOocakrDAxruQBJUzqO5zh0/va/kY/+ENPztdmFgbf+WsSG1+F5Rt/nR1/EwZNgConjQVRCvuQQtcxBRU0EsKwxzULXoyZmsXFZ06CGdbxCtcHvMTjfvz6xc8D57rp6HZ6UfOP3++jMFf0Fj/qsGpPRsG/7SJnKbPCJWEjr8jRNwKw34OGk5//b9jy+v+XAuJaejJaJDL2NFw2JGBHTIGGZYXtPw1A+YMFRGtWulJgokZEldcoADja5XLpsHk8dOEEgiRd6krLP+wZliLvyJdKxCHWxCAhBa28B2/PCL7I8Dy4RMSm5XuihjhRn0WpEKYcoZEdtvIV/jR35ErWxCPmSE24v2J4kZqnHpZREjEopVYBFdUmWzUlTOLKPHd//MoXWQ+Fj5tzF1Lz+T+lKN1N0PSTekNna0v9fyVWK8EXbxUwNbcAsQ/Da1YsGGLDyUG3edjmRLeI4Lv/6+A6uWbWAM+bW0Fu0QyOcHig7PuT5hgv9/v1r13P7JWf6SmxtCCm59PQmbtu4ctSGbzpkg4/nQlqXp2kCZr0Bhz4DYQiQHnzz2X18/LXrp3JIU9796WTcct4yvvTYdizDwDL8JDbHpeTI0HhIVJJWe6bA4rpkGGLvP7nctnEV927eQ0eupDx5ZFguFrxGQFjr210oMb8mTq7k+nXg6gmWIYhGLQq2i+P1lZeNNoKOYWKffj44NkTjJ39+GUKo/WXX9bBMVVIWRRnSmGkwLx1nnq8+15kt4PnCKcF3e82Z6ru97g/f0me8DZPGy65nyWvewsGeAiJfqljoDBgDQcIcgCRqmpRct6K+uz9DRXTu3rSVR3Yc40h3rk9uFSg4Bb7y5E7SMYv1i+YgfSO88bQ5XNc09Ac+mtBvU02Cvx2HMPBMywafDgsSzfRg1htwlcncl9EsgY58acrDTxO5hz0eNNUkOLOpjsf3HccUQgmrVCSyqQURKAnMAx2ZMGmq/+TSVJPg7RuWc+8zu+kp2NiOFxqK8qSvvONiCo94xCJXcmjLFPpC40J1kwNorokjgGM9+VAYZtiENruoVm7Rss/VMCF6EndyEKQE13PJlJwwAhAxDWKGYG4qxmI/ErG0IUW9JclJgxvXncaiuiS3nL+MxmSMuzdtpX3DH8JTT2HWNyGvejttjYuQPQW1LeBf71CV4sFnrz5D9bzF9ckh65eHiugEodoj3ZVyqwXHxXFVlnim6LDnRA8r5tUiJfx6dyttJwT3nD3wfaYq9DsZ2eDjwWDbFMCAYzNtQaIZO7PegBv+pKbCteoOT0bMUw4/jfee4XTkazdv5LJ/foiOXAnb9cKyn7A+G+F7pGZFw43BJpe/vWYdhoBNO1t49lAbBWfgbOt64CJxXJvegk3UNEhaQi0c/BC+aajGIAtqkxzoyoXjKf+zAj9hDWRFffdYkUDW9rA9G9tPisyWHCKGYF4/D1gIuPmcpfzNH5zNoUOHmJcu70IWhes+iDt3CS5qH7vkesxLxUMv33G9AYpt4XrG/1fUMpiTjPHLP7+WezfvHlVE5/7n95O3XboKpTLN8YFtPNtyRU7zv1vLNHj+eI4TmQJLk8kB5wsSH49nCrieij40peNh9nv/6Mx43EfTfUtqsG0FV0q+9Nh2JLCkLolpGOFWwwWLG8P8jqGYDgsSzcQz6w2451X6MYaAnqLDj186xPsvO3PUpRgzrbxjuAl0QV2Sd124nP94ajclx1WJWYbEln1hUctfIBVsl+2t3TSmYiyoSQyYXIKIw/s2ruJd9/+O3+w+rurKByEsz3I9YlaEeL9fcWe+pOqbfYbzvMuzzSMHt2KvuGiUn9DgBNGARMSkYDtI4Fh3jtPn1KiwOXBxc4pravNceeWV5HI5fviLX4ZdyAwhEPNVX3DH3yMvOh4nMgWaauL0FGwsQy1egms0DeEbRfX5p6MRVsxN84Y1i1nckBp1RKe7UOJEtlght1ry69v7xHPU91ye5+B4Hg9uOcSHr26sOF9nvsjBzqzqsubXeUspac8WqU9EWdqQCqMz430fTectqcG2FQ75GgvSTwJZ1pgO76mnDrRh+cmQ03FBopk8Zr0B7z+xW4aBkJIDnRnu3rR11KUYM6W8Y6QT6F1Xr+M3e1rpLdr+pGyCvwdt+XXNOdvxvQXJ8V4P1/P4ypM7B52Em2oSXLKsiSf2tYUZ6UOhvF3l3cYsMzQyeT/zfVgGKRWzl6wd24c1xNhKrgfSY0FtkgW1CVozRQSQjJqcNzfJ4Z/dx5s//INQzvQ9H/prOle8LhQyKfoysQECJZbTky9Rn4wppTvbA0vpq3vgJxYa1CeinDGnhitWzK8wTuURneO9eb793NDebV08iuO4VAZqK71v6SvNuWXjNISgpzhw/3XzwXbac0VMIcJoTfBnUPd9WkOK2nhk3O+j0WxJDbZoPVly3lgZbFuh5Hph1EMIQVe+VFGjr+YoeNXSeTx3uGPaLUg0k8esN+D98fyJc346Pur9uJlU3jHSCdQ0DK4/ewn5kkNbtojrSeYkVYONE9kCJcfDMPz9WiGYk4yxtD415CTseh6/2d1CpuSMSIDF86SvVuYSt0xcr9LowSDe9ynUeY+G0M4J4XdOUwZ8TuYo//GFf6XYdjR8btMZqziyZAOeH1YuN94qI9/3eqUy4ovqEqyZX0vedhEIYpbBGXPSHO0psHJuDQv9PfXBvOuRLs5uOW8Z//r4DiR2mREPVNL6zmcZAtPs+817UlIbq9wiOd6bp7doq334Qb5YwzdUi+qSXLOymQ/8cPOE3EfDbUkN97mcLDlvrAyWUd6aKVREPeQgNfyulCysS3LHlWumZY6MZnLQBrwfjn8ztfQWOK1hdHvhM6W8Y7QLkSBJaEmk7+dUdFzlIVqq/jtiCFbNqSFjuxzozGIagkd2HON9G1dVTMJ3b9rKnvbMiBXUgnIzx1P7s7Z3kiYgE2y8yxP4DN/TbO3NKyPnOWR+82Pan3s4FJuRhkVi45tYeO1NbGntJld0MJEEKQDlPdGDvzueZHtLN2fOr1OLFn/R2VST4J/+8KKThpZHujhrqklww9olfPWpnX7HN8I6dlCfe8Q0sEwlQhNgGQY3nVMphnT/8/sxhQjr3Ae7QzwpScctHtnVMiX30XCfy3DJeafCYBnlgaRuQP9Sw2BcPQV7WufIaCae6tmMnUQEkq68SswaTSnGTCnv6N9tbDDKu40FSULBxF50XHae6AnD2JahOo4d6MrRlinQlS/RlinwUksX7/7OE2ETk2DhoCRVRz5eD2XUVItRda5yZbiQSfS8g/1dhNoPd4/tgQfupvjsL/qM97zTEDd9lOK5r8VGUBePgpTY/mIkKKMrL6XDv9aco2qyYeRd6mB0izOAu994HucvnhMulAyh6vED463EZyJhMyDH9TivKTEgYS+4N5Y2pmlIRP0sekWQVd+YjHHxaXMn9T463pvn//12O3f+5Pd889l9DPWzV8l5eU74mv3jxWCd2gJJ3YDyHu0BOstcA9qAD4rjqRBVa29hVDfJaNomTmfGMoHedfU6XrOymQOdWba1dpMp2jhSGa+C45Gz3YoGFUIon+7llu7Q6AQLh6Z0fEyJfn7VFBGhEroMQxDOe449KcZb0leWCL5OeylP5H++DN3HARBmBF51I94N/wsaFyJ9Tf4FtQki/SbvAddHX7Jayalsdt7f+A7GaBdnpmHw8Puu5rZXrVDNRhIRFtelaE7HiVtm2D8gKGu6asV83n32vAHnDO6NQPltzfw65qbj1CeizEvHWTO/jtMaUtQnYpNyH7mex6cefpFbv/kYP9x6kB9uPcTx3jzbWrvY35EZ9DsIkvPGk/K2ugHz0/GKBWz/Hu2gs8w1Ch1CHwSJ9EuT5KhukmqpNz0ZY6kzNQ2DXMnF9RNqTMMAqUREciVHeaGeqok2kbj4MqemHYbSg4VDzDJJRSxKbmlU43Zcj0TEwJWqA5grhR/6lWBaeLVzMU9kx8V4DxfiV0IqykMFcKMJnAveROSp75NcvILoNe+kM9YYysYJVNg0ZpnMr0lwsDM7bOlbINSSt90Bj50stDyWxZlpGHzuug3cceXZFfutV69q5pGdLRX7rylDsn379gHn7H9vRE0jzFovf9/g3pjo+6h/uDwM2UtVxUCZbkH5+AZLzjsVBitxK5fUlVLSmIxNWJMZTXWjDfggSN8ArJpfO+rmBtO53nSkjHYh4noed/3sOb721O4w4cqVkpLjUXL6MsldCa4TJJipcGxvXnUiu/Wbj/HGNYvDhcOZTTX8/lAHzihl1GxXdTLrS//yEQJn0VlIK4rbsHBcPO/+RtwQKuztloqYpiAaSVDyL7fhomuoXTCP4ukX0J63KTkuwZQsIUwCW9qQ4mhXdkAb0eD9At0CIVSJWn+GCi0HmdVPHTjBwa4szTWJMOzdn6G828H2W9c0V0oO53K5Qc852ntjIu+jwbYRgv7sQWSoXLcgYLDkvPFgsBK3JQ0p3wsXLK5LhO+vs8w15WgDPghCQDoW4T9u3jjq107netPBGKrOezQT6N2btvL9LQfxpKTUT750OPPrSbClRHqS3+xuZXdbL4vrEsQsi3jEorkmzuGeocPBg1EqDw+XFywDCIHbvGJU5wN/zxdloJ1hqtOkhPiJvUR/+y3cJWdjXP5WmpJRPKnqdY81n4PMlSi4HkXH7/3tSiKmoOS4HOrOKcNuGtj+GwXRVYH6HXm+p2iZIpSWLae/8e2fWW27Hu3ZIh25IvVxFf7u749PVJRoNPfGRN5HgyWbNqXjtGf7WpgG2xrlUYLBkvPGg+FK3KREZ5lrhkQbcJ8wUUgoJbY/vWgFC+pSw75mMKa7BGrAyUqJPvIalW57sgk08Gak9DuCuXJUCWjloeJDXTkOdeWYn46xuqnOV1VjyMSi/lTYVrtIZN9zyutO1Y98QIOdN5Q3E4iyJUnFsOwisWd/Smz7bwGwXn6U0hnrWXTBRRzPFGjpyWGZJnnbCb1yiYpUuI7kRKZAxDTI2y4GEgOVOBUk+AUlZBKwTEHCqsz8DuhvfO/62fN8f8tBlQhlGsxPx2lIROnIFQcNFU9klGioeyMIxX9m09aKReRE3UeDbSPELJN6/3MxfC+8vLZ9qOS88WSojHKdZa4ZillvwIMsX+HLFxpAc22CD53iTTPdyztGWko03AR6vDfP7d97mj3tvfTmHYqOFybkyCGVuk9Oa6ZIZ+44iWiEmqhFd9EZ3QnKss2jezZTWn7hKRnx4DqCxiOgPDTXzxI3W3YT/923MXvbw9e488/ATNVzrCdPZ76I40HethnKgQ8jFygvP2oZfgvWPqnYiOWXE0moT0QHhMDLjW//bY0g3N+eLVAXi9CQjNGdL9GZL7HQ9bAMcVLvdqSyph15my8/sYucx6DPC+6NYBH5gf/ePGQ9+kTcR0PleCxtUAv2rnxJfde+7K9lGFy1Yv6E1IFrNKfCrDbgArAEODKYnKE5FWdBbZK3f/t3VSl9OhJGW+c9XIvJvW299BRspbaGElYRg2t1jIqSB47fprImatJbGpiwNSj9S8XMCNKKnuJoFEFZl2UKSo5E2EXiv/8J0VceC58jzQilDdfhrLmcZDxKd8EGVFp6YLwHS4AL8gYM4RtvQ1Abj5Ip2uAnVbqehyslqaiJZRgUHCVeExiZVy2dRzoW4X8//CJPHTjB9mNdlZn/qDfuKtg0JKKcNb+O1t4CNTGL161edMrCL67n8dlfb+fhl44QSyaJmOaw8qfli8j++ug/33YYmBjVwqFyPIQQLGtMU3Rc2rJFbly7JBTEGSo5T6OZSma1ATeESh5yXY+IAfNrkpzRmA71natJ+nQ0jEZw5pbzlg3wur7y5M5w4rUsE4mNIQSGUC1AjXFyVDwJ2aJDyRtm47kc33gbRZXFPTGlYn7jlKM7ST5+P0amI3zEnb+c/KtvQdQ1he1Vk5aFlFSoww328UhUTacpBHHLREpJzDQgatGWLWJ7Hr7zje1K8iWbtqxkcV2K689eTGtvnt8fbsf1VBLfyy2dFGwPIaiQmQXCJK2FdUlOa0ixcVkTHxjGyx1ptObuTVv59e5WP19gePnTYBFpCsH+jkyojy5Rne2OdOfY3Z7hxrOXsG5R48BBnQIny/EwDcEfX3B6xX0/VHKeRjOVzGoDbvolS/Ggz3S+xIHObLgnWE3Sp6NhJKVEAvjxy4f4ycuHKryu7724n2M9eZprkhzrVUlXpTLxlNHsWY+E0RpvMQHGW22xgOn/yaHtpH/xr+Hj0opS3HAdpdWXgTDU8/z/ahMRWnryJ/1MBFQI2AghaMsVQzEYUIbF8ySelLTlSiRtj8ZElJ9uP4LjeqEnu7Oth4Lt4UqPoFQ83i/hLUjSWlSbGLaeeqTRmm3HOtXzTIPBiv/630vBIvJgZ5aOXBEhlHxskAApBHTlSrzm3x7hA69ePe6RsGpLNtVoBmNWG3DbU60eLb8NpetJjvXkkcDC2gQxy6wK6dPRMlydd8n1aM0UaMvkMQ2D1U114V6rIQStvQWOdOc41pMPPTvPk2HjkSFaTp8SJ5VVnUCFNQHELIOIYZB3HFwJ5sIzcectwzyxH6d5BflLb8GrnQuoRSGoLZlENMKiuiRHurMnfR+1/eBRk4jieirb3PY8IkZfn/NAgMXxW7f2FEtsP96D7Xqcu6Cew5kcXfkSedvxa+HVuUuOR8QwKgRDgiStk2WcjzRa8/GHXhyxQMwt5y3jFzuOsLetl7Zckahp+ip6avsleCvDEORKzoSE06sl2VSjGY5ZbcBVwpAM2zWCmoBbevJ050thi8PpLn06WvrvAZZcj9bePO25IkXHwwAKjksyarGttaui3KgtW1TGQUqiEoqu6jzWl+g1/uOdKuMNaj86JkAaAgOQCAzLoHj5H2Me2UFp9aV4Qi1wAgEXUGIcq+fVKkGZEQYRHAnJiIUp4Hi2gCnKtN39D8EwRF84XAgKtovrSV481kXUVCFuYRhIVzWRQaptjbzjko723e5KMU5w+fKmYaNL5dGaouMO2sc7aEQS9LEeivKozoHOLCeyRYp+SR0wYJEg/f/ac6UJi4RN92RTjWY4ZlZ21jjglbVL7MgV2d+RmfbSp6Ml2AO0XY/9HRm2tXZxuDtHb8Gh5CrZU6BCmepAR4aS61F0XJAqzJmzHYquhzuFyblGpn1C5VFjx3Yg7/8EzpHdvkaqBCkRDc2U1rwa6RtvUyjNd8NQAivNNSqCs+NE74gT+iRwrCdHZ77E2uZ6mutSxCyTiKGS4Mxy401lZEIZQfVo1DTK9NNVKZ7rSRxXfa/SlzR9yzmnnTRUXBeP4nrqd7K9tbtCy357azf7OzK4nkd94uTyp/s7MhzoyKr6f9cLt14CQ+1KtT0QVu2h9u/Vvn6fvKtGo1FoA96P8jnIEILugs01K5unbkATxF1XryNiGXTmS7iu9MOfyjgZvgJU0d9ADZKejnbnsPqURSoEW6YKr2Eh9pK142+8S3kSv7sf8dN/RvS2Yz76TTy75H9EAsftSyoLpE0NwyAZscI+6Ps6MuRKzqi2FVwJvUWHHce7sW2HuakYgSBsf8rLwyT09UD3vxPXU8V8an9dEItY1CeizEnFuO1VK/jcdRcM2FcOmnv874df5P/9djvXrGzmUHcu7Nc9WB/vw915PvW6cwdoepdTdFy6CzYLahMc6MjQU7AZTAjOk/jJbKpNqfBL96qhCZBGM9nM6hD6UARhYAnUxyM8squFNQsahn1NtdGeVb2p18yv8zuHuRiGgWWoumPb9VSLTtQqT0pJd9HGMgwipqTg9CWuTXV1rDd3CaXGhWAMVCYbC9bhbSQe/y5GrqvvPZL1yGIBkhFsf8sgkDb1/DB30fHwHI+Ur2NdtN0wNwBO/lmpJED1rIItaXWLzEvGwnD3YM+PR0wyRQdTqJ7rBcetSAQLfssWglTU4ow5NVy+vGmA5z1UqdgPthygKze0Jr3wxzs3HeeK5fP5xfYjA55TdFx2HO9BSsnR7hwd+RIGKrEuW3IHLamLmCLMsWhKx6uiCZBGM9loAz4Inj/7NcQjLG1Mz8iVf5CcFLNMamKRfglIHrbb59XFg5Cs73HGTIOS4+ExBcbbLmLkuvDq5lceHw/jXcyR2PxDorueDg9JK0b+ohuxV22kf4G72puWFYbSBhzXCTW1I6YRJqEFiZJDfWaqfziACDuadRZKxCMmvQU71EEP3jvie6YxS30frtenHqteL8IMeokkGbX49tsvGzRJa6hSsSM9eVwpVXKe3yI1WIgI/x5ZXJ/i/uf3c9fV6yjZDj/bso/DXTk8JD15G9dTSXcIQUtvIUzOi1kmMUuGv6VyXE9FheamYuE++3RvAqTRTDbTOoR++PBhbr/9di666CI2btzInXfeSXd3NwDbt2/nbW97G+eccw6XX345995777i9b9Q0WDO/jmWNaeQMXfmXJycFjRwCTMNQnbSk7OtdDdTFI6ENi1pT8NPxE9Yie5/D6Dw6rqe2Dr1MzQ//ocJ42wvPpPcPP4p95iWVmuplqIS+ymMl18M0DN9Ll8T8z+pk2w1SytBwSakaaUQN1W876GwWnMMyBPGIaud57sIG1YZUKkPtle0lR0zleScjFnOSsUHHMFypmOt6YWb9ijnpAS1AlzWmMfuFtwMtvu6CrfIpDANDGP5vTC0AbFf1b4+ZBpF+vyUJ4b63lBLbdU+abKfRzEamtQd+++23s3btWn7961+TzWa5/fbb+fznP8/f/u3fctttt3H99dfzH//xH+zatYvbbruNRYsWce211475/YIQZlM6HpZOVUP7z7FQXkoWNHJwPS+U7QxquhF+0pNh0FybQPgJUZ7ncbinMHkD7pdtbh3bRalu/rh43rEXHyb+3M/Cf8tIjPxFf4i98lVDGu7hcDyVxe+4Hi4Q8ZPbTlZmFaShSVRGesn1mJ+Oc9P6ZbzU0sWWo52UXI9ExCRmmWGP6KM9efW9+NGA/iOWUtKQjGEaYtCSyOFKxcygP7mUdOZLLKkf2B8gCG8HQi6mIZifjNNZsMOOaSVXlbbhh/qFn0MRNUVYM1++1RCE0DsLNqdb5ojrskcq9zqbCT6jtt4s+a4O/nJJgaXJ8RQ70kwW09aA9/b2snbtWj784Q+TSqVIpVK8+c1v5utf/zq/+c1vsG2bO+64A9M0Wb9+PW9961v57ne/e0oGPPBYFvodiKql/edYKC8lCxYr2bL+0oERb0xGSUQMFtQmmV+T4NLT5/JSSzfbjnZN3v73YKViyy8ctz1ve8laYi88hPBc7EVnkb/krcj02HMePNR+ePDZ2L7hFoBpoELdg7wu+DwNoRaSJceju+CwsC7J3W88j7s3bQ2FR4QQ7G/vpbtgI1Eyq0EyHRCWkNmuJG6ZLG1IqdrxQbaDhhP2mZ+O054tAGLIBYjrSVp6cjzwwgEMASnh0Zkphr8hgIhp4jquki72j0m/DFEtHtR2Q9Q0sT0vDJ03peMYCNqzxWHvw8H28AuOy78+voNF9UnetGYxf3z+6dPyXp6sRUf/z0hKSSaT4bkHn+GqlQtmpGz0TGfaGvCamho++9nPVhw7evQojY2NbNu2jdWrV2OafRP4mjVrePDBB0/5fWOWieXvLc5kRaZyOcnDXTlfh1tU1MQDnMgUqI1HWNZo0JEr0nVINXqwy/ZCJ5QJrvMG8BoXUthwHTKSwF558Zi87v6UZ4iXH3M9iBgGCEmpX/1dYLyDnHPDUN7qNSubBwiP/PilQ5iGYMXcGna392IaBp70/DA6ICVRUyUlIlRiomUw6HbQcMI+UdOgPq66dPXPMpdSsq8jg2kIvrflIJ2+HOoJz0OYBsL3uMNzGQLTNHF84R9DqGx+UMY7Zhog1KKhf4e0k4kple/hi0CetVBCSmjtzbOvrZefbTs8rfobjFRjfrzon+cQ3POenLmy0TOdaWvA+7N161a+8Y1v8KUvfYlHHnmEurq6isfr6+vp6urC8zyMUfzoLUNgCoFlKC+hLh7hitPn8qcbTmduOk6xMIlh4lMkn89X/HkyPnTJCjozeV461kXJUZN/ef/piKlu7p6Cze4TvZw+J43jeWxt6aa3OHRnrXFjAoy3dWAr0VceI3f1n4HZ9/MvnX3VKQ+3P4MtbiRKHrYpGSFXssmUNVpTCWf+DrJUv83amMXPXz7IsroYAGkTLltSx788th3Hk+xtz2D73cRsF0RZrbjlC7t4UnK4M0PENDjQ3s3/2fQiN52zJGyNeePq+fxo64EhPezFdQmkhKZkFNt1w3Me6crheZLT6pMc7MqpLRhX4kmVjS8EpKJ9CwZhGNTELFbOSbGzLUPedjB8hTjDUImStfEIi+sSYRvVgPbe7JB65CcyBX6985hSl/M8DnRm6cqXKkreugo2uaLNL7YfoWQ7fOyq0Ym3jPbeGgmf/fV2fr1byc8CoQiO48oxj3Mo+n9GAJ6vC+C5LoZp8pudx3jH+iUT2jJ1MpiI72oyKW9AdDKqwoA/++yz3H777dxxxx1cccUVbNq0aVzOawrBvIRSvmqMW0QMNTFlOts5cQhOjMu7TD779+8f8XONfA8LEgZ7bQdTqMnO8rOhXdfD9ufRoz15irZNV8HBnoy4+Tgbb1HIEH/6+0T3Pgeofe/i+W8Yt+GOlracTcISRI0ytTX6RFZMIWiMG8yLCfYcPsb27Q4nsiU+/fQxXm7PU3A9LAG2pxLpLENlpLpl3eBKfsja9iTHSjYNMZPvv7Afx5P882PbuXJxDf/f+vmYhuDMlOTplizWIBOHIyWXNydIRgxebFO67ivqomQiEksIctksXbkiuVJf73LPz3/MFm1ipvAbBEk8B9wSLK+N4EiLpGmwr6eIZQT3IOT7GWpPSnJdxpDdwO5/pZ2eTAbDjzR0ZouDlKZJjnT2Mj8Z4ZGXD3B5nU1jYvTJqaO5t4ajI2/z8EtH1Pc0xHNOZZz9Kf+M+lPwnRRPSr70yO+5ZfWcU36/6cB4fVdTQTQ6sg6K096A/+pXv+Kv//qv+bu/+ztuuOEGABobGzlw4EDF8zo7O2loaBid9y3gjDk1GGblXmqyvpGzzqo+ecV8Ps/+/ftZtmwZicTI9tASLdsoigyxiFeR/FRwXEple7USOJEfZV/usSIlkb3Pjpvxtva/SOLJBzAKmfCY2Xmsr+ZqCpAowxq1TGL+frchBPGIQW0swoLaOFLC8UyRvXnB32xuZ1trNz0Fm5JUSW5u2fAdqRIuI4IwLI0Q2FIZ4KhpUJQGBUdFV3Ie/GhfD/uLBj951+V8/szVfP7RV3hi3wls2RfOtYTAQ3KgCG4BIvEknpQ8cSJPyYWlDcr79hAIIyiz63u9I8FEEPfvsUUNNUQtE8f1eO2K+bz3wtN594PPDCvBawJ/ec2GIT3DRMs20p3qmo905xCmOWh5jWFFSKZSeFLyYiHB/3f+yhF/X2O5t4bjy0/sIpZMDqsxP5ZxDkX5ZxSe33UpFArE4/FwDqzWua+c8f6uJpvdu3eP+LnT2oA/99xzfPSjH+WLX/wil156aXh83bp1fOc738FxHCxLXcKWLVs455xzRnV+R0qOZYrM92UvQd00c2pSJKs4KzORSIx4/HNrUmHotOgXEgdCIFMm0CIEzsIzie59FmlFx2y8RSFD/KnvEd33fHjMiyYpvOrN2GdsmDLjDUHzEpWJHbcMElGLNfNV45iC7bDjRC95Wy2Yiq5HT6FE3nHV/jmEdeae/0UZhsD1PJIRk6hhYrsqw7vkSiJ+WWCQ7U3wpxBsOdbNp3+9nc9dt4FPvfGCAc09jnZnefJAG6ZhhEbRRC0eeooO+zuyZEqOWjwYKmkueIPAODm+jOu8dIJYxMIyDP5gVV/S1FUrFwzZ2tPxPK5ctYClTUO3FJ1bk0KI9nA/dzDjLaUkYhqYhuoWl/fEmO7x0dxbw5Hz1JbdcJzKOPtT/hn1xzBNP4ei+ue+csbru5psRho+h2lswB3H4W//9m+58847K4w3wOWXX04qleKee+7h/e9/Py+//DIPPPAA//RP/zSq9/Ckas7RketrXDJTy8aG4uZzT+PTj7xItuR711JOqbZ5gKyZQ2n5BmQkPibjbe1/gcSTD1Z43faSteQvuRmZrBvmlZOHA+B5OJ5gbipOxFDJVy29eUqOhzDUdsbxjJ+HIVVnMtM3xkHGuYfqCGegvPq4ZWIaUJeI0p4t4HhqT61QpvQSNX2DLCU/eukwd1x5Nk01iYrmHsd789z6zceG7JmNlLTlisow+q15JS5O8AOSQWa5quy4ef0yGhLRAR2/TrW1Z3lFRVD21n8KFH65JPSVvU0lwyUOBoznOPs3MBqM2Tb3zQSmrQF/4YUX2LNnD5/85Cf55Cc/WfHYQw89xFe+8hX+7u/+jo0bNzJnzhzuvPNOrrjiilG/T7mmsyclf3rximlZajIRHO/N8yff+h3ZoirlmVJZVNdRZWFlE5pMD+11DYmUJH77DaJ7nw0PebEkhYvfgn3GBVPqdQ+G43vhQijt9PZsAdvvJGb5hfhSqvaiwvdqXVclHPZPIgzK1zzPIxmL+l6nie055EpOWWa89PuHK6Nbct1R14cH2gFSqkWD6ddzxy0TR/ghfj+60JCMct2axfz9awfPcD7V1p7lFRVh2VvZD9mTksZkLIyyTQdDNdkGtfwzGirSMVNLZmcy09aAb9iwgR07dgz7nPvvv39c3ktCmA373otXjMs5pzOu5/HpR7Zw3zN7ONKdCyeRKTPefsKa27AAt3nFqRlZIfBq5vad+rRzyG+8CZmsHYeBjj9JSxCNWCQtg2OZAlHTwpUOUcsM27oGcqieVMpuwX6x7wQPKFUzDIOGeAQJ5Eo2jl+PXRE+x0+esx2sVGzU9eExy6Q+ESVv55GeR3lnEttVURzLVK9tzxb575cOkopZw5ZGnUprz3Ivvi4Woatg+zK3gsZkjKUNSoBmuhiqqTCo/SMdoBY3JnDlDC6ZnclMWwM+GRhCKJlKXzDCMgQPvHhwxvcHvnvTVu57Zg+tmQII4dcen1wpbEIoyza3WnaDaeE2nX5Kpyyeey1W615KZ16Cffp5087rDogaELEspJS0ZIu4HtTGLCrU0kPlMmWty71uQwik6PveDFQr04ipVPNsV5VUAYN2ChMoT7kuHhl1fTjA0oYUtuPSXiiF3n3BdnH8/ea47/EaQjAvFZ/QWuNyL/5bz+3jpy8f5kh3jjnJKPGIpQzVNNN2ONWtg9HSP9LR3psl32Xwl9ds4LRhcgw005dZbcBBtSqcl4qFamQzsXFJOcd78zy84yg9BXuAV2YwMCw7oQxSKub2b1IyHFIS2fccIt9L6ewr+46bFtnX/cW0NdwAMcvAok+hLdg3zjlORR1o1DTCrHJD9Omu9/fZBEqfXmmiS7VvLgQRQ1Aq605WjpQSyzToKTqDhmpPFuYVQnDm/HpWN9XyP68cxfE8Co5LwjSw/E5i/cPXv91znOO9+QnzgJtqEvzVFWv4qyvWjDkkP1mc6tbBWAkiHblcju3btzO3yuu+ZzOz2oD3T2JbUp+c8uSWU+FEpsD9r7STaNnG3JrUoJKM9z+/n5aegmosEXh3/mODqYdNGKdY5y1yPSSefIDIwa1Iw8RZsAqvcWHZE6av8Qa1V+z6QhoSFW52HI+EaZEpyyI0IGzxKhAYQoZ9yIEwdyFm+UpmKMMa9HdPREzckqM8af+xoHFNxDSIGILF9clBDepIw7x3Xb2Opk1b+eaz+9QCwm9C0j98HbzmZKpq48WphOQnk2oZp2b6MasNOFQmsYkqbVkYSDL+etcxenozpDs9hGgPJRnfe/FKHnjxAN2FEk/tP4HtKi3tqNknZQnKeE+KB34qxtuvEY8/9X2MkhL8EJ5LZP8LFMsN+DSnXGlMCMHpDWl2tfUSi1pYppKtDULXMcsEv8+36gzW1ybUMgSWAKusJElKFVkK+oqmoyqEbPv614ZhYPqvqYtHeNOaxQPGF3iFnpSkYhaZgqMU0wYJ8waeZG/R5r+3HCRfKJKIx2iuSRC1KkuljCH02DUazeiZ9QY8QBnyaVA/NQYCjWNBX+2tIQR52+UfH93OPz66nfqEUvbpyZfoLdkYQpCIWGXe3fT3vEWum8QTDxA59FJ4zIunyW+8CWfZ+oka8YSgjDEUHZd4RHXdqotHmJOIhh28unxtceGXfTWlYyyfm2ZvWy8R06ApHac1W6QtUyn3G5RMCSFozxaUDGpNgqZ0nOOZgvLOy2LquaLN8V4lO/mt5/bx022HOdKVY04qRtwy8fwuZ8moxUWnzR20FAxgUV2KBbUJCpYkmUoOmqw2HUq4NJqZwqw34AXbxTAEc1MxFtenJi28N16U93IOvDopZV89se9h9xbsMETuPwvHtamJR5COq7TQJ3qwYzXeUhLZs5nE0z9AlPr0jUtnnE/h4rcg4+lhXjz9MAXYroeN0iuXUn2PjckoyZiFAE5rUMbwaE+enoJNUzrGn5x/Bu+7ZBVfeXJnGNbuXzbVf8+5Ph6lM1+iKR0nZpksrksqrfCCHT73kV0t3Lt5jy8uI0Md8RPZAvXxKEsb01hCkCnaGAI+MMT9cct5y/jR1gODPhYwHUq4NJqZwtS35JlihICIIegt2hzqzNKVL071kEZFUKtbzsGuHG25YthZzPNrhiWVIXIPyJRspJST4n0LzwHP3/cdofEWhSzJTV8l+di3QuPtJWrIvuZPyV/xzqox3gKVkBZkZksg7nfg8jwPS6hb8UBHhtXzarh+7RJilknEFJw5r4bFdSl+tbuFW7/5GJ6UvGblAgyhasXr/WxxgNpYBNPvxnWoK8u8VIzzFjeS8DOxD3Rmac8VIdifbkxzqDNLR67Eid48xzMFDF/lDQmd+RIHOpQYjmUYYRLaYDTVJLh02TycIbLeHM/j8uVNU17CpdHMFGa9Bx7zs2WRah/86YNtUz2kUdG/VrfkenQX3FAO9WSVYa4H7iRtHchYCnvlRVgHtmIvPWdEYXNpRTF628N/l864wPe6U8O8avphCVhYm2BBbZKXW7tIWwY9hVIYIQGPzrxN0XG5d/NemmvizEvFWFyXoui4HOrK4npqb/uh7Ud4w5rFfOtPLuP+5/fTlS/y9IETbGvtoadg01u0w/C4JyWvX7OYP3vVSv7j6V3ct3kP8/1wetQ0KLkeXYWSKiOUKjIQNc2yrnTQVbApuZ7KiD9JEtqdV6ym7cRxdmYIS9AmsjRKo5nNzHoDXo4hBJmCM6FlLuNN/1rdzqKrsoylZHL86tGhjPgoem5bEfKX3Ury1/9J/lU34SytTgNg+hniR3tyCCRZx6PoSVypsstLrpKyDZqBHujMcawnTyKibtFgL1xKSXu2yInNe3nvxStDQ/qph1+kM2cTtUxc18Py98ijpsGvdh3DEFAbj7KkPlWx4GvNqD3yPmlWQcn1iFt9wTkpJa2ZAkvqkidNQjMNwXvXNTF3yen88JXWaVvCpdHMBLQB9wn2A01DVNU+eP9aXdfXvUaIYWUaJwW7iHV8H86CVVCe0DSU8ZaSyK6ncJtOx6tvDg+7Tcvo/aO/q+jfXU0IVMlWR76kau09tbhyPdW/W8pydbW+L63kSkquTczqC70HVRNt2QLvfeBJfnbbH4R5EMmoRTI68DMKQt/nLW4YIMriun1d6EQQiuq38BP+82DkSWjz0vGquYc0mmpl1u+BBzWxQb1qtZW5BLW6uZLNke4cGdujYDtEBlHemlT8hDXz+D4iB15UrbeGQWQ6SD7ybyQf/w6J33174POr2HgbhiBiGpw5r5aCqzYsgvwEgoRw/7lBgni5GS06HnnHrdgOMYVgx/HusNzrZCp6juexpy0T7pWH5/GbfwBlv5nK346kL4Kgk9A0mulDdc6K44QhYG6qsl612spcXM/Dk3AiW6QjW8T1PGwPHHuydrYHoV+2uch1g1sCYxDFJymJ7HySxOYfImyVQGidOIB1ZDvOkrMnc9QTQsQ0SERM9R1lCkSEQdFVzWMkhAa13GT21zYHKDkerifDBiSgvPH7n98/rGZ5gCEEK+fW8PvDpYrITHkWu2kYREzpN1HpQwjB/HR82uiIazQaxaw24JYQLKqrrFetNg/j7k1b+dWuYyxtSDM/HeNwRy+9jqRge3hTsQs+VKlYZKDxFpkOEo9/h8jRvqY1XrKO/CVvnRHGG/y9a1TYvDWjOo0FxrvieWV/71+TH4i2AH6/bZd4xKK5JuHvMY+sNeWCuiRXxKwKZbWoaYSlZlJK5vv140ENOkJQH7OIWyaXL2/SSWgazTRiVhvw/lSbh1FeA15yPY5nirgSGhNR2ilRdCdV2Xzkdd5SEt3xBPHNP0I4fWV7pZUXk7/wxjH1/56OBDX3BV9FLTDcQbh8pIur8v7WQqguYvNiSoSnNh4ZVWvKOakYUNlAY0lDyl8gCBbXJTANg7zt0JErsaguyXVnL+aPLzhdJ6FpNNOMWW3Ag/muWstc7n9+P44n2dfWS1uuqPZIpUek5FJwpqfxFr3tJB+/H+vYrvCYl6wnf+lbcRavmdQhTzQS5YHbQRG+j0Alr3mDeOLB48Hr+3alRfhvy1DKbIFRHm1ryqEaaEjJtG7+odFoKpnVBjxhGVx++lzV+KMKJ6vOfJGtRztV3a+PK6F0koSxcWcUCmtGtgvz2O7w36VVG8lfeANEq+uzHynCLwzz/KYxpgFSKrMsRF/5lgyfr5LeLN/AlzxJIGtuGSLsE15yZYUoymhbUw7VQENnjms01cOsNuDpqMlHrzqLZLI6Q7a/23ucnqLte2fypKItE4V15JURy6O6zcspnfVqIge3kr/0bTiLVk/mUCcfofa/hYCIZYTiKbb/ZQm/5afrx79VcxKBLSWOVHrpoM7hANJ2iEcszm6uqzDKU9WaUqPRTB2z2oBXE8HE3F0oURePcs3KZvZ3ZMKey1NZ8u0sXoMoZhFOqdJ4S4/IvhewT18Poi+0W9hwHYXz3wjRmd2HOGjvmUpEiFsG7blAeU2QsAS264EQpKIWq5vq8KSkPhHhqf1tOI5LOhbBAL+yQIZ73LWxCPe+7ZJBm4Xo1pQazexBG/BpTtAq9NE9rbhenzTlv/zuFY525yp6Q08ZVgR7+YXg2qHxNnraSPzu21ite8gXeimtuaLs+dEpGujkEPHbbp4+J0VdLEJrpkh7toDjeaGamu2BCcQtg4ZkjKhpYAjBF950Adf/56/pyJXC/W/TMPDXAmHv8PGi/8JwsB7yGo1meqIN+DQnaBVqGUZFq9C2bJGSO0Wet130N2XLDLEVUf9Jj+i23xJ/9qcIV+3Nx5/9KfYZG6pOv3y0qD1ugenXal+3ZjEPbjlIR64Y1lgHvdgl4EpJyfUqaqwf2dXC4voUUioN8qAMTaLC7Q3xyLh0zRtqYRj0kA/6fGs0mumLNuDTmOO9eR7ecZTW3kLYyKI+HqEtWyRbmiK1OD9hDcOktOLCCiNudB8n8fj9WK17w2NeupHcZbfOeOMNak0jIawA2Hyw3d//VguvmGngAbbfujWoDzeE4OpVzdx19To+s2krphAsa0xTcj1aM4UB2ubAKasFDrUwlBI27TwGqGz1/miPXaOZPmgDPg0YbFKck4rx7u88wcst3b7WpqBgO+z3k5/cqXC9+2WbRw6+hH3G+eB5RLc/SvzZn4VeN0Bx9aspbLgOIrEpGOzko8r4lMdccjyeP9IBqJIxUIbdC5LXgv+EwDIIPd5yUZaoabCkbmAy4KmqBZbrBwxGedvQwDhrj12jmX5oAz4BjNRLGW5SlMArLV0UHRcpwZWean5BZU/vSWOQUjF70Wrldf/u21jH9/VdV80c8pfegrtg5VSMdEoxUGVg0hc4F4iwTCyo4Y4YBpYpkJ6HaZrsbc9w96atfPzac0clyjJWAu304ZTb+rcNHavHrtFoJo5ZbcB7Si5ffmIX77h41biEAU/mpbz34pU88OKB0LAf7c7x1IETAybFgu3y7JEOPE/i+e0mA497qva8h6rzjj7/8wrjXTzrcgoXvGnWeN3lmEKoMLpUGuiGCMRaBDnbIRm1wu5BShBVCb1EzEqPdzSiLEMx3CJypNrpQZh+NB572hz2tBqNZhyZ1Qa85Hj8ePtRHtrVOi5hwKG8FE/Cfzy9m/94ejcRw8CTEs+TdBVtGhNRljamK5pZvHKiB8dPdgrKxKaMk4i0FC54E5FDLyOFQf6yW3CbV0zdWKeQcnsYMVX4uzYepTtfouh4SInqs20a/V4naPKT2AKPd7SiLOWMJNQ9Uu30IEw/Go/9tg1LR/JxaTSacWBWG3Do83i/vnkPv9ndyvVrl4wpMWc4L2V/R4bjmQKO65GwDCzTpGC72J6nQuTA6Y1pAIqOS77khOHJKS0T62+8I3GcOYsrRVoicbLXvA8v3Tjjy8P6I1Ad7YSKlfsSpyZBM6+FtQkEcKwnpwx8/4WYlNTFI2F3scDjPRVRlpGEut+3cdWowvSj9dg1Gs3kMKsNuAQOdGbp8ct1OvM2BccdU2LOUF5KyfVo7c3j+DFwR6oPPSgLclxJa2+BRXVJoqbB8Uwh7AU9pQItTqnCeOPaRA69TPz5/6H3xo8ga+aET/Xqm6dokFOLAFJRi5KrysNUj2+XqGnQmIwRs0yWNqToLtj0FEq+mCphl6+aqMlp9WoxNFhi2mhFWUYa6n7fxlWjCtOP1mPXaDSTw6xOG3WlpDNfAggFNk5ki6G3cvemrSM+11BeytHuHLYvpSl8T0i9n99qUqgJ82h3To3Jk8ojm2qBFtPCS9aC9DDbDhJ76ddY7YcQTpHEEw9M5cimFMPXL7eAqGWAL4UafJ+2q0LeSxtU2ZwQgtVNtaQiJgtqEtQnosxLx1k9r4aF6WhYYjYebWyDReRwBKHuu65ex9WrFoQhdiA00v3D9LectwzTGN4Dr7Y2vBrNTGBWe+CepMLoCsD1W3AOVkozHEN5Kd1FO0xBllKGJUURQ2C76jlSqueVXI/ekk3RdqZ23xtAGHi1TcSf/QlmT1t42K1rorj+dVM4sKklsI+JqIlhGNQnoliGEtYp2C5RU3UesT2p/o4Sdzn/tLk4rhd6vK7nEXRSHa82tqMJdY8mTD+axLpcLndK16DRaEbOrDbg/ZGAWZZk1L+UZjiGLP8p7wmJMtxAhTIXQlAouWxr7cJxvalTWAvwXGJbf0nshYcQnlplSCEonX0VhfNeP+v2uvtjQLi1IoAl9SkW1yU50JmlK1/C85QAy6LaRJh49pHXnM3nfvVymJgGyuM1gSvHqY3tWELdIw3Tn0pinUajmRi0AaesWQTKAy86LjHLHFVizlBeSl08Qk/RDsuFyvfUg2YXRcfDxQNpYLvelJWKRfa/iJduJL75h1jth8KH3Lr55C+7Fbdp2VSMbFphiL5FWPmCT/jqaUXH5XimwJK6JDesXVLh0ZZ7vO29WfJdBn95zQZOa2ocl7FNZA257nam0Uw/Zr0BLzgujm80o6ZBZ65IV75EfSLKkvrkqBJzBvNSmmsTtPTmkYjQYAcIIYiYBp7nsaAuietJMlMhkRpkmxd6iT/z3xglFQaVQlBc+xqK61+vdM5nOQJA+lnnqO9vfrqyo1rMMllUl+T6tUv4wCCebeDx5nI5tm/fztz02DuyDVbrPR415MOhu51pNNOHWW/Ag6SjiN+AIgg+duSKCCFG5a0M5aVcumweD7x4gN6iEzancDyVtSyBZDTCwtoEO070IGXQ3XuSKC8VEwb2snOJ7XwSt75Zed3zdF1vQPCdeNLDkwaNiWioTV7ORCd0DVfr/eoz5vOalc38bu8JHerWaGY4s96AR02DqGUOSMdX2cFjM6P9vRTX86iJR9i0s4Uj3Vnac0UKjochVFJRruTwzMG2ydc3L2SJ7nwy1C+X0QTFNVfgLFqNs2QtmLPn52GIvgS14YiYhlrkCVjq1+6XM14JacMxXK33r3Yd4+pVC/jWn1ymQ90azQxnVpeRGQLiloksq7eWAELQkIiGbRtPlcAz/87bL8M0VMIagOupBhclT0668TZa91Lzo88R3fkkSFmhsOYsWz+rjHfQWMQyBAYVOYfh3wWqd/eS+iTnL5nDnGQMx/VOWoI13oy01hvgg5efxcevPYcPXH6WNt4azQxk9szSg+BJNfEKITAMQW3UImKZJ23bONaWiv/30W0c6szhoeq9pyRZzXWIPf9zYlt/hfBH4Pa0kd/4R5UKa7MAQ8CiugRL6pIc7s4jhKC3YJMplih5anUrUYa9OR1n+bza0KAvqU9x9cpm0vHopHq5Y2lEotGUE8xfbb1Z8l0d/OWSAkuTs+venynMagMOhHrjrq89vrisfWP/kpvRtFTsb+SvWdnMvc/sxXa9sG/0ZGO0HSL52Dcwu1rDY16iluKZG2ed8Q44Y04Nj/z5Ndy9aWuYfLivPcOJbBGQJKMWZ8xJ05m3OdCRwTQN5vsLPA8x6UZSy5pqxkr/+UtKSSaT4bkHn+GqlQt0S9gqZNYb8KLjqj1wIejKl8ISMhiYjDQSnem7rl43qJH/4m+30eWrvo1kr3VccR1iL/yC2NZNCKlqkCXgLDyT3OVvh0TNJA9oemAJwYGOLO3ZYkXy4S92HGX3iR6aauIc686zu60XvzsoEmjPFqiLRXjT2Ysnfcxa1lQzVvrPX64M5ifdErZamfXLLduV5EoOBcfFk5KjPXkOdWXZ295LxOqbJEe693jXz55j085jyDKVN9XQok/jfDIx2w6S/vEXiG95ODTebs1cimdfRe7Kd81a4y1Qfbu78iW++uROoC/58L63XcJpDSmOdeeV1K6s3AtHQlfBpqVn8lXHtKypZiyMpiWspnqY9R64ZQpcr68ZRbEnTzxi0pCIkiu53PrNx7hi+XzSschJ9x5ztsMPXzrE4rpUeExKyYtHOyn6Eq2TidHVSuqn/9jndQuD4vrXUlx3tdJvnWG13QaQiBjkbY+hPm0DZbgBHE/iepInDrRVPKepJsEFi+fw7OF2DAZ+356UNCZjPHe4c8RSu+PFePUL18wudO7EzGTWG3BQWtWOpyb02kSE1fPrwiS2IDxumcZJ9x5PZIs4/Qz1nrZeeopTsx/p1c/HXnoO0f0v4DYuJvfqW/EaF03JWCaSmGUQMw2KrkfBVe7yYN07+xcGSgm2Jwc+EWiuTdAQj9Ltd6oLmt0IIWhMxljakJqyCU/LmmpGi86dmJnMagMuUUYbqcq4BFATswaIc1iGweGuHHOSURKRvo8skM10PemXhznEowMfn7Q9b9cBw1StzgDsIu6cJdilAvlXvQVZ1zRJA5k8hF8KGIS2VXtPD8f1cPu1Y5VAefRZdRDzuHTZvAHn7S3anD6npuI7tgyhKhT8HAnB4FUKE42WNdWMFp07MTOZ1QYcVIi73AHrHsJbnpuK0ZYtsLjOQkoZNq4o986KnsccIejJF9nXkaVzEid38/h+Er/7NsVzr8VevqFPYc21cZuWETm6g1LtXBDVnfYQMURolC1DEDVNVQoIxCJKkCduGhQAKd0B9fXl05eUkIpb3LZx1YD3CSa8mGWypD414HGY+glPy5pqRspE6uRrpo5pP5s/9thjXHLJJfzVX/3VgMd+9rOf8drXvpZ169bxpje9iccff3zU5/fK+m4LoRqLlAbZr45bJovrVNj0QGeWjlzRf40IXzw3GaelN89zR7smz3g7JeKbf0Tq5/+E2d1K/OnvI3pO9MmjohTWSmdcUPXGG9R3lYqYpKMWccvE8L8z0xA0JqLhdxk1DVUi2M/hEKJ8AQDvvnDFoPvFOllMM5MIcieCTnj9cTyPy5c36dyJKmNaz+j//u//zqc//WmWLh2ox/3SSy/xkY98hA9+8INs3ryZd77znfzFX/wFLS0tp/SeEcOgNVMYcNyTkjedvZhXLZ1LV74UGu5y5bbeoj2pimpm6z7SP/oCsZd+hQgUwdKNRHf/vtJ4+wpr1Y5ACeAUHD8pz7/mRMSkNh5laWOahkQU/Nr+QFnN8PfETaGORQxBMmKy8fQmPvum8wd9Lz3haWYad129jqtXLQhzJqCvpa3OnahOpnUIPRaL8b3vfY/PfOYzFIvFise+//3vc/nll/OGN7wBgJtuuokHH3yQH/3oR7zvfe8b0/tJv3e3O4gHbhkGf3z+6dz//H5Wz6/jRLaI63pYpkFDIkpLT57uyfS6n/sZ0ZcfDdXUpGFSOOdaiMYRJVUKMtOMtyEElqmMczpqEY8o1byW3kKYuLasMU3JVf24Hdejt2jjeBLP84hZJumYRdQ0uXHtYj7zxvOHFa7QyWKamUT/3ImJaGmrmVymtQF/xzveMeRj27Zt4/LLL684tmbNGl566aUxv58EiraLkVSqRQGO63HVivmkTUlbb5aIIVhYE0dKyf7OLFs6s5NWJma27CHx+P2YPSf6xjdvKflX/RHW8f0z0vMuJ+o3E4laBgtqlfc7PxUFQ4TfmSlgYU1fm07bdWnLFnnj6gUsqktx07rFzE3HKRYGRlr6c8dlK3nH+iU8uOUQPUWbunh0VK8finw+X/HnTEFf1/QnbcJtG5aSz+fZv1+QMiW53ORrGkwU1f5dBXlVI2FaG/Dh6OzspL6+vuJYXV0du3btGtV5BH7Str8X7rgeSeGSy2ZDb+u8pgTXNUkef24Lj+84xq7OAkII2vI29iSWd0d2P0PisW/3ed2mReG8N1BacwXRXU/NSONd/jM2BUhP9W7PF4rkLIkjJRc3qySzp1syWIP88B0pec3CFH+8NArYnDi0jxMDnjU8VzUEoxnb64di//7943Sm6YW+ruphJl4TVPd1RaPRET2vag34UCuUka5cAgwh+tpReZJFdXHeftGqCm+rIRnj84++wuP7u8hi0VlUiW6TrarmLDoLGUsiilmcecvIX3YrXv189VjzCiL7X0BG4jPGeINaXAkhiBsCyzSQqvaLeCxGTSrJJafP484rVgPw+Udf4Yl9J7Bln4RtRIjwOSdLSptMlPezn2XLlpFIzJx9dH1d1cNMvCao/uvavXv3iJ9btQa8oaGBzs7OimOdnZ00No5uL8eTfWnoMcvg3Rev5MNXV+oBf+rhF/ntvhNYhkFbzp48SVQpKU+jloka8pfcjJHpoLTmSijbv/Xqm7FPPw8vXjNjjDeo/tvzUjHmxwSReJwTORvP83j3hcv5s0tWVdQ9f+qNF1RdbXQikSA5AztB6euqHmbiNUH1XtdonNCqNeDr1q3j5Zdfrji2detW3vjGN47pfKrfs8Vtr6qsCS7XEC65Hl2F0iDimuOPeWwX8Wd/Su7q25DxdHjcWbZe/cVzB7zGq5s/CSObPAwBC2oSLK5LkM/liJgmC2pMrl61gL8ZoumCro3WaDSzhWldRjYcN910E48//jg///nPKRQKfOMb3+DgwYPceOONozpP4ElHLIOamMXXnq7cQw80hAFaM4VhhRDGBbtI/MkHST/0z1gn9hN/6vuDPie64wnMEwcmeDATx0gWQamIRXNtAuGHw3W5i0aj0fQxrT3wdevURO04DgCbNm0ClKe9atUq/s//+T/cc889fOQjH2H58uV85StfYe7cuaN+H796jGWNqbAjT1DfW64h7Loenje40Mt4YB7dSfLx+zEyHeExI9cFdhEiMXUgUFgrZrEOb0MaJt6cyW9rORqCRMFySdmTrYNMASvmpmmuSXB2U1qXu2g0Gk0/prUB37p167CPX3vttVx77bWn9B6GnyRVcj22HuvinIUNFQ0qAklNgZJZ7S0NDF2fMnaB+OYfE9vRpyQnrSiFC66jdNZlfQpqZcYbVLa5l57+Bi3IExzx81F19z1Fh/WLGrnjshVs376duen4SV+r0Wg0s4VpbcAnAwkgJQJBb8khV3IqGlScv7CeuzdtobdgU5oAmTXz6A6Sv7sfI9uXkOc0ryB/6S14tWXRhEGM93TPNu/roa0kTQ3ksI1dArEWkERNgeN5urnCNCJIEOwulKiLR7nlvGVaiU6jmUK0AZdBCF01NdnbnqE2HqHkOLz+33/Fi4c7yJQc1XZynIlv/hGxl37VN5bBvG6oKuMdswxKjhdm6qsIh3pMIDDEQCMehNiFXyIW8Vu3Rk3T1xqf7II9TTmu53H3pq08uqc17CntSclPXj7EFcvnc9fV64ZVtNNoNBPDrDfgAYGJyJYcbjlvGa//91/x7KF2TCFCr3C8cRsWhH93mleSu+xtyJp+e/jT1HgL/38Rw8CVHgKBZQjWzq/jpdZuCo4Xbk+ErxGAFFhInLLzhJ+vb7xjpgFCcMPaxTTVJGaUSlQ1cvemrWzaeQzLMMJ8EEMIpIRNO48B8PEhqgI0Gs3EoQ14PwwBO1q7eOFwB54nybuu6hk+AdjLL6R0eDtu83JKZ14ysFvYNDTepgh6bkskkppYhFXzanFcj1TMImqZnGeZPHOwTb3AzxAMStojpiBmWRQdlxWNaY70FMg5DgLVZMQwDCRw/uJG7n7j4I1GNJNHeRnlYFiGMSDxU6PRTA7agPcjYsA1/7YJe5xttnV4G9axXRQuvKHvoBDkr3znkK8RdhHhqCYu08F4RwSkYmpPOmaCEAapiEnMMohaBlJK8iUnbBrSW3AwDDANgYHwQ+PgSsnFS+dy1YpmfrvnODnb4US2iOO4RC2TG9ct4TNvOG/IsKzei508gjJKYxhxCcfzKhI/NRrN5KANeD96SuNcIlbMkdj8Q6K7ngZUgpqz5OwRvVQmaymtuIjIwZconX7elBlvA+U9G4ZgTipGUzrO8UwBx/M4b9EcVs5N8+SBNj/Eql5z7sJGthztpLdoIyXEIqbfwlBwwZI5/M9tryFqWaNSTnM9yaceflHvxU4i5WWUQ2EIUZH4qdFoJgdtwCcQ69DLJJ74LkauOzwW2ff8iA04gEzW+eH1ydXxFmV/GoYIO+Q0pePELJMl9SkMIfjU687lAz/cPCDEagjB+kWNZIo2+zszrJpXx/x0nE+9/lzOam4Inzca5bTPP/pKKGmr92Inh6CMcjgj7kmpqwU0milAG/CJoJgj8fQPiO7ZHB6SkRj5i/4Qe+Wrhn6dXcRsP4Q7f3mlwZ5k4w0qmxyoyL6fm4wRs0xAhU2vXrWAR3a1DBtiTccirJlfz43rTjulEGtH3ubxfV16L3aSueW8Zfzk5UPDKhBahuFXC2g0mslExxvHGevgS9T892crjLe9aDW9N34Me9XGoY2xn7BmHduFdXgbE6/ZejIEMcvE8r3vdMxi+dya0BsLJE0nK8T6iwM9OCf5TIK9WM340VST4Irl83G8wbeWHM/j8uVNetGk0UwB2gMfL5wSiSe+S3TP78NDMpogf9GN2CsuHt6L7pdtbvScAKfUJ586wfQvkjOA5po4UkqiVpxrzlzAGXNqyBSdAfvUkxVizdie3oudIgLt+d/uOY7jeWHugWUYWpteo5lCtAEfL8wIIp8J/2kvXkP+krciU/XDv26oUrFxNN4GjKoFal08wh+du5RFdcmTtuOcrBBrOmLgSRdzmOfovdiJwTQMPn7tuVXXqlWjmeloAz5eCEH+0rdi/vyLFM5/A/byC0++dz3Bdd4GhAbN9TzytosbKs8p+ht20xA0JKM8e7iddMyiMTn8QiIIsQZCH/0J9spPNcT62qW1PNfVNexz9F7sxKJbtWo00wttwMeIdeBFZCSBu7Cvf7hMN9L7Rx8HYzg/0WcSRFrS0b5xmIZBIgJFxyMZtfCAou1guxIPJWATMQ0SlklzbXJUmd0TFWINPL623iz5rh7OW9jA7490TuhCQaPRaKoFbcBHiShkiD/1PaL7nsdL1dN748cgWtYla5oY77qYhTCUuEqwx20YBqc1JpifTuBJydHuHK2ZglJBMw2EgPpElKipDORIM7vHO8TaX3tbSkkmk6Em7WKZJgIq6sD1XqxGo5mNaAM+Cqz9L5B48kGMgtrrNrJdRPdspnTWq0d1nsihlybMeAsgHbMQQrBiTprOgo3relimwYKaBPe//dV87eld/HbPcUyjT3dcCEF9PMLSxnTF+UajsjVeIdb+2tuunyQnETiux6uWzmVhXUrvxWo0mlmNNuAjQOR7lde9/4XwmBdLUrj4LdhnXDDq89lLziZayIL0xs14GyidcvzmK1JKOvMlltSnAGWI/2BVM6YhqI1HOX9xI0e7c9QmosQsk6Z0PPS8K847yZndI9Hefu5wJ3dcebYOl2s0mlmNNuDDISWRfc8Tf+p7GL7HDGCfto78xpuRydqxnTcSp7TyIvC8UzbeQYZ51DJpSsew7RJ5F6QQuJ4MQ8yvWdmMJ+HWbz4Whp9LrkdPoURDIkZkEOMNk5/ZrbW3NRqNZmRoAz4EIt9L4skHiBzYEh7zYikKr3oL9unnj04dzS6CaVXuj0fiQz9/qDH5/0Utg4hpYLsqhzwZtTizqQ5LQC4LkXic49kSK+fV8rozF3LL+cv4tyd2DmgJOb8mTkeuSGe+BB0ZlvULn8PkZ3Zr7W2NRqMZGdqAD4VTxDrySvhPe+k55DfehEyM0uv2E9ZkNK7C7SNJcuuHISBhmURMg1TUYm1zHTvbejENoyL07fpqWRHTZFlDmvvedglNNYkhw9Ixy6Q+EaUjV6SrYFNyvYow+lRkdmvtbY1GoxkZ2oAPgayZS+GC64i98BCFjTdhL1s/ek3ysmxzUcxiHXoZZ+k5J33ZvKRFKhbra6INWIZqJBKPWNx362V85cmdQ9deux5/UGZ4hwtLL21Qe+Rd+RItvXlOq09NWWb38d48vYUSh7qyGIbB/CH25XW9t0aj0WgDrpASa/8LOIvXVCiglc66DPuMC5Dx1OjPOUipmNO8YsinC2BxXQJDCAxD0FyT4HimgOtJTEMwLx3HMESoOz1U7bUhBFetmF9heIcLSwshWNaYpui41CaibFw6b9IzuweWjSlj3p4tUB+Psriubxy63luj0WgUs96Ai1w3iSceIHLoJYpnXU7hVW8pe9AYN+M9VLZ50Gs7YhoYhmBpQ5qDnVleae0Oy6c8KenIFjl3cSMfeY1qRTpY7XXShHPjOS4576yKvtgjCUtHTIPXnrlwShLD+peNlUcFOnJFPCmZE5GYwJW63luj0WiA2W7A7SI1//1ZRCkPQGz7bymtvhSvvvmUzjlS4x302gYoOR49eYcDMoNhGJzVXE9rphDWcDel4ziux+d+9TLv27iK+5/fT3ehRF08yi3nLaOpJkEul2P79u0D3mei9MqDxUP/cYz2HP3358ujAsf9z+Di5hQfecNFnNbUOKrzazQazUxlVhtwo5AJjbcXT5O/5OZJM97g65BLiRACIaDk2HQWJGub64maBkvqKl8npeTrm/fy8+1HaMsW8TyJYQh+sOUAV69awIcuGTxEP9565f1D3kGU4CcvH+KK5Sp8bw5Rx92f4fbnY5bJkvoUtuuSiJjMTY8+c1+j0WhmKrPagAeUzriAwsVvGVu4PMA33hE7h2kaFM0YzoqLsBJJXG/oTmCuBCElEUNQcCWWdGnNFAZN4Np9oodjmQKHurJYhiBiCAzDoCNX5EBnlmLJ5g8XDB4mH0+98v4hb8AXjxm5fnrASMvGsvbg/ag1Go1mtjKrDbgUBtnXvBdn6TjsqZoWMhrHKWaxo3HsFRchY0m8EdgdCZQ8iQEgoC1TCBO4ljamQUr2tGc42lvwFwIS25XYLkRMScw06CnYfPv5g1xRv3Dw4Y2TXvlIlNJGop8eMNKysVRk9OV3Go1GM5OZ3QY8VTc+xhvAMLHPuADr0Mu4zSuQI1BYC5qM+Aqo+NvhqtWnJBRYATieyVd48cI3eLarVghxy6QrX+S/d3dy6flDv+ep6pWPt1LaSPbnI0Lw2qVjVL3TaDSaGcrINipnKmLkl19urgyh+mb3t2HCMHGXnjNieVRDiPC8KqSt6r3L37M9V6QjX8KTfcdE2RsLIbBdD9fzkEKwpS0/4msaC+OtlBbszztDhCocz+OS0+fRmNDCLRqNRlPO7Dbgw2AZELcMIkI1CYlHTOKWSTpmEbVMLKdEZO9zCKdQ8TrJ0Pvd/fGkVB64UK+JGIKoYeJJiet5FByXXMkhW7RDD1WIysVEgO1JJfwy0jcfI0HIezhGq5R219XruHrVgnBfPjiHIQRXr1rAnVesPqUxazQazUxkVofQh8IQYApB3DKpr0kgpaQjX6I+EaW3aGM6RexdTyNzvZDPUFp5EWIM2uaBGRSohLSYZVIbt+gu2PSWnH72WP1NDOL9Cj+BTAjBurkTK3AyESVpJ9ufz+Vypz5wjUajmWFoA+5jCGV4lOcHqWiE1U21WKaBKQSn+57vnqMn6H7lKUS+V3nP0iPIVAv2s4fzTwfuewuSUUt1FZOSnoKjkraiEUquh+25CAmJqEWm5OD5ymzlSF9ytSEe4c0rGybg0+ljvEvS+p9bdxjTaDSakTHrDbiBElNJWCaGIWhIRFlcn+KaVc1IRIUnuK+ljb/5x3/lhYjLccvEtmLIlReDGUeiFgEniS6HCAHNNQlyJSc8JoXA8dywhjpuGUSkQAiBAURNg5LrqQWCL5MevN+cZJS3nbeUxsQo9dp9RiPKMp4laRqNRqMZG7PegAtDhZ/zjks6ZrGwLsHVq5r52351zJlMhk0/+W/OaYhwzoXLKYgo3841U7JiHOvJh4IsHnKAERdA0hIUPYnjqX/XxiKsmlfL/o4MnfkSUkpMw8ArC5FLYE5SabN35IrELTM8X+DLS6k81/dcvIIPXbKCnTteYTSMRZRlvErSNBqNRjN2ZrUBF6gENdM0sHxDHrXMAR5kJpPh/vvvp729HYC6ujpuv/VWzKf3s2nnMQwhaOnN43oSgUAgy/a3/f7dlokpJSW/7OvMJlUWtaQh5WezK++7txAktqmQeFAHDkobPG4a1CaiREwDKSVnNtXxH2/dSHNtckx7xaciyqJD3hqNRjN1zGoDbhmCdDSC6yuhLWtIYSBozxbD8PFgxvvWW2+lrq4uNPSP7mkFKekq2BRsF8fPPktGLep9Y+t5Ess0aExGufyMJhbWpSo8Vynh9u89zfOH24lYZkWfb8q0wVt7C6ycV8PrVi86ZY93vEVZNBqNRjN5zGoD7ngy3MO1XY/d7Rnq4kW+9dw+/uqKNQBs2bJlUOMNfaHk9/mh5GPdOXa19bJ8ToqXW7vpLTiYhqjYI758edOQWuH/+kcXc+s3HxtyHz1mmSxrTHPfLZeOi0Edb1EWjUaj0Uwes9qAQ19ZVvBnZ77ET7cdDg34xo0byWQy7Nmzp8J4lzNUKHm0e8QTmeE9GOMtyqLRaDSayWPWG/D+CCE43JULw8ZCCK655houvfRSUqnRNTsZyx7xZGZ4j1SHfDSiLBqNRqOZHLQB749TpN6TFWFjIcSojfdYmcwM74nqE67RaDSaiUcb8DKkXSCx5/dkI5KjK5qBqdv3nYwM78kO2Ws0Go1m/NBa6Pj65U6RxJ7fU0sR1y5xdMtTSuFshnMyHXItyqLRaDTTk1ntgRtCUB+PYHk21oEXgCIAViLF39/+7kF1x2caWpRFo9FoqpOqNuCHDx/m7//+73n22WdJJBK8+c1v5o477sAYoq65P5aARUmT7hcfx8mrvtsiluS6t9zEisXNEzn0aYcWZdFoNJrqomoNuJSS97///axYsYJHH32UtrY2brvtNubOncu73/3ukZ6FjucfReYzqp1nIsV1b7mJ/33Dxokcukaj0Wg0p0zVGvCtW7eyY8cO7rvvPurq6qirq+O2227jvvvuG7EBj3s2y5PgJNLU1dXx6b/6c5Yvml2et0aj0Wiqk6o14Nu2bWPRokXU19eHx84++2z2799PJpMhnU6f9BxSely4pJG5c+cOKdKi0Wg0Gs10pGoNeGdn5wCDG/y7s7PzpAbctm1SqRRXXXUV6XSaAwcOTNhYJ4sga37Xrl0zKgFvJl7XTLwm0NdVTczEa4Lqvy7btkc87qo14Kf6xQghME1zRnndQgii0ehUD2PcmYnXNROvCfR1VRMz8Zqg+q9LCDHz8vweXwAAC1xJREFUDXhjYyNdXV0Vxzo7O8PHTsZ55503EcPSaDQajWZSqFohl3Xr1nH06NHQaIPqHLZixYpJkz3VaDQajWaqqFoDftZZZ3HOOefw6U9/mp6eHnbs2MFXv/pV/viP/3iqh6bRaDQazYQjZBXrhba0tPB3f/d3PP3006RSKW699Vbe//73T/WwNBqNRqOZcKragGs0Go1GM1up2hC6RqPRaDSzGW3ANRqNRqOpQrQB12g0Go2mCtEGXKPRaDSaKkQbcI1Go9FoqpBZZ8APHz7Me97zHtavX8/GjRv5whe+gOd5Uz2sMfHYY49xySWX8Fd/9VcDHvvZz37Ga1/7WtatW8eb3vQmHn/88SkY4eg5fPgwt99+OxdddBEbN27kzjvvpLu7G4Dt27fztre9jXPOOYfLL7+ce++9d4pHOzJeeeUV3vWud7FhwwZe9apX8cEPfpDjx48D8OSTT3L99dezbt06rrnmGn784x9P8WjHxt13382ZZ54Z/rtar+vMM89k7dq1rFu3LvzvU5/6FFC91xTw5S9/mcsuu4zzzjuPd73rXRw6dAio3uvavHlzxfe0bt061q5dG/4Oq/W6RoWcRXieJ2+44QZ5xx13yK6uLrl792551VVXyf/8z/+c6qGNmq9+9avy2muvlW9729vkhz70oYrHtm7dKs8++2z5s5/9TObzefnAAw/Ic889Vx47dmyKRjty3vSmN8mPfvSjMpPJyNbWVvnmN79Z3nXXXTKXy8lLL71Ufu5zn5OZTEY+//zzcsOGDfIXv/jFVA95WIrFoty4caP853/+Z1ksFmV7e7v8kz/5E3n77bfLlpYWee6558qvf/3rMpfLyV/+8pdy3bp18sUXX5zqYY+Kbdu2yYsuukiuWrVKSimr+rpWrVolDx06NOB4NV+TlFJ+61vfkm95y1vk4cOHZVdXl/zoRz8qP/nJT1b9dfXnX/7lX+QHP/jBGXddQzGrDPiLL74oV69eLTs7O8Nj3/72t+W11147dYMaI1//+tdlT0+P/MhHPjLAgH/iE5+Qt99+e8Wxm266Sf7bv/3bZA5x1PT09MiPfvSjsq2tLTz2zW9+U15zzTXy5z//ubzooouk4zjhY1/4whfkn/7pn07FUEdMV1eXfOCBB6Rt2+Gxb3zjG/Kaa66R//7v/y6vv/76iud/6EMfkh//+Mcne5hjxnVdedNNN8kvf/nLoQGv5usayoBX8zVJKeVrXvMa+cILLww4Xu3XVc6RI0fkRRddJI8cOTKjrms4ZlUI/WQ9xKuJd7zjHdTU1Az62LZt2zj77LMrjq1Zs4aXXnppMoY2ZmpqavjsZz/LnDlzwmNHjx6lsbGRbdu2sXr1akzTDB+rhmuqq6vjpptuwrIspJTs3buXH/zgB7z+9a+v2u+pnO985zvE43Guu+668Fi1X9c999zDZZddxmWXXcbHP/5xstlsVV9Ta2srLS0tHDhwgGuvvZaLL76YD33oQ3R2dlb1dfXnH//xH3nLW97CwoULZ9R1DcesMuAn6yE+U+js7KxYpIC6zo6OjqkZ0BjZunUr3/jGN7j99tsH/e7q6+vp6uqqihyGI0eOsHbtWt7whjewbt06PvjBDw55TdXyPbW1tfEv//IvfOITn6g4Xs3XFeTGPPTQQ3z961/nhRde4BOf+ERVX1NLSwtCCDZt2sR3v/tdfvjDH3LkyBE+/vGPV/V1lbN//342bdrEe9/7XqC6f4OjYVYZ8Gps7j4WhrrOarr+Z599lve85z3ccccdXHHFFVU19sFYtGgRL730Eg899BB79+7lr//6r6v+e/rsZz/LzTffzBlnnFFxvJqv67vf/S4333wz6XSa5cuX8+EPf5if/vSnOI4z6POr4Zps28a2bf76r/+ahoYGFixYwAc+8AE2bdpU1d9VOd/61re45pprwlbSM+W6TsasMuCn2kO8WmhoaBgQUejs7Kyaa/zVr37Fn/3Zn/E3f/M3vPOd7wSG/u4aGhowjOr4GQshWLZsGXfeeSc//elPsSxr0Guqhu/pySef5KWXXuLP//zPBzzW0NBQtdfVn8WLF+N5HoZhVO01BdG4dDodHlu0aBFSSmzbrtrrKucXv/gFr3vd68J/z6Tf4HBUx8w3TsyWHuLr1q3j5Zdfrji2detWzjnnnCka0ch57rnn+OhHP8oXv/hFbrjhhvD4unXr2LFjR4UntGXLlml/Tc888wxXX311xbiDkP8ll1wy4HuqhmsC+PGPf0xLSwuXX345F198MW9+85sBuPjiiznzzDOr8rq2b9/O5z//+Ypj+/btIxqNcuWVV1blNQEsXbqUdDpdMf4jR45gWVZVX1fArl27OH78OBdddFF4bLA5sNqua0RMdRbdZHPzzTfL//W//pfs7u6Wr7zyirz00kvlt771rake1pgZLAt9x44dct26dWEZ2X/913/J888/X544cWKKRjkybNuWr3/96+WDDz444LFisSivuuoq+Q//8A8yk8nIp59+Wq5fv17+5je/mYKRjpze3l55ySWXyH/4h3+QuVxOtre3y/e85z3y1ltvlW1tbfL888+X9957r8zlcvJ//ud/5Lp16+T27dunetgnpaurSx47diz87/nnn5erVq2Sx44dk0eOHKnK62ptbZXr16+X9957ryyVSnLv3r3yjW98o/zMZz5T1d+VlFJ+9rOflTfccIM8duyYPH78uHzrW98qP/axj1X9dUkp5Y9+9CN55ZVXVhybCdc1EmadAT927Ji87bbb5DnnnCM3btwov/SlL031kMbE2rVr5dq1a+Xq1avl6tWrw38H/OIXv5DXXnutXLt2rbzhhhvk5s2bp3C0I2Pz5s1y1apV4bWU/3f48GG5c+dO+ba3vU2uW7dOXnnllfLb3/72VA95RGzbtk2+853vlBdccIG8+OKL5Qc+8AHZ0tIipVTXfP3118u1a9fKa6+9Vj788MNTPNqxcejQobCMTMrqva5nnnlG3nzzzXL9+vXyqquukl/4whdksViUUlbvNUmpFsCf/OQn5YUXXigvvvhi+bGPfUz29vZKKav7uqSU8mtf+5q88cYbBxyv9usaCbofuEaj0Wg0Vcis2gPXaDQajWamoA24RqPRaDRViDbgGo1Go9FUIdqAazQajUZThWgDrtFoNBpNFaINuEaj0Wg0VYg24BqNRqPRVCHagGs0Go1GU4VoA67RaDQaTRWiDbhGoxkTb37zm7njjjsGHP+///f/ctVVV1EoFKZgVBrN7EEbcI1GMybWr1/P1q1bK44dOnSIe++9lw9/+MPE4/EpGplGMzvQBlyj0YyJ9evXc+DAAbq7u8NjX/jCFzj77LN54xvfOIUj02hmB9qAazSaMbF+/XqA0At/5plnePjhh7nrrrumcFQazexBG3CNRjMmTjvtNBobG9myZQue5/GZz3yG66+/nnPOOWeqh6bRzAqsqR6ARqOpXs4991y2bt3Kgw8+yMGDB/nqV7861UPSaGYN2oBrNJoxs379er7+9a/zwgsv8J73vIf58+dP9ZA0mlmDDqFrNJoxc+6559LR0UEsFuO9733vVA9Ho5lVaAOu0WjGTGNjIwB33HGHLhvTaCYZIaWUUz0IjUZTnXzwgx+ktbWV73znO1M9FI1m1qH3wDUazagoFArs3LmTX/7yl/zyl7/ke9/73lQPSaOZlWgDrtFoRsXTTz/N+973PhYtWsQ999zD6tWrp3pIGs2sRIfQNRqNRqOpQnQSm0aj0Wg0VYg24BqNRqPRVCHagGs0Go1GU4VoA67RaDQaTRWiDbhGo9FoNFWINuAajUaj0VQh2oBrNBqNRlOFaAOu0Wg0Gk0Vog24RqPRaDRViDbgGo1Go9FUIdqAazQajUZThfz/uK0xMlKVr2UAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 800x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot Metrics for Lasso Regression\n",
    "plot_model(lasso, plot='feature')\n",
    "plot_model(lasso, plot='error')\n",
    "plot_model(lasso, plot='learning')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_7ed85\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_7ed85_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
       "      <th id=\"T_7ed85_level0_col1\" class=\"col_heading level0 col1\" >MAE</th>\n",
       "      <th id=\"T_7ed85_level0_col2\" class=\"col_heading level0 col2\" >MSE</th>\n",
       "      <th id=\"T_7ed85_level0_col3\" class=\"col_heading level0 col3\" >RMSE</th>\n",
       "      <th id=\"T_7ed85_level0_col4\" class=\"col_heading level0 col4\" >R2</th>\n",
       "      <th id=\"T_7ed85_level0_col5\" class=\"col_heading level0 col5\" >RMSLE</th>\n",
       "      <th id=\"T_7ed85_level0_col6\" class=\"col_heading level0 col6\" >MAPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_7ed85_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_7ed85_row0_col0\" class=\"data row0 col0\" >Linear Regression</td>\n",
       "      <td id=\"T_7ed85_row0_col1\" class=\"data row0 col1\" >2.9657</td>\n",
       "      <td id=\"T_7ed85_row0_col2\" class=\"data row0 col2\" >21.3433</td>\n",
       "      <td id=\"T_7ed85_row0_col3\" class=\"data row0 col3\" >4.6199</td>\n",
       "      <td id=\"T_7ed85_row0_col4\" class=\"data row0 col4\" >0.7303</td>\n",
       "      <td id=\"T_7ed85_row0_col5\" class=\"data row0 col5\" >0.3844</td>\n",
       "      <td id=\"T_7ed85_row0_col6\" class=\"data row0 col6\" >1.5128</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fc9cc9cb1c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_99772\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_99772_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
       "      <th id=\"T_99772_level0_col1\" class=\"col_heading level0 col1\" >MAE</th>\n",
       "      <th id=\"T_99772_level0_col2\" class=\"col_heading level0 col2\" >MSE</th>\n",
       "      <th id=\"T_99772_level0_col3\" class=\"col_heading level0 col3\" >RMSE</th>\n",
       "      <th id=\"T_99772_level0_col4\" class=\"col_heading level0 col4\" >R2</th>\n",
       "      <th id=\"T_99772_level0_col5\" class=\"col_heading level0 col5\" >RMSLE</th>\n",
       "      <th id=\"T_99772_level0_col6\" class=\"col_heading level0 col6\" >MAPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_99772_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_99772_row0_col0\" class=\"data row0 col0\" >Ridge Regression</td>\n",
       "      <td id=\"T_99772_row0_col1\" class=\"data row0 col1\" >2.9778</td>\n",
       "      <td id=\"T_99772_row0_col2\" class=\"data row0 col2\" >21.3486</td>\n",
       "      <td id=\"T_99772_row0_col3\" class=\"data row0 col3\" >4.6205</td>\n",
       "      <td id=\"T_99772_row0_col4\" class=\"data row0 col4\" >0.7302</td>\n",
       "      <td id=\"T_99772_row0_col5\" class=\"data row0 col5\" >0.3830</td>\n",
       "      <td id=\"T_99772_row0_col6\" class=\"data row0 col6\" >1.4887</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fcae509cdc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_7017f\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_7017f_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
       "      <th id=\"T_7017f_level0_col1\" class=\"col_heading level0 col1\" >MAE</th>\n",
       "      <th id=\"T_7017f_level0_col2\" class=\"col_heading level0 col2\" >MSE</th>\n",
       "      <th id=\"T_7017f_level0_col3\" class=\"col_heading level0 col3\" >RMSE</th>\n",
       "      <th id=\"T_7017f_level0_col4\" class=\"col_heading level0 col4\" >R2</th>\n",
       "      <th id=\"T_7017f_level0_col5\" class=\"col_heading level0 col5\" >RMSLE</th>\n",
       "      <th id=\"T_7017f_level0_col6\" class=\"col_heading level0 col6\" >MAPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_7017f_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_7017f_row0_col0\" class=\"data row0 col0\" >Lasso Regression</td>\n",
       "      <td id=\"T_7017f_row0_col1\" class=\"data row0 col1\" >3.1960</td>\n",
       "      <td id=\"T_7017f_row0_col2\" class=\"data row0 col2\" >24.5721</td>\n",
       "      <td id=\"T_7017f_row0_col3\" class=\"data row0 col3\" >4.9570</td>\n",
       "      <td id=\"T_7017f_row0_col4\" class=\"data row0 col4\" >0.6895</td>\n",
       "      <td id=\"T_7017f_row0_col5\" class=\"data row0 col5\" >0.4153</td>\n",
       "      <td id=\"T_7017f_row0_col6\" class=\"data row0 col6\" >1.9434</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fc9c76ec550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VendorID</th>\n",
       "      <th>tpep_pickup_datetime</th>\n",
       "      <th>tpep_dropoff_datetime</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>RatecodeID</th>\n",
       "      <th>store_and_fwd_flag</th>\n",
       "      <th>PULocationID</th>\n",
       "      <th>DOLocationID</th>\n",
       "      <th>payment_type</th>\n",
       "      <th>...</th>\n",
       "      <th>mta_tax</th>\n",
       "      <th>tip_amount</th>\n",
       "      <th>tolls_amount</th>\n",
       "      <th>improvement_surcharge</th>\n",
       "      <th>total_amount</th>\n",
       "      <th>congestion_surcharge</th>\n",
       "      <th>airport_fee</th>\n",
       "      <th>PU_DO</th>\n",
       "      <th>duration</th>\n",
       "      <th>prediction_label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14000</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-01-15 20:50:18</td>\n",
       "      <td>2022-01-15 21:04:55</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.56</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>186</td>\n",
       "      <td>141</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>16.299999</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>186_141</td>\n",
       "      <td>14.616667</td>\n",
       "      <td>13.217827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14001</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-01-15 03:27:12</td>\n",
       "      <td>2022-01-15 03:38:24</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.25</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>79</td>\n",
       "      <td>90</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>3.45</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>17.250000</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>79_90</td>\n",
       "      <td>11.200000</td>\n",
       "      <td>10.910857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14002</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-01-10 10:29:47</td>\n",
       "      <td>2022-01-10 10:55:52</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.10</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>143</td>\n",
       "      <td>162</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>16.799999</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>143_162</td>\n",
       "      <td>26.083334</td>\n",
       "      <td>13.397337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14003</th>\n",
       "      <td>1</td>\n",
       "      <td>2022-01-25 08:29:21</td>\n",
       "      <td>2022-01-25 08:36:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>229</td>\n",
       "      <td>237</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.45</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>12.250000</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>229_237</td>\n",
       "      <td>6.650000</td>\n",
       "      <td>8.292283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14004</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-01-07 16:33:06</td>\n",
       "      <td>2022-01-07 16:36:26</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>264</td>\n",
       "      <td>264</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.86</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>11.160000</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>264_264</td>\n",
       "      <td>3.333333</td>\n",
       "      <td>7.309864</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",
       "      <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>19995</th>\n",
       "      <td>1</td>\n",
       "      <td>2022-01-08 00:31:27</td>\n",
       "      <td>2022-01-08 00:33:46</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.30</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>246</td>\n",
       "      <td>68</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>7.300000</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>246_68</td>\n",
       "      <td>2.316667</td>\n",
       "      <td>5.140245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19996</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-01-13 20:03:37</td>\n",
       "      <td>2022-01-13 20:11:56</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.34</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>113</td>\n",
       "      <td>170</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.26</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>13.560000</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>113_170</td>\n",
       "      <td>8.316667</td>\n",
       "      <td>9.732448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19997</th>\n",
       "      <td>1</td>\n",
       "      <td>2022-01-10 16:45:42</td>\n",
       "      <td>2022-01-10 17:00:02</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.80</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>161</td>\n",
       "      <td>79</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>16.299999</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>161_79</td>\n",
       "      <td>14.333333</td>\n",
       "      <td>13.026214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19998</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-01-15 03:41:13</td>\n",
       "      <td>2022-01-15 03:41:17</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>233</td>\n",
       "      <td>264</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.95</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>-4.750000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>233_264</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>6.187525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19999</th>\n",
       "      <td>1</td>\n",
       "      <td>2022-01-03 12:41:30</td>\n",
       "      <td>2022-01-03 12:47:28</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.40</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>237</td>\n",
       "      <td>262</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.95</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>11.750000</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>237_262</td>\n",
       "      <td>5.966667</td>\n",
       "      <td>8.185906</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6000 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       VendorID tpep_pickup_datetime tpep_dropoff_datetime  passenger_count  \\\n",
       "14000         2  2022-01-15 20:50:18   2022-01-15 21:04:55              1.0   \n",
       "14001         2  2022-01-15 03:27:12   2022-01-15 03:38:24              1.0   \n",
       "14002         2  2022-01-10 10:29:47   2022-01-10 10:55:52              1.0   \n",
       "14003         1  2022-01-25 08:29:21   2022-01-25 08:36:00              0.0   \n",
       "14004         2  2022-01-07 16:33:06   2022-01-07 16:36:26              1.0   \n",
       "...         ...                  ...                   ...              ...   \n",
       "19995         1  2022-01-08 00:31:27   2022-01-08 00:33:46              1.0   \n",
       "19996         2  2022-01-13 20:03:37   2022-01-13 20:11:56              1.0   \n",
       "19997         1  2022-01-10 16:45:42   2022-01-10 17:00:02              2.0   \n",
       "19998         2  2022-01-15 03:41:13   2022-01-15 03:41:17              5.0   \n",
       "19999         1  2022-01-03 12:41:30   2022-01-03 12:47:28              1.0   \n",
       "\n",
       "       trip_distance  RatecodeID store_and_fwd_flag PULocationID DOLocationID  \\\n",
       "14000           2.56         1.0                  N          186          141   \n",
       "14001           2.25         1.0                  N           79           90   \n",
       "14002           3.10         1.0                  N          143          162   \n",
       "14003           1.00         1.0                  N          229          237   \n",
       "14004           1.00         1.0                  N          264          264   \n",
       "...              ...         ...                ...          ...          ...   \n",
       "19995           0.30         1.0                  N          246           68   \n",
       "19996           1.34         1.0                  N          113          170   \n",
       "19997           2.80         1.0                  N          161           79   \n",
       "19998           0.00         1.0                  N          233          264   \n",
       "19999           1.40         1.0                  N          237          262   \n",
       "\n",
       "       payment_type  ...  mta_tax  tip_amount  tolls_amount  \\\n",
       "14000             1  ...      0.5        1.00           0.0   \n",
       "14001             1  ...      0.5        3.45           0.0   \n",
       "14002             2  ...      0.5        0.00           0.0   \n",
       "14003             1  ...      0.5        2.45           0.0   \n",
       "14004             1  ...      0.5        1.86           0.0   \n",
       "...             ...  ...      ...         ...           ...   \n",
       "19995             3  ...      0.5        0.00           0.0   \n",
       "19996             1  ...      0.5        2.26           0.0   \n",
       "19997             1  ...      0.5        0.00           0.0   \n",
       "19998             3  ...     -0.5       -0.95           0.0   \n",
       "19999             1  ...      0.5        1.95           0.0   \n",
       "\n",
       "       improvement_surcharge  total_amount  congestion_surcharge  airport_fee  \\\n",
       "14000                    0.3     16.299999                   2.5          0.0   \n",
       "14001                    0.3     17.250000                   2.5          0.0   \n",
       "14002                    0.3     16.799999                   2.5          0.0   \n",
       "14003                    0.3     12.250000                   2.5          0.0   \n",
       "14004                    0.3     11.160000                   2.5          0.0   \n",
       "...                      ...           ...                   ...          ...   \n",
       "19995                    0.3      7.300000                   2.5          0.0   \n",
       "19996                    0.3     13.560000                   2.5          0.0   \n",
       "19997                    0.3     16.299999                   2.5          0.0   \n",
       "19998                   -0.3     -4.750000                   0.0          0.0   \n",
       "19999                    0.3     11.750000                   2.5          0.0   \n",
       "\n",
       "         PU_DO   duration prediction_label  \n",
       "14000  186_141  14.616667        13.217827  \n",
       "14001    79_90  11.200000        10.910857  \n",
       "14002  143_162  26.083334        13.397337  \n",
       "14003  229_237   6.650000         8.292283  \n",
       "14004  264_264   3.333333         7.309864  \n",
       "...        ...        ...              ...  \n",
       "19995   246_68   2.316667         5.140245  \n",
       "19996  113_170   8.316667         9.732448  \n",
       "19997   161_79  14.333333        13.026214  \n",
       "19998  233_264   0.066667         6.187525  \n",
       "19999  237_262   5.966667         8.185906  \n",
       "\n",
       "[6000 rows x 22 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Predict on Test Data\n",
    "predict_model(lr)\n",
    "predict_model(ridge)\n",
    "predict_model(lasso)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_val = df_val.sample(n=20_000, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_ea815\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_ea815_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
       "      <th id=\"T_ea815_level0_col1\" class=\"col_heading level0 col1\" >MAE</th>\n",
       "      <th id=\"T_ea815_level0_col2\" class=\"col_heading level0 col2\" >MSE</th>\n",
       "      <th id=\"T_ea815_level0_col3\" class=\"col_heading level0 col3\" >RMSE</th>\n",
       "      <th id=\"T_ea815_level0_col4\" class=\"col_heading level0 col4\" >R2</th>\n",
       "      <th id=\"T_ea815_level0_col5\" class=\"col_heading level0 col5\" >RMSLE</th>\n",
       "      <th id=\"T_ea815_level0_col6\" class=\"col_heading level0 col6\" >MAPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_ea815_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_ea815_row0_col0\" class=\"data row0 col0\" >Linear Regression</td>\n",
       "      <td id=\"T_ea815_row0_col1\" class=\"data row0 col1\" >3.8077</td>\n",
       "      <td id=\"T_ea815_row0_col2\" class=\"data row0 col2\" >36.1142</td>\n",
       "      <td id=\"T_ea815_row0_col3\" class=\"data row0 col3\" >6.0095</td>\n",
       "      <td id=\"T_ea815_row0_col4\" class=\"data row0 col4\" >0.6310</td>\n",
       "      <td id=\"T_ea815_row0_col5\" class=\"data row0 col5\" >0.4002</td>\n",
       "      <td id=\"T_ea815_row0_col6\" class=\"data row0 col6\" >1.2396</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fcae45c9040>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_09749\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_09749_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
       "      <th id=\"T_09749_level0_col1\" class=\"col_heading level0 col1\" >MAE</th>\n",
       "      <th id=\"T_09749_level0_col2\" class=\"col_heading level0 col2\" >MSE</th>\n",
       "      <th id=\"T_09749_level0_col3\" class=\"col_heading level0 col3\" >RMSE</th>\n",
       "      <th id=\"T_09749_level0_col4\" class=\"col_heading level0 col4\" >R2</th>\n",
       "      <th id=\"T_09749_level0_col5\" class=\"col_heading level0 col5\" >RMSLE</th>\n",
       "      <th id=\"T_09749_level0_col6\" class=\"col_heading level0 col6\" >MAPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_09749_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_09749_row0_col0\" class=\"data row0 col0\" >Ridge Regression</td>\n",
       "      <td id=\"T_09749_row0_col1\" class=\"data row0 col1\" >3.8367</td>\n",
       "      <td id=\"T_09749_row0_col2\" class=\"data row0 col2\" >36.2711</td>\n",
       "      <td id=\"T_09749_row0_col3\" class=\"data row0 col3\" >6.0226</td>\n",
       "      <td id=\"T_09749_row0_col4\" class=\"data row0 col4\" >0.6294</td>\n",
       "      <td id=\"T_09749_row0_col5\" class=\"data row0 col5\" >0.4029</td>\n",
       "      <td id=\"T_09749_row0_col6\" class=\"data row0 col6\" >1.2281</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fcad44b5a60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_65ecf\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_65ecf_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
       "      <th id=\"T_65ecf_level0_col1\" class=\"col_heading level0 col1\" >MAE</th>\n",
       "      <th id=\"T_65ecf_level0_col2\" class=\"col_heading level0 col2\" >MSE</th>\n",
       "      <th id=\"T_65ecf_level0_col3\" class=\"col_heading level0 col3\" >RMSE</th>\n",
       "      <th id=\"T_65ecf_level0_col4\" class=\"col_heading level0 col4\" >R2</th>\n",
       "      <th id=\"T_65ecf_level0_col5\" class=\"col_heading level0 col5\" >RMSLE</th>\n",
       "      <th id=\"T_65ecf_level0_col6\" class=\"col_heading level0 col6\" >MAPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_65ecf_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_65ecf_row0_col0\" class=\"data row0 col0\" >Lasso Regression</td>\n",
       "      <td id=\"T_65ecf_row0_col1\" class=\"data row0 col1\" >4.0422</td>\n",
       "      <td id=\"T_65ecf_row0_col2\" class=\"data row0 col2\" >39.2687</td>\n",
       "      <td id=\"T_65ecf_row0_col3\" class=\"data row0 col3\" >6.2665</td>\n",
       "      <td id=\"T_65ecf_row0_col4\" class=\"data row0 col4\" >0.5988</td>\n",
       "      <td id=\"T_65ecf_row0_col5\" class=\"data row0 col5\" >0.4254</td>\n",
       "      <td id=\"T_65ecf_row0_col6\" class=\"data row0 col6\" >1.4393</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fc9d5c40f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VendorID</th>\n",
       "      <th>tpep_pickup_datetime</th>\n",
       "      <th>tpep_dropoff_datetime</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>RatecodeID</th>\n",
       "      <th>store_and_fwd_flag</th>\n",
       "      <th>PULocationID</th>\n",
       "      <th>DOLocationID</th>\n",
       "      <th>payment_type</th>\n",
       "      <th>...</th>\n",
       "      <th>mta_tax</th>\n",
       "      <th>tip_amount</th>\n",
       "      <th>tolls_amount</th>\n",
       "      <th>improvement_surcharge</th>\n",
       "      <th>total_amount</th>\n",
       "      <th>congestion_surcharge</th>\n",
       "      <th>airport_fee</th>\n",
       "      <th>PU_DO</th>\n",
       "      <th>duration</th>\n",
       "      <th>prediction_label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2022-02-09 08:56:03</td>\n",
       "      <td>2022-02-09 09:07:16</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.70</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>237</td>\n",
       "      <td>236</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.46</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>14.760000</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>237_236</td>\n",
       "      <td>11.216666</td>\n",
       "      <td>8.768399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-02-15 21:52:03</td>\n",
       "      <td>2022-02-15 22:01:45</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.98</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>90</td>\n",
       "      <td>107</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.56</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>15.360000</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90_107</td>\n",
       "      <td>9.700000</td>\n",
       "      <td>9.378112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2022-02-28 13:26:05</td>\n",
       "      <td>2022-02-28 13:26:35</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.90</td>\n",
       "      <td>5.0</td>\n",
       "      <td>N</td>\n",
       "      <td>265</td>\n",
       "      <td>265</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>115.300003</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>265_265</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>48.252813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-02-15 23:21:02</td>\n",
       "      <td>2022-02-15 23:33:27</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.57</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>186</td>\n",
       "      <td>233</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.56</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>15.360000</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>186_233</td>\n",
       "      <td>12.416667</td>\n",
       "      <td>10.230563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-02-24 14:38:15</td>\n",
       "      <td>2022-02-24 14:44:37</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.23</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>90</td>\n",
       "      <td>158</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>11.800000</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90_158</td>\n",
       "      <td>6.366667</td>\n",
       "      <td>8.171912</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",
       "      <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>19995</th>\n",
       "      <td>1</td>\n",
       "      <td>2022-02-14 07:04:17</td>\n",
       "      <td>2022-02-14 07:11:44</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.60</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>100</td>\n",
       "      <td>249</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>10.800000</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100_249</td>\n",
       "      <td>7.450000</td>\n",
       "      <td>10.379131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19996</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-02-08 14:58:58</td>\n",
       "      <td>2022-02-08 15:09:54</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.74</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>211</td>\n",
       "      <td>234</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.46</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>14.760000</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>211_234</td>\n",
       "      <td>10.933333</td>\n",
       "      <td>9.290158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19997</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-02-09 22:12:29</td>\n",
       "      <td>2022-02-09 22:27:30</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.48</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>249</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>3.56</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>21.360001</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>249_50</td>\n",
       "      <td>15.016666</td>\n",
       "      <td>12.118456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19998</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-02-12 22:36:46</td>\n",
       "      <td>2022-02-12 23:04:54</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.41</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>79</td>\n",
       "      <td>193</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>5.46</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>32.759998</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>79_193</td>\n",
       "      <td>28.133333</td>\n",
       "      <td>18.071008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19999</th>\n",
       "      <td>1</td>\n",
       "      <td>2022-02-05 09:43:25</td>\n",
       "      <td>2022-02-05 09:47:38</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.10</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>141</td>\n",
       "      <td>161</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.76</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>10.560000</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>141_161</td>\n",
       "      <td>4.216667</td>\n",
       "      <td>8.225852</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20000 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       VendorID tpep_pickup_datetime tpep_dropoff_datetime  passenger_count  \\\n",
       "0             1  2022-02-09 08:56:03   2022-02-09 09:07:16              3.0   \n",
       "1             2  2022-02-15 21:52:03   2022-02-15 22:01:45              1.0   \n",
       "2             1  2022-02-28 13:26:05   2022-02-28 13:26:35              1.0   \n",
       "3             2  2022-02-15 23:21:02   2022-02-15 23:33:27              1.0   \n",
       "4             2  2022-02-24 14:38:15   2022-02-24 14:44:37              2.0   \n",
       "...         ...                  ...                   ...              ...   \n",
       "19995         1  2022-02-14 07:04:17   2022-02-14 07:11:44              1.0   \n",
       "19996         2  2022-02-08 14:58:58   2022-02-08 15:09:54              2.0   \n",
       "19997         2  2022-02-09 22:12:29   2022-02-09 22:27:30              1.0   \n",
       "19998         2  2022-02-12 22:36:46   2022-02-12 23:04:54              4.0   \n",
       "19999         1  2022-02-05 09:43:25   2022-02-05 09:47:38              1.0   \n",
       "\n",
       "       trip_distance  RatecodeID store_and_fwd_flag PULocationID DOLocationID  \\\n",
       "0               1.70         1.0                  N          237          236   \n",
       "1               1.98         1.0                  N           90          107   \n",
       "2              12.90         5.0                  N          265          265   \n",
       "3               1.57         1.0                  N          186          233   \n",
       "4               1.23         1.0                  N           90          158   \n",
       "...              ...         ...                ...          ...          ...   \n",
       "19995           1.60         1.0                  N          100          249   \n",
       "19996           1.74         1.0                  N          211          234   \n",
       "19997           3.48         1.0                  N          249           50   \n",
       "19998           6.41         1.0                  N           79          193   \n",
       "19999           1.10         1.0                  N          141          161   \n",
       "\n",
       "       payment_type  ...  mta_tax  tip_amount  tolls_amount  \\\n",
       "0                 1  ...      0.5        2.46           0.0   \n",
       "1                 1  ...      0.5        2.56           0.0   \n",
       "2                 1  ...      0.0        0.00           0.0   \n",
       "3                 1  ...      0.5        2.56           0.0   \n",
       "4                 1  ...      0.5        2.00           0.0   \n",
       "...             ...  ...      ...         ...           ...   \n",
       "19995             2  ...      0.5        0.00           0.0   \n",
       "19996             1  ...      0.5        2.46           0.0   \n",
       "19997             1  ...      0.5        3.56           0.0   \n",
       "19998             1  ...      0.5        5.46           0.0   \n",
       "19999             1  ...      0.5        1.76           0.0   \n",
       "\n",
       "       improvement_surcharge  total_amount  congestion_surcharge  airport_fee  \\\n",
       "0                        0.3     14.760000                   2.5          0.0   \n",
       "1                        0.3     15.360000                   2.5          0.0   \n",
       "2                        0.3    115.300003                   0.0          0.0   \n",
       "3                        0.3     15.360000                   2.5          0.0   \n",
       "4                        0.3     11.800000                   2.5          0.0   \n",
       "...                      ...           ...                   ...          ...   \n",
       "19995                    0.3     10.800000                   2.5          0.0   \n",
       "19996                    0.3     14.760000                   2.5          0.0   \n",
       "19997                    0.3     21.360001                   2.5          0.0   \n",
       "19998                    0.3     32.759998                   2.5          0.0   \n",
       "19999                    0.3     10.560000                   2.5          0.0   \n",
       "\n",
       "         PU_DO   duration prediction_label  \n",
       "0      237_236  11.216666         8.768399  \n",
       "1       90_107   9.700000         9.378112  \n",
       "2      265_265   0.500000        48.252813  \n",
       "3      186_233  12.416667        10.230563  \n",
       "4       90_158   6.366667         8.171912  \n",
       "...        ...        ...              ...  \n",
       "19995  100_249   7.450000        10.379131  \n",
       "19996  211_234  10.933333         9.290158  \n",
       "19997   249_50  15.016666        12.118456  \n",
       "19998   79_193  28.133333        18.071008  \n",
       "19999  141_161   4.216667         8.225852  \n",
       "\n",
       "[20000 rows x 22 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Predict on Validation Set\n",
    "predict_model(lr, data=df_val)\n",
    "predict_model(ridge, data=df_val)\n",
    "predict_model(lasso, data=df_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Initiated</th>\n",
       "      <td>. . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>08:30:41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Status</th>\n",
       "      <td>. . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>Selecting Estimator</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Estimator</th>\n",
       "      <td>. . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>Stacking Regressor</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                   \n",
       "                                                                   \n",
       "Initiated  . . . . . . . . . . . . . . . . . .             08:30:41\n",
       "Status     . . . . . . . . . . . . . . . . . .  Selecting Estimator\n",
       "Estimator  . . . . . . . . . . . . . . . . . .   Stacking Regressor"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_cacec_row10_col0, #T_cacec_row10_col1, #T_cacec_row10_col2, #T_cacec_row10_col3, #T_cacec_row10_col4, #T_cacec_row10_col5 {\n",
       "  background: yellow;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_cacec\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_cacec_level0_col0\" class=\"col_heading level0 col0\" >MAE</th>\n",
       "      <th id=\"T_cacec_level0_col1\" class=\"col_heading level0 col1\" >MSE</th>\n",
       "      <th id=\"T_cacec_level0_col2\" class=\"col_heading level0 col2\" >RMSE</th>\n",
       "      <th id=\"T_cacec_level0_col3\" class=\"col_heading level0 col3\" >R2</th>\n",
       "      <th id=\"T_cacec_level0_col4\" class=\"col_heading level0 col4\" >RMSLE</th>\n",
       "      <th id=\"T_cacec_level0_col5\" class=\"col_heading level0 col5\" >MAPE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >Fold</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_cacec_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_cacec_row0_col0\" class=\"data row0 col0\" >3.0721</td>\n",
       "      <td id=\"T_cacec_row0_col1\" class=\"data row0 col1\" >23.6270</td>\n",
       "      <td id=\"T_cacec_row0_col2\" class=\"data row0 col2\" >4.8608</td>\n",
       "      <td id=\"T_cacec_row0_col3\" class=\"data row0 col3\" >0.6917</td>\n",
       "      <td id=\"T_cacec_row0_col4\" class=\"data row0 col4\" >0.3933</td>\n",
       "      <td id=\"T_cacec_row0_col5\" class=\"data row0 col5\" >1.7663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cacec_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_cacec_row1_col0\" class=\"data row1 col0\" >3.1493</td>\n",
       "      <td id=\"T_cacec_row1_col1\" class=\"data row1 col1\" >24.2897</td>\n",
       "      <td id=\"T_cacec_row1_col2\" class=\"data row1 col2\" >4.9285</td>\n",
       "      <td id=\"T_cacec_row1_col3\" class=\"data row1 col3\" >0.7028</td>\n",
       "      <td id=\"T_cacec_row1_col4\" class=\"data row1 col4\" >0.4091</td>\n",
       "      <td id=\"T_cacec_row1_col5\" class=\"data row1 col5\" >1.3267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cacec_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_cacec_row2_col0\" class=\"data row2 col0\" >2.9548</td>\n",
       "      <td id=\"T_cacec_row2_col1\" class=\"data row2 col1\" >22.3303</td>\n",
       "      <td id=\"T_cacec_row2_col2\" class=\"data row2 col2\" >4.7255</td>\n",
       "      <td id=\"T_cacec_row2_col3\" class=\"data row2 col3\" >0.7239</td>\n",
       "      <td id=\"T_cacec_row2_col4\" class=\"data row2 col4\" >0.3775</td>\n",
       "      <td id=\"T_cacec_row2_col5\" class=\"data row2 col5\" >2.0920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cacec_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_cacec_row3_col0\" class=\"data row3 col0\" >2.9833</td>\n",
       "      <td id=\"T_cacec_row3_col1\" class=\"data row3 col1\" >21.0995</td>\n",
       "      <td id=\"T_cacec_row3_col2\" class=\"data row3 col2\" >4.5934</td>\n",
       "      <td id=\"T_cacec_row3_col3\" class=\"data row3 col3\" >0.7370</td>\n",
       "      <td id=\"T_cacec_row3_col4\" class=\"data row3 col4\" >0.3885</td>\n",
       "      <td id=\"T_cacec_row3_col5\" class=\"data row3 col5\" >1.8926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cacec_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_cacec_row4_col0\" class=\"data row4 col0\" >2.9087</td>\n",
       "      <td id=\"T_cacec_row4_col1\" class=\"data row4 col1\" >20.1539</td>\n",
       "      <td id=\"T_cacec_row4_col2\" class=\"data row4 col2\" >4.4893</td>\n",
       "      <td id=\"T_cacec_row4_col3\" class=\"data row4 col3\" >0.7635</td>\n",
       "      <td id=\"T_cacec_row4_col4\" class=\"data row4 col4\" >0.3294</td>\n",
       "      <td id=\"T_cacec_row4_col5\" class=\"data row4 col5\" >0.8553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cacec_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_cacec_row5_col0\" class=\"data row5 col0\" >3.1658</td>\n",
       "      <td id=\"T_cacec_row5_col1\" class=\"data row5 col1\" >33.0938</td>\n",
       "      <td id=\"T_cacec_row5_col2\" class=\"data row5 col2\" >5.7527</td>\n",
       "      <td id=\"T_cacec_row5_col3\" class=\"data row5 col3\" >0.6078</td>\n",
       "      <td id=\"T_cacec_row5_col4\" class=\"data row5 col4\" >0.4105</td>\n",
       "      <td id=\"T_cacec_row5_col5\" class=\"data row5 col5\" >1.8115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cacec_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_cacec_row6_col0\" class=\"data row6 col0\" >2.9141</td>\n",
       "      <td id=\"T_cacec_row6_col1\" class=\"data row6 col1\" >19.0335</td>\n",
       "      <td id=\"T_cacec_row6_col2\" class=\"data row6 col2\" >4.3627</td>\n",
       "      <td id=\"T_cacec_row6_col3\" class=\"data row6 col3\" >0.7589</td>\n",
       "      <td id=\"T_cacec_row6_col4\" class=\"data row6 col4\" >0.3676</td>\n",
       "      <td id=\"T_cacec_row6_col5\" class=\"data row6 col5\" >1.1048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cacec_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_cacec_row7_col0\" class=\"data row7 col0\" >2.9851</td>\n",
       "      <td id=\"T_cacec_row7_col1\" class=\"data row7 col1\" >23.4743</td>\n",
       "      <td id=\"T_cacec_row7_col2\" class=\"data row7 col2\" >4.8450</td>\n",
       "      <td id=\"T_cacec_row7_col3\" class=\"data row7 col3\" >0.7209</td>\n",
       "      <td id=\"T_cacec_row7_col4\" class=\"data row7 col4\" >0.3873</td>\n",
       "      <td id=\"T_cacec_row7_col5\" class=\"data row7 col5\" >0.8960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cacec_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_cacec_row8_col0\" class=\"data row8 col0\" >2.8073</td>\n",
       "      <td id=\"T_cacec_row8_col1\" class=\"data row8 col1\" >17.6822</td>\n",
       "      <td id=\"T_cacec_row8_col2\" class=\"data row8 col2\" >4.2050</td>\n",
       "      <td id=\"T_cacec_row8_col3\" class=\"data row8 col3\" >0.7649</td>\n",
       "      <td id=\"T_cacec_row8_col4\" class=\"data row8 col4\" >0.3639</td>\n",
       "      <td id=\"T_cacec_row8_col5\" class=\"data row8 col5\" >0.9614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cacec_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_cacec_row9_col0\" class=\"data row9 col0\" >3.0283</td>\n",
       "      <td id=\"T_cacec_row9_col1\" class=\"data row9 col1\" >21.5162</td>\n",
       "      <td id=\"T_cacec_row9_col2\" class=\"data row9 col2\" >4.6386</td>\n",
       "      <td id=\"T_cacec_row9_col3\" class=\"data row9 col3\" >0.7509</td>\n",
       "      <td id=\"T_cacec_row9_col4\" class=\"data row9 col4\" >0.3774</td>\n",
       "      <td id=\"T_cacec_row9_col5\" class=\"data row9 col5\" >0.8217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cacec_level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
       "      <td id=\"T_cacec_row10_col0\" class=\"data row10 col0\" >2.9969</td>\n",
       "      <td id=\"T_cacec_row10_col1\" class=\"data row10 col1\" >22.6300</td>\n",
       "      <td id=\"T_cacec_row10_col2\" class=\"data row10 col2\" >4.7401</td>\n",
       "      <td id=\"T_cacec_row10_col3\" class=\"data row10 col3\" >0.7222</td>\n",
       "      <td id=\"T_cacec_row10_col4\" class=\"data row10 col4\" >0.3804</td>\n",
       "      <td id=\"T_cacec_row10_col5\" class=\"data row10 col5\" >1.3528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cacec_level0_row11\" class=\"row_heading level0 row11\" >Std</th>\n",
       "      <td id=\"T_cacec_row11_col0\" class=\"data row11 col0\" >0.1054</td>\n",
       "      <td id=\"T_cacec_row11_col1\" class=\"data row11 col1\" >4.0146</td>\n",
       "      <td id=\"T_cacec_row11_col2\" class=\"data row11 col2\" >0.4013</td>\n",
       "      <td id=\"T_cacec_row11_col3\" class=\"data row11 col3\" >0.0451</td>\n",
       "      <td id=\"T_cacec_row11_col4\" class=\"data row11 col4\" >0.0225</td>\n",
       "      <td id=\"T_cacec_row11_col5\" class=\"data row11 col5\" >0.4661</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fca6c560eb0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_1e3f5\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_1e3f5_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
       "      <th id=\"T_1e3f5_level0_col1\" class=\"col_heading level0 col1\" >MAE</th>\n",
       "      <th id=\"T_1e3f5_level0_col2\" class=\"col_heading level0 col2\" >MSE</th>\n",
       "      <th id=\"T_1e3f5_level0_col3\" class=\"col_heading level0 col3\" >RMSE</th>\n",
       "      <th id=\"T_1e3f5_level0_col4\" class=\"col_heading level0 col4\" >R2</th>\n",
       "      <th id=\"T_1e3f5_level0_col5\" class=\"col_heading level0 col5\" >RMSLE</th>\n",
       "      <th id=\"T_1e3f5_level0_col6\" class=\"col_heading level0 col6\" >MAPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_1e3f5_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_1e3f5_row0_col0\" class=\"data row0 col0\" >Stacking Regressor</td>\n",
       "      <td id=\"T_1e3f5_row0_col1\" class=\"data row0 col1\" >3.7731</td>\n",
       "      <td id=\"T_1e3f5_row0_col2\" class=\"data row0 col2\" >35.9851</td>\n",
       "      <td id=\"T_1e3f5_row0_col3\" class=\"data row0 col3\" >5.9988</td>\n",
       "      <td id=\"T_1e3f5_row0_col4\" class=\"data row0 col4\" >0.6324</td>\n",
       "      <td id=\"T_1e3f5_row0_col5\" class=\"data row0 col5\" >0.3995</td>\n",
       "      <td id=\"T_1e3f5_row0_col6\" class=\"data row0 col6\" >1.2939</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fc9d49fce50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VendorID</th>\n",
       "      <th>tpep_pickup_datetime</th>\n",
       "      <th>tpep_dropoff_datetime</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>RatecodeID</th>\n",
       "      <th>store_and_fwd_flag</th>\n",
       "      <th>PULocationID</th>\n",
       "      <th>DOLocationID</th>\n",
       "      <th>payment_type</th>\n",
       "      <th>...</th>\n",
       "      <th>mta_tax</th>\n",
       "      <th>tip_amount</th>\n",
       "      <th>tolls_amount</th>\n",
       "      <th>improvement_surcharge</th>\n",
       "      <th>total_amount</th>\n",
       "      <th>congestion_surcharge</th>\n",
       "      <th>airport_fee</th>\n",
       "      <th>PU_DO</th>\n",
       "      <th>duration</th>\n",
       "      <th>prediction_label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2022-02-09 08:56:03</td>\n",
       "      <td>2022-02-09 09:07:16</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.70</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>237</td>\n",
       "      <td>236</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.46</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>14.760000</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>237_236</td>\n",
       "      <td>11.216666</td>\n",
       "      <td>9.025422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-02-15 21:52:03</td>\n",
       "      <td>2022-02-15 22:01:45</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.98</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>90</td>\n",
       "      <td>107</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.56</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>15.360000</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90_107</td>\n",
       "      <td>9.700000</td>\n",
       "      <td>9.362918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2022-02-28 13:26:05</td>\n",
       "      <td>2022-02-28 13:26:35</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.90</td>\n",
       "      <td>5.0</td>\n",
       "      <td>N</td>\n",
       "      <td>265</td>\n",
       "      <td>265</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>115.300003</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>265_265</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>45.109824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-02-15 23:21:02</td>\n",
       "      <td>2022-02-15 23:33:27</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.57</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>186</td>\n",
       "      <td>233</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.56</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>15.360000</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>186_233</td>\n",
       "      <td>12.416667</td>\n",
       "      <td>9.932593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-02-24 14:38:15</td>\n",
       "      <td>2022-02-24 14:44:37</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.23</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>90</td>\n",
       "      <td>158</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>11.800000</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90_158</td>\n",
       "      <td>6.366667</td>\n",
       "      <td>8.090835</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",
       "      <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>19995</th>\n",
       "      <td>1</td>\n",
       "      <td>2022-02-14 07:04:17</td>\n",
       "      <td>2022-02-14 07:11:44</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.60</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>100</td>\n",
       "      <td>249</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>10.800000</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100_249</td>\n",
       "      <td>7.450000</td>\n",
       "      <td>10.375201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19996</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-02-08 14:58:58</td>\n",
       "      <td>2022-02-08 15:09:54</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.74</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>211</td>\n",
       "      <td>234</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.46</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>14.760000</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>211_234</td>\n",
       "      <td>10.933333</td>\n",
       "      <td>9.015113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19997</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-02-09 22:12:29</td>\n",
       "      <td>2022-02-09 22:27:30</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.48</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>249</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>3.56</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>21.360001</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>249_50</td>\n",
       "      <td>15.016666</td>\n",
       "      <td>12.629226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19998</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-02-12 22:36:46</td>\n",
       "      <td>2022-02-12 23:04:54</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.41</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>79</td>\n",
       "      <td>193</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>5.46</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>32.759998</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>79_193</td>\n",
       "      <td>28.133333</td>\n",
       "      <td>19.533936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19999</th>\n",
       "      <td>1</td>\n",
       "      <td>2022-02-05 09:43:25</td>\n",
       "      <td>2022-02-05 09:47:38</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.10</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>141</td>\n",
       "      <td>161</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.76</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>10.560000</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>141_161</td>\n",
       "      <td>4.216667</td>\n",
       "      <td>7.846375</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20000 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       VendorID tpep_pickup_datetime tpep_dropoff_datetime  passenger_count  \\\n",
       "0             1  2022-02-09 08:56:03   2022-02-09 09:07:16              3.0   \n",
       "1             2  2022-02-15 21:52:03   2022-02-15 22:01:45              1.0   \n",
       "2             1  2022-02-28 13:26:05   2022-02-28 13:26:35              1.0   \n",
       "3             2  2022-02-15 23:21:02   2022-02-15 23:33:27              1.0   \n",
       "4             2  2022-02-24 14:38:15   2022-02-24 14:44:37              2.0   \n",
       "...         ...                  ...                   ...              ...   \n",
       "19995         1  2022-02-14 07:04:17   2022-02-14 07:11:44              1.0   \n",
       "19996         2  2022-02-08 14:58:58   2022-02-08 15:09:54              2.0   \n",
       "19997         2  2022-02-09 22:12:29   2022-02-09 22:27:30              1.0   \n",
       "19998         2  2022-02-12 22:36:46   2022-02-12 23:04:54              4.0   \n",
       "19999         1  2022-02-05 09:43:25   2022-02-05 09:47:38              1.0   \n",
       "\n",
       "       trip_distance  RatecodeID store_and_fwd_flag PULocationID DOLocationID  \\\n",
       "0               1.70         1.0                  N          237          236   \n",
       "1               1.98         1.0                  N           90          107   \n",
       "2              12.90         5.0                  N          265          265   \n",
       "3               1.57         1.0                  N          186          233   \n",
       "4               1.23         1.0                  N           90          158   \n",
       "...              ...         ...                ...          ...          ...   \n",
       "19995           1.60         1.0                  N          100          249   \n",
       "19996           1.74         1.0                  N          211          234   \n",
       "19997           3.48         1.0                  N          249           50   \n",
       "19998           6.41         1.0                  N           79          193   \n",
       "19999           1.10         1.0                  N          141          161   \n",
       "\n",
       "       payment_type  ...  mta_tax  tip_amount  tolls_amount  \\\n",
       "0                 1  ...      0.5        2.46           0.0   \n",
       "1                 1  ...      0.5        2.56           0.0   \n",
       "2                 1  ...      0.0        0.00           0.0   \n",
       "3                 1  ...      0.5        2.56           0.0   \n",
       "4                 1  ...      0.5        2.00           0.0   \n",
       "...             ...  ...      ...         ...           ...   \n",
       "19995             2  ...      0.5        0.00           0.0   \n",
       "19996             1  ...      0.5        2.46           0.0   \n",
       "19997             1  ...      0.5        3.56           0.0   \n",
       "19998             1  ...      0.5        5.46           0.0   \n",
       "19999             1  ...      0.5        1.76           0.0   \n",
       "\n",
       "       improvement_surcharge  total_amount  congestion_surcharge  airport_fee  \\\n",
       "0                        0.3     14.760000                   2.5          0.0   \n",
       "1                        0.3     15.360000                   2.5          0.0   \n",
       "2                        0.3    115.300003                   0.0          0.0   \n",
       "3                        0.3     15.360000                   2.5          0.0   \n",
       "4                        0.3     11.800000                   2.5          0.0   \n",
       "...                      ...           ...                   ...          ...   \n",
       "19995                    0.3     10.800000                   2.5          0.0   \n",
       "19996                    0.3     14.760000                   2.5          0.0   \n",
       "19997                    0.3     21.360001                   2.5          0.0   \n",
       "19998                    0.3     32.759998                   2.5          0.0   \n",
       "19999                    0.3     10.560000                   2.5          0.0   \n",
       "\n",
       "         PU_DO   duration prediction_label  \n",
       "0      237_236  11.216666         9.025422  \n",
       "1       90_107   9.700000         9.362918  \n",
       "2      265_265   0.500000        45.109824  \n",
       "3      186_233  12.416667         9.932593  \n",
       "4       90_158   6.366667         8.090835  \n",
       "...        ...        ...              ...  \n",
       "19995  100_249   7.450000        10.375201  \n",
       "19996  211_234  10.933333         9.015113  \n",
       "19997   249_50  15.016666        12.629226  \n",
       "19998   79_193  28.133333        19.533936  \n",
       "19999  141_161   4.216667         7.846375  \n",
       "\n",
       "[20000 rows x 22 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Predict on Stacked Model\n",
    "stacker = stack_models(estimator_list=[lr, ridge, lasso])\n",
    "\n",
    "predict_model(stacker, data=df_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Final Model\n",
    "final_model = finalize_model(stacker)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING: For shap='linear', shap interaction values can unfortunately not be calculated!\n",
      "Warning: shap values for shap.LinearExplainer get calculated against X_background, but paramater X_background=None, so using X instead\n",
      "Generating self.shap_explainer = shap.LinearExplainer(modelX)...\n",
      "Building ExplainerDashboard..\n",
      "WARNING: the number of idxs (=6000) > max_idxs_in_dropdown(=1000). However with your installed version of dash(2.8.1) dropdown search may not work smoothly. You can downgrade to `pip install dash==2.6.2` which should work better for now...\n",
      "For this type of model and model_output interactions don't work, so setting shap_interaction=False...\n",
      "The explainer object has no decision_trees property. so setting decision_trees=False...\n",
      "Generating layout...\n",
      "Calculating shap values...\n",
      "Calculating predictions...\n",
      "Calculating residuals...\n",
      "Calculating absolute residuals...\n",
      "Warning: mean-absolute-percentage-error is very large (9152465771363.936), you can hide it from the metrics by passing parameter show_metrics...\n",
      "Warning: mean-absolute-percentage-error is very large (9152465771363.936), you can hide it from the metrics by passing parameter show_metrics...\n",
      "Warning: mean-absolute-percentage-error is very large (9152465771363.936), you can hide it from the metrics by passing parameter show_metrics...\n",
      "Calculating dependencies...\n",
      "Calculating importances...\n",
      "Reminder: you can store the explainer (including calculated dependencies) with explainer.dump('explainer.joblib') and reload with e.g. ClassifierExplainer.from_file('explainer.joblib')\n",
      "Registering callbacks...\n",
      "Starting ExplainerDashboard inline (terminate it with ExplainerDashboard.terminate(8050))\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"1000\"\n",
       "            height=\"800\"\n",
       "            src=\"http://127.0.0.1:8050/\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "            \n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x7fca981d6670>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Create Dashboard for the Linear Regression Model\n",
    "dashboard(lr, display_format='inline')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.16"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}