Analysis/Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import r2_score, mean_squared_error\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "pd.set_option('max_columns', None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## In this file we will try to find answers to following questions:\n",
    "1. Is it possible to accurately predict the most attractive price of your Airbnb?\n",
    "2. What services will actually help you raise the price?\n",
    "3. What are the most favored neighborhoods?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>host_since</th>\n",
       "      <th>host_response_time</th>\n",
       "      <th>host_response_rate</th>\n",
       "      <th>host_acceptance_rate</th>\n",
       "      <th>host_is_superhost</th>\n",
       "      <th>host_total_listings_count</th>\n",
       "      <th>host_has_profile_pic</th>\n",
       "      <th>host_identity_verified</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>is_location_exact</th>\n",
       "      <th>accommodates</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>beds</th>\n",
       "      <th>price</th>\n",
       "      <th>security_deposit</th>\n",
       "      <th>cleaning_fee</th>\n",
       "      <th>guests_included</th>\n",
       "      <th>extra_people</th>\n",
       "      <th>minimum_nights</th>\n",
       "      <th>maximum_nights</th>\n",
       "      <th>availability_30</th>\n",
       "      <th>availability_60</th>\n",
       "      <th>availability_90</th>\n",
       "      <th>availability_365</th>\n",
       "      <th>number_of_reviews</th>\n",
       "      <th>review_scores_rating</th>\n",
       "      <th>review_scores_accuracy</th>\n",
       "      <th>review_scores_cleanliness</th>\n",
       "      <th>review_scores_checkin</th>\n",
       "      <th>review_scores_communication</th>\n",
       "      <th>review_scores_location</th>\n",
       "      <th>review_scores_value</th>\n",
       "      <th>requires_license</th>\n",
       "      <th>instant_bookable</th>\n",
       "      <th>require_guest_profile_picture</th>\n",
       "      <th>require_guest_phone_verification</th>\n",
       "      <th>calculated_host_listings_count</th>\n",
       "      <th>reviews_per_month</th>\n",
       "      <th>24-Hour Check-in</th>\n",
       "      <th>Air Conditioning</th>\n",
       "      <th>Breakfast</th>\n",
       "      <th>Buzzer/Wireless Intercom</th>\n",
       "      <th>Cable TV</th>\n",
       "      <th>Carbon Monoxide Detector</th>\n",
       "      <th>Cat(s)</th>\n",
       "      <th>Dog(s)</th>\n",
       "      <th>Doorman</th>\n",
       "      <th>Dryer</th>\n",
       "      <th>Elevator in Building</th>\n",
       "      <th>Essentials</th>\n",
       "      <th>Family/Kid Friendly</th>\n",
       "      <th>Fire Extinguisher</th>\n",
       "      <th>First Aid Kit</th>\n",
       "      <th>Free Parking on Premises</th>\n",
       "      <th>Free Parking on Street</th>\n",
       "      <th>Gym</th>\n",
       "      <th>Hair Dryer</th>\n",
       "      <th>Hangers</th>\n",
       "      <th>Heating</th>\n",
       "      <th>Hot Tub</th>\n",
       "      <th>Indoor Fireplace</th>\n",
       "      <th>Internet</th>\n",
       "      <th>Iron</th>\n",
       "      <th>Kitchen</th>\n",
       "      <th>Laptop Friendly Workspace</th>\n",
       "      <th>Lock on Bedroom Door</th>\n",
       "      <th>Other pet(s)</th>\n",
       "      <th>Paid Parking Off Premises</th>\n",
       "      <th>Pets Allowed</th>\n",
       "      <th>Pets live on this property</th>\n",
       "      <th>Pool</th>\n",
       "      <th>Safety Card</th>\n",
       "      <th>Shampoo</th>\n",
       "      <th>Smoke Detector</th>\n",
       "      <th>Smoking Allowed</th>\n",
       "      <th>Suitable for Events</th>\n",
       "      <th>TV</th>\n",
       "      <th>Washer</th>\n",
       "      <th>Washer / Dryer</th>\n",
       "      <th>Wheelchair Accessible</th>\n",
       "      <th>Wireless Internet</th>\n",
       "      <th>translation missing: en.hosting_amenity_49</th>\n",
       "      <th>translation missing: en.hosting_amenity_50</th>\n",
       "      <th>Allston</th>\n",
       "      <th>Back Bay</th>\n",
       "      <th>Bay Village</th>\n",
       "      <th>Beacon Hill</th>\n",
       "      <th>Brighton</th>\n",
       "      <th>Charlestown</th>\n",
       "      <th>Chinatown</th>\n",
       "      <th>Dorchester</th>\n",
       "      <th>Downtown</th>\n",
       "      <th>East Boston</th>\n",
       "      <th>Fenway</th>\n",
       "      <th>Hyde Park</th>\n",
       "      <th>Jamaica Plain</th>\n",
       "      <th>Leather District</th>\n",
       "      <th>Longwood Medical Area</th>\n",
       "      <th>Mattapan</th>\n",
       "      <th>Mission Hill</th>\n",
       "      <th>North End</th>\n",
       "      <th>Roslindale</th>\n",
       "      <th>Roxbury</th>\n",
       "      <th>South Boston</th>\n",
       "      <th>South Boston Waterfront</th>\n",
       "      <th>South End</th>\n",
       "      <th>West End</th>\n",
       "      <th>West Roxbury</th>\n",
       "      <th>Apartment</th>\n",
       "      <th>Bed &amp; Breakfast</th>\n",
       "      <th>Boat</th>\n",
       "      <th>Camper/RV</th>\n",
       "      <th>Condominium</th>\n",
       "      <th>Dorm</th>\n",
       "      <th>Entire Floor</th>\n",
       "      <th>Guesthouse</th>\n",
       "      <th>House</th>\n",
       "      <th>Loft</th>\n",
       "      <th>Other</th>\n",
       "      <th>Townhouse</th>\n",
       "      <th>Villa</th>\n",
       "      <th>Entire home/apt</th>\n",
       "      <th>Private room</th>\n",
       "      <th>Shared room</th>\n",
       "      <th>Airbed</th>\n",
       "      <th>Couch</th>\n",
       "      <th>Futon</th>\n",
       "      <th>Pull-out Sofa</th>\n",
       "      <th>Real Bed</th>\n",
       "      <th>flexible</th>\n",
       "      <th>moderate</th>\n",
       "      <th>strict</th>\n",
       "      <th>super_strict_30</th>\n",
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       "      <td>3.0</td>\n",
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      "text/plain": [
       "   host_since  host_response_time  host_response_rate  host_acceptance_rate  \\\n",
       "0      735703                 0.0                 0.0                   0.0   \n",
       "1      734661                 1.0                 1.0                   1.0   \n",
       "\n",
       "   host_is_superhost  host_total_listings_count  host_has_profile_pic  \\\n",
       "0              False                          1                  True   \n",
       "1              False                          1                  True   \n",
       "\n",
       "   host_identity_verified   latitude  longitude  is_location_exact  \\\n",
       "0                   False  42.282619 -71.133068               True   \n",
       "1                    True  42.286241 -71.134374               True   \n",
       "\n",
       "   accommodates  bathrooms  bedrooms  beds  price  security_deposit  \\\n",
       "0             4        1.5       2.0   3.0    250                 0   \n",
       "1             2        1.0       1.0   1.0     65                95   \n",
       "\n",
       "   cleaning_fee  guests_included  extra_people  minimum_nights  \\\n",
       "0            35                1             0               2   \n",
       "1            10                0             0               2   \n",
       "\n",
       "   maximum_nights  availability_30  availability_60  availability_90  \\\n",
       "0            1125                0                0                0   \n",
       "1              15               26               54               84   \n",
       "\n",
       "   availability_365  number_of_reviews  review_scores_rating  \\\n",
       "0                 0                  0                  94.0   \n",
       "1               359                 36                  94.0   \n",
       "\n",
       "   review_scores_accuracy  review_scores_cleanliness  review_scores_checkin  \\\n",
       "0                    10.0                       10.0                   10.0   \n",
       "1                    10.0                        9.0                   10.0   \n",
       "\n",
       "   review_scores_communication  review_scores_location  review_scores_value  \\\n",
       "0                         10.0                    10.0                  9.0   \n",
       "1                         10.0                     9.0                  9.0   \n",
       "\n",
       "   requires_license  instant_bookable  require_guest_profile_picture  \\\n",
       "0             False             False                          False   \n",
       "1             False              True                          False   \n",
       "\n",
       "   require_guest_phone_verification  calculated_host_listings_count  \\\n",
       "0                             False                               1   \n",
       "1                             False                               1   \n",
       "\n",
       "   reviews_per_month  24-Hour Check-in  Air Conditioning  Breakfast  \\\n",
       "0               1.17             False             False      False   \n",
       "1               1.30             False              True      False   \n",
       "\n",
       "   Buzzer/Wireless Intercom  Cable TV  Carbon Monoxide Detector  Cat(s)  \\\n",
       "0                     False     False                     False   False   \n",
       "1                     False     False                      True   False   \n",
       "\n",
       "   Dog(s)  Doorman  Dryer  Elevator in Building  Essentials  \\\n",
       "0    True    False   True                 False        True   \n",
       "1    True    False   True                 False        True   \n",
       "\n",
       "   Family/Kid Friendly  Fire Extinguisher  First Aid Kit  \\\n",
       "0                 True               True          False   \n",
       "1                 True               True          False   \n",
       "\n",
       "   Free Parking on Premises  Free Parking on Street    Gym  Hair Dryer  \\\n",
       "0                      True                   False  False       False   \n",
       "1                     False                   False  False        True   \n",
       "\n",
       "   Hangers  Heating  Hot Tub  Indoor Fireplace  Internet   Iron  Kitchen  \\\n",
       "0    False     True    False             False     False  False     True   \n",
       "1     True     True    False             False      True   True     True   \n",
       "\n",
       "   Laptop Friendly Workspace  Lock on Bedroom Door  Other pet(s)  \\\n",
       "0                       True                 False         False   \n",
       "1                      False                  True         False   \n",
       "\n",
       "   Paid Parking Off Premises  Pets Allowed  Pets live on this property   Pool  \\\n",
       "0                      False         False                        True  False   \n",
       "1                      False          True                        True  False   \n",
       "\n",
       "   Safety Card  Shampoo  Smoke Detector  Smoking Allowed  Suitable for Events  \\\n",
       "0        False     True            True            False                False   \n",
       "1        False     True            True            False                False   \n",
       "\n",
       "     TV  Washer  Washer / Dryer  Wheelchair Accessible  Wireless Internet  \\\n",
       "0  True    True           False                  False               True   \n",
       "1  True    True           False                  False               True   \n",
       "\n",
       "   translation missing: en.hosting_amenity_49  \\\n",
       "0                                       False   \n",
       "1                                       False   \n",
       "\n",
       "   translation missing: en.hosting_amenity_50  Allston  Back Bay  Bay Village  \\\n",
       "0                                       False        0         0            0   \n",
       "1                                       False        0         0            0   \n",
       "\n",
       "   Beacon Hill  Brighton  Charlestown  Chinatown  Dorchester  Downtown  \\\n",
       "0            0         0            0          0           0         0   \n",
       "1            0         0            0          0           0         0   \n",
       "\n",
       "   East Boston  Fenway  Hyde Park  Jamaica Plain  Leather District  \\\n",
       "0            0       0          0              0                 0   \n",
       "1            0       0          0              0                 0   \n",
       "\n",
       "   Longwood Medical Area  Mattapan  Mission Hill  North End  Roslindale  \\\n",
       "0                      0         0             0          0           1   \n",
       "1                      0         0             0          0           1   \n",
       "\n",
       "   Roxbury  South Boston  South Boston Waterfront  South End  West End  \\\n",
       "0        0             0                        0          0         0   \n",
       "1        0             0                        0          0         0   \n",
       "\n",
       "   West Roxbury  Apartment  Bed & Breakfast  Boat  Camper/RV  Condominium  \\\n",
       "0             0          0                0     0          0            0   \n",
       "1             0          1                0     0          0            0   \n",
       "\n",
       "   Dorm  Entire Floor  Guesthouse  House  Loft  Other  Townhouse  Villa  \\\n",
       "0     0             0           0      1     0      0          0      0   \n",
       "1     0             0           0      0     0      0          0      0   \n",
       "\n",
       "   Entire home/apt  Private room  Shared room  Airbed  Couch  Futon  \\\n",
       "0                1             0            0       0      0      0   \n",
       "1                0             1            0       0      0      0   \n",
       "\n",
       "   Pull-out Sofa  Real Bed  flexible  moderate  strict  super_strict_30  \n",
       "0              0         1         0         1       0                0  \n",
       "1              0         1         0         1       0                0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_original = pd.read_csv('../Data/boston_listings.csv')\n",
    "df = pd.read_csv('../Data/boston_listings_updated.csv')\n",
    "df = df.drop(columns=['Unnamed: 0'])\n",
    "df.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's have a look at our target variable 'price'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    3585.000000\n",
      "mean      173.925802\n",
      "std       148.331321\n",
      "min        10.000000\n",
      "25%        85.000000\n",
      "50%       150.000000\n",
      "75%       220.000000\n",
      "max      4000.000000\n",
      "Name: price, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x136e7c370>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(df['price'].describe()) \n",
    "df['price'].plot(kind ='box')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have some outliers in the data and some extreme values as well. <br>\n",
    "Let's use _Tukey rule_ to detect the outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Min value -117.5 , Max value 422.5\n"
     ]
    }
   ],
   "source": [
    "Q1 = df['price'].quantile(0.25)\n",
    "Q3 = df['price'].quantile(0.75)\n",
    "\n",
    "IQR = Q3 -Q1\n",
    "\n",
    "Max = Q3 + 1.5*IQR\n",
    "Min = Q1 - 1.5*IQR\n",
    "print('Min value {} , Max value {}'.format(Min,Max))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, we have outliers at higher price values but non for lower(negative min value). <br>\n",
    "Let's see the count for outliers for values higher than 422."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total count for price higher than 422 = 124\n",
      "A look at the outlier prices :[ 425  429  449  450  451  459  464  479  480  499  500  506  524  525\n",
      "  536  550  559  568  570  575  599  600  603  625  650  680  690  695\n",
      "  699  700  725  750  769  800  849  872  975  999 1000 1235 1250 1275\n",
      " 1300 1345 1372 1400 3000 4000]\n"
     ]
    }
   ],
   "source": [
    "print('Total count for price higher than 422 = ' + str(df[df['price'] > 422]['price'].count()))\n",
    "print('A look at the outlier prices :' + str(np.sort(df[df['price'] > 422]['price'].unique())))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have a lot of values at range below 422, lets take upper limit to 500. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.query('price < 500')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Is it possible to accurately predict the most attractive price of your Airbnb?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use **pycaret** library for our prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pycaret\n",
    "from pycaret.regression import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Initializing the environment in pycaret. Creating the transformation pipeline to prepare the data for modeling and deployment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " \n",
      "Setup Succesfully Completed!\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_cde21108_9cd0_11ea_89b2_acde48001122\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Description</th>        <th class=\"col_heading level0 col1\" >Value</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_cde21108_9cd0_11ea_89b2_acde48001122level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_cde21108_9cd0_11ea_89b2_acde48001122row0_col0\" class=\"data row0 col0\" >session_id</td>\n",
       "                        <td id=\"T_cde21108_9cd0_11ea_89b2_acde48001122row0_col1\" class=\"data row0 col1\" >2347</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_cde21108_9cd0_11ea_89b2_acde48001122level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_cde21108_9cd0_11ea_89b2_acde48001122row1_col0\" class=\"data row1 col0\" >Transform Target </td>\n",
       "                        <td id=\"T_cde21108_9cd0_11ea_89b2_acde48001122row1_col1\" class=\"data row1 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_cde21108_9cd0_11ea_89b2_acde48001122level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_cde21108_9cd0_11ea_89b2_acde48001122row2_col0\" class=\"data row2 col0\" >Transform Target Method</td>\n",
       "                        <td id=\"T_cde21108_9cd0_11ea_89b2_acde48001122row2_col1\" class=\"data row2 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_cde21108_9cd0_11ea_89b2_acde48001122level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_cde21108_9cd0_11ea_89b2_acde48001122row3_col0\" class=\"data row3 col0\" >Original Data</td>\n",
       "                        <td id=\"T_cde21108_9cd0_11ea_89b2_acde48001122row3_col1\" class=\"data row3 col1\" >(3495, 135)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_cde21108_9cd0_11ea_89b2_acde48001122level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_cde21108_9cd0_11ea_89b2_acde48001122row4_col0\" class=\"data row4 col0\" >Missing Values </td>\n",
       "                        <td id=\"T_cde21108_9cd0_11ea_89b2_acde48001122row4_col1\" class=\"data row4 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_cde21108_9cd0_11ea_89b2_acde48001122level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_cde21108_9cd0_11ea_89b2_acde48001122row5_col0\" class=\"data row5 col0\" >Numeric Features </td>\n",
       "                        <td id=\"T_cde21108_9cd0_11ea_89b2_acde48001122row5_col1\" class=\"data row5 col1\" >29</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_cde21108_9cd0_11ea_89b2_acde48001122level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_cde21108_9cd0_11ea_89b2_acde48001122row6_col0\" class=\"data row6 col0\" >Categorical Features </td>\n",
       "                        <td id=\"T_cde21108_9cd0_11ea_89b2_acde48001122row6_col1\" class=\"data row6 col1\" >105</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_cde21108_9cd0_11ea_89b2_acde48001122level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_cde21108_9cd0_11ea_89b2_acde48001122row7_col0\" class=\"data row7 col0\" >Ordinal Features </td>\n",
       "                        <td id=\"T_cde21108_9cd0_11ea_89b2_acde48001122row7_col1\" class=\"data row7 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_cde21108_9cd0_11ea_89b2_acde48001122level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_cde21108_9cd0_11ea_89b2_acde48001122row8_col0\" class=\"data row8 col0\" >High Cardinality Features </td>\n",
       "                        <td id=\"T_cde21108_9cd0_11ea_89b2_acde48001122row8_col1\" class=\"data row8 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_cde21108_9cd0_11ea_89b2_acde48001122level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_cde21108_9cd0_11ea_89b2_acde48001122row9_col0\" class=\"data row9 col0\" >High Cardinality Method </td>\n",
       "                        <td id=\"T_cde21108_9cd0_11ea_89b2_acde48001122row9_col1\" class=\"data row9 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_cde21108_9cd0_11ea_89b2_acde48001122level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "                        <td id=\"T_cde21108_9cd0_11ea_89b2_acde48001122row10_col0\" class=\"data row10 col0\" >Sampled Data</td>\n",
       "                        <td id=\"T_cde21108_9cd0_11ea_89b2_acde48001122row10_col1\" class=\"data row10 col1\" >(3495, 135)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_cde21108_9cd0_11ea_89b2_acde48001122level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "                        <td id=\"T_cde21108_9cd0_11ea_89b2_acde48001122row11_col0\" class=\"data row11 col0\" >Transformed Train Set</td>\n",
       "                        <td id=\"T_cde21108_9cd0_11ea_89b2_acde48001122row11_col1\" class=\"data row11 col1\" >(2446, 157)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_cde21108_9cd0_11ea_89b2_acde48001122level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "                        <td id=\"T_cde21108_9cd0_11ea_89b2_acde48001122row12_col0\" class=\"data row12 col0\" >Transformed Test Set</td>\n",
       "                        <td id=\"T_cde21108_9cd0_11ea_89b2_acde48001122row12_col1\" class=\"data row12 col1\" >(1049, 157)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_cde21108_9cd0_11ea_89b2_acde48001122level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "                        <td id=\"T_cde21108_9cd0_11ea_89b2_acde48001122row13_col0\" class=\"data row13 col0\" >Numeric Imputer </td>\n",
       "                        <td id=\"T_cde21108_9cd0_11ea_89b2_acde48001122row13_col1\" class=\"data row13 col1\" >mean</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_cde21108_9cd0_11ea_89b2_acde48001122level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "                        <td id=\"T_cde21108_9cd0_11ea_89b2_acde48001122row14_col0\" class=\"data row14 col0\" >Categorical Imputer </td>\n",
       "                        <td id=\"T_cde21108_9cd0_11ea_89b2_acde48001122row14_col1\" class=\"data row14 col1\" >constant</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_cde21108_9cd0_11ea_89b2_acde48001122level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "                        <td id=\"T_cde21108_9cd0_11ea_89b2_acde48001122row15_col0\" class=\"data row15 col0\" >Normalize </td>\n",
       "                        <td id=\"T_cde21108_9cd0_11ea_89b2_acde48001122row15_col1\" class=\"data row15 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_cde21108_9cd0_11ea_89b2_acde48001122level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "                        <td id=\"T_cde21108_9cd0_11ea_89b2_acde48001122row16_col0\" class=\"data row16 col0\" >Normalize Method </td>\n",
       "                        <td id=\"T_cde21108_9cd0_11ea_89b2_acde48001122row16_col1\" class=\"data row16 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_cde21108_9cd0_11ea_89b2_acde48001122level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "                        <td id=\"T_cde21108_9cd0_11ea_89b2_acde48001122row17_col0\" class=\"data row17 col0\" >Transformation </td>\n",
       "                        <td id=\"T_cde21108_9cd0_11ea_89b2_acde48001122row17_col1\" class=\"data row17 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_cde21108_9cd0_11ea_89b2_acde48001122level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "                        <td id=\"T_cde21108_9cd0_11ea_89b2_acde48001122row18_col0\" class=\"data row18 col0\" >Transformation Method </td>\n",
       "                        <td id=\"T_cde21108_9cd0_11ea_89b2_acde48001122row18_col1\" class=\"data row18 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_cde21108_9cd0_11ea_89b2_acde48001122level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "                        <td id=\"T_cde21108_9cd0_11ea_89b2_acde48001122row19_col0\" class=\"data row19 col0\" >PCA </td>\n",
       "                        <td id=\"T_cde21108_9cd0_11ea_89b2_acde48001122row19_col1\" class=\"data row19 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_cde21108_9cd0_11ea_89b2_acde48001122level0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
       "                        <td id=\"T_cde21108_9cd0_11ea_89b2_acde48001122row20_col0\" class=\"data row20 col0\" >PCA Method </td>\n",
       "                        <td id=\"T_cde21108_9cd0_11ea_89b2_acde48001122row20_col1\" class=\"data row20 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_cde21108_9cd0_11ea_89b2_acde48001122level0_row21\" class=\"row_heading level0 row21\" >21</th>\n",
       "                        <td id=\"T_cde21108_9cd0_11ea_89b2_acde48001122row21_col0\" class=\"data row21 col0\" >PCA Components </td>\n",
       "                        <td id=\"T_cde21108_9cd0_11ea_89b2_acde48001122row21_col1\" class=\"data row21 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_cde21108_9cd0_11ea_89b2_acde48001122level0_row22\" class=\"row_heading level0 row22\" >22</th>\n",
       "                        <td id=\"T_cde21108_9cd0_11ea_89b2_acde48001122row22_col0\" class=\"data row22 col0\" >Ignore Low Variance </td>\n",
       "                        <td id=\"T_cde21108_9cd0_11ea_89b2_acde48001122row22_col1\" class=\"data row22 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_cde21108_9cd0_11ea_89b2_acde48001122level0_row23\" class=\"row_heading level0 row23\" >23</th>\n",
       "                        <td id=\"T_cde21108_9cd0_11ea_89b2_acde48001122row23_col0\" class=\"data row23 col0\" >Combine Rare Levels </td>\n",
       "                        <td id=\"T_cde21108_9cd0_11ea_89b2_acde48001122row23_col1\" class=\"data row23 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_cde21108_9cd0_11ea_89b2_acde48001122level0_row24\" class=\"row_heading level0 row24\" >24</th>\n",
       "                        <td id=\"T_cde21108_9cd0_11ea_89b2_acde48001122row24_col0\" class=\"data row24 col0\" >Rare Level Threshold </td>\n",
       "                        <td id=\"T_cde21108_9cd0_11ea_89b2_acde48001122row24_col1\" class=\"data row24 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_cde21108_9cd0_11ea_89b2_acde48001122level0_row25\" class=\"row_heading level0 row25\" >25</th>\n",
       "                        <td id=\"T_cde21108_9cd0_11ea_89b2_acde48001122row25_col0\" class=\"data row25 col0\" >Numeric Binning </td>\n",
       "                        <td id=\"T_cde21108_9cd0_11ea_89b2_acde48001122row25_col1\" class=\"data row25 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_cde21108_9cd0_11ea_89b2_acde48001122level0_row26\" class=\"row_heading level0 row26\" >26</th>\n",
       "                        <td id=\"T_cde21108_9cd0_11ea_89b2_acde48001122row26_col0\" class=\"data row26 col0\" >Remove Outliers </td>\n",
       "                        <td id=\"T_cde21108_9cd0_11ea_89b2_acde48001122row26_col1\" class=\"data row26 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_cde21108_9cd0_11ea_89b2_acde48001122level0_row27\" class=\"row_heading level0 row27\" >27</th>\n",
       "                        <td id=\"T_cde21108_9cd0_11ea_89b2_acde48001122row27_col0\" class=\"data row27 col0\" >Outliers Threshold </td>\n",
       "                        <td id=\"T_cde21108_9cd0_11ea_89b2_acde48001122row27_col1\" class=\"data row27 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_cde21108_9cd0_11ea_89b2_acde48001122level0_row28\" class=\"row_heading level0 row28\" >28</th>\n",
       "                        <td id=\"T_cde21108_9cd0_11ea_89b2_acde48001122row28_col0\" class=\"data row28 col0\" >Remove Multicollinearity </td>\n",
       "                        <td id=\"T_cde21108_9cd0_11ea_89b2_acde48001122row28_col1\" class=\"data row28 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_cde21108_9cd0_11ea_89b2_acde48001122level0_row29\" class=\"row_heading level0 row29\" >29</th>\n",
       "                        <td id=\"T_cde21108_9cd0_11ea_89b2_acde48001122row29_col0\" class=\"data row29 col0\" >Multicollinearity Threshold </td>\n",
       "                        <td id=\"T_cde21108_9cd0_11ea_89b2_acde48001122row29_col1\" class=\"data row29 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_cde21108_9cd0_11ea_89b2_acde48001122level0_row30\" class=\"row_heading level0 row30\" >30</th>\n",
       "                        <td id=\"T_cde21108_9cd0_11ea_89b2_acde48001122row30_col0\" class=\"data row30 col0\" >Clustering </td>\n",
       "                        <td id=\"T_cde21108_9cd0_11ea_89b2_acde48001122row30_col1\" class=\"data row30 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_cde21108_9cd0_11ea_89b2_acde48001122level0_row31\" class=\"row_heading level0 row31\" >31</th>\n",
       "                        <td id=\"T_cde21108_9cd0_11ea_89b2_acde48001122row31_col0\" class=\"data row31 col0\" >Clustering Iteration </td>\n",
       "                        <td id=\"T_cde21108_9cd0_11ea_89b2_acde48001122row31_col1\" class=\"data row31 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_cde21108_9cd0_11ea_89b2_acde48001122level0_row32\" class=\"row_heading level0 row32\" >32</th>\n",
       "                        <td id=\"T_cde21108_9cd0_11ea_89b2_acde48001122row32_col0\" class=\"data row32 col0\" >Polynomial Features </td>\n",
       "                        <td id=\"T_cde21108_9cd0_11ea_89b2_acde48001122row32_col1\" class=\"data row32 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_cde21108_9cd0_11ea_89b2_acde48001122level0_row33\" class=\"row_heading level0 row33\" >33</th>\n",
       "                        <td id=\"T_cde21108_9cd0_11ea_89b2_acde48001122row33_col0\" class=\"data row33 col0\" >Polynomial Degree </td>\n",
       "                        <td id=\"T_cde21108_9cd0_11ea_89b2_acde48001122row33_col1\" class=\"data row33 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_cde21108_9cd0_11ea_89b2_acde48001122level0_row34\" class=\"row_heading level0 row34\" >34</th>\n",
       "                        <td id=\"T_cde21108_9cd0_11ea_89b2_acde48001122row34_col0\" class=\"data row34 col0\" >Trignometry Features </td>\n",
       "                        <td id=\"T_cde21108_9cd0_11ea_89b2_acde48001122row34_col1\" class=\"data row34 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_cde21108_9cd0_11ea_89b2_acde48001122level0_row35\" class=\"row_heading level0 row35\" >35</th>\n",
       "                        <td id=\"T_cde21108_9cd0_11ea_89b2_acde48001122row35_col0\" class=\"data row35 col0\" >Polynomial Threshold </td>\n",
       "                        <td id=\"T_cde21108_9cd0_11ea_89b2_acde48001122row35_col1\" class=\"data row35 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_cde21108_9cd0_11ea_89b2_acde48001122level0_row36\" class=\"row_heading level0 row36\" >36</th>\n",
       "                        <td id=\"T_cde21108_9cd0_11ea_89b2_acde48001122row36_col0\" class=\"data row36 col0\" >Group Features </td>\n",
       "                        <td id=\"T_cde21108_9cd0_11ea_89b2_acde48001122row36_col1\" class=\"data row36 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_cde21108_9cd0_11ea_89b2_acde48001122level0_row37\" class=\"row_heading level0 row37\" >37</th>\n",
       "                        <td id=\"T_cde21108_9cd0_11ea_89b2_acde48001122row37_col0\" class=\"data row37 col0\" >Feature Selection </td>\n",
       "                        <td id=\"T_cde21108_9cd0_11ea_89b2_acde48001122row37_col1\" class=\"data row37 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_cde21108_9cd0_11ea_89b2_acde48001122level0_row38\" class=\"row_heading level0 row38\" >38</th>\n",
       "                        <td id=\"T_cde21108_9cd0_11ea_89b2_acde48001122row38_col0\" class=\"data row38 col0\" >Features Selection Threshold </td>\n",
       "                        <td id=\"T_cde21108_9cd0_11ea_89b2_acde48001122row38_col1\" class=\"data row38 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_cde21108_9cd0_11ea_89b2_acde48001122level0_row39\" class=\"row_heading level0 row39\" >39</th>\n",
       "                        <td id=\"T_cde21108_9cd0_11ea_89b2_acde48001122row39_col0\" class=\"data row39 col0\" >Feature Interaction </td>\n",
       "                        <td id=\"T_cde21108_9cd0_11ea_89b2_acde48001122row39_col1\" class=\"data row39 col1\" >False</td>\n",
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       "            <tr>\n",
       "                        <th id=\"T_cde21108_9cd0_11ea_89b2_acde48001122level0_row40\" class=\"row_heading level0 row40\" >40</th>\n",
       "                        <td id=\"T_cde21108_9cd0_11ea_89b2_acde48001122row40_col0\" class=\"data row40 col0\" >Feature Ratio </td>\n",
       "                        <td id=\"T_cde21108_9cd0_11ea_89b2_acde48001122row40_col1\" class=\"data row40 col1\" >False</td>\n",
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       "                        <th id=\"T_cde21108_9cd0_11ea_89b2_acde48001122level0_row41\" class=\"row_heading level0 row41\" >41</th>\n",
       "                        <td id=\"T_cde21108_9cd0_11ea_89b2_acde48001122row41_col0\" class=\"data row41 col0\" >Interaction Threshold </td>\n",
       "                        <td id=\"T_cde21108_9cd0_11ea_89b2_acde48001122row41_col1\" class=\"data row41 col1\" >None</td>\n",
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      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x140670b80>"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bnb_setup = setup(df, target='price')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comparing all models in the model library and score them using K-fold Cross Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "        }</style><table id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Model</th>        <th class=\"col_heading level0 col1\" >MAE</th>        <th class=\"col_heading level0 col2\" >MSE</th>        <th class=\"col_heading level0 col3\" >RMSE</th>        <th class=\"col_heading level0 col4\" >R2</th>        <th class=\"col_heading level0 col5\" >RMSLE</th>        <th class=\"col_heading level0 col6\" >MAPE</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row0_col0\" class=\"data row0 col0\" >CatBoost Regressor</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row0_col1\" class=\"data row0 col1\" >32.926100</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row0_col2\" class=\"data row0 col2\" >2275.892600</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row0_col3\" class=\"data row0 col3\" >47.671700</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row0_col4\" class=\"data row0 col4\" >0.725600</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row0_col5\" class=\"data row0 col5\" >0.295400</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row0_col6\" class=\"data row0 col6\" >0.247500</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row1_col0\" class=\"data row1 col0\" >Light Gradient Boosting Machine</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row1_col1\" class=\"data row1 col1\" >33.767200</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row1_col2\" class=\"data row1 col2\" >2432.534500</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row1_col3\" class=\"data row1 col3\" >49.303700</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row1_col4\" class=\"data row1 col4\" >0.705900</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row1_col5\" class=\"data row1 col5\" >0.302000</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row1_col6\" class=\"data row1 col6\" >0.249600</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row2_col0\" class=\"data row2 col0\" >Random Forest</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row2_col1\" class=\"data row2 col1\" >34.349500</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row2_col2\" class=\"data row2 col2\" >2520.858300</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row2_col3\" class=\"data row2 col3\" >50.176000</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row2_col4\" class=\"data row2 col4\" >0.695600</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row2_col5\" class=\"data row2 col5\" >0.311000</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row2_col6\" class=\"data row2 col6\" >0.263800</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row3_col0\" class=\"data row3 col0\" >Extreme Gradient Boosting</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row3_col1\" class=\"data row3 col1\" >35.839400</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row3_col2\" class=\"data row3 col2\" >2609.251900</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row3_col3\" class=\"data row3 col3\" >51.034800</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row3_col4\" class=\"data row3 col4\" >0.685500</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row3_col5\" class=\"data row3 col5\" >0.315900</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row3_col6\" class=\"data row3 col6\" >0.272900</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row4_col0\" class=\"data row4 col0\" >Gradient Boosting Regressor</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row4_col1\" class=\"data row4 col1\" >36.028600</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row4_col2\" class=\"data row4 col2\" >2632.604600</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row4_col3\" class=\"data row4 col3\" >51.248600</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row4_col4\" class=\"data row4 col4\" >0.683000</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row4_col5\" class=\"data row4 col5\" >0.317000</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row4_col6\" class=\"data row4 col6\" >0.273400</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row5_col0\" class=\"data row5 col0\" >Extra Trees Regressor</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row5_col1\" class=\"data row5 col1\" >35.880400</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row5_col2\" class=\"data row5 col2\" >2800.798400</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row5_col3\" class=\"data row5 col3\" >52.884100</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row5_col4\" class=\"data row5 col4\" >0.660600</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row5_col5\" class=\"data row5 col5\" >0.328000</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row5_col6\" class=\"data row5 col6\" >0.272400</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row6_col0\" class=\"data row6 col0\" >Orthogonal Matching Pursuit</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row6_col1\" class=\"data row6 col1\" >40.410100</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row6_col2\" class=\"data row6 col2\" >3163.407700</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row6_col3\" class=\"data row6 col3\" >56.204900</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row6_col4\" class=\"data row6 col4\" >0.618300</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row6_col5\" class=\"data row6 col5\" >0.369700</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row6_col6\" class=\"data row6 col6\" >0.308800</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row7_col0\" class=\"data row7 col0\" >AdaBoost Regressor</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row7_col1\" class=\"data row7 col1\" >55.022200</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row7_col2\" class=\"data row7 col2\" >4328.770900</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row7_col3\" class=\"data row7 col3\" >65.746000</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row7_col4\" class=\"data row7 col4\" >0.477200</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row7_col5\" class=\"data row7 col5\" >0.491400</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row7_col6\" class=\"data row7 col6\" >0.548000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row8_col0\" class=\"data row8 col0\" >Decision Tree</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row8_col1\" class=\"data row8 col1\" >49.037200</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row8_col2\" class=\"data row8 col2\" >5466.341700</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row8_col3\" class=\"data row8 col3\" >73.910900</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row8_col4\" class=\"data row8 col4\" >0.336000</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row8_col5\" class=\"data row8 col5\" >0.447700</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row8_col6\" class=\"data row8 col6\" >0.353700</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row9_col0\" class=\"data row9 col0\" >K Neighbors Regressor</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row9_col1\" class=\"data row9 col1\" >62.015000</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row9_col2\" class=\"data row9 col2\" >6751.710000</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row9_col3\" class=\"data row9 col3\" >82.133200</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row9_col4\" class=\"data row9 col4\" >0.184800</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row9_col5\" class=\"data row9 col5\" >0.553500</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row9_col6\" class=\"data row9 col6\" >0.535400</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row10_col0\" class=\"data row10 col0\" >Lasso Least Angle Regression</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row10_col1\" class=\"data row10 col1\" >66.498500</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row10_col2\" class=\"data row10 col2\" >7001.499900</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row10_col3\" class=\"data row10 col3\" >83.598900</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row10_col4\" class=\"data row10 col4\" >0.156700</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row10_col5\" class=\"data row10 col5\" >0.569000</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row10_col6\" class=\"data row10 col6\" >0.614600</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row11_col0\" class=\"data row11 col0\" >Support Vector Machine</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row11_col1\" class=\"data row11 col1\" >72.542800</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row11_col2\" class=\"data row11 col2\" >8545.591100</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row11_col3\" class=\"data row11 col3\" >92.370200</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row11_col4\" class=\"data row11 col4\" >-0.029900</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row11_col5\" class=\"data row11 col5\" >0.602700</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row11_col6\" class=\"data row11 col6\" >0.620900</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row12_col0\" class=\"data row12 col0\" >Passive Aggressive Regressor</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row12_col1\" class=\"data row12 col1\" >135.280400</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row12_col2\" class=\"data row12 col2\" >40121.891200</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row12_col3\" class=\"data row12 col3\" >182.553500</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row12_col4\" class=\"data row12 col4\" >-3.830500</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row12_col5\" class=\"data row12 col5\" >0.972900</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row12_col6\" class=\"data row12 col6\" >1.182000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row13_col0\" class=\"data row13 col0\" >Random Sample Consensus</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row13_col1\" class=\"data row13 col1\" >129.717000</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row13_col2\" class=\"data row13 col2\" >20553886.473200</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row13_col3\" class=\"data row13 col3\" >2070.655700</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row13_col4\" class=\"data row13 col4\" >-2230.520500</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row13_col5\" class=\"data row13 col5\" >0.402000</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row13_col6\" class=\"data row13 col6\" >1.016100</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row14_col0\" class=\"data row14 col0\" >Huber Regressor</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row14_col1\" class=\"data row14 col1\" >239.872700</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row14_col2\" class=\"data row14 col2\" >71460582.388500</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row14_col3\" class=\"data row14 col3\" >3851.483700</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row14_col4\" class=\"data row14 col4\" >-7757.396100</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row14_col5\" class=\"data row14 col5\" >0.599700</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row14_col6\" class=\"data row14 col6\" >1.940700</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row15_col0\" class=\"data row15 col0\" >Elastic Net</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row15_col1\" class=\"data row15 col1\" >265.309700</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row15_col2\" class=\"data row15 col2\" >116466543.444800</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row15_col3\" class=\"data row15 col3\" >4875.654700</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row15_col4\" class=\"data row15 col4\" >-12643.527000</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row15_col5\" class=\"data row15 col5\" >0.452400</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row15_col6\" class=\"data row15 col6\" >2.139200</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row16_col0\" class=\"data row16 col0\" >Lasso Regression</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row16_col1\" class=\"data row16 col1\" >285.206900</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row16_col2\" class=\"data row16 col2\" >146441352.111500</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row16_col3\" class=\"data row16 col3\" >5456.221200</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row16_col4\" class=\"data row16 col4\" >-15897.807800</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row16_col5\" class=\"data row16 col5\" >0.399500</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row16_col6\" class=\"data row16 col6\" >2.273200</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row17_col0\" class=\"data row17 col0\" >Linear Regression</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row17_col1\" class=\"data row17 col1\" >298.231400</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row17_col2\" class=\"data row17 col2\" >165173029.530200</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row17_col3\" class=\"data row17 col3\" >5789.148600</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row17_col4\" class=\"data row17 col4\" >-17931.454500</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row17_col5\" class=\"data row17 col5\" >0.413400</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row17_col6\" class=\"data row17 col6\" >2.375000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row18_col0\" class=\"data row18 col0\" >Ridge Regression</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row18_col1\" class=\"data row18 col1\" >302.035800</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row18_col2\" class=\"data row18 col2\" >170122988.397000</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row18_col3\" class=\"data row18 col3\" >5874.519300</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row18_col4\" class=\"data row18 col4\" >-18468.858800</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row18_col5\" class=\"data row18 col5\" >0.412100</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row18_col6\" class=\"data row18 col6\" >2.406200</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row19_col0\" class=\"data row19 col0\" >Bayesian Ridge</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row19_col1\" class=\"data row19 col1\" >309.958900</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row19_col2\" class=\"data row19 col2\" >180881721.569100</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row19_col3\" class=\"data row19 col3\" >6056.031000</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row19_col4\" class=\"data row19 col4\" >-19636.907200</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row19_col5\" class=\"data row19 col5\" >0.414400</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row19_col6\" class=\"data row19 col6\" >2.468500</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122level0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row20_col0\" class=\"data row20 col0\" >TheilSen Regressor</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row20_col1\" class=\"data row20 col1\" >330.230600</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row20_col2\" class=\"data row20 col2\" >197796980.526600</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row20_col3\" class=\"data row20 col3\" >6337.264600</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row20_col4\" class=\"data row20 col4\" >-21473.367200</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row20_col5\" class=\"data row20 col5\" >0.513600</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row20_col6\" class=\"data row20 col6\" >2.670700</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122level0_row21\" class=\"row_heading level0 row21\" >21</th>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row21_col0\" class=\"data row21 col0\" >Least Angle Regression</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row21_col1\" class=\"data row21 col1\" >4168209719964905751068791528335867904.000000</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row21_col2\" class=\"data row21 col2\" >182108245822493934045890452105120330729847447583203757717190644227100377088.000000</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row21_col3\" class=\"data row21 col3\" >6035035141943979797889023702640623616.000000</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row21_col4\" class=\"data row21 col4\" >-20833247305056558304941373071703526695613889360431373272793224239382528.000000</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row21_col5\" class=\"data row21 col5\" >24.212600</td>\n",
       "                        <td id=\"T_1bf79ae2_9cd2_11ea_89b2_acde48001122row21_col6\" class=\"data row21 col6\" >40486853681480591074327979569971200.000000</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x14016c190>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compare_models(fold=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have selected highest scoring model for perdiction. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>MAE</th>\n",
       "      <th>MSE</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>R2</th>\n",
       "      <th>RMSLE</th>\n",
       "      <th>MAPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>29.2969</td>\n",
       "      <td>1790.9440</td>\n",
       "      <td>42.3195</td>\n",
       "      <td>0.7527</td>\n",
       "      <td>0.3115</td>\n",
       "      <td>0.2855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>33.0991</td>\n",
       "      <td>2200.3129</td>\n",
       "      <td>46.9075</td>\n",
       "      <td>0.7445</td>\n",
       "      <td>0.2817</td>\n",
       "      <td>0.2384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>31.9811</td>\n",
       "      <td>2289.8277</td>\n",
       "      <td>47.8521</td>\n",
       "      <td>0.7415</td>\n",
       "      <td>0.2851</td>\n",
       "      <td>0.2252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35.5426</td>\n",
       "      <td>2604.3860</td>\n",
       "      <td>51.0332</td>\n",
       "      <td>0.7265</td>\n",
       "      <td>0.2928</td>\n",
       "      <td>0.2504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32.6938</td>\n",
       "      <td>2266.2824</td>\n",
       "      <td>47.6055</td>\n",
       "      <td>0.7163</td>\n",
       "      <td>0.2647</td>\n",
       "      <td>0.2059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>32.7805</td>\n",
       "      <td>2553.4094</td>\n",
       "      <td>50.5313</td>\n",
       "      <td>0.7313</td>\n",
       "      <td>0.2736</td>\n",
       "      <td>0.2271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>31.7081</td>\n",
       "      <td>1925.8955</td>\n",
       "      <td>43.8850</td>\n",
       "      <td>0.7454</td>\n",
       "      <td>0.3033</td>\n",
       "      <td>0.2465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>34.3131</td>\n",
       "      <td>2698.7546</td>\n",
       "      <td>51.9495</td>\n",
       "      <td>0.6824</td>\n",
       "      <td>0.3061</td>\n",
       "      <td>0.2532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>33.0470</td>\n",
       "      <td>2072.2955</td>\n",
       "      <td>45.5225</td>\n",
       "      <td>0.7199</td>\n",
       "      <td>0.3202</td>\n",
       "      <td>0.2837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>32.0026</td>\n",
       "      <td>2134.4965</td>\n",
       "      <td>46.2006</td>\n",
       "      <td>0.7185</td>\n",
       "      <td>0.2963</td>\n",
       "      <td>0.2242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean</th>\n",
       "      <td>32.6465</td>\n",
       "      <td>2253.6604</td>\n",
       "      <td>47.3807</td>\n",
       "      <td>0.7279</td>\n",
       "      <td>0.2935</td>\n",
       "      <td>0.2440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SD</th>\n",
       "      <td>1.5655</td>\n",
       "      <td>279.9839</td>\n",
       "      <td>2.9550</td>\n",
       "      <td>0.0194</td>\n",
       "      <td>0.0165</td>\n",
       "      <td>0.0244</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          MAE        MSE     RMSE      R2   RMSLE    MAPE\n",
       "0     29.2969  1790.9440  42.3195  0.7527  0.3115  0.2855\n",
       "1     33.0991  2200.3129  46.9075  0.7445  0.2817  0.2384\n",
       "2     31.9811  2289.8277  47.8521  0.7415  0.2851  0.2252\n",
       "3     35.5426  2604.3860  51.0332  0.7265  0.2928  0.2504\n",
       "4     32.6938  2266.2824  47.6055  0.7163  0.2647  0.2059\n",
       "5     32.7805  2553.4094  50.5313  0.7313  0.2736  0.2271\n",
       "6     31.7081  1925.8955  43.8850  0.7454  0.3033  0.2465\n",
       "7     34.3131  2698.7546  51.9495  0.6824  0.3061  0.2532\n",
       "8     33.0470  2072.2955  45.5225  0.7199  0.3202  0.2837\n",
       "9     32.0026  2134.4965  46.2006  0.7185  0.2963  0.2242\n",
       "Mean  32.6465  2253.6604  47.3807  0.7279  0.2935  0.2440\n",
       "SD     1.5655   279.9839   2.9550  0.0194  0.0165  0.0244"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "catboost_regressor = create_model('catboost')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's tune the model to get the best hyperparameters and select the best model based on Mean Square Error."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MAE</th>\n",
       "      <th>MSE</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>R2</th>\n",
       "      <th>RMSLE</th>\n",
       "      <th>MAPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30.6531</td>\n",
       "      <td>2076.5713</td>\n",
       "      <td>45.5694</td>\n",
       "      <td>0.7133</td>\n",
       "      <td>0.3323</td>\n",
       "      <td>0.3175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>35.4052</td>\n",
       "      <td>2449.0869</td>\n",
       "      <td>49.4882</td>\n",
       "      <td>0.7156</td>\n",
       "      <td>0.3005</td>\n",
       "      <td>0.2469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>33.6164</td>\n",
       "      <td>2435.3448</td>\n",
       "      <td>49.3492</td>\n",
       "      <td>0.7251</td>\n",
       "      <td>0.3074</td>\n",
       "      <td>0.2414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>36.7964</td>\n",
       "      <td>2796.4037</td>\n",
       "      <td>52.8810</td>\n",
       "      <td>0.7064</td>\n",
       "      <td>0.3054</td>\n",
       "      <td>0.2599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>33.0873</td>\n",
       "      <td>2356.0209</td>\n",
       "      <td>48.5389</td>\n",
       "      <td>0.7051</td>\n",
       "      <td>0.2736</td>\n",
       "      <td>0.2080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>34.1076</td>\n",
       "      <td>2676.7304</td>\n",
       "      <td>51.7371</td>\n",
       "      <td>0.7183</td>\n",
       "      <td>0.2783</td>\n",
       "      <td>0.2347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>32.1869</td>\n",
       "      <td>1936.2267</td>\n",
       "      <td>44.0026</td>\n",
       "      <td>0.7441</td>\n",
       "      <td>0.2987</td>\n",
       "      <td>0.2449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>35.3209</td>\n",
       "      <td>2809.2780</td>\n",
       "      <td>53.0026</td>\n",
       "      <td>0.6694</td>\n",
       "      <td>0.3183</td>\n",
       "      <td>0.2616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>32.5733</td>\n",
       "      <td>2095.9955</td>\n",
       "      <td>45.7820</td>\n",
       "      <td>0.7167</td>\n",
       "      <td>0.3314</td>\n",
       "      <td>0.2840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>34.6072</td>\n",
       "      <td>2487.6999</td>\n",
       "      <td>49.8768</td>\n",
       "      <td>0.6719</td>\n",
       "      <td>0.3156</td>\n",
       "      <td>0.2458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean</th>\n",
       "      <td>33.8354</td>\n",
       "      <td>2411.9358</td>\n",
       "      <td>49.0228</td>\n",
       "      <td>0.7086</td>\n",
       "      <td>0.3062</td>\n",
       "      <td>0.2545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SD</th>\n",
       "      <td>1.7074</td>\n",
       "      <td>287.2619</td>\n",
       "      <td>2.9498</td>\n",
       "      <td>0.0216</td>\n",
       "      <td>0.0187</td>\n",
       "      <td>0.0281</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          MAE        MSE     RMSE      R2   RMSLE    MAPE\n",
       "0     30.6531  2076.5713  45.5694  0.7133  0.3323  0.3175\n",
       "1     35.4052  2449.0869  49.4882  0.7156  0.3005  0.2469\n",
       "2     33.6164  2435.3448  49.3492  0.7251  0.3074  0.2414\n",
       "3     36.7964  2796.4037  52.8810  0.7064  0.3054  0.2599\n",
       "4     33.0873  2356.0209  48.5389  0.7051  0.2736  0.2080\n",
       "5     34.1076  2676.7304  51.7371  0.7183  0.2783  0.2347\n",
       "6     32.1869  1936.2267  44.0026  0.7441  0.2987  0.2449\n",
       "7     35.3209  2809.2780  53.0026  0.6694  0.3183  0.2616\n",
       "8     32.5733  2095.9955  45.7820  0.7167  0.3314  0.2840\n",
       "9     34.6072  2487.6999  49.8768  0.6719  0.3156  0.2458\n",
       "Mean  33.8354  2411.9358  49.0228  0.7086  0.3062  0.2545\n",
       "SD     1.7074   287.2619   2.9498  0.0216  0.0187  0.0281"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tuned_catboost_regressor = tune_model('catboost',optimize ='mse')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'iterations': 500,\n",
       " 'learning_rate': 0.2,\n",
       " 'depth': 7,\n",
       " 'l2_leaf_reg': 10,\n",
       " 'loss_function': 'RMSE',\n",
       " 'border_count': 5,\n",
       " 'silent': True,\n",
       " 'random_state': 2347}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions = predict_model(tuned_catboost_regressor)\n",
    "\n",
    "final_model = finalize_model(tuned_catboost_regressor)\n",
    "final_model.get_params()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our RMSE for final model is 41.25 dollars, meaning that our regressor is wrong by that much on average.\n",
    "<br><br>\n",
    "But our motive here is to understand important variables that contribute towards price of the AirBnB. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save the model for further use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Transformation Pipeline and Model Succesfully Saved\n"
     ]
    }
   ],
   "source": [
    "save_model(final_model, 'airbnb_catboost_1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot interpretation of the model based on the SHAP (SHapley Additive exPlanations), which is a unified approach to explain the output of the machine learning model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x684 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "interpret_model(final_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "RMSE for final model is 41.25 dollars, meaning that our regressor is wrong by that much on average. But the SHAP analysis tells \n",
    "\n",
    "* Clearly, an Entire home or Apartment has the biggest impact on the price, so it is for the number of bedrooms and bathrooms. These parameters alone can shift the price of your listing.\n",
    "Latitude and Longitude meaning the location is important as well, we will discuss this later in the post.\n",
    "* Availability throughout the year and acceptance rate of the host is an important factor. Higher values for both will benefit you.\n",
    "* As expected, the higher the number of minimum nights less popular will be Airbnb.\n",
    "* It is interesting to note that rentals with higher rents tend to charge little or none for the cleaning fee."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Important of feature for pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = final_model.get_feature_importance(prettified = True).set_index('Feature Id').to_dict()['Importances']\n",
    "\n",
    "#sort by decreasing importance \n",
    "feature_importance_dict = dict([(v[0],v[1]) for v in sorted(features.items(), key=lambda kv: (-kv[1], kv[0]))])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. What services will actually help you bump the price? Let's check the significance of annementies in price of the AirBnB."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Importance')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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g2wH2pg/X38in0of2H3QgvTFhVlMjy+Oq6mlJ/ij9gHrui9NekH6NiI3cJL0RammmHe0Tpx3HndIP0G6dfrAzZ8D2riTfW33qtIvSG8fftLb3WvpncLYpzI6kqq6c3th5eg6dAL81/f/3ryX5yar63jbPBTFvmD5ibBXCtaSH3N9bVZdrrc19LY0jaq3tXXYNa7XWnrXsGjbSWjt12TWs4wlr7rf0xohTN1j+vCRnbF85hxw2EiRJHlJ9iq7DXWwkyHbXNblqLt5AvZ6rZ/sbqA93h5m3N+pa6T23D/r29A4ez0yS1tpnp5OPe85Uz/Vy9OsT/VcO9ZKf23enT3P1gCVt/3B3yPoNKMt2Xvqx30ZOSrKZqbsW0lr7cuP8ZkbeLsl7ktylqi6fvt+/b/po27Uh5Ynpo462TWvttxZ8/9IukD5NDfQj6Z0orp2+H3lFeoPZI5M8uKru0dr/Z++8w62orjb+W6DYEWJUlKiY+Fliib0CYkXsJhq7IkYlGns0VkCNYu8YMVGR2HuLXcCCvbfYJWLXGFFjw3vX98e7hzv3MOecC9zZMzec93nm4U45znLPzC6rvK9XjmMzBHcfmLJhN0R5WKr3q2xzwxSeQ4l7yyFH4vbAw95aJ64XLXqTeeNVtL6/Cznwb3H372v9wESrOhAFV581sz3zyKgOeJ76a+4lUJA3Bt5ETvK/AZe5+xP1fhAqtHZAa8vbzexgdz83XzM5Ec3/1kk78l0sNuNDUP4F4ER3P8DMtkPvws7kE2Dri6pra+FhFLiMhnRfVlLsieYMq3i5tDb7IKd+ZXANAHd/PCSG9c/TiPSaNsx1Li3bWJTCaojK9qKiDalA2dZEpUsEDrgfJSeUDb0opx9/AlBP+mcNpGVXBI5E8gtHAXua2fNUZ9tpd/mDDBSaoN8IsLU/PkMBmCNrXLMxyvxIkAiZRkOgZNkULSY3Re+Co3LrmBgD7GhmS7n7VJ2CSX9mA5RFWigCZeQWqOpvjXA4tpD9OajU9T2UdTUncHjFNWsiGrFCYGYroUn9jsgZ/Q1aPF3g7s+Z2dzIWXEc4mrvG8Gsz4Gai9zIGIom1Nea2YEh2N1AA+2BJCBjqH8dhRw9lUgyEF/zeFS3/VJ/l60S5J/A9mY23DN0NkPF9/aICiIa3L0udV/B+B5lmCfoQ4v2UoIvka5EDLwHbGVmR2clL4QAxFYogFMEZqd1dUXR+Flb3rFQrT8ohkEBdwKDzay3Z+iUmtkAlOR0ckSbQIu2ZyPfsy24CNGFvYECyL2Yuu9cBYiq+VR2hAqUrdCcdUNU6fQuysb9m7t/EtZL2yEB99Oo7+SYbqSDuQ20CacCY1HgKMEZyR9h3bYOqj6KgZeB37j783WvDAgUfkeF6pp9aT2etjdOQqwsA7Iqv8xsM+T43TpHG9L4O6KYazNlWUgovQi4yMy2Jg5jyzbAldWqZNz9BzO7Da17D3D3b8zsflS5kgdmo36l70K0aK02IMwHXFiy4BooeFsvkfxdREEaC4ujQEJZ8QNTV1SXAaVaE5U0ERjEdvCESQf+xFh0v21AWf34twCHm9l27n5d5Ukz2wNVWh+dsx3VMDD1dw+qj0+OEh1yRdEJ+o0AW/vjBuAAk9DgUZ4ScQ08rcNR1u15qd+sghbFuSM4CfdEToKFkRP2I+SE/au7vxPDjhSGo4Xrg2FxsXCwc1kUeBmKqG9Oj2zXFJjZL1BQbXeU1WrIiXcJkUvj3f1WMxuMMvcArnD3y1O29kNZFHfHtMvM5kQLi33Q+2zIYX0hykqcUmHn0rI4NfAYx8hiALgdUeVZGQbxsPjaGzkG3g7cylmDs3sBYqUJzKw7MHdWwKFIhPft14guL5nYPIMyv2PRjlbaNCuisEns+WcRJfJpZ3mgtbq5LEGakleCnIaoiZ41s3PRYigRNl4X2B8tgk8rzMJy4h2kEZHgN8AbFfQaixCP6vMylLwxxsyOAsa7e1NwtPZGmeg/R3OLIvASqiQqCy42sw/cfWy1C8zsQjT/iRlgG46qFO4xs/MIgfjgAO4L7IeqUvKoDqiKohdt1eDul5l0eZK54fmk1hlmtjaqTIma7R3mEAsBb6UreYIzYGtEeXN2WypX2tmuRVGC4SBaHAF3k6HtFP6+NlCuHRjRxqURnezc7v73WPdtC8oyN3T3h8xsc/QsHa2J0oGjtVEyxU2R7NlqBn77A6IWbDeEishK3Ikqv+5n6nnO+sBt1NGnai+4+wx9Tx70JSNgPupLVcwarkvwEfn52Z4FdjCz8919qoSP0FdtDzyV0/3rwqSr12qNlhPF7rTgTUTdWjZ8AKxe55pViVeJm7BclRmPUJ0yv0iUbU1UVgxFCSnHAYPM7DmyA7qxKp4SlNWPfypaE11lZtsSGHfM7A8oiPvrYMN5Vf8L+SKazmGHgLs3tnbcUHbJ0ygDfzIq6Xw8/Ds5HH8G6BquXwgNEoNztGkWFMS6G1U9NYd/70RZWZ0LbrNNkD5WU9iaU/9+DqxfgE1dUMBoTMqWySiDYDOgU9HvWpk2NCg2oYyia4F+bfjNEUBzJPvmQ3QdI5FToOj26o34p5tDm01Ek7KptgJsmxtlAH8UnumPqXNrIMqklQtsu02R7kbyXSZbUzi+eWR7uqJA8n9TfVhT2L8Q6Fb0+1bGDTnsVyjajgqbDkbZh00VW3M4fnCBtiUC1Tcgao0bw/5iBbfZAaF9HkdUJE1IeDl9zfPAfZHsmRVVeSd9wmTkQJycepY3A7MU1F7boqruXxb53FL2vBzG7+WqnD8rtNn1Bdi2MnKIVfbzzWghuXxBbdYJBdwfQ47D9Bi5EnABsGTRz7bC5i5oQR71vUcBq6+AOVLH9q8Yv6N/D6n7f4ocF4u34TdR5qzIefNUegxKnVs3tNcWBbxDpZ4bNrbMZ9ZM9nym3tZUtO1l2sI4ORGYp8r5ruH8K6ljo4D3c7JnQ+TH+R4l+w5E2vADUSXz92HOs0EBbbUYokitfO+awvFeBT7HwaHP71H0O1Vh14jQPkdQ4ZML841Dw/kROdowBEme/CS135bt2ILa7Fdonb1r0c+vwq5SrYkq7jsXmqP2KUE7tWUcij4WUUI/fsq2RZEWY1Y7PQD0LPq5lm0D5g/9/jmIlSJ9fHVSa5P23CzcpIF2RKAgOhw5EH+eOvU2EtI71adBAHwG7FgaVV7tirLRDGXJXIJestKUVptZN9Rea6JgyCTkvLjU3aOJYIbKub2AXVCWkwH/Ai5GIqof1Pj5TAszm4gyo//q7m3Sfws8+t09QpZU0H7qhiZk3yDnXLVMmQ0i2DMeZaPtiWhHYlONZsLM5kW8/csibYsuwDLu3jmcnxM5qy9098MKsG9lNJHpjLS5xqCMvoVQxtiOaDK7jrs/HcGersB41F5foazSxJ4V0UTtFWBtb62TGAVm1gv4JfCAh8o+M5sFLaKS6oHT3D1KdndHQKjy3gUtQualpTryihh9VRWb9kJ0ul3QmJTGD8CB7j4yumFMqdy8DGVLG8p+/62HapWgjfMCWmD+OaJdO6FK/crneKm7x9L1y7Irqb7qjyb8T1OFhsfdH8w63s72LAo8ihZoa3oqy9bMTkZz2X8A23gBIvKh8nAzpNWQnhveUpA9XVByWj9aqKcXSo2R3VAA4hR3j1YlGSpVnnP3qtpJ4Vtc2fPTd8q65wvA2+6+derYv1BfsROqHhuN5kG/i2jXoygQeq3X0ciKiaDH9QSa4/wVWBIYkHq/DDny73f33SPaVeq5YUdDWJ8PQOuRqz0nyjozm+53xAuo1g39/Wzu/k3F8fURles3wEUemW3HzA5EySavoir48bRU/vVGtFxLAoe4+znhO30HeNndN8vJpm1R0mh3WuvcGEpa3tvdb8jj3jVs6gE8iXTXJ6AKyWRN1AdVOXwArNpWX0EONp6J3qXj0Zww89uL6SML7fY0Gg/fRYGZD8N+b1TB/xFqt1yq2AKriKN+/fWw3xZ4Mg7EhJkNQTpsm6Kxqdpc2t39hIh2lW5NFKpJz0EyN51Rm8wSzvVG/rt93X1cDHvCfdvM5BF77V0WP341mNkKVKyJYvi8OhrMbE/kO5kdfYuemrMuhwLde7v7xe1+86Iji//rG9Kg6kmVrKec751EtSejDn5LGpVXtdrrMVqy+35AFQKbgALRZdrQIHl16BzeTB1fBg0KUbMYyv5eUbJMGVS9Nqrodsmw69TQDruF/aGVbYLoNp8pyL4bgG+RIzjr/Brh/A2R7Bke2msEFZVqyKl/Xjg/vKD2uhTxhM+SOjas4p2fXK09I9hXmkoQYDegfxHtUMeuDcK49AVyCPQDlgr/Hh+O/0gBmcoVdnbNmueg5J5fAfMW3ZZl2JJxJj3mVNsi2vSr8B69SEtW5nHBvnuALkW3W1k25EhtRpnbnUJ/WjlG3oMWvLHfqyF1rjk65nsV7vlv4KzU/i+DrYeljl0DvF70sy3DBlxBqqKvyhzsOlKVMpHsKu3csEzziAzbhiBH+U9SxzZE89Skr38LmK/od68MGwpifZeeLyBarPSY+SmwSAG2XVBjzG5GweXk2gXR+iBXBh5UkbITktH4a/h3J2Cugp7fiKR/Z+pKrM7AH8P58wt8x3ZHyTFV517pPiSiXb0Q01SWb+Ju2lBlPYP3Xzdss1fs190Keo6l8utk2FeKNREKbr+P1ok3omBkuip+VkRX+Zci2qnsGwX68TviRkn800hHrwklvm8ZxqbKOesLwG153L+hwZYz3P0rVNlQBCbSUnlVNO91R8DqKOPsb6jN2iy6HAshK24UqrAALdLSYqr/QSLWBpwSyy4vSQVWNXj5hOO/RhP8suHXwN1eO8P9XyhrrAj0Aa5z98eyTrr742Z2PaoOiYFfI0fqfhm2TAL2D1V3v6G2YG5eWAtluv8IYGadgH1RFu7GKDvyPkSNuH1MwzIqQb5CFFQJ3kH6OJ8SRy/rEhQQjapf2QYchtpmFXd/K3X8NWBc0Nl7Olx3fwH2AeBVKjTd/TMaWgNpHE/rbPPC4e7Ph2z4fwC3horvY1EW9VYubaAGhJ2Rrt/xAGaW9SzfQYvMsqEz8d+9OZDDPME6wYb7UsfeAjaPaZSZvY0Cf1X1KsxsP+BQd/95tWtywAbAje7+So1rJiLnQUyUcm5YwnlEJQYAr3prJpbh6BsYiuZg+yJtvyHxzSsd+gJjvXVF31CUAHIgaq/hwCFo3hoN7r6vmV2JaBhXREl0XyIH3mhPVZwH/0Huc34XM8WVYSsDNgPucfeptIrdvQk43cw2RP39H2IbZ2a/Q1V/PyKqtQ/C34XD3ScA/c2sJ62ZF5711vpded3/gVr71WBmc9e/KhesV9B9a8LMLgFedPezss4XsCYaCiwAbOTuY81sKPINJPZMNrOH0NysgQoU7MefAjObA9EafpS1JgtVdwsCn3gB1XUl9E//CSU3revuX5pZll7jC6S+hfZEI8D2v40JwIQyB9fMbDByDPbxDPrFMNF4EDjJ8yjhbI29gSfd/fmc7zMj2BdRfl6COLkPRo4wANz9o0A/uBk5dmBVhLPbhDoL9JkFd6DMr7LhZ6hKrBa+JoirFoB5kXOpFt5FmWMxsBj12+sBIjsCUlgQOb0SrIiy544L48J7ZnYLClzGxmFogTQM+DMt/P8AuPsXZvYgCpbGcIx9hDLhy4bVEY3ZW1kn3f0tM7sOBXEbCAgUGjuhrLm53H3DcLwXatN73f0/se1y92Gx79kWuPt9gU7jMtQfPAZs6u7fxrh/B5pTLI4CkbXwOfCTCLZMK5ZEi9yYeB9YOrXfHzml0/Ps7mgxHhO9wn1roRsa42OiO1BvzWaIojEmyjo3LNs8ohK9gCkU3GFNuwpwpgd6sEAXuTWNABvAIogGHgAz+zmq2D/e3S8Px/oidpno82p3fxhVgJQKZtYd6YvXWx/ljR6oCrcWnkYB8SLwR+ATRNsflWa0rQjBtNwDalkwsy3c/bZpuH5O5M/om59V2WhrALAA7IQqccuCTYFb3X1sjWveJWc/QKCjB+lSNqX268JLJGlUIIYAB6FquqwE/blQ8vTpFDOXKIV/OoVVEf12LXmW99CY1e5oBNhygJmtiyb9q6PFUpbTzj3w3+aIPhSY0d5G7AR8mBVcA000zOw9FBHPO8A2Ei3Syhxg2xPZt5e7e5Xs6TfIv4JnFNOeCW3hN40Am4SMx5vZCODwkIVYBnyFMp1qYXGKq0j5APWrtbAqylqJgf9Sv73mR5RPRWBWWn+nSfXAmNSx9xCFRGyUrRLkLmA9M+tUsorcOaj/vX1K60yx3BAqP6YH7u6/aFdjqsDMjgeOomXulX63OiH9xoNQxeJMheCcrIZ3gSTgfjKwipISBc9XE24UHWNO8R0KvNTColTR1WtPhEzpNLYOAeRKdA429aF+cLC9MRbY3cz+gNpuS0ThnO5jf0H9xJkiMA+ii4+Jj4El6lyzLPHbq6xzw7LNIyrRndbOsGQOdnvq2NPAPjGMmYbxO9p4XYGuKACfIGmvu1LHXqak1SsxESqHjkPfwPyonRJdpTVQQPkYd38molmTqJ+UsChVdM8iYDHgb2UNrsEUvayV0DxjEqLdjZUof7WZbVCNJSaNUDFzK43Kp0pMoP5YGRMLIr9gLUxGAZo8MYGg8Qe8ntqvhyn9WiyUyI+fxgDgvopq+LQxn5vZfag6uIgAW1n80wm6IB9dLXRDNJLtjkaArZ1hZpsBN6MF7buIxqkU5eclxVLA9XWueQHYNoItFrYyYylgpAfy2Cr4BE2288QeOf/3c4GZ7QD8jhb6hS/R4vZid786oilXI4fFYGA3M3ud7AWHu/sGEe16EtjczOYJZfGtYGYLoWyo26f6ZRzcAQw2syOA0wLlSGJbJ5QxsyFwYSR7ngS2M7NT3H2qCayZ/QL4LfBoJHsq8R6wQmp/U+Azd/9n6tgCtHZoxELZKkGORlU7F5vZYYHGowz4F7B+nWvWQ/ONGOjE9NHMRRlbQx9/DKL6/BOiPj0iOe/ub5vZU8jRX1iALQihb4AWm3N7EGE3s9mRk/GznAK946j//IxU1UUKeQrZd5Q5xXPAxmbWpQpNy7xoAfnIVL9sfwxM/e2oQnnFKtc68Djxqz6Go+rac9B79TVKZAPAzLoCvZFeaK7IyJjuViWLOglI/gaJ2sfEGGBHM1vK3V+rPGlmq6F+Y0Rku8o6NyzbPKISn6KM8wTrIWfm46ljXYhXPV9t/O5GS/XhB8jGIvAheqYJEr26p1PH5qYgv4qZdUbr8O5UGQ9zTkRJ7JgXVdIti8akz9BcIsGLKKFiRyBmgO1hYFszu8DdpxoDQ+BvO+IneiR4HyUelg5mthhK9J6K/tfM7gUGBxrJvHGbmfVx91erXRDmrzegtcm9EWzqSLgS+Sm6F8GSkYHPUWVwLSyJWFzyxGg09kyq2C8VSuzH70X9opnX0Xy6CJTFP51gAmILqIU10PNtdzQCbO2PYWhiupm731OwLR0B81I/0/dL6lO5zCz4EZi9zjU9kRMjN7j7ZXn+99sbgRt4NKqYNJSx8Cmiy9sAWD9QI+wcyaR+qb/nQgG/LMSefJyD9CzuMLO90yfMbBkkoj07cG5kuxKcgKh0TgT2CbzhH6IS795oAvIRogqKgdOAe4Anzew8lK2f2NMP2B85A06PZE8lbgcONrPTUfXARkztyFyS1jSSsVCaSpCAq9DEfzdgBzObgN6lym8wdtD7JuBwM7sAOMrdp7RHcE6fgLLsTo1hjLv3inGfGcABwJsE7TAz2ybjmn9SHEURZrYJqsjvQUsV1gnh9IrAeFS1f1UOty+dBhx0qDnFRYgC64pAqTkFZtYN9a/diZPkkTiiDQWCzkZjeCWagP8UUSnv7u+Y2bK0JMndWkH3swRyKsbQEJpA63f/wLBVgyGtp5gYjhzQD5rZMGBhgNCGfVFVylfEn1OUdW5YtnlEJZ4DtjSz5ZCt2wMPV1Dv9iIS60Kt8dvMlkDPby7iZZlX4jGM9s9kAAAgAElEQVTUXpuj9toW6QinA36LUwCFnpkdixIU6tGg5pmIkuBoFFwb6O6jg67SlKoFd//GzB5A69uYOBFRgD1gZlcz9ZpoR6AZafEUgdHA76olChQFM+uBgpM90Tj1IGq3hVCgdGPgYTNb1d3zDIT8Fq057jKztatIt3RGScKbBju3ytGemgiJHceg/qon2dTJsSuNhiMmnbFmdgySnvk44v0rMR71qT2y3h0z+z9EuXt5nka4+8Ba+yXCMMrpx58V9Z214NT3EeeFUvinU7gF+U62c/frKk+a2R4oAf3oXO7u7o2tHTeUaXVp0XYEW5qBIUXbUcfGd4Db6lxzGzCx0V4OqoR5BbCwPxRoSp2fHVHH3Fu0rWXaUKVYM8rCXR/oHI53DvtPICfU4KJtLXoL71RzaI/vwr+fhX+bgcMKtq8Xqk5pztjuBhaPbM8+qXZKb83A98DvC2yrBVCwIWmficDCFed/AE4vwLaxKLDXJfXepfuyecN7d2ske7Lep6ytKYY9Kbu6IkqkZhQAfBC4Bmn7fRGOvwJ0Leo9K9OGnM/npfaHVj4z5Nz5tiD7Vg39xXsoGHh5hn1vIt29wtuzsWU+w0tS/ftHob9/Cs3/m9PvX0SbhgJ9i26bMm+IhvTS8G8z8GzYr9z+hjQiNi7Izk2QVl56LpH8+zmwfkF2lW5uWLZ5RIZ9fZDjKT03HJA63xlldV9RhH0Z9s4exp/hBd1/eUSpnrTVZGCNCvsmIdaRmHYdHt7x/4T+44Twrk21RbLnTeDO1H7WPGcE8HEBz3Bz4N+pviLdj30GbFnEuxVsmwWxJj2OAn7zFGVLxrNqRrR0nSvOdUbacc3A+RFs2SPc6wWgW8U5QwlGzcgfNVeBbdYTBdp/RNR0zcin+Cpa1zaj6s2xke2qHLerbT9GsmeN0I++hmgGTwn3nyvsv4rG82WLepZl2iiRH7/CrheBJ+pc8yTwz4LsK5V/GiU6vhP6h2sQzXQT8IewPzm8+7n0YY0KtvbH12SLDxaFfmkNjTbAPVAVRcJYYFcz6+0SD24FM+uDBoBcMytSqEYZUxUeV3zz78D5wFlm1iqzNmQVnYkyXo/I+O3MjEEoK6yvpzJHXRSDYwLf8kuIQzgWvWAp4e7HBVH4A4A1gflQVswdwFnuPqbW7yPYNwHoH8TiE6rPScCzLnHo2PaMNLM7kbhrK3uAy929iOqwxLZPzGx5WrJYH/DWWZs/RQu6u6MbV65KENw9FkXTNMHdvzSztVGF2s60pn/4BlUOHOG1hXxnJhj1s/wWRAvKInAsem6rukSfh2Zc8ySwclyzyomgM7MNU/etN7l7rEzIVnD3QWGMPBBlQBp6Xi8DZ7p77nSHGTYdF/ueHQ2eypg2s93QO3R8cRZlw93vMrPFgd1pmYNNQtU9l3oVDY4IdpVxbliqeUQl3P2hUI21F2qrK9z9ztQlayMncRYlb3S4+3eBjm5H4MgC7v9ioBHcPRy6xt2fTF2yEqJRzaO6uxb2Qs9pZXf/NPK9s/AzRNFXC19Tv9qu3eHutwc/ylZoXEyP2zd7sZrj34d/jUC3VsVH5h636mkz4B53Py3DkCbgdDPbEAUv/5CnIe5+aaioOxG41cw2cvek3f6G+oZngU0KfpZDUGVkf3e/z8ya0fh4fNCx+ytKyI1dxfkQJWKJcPfHzWwf4C+0pnBO1ow/AoPc/eXoxqUQ/HDrlmBOVjY/foJbgSPM7HB3n4qxJkinrEwkNpsMlMo/7e7/Ce/UaMQKkSBhWXgI2CmvPiyJMjbQTggl8Yu6+9olsKUZdfJtibAl17m7x6A3AMDMlkIZJp2BC1CE+X2UmTIA+D2KOK/qrXWD8rAlaa9pQdRJWOik/oHoAj5E2fr/hxZna6LO6xZ3z6LFak87pleXwr0A4Wwz+xpxAx9a45ozgH3cfe54ljXQwMwLM7sE6QhNRpnB86OF27LAbMAId9+/MANLhqB7sBQtDovXvDV1UmEIguerobF7tqxr3H10BDueRZmhq4X9oagyvXPY74QoIj919+hc9WaWVFMMyrIvHDsVVVN3jW1fmWBm2yHHeDdaz2MdVW/u4+71NHxzhZnNgRz4kwp2NiX2FKoRZGZ/RFn20xXANrOVgB4VQYh2h5ntDnzk7kUklzTQjmjMI9oXge58L3cvimqqdDCz74C/luU9MrNPgdvdfY+wnzWPuBZY291/VpCZpYOZjaONfh53Xy9fa1oQ3q8z3L0qXZmZnQgcGuu7NLNzkMzBLUiP9Dzkk3sZBUIKDUIEGv+X3X2zsN8MDEsCNCE56yU03z6gMENLgkAFuS9TJ+2c7xl6r7GR1YcVZEdp/PhpmFl3NK9ZJPx7Dy3+8v5IXuBdlAQSXfuvLP7pKratAKxF6r1396dr/2rG0Khga3/8CXgi8O6e6MVHMB8IWynh7q+Z2W+R/sJBtNZDMJRhsVPewbUUvqQ4rv66cPemkA15DMpiWiic+jWy+wRatFzyRJZQdpeUPQl1zE9pcfJ8iMr2i0BbAs3TVOrZHjCzHYDf0ZKd/yUS877Y3a+ObU9HQshQWwk5XycBz7j7e5FtuARlZN5a45rNgV8nDvUGWlDGSpCyIWQCf+HuX4Zg2ksZ18wDdI9cTZ2+/yCUNVdNKzXRGcs9wAZcC/zZzA519zMyzh+FdJ+ytKpiYG40NtbCnGiMjQJTCve2tOhYZAVI3SNqD5rZRqhKoRm9N+MQHWMPYD2kp3qVmX3h7vfFsqsSoSL+27oXRkBJNIJORLqf5wJ/9wwNl0qE929jROW9JdJEyDXAhjLxz6eY6u0OBzNb0IvVkamKxjyi/WBmP0UVwxOLtqVk+Jhy+cyeBDavpiUWtKk2pXXFSnSEuWk3lIBSOMuCu/cr2oYqmAQsVueaRcN1UeDuB5rZgkiX7SVgaeANYMOig2sBPdB8P0ETMEey4+5fh2rcrVDV9UwNd38DzQ8bqI2y+fGBKRVZ/ZC/fE00x0n7Nh8BdikiuBbsK4t/Osu2FxDlbTQ0KtjaGcHh2gtYF/HCP0d2wMbdfc+M4+1pS6tsjjLDzOZDGYhroMnYFyiz4jJ3/3ckGzpMe8EUp8SStETkXw1UAkXY0hW4D5WaH4lEvJtCRkMfJPraCU3MogsLm9kTSG9qGW8tLp6cnwMtxj9z99Uj2GPIabgTGhwrA5IOXO3uO+dtSw0b56R2FnxRzvzFgJHARhmn70WVHxMi2VK3zzCzo4Hji8zKMrPVqO88z3U8qocyVYKE4G2tSqzcKkAybGlC71jViWmR75iZbYIowl5G2lRnADcjXct+yHF+HXCHu18WwZ45kKj3r5AulqPKurPQWLQqmlus6+4/5m1Phn1vobE6ybrNyjx/GGnqrRDBntnQ8+tHSyC0slqsCHaDh9ACso+7P5NxflWkR/iUu/eNZVdZYWaHAyejueAtyEGe+X57jnSSZrYkooLZFM1rHgEeRt/ih6jCaHY0b10aOQo2QM6yfyPdhpHuXo/mdUbtfA+4wd0PrHtxZIQq2/0QJfAySCNilnBuJURXd7a7vx7Rpu9Rvz7SC6YJr4Wi5xFmtqq7PzUDv58d6QjnklRqZkOqnJoFZcZvhQL0R2bRUOVgz7YzUoUcAkm93P3RdjQr6z6nocDjst5Cl1cYzKw/SkIYD+yNgiBD3L2zmS2D6PHWRLIIj0S2bRakGfY7YPHUqXdQYsPpRcy9ygwzuwHRRK6f9bxMtKkPAP9w999EtGtWVJWyIXp+fb0AKYYsmNknwJXuflBq/y533y11zenAvu4+Z0E2zoX8dHO7+0NF2NBRUKIKttL48avBzFZG/fsUf3nWOqkolME/HZL8/hKxQKc1vATCff9LG8q2bcvWFMmWIUW3SUfZGu01Q213HvA6QWg84/zsKPPp3ILsGxye75PImTNLON4ZZcM/hpxBgwuwZ32CqHGwZ33knI5mT4Vtu6JstcLFeTNs64Ech83A20hsfHj4961w/D1EMRXDnrp9BnA88ENB7WXAZbQWXG41DsUajzrChoJBL9d596O2VRvfsaOLeoYoqP0pQSy+0l6kazkZ6B3RpnlDnzC54n3/MXwPhQnbo6qZH5P2YGoh6AHB1pMi2TMk3O844CfJ8wt97Q5Iu/TKZIyK2E5fA5fUuWYU8HUBz3BdVBnwSXjHCh8j0fxqIjB/7PaoYs/aSBvru9RYU7kl3+U/UYZ5tO8SOXpfADoV3VYVdnVB2kBNoV99r6J/6Bba9LjIdr2Yeo6vA4cC8xXdXmXbQhvdBKwxjb+bF1XgfVBvvJ8Om5qAY1P21dq+iPluhXs+DWwPzDYNv1sKJc38N/l/y9nOOZFmy20oAFqGd21o6pv8jpZEzaRvPawAm7ogjbwmNM+ZADwa/v0xHB9HFV9BBPvORYm2hT+/CrtWDs9wMtIyGoTmgnugOesP4fwqOdsxJmNL/BGvVDl/f0Ft9iRKkkn27w3j5Zxhv1MY418vwLZEI/EHKuaDSEf7FaBfZJs6A78E1gH6Zm1FPMeUfa3WQgXaURo/fmOb4efYhBIxd4495pSp3P1/BYvXv6SBBqYPoSJsNnf/puL4+ijz8BvgInd/J7Jp2wBXuXsmBaRLOPsW5LArolR/JKpe2BHxFjeb2efIodgJBSKudfdYQuiD0IKjr6cq6lwZHmNMwpwvIed0NHF2MxuIqlCa0GKyahZ8QTgWVRb9CVH/TMmICd/GwYiqLilRj4GqZeChOqQvojYrAn9AAdPRaGH5FHA2otXoh8Rm76AAMfuywczWRE7zT1EQZH+UMfoa6juWQSLDzxZlYw30QI6mIrAy4lVPVyZPoTd094vNbFcUBBwQwyB3nwQMNAktr0ZLFt0T7v5pDBtqYDgaB+8JWje9AMxsM9RX7Icqfc6MZM92iF53aLADAHf/CLg6VH8/hyi8syg388L3qB1q4YNwXTSE53Qzcla8i/qHMoyRiyCNoKLfbwBcWfiPmNlg9F73RhRX8yFKzU+Q82ucu79cgIlHo8Sqi83sMHevR9saC4ehpK9hwJ9RsPvY5KS7fxHoEPsjh1QUuPvyZrY2qpbZDjgNUfHeiKraolV1pxF0SRYC3vJUZZGZ7QFsjcbFs939iUgm7YloUh8xs9eBa1CV0VOeom6yFq3ENdGz3AIlIl4HtDelpdFSlVxNV6oZVZe+6nGrizZAgbKrgElhnTie2hWv/VEl+mRE9Xx2BDtfAmZFOjKbmtkkqlc1RNEZd/fjQl9wAC26So7m9Gd5MZWmh6C1xe1IL+yN5ISZ/QLNIbYI151cgH1/APYzs/HIL3BdNb9FTLj7M2a2LQqm7YzYbRIY8DkwyHPWDULPrhqWDlsliqJCux/Y28xmddHnX4bWuo8EasjeSIfzpJhGharax4EF0ZpxAaT/lODxcGx7FGyOYVMZ6MPrYQIKhhSNhh9/OmBmzyN/5eVeAFNZBrYD9kFzjHWAc8xsFFon5a452KCI/B9GB6Q8LJSWqyO0l5mdhURmFwxOxETH6wpaFlD/RiKX0Tj0zexbtMg+qMY15wB7u/sc1a7JG2a2IwpuJZpnk5DD/BJ3vyqiHV+j9jq0xjVnAPu4+9wR7XoJOSt6e1Fl1TVgEjV+1d03qXHNXcDS7t4rJxveTu32QovsrIV2Z2B+1J9d6O775WFPLZjZU4heapmwXykCvRJyMg72AnRKQiD5MGB1ROuUpTvlHuixcrblFuR8WtrdP0i3VaA7OA45BdbI2yFsZruldkchh/7NGZd2Rk7rg4BX3H2dPO3KglWIs5vZN+h9PyR1zenIOfCT2PaVEYHe41rg56nDCRXjW0iz8cVItnyDFhwHhv0mVD13bOqaq4Dl3H35GDaFe94MdPMamilm9gDwuUcUzTazJ5HTZmt3vyfWfevBzP6FaFh/X7QtHQFmNgYlWC2PsswnoESYykWxe1ztwVeAf7t7n7CfRSE7EtjC3ReOZVeFjfMCu6Fg27KozV5DDuvRHlEDxMz+AuwCLJAkq5nZ/ijokqyJvgNWdfdXItk0F6pG2wcFvpN3ajItAaOuyeUooe024DTPgeqw7GvbML/aESWXrEVth72h+fYoxIgyIW/7YMrao00OM3efaR20Zpbo3KzoGTS/gf72OeR/jDafSN3/N7Q4XUHf4ygiOV3rIfQdW6HEtbSP4maPQHkb1mTTDHd/oL1tqQcz+z+k7zTa3T8Mx85CyZHJWvJqtPb4LqJdF6JEi43dfWyVMfwm4Bcehwa+FPThDbQPQgB3A2pLfkTXOjOzyei7+wZ9dyN9Buiy2wtmtjiaq+6OkpEdJU9fCNwUgvPtjkYF2/82LkMTmVLDzDZGGWxZmTFpFMrLWxL0BcYmwbWAoWjBcSDqPIYjR3BMMdO3gG3NbGiFbcCULNNtEa1fdJhZX+DLEESLFkirgUqdmyzUO58HlgBGlTG4FtADBZNr4WlqZ+HNKDrRstBOnmPWs5qMaJXuR5noRWBpNA6kMWXcd/dnzex2YF/aP2u6JkpYCbIWcKu7f5A61gk0WwWGmNkAFGjbNmdbRtH6HdsqbJVI3rtvgl1F4CMUSE7wIcrMT2NeIo3fZjYM6YE+FjkTv80I2cpLIc2NtWipsHsMVQPGtHsyckAn+IrWzxOkQ7BlNIuEPwGPmtnJwAlp51JwQg0FlkNUhDGxHNJHLU1wLeBaYBszm81LoBHUAdAv9fdsqM+q7Lcgfnb+4kjzphYS9oVCEOb45wHnhaq2vZAG1JnASWZ2HXB+JOfKOoiiLK2t/EfgfVQF0gNVNRyCNKFyR+irTgp910ZIv6iygvNNQgUn6vPfi2FbGRHmV1cCV4ZxMau9plS8AmM8Q0s7Zxt7xbxfB8YSwHlZwTUAd282sztRECQ63P0G4IYKp+shwMEhYSdXp2sb7Psv4Vso6P7RA2XTi1AdeUrFsYPN7CSUvDbB3T8uwLRN0VpybI1r3kXMKDGwFxoPVy4LwwGAme3s7vX8OZjZJe4+KIZNZYeZHYeYh9Lxm0Q7O/139AAbokXdE82z9gQGmdmzKPHqyhgJAllwMbodGao4t6IlwWJd4DMzuxQlWLzVnvdtBNhmEGa2aPjzfXdvSu3Xhbu/m5NZyX9/jzz/++2BMtFyuXtW9UTZsAgSjQfAzH6OnALHu/vl4VhfYBPiBtguRBR0T5jZiajM+2NUIr8uouPpgahTisBY1MnvW9D9K/FP4NdmdnTWQtEk1L414umOic+JTLk1jZgELFbnmkXDdbkgvdAOmcFnlTUzOCDdFv9lasfcG0h7LDaGIef+ZiVxVs+LFj0JfgDmqrhmPK2pW/JCMnYbomy9GWUeVqIJVSw/6u5ZVZQx8DKtHdMPATuYWR93f8jMlkPO11g0cAml2rdm9hDq++9HNIiloWxw0dveGrYi8R7KhEzwOq3pbEAV35/naYRJWLwSL6AK173N7Bla5hRJdveDwOFoMRcLX5NzW0wnhiKasOvN7ACPTxOeiVAROaxWRq2ZHY10n6KtSUs83/8O6azVwqJkV80Xgc9QFch3wBwoWLkbsKuZ3YaqB/L8Xnqi/h0AM/slWif9yd0fDse2Q8mJURGCDHeHrYE2IFQRvQaMKNqWMiNU1N1HmN+4KJ3LgB+Aeqwrc6G5f2EoyunaVphZV0IFm7t/WYQNHRUhiFRkIGlBtK6uhclMvb7MC6WiD0/hYjP7oFYgMlQD7o6Yp3JBmf34aZjZzmhtOwaNjzegZNx7UMLYnohiemQsm9IIweyTUHJRf5TAsAXyD59uZlcgCaNCCn9C4mqSYLF6+LsnWkP+MbBfDfV2ouFtBNhmHBNQtHgZ5JhI9uvBabQ/SPvnO2C1QMu1P6rQqqTlOrpII0uErkB6srUOepfuSh17meo8+7nA3c8P5fr7k10JYyir7YKYdqXwGcqCLAsuAS4AHjSzI4AH3P1Hky5DX1SFuBjSE4uJ24F+ZmZlckan8DCqlLzApfHSCma2BuJdrpcB3l5YD/X5ZcX7tHaevw2sUnHN/1GMflfZKkE+QTSV6f1KLY1ZkRMxV7j7lKpDM9sd0cOMzvu+04k7gbPNbOFQ/Xcq+gbHWYvOpRGvinNTlAG/Pgoc90dj5CQzG0eLKHtZq3RjYzxqrwQ3I22lvwE3oYXbhuSfUT2wxrlu6HlWYl00XsYMsN3P1AHIMqB0GkEB1Sq8s65rQKwjG5tZF8/QBgr0jP1JJdrFhpnNCiRUa33Rs3sdZU2PAlZETostkSNoxxzNmYPWFbjJmui+1LG3gM1ztKEjYKCZ9ZuG66NSozYwzZgdOZ33ADCz19DYNAb5UIoKwL+A1mjDshz6ZvZTxADxfHTLMhDb6VoLZtYFJRQNImjzhuMTkM/gtKwxoYHS4XMU1KqFJYmnzf4x5fQ3vwXcZGa93f2lypOB7nNv4Mac7ZhAx/Dj/x4lRG4S/IWgKs2rkV72Tcj3VThLl7vfDdxtZgui9dmeaL64T6DZvxD5f6JRt8IUCtx9gG1QMtiniBVrZeQ76G9mu7j7NTN6rzJ+cB0NownOm4r9BtqGMtFydQR8SGsBzg1R4Cg9+ZubAqjW3P1AM7uaqTXOnkG0g4U5BBCtSGwqqVoYiSo0d0TZJ80pZ3Qn5LC41t0vjGzXkcjheqGZHeruX0e+fz2ciGjVHgjv2lj0TfRAjuAdkVh7FFHjDkCn8QStA2p3AoeFrM0bUZtthQKrsVG2SpDXaR1QewwYYGZLuvvrZtYDORXrZSa2K9w9arLEdGAkypr7D4C7v2JmGwDHoPZ8Cjg7TLhzh7vfRUg4MbOfoMDMBmHbmkC1aWYfuXvPav+dvGFmq9KiPZhFnxmLR/9KYBEz6+XSszkbtVHivDNEZ3ZEznZ0FN2aP6FK/WOAE0uUiNIJzfvSGbVZQasyBrK60zpIMjPjIrTgv8LMWgWOzawbSmDrjhwUUWFmSyCH10BE3deMAvIXuPv9qUvHoQSL6xGbRp54n9byAv1REmLagd+dghLsTFp/o2olyJjZLqjSLyuJoL3Qi5TTvg0oS79WCKxFB/cmd//KWuvi1kSMZCh372Fmy6J5zYZoPbkfYmlpNrPnCQG3WHOvgPOR/s4TZvZnpl6jHYMoqA+IaFNNxHS61rBhHvS8VkHf3rsoANMDJdseD2xpZhuUcF1eGMxsTuS8XxFR1M2acVnsZIHx6Fn1yKosDcnomwCXR7KnrPThA4BHgTvNbE13fz85EeiVD0QBox1ytqOj+PGXB67y1hICU9aO7n63md2NgvS3xTYuC+7+sZkNR4Uf56PkhdWB1YDTzOxEdz87TxtMEkUD0dx1SbQGGg/8BbguoQMOCRY3InalGe7rrTxrwwZmRpjZ98AZ7n5U2P8OVTodlrrmbGAnd1+gIDNLAzO7CpXc7oAcEregbPwtU9fcDvRy9+WKsbJ8CBOax1E27fFF8atXwsx2ZOqA5LPAJS69uLzvPybjcDfgV0jX6Q2qZ8EXkt1qZpsjXbHutJ4EGQrYDHL3KLRrwck6FFisIkkgOd8TeAc41t1PqTwfwb6tUTXkpu7+Tgg4PIUcLYl+3OdAb3d/NbJtVwOLunspAt9mdhiqslrI3T83s3UQXfH3iKr1/4B5gD1iVpOZ2coo+36kZ+gJhMDf3ihRpfSaq0UhTLJ3Q0kEC6A+LLqua6D+uRFVv9YKdhRiH4CZzYKCbEugbM7b3P2bImwpGwKVZS9UPfcvVHFUbYyMWVlXGgSa8gTjUFXTqIxLOyO6w5OBD9195bxtS8PMOiGn9M4oe3muhKbSzFZC2iVnu/vrke26BDkEJqPEhfnRvHBZ5AAe4e5RNYzM7H7kIDfkMP8rovuZat6T+s2RwJ/z7MfM7CJEIXUoWhNdANzg7jumrrkHmN/dV8rLjhr2NSOK1Ko04oEi9fi82inYMIqp9XhroogEMjMb0obLmlEQ9Z+I/aPdq3pCmzmwTEiwSvZr/ozi5hWdgFVRwG19VMk5W7AnajK9SQPrCLLby4BT3T3vhJ2amA6n61fuvkyO9pwJHBTudZinKJ5NenGnowDgWe5+aF52dCSY2QooQXl+SjSXDkw6DyPGmIPQuPlHxEDVFzgLzSFXcffcqfNDEPJuNE8tDX04gJn9Cq2zJwLruPuXJp2xY1EV+uaNqk3BzL5B3//RYf+/iPrzoNQ1pwCD3X3egsycguD/SvTYeqJx+x+oGndlYDD6dk9y92NzuH8f1L//BlV8f42C2n9x9xer/OYERC/eZYbv3wiwNVAkzGwicLu7/z7svws85e6/Tl0zAtjN3ecpyMzSwMyWR4Gi2cKhZuQcfzycnx2Vg18/szp3shAcFkugRcfHKLv1I6ZeAMxUTrGwaJweFOb8BTCzuVA1SmVg8maPKKRqZo8DX7r7RjWuuRvo6u6loBULFFN7oeqiCcBod/+wADsWQxV251GCSpAQ+FgGeMXdvwrHtkG0V0lbneXuF0W260qgNwriTtVGJp6ICcjR1OYs6/91hLGwNy3VayuhKp/vkSPlfnc/uQC7RqLv7yFUjTKRKhXnHaBCdqbDNIyZhY6RRaKNzugpl6N57G7unjcNactNRcd1J3J+JdqzCyXPLFSLfQSc4u5DY9mVsm8gyuBegRbn4cvAme6eRcOetz3NqBrlAjTPamrDb5ZDTsRpCuxMo12Lo6ShbqidvkaSA6+F813Rc7zU3ffLy44a9rUlwPZn5FSfrdo1edtQFmT0HWnHeeVxRxq0+7d3dVH4/hy4MVSw7d7W3+b5vteCSX9ww7CtixLCCtGbNLM1kWM1K3n00dj2pOwq1Olaw66JwKfVkkzCPP8Z4KfuXo9+cKZASBBeFyW5jiZoaBVrlWBmg1CwNiu4/SNKBL4ip3u/nXE4oQ8HfYtloA8HwMw2RIGXRxHF7TC0PtrE3csk7VIozOwttM4fFPb/iRLT1k9d83eUUD1fQTYaqkzcJ/w7CwUTCjcAACAASURBVPK5/g0lZE1MXZtU7f7M3RfO+M/NqC3JWu1l9C2O9jrVv2Y2GPX1M8yo0giwNVAoQlZkk7tvHPavRR/lKt5Cy/Uc8EHs7NayIgTZksn+Ne7+ZOrcWog7fIS735f1+5xt6wwsRXXaK9z9wahG0XCK/S/AJDy7Glr0PpkeqIuCmf0buNzdD6xxzVnALu4+fwR7DgAec/cn8r7XjKJRCdI2mNk7wIPuXtXBYxJk71vE4qhsCNUAGyD66dnQYvZJgvYa8EiRGZFm9hHi0V/d3ac3wSE3hMzuuWP3r9ZxhMYXa+u17v6vPG2phaKeY7j3MFoqpIegKrasYHETcpCP9fgV1Eej5IlhqHJ5CKo075y65h6UHLNmRLsWBX7wQC1lZnOg+fSkmMlDGXYtlQStyoawTkwkBG5N9wehAnxX4Mr0Wimibc1Iw2kqut/gjFoUaV12dfclcrShowTY1kWB5U2R0/xh5KBbENEg7oqcsVeiLPj9kQ7f+u7+UBE2F4XQVyQJROujNjJEL5jMd8YUkUBXVhTtdK1xj29RtfSRNa45GVUgzZmXHR0JoaLndnf/bdG2ZMHEnLQvsCaiVJ6EpAfOz3MsNWn2TZdzP893vBZMNMlJYsLjwEZFznfKCBPl9qLuvnrYH4GSBQbRIvlxPTDe3Tes9t/J0b5jUVLFImgcehAlZN3orWktK38zLA+/a0hO/ktR84JGgG0GEZyE04OZ2mmYwEpKy9XAtCN0lAejbLWqKCKAVTanmEmjaGfgGM+mFlwYfRej3X1c3vaUHWZ2OqJaSLJZHVUTHVb9V/kjTPDPqbMoGg4c7O6zR7CnlSPFzJrCfgwtp2lCI+jdNoSF95keaCGqXHMiesdyX3iH59YM/NLbTp0EkaiKUvbcC5yLgpOl0awIz/P8ovuuNMxsbqRzuzOi7JjyrEx0N0PRWPVMjjZMDyUXRHqvEpjoD7/0EtKxluE5Ztj0Dhqrz411z7bAzF4B/u3ufcL+UGBIRYBtJLBFHtm1NexqAi5LspTLgpAVf4e7/6FoW8qOir7LqN+PGaJJOiZHezpKgG03ROW/dlZFkYlSbDyiwbo8JJw+Ddzp7lvlaFdfYEKtZA4zWwRYPEYSqZm9Afw87H6GqkuTgNpbed+/o6Jop2s1hGqUcR7YnKpc8xdgXXf/ZTzLygsz+wSNlaWZSzdQHdaaOjwLB6Ekij2pSLaNnZhvktMYRH2d7GhSKaGq+gJgWZfkxyKoKrh76rLJQD93fyyWXSn7Eurmv6M+9pU2/GZTYDt33yMHe3YDPva4GqRTEJWX+X8UA6scT7I3qx131IlEhZltQWu9gSXC8WWQttcVnhKajICRKMo9GcDdx5vZdiizdDlEfXV4I7hWbpjZ4cixMwl1rlVpr4pAkZnkVbA/sHRWcA3A3T8I1YjzouzvwhEy4n+InVVk0qk7BPWZr6L+cyngEDN7xiNo1dXAeyg7rRbWBGL1qd/RQh8Laqta3PRFopBMuUqY2akoA/oQr6LNaKITOwP4r8fXjvgGOctrYX6UlBIDD6Jv8ZuK/bJgIsqg2xjRq90fKuXHxKx0qoE3UKZ5KWCijH0Y6Ts9h5x1aa2RF9Gid0dEUZQXOorQ+Fg0b923aEPSKNFzbIWiMqLbgMVRJUwtfA78JIItaXyBnl3ZMD9ynjRQH+kxsS+qKJqQcV1SwXk/olBqQEma12YF1wDc/Xkzuw6tCS539xfN7B+I/j9PjEVr3FpByt3C+RgJYb+gJZHobEQdVjidWgiA7oQc0/MjGz9FVSlXVnuuseDuOxV5/xr4K3C0mZ3o7u9VngzVitsj31gDwhhgjaKNaKDNGEfbkk1uyjgeU0NvaWRrXW2/KAYlN3MfRUrL2N0nmtlqSH82kbG4oMA+djCKIbTZR+judwB35GTPJUiCpBFg66CoXDx2QgKWfVD29DjE/94Didrvjybfh8QzcQoVxShgl3DoW+RUTPAf4CTUmZwSyy53/xJNvNLHbiK7g53pECLwADe5eODbrLETOSi5FwoirOzun0a8b0fFykjAtRYeRk7iaAiVdf2B4e7+n3BsAeA6pGf0o5mNcPeY/dfvULC2v7uPDTZtiPRT9gSKDLDdBexnZtt7hgaEme2AKBAviGTPO0B/MzvX3T8Ox8ropAZYjIIrQcxsEzQ5PahacA3A3X8ws1eBc83sfne/N5qRcpZvZWaHZFVimTRmtgrX5Q5371drv2i4+2Jm9gtaqJM2RklFHqow7g/bWHcvwpE9AjjZzHpGTmaqhqNRUGagu49OKnmSk+7+jZk9gNoyN7j7wFr7JcJnaP5cNpTiOVaDmc0a7r0Moq48IRyfHegKfOZxKVO/Q7pdtbAo2bTFeeIxpFtUNryMnDiFwsyGoDnNiMB6MqTebwI8ViV/ekwMWd2XFlw9tgeR5gftgKWo73D7AEjTwr0BbJabRUJbEtXaUq3YXjgB0UKuB2wETDZpQifzm8c8oh6VSR7iPERZlpXYty5wmJldABzokemzzOz3KGH11GScMbMDER1pJR7Io6KiDm5EwfhnzOxs5CdMqFET2tQHgJusgj67vRLHpqEvrUS0vrUCRwGPm9kRSCu1FGvdyudTBc1o/VtY0kqYky0HfONx6J+Pp7z+iDROBxYATgYuAibG7EunBe7+DlAWVoE1gblQDKQM+AjFZApBgyKynWFmBwPHoEDDVFUzJkHmp4Hj3f3siHbthyY/lyCH4sFMrTfwAHon6pXxNhAJ00mbZESmVTOz74C/uvv+se7ZkRHa6/RalDAm4fND3X2OatfkYNfNwHKe0oIws9EoMP8mMDea8O/o7tdGsulTRJ2xXcXx61Ep/E9j2JEFM+sJvIAcdbeggNv7QE+kJbklSl5YMSsrMQd7DkDZrNNCTwSRadZgChXWSHcvrBIkvNsDgIVrBdjCtbOiisV73X2XWte2J8xsexREfhzYx91fSJ37FaqmWQ3p/BUZbC4tzGwFWnRK+iDa6WZ3nzXCvbMW26ehzNvj0Hww04kfo+LOzN4E3nD3AWE/iypvBLCtu5em8q4omNk1SAdhraJtSaPMzzEkMlyMEg1bzU/NbE1E+Ra1/zKzsYhi7f9CAkWr9goVgW8hzcYtI9q1GvAQ8Ad3L01Vk5nthKqs1kyPQQXYkbUmaguirokSmOjpv3D3SXUvbgAz+xh42d3Xr3HNWERRvWDYHwHs4O7z5WhXXZpNM/sr6l+7V7smB7vmQgGY9cP2q3Dqv6gfud/dz4xgx1koCPQDcC1KLn8f9fcLB9u2BWYFznD3w/O2KWXbykiHd3h6zR36/KEZP3FglZjJf6l+rdqardrxdlu7VelL0/e0jOPR/U1pmNlSwCNoDv0cLQwIabhHlORpo58uwUcouHpcXgl/ZvZb9O0NdvfPw7FfoCTlJGnmFuC3XkUra2aCmU1C0gJbFG1LR0Lwa57lNSRTYsLM/oYqqVeMnLwHNCrY8sDeiN4gk5LOxZt6XbguWoANVXo8D+zl7m5mWZ3/G6h6pYHyYBAaqBOR4thZVW3Fx5SsPwkO/GlFrEDDJERjVguLoEVSTPwKZckBYGZzoInZve7e38zmQVRTg9EiKga6I2rISrwKbB3Jhky4+/tm1h9V+G2NKokSGCrZ3y5GcC3Yc66Jl34ztKhdj+r0REWjDJUgawH31QuuAbj7ZDMbQ31K0HaFu19jZgMQ/dCzwQGVBHETUfvRRQXXQlXY2V5DYykk+Bzq7j+vdk3OmAU5drqEf414mW0TqO4wqeVAd+KMqT8DbqhzzdfU0VaNhUDfMgBRlF5dgOP6GJQ1fQJKlKvbd0RCKZ+jma0K3Iz6+4PRgnfH5Ly7P2bSaduGuNXoFwFXAFeYWSvnm5l1Ay5Fc48LI9oEerfHASND1cUTyAlX2YfErhp4D7EujDdp0z1Zxa689VLWC/++W7FfSlTzBTRQFXcAu5nZScCJnqKbCsGkY1ClT5qhJZGTaFdkVPT0ExnQVOiMql13QMwj0RDaZwrNlkk7aFfgSNSXbALkGmAzsyUQO9O/gE2qVMNcEpJG7wIONrML3f3tPO1KYUcU+MvyuTktc0JQnz8RJZXGrPosAyV2Vl96MLApGivH0ZqRaydEsxzTlzkFZvYzNLfoHrZqdNSxJXlGI4aWdWkJ/CXViCuihNxxyL+zPLAfsLmZre75MEANQkmkn6eOnQEsgWg250O+iz0QVenMDgPq6ocVBTPrjZ7VSmg+PwlpsV3q7lHHnwpMQJV/ZcHRiBHiYjM7LDZjTakc4v8j6EV9SpH/hOtiYilULVBrAP+E+lovM4TgjJseuLsXTk8SGy7O3fT+ZQWZUg/XAtuY2WzuHksLqB6mR3sqll7VE8DWZtbD3T+aygizhVHAZnwkexIsgOhXEqwBzE7gfXbRlN6OnGGx0Img0ViByZRAX8zdnzKzJZGG5Zpo8vwFGthvi+2AdfergauhNPRE1TAOWLtgG3qi4GhbMYHWQdQocPeBZvYIcmIsixa4AC8B5xZc6dCL+lRr3dCCMwrC95hQRPZDi+8kA/hlWmiUYqAMjpNa+Ir6i6LFiawLFZybv0eC3knW7YbAbShQCnB4cEr8O6JpR6Lv7ihgTzN7nuoBkJhOnVI+R+BYFAxd1d0/ClUDlXgS0WZHg7tfZWYbIR3tpNIcM3sK9bGzIRrCvPQhqmFY6u+VqE4X6cTV4xlHS4VFoolbDblVM7j7A7X2ywozWwiNRz1prZObIHbAtKw4Ejnv/wQMNrMXaHFOr4DmEu+i/jdp1yXIJxA+LPW3o7lEvxrXvw/E1uhNqm37ofdrQ+TvSdZGMcbGXcP9BtaimnP3V81sd6Rntwu19ezaE32AR6s5WCuqGz4zs/vCb6LBS0CJXdmXmiRJNkJVy5W6rZeZ2fmIyvLGSCZW4mz0rl8CXIZ8F2WowBoOPIooBrOSBI5FsiprIWagY1El5ZHkIx/0S6TVmNjQFQVNr3X3HQI7y3NEDrCFKrp1gH9kzeHN7KfBzocjBuNBrCJLRbxfm2Fm5yH950rf14rAQJOEywHxLQPgSjRmd/cgMVMwrkLBx92AHcxsAtXXau1Ond8IsLU/PkNVYJklkqb0p/7EmfSk8SNylNdCT5Thmic6MX3OpsId6Q3UxFAUXLjezA5w8QIXCncvjHu3DTgPVRk9ZGaHAne7+/dmNhvKODwD0TFWrQrJCd/TWpuxD/pe0xnJXwI/iWkU5XZQE4JoN1LcQqMajkOOsTKiDJUgSfZqW9GZgt5Fd78IuMjM5iQEcd39myJsmQ7MgzKIc4eZTUTVm8mc4W1U2TMGGJNThmhVlMFxUgdPouzZedz9q8qTwYG5KXB7ZLsGAK9WZN0OR9/fUBRk3hfRUk2vdsj0YGDq7x60BLsrETtruqzPcR3g5qxEohQmkr+G0lRw90Fm9iB6h1ZAfcbKKAh/prtfGtsmyluR1VH0U0oHMzsOBV7SPpc05Vvy90wfYAtB+NWQc3oHVK2W4FuU7HeEu38Srv8Q+S7yQPItGpo/jEKO/Eo0IZ/Oa7GoqEx62cm2MvKtGPLh3Bnsvd/dn49gztrAP9sS7Hb3B8zsFaTpHQv/B/w943iWVhwoka5UFNAF4WDgmozgGjAlufTacF1W++aN9ZHv5HcF3LsWTgaed/ejKk+EYNsRZrYGcLK7/xo4zsy2Qkm6eQTY5qeFBQv0bs9CSMQN7Cz3kmIWiIQjUCJ5NeaCSUgP7QaUbBcLxwN3m1k/dx8X8b41YWb7o2rHt9FcYRytK0qPAfYzs9fcfUQBJg4HVgXGmtkxwJPu/nEBdiTol/p7NhQ0zQqc5jKvbQTY2h/XAQeFQedP6UCDSX/tFLSQiy0C+AqiN7CsKjaT0Pj6qMw0N7h7rzz/+w0UhpeQo3phYNPAYZxVyTlTViJWwt3vCYGFY4GbADez/9BSaWHACe5+V2TT3kH9QILfIF2X91PHFiF+FvwwMxuWdaIKFWgsqs9KW7oDc7v7xNj3roKbvA16KWa2p7tfHMOgFMpQCfIxsPQ0XL8MsrEwhKBaoYE1m1pXrFvGMWihTvoNWhTEQGeUSZc4mHLXMevgOAc55O4ws73TJ8xsGZTROjvxkz16obExsaUnsAoKfPw5HFsaLdBjBtiq0RAVjbI+x7mpP1+Yk4LEyANLxKhAh90dmJTOOC/AnlJWZLn7sKJtqIeQwNqDKkkzRYwFZrYzmuePAUYgZ+Eo4B7kANoT+Q1GxratrAhJMHua2WDkEJsXJfa9GjMRK/0tmtllKFGgLN9nUpHyPdJaG4Oq8p9w9+mRR5gRLI3e57bicVQZFQvzoArvSlyKqukq8UX4zcyOpQjUozXwAbBdnWvyQickWVE29KV+Re0jSGojwWPkJwHzFa2pwddF6+w0neB3xH/n+1FDoiEV+Kuqx5kTFkGadPeY2VXU1skenXU8JwxG39uq7p62519oDnsr+h72RXON2Pgu/Guo/ahCqRzFP1d0kUUjwNb+GIIyc7ZFlHnv00Jv0BM5f56kNfVADPwdOB84y8xaZUiYWWfE0b0wBdAbNDBtMLPVEX3Gikh3I+s7jh1g6ISqJNML2KyetVGJGODuQ81sPKJ9WwNVpXyOJlrnufu9tX6fEy4Dzjazx1HFyfKoCiqNFYCqNCA5YVrfm2jvmZnNjdpoZ5QpNkU7KWSpDQWOqZYFmDPuMLM1vYYGnJntihYDsQNsA1N/F1UJ8iiial2wXqaVmfVA2cKFVCma2fwoULUMMFeStRmOLw686O6xNO0m0DoQemDYqiGhFcsd7r5wjPtML8LzWgZ4tkqlUVc0tr8SgzPe3e8OFRZDUcB7crDjM1oSPv7k7o/kbUsFuqPxMME66J1LV2A9DewT0ygvqaZSiZ/j+4hysRZWJF4APhOh7yxaEzSh5XquVmKMmS0HrBzZuVNamNl2aO26HNX9GrE0LSvxe6Rft4m7/xgcThMSKm8zuwnpGBWioVpmBMfrS0XbAeDuZdM/PxkF1Ma7+3f1Ls4Z3ZDESFvxMRqTYuErMlhXwlieNZ7/hPj65wR2ij1p8e1kJQrkQmdWBV+ieVct9CZ/5qtqeAz1+WXDbFRfzyZYiNZUwV+TH73lG8CAwJDkwG+BFyrWF4sxbd9we6AncH2da95FFN4xMYoWOuxdw1aZ+JtUncecg/0cuKgiuDYF7v65md2A6EeLwEM0WA6moBFga2e4+9dBgPCPKBvhFyiDG8S1eylwhrtHoUtKYSTqpA5A2SZfAZjZ9Yjab2HgFne/IrJdrRCcS/OiLNIvi7SljDCzbVFZdyfk5HyCEnBONyoTpw1m1hf40t3vYdoy//LGX1B/sD2aQNyGqm6BKY6d5YlYMVB0FkotBO2Dh5ED8TmUqb9M6pIXEc3mjkARAbZ5gLvMrHfWpCw4pi5BE/DYKEMlyCj0bC43sy2rBahChfdotCAaFc26lvvviSpPZqdlYp/QoiyIAoV7Ey9ImuiKGeI3f4FsQfiEOun+0Nc1IBqPgWiBnYUm1O/+DTg0hkHuflygyjsA9f/zoed7B3CWu4+JYUcFPqU19dd6KGj0eOpYFwqqfCojSvoc70S6DL09Q4DdzAYgirGTo1vGlASZbZhaMP4mdy/CcTgKJWDWqjzfCtEYRQ2whUrI1WlJJPoUeNxr6C5FsGk/NDb+iDSL36cEa6IUlgeucve0TVM06kJg/G7gMNTv5wozGwOMqhWcNbNdgEHuHrtyoNKO3ui77Ia+y2ey+pCZEVn0cwViLqYtOeF7VLUcCxNQv9VWrB5+Ew1mtgLyBcxP7QTRmA7sfyBdp9OB49IJYWY2Dxqn1kF+zSJwNJLZ2CEkLJQFzwPbm9lZ7j5VgkB41r+l9ZqpFxpP88BF6Bm9gebQvRCtZxqrIGrsmPgB6FrnmnmIH7QpWzJFgn9TX2rhB+IzTAHg7v2KuG9p4e6NLccN0aP0RLRhRdsyCxoQPwOaU9vnqAJjloLs6oIGyreQcynZ3grHuxTddmXZUDbfJKB30bY0thl6jk3ABUXbUcO+rsA8Gcd/CvwKmLdoG8uwAaeGPnS3sD8UaKq45nbkGCjCvvVR2f64yn4UOel+QJPuhYpuywKf4U3hGb6KglY/D2NSFxQE/F041wTcWIB9G4V7P4uSZEZkvGMvALcV1H7NwJCin2OGXTsA96FFyY9hnnMvsEPBdr0CXF/nmuuAlyLZ0xdYsejnlWHXbeHZLQcsEZ7ffRXX3IR0b4qwbwuU7PQ88Gbq+DLA4UDPotuwDFtY/3yGaG1PAa4J/dlmYf9rFBT5aQG2bRfesSZar4mSxIBtC7Cpbn+KEpx+jGhTb1Qt2lRlewJYq6D3602kL7N4Efdvg33fACem9v8LnF1xzSkoqbQs79fRlXOMyG22Shgnk/erOfX3K4giqwi7FgrzrzdRQCnrW4j2Xabs+lkYj3YNc8SfRb7/NM0ByVgn5WzfmeHZrNmGa9cK/z9nRG7DMcHGY1BSfufY71GGTQvQ4pf7Aq0jrwn/fhHa6U1ggYLsG4LW103BpjPCscrt2Mh29Q82fYuouQciTeGBKHHuW7Qm6R+unyO0599ztOkkNA/7DNGJW+rc2uFZ/jFyOz2MAtmzVjnfJZx/rIj3q2wbYqF7rU57vY4YsAq3t+gtjIXXIOmbr1Fh0duILWCzvO9vwYgGZiIEnvolUXbrJMRrHpuzO7FlHkRzsArKUphIi2jjIiiT5ylgAy8mm7RUMLPvUPbh4LoXF4gSalGVCmb2MXC5u0epUGggH5jZm0ijbkDYH4oWmp1T14xAjroFC7JxZ5TpfoO7/zYcG4Cc0x8BfWbm7zRUMFwPbEz1TDkD7kbPMSp1jJndhzQufunuX1Z5xy5HDs6ZXt8yzG9GAzvx/+ydd5hkVfW138UIKEFAAZHkIAiCiGQUCUOSqCJBojCAIiKgiAQZwpBEBAEDKAYYogiSFMlhyEmiShQYkoDwAcPwQ/L6/tinpqtrqqq7h77n3p6+7/P0Q1fV7alN9617z9l77bXj7/Yusamck+gcMHC27W1Kim8SIa7Yt8sxRwG72J6t0zGDGM+7wEm2dy36vQaCpFWJ+SjNau6NbF+aXh9BJNavzPm3TOfXOGDb9NT/gA81Po/JSvZpYIzto9r+I8XENbs7WMeUjaRlgXMI8UKDRgfso8AmtrPOUpG0DtFd9x5wJlMOjG9cP9a3fVXGuN4Dxto+tMsxZxDJubkyxLM+YYs8I3Gu30kUREU4nyxHJAjfBL7izNbmkv4H/M72Hjnft79IehS4zvaO6fEDwLNu6g6TdDqwge2PZoinP+fX4cDetmfsdExRSFqE2PN/mEjAXkNc5z9OCMZWIfIWK9rO5ryQZoDeTjgG/IvoTHyCOO8/SQiY7yEKpWtkiukThDtRu3lmVxJriAkZ4niP+H9v52LQjmWApZrXsEUi6VPAA0R+aX3bD3Y4bjHgMqJguUTm8+t14OLGHq0qSPoocCRxP2zuOnyduG/ub/v/lRTbe/081LnOtQaStiQcgWaj975SxPXru7bPSsfORjgOPJTj89qKpBmIe/j/uXenddHvuwtwIjGv6zu2n2t6bR5idMWXgd1tn5grrqqSHN6uItZhPwJuse20J1mZ+JzOCKztNuMHMsb5CZpcDpx59q2kmYn9xnp07gY20aG7hQsarVFbRA5DHFXV0iw9WjgEWJ7YwO1t+/HGC5IWAo4h7FsOIZNdUsV5jjRbo2pUfBZV1RhP3BBrhjbzE0Pru/EavQcMZ8X2mZLmB46UdByh+DuPKDqsWXZxTdKXiWtGY7bYIun5xYnF9Zm2nynq/R22zusTG8idiXmIM6SX3yJ8/n9L2DyVoUhanigIdbNMfpq+Pf+HC98mzqc7iVml19l+NxVkVies6LaUdIPtvgaRF4HpOb86MQNNNmIF8yIVmD3Viu0bJG1EzBMwcR24tOmQlYlE/wWZQ9uV6BQ4mViT7gkc2HjR9nNptmqjQysXzyqGnJ8KXGa7v8mnwrF9V0pebkh0CDTEfbcS1vRlWPodRCTHV22zJj1V0q+A69NxhRbYJJ3c8tTGkka2OXQE0d2wKpEcKJSUqPgDMQvoUOCYVqFjWvfvA+wPnCxp0aISFh14it6zbKrG3fSeE3QNsHOafXs+MIqY2X5TxpjarmNSom5BYAPi91oGBxK2YFvYPrfltbFNYxIOALbPGNdBxBprXdtXpeT+KbYPTevr3xHWa1nmY6UE9I1Eh/AE4lrVKESuSgjGbpS0fHPiukCWTl/9Jdta2vYjkg4jchB3SzqXEO809hXzEn+3zYhrydicxbXEa7SfB1cqqXi2s6RdCaFfw0b5wZLu281kKWRPDbbPlnQx4RTTsJ9+lbgfXNRcALE9kRBwloJjZFHusUUQ++pNiN/ROpLuIz6T8wFLEQXdq4hCW3bSTMRNaLEpJuzDs89oJAQMMxDX+BuAdxQzluekp57zLHBvmvXawEWLbyXNSawBtyI6X5tfe54oxh9p+6U2Pz7YnER0jL6Qvh9Pb1HYmkSuZ0OiCD66iCDqDrb3iaSpHcxd+Ak/FJD0FFHhXrbD6yIuaHPaXiBrcBVE0tFE0nkp55/j15E2s6hmABZvUnXPRAw2/o3tvUsLtCIkRd1thN3IoY5h3mXE8Rix0Vnb9uMDuJ7V1y9A0guE6nCH9Lhdd9E5wMq25y8pzEYcvyKSw28TdhSrd1JyZoqnqp0gI+gZiP5SWd3dTfG8Dvy60e3a4Rz7DbC17b787IuK8SPAjsTsijloXxyyMwxnl3Q7seH4TLtEr6QPEVbLL9leoeh42rz/HUQheYkOr4tQyL9leyAJq6mN50/Agra/UPR7TQtIuovYqC2b1KPtPo+/JxKx2dasqTNmMeJ+/l/gDOC03J1hQwVJrwHnNDqLOhwzjuhanqXgWJqLoY3Ovk6YWDtua3tqJI8hcAAAIABJREFU95/9jWs0UUgeY/vIPo7dHzgMGG379CLjannfMYSo4jNlqrY7kX6HJxLxPS5pASLJOkfTYW8Do2zfWlAM79FT0BB9FzcE/Nj2AUXE0/WNpWcIZf5mXY45j7D7m6/TMQXENQH4l+0N0+NenYCp0PxP4C85uikVzhjfIURExzavU9Madk/Cwv5E27sVHMtUFTptnzrYsXQj3avHEMno1s+ACNu+w7t1dxYY29nAvLZXy/3eQxVJCxLr5BwF5JoCkDQ9Icz/Dr2FyK8Q981DysiPSdqAEKt9hN7rMRN29TvYvjhzTBOYSmGC7cJm3qd85pX0uM69Q1isi/j9Na63TxC5xsLWrZKWJmoG9wHr2G4711DS3CnmJYFlbHebeTxV1B1s75/pmLoTvtsG6n2jGGQ8NWRJgjUxJ5EI6BiMYgB0Je0/SuBgQp12jqTv2a6K4mkMUVwbbfu0RtKp8aLt1yVdRyZ13xDgR8RmbH9gJ0n3Et2JrdcS296pwDhar1/9vZ4Vev0aQtwBbCRp1nbJHUkfJ9TAWRdiHdidUO+sSljullZcS1SyEyQlKooaNj01TCBsuLqxEiV1pUv6NKEQq8pw9iUIy8O2XRS2/yfpQiIpWwZ/JrpJf0V07U+OMxX/jiEKJbmSmwcAtyWFd2lij4GgsKB+qyQV6WLE+dXtfP4v8XnIhu3FJa1AqDG3IK6pP5B0D5EoOMt2KcPPm0k2N7MRVmrdunKL5k1C7duN/6TjiqaR/BAxI+J4Yk5KK+8CL2c879cnkkk/68exPwN+QKx3shXYiI7kZYGrJO1DzLutTKHN9jhCSNR4/FT6nO4FLEzc308suBB+PT3339WAJ9P7ttKYPXg1MSuoDOYkZt5240FgowyxNDMPYTvV4F3CVg2Y7IRwJdGNkSNfsSFwhe2jW19Ia9hjJK1N/J4KLbDlLpRNLbYPkXQaIQZbmR7Xh+eIDtJxRYsWurA/sQ7bDziqJLeMocbjxLW1yBzJNEHKRRxAzIebj/YuGradtS6Q9hv7SzqA6JCcnSiuPViWC4PC0vx8Qih6JlPaFG8F/FnSF23fmSsu2yNzvVd/kTQd8TtakMgDHA7c2GgAkTQjkXMaQzjInEGx7l1bE2ud7ToV1wBs/1fSdoTYaWuiIDeo1AW290kVT/jEqKn8udw39QnEBbUbs9F+MzDsSIWqnQl7g8ckvUK0Lbc5NGuH0SbA5bZP63LME0D2joGKMrrp+3nobO9mClw8tl6/Knw9qyo/J+a4XJI+l5NJFoe/Az4I/CJHMC1K5W60sxDIvR7YCbgX+FYSUrSL+xFiQzCcuQjYR9LmntIyCUk7EHYaY7JHFhxDWEL8hLD8eKrkrr++OkDox+tF8gtig/YdwgruenpsUVYjiuD3Ekn2HFRF7NELSWsRn/0jbb+cnpsbOJeYw/OOpBNs/yBXTIl3iGt6N+YjLJ+yYvsO4A5J3ycGfG9PzCE4Hjha0qVEEvHCnHEpZnzsTSQ2RzY9P4EQWBxdgiPDDcAX+zjmi0RxolCahXKSDgGurYh4bing+v78bWy/ma5lnys+rF7v+27q5jmXSITRsrZpOjT7GqctjlEIhRY9Wt5vVOP7ZmvDXO8/QF4gRDLd+DRhbZyTV+mdlH6ZuM43M5F8wop5iMRmN+5k6nNB0yTps3dgnwdmxvZjklYBbga+lYQxnXI7hazDkjDfwPa2nx6AUD+3ML/BK4QgoJJIWp6+XT0OyxBH6/zIGekwP7LoWDqRimn3l/X+LYwhPgertukqH5fWG+OJPdOmmWOrGl8ixlicA2zVKgyw/SYhfroa+BOwqaR1XNys3pUIkVWfBTPb90q6k5h/OOhUYrFZM/jYnq7sGPrJ74Axko6w/XTri6kFfAvCemTYkxZglxHexO8Qg2bb7SZzJxArP4uqYhTWrl2TD9uXp4TYwUSS+m0AhS/2HMTncF/bN2cKqVmpXHUq2QlSQX4KbAn8UTF/ZDYASbsRyrBNiELkL0uKb1Xgb7b3L+n9W3kA2ETSmHZdbKlLbGNK2sylDrpRhP3J14m/bYP3gLOA3Tp14BXA6KbvSxN7tGF3YEnb+zQ9dwxxvv0bmAX4nqRbbZ/T7h8oiPuBUZLU7tol6YOEyvXujDH1IqmCzwPOkzQXMZPwG0TRbSMy7v0kzUp0xCxHnENPEgXceYBPELO9viJpLbfM9yqYfYFbJP0EOKy5K0wxe+xgwj4m66xc24fkfL8++BgDm/X2GJkT+pK+SnQFjyA6Gv5D7I1KIxWUbwQmAet16gpOx10KzEwk83J0Dy9EJKaryjXA1pK2tH1264uSNiW6xPoqLg02TxAWWA3uBdaUNFMSvk5HJBunyGMUxETi+tmNBSkxaV7TfxRz/C4k9o1z0DlHUOQ6bFT692dqetwfytpz3krMx6oUqUv/fGJGXF+uHjlym5WaH9lKsolci5jDPkuj6JjW0h8GXszczbYqcG6b4hoAtm+T9GcqIv5Nv78lgddt53ay2ZQo1O7eLZeTRNS7EXuQzQh7xiL4FPDXARx/JzF2adCpC2w1ZXM+odq+S9LxRIL4eWJjtzrwPeA64IJUbJuM7Sczx1oFjiIGjm9HWP5UZZD9JFoGW7ZhIfKrDitJRdTJNYNAsh25nrCF+TzwUWLhfAlwnO2pteudmlhG5XqvQaCynSBVwvbLklYHTgM2b3qp0RV5AzF/rQy7PIgNZFWUhxAdMScC1ye7netsv5PmkqwGHEkkpn5aVoC2XyGSiN8jurobtii3l2DjV1Wxx+eItR8wuTC6GXCl7XVT4eYfwC70tu4qmtOBXwHHSerVPZfOsWOJLsT9MsbUjRcJ1fIDxCY8977vEELhej5hifp44wVJCxFF06+l4/bKGNe+hC3M3sDOitl6jb3HsoSQ4Xpg3zbd3oOe4JQ0Xbv1vGK+8cFE0VbEZ+KwbvY3g8isxNq+v0wiCt85GUsIDTe0fWPm9+7EtkRB+cvdima231LM1b6EKIKPKzqwlm7J6emx5ZoIPJCpyNeNQ0kFNEnfJdxaniUSxKOI7uVJhBVVTq4mrhPTp9/RqcSa7OZkDbkKMSbhx5niuRHYTNKJ7QR8klYi1osDKZDXlMfxhOjwZOLcyi4UaBXmDwGh/ljgBknftF2WpW07jibu1zcApwBPUa7oY13gMttXtb6QOhU3JwTCh5B5HI+k9YA/ENf3xnzQRtFxacK6dVvgjxnDmo34m3XjSaL4lw1JXyf2QLvYfik9tzAh0lk4Pb4I+LrtXOfbssBN/VmPJlvGG9PPFMVsDGzEx4v07aI3VdQFtvdJ8vAEuMD2pKbHfdKHnd5w4TF6LJ3aKTlEVLy/0vK8GZ7n7+eAP9ruOLeuJIbSLKqaDtTXr6nD9rVEIqCm/1S+E6QqJDHJKElLAV8girgTgVud0QO+A3cSiYGqcBKhQNwKuAJ4T9JLxLDl6Yg1xTm2f1NeiEHalFxScgxVFXvMTSSZGqxEFOTHAaT19sVEcSYnJxHr0T2IBOYkgKRo/TxRXLvIdu4Oi14oZiNuTyQn5iXO+38TybucbA7cY3uz1hdsP566cu8iujlzFthGN30/O3GvaWX19NXMoHcQSNodOF7Ses3WOanDaTxh1dio8i0JfEnSsrZfH8w42jCCgXcntLPCKpLFgNMqVFyD6Cp/zHaf13bbl0l6hPicjCs6MJjcYfFToqu1WeT0hqTTgf2SCCQ7tv+tmB12GmHR+kV62z4/RFjYPZI5tD8QtpBzAs/aPkPSckSn9VLpmLOBIzLFcwQxh+06SWczZSFyK6IjPlfBr+b9sSYxZuObZQcyhFifuD+eJOk7hA1iJ3vznC5YXyXWNGtURARftfmRwGQLzQuJIseehJ3mVk1x3SrpcWKNn7PA9p8USzeWp+8ZvoPNjsC8jeJa4mfAIkTn90eJv+EORFdiDhYgxB795V80/Y0L4EPAQOzm36ZvofdUMRwLFIPNOOJifiux0W487kajSp89QS1pG+JDugxRfX+VuBGcUlJC4DSGjqVZFXiNGDpeNSo1i2qoIOnLhHJ1cWBm24uk5xcn2pbPtP1MxpDGUeHrV9VIBcl7uvk9S/ossExdkJyCodYJUjrpPBv0Ybzvk0OByyWNsj2+7GBSsXabVHxprHU+QhQk7wZOtp1zs9YLSe8CY7tt+CWNAQ5xRWYGlcSbNCUBiKKp6T0T61Xib5sNx8ynjYiB8bsRg88hkuqvEEKxUizNJc1BbF63JxIAIn5HfyBmr+WyKm5mTmKweVuSdczlZFZOU63OzVWBF9rMpdiJENU9QCTyJxH3w42B7xJK+aIZKWm1/h5bZCAdeJGBJVRysAwDE05cTwgQCycV124iuq0mEV0WzxLXsaWBnYFVJK1s+9UcMbXimCW5uKSV6ekmnQjcbfumkmJ6hHCQaX5uT0k/JuYYTbD9fMZ47krihFOJPeTWTS+LyBPsWAEBVk3/mI7oyB8yJBeBbYFv2l6uhBDGNn2/DJ3tInNZMTaYDTi9IsU1qN78yAYHEt3ny9t+TtLBbY65g2I7ntpxCbBLckA52k0zxZMV8J7A2kBukeYSNFkrpnv5BoRgdMvUkX4PeQtsH2ZgltOvEM4I0zzDefM+WOxIXLwblewdSoylI+mD92di/oIIBcMLxOZ3TWCNRvtpTosI26Nzvdc0wiVMqagtHVdvFlWlUXgOjSMWpwD/o3cy8WVCeShaNnUF0+n6NTthZbYlMdulth0JxhGL/G5Fj68QRYjSCmypg3QtYlE9Y5tDciv8YIh0gpSNpMeA4213FCckK6W9bH8yX2STWQC4CLhC0h+Jjra2C+6cReZURCutkNYF0b8ZqbnnqPa8sbQFoeCcDhhv+6ISwnic3l1FmwKPtAhOFqAE2+lkvzI2rXkWpaej9MHmzXhOJJ1HbLZnIPYkVxH3pwtsv1FGTIkJ9G3BMls6LhsV69z8HL0Lxw22IP6W29v+O0z+bD5JKJVzFNi2T1/9oSG+ysl5wHpN1n1VYE7CbrS/PE9cQ3LwI6K49mtgTHOnWrIjPZwo3v4ofZVG2i9Wes+YutBz2LW2e++L0+iMr9JSiAQuLNE2vGbg3Ep0J1ceSZ8HvkV0nc9MeSL5NUp63754hLCargpVmx/Z4IvEdeq5Lsc8RXTq5uQwQsR0BPBtSQ0RyjyEFfBIolMyt03xXPTumvsCUcc5G2L2cupELLJDrJUZiHpCf3mP3sXeIhitmHPeH0YWFURdYHuf2B7X8ji3/Up/+RHREXNr+v7GpMQdQagnjySKb/uS/6JR03/2A26SdAKwT5UW0FWaRTUE2JWwZzmZsEXak1DzAJDUPDcRC4tsBba+rl+STiGKa3UnYv+ZGpulQSMlgfej9/2+ORHW6nuehap1gkj6BfBr2w/kes9+MpK+E9Sz0/fA+6IYR4+F0zfSV+v5Xne9Dow5gEKLIpK+CexPbNruAL5DFLWuINaEEH+3PST9BdiknZVrgZxK2ObdRnSofJaYEdHMUoRlWDZSQvMV26+m38cU75+U3XM475zgr6VYTiXU0zk737vxO2CMpCNsT5HASb/PLSip668izAU82vxESnqtADzRKK5BFHclXcaUtvlFcD3Vdxg5gLCPPVfS921PKDkeCMHcQGbRzULB1/smNiGspb/b+oLticDukpYlBA2lFdgkfYL4XJjo7qzMzPVkv7s4MIvt08uMJeUAzkpfNUOXMcQ8sS1tn112MK2k4vs3iMLaksTa8P+I9X+ubple2L6u76NK4QTgJ5Lmq8g6rGrzIxvMQt8CuZkIoV82Uv7ti4QIeB2m3FtfScxBy20ROYkQUTRYnbg/Nls0vkH+DrGqrRFHMrDCWSHx1wW24cN2xPyFUbYn22kkte34VO39JzGXoJQCW5UWrRXmbOIiuwuwnaSHCcVaK7a9VtbIqGdRDYCdCBXRt5JFUrsL/CPEcNrKYPvqlNw5lPYzS2qmZFGiIzE7yRL4QMKf+wRC7T2OSKKPIs7Dc4mFZHYq1gmyG/DdVNg+CTi3+V5ZcWalPJusSnbtV4k2FmudbNdGAAsSlk+FFY7SYPHfNj01inA4OBNYjbAQu40o9G1CJPN3JKwGc/FrQqizBZHM+StNYhNJSxJFt4MyxgRRhBxL94LQHsQ9Muc8qi/Yvi3j+/WX84lz6i5JxxNFm+cJhffqwPeA64ALUrFtMjmS6kmN/03CXmp24v5zJ2Gbn6t7ZmamPFc+Tbga3NLm+GcJa55CsT2q6PcYBP4BTE8U2b4s6RU674kWzhTTU4RFa39ZnuhKzMEniHVgN64jRH9ZkTQnIfrYipjB2fza88T96ciWGTTZkLQ08Ht6W9Gdnl5bnRiTsIXtv5YQXs3QZkNin3ampF2Ie1Cn61hO4eEXCdvYzYgxHw1nhcuIc31SrliGEJcS+ZGb0t62m6tHIdf9lo7uqs2PbPAMUdjrxtLAYxli6UUS6qwraT7iet9sU1xW0fQRYH1JMxJFoa8D99luLlJ+Avhv5rjGShqb+T07UZmu1rrANnyYH/hlp4Sh7TclXURYQ2SlXrQOiFFN389Md8/pmuqyGHBSHx0B/yW/J3Z/eJgo8A5LJJ3c8tTGkka2ObSRLF+V8iw1v0PYPqyXlO8QsyLOBs6WdEGKLbuVXgU7QTYHvk1YaX4R+LmkccDvbJfRJdPM7G2eg55zbFNK2IRA+V37kt5j6u53zjjjbDw9MZrutmsibDT2KjCe3Qgb5y0JNeY6xAZ7L+AE27tPDkY6jEhib0fGAltKDmydkk1uk8h5jlj/TMgVU6K/Fp9ZqWhxDeK61OhwbZcYFFHAbe3IMgXvUSUdTnTptP49lwZ2lHSU7f2LjCHxIrEmbGal9N+72hz/QQY292JaZjrgHXoXqNp9PnN+ZscDu0pavrn7sB0p0bky8MscgRFdJ3P3ccxcxGycbEj6FHEvWoD4W70D/L/0/UcIe64fAJtKWtt21vWOpEWJv+sIYub4osD6TYdcT8w924wQgwz2+283tT+b05q7gaSlCOvb+YkCeCtl2NJXmbFN36+WvtpRuNtImuW6PSE8WZz4DD4HnEh0P90HPF2V4lraG21Hb5HMXUQnfxlW0BPoWfP8vstxRa5xXk7WhtcAV9uuzPzIJi4lZp2tYvvG1hclrU/cG3+SPbJEKqZVoQsRQhR5ClFoe5vo0moVwiwH/CtvWANeWxWWn65SV2tdYBtk0ryU/pBTTQfwH9ovcpqZPh2XjbIXrUMN21lbpWsK4x0iSdKN+YDXMsQyUJZgeBdwRzd9byIZt3SHY010gmRXAyc+C/wxdYo1mKyUT7MTLwf2Jv/1tVKdILbPA86TtBCh2NyeSOjsKek6YqDxBZnmvEyg92fse+mrEyJiHY4MBRuzQ+nZcB9ErHnabQTeJZKK19p+sMB4lgf+ZvuC9PgCSX8jZrkc33yg7QmSLiTme2XH9qsdnn+REuav9ZN5iER2oSSxx4W2/9L03KLAp5ufa3pte2KeV87u89Oo4OdT0uZEt8wTxD3oGqIz7OOE+vxAYF9J99g+p+Bwbgc2kLSE7fvTjN7RxO+tnSPEEmTeq1UV2yPLjqENvyLETedK2qCT7XRybTmXuO6fmCm2O4DNU/H4kTYxLUwo49t1ThZCskM9kxALjSdcdG5sCIKTYn9VwkZvdeAMIvGak4OJ2THLp8/owTTlKpITyS2ErWsRjGPg19Hs1tySPkIIpNdriqEd2W3pK04lOi8knUG4FnyQcMU4nzj3Lmu4iiShZiWQ9C1iZMUM9D7XNgYOkPQ927kdWqqw5vkA4YD0JQBJLxNriUbB7WGXOD8ycSQh8rtC0i9Jtn6SNiQKzN8l1mTHlhVglbB9qqTFiPwExDpjsjBH0srAIvR2Jyk6pjon3YG6wDb4TEf7C+vs9Hin/oeoPufkLGLw30HtEhaSZieKWDktgKD8RWvNVJC6Bt4DlrD98AC6CHJ2DVSZ+4FRktSui03SB4kkz93ZI2tD2gAvQPivr08oj4YrC6X/ilDnH0+IA1p5F3jZ5c5JnJ5I2Df4H709vCGsgcvoSKxqJ8jjwI8kHUgUHBpdbasDL6Y5hL+z/WiXf+b90tigiVBm3gfc0+a4RkHmattXFBhPn0iai+ikWxyY2fY3m55fCPiH7f8N9vsOBRsz22Mb36cix4W2y5xj+VGm7BhtPJ7Q5vgnmPK6kYWyzquWGFq7B5bu0FHQ6Cjdluj6K5rRxN+ruZi2FVHEbSdKGElcx7Jhe3TO9xsAuxNWlSu0WOxMAE5WzB38J5HkKbrAdiJxr7lJ0rWEqvyzwD22e3WwpbXhKhliqplKbD8k6VBCQHS3pD8Tic3GDML5iDXFpsCMwEEZu+SPJizC70hJzWuJJOY8hEPK7sRsnGMyxQORBF6eOKe3at0T2X4TuErS1cCfiC62dWxfmTHGtYDzbd/f5ZiniG7wIhgqVtzHE3vEq4hC6DOEoLSmCxXqvNiayO0cBRxlu7Kd0pLWIoSPk4jrWqtIZg/gBEn/tn11rrgqsuaZnXBiWZO4di1H3G82AZD0DDGb7Wpi/5h7nhi2n5H0JeK6v3fTS38h9r6PErOfCxXSJaGagf1tP9/GpagTtr1TgaG1e8P9CWFYO/5O2PqXmXOqSdSJ7kGmm5pO0iKE0mJm8s9WOpQYTHp7Wvi3zkE4kFBR5lYUlb1orZk6Gl0Dr7c8rukfpxPqk+Mk9eo8kTSCUOzMC+yXM6h+FEpFJPT37nLMNE2z5UTyV7+2JBuK/tDYbDR4kh7P9QbzUt0NcJZOkHakrr9GV9uK6fv5gH2AH6ZZhAfbvrOA9x7d+D4l8i+wfehgv89gIWknYm3TmNFgwl4GYo1xC6G6yy3gqRy2F+r7qMIZQRTbm3kDJs/lbeVtSiiGV+i8Gkdvi8+vpq9WGr+j14FDCo6p5v3xOeC0Tskb2y9KOpcQOBSK7SslHUCcMxunp5+gvY3sFsQeslRBRVVJ9maz2H6qzDhsHyrpHUJEujVR+G5GxHV1jO0jM8Z1taRdCVFYa7KuEdNutq/KFROR+H0T2L2bbX4S3O5G2MluRlhK5mIOegqknRAhGB50yrbiHgAbATfb/lLZgdRMFa8S8z1/SAiJxhGCsDdLjao9exPFteVaBI8PAeMlnUrMP9ubKCQNG2y/QU8BbUwat7A6UXBbkxDwbE9a30h6CLjK9h6Z47wrdWVtCHyBnjnstwIXtbjvFMVoYl1/FJEXH93PnzMxxz4LkhZ0l3EZqeP7rWStOZxF8JWgLrBlxPa/JW1CqCIPJrz/C6FLolyk+WZtnv8UkXTJeV6UumgdiqRuou8C29Cj6v5Aem0ZosvoeNsPFxVDa9fAUOgiqBgnEZvEPYjZT5MAktL180TR4yLbZ2aOq1Oh9D1iSO7twCnJWmDYY7vqSdS7CWFFg2uAnSV9g7D+GEUkKm7KEUyFO0HakmaAfhv4GqE0f4GwMlqWsMxbV9K2tv9UVAxVt2CQtA5hSXEfsa5Zl6aOSNv/lPQvInE87AtsNf2jYudVo3tAwMnAhcBFbY5rdJTeUmXldw0Q+5y+5ky9Tqb9kO0fJ3uulYhz6Fbb7eK7n7gf5SyAVBpJsxDFyW2I+WGTZ9tIWom4fhzQ2g1YNOlveiawI9FN0BA7PQvcSKyls4uzbJ8k6VLgG8TcotmIpObdwBklxLQscFN/9hW2/yvpxvQzOXmesN/qxmcIQfBwZgRwc9lB1Ew1Hyds+3amx2JwoqSzievVHWUG18KKwDmd3ERsP5pEMpvmDat6OOblXZy+kDQnYUu6JvE3/jQxBzZrgS3F9i7RtTaFrXkmGqLHZ1oeV41LJa1se2KnAyStTYiBZ8oXVk076gJbZmy/IelKQs1WWIGNodNRVC9aB4CkGQhlwihiNt0kws6jwePEZu4FYlOZK67VgFdtt7Mxq2nB9ruSNgIOAHajZ+O9CTG8/jBK8KevC6VTh6Qv07vgvUh6fnHgy8CZjmG5ubkYOFHSQsn68CeEAn5c+oJQLB+QKZ5xVLwTJCngRxMbzEVTLDcBvwbOdZrBlrrazidsoAorsLXE9mniHJvFdjuhTBnsSyQMV7f9ahJ5tHIfoU4cdCQdNJU/atuFX2MlXUOc69vbfjo97g+2vVaBoY1M9+3JjwEkrcqU3WojC4yjE6WeV800dw80WXxmm2tTUwiPAhtJ+pHt91pfTEK2DdJxWUjq5I4K5XRMlRKcpSNpNqJY9RnCRvlF4h7Z4B/E/K6tgKwFNpjseJBtL9Zf0rl2RNlxJBYg/ob95V9M2RFYNNcAW0larJ2dp6QVCEeeEzLHVTXuIixuawaIpI8Te7F1CbeMdsLyQsdsJLvtU4BTJH2GEBhuQ4ibvi3pQXrs68vmQ/Q9h/eFdFx20jWh8becsc0h2S0Gm1iAELEuCMxdUgyVoFVQUmFHooWBvyR75LdaX0x7twuJHGJNydQFtnJ4h7C+KowhlCivF60DY29CdTKWGAR9EGHvCYDtVyRdT9zUc27qriW6snbN+J5DmtT6PjbZDC5KT2v8gx1sumoqhmLa8zii0wqiA7h5Mf8y8GNiM3JU1uAA2+PoKaRh+6l0Td2LWKxNAE60natLrLKdIGlxujOhdvwg8BpxTft1u9+P7dvTPLZ9M8S2NPB7Qm3e4PT02uqE6GIL238tOpY2LA+c7TazXZt4muLWPGOn8udMHhHDqPReMzU97g9FC6S2Z0oLOgHj2xzbsGfMSdnnVVtsr5Hz/WoK4yzi3nyRpB/YfqTxgqSFiZkuSwBjSoqvpn+MIYpro22fluZ4TxZd2H5d0nXEPrKmmnyYgSUFXwFmLSiWThxJuI1cL2ks4TJCKkKsRuy3J1HQ7DpJj03lj9r2woMaTHcOAy6RtIrtgRRNhzWS5iMcYj5GFJBnJGyC3yQKlh8gBAQdu1cGG9v/AvaQtDc4508sAAAgAElEQVTwdWJ/9MX08mhJ8xD7kotLylk8QXRgdWMN+hCtDDYteYHG2rm5IOmm57MU2JI4s2EPOYpwDxPwHNE5dm36yo6k5YluxDloPzs4ixiyKZ6DgPG2r+9yzKrAGplHN+xI7PvPID6PzfF8HvgbIUpeO2NMNR2oC2yZSW25X6PuyGpQ6qJ1CLINYaVxKICkdkmvx4mumZy8yJQzXWo6IGlB4BXbr6aZA+2Ky7MCc3TzXK4pnV0Jm52TiaLVnvQueD8n6SbCXzx7ga0dqZNtt5Leu8qdII0h4/8iutVOs/1aHz/zDD22EoUgaVGi6DGCmJuyKDFEvsH1RDfzZkAZBbYZ6HtO3uxE0bQIKl3waLX4rIjl51BwOCj7vKqZtjkWWI+4N68v6T9Ex+Q8hOJ8OqKr5tjSIiwZSRcRdkMX236p7Hg6sAlweR/riCeAFTLFUznSfmPAZNx7zMDAruPvkXlshO2HJG0K/JGYnw2RoL4v/fcVYJMCf2fTMXX37KydRravkbQlcIGki4mOtrZFoQqt/avAQcS9Z13bV6UxL6c4ZjnOD/yOcBLILhRI89dOB05PhZpvE8WjDYku7+eIe2ZuLgD2kXQisH+zGFPSh4li74rATzPHtRuRFziNmCH8d+B44ByiuLUfcAnFjgn6BD0FtTUIlyQB/yXspccT8+OnyD3lIv2Nzk/xdbtO5RJDNhibvjoW2OjJT2crsNk+K10LfiLpONt7AkhajhDZvg2sY/v+XDFVmbKd1eoC2yDTxa7oA0RL7lcJv/Mi7SGHDBVYtA41FiJUCt14CfhIhliaGQ+snPk9hzKPEzfwbouGPYibdztFTyFIOrkfh71HDEJ+APir7eeKjarS7ATcC3zLtjsUvB8hOkprmqhgJ8ifiG6+G/r7A7Z/A/ymuJCAWMTPACxv+/6k0J9cYEvn3S2Ul0CcACzXxzEr0UZEMBjYvq7vo4Yekha3/UAR//YQcTiYQInnVTdS1+je9Khu2xVNC7VyaqJ1juXSAGnOZmvSYukM8QwJbL+V5vz9kFAGLwzMn15+lBDNHNOwBB6mrEaI9d6RdAORDLuwJLvrTsxPFAG78Rqx7x6uTGDgxZnJc+wyUXXBB7Yvk7QQ0fn9eXpcR24liiGFFaFtjyzq3x5M0hiLrxL3xUaXfOvfttG9UxfYelgXuMz2FLM1k7X45sA/Ccv87HOymmJ5ENhT0r6EqG9n4j5RBkcSs+x3AbaRdC89IpnPEZ2xD6bjcrI98JDt0QDR0MYrtm8FbpV0OXHNuJKw4yyCRsfr/yMKRdcSBbUqFV+OJgqANxC/h6cIh7ehwPRELiwrtn8qaQGis/Rp4Ir0JWA92/fmjqnClOqsVhfYBp+xfbz+KnC47dyKCmCyx/NadPcEzjr7qcxF6xDkDUK13Y0Fye/BewBwm6TDgEOHeVKiP4hqeJi3MpqezVC7+FqtDn4l6QDbRxcdWEVZDDgpdSF24r/AXJniaUuFZ8RVBtu5Z3r0l7WA8/vYGD0FrJMpnlYuIlSkm9s+t/VFSTsAS1Ehq7V03u/cUABmeL/Dbfd7zqGkRQiVaRmq4KpQyfNK0oaEte0IwnroIcpNCrSbY9mwKGqlDKvPSpK6et6yfSRwpKRZiCLMxH50Lg8X5iISYF8jEpm/BH4h6e/0FNtKU8AnJtH3DJmF6HtWz7TMk0z5uZ+dON+rMm9mbHKxqTSpS+bn6avySPpAGkeQiyOJXM79hGjtPwydpHmZzEN0ODV4l6ZxA7Zfk3Qlca8vrcDWFM9bhM3yWZI+VVIMr0pamehQ2wZYpenl14muv/36sBkvgk8Dp7Y8Nznfbvvu1N25K8UV2BprvfuAO4kuugcLeq+p5atEh+sabjMHt+IsS3lrij2IveFRxP5nBqK4Vs/n7U2pzmp1gW3w6aTKf4+Yx/Ng5sXOZNKsp/3o/Xdv3nA3vs/tdfu47dMZQovWErkH+JKkGToMuZyNUELdnDmuHxHqqv2BnZKS6Dmm3NSVOdR1qDEPfVtkDTYLAz8jFqrHExZJzxO+8KsC3yPURj8h1PAHEu3qD9tuN0trWucdYl5XN+YjFNTZqfqMuCp1gkh6FxjbTWAiaQxwSKbOlAZzELOmuiEyWyY18VNgS+CPkjYjdQpI2o24ZmxCdHH+sqT4SPHMSM8ciUa3dZYCG7C/pGds/7qvA1Pi/2oyzxarIFU9r8YSViwb2r4i83u30prEqRzpunq27W3KjqWFx4nf344QCUxKuk9XlbRXbSikv5OSmV8DNiYS6T+W9CA9xbY7SwjzDmAjSbPantT6YhKVbgBcnD2yitCu+ykVsw60vVD2gNozUMFhLRToQpojuTOwHWEPl4stgX8AK7TLUdR05FV6r+FfZkqB1URKFmu2o3l+aQnvPRH4dloXLkYSyRAdZGUKvZttUf+PKV2lHgG+VOD7b02IM9cEjiCul6+lTvRrgWts313g+/eH2YDTq1Bck3RNy1OjJY1qc+gIwo3uE4TzWnaSa81WxD5xGWCjet5lW8ZTorNaXWAbZKpqVyRpGyIZfg1wAmGpMY7YOI0irM7OJdopc3IAkciv6R+/Bc4EzpTUq1AlaXZCDTMHxduWtTK66ft56JwczDbUtWq02DjBlNZODUYQXYjbEhuVnGxMJC+XbuloeoiYk3gacDdwg+3jk9XB/YTn+HAssN0PjJKkdl1skj5ILHDLWshWdkZcBTtB+ttVmrvz9HlgkT6O+QwlzXW1/XIqlJ5GzFNt8Iv03xuArW3nFgsAIGlJItG0DaHaF2Gf8oeMYTxOdH48b/v8TgelQffXEhu4I3IFV0UqfF4tSRSMyi6uYXuHsmPoB5OI63vVeIXh3dU0YGzfTIj39k7X1U2JNeMYQkTwNKnYBlzfR2f/YPFzYv7IJZJ2bn4hdSr/jhBB/aLNzw5nKlOgqshc0ikYArPreiFpeuIz+S0iryPyzyidHTirLq4NmCeIdV+De4E1Jc1k+3VJ0xEFmb7EdsOSVEz7Z9lxJJ6hd3H0Maa0O/8UBQqobZ8NnA2T57GtTeQiRhGCE0t6hZg93ii4/auoeDrwCCHergKjmr43Me9wZJvj3iNsN/9EwQJNSY/1cciHiFj/kGxIG9j2woUFNnQo1VmtLrANH75D3JjXs/1O+jBOaFyEJV1AzPbKXZF/hvBJrukHtv+Y5kaMJixbXgZIli2fIWw/T7B9SebQqqKCrDLj6NnUmvbWTtCTwH+d8FvPyc7AuZ3sAm0/JencdNzxtp9MVgdl2dOVzenE7MjjJP2g+QVJI4BjgXmJzuEyqPKMuLFUpxOkv8xB2PTm5BpgK0mLtbPjkrQCoVQ8IXNck0kJpVGSlgK+QJPNcxldDZI+RCipdya6IxvX1HuBvWy3qhWLZl3gJkIYs67tKYZnS5qbUCQuBBxn+8DWY4YbVTuvEq8Rc25r+sfdwBJlB9GGWwn1b81UYPufpJlAkkYSHaVfA3YnLIxeJEPyzPblyZ3l4BTP2wCSXiTu1wL2TcXBmpqBMIHqz65D0mJEUW074h4pomBzCiGuy8kD5O2Ym1a4GthZ0vQpEXwqIS66OVlDrkLkeH5cYoyVRdIqxP18dmKNeFeJXT2307ugdikhSjmQEKCMInI/WbqqbT9BCAr/AJOFJ43uttVTLEh6wXZO54wTCAek+coeUdEs8pD0HuFmc2iJIUE4+nS7/7yRvlpFv1UcP1MGpTqr1QW2DEjagkjyTAeML8lK7bPAH1vsKUc0vkmblMsJu66/ZozrAuArkj5kuzSv1KGE7R0lXU/Y9S1FXEyXBf4FHGu7KE/nXqTuq3ts35du4DXdaajNRWx6LqR919e7hELmluT3n5OR9LY2aMcr9C6oTgBmKSieqnMSUejeg+iymAQg6c/EPMl5gYtsn1lSfFWeEVd6J4ik1uHcI9s8Bz1dpdsQnXY5OZI4t65Ptk7zAkj6DDFc/GDivDsmc1xTYPs+wvO/FCR9jiiqbU0Id0TMHxhHWAneUUJxDdv/lrQBYVlxoaTVbU/uTpb0EWLm2qLAb2zvlTvGKlP2edXC1USxr6Z/HAX8VdI6tq8sO5gmxgI3SPqm7d+XHUw7JM1EFIlGtHu9rE6ZVmxPIMRExyahwMbpK9f7H5L2RHvQM8fbwCWEWCH7Nb9mmqCys+skzUCsC3cmii8C3kr/PRfYMlMHaSs/A34naVHbD5fw/kOVPxCC6TmBZ22fIWk5QrCwVDrmbIa5s0Er6Xd0OrHXhaaxN5IeAraz/ffMYZ0HLC9pIduPE3bnXycE02NTjC9RkvDW9gPAA6mxYh2iE+uzFJwHaNMRfClR5LspiWTuJPJLU5B5rbMD5bkOTaadtXPNgBjd9H12ZzWVc/+d9pD0TaJKOhfhCf8dwhboCsJyDXou/H8BNsm5+JH0OrHRGJMe/x/wO9vfbzrmKGAX27NljGtWQqH/EqEsr0qLdyVRz2D259LjDxEb8Im5rZLaqTwkbQ9sb3vNnLEMNSRdC5xi+7SyY2lG0lPA87aX73LMncDHbM+fHv+GuJ71NWh+mkTSB4hW9N3o7bP+CpHUP8zlzd2cCJxpe9f0+GDgINsjmo45legimzNzbC8Ap5VZTEjXsP7eh0XYQ2xn+6ziomrzxtJ6RHd5o9u7sZYQcZ5tVrUkoqSPEgXA14GrbBdmVZTWXzsTqlERKrUzgXEN25P0t/697Z07/kMFk7rP/0p0d6ycOoBnI9ZAywCnDhHbv8JJa5u5gOfa2U0pZup9DPiv7Wxdpclu53bi2n5ESUnMIUMSYm0OrE+Iiu6gvYqUnOshxfznlYkE0z3E37STujXbXOoU2zeAfYHFuxxm550FWkmSIOZV2/eUHctQot1asKZv1DO7rpTfm6QliLXOtvR0aDZERH8k1halrXPS53EPokPm5ym2tqLNdp38Nb2RNBfwScJx6vmy46kSkhYB/k7si24k1tHPEh2UaxKF54nAii5xThxAWud/i5hzP4HY+z6bOYaPAmsQv5u16Bk90NhP3me7sK7+LvttdXi+Qb3WYcr8b0130l6tXxTRJDLsT9jBICW/ftv01Cjgz0SCZzXCGug2YjG0CdHxsCN5Z4A0bjoNnqRHFdNgXvLPwLmXGOy6LHCvpDeIjop2m9zaU3bKwez/A6rU+TeSaDmv6YLtNcqOoQPnAXtIOgPYv1k1lG7uRwJLE8nFBssRNoPDklQ8G5sUWIvSY2P2YJFFhX5S5RlxVegEOZSeQtVBRIdRuzmqja7Sa20/mC26hO3LJC0EbE+PQn8iYXN2iu3SLOskfYdQiq3fiCOpSi+jp+D8d0lrFigC+S1R/DyPuD9eVoHP3hTYvlLSDsAZwOWS1ieSYssQnv47lhlfxTgI+D4xy6Ld+T0z8CDRuXlQxrgOJtwCDgF2lHQP7VW3hdiODEHG0XON3SR9Qe81fiPBklNwNLbp+2XobBdpIFuBTdJowuHgXWLO4FOUO5u06lxLOAnsWnYgNcOC0gQVkm4k1swNEdGxNImI0jElRTeZ8fReU3f7fdXF3T6w/QLwQtlxVJQDgVmBLWyf2/LaWEmbEV1/BxB7p9KwPZHMLiOSZiHycY2C2pL0njX+KLEPv4aYwVb0TNrTqNDszwZNrjW3236jg4tNWzKLBHrlf2u6U7azWl1gGxx2I3zftwSuJBSRZwN7EfOwdm8cmIbt/YPwys5ZYLubuLg2uIbwe/4GPZ7AmxHFwJxMR/zuWtt/a0/Z9tSD2WuK5CCi43ZrYAtJzwDPE50C8xEbonvScUj6OPH5Pb2UaCtEKmDltg/siyrPiNsXuF3SAZTUCWJ7bOP71H17oe1f5I6jPyS72J+nryqxBXH6NxdBjiYERacQ144NgV0I+6CiECEaWhK4ixAVVQ7HHNW5geOIAtEMhKvBtlXuhpL0gcyduOsTnY9ti8e2X5J0FbAReQtso5u+H0n7QehQkO3IEKSqHZlVFTn9kLAKWyVZOdV050WqJTKsqSmKlQkh0U+ILrr3So6nHQ3RWs0QR9LJxJ7oL12O2YhwsCkj6b82cEGb4hoAtv8s6aJ03LBC0i2E+HkEPfnTZ+kpqF1t+6mcMdkenfP9BsB44pq1OPBw0+P+kFMkUOd/hxB1gW1wWB74m+0L0uMLJP2NGBx5fPOBtidIuhDYIHOMFwMnNnkC/4RIjI1LXxCJ8gNyBlV7zA6YejD7NIKk1YmZhysSyejp2hyWtTXe9quSVgb2IRRfnyRmTwE8RiiQftqw5EoWByvniq+KpIHBKxJ2ZibUhrfZrkKxrcoz4irVCWJ7ob6PqmnDp4C/NR5ImpNQTf7e9rfTc7cRRfuiCmyrAN8mREJHAoenwfDjiATBFBaDZWL755LmIYrMlwGbV6HjTtL3CRXnzS3Pz0zMy/1SxnBGEsmAbjxM/O1zUl8nBoDtU8uOoR2223UqV4FFiK6UurjWP8YzzNegfSGp472ly2u1LVf1+DdxfdgP2ETSOOB02/8pNaommkVrNQMjzeLdkZ6cQLvkvW2vlSmk0YSdYccCG/A5IldQRoFtTkKk1o0HCRFWobSZL9YvXNxssZUIoc54UlGtDPeVIUJDFPBiy+OqUed/u9D0GXzG9rsD+UwW8TmsF0+Dw0eZsnOh8XhCm+OfIIbkZsP2OHoKadh+StIKRJddwxP4RNv/yBlXzYAZS8UHs9f0jaQNiXkkI4juzYeoiA2Q7TcJS6TD0ozEDxMzLiaVG1m1kLQK0U20dIfX7wS+Z/uWrIE1kRYZG9EzI65hE7wJUcw6jIz2Vy2Mbvp+JBXpBJE0PWGnsTgwS2P+TrLT/DDwYk7lsKSvANvQpohLzNe7OFcsHfgoYevc4Ivpvxc0PXcDvf/eg0oqCN0saQ/gG8Ssg/WAdYGXJZ1T1Hv3h25JTCLGN9pYO5WR4DwWcOpcPho4EZidKAIumzmW6Qm1fjcMfDBDLD1vWLLtSM00z0vAm2UHMYQ4ALgtucMcavvtsgOqIFPjAFO7xlQM24tKGkWIiTamt5joFOCiEsOreR9I+jRRDJmL7p+9qiX+ZyTsjMvgBWCJPo75NHm6fiYw8L+NKS4PvzxwdxVdMVJOYg7g7E73a0kzEE0gL9n+W7tjBotWUUCFRQJjqfO/3ZhA707ExuO+KORzWBfYBocRTGlR0ejwaHfjeZsKLF5TJ9tuZcdRMyDWJxZhJ6XZN2UPZq/czXuIMJa4Dmxo+4qSY+lIKqrVhbUW0uyk84nNxf+IQd7PENf1eQlrhuWBayR9xfaVZcVa4RlxlesESfNU/wDMQ89coMZ1dGnCQnlbYm5W0bHMDJxDFIpa1wszEwXJr6du+S3SPM4yeIlQkjZYnSiMNHdBZSmEpDkHvwJ+JWklYGfg64Q9JcD6kvYiBoznnGsxVBKc2xAdiV8jxAM7ADMR1422NjwF8hh9z3MdRQjWhj2SHgMutf3dsmNph6S5gE2Jze/Mtr/Z9PxCwD/KuIZJGkkU5ZchhI8TCUv9M9IeKTcX02Vuas0U/Aj4J7A/sJOke+m8JxqWlq2227lj1AxBbI8Hxqdup9H0FhO9Qpz3M5UVXzNJhLgMIdKZCNxl+8Zyo6osxwBzE+5SvwWeqoKrAV1yPJJmBFYjrrdlcA2wtaQtbZ/d+qKkTQknsRwOLU8y5e9qdmJNkX2Navuu3O/ZHyR9lhCX/9p2x9Eitt+StDywq6Slm+dMDmOqlv+tGo0ZfxNbHpeC6vX7+0fSe8BY24c2PXcwcJDtKVq8u702rSNpu/TtBbYnNT3uE9s5B6BXknSu9QcXfX6lWAas2KltR0DS/wj1TlXnk9R0IBU+HiE2Q4cDx9h+reWYWQibzf0J3/NFS0oevkucZ9vkfu+hRlrM30ioHX9KdIxt1XwdlfRvIknw9QzxnEHYKr5AWH2Op3cRd02igPRRwiZodNExdYjzakIluhShZL0fmGB75aZjzgWWsb1ICfHNShRFv0kke0yIG/5qe/Pc8QwVJO0M/Ib4fZ1pu99rtUF6/yMIK6wf2f5pm9f3A44gLIt/lDO2KiJpEvBL2/uXHUsrknYCfkEU2UXT+lTSksC9wM62c86lJhXbjyC6JVsL2m8T596xmWP6KCHkuA7Yq3VtURZpHs8/bB9XdizNVGlPVDPtU8X8jaRVibXgpsQ11oT9+h+ItWHbOaYFxrMcMf95scZT9OQKHgK2s/33nDFVHUkTgettf7nkOB5rejiSKNq2s+4fQXTbzQj8pgxhj6RFCHHrLISg71pivz0PIb5ahRAIr2D7kRLiG0vMSqzMtaJsJB1HdOAuaLtrZ2EaN/AE8Fvbe+aIr8rUa52hRV1gGwTSST+OJgtGQvm7HXGRb9247UAsMIougMxAJAwnAev10Yp7KaGKX7VIi42moszith/uZ5Gm14Z8OJPmdvWLomdMDOBi34taTQmSXiC6KPYqO5ZWqjgbrkpIGg2cDIyxfWQfx+5PdECN7qbWKgpJrxBKsWGfgO6LNAx7VWAJ28+1S6RI+iOwrO3FOv07gxTL0sBdwH3AOp26rSTNDVwJLEkUsO4rMq4OMXyFUCS+SdjczgRsb/uMpmOeImZ7bZo7vmZS4ufbwFbATMN1TSFpIeJc/6vtl9u8PjuxdlyCmOPwGvG5+L+MMc5BdBItkP57BVFgno9Q6y9NqIaXbff/MMixTI2SPOs9UtKtwBO2t8j1nv1B0jqExeh9xNzNdYFdWq6r9xGxZ0suStqKULa/TBT/xhNq4HmANYi5pbMBW9v+U8a4riGU758DXifEPJ1mk+aaxYOkN4DjqraWkPSJ/h5b27vWDIShcN1vJt23tyfERJ9JT79hO1tHWyp6/J2wU7+R6DJ6lrCnX5MoekwEViyj6FFVJL1K7NX2LTmOCfTk5BYEXqX9/edd4P8R870Ot/16lgBbSKNuTqOnmGt6cq4PEXuR20uKrXLF+LKRdDfwrO0N+nn834D5bX+uwJiumcofzb0Gq0z+t6ZvhmWCtCC2T1/NiNi0tdKs5imSbQmrsi93K5qlVtyjgUsIi6BxBca0I/H//mx6XHfwDIAqXTTrQtn74mrgC2UH0UqVZ8NViPUJW7yf9ePYnwE/ADYgFJ25uZu+Peprgi8CF9ruZnfyFLBhhli2Ju6T23WzMrT939QFfnf6mewFNtt/kbQLoaCG6HZqLq6NIhSml+eOrRXbdwI7S9qTKLINV34IbAmc1fqCpJmIteASwBji73YHYbPZn2veoGD75XTunAV8npgB15w8uRnYtujiWmIoWHz+Avi9pKXKKLR3YV9ivb+67VcltRvSfh/510N7EcW1ZVsKLw8B10k6lVDH/xDIVmAjRJkNZqbDjFfyW99MILr2K0VdNKspkKFw3Z+M7VcIa+efS/oCISbaLHMYBwKzErblrbbSYyVtBpxNzE5szZkNZ+6kp0hUGrZHNr5PIurjmt25qobtO4DFJa1MrBEn2zzbvqnU4GrasTCR/+ovDxBF+SIZ1eH55v1Gu+ezrsGqlP+t6Zu6wDY4XE81Z1FtAjxm+5K+DrR9maRHgM0psMBme5ykBUkXLdunFvVe0xKSRhAJ1BWJc+02wvqtCh7dNQNnX+B2SQcAR7g6rcRjGQKz4UpmKcLK462+DrT9pqTrCTV6GRwF/FXSOi5xDlyDiiuCZ6HvYdgz0b6jc7BZibCi7DNRbvteSXcSRYhSsP1bYm5Eu9fGE52wlSF1Yg3nIdFrAlc75jNORtIHgPOIc2lyh25SeG5GxgIbgO0JwMqSlk0xzU6oqW91xhkTQ0RM9DRwFXCTpJOIomi7+QzYvj5jXMsTa9VXuxzzNNE5lpMlgFM7FWhsP56sbb+RM6gKn2tnAbtImiNTUbsrqVtnd3r2RLcCJzjmcNbUvG8q/FnsE9u3ALdI2iPzW69NjABpO7PV9p+TW8TaecOqPIcCl0saldbMVWANQlhReWzfTO+5zzXVZAagz9xJE28RNqSF0XqdT65u5xDOMIcxpbvBGGLua+GjImreP6nLdV3C/aTduWQXMJ+3LrANArZHlR1DB5YhlMj95Xqi06JoHicS+ZOHMKbW19WrrJQpC0kfJLylV2w8BXwX+K6kNW2/UVpwNVPLwYRH/iHAjpLuobMNUM7B7EsSybC6uNaZjwF/G8Dxj9FZIVU0cxP2XJdKupDuSdccMy6rrAh+hh5bnU4sTfw9i+ZTwF8HcPydQKmzG2qGFPMDF7V5/lRiI7Kfe889u4tQw5dCKqZVcmh7hRhPj7L2B3QX/eW0LJoB6MtadHbCcionk2i/5mrmZcIiqwaOJIql1yZh2B22ny8jkFRcu51QwzfWBxsC20taKXXy1NQMe/oQNhTBnMCDfRzzILBRhlgqS3KeaOUi4IpkRX8nHe5PmfZqHTtmJE1P5Apet/1Qjli6kWyC5yLWPC/YfrLkkGo68xJhPdpfFiSsSHNyILHWWbJlLfEEME7SX4B/pOMOyhwbqUllOyLHPzvRsXkXMW+z7uhPSBLRMLQtPR2Hzfmk5k7EusBWMyDmBAayAXoe+GhBsTQjpkyajiIuVHWBbUr2JDoangfOJ353m6Tn9iQ2vjVDi9FN349MX+0o5MLfhdeIBVBNZ2YlknP9ZRLRHVUG4+hZRGySvqB38rWxwCh801ZxRfClhEJ/Fds3tr4oaX1gZeAnGWKZDehoDdmGF4mFdnaS5dsXCGvIiem5mYETga8Sc4SOsv3zMuKracv/aOkYkvQrwjZzH9vHtBw/MwWrSFuR9CEiafJcu25hSTMSYof/1iIjINbOVemEb2YCYVXfjZUIa8acXEEUk9vOFEub8y+l42qg8RkTqTgfv6IpyNFxvi+wCHA/IQoQsaZeLL1WqTlxNTXDiBfo25b+0/TtFjGtM44p79eNC+o30le717Ps1QAkfZ1wLtjF9kvpuYWJvdLC6fFFwNdb3RAyxDYnsD+xZpVJtSYAACAASURBVJ275bXnifmqRzbirqkMdwHrSJrR9pvdDkzNBesQwuCcbAOc10moY/slSX8mCjdZC2ySvkXYwc9A7zz6xsABkr5n+6ScMVWY3Yjr6GnE7+zvwPFEd+IoYD+iCamQ9WJdYJu2+R8DS+zOQs8mqqY6bEooaZduKEYlHUZ0QG1GXWAbiixUdgAdqORsuIoxgoEnMssaclzPuOw/RxJzqa6Q9EtS0TvNJVyN6Bp+Fjg2QywfYmA2Gm8DHywolr7YF1jV9olNzx1JLGxfI0Q7x0p6oO6MrQz3A5tLOh24BzgC+BbRudZaXIOYgfB0xvggNq7fJ2w92iVJZiaU8MdQgoq0atgeW3YMHbgI2EfS5u1swyTtQNguj8kc1z7AzalbYL9m5W9SCB9FiBb2yRxXI4YtgW8SKuXZiE66O4E/2D67hJBuoDoF3I2IjvMVbb8OIOlE4nrwZeoCW01NWVwDbC1py3bXKUmbEsKrM7NHVi2Gwt5sR2DeliLVzwhxwzXE2v6rxP/L73IFJelTwJXAAkSR4R2iy0nARwjx2A+ATSWtbTuH60hN/7iAcEo7gphv241DiUaR84oOqoV56Xv//Tbw8QyxTEbSWsBvCNH20cRn8NkUx5rAHsAJkv5teyBz7qZVtgcesj0aJgvCXrF9K3CrpMsJa/ErgVMG+81VndE/NYONpH8AE233a0CkpBuBD9tequC43gPGNttBSjoYOMh2WYnoyiLpVeBPtr/V8vwfgM1tf7icyGqmNZLVwu3AL6nWbLjKkK5f4+j/rModgO3qa1v1SbOezgE+2fR0owPwUWAT2//IEMcU98g+ji/t/inp38RMrG3T4+kJdfIDhErsI8DdhKXYsLYFqgqSvgJcSE/CvJGkOA8Y3awslbQ7ofo7wXa2eS7JNvkp2x2tT5NVy/y2l80VV83AkDQHoVpegDi/ZiNUyd8DViW6qh8FlkuzEXPFdQ0xG3Ipwp7yScIl4mOELdEI4D5C3NaMba9VYFwiFLdbE5/Ld4nr6Zz0iHvOtr1NUTFUHUmvAafZ3rXl+ROJtVZZjgE1NcMaSYsQQoBZiJlY1xJJ4HmI9eAqRIJ4BduPlBRmTT+Q9CRwZWNMhaQPE/ei821vmdb69xB5xpUzxTQdkRRfnrDFPhy4seFykJwNViUEO6sTe5PCY6v4fPHKkM6Ze4lu87OAQ1uvA+kachDRSfYA0VyQrUMy7WnfIywiO7ln/JOooSySMa7LiFnUy9l+tM3rCxPX3lttr5crrqqS1omn2v5uevwecLjtg5qOOQ9Y0PYKg/3+w+qDPQwZD+wqaXnbf+92oKTlCPurX+YIrGZAzEJ79fhThIq7pmawqOpsuKqxffrqDw1bj5qKY/suSYsR81y+QCg0JxIbuosy26CMljSqn8eOLDCOvpib3ven5Qkb1ZOSdd9/ko3MsF/wVwXbf5G0M7ArocQ8nCg2jAOWk3QGkUgZRRRAXiUUkzkZSXRUd+NhImFX04SkVWiZz9DO9jYHtl9OM5ZPAzZveukX6b83AFvnLK4lRjV9/wFCVPHJlmM+1+bnir6Xf5tILN1JdAdfZ/tdSSOIhOFPgC0l3WD7NwXHUlVmImbJtvI80f1dU1NTArb/LWlt4nr/xfTVPPvmIWD7urjWN0kItSbxu7vO9vmZQ5iLKI42+AJxrzwbwPbbkq4kbBpz8SVij3EOsFWrCDiJw66SdDXwJ6KLbR3bVxYcV5Xni1eGdM5sTKzttyG6XZ+hZw85HzEjWum5jXPbjxK204cA10jaH7ipaQ22CtF990kiX5aTFYFz2hXXAGw/KulcwvWsJpjY9P3/EYLfZh4hrimDTl1gm7b5FfAd4FxJG9h+oN1Bkj4NnEsoJU9sd0xN6bTb1NdJ+5rBZnTT9yOpzmy4KnE9Q/izJ2k+YFlgOuBm2wOZ9TXNIOlY4LKGbWGyBXslDYX/S/oqk5EMrHBW1jlpeq8lV0nPNQ9If4HYrNdUBNu/B37f/JwkAycAY+lJij1PzNh4KnOI0xMq0m6Y8qxRK0cSyp1OqIOhSdwh6SGiu6er2K4IbD8JjJK0FC3CBdt35o4nxVTVeaA7EnPrVrP9v8aTtt8lEj6rE+rpnQi7oOykGZuLArPYvqGMGGpqaqqJ7TuAxSWtTOw1ZiOu93fbvqnU4CqEpC8DewMH2r6u5bVxhM16owizm6QLbedMnk8i/nYNVifWE81inTcIQV0uNgXeBHbv5rBj25J2A75CjFIptMBW4fVE5bD9cFqrHkHMMZs/fTX4/+ydd5hcZfmG7ycgvYPSpAkqRZEuQoDQpVfpvclPiiCCdEKRIiAgFkANHSIqJaCAlITeq/Teu0BAigTy/P54v8lOJrMt7Jxzdue7r2svcs751nmdnTnlLc/zP+I+9lDbb5UQ4vGEb/D6xATuWEnvEsWZQcR3cgTF+LHXMyXde1e+TW4yqvEqUbCt8RwT+kF/kyi89Tm5wDaAsf2kpKOIZMkDyZTxRsbvFFiVuGBNTkhMFWU03tidPy+Mk21pRktlWfoB80pasXEfgKQVaNIJY/vmAuLK9JB+IiFQVW+4ymB7SNkxdEdKZO5DFDXuAU62/VHybjyAjmv/GEkH2T6lpFDLZB9iOrPmC/Y8ca08uqyA6li57AB6wUuEbEWNDYBXGnwP5mBCqbWWIGleYGGi4/ejtG9S4DDCCPoj4ETblxURT3/C9vmSriQeLOcgEv1X2f5vCeE8RyR0umII8GI3a9qCJKtzAzAdkQBr9GcYDFwnaZmypgdsP0zILmY6Z2Fi+veTZgdtfyLpcmLSrVAkfR04jfA5q8lVTpqODQbOAn5ie1QB4SwmabvGfSmW+sT0OGyfV0BcmUwGsH07IROZac76RAHyrvqdktYFtiPuVU8hCl27ARtK2tL2xQXF9zSwVpLEM7AZ8LDt+iT/PECRRZAliImibhtDbb+VrG+yhHjFSIWzXZME/VJ0+Jm9Dtyb1E/Kim0M8V3birD4WJworo0m5M7PLvA7WM+LxL18V6xMPJNnwu6mvqB2NbC/pMOAS4nnxw2Aq1rx4rnANsCxfZSkz4lR1q2YcJRbhETQIbaPKzC0eWnenT+kk/X9dmKkj+hMkk6EFGgjjVMFmfKpvISA7Zys7OekieRbCflYEYbCS0gaTujSfwT8m5CEmw84SdJDtjtrbhio/JeQmqohKiLZ0djNWnEuAY5MDTyfEhMqpzasWYjwWSqCI4jExax1+w4lCmw1LpG0gsPsOFOH7fcJeaeyGQEcKOkA279qPCjpQCJxMsGxNuUwopN8c9t/bTg2VNKmhLTTofRc3jhTPPVyap1R+HVK0uxEInhW4rv5NeJcX+OutG9zmj+T9DUbpJ9GROf+uFU4r2UyX5omxeVmjCXknR8vo6ki+S0tSIdU8eMpeZ0JlgFuaVJM2Im4Duxo+28Aks4n7qG3BopK7p8FnE0U2sYQObt9G9YsSdhKFMVcjD9B1x2PUqyEZaYXpM9+KfLl3WH7IsInripcBhyQvGYPTs9qwDh/xKOJc0p+Jgr+DiwlaT7bzxPvy2aE/OdQ4l7xXeDAVry4upiwzQwgJM1DXLSXZ/xOgVuJanxhifUkcdJr+lnSsc+QNIqJKDDabtkURBeTht3R7pOImUxLSdIi2xESwf8CVgf2BB4jOg03tj06rd2QuAm5wvbGpQRcEpLuJ/wttyWuhS8QhaHG4tAEJMmzDCBpGuBaOpKtDwIr133G5gOeAY6zfWgB8TxBdNlulrYHEX497xBa67MB1wPX2t681fFkJg5JMwIPEAmVB4hzWU3yY01iWuUlYAnbhUxHprgmASa3/XHD/lWIhP/HwFnpga4wko/FHbY37WLN34Flbc/Z2ZpWkBKtGxAP/zMS00+NtLuvKwCS7iYKVQs1m2KTNCWRNHzH9jIFxnUGIUu5hu2Rko4gVE8mqVtzGTC/7UVbHMvZE/N7tnfs61gymTKQNJbe5QUeBfYoQtI1JXt/Rdxb10s4f0pIvx1YnxxuVyS9CZxv++cN+98hkr+z1MsgSroEWL7I67ekY4npOYALgX1qMSUJ0FuBA2yfVFA8nwIn2O6R/5WkI4n4smxepl+Tzqt3EA2rHwIPEbmL2Qi/4OmAJ4h7/A/KirPKSJoe2BWYn8j5nGf79S5/aSLJEy4tRNJUdP4wWWiSLhXQijZkbEq7FsomlopK0g3pZH9n3be1/bmiXyGS7xTAq8nEde4uf6GOXGSoLCsREhp7p+2rJC0BLEd0RI4zfbV9uaSrge+XEGfZnAhcwPgSNj9NP12Rp4PrSPKBy0v6Ttr1mO167ywDGwNFeT/NyviygYsBswBH2n4FeEXSFcAKBcWTmQhsv5dkxC8iJEiXYPz7i9uBbYosriVOAv5P0qx1ReQtiORTLbZdJC1RsG/dLMTDdVc8AaxbQCzjkDQH4X+yIF1PXrWzr2s9wwgv7JvTlOZNtj9Phd0VgeMIWa6iu5TXBkbYHtnFmpco4LyaC2WZDDsSktcbEA1DtxJ+qbMS38FVgcuB24hr52bAtZJ+YPuhVgWVksC3AYsQSeBb6JAqXowo1gyWtFxOAjMj8Fn9jvQMPhNwZROPsecJdYbCsH0wcHAnh+8l/j+0xMOoEyYDemO3MTb9TibTayqWx/8gFbV/RUyyDq47/DHwR6J5od3Pq52SntkKaQbISaIWkPTff0FUmTsjJ+ky/ZZGQ1dJkxFSYd8hxpRHEVMDsxGawIcQxuybFRpopjteIM5FCwFP1W13Rz5/VZfZCX3peu4mCmzNpDweIyZ72grbF0t6HliHmIrZgfAHerDMuPorth/pZP8LxHmlKL7C+Oew5dN2/dT1K3RM8mcqSvrsLJcaBJYlpKbeB+60fX9JYa0IjKxvVCCa194nivOzEUWQnzGhnFIreZvw7+qKBeneJL2vOZm4v7iYSAC8DHxecAz9iTOJBPmWxNTmWEnvEknXQUSR8hLbZxQc16yEVFhXjCGkqTOZTGt5G1gLWMv2tY0HJf2QKLD90fbJkoYR55NfEHYhreIgorj2B8J6pF7GbHrgGGCPtO6gFsbRH/gQ+HrDvppn0AOd/E5h3lRJqeg224c1O277MxoKhAWRG7UzLaUqeXxJX6mX1U3PHT+WtCfwbWB6Qn73ySy/Wy1ygrSPkbQD0YH4BdG5kx8mM+3AYYRR6XcapB9eBM6RNILwfToMOLyE+DLNOY+4SRjdsJ3pv0xGx9+zxgcAzSSniO7Dpt1ZA53kwXUnjLt2X2b7qFKD6gdIWhF4oafde5IWBRazXYQHzitAvUTZ2oSc2uN1+75G+k5kqk8qpk1QUJM0A7C97dMKDGcu6qZeJX2DeNA9yvYFad+KwA8ptsB2I7CVpC1sD288KGkTYtrhwgJjgmjeuNn21gW/br8kTS1sLekqQtZ/caK4NppIug6zXZQHTz3vEp/9rvgW0ViXyWRayyHApc2KawC2r5F0KeG5ebXtGyRdRyhctJKNiQaYPZrENBrYKzXMbEIusP0bWEfSNEkJAmAj4hm8mS/VfMQ0YFEsS3o+qhhDJQ0tO4jMwKRiefz3JN1C3N/fUGssTMW0pk2t7UxvVLjqacUkYi6w9T0/B94DBjckdDKZgczWwN8701W3/a6kvwHbkAtslcH2Dl1tZzJtxI503jWaGZ+RhFHwuGKkpF8QXgczN1m/EXHeL6LAdhWwr6STiG7f1Qmj9nq+xfgykpl+hKQVCKmpTYDJgSILbNMxfnG2NiF5Td2+R4nJ/SI5ilRAk7QH8R2t+TMMIeRkPiQmCIpkCuCugl+z35OKaGUU0jrjNmB9SbPZnqCIJumbRFH5gsIjy2Taj+8R5/iueA5Yr267iOvSPISvc1fcRLHNJ1XlQmJi+SZJ5xL3pVsTTQrj/W0libiG31FgfE/TfVNFGXQlNd2M3DSc6Q1VyuNPSnhOrwEg6T1CIewGouD2VHmhVZIX6P33vSWTiLnA1vcsAJxTgS9lJlMkc9C9VMAYsixXJlME+YGil9g+t+wY+hHNHnCnICT8yuZXhDfJz9L2q9T5z0r6GvAD4DfFh5aZWCTNRMi47kokogT8Fzi/4FBeJzrJa6wGfALcV7dvGgrueLX9jKTViCL28nQU/mrf1SeJab/uZP76mkeIpGumf3MiUcC9SdI+wFQAkqYmZFNPIfxuTi4twkymffgM+G43axYlnrtrTEp49bSSjwiFgK74agFx9Af+TEz8rUn404n4e/3UdqPP2KpEs8z1Bcb3J+BISXNXxXO90Z4kk2kBVcrjz0Dcy6+SfpYizhkbAUh6lVRsIwpuRU64VpGXmDD/NQMhpVloU20usPU97wL/KzuITKZgXgE2kHRI0uUeD0mTEw/nrxYeWabHSPoCGGr76C7WHAIcaTtfP6pLUwmN9PfNdIOkrxO+bJM3O2775mIjyvQU229J+i6RkAC4yfaHdUtmAfYHmkorZaqFpJWJabUNCflbEZ35xwJ/sf1RwSHdSUzyrEtMSG5KPNjWJzLno4R7Hdv3AAslI/Ql6PBneMD2bUXHkzgROE/SwrYfKymGCZA0I9Hw9azt/9Xt35H4rH0EnGr77pJCrBS275L0Y8Jb6aq6Q7Vpzs+BnWw383nNZDJ9y0hgQ0m72T6r8aCk3YF1Gd+PeUHiWb2V3AP8SNIJzZo5JM1PeLEXOYlVSWyPlbQO4be5HPAfQvazmQ/0LMSk/ogCQ7ySUIC4TdIJxN/2DZo0cFalAJfpP6R8xPAKyodXJo9v+1M6CmhImpaQ+a0V3L4LbA9sl44/CVxve+9SAi4Z2/M27ku5sMNszzfBL7SQnCDte64ChkhS0tLPZNqBcwnJsBslHUwY434haRJC1uCXwDeomyTIVBLRM/mF3ko0DGiSx9RWhCHu1LZXS/vnBZYBrrP9XpEh9XJ9vlYBktYgOvEX7GZpKZ51aVrgW8A0tm8pI4b+QPIavKqTY48BlUn0ZyZE0leJabVdiG5SEYmdC4H9gBttDyspvGOJZqEr0vZY4v4GAElTACsAf2tlEMlfZ7jtS9L2OF9E27dT5xNXJCmOet4iEnW3SzqNmPTrTEq8yMaFYwnJ8nHTFpL2Ak6l4/q5oaSlqlQYLBPbw5IfyE8If56ZiQLuncBvbT9ZZnyZTBtxICH3+AdJ+xEFqzeBWYkJ/QWI8+zBAJJmra1vcVwnAv8C7pF0OhNKFe9FTHif1OI4+gW2xxL3NV16oyZf1Qm8VVvMc3RMwXclw90SibXMgOdDYuKoalQ2j5+aRa9KP0iahTivr0LISC5IeEK3ZYGtE0r5G+YTYt9zEKFVf4ak/eqMSzNNSA/jH3TSsZPpPxwPLAmsT9xQj5X0LmHOPoi4QRuR1mX6NzMSnfsZQNJRxENsTbqi/mI+iPBR2Qc4vYh4soTGxCFpWeKm9W3gt0Qi4CZCWm0Fong6ghJ82tJE3WmEn8Yk1D3QShoMnAX8xPaoomOrGnkKt2skjWXiHjjc6vcrSRzuRtxHTEbIYF0KnANck5p29mtlDN1h+9+Svk90jUJM0d1Tt2RxwpC81f5ZGwL1980T+CKWxCiaf74EHNbJsRpFNi4sT0weflK37+fE5OFWREL4PEJqdpcC46o0aSqlNP8kSesDT2TvkUw7Y/tpST8Afk8kWL/ZsGQUsEfd9+QtYFrGl4xsRVw3SPoJcb96cPqpUZNA3NN2kVKHmYnjPHLzZaZ1PAAsXHYQTehPefy5gLnTT3fSvJkCabvkQgH8ldCW3gXYStLTNO/WtO1Vm+xvN0YSJq8/KTuQzMST5JE2lLQVsCORZJqJ6G69Hzg7mbZngDTZN7ntjxv2r0J0x38MnGX7+QJiaew4n7fJPojk19yECXPuVAYkbQEcSsjN/QLYnOgsBcD2c5LuJRLGhRTYMhPNQUTheGnbr6VphpG2j0oG40cSydZDigxK0uzAXURn8gjiJvoHdUvuSvs2J5Iq7U6ewu2am5kwaTIj4dcyFniZmBabjXh4GwQ8TJh+t5p/pdjuJ4pqFxU8+dsjbP+bKMY0O3YHyR+hxYwGpqvbrsrn+Sj6R1JuTpLsDoCkhYnP+y9s35r2/YjwF8tUh8uoKyRLeo6Q8syempm2Ik2MrpoasBYjJIE/AB60/XLDWlOQ7JntMyVdDWxL5ALGSRUDF9gu1AsnM3HY3qHsGDIDmhOAKyWtbvu6soOpo7J5fEkL0iEPOYR4dqspfIwgcuoji4wp05xcYOt7htT9e2ripqcZ/eEBtAjeIQziM71E0lTEybVp128Zmti2LwIuKvp1+yEnAf8naVbbo2FcseZCOhJlu0haovFBqQWMouN8ZKIzf/tO1opIwpY6RVAh9gaeATaw/ZmkZonVxxn/upCpJj8ARth+rW7fIBiXnDhc0lpEcm/TAuM6giigrW57pKQjqCuw2R6TZLuWLzAm6N/3MG07hWt7SP12KuDeTkyK7V/f1CFpPuJatTjww6JCJDwQ/kM85PYLJH0F+A7wcUFSeY8DW0q6h5Dggs6bY8ajlVKMtoe26n+7j5mS8c8ByxOfvfrJimcJH6NMIjWHfZuunz1aKfU5BvhK3fa8hIl9JtOW2H6F1nur9QhJhwPP2z6fOunkTCaTaeBrwDXA1ZIup2uPv/MKjGtI3b9LzeNLmoeOgtrKhG+wiInk64n83cgsz109coGtj8nyXL1mFGHumukhkrYlpmUW6mJZ1sSuNisSF8XRdfuOILpkfkpMDxxHTMy0Wo6n1nEu4HDiO3lTk3VfEEnPkbafaHFM/YXvAufY/qyLNa8R00eZajM94+vBf0bcXNdzGyEfViRrE4W/rrrSXiJkLItkaDIPHo8k0VgoeQr3S3EC8J7tCYrGtp+XtCkxUXYCnTde9BXbArsCqwOrAaMlDQfOtX1Xi1+7R0jajCiw72773bRvfuBqYP60fQWwme3PWxjKUOByxm9o6qo5pp7CpBglzQ28b/uDLtZMC8xYcFPYq4zvtbkmMf3xUN2+GckNgOOQdBhxPzp9N0tb+fl6CRgsaRLbtWtNf272yGQGEocSPpaZTCbTFefQkXvaOP3A+Ndzpe3CCmwVy+M/l/77H0J9ZCSRg8u+wBUnJ+AzZXMocJeko4GjktRgphMk7QAMI4odtxByTq1M4mRaw1zE1AAAkr5BdAUfZfuCtG9FYmqgpQW2+o5zSdsDl2e5nR5Tm+jrillp02mZfsZbREK1fnv+hjVfISYfimRW4Olu1oxhwmJgq+mtJF0rk6CjyFO4E8uaxD1FU2xb0rWE9HNLsX0hcKGkbxFebNsBuwM/lvQEcG6rY+gBOwFz1IpriZOBBQjvtZkJmecdgT+2Kgjb/5K0EFGInJMouN1E8+aYMnmeiK1TT0RiEvwoivVgGwlsL2lP4vq8PvB32/XX8/mJe+y2R9IBxPT2aOB8ynv2uJjw8ntX0n/Svn0ldXd+su3G63km02+RNBNxPVqGzidKi5Yxe5XxpYsz/QRJw4j754Ntv5m2e4Jt79zC0DIDk5Y/UwwAagXGh4H7gHuB3ODeD8gFtkzZHAQ8Qhjh7izpIZqPCOcLePBzwgtlsO3HywpC0lgiSbmw7afSdk8SqLadzzvxAFLf0V2TJ7qmbt+jxEh4Ydier8jXGwA8TRcTuJIGAYOJv2Wm2jzF+AW1O4G1JH0rneNmAzah+2JXX/MuUZDvim8R181CqFiHH+Qp3C/DtHQ/kTJ9WlcItp8Cfi7pIOI7tysh23Ic8XceLGkT4IoWT4k1Y2FgnF+EpOmIKdNLbG+RpCIfpMUFNoDkZfPnFMdQYJTto1r5mhNBTz0Ri+Y44rN1GhHff4lCIDDu7zoYOLvIoHqY1BxL3D8+Dlxpu4hz/65E8nwJ228X8HqdcTQxVbgOMAcd5/3uPmNV/AxmMhNF8uIZBXyVrj/bRU93XgasL2lK23n6t3+xA/F5OQF4M233BAM5P5fpFbar0DBXdbYCViUkIn9JfNf+m2whRgI32n6gxPgqRVcKOl0ca0leOie6vyR1MkR32/60J/4HNVqsU99f2KHu37Oln2bkC3iwACFJV1pxLXEz8Tf5uGE70zNeB+qLWasRSYP76vZNQ8EdwpK2TlMEXa35CvAr262WruwPXAIcI2k/2yc3OX4w8Z09rdiwMhPBNcTfcqY0nXIaIVnxgKTHgG8SRYYDCo7rNiJhMVuzRKqkbxKTrhcUHFdlyFO4X4rHgc0lHdfM7zN5AGwOFC5JkhQNhgPDkwzjbsRk4oLEufcdSefZ3r/AsL5Kh+cZhB/ipCnOmifidcCWBcYE0YzzQsGv2VfMBnxU5Asm+dNF6PDTHNEgUbkAcCbFewrvQMe9dLPEuRv2/1bSobZPbHFccwF/LLm4RiqoH59+as1+p1SwsJzJtJKTCA+j44GzgJfrJFPL5AhCrvzy9Fz0SNkBZXpMLSfxSsN2JtN2SNoC2IXwoJ6eaGq6D/iz7eFFxJBeZ3iKZx4iV7gK0XC4NmBJ7xMNpbWCWzs3dE9MI1VLmq9k55z4l6FucmehXk7yYLtIOZRKkk4YPSJ167Y1kl4D/mr7p2XHkpl4JF0MrAdsQcgTXQHcYHv9ujVXAfPa/k6BcY0l5ML2tD2BrKGk+YC/AEvm8xdImpIogHyPGN03sDRwCvGQuRQxCbVSCZMWmV6QJhYWAh6z/WHatxHRMT8/kbw+xfZZBcf1feBWQot9H+LG+ufEFOyKxGdtXuI72c431pmJID1EXkRMSv6GaJZ5k5AmXQnYi5Cf2sr2X8qKs4akSYENiWLbqlDsvbSkt4C/2N4rbR9LeOLOavudtO94YG/bUxUVV5WQtF3d5jmEV9zlTZbWPBH3Ic67y7c+umqT7rFOJqbnTiXO/bXv4wqER+8tRGJ9MUIucU5gY9tXtDCuF4F/2v6/Vr1GD+NYFHjD9ltp+2zgMtsjyowrkykSSaOBm22vpLOqXgAAIABJREFUV3Ys9Uh6DpgMmD3t+pSQW2+mSpQlWytGunY/aPvhsmPJZMpAkgjPt62I4ssXwDvALMQ9q4HhtrcuLUggScTXpttWAmZIh9623dmwSqYgcoHtS5JkWQycbvvduu1usX1kC0PLDEAknQV8H1jMJX95JU1l++PuV473Oz+y/ddWxdRfkPRd4C5g8rRrLCH7eVc6PgWRVPlbkdKokkYSF+pHgc3rjVQlbUZ0dE9PFBqyhxEgaXpi2mlrxvdAGAtcSBQrPywjtszAQNJOwB9orjrwObBTd5On7UaatF2VKJpOY/votH8Kojj5ToPfUtsiaV8iYd/4+RLh73eg7VMKD6wbUjFiZ9uHFviatxHFjkWIe/3HgA9tL1635mLgB7bnLSquKtGLRsNa5+jHRIHoX62Lqn+QvosHE/f4rzY5PhfwAHCM7VMlzU18Bu+wvXoL4zoR2AhYxPb/WvU6PYjjC+DI2sSapBsJVY/zyoopkykaSR8Af7D9i7JjqUfSC/Q8B5YnpCpGunYPrZ8ITqoQ29tepbzIMgONVIzvCYUW4yXtDvyemFb7BXCT7S8kTULkx44HlgT2sH1GUXF1hqQ5gdWBfYHvEu9X2zfAl00usGUy/QhJMxMTMzcB+9n+b4mxXA2s21NZCknrE9N3k3e7uA1IRbbt0+ZfbN9Td+wHhBTd72xfX2BMInxIDiE6D39KSM+dTki0vgvsYPsfRcXUX0iG40sDMwOjCdngUuWUMgOHJAX5E2BZOj5jdwK/tf1kmbFVDUk/JLypZiOZRNceOCQtS1xDt7F9cXlRVoukJrANHXIoo4H7gQuzekAHKdl0NiGjNIaYHt23XpJU0lPA07bXKSXIkknvEcR3bxgxvdZsuqrmiXiH7fdbHNPhROL3d6kZ8vAe/qprxfkikPQ44RH5ky7W/IGYil84bQ8HVrc9cwvjmgq4FnifmM58vlWv1U0cY4BjbR+RtidICGcyA53UDDna9oZlx5IZOHRSYDsCODwn7TN9SRfF+Bno8IV+DRhTZDFe0t3EtNoizXwkk3LRI8C7tpcuKq6615+ZkIRfhWgiXaB2iHg/H65v+MuUQy6wZSpBSnrVtG5nIJI79wFn2769zNiqROrWnIGQpPsYeJp44G3EtldtcSxjgfNtb9+DtWsAI4Cx7Sqb1J+QtDJRWJuNSIDNTMgSbWX7tTJjqxI98axL64bZ3qmImDJ9R2oKWIW4cb3Z9t9LDinTDZKWImTV3gF+BSwDbFmfHJD0DHC/7c3KiTLTn0mykLulzQuBfWqKApKWIz5/B9g+qaQQK0NKBJ9d9oRRJ3L+PaHQbmBJnwCn2j6oizXHEZ+5KdP28USRt8+a1zrpLv8KMEf692g6f/ZoWbe5pOeJBNIGaTsX2DJtR3pGuxZYw/aoksPJDBBygS1TBSQtQEjWTw2s2cyypIWv/V/gzK5UmiSdDPzY9jQFxDMNMTlXK6h9h8hJ1BQgngVuAG4kPNjeaXVMme5pJjeUyRSKpGOAg5jQaHAxYCdJJ9g+uPjIKsmQun9PTbxHzSiicn4NsI2kN20f0NkiSUOAy9LmjwqIq9+SZM2+A3xc5lSK7ZGSTgeOJTp53iYX15rxZ0mv2R7Z2QJJZxCTirnAVjEkrQfsDxxm+6aGY2cD29FxXdpT0uW2Nyk4zEzvOIxoPlnK9hspOdDIPcASxYaVGSik+9HO7knvJTzrPiououpie+WyY0jU4nipYbtqvEPI/XRaYAPWIBqfatSaEvuSQUz4HPE5He8fTPjM1tm+vuRK4lr8OPB62rdDes7oipY3HWYyBTIXMRX8ryRJfB/NC96U3dyQyWQyvcH2M5I2JibFjqDr+6E+f3m6v49p9X1OvIh0ByFHOUnda75OR0HtBtsvFxFLpnfkAlumVCT9iEhUvAgcTZwwXicMclchkmW/kPSg7UtKC7Qi2B5Udgx1bAqMBPaT9Hozj5YkdXgl0fm6ZZYWDJKf2abA7rbfTfvmB64G5k/bVwCb2f684NimBs4CtiDG829O/75P0vbZJ2U8ngUukzTY9iONByWdQkw6XFp4ZJmesD5RaLmrfqekdYmi6EfAKcCHxN9xQ0lbtlJaMHnqTBS2X+p+1YBneeBy2290seZloC3l+5ohaRCwB+EjuRAwte1J07HFgV2JqZqnyouyf2D7M+CzsuOoEpJWIr6Xtemn14DbGpsaWknjaxX52r3k78Deki4ADq4/p6drw3FEY93pdb+zJKFm0WdU2D/wEGAy4vy9EpEMmzf9dEWW68kMJM6hIxG8bfpp/IzXJMNaVmCTtN3E/m4u/GUymc6w/amk64AtKbbA9jiwsaRDupCI3JDwvm013wfeA0aRimq2nyjgdTNfklxgy5TNXsCbwNINY60vAMMkjSA6GPYA2r7AViVsfyxpbcLP5sQ0yXZR7bikJYmC0ZSEQW6WV+tgJ2COWnEtcTKhpXwjIcm4AbAj8MeigkrJ1L+kOK4BtrP9TvoengX8U9KvgYN66r03wFkLuAO4WtKytl+tHUiyTT8F/kEUKDPVYxnglibyEzsRiYkdbf8NQNL5REF1a6CV3l0vMHHJQJPv6QCmIaZAumIqYkKj7ZE0GXGdHkJ4bH5IvIc1nie+D28TnaQZQNKiwFZ0FCRXS/vnJc4r19l+r7QAK0AqrP0B+HZtV/pvTU7zCeD/bN9cQnhV5XBgBeKztbmkV4lnpFmBOYlO5gfTOiTNTngBnl9KtAVj+0Ng99p2lojMtCk7lh1A4hzGv18V3d+/trzwl/lS5GaETFX4nLAqKZJhwO+BmyUdCNxk+3NJkwArEk1O8xAWBK1mKeCBmgR9pv+QPdgypSJpNHCe7b26WHM6keifvrM1mfKQNA9wO1EUWs/2dZK+S0y3zQTsZvtPZcZYNSS9RCTgdk7b0xFJ4Uttb5GkIh8kTKyXKzCuT4nE8yG2T2w4tgAwnJj4udv2skXFVWUkfQ+4iZiKWd72B5KOJKZvrwfWTVMNmYoh6U3CR/LnDfvfIZIAs9Tf2Eq6hPgbz9nCmM5hwgfc+Ygb+9HEeeEN4qFjMcIM+mbgedtVSbqUhqRngSdsr5O2J/CPkHQrMJ3tRUsKszJIOoRQDxgKHEMk7g9reL/+Rbxf+ZwPSDqKUF6oFWnH+XRJ+gYxTbSP7dM7+Z9oZWyVKPxJ2oRoRJiUUKUYSVwjIeTNhhATbZ8DW9gubcpb0lSErGdTj5miJ4MlTQ4cQExRf6Pu0HNEUvpXRXqSdEXZsuZV8fjLZNoRSc082DcG1iOei0bRcb+6MnEfOwK4zPa5BYWZ6SF1XqW9wTXFg0ymr5A0C/G8+4ntbxb4ugIuICbnDIwlmg9nIu75BVxiOzdOZzolnxAzZTMp4ZfSFR+TP6vjIWkLYBdgcSLB+gGhwf5n28OLjMX2i5J+CNwC/F3S7oSs2kxEkikX1ybkq3T4RwD8gPiMDwewPaZuNL5IXieSXXc1Hkia2D8ATiQmTzOA7YckbUpMqo2QdCNRXLsF2CAX1yrNjDTIuSUZrpmAK5t0jT1PyEq2DNs7NMTzbWJK8hTgSNsf1B2bDjiS8IrbrZVx9SOuBnZPsq23Nh6UtBawHHB84ZFVk60Jub6jACQ1S648TyTMCkPSMODfzaSnyyTdex0KXAv8AtgcOLB23PZzku4lzhOFFtiaFf7qDg8iCl77tDouSXMA5xLFs72APzVOvCdZ0p2BU4HzJN1ZtMerpG2Jv+FCXSwrfDLY9v+IovfRkqYFpgM+SNNbpVBVWfMKefxlMm1HY5Esqdr8kHj2ubJh+ZGSNiDUiM4oKMRM7+mtv1QhflSZgYWkwzs5NCnRhLUBkd8sUh6S9Ny/taSrCPWOxYmcwGjgAWBYK20iMgODXLToY1Jy7rNu/D8yHTwLrCvpINtjGw+mh/C107q2J3VWnEd0KAv4gpBumgVYFVhF0nq2ty4yLtv/lrQ+ISt4fortQNu/KTKOfsSHxI1DjZqXRH1C+FNg2iKDAha33dQoG6LwB+wj6foCY6o8tq+XtDORVFwBuBNYu5l+d6ZSfAh8vWHfkum/D3TyO0VPDhxPFBr2azyQim37Jjne44nO4XbnOEKS9V9p+n1eAEnrEN3TexCNBL8uK8CKMR/RHNAVte7NItmKKCpXjb2BZ0jNE5I2arLmcWJCqzAqVvjbh5Bh3cT2Zc0WpPv9P0p6m/Ao/WmKuxAk7UBIAX1BNMO8TBQEK0UqqpVWWKujkrLmUA2Pv0wmA4RH4mVNimsA2L5C0uVEE+I1hUaW6RbbWTo9UxRDuzn+AXCM7SKkGCcgFdFyIS0zUeQCW9/zPJFk3ansQPoJFwHHAldI+pntcUbdqTvyRGBh4qYtAz8mOs7vI5IRN9n+ImkDr0QkWbeQdIvtlnaISVqxye4ziUTJNcCdzdZkvw0gJKTWSlJABjYDHm7wIZwHeKvIoLoqrjWsu6rVsVSRTj7zNV4CriAKbMcDS0Y9PMif+0ryb2AdSdPY/m/atxETFrtrzMf4k6dFsCLdd/veSlwb2h7br0pag+iS3r/u0Aii8eNZYOOGc2078ykwQzdr5gZ6dG3oQ14Avlbwa/aE7wLndDOZ/BrhmVUkVSr8/RC4q7PiWj22L5d0F+FnWliBDfg5YR4/2PbjBb5uf2Vh4LraRpqeXpsklVQna15YgS17/GXaBUnPEZ/r1Ww/n7Z7gm3P38LQGvkeIQfcFc8Q545MJtO+dDZ9Ppa4N3uiyGn4TKYvyQW2vud9wksp0zN+TTyMr0MUHF4jEpizEYbeg4jkYe42D3YiEk8r1k/HJPmdG9MD5yOE9E6rJRhG0blW95rpp5HC5XYqylnA2UShbQwxZbFvw5olgUdbHYik7YAHbT9ct28yYIp6Obq6YysBK7Wpqfwoembg3Syx2NTfJVMqFxJNATdJOhf4FtHA8AYNSYI0PTyYkGssksnp3uR59rQuA9i+P0lrrgssS0xXjCYmS6/ID23j8SCwhqTJmhWNJE1PXMtvLziuiwipzxmL8AzrBSISAF0xK8VPulap8DcP0Btp8NuBXVsUS2csQLxflSuupXus/QnPvBnpkPysp2jfm0rJmvfQ428h4HpJpXr8ZTJ9wCAmlPztiVdW0fJ9nxFFtq74HvHcm8lk2pQqTZgntbThxDl1m6TW1GzdZIRKl7MHW6YrcqK777mT0GvN9IDUabs60U26E6HlX5PsepaQcDmps5NdG7IwcGZn0nO2P0nyC0VMM9xM781wM4RufUoA13yTfkudbJOk5YgE0FkFhHMOMar/cN2+g4DDaV4UGpKOtWOB7SjyZ34g8WdCVnFNYDEiGTEG+GmjZxAhwTsbULQ86gPEVPJvbU8gW5nkITcH7i04rsqRJLqXJr6j99i+gpgqzXTOWUSh+cIkczsOSTMQjSAzUrxnynHAUsBISYcSf883C46hGU8THn5NSQ/qgymgOabxpalO4e8rNHhbdsMYim9AeRf4X8Gv2S1JyvZy4v14CXiSakhXVkbWvL94/GUyfYXtebvarhA3ABtL2hP4Xb2PcWpS25OYVv57SfFlMplMI5ukn+26yjennPXlwAWS/mo7n8cyTckFtr5nKHCLpF1s96aDs21JJ7PjgOMkTUM8xI2uk+zKdGC670grpGPN9pAiXmegYvtg4OBODt9LJDU/Ki6iTHfYHlp2DJm+w/bYlNDckkia/we41PaDTZbPApxGSA0WyZF0SO5eSDQ2vEkky1civKoGpXVti6STCO+ncTJhkk6xvX8Xv9b22L44NTntQPhzvQeQ/LoWISYjf2f7nwWHVisEiVQkrZfcraPoSZ5LgGMk7Wf75CbHDyaaY04rMCaoVuHvdWKirqcsQkwNF8lVwBBJqk8CV4ChRMFxHdv/KjmWeqoka155j79Mpk05kJB+O43w676VjvvVwYTM+rvU+YNmMpn2RdKywC7EcMoMhNrIfcDZtotSztiMUHi4qAdrhwMnEM/eucCWaYqq9VzR/5F0OPGQuzohvXM38eDY+Ebb9tEFh5fp50i6m/AlWajZFJukKYkEyju2l2lxLLNkH5v+j6SxwNB6yUdJRwCH256gq7yrY5lMpu+RtCkhZTkj499LiCiI7NbOnXSStiSmsAw8QbwvNV+ebZJZdaYLJO1AJKEXpaNI+Sjwa9tnlxDPKHo4LWy7My+HPifdY91GyFzdS8S4NHAK4b+5FKFksVKRUqSSDgKOAQ6wfXLjdTpNAR4J7GX79y2O5Wwi+fA92090s3Yh4CHgQts7tjKuhtedmfg73gTsV5WGPkmfAMOLfC96gqTtiWnWV6iTNbf9m7o1TwFP216nxbE8DHxk+wc9XH8HMLXtRVsZVyZTFJK2tn1hD9YNs71TETHVveYCwO+B1Zocvg7Yw/YzRcaUyWSqh6RjCMWkZt1zBk5IzeitjuMF4Cbb2/dw/TnAyrbnaWVcDa/5EKEkcoHtD4t63czEkQtsfUxKVvcE5wR1B5KmIqS66jsY7gcus52neBKSdiduXO8jOsBusv25pEmAFYlJwKWJG9iWSjpJ+pyYpriM8LV5qZWvNxCRtCiRiFqISACslvbPS/hvXNdq/5lcYOsbJA2m4fxl+9aufyuT6RmSpgY2AJYgTXkT18gr2v0aKekGorixpu2Rad9qwNXENbJZoifThFRAmpFQEWjrz1VnJF+60wi/xvrr4Fii0Ltn0Q/AVSr8Jdnae4DngPVtP9bJuoWAK4mphu/bbpnMraQbm+yegXi/PiYmtN5vssa2V21VXI2kiavzbO9X1Gv2FEnH0iFrfiGwT236L8ma30oUeE9qcRyjCVnIHr1Hkk4GdrU9XSvjymSKQtKnwFq1+51O1pxBfO5LeVaTNCfxTFS7X33A9qtlxJLJZKqFpB8BfwFeBI4GbiTUD2YHVgEOA+YGtrR9SYtj+RQ42fYhPVz/S+BntqdsZVwNrzmGUKv5mJiiO7OV98yZL0cusPUxyZy6R1TJ4LFMJK1N6OnPxPhdDCakBHa0fVUZsVWNpGF+ASFpZiKh8y7x3g0i3r9LijDflPQ7IuE7R4rlAWJc+vIqGsdXDUlHEXJSNQP7cUV3Sd8gEj772D69k/+JvoojF9i+BCmZeD4dEzOiY/LiSULTO98EZTItIiWlR9n+UcP+vwFDbM9STmSZgYykmYgi1sxEAvFu22+XGE9lCn+STgD2J7zYLiW8eV5Oh+ciphs2AiYjEhstlXLtRfNjI4U2Q0oaDsxtu1O5zyoiaTJgSmKyrNUF3I+B02wf1MP1xxOTm1O3Mq5MpigkPQrMCQy2/UiT46cQE+mX2t606PgymUymKyTdDHwT+G4zNSxJswCPAE/a7nFufSJj+QA4y/bPe7j+JEI5prCmHUmzEr6yuxAKArW865nARbkhslrkAlumVCQtAdxOJAMuZsIOhi2BL4Dlbd9XVpxVI0li7URDdxgwrGg5LEnLENOHGwLfIk76TxNJlcts31NkPP0BSVsQWs/XEr4QmwMH1idyJN0FfGB79RbHkgtsE0mSQrkXmI7o3m48fw0mvpvL2H66rDgzmYFMmqY+zvZhDfuPIc6r2W+4F6TO8yWI5o/byywaZXpPVQp/STL/UMLvu/FhU8S9/bHE/Ud+GAUkzUNYC5wO/DK/LxMi6Vngcdvr9nD9lcDCtudvbWSZTDFImhu4g2ieWLZ+MiwVlA8A/gFsVKRUcUOMCxLqLNPYPr+MGDKZTDVJk+jn2d6rizWnE03K07c4lieAl3uab5N0HTCX7QVbGVcXr78moSawHpE//y/RRHeWm3vIZwomJx0yZXMI8eC9gu07G46dk6akRhGTPpsUHFtlSUW0SvjK2L6bSAgcKGlhOoptBwK/kPQaISN5GSHXNbGdxAOJvYFngA1sfyZpoyZrHgeGFBTPDOmBbdw2gKS5mFAbe4aCYuoPHAZMC2xu+68Nx4Ym76zhRJKxR9remfZG0nbpn5fZ/rBuu1tsn9eisKrOIMIXqJExNNf2b3uSPPE+wFcJOb+TbX8k6WgiOVd7Phgj6SDbp7Q4nsOJe8Hf2X43bfcEO/sZj4ftd4nmnbLjOErSuUQz2PJE4wmEL/WtwDm2ny8rvopyBOF9eCSwk6QH6Vy6cudCI6sONwNbSVqwhx5/axLJp0xmQGD7paT+cxNwjaTlbX8g6Uji+n09sEkZxTVJiwF/IhqAa5yfjq1ESHdvbvvKomPLZDKVYVJC7rArPqaYWsVtwLaSFujOHzI1Vg8BzikgrqbYvha4tm6qbWfgx8CPJd1DeLUNt/1pWTG2O3mCrUWkZPV2TOgpdr7tF8uMrUpIegu4xnanSURJ5xPeKl8rLrLMlyV9B2rFtuWJJOh7wAgiedy2N9eSPiSSS3ul7QmmwpLfxb6t1nhOE2y9vhDkCTaQ9CpwR1cSLJL+TnSYzllcZJn+St33cSHbT/Xw+yna2Nc1vUdHNBZa8rRtc1Jn+d3A1HRI2l5JNANcBHxEyNvOSPhjGVjddjMfq76Kqdnnvie09HMvadhE/mo7F0AqT/q7Xm57RBdr1gU2tr1TgXFV4nPfDEmzE81CaxLydJN1EldLE2JV9PjLZMogec3+g5hmuxEYCtwC/ND2JyXE8y3i3mIS4I+Eqs1adfYHIqSCb7Cdmw4zmTZF0sPEeeK7zRrvJQ0CHiLuKRZtcSxLA3el11vT9ludrPsq0bz2PSKvU7pCVzqnrg/8lrgvgw6LpV/aPrWs2NqZPMHWAiTtCvyGePio757eEDhU0k9tn1lKcNVjejp8GTrjJUKCLdOPsP0ScCpwatJS3pDw3NiSmOZp56SnCGmPrpgVKKL75CUmosCWAWAWoMsO6nS8R1JGmQwx7WFCahRgxxJj6U8MlTS02QFJXzTZ3fJEcIU5EJiGeCD7F7A6sCcwPzCSKCqMBpC0IeGtuieRwGsVK6f/vtSwXTY7TOTvmegqbQm58Pel2QF4gWj46ozvEfeqhRXYiGJQ5UiysXcT96WPApMDLwL/A75B5BMeJJpJW4rt+ySdSHj83S+pJx5/ubiWGXDYvl7SzoSP/QrAncDaZRTXEkcQ37mlbD+WmpzWqh20bUl3EBLGmUymfbmIkAi/QtLP6m00JM0PnAgsTCidtRTb90g6k5gCezT9+0bglbRkTmBVQpZxZuCMsotr6Z5sF+I5Y04ipzgCGEZI/O8OnCxp5kb7hEzryRNsfYykVYmExYdEka3Rk2dvIrGxpu0byoqzKkh6HnimK91bSdcC37JdyQfPTO+QNA3xAHBJ2bGUhaQHgM9tL522x5u0SJ07jwNv2x5cXqSZrpD0CuFvs3EXa/IEWybTQnox9TEetgf1dSz9gXTf9YrtFer23QIsR5NJD0lXAYu34zkseWJNFK1Uq5jYzzxtPOlaTzPv2SZrjiI8HJtNarUVKeG0C/Hsen39+yfp68S0yrzAcrbfKyim7PGXaQskrdjNkn2IAtvONEjK2r65VXE1IukN4Hrb26TtZuosvwZ2sp3tBjKZNkXSZES+fEWiOPQakS+fjSgYDSKkxFez/VkB8UwK/J64z+nsfkHEvc5PbDdr3GwpaVptLaIQuBZx7/MmIcl7lu2X69ZOSzQefd32HEXH2u60a/duK9mfKK4tafvZuv1PAqOSH8F9aV3bF9iAfwK7SzoQOLH+hJWKDPsSnYhnlBRfpgdIGgH8gZD77PJB1vZ/gbYtriUuAY6RtJ/tk5scPxhYADit2LAyveRGwgtkC9vDGw9K2gTYgOz/kcm0jHYtlH0JZgcubdh3N1Fge7TJ+seANVodVBWpsKR7bjj78nR6ryppciLx80Zx4VSaNYn7++sbD9h+RdKPgEcI77i9iwgoe/xl2ohR9Ewq/LIm+4tsqJiRjqmPzhDN5WUzmUybYPszSasDPyeu4fMDX0+HnyUmsU6y3cxfuxXxfA7sJukcooBVf0/xOnFPcZbt24uIpxFJhxENFHMR59CbiYLgpc28NpOH+5WEbHCmYHKBre9ZBrikobg2DtvPSvorsEmxYVWWownpwF8S5oy30NHBMJjoiHwDOKasADM9Yl1gHeBlSX8E/mw7JyY651TgR8CvJG1GenCSdBLRhbgUIfVxVmkRZnrCUaQCmqQ9CHm12vlrCHEO+5B8/spMJJIOsf3LbtbMAAzrapIyk6ljMiaUcvsAoBNpqY9ob0nnylHhwl9lkfRcw659JTWT4J0E+Cohg9jS5r7kVQzwqu0v6ra7JcmwF8VsjN8Y9wUwzh/Y9n8lXUfcDxVSYEuv+yIhS5fJDGSOon9I+b9JNId2xSJ0bw2SyWQGOKl4dhxwXFK3mh4YnRrxy4rpdqCUIlo3HEk8p/0e+ENn3rMN3Aec19KoMk3JBba+Z0rgnW7WvE3dg0k7Y/sNScsDZxI+II1yPNcBu9t+fYJfzlSJpQi93y2IounhqXPiTNvXlRpZBbH9iaSViQm1relIXv6MGJW/ANizWVdKpjrYfiaZjJ9HdDstTzwE17w3nwS2r9cWz2R6ydFJHmjbZsbLkpYFLgZ6nJjNZPoDkmYnJODWJCRjmnW9l+KlJ2k94tq9EDC17QXS/oWA9YALbb9adFyZLhlER5K6dp1Wk3VjgH8TKiOtbo55IcWyEPBU3XZ3mGKf4T9g/O/fe8R3sp7RRGEyk8n0IbaHlh1DD7kR2FLSt20/2XhQ0tKEl9HvCo8sk8lUllRUK62w1g/YnXiu+Kinv2D7n4RSXKZgcoGt73mR8FrripXpMHJve2y/AKyZDBsXJ3UwAA/kBEX/wPb9xGj1z4ik027AxsBGye/lLOBs22+XGGalsD0a2CG9Z0sTxqmjCU+v/D71E5LR7UKSliOMZevPX7eVGlxmIHABsA3woKRt671bJR1ANDSY8ODIZHpKpbvh0/3g3cCshGzl5MT99f+AbxDPLw8y4SReq+MScA7xnQT4hPFFGzUZAAAgAElEQVQb5t4jfJ8EnFBkbACSBgM7Mv699P2EXN6tRcdTJWzPW/t38hA7pSsPtoI4j/gujm7YrhovEtJENR4CVpE0le2Pk6T/GnQvD5fJZAYuxxHqLDdLGgrMASBpEUJy9whC1eOksgLMZDKZ/obtrGjVj1D2/e1bJB0HHEBMZB1s+/26Y9MRybA9gV/ZPqicKDOZ1iNpSULHeAtgaqIr+HLgDNujSgwtk8lk+g1Jxux0YAoigfF7Qp9+TeAZYHPbD5QXYaY/kYoLvb75t12YTKSkMwmz8TVtX59iHpp8l75OGI3PCyxn+70C49qD+C4OA/YjfIIPq39vJN1EPF+tWFRc6XVPB35C86ksA7+zXZh8X5WRtBLwQpba7BmSjica52a1PUbSNkQx8GFCaWQwYZFwrO3Dyos0k2kvUlPF4sAMpIaKMpspJP2QUFWYrraLjonh94FNbd9YUniZTKYkJE3M9962V+3zYDKZFpILbH1MKqLdQch9fEh0+dU8eb5H3HA8ASxr+4Oy4sz0TyQN68GysYScy+PAlWV7oaXJnr/QISdj4DEiKXV5aYFlMplMPyFJz/2F8K/4nJjguQj4vzL16jP9j1Ss6i0uuMD2AvCo7XXS9rgCW9qeBngEGFFk0UjS/USicAnblnQEcHhDge1PRGFwrs7+d1oQ116E5PRzRCPfKMK/eDZCNeNQYD5gb9tZnqufI2km2+8W+HrfJFQpzqtJ9ks6BdiLkN4EGA7sZPvTouLKZNqV1MR6PvDt2i46GmeeBLazfW9Jsc0AbA8sS4c6y52Ekk1h561MJlMdunj2qLfWmOBYkc8eVaGJZ3BPse35+zSYTK/JBbYWIGl64FeEVN5UdYc+Bi4EDiyy47ZK9LBA1Azb3rlPg+mHNHSed9alXL9/DHCo7RNbHVs9kqYANicm2L6fYnoIOJuQ0duc8HPYx/bpRcZWBvlzP7BJfjyLAV8HvtJkSf47Zr4USZruV8TUDISX6yq2Hy0vqkymNUj6FPiN7QPS9hjgpHrlB0l/BNaw3ejd28q4PiK8ZX+WtpsV2I4FfmZ7igLjepSQhPxOvXJG3fGZCF+x920vUlRcmd4h6XTbe3WzZgbgettLtTiWr9ge082arxKSrS/YfrOV8WQymUDSAsC9RNP2rYT32evA7IRNyWCiqLVM9oDOZDJVICkHNLIDsB1d2CvZvqlVMVWV1GQ4UUUa2/P1bTSZ3pI92FpA8lb6saQ9ic6img/Ck909rLQBO3Syv7Puhdp+AzlBDfMDJxM3z6cSN9ZvEj4lKwA/BW4BjicS/ocBx0t6yvYVrQ5O0sJEUW1b4nP/GTFl8Qfbt9etOwz4F/AzQm5poLPDRP5e/txXGEnzAFcSU0WddV9B/jtmvgTJj+oi4rz/b+AfwP7A3ZL2sf3HMuPLZFrAB0QTTo336JiCrzEa+GphEQWfE1KtXTEnxZu1fwM4q1lxDcD2u5L+DuxabFiZXrKHpJc6a4pLk5vXEJJwreY9SbcQyfsbktfyeCS/4OwZnMkUy2HAtIQ8+F8bjg2VtCkxUXooMUmWyWQypdKsUCZpSGfH2pl6z+BM/yMX2PoYSXMTHaIfpGLaI03WTAvMaPulwgMsn8aq+iDgFKI49BsmlLXZC7iZKMRkYEPivVrM9qt1+58kTIXPAx4AbrF9qqRrCTnGPYGWFdgkbUv4MyxHFBqeJ4p8w2y/07je9suS/kLc/LcD/aqbJHUlb0JI3U5te5e6/fMB/7b9SYkhVoXfAN8h/HjOA14lErCZTJ8gaV1i8ndmwtt1H9v/k/RPwufiDEmrAbtm2enMAOJFoF5i8SFgFUlT2f5Y0iBgDeCVguN6DBgiSW4iAZKm91ch7sOK5D9EQ1NXfAZMcD+WqRSXAsdJetn28PoDkqYCria8zvYvIJZJCZ/PNdLrv0c8o91AFNyeKiCGTCYzIasBlzUprgFg+2+SrkjrCkHSZMC0tv9Tt28qIo+zDJHvGUlMgP+vqLgymUymp0jaDnjT9rVlx5Lpv+QCW9/zPDCU8EDojL2Bo4C205RtNBSXtC9RMFqi4diTwE2SzgXuAzYgJrband2AvzYU18aRCld/TetOtf2SpKuA1Vsc17mE99s/gT8AVzdLPjXwJDFtN+Bp/NxXGUk7E4WjKeiYHt0lHZ6V8JjcDfhzKQFWi1WAa2sFyEymBYwgpnnG61S2fauk7wHnAD8CliImnDOZgcANwG51MnXnEk0Mt0u6jpjmXAQ4tuC4zgd+C5wiabzGL0mTAL8G5gAOLDiuy4H1JR3cTCkjJT/XT+sy1WVr4HrgHElv2B4F4wq3VwLLE5KkJxcQywzp9VZJP0sRPmwbpZheJRXbiILb6wXElMlkYBbgiW7WPAGsW0AsSDqaaISeIkmbbQs8SjwvfpsOhY/1gS0lrWS7u4aQTCaTKZphhLJWLrBlJppcYOt7RNdSYZnx2Q24pLMChO3n6wtGhUZWTeYlZJG64n3Gn5h6AZimRfHUOJ7oSutxIcn2xcQERqYiSFodOAt4GDiC6F7evXbc9iPJ62VDcoENwuPw32UHkRnQ3AtsZvuFxgPJLH59SfsQ5+BMZqDwZ0IWchbgddsXSFqS6IZfNK0ZDvyy4LjOJJKEexOF7Q8BJP0NWJYorl1h+8KC4zqYmBK4XtJBwB22nbwblwOOI97PgwuOK9ML0nTyesDtwGWSViCa0S4nVD2OtX1MQbF8SkcBraa+shIdBbfvEvJz26XjTxLecHsXEV96zRt7sGws0aTyODH1c29ro8pkWs7bwMLdrFmQAiaWJW0FHJI23yXyDxcTsubfTv+9C5gR2Im4Tu1JNKNkMplMlXiDmLYtlTRJN1HYPq8vY8n0HnU/ZJLpDZLGAkNtH9XFmtOB7W1PV1xk1UTSJ8Sk1UFdrDke2Nv2VMVFVk0kvUyMLndqbi7pPmBW219P22cAG9v+WkFhZnpISqRsTYcU4wJp/0LAesCFnU0rtiie64mHsoVtfyDpCKJbepK6NRcAP7Dd9tMykq4EJrG9dtmxZAYmdRM83a1boplHTiYzkEgyxd8AXrD9ZkkxTErIW+8JzFR36H2i8/Vo2y2VCpb0XJPdkwGzp39/TiRXZ6GjmfJ14H/52l19kr/rHcAXRBPPD4lnpcrI5UuahSj6rULISM4HuP5+sYAYxqZ/dufjXb99hu09Wh1bJtMqkh3EVsA2jVKy6fgmwF+IZ8iWerBJugH4PrCC7QckLQHcRHzvjq9vCJA0I/AU8KztZVsZVyaTqT7N8kxlIulPRBPAYrbHdre+hXGMJe5Xxu1q2G76axR8D5ZpTp5g6wOaVJkX66TyPAkwN7ANeeqhxjvElEzTAlvqvl2T8JfIwN+BvVOR4+B6H7/k/3ccsBiR5KmxJPB0oVFmuiR9rs8hzgUAnwBT1i15j5C+EnBCgaEtBQzvxsvpFcIjMQOHA7dI2qLZQ24m82WpL65J+jqwOCHdNRq43/YraV0urmUGPLbfJrr3y4zhc2CopCOBbxH+iKOBJ2x/UVAYg5jwYXsM0Ojt/FqT38vUkXyCZqQT2f4y/LJtvyhpLcKDek3gD1UqriXmIp5p5wbKauCbgphkXYiwZrgVeJOQM1+BKIQ/Rky+LkJMeu8u6V7bZ5cScSbz5TmKsM64UNIehLfZ68Sz2RBCQvlDoIhp1+8BI2w/AHEvmpoPNyekncdh+710bOMC4spkMpnecghwJ/BnSfvbLsu3eMcm+zYmmu9vIvxw3yDO+SsDKxKWEpcVFF+mC3KBrW84h44HXRM3PRs0WVfrovsYOLL1YfUL/grsI+kS4Be2n68dkDQfUVxYFDilpPiqxuHEQ+NWwObJA6H2MDknkSB4MK1D0uxE0uX8vgyih7IszbDtVfsyln7KTwiN+mHAfsC+wGG1g7bfkHQbsA7FFtgmAz7qZs0MRFd125O6NVcF/iHpx8D9NJdwte2ufDkzmU5J0wxn0sRLM/lR7d5MQjKTyfQtkr4gmlC2Tj6zT5YRh+15y3jdgYSkbYFfEMWZzjAtfFaWdHg3S+4mmubealhb+D2FpAXpkIccQhQlRSR5RhBJ/pFFxkTcNy8FfKehMewlovhwFfAIcY08XNL9xHd2FyAX2DL9EtvPSFqN8CVdPv3UT2s+SSglFdFcOwPQOFFdy+W80mT9K8C0LY0ok8lUDknDmuxerItjEPc6O7cuqgm4mMjjbAdskTwl32DChraW5jNtj9ecIGltQslgA9tXNiw/UtIGwCXAGa2KKdNzskRkHyCpNn4vImF+OXBFk6VfEJNYd9h+v6DwKo2k/2fvvsPlKqs9jn9/9KtcaQGiKBBQAQUpgkgPRZFelCtFISBiQ6WIIEq3wUVp16sgJcClKr1JCaEFEaSLNGkiEDCU0EQg/O4fa0/OZDKnJJzZe8856/M88ySz93ucJZkzs/e73netuYFriRukKUC7hNFtwPq2X60qzjqRNCfwfaL3wRJNpx4lLraPKHondDKGmd02nVuXgeImX8BKRZ+UdqUYTwQ2tP2hEuP6K/Cc7dHF83Zx3Qm8bXuVsuKqK0nzAOcTq4f6ku/7NFMkjSS+Axch+mneQKxUfj+x2GIUsUtlZdsTKwozpUEnaX56erb0tsOo1EU7kl4idhP1WtY81Z+kMcT92hRgAvAkUVZzOrbbrSQerDhqey1dLOxoJNTWJb5zBDxHrJ6+Dhhvu5IkM0wtlXqx7T36GHM0sLntJYrnpwOb2Z63pDBT6hhJqwMrAfMQE8N32p5Q4utP1xqlr7JvdSsJl1Iqx0xe71RVdro/Zcc1AXjS9rZ9jDkH+KDtNcqKK7WXO9gGQXOWuUi2XZgNBgfG9quS1gS+R2yHXZIoNwLwN2KF4S9sv1lRiLVj+99EKZTDiqbj7wNetv1KiTFkmaF3ZyngePe9wuE5YMGS4mm4CPi+pG1s/671pKSdiR2lP5zuJ4eno4iJp2uIXaJP08skXUoz6QAiubYv8MvmEnSSZiV2vx5BT0+olLpesVvmOuI7sF1vpYayVwneCXys5NdMg+97RCnuNW3fX2Ec/S3OqVJjV8rzxMKO8URC7a/VhTSdDxBVOvryFj29CSF20MzVsYhSKpHtm4Gbq44jpZT60bHFSoOlxvOby9N/hYC/ARuXEEvqRybYBpntOt8s1VKRPPsp8NNiR9s8wOTcsda/IqlWWmItDZq36f8GfxGg7N+BI4BtgbMkfYH4XUTS7sRuma2Jfn7H9fq/MLxsCtxs+7NVB5KGrE2Aq2z/d+uJItl2ZFEqaFMywZaGjiOJvk4/B04gVm7WoTTx4cAlkj5j++qqg2lVVDhYhbh+mLPdmFwACMCHgbEVJ9ewfX2Vr98PEQnse4DbgT8DD1Qa0fSeAjaT9IOiN+I0JM0ObM60vQgXBLKKTEqDZwVJOzY/h6lleFsXyKxQWlQppdpoLXuYZsibRJKtL8vT/4KjVIJMsKVaKZJqmVhLQ91fgdGS1G4Xm6S5iLI8d5YZVNGAejTRmHqbplPHFn/eCGxvu78+bcPFf5ArR1NnjQTO6GfM7URPnJSGirWAy2zvX3UgLRYC/gBcIelConxru/4MpSeyJO1CLJKZr7chRJyZYIMXgH9XHUSror/xBNsH9Du487YH1ieuRX9CvHdelXQjsZL6WtulXqO2cTrRc/pqST8iWjC8I2kWYHXgx0Qytblf3erAfaVHmtIgk7QZkbD6IDB7myFl9S/aong0EzC2zdjG91BKKaWBGQdsXSx4/1Xz3KEkEQtsNwLOqyi+1CQTbB0g6b3AN4EN6X0VqW0vWWpgaUiQtA6wDz19SdptZ7btTjZmX7v4662232h63i/bN3QorG5yOvA/wFGS9mo+UZR9+yVR+ma/sgOz/QSR/PsEsBqwAFHX/xbbt5cdT83dybR9EFMabJOBxfoZs2gxLqWhQsRClLoZS0wOitjRvXVxvHnCsPRElqTPAScSiYOfAL8g+kHfSiTfPwv8Dri8rJhq7lL6WORUoU8Dt1QdBIDts4GzYWo/tg2IZNtoogyRi56E19OTcCs7cfVToof3xkQZy3ckvQDMT9wbiUiI/xRA0vuBu8lJqNTFit/HS4CP038J5U4n2HJXSkrpXZG0BbCF7V1Kft1umc/cjygpfgywh6SbgGeBhYE1iX7sL1DBvGGanup1X9H9JM0L3ET0aHiZ6I81GZiD2O0AUariLdujKgmyZuqQMOoWkjYhJk1mBf5O343ZO1autGgCamAZ2w81Pe9XNjaemkS7jJj0eoYo8/kR4AJiguUDwEW2tyo5rjqtnq49SZ8hJurWt31T1fGkoUfSeUSZyPWKXhut51clJjgvs/35suNLqRMkjSdKhW9ZdSzNij7LA1JmORxJVxM7GZaw/UpxTXaw7UOL818BfgOsm99VIGkBYALx2bl3XUrSS7obuMf2l6uOpS+SlqFnd9s6wLzFqX/aHllBPF8GdiJ+B+Yh7r/vBE7LkqhpqJF0EbAZcDKxkOMpep8LeKLE0FJKaYZJOgg4sOw5wm6az5T0YeB/icVOra4GvmX7b2XGlNob9gmLDvgRkVz7CrHSdQpwFFGeYlVi18prxO62Ya9NwuhBerlITAAcTNTX3cT2VRXGcSjxBTSp5XkaANtTJG1KfF7sTk8D9q2J3hCHMW1Jm7LUZvV0l3g/kWC7VtKZRKm+tjuJcpInzaSfEAm26yWdTewUeIYoHTka2A54h2KFfkpDxKHAlZJG276u6mAaatxDYiViUU5zT96pi9Vsn1QkIX5IlJEZ7n4HvA7sCmwv6WHa9+Wy7fVLjOtE4BBJi9r+e4mvO0OK3nX3S7oA+AywJ7Ac0d+sinhOJypDpDQcrAdcaXvXqgNJKaUu1jXzmUXy7LOSFgFWJBYTTQbutP1UpcGlaeQOtkEm6SHgaduji+etq0gXAu4FTshdIiDpNqLEwZYVJ4y6gqR/AWfb3rnqWNLgKGonf5SeUowP2J5SUSxdsXq6LppWOjWXaGn9UhUxSTfsd26mmVMk408ldni3lqJ7AdjF9sVVxJbSYJC0Y5vDmxF9Xc4iFi+0S4Dk4gVA0hvAL2z/sHj+OvAb23s1jTmS+KyYv6Iwa6P47h6IUr+7JS1O9LxdETicvnv8lZ6AK3b+rUtM8K9P9DeDnrKo99hesey4UhpOijKoJ9r+ftWxpJTSu1XVDraUOiF3sA2+DxF1sRveoakHm+3nJF0BbAsM+wQbsCyRMMrk2sC8Skyopi4maQrxvt+h6P/xYNUxFbpi9XSNZKI7dZztSyUtSiQbVqJp1Rpwoe3XqowvpUEwlvaLEwC+XDzaLl6gxF5nNTaRaXcPPQMs1TJmHqJaxLBnu10p+jp4lJ5FO8f0Mc6UcA8vaW6iBGQjobZsEVvjd/MRYBxwLdGDbVK7/50ySHoPsQil7Xs8r2nTEDKB+F1MKaVUIkmzAJvZvqjqWFI9ZYJt8L1OJNUaJhOlnJo9CyxSWkT1lgmjGTMOWK3qINK79gpRErVuLiHK/UyQVLvV03VT43JhaYgpkmhnFo+UhppaLlaQ1Eh4bGD7seL5QNj2kh0MrdV9TJtQuxHYVtJatm+UtCzwX8W4VF+nUZPyRJL+CHySSFg1EmrP0JNQG2f7yYrCm6oofbovsEwfw0pJSKZUkgOBGyVta/vsqoNJKaV36S5qvlhO0mJEWfGdiRYhZfdgmx/YBfgUvS8mKruseWojS0QOMkl3ESXeti2e30gk2Jay/U5xbALwfttLVBdpPRQ9ZRa1vXrVsXSD4sP9VuA44Ceu0S+wpA8SfRhWAD4IzN5mWNmTTrUkaTzwsu0tqo6lWUvJw77eW7adkxUppZSGLEmPE9+F6xUJtsbzftke1cHQpiFpd+Bo4nr6aUkfIxbIzEUsYpuf+F7f1PblZcWVuldxPfgicB1FUs32A5UG1ULSGOBkot/5BOBJeunjnaX101AiaVXgMqLtyB207/9s21X0804ppa4naVaiesxuwAZEb2MD19jesMQ4liauxRZk2rYkrbIlSQ1kgm2QSTqGWCX6AdsubnqPBa4idoeMBrYGfm1798oCrYk6J4zqSNLJwOJEyZYniBUfvTVm/0qJcY0GLicmc94mdmn2dpNb2qRTXUn6HPF5sLHtq6uOp0HSWAY+eZiTFW1I2pwopyTgBtvnVRxSGgIkrUz/q9ZyIiWlYUjS7EQS7UXbbxbHPg38CFgSeBw42vaVlQVZQ5K2JVYkNxrGv0z0+ztpuO8MkbQScGed78sk/YVYSb6m7furjielMkiaBzif6IfYl5xsTSl1jWIO5XHb91QcxxLAV4ExwELF4UnA8cT14RMlx3MpsDHwc+AE4EnbU8qMIQ1cJtgGWXFD8lXgp7aflDQbcC6wZdOwCUTt1rYN24eTuiaM6qrGjdlvBZYHvgKc2ditmdqTtCOwDbARcCF9l2Ks9Zb54UbSZsA+wAG2r285dwqwIz2ri0z0yPp8uVGmoULS++iZSMlVa2lYkPRJYBPgeNvPtjk/klhRerHtuzocy+y23+rka6RySRJRjmh74nN1CjF5MoJYwGCKPrmVBZn6JekNYKztr1cdS0plKeZOxgDXAKcDT9P7otbr2x1PKaW6KeY5T7S9WwWvPRuwFXFvsS6xW+1NYkH856uKq4htMrFoe7MqXj/NmEywlaSYLPgwsYr0tkxAhLomjOqq2PE3IGWurpD0L+Ac22PKes1u1lKKsVnzB7LI933tSPotsB0wwvYbTcc3BS4GXgOOIvrs7QYsAXzJ9lkVhJu6nKTjiUU7NwKn0HcJrJxISUOCpDOBNYHF2u2gKRIkjwPX296xw7G8Svz+NfpO3dHJ15sZxaKdu/pa9Vv0YVspF+2ApK8D/0vsVtuXeB9NKcoBrUOsEv4k8C3bv6kgvlWADYl+3XO2GZKLDgFJTwO/s/3dqmNJqSySngMesr1m1bGklNJAFLvC+vM3YmPK/o0Dtgfa+3imSPoIcZ+9E7HISsS14Vhi08CLVSb+ihhfJqrf7VvF66cZkwm2VKm6JozSjClucs+2vVfVsXQDSTsNdKztUzsZS5oxku4Gnra9Ucvx84k63V+0/fvi2EjgEWC87U1LDzZ1PUkTgX8An8qFOWm4kPQYsVqz1+/KYsfw2p3u61rskpmDngUwzX2pxtl+qJOvPxDFzf/Btg/tY8wPgUNz0c7UqgsjgI/b/leb8/8B/AV4wfYqJcYlYlLnS/T0wm1eiDW1R27+O4KkE4BVgRXqXMoypcEk6RVisvX7VcfSTrFQYSl6L2mO7RtKDSqlVKmmxeUzwrZn60Q8DU1xPQucQeyKv6/NmCoTbOOByba37HdwqlxH37DDkaQjgFOyFvzAZNJsyLiUWPWbBqAuSbOizIiB/W0/WzwfiOG8enok0K5v3tpEedupPddsT5R0GbBGSbGloWce4PRMrqVhZiSRWO7L00T/pU6bl/gMX694rEz0Ut4KQNJTFMk2IuH2TAkxzYxG6cMEHyPKj06XXAOw/S9JFwJfKzcsdge+TJSvPBb4M3A0saJ7NLAf0e/4ByXHVVc/INou/EbS3rZfrTqglEpwJ1Edo3YkHQDsSVy79mXYLxBIaRh6FeirCsQ6RMuUB8sJZyoDVwDntSbXauJQ4EpJo21fV3UwqW+ZYBt83wP2lnQ7cCpwlu0XKo4pdSlJixZ/faooX7Nonz/QxPbfOxRWO/sDt0j6FfB926+V+Npp5o0hLioOJ1bujBngz5notzcczUfU5J6q+L2cH7ikzSrqx4DNS4otDT0PAwtXHURKJXsdWLCfMQsC/+50IEUp4EYCDUn/SUwCNBJuyxGlZXYszj8IXGP7O52ObQZ9lNh9l9qX6G7V3/lO2Al4sFFuPTa08ZLtW4hr7CuBW4hFPqdUEF/d/I74rNgV2F7Sw/Tex3v9UiNLqXMOAy6VtKbtm6oOpkHS94FDgMlEb7heS5qnlIadk4FdgGeI8tvTXY8WO8UuLXmn2AHEnNbOwJjiGn4ssbi1LgvmPgRcBFwl6SyihGW7ax2yDHz1skTkIJP0ReIG6TNEc8S3iN09pwKX255SYXipyzRtW17G9kMzsL2641uqW0n6MPAnopTSQ8QFdru48ia3JppKtP6jSOBmydZ+SHoeuKy574+krYida4faPrhl/OHA12zPW2qgaUiQ9DWiH9Cytp+qOp6UyiBpHLAssGS7XSmS3kckn/9qe92y42uJZQTREH094LPAKEoo4dey43wMcFfxaDUrsCiwFvHdtUUn4+oGRYnIhYhr695KRN4HTLL9qRLjehU41fa3iufvAD+2fWDTmPOARcssXVlX2cc7DUdFz80tgM2AM4nJ1nb33KVOthYJ7rmIXp//LOt1U0rdQdJmwAnEXOZuti9tOV9ZKUZJGxK92DYDZgemAFcRc/hnVxVXEVtj/re1ZPg0w8hrnVrIHWyDzPY5wDmSFibKfOxETymZSZLOAE6z3e4meFiQNDNJxtITRjVxGvEBOrnlea1I+jgwntjdA7BiL0NrF3sZJD1K/H/fwPZjxfOBcCf7y9h+orhRuwu4Z7gmzWbQvcAmkuZumvjdivj3bbeSdBSxWiulmXEFMXE/QdIh9L1qrcxdyyl10gnAWcDVkr5m+57GCUnLA8cTPbROqCi+Zh8iEliLEkmbsoxp+ruBFYpHOyYWQO3Z4Zi6xcnA/wI3SNoPuN7220XvoLWBnwGLAUdUEFvzRPlrxO74Zg8Tidxhz/YsVceQUgXG0jPZumPxaDvZSswblOVDwG8zuZZSasf2JZKWA34LXCTpdOC7ttsuECiT7SuJMowLETvtdgU2Aj5HcY0t6ZO2b68gvJ0reM00k3IHWwkkrUjcCG9HTAgYuNd2bzfCQ9oMrDicRt5I1VdRtmYD4CBipcfTuVuzh6THid/79YoEW+N5v2yP6mBojd/Hg20f2hr2faMAACAASURBVHRsJ2An2+t18rW7kaSvEpO7dxLv9Y8C3yBqhi/a/L5X1Hd6Cvij7c9XEG7qci2r1vr6zBiui1DSECVpLD0Th88Sn6WLECVTRSxWG1NBXEvTUx5yNLGwSMR3wPjGw/YjHY6jseNcwKNEr65j2gydAryYpbt7FN/N/0fclxl4B3iBSGbNQvw3Pdf2tiXH9SBwi+2diud3A6/bXq1pzHnAGrZHlhlbSqkeinu0ASmz57ekJ4hqTd8o6zVTSt1J0hjiuvUVYFfbV1a5g60dSesDuxE7hucgrhfvIWL8VZWxpfrKyZgS2L4TuFPS3sB3gZ8SPRuGpUyUdZ6k+Uvu/bcacL7tH5f4ml3D9uJ9Pa+hxYkeM2l6JxG7kjckdguIKAX83TZJ5fWBkcA1pUaYhpJa7lpOqdNsj5F0M/Bt4OPEZynAX4BjbZ9YRhxFIquRUFsXeD/xuf8c8dl+HZFQK7Upe/OO82J36/jchT4wRa/UHSRdSqxUXpFIrk0mFs+cbPusCkK7Ffhk0/MrgH0kHQCcTyR0tyBaD6SUhqEyk2Yz6FxgK0lz2u54f9SUUveyPVbSeIo2SpJq11fW9jhgXFEKfgyxq2154FggE2yprdzBVgJJ8wCN3myfJm7MJ9uer88fTKmFpONsf7ufMfMC19heuaSwkDQJOMX2PmW9ZreRNLvtt6qOo1UvO9gOAg7MOs7tSZqFWPm+OvA8kVyeruyvpG2BVYEjs39WSinNHEnvAeYFXrL9esmv3Vg48TxwAz071P5aZhxp6JO0JVGecuOi2sH8wJ+JRU+NncwvAGvafqCyQCsiae3ir7fafqPpeb9s39ChsFJKTP2evpIoY/4d249VHFJKqeaKigJ7AT8G5qRGO9jakTSa2HH3pQpee0Hg88AywHtt79p0fBRRIW+6vsKpXJlg65BiAnZDIqm2OfGBYeBaIlN/fv4CpBlVJEP2tf3fvZyfm1hRvUqZyRFJvwcWtJ27nnpRNK+/kfgMGGf7jopDAjLBllJKKfWmqUzreGAc8R1+m+2ZKnc+SDEtWvz1KdtTmp73K3s1dpdikeZXgSWBx4nSqMOyr2vT7+Iyth9qet6vvJ5NQ5mkzYld1gJusH1eBTE8CswOfKA4NJn2PYM72l88pdR9JH0E+BjwuO27q46nbiR9hdg5NxdF24jGdY2kZYG7gd1sn1RdlAmyROSgKxo37gjsQE+fiIeIMk+n2f5HheGl7nc+8DNJT9o+u/lEsXLsCuBTQNk7yb4P/KloFn+4M3PfzmxE0v2zAJJeJEpLjSMSbg9VF1pKqe4kfZAoZTYvMXFxR15TpNRx2xPlftcDfkJM6L8q6UYi6XZtUQq+TI8XcSxD3GM0nvfH5L1fLRVJ0lWIf6PbbD8JYHsycGSVsdXIocR/n0ktz1Ma0iRtRtzbH2D7+pZzpxBzTyoO7S7pwgp6P88CvA00L+JQm3HtjqWUhjHbDwMPVx1HHUn6DHAC0f/tIGI+8euN87b/Iuk+YEuilUmqUO5gG2TFajqIya9zgbG2/1hhSGkIkTQnxQ414HO2ryuOzwVcRvQHObDsXmiSTia2Jq9NTPTcRfwOtLLtr5QYWm0U/0Zr0NPLZWVgVnomB56iSLYRCbdSVigXn1kH2T6s6VjuYEupJooeUMcDn2lz+mrg67YfLzWolAZRsfJ9ZpS6Er74XdyA+A4fTfRjM7FK/3p6Em73dTiOscXr7mf72abn/bK9cwdDSzNB0pHAHvRMPBs4Ksuup5QAJP2WKE0/wvYbTcc3BS4GXgOOAl4BdgOWAL5UUS/JlFJKg0TSNcDSwMdsv9xunk7S/wGr5e7g6mWCbZBJ+gMwFrggG7ymTih6rN1MTOysBTwIXELsjPqp7R9VENNASyU5kzZB0n8C69CTcFuOYst3MeRBopfedzocx4BL7DSx7VwFn1KHSRoJ3AYsQixeuAF4hp7P/1HA08DKtidWFGZK74qkx5nJnSi2Rw1uNAMnaRl6dretQ+wuBfin7ZFVxZW6h6TtgDOI9/8DxHXgUsXpnCDvg6Q5B3KvLWnxXISSupmku4GnbW/Ucvx8YAvgi7Z/XxwbCTxC9ArdtPRgU0qpF5K2IhaovQ38wfbVvYzbCdjJ9nolhldLkl4Czrb99eJ5uwTbz4Fv235vRWGmQk6QDjLbn6s6hjS02X5J0kbAH4mSkPcSybWjq0iuFSqb4OpWtl8BLi0eSBpB7EBcj/j3XJqYZOlogq0wo+U6srxHSuU4gEiu7Qv80vaUxglJswJ7AkcAPwJ2ryTClN4l24tXHcPMsH0/cL+kC4gdpnsSi2UWrDSw1E12JSaaNrQ9HkDSBsT1/VeATLD17gzgC30NkPQhom/iEqVElFJnjCQqFrRam9hBPbXnmu2Jki4jqqZUplhIOi8w2fbLVcaSUqqWJAHnAJ+nZx5pj+Kzakfbrf0aFycWriWYg9il3Jd5gSn9jEklyARbB0lamuiPMLft06uOJw0dtp8okmw3EHV4f217ryrjqeq1h5APAYsWj4XKelHbs5T1WimlGbYJcJXt/249USTbjiwmYzclE2wplULSAvQsiFkf+HDjFLET6Z6KQkvd5xPARY3kGoDtayRdRKzyTr3bWtIxtr/b7mSxk+daYpFKSt1sPuDN5gNF38b5gUva9D5/DNi8pNiaY5oN+B6xcGBU0/HHgBOBI22/XXZcKaXK7UwsiHkS+A3wFrATcf96k6T1bD9XYXx19jjwyX7GrEpUv0oVywRbB0hagbiIWLHp8OnFuXWIVYlftH1JBeGlLiLpwH6G3AqsADzXMtbNPbVS/RQJ+EZ5yNHEzZOAiUQ9/fHFI6U0fI0kVun35XZyIjYNYZLmIxarPVnR689NT0nn9YFlie/rxircR4j+qdcSPdgmlRzfyQMcOmz74NbYfERpyFYPEA3rU++OA3aX9KTtI5tPSFqIuIYeBexQRXApDaJXgA+2HGtMuN7Zy8+80cvxjpA0B/AH4rvSxER6o6T54sBPgM9J+qztN3v730kpDUk7E7ttV2kk0iQdBRwO7AVcUyTZSr1+7hIXAd+XtI3t37WelLQzsVjrh6VHlqaTCbZBJumjwHXArMAxwEeB5nrZNwAvEBn8TLCl/hw8wHEHtTw3UEmCrShbNgKYs915238vN6J6kLQYPQm1dYkbDgHPAdcQnxvjbefqk5RSw2RgsX7GLFqMS2nIKJJahxCT4wsS1zWzFedWJa57fmT7jg7H8UdiInNWehJqz9CTUBtXVeKvyZh+zpue3XWZYGtSg2ojsxAruVu9RZbj7s8eRNLh55L+YftsAEnzE9fVHwV2tn1OhTGmNBjuBTaRNLftV4tjWxGf6Te1GT+K+J4q017EYq9Lgb1tP9w4IWlJ4BfAZsW4n5ccW0qpWssBv2/epVZUYvmepL8DRxNJtnVtv1hVkDV1BLAtcJakLwDzAEjanejHvjXwMLHoKFUsE2yD7yCiTurKtv9aNCGcmmCz7eJmfZWqAkxdZd2qAxgoScsRF8zr0ktyjaYJsmHo0eLP54lE+3giofbX6kJKKdXcTcAXJP2v7ZtbTxaJhm2Ay0qPLKUOkTQP8d7/OHAXMIlIgjTcS9xUbgd0NMFGlF15kVgEM47YodZux1GVeuuDOy9xv3EAcDOwX2kR1VzNqo20lndLA1DcU29P/F6OlTSR2M1zNbHL9Ou2T6syxpQGyRnA8cD1kk4lksc7EFVPpql2UvQ6WpPo1V6m7YG/AFvafqf5hO1HJG1NfJ/vQCbYUhpu5gCebXfC9rGS3gGOBa4uWh+kgu0Xi+vS04h7/oZjiz9vBLa33V+ftlSC4TrR3UnrA+f3M2n+JNEMPaU+2b6+6hgGQtIyxOQNxI3tZsDdxBfpSsSOtvHAsNy9VmjuzXI78GfalwVKKaWGnxB92K6XdDbxOfoMUTpyNJFgeAf4aVUBptQBPySSa2Nsn1YsVptaBtv265KuJ665O21l4M42PW5qo48+uE8Ad0u6krj2uAY4qbTAaqqG1UYOlnRwuxOS2jWtt+28hwds/1vS5sAE4AJiMduKwJ62T6g0uJQGz0nELoUNidYQIna5frfYBdJsfeIa8ZpSI4xepMe1JtcabL8j6Qrg2+WGlVKqgaeIiitt2f6foofjL4Erie/0VCgqgI2W9AlgNWABonrNLbZvrzS4NI28OB988wH/6GeMiCx+SjNE0rXABNsHVB1Lix8BsxN1le8tVqFcYPtQSe8lVlhsTP9ljIay7YmbnvWISXMDr0q6kZg0v9Z2b3X0U0rDkO07inIQpxKrfrdvOi1iEniXvLhOQ8zWwJX97D55ghKqQXS6BGUZbD8p6RLgu2SCDepXbWRGS0Fm6cgmtl+QtCGxY2cF4Ae2j6k4rJQGTZGc2oRYVLU6UQ3lfNt3tRk+glg4cHGJIQK8Cczdz5j30r4kbkppaLuXfipz2T5a0pzAz5i2ukAq2L6HWDCXaioTbIPvWWIFT18+TuxiS2lGfRq4peog2hgNXGr73qZjArD9mqSvEV8GhzFMk2xFb4hGf4jFgA2IZNtoIvloSS8B19OTcLuvmmhTSnVh+1JJiwJbEDuC5yFWrd0JXJglIdIQ9EHgvH7GvErRhyANyLPAR6oOoiZqU23E9iydfo2hQtLJ/Qx5gkicLtUy1raz92DqasXOsDOKR1/jpt5vluweoqT5wbb/2XpS0ghiV/DdpUeWUqra5cCWkjax3WtbA9uHS5qD6MFc28oRKfUmE2yD71pgO0lL2X6w9aSkVYgbu1+VHlkaCh4GPlR1EG2MIGJreBt4T+OJ7bcljScaMg97RTmnk4pHo8RmY3fbOsREOpL+aXtkVXGmlOqhSKKdWTxSGupeARbqZ8woojdb6oekWYnri8lVx1ITWW2kO42ZyXEGMsGWUmf9D5HYu1XSj5m+pPmPgAWB71QVYEqpMucTZbn7XRRq+zBJfwcW73RQdSTpQOK65VfFDv0D+/uZgm0f1sHQ0gBkgm3w/YxoPnhDUU//AwCSPg6sTZQleQU4sqoAU1c7EThE0qJFLd66eIFpy0JMYvo6y2+Sq83bsn0/cL+kC4gV03sCyxE3IimllNJwchuwqaT/tP1K60lJ7yd2fl9aemQ1JGntXk7NRizK2pkonXdiaUHVW1Yb6U6jqg4gpdSe7XMlrQDsB7TrfyjgCNvnlhtZSqlqtl8Ajp+B8ad2MJy6O5hIsJ1DzLEePMCfM1EtLFUoE2yDzPaDkj4PnEWs5IG4oLin+PMlYOuaJUdS97iESMBMkHQ4MQk1kTZbqEt+jz3CtKtMbgc+I2kh288Vfdi2AB4rMabak7QAUY96PWIHW2PCR8S/adZYTmmY6WOyvE+2bxjsWFKqyDHAFcDlknZrPlHs+P4tMBfR3zXBdfRdSkfADcA+pURTf1ltpAsV1R9SSjVle39JFxM7Rldk2pLmJ9v+Y5XxpZRSF2j0qvt7y/PUBWRnadNOkDQvsBPRM2sB4uLiFuCUIoOf0gyT9A4xidJIwPTGtktLoBdbl78PLFz0XNuESAY+A9wMfBJYDNjb9tFlxVU3kuYmSkA2EmrLEv+WjYb1jwDjiMmfa21n+auUhpmmz/kZYnvWDoSTUiUkHURUfTDwFjA78CJR3k/Avrb/u7oI66OomNHuM+Md4r/ZrbZvLTWoGpO0FHAH0cfvYGJ3367AJ+ipNjIX8IlcEFlfkh4FrrD9rapjSSmllFJKw1sm2FLqIpLGMsCJV9s7dzaaHkW5prWBcY2kkKTvEpMU8wKvEyvNf1Q0aR52JP2RSDTOSk9C7Rl6EmrjbGc5opSGuSLB9hZwNTE5PiC2v9yxoFKqgKR1iX4trYvVjrJ9bZWxpe4m6XNEtZH3NQ7Rs4DtJeAL+R6rN0mvAMfZ3r/qWFJKKaWUBpuka4EJtg+oOpbUv0ywpZRmiqQdgbts91rGUNKswAjgOQ/zD5ti0vxFopTTOGKH2gOVBpVSqh1JjxC9Zt4AzgN+m+UfU0ppcGW1ke4m6RbgCdtfrDqWlFJKKaXBJul14OhcTNQdMsH2Ls1srxTIfimpuxUJo4NtH9p0bCdgJ9vrVRdZPUlaCbhzuCcaU0r9k7Q+sBvRu3J24CHgRODULB2bUuqPpM2JctQCbrB9XsUhpTSoJG1PfC9+uq/FfimllFJK3UjS3cA9WammO2SC7V2a2V4pkP1SUnfrJcF2EHBgvrenV3w5ngdcYPvequNJKdWfpAWAMUTD+KWBN4GLiF1t11QYWkodJ+kTwPbAMsB7bW9QHF8c+BRwte0Bl1EdSiRtBuwDHGD7+pZzpwA70lOO2sCFtj9fbpT1JGlj4A/DtWT5UFEscv0esC5wPHAbMJE29+W5qDWllFJK3UbSt4FDgBWyL3D9ZYLtXeqjsXi/bB8yuNGk4ULSKsCGwCLAnG2G2PZXOhxDJthmgKT7iElCA48D5xPJtpurjCul1B0krUXsavs88bn/GPB523dXGlhKHSDpUGB/YJbikBvXFpKWAB4G9rB9XEUhVkrSb4HtgBG232g6vilwMfAacBTwCvG5sQTwJdtnVRBurRTXr88AZwCn2f5LxSFNQ9LswPrENePctg8rjs9F9IyblMnBaRa5NieS28r7kpRSSil1m2JR4bHAisDh9L2YKBNwFcsEW0pdRJKAscCXmLYhe0PjuTt9M5kJthkn6SPA1sCWxOp7gOeAC4ELiL5sb1cUXkqpC0j6NHAuscBiK9sXVxxSSoNK0rbAmcCVwL7AF4H9mq8tJP0JeNn2Z6qJslrFrvinbW/Ucvx8orTsF23/vjg2EngEGG9709KDrRlJvwb+C5iPuG6+AzgVOMv28xXH9jngJGAkLdfzxWf/BDJRCszYItdc1JpSSimlbtOymKivax7bnq2cqFJvMsGWUhcptggfA5xGrGT4M3A0Mdk6GtgPuBz4ge0nOhxLJtjeBUkfALYqHmsDswIvA5cSybY/2H69ughTSnUhaU5gG+CrwJrERfaNwFdtP1RlbCkNNkk3AyOAZW2/2e7aQtJYYLTtxauJslqSngVOt/29luOTiM+HEc09XyWdC6xhe5FyI60nSXMQicidgM8S12BvEdfQY4HLy17wJGll4CZgEnAEsRBru5b3/d+AO2z/V5mxpZRSXyTNafvfAxi3uO3HSwgppZS6XnG/M9DFRDt3NprUn8xwDoKiBvzjA92SKWl5YHnbp3U2sjQE7QQ8aHsMQGxo4yXbtwC3SLoSuAW4GjilhHgyQz+TbD8N/Ar4laR5iYmerYgdbjsA/5J0FZFsu9T2C5UFm1KqhKTliKTaDsRui0lE2bff2n6wythS6qDlgLG23+xjzNPAwiXFU0fzET0Zp5K0KDA/cImnX0H5GLB5SbHVXvHe+h3wO0kLEZUhdiIqDGwBPC/pTNt7lBjWAcDrwMq2JxaJ5Va3ASuVGFNKKQ3EGcAX+hog6UPAtUTJ4pRSSv1ozPum7jBL/0PSAIwHxjQfkLSvpN7KjGxJOcmPNPQsTVyYNpuaKLd9J7ED6pslxXOwpCmNB3AgQPOxlkeWP2zD9ku2T7W9JbFq/wtEYm0dYiX1MxWGl1IqkaT3Stq1KIF3F/AtooTZtsAitr+XybU0xAnor8fUwsAb/YwZyl4BPthy7JPFn3f28jPD+b9Xr2w/Z/uXtpcnelwcC8wDfLvkUNYALrQ9sY8xTwLvLymelFIaqK0lHdPbyaJU8bVEefOUUkq9kPSopO+0HFu02NiTaix3sA0OtTk2FzBv2YGkYWFy099fI1YrN3uYKHdThnbv/cEcPyQVq8zfbDeJYvtfwPnA+ZJmA9YjkvIppeFhIvCe4s+fASdmOZ00zDwMrN7bSUmzEKVS7ystovq5F9hE0ty2Xy2ObUVUFripzfhR5GKdPkn6KNGbbWtgdsqv0jA3sUu5L+8hF8hOVfSm/gKwITFxP2ebYba9fqmBpTT8HAfsLulJ20c2nyh2CY8nvod2qCK4lFLqIoszfS5hZ2IzQ7biqbFMsKXUXZ5i2pVfj9KzYrnhI0TiraNs5w3+zHsMOBXYpa9BRf+Pq4pHSml4eC/RC+gJoj/j2kU54L7Y9jqdDiylkpwL/FjS3rZ/0eb8/sCHiZ60w9UZwPHA9ZJOBT5KTFxOJCYypyqSEGsCfyw7yLorSnRvS5SH/BSxEOxl4CSigkCZngI+3s+YFYhr/2Gv6E16OdGDWkRCtPnL0k3HU0qdtQexq/rnkv5h+2wASfMD1xDfUTvbPqfCGFNKKaWOyQRbSt3lVqZNqF0B7CPpAGLX02iid8Sl5YeWZsBL9L9KOaU0fM0OfHoGxucEYhpKjga2AY6Q9F8U729JRwJrASsT/WZPqCzC6p1E7LTakEi6iEjMf9f2lJax6wMjiUnOYa/YAbkRkVTbDJiDeI+NI5Jq59uuopzmFcDXJa1pe7pdiJI2InZ2/rz0yOppX2Bd4DAi2T4JOJj4XBhN/He6GfhyNeGlNHzYtqTtKT5HJU0kyhVfDSwLfN32aVXGmFJKKXVSJthS6i7nAStLGmX7MeAIopzNIcRNpYAXgP0qizANxC1En4+UUmo1quoAUqqS7X9JWpeYNN+BnnIoexG92f4P2L3Y5T0s2X5H0ibAdkTS5XkiMXRXm+EjiP+WF5cYYp09DSxIXDM/RFQUOM32U5VGFSWBtwWuknQcUSKI4t95baIf5zPAL6sKsGa2Ae6wfRBAY6d3UX79bEm3En1M9wDa7YRNKQ0i2/+WtDkwgegl/ihxv7un7eG8ICallNIwkAm2lLqI7QuBC5uevyBpReCrwJLA48QkQfbZqLeDgRsl7Wr7xKqDSSnVh+0nqo4hparZngyMkbQXsAqwANGD9lbb/6w0uJqw/Q5RKvKMfsadDZxdSlDdYS7gt8BY27dUHUyD7ackfZYokbpP06mLiWTgI8DWtrMCQliS+HdsMLH7O57Yj0q6DBhDJthSKkUxN7EhUZJ4BeAHtodzOeeUUkrDRCbYBk+WZ0odJWlRYpLJwG22n4Spk1BH9vWzqXY2Aq4Djpf0DaL050Sm/xyx7cNKji2llFKqBdsvAFdWHUcaUha2/e+qg2jH9h2SlgI2AVajJ7F8C3DRcN612cZbQHMpz1eInYnNngA2Ly2ilIYJSSf3M+QJovzuUi1jbfsrnYsspZSGhNEtPdhHAxStgdo1Z895wxqQnXmhd0vSO8xEgs32rP2PSmlq35E96PkwNXCU7X16/6lUV8VnxkA4PydSSimllFLqIek+4E7bXyqe3wrMaXv5pjFXAh+z/aGKwkxpSJqBe9lWeW+bUkp9mMHPVxNzxPnZWgO5g23wtMsi9yUzm2lAJG1H9B0x8ADxXlsK2EvSHbbPqjK+NFPWrTqAlFJKqa4kzQ/sAnwKmI+ePmzNbHv9UgNLXUfSjsVfL7D9StPzftk+rUNh9UvSfMDcjYoVaToTgA2anl8I/FjSiUT/p9HF+TPLDy2lIS/7BaeUUmccUnUAaebkDraUak7SOGAtYEPb44tjGwBXANfb3qCvn08ppZRS6haSlibKKC9I3wvYcrVm6ldTpZFlbD80wMojlawGljQ3MbGyA/H+t+3ZinOrAgcBP7J9R5lx1ZGk0cC+wDdsPy7pPcB4esrpC/gbsK7tpyoLNKWUUkopDXmZYEup5iT9E7jO9jYtx38PjLY9oprIUkoppZQGl6RLgY2BnwMnAE/anlJtVKlbSRpDJFzOL3aw7TTQn7V9ascCayFpHuAm4OPAXUT/omUaSb4igfQs8JssEd+epNmALYAPA48Dl9h+vdKgUkoppZTSkJclIlOqv/mI0pCtHgC2LDmWNIgkfQLYHlgGeG9jN6KkxYmyWFfbfrGyAFNKKaXyrQVcZnv/qgNJ3c/22JbnpSXNZtAPieTaGNunSToIOLBx0vbrkq4Hhn1ZVElrEdfJBv5kewKA7beB86qMLaXhSNI2wDeAL9l+us35RYDTgF/ZPr/s+FJKKaVOywRbSvU3C/BWm+NvMeO9/1JNSDoU2J/494VpyxXNApwF7AEcV3JoKaWUUpUE/LXqIFIq2dbAlf30fXuCKIE4LEmaFTgH2Krl+PnAF22/U0lgKaVdgXnbJdcAbD9V7NLdFcgEW0oppSFnlv6HpJRqIGu5DiGStgV+BFwNrAD8rPm87UeBPwOblx9dSimlVKnbgaWqDiKlkn0QuKefMa8C85QQS119jUhEvgH8Abiy+PvWxbmUUjWWI+5d+3Ib8IkSYkkppZRKlzvYUuoOB0s6uN0JSe36kkxtip5q6TtE4/UtbL8paas2Y+4HRpcaVUqpdiTND+xClMOaD5i1zTDbHvZlw9KQcShwpaTRtq+rOpjU3SQ9OpM/attLDmowfXsFWKifMaOASSXEUlc7AP8CVrZ9P4CkjwO3Al8Gfl1hbCkNZ/MDz/Uz5nkge8enlFIaknICPqXuMKOlILN0ZL0tB4y1/WYfY54GFi4pnpRSDUlaGrgOWJC+P9dzl3PqWpJ2bHP4IuAqSWcRO9peavez/ZTTSwmiYsvMfEaWfS19G7CppP+0/cp0wUjvBzYGLi05rjr5GHBeI7kGYPs+SRcAm1YXVkrD3iTgI/2M+Qi9fJenlFJK3S4TbCnVnO0s5Tr0COivT8TCRNmblNLwdSSxo+HnwAnAk7bb7VpOqZuNZfoESCO58eXi0e68gUywpT7ZXrzqGAboGOAK4HJJuzWfkLQM8FtgLuDYCmKri/cBj7U5/igwd8mxpJR6TAA2l7S07QdaTxafYVsAl5QeWUoppVSCTLCllFL5HgZW7+2kpFmANYH7SosopVRHawGX2d6/6kBS6qCdqw4gparZvlLSIcBBwF+AtwAkTSLKAwvY1/bN1UVZOQHtFplMIat3pFSlI4leiDdJOpTokfgUsAiwEXAAUeL8yMoiTCmllDooE2wppVS+2+kQNQAAIABJREFUc4EfS9rb9i/anN8f+DCxmjmlNHwJ+GvVQaTUSbZPrTqGlOrA9iGSbiB69X4aWIDYqXk5cJTta6uMrybmlbRo6zEASR+iTaLN9t/LCCyl4cr2bZK+CfwKOKp4NJsCfMP2n0oPLqWUupykHYie7CsSu/lfBu4ATrF9RpWxpR6ys21HSimVSdJ/EKU0lgf+TEyerELcjKwFrAzcAqxj++2q4kwpVUvSeGCy7S2rjiWlTpI0u+23qo4jpapIWht42fZdVcdSV5LeYcb76dl2LipOqQRFKchvAqsSie+XiHvaXzf3TkwppdQ/SbMDvyf6zDZ28U8CRhC7gk305v1C3kdVLxNsKaVUAUnzEDvUdiC+HBveAc4Adm/X5D6lNHxIWhe4Evis7esqDieljpH0KnAjcC0wzvYdFYeUUqkkTQGOt/3NqmOpK0mPM+MJNmyPGvxoUkoppZQ6R9KBwMHEQoUfADfZniJpVmJh/s+ATwEH2f5xZYEmIBNsKaVUKUnzE7vXFgAmA7fa/me1UaWU6kDSjsBmRGP4s4DbidXA07F9WomhpTSoJL0BzEHP5PmLwHXAOCLh9lBFoaVUCknPAv9ne++qY0kppZRSStWS9DdiAf6ytt9sc35Oom+vbH+47PjStDLBllJKKaVUQ03lsJp7yrReuIkogTUrKXUpSXMBawDrFY+V6Sl9AvAURbKNSLg9U0WcKXWKpHOARW2vVnUsKaU0MyR9GtiV6BM0L7F49HaiT9DNVcaWUkrdpliAeJztffoYcyTwLdv/UV5kqZ1MsKWUUskknQy8RmzlfqGXMVsAW9jepdTgUkq1IWmngY61fWonY0mpTJL+E1iHnoTbchTJ5GLIg8A1tr9TTYQpDS5JHwH+BPwKODR7aaSUuomkHxMlzNTmtIHDbe9fblQppdS9JD0KXGx7jz7GHANsanvJ8iJL7WSCLaWUSta0K+VhYGPbj7YZcxBwYO5KSSmlNNxJGgGsSyTbPguMIndupiGkWHz1YWIn57PA3cBEpt+1bNtfKTm8lFLqlaRtgHOAJ4DDiH6qzwDvJ763DwAWBbazfW5VcaaUUjcpFi6MAT5m++U25+cF7gNOsn1gyeGlFplgS6kLSZodWB9YBpjb9mHF8bmA9wGTbL9TYYipD0WC7Q7gE0SfmS1t/7FlTCbYUkopJUDSivTsZlsbeC+ZYEszQdLiwMeA622/VhybjZgA3pKoMPDfti8oOa6BXrfn+z6lVCuSbgA+Aixne1Kb8yOIPkEP2l6n7PhSSqkbSZoDOBdYGjgUuIFYhLUwUenjAOB+4L+y8kH1MsGWUpeR9DngJGAkLb13irrnE4Av2T6ruihTX4pJlIOBm4HzgNmBMc0r+jLBllJqkPQeYGum7WlxB3BBY4I4paFE0tL0JNRGA/MR1zwTgfGNh+1HqooxdSdJpwCbAwvbfrs4djDQvPJ3CrCW7VtKjGuxgY61/UQnY0kppRkhaTJwmu1v9zHmOGBH2/OUF1lKKXUvSVMaf2X6igZ9Hbft2ToWWGor/4On1EUkrQxcCEwC9gQ+BWzXOG/7FkmPAVsBmWCrOdvXSFoDuAw4U9Io24dXHVdKqT4kbQycCszPtH0tDBwlaWfbl1YSXEqDpEguNBJq6xJlpQQ8B1wDXEck1B6sKsY0ZKwGjGtKrs0CfBN4gCg/OpJ4z+0JfLGTgUjaEbjL9j2ZNEspdbHZgNf7GfM6Of+YUkoz4kbaJ9BSDeUXXErd5QDi4nRl2xOLXU6tbgNWKjesNLNs/0XSqkSS7aeSlgS+UXFYKaUakLQScD4wK3AG0/e02A74vaQ1bN9eWaApvXuNXqTPE+VPGjvU/lpdSGmIWpjoE9SwAjACOMT2P4B/SLoIWKuEWMYSFQ3uaRyQtBOwk+31Snj9lFIaDI8Am0r6Qbs2FcVCho2LcSmllAbA9uiqY0gDN0vVAaSUZsgawIW2J/Yx5kli8jV1ieLfcy3gcmBXItmW5TNSSj8kVq2tZXtH22NtX1n8uSOwZnF+/0qjTOnda+zOvAe4HfgzsaMopcE2O9OuBl6jeH5t07F/UN219OJEX42UUuoWZxK94S+S9JHmE8Xi0d8TvS/PrCC2lFJKqeNyB1tK3WVuojxkX95DJs+7ju3XJW0BHAN8C9ig4pBSStVbC/hdb32AbP9J0u+BDcsNK6VBtz2wPrEz8ydEwuNVSTcSu9mutX1nhfGloeMfwCeanm8MTLJ9f9OxhYCXS40qpZS61y+BzwGbABtJepqouDASWISYm7ipGJdSSmkGSZodWJqefuz3236r2qhSs0ywpdRdngI+3s+YFegptZTq6QngpdaDRUmNb0t6BPhF6VGllOpmHmJXcl/+DryvhFhS6hjbZwNnw9R+bBsQybbRRALEkl4Crqcn4XZfNdGmLncpsKekI4E3gM8Ap7SM+SjTlpFMFZM0s/c2tr3koAaTUpqG7TclfQb4HrALsCTwweL0I8DJwJE5GZxSSjNG0vuAI4AvA3M1nXpD0unAfranm1tM5csEW0rd5Qrg65LWtH1T60lJGwGrAz8vPbI0YLZH9XP+aElnMe0XaEpp+Hka+FQ/Y1YmVgmnNCTYfgI4qXggaRl6dretA2xRHP+n7ZFVxZm61hHAlsBexfOngKk9jSUtBKwGHFt+aKkPszBtaU+AOegp5TmFqPIxguhbCvHd+GYp0aU0zBXJs58BP5M0N7FIbLLtVwEkzSXpP2zn7uCUUhqAIrk2gdhk8QpwIz392FcAdgPWlLR6frZWL8vIpdRdfkbsfLpK0uFELXMkbVI8/x3xgZvlF2pM0qLFBE5f/sX0EwkppeHlcmA9SftJmrX5hKRZJO1N7PS5vJLoUiqB7ftt/w/wbWBv4C9Ez7YFKw0sdSXbzwHLAZsXj4/ZfrppyAhgH+DEskIq6XW6mu3FbY9qPIDlieToLcC6wFy2308sTlsP+BPTlwNNKZXA9qu2n2ok1wq/Bl6oKqaUUupCPyCSa78GFrM92vZ2tkcDiwG/IuaEf1BdiKlBdl7Tp9RNJK0EnAss0XTYxGTTI8DWtu+tIrY0MJLeAd4G9iomDduNOQg40Pas7c6nlIY+SSOB24keFn+nZ9XaSGBNYHFgIrCy7dzFloYUSQsQE+frETvYPtw4RVz33GN7xYrCS+ldK64HZ/Rm3LaHfRUaSccR/UeXtT3dLjVJcwH3AlfY/k7Z8aWUpiXpFGDHvLdNKaWBkfQg8Lzt1fsYMwFY0PZHy4sstTPsL85T6ja275C0FNFEeDVgAaLJ5S3ARbbfrjK+NGCzAsdIWtL2nlUHk1KqH9sTJa0BHE/0CVqsZcjVwNczuZaGgqKk1Dr0JNSWJZJpKoY8AowDriV6sE2qIs7U3SRNAQ62fVgfY34IHFJSIkv9D3lX44eqrYCz2iXXAGy/IekiYFsgE2wppZRS6jaLAef1M+Z6IOcTayATbCl1IdtTgIuLR+pORxPlbb4raXFge9v/qjSilFLt2H4c2FDSIsCKFD0tgDttP1VlbCkNFkl/BD5JLD5pJBCeoSehNs72kxWFl4aW5qRtf+M6yna2a5h5CwCz9zNm9mJcSimllFK3eQ3or7XMgsDrJcSS+pEJtpS6mKT5gLlz0qkrTQY+B5wAjAGuk7S57WcrjSqlVEtFMi0TammoWhV4EbiOIqlm+4FKI0rD2XzAG1UHkfr0CPAFSQfZntx6srhH+gLwaOmRpZRSSim9e7cB20g63PbDrSclLQn8f3t3Hm1ZWZ95/PswRxAEB0KIZYGyFElQEByIKAoRB6JI0r2aqEyGiIBDSJM2olgCiROKGscoCGgHOooCEkpsZlERaFToKKhAASLQAgqIqAX16z/2vtapw53Orbpn33Pv97PWWXcP797nKS51ap/92+/7/lfg20NPpkewwCaNmHYIpXcDr6F5WqFo/y4neQ7wLuAdVXV1ZyE1Le1wngcluQE4Bvh2kldU1Q87jiZpjknyRB7Zg82HKzRf7ETz/7STQ2uNS/KCvk2Lx9kGTQ/KRTTX2NfPejCtjk8BHwWuSPJPwKXAncDmNEPNHkUzX+k/dZZQkiRp5j4AfB24sp179iJWzse+G/AmYCPg+K4CaqX4PVYaHUk2AS4DtgO+B6wHbDs2WXCSR9F8ufxUVR3ZWVBNqp3UfklVHdOzbV/gJOBB4L8AzweOdiJoaWFLsg3wCZp5qfpdCBxWVT8abipJGh3tddd0v/QGWAHsV1X/NnuptLqSfITm5tJ4v9sA/1JVbxluKknjSfI5ms9Vv9tK0jQleQPwER45LHaA5cBbq+qTQw+mR7DAJo2QJO8H/jtwQFWdmuRd9BVhkpwD/FFV7dhVTk1uvAJbu31X4Eyap1CuAXb0S4i0cCV5Cs2QD4+lGQ7rMuAOmqfWng88GbgL2KWqftJVTkmay5IsoSnCBDiaZijSS8Zp+jBwN3CRQ5SOhiTPAw5i1R7eVwMnV9W3uswmzWdJHp7JcX63laTBJFkEvI6+0WyAL1TVzV1m00oOESmNln2A86rq1Ena3AzsPKQ8WoOq6hvtjYJzgWcx/aetJc1P76Eprr0F+HhVrRjbkWQtmif3TwD+mWb8dUlSn6paMracZH/gzKr6aHeJtKZU1bdx7hGpC5nBMX63laQBVdUtOOT1nGeBTRotfwycMUWbX9E81aC560CaIT4foap+1M6ldyzwB0NNJWmu2R04t6r+pX9HW2z7SJI9gT2GnkySRlBVbdV1BkkadVW1VtcZJGk+S3ISzUNhZ0/SZi9gn6o6aHjJNB4LbNJouR94whRttqIZMkxzVFWdMsX+u4FDhxRH0ty1HhMU43t8F9h1CFkkad5Isi7NQwzbAhtV1bHt9g2AjYG7ensNq1vt8EgAt1XVwz3rU2qf/JYkSRolBwDLgAkLbMAzgP1phstWhyywSaPlSmCvJI+uqvv7dybZAng5cM7Qk0mS1rTvA0+Zos1TaOZslCRNQ5KXAifSzGcZmmHLjm13PxP4JvBa4LROAmo8y2h+T9sCP+pZn0rhPQ9JkjQ/rU8zh7A65sWmNFo+AiwFzk3yt707kmwLfAbYAHBeiTms7eo9HVVVr5/VMJLmsn8GvpLkZVW1tH9nklcArwb2HnoySRpBSXYCzqQZ7eHvgGcD+47tr6rLk9xE89lqgW3uOJWmWHZv37okSdJ8NeG1TpL1gRcAdwwvjiaSKq9LpVGS5F3Au2g+aJcD6wK/ADaleQr3f1TVB7pLqKkkmWrIoaJ9orqq1h5CJElzQJL9xtn8l8BewAXApcCdwObAC4EXA18FvlxVpw4rpySNqiRn0Qyr+/SquqO9rj6693oryWnAjlX11K5ySpIkaWFJcmPP6mLgl+2r39rA42l6sH2qqg6b/XSajAU2aQQleRHwZuC5wGNpnua8HDihqi7sMpumluRJE+x6DLAz8E7gW8DbqurmoQWT1Km2+N5/YZZpHGoxXpKmIcldwNljk8FPUGB7P3BIVW3cUUxJkiQtMEmWsfJ+wCLgPsYvsD0M3E3zEO5xVfXroQTUhBwiUhohSV4A3FdVFwEXdZ1HMzNJ0exm4PtJzqOZU+l8mjlCJC0MB3YdQJLmuY1ohoeczKOAtYaQRZIkSQKgqhaPLbcP355QVcd0l0jTZYFNGi0XAZ8GDu06iGZPVd2a5KvAW7DAJi0YVXVK1xkkaZ67DdhuijbPBG6coo3mgCQ7A3sCW9IMk9TP+YwlSdIoehGwrOsQmh4LbNJouQt4sOsQGoo7gW26DiGpO0lOAq6tqhO6ziJJ88RS4JAkz6+qy/p3JnkZsAvw3qEn07QlCXAy8FraeYtZdUjl6tlugU2SJI2Uqrqkdz3JxsAmwL1VdV83qTQRh76QRsvFNF/6NY8lWRt4Mc3cepIWrr8GntB1CEmaR95DM5fF15O8D3g6QJJXtOtfBG4HPtRdRE3D4cDrgM8DO9EU0z5M8z3p7cD9wOnA1l0FlCRJWh1J1ktyVJIbgF/Q9Gj7RZIb2u3rdZtQY1JVU7eSNCck2Qb4DvBx4JiqWt5xJM1AO5feeNYBnkgzD9OuwGer6g1DCyZpTklyHfBNh7eSpDUnyY7Av7Nq8WWsx9MNwD5VdW0X2TQ9Sa4CNqyqbdv1FcCSsXlKkuwAXA4cUlWf6y6pJEnS4JI8GrgAeBbNdeqtwB3AH9LcNwxwFbB7Vf2qq5xqWGCTRkg7XNhTgD+jGULw+zQfsP1/kZ1vYA5rbwJM9uEb4FLglXb9lhauJEcDhwDbVdUvus4jSfNFO1rAXsBzgcfSjBpwOXBWVT3UZTZNLcmvgFOq6rB2fQVwXFUd3dPmDGBRVe3cUUxJkqQZSfIh4K3Al4Ejq+qmnn1bAccDrwZOqKq/7yalxlhgk0ZI++VxOqqq1p7VMJqxJEsYv8C2gqbb9xVVdcVQQ0mac5KsC5wBLALeAVxZVXd2m0qSRlOSRcDONNdgV1bVrR1H0gy1BbaPVtXb2/X7aQpuh/e0eS9wWFU9uqOYkiRJM5LkVuDnVbXjBPsDXA08rqqeONRweoR1ug4gaSBbdR1Aq6+qlnSdQdJI+E37M8BZAM119CNUVXlNJ0kTSHI8zVPAYx+ileSEqjqyw1iauduALXvWb6QZQqnXNsADQ0skSZK05jwO+MJEO6uqkpwHvHl4kTQRb8ZIc1yS/YDvVdU1VXVz13kkSUPzDSYfTlaSNIUk+wJH0HyeXkdTZHsqcESSq6vqtC7zaUauYNWC2lLgyCTvpBlKaTfgVcA5w48mSZK02pYBj5mizSZtO3XMISKlOa5/0u522/7A/lX14u6SaU1KsiWwI7AW8K2q+nnHkSRJkkZekguAXYE9q+qidtseNEWZS6pqjy7zaXBJ9gbeA7y8qm5KshlwFbCYppAa4B7g+VV1XWdBJUmSZiDJEcBRwDOq6qfj7F8EfA84tqpOGHY+rcoebNJoWgy8sOsQGkyS7WmGJ3o8cCXwwap6IMmxwD+w8jN5eZJ/9B9JSZKk1bY9cNZYcQ2gqs5PchZNTyeNmKo6EzizZ/2eJDsABwNPpnma+9Squr2bhJIkSavly8ALgKuTfBi4FLgT2JzmfvBbgEuAr7TFtt+rqluGnHXBs8AmSUOQ5GnAZcCGNE/VvhzYMcnpNE+lPABcC2xKM9fe8Um+X1UXdhRZkiRpPtiUZmjIftcBew85i1ZTexNpZ5qealdW1a0AVXUvcHyX2SRJktaQG1nZK//YcfYHeGX76lVY7xk6/4NL0nC8DdgI+BjwdeDPgcNpnrK9CNinvTEwNuzNGe1+C2zSApdkC2B3YEtg/XGaVFWNd9EtSWqG314+zvblNDcnNCKSHE8zGsTY762SnFBVR3YYS5IkaU07FedjHxkW2CRpOF4IfLOq3tyun5NkR2AX4MCx4ho0w94kWQo8p4OckuaQJO+mKdD3XrOFlRfbY8sW2CRpYt6gGHFJ9gWOoPldXkfz799TgSOSXF1Vp3WZT5IkaU2pqgO6zqDpW6vrAJKmxZsCo28L4Iq+bWPr/zlO+x/QzNUmaYFK8hrgncA3gL+iuZl4CvDXwGeAFcDpwIu7yihJI2JJkod7X8DRAP3b29dDHefVI/0N8BCwR1VtV1VPB/ak+bfw9Z0mkyRJ0oJlDzZpNCxJsqR/Y3tzYDxVVf79nlvWA+7t23YfQFU9OE77B4C1ZzuUpDntjcBPgZdW1UNJAJZV1enA6Um+AvwH4FP7kjS5QYeCdOjIuWd74KyqumhsQ1Wdn+QsYLfOUkmSJGlB8wa8NBq8KSBJC8+fAqdVVW9Pit8X3qvqvCTnAUcCXx12OEkaBVXlqC3zw6Y0Q0P2uw7Ye8hZJEmSZl2SnWl67E82H7s9+TtmgU2a47wpMK841KekQawL3N2z/iCwSV+b/wscMrREkiR1Yy1g+Tjbl+PDhZIkaR5JM3zNycBrWTnveu/1TvVst8DWMW/cS9LwLJnu3B9j+yQtaLfTzN845haaIbJ6/RHNnDSSJM13PqwmSZIWgsOB1wGfB3aiKaZ9GNgFeDtwP8187Ft3FVArpcprVEmabUlWzOCwqirnYZMWqCRfAhZV1bPb9Y8DfwscBHyZZs6ZLwHfrKo9usopSdJsa6+lB7154bzUkiRp5CS5CtiwqrZt11cAS6rqmHZ9B+By4JCq+lx3SQX2YJOkoaiqtWbwsrgmLWznAH+SZKt2/b3AvTRDRdwHnE3zJNs7OkknSdJwZcCX9zskSdIoehpwYd+23z80VFXfpblfcOgwQ2l8Ps0lSZI0B1XVyTTFtLH1W9tJjv8eeDKwDPhEVV3bRT5JkobFeaklSdICc2/P8gPAZn37fwy8ZHhxNBELbJIkSSOiqm6iGY9dkiRJkiTNP7cBW/as3wg8q6/NNjSFN3XMp8AkSZIkSZIkSZK6dwWrFtSWAs9O8s4k2yU5DHgVzTxs6liqBp0nWJIkSbMhyXrAZcD9wEuravkk7ZYCGwK7TtROkiRJkiSNjiR7A+8BXl5VNyXZDLgKWAwUzVyz9wDPr6rrOgsqwAKbJEnSnJHkIOAzwF9U1blTtH0pcC5wUDtfmyRJkiRJmmeSbAIczMr52E+tqts7DSXAApskSdKckeQc4KlVtc00218P/KSqXjG7ySRJkiRJ0lyQZANgvaq6r+ssC51zsEmSJM0dOwAXD9D+UuCZsxNFkiRJkiTNQZ+kGSZSHbPAJkmSNHc8DrhzgPZ3Ao+dpSySJEmSJGluStcBZIFNkiRpLnkQ2GiA9hsBv5mlLJIkSZIkSZqABTZJkqS541ZgpwHa7wTcMktZJEmSJEmSNAELbJIkSXPHxcDzkkxZZEvyLGAX4KLZDiVJkiRJkqRVWWCTJEmaOz4GFPDFJNtO1CjJ04AvAg8DnxhSNkmSJEmSJLXW6TqAJEmSGlV1fZJjgCXAd5N8CbgQ+GnbZEtgd+AvgfWBo6vq+i6ySpIkSZIkLWSpqq4zSJIkqUeStwPvAtal6dG2ym5gObCkqt4z7GySJEmSJGnNSfLwTI6rqrXXdBYNxgKbJEnSHJTkScBBwJ8BW7SbbwcuAz5XVTd3lU2SJEmSJK0ZSVbM4LCywNY9C2ySJEmSJEmSJEnSANbqOoAkSZIkSZIkSZI0SiywSZIkSZIkSZIkSQOwwCZJkiRJkiRJkiQNwAKbJEmSJEmSJEmSNAALbJIkSZKkziWp9nVx11kkSZIkaSrrdB1AkiRJkqSpJNkbeGa7+uGq+mWXeSRJkiQtbBbYJEmSJEmjYG9g/3b5ZMACmyRJkqTOWGCTJEmSJHWuqtJ1BkmSJEmaLudgkyRJkiRJkiRJkgZggU2SJEmSJEmSJEkagAU2SZIkSZqBJBsneU2SE5N8N8kvkyxPck+Sq5N8MMmTpzjHsiSVZFm7vkGStyb5TpK7k9yX5P8kOTzJen3Hbp3kI0l+mOSB9n3PS7LHAH+G7ZJ8KMn32uN/m+S2JGe3f7YJvzMmWdxmryQnt9sel2RJkmuT3N++rk7yj0keNUWWsXNd3Lf95CTFyvnXAG7qad+f4b092/aZ5n+Ha9r2DybZdDrHSJIkSVrYUlVdZ5AkSZKkkdIWu+4D1p+i6UPAW6vq4xOcZxnwJOBm4HnAfwA7THCu84BXVdVvk7wK+J/AhhO0fVNVfWyS/OsAHwQOZ/IHL78D7F1Vd4xzjsXATe3qKcDHgDOBLSc41/eA3avqngkyjX05vaSqduvZfjKrFtcmckpVHZBka+AnQIClVfXyyQ5K8hzg8nb1C1X1umm8lyRJkqQFzgKbJEmSJA0oyQbAg8DPgP8NXAPcCawAngjsArySpsgDsE9VfWWc8yyjKbD9jJVFtnOBrwL3AE8D3gQ8rj3kmHb/Ze37nwRc1e57GbBv+57LgWdU1Q/Hec8AZwCvbjfdDpwOfB/4dZvnvwHPavf/ANi5qn7dd57FrCywXQj8CbAZ8L+Ai4BfAU8HDgMe27b7fFXt15+pPd9EBbYdgUXAm4EXtZvfAPy/vlPcUlVXt8ecB7yE5vexVVXdMt57tm0/C7y+XX1hVV06UVtJkiRJGmOBTZIkSZIGlGRt4M+B82qCL1VJtqfpdfaHwI3ANlW1oq/NMpqCFkAB+1XVF/rabEPT++tRwL3Az9tdL66qW/vavpOmCAfwyao6dJxcbwE+3K5+AXjDOMWzAMcBb283va+q3tbXZjErC2wAvwReWlXf6Wu3FXA18BjgYWBRVf1snFzjFth69p/Myp5sW1XVsv42PW33oSkiAry7qpZM0G4jmgLjRsD1VfW0ic4pSZIkSb2cg02SJEmSBlRVD1fV1yYqrrVtrmFlgWprml5tk/nX/uJae54f0xTCADYBngK8pr+41voAcH+7vGf/zrbn3VimK4H9+4tr7XtWVR0FfKPd9Mb22Mm8ub+41p7rJmBsiMy1gd2nOM+acDZNr0CAAyeZS25fmuIawGdnPZUkSZKkecMCmyRJkiTNnm/1LD9nirYTzpkGfLNn+aqqumK8RlX1G1YOGbnVOEWxPYEntMsn9PeoG8dYYW9j4LmTtPs58G+T7L+wZ/npU7znaquqh4AT29VFjFNsbB3c/vwdcPIsx5IkSZI0j6zTdQBJkiRJGlXtMIn7A7vRzJf2GGCinl5/PMmpHgD+c5L9d/Ysj1tcG6dt2jx39OzbtWd50yR7T3GuLXuWtwUunqDdVVX18CTnua33fad4zzXls8BRNA+WHgws7d3ZDuG5c7t6ZlXdNaRckiRJkuYBC2ySJEmSNANJ3gq8F1h/modsPMm+eyYbbhL4bc/y3VO8T2/b/mLf4p7ljzOYyQpjUxWnJss0K6rqliTnAnsBeyXZvKp6C5UH9yx/ZhiZJEmSJM0fFtgkSZIkaUBJXgOc0LPpG8AlwDKaOdB+125/AvDpdnntSU451VCNM23bb5PVOHa9SfatTqbZ9GmaAttd8s+4AAADmElEQVS6wAHA+wCS/AHw2rbNjcAFXYSTJEmSNLossEmSJEnS4I5tfz4EvLKqlo7XKMl2w4s0Lb/qWd66qm7qLMlwnAvcQjMP2+tpC2zAX9EMnwlw4hS9ByVJkiTpEdbqOoAkSZIkjZIkWwNbtatnTlRcaz1pCJEG0TsX2mRzws0LVbWClcM/bpNkt3b5b9qfDwEnDTuXJEmSpNFngU2SJEmSBrN5z/INU7TdczaDzMAlPcsv6SzFzPQOQ5kBjjuRppAGcHCSpwIvaNfPqao71kQ4SZIkSQuLBTZJkiRJGsyve5afPFGjJE8EDpz9OAM5F7irXT40yRZdhhlQ7/CWG073oKq6HTi7Xd0HOLJn92ceeYQkSZIkTc0CmyRJkiQN5ofAA+3yq5I8u79Bks2BM4FHDzPYVKrqAeDd7epmwNeSbDPZMUmek+T9sx5uar3zxe044LGfan9uQDMXG8CtwNdWN5QkSZKkhWmdrgNIkiRJ0iipqt8l+TRwBLAucGmSk4ArgeU0xZ8DgccApwL7dZV1PFX1sSQ70+TaHvhBkrOBS4HbgbWBxwN/CuxOM9/cDcA/dJP49y7oWX5/kscD17Ny+MfbquraCY49n+bP0Nvj8MR2jjZJkiRJGpgFNkmSJEka3FHADsCLgPWBN7avXp8G3s8cK7C1DgB+DLyDJv8+7WsiPx1CpklV1TVJTgP2pZkH7/i+JqfQ/LnGO7aS/CvwvnbTCuCkWYoqSZIkaQFwiEhJkiRJGlBV/QZ4CXAo8G3gfuC3wM3AvwN7VtUhNIWcOacax9H0TjsauAS4A/gd8Buagtr5wLHA86pqt46i9nsdTSHzYpq55B6atPWqzu9Z/lpV3boGc0mSJElaYFJVXWeQJEmSJGlWJTmOpuchwKur6swu80iSJEkabRbYJEmSJEnzWpL1gGXAFsBtwOKqGqT3myRJkiStwiEiJUmSJEnz3SE0xTWAT1pckyRJkrS67MEmSZIkSZpXkmwGPBtYH3gu8Hft8t3A1lV1X4fxJEmSJM0D63QdQJIkSZKkNWx7YGnftgLeYHFNkiRJ0prgEJGSJEmSpPnsLuAC4EVVdUbXYSRJkiTNDw4RKUmSJEmSJEmSJA3AHmySJEmSJEmSJEnSACywSZIkSZIkSZIkSQOwwCZJkiRJkiRJkiQNwAKbJEmSJEmSJEmSNAALbJIkSZIkSZIkSdIALLBJkiRJkiRJkiRJA/j/yXcwmdHCFNcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 2160x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "amenity_list = ['24-Hour Check-in', 'Air Conditioning', 'Breakfast',\n",
    "       'Buzzer/Wireless Intercom', 'Cable TV', 'Carbon Monoxide Detector',\n",
    "       'Cat(s)', 'Dog(s)', 'Doorman', 'Dryer', 'Elevator in Building',\n",
    "       'Essentials', 'Family/Kid Friendly', 'Fire Extinguisher',\n",
    "       'First Aid Kit', 'Free Parking on Premises',\n",
    "       'Free Parking on Street', 'Gym', 'Hair Dryer', 'Hangers',\n",
    "       'Heating', 'Hot Tub', 'Indoor Fireplace', 'Internet', 'Iron',\n",
    "       'Kitchen', 'Laptop Friendly Workspace', 'Lock on Bedroom Door',\n",
    "       'Other pet(s)', 'Paid Parking Off Premises', 'Pets Allowed',\n",
    "       'Pets live on this property', 'Pool', 'Safety Card', 'Shampoo',\n",
    "       'Smoke Detector', 'Smoking Allowed', 'Suitable for Events', 'TV',\n",
    "       'Washer', 'Washer / Dryer', 'Wheelchair Accessible',\n",
    "       'Wireless Internet', 'translation missing: en.hosting_amenity_49',\n",
    "       'translation missing: en.hosting_amenity_50']\n",
    "\n",
    "amenity_importance = []\n",
    "for amenity in amenity_list:\n",
    "    for col in feature_importance_dict.keys():\n",
    "        if amenity in col:\n",
    "            if 'False' in col:\n",
    "                amenity_importance.append((col[:-6], -feature_importance_dict[col]))\n",
    "            else:\n",
    "                amenity_importance.append((col[:-5], feature_importance_dict[col]))\n",
    "    \n",
    "    \n",
    "amenity_importance.sort(key=lambda tup: tup[1], reverse = True) \n",
    "\n",
    "pd.DataFrame(amenity_importance, columns = ['amenity','Importance']).drop_duplicates().set_index('amenity').plot(kind='bar', figsize = (30,15))\n",
    "plt.xticks(fontsize=20)\n",
    "plt.xlabel('amenity', fontsize=30)\n",
    "plt.ylabel('Importance', fontsize=30)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Result:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* We can see that mentioning basic amenities such as hangers, lock on bedroom door, hairdryers have a negative impact on the listing. This could be because there are very basic necessities and should not be mentioned as a special feature.\n",
    "* The easiest things to increase price are to include a TV, Cable, and an Indoor Fireplace. Also, including a free on-premise parking space add a bonus.\n",
    "* Investing in safety devices like Fire Extinguisher, Safety card, Intercome, and smoke detectors will not only help you safeguard the place but also earn a few extra bucks."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What are the most favored neighborhoods?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_original['price'] = df_original['price'].map(lambda p: int(p[1:-3].replace(\",\", \"\")))\n",
    "df_original = df_original.query('price < 500')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "neighborhoods = df_original['neighbourhood_cleansed']\n",
    "\n",
    "for n in neighborhoods:\n",
    "    n_price.append((n, df_original[df_original['neighbourhood_cleansed'] == n]['price'].mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Average Price')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.DataFrame(n_price, columns = ['Neighborhood','Average Price']).drop_duplicates().set_index('Neighborhood').sort_values(by = 'Average Price').plot(kind='bar', figsize = (30,15))\n",
    "plt.xticks(fontsize=20)\n",
    "plt.xlabel('Neighborhood', fontsize=30)\n",
    "plt.ylabel('Average Price', fontsize=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Result:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As expected, the areas near the city center cost on average $50 or more than the ones far from the city. It’s the premium you pay to be ‘right-there’ location wise which honestly sounds good to me."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}