Training.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0f3f334c",
   "metadata": {},
   "source": [
    "# Import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "b7a21daa",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "e84209fe",
   "metadata": {
    "scrolled": true
   },
   "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",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Type</th>\n",
       "      <th>Build</th>\n",
       "      <th>Dir</th>\n",
       "      <th>minArea</th>\n",
       "      <th>maxArea</th>\n",
       "      <th>representativeArea</th>\n",
       "      <th>floorAreaRatio</th>\n",
       "      <th>minDeal</th>\n",
       "      <th>maxDeal</th>\n",
       "      <th>...</th>\n",
       "      <th>CONVENIENCE</th>\n",
       "      <th>WASHING</th>\n",
       "      <th>BANK</th>\n",
       "      <th>OFFICE</th>\n",
       "      <th>PRI_SCHOOL</th>\n",
       "      <th>PUB_SCHOOL</th>\n",
       "      <th>Old</th>\n",
       "      <th>sector</th>\n",
       "      <th>divisition</th>\n",
       "      <th>city</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>테라156</td>\n",
       "      <td>OPST</td>\n",
       "      <td>202103</td>\n",
       "      <td>EE</td>\n",
       "      <td>31.70</td>\n",
       "      <td>34.73</td>\n",
       "      <td>31.0</td>\n",
       "      <td>249</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2021</td>\n",
       "      <td>가락동</td>\n",
       "      <td>송파구</td>\n",
       "      <td>서울시</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>아키죤</td>\n",
       "      <td>APT</td>\n",
       "      <td>200211</td>\n",
       "      <td>EE</td>\n",
       "      <td>86.83</td>\n",
       "      <td>86.83</td>\n",
       "      <td>86.0</td>\n",
       "      <td>0</td>\n",
       "      <td>89000.0</td>\n",
       "      <td>96000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2002</td>\n",
       "      <td>가락동</td>\n",
       "      <td>송파구</td>\n",
       "      <td>서울시</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>강남팰리스</td>\n",
       "      <td>OPST</td>\n",
       "      <td>202102</td>\n",
       "      <td>EE</td>\n",
       "      <td>29.23</td>\n",
       "      <td>33.19</td>\n",
       "      <td>29.0</td>\n",
       "      <td>249</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>34</td>\n",
       "      <td>14</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2021</td>\n",
       "      <td>가락동</td>\n",
       "      <td>송파구</td>\n",
       "      <td>서울시</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>송파성원상떼빌(주상복합)</td>\n",
       "      <td>APT</td>\n",
       "      <td>200606</td>\n",
       "      <td>EE</td>\n",
       "      <td>113.32</td>\n",
       "      <td>182.06</td>\n",
       "      <td>182.0</td>\n",
       "      <td>545</td>\n",
       "      <td>118000.0</td>\n",
       "      <td>200000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>33</td>\n",
       "      <td>11</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2006</td>\n",
       "      <td>가락동</td>\n",
       "      <td>송파구</td>\n",
       "      <td>서울시</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>가락현대TWELVE</td>\n",
       "      <td>APT</td>\n",
       "      <td>200501</td>\n",
       "      <td>EE</td>\n",
       "      <td>99.91</td>\n",
       "      <td>99.91</td>\n",
       "      <td>99.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2005</td>\n",
       "      <td>가락동</td>\n",
       "      <td>송파구</td>\n",
       "      <td>서울시</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 39 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Name  Type   Build Dir  minArea  maxArea  representativeArea  \\\n",
       "0          테라156  OPST  202103  EE    31.70    34.73                31.0   \n",
       "1            아키죤   APT  200211  EE    86.83    86.83                86.0   \n",
       "2          강남팰리스  OPST  202102  EE    29.23    33.19                29.0   \n",
       "3  송파성원상떼빌(주상복합)   APT  200606  EE   113.32   182.06               182.0   \n",
       "4     가락현대TWELVE   APT  200501  EE    99.91    99.91                99.0   \n",
       "\n",
       "   floorAreaRatio   minDeal   maxDeal  ...  CONVENIENCE  WASHING  BANK  \\\n",
       "0             249       NaN       NaN  ...           12        4     1   \n",
       "1               0   89000.0   96000.0  ...           20        6    12   \n",
       "2             249   30000.0   30000.0  ...           34       14    13   \n",
       "3             545  118000.0  200000.0  ...           33       11    20   \n",
       "4               0       NaN       NaN  ...            6        4     5   \n",
       "\n",
       "   OFFICE  PRI_SCHOOL  PUB_SCHOOL   Old  sector  divisition  city  \n",
       "0       3           0           2  2021     가락동         송파구   서울시  \n",
       "1       3           0           1  2002     가락동         송파구   서울시  \n",
       "2       2           0           1  2021     가락동         송파구   서울시  \n",
       "3       1           0           1  2006     가락동         송파구   서울시  \n",
       "4       1           0           1  2005     가락동         송파구   서울시  \n",
       "\n",
       "[5 rows x 39 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./s_h_p.csv')\n",
    "\n",
    "ori_df = df\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a1fe3bb",
   "metadata": {},
   "source": [
    "# Select Feature & Label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "294ae8e6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 16561 entries, 1 to 17784\n",
      "Data columns (total 20 columns):\n",
      " #   Column              Non-Null Count  Dtype  \n",
      "---  ------              --------------  -----  \n",
      " 0   Type                16561 non-null  object \n",
      " 1   Dir                 16561 non-null  object \n",
      " 2   Old                 16561 non-null  int64  \n",
      " 3   sector              16561 non-null  object \n",
      " 4   divisition          16561 non-null  object \n",
      " 5   representativeArea  16561 non-null  float64\n",
      " 6   floorAreaRatio      16561 non-null  int64  \n",
      " 7   BUS                 16561 non-null  int64  \n",
      " 8   METRO               16561 non-null  int64  \n",
      " 9   INFANT              16561 non-null  int64  \n",
      " 10  PRESCHOOL           16561 non-null  int64  \n",
      " 11  HOSPITAL            16561 non-null  int64  \n",
      " 12  PARKING             16561 non-null  int64  \n",
      " 13  MART                16561 non-null  int64  \n",
      " 14  CONVENIENCE         16561 non-null  int64  \n",
      " 15  BANK                16561 non-null  int64  \n",
      " 16  OFFICE              16561 non-null  int64  \n",
      " 17  PRI_SCHOOL          16561 non-null  int64  \n",
      " 18  PUB_SCHOOL          16561 non-null  int64  \n",
      " 19  price               16561 non-null  float64\n",
      "dtypes: float64(2), int64(14), object(4)\n",
      "memory usage: 2.7+ MB\n"
     ]
    }
   ],
   "source": [
    "label = 'price'\n",
    "columns = ['Type', 'Dir' ,'Old', 'sector', 'divisition', 'representativeArea', 'floorAreaRatio', 'BUS', 'METRO', 'INFANT', 'PRESCHOOL', 'HOSPITAL', 'PARKING', 'MART', 'CONVENIENCE', 'BANK', 'OFFICE', 'PRI_SCHOOL', 'PUB_SCHOOL', label]\n",
    "\n",
    "\n",
    "df[label] = (ori_df['minDeal'] + ori_df['maxDeal']) / 2\n",
    "\n",
    "df = df[columns].copy()\n",
    "\n",
    "df.dropna(inplace=True)\n",
    "\n",
    "train_df = df\n",
    "\n",
    "train_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6fbca12d",
   "metadata": {},
   "source": [
    "# Outlier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "34110609",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     16321.000000\n",
       "mean      97388.773666\n",
       "std       78832.002092\n",
       "min        6050.000000\n",
       "25%       33250.000000\n",
       "50%       80000.000000\n",
       "75%      135000.000000\n",
       "max      450000.000000\n",
       "Name: price, dtype: float64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def remove_out(dataframe, remove_col):\n",
    "    dff = dataframe\n",
    "    for k in remove_col:\n",
    "        level_1q = dff[k].quantile(0.25)\n",
    "        level_3q = dff[k].quantile(0.75)\n",
    "        IQR = level_3q - level_1q\n",
    "        rev_range = 3 \n",
    "        dff = dff[(dff[k] <= level_3q + (rev_range * IQR)) & (dff[k] >= level_1q - (rev_range * IQR))]\n",
    "        dff = dff.reset_index(drop=True)\n",
    "    return dff\n",
    "\n",
    "\n",
    "train_df = remove_out(train_df, [label])\n",
    "\n",
    "train_df[label].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "a36a4161",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Dir</th>\n",
       "      <th>Old</th>\n",
       "      <th>sector</th>\n",
       "      <th>divisition</th>\n",
       "      <th>representativeArea</th>\n",
       "      <th>floorAreaRatio</th>\n",
       "      <th>BUS</th>\n",
       "      <th>METRO</th>\n",
       "      <th>INFANT</th>\n",
       "      <th>PRESCHOOL</th>\n",
       "      <th>HOSPITAL</th>\n",
       "      <th>PARKING</th>\n",
       "      <th>MART</th>\n",
       "      <th>CONVENIENCE</th>\n",
       "      <th>BANK</th>\n",
       "      <th>OFFICE</th>\n",
       "      <th>PRI_SCHOOL</th>\n",
       "      <th>PUB_SCHOOL</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>APT</th>\n",
       "      <td>11458</td>\n",
       "      <td>11458</td>\n",
       "      <td>11458</td>\n",
       "      <td>11458</td>\n",
       "      <td>11458</td>\n",
       "      <td>11458</td>\n",
       "      <td>11458</td>\n",
       "      <td>11458</td>\n",
       "      <td>11458</td>\n",
       "      <td>11458</td>\n",
       "      <td>11458</td>\n",
       "      <td>11458</td>\n",
       "      <td>11458</td>\n",
       "      <td>11458</td>\n",
       "      <td>11458</td>\n",
       "      <td>11458</td>\n",
       "      <td>11458</td>\n",
       "      <td>11458</td>\n",
       "      <td>11458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OPST</th>\n",
       "      <td>4863</td>\n",
       "      <td>4863</td>\n",
       "      <td>4863</td>\n",
       "      <td>4863</td>\n",
       "      <td>4863</td>\n",
       "      <td>4863</td>\n",
       "      <td>4863</td>\n",
       "      <td>4863</td>\n",
       "      <td>4863</td>\n",
       "      <td>4863</td>\n",
       "      <td>4863</td>\n",
       "      <td>4863</td>\n",
       "      <td>4863</td>\n",
       "      <td>4863</td>\n",
       "      <td>4863</td>\n",
       "      <td>4863</td>\n",
       "      <td>4863</td>\n",
       "      <td>4863</td>\n",
       "      <td>4863</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Dir    Old  sector  divisition  representativeArea  floorAreaRatio  \\\n",
       "Type                                                                         \n",
       "APT   11458  11458   11458       11458               11458           11458   \n",
       "OPST   4863   4863    4863        4863                4863            4863   \n",
       "\n",
       "        BUS  METRO  INFANT  PRESCHOOL  HOSPITAL  PARKING   MART  CONVENIENCE  \\\n",
       "Type                                                                           \n",
       "APT   11458  11458   11458      11458     11458    11458  11458        11458   \n",
       "OPST   4863   4863    4863       4863      4863     4863   4863         4863   \n",
       "\n",
       "       BANK  OFFICE  PRI_SCHOOL  PUB_SCHOOL  price  \n",
       "Type                                                \n",
       "APT   11458   11458       11458       11458  11458  \n",
       "OPST   4863    4863        4863        4863   4863  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group_df = train_df.groupby(by='Type')\n",
    "\n",
    "group_df.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "baf89f43",
   "metadata": {},
   "source": [
    "## Scaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "61ea4a7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Type</th>\n",
       "      <th>Dir</th>\n",
       "      <th>Old</th>\n",
       "      <th>sector</th>\n",
       "      <th>divisition</th>\n",
       "      <th>representativeArea</th>\n",
       "      <th>floorAreaRatio</th>\n",
       "      <th>BUS</th>\n",
       "      <th>METRO</th>\n",
       "      <th>INFANT</th>\n",
       "      <th>PRESCHOOL</th>\n",
       "      <th>HOSPITAL</th>\n",
       "      <th>PARKING</th>\n",
       "      <th>MART</th>\n",
       "      <th>CONVENIENCE</th>\n",
       "      <th>BANK</th>\n",
       "      <th>OFFICE</th>\n",
       "      <th>PRI_SCHOOL</th>\n",
       "      <th>PUB_SCHOOL</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>APT</td>\n",
       "      <td>EE</td>\n",
       "      <td>2002</td>\n",
       "      <td>가락동</td>\n",
       "      <td>송파구</td>\n",
       "      <td>0.156388</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>92500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>OPST</td>\n",
       "      <td>EE</td>\n",
       "      <td>2021</td>\n",
       "      <td>가락동</td>\n",
       "      <td>송파구</td>\n",
       "      <td>0.030837</td>\n",
       "      <td>0.199359</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>34</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>30000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>APT</td>\n",
       "      <td>EE</td>\n",
       "      <td>2006</td>\n",
       "      <td>가락동</td>\n",
       "      <td>송파구</td>\n",
       "      <td>0.367841</td>\n",
       "      <td>0.436349</td>\n",
       "      <td>18</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>33</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>159000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>APT</td>\n",
       "      <td>EE</td>\n",
       "      <td>2018</td>\n",
       "      <td>가락동</td>\n",
       "      <td>송파구</td>\n",
       "      <td>0.209251</td>\n",
       "      <td>0.228183</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>306750.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>APT</td>\n",
       "      <td>EE</td>\n",
       "      <td>2001</td>\n",
       "      <td>가락동</td>\n",
       "      <td>송파구</td>\n",
       "      <td>0.277533</td>\n",
       "      <td>0.239392</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>24</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>227500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16316</th>\n",
       "      <td>OPST</td>\n",
       "      <td>SS</td>\n",
       "      <td>2013</td>\n",
       "      <td>흥인동</td>\n",
       "      <td>중구</td>\n",
       "      <td>0.061674</td>\n",
       "      <td>0.661329</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>32</td>\n",
       "      <td>5</td>\n",
       "      <td>40</td>\n",
       "      <td>23</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>16250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16317</th>\n",
       "      <td>APT</td>\n",
       "      <td>EN</td>\n",
       "      <td>2014</td>\n",
       "      <td>흥인동</td>\n",
       "      <td>중구</td>\n",
       "      <td>0.325991</td>\n",
       "      <td>0.557246</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>43</td>\n",
       "      <td>2</td>\n",
       "      <td>41</td>\n",
       "      <td>25</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>202500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16318</th>\n",
       "      <td>APT</td>\n",
       "      <td>NN</td>\n",
       "      <td>2016</td>\n",
       "      <td>흥인동</td>\n",
       "      <td>중구</td>\n",
       "      <td>0.030837</td>\n",
       "      <td>0.639712</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>26</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>25500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16319</th>\n",
       "      <td>OPST</td>\n",
       "      <td>NN</td>\n",
       "      <td>2016</td>\n",
       "      <td>흥인동</td>\n",
       "      <td>중구</td>\n",
       "      <td>0.048458</td>\n",
       "      <td>0.639712</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>26</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>22000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16320</th>\n",
       "      <td>APT</td>\n",
       "      <td>WN</td>\n",
       "      <td>2016</td>\n",
       "      <td>흥인동</td>\n",
       "      <td>중구</td>\n",
       "      <td>0.030837</td>\n",
       "      <td>0.639712</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>26</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>25500.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16321 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Type Dir   Old sector divisition  representativeArea  floorAreaRatio  \\\n",
       "0       APT  EE  2002    가락동        송파구            0.156388        0.000000   \n",
       "1      OPST  EE  2021    가락동        송파구            0.030837        0.199359   \n",
       "2       APT  EE  2006    가락동        송파구            0.367841        0.436349   \n",
       "3       APT  EE  2018    가락동        송파구            0.209251        0.228183   \n",
       "4       APT  EE  2001    가락동        송파구            0.277533        0.239392   \n",
       "...     ...  ..   ...    ...        ...                 ...             ...   \n",
       "16316  OPST  SS  2013    흥인동         중구            0.061674        0.661329   \n",
       "16317   APT  EN  2014    흥인동         중구            0.325991        0.557246   \n",
       "16318   APT  NN  2016    흥인동         중구            0.030837        0.639712   \n",
       "16319  OPST  NN  2016    흥인동         중구            0.048458        0.639712   \n",
       "16320   APT  WN  2016    흥인동         중구            0.030837        0.639712   \n",
       "\n",
       "       BUS  METRO  INFANT  PRESCHOOL  HOSPITAL  PARKING  MART  CONVENIENCE  \\\n",
       "0       14      2       7          1         0       14     2           20   \n",
       "1       17      1      13          0         1       11     4           34   \n",
       "2       18      3       8          1         1       14     2           33   \n",
       "3        9      1       9          0         0        8     1            9   \n",
       "4       15      1       9          1         0        7     3           24   \n",
       "...    ...    ...     ...        ...       ...      ...   ...          ...   \n",
       "16316   15      2       6          2         2       32     5           40   \n",
       "16317   16      2       6          2         2       43     2           41   \n",
       "16318   15      2       4          1         2       48     0           38   \n",
       "16319   15      2       4          1         2       48     0           38   \n",
       "16320   15      2       4          1         2       48     0           38   \n",
       "\n",
       "       BANK  OFFICE  PRI_SCHOOL  PUB_SCHOOL     price  \n",
       "0        12       3           0           1   92500.0  \n",
       "1        13       2           0           1   30000.0  \n",
       "2        20       1           0           1  159000.0  \n",
       "3         9       1           0           1  306750.0  \n",
       "4        18       1           0           2  227500.0  \n",
       "...     ...     ...         ...         ...       ...  \n",
       "16316    23      13           0           1   16250.0  \n",
       "16317    25      13           0           1  202500.0  \n",
       "16318    26      12           0           1   25500.0  \n",
       "16319    26      12           0           1   22000.0  \n",
       "16320    26      12           0           1   25500.0  \n",
       "\n",
       "[16321 rows x 20 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler as Scaler\n",
    "\n",
    "def scale(df, col):\n",
    "    scaler = Scaler()\n",
    "    scaler.fit(df[[col]])\n",
    "    train_data_robustScaled = scaler.transform(df[[col]])\n",
    "    return pd.DataFrame(data=train_data_robustScaled, columns=[col]), scaler\n",
    "    \n",
    "repre_df, repre_scaler = scale(train_df, 'representativeArea')\n",
    "floor_df, floor_scaler = scale(train_df, 'floorAreaRatio')\n",
    "\n",
    "train_df['representativeArea'] = repre_df\n",
    "train_df['floorAreaRatio'] = floor_df\n",
    "\n",
    "train_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "316e4ba3",
   "metadata": {},
   "source": [
    "# Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f5e8996",
   "metadata": {},
   "source": [
    "## 방향에 따른 가격"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "bb22151b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/y8/ldvzwhm13037mvvt3p2d9n0m0000gn/T/ipykernel_5241/651298774.py:10: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_yticklabels(ylabels)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[Text(0, 20000.0, '2.00억'),\n",
       " Text(0, 40000.0, '4.00억'),\n",
       " Text(0, 60000.0, '6.00억'),\n",
       " Text(0, 80000.0, '8.00억'),\n",
       " Text(0, 100000.0, '10.00억'),\n",
       " Text(0, 120000.0, '12.00억'),\n",
       " Text(0, 140000.0, '14.00억')]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize':(15,15)})\n",
    "\n",
    "sns.set(font_scale = 2) \n",
    "plt.rcParams['font.family'] = 'AppleGothic'\n",
    "ax = sns.lineplot(x='Dir', y = label, hue='Type' , data=train_df)\n",
    "ylabels = ['{:.2f}'.format(x) + '억' for x in ax.get_yticks() / 10000]\n",
    "#remove '구'\n",
    "# xlabels = [x for x in ax.get_xticks()]\n",
    "# ax.set_xticklabels(xlabels)\n",
    "ax.set_yticklabels(ylabels)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77a83367",
   "metadata": {},
   "source": [
    "## 구에 따른 가격"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "72a936f2",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/y8/ldvzwhm13037mvvt3p2d9n0m0000gn/T/ipykernel_5241/1627207721.py:5: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_yticklabels(ylabels)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[Text(0, -50000.0, '-5억'),\n",
       " Text(0, 0.0, '0억'),\n",
       " Text(0, 50000.0, '5억'),\n",
       " Text(0, 100000.0, '10억'),\n",
       " Text(0, 150000.0, '15억'),\n",
       " Text(0, 200000.0, '20억'),\n",
       " Text(0, 250000.0, '25억')]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KgSRW2GjHAIBOr4SUAnlpZvwMiYiIiGaHhSERrYTSePQKg1aqIYTAeisUhnu9ElIIGOvhPTeTEtHyMdZhey+f9dMgojnHwpCIVoKxIa6ilWoAwHq7LgwNhBAAPEPuiWgp5aXF5nZv1k+DiOYcC0MiWgmlcejlBq0kFIYbQx3DQMBaFoZEtHys8+hWZ6yJiI7CwpCIVkLoGJqmY7jWigAMFYYesI4LaIho+Xjn0c8NHMfliegYLAyJaOl576s75qEw9N5DSoEs0ejUhaEADEdJiWgJldbBWAdrefOLiI7GwpCIlp7zHs76cMYw0cgLi62dPtYyPTRK6mEYWUFES8hUhWFpePOLiI7GwpCIlp5zHv0qjqKdRrAudAzXsmgk5J5ZhkS0jErj4T1gOC5PRMdgYUhES2948UIr1XDeQ4rRwlBKgaLkcgYiWj7WOigpOBVBRMdiYUhES89VixcAVGcMw8fbWYS9noH3HkoKFIaFIREtH+McokghL3iNI6KjsTAkoqVXL54BgFYSAu6FANZSDWMditKFjiHvphPREiqNQ6wlck5FENExWBgS0dJzzqM31DEEAKVk8997vRJSCBjr4bnOnYiWjLUesVa8+UVEx2JhSERLr7QO/WqEqg64V1I0/73XKyGEABBiLYiIloVzHh6AUuEcNW9+EdFRWBgS0dIrTQi3TyIFpSTggSzRyIYKw0DAWr5pIqLlYZ0HvIcQosl0JSI6DAtDohkpjeN5jykxxqNfGLSrcHsIII01snR/YQhYrnMnoiXinAeECP9DCBiG3BPREfSsnwDRqur0S4idPn8Ip6C0Dr3cIqs2kmolkMQSsZaQQjSFofcehnfTiWiJuH2jo4ZTEUR0BHYMiWbEGIeSHcOpMDaMkrZTDec8Ii2hlYKAQDvT6FSFoRBgzhcRLRXrPET9PzzYMSSiI7EwJJqR0jiUfIGeCmMdurlBK9Gw3kNLCaXCW6XhkHtmGRLRsnHON11DqcAsQyI6EgtDohkprYMxHOmZNO99KAz7Bq00ajqGSh4sDKUUKEsW60S0PKzzzRFDJcNmUiKiw7AwJJqR0lh2DKfAOo/SWFjn0Uo1nPeIIhm2kwpRFYYh45AdQyJaNtY6yKoyVFIi5zWOiI7AwpBoRkrrYfgCPXHOeXTrcPsknDHUUkJLAXignUXo9Es456GkZAA0ES2V0jjIakJCK4GcUxFEdAQWhkQzYoyDdZ5hwxNmnUc/r8Ltm62kElIKCAGsVR/r5gZSChjL7wkRLQ9j3VBahYB3nrE8RHQoFoZEM+CcR1167F8lTuPl/FDHMNUQQFUUCigl0Mr2ZRkyAJqIlkhpfTNKCgAejKwgosOxMCSageG7tY5FyETZoVHSdhoBQLN4JtYSrWRfYSgELN80EdGSMHYwSlqzPN9ORIdgYUg0A3VHSgz9N02Gcx55PUqaaEAMCkOtJVpJKBbrLEMAMByzIqIlYYaWzwAAvEfJm19EdAgWhkQzUHcJPQDWIJNVGoteYaCkQBxJAKK5ex4rhTRWAAYdQ+89O4ZEtBR8NRo/3DEUUqBkZAURHYKFIdEMmLpL6HnGcNKMcegXNpwvbFa2h/8fRRJaS0RaNoWhEOEOOxHRonPeA/teYpQUyFkYEtEhWBgSzYBz1Yu14BnDSSutRy83TVSFqhbPAECkJLwDswyJaCk55wEBfOCj9/FgLwdQF4a8+UVEB7EwJJoBYxxk9dPHM4aTZaxDLw8dQ+c8Ij247CkV/jsUhqFjqKREyTdNRLQErPMoSouf/tWP49c/8CwAQCmBvDQzfmZENI9YGBLNQGkspBSQQsBYdqcmqTQO3dyglUZw3iNSg8ueFAIQoTCsl89IdgyJaEk4B/SqrcybO30A4eaXsZ7HGIjoABaGRDNQVFvipBQwhi/Ok2SsQ7dv0D60YxhGSkc7hgIFO4ZEtASc9+hVW5nrwrDGyAoi2o+FIdEMhFHSumPIF+dJcd6jNA55GUZJrffQQx3DsITGYy3T6Be2+b4Yx7vpRLT4rPPoF6MdwxpD7oloPxaGRDNQWg8p0RQhNBnO+WaMql4+M9wxDCvcBdaykGW416+yDBlZQURLwFrXFIY7e8XIjUjelCSi/VgYEs2AMdUoqRAoDV+cJyXcLa/C7VMNeIx0DKUQUFKglVaFYR1yLwQsAyaJaMGV1qFfRVN4ADudovoVz9ceIjqAhSHRlFnnQlJFdcaQ5zwmxzmP7lDHsP6aD9NKopVoAGgW0ADcFktEi89ah7wYLNN6sBcKQyXlyMeJiIAFLQzf8Y534L3vfe+snwbRuQznFkrBAmSS7PAoaRrBe9+E29diLdFKFQA0WYaeo6REtARK65qpCQCDLEPFkHsiOkhP+gE+/OEP4wd/8Adx7949lGWJF7/4xfj2b/92pGmKf//v/z1+6Id+CEII3Lp1C9/1Xd+FjY0NAMDm5ibe9ra3YXNzE845fPVXfzVe//rXAwCiKEIcx5N+6kQTMVwICiHgEYrF/Z0suriRM4aphvcHv86RlkjicCmsR0m5FIiIloExDr3c4PJajJ1OMdQxFCg4SkpE+0y8MOz1eviWb/kWPPTQQwCA7/u+78O73/1u/Nk/+2fxj//xP8ZP/MRPIMsy/If/8B/wPd/zPfie7/keAMDb3/52fM3XfA1e/epXoygKvPGNb8RrX/taXL9+HULwDTQtLrevQygQikUWhuMXwu0Ho6SdfnmgYxhFEkKEX99jliERLRHrgH5h0U4jCCGwXXcMpUA/N/De8z0VETUmPkr6yle+sikKAeAFL3gB8jzHT/3UT+Ev/aW/hCzLAABf8iVfgg9/+MPY29sDANy+fRuvfvWrAQBxHOPlL3853vOe9+BNb3oTfuZnfgaOiyFoQe3fQlp3DGn86sULWaKaDaQHCkOl4B2zDIlo+ZTWoZeHuJ6rG2nTMQzTKp5HGYhoxMQ7hsPe//7341/+y3+J7//+78ff+Tt/B3/+z//5kV9/4QtfiE9+8pN4/PHH8fjjj+Pnfu7n8PrXvx63b9/GBz7wAfzIj/wIWq0WfviHf/jcheG1a2vj+KuM3Y0b6yv52Kv4+Gqnjwc9g0vtBABwaSPD1WvtZjPmNC37175nPawD1loxLm20ECcGN29ujDx+nMXYLSwubyToFxZXLrdRGAsx4ee37F/7eX78Vf67r/rjr9Lf3TmP9ftd5KXFY+vrEAD+4JNbuHK5DQCQkcblK21kyXTeCq7S156PPz+Pzcc/m6lcDYqiwLve9S7s7u7ih37oh9But+GcO3R8of7YN3/zN+PHf/zH8da3vhXtdhvf/d3fjVar1XyeP2f49P37e3PXnblxYx137+6u3GOv6uPffdDF7k4PrjS4crmN7e0ubt+Jmiy9aVmFr/3tu7vY2cuRRgr3t/bgHJrHrB+/0y+xvd1DoiXubHax9aADV8VcXF+bzFnmVfjaz+vjr/LffdUff9X+7sY6bG930emVUAK4vJ5ie6/A3fu70Epit1vi+ds7U3ntWbWvPR9/Ph6bj384KcWRjbKJF4YPHjzA3/ybfxPf8A3fgM/7vM9rPv6iF70IH/vYx/CSl7yk+diTTz6Jxx57DEAYH83zHFtbW9jd3cXb3/52AIC1Fh/5yEfwtre9bdJPnWgiSuNGzhN6AO6cNzroeMY6dHODq+tpFW6vDnxOPVpaj5LWC2qs83DeQ/L8DREtIOfCduXCOLQShasbKYCQZXh1IwU8GJdERCMmXhi+4x3vwDd90zfhFa94xcjH/8Jf+Av4tm/7NnzhF34hkiTBL/3SL+Hxxx/H2tqggn3zm98cnqQefZrvfOc7EUXTH7sjGofSupFiQwoxd13sZWFsyDF87IauCsODx6qHC0NjPfLSIo01UEVWSM3CkIgWj3UevSqqIkt0Uxg+2AuFoVRhMc2lWT5JIporEy8M3/e+9+FjH/vYyMde+tKX4ju+4zvwVV/1VXjDG96AOI7x0EMP4bu+67tGn5w+/OlprblFixbW/o4hBBiNMCFladHrG7RSDec9InVYYSgB75txqr2eCYWhELDOIVrMuFciWnHOe/SLQVzPoDAcbCYtmGVIREMmXhj+7M/+7JG/9mVf9mX4si/7sjP/mcaYc58xJJq10jjE0aDYYGbeZDjn0S8dnA9vipzDoR1DKQWEEGhngyzD65fS0DFkJ5eIFlTIcR10DC+tJxACQ1mGEjljeYhoyFS3ko7LW97yFnYMaWFZ6yHj4VFSoDQsQMbNOo9+lWHYTiN476EO6RgCQBwptKrNfJ0qsgII3ysiokXkhq6BWaKhpMBGKx7JMqxHTYmIgCnkGE4Ci0JaVNY5eIz+G5ZScAHABDgXzhcC4U0RxMEMw5pWslnZXmcZCsGQeyJaXKV16FeFX33j6/Ja3HQMpQzn2y1zoYmospCFIdGiOmzJjBAChi/MY2edR6/pGIY3RUcVhpGWiCMJKUVTGEopUDLknogWlLEOeTl0cwzA5bWkOWMYeBhORhBRhYUh0RTVZ9aK0uJf/crHsL2XQ0rwhXkCnB8Uhq1EQwCjS3+GRFrAe2At1U1hqKRAYdkxJKLFZG3IY420bM5XX15PsNsth861c2KFiAZYGBJNUV0YPne/i9/72CY+8vSDsHzG8IV53OzQ4oVWquHhj+wYxlrBuUGWIVB1DPl9IaIFVRqHXmGbbiEQRkmBkGUIAPDsGBLRAAtDoimyzsN7j04/FB+dXgkhBLz3DLkfM2MdeoWBVhJxpACIIzuGSkl479HOomb5TFjlzsKQiBaTcQ793KKVqOZjl9YSAIPNpELyLDURDbAwJJoi5zykEOj0wohjpx/+P8Th5w/p/Ixx6OdVhqHzkFJAHrG4SkkBCFF1DMP3REoB61iwE9FiMjbkGB7WMRzOMsy5mZSIKiwMiaaoMBZCAHtVx7DbH0QjsDAcr9KGMar2MeH2NSVFE3Lf6Zcj3wtGVhDRIjLGoZePFoYbrXhflqFAzskIIqqwMCSaImMcpBTNuGKTmefBMPUxG35T5Jw/NNy+FjqG4Yyh92hiLgBwlTsRLRxXHVvo5raJqgDCJMSl9lCWoRLN5lIiIhaGRFNU2row3D9KKjiyOGalDYVh3THUx3QMpRSAD6OkwCDLkIsZiGgROR8Kw3qUtCgtNnf7AMI5w0HHUMJYjswTUcDCkGiKSuMgheAo6RQY49DNDVpNx/Dw84XAoGPYzkZD7gF2colo8VjnkZcO3oetzNZ5+OpaFkLu89HPZ2QFEYGFIdFUlcZBShw6SsrCcHy89+iXFkXpquUzOLZjKISAVgKtNBSG9fdFCIGSG/uIaME4N8hxrcfppQwbsC+v7csy5GQEEVVYGBJNifce1lZbSasR0m71/4UADAvDsXHeo99kGIZzg5FWx/6eSEtk8WjHUEqBkosZiGjBWBfGSIFQGIZroIRz/mCWoRBDgfdEtMpYGBJNSTjzEbqGpXGII4m8tLA2dBEZcj8+zvlmgUwr1YDAkeH2tUjLkHmoZVMYKilQWHYMiWixOO/Rq2IoWolqzllb5w9kGQIeJV9/iAgsDImmxjkPiEE36salDEDYgCl4x3as7NAYVT0eelSGYS1WEq6KrKizDJUUfMNERAvHOd/kE2aJhkC4+WWsPyTLUDLLkIgAsDAkmpr6DEc9RnrjcgoA6OUWUgoYxiKMjXMevWqMql7VfnLHUME6j3YWjYySFhwlJaIFY4wb6hiGa2A4b+0PZhkqgbxkYUhELAyJpqZeB14vNrl+edAxlIKjpONknUevKsDbqQa8h1Inj5J6F7IMO0OFobWOi4GIaKGUxqFfWAgBJLECBJDG4bz1gSxDKVDw9YeIwMKQaGpsFThcd6OuX6o7hgZSCG6FG6OwkS+8KUqrhTLyhI5h/etrQx1DAIAQDLknooVi3CDDUFRj9HWBCOzPMhQoSgvPLEOilcfCkGhKrPUQYjBKOlIYSsGtpGNUWodeYZDFGkIAUskTzxgqGS6Ha5lGv7AjHVwW7US0SIwNHcN6jBQQSGMF4GCWoRACHp6ZrUTEwpBoWkprQ1RFr0SWKKxlEYAQWSGEgKs6inRx4U2RaYKdo2MyDGt1yH39fWm6hp5vmIhosZTGoZ+bKqrCQ4owLi9wRJYhuACNiFgYEk2NMQ5SCuz1S7TTqIpHEM32TAAsQMakNGErab1s4bhw+5pSAvBh+QyAkXFSfl+IaJEYG5bPtJLBNVAIgThSsPaQLEOG3BMRWBgSTU1RFYadnkE7iyCEQDuNRgpDdgzHw1iHXm5DYehP1zGUUgBCHOgYCiFQGm7sI6LF4L2vzlkbZPU1UIdrYBYrGOcOyTIMi7aIaLWxMCSaEmNdM0q6VmXrtbII3TwUHQLsTI1LaRy6uanulgNRdPz5QiDkHEopmtzDJuReCZSMrCCiBeG8h0c4v54lCs6h2cqcxCGWZ3+WoVRAzhtgRCtPn/wpRDQOpXFIIhVGSauuVDvVTcfQezAWYUxKY6tR0gjOn26UFAAiJaGr7aTDWYZ8w0REi8I5j9I4GOvDzbGhqYkkUnAWB7MMpUCe8zpHtOrYMSSaAl+d3zDWoShdUxi29o2Ssi68OO89un0D70Ph7R1ONUoKVMsZRAiE7vTC90UJhtwT0eJwDs3rSlafMaxGSbWWgDgsy1DyBhgRsTAkmgbnPeBDmD2AZpS0nUbNx8B14WNhnUc3D92+elX7SRmGtVhLWOdHsgylFCh59oaIFoR1HnkRXlfqjqGu4niGpyf2ZxnmvAFGtPJYGBJNgbUeEECnKjaaUdIsjJJ6H36dh/8vrg63B9CcF6wzCk+ilYTzHmut0cLQWccxXyJaCM57dItwDcwSDfhBQajV4CbZcJahlCEyyTq+BhGtMhaGRFNQdwL3qnD74VFS74G8sFXIPV+UL8pW2/iAqjAUVUbhKURawu3rGAIAhOAbJiJaCNZ59EeugaKZmlBSQlVF4MEsQ0ZWEK06FoZEUxBGSX3TMRweJQXCiKkUAtaw+Lgo54cLwwjw/tSjpFrJcDYx09jrlSPxIXzDRESLwFqHfjE4YwiM3hxLIgnr3CFZhoysIFp1LAyJpsDasD68GSWtCsJWFl60e1VhyOLj4sIZw2qU9JA3RcdRSkBUWYbhnA6jRIhosRjn0C9CgZclCsDoOes01jDWH8gy9OwYEq08FoZEU2CsgxBAp2+QRKrZEDfoGFoIAZR8Ub4w5zz6hUGkJXRV6J22YyhF+Lz9IffeszAkosVgjENehNcaJSXg/WjHMFaw9pAsQylQcDMp0UpjYUg0BcY5SCmw1yuxlg3iQ+vCsJebcMaQYzwXZoxDP7dop2FNe6RPf5mrFzPsLwyFDNmIRETzzliPfmGbbiEw2jGMtYLz/tAsw6LkdY5olbEwJJqCsnSQQqDTG4TbA0ArGyoMBQvDcSiNQ68wg2BnrU7+TZX6zdOgMKyyDPmGiYgWhLEevcKilWh478PUhBgUhlrJZpJiNMtQNCOoRLSaWBgSTUFpQsew0zdNlxAYnIHr5gZChHHF4YUndHbGOvRyg1aqYZ1HpE43RgqEjX0CQLtaDjQcWcGQeyJaBPU1cH+4fS3Sg2viSJahEshLAyJaXSwMiabAOAclDo6SSimQxgq93EBUd3QdC8MLKW3IMWylEZwbDXQ+Da0lYi2b0V8AUEKg4MZYIloAZV0YphrO48DNMVVdE733I1mGSkoY6/kaRLTCWBgSTUFZOngfzn0Mj5ICoWvY6w/u0jIu72KMdej2Q8fQeY8oOttlLtYKHmGctDPUMSw55ktEc857D+eqm2PVOP3+jqEUoslsPZhlCEZWEK0wFoZEE+a9h3Ee3Spbrx4ltc6hNA5ZoptfA8JWTTq/bm5QWhfO1zggOmPHMFIC1nmsVVmGQCgMnXX83hDRXLPOw1mPvLTNKOlh18A0UrDOH5JlyMgKolXGwpBowqzzgA9RFcDg/Fq3b9Dpl8hSjV5uRz+fzsV533T5WqmGEDh1VEUtiiScCx3DujAEAAgBy3YuEc0x5zz61TnBumOoDikMk0gdmmUIcAka0SpjYUg0Yc55QACdfhVuX42S+vDhMEo63DHk+Y5zCyNU1dc51fD+9OH2Na3CiNWBwpB30olozjkfNpICOLZjmMSqGiUdzTIUIixLI6LVxMKQaMJCx9CjU0Uf1FEIQghAAFmimlHS+nwInY91o2+KcJ6OoVbwPhTw3dwMvh9CsJtLRHPNOY9+9XqSpQrwhy/giqNwndufZciQe6LVxsKQaMLqYqLuPtWjpHW5kiUapXEwNmQdsmN4fvXSBWBwllPJs13mlAwF+1oWwfsw8gsA8FzKQETzzTqPfnUNbCUaqPIK96uvcweyDJVAzsxWopXFwpBowpzz8AijpJGWiKMQuF6Xf3WWYa/KMjQc4zk3O3S3vFUV4GcdJVUHQu5DQS8kR6yIaL4559Erqo5h9doyHG5fi7RE/So0kmUoBXKG3BOtLBaGRBNmrIMQQKdnmmIDQDhkiMGLd7fKMmQswvk559HthwI7iUJY/VlHScPn+wOFoeKIFRHNOetCLBIwuOl42M0xJQUExCFZhgJFaeE5uUK0klgYEk1YaV0IS++XzRipcx5SSWgpkcahg9jrG0gJLji5AGMdeoUJURUeB/K7TuOojqGUAkXJop2I5ldpHPqFgZIhqxDeH3pzTAiBOFKwdjTLUAgBD8/z1EQrioUh0YQZE84Odnum2Uhab4qLIom0Gi3t5RZScFX4RRjn0CvsINxena1bCNSFoUArCd+X0Y4hvzdENL+MdehX10BRjZAeNU6fxQrGuYNZhoysIFpZLAyJJqw0g47hWh1u7z1iLRFpiaTqGHZzA8msvAsxxqGXG7TSCM6FDaNnJYSAlgKRVogj2eQiKilQcpSUiOZY6BiGcHvvPcQRy2cAIElCyP2BLENG8xCtLBaGRBNWWgdRbbdsZ4NR0jiS0EoiaTqGhgtOLqi0VWGY1B3D813iokjC7ssyFELAeTBOhIjmlnWDwtB5f+w4faIVnMWBLENAcAMz0YpiYUg0YaVx6Ffrv+tRUus8Yq0QawWlBLQS6NUdQ96pPbe6Y9hOq2Dnc5wxBKqQe39IyD3Aji4Rza3S+sHNMQfoY8bptZaAOCzLEMg5HUG0klgYEk2Q9+EQfx1gX4+SehfWhUdawrmwmbRXbSVlyP355aVDL6/OGLrzLZ8BgFhLuKZjaAa/wBErIppj9c2xLFGhY3hMjmsdfH9oliEjK4hWEgtDogmyzgMezTm1epQUAlAqFIbeh7Xi3Xxwh5Yh9+ez1w1f51aqm/Dm84i1GioMhzqGQnBbHxHNJec9nAs3x7Lk5KmJ4W7iaJahRFGao34bES0xFoZEE+ScB4RHpx9eZNtDOYZKDpYC1B1DIJxlY/Fxds55dPOqMEzC11mfszDUVSe3nUXISzs49+nBszdENJec8yiMg/O+OWetjzlnraSEkgLOHcwy7DOah2glsTAkmqC6Y1h3nepRUsBDSQFVvWgPF4YcJT0f63zzNWyl+lzh9jUlBSAGWYZ1x5fLgYhoXlnn0SvCNfA0HUMASCIJ69xIlqGsikW+DhGtHhaGRBNUd/46vRJaCcRR9SPnBZQSTQESRkkHozscJT075zx6RRjHbaUaHkfnd51ESQF4YK0a/R3NMuRSBiKaP8559KsjCVmqAY/m5uNR0ljDWH9IlqFHyekIopXDwpBoguo7rp2+QTuNmuUyQgzGeAAgSxV6uYH3nufYzil0DKvtr2ko6M7fMZQjHcORwpAjVkQ0h5z36Nc3xxINQJx4cyyJFaw9LMuQkRVEq4iFIdEEGesAEQqL+nyhc4NsKTV0xtB7IC8s4AHPwvDMnB+MkqaxgkBV4J2DlAKo4iqAQWEopUDBUVIimkN2eGoiqW6OieMLw1iH7aX7sww9uIGZaBWxMCSaoNKE8xqdXtmMJdYZhkAY8xEAsjj8Wjc3EAIc4TmH+oxhEilIKc4dVQGEde1Atd0U+zuGHCUlovkTRkkHZwwhTh6n10pCCHEwy1BwbJ5oFbEwJJogYx2kEM0oKYADCwGUEkiTUCj2cgMpwZD7c7A25HfVGYbRMcHOJ5FCQCoJAYFWqtHpDTbGejDknojmj7EO/erGVRqH15STxukjLZrP259lyJtgRKuHhSHRBJXGQQqg0y+bDEPrPeKhwjDSEkkUXsS7uYUUIoyg0pmUVbBzq9rGd5GOIQBESsL5Q7IMwcKdiOaPsQ79wiJLVFMQntQxrJfTeO/3ZRkK5DxPTbRyWBgSTVBZvVB7jyM7hlrK5u5uLzcQUjAS4RyMdegVNnQMvUd0wja+k0RaHh5y7z2XAxHR3ClNGCUNZ9b9qSJ7pBDNtW4ky1AJ5OwYEq0cFoZEExS6WOHFda0Jtx89/zbcMezlBlIIjiqeQ9MxTE+X33WSIwtDbo0lojk06BiGm2PqlOP0aaRgnR/JMlRSojSO0UlEK4aFIdGE+KqzVOcT1hEKAKCH7uJqJZsippsbSAGUHFU8s9LapjD0HoiqBT/nFSkJ6z3amcZerwxRIgDgwTXuRDR3jB0ep8epb44lkToiy5Bj80SrhoUh0YSErpJHpx+6Te2mYzgao1C/eGeJqpbPMD/qPLp9A2M9WkkItz9vhmFtuGNonQ9RIgCkBLf1EdHcacbpq1FSfcq4niRWcG6QZbhVjZPCe553J1oxLAyJJqQJt6/GEJtR0n0jPkpJwIf14r2+gRACzoMjPGe0160K8Oos50lLF06ilYT3B0PuJUPuiWgOWRsie7IzjtPHkYL3aDqG23XIPbgIjWjVsDAkmhDrPOCBvV7oAtYLZiDESDdLSQEIgSzRzdgp4JvCkk5mnUO36uhl6emCnU+ilIDAwcJQMeSeiOaMcx6ldSiNa84Y6lMu4AqvQRjKMgwdQyHARWhEK4aFIdGEWOchhEC3X6Kd6tAJdB5KipGipe5stRLdLKoBBAvDM3BVuD0AtJNQGOoL5BgCVWEpxFBhGP58ya2xRDRnhsfd6zOGp43sCZ1F32QZNiH3HJsnWjksDIkmxDkP7z32+qYpLqzziKPRH7u6e5gluiluAI6SnoUdKgxbqT7VmvaT1IXloR1DrnEnojkyfHMsS8Jo6Fk6hgJiKMuwiqyQkpEVRCuGhSHRhBjrABHOGNYbSQ8796GkALzfN0oKdgzPwDmPXr9+UxS+1uqUixeOUheWaRzCouvCUAgBDzBShIjmhnUevXqcvrkGnu7mmBACcaRgq82k9RlDpQTygtc5olXCwpBoQkrjIKUIhWHVdXLeI1ajMQqyGSVVKI0LBaUHs/LOoH5TJKVApMWp87uOo6RE/accyDIE17gT0fxwfmhqIqnPWZ/+92exgnFuX5YhpyOIVg0LQ6IJMdZBAuj0TbMp0zqPaP8oqRAQ1fIZAM2LO+vC06vHqMKadnHhcPuaPirkHizciWh+OOfRz4emJsTZxumTpA65rzaTdopqOoKRFUSrhIUh0YSUJmyIs85jLQtFn3eHB69rLZutpd3cAMIzy/AMyirYuZ2ebRvfSSIl4HwoDDvDhSE7ukQ0R6zz6A8tnwHOFtmTaAVn0WQZ1ucMAcHpCKIVwsKQaEIK49Cttow24fbi8BfrSAkkVWHY64d4i5KF4amFYOdBflc8po5hrFVT2O/vGPIuOhHNi9Ja9AuLSMtmG+lZOoZaS0AckmXoPV+LiFYIC0OiCTFVFwsYhK7D+0MLQ60l0qgqDHMLKQQ7hmdQGo9ebtFOQmF42jXtJ9FKwrlwxrDTN81CIK5xJ6J5Yo1Hv6zH6X3YzHyGLNd6ymJ/lmHoGPK1iGhVsDAkmgDvPazzzZbRtaZjKA4tDGMlm45hNzeQgh3Ds6iL8FYawXmPSB0c1z2PKBqcMQSATj90DaUUKEt+f4hoPhjr0M8tskTD+yqC4kyFYfjcw7IMc94EI1oZLAyJJiCcP/NNIdGuzhgCOHRjptYSsa47hgZSApavxafWL8IYVSut3hSNYSspAERKwnscCLlXUqBgyD0RzYnShjOGWaLONTWhpISSAs7tzzJkZAXRKmFhSDQB9WKSvZ6BEBgZ7zksXy/SClpJaCXRzQ2EEDzDdgZ73XB3uw63P8vSheMoFc7dtA8JuS9ZGBLRnKjPWbcSXU1NnP3tXRJJWOf2ZRlKFKU54XcS0bJgYUg0AeEsWpVhmEYQQsA6jzg6fMRRyrD9spWo0DFkYXhq3nvsVZ3ZVqIhhDjT2Zrj1H9OM0raG4ySMt+LiOaFsQ69/mABlzpHYZjGGsb6A1mGOcfmiVYGC0OiCag7hp1+iXYaxkid80fm6ykpIBDyp8IoaSgkveea8JNY59HrV2vaUw2PcXYMBYBB3EjdMRRCwHvAOr5hIqLZ8t6jNA69IpwxdN4j0me/BiaxgrWjWYb1a5FjPA/RSmBhSDQB1nnAe+z1TDOGeFyMQt2ZylLdLKyBBxwLwxM559Erhra/+rOtaT9OXWBGWiGJ1GhkhfDM9yKimXPeIx/KMHQO58pyjbWC8/6QLEOG3BOtChaGRBNgrQMEqlHS0G2y3iOKju4YojqLWHe/IMC7tKdgnW9iQbJEHZkVeR7hz6nHSfdnGQoYFoZENGPOefTLQWHoPc51xlArCSHEIVmGPNpAtCpYGBJNQGkcpKhGSauOoXdoNo/uV3e46lHSGicVT+b8oDBMYw2tzram/TiiihdxzqOdRaOFIUdJiWgOjN4c0xDifFMT9fjp/ixDD8+bYEQrgoUh0QSU1sHY8GJaLy457uxb3ZnKEo1eYZoNppYdwxPVb4rSWEEAR57jPK94KMuwM9Ix5PeHiGbPOY9+HjqG2QXOWdcLa4TAaJahECiYZUi0ElgYEk1AaVxzB7ceJYU/PKoCGHSmsljB+5DL58EzhqfhnEc3DxmG1nvoI77G56WlhPWhMKxzDIEQ/Mw3S0Q0a86jOWfdSsLrzXk6hlIIRFoezDJU3MJMtCpYGBJNQL0hDhhk4J109k0riTQehNzDcxPcaRjj0K/zu47Z/Hpewx3DvLQoq2JQSYGSa9yJaMasC+H2QHXOGuc/Z51GCsb50SxDRlYQrQwWhkQTUGdKAcBaWhWG8FX8weG0kkiqwrCbG0BwVPE06u5sK43gvIcec2Go9aAwBNB0DaUUKBhyT0QzZm24OSaFQBKpC21mTqI6smIoy1AJ5OwYEq0EFoZEY+a9h3W+iZ1oZ7rKIxTH3sWN9nUMQ8g9X4xPYqxDL7doV8HO59nGd5xYh/HedlMYhnOGiiH3RDQHympqou4WQgwikM4qiRWc25dlKARK43i0gWgFsDAkGrPQ5fNNAdGuO1knbMvUWiCJwvmQXm4hpYAxfCE+SWEcurlBlmjAny+/6zhSCnig6RjWC2ikDG+WiIhmyViHfhVu7324aXXezcxxpOCBkSzD+s9ibivR8mNhSDRm9fhnpx/Ovckq7uCocPtapCXiKuew2zcQAlwRfgqdvglxEun5ly4cR8tBjiEw6BgKIeA9mO9FRDNl3KAwdM5f6OZYPdVyMMuQIfdEq4CFIdGYhYUxIoTbV8WEOybDsBYpiUQrCDE0SsqcvBPtdsMbl1ZVGI4r3L5WF5rtdHSUFAAgPM+BEtFMmeactYbzF1vAFX6vP5BlCDDknmgVsDAkGrO6UNgbCre3ziOKjv9xk1JASIE01k1hyFHF4znv0emHQq2V6hM3v55HHTEipUA71aOFIQTHq4hopkrr0ctt2MzsL94xFBAHsgyFAF+PiFYAC0OiMbPOA96j0zPNRtIwSnp8x7AuaFqJQjc3kFLAsmN4LFeF2wNAK40AiLGPkiopgGrpwv4sQ3jwe0REMxUWcJlqlBSI9PmvgUIIxNVm0uEsQ+a2Eq0GFoZEY2atgxCjo6T+FHdx64ImS0LHUAge9j/JSGGYTG6UVEoB7z3aWTTSMRSMFCGiGXLeoygtrPPIEnXhjiEAZLGCcW5flqFkZAXRCmBhSDRmpXGwzqEwrjmXBuDYDENgUNAMCsOwDZMh90ezQ4VhFqsLbeM7TqQVbJVl2BkuDHkXnYhmaP/NMe9w4cieJAnXu5EsQymQF5yOIFp2LAyJxqyoFgEAg4gDCHFirpSUAvChMOzmVbHhudzkOM57dHMLJUNG5LijKmpaDULu93pllUsJKCFQlHyzRESz4ZxHr6hujiXhnPVFx+kTreAsRrIMlWJuK9EqYGFINGbGOvSK8AJaj5ICgD6hYyiFAASQJaopLCEEQ4WPUXcM26mGB06MBDmvSNeFoYZ1Hv3q+yslC0Mimh3rPPI8XIOyMUX2aC0BcTDLkPE8RMuPhSHRmJVDHcNmlNT7ZrvlUYQQ0FIgizVK42CqDXAcJT2atR793IY17e5ia9qPE2sJ533TAa7PGSopOEpKRDPj/KBjWJ+zPmk65ST15MWBLEOeeydaeiwMicastA7dflUYZhGc85BKnuourtYSaRy2l3ZzA3jPjuExSmur/K4IdgxLF44SaQnrfBM/UheGUgoY45rRUiKiaQpTE+Hm1LgWcNXTLQeyDL1nti7RkmNhSDRGzns450NRB6BdBw6fsmCJ1KAw7OUGHtx6eRxjHHqFmXjHUCsJ+MGZ0XoBTT1exe8REc2Cdx794TOG/uKjpErKprgczjIERDPJQkTLiYUh0RiFsU+PTq9EGqtmaclpz75pLZFEg46hgOAo6TFK69DNTbhT7jGxjmH9Rmv/KCkAQHBBEBHNRmkd+oVFEqlwnRLjiexJIgnrHC7vyzLMOTpPtNRYGBKNUSgQBPZ6pjlfaJ1HHJ2+Y5gMdQyF5GH/4/Rzg6J0aKU6bH4dc4ZhrX6jlVaRGKMh94LnbohoJox16A9NTYwrsieNNYz1+7IMGVlBtOxYGBKNUd3d6/QH4fahY6hO9fuHO4a93EIKFobH2elONty+pqQARHjDtbYv5B4CsDx3Q0QzYGzYkjyucPtaEitY63FpOMtQCRSlOfk3E9HCYmFINEb1SGGnVzZjh87h1GfftJJI41DkdHMDKQQMu1FH2uuGO9mhO+sn1zFUIWMSCAuFhgtDAZ4xJKLZMMZVhWHoGI6rMIyjUGiOZBlKiZzxPERLjYUh0RjVBcLwKClw+mUAdUi7VhK93DRbL+kg5z26/VCgtdLJdgylEBAC8FVkRWe4MJRAXvDcDRFNn3EhHqmVnG3R2Um0lBBC4PJQlqGUAtY6nnsnWmIsDInGyFgHax3y0g7C7QVOzDCs1flTrUSh1zcQQnA9+BHc8Jr2VAMQEysMhRBQSlRZhnqkY6iEQMHinYhmoM7NDR3DQdTERUU6/DkHswwFjzcQLTEWhkRjVBrbrA6vR0kBH0YRT0FVW+WyRIdRUgmOkh7BOo9e1amrl8KMY+nCUWIdNsyuZRE6fdPcNZdSoGRhSEQzUJQOeelCYejHF9mjqs7jWhZBCjGUZcjXJKJlxsKQaIxKM+hiNaOk/vSdrHrkNEt1GCUVHCU9ynBeZBKpiUVV1LSWcG4oy7AaY1VSoOAKdyKasjA1MRin9y5cp8ZBCtEUmRvtqMky9GDIPdEyY2FINEbGhsB1AGhnGt57iDPkStWf10pCYRgC1D2c5x3a/azzzQgVcPoFP+cVKwVbdQwBNJEV9TlQz+8REU2RdWEjKVBtZhYXD7cflkYKxvnRLEMhUJS8EUa0rFgYEo1Rfd4DANbSKKwP1/LUI45SCggAWazQrTqPEOBh/0O4qjBsjXkb31EiLeG8R3tfyL0QAh4MuSei6XJ+ME6fTSCyJ4lCZMVIlqFiYUi0zFgYEo1RaR26/bpjGJ0pw7CmtUSaaPQL03ShWBgeZGy1jS8NZ2viCXcMIy3hh0dJh7MMIVgYEtFUuaGOYZZowPtmgdk4JLGCc/uyDKVgZAXREmNhSDQmzvtw7q1vEEcSkZaw7uzLALQSSGMF74F+YSFYdByqtCG/q53qM2VFnlc9orVWbZsdCbn3gOWmPiKaIus8+tWESitR1Qbs8RWGcaTggX1ZhgI5O4ZES4uFIdGYOOcB77HXN1hLo+ZjZ+1khZD70GXs5gYe4Pm1Qxjj0K06ht77ZovepCgpAQFEWiGJ1GhhKADD4p2Ipmh4AVc9SjrOM4Z1kbk/y7DkmWqipcXCkGhMrPOAEOj0yuYc2nk6hpGSSKJQGPaqsVR2DA8qjG1GSSEml2FYG44c2Z9lKABujyWiqXLeIy8slKw3iI73Ohj+TD+SZSiEALxnZAXRkmJhSDQmtnqh7PTLJtzee5x5KUoUSSRDHUN4njE8zF6vhPfVNj6Md4TqMOHPD9+HtSwaLQwlmGVIRFNlTBinD1MT1fKyMZ4xVFJAQBzMMmTIPdHSYmFINCZ1pMRebzBKKoQ4dbh9TcuhjmFuIAQ7hofZ7db5XeFrPc4RqsOEP78+ZzhaGCohULAwJKIpKo1Dv6g2M3uPaMzj9EKIcM7Qj2YZwvM1iWhZsTAkGhPrPKwNEQr1KClw9k6WVhJZPFQYsht1qL3uINgZ8BPvGMpqXNVVWYYjhaES/B4R0VSZagFXNsHInixWMM6NZBkKAXYMiZYUC0OiMSmNRb8Ot0/DeCO8D0tLzkBKgSTWEALo5haSYzsHDC9dCKOkYuIdQyAU7c6FLMOidE2el5QCheGmPiKaHuM8enkoDL0HojNOp5xGkihYN5plKCWa1zoiWi4sDInGxFiHXhG6SE3H8ByjpKo6J5LGGr3cQEoB41gYDrNVuD0AZLGGlGKs+V1HiauQ+ybLsFoOJAU39RHRdJXGoTc0SjqJjmGiFZzFvixDycgKoiXFwpBoTEoT7t4CqLL1wnjjWQuWuvPVSlQoDAU3Xu433DFMEzX2szVHqbMp68KwHicNCx88z90Q0dSUxqKfG2RpCKKfRJar1iGm50CWYcHXJKJlxMKQaExK45ou1loWhQzD6Ow/YkqGdeBZotHNDaQQ4CTpKFuNUEVaDq1qn7woGu0YjmQZQjSbaYmIJsn7cA10Hs0oqZ5EYagkPPxIlqFSohmjJ6LlwsKQaEyMdU0Xq51FsP58d3DrjmGWaPT6BkIIlKwMRzgfRkknOUJ1mEgpeIfDC0MPWI78EtEUOO+bc371dXAS56y1CpEV+7MMvecCGqJlxMKQaExK69DrG2glw1k05xErdeY/RwoBqWQoDKszhs55nl8bUp8xbFUju5Ge/PlCIGwf9QibUIXYVxiKsAyCiGjSnBscXcgSDYHJZLkqGaYy2un+LENwQoJoCbEwJBoD5z2cdej0DdYyDSEErPOIzjFKCgBRFVnRrV74mRs1Kiz6qQtDTK1jWL/xklKglWh0RkZJPc+CEtFUWOfRqzqGWRK2YE9qAVcaK3j40SxDgEvRiJYQC0OiMXCuDrcv0a4C173DuZeiaCWRJgrGupCPJ8CO4RBjHHq5Rate067P3pk9j+E78geyDCWzDIloOpwD+tWNw1aqAYGJRfYkkYKxfiTLEAAMO4ZES4eFIdEYWOsBIdDtD4XbC0CdszCMlEAahbvA9UIbdgwHSuuqUdLwtZ50uH3tuMJQSoGiZGFIRJPn/KBj2Ko6hpO6DiaxgrX7sgwFUHIBDdHSYWFINAbWeQiEjuFaNhxuf74Xaq0kkjh0wbq5AXy4Q0xBPzcojRvcKZ9ChiEQztug6tyGwtAM/ZpAYRj6TESTZ51HPzcQIox6AmJiHcM4UnDe78syFOgVLAyJlg0LQ6IxcD5k2HVz04ySQohzF4aRlkiiUBj2cgOI8BgUbHfCXet2Otk75ftJKaqNfB7tqmNYj/hKKVBwlJSIpsBah35hkcbV+cJzZOaelpYSQojRLEMlUJS8EUa0bFgYEo2BtQ793MB7DEZJEbZYnofWsroLXBWGnuHpw3a7oTBsJRqY0Jr2o8RRCJOusyr71V1zKQSM5fZYIpo84xz6pUUrCdcjPcFrYL31eSTLUErkHJ0nWjosDInGoBzKMFzLNLz31frw8/2ISSmaO8HdvqlWg/NFGAhLeOoRzlbdMTxnAX4eWklYdzDkXggBgAU8EU1eWMBlkFUZhufJzD2t+qz8pfYgy1BKAWtds3iNiJYDC0OiMTDVWA8AtNPQSYqj82/KVFIgTQYdQykEV4NX6nB7IKxpl0pO7YwhEMZ8nffNWdKRLEMIZnsR0cSV1WtOlkw+skcKgUhLtFO9L8tQMOSeaMmwMCQag7L06OahQGhnUcgwvMAdXCkEIiURaYlubsPdWZ5fAxCiQerubBqrc0eCnFekRTNKCowWht4DlgU8EU2YtWH5TKvqGOoJT02kkYIDDmYZ8kYY0VJhYUg0BiE+IXQM11INd8HCUEkBj9ARazqGfAEGUAU7V9v4Yi2nFm5fi7WCczi0MAQ8DEeriGjCjPXo5RZZ9XqjJzhKCoQsQ3tYliFvhBEtFRaGRGNgqlw9JUXIfPL+Qp0sKQXggVaimiKoZGEIIHQMe3kYofIImY/TpJSE9x5JrKCkGCkMhQhnf4iIJqlXGJTWNR3DSJ3/6MJpJHFYcjOcZSgAlLzeES0VFoZEY1Aai04Vbi+EgHe48BlDiNAx7OYGUvIsR63uGLbTcLYmiqZ7GQvfmxBbsZZF6BzIMmS2FxFNjvce3d7gnLX3k4/siSMFD4xmGSqBglmGREuFhSHRBTnv4ZxHp19irdqS6XGxTZmiykDM4uFRUhaGQN0xHD5bM4PCcCTkftAxlFKg5Ap3Ipog6zx6RbjutBINAUw8sqcuPEeyDKVAv2RhSLRMWBgSXVC9hbLTM2jVGYYeF86V0koiTRR6uYUQoSBiRl51nrMwaKURvMPUl8/U3VzgYGHIjiERTVrITw03oLJEAdWNxEkKZ+b9vixDgZyFIdFSYWFIdEHOeUCIkY4hxPkzDGtRFXLfL8LIkEfoTq660oRFP3WG4TTD7ZvH8+ExD+sYFjxzQ0QTVI/TA0A2peugkgIC4kCWYWkcb1gSLREWhkQXZJ2Hdx6dXjhjGPgLh65rFQpD79FkJHIBHFCUDv0ijJICFy/Az6ruGHrv0c40un3TRFQoKWEsO7tENDl+KMu1lWjA+4l3DIUQiCOFVjLIMhQijNVzYzbR8mBhSHRB1oWgYec92mldGF58tCfSAkkUip86t88xCgG73QLeI3QMxeSXLuwnhIBWogq5D9/vbn+wgAbew/L7REQTYp1HXt0szJLpTU5ksYKDH80y5Pl3oqXCwpDogoYD19eyKlNKha2VF6GVRBKHzaa9qvDgKCmw262WLqTTWbpwmEjLo7MMhWjOnRIRjVu9gCvSMox4CgF5wdeb00iSw7MMeSOMaHmwMCS6oNK6pjBsZxGs84jHEDYcKYm0irzo5gYCfAEGQscQCIWhn8II1WFCYeiPCLlHM1pKRDRupQ1TKq1kOuH2tUQrOIeRLEN4sGNItERYGBJdUGkc+nVhmEZwziPSFw8bllI0Y0K93DSxGKvMe49Of3C2Rggxk45hrOTIKOneUJah55kbIpogaz36pUErrcPtp3MN1FoCYn+WIZqxViJafCwMiS7IGDcySmqdH0uEgpICaT1KWmUZrvooqXUe3Tx059JITe1O+X6RVrDON8uGhjuGQvAOOhFNjrEO/dwiSzScw9Sug1pJePh9WYYSBSMriJYGC0OiCyptOO8hBKoXao8ouviPlpQCSawgBNDNLSBCEbrK3NCa9jRRiMfQmT2PSMuQoaglkkihwyxDIpqS0jj0iqow9OO5EXkaWoXIiv1Zhj12DImWBgtDogsy1RnDdhpBCAHvxzNKWi8VyGLddAzLFe9Ehfwui1hLSCGnNkK13/D46mFZhmW52t8nIpoc4xx6uUErUXB+emcMlQzLbjZagyxDpQRK3ggjWhp61k+AaNEVpUW3b7CWDX6cxrEQpS4+sqQuDLHyZ9dcld/VSsPIrp7SnfL9hrMT1zI9UhgqKdkxJKKJCVmutplQ0VPMck1jDSUxkmXoXLhBOqvrMRGND3+KiS7AOV8tRCkHGYbi4hmGQCgwBIAsUejmBlIKWHYMq8IwCiNUMzpjWIfcAwc7hkoKFOwYEtGE1KPrWaIBj6kWZEkk4Tz2ZRmCET1ES4KFIdEF1PERnZ5pFpEA4SzGOCgVNpP2+mGU1Kx4DIJzHr3ChqiK6ozfLCglgGoR0GGjpMb5lV8URETj54YWcLUSDUx5M3MSH55luOqvTUTLgoUh0QU45+ERtlK2R0ZJx/OjFWmJNFZhuY0MSwdWmTHhbE070RBiNuH2QPW4VaB0O4tQlG50Mx+jRYhoApz36OfhWpOl4TVnmlmucRTONY5mGTKih2hZsDAkugDrPIrSwTqPtTSC9x5Sju8OrpYSaaLRzS2kECs/rlNWhWGWang/3TdEw2R1l3445L7OVwQACMHICiIaO+s8+tVNqFaVcyvF9K6DWkoIIUayDMPCLZ6rJloGLAyJLsA6h25VELSzaGwZhrVIS6SRgrEOxobzjKvcier2Sxjr0U41MMOOIQBESo4UhsPjpMBgzJiIaFyGI3vCGUM/1etgpMNjjWYZCuQsDImWAgtDogtwLiyeAYB2FjbExWM896aVHAm5B7DSZ9e2u0NnazC+kd3ziLSE84cXht77le/uEtH4DY+SDq6D0ysMVXXj81I7FIYP9nIoJZBz4RbRUmBhSHQBpXXoFaFgW0urjuEYC8NISyRVYdjNTdj+tsKdqN1uONPSqjbAzmqUFABiLcMI8SGFoRDgKCkRjZ11Hr0iLCOLdMi6nWbHMDyuxEZ7KMtQSvQLc8LvJKJFwMKQ6ALK0jWdvHYWwTmPKBrfj5VSYZQUqDqGHis9SrrbqzuGCgKzHSXVKnQMW2lYhMMsQyKatDBKapElCh6YWrj9sDRSyBLdZBnWUUqr/NpEtCxYGBJdgLEudPIQxnqcA2KtxvbnKxniKoCqMBRiZUdJnfdNfleaqJm8IRoW6XDGUEqBVqKb5wYwy5CIJsMYh34ZInucG1800lkkkYI/kGXIhVtEy4CFIdEFlNaj1zdopbrpXo1zvFEKgbQqDOslN6t6V3Z46UIaK0QzeEM0TCtZRxkemmXIjiERjVtpHfq5QZZoOO+nGm5fS2IF5w7JMuS5aqKFx8KQ6AIKY9HtG6xVZ94gxrsQRUkxunxmhUdJbTVCJYVApGbfMVQqnO8BgLVWtG+UlB1DIho/Yz36hQ2F4Zi3YJ9WHIUx1pEsQzDknmgZsDAkugBjHDq5GQq391Bj7GRJKaBlOOzfzS2ECHeMV1HdMWylGh5ArMY3snsew9lhoWM4WL4gpYB1fmXHfoloMox16BcmHF2YUcewHl8dzjIEPIxZzdcmomXCwpDonJwLuYKdXol2tZkSEGMdJa3/rFai0csNpMTKxiDU2/jC2Zrxbn89j+GzPfUoqR8uBBlZQURjVpS2Wj4zu+tgKEb9SJahVhJ5wfF5okXHwpDonKzzgBDo9A3aaQTvPQTGfMZQhnHFLFHo5QZihQ/4O1ed56yW/Mx6lHR4I+patZG2P/zGSAhYjlYR0Rj1cwPrwjZk+EGu4DQpKSAgRrMMGXJPtBRYGBKdk3MeRWlRGoe1rBrr0bI5dzYuWktksUY3N5BSrOwoqbEOvSJs44OYbVQFEM6SCoQw+zrLcLdbjnzOKmdOEtH4daolZFmiATHeCZXTEkIgjhTWW8NZhgIFR0mJFh4LQ6Jzss41m0LbaZVhOIFNmZESSKuOoRQhL2oVGeeaM4YAoGdcGAKhaHfON6PEwwtoOEpKRONknUO3qOORqizXMd+IPK0sVsgSNZJlmJd2dJyeiBYOC0Oic7JD8QntLIJ1fqwZhjWtJdK4OmMoQkTGKsoLi35h0ariO2bdMQRCZqUb6hgOZxkKsbrdXSIaP+eAfh7GNbMkLOGaRccQAJJkNMtQCAF4z8gKogXHwpDonJzzzVjPWlYtAojG/yMVKYk0VuhVbwhWdTxxtxvWoreqaJBZvSEaFqmwfXSt2kp7IMuQyxiIaExctYALqEZJMbsbZIlWcA6jWYYrfAaeaFmwMCQ6p9K4QccwnXTHMPy5eengVjQGYacTCsN2Oj8dwyiScA5IIgWtxMEsQ8vCkIjGwzrfdAzryYlZ3SDTWgLiYJbhqt64JFoWC1sYvuMd78B73/veWT8NWmGlceg2o6Qa3mMimVKRUkiGQ+7hVzLkfqda7JJVZ2uUnP3lS6twxlAI0URW1KQUKLmMgYjGxPlBxzCNqzOGsyoMq8iKkSxDD3YMiRacPvlTxuf9738/3vnOd+Ld7343AODnf/7n8YM/+INYX19vPudLv/RL8YY3vAEAsLm5ibe97W3Y3NyEcw5f/dVfjde//vUAgCiKEMfxNJ8+0QhjQ8cwS1TYUCnEWMPta0oKJFEoDLu5QRwloTCcbb771O1VhWGaqJlHVdQiHc7ZAOGc6f6OYZ+jpDRHvB+ci6bF45xHPzdhgkRgIq83pxVyXMVIlmEaK2YZEi24qRSGnU4HX//1X4/bt2/jhS98YfPxbreLL/mSL8Gb3/zmQ3/f29/+dnzN13wNXv3qV6MoCrzxjW/Ea1/7Wly/fn3skQBEZ1Xa0DFsp1HzsUmM9UgpmrGhXm5weS1ZuVFS53zTnc2iaCLbX89DSQFUT2Uti7C1kze/JmU4f+i8n9nmQKJhvdzgztMPcHM95mvoAjLWoV/U4faYSbh9TUkJJQU2hrIMH7u+hoJZhkQLbSpXlXa7jZ/6qZ/CP/2n//RMv+/27dt49atfDQCI4xgvf/nL8Z73vAdvetOb8DM/8zNwDI+mGSqMQ7dfNlEFkxpvVFI0Zwx7uQE8Vm6UdHgDbBLLuekYqn0h9yNxFQAjK2iu3NvuY69XoCj52rmIjBsqDL2HnvE4fRprrLfC61+dZZgbFoZEi2yqo6SH+a//9b/ib/yNv4H79+/jypUreOtb34oXv/jFAIDHH38cP/dzP4fXv/71uH37Nj7wgQ/gR37kR9BqtfDDP/zD5yoMr11bG/dfYSxu3Fg/+ZOW8LEX+fGf3uyhVzi84GYLVy63IXWOhx/aOPOZj5MePy8tbt7vhv8hJS5dznDl6lpzp/YiFuVr388NLEKBfPlyG1fWk7E894v+Gf3C4O5egUvtBNevtND98F1srGdQ1VlTGWlcudpuNgiO87EvapUffxX/7nlp4e91oeGxtpHh8noy9edQ4/f+fHrWo7QOl9YSbKxnuHSO6+A4//5d6xGnEaQU6Jce166todMvj3yMRf7a8/EX97H5+Gcz08LwVa96FdbX1/G6170OcRzj/e9/P77xG78R//bf/lsIIfDN3/zN+PEf/3G89a1vRbvdxnd/93ej1Wo1v/88Qar37+/NXbflxo113L27u3KPvciP75zH1oMudjsFIiVwf3MP/cLi/v29sT++sQ55L4cQwP0HXWxv93An1ci7FysMF+lr3+0bbO/20Uo0trY60HC4e8Fx0nH8/Y112N7uwpUGuno6Tz+/3RTtu50Ct+9EI+PG43rsi1jlx1/Vv/udB13s7vRw5Uobn3x2C+XV9tSfA8Dv/UUe/969Pex1S1xdT7D1oAPpznYdHPffv7vXx4MHXWy0Ijx/fw/b213sdko8f3v7wPTMon/t+fiL+dh8/MNJKY5slM20MHzhC184cubwcz7nc5CmKXZ2dnDp0iXEcYw8z7G1tYXd3V28/e1vBwBYa/GRj3wEb3vb22b11GnFWedDwVZatLMIzvmJbCQFwg+wEBJZotHrWwAedsU2v7lqaUaWhu2vk4gFOY8wSirg/WiW4XA3l4HPNGvOedzb6iFLFWItsb3Tn/VTonMobdhK2pqDM4YAEEcKzvt9WYaAMR6KuwGJFtJMC8Mnn3wSrVYLN2/eBAD8xm/8BrIsw6VLl5rPqRfTaD36VN/5znciikbvwhNNi3UO3TrcPtWw3iOZULEihYCUAlmi0c0NpBQwK3a+tj5jeGU9gcd8ZBgCgBACWgl4j+as6fA5QyEESp65oRnb7RawLpyBjrREL7dwzs/NzxGdTl4YFKVrbpCpCd2MPC1dbeO+vBbjo8/uNB83ziFZtbXZREtipoWhcw7f+Z3fiW63i6Io8PDDD+Md73jHyOfsLwiHP86tajQrzvmmMKw7hnE0uRfpSElksUIvN5BCwKxYPp61Dr3c4tb12YY6HybSEtZ5rFWFYWd/liEXfdCM3X3QQxKH61P9upmX9tCzrzS/6ptO2YzD7WtRNT8/mmXoOSVBtMCm+qrw2GOPNRmGAPCSl7wE3/d933euP8sYc64zhkTjYJ1HJw8v0u1Uwzo/0fFGrSTSRGO3U4TCcMVeeIvSopcbtKqzevMU/xBpiX5hsZYe7BgqKVBYdgxpdrp9g25usdEemrDxHgULw4XivcdedTOyji+a9XWw7lheGsoyTCKFkpEVRAtrYV8V3vKWt7BjSDNjnUe3N+gY+gmf96g7hrc3DaRcvXNr3b6BdR6tNFyy9JzkGALhe9N1BkmikMYKe71BgLiSAuWKdXdpvmzt9g/8vGgt0emXuLQ2u82kdDa2CrcHqo6hAGacVgEpBCItcamKrHiwl+PRa23kLAyJFtZ8hIGdA4tCmiVjHHpFfcYwAsRkz3toLZBUo6RCiDCys0K2OwWA6k65n58zhkBYhFNvOt6fZSilYGYczUxpHDZ3cmTJ6DRDpOXIDQyaf9579ItQcLXmZJQUANJIYa1VdQz3CmglkPOaR7SwFrYwJJql0jj0coMkUk3Y+iRfpCMtkcYKxobA9FUrDHd7VWGYaggx+xGqYVpL1LuA2ocUhtY6OI690wxsd8KmyP03UrWSyEsLu2JLrBaZdb65GZlVkxPzcIMsiRWyWEMKgQd7OZSUyEvedCBaVCwMic6htGEraTsbTGNPtDBUEmkUHqtfWljnV+qM7U7VMUxjBaXEXE0MKCmA6umsZdHI8hkAgBCwKzb6S7PnvMfdrR5a6dFnn/OCheGicM6jn4eOYRYrCOBAVuAsJFH497XRjvBgrwhbs63nzTCiBTX7qwrRAiqNDYVhHVzuATXBc29SCqTVOFivbwCPlXnh9d6jU429pbGeeXbXfkoKoPpWrGV6pGNYW7UOL83eXq9Ead3RI+4e6Bfs7CyKOrJHKwGt5ERfb84ijhQ8MJplCKxc1i7Rspivd1hEC6K0YSvpWhbBew8hJnv3VkmBNA4dw25uAIHmXNuyc96HvzOANFHQc3CXfJiScqRjWBiHYnj5gvewK/K9ovlx70EPSXx0tzDSAp0+C8NF4apCPks0nPPQM84wrNWLjS6vxdjeK5qPl4bXPKJFNB9XlhVWGof7271ZPw06I2McOr0wSuqcb84ZToqUAln1Jq9XFUmrcjzIVXfKlRTQUkw0L/I8pBSAHyyfAXCga8jCkKaplxvs9cpmzO8wUSSx1yuO/HWaL9Y69AuLVqLhvEc0N4WhBOBHswwRQu6JaPHMx5VlhRnrsLWbn/yJNDecC8tf+oVFO40mnmEIVB3DahNdXRiuSrERli5YtFIND0y8CD+r4ZGu9iGFoRACpeH69nlRGrf0ESJbu/mJo4ZKShjrOOa8IMpqE3aWTudm5GkpKSAgRrIMAQ+z5D9jRMtqPq4sK66fm5VaJLLorHPoVUsA1rIIzvmJn3uTUiCt7v7XY5WrcsbQOY9e34Q75c4jUpMtws9KCgGpJJzzTcewM5xlqARKrm+fG/cfdPH03b1ZP42JMdZhc6ePLD5NTLFg5tyCMHPaMRRCII4UNrJBlqGSEnnBf1dEi2g+riwrjndtF4t14XwhALRTDes94kkXhkJAa4lIy9Ax9H5lzhjWa9pbqYb385HdtV+kJJz3h46SSimQs2M4Nzq5weZuH53+wSVBy2C7U8B7f2iUwTP3Org3dHRBYDCBQPOttA793FZnDCe77OysslhhrQq5394roBRvOBAtKhaGc8BYz4PaC8Q5j261tKE9pY6hEOF8XZbo0K0UqzNKWp8xbKVRWNE+R2+IapEOHcNWEnIWhwtDJcTSjy4ukk6vRBZrPHevu3STGiGioossOdgttM7hJ3/hD/D/e+9Hmo9FWi5tgbxsjLFhlDQJN8jmpWMIAEkymmWopUDBax7RQpqfK8sKM8ahZMdwYZihwjB0iMRUzntoLZHFCt3cQAqxMuvAS+vQyw3aqYYQYq7C7Wt1YSilQDs9GHLPN0nzwTqHsnRopRrd3GD3kGiRRdbtGxTm8PNnTz63i15u8dRzO01BHGmJTo9HGRbBXt/Ae6BVxRbNQ7h9LdEKgBjJMixKy39XRAuIheEcsN4hLznOsyhGOoZpuDOvp/AiHSkZOoZ9Uy00WY1iIy8N8tKFO+WY31FS22wm1QcKQ2fdyoz+zrPSONT3FbJE4rl73aU6q3v3QQ9xdPjPxxNPbQEAOn3TLDyTUsA4vzLXkkW22wnXlLobPE/XQa1DZE+dZSiEgAdjeogWEQvDOZHnnMdfFMY49PISkZaIq4Uwk8wwrGktkcYKvdxAyjCCvAq29wbnOeHn6055re4YAmG8uLO/EyUELNe3z1xpHOqfmkgrFKUdCeVeZHlhsXtERIVzHh966gFuXs4AhLOGNQGwoz3nnA/nrIGqMBTzdR2sIytGswwFdycQLSAWhnNAK4keN3gtjNJYdHPbLBqB91M59xapUBjWo6SrkhO10w1vNFppeEM0T3fKa1rJOsoQa1l0IMcQWJ1Cfp7lhYXE4N9Plio8f7+7FEX71m4fSgiIQ0atn7q9i25u8IWveASxlnjm7lBhKIBuvlwjtcvGOY9+VRi25rFjqAQAcTDLkNc8ooXDwnAOKBk2eC3TSNMyK6xDt182Y6QQYip3b7WWSGKNfmEBj5W5G7tTjVClsYaWh7/xnTWlRFNurGVRdR5o6OfZc6xqHvRyM7IoSisJax22dha7a2isw73tPrLk8CiXJ57cQqQlPu0Fl/CCh9ZHCsNYS3S6PMowz6zzTURSNoeTE0pKKClwqT2UZbhCr1FEy4SF4Ryo3+iuyjKRRWeMQzc3zUZSJaezEEVXHUMgjH6tyovuXi90DLNYIYrm85IlhUB9eK3OtuyNjIcLFoZzoJObA4tZ2lmE21u9hT5nt9st4I6IqHDO44mntvCyW5cQaYVPeXgdz292m+tHpCU6eclFIXPMOY9+FSuSxSqMks7ZDbI01lhvDbIMpQKzDIkW0Hy+y1pFHgv9xmSVlNaj0zdYSyNY5xFPqVhRUjSh1b3CwK7AmI73vgmLT2IFPYWznOehh0aJD8syFDKMINPsOOdRlrY6DzUgpYD3HveH8v0Wifced7b6h0ZUAMAn7+yh0zf4zBddAQC88OENWOdxe7MLINyYdN6jKPn6M6+c9+gXFkIASaSg5nByIolkc+3b3iugqs2kRLRY5vNd1kryKFfgjf4yKAqLbt+glempZBjWpBBNx7BfWHhg6Tdd2irDEAhviKb1tT6r4U5N+5DCUEmBkm+SZqo0runq7tdKNe5u9xYylLubG+SlOfJn44mntqCVwMseuwQgFIbA/gU0AjlvXMwt6zx6hUUWhwzD/Tc35kESj2YZKin5b4poAc3f1WVFSSkYWbEArAtjpAAGHUN1+LmecVNVwD0Q3gyuwrk153wV6hzGp+bxDREQztgIhO5Nfde8sy+yImdHZqYKE87mHkZKASUl7j7oTvdJjcG9B/0ji0LvwxjpS29daraVXl5PsN6KRs4ZKgl0GXQ/t+pR0izRcH56NyPPIo7CNfrSWowHVceQ1zyixTN/V5cVpZVsOiM0v5zzTWHYrjuGUxollVIgrZZL9PoGqEbAllndMWwl0+3OnoeuIivWslC8j3QMBUPuZy0v7VENQwBAlihs7uQLdR0uSoudTt5MEuz39N0OdrslPvOFV0Y+fut6e6QwDEH3LAznlbEO/dIiSxWc83N5g0xLCSFEVRjmIb/V+aXY+Eu0Subv6rKilBLIC15A550dDrfPIniEPLRpUFIgrc8YVh3DZR8ldT4scWmlETCnI1S1SIVCPYkUtBIHQu7LFVkWNK96uYXWR1eGQghEWuL5zcXpGtZh4kedN/vgk1tQUuBTX3Bp5OO3brSxuZs31zKtJLq5XfrryaIy1g1ukHkgOubf8azUz+nyWjKUZcjICqJFM7/vslZEvzDNZktGVsw/6zw61cjVWhpBYHp5UlIKpJGscsfCG7pl//cy3DEE5mtF+36xVrDOQwhxIMtQSgFnHd94z1AvNyfeWMgSjZ1O0fyMzzPrHO4+6CFLD78xVY+RvuTRjeaGUu3W9TaAwTlDIQQ8sJBnLFdBacLymXqUdB5vkKnqOV1qxyNZhty2TrRY5u/qsmL+3rvfh9/64O3mjq/huNlcG+0Yanjvpxo0HEUKWaJDx1AsfwRCPbrbSucv1Hk/rSTqqan2YSH3QnCsakac98hLe6p/P2ms8Ny9ztzHN+x2SzgXzrce5tl7Hex0imYb6bBHr7chBPDM3b3mY8J7jjvPKVt1DLNEwzsgmsPCUFYd90vtajNppwA8l+oRLZr5u7qsGCkFnq5fnBkIO/fqQkVJEZY5CDESVTBpWglksQoZeR7wS14YlqVFvy4MxXwXhlEkm47gehY1MRsN7zlWNSOlcfDwp1rxn8QK3dxit1uc+Lmz4r3H3Qc9JPHRL+EffGoLUgh82gsuH/i1OFK4cTkb2UyqtUSnN79/51XWKwyM9WFyQszv5EQaKay1Qsj9g70cgtuYiRYOC8MZe+hKhs0mP4t31+ZdaSx6fYO1LGreZB51x34StJJIE41ubkI23pLfSNjtlnAeVcdQzO0bIiDcxa+bTPtHSQGsRId3XpXGQeD0/3ayROK5+725HdXu5Ra93IZNkIfw3uNDTz3Aix9dPzLf8Nb1Np4d6oxGSmJv/80Mmgv1TYp5n5xIYoW1dDTLkOPJRIuFheGM3byS4f52H957SCXQX6CNeKuoNCGuoh4jFZju3dtIyapjaCDF8neYd6o3RO3qzca8viECqjM21dNrZxG6uRkdHfVgYTgjeWnPUBaGhVJFafFgL5/Yc7qI+zu9YycVnt/sYWs3P7CNdNitG230covN3fB31FoiLy3HnedQPX2QzflZ6yRSaCXDWYaMrCBaNCwMZ+zG5Qz9wqKbG2gp0WeW4VwrrUO3b9CuMwyPuGM/KVpLJE1hKJZ+NHGnG7puaayg5NHbF+eBHHpugyzDwc+zkOBY1Yz0c3Pmke8sVXj+fnfuCqXSWGzt5iHb8whPPLUJIYBP/5TLIx/33mOnE262NAtohmIr4D23Y8+Z4YikpjCc0+tgHCkIKQZZhor5zESLhoXhjN280gIA3N/uQyuBfs4X5Xlmqo7hWhbNJFcvUhJprMICHLH8y4rqEaosVnO5iW+YUgJ1gnpdGI5kGUoRQtZnLC8sPv7Mg7kreCap0z95I+l+WklY67C1M19dw+29AgJH3yTx3uODT27hRQ+vh5iXIXlpUZQhluLG5QyRlqOFIQT6Bd/IzxPrPPIiXDdaiQb8/HYM65svdZahkhLG+rkdySaig+b7ndYKuHklAwBs7oRA2MIwsmKe5aVFp1+inenQMZxyYaiVRBqHx7bOwyz5m/vdqrBKYjX1r/VZDY+5HhZyL6VAMeOxqk6/xEee2cadB72ZP5dpaTaSnmNJVDuLcHurh3JObsA453HnQQ+tIyIqAODOgx42d3I8fsgYaWk81loRSuMgpcCj11p45t5gM2mkBTp9FobzZLRjqOZ6CVe4+eIPZBkysoJoccz3O60VcG0jhRQC93f6jKxYAJ2egffhzJtzHlE03R8hKQWyKpMsL+xSj5I679GtRjHTWE29O3tW4c1a+BkejJIOdQyFmGkcwNZuHx99ZhtxJKCVXJlognA9Pd1G0v2kFPDe436zIGy29vohH04d0/184sktAMBnHFIYenhcWkuaQvfWjTU8v9lrXnMiLbHHzaRzxfmQYQiErZ/zPFKvpICAOJBluMyvU0TLZr7faa0AJQWubCTYrMaVPCMr5pZ1DnvV3fS1LIL3IdR8mpQUSKuzRf3CLvVNBOc8ekUJrWTzf/NMCAElBZzzaFeF4e6+juEstsh673F7q4NP3N5DO9OItIKSEr18/kPcx6Ewrp7wPZd2pnF3uzcX2xXvbvWQxMdfc554agsvfGituTlRc85DS4krG2nz5bh1ow3nPJ7f7AIIC5SMdXwNmiPWefRyg1hLCCnm+joohEAcKWy0hrIMwfc0RItkfq8wK+TapRT3d/oAQr9hXsaWaJRzHt1+eDPdntHa8OGOYb+w8MDSjh6HN0QWrTSMzs57xxAA4irLUFdnQTv7CkPnfJN1OA3WOTx9Zw+37/ew0Y6aaJVYy5HntsyK0jYdlryw+Ne/8tEzbX8OBb/E3QfdST3FU+nlBp1+GfJTj3D3QQ93H/Tx+IuuHvi1vLTYaMXIhgrLZgHNvdFzhvNQBFPgnEe/COH29bVlnmWxwlpVGIatvp7vaYgWyHxfYVbEtUsZNnfyKrICzdgIzRc7dNajXXUMp5lhCFQdw+qNXS83AKZbaEyTq+6Ut9OwcOG48bl5oaWE9YMFNIflwk1r6UtpHJ58bhcPOgXW29HI+JnWEr3CNhl2y6xfmOZ84Uee2cZ73/80futDd870Z2SJwuZOXv3MzcbWbn7iOcknngpjpI+/8PKBXzPWY6MdQymJJFIojcNGO8Z6KxpZQCOAmf49aZR1YZS0lWo47xHN+XUwSVTTrQ5ZhrJZnkNE82++rzAr4tqlFMY67HTD2By3ws0n63zYBorqDJnAuRZaXIQcKgzrInVZC8N6hCpLNFCNac67umMIHBFy7/1Uztv0C4OPPruNorRYb0UHziRJIeD9akwndPPBRtLbW6Hr974P3z1TgS6EQKRlM3I5bcY6bO70w1bKYzzx5BZecHMN6634wK9575uIi41WhLLakHvrRnukYxhpiU5/NbrJi8BYi35uq44hoPV8XwcTrdBKokGWoWIHmmiRsDCcA1c3UgBVZIUU6PHu2lyqC8NBceanP0oqRDPGGu7qi6UNTQ/b+OzMxnbPQ+sTCkMx+e/XXq/ER57ehgCQpUcXEt77pV9A46vFHfUa/TtbPSglsNst8aGnHpzpz8oSjZ1OMZOiabtTwAPHLh25v93H7a3eodtIjXWII4WoOhPdyiLUx75uXW9jazdv/l6RltWSreW8riwaYzx6hUErWYyOodYScjjLUM526RYRnc18X2FWxLVLoTDc3OlDKYnSuKU9N7bI6jOG7ebN9my6WEmsEWuJXtW9XNY3cNZ59HNTdUn83GZ3DYu1Qv3taB/aMZzs6vbNnT4+9sw20liduKREiuXPrDPWwftBQXV7s4fPeul1XFlP8JtPnG2cFADSROG5e52p/sw573Fnq3tsoD1w/BhpUVpcbg+6iEmkUP9DvXVjDcAg6F7KcPOCC0Pmg7EO/SJ0DL0Lhdc8qyMrBlmGAkW5GmPrRMtgvq8wK2K9HSPSEvfrIGXvl3rb5KIqjEU3NyGqwntoNZu14ZFWyBIdRkk9lrZj2CtKFMahtUAdQylFs/FxLdMojUMxNEYl5GTGN533eP5+B5+8s4e1VnSqN49ai+bmwrIa7lT0C4PtToFHb6zhtY/fxCfv7OHZkaUrJ0sihW5usdudXqRDt29QGnfi0pEnntrCrettXFpLDvyasR5rQ+OlcdXVcc7j0WstCLF/AQ2Qr0jO5bzLy7owDBmG836DLHQ0RZNlKISAx3RG6Ino4lgYzgEpBK6uJ9isNpNCzGatPR3PGBcKwyxsh5tV4HqkwihrvSBiSetC7FQBya00wqy6s2elR0LuwwKG4a5hGKsa76i4dQ6fvL2H21th8+hp3zhGWmJvyc+SFaVtoirubIUswkeut/GKl11DpCV+6xxdwyyRePZ+d2pTHXe3eohPyEvd2s3x3P0uHn/RwTFSABAYnE0GQgd1LQtB93GkcPNyhmeHF9AIoLsicSbzrs6VXJQbZFKGa/VolqFgB5poQbAwnBNXNwaRFfAMuZ9HpXXo9g3W0gjW+alnGNa0kkgTjW5uIcRkRxNnqc7AyuL5DnUeJk9TGI6xE1Mai48/u4vdboFLa/GZvkZKShiz3COD/dw0yzrqwvDR622kscYrX3YNv/fxzTPHdkRaoSxdtYp/svqFwW6vQBqfsHSmGSM9WBiWxiJL1YGO41orHgq6Dwto6nG/uDpnSLNXZ6Fm1eIhuQDXwTTWWM+YZUi0iFgYzolrlxJs7eawzkEpoLfkZ38WUVFadPsG7SyCc0B0wl38SYm0bDqGkxpNnAc71RuKNNFzn91VG44vOawwlGNcxNDLDf7w6W0Uxja5YWe33BljYSNpdb5wq4c0VrhcjVq+5jNuwjqP3/6Du2f+c7NU4fn73YlHjzzYzaFPEYnzwSe38Mi1Fq6sHxwjzcsQS7FfFqtm2uDW9Tb6hcVmdZyh3kzKc2Gz5b1Hpxr3zqqz1vPeMQSAJJJYH84y5M1uooWxGO+2lljdYbi2kcJ7hC1eSqKfczPpvNnrlbDOD42SzqhjOFQYSiGmlos3bfWd8jRWCxFuD1RjXkM5hgBGsgzrRQwXtdPJ8ZFntqGVaEbMzmscz2ceDTaSDqIqbl7Jmq7q9csZXvroBn77jNEVQOjaW+uwtTO5rqGxDve2+0hPWDrzYC/Hs/c6h3YLAcA5YC09eOMgjhTq5lOzgKY6ZyiECFtrec5wppwPC7gANFEl837GEACSeDTLUPJmN9HCWIx3W0ssUhLwByMr+kv6Zm1Ree+x2wmFyloawXs/sy5WHZfRL8L5qWU9j7rXHRSGi9IxlFKEBTTeI0s0hBjtGIZFDOcf//Xe4952Dx9/bhdZohBHZ785sdMpcKfK41NKNtmcy8ZYD+cHRc6drR4eupKNfM5rP/PmuaIrgLB19vZWb2Id151uAedP3sb7oXqM9JDzhd57CIFDR1F1FXRvjMP1SyliLfHM3b2hzxDIx3wels7GVeH2QF0YLsZZ6zhSyFI9yDKUAjlvdhMthMV4t7XEpBSII9WsEm8iK0q7tMHli8gNjfS0qzuh0w63rykpkFVv9ApjYJdw25urwu2FCOedojkPdR4WaQXrwhv6dhodeobNnONn23mP5+538cy9DtZb0bmKZe89/vkv/iF+5Gd+r3quYmmXjJRmsHhme69AUTrcvNIa+ZyX3bqEq+sJ/ssTt8/859c3AO5v98bxdEd473F3q9+cKzvOE089wENXMlyrbi4OK4zDWnb0QqL1LEJhHKQUePR6u4msAAApge6SLyead7a6DgKhC7coZ621lFBSDmUZyqWPxiFaFiwM50Ar0825sSayQixvJ2gROefRyevCUANidndupRhsGOwXbinXgNdviEIBLGY2tnseWp0Qcu/9mTuGxjp84vld3NvuY6N1+s2j+/23j23i9lYP9x70wmIWJdHN7VLmpg538m5Xi2f2dwyFEHjN4zfx9J3OmaMrgHAtuLvdQz7mCY9O3yAvzYkj1DudAp+8s3fkNtLyiPOFtXYWNUtBbt1o4/mtXnMWLCygYWE4S84B/cJCSgGtxMJMTtQ38i6txXiwGzqGnIIiWgyLcZVZclmiYb3HtY10EFnB9c5zxVjf3D2vz+uoUyyFmAQlRdNJ6Bd2Kf+dOOfRKyxaqYZHGHlcFJEeLgz1wcJQiDMV80Vp8fHndtDpGWy0o3N3DIx1+OX3P9PErDy/1av+rOVcQNPtm6arf2crjM7e2FcYAsArXnYNsZbnCrwXQkBJibsPuhd7svvc3+6fKg7nQ594AAD4zKPOF2JwNu0wSTx0zvB6G855PFeNGTc3DTi5MjPOhxtkrUTDeyxMYVhfry+3Q8dQSgFn/dKehydaJotxlVlyWaLhrMe1S+lQyP3ybptcRM57dPthtDFLNOBntx1OSoF0qDC0zi/d9sC6Y7go2V3DYi2bDtzhHcPTnzHs9g0+8sw2jHVoty62ZOa3P3wX250Cr/+8FwIAnr9fFzNiKa81vcKObCS9sp4gOeRMZhprvOJl1/H754iuAIAsUdjcyZuRv4vKS4vtTo4kPrlL/sSTm7hxOcX1ywcLXuc8lMCxf06sJaQIQfe3brQBoBknrc/DjrsbSqdnqzOGWaLgvEc0o+MLZyWFQKQlNtox9npl6EILLOV0C9GyYWE4B2KtACFwdSPBTqdAaSyUAmfy54h1oTBsNsMpObPtcEoORkl7uQE8lm4U0HmP7lBhuAjZXbVIS9iqy9LOInR6ZqRwlxLIT1GIbe/l+OgzD6CVONVZs+PkhcWv/u5zePEj6/isl17DeivC7aozJIBm8+Ey6RWm6bDc2erh5iHdwtprHj9/dIWo3gQ/vzmeruGD3RxKyBM7w3u9Ek/d3jtyG2lRWmy04mN/doQI52BL47DeirHRjpvNpAAgvB9bvAqdnbUOvcKEm8ducTqGAJBGCuutMMa83SkgsLyZu0TLZHGuMkssjiRQjZICwOZOzsiKOWNt6GCtZRGc82Gb7IwIIZpx1l5uAIGlG/eyzqOf26YQn9Win/PQ1aZhIHQM63GwmpQCxTE3fbz3uPughyef30WW6nNtHt3v13//eXRzgy9+1WMAQjxBXchEWqKzZAtojHVwNixVMcbh/k7/2MLw+qUUL721gfd96OzRFUCYItjpFOhccFmLdQ73tnvI0pO/58dtIwXCxMlx5wtra61oEHR/vT2ymVRriU6vOM1TpwkorUO/sNUoqV+Y2B4ASBPVhNw/2MvhAZTsGBLNvcW5yiwxrcLd4Tqc+P5OH1oJ5v7MkdIOwu2t96c6/zNJrVRDihDgDaDpUC2L0lj0CoNWGmFRQp1rw53ko7IMjxrPc87j2XsdPHeBzaP77XVL/Offv42Xv+gKHr0exgVv3VjD3Qd9GOugtVi6yIqidKgPz93d7sH7g4tn9nvt4zex1yvxxJMPzvWYaaLw3L3Ohca6dzsFrDtdVt0TT23h2kaCm4eMkQ6e08md5vr8GhAW0DzYGxS4kZIj/3Zpuqx16OehY+h9KNQXRazVUMh92Eza45Zborm3OFeZJSaEQJooXGoiK3IoKWGMW7pO0KIyxqGbG7TTKtw+mu2PThwppIkejJIu2YTObqeE90A7DVtJFyHUuaYOLQwHb4ikFMgPCQ431uGp2zu4v9PHevv8m0f3+5UPPAtrPf7459xqPnbr5hpc1ZlUUqI0bqmWGJXWoa52bm/WG0lbx/2WJrriN88RXQEASaTQzS12u+frsHnvcXe7jyw5+drS6Zd48vldPP6iK4eOnFrnoLU81Q2s4Y70/nOGWkvkpeXSkBkpjG2WcJ0m03KexJFCK42aLMNYywt31Ilo8lgYzoks1pBSYL0V4X69mZSRFXMjLy06/RLtapR01vEJWklksUIvt2GUdMnOGG53whKmNFYhNH6BzhgqKZpu1WGFoZIC5b6OYV5YfPSZHXT7FhvteGxZZfd3+nj/h+/hcz79Oq4O5dzdurEGAHi+KpqEEKHLtiR6eTnYSPqgB61kM5FxlCa64m5n5JzdWWSJxLP3u+f6eezlBr3cIDrFteXDn3gA74HHX3j10F/PC4dLp/x3pJVEEoeg+0eutiAERvIM4T3yYnn+bSySbt/AOY8s0RBYrCVcWoUbepfWYmzvFYj08kbjEC0TFoZzIokVjHW4up4MCkNuJp0b3dzAWF+dMcTMz3pEWiCNdRgl9X7pOss7ndB1yRI90/Oc56GUGDljCIwWhqLaAll3YTr9Eh95Zhveu5CROUa//P5noJTAf/dZj458/PrlLCxMqTeT+ioQfkn0ctuM4d7e7OLm5fRU3ZZXvuw6Yi3xW+eIrgCASCuUpcODvfzMv/f+Tv/U15UnntzClfUED189fIzUWo+11snnC2trVdB9HCncvJztK4wF8pLjpLOw1w3XjWbp2QLdIAs/f74Kuc+rLbce5RLdgCJaRov1jmuJJZECvMC1Syk2t+s3FcwynBfbe6FQadfxCTMuVrSSSBPVLJ9ZtjOGu1UhlcZqoTbxAeHNmxBhNDCOJLSSh2QZhjfvW7t9fPSZbcSRONV5sLN49l4HH3xyC5/78oewVp31GX6OD13NmgU0Sgl0luicYS/ft5H06vFjpLUkVnjlp4boigPfs1PKUoXn73fPNH5ZGosHe3mzbfg4vdzg48/t4vEXHj5GGnhk8en/Pa3tC7p/5u7grGSkBc8Zzshe9TOZJRoQi9YxlBAQTZYhAAgvkC/RDSiiZbRY77iWWKQlIICrGym61UgRIyvmg/ceu9Wd23YWzcULdKQk0iicMZRCoLTL9WJbf73TWM28O3tWQggoJeC8DxtkM30gH8974M6DLj5xew/tVJ9qfPCs/uNvP41WovH5L3/40F9/5GoLtze7zbbD7pJsJjXWwbqwkXSvV6LTN8cuaNnvNZ8Roivef47oCiC8IbbWYWvn9F3Drb0cAuJUo58f/sQDOO+P3EZqjEMS6zP93CSRCrklCGPGeWlxfztMrkTcTDoTdXYuELIygdMtJZoncaSw0QpZhqVxkJLvaYjm3WK941pi9Yv4ILKiD61kOENGM+W8b97Yr83JlkwpBbJEods3EAKwZrk6ht1qSUESKUR6sd4MAVXIvTs65F6IsGRqox1NpPv80We38fHndvGFr3jkyIDzh662UBiHrd08bEFekvM/pXHNKO+drWrxzBEjl4e5Nhxdcc6JjXYW4fnN7qmOAjjnce9B/1QRFUDYRnqpHePRa4d3QfPSNovMTiuOJFQddF9trq3HSZWSKO1yLSdaBM559PLhUdLFWsIFAFmssN4O0wpbu/3qJgMLQ6J5xsJwTigpoaTAlbXwgn5/J4dSgnfX5oC1vomFaGca8LN/gQ4h9xrWeRi3XG/anAtf70hLKCkXbpQUCNsc60nCwwrDS+1krEtmhnnv8R/f9wwur8V41affOPLzHq7GK5/f7FbPYznO/5TG1XUhbm+FUdnjMgwP89rHHwrRFVVW4FnV14f7270TP3evV8IYByVP/nfeLww++uzOkdtIgfDz086iQ3/tKE3QvXW4filFHMmRBTTeHx2xQpPhnEevCF/zNFm8JVwAkCSqOWdd3+zu9ssLRboQ0WQt3juuJZYlGmutCEKERQSMrJgP1g2P9GiIORgllTJEnABhA+EyBQdb59HLLdppyO5atOUzABAr1Zz7DIXh9G7w/P7Ht/D8Zhdf9Nm3ji2qb17OIASac4bwAsUSnP8JY/jVRtKtHtayCO30bIXSy25t4OpGgv9yziU0QLiJdHe7d2JBdfdBt/lZPskffHIbznk8/sLDx0i994AQpzqruN9aK4IpQyTCo9fbIwtopAD6xeL/21gk1nn0q4mhRKuFvA4mkcZ6Vsdw9SGlgONSPaK5tnhXmiVWnyO4vJZgsz6fIgQvojMWznqUzcpwreVEOj1noaRolkvkhV26jmEvN80mvlkv+jmPSMtmLLOdRejl5txjiWdhrcMv/84zeOhKhj/yksOjDGpaS9y4nDWbSYUM/5YW3YHFM/u6hUVpT9zAKoTAax+/iWcuEF0hhICSEncfdI99rp2+GckSPM4TT25hvRXhsSpvcL/SOLSS8y1syhINW/2bvXW9jdubvea1J9ISezxnOFXOe/QLE4p8gcWcnFAC7SxkGW4129Y9Cr6nIZpbi3elWWJZrGEtcHUjwebQRZRZhrNlrUcnN1jLQsjwrDMMgdGOYb+wUyk6psVWo6StNHRnF218CghvpP3QKCmAqWz9/O0/uIet3Rxf/KrHTnXz4uGrrSbLMFLLEUDdLQwiFc543tnq4aF9hWGvMOjlJ/+8vOKl1xFH54+uAMLNvs2dPGwPPkQYrzvdv++8tPjIM9vHbiMtS49L7ePzGo+SRKreP4PHbrThvMdz90NRzLNh01ePkrYSDedw6n8n80QrCSmBS2txc7NbCB6RIZpnLAzniK42k17bSHF/u1/N4YulyhdbRPUoaTuNYJ2fiy2ZUgw6hv3CLFVchfMe/dyglUbwmP3Y7nkMn0E9LMtwEvLS4ld/91m88OF1vPTWxvGfW3UGH76aYa9XYq9Xhjf/Cx5ZYayDNWEccnOnD+v8oecLpRQnxkkkscIrX3Ydv/fxzSZP7qyEECEvcvNg17CsFv9kp4wp+cNPbsO6o7eRAoDzDq0zjs3WmqB763Dr+hqAwQKa8PXyfC2aIufCdTBLwg3JaAELwzD+KqrCcGjL7RLcgCJaVrN/h0uN+gzB1Y0UhXHY65VhAQ03k85UaS16uUE7i+CcRzwHhaEQoglD7xUWHlias6ihY2jRSjXg/cwX/ZyHkrJZ/79WfZ8mXRj+59+/jU7f4E++6tax3ULrHB7s9VEah4eGFtDUxdIijyWXxjVf99v1RtIrg+2d3ntIIXBtIznVdfU1n3ETznn89jmjK4AwornTKQ68Gd7u5PDAqcfSn3hqC2tZhBfcWDv01y9yvrC2lmkUpcNaK8KldrxvAY1HvgTLiRZFaR36hUWWajjnw43jBSOlgJICl9qDwlDr5cpMJVo2i3elWWL1hf/qehgF2twJa+T73AY3U2Xp0OkbrKV6bjqGALBRhZb3cgOB5Qm57/ZLGOuGzhguYmE43Y5hp1fiN37veTz+wiu4dUThUOvnDpfXU5TGDTaT3q87WgLFAr/5L61rNh7e3upBCOD65XTw68ahnUa4vJbgNPuarl1K8bJbG/jtD58/ugIIWyWfuzcIjXfe4+5WrzlXfpKiGiP9jBdePvJGSVE6rGfRhW6ktLP4QNB9TUpx5EgsjZ+xbjBK6j20mv0RhvNIY42NVoydTtFs37WMPyGaW/PxDpcAhPHAJFK43ERW9BlZMQe6/RDO284ieI+5uXObRBqxluj1zVJ1DHc6YclFK9GQSi7kGcNQzA6WzwCTLQx/9QPPobQOf/xzbp34udY5XNlIYK1Dlmhcase43WwmBfJyca83/cI0RfmdrR6uX0pHlnaU1mGtHSGNFWItT/XmtI6u+OA5oyuAcH6v2zfY7YZ/251eidK6Uy8U+egzOyiNO3IbKRAKw40z5hful0aq6WDeut7Gdqdo/t3GWk68600D1oYlXFmi4P1ijtQDQBJJrFc3MR/sVUv1vEDBG95Ec2k+3uFSI4sVWqmGkmIQWWH9iedhaHK2q0KlnUUQQkDPyQt0pCWyRDcZi8sQTg4Mvt5pspgr2oH6jGH4d6KVRBordCb0pnprN8f7PnwXn/2p13H9Unrs55bGIY01Lq8lQFUAPHyt1ZyB03qxu0KjG0m7uHllNATeOaCdhp/j6xvpqcZJX3prA9c2EvzmBZbQAECWKjx7vwvnPO4+6CE55SZSAPjgU1topRovfGj92M9rpac7r3iUOJIQIoyN3qo2n9Zdw/psGDPopqOfG5TGNduwF3GkHghnda9UU1B3HoTxbsH4E6K5tZjvupZYOGhebSbdzpuPG8MX41lpCsPqTddpgqinoS44erkBvF+aUdK6Y5hG51u7Pw+kCGdr3BSyDH/5d56BFAKve+WjJ35uP7e4dilFEofzm957PHy1hfs7OYrSQqvFXkDTyy20DlMWD/aKAxtJATQF2Xo7xmlutwkh8JrHHwrRFXf3zv3cIq1Qlg7P3dtDp1ciOeVZwNI4/OEnH+AzPuXoMVLnPJQSZyo2D1MH3RfG4ZFrLQgBPHNvr/k1z6iBqdmtbiQ1I/ULWhjGUSgMlRR4rhpZV0o02cBENF8W813XEovjMDZydSPF5i4jK2bNe4/daiPhWhbN1TKUSIfCsJtXy2eW5E7+Tne4YzgfX+vz0FVkAlAXhuPvGD53v4vf+9gmPvflN7HeOn6MMHR6PDZaEZQUSGINY31zzvD2Vi+caS7sQv5bss6hrM4w3akWzwxvJK07XXVmYBIptBN1qpG2V7zsGuJIjqdreK9zpmvIx57dRnHCGGleWqy34rHkq65nEcrSIdIKD11pjZwzBARyjgBORV0YZomGEGJuXnfOSkuJSCs8fK3dFIaRls2kCxHNFxaGc6Yenbu2kWJzJ4dzHkJwHn9W6qgKoDorJsTcLENRSiKNNXq5gRRiac4Y1iOXaazn5jznecRDIfdrrckUhr/0208jSxQ+///18ImfWxiHtSxGVOVwrmVRtYAmFE/Pb3abwqJcwAU05VAn606zkXRQGIaFRmrkDfbVjbSJ7jhOEoXoit9/cuvc0RVAuFmQJfpMI58ffHILaazwokeOHiMtzcXPF9ayVKO+kty60caz97pNUS1lOHNNk1f/O2ulGt77he0YRjo878duruH5++HfklYSeWl5RIZoDi3uu64lFTZeelzdSGCdx3angJTiVG9eaPxcFbYOhPOfSoq5WYaipECWVKOkAkux5c17j07fQIjwszAvG2DPI9KyGe+dRMfw48/t4KPP7uCPfdYjSOOTC428cLg6dAaxnUWwNhQUWaIGm0k9UCxgXt3+wjCJ1EixVC+QGraWxfDAqc7Nvebxi0dXAOGGx2k7e8Y6/MEnt/EZn3L5+BF2IZBdIKZiWBKp5utx63obeWlxbztMr8RaTuysLA0455uzvnXO5aJ2DFV1s/uxm2vo5qY5KgDvF3oDMtGyWtx3XUtKKwkpZXNY+/5On5EVMxQ6hiXSWEEKgTianx8ZKQTSWIdD/H45CkPnwxuiVqIBLxZ2+QwARNFQx7Dqzo2r8++9x39839PYaMd4zaffPNXnCzE4JwuEN/kQAkIIPHS11WwmlRLoLeAm5Lyw9T4d3N7q4aGr2UgBZqxHKxktDCMtsd6KTzUeeW0jxcseu3Th6Iqz+NizO8hLe2yovbUOsRLNiOxFaSWRRDoE3e9bQKOVRDdfzFHjReK8b7aRZ3Hocs/LDcmzkkIg0hKPXg//lp5ronHASSiiObS477qWWBaHFfJAnWUoF3pT4CKzLnSw2mkE6+cnwxAIHcO0ykErSotyCRYUhTvlthm1W9S75AAQKQVf1Q/jzjJ84qktPHu/iz/+2Y+eaty2X1hcXotHlvkkkRpZQHN7qwfrHLSW6E5oUc4khY2kYUHKna3eyPlCICxPOezGzrVLKcrydD87r3385oWjK87iiae2kEQKL35k48jPyUuLS+1krI+73tIojcP1SymSSOGZe6EwrAttvqGfLOs8etWUUBKppuu2qNJI4caVDEIAz23WC2gkF9AQzaHFvtosqTRWSCKFOJLY3OlDSgHrGFkxC646Y9jONJzziOcoZFjKwfhYXtqpdTEmyVaju600FFKLeq4GCJv36nKjnYVCdxyFoXUOv/T+Z3DzcoY/8pJrp/o9pfG4sj4aZSGrBTS2WkBjncf97RyRWszFEJ3cIFIS250CeWnx0FBURbN4Rh/8+W2nuoloOMlLH62iKz54sSU0p2Gtw4c/8QCf/imXj93Oa104wzpO7SyGMQ5CCDx6fd8CGu+RcwRwopzz6BdhS7BSYqGXcAFhkZgUEjcuZc3IeqQlOjnHkonmDQvDOZRWm0mvbaS4X53tABhZMQvGOnRzg7UsgnUe0RyNkiopmrNl/cLCLMGNA+c8+vUoqZifaJDzGC5qx9kx/J0/uIfNnRx/4lW3TtVRtc5BK3HowpO1TKMwrtlM+vxmN9yIsm7kzN68c86jLC2Ukrh9yEZSYz2yfYtnakpKXF5LTpWr1kRX3Ovg6QtEV5zGx5/fRb+wx24jBUJBW59DG5ckkqhzOG/dWMPtrS7K6typUjxnOGnW1SP1Cs5hoZdwAeGGjHEOD19rNaOkWgn0OJZMNHcW+2qzpOLq8P/VjRT3dwaFISMrpq+0LnQM0wjeodnoOA+kFM0bwn5pYOziv8DWHcN2utihzkAoDOtnPygML9aJK0qLX/nd5/CCm2v41Mcuner39HOLqxvJoWeU2mlYQHP9UgqtRBN0D4imEFgEpXGoDxgeFlVRGod2cnRX7fJ6cupR7Dq64rcuGF1xkiee3EKsJV766NFjpHUA+rjzPuNIQchB0L33g7NhsZYT2bBLA86HG2Qh19gv9FlroIqI8cAj11rY65XY7RYcSyaaU4t9tVlSkZaAAK5tJNjuFDDWQYAX0Fno5wZ5acMooJi/0cZ6hKyfW5gF6vAcxRiHfmEXfkU7EP6t1KVGKwnjihd9Q/1fPngbe70Sf/LVj516s6V1OPIMWhwpeIQC/OaVwZgXgIXKqyutQ/3FvrPZxeW1eCTs3VqHVnZ0YdhKNbTEqSJfkkjhs8cQXXEc5zw+9IkH+LQXXD62W5SXFpfWxhNTMUxWQfelcXjs+r4FNJpRA5NWj5JmaTjCsOgdQ60EIICHrw0mE2rFErxuES2Txb7aLKlISaAaJfUe2NrNGVkxIw/2wmrtOtx+3gqVjerNbq+w8N4v/FjOdhVuv+ihzkA1Blt9P4QQWMuiC43gdfslfv33buPTP+UyXnBz7VS/xxiHJFJIj4gyiCPZFJgPX23h+c0qZ0yLhVoM0S/M6EbSofOFAAAhwhbWI0ghcHUjPfWSr3FFVxzlyed30cvNsdtIAcC50PWdhPUsQlGGiI/La3GzgAZAOGdY8A39pBjj0CssWlXHUC/wSD0QttnCoxlZr7vPUgK9BbrOEK2Cxb7aLCkpBaJINXeCN6vIih4Lw6nb6eYAqjX/QsxdYdjKNKRAk2W46CH323vh653FauHvkksZoiC8H0+W4a9+4HkUxuJPfM6tU/+eXm5x7VJyZHdRSYkkUjDVOcN+YbHdKRApic4CvWHr5RZaCRjjcH+nf2AjKbwf6SAe5tJagtNOY1+toive96E7E1n69MRTW4i0xMtuHT1GWkeQHFX0X1Sa6JE8w5EFNBDIy8X597FoSuPQL+rYHox9VHjaQgxXuDlzbSNtCsNFu84QrYLFvtossSxRTWTF/Sqyor+A2WKLru4Y1nfl1Zxth4u1QproptNhF7ww3OmEwilN1LEdnkURR6op1tsXKAwf7OV434fu4JUvu44bl7OTf0PFw2Ojdfyo4VqmUVqHh6q7+bc3u1BKIC/twtxo6OUGWkvc3e7De+Chq0OLZ4xDEusTu89pHP7NnTYP9LWP30Snb/DBJ8cbXeGcx4ee2sKnPnbp2DPNpXFYS6OJLWhKY1Xvn8GjN9rY7hTN6KzW4sLnZeloRTVSnyXhhuQiT07U0ljBWo9HrrVGNpP28vJUG4GJaDoW/53XkqoXCrRSjfvbjKyYBe9980aoVS1DmbctmVpLZLEK8QJ+8TuGO9XXO4v1wi9cAMKd8rpYv0jH8L2/8yyEAF73ykdP/Xvy0qKdRicGn7fTCMZ4PFR12Z673x0shliABTTOe+SlhZICd7bCG86bQ6OkpXVYy07e2imEwPWNFP38dH/nEF2R4r+MeQnNJ+7sodM3J24jLUqHjQmcL6xpJRFpBWsdbtXnDO+FTayxluj0iok99qrr9Et4j2a52LxNqpxHGutmM+l2p0C3X0IIAeexUBuQiZbd4r/zWlJJFA6dX9tIsTm8mZQX0KmxVYYhEJaHnPQGexYipZDGOpzTEGLhzxjuVW8240gt/PgUEO6Iu2aUVGOvd/a747c3u/jAR+/jtY8/hI326QuBonS4dik98fPCAhqPOFK4tpHi9mZv8GcswPUmXBM9hBC4vdWDVgJX1wfLdozxpz6Ht96KT/0zJITAax+/iWfHHF3xoae2oJU4ceus8z6MGk7QehahMA6PXGtDCoGnq3FSpSRK607dXaWz2a3OWtcRM8vQMcySQccQGATdA4txnSFaFYv/zmtJRUoCQuDqRoL7O3nz8WWIJFgUropOiHU4HxHN4WhjyDJU6FVdjkXvGNYdtSRWc/n1PqtIi+Z7svb/Z+/Pg2RLz/Je9PmGNeZQWXPtubt3d6tbAiM0AIZr4CAZjn0UNoPj2phL2A5dBhvb2IHDEdgRhP+4Bv9xAyIcPsc+OMLYBxMhri/Gxj74+BohYTxJgARCanVLvXf3Hqp2Ve2qrBzX/H3f/eNba2Vm7apdU2ZVrsz1iyBC1K6uysphre993+d9HseAUjhzePxvfm4TtsnwLV+7cer/Zniv8SRGDGiW3dwxkBIUQr6uC8NBVMVqwzl0kFanbupYJoNr8VM7QP+Rl5dhGWxs0RVKKXz5wQFevrHw3McspQKlBNaE9gszqo6BJJEwOMX6kjNiQKMUKZRzbZHoZsoJiwFKHRk1UzQc24CUAwOaTE5KiHb/LikpmQ6Kf/KaUQyuHQ2X6zZ6fowwFmVkxSWjJ4YxKo4BKdVU7rzpLMPZkJIqpSe0lsHACC28+Qygd0Az9fd5Qu4fbHfx1cdtfMvXbpwpxDyIBBYq5qmmrqMGNA7a/Qh+mMDgFP0C7JFl10ZAT1fXDxvPEALTOP17aXnBPrUDtGUwvP+VZXzp3YN8ynMRHj/to+vFJ7qRRolAzTUmXjBYJsvzIW+sVLC118+vMbpxUN6Pxo1SKjdkmSUpqX4vKTgWR6NqHjKgKXMxS0qmheKfvGYUzrSj4VJdS6KanQCMkUJ08GeFTEpacTiEUjDOcLi8LPTEMDOfUUgKXBgKqeCHiZZPTWFm5HlgjObTu8oZC0OlFH7j9x6j5hr4htfXz/R740RiqX6yjDQjM6DJu/lND5zRM083r4IgTMAYQd+P0Q+SEUfSROi4jrPsBlcdEyA4teT3w6+l0RVvXTy64o13D8Aowas3G8/9vjg52VRoHGRTyyzoPool9tp6tcHgNJd+l4wPqVR+n3dMVvjYngzLYLlL87XlyqAw5KUzaUnJNDF9J90SAHp/xbZYHkzd7IRglCAos6MuDSkV+mGCqm1AST39mTYo1e+TzJioyCH3mXTXnaEuOaMkn7hkE8PTTuHeetjC5tM+vv39188kq5VSgVNypv2zzIBmuDCklEAKOfV7zf0ggcEodg70buSI8UwiddTMGTA4RdUxEcWn+7uX6jZeubmA33vr6YV27jIZ6UvX6ydKRBUUXGsy+YXDUEJQTYPuBwY0Wk5alIly0ZBS5QZI1gzE9mQQQuBa+r20sezioBsiCBNtrFeA60xJybwwG1ecGcUxOWquvvnvd4IysuKSSYRMJ4YGFKazUGGUwDH1wTeIi20GIaRCkE0MMRuGC4ySPOT+LFJSKRV+83ObWFmw8XUvr5zpd/pRgqW6fabnT0+GFCqOgZpr5Ps/IGSqnUlzR1I2cCQdjapQ+fvpLCzX7VMXhsB4oiu29vro9CO89wQZqZASnNIzyWMvQs3VBjTLCzYsg2EzNdrJnLLjKX5/FBEp9WeYEN2MNKYsIukiVGyOOJG5AU22zzzt15mSknmiLAynGNviIASoV8xBZIVQhT78F4kgShBEQk8c1HQWhnpiqA++YSyQFDjORCoFLxT5QX4an++zoieG+n+bnKbyu5MLw99/ew977QDf8YEbZy6QhQTqFevkbxzCNGg+2VxfcnNnUgKcet/uKsgm5IQQ7B74qNh81IGUaIfns1Jx9LX3tHLSl67XsbJg47Nf3jnz78p448EBKCF49Vbjud8XxRILFTM3DJo0tsWhpHZ9vbE6GnSvlEJ4hgK65GSEVHmGoYKWo88Kjs0hJHBtadSZdNqvMyUl88TsXHFmEMtggCJYHnYmJSgLw0uinYbbVx0j3Xmbvo8LJQPJYBglhZaSJkIOdgwxnc/3WaGUAEof4Akhp8oyjBOB3/r9LdxcreA9txtn+n2JkDA51W6GZyA3oEn3DJ+2fcSJBGMk3V+dTqJE5hPZnQMf60vu6DconGuyxihFo2qd2lyFEIIPv76GrT3vXNEVSil8+d0DvHi9dqLJUCIUapewX5hhDbmj3lipYLfl5yZolJLSUXLMSJXuWls6smoW8lwzLK4NaCqOgfqQMoEzUu4ZlpRMCbNzxZlBONPThizLUCkFqDKy4rJo93VhqE1DFNiUSnoyuXEQikK/N4JQQEidzUYwQ1LSoclTJc0yfB6f/fIuul6Mj3zo5pmnQkEosFK3zzVNymReG0sOlAKetvypN4aIYqFDsqXC05Y/YjwjhIRh0HPnYTZqFuLk9J+nr7uroys+e47oiu2mh1YvwntPCLUH9HvprIX/RTDSSbcQEjdWK1AKuXGIySm6Z3DZLTmZ4YmhVGpmdgwBwDAoaGpAs/GMAU35PioZP0optLrhyd9YkjM7V5wZJDOcWKrbCCIBP9R7B2VkxeWQF4Y2B0CmVtpYdfT0IIhFoV1JWz3tduhYfGYOQ4QQcEaGQu4N9J9zkPbDBP/lC9t45eYC7qzXzvz7JIBa5XzTJNfmzxjQcEYRRsnUxqAEkXYkbXZDJEKNRFVo45nzG7S4Ngenp4+AMdPoijfeOXt0xRvvHoAQnDgh1i6rHMYlG2HVXBNRogtDYNSAxgviU0tuS05GpMoJZwYnhnTIgObasou9doAoFmCMIi74jnzJdBIlEo93u+V76wzMzhVnBmGUgjOCRk0f9PY7YRlZcYl00sLQtXgeHzKN1F0tPfNDASlVYQ9p2fNtm7NluGBwOpJl+LyJ4X/5wycIY4GPfPDmmX9PFAtULD4i/TsLtskBKCzWLJgGLYQxhBcm4IzmxjPDjqSJUGd2JB2GEoKlun0mKe2HX1uDVGeLrshkpC9s1OCeUMiGscBCdfJupIfJgu4rtoFG1cz3DAnRTY+owBL2aSOREkEk4FpM77bPUGEIpMoEoZ1JAeRuwoA6k+FTSclpCGOBrhchLAcqp2a2rjgziG1yNNIJwH47AGdlZMVloJTKD/C2xacy3D7DMjhMg+YHWDGl052TaPfT59ucnYkhkBWGg4mhH4ojd0Hb/QiffWMXX3d3eUQSeVqCSGC5fjbTmWEyAxpCCDaW3Hz/R6npPLAppSV3nBHsNH0QAqwu2EP/rpsMF6FesXAWdfZ5oit2D3w0uyHe+8LSid8rhcoVApeJaQwF3a9Wc2dSAFCKlIeuMZIkEkGkJ4Yg06tUOS+OzSGHDWj2MzMjUjrclowdL4iRCDXVu/LTxuycvmYUx2KoOAYoIWh2AzBK4ZcTw4mThdtzRsEpmcoMwwyDUzh5yP3pnRSnjbwQNxnMGeqSm4yOSEkBoHtEMPhv/f4WAODbv/76mX+HUgoEBNULmJIcNqDZOfAhpZpaA5pESEg1cCRdWbBHGwpE5QHt58WxGExGziRD+ob3ni264o0Hp5OR6s81uXCxex6yXEWlFG6sVNDx4lwuyxjgT/EeatHwwgSJULkJEZ1Spcp5sTiDgkLNNVCxeb5nyDmZ2T3DRMhc1VByufT8GFXHQNebzffWJJid09eM4pgcUASLNRP77TANnS4jKyaNDrePUXU4pMJUT7A4p7BMBq/gE8PsoGkb7NJ3qCaJwVn+mlSywrA/Whg+bfn4g7f38OHX17BQPfvUL4wEFqrmuY1WMgYGNC7iROKgG8JgNH9vTRNRIrMkEOwceFhrDBnPSKmbOhd8PgghWFlw8sDx0/DStTS64o2dUzVpvvzuAW6v1/KmwXEkQsK12YX/pvNACUElC7rP9gxTOanJKXpHNDpKzkfX05+1WYrtGcYwKEj6yb22PFAmGIyi50/fdWYc+GGCnf1+YZu2RUVKBT9IULEN9IM4b9CWPJ/pPe2WABg1oGl2tDlHGVkxebKJYcU2IOXFJw+ThFICx2R6qqNOb5YxbfSDBJQSfaCf4kL8rBicQg3tGAJA55A5yW/+3iZMzvB/+9pr5/odcSKxWDu/jDQjM6BZP2RAM40GI1EsAKWL4lYvwtpQVEWcSFQvYDwzTM01z/S359EV+95I5t9RPG352GsHp3IjDSOJhTPmU46TqssRJRLXllxQSvA4/ds4o/BCUR66xkTPG+y2A7PhzjxMFrEUJxIbyzr+JEmjcabZ6Ooi9LwYXpCU57ZLJhyK1VGyNG48LbNz+ppRDE6hiC4M9zthHlkRl8v+EyUvDB0DSk23MxxPQ+79dKpRxPuqVApeEOuoCkpm6jA0/LfkUtL+QNbycKeLtx618M1fu5FPCc6ClAqUkgs5cGboMHiFtYYNSgm2m55WKajpa0YFoQDnBLstbV4x7EiaJAruCRO402KZDLbFz7T/dNroikxu+tqdxok/UwHnen+MC9cyoKSOT9hYdHJn0syUqzx0jYcs/iOTks7axBDQsT2x0M6kSgE7LV+/j6bY6OoitL0IhJDy3HbJaKPG7POjEJ4yl3bemd7TbgkA3Y0lAJbrFhIhtU6aqPICM2FkPjGc/q5tNjH0ggSAKqSUVEoFLxT58z1LhyFGB5fZ7O/LZLNKKXzy9zZRdQx84+tr5/r5fpRgsWaP5T1qGhQKBIxRrC7YucwLwNQ5T3phDM70fiGAEcMehYsbzwyzXLfPJCfV0RUreOPd50dXfPnBAW6tVU8MrM8mlto59moYCbpfreDJXj+f7iilEE6hQVHRUErlsm3bZDOT53oY1zZGDGim3ejqIsSJQJxNRMvmyaXS82MYXH9+OKcnZgiXaMrCcMohhMAyGBrp3tF+JwBntIysmDBRLOBHemI47c5wjBLYJkcYazmXmLLJzmkQUruGOTNZGJK8ackZhWOxPJrjK4/beLTbw7e9//q55cpCqPz6cFE4GzKgWXbzyAoCTFW3deBISrFz4MEyGBaG8xuVGqthVM01AXI2Y6cPv7YKqRR+95joiv12gN0DH+994WQZaZRIVB3jSosEg2uJtw66ryJKJPbaer2BM/rcfM6S0yGkQpAVhhabKUn9MFb62VyomrBNljuTckbghbP1PsqUPJzRqTTxmmV6fgzD0J8hk9NnVjhKjmY2rzozhmNx1F0ti8oiK8oLzGRp9yMoBVQdXajwKc7Vo3TgVBglAoksXmEo08KwMoPyKcaIzk5I0Q5pEaRU+M3fe4zluoX3v7J8rp+dCKldaa3xFUFVZ2BA0w8SdL0InBH0p8h5MhF6Mk6IjqpYW3RySaN2UqX5fvY4MDhF1THPNM1Yqtt49dYCPndMdMWXH2Qy0lMUhpEcLXyviJqTBt2vZAY0OrbCLLvxY0FfB3UhYXE21fedi5Ad1gFtQJM5kxp89hoMPV9fP42yMLxU8kltqthhjCJJZKm2OwVlYVgAHJPBsTk4ozrknlIEMya3mDZavRAA9N6WUiNywGmDEpLvo4SRPDIjb9oRUsGPRB7wPUvyKUpJngEHaGfSTj/CF+7t42krwHd84Oa5319BKLCyYOdF0TjIDGg2cgMaH5xP16EmTgQICJRS2D3wR/YL40Si4oxfcrlct88sc/vw68dHV3z5wQFurFZOV/CRwc7ZVVJ19HtjqW7BNlm+Z8g5RRgLiAI2paYJqRSCKIFpUBBKwKf4vnMRtAHNQJmwe+BDCO0k7Edi6oyuzotSCq1eBNPQ0/ZZ+tumnSOl7aSU856G2bzqzBimyQAQLNctNDuBNoOQcurMIGaJdk9LDio2B2V0qgsVQkhuShFGAuIsidxTQhwLhJGAY+m9mmkuxM8KJST9zA6yDA+6AT79+1u4vlI5lfHIcUilUHfH61RpGRwgwPqSLrYyZ9JpOvhnXd9OP0IYi5H9wjiRYzHiOYxr8zPLSbPois8ciq446IZ4su+dyo1USgVKxrszeV4sUz8HhBDcWKmMuq7O4H7YZSOzBpnFIaUa69R72qg4BqJE4vpyBUIqPG0FIEQ3e6Ztn/m8RImEEHpqRQmBKo0DLw0vjJ85t1GiA+9Lns+Zrzq7u9plLUkSdDqdsT+gkmfJHDFHIitQOlxNkna6A2ZbfKodSTMyt0s/ShAXsDDMtP+OxbX0csYwGB0pDNu9CJ1+hI9+8Ma5p31RrA+Q1pgLBtOgUEobnSzWLOw0hwxopuTg74UJGCPYSY1n1oeiKpRSEzFp4YyiUTURnGHXkhCCb3h9DU/2vTzeARjISF8/jYw0FqhXzLFOhc/LSND9qo4aGLiRknL3/YJkO4aOxSGVupLMysvCtQwIobCxrD+7T7LrjCIz43AbHKGyiMuG/qXQ8xKYhxorpkGfawZWojn1VefevXv403/6T+PP/tk/CwCQUuInfuIn8Fu/9VsTe3AlGoNTQAFLdQsH3Uh37afQPn6WyC4ersmeubhMI5mrYRiLQr4v2kOFYRGe77NicJrnvGVF/N0bdbxwrX7unxlGAssL9lge3zCcUZgGRSIk1pecEcfAaWlG+aEYdSQdCrcHIbCMybyHFms24uRsjZc/ckR0xRvvHuDasovGKbIno0SifoX5hcMMZ9DdWK1AKWArfX9wTtCf0YDyy0JKbarkWhxKYqYnhpbBQAjBUs2CaVBspwY0hOoG5yzQ8aKR13DaTLxmFakU+kH8zOdHZ/LOZlbmODn1Vefv/b2/h3/wD/4BFhYWAACmaeJnfuZn8I//8T+e2IMr0XBGQZm+gMpUs07I9NnHzwpKKfT8GIwScD5eE4tJUU/3lIKwmIVhJ5Xu2uZsOvGZnOYxIisLNhgj+MgHbp7752lZIkHVmYwhSdU2cgOaZjdEGIupcgz0oyR1JPXRqJr5JCvLdJzUpMW1OTjFmQ4WpsHw9a+u4MtpdEWzE2Brr3+qaSGgD5PTICPNqFX0e+NIA5pSpnUhEqngR9qdWSltmDGrDBvQbCwdNqApfmGolEK3H424TbPSOPBSyCbOh1UW2f9/FtXHPHLqq06/38frr78+8rWVlRV0u92xP6iSZ3FMjoWqPgQ2OwEYI0fKFEoujpA6S6qS3pzNCU0fxknV5qBEH5iFVIVbcM+kpLbJCiHdPSucDSaGr9xcwP/rR745l1CdhzAWqFeMiTUtDhvQ7KR7hl5w9TfUREhIIUEpwe6Bh7XFwfMYC4mKxScmu6SEYKlun3mi8eHX1vLoii+8vQcAeP0UMRVCSHBORzIErxrXMiCkgmsbWKxZuQENYxRRQRUL00KSSAShnhgCs+XOfJhhA5pryy62m77eq2QUXpgU7h52GL2TPWqkps11ynPbpAljOeIEPgwhpeT9JE59qqjVamg2myM33M997nNYWlqayAMrGcUxWS4n2m+H4Kx8c08KkYXbOzqE1xhjHtqkMAwG2+K51bks2E216+lJg23M5sTQ4IMdQ0LIhXfgolhisT5+GWmGnZqMDDuTGpzCD+MrP7BFsQQIQSJ0jt5h45lMqjsp6hXrzAZPizULr95awO+99RSfe2sX64sOlk/x+oWxxMKUyEgzLINmsZzPGNAQzM5+2FUQxgJhLHIHWjoFe6WTpOLo6fO15Ur+ec6MuoreYDhqMqijxkTh7s9Fo+9Hx54jOCfo+uWe4fM49enkb//tv40f/uEfRrvdxq/+6q/ii1/8Ij796U/jn/yTfzLJx1eSYlsMJqOwTYZmGnLvTVGu2CwhpYIXxKinoeFF6Nry9L2R3YykBIo0eMsy0CyTjzWYfFrgjB7XwDwz2qWSoDoB580M09B7zTXXgGtxbDc9EEIgUlc98wonWLHQ3eC9VgClMBJVoZQ2jJokjqX3jpPUXv+0fMPr6/jKo6/AC7r49q+/fqr/RgiFqjvZQvesGFw3b4TUe4ZffKeJTj9KDXIwEjtTcja6/cGuNUgx7j0XwbUM7Ap/qAHl6UaPUghjWYim7HF0+tEzaiM9WNG72tOkApg1en5yrJrG5Aw9X0+kp8HQaxo59V3tfe97H37+538eP/ADP4A//MM/xM2bN/Gv/tW/wiuvvDLJx1eSYnAGQrQz6X5H2zpLVfyu2jQipEI/TFC1s5vz9FdYjBI4JoeXF4bF6kj2/Ri2qd/j0xwNcl4YI2O7CQVRgqW6PdHniTMKw9BTzo1lN3cmJVNgJe+HcepIqh/TcGEIYOLmRYQQrCw4CMKzTcZevFbDSmoWdJqYCo2CMwGH1YtSdUzE8dCeYZ5nSGYuoPwy6Qap6Zmli4ZZvBYOYxoUBAQrCzY4o3iSGdAUXO4nlULXj49cQykjKyZLIiTCSORNu2YnQKsb5v9OKYEU8srvY9PMqe84n//853H//n18/OMfz7/2K7/yK3j11VfxtV/7tRN5cCUDDE4BAizXLTzY6aVf1ZEVs2xpfRUkQsJPpaSAKkR8AiUEtsnyXT1RoMJQSgUvFHBtvRs2i13ycUrChFBoVCdjOjNMxTbQ9yOsLzn47Bu7EFKCpAHBk5ZrPg/tSEqxe+CDUb3zBwzy/i7DLKrmmvkh9rQQQvCdH76FzX0PKw3nxO9PEgnL5FNpflV1OFrdEBtLLigl2HyqzXR0N74sDM9Lz9PFUC4lnb6XfqyYBktzMYGNJSc3oOGcwA8SYOGKH+A5CSMBKY+eSBGCK7+GzjKHA+x/+TffRqNm4/s/8vLgi4QgjEQ5tT2GU192fvZnfxbvf//7R772oQ99CD/7sz877sdUcgT6cKAPQZ1+lHecyonh+Ol5EaTSB2OoYhQqjBI4Bd0xzLK73Bneq+Fjai6IVL7oTFguCQAVmyNOFK4tuRBSSzc5J1ceEOyHA0fStUUnn6okQsK1jUuRB1mm3umNk7NNDV++uYDv/raXT/5G6APOQmXyDYDzkO2gck6xseTkzqSU6t3Psz4vJZqsqHYsBoJiqFUuwrMGNB6UUjAK7nDrhcmx9zGjXAOaKH6YIHvq+36Mp60A7251IIbOyoyWyobnceqrTrfbxd27d0e+dufOHRwcHIz9QZU8CyUEJmdYzJxJu4HOxCkX/cfOQVdP3So2BynIngelemLohQmklIWSksrUBTbbSyrC831WxiUJC0KJ5YZ9KcWPbTIoDMLjt5seDEbRv8JDTSIkhMwcSf1njGcqzuXJLpfr9pnlpGdBKqSqhekj67QrpXBjpYqtfW9groTUFbDkTEil8h1x22SFUKqMg8yAZmO5giiWaHZDMEoRJ7Kwje9OPxqJ4/h3//VdfP4tnWPKGS0jKyZIzxvkFz7a1Q2rWMg8bxXQ6wbdsjA8llMXhnEco9VqjXxtf38fdMY7WtOEbVEspIYozXYIxkgZljoBWn2tR3dtDs5pIRaUGSWwLQ4pFYRQhZsY+mk8CBRm8kDEqHZyvKijp1ASdfdypkimoacWy3W9/7Pd9MCYPrAJeTUHtjiRgAL6QYyeH48UhlLiUvfxaqkpzCRcWrOf6VjTKXWidDDpubFaQZxIPG35AABCyyil86DD7dPC0JhOCfEkcC0DidDKBADYTg/whBDtQFwwpNQ5yNmuc6cf4fNf3cPvvrkDQN/fwrh0Jp0E6lCw/cPdXt6UfbA9iNbjnCKMymid4zj1ledHfuRH8PGPfxyf+cxnsLe3h//+3/87fuiHfgg/9mM/NsnHVzKEY3HU08PIfupMWmbijJ92L3OGYxM3shgXlBI4aQh2EAskBVqsFlLCD4WWT5HZlJIC+mZ0kUlunGgb+4tGXZyWzIBGKYX1RSc/sCmlruzAFicSCsDugS5C1ocyDEHUpbqlGpyh6pgTeS7iRMK1+FRLCWuu/ttvrI4a0BhlN/5cCKngRwKMEjBGwKf4tR8npqGbr6sNG4ySfM8QCgjj4p1vsuI+ayjf2+oAAJ7s9Ue+Hhew6J12okSrpbJi8NFODzdWKlhfcvFwtzf6zaRU3B3HqU8Yf+pP/Slcu3YN/+yf/TM8fvwYt27dwk/+5E/iwx/+8CQfX8kQtsHBGEXVMdLIClJq1SdAp5+FrXMYbDo79keRLbMHkdCW/gXBCxNIpeBaxljdO6cNkzMIKXDed1QQCtxYrY71MZ1EZkCzsezii/eb2uIb+gbsXEG8nh8m2pG0mRWGemKYWY9fdiNnecHGu9tdWOZ4rxNRLLGxPLmcynHg2gak8rFUs+BYDJtP+/jAq6swOYUXxKUd/BmRUiEIdfNHKcxknutRZM0cSgnWFp3c1EnvMydYrF3lozs7XpBgkPQJ3N9sAwAOuiGCMNFxOkrLG61z3w1KjiKMBVT63MeJwJOmhz/6vnVIRfC5t3ZHikYCaJPBMlrnGZ575XnzzTfz//3JT34SnU4H3/d934cf//Efx/d+7/ei0+ngk5/85MQfZInG4Ppys1S3sN8Jy8iKCdH1YxCig5yPspueVrJ9pDBOkJwxgPsqySa0lslmWj5lMHJut1ilFED0lOYyqdgciVDYWHIRxgKtXgTGCPzwaiZCmfHMbstHxeb5e15P2C7HeGYYN91DHrecVKWNkmnGSq+NhBBcHwq6z+5LpR382ZBKS0ldi0MqBWNO3MYpIXCGDGie7A8MaK5yn/m8tPshDENfh6RUuP+kk8vOdzO5NUGh4zimFS+IkUVfbu7pvedba1XcvbmAMBa50gTQk+pS2XA0z50YfupTn8Jrr70GAPiN3/iNY7/vIx/5yHgfVcmRZLEUy3Ubbz1q6S+mmThlZMV4UEqh78eo2Aakuhzr+3GR3XyKpp1v9/ROp2OxmZZPGQaF9E/+vqOIYomqY176+9E2GaTSVvIAsNP0cPdGHX3/ag41fpTANCh2syDslFhILFQuf4TJGUWjaqLnx2NzitVNAAJ7SvcLMwzOwCjRQfcrFfznzScIY20BrxTJ/3fJ6dC71lpSL+X8TAwBoGIZOOgFuLZcwee+sod2L0KjZqEbxhBSTrWkephESHihQDU1wdpuevBDgY98cAOf/L1N7Bz4uL1eK3wcx7TS82MYaWX4aEfvFN5aq8JJ5S0PdrrYWNbrBwan6PkxpFIzu75yXp57J/vLf/kv5/+7Xq/jJ3/yJyf+gEqOhzMCSgmW6ha8INEL/oQgFhInp2KVnAYh9fJyxU7lPAUquDNTkqBghWHXy6S7sz0x5Oz8O4ZRLHEtDRO/TLTMS2Ft0QEhwJOmh/fcbqAfJJcuFRRSIkkULINgt+XjQ6+t5f8mJeDYVxMEv1izcdANxyatjWKJumsU4rBScw30/TiXOG/t9fHitToY0zKtyzJKmgWkVPCjBGuLjr73zKA783G4joGnbT8/tD9pemjU9AcqiiUcqxj3hWxn7fB+4ftfXsF/++JOPrEyGIU3QUfjeURKBT9IUE0b5I92e1ht2HAsjsW6jYWKiYc7PXzje9cBDF6jMBKXEv9UJE79afvkJz+JKIom+VhKToCkIeZZuPV+N9T7PuUC7dgQaXRCxdGytCI5ZGYTQ79ghWGnr+UctslGLL5nDYMznEdxqIOStazzsuGMwuAUlBAs123sND0QQqBSpcJlEsUSIHpXJxEq3y/MsPjVTKdci4NTMraImCiWqE9pfuFhqo6BOFG4kTYtMjmpySl6fnleOAuJkAgiMchznaPC0EoNaNYbaQMqN6ApliS578cYftnub7axseSi4hi4tlLJC0PGKKI4KVSs1LQzXJRLqfBot49ba4Od/DsbNTzc6T4j+w9LSe8znPoU9mM/9mP4kR/5EXz2s59Fs9lEr9fL/6/k8rBNnkummm1tQOOXnaexIaWCFyS5kUuRMvVsk8M0KIIwgSjQjmEWZGwZrFBmP2eFUQKc4+0URAKLNevK5FTVPGfMHXUmveQDWywklFLYOdCPYW3IeAbAlTUVtIrDHp9DNEFhOti2yaGg4NocSzUrdybl6USktOQ/PXEiEIRJ/toX6d5zUcy0qcMYwVrDwXZqQMMYhVegPbB2P8rNdMJY4NHTPl66XgcAXFvWhWFemBCCKCnPbuNC72zqz8zTlo8wFri9PnAuur1eRT9IsN8J869xRtC9orWIaebUd59/9I/+EQDgb/yNvwHDMMAYy0expQHN5eFYHBVHGx7sdwK8xhYLaek8rSRCwguyTD1VmN0GQB/GbJPDjwSUUiMOXNNM14vBKAFndKYPQ+f92xKhclnVVVCxDbR7ETaWtDOpF8SghCCIBg2UyyCIEjBKsHPggxBgdUEXhomQcC12pdLLesXKjSUugpQKjJLC7OZZBksnyAo3Vit450l3RGIcxeLS4lWKTs8XkEpPoEHma2JIaWZAo7Cx7OLtx+3UgIbAuyKjq7OSCAk/TPJp/4PtLqRUuHtDF4bXVyoIY4F2P0KjauWqC7sY4oCpR+8XpjEVaTTF8MQwKxIf7nSxsqAdn02jVDYcxalPvf/yX/5LLC8v49atW/jABz4A13Xx6quv4l//6389ycdXcgjTYOCMYaFiYr8TgjOCIBQTCVmeR/pBAiGVdjssmJQ0yzL0Uye3InTrlVLw0p1OSshMH4b033a210RICc7IlU6QbFNLYDeyAOqmPzBPuERyR9Kmj+W6nZtzxInM3UmvCsfS+7EXlXCHsUDdNQsT83A46L7nx+h4+iCvlEJYZrWdmmzXeh4nhoA2oIkT7UzaDxJ0vbhQk+cgGp3+3d/qgDOaFyfZjvhOKielVO+3lYyHnh/nqpGHuz1UHSNfuwKA5bqFis3xcGegcmSUIk4k4nJyO8KpC8O/+3f/Lj7+8Y/jl3/5l/FzP/dz+Pf//t/jm77pm/BTP/VTk3x8JYcwhpxJm50g79ae1wa/ZJTMIVOHS5NCGEBkMEpgWxx+mICQ80cjXCZSKXihgGvP/mFI/21n+/uCUGK5bl/p+9A0GEBU7ky63fRSK/nL7eT7oQBn2nhm2JE0EVcf7UAIwfKCjeCCsv44Kc5+YUbVMRElcmjPUB+8eMFkgFdNNrlwbJ1zN8tNsqNwHQOJkHkD6km6zwwUIwy+50XgQ43ke5ttvLBRzQ3sri3rz8ewAU2/INPQaSdOBOJk4F77aKeH2+vVkQYbIQS312t4sN0d+W8JyDNF/bxz6sJwa2sL3/Vd3zXytb/wF/4CvvSlL439QZUcD09H5csLNvY7QT4pvGwjiFnloDsoDIvmkJlNDL0wyaWk046UCn6YwLXTCe0MH4ayv+0s030hr75Q4IyCMwrLZKi7BrabXtppvbwMVSEl4kQikQoH3XDEeIYQMhV5owuudWHlRmYwViRcm0MKYH3JBaNkxICmzAk7Pb30uXJMpqWkBWpKjgOTU4CQgTIhM6ABEBZgotPux/l+YasbYr8T4qXrgzwK2+JoVE3sNvXfxTmBV8CcxmlkWJnQ7kdo96MRGWnG7fWq/vfeYM+QMpSvwyFOfTe9ceMG7t+/P/K1IAjQaDTG/ZhKngOjFJwRNKomoljqPLE0sqLk4rR6Wde2eNEJjOpDZWZGVAT5jUgt2t056JITQsAZObUzqd4/4VNhRFK1DcSxxPqSi51mdmBTl9aQyn5P1m1fSw+PWSFmTsFOnmUy2BY/tywpERIGp1Pxt5wFOy1kOKPYWHJzAxrGdJahkOW96TRk2aCOqV1uiyInHheWwUCg8+VWFuzcmZQS6GiuKSZOBMI4yaeD95/omIpsvzBjbdHJd5EZpUiEKj8fY8AL4/zs8Gjn2f3CjMGe4UBOqhtY5Z7hMKc+cXz0ox/FD/3QD+FjH/sYXnjhBRBC8Nu//du4c+cO/s2/+TcAgO/+7u+e0MMsGcYxORaq2oxivxNgecFGFAnAveIHNgN08kw9rjuYBYJSAtvk+jCmUAwpaZo9lBkuzPLEENCHntO+LkEocH318rMLj6LiGGj3I20MsdnOi58ovpwMqDiRUGpQGK43MuMZBeeKjWeGWa7b2Nzr5yHLZyGKBZZq9gQe1WTJgu6l1AY0n//q3sD4SqlC5dBdFZlyAtAF0jyF22fQtLGZCIVry24u+dOy9ekuDP1IYHhN4N5mB3XXyE1OMtYWXXz1cRuJkHkRWX4+Lk7PS/Lz2qPdHgxO88nzMOuLDiyD4eFOD197dxmAbmj1/ARCykKZDU6SUz8L+/v7+J7v+R4YhoHNzU08fvwYL774Il588UU8fvwYjx8/nuTjLBnCsTgWKnqnptkJwClJL0wlF6XT14WhZbDcQrsoUELyXb0gTKAKUBiGsUCUSDjpTuesd8kNTk81ydWTMIW6e7W7cxnWkAFNVqAxRi9NghNGApQCuwceTINiITUViBOJyhXvFw5Tcw1AqXNJShOhUC1oIHzNMRAlAjdWK4gTOeTQSlIb+ZLnoZUT+h5uGjQvGuaNqj2Ixul4Mfp+DINTeEE81QZ7w/uFUiq886SDl24sPHM/W190oBSw1woA6Ot8qfa6GFIp9IM4V3g92u3i5mrlSPURpQS31ip4sDPYMxwE3ZevQ8apW71/9a/+1Uk+jpIzYJsMFdsEowT7nRCM0TKyYkz0/BiuzUFACiclBQYh6GEkCnHD6aaFuGOyqdgTmzQGo/DkyZ/VKJaoOua5Jk+TwMoNaAbOpGuLzqVZyWtHUh1Vsb7o5jdzISTcK3YkHcbgTJuxxALWGXcFCYq3X5hRcQy0etFI0P3GkgvOCfp+gsXaCT9gzpFSIYiS/PWf18LQdQzstX1cGzKgefnGAmQa7TCtMut2P4KV3r+29vsIIoG71+vPfF9mmrXb8rGxrHdygyhBvaANoWkgGgq2DyOBnQMff+yPXDv2+2+v1/D25ib6QYyKre8dBBistJScfmJYMj0YnIISgsWahf1OUEZWjIms81S1DS1rLODNOZs4hHExQu5baWFoWxx8DmQcJmenMgUKY4nlhemRFWYGNDWXwzIYtpseOKN5Zuak8SMBTgl2D0YdSUHI1Em+lxbsM8c0xImEbbHCFgRaTqywWLPgWjzfMzQ5Re+S3WuLiJAKQerOLNP8vnnksAFNtmeolEI0pQZ7YSyQCJWfF+5v6f3CF6892w1Zrts6izU3oKHwyoD1CxHGEtni/uOnPSiFkWD7w9zZ0P/2aGjP0OBlnuEwxbwLzTkGpwB5NrIiKUAhMM1IqXS4vZOF2xfv5lxLpyf6ZjWdN9JhBjudxTP7OQ+cU5zkNaADwjF13cuqbegA6iUH2/teft2Z9IFNSoUwStAPBYJI5I6kSilAqakLg6/YHISczX02jEUujy0iw6/B9dVKHlnBGEUcy0Jci64SqbQJl2NxSDm/E0PLYPozbTIs1qzcmZSS6ZUkB4eaY/c2O7i+7Gqn7UNQSrDacEYiK7wpN9aZdvp+lO/kPtztgRDgxnN2868vu+CMjMhJDU7R86ZbrnyZzOfVp+BkN42luoVmJ8wnEOXN92KIvDAsbnRCtpPmR7IQESadvp4m2GZxpyVngVFyYpRhEAksVq2pez5cx0CSaDnpzoGfXnfIxN9ncSIBMuiyZxNDIRQsk0+dky1nFI2qeaZsLCl14V1UKCVwbK6D7lcqeNoKRsK7M7lXydHIbGJocSg1yCueNyglsEyeG9A82deT56vITT0tnX6UNzXDSODx0x5eOuRGqpTKC9u1RWco5J5AiMuL/ZlFen4y2C/c6WFjyX2mWTh8LWKM4sZqdcSZlFICkRpllZSFYSEhhMAyGBarFoRU6PQjKJRZhhdFSgUvTFCxDRBou/WiUa9ot9owSgphg93z9M3eMuZjYsgoAU5oSsaJQqNmXc4DOgO2ySAVsLHsIhES+50ABJO3ko+FBIYcSbPCMBYSVWe6pqoZizX71NfjbEJ81p3EaaPqGCNB91vpoZ4QlOZoJyDSHUMndWeetmbHZVJ1tAHNtWUXrV4EP9QH//4USi6VUuh6UV6IvPOkA6Uwkl8I6MKkm6pj1hYd9Px4yLjr8mJ/Zo1ESISRAGcUQkps7vWfiamIE4lWLxhZ4bi9XsV20xspGKG0cqOkLAwLi2Nx1FPp0X4nAKWk7MpeEC9MtMthKuEronVxxdETlCASiAsgLc7yg+ZnYkifOzEUUoIzMnUyUkDvRxIorKf7PztNTx/YJmxAE0QJCNGF4ULFhG3q5yYRKjcPmDbc1GX3NPukcSJRtY1CXm+GqdgGpBjIuLKge21AM53TnmkhFgJ+NIh+KaJaZVxUHANCaGdSANhueulkbfpUMFEskUiZF/L3tzowOcWtQ1LGzHlbSJlL4XcPvJF/Lzk7w4XcdtNHnMhnCsMoFqhXrJHz8Z31GpTS0RYZOrai3DMEysKwsDgWy52s9jthGVkxBrJwe9fmhZ1eMUZ1yH2UQBRAntLz4zyHbh665Fm223H4ocBS3ZqaXL5hDE7BOcVSTTsiP2l64JxMPLLCD0XqSOphfWnIeEapqXUppJRgqW7DP8VeVBTLvMlXZCyDQRHdtFyqW3lhaHKGXlkYPpcwbUq6ln4/z8O18DgyA5prhwxotGx9us44XhiDDOcXbnXwwrXakcZ1jsURJzJXPORyUkYmrrqYVfxQNw0B4FG6M3i4MJRSK3CGi++bqxVQQvBwd7BnaBoUXa98HYCyMCwslsHgWDqEvdkJtENgeXG5EO1eCEBHJ0xLTMBZYZTAMRn8UEAqnCoz76pQSu90umkO3Tx0yU+SJysJLFSmT0aaUbUNSAWsNhzsNH0wSpEkk92R8cMEIMB+O3zWkXSKI04WKtapnIEV9ISx6BicgmdB9ysVbO71oZQOuk+EnLpD/TTRSSX15cRwYECj85rN3ICGEJxpb/cy6Hpx7iDb7AQ46IbPyEjjRMI2ORZrNpJEoeoYcC1eGtCMgSznEgAe7fbRqJqoVw432XSTbvgTZRoMG8suHmyPTgyjJCn3PVEWhoWFMwpKCZYXbOx3AjCmpaSlq9L5afV06KxjFXdiSAmBbfK0SaBOJWW7KoRU8MM0O2hO9mooIaCMHvm6JGlO1zRn2eUGNMsutvc9fb0hmNjSvlQKYSxw0A0hlcL6op4iJELCMthUyy8dS+/NPm9yL+Vs7BcCevd9EHRfRc+P0UnjaAhw5giPeaKbFoauzQE1H9fC48gMaITQ15lsYsgZQX/C6oSzIJVCx4tgpk3kLKbicH5hGAk0aiYcm6f7xGTEgKZs6p8PpRR6aWGolMLDne6R+4W2xeDaHCzdQ8y4s17F1l4fybCMV01f8+EqmN67aslzMTgFFLBU086kZWTFxWn1UodMi01dNtppYZTAsVh6ozndjtNVIaSCH4mhnc75OAwZxxSGfiiwvGDl4e3TiG0yKAAbSw68MNEHWoWJTYP0TpHC05Zu2uTGM0O7wNMKIbpxF4THF0RRIlB3zamUDp+HSto4yPcM0zxDQku53PPo+kMTQ4KZeT+cl6rDEQttQLPfCRDGQmf+TVFhGEZalTO8X7hQMbFUH1V8KKVQtU0te09f17VFB09bfj5RF7KMdDkrUSIhpX7+Droh+kHyTH5hFAssVEwQQtComiMNzNvrNQip8msUUF6nMop5+i0BZxSMESzWbbR6ob6okMlbx88ymWuYXWCHTEr1xDC7gU6zlFQIOZgYopjxIOfB4BTiiMJQQeV7w9OKybXMKwug3m56YBPs5MeJhFLAzoEHRgmW6zYAIEnUVBr0HKbumhDq+GtyHMs8YmYWsE09FdlYdMAoweOng7iBbrlneCyZOY9tMjBKpro5dBlUbANJIkeuM1rqJ6bGbdsLk/wALaXCO0+6uHujPvLaZYVL1mymab7p+qKDOJE46Ibpd5IyKuGMhLGASgWimYnMM/uFCnmeZNUxRwYnt9f19w7nGZqc5rLueaaYp98SAPom3KiaUAo46IbpxLC8uJyXbj/SN2bG8sDUosHSm5Cfhu5O9cQwLQyd1MFxXg5DBn92YhjGAlXHmFozlQyDUzBGsbKgC7Tt1JnUm5AzaRQLUEKw2/Sx2nAGEjsCWMb0F4a2yVPTiWMmqgRwrNkpDC1TG4dQSrCx7OYGNAaj8IIyQPoolFJ5Y2Ve3JlPwjS0MuFa5kyaGdBMUdZcpx/BSHecN/f6CGOBlw7LSGOBekUrAgghcCxjxIAm2zMEJqe6mFW8IEZmBfFwpwfbZFht2Pm/Z9eabDXDsdjI9cexONYWnZE8wywvc5ob6pdBeQUqMI7JsJAu2jY7IVgZWXEhun6sw+0VwAs6vSKEwLUMSKkQx/LIydS00PViKKXfx/N0GDIO7ToAekdvqW4f819MF1kkylLNSjv5JDU7Gv97zQ8TMEawc+DnNu8AAIWpNp4ZZrluI4iePcwKKcEoLczfcRoYpXAshkQo3Fyp4Mm+B5Ha+UupSlv+IxBS5fK1eclzPQnToHpn1TVRdYyBMymZjjOOlHq/LVs5ubfZBiHAi9dGC8NEqBEzMddmiIXEamPUmZRRnMrBuGSA3i/URd+j3R5urVVHmsuJ0C6/2dnC4AymwUaGJ7fXq3i828sbtdl/H875nmF5BSowjs2HIiu0AU25xHw+ZNq1raZGKNNsanESlTT02093IKaVVuoCaxfY7Oc8GJxCDBVRWRez6hRjcqRlXjrPcKfppzfTyYQ094MEcSzR82OspVEVQkgYBi1MM6HmGoBSz0zLoliiUTVnblKuA8oFbqxWkAiJ3YMg/ZfpONRPG0opBJGAwbWhHC/wvWdcMEphGQxJGnSfTQwZnQ4DmiASQGokA+iYiuvLldxVNid1V81wLQNC6JidpZqVTww5o/CC8rNxWqRU8IMEnBF4QYy9dvCMjDSMJWqHHEoXXPOZPMMokdhuDjIltQHN1b/HrpLyClRgDE5hWxyuxXVkBaVlluE5kVLBC9KJIdSJsQLTTC0tMIIonuosw2a6X2EbxTX7OQ+HC5ogEmhUzcIUOo7FtQHNsouDbqhvomr8h/7MkXS/c5TxTDGKaEB3qquO+cy0LE4UqlO+U3oeXNtAIoAbq/qgtvlUS7Uow1SZh0wL2oRLS+qlBDgv7r1nnGQGNBvLLp62fcSJmKhs/Sx4YZwXhX6YYGuvj7s3RqeFcSKeaXoaaUYjAKwtOXnIvXYmLaXWpyULtieE4NGulqvfWj+cX4hn7hNV14AYuk0dtWdocDL3uavFOImUHIk+SCosLVjYb5eRFRdBSAUvTNKpTbGNUKqOPmyGsUQ8xYVhu6sP/LY1X1LSw02HOJFYrBVDRgoMG9CkcqimD0LHL7/JbMR3W7qrPoiqUFPvSHqYpQUb4SE5KYGCMwMxFYexDQZCgEbVhGvx3PXPYHTuD1xHIaWCHwq4FoNUaq7UE88jUyZcW3JTAyp/orL1s9DuRbkE/N0nXSiFZ/YLo0hhsTrqUGoZDCR97GsNB/udEHEicql16Sp/OvRET99HH+52wSjBjeVK/u86FgTPRD/ZJgfI4DmuuSYWaxYeDuUZGgbN42PmlfIKVGD0DYRguWaj2U0jK4DSgOYcBKFAFEu4FgcvuBFKvaK7ZGEkpnpi2O5qF9h526uhh1zrOCWFcNjMyAxo1hoDx0CD6aX9cRILCSiF3QMfFZvnUlulnr3hTzsVm4OQgWw4SSQsk+c7MrOEwSkYJVAKuLFaGRjQcB3kPc2GWFeBTKVrjsWhpC6gSzIDGpUb0DzZ9/L78lVKkoWU8MIkv2fd22rDMlge0ZJ/n5KpAmlAltGYCJk3urIoHoCUBjSnRO8XDhxJry27I4aBcaL3Cw+vBBmcwjL4SHbhnfUqHu728mszoxSJVHMtey+vQAWGEgLLYGjULHS9OH8jx0l54z0r7b6WNToWz53Gikq2dxpEYronhtlzburw2XmBD00M/TDBUt0uXG5ZxeGwDIqKzXNn0nHv/oSRACEEOwdeLiMFdKzHtLu3HoYzikbVzMOTw0TkDZxZgxCCisPToPsK9toBgijRh3o1kIGVaLT5jMgzDEmB1SrjJDOgqVdMOBbP9wzVFTuT6v1C5NnR9zY7eOFabaQIyRp+1hENrIrNtTPp0rPOpKU50+no+TEMgyJOJLb2vGdkpFEsR0x/hlmomAiHCvDb6zX4YTJUoOuh4jxfp+bnNDajOCZDo5oZ0IRAGVlxLlo9Pb1yZ8AIpZ4uXAdxMqKnnzY6/QgGozAMVmjp7lmhVB+QAUAoYKF69A1smqnYht7/WXKx0/QmEtKsiwlg9yDAWtpdF1LC4MUxnhlmsWbnnWopVC75nkVqjqmD7lf0FGVrLz3UQ829scNhhJDwoyRXDczTtfB5ZAY0QuipYeZMetUme/0gBkkvP81uiHY/wt0jYyqsIxt+jsUhhJaZckaxczD0d5U7uCcSJwJxoh2dt/b7kFLh9tposL3OLzxahVNxDMgj8gwfDu0ZUoaxK2CKRPHuriUjWNZwZEUASgmCuLy4nJVWb7DvZrJiTSMOo+USFEEoprpJ0PNjOHN4GGKUglCCOJEwOS2cLBJIdzUUsL7kYrcVpJLl8YY0e2GCnh+nsquB8Uy1QMYzw7gWTwtoBRBSyNf9tNimNijKCsPMgMY0KB5ud9Hph+UufEoYC4SxyN0ri6YemCSZAc21ZRe7LR+JkKls/erOOJ1+BCtVFd3f7AB4dr8wSRTq1aMbP7bJoJRuEK4t2vnE0OBkKox1pp1w6B7zaCcLtn92v/CoaS2QrSEMPmOLNQtVxxjJMzQ5m+s9w7IwLDi2wVE7FFkRhlM8JppS2n09MXTM4ktJGSWwTQ4/mt7CUEiJnhenJiJqEFw+JxiMwvMTrCzYhdxntQyaB1BLqbQMRwHhmJpSSml53WFH0iRRcAsS63EYSgkWazbavXAkX2sWsUz9t9kWx3Ldzg1obFMrMt7Z7uKdJ50yXgm6QaaUbhwA89UkO4ncgCa/zvi5M+lVNBYSIeEHIt8NvrfVwWLNeiaDVkHlr+dh9H+bGtAsunmWIWfaVb5smDwfL4zz88Kj3R5WFmy4Q81CvV/Ij40c44zCsVm+z0kIwZ31Kh7sdPPn3uAUQSSeyRueF2b3zjQnGJzC5BT1iolmJ8wvLiVno9PX3SHHYoU3hKCUwLYYgjCBkM/mp00DfijghfHcyqcMg0JC5U2domFwXdispUHN200PnI9P4pUICQW9f0MI8kBoheIZzwzTqJqIhTh2/2VWYFRPwuNE5gY02XXI5FrlEsYCX33cwpP9/tQ2sC6Drpc2JbOJ4ZxdC5+H3iVW2FgaNaBRUl3JPp7eEdbvYyEl3n3SedaNNBao2PzYxk9m3iWlwlrDgRdoZYTeWSzNA0+i5yUwOYVSKg+2HyaOVa6iO456xRyZPN7eqKHrxflKEaBNzuY16L4sDAtOtg+3VE8jKyhBWEZWnJlMHsIZLXyRwiiBY3J9SFe4cmvvo+j0I3hBknZVydwdhizOUHdNWAUzURmm4minUINTXRiOUeKVHfp2Dnws1+3B3q9SOi6joDgWx3LdGelwzyo1dxB03w8StIcOXYB+Lmqugf12gLcettDsBFN5rZo0PS9rSs5nk+x5mIbO/VusWbAMlhvQgGgp/mXT8yPQ1Dzs8W4fUSKf2S+MYnni3rhraYnsemlAcyakUugHMQxO8bQVIIjEM4WhhDrR5btiG1BDT/ORe4YEcztkKQvDgsMZBaUEyzUL+50gl6WVXaez0fNjVGwDhBQ7wxBIJ4YmgxcmAMHU2cMrpdDqhfDTwpBSMnd7NbbFsLxQnOzCo6jYBoRUWF900sKQIIjGkzEWxdr5b/fAz2WkUiowRgttDkUIwd2bC4WKJzkvrm1AyKE9w1ROOgwhBFXXgG1SbD7t4+3H7bnLOuykf69tahOuIkrLJ0VuQCMVNpZdbKWFISFAcAUy5E4/hjUkIyUEeOHaIeMTPBusfpiKY2hn0vTalhnQEMzvlOo0RCPB9non8PaQI2k2ELHN519fbZMBQ/FBaw0HtslG9gwNg6Lbj477ETNNce+wJTm2qSMrgkjACxIQlJEVZ6Xnx6g4BhRG4wSKCCUEjsVzWd+0yeSDSCBIDRdsi89lbtf6UuVEucu0Y5scSukD23bTy78ej8GAJgj1fsdBN8wPT3EiUXGKX1AVXap+WiyDAUphfckBZwSPnz5bGGYwRlGrGAAU7m228WCnOzd28d5QYTjLe6fnJYt3uLasHZAzZ+LLbiDEiUQYJXle3v2tNm6uVkeKkCym4iS5u20yKKkLyKpj5BNDxkjp2vscwlhn2wJ6ulexORZrg+lsZk52kgKJUaqdtdPpLCEEt9M9w4wsm3ce1XflVWgGcCyeHzL3OwGUUlOdXzdtZPKELED7uKXlokCIDkwPYwkhlHZBnCL6fpwbJOmdzmI/3+eBzsBkIDOg2VhyEcW6iIMCojGENHthjFZP51xmQdBxIk/sxJdMDyanoIyCgGBj2cXmXu/k/8ZgWKia6PsRvvLoADtNb6bVL1IpeOm1MFtlKBnFtbk2oFlyIaTCXjvQ5l2XPDHUBZu+ZntBgq0975n9wjAWaFTNE6/tJmdQJDOgcfLCkF/B31Uk+n6UF+aPdnu4tV4dea6jWKYNppOpV4wRF+3b6zU0O2Eu7aaUQCpcaWbmVVFehWYA2+S5mUEWWTEud8B5QEoFL0hQsTkomY3l/0pa5AZxMnV7O81uiCQdY9pG2SUvKocNaHaaPigF/At2vJVSCKJnHUmVUidKhEqmB0KIjhtIJG6sVLG9753a5c+1DVRsA7stH1951EKrN5vxFlKqPK/T4BRGwdUqk8AytHP1xrJuEG3vp7mpQl7qnmHXi8HS1+edJzqm4vB+YSJOZyhmGLphopTC2qKDpy1fTxsZgR+OR44/i/T8BAan6HoRWr0Itw/tFyql4FqnKwxdyxi5ptzJ9gx3B1NDpRSCOVEuDFOeyGYA02CoVUwQoieGnNEysuIMRJFAEOkcqVmReWVZb0GYTNWOYRQLBFGS7wrYFoPBy8NQUcmkPIQAT5oeONcxHBchEQpSKuweBDANikaWB0ZInh9WUgyqjok4kbi5WkEiFHaa/sn/UQqlBDXXgMkpHu50cW+zA2/GAsClVPBDvWutFCmbZEdgmdqAJjOhejJkQDMOdcJp6fTDQX7hVge2yXB9ZTQ/D2pgIvQ89LoHQyL0jnYiFJrDHhGlAc0zJEIijAQ4o/l+4a31wX6nUmfLh9V7hiQvDjeWXRic4sH20J4hp+j787dnWF6FZgAjddJcrFlodkIwRubWTek8tNIFY9fiMyNrrLm6MPQjMVVS0n4QgwC5XMY2eKFdJuedimNAKWB1wcFO0xuLxEvnSxHsHHhYW3RACNHGM5TMTONmXnBMBqkGBjRbRxjQnARP45gSKfD2ZgubT3t5BlnREVLlTUmp1Mzcf8ZJZkAjlY6tyApDgiw+YvJEsUAktPmVUgr3tjp48Vp9RF0UJxLuc2IqDlOxRg1odltp00RdTRTHtDO8c/xwpweDU2ykrq6AdnOtOifvF2ZQSlB1jPy5ZpTi5mplxJnU5BSdOQy6L69CM4DBKaAUluv2SGRFKUc4HVm4vW0xmDNyY87kLGEkkIjpOUS1ehFMg+Wdf9viYGWXvLBkBjTrS9qZdBwSrziRUEph98DHeiPdLxTy2MDokunFMhkIARaqJlybY/M5BjQnYZs63qLVi/DWwxb22v5UqSHOg1SDwlBJ5PtTJaMMG9BsNz0opWWX3iUZ0ASRyE1P9tsBOv3oiPxCiUbt9Pmkrs0hhMLqggNCkAfdE0JyRU3JAD/UkmtA7xfeWKmM+EHEsUT9jIZuCxVzxCzt9noNOwd+7njLGEUciyuJRrlKyqvQDEApAedUTwy7Yf51McNL++OknZpcOBaHMSNStXq6gB3GAsmUONQmQqLnxzANnXdHyMCivaSYWGnO2Mayi64Xo+/HAMiFJjp+lMCPYgSRwNrSwJE0M4cqKQ553IBQuLFSOTKy4iwQQlBxOFybY2uvj688bqHrRYXdPxTDUlKomdhvnwSZAc3Gkos4kdjvhFrmF1xOYdjpR/k0996W3i88XBgqpQvY02IaDIAC5xTLdRu7zYEzqV8a0DxD39f5hWEssN30cGv9UH6hUmduHjoWx/ApebBnOGqUNS8OyRmzcQougWNyNKp6n6PrxYDC3HU5zkvmfuiYsyNrrLu6cxlECRIxHYcmP0yglAIhBH6Q5LmR85ZhOEtkOaqZAU0WW3GRG6kXCDQ7mSNpZjyjp8slxaPmGIjSoPu9dgB/DHuClBLUKyYY1fte7253C2nzr9LC0LE5CMpw++PIDGiupQY0T/b7epqTqIm71iql0PEimMagMFyqWyMxCUJKMEZ0RMsp0YWmfr3XFp1cSqpjEor3Xp4kSin00sJw82kfSmEk2D47V1in3C/MsEwGOpT1fGO1CkrJSJ4hoQReOF9y0rIwnBEci+djdO3mpxBPSUEw7WRSUtfmM3Njdiw9iQsikTuAXjXDXdd+GOfOqbPynM8jhBBULI7lBRuALgw5JxcyCfGjBPtpYZjt3wCYGZn3vFFx0qD7Vb1n+Jk3noxtwmdwHW8RRAm+8qiF7f1+oeItoiQzPtMH2rJJdjSmQaFAsNKwwSgZMqCZfJxAFEtd+FGKREg82O7i7vWFke8JI4lG1TpTBBFnFAbX0vu1RQcH3RBhLMBYuQp0mCiRkFKlRVsXhAC3VgeFYRRL1BzjzJ8fSrTBVWZiZHCK6yvuSJ6hySm6/bIwLCkgtsXRSAvDZicEZWVkxWnp9CMYTEueip5hmMEohW0yBOF06OOlUmj3wryj2vcTLQ1UKrcALykmVccApxQLFRPbTf9CHe9ESEghsdvysVAxYZtcHwgISmOOgqK7+Aq312q4uVbBv/3P9/EL/+FNPNm/mKx0GMfS+4d77QBvPWqh2Q0KcbD2wwRCphI4UjbJjoOn92eloPeZs8JQYeLnHD/UCixA77bFicRLNw7HVJx9vw3QAfexkLky4umBD0IIFNRU3LenhTAWUOl09dFuD+uLzsh0MDrHfmFG3TURx8OxFTU82fPydQiDa0O1ou8zn4XyTjsjGIyg6pjgjOjICkpLnfop6XgxXIdDATNTpDBKYFscQSSmYtc0CLU7Kk2Nkbb2+rixVgUIKfdqCo6dOipuLLnYaXqDjvc5bqRRLAFCsNv082lhIiTcVHZcUjxMTkEJAaMEf+lPvIY/98dfRbMT4p/+uy/j3/+3d+GNaU+MEIKqa8A2KB7v9HBvs31pO2jnpZ1OIrKIg/JaeDxZJua1pQqe7KcGNHzy+3hdL86bUve3OqCE4IWN0ZgEcoaYhGGc1FRnbVFLZHMDGpCyMBzCC2JwpiWfj5/2R2SkAKDI6WJCjsJJmzIZt9erkEr/HkBfV5RSc7VnWBaGM4LBKQglWMqcSRlBGJUXltPQ8+M090/NTMeWUgLHZHlH+qrp+RFI+ty+/bgNIRW+9qUVUFruGBYdy6AghGB9ycFeO8gPNOfJGIuFhBACe+0A60PGMxWn3C8sKjroXlvzE0Lwje+7hr/6PV+Db3zvOj7/1T38o3/9RXz2y7tj68gzRlGvmpBK4e3NNh7udKf2UNdLrfC1aUbZJHsemQHNtRUXYSzQ6mmlT++CuanPY7BfqIu++1sd3FyrjOwSRolE1TbOlUFpmxxQQKNqwuQUu3lhiELuzE4KvV/IsN30ECcSt9ZGC3NKcOb9wgytFCP59ScrOkf2DEHm6vUoC8MZgTMKAmCpnmYZlpEVp6bvx+nBk8xMYcio7mD6UQIFXLkM4qAb5R3VNx8coGJz3F6vwSijKgpPZkCznna9s8PNebK4gjBBqx9BKoW1NKpCSm0MVVJcqq45MgGxLY7v+oZb+NE//T5cW3bxf33mIX7+372Bd590xvY7LYOh7hro+hG+8qiF3ZY3FeqJYXpBGpVksrJJdgKWoSc7G0vDBjQEYTQ5mV8QCUilG639IMaTfQ93D8dURBIL1fPJGE1OoQgBIUQb0BxoiexlTEKLgpQKfpCAMzIUbH/x/cIMQghqrpk3j2yTY2NpdM/QMEjexJkHylPZjEAIgW0xLFYtHHTDLHIHSSlHeC5SKvSDGBXbAGdkZuRqlBI4FtcmIEpd6dQwjASiOAFnFEki8dXHbbzndgOK4Fxd1pLp4igDGkrO1/H2wmTgSJqFFxOVd+xLioljMhzVo1xtOPh/fOer+L//T3cRxQL/x3/8Cv6/n76XRwhdFEIIKraBis2xs+/hS+/sT1XnPztsWiYrm2QnYBoUSmlDKkq0AQ0hBIqQiU2E/TDJ9wvfyWIqbowazyiiULHPF6VjcApG9NRrbdHBzoGfZjRS+OF0Trkvm+y1JUQXhgsVEwtD+4RRLPPc5vNSr4w2rm6vV/F4t583kkzO0PXjwsbinJXCXol+7ud+Dp/+9Kev+mFMFZahIyukUjqCQaFQDm1XQRQn8EMB1+Yz53ro2oaOiACudHKs93x0wX3/SQdRIvHa7UVIqUpDkRmh6hhwLAbb1HIfg1P0zyHx8sNES+EpwXLdzvd3Zu2zOW88r7AnhOC1O4v4y9/9Nfj2r7+Orzxq43/91S/hP//B1tj2rCglqFVMQCHfT5sGMpMmx2Rlk+wEOKMwDa2MWlu0B86kanJGLZ3+aEyFYzFcSyeWgM6KNtLHdR4IIXAsI98zDCKBrheDMzrRSWiR0I0cvef3cKf3TH4hLrBfmGGbbHjNEHfWa0iEzN9jlBIIOT+GQJd6Jfrc5z6Hv/gX/+LI1/7jf/yP+N7v/V583/d9H/76X//r6HQGUpJms4kf//Efxw/+4A/iB37gB/Drv/7r+b8ZhgHTvFiXYNZwLIZ6RWfr6MgKlJEVJ3DQzcLt2cxkGGZUbQ6pBlbPV8VBN4Rl6kvNmw9bsAyGF6/V0sJwNia0845tcSilZV7bTQ+caSe3s5AIiSR1JF1t2KBUGzC4Vmk8U3Q4o7BM9lwFi8EpvvXrruOvfM/78OqtBXz681v4x//mi3jzwcHYCjnXNtDpR+j5Vy8Lk1LlUyHT4DBmxPhsklRsA1EisbFcwXZmQMMmkzMnlULXj9NJpcL9rQ5evFYf2QMNY4HFM8ZUHKZi8xFn0syABqUBDYBsv5Cg1dOf22Hjmcyx+rz7hRkmp2CcQqTRXlnxOSwnVQoIpnRXedxcSmHY7/fx5/7cn8NP/MRPjHz90aNH+Bf/4l/gl37pl/Arv/Ir+NjHPoaf+Zmfyf/9p3/6p/GDP/iD+MVf/EX8wi/8Aj7xiU9gb28PAMqDwhFYBkOjmhaG7RCU6Z2dkuMZFIYcxjm7ftNKxdXyluAKDWgSIeEFCQxOIaXCWw9beOXWAhijEFKVXfIZweQUIAQby26+YyiFPNPBJk4kCPSOYubSFwuJamk8MxPooPuT3w+NqoU/8+138YPf9SpMzvD/+dQ9/NJ/+iqetvwT/9vT4NoMm3v9K5/GCKnghzEsg4EQgJeF4YlUMgOaJRdemKCTOob2J1Doh5HIFQtPWwG6XvzMfqEQCtULyhgdi0MIlbswZ3uGwPkMvGaNnh/DMGi+X3h7qDCMEoGaa154N5cQgkbFzDMxq46B5bo9YkDDGeBNQUPpMriUU1mlUsEnPvEJ/OIv/uLI1z/xiU/gL/2lvwTH0R+I7/zO78Rbb72FXk+/GDs7O/jQhz4EADBNE+973/vwy7/8y/jRH/1R/Nqv/RrklAR3TwsGp7BTOVezqyMrgjLL8Lk008mqbfKZmxjW0gB5P0yuTEqaTY0I0cG0fpjgtdsNAHqvotyrmQ2yXZm1hoNEKOy1A4CQMx1s4kTCCxN0vTjvnkupp5ElxafiGGdabXjxWh0//Kfei//5G29ja6+P//3fvoH/+NlHF94RNDhDFEscjGmP8bxIpfJweykVeCmXPhHbTA1olnXjaHu/n6oTxm+01w/ivOC4t9UGMLpfqJQCCIFjXXBalbo6OxZH3TUGzqRUF6fzTJzoHGZGKR7udGEZDKsNZ/DvsULNPd9+52GqrolkSGF3Z6OKhzu9vIFkcobOnBjQXOkd991338X3fu/3jnztzp07ePToEV5//XW8/vrr+PVf/3X8yT/5J7Gzs4MvfOEL+Kf/9J/CdV38/M///LkKw+Xl6snfdAWsrtZO/qYTSITEbjfC2qKLjhdjZbmKOJEn/uxx/O6LcJW//zNvPQUAbKxUsb5WR6NmXfpjmNTff3NDdzepwbG4WMFS3b60353R22pjZbkC1zbwqd9/AoNRfOi912GZDO1+iLW1+rmDacfBVb73Zu1z1w4FknRToxcKbKzVUK05WBm6kT/v9yekhyxh56Vbi1hsVECMADc2FsZeHM7ac1+E31+rJzhIcwUXG5VT/3ff9Uer+Javu4Ff/2/v4n988Qm+9G4TH/uWF/Hh926ca1Kw2KigXtNNiMaiC+OSG4LZc+8FMRKpUKtYqNUdbKwvHHmNntTvvwou+rsTIbHXj7CxWgMhwEE/wdJiFcwMsdBwdeE4pt9/4CdYW6nBMhke7faxvuTihRuL+b8HUYLFRYaN9YXn/JSTf7+QCrudCPWKiRtrNex3Qiw2KnCcBKbDx/p6Fe217/QjNBZCLFQtbO17ePF6HctLgzM85SFu3Vg81Y7hSb9/IRY48JJcdffaiyv43Ff2EAjgxpK+XrV6IRaXKudSOl31Nf8sXGlhKKU8UhKafe1v/a2/hV/4hV/A3/ybfxOVSgV//+//fbjuYPH3PHsH+/u9K5eQHGZ1tYanT7snf+Mp6HUD1F0Dj3Z76HZ9dL0YO/XjR+3j/N3n4ap//0E6MRSxQKvVR5zah18Wk/z7Vdqdb7Y87Ox2IA7tYUz6uZdS4d3HB3AtjsAP8Qdf2cWL1+vwvACepwvWg2YfoXc1nfurfO9d9ft+Er8/CWJASjBK8PajA1xbtPF4S0IdoVo46vc/ftLBwye6M18xKJoHPfT8BO22h+4YVwdm8bkvwu9XSqHXCVB3LLQ73sn/wSH++Idu4GtebOA/fOYhPvGfvoL//PnH+J+/8TZurp6+2bvYqOCgpYOre16ML78tcW359EXqRRl+7nt+jK4XoWobaLU8HDj8mWv0JH//ZTOu3+31Q8RBhJUFG+9stnDQ6qPbj7H1pP1cd8qz/H4hJR4/aaPqcHQ6Cm8/buGDr67m7x1AB9/fXK2c+mc+7/cHfoQoiLBYNfHWwwPsNbuAAg7aCvUL7s+d5vdPmvP+7t2Wh07XR78fYHvfw+u3G/lroHd0E3TbHnon3B9O+/t9L0QcRuCMYqWmJ5FffPspXEP//G4/xuZWC+4ZXWiv+pp/FJSSYwdlV6pdeOGFF3D//v2Rr7377ru4efMmAC0fDcMQBwcH2N7exk//9E/jh3/4h/Hxj38cv/RLvzQ1zmLThGNxNGom2v0o3+8pIyuO56CrMx9Ng85MhmFGPb1JhlFyJe60fpRASn0B2tr30PFivH6nMfI9s/aczzO2xUEJwfqig53MmTQ4vezPj7QjqWtzVByOREi4Fiuz3WaELDriIntT15Yr+Et/4jV89x97EZ1+jH/2f76Jf/tf3jmXmUzF4Xja8q8sL05KhSB1xCZA+T4/JRVbu3heW65gu5m5Rurrx7jQ+4XpCsRuD4lQeOnG6H4hlDpzgXAcwwY0Uirst0MwRhEnMjdEmUd6XgKT06H8wsHULYoF6q45Vr+RBddElBrMNKoW6hUTD4cMaCghZzZVKyJXOjH8M3/mz+Cnfuqn8Mf+2B+DZVn4zd/8Tbz++uuoVgdV7F/5K38FAMD56EP9h//wH8IwxvOhnCUck6GROpMedEM4lj5glTlgR9PuhajYHIQQMDpbOx6VdMcwiMSIdv6y6HoRMsXFmw8OQAjw6q3GyPfQsjCcGbJIifUlF28+PACjBH4Qa+e4E15nISXiRGG3FWBt0QEh2pHvMqR1JZdH1TXgXdDZjxCCP3J3Ge+53cBv/8ET/I83dvDmgxa+9f3X8A2vr536Ok4IgcEptpseXtioXbqhnd4xTHIZXNkkOx0Vm6PVDbGx5OIL9/bR87RzaN9PgMZ4focXJMhejntbbVBKcGeoKEmEhMEprDGdqxyLo9kJRwxo1hYdKOicPsearbPJaZBKZ0xXHY5Huz1QQnBjZaAYjBM5tv3CjKpr6P34lDvrVbzzpJubEHGDoOvFWFk4ej1iVrjSd9vdu3fx/d///fjzf/7P4/u///vxa7/2a/g7f+fvjHwP5/yZojD7eulM+iy2xfOdrf1OACiUlsfPod2LdAGlFNiMucKZ6Y0riMSlT42VUjjohrBTGcybD1p4YaM2ugugysPQLGFwCkqA9UUHfij0ov4pDWiiWEIphactPzeeSYSCa5XNv1mi5pqwDIZOP7rwSodlMHz0Qzfxl7/7fbi1XsV/+p3H+N//7Ru4t9k+9c9wLH5l8RVhJBDGUl8TSdkkOy22yaAAXEsNaJ40+3k8zrhUZO3+IGLp/mYHt9eqI831MBZj9SPI7pMrCzYoIXlkBYF2Zp5HoqFg+4c7PVxbHt0HVgRwxnx/0OZGg/fQ7fUaen6MZupeb3KKfhBfaS70ZXCpE8ObN2/in//zfz7ytY997GP42Mc+duaflSTjuwjMEgan+QVrvx3g9noVQSRw+vXo+aLjRXBtDsrozEl5KCWwrbQwvGQ5ShgLxEIfep62fOx3AnzDe9fyfxdSwuK0PAzNEIQQuLaB5Yae8m3ve7i27CKKJewT/IViIXP5+3oaVUFAzh0cXTKdWAbD6xsLIEJgu+nB5PTCxkLLdRt//qOv4CuPWvj/ffYRfuk/fRXvudXAd37DLSye4vDuWAxbex5euWlc6vWo09f77G45MTwTpqHDyDfSoPkn+x5eudmAlCqd5F1sipcICS8UqDocPS/GzoGP7/jAjZHvEQLP3Wc8K/oxKzBGsbJg586klGiTm/oYf1dRCGMJKP2abu318eHXB+cHKRU4pWO/P+gpMEeSSHBOcTvNM3y43cVy3QYhBErqovUko6MiU9i77l/7a38N3/It33LVD2PqMJieElUdA81OAM7ole1QFIGeF6Fi85mMTWCUwDEZgjC5dClpP0hAU4fKNx8cAADeMyQjDUJxrFtlSXGpOByLqavbdtMDY+RU158gStDs6MPQ2qKTWsGjlMDPIIwSrC26ePVWAwbX08Nx7FG9equBH/3u9+EjH7yB+086+N9+9Yv41Oc288nDcZgGQxiLS4+v6Hi6MHQsDqhyYnhaOKMwOAFnBEt1C9v7qZGRUrqYuCBBJIBUOnj/SQcAcHdov1ApHarujLEwMDgFYzrrd23JyQtDzik8fz7Pb30/AucUT/Y9CKlG8wtjgZprTEQ1uFAxEabXjJUFG67FR/IMgfG8z6aZwp6GSxnp0WRZSEt1C/udEJwSBBfc6ZhVEiHR82O4tpHvR80SlBLYJod/BVLSVjfIu3lvPmzhxmplJJZCSIWF6uVHg5RMFtcyYHCG5bqF7aYHI5V4nYQfJthvhyAEWG3YSISCUxrPzDS2yfHi9TpurlbhBwLeGYyKjoMzim/52mv4se/5Grz3hUX89hee4H/7N1/Cl95pPldhVHEYtve9S1276KaZaI7FQCkp3+tnoOKY2oBmycWTtDAkhFw44xIA+n6cF+n3NttwbZ5PJwEte6+5458uu5Y2oFlrOGj3IwRhAs7IWE11ikTPT2Bwmpu/3BoqDONETmyKWnGMXOZOCMHt9SoeDBnQcE7R8y7Xvf6ymb3T8JxDCYFlMCzWLDQ7Qe5sNeua6PPQ7UdQSl+QZ1GyRtPQ3Gz34rLeA3Ei4QUJTIOh1QvxZN/LQ+0BLSPljMK1Z1eKMa9kDZaNJRc7TU/v/gTxibJ/PxTYa/tYqtkwOEOcSFTK/cKZhxKCpbqNV281UHEMtHvRWJpY9YqJ7/nWl/AX/8R74Focv/Jb9/F//F9vYetp78jvZ5RCKYW9tn/h331asr1Gy2DnykWbZyo2RyIUrq1U0O5H8IIEnJOxNBfa/QimwaCUwv2tDl66Xh8ZRESxwMIEsnerjnZbzQ1oWj4YpUgSdSWu4ldJIiTCSIAz7Ui6XLdyMz0AAMHEpJx6h5Xk96zb6zW0elEu/TY5zaf9s0p5NZpBHJOhUTXRDxIEYQJAlZEVR7Db0ocA22IwLznk+LKo2FxL+QguLb/TCxNkv+mthy0AwGt3hoKBQ4GlulVO/WeQzIBmbdFFqxchjAWkwnMPNtqRVOJp6kgK6O93nbIwnBdMg+HOeg0vXqshSiR63snNhNNwe72G/+fHXsf/8kfvYLcV4Oc+8Tk0O8GR35vFV4xj6nQa+mlhaJsMxgwqViaJbTIoNdgz3M7jcS5mIhQnEmGsJ1U7Bz76QYK710djKlS6Sz1uLJNBSe3qDCA3oAHU3BkIZlJOpRQe7fZHpoVSKrAJ7Bdm6KY1y+9ZdzbSPcN0asgYRZLImX5NyqvRDOJYHAtpZMV+NwRA5tbZ6nlkGUiuaczsjbniGIhiCSHkpRWG7V6YX7S//OAAaw0Hy0OxA0Iq1N1SRjqLZAY0q6kBzU76GYuecxONE4koFmh2Q6wvOdkPmkl5d8nzqVcsvHqrgaW6jW4/PnE/8DRQSvDB96ziR/7Ue0EJwW9+bvPI78viKzJp4iRRSqGfSqwtk82cI/akMQ0GBTVwJt3v6+makBearummgH4t7m/p/cKXhgrDJJGwOJvI7rNpMCiiUHcNWAbL9wwV5s9Z3g8TEALstQP4YTKSXxhOIL/wMPWKme8Rri+6MA2KB0N7hgqD4nUWKe+8M4hpMCxUtdShmUZWlBPDUaTU1vgA4NocbEalPNW0sxlGEuISCkMpFTr9CJbB0PdjPNrt4bWhUHshJQxG4VizOaEt0ZOXpfrAgIZAW/MfR5xI7KfZUeup8QxRamwZYSXFgjOK6ysVvHxzAUqRsURbAPqw9+0fvIU33j3A5jGS0suKr5BKIQgTMErAKJlJ87NJog1oKExO0aiag2JekQs1E/p+nGfv3ttsY63hjLiPhrFAozqZ3TaDU5C0KF0fMqA5rYHXLNH3YxhDwfa3D+0X1iYg5R2mYhtQ6ZGZUoJba9WRoHtGCbwLTqenmfJqNIMYnKKRZRm2AzCGuV1gPg4/StBL3b5cm8+sVXg1leN5kbiUeBcvTCCV7r6/9agFpYDXbo/KSBdLGelM45gcjmmg6hjYbvrgjKD/nN2fMBI6cxVagiqEgmXy0qVxznFtA6/cXMDGsou+H4/lcPw/ffAmKjbHb/zu42Ovh47FsPm0P9GdbCkVgkjAtTmUGpjGlZyebCdvY9kdOJMidRU9J610vzBOBB7u9PDSjVEZqZAYe6h6hvYE0BLGtUVdGCqlTm3gNSsopdDLCsOdHlx70GgE9Dw3y32cFLbJAIKRPcOnrSDfYTUNiu4M7xmWV6MZxOAUPO2kNTshGKMIwtkde5+HrhchiJLUuZPObGGY3cSCKLmUiWGnHyFb13zzYQuNqjmQB6KUkc4DWmalsLHkYLvpgfPnR+b4YYL9TpB3/2Mh84ZGyXxDKcFaw8UrtxowOUO7H0FcQCpomxzf+v7reLDTw1cft4/8niy+otWdXHyFkAp+lMCxOKTSmWwlZ6NiG9qAZslFsxsiiLQBzXkbCHEiEMXa8OTBTg9CqhEZqVIKZIKmJwBQsQzEicL6ooswFmj3o7mLHIsSvfZCKcHD3R5urVXzRrKQEiyNZJskjFJUbCOX8N5J8wwf7eqpoTZVSy5tPeeyKa9GMwhnFJRSLNVs7HcCcFZGVgyjlEKrGyKIBGqOAQI6szseWUREEIqJX8SUUmj1QlgmQxAleGerg9fuLI5c1EsZ6exjcgpKCdaXXOy1tJQ9jMWxWXV+JLDX1sYzhBAkiUSldKwtGcI2OV66XsettSqCSMLzk3MrID7w6gqW6hY++XuPj70mVmyGJ/vexNwgpdTX5CzDsHQlPTu5Ac1yBQCw3fRTA5rzFVF+KPJ71f2tDhgleUEA6GvYQsWcqJLBtTmEVLkJ186BD0oJhLzY7mSRCGMBBYKeF+OgG44Yz0SxnIgj7FHUK9qfAQCur1TAKMn3DLP3yazuGZZXoxnFNigW63piSAlBHE++MCgKYSwQCQkvTFB1TRCiO0SzSHVoYphM+PUPIoEkdQx7+3EbQqqRmIpSRjof5AY0CzakUrn7b3REKLCUCkEY42nLx/qiNpJQKIPtS56FEIKlmo1Xby2g6hroevG5TDkYpfjIB27iaSvAH7y9d/T3MB1fke2hjxup9MTQtThASCmbPgemwQAyMKDZ3u+DM/rcJtTz6PkROBvkF95er8IYciuPYjnx7F3TYIBSWGukkRW5Myk58vo5i3hBDM5w/H7hhPILD+NaRt584ozixmoFD7cHe4aEzO7u52yehkvgWByNqoUwFrqDRlA6k6b0gwREEfT9GFXHmOn9jiwENognH3Lf9+P8gvLmwxYqNh/p9pUy0vlBG9AMnEmVOtpyPU4k+oGAH4q8S04Imclc0ZLxYHCG2+s1vHitjiSR6PbPHm3x2p0GbqxW8Onf30KcHN31n2R8hZAqnRjqwmNWVxkmCWcUPFWg1FwjN6BRSp25iFJKpfuFFJ1+hKetAHdvLDzzfe6E1S7aHZ3ASiPHdpuZqY6am/Ob3i9keLjbBWckL/wBAIRMfL8wQ+8ZDvIM76zX8KTp5VNCzsnETaquivLuO6PYJs+LAm3sQOZGinASrW4I09Bud1XXmNkMQ0Bf3BglCEIx8df/IJWRxonEVx+38drtUkY6rzjp9cc0qN4zZARe+OxNNBYyn8qsLTlIhIRlsJmd4JeMj5pr4pVbDaw0bHS8+EyyLkIIPvqhm+h6Mf7HG7vHfo/B6YixybhIEoEg3TEEUE4Mz0k13QO7tuzm8VMEz4/HOYookRCJBKM0j6m4eyimwjb5yARxEnBGYRgUIjOgaQ07k85mETKMkBJ+kIAzgke7PS3hTGXWQkgYjFyamoRSgqpj5O+l2xtVKAU8TieZJmfo+ePJW502yrvvjGIaLLdVbrb1ns+8ZeEcRSIk+kGMVi9CP0hwe602sxmGgL7R2CZDECdIziGvOS1RLOCHOhj4/pMO4kTiPUMxFaWMdL4wDQZCdAaUdial8IJnD+5BlOSOpOsNB3EiUXXK/cKS08EZxbXlCl650QAlBJ1+fOqViTvrNbznVgP/9Q+fHBuM7lgcHW/88RX9IIZU0FJSpcqJ4TlxHQNJorCx5GKvHSCKBRij8M74egWRQPauub/VQcXmuYIB0IqbRu1yJIwViyMWEmuL+m9KhDz2+jlrZJPeOJF4su/h9siO5+XtF2YsVExEkX5Mt1arIAR4mO4ZUkoghDxzE6IIzO6JeM4xGEW9YoFSgv1OCMYwEUlM0dB2wwT3sq7grcZMB2lr11WuJ4YTvIANZ/q8+eAAlsHw4sYglLaUkc4XJqcglGBjycFOOjH0w2e7q34osN8OUK+YsC2OJFFw7dKRtORsuDbH3esLuL7soh+cPtriOz54A3Ei8dt/8OTY77HN8cdXdPra6t61y4nhRbBNBqmAa8sVKKXNWgxO0T/jdK3TD2FyvVd6f6uDuzcWRpqYSipU7UvabUuLXZ3pCuy1Au1MOgfnN31GJdjc60Mp4Nba4AyRCIXqJe0XZjgWh0rfBqbBcG3ZxYOhPEMQ8tyM3qIyuyfiOcfgFJQASzULzY6+sJSRFUC7H8LgBG8/bmNt0UHVNWZ6YsiozkbyowSTVJIe9HSovZQKX3nUwqu3Fg5JQEoZ6TxBCIFrGVhpOIgSiYNeBHGEasEPEzxt+1jPu/NqpqXdJZODUoKVhoNXby7CMhg6p4i2WG04+PpXVvC7bz1FM51cH2YS8RUdTxcutslACAEtlRTnwjIYyJABzZP9rAklTl3IK6XQ9WKYBsN204MXJiMxFVIqEEpgX9L9yzIYlBo4k+62tDOpFHLmVV96v5DkxjO3VitD/6rgXNJ+YYZlMlCCXIVwe72Gzaf9fC2HUe1VMWvM7ol4zqGUwDAYFmsW9juB1qjPQcfpeUip0OpFgAIe7vbwcrpcPsvmM4QQOJaeGE5qeV1IiZ4fwzQoHux04YcCr90ZCrWPShnpPFJ1OJZrekq80/RAlBqR3Uil4AUx9jthfghSKI1nSi6GZTK8eK2O22m0Rf+EaItve/91MErwqc9tHvs9446vyA6Ttslm+v4zaTIDGtdmcG2O7f1+fp+JT2lAE8YCQujcvHubWkn00rVBYRhlMRWXdP+yDApFCJbrNhgl2MkMaECONUqaFXp+DMOgeLjTw9qiAzvdwRVCwjTYxHc8D0MJQc01EKXP+531KoRU2NrrA9DKmG5ZGJYUCTstDLPIiiQNDp1X/CiBUsCDnS6kVHj55oLOkJpxGY9rc3ihDmOdxKK0H4o0/JfgzQctcEZGFveFVFiolDLSecOxOBZrNigheLLvgRAyYhASJxIH3QBSatmUSI1nyky3kotCCEGjZuPVWw3UXQOdfnzsobrmmvim963jS+8eYDM98B0mi6/YG1N8Rc/XTVrLZDBmNEP3sqgOBd0/GTIKCk9ZRA3Lju9vdbCeKokyYnG59y/OKBgBCAFWG/ZQZMVs+0TEiV53ISB4/LQ3ElMRxjI3U7xs6q6JONHnpkzamuUZck4RRpM39rtsyjvwDONYHAtVE0IqtPvR3EdWdL0IjAJvb7ZhGQy31ioznWGYUbENBGECJRXEBBoDnX4Inh6c3nx4gLs3FnLnsExGelkW0yXTg2kwcE6w2rD1niEnI7uocSLxtKXle2uLLqIy2L5kzBic4tZ6DXdvLCARCp1+eGRz7Ju/ZgOuzfEbv/Po2OZZxeHYHVN8RS+fGPJyYnhBsp28aysunrYCJIkEpYB/yqD7jhfBMAiiWODhbg8v3aiPfoNSuXvsZaBVPtptdW3Rxc7BwJnUO+XfVETCWEJBZzdGsRyNuhJqpFi/TIZfe9fmWG3YeDi0Z6gwe0H35RVphrEtjkZlKLJijp1JlVJodUNYBsVXH7fx0vU6GKVQ0BfcWabqGJBKW3KPe2Io0+fVNhm29vroejFePyQjXarbpYx0DjE5BSEE60vamdRgVGeqpkSx0DJ3SrC8YKXGM2VhWDJ+qo6BV281sNyw0fefPVxbBsO3fd11PNjp4auP20f+jCy+Yqd5samhVApeOqUyGIVRTsgvRG5As1SBVAq7rWevNcchs/1CzvDutlYS3b0+yC+MEwnb4pfuQ1CxM2dSBz0/hhckMPjRkT+zghfGoJTgYbZfOORIehX7hRk6Ponkars76zU82u3l/z8lp29CFIXyijTDGIyiUdMh0812iHnOMgxjgUhI7HdDdL0YLw91BWfdEa6Wdtq0Ac14C8MgFEik3s/48oMWKCF45ebgxiqkQv2SLaZLpoPMgGa1YaPnx/BDgTiREGlsih8m2GsHWFmw8yZNOVkumRSMUlxf0YfNo1YqPvCeFSzVLHzy9x4fu3LhWBztfnih+AopFYIw0WZcBLlJV8n5MDkDgcLGkAGNwemRLsiHCSMBpfQZ4P5WB5zREQljFEksVi9/DcKxOIRQuSnX7oGnDQQjMZO5eQDQ8xKYnOLRThd118ijKRIhYRqTz5A8DkIIaq6ZTwVvr1cRxTLPzTSN2dszLK9IM4zBKVyLweA0N6CZ18iKfpCAKO1GCgAv31yAlAqc0pl3hKs6ujAMQoFxRxn2gkgf6lMZ6QvXarn0opSRllRsjuW6bk5tNz0opfKsKi8tDNeX9IGOAJcWXlwynxicYW3JOXKaxCjFd3zwBp62AvzBvf1jf8ZF4yukVPAjAcfikFKVE8MLYnAKxilqLodtMjxJDWjkKRRSXpggu/vf3+rgzkZ1RNorlETFuXwJo2lotUVmyrVz4Od/0yw296VS6AcxONMTw1tr1VxlpM1/rjbCqF4x8/fS7XW9Z5jlGRqcoufHY42zuWrKK9IMwxkBoxTLde1Mmtk4zyOtbgjLpHj7cRvriw5qrolEyEvdHbgqsoldECVjv3gddCJYBsXTVoBmJ8Rrtxv5v/mljHTucWyOpbo+3Gw3PRAMJM2tdHq/lhrPGJyUxjMlE2cpNUQSR3TJXr+ziBsrFXz685vHmtWYBkN0gfgKIRWCKEnD7VG+58dAZkCzsexie8iA5qTw8U4/gmFQtHsh9trBiGmabhwTWFfQ2DQ5A5RCxeZwLD5iQDOLgepROo3reDG6Xoxb60MZyAJXUpwPY5ssbyDUKyYWa1a+ZzhcwM4K5RVphiFEZ+8s1mw0O+HcTgzjRKIfxBBS4dFuX7uRAohiicX67LtlZm5efjheKWkYC4SJAOcUbz48AAC8Z6gwlAqljHTOsQwGx6JoVE1sNz0dmxPGiBKJvXZmPOMgTq6mM18yf3BGsb7kwDti15AQgo9++Ca6XozPvLF77M9wLxBfoZRCEOqJIQiZ+VWGy6CSGdAsabMWISUIgCA8/rwjpdIxS5zi/lYaUzG0XxjGAvWKdSWKIkoJLJNDSmB90ckNaKBmqwDJCGMJKIVH6RRuWM4LKNjm1TbwLUPHymTNpNtrVTzc6Y3Iep/3XisaZWE44zgmR6NqotULAYW5jKzQdtQE7z7pQiqVF4YSV9+JugxyKWkkTgx8Pgt9P867aG8+aOHmagW1tAjVMlJSykjnnGEDmp2m3v3p+wniWOBpav2/vuggEQoVe/Y/iyXTwWLNAmP0yOvhnfUaXr3VwH/9w+0RF91hLhJfIaSCn00MgZlfZbgMbJNBAbi27EJIhaetQF9rnmPWEkQJkMYs3dvqoObqfeiMOFGoV6+usTlsQPO05UMpBc7JSLzGrND3I3BO8Wi3B9OguYQ2SSQs8/LNf45ioWLmaxC3N2r5KgSg1XndIxpNReXqn+2SiWKZDAsVE0oBB90QIGTunEnb/RAmJ4OYitWqlokQzIWUNCvWglggGeOSYbsXwTQoWt0Q201vJNTejwSWaqWMdN7JDGjWGg72O2G6X5UgiBLsdwK4FkfVMaAUrkSyVTKfMEqxseTCO2a14iMfvIEoEfjtP3hy7M84b3yFNp8RcFIHXlZODC9MJr3cWK4AALb3B02o4/CCBJRQSKnwzpMOXrpeP3S/UnCucFLl2hxJog1odOarjoXyZrAw7PkJDE7xcLeHm6vVfIoeJgL1K94vzKi6JhKhhyq3U8fUbM/QNCh6fnRlj23clIXhjGMZDI3UVWu/EwJKzVWWoZQKrV4Eg1O8vdnG3Rt1UKozi2oVay4KF4NTWAZDGAmIMTUFEiFzGU4mI32tlJGWHEHF5lhe0Neg3VYApYBWL8JeM7uhPQABAABJREFUO8DaoqM/g0TBKo1nSi6RRtUCp0c7da82HHz9Kyv4nbeeotkJjvzvCdE7sWeNr/DCBHG2365UKSUdAwanYIxisWrC5BRP9j1QSiCEPLYR3u7r/MInTQ9+KEb2C+NEoGJf7aQqux6u5c6kPjgjM+dMmgipQ+ITid0DPy+6gDS/0JmOc4Rjsvx5X6pZqDpGvmfIKEWcyGP3kotGWRjOOAanWKzpQ1mzE4AQMjNv3tPgRwmUAp62gzSmIt0vTGRuhzzrMKp3TYMoyTteFyXrWhJC8ObDFtYXHSyl7pOljLRkGMfmWFlIDWj2tTNpz4uw1wqwvuhASAnGaGnCUXKpUEqwsezCD46+H37b+6+DEoJPfW7z2J/h2hyt3tniK7qe/t5MSlpODMdDxdHSy41lF0+afQCAAkF0xHlHSAkv1FOq+5vaqfzFocIwjCQWriCmYhjt0Kyw2hh1JlUzlkedxUA8eqpfs1sj+4XTE2FkcAbTYEiEBCEEt9ereJBODAGAQBfts0B5J55xDE5hW9rZar8TgFKCYI6cSbteBEaRx1TcHcovdKzpuOBMGkoJHJPrHLkxFYadXgTOCXp+jIc7vRHTmVJGWjKMxRkqjr4GbTc9UEKw2/T1/sySiziRqJb7hSVXwELFgmHQIw/aNdfEH33fOr707gE29/rH/gzHYtjaO318RdfTkjPbZCCl+czYqNhDBjRNH1IqUKKzCg8TpPmF2X7htWV3ZMdZpj/vKuGM5g2zpZo1cCZVaqacSf0wASHAo90uCAFurGg5cJxI2Cafqobhgmvm5j+312vo9CPt3wGAMi1PngWm5xkvmQiMUjBK0siKUEsRZtDV6iiUUjjohrBNhrc329hYcvOYCusKA1OvAsdi8KNkLBlIUim0+yEsg+Gthy0A2uZ98O+ljLRkgGFQUEKxseRgu+mBc4Ktp7rTurboIEkUXHv2d31Lpg9KCTaW3GMNPb75azbg2hy/8buPj5XvmQZDECanjq/o9NPC0GIjmXklF8M2ORSAjWXdbMoiuo7KrOz7MRjVRePj3T5eOiKmYhomVa41MKDZPdAxHISQmXIm7fux3i/c6eHasptn2UZTtF+YUXUNiPSpv3N4z5BTdGdkz7C8Ks0BjsXRqFlodgJwRucmsiKMBeJ0x+DRbi93Iw1jgYXqdF1wJk3FNuCHYiyFYRAmuhtLCd58eIDFmpXvQZQy0pLDUELgWhyrDSfdk6Fop4fjtYYNhfkwgSqZTuoVE5bBj1yxsEyGb/2663iw3cXbqeTwKCoOx5Pm6eIreqnTqW0wcFZOC8eFZWgDmmupAc2TzIDmCGfZdj+CaTC8u62dyof3C8NYoFE1p0LxUnUMxIkuDPc7IeJEpJE/s3GGU0pHhlACbO31R2Skcor2CzNskwNEN4jWFh3YJsODbb1nyBmFH4oj81GLRlkYzgGOxdComOh6MRIhkQg1E2/ek+gHCYgiuP+kA6WAl1MZqZSYugvOpKnYHEGaY3jRxfWOF2lJcpTgnSddvHa7kd9EiyIjVZGPqHm842DJeKk6BlYWbAipsN8JsHvgYblu6am9UtpVsKTkCqCE4NrK8buGH3x1BUs1C5/83c1jo54Yo1DydPEVmVOmNWUyuaKTGdAs1SxwRvBk3wNjFFEsRwr2REgEodD7hVttGJzi5lBBkiQqd/K+aiyTQUlgfdEFAB3DweiRU9AiEqXxaTsHPhKhcHutNvSv09dg1kZ+HEmi9wxvpXmGwCDoPoyKf7Yur0pzgGNyLFQyAxotd0mS2XG1Oo5WN4BlUnz1cRu2yXBztZoXRfOyX5hRcQxEiYQQ8tS7MEehlEKrG8IyGb76uA0p1UhMRVFkpCoJITr7M+XuNs0MG9DsNH082etjbdGFlAqM0anIqSqZX2qOAcfmR0r0GKP4jg/ewG7Lxxfu7R/7M7L4iqN22obpp0Y1lkFhlIXhWKk4HEIqrC+62N7Xe6EEamSHNIgEFPR1/95WBy9s1PICfXA+mA4Fg2kwgCisLQ2cSRkjCGNxofv4tBDGAgoED3d1cZVNDONEaqn1FH4+FipmbphzZ6OG/U6Qm08RaMPDojN9z3rJ2OGcYrGeRVYEIMDMR1bEiUQ/0K5j9zY7uHtdx1RkRheMztdbv+pq6awfCVxkWBzFEpGQ4IzizQctVB0DN1e1dKdIMlIVBZChBySzsRMw7VicYbFmgTOKRzs97LV8rKX5XJVyv7DkiiGE4NpyBf4xxmyv31nE9ZUKPvX5zWNdvbP4iu2md+zvycLtDU5BKZnKg2+RqdhaenltxcV2008LvdGdvJ4fgTGCg26IZicc2S+MEwnXnp5Jrm6YETQqpo5GOfDSyZSaCWdSL4jBGfBot4fFmpWfU6JYTK1rfMUxcuXA7bSQfZRODQ0+G3mG0/HuL5koBqNoVPWHbD/NZJql5eWj0Bp8gu2mj54f5/uFUSxRr07nBWeS1FPpbBAlx8qhTkMviEGULrDf3mzjPUMy0iCShZCRAgBiH0oKyOhsGWQl58Mw9EF4bdHGl95tQgF5cHPFma9935LppGJzVB3jyIkfIQR//EM30fVifOaN3WN/xknxFVm4vWtxSKnKSfmYsU0OpbQzaRgLHQrPycieYbsXwzIY7m91AGBkvzCKJRq1q42pGEbvZzNIpbDWsAfOpCAzURj2/DhvFt4e3i9UgDulTtW2yaBA9Pts2YXBKR6keYYGp+h5ceGVSOVVaQ7gnMLgDHXXQLMTglJyotyl6LT7IUxO8PbjFgDgbppfKDHIj5onsp2JILyYBCWT597faiNOJF6708j/TShVCBkpAKg4ALVcwO9c9UOZC7IDzlrDybOe1pdcKKX0Qn9JyRVDiM41DI9pmt7ZqOHVWwv4r3+4De8IQ5OM58VXCCnhR0kabq9lqiXjwzJo6kw6MKDhjKKX7nXGiUAUJ+CM4v5WB/WKieUFO//vlcLUKRhcy0CcKKwtudhJC0MCFN5EUEgJP0jQ7kfwwgS3UpdPpRSUmp78wsNwRlGxdZ4hYxQ3Vit50D2lBHIG4kTKq9IcQAmBZWgpV2bhPMuRFVIqtHoRLJPh7c0Ori+7qKbjf06m94IzSTKJRhBpA5rzkMlzTYPhzQct2CbDCxt6WbxQMlIpoZIY1HIhg07hu3tFoeIYWGnoQ5hppCoGQmAZ5W2oZDqo2AaqjoHgGNfHj3zwJqJE4Lf/4Hjjqjy+ovdsfEU2MdQ77qQMtx8zBtd7aSt1C5SStDDUO3laxisAEEip8M4TvWKSKVyElGBMn5WmCdcxIITEWsOBFyTplI0cK3suClGsi6fHh4LtEyHh2tO5X5hRq5gI08d/Z72G7aafF+pKHZ2dWSSm95kvGSuOyUYiK2bF7vgo/CiBUnrJ/PHTHu4OxVTUKlYxpI5jpp4Whl54fimpDqIlEFLirUctvHqrke9qBpHEcr0gMlIZgxACQimIkkByuvyxkovhWgaW67owvLZcgVIo96xKpo6NJRfhMR3/1YaD97+8gt956ykOnpNbWHE4nuw/G18h0x3DTLVCi3C9LBgVm0MqLVV/st/P70lRLNDz9H7h5l4fQSRG9gvDSKJRnb7zgZnKjdeHDGg4L/4ZThdSBI92unAsjpV0chvGcuqVR1XbgEo/2rfTSeej1EBHT6iLvWdY3pHnBMfiaFQt+KFAEOnu2axGVnS9CIwB97eymApdGCZCTe1C86SppX93GJ1fStrqhTAYwYPtHoJI4LXbjfzfhJoei+8TEbpxAAAKpNwzvCQsg2G14YIQ4PpKFbGQcC0+dQexkvnGtQ00KtaxB+9v//rroITgNz+3eezPYIxCimfjK4RUCCIBx+YAQTkxnACVNPtvY8nFdtNLpYkKYSTQ7kewDJrvFw4XhomYzoLE4BQKJM8K3jnQ8tjwgn4BV03Pj2Fwgke7Pdxaq+T3ASn15H6asUwGEC17vblaAaVkEHRvDKTLRaUsDOcE02RoVLPICm1AM4uRFUopHHRD2IaOU3Ashhsrlfzf5i2mIsMxGRgl8COB5Bz6dykVOn0tz33z4QE4o7ib5kIWSUYKAErEeUgtYQbgHx9cXTI+DEPHUnzft72E7/jwLcSJRK00nimZQjLH3KNk5jXXxDe9bx1feqeJzb3+sT+j4nA8PRRfEScSQSQGE8OyMBw7uQHNsgs/1MUgYwQHnQCx0PE497bauL5SyWMplFIgZDrvYZxRmAaFbTBUbD4woCHFNqDp+TGiRGC/E+YyUv06TP+6D6M0d8A1OMP1ZTc3oOGMIoiSZ9QCRaIsDOeEo5xJZzGyIowFYiFBKcG9zTbuXl8ApQRJImGbXAdqzyGMUdgmQxiJc73uXpgga06+9bCFl2/W8+eyUDJS6KgKQtL3ATchgx6Umr3PwrSRGdC8cnMBKwuONhiYQyOokulnWGFzFN/yNRtwLY7f+N3Hx+4oU0rADsVXZHuHWUFSTgzHj2VQIDUSAoDtfQ8Go+j6MaAUgjDB5tP+iBtpFmM1rbL2isURC4n1JXdQGCogOiY6ZdqJE92g3trTn43b67X06xKuxQoRJ1avGPme5O31Grb2vLxQ1+ZAxXxtgLIwnBsMTrFQNUEIsN8JQTCbkRX9IAGFXjrvB0keUxFOcS7OZUAJgWPxtJN19klxL5Xnbj7to+vFeO32INS+UDJSAIh8gOmDGSEUREogLvcML4NM5pVhlnb9JVPK2qKDRBw9NbRMhm99/zU82O7i7c3jFQeH4ys6eWHIQFBODCeBwbU6ZnXBASHamdTgFF4Qw+AU7253oRTw0o3R/cKFKY6xch0DSaKwtujgacuHlAqEFtfkJIwlFICHuz0wSnAtLeKjWGKhMj1xIc/DtYz82nB7vQopFTafajkpoeRYA6siUN6V5wTOdGRFo6oNaBibzciKVjeAadD8Zp3LHeXAmXNecS0OPxQQZ5wYKqXQ7IawTYY3H7ZACcGracFdNBkpAMg4AOhgUqUIIKPjQ6lLxodrGUiEglQKlKDMcSuZWmyTY6luwTvmgPfBV1exWLPwyd/dfO6u13B8RTYxtE0GxsqicFJkkROrjYEBDWcUlsFwb6sDk1PcXK3k36+Imuq9NstgUEphfdFBIlRqIkgKa0DjhTEoJXi008P1lUo+qdX5hcVQkdgmA4jOM8wyGB9ke4acouMdH2kz7ZR35TnCMTmWUmfSbN9slhiOU/jqY71DULGNwujWJ41rc/hRguSMpkNBJCCEAiUEbz44wAvXarkEsHAyUikBEYHQwXuBMAvwyjzDy8A0KAgI4ljCtY3CvG9K5pPVhgsh1JFTQ8YovuMDN7Db8vGFe/vH/ozh+IpOX7sVWgYvmyITJNv/urbk4sm+NqBpVHWExf2tDl64Vs/lirq5qff4ppVMHpsZ0Oy2fBiMol/QwrDnJSDQ09zb66P7hVZBzmmUElQdA1EiYVsc64tOnmdocIp+EF8oM/oqmd5PQsnYsU2KRs3CficEo6TwAamH0d0zAi/QOwQvp9PCKJGoOkYhdOuTpGIb8ENx5oX1fhCDEOBpK0CzG+L1OwWWkYr42WKEm5BhTxeNJRPFNLSbWxQLVJxidIZL5hfLYFhZcOAFRzdR3/vCIq6vuPjU5zefe1110/iKdiYlNRn4nN+PJoltaQOajWUX/TT7D9DGewfdcGS/MIwFFqcwpmIYzigoAZZqNggBdg58MEYRJ7Jw7vJSKfSDWEtilcqNZ/R+IS/UOW2hYiKK0jzDjRoeP+1DSJm/l4qqyivOK1ByYWyTY6Fi5pM1KdWZZYXTTKsXwuQE97e0jPSVVO4YRXKu9wszqk66Y3jGwvCgG8IyKb784AAA8J5bDQDa3rtoMlKIGEqNHgD0RVwBSXA1j2mOyAxogljAMcvCsGT6WVmwIZU8Ui5KCMFHP3QLXS/GZ97YOfZn8DS+otPLJoasnBhOkGzClu2uPdnXqwJ5TMXQfqEQCtUpb24SQlKFBbBct7HbHMSgFM2ZNPO2yHL/8sIwLl6cmGNxZMeJ2+tVxInM32tQKOzwpbwyzRGmwUacSRX0NG0WkFLpjCJTy0hdi+P6ymCHoCi69UlSdQ0opaWhp80/ihOBIExgcIa3Hh7g1lo139UMQ4nlheLISAFADkVVDEMIgQzKPcPLoOIYIEinhyUlU45pMKwuOPCCow95L2zU8MrNBfzXP9yGFxy/V1RxuDZHIwDnZGodMGcBzigoJVhtaOlldli/t9lBo2piqaYNTpRSACGFiLFyU2fStUUHu0P5mEU7w4WxBJTCw90eVht27tAroQp3TrNMBkr0+fP2mnZWzfIMDU4Km2dYXpnmCINrKSkANDta0lK0btNx+NEgtPzeZgd3b9T1YV8qMEZglYfQXPIZRMmpte99P4aCnhpuN/1nQ+2dYnX4EPsg5IibD7OAoMwzvAxcy4BtsdKRtKQwLC/oAuO4htpHPngTUSLw2194cuzPoFTfjxyLQykCXr7/JwYhBBWLg1KC5bqN7X0PQki8s93B3esLeTOzSGsmjsUhhHYmPeiGCGMBSoo3ler7ESgleLzbH8kvBLSqrUhQQlB3TUSJQNU1sFy3BnuGBkXXi674EZ6P6f80lIwNg1HUHQOcEex3gpmKrOimcQpbe3144WhMRd01CzXVmhTVNEw8iATEKSeG7X4Ey9Ch9gDwWrpfmAgJkxdMRgoAUQiwIx4zMyDDPpScjc/DNGOZDIsFMiwqKTE4xWrj+Knh2qKD97+8gt958ykOusdH3/SDGK7NoRTKieGEqToG4lji2oqLJ00PD7a7iGI5IiONYpmrqKYd06AghGA9NaB5euCDM1o4Z9Ken6Dd14XtcH5h1TYKGd9Scw3ESRZbUcPDnR6UUmCUQkiFuIBZk+WVaY6glMA0ORZrg8iK50lfioJSCgfdELbB8Pam3iG4e10XholQqBfkwj9psomhHyankpIKKdHxYpgGxZsPWlhfdLCYTpzDUGKpYDJSAJCxPxJVkZH/HXG5ZzhpLIPhzkb95G8sKZkiMtn8cdfOb3v/dVBC8KnPbR77M7wgLsPtLwnb4pBK4dqSi04/wu+9uQNCgBc3aoNvUgquNb0xFcOYnAFK5fLY3ZYPzsmxzYppJBESYSSwmQbbZxPDKJaoVYrxOhxm+P1ze72KIBIDqa8qZtB9WRjOGY7FsFizsN9OnUnD4r1pDxPGArGQYIziq4/buLFaGWjVlSpNLlJq2cQwlqeSkvqhAJRC30/waLeXTwsB7SxWNBmpkgKQyUhUxTAEFDLsX/KjKikpKQKcUawvOugfcxCvV0x80/vW8MV3mtjaO/o60g+SvDAs68LJYnI9YdtIDWg++8Y2bqxU8qglISQMTgsVj2CZHDXHgMkpdpo+GKVIEoWkICaC4ZDxTNUx8mmtKlCBfhjToGCpTDybgD7c1nuGlGmVQNEoC8M5w7E4GlULB90QlBQ3IHWYfpCAgqAfxNja6+PlG+m0MJGwzDIvKqOe7RiecmLY6YdgjOKtRy0AwGt3GgBSN9IiykiFzk46FsOC8so9w5KSkqNp1HQW3nERAd/8NRtwLY7f+N3HR2Yfen4M1+IAQSFlc0XC4NqAJpNeJkLhpaGYiiAWuedCUajYHIlUWF10sHuQmqWR4nhF+GECQoBHOz3cXq+CpAHxIAU8T6QQQlBzTYSxQKNqou4a+Z6hyVkelVIkyhPznGEZHI2KCakUOl6ERMrCdJuOo9UNYBoU91IZ6StD+4VF2R+4DDI3Uf8UrqRKKbR6EWyT4csPDrBUs7CWSliKKiOF0EY6x0GYARUHUKL4zZKSkpLxk08Nj3EbtE2Ob33/Nby73c3XGjKUUuiVUtJLIzOgYYzmKxB306YxAAiBYmXwQrurJ4nC+qKDnQMfSikopQpTGPb9GH6YoN2PBjLS1ACoyI2SehoDRwjB7Y0aHqR7hgan8ENRuFi4sjCcMww2cCbd74QgQKELwziR8MIEpsHw9mYbFZvn2UVSamv8Ek0mmwmiBPEJr3kQCSRCIooF3n3SxXvuNPJCsIgyUgCQySkcwhTKPcOSkpJjWaxZ4Iwee9/84KurWKxZ+OTvPh5pwMWJhBAqj0Yo8kG4KFQdA3EicX3ZhWNx3EgjrJRSoASFWzPJ3NXXFl0EkUDXi8FYMZRfSin0/BjbzdH9wjiWqBcsv/AwtslyNdLttSp6fpybUCmlm/FFoiwM5wyDUyxWs8gKnWVYlG7TUegLotZ339ts4+6NhVyeQAp44Z8khBA4JtOupOL5E8OuH4ERiq88bkMqhddvD9xICykjBYAkODqqYghCKWTYu6QHVFJSUjQYpdhYcuEFRx/2GKP4jg/cwG7Lxxfu7edf99LDu2NxEGir+5LJYlvaAfajH76FH/3eP5IX41EsUXOLN6UyDQYQlctjdwrkTBolElIqPH7ag8H1ZwjQjWbXKvY5zTIYOKcQUuLOxmieISWAX7A9w7IwnDM4I3BtDstg2O8EMBjFXjs4ch+iCLR6IQxGsLXXhx+KfL8wSiQqBZcnTALX4gjC5MQpcasbwTIp3nx4gKpj4Maq7rQWVkYKAJEPsBNuQNws9wxLSkqeS6NqwWAEyTFN1fe+sIjrKy4+/fnNvPGaHd4dk4NRUsxraMEwOYUCsFAxc2MQQMd0LRRwSsUZBaMUKws2AGD3wIPBaN50mGbCWECB4OFODzdXK6A0a+CTwhgAPY+FiokollhZsOFYHA/SPcOiTHSHKQvDOYMQAsfmWKpbaHZCuLaBTj8q5IKslAqdfgTLZPjqZhuEAHfTjKIokoW88E8a1zbgR+K5UtIoFghiAQWFtx938NoMyEgBQMahtgl7DoQZQBxAieJ9HkpKSi4HSgmuLbvHRgUQQvDRD95Ex4vx2S/vABhMDG2TleH2l4TBKRjBMzv1ihA4djHXTCo2B2cUNdfA7oGfmyFN+0qQF8QQQkc5DMdUVB1jJqbnVddEnOhC9/Z6NZ8YFpHy6jSHOKZ2Jt1v610q12bY3OufyqlymvCjBNrQiuBeGlPhDEkS8siKkpyKrSeGz1uG7gcxiFK4t9lBIiReG5aRGrSQMtLDURVKxMdOyRUhUOWeYUlJyXOoVy2YJjt2FeOFa3W8cnMB/+UL2/CCGH4qPbVMBqMMt78UCCFwbWOkEZoICYuzfF+vaFTSv2d90cHuwSAvb9pXgnp+jN1WAKWQT2+jGdgvzHBMBqT2dnfWqzjohuh6p/A1mELKq9McYlscC1UT7X6EKBEwOEMUCzQ7xToMd/sRKNMXnK19D6+kMlIpFRgjhb3wT5Kqk00Mj28CtHoRTJPhzYct2CbDnQ3d3QtDiaW6VUwJlIjz5XAVB/D/w/8b/r3PH/mthHCooHt5j62kpKRwUEJwbcl9rkzsIx+8iSgR+O0vPMm/zzIoWFkYXhqZAU1GGBXbrdwyGZTUBjRP20EenRJNcWEohIQfJNjc64EQ5KspICj8fmGGwRlMgyERcpBnWNCpYXl1mkMsg+UGNPstXQxWHQPbTQ9RXAz3JKUUDnohbIPh3qbeCXt5KKZioWIWs4CZMFVX3ySPe50TIdHzYzBK8JVHLbznVgOM6stEkWWkEEkeVSE7T4E4RPDojaO/1zAhyz3DkpKSE6hVTNgmP/Z6urbo4P0vr+B33nyKrX0dem8ZDAYv702XhZMa0GQICdTcYspIgVEDGikV9tthusc2vesPWbD9490+NpZcWAbLDQJnYb8wo1ExEcUCG0suTE7zPcOiURaGcwhnJM/1edrSUgRKCSgh2EmthKedMNZ7coxRvL3ZQdUxcperOFGozYg8YdzU0viOfnB0yL0fJoACHux0EURiNNS+oDJSQEdVkHRmqLpPAQDh9jtHykkJ5SBJAnWaeIuSkpK5hRK9axhEx09rvu3910EJwR+8vZ9ePwl4OTG8NMyhfc6sGLEL7FZucAqAYLUxMKDRzqTT29T3wwRCAo+f9kf2C2szsl+YUXEMCKnP0zfXqni4XU4MSwqCwQdZhk8PBoWgYzM0eyG8Aljr9vwYdCSmop5PCAlUIWIqZNBF3Nm71N9ZTQvDIEogjyiK2r0InBO89aAFg1O8dD2dwhZZRgpoR9J0v1B29XMu/S5Uv3nktyug3DMsKSk5kapjwLVYPhU5TL1i4pvetwZAHxyVQrljeIkYnIKmBjSZmqjIbuWUELgWQ6Nq6mZ+Glkxzc6krW6Ig66PRMjRwtCdrQa+9rgY7BnutvzCOZICZWE4lzBKUbE5KjbPJ4aAXtS2zdSIZsrjK9q9EKZBsbnXRxANYiriRMIyedpVm25U0If0LldqkEloglBAHJoYSqXQ6YcwOcWbD1t4+cZC/jwWWkYKAEkAUN0skN2ngKEbI3LvwZHfThiD8juX9vBKSkqKCSEEG8sVBM+Z2Hzz12zAsXjemCtyYVI0cgOaRCKKJRbSNZoi41oGpCJYWbBzZ1Ip5FQa0HT6IZqdANtNfdbMCkMQjJgFzgKcUVgGR5JI3E7zDDef9q/4UZ2d6T89l0wE2+RYrFnYPRiVjloGgx8kaPXCK3pkJxMnEl6YwDQYvvpYx1S8dF3HVISxQKNWkAIm6uP/z95/BsmWnee54POt7dJVlj/etYVtuEYDBBqGFEEjGpGQMDSSwCtKCClifumPYiTdmIj768aNuBH3xkRMcGKuSEqiE40omiFFSgQFNLw3Dd99uvuYOqa8TbPdWvNjZWZVVu6q48pl1XoC5pzMvTP3SbNzf+v73vfV8cGO7tY6K3StZHCUtB1n5Npwe7HJRis9NmOkADpp9zIMzcYC3qknUaUq+cK14h38CN1aG9p8T4fDcXBUS7boi5Pi4rAU+vzyjz7Jz3/gCRDwXGF4oFTLmzrQ8hD/jnWplAPyXHNqqzOpyJErDNMs5+Zcg1o55Ob8BmO1kHo1RGuDOmb6wi6j1ZA4zTk/WcVTwsz88I2TusLwhFKOPMZHIha2dAy7VMs+dxabRzYXx7bm7Q/rK7dWuTBd6608aQ21IcgnMsagkxYmSzD64F7n+i4dw/VmilLC968vo5Tw1IXjMUZqdAZGI6JsTEVjGTUyTXjmcfTiDh1D5SE6g9zpDB0Ox+7YrmGF9g6FIcCFUzWudEbzXcfwYCmHPnGWUwp9a94y5HR1k6fGy6w2Etodb4A0Ozo6Q2MMM/MbKLG+FjdnNzbHSLOcWuV46Qu7VMsBWht8X3F+usqM6xg6hoVy6DNajVhvprST/hloz1MYbfrGTI8SKxsxgSdsNFPuLDZ7BcxQCcvzFNEaMAdafHRNeYo6hsud8dzv31jhsbMjvddx6MdI8wzpdP7MxiIAMjJFdPoxTHMV3Vwp3M0YMInTGTocjntTLQXUq6G9SL8HrmN4sISBh8DwTBPdgzBQIMKp8TIAcystPE9oto+Onm1xrc16I6Vc8llYbdNoZ70YhzQ11I+ZvrBLKfQwCMYYLp2ucXepOXCNfdRxheEJJfAVE3U7a3/tzqDOrVq2+sOjJpzV2rDWSIhCj6vdmIqOvrDncjUMP7pZgkEAweQHZ/ZTDn08Tzpjo/3ZTmmas7QWs7weH4tQ+y4mTzGdj0TXeEaNTBGeedzetqPOMMC0XGyFw+G4P06PV0gyvfsIunEdw4Mm9BXVcjAU00T3g6cUga+YHrXOpNaARo6MAU0rzri90KDWmVB6rXOt1tMXcvz0hV18T1EtbeYZGgOv3R4uvwJXGJ5QfE9x5Wyd6fEy/+Nrt/qKBLCjMYGvuLPYOFI6q1aSoY09vqu3VqmVA05P2FUzKywfjlUoncWIACKY5OD0nCJCOfRpJTn5llHhRjsFhO/fWAHgdZfGgOEfIwUwW6Iq9Po8IEhtkmD8DAQl8h3GSfFD61x6hD7/Dofj6FIp2UmcnUZKjTEgHMsRuqOMiHD5bJ3SMSpGqpFPKfSIAo+55RaBr2gn2aH/XuVac2N2gyj0egsgr91ZpRR6TI+VrL5QWT+L48pINSRONZdO1bh0uka1PFwLEq4wPKEEvsJTws8+/xgLq22+9tJgbEI58llvpaw3j47Oar2R4Hm2c/jq7TWevDDaK1qMGCrRkHwBkyYoH/F8++cDpFLyaSd5n1B9eT0mChXfv77MxVO1nnve0I+RAiSbjqRmfQGpjiFegCiFmry0c8dQeYjJITu6RkwOh+NocWq8TLpD11AbO0Y6zAttw8pIJTxWBXmlk5nXNaAREbTh0L0h7i42SbK8r/B79dYaF0/VEBGSNKdeCY/1d6BWCtDajjD/4t95sjdCOyy4wvCEIiJEgcfrLk9w+cwIL3z9duEcdCXyubXQGOgoHgbGGJY3YkqBx8z8Bu0k56nOGGmuNb5SdvZ+CDBxA7zAjiumB1wYRj6tOOsVhlmuabYz1psps8st3nCM3EgBSLdmGM4jI9O9u7zJy5iNRUy7ODbEGNDJ0dTaOhyOo0c58hmvRYUyDK2NC7d37AlR4GGM4fSELQyNMRjDoTqTrjViFlbb1MqbndlmO2VuudkbI00z3YvNOq5EoYcIh969fVjcGeoEU4588tzw489doBlnfObFuwPbBL4izTSLa4dvwhGnOVlm8DzFyzOrKBEeO2dXYpJUM1YbjlUoo3M73ujZjqFJD9aZtNrpGGa5PWk12xkG+MHNFYCevvA4jJEC6DQGz8cYjdlYRI1M9e5TU5cByBdvFO4rfgguz9DhcDwAp8YrZLkZuDDU2gxFxq7j6BN1DWjGysRpzmojQbDXSYdBN5qiWvL7rhluzllXzounbWFoBMrDMtn1kNis8ODIxYfcL+4MdYIph1Yge3ayyluemOSL351lZX1wbK5WDphdbPVygA6LjVZK93zzyq1VLp6q9pwzs8z0MvqOPFmCmM0TpwgH6kxaLQe0k4ysc9JabcSEvo2pODNRYWzEmhLlRg+9c5jRGaJzG1XRWAado7Z0DNXYWfCCHcdJ8UN0ex1jhvME73A4Dp4o9Jisl2i1+38ztXEdQ8fe4HsKJTA9Zj0WZpdb+L4cimFgXzTFloUPYwxf/cEcoa84N1m1HfMhmux6FOrVgCQdzuuG4//uOHYkDD00dkXz77zjPCLC337t1sB2SgmeJ8wuHezI43ZWO3EK682Eu0stnuzEVAAghvIwxFTQdcncXEk2ndsOipFyQDvJibMcrQ2rGwlJppmZb2yG2meaMPCGXyCeWVMd2HQkldpmx1CUh5q8SL6TzlAUog2kTmfocDjun6mxMrnu1xrajuFwT2A4jgYiQqUU9Nzl55Zb+J46lMJwazTFVr76g3mu3lrjp9/3OIGvSNKckUow9FNI90MlCtwoqWP4CLasXNarIe9582m+89oSM3MbA9uWI4+ljbjjXnnwpJmmGWeEgcfVW3a0r1sYppmmFPpDM6JjkiYimwWXcLDOpLVKiDHQaKY9l9eXto2RtpOcqXpp6E/gRme9ItxsiarYijd5GbM2i9nBBMiIQR+wQZDD4RhuosBjaqxMc0vX0GkMHXtJJfJRShirhcwtNfE9oZXkB1qQbI+m6LKw2ua/f3mGJ87Ved9bzwH2Wm3Yp5Dul1LoWdf5IawN3RnqBBP4CrZ8aJ9/8xlq5YD//uWbAycWG3PgcXuhgT6ET7rN57FFytWZVUYqAac6IxRxmg9NTAUAHeOZHl5woM6kXcfR9VbSc3n9/o0VJuoR02M2F0kbM3CiH0ZMGvdHVUQ1JCz3bXNPnaEXQdPpDB0Ox4MxNWrt+bW2v5k5pm9B1uF4FMolH911Jl2xzqQHaUBTFE3Rvf1PPvUqga/4e++7sukGK/TkP8cdpYSRckCSHa4E62FwZ6gTjO8plCe9H60w8PiRt59jZr7B964vD2wfBh6tdlaoQ9xvVjdiAk/ItbYxFee3xFQYhia41hhjXS69LSdH5WPSg3O+7BaGzXbG0nqM0YZrd9Z5/aVxRIQs00SBfzxO4Gl/VMX2biGAGj8PyttFZxig440DNQhyOBzDT+B7TI+XaLbteJ/gwu0de0fYmZI6NV5mYbVtoyqMIT2gyIq7i03SbdEUAC984w53Fpv8zHsvM9LpEGpt8E6IvrBLvRqSDqHO8OS8Q45CqlHQl3vz1ienOD1e5uNfmSnMw6mWfe4sNg80K0drw1ojIQo9bs41iNOcpzpjpN3O5tAUMXmKaIPI5lfPOpPGB1Z41DudwFY7Jc8Nr95ZRxvTi6loJzmTHd3C0JO2QVlbb70x32c800W8ADV+Hr1D0L19rwxkh+/M63A4hovJup1Q0NpAJ8fQ4dgLQt8DYzg1VsYYWFhpIyLEyf53qbrRFNVy/7XXjdl1PvutO7ztyUnecHm8d3t8AvILt1PujPoOG64wPOFUyn4vtgDsauaPPXeRlY2EL31vbmB7z1MYY5hfObgOV1cHJyJc7cZUnK0DNqaiXgmG58u3zXimi3UmPRj9ZncFr9nOEIHvXV9mpBJwbqoKgOZ4jJEC6LRtu7PxBqQxUtAxBFBTV9ArdzA7mMwIgo6dztDhcDwYga84NV6m0cpAOFEXxo79RSkhCn0m61YCMrfSwvP235k0SYujKeIk508//RpjtYifePelvn3STDNSHSLJzx4QhcNp4OcKwxPOSCXsxRZ0efxcnScvjPLpb96hWWA2Uy37zC+3Dsz9qquDA7h6a5VLp2tEndD1JNPUh+hko3coPKwz6cFEVnRfr3aSoxS8cmutf4zUPx5jpCbPEK0RUVZfCIUdQwBv6jIYg166WfxgfgSt1f06VIfDcYyZqJdQSjDGuI6hY0+plnzq1RBPWef4wFM02vt3baaN4dbCBiKmL5oC4K+/dIPVRsLPv/+xgYJI6BiynCCUyFBdn3ZxheEJp14NqZT8gVDUH3vnBZIs54Vv3BnYR0QIA8Wdxca+u18ZY1jeiCkFHmuNhLnl/pgKg6EyTGGpSQNRg8cryI7dqr2mqzHMcs312Q2yXPdiKo7VGOmWDmwvqmKnjuHEBRDZMbYCL0DHTYwePiG5w+E4XHxPcXq8jCDDM93iGAoqHQOa6bESc8u2Yxin+b6ZBC6t2miKyjZfh+9eW+KbVxd53zNnuXiq1ndfnms8T/U0kSeJkWGaaOtw8t4lRx8iwpnJ6sBM+vRYmWefnuYrP5hjoWBstBT5bLQy1hr72+WK05wsM3ie4uot27F58rwtDHOtCYZMzGziZr8jaRcvgAMaVVRKKIUejVbK968vU448Lp8eAY7XGKnJU0zHkdSsL4AfIqWRwm3Fj1Bj53bRGXZO7KnTGTocjgdnbCRivB65jqFjT4kCDwOcGq/0nEnB7IszaSvOuL04GE2x1kj4y89f59xkhQ+87ezAfu00Z7R6svSFXUYqIVOj5XtveIQYnitqx75RKwfUygHtbaOhH3zbOQJf8fGvzhTuV4lsfEW+j6YpG62U7rnk6swq9WrYi1SIE81obXhONkZrG5/gFYxpHrAzaSXyWWskvHRzlacvjqHU8RojBTBZ0vvs6HVrPLPbZ0VNXkYv38LsoPW0OsPGfhyqw+E45vie4vHzY0Pze+UYDsLAQ8RwarzMejPtOODKnheGO0VTGGP4889eI8sNH/7A43hqsKzIct3zNjhp+J6iFA3XNZUrDB0AnJmokqS6bzS0Wg543zNneenmKq/dGcxx831FlmsWV/evi7K6EROFijzXvHpnjae2xFTkuRmuk02esNMlwUE7k1ZKPi/dWCZOc15/eTPU/tiMkQKkrb6oip3GSLt4U5dB5+il4oUQ/BDTdDpDh8PhcBwNfE/hKcX0aMeAZrkJmAF50KNyd7FJkg5GU3zpe3O8enuNH3/uIpOdY9hKlmuUCOUhK45OMq4wdAC2UBgdiWjF/SeTH3rjaUarIX/z5Zu9vMOtVMsBs8vNPT8JgXWxarYzAt/jxtwGSap58kK9d7/BDNXJxmQJZpfF4oN0Jq2WfJJME/iKJzoOr8dpjBTAJNaR1KRtTHt9R+OZLmrSuqjtOE7qh/ax9MGYLjkcDofDcS+qZZ+JjjPp7HKLwFO97My9oBtNUav0X2/NLbf4+FdmeOrCKO94enDh1RhDo5Vx+ewIwQnUFw4r7p1y9Dg9XibL+7uGvq/40WfPc3epxYuvLA7so5TgKeHu0t7r45pxRvdIrt5aRanNmIo0y6mUfHxveD7CJmkhsrMrl4GDKww7BjRPXRi1nd/jNkZqDKaTYXgv45kuEpaR0dM7G9CAfZMSpzN0OBwOx9GgGgWEgaIc+cwtt/B9tWeu8b1oinJ/NEWWa/7kU68ShR4/+/yVwhHpZitnYiRioj5cGruTzvBcVTv2nVInD6fV7u/+vemxCc5NVfnE12+RFHQGy5HPynrMRmtvi5rVjbjnYnV1ZpXLp2uEnTGGJDGMVYds7DFu2ky9HRDE5u4dAF1n0tdfGgOO4RipzhAMIgpzj6iKrXiTl9FLN3fsCopS6HhjTw/V4XA4HI6HpRT5YITT42Vml1t4am+cSfuiKbYtwn/i67eYXW7xs89f6V1PbCXNNKLgbCcf2TE8uMLQ0cfUWJlc93cNRYQff+4C682Uz39ndmAfEaEcedyab+yZRbLWhrVGQhR6rG7EzK+0e26kANroAbvko4wxBp3s4EjaRflWF3cAnB6vUAq9XvTHcRsjJd8s7PT6AigPqYz1bjM6LyzC1dQVyDP0ymBMC+B0hg6Hw+E4UgS+go4BzfwWF/k0fTTPgp2iKa7dWePz357l2aened3FsYH9jDE02ymXTo8M1VSXw+LeMUcfUeAxNVam0ervmFw6PcIbLo/zuW/fZb05GFERBh5xmrO8vjdZfM04QxtbdF69ZY1vukWMMQZEKEVDFJaap4jOEdnlK+cFNs7iAHj+LWf5l7/8dkqhf+zGSKETVdFZozDrC0h1AlFbPi9pCx0PFuHeVEdnuMM4qXgBpPGOzqUOh8PhcBwkdrJKODVWJs20vQ4zkGQP7/3QbBdHU7TjjD/9zDUm6hE/9tyFwn0brYzpsUphJ9Fx9HGFoWOAbubKdrOZH332PFobPvH124X7Vcsedxebe2KTvNFM8DrX8VdnVhmthkx1HK/iNKdeDVDDZPudp7sazwCgPGtQY/bfmTTwVK8QPHZjpIBJk14R3o2q6LtfG0R5A6+1RDWkNrWrztAIVr/ocDgcDschIyJUIo/JUfs7PrfcQoSHNgXMcs3NucFoCoD/+sUbrDcTPvz+x3rSnq0kaY7vK05POF3hsOIKQ8cAga84NT7YNZyol3juDaf4xssLhWYznlJg6BtleBiMMSxvxJRCj6wbU3FhM6YiTTWjwxRTAej03p1UEUEwkO1/N2rryf7YjZECZC1baOcZprE8YDwjYpCwDNlg99ubuoxevLFjgS7iYdrr+3LYDofD4XA8KJUoYLRmC8PZ5Ra+L7Qe0pl0dqk4muLbry7y7VeX+ODbznF+ujawnzGGVpJz6VStMM/QMRy4d85RyES9hFIyEF7//recpRx5/M2Xb/bpELtUyh7zq61HcsSK05wsM3hKcWN2gzTTPHFhtG+b8hDpCwFImoi69zEflDOppwQMx3KMFLZEVTQWATPYMTTg1ycLR0LV1GXIYszqoJ4WgCDCtAZzPR0Oh8PhOAwq5QBPhImRiLlOZEXjIa7DVjdiFtcGoylWN2L+8vM3OD9d5X3PnC3cd72Zcnq8PFT+D45BXGHoKMT37CjA9q5hOfL54NvO8dqddV6eGTThEBGiQHF7oVFYON4PG62U7pTo1VureEp47MwIAHmu8X3VcysdFkzc2NWRtMtBOZMqJSDHdIy0IKpCbekYmjxDggivVEUKPqNq8jLAjuOkonxIU0xBt9HhcDgcjoOme010arzM3HITz1OkaV6YP70TSZozM9+gUuqPpjDG8KefuYY2hg+//7GB8VKAdpJRjnxOjVUe/R/jOFSG6+racaCMj0T4niLL+7uGz75umsl6xMe/MjPQUQQbe9Fopaw2Hu7CeXUjJgq3xFScGenNssepZrQaFWbmHFWM1pg0tq6j9+KAnEmViO0Im2M4RloQVSG1yc378xSJKkgQYUQGRkZVZRSpjKEXru34FAanM3Q4HA7H0SAMFIhwarzM4lpMmuUgct8GNNoYZuY3EGHASfTz35nl+t11fvLdl5iolwb31YY01VyYrhUWjY7hwhWGjh3xlOLMZGUg19BTig+98wILq22+9tJC4b6Vks/thcZAUXkv0kzTbGcEvsfKeszCan9MRa4NI8NWyOQJItxfMXuAzqSB71EOj98YKXlGd41Ury8glTHE36JJ1RmEVUR5qKhaOLqrpi6TL17fsestnufGSR0Oh8NxJPCUIvAV0x2TvvkVu3CZ3KcZ4NJqm41mSqXUfz1wd6nJJ752i9dfGuNtT04W7rvRTDk7VaUcHbNriROKKwwduzJWiwgCZVeftvD0xTEunxnhha/fpp0MzrH7viLXhoXVB+t+NePNi/qrt+yo6pNb9YWGoTv5WKfR+1xFO0Bn0tBXTIwcrzFSoE83qNfnB4xnjDGobqFYqhcb0ExehqTV6zgO4Efo1tpDj0s7HA6Hw7GXVEsB4yP9zqRJcu+O4U7RFFmm+ZNPvUo58vmZ914uXNxuxRnVclDYSXQMJ64wdOyKEuHsRIVW3H9y6YbeN+OMz7x4t3DfaslnfrlFfB8npi6rG7EdicAWhuMjUU8Dl2Y5lZI3dIGpJm0j9zleYZ1JORADmsnREvXqcLm73g8mTRAUxmjM+uKA8YwAdApDFZUpKu3UVEdnuLiTztBDdAa50xk6HA6H4/CplHxq5RDfU8wuNzsGNLtfS2S55sbcemE0xd9+bYb5lTZ/7/krhYYyea7Jc8OFU7Xhig9z7MpwXWE7DoV6NaQc+STbMnHOTlZ5yxOTfPG7s6wUBNsrJXieKoy2KEJrw1ojIQo8skzz2p11njxf761SJYlhtDqEHa52A+7DkbSLOaDIisnRcmEO0dCTNK0jaXMVdNZvPGM0RinwOu9HEAFmoPMn1QmkNLJj0D10dIaJ0xk6HA6H4/AphR4icGqsxFwnsqJ5j8iK2aUmaaoHoileub3KF787x3OvP9U/tbWFRivj3HR1YF/HcOMKQ8c9ERHOTlYHuoYAf+cd5xER/vZrtwr3rZR8VhsxG617FzrNOEMb+3zX59ZJM913QtJGD6UNsk6a4Pcft0nbxF/8A7KN5YHtBUFn9849dBTTdSTtGc9s7RjmGSos9xYbRPlIULa6wy2ICGrqMnphF52hCjBtpzN0OBwOx+ET+psGNLPLLTylyHJTaBIIdkJrYXUwmqIVZ/z5Z64xNVriQ+88X7hvs5UxWosYrw3hYr1jV1xh6LgvqiWfWjkY0BPWqyHvefNpvvPaEjNzG4X7lkKPW/ONe9ombzQTvM7C09UZG1NxpRNTYYxBlFCKhmtlyuQpYnJE+r9q+dyr5Le/R/v6twd3Ur7tejkeGGMMJrMOsEVRFeQphNW+faRch4JCXE1exrTXMY3B4h0AP0Q3nc7Q4XA4HIeP7ymUwPRYmWY76y3IJ+lgYdiNpqiWB6Mp/uJz12m0Mj78gccI/MFrrizTGIFzU5Whcoh33B+uMHTcF7ZrWCEuOME8/+Yz1MoB/32H0Psw8IjTnKX1ncfujDEsr8eUQnsSujqzxpUzI72TUpzm1Cvh8M2xZwmGwWPWSzMAJAsFnVbPx8T7H1lxLMlTpNN11uvzEFWRcDNXyegcCct9u6ioiilYUfU6OkO9m87Q5IVFpcPhcDgcB4mIUCkFTHacSeeWWxhjSLc5k+4WTfHiK4t87/oyP/z2c5yd7F9EBXut1owzLk5XC4tGx/DjCkPHfVMpBYxVI1pxf9cwDDx+5O3nmJlv8L3rxd2Vatnj7mJzwN20SzvJyXKDpxTL6zGLa+2+MdI01dSHLaYC0GkMBfYmetkWhuliQWGofEwWH4gz6bFDZxixr7dZX0DV+h1JReiPrgAIIqTgPZKRKQjLOwbdAxgDOnFFvMPhcDgOn0rk95xJZ5ebeEpop/3XbIurrcJoiuX1mL/64g0una7x3jefKXz8Ritjoh5RH0a/B8d94QpDxwNxarxMmumBzuBbn5zi9HiZj39lpjC70FMKEWFuufgiutFO6TYDr84UxFQA5SHUF5I0kW3GM0bn6OXboDyy1TnMtriEg3QmPW6YPANjzWT0+nz/GCnY7u02vad4AQSR3Xfr7aLwJi/vGnQvfgguz9DhcDgcR4ByyWYTV0t+x4BG0Wxt/rY12xl3FpsD0RRaG/70068hCD//vscKg+rTLMdTwpmJwU6i4/jgCkPHA1GOfMZrEc1tXUOlhB977iIrGwlf+t5c4b6VksfiarvQJWt1IyYK7cfx5VurTIxETHZycfJc4/tqKJ2vTGwdMvtuW50FneGdfxMYg14djPs4KGfS44aNBvEgaUDa7jOeMToH5SNqMAdTdsgzVFOXMc0VdHO1+An9EN1ed91dh8PhcBw6oW+vo05PVJhbbhF4qne91o2mKBVEU3zu23e5ObfB3/2hS4wV5BsbY2i1cy6eHhm6yDDHg+HeXccDc2q8Qp4PWvw/fq7OkxdG+fQ379BsDxY1IkIYKm4vNPr2TbOcZjsj8D3STHPtznpftzBO86F0vjJa94xQtpIv3QTAf/w5APTK7YF9rTOpy8h7YJL2rsYzKqoU7ialmi0ct3FPnaEoRGtInc7Q4XA4HIdL2NH9nRorMb9iJ7Ty3OoM7y41yDI9EFN1e6HBJ79+mzdeGeeZxycKH3ejmTI9XqZWHsLJLccD4QpDxwMThR5To2Wa7cEL6R975wWSLOeFb9wp3LcU+jTilNWNzQvpZpz3FF7X766T5Zonz28WhlluqFWGMIg9TxHMgGuXXp5BSiOo8Quocg29XPBaOWfSh8KkrU5hWBRVMehI2kWCCJECneHoGdsV3E1nKGIjSRwOh8PhOESUEqLAXqNluWFprQ0Y5pYbLK7GVMv9C9VplvOnn36Natnnp99zudBlNE5zwsDn1Hh54D7H8cMVho6HYnqshNZmIIJieqzMs09P89UfzLOwWuxCWi353F5s9rSIqxsxYWA/ildvreJ7wuWtMRVIz610mLAGMoO366UZ1MQFRIRg8kJhx9A6k7pi40HYjKrwMOsL4Ic2iqJ7PwYVlop39kKM8ge6hiIKNXmJfIeOIYB4ITSdztDhcDgch0+t7DNRt1NWc52u4exicyCaAuDjX5lhYbXNz73vMcrRoMxCa0Oc5Fw8VcNTrmQ4Cbh32fFQBL7H9HipUC/4wbedw/eFj39lpnBf31PkuWFhtUWuDWuNpKcfvHprlStn6wSdOfk001RK3lDOtFu9W/9xm7iBaSyjxi8AEE6dx6wvDBjQOGfShyBPESO9qApVm+r7EZQC45nefSKo8kihztCbumLfo7g4pxM/QMcbhZEXDofD4XAcJOXIZ7QaIQKzyy1KkUfoD15HvTyzwpe/P8+733iax8/VCx9ro5VxZrIy4GDqOL4M39W248gwNVpGRMi3XRBXywHve+YsL91c4bU7xZ2Uatk6Zi2tttGd3LmltTZLa3HfGGmSakaHUF8IQNyAbY6k3fxCNWELw2DyAmDQq7N92zln0ocgTzGdOtCsL9i4iQ7GGDuu7O2ij4hGMAWvt5q0OsN84UbhbiIKMJDtnNPpcDgcDsdBEAU+nqeYrJeYW2oR+B6lbd3ARjvlzz9zjVNjZX70HecLH6cdZ1Qin6lRN0J6knCFoeOh8T3F6fFynxVylx9642lGqyF/8+WbA+OmYOfgA19xe2GDbkbq1VvW+fGpLcYz2hiqwxhTQSffbpsjab48AyKosbMABJP2hFw0TmoMzpn0AehFVaQxprXWbzyjMyQodYq4YlRYokBegRo/C56/owENdMyC3Oivw+FwOA6ZMFCIGE6Nl3ujpFsxxvAXn7tOO8n58Acew/cHfxdzrUlzzYVTVVTRD6Pj2OIKQ8cjMV6P8Dw1kF3o+4offfY8d5davPjKYuG+5cgnzTSl0BZPL8+sMlmPeuGsWhsrpB5GfWGeIXlqoxO2oJdmkNEzvZB1rzoKYQW9MmhAI4JzJn0ATBYjSqE3rCPpduMZCYsdSXv4IUbUwPiuKB81cXHXoHv8EFo7RFo4HA6Hw3FA+J7CU4qpsRLL6zFx2q+d/8bLC/zgxgp/5x3nOT1R/Lu40co4N1XrXZ85Tg6uMHQ8Ep5SnJmoFDqUvumxCc5NVfnE12+RpIP3A4zVIpQS0izn+t31bWOkOaOVcDhXq7IYs607ZYxGL9/C6+gLoaNtGz9XbEDjnEkfjMQ6kpqCqAqjc9ghqqKLiEJF1R10hpcxq3cxyeDqq90gRMfNwsgLh8PhcDgOkmrZZ6qTBT2/vPm7tbTW5q+/dJMrZ0b4oTedLty32U6pVwImCvIMHccfVxg6HpmxWkTgC2m2rdMiwo8/d4H1ZsrnvzO7w96Wa3fXyXLTl1+YZJr6kOoLizp9Zn0esqSnL+yixs5i1ucH9W3OmfSBMGl7M6pCFFLtz2NS/n1EnpTrmLwg6H6ym2e4k86ws3iROp2hw+FwOA6XahT0guq746RaG/7kU6/hKeHn3v9YYTRFlmuMEc5P1Qrvdxx/XGHoeGSUEs5OVmnFg1rDS6dHeMPlcT737busN3cei7w6s0bgKy6fHtm8UYTyEI6RAhA3B8dIF/uNZ7qosXNgBg1onDPp/WOM7ouqkNpE3+svGDvueQ9UWCyyVxMXQNTusRUIOm48+ME7HA6Hw7GHlCKfeiUk9BWzS7Yw/PSLd7i10OCn33OZ0erg76Exho1WxvnpKmEwpNdejkfGFYaOPaFeDYkCv3Bk9EefPU+uDZ/4esG4JPZkdPXWKlfOjPRE0FmuCX01tCcnkzQHHDDz5RkIy4OdrI4RzfZxUutMaiAfLLgd28gzBDajKrboC43RGPGQ3RxJu/glQDDbAijFC1Dj53cNuscPMU2nM3Q4HA7H4RL4ChGYHi8zt9zk+p01PvXN2zzz+ARvemyicJ9mO2diJCosGh0nB1cYOvYEJcK5qQqtZLAwnKiXeNcbTvGNlxe4uzQ4Grm0FrO8HvePkaY5Y0N6cjJG98Yat6KXZlDjFwbGM6Q8CmG50IDGGCnUvDm2kacYBKMzTGMJqU313afuZTzTQZRCRZXCmBA1dRm9csd2JovwAkzawmhXyDscDofj8Ah9hYhwerzM7HKL3/lv36deCfm7775UuH2aaUTg7GTVjZCecFxh6NgzauWAahQQFxSH73/LWcqRx998+eZAN+blGdtl2Wo8k+eGWmU4C0OyFMH0nVxN0sKsz+NtGyOFjgHN2A4GNGKcM+l90Iuq2FgCY/qjKvIUStX7f7BSfcege4zuZVFuR0TAAInTGTocDofj8JCOFGdytEQ7yVlcafFz739sIM8Q7NRWs51y8VSNoCC6wnGycJ8Ax54hIpydqtAuKAzLkc8H33aO1+6s9wrBLldvrTI1WurFVBhjQITSkOoLTZ6wPbmxW/Rt1xd2UWNnMWuDBjSiAudMeh+YtI0oD91zJN0ySqoNEpTu+7FUVMEwqOtUExcB2TW2QpRyOkOHw+FwHDrVctBzJv2RZy9y5cxI4XYbzYyp0TIjw7oY79hTXGHo2FOqpYB6NSw0onn2ddNM1iM+/pUZcm0vvOO0E1OxZYw0zTSVyMf3hvPjaZIY2fbV6naZ1Pj5wn2sAY1Gr8313+H5O0ckODZJ29Z4ppdhuNkxFDG93Mj7IoiAwVEaCSLU2Nl76AwjpzN0OBwOx6FTjgJOj5f56E88zU89/1jhNkmaEwRqxzxDx8ljOK+8HUea0+MV0lQPjIx6SvGhd15gYbXN116yF/BXb66Qa7Mtv1AzWhvilat4Hbz+cY18aQYZmd6xc7WTAQ3Kx6TtgdfS0Y9JW+DZqAopj/YVgsYA3v1/nkT5iB8NxofQ0RkuzxTeB9akhrS94/0Oh8PhcBwEoa9AhMfO1vHU4GKnMYZWknPxVG1oF+Ide4/7JDj2nErJZ3QkKuwaPn1xjMtnRnjh67dpJxnfu7ZE4Csuna71tjHYzuOwopNWnyOpMcYaz+wwRgoglTEISujlfgOaTWdSV2jshI2qSBHlo9cX+rqFJs+QIELUg53qpFysM1STl0Hn6OVih10AI51MRYfD4XA4DokwULsayaw3U06Pl4f6esux97jC0LEvnB4vk+Vm0Pa/E3rfjDM+8+JdvndticfO1nurVVoblEA0tPrCDMmzvgw901iCtFVoPNNlNwMa50x6D/IMwdgCcX2hT19IniLRg4/ISKmKMYNaWW/KOrrp3fIMxcO0Nx74OR0Oh8Ph2Cs8pQh8RZ4PaubjJKcc+ZwacyOkjn5cYejYF0qhz2S9RKs9eHF9drLKW56Y5PPfucvSWpuntsVU1KshaljtkvMEs+3Q9dJNoGtesjPWgGbOOmxuxTmT7k6eYgDTWrOF4FZHUp1B+ACOpB3Ej5CC6V0JK0j9FPnCtZ139iNMy+kMHQ6Hw3G4VEsBadZfGGptiNOcC9M1VMGIqeNk4wpDx74xPVYm0xqtB6+w/847zuN1xvuePF/v3Z5kmno1OrBj3GtMlrDdklQvzYAf9RcsBajxrgHNbN/tzpl0d0yeggGzPg/QF1VhDKgHMZ7pIH6I8YPCTEJv6jJ68SZGDy56AIjnQ5rYz4LD4XA4HIdEpeST5f0XJRvNlHOTVcoF0RUOhysMHftGGHicGivTbA9eXNerIR9653ne/vQ0o7UthWAne2dYMe2GLQy2kC/NoMbPI7L7161rQGO2B907Z9JdMWm8Y1SFYOAhCkMAVRrZWWeYp+jt79PWY8LpDB0Oh8NxuJRCr2+tuhVnVMsBE6P3H+HkOFm4wtCxr0yOlgEKu4bvesNpfuWn3tj7e5ZrQl8RBkNcGCbNfuOZLMGsze5qPNNFKuPWgGZ7weGcSXcnadrXaH0BwjIS2dFRYzRGqb7340GQ0siOHUO4h87Q8zGt9Yd6XofD4XA49oJwS2B9rjV5brhwqja8ch3HvuMKQ8e+EviKUxNlGgVdw+3Eac7YEMdUGKNtl0htdgz1ym0wZlfjmS7WgObsgAGNcybdHZPF4Hno9fltxjMZKizv6sq2GxKWkKI8w9IIUpvcNegeP0K31h7qeR0Oh8Ph2At8T6GUoLWh0cw4N10lGuLFd8f+4wpDx74zMVJCCYXOWFvRuaFWHt7CkDxFoK8QuVew/XbU2Fn02txAp8p0Ht/Rj9EakyaFURXk6UMZz/TwQoyoQi2hmryMXryOMcWfaVEeolNbtDocDofDcQiICJXIZ3mjzWgtYrw2vB4OjoPBFYaOfcf3FGcmq7t2DY0xVl8YDe9KlskSzDbnGb00g1QneuON90KNnQOdY9bmBu7TqSsyBtApIoKJG5A0+zqGRudIWH7ohxYRqzMsKMi9qcuQxoXvU+/5AZM4naHD4XA4Do9q2SfwFOemKg89QeM4ObjC0HEgjNciAk+RZcUdljTTVEtBz6l0GDFJjGz5ShljrPHMfYyRduka0GzXGYrywRnQDJJnGMym8Uxts2MoYt1FH4nySKG7qOroDHcbJxUVYNpunNThcDgch0e9EvLUxTECf3gX3h0Hx/BehTuGCqWEs5OVQodSgCTVjA6xvhCApAFbHElNaxXijQcqDKU6AUE0aEDjBdbYxtGHyVNAelEVW0dJDQL+wxnPdFFBqUBlCKoyhpRH0bvqDEN0c82ZBjkcDofj0CiFPiNDHAPmOFhcYeg4MOq1iCj0SbNBzZYGKkOeqaPjfkfSrr7wfoxnuogIanTQgAblYbLYFRnbMEkbEWU7hl6AVEbt7ToH5dtO66MQRBiRQi2hmrpMvnh9x/fE6gzzwsgLh8PhcDgcjqOGKwwdB4YS4exkmVa7vzDU2uCLzdsZVozOQGeI2vw36KUZ8HykfvqBHkuNnUWvzvaZnogoxGhnQLOdtAXKR28sILXJzazIPEVFlUd+eBGFimqQ7aAzjBuYjYUd9zeAdiPADofD4XA4hgBXGDoOlJFKSKXkE6ebRU+c5oxUo+EWRWcJFBjPqLFzfcXi/bCTAY1zJh1EJ23wfExBVMUjOZJupTwC+Q5B97DrOKn4IbjYCofD4XA4HEOAKwwdB4qIcGaySpxsFoZpphmtDre+0GRJX11o8gy9emdXfaExGqMLRhR3MKAB50y6FaM7HVSdY5qrqD59oUbCvdFUqLA84DYLILVJiKrkuwTd44fo9tqOsRYOh8PhcDgcRwVXGDoOnGrJp1YOaMebRjTDHFMBYJJmn55Nr94FnaPGdykM4wZ5a33gdqlN2ILCOZPujra5kd1RTtnSMRSzB46kXYISIANaQhHBm7qCXthFZygK0RpcQe9wOBwOh+OI4wpDx4EjIpyZqBBnmjTTRIE//DbKcbPPkbQXbL9Lx1AwFFleiiirMxwwoPGtps5hyVIMshlV0ekYGmMwIn1GQI+CKA8JSoVjvGrqMqa1hmmu7Li/EXE6Q4fD4XA4HEceVxg6DoVKKWCsGrG81ma0tjcX8IeFMcZe+G91JF2eQcp1VLlevI/OMeKh/NAa12xDjQ4a0OD56LTtnEk76DwFMej1eRCxnVawJkBBadOIZg+QymihztC7H52hF0Brdc+OxeFwOBwOh2M/cIWh49A4NV4mCj1q5eHWF5InCPSZ5+h7BdtnMaoyilcdg7TA2GT8HOisl88H3bFE4wxouqQtRHzM+gJSndgc5c1TJHx0R9KtqLCC0QU6w/o0BKX70BluOJ2hw+FwOByOI40rDB2HRjnyeeLC2NDnF9qRxs2iwbTXMc2V3fWFOkPKdbzySHHHcAcDGiOuMOyRxOB56PWFfuMZncMeRFX0EZTY7joLtlj3Ji/v3jEUZceGnc7Q4XA4HA7HEcYVho5DZWwkQqkhjqkAdNpGtogF86VbwD30hQYkLCNhqUhmaB0vCwxoALQLTAfs625QmI3FPuMZALVXxjMdxPORIMLspDNsLKF3jaUQdNzY02NyOBwOh8Ph2EtcYehwPCpxs19fuHQTOgYyRZg8hbCMeAEqiDBeMNA1FFGo0TMDBjSifPt8Jxyjc9Cp1e4Z3dcxFAzscWEIIOWRTl5lP2qqozO8xzipyzN0OBwOh8NxlHGFocPxiOi4MWA8o8bOWtORIrI2Uhnr/VXtVHCMnUOv3u03oHHOpJY8Q8Aaz7AZVWGMxoi382v/CEhU638vOqjRM7a7u8s4KV6IjhuFuZUOh8PhcDgcRwFXGDocj4DRmXXBVF7n7zl6+fY9gu0NaosGTsp1TL6DzjDPejl9gHMm7ZKnGCOYblRFbbJ3u9pj45kuEpT6RoZ7tysPNXGRfFedoYAxrqh3OBwOh8NxZHGFocPxKGSpHV3sYNbmbHGyg/GMdaYUCMq92ySIkAKhYc+AZnlTZ+icSS06S3pRFVIasTmDAHm298YzHcQPMZ5f2DX0pi5j1ucxu+gIRQQdu8LQ4XA4HA7H0cQVhg7HI2CyhK3Nu3z5HsH2WYIqjyJqy1fPCzFqsOCQkSnwggGdoXMmxY7jdqMqthjPGJ0jYXmXHR+NHcd+p64AkC/e2HlnP4LWyv4cmMPhcDgcDscj4gpDh+MRMEmjN0YKNr+QqNqnIezbPktgW+i9iKDK9YGCY9OAZrszqThn0iTGKA+90R9VIWI7e/uFlEaKnUnHzoHy76EzDNBJszCexOFwOBwOh+OwcYWhw/EoDDiSzuCNX+gLu9+KCKhosKMlpRGMLig4xjsGNFvC0UW8E69V02kLkiZkSX/H0ADePhaGQalw7Fc8HzVxYdeg+95nImnv09E5HA6Hw+HYSpH8w7EzrjB0OB4SYww6bYPn278nTczG4o5jpEZnGC9A/Gjgvp2MTdToWWu0st5vQHOSIytsVEWGaSwB9DqGJs+sXlPt42nNDzGi+gr1LmrqMmblLibdufATUS7P0OFwOByOA8DkGfHd15xh3wPgCkOH42HJU8RoROzXSC/fI9g+jVE7jJj2Co5tK1tqvGNAs3Wc9KQ7k26LqlDdjmGeIvtkPNNFRFClYp2hN3kZMOjFmzs/gB9hmqv7d4AOh8PhcDgAu2Cvm6uFv9mOYlxh6HA8LNuNZ5ZmAEGNnyvc3OjcFhUFWJ3hYMEhtSlbCG4xoDnxzqR5igHbRQ1KEFXt7TqDfYqq6GMnneHEBRBFvnBtx13FCzBpu3B/h8PhcDgce4dpraGTGJPFh30oQ4MrDB2Oh0Sn7T4toV6aQUZPFY6KGmNAgLC08wOW6gMFgyiv0IDGCCe2MOwa7+j1edTIVO89MAZUwWu/16iwjBR0a8UPrSZ0F51hFx07naHD4XA4HPuFMQbdXEWVKph447APZ2hwhaHD8bBsMZ4xRqOXrfFMIXmCiqqI8nd8OBUWG5uosXPolTsDurYT23VKWojy0duiKkQM7KMjaQ8/wogUjvKqycvo5dvWfXYHRHnkrbX9PEKHw+FwOE42aRvROSooYVquMLxfXGHocDwkJm1uGs+sL1oN4U7GM1kMO+kLu/hRobGJGusY0Gws9m4T8TDJCTWgydqYLIW4sWk8YzRGVJ9D7H4hSqGiKuQFOsOpy9BZJNgRPyLfcDpDh8PhcDj2Cx03QATxA0zWdu6k94krDB2Oh8DoHJMlvQ6gvkewvQDqHvo3a2wymGeoxooNaE6qM6lOY3RzGdhqPJOhgtKOMSF7TsH7BKAmLwFCvkueoXg+Omvv2lV0OBwOh8Px8JjGMmyVlzid4X3hCkOH42HIEmTLJKFemoGghNQmBzY1OseIZ41S7kV5ZKBgkJFpG56+vGlAc1KdSY3OQOewYaMqpBtun6cQ1Q7sOFRUpuiVl6CEjJ3ZPegewIBur+/LsTkcDofDcZIxeYpJWsgWeYl2GcL3hSsMHY6HwGRJX2GQL82gxs/3oiv6yGJUZfS+ulkqLA/oDK0Bzem+jqF1JtXWifMkkWcIxkZVKB/pjOcanSNh+eCOI4gAU1iYe5OX0UszmHzn98Yr1zDLtzGpW8F0OBwOh2MvMUmr7+/ihdB2OsP7wRWGDsdDYJIWojz75zTGrM3trC/MM6Rcv78H3iFAXY2dQ6/2G9AYkZOXzZNnGLDGM7XJXiEuQt/K4H4jykeCcmFhrqYug876IkYG9/dA+eRLMwPvtcPhcDgcjofHtNaQrZ4DXoB2zqT3hSsMHY6HIWn0jE5sAWDwdtQXmvvuZomowgB1NXbW5iZ2RigBMObEOZPqLEEQTCeqootBwN9/45mtSLlYZ+hNXgK45ziphGVM3ECvL+26ncPhcDgcjvvDGI1urvS5lIvykDx32v77wBWGDscDYoxBJ62eI6leugmAKoiqMHkKQbl/5epeFASoFxnQiPJPnjNp0rIOpM2VnvGM0bkdK90lCmQ/UFG10OVMoioyMk1+H3mGElUxK7cGxl4cDofD4XA8BGmMaN2b6upixJy8KauHwBWGDseDkqf2pNMZY9RLM0htqrgrmMZIdeyBHr4oQF3qp0B5/eOJng8nraBIW5imzQDcajyjot0dX/eFIEIKLWhsbIVevHFPe2xRno2vWLqJ0W6k1OFwOByOR0G3bUzFdkQU+qQtpj8ErjB0OB6ULLGji9juYb40s7O+EG0z7x6EIMKofp1hkQENno9OWifKmXSnqArCB3yN9wDxAvteFZjMqKnLkCXo1bv3fpyghEna1lDH4XA4HA7HQ2Oa22IqunghODfwe+IKQ4fjAdFZ3FuMMs1lSJqFhaEt7ASCB3PLFOkEqA/oDM+hVzYNaEQUYk6OM6nRGRiN2VgEpBcNYtBIWPAjcADIjnmGl4F76ww3H6eGWb178kaDHQ6Hw+HYI0yeQtIsNqPzAnTcdIZv98AVhg7Hg5I0oRtsv2SD7QuNZ7IEVR5B1EN8zcp1TF5kQBPb0NYOhhPkTJqliAGzPo9Ux5GOxlNMx4r6EJBSrXBcVJXrSHUcfR86Q7BFPn6JfPHmPcdPHQ6Hw+FwDGKSVm+iazsiYuUfJ+Wa6SFxhaHD8YCYuNFzwNRLM+AFNoR++3ZZAuXRh3oOFVaQbROiauycfc6t46QnyJnU6AwjxkZVdPSFxhgb23HAjqRdJIiQ7W9UBzV1hXzhxn2vTkoQYdIYvT63l4focDgcDseJwDRXdjX7MwZM6oLud8MVhg7HA2C0tTvuOmDq5U6w/Tb3K7Da54c2RfEjjEi/zrA+PWBAI8o7MY6WJktAG8zG4mZUhc6QoNQzAjpwvBCj/MIunzd52ZrlrN2/dlBKNczKrF18cDgcDofDcV8Yo9GtNQh2lpaI52Nc0P2uDGVhmKYpH/nIRw77MBwnkSxBTMd4Jk/RK3eL9YU6w6gAKRJA3weiFCqqQZZuuc1H1U9tM6AJ7GjrSSBpYeIN0PkW45kUCQ/BkbSDiKBKtWKd4ZTVGd5PbMXm4ykIKx2X0pOhHXU4HA6H45FJ232O8YV4oVt4vQcHG/xVwD/8h/8Qz+vvtvy7f/fvKJVK/Nmf/Rm///u/TxAETE5O8r/8L/8Lo6OjBEFAGB6OpshxsjF5ihE7wa5X7oDRhfmFpDGqNvFoT1YegZU7fatfauwc2a3vYIxBRHrOpIP9ymNI2u7pK3ujpDpDDiOqYiulOqaxgtBvMiSVMaRctwY0j7/rvh9O/BDdXkevzuKNn9/ro3U4HA6H49ih201QxfrCLuL56FYTk2c9nwJHP4f+qszPz/M3f/M3A7e/8sor/NVf/RW/9Vu/RRAEfPKTn+TXfu3X+Df/5t8AoB7G0MPheERM0kLElmG7Gc8YrfFKI4/0XCqqkG2TUauxs3Dtq5jmMlKdsCtjJsfk6a5z9ccBncboxraoCgRV5D52gKiwhCn4LRIR1ORl8vnXNgv5+0SiGmZtHlMeRUq1PTxah8PhcDiOHzamonTvDQXIYpsF7RjgyFZXV69e5f3vfz9BYC923/Wud/HKK6/wr//1v+Zf/It/gXZh0I7DIN6w45t0gu0rYwMX7sYYa5UZ3scJajf8EiB9OYVFBjQG4Jgb0Jg8Q3SO2VhESjUksK+tYOCQC0P8ECOq0GTGm7oM8UYnYuP+EREIK2SLNwpzEh0Oh8PhcFhMltiYivtYIBcEfUK8GR6GQy+XjTH87//7/87LL79Mo9HgQx/6EL/6q7/KW9/6Vv7n//l/5md+5mcYHR3ld37nd3jf+97HP/kn/wSAj370ow/1fJOTR3P1fXr60bpLw/rcw/T8xhha64KK6ohS3F29RenMY4yP948y6jRBxqYpnR5/5OdvZ1OYPEN1xklN/Qq3lUfYmme087x5MyUcDfFrD/46Dstrr+MWrUaFpdYS/vgZxscrGK3RJU3lzMOP7O7Vvz/OT6GTNmrbYkAqr2fuG39BqXWH6qVLffdt/9wMUiFvreOpNaLpy3tynFsZlvf+uD23e3733p/U5z/J//aT/vz7/dx5Y5V2o4JfLf5d3fp7qxOFCjXRAb4eh/3ePwiHXhj+q3/1r3jqqad4/PHHabfb/Nt/+2/5kz/5Ez784Q/zL//lv+R//V//V9I05dlnn+WXf/mXe/s9bMdwcXEDrYvt5Q+L6ekR5ufXT9xzD9vzmywhX24gZYVurZI3VslqZ1he7jd/0e011Ng5vPt43Hs9f94OMGsLSLR5UpGRaZp3b6A7z2viBNELeGMP9nUeptdet9fJlpuky7P4F9/C8nITk8WIF9J4yH/DXv7787aHXl5Blep9txtThbDC+o2XSU4907t9fLwy8LkpwhiFWb6B1/JQlYeLPilimN774/Tc7vnde39Sn/8k/9tP+vMfxHPnizOYdoYUmPFt/701RmOSOXxv+oEkHg/LYb/3RSglOzbKDr0w/Imf+Inen0ulEn/v7/09PvOZz/DhD3+YsbExbt++TRiGfPKTn+SFF17AGEOe56yvH60X2XEC2Go8s3QLoNB4RgyoqLonT6miMpnerjM8R377u1sMaAJIjrfLlkljJGlAFveMZ8gzqIwd6nF1UWGZoqUqEcGbunzfQfdF+xNWyZdnkKhy7HWkDofD4XA8CN2Yivt1KLfeDBryBB7SOf44c+iF4Ze//GXe+c53IiK0223+4A/+oNcZvHDhAr/xG7+B7/t9ZjPGmL7uocNxEOg03vzz0gwoDzV2pm8bYzRGKQgeUV/YJSiBSJ95iRo/S379azbItTp+MpxJ0za6uQJsGs8Yk6PC8i47HSBb9KDbVyDV1GXy299DN1dQD1HIihdAFqNX7qAmLh7ICqfD4XA4HENB2gZzj5iKbRgjdsHZFYYDHHph+M1vfpNf+7VfwxhDlmX80i/9Eh/4wAcAu1peFEshIj1TGofjwEgaiOoYzyzPoMbO9oLue6QxqjK2ZxfvojxUWMbkac9kZasBjaqO25Ohzo61/bJJWtZxjM2oCjE22uEoYHMnK33vUxdv6gopoBeuoy6NPdzjRzV0YwlKdbzqwz2Gw+FwOBzHDd3eQB7QS1OUwsRNKNfvvfEJ49CvIj/2sY/xsY997IH20VqTZc6pz3GwmLgJXoDROXr5Nv5j7xzcJs9Qe32iKY/B6t3NwrB+CkShV27D+Tfa5xWxYxHHtTDMYvTGEvgR0okBMQDe0SgMASjVYXV2oDCU+ikISuQL1/EvvfWhH17Cml2QiCpHpiB2OBwOh+MwMY3lvrzn+8IPMe114Mw9Nz1pHNm4it1QSvF7v/d7h30YjhOE0dqOHXg+ZnUWdIYqyC8UMb0ohb1CRWXMFgWbeAFSn+6LrMCAyY5nZIWNqtCYjUXUyBQiYm8LIuQI5ZmqqNL3PnURUajJSw+tM+w9jucjIujl230RJg6Hw+FwnERMlkAa76i/T178K9a/+YnBO5SPSdsYF303wNG5qnpAnM7GcaDkSc8AJl+6CTBQGNoxwmjvuzndqIpteYZ6ZbNAEOVhjmsuT56CgFmfR7rB9nmKRPcnND8wgggoPi95k5cxG4udFcqHR8IqurmK7ozVOhwOh8NxUjFJC7NDOWDa62SvfInG9z8/sJgqIjYHOYuLdz7BDG1h6HAcJCZLeicfvTyDlEaQ8rb4gDRG9sElU5SPBOW+EHs1dhaSFqa1am/wfDimhaHJU3SSYNobqK4jqc7gPh3IDgpRPuJHdoFgG2rK5hDmizce/XlKNfTSLYz7QXM4HA7HCcY0V3veD9vJZr4DGPLGih033b4vgk7b+3yEw4crDB2O+8AkLUSs76demkFNXBjoWhujUaXiXJhHRSqjkCW9v281oAHAC9AF+T3HAZMl0FgENo1njAF1BN3EpFzve5+6qLGz9j1auPboz6E8RHm2ODRuDMbhcDgcJw+jNbq1uqO+MJ95ETrRYXrhtYH7RfkQb+zrMQ4jrjB0OO6HuAmej4kbmMbyQH6hMRpEINif+AQV9uvX1OhpELEGNFgdm+gckx9DU6a0NRBVIWIGTF6OAlKqYkw+eLvyUJMXyRceTWfYe7ywgm6vozfcSKnD4XA4TiBpCzCFMRV6fQG9fJvgqfeiyjXyokVZP0S3XGG4HVcYOhz3wBhju3FeYPMLGdQXkiWoUm3/zFBCm5PXRbwAGek3oDGCdSY9ZpikbcdAlIdUxmxWpCg4gmHv4kfIDr4w3uRlzNocZo86uxLVMMu3MG4UxuFwOBwnjN1iKrKZbwHgXXiG6MwT6PlrgzpD5SE6LZR/nGRcYehw3Is8RXSOiCJfngEROxq4BZMlsA/6wi5WZ1jqO4GpsXP9DpXH0JnUGINJ2+iNRaQ2iSgP8gwVlI6kAZX4IcYPMHqwc7uXOkOwP2p4AfnSjBspdTgcDseJwjRXCsdIjTHkN19ETT+GKteJzj6Baa9jNhYLthVInV5/K64wdDjuRZ5uGs8szSCjZwacR0UEtc9mKFa/tnkCswY0TUxrzd5/HJ1JdYZgelEVAOQZHDVH0i2o0kixznD8PCgPvUfjpAASljFxE72+sGeP6XA4HA7HUWa3mAq9fAvTWMa/8AwA4dknAArHSUUEfdyumx4RVxg6HPdAd1aTjNHo5Vt42/WFOsMof981byqqYnR/ZAXQ0xkeS2fSPMPkGaax3IuqMDpDwuohH9jOSGmksGMoXoAav7CnhaF9vhpm5e7xWxRwOBwOh6MAkzR3jKnIb74IysM7/0YA/PoUUhopNn/zA3jEGKnjhisMHY57kTQRFWDW562WcLu+MI1RldH9H20MSsCWwnD0NCCbOkPlHztnUpOnncw+0+sYirD3WZF7iAQlZIc8QzV1Gb1yZ08tskUU+CH50k0X1utwOByOY49priHe4HWA0TnZre/gnXkaCUpAZ6Jr6gr5/GsDOsOuo7uTY2ziCkOH4x6YuAGev6PxjNE5Uh7Z9+MQz0eCzZw88UOkvmlAY4XUx8uZ1KQJpuO8qWqdqArErvIdVfwQIwqjB91JvanLgCGZ3eOuYVCyWsz1uT19XIfD4XA4jhK9mIqCBWI9/yrEDbyLb+m73Zu+AnEDs012IaIQowvlHycVVxg6HLtgtMakMSiffGkGwjJSndi83xgQsQH0B4CUt+UZjp5Fr2wa0Fhn0mNkQJO1esG0MjJliy3l2/yhI4qIWJ1hUdD9xAUQIb47mKn0yM8b1TCre+d66nA4HA7HkSNtgSmOqchufguCEt7pp/puV1OPAZAX5BkasNd5DsAVhg7H7uSJHV0UscH249uC7fMUFZYR72AKle05eWr8rF0F687Im44o+5hgYlsYSmXMiszzFHWEjWd6lEcKLbDFj1Bj50hmX93zpxRREETkizcLu5UOh8PhcAw7ur1RGA1msoT89vfwzr9x4JpMquNIuV6oMxTxMW2XZ9jFFYYOxy6YLMEYwSQtzPo83kB+YbyvMRXbkaCEmJ0NaER5xybXzhiDyWIbVdExniHP4Agbz3RR296nvvumLpPM37QjynuM+BEmjdGrbqTU4XA4HMcP01wBfzCmIr/zfchT/G1jpLBVZziYZ4gfYGJXGHZxhaHDsQsmbSNKeoXXgPGMsW6hB4V4AQSlno5QjZ6hz4DG8yE+JqOEOgOT90VVGDQSDv4gHDmCCCNSKGj3zr8JRGi/8BvoglylR0VKNcz63L4Ung6Hw+FwHBa7xVRkN7+FlOuoyUuF+3rTj9mIr21afPEC2wQocBM/ibjC0OHYjXbDulZ1jWfGz/fuMkZjlBQGrO4nUh7p5RmKHyIjU/3OpOkxiS3IU3RrDXSG6nQMBQqdyI4aIgoV1SAbHCf1xs8z9ZP/HJO2aH/y18n3Or5CFARlsoUb7ofO4XA4HMcGHTcpmsUxcQM9dxXvwjOF2kMANXUFgHz+WsEDGBd038EVhg7HLuikCV5AvjSDjEz37I8BG1NRru94EtovJKr16wzHzmK2jJJKJ/tv2DF5htlYAjrGM8YcfUfSrZRHIC/We0anr1D64MeQsEz82d+ygvk9RPwQdIZend3Tx3U4HA6H49BorRTGVWW3vgPG4F98ZsddVXUcqYztoDOUPY2RGmZcYehw7IDJU8TkQMd4ZntMRZ5al9ADxuoMN/+uxs5i2hubBjTHxJnUpHHPkVSNTIHOED868EL8YVFhGVO4ttm5vzZJ6YMfQ42fJ/nKH5P+4FOD2odHQKIaZn0B7cJ7HQ6HwzHk2JiK9cKYivzmi0j9VEdeszObOsNtMg8vAGdAA7jC0OHYmSzBIJjGEqStAeMZASQ8mJiKvuf1QwjC3phgz4Bm2XYNzXFxJk06URVRFQkrkKfIAeo5H5mgBMiuxZ5EFaLnfwXv4jOk3/0fJF/7sz0b/xQRCCvWpfQYdJAdDofDcYLpyGS2Lw7rxhJ6aQb/ws7dwi7e9BVIW5i1bQZtXoBub+zp4uyw4gpDh2MHdJYApjDY3uQpBFHhSMNBIKU6pLb4U2N2hWxr0P1xcCY1abvfeEZnMAxRFR1EeXb0+B7dW/F8wmf/Pv7rP0h+4xvEn/tdTLI3OlHxAsTkmxpUh8PhcDiGEN1e748L65B3pBjeLmOkXXp5hvP9eYaiPDshdgymrR4VVxg6HDsRNxAVoJdugh9uRiYAZDFygDEV25FSrddZEj9Cav0GNMPuTGqMQact9MZiz3gGBHVIhfjDIpXRHXWGfduJEL7hRwif/Xn0wnXan/oNdGeM9pEJa+iNRfLm6t48nsPhcDgcB4yNqSj132YM2c0XUZOXUfdxTaYqo0h1HF1gQGMAkzkDGlcYOhw7YOImeD55L9h+8+tijEGVaod2bBKUEDZXztT42V6kBt4xcCbNUyRuQdpGOh1DwRRqC44yKqxg9P2PpviX3kb0/Ecx7Q3an/x35J1u9aMgIkhURS/dPB4jxg6Hw+E4UZgshiwZCK43K3cwG4u7ms5sR01dIV+8PqAzFPHsdd8JxxWGDkcBxmhMFmO0xqzN9o+RGg0IBAevL+wifojx/E2d4ehZTHsd095AlAc6H+6oAp2RN2zGnxqZtu+HeIXZRUeaoAS7GNAU4U0/RumD/wwJIuJP/weyW9995MMQL0Cw48ZOQ+FwOByOYULHTYp+urKbL4IovPNvvO/H8qYfg7SNWb277Y4AnFmbKwwdjkKyFMFgVu+AMf3GM1mCiqqIOtyvjyqPQKcDpMY7BjTd2ApMYYbesGCytC+qgjxFhcOjL+wino8E0QObv6iRaetYOnaG5Et/SPrSZx+5oJOohm6uoN1IqcNx4jBpm2TJLQw5hpTmYEyFMZr81rfxzjxlDerukx3zDL0AnbQwWg/sc5JwhaHDUYDJYoyhONg+S6By8DEV25FyvVdwdC2auzpDQ8cgZ0gxWWzdYP3QGu3kw2U8sxUpj8BD6BYkqhK973/CO/9G0u/8Dek3/gKj83vveI/H1Es30S7I1+E4Uej1BZLZ6+j1xcM+FIfjgTA6R7cbA1ISPf8apr2Bd/EtD/R4qlxHapODBjQiNgrshOsMXWHocBRg0jaiFHppBqlO9MUkCKCOQGyCBBFdgy4JSkhtcrNjKN6eOVseCkkb01hG1aYQEYzJDyUaZC+QqPbQBZ14AeFzH8F/+n1k175K/PnfeyTHWVE+Iopk4abrHDgcJwSTJejGMn59ArNyC91aO+xDOnGYIZ7gOXSSFqAHYiqym98CP8Q78/QDP6Q3dQW9eGPgt9lgTvzCqSsMHY4i4gZGOsYzW/WFOsd4/tEwQfFCjPJ7JzY1drbfmTQZXhG16TiS9oxnDMOnL+yw3SjogfcXRfimDxG+/WfR86/S/tRvPtI4qERV8sZaLxPK4XAcb3RzBbCW/IQV8oXrxyLSaFjIGyu0rn/bOUM/JLq9gYjXd5vJU/Lb38U798aHujZQ01cgi9HbdIbiBRCf7KB7Vxg6HAXopIVJmhBv9BWGpDGqMlaYpXPQiAiqXN/UGY6dw7TWMHHDOpMOacfQGG1XtNvrvagKIxyNYvwh2DQKerQxUP/Ks0Tv/ceY5irxC/8OvXz74Y9JeU5r6HCcAIzW6LW5ngZLvADxAlscPqD22fHgmDRGL920WvP518hXZ920xgOim8vgR3235XdfgizBv48x0qLX2+voDPW2cVL8AH3CDWhcYehwbMPkGZKnmM5Ypje+1ZE0s5qxI4KURjDajqiosbOANaAZamfSPIMtxjMmz2xWo/LusePRZatR0KPgnXqC0gf/KYhH+9P/nuzODx7ueKIyemNxwK7b4XAcL3RrDbTuO39KUII8QS/NuHPAPmK0Jl+6iSgfFURQrqNX79rX/REXCk8KJouRPB2IqchvvIiUarbzt9v+eUq2ujAYTVEaQUamyBeu9d+ufCRLh9qj4VFxhaHDsZ0sxojVF+L5yOhpoLvqJMghxlRsZ+uY4mZhaMdJxQypM2me2hVCrDsneYoMqfFMFynV9uyHRtVPU/rhj6FGpkm+8Pukr3zhwY9HeWA0uMwmh+PYYozBrM3aQnAbEo2gW2votdlDOLKTgV6fxSTNnj5eRCGlOrq5Qj7/msuVvQ+KYipM0iSffRnvwjMDusPtmKSFV6kXLsx6U4/toDMETrDO0BWGh4wxemhH/o4runMC0UszqLFzmyuteWoLsW0rV4eKH2JEYXRuj6060RsxNDKczqQmz6xzniikOg46gyGMqtiKBGX2cvpYSiNE7/8neGdfR/riX5N8878+8Mq/KL+nPXI4HMeQpGmN1HYYw5dSDb06S95YPuADO/6Y9gZmZQ6Jan23iwiqNAJpQjZ71UpWHDvTGIypyG59F4y+Z6i90TkoRTB+urAItzrDpGfa10VE0MnJ1eC6wvCwSWPat17GtE+22PVIETfBGPTqnX59YRYjlfHDO64CrM5wS57h2Fn0aqdjKGooDQZM2sY0l5HaBKI8jAG1TV8wdHQL+D0c2xI/JHz3L+A/+R6yV79E8oXfxzyIzXZQQjdW3EiTw3FM0WvziLezNltEWdfkxRuuQNlDTJ6SLV6HqLJjR0uiCqI88rsvkzdWDvYAhwSjc3S8MagvvPkiUptCRs/uvn/SRNVPoSojNtt5G5s6w2vb7ghPdNC9KwyPACaLyedesaYhjkPHJE2boadz1BZ9IcagSocfUzFAqd7rDKqxc5jmKiZuggpgGD9TaRuzsdQznhExQ2s806W3SrzHo0MiivCZnyB460+R332Z9qf/w31b0dsLFu3OOw7HMcSkMaa1BgVjpFsR5UNQJpu/5kYb9wBjDHrpFsKmk7bJksJFQfEjiKqYhWvOlKaIpAXG9Jn96eYKevEG/sVndjUB7L7eqjKG8kMIKwOfb4mqSP0U+UKBAU3SOLHvhysMjwLKtxbSc6+6i7RDxhiNSds9nV63Y2iMxigFwdHrXKmw1BtT3GpAM6zOpDpu2I7hyJR93UXBkEZV9FEa2bfR3uDxdxG955cxG4vEL/z6gAX3TogXYjpGPw6H4/igN5ZAqfty0BY/RIB88QZGOzOaR0FvLKJbq0hoF5F1c4XWf/9/Mf8Xv4YpiEEQ5W8xpbnpJji2oNvrA6Zz+c1vAeDdY4yUpIWqTfTGUKU6Xhhcb/MMb/YZ9YkoROs9X8gdFlxheEQQLwA/Ipt71Y10HCZZimDQy7eQct3GQYCNqSjX7yl0PhT8qDemuNWAZhidSY3RtqgxpmM8k6GC0pGIB3lUVFi2hkD7hHfmaUrv/1UwhvanfpN89uV77+RH6NbaUGpRHQ5HMSbP0BsLfdpsk7Z37YBIWMHETfTyrRPbKXlUTNLCLN/u6QpNnpF86Q8hT8mW7tD+5K+j1+cH9hNRqPIourlqp8dOaEGyFWMMurEysBifzXwLNXERVZ3YdV90jqpN9m5TUZUBFxtATV2xhnfb4p8MPJg04xhxBK9yTy7ih4gfkc2+ghnCTs9xwOQJho7xTF+wfYqURw/vwHbBjila1y0Jy0h1vCemFsNwOZNuiapQI1P270PuSNrDjzAi+3rRpcbOEv3wx5DqBPHnf4/0ta/sun234NZO4+xwHBt0a7Uzgqc6f1+j9df/Byuf+aPdi8PSCHpjCb2xeFCHemwwOiNfvAFbopXSb/01evk24bMfZuqn/gUmT2m/8BsDEQldVGkE8oxs9mU3PZYliM5sR7WDXr2LWZu7d7cwbSPl0X433qBUmCe8U56hKL+ww3sScIXhEcMWhyHZnCsODwOTxBC3MM2VPn2hGHqW00eS8khvlVGNne2Nwhoxw9UNylN0o5NhWJvC6KwXzDzsiFJ21TLf39VgVR6l9P5fRZ16kvQbf0Hyrf++q+mNBCXM+sK+HtNRwuSZC/Z2HFvs1MVc33kzu/oFyBKaL3+F7JUv7riviNhonaVbzhDvAdHLdzBZgnQ6XNmNb5C99hX8p57HP/cGwulLlD74MSSqEX/mt8hufLPwcSSsICogn716ok1pdDyo8ctuvgii8M+/afed8wRVn+q7SURQlTHYZsgnUQUZPT1YrPsBpnUyvwOuMDxk9ModdLpNEOtHiArI5l8dSlfJoSZe7+U69fSFeQZBtKPl91FAheUtOsNzmOaKzU8aMmdSk6fojSWkMmpfb+kI9I8LpeI8pb1Ggojoh34J//HnyK5+juRLf7TjeJL4ISStEzM2ky7dIbv9PfL1Baenchw7THsD8qQXq2SSFtm1r+CdfxOlS28i/fZ/I597dcf9RXkQVcgWrmFOcJbbg5A3VtAbi70RUr16l+Trf4GaukLwxr/T205Vxyl98J+hJi+RfPVPSL//QmEHV/wQoip68Rr56t09dbMeGpqrfb/9xmjymW+jTj2BRDubAJostiPUBQvKqlwv1HDaPMObfQuG4gWYrH0iNZ+uMDxkWn/z/2b5E789cAKWIELEs51Dd3I+MHTSQq/cBVE9vR5ZG6kczTHSHlviEPqC7ofMmdSkMaaxhNS6q30C/jEwnumgonKBafb+IMojeMtPETzzE+S3v0f8mf+4YxfAiKCbx9+e2+iMbG0RCcro5dtkd35A3lx1mirHsUGv9gfaZ699GbKE4On3Mf7BX0RqU8Rf/iP0LtmF4gWIeOQL14ZKo34YmCxGL91ESjVEBJO0iL/4B0hYJnrXRwbMUyQsEz3/j/EuvoX0e58g+dqfFb7GonykVEevzg6Yoxx3NmMqNhfj9cJ1TGsN/+Jbdt83baNGTxf7EoRlEBkotNXUFdAZenlmcJ8TsmC6FVcYHjLRD/0S2fJd4i/+/sB4kwSlTnH46olZzT9MTJ4heYZeuYUaO7NpNW20nf0/wogoVKkGWYIa3VIYev5QBbWapNGLqrDhtH6fxmDoCSI4sNLQjs8ET76H8N2/iF6bpf3Cr6PX5ga3C0qYxsKxL5B0ax2DQTzfmkl5Pnr+mosLchwLTNKEpNXrtJg8JX3li6jTT6LGzqKCEtEP/RIYQ/yF39/V5ETCMmQpeunWyexY3QdGa/LFm7aIUz7GaOKv/gmmuUr4rl8YCLfvIsonfPbDBK//YfIb3yD+3O8WSod6pjStNetaf1KuAwtiKrKb3wIvwDv7uh13M3kKXoiUdnrdvb7c5y7e1GWgIM8Qhur6aa9wheEh4198hvq7fgY9/xrJV//LwAlYghICZLMn6KRwWOQJ2mj08m3UxEWg426F3DML6kjQyTOUqIJUxtArtzvOpOnQjEPotTnQWcd4JkUdF+OZDqJ8JChjDtgQyD/3BqL3/yomz2h/6jeI71ztPy4vgDQZ0F8cJ4wx1rhgi1ZYvABVGYU8s5qexZvuPOsYWvT6oo2/6pBd/wbEDYKnnu/dpmqTRM99BLM2R/K1P72HGU3N5sYVLCY5QK/PYeJmz38ge+kz6LsvEbzlJ/EmL/a2M8YMnPNFhOANP0z47M+jF67T/tRv7tjFtaY0Odns1ROxgKXba32dVpNn5Le/i3fuDbtKekzaQo2e2t09vjw2cI6XsIyMnR3IMxQvhBOotXWF4RGg/NhbCd78Y+S3vkv6zb8aOFFLWLYZQ/OvORvjfcRkiTXhyNNN45ksQUW1gXGQo8jWOIStBjRiZCjyeLqmCQDSiaog3FlLMKxIuY4+hPFwb/w8pR/+GFIaYekTvzs4mqSUdTM8riRN61ZXMJosQckurLTWyG//wOp6TtDolmP4MVmCaSzZcTnsOF529XOo8fN2VG4L3uknCd70IfJb3yV76TO7Pq6URjCrsyfaCKUI097ArM72ulP53Cuk3/0feBefwX/suW3brqOTNiYZLOr8S28jev6jmPY67Rd+nXz5VuHzSVjeYkpzfLNnbUzFal9MRT77MqRt/F3cSO3it7IGM7ugogrC4JipN3UFvTTTb9bnBXak9YThCsMjQvDU8/hPvZfstS+Tff+FgfslLIPWrjjcR0y7gRkwnkngqOsLuwQRRnV1hucwjWWbq8SQOJPmKaZjk65GpjBoJDxGxjMdVFSFQ+rgqsoY4Zt/At1ukN/5Qf+dQRm9vnhsx8Z0Y9lqbndARKypQamGXpu3+sON4/t6OI4XurEMshlon9/+LqaxjP/0+wr1Vv5T78W78GbS7/4t+d2XdnxcEQVRFbN4wzmldzB5SrZ4HYIyIgrdXCH+8n9G6qcI3/azfa+3iZtIVKF08fU2sqigA+VNP0bpA/8M8QLiT/97stvfL3xea0pTQy/eIF+9czzPTVmM5GmfhCS/+SJEVdT04zvuZpKm7RbeYxFf/NBeK227JvKmroDO0UubOkNRHpLnJ+6a2xWGR4jgTT+Gd+ltpN//JOmrXx64X8IK6NwWh8NwoT9kmKSJXp2FqIp0Vp0EMzTjjCKdOIQs2TSgWb2DqCFxJs0zWxiGFSSqInRGOY4bQXSAKsNB1Okn8KpjZK99te92O3acQ9w8pCPbP0ye2gvn8N4j4VavO4L4EXrpFvndl9Ht9WOvv3QML0bn6LX5nhOjMYb0pc8gtakdNVkiQvj2v4eMniH+yh+jd4msEeVDEJHNXzvx1x7GGPTSbcQYxA83Q+y1Jnr3L/aNOposwQh4k5dQYQlv+gpSqqBbawPnE1WfpvTBj6Hqp0m++PukV79Q+PyivI4pzfyxNKXRcQOztbBOWuR3X8K/8OYdi75ugXyvbmEXqU5C1n9NpKYuA0K+Lc/QiBmKiau9xBWGRwh7ov5Z1JmnSb/5l2S3vjO4TVgBnXWKw+N1QjhMjNGYtI1evoU3fsG6i+kco3wYpriEch2TbykMl+9YzckQrPR2L97VyJTVZBwzR9Iu4gWoIDq076+IovK6d6HnX+1lRvbuUz66uXIox7Wf6NZaX+D3/SDKGtSAkM+9ip5/zXVMHEcS+/nWvQtnPfcKZnWW4Onnd/3Mix9aMxrxrBnNLguI4kcImnzhxomOedEbS+jWSs9YZjPE/udRtcnedkZnkLbwp6/0ikVRPmryCqo6jikoDqVUI3rf/4R39vWk3/prkhf/qrAraE1p6pjW+vEzpWmu9hXX+e3vgc7xLuwSap+0ULWJ+44UU6Uq6G2vfVBCjZ9Fb8szFFHo5Pgtlu6GKwyPGKI8ouc+gpq4SPLlPyafe2Vwm9B2hfKFa6443Cvy1Ga5bSz2xkhJY+tcWGR7fERRYQUx2I5beRS9crszJ3/0T2wmaWM2lpCRKdCZvRB5gAv5YcKrjVkH1rhxKMZA1afeCQjZta/13xGW0I2VoTEruh+MMei1uZ5BxIMifogqj2LSmPzuy+RLMydutMhxdOmaKm39fKcvfQYpjeDtosnqoipjRO/+BUxjkeQrgwZ4W5Gwikka6NU7J7KDbpIWZnmmVxRm1/tD7HvbGQ1xA5m8bBfztyBKoSbOo+rTneJwm+GgHxK++xfwn3wP2StfJPnCH+ycQVuqWVOauy8fC1MaozOr6dsyKZTdfBGpTqDGzxfvYwzovK8ovydBCaO8gd85NfWY1Rlufb29ENrHP8ppK8fzqmuY8AJEeX0fRPFDovf8Q2RkiviLf1AoRpaoBmnb5QztFVlKvmrNWnr6QpMd/fzC7fgRppPT0zWgGRZnUrOxAGkL1TGekSEZ4X0Ygomz+KefRGoTmCzGtNcw7fUDG9PyqmN4Z54iu/71vs+FiAKTH4uLjB5xA7K0Fz/zsEhYhtIIprli9Ydrc0f+O+U4AcQNTNrufb7zpRn0wjX8J99z31E/3tQVgmd+kvzuS6Tf++Su20o0gl5fGJg2OO4YnZMv3gC/hCgPvXKH5BuDIfZgzWZk9DRedazwsaSTk6zGz9pz/7bziIgifOYnCN76U+R3XyL+9L/H7FCcSFhGvNCa0nQ0+kNL0gZDbzFet9bQC9fwLj6z8wJ92kbK9b7sznshItaNepsJnDd1BYxGL93ccqNdWD+Wes4dcIXhISOeT3T2CUhbfQWehGWi9/5jJCwTf+53C+f/e8Xh/HVXHD4iOmljVu4Cgho7Z280IMHDdRkOC1EKFdUgS1Hj5zCNJXvRMATOpPnybaBjPKNTiI6fI2kXEYVEVbyxs/jn3oB3+ilk9CwYjWmtWQ1KFu/rqrx/5VmIB01oxI+su+ExQW8sPXJR2MUa1NSQsIJeuWsLxMbKibpocBwt9NpcL7cQbGQCQcl+v7dgjEbv8hvgP/4uvMtvJ/vBp8hufXfH7brfAbM0U2ikclzRq3cxWYIEkQ2x/9IfFobY6/Y6qjqGqp/e9fFEBK9+ykZjtTcKp7+Cx99F9EO/hF5foP3J4gxa2GpKc5N8eXhNaXRrrd90ZuZbALuG2ps8RtWnH/i5pDI6cN2sJi+BCPmWPEMRQTADReRxxhWGRwCvVEUmL9uVvy1faFWuE733owDEn/ttqyPYhkQ1a5qyeMOtXj8KSQO9ehepn7In/izprMQNYbh6eQS26gxX7mDgSJsGGL0tqgJB3adeYNgRESQs49Wn8M++Du/c61CTFxEvwMQbtkhMWnv+Y69OP4mU62TXt42T+pENgj/Cn5f7xWQJprmy5zmkNii5jngBevE6+ezVE3WR7DgamKRlu1Odz7denye/8338x9+FBP3aeBM3MHFr57FEEcK3/jRq/ALJV/8E3XHoLtxWeRBWyBauHS992w7kzVX02ry93toaYv/u/hB7k7SQIEKNn79vCYpXm0RNX4GkUfjeeGdfR+kDvwomp/3CbxTKi6BjSlOuo9fn0Qs3hk5mZIyx+vYtn9vs5rdQ4+d2HBM1WWylVeGDTxdJWAahb/FVggg1dh69Lc/QGE7E57yLKwyPCF51DBk9Y8fJtnxQ1cgUpff+Y7tC9bnfKTQ/UKURdLvRcahyxeHDkLcb6OXbeF19YR4jlfHDPaiHREUVDKavMBQlR9uZVKe2S+UFdiwEAyekMNyO+BFedRzv1OO2mzh9BYmqmLiJbq/tmS5RlId3+e3o2at9wcr2gsagj0Gho5trGJF90wmLF6DKo6AN+dwrdnrjKH/PHMcK3VjqC7RPX/4cKJ/giXf1bWfPF7JlOqn4/CGeb4udILJmNLuYbogXIOKRLxzviSWTxejFG3ZSQKQ/xH5iS4h9lmDQeFNX7nuEt4uqjOKdftJOiRR0ptTYOaIPfgypjBJ/7nfJrn+98HF6pjTxBvncK4WPdWTJYkRnmwZKa3OY1bt4u3UL0zZq9PRDnd9F+ahoZGCSSk1fQS/f7isExfNP1MKfKwyPEKp+ClWdGJglV+PniN79S5iNReLP/17hqpIqjdh57KWbJ9ox7GEwOsOsz0EWbxrPGIMqDanGzS8BAmHVriCu3D76zqSdqAoZmQLAiLdn43/DjHjWGdObuoR//o34048j1XFM2rbGBXHxCNL94l9+O8DAhYb4US9TclgxRqPX5x/adOZBkCBCyqOYuEF+5yU7znUMOq6Oo4vJU/TGZqC9bq2R3/gm/pW393WxwHYL1egpvOooMnmpswBdfJ2gynXCd/8iprVG/OX/vOsilIRlTBrbC+ljaEZjjCZfnLHdOM/fMcTe6NwGsE9duW9nzO1IVMU//STG5MUNgMoYpQ/8U9T0FZKv/RnJd//Hjq+5RDXQmmz25aEpaHTcsE7kHbKb3wIR/PNvLtze5Cl4oTXgeViqYwOdwJ7OcHGrzjDEnKCge1cYHiFEBDV+znYHkn7zB+/U44Tv/PvopZvEX/qjwpO1KtfRTVccPjBZYosnrPGMMRojas/Hzw4KqzOs9MZJ9cod8PxdV38PGxtVsYQamYI8RT3EaMhxR5RCSjW88XP459+Id+ZJpH7KmsW012zW3gPqSFVlDHX6KfLtJjR+ZEfbh3l8Jm7CtqDk/UaiCpRqmI1Fqz9cX3DnYse+oBurAD3n5uzq5wGD/+R7+7YzOgdRqOoEAF51HDV6eteCwZu4SPjWn0bPvUr6nb/d9ThUaQTdWN5R/zbM6LV5TNywmuIdQuyN0Xacd/IC8oi6eAnL+KefAKUGrgHBRipE7/lHeJffQfaDT1kX2R0WBiUsI35kJxmGYZGvudIrqo3R5DMvoqYf37HwM2nLBto/gnO5iips7zVanaHqyzMUz8ekydCN5z4srjA8Yojy8KYuYVADYwD++TcRvPWn0bMvk3ztzwtX/GxxuIpenhlaAfJBY7IEs3wHghJSm7QxFaX6cEcllEY7heE52/nJM0x+dJ1JdWMV2uuo2hTkGRxjR9K9wOoSK3j1U1aXeOZ1qPFzILJpXpO27+sc4F95FtNeJ599ue92IwrdGl6bbr02X7h6r9NkX7sbIraAl6CMXr5tC8Tm6rHsqDgOB6M1em221w03SYvs2lfxzr8ZVe2XQNhu4Zk+vbwaPd0bOdwJ/8o78B9/juzq58huvrjr8UhpBLNyF91cfYR/1dHCtDcwq3eRUm33EPv2BmrsNF6n8H5UxI/wTj1utd4FTqSiPMK3/yzBG3+UfOZbxJ/9LcwOcVTiBdaUZukmyeKtI3sOsjEVzV5MhV68iWmu7mg6Y69jlHUWfQTEjyCI+qY7xA9R4+cH8gwRYJgXSh+AIb7yPb6IF+BPX8HkycA4UvD4cwRv+GHym98k/fbHC/dX5bpdwVu65YrD+8AkTfTq3Y5gXGF0OnwxFdtQURmjt+gMV+8iBpvXeAQxyzOANZ4xJj+Q8b/jhAQRXm0S/8xTeOffgDd1GQlLmLjRMa/Z2W7bO/MUUqqRvfbVbY9ZwmwsHtmLid2wESDr4PcbcOQL17jz2/9P2n/9fxB/6Y9IX/miNWfah/PkpkGNj56/ZjU/xykGxHFo2ED7vNcNz177MmQJwdPP923X6xbWxvpuF1FWNuEFu2pig2d+EjV1meRrf47uuEYXIaIgqloTvKMsWbhPTJ6SLd6AoIyIIn3xr4pD7OMNVLl+TwfSB0W8AG/6Ssc/Ym3gHCwiBK97P+Fz/wC9fIv2p37DjhUXPZbykFKdbOkueuX2kbwmNEkLjOl1YfOZb4Hn4519/Q7bN1H16T2ZBpHq+IDjqJp+zL5WW74bIoI+Bp/t+8EVhkcUCct21jluDnR5/Nd9sLeSl7702eL9S53i8JjO/u8lZmMZs77Q0xeKYfgLk6AEIjYCga4zqRzZYO58uZMhOTKFGHH6wkdAvMCaGUxdsbrEU48j5bpdAGkP6hL7TGiaK32PQxbDEJqp6MaK/fxvMyVIv/8CqlxFTV1GL90kffGvaH/i/0vrL/432p/9bdLvv0A+/9qefk+67wd5Rj57lfjua/bc3FqzXYmkhUnjTkf/6F20OY4WxhjM+hziW6mDyVPSq19AnX4SNXqmf9tut7DgAlo8H2/qsv3c7bBgKMojetcvIFGV+Iu/v2uHUTwfvNBmKx/RBcj7wRiDXr6NGI34oQ2xv/ZV/Kff1x9in7TAC1CTF/ZlukiUj5q8hKqM26zDgoLOv/AM0fO/gombtF/4dfKturitjyUKVR1Fry0cyYaBaa72PqNGZ2Qz38E7+/oBZ12wY6YYM9AZf1hUqQbbXg+rMzToxRtbNjw5QfdD6MV/clDlEczEBfTyTSiN9i5yRITgLX8XEzdJv/M3SFTpmUh0EREojaA3FkFsNt9+OfMNM8YYsoVrgMGbuGAvmIPwoQXkRwVRnnUnzXOkNIJeuY1/4RlMmsARq3mN1uj1OXshX5uwWsghf/2PCqI8KNXwSjU7apq00O11K/TPda8A9y+/g+wHnya7/nXCN/zI1gdAt1bxhmihxH6e5pFtOtV8+RZ6/jXqz/002QVrHKGbK+jFG+jFG+SLN0i/9wm7cTeAevISavIS3uTFAUOPB0WCEsaP0O0N8sU1topbun80BhAB5SOe37nYDux/OwYYKM9eiCoPRIFSIJ47v58UkiYkLaRsp1qy61+HpEnw9Pv6NjM6BzXYLdyKBCW8qcvo+dcwpZHCAkeiKtEP/SLtT/0m8Rf/iOh9v9KX29f/eJGN2Fm6iZq6MpRyDN1YQjeWUZWxzRD76ccItpwXTZ5iTI4/9fi+aphFdTu7vtVwFrxH3tRlSj/8MeLP/S7xZ/4D4Tv/Pv75Nw0+log1o2ssg9GoiYuIOvz3xxhj8ws7RWA++wqkLfwLzxTvkLRQtYm9u0YLShilQOe9z7WavAjKI5+/hnfmabudH6DjBmpLZ/O44grDI443MglZjF5fQMr13u0iivDZDxMnLZKv/zmEFfyzr+vb1xaHdfT6AqBQY2eO/Qf6QTFZglnpdKvGz1vL5JHizJyho1SH1VlrQLNsDWhIGsDUYR9ZP7mNqpCORkP8cMcLD8fD0x338qIqQSnBvPR9pGwLQ1UdR516wprQvP6DmxcfQRm9sYSqP5rI/yAx8QZoPfAZ6gZ/V1/3blYbdoVYVcZQlTHoaFlM0kIvzZB3isXs1S/B1c8DINWJTpFoi0WpTT7w+VREUFEFtUOdLXRytYwGo+1CTtLu/V0b06kixY6Gi+k4+RlbTCof8TxQAfg+qBDxfRDVKyZ1GtqOjqjO7e43YZjQ6ws9LZbROdnLn0ONX0BNXu7bziQNuyB8j8JFleuYsfPo5RkojxZ+HtTYOcK3/xzJV/6Y9MW/JnzbT+/4eBLV7Kjryl288XMP8S88PEzSwizNWM3k1hD75/5B73xidA5JE//UE4Udrb1GRPDGzoIKMMsztoDfdm5TtUlKH/xnxF/4fZIv/RHmTcv4Tz0/8F72isPmqu26TV48/N/atN2JqbALefnNFyEso04/ObCpMQZ0bk3q9ggRhaqMYVprvTxE8QLU+IW+PEMRZVfu8mRAonDccIXhEKDGzkCWYOKNvlVr8Xyid/8i8Wf+I8mX/gh5/qN4U/0/DpvF4RwoUHVXHG7FpNaRVGqT1nmsvYb3iJ2Bo4KKKmRo1Ng58rsvdSy1j+CMfJ5iOsUHWYqUj8frf5TxK6MgCrNlldS/8izJl/4QPXu1t0oqyrNd9KQFj+i4d1CY9QVrKrAFvT5Pfvt7+K97PyosQWMHs4awjHfmKbwzT9nHyjP0yp3NruLdH5Df+IbdOKz0ikQ1eckuvO2F5kUExAM82HbNVnTm3uw2atAak+eQpRB3i8nOmJQRRKDdrpCvNjEYMIDnWx2S8nqdSZRvb/e2FpWqV0x2/zwsiwXHBZPFmOYqlEYAyG99F9NcIXjmJ/t+143OQHn3PW6nRiYhbaEby30L0FvxLz6DXr1jC9GxM/hXnt3x8aQ0Yq85OtrnYcDonHzxhr3oF+mF2Ecf+NXedZcxBhOv227bo8QkPARefYrcU5iFm5io2mcmBJ3O7vt+heSrf0r6nY9jGssEb/2pwsJPlevWxXr+Gt705QN1bt6OTpoYOotiaUx+9wf4l95eXLCmbZtzvMeO8VIZRTeW+s6vavoK2fc/hUlamyZPxh7j9t+X44YrDIcAEYWavEA+92rfhxTs6Eb03n9kxzy+8HuU3v+rAzqD3ljpyhwgeNvuP8nkSQu9cgfvzNMdLab0cqGGns5qpnQMaMz6AtTG+4qBo0CetjHNFeT8G0FnvVU7x/4hno+qT2HW5yGyF5ne2ddBVCV77aub4zNYrYtuLOMNQWFo0rZdQCv1X9xuBn+/+4EeTzwfb/Ii3uRF4Hl7Ybix0Bs91Ys3yO98327s+Z3OTaerOHFhzy9gdj1WUeAVjAJu+7tXqSCx119QGm31jflmdxJj0Oi+R+oY9Nv/dAuR7tir8jtFpQd+0Olgqs3R105BabT7fj8sNtDednmNMaQvfwYZmcI7+3TfdiZpWjO1+zzPd6OyTBZj4qaNXSkgeNOH0KuzJN/4S2RkGm/y0o6PRzSCWZrBBKVHjnE4CPTqXUwao8p10h98yobYv/Wn+kPs2+uokVOHVux61Qm0smZWJigNjFOKFxA+9w9Iq+NkL30G3Voleu7/VtjZVKURTLxhxyWnLh+err+50tPL5ne+B3mGd7F4jNTkCV69+DP3KGwWfptjot7UFTJeIF+8jt8xwRGlrAPsDosnxwVXGA4JoqxQPJu9ClnSd0KQqEr0/EeJX/gN4s/9DtEH/tnASqGIgvIIenUWWxzurYvWsJLO37Qz6xMXIUtQUfVIFU2PgigfCcqokWnAGtD4tXHrTHqE/o1mecZqHkamMAa8Y74ad1RQ1XHy1dnej6EoD//y28le+iy6tYbq/viFJXRjBTV29sh/N3Rj2RYgW29rrdrg78fe+eg6QRFkZBo1Mt3rmJj2eqdIvGnHT1/6NFnH8EtGT/d3FctHz+14s2gruG+3/dg0gsBoO56axb2isjf6inT+Y1+TdjqBCaaP/ar7XmN0hl5b7BVteu4qZnWW8B0/19e57XULK2MP9PiiPLzJS2SzLw9cY/S2EUX03Edof/L/Iv7iH1L6kX++eZ4oeDwTlMkWruGffuqBjuWgyZurVpdcGiWfvVocYh9voMo1O8F1iKhyHTn9BNn8a2D0wOKTiCJ804dQlXGSb/4F7U//JtF7/iGMDxb7EtUwSYN8/jW8qSsH7q1gchtT0T0vZzdfRCpj9nps+7ZZbHXj+7BwLMpHRTXMljFRNXHBjt7PX4OuO6ofWrdrjndzxc2BDBHiR/hTVyBt2ZP/FlRljOj5j2LyjPizv13oHmbztUbQq3fJ1+YP6KiPNvGMXe23xjMJDHlMxXakMmo1e6Vax6qaI+cYp5esDbqMTCNinPHMASF+1LHq3hwv9i+/AzDk17++uZ0oe+GfFI9fHhWMztHrixD0d/yzq1/ABn+/Z1+eV0oj+OffRPiWn6T0I/+c8s/8a6Lnf4Xg9T+MhFWyG98k+fIf0/7r/5PWf/s/WfrE75L+4NPkd19CD3m+oYiyY6heYM8zQQkJK0hURUoj1sq/PIIqjSCluu3k6ozs7ssuuuMBsRmBulcEpi99BinXB7orJm52nEgffBFH/BB/+rHONUZx5q2EZaIf+mXIYpIv/sGuvyfihwhinUqPaIauyRL00k0krGFaq8Rf+ePBEPu0bScCJi4difFpiar4p5+0Eww7nJf9x54les8/wjSWiT/566RLd4ofK6xax+T51zAHnNNn0hZgFyZNex099xrexWcK5U4mbaNGT++fFKoy1pcdLl6AmrhIvjXPUPl2KuWIfpb3isP/hDseCImqyORlaG8MWA6r+imi9/xDTHuN9ud+tzCfqFccLt8iX184qMM+kpikRTx/A7zAFiUY1BCMvDwIKqxg0MjoWfTKHZvTmBytkFa9an+wpDqBEWV1To4DQdUm+7ItVW0CNf0Y2fWv9Z1fxAswO+RkHRV0a92uoG+5IDZxk+y1r+BdeGbP7M3vRTegOnjDD1N6369Q/un/B6Uf+ecEb/lJ1Ph5kvkbpN/9W+LP/x7t//Z/0vrL/432p36T5Jt/SfbaV8gXb/ZdoBw3VFRBvJB89ip542h/po4Kxmj06hzSWfTIl26iF67jP/mePn2YyTNbwDxgt3ArElaQyUuYeH3HWANVP0X4zr+PXr5F8o2/3HVxQ8IKJo1J5m8euUUQYzT54s1esVcUYt+N8/Cmrgzo+g4TCUr4p58A5e0YI+KdfpLSB/4pAPP/9f9DvnC9+LHCCmhNNvvqgZ57tsZUZDPfBgz+hcFQe/u5DvdV16miKttrTm/6Mczq3V7xLSJ28uGIxn7tFUfnU+64b7zqGHl2Fr16F0r1vhUUb/IS0bt+gfgL/4n4i39A9J5/NChSFgXlOnp5hnS1BhycDuYoYIxGry9hVm6RLd2xbqSAEe/4uU2FJcDqR7LZqxijkSPU+bFdHuu4aw0wAmeOdJB0RnPMltEx/8qzJF/+z+i5V/C6I2BByY6X5tmRujjailmfHxiryl77EuQpwVPP77DX/iPKQ8bOocbOwRM/xPh4haW5JfTaHHp1FrM2i16dJbvxzb4LDqmOo+qnkfop1Ohp++faxJHoWDwq4ocY5aEXbkASo8ZOH4t/135h4gbkaU8Llb30WQhK+Ffe0b9d0kJNnHvkkW+vOg5ZG706v7MZzbk3oF//QbLvv0A2dnZX/a5ENfL1JfLmAlIaQUo1q3vzwkM93+u1eZv1WK6TfP3/Z0Ps3/1LvRB7o3OIm3inHj9QvfD9In6Id+oJ8oXr6PY6qmNKtBU1eobog/+M7Au/S/zZ3yZ810d6mrm+xwrLkLbJ5q7iTz++71nOxhh0c7X3uuY3v4WMnkHVpwe3TTua2X08R0gQ2VHRLb9xauqKPbaF670MS4Og0/ZQRTg9KEfzF95xT6yDY1LoIuadeZrwHT9H8tU/JfnKfyF810cGvlDWun6EZO46uYzZYPEjrh/aC0zSIl+6iUnaEJRIl+7gP/VeyGJUpdiqe5ixOsOS/bxg0BuLSHCERjXzDLOxhIxMQZ5Beeywj+hEISJI/RR64VqvMPTOvR7CijWh6RSGXbML3d7Aq44d4hEXY5IWJM1ethvYEbH0lS+izjyNOmKaaglKeB2Tmi7GGExzBb021ysW9eos5s4PoKPPw/NRI6eQTqFoC8ZTQ2HusR1Rnl2gXJ+359+JC0d20eGw0auzPU2mXpsnv/N9/Nd9oE+nabsqHqqyR8Hf9dOQxANu6FsJXv9BzOpd0m/9Nap+Cm/6scLtRASvOgqtNUxjBb2+iIjBiGeLmfIIKihBEB3YAoGJG5iVu0h5ZFuIvS2ajDGY9gZq4jyqPFhwHRXE8/Gmr6CXZmyhVRoZuI5RlTGmfvr/ztxf/TrJF/4A3v6zA4sKYM9LpDHZ3Cv4px4fyILdU9I20jHC0+sL6JXbBG/+8YHN7NimQh2AzEcq49aQzbOfdzV+3mZILlyDTmEoyrdB9wc0gXIYuLPwkGJdxM7bHL6kYefEt+BfehsmbpJ++7+TfuMvCd72M4OZNsrDq1TRd2fRG0t44+ds5+aYFUewGaJuVmfBj1DlurWmNho1fgGjsx1F9MOOlOuoms0INGuzmNqkdSA8ApgsxjSW8Kav2PfAOZIeOKo0glY+Rmd2IUH5+JffRnb1C5j2OtJZhZYggsYiHMHC0Lo19v+cZde/BklrIPj7qCIitktYHYctmbQmTzHr871CUa/Nkd/5Qb8OtFRDeoWi/X+pTR35QktEIeU6Jl4nn3vFjusdQDbcMGGSFsSN3qJH+vJnwRt02DVJEzVxfs9Cy0VsuHo+9wombRd2zLp5yu0Xfp34S39I6Yf/+a4j2+J1YlC6N+jcFmjNFQzW6VZFNVsohmUISvuyYG3yjGzhBhKWMauzxSH28QZqZLLXPTzKiPJ6oex6YxFKIwMFtleycRbxF/+Q5Ot/jokb+E+/b/C6MIggE7LZTnG4T4tOOm7Qnd3MZr5lj/HCmwc3TFqo+vSBRGqoUo1sbbb3+ZSOrjSf38wzxA/R7cb2JKFjxdH+1XDsiiiFN3WJfPZq4Yk7eOq9mLhB9vJnoVQj3HLS2/oYqlzH5Cn5wjVUaQQ1fu5Ijk08LCZukC/N2PyZLSdMvTQDgDdxHozZ99GJw0JFVfKgClHVOpOef6MNaT0C5GvzdkRqZMo6FzrjmQNHlELVT6NX7/SKQP/yO8he/hzZ9W8QvO79djs/wrTW+sZOjwJGZ+iNpb4LmF7w97au3DAiXrA5iroF095AdzuL3XHUV74IXWMEUcjIFKp+arNYHD2NGTt65zmJRjBpm2z2ZfypKweeEXeU0RuLvUUP3Volv/lix2F3y+c9z8AL9qxb2EU839r2333Jxo4U6L8lKBG9+5dov/DviL/4B5Q+8E/v+/wgyoOwjND5TBqNyRJYuWOdfQUkKNvFzahqO4qPqEE3xqBXbiEmB01xiH3SQMIKauzc0CyUi6hOh8uz7vOl+uCkmB8RveeXbdbhd/8WEzcInvnxgu1CECGfvYqafnxfOqamsQx+hDGG/OaLqOnHBhbnjdEYo+1o80EQlkGUldx0XhNv+grpd/8HJm5Yjw/lgba600OL+NhnXGE45IgX4E0/Rnb35cITd/CmD2GSJtn3X0CiKsHj79rxcaQ8akct77yEjFpL9sMMPn1UjM7Qq/OY9TkISgMnHb00g1cb7wXaHtcvOUEJEVBj1oDGcHScSc2yLc5VbcpOyx2hguMkoaqj6NU7vR9ENTKFmrrSGa96vvcjaUTQrTW8kalDPuJNdGsd62y3eXGT3/wWprVG+LafObwD22ekVMMr1fBOPdG7zegcs7HUXzAu3SSf+XZvm7tRBUbPoMbOocbO2hiSyvihXwBLUII8JZ97FZk4PzTB6PuJyRJr+tQplHdy2DVJEzV5Yc+6hVuRIMKffox89hVMebATBaBGpoie+wjx536X5Gt/RvjcRx7q8ySibP5uEHXiUAzoDLO+YDs5xtj7ohGkPPJQOkXdWEJvrCDlEeLP/6fBEPs0xqDwpy7ty+u5n4gI3uhZUIHNkCyNDHRcRfmE7/z7pFGV7JUvYJIG4Tt+fnA7L8CEFfT8q5ipK3h7OMpp8szqYct1u2jfWCZ43QcGN0xaqNrEgS1EinQaJe1GL896U2d4Df/8m+zxGyCNj61R3vBe9Tt6SFDaPHGXan1fcBEhfNvPkMRN0m/+VySs4Be167vbh2WMidBrC+iNJdTY+aHU3pn2BtniTdBZX5cQbMGYXf0i+exLlK88A3mM1I9vLo14PhJEqNHTZLOvQJ4fGWdSvXwLsI6keMFQL0QMM+IFqOo4prkKnU6Ef+VZkq/8MXr+tV7xIUEJs7EIR6QwNMZg1jbdGu1t2gZ/10+j9jA/bTOzz2AT3rf/Wff+3HNfFKGnDwTECHmUoVstO1a3x5oqUR5Sn7YGDlvO8yZpWbObtVmC1gKt2RtkL3+uc8zYhbNOkdgtGKU6fuCGMOIFmEihl25CGndiF4br4nwv0c0VO14pyi7wvvYVvAtv7hvXNHlqu4X7qM+WUg2ZOI9eugU7yE28008RvOlDpN/5uDWj2YMRbhGxF99e0BvvMzrDtFbRjUUEwYiyOsVSzY6f+tGOnxmTtDBLt5BSjewHn0bPvtwXYm/yDPIE//STQ71Q7I1MkSsPs3gDE1YG/i0iiuCZn0SimnVITlpE7/qFgQLMFodVzPw18qlLe9a5M0mzF5Ka33wRlIfX0fD1oXPUAf/WSGUM3VjpdbFtFzawOsNOYSii0EkT75hONrirsGOClGrI5AX7g1rqL+REeYTv+gjxZ3+b5Cv/BQnLfavMA4/VibQweYZevIHZqFr94RCMWpo8s12xxpIdQdl2zPndl0i+9d8wG4uoM08z+s6/y8paG680fOYND4KUR1G1acCgG0tHRjehV2chKIEfoiKnLzxMVG2SfGOx93drQlMmu/bVzcLQCzCtVUzSOhrng6RpTQy2mM7kd17CrC8QvvMfFOdhZSmmvd4p1zpFm+mGsPe26vyv2KJPBDpB8KI6/+/5oDxQClCbfxaF8nzsA0onQF56j1GaGsELFq3RTGsNtLb3+dG+rYxLWMabuow3dZnx8QosN+2q/doceuU2euUOeuVO/yiqH20pFm3BeBCuqKI8KI2i1xcga9vsuCOuldwPjM7Ra3M9A5Ds1S8XOuyatGVfo30uoFVtEpJWoeFdF/+p59Erd0i/83HU6OlNV+M9RJQP4RadYidj1bRsJqjVKVagNIqKynbxRfkYnZMv3bQasfnXSL/3ib4Qe2M0xA3U9JWjcW57RLzqOFr56PnXMIEB+n9fRYTgde9HogrJ1/+C+DP/0brYb/sdFs/HlGqYhRvkRu9JJ9+01nrvSTbzbbwzrxuQL5mkZT0vDljW1P2+GWPzFa1+8xL5/LXNjfzAGtDUTx3osR0UJ+9se4zxapOQJuj1QYtp8QKiH/pl2p/+98Rf+H2i9/8TvE5Mw06I51tjgLRNfvdlZGTSWqYf0R9p3VwlX56x4ybbYjz0xiLJi3+Nnn0ZqU0Svecf4Z15ClUpwVpsi5NjjJSqSMcG2qzNYSZ2f+8Pgm5UhRqZRnQO4fEuzo86EpbtglBHryxegH/xrWSvfgnT3tjUfSnPjpMegYsnvbEEanM13BhD9tKnkcoY3vk3DmxvjCFvN1Dj52whJ6q/cKNbAMqWYlD2tBhSYWesvVxHGQ1pjI4bmMYyut0di+10E/fRKVo8Hxk/Z1+LDkZnmLX5TqFoC8bs1S9tKRZD1PYx1JGpPS8WRaRjSrNBPne1Y0pzvM/R29GtddA2l7PnsHv6KdTo5nSLyVNQwYEYp20a3sWYpFnoWCkihO/4OdobC8Rf/s/WjGafFyHt+Glp8/NhtH1dVu+QYQBB/Ig4HbVu5EYPhNhbB9J11PjZA3G/PChUeQQ58yTZ3KvopFW4jX/lWQgrJF/+z7Q//ZtE7/3owGsgysOUarbxoA1e/eG7eMZodHMFCcrouVchaeJdfGZwuzzBqx+8Plw8H1WqYvKkF1/mTT9G+p2Pb/4OegE6bqC2aBGPE0fzCt/x0Kix05AlHTfB/ja3hGVKz3+U9gu/Qfy537HBp+OX7/mYEpQwfojZWCZrLKPGz6EqY0fmC2GyBL16t2dAsXVswqQx6Q8+RXb189bJ7c0/jv/Eu3ojiyaNUeXRI/Nv2S8kKFmzgrBiLfDTpGMDfXiYLME0llBnX49Bo0LnRnjYqPopsrlXehdZ/pVnyV75AtmNb2yOhgUlO2Zenz7U743JU3uBscWEQy9cQy/fInjrTxcXVUkLf/IUyjsaHXMRBWHZFtkjU7aLl7YwzdVONzHDILaTeACZb6J8pFPwgbWzNzq3rqhbi8VrX7HxMmDHGEfPbI6hjp+1jqh7UNRKVLOmNHdfxpu6cqRjA/YSOyI92/seZje+AUlzYDzTagsvH9i4rSiFN3mJbPYqZHFfXEZvGz+0ZjSf/L+Iv/CfKH3wYwda1Isoe0HvR5vjp3lq3V2DMsln/v1giH17HVWdQI0MZugNOxJW8E8/BcndHQt6/9wbkOc/Svz5/0T8qd8geu8/7kRcbXkc5UHJ5l9jchuT8zDnozRGOgse2cyLEJQGOssmS+xxHpZLeWUM8/9v777jpKrOPoD/zrn3Tt3e6CxLV5oIooAVjRKBiAUrCIKKMe+rRhMbFixB1EQNESK+tkRM1NiILXYsEKQooBSRsrD07WXqLef948xcdrYxs+yy7fl+Pn5kZ+7MuXfmzr333POc5yndZ+/fMfMMuw+OnPcihe7b4Q0r6hi2M4xx8MxuMA/tqDPci7mS4Rw7DcGvXkBo+RKYmb8BcOTwJca4nABvGbCKCyAqi2V4aQvWz4oWSLVK94BBhktGD1RCWDALNiD846dAqApKzxPgGHS2nXUxytJ1wN065ks1J6ZoYA63nYCGMRlS15KMyjI5uTw5W35/CiWeaXFOD5jmtDOu8ZRs8MyeMPK/g9pvbCS0RoWw/EA4YM9HbAlWoEJmE67WOdW3fgM4vVBzT6i1vBAWYOnQMjoD5cYxXNP4MUUFU5IBVzK4EIAhRxPhL4cVqpLhTVyxw+OOyTpxRRaeTu0M5A4HEE1yU2SHoFql+2Ds+h7YsUq+SFHBUzrLEZjULjLTdXJ2ozqLTHPJUepDOyAyuoEnZba5Oe8JC/kiNy1TDmfYzegOXr3upanLEVzPsS2zxFQH1OxeMA/8DMGVOvdD7k2Hc9SlCC3/O8Jr34bj5MuO6TrWxBQN3OmB8cNrtYvYh/xgTo/cR9vpfsU0J5w5/YHKH2KjP6pRsnrBdfo1CC5fguBXL8I5+koomT1i34dxWXu07IAs9ZXaOeHPzArKMhXCCMPctwVKjyG1otCEHgTPym2x74M7vYfniUMm7oPqkPMMI3O3hRAy0z11DElbwHgkxfTBn4E6UsvzpEy4xlyF4Ncvoeij56AMGg+enRfXj5BxVWYv1YMwD24DS8qUadCP8URtYYRhle6TBV2d3pgDi1m6F/r6D2GV7gFP7wbtlMuhZHSv753kPIQOgLlTwJOzYRTugGXqMi04Wu5EGC7cJdcrKUvO5VLb7mT/9oIxDpacDat0r/2bVnuNQHjt27CK8u0C1owpsPxlUFqoYyiEiMy/Ovzbtcr2wzq0HdrxZ9d9PAr7ZfkGhxtA5bFb2UZijMm76ZoLSMoEt0w5xytYCctfBoT9cvqj6gBUxzEdvZVJbmTNRPQ8AUAktXxlceycxd3rAWO1fBFXwFM7wzd4LETmwITWlylaJJRtL2CEwFO7tOukNFZlkb0Pm3s3QvjLoA0dH3OOtkcLW2DUnjk8YJm5sIrz6yyLAMjwO23IedA3/Af6li+BMROO+XpW5/t5Te0i9kYIggFqZs9mDdtuDbjqgJLTG1bxHljBCpnZtcY1H0/tDNcZMxFavgSh5X+Dc9RlUDrHjubJzmEyrIpDsnOY1iWhfVD4ZZkKc/8WwNSh9hga+3y09EpLRgeoTghFk8kLuRqZZ5gbU8+QKRpEsApoR6HHUdQxbKfkXb28yF09XuuuHk/rCucpV0Bf/S8Yy/8OlpQJNW8k1J4nxDXxWoaXOiH8ZYfDS49BFjshZPIUq3SfHB2t9qMUwSqEN30mCz87vXCceAGUnsPqXCd7onlyep3hMO0Rc0bmGQoBVBbDCgcBtFynOFy4W66XNw1MbdrsjKTxuCcVVul+CMsE44qcq7fhQ5mEJtIxhMMNy1fachfoIR9g6GDuw/uvvvUbQHVC7X1SrcVlPSwBpQ2HislQrkiJirQuEEYIVsgPBMphBSojyRJ4k9R6a9T6MX44I2rPYQAin3tViR2GahbuQNnXr4MlZ0MbdA6Uzv3jHhVgXJGjFVXFEOEglKyebTpzZH2EHoQIVACuZAghoG9dDpacDaVz/8PLmLrMNH2MRwurU7xpgNEJVnntnAZRau+TYZUdgLHlSwS65gKpvWstI6IZfaP/WYf/LWL+FjHLiLheY9m1Ef0/fhxTxF5YBqAHoXbu16rqsjYnxlU56ly6F5avBHDVLj/CvRlwnT4TwRVLEFr5TzhOvABq5Pdsvw/jMqy0ogiwLPCMbnGdv4WpA5GkMkbBD/JmdY06s0L3g6fH937NhTEGnpQOUVkMOOW1s5LdC/rBn2EFKuScXsUBEapqsXVsTtQxbMeYwwOW1QuicCeEu/ZdPSU7D5mXzUHRxtUwdqyB/sNH0Dd9BqXbYKi9TzpychrGAGcSYJmwSvZCVBZByejebOGlQg/JmjehKjDn4bIcwjJh7FgFffMyeQeq7xhoA0+vc4hfCCEzGVoGWHIOXF3yUFUabJb1bW2Y5pJ3+CHvSFshP8BbrmOoF+8FuCo7rJSRtNVgXAVPyYKoLAScyTIJTc9hMHauOVzkl3EZThj21QrPPhasqpKYToFVVQxz7yao/cbU/bsP+eScyHZ0AchUJxTVCXjTwS0L0AOwglUQ/jKIQIVMu6FoTV4SI6F1ZFyO0iZnAT2GQAgBd/l2lK76AOGV/wTP6AFt8C+gZMaXZIIxBuZKgQj7YB6MJKVpBUmQmpJMqMTBGJM3disOyjpz1UsuteBoYXU8pRMQDkOEKu06gNXJclkTEKosRMkXS+Tcv5odumrlXJqTkpQOR6SIvRAWEKwCy+pV55y79oxxDp7RHVA1GRJaV61DVxJcp81A6NvXEF77NkTIB63fmNhlGJM3anylcuQwo8cRbxKKcEB+26EqWIe2Qe07pkYpMRMAbxUJgLgrGUb5ITumKjrP0CrKB+8xFExRYQUDEJFRxfakfW0NqUXxpMJM7wqrbH+tTJ0AwFQNas8ToPY8QYb+7FwDo2ADzN3rwNO6Qs0bCaX74AYvqBhXZAY5IySzl0bDS5voIkwIC1ZlCUTZvkiIQbXU9Ae3IbzhPxBVReCd+sIxZHy9dW9EOACYYTBvusyuqjnBVA1AB+kYqg6w5Ew52lNxUHYM3c2bhKPW3eDo35YFveQAWFImIMyWm2RO6sS96TDLD9kpu9VeJ8LY/i2M3evtCwSmyIRUOMYdQ2GEZTiS6/AohfHzCoBzaH1Pqb28ZQKMgSe137nEjHPA6ZWhvamd5GcUDsh6bzVKYtRMW39M15MxuPOGIpCSB2PX99A3L0PoqxegdB4AbdDZtRJe1Ps+Di+EHoJxcBuUzJ6t4kKyKQjTgFVVbN9c1bd+A+ZOgdKjWk1KIyxv8rXgaGGUndPg4HY7m3GtZRQNzlMuh1KwGkF/0C77Ei3twqr9+3BGYB7pHPNay9tlY+p47PBrWK3XZXTKRlmlnFcvgpXgaV3kqGcHxBiDktoZ4BpE6R4Ih7fWPD+mueAcfRXCa96C/uPHsnM46JzYUmiR7MGWvxwQAjyzR4MhuSJQAaZoMAo2AELUCiNF2C+T2rSGjpbmlnMhI5lHeVoXQHPCLMyPXW89ZI8qthfta2tInXhytkyJ3kD9IUBOsHUMnwRt8C9g7F4PY+cahL//N/Djx1B7DoOaN7LBrF1MdUIoDohgBQx/mUw6kJRxVKFmIhyQo4ThgBypiBx0rKoS6D98BPPAT2DeDDhPuQK8nrAkYYRlrSdXMnh2boe7Q1gdd6eCp3SKZCYNQTitOr8fUUfoTrRjFw3hEZYFWEbkP0tmKRQmhGnKi3FhytfIMsQAGCBkyThAwKwoAk+Xcz+51jHCedsKpjrBPKkQIT/gcIOndALP6CHn6PQdHZn/5oQVKAM3ux7TEjaWv0IWtY4mmgpWwti9TobB19FJFWGfrMHXSsvsNAemOuSNOU/q4ZIYQR+EvxSGvxxWMHBMSmLUu35cgZY3EmqPoTC2r4S+dTnMz/4KpecwaMedFVdHj2lOmZSmMB8ivRt4cttPSmP5y+yESmZxAaziXdCGnBdzoSz0AHhWrxYfLYyycxoc2Apwpc7wXuZKRuqoibBK/S2whpF1UDUAOqxgJbg3Pe6bEO2ZkpwJS1FhFeVDaO46CtyrcIy6BPr6D2D8vBwi5INj+KRaxwzuToEVrIQozIeSnVtnx84uU+HwwCz4ASwlBzy1U8zzEALcm948G5sgxjm4OxUi5AMcbjDGoWTmygQ00WUAWHqwxebaN5eOc6bswA7XHwrL0K8j1Itjmgtan5Oh9h4Fq3iXHEXcsRrG9m/Bs3rJMNMuA+u8oGCMyXp0lgmrbB+ErwhKevc6s2A1RFgWrMpCiPIDstBypEMrjBD0n76BsW0FwBVog86B2ueUOi/6ZIpqP5jmgprTB3B62/yFw9FiriSwlBxYO9fAMnRYpQWyU2dZMjTQMmVHL1r/SQBgsjcX7dDZ74Vo7bfqd2jl/5mqoL76bwyys276yqH1PEGGarSjEL/2gidnwfD/DBaZh6r2OhHh75bCKt4lQ/gYhxAMVrDqmN15l9EDhTHhg/q2lYBlQa1R+BvA4TpvreRioyXElMRIyYInw4WqfYUQgUp5p98y5MhwSySxUR3QBpwOtddI6Fu/hrFjFcw9P0DtfTK0Aace8SYeU1QId7JMoa8H5NykNpqURggLVkWhHVZv/PwNoLmg9jrx8DLR0cJWVraDaU6Z0+DgdghXUovcbIiHCAfk3Mz0bh3+WiCKe1LBOvWFUbgTEFatUV/GuCz/40yCvmUZwmE/HCddUjupoStZ1h0tzIeSlVv7BkGkTIXwlcEq3QNt0Dmxz4cDYEkZrSrcn3nTZILDyN88Ow/mga2wAuUyck3RgGAV0My1Oo816hh2EIxzKFk9YR6qP+Sj1msYg5LVC0pWL4ghVTB2fQdj51qEV/0LzJUEJfdEqHkjYkI7D7cXDS8Nwzi0DdydDp7eOa5ELyLkk6OERggsMjlaCAFzzw/Qf/wEIlgJpcdQaIPOqbOwb3TuE7gGnpkL7qk7a1pHxDQXlJROMIUVGXlBHR06V50nzaY8jYqqYgBCZiRldd9lJi3M4QFzeOTFqOqA0m0Q8MN/ZBKayHwLpjoAXwlwrEKyQn7A1O2OoQgHYOxcDaX7IPCkjFqLH67z1jovVFsCUzRwV6QkRloXwAjDCldLYgMZPgzFecwu0pjTA8eQ86D2ORn65i9gbFsBY9daaP1Ohdrn5IanMjAOuFNh+UohjBCUzJ6t6uIyXiJYZe/bVsUhmPt/gjrwjJhz5uE0/q3vfMZcSWAZ3WTmWHftaSstzTLCELCgZvWh40ENzOmF2qkvjEM7gUj5jpjnGYN23JmA0wt9/fsILX8ZztFX1i6H5kyS838Ld8qbh9V+h1bQB3AGc9cPAACl++CY18Iy7BIirQVzuCPRTpKS1Qs6AKswH7znMFnCIlgFHply0V5Qx7ADYYoWCfn4ud6Qj3pf60qSd3b7nwrrwM/Qd66B8dNXMH76GkqX/lDzTgLP6V3rhBUNaRIhH8x9P4GldZZ1qOo4MAvLhFVxCKL8EKC5wCNziKyy/Qiv/wBWSYEMdx11aa36OkAkA15I1sjhaZEsqXQCiMFUB5DeFQBglB0A69TpCK9oHlZloVwfTxp4Bw7tbc0YY2DJObBKdtu/Y7XHUBj530EM/aVMbqU5IYIVduexuVkVhTHtGDtXA0YYWl2jha1oLlZrFQ0JVrRqSWyMIKygH8JfKucnMhyzsFPuSYNzxIWw+o5BeNNn0Dd9BmPHKmgDz4CSO7ze9qNznUTYB+PgdqjZbS8pjVV+UIbHAtB/XgEoKrTeo+zn5f7sbHWjhdXxpExAD8rkUA1MWzkWhLBkAXJTh4CAcCVBrdFZIYcxzQW1Ux+YRfl2gr+atN4ngTk9CK95C8GvX4RzzNRaN+eZwwsR8svOYXae/XkLfykEd8Ao2ACelQvuSbNfIyKZSlvbb5YpGuBw2+c3ltoJ0FwwC3dCjWa8FyYQqSnaXlDHsINhmkuGfBzaAeFM/K4jYxxKlwFQugyA5SuVYaa7voe5/ycwb3qk5MXw2necnB4ZKlN2AFZlMZT0rjEnDhGsgllSAGHoYO7IKGHIB33T5zDy1wIODxzDfwUl94RanU+ZadQn0yanZoMnZXeo+USJUjK6A5oL4aK9QKdhR35BM7Aqi2SoqSsZoIykrRZ3p8CKZCBlXIHaa4QMK9+9wU70IgRgBSuhNPPdXmGEIIKVdrIbYerQt60E79RXjnzVXF4PQKnjZhWpH+MccHigODxAShaEaUDogWphp/oxqZ3IUzvBNfpKmEW7oG/8FOF174Ft+y+048+G0vW4eu/OM4cXiCZBy8qF0kaS0oiw307jb/nLYRZsgJo3MibDtwgHoOTkter9mUVuygo9FNe0laYkLAMwwhCmAcYg5yE7vUBKNhTNBXfXbPiKW26OY1vA7FqHu+utdah2GwTmcCO08lWEvnoezjHTaiX8Y06PjOY4uB1qTm8IwyWzwQd9EFXF0PrGZjgVZhhKSnyZiY815k2HVbZPdgwZh5LVK2aeoYA8N7WnGw509dwB2SEfxQUQovEHbu5Nh2PwL6AddxbMfZtg7FwD/cdPoG/6HEq3QVB7nwSe3t0+sDDG5V1dU4dZtBPcnQozpQ/M0r0QFUUyyYXbDWGZ0HeslOUnjDDUPqdAG3hGnXeTRDgAGGGwpAzw1JwOU5PwaHBPKnhKDsKFBVAD5TGZQiEEgMj/7VpR1TOLCtRMQtOY580DW6EkZwCctbq7hOQwxjl4SmdY5fvBXMngqZ3B07vJJDR9TpYjNQ43RGVRs8+zsHxlkXmr8nhi7PoeCPuh9T+11rJCD0bKoCQ2t5nEYooKptQMOw1Ewk4rmj3sVMnKBT99Jsz9P0Hf9BnCq14HT+8mayBGa2rWXGfVCcEViMJ8mGmdZZbDVh7mZVUWAZGbmca2/wIQUPuOtp8XRhjM4WqR0jCJYpxDyewB4+A2wAg1yzlZCCFHacywzBoJQCgauDsF3JUsR15VZ2wGTYoeiousddirwVqHSnZvuE6dgeCKJQh+9QKcY66qVd6MOdyAHoRxaBsMTx4EuMxGGq2NGyH3bXerzUzOXUkxBVV4di+Y+7fA8peBe9LAmCKTtLWB32a8qGPYQSlJmYAehumvAHB0J3SmqFB7DIXaYyis8oORkhfrYRZsAEvtDC1vJJQeQ+wTBFM0MHcaRNiP0J4tEFVhe06CeWgHwhs+hKgsBM/pI8tPpNTOhCr0EIQRkFk222DYUEtimhM8tTOMHatg/OfJFlsP18DRsASj+YWtHPemwirfb6ftVnuNQPj7f8MqKZDzuRRN1s2Lc+5yY0STUUWTkQjLhPHzCvCM7uCZubVfoIegZPZo9R2CtiQ27DStdthpsBKAiISdOpos5TxjDGrXgVC69Ie5ez30zV8g9M3fZHmiQeeAp3au/RoeSUpTfgAwwuDpXVttx0AYYQifLL8iwn45h7f7kJiESYdHv9vG/sxUB9TsXrIOI1eOel+IhoUKMxxtAUxzgSVny4yQmpPOI03ocK1DFVb5QVnPtmYm0vSucJ0xC6HlLyP09UuyJElOn9j30VyAHkL40G6Acxh7foTSqV/M9drhebOtdN9WnRBcBSJRM0pWXmSe4U7w3OFA5PyH1JaZltMcqGPYgfG0TlAsFWLvfgjG5MFbdRzVCZSndoLjhAnQBp0DY88PMHasRnjde8CPnxwueRFJE80cHiheD1jYD8tXivCPH8PctxnMmw7HyZdD6TKg1sFCZhoNgDncUHP6JpztlABQHFD6jIK3czcE/IZMPGPXg6pW/ymaVbTGY03zPENaZjKK9x9oV7H57RFTZFZP4S+XtfK6V0tCEy1MzhmsQAWU5uoYhqoAy7KPTebejRD+MmhDx9c+RoT9YJ60mDA80vSOHHbqb9KwU8Y41NzhULoPhrFjFfSfvkbw82eg9BgC7bhxtTLPygiVVFj+MjspTWtk+Q4XtNd3rAZMPWbOrDBCMglGGxv9Zg4PWGYurOL8mJqj8TgcFqrLYkecg7uSwFzZ4A63HA1spR399kLWOuwiax2W7IVweWt18HlSJlxnzEJw+RKEVrwCx8gLoXYfEvs+mhOK1wmxbxMQqoLS4/DzwjQitalb72gbY0ye/6pKZGb7lGzA4Zb1DKMdw1AVhFV36a+2iDqGHRhjHM5OvaCYSbD0IBCohBWqiqQvj4ReqI5G3YljmlPWqeo1AlZJgRxFzF8LY8cq8MxcqL1HQul6HCwjjPCmz2WBasagHT9O1kmr0aawDDlcr2hy4nIrzHrWVkSL23pysqC34JQLYRoybp9O8K0eT8qEWVUMQIbqqT2Gwti1DmLIeHn3V3PDqiwGT85ult+lqCyyIw6EELLwd3I2lM79Y5eLhJjxVKpRdqzVCjs1w7BCtcNOhXF0IztM0aD1Gws190ToPy+HsX0lzD0bofY+CdqA02vdEJBp9P0wDm6Dld48Ny4aSyZcK7Kz/+rbvwXv1C+2vpsebFOjhdUp3jTA6CxHnVB3xzYmLNSywCAgVEe1sFBX5MZC29v+9kBJzoKpaBD11Tp0JcN12gyEVr6K8Oo3IUJ+aH1Ojl2GKzKMVHXGHLOF7o+UDmndHSruSoZRWQgGxMwzFNFspAKAEQLaSeQadQw7OMY4WLTGlTcdPHKQFnoQIuSHCFTAClYAIhJKpDgAVYv7h8wYg5LZE0pmT4gh58HYtQ5G/hqEV78JOL0IqypMXzmU7kOgDT6nVukLO9Mo5+AZ3cA96e3mrkxLYq5kWEYhWvIQIEz9iHXKSOvAHG4wV7IdLqr2OjESMr4BWp+TZeie6ZMJBpp4pE4ei6rAolmKD/4MUXEIjhGTax+Hwj6wpEwKLW9hjDF5EahGwk6FJbNVBv0QCMnQY8hQs8bOTWQONxyDzoHaexSMLV/K0Phd30PrN0beXKw2t405PYARRmDPFpimR2ZEdHha/FxiBcpl7TiuQN+5ptacWTla6Glzo4XV8ZQcQA/BDFQCUOoMC+UON+ChsNDWSvGkQnTqC+PQjrprHTrccI6divDqN6Fv+BAi5IN23Fl2Z14YOsx9m6F0Pd7+boVlAuDgbSFBlMMNgNvTKXh2Hsx9myH8pWDeDAgIWei+nZx3qGNIYsgTukxND3cKkNZZhnXoIZl0IFgJK+QDhCXvlihaJPz0yLsSc3qh9R8Ltd9oWIe2w9i5BgoMqCdeBCUrdp6QEBYQ9kMIAZ6SA56c2WRzVkjkgsxo2XUQhg44O27h8baGpWTDOrRTloBI6wqe1kUmoek9Siah4Sosf7m8uGtClq9Uhh9H6Fu/AXOnQqkRsiSEBWFZUFJotLC1Yexw2Kk7OxmVahGsYBVEVQlEoBwCTHbkGjEyxN0pcAyfBLXvaOibPoO+eRn0HatleaW8EfZ5g6kOKG4XxKESGL4SyIvSFDBvuux8HePIBSEERMUhmbzJMmFsWwGe0SPmXCj0ENQ2OloYxZi8qctC+yECpZGw0ORqYaGuFu+gkyNjTi/Uzv1krcOwv9ZNXaZocIyagvC692D89BUQ8kE7YQIY4wjs3iQTCfYYevgFYb9MDNUGrusYV8DdSRDhIKC5YusZejPkdXDIB3jbx/VM6/9GSItjXAWcqrzgS86So4pGWN7JD1bK+OqQDwLc7lhC0epPKc44lE79oHTqh/R0D0pLD8czytITAcDSwZKzoCRnt6s0wK2G6gC4AmHqsfMBjykBTllk2wzm9Mr5FKYOpmgyCc2692CV7rVLoFi+EvDUzk12oScsE1ZlsT0CaBbtglW8G9rQX9a6kBchXyQzMR0vWjumOqEkOYGkTAgjLG82+mQCG4HovERnQh0inpwF58mXwSzZA33jp9A3fAhj+0pox4+D0m2QjI7hHMzpkVkshQUR9NnZbrk7FcybJjuJx6LcUcgHoYfA3akwCjZA+MuhDT3fflrocm5hU4/AtwTGVbi69YOillNYaBt2pFqHjCtwDP8VdGcSjK1fQ4T9cIy8CIEd38us1tm9AMDOdl5zXnBrxjzpsAK77KRHcHphFu2E2utEQNVgBSrRXibFUMeQJCyanY5pTiASBiBMPTKq6I+MKlbJkgUJJLURehDQQ7LoeVqnZstwSOR3qKXmAJV75ARwy5CdcvkkWPUEzYJBsNi/ZWwxwGTUvUwqE/0/q+PvyL9rdT7pIr7NYIyDp+bAKt0LpmhyxO6Hj2QSmozuYFyBsORIP5ooKZQVqLRD7QA5WgiHR076r0ZYpry4T8qq621IK8ZUBxTVIacymDqsYKSTGKo6HJWiOeO+caVkdAc/dTqsQ9sQ3vgpwqvfBNu6HI7BvwDSD48yy1FMNxjcspMY9sPyl8n9yJkk18flbbawRqvikCytETNntp/9vDCCUHP6tJtOFFM0ec1A2jSmOqBk94ZVshtWoALMFVvrkDEGx6CzwVxe6Bv+g9Dyl2GV7o1ElkR+w+EAWFJGm7qJxxxusMg1EmNMzjMsjMwz5Cpg+uybpm0ddQxJk2CKBigaFFcSkJITSWUeiiupjTDCEHoAzOmVKebbwR3StkDL6AzVrFZAWQgAIlJrMFJ7ELKeYfXH7dqGELIjYFmAMOV/MX9bkedN+X8RqYsYwbypQDs4iHYk3JMKqzRSukJzQuk+BOaeHyCGnCfvpCoaLF+JPA40AVFZaN8gssoPwDr4s5y7UuOCQoR94Gldj81ID2k2TNFkwhJvGrhpQIT9EL4yWIHyhDqJjDEonfrBldMH5p4foW/6HKHlL6NwRx+w3FHgnfvFvAdjHNBcYJpLhngaIYiS3fJ85fICnnSZFbOJLmRFOAARrARzp8I8sLXWnNloHc72MFpI2h+mHLnWodbnFDCHB+G17wDCig0jtQzwZq5729SY6gAiSaKY6pD1DPduhPCVgCVlQoABeqhdXNPQWZQ0C5nKvL6kNj6Z0jxYAcOnAxBQsvNq3XkixxazR/aOsNxRthPtWLqyk1FV3IJpUUnCGFfBkzMhqooBZxLUvBEwd30Ho+AHaL1PAjQnLH85eLpx9LXLwgE5lyWSkEr/eTmgOqD2Pil2OdMAuNqmwpLIkTFFBXOnAO4UcMuMdBJLYfkr5I0JrgIOV4OdRMY41B5DoXQ9Hkb+GhjbVsBa+U8wbzrUvJOg5g6vlahIRsRU6ySaYYiSPTKGwuEGS8oAdyYd1eiXVVUMRH4fcs5sSsycWWGEoGZ0p/MhabXsWoeKCqui7lqHao+hYK5kOAMHoUfqjQo9KK/12mCiFuZJhyjfD6iO2HqGSZlgjMEKB5vspmhLoo4hOSZqJ7XpAmEZcKc54SvXW326YtJ0ZG1DUJmKNoonZcCsOCQTQ6V1BUvtLJPQ5I0EY1xeTAd9YEeZbU7Wd1Mj/y6FuedHqJG70NWJsA88M5f2p3aMcQXMFSmFkS7DlS1/maxRGK1vqbnq3QeYokLrcwqyTzwDRRtl2ST9x4+hb/4Cao+hUHuPiikRYb8ukl3VLpVihGGV7oOAkI97M8FdXtl2nJ04YeqwfKVgTi/M4t1yzuyQ8fa602ghaSsYY1DSusi55/XUOlSy85CcPsjOJSGMkJyT3gZxlxdmWSScNCkTcCbBLMqHmjdSTosJVgIpbX86A3UMSYthXAV3esBYZUuvCiEkTkx1gnnSIEJ+MIcbaq8R0Ne/D6tsH5T0bnLeVFWxPf+4MYRlwKoqscPKjW0rADCofUfHLmeEZZZUT2IFtEnbxTgHXElQXEng6V2BcACWvxyWrxRCmJGwUHednUTGFajdB0PtPhhW2X7oO1bB2L0eRv5a8KxcqL1HQekysP4OZvTmJmQHT5Tvh1kuIBQN3JsB7k45YifR8pXL92IcxtblgOaWCSwihBGEmtmPRgtJm6EkZ8HkKkTxbgiHu955dsIIt+2ESpoLgiuAZYJxBUp2L5iFO+U0HEWDFfaBR2sbtmE0TEMIISQhPDnLrkOm9hgCKBqM/LXySdUhsxUb4QbeoWFWoBKAkCOQwSoY+d9D6TlMXnhXI/QAeFoXijjooBjjcm56eleo3Y6TyVqSsuQcwUAFRLBKhhrXgad1gfPEC+D+5a3QBv0Cwl+O8Kp/Ifjxn6H/9BVEqKrhthVNhsS5UsC4BlFRCPPAzzD2bYZZtl9Omag2pxqIFrQ/AObwwKo4BPPAT1D7jDrc2dSD4K4UmmdP2hzFmwalUx85XUgP1bmM0ANgKZ3abMeJMSanLOhBAICSlSezC1cVyWNRpEZnW0cjhoQQQhLj8Mg5WJERO6XbIJgFP0AMPg9Mc8ICgxWshNKIBAN2fTdNzkHRt38LWAa0fmNjl4uE3DFXcpNsEmnbGOOA0wvF6ZVhoXoQVqBChiTrfgAMwqg9ksEcHru+rnlgK4ztq6Bv+hz6li+hdBsMtc8oKOndGm5bUQElMrfIMiGqSmBUFgKMHx5JdHhg+irsLLvhn5cDigqt96jDb2SEwDN7NOGnQsixw5xeqJ36wijMr1XrUJgGoDjA2/jxmrtTYFQWgQF2+Q2zMB88ORsCMlS2rWffpY4hIYSQhDDGwFI6wSreDaY6ZBKa3evkPMC8EbLTWFUCNCbzXNgP6EEwdyqEHoSxcxWUrseDJ9eYu2GEZBbjNnr3mTQfxpid/Iyn5MhOYtAHYQVkin3VUascEmMcapeBULsMhFVRCGOnDDM1C9aDp3eTYabdBh0x8y3jCuD0ylqJlgnhK4NRVQSAI5zmAVNdsPxlMAt+gNr7JHt00E7KQaOFpA1jDnedtQ5F2A+e0a3Jaty2GIcbYExmSfZmgLmSYRXtBHqfBMZUGWngbttTG9r4N0QIIaQlcHcKwDmEZYKndwdLybHDSZnqAPSArE2aIKuqBOByZMfYuRbQQ1D71xgtDPvB3Gl0EU2OiDEG5nBDScmCq+dxUHN6y0yKgbJ690+ekg3HsAlwj78V2tBfQuhBhNe+jcBHTyK86XNYgYr42uYKmNMTCQ/1gKkamOqAsW0lABE7Z9YI1ZkAh5C2JlrrkDk8sAIVkTqzHPwoE5K1Bowr4K4kwAjJ0NLsPJiReoZQNYhAwyHobQF1DAkhhCSMcQ6ekiM7aYxB7TUCVtk+WGX7okvEfQEdJUxdFhl3uCBMHfq2/4Jn944J5RNCAKYBnprTdBtDOgTGGJgrCUpOH6id+oGpDgh/uawrGCleHbO85oLW52S4zvkNnGOmgqd3g/HTVwh+9BRCq/4Fs2hXna+ru20OrjkhQn4Y+WuhdB8C7kkDIEuz0GghaU+YooJn9QL3psGsLAFPyTrqEkathifdnkOvZPUCwn5Zc1fRIIyg7Ai3Ye3kWyKEEHKscU8arLIDEJECxvqPn8DI/w6OE7oCmhtWZTF4cnbc4Z5WoAIQMumMsfs7IFQFrf9FsQuFfWDe9DZZB4u0DowxOR8xpzdE2A+r/BBEoByCa7JWYY39lTEOpVNfKJ36wvKVwNixGsau72Hu3QiW2gla71FQug+xk8g0xNi5CjB1aNVHwY0weFbPpt5MQlpUtNahwxkGD7SfckLc6UH0dhDPzgMAmIU7Zdg6ABghoEZZpbaERgwJIYQ0ClMd4EkZQDggw/W6DYJRsEEmpVFUMFMH9EBc7yWEgFVxCMzhhhAW9J+Xg6d1tU+8chkLsCwKuSNNhjk8ULJ7QencH8ydBAQrIxlF6x4J5N4MOIacB/f4W+E4YRIgBMLfv4vAf55A+MePYflK623L0sPQt38L3rk/eIrch0U4AOahTKSkfWKMQ0vNrreERVvEVAegOSFMHcyTBuZOhVWUbz9vhROfQtGaUMeQEEJIo/GkTMCSJQHUvBGAEYa558fIkwosf3l8bxTyAYYOpmgw926C8JVC7X9qzOiNCPnAUrPjGpkhJBHM4YaS2RNKl/5g7pRIB7GqVskJe/lI0iXXuF/DedoMKNm9YWz7L4If/xmh//4T5qHttTqX/q2rgHAAWv9TDz9ohOlGByFtDPNmAEYwMs+wV2SeoQWmOIBg255nSKGkhBBCGi1asFjoIfCMHmDJWTDy18qi3ZoLVlUxeEqnI2ajs6pK5BwNIaBv/QYsKRNK14H28zKBAQNPymrgXQg5OkxzQcnsAZGaA6uyCKKyGIJzWaKljnqZjDEoWb2gZPWCFSiHsXMNjJ1rYR74CSwpC2qfUVB7DAMUFVU/fgWe0QNKpgwblaOFqTFp/QkhrR93JcEskzd+lKw8mLvXyzJLydmwQlVoy4GzNGJICCHkqPDUTrJ+UzQJTeleWOUHZOp+y5IlKBogjDCEv1R2JA9thyg/AK3f2JgLcRHygad0blchSaT1YqoTSno3KF0HgiVlyvDSYP0jiADA3alwHH+2DDMdcSGY5oC+/gME/vMnhP77D5i+Mnu0UCZRClMSJULaIs0JwRWZlbtaPUPGFTDTtJPTtEXUMSSEEHJUmNMLKBqEacjREa7AyP9OPqeossh4Ayx/BQTjYIzJ0UJXMpSeQ+3nZXFkFTwpvVm3g5CamOqAktYFapcBYClZECEfrGBlg5kHmaJC7TkMrjOvh/OMa6F0GQircCe0jC7gnfvJhfSALLlCo4WEtDksWn5DD4F70sA8abKeIQDBBIQRauE1bDwKJSWEEHJUGOPgqTmwSvaCu1OgdDseRsF6aIPOkaOA/nLwdKPOdOVCWLAqC8EcbpglBbCK8qENOS9mWaH7wTO6yxFIQloAUx1QUjuDJ2XB8pXITKbCAnN6G9wvlYzuUDK6QwwZj7R0L8r9IjJaqNNoISFtGPOkyikQkNlJzX2b5TGBcYiQH3Alt/QqNgqNGBJCCDlq3J0KMCZLV/QaAeghmHs3yXBQIeSJsg4i5ANMHYyrMLYuBzSXnJ8Yfd4Iy+ynkZpvhLQkpqhQUnKgdhsIntYVQg/AClZARBIw1fs6pwfcGSmxogdkNkMquUJIm8UcboDJsHAlqxegByHKDwKKAwi13QQ01DEkhBBy1JiigidnAWE/eGYuWFImjPy18jnVAVFZXOfrREURmOqAVXEI5v4tUPucDKY6Dz+vB8BTu9SZ+IOQlsK4CiUlC2rX48DTu0EYYViBChn23AB7tDCFRgsJacsYV8GdyTKzsD3PcCegaLBC/gbnI7dmdKYlhBDSJLg3A4jMvVJ7jYBVUgCr4hCgOiGClbUm5AsjBBGsAFQn9J+XA4oKrfeow8/rQTCHR5YPIKQVYlyBkpQJtctA8MzuEKYuRxBNve4XhANgXhotJKRd8KZBGCFwdyqYNwNmUT4YY2BCAHrbnGdIHUNCCCFNgmlOMHcqoAeh9owmoVkLxhgEAKtGfSfLVwYwDhEoh1nwA9ReI2ILfRshKOldY2oZEtIaMc6heDOgdhkAntETECasQHnMzRAhBGAZ4Mk0WkhIe8CdHkTPTjy7F6yiXRCWCQG02QQ01DEkhBDSZHhKNoQZBnN6oXQZCGP3eghTB9NcEFWHw0mFJZPOwOGBse2/AAC17+jDz4cDYO6U2I4iIa2c7CCmQencHzyrFwABy18OYYRghfw0WkhIO8JUpyxdYepQsvIAIyRLNSkqRBstdE8dQ0IIIU3H4ZGdQCMMNW8EoAdlEhrVAegBiEh4jQhVyRqHehBG/ndQegyxE8wcztrYuQU3hJDGY4xD8aRC6dwfSqfeAOOAadDcQkLaGeZJA/QQlMg8Q6swH1Ad8hzXBlHHkBBCSJNhjIGldILQg+BZvcC8GXZNQ4DBClQCAERlIZjqhL5jFWDq0PqNPfwmuh/Mm04jK6TNY4yBu5KhdOoLV8/jwTRXS68SIaQJcXcyBCwwVzJYUibMop1gXIXQw0dMRtUaUceQEEJIk+LuZIDLMhVqrxNhFe+SYaOaG1ZVIaxQQJapYICx/VsoXQbYIylCWHJkhWq8kXaEMQbuoE4hIe2O5pJz5YUFJTsPVvFuCMsEGIA2OM+QOoaEEEKaFOMKeEoORNgPtecJAOMw8r8DU1QwQ4deut9+DHoQav9TD7845AdLyYkpWUEIIYS0RoxxOQ0iEiUDIwyrbB8YGKxwoKVXL2HUMSSEENLkuCdNpux2eiJJaNbJsBrOYVSWQigOGD+vAM/qBSWjBwBEsrkJWQ+REEIIaQOYOwXCMmLnGSoOIFjZouvVGNQxJIQQ0uSY6gDzpgPhoExCEw7A3L8ZcHjAHU5YezdCBCuhVRstFCGfLGavaC245oQQQkj8mMMDgAEOL1hyNsyifEDVYIV8MplaG0IdQ0IIIc2CJ2fJ7KLZeWCeNBg714IxLpPObP0GLLUzeE4fAJCjiYoCnpTewmtNCCGExI8pKrjTA5hhKNm9YBXvBoQFJkRMLdO2gDqGhBBCmgVzuAGXFzB0mYSmKB9WZRGCuzdCVBVD63+qXbxehP3gaV3AuNLCa00IIYQkyJMOoYfAs/IAU4dVug9CACIcbOk1Swh1DAkhhDQbnpIDYQSh5g4HGIOx6ztUblgG5k2H0u14AIAwwmCaA9yd1rIrSwghhDQCd3rBGKBk5QIArKJ8MM5hBn0tvGaJoY4hIYSQZsNcSXISvuaG0nkAjO3fQi8qgNZvLBiTpyARDoCldgHjdEoihBDS9jDNCagOQHWCpeTALNwJMAYhzJZetYTQWZgQQkizYYyDp0ZKV+SNACwT3J0MpecwAIDQQ2BOD7g7pYXXlBBCCGk85kkHjGCknmEBFbgnhBBCauLuVIAxsOw88JzeSBkx3s48KowAlLQu9lxDQgghpC3iriQIy5L1DC0DVtn+ll6lhFHHkBBCSLNiigqelAWmB+EaezW8/U8CIENIuTtVhpsSQgghbZnDDXAOntkTAGCV7G7hFUocdQwJIYQ0O56UAViWXdNJCAEYYfDUzi28ZoQQQsjRY4yDu1NkWabUzrCKqGNICCGE1MI0J5g7BdAjqbvDfrCkDFnSghBCCGkHmCcNwtShZPWCVbYPwtBbepUSQh1DQgghxwRPzoIwdQhhAZYBnprT0qtECCGENBnm8ABAZJ6hifDB/BZdn0SpLb0ChBBCOginF0xzwqwsA0vOAVOdLb1GhBBCSJNhigru8gIZ3cDSuoJpjpZepYTQiCEhhJBjgjEGltJJFrNPyWrp1SGEEEKanicNAOAaOxWOnNyWXZcE0YghIYSQY4a7U+BKzYCv0mrpVSGEEEKaHHd67URrbQ2NGBJCCDlmGOcyzIYQQghpj1QnhKIBltnSa5Iw6hgSQgghhBBCSBNgjIF70yH0UEuvSsKoY0gIIYQQQgghTYS7kwG0vSkT1DEkhBBCCCGEkKaiuQHN1dJrkTDqGBJCCCGEEEJIE2Gcg7vTWno1EkYdQ0IIIYQQQghpQsybBsbbVgEI6hgSQgghhBBCSBNirmRoaTktvRoJoY4hIYQQQgghhDQhxhgYV1p6NRJCHUNCCCGEEEII6eCoY0gIIYQQQgghHRx1DAkhhBBCCCGkg6OOISGEEEIIIYR0cNQxJIQQQgghhJAOjjqGhBBCCCGEENLBUceQEEIIIYQQQjo46hgSQgghhBBCSAdHHUNCCCGEEEII6eCoY0gIIYQQQgghHRx1DAkhhBBCCCGkg6OOISGEEEIIIYR0cNQxJIQQQgghhJAOjjqGhBBCCCGEENLBUceQEEIIIYQQQjo46hgSQgghhBBCSAdHHUNCCCGEEEII6eCoY0gIIYQQQgghHRx1DAkhhBBCCCGkg6OOISGEEEIIIYR0cNQxJIQQQgghhJAOjjqGhBBCCCGEENLBUceQEEIIIYQQQjo46hgSQgghhBBCSAdHHUNCCCGEEEII6eCoY0gIIYQQQgghHVyb7Bjquo5LLrmkpVeDEEIIIYQQQtoFtaVXoLKyEvfddx92794Ny7Jw3XXX4fzzzwcALF26FK+++io0TUNmZibmzp2L1NRUaJoGh8PRwmtOCCGEEEIIIe1Di3cMH3nkEYwfPx7nnXce/H4/rr32WgwePBi6ruPDDz/E3//+d2iahmXLlmHRokW46667AACct8nBTkIIIYQQQghpdVq0d1VVVYUtW7bgvPPOAwB4PB5cc801eO2117Bt2zacdtpp0DQNADBq1Chs374dd955J2bPng3Lslpy1QkhhBBCCCGk3WBCCNFSjW/ZsgWLFy/Gk08+aT+2fft2/OlPf8J9992HOXPm4IknnkBqaiqeffZZOBwOzJgxAwAwbdo0vPzyyy205oQQQgghhBDSfrRoKKllWWCM1XqcMYbOnTvjlltuwbx586DrOkaMGIErrrgi5rWNUVxcBctqsb5wnbKzk1FYWNnh2qb26bvvqO135G1v6fY78rZ39PY78ra3dPsdeds7evsdedtbQ/t14ZwhMzOpzudatGPYo0cP5Ofnxzy2fft25OXlAQDS0tKwb98+OBwOLFu2DF9++SWEEDBNE5WVretDJoQQQgghhJC2qkU7hsnJycjLy8Nnn32Gs88+G8FgEC+//DLmzZsHAOjevTuef/55qKoak2xGCBEzepgIzmuPULYGLbleLf2ZUPv03XfE9jvytrd0+x152zt6+x1521u6/Y687R29/Y687a2h/ZoaWp8WnWMIAGVlZbjvvvtw8OBBu1zFueeee8TX0RxDQgghhBBCCGkaLV6uIi0tDQsWLEjoNZZlwTCMZlojQgghhBBCCOlYWnzEsLGEEHUmriGEEEIIIYQQkpg2WyWeOoWEEEIIIYQQ0jTabMeQEEIIIYQQQkjToI4hIYQQQgghhHRw1DEkhBBCCCGEkA6OOoaEEEIIIYQQ0sFRx5AQQgghhBBCOjjqGLZiQgjEW00kkWWbo/36WJbVYm0fjWPdfs3PqaNsv2VZ0HW9RdquT1O1b1lWo/b/ltz+9vLZJ9JeU7ZvWRbC4XCdj+u6br93c25nPO/dnL+7eN6nrmWO5XdfXzumadZa7lisU3v43dW1TzVH+0fTTrStuhzNdy+EgGVZME0ThmEgHA4f9Toey99CPG3Fe2w7UluNabu5tPTn3NLbX58WL3DfHhUVFWHKlCn47LPPwHn8fe9LLrkEb7zxhv33pk2b8NBDD8HhcEBRFHDOYRgGTNPEli1b8MEHHyAnJyfhZeMV73uuWbMGjz76KFwuFxwOB1RVhaIoEEJgw4YNePvtt+22i4qKcPfdd4Mxhrvuugu9evUCAPzpT3/CqaeeipNPPjnh7WmopuWsWbPw/PPPH3Fbly5div79++O4445r1Oe5bNky/OMf/4BhGDAMAwDgdDoxbdo0nH766TFtvf7669A0DRdeeKH92JVXXolXX3014c8+asuWLXj22WdRUlICy7IghEBKSgquv/56DBs2DADw1VdfQdd1cM5hmiZ0XUdaWhpGjx6NZ599Ft26dcOECRMSaj+Rz37q1KnQNM1+P9M0IYSAqqp49tlnG7Xt8bZ/6NChevf/KVOm4F//+lej2i8qKsLtt98Oh8MBy7KwevVqjBw5Eg6HA+FwGHfeeSf69OlTZ7v1aYrf8mWXXYbXXnstoXYb03ZJSQkyMjKO+L4fffQRlixZAs45XC4X7r77buTm5uKpp57CCSecgDPPPDPh9uv67qOPzZgxAy+99BIAwDAMMMagKErMcgDwl7/8BccffzzOOeecRm1/TT/++CMef/xxOJ1OqKqKFStWYNSoURBCIBwOY968eejRo0eTHa+XLVuGzZs349e//rX92BNPPIFhw4bZ29Qcv7tE2p81axaeffbZmM9/165deOqpp/DUU081uv2ax+ya3nnnHei6jnHjxuG+++7DwoUL7eemTp0a045pmgiHwxBCYMmSJY1ep2nTpuHll19u8DsTQuDyyy+P+X021f5Q12diWRauv/56qKqKAQMG4Le//W2t1yXafrz7VE2GYUBV1bivdTZu3Ai/34+RI0fC5XLF3U5Tffdz586Fy+WCqqr2Ou3YsQNVVVX46KOPkJOTg3PPPRedOnUC5xyMMbuD2KdPHzz44INH/M7q0hT7wzXXXINnn30WmqbZj+3YsQN//etf8fjjjyfcVrzHtpqix2TLsnD55Zfj9ddfb9LtvOCCC7B06dKYx1r6OrO5jnnHCnUMm1gwGMT999+PM844A3PnzsX9998fs3Pcdddd2LNnD1wuFxhj0HUdHo8HCxcuhKrGfh2DBg2K6SxUd/HFF8fsMPEuGw6HMWvWrDqX6969Ox555JGE33PkyJExF9bVTZs2DVlZWQCADz74AKWlpTjrrLMAAF9//TVWrFiBK6+8EpzzmM8p3raLiopw5513QlEUmKaJQCCAzMxMLFiwAADgdrtrvf6jjz6Cw+Gw1wOQB8zoeibSPgDs378ff//737Fw4cKY9kpKSjBjxgwMHz4cycnJMW1FO2tRR/PdCyFw1113YdGiRejSpYv9eGFhIWbPno2XX34ZXq8Xfr+/1l3R6qqfQOJpP9HPXlEULF68GA6Ho951SGTbE2n/tttuq/eCzev1Nqp9AMjKysILL7wAAFi7di3Wr1+Pc889F1OmTIl5XXP87mbPng0hBDjnCIVC6Nq1K/7whz8AQMxnvGHDBsybNw9ut9u+mBNCwDAMVFVVYd68eejdu3fC2w4AM2fOxDvvvFPn8lE+nw8vvvgilixZAlVVsXfvXtx///147rnn7BtJiW47ADz22GPYunUrNE2DZVnYu3cvZs2ahYsuuijmhtz8+fOxefNmBAIBHDp0CL1794Zpmpg3b559Qm5M+3UZOnSovZ+9+uqrKC4uxrhx43DllVfGLHe07UStX7++1v6rKErM99/Uv7tE2weAGTNm2J+1ZVmoqKhAz549427/nHPOwcaNG2PWofox++mnn8bKlSvhdDohhMDDDz9s3wyoeVMAADjnePHFF2MeC4VCuP7664+4TnfddRdKSkoAAPfeey9KSkpw7733YujQoTH78tatWzFv3jx7FOlXv/oVrrjiCjDG4HK54t72+j77eM5j0W197rnnYh6reZGbaPvx7FOzZs2CEAK7du1CWloa0tLSMH78eEyZMqXWeai+9qdPn46Kigr8/e9/r7ed5vjuDx06hGeffRZDhgxBSUkJxo4dax/TH3zwQaxatcr+TDIyMo54MwBouuPwwIEDcdNNN9nvEb0RbFkWXnzxRfs7EUJg5syZUBTFPkf6fD707ds35v3i/e7jPbY9/vjj2LFjh71/+Xw+/O1vfwPnvNY1Qbxtr1mzBn/961/hcrlgGAauuOIK+2aix+OJeV1ruc482mNeIueBpkYdwyayZ88efPnll/jwww9x4403YsyYMXj77bdx1VVX4dRTT8WYMWNw4oknYt68eVi5ciVGjx4NAPj2229x4oknAkC9dyNqKi4uRnZ2dqOWdTgceOmll8AYqzWaWd+F69G0X72dAQMGwDAM5Ofn4913363zzmWibWdlZcWc9D788EOUl5fbf9c1TP/jjz/ilFNOiXkseoGdaPuAPEnquo5gMBhzgIjeHa1+8gyHw1i+fDl27dqFX/7yl/bj8YYc1tV+dBudTmfM416vF5qm2ds1fvx4PPzww7jnnntQUFCATz75BE6nEzNmzMD+/fvx+9//PqH2E/3soyewmizLatRnn0j7DV3A1LxwiLf9KNM0sXTpUrzxxhv497//jYcffhgHDhzAtGnTkJaWZrff1L+7vXv34r333gMgP8PqJ+jq2z548GAsWbIEX375JUaOHInU1FSUlZVh1apVOPfcc49q22te4NYlegc9uo9bloVAIABA3hCJ57hXV/t33HEHwuEwtm3bhuOPPx6vvPKKPXpZ/fd0zz33AJAX0q+99prdkQcQd0RHvMe8H3/8EevWrcPKlSsxbNgwvP7663jllVdwww03YMSIETj99NMxYMCAo24HkBdcn376KVwuF2bOnIl//vOf+OKLL7Bjxw77nAI0/e8u0fYB1Lpw3rNnD5588sm4trO4uBiMsVq/4erH7IKCAsydO7fWRW996tvn4gnreuSRR1BcXIw5c+bgmWeewcKFC+1jSPXPc968eXjggQeQm5trj9qNGTMGubm5TXKuP9J5bPPmzXj88cfti9M1a9bgpJNOQjgcRjAYxIIFC+zjU6Ltx7NPRUdQrr32Wpx33nkxN8saukFZXTgcrtXRrdlOc3z3CxcuxO9+9zt7BOyWW26B0+nE0qVLsWXLlpgokHiPIU11HE5OTq63M1HT888/H/O72blzZ4MjrTXbqv7dx3tsmzFjBvx+vz2VovpNz5o3wONt+6mnnsKzzz4Lj8cDy7Iwffp0jBkzBg6HA4cOHcKMGTNw3XXXYezYsa3mOvNoj3nxngeaA3UMm8i7776L3NxcvPjii9A0DY899hhuv/12jB8/Ht98843942SMYf78+XjzzTehqir+9Kc/2UPr8cYar169GsOHD2/0sjNmzICu6/YBMtpuNASyqdo/cOAAOnfubP8dPZguX74cJSUl6NOnD1599VU88sgj2L59O8aOHXtUbYdCIbzxxhv485//bD+2fv16TJ06FTNmzLBDm9avX1/r4iwantKY9nNycvCb3/wGN910E8rKyuD1elFZWYns7GzMnTs3psP27LPP4qKLLsL27dvxzjvvYPLkybAsC1u2bMGll16K22+/HSNHjkyofc457rnnHvz+97+HEAKpqamoqqqC3+/HDTfcENNZ3b9/P6qqqrBr1y4Eg0GcffbZOOecc2rdUU6kfSC+z37AgAG4/vrrwTnHpk2bcNxxx9mht0899VTMvpJI2/G0X5foHJDCwkLMmDEDkydPxuTJkxNq//3338err76KU089Fc8//zyefvppPPXUU3jrrbdw4403YtKkSbjiiisANP3vrnqHtubFSfVjCeccnHN8+eWXyMrKwrBhw7Bz50588803cV+Q1PfZx3OBm5ycjGuuuQbXXHMNOOewLAt33nlnQu9RX/tVVVV48cUX8fjjj6OkpAQnnHACABlaffXVV2P69Ok4++yzAcgQ7v379+PgwYPo1KmT/R7x3JSJ95iXmpqKPn364KKLLrLvZE+dOhWXX3451q1bd8Sw20SO7Q899BBuvvlmbN26FQsXLsT//u//4qqrrsKTTz4Z8/031+8u3va9Xi+uvPJKaJoGVVURDocRCoXqDQGtq30hBEpLS2Mer37MjobxxSsvLw/Tpk2zO03RcMLoiM2RLFu2zB4pDQaDMdEWUYwxex+Idmyjv/WCggIsWLAA5557LgYOHFhvOw3tD0c6jx133HH2TZAPPvgAK1euxOTJk+3zYDzqaz/efer999/H0KFDsXLlSowZMwbdunUDAJSWlmLWrFmYNm2aPfJTl6SkJOzcuRPXXHNNve00x3dfUVERc4xIT0/HqFGj8Ktf/QrXXnttTMcwMzMT1113HTjnEEJA13UYhgGv14tnnnnGXq6pjsM1R8jqoygKfv3rX0PTNAQCAbhcLvh8PuTm5sbdVvXvPt5j25NPPok+ffogPT0diqJg5syZ9nts2rQJ06ZNw8yZM2NGuo/Utq7rCIVC8Hg8MAwDoVDIPm5nZ2fj8ccfR1JSEoDWcZ3ZFMe8eM8DzYE6hk3A5/Ph/PPPh8vlQllZGQA5fH399dfDNE0MGzYMuq7D5/PB6/WiS5cuKC0tRXZ2tr0zA7Ij9Yc//AEXXHABiouL8X//939wOp12SIRpmlAUBfn5+VBVFV988QVmzJgBt9sd97Ljx4+Hpmn1jl5Effnll3G950knnYTvvvvOjjuvOQlb13VMnToV//u//4uTTz4Z27dvx7p163DhhRfi5Zdftg8s1S8m4m07uj0AUFZWhvvuuw8333xzzGfar18/PP/88/Z2bt++HUIIfPHFF5g4cSKmTZsGv9+PwsJCnHHGGQm3P27cOAghMHLkSMyePRvfffcdbrzxRixatAgnnHACBg0ahGAwCM45XnjhBWzduhV//vOfEQwG8T//8z8oKSnB1KlTMXDgQPzjH/9o9PaPGDECzz//PMLhMCoqKpCUlFTnaE737t2xZ88ebNu2DZmZmXjhhRewfPlyFBQU4Pbbb29U+0f67AF58R0duQGASy+9FM8991xMZ1wIga+++qpZvvtFixbhgQcesCd7CyHgdDoxffp0pKWl4cUXXwRjLKFtHz16NPLy8nDvvfdCCIGdO3fik08+wXnnnYe+ffvi1ltvhWEY9g2SpvzdzZgxo8GRzvLycjz33HMYMWKEfYLJy8vDgQMHMGzYMOzevRv9+vVrdNvRz764uBjXXHMNhBAxowC6ruN3v/sdRo4cif/+979ISkrCrFmz7IuwjRs34qeffmp0+yNGjMDevXtRXl6OQ4cO4aOPPkJKSgpWrFgBp9OJ/v37429/+5v93kuWLEHXrl3xm9/8Br/73e+wcOFCpKSk2Mepxm5/devWrYuZax39HKKdBsMwEAgEMHv27KNqJxwO48EHH0SXLl1wzjnnYNy4cZgzZw4WLlyI3/zmN/Zy0Qunpv7djRs3Lq72owkpouFWDWnos9+6dStKSkrw8MMPQ1VVvPLKK7WO2XX9nhq60frAAw/U+1w4HAZjDCtWrKh3nbZt24aKigpcfvnlcLvduOyyywDIc/jrr7+OwYMHY86cOZgzZw5CoRBM08T48ePti9bu3bvjpptuOuK217c/xHMeA+Rx4I033sCKFSvw4Ycf4qGHHsLatWtxySWXoHfv3gkf86IdmCPtU5WVlXjhhRfg9/tx5513orS0FHfffTfGjh2LadOmITU11T431NW+YRhQFAW7d++G0+lEIBBAampqnftuc3z3AwcOxJlnnolOnTohHA4jEAhg/fr12LNnj31jde3atTj//PNx2223QVEU+3cevf7RdR2bN29Gly5dYkZmj+Y4vHPnThw8eBAnnngiunXrhuzsbPvzCofDWLBggT3SVH3O20UXXRRzPIynrerf/Zlnnokvv/wyrmOboiiYMGECOnfuDMMwUFlZiYKCAvTo0QN9+/a1R9IS2e9uv/123HzzzXbo7bXXXmtf35SWluK9997DmDFj7BslLXWdGT2/He0xL57zQHOijmETOHDgAN5///2YL3jy5Ml48803YzpJEyZMQF5eHnJyclBSUgJN05CZmWlfVGVmZuLqq69GRkYGBg4ciNGjR8eEAdScsB3dwU3TjHtZAHXG3dc0duzYuN+zobt10XBKIQQ+/vhjvPnmm5g/fz7S09Px8MMP47HHHrNDGKMXMom0XVxcjHfffRcrV67EbbfdVusgG71LF33/efPm4e6778bixYuxZcsW+yD1xBNP2O+ZSPs//fQTPv/8c/vOUGpqKv75z38iNTUV27dvx08//QTTNHHWWWehoqICTzzxBBhjcLvdeOaZZ/DOO+/UOokl0v6uXbvw29/+FoyxmP+iHfTo/nfffffh5ZdfBmPMnpDudrvRtWtXvPDCC3jssccS3v7i4mK89NJLR/zs169fj3//+99QVdWeFzFy5Ej86U9/sg/0wWAQEydObLbv/q233kJ9xo4da+/DibR/8OBBe45b9Pu/6667UFxcDNM07f+id8mb+ndX/W40EDvypaoq+vfvj6ysLPz444949NFHUV5eDs45/vWvf2Hfvn1wOBx4//338eijjyI3NzehtqM+/vhj+9+XXnqpHf0QTXIEyHlP0e1fsmQJ5s6di9zcXKSnp+Pdd99t1LZv3boV27Ztszv3ycnJ6NmzJ5KSkpCTk2OHqJqmifvvvx+MMdx///1QVRXXX389rr76arzwwgsxHdrGbH91Q4cOxZIlS+zv2LIsnH/++fjPf/4T8x0ZhnFU7ei6jtGjR9uJojjneOSRR+x5b9EbDz/88EOz/O78fn9c7a9cuRIvvfSS/ftwOBxYt24dhg8fDiEEQqEQqqqqcO2119bb/uuvv47rrrsOixYtwuLFi9GrV686j9l1hTZaloXnnnsOb731lj2asXLlSvzlL3+B2+22v6eNGzfi+OOPB+ccuq7DNE07LK3mOl188cV48803sX//fjzzzDO44447EAwGEQqFUFFRAafTiYEDByI9PR1dunSpN2wvPT3d/nei+12857Hvv/8ef/nLXzB+/HgsXrzY/vvjjz/GggULcOONN2LAgAEJtb9hw4a49qkRI0ZgxIgROPXUUwHIeXiLFi3Ctm3bACAmSUld7Z9//vn2jbdoO7qu17nvNsd3P2vWLEyYMAEbN25EdnY2TjzxRFxyySVYtWoV5s6di6lTpyIYDOKzzz7De++9FzNXOTqiGU0icv755yMtLa3JjsPR4xXnHIsXL8YNN9wQs+3Lli3DggUL4Ha77Tl1oVAIM2bMsNdL13X8+te/xplnnhnXd29ZFq677rojHtsAOR/w7rvvhtPpRHJyMrp06YJhw4ahW7duMeeoRK8x65tn6vF4cM0119jLf/LJJy12nbl8+fKjPubFex5oTtQxbAJ9+vTBTTfdhMLCQrz11lvYuHEjKisrkZycjMGDB+Oiiy5CTk4OvvvuOzz88MPIz8/Hhg0bYJomKioqcOmll+LWW28FY6zOrE5RNS8qoxezdYVA1rcsIA8sV199NTRNw08//YTjjz/eTjn89NNPIzk5OeH3vPjii+HxeOzsXVE+nw+vvvqqfZdtwYIFdmjlPffcg61bt9rLR38EibTt8/mQl5eHMWPG4N///jd+/vlnBAIBpKSkYMSIEZg/fz4AeTCYM2cOTj/9dBx33HG49dZbccstt+CZZ55BTk5OTGmBRNofOHAgBg4ciD179uD111/H9u3b4fP54PF40K9fP1x00UV26EZ0RC5K0zRMmTIFfr8/5jNLpP3c3NwGOz3VjRw5EhUVFXj99dexYcMGHDp0CIcOHcKjjz6KiooKey5HvO37/f64Pvthw4Zh2LBh8Pl8eOutt7Bu3Tp7VHPQoEG45JJLGpzrcrTffdT8+fPx3Xffwe1229sohLDvdCay7QDQuXNnTJ48GWVlZXjrrbewYcMGVFRUwO12o3///rjwwgtjJps39e+uqKgIV1xxhZ0RtfrvUdM0Oxtu9+7d8fe//91+/oILLsAHH3xgb39Uor/5mqqH01X/91VXXQVA3o3/+OOPMWrUKPz1r3/FqlWrkJ+fj8GDByfc/oABAzBgwAAcPHgQb7zxBjZv3gy/34+UlBQMGzYM9913n/36//mf/4kJlzzttNMwfPhwJCUlxWQRPtrtj/6GCwsL8dprr2Hr1q04cOAAbrrpJgwaNAiXXnop0tPT65zvmkg7Xq8XEyZMgGEYeOedd7B69WqUlJTA4XCgZ8+eOPfcc+1R4ub43SXS/mmnnRYzH+yKK67AH//4RwANZ/mLfp7xHrOjHe6ysjIcOHAABQUF8Pl8uO6663DGGWfY+8Mpp5xSa17e1Vdfjccffxypqan1rktUZWUlJkyYYIdsXnPNNXC73fB4PJgyZQq8Xi+GDh1qL3/bbbfB5/PZHYfoyE7183wi+10i57Hhw4fHzKeNTmE599xzY0IXE2k/kWN5dC5Y9XnE0fWrvt/V1X5ycjLuvffeuNp5++23m+W77969O7Zt24bXXnsNCxYsQH5+Ph5++GFcfvnl9nzGwYMHo6SkBP/617+wbt06+Hw+JCcnY8iQIZg2bRpSUlLs9xs0aFCTHIer//vzzz+v1TE888wzGwzPPZK6vvvoY0c6tgFyn6+qqsI//vEP/PDDD9iwYQO2b9+OTZs2xcz3S/R4W1VVhTfeeAPff/89Kisr4fV6cdxxx8Vk+Wzp68zTTjvtqI55iZwHmpUgTSIUColf/vKXYunSpaKoqEjoui7KysrE559/LiZOnCiKi4tFOBy2n6vJsiwxadKkBtu47LLL4l6feJedNm1ak7znzJkz63x86tSpMX+/8sorYu3atbWWW79+vTh48GCj2t69e7eYOnWq+OGHH4Tf7xdCCFFeXi7ef/99ccUVVwghhMjPzxdvvPFGzOvWrVsnVq5cKYQQYv78+WLFihWNat8wDDFlyhSxfv164ff7hWmaoqqqSnz77bdi0qRJIhAI2MvOnDlThEKhmNfrut5g20dqXwghpkyZInw+X4PLCCHENddcI/7973+LkpISYRiGqKqqEmvWrBG/+tWvxNatWxNuP57PPmr27NniH//4h/0bKCkpqXO5eNtOtP077rhDFBQU1HqPmvtoIu1Hn//8889FVVWVsCxLVFVViZUrV4rzzz9flJWV1fmapvjdmaZZ72suvPDCep+74IILjrptwzCEZVkxj1188cW1louu46JFi0RBQYH49NNPY55fsGCBWLZsWcLtCyF/N1OmTBFffvmlKC8vF7qui8LCQrF06dJa3+mtt95a53fx4Ycf1nk8iqf9+kyaNEm8/fbb9jH/4MGD4s033xRTpkxp0nbmz58v5s+fLw4cOCB0XRd+v19s2LBBXHbZZWL9+vX2cs3xu0uk/ZkzZ4pp06aJq6++WkyfPl1Mnz5dXHzxxeL2229v8P0nT54c9zH7iSeeENOmTRO//e1vxWOPPSZef/11sWTJEvH222+LoqIiMXv27Jj3eeaZZ0RhYaEQQogrr7xSlJaWNrguNT+Tt956S5SXl8c8p+t6rf0uHA7X+T71nS9rtlNTvOexmr9NIUTM/mdZlgiFQsIwjITaj4p3n6prPWquS0Ptx9NOc333H374objlllvE7t27hWVZ4tJLLxUbN24UkydPFpWVlfZyl156qfjwww9FWVlZzDpeddVVDW5jUxyHL7roonpfs2bNGnHDDTeI6dOni2nTpokZM2aI6667rsHjbUNtCRH/se3WW28Vb775pn2dUV5eLj744AMxffr0Rrc9e/Zs8frrr4uioiJhGIaorKwUy5Ytq/M41lLXmVGNPeY15jzQHGjEsImEw2EoioLTTjvNvnOSmpqKU089FS+99BJ8Ph8yMjKQmZlZ5+urZ+2rTyIFU+ta9osvvsBf//pXqKpqj+zt378fV199tX0nz7Is/Pa3v7XrvMTbvqhjyNswjFqPl5SUIBgM1lq2+l3WeLcnKnrnpn///vbd+JSUFIwdO9YO5cnNza016bp6yQhxhEKjDbUfDe3o1auXnejF6/Vi0KBBcLvdMe9rGEatORGqqtpZahvTfvR9j5SOHpB3vIcOHWrvo16v1w7zqKioSLj9eD77qLKyMgwcOND+DaSnp+OUU07BokWLGlzno/3uo6I1sES1u3b1ZdeLt31d11FYWIhBgwbZySiiowYejweVlZX47rvvmuV3F082vM8//xxPP/10TIZaxhhmz55th7j7fD785je/qfMuc31tf/nll1i8eDGSkpLs90xLS8MNN9xgj8IFg0HMmjULv/jFL+wQsmgimKhLL720VrmDeNqPPudwODBmzBj77m9WVhZGjBhhz9eNKigoqPPu65HmbiRapDpaM3HcuHH2aEFOTg7OPvtsvPjii/YckqNtB5BZ7qZMmWInyVBVFUOGDEH//v2xf/9++5jaHL+7RNqvq47stm3bapUMqIlzjosvvjjmsfqO2XVlHnzvvffs33zNbfn+++9x+eWXA5CRHA3tg9VF32f16tV2mGSUqqq1trWupDSJtFNTvOexr7/+GosXL46Z3pKeno5rr73WDp82TRM33HBDnck4jvTdx7tP1TficaSEW9H242mnub77QCCAgQMH2qO7hmGgb9++tebuV1ZWYtiwYfaoY3p6OkaMGIHFixfbyzTFcfjzzz/HwoULY0r87N69G1deeWVMyYqrr74akyZNwh/+8Ac89dRTMVErZWVlmDlzZsw81Lraqksix7ZAIIBhw4bZ1xkpKSkYPXp0rekP8bYNyCRPY8eOtfeFpKQkDBkypM59qaWuM6Mae8xrzHmgOVDHsIkkJSXh/vvvt8MfLMuCoihQVRWXXXZZgyGiUc3dMTzrrLMazATVmPeMKi0txZVXXhlTt8U0Tfj9/pjlhBD44x//iIyMDHsuSjTmfdCgQbjjjjsSbrt///6YMGECbr75ZoTDYfsgqapqTPbDhhypg9BQ+w6HA7fddhvuuOMOBAIBe1K2oii45ZZbYrKCmqaJWbNmxZwwRaRA7KJFi2LmnsTbftS0adNqPSaEwOOPP27vfw899BD++Mc/orKy0j45hcNhjB8/HiNGjEh4+xP57B977DEsWLAADz30ELxer31yu/feexvcrqb67hljuOWWW5CcnByzjxYUFDS6fU3T8MADD+Duu++OyZTGOcdVV12F7t27o3v37s32uzuScePGYdy4cU3edqLvaxgG7rrrLng8Hvt3b1kWQqEQTjrpJNx4440JtQ/I2lEzZszATTfdhHA4DF3XoSgKUlNT8dBDD8Usa1kWbrvtNrhcLjt8NxgMIhgMYuTIkbXCvONpvy7Reaa///3vY17rcDgwZ86ceueYNuY7vuuuu/DnP/8ZL7zwQkxmxuHDh8dknmyO310i7TfWkdo/0jF74sSJAORFYl3vdeutt8acr4LBIHw+H+bMmVPvBWT0fYQQ+PWvfw2XyxUz7wqonaa+LtUzXjbUTqKin8npp59uh5I3xpHab+w+VX0942m/se00xXf/q1/9Ck8//TRmzJgBzjl27tyJm266CTfccENM4pH58+fj4YcfRlVVlf0bUBQF999/v71MUxyHzzzzTJx11lkNhhdWn1Jw55134pFHHkE4HIZhGPact9/97ndHbKsuiRzb7rjjDjz22GP2dUb0v6M55sydOxePPfYYfD6fvT0ul6vOZEKt9TrzSFpLx5CJI90uJyQO4gix04kud6zFW9PraEVHF6sniokeYOKtp3ek942mzY7+dzTvS0hTOFa/r/o0dNxp6XXriCoqKrB9+/ajSsne0t9b9WMsEH891ObU0p8JIa1Ba7zObIpj3rFCHUNCCCGEEEII6eDo1hIhhBBCCCGEdHDUMSSEEEIIIYSQDo46hoQQQgghhBDSwVHHkBBCCInD0KFDcdtttzW4zKJFi7B58+Z6n7/tttsQDofjfpwQQgg5Vij5DCGEEBKHkSNHYs2aNU3yXt9++y3efvttzJ8/v0nejxBCCDlaNGJICCGEEEIIIR0cdQwJIYSQOrzyyisYN24cJk2ahLpmxKIAAAOfSURBVAcffBCAHDVcvXo1rr322phlFy5ciKVLl+LOO+/Ep59+iq1bt+Kqq67CpEmTMHHiRLzyyisAZLHrDz/8EHfddRc+++wzTJw4Ef/9738xbtw4VFRUQNd1PPbYY5g4cSImTpyIp59+2i4IPmnSJHzxxRe48MILce655+Lee++FYRjH9kMhhBDSblHHkBBCCKlh+fLlePPNN/H222/j3XffxZgxY+znRo4ciYKCApSWltqPLVu2DOeee6799/3334+bb74Z7777Lv75z3/GFFweO3YsHnnkEZx99tl47733MHr0aPu5J554AuXl5Vi6dCneeust/Pzzz3juuecAAPv27cPSpUvx6quv4oMPPkBpaSlee+215vwYCCGEdCDUMSSEEEJqePnll3HbbbchNTUVAHDOOefYzzHGcP755+OTTz4BAGzevBl9+vSB2+22l0lLS8PXX3+NsrIyJCcn48orrzxim0IIvP3225gzZw4URYHD4cDcuXPt0Uafz4d77rkHTqcTqqpiwoQJDSa6IYQQQhJBHUNCCCGkht27d6Nfv37239FwzqjJkyfjgw8+AAC89957mDx5cszz8+fPB2MMM2bMwLRp0/Dpp58esU3TNJGamgqPx2M/lp6eDl3XEQ6HkZOTg6ysLPs5TdMolJQQQkiToY4hIYQQUkOnTp2wZ88e++/8/PyY53NzcxEOh1FYWIh169bh5JNPjnk+NTUVt956K9555x3MnTsXc+fOxaFDhxpsU1EU+Hw+BAIB+7HS0lI4nU44HA6oqnr0G0YIIYTUgzqGhBBCSA0XX3wxnnzySfj9fhiGgT//+c/gPPaUOXHiRDzyyCMYOXJkzBxCAPi///s/lJeXAwCSkpLgcDhiRgJVVUVlZSUAGUIKyBDVK664Ao8++igsy0I4HMYDDzyAq6++ujk3lRBCCAEA0O1HQgghpIaJEydi3759uOiii6BpGmbPno0ffvgBFRUV9jITJkzAI488gqVLl9Z6vaqqmD59OizLgqZpePDBB5GUlGQ/f/zxx6O4uBgTJ07EnDlz7MdvuOEGLF68GBdeeCGCwSAmTZqE6dOnN+/GEkIIIaAC94QQQgghhBDS4VEoKSGEEEIIIYR0cNQxJIQQQgghhJAOjjqGhBBCCCGEENLBUceQEEIIIYQQQjo46hgSQgghhBBCSAdHHUNCCCGEEEII6eCoY0gIIYQQQgghHRx1DAkhhBBCCCGkg/t/+46tfl1fQbwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(font_scale = 1) \n",
    "plt.rcParams['font.family'] = 'AppleGothic'\n",
    "ax = sns.lineplot(x='divisition', y = label, hue='Type' , data=train_df)\n",
    "ylabels = ['{:.0f}'.format(x) + '억' for x in ax.get_yticks() / 10000]\n",
    "ax.set_yticklabels(ylabels)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8b7b8e9",
   "metadata": {},
   "source": [
    "## 아파트 오피스텔 별 가격"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "7936369f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/y8/ldvzwhm13037mvvt3p2d9n0m0000gn/T/ipykernel_5241/3413037222.py:5: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_yticklabels(ylabels)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[Text(0, -100000.0, '-10억'),\n",
       " Text(0, 0.0, '0억'),\n",
       " Text(0, 100000.0, '10억'),\n",
       " Text(0, 200000.0, '20억'),\n",
       " Text(0, 300000.0, '30억'),\n",
       " Text(0, 400000.0, '40억'),\n",
       " Text(0, 500000.0, '50억')]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(font_scale = 1) \n",
    "plt.rcParams['font.family'] = 'AppleGothic'\n",
    "ax = sns.violinplot(x='Type', y = label, data=train_df)\n",
    "ylabels = ['{:.0f}'.format(x) + '억' for x in ax.get_yticks() / 10000]\n",
    "ax.set_yticklabels(ylabels)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "521e1dd4",
   "metadata": {},
   "source": [
    "# Label Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "e2a354f1",
   "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>Type</th>\n",
       "      <th>Dir</th>\n",
       "      <th>Old</th>\n",
       "      <th>sector</th>\n",
       "      <th>divisition</th>\n",
       "      <th>representativeArea</th>\n",
       "      <th>floorAreaRatio</th>\n",
       "      <th>BUS</th>\n",
       "      <th>METRO</th>\n",
       "      <th>INFANT</th>\n",
       "      <th>PRESCHOOL</th>\n",
       "      <th>HOSPITAL</th>\n",
       "      <th>PARKING</th>\n",
       "      <th>MART</th>\n",
       "      <th>CONVENIENCE</th>\n",
       "      <th>BANK</th>\n",
       "      <th>OFFICE</th>\n",
       "      <th>PRI_SCHOOL</th>\n",
       "      <th>PUB_SCHOOL</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2002</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>0.156388</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>92500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2021</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>0.030837</td>\n",
       "      <td>0.199359</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>34</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>30000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2006</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>0.367841</td>\n",
       "      <td>0.436349</td>\n",
       "      <td>18</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>33</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>159000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2018</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>0.209251</td>\n",
       "      <td>0.228183</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>306750.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2001</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>0.277533</td>\n",
       "      <td>0.239392</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>24</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>227500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16316</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2013</td>\n",
       "      <td>312</td>\n",
       "      <td>23</td>\n",
       "      <td>0.061674</td>\n",
       "      <td>0.661329</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>32</td>\n",
       "      <td>5</td>\n",
       "      <td>40</td>\n",
       "      <td>23</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>16250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16317</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2014</td>\n",
       "      <td>312</td>\n",
       "      <td>23</td>\n",
       "      <td>0.325991</td>\n",
       "      <td>0.557246</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>43</td>\n",
       "      <td>2</td>\n",
       "      <td>41</td>\n",
       "      <td>25</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>202500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16318</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2016</td>\n",
       "      <td>312</td>\n",
       "      <td>23</td>\n",
       "      <td>0.030837</td>\n",
       "      <td>0.639712</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>26</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>25500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16319</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2016</td>\n",
       "      <td>312</td>\n",
       "      <td>23</td>\n",
       "      <td>0.048458</td>\n",
       "      <td>0.639712</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>26</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>22000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16320</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2016</td>\n",
       "      <td>312</td>\n",
       "      <td>23</td>\n",
       "      <td>0.030837</td>\n",
       "      <td>0.639712</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>26</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>25500.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16321 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Type  Dir   Old  sector  divisition  representativeArea  \\\n",
       "0         0    0  2002       0          17            0.156388   \n",
       "1         1    0  2021       0          17            0.030837   \n",
       "2         0    0  2006       0          17            0.367841   \n",
       "3         0    0  2018       0          17            0.209251   \n",
       "4         0    0  2001       0          17            0.277533   \n",
       "...     ...  ...   ...     ...         ...                 ...   \n",
       "16316     1    4  2013     312          23            0.061674   \n",
       "16317     0    1  2014     312          23            0.325991   \n",
       "16318     0    3  2016     312          23            0.030837   \n",
       "16319     1    3  2016     312          23            0.048458   \n",
       "16320     0    5  2016     312          23            0.030837   \n",
       "\n",
       "       floorAreaRatio  BUS  METRO  INFANT  PRESCHOOL  HOSPITAL  PARKING  MART  \\\n",
       "0            0.000000   14      2       7          1         0       14     2   \n",
       "1            0.199359   17      1      13          0         1       11     4   \n",
       "2            0.436349   18      3       8          1         1       14     2   \n",
       "3            0.228183    9      1       9          0         0        8     1   \n",
       "4            0.239392   15      1       9          1         0        7     3   \n",
       "...               ...  ...    ...     ...        ...       ...      ...   ...   \n",
       "16316        0.661329   15      2       6          2         2       32     5   \n",
       "16317        0.557246   16      2       6          2         2       43     2   \n",
       "16318        0.639712   15      2       4          1         2       48     0   \n",
       "16319        0.639712   15      2       4          1         2       48     0   \n",
       "16320        0.639712   15      2       4          1         2       48     0   \n",
       "\n",
       "       CONVENIENCE  BANK  OFFICE  PRI_SCHOOL  PUB_SCHOOL     price  \n",
       "0               20    12       3           0           1   92500.0  \n",
       "1               34    13       2           0           1   30000.0  \n",
       "2               33    20       1           0           1  159000.0  \n",
       "3                9     9       1           0           1  306750.0  \n",
       "4               24    18       1           0           2  227500.0  \n",
       "...            ...   ...     ...         ...         ...       ...  \n",
       "16316           40    23      13           0           1   16250.0  \n",
       "16317           41    25      13           0           1  202500.0  \n",
       "16318           38    26      12           0           1   25500.0  \n",
       "16319           38    26      12           0           1   22000.0  \n",
       "16320           38    26      12           0           1   25500.0  \n",
       "\n",
       "[16321 rows x 20 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "def labelencoder(df):                               \n",
    "    encoders = {}\n",
    "    i = 0\n",
    "    for c in df.columns:     \n",
    "        encoders[i] = None\n",
    "        if df[c].dtype=='object': \n",
    "            df[c] = df[c].fillna('N')\n",
    "            lbl = LabelEncoder()\n",
    "            lbl.fit(list(df[c].values))\n",
    "            df[c] = lbl.transform(df[c].values)\n",
    "            encoders[i] = lbl\n",
    "        i += 1\n",
    "    return df, encoders\n",
    "\n",
    "train_lbl_df, encoder_lbl = labelencoder(train_df.copy())\n",
    "\n",
    "train_lbl_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4020c7d",
   "metadata": {},
   "source": [
    "# One-Hot Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "d276eb6e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 16321 entries, 0 to 16320\n",
      "Columns: 419 entries, Type_APT to price\n",
      "dtypes: float64(3), int64(416)\n",
      "memory usage: 52.2 MB\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "one_hot_col = ['Type', 'Dir', 'Old','sector', 'divisition']\n",
    "\n",
    "def encode_one_hot(df, col_name):\n",
    "    ohe = OneHotEncoder(sparse=False)\n",
    "    trained = ohe.fit_transform(df[[col_name]].copy())\n",
    "    res_df = pd.DataFrame(trained, columns=['%s_%s' % (col_name, col) for col in ohe.categories_[0]]).astype(int)\n",
    "    res_df.reset_index(drop=True, inplace=True)\n",
    "    return ohe, res_df\n",
    "    \n",
    "one_hot_df = train_df[one_hot_col]    \n",
    "\n",
    "temp_df = train_df.drop(labels=one_hot_col, axis=1)\n",
    "temp_df.reset_index(drop=True, inplace=True)\n",
    "\n",
    "encoders_hot = []\n",
    "encoded_dfs = []\n",
    "\n",
    "for col in one_hot_col:\n",
    "    ohe, p_df = encode_one_hot(one_hot_df, col)\n",
    "    encoded_dfs.append(p_df)\n",
    "    encoders_hot.append(ohe)\n",
    "    \n",
    "encoded_dfs.append(temp_df)\n",
    "\n",
    "train_df = pd.concat(encoded_dfs, axis=1)\n",
    "\n",
    "train_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "8e50ce5f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Type_APT</th>\n",
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       "      <th>Dir_EE</th>\n",
       "      <th>Dir_EN</th>\n",
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       "      <th>...</th>\n",
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       "      <th>MART</th>\n",
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       "      <th>OFFICE</th>\n",
       "      <th>PRI_SCHOOL</th>\n",
       "      <th>PUB_SCHOOL</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>0</td>\n",
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       "      <td>11</td>\n",
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       "      <td>2</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>33</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>159000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>8</td>\n",
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       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>306750.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>24</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>227500.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 419 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Type_APT  Type_OPST  Dir_EE  Dir_EN  Dir_ES  Dir_NN  Dir_SS  Dir_WN  \\\n",
       "0         1          0       1       0       0       0       0       0   \n",
       "1         0          1       1       0       0       0       0       0   \n",
       "2         1          0       1       0       0       0       0       0   \n",
       "3         1          0       1       0       0       0       0       0   \n",
       "4         1          0       1       0       0       0       0       0   \n",
       "\n",
       "   Dir_WS  Dir_WW  ...  PRESCHOOL  HOSPITAL  PARKING  MART  CONVENIENCE  BANK  \\\n",
       "0       0       0  ...          1         0       14     2           20    12   \n",
       "1       0       0  ...          0         1       11     4           34    13   \n",
       "2       0       0  ...          1         1       14     2           33    20   \n",
       "3       0       0  ...          0         0        8     1            9     9   \n",
       "4       0       0  ...          1         0        7     3           24    18   \n",
       "\n",
       "   OFFICE  PRI_SCHOOL  PUB_SCHOOL     price  \n",
       "0       3           0           1   92500.0  \n",
       "1       2           0           1   30000.0  \n",
       "2       1           0           1  159000.0  \n",
       "3       1           0           1  306750.0  \n",
       "4       1           0           2  227500.0  \n",
       "\n",
       "[5 rows x 419 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "75d09baa",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>16321.000000</td>\n",
       "      <td>16321.000000</td>\n",
       "      <td>16321.000000</td>\n",
       "      <td>16321.000000</td>\n",
       "      <td>16321.000000</td>\n",
       "      <td>16321.000000</td>\n",
       "      <td>16321.000000</td>\n",
       "      <td>16321.000000</td>\n",
       "      <td>16321.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.702040</td>\n",
       "      <td>0.297960</td>\n",
       "      <td>0.160468</td>\n",
       "      <td>0.054715</td>\n",
       "      <td>0.183812</td>\n",
       "      <td>0.048036</td>\n",
       "      <td>0.261320</td>\n",
       "      <td>0.045647</td>\n",
       "      <td>0.157037</td>\n",
       "      <td>0.088965</td>\n",
       "      <td>...</td>\n",
       "      <td>1.500705</td>\n",
       "      <td>0.591447</td>\n",
       "      <td>12.201581</td>\n",
       "      <td>1.992831</td>\n",
       "      <td>21.203480</td>\n",
       "      <td>8.060168</td>\n",
       "      <td>3.277985</td>\n",
       "      <td>0.249556</td>\n",
       "      <td>0.783163</td>\n",
       "      <td>97388.773666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.457376</td>\n",
       "      <td>0.457376</td>\n",
       "      <td>0.367051</td>\n",
       "      <td>0.227430</td>\n",
       "      <td>0.387343</td>\n",
       "      <td>0.213849</td>\n",
       "      <td>0.439367</td>\n",
       "      <td>0.208724</td>\n",
       "      <td>0.363847</td>\n",
       "      <td>0.284702</td>\n",
       "      <td>...</td>\n",
       "      <td>1.274184</td>\n",
       "      <td>1.744455</td>\n",
       "      <td>13.865183</td>\n",
       "      <td>1.559486</td>\n",
       "      <td>12.115754</td>\n",
       "      <td>6.825723</td>\n",
       "      <td>2.627202</td>\n",
       "      <td>0.504305</td>\n",
       "      <td>0.734881</td>\n",
       "      <td>78832.002092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6050.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>33250.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>80000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>135000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>129.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>450000.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 419 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Type_APT     Type_OPST        Dir_EE        Dir_EN        Dir_ES  \\\n",
       "count  16321.000000  16321.000000  16321.000000  16321.000000  16321.000000   \n",
       "mean       0.702040      0.297960      0.160468      0.054715      0.183812   \n",
       "std        0.457376      0.457376      0.367051      0.227430      0.387343   \n",
       "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "50%        1.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "75%        1.000000      1.000000      0.000000      0.000000      0.000000   \n",
       "max        1.000000      1.000000      1.000000      1.000000      1.000000   \n",
       "\n",
       "             Dir_NN        Dir_SS        Dir_WN        Dir_WS        Dir_WW  \\\n",
       "count  16321.000000  16321.000000  16321.000000  16321.000000  16321.000000   \n",
       "mean       0.048036      0.261320      0.045647      0.157037      0.088965   \n",
       "std        0.213849      0.439367      0.208724      0.363847      0.284702   \n",
       "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "50%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "75%        0.000000      1.000000      0.000000      0.000000      0.000000   \n",
       "max        1.000000      1.000000      1.000000      1.000000      1.000000   \n",
       "\n",
       "       ...     PRESCHOOL      HOSPITAL       PARKING          MART  \\\n",
       "count  ...  16321.000000  16321.000000  16321.000000  16321.000000   \n",
       "mean   ...      1.500705      0.591447     12.201581      1.992831   \n",
       "std    ...      1.274184      1.744455     13.865183      1.559486   \n",
       "min    ...      0.000000      0.000000      0.000000      0.000000   \n",
       "25%    ...      1.000000      0.000000      4.000000      1.000000   \n",
       "50%    ...      1.000000      0.000000      8.000000      2.000000   \n",
       "75%    ...      2.000000      1.000000     16.000000      3.000000   \n",
       "max    ...      8.000000     30.000000    129.000000     10.000000   \n",
       "\n",
       "        CONVENIENCE          BANK        OFFICE    PRI_SCHOOL    PUB_SCHOOL  \\\n",
       "count  16321.000000  16321.000000  16321.000000  16321.000000  16321.000000   \n",
       "mean      21.203480      8.060168      3.277985      0.249556      0.783163   \n",
       "std       12.115754      6.825723      2.627202      0.504305      0.734881   \n",
       "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%       13.000000      4.000000      1.000000      0.000000      0.000000   \n",
       "50%       19.000000      7.000000      3.000000      0.000000      1.000000   \n",
       "75%       28.000000     10.000000      4.000000      0.000000      1.000000   \n",
       "max       80.000000     91.000000     20.000000      3.000000      4.000000   \n",
       "\n",
       "               price  \n",
       "count   16321.000000  \n",
       "mean    97388.773666  \n",
       "std     78832.002092  \n",
       "min      6050.000000  \n",
       "25%     33250.000000  \n",
       "50%     80000.000000  \n",
       "75%    135000.000000  \n",
       "max    450000.000000  \n",
       "\n",
       "[8 rows x 419 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e65e47f",
   "metadata": {},
   "source": [
    "# To Binominal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "d711b8e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Type_APT</th>\n",
       "      <th>Type_OPST</th>\n",
       "      <th>Dir_EE</th>\n",
       "      <th>Dir_EN</th>\n",
       "      <th>Dir_ES</th>\n",
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       "      <th>Dir_WN</th>\n",
       "      <th>Dir_WS</th>\n",
       "      <th>Dir_WW</th>\n",
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       "      <th>OFFICE</th>\n",
       "      <th>PRI_SCHOOL</th>\n",
       "      <th>PUB_SCHOOL</th>\n",
       "      <th>price</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>1</td>\n",
       "      <td>92500.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>30000.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>159000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>306750.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>227500.0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>16316</th>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>16250.0</td>\n",
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       "    <tr>\n",
       "      <th>16317</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>202500.0</td>\n",
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       "    <tr>\n",
       "      <th>16318</th>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>25500.0</td>\n",
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       "    <tr>\n",
       "      <th>16319</th>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>22000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16320</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>25500.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16321 rows × 419 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Type_APT  Type_OPST  Dir_EE  Dir_EN  Dir_ES  Dir_NN  Dir_SS  Dir_WN  \\\n",
       "0             1          0       1       0       0       0       0       0   \n",
       "1             0          1       1       0       0       0       0       0   \n",
       "2             1          0       1       0       0       0       0       0   \n",
       "3             1          0       1       0       0       0       0       0   \n",
       "4             1          0       1       0       0       0       0       0   \n",
       "...         ...        ...     ...     ...     ...     ...     ...     ...   \n",
       "16316         0          1       0       0       0       0       1       0   \n",
       "16317         1          0       0       1       0       0       0       0   \n",
       "16318         1          0       0       0       0       1       0       0   \n",
       "16319         0          1       0       0       0       1       0       0   \n",
       "16320         1          0       0       0       0       0       0       1   \n",
       "\n",
       "       Dir_WS  Dir_WW  ...  PRESCHOOL  HOSPITAL  PARKING  MART  CONVENIENCE  \\\n",
       "0           0       0  ...          1         0        1     1            1   \n",
       "1           0       0  ...          0         1        1     1            1   \n",
       "2           0       0  ...          1         1        1     1            1   \n",
       "3           0       0  ...          0         0        1     1            1   \n",
       "4           0       0  ...          1         0        1     1            1   \n",
       "...       ...     ...  ...        ...       ...      ...   ...          ...   \n",
       "16316       0       0  ...          1         1        1     1            1   \n",
       "16317       0       0  ...          1         1        1     1            1   \n",
       "16318       0       0  ...          1         1        1     0            1   \n",
       "16319       0       0  ...          1         1        1     0            1   \n",
       "16320       0       0  ...          1         1        1     0            1   \n",
       "\n",
       "       BANK  OFFICE  PRI_SCHOOL  PUB_SCHOOL     price  \n",
       "0         1       1           0           1   92500.0  \n",
       "1         1       1           0           1   30000.0  \n",
       "2         1       1           0           1  159000.0  \n",
       "3         1       1           0           1  306750.0  \n",
       "4         1       1           0           1  227500.0  \n",
       "...     ...     ...         ...         ...       ...  \n",
       "16316     1       1           0           1   16250.0  \n",
       "16317     1       1           0           1  202500.0  \n",
       "16318     1       1           0           1   25500.0  \n",
       "16319     1       1           0           1   22000.0  \n",
       "16320     1       1           0           1   25500.0  \n",
       "\n",
       "[16321 rows x 419 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "binomial_header = ['BUS', 'METRO', 'INFANT', 'PRESCHOOL', 'HOSPITAL', 'PARKING', 'MART', 'CONVENIENCE', 'BANK', 'OFFICE', 'PRI_SCHOOL', 'PUB_SCHOOL']\n",
    "\n",
    "for head in binomial_header:\n",
    "    train_df.loc[:,head] = train_df[head].apply(lambda x : 1 if x > 0 else 0)\n",
    "\n",
    "train_df\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f3b1936",
   "metadata": {},
   "source": [
    "# K-Fold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "016c7a89",
   "metadata": {
    "scrolled": true
   },
   "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>Type_APT</th>\n",
       "      <th>Type_OPST</th>\n",
       "      <th>Dir_EE</th>\n",
       "      <th>Dir_EN</th>\n",
       "      <th>Dir_ES</th>\n",
       "      <th>Dir_NN</th>\n",
       "      <th>Dir_SS</th>\n",
       "      <th>Dir_WN</th>\n",
       "      <th>Dir_WS</th>\n",
       "      <th>Dir_WW</th>\n",
       "      <th>...</th>\n",
       "      <th>INFANT</th>\n",
       "      <th>PRESCHOOL</th>\n",
       "      <th>HOSPITAL</th>\n",
       "      <th>PARKING</th>\n",
       "      <th>MART</th>\n",
       "      <th>CONVENIENCE</th>\n",
       "      <th>BANK</th>\n",
       "      <th>OFFICE</th>\n",
       "      <th>PRI_SCHOOL</th>\n",
       "      <th>PUB_SCHOOL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 418 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Type_APT  Type_OPST  Dir_EE  Dir_EN  Dir_ES  Dir_NN  Dir_SS  Dir_WN  \\\n",
       "0         1          0       1       0       0       0       0       0   \n",
       "1         0          1       1       0       0       0       0       0   \n",
       "2         1          0       1       0       0       0       0       0   \n",
       "3         1          0       1       0       0       0       0       0   \n",
       "4         1          0       1       0       0       0       0       0   \n",
       "\n",
       "   Dir_WS  Dir_WW  ...  INFANT  PRESCHOOL  HOSPITAL  PARKING  MART  \\\n",
       "0       0       0  ...       1          1         0        1     1   \n",
       "1       0       0  ...       1          0         1        1     1   \n",
       "2       0       0  ...       1          1         1        1     1   \n",
       "3       0       0  ...       1          0         0        1     1   \n",
       "4       0       0  ...       1          1         0        1     1   \n",
       "\n",
       "   CONVENIENCE  BANK  OFFICE  PRI_SCHOOL  PUB_SCHOOL  \n",
       "0            1     1       1           0           1  \n",
       "1            1     1       1           0           1  \n",
       "2            1     1       1           0           1  \n",
       "3            1     1       1           0           1  \n",
       "4            1     1       1           0           1  \n",
       "\n",
       "[5 rows x 418 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import train_test_split\n",
    "from tqdm.notebook import tqdm\n",
    "\n",
    "def get_rsme_score_fold(model, kf, X, y):\n",
    "    X = X.values\n",
    "    rsme_history = []\n",
    "    for train_index, test_index in tqdm(kf.split(X)):\n",
    "\n",
    "        X_train, X_test = X[train_index], X[test_index]\n",
    "        y_train, y_test = y[train_index], y[test_index]\n",
    "\n",
    "        model.fit(X_train, y_train)\n",
    "\n",
    "        y_pred = model.predict(X_test)\n",
    "        rsme_history.append(np.sqrt(metrics.mean_squared_error(y_test, y_pred)))\n",
    "    return rsme_history\n",
    "\n",
    "def get_rsme_score_random(model, X, Y, count = 5):\n",
    "    rsme_history = []\n",
    "    for i in range(count):\n",
    "        x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.2)\n",
    "        model.fit(x_train, y_train)\n",
    "        pred = model.predict(x_test)\n",
    "        rsme_history.append(np.sqrt(metrics.mean_squared_error(y_test, pred)))\n",
    "    return rsme_history\n",
    "    \n",
    "                            \n",
    "                            \n",
    "X = train_df.drop(label, axis=1)\n",
    "Y = train_df[label]\n",
    "\n",
    "lbl_X = train_lbl_df.drop(label, axis=1)\n",
    "lbl_Y = train_lbl_df[label]\n",
    "\n",
    "kf = KFold(n_splits = 5, shuffle = True, random_state = 15)\n",
    "\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a126791d",
   "metadata": {},
   "source": [
    "## Linear - OneHot x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "7b04ca3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a4dd1bc7c1f34ae7b3e49669fb4c4b51",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 RMSE: 47049.5501385541 47602.468963256375\n",
      "1 RMSE: 49635.49247349249 48287.66381975599\n",
      "2 RMSE: 48920.8320684698 49183.75305155174\n",
      "3 RMSE: 50290.71811506801 48001.23853399174\n",
      "4 RMSE: 49563.21183970679 49401.506224120014\n",
      "AVG: 49091.96092705823 48495.32611853517\n",
      "MIN: 47049.5501385541 47602.468963256375\n",
      "MAX: 50290.71811506801 49401.506224120014\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "rmse_history = get_rsme_score_fold(LinearRegression(), kf, lbl_X, lbl_Y)\n",
    "\n",
    "rmse_history_r = get_rsme_score_random(LinearRegression(), lbl_X, lbl_Y)\n",
    "\n",
    "for i, v in enumerate(rmse_history):\n",
    "    print(i, 'RMSE:', v, rmse_history_r[i])\n",
    "print('AVG:', np.mean(rmse_history), np.mean(rmse_history_r))\n",
    "print('MIN:', np.min(rmse_history), np.min(rmse_history_r))\n",
    "print('MAX:', np.max(rmse_history), np.max(rmse_history_r))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb5fa20e",
   "metadata": {},
   "source": [
    "## Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "e1220e8f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "704a3adbe0864427aa9bfe485f5673cd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 RMSE: 15822.093964692698 13722.87340050579\n",
      "1 RMSE: 16783.73893891826 17374.500821183286\n",
      "2 RMSE: 13486.522440222952 13750.78426481183\n",
      "3 RMSE: 16249.279883346475 14902.05404144715\n",
      "4 RMSE: 16359.911779861557 16170.338072402938\n",
      "AVG: 15740.309401408387 15184.1101200702\n",
      "MIN: 13486.522440222952 13722.87340050579\n",
      "MAX: 16783.73893891826 17374.500821183286\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "rmse_history = get_rsme_score_fold(DecisionTreeRegressor(random_state = 15), kf, X, Y)\n",
    "\n",
    "rmse_history_r = get_rsme_score_random(DecisionTreeRegressor(random_state = 15), X, Y)\n",
    "\n",
    "for i, v in enumerate(rmse_history):\n",
    "    print(i, 'RMSE:', v, rmse_history_r[i])\n",
    "print('AVG:', np.mean(rmse_history), np.mean(rmse_history_r))\n",
    "print('MIN:', np.min(rmse_history), np.min(rmse_history_r))\n",
    "print('MAX:', np.max(rmse_history), np.max(rmse_history_r))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0faf2050",
   "metadata": {},
   "source": [
    "## Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "8dfa09d5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "12e8f7a4a21f43339b37628bf1c7e90c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 RMSE: 12613.106201620598 12812.183162161278\n",
      "1 RMSE: 14276.675708757251 14265.655395250473\n",
      "2 RMSE: 12639.414467432354 13914.323060610248\n",
      "3 RMSE: 13531.32857800177 15009.719940952327\n",
      "4 RMSE: 13366.839213717032 13768.991130719833\n",
      "AVG: 13285.472833905802 13954.174537938834\n",
      "MIN: 12613.106201620598 12812.183162161278\n",
      "MAX: 14276.675708757251 15009.719940952327\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "rmse_history = get_rsme_score_fold(RandomForestRegressor(n_estimators = 30, random_state = 15), kf, X, Y)\n",
    "\n",
    "rmse_history_r = get_rsme_score_random(RandomForestRegressor(n_estimators = 30, random_state = 15), X, Y)\n",
    "\n",
    "for i, v in enumerate(rmse_history):\n",
    "    print(i, 'RMSE:', v, rmse_history_r[i])\n",
    "print('AVG:', np.mean(rmse_history), np.mean(rmse_history_r))\n",
    "print('MIN:', np.min(rmse_history), np.min(rmse_history_r))\n",
    "print('MAX:', np.max(rmse_history), np.max(rmse_history_r))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28bc4d66",
   "metadata": {},
   "source": [
    "## Predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "b84abfe1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def encode_lbl(value, encoder, delta = 0):\n",
    "    for i in range(len(value)):\n",
    "        if i + delta < 0 : continue\n",
    "        \n",
    "        if encoder[i + delta] != None:\n",
    "            value[i] = encoder[i + delta].transform([value[i]])[0]\n",
    "    return value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "d1a8bc30",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 13653.034644165044\n"
     ]
    }
   ],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.2)\n",
    "\n",
    "model = RandomForestRegressor(n_estimators = 30, random_state = 15)\n",
    "\n",
    "model.fit(x_train, y_train)\n",
    "\n",
    "pred = model.predict(x_test)\n",
    "\n",
    "print('RMSE:', np.sqrt(metrics.mean_squared_error(y_test, pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84d0a625",
   "metadata": {},
   "source": [
    "## Important"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "a3773bf7",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "representativeArea 0.5523\n",
      "divisition_강남구 0.0862\n",
      "divisition_서초구 0.0422\n",
      "floorAreaRatio 0.0316\n",
      "Type_APT 0.0247\n",
      "Type_OPST 0.0206\n",
      "divisition_용산구 0.0172\n",
      "sector_여의도동 0.0108\n",
      "divisition_송파구 0.0085\n",
      "Old_2018 0.0076\n",
      "divisition_성동구 0.0070\n",
      "divisition_마포구 0.0067\n",
      "sector_잠원동 0.0052\n",
      "sector_한강로2가 0.0049\n",
      "Old_2020 0.0046\n",
      "PUB_SCHOOL 0.0043\n",
      "METRO 0.0043\n",
      "divisition_광진구 0.0042\n",
      "sector_잠실동 0.0040\n",
      "sector_청담동 0.0038\n",
      "sector_한남동 0.0036\n",
      "HOSPITAL 0.0035\n",
      "divisition_동작구 0.0035\n",
      "sector_압구정동 0.0035\n",
      "PRI_SCHOOL 0.0035\n",
      "Old_2021 0.0034\n",
      "Old_2015 0.0032\n",
      "Old_2017 0.0032\n",
      "sector_논현동 0.0030\n",
      "Old_2012 0.0030\n",
      "sector_신정동 0.0030\n",
      "Old_2006 0.0030\n",
      "MART 0.0029\n",
      "Old_2004 0.0028\n",
      "sector_목동 0.0027\n",
      "Old_2008 0.0026\n",
      "Old_2014 0.0026\n",
      "sector_신천동 0.0024\n",
      "Old_2003 0.0024\n",
      "PRESCHOOL 0.0023\n",
      "OFFICE 0.0023\n",
      "divisition_영등포구 0.0022\n",
      "sector_대치동 0.0021\n",
      "Old_2013 0.0020\n",
      "sector_흑석동 0.0020\n",
      "Old_2019 0.0020\n",
      "Old_1987 0.0019\n",
      "Old_2022 0.0019\n",
      "Old_2016 0.0019\n",
      "sector_이촌동 0.0017\n",
      "divisition_중구 0.0017\n",
      "Old_2007 0.0016\n",
      "sector_자양동 0.0015\n",
      "sector_개포동 0.0015\n",
      "divisition_강동구 0.0015\n",
      "Old_2011 0.0013\n",
      "Old_2010 0.0012\n",
      "Old_2009 0.0012\n",
      "divisition_동대문구 0.0011\n",
      "INFANT 0.0011\n",
      "Old_1998 0.0011\n",
      "Old_1986 0.0011\n",
      "PARKING 0.0010\n",
      "Old_2002 0.0010\n",
      "Old_1988 0.0010\n",
      "sector_삼성동 0.0010\n",
      "sector_자곡동 0.0010\n",
      "Old_1984 0.0010\n",
      "sector_역삼동 0.0009\n",
      "Old_2005 0.0009\n",
      "sector_반포동 0.0009\n",
      "Old_1997 0.0009\n",
      "sector_길음동 0.0009\n",
      "Old_1999 0.0008\n",
      "Old_2001 0.0008\n",
      "Old_1978 0.0008\n",
      "sector_석촌동 0.0008\n",
      "sector_신도림동 0.0007\n",
      "divisition_은평구 0.0007\n",
      "Old_1996 0.0007\n",
      "sector_광장동 0.0007\n",
      "sector_공덕동 0.0007\n",
      "BANK 0.0007\n",
      "sector_염창동 0.0007\n",
      "Old_2000 0.0006\n",
      "Old_1982 0.0006\n",
      "sector_옥수동 0.0006\n",
      "Old_1975 0.0006\n",
      "divisition_구로구 0.0006\n",
      "sector_서초동 0.0006\n",
      "Old_1992 0.0006\n",
      "sector_성수동1가 0.0006\n",
      "sector_성산동 0.0005\n",
      "sector_이태원동 0.0005\n",
      "sector_금호동4가 0.0005\n",
      "sector_세곡동 0.0005\n",
      "sector_방배동 0.0005\n",
      "sector_고덕동 0.0005\n",
      "sector_문정동 0.0005\n",
      "divisition_서대문구 0.0005\n",
      "divisition_양천구 0.0005\n",
      "sector_상암동 0.0004\n",
      "Old_1983 0.0004\n",
      "sector_길동 0.0004\n",
      "sector_당산동5가 0.0004\n",
      "sector_도곡동 0.0004\n",
      "Dir_SS 0.0004\n",
      "divisition_성북구 0.0004\n",
      "sector_일원동 0.0004\n",
      "sector_순화동 0.0004\n",
      "sector_남가좌동 0.0004\n",
      "Old_1994 0.0004\n",
      "divisition_종로구 0.0004\n",
      "divisition_도봉구 0.0004\n",
      "divisition_강서구 0.0004\n",
      "sector_신월동 0.0004\n",
      "sector_원효로1가 0.0004\n",
      "Old_1995 0.0003\n",
      "Old_1974 0.0003\n",
      "sector_성수동2가 0.0003\n",
      "sector_북아현동 0.0003\n",
      "sector_현석동 0.0003\n",
      "sector_화곡동 0.0003\n",
      "Dir_WW 0.0003\n",
      "divisition_강북구 0.0003\n",
      "Dir_WS 0.0003\n",
      "sector_마천동 0.0003\n",
      "sector_한강로3가 0.0003\n",
      "sector_명일동 0.0003\n",
      "Dir_EE 0.0003\n",
      "divisition_금천구 0.0003\n",
      "Dir_ES 0.0003\n",
      "Old_1993 0.0003\n",
      "sector_동작동 0.0002\n",
      "sector_양재동 0.0002\n",
      "sector_상일동 0.0002\n",
      "sector_가양동 0.0002\n",
      "sector_명륜2가 0.0002\n",
      "divisition_중랑구 0.0002\n",
      "sector_천호동 0.0002\n",
      "sector_가락동 0.0002\n",
      "sector_송파동 0.0002\n",
      "sector_망원동 0.0002\n",
      "sector_풍납동 0.0002\n",
      "Dir_NN 0.0002\n",
      "sector_내발산동 0.0002\n",
      "sector_염리동 0.0002\n",
      "sector_전농동 0.0002\n",
      "Old_1985 0.0002\n",
      "sector_사당동 0.0002\n",
      "sector_방화동 0.0002\n",
      "sector_가리봉동 0.0002\n",
      "sector_중계동 0.0002\n",
      "Old_1991 0.0002\n",
      "sector_대림동 0.0002\n",
      "sector_용강동 0.0002\n",
      "sector_행당동 0.0002\n",
      "sector_상도동 0.0002\n",
      "sector_북가좌동 0.0002\n",
      "sector_장안동 0.0002\n",
      "sector_도화동 0.0002\n",
      "sector_본동 0.0001\n",
      "sector_암사동 0.0001\n",
      "sector_신공덕동 0.0001\n",
      "sector_구산동 0.0001\n",
      "Old_1981 0.0001\n",
      "sector_장지동 0.0001\n",
      "sector_구로동 0.0001\n",
      "sector_남현동 0.0001\n",
      "Dir_WN 0.0001\n",
      "divisition_노원구 0.0001\n",
      "divisition_관악구 0.0001\n",
      "sector_시흥동 0.0001\n",
      "sector_보문동1가 0.0001\n",
      "Old_1989 0.0001\n",
      "sector_방이동 0.0001\n",
      "sector_충무로4가 0.0001\n",
      "sector_홍파동 0.0001\n",
      "sector_하중동 0.0001\n",
      "sector_답십리동 0.0001\n",
      "sector_응암동 0.0001\n",
      "sector_창동 0.0001\n",
      "sector_구의동 0.0001\n",
      "sector_문배동 0.0001\n",
      "sector_청량리동 0.0001\n",
      "sector_신사동 0.0001\n",
      "sector_오류동 0.0001\n",
      "sector_당산동4가 0.0001\n",
      "sector_봉천동 0.0001\n",
      "Dir_EN 0.0001\n",
      "sector_항동 0.0001\n",
      "sector_황학동 0.0001\n",
      "sector_증산동 0.0001\n",
      "sector_보광동 0.0001\n",
      "sector_도림동 0.0001\n",
      "sector_성내동 0.0001\n",
      "Old_1977 0.0001\n",
      "sector_하월곡동 0.0001\n",
      "sector_녹번동 0.0001\n",
      "sector_서빙고동 0.0001\n",
      "sector_홍은동 0.0001\n",
      "sector_상수동 0.0001\n",
      "Old_1990 0.0001\n",
      "sector_등촌동 0.0001\n",
      "sector_중동 0.0001\n",
      "sector_한강로1가 0.0001\n",
      "sector_쌍문동 0.0001\n",
      "sector_문래동3가 0.0001\n",
      "sector_마곡동 0.0001\n",
      "sector_불광동 0.0001\n",
      "sector_사근동 0.0001\n",
      "sector_개봉동 0.0001\n",
      "sector_합정동 0.0001\n",
      "sector_독산동 0.0001\n",
      "sector_거여동 0.0001\n",
      "sector_영등포동8가 0.0001\n",
      "sector_아현동 0.0001\n",
      "sector_오금동 0.0001\n",
      "sector_내수동 0.0001\n",
      "sector_신수동 0.0001\n",
      "sector_장충동1가 0.0001\n",
      "sector_월계동 0.0001\n",
      "sector_홍제동 0.0001\n",
      "sector_삼전동 0.0001\n",
      "sector_구수동 0.0001\n",
      "sector_신길동 0.0001\n",
      "sector_신당동 0.0001\n",
      "sector_방학동 0.0001\n",
      "sector_역촌동 0.0001\n",
      "sector_서교동 0.0001\n",
      "sector_수색동 0.0001\n",
      "sector_고척동 0.0001\n",
      "sector_당산동3가 0.0001\n",
      "sector_용산동2가 0.0000\n",
      "sector_문래동6가 0.0000\n",
      "sector_온수동 0.0000\n",
      "sector_묵동 0.0000\n",
      "sector_정동 0.0000\n",
      "sector_대흥동 0.0000\n",
      "sector_수송동 0.0000\n",
      "sector_금호동2가 0.0000\n",
      "sector_휘경동 0.0000\n",
      "sector_미아동 0.0000\n",
      "sector_이문동 0.0000\n",
      "sector_하계동 0.0000\n",
      "sector_공릉동 0.0000\n",
      "sector_냉천동 0.0000\n",
      "sector_청진동 0.0000\n",
      "sector_용두동 0.0000\n",
      "sector_마포동 0.0000\n",
      "sector_양평동3가 0.0000\n",
      "sector_연희동 0.0000\n",
      "sector_홍익동 0.0000\n",
      "sector_돈암동 0.0000\n",
      "sector_망우동 0.0000\n",
      "sector_둔촌동 0.0000\n",
      "sector_면목동 0.0000\n",
      "sector_효창동 0.0000\n",
      "sector_금호동3가 0.0000\n",
      "sector_영등포동7가 0.0000\n",
      "sector_신대방동 0.0000\n",
      "sector_화양동 0.0000\n",
      "sector_번동 0.0000\n",
      "Old_1976 0.0000\n",
      "sector_신내동 0.0000\n",
      "sector_수서동 0.0000\n",
      "sector_종암동 0.0000\n",
      "sector_하왕십리동 0.0000\n",
      "sector_만리동1가 0.0000\n",
      "sector_대방동 0.0000\n",
      "sector_공항동 0.0000\n",
      "Old_1973 0.0000\n",
      "sector_상계동 0.0000\n",
      "sector_당산동 0.0000\n",
      "sector_천연동 0.0000\n",
      "sector_흥인동 0.0000\n",
      "sector_대현동 0.0000\n",
      "sector_율현동 0.0000\n",
      "sector_중곡동 0.0000\n",
      "sector_평동 0.0000\n",
      "Old_1971 0.0000\n",
      "sector_중화동 0.0000\n",
      "sector_삼선동3가 0.0000\n",
      "sector_구기동 0.0000\n",
      "sector_신림동 0.0000\n",
      "sector_당산동1가 0.0000\n",
      "sector_회현동1가 0.0000\n",
      "sector_가산동 0.0000\n",
      "sector_용산동5가 0.0000\n",
      "sector_성북동1가 0.0000\n",
      "sector_인의동 0.0000\n",
      "sector_양평동5가 0.0000\n",
      "sector_용답동 0.0000\n",
      "sector_토정동 0.0000\n",
      "sector_평창동 0.0000\n",
      "sector_천왕동 0.0000\n",
      "sector_정릉동 0.0000\n",
      "sector_현저동 0.0000\n",
      "sector_연남동 0.0000\n",
      "sector_양평동4가 0.0000\n",
      "sector_우면동 0.0000\n",
      "sector_상봉동 0.0000\n",
      "sector_합동 0.0000\n",
      "sector_갈현동 0.0000\n",
      "sector_동소문동5가 0.0000\n",
      "sector_영등포동2가 0.0000\n",
      "sector_영등포동1가 0.0000\n",
      "Old_1979 0.0000\n",
      "sector_창전동 0.0000\n",
      "sector_응봉동 0.0000\n",
      "sector_당산동2가 0.0000\n",
      "sector_마장동 0.0000\n",
      "sector_강일동 0.0000\n",
      "sector_군자동 0.0000\n",
      "sector_수유동 0.0000\n",
      "sector_당주동 0.0000\n",
      "sector_장위동 0.0000\n",
      "sector_제기동 0.0000\n",
      "sector_원효로2가 0.0000\n",
      "BUS 0.0000\n",
      "sector_상왕십리동 0.0000\n",
      "sector_영등포동 0.0000\n",
      "sector_회현동2가 0.0000\n",
      "sector_노량진동 0.0000\n",
      "sector_교북동 0.0000\n",
      "sector_원효로4가 0.0000\n",
      "sector_대조동 0.0000\n",
      "sector_오장동 0.0000\n",
      "sector_원효로3가 0.0000\n",
      "sector_양평동6가 0.0000\n",
      "sector_영천동 0.0000\n",
      "Old_1972 0.0000\n",
      "sector_후암동 0.0000\n",
      "sector_청암동 0.0000\n",
      "sector_명륜1가 0.0000\n",
      "sector_충무로2가 0.0000\n",
      "sector_금호동1가 0.0000\n",
      "sector_송정동 0.0000\n",
      "sector_신계동 0.0000\n",
      "sector_내곡동 0.0000\n",
      "sector_우이동 0.0000\n",
      "sector_양평동1가 0.0000\n",
      "sector_동소문동7가 0.0000\n",
      "Old_1965 0.0000\n",
      "Old_1970 0.0000\n",
      "sector_문래동4가 0.0000\n",
      "sector_능동 0.0000\n",
      "sector_충정로2가 0.0000\n",
      "sector_신원동 0.0000\n",
      "sector_안암동1가 0.0000\n",
      "sector_양평동2가 0.0000\n",
      "sector_만리동2가 0.0000\n",
      "sector_창천동 0.0000\n",
      "CONVENIENCE 0.0000\n",
      "sector_성북동 0.0000\n",
      "sector_석관동 0.0000\n",
      "sector_신창동 0.0000\n",
      "sector_영등포동5가 0.0000\n",
      "sector_영등포동3가 0.0000\n",
      "sector_동자동 0.0000\n",
      "Old_1969 0.0000\n",
      "sector_용문동 0.0000\n",
      "sector_회기동 0.0000\n",
      "sector_도원동 0.0000\n",
      "sector_동빙고동 0.0000\n",
      "sector_당산동6가 0.0000\n",
      "sector_문래동5가 0.0000\n",
      "sector_궁동 0.0000\n",
      "sector_상월곡동 0.0000\n",
      "sector_도봉동 0.0000\n",
      "sector_갈월동 0.0000\n",
      "sector_도선동 0.0000\n",
      "sector_연지동 0.0000\n",
      "sector_산천동 0.0000\n",
      "sector_충정로3가 0.0000\n",
      "sector_남대문로5가 0.0000\n",
      "sector_삼선동4가 0.0000\n",
      "sector_동선동4가 0.0000\n",
      "sector_상도1동 0.0000\n",
      "sector_신문로2가 0.0000\n",
      "Old_1968 0.0000\n",
      "sector_동교동 0.0000\n",
      "sector_노고산동 0.0000\n",
      "sector_경운동 0.0000\n",
      "sector_문래동2가 0.0000\n",
      "sector_서계동 0.0000\n",
      "sector_청파동3가 0.0000\n",
      "Old_1980 0.0000\n",
      "sector_익선동 0.0000\n",
      "sector_충무로3가 0.0000\n",
      "sector_동소문동4가 0.0000\n",
      "sector_신영동 0.0000\n",
      "sector_필동1가 0.0000\n",
      "sector_충무로5가 0.0000\n",
      "sector_동소문동6가 0.0000\n",
      "sector_영등포동4가 0.0000\n",
      "sector_쌍림동 0.0000\n",
      "sector_동선동1가 0.0000\n",
      "sector_견지동 0.0000\n",
      "sector_원남동 0.0000\n",
      "sector_영등포동6가 0.0000\n",
      "sector_연건동 0.0000\n",
      "sector_저동2가 0.0000\n",
      "sector_동선동3가 0.0000\n",
      "sector_인현동1가 0.0000\n",
      "sector_을지로2가 0.0000\n",
      "sector_광희동1가 0.0000\n",
      "sector_낙원동 0.0000\n",
      "sector_묵정동 0.0000\n",
      "sector_을지로3가 0.0000\n",
      "sector_을지로4가 0.0000\n",
      "sector_관수동 0.0000\n",
      "sector_관철동 0.0000\n",
      "sector_부암동 0.0000\n",
      "sector_을지로5가 0.0000\n",
      "sector_청파동1가 0.0000\n",
      "sector_을지로6가 0.0000\n",
      "sector_삼선동5가 0.0000\n"
     ]
    }
   ],
   "source": [
    "pair_item = []\n",
    "for i, item in enumerate(X.columns):\n",
    "    pair_item.append((item, float(model.feature_importances_[i])))\n",
    "\n",
    "pair_item = sorted(pair_item, key=lambda item: item[1], reverse=True)\n",
    "\n",
    "for k, v in pair_item:\n",
    "    print(k, \"%.4f\" % v)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87bc4f54",
   "metadata": {},
   "source": [
    "## Prediect"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "2d193046",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Type_APT\n",
      "Type_OPST\n",
      "Dir_EE\n",
      "Dir_EN\n",
      "Dir_ES\n",
      "Dir_NN\n",
      "Dir_SS\n",
      "Dir_WN\n",
      "Dir_WS\n",
      "Dir_WW\n",
      "Old_1965\n",
      "Old_1968\n",
      "Old_1969\n",
      "Old_1970\n",
      "Old_1971\n",
      "Old_1972\n",
      "Old_1973\n",
      "Old_1974\n",
      "Old_1975\n",
      "Old_1976\n",
      "Old_1977\n",
      "Old_1978\n",
      "Old_1979\n",
      "Old_1980\n",
      "Old_1981\n",
      "Old_1982\n",
      "Old_1983\n",
      "Old_1984\n",
      "Old_1985\n",
      "Old_1986\n",
      "Old_1987\n",
      "Old_1988\n",
      "Old_1989\n",
      "Old_1990\n",
      "Old_1991\n",
      "Old_1992\n",
      "Old_1993\n",
      "Old_1994\n",
      "Old_1995\n",
      "Old_1996\n",
      "Old_1997\n",
      "Old_1998\n",
      "Old_1999\n",
      "Old_2000\n",
      "Old_2001\n",
      "Old_2002\n",
      "Old_2003\n",
      "Old_2004\n",
      "Old_2005\n",
      "Old_2006\n",
      "Old_2007\n",
      "Old_2008\n",
      "Old_2009\n",
      "Old_2010\n",
      "Old_2011\n",
      "Old_2012\n",
      "Old_2013\n",
      "Old_2014\n",
      "Old_2015\n",
      "Old_2016\n",
      "Old_2017\n",
      "Old_2018\n",
      "Old_2019\n",
      "Old_2020\n",
      "Old_2021\n",
      "Old_2022\n",
      "sector_가락동\n",
      "sector_가리봉동\n",
      "sector_가산동\n",
      "sector_가양동\n",
      "sector_갈월동\n",
      "sector_갈현동\n",
      "sector_강일동\n",
      "sector_개봉동\n",
      "sector_개포동\n",
      "sector_거여동\n",
      "sector_견지동\n",
      "sector_경운동\n",
      "sector_고덕동\n",
      "sector_고척동\n",
      "sector_공덕동\n",
      "sector_공릉동\n",
      "sector_공항동\n",
      "sector_관수동\n",
      "sector_관철동\n",
      "sector_광장동\n",
      "sector_광희동1가\n",
      "sector_교북동\n",
      "sector_구기동\n",
      "sector_구로동\n",
      "sector_구산동\n",
      "sector_구수동\n",
      "sector_구의동\n",
      "sector_군자동\n",
      "sector_궁동\n",
      "sector_금호동1가\n",
      "sector_금호동2가\n",
      "sector_금호동3가\n",
      "sector_금호동4가\n",
      "sector_길동\n",
      "sector_길음동\n",
      "sector_낙원동\n",
      "sector_남가좌동\n",
      "sector_남대문로5가\n",
      "sector_남현동\n",
      "sector_내곡동\n",
      "sector_내발산동\n",
      "sector_내수동\n",
      "sector_냉천동\n",
      "sector_노고산동\n",
      "sector_노량진동\n",
      "sector_녹번동\n",
      "sector_논현동\n",
      "sector_능동\n",
      "sector_답십리동\n",
      "sector_당산동\n",
      "sector_당산동1가\n",
      "sector_당산동2가\n",
      "sector_당산동3가\n",
      "sector_당산동4가\n",
      "sector_당산동5가\n",
      "sector_당산동6가\n",
      "sector_당주동\n",
      "sector_대림동\n",
      "sector_대방동\n",
      "sector_대조동\n",
      "sector_대치동\n",
      "sector_대현동\n",
      "sector_대흥동\n",
      "sector_도곡동\n",
      "sector_도림동\n",
      "sector_도봉동\n",
      "sector_도선동\n",
      "sector_도원동\n",
      "sector_도화동\n",
      "sector_독산동\n",
      "sector_돈암동\n",
      "sector_동교동\n",
      "sector_동빙고동\n",
      "sector_동선동1가\n",
      "sector_동선동3가\n",
      "sector_동선동4가\n",
      "sector_동소문동4가\n",
      "sector_동소문동5가\n",
      "sector_동소문동6가\n",
      "sector_동소문동7가\n",
      "sector_동자동\n",
      "sector_동작동\n",
      "sector_둔촌동\n",
      "sector_등촌동\n",
      "sector_마곡동\n",
      "sector_마장동\n",
      "sector_마천동\n",
      "sector_마포동\n",
      "sector_만리동1가\n",
      "sector_만리동2가\n",
      "sector_망우동\n",
      "sector_망원동\n",
      "sector_면목동\n",
      "sector_명륜1가\n",
      "sector_명륜2가\n",
      "sector_명일동\n",
      "sector_목동\n",
      "sector_묵동\n",
      "sector_묵정동\n",
      "sector_문래동2가\n",
      "sector_문래동3가\n",
      "sector_문래동4가\n",
      "sector_문래동5가\n",
      "sector_문래동6가\n",
      "sector_문배동\n",
      "sector_문정동\n",
      "sector_미아동\n",
      "sector_반포동\n",
      "sector_방배동\n",
      "sector_방이동\n",
      "sector_방학동\n",
      "sector_방화동\n",
      "sector_번동\n",
      "sector_보광동\n",
      "sector_보문동1가\n",
      "sector_본동\n",
      "sector_봉천동\n",
      "sector_부암동\n",
      "sector_북가좌동\n",
      "sector_북아현동\n",
      "sector_불광동\n",
      "sector_사근동\n",
      "sector_사당동\n",
      "sector_산천동\n",
      "sector_삼선동3가\n",
      "sector_삼선동4가\n",
      "sector_삼선동5가\n",
      "sector_삼성동\n",
      "sector_삼전동\n",
      "sector_상계동\n",
      "sector_상도1동\n",
      "sector_상도동\n",
      "sector_상봉동\n",
      "sector_상수동\n",
      "sector_상암동\n",
      "sector_상왕십리동\n",
      "sector_상월곡동\n",
      "sector_상일동\n",
      "sector_서계동\n",
      "sector_서교동\n",
      "sector_서빙고동\n",
      "sector_서초동\n",
      "sector_석관동\n",
      "sector_석촌동\n",
      "sector_성내동\n",
      "sector_성북동\n",
      "sector_성북동1가\n",
      "sector_성산동\n",
      "sector_성수동1가\n",
      "sector_성수동2가\n",
      "sector_세곡동\n",
      "sector_송정동\n",
      "sector_송파동\n",
      "sector_수색동\n",
      "sector_수서동\n",
      "sector_수송동\n",
      "sector_수유동\n",
      "sector_순화동\n",
      "sector_시흥동\n",
      "sector_신계동\n",
      "sector_신공덕동\n",
      "sector_신길동\n",
      "sector_신내동\n",
      "sector_신당동\n",
      "sector_신대방동\n",
      "sector_신도림동\n",
      "sector_신림동\n",
      "sector_신문로2가\n",
      "sector_신사동\n",
      "sector_신수동\n",
      "sector_신영동\n",
      "sector_신원동\n",
      "sector_신월동\n",
      "sector_신정동\n",
      "sector_신창동\n",
      "sector_신천동\n",
      "sector_쌍림동\n",
      "sector_쌍문동\n",
      "sector_아현동\n",
      "sector_안암동1가\n",
      "sector_암사동\n",
      "sector_압구정동\n",
      "sector_양재동\n",
      "sector_양평동1가\n",
      "sector_양평동2가\n",
      "sector_양평동3가\n",
      "sector_양평동4가\n",
      "sector_양평동5가\n",
      "sector_양평동6가\n",
      "sector_여의도동\n",
      "sector_역삼동\n",
      "sector_역촌동\n",
      "sector_연건동\n",
      "sector_연남동\n",
      "sector_연지동\n",
      "sector_연희동\n",
      "sector_염리동\n",
      "sector_염창동\n",
      "sector_영등포동\n",
      "sector_영등포동1가\n",
      "sector_영등포동2가\n",
      "sector_영등포동3가\n",
      "sector_영등포동4가\n",
      "sector_영등포동5가\n",
      "sector_영등포동6가\n",
      "sector_영등포동7가\n",
      "sector_영등포동8가\n",
      "sector_영천동\n",
      "sector_오금동\n",
      "sector_오류동\n",
      "sector_오장동\n",
      "sector_옥수동\n",
      "sector_온수동\n",
      "sector_용강동\n",
      "sector_용답동\n",
      "sector_용두동\n",
      "sector_용문동\n",
      "sector_용산동2가\n",
      "sector_용산동5가\n",
      "sector_우면동\n",
      "sector_우이동\n",
      "sector_원남동\n",
      "sector_원효로1가\n",
      "sector_원효로2가\n",
      "sector_원효로3가\n",
      "sector_원효로4가\n",
      "sector_월계동\n",
      "sector_율현동\n",
      "sector_을지로2가\n",
      "sector_을지로3가\n",
      "sector_을지로4가\n",
      "sector_을지로5가\n",
      "sector_을지로6가\n",
      "sector_응봉동\n",
      "sector_응암동\n",
      "sector_이문동\n",
      "sector_이촌동\n",
      "sector_이태원동\n",
      "sector_익선동\n",
      "sector_인의동\n",
      "sector_인현동1가\n",
      "sector_일원동\n",
      "sector_자곡동\n",
      "sector_자양동\n",
      "sector_잠실동\n",
      "sector_잠원동\n",
      "sector_장안동\n",
      "sector_장위동\n",
      "sector_장지동\n",
      "sector_장충동1가\n",
      "sector_저동2가\n",
      "sector_전농동\n",
      "sector_정동\n",
      "sector_정릉동\n",
      "sector_제기동\n",
      "sector_종암동\n",
      "sector_중계동\n",
      "sector_중곡동\n",
      "sector_중동\n",
      "sector_중화동\n",
      "sector_증산동\n",
      "sector_창동\n",
      "sector_창전동\n",
      "sector_창천동\n",
      "sector_천연동\n",
      "sector_천왕동\n",
      "sector_천호동\n",
      "sector_청담동\n",
      "sector_청량리동\n",
      "sector_청암동\n",
      "sector_청진동\n",
      "sector_청파동1가\n",
      "sector_청파동3가\n",
      "sector_충무로2가\n",
      "sector_충무로3가\n",
      "sector_충무로4가\n",
      "sector_충무로5가\n",
      "sector_충정로2가\n",
      "sector_충정로3가\n",
      "sector_토정동\n",
      "sector_평동\n",
      "sector_평창동\n",
      "sector_풍납동\n",
      "sector_필동1가\n",
      "sector_하계동\n",
      "sector_하왕십리동\n",
      "sector_하월곡동\n",
      "sector_하중동\n",
      "sector_한강로1가\n",
      "sector_한강로2가\n",
      "sector_한강로3가\n",
      "sector_한남동\n",
      "sector_합동\n",
      "sector_합정동\n",
      "sector_항동\n",
      "sector_행당동\n",
      "sector_현석동\n",
      "sector_현저동\n",
      "sector_홍은동\n",
      "sector_홍익동\n",
      "sector_홍제동\n",
      "sector_홍파동\n",
      "sector_화곡동\n",
      "sector_화양동\n",
      "sector_황학동\n",
      "sector_회기동\n",
      "sector_회현동1가\n",
      "sector_회현동2가\n",
      "sector_효창동\n",
      "sector_후암동\n",
      "sector_휘경동\n",
      "sector_흑석동\n",
      "sector_흥인동\n",
      "divisition_강남구\n",
      "divisition_강동구\n",
      "divisition_강북구\n",
      "divisition_강서구\n",
      "divisition_관악구\n",
      "divisition_광진구\n",
      "divisition_구로구\n",
      "divisition_금천구\n",
      "divisition_노원구\n",
      "divisition_도봉구\n",
      "divisition_동대문구\n",
      "divisition_동작구\n",
      "divisition_마포구\n",
      "divisition_서대문구\n",
      "divisition_서초구\n",
      "divisition_성동구\n",
      "divisition_성북구\n",
      "divisition_송파구\n",
      "divisition_양천구\n",
      "divisition_영등포구\n",
      "divisition_용산구\n",
      "divisition_은평구\n",
      "divisition_종로구\n",
      "divisition_중구\n",
      "divisition_중랑구\n",
      "representativeArea\n",
      "floorAreaRatio\n",
      "BUS\n",
      "METRO\n",
      "INFANT\n",
      "PRESCHOOL\n",
      "HOSPITAL\n",
      "PARKING\n",
      "MART\n",
      "CONVENIENCE\n",
      "BANK\n",
      "OFFICE\n",
      "PRI_SCHOOL\n",
      "PUB_SCHOOL\n"
     ]
    }
   ],
   "source": [
    "for item in X.columns:\n",
    "    print(item)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "fac0fb97",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Type\n",
      "Dir\n",
      "Old\n",
      "sector\n",
      "divisition\n",
      "representativeArea\n",
      "floorAreaRatio\n",
      "BUS\n",
      "METRO\n",
      "INFANT\n",
      "PRESCHOOL\n",
      "HOSPITAL\n",
      "PARKING\n",
      "MART\n",
      "CONVENIENCE\n",
      "BANK\n",
      "OFFICE\n",
      "PRI_SCHOOL\n",
      "PUB_SCHOOL\n"
     ]
    }
   ],
   "source": [
    "for item in lbl_X.columns:\n",
    "    print(item)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "f6403fdb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.19383259911894274 0.18975180144115292\n",
      "9 억 6000 만원\n"
     ]
    }
   ],
   "source": [
    "rep = repre_scaler.transform([[103]])[0][0]\n",
    "floor = floor_scaler.transform([[237]])[0][0]\n",
    "\n",
    "print(rep, floor)\n",
    "\n",
    "pred_value = ['APT', 'WS', 2022, '쌍문동', '도봉구', rep, floor, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1]\n",
    "\n",
    "#금천구(시흥동, 가산동), 도봉구(쌍문동)\n",
    "\n",
    "#마포(성산)\n",
    "\n",
    "#강남구(개포동, 대치동), 용산(보광), 서초구(서초동)\n",
    "\n",
    "value = []\n",
    "#Label Encoder \n",
    "# value.extend(encode_lbl(pred_value, encoder_lbl))\n",
    "\n",
    "#onehot Encoder\n",
    "for i, item in enumerate(pred_value):\n",
    "    if i < len(encoders_hot):\n",
    "        temp = encoders_hot[i].transform([[item]])\n",
    "        value.extend(temp[0].tolist())\n",
    "    else:\n",
    "        value.append(item)\n",
    "        \n",
    "price = model.predict([value])[0]\n",
    "\n",
    "print('%d 억 %d 만원' % (int(price // 10000), int(price % 10000)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c4d644f8",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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