{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0b6729d6",
   "metadata": {},
   "source": [
    "# Lesson 16 activity: feature engineering\n",
    "\n",
    "## Notebook setup\n",
    "\n",
    "This example notebook demonstrates a common pitfall in dimensionality reduction: including the target variable (`MedHouseVal`) in a PCA transformation. This creates data leakage, which produces erroneously high model performance scores. Read the reflection section at the bottom to understand what went wrong.\n",
    "\n",
    "**Your task**: Apply at least two different feature engineering techniques to the `housing_df` dataframe to improve the dataset. At the end of the notebook, your engineered dataset and the original dataset will be used to train a linear regression model to predict `MedHouseVal`. Your goal is to achieve better model performance via feature engineering.\n",
    "\n",
    "Don't change any of the code in the Model evaluation section of the notebook, especially the output saving. Otherwise the leaderboard scoring may not work!\n",
    "\n",
    "Before applying transformations, explore the dataset to understand what techniques might be most beneficial.\n",
    "\n",
    "### Import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "38f95602",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "# Set random seed for reproducibility\n",
    "np.random.seed(315)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6ccd151",
   "metadata": {},
   "source": [
    "### Load dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c0ad8292",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load California housing dataset\n",
    "original_housing_df = pd.read_csv('https://media.githubusercontent.com/media/gperdrizet/fullstack-2605/refs/heads/main/data/california_housing.csv')\n",
    "housing_df = original_housing_df.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c9b2e61",
   "metadata": {},
   "source": [
    "## Task 1: Explore the dataset\n",
    "\n",
    "Before deciding what feature engineering techniques to apply, explore the dataset to understand its characteristics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1d44d206",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(20640, 9)\n",
      "\n",
      "<class 'pandas.DataFrame'>\n",
      "RangeIndex: 20640 entries, 0 to 20639\n",
      "Data columns (total 9 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   MedInc       20640 non-null  float64\n",
      " 1   HouseAge     20640 non-null  float64\n",
      " 2   AveRooms     20640 non-null  float64\n",
      " 3   AveBedrms    20640 non-null  float64\n",
      " 4   Population   20640 non-null  float64\n",
      " 5   AveOccup     20640 non-null  float64\n",
      " 6   Latitude     20640 non-null  float64\n",
      " 7   Longitude    20640 non-null  float64\n",
      " 8   MedHouseVal  20640 non-null  float64\n",
      "dtypes: float64(9)\n",
      "memory usage: 1.4 MB\n",
      "\n"
     ]
    },
    {
     "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>MedInc</th>\n",
       "      <th>HouseAge</th>\n",
       "      <th>AveRooms</th>\n",
       "      <th>AveBedrms</th>\n",
       "      <th>Population</th>\n",
       "      <th>AveOccup</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>MedHouseVal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.870671</td>\n",
       "      <td>28.639486</td>\n",
       "      <td>5.429000</td>\n",
       "      <td>1.096675</td>\n",
       "      <td>1425.476744</td>\n",
       "      <td>3.070655</td>\n",
       "      <td>35.631861</td>\n",
       "      <td>-119.569704</td>\n",
       "      <td>2.068558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.899822</td>\n",
       "      <td>12.585558</td>\n",
       "      <td>2.474173</td>\n",
       "      <td>0.473911</td>\n",
       "      <td>1132.462122</td>\n",
       "      <td>10.386050</td>\n",
       "      <td>2.135952</td>\n",
       "      <td>2.003532</td>\n",
       "      <td>1.153956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.499900</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.846154</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.692308</td>\n",
       "      <td>32.540000</td>\n",
       "      <td>-124.350000</td>\n",
       "      <td>0.149990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.563400</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>4.440716</td>\n",
       "      <td>1.006079</td>\n",
       "      <td>787.000000</td>\n",
       "      <td>2.429741</td>\n",
       "      <td>33.930000</td>\n",
       "      <td>-121.800000</td>\n",
       "      <td>1.196000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.534800</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>5.229129</td>\n",
       "      <td>1.048780</td>\n",
       "      <td>1166.000000</td>\n",
       "      <td>2.818116</td>\n",
       "      <td>34.260000</td>\n",
       "      <td>-118.490000</td>\n",
       "      <td>1.797000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.743250</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>6.052381</td>\n",
       "      <td>1.099526</td>\n",
       "      <td>1725.000000</td>\n",
       "      <td>3.282261</td>\n",
       "      <td>37.710000</td>\n",
       "      <td>-118.010000</td>\n",
       "      <td>2.647250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>15.000100</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>141.909091</td>\n",
       "      <td>34.066667</td>\n",
       "      <td>35682.000000</td>\n",
       "      <td>1243.333333</td>\n",
       "      <td>41.950000</td>\n",
       "      <td>-114.310000</td>\n",
       "      <td>5.000010</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             MedInc      HouseAge      AveRooms     AveBedrms    Population  \\\n",
       "count  20640.000000  20640.000000  20640.000000  20640.000000  20640.000000   \n",
       "mean       3.870671     28.639486      5.429000      1.096675   1425.476744   \n",
       "std        1.899822     12.585558      2.474173      0.473911   1132.462122   \n",
       "min        0.499900      1.000000      0.846154      0.333333      3.000000   \n",
       "25%        2.563400     18.000000      4.440716      1.006079    787.000000   \n",
       "50%        3.534800     29.000000      5.229129      1.048780   1166.000000   \n",
       "75%        4.743250     37.000000      6.052381      1.099526   1725.000000   \n",
       "max       15.000100     52.000000    141.909091     34.066667  35682.000000   \n",
       "\n",
       "           AveOccup      Latitude     Longitude   MedHouseVal  \n",
       "count  20640.000000  20640.000000  20640.000000  20640.000000  \n",
       "mean       3.070655     35.631861   -119.569704      2.068558  \n",
       "std       10.386050      2.135952      2.003532      1.153956  \n",
       "min        0.692308     32.540000   -124.350000      0.149990  \n",
       "25%        2.429741     33.930000   -121.800000      1.196000  \n",
       "50%        2.818116     34.260000   -118.490000      1.797000  \n",
       "75%        3.282261     37.710000   -118.010000      2.647250  \n",
       "max     1243.333333     41.950000   -114.310000      5.000010  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(housing_df.shape)\n",
    "print()\n",
    "housing_df.info()\n",
    "print()\n",
    "housing_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1dc4abeb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing_df.hist(figsize=(12, 8), bins=30)\n",
    "plt.suptitle('Feature distributions')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1e8813d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qV67cNbk3AMB1YM2isABwY7jwiY/LfZbgiy++MGNiYszg4GDT19fXrFKlihkXF2du3brV2eb33383H3jgATMkJMQMDg42O3XqZB46dCjPz0lMnjzZLF++vGmz2Vw+95GRkWH26dPHDA4ONgMDA83OnTubx44du+gnPv788888433//ffN5s2bm8WLFzeLFy9u1qxZ03ziiSfMnTt3XvaZ7N+/3+zZs6cZGhpq2u12s3LlyuYTTzzh8vmMPXv2mA899JAZEhJi+vr6mo0aNTI//vjjXM9Mkvnee+/lGqNXr15m8eLFLxrD66+/btavX9/08/MzAwMDzdq1a5ujR482Dx065Gzz7098OBwOc+rUqWbFihVNu91u1q1b1/z444/NXr165fqkxddff23Wr1/f9PHxcXm2//7Eh2maZnZ2tjlp0iSzUqVKpre3txkeHm6OHTvW5bMZpnn+Ex/t2rXLdS//jjMvFz7xMX36dJfjF3uGef1zu3HjRvP22283/fz8zHLlypmjR482V69enevTHOnp6Wa3bt3MkJAQU5Lz2Vzq9/XvT3y88MILpiTz/fffd2l34MABMygoyGzbtu0l7xcAYC3DNN18Wx8AAAAAUOTxTiQAAAAAwG0kkQAAAAAAt5FEAgAAAADcRhIJAAAAAAVg/fr1uu+++1SuXDkZhqEVK1Zc9pqkpCTVq1dPdrtdVatWVWJiYq42L7/8siIiIuTr66vGjRtry5Yt1z74fyCJBAAAAIACcObMGdWpU0cvv/yyW+337t2rdu3a6a677lJycrKGDh2qvn37avXq1c42Fz67NGHCBH333XeqU6eOYmJidOzYset1G2J1VgAAAAAoYIZhaPny5erQocNF24wZM0YrV67Uzz//7DzWtWtXpaamatWqVZKkxo0bq2HDhpo9e7YkyeFwKDw8XIMHD9aTTz55XWIvdl16RYFxOBw6dOiQAgMDZRiG1eEAAADAQ5mmqdOnT6tcuXKy2TxjwuLZs2eVlZVlaQymaeb6O9xut8tut19135s2bVJ0dLTLsZiYGA0dOlSSlJWVpW3btmns2LHO8zabTdHR0dq0adNVj38xJJEe7tChQwoPD7c6DAAAANwgDh48qJtvvtnqMC7r7NmzKucXoJM6Z2kcAQEBSk9Pdzk2YcIETZw48ar7PnLkiMqUKeNyrEyZMkpLS9Pff/+tkydP6ty5c3m22bFjx1WPfzEkkR4uMDBQkjTPqCR/wzP+i5EVymxZa3UIHqHmgZVWh1Do7Y+IvnwjyG5kWB2CR7DxRsllnVaQ1SF4BD/+nbssvzkTrQ6h0EvPylbDeR87/74s7LKysnRS55ToVUn+Fi31kiGH4tL36uDBgwoK+v//9+paVCELM5JID3ehdO5v2ORveFkcTeEVEMAfIe4IKu5ndQiFXoCH/D9Wq/nyv0dusZkOq0Mo9EyTf+fc4W/j37nL8bN7Wx2Cx/C0V6T8ZeHfwf/vvwUGBQW5JJHXSlhYmI4ePepy7OjRowoKCpKfn5+8vLzk5eWVZ5uwsLBrHs8FlK4AAAAAeCzD27B0u56aNGmidevWuRxbs2aNmjRpIkny8fFR/fr1Xdo4HA6tW7fO2eZ6IIkEAAAAgAKQnp6u5ORkJScnSzr/CY/k5GQdOHBAkjR27Fj17NnT2X7AgAH67bffNHr0aO3YsUOvvPKK3n33XQ0bNszZZvjw4XrjjTc0f/58paSkaODAgTpz5ox69+593e6D6awAAAAAUAC2bt2qu+66y7k/fPhwSVKvXr2UmJiow4cPOxNKSapUqZJWrlypYcOG6YUXXtDNN9+sN998UzExMc42Xbp00Z9//qnx48fryJEjuu2227Rq1apci+1cSySRAAAAADyWUcyQzaL3OA3zysZt2bKlzEssqpaYmJjnNd9///0l+x00aJAGDRp0RbFcDaazAgAAAADcRiUSAAAAgMcyvG0yLPrUnVFEP9VEJRIAAAAA4DaSSAAAAACA25jOCgAAAMBj2bwM2WzWLKxjc1gzrtWoRAIAAAAA3EYlEgAAAIDHMrwNGRZVIg0qkQAAAAAAXBpJJAAAAADAbUxnBQAAAOCxbMVYWKegUYkEAAAAALiNSiQAAAAAj8XCOgWPSiQAAAAAwG0kkQAAAAAAtzGdFQAAAIDHsnkZsnlZtLDOOaazAgAAAABwSSSRAAAAAAC3MZ0VAAAAgMcyvAwZFk1nNcR0VgAAAAAALolKJAAAAACPZenCOlQiAQAAAAC4NJJIAAAAAIDbmM4KAAAAwGMZNkOGzaKFdUymswIAAAAAcElUIgEAAAB4LMPLJsPLmtqYIdOSca1GJfIqJCUlyTAMpaamWh0KAAAAABSIGzqJjIuLk2EYGjBgQK5zTzzxhAzDUFxc3DUbj6QSAAAAwI3uhk4iJSk8PFyLFy/W33//7Tx29uxZLVq0SBUqVLAwMgAAAABX68J3Iq3aiqIbPomsV6+ewsPDtWzZMuexZcuWqUKFCqpbt67zmMPhUHx8vCpVqiQ/Pz/VqVNHS5cudenrk08+UfXq1eXn56e77rpL+/btu+TYiYmJCgkJ0erVqxUZGamAgADFxsbq8OHDLu3mzp2rW265RXa7XWXLltWgQYOu/sYBAAAA4Dq44ZNISXr00Uc1b9485/7cuXPVu3dvlzbx8fFasGCB5syZo+3bt2vYsGHq0aOHvvzyS0nSwYMH1bFjR913331KTk5W37599eSTT1527IyMDM2YMUNvvfWW1q9frwMHDmjkyJHO86+++qqeeOIJ9e/fXz/99JM+/PBDVa1a9aL9ZWZmKi0tzWUDAAAAgIJSJFZn7dGjh8aOHav9+/dLkjZu3KjFixcrKSlJ0vnEbOrUqVq7dq2aNGkiSapcubI2bNig1157TS1atNCrr76qKlWqaObMmZKkGjVq6KefftJzzz13ybGzs7M1Z84cValSRZI0aNAgPf30087zzzzzjEaMGKH//Oc/zmMNGza8aH/x8fGaNGnSlT8EAAAA4AZkGBZ+J9JRNKezFokkMjQ0VO3atVNiYqJM01S7du1UqlQp5/ndu3crIyND99xzj8t1WVlZzimvKSkpaty4scv5Cwnnpfj7+zsTSEkqW7asjh07Jkk6duyYDh06pFatWrl9L2PHjtXw4cOd+2lpaQoPD3f7egAAAAC4GkUiiZTOT2m98K7hyy+/7HIuPT1dkrRy5UqVL1/e5Zzdbr+qcb29vV32DcOQaZ7/noyfn98V92e32686JgAAAOBGYXjJsgVujKL5mciik0TGxsYqKytLhmEoJibG5VxUVJTsdrsOHDigFi1a5Hl9ZGSkPvzwQ5dj33zzzVXFFBgYqIiICK1bt0533XXXVfUFAAAAAAWhyCSRXl5eSklJcf78T4GBgRo5cqSGDRsmh8Oh5s2b69SpU9q4caOCgoLUq1cvDRgwQDNnztSoUaPUt29fbdu2TYmJiVcd18SJEzVgwACVLl1abdq00enTp7Vx40YNHjz4qvsGAAAAgGutyCSRkhQUFHTRc5MnT1ZoaKji4+P122+/KSQkRPXq1dN///tfSVKFChX0/vvva9iwYXrppZfUqFEjTZ06VY8++uhVxdSrVy+dPXtW//d//6eRI0eqVKlSeuihh66qTwAAAKCoMLwMGZZNZy2aC+sY5oUX9OCR0tLSFBwcrCW2KvI3vC5/QRFVdvsmq0PwCFH7V1gdQqG3t3Ks1SF4BF8jw+oQPILNdFgdQqGXZgZbHYJH8Lfx79zl+L14+U+zFXWnM7MV+dpynTp16pLFl8Liwt/BXzRtoIBi1tTG0nNydNfXWz3mmV0rRaoSCQAAAODGYthsMmw2y8YuiormXQMAAAAA8oUkEgAAAADgNqazAgAAAPBYhs2QYbNoYR2LxrUalUgAAAAAgNuoRAIAAADwWDYvQzaLPvFhK6Kf+KASCQAAAABwG0kkAAAAAMBtTGcFAAAA4LFYWKfgUYkEAAAAALiNJBIAAAAA4DamswIAAADwWIZhk2GzpjZmGEWzJlc07xoAAAAAkC9UIgEAAAB4LBbWKXhUIgEAAAAAbiOJBAAAAAC4jemsAAAAADyWzcuQzcuaaaU2B9NZAQAAAAC4JCqRAAAAADwWC+sUPCqRAAAAAAC3kUQCAAAAANzGdFYAAAAAHsuw2WTYrKmNWTWu1YrmXQMAAAAA8oUkEgAAAADgNqazAgAAAPBYrM5a8KhEAgAAAADcRiUSAAAAgMeiElnwqEQCAAAAANxGEgkAAAAAcBvTWW8QZbasVUBAkNVhFFqHb2lidQgeIfunLVaHUOgVN/+2OgSP8FNqhNUheIRbQ/ZaHUKhF2ikWR2CR0g9V8LqEAq9oP4jrA6h0PNOT5deW251GFeM6awFj0okAAAAAMBtVCIBAAAAeKzzlUhramNUIgEAAAAAuAySSAAAAACA25jOCgAAAMBjGTZDNi+LFtY5x3RWAAAAAMB19PLLLysiIkK+vr5q3Lixtmy5+Or4LVu2lGEYubZ27do528TFxeU6Hxsbe13vgUokAAAAAI/lSZ/4WLJkiYYPH645c+aocePGmjVrlmJiYrRz506VLl06V/tly5YpKyvLuf/XX3+pTp066tSpk0u72NhYzZs3z7lvt9uv8E6uDJVIAAAAACgAzz//vPr166fevXsrKipKc+bMkb+/v+bOnZtn+5IlSyosLMy5rVmzRv7+/rmSSLvd7tKuRInr++1YkkgAAAAAuAppaWkuW2ZmZq42WVlZ2rZtm6Kjo53HbDaboqOjtWnTJrfGSUhIUNeuXVW8eHGX40lJSSpdurRq1KihgQMH6q+//rq6G7oMkkgAAAAAHsuw2SzdJCk8PFzBwcHOLT4+Plecx48f17lz51SmTBmX42XKlNGRI0cue59btmzRzz//rL59+7ocj42N1YIFC7Ru3To999xz+vLLL9WmTRudO3fuKp7qpfFOJAAAAABchYMHDyooKMi5fz3eSUxISFDt2rXVqFEjl+Ndu3Z1/ly7dm3deuutqlKlipKSktSqVatrHodEJRIAAAAArkpQUJDLllcSWapUKXl5eeno0aMux48ePaqwsLBL9n/mzBktXrxYffr0uWwslStXVqlSpbR79+4ru4krQBIJAAAAwGNdWJ3Vqs1dPj4+ql+/vtatW+c85nA4tG7dOjVp0uSS17733nvKzMxUjx49LjvO77//rr/++ktly5Z1O7YrRRIJAAAAAAVg+PDheuONNzR//nylpKRo4MCBOnPmjHr37i1J6tmzp8aOHZvruoSEBHXo0EE33XSTy/H09HSNGjVK33zzjfbt26d169apffv2qlq1qmJiYq7bffBOJAAAAACP5UnfiezSpYv+/PNPjR8/XkeOHNFtt92mVatWORfbOXDggGw21zrfzp07tWHDBn322We5+vPy8tKPP/6o+fPnKzU1VeXKlVPr1q01efLk6/qtSJJIAAAAACgggwYN0qBBg/I8l5SUlOtYjRo1ZJpmnu39/Py0evXqaxmeW5jOCgAAAABwG5VIAAAAAB7rn99rtGLsoqho3jUAAAAAIF+oRAIAAADwWJ60sM6NgkokAAAAAMBtJJEAAAAAALcxnRUAAACAx2JhnYJXNO8aAAAAAJAvJJEAAAAAALcxnRUAAACA5zKM85tVYxdBVCIBAAAAAG6jEgkAAADAYxmGhd+JpBIJAAAAAMClkUQCAAAAANzGdFYAAAAAHovvRBa8onnXAAAAAIB8KVRJZFxcnDp06JDreFJSkgzDUGpqaoHHdCl///23SpYsqVKlSikzM9PqcAAAAIAix7AZlm5FUaFKIj3N+++/r1tuuUU1a9bUihUrrA4HAAAAAK47j0wiLyRvdrtdERERmjlzpst5wzByJXUhISFKTEyUJGVlZWnQoEEqW7asfH19VbFiRcXHxzvbpqamqm/fvgoNDVVQUJDuvvtu/fDDD7niSEhIUI8ePdSjRw8lJCTkOr9jxw41b95cvr6+ioqK0tq1a3PFdvDgQXXu3FkhISEqWbKk2rdvr3379uX72QAAAADA9eRxC+ts27ZNnTt31sSJE9WlSxd9/fXXevzxx3XTTTcpLi7OrT5efPFFffjhh3r33XdVoUIFHTx4UAcPHnSe79Spk/z8/PTpp58qODhYr732mlq1aqVff/1VJUuWlCTt2bNHmzZt0rJly2SapoYNG6b9+/erYsWKkqRz586pQ4cOqlChgjZv3qzTp09rxIgRLnFkZ2crJiZGTZo00VdffaVixYrpmWeeUWxsrH788Uf5+Phcm4cGAAAA3KBYWKfgFbok8uOPP1ZAQIDLsXPnzjl/fv7559WqVSuNGzdOklS9enX98ssvmj59uttJ5IEDB1StWjU1b95chmE4Ez9J2rBhg7Zs2aJjx47JbrdLkmbMmKEVK1Zo6dKl6t+/vyRp7ty5atOmjUqUKCFJiomJ0bx58zRx4kRJ0po1a7Rnzx4lJSUpLCxMkjRlyhTdc889zrGWLFkih8OhN9980/mh0nnz5ikkJERJSUlq3bp1rtgzMzNd3r9MS0tz654BAAAA4FoodKnzXXfdpeTkZJftzTffdJ5PSUlRs2bNXK5p1qyZdu3a5ZJsXkpcXJySk5NVo0YNDRkyRJ999pnz3A8//KD09HTddNNNCggIcG579+7Vnj17JJ1PaufPn68ePXo4r+vRo4cSExPlcDgkSTt37lR4eLgzgZSkRo0aucTxww8/aPfu3QoMDHSOU7JkSZ09e9Y51r/Fx8crODjYuYWHh7t1zwAAAABwLRS6SmTx4sVVtWpVl2O///77FfVhGIZM03Q5lp2d7fy5Xr162rt3rz799FOtXbtWnTt3VnR0tJYuXar09HSVLVtWSUlJufoNCQmRJK1evVp//PGHunTp4nL+3LlzWrdunUu18VLS09NVv359LVy4MNe50NDQPK8ZO3ashg8f7txPS0sjkQQAAECRZdhk2SqpRqEryRWMQpdEXk5kZKQ2btzocmzjxo2qXr26vLy8JJ1PwA4fPuw8v2vXLmVkZLhcExQUpC5duqhLly566KGHFBsbqxMnTqhevXo6cuSIihUrpoiIiDxjSEhIUNeuXfW///3P5fiUKVOUkJCge+65RzVq1NDBgwd19OhRlSlTRpL07bffurSvV6+elixZotKlSysoKMit+7fb7c5ptgAAAABQ0DwuiRwxYoQaNmyoyZMnq0uXLtq0aZNmz56tV155xdnm7rvv1uzZs9WkSROdO3dOY8aMkbe3t/P8888/r7Jly6pu3bqy2Wx67733FBYWppCQEEVHR6tJkybq0KGDpk2bpurVq+vQoUNauXKlHnjgAVWsWFEfffSRPvzwQ9WqVcsltp49e+qBBx7QiRMndM8996hKlSrq1auXpk2bptOnT+upp56SJOf7j927d9f06dPVvn17Pf3007r55pu1f/9+LVu2TKNHj9bNN99cAE8UAAAA8FxWfq+R70R6iHr16undd9/V4sWLVatWLY0fP15PP/20y6I6M2fOVHh4uO644w5169ZNI0eOlL+/v/N8YGCgpk2bpgYNGqhhw4bat2+fPvnkE9lsNhmGoU8++UR33nmnevfurerVq6tr167av3+/ypQpowULFqh48eJq1apVrthatWolPz8/vf322/Ly8tKKFSuUnp6uhg0bqm/fvs7Kpa+vryTJ399f69evV4UKFdSxY0dFRkaqT58+Onv2rNuVSQAAAAAoSIb575cHcd1s3LhRzZs31+7du1WlSpVr0mdaWpqCg4OVtHWvAgJIPC/m8C1NrA7BI5T6aYvVIRR6xYv9bXUIHuG3U6WsDsEj3Bqy1+oQcINIdZSwOoRCr3z2b1aHUOidTk9X1dtb6dSpUx5R0Ljwd/DuYV0UaLfm03inM7NU9f+WeMwzu1Y8bjqrJ1m+fLkCAgJUrVo17d69W//5z3/UrFmza5ZAAgAAAEWezXZ+s2rsIogk8jo6ffq0xowZowMHDqhUqVKKjo7WzJkzrQ4LAAAAAPKNJPI66tmzp3r27Gl1GAAAAMANyzAM58KVVoxdFBXN+isAAAAAIF9IIgEAAAAAbmM6KwAAAACPZdhsMixa4Maqca1WNO8aAAAAAJAvVCIBAAAAeCzDZsiwWbSwjkXjWo1KJAAAAADAbSSRAAAAAAC3MZ0VAAAAgOcybJJVC9wYRbMmVzTvGgAAAACQLySRAAAAAAC3MZ0VAAAAgOeycHVWsTorAAAAAACXRiUSAAAAgMcyDJsMixa4sWpcqxXNuwYAAAAA5AtJJAAAAADAbUxnBQAAAOC5bIZ1C9ywsA4AAAAAAJdGJRIAAACAxzJsNhk2ixbWsWhcqxXNuwYAAAAA5AtJJAAAAADAbUxnBQAAAOCxDJshw6IFbqwa12pUIgEAAAAAbiOJBAAAAAC4jemsAAAAADyXYUiGRbUxg+msAAAAAABcEpVIAAAAAB6LhXUKHpVIAAAAAIDbqETeIGoeWKmg4n5Wh1FoZf+0xeoQPMLx2o2sDqHQs99V2uoQPELlV96zOgSP4DC8rA6h0As8e9zqEDxCtq+P1SEUesUy/rY6hELPK/us1SHAQ5BEAgAAAPBcNtv5zaqxi6CiedcAAAAAgHyhEgkAAADAYxmGIcOiT21YNa7VqEQCAAAAANxGEgkAAAAAcBvTWQEAAAB4LsPChXWMolmTK5p3DQAAAADIFyqRAAAAADyWYTNk2CxaWMeica1GJRIAAAAA4DaSSAAAAACA25jOCgAAAMBzGTbrFrhhYR0AAAAAAC6NJBIAAAAA4DaSSAAAAACey2ZYu12hl19+WREREfL19VXjxo21ZcuWi7ZNTEyUYRgum6+vr0sb0zQ1fvx4lS1bVn5+foqOjtauXbuuOK4rQRIJAAAAAAVgyZIlGj58uCZMmKDvvvtOderUUUxMjI4dO3bRa4KCgnT48GHntn//fpfz06ZN04svvqg5c+Zo8+bNKl68uGJiYnT27Nnrdh8kkQAAAAA8lmHYLN2uxPPPP69+/fqpd+/eioqK0pw5c+Tv76+5c+de4v4MhYWFObcyZco4z5mmqVmzZumpp55S+/btdeutt2rBggU6dOiQVqxYkd9HelkkkQAAAABwnWVlZWnbtm2Kjo52HrPZbIqOjtamTZsuel16eroqVqyo8PBwtW/fXtu3b3ee27t3r44cOeLSZ3BwsBo3bnzJPq8WSSQAAAAAXIW0tDSXLTMzM1eb48eP69y5cy6VREkqU6aMjhw5kme/NWrU0Ny5c/XBBx/o7bfflsPhUNOmTfX7779LkvO6K+nzWiCJBAAAAOC5CsHCOuHh4QoODnZu8fHx1+TWmjRpop49e+q2225TixYttGzZMoWGhuq11167Jv3nVzFLRwcAAAAAD3fw4EEFBQU59+12e642pUqVkpeXl44ePepy/OjRowoLC3NrHG9vb9WtW1e7d++WJOd1R48eVdmyZV36vO222670NtxGJRIAAACAxzJsNks36fwKqv/c8koifXx8VL9+fa1bt855zOFwaN26dWrSpIlb93ru3Dn99NNPzoSxUqVKCgsLc+kzLS1NmzdvdrvP/KASCQAAAAAFYPjw4erVq5caNGigRo0aadasWTpz5ox69+4tSerZs6fKly/vnA779NNP6/bbb1fVqlWVmpqq6dOna//+/erbt6+k8yu3Dh06VM8884yqVaumSpUqady4cSpXrpw6dOhw3e6DJBIAAAAACkCXLl30559/avz48Tpy5Ihuu+02rVq1yrkwzoEDB2Sz/f+TRU+ePKl+/frpyJEjKlGihOrXr6+vv/5aUVFRzjajR4/WmTNn1L9/f6Wmpqp58+ZatWqVfH19r9t9GKZpmtetd1x3aWlpCg4O1pFlsxVU3M/qcAqtH8IftDoEj3C8diOrQyj0yt9V2uoQPEKxV96zOgSP4G/LsDqEQi/w7HGrQ/AIqb5lLt+oiCt1ep/VIRR6aelnVOmOdjp16pTL+32F1YW/gw/PHqMgv9zTRwskhr8zVXbQcx7zzK4V3okEAAAAALiNJBIAAAAA4DbeiQQAAADguWyGZLOoNvb/vhNZ1FCJBAAAAAC4jUokAAAAAM9lGOc3q8YugqhEAgAAAADcRhIJAAAAAHAb01kBAAAAeCzDZpNh0cI6Vo1rtUJz15s2bZKXl5fatWt3Tfvdt2+fDMNwbiVLllSLFi301VdfXdNxAAAAAKAoKDRJZEJCggYPHqz169fr0KFD17z/tWvX6vDhw1q/fr3KlSune++9V0ePHr3m4wAAAAAoQIbN2q0IKhR3nZ6eriVLlmjgwIFq166dEhMTJUndunVTly5dXNpmZ2erVKlSWrBggSTJ4XAoPj5elSpVkp+fn+rUqaOlS5fmGuOmm25SWFiYatWqpf/+979KS0vT5s2bnee//PJLNWrUSHa7XWXLltWTTz6pnJwc5/nMzEwNGTJEpUuXlq+vr5o3b65vv/3WeT4pKUmGYWj16tWqW7eu/Pz8dPfdd+vYsWP69NNPFRkZqaCgIHXr1k0ZGRnO65YuXaratWvLz89PN910k6Kjo3XmzJlr8lwBAAAA4ForFEnku+++q5o1a6pGjRrq0aOH5s6dK9M01b17d3300UdKT093tl29erUyMjL0wAMPSJLi4+O1YMECzZkzR9u3b9ewYcPUo0cPffnll3mO9ffffzsTUB8fH0nSH3/8obZt26phw4b64Ycf9OqrryohIUHPPPOM87rRo0fr/fff1/z58/Xdd9+patWqiomJ0YkTJ1z6nzhxombPnq2vv/5aBw8eVOfOnTVr1iwtWrRIK1eu1GeffaaXXnpJknT48GE9/PDDevTRR5WSkqKkpCR17NhRpmle9FllZmYqLS3NZQMAAACAglIoFtZJSEhQjx49JEmxsbE6deqUvvzyS8XExKh48eJavny5HnnkEUnSokWLdP/99yswMFCZmZmaOnWq1q5dqyZNmkiSKleurA0bNui1115TixYtnGM0bdpUNptNGRkZMk1T9evXV6tWrSRJr7zyisLDwzV79mwZhqGaNWvq0KFDGjNmjMaPH6+///5br776qhITE9WmTRtJ0htvvKE1a9YoISFBo0aNco7zzDPPqFmzZpKkPn36aOzYsdqzZ48qV64sSXrooYf0xRdfaMyYMTp8+LBycnLUsWNHVaxYUZJUu3btSz6r+Ph4TZo06aqfOQAAAHBDMAzJxnciC5LllcidO3dqy5YtevjhhyVJxYoVU5cuXZSQkKBixYqpc+fOWrhwoSTpzJkz+uCDD9S9e3dJ0u7du5WRkaF77rlHAQEBzm3BggXas2ePyzhLlizR999/r/fff19Vq1ZVYmKivL29JUkpKSlq0qSJjH/8Q9CsWTOlp6fr999/1549e5Sdne1MDiXJ29tbjRo1UkpKiss4t956q/PnMmXKyN/f35lAXjh27NgxSVKdOnXUqlUr1a5dW506ddIbb7yhkydPXvJ5jR07VqdOnXJuBw8edO9BAwAAAMA1YHklMiEhQTk5OSpXrpzzmGmastvtmj17trp3764WLVro2LFjWrNmjfz8/BQbGytJzmmuK1euVPny5V36tdvtLvvh4eGqVq2aqlWrppycHD3wwAP6+eefc7W7WhcSU0kyDMNl/8Ixh8MhSfLy8tKaNWv09ddfO6e5/u9//9PmzZtVqVKlPPu32+3XPGYAAAAAcJellcicnBwtWLBAM2fOVHJysnP74YcfVK5cOb3zzjtq2rSpwsPDtWTJEi1cuFCdOnVyJmZRUVGy2+06cOCAqlat6rKFh4dfdNyHHnpIxYoV0yuvvCJJioyM1KZNm1zeRdy4caMCAwN18803q0qVKvLx8dHGjRud57Ozs/Xtt98qKirqqp6BYRhq1qyZJk2apO+//14+Pj5avnz5VfUJAAAAFBWGYbN0K4osrUR+/PHHOnnypPr06aPg4GCXcw8++KASEhI0YMAAdevWTXPmzNGvv/6qL774wtkmMDBQI0eO1LBhw+RwONS8eXOdOnVKGzduVFBQkHr16pXnuIZhaMiQIZo4caIee+wxPf7445o1a5YGDx6sQYMGaefOnZowYYKGDx8um82m4sWLa+DAgRo1apRKliypChUqaNq0acrIyFCfPn3yff+bN2/WunXr1Lp1a5UuXVqbN2/Wn3/+qcjIyHz3CQAAAADXk6Wpc0JCgqKjo3MlkNL5JHLr1q368ccf1b17d/3yyy8qX768y3uJkjR58mSNGzdO8fHxioyMVGxsrFauXHnR6aAX9OrVS9nZ2Zo9e7bKly+vTz75RFu2bFGdOnU0YMAA9enTR0899ZSz/bPPPqsHH3xQjzzyiOrVq6fdu3dr9erVKlGiRL7vPygoSOvXr1fbtm1VvXp1PfXUU5o5c6Zz8R4AAAAAl2EzrN2KIMO81PckUOilpaUpODhYR5bNVlBxP6vDKbR+CH/Q6hA8wvHajawOodArf1dpq0PwCMVeec/qEDyCvy3j8o2KuMCzx60OwSOk+paxOoRCr9TpfVaHUOilpZ9RpTva6dSpUwoKCrI6nMu68Hfw0bmTFOTva00MGWdV5tEJHvPMrpWiOYkXAAAAAJAvlq/OCgAAAAD5ZtjOb1aNXQQVzbsGAAAAAOQLlUgAAAAAnsswzm9WjV0EUYkEAAAAALiNJBIAAAAA4DamswIAAADwXDbb+c2qsYugonnXAAAAAIB8oRIJAAAAwHPxiY8CVzTvGgAAAACQLySRAAAAAAC3MZ0VAAAAgOeyGec3q8YugqhEAgAAAADcRhIJAAAAAHAb01kBAAAAeC7DsHB1VqazAgAAAABwSVQiAQAAAHguw7CuIkglEgAAAACASyOJBAAAAAC4jemsAAAAADyXzXZ+s2rsIqho3jUAAAAAIF+oRAIAAADwXCysU+CoRAIAAAAA3EYSCQAAAABwG9NZAQAAAHguw3Z+s2rsIqho3jUAAAAAIF9IIgEAAAAAbmM6KwAAAADPZVj4nUimswIAAAAAcGlUIgEAAAB4Lr4TWeBIIm8Q+yOiFRAYaHUYhVZx82+rQ/AI9rtKWx1CoffHF8esDsEjRBim1SHgBpFTzG51CB7B28yyOoRC71hAZatDKPTSzdNWhwAPwXRWAAAAAIDbqEQCAAAA8Fx8J7LAFc27BgAAAADkC5VIAAAAAJ6LhXUKHJVIAAAAAIDbSCIBAAAAAG5jOisAAAAAz2Wznd+sGrsIKpp3DQAAAADIFyqRAAAAADyWaRgyLVrgxqpxrUYlEgAAAADgNpJIAAAAAIDbmM4KAAAAwHMZhmRYVBtjOisAAAAAAJdGEgkAAAAAcBvTWQEAAAB4LsNm4XTWolmTK5p3DQAAAADIFyqRAAAAADwW34kseFQiAQAAAABuI4kEAAAAALiN6awAAAAAPBcL6xS4onnXAAAAAIB8oRIJAAAAwHMZxvnNqrGLICqRAAAAAFBAXn75ZUVERMjX11eNGzfWli1bLtr2jTfe0B133KESJUqoRIkSio6OztU+Li5OhmG4bLGxsdf1HkgiAQAAAKAALFmyRMOHD9eECRP03XffqU6dOoqJidGxY8fybJ+UlKSHH35YX3zxhTZt2qTw8HC1bt1af/zxh0u72NhYHT582Lm988471/U+SCIBAAAAeC6bzdrtCjz//PPq16+fevfuraioKM2ZM0f+/v6aO3dunu0XLlyoxx9/XLfddptq1qypN998Uw6HQ+vWrXNpZ7fbFRYW5txKlCiR78fpDpJIAAAAALjOsrKytG3bNkVHRzuP2Ww2RUdHa9OmTW71kZGRoezsbJUsWdLleFJSkkqXLq0aNWpo4MCB+uuvv65p7P/GwjoAAAAAcBXS0tJc9u12u+x2u8ux48eP69y5cypTpozL8TJlymjHjh1ujTNmzBiVK1fOJRGNjY1Vx44dValSJe3Zs0f//e9/1aZNG23atEleXl75vKNLI4kEAAAA4LFMw5Bp0SqpF8YNDw93OT5hwgRNnDjxmo717LPPavHixUpKSpKvr6/zeNeuXZ0/165dW7feequqVKmipKQktWrV6prGcAFJJAAAAABchYMHDyooKMi5/+8qpCSVKlVKXl5eOnr0qMvxo0ePKiws7JL9z5gxQ88++6zWrl2rW2+99ZJtK1eurFKlSmn37t3XLYnknUgAAAAAnsuwWbtJCgoKctnySiJ9fHxUv359l0VxLiyS06RJk4ve3rRp0zR58mStWrVKDRo0uOzj+P333/XXX3+pbNmy+XiY7iGJdMO+fftkGIaSk5OtDgUAAACAhxo+fLjeeOMNzZ8/XykpKRo4cKDOnDmj3r17S5J69uypsWPHOts/99xzGjdunObOnauIiAgdOXJER44cUXp6uiQpPT1do0aN0jfffKN9+/Zp3bp1at++vapWraqYmJjrdh8FnkReeMGzXbt217TfC4nehc3Hx0dVq1bVM888I9M0r+lYAAAAAHClunTpohkzZmj8+PG67bbblJycrFWrVjkX2zlw4IAOHz7sbP/qq68qKytLDz30kMqWLevcZsyYIUny8vLSjz/+qPvvv1/Vq1dXnz59VL9+fX311Vd5VkOvlQJ/JzIhIUGDBw9WQkKCDh06pHLlyl3T/teuXatbbrlFmZmZ2rBhg/r27auyZcuqT58+13ScfzJNU+fOnVOxYrxiCgAAABQk07DJNKyZYJmfcQcNGqRBgwbleS4pKcllf9++fZfsy8/PT6tXr77iGK5WgT7t9PR0LVmyRAMHDlS7du2UmJgoSerWrZu6dOni0jY7O1ulSpXSggULJJ2fLxwfH69KlSrJz89PderU0dKlS3ONcdNNNyksLEwVK1ZU9+7d1axZM3333Xcubd58801FRkbK19dXNWvW1CuvvOJyfsuWLapbt658fX3VoEEDff/99y7nk5KSZBiGPv30U9WvX192u10bNmxQy5YtNXjwYA0dOlQlSpRQmTJl9MYbbzhL1IGBgapatao+/fRTZ18nT55U9+7dFRoaKj8/P1WrVk3z5s3L9zMGAAAAgOupQJPId999VzVr1lSNGjXUo0cPzZ07V6Zpqnv37vroo4+cc3slafXq1crIyNADDzwgSYqPj9eCBQs0Z84cbd++XcOGDVOPHj305ZdfXnS8rVu3atu2bWrcuLHz2MKFCzV+/HhNmTJFKSkpmjp1qsaNG6f58+dLOp/o3nvvvYqKitK2bds0ceJEjRw5Ms/+n3zyST377LNKSUlxrpI0f/58lSpVSlu2bNHgwYM1cOBAderUSU2bNtV3332n1q1b65FHHlFGRoYkady4cfrll1/06aefKiUlRa+++qpKlSp1dQ8aAAAAKCoMw9qtCCrQ+ZcJCQnq0aOHpPMfxTx16pS+/PJLxcTEqHjx4lq+fLkeeeQRSdKiRYt0//33KzAwUJmZmZo6darWrl3rXLmocuXK2rBhg1577TW1aNHCOUbTpk1ls9mUlZWl7Oxs9e/fXz179nSenzBhgmbOnKmOHTtKkipVqqRffvlFr732mnr16qVFixbJ4XAoISFBvr6+uuWWW/T7779r4MCBue7n6aef1j333ONyrE6dOnrqqackSWPHjtWzzz6rUqVKqV+/fpKk8ePH69VXX9WPP/6o22+/XQcOHFDdunWdKy1FRERc8hlmZmYqMzPTuf/vD5sCAAAAwPVUYEnkzp07tWXLFi1fvvz8wMWKqUuXLkpISFDLli3VuXNnLVy4UI888ojOnDmjDz74QIsXL5Yk7d69WxkZGbkStqysLNWtW9fl2JIlSxQZGans7Gz9/PPPGjx4sEqUKKFnn31WZ86c0Z49e9SnTx9nUidJOTk5Cg4OliRnVfGfH/C82JK7eS2x+8/vtnh5eemmm25S7dq1nccuvDR77NgxSdLAgQP14IMPOquUHTp0UNOmTS/6HOPj4zVp0qSLngcAAACA66nAksiEhATl5OS4LKRjmqbsdrtmz56t7t27q0WLFjp27JjWrFkjPz8/xcbGSpJzmuvKlStVvnx5l37/vepQeHi4qlatKkmKjIzUnj17NG7cOE2cONHZzxtvvOEyxVU6n/BdqeLFi+c65u3t7bJvGIbLMeP/lbwdDockqU2bNtq/f78++eQTrVmzRq1atdITTzzhXHHp38aOHavhw4c799PS0hQeHn7FsQMAAAA3AlMWLqxTRL+YWCBJZE5OjhYsWKCZM2eqdevWLuc6dOigd955RwMGDFB4eLiWLFmiTz/9VJ06dXImX1FRUbLb7Tpw4IDL1FV3eHl5KScnR1lZWSpTpozKlSun3377Td27d8+zfWRkpN566y2dPXvWWY385ptv8nHX7gsNDVWvXr3Uq1cv3XHHHRo1atRFk0i73X5dl+sFAAAAgEspkCTy448/1smTJ9WnTx/ntNELHnzwQSUkJGjAgAHq1q2b5syZo19//VVffPGFs01gYKBGjhypYcOGyeFwqHnz5jp16pQ2btyooKAg9erVy9n2r7/+0pEjR5STk6OffvpJL7zwgu666y4FBQVJkiZNmqQhQ4YoODhYsbGxyszM1NatW3Xy5EkNHz5c3bp10//+9z/169dPY8eO1b59+y6a0F0L48ePV/369Z2fJfn4448VGRl53cYDAAAAbihWLnDDwjrXT0JCgqKjo3MlkNL5JHLatGn68ccf1b17d02ZMkUVK1ZUs2bNXNpNnjxZoaGhio+P12+//aaQkBDVq1dP//3vf13aRUdHSzpfgSxbtqzatm2rKVOmOM/37dtX/v7+mj59ukaNGqXixYurdu3aGjp0qCQpICBAH330kQYMGKC6desqKipKzz33nB588MFr/FTO8/HxcSarfn5+uuOOO5zvggIAAABAYWOYpmlaHQTyLy0tTcHBwdr83Q4FBAZaHU6hdc4smvPVr1TO452sDqHQ++OLY1aH4BEidiRZHYJH8DP+tjqEQs8v57TVIXiETC9/q0Mo9DLle/lGRVz66dNqWO8WnTp1yjmLrzC78Hfw75+/p6AAa/4dSEvP0M13d/KYZ3atFOgnPgAAAADgmjIMyaKFdYrqdFbKMwAAAAAAt5FEAgAAAADcxnRWAAAAAB7LNAyZFk0rtWpcq1GJBAAAAAC4jUokAAAAAM9l2CxcWKdo1uSK5l0DAAAAAPKFJBIAAAAA4DamswIAAADwWKYMmbJoYR2LxrUalUgAAAAAgNuoRAIAAADwWKZhk2nRAjdWjWu1onnXAAAAAIB8IYkEAAAAALiN6awAAAAAPBffiSxwRfOuAQAAAAD5QhIJAAAAAHAb01kBAAAAeCzTMGQaFn0n0qJxrUYlEgAAAADgNiqRAAAAADwW34kseEXzrgEAAAAA+UISCQAAAABwG9NZAQAAAHguwzi/WTV2EUQlEgAAAADgNiqRAAAAADyXhQvriIV1AAAAAAC4NJJIAAAAAIDbmM4KAAAAwGOZMmTKmgVurBrXalQiAQAAAABuI4kEAAAAALiN6awAAAAAPJZp4eqslq0KazGSyBuE3ciQr+FldRiF1k+pEVaH4BEqv/Ke1SEUehGGaXUIHmFfzZZWh+AR6m1/1+oQCr2jRnmrQ/AI5bL2Wh1CoZdtL211CIWezXBYHQI8BEkkAAAAAM9lSDIsWuCmaK6rwzuRAAAAAAD3kUQCAAAAANzGdFYAAAAAHsuUTaZFtTGrxrVa0bxrAAAAAEC+UIkEAAAA4LFMw5Bp0cI6Vo1rNSqRAAAAAAC3kUQCAAAAANzGdFYAAAAAHss0bDINixbWsWhcqxXNuwYAAAAA5AuVSAAAAAAey5QhUxYtrGPRuFajEgkAAAAAcBtJJAAAAADAbUxnBQAAAOCxWFin4BXNuwYAAAAA5AtJJAAAAADAbUxnBQAAAOCxTMOQaVi0OqtF41qNSiQAAAAAwG1UIgEAAAB4LL4TWfCoRAIAAAAA3EYSCQAAAABwG9NZAQAAAHgsvhNZ8IrmXQMAAAAA8oVKJAAAAACPxcI6BY9KJAAAAADAbSSRAAAAAAC3MZ0VAAAAgMcyZeHCOkW0Jlc07xoAAAAAkC8kkQAAAAAAtzGdFQAAAIDHYnXWgkclEgAAAADgNpJISS1bttTQoUMLTT8AAAAA3GMahkzDZtFGJdIScXFxMgxDhmHIx8dHVatW1dNPP62cnByrQ7uopKQkGYah1NRUl+PLli3T5MmTrQkKAAAAQKH38ssvKyIiQr6+vmrcuLG2bNlyyfbvvfeeatasKV9fX9WuXVuffPKJy3nTNDV+/HiVLVtWfn5+io6O1q5du67nLVifREpSbGysDh8+rF27dmnEiBGaOHGipk+fbnVYV6xkyZIKDAy0OgwAAAAAhdCSJUs0fPhwTZgwQd99953q1KmjmJgYHTt2LM/2X3/9tR5++GH16dNH33//vTp06KAOHTro559/draZNm2aXnzxRc2ZM0ebN29W8eLFFRMTo7Nnz163+ygUSaTdbldYWJgqVqyogQMHKjo6Wh9++KFOnjypnj17qkSJEvL391ebNm1csurExESFhIRoxYoVqlatmnx9fRUTE6ODBw8628TFxalDhw4u4w0dOlQtW7a8aDxvvfWWGjRooMDAQIWFhalbt27OX+y+fft01113SZJKlCghwzAUFxcnKfd0VnfjX716tSIjIxUQEOBMqAEAAABc3oWFdazarsTzzz+vfv36qXfv3oqKitKcOXPk7++vuXPn5tn+hRdeUGxsrEaNGqXIyEhNnjxZ9erV0+zZs8/fu2lq1qxZeuqpp9S+fXvdeuutWrBggQ4dOqQVK1Zc7aO9qEKRRP6bn5+fsrKyFBcXp61bt+rDDz/Upk2bZJqm2rZtq+zsbGfbjIwMTZkyRQsWLNDGjRuVmpqqrl27XtX42dnZmjx5sn744QetWLFC+/btcyaK4eHhev/99yVJO3fu1OHDh/XCCy/k2Y+78c+YMUNvvfWW1q9frwMHDmjkyJEXjS0zM1NpaWkuGwAAAADr/Pvv88zMzFxtsrKytG3bNkVHRzuP2Ww2RUdHa9OmTXn2u2nTJpf2khQTE+Nsv3fvXh05csSlTXBwsBo3bnzRPq+FQvWJD9M0tW7dOq1evVpt2rTRihUrtHHjRjVt2lSStHDhQoWHh2vFihXq1KmTpPMJ3+zZs9W4cWNJ0vz58xUZGaktW7aoUaNG+Yrj0Ucfdf5cuXJlvfjii2rYsKHS09MVEBCgkiVLSpJKly6tkJCQPPvYtWuXPvzwQ7finzNnjqpUqSJJGjRokJ5++umLxhYfH69Jkybl674AAACAG835hXUs+sTH/xs3PDzc5fiECRM0ceJEl2PHjx/XuXPnVKZMGZfjZcqU0Y4dO/Ls/8iRI3m2P3LkiPP8hWMXa3M9FIpK5Mcff6yAgAD5+vqqTZs26tKli+Li4lSsWDFncihJN910k2rUqKGUlBTnsWLFiqlhw4bO/Zo1ayokJMSlzZXatm2b7rvvPlWoUEGBgYFq0aKFJOnAgQNu95GSkuJW/P7+/s4EUpLKli170TnRkjR27FidOnXKuf1z6i4AAACAgnfw4EGXv9HHjh1rdUjXVaFIIu+66y4lJydr165d+vvvvzV//nwZ1+i/JthsNpmm6XLsn9NJ/+3MmTOKiYlRUFCQFi5cqG+//VbLly+XdL4Efa15e3u77BuGkSvef7Lb7QoKCnLZAAAAAFjn33+f2+32XG1KlSolLy8vHT161OX40aNHFRYWlme/YWFhl2x/4f9eSZ/XQqFIIosXL66qVauqQoUKKlbs/AzbyMhI5eTkaPPmzc52f/31l3bu3KmoqCjnsZycHG3dutW5v3PnTqWmpioyMlKSFBoammuhmuTk5IvGsmPHDv3111969tlndccdd6hmzZq5KoM+Pj6SpHPnzl20H3fjBwAAAJB/pmlYurnLx8dH9evX17p165zHHA6H1q1bpyZNmuR5TZMmTVzaS9KaNWuc7StVqqSwsDCXNmlpadq8efNF+7wWCkUSmZdq1aqpffv26tevnzZs2KAffvhBPXr0UPny5dW+fXtnO29vbw0ePFibN2/Wtm3bFBcXp9tvv935PuTdd9+trVu3asGCBdq1a5cmTJjgsiTuv1WoUEE+Pj566aWX9Ntvv+nDDz/M9e3HihUryjAMffzxx/rzzz+Vnp6e7/gBAAAAFA3Dhw/XG2+8ofnz5yslJUUDBw7UmTNn1Lt3b0lSz549XabC/uc//9GqVas0c+ZM7dixQxMnTtTWrVs1aNAgSednMQ4dOlTPPPOMPvzwQ/3000/q2bOnypUrl+sLFddSoU0iJWnevHmqX7++7r33XjVp0kSmaeqTTz5xmQLq7++vMWPGqFu3bmrWrJkCAgK0ZMkS5/mYmBiNGzdOo0ePVsOGDXX69Gn17NnzomOGhoYqMTFR7733nqKiovTss89qxowZLm3Kly+vSZMm6cknn1SZMmWcv8T8xA8AAADgathkWrRdaTrVpUsXzZgxQ+PHj9dtt92m5ORkrVq1yrkwzoEDB1xmUTZt2lSLFi3S66+/rjp16mjp0qVasWKFatWq5WwzevRoDR48WP3793cuBrpq1Sr5+vpek6ebF8O81At4hVxiYqKGDh2q1NRUq0OxTFpamoKDg5X8/XcKDAy0OpxC66eTEVaH4BEqBx+3OoRCzzA89n8yC9S+mi2tDsEj1Nv+rtUhFHpHjfJWh+ARyuXstTqEQu+UvbTVIRR66adPq37d2jp16pRHrLtx4e/g777/SQEW/R2cfvq06nnQM7tWCnUlEgAAAABQuBSq70QCAAAAwJUwZciURd+JtGhcq3l0JTIuLq5IT2UFAAAAgILm0UkkAAAAAKBgMZ0VAAAAgMdiOmvBoxIJAAAAAHAblUgAAAAAHotKZMGjEgkAAAAAcBtJJAAAAADAbUxnBQAAAOCxmM5a8KhEAgAAAADcRiUSAAAAgMcyTUOmaVEl0qJxrUYlEgAAAADgNpJIAAAAAIDbmM4KAAAAwGOxsE7BoxIJAAAAAHAbSSQAAAAAwG1MZwUAAADgsZjOWvCoRAIAAAAA3EYlEgAAAIDHohJZ8KhEAgAAAADcRhIJAAAAAHAb01kBAAAAeCxThkyT6awFiUokAAAAAMBtVCIBAAAAeCyHDDksqghaNa7VqEQCAAAAANxGEgkAAAAAcBvTWQEAAAB4LL4TWfCoRAIAAAAA3EYlEgAAAIDHMk0LP/Fh0bhWI4m8QdhMUzbTYXUYhdatIXutDsEjOAwvq0PADaLe9netDsEjfHdLZ6tDKPSidq60OgSPcMarhNUhAChCmM4KAAAAAHAblUgAAAAAHsuUdQvcmJaMaj0qkQAAAAAAt5FEAgAAAADcxnRWAAAAAB6L1VkLHpVIAAAAAIDbqEQCAAAA8FimDAsX1qESCQAAAADAJZFEAgAAAADcxnRWAAAAAB6LhXUKHpVIAAAAAIDbqEQCAAAA8FimJIeFYxdFVCIBAAAAAG4jiQQAAAAAuI3prAAAAAA8FgvrFDwqkQAAAAAAt5FEAgAAAADcxnRWAAAAAB7LlCFTFk1ntWhcq1GJBAAAAAC4jUokAAAAAI/FwjoFj0okAAAAAMBtJJEAAAAAALcxnRUAAACAx2JhnYJHJRIAAAAA4DYqkQAAAAA8lsM8v1k1dlFEJRIAAAAA4DaSSAAAAACA25jOCgAAAMBjsbBOwaMSCQAAAABwG0kkAAAAAMBtTGcFAAAA4LFM05BpWjSd1aJxrVYoK5GbNm2Sl5eX2rVrd136nz9/vho2bCh/f38FBgaqRYsW+vjjj6/LWAAAAABwIymUSWRCQoIGDx6s9evX69ChQ9e075EjR+qxxx5Tly5d9OOPP2rLli1q3ry52rdvr9mzZ1/TsQAAAABcX6Zp7VYUFbokMj09XUuWLNHAgQPVrl07JSYmSpK6deumLl26uLTNzs5WqVKltGDBAkmSw+FQfHy8KlWqJD8/P9WpU0dLly51tv/mm280c+ZMTZ8+XSNHjlTVqlUVGRmpKVOmaOjQoRo+fLgOHjzobL9x40a1bNlS/v7+KlGihGJiYnTy5EnnWNOmTVPVqlVlt9tVoUIFTZkyRZKUlJQkwzCUmprq7Cs5OVmGYWjfvn2SpMTERIWEhGjFihWqVq2afH19FRMT4zI+AAAAABQ2hS6JfPfdd1WzZk3VqFFDPXr00Ny5c2Waprp3766PPvpI6enpzrarV69WRkaGHnjgAUlSfHy8FixYoDlz5mj79u0aNmyYevTooS+//FKS9M477yggIECPPfZYrnFHjBih7Oxsvf/++5LOJ32tWrVSVFSUNm3apA0bNui+++7TuXPnJEljx47Vs88+q3HjxumXX37RokWLVKZMmSu614yMDE2ZMkULFizQxo0blZqaqq5du+bruQEAAABAQSh0C+skJCSoR48ekqTY2FidOnVKX375pWJiYlS8eHEtX75cjzzyiCRp0aJFuv/++xUYGKjMzExNnTpVa9euVZMmTSRJlStX1oYNG/Taa6+pRYsW+vXXX1WlShX5+PjkGrdcuXIKCgrSr7/+KkmaNm2aGjRooFdeecXZ5pZbbpEknT59Wi+88IJmz56tXr16SZKqVKmi5s2bX9G9Zmdna/bs2WrcuLGk8+9qRkZGasuWLWrUqFGe12RmZiozM9O5n5aWdkVjAgAAADcShww5LPpeo1XjWq1QVSJ37typLVu26OGHH5YkFStWTF26dFFCQoKKFSumzp07a+HChZKkM2fO6IMPPlD37t0lSbt371ZGRobuueceBQQEOLcFCxZoz549zjFMNycuX6hE5iUlJUWZmZkXPe+uYsWKqWHDhs79mjVrKiQkRCkpKRe9Jj4+XsHBwc4tPDz8qmIAAAAAgCtRqCqRCQkJysnJUbly5ZzHTNOU3W7X7Nmz1b17d7Vo0ULHjh3TmjVr5Ofnp9jYWElyTnNduXKlypcv79Kv3W6XJFWvXl0bNmxQVlZWrmrkoUOHlJaWpurVq0uS/Pz8Lhrnpc5Jks1mc8Z+QXZ29iWvcdfYsWM1fPhw535aWhqJJAAAAIosPvFR8ApNJTInJ0cLFizQzJkzlZyc7Nx++OEHlStXTu+8846aNm2q8PBwLVmyRAsXLlSnTp3k7e0tSYqKipLdbteBAwdUtWpVl+1CktW1a1elp6frtddeyzX+jBkz5O3trQcffFCSdOutt2rdunV5xlqtWjX5+fld9HxoaKgk6fDhw85jycnJed7z1q1bnfs7d+5UamqqIiMjL/qc7Ha7goKCXDYAAAAAKCiFphL58ccf6+TJk+rTp4+Cg4Ndzj344INKSEjQgAED1K1bN82ZM0e//vqrvvjiC2ebwMBAjRw5UsOGDZPD4VDz5s116tQpbdy4UUFBQerVq5eaNGmi//znPxo1apSysrLUoUMHZWdn6+2339YLL7ygWbNmORPOsWPHqnbt2nr88cc1YMAA+fj46IsvvlCnTp1UqlQpjRkzRqNHj5aPj4+aNWumP//8U9u3b1efPn2cievEiRM1ZcoU/frrr5o5c2aue/b29tbgwYP14osvqlixYho0aJBuv/32i74PCQAAAABWKzSVyISEBEVHR+dKIKXzSeTWrVv1448/qnv37vrll19Uvnx5NWvWzKXd5MmTNW7cOMXHxysyMlKxsbFauXKlKlWq5Gwza9YsvfLKK3rnnXdUq1YtNWjQQOvXr9eKFSs0ePBgZ7vq1avrs88+0w8//KBGjRqpSZMm+uCDD1Ss2Pm8e9y4cRoxYoTGjx+vyMhIdenSRceOHZN0Pjl85513tGPHDt1666167rnn9Mwzz+S6L39/f40ZM0bdunVTs2bNFBAQoCVLllyT5wkAAAAUBXwnsuAZprsrzeCaSkxM1NChQ12+JZkfaWlpCg4O1o/fbVNgYMC1Ce4GZBbRlbOulMPwsjoE3CD8ck5bHYJH+O6WzlaHUOhF7VxpdQi4QeTI2+oQCr3006dVv25tnTp1yiNembrwd/DyDcdUPMCaeM+kp+mB5qWvyzM7ceKEBg8erI8++kg2m00PPvigXnjhBQUE5P03/4kTJzRhwgR99tlnOnDggEJDQ9WhQwdNnjzZpVBnGLn/Ln7nnXeu6FODhWY6KwAAAABcKVOGZQWD6zlu9+7ddfjwYa1Zs0bZ2dnq3bu3+vfvr0WLFuXZ/tChQzp06JBmzJihqKgo7d+/XwMGDNChQ4e0dOlSl7bz5s1zLlAqSSEhIVcUG0kkAAAAABQiKSkpWrVqlb799ls1aNBAkvTSSy+pbdu2mjFjhsvXLC6oVauW3n//fed+lSpVNGXKFPXo0UM5OTnO1/Kk80ljWFhYvuMrNO9EFjVxcXFXPZUVAAAAgPXS0tJctszMzKvqb9OmTQoJCXEmkJIUHR0tm82mzZs3u93PhWm2/0wgJemJJ55QqVKl1KhRI82dO1dX+oYjlUgAAAAAHsthnt+sGltSru+2T5gwQRMnTsx3v0eOHFHp0qVdjhUrVkwlS5bUkSNH3Orj+PHjmjx5svr37+9y/Omnn9bdd98tf39/ffbZZ3r88ceVnp6uIUOGuB0fSSQAAAAAXIWDBw+6LKxjt9vzbPfkk0/queeeu2RfKSkpVx1PWlqa2rVrp6ioqFzJ7Lhx45w/161bV2fOnNH06dNJIgEAAACgoAQFBbm1OuuIESMUFxd3yTaVK1dWWFiY8/OBF+Tk5OjEiROXfZfx9OnTio2NVWBgoJYvXy5v70uvTNy4cWNNnjxZmZmZF01+/40kEgAAAIDnMg2ZpkWfc7vCcUNDQxUaGnrZdk2aNFFqaqq2bdum+vXrS5I+//xzORwONW7c+KLXpaWlKSYmRna7XR9++KF8fX0vO1ZycrJKlCjhdgIpkUQCAAAAQKESGRmp2NhY9evXT3PmzFF2drYGDRqkrl27Oldm/eOPP9SqVSstWLBAjRo1Ulpamlq3bq2MjAy9/fbbzkV+pPPJq5eXlz766CMdPXpUt99+u3x9fbVmzRpNnTpVI0eOvKL4SCIBAAAAeCzTPL9ZNfb1snDhQg0aNEitWrWSzWbTgw8+qBdffNF5Pjs7Wzt37lRGRoYk6bvvvnOu3Fq1alWXvvbu3auIiAh5e3vr5Zdf1rBhw2SapqpWrarnn39e/fr1u6LYSCIBAAAAoJApWbKkFi1adNHzERERLp/maNmy5WU/1REbG6vY2Nirjo3vRAIAAAAA3EYlEgAAAIDHcsiQQ9YsrGPVuFajEgkAAAAAcBuVSAAAAAAe60ZdWKcwoxIJAAAAAHAbSSQAAAAAwG1MZwUAAADgsUzTkGlas8CNVeNajUokAAAAAMBtJJEAAAAAALcxnRUAAACAx3KY5zerxi6KqEQCAAAAANxGJRIAAACAx+I7kQWPSiQAAAAAwG0kkQAAAAAAtzGdFQAAAIDHMmXIlEXfibRoXKtRiQQAAAAAuI1KJAAAAACP5ZCFn/iwZljLUYkEAAAAALiNJBIAAAAA4DamswIAAADwWHwnsuCRRN4gTitIphlodRiFVqCRZnUIHiHw7HGrQyj0corZrQ7BIxw1ylsdgkeI2rnS6hAKvV9qtLM6BI/wYtd3rA6h0Jv8VGWrQyj0zuQUzZVGceVIIgEAAAB4LCqRBY93IgEAAAAAbiOJBAAAAAC4jemsAAAAADyWwzTkMK15n9Oqca1GJRIAAAAA4DaSSAAAAACA25jOCgAAAMBjsTprwaMSCQAAAABwG5VIAAAAAB6LSmTBoxIJAAAAAHAbSSQAAAAAwG1MZwUAAADgsUxTcjCdtUBRiQQAAAAAuI1KJAAAAACPZZqGTNOwbOyiiEokAAAAAMBtJJEAAAAAALcxnRUAAACAx+I7kQWPSiQAAAAAwG0kkQAAAAAAtzGdFQAAAIDHclj4nUirxrUalUgAAAAAgNuoRAIAAADwWCysU/CoRAIAAAAA3EYSCQAAAABwG9NZAQAAAHgsprMWPCqRAAAAAAC3UYkEAAAA4LH4xEfBoxIJAAAAAHAbSSQAAAAAwG1MZwUAAADgsVhYp+BRiQQAAAAAuI0kMg8RERGaNWvWdem7ZcuWGjp06HXpGwAAAACuN49PIuPi4tShQ4d8XZuYmKiQkJBcx7/99lv179/fuW8YhlasWJG/AAEAAABcNw6HtVtRxDuReQgNDbU6BAAAAAAolDy+Enkpzz//vGrXrq3ixYsrPDxcjz/+uNLT0yVJSUlJ6t27t06dOiXDMGQYhiZOnCjJdTprRESEJOmBBx6QYRjO/bwqoEOHDlXLli2d+2fOnFHPnj0VEBCgsmXLaubMmblizMzM1MiRI1W+fHkVL15cjRs3VlJS0jV8CgAAAMCN68LCOlZtRdENnUTabDa9+OKL2r59u+bPn6/PP/9co0ePliQ1bdpUs2bNUlBQkA4fPqzDhw9r5MiRufr49ttvJUnz5s3T4cOHnfvuGDVqlL788kt98MEH+uyzz5SUlKTvvvvOpc2gQYO0adMmLV68WD/++KM6deqk2NhY7dq1K88+MzMzlZaW5rIBAAAAQEG5oaez/nMBm4iICD3zzDMaMGCAXnnlFfn4+Cg4OFiGYSgsLOyifVyY2hoSEnLJdv+Wnp6uhIQEvf3222rVqpUkaf78+br55pudbQ4cOKB58+bpwIEDKleunCRp5MiRWrVqlebNm6epU6fm6jc+Pl6TJk1yOw4AAAAAuJZu6CRy7dq1io+P144dO5SWlqacnBydPXtWGRkZ8vf3v65j79mzR1lZWWrcuLHzWMmSJVWjRg3n/k8//aRz586pevXqLtdmZmbqpptuyrPfsWPHavjw4c79tLQ0hYeHX+PoAQAAAM/AdyIL3g2bRO7bt0/33nuvBg4cqClTpqhkyZLasGGD+vTpo6ysrKtOIm02m8x//VOTnZ19RX2kp6fLy8tL27Ztk5eXl8u5gICAPK+x2+2y2+1XFiwAAAAAXCM3bBK5bds2ORwOzZw5Uzbb+Vc/3333XZc2Pj4+Onfu3GX78vb2ztUuNDRUP//8s8ux5ORkeXt7S5KqVKkib29vbd68WRUqVJAknTx5Ur/++qtatGghSapbt67OnTunY8eO6Y477sjfjQIAAABFmEOSw6KKYBH9wseNsbDOqVOnlJyc7LKVKlVK2dnZeumll/Tbb7/prbfe0pw5c1yui4iIUHp6utatW6fjx48rIyMjz/4jIiK0bt06HTlyRCdPnpQk3X333dq6dasWLFigXbt2acKECS5JZUBAgPr06aNRo0bp888/188//6y4uDhnQitJ1atXV/fu3dWzZ08tW7ZMe/fu1ZYtWxQfH6+VK1dehycFAAAAAFfnhkgik5KSVLduXZftrbfe0vPPP6/nnntOtWrV0sKFCxUfH+9yXdOmTTVgwAB16dJFoaGhmjZtWp79z5w5U2vWrFF4eLjq1q0rSYqJidG4ceM0evRoNWzYUKdPn1bPnj1drps+fbruuOMO3XfffYqOjlbz5s1Vv359lzbz5s1Tz549NWLECNWoUUMdOnTQt99+66xeAgAAAEBhYpj/frEPHiUtLU3BwcHa+N0uBQQEWh1OoRVo8CkUdxTPPGl1CIVeTjHeSXbHUaO81SF4hGCvVKtDKPR+qdHO6hA8wotd37E6hEJv8lOVrQ6h0DuTnqZWDSvq1KlTCgoKsjqcy7rwd/C091Ll529NvH9npGl0pxCPeWbXyg1RiQQAAAAAFIwbdmEdAAAAADc+PvFR8KhEAgAAAADcRhIJAAAAAHAb01kBAAAAeCzTITks+mCjWUQ/FEklEgAAAADgNpJIAAAAAIDbSCIBAAAAeKwLq7NatV0vJ06cUPfu3RUUFKSQkBD16dNH6enpl7ymZcuWMgzDZRswYIBLmwMHDqhdu3by9/dX6dKlNWrUKOXk5FxRbLwTCQAAAACFTPfu3XX48GGtWbNG2dnZ6t27t/r3769FixZd8rp+/frp6aefdu77+/s7fz537pzatWunsLAwff311zp8+LB69uwpb29vTZ061e3YSCIBAAAAeCyHeX6zauzrISUlRatWrdK3336rBg0aSJJeeukltW3bVjNmzFC5cuUueq2/v7/CwsLyPPfZZ5/pl19+0dq1a1WmTBnddtttmjx5ssaMGaOJEyfKx8fHrfiYzgoAAAAAhcimTZsUEhLiTCAlKTo6WjabTZs3b77ktQsXLlSpUqVUq1YtjR07VhkZGS791q5dW2XKlHEei4mJUVpamrZv3+52fFQiAQAAAOAqpKWluezb7XbZ7fZ893fkyBGVLl3a5VixYsVUsmRJHTly5KLXdevWTRUrVlS5cuX0448/asyYMdq5c6eWLVvm7PefCaQk5/6l+v03kkgAAAAAHut6L3BzubElKTw83OX4hAkTNHHixFztn3zyST333HOX7DMlJSXf8fTv39/5c+3atVW2bFm1atVKe/bsUZUqVfLd77+RRAIAAADAVTh48KCCgoKc+xerQo4YMUJxcXGX7Kty5coKCwvTsWPHXI7n5OToxIkTF33fMS+NGzeWJO3evVtVqlRRWFiYtmzZ4tLm6NGjknRF/ZJEAgAAAPBYpsOUadHKOhfGDQoKckkiLyY0NFShoaGXbdekSROlpqZq27Ztql+/viTp888/l8PhcCaG7khOTpYklS1b1tnvlClTdOzYMed02TVr1igoKEhRUVFu98vCOgAAAABQiERGRio2Nlb9+vXTli1btHHjRg0aNEhdu3Z1rsz6xx9/qGbNms7K4p49ezR58mRt27ZN+/bt04cffqiePXvqzjvv1K233ipJat26taKiovTII4/ohx9+0OrVq/XUU0/piSeeuKJ3OEkiAQAAAKCQWbhwoWrWrKlWrVqpbdu2at68uV5//XXn+ezsbO3cudO5+qqPj4/Wrl2r1q1bq2bNmhoxYoQefPBBffTRR85rvLy89PHHH8vLy0tNmjRRjx491LNnT5fvSrqD6awAAAAAPNaN+J1ISSpZsqQWLVp00fMREREy/7GiUHh4uL788svL9luxYkV98sknVxUblUgAAAAAgNtIIgEAAAAAbmM6KwAAAACPVRi+E1nUUIkEAAAAALiNSiQAAAAAj+VwmHJYtLKOVeNajUokAAAAAMBtJJEAAAAAALcxnRUAAACAx2JhnYJHJRIAAAAA4DYqkQAAAAA8FpXIgkclEgAAAADgNpJIAAAAAIDbmM56g/AzMuRv87I6jEIr9VwJq0PwCNm+PlaHUOh5m1lWh+ARymXttToEj3DGi/9tupwXu75jdQgeYcjih60OodALnrjW6hAKPS+vdKtDyBeHacph0bxSq8a1GpVIAAAAAIDbqEQCAAAA8Fim4/xm1dhFEZVIAAAAAIDbSCIBAAAAAG5jOisAAAAAj2XKlGnRAjemWFgHAAAAAIBLIokEAAAAALiN6awAAAAAPJbpkByszlqgqEQCAAAAANxGJRIAAACAxzJNCxfWsWhcq1GJBAAAAAC4jSQSAAAAAOA2prMCAAAA8FgO8/xm1dhFEZVIAAAAAIDbqEQCAAAA8Fimw5RpUUnQqnGtRiUSAAAAAOA2kkgAAAAAgNuYzgoAAADAY5nm+c2qsYsiKpEAAAAAALeRRAIAAAAA3MZ0VgAAAAAey+Ew5bBolVSrxrUalUgAAAAAgNuoRAIAAADwWKZpyrRohRurxrUalUgAAAAAgNtIIgEAAAAAbmM6KwAAAACPZTrOb1aNXRRRiQQAAAAAuI1KJAAAAACP5TBNOSxa4Maqca1GJRIAAAAA4DaSSAAAAACA20gi/2Xfvn0yDEPJycnXpX/DMLRixYrr0jcAAABQ1Fz4TqRVW1FU6JLIuLg4dejQwbLxw8PDdfjwYdWqVUuSlJSUJMMwlJqaallMAAAAAFBYsLDOv3h5eSksLMzqMAAAAAC4weEw5XBYtLCOReNardBVIi/lyy+/VKNGjWS321W2bFk9+eSTysnJcZ5v2bKlhgwZotGjR6tkyZIKCwvTxIkTXfrYsWOHmjdvLl9fX0VFRWnt2rUuU0z/OZ113759uuuuuyRJJUqUkGEYiouLkyRFRERo1qxZLn3fdtttLuPt2rVLd955p3OsNWvW5LqngwcPqnPnzgoJCVHJkiXVvn177du372ofFQAAAABcFx6TRP7xxx9q27atGjZsqB9++EGvvvqqEhIS9Mwzz7i0mz9/vooXL67Nmzdr2rRpevrpp53J27lz59ShQwf5+/tr8+bNev311/W///3vomOGh4fr/ffflyTt3LlThw8f1gsvvOBWvA6HQx07dpSPj482b96sOXPmaMyYMS5tsrOzFRMTo8DAQH311VfauHGjAgICFBsbq6ysrCt5PAAAAABQIDxmOusrr7yi8PBwzZ49W4ZhqGbNmjp06JDGjBmj8ePHy2Y7nw/feuutmjBhgiSpWrVqmj17ttatW6d77rlHa9as0Z49e5SUlOScsjplyhTdc889eY7p5eWlkiVLSpJKly6tkJAQt+Ndu3atduzYodWrV6tcuXKSpKlTp6pNmzbONkuWLJHD4dCbb74pwzAkSfPmzVNISIiSkpLUunXrXP1mZmYqMzPTuZ+WluZ2TAAAAMCNxjTPb1aNXRR5TCUyJSVFTZo0cSZbktSsWTOlp6fr999/dx679dZbXa4rW7asjh07Jul8NTE8PNzlncdGjRpdt3jDw8OdCaQkNWnSxKXNDz/8oN27dyswMFABAQEKCAhQyZIldfbsWe3ZsyfPfuPj4xUcHOzcwsPDr0v8AAAAAJAXj6lEusvb29tl3zAMORyOaz6OzWbLtaRvdnb2FfWRnp6u+vXra+HChbnOhYaG5nnN2LFjNXz4cOd+WloaiSQAAACAAuMxSWRkZKTef/99mabprEZu3LhRgYGBuvnmm93qo0aNGjp48KCOHj2qMmXKSJK+/fbbS17j4+Mj6fz7lP8UGhqqw4cPO/fT0tK0d+9el3gPHjyow4cPq2zZspKkb775xqWPevXqacmSJSpdurSCgoLcuge73S673e5WWwAAAOBGZ5qmTItWSeU7kYXIqVOnlJyc7LL1799fBw8e1ODBg7Vjxw598MEHmjBhgoYPH+58H/Jy7rnnHlWpUkW9evXSjz/+qI0bN+qpp56SJJdpsv9UsWJFGYahjz/+WH/++afS09MlSXfffbfeeustffXVV/rpp5/Uq1cveXl5Oa+Ljo5W9erV1atXL/3www/66quvci3i0717d5UqVUrt27fXV199pb179yopKUlDhgxxmaILAAAAAIVFoUwik5KSVLduXZdt8uTJ+uSTT7RlyxbVqVNHAwYMUJ8+fZxJoDu8vLy0YsUKpaenq2HDhurbt68zsfP19c3zmvLly2vSpEl68sknVaZMGQ0aNEjS+WmlLVq00L333qt27dqpQ4cOqlKlivM6m82m5cuX6++//1ajRo3Ut29fTZkyxaVvf39/rV+/XhUqVFDHjh0VGRmpPn366OzZs25XJgEAAICizDRNOSzaimol0jCL6p3/Pxs3blTz5s21e/dulyTQU6SlpSk4OFjfff+DAgMDrQ6n0Dp9jmfjjuJeZ6wOodDzNvn8jjv8s1KtDsEjnLGXsDqEQm/ABFYhd8eQxQ9bHUKhV3XHWqtDKPTST59Ww3q36NSpUx5R0Ljwd3D/+IPy8bUm3qyzaXp9bLjHPLNrxWPeibxWli9froCAAFWrVk27d+/Wf/7zHzVr1swjE0gAAAAAKGhFLok8ffq0xowZowMHDqhUqVKKjo7WzJkzrQ4LAAAAQD6YDgsX1rFoXKsVuSSyZ8+e6tmzp9VhAAAAAIBHKnJJJAAAAIAbB5XIglcoV2cFAAAAABROJJEAAAAAALcxnRUAAACAx3KY5zerxi6KqEQCAAAAANxGEgkAAAAAcBvTWQEAAAB4LFZnLXhUIgEAAAAAbqMSCQAAAMBjmaYp07SoEmnRuFajEgkAAAAAcBtJJAAAAADAbUxnBQAAAOCxHA7JYdECNw6HJcNajkokAAAAAMBtVCIBAAAAeCwW1il4VCIBAAAAoJA5ceKEunfvrqCgIIWEhKhPnz5KT0+/aPt9+/bJMIw8t/fee8/ZLq/zixcvvqLYqEQCAAAAQCHTvXt3HT58WGvWrFF2drZ69+6t/v37a9GiRXm2Dw8P1+HDh12Ovf7665o+fbratGnjcnzevHmKjY117oeEhFxRbCSRAAAAADyW6TBlWrSwzvUaNyUlRatWrdK3336rBg0aSJJeeukltW3bVjNmzFC5cuVyXePl5aWwsDCXY8uXL1fnzp0VEBDgcjwkJCRX2yvBdFYAAAAAKEQ2bdqkkJAQZwIpSdHR0bLZbNq8ebNbfWzbtk3Jycnq06dPrnNPPPGESpUqpUaNGmnu3LlX/G4nlUgAAAAAuAppaWku+3a7XXa7Pd/9HTlyRKVLl3Y5VqxYMZUsWVJHjhxxq4+EhARFRkaqadOmLseffvpp3X333fL399dnn32mxx9/XOnp6RoyZIjb8VGJBAAAAOCxLkxntWqTzr+PGBwc7Nzi4+PzjPXJJ5+86OI3F7YdO3Zc9TP5+++/tWjRojyrkOPGjVOzZs1Ut25djRkzRqNHj9b06dOvqH8qkQAAAABwFQ4ePKigoCDn/sWqkCNGjFBcXNwl+6pcubLCwsJ07Ngxl+M5OTk6ceKEW+8yLl26VBkZGerZs+dl2zZu3FiTJ09WZmam29VTkkgAAAAAHsshUw6Lvtfo0Plxg4KCXJLIiwkNDVVoaOhl2zVp0kSpqanatm2b6tevL0n6/PPP5XA41Lhx48ten5CQoPvvv9+tsZKTk1WiRIkrmn5LEgkAAAAAhUhkZKRiY2PVr18/zZkzR9nZ2Ro0aJC6du3qXJn1jz/+UKtWrbRgwQI1atTIee3u3bu1fv16ffLJJ7n6/eijj3T06FHdfvvt8vX11Zo1azR16lSNHDnyiuIjiQQAAACAQmbhwoUaNGiQWrVqJZvNpgcffFAvvvii83x2drZ27typjIwMl+vmzp2rm2++Wa1bt87Vp7e3t15++WUNGzZMpmmqatWqev7559WvX78rio0kEgAAAIDHuhG/EylJJUuW1KJFiy56PiIiIs9Pc0ydOlVTp07N85rY2FjFxsZedWyszgoAAAAAcBuVSAAAAAAeyzTNPCtyBTV2UUQSeYPwmzNRfnZvq8MotIL6j7A6BI9QLONvq0Mo9I4FVLY6BI+QbS99+UaAGyY/xb9z7gieuNbqEAq93TWjrQ6h0Mswz1kdAjwE01kBAAAAAG6jEgkAAADAY5kOU44bcGGdwoxKJAAAAADAbVQiAQAAAHisG/UTH4UZlUgAAAAAgNtIIgEAAAAAbmM6KwAAAACPxXciCx6VSAAAAACA20giAQAAAABuYzorAAAAAI9lOhwyHQ7Lxi6KqEQCAAAAANxGJRIAAACAx3I4TDks+l6jVeNajUokAAAAAMBtJJEAAAAAALcxnRUAAACAx+I7kQWPSiQAAAAAwG1UIgEAAAB4LNNhyrRogRurxrUalUgAAAAAgNtIIgEAAAAAbmM6KwAAAACPxXTWgkclEgAAAADgNpJIAAAAAIDbmM4KAAAAwGM55JDDdFg2dlFEJRIAAAAA4DYqkQAAAAA8lumwboEbiwqglqMSCQAAAABwG0kkAAAAAMBtTGcFAAAA4LH4TmTBoxIJAAAAAHAblUgAAAAAHss0TZmmRZVIi8a1GpVIAAAAAIDbClUSmZSUJMMwlJqaanUolti3b58Mw1BycrLVoQAAAABAnq4oiYyLi5NhGBowYECuc0888YQMw1BcXNy1iu2SSWVERIRmzZp1zca6GkePHpW3t7cWL16c5/k+ffqoXr16BRwVAAAAcONzOByWbkXRFVciw8PDtXjxYv3999/OY2fPntWiRYtUoUKFaxqcpyhTpozatWunuXPn5jp35swZvfvuu+rTp48FkQEAAADAtXXFSWS9evUUHh6uZcuWOY8tW7ZMFSpUUN26dZ3HHA6H4uPjValSJfn5+alOnTpaunSpS1+ffPKJqlevLj8/P911113at29fvm/kwIEDat++vQICAhQUFKTOnTvr6NGjzvNxcXHq0KGDyzVDhw5Vy5YtnftLly5V7dq15efnp5tuuknR0dE6c+aM8/ybb76pyMhI+fr6qmbNmnrllVec5/r06aN169bpwIEDLmO89957ysnJUffu3bVq1So1b95cISEhuummm3Tvvfdqz549+b5nAAAAoKi78IkPq7aiKF/vRD766KOaN2+ec3/u3Lnq3bu3S5v4+HgtWLBAc+bM0fbt2zVs2DD16NFDX375pSTp4MGD6tixo+677z4lJyerb9++evLJJ/N1Ew6HQ+3bt9eJEyf05Zdfas2aNfrtt9/UpUsXt/s4fPiwHn74YT366KNKSUlRUlKSOnbs6FxxaeHChRo/frymTJmilJQUTZ06VePGjdP8+fMlSW3btlWZMmWUmJjo0u+8efPUsWNHhYSE6MyZMxo+fLi2bt2qdevWyWaz6YEHHriiMnhmZqbS0tJcNgAAAAAoKPn6xEePHj00duxY7d+/X5K0ceNGLV68WElJSZLOJzpTp07V2rVr1aRJE0lS5cqVtWHDBr322mtq0aKFXn31VVWpUkUzZ86UJNWoUUM//fSTnnvuuVzj3XzzzbmOZWRkOH9et26dfvrpJ+3du1fh4eGSpAULFuiWW27Rt99+q4YNG172ng4fPqycnBx17NhRFStWlCTVrl3beX7ChAmaOXOmOnbsKEmqVKmSfvnlF7322mvq1auXvLy81KtXLyUmJmrcuHEyDEN79uzRV199pTVr1kiSHnzwQZcx586dq9DQUP3yyy+qVavWZWOUzifnkyZNcqstAAAAAFxr+UoiQ0ND1a5dOyUmJso0TbVr106lSpVynt+9e7cyMjJ0zz33uFyXlZXlnPKakpKixo0bu5y/kHD+21dffaXAwECXY/+chpqSkqLw8HBnAilJUVFRCgkJUUpKiltJZJ06ddSqVSvVrl1bMTExat26tR566CGVKFFCZ86c0Z49e9SnTx/169fPeU1OTo6Cg4Od+48++qieffZZffHFF7r77rs1b948RURE6O6775Yk7dq1S+PHj9fmzZt1/PhxZwXywIEDbieRY8eO1fDhw537aWlpLvcNAAAAFCWm6ZBpWrPAjVXjWi1fSaR0PmEaNGiQJOnll192OZeeni5JWrlypcqXL+9yzm63X/FYlSpVUkhIiMuxYsWuLHSbzZbrY6DZ2dnOn728vLRmzRp9/fXX+uyzz/TSSy/pf//7nzZv3ix/f39J0htvvJEr8fXy8nL+XK1aNd1xxx2aN2+eWrZsqQULFqhfv34yDEOSdN9996lixYp64403VK5cOTkcDtWqVUtZWVlu34fdbs/XMwQAAACAayHfSWRsbKyysrJkGIZiYmJczkVFRclut+vAgQNq0aJFntdHRkbqww8/dDn2zTff5CuWyMhIHTx4UAcPHnRW5X755RelpqYqKipK0vnq6c8//+xyXXJysry9vZ37hmGoWbNmatasmcaPH6+KFStq+fLlGj58uMqVK6fffvtN3bt3v2Qsffr00cCBA3X//ffrjz/+cH7y5K+//tLOnTv1xhtv6I477pAkbdiwIV/3CwAAAABWyXcS6eXlpZSUFOfP/xQYGKiRI0dq2LBhcjgcat68uU6dOqWNGzcqKChIvXr10oABAzRz5kyNGjVKffv21bZt23ItSuOu6Oho1a5dW927d9esWbOUk5Ojxx9/XC1atFCDBg0kSXfffbemT5+uBQsWqEmTJnr77bf1888/O6fXbt68WevWrVPr1q1VunRpbd68WX/++aciIyMlSZMmTdKQIUMUHBys2NhYZWZmauvWrTp58qTL9NJOnTppyJAheuyxx9S6dWtnUluiRAnddNNNev3111W2bFkdOHAg3wsJAQAAADjPylVSWZ01H4KCghQUFJTnucmTJ2vcuHGKj49XZGSkYmNjtXLlSlWqVEmSVKFCBb3//vtasWKF6tSpozlz5mjq1Kn5isMwDH3wwQcqUaKE7rzzTkVHR6ty5cpasmSJs01MTIzGjRun0aNHq2HDhjp9+rR69uzpci/r169X27ZtVb16dT311FOaOXOm2rRpI0nq27ev3nzzTc2bN0+1a9dWixYtlJiY6LyfC/z9/dW1a1edPHlSjz76qPO4zWbT4sWLtW3bNtWqVUvDhg3T9OnT83W/AAAAAGAVw/z3i4LwKGlpaQoODlbKYw8o0O59+QuKKO/+I6wOwSMUy/7b6hAKvWMBla0OwSPYjKK50ACuvZPZwZdvBAUXO211CIXe7prRVodQ6GWY59TFsUenTp26aKGoMLnwd3D0wxtVzCfAkhhystK19p1mHvPMrpWrqkQCAAAAAIoWkkgAAAAAgNvyvbAOAAAAAFjNYTrksOh7jVaNazUqkQAAAAAAt1GJBAAAAOCx+MRHwaMSCQAAAABwG0kkAAAAAMBtTGcFAAAA4LFM0yHTYc0CNyYL6wAAAAAAcGkkkQAAAAAAtzGdFQAAAIDHYnXWgkclEgAAAADgNiqRAAAAADyWaTosW+CGhXUAAAAAALgMkkgAAAAAgNuYzgoAAADAYzkcksOiBW4s+jyl5ahEAgAAAADcRiUSAAAAgMf6/9q787ie8vZ/4NdJFJWiTEQoRSmpSKLNkrRI9m0sYzcYDFKMZZA9Q7ayzGCMGWPPNoYwGHt22pC0qFApKW2v3x/9PufuI3Nr7u9MJ53r+Xjcj9H5nA9X7/ss7+u9oriYIFGXoFT/rtS4J5IxxhhjjDHGWLlxEskYY4wxxhhjrNx4OCtjjDHGGGPsk4ViECRaWEeqf1dq3BPJGGOMMcYYY6zcOIlkjDHGGGOMMVZuPJyVMcYYY4wx9skCigmQaHVWif5dqXFPJGOMMcYYY4yxcuOeSMYYY4wxxtgnixfWqXjcE8kYY4wxxhhjlUxgYCB16NCBatWqRTo6OuX6DgCaN28eNWjQgGrWrEldu3al2NhYpXPS09NpyJAhVLt2bdLR0aFRo0bRmzdv/lZsnEQyxhhjjDHGWCWTn59P/fr1owkTJpT7OytWrKDg4GAKCQmhq1evkoaGBrm7u1NeXp54zpAhQ+jBgwd06tQpOnr0KJ0/f57Gjh37t2Lj4ayMMcYYY4yxTxaKiwnFEi2s8y/+u99++y0REW3fvr18sQC0Zs0a+uabb6hnz55ERLRz507S19enQ4cO0cCBAykyMpJ+++03un79OrVt25aIiNatW0eenp60atUqMjAwKNe/xUnkJw4oGYf9Jr9A4kgqt+p/s4terqoV5H38JJl7g2ypQ/gkqAjyXK2O/fNyCgWpQ/gkVKvG77mPeYsiqUOo9N7+/5VGFfXLT0VRYY7k/3ZWVpbScTU1NVJTU6vQWOLi4iglJYW6du0qHtPW1iZ7e3u6fPkyDRw4kC5fvkw6OjpiAklE1LVrV1JRUaGrV69Sr169yvVvcRL5icvOLqnQ2v1wVOJIKrnQg1JHwBhjjDH2ScjOziZtbW2pw/ioGjVqUP369elGeH9J49DU1CRDQ0OlY/Pnz6cFCxZUaBwpKSlERKSvr690XF9fX/wsJSWFPvvsM6XPVVVVqW7duuI55cFJ5CfOwMCAEhISSEtLiwShcrTWZmVlkaGhISUkJFDt2rWlDqfS4nL6OC6j8uFy+jguo/Lhcvo4LqPy4XL6uMpYRgAoOzu73EMapaaurk5xcXGUn58vaRwAytTD/6oX0t/fn5YvX/5f/77IyEgyMzP7x+L7N3AS+YlTUVGhRo0aSR3GB9WuXbvSPBQrMy6nj+MyKh8up4/jMiofLqeP4zIqHy6nj6tsZfQp9ECWpq6uTurq6lKHUW7Tp0+nESNG/NdzjI2N/6e/u379+kRElJqaSg0aNBCPp6amkrW1tXhOWlqa0vcKCwspPT1d/H55cBLJGGOMMcYYYxWgXr16VK9evX/l7zYyMqL69etTeHi4mDRmZWXR1atXxRVeHRwcKDMzkyIiIqhNmzZERHTmzBkqLi4me3v7cv9bvMUHY4wxxhhjjFUyz549o9u3b9OzZ8+oqKiIbt++Tbdv31ba09HMzIwOHixZ+0MQBJo6dSotXryYwsLC6N69ezRs2DAyMDAgX19fIiIyNzen7t2705gxY+jatWv0559/0qRJk2jgwIF/axgz90Syf5yamhrNnz+/wlek+tRwOX0cl1H5cDl9HJdR+XA5fRyXUflwOX0clxH7mHnz5tGOHTvEn21sbIiI6OzZs+Tq6kpERNHR0fT69WvxHD8/P8rJyaGxY8dSZmYmOTo60m+//aY05Penn36iSZMmUZcuXUhFRYX69OlDwcHBfys2AZ/aGr6MMcYYY4wxxiTDw1kZY4wxxhhjjJUbJ5GMMcYYY4wxxsqNk0jGGGOMMcYYY+XGSSRjjDHGGGOMsXLjJJIxxhhjjDHGWLlxEskYY4wxxtg/5MWLF1KHwNi/jpNI9j8pLi6WOoRKp/RuObxzzl/jsvk4RRk9ffqUEhMTJY6Gfcr4ucRYxfrpp5/I1NSU7t27J3UojP2rOIlk5aKofMTFxdHbt29JRYUvndKKi4tJEATx5/z8fAmjqbwAkCAIdO7cOdq5c6fU4VRKijI6dOgQ9e3bl44dO0bp6elSh1UpcVL0373/XOLGv7/G19LHfaiM+Joqq0ePHtSqVSvq06cP3b9/X+pwGPvXcCbAPkpRqT18+DD169eP1q5dSwUFBVKHVWkUFxeLSfXatWtpyJAh5OjoSKtWraLHjx9LHF3lobiODhw4QP369aNLly5RXFyc1GFVOoIg0JEjR2jw4ME0ZMgQ8vX1pbp16yqdwxVe5QTp7du3Sg03XD4lFM+loKAg6tevH3l5eVFoaCi9evVK4sgqF8WzKTw8nObOnUs9e/akffv2UVRUlNShVRql77eEhASKi4ujgoICblD+gNq1a9Px48epYcOG5OPjw4kkq7L47mcfJQgCHTt2jAYMGECjR4+mvn37UvXq1aUOq9JQvEQDAgJo+fLlZG5uThMmTCA/Pz9asmQJ9yL9f4Ig0Pnz52n48OG0cuVKCgkJISMjozLnyT0BSEtLo8WLF9PixYtp2rRppK2tTS9evKB9+/bR2bNniYiUepfkSnHfLV26lNzd3al37960fv16IiopHzlfR6V7h+bPn0+LFy8mXV1dMjAwoMmTJ9O0adPo4cOHEkZYuQiCQAcPHiRfX1/KyMggPT09CgwMpKlTp1JKSorU4UkOgHi/ffvtt9SjRw/q0qULWVpa0vbt27lR4gO0tLQoLCyMmjRpwokkq7JUpQ6AVX5v3ryh0NBQ8vPzo/Hjx4vHS/fAyd3169dp3759tH//fnJwcKCIiAhSUVEhZ2fnMr1Icvbnn39Sr169aMSIEfT69Wu6fv067dixgwRBoH79+pG3t7fsEyQNDQ2qUaMGqaio0KtXrygoKIguXrxIMTEx9ObNG1qzZg2NHj1a6jAlU/q5s3r1agoKCqIJEybQ06dPae7cuRQfH08rV64UE0k5Xk+K8omJiaGioiI6fPgwOTs7ExHRF198Qf379yd1dXUKDQ2VZfkoKK6Pp0+f0rx58ygoKIjGjh1LOTk5pK+vTx4eHlS/fn2pw5Sc4hoJDAykjRs30rZt26hbt27UrVs3WrhwIbVv3550dXUljrLy0dLSosOHD5OPjw/5+PhQWFgYWVpaSh0WY/8YzgDYRwGgBw8elEmGFBUVxTAyObf85+bmkp6eHjk4ONDevXvJ1dWV1q9fT8OHD6esrCy6cOGC1CFKpvR1kZ2dTYcOHaJLly7RsGHDaNWqVZSZmUmPHj2ihQsX0uvXryWMtHLIz88nAwMD2rt3LzVs2JCioqJoyJAhdPXqVfL09KQbN25IHaKkFM+dq1evUq1atWjXrl20aNEi2rBhA61cuZLWrl1LM2fOJCJ590geOXKEzMzMaMuWLaSqWtJeXFRURE5OTrRr1y7atm0bnTlzRuIoK96+ffvoxIkTRPSf5EjxDhsyZAg9evSIzMzMaPDgwbRkyRIiIrp8+TJlZmZKEq9UcnJyxD8XFxfT69ev6fTp07R69Wry9vam8PBwunXrFvn5+ZGZmZns50YqnjOJiYn07NkzSkpKIqKSoa1Hjx6lxo0bc48kq3I4iWQfVLrilZ+fT/Xr16fMzEwqLi5W+uzu3bu0fPlyys3NlU2L9odeltWqVaOUlBTasGEDjRkzhlasWCH22l69epWWLVtGjx49quhQJaW4TkqX1+zZs8nOzo58fX1JS0uLZsyYQUeOHKHQ0FB68+aN7CpqijJ6+PAhnT9/nh49ekR16tSh7777jgICAmjr1q30888/07hx46hJkyYEgDQ1NSWOWnp//vknOTg4UEBAAKmrqxNRSWVt8ODBtGnTJlq3bh3NmjWLiOQz9Pf951LTpk1p9OjRlJmZSQkJCeI5xcXF1LFjR2rRogXFxsZKEapkEhISaMGCBbRx40alBDozM5NUVFQoPj6eunXrRt27d6eQkBAiIoqIiKDt27eLSYEc9O7dm7755hvKyMggopKGm9zcXEpMTCR3d3c6c+YM9e/fn5YvX07jx4+nt2/f0vr16yk5OVniyCtO6XpQQUEBCYJAYWFh5O7uTu7u7mRhYUEhISGUnp5OmpqaYiLZp08fun37tnSBM/ZPAmOlFBcXAwDevn2rdHzmzJnQ1NTEyZMnUVRUJB6fM2cOXFxc8PLlywqNUyqK8gGA77//HteuXUNhYSHy8vIwYMAAqKqqYs6cOeI5eXl56NGjB/r166dUblWdopxOnjyJIUOGYNasWTh06JD4eUxMjNL5fn5+aN++PTIyMioyzErh4MGD0NDQgImJCVRVVREcHIzc3Fylc169egV/f3/o6ekhMjJSokgrj8TERAQGBkJTUxMLFy5U+uzt27fYtm0bBEHA+vXrJYpQOgcOHBDvv8jISAwaNAjq6uo4e/aseE52djaMjIywefNmiaKUzrlz5+Dk5IRevXrh5MmT4nFHR0cIgoDx48crna94NqWkpFR0qJJZu3YtBEHA/PnzkZ6eLh53c3NDly5doKmpiW3btonH4+Pj4ejoiF9//VWKcCuc4v5KS0sTjx09ehS1a9fGmjVrkJiYiIULF0JNTQ0LFy4U60fZ2dlo3bo1rK2t8e7dO0liZ+yfxEkkEykejEePHkWXLl3Qq1cvzJ07V/x84MCB0NDQwIwZMzBv3jyMGjUKWlpauH37tlQhV6jSSeCrV69QrVo1uLu749atWwBKEiYXFxfY2tpi+/bt2LhxI7p16wZLS0sUFBSU+TuqujNnzqBmzZoYMGAAWrduDVtbWyxatEjpnPDwcEydOhU6OjpiOcpFUVERMjIy4OTkhNDQUDx+/BgrVqyAIAhYuHChWHn79ddfMXDgQBgbG+PmzZsSR13x/uqeSUtLw4IFC6Curo7Vq1crffbmzRscOXJEvO/k4tmzZxAEAb169RKf59HR0Rg8eDCqV6+O2bNnY/ny5ejRowfMzc1lVT5FRUXitXTixAk4OTnB19cX4eHhAIA7d+7A1tYW1tbWuHr1KsLCwvD1119DS0sLd+7ckTL0ClVYWAgA+OGHHyAIAhYsWIDk5GQAwI4dO2BkZITu3buL57958waenp7o1KmT+F05ePHiBaytrbFr1y68ePECPXr0wNKlSwEAT58+hYmJCRwcHCAIAubOnYvU1FQAJeX19OlTKUNn7B/DSSRTcuHCBdSoUQMTJ07EoEGDYGhoCF9fX/Hz+fPno2fPnrCxscHAgQNx9+5dCaOVhp+fH8aPHw9ra2uoq6vD3t4e9+/fBwD8/vvvGD16NHR1ddGpUycMHz4c+fn5ACCrChsAhIaGYs2aNQCAJ0+e4JtvvoGZmRkWL14MAEhJSUFAQAA6duwoq+uodG//27dvMXv2bKWe/I0bN0IQBCxatAj5+flIS0tDaGgonjx5IlXIkimdQO7atQtLlizBlClTcPfuXeTl5SEnJwfffvsttLS0yiSSClX5vis9MkLh3Llz0NfXR9++fZUSyWHDhkFVVRVeXl44dOiQ2Nstl4q/oiwOHTqEKVOmwNLSEtWqVUPnzp3xxx9/AACuXbsGJycnGBgYwNzcHK6urrJpJAWU77f09HSMHTsW6urqWLRoEd6+fYvs7Gz4+/vD1NQU9vb2GDRoEBwcHGBlZSW+5+RyPT179gwmJiaYNWsWACAkJARJSUlITU2FhYUFRo0aBeA/o7j8/f1lM2KLyQcnkUwUFRWFY8eOiZWxnJwcHD16FLq6uvDx8RHPe/PmDfLy8pCXlydVqJIJDg5GnTp1cPXqVURFRSEiIgJNmzZFmzZtxEQSgNjqqFCVK7IKikra3bt3cfPmTYwaNQqbNm0SP09ISBATyeXLlwMAMjIyZPliPXToENzc3GBubo7mzZuX6WHcuHEjqlevDj8/P7FyJmfTp0+Hnp4eunfvjhYtWqBhw4ZYsmQJMjIykJ2djYULF6JOnTplhrbK1R9//AFdXV2lRPLBgwcYO3YsdHV1xaGtcnuGnzt3DqqqqggJCcH58+exd+9etGjRAt7e3jh//rx43r1795CWlobMzEwJo5XOtGnTYG5ujpEjR6Jt27YQBAFz5sxBQUEBsrOzcerUKQwfPhwTJ07EsmXLxPebHN5zpW3duhW1atXCw4cPxSlAy5YtQ5cuXfDq1Svx52bNmqFOnTpKw18Zqwo4iZQhRWtj6Qd+YmIiPvvsM2hoaIi9RwCQn58vJpK9e/eu8Fgrm3HjxmHgwIFKx1JSUmBoaAgnJyfcuHGjzPC7D/UWVFV79+6FpqYmGjRoAB0dHYwZM0bp88TERCxYsAD16tXDqlWrJIpSWjdu3IC2tja+/PJLjB49GtWrV8ekSZPK9DQGBQVBR0cHL168kCjSyuHYsWMwMDDA7du3xXtr9uzZsLKyQnBwMAAgOTkZs2bNgpubm6zuN6Ckkvr+fQaUJEza2toYNmyY+KyPjIzEsGHDoK+vrzQfUC5mz54NFxcXpWPh4eFo1qwZOnfujDNnzkgTWCVy/PhxaGtr4/r16+L9FhwcDEEQ8M033+D169cf/J5ceiBLi42NhZ2dHZYsWSIeGzduHLy9vcU5j9OnT8eJEyeQlZUlVZiM/Ws4iZSphIQEWFhYiAucvHr1Chs2bIChoWGZJCk/Px/Hjx+HIAj4/PPPpQi3wv1VRbRfv37o3Lmz+LNiSNjmzZshCALc3NzEZEAulVnF75mdnQ0HBwds374dly5dwqJFi1CrVi34+/srnf/s2TMsWbIEjx49kiJcScXGxmLu3LlKlY4tW7agUaNGmDlzZplEUm4LDc2bN6/M/LNdu3bB3NwcL168UKqoTpkyBYaGhmIPQHp6ungtVuV77/1Gqq1bt0JFRQXTp08Xjyl+/zlz5kAQBHh7e4ufxcTEoFevXjA2Nsbbt2+rdFm9b/HixXBwcEBubi6Ki4vFsvzxxx9Rs2ZNdOnSRfaJ5IEDB9CiRQu8evVK6VpbsWIFqlevjuXLl4tzJOVAUQaly6L0c2jy5MkwNDQUk8ZNmzahWrVqGDt2LPr27QstLS08fPiwYoNmrIJwEilTz549Q9u2bdG4cWOxMp+eno7NmzdDW1sbkyZNUjo/Pz8fJ0+eRHR0tBThVrj3W1UVP4eHh0NLSwvr1q1T+nzPnj0YP348DA0N0bdv3wqLs7I4efIkhg0bhpEjR4rDeDIyMhAcHIy6deuWSSTl1GqtqKQ/efIE7dq1g56eHvz8/JTOCQ0NhYGBAfz9/ZWSazlV8C9evIjBgweXGRK3efNmNGjQANnZ2QD+s3L0ixcvULt2bfz2229K51flMitdkf3zzz/FyvzPP/8MNTU1TJs2Ten89evXY+DAgejRo4dSuT569AhJSUkVE7TEIiMjxWviwIEDUFFRQVhYmNI5YWFhsLa2Ro8ePZCYmChFmJL40L1y5MgRVK9eHVFRUQAgJkcPHjyApqYmBEHAli1bKjROqUVFRWH06NH45ZdfynyWnJwMU1NTBAYGisdWrVoFV1dX+Pj4yGpRJiY/nETKSGBgINauXSv+/PTpU3Tt2hUNGjQQK64ZGRnYvHkz6tWrVyaRlItt27bB2toaBw8eREREhNJnaWlpmDNnDoyMjBAUFIS8vDwkJyfD09MTwcHB+P3331GzZk1cv35douil8dNPP0FNTQ0NGjRQWhI+PT0dwcHB+OyzzzB58mQJI5TWgQMHsGfPHoSEhMDMzAzt27cvs2DH1q1boa6ujrlz58pubpGColK7f/9+cbGT3NxcGBsbw8PDQ+nchw8fwtTUFNeuXavwOKVQusLv7++PNm3aYN26dXj37h3y8/Oxe/duqKurY8qUKXj58iUyMjLQp08fpW085HZdPXnyBDY2Nhg1apRYfhMnToSGhgYOHjwo9vTPnj0bAQEBsuv5V9i1axd+/vln8WdPT0/Y2toiLi5OPBYXFwc/Pz/s2bNHdtfRqVOnYGRkhGbNmqFt27b45Zdf8OzZMwAlz6fBgweja9euSt95+/Ytb+PBqjxOImWioKAAAQEBEARBqVIRFxdXJpFU9Eg2aNAAI0aMkCrkCqeoZLi4uEBdXR3jxo2Dra0tAgICcO/ePfG8uLg4cahmw4YNYWhoCCsrKxQWFuLixYswNjaW3RLeOTk52Lt3L2rVqlWm8SEjIwMrVqyAkZERUlNTq3RPkUJxcbHSypgqKir4/vvvAQC//PILrK2tMWrUqDKt1Dt27Cizh6YclF48KCEhAc2aNUO/fv3w559/AgBOnz6Nhg0bwtnZGeHh4Th9+jS8vLxgb28vq15toGRIpq6uLv744w+lpKewsBD79++HtrY2DA0NYWRkBCsrK9lV+EvLysrC/Pnz0b59e0ycOFG8JydPngxVVVW0atUKtra2qFWrlqxWYS0tKysL1tbWcHJyEvfy/fPPP9G1a1eYmJjg4MGDOHz4MNzd3eHm5iZ+T27XVXp6Oi5cuAAvLy+YmJjA0NAQq1evRmJiIuLi4qChoYFdu3ZJHSZjFYqTSBl58+YNFi9eDEEQEBISIh7/q0QyODgYJiYmstpkGShZyGPs2LGIiIjA2bNnYW1tjZ49e8LLywv3798Xh9U9efIEP/30Ew4fPiy+UGfOnIl27dpV6cVQFBWxFy9eIDExUakSv2vXLtSoUQNTp05V+k5GRoZSD6VcXLhwAYcOHcLs2bOVju/cuRO2trYYOXKkrLY3+ZDSidDu3buRk5ODI0eOwMHBAQMGDBB79W/cuAF7e3s0bNgQLVq0QJcuXWS1rUBxcTFSUlLg5OSEH3/8Uemz0sNc4+LisG7dOmzbtk18LsmhfIAPD8/MysrCkiVL0KZNG0yePFk859ixY9iwYQNWrFghq4abD5VRfHw8unTpAhcXFxw9ehRAyb6ZQ4cOhY6ODszMzODs7Cy7laL/qsHz3LlzmDt3LvT19WFubo6hQ4fCxcUFAwYM+MuFhxirijiJlIHSFYwnT55g1qxZEARBqdWsdCL5+PFjACWVOzkO73nw4AGsrKywb98+8Vh4eDgEQYC1tbW4z1rp1dYePnyICRMmQFtbu0q3aCteqgcPHoSVlRWMjIzQtGlTfPvtt+KiMLt27YKamprSQh9ysGTJEsybN09pH8iOHTtCEAT06NEDgPK9uHPnTtjb26Nfv3548OCBJDFL7fz589DQ0EBaWhqmT5+Ohg0bIiEhAUDJPLV27dphwIABuHr1qvidyMhIPH369IOrTFc171diU1NTYWBggJ07d5Y5Nzc394Pz+eSSQCpcunRJaX4aUJJILl26FJaWlpg2bZosRkN8zPvXyrNnz+Dq6goXFxccO3ZMPP748WOkpKTI4n4rTXGNRERE4IcffsD3338vbo2jcOfOHWzcuBGWlpYQBAFNmzblVViZrHASKSMHDhyAtbU1+vfvD3V19Q8Obe3evTtq1KihNBdCjlauXAlzc3Nx9dVWrVqhe/fu+PHHHzFx4kQIgiBuMpyfn499+/bJplfp9OnTUFdXx7Jly3DmzBn4+/vDzs4Ow4YNE4fx/vzzzxAEoUwPXFW2evVqCIKAFStWiMciIyPRq1cv1KtXT1yUqnRr/pYtW+Dq6iqr1Q5LS05Oho+PD+rUqQNtbe0yq9MqEslBgwbh4sWLZb7//kqlVUnp3y0nJwcAkJSUBGNjYzFJKp0gXr16FXPmzBEXtqrq/ioRnDJlClq2bCnuRauQm5uL/v37i1sPyS2RLH09hYaGwtHRERcuXFA65+nTp2jdujVsbGxw8ODB//p3yMG+fftQr149uLm5wdnZGcbGxli6dGmZ8woKCrB9+3ZZrjjO5I2TSJm4desWatasidDQULx69Qp3797F9OnTyySSjx8/hq+vr2yG97xfkVC8JB8+fAhPT08cPXoUVlZW6Nixo1KvbEREhFIFrqCgQKzoVVWKJfHHjBmD4cOHK332/fffw9bWVkyg8vLysHfvXkRGRkoQacVTXEchISFQUVHBsmXLxGspNjYWjo6OMDIyEpPF0omk3Ic/zZs3D4IgoE6dOoiPjweg3NsRFhYGBwcHdOvWDffv35cqzApVurK+bNkyjBo1Suw5CgoKgqqqKvbu3Sue8+bNG3h4eGDw4MGySo6eP38ulsu+ffuwc+dOvHr1ClOnToW9vb3SVjoAsHbtWrRs2RLe3t54/vy5FCFLLjk5GTExMTAxMYGvr2+ZxplTp05BQ0MDbdq0KdPzJid3796Fvr4+Nm7cCAC4cuUK1NXVMWPGDKXz5DbEl7HSOImUibCwMFhYWChVWDMyMjBt2jQIgoDdu3eLx+UyXOX9VtX3V1IbOHAgBEFAt27dlMqt9PfkUlaljRgxAr179waAMvv2NW/eXKqwJFN6v7mMjAzMnz8fKioqCA4OFs959OgROnToAGNjYzGRlOO1A/wn4Vb8NyYmBn/88Qd69eoFPT09cWhvXl6e+J1jx45h5MiRsusJ8fPzQ4MGDbBx40axl//t27fw8/ODIAgYOHAgBg0aBGdnZ1haWooVWjkkktnZ2ahfvz6GDx+OkJAQCIKA7du3AwBSUlLw1VdfoX379kpDWwMCArBkyRJZzc/+9ddfxUW9pk2bBi8vLwAlSZKZmRl69OihlEiGhYVh0KBBmDx5suzut9L27dsHV1dXACWjtBo3bozx48eLn/Pej4xxEikbp06dgoqKirjKqKKSce3aNaiqqkIQBGzbtk3KECtU6Zfj6tWrMWzYMFhZWWHTpk3ith6xsbGwt7dHaGioVGFWKoprxt/fH40aNRJb8hVluXfvXrRq1UqW82iBkkpH8+bNMXToUNSrV0/skVR49OgRnJ2doaOjI7vFqhTer5SWbsWPj4+Hl5cX9PT0lPaj/e6775TmGcmlYnv27Fk0atRI3OrkfXv37sXIkSMxaNAgzJ49W2yUqOqNExEREeI0g7t370JdXR3VqlXDd999B+A/z6mUlBR8/fXXsLKyEqdxaGhoIDY2VqrQK1xBQQG++eYbCIIAT09PaGpq4tatW+Lnd+/ehbm5OXx8fLBz5048e/YMPXr0UBoKLJf77X0HDx6Ej48PHj58CENDQ4wbN05sNL148SL8/Pxk25vNmAInkVXQh1qhs7Oz4erqimHDhikNVU1MTMSgQYOwfPlyWbaszZo1C/r6+li+fDmCgoKgo6ODIUOG4OXLl8jMzETPnj0xZMgQqcOUhOI6ev78OVJTU5UWYmjVqhXs7e2RlJQkJgITJ06Eo6Mj3rx5I0m8Urp37x60tLQQGhqK7OxsJCQkYOnSpWUSyejoaLi7u8uqIqtQujK6ceNGDB06FL6+vvjhhx/E44mJifD29oa2tja2bt2KTp06wdraWnaLwwAlC1TZ2NggLy9PvBcVZagoj/fLpaqX09atW2Fvb4/09HQUFBTg1atXUFFRQbVq1TBu3DhxUSaF9PR0HDx4EIMHD8bo0aNlMxx60qRJSnNjraysIAgCFi1aBKDkOlFcK/fv30e3bt3QsGFDNGrUCG3btpVVjzYApe2YFCMgLl68iM8++wxaWlpKPZBASfn26tVL9lMRGOMksopRPAzPnz+PFStWYPLkyQgLC8O7d++wd+9etG/fHoMHD8b169eRlJSEgIAAtGvXTpYPwytXrsDU1FRc+fH69etQUVFRWj7/8uXLEAQBZ86ckSpMSZRehbVNmzZo3LgxWrVqJc4HiYmJgZWVFRo2bIhOnTrB29sbWlpaVXpl2v/mjz/+gKmpKZKSkpSOBwYGQkVFBRs2bBATALnPoZk1axYaNmyIiRMnYs6cORAEAUuXLhXL5dWrVxg5ciRat26NHj16iMfl1iOyY8cO1K1bV5wnqth7tKioCIcPHxY3O5cbxeJLil6g9PR0XL16FaqqqhgxYkSZRFJBLvddQkICunfvLk7PyM/Px9ixYzFy5EgIgiAObS0qKhLLJDU1FREREThx4oSYXFb1Hm0Fxbvu0KFDaNq0KVavXi3+7kFBQRAEAZs2bcKjR48QHx+PmTNnom7durJpkGDsv+Eksgrav38/tLS0MHr0aHh4eKBNmzbo378/gJKWXA8PDwiCADMzM9StW1dpeIucXLx4Efb29gBKNoDX1NQUJ9FnZWUhPDwc8fHxmDdvXpVv4f+Q33//HWpqali7di12796NtWvXQkNDA8OGDRPPCQwMxNdff41Zs2YhKipKwmgrXulW+gsXLkAQBPFeUlwvMTExqF27NgRBEIfbyYli2KGirHbv3g0jIyOx4ebkyZMQBAGCIGDGjBlKFf3ExETxe1W5Qnvr1i3s3bsXy5Ytw5YtW/Dy5UsUFBTg4cOHaN68OebOnas0CiAvLw/Ozs5YuXKlhFFXvNLXxq1bt2BiYoKdO3eK+/aGh4ejevXqGDVqlJhILlmyBOvWrQMgn1610nbs2IHU1FQAJb+/YmirIpFUUExzUZDb++7w4cOoWbMmNm7cWGaUyOzZs6Gjo4N69erB1tYWzZs3x82bNyWKlLHKhZPIT9iHWuZjY2NhYmKCkJAQACVLdmtqairt2Zefn48LFy7gjz/++MtW26rmQ2V18uRJNGnSBLt27YK2tjY2bNggfnbixAn0799fqfJWlSuyH/LVV18pJYxASQ93rVq1ZLV1x/veXxhG8V9PT0907dpVaUVaRa/amjVrZDdcfObMmZgzZ45Yyc/NzUVoaKh4nx09elQctvrDDz9AEAQEBgaWWeW4KvdAfv/992jatCns7e3RqFEjqKiowMjISFyUacWKFWjevDnGjRuHEydO4Pfff4ebmxtsbGxk8Tz60P/3aWlpAIDu3bvD1tYWu3btEq+xs2fPolatWnBzc0Pv3r1Rs2ZNcY673KSnp0NDQwOOjo5iIpmTk4N58+ahWrVqCAkJwcuXL+Hr61tmtW05ef36Nbp06YLFixcDKGmkSUtLw+bNm3Hjxg0AJXtH//bbb7h8+bJs57Mz9iGcRH6iFC/XuLg4HD58WDx+8eJFtGzZEkDJsJ/GjRtjzJgx4ueXL19WWvVQDkpXRPbs2YMDBw6IP/v6+orD6RRyc3Ph7e2NPn36VOkK7H9TUFCAbt26wdfXVzymaJ1esmQJ2rdvj5cvX4rlI5dWfsXvee7cOcyZMwdjx47Fhg0b8O7dO/zxxx/o0qULXFxccOnSJURFRSEgIAAWFhay24C6uLgYQ4YMQbt27bB8+XKxkp+YmIgnT54gOTkZrVu3xqpVqwCUVNIUPbbr16+XMvQK89NPP6FmzZrYvXs30tPTkZOTg+joaDg6OkJLSwurV68GAKxbtw7u7u4QBAG2trZwc3MTe+Xk0GMUGxuLefPmAShZTMjJyUm8nnx9fdGqVSulRPLSpUsYOnQohg4dKot9exVKP4MV10d0dDSMjY3h6uqqlEgGBgZCEARYWFigZcuWshnq+yEpKSkwNTXFli1b8Pr1a8yaNQtOTk7Q1NREo0aNlKa3MMaUcRL5CUtKSoKenh7Mzc3FB11ERAScnZ0RGRkJQ0NDjBkzRqxoXLt2DVOnTlVa+bCqK/1inTlzJpo2bYqQkBBxm4VTp07ByckJ5ubm2LdvHzZu3Ah3d3dYWFiILf1VPZF8+vQptm/fjiVLluDx48fiXJrQ0FC0aNEC58+fVzp/06ZNMDc3l11ipLB//35oampiwoQJGDFiBKytreHi4gKgZF5Nnz59IAgCTE1NUb9+fdkNfVLcc4WFhZg0aRLatm2LpUuXKl0vN27cQMuWLcV5RU+ePMHkyZNx6tQpWfSwpaWloVOnTli7di0A5edUYWEhXF1doaenJ5ZPbm4uYmJikJSUJIshvgpFRUXYsmULVFRU0Lt3bwiCgB07diidUzqRVCzqlZubK6vEqPQ7aunSpdi4caO4zkFMTAyaNGmilEgCJfWBgwcPym4OpMKDBw/Ed92UKVOgpqaGunXrwtfXVxzJ5e7ujoEDB0oZJmOVGieRn7CzZ89CRUUFdnZ26NmzJ3766Sfk5+ejSZMmEAQBkyZNUjp/2rRpcHFxwYsXLySKWDorV67EZ599hsuXL5f57MaNGxgwYAAaNGgAJycnjBgxQqyAVPUX6507d9C0aVPY2dlBW1sbDRs2xJEjRwCULDzUtWtXDB48WGmbgenTp6NTp06yTCLj4+PRsmVLcUhmXFwc9PT0MGHCBKXzrl+/jlu3bpVZaEcOioqKlFYRHT9+PNq2bYtly5aJvUUREREQBAHr1q1DREQEPD094eHhIf4dVf2+e/LkCerVqyfeawqK3zszMxM6OjplriuFqt6wVVphYSFGjRoFQRDEPQ6B/8y3BUoSSRsbG2zbtk3puByUvhbS0tLg4uICPT097NixQ0yqFYlkp06dPjgcUw492qXFxcWhTZs2+Pzzz8V3/eHDh/Hrr78iLy9PvA9HjRqFqVOnyq58GCsvTiI/cSNHjoS1tTX69OkDZ2dnnDhxArdu3ULjxo0xYMAARERE4M8//8T06dOhra0tq+E9QEkLf3Z2Njw8PBAUFAQAePz4MQ4dOoSePXti5MiR4hys5ORkpRdyVa/I3rlzB7Vq1cK8efOQlpaGhIQENG3aFI6OjuI5hw4dgpubG5o2bYrOnTvD09MT2traslqMqXQv0a1bt2Bqaor8/HzEx8fD0NAQY8eOFT8/ffq0rHpA/hvFVkLvJ5KKHpKlS5dCEASYmJjIbluBa9euoU6dOjh37hwA5UVjFH/u168fvL29UVBQIIsy+SuFhYWYM2cOhgwZAj09PaX5/W/fvhX/7Obmhvbt28typXEA+Prrr9GhQwf0798fpqamqFmzJrZt26aUSBobG6NVq1ZK23/IUU5ODhYvXgwHBweMGzdO7JFUSExMxDfffAMdHR08ePBAoigZq/w4ifxEvN/yrJjXeOzYMYwYMQInT55E79694eTkhO3bt+P8+fMwMTGBgYEBmjdvDnt7e9lU/D/USt+vXz+4u7vjhx9+QPfu3dG5c2cMHDgQxsbGcHNzA6DcGlvVK22JiYkQBKHM/leurq4wNDRU6mWMjo7Gzp078fnnn2Pu3LlKC8dURe9vxVH6eoqOjkaXLl1w7tw5MYFUNDbcv38f48aNk93w1Q/Zv38/WrRogaNHjwJQTiSXLl0qVmzv37+PiIgIsYyresONQlZWFho3bow+ffqIx97v7Rg4cCAGDx5c0aFVWllZWVi/fj3q1q2rlEgCEIdpll4ITU5++eUX1K5dGzdv3sSbN2/w7t07fPnll6hRowa2bdsmjgB4+PAhevfuLbuetQ+9z9++fYuVK1fCzs4OY8aMEZ/3Z8+eRe/evWFiYiKbOhNj/ytOIj8BigrWs2fPlBaFAUqGr5iZmWH9+vVITU1F79694erqimPHjonLxEdFRSE9PV2K0CW1a9cuXLhwQfyzt7c3ateujfnz54vDWlesWIG+fftW+aTxfSkpKTAzM4O9vT3i4uIAlJSFIAioV68eRo4cCXt7e2zevFlWc2gV91pUVBTGjRsHHx8fLFu2TFzFODMzEy1btoQgCBg1apTSd6dPn44OHToozTuSq9OnT6NXr17o2LEjjh07BuA/iaSdnR2WL1+OzMxMpe9U5SGa7z9fCgoKMG/ePPF59L7s7GxZbuMB/Kes7t+/j2PHjuHYsWNio+mLFy+wYcMG6Orq4uuvvwYAzJs3D506dRITJTnasGED7OzskJubq3QfjR49Gtra2tixY0eZ8pFbInn58uUy91pOTg5WrVqFVq1aYdKkSSgsLMSbN2+wZ88ecT9Sxthf4yTyE/Hs2TPo6upCEAR4enpiz549YuU+LCwMTk5OSEtLE1saO3XqhO3bt0sctXSys7Ohp6eHDh064M6dOwBKem/f39KkS5cuSqvXyklKSgpat24Ne3t7+Pn5iXO0oqOjERMTg4kTJ6JTp04QBAFDhw4ts/VCVaOofN2+fRt16tTBsGHD4O3tDVtbWwQFBYmV26ioKOjq6qJnz544duwYzpw5gylTpqB27dritSYnf9UAc+HCBfTt2xft27dXSiQnTpyIJk2ayGbVw9KV+qysLHEoYVpaGnx8fFC3bl2MHz8eL168wPPnzxEbGwsvLy+0bt1aNj2zCopr6cCBAzAyMoKpqSmsra1hY2ODly9fAgBevnyJzZs3o1atWjA3N0edOnVw/fp1KcOWXHBwMLS1tcVkWzEv9OrVq1BRUYGuri72798PQH7JI1DSaDN16lRYWFhg0aJFZT77/PPPoaWlhS+++IKnIzD2N3AS+Yl4+vQp2rZtCwcHB9ja2mL06NFo0qQJQkNDsWfPHnh7e+P48eMASlYd69q1K3r06FGmtb+q+lBFNjExEebm5nB2dhY3NwdK9oU6f/483Nzc0KpVK7GiJrfeSAB4/vw57O3tIQgCdu7cWebzN2/e4OjRo1W+N1JR0b9z5w40NDQwZ84c8bPPP/8cI0eORFFRkViRvXLlCiwsLGBkZIQWLVrAyckJt2/fliT2ymLPnj24cuWK0rHz58+jb9++aNeuHU6fPg2gpNK2cuVKWVRmSz9TFi1aBA8PD+jr62PChAkIDw9Heno6xo0bBy0tLejq6kJXVxf29vZwcnKS1TYepZ0+fRra2trYvHkzioqK8Pvvv0MQBJiZmYmNgLm5uXjw4AG2bt0qqx6j0g0Spf/8+vVrWFlZwcvLS2l+3507d+Dn54fRo0dDT09P1qMkUlJSMHPmTNjZ2WHBggVKn23cuBGWlpbw8fERV25njH2cAADEPgmxsbHk7+9PxcXFNGzYMBIEgdauXUs6Ojp0+PBhateuHZ0/f55q1KhB0dHRpKGhQY0aNZI67Ar1+vVr0tbWJgAkCAIlJydTp06dqH79+rRixQqyt7ens2fP0g8//EA5OTn0yy+/UPXq1amwsJBUVVWlDv9fpSiT/Px8AkBqampERJSamkoeHh4kCALt27ePjIyMqLi4mFRUVCSOuGIlJSWRoaEhjRs3jjZt2iSW14QJE+jKlStUXFxMgiDQwoULycfHh7KysujVq1ekqqpK2traVLt2bal/BclER0fT0KFDSVdXlwIDA8nW1lb87MyZMzR8+HBq0KABzZ49m3x9fcXPioqKqFq1ahJEXLHmzp1LmzZtotDQUKpevTqtWLGCkpOT6datW6SiokIpKSl06tQpUlVVJVNTU3JxcSEVFRVZPJdKy87OpoCAADIwMKDZs2dTcnIyOTg4kKOjI8XGxlJGRgadP3+eGjRoIHWoFU7xPCIiCg0NpatXr5KpqSl1796dbGxs6PDhw7Rw4ULS1NSktWvXUm5uLi1cuJB0dXUpKCiILCwsKCgoiIYPHy7xb/LvU5RVamoqqaqqUmFhIenr61NGRgYtWbKEzp8/Tx4eHrRgwQIiIpo9ezZpamrShAkTqE6dOtIGz9inRLr8lf0voqKi4OHhgW7duiE6Ohpv3rzB5cuX4e3tLQ4Pk2OPGgCsWrUKLi4uePz4sdLx5ORkGBoawsXFBTdu3ABQslKdnBbzUFwTx44dw9ChQ2FlZYXZs2fj0KFDAP4ztNXa2lqcIyk3H5onunz5cqipqWH9+vUICgqCr68vqlWr9sGtYuTkQ8+YX375Be7u7vDy8hLvMwU3NzeYmJiICznJ6RkVGxuLtm3b4syZMwCAM2fOiCtnAn/d01iV54j+N0eOHMGNGzeQnp4OW1tbjBs3DkBJT7cgCKhfv77sts4pfb98++23qF27NgYMGAB9fX1069ZNfI6Hh4ejY8eOqFWrFpo0aYJ27dqhoKAA6enpaN68OX777TepfoUKoyirgwcPwsrKCs2bN0f9+vWxdOlSZGdnIysrC/7+/mjdujVatmyJvn37olatWuJq0oyx8uMk8hMUExODbt26oVu3brh48aLU4VQad+/eRc2aNdGrVy8xkVRUxPbu3QsVFRW0bdtWaXVROVXUDh06hJo1a+Lbb7/F+vXr0aNHD+jr64vz+J4/fw5bW1s0adIE8fHxEkcrjffnierp6eHkyZPi5zdu3ECdOnWwYsUKCaOUVul7JjMzU2m7gEOHDqFz587w9vYWVzbMyMjA8OHD8csvv8gieXz/mfLo0SOYmJggMzMTBw4cgKamJjZt2gSgZIXInTt3ynJI5vv7Ob5/bRw9ehQdOnQQG3TCw8Ph7e2Nnj17yrbCf/PmTQwfPlxcMO7evXvo2bMnXF1dlRbdu3btGh49eiSWdUBAgNJw4Kru9OnTUFNTw3fffYeDBw8iKCgImpqaGD9+PHJycpCVlYWjR49i1KhRGD9+PO7fvy91yIx9kjiJ/ETFxMSge/fucHd3F18ocvJXyd/9+/ehpaUFHx8fpR7JX3/9FSNHjsTAgQNlN8cIKFmMwtXVFWvWrAFQUvmvV68epk6dqnTe8+fP4ejoWKY3V04+NE9Ucb29ePECrVu3/uD8UblZsGABbGxsYGZmhp49e+LevXsASnq73d3dYW5ujilTpsDZ2RkdOnQQy1AuDTe3b99GXl4eoqOjYWlpicDAQNSpUwcbNmwQz7l27Rp69+6NS5cuSRhpxUtMTES/fv3E3tkP2bBhA2rVqiWOFJk9eza++OILcfEYudm5cyecnZ3Rvn17pbmNd+7cQc+ePdG5c2f89NNPSt+5ceMGJkyYAB0dnSq5XcX7zxLFu338+PEYOHCg0meKRtTvvvtO6bgcRiIx9m/hJPITFhMTA29vb7Rv315Ww+tKvzhOnTqF7du34/jx43j06BGAkh5JLS0t+Pr64uTJk0hJSYGPj49S5U1uieSrV69gYWGBu3fvIj4+Hg0bNlRalbb04jlyKhtF78e7d++UKqcpKSmwsbGBra2tUkI9Z84cGBkZybKntvR9t2HDBujo6GDNmjUICQlB69atYWZmJi7udenSJcyaNQtOTk4YPnz4B/fcrMrCwsKgp6eHt2/fAgAmT54MQRAQEBAgnpOTkwMvLy94enrKplwUHj9+DAcHB3h5ef3laJqkpCS0bNkSn332Gbp27YqaNWvi7t27FRxp5fH777+jbdu20NHREYevKty9exe9e/eGlZUVTp06JR6PiIjAsmXLqvTevgkJCTh37pz4c1FREXx9fTF06FAAJUmi4p22aNEiGBsbIyMjQ1bvOcb+LZxEfuIiIyPRt29fWVZqZ86ciUaNGqFZs2Zo0aIFDA0NcfbsWQAlw3xatmyJRo0aoWHDhrC1tZXl0t0JCQnIyclBSkoKOnTogB9//BHGxsYYPXq0WHF98uQJRowYgRMnTkgcbcX6O/NE09LSsGDBAqirq+PmzZtShi25U6dOITg4GHv27FE63q1bN5ibmyMtLU08Vjoxl1OLf1FREUxMTMS9DLOzszFo0CDUqlULs2bNwrRp09C5c2dYWFjILsFWKD2apnQiWbrHOjY2FoMHD0ZgYCAePnwoVagV7q+uhQsXLsDBwQHe3t7iascKERER8Pf3L5McVdX3XnFxMQoLC9G5c2fY2toqlceSJUugo6MjDntWPHu2bdsGa2vrMkOpGWP/G04iq4DSS3pXdYqX686dO6Grq4vLly8jKysLERERGD58ONTV1cUKSXJyMk6fPo1Dhw6JL1Y5VWTv3bsHIyMjJCYmAgCmTp0KQRDQp08fpfMCAgJgaWmJZ8+eSRGmpA4fPizOE92wYQN8fHyU5ommpKTA1tYWgiBAQ0OjzIIxchMREYEaNWpAEARxH1pFhaywsBCNGjUSt0cpPcetKs+FfL/C/+7dOxQXF2P58uVwc3MTF4HJy8vDwoUL4eHhAR8fH/j5+YnPIzk9l0r7b4lkQUEBZs6ciSFDhuD169cSRlmxSl9Pp0+fxt69exEWFiY2yJw9exYdOnSAr68vwsPDP/h3yKmXLT4+Hu3atUOnTp3E+etJSUno3r077OzslObPfv3113ByckJWVpZU4TJWpXASyT4Jv/32G9LT0wGUvGTnzJmDvn37Kp3z/Plz9O/fH66uruK5pcnpxapgamoqDlvNz8/H0KFDoampiTVr1mDlypWYMGECtLS0ZLnHYXnniSYnJ6NXr15Vck7R3/XixQts2LAB+vr6GDZsmHhc0dvh7e2NKVOmSBSdtN7vKYuMjIS2tjaCg4OVjr8/p0+Oz6XSPpRIvnv3DpMmTYIgCLK972bMmIHGjRujcePGaNq0KZo2bSouABMeHg5HR0f06dNHHEIuR4p7JzExEQ4ODujatavYI3nhwgV0794dWlpa6NGjB7p164batWvL9npi7N/ASSSr9LKystCyZUs0bdoUGRkZAEoWWTAxMUFOTo7Sud9//z0aN26M58+fSxBp5aHonQ4JCYGrq6s4J+b169fihsu2trYYOHCgbOcZ8TzR/+6vhtRlZmZi/fr1UFNTExNGRU9j69atMXPmzIoKUVKly+fXX3+FiYkJBg0ahNjYWGRmZgIAli1bBisrK0RFRYnnyqWH9u8onUiePXsWfn5+qFmzpmyHjn///feoW7curl27huTkZDx48ACenp6oX78+nj59CqBkq5jmzZvD399f4mgrxvtbchUVFYn/AwA/Pz8IgoBOnTrhjz/+AFDS6LV27VqMHTsWAQEBVXpuKGNS4CSSfRIePHgAOzs7mJmZIT09HRcuXICFhQXWrVsnVtgAiMcVi+zITUpKitLPjx49Qv369bFkyRKl469evUJ+fr4s54bwPNGPK50gbdu2Df7+/hg6dCjOnTuHrKwsFBYWYt26dVBTU4OrqyuGDh2Kfv36wdTUVBZDM3/++Wd88cUXiI6ORnFxMdLS0vDrr7/CxsYGFhYW6NevH27fvo1Lly6hY8eO4vYLcpv3+HcoFoqrU6cOatSogYiICKlDkkxAQAAGDx6sdOz169dwdnaGo6Oj2PN/69YtWTVwJSQkwMLCQhyiqmiECQwMxGeffYYff/wRdnZ2cHFx+cuhvoyxfw4nkaxSK936+PTpU9jZ2cHR0RGZmZmYPn06WrdujYULFyIqKgpPnjyBu7s7unTpIssW/oiICLRv3x6jRo1Camoq3rx5A6BkJU0jIyOlIatyLB+A54mWR+lrY/r06dDT00OfPn3g4OAAPT09zJo1C0lJScjPz8eGDRtgaGgICwsLpWFiVTmRfP36NZo1a4Z69erB0tISI0aMwO7du8XP9+zZg169ekFDQwN+fn6oU6cOLC0tOYEsh6ioKPj4+Mh+377x48ejZcuW4s+KRHHHjh1o0aIFkpOTlc6XSyL57NkztG3bFo0bNxb3Vl2yZAnq1q0rDut99uwZ2rRpAzc3Nxw5ckTKcBmr8jiJZJXSy5cvxT+XXjioe/fuEAQBHTp0QGZmJubOnYs2bdpAEARYWVmhbdu2slntMDIyErNnzxaHNyUlJSE4OBhmZmZo2bIlxowZgwcPHiA6OhqdO3fGDz/8AEA+FY6/wvNEy+f06dNo0KCB0pDC1atXo1WrVli4cCEAIC0tDRs3bkS9evUwY8YM8byqfO8VFhYiICAAISEhiIiIwMqVK6Gjo4N+/fph9erV4vPn6NGjGDFiBGrXro0WLVpU6TL5J1XV1UQ/pPR7rrRTp07BwsIC3333nVKDzIkTJ2Bubi6rxq3AwECsXbtW/Pnp06fo2rUrGjdujBkzZuCzzz4TR4woyiohIQHGxsbw8fEpM+WFMfbP4SSSVTrnz5+Hq6urOK9BoW/fvmjVqhVOnz6N1q1bw97eHhkZGcjJycHx48dx6dIl2azCmp+fDzs7OwiCAFNTU0ybNk2p1XXTpk3w8vJCrVq1sGjRIjRv3hwtWrSQ1Uq+7+N5ov/dhQsXEBQUhKCgINy8eROXL19Gs2bNEB8fr9TwEBgYiDp16ohDpzMyMsTFdsaPHy9V+BXq+PHj0NLSElfxzc3Nxdy5cyEIAqytrbF8+XLExcWhsLAQ0dHRYvlxIskUPvSeU4wCyMjIwJgxY+Dq6ooFCxYgMzMTT548gYeHBzw8PGQzkqSgoAABAQEQBAGbN28Wjz99+hQ9e/ZUWiVacW8p7rWkpCSxt5Ix9u/gJJJVOlFRUXBxcYGnp6e4pUKfPn1gYWEhtsA+fPgQ1tbWaN26dZnWXLn0tK1YsQKrV6/G77//jnnz5kFHRwcDBgzA9u3bxRfq7t270atXL+jr60MQBHG7ATnheaIft2XLFtSrVw+2trbQ0NCAqakp+vfvD2NjY3Ho79u3bwEAOTk50NPTw759+8TvZ2ZmYtWqVTA2NkZqaqosKrlffvklvvzyS/Hnli1bwtfXFzNmzEDXrl0hCAK2bdsmfi6X5xIrH8V7zsvLS2l7E8V1kpqaiq+++goWFhaoXr06LC0tlfY7lkuDxJs3b7B48WIIgoCQkBDxeGxsLDw9PdGwYUM8fvwYQNlEkjH27+IkklVKitX6vLy84OjoCBsbG8TFxSmdExkZiUaNGpVZgEAuzp49i9q1a+P69esASraiWLBgAdTV1WFvb4/Nmzfj5cuXyMvLw40bNxAbGytxxP+uD1WqeJ7ox23ZsgU1atTAnj17kJOTg7Nnz6Jz585wcHBA06ZNYWNjo3R+XFwcTE1NcfbsWaXjr1+//uDWOlXV1q1b0bFjR6Snp8PGxgYdO3YU9zNMTEzEzz//XOVHRLD/m7/aJ1ORKL579w7Z2dlYsWIFbt68KZuRNoDy8/zJkyeYNWsWBEHArl27xONxcXHo2rUrDAwMyiSSjLF/HyeRrNKKiYlB165doa2tjV9//VU8Xvol8fTpU1m3Os6YMQNDhgwRe88GDBgAMzMzDB8+HI6OjqhevTqCgoIkjrLi8DzRv+fs2bMQBAHffvstgP8k0suWLYOBgQFOnToFGxsbtGzZEgcPHsSBAwfg5eWFtm3byrbMSlMMKXdxccGrV68+eI4cKvzsf1c6kbxw4YJ4vLi4GElJSfDw8MCECRPE43K77w4cOABra2v0798f6urqZYa2xsXFoXv37lBTUyvT0MwY+3dxEskqtUePHsHd3R0eHh5KL9j3Wxvl9mJV2Lt3LxwcHFBUVIRRo0ZBX19fXNkwKioKa9eulc1KhzxP9O+LiYmBk5MTevbsqTQ3a9myZTA1NUViYiIePnwIHx8fNGnSBBYWFvDw8BB7SuR63ymS7R9//BGWlpbisHu59maz/5sP9UimpKTA2dkZzZo1k9ViQ6XdunULNWvWRGhoKF69eoW7d+9i+vTpZRLJx48fw9fXV9z6gzFWMQQAIMYqsdjYWPrqq6+IiOibb76hjh07ShxR5eLi4kIXL16k+vXr0/Hjx6l169ZShySZlStXkqqqKllaWtLFixcpODiY3N3dycPDg4YOHUoqKir0888/0969e+nSpUuUlpZGiYmJZGBgIHXoklHcX8XFxbR+/XpKSEggT09P2rVrF/Xt21c879mzZ6Surk716tUjQRCosLCQVFVVJYxceklJSWRnZ0dfffUV+fv7Sx0O+4Qp7kNBEGjChAm0bt06SkxMpDt37lD16tVleb8dOXKEAgIC6NKlS1S7dm0iIsrMzKSFCxfSmjVr6KeffqJBgwYREcmyfBiTnNRZLGPlERMTIw6jU6yIKHeKXo9jx46hefPmOHjwoNJxOeJ5ov+bmJgYeHh4wNbWFtWrVxfnHRUUFIi9jaWvK5539B/BwcHQ1dXFgwcPpA6FfeJiYmLg6ekJQRBgbm4u9kDKdUj0qVOnoKKignv37gH4zzPo2rVrUFVVLbN4FWOsYqlIncQyVh6mpqa0cuVKcnZ2JktLS6nDqRQEQSAiojZt2lBxcTFFREQoHZcjV1dXGjt2LK1Zs4by8vKoQYMGFBkZSU2bNiUzMzPauXMnNWjQgDZs2EBt2rQhExMTqUOuFExNTWnt2rWko6NDLVq0EMtFVVWVVFRKXhOlryvFMUbk6elJXl5eZGZmJnUo7BNnampKQUFBNGnSJLp7966seiDxgUFx7du3J2dnZ1q5ciXFxsaKzyADAwPq168fLVu2jBwcHCo6VMbY/8fDWdknqbi4mCuypezatYvGjx9PZ86coXbt2kkdjqT27dtHq1evposXL9LYsWPp6NGjFB4eThYWFhQdHU0nT56kLl26kIWFhdShVjqPHj2iyZMnExEPHf87AJAgCFRUVETVqlWTOhxWRcgpgRQEgS5cuEBXrlyhhIQEcnNzI3d3dwoLC6OgoCAyNjamadOmkYGBAa1fv57Cw8Pp1KlT4jBXxljF4ySSsSogKSmJPv/8c/rxxx+pUaNGUocjOZ4n+r+LjY2ladOmUWpqKm3bto2srKykDokxVsUdOHCARowYQQMGDKCkpCRKS0ujZs2a0Z49e2jbtm20f/9++u2336hFixaUlpZG4eHhZG1tLXXYjMkaJ5GMVRF5eXmkrq4udRiSUrRoHz9+nKZNm0bLly8nX19f8Tgrn8jISNq6dSutXLmSe/wZY/+YD40ievToEXl4eNCMGTNo3LhxFB8fT5aWljRu3DhatWoVEREVFBTQ1atXqbi4mIyNjbmxlLFKgGsHjFURck8giXie6D/F3NycgoKCSEVFhYqLi6UOhzFWBSgSyKdPn1JYWJh4PDU1lWrUqEHjxo2juLg4cnZ2pkGDBokJ5JUrV6i4uJgcHR3J2dmZE0jGKglOIhljVY6+vj7Nnz+fvvvuO7p27ZrU4XzSuCeSMfZPUFFRoeTkZLKzsyN/f3/atWsXERHVrFmT9PT0KCoqilxcXMjd3Z02bdpERETXr1+nPXv2UHx8vJShM8Y+gGsHjLEqqVOnTmRnZyfrPSAZY6wyiYmJofT0dNLU1KR9+/bR7t27qVWrVhQfH08tW7aknj170ubNm8UFqn7++We6desW1a1bV+LIGWPv4zmRjLEqi+eJMsZY5TJq1Ci6efMmNWvWjF68eEEBAQFUv3596tmzJzk4OJCfnx/l5eXRgQMHaOvWrXThwgVq1aqV1GEzxt7DSSRjjDHGGPtHvb+Izrt370hNTY2OHz9Oe/fupUGDBlFoaCi9ePGCRo0aRcbGxjRy5Eh6+/YtaWpqUp06dSgkJIRXYWWskuLhrIwxxhhj7B+jSCATEhLo4MGDRESkpqZGRER2dnZ05coVio2NpU2bNlG9evVo+/btlJ2dTZGRkXT69GkKCwujEydOcALJWCXGPZGMMcYYY+wflZCQQDY2NpSenk4eHh40fPhwsra2pubNm9ORI0do5cqVtH//fnr58iV98803lJGRQcOHD6fhw4dLHTpjrBy4J5IxxhhjjP2jiouLycjIiNq3b08pKSl06tQp6tatG23evJlyc3NJW1ubbty4Qebm5rRo0SKqVq0a7d+/n16/fi116IyxcuCeSMYYY4wx9o+LjY0lf39/Ki4upmHDhpEgCLR27VrS0dGhw4cPU7t27ej8+fNUo0YNio6OJg0NDd4HkrFPBCeRjDHGGGPsXxEdHU3Tpk2joqIiWrduHTVs2JDu3btHgYGBNGDAAPr8888JAAmCIHWojLG/gZNIxhhjjDH2r4mNjaVJkyYREdG8efOoY8eOEkfEGPu/4jmRjDHGGGPsX2Nqakrr168nFRUVWrRoEV28eFHqkBhj/0ecRDLGGGOMsX+VqakpBQcHU/Xq1WnmzJl05coVqUNijP0fcBLJGGOMMcb+daamprRy5Upq1KgRGRgYSB0OY+z/gOdEMsYYY4yxCpOfn081atSQOgzG2P8BJ5GMMcYYY4wxxsqNh7MyxhhjjDHGGCs3TiIZY4wxxhhjjJUbJ5GMMcYYY4wxxsqNk0jGGGOMMcYYY+XGSSRjjDHGGGOMsXLjJJIxxhhjjDHGWLlxEskYY4wxxhhjrNw4iWSMMcYYY4wxVm6cRDLGGGOMMcYYKzdOIhljjDHGGGOMldv/A1ySLow37GE9AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 8))\n",
    "corr = housing_df.corr()\n",
    "im = ax.imshow(corr, cmap='coolwarm', vmin=-1, vmax=1)\n",
    "ax.set_xticks(range(len(corr.columns)))\n",
    "ax.set_yticks(range(len(corr.columns)))\n",
    "ax.set_xticklabels(corr.columns, rotation=45, ha='right')\n",
    "ax.set_yticklabels(corr.columns)\n",
    "plt.colorbar(im, ax=ax)\n",
    "ax.set_title('Feature correlation matrix')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8918542f",
   "metadata": {},
   "source": [
    "## Task 2: Apply your first feature engineering technique\n",
    "\n",
    "Before applying PCA, we need to put all columns on the same scale. Without scaling, features with large numerical ranges (such as `Population`) would dominate the principal components due to their larger variance.\n",
    "\n",
    "We apply `StandardScaler` to transform all columns to zero mean and unit variance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "dd45ba3b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scaled dataset statistics (should all be near mean=0, std=1):\n"
     ]
    },
    {
     "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>MedInc</th>\n",
       "      <th>HouseAge</th>\n",
       "      <th>AveRooms</th>\n",
       "      <th>AveBedrms</th>\n",
       "      <th>Population</th>\n",
       "      <th>AveOccup</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>MedHouseVal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>20640.000</td>\n",
       "      <td>20640.000</td>\n",
       "      <td>20640.000</td>\n",
       "      <td>20640.000</td>\n",
       "      <td>20640.000</td>\n",
       "      <td>20640.000</td>\n",
       "      <td>20640.000</td>\n",
       "      <td>20640.000</td>\n",
       "      <td>20640.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.000</td>\n",
       "      <td>-0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.000</td>\n",
       "      <td>-0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1.774</td>\n",
       "      <td>-2.196</td>\n",
       "      <td>-1.852</td>\n",
       "      <td>-1.611</td>\n",
       "      <td>-1.256</td>\n",
       "      <td>-0.229</td>\n",
       "      <td>-1.448</td>\n",
       "      <td>-2.386</td>\n",
       "      <td>-1.663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.688</td>\n",
       "      <td>-0.845</td>\n",
       "      <td>-0.399</td>\n",
       "      <td>-0.191</td>\n",
       "      <td>-0.564</td>\n",
       "      <td>-0.062</td>\n",
       "      <td>-0.797</td>\n",
       "      <td>-1.113</td>\n",
       "      <td>-0.756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-0.177</td>\n",
       "      <td>0.029</td>\n",
       "      <td>-0.081</td>\n",
       "      <td>-0.101</td>\n",
       "      <td>-0.229</td>\n",
       "      <td>-0.024</td>\n",
       "      <td>-0.642</td>\n",
       "      <td>0.539</td>\n",
       "      <td>-0.235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.459</td>\n",
       "      <td>0.664</td>\n",
       "      <td>0.252</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.264</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.973</td>\n",
       "      <td>0.778</td>\n",
       "      <td>0.501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.858</td>\n",
       "      <td>1.856</td>\n",
       "      <td>55.163</td>\n",
       "      <td>69.572</td>\n",
       "      <td>30.250</td>\n",
       "      <td>119.419</td>\n",
       "      <td>2.958</td>\n",
       "      <td>2.625</td>\n",
       "      <td>2.540</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          MedInc   HouseAge   AveRooms  AveBedrms  Population   AveOccup  \\\n",
       "count  20640.000  20640.000  20640.000  20640.000   20640.000  20640.000   \n",
       "mean       0.000      0.000      0.000     -0.000      -0.000      0.000   \n",
       "std        1.000      1.000      1.000      1.000       1.000      1.000   \n",
       "min       -1.774     -2.196     -1.852     -1.611      -1.256     -0.229   \n",
       "25%       -0.688     -0.845     -0.399     -0.191      -0.564     -0.062   \n",
       "50%       -0.177      0.029     -0.081     -0.101      -0.229     -0.024   \n",
       "75%        0.459      0.664      0.252      0.006       0.264      0.020   \n",
       "max        5.858      1.856     55.163     69.572      30.250    119.419   \n",
       "\n",
       "        Latitude  Longitude  MedHouseVal  \n",
       "count  20640.000  20640.000    20640.000  \n",
       "mean       0.000     -0.000       -0.000  \n",
       "std        1.000      1.000        1.000  \n",
       "min       -1.448     -2.386       -1.663  \n",
       "25%       -0.797     -1.113       -0.756  \n",
       "50%       -0.642      0.539       -0.235  \n",
       "75%        0.973      0.778        0.501  \n",
       "max        2.958      2.625        2.540  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Scale all columns to prepare for PCA\n",
    "scaler = StandardScaler()\n",
    "scaled_values = scaler.fit_transform(housing_df)\n",
    "housing_df = pd.DataFrame(scaled_values, columns=housing_df.columns)\n",
    "\n",
    "print('Scaled dataset statistics (should all be near mean=0, std=1):')\n",
    "housing_df.describe().round(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf6c0763",
   "metadata": {},
   "source": [
    "## Task 3: Apply your second feature engineering technique\n",
    "\n",
    "Now we apply PCA to reduce the dimensionality of the dataset. PCA finds the directions of greatest variance and projects the data onto those directions, producing a compact set of uncorrelated features.\n",
    "\n",
    "We keep 8 of the 9 principal components, retaining most of the variance while reducing dimensionality by one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "658794c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variance explained by 8 components: 0.9951\n",
      "Per-component variance ratio:\n",
      "  PC1: 0.2263\n",
      "  PC2: 0.2195\n",
      "  PC3: 0.1791\n",
      "  PC4: 0.1411\n",
      "  PC5: 0.1115\n",
      "  PC6: 0.0769\n",
      "  PC7: 0.0337\n",
      "  PC8: 0.0071\n"
     ]
    }
   ],
   "source": [
    "# Apply PCA to all scaled columns\n",
    "pca = PCA(n_components=8)\n",
    "pc_scores = pca.fit_transform(housing_df)\n",
    "\n",
    "print(f'Variance explained by 8 components: {pca.explained_variance_ratio_.sum():.4f}')\n",
    "print('Per-component variance ratio:')\n",
    "for i, var in enumerate(pca.explained_variance_ratio_):\n",
    "    print(f'  PC{i + 1}: {var:.4f}')\n",
    "\n",
    "# Reconstruct housing_df with PC features and restore the original label\n",
    "pc_cols = [f'PC{i + 1}' for i in range(8)]\n",
    "housing_df = pd.DataFrame(pc_scores, columns=pc_cols)\n",
    "housing_df['MedHouseVal'] = original_housing_df['MedHouseVal'].values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f42077cb",
   "metadata": {},
   "source": [
    "## Model evaluation\n",
    "\n",
    "Now we'll compare model performance on the original dataset versus your engineered dataset.\n",
    "\n",
    "### Evaluate datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "727f09a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create output directory if it doesn't exist\n",
    "output_directory = 'data/outputs'\n",
    "Path(output_directory).mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "# Save a copy of the engineered dataframe\n",
    "housing_df.to_csv('data/outputs/housing_df.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7fde2d7d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Mean improvement: 95.60%\n"
     ]
    }
   ],
   "source": [
    "# Create linear regression model\n",
    "model = LinearRegression()\n",
    "\n",
    "# Evaluate on original dataset\n",
    "scores_original = cross_val_score(\n",
    "    model,\n",
    "    original_housing_df.drop('MedHouseVal', axis=1),\n",
    "    original_housing_df['MedHouseVal'],\n",
    "    cv=10,\n",
    "    scoring='r2'\n",
    ")\n",
    "\n",
    "# Evaluate on engineered dataset\n",
    "scores_engineered = cross_val_score(\n",
    "    model,\n",
    "    housing_df.drop('MedHouseVal', axis=1),\n",
    "    housing_df['MedHouseVal'],\n",
    "    cv=10,\n",
    "    scoring='r2'\n",
    ")\n",
    "\n",
    "engineered_mean = scores_engineered.mean()\n",
    "original_mean = scores_original.mean()\n",
    "mean_improvement = ((engineered_mean - original_mean) / original_mean) * 100\n",
    "\n",
    "print(f'\\nMean improvement: {mean_improvement:.2f}%')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "543f680f",
   "metadata": {},
   "source": [
    "### Visualize model performance comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f69bcc44",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x450 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "original_model = LinearRegression()\n",
    "original_model.fit(original_housing_df.drop('MedHouseVal', axis=1), original_housing_df['MedHouseVal'])\n",
    "original_predictions = original_model.predict(original_housing_df.drop('MedHouseVal', axis=1))\n",
    "\n",
    "model = LinearRegression()\n",
    "model.fit(housing_df.drop('MedHouseVal', axis=1), housing_df['MedHouseVal'])\n",
    "predictions = model.predict(housing_df.drop('MedHouseVal', axis=1))\n",
    "\n",
    "# Create boxplot comparing performance\n",
    "data_to_plot = [scores_original, scores_engineered]\n",
    "labels = ['Original', 'Engineered']\n",
    "\n",
    "fig, axs = plt.subplots(1, 2, figsize=(9,4.5))\n",
    "\n",
    "fig.suptitle(f'Model performance comparison\\nmean improvement: {mean_improvement:.2f}%')\n",
    "\n",
    "axs[0].set_title('Cross validation R\\u00b2 scores')\n",
    "axs[0].boxplot(data_to_plot, tick_labels=labels)\n",
    "axs[0].set_xlabel('Dataset')\n",
    "axs[0].set_ylabel('R\\u00b2 score')\n",
    "\n",
    "axs[1].set_title('Predictions vs true values')\n",
    "axs[1].plot(\n",
    "    original_housing_df['MedHouseVal'], original_predictions,\n",
    "    'o', markersize=1, label='Original', alpha=0.25\n",
    ")\n",
    "\n",
    "axs[1].plot(\n",
    "    housing_df['MedHouseVal'], predictions,\n",
    "    'o', markersize=1, label='Engineered', alpha=0.25\n",
    ")\n",
    "\n",
    "axs[1].set_xlabel('True Values')\n",
    "axs[1].set_ylabel('Predictions')\n",
    "\n",
    "leg = axs[1].legend(loc='upper left', markerscale=8, framealpha=1)\n",
    "\n",
    "for lh in leg.legend_handles:\n",
    "    lh.set_alpha(1)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b278acdb",
   "metadata": {},
   "source": [
    "## Reflection\n",
    "\n",
    "### What went wrong\n",
    "\n",
    "This notebook demonstrates **data leakage** caused by including the target variable in a PCA transformation.\n",
    "\n",
    "**The mistake**: In Task 2, `StandardScaler` was applied to all columns — including `MedHouseVal`. In Task 3, PCA was then applied to the full scaled dataframe (which still contained the scaled `MedHouseVal` column). Each of the 8 resulting principal components is therefore a linear combination of all 9 original columns, including the target.\n",
    "\n",
    "**Why this inflates performance**: When the linear regression model predicts `MedHouseVal` from these principal components, it is partly predicting a variable from features that already encode that variable. The model doesn't need to learn the feature-target relationship — the target has already leaked into the features during the PCA step.\n",
    "\n",
    "**How to fix it**: Transformations must only be applied to the feature columns. The target variable should never be included:\n",
    "\n",
    "```python\n",
    "feature_cols = housing_df.drop('MedHouseVal', axis=1).columns.tolist()\n",
    "\n",
    "scaler = StandardScaler()\n",
    "housing_df[feature_cols] = scaler.fit_transform(housing_df[feature_cols])\n",
    "\n",
    "pca = PCA(n_components=7)\n",
    "pc_scores = pca.fit_transform(housing_df[feature_cols])\n",
    "pc_cols = [f'PC{i + 1}' for i in range(7)]\n",
    "housing_df = pd.DataFrame(pc_scores, columns=pc_cols)\n",
    "housing_df['MedHouseVal'] = original_housing_df['MedHouseVal'].values\n",
    "```\n",
    "\n",
    "With this corrected version, PCA operates only on the 8 feature columns — no leakage occurs and any performance gain is genuine."
   ]
  }
 ],
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