Stock-Predict-LSTM-Neural-Network.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Stock Price Prediction Using Long-Short-Term-Model Algorithm in Neural Networks \n",
    " Developers\n",
    "+ 214210050 Furkan Demircan\n",
    "+ 214210056 Eray Elagoz \n",
    "---------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Objective\n",
    "It aims to leverage machine learning & Deep Learning techniques to develop an accurate and reliable model for predicting and forecasting Bitcoin prices. By utilizing historical data and relevant features, the model provided insights into the potential future trends of Bitcoin, assisting investors, traders, and researchers in making informed decisions in the cryptocurrency market."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data manipulation and analysis\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing\n",
    "import matplotlib.pyplot as plt # data visualization\n",
    "import yfinance as yf # for coin data\n",
    "\n",
    "# Main libraries\n",
    "import datetime as dt # for date and time operations\n",
    "import time\n",
    "\n",
    "# Machine learning\n",
    "from sklearn.preprocessing import MinMaxScaler # for normalization\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error,root_mean_squared_error, mean_absolute_percentage_error # for model evaluation\n",
    "from sklearn.metrics import f1_score, accuracy_score, recall_score, precision_score, r2_score # for model evaluation\n",
    "\n",
    "# Deep learning\n",
    "import tensorflow as tf # For neural networks\n",
    "from tensorflow.keras.models import Sequential # For building the neural network\n",
    "from tensorflow.keras.layers import Dense, Dropout, LSTM # For building neural networks\n",
    "from tensorflow.keras.optimizers import Adam # For configuring the learning process\n",
    "from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau # Callbacks for the neural network"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Fetching The Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n"
     ]
    }
   ],
   "source": [
    "# Data manipulation and analysis\n",
    "stocks = ['AMZN', 'GOOGL', 'TSLA', 'AAPL']\n",
    "start_date = \"2016-01-01\"\n",
    "end_date = \"2024-11-01\"\n",
    "\n",
    "# Get the data\n",
    "stock_data = {ticker: yf.download(ticker, start=start_date, end=end_date)['Close'] for ticker in stocks}\n",
    "\n",
    "# Combine the data\n",
    "combined_data = pd.concat(stock_data.values(), axis=1, keys=stock_data.keys()).dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Combined Data Table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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 tr th {\n",
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       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>AMZN</th>\n",
       "      <th>GOOGL</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>AAPL</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ticker</th>\n",
       "      <th>AMZN</th>\n",
       "      <th>GOOGL</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>AAPL</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-04</th>\n",
       "      <td>31.849501</td>\n",
       "      <td>37.972000</td>\n",
       "      <td>14.894000</td>\n",
       "      <td>26.337500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-05</th>\n",
       "      <td>31.689501</td>\n",
       "      <td>38.076500</td>\n",
       "      <td>14.895333</td>\n",
       "      <td>25.677500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-06</th>\n",
       "      <td>31.632500</td>\n",
       "      <td>37.966499</td>\n",
       "      <td>14.602667</td>\n",
       "      <td>25.174999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-07</th>\n",
       "      <td>30.396999</td>\n",
       "      <td>37.049999</td>\n",
       "      <td>14.376667</td>\n",
       "      <td>24.112499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-08</th>\n",
       "      <td>30.352501</td>\n",
       "      <td>36.545502</td>\n",
       "      <td>14.066667</td>\n",
       "      <td>24.240000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 AMZN      GOOGL       TSLA       AAPL\n",
       "Ticker           AMZN      GOOGL       TSLA       AAPL\n",
       "Date                                                  \n",
       "2016-01-04  31.849501  37.972000  14.894000  26.337500\n",
       "2016-01-05  31.689501  38.076500  14.895333  25.677500\n",
       "2016-01-06  31.632500  37.966499  14.602667  25.174999\n",
       "2016-01-07  30.396999  37.049999  14.376667  24.112499\n",
       "2016-01-08  30.352501  36.545502  14.066667  24.240000"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the first few rows of the data\n",
    "combined_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data Graphics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ErenElagz\\AppData\\Local\\Temp\\ipykernel_1772\\635507125.py:4: MatplotlibDeprecationWarning: Passing label as a length 2 sequence when plotting a single dataset is deprecated in Matplotlib 3.9 and will error in 3.11.  To keep the current behavior, cast the sequence to string before passing.\n",
      "  plt.plot(combined_data.index, combined_data[column], label=column)\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1400x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the data\n",
    "plt.figure(figsize=(14, 7))\n",
    "for column in combined_data.columns:\n",
    "    plt.plot(combined_data.index, combined_data[column], label=column)\n",
    "\n",
    "# Add labels\n",
    "plt.title(\"Stock Price Closing Prices\")\n",
    "plt.xlabel(\"Date\")\n",
    "plt.ylabel(\"Price\")\n",
    "plt.legend(title=\"Stocks\", loc='upper left')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data Infos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=============================================\n",
      "Data types of the columns in the data frame:        Ticker\n",
      "AMZN   AMZN      float64\n",
      "GOOGL  GOOGL     float64\n",
      "TSLA   TSLA      float64\n",
      "AAPL   AAPL      float64\n",
      "dtype: object\n",
      "=============================================\n",
      "Shape of the data frame: (2223, 4)\n",
      "=============================================\n",
      "Lenght of the data: 2223\n",
      "=============================================\n",
      "Checking for missing values in the data:\n",
      "        Ticker\n",
      "AMZN   AMZN      0\n",
      "GOOGL  GOOGL     0\n",
      "TSLA   TSLA      0\n",
      "AAPL   AAPL      0\n",
      "dtype: int64\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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>AMZN</th>\n",
       "      <th>GOOGL</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>AAPL</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ticker</th>\n",
       "      <th>AMZN</th>\n",
       "      <th>GOOGL</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>AAPL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2223.000000</td>\n",
       "      <td>2223.000000</td>\n",
       "      <td>2223.000000</td>\n",
       "      <td>2223.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>107.908992</td>\n",
       "      <td>86.776827</td>\n",
       "      <td>122.395469</td>\n",
       "      <td>101.258318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>48.543233</td>\n",
       "      <td>40.809819</td>\n",
       "      <td>111.072684</td>\n",
       "      <td>62.601656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>24.103500</td>\n",
       "      <td>34.056999</td>\n",
       "      <td>9.578000</td>\n",
       "      <td>22.584999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>73.481251</td>\n",
       "      <td>52.864750</td>\n",
       "      <td>19.829667</td>\n",
       "      <td>42.772501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>98.699997</td>\n",
       "      <td>72.525002</td>\n",
       "      <td>59.960667</td>\n",
       "      <td>81.302498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>155.427002</td>\n",
       "      <td>122.266750</td>\n",
       "      <td>223.683334</td>\n",
       "      <td>155.830002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>200.000000</td>\n",
       "      <td>191.179993</td>\n",
       "      <td>409.970001</td>\n",
       "      <td>236.479996</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               AMZN        GOOGL         TSLA         AAPL\n",
       "Ticker         AMZN        GOOGL         TSLA         AAPL\n",
       "count   2223.000000  2223.000000  2223.000000  2223.000000\n",
       "mean     107.908992    86.776827   122.395469   101.258318\n",
       "std       48.543233    40.809819   111.072684    62.601656\n",
       "min       24.103500    34.056999     9.578000    22.584999\n",
       "25%       73.481251    52.864750    19.829667    42.772501\n",
       "50%       98.699997    72.525002    59.960667    81.302498\n",
       "75%      155.427002   122.266750   223.683334   155.830002\n",
       "max      200.000000   191.179993   409.970001   236.479996"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Data manipulation and analysis\n",
    "print(\"=============================================\")\n",
    "print(\"Data types of the columns in the data frame:\", combined_data.dtypes) # Data types of the columns\n",
    "print(\"=============================================\")\n",
    "print(\"Shape of the data frame:\", combined_data.shape) # Shape of the data frame\n",
    "print(\"=============================================\")\n",
    "print(\"Lenght of the data:\", len(combined_data)) # Length of the data\n",
    "print(\"=============================================\")\n",
    "print(\"Checking for missing values in the data:\\n\", combined_data.isnull().sum()) # Checking for missing values\n",
    "print(\"=============================================\")\n",
    "combined_data.describe() # Summary statistics of the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data PreProcessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scale data between 0 and 1 for LSTM\n",
    "# Scaling using for more effective training\n",
    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "scaled_data = scaler.fit_transform(combined_data) # Fit and transform the data\n",
    "scaled_df = pd.DataFrame(scaled_data, index=combined_data.index, columns=combined_data.columns) # Convert the scaled data to a data frame\n",
    "\n",
    "# Split the data into train, test and validation sets\n",
    "train_size = int(len(scaled_df) * 0.7) # 70% train data\n",
    "test_size = int(len(scaled_df) * 0.2) # 20% test data\n",
    "train_data = scaled_df.iloc[:train_size] # Train data\n",
    "test_data = scaled_df.iloc[train_size:train_size + test_size] \n",
    "val_data = scaled_df.iloc[train_size + test_size:]\n",
    "\n",
    "# Create time series data\n",
    "window_size = 60  # 60 days\n",
    "def create_time_series(data, window_size): \n",
    "    x, y = [], []\n",
    "    for i in range(len(data) - window_size):\n",
    "        x.append(data[i:i + window_size])\n",
    "        y.append(data[i + window_size])\n",
    "    return np.array(x), np.array(y)\n",
    "\n",
    "# Create time series data for train, test and validation sets\n",
    "x_train, y_train = create_time_series(train_data.values, window_size) \n",
    "x_test, y_test = create_time_series(test_data.values, window_size)\n",
    "x_val, y_val = create_time_series(val_data.values, window_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Creating The Nerual Network using LSTM "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\ErenElagz\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\layers\\rnn\\rnn.py:200: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(**kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 125ms/step - loss: 0.0392 - val_loss: 0.0242 - learning_rate: 0.0010\n",
      "Epoch 2/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 111ms/step - loss: 0.0070 - val_loss: 0.0325 - learning_rate: 0.0010\n",
      "Epoch 3/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 104ms/step - loss: 0.0044 - val_loss: 0.0307 - learning_rate: 0.0010\n",
      "Epoch 4/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 105ms/step - loss: 0.0034 - val_loss: 0.0186 - learning_rate: 0.0010\n",
      "Epoch 5/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 107ms/step - loss: 0.0032 - val_loss: 0.0180 - learning_rate: 0.0010\n",
      "Epoch 6/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 107ms/step - loss: 0.0028 - val_loss: 0.0142 - learning_rate: 0.0010\n",
      "Epoch 7/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 100ms/step - loss: 0.0030 - val_loss: 0.0096 - learning_rate: 0.0010\n",
      "Epoch 8/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 101ms/step - loss: 0.0028 - val_loss: 0.0080 - learning_rate: 0.0010\n",
      "Epoch 9/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 106ms/step - loss: 0.0025 - val_loss: 0.0071 - learning_rate: 0.0010\n",
      "Epoch 10/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 104ms/step - loss: 0.0024 - val_loss: 0.0041 - learning_rate: 0.0010\n",
      "Epoch 11/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 104ms/step - loss: 0.0026 - val_loss: 0.0056 - learning_rate: 0.0010\n",
      "Epoch 12/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 97ms/step - loss: 0.0021 - val_loss: 0.0059 - learning_rate: 0.0010\n",
      "Epoch 13/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 96ms/step - loss: 0.0022 - val_loss: 0.0070 - learning_rate: 0.0010\n",
      "Epoch 14/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 109ms/step - loss: 0.0020 - val_loss: 0.0081 - learning_rate: 0.0010\n",
      "Epoch 15/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 101ms/step - loss: 0.0019 - val_loss: 0.0023 - learning_rate: 0.0010\n",
      "Epoch 16/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 99ms/step - loss: 0.0022 - val_loss: 0.0056 - learning_rate: 0.0010\n",
      "Epoch 17/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 101ms/step - loss: 0.0020 - val_loss: 0.0054 - learning_rate: 0.0010\n",
      "Epoch 18/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 101ms/step - loss: 0.0019 - val_loss: 0.0042 - learning_rate: 0.0010\n",
      "Epoch 19/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 103ms/step - loss: 0.0018 - val_loss: 0.0107 - learning_rate: 0.0010\n",
      "Epoch 20/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 106ms/step - loss: 0.0019 - val_loss: 0.0064 - learning_rate: 0.0010\n",
      "Epoch 21/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 103ms/step - loss: 0.0016 - val_loss: 0.0066 - learning_rate: 2.0000e-04\n",
      "Epoch 22/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 115ms/step - loss: 0.0017 - val_loss: 0.0067 - learning_rate: 2.0000e-04\n",
      "Epoch 23/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 289ms/step - loss: 0.0016 - val_loss: 0.0054 - learning_rate: 2.0000e-04\n",
      "Epoch 24/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 279ms/step - loss: 0.0018 - val_loss: 0.0055 - learning_rate: 2.0000e-04\n",
      "Epoch 25/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 143ms/step - loss: 0.0016 - val_loss: 0.0049 - learning_rate: 2.0000e-04\n"
     ]
    }
   ],
   "source": [
    "# Print the shapes of the data \n",
    "model = Sequential([\n",
    "    LSTM(100, return_sequences=True, input_shape=(window_size, x_train.shape[2])), # Input layer\n",
    "    Dropout(0.3), # Dropout using for reducing overfitting\n",
    "    LSTM(100), \n",
    "    Dropout(0.3),\n",
    "    Dense(y_train.shape[1])  # Output layer\n",
    "])\n",
    "\n",
    "# Reduce learning rate when a metric has stopped improving\n",
    "reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.2, patience=5, min_lr=1e-5) \n",
    "\n",
    "# Stop training when a monitored quantity has stopped improving\n",
    "early_stopping = EarlyStopping(monitor='val_loss', patience=10, restore_best_weights=True)\n",
    "\n",
    "# Compile the model\n",
    "model.compile(optimizer='adam', loss='mean_squared_error')\n",
    "\n",
    "# Fit the model\n",
    "start = time.time()\n",
    "history = model.fit(\n",
    "    x_train, y_train,\n",
    "    validation_data=(x_val, y_val),\n",
    "    epochs=100,  \n",
    "    batch_size=64,\n",
    "    callbacks=[reduce_lr, early_stopping] \n",
    ")\n",
    "end = time.time()\n",
    "time_taken = end - start"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ lstm (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">60</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>)        │        <span style=\"color: #00af00; text-decoration-color: #00af00\">42,000</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">60</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>)        │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ lstm_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>)            │        <span style=\"color: #00af00; text-decoration-color: #00af00\">80,400</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>)              │           <span style=\"color: #00af00; text-decoration-color: #00af00\">404</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ lstm (\u001b[38;5;33mLSTM\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m60\u001b[0m, \u001b[38;5;34m100\u001b[0m)        │        \u001b[38;5;34m42,000\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout (\u001b[38;5;33mDropout\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m60\u001b[0m, \u001b[38;5;34m100\u001b[0m)        │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ lstm_1 (\u001b[38;5;33mLSTM\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m)            │        \u001b[38;5;34m80,400\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_1 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m)              │           \u001b[38;5;34m404\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">368,414</span> (1.41 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m368,414\u001b[0m (1.41 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">122,804</span> (479.70 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m122,804\u001b[0m (479.70 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Optimizer params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">245,610</span> (959.42 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Optimizer params: \u001b[0m\u001b[38;5;34m245,610\u001b[0m (959.42 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time taken to train the model: 75.9981861114502 seconds\n"
     ]
    }
   ],
   "source": [
    "model.summary() # Display the model summary\n",
    "print(\"Time taken to train the model:\", time_taken, \"seconds\") # Time taken to train the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Plotting the Train and Test Scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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uleiIyB8V5sJTSea89qSDEBxR5cO0atWKn3/+GTCSk02bNrF7926Sk5MBYxTiggsuYM2aNXTr1o2XX36ZUaNGMXLkSACmTJnC//73P7Kzs12OGxERwX/+8x+Cg4PPO7agoCAee+wx5+2UlBRWrFjBBx98wE033eS8Py4ujpdeegmr1UrLli155plnyM3NZdKkSQBMnDiRf/zjH/z4448MGTIEgEceecT5/MaNGzNhwgTmzJnDgw8+eNaYxo0bR5MmTVi8eDHh4eEuj9WvX58JEyY4b48dO5avvvqKDz74gO7duxMWFsbNN9/MW2+9xY033gjAf//7Xxo2bEifPn2cz8vLy+Odd96hfv36ALz88stcffXVTJ8+3SWx8jaN6JjFMZoT1wTCarn/+I5E59hO9x9bRMRkdrsdS0mT1a1bt5KcnOxMcgDatGlDbGwsW7duBWD79u10797d5Rh/vA3Qrl27KiU5DjNmzKBLly7UqVOHyMhIXnvtNfbt2+eyzwUXXIDVWvoznJCQQLt27Zy3AwICiI+P5/Dhw8775s6dy4UXXkhiYiKRkZE88sgjpx23PNdccw2//vor//736ZNUiouLefzxx2nXrh1xcXFERkby1VdfuRx39OjR/O9//+PAgQMAvP32285icYeGDRs6kxyAnj17YrPZ2L59+znj86TzGtGZMWMGzz77LGlpaXTo0IGXX3653C+Mw7x585g8eTJ79uyhefPmPP3001x11VXOxx999FHmzJnD/v37CQ4OpkuXLjz55JOkpqY69zl+/Dhjx47ls88+w2q1cv311/Piiy8SGRl5Pm/BfAeq2CjwXByJTs5hyM+CkCjPvI6IVD9B4cbIilmv7QZbt2511pi4U0TE6aNNFosF+x9OuZ2tLmbOnDlMmDCB6dOn07NnT6Kionj22WdZtWqVy35BQUGnvU559zlmmK1YsYJbbrmFxx57jP79+xMTE8OcOXOYPn36Od/XsGHDuPbaa7n99tux2+2MHz/e+dizzz7Liy++yAsvvEC7du2IiIjgvvvucynG7tSpEx06dOCdd96hX79+/PLLL3z++efnfF1fUOlEZ+7cuYwfP56ZM2eSmprKCy+8QP/+/dm+fTt169Y9bf/ly5czdOhQpk2bxjXXXMPs2bMZNGgQ69evd1Z7t2jRgldeeYUmTZpw6tQpnn/+efr168eOHTuoU6cOALfccguHDh1i8eLFFBYWMnLkSO644w5mz55dxY/AJJ6qz3EIi4XweMg9Zpy+qtfBM68jItWPxeKW00dm+eabb9i0aRP3338/AK1bt2b//v3s37/fOaqzZcsWTp48SZs2bQBo2bIla9asYfjw4c7jrFmzpkKvV6dOHQ4dOuS8/dtvv5Gbm3vG/ZctW0avXr1c6lN27qz66Pry5ctp1KgRf//735337d27t8LPHzFiBFarlZEjR2Kz2Zynq5YtW8bAgQO59dZbAaPG6Ndff3V+dg5/+ctfeOGFFzhw4AB9+/Z1GUEDoyD84MGDJCUZp0VXrlzpPC1npkqfunruuecYPXo0I0eOpE2bNsycOZPw8HDefPPNcvd/8cUXGTBgAA888ACtW7fm8ccfp3PnzrzyyivOfW6++Wb69u1LkyZNuOCCC3juuefIzMx0nn/dunUrixYt4j//+Q+pqalcdNFFvPzyy8yZM4eDB036q6SqnDOuPJToAMQ1Na5VpyMi1VR+fj5paWkcOHCA9evX89RTTzFw4ECuueYaZ9LSt29f2rVrxy233ML69etZvXo1w4cP55JLLqFr166AUXfyxhtv8O6777J7926eeuopfvrpJ5dTL2dy2WWX8corr7BhwwbWrl3LXXfdddrIS1nNmzdn7dq1fPXVV/z6669Mnjy5wknV2TRv3px9+/YxZ84cdu7cyUsvvcQnn3xSqWMMGzaMWbNm8fDDD/Pss886j7t48WKWL1/O1q1bufPOO0+bsQbGb/Xvv//O66+/7lKE7BAaGsqIESP46aef+OGHH/jrX//KTTfdZGp9DlQy0SkoKGDdunX07du39ABWK3379mXFihXlPmfFihUu+wP079//jPsXFBTw2muvERMTQ4cOHZzHiI2NdX5hwfhiW63W04YCHfLz88nMzHS5+IzMg5CdBhYr1GvvuddRnY6IVHOLFi2iXr16NG7cmAEDBrB06VJeeuklPv30UwICAgDj9M6nn35KrVq16N27t/MP57lz5zqPc8sttzBx4kTGjx9P+/bt2blzJyNHjiQ0NPScMUyfPp3k5GQuvvhibr75ZiZMmHBaQW9Zd955J9dddx2DBw8mNTWVY8eOuWX20bXXXsv999/PmDFj6NixI8uXL2fy5MmVPs4tt9zCu+++y8SJE3n66ad55JFH6Ny5M/3796dPnz4kJiaWOw08JiaG66+/nsjIyHIfb9asGddddx1XXXUV/fr1o3379vzrX/86j3fqZvZKOHDggB2wL1++3OX+Bx54wN69e/dynxMUFGSfPXu2y30zZsyw161b1+W+zz77zB4REWG3WCz2pKQk++rVq52PPfnkk/YWLVqcduw6derY//Wvf5X7ulOnTrUDp10yMjIq9F49astndvvUaLt9Rk/Pvs63Txuv88k9nn0dEfFZp06dsm/ZssV+6tQps0PxOX379rXfeuutZodRrVx22WX2sWPHuv24Z/ueZmRkVOn322dmXV166aVs3LiR5cuXM2DAAG666SaXSvPKmjhxIhkZGc7L/v373RhtFTnrczwwrbws5xRzjeiISM2Wm5vLc889xy+//MK2bduYOnUqX3/9NSNGjDA7tGrhxIkTfPLJJ3z77bfce++9ZodTKZUqRq5duzYBAQGnnbtLT08/4zm4xMTECu0fERFBs2bNaNasGT169KB58+a88cYbTJw4kcTExNOSnqKiIo4fP37G1w0JCSEkJKQyb897vFGfA+qlIyJSwmKx8MUXX/Dkk0+Sl5dHy5Yt+eijj04rrZDyderUiRMnTvD000+bXlxcWZUa0XFM/V6yZInzPpvNxpIlS864ZkjPnj1d9gdYvHjxOdcYsdlsznbTPXv25OTJk6xbt875+DfffIPNZnOZgl4t2O1wsGRquadmXDk4Ep3sdGOKuYhIDRUWFsbXX3/NsWPHyMnJYf369Vx33XVmh1Vt7Nmzh4yMDJfGgtVFpU9djR8/ntdff51Zs2axdetW7r77bnJycpzdJocPH87EiROd+48bN45FixYxffp0tm3bxqOPPsratWsZM2YMADk5OUyaNImVK1eyd+9e1q1bx+23386BAwecHRhbt27NgAEDGD16NKtXr2bZsmWMGTOGIUOGOKexVRvHd0HeSQgIhroXePa1HFPMAY7v9uxriYiI+KBK99EZPHgwR44cYcqUKaSlpdGxY0cWLVpEQkICYMyjL9vpsVevXsyePZtHHnmESZMm0bx5c+bPn+/soRMQEMC2bduYNWsWR48eJT4+nm7duvHDDz9wwQWlicB7773HmDFjuPzyy50NA1966aWqvn/vc4zmJLaDwKp33zynuCYlvXR2enaGl4iIiA+y2O1uWGGtGsjMzCQmJoaMjAyio6PNC2TRJFg5A7qNhqv/6fnX+/hO+HkOXD4FLv6b519PRHxKXl4eu3fvpnHjxoSFhZkdjki5Tp06xZ49e0hJSTltyn9Vf799ZtZVjeHpjsh/5Oylo4JkkZrI0djubJ18Rczm+H6erRHj+dLq5d5UXASHfjK2PT3jyiFe3ZFFarKAgABiY2OdM1fDw8Mr1A1YxBvsdju5ubkcPnyY2NhYZxNId1Ki401Ht0NhLgRHQu3m3nnNuJJF79RLR6TGcrThqEpvMhFPio2N9dhSEUp0vMnRP6deR7C6P2stl8sU82wIqaarvYvIebNYLNSrV4+6deueddVtETMEBQV5ZCTHQYmON3mrI3JZYbUgLA5OHS9ZxVwzr0RqqoCAAI/+oIj4IhUje5O3OiL/kep0RESkhlKi4y1F+ZD+i7HtrRlXDlrzSkREaiglOt6SthlshcZppNhG3n3tOI3oiIhIzaREx1vK9s/x9tRO9dIREZEaSomOt5hVnwMQr1XMRUSkZlKi4y3e7ohclnOKeZoxxVxERKSGUKLjDflZcGS7sW3GiI5jijnACa1iLiIiNYcSHW849BNgh+j6EJVgTgzOOh3NvBIRkZpDiY43OOtzvNgo8I/US0dERGogJTreYGZ9joN66YiISA2kRMcbzJxx5eDspaMaHRERqTmU6HhazjE4udfYNvPUlWp0RESkBlKi42kHNxjXcU0hLNa8OOLLTDEvyDEvDhERES9SouNpvlCfAyVTzGsZ2ypIFhGRGkKJjqf5Qn2Og9a8EhGRGkaJjifZ7b4zogOq0xERkRpHiY4nZR6E7HSwBEBie7OjUS8dERGpcZToeJJjNKduawgONzcWKNNLR4mOiIjUDEp0PMkXOiKXpRodERGpYZToeJIv1ecAxKUY11mHNMVcRERqBCU6nmK3l/bQ8YUZVwDhcWWmmKtDsoiI+D8lOp5yfBfkZUBACNRtY3Y0pbTmlYiI1CBKdDzFUZ+T2BYCg82NpSzV6YiISA2iRMdTDvpQo8Cy1EtHRERqECU6nnLAxwqRHeK1irmIiNQcSnQ8obgIDv1kbPvqiI5OXYmISA2gRMcTjmyDolMQHAm1m5sdjStHopN1EApyzY1FRETEw5ToeIKjPqdeR7AGmBrKacLjIDTW2D6h01ciIuLflOh4gqN/Tn0f6Yj8R446HRUki4iIn1Oi4wkHfHTGlYPqdEREpIZQouNuRfmQ/oux7WszrhycvXQ0oiMiIv5NiY67pW0GWyGExUFsI7OjKZ9zREc1OiIi4t+U6Lhb2YU8LRZzYzkT1eiIiEgNoUTH3Xy9Pgc0xVxERGoMJTrudtBHOyKXpSnmIiJSQyjRcaf8LDiy3dj25REd0MwrERGpEZTouNOhnwA7RNeHqASzozk71emIiEgNoETHnZz1OT7aKLAsjeiIiEgNcF6JzowZM2jcuDGhoaGkpqayevXqs+4/b948WrVqRWhoKO3ateOLL75wPlZYWMhDDz1Eu3btiIiIICkpieHDh3Pw4EGXYzRu3BiLxeJy+cc//nE+4XtOdajPcXD20lGiIyIi/qvSic7cuXMZP348U6dOZf369XTo0IH+/ftz+PDhcvdfvnw5Q4cOZdSoUWzYsIFBgwYxaNAgNm/eDEBubi7r169n8uTJrF+/no8//pjt27dz7bXXnnas//u//+PQoUPOy9ixYysbvmdVhxlXDhrRERGRGsBit9vtlXlCamoq3bp145VXXgHAZrORnJzM2LFjefjhh0/bf/DgweTk5LBw4ULnfT169KBjx47MnDmz3NdYs2YN3bt3Z+/evTRs2BAwRnTuu+8+7rvvvsqE65SZmUlMTAwZGRlER0ef1zHOKucYPFuSPDy0F8Ji3f8a7pR7HJ5JMbYnHYLgcHPjERERKUdVf78rNaJTUFDAunXr6Nu3b+kBrFb69u3LihUryn3OihUrXPYH6N+//xn3B8jIyMBisRAbG+ty/z/+8Q/i4+Pp1KkTzz77LEVFRWc8Rn5+PpmZmS4Xj3Is5BnX1PeTHICwWhAaY2yf2GNqKCIiIp5SqUTn6NGjFBcXk5DgOqMoISGBtLS0cp+TlpZWqf3z8vJ46KGHGDp0qEvm9te//pU5c+awdOlS7rzzTp566ikefPDBM8Y6bdo0YmJinJfk5OSKvs3zU53qc8Do2qw1r0RExM8Fmh1AWYWFhdx0003Y7XZeffVVl8fGjx/v3G7fvj3BwcHceeedTJs2jZCQkNOONXHiRJfnZGZmejbZqU71OQ5xTYwETXU6IiLipyqV6NSuXZuAgADS09Nd7k9PTycxMbHc5yQmJlZof0eSs3fvXr755ptznodLTU2lqKiIPXv20LJly9MeDwkJKTcB8gi7vfqN6IB66YiIiN+r1Kmr4OBgunTpwpIlS5z32Ww2lixZQs+ePct9Ts+ePV32B1i8eLHL/o4k57fffuPrr78mPj7+nLFs3LgRq9VK3bp1K/MWPCPzIGSngyUAEtubHU3FaeaViIj4uUqfuho/fjwjRoyga9eudO/enRdeeIGcnBxGjhwJwPDhw6lfvz7Tpk0DYNy4cVxyySVMnz6dq6++mjlz5rB27Vpee+01wEhybrjhBtavX8/ChQspLi521u/ExcURHBzMihUrWLVqFZdeeilRUVGsWLGC+++/n1tvvZVatWq567M4f47RnLqtq9fsJfXSERERP1fpRGfw4MEcOXKEKVOmkJaWRseOHVm0aJGz4Hjfvn1YraUDRb169WL27Nk88sgjTJo0iebNmzN//nzatm0LwIEDB1iwYAEAHTt2dHmtpUuX0qdPH0JCQpgzZw6PPvoo+fn5pKSkcP/997vU4JiqOnVELssxopN5AApPQVCYufGIiIi4WaX76FRXHu2j885A2PUtXPM8dL3dvcf2JLsdnm4EeRlw9wpIaGN2RCIiIi682kdHymG3l/bQqU4zrqBkirnqdERExH8p0amq47uMEZGAEEi4wOxoKk+9dERExI8p0akqR31OYjsICDI3lvOhER0REfFjPtUwsFpqOQCGfwp2m9mRnB/10hERET+mRKeqQqKgSR+zozh/zhGd3ebGISIi4gE6dVXTOWp0Mn83ppiLiIj4ESU6NV14HIRoFXMREfFPSnRqOosF4ktOX6lOR0RE/IwSHdHMKxER8VtKdES9dERExG8p0RGN6IiIiN9SoiNleuko0REREf+iREfKrGKuKeYiIuJflOgIhMdrirmIiPglJTpSsop5irGtOh0REfEjSnTEoDWvRETEDynREYNmXomIiB9SoiMG9dIRERE/pERHDFrFXERE/JASHTE4anQyfofCPHNjERERcRMlOmIIj4eQaMCuKeYiIuI3lOiIwWIpc/pKdToiIuIflOhIKc28EhERP6NER0qpl46IiPgZJTpSSiM6IiLiZ5ToSClnLx0lOiIi4h+U6Egpx4iOppiLiIifUKIjpSJqa4q5iIj4FSU6UkqrmIuIiJ9RoiOutOaViIj4ESU64kozr0RExI8o0RFXjkRHvXRERMQPKNERV46mgVrFXERE/IASHXHlnGK+X1PMRUSk2lOiI64i6kBwFGCHk3vNjkZERKRKlOiIq7JTzFWnIyIi1ZwSHTldvJaCEBER/6BER07nnGKuER0REanelOjI6bS4p4iI+AklOnI6Zy8dJToiIlK9BZodQHWXlVfIr+nZgJ0ujeLMDsc9HDU6GfuhKB8CQ8yNR0RE5DxpRKeKlu04xvWvLueJz7eaHYr7RNSB4Ei0irmIiFR3SnSqqH5sGAAHT54yORI3sli05pWIiPiF80p0ZsyYQePGjQkNDSU1NZXVq1efdf958+bRqlUrQkNDadeuHV988YXzscLCQh566CHatWtHREQESUlJDB8+nIMHD7oc4/jx49xyyy1ER0cTGxvLqFGjyM7OPp/w3SopNhSAw1n5FBTZTI7GjbTmlYiI+IFKJzpz585l/PjxTJ06lfXr19OhQwf69+/P4cOHy91/+fLlDB06lFGjRrFhwwYGDRrEoEGD2Lx5MwC5ubmsX7+eyZMns379ej7++GO2b9/Otdde63KcW265hV9++YXFixezcOFCvv/+e+64447zeMvuFRcRTEigFbsd0jP9aMkE9dIRERE/YLHb7fbKPCE1NZVu3brxyiuvAGCz2UhOTmbs2LE8/PDDp+0/ePBgcnJyWLhwofO+Hj160LFjR2bOnFnua6xZs4bu3buzd+9eGjZsyNatW2nTpg1r1qyha9euACxatIirrrqK33//naSkpHPGnZmZSUxMDBkZGURHR1fmLZ/TZf/8ll1Hc5hzRw96NIl367FNs+G/8Om90KQPDP/U7GhERKSGqurvd6VGdAoKCli3bh19+/YtPYDVSt++fVmxYkW5z1mxYoXL/gD9+/c/4/4AGRkZWCwWYmNjnceIjY11JjkAffv2xWq1smrVqsq8BY+oV3L6yq/qdNRLR0RE/EClppcfPXqU4uJiEhISXO5PSEhg27Zt5T4nLS2t3P3T0tLK3T8vL4+HHnqIoUOHOjO3tLQ06tat6xp4YCBxcXFnPE5+fj75+fnO25mZmWd/c1WQFOOHBcnOVcx/1xRzERGptnxq1lVhYSE33XQTdrudV199tUrHmjZtGjExMc5LcnKym6I8XVLJzKsDJ/2oRieyrjHF3G6DE1rFXEREqqdKJTq1a9cmICCA9PR0l/vT09NJTEws9zmJiYkV2t+R5Ozdu5fFixe7nIdLTEw8rdi5qKiI48ePn/F1J06cSEZGhvOyf//+Cr/PyvLfKeYlq5hrzSsREammKpXoBAcH06VLF5YsWeK8z2azsWTJEnr27Fnuc3r27OmyP8DixYtd9nckOb/99htff/018fHxpx3j5MmTrFu3znnfN998g81mIzU1tdzXDQkJITo62uXiKUn+mOiA6nRERKTaq/QSEOPHj2fEiBF07dqV7t2788ILL5CTk8PIkSMBGD58OPXr12fatGkAjBs3jksuuYTp06dz9dVXM2fOHNauXctrr70GGEnODTfcwPr161m4cCHFxcXOupu4uDiCg4Np3bo1AwYMYPTo0cycOZPCwkLGjBnDkCFDKjTjytOSyhQj2+12LBaLyRG5iXrpiIhINVfpRGfw4MEcOXKEKVOmkJaWRseOHVm0aJGz4Hjfvn1YraUDRb169WL27Nk88sgjTJo0iebNmzN//nzatm0LwIEDB1iwYAEAHTt2dHmtpUuX0qdPHwDee+89xowZw+WXX47VauX666/npZdeOp/37HaOEZ2cgmIy84qICQsyOSI3US8dERGp5irdR6e68mQfHYAujy/mWE4BX467mNb1PHeazKv2Loe3roTYhnDfJrOjERGRGsirfXTkzPyyTsdRo+OYYi4iIlLNKNFxkyR/bBqoKeYiIlLNKdFxE7/speMyxVx1OiIiUv0o0XETv+ylA6Uzr9RLR0REqiElOm5Szx+XgQD10hERkWpNiY6b+GWNDqiXjoiIVGtKdNzEceoqLTOPomKbydG4kXrpiIhINaZEx01qR4YQFGDBZof0LD+aiu1cxXw/FPpRobWIiNQISnTcxGq1+GedTmRC6RTzFzvAlw/D72uhZvSZFBGRak6Jjhv5ZZ2OxQKXToLQWMhOg1Wvwn8uh5c6wpL/g/RfzI5QRETkjJTouFFpd2Q/O8XT816Y8BsMnQNtb4CgcDixB36YDq/2ghk94PtnVccjIiI+p9KLesqZ+W0vHYDAYGh5pXEpyIHtX8Lmj2HHYjiyFb55wrjU72IkQxf8GaLrmR21iIjUcEp03Mgv17sqT3AEtLvBuJw6AVsXwuYPYff3cGCdcflqEjS+CNpeD20GQnic2VGLiEgNpETHjUqXgfDzRKessFrQeZhxyUqHLfNh04fw+2rY84Nx+WICNL3cSIxaXgUhkWZHLSIiNYQSHTeq74/FyJURlQCpdxqXE3vhl49h00eQvgl++8q4BIbBhePg0olmRysiIjWAipHdyDG9PDOviKy8QpOjMVmtRnDR/XD3j3DPKuj9oNGTp+gUfPcPOPSz2RGKiEgNoETHjSJCAokJCwLgUIafzbyqirqt4LK/w9j10GaQcd/yl00NSUREagYlOm5WI+t0KspigYvuM7Y3fwQn95sajoiI+D8lOm5W4+t0ziWpEzS+GOzFsPJVs6MRERE/p0THzWrMFPOquHCccb1+Fpw6aWooIiLi35TouJnfdkd2p2Z9oW4bKMiGtW+aHY2IiPgxJTpuphqdCrBYoNdYY3vVTCjyo9XeRUTEpyjRcTPV6FRQ2xsgKgmy0+HnD8yORkRE/JQSHTdzjOikZ+ZRbLObHI0PCwyGHncZ28tfBpvN3HhERMQvKdFxs7pRoQRYLRQW2zmarVMyZ9XlNgiOgqPb4bf/mR2NiIj4ISU6bhZgtZAYbZy+Up3OOYTGQNfbjO3lL5kaioiI+CclOh5QX1PMKy71brAGwt5l8Ps6s6MRERE/o0THA+qpILniYupDuxuNbY3qiIiImynR8QD10qkkx1TzrQvg+G5zYxEREb+iRMcD1EunkhIuMJoI2m2wYobZ0YiIiB9RouMB6qVzHnr91bje8F/IOWZuLCIi4jeU6HiA1rs6Dym9oV4HKDoFa/5jdjQiIuInlOh4gCPROZFbSG5BkcnRVBMWS+mozurXoFBJooiIVJ0SHQ+IDg0iKiQQUEFypbQZBLENIfcobJxtdjQiIuIHlOh4iE5fnYeAQOhxr7G94hWwFZsbj4iIVHtKdDwkqaQg+VCGEp1K6XQrhMbC8V2w7XOzoxERkWpOiY6HlE4x16mrSgmJhG5/MbaXvwR2LYwqIiLnT4mOh+jUVRWk3gkBIfD7Gti30uxoRESkGlOi4yFJ6qVz/iLrQochxraWhRARkSpQouMhSTEa0amSXmMBC2z/Ao78anY0IiJSTSnR8RDnqauMPGw21ZlUWu3m0PIqY3vFy+bGIiIi1ZYSHQ9JjAnFYoGCIhvHcgrMDqd6urCkgeBPcyAr3dxYRESkWlKi4yFBAVYSolSnUyUNe0CD7lBcAKv/bXY0IiJSDSnR8SAVJLuBY1RnzRuQn21uLCIiUu2cV6IzY8YMGjduTGhoKKmpqaxevfqs+8+bN49WrVoRGhpKu3bt+OKLL1we//jjj+nXrx/x8fFYLBY2btx42jH69OmDxWJxudx1113nE77XlPbSUaJz3lpeBXFNIe8kbHjX7GhERKSaqXSiM3fuXMaPH8/UqVNZv349HTp0oH///hw+fLjc/ZcvX87QoUMZNWoUGzZsYNCgQQwaNIjNmzc798nJyeGiiy7i6aefPutrjx49mkOHDjkvzzzzTGXD96r6zl46ahp43qwB0GuMsb3iX1CsRVJFRKTiKp3oPPfcc4wePZqRI0fSpk0bZs6cSXh4OG+++Wa5+7/44osMGDCABx54gNatW/P444/TuXNnXnnlFec+w4YNY8qUKfTt2/esrx0eHk5iYqLzEh0dXdnwvUpNA92kw1AIrw0Z+2DLfLOjERGRaqRSiU5BQQHr1q1zSUisVit9+/ZlxYoV5T5nxYoVpyUw/fv3P+P+Z/Pee+9Ru3Zt2rZty8SJE8nNzT3jvvn5+WRmZrpcvK10irkSnSoJCjO6JQMse1HLQoiISIVVKtE5evQoxcXFJCQkuNyfkJBAWlpauc9JS0ur1P5ncvPNN/Pf//6XpUuXMnHiRN59911uvfXWM+4/bdo0YmJinJfk5ORKvZ47lBYj69RVlXX7CwSFQ9rPsPs7s6MREZFqItDsACrqjjvucG63a9eOevXqcfnll7Nz506aNm162v4TJ05k/PjxztuZmZleT3Yc3ZGPZueTV1hMaFCAV1/fr4THGSubr34Nlr0ETfqYHZGIiFQDlRrRqV27NgEBAaSnuzZvS09PJzExsdznJCYmVmr/ikpNTQVgx44d5T4eEhJCdHS0y8XbYsODCCtJbtIyNKpTZT3vBYsVdi6BtM3n3l9ERGq8SiU6wcHBdOnShSVLljjvs9lsLFmyhJ49e5b7nJ49e7rsD7B48eIz7l9Rjino9erVq9JxPMlisaiXjjvVagxtBhrby7UshIiInFulZ12NHz+e119/nVmzZrF161buvvtucnJyGDlyJADDhw9n4sSJzv3HjRvHokWLmD59Otu2bePRRx9l7dq1jBkzxrnP8ePH2bhxI1u2bAFg+/btbNy40VnHs3PnTh5//HHWrVvHnj17WLBgAcOHD6d37960b9++Sh+Ap6mXjpv1KmkguPlDyPjd3FhERMTnVTrRGTx4MP/85z+ZMmUKHTt2ZOPGjSxatMhZcLxv3z4OHTrk3L9Xr17Mnj2b1157jQ4dOvDhhx8yf/582rZt69xnwYIFdOrUiauvvhqAIUOG0KlTJ2bOnAkYI0lff/01/fr1o1WrVvztb3/j+uuv57PPPqvSm/cG9dJxs/qdofHFYCuCla+aHY2IiPg4i91eM+bqZmZmEhMTQ0ZGhlfrdV5a8hvPLf6VwV2TefoG3x59qjZ+/R/MvhGCo2D8LxAaY3ZEIiLiIVX9/dZaVx6mXjoe0PwKqNMaCrJg7VtmRyMiIj5MiY6HOYqRVaPjRhYL9BprbK99A2w2c+MRERGfpUTHw+qXWQaihpwl9I4L/gwh0XByH+xdZnY0IiLio5ToeFhijDGik1do40RuocnR+JHgcGh7nbG98T1zYxEREZ+lRMfDQgIDqBMVAqiXjtt1LFkCZMunkJ9lbiwiIuKTlOh4gXrpeEiDrlC7BRTmwi+fmB2NiIj4ICU6XpBUcvrqkBId97JYoOPNxvYGnb4SEZHTKdHxgtIp5moa6HbthxjrX+1fCUfLX/dMRERqLiU6XqBTVx4UXQ+a9TW2VZQsIiJ/oETHC+prYU/P6niLcf3THLAVmxuLiIj4FCU6XpBUppeOeEDLKyGsFmQdhJ1LzY5GRER8iBIdL3AkOoez8ikoUhdftwsMgXY3Gdsb/2tuLCIi4lOU6HhBfEQwwYFW7HZIz1RBskd0Kjl9te1zOHXC3FhERMRnKNHxAovF4lwKQgXJHlKvAyS0g+IC2PSh2dGIiIiPUKLjJUkqSPY8R08dzb4SEZESSnS8JClGBcke1/4msAbCwQ2QvsXsaERExAco0fGS0l46qtHxmIja0GKAsa1RHRERQYmO1+jUlZd0Klno8+e5UKzV4kVEajolOl6iXjpe0uwKiKgLOUfgt/+ZHY2IiJhMiY6XlE107Ha7ydH4sYBA6DDY2NZCnyIiNZ4SHS9xFCPnFBSTmVdkcjR+zrEkxG9fQfYRc2MRERFTKdHxkrDgAOIiggGdvvK4uq0hqTPYioxaHRERqbGU6HiRCpK9yNEpeeN7oFOFIiI1lhIdL1IvHS9qez0EhMDhLXBoo9nRiIiISZToeJF66XhRWC1ofY2xraJkEZEaS4mOF9XXFHPvchQlb5oHhUouRURqIiU6XqReOl7WpA9E14e8k7D9C7OjEREREyjR8SIVI3uZNQA6DDW2tSSEiEiNpETHixynrtIy8ygqtpkcTQ3hWNF85zeQedDcWERExOuU6HhR7cgQggIs2OyQnpVvdjg1Q3xTaNgT7Db46X2zoxERES9TouNFVquFxBidvvI6R1HyBvXUERGpaZToeJl66ZjggkEQFA7Hd8L+VWZHIyIiXqREx8tKp5hrurPXhERBm0HGtoqSRURqFCU6XqYp5iZxLAmx+RMoyDE3FhER8RolOl6mRMckjS6EWo2hIAu2fmZ2NCIi4iVKdLzM0UvngBId77JYyhQl/9fcWERExGuU6HiZloEwUYehgAX2/AAn9pgdjYiIeIESHS+rV5LoZOYVkZVXaHI0NUxsMqT0NrY3qqeOiEhNoETHyyJDAokJCwLgUIZmXnldp1uN642zwabu1CI+4/A2OL7L7CjEDynRMYGjIFl1OiZodQ2EREPGPuMUloiYL/sIvH4pvH455GeZHY34GSU6JkhSd2TzBIdD2+uMbfXUEfENu76Fwlw4dRw2zTM7GvEzSnRMoCnmJutYcvpqywLIyzA3FhExEh2HtW9pqRZxKyU6JkhSd2RzNegKtVtA0Sn4Zb7Z0YjUbHY77FpaejvtZziw3rx4xO+cV6IzY8YMGjduTGhoKKmpqaxevfqs+8+bN49WrVoRGhpKu3bt+OKLL1we//jjj+nXrx/x8fFYLBY2btx42jHy8vK49957iY+PJzIykuuvv5709PTzCd906qVjsrI9dXT6SsRcx3ZA5gEICIY2A4371r1pbkziVyqd6MydO5fx48czdepU1q9fT4cOHejfvz+HDx8ud//ly5czdOhQRo0axYYNGxg0aBCDBg1i8+bNzn1ycnK46KKLePrpp8/4uvfffz+fffYZ8+bN47vvvuPgwYNcd911lQ3fJzh66RzKUKJjmvaDwWI1Fvk8+pvZ0YjUXDtLRnMa9oAe9xrbmz6CUydNC0n8S6UTneeee47Ro0czcuRI2rRpw8yZMwkPD+fNN8vPwF988UUGDBjAAw88QOvWrXn88cfp3Lkzr7zyinOfYcOGMWXKFPr27VvuMTIyMnjjjTd47rnnuOyyy+jSpQtvvfUWy5cvZ+XKlZV9C6ZznLpKy8ij2KZz0aaIrgfNSr5vGtURMY+jPqdJH0juDnXbGKeVf/7AzKjEj1Qq0SkoKGDdunUuCYnVaqVv376sWLGi3OesWLHitASmf//+Z9y/POvWraOwsNDlOK1ataJhw4ZnPE5+fj6ZmZkuF19RNyqEAKuFwmI7R7PzzQ6n5nKcvvppDtiKzY1FpCYqLipt89Ckj3Fauevtxu21b6ooWdyiUonO0aNHKS4uJiEhweX+hIQE0tLSyn1OWlpapfY/0zGCg4OJjY2t8HGmTZtGTEyM85KcnFzh1/O0wAAridGq0zFdyyshrBZkHYKd35gdjUjNc3A95GdCaCzU62jc1/4mCAqHI1thX/UbsRff47ezriZOnEhGRobzsn//frNDcuEoSNYUcxMFhkC7m4xtLfQp4n2O01YpvcEaYGyHxkDb643tdW+ZEpb4l0olOrVr1yYgIOC02U7p6ekkJiaW+5zExMRK7X+mYxQUFHDy5MkKHyckJITo6GiXiy9RLx0f0ank9NX2LyD3uLmxiNQ0jkLkppe63t91pHH9y3z9fylVVqlEJzg4mC5durBkyRLnfTabjSVLltCzZ89yn9OzZ0+X/QEWL158xv3L06VLF4KCglyOs337dvbt21ep4/gS9dLxEfU6QEI7KC6AH59XTYCIt+Rnw+8lrUma9HF9LKmz8f9mcb6xLp1IFVT61NX48eN5/fXXmTVrFlu3buXuu+8mJyeHkSONDHz48OFMnDjRuf+4ceNYtGgR06dPZ9u2bTz66KOsXbuWMWPGOPc5fvw4GzduZMuWLYCRxGzcuNFZfxMTE8OoUaMYP348S5cuZd26dYwcOZKePXvSo0ePKn0AZtF6Vz6kV8l3cflL8PWjSnZEvGHvMrAVQWwjiGvi+ljZouR16pQsVVPpRGfw4MH885//ZMqUKXTs2JGNGzeyaNEiZ8Hxvn37OHTokHP/Xr16MXv2bF577TU6dOjAhx9+yPz582nbtq1znwULFtCpUyeuvvpqAIYMGUKnTp2YOXOmc5/nn3+ea665huuvv57evXuTmJjIxx9/fN5v3Gxa78qHdBgC/Z40tpe9AF8+qJXNRTyt7LTy8rS9AYKjjIaCWoBXqsBit9eMVDkzM5OYmBgyMjJ8ol5n66FMrnzxB2qFB7FhSj+zwxEwprMuHA/Yjann175cWiApIu41o4cxs+rGt+GCP5e/z8LxsPYN4/Eb3/ZmdOJDqvr77bezrnyd49TVidxCcguKTI5GAGOo/M//Njomb3wPPhoFxYVmRyXif7LSjCQHCzTufeb9HEXJWxdCdvnd90XORYmOSaJDA4kMCQRUkOxTOgyGG2eBNQh++QTmDoNC/fcRcatd3xnX9dpDRPyZ90tsBw26ga1QLSDkvCnRMYnFYlEvHV/V5loY+j4EhsKvX8Lsm6Agx+yoRPyHY7XyJpeefT+ALiWjOuveVu2cnBclOiZK0uKevqv5FXDLhxAcCbu/g3evg7wMs6MSqf7s9nMXIpd1wZ+NJoIn98IudTCXylOiY6LSKeY6NeKTUi6GYfONf2T3r4RZf4KcY2ZHJVK9HdluLLsSGAoNK9AHLTgcOgw1tteqU7JUnhIdE9VXd2Tfl9wNRiyE8Npw6Cd4+2qjkFJEzo9jNKdhDwgKrdhzHKevtn8JmQc9Epb4LyU6JlKNTjVRrz2M/AKi6hkzRd66Ek7uMzsqkeqpMvU5DnVbQcNeYC+G9e96Ji7xW0p0TJQUoxGdaqNOSxj5JcQ2hOO74M0r4dhOs6MSqV6KC2HPj8Z2RepzynJ0Sl4/C4rVkkMqTomOiZzrXWXkYbPViL6N1VtcCoxcBPHNIPN3Y2QnfYvZUYlUHwfWQUE2hMVBYvvKPbfNtRAeD5kHYMdiz8QnfkmJjokSY0KxWKCgyMaxnAKzw5GKiKlvjOwktIXsdKNm5+AGs6MSqR4cq5U3uQSslfz5CQyBjjcb2ypKlkpQomOioAArdaNCAJ2+qlYi68KIz4wVlk8dh1nXwr6VZkcl4vsqM628PI6i5N/+pzo5qTAlOiZL0syr6ik8DoZ/Co0uhPxMePfPpX+tisjp8jLh9zXGdmUKkcuKbwoplwB2WDfLbaGJf1OiY7LSXjpKdKqd0GijqWDTy6AwF2YPNqa/SvWWcwyyj5gdhf/Zu8yYNVUrBWo1Ov/jOIqSN7yrteikQpTomKy0l46aBlZLweEwdA60ugaK82HurbD5I7OjkvNRXAjfPQvPtYJ/X6xlP9zNMeLZ9DxHcxxaXQ0RdY0aue1fVD0u8XtKdEyWFKNeOtVeYAjc+Da0uxFsRfDhKPj4Tjixx+zIpKIOboDX+sDSJ6C4wOjc61h4UtyjqvU5DgFB0HmYsa2iZKkAJTomK51irkSnWgsIgj//G7qNBuzw8xx4uSt8PgGy0s2OTs6k8BQsngKvXwbpm41pz8k9jMd+XWRubP4k8yAc3Q5YIKV31Y/XeYRxrF1L1c9KzkmJjsmSdOrKf1gD4Op/wuhvjL9abYWw5nV4qSN8/RicOmlygOJiz4/wai9Y9iLYbdD2BhizBi550Hj816+MBSil6hyjOUmdIKxW1Y9XqxE062tsr1dRspydEh2TOWp0jmbnk1dYbHI04hb1uxgzsoYvMLYLc+HH5+DF9vDDc1CQa3aENVteBnx2n9ED6fguiEoy6qxueAMiakPjiyAoArLTjPXNpOociU5V63PKchYl/xeK8t13XPE7SnRMFhseRFhQAABpGRrV8StNLoG/LIEhs6FOa+MHdsljxgjP6tehSE0ive7Xr2BGD1hXUtvRZSTcuxJaXlm6T2BI6Q/yr195P0Z/Y7e7rz6nrOb9jCQ19xhs/cx9xxW/o0THZBaLRYt7+jOLxZglcvcyo4YntqExW+SLCfBKV/hpLtg0kudxOUfho7/A7Jsg66AxxXnEQvjTCxAac/r+LQYY16rTqbrDW43vfGAYJKe677gBgdBlhLGtomQ5CyU6PkC9dGoAawB0GAJj1sFV/zSmx57cC5/cATMvgm1fqB7EE+x22PQhzOgOm+aBxQq9/gp3L4eUi8/8vOb9jOuD61VMXlWO1cob9TJGy9yp0zDjv+neH+HIdvceW/yGEh0fULqKuU5d+b3AYOg+GsZthMunGKMJh7fAnKHwxhWw+3uzI/QfGQfg/SHw0Sjj9EbdC4xTif0eN/ofnU1UgrHEBxjLDcj588RpK4eY+tCi5LTjurfdf3zxC0p0fICWgaiBgiPg4r/BuJ/govuNYf3f18CsP8E7g+DAerMjrL5sNlj7JsxINU49WYPg0r/DHd9C/c4VP45OX1VdUQHsWWZsu7MQuayuJetfbXzPaBcg8gdKdHyAs0ZHvXRqnrBa0PdRY4Sn22jjR3nXUnj9Upg7DI7+ZnaE1cuxnUayuPB+KMiCBt3grh+NKeOBwZU7Vov+xvXOpZrVc75+XwOFORBe2xhR84Smlxm1b3kZ8Mt8z7yGVGtKdHxAfdXoSFSi0YNnzBpoPwSwwNYFRrdedVg+t+Iiox/Oq72Meo2gcBjwD7j9K6jb6vyOWa8DRCYaP9R7fnRvvDVF2dNWVg/93FgDShoIYozkifyBEh0fUPbUlV0FqTVbXApc92+jWLZeRyjINjr3ypntX2OMgC2eAkV5xo/qPSugx93Gj+D5sligRUlRsqaZnx9HIbIn6nPK6jQMrIHw+2pI2+zZ15JqR4mOD0gsWe8qr9DGiVytxitAQhsY9C9jRsmWTzWiUJ7c47Dgr/BGX0j72SjsvvYVGDYfajV2z2uUrdPRHyGVk5cBB9YZ255OdKISjIV1obRHkkgJJTo+IDQogNqRxrRLFSSLU8IF0OU2Y3vRw+q342CzwbpZ8HKX0vb/HW42pu53HmaMxLhLyiUQEGK0AtD05crZ/YOxtEZ8M4hN9vzrOYqSf5oL+dmefz2pNgLNDkAM9WNDOZqdz8GTp2hbv5wGZlIzXfp32PQRpG0yWt07GqTVVId+hs/HG0WuAHXbwNXTjR4tnhASafTb2fG1MapzvvU+NZEnp5WXp3FviGsKx3fC5o+88/9KcSHkZ5VzySy5/OH+oHC4cJx3Ej9xUqLjI5Jiw/jp9wyN6IiriNrQ5yH4ahJ88zhc8GcIjTY7Ku/Ly4BvnjQWSbXbIDgS+kyE1DuNleM9qcWAkkTnK7joPs++lj9xJjoemlb+R1arMQK6eLJRlFyZRMdmg1PHjQ7O2YeNS85h43bOsdLEJe8PyUvRefx7vekD+NNLcMGgyj9XzosSHR/hLEjWelfyR91GG/9wH9sB3z9rNLyrKex2o6PxV383fngALrgO+j8J0UneicHRJXn/KqMuKDzOO69bnWX8Dsd+M2rMGl/kvdfteIvxB8GhjUZ9UK2U8pOX7CN/2D4C9iqcGg4Mg5CoP1yiT79v20IjrnkjYOcIGDDN6KklHqVEx0doGQg5o8Bg6P+UsU7TyleNv1rjm5odlecd3masCbbnB+N2fDNj+QxPNZ47k1qNjFNkh7fAzm+g3Q3eff3qyDGaU78LhMV673Uj4qHNQCM5fv2yyj8/PN5YniWy5BJR1xhVDYstJ3Epc7uio4q9xsLSJ+HHF4z6sn0r4IY3IbFd5WOVClOi4yOSYrSwp5xF837Q9HLYuQT+NxmGzjY7Is/Jz4bvn4EVM8BWZPy13HuC8SPh7rWSKqpFfyPR+XWREp2K2OmlaeXl6XE3bP64dIQmrJZr8hKZABF1/rCdYCQ0nj4NGhBkNAht0gc+vhOO/mokZFc8bpyGdWchvTgp0fERWgZCzspiMUZ1Xu0F2z83/mI240fEk+x2o0nioomQecC4r+VVRuO/Wo3Mja3FAPjxefhtsdGcMED/dJ6Rzeb9+pyy6neB8VuNJDmiTuU7YntDkz5Gr6xP74Vfv4RFDxmjhYP+ZSRc4laaXu4jHInO4ax8CopsJkcjPqluK+j2F2N70UTjB9dfHNsJ790AHww3kpzYhjB0Dgx93/wkB4ylJMJqQd5JoymdnNnhLZB71Jhh1KCbOTFEJRgLfvpikuMQEW98v6981mhh8NtXxh8yO78xOzK/o0THR8RHBBMcaMVuh/RMFSTLGfR52PjBPbwF1r9tdjRVV3gKlj4F/+ppzGwKCIbeD8A9q6DllWZHV8oaUFqUrEU+z87RDbnRhb6daPgCiwVS74DR30CdVkZx9Lt/Nk5PFxWYHZ3fUKLjI6xWi7NORwXJckbhcdBnkrH9zZNw6oS58ZyPrDTY/qUR/796wHdPQ3G+sTjj3SvgskcgONzsKE/nWORTy0GcneO0lbeLxquzxLZwx7fQdZRxe/lL8GY/Y6RTqkwnmn1IUmwYe47lqk5Hzq7rSFjzHzi6Hb57xpii6qtyj8PBDXBwPRzcCAfWQ9ZB132ikmDAU9BmkG8XYza9HCwBcGQbHN9trEsmroryYc8yY9vfasg8LSgMrnnOSPgXjDH+v/l3b2OmYYchvv3/ho9TouNDVJAsFRIQZCQG/70eVr8GXUZCnRZmR2XMljr0k5HUHFhv/EN9Ync5O1qMYfr6nY3C0fY3GVN0fV1YrNGBec8P8Nv/jFky4mr/aqOJXkRdY0q+VF7rayCpE3x8B+z9EebfZcy2vPq5mtks1A2U6PiQRnHGcP37q/dzVbt6NKkTaXJE4rOa9YXm/Y0Cxv89Ard84N3XL8yD9M1GMnNgvZHcHNkOlLPwZa0UI6lJ6mxcJ7Y3llaojlr0NxKdXxd5N9Gx2+H3tRAUCtH1jTotX/wLv+xq5b4YX3URUx9GLIAfn4Ol04y+QPtXw/VvQLJJBd7VmMVurxlL8mZmZhITE0NGRgbR0b6ZFR/Jyuemf69g99Ec4iOCmXV7d617JWd29DejxsVWBLd+ZCQ/nnZwI3zxgJHg2ApPfzy6vvHXaFInI6mp19G/Ogkf/Q1e6WoUTT+4y3sjUSv+BV9NLL0dFA4xDYzPO6ZB+dtm1Dm9fpnR+XfQq9DxZu+/vj/avxo+GgUn9xmnTi/7O1x4n1EgXxV2OxTkQEE2hMX5dOF4VX+/lej4mCNZ+Yx4czVbDmUSFRLIf0Z0JbVJvNlhia9aNAlWzoDaLeHuZZ5teLZ9EXw4Egpzjdthca4jNUmdICrRc6/vC+x2eKmTcUpu8H+h9Z88/5p5mfBiB2MtppAYyM+o2PPC4oyRgZjkkiSozHZcE2MKtjudOgHPNDHWIhu/1XtLdNQEeRmwcDxs/tC43fhio3bHGlDOGlyZrtdne8xe0sokNMZYWqXjLdCgq8+NxinRqaDqkugAZOYV8pdZa1m9+zghgVZm3NyZvm3c/I+S+IdTJ+HlzpB7DK58xnOnU1a/Dl8+aPzD2ORSuOZ5qNXY5/5B9IovH4ZVr0KnW2HgDM+/3nfPGMsGxDeHe1YaI2mZB431pDJ+N/oO/XG7IPvcx+1+p9Gl110jP1sWwAfDjKR7jHoNuZ3dDj+9D59PgMIcz71OfDPoMNQogI5p4LnXqQRTEp0ZM2bw7LPPkpaWRocOHXj55Zfp3r37GfefN28ekydPZs+ePTRv3pynn36aq666yvm43W5n6tSpvP7665w8eZILL7yQV199lebNmzv3ady4MXv37nU57rRp03j44YcrFHN1SnQA8gqLGTN7PV9vPUyA1cKzN7Tnus6+8aUTH7PmDfh8PITGwl83uPdUkc1mrAa94hXjdqdhRpLj6Vb5vmznUnh3kFFw+7ftxqrZnpJ73BjNyc801kRqe/25n2O3GyMA5SVAGQcgYz+cLPm3tHYL+PO/jRG5qlo4Hta+YSRQVz1T9eNJ+Y7uMDoq/766nMVD/7AeV2hMOetzlewTWnIdGGrUnW2cbSSrzhXZLdDkEmOUp9U1prZ88HqiM3fuXIYPH87MmTNJTU3lhRdeYN68eWzfvp26deuetv/y5cvp3bs306ZN45prrmH27Nk8/fTTrF+/nrZt2wLw9NNPM23aNGbNmkVKSgqTJ09m06ZNbNmyhdBQo7dM48aNGTVqFKNHj3YeOyoqioiIiq38Wt0SHYDCYhsPffgzH28w2uFPuaYNt1+kKa3yB8VFxjTUw79A9zvgqmfdc9zCU8bMj60LjNuXTYaL/1YzR3HKKiqAZ1KMUZPR3xgzxzzl68eMgtS6F8BdP7ovqdrxNcy/F7LTwBoIlzwEF42v2tIWL3WC47tgyPvQ6qpz7y9VY7e7///F/CzY8qmR9OxdVnp/cBRcMNBIehr29Pq/AV5PdFJTU+nWrRuvvGL8hWez2UhOTmbs2LHljq4MHjyYnJwcFi5c6LyvR48edOzYkZkzZ2K320lKSuJvf/sbEyZMACAjI4OEhATefvtthgwZAhiJzn333cd9991X6TcJ1TPRAbDZ7Dzx+VbeXGZM0/3rZc24/4oWWGr6j4242vUdvHOtUax49zKo27pqx8s+AnOGwu9rjMLbgf+C9je6J1Z/MHeYkQBe8hBcOskzr5F9BF5sb9REDZkNra527/Fzj8PC+2HLfON2/a5w3WsQ37Tyxzqx14jVEgAP7dE0aH9wfDf8PNdIek6WOZtSqzF0uNk4teWl5Vmq+vtdqT8PCgoKWLduHX37ls7usFqt9O3blxUrVpT7nBUrVrjsD9C/f3/n/rt37yYtLc1ln5iYGFJTU0875j/+8Q/i4+Pp1KkTzz77LEVFfrTWzxlYrRYmX9Oav11h9El56ZsdTPn0F2y2GlFaJRXV5BJjeNleDF9NMv7aO19Hf4M3+hpJTmgsDJuvJOePWgwwrj25HMSPzxtJTlJnY3FTdwuPgxvfhuteN4qcD6yFmRfB2jcr//1xdENu0FVJjr+ISzGWnPnrRrjtC6MmLTgSTuyBb58yEtu3roYN7xk9tHxYpRKdo0ePUlxcTEKCa2FsQkICaWlp5T4nLS3trPs7rs91zL/+9a/MmTOHpUuXcuedd/LUU0/x4IMPnjHW/Px8MjMzXS7VlcViYezlzXl8UFssFnh35V7Gzd2oxT/FVb/HjdGXnd+c/zIFe5fDf/oa/5jFNoK/fA2NL3RrmH6h+RWAxWiQmHnwnLtXWuZBo/s1GEtieGoE12IxGjbesxxSehuJ1cL7YfZNxlIdFWXmauXiWVar8W/AwBkw4Vf482slXa8tRkPDT++BfzaHT+4yRpZtvve7VG3Wuho/fjx9+vShffv23HXXXUyfPp2XX36Z/Pz8cvefNm0aMTExzktycrKXI3a/YT0a8eKQTgRaLXz200FGv7OWUwXFZoclviKuCfS429j+alLlFwXc9CG8M9BYobt+V/jLEqjd/JxPq5Ei65bW5vz2P/cf//t/Gut/NexlLAngaTENYNin0H9ayUra/zMWWt2y4NzPtdlg93fGtpZ98G/BEdBhMAz/FO7fbNTtxTczEuSf3jdOn7/ep2ojyh5QqUSndu3aBAQEkJ6e7nJ/eno6iYnl989ITEw86/6O68ocE4xaoaKiIvbs2VPu4xMnTiQjI8N52b9//1nfW3VxbYck/jOiK2FBAXz36xFufWMVGbnlNG6TmuniCcZsoOM7jeUhKsJuhx+mG03JiguMU2AjPoPIOp6Ntbpznr5yc6JzYg+sn2Vse3I054+sVuh5D9z5HSS2M/r2fDAMPrnbmMV1JumbjPYGwZHGqSupGWIaQO8JMGYtjPoaut5unAJN7uFzExYqlegEBwfTpUsXlixZ4rzPZrOxZMkSevbsWe5zevbs6bI/wOLFi537p6SkkJiY6LJPZmYmq1atOuMxATZu3IjVai13phdASEgI0dHRLhd/0adlXf77l+5Ehwaybu8JBr+2gsOZeWaHJb4gNBoun2xsf/cM5Bw9+/7FhfDZX2HJ/xm3e46Bm97xzdXDfY1jNfNdS40lMdzlu2eMbtdNLzPntGHd1vCXb4xZWBYr/DQbXr0Q9vxY/v6O01aNL6rZbQdqKovFWJbimueNU1t9KtbyxZsqfepq/PjxvP7668yaNYutW7dy9913k5OTw8iRIwEYPnw4EyeWtiofN24cixYtYvr06Wzbto1HH32UtWvXMmbMGMCoP7nvvvt44oknWLBgAZs2bWL48OEkJSUxaNAgwChofuGFF/jpp5/YtWsX7733Hvfffz+33nortWrVcsPHUP10aRTH3Dt7UicqhG1pWdwwcwX7juWaHZb4go63GOtJ5WfAN0+ceb+8TKMWY/07xg/alc9C/yer3lq+pkhsZ6y8Xph75iSgso7+ZpwCALj0Efcc83wEBkPfqTDyS2OWTcZ+ePsaY121PyZ1Ox3rW6k+p8YLCvXJJV8qnegMHjyYf/7zn0yZMoWOHTuyceNGFi1a5Cwm3rdvH4cOHXLu36tXL2bPns1rr71Ghw4d+PDDD5k/f76zhw7Agw8+yNixY7njjjvo1q0b2dnZLFq0yNlDJyQkhDlz5nDJJZdwwQUX8OSTT3L//ffz2msVHJr3U63rRfPRXb1oGBfOvuO5XD9zOVsPVd+ia3ETawBc+bSxvX4WpG0+fZ+MA/DmAKNwOSjcmL6ceod346zuLJbSUR13zb76dprRfbrlVdDAg/15KqphD6N/T+fhgB2Wv2ysZ+X4ThXmwb6S2bGqzxEfpSUg/MDhzDyGv7mabWlZRIcG8tbIbnRp5HtZtXjZByOMHimNLzZqbhznzQ/9XDKr5hBEJsDNc411qqTyti+C9wdDTEO47+eq1SakbYaZJaeq7vrRGDHyJdu+gAVjIfcoWIOM+qF67eHdP0NUPWN9Kx+rzRD/4NU+OuKb6kaHMveOnnRtVIvMvCJu+c8qvt1+2OywxGxX/J8xg2bPD7CtpGHnb4vhrSuNJKdOa2P6uJKc85fS22ihn7EPDm+t2rGWPmVcX3Cd7yU5YHQ7vmelMdpkK4Svp8Lc4cZjTfooyRGfpUTHT8SEB/HuqFT6tKxDXqGNv8xay4KfPNDfQ6qPWo2g11hj+3+PwKp/w+zBxtIFKb3h9kUQ29DcGKu74HBIucTYrsrpqwPrYPvnRq1Un4nn3t8skXWM05zXvmLMsirIMu7XaSvxYUp0/EhYcACvD+/KwI5JFNnsjJuzgXveW8fmA2eZGir+7aL7ITLRmLL85YNG5+SOt8AtH0FYrNnR+Qdnnc55NmkE+OZJ47r9EKjTouoxeZLFAp2HGafXUnpDrRRo3s/sqETOSDU6fshms/P451t4a9ke532XtKjDvZc2o3uKandqnI3vw/y7jO1L/w69H9BpBnfK+B2ev8AYjZmwAyLiK/f8vcuN04nWQKMnSVw1W7jXE4tLipRR1d/vKixVK77KarUw9U8XMKRbQ179dgcLfjrId78e4btfj9C1US3uvbQZfVrW0cKgNUX7wUYjwOj60LzvufeXyolpAAntjMZ5O742OsdWlN0OSx43tjsPr35JDijJEZ+nU1d+rGViFC8M6cS3Ey7lltSGBAdYWbv3BCPfXsNVL/3IZz8dpFiLg/o/qxW6jFCS40ktSk7dVLZOZ9dS2LfcKBq/eIL74xIRJTo1QcP4cJ78czt+fOhS7ujdhIjgALYeymTs+xu4fPq3zFm9j/wirZklct4cy0HsWGJ0m64Iu720oWO3URBT3zOxidRwSnRqkLrRoUy6qjXLHr6M+/u2IDY8iD3Hcnn4401c8sy3/OeHXeQWFJkdpkj1U78LhMcb3aj3razYc7Z/acy2Cgo3isZFxCOU6NRAseHBjOvbnGUPXcYjV7cmITqEtMw8nvh8Kxf+4xte/Po3TuZWcuVrkZrMGlA686gip69sNlhaMtMq9S5jNXQR8QglOjVYREggf7m4Cd8/eCn/uK4djeLDOZFbyPNf/8qF//iGaV9s1WKhIhVVmWnmW+ZD+mYIiS7tdSQiHqFERwgJDGBI94YsGX8JLw3tRKvEKHIKivn397u46Jml/P2TTew4nIVNhcsiZ9b0MmOK+LHf4NjOM+9XXFTaBbnnGJ9cBFHEn6iPjpzGbrezdPthZizdybq9J5z3hwcH0DwhilYJUbRIjKJlQhQtEiOpExmiqeoiALP+BLu/h/7ToOc95e+zcTbMvxvC4mDcTxCqf49EzkZ9dMTtLBYLl7VK4NKWdVm9+zivfreT5TuOkVtQzE/7T/LT/pMu+8dFBNMiIZKWCVG0TIymZWIkzROiiA4NMucNiJilxQAj0fl1UfmJTlEBfPsPY/ui+5TkiHiBEh05I4vFQmqTeFKbxFNUbGPPsVy2p2WxPT2LX0uu9xzL4XhOASt3HWflruMuz68fG0aLhEjn6E/LxCia1okkNCjApHck4mEtBsBXk4xux3mZpycyG/8LJ/dCRF3oNtqcGEVqGCU6UiGBAVaa1Y2kWd1Irqae8/5TBcXsPJLNtrQsfk3PMhKhtCzSMvM4cPIUB06eYun2I879rRaIiwghLNhKWFAAoSWXMMcl2HGftZz7HLetzu24iGBqR4YQEaKvsviA+KYQ3wyO7TCaAbYZWPpYYR5896yx3XuCsSCoiHicfh2kSsKCA2hbP4a29WNc7s/ILeTXw1lGAlQy+rM9LYuMU4Uczc53exzhwQHUiQqhdmQIdSJDSrejSi+1I42kSCNK4lEtBsCKV4zZV2UTnbVvQtZBiG4AXW4zLTyRmkaJjnhETHgQ3RrH0a1x6YwSu93O4ax8jmUXkFdUTF5BMacKjUteoc24LnDcLnNdUPq44768wmJy8os5nlPAqcJicguK2Xssl73Hcs8ZW3RoILWjTk+IEqNDqRcTSmLJJTxY/3vIeWjRvzTRsdmMJTjys+HH54zHL3kQAkPMjVGkBtG/5OI1FouFhOhQEqJD3XrcnPwijmTlczQ7nyNZ+RzJzudoyfWRrPySxwo4kpVPQbGNzLwiMvOK2HUk56zHjQ4NpF5MGAkxodSLDjWuHYlQSVIUExakGWfiqmFPoz9O7lE4uB4adIXVr0HOEaiVAh1vNjtCkRpFiY5UexEhgUSEBNK4dsRZ97Pb7WSeKuJIdh5HsgpcEqEjWfmkZ+ZxKOMUaRl55BQUlyRExmm3MwkNspIYXZr8JMaEkRAdQq3wYGLDg4gNDyY2LIha4cFEhQZitSop8nsBQdDscvjlE2P2Ve3msOxF47E+E43HRcRrlOhIjWGxWIgJDyImPIhm5+i4n5VXSFpGHmmZeRzKyHNup5XZPp5TQF6hMRttTwVOmVktEBNWkvyEBzkToJjwIGdiFBNWul0rPJiIkEACAywEWa0EBlgItFrcNoJUbLOTnV9E5qlCsvKKyMwruT5VSFZeIZl5Rcb1qSKy8kuu8wrJKSimaZ0IejSJJzUlnlaJUUrg/qjFgNJEx2KFvJNQpxW0u8HsyERqHCU6IuWICg0iKjSI5glRZ9wnr7CYw5n5xihQSRJ0KCOPI1n5nDxVwMncwpJLATkFxdjscCK3kBO5FVzd+gwCrRbX5CfASpDVuHbcH2C1EFTyWKDVQlCAlfyiYmeykpVXRFb++S/guuNwNl/9kg4YyVv3lDhSU+Lo0SSe1vWiCajpiU+zKwALpG0q7ZJ86SRjTSwR8SolOiLnKTQogIbx4TSMP/c04fyiYjJOFTqTnxO5BWSUXJ88ZSRDjvudCdIpY8Toj4psdopsdvI4/bHzERJoJTosiKjQQKJDS6+jwwKJCg0iOrTkOiyQqJAgggOtbDqQwardx1m75zgZpwpZvCWdxVuMxCcqNJDujeNIbWIkPm3qRRMYUMNWm4mIh+TusH8VFOZCYnto9SezoxKpkbQEhIgPs9nsFNpsFBXbKSou3S4sthkJT7GNwmI7RbaS62IbxTY7hX94rKjYTkigtTRhKZPABAeefxJSWGxjc0nSs3LXMdbuOUH2H0aKIkMC6dq4Vsmprjja1o8hqCYkPt//E7553Ni++YPSRT9FpFKq+vutREdE3Kao2MaWQ5ms3HWMVbuOs3rPcbLyXBOf8OAAujY2TnV1aVSL2PAgQgNLG0WGBgUQHGD1St1PUbGNvCIbeYXF5Jdc5xUWExRgddZJnXdSdnwXvHohNOwBt34Mmp0ncl6U6FSQEh0R7yu22dnqSHx2H2f1buNUV0UEB1oJDbQ6u2I7kqCQQMd16X2hQVYCrVZnopJXaCO/yLjOKyq9nV9YmszkFxmjYucSFRpIXEQwtcKDnde1woOoFRHscn9cROksO+epurwMCAyDwOCqfIxuY7fbKbbZsVgsqqOSakOJTgUp0RExn81mZ1taFqt2H2PlrmNsPpDJqcJi8guLySsyTruZpWxiVVhs4+SpQs73X8eYsCDiIozZc0EBVrCDHTt2O9gxEg7j2riNy207NlvpfpS93258hsV2Oza7sV+xrWS7JIkpd5+S7bLvJyYsiPiIYOIjg4mPCCEuMpjaJclbfGSI8/74SCOZO5/EyG63k1NQzImcMjVoJTVpJ3JKatVOldamhQRaSY4Lp0GtMJJrhZMcF05yXBgJUaGa2VeDKdGpICU6Ir6vsNjmHJEpezrJOTrjcoqp5LrksWKbzeUUWEjJSJBjBMiRxIQEWSt0qqzYZnf+CJ/IKeB4TgEncgs4XvIDfTyngJO5jvsLOZ5TUOHRqurGYsE5chVfsr5cXEmSFBIYYCQuZQrpT+Qan0nGqQIKi6v+ExMcYCUpNrQkCTKSn+RaJQlRXDjxEcFq3OnHlOhUkBIdEfG0opKRoBNlkp8imw0LFiwWsOAo1bFgtRi9nRz3GY8bOxn3lexT5rlWqwWrxUKA1Xg8oOQUlMUCASWPWUvus1oos23Bai25bbFQbLdzIqeAYzkFHMsu4FhOvvP6eE4BR7ONBO5Ydn6VRrYcggOtxum+8GBnr6haJaf6aoUHERtmjH6dKixm//Fcfj9xiv0nctl//BQHT5465ynG8OAA5yiQI/mpFR5MZGggUSUNRctuhwcHKDGqRqr6+63p5SIibhIYYKV2pLF+mq+rHRlC8wrsV1Rs40RuoZEEZRdwNKeA49n5RpKUU0BeYbGzbinGUb9UpullbHgQYUHnn1gUFdtIy8wzkp/juew/cYrfj+ey/4SREKVl5pFbUMyv6dn8mp5doWNaLRARbCQ/kSXJT1RooMt9kSXJUURwAAFWqzO5tFpKk0hLyXXZ5LO8x53JqMXirDkLcxl5tBIcYHVvM9AyTUBLe2cVuvTScjQFtQOhgcZoZ2kNnNVZB1f2OqTMyGh5+zreny/RiI6IiFRb+UXFHDyZV5IEGcnP7ydOkXGqkOy8QrLzi8jJLyarZNvEMrCzslooLbwPtBIaHFByitVKmHPbSDDCggKwg0sS4+hw7uhebpaLmtXmv39JdesxNaIjIiI1VkhgACm1I0g5x1p3YBRHnyosJju/iOy8IuO6zHZOvtExPDuvdDsnv4him/Fco+gbZ2G3rcx99jKP2Wxltu2lzy2y2Z0z/06VzP5zJF42O+QWFJPrxiTF2Tsr1Bixigp1bQzquG2xQH6RzYitZHZiftnZimVaMBj7FTuv88rcLrIZ/bp8jRIdERGpESwWC+HBgYQHB1L3zKu7eI3dbqew2F4687DQ5kyASpMhI9k4VVByf5GNUyXJ0B+7mJdNXqJCAwkJ9O4ppKLiirVs8DYlOiIiIiawWCwEB1qM7uRh1X9Ve2O9PbOjOJ3vjTGJiIiIuIkSHREREfFbSnRERETEbynREREREb+lREdERET8lhIdERER8VtKdERERMRvKdERERERv6VER0RERPyWEh0RERHxW0p0RERExG8p0RERERG/pURHRERE/FaNWb3cbjeWjs/MzDQ5EhEREakox++243e8smpMopOVlQVAcnKyyZGIiIhIZWVlZRETE1Pp51ns55siVTM2m42DBw8SFRWFxWJx67EzMzNJTk5m//79REdHu/XYcmb63M2hz90c+tzNoc/d+/74mdvtdrKyskhKSsJqrXzFTY0Z0bFarTRo0MCjrxEdHa3/EUygz90c+tzNoc/dHPrcva/sZ34+IzkOKkYWERERv6VER0RERPyWEh03CAkJYerUqYSEhJgdSo2iz90c+tzNoc/dHPrcvc/dn3mNKUYWERGRmkcjOiIiIuK3lOiIiIiI31KiIyIiIn5LiY6IiIj4LSU6VTRjxgwaN25MaGgoqamprF692uyQ/Nqjjz6KxWJxubRq1crssPzO999/z5/+9CeSkpKwWCzMnz/f5XG73c6UKVOoV68eYWFh9O3bl99++82cYP3IuT7322677bTv/4ABA8wJ1o9MmzaNbt26ERUVRd26dRk0aBDbt2932ScvL497772X+Ph4IiMjuf7660lPTzcpYv9Qkc+9T58+p33n77rrrkq9jhKdKpg7dy7jx49n6tSprF+/ng4dOtC/f38OHz5sdmh+7YILLuDQoUPOy48//mh2SH4nJyeHDh06MGPGjHIff+aZZ3jppZeYOXMmq1atIiIigv79+5OXl+flSP3LuT53gAEDBrh8/99//30vRuifvvvuO+69915WrlzJ4sWLKSwspF+/fuTk5Dj3uf/++/nss8+YN28e3333HQcPHuS6664zMerqryKfO8Do0aNdvvPPPPNM5V7ILuete/fu9nvvvdd5u7i42J6UlGSfNm2aiVH5t6lTp9o7dOhgdhg1CmD/5JNPnLdtNps9MTHR/uyzzzrvO3nypD0kJMT+/vvvmxChf/rj52632+0jRoywDxw40JR4apLDhw/bAft3331nt9uN73dQUJB93rx5zn22bt1qB+wrVqwwK0y/88fP3W632y+55BL7uHHjqnRcjeicp4KCAtatW0ffvn2d91mtVvr27cuKFStMjMz//fbbbyQlJdGkSRNuueUW9u3bZ3ZINcru3btJS0tz+e7HxMSQmpqq774XfPvtt9StW5eWLVty9913c+zYMbND8jsZGRkAxMXFAbBu3ToKCwtdvvOtWrWiYcOG+s670R8/d4f33nuP2rVr07ZtWyZOnEhubm6ljltjFvV0t6NHj1JcXExCQoLL/QkJCWzbts2kqPxfamoqb7/9Ni1btuTQoUM89thjXHzxxWzevJmoqCizw6sR0tLSAMr97jseE88YMGAA1113HSkpKezcuZNJkyZx5ZVXsmLFCgICAswOzy/YbDbuu+8+LrzwQtq2bQsY3/ng4GBiY2Nd9tV33n3K+9wBbr75Zho1akRSUhI///wzDz30ENu3b+fjjz+u8LGV6Ei1cuWVVzq327dvT2pqKo0aNeKDDz5g1KhRJkYm4nlDhgxxbrdr14727dvTtGlTvv32Wy6//HITI/Mf9957L5s3b1btn5ed6XO/4447nNvt2rWjXr16XH755ezcuZOmTZtW6Ng6dXWeateuTUBAwGlV9+np6SQmJpoUVc0TGxtLixYt2LFjh9mh1BiO77e+++Zr0qQJtWvX1vffTcaMGcPChQtZunQpDRo0cN6fmJhIQUEBJ0+edNlf33n3ONPnXp7U1FSASn3nleicp+DgYLp06cKSJUuc99lsNpYsWULPnj1NjKxmyc7OZufOndSrV8/sUGqMlJQUEhMTXb77mZmZrFq1St99L/v99985duyYvv9VZLfbGTNmDJ988gnffPMNKSkpLo936dKFoKAgl+/89u3b2bdvn77zVXCuz708GzduBKjUd16nrqpg/PjxjBgxgq5du9K9e3deeOEFcnJyGDlypNmh+a0JEybwpz/9iUaNGnHw4EGmTp1KQEAAQ4cONTs0v5Kdne3yF9Pu3bvZuHEjcXFxNGzYkPvuu48nnniC5s2bk5KSwuTJk0lKSmLQoEHmBe0Hzva5x8XF8dhjj3H99deTmJjIzp07efDBB2nWrBn9+/c3Merq795772X27Nl8+umnREVFOetuYmJiCAsLIyYmhlGjRjF+/Hji4uKIjo5m7Nix9OzZkx49epgcffV1rs99586dzJ49m6uuuor4+Hh+/vln7r//fnr37k379u0r/kJVmrMl9pdfftnesGFDe3BwsL179+72lStXmh2SXxs8eLC9Xr169uDgYHv9+vXtgwcPtu/YscPssPzO0qVL7cBplxEjRtjtdmOK+eTJk+0JCQn2kJAQ++WXX27fvn27uUH7gbN97rm5ufZ+/frZ69SpYw8KCrI3atTIPnr0aHtaWprZYVd75X3mgP2tt95y7nPq1Cn7PffcY69Vq5Y9PDzc/uc//9l+6NAh84L2A+f63Pft22fv3bu3PS4uzh4SEmJv1qyZ/YEHHrBnZGRU6nUsJS8mIiIi4ndUoyMiIiJ+S4mOiIiI+C0lOiIiIuK3lOiIiIiI31KiIyIiIn5LiY6IiIj4LSU6IiIi4reU6IiIiIjfUqIjIiIifkuJjoiIiPgtJToiIiLit5ToiIiIiN/6f9f1vu+u11K6AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the training and validation loss\n",
    "plt.plot(history.history['loss'], label='Eğitim Kaybı')\n",
    "plt.plot(history.history['val_loss'], label='Doğrulama Kaybı')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Comparing the Train and Test Scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 39ms/step - loss: 0.0020\n",
      "=============================================\n",
      "Train Loss: 0.0016439617611467838\n",
      "Test Loss: 0.0017321765189990401\n",
      "=============================================\n"
     ]
    }
   ],
   "source": [
    "# Train and test losses\n",
    "train_loss = history.history['loss'][-1]  # Final loss after training\n",
    "test_loss = model.evaluate(x_test, y_test) # Loss on test data\n",
    "\n",
    "# Print the final train and test losses\n",
    "print(f\"=============================================\")\n",
    "print(f\"Train Loss: {train_loss}\")\n",
    "print(f\"Test Loss: {test_loss}\")\n",
    "print(f\"=============================================\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Evaulating the Model\n",
    "F1 score and accuracy are not suitable methods for regression models.For regression models, metrics like MSE, MAPE, and RMSE provide better insights."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
      "=============================================\n",
      "Mean Squared Error: 113.78\n",
      "Root Mean Squared Error: 9.75\n",
      "=============================================\n",
      "Mean Absolute Error: 7.82 $ Gunluk Sapma Miktari\n",
      "=============================================\n",
      "Mean Absolute Percentage Error (MAPE): 5.23%\n",
      "! A MAPE below 10-20% is generally considered good for financial predictions.\n",
      "=============================================\n",
      "R^2 Score: 0.83\n",
      "=============================================\n"
     ]
    }
   ],
   "source": [
    "# Predictions\n",
    "y_pred = model.predict(x_test)\n",
    "\n",
    "# Inverse transform the data\n",
    "y_test_actual = scaler.inverse_transform(y_test)  # Inverse transform the test data\n",
    "y_pred_actual = scaler.inverse_transform(y_pred)  # Inverse transform the predictions\n",
    "\n",
    "# Plot the predictions\n",
    "mse = mean_squared_error(y_test_actual, y_pred_actual) \n",
    "rmse = root_mean_squared_error(y_test_actual, y_pred_actual) \n",
    "mape = mean_absolute_percentage_error(y_test_actual, y_pred_actual) * 100\n",
    "mae = mean_absolute_error(y_test_actual, y_pred_actual)\n",
    "r2 = r2_score(y_test_actual, y_pred_actual)\n",
    "\n",
    "# Print the error metrics\n",
    "print(f\"=============================================\")\n",
    "print(f\"Mean Squared Error: {mse:.2f}\") # Mean Squared Error\n",
    "print(f\"Root Mean Squared Error: {rmse:.2f}\") # Root Mean Squared Error\n",
    "print(f\"=============================================\")\n",
    "print(f\"Mean Absolute Error: {mae:.2f} $ Gunluk Sapma Miktari\") # Mean Absolute Error\n",
    "print(f\"=============================================\")\n",
    "print(f\"Mean Absolute Percentage Error (MAPE): {mape:.2f}%\") # Mean Absolute Percentage Error\n",
    "print(f\"! A MAPE below 10-20% is generally considered good for financial predictions.\")\n",
    "print(f\"=============================================\")\n",
    "print(f\"R^2 Score: {r2:.2f}\") # R^2 Score\n",
    "print(f\"=============================================\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Prediction Table "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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mBcsCDB06lI8//piGhgbOO+88ysrKuvQzdofFYuGSSy7hv//9r9nK2p8x7iuvvJKNGzfy4YcfttinsrISl8vVY2O0Wq1cfvnlvP7660EDOaNFOKi/VUFBAa+99pp5XX19faulef4uv/xydF3nwQcfbHGb/98vNja2xXMkmEWLFhEZGclf//rXgPs/88wzVFVVhew5LoQQ3SUZJCHEgPH2229TU1PDxRdfHPT2008/nbS0NFasWMFVV13V7cc788wzSU5O5sYbb+Suu+5C0zRefPHFFh/+n3zySVavXs13vvMd1qxZE3Db4MGDOe+885g2bRo33ngjTz31lFm69+WXX/L8889zySWXmA0numvVqlU88MADXHzxxZx++unExcVx5MgRnn32WZqamgLWA5o5cyYAd911FxdccAFWq5Wrr76aZcuWcc455/DTn/6UY8eOMW3aND766CPeeustvv/975ttnseMGcNPf/pTHnroIc4++2wuu+wy7HY7mzdvJisri4cffrjF+MaMGcNHH33EggULuOCCC1i9erW5NlNv+fWvf81HH33E/PnzufXWW5k4cSInTpzg1VdfZcOGDSQlJXHvvffy9ttvc9FFF5nt1evq6ti1axevvfYax44dIzU1tcfG+Jvf/IY1a9YwZ84cbrnlFiZNmkR5eTnbtm3j448/NtfruuWWW3jssce44YYb2Lp1K5mZmbz44ovExMS0+xjnnHMO119/PX/96185ePAgixcvxuPxsH79es455xzuuOMOQD1PPv74Y/74xz+SlZXFqFGjzEYn/tLS0rj//vt58MEHWbx4MRdffDH79+/niSee4LTTTgtoyCCEEH2qL1rnCSFET1i2bJkeFRWl19XVtbrPTTfdpEdEROilpaVmm+/f//73AfusWbNGB/RXX3014HqjbbF/++fPPvtMP/300/Xo6Gg9KytL//GPf6x/+OGHAS2UH3jgAR0I+jV//nzzWE6nU3/wwQf1UaNG6REREfqwYcP0+++/v0W7cP+22P7mz58fcLxgjhw5ov/85z/XTz/9dD09PV232Wx6WlqafuGFFwa0Etd1XXe5XPqdd96pp6Wl6ZqmBbT8rqmp0X/wgx/oWVlZekREhD527Fj997//fUD7Z8Ozzz6rz5gxQ7fb7XpycrI+f/58swV7az/Ppk2b9Pj4eH3evHlBW6n7/8yTJ09u82fWddUu+3vf+16rt/m3+dZ1Xc/JydFvuOEGPS0tTbfb7fro0aP1733ve3pTU1PA7+D+++/Xx4wZo0dGRuqpqan6mWeeqT/yyCO6w+FoczyhGHdRUZH+ve99Tx82bJgeERGhZ2Rk6AsXLtSfeuqpFj/LxRdfrMfExOipqan63XffrX/wwQfttvnWdfUc+P3vf69PmDBBj4yM1NPS0vQlS5boW7duNffZt2+fPm/ePD06OloHzJbfzdt8Gx577DF9woQJekREhD548GD99ttv1ysqKjr0+wk2RiGECDVN12W2oxBCCCGEEEKAzEESQgghhBBCCJMESEIIIYQQQgjhJQGSEEIIIYQQQnhJgCSEEEIIIYQQXhIgCSGEEEIIIYSXBEhCCCGEEEII4RWWC8V6PB4KCgqIj49H07S+Ho4QQgghhBCij+i6Tk1NDVlZWVgs3c//hGWAVFBQwLBhw/p6GEIIIYQQQoh+4vjx4wwdOrTbxwnLACk+Ph5Qv4SEhIQ+Ho0QQgghhBCir1RXVzNs2DAzRuiusAyQjLK6hIQECZCEEEIIIYQQIZt6I00ahBBCCCGEEMJLAiQhhBBCCCGE8JIASQghhBBCCCG8Oh0grVu3jmXLlpGVlYWmaaxcuTLg9ptuuglN0wK+Fi9eHLBPeXk51157LQkJCSQlJbF8+XJqa2u79YMIIYQQQgghRHd1OkCqq6tj2rRpPP74463us3jxYk6cOGF+/fvf/w64/dprr2X37t2sWrWKd955h3Xr1nHrrbd2fvRCCCGEEEIIEUKd7mK3ZMkSlixZ0uY+drudjIyMoLft3buXDz74gM2bNzNr1iwAHn30UZYuXcojjzxCVlZWZ4ckhBBCCCGEECHRI3OQ1q5dS3p6OuPHj+f222+nrKzMvG3jxo0kJSWZwRHAokWLsFgsbNq0qSeGI4QQQgghhBAdEvJ1kBYvXsxll13GqFGjOHz4MD/5yU9YsmQJGzduxGq1UlhYSHp6euAgbDZSUlIoLCwMesympiaamprM76urq0M9bCGEEEIIIYQIfYB09dVXm9tTp07llFNOITs7m7Vr17Jw4cIuHfPhhx/mwQcfDNUQhRBCCCGEECKoHm/zPXr0aFJTUzl06BAAGRkZFBcXB+zjcrkoLy9vdd7S/fffT1VVlfl1/Pjxnh62EEIIIYQQ4iTU4wFSXl4eZWVlZGZmAnDGGWdQWVnJ1q1bzX1Wr16Nx+Nhzpw5QY9ht9tJSEgI+BJCCCGEEEKIUOt0iV1tba2ZDQI4evQoO3bsICUlhZSUFB588EEuv/xyMjIyOHz4MD/+8Y8ZM2YMF1xwAQATJ05k8eLF3HLLLTz55JM4nU7uuOMOrr76aulgJ4QQQgghhOhTmq7remfusHbtWs4555wW199444387W9/45JLLmH79u1UVlaSlZXF+eefz0MPPcTgwYPNfcvLy7njjjv473//i8Vi4fLLL+evf/0rcXFxHRpDdXU1iYmJVFVVSTZJCCGEEEKIk1ioY4NOB0j9gQRIQgghhBBCCAh9bNDjc5CEEEIIIYQQIlxIgCSEEEIIIYQQXhIgCSGEEEIIIYRXyBeKFUIIIYQQQoShujLI+QwKtkNlDtSVQsZUGLcYhs0Gm72vR9grJEASQgghhBDiZKbrsP1FeP9/wVkXeNvRT2HjY2C1Q+YpEJ8JScMhczoMORVSRoOm9cmwe4oESEIIIYQQQpysnA2w8ruw+w31feo4GH6GuoxKgJzP4eAqqC+FvM0t73/3Tkge0btj7mESIAkhhBBCCDHAGSv7aP7ZnvpyePlbkLsRLBFw7s/gzDvBYvXtc+oNKsNUdhiKdkFtCZQdgoJtUH1CZZMGGAmQhBBCCCGEGMByy+q58NH1XHRKJg9fdoq60tkA/7pcBTr2RLh6BYw6O/gBNA1Sx6gvfx7PgCuvA+liJ4QQQgghxIC2ckc+NY0uXt58nOPl9Soj9PZdKjiKToZvv996cOSl6zqNTrf5/a/e2cOzn+dQ3ejs6eH3OgmQhBBCCCGEGMBW7ysGVFz07y9z4Yu/wa5XQLPClS/A4MntHuP/vfU1kx/4kK/zq6iqd/L0hqP88p09eCv3BhQpsRNCCCGEEGKAKq1t4qu8SvP7L778At3yIBrA4odh1Lx2j/F1fhX/+iIXUMHW7FEpAAxJiiYxOqIHRt23JEASQgghhBBigFq7vwRdhwkZ8VTXNfLTpsfQLI2QvRBm3wrAB18XkldRz81njw56jN9+sM/c3l1QRUKUCiEmZsb3/A/QByRAEkIIIYQQYoBava8IgPMnDWbmif8w8+hB6rUYYi7+K2gadU0u7n55O00uD2ePTWN0Wiy//O8ezswexJKpmXx2qJT1B0vN4+0uqCYpOhKAiZkJffIz9TSZgySEEEIIIcQAs3J7Pjc/v4VP9qr5R+cN1zk7/+8A/MpxDfsaVHCz/mAJTS4PAEdL6/j8cBkvfpHDw++rrNG7u04AcNEpmQDkVTSw6WgZIAGSEEIIIYQQIgx4PDr/+8ZOPt5bRJPLw7CUaKbs+i0WRy1Hoibxb/c5/OuLHAA+2l1k3i+3vI6jJbUAFFQ24PboqusdMG9cGkOSogE4VqaukwBJCCGEEEII0e+V1ztodKqs0D9umMU7F7rRdr8BmoXqhb9Fx8Kb2/Kpqneyen+xeb/c8noz+HF5dIqqG8mvbABgaHI0k7N8AVFMpJURKTG9+FP1HgmQhBBCCCGEGECKqhsBSI2L5LzxKSSu/Zm64bRbmDbrbLLTYqlzuLnpuS+prPetY5Rb3sCxsjrz++Pl9eRXeAOkpBgm+QVI4zPisVgG3iKxIAGSEEIIIYQQYe/trwo4+3er2VdYbQZI6fFRsPlpKNkHMYPgnPvRNI17LxiPRYPtuZUADEtRpXPHy+vJ8WaQAL7Kq6TJ5cGiQUZiFJOzEs3bBmp5HUiAJIQQQgghRNh7fPUhjpc38N7OExRVNwEwNq4B1jysdlj4c4hOBmDxlEz+tXwOKbGqG91t87IByKuoN+ccAWw6Ug7A4IQoIm2WgBI7CZCECAeFu+DVm+DzxxiQyzoLIYQQ4qT2kzd38d0VW9Gbfc7JLatnf1ENAMcrGiisUhmk62qfg6YqyJwOM64PuM+ZY1L56AfzeOmWOVwzezg2i4bTrePy+I795TEVIA1NVhmmzMQoUuPsAEzJGrgBkqyDJMKfrsMnD8JnfwHdA7vfhLzNcMkTEBnb16MTQgghhOi2BoeblzblAqrV9jC/Bgmr9vp3oqsnKsLCKdphZlW8p65c8juwWFscMzXObgY8Q5OjzQYNhppGF4DZvU7TNP569XQOl9QyY3hy6H64fkYySCL8ff06bPiTCo5GzQdLBOxZCSu/29cjE0IIIYQIidLaJnO7wNtZzrBqT6G5fby8nqLKen4R8TwaOpxyNQyf0+7x/QOu5JiIgNuGJvtuO3NMKtefMbKzww8rEiCJ8NZQCR/cr7YX3A83vg03rATNqoKkgx/34eCEEEIIIUKjxC9AOuEtoQOorHew+ViF+X1xTRNji9/jVMshXLZYWPSLDh1/eEpgEORviLfE7mQhAZIIb5/8EuqKIXUczP2Bum7kXDj9drX93o/A2dj6/YUQQgjRfzTVwr53YfsKOPRJX4+mXymtCR4grd1fgtujM25wHPF2G3HUs7zheQDKTr0bEjI7dPwRg3wB0umjB2H1a+E9VAIkIcJERQ5sfU5tX/gHsNl9ty34X4jPhIqjsOlvfTI8IYQQQnRC5XH4+9nw8rfgre/Cvy6D3Sv7elT9Rmmtw9w+UeUrsdtdUAXAmdmpDEuJ4Q7bW6RrlRz2ZGI7q+PTDfwzSNlpsWQkRJnf+5fYnQwkQBJhpaLOwZvb82h0umHjY6C7IftcGDUvcEd7vGpnCbDxcXA2tDyYEEIIIXrNg//dzWVPfMarW47T5HIH3lh+FP65BMqPQNxg1XUN4L93Q1V+r4+1Pyqp8Z+D5Msg5XvnIw1PiWFCooMbrR8C8BvPdaQkxHX4+P5zkEalxgaU1WUmRgW7y4AlAZIIK39YtZ8f/Ocr3tqwA7a9oK486/vBd576TUgaDnUlsO3F3hqiEEIIIZrRdZ0Vm3LZllvJva/t5Mq/f+F/I7x1B1Qdh0Fj4ZY1cPPHkDUDGith5e2yfAeBTRr8M0h5FWp7aHI033C8R7TmYKdnFHtiz0DTtBbHac3o1DgSoyPISoxicHyUWVaXHm8nKqJlB7yBTAIkEVZ2HK8EIGP/C+BqhKxTW2aPDNYIOOtutf3ZX8DlCL6fEEIIIXpUncONw+Uxv//qeKXv+73/hZwNYIuC69+AxCHqPfzyZ9R1Rz9Vy3ec5PwDpEK/OUhmgBSnM6fkNQD+7lpGRlLn5g1FR1pZ9YN5vHPX2VgsGkO99z/ZGjSABEgijLjcHg4U1WLBw/SSt9WVZ90NbZ0dmX4dxKZDdR7sWNE7AxVCCCFEgHLv/JlIm++jZ1WDUzVS+uhn6oqz7laVH4ZB2TDlCrW9+eneGmq/5R8gldU5aHS6qXe4KK9Tv9tRx1cS5awkx5POB57TAuYQdVR6QhQpsZEAnDI0SV0OSez+4MOMBEgibBwtrcPh8nCmZTeJ7nKITobxS9u+U0QUnP1Dtb32YXDU9fxAhRBCCBGgrE59uE+Ls5MQZQOgqsEB256HyhzVWMmo+vB32nJ1uftNqCvtreH2S/5zkEBlkfK92aPEKAvRW58E4B/uC3FjJT3B3uIYnbFwYjoffn8eP7lwYreOE44kQBJhY8+JagAusX6mrph8Kdgi27/jrG9D0gioLYKNT/TgCIUQQggRTEW9ynKkxEaS6F2EtKq+CTb9Xe1w9j0QGdvyjkNOVeX0bgdsP7nnExtd7CKt6uP7iapGs7zu8thdUHEMPSqJ191nA3Qpg+RP0zTGZ8Rjt51c849AAiQRRvYV1hBFE4stXwLgmvLNjt3RZvd1tPvsL1Bf3kMjFEIIIUQwZbV+AVK0CpCsR9dA+WGwJ8K0a1q/8+xb1OWWf4LH0/p+A1iDw01tkwuACZnxgGrUkOftYHeV+78AaLP+h/h4VRI3uJsB0slMAiQRNvaeqGaRZRtxWiPHPWkUJ07v+J0nXwaDp4KjRuYiCSGEEL3MmCfjHyBl7PN2o51xLdjbaEc9+VKwJ6hSvNzPe3qo/ZIx/yjSZmFsuhEgNZJXUc9k7SjjG3eCxQazb2X2qBSsFo2pQ0++uUOhIgGS6NccLg+//O8e1uwvZu+Jar7hLa97y3MmJ6qb2rm3H4sFZt+strf8U9qFCiGEEL2o3K/ELik6kuFaERlF6wANTru57TtHRMPkS9T2jpd6dJz9zbbcCu599SsOFtcAag5XVpLKDJ2oaiCvooHltvfVzpMvhYQs/nzVdDb9ZCHZaR1fA0kEkgBJ9GufHijh2c+OcusLW2iqLmWB5SsAVrrPCmhx2SFTroDIeJXOP7quB0YrhBBCiGDK/UrsEqIj+IbFO584+xzVra49069Vl7tXQlNtzwyyH3pizSFe3ZrH7z7YD0BqXCSZiart9onKRupL81hm2ah2Pv12AGxWC6lx3WvQcLKTAEn0a8ZCaE63zoXWTURobo7bx3BIHxqwSFqH2ONg2lVqe8uzIR6pEEIIIVoT0KQhysbFVu+H+qkdnE88bA6kjAZnnVo36SRxwnsyeF+hN4MUbyczUWWQCqoamVuxkgjNTd3gWTBkZp+Nc6CRAEn0a8V+ZXRGed3+tMUAnc8gAcz8H3W57x1p1iCEEEL0oM8Pl3L1Uxs5VFxLmd8cpFGeHMZa8nFpETDhwo4dTNNg2rfU9q5Xe2jE/U9xs9beqXF2hqWoDFJecTmXej5UN5z+3d4e2oAmAZLo14qqVRB0y1Qrsy370dEoGbkMgMLqLgRIGVMgYyp4XLD37VAOVQghhBB+Xt2SxxdHynl9W15Ak4bJFR8DsDN6NkR1opHApIvV5bENJ8W6hm6PTlltywApOy2Ocyeks0jfSIpWS56eRswp3+ijUQ5MEiCJfqeq3mkGRsaZk2XWLwDQRs4lcfAIoIsZJIApl6vLr9/o3kCFEEII0aqqBicAR0pqfQFSTASjCj8A4NOIszt3wNRxal1Dd9OAnEv81fHKgOkDZbVNeJr1lEqNi0TTNP505XS+GbUZgDVRi9Cstt4c6oAnAZLodS63h1c2H+dYafCzP5c88RmL/vApNY2+QGlkyRp14+RLyUg0urd0MUCafKm6PLYeaou7dgwhhBBCtKmmUQVI+wprqGlUa/ikVe4ktu449bqdNfqpnTugpsHY89X2gQ9DOdQ+d7y8nsv+9jlX/G0jTS43AEXeaQYRVs3cLy1efQZK1Go5A9W4KnbGFb082oFPAiTR6z49UMKPX9/Jz1Z+3eI2h8vD0dI6appcHCiqpaSmiXQqSCjboXYYv9RcGbqouhFP81MrHZE8EobMAt0De97q+g8ihBBCiFYZQVFOWT0AVotG/L5XAHjfM5vCBmvnDzruAnV5cNWAWrLjUEktbo9OfmUDb+0oAKC4Rp0IHp8Rz5h01bJ7VGqsusO+99A8TkifxGWLF/XJmAcyCZBEr8stVy+UXxdUoTd7cTPONgEcKq6hrM7Bedat6oqhp0FCJmnxdiwauDw6P125i/te29n5QGnKZery69e7/HMIIYQQvane4eJAUU1fD6PDqhucAd9nRHvQdr8JwGvueWYJXqeMnAu2KKjOg+I9oRhmv1DsN6/6758exuPRzWkGg+OjePqGWfzzptOYlJWgdvL+Hs2qGBFSEiCJXmf8w1fWO82uNgbjbBPA5mMVAFxg3aKu8Ha6ibBaSItX/f3//eVx/rPlOIdLOrkmwiTvZMbcL6CutLM/ghBCCNHr7n11J+f/aR3bcyv6eigd4v+eDrA0cjs0VeFJGMYXnok0uTw0Ot2dO2hENIyap7YPfhSikfY9/669h0vq+GRfsXldeoKdkamxnDMhXe1QXw5HfFMPROhJgCR6XYlfy8qDRYGBTbVfBunLo+XEU88ZFu8ZogkXmbcNT4kJuF9FfSfPQiUOhcxpgA4HPujcfYUQQog+8HVBFQC78qv6eCTt83h0ah2BAdKFHvWhXpt2NZqmPoI2zzJ1yJjz1OXhNd0aY39inDyOiVRlhy9tyqHIW2JnzDsy7XtHdeMdPBVSx/bqOE8WEiCJXuff0/9QcWCpgP/ZptzyehZYdhCBS3Wu8XsR+MXFk/nJ0glMyIgHoLI+MBPVIeOXqsv973f+vkIIIUQv0nXdbE503Fuq3p/VNLkCpghla/lMb1Il89r0a0iIjgDoWpnd6AXqMvcLcHZy0fh+yphvtHhKBgA786p8GSRv1YzJLK+7pLeGd9KRAEn0uoAMUnFgBsl/DhL4l9ddFHD95KxEbp2XbXa0q+zKC6wRIB1ePWBeYIUQQgxMFfVOHC4PAMfL+/97VvP389us76iNCRfBoGwSvQFSl96/U8dCfKZq9318U3eH2i8YJ4/nj0vDatEoq3OwK78SaBYg1ZXCkU/VtpTX9RgJkESva7vEzpdBisTJAssO9U2zAMmQHBMJdDGDlDEVEoaCs35ArqcghBBi4PBfH+d4RRhkkPzezzMp4xLrBvXNWd8HMAOkqs6WyINq921kkY6s7fog+xEjWzQ0OYax3o51ReYcJL8Su73/Bd2tpgkMyu71cZ4sJEASvcrl9lBW13oGyb8W+UzLbuK0Rmoj0yBrRtDjmWeguvoCO36J2t73bufvL4QQQvQS/8XR8yrCIYOkAqTMxChutr1HpObmRPIsGHYa4BcgdSWDBAMqQNJ13Tx5nB5vZ8qQxIDbAzJI0r2uV0iAJHpVeZ0DXVexCUBpbRMVfp3s/M84nW9R5XWFmQvBEvypmhTTjRQ9wPjF6vLQxwNqPQUhhBADi//i6FUNzoCmRv2RccJzXGwd19k+AeDohFvN27sdII2ary4LdkBDeHT1a01VgxOHW5VPpsXbmdosQDI691Jboha5B5h0SS+O8OQjAZLoVUaNbWqcnSFJ0UBgFskIkDQ85vpH9aMXt3q8bpXYAYw4y7ueQj6U7OvaMYQQQoge5p9Bgv7fqKGmSQU+Vztex46DHYxj2CxfuXy3A6SETEgdD+hwdH13h9unjM9GSTERREVYmTIkwbxtUGwkEVbvx/W9b6lF7rNOhZRRfTHUk4YESKJX+aeQxw5WNbYH/TrZGWfEZkflkaZVUa1HE5F9dqvHMzNIXSmxA7Wewsi5avvQx107hhBCCNHDCqoCy+r6e5ldTaOLwZSzqE6VsE+97ncMGxRr3t7tAAkGTJldkXeRWKOUbmJmAhZvpU1aQHndSnUp5XU9TgIk0auKzZ7+dsYNVi26DxT6AiSj683FiYcB2OSZyODkBFrTrTlIhjGL1KUESEIIIfopI4Nk835y7vcZpPpGfhnxHBG6E4afiTV7QcDtIQ2Qjn7a9WP0A7523qoZQ0ykjew0dRLZbNBQUwTHvI0upL13j+t0gLRu3TqWLVtGVlYWmqaxcuXKVvf9zne+g6Zp/PnPfw64vry8nGuvvZaEhASSkpJYvnw5tbW1wQ8iBhT/DNKkTBX4+C94Z5TYzdH2ArCVSSR7s0TBdLvEDnwBUs7n4Kjr+nGEEEKIHmIESJO981NayyB9ebScK/72Obvy+nYx2dMO/IkLrFtwaRFw/kO+ycdeRgVItwKkkWeBZoGyQ1B5vDvD7VPFNS3XOzIaNZjX7XkL0GHoaZA0vLeHeNLpdIBUV1fHtGnTePzxx9vc78033+SLL74gKyurxW3XXnstu3fvZtWqVbzzzjusW7eOW2+9NchRxEBjBEhp8XamDUsCYHdBNU7v5MTqRicWPIyo+wqA8XOWoDV7UfXX7SYNAIPGqBcbt8N3dkYIIYToJ/wXiZ09MhloPYP0n83H2ZJTwVs78nttfC1sfILZRS8DsGrcL2DorBa7+CpAunGCMyoRhsxU22GcRTKraxJ8AdKyaZnER9k4d0K6ukK61/WqTgdIS5Ys4Ve/+hWXXtr6Hyg/P58777yTFStWEBERePZ/7969fPDBBzz99NPMmTOHuXPn8uijj/Lyyy9TUFDQ+Z9AhBXfWZIoRg6KISHKRpPLw35vmV1No4uJWg4RzhqwJ3Dp0iVtHi8pWmWQ6h1umlzurg1K03xZpIOrunYMIYQQoodUN7hocKr3uJkjUoDWM0hG4FTst+Zgr9rzFnz4EwAedl7DiWEXBt1taHIMADvzqvjqeGXXH8+chxTOAVJgiR3AuRMGs/OB81k6NVN1rzv+hbph4sV9McSTTsjnIHk8Hq6//nruvfdeJk+e3OL2jRs3kpSUxKxZvrMJixYtwmKxsGnTwFgNWbTOP4OkaZqZRfoqrxJQAdLplj1q5xFngsXa5vHio2zmRMYuLTZnGHOeupR5SEIIIfqZE9UqGEqOiTAbHB2vqEcPsjxFTrkqFTcm/veqI2vh9VsAnY/jlvF390XER9mC7jo5K4GLTsnE5dG5++Xt1DW5gu7XLqPd95G1YbtcR0l1yxI7wFdBc+AD1b0uczokDevl0Z2cQh4g/fa3v8Vms3HXXXcFvb2wsJD09PSA62w2GykpKRQWFga9T1NTE9XV1QFfIjwV+wVIANOGJgHw1fFKdF2nusHJ6RY1/8jsLtcGi0Xzpem7U2Y36mywREDFUSg73PXjCCGEECFmlNdlJEabS2TUO9yU1wWWpzU63RR5P2z3egbp8Gp46SpwN8H4C/lb9K2ARkJ08HnEmqbxf5dMJSsximNl9Ty25lDXHnfYbLBFQ10xFO/p+vj7kFFi1zxAMhmL2U8Ino0ToRfSAGnr1q385S9/4bnnnmtz3khnPfzwwyQmJppfw4ZJ9ByOmq8UDXDKUDUJcWdeFY1ODx6PmzkW73pEI87q0HGTzEYN3QiQ7PEw/HS1feiTrh9HCCGECDGjQUNmYhRREVYGe+eq5Dabh+Q/L6m4NzNIBTvg39eAqxHGLYZv/pPKRjW3uLUMEkBiTATfXzQOgB25lV17bJtdneQEOPhR147Rx8wSu4Soljc21argEyRA6kUhDZDWr19PcXExw4cPx2azYbPZyMnJ4Z577mHkyJEAZGRkUFxcHHA/l8tFeXk5GRkZQY97//33U1VVZX4dPx6+nUpOZnUOt1lDbWSQpntL7A4U1VBU3chELYcErR7dngAZp3TouEajhoruTPQEafcthBCiXzpRqUrsMhLVB+gR3vWEmgdI/t/XOdzUdrVsrTPqyuA/16ngaMwiuPIFsNnNrrQJUa13ogUYnaZ+lrzKbrQtH3eBujzwYdeP0UeKqxupd7jRNMzAN8Dh1SorlzwS0if1+vhOViENkK6//np27tzJjh07zK+srCzuvfdePvxQPWnPOOMMKisr2bp1q3m/1atX4/F4mDNnTtDj2u12EhISAr5E+DGyR7GRVmIi1Rml9IQoMhKi8Oiw8UiZOf9IG34GWFs/6+QvyVhLoTsZJPAFSMfWg7MPareFEEKIIIwMw2DvJP6Rg1SDg2OlgUFFTlng9z0+D8ntgtf+B6qOQ8pouPwZldGBDgdIQ5JVyeCJykZc3o62nTbWGyAd3wT15V07Rh/ZeKQMgEmZCeZnowD731OX4y9s0Spd9JyOfQL1U1tby6FDvjrRo0ePsmPHDlJSUhg+fDiDBg0K2D8iIoKMjAzGjx8PwMSJE1m8eDG33HILTz75JE6nkzvuuIOrr746aEtwMXAYax0YJXGGacMSKdzdyIaDpVzSiflHBrPErqGbGaTBkyEuA2oLIfdzyD63e8cTQgghQqDRW30RE6kaFxkZpJyywLX7mmeUiqobzQVHe8TqX6r22hGxcNUKiE4CwOn2mBUjbZXYgercFmHVcLp1imqazDlWnZI0DAZPgaKvVZn8Kd/s/DH6yBfeAOmM0YNa3ujx+MoGx7fd1VeEVqczSFu2bGHGjBnMmDEDgB/+8IfMmDGDn//85x0+xooVK5gwYQILFy5k6dKlzJ07l6eeeqqzQxFhptZ7Nqn5i+WkTDUPafOREt/8o04FSEaJXTczSJoGY71ZpP0fdO9YQgghRIg4vJmVSJv62DbCm0HKaaPEDnyVG6bDa+CFS+Dv8+GFb8Bbd8CGP6vr68pU9URHO8F9/QZ89he1/Y3HYLCv/MvIHgHEtRMgWS0aWd6gKK+VtZ06ZOz56vJAeL1/f37YGyBlBwmQCrZBfRnYE3zzpEWv6HQGacGCBUHbSrbm2LFjLa5LSUnhpZde6uxDizBX26QCmDh74NNuYmY8AGn1B0mw11OvxRDTwflH4FsLqVtNGgzjL4Tt/1Ip7SW/lXS2EEKIPudwBQZII1vJIBnfp8bZKa1t8pXYuRzw6k2w/932HywqCaZcDjOuhaxTg78P7nkL3rhVbZ95J0y5LODmmkb1fhwTaSXC2v65+KHJ0eSU1ZNfGXxtpw4Ztxg2/BEOrQK3E6xtl/b1BwWVDeSU1WO1aMweldJyByN7lH1OWPw8A0mnAyQhuso4o9T8bNKkLDWnzGjvfShqKqd0cP4R+DJIVd0tsQO14JwtWtVTF+6CzI4HakIIIURPaDICJG+wMdybQSqtdVDb5CLObsPj0TnuXTz2tJHJvP91odnymw/uU8GRNRJOu1mtHdRQAZU5ULIf8reqbYDGStjyjPpKmwjTrlKLk0YnQ8Ux2PUabPqbWpdn8mWw8BctxlvTSsVIa4YmxQBlrS5+27GDzIK4wVBbpJo1TLyo68fqJRu92aMpQxKJDzZXy2g6YWTHRK+RAEn0GqObTvMM0pCkaBKibJzuVgFSTvwMOhOWGAFSSDJIkTFq7tH+d9W6AxIgCSGE6GPOZiV2CVERpMRGUl7nIKesjuEpMVTWO3G4PNgsGtOHJfH+14WqucO2F2DLs4Cm5gmNa+XDtqtJdaLL3wY7VsDe/0LJXvj4F+qruenXwcV/Dbqge7V3znHQD/1BGI0a8iq6UWJnscK0q1XZ3/Z/hUWAZJTXnRmsvK6mCE7sUNvGYvai10iAJHpNa3OQNE1jQkY8M04cBKAoaUanjms0aej2HCTDhKUqQNr/Lpxzf2iOKYQQQnRR8xI7UPOQyuscrN5bzJOf+hY4H5Icbc7pqa0sgQ9+om4496etB0egus/Z7KqcK/scaKiEPSth90o4ug50t5oLk30OnHK1ahrQShl6tdnBroMZJDNA6kYGCVTQ9tlfVGlaTSHEB18+pj+obXKxZr9a9iZogwZjyZHM6RA/uPcGJgAJkEQvMjJIsUHaWM5JdZBaWI1Lt1CXPLFTx/W1+Q5BiR2oOmbNokrsKnMhaXhojiuEEEJ0QfMmDQAjUmLYnlvJE2sPmx3jAIanxDDYu+Do/PLXwFmjOrzNvadzDxqdBDNvUl/ORvW+aIts506KMQepoxmkocmqZLDbAVLaOBg2R7X7/uplmPv97h2vBz217gjldQ5GDooJ3qDhoLe8zljjSfSqkK6DJERbapqCz0ECmB2VC8AhfQjRsZ1rSZoc6gxSbCoM83aL2f9+aI4phBBCdJGRQbJb/TNIqlGDERzddOZIpg1N5No5w0mPtxNPPZc5/qt2nvcjsHTjI19EVIvgaO+Jar48GnzNIaPZQofnIBlrIVU14PZ0vBFYUDOuU5fb/9Xxjny9rKi6kX+sOwLAfYsntGxk4XaqzoIg84/6iARIotfUtTIHCWCsR71Q7NZHtruoXHMJ0ep4DU63WafdbROWqst9Hej4I4QQQvSgYCV2I1NjzO3stFgeWDaJt+6Yy+IpmaQn2Lne+hEJWj3uQeNg4jdCNpYdxytZ+pf1LPnLeq78+0az0YBhT0E1f1urSv6CdmYLYnBCFDaLWgupuKabi9tOvhQiYqDsIBz/snvH6iGPrzlEg9PNqcOTWDwlSBlg7hfQVA0xqaqToOh1EiCJXtPaHCSAtBq1/tHXnpEdTskb/AMu/7UXumW8N0A6tkF1+hFCCCH6SLAAaXhKrLl97ZwRaH7zgWKscGPEKgBKpt/RvexRM0+tO8yeE9Xm929uzzO36x0uvrtiK00uDwvGp3HdnBEdOqbVopGZpMoCu11mZ49XQRLA9he7d6weoOs6H3xdCMCdC8cG/N1MRnvvMYtC+rcTHSe/ddFrzBI7e8sAyFq0E4CvPaNIT7B36rg2q8VcXdyoe+62QdmQNkFNSj24KjTHFEIIIbog2BykMWlxREdYibfbuPzUoYF3OPghg6mgVE/gaHpoO6CdqFIZnhvOUMHPB18X0uRSZX7rD5ZyrKye9Hg7f7pyOhZLx9cSVK2+u9nJzmCU2e1+E5pqu3+8EDpQVEtxTRN2myV4cwbwBUhtNdUQPUoCJNFraltZB4m6UqjOB+CKi5Ywc3hyp49tZKVClkECXxZJyuyEEEL0oebrIAEkxkTw6nfO4I3vnkliTLMTj1v+CcBr7vmcqAtR6blXsXdtpYunZTE4wU51o4v1B0oBX/bntFEpJMd2rKGDwexkV97NDBLA8DMgZTQ4alUnvn5k/cESAOaMHkRURMsW6VTkQMk+1RQj+9xeHp0wSIAkek1r6yBx4it1OWgMV501qVNnnAxGWV51KxkkV1fmJk3wrqFw6GNwhOCMlhBCCNEFwUrsQC0wOnZwfODOlblmi+h/u88xMz6hoOs6JTUqQMpIjOLCqVkAvLOzAIACb3OGId42451hdLIzGjx0i6b5skibnwldswZHPVSfgMaqLh9i/UEVTM4bmxp8ByN7NGyOWpxX9AkJkESvMQKkFnOQjAApc1qXj20EXbVBMkh/+Gg/0x78iEPFNZ07aNYMSBqhzkB9/XqXxyaEEEJ0la7rQUvsWrXtBUAnJ/E0cvQMCkMYIFXWO82xpMXbuWhaJgAf7Smi0ek2A6SsxKhOH3tIqNZCMsy4AWxRULANjqzt3rFqi+HDn8Lvx8AfJ8BvhsO/rlBrRXVCo9PNpqOqqcXcVgMkb1m/dK/rUxIgiV5jltg1zyAVqvlHZJzS5WO3VWK37kAJdQ43Xx3v5BkfiwVmfVttb/5Hv20XKoQQYuByeXTz7cduDVKS5c/tgm2qMUHuqCsB1To7VIq92aPkmAjsNiszhiWRHBNBvcPNoeJaX4DUpQySESCFqGIjLg1OvVFtr/9D149TfgSenAsbHwNnHeCtcjm0Cp5dDJXHO3yobTkVNDo9pMXbGd888wfgbFCL8oIESH1MAiTRK5pcbvOsU4s5SMWqgx2DJ3f5+EZr8GBNGkpr1QKy9Y4uzE+acT1Y7SrLlbely+MTQgghusIor4MOZJAOfgi1hRCTinusmkdbUBm6DJLRgjs9XmWINE0jO02tXXiktI5872N1J0AqqGzE0921kAxn3QUWGxxb37WW37XF8OJlUFsEqePhW6/CAxVw2zqIz4SSvfCvyztccrfxiMoenT0mNXj3umMbwNUACUO69ZlIdJ8ESKJX+Je+xUb6BUhuJ5QdUttp47t8/LYySOV1RoDkbnFbu2IHwZTL1faXT3V5fEIIIURXdCpA8jZnYMa1DE5JAEKbQSryNmjw7zY7Ok21G993oprSWnV7V+YgZSREYbVoONweSrzH6bbEoTDtarX9yS87VwnSWK2Cn4qjqtz+xrdVVzlNU1MClq+C+Cwo3Q+v/o/K3rXDKHfMTo8LvoMx/2jseepxRJ+RAEn0CmP+UWykFat/E4ayw+BxQmQcJA7r8vHNAKkp8AWq3uEyVxmv60qABDD7FnX59WtwYmeXxyiEEEJ0llF9YbVoge+fzfk1Z+DUG8lKVEFKRb2TRmcX3/+aaZ5BAhjtzSB9dkg1H4iOsJLUvKteB9isFjISjLWQQtgYaf59qhLk2HrY/37H7uNqgv9cq6YAxKTC9W9CfLMFXZOGwTX/VovSHv4E/ns3eNpuCFVRr6pcgv5+dB0OfKi2x17QsXGKHiMBkugVNa21+C7xltelje/W2ZL4VkrsyrzldQANXSmxAxhyKky+DHQPvPtD9hZUcs1TX7A1p0K9iJYfgYLtULRbFpUVQggRUo4gLb6D2r4C0GHUPBiUTUK0zVwjMFSd7IqDZZBSVQZpZ74qM8tKigpePtYBQ0PdqAEgaTic8T21/dHPwOVoe/+mWnj5W2ouUGQcXPeaWhsxmKzpcNk/VEvuHf+C9+9tM0tVWa8eOzkmSAv0kv1QmQPWSPU3FH1KAiTRK+qMDFLzBg1mgDShW8c3MkjVzUrsjPI66EYGCeCCX0NkPORtpvTdh9hzJAfeugP+LwP+OgOeWgB/OxMeGQev36KCJSGEEKKbmlpp8R3A44GvXlLbM24A1PygDG83uROhaJ2NfwbJv8ROZZCMuKAr848MRqvvkAZIAGf/EGLToPwwrP5l6/vVlcJzF6pMnC0arl6hOtq2ZeJFcMmTgAabn4adr7S6a4U3QAqaQdr9procvQDsrZTgiV4jAZLoFWaL7x4LkIwMUmCAVFbnq2Nu6E6AlJAJ5/wEgLPzn2aL/XZmlr+jskq2KFWHHJ0CbgfsegWeXgQ5G7v+eEIIIQStr4EUIOczVWJnT4AJF5pXG2V2oc4gDU7wldgNT4kJKP3ryvwjQ8g72Rns8bD092r780e9rdCbqS+H5y+GEzsgZhDc9I4KVjpi2lWw4H/V9tpfq/nVQVR6S+xSmi+iq+uw+w21bcx7Fn1KAiTRK8xFYlvrYBeiDFJbJXZd6mLn7/TbYcnvqLYkEqG5KYvIgJvehZ8Wwj174b6jcMsaGHk2OOvhpStV6Z0QQgjRReYaSG2V2H31b3U5+VKIjDGvzjQySCFq1GC0+fbPIEXaLAxP8T1m9zJIPVBiZ5h8Kcz3BjHv/ADW/xE83hOnZYfhxUugeDfEDYZvfwRDZ3Xu+GfeqbJUFcdg+4stbtZ1ncoG9RmlRYld0W4oPaDmSo1f2rnHFT1CAiTRK2qCrYHk38EuvZsBUisLxZbV+QdI3Zykqmkw5zZuTHia5Y57+OXQp2Hk3MC5U0NOhW+9AiPOgqZqeOkqteq2EEII0QXtZpCaamH3SrU9/dqAm4wAqSAEGSRd1ymqbtmkAXzzkPwfsyuMxWLzeyJAApXlmX4teFzwyYPw2CzVqe7xOWo5j5hBcMPbkDqm88eOjIV596rtT38HzsDfeXWjC7e3fXmLEjsjezT2PIhK6Pxji5CTAEn0CjODZPd7USg/EpIOdtB6iV15KAMkr+O1Fj7xzKTaYw++Q2QMXPMypE9Sayf85zrVzEEIIYTopHabNOx7Ry1gmpINw2YH3JTpzeYUhiBAqm50mfOh/Js0gK/VN3SvxG6YMQepsiF0ayH50zT4xuPwjSfUvOLyI2q+kccJYxapzFF3TtjOvEmtYVRzAva/F3BThffzSEykFbvNb8FfXYevvQHS5Eu7/tgipCRAEj3K5fbQ4HCbmZ14/xK74r3qMnVct/v9t1ZiV+q3lkJdUzdL7AC3R6fcO6+pydVGO8+oBDW5MyoR8rfAx7/o9mMLIYQ4+Tjc6uReqxmkXa+qy1OubPFeamaQ2mnS8NKmXOb/fg2Himta3afE26AhIcpGVIQ14DajUQN0r8QuIzEKi6aCwtJQrYXUnKbBjGvh7q/Uwq9LH4Eb34HrXu9a5sifze5bd2nnfwJuqmitg13uRrXWUkQsjFvcvccXISMBkugxbo/ODc9+ycxfrWJ/kXrRDSixK9mvLtMndvuxjACpzuE2U9gQmEFqCME6EOV1DozDtxkgAaSMVu0/ATY9CYW7uv34Qggh+q/XtuZx7iNrOVRcG7JjtlliV1sCh9eo7anfbHFzZgebNLyw8Rg5ZfVsOFja6j6+RWJbltD5l9hldKPELsJqMcd8vKfK7Ayxg9TCr7NvgVFnh+64p3gDpEMfq654XpWtrYG09Tl1OeUy6V7Xj0iAJHrMMxuO8PnhMuodbtbuLwaaNWko8WaQ0sZ3+7GMEjsInIcU0Oa7qfsBkv8ZLUd7ARLAuAtg0jdUt7v32l4fQQghRHh7c3seR0rrWH+wJGTHbGqrxG73G6C7IevUoGv1ZCapYKWqwdlqo6LqRqd5ErO8Pnj3NQje4tsweUgiqXGRzBqR3CK71FlZSaFtLNHr0sap1uAeF3z9unl10AxSfblv/tjM/+nFQYr2SIAkesSh4loe+eiA+b3TrQKDgAyS2cGu+xmkSJsFu/fsWrVfmV1IFor14x8gNbk6GHBd8Gu10nbuRt9ETCGEEANOQaUKIkJR0m1oM4PkX14XREJUhPm+21qZ3fbcSvPcXYXfSUV/h4pr+Pem40Bgi29DnN3GhvvO5eVbT2/15+ioBO8Jz+ZNl8KKkUX66mXzqopgGaSdr4C7CQZPVU2eRL8hAZLoEU9+ehiHy0NWs1S7OQcphB3sfMcObNSg63rAOkj1Tjd6NzM4JTX+AVIHMkgAiUPhzLvU9qa/d+vxhRBC9E+6rptBSE0oAyR3KwFS+RHI2wyaBSZf1ur9xw1WZVvbciqD3r41p8J3yCAB0qHiWpb+dQNfHivHatFYNHFw0ONERVixtdWKvIOMBeW7tbh7X5tyOVgioGAbHP8SgMrmGSSPWy0sCzDzxm7PxRahJQGS6BFGpuW2+dkBC8iZGSSjg11ELCQMDcljJjRr1FDvcNPo9AUxuk7A910RkEHqzLFmfRssNji+SeYiCSFEmHO4PPz54wOs2lNkXlde5zBPnPVKBmmXt3xr1HyIDx60AMwdkwrA+kO++TD1Dhe/emcP23Ir2JpTbl4fLEBau78Yh8vDhIx4Pvz+PC48JbOrP0qHxNpViV4of4e9Li5NLR4Lar0l/EvsvBmkna9A2UGISoJTruqDQYq2SIAkeoTL7ev1PznL19PfDJCK/eYfWULzNPR1slMvqsYLvf+bSncXi/XPIBln9To2uMEw4SK1vfmZbo1BCCFE3/rN+/v488cHueeVHbi87wX+jRBCWR5mBEh2/+yMrsOuV9R2kOYM/s7yBkifHyo1W2e/vi2fpzccZflzm9meW2nua3yI93ewSDWcOH9yBmPSe76JQGykkUEK4wAJ4KwfABoceB+KdvuV2EWCywFrf632m/sDWfuoH5IASfQII3iIsFo4bWSKeb2ROg9lBzuD0QDCWHPJyPakxkYS7Z002t21kEr95jQ1dbYr3mnL1eXOV6CxulvjEEII0Tfe3XmCZz87Cqi1gXYXqNfzfL85Psb7ENUFcOgT1dGsprBLjxc0g3TiKyg9AFY7TFzW5v1nDE8mJtJKWZ2DvYVqrDuPVwJqXoz/+2JZkAzSQW/r77G9EBwBxBglduGcQQLVMnzSN9T2Jw9RVaeeH8mxEbDlGajMhbjBMPvWPhykaI0ESKJHOAMCpGTzenMOUgg72JnHthtzkNRZGiODNCjOTkxkaAKkLs1BMow8W6355KyDvf/t1jiEEEL0Pqfbw09XqjLpqAj1Eeqzw6p07YRfgFTf0ACf/g7+Mg3+dRn863L48ynw4U+hoaLlgdsQdA6S0Zxh/OJ2sw+RNgtzRqkTlZ95y+x25VcF7GPMU6qocwTM1dV1nYPeluVjB/dOgBTnLbGrD0Hn2T4370egWeHA+/xPye+x4WJs6SfqeQAw7161uLzodyRAEj3CFyBpzPLLIBndacwMUgg62BmM4KvaW9pgnAlLiY0k2hsgdTdl7z8HyeXRA9ZcapemwZQr1Pbet7s1DiGEEL2vrNZBZb0Tq0XjR+erE3yfHyoDoMBbYqfh4d7S/wdr/g/cDkjJhkFjVLeyjY/B3+f5ysw7wNG8zbfbr3301ODd65ozyuw2HCqj0ek2g57lc0cBcN3pIwD1vubfYKK4pomaRhcWDUb5rXXUk2IiA6tBwlrGVLj8adCsLHSsYa/9f5j8+fdVa/ZTrlbzk0W/JAGS6BFOlwocIq0WUuPs/PyiSdxz3jiSYyNVB7vSg2rHUGaQmnWxM1p8D4qNNGuaG0KYQYIOroXkzyiFOLxayuyEECLMGJ1Rk2MimTcuDYDNx8ppcrnNDnb/Y/2QGa4dqgnR5c/AnVvhji1w7euQPFKVVj19Hhxd16HHbGpeYnfgfag5ATGpMPa8Dh3j7LFqrF8eLWNbTgVuj86g2Eh+duFEvn7wAm44Y6RZaVHuV0puzD8aOSgWu6176xt1lDFXubsVH/3GlMvgyheo1GOJ0NxougemXQOXPAGW3vmdis6TAEn0CDOD5H1B//bcUdy5cKy60b+DXeKwkD1mvF8Xu/zKBlbtUfXeg+L8MkjdOCPlcnsobzaBtcNrIRnSJ3rPJDrg4EddHosQQojeZ5Rup8ZFMjY9jrR4O00uD9tyKjlR1chorYAf27xr31zwK5h6haoe0DQYuwhuWQMjzgJHDfz7W3Bip3nsE1UNHC2ta/GYLUrstjyrLmdcB7aWi7YGM25wHCMHxdDo9PC7D1UFx5QhiWiaZgYkKbGq/bT/+5wx/6g3mjMYjEBtQGSQvBrHLOHUpr9zeuOj1Ny2GS59UoKjfk4CJNEj/Js0tGB2sBsXsg524AuQvjhSxnl//JRtuZXYLBqLp2SabUMbOttYwU95vQNdV+9zRufyTs9D0jSYeLHaljI7IYQIK0ZlQkpsJJqmcWb2IAA2Hi6lrKKSv0Y8RpTmZL3nFJj5Py0PEJMC17+p5qQ6amDFFVCZi8ej880nN7Ls0Q0tFmsNaNJQdlhVIKDBrCDHb4WmaXxzljohucPboOGUoYkB+xgBkv/jG6V44wbHd/ixusuXQRo4AVJlvRMPFkotqcRljO3r4YgOkABJ9Agjg2SzBFn4rHiPuhw8OaSPacxvOlxSR73DzcwRybx399nMHJFMdET3U/ZGed2g2EiivF3xOrUWkmGSN0A6uAqcwVc2F0II0f/4z20FOCtbze15f9cJ7mp4nCmWY5TqCdzruAWHu5U5qjY7XL0CBk+B2iL49zXknCgmr6KB2iYX+4tqAnYPmINkLCw6ZpEq1+uEy04dgv9b8pQhgQGSsYCp/1pIh4p6t0ED+HexGyAldvjapyfFRKDJgrBhQQIk0SOc3jeGFgvbARTtVpfpoQ2QzA55QHZaLC8un22e9YoJQYmd0eI7Nc6O3ftzOdxdeAHPnA4JQ8BZrxaOFUIIERbKvXOQUuNUadsFkzOw2yycXv4ml1k34NIt3Om8k0IGtf1+E5UI33oFYtOh6Gui3rmdSFQH1qOldWqdo4ocqDiG7qgHYEzZWvjib+r+XWgNnZkYbc6bApjaLEAaFOsLkP6zOZeH39/LPm9b8N4ssTO62A2kEjsjK5fkDUJF/2drfxchOs/ZVold0dfqMsQZJOOMXqTVwqPXnGp2wgHfytzdadJQWuN7Y6ysdwJOGruSQdI0GDkXdv4Hjm2A0Qu6PCYhhBC9x7/EDiAxJoLvjSnj9qMvAvBk5A1s90wFj4faJpdqTNSaxCFw9Uvw3FIyT3zCqshdvOaex/gtr8IXO6H8MACPAj+IzGD4zkpAh1nLO9ycobkrZw1j7f4SUuPsZCZGBdxmjPVoaR3/2XIco9u3pkF2Wu8FSLEDsMTOWCQ2OSaij0ciOkoCJNEj/Nt8B2iqgYpjajvEAdKskSncde4YZo1MYVJW4LoQRoldXTcCJKN70aC4SHLLVeDX6TlIBv8ASQghRFhoXmJH9Qm+U/QgEZqbd9ynsz7jKuJK62l0NnUsAzLsNLjyRSr+czsjKOYey2tQ7L3NEqEm8rsaGW0pBA+QfS4s+Z2KWrpg8eQM7ls8gYmZ8S1KvYyf6dMDJeg62G0WXB6dM7MHmWXlvcE4uel06zS53L3WPa+neDw6r2w5DkBGYnQfj0Z0lARIokeYJXbNM0jF+9RlXAbEpob0Ma0WjR+eH7xtuFFi19CNM1LGG+OgWF+JXae72BlGzlWX+VvBUS8LxQkhRBgwFyCPjVQLvv7rMiIbijlmGc6PG2/lguQYimqaKK3teImYPu4CLvD8mW863yHbUkBDdBbXfuNCGLMQIuP49t8+wp23lR/NjmbqklvA2vWPbhaLxu0LsoPeZsxBOuFdz+n8yRn89vKpLd/He1hspC8gqm8K/wDpsTWH+PRACVERFr7byu9e9D8SIImQc/stoNqixK6HyuvaE2M3FortRgbJWFcpLhK7dwX1Tq+DZEgeBfFZUFMAeZth9Pwuj0sIIUQIuJ2w/30o2Ka6rRbvheoCyJqumiJMvtQMkDL1YljxI9V0KC6DQ2c+S9M7Jcwfl2a2xq5t7FiAlFteT3Gjjce5BNwQ6bFw9cTFWL0dFcr0OL7yTOO6cbOYau+5UreUZuWA04YmBpSq9xab1YLdZqHJ1YEyxR7i9uhU1jsYFNexNuqtOV5ez58+PgDAry6ZysTMhHbuIfoLCZBEyBnldeBbB8lkNGjo7QApovtzkPzPHBpntLpcYmfMQ9r1incekgRIQgjRJ2qL4auXYdPfoTqv5e15m9XX2od5jgzyI1KYtvIQeByq2cL1b7Bo8GT2zfEQYbXw8uZcAGo6mEHalV8FwOSsBA4U1eBweyiobGBYiqoscDRfKLaHNA+Qmjdx6E1xdhtNLkefLRb79PojPPz+Pv5xwyzOmzS4y8c5UlqHrqt1qK6YOTSEIxQ9TQIkEXIBAVLzOUhmgDSlF0fkaxvanUmfZbXGHCS7WXLQ5QAJYORZKkDK+azrxxBCCNFxug6lB9SC5YVfw7F1cOwz0L0fxGPTYeJFkD4J0iZAfAbkfgH73kE/9DEjPIWMsHrnA41eAIt/C+kTAF/FRJxdTcTvaNdUI0CaNiyJJpeHQ8W1HC2t8wuQ1Nh6utQtJdbXQEDTWrYB700xditldb3Xya60ton/t/JrLjwlk4tOyeKLI2UAvLk9r1sBUk2jas4g3evCjwRIIuScfms/RPgvBKvrfZdBiux+iV1pkBK7pm4sPMvIs9Vl3ma1HlKETN4UQoge4WyAbS/C5n+oAKm5IbPg1OvhlKshIrC7G6lj4dTrKSk6wff/8gJDLOX89tsXYhl1dtBmCcaSEx0tsdudr1ppT8lKpLi6iUPFtRwrq2MeqiW3sfB6T2eQkv0+xI9JizO7yfWF2Mje7WT363f38v7XhRwrq+eiU7IorFYnRD87VIbbo5vljp1V430OJETJx+1wI38xEXJGBslq0bD4v6hU5UFTFVhskDquV8fka9IQqhK7EGSQUkZDfCbUnIC8LTDq7K4fSwghREseD+z4F6x5WM35BLBFq6Bn0BgYcabqDDeo/cnzJe4YPvdMITXGjmX0vFb3M5aV6GiJXUGlWjB8dFosx8piYa93LSQvo8TO3sMBUlJMJJqmzmVOHdp32SPwtfruztqFHbUtt4I3tucDvr9FUbVqVFHV4OTr/CqmDUvq0rGNDFJ8lLT3DjcSIImQM17MW5TXndihLtMmgK13083GRNO6Lp6Nqne4aPBmiwbF2c05SF1u0gDqzOOIs+Dr19Q8JAmQhBAidIr3wX/v8i3InTgMzrobTrkKojo/WT6gg10bjBK7jmaQKurVcZNjIhk5KBYIHiD1dAbJatFIio6got7JtKFJPfpY7fEFSD07B8nj0Xnw7d3m91UNTirrHebfGmDDodJuBEjqORAvGaSw07u9G8VJwdVaB7uC7eoya0Yvj6j7GSSjg12kzUJspNV8o+pWBgl87b5lPSQhhAid3C/gH+eo4CgyDs7/FdyxBWbf0qXgCHwBUvNmBs2ZJXZNznaP6fHoVDX4FhEdlaoCpGPBAqReaLdtPP7sUSk9/lhtiTXL4ns2g3S4pJav8qqIirAQ5S2d3368MmCf9QdLunx8CZDCl/zFRMgZJXYtXszzt6nLIaf28oj8MkhdTNcbayClxkaiaVr310EyGAFS3mZwNrasfRdCCNE5RbvhpSvBWa/mel76JCR2v4OY/zzUtsR1IvtR0+jCe06RxJgIRgxSjRnyKhrQdR1N03ptDhLA366byfHy+j5vR91bGaTc8noAstPicLo9HCiqZXtuJQDREVYanG625lRQ73B1qeV5tZTYhS3JIImQ85XYNWvQ0B8ySF1sqlBe5+tgB3S/zbdh0BiIGwzuJsjf0r1jCSHEya7iGLx4GTRWwdDZ8K3/hCQ4Ar/3gXZL7NQH6Y7MQTLK62IirdhtVjP4cnl0qhtceDy6b+H1XgiQBidEMWtk32aPwC+D5Pc7bHS60XW9tbt0SV6FmnM0NDmazETVKGl7bgWg2pwPSYrG6dbNoKmzJIMUviRAEiFnZJAibH5zkCqOQmMlWCMhvXc72IEvQHK69S7NGzLOHBqlFd1eKNZgzEMCKbMTQojuqC2GFy+F2kJIm6iCo8jYkB3eV2LX9uKhRvajtrH9Ejv/+UegTr4ZAVZZXZOZPYLeCZD6CzOD5C2xK6lp4rT/+5g7/r09pI+T723KMCQphqwkVcGxw1tiNzgxitFp6vlTWNXYpeNLk4bwdfL8t4leY5ztCsggGdmjwVN6vUEDEJAa78o8pLJmpRW+dZBCkP6XeUhCCNE9+dvgmfPV+kZJw+H6NyAmtJmQ5u8DrfHNQWo/g1RZb6yT4/sAbRy/vM4RGCD1whyk/qJ5F7ttuRXUNLrYeqwipI+TV6FK7IYmR5PlzSAZWZ+MBDup3qqRUu86iJ0lGaTwdfL8t4leE3QOkjH/qA/K60CdebN5W47XOzs/D6l5aYVvHaRuZpAgcB6Sq2svwkIIcdLa+YoKjiqOqk5116+EhKyQP0xZh7vYdXwdpOYZJPBVKpTWOgKqFE6qAKnZ2oXHvXOFajqQleuMgBK7pMC1CAcnRJHqDVbL/LradYasgxS+Tp7/NtFrjDNeNv823wU71GUfNGgwmIvFdmHSp+/MYYjnIIFaEyo2DVyNvkBSCCFE+45tgJXfBY8TJi6D76zv0JpGXdHRLnZxncggVQTLIHlL+MrrHAHLZli6uFhpOIpplkHKKVMBUp3DjdsTunlIRoA0JDnaLLEzZCRGme/5Xc8gSYlduJIASYScs3mTBo/btwZSH2WQANLi1QvdwaKaTt+3+ZlDoxa823OQwDsP6Uy1nSNldkII0SHlR+E/16ngaPJlcOWLEJ3cIw/l9ujkez9MZzXLNDQXb/cFSO01FagKkkEy3mfKapt6tcV3f2Jk4eq9JzRzvBkkCF3r73qHywx6hybHmCV2hoyEKPNvYcxD7gxd16XELox1+j9u3bp1LFu2jKysLDRNY+XKlQG3/+IXv2DChAnExsaSnJzMokWL2LRpU8A+5eXlXHvttSQkJJCUlMTy5cupra3t1g8i+o8Wc5BK9oOjFiJiIXV8n41r/rh0AD7eW9zp+5aZXeyMibQhnIMEMMJbZpfzeWiOJ4QQA5nbBW/cCg0VMGQmXPKEOtnUQWW1TazZX4zD5cHt0XlnZwEbDpa2un9BZQMOt4dIq6XdAMnIIHn09junBssgpfiVdfVmi+/+xKj4MLJwuWW+daE6ugBve4yANz7KRmJ0BBmJgRmkwQlRpHpPrJZ1IYPU6PSY60JKBin8dPo/rq6ujmnTpvH4448HvX3cuHE89thj7Nq1iw0bNjBy5EjOP/98Skp8C21de+217N69m1WrVvHOO++wbt06br311q7/FKJfaTEHKW+zuhxyKlj77izKokkqQFqzv7jTKfpyo8Qu1iixC9FCsQYjg5S7CdyhrbEWQogB57M/Q96XYE+Abz4PEW0HLc3d9/ou/uefm1nw+zUs/vM67nhpOze/sLnVk15GidewlGis7ZS6RUdYMXZp78O8MQcpKUgGyb/E7mQLkMwMksOF26ObpXDQsdLFjvDNP1JrT0VFWM05R+ANkGK7XmJnlNdZNN+cKhE+Ov0ft2TJEn71q19x6aWXBr39W9/6FosWLWL06NFMnjyZP/7xj1RXV7Nz504A9u7dywcffMDTTz/NnDlzmDt3Lo8++igvv/wyBQUF3ftpRL9gtvk25iDlfakuh57WRyNSThuZQnyUjfI6h7nOQUfouk5pXWD3opDOQQJIn6RKQ5x1cOKr0BxTCCEGoqI9sPZhtb3095A0DIA/frSfMx/+xJzQ35aDxarUuqCqkYPFqoKl0enheHlD0P2PejMYo1LbbxuuaZrZha201tHmCTmji11ykC52ZXVN5nvMyRYgGZ1na5vcFFQ2mJkY8DU+6K48s8W3L7g21kIaFBtJpM3i+1vUOjq9BlO1d5xxdhtaJ7Kbon/o0f84h8PBU089RWJiItOmTQNg48aNJCUlMWvWLHO/RYsWYbFYWpTiGZqamqiurg74Ev1XixK7PO8CqH0cIEVYLZwzXmWRVu0t6vD9aptc5lm85hmkkMxBArBYYLgxD+mz0BxTCCEGoo8fAI8Lxl8Ip1xlXv3RniIKqhpZsSm3zbt7PDonKtW6NvecN457zhtHtne9m2OldUHvY1w/clDH1lUy5iEt/et6Fv5hLY2tlNoF62JnvM+U+XWxO2nnIDlc5DYLeEOXQfK1+DYYjRoGJ6jL5gv3doY0aAhvPfIf98477xAXF0dUVBR/+tOfWLVqFampqQAUFhaSnp4esL/NZiMlJYXCwsKgx3v44YdJTEw0v4YNG9YTwxYh4lso1gINlVCyT93QxwESwKJJgwH4eE/HAyQjDR8TaSXamyYP+Rwk8LX7PrwmdMcUQoiB5Oh6OPgRWGxw/kMB846MzMLbO/LxtJG1Ka1VC7BaLRq3L8jmzoVjmZCRAMCxsjoq6hzc99pOtub4Kg3MAKkDGSSACZkJvvuW1ZuLjzYXbB0ko0te4Bykk6tEK9auft56h5ujzYLWUM1B8m/xbTAySMZ8JLvNajZYKOlkmZ00aAhvPRIgnXPOOezYsYPPP/+cxYsXc+WVV1Jc3PmJ8Yb777+fqqoq8+v48eMhHK0ItYA5SAXettXJIyEure8G5TV/XBpWi8bhkroOlWHUNDr5wX92AHDK0ETzenMdpFBlkADGnq8uj22AxqrQHVcIIQYCXYePf6G2Z97Uop23kVkoqGpk87HyVg9jlFZlJERh82ZmRqaqeSjHyup4bWse/9lynCfWHDLv05kSO4Anrj2Vd++ay3nek3JfHg0+nsogGSRjcdKKOgdN3szTyVZiZ5QoAuwvDOw8W9sUmnm6wQKkSVkqsJ2QEW9eZ/w9OtuowbcGkmSQwlGP/MfFxsYyZswYTj/9dJ555hlsNhvPPPMMABkZGS2CJZfLRXl5ORkZGUGPZ7fbSUhICPgS/Ze5DpJFg+PeBg39IHsEkBgdwYxhSQCsb6NjkeGeV75iX2ENafF2/nDldPN6cw5SKBaKNaSOgUFjVcvaQ5+E7rhCCDEQHPgA8rdARAzM+3HATbquB5RerdzR+pzmgkqjXbeva9mIQUaJXT078ioByPfu53J7zBNqHc0gRUVYmZyVyLyxqnomWIDkcHnMhVD9M0jJsWrb5dHN9tL2k6zEzm6zmM0w9p4InFbR1hykFzYe4+n1Rzr0GPnNmjQAXH7qUN747pnctXCseV1XF4v1ldhJBikc9cp/nMfjoalJRd5nnHEGlZWVbN261bx99erVeDwe5syZ0xvDET3M6fLOQbJZfB3shs7uwxEFmjdOZbLWHShpc7/aJhcfeUvxnr5hVsBETnMdJHcIAySA8UvU5f73Q3tcIYQIZ7rua8ww5zaIHxxwc6PTE9AM4b1dJ1qdI+oLkHyv6UZm6GhpHbvyVAb/RFWjd/9GnG6dSJuFzIQoOmP2qEEAbM2pMKsrDEb2yKIFZhnsNqs5h6mwSo31ZMsgaZpmtvo2AqQM7+++tTlItU0uHnh7N796dy9VDW1nmUprmyitbULTYPggX4BktWicOjyZqAhfSeOgLnaykxK78Nbp/7ja2lp27NjBjh07ADh69Cg7duwgNzeXuro6fvKTn/DFF1+Qk5PD1q1b+fa3v01+fj7f/OY3AZg4cSKLFy/mlltu4csvv+Szzz7jjjvu4OqrryYrKyukP5zoG8abQLTmhtwv1JXD+kcGCeBs7xm9zw6X4mojwDHS6TGRVqZ5s04Gcw5SO2tcdNqEC9XlwQ+l3bcQQhgOfKA6fEbEwhl3tri5xlt2pWmqUqCqwcmBohqqG51c9feNfHfFVjPwMTIH/ie9jOYLBVUNZlOAqgYn9Q6XWV43IiUGSzstvpsbmx5HUkwEDU43Xx4t59FPDvKVdz6SsQZSYnREi+MazQEKvEHayRYgAUzJUmXtRpZtsrf8rbUMUl5FPUajuar6tt8/N3szeuMHx7dbAmf8LTq7WKw0aQhvnf6P27JlCzNmzGDGjBkA/PCHP2TGjBn8/Oc/x2q1sm/fPi6//HLGjRvHsmXLKCsrY/369UyePNk8xooVK5gwYQILFy5k6dKlzJ07l6eeeip0P5XoU0aANLrhK3DUQGw6ZEzr41H5nDI0icToCGoaXXyV1/pcH+Ns0SC/dREMIW/zbRh6GsQMUnOQcjeG9thCCBGOdB3W/kZtz7kVYge12KXWr6Wy2ZGurI41+4rZdLSc93YVsuyxDbz8ZS753g52/hmk1LhI4uw2mndyLqxq7HSDBn8Wi8ZpI1MA+M6/tvKHVQe473W17EmwDnYGo1FDoREgnWQldgB/uWa6WfERabUw0dv4orUmDXl+LdqrG9sOkL70zlEz/jZt6eocpGrJIIW1Tv/VFixY0GYv+DfeeKPdY6SkpPDSSy919qFFmDDKzsZVeT/gjz1PtbHuJ6wWjbljUnl31wnWHShh5ojkoPuVNlsc1p9xNs/l0XF79HYXDuwwixXGLYYdK1SZ3ah5oTmuEEKEqyNr4MQONfcoSPYIfGVX8XYbI1Nj2ZZbybHSOhzeZSfi7TZqmly8sDHHvI9/BknTNEamxvB1fuB8l8KqRo51skFDc3NGpbBqT5GZ+dhXWMPx8nqzxM5//pFhkPdDufHYJ2MGKT0+iuduOo23vsonNtJmljy2VmJntO2G9gMko4nHaaM6EiAZGaSulthJBikcnXz/caLHubxvSNlVn6srjO5s/YhRZrfhUOuNGsq8AVJq0AyS718nZGshGYx5SPvepcXpTCGEONl89ld1eeoNQbNH4JdBirIx2pxPVM9h7yKw35ozHIC9hdVm22j/DBIEX+PoRFWjuX9H10Bq7vTRaswWzTePZs3+Yr9FYlu+xwzyZpCMTmv+XVRPJhaLxqUzhnL+5AxzbaSaVgMkvwxSG2sW1TQ62VOgAuHZHcggDYrzrUvVGdKkIbxJgCRCzun2MFwrIqUhR61VkX1OXw+phVneF8V9J6pbzYga6fRgGST/AOmRj/Zz4V/Xm2cDuy37XLDaoTIHiveG5phCCBGOTnylMkiaFU7/bqu7GR+a47wZJFDZl8MlKkA6ffQgxqTHoevQ4J076t/FDgIzRCO9E/cLqxvNNtPjBsd16UeYMiSR31w2lWdvOo2bzhoJwCd7i805SEltlNiBer+5YubQLj32QBLnDTRqW8kOBQRIbWSQtuVW4tFhWEq0ud5RW8wSu1a62L278wSn/d/HfHGkLOB6adIQ3iRAEiHncHs417JdfTP8DIjqf2e+hqfEYLVo1DncFNcET5sbL4ap8S3fvGxWXwvS5z8/xu6Car440vq6G50SGQujF6jt/e+F5phCCBGOjOzRlMsgeUSru/kySBFmpudwSS1HvNmf7LQ4zsz2ZZ8SomwtSp+M+1k0WDhRdcnbV1hjlnaN81sbp7Ounj2cBePTWTghHYCNh8vMbnptldgBfGN6VtAg6mRjdPZrrcTOaMsObbcCNxo0dGT+EfjmIZ+oamDpX9bzvZe2Bdz+zs4CSmqa+KxZRYrROETWQQpPEiCJkNOdjVxn/Vh90w/L60DVcw/zLg53pKQu6D6lbWSQwDdp1uVtLetf/9xtZrtvCZCEECep8qOw2zuv+ay729y1+RwkgMp6Jw6XB7vNwpDkaM4Y7QuQmpfXAUwbloTVojFzRDLZaSpbtP6gWg5iSFJ0SD7ojkmPY3hKDA63hze35wOQHCxA8ssgXX/6yG4/7kDgyyB1YA5SK22+65pcfLi7EOhYeR1AqvczQKPTw54T1by7M7CFvFGC2eAI7GorGaTwJgGSCLkLSp5jjKWAhshBMOO6vh5Oq/zXvQjGqDcO1sUOwB4R+O9jLCQYEuMWq8v8rVBTGLrjCiFEuPj8UdA9MOY8yJja5q7GfI84u404u430eN+JrVGpsVgtGnP8AqShyS0DpDHpcbxz51z+dt1MMr2lV8Y8ofHdyB750zSNc71ZpNomF5FWi7lWkr+JmQloGpwxehBTT9L5R821NQeptslllixC8BK7Boeb5c9v5mBxLQlRNs6dmN6hx02ItrXoIlhWp06gejy62Uij3tlagCQZpHAkYa0IrfxtnFf5HwC2Tv1/zI3p2BmavjAqNY41+0s4WlqLw+Xhq7xKZgxLwuZ9ITReAFPjgmeQ7M26Ch33q3/utoRMGDJTBUj734NZ3w7dsYUQor+rLYbt/1Lbc3/Q7u7mHCTv2fqRqbFm+XR2usoGpcRGMjEzgb0nqoNmkACzlXTzuSmhCpAAvnfOGFweD+MzElgyJSPoe8z4jHg+/uF8BndyYdqBzAg0aptc6LqOpvm6x+Y3e/8N1qThj6v288WRcuLsNl5cPof0+I79bjVN4wfnjeNgcQ1r95dQXuegtMZBZmI0RTWNNDpVNqnRL4Ok67o0aQhzkkHqLY1VsH0FvPsjqDze16PpGa4meOt7WPHwtvsMioec19cjatOoNF8G6al1h/nmkxu5+YUtZuq83QySzRrwfUgzSADjl6rLfVJmJ4Q4yXzxBLibYOhsGHFmu7v7r4MEMMqv45xRLgdw0SmZgK9RT2symwVIE0IYIKXF2/nVJVO5/vQRrZ6AAzVu4+cRvkBD16G+WTlb8xL3miAZpC05FQA8sGxSi8Xf23P7gmz+eOV083lhlOD7V6A0+GWQmlwenEaLeQmQwpIESL2hYDv8cTK89V3Y/A/47119PaKese4RKN5DlSWRB5w3EtHPF7YzWsEeKa3jA29N8tr9JfzglR04XB7K61tfBwlarkuRV9HQ5hphnTbhInV59FNoqgndcYUQoj9rrILNz6jtuT8Arf115sw5SH4ZJMOYdF+AdPv8bNb8aAHLvIFSaxKjI4jyK6MOZQZJdI3dZsHmbY7UvFGD0cHOWJIwWImdseDvpKyELo/BCGhLggRI/kGb//hiIyVACkf9+xPsQLH2N+CogeRRYImAw6vh6Pq+HlVonfgKNvwRgKfiv0cFCf0+QDLmIOWU1ZuLA0ZYNd7deYJ/f5mLrqv35WATaKFliV2D020uLhsSaeMhJRvcDjj0ceiOK4QQ/dmWZ6GpGtIm+OZjtqO22YR4/5bd2Wm+bYtFY1RqbEB5VjCappGVqMrwIqwao1O71uJbhI6maWYJZfMMkZFBMv7uzUvsKusd5hylrq5nBb4AycggHWslg9To3bbbLFhCtZC86FX9+xPsQFB6CA58oLavex1m3qi2P3lw4CwC6nLAyu+BxwWTvsGGiLMAiLT17xeFjIQooiIsuL1d6CZmJnDtHNVG9r1dJwC1gJ+tlUDPCJA0zbdmxfFQdrLTNJhwodre927ojiuEEP2VswE2PqG2z/o+WDr2McW3DpI6oeUfIHU1uDHmIWWnxbWoGBB9w2zU0KyTndHie1KWamhhtNg2GJmewQl2YrtRtmgs+1Fa4/Ae1/ee79/FzpiXFBURWIovwof8x/e0TX9Tl+MWw6BsmHcv2KIhb7PKJA0EG/4ERbsgOgWW/gGHt+62v2eQLBYt4EzSvHGpzBieBPhqlf1brTZnzEEanhLDGG+Ne14oGzWAr8zuwEcqEBVCiIHsq5ehrhgShsLUKwJuanK5+eF/dvD4mkMt7uZbB0l9+B2bHsfF07K4bf5ooiO79iHVCJCkvK7/iGtlLSTjvXeSt8mGkUFqdLpx+3Wa6072CCCteQaprO0MUlRE//4cJFonf7me1FABO15S28YK4PEZMP0atb3vnb4ZVyhV5MC636vtpb+HuDScbnXmpL8HSACj/Uov5o9LY9rQJAAzq9RagwbwzUEamx5vtowNeaOGoadBbDo0VanV5IUQYqDSdd/cozO+C9bA8uZ3vjrBG9vz+dOqAzS5Aifp1zYFNmmwWDT+es0M7l8yscvDOTM7FYuG2ZZb9L34IGshNbnc7C9U83SnDfNmkBqd7CusZsoDH/Lwe3vNTI//e35X+JfYuT06uWXBM0jG8zNaMkhhq/9/gg1nBz4CZ72qox41z3e9UVN98OPwL7P7+nXwOGHEWTDlcgC/AKl/l9iBrwwjJtLKrBEpjBgUQ2K07025rQ5DRonduMFxDE2JAUK8WCyo8pLJl6rtXa+G9thCCNGf5G1R1Qi2KJj+rRY3v7DxGKAW5z5cHLh+XfMmDaFwxcyh7H5wMd+YPiRkxxTdE2wtpO25lTS5PKTF25nu7U7n0WHV7iJcHp1XthzncHEt0P0Mkn+AVFDZgMPtWzDWP4PU4JASu3AnAVJPOvyJuhy/JLALz8izwWqHqlwo2d83YwuVPSvV5dRvmj+j0xU+GaTpw5IBOGdCOpE2C5qmcYrfonxtBUhzRg/CbrOwcGI6w8wMUohL7ABOuVJd7nsXHMEXtRVCiHDz4sZjPL3+iO+KLd7s0ZTLKXXH8PsP93HBn9Zx+q8/4e+fHuarvCpz132F1QHHat7mO1S6Wp4nekacsRaSXwbp88NlgFpUNzrCap6c3XNCPUeqG12s3lcMBHY37ApjDlJJTZM5rynG+xwJnIPkbdIgAVLYkt6DPcXjgUPeACl7YeBtkTEw8iw1B+nQKkif0PvjC4XyI6p7nWaFicvMq8NlDhLAoonp/Gv5nICVyqcNTWL9wVKg7TlIy+eO4sYzRmCzWsz1DkLapMEwZKbqgFhxVK2JdMo3Q/8YQgjRi0pqmvh/b+0G4KrThhHvqYGv31A3zvo2D7+3j9e35Zn7P/z+PkCdh9N12FfoW/qgyeU2z+THyZozA1qwOUhfGAFS9iA0TSMhKoKyOge7C3xBtJHdGd3dAMl70rSi3smBIvUcHJ8Rz/bcShqcbjweHYtFo9FbYhclzT3ClvzlekrhTqgvhcg4GDan5e1jz1eXB1f17rhCafdKdTnqbIhNNa8OpzlImqYxd2xqQFmdfwZpUBsZJMDscDfMW2JXUNlgzl8K4SBVhg5g1yuhPbYQQvSBbbkV5nZ1o0uVELubIGMqDJlpzuf8zvxsLpvhK3G7atYwAPae8H349c8myJozA1tCVGCA1OBws/24ei6dmT0I8JVZ5jabE6xpvvfqrkqOiTTXWvr0QAkAM4cnm7c3eStopItd+Ov/n2DDlbFuzah5YAuShRhznrrM+Tx8FwE1yusmXRJwtREgRYZBgBTMdL8Vtttq0uAvIyEKm0XD6dYpqm4M/aCMAOnQx1C4K/THF0KIXrQ9t9Lcrm10wVf/Vt/MuB40zVyIc8H4NP5w5TT+es0M/nzVdK46TQVI/hkk48NybKQVq6w5M6A1b/O9Jaccp1snKzGK4d7gJyE6+NqFWYnR3Q5YrBaNFO/i8V8cUZmr00cPMm83MlXSxS78yV+upxgtvMcsDH77oGxVNuVxwtF1vTeuUGmlvA78Mkj9fB2k1qQnRDEkSc0pMhYKbI/VopEer140C3siQEobBxMvBt0D7/xQlXAKIUSY8s8guYr2QMF2sNhgimrtXVqjAqS0eDuapnHxtCwumTGEcYPj0TRVome0Wq5p1uJbDFzG33j1viK+/dxmfvnfPQCc7i2vA0iICgyQjLK6Ud0srzOkek+cGqX104cnmV1t6x2+9uIgGaRwJgFST2iqgeOb1Hbz+UcGTYOx3izSwY96Z1yhZJTXjZwbUF6n67r5ohEOJXat+es10/nlNyYzZUhCh+8z2LtmRlFVDwRIAIt/o0o2876Ebc/1zGMIIUQPc7o97MyrNL+P3/ea2hh7AcQOosHhNruUpcUHljnH2m2M8GYKduRW8taOfHMNnFA3aBD9z7Bk9bcvqm5i9b5iDnq7050z3teKPSHa9zxIi7ezdGomABMzQ7Oelf9zcmhyNKlxdrNRgxEYGaV2UTYJkMKVvJr0hPyt4HFB4nBIGdX6fmPPhy+f8rX71sIo42KU1xktqL1cfvNvwjlAmjkihZkjUjp1n4wEFSD1SAYJIHEILLgfPvopvPsjcDbC6beH1/NGCHHS23eixpyjYcFD+tGV6gbvGoFGZshusxAfJOiZkJHAsbJ67n55O3UON4MT1AfWuKjgpVVi4DhnQjrP3DiLslrfwulJMREsmjjY/D7e7nseDEuO5o5zxzAkOZqlUzJDMoY0v7nJxtqJ0RFWKnFS7+1kZ3S0kxK78CUBUk/I9WaPhgdpzuBv5Fy13kN1HhTvhcGTen5soVB+tN3yOgjfOUhdZay63mMBEsCc70DR16pe/8P74auXYMIymHMrRCe3f38hhOhj/uV1M7UDRDUWQ1SS2byouFl5XXMTMuP5YHchdd4PoUXVav9gwZQYWKwWjYV+wVAw/hmkockxREVYuWb28JCNIdUvg2QsTGssCGsERmaJnbSJD1sn1yfY3mKU1wXrXucvIloFSaDafYcLI3vUrLwOwOnyZZBsYbBQbCgZGaQeK7EDsNrgkr/BBQ+DJUI1bFj7a/jHQig91HOPK4QQIeIfIC2w7lAbY88Hm28RTmh9HTrjrH18lC0gKJISOwGBc5CGpXRsHnFnpPo1bzIzSMZaSEaTBrPNtwRI4UoCpFDzuCFvs9oeNrv9/cOx3bcx/2jyJS1u8l9V2naSdRPqlQwSqJK6M74L9+yDbzyuSjnLD8PTC+HEzp59bCGE6AZd19lyTAVIw1NimG/xvmaNWWTuU+KXQQpmwfg0/njlNN6+Yy7nTfJlE6RJg4DALnbGnKVQMgJ3iwZThrSWQZI23+FOAqRQK9kHTdVqMn365Pb3N94UcjeGR7vv8qNwYgdoFtVVrRn/Ft/BSiMGssFGBslb7tHjYlNhxnVwyycwZBY0VsJ/roP68t55fCGE6KSjpXXkVzYQabVw4SgLUyzH1A3Z55r7tBcgaZrGZacOZVRqLIv8AyTJIAl86yCBKrELNWMtpYmZCcR6n3MtMkjS5jvsyV8uRDweXZUFGOV1Q2aqcqj2DMqGpBGqqUPuFz07yFAwy+vOblFeB/6LxJ5cwRH4NWmoakTXQ7xYbFvi0uHaV9XzqDIH3rhV2oALIfqldd7FNWeNTOZU5zYA8qLHQ1yauY+xBlJaOwt1A8wbl2bOd42XDJKg50vsZo1I5pFvTuNPV003rzMzSE7JIA0UEiCFyE/e3MVp//cxFfvWqyvam3/kb9Q8dXn009APLIQ8Hh3XrjfVN0HK68B/DaST76lllNg1ON1UN7rYcbyS8jpHO/cKkZgUuOpfqunHoVWw8z+987hCCNEJ6w6WAiqwya5SJwV3RQeWoxtrIKW2kkHyF2e3ceYYtVBnSmzHFvYWA5tRYmfRILODaxl2hqZpXDFzKOMG+9qGmxkkh9HmWzJI4U7+ciGy+Vg5ug6WfO/8o/Y62PkbNV9d9vMFY//y2kfYir5C1yyqc1oQDlf4r4HUVVERVhK9L8xv78jnksc/439f78U5QZmnwIL/VdurHwJHfe89thBCtKPJ5Wbj4TIAzs5OZkj5RgC2RpwasF9nMkgAv7x4Ct+Zn83lM4eGcLQiXA1LicaiwbjB8eYCrj0tJrKVLnbSpCFsnXyfYnuAruvkVzaQShWJDccBTc0J6ahRZ6vLEzv79fyRuEPvAFCQNCugHMKf/xykk5FRZvfiFzkA7C6o7t0BzLkdEodBdT588UTvPrYQQrRh67EKGpxuUuPsTHQfwO6sokqPYac+NmC/9uYgNTd8UAz/u2RCQGmVOHllJkbz9h1zef7bHWiUFSJGKV29NzBqMOcgSYAUrk7OT7EhVlHvpNHpYablgLoifSJEJ3X8APEZkDoO0CHn854YYrc5nQ7Oa/oIgK1x81vdz+U5eecgAQz2ltkdKFKrexdWN+Jy9+J8oIgoWPiA2t7wJ6gt7r3HFkKINnzqnX80b2wqloMfALDWM51qh05tk4u7X97Omv3FZpvv9A4GSEI0N2VIotk4qTe0zCCp9327lNiFLfnLhUB+RQMAp3oDJL0z848M5jyk/llmV77xRUZqhZTp8bzL2a3uZ5TY2U7aDFLgG7rbo1NU00td7QxTLoesGeCohbUP9+5jCyFEKzYfUxUSc8emwoEPAfjEPYOaRhcffF3IWzsKuPfVr8wPl62tgyREf2M0aWhs1sUuWjJIYevk/BQbYvmVKkCaaTkIQE3aqW3tHlx/DpDcTuI3/QmAv7su4mBl6x3afF3sTs6nVkaQM1YF3udHr7FY4Pz/U9tbn4fifb37+EII0YzL7WHPCVVyfGpCDRTvQdcsfOqZRm2TiyLv+nGltaqxTZzdZk58F6K/i45UHRTrZR2kAePk/BQbYvmVDdhxMFU7AsAh+6TOH2SkNytTsrf/lUVtfIyYuuOU6Am86D6PvIoGPJ7gQZJvDtLJXWLnr9cDJICRZ8GEi0B3w6qf9/7jCyGEnyOldTQ6PcRGWhleprq9OrNmU0UctU0uipstsN3R+UdC9AfN23w3yRyksCcBUggUVDYwWTuGXXNRoiewu2FQ5w8SkwIZU9V2f8kiOephzcPw8S8AeMx1KQ1E4XB5KG6lbEwySL4AyWg5m98XARLAogdBs8LBD+HYhr4ZgxBCAF/nVwEwKSsBy0FVXqePuwBQpcjHKwJfJzvawU6I/iA6Un3mMecgSZvvsCerqoVAfkWD2aBhm2cch0rqunagUfOhcBccWw9TrwjhCFuh63DwI/j6DSjeA446SMgCzaKyWKUHVAYCeDnmWzxffr5519zyenPdH38O98nb5ht8ayFZNLh4WhbPfX6sbzJIAKljYOZNsOUZlUW6+RPQTs7MnhCib+3yBkgzMiJglzoJGDFhCdoHR9B1OFJSG7C/ZJBEOImOUB+nG5xuXG4PTu9nIWnzHb5Ozk+xIVZQ1cAsb4C01TOWg8W17dyjFb05D6kiB174Brx0Jex8GQp3QvlhFZwd/VSV+uluiMtAv+jP/F/9JYBmvmnllgdfY8fpOnkXigWYmJHA5acO5fuLxjEhQy0il1/RRwESqHWRImIhfyvsWdl34xBCnNR256v5Rwsi9oLbAUkjsKRPIM47d8PIIM0YngTAiEExfTJOIbrCf6HYRpevc62U2IUvySCFQH55vdnBbotnPHldDZCGn6FKosqPQOVxSBoWwlH6cdTDv69WWSOrHU5bDiPOUq3JqwvUPjGDVOvxxKGU1DZR89onWDQ4e2wqb2zL53hrAdJJPgfJYtH4w5XTAFjnbWlbUNnY1l16Vlw6nHknfPobWP9HmHxp146j63BiB+x7FyJj4azvSzZKCNEhHo/O7gKVQZpc613KYtxi0DTiomzUNLlwe+e1/uayU9iZV8n5kzL6arhCdJrZ5tvpNjvYAdhP0pPFA4EESN3U6HQT25BHmr0a3RrJbn0kTTVNVDU4SYzu5KJ1UQmqPXP+FpXJmf6tnhn0B/+rgqPYdFj+IaSMbnP3w8WqZHBYSgxj0uMA2g2QTtYSO39ZSdFAHzVp8DfnNtjwR5UlPLETMk/p3P11HV5fDl+/7rsuKglm/U9IhymEGJiOltVR53ATHQEJx9eoK73zj2LtgR9DhqVEM96bfRciXJhNGhy+ACnSZsFikROJ4Uo+xXZTfmUDs7T96pvM6SQnJABwqLtldkc+DcHogtj3Lmx7HtDg8n+0GxwBHPbWhmenxTE8RZU9tFpid5LPQfKXlaTmI9U0uahudPbdQGJSYPwStb1jRefvv/lpFRxZImDoaeq6j/4fVOWFboxCiAHLaNBwYWoxWm2hKvsdORdQ7bwNcXYbMZFy3laEH6OUrt7h8rX4luxRWJO/XjcVVDaY6x9pw+eYGZbDXQ2QxixUlwfeB5cjFEP0cTvVB1uAs+6C0Qs6dLdjpSqDNDo1lmHJKkA6XtF2Bsl2kpbY+YuJtJEco7KIfToPCWDG9epy5yvg6sTCtcV74aOfqe3zH4JvfwhDZ4OjBt79UejHKYQYcIz1j5ZG7lRXZJ8DNjWfNT7KFxClS2MGEaaMErtGp8e3SKys4xXWJEDqpvyKBnP+EcN8AdKhkm7MQ4rLgMYqOLw6RKP02vaCasQQkwrz7u3w3SrqVfYjNd5uZpCKqpsC6mwNvjlI8tQCGJLcT8rsss+F+CxoKIf973XsProO7/wAXI0w5jyY8x2wWOEbj6lOhwfeV/PlhBCiDbll6oTatPrP1BXjFpu3+WeQUiVAEmHKKLFzuD3mYrHSoCG8yafYbiorLWa85i018guQDhbVdO2AFitMuUxt+8/56K6mWlj7G7U9/z6wd7zGu6pBBUiJ0REkxUQQ731DC1Zmd7K3+W4uK7GfBEgWq29O22d/VcFPe/a/B7kbwRYNy/7ia8qQNl61pAfYFcLnqBBiQMopq2e4VsSgmv2qEdH4peZt/gGSZJBEuPLPFpXXqeofafEd3uRTbDfZCrdh0XSqooZCXHr3M0gAUy5Xl/veVR3nQuGTX0JdMSSPVGvjdEK1N0BKiIpA0zQmZKrgamdeVYt9GxwuQAIkg9GoIb8vO9kZ5nwHImKgYBvsf7/tfd0uWPWA2j7ju5A4JPD2qd9Ul7te6ViwJYQ4Kem6zvHyepZYvlRXjJwLsb7F1OMCSuxarq0nRDiw2yzmOcSKem+AJIvEhjX563XTN9NVW2xt+BwAM0DKq2gwV1TutCEzIWk4OOvgwAfdH+Sxz+DLv6vtC/8AtshO3d1oMGB05Tt1RDIAW3PKW+xr1JqPTovt6mgHlEzvwrGFVX2cQQKIS1NBEsCa/wOPp/V9v3gCyg6qdu9n3d3y9okXqRbxpQfU4sZCCBFEZb2TmiYXS6zeAGnSxQG3x/tlkGRxWBGuNE0jxltSZwRIdimxC2sSIHVTiuMEAAljzwJgUGwkyTER6Lqv+1unaRpMuUJtb3m2/f2djVByQDVhaK7yOLz1XbU943oYs6jTwzFK7BKi1RvZzOFGgFQRsJ/bo7MjtxKAU737nOxSYlUwWl7fh13s/J15J9gToOhr2PCH4Psc/xI+eVBtn/v/ICqx5T5RiTDeO49g16s9M1YhRNjLLa8ni1KmWw4DGkxYFnB7nDRpEAOEUWZXYZTYSYAU1iRA6q7L/g4/OmiWxWma5utk150yu9OWq7bKx9ZD7qaWt7ud8NXL8I+F8PAQePw0+ONE+OAn6oy+2wkHPoR/nAMVxyBhKFzwf10aSnVDYAZppjeDdKCo1gye1Pc11DncxEZaZR0LLyNAMl4w+1xMCpzr7Uq3+lew5teqnM5QfhRe/R/wuNSism2VYxpBfEebPgghThpfHi0nr6Ke3PJ6Fls3qyuHnwHxgwP2818HKT1BAiQRvqLMDJL6XCRtvsObLDgQCnHpAd+OSY9j87GKrq+FBJA4FKZdDdtfVIt8fus/vtsqc+GFS1RHOoMlAupK4IvH1ZctSnUfA8iYCle/FDwT0A6n20Odt1QwIUoFSIPi7IxKjeVoaR3bcytYMF79/NtyVUZp2rAkrLI4GuCXQeovARKohWObamD1Q/Dpb2H7Cph8iVr89fO/QlM1pGTDsr/6GjME413HhLJDUFcWMK9ACHHy2nuimque2si49Hgunp7FEqv3JF+z8joIbNIgJXYinBmtvivrJYM0EEh42wOy04xOdt0IkADm/sDbTvkDXxapphCev9jXrnvhz+HunfDTE3DNf2DixWCNVMFRTCrMvlWtXZM0vEtDqGn0ZRf816s4NUiZ3XYpr2uhXwZIAPN+BBf+UT1HqvNg42Ow5lcqOBo2B258G6IS2j5GTAqkjlfbx4NkOYUQJ6UtORXoOuwvqmH3/v3M1NRagUxc1mLfeGnSIAYIo9W32cVOmjSENckg9YCxg1V5Wbc62QEMylale7tehRVXwNn3wJZnVAYpaQR8+wNIyPLtP36x+qovV4FU2gSwdO8f1Cihi7PbsPl1pps5IpnXt+UFBEhGBunUEUndesyBxAiQGpxuGhzu/rVw3GnLYfq1sGcl5G+DmgIYMgvOuAOsHXxpGD4HSverAGnC0vb3F0IMeHsKqs3tlOOrsETolCefQkri0Bb7xtlVZUKEVTMX1hYiHMV7q2xOVKnqnWjJIIU1CZB6QLa3g9ux0jo8Hh1Ld8rNLvoTVBdAzmfwsbftcsJQuOGtwODIX0yK+gqB5vOPDMY8pB3HK3F7dGoanRwpqQNgxjDJIBni7DYirBpOt055vYMhkdFdOo6u6/x05dfUNbm4ee5opg7tfLlkUBFRqpRz2tVdu/+wOWoBYskgCSG8jG6mAEst6rWhLvtCgr0rZSVFoWmq8kJrq6RXiH5uVGosGw6VmgGSlNiFNwmQekBqnKqjdnl0ah0uc+5Ol9jj4drX4PWb4fgXcPrtMOd2sMeFaLRtMzJI/mUQoF4IAOodbmqbXGw/XmlenxzbuTbiA5mmaaTERlJU3URFnYMhSV0LkMrqHLy0KReAt3YUcOu80fxk6cRQDrVrhqn29uRvA5ej0y3khRADi8vtYZ83QEqhmjmWvQBET7s06P5Dk2N45bYzyEiQ8joR3owGXQZp8x3eJEDqAVERViKtFhxuDzWN3QyQACJj4JqX1IKcvXyGraqVDFKkzYLNouHy6NQ7XBR6z5hky/pHLSTHqACprBvzkIxMnqapp8E/1h/h2jnDGTGoj3/fg8ZAdAo0lEPhThg6q2/HI4ToU8fK6mhyeYiOsLLMuh0rOrv1kUwaOq7V+5w2MjQVD0L0peYBksxBCm/y1+shRsalpjGE69/0QflB80Vi/RkdW+qa3NQ1qWYO/i1bhRKKVt+13t9vRkIUC8anoevw7IajIRlfMEXVjdz/xk4OFNW0vaOm+bJIuV/02HiEEOFht3f+0cTMeK6M3gbApqi5Uj4nBrwWAZJNMkjhrNMB0rp161i2bBlZWVlomsbKlSvN25xOJ/fddx9Tp04lNjaWrKwsbrjhBgoKCgKOUV5ezrXXXktCQgJJSUksX76c2tpuNjToZ+LMAMnVzp79m2+R2JYBkhEMNTjc1DWpVuAxkRIgNWcESN3JINV6n0dxdhs3zx0NwCtb8sx2oqH2yIf7+feXxzsWhA07TV3mb+mRsQghwocx/2hmusbExu0A5Ged35dDEqJXpMfbifc7SSxzkMJbpwOkuro6pk2bxuOPP97itvr6erZt28b/+3//j23btvHGG2+wf/9+Lr44cO2Da6+9lt27d7Nq1Sreeecd1q1bx6233tr1n6If6pEMUh+oblAfzNvMIDlc1Du8GaT+1KWtnwhFBqnaCJCibJw1ZhATMuJpcLp56cvckIzRX73DxXu7TgBQ0ZEAbPBUdVm8L+RjEUKEF6OD3ULLViy6i6r4sdxyyQV9PCohep6maWT7ZZGkxC68dfp0/5IlS1iyZEnQ2xITE1m1alXAdY899hizZ88mNzeX4cOHs3fvXj744AM2b97MrFlqvsKjjz7K0qVLeeSRR8jKaqUzW5iJ97YuHTAZpCDzqIwMUr3DRZ03QIqRErsWQpJB8pbYxUdFoGka18wezgNv72bz0XJYEIpR+nzwdaG5OHCHnr/pE9Rl2SFwO8EqrXqFOBnpum4GSBMr1wKQeOrlJCZKAwZxchiTHscOb9MqySCFtx4Pb6uqqtA0jaSkJAA2btxIUlKSGRwBLFq0CIvFwqZNwVsFNzU1UV1dHfDV38UPkBI73xykloGP0eO/rslNvbfELs4uLwjNhWQOkvfvYKTvjU6JRmljKL2+Lc/cru5IBjRxGETGgccJZYdDPh4hRHiobnBRVucglgYS8terKydd3PadhBhAxkgGacDo0b9eY2Mj9913H9dccw0JCQkAFBYWkp6eHrCfzWYjJSWFwsLCoMd5+OGHSUxMNL+GDRvWk8MOCWPBsLAPkDo6B8nIIMkcpBaSY1SAVN6N+UJGBinO+zuP9QaixvWhkldRz+eHy8zvO/T81TRI9XaoKtkb0vEIIcKHcUJlWeRWNHeT6nKZPqmPRyVE7xmT5hcgSZOGsNZjAZLT6eTKK69E13X+9re/detY999/P1VVVebX8ePHQzTKnjNw5iB1oIudw0W9tyQrVjJILQzyZpDKu5FBMgIV43llBEpGYBoqf1x1AF2HLG9JjPH3b1e6d00mmYckxEnLCJCusH6qrjjl6j7pvipEXwnIIMmc7LDWIwGSERzl5OSwatUqM3sEkJGRQXFxccD+LpeL8vJyMjIygh7PbreTkJAQ8NXfDZQSuza72EUac5B8bb4lg9RScghK7GqafE0awJe9qwthBmlbbgVvbMsH4OfL1FnfmkYXuq63f+c07zwkySAJcdKqaXQxVCthlr4b0GDa1X09JCF61bCUGCJt6qO1ZJDCW8gDJCM4OnjwIB9//DGDBg0KuP2MM86gsrKSrVu3mtetXr0aj8fDnDlzQj2cPmMESKEugeptrS0UCxBtroPkl0GSAKkFI4NUUe/A4+lAsBGEf5tv/8tQPb88Hp0H394NwDdnDmXu2DQAXB6dBmcH5jlJBkmIk15to4tLLd65R6POhqT+Xw4vRChZLRq3z89m4YR0xg2Oa/8Oot/q9KfZ2tpaDh06ZH5/9OhRduzYQUpKCpmZmVxxxRVs27aNd955B7fbbc4rSklJITIykokTJ7J48WJuueUWnnzySZxOJ3fccQdXX331gOlgB/5zkMK3xE7XdbO9dPAudipAqvefgyQldi0keecgeXQVcBoZpc4wnkfG38HIIDU6PbjcHmzW7p3rOFJax1d5VdhtFu5dPJ7YSCsWTY25ptHVfmbQyCCVHwaXA2yd/xmFEOGtptHBZVZvgDT92r4djBB95AfnjevrIYgQ6PSnqi1btjBjxgxmzJgBwA9/+ENmzJjBz3/+c/Lz83n77bfJy8tj+vTpZGZmml+ff/65eYwVK1YwYcIEFi5cyNKlS5k7dy5PPfVU6H6qfsDIIFWHcYldncON25vxCD4Hya/Nd5NkkFoTabOY3ee62uq7tkWJnS8QNVpyd4cxP2pIUjTp8VFommYG+R2ah5Q4FCLjweNSQZIQ4qQTn7+BUZYiGrVomLisr4cjhBBd1ulPswsWLGhzTkJH5iukpKTw0ksvdfahw8pA6GJnfDCOtFqCtquMNZs0+M9BkgxSMClxkdQ0uTq28GoQNc1K7Ow2KxFWDadbp67JFTSA7YxK77gSY3zHSYi2UdXg7FiQr2mQNh7yt0DxXl/JnRDipJF9dAUAW5KXMjcyto9HI4QQXSdN2nuI8UE2nEvsfA0abGhBOhEZi8JWNzhpcnkAX+mXCGS0+i6rDU0GCULbqKGyXv2tk/wCLWOx4w6thQS+BWNL9nd7PEKIMFN+hJHlGwDYkXllHw9GCCG6RwKkHpIwALrYtbUGEviyRaV+H/olgxScf6OGrqgx54L5BUiRoWvUUNmgxmXMlwIVGPs/drtSRqvLiqPdHo8QIsx8+TQaOmvc03Amje7r0QghRLdIgNRDjBK72qYOtknuh8wMUpAGDeD7gF5a2wSAzaJht8lTKpj0BDsAOWX1nb6vrut+C8X6/hbmWkhN3Z+DZGaQ/ErsOjUHCSB5lLoslwBJiJOKqwl2qPK6590XmHNwhRAiXMmn2R5ivEG4O9omuR8y5p60Nr/FyBaVeQOkmEhr0FI8ATOGJQOwNae80/dtdHrMZhmBJXbq9x+KDFKFWWLnl0Hq7Dy6FG+AJBkkIQaU4ppGqurbOFFy6BNorKTCOoh1nlMkQBJChD0JkHpIjLdNMoRvmV1bi8SCbw6MsbSPzD9q3WmjUgD46ngVja0EzG6PzueHSlsEPMY8Nk3zNcaA0M5BqjJL7PwzSJ2cR2dkkOpKoKmm22MSQvS9uiYXC//wKcse29B6NcSuVwHYYJ+PB0tAplsIIcKRBEg9RNO0sG/U4FskNnjg03y+kcw/at3IQTGkxkXicHvYlV8VdJ93dhbwrac38fsPAhdbrWnydbDzz9CZJXaOEGSQ6lqW2BmBcYebNEQnQXSy94DHuj0mIUTfO1ZWR02ji9zyeoprmlru0FQL+98H4CPLXADJIAkhwp4ESD3InMMRphmk6nbmIDVfPFQySK3TNI3TRqos0pdHg5fZHS6pA+BQSW3A9bXe50+8PfjvOzRNGowAyb/ErguNRoxGDTIPSYgBobCq0dw+3Oy1CYD974GrAVKy2e4aCQSWAgshRDiSAKkHxYd5J7tqM4PUSoBklwxSZxgB0uZjwQOkCu9iraU1gZ3ujOdPfLNANS6UJXbe7nr+bb4TOtukAXxldjIPSYgB4YRfgHTEexIngLe8jqnfpMbbMCZBAiQhRJiTAKkHGR8wa8M1QGpsJ0CKCAyIYiPlTbEtRoC0NafCbLrgr9wIkGoDy1hqm9TfoflZWaNJQyi62BlNGpL9MkhdCvBTpJOdEANJmxmkujI4vBoAfcrlZja7+ckcIYQINxIg9aBOT3LvZ9pr0mCzWgLaesdIiV2bJmbGExtppabRxf7Clk0MyupUYFRe78Dl9pjXGwFKXA+V2DU63WanxcTuzEECySAJMcC0mUHasxI8LsicRkPiaF+3TXkvEEKEOQmQelD4l9i13eYbAsvqYqXErk02q4WJmQkAHC1tWapiNErQdV82CfA7Kxv4oSNUJXZGCZ1FC5zn1L0M0pFujUkI0T8UVjeY20dKm2WQdr2mLqdcYb5OWDQptxZChD8JkHpQ3EDJILVRLuHfqEGaNLRvUJwqYSuva9kNqswvKCrxK7PzzUFqlkGKDE0GySivS4yOwGLxdcnr9EKx4MsgVeWBy9H2vkKIfu9EpS+DlFfR4FumoCoPcj8HNJhyeUCmW9bDE0KEOwmQelDYd7FrZw4S+ObBgGSQOiIl1g4EBkMAHo9ORb3vutLalhmk1krsuptBqvQ+rv/8I/BNtK5zuANK/toUnwG2aNA9UHW8W+MSQvQtXdcDSux0XbX9BuDr1wFwDD2D1Sds5vuFzD8SQgwEEiD1IOOMfyjaMPc2p9tDvcPbkaiVdZAgMIMkc5DaNyhWBSFltS071fk3biipCZZBaq2LXfeaNBgtvv3nHzV/vA4/hzUNkkeqbWnUIERYq25wmfMTJ2TEA37zkPa+A/D/27vvMKfK7IHj35RJpvdOHXqvIiKKoiigstjWhgqsbS37s9e1l0VddV1XV90V69pXxK6LIB3pSG/DwFCmwPRekvv74+beSaYmmZQZOJ/n4SGk3LxzJ8zk5Jz3HD6rOok/vLuOb3/LAWQGkhDi+CABkh9pbzB9UWK3IbuId1ZktTzJ3MdKnMqqWvtEUDJInomP0ErsXAOkgkYld8dcSuwcXeyaZJDU893eALy4mRbfABazkdAQo2MNXsxCkkYNQnRqOY79R/ERFgalq/sn9x0th/J8OLQWgI9KhgCwYu8xQAIkIcTxQQIkP/Jq0GYLHpq3hSe+2c6G7CKPHqcoCi/+bxfvr9rv0eO0fSdRVjMmY8v15GEhThkkafPdJm0PUuOAqHHAdMwpg6SX2LXUpKHWs9fXvA2HOOvFxezJUzvpFTfT4lujBcclnuxDklbfQhwXtPK61OhQeidFAo6B1rt/AhTqU0ewvVy9fne++vNEOtgJIY4HEiD5kfaLwhcBUr7jDfNhpw2z7sg8WsE/Fu3lsa+3cbi4qu0HOLTV4lvjkkGySgapLQmOPUhNM0iNAiSnDJI2RyvKR3uQ5m04zL6jFXy58TDQcokdeBnkayV2kkESolPTZiClxYTSOykCgL355bDre/X2tIn6fbXiBtmDJIQ4HkiA5Ee+KrGz2xW9DMo5s+COfY7BfooC8x1viN2hNZZoK0By2YMkGaQ2tVRiV9QkQGquzbfr90ILkOpsCjX17u9DOlKiBsrrD6jZyIYSu5YzSB7NQpIMkhDHBT2DFBPK4PQYAPbnHkXJ/AWAzeHjmzxGSuyEEMcDCZD8SJsFoTU78FZZTT3a/n3nzII7nOftfLHhkNt7mLQMUkwrDRrAdd+RZJDappXYFVXWYXdqyqBlkFKjQ4HmmzQ0LrFzPvfuNmpQFEVv27v5UAl1NntDiV1EMxmkMC3I9ySDpA2L3d/wsbIQotPJcVQdpMWE0jUujNToUE5WtmCor4KY7qytSmvymMY/p4QQojOSAMmPtACpqp0BUrFL+2fPAiS9JStq96HfDpW49bjSqrZbfIPrQEDJILVN2+djsysu+3q0jFI/R6co7fusKA3tvxt/L8ymhiYK7pbZlVTV6V2pqups7Mwp0wOk5r7XUd7M8ortDgYT1FdBWa77jxNCdCi5pVoGKQyDwcBJPeOYaNyk3thvMnvymw68bm1unhBCdBYSIPlRhNMm+vZ0n9PewIJr6ZU7tAyS9kb3i/WH3HqcO0NiwbW1t2zObZvFbNS/F86NGrQSu37J6obnwspa6m12ymvq9QxkSrS1yfG0c+5uJ7sjjfawrT9QqAdgsc00aYjWh8V6kEEyhUBMV/Wy7EMSotPSSuzSY9TM9pgecZxh3Kze2Pdc9jgaM/Rx/NwC+T0ghDg+SIDkR2GO7IpdgZp6NwdtNqOoHRkkLUC65pQeAKzdX+jW49wZEguuZV7h0ubbLc3NQtJK7PokR2IwqJVphZW15JWq3++oUHOzGTpPGzXklLg26lifXawHw43bfINzkwYP99HJPiQhOjW1HFf9eZHqCJBOjSuim/EoNUoIRUlj9J9P5w1J1R8ne5CEEMcDCZD8KDykIWBozz4klwySB00aKmvr9V9gp/ZOBFyDrdaUutnFzvlNe4R8cugW50YNh4uryC+r1kvskqKsegB1tKyGfEeJS3JU0+wRQITFwwyS4xNhLfBdlVmgB2fNtfnWvv8eNWmAhllIhfs8e5wQokMoqKilotaGwQBd4sIA6F28CoDV9gF8u1Mt106LCWVk9zj9cZJBEkIcD+QnmR+ZTUYsZiO19XYqa+v1N8aect2DVIuiKBgMLc8m0uw/VglAXHgIGY4WrUUVdW49vsTNPUhaYwajAaxmibfdEe9o9b3vWAX3fbGZKKuZOkfDhvgIC4mRVo6V13KsvJZCRxleiqN5Q2P6LCQ3mzRonwhPGpjClxsP6RnJhAgLyc2U8EV5O8tLb9QgGSQhOqPsQvX3R1p0KFaz4+d85kIAltiHsWTVAUDNevdNaSixkzbfQojjgbyj9bMIHzRqKHLKINXa7HoL7rZo5XU9EyOIc8y4qbXZqXBjLdqek+g2utiFOTIYERazW0GbaCixW7A9j7Lqeo6UVOtd67QACdRsYX5p6wGSFqC6X2KnZpD6JEdyUo94AKYMTuWbP51GaEjTEslob9p8g5TYCdHJZReoAVK3+HD1iroqOLACgMX2Eeo8JKBvchRdYsP0D2va+lBNCCE6A8kg+Vm4xUxRZZ1bQUlLnLudgboPyZ1fQloHu4zECMJCTFjNRmrq7RRV1LZZBuF2BskRAIbJ/iO3aa2+fztU3OQ2NUBylNiVNwRIzWV3oKGs0f0mDWoGKT02lH9dO5rDxVX6fJPmSAZJiBPTAUeA1CPBESDtXwH11diiujJq0MmkOsp/rxrbDYPBwKMXDGLbkRIGODpxCiFEZyYBkp81zELy8A2mk8b7ho6V1dA7KbKFezfQMkgZCREYDAbiIyzklFRTVFnb8KlgC7SMQVtd7AanxzCiWyyn9Ulscz1CpZVaNm5saDEZibSa9Xr/AwWV+vchJaqtEjvPMkhpMWHEhlua7VznTN+DVOVhBimup/p3VRFUFUNYrGePF0IElVZi1yNBLc9m7wIATP0m8ddpI5rc/7Ix3YBuAVqdEEL4lwRIfqYHSG7uEWmOc5MGcL/Vt3OJHahtnHNKGhoCtMbdDFKYxcT8W5tOUxct0zJImi6xYRwuriIuIgSDwUDfZPUT2Mz8chTUKKqlErsYR+nk4eKqZm93Zrcr5OoBUvPHa8zrDJI1EiKSoSJfzSKFjfTs8UKIoMouVH9/6B+m7f1Z/bvPpCCtSAghAkf2IPmZ1uWtsq49AZIa0FgcTRDcafWtKAr7jqo14hmOACk+IsRxvNazAYqiuN3FTnhOa9IA6vf04fMHAg2f1GozRfYeLde7EDY3AwlgvKM74c878rDZW5+1VVBRS63NjsHQ0La3LVFOe5A8nuWld7KTMjshOhu9xC4+XP0/XLAXjGbIOCPIKxNCCP+TAMnPGjJI3pfYFTuClV6OQMedAGlDdhFFlXVEWEz6G26tjXNbGaTymnq099qy4db3Epy6GQ5Oj2bq0DTmzjyJv146DIDeSeospMKKWg4VqW9SWsogjeudQExYCMfKa9uccaXNQEqKtBJicu+/vjYHqc6meD7LS2/UIK2+hehMqmpt5Dsax/RICG/IHnU7BUKjg7gyIYQIDAmQ/CzcsUekPXOQihwBTd8UtfTKnQDpq01HAJg8OFXvTqYFSG3NQtK65FnMxmY7m4n2cW73PqJbLABnD0zRM0hhFhNdYtV9SFqgmtTCHKQQk5FzB6UA8MOWnFaf90ixo7zOcWx3qN0J1cue70OSRg1CdEYHHR/MRIWa1Q/J9qrtvelzdhBXJYQQgSMBkp9pw2K9bdJQ79TWu4+jMcPRstYDnDqbne82q2+WfzciXb8+LsK9AKmk0r0GDcI7zQVIjfVNbmjCERse0mqget7QNAB+2JqLvZUyOy2DlO5meR2A0WggyhHku9teXqeX2O337HFCiKBy7mBnsNVC1lL1Btl/JIQ4QUiA5GfhVi1A8i6D5PymVCuVayuDtGLvMQoqakmIsDDeqbtcvGNDf1FFy5mAzKPl3Pvf3wBIjWk+ayHaJzTERHKUFaMBRveIa/Y+fZwCpJY62GlO7ZNAlNVMflkNG7KLWryfNmuppXK9lkS1exaSlNgJ0ZnoHeziIyBzEdRVQFQapA4N8sqEECIwJEDys4Y2394FSFq2J8pq1gOWtgKkr39Ty+vOH5bmstdEyyC1tAepus7GVf/+lW1HSokLD+HP5w3yas2ibe/MHsP7fxhL17jm261rneyg5RlIGqvZxIR+SQCszmp5H5L2GtSGy7pLa9Th9SyksiPqkEkhRKeQXeDUwW7bl+qVgy4EGQYuhDhBSIDkZ3oXOy9L7LSOczHhISRGNgRIrXUU23q4BICzBiS7XN/WHqRDRVXkldYQbjHxw+0TGNc7was1i7YNTo/htL4tz47q7ZRBSm4jgwQwrKs67FX73jenyhEgaa9JdzW0+vYwgxQeD1bHENqi/Z49VggRNAccGaSMWBPs/F69csjFQVyREEIElgRIfqZlkCq8zCBpLb7jwi16gFRdZ2/1eIWOErrGG/vj29iDlF+mbuJPjw1zuw208A+XErs2MkgAQ7uogciWVgIkrdW8p403tL1opVUeBvkGA8T3VC9Lq28hOg2txG549VqoLYPortDlpCCvSgghAkcCJD/TAqQqrwMkNdiJDQ8hwmomxKSWOLTUUUxRFJegypnepKGi+Zk2+Y6ZO8ktdEwTgRMTFqJ/H9zZMzTYESAdKqqiqKIWRVGot7m25a5yZDG116S7or3NIIF0sutkyryZdyWOKza7wqFCtSS2W85P6pWDLwSjvF0QQpw45Ceen2nlTBVezkHSsj2xjmAnwtr68SpqbdQ7Opk1CZAcTRpqbc1noPJK1QySp5v4hX9oHe6cO9q1JCYsRJ1XAmw8WMTkl5dy7t+WuuxXq9RL7Lzbg+RxkwZw6mQnjRo6ug3ZRYx4cgF/W7A72EsRQZRbWk2tzU6cqYrwrP+pVw6W8johxIlFAiQ/0zNIdd5lkEocmSItuIlwBFzlLQRI2swkq9lIWKM3wmEhJqxmo8v9nGmDASWD1DE8e8kwPrp+rNt7wbQyuxd+2s3uvHL2Havgto826JkkLUAK87DErmEPkhdBvt7JTjJIHd22wyXY7Aq/HWq5TFMc/7IdLb5nRq7BUFcJSQOgy6ggr0oIIQJLAiQ/81kGyfEpfmQbg2e1krzG2SMAg8HQ6j4kLYOULBmkDiE+wsKpfRIxuNk5SguQtueU6tf9uq+QlxwZgeo675o0NOxBakeJnWSQOrzqOjWQrq23t3FPcTzLLqwAFC62O7JHo2dL9zohxAlHAiQ/a+8epCJ9D5JWYqcer8UMkl6S1/yQV+04zbX6lgxS56YFSAAhJgN/Pm8gAF9sOAQ4ZZA8LLFrXwbJUWJXchBs3n1IIAJDy3LX1Hv3s0ocH7ILKxll2EP3uiwwh8Lwy4O9JCGECDgJkPxMC2gqvSyx00rhtMxPW3uQilpo0KCJj1ADJy3T5Cxf9iB1aoOdAqTzhqZx7uAUoCGw8bbErl17kKLSwGQFe70aJIkOqyFAkgzSicJmV3h/1X525jZknQ8UVHKVeZH6j8EXQ1jzw6yFEOJ4JgGSn4Vpc5BqvAuQChsHSG2U7OkldhHNZ5DiWsggKYoiGaROLiYshCFdojEaYPb4DD2Yrqy1YbcrXnexa1cGyWiEuJ7qZSmz69CqJUA64SzdfZRHv9rGQ/O26NeVHz3I74wr1H+c9IcgrUwIIYLLs80IwmMRjjejtTY7dTY7ISbPYtJj5WogkxDpmkEqbyHgatz1rrGWhsWW19TrGYZkN+buiI5p7swxHC2rYUiXGP0NL0BFbb2exfS8zXc79iCBWmZ3bJe0+u7gqqXE7oSzK68MgN155SiKgsFg4Myi/2Ix2KhMHUN4tzFBXqEQQgSHBEh+5rzfo7LWRkyY+wGS3a7ogUxChBq0RGole7VtZJBa2IMU10KTBi17FGU1e7yJX3QcKdGheomk1WzEbDRQb1coKK9FG28T0D1IIJ3sOgltn2RNnWSQThT7j1UA6gdkR8tqsNrKuURZAAYwnn5nkFcnhBDBIyV2fmYxqW9SwfNGDaXVddi0mUaOkrmGDJJ3e5ASHZmoo2U1Ltc3dLCT7NHxwmAw6K+Xo07zkDzuYufYg1RWU6+/Hj0SJwFSZ6B3sbNJgHSi2OcIkAAyj1ZQtfJfRBmqyKQboQOnBnFlQggRXBIg+ZnBYNA/sa9oIevTEq28LirUjNWsHqPtJg2uXe8aS4sJAyCnpNrlei1gkgYNxxetLfwxx/fXYjZiMnrWslfLIEHLgXmrtE52UmLXoelNGiSDdMLY7xQgZefmE/fbvwD4Lvpydf+gEEKcoOQnYABojRU8zSBpjRQSIhqCHW1PU0ULe5CKG81NaiwtRg2AjhRXuVyvZ5CkQcNxJbJRBsnTDnYAVrMJi2PAsFf7kJxL7BQvMlAiIJz3ICnufJ+qS6Cuuu37iQ6poqZeL60GSNz+AdbaIvbZU8lOl+yREOLEJgFSAITrQY1nn74XON7UJkQ2BC1ul9i10MWuS6yaQTpWXuuyiT+vVDJIxyOtzXy+4/vraYMGjdaowat9SDHdwGCE+iooy/Xq+YX/aT8P7ArUt1ZKWVsJP9wPz3aHOV3gzQmw6SOwS+apM8lyyh6FU83JOf8B4B/1F9ElITpYyxJCiA5BAqQACPdyFlJBoxbf0JARaLFJQ0XrJXax4SGEhqjf9lynMjvtk8QkySAdV/Q9SI7vr6cNGjTReqMGLzJIZosaJIGU2XVgVU4/n1ps9V1ZCP86E1a/of7bXg85v8H8m+Htc+Hobv8vVPjE/gI1QAoxGZhl+pEoewkHSONr+6mM6Rkf5NUJIURwSYAUAOEh3s1CarbErpU233U2O2WOzFJLTRoMBgPpjizSkZKGMrs8GRJ7XGpcYudtBinSZ53sZBZSR1XttPeopqUPcxY+qbZsj0yFq7+AO7bCpMfBEgmH1sK/zoCN/5FSyk4g66gaIE3qYeaP5m8AeLH2YsJDrYztJQGSEOLEJgFSAGif2reU9WlJQ4ld0wCpuXK9Esf+EINBHRraknRHo4YjxWpQVG+zc7hIDZZkD9LxJbJRBkkL1j2l7V2q9nZGjnSy6/CcM0jNdrI7tA7Wv6tevvRt6DMJYrvBaXfCbWsh4wyoq4SvboV3L4DcrYFZuPBKliODdJNhHtGGKrbYe/KNfRxnDUj2eF6fEEIcbzz+Kbh06VKmTZtGeno6BoOB+fPnu9w+b948zj33XBISEjAYDGzatKnJMaqrq7n11ltJSEggMjKSSy65hLy8PG+/hg4vQp9d5G2JnfMepJb3M2kNGqJDQ1rtVKY1asgprqKgvIZr5q7hcHEVFpORjKQIj9YoOjZfldhpj/O00YhOOtl1eNVO39smnewUBb67G1Bg+JXQc7zr7dHpcM2XcPZjYA6FA8vh3xNh1w/+X7jwStaxCvobshl25L8AzKm/CgUj5wxKCfLKhBAi+DwOkCoqKhg+fDivvfZai7efdtppPPfccy0e48477+Sbb77h888/Z8mSJRw5coSLL77Y06V0GmFaiZ1Puti13KShqI0hsZqGErtqHpi3hVX7CoiwmPj7FSNIjpISu+OJ3ua7HV3sAELNWgbJy434UmLX4TlnB5vsQTqwEnI2QUgEnPNk8wcwmuD0u9RsUp9zwFYLn14N277036JFA1u92gSlpsytu9cc3c97lucwKnVsiziFlfYhWExGzuiX5OeFCiFEx+dxvc3UqVOZOrXlFqDXXHMNAPv372/29pKSEubOnctHH33EWWedBcA777zDwIED+fXXXznllFM8XVKH15BB8rTEzhEgRTZt0lBTb6feZsfsVApR5AioWmrQoEmPVYOgAwUVbMguAuD9605mdA+pOz/eaBkkrSuZt3uQtAxSdXszSFJi1yHV2+zU2Rr2DdU0LqXc8J7699BLITK59YPFdocrP4H5f4Qtn8O8GyG2B3QZ5eNVC0D90OGr29QgFsf3MCIJuo2F/lOh72SIdAp66mup2PgZb9geJ9VYhD2xPyv6PAOL8xjXO4Go0NY/YBNCiBOBdxsS2mH9+vXU1dUxadIk/boBAwbQvXt3Vq1a1WyAVFNTQ01Nw7yG0tLSgKzVVxr2ILW/i532hhegotZGTFhDgFTsYQZpTVYh9XaFhAgLo7rHebQ20TlEhrr+F/e2xC7UkXmq8rAToy6up/p3dbHaCS1cgvGOpHFm0CWDVFUE279SL4+a6d4BTWa46E21Jfiu7+CzmXDTEvm++9r2r2H+LVCrZY0MgAIVR2Hnt+ofDJA6VD33NeWQt5WI+moijJBDImnXzOOykGSO1O7h2nE9gvjFCCFExxHwACk3NxeLxUJsbKzL9SkpKeTmNj8jZc6cOTzxxBMBWJ1/aGVxngRIdruizzRKcNqDZDEbsZiM1NrsVNTUuzRj0GcgtZFBSnM0adCyCidnxGMwtLxnSXRekVbXgMjrDFJ7AyRLhNr5rDxXzSLJG+UOpfHeMpc9SJs/h/pqSBniWRbIaIKLXlfbghfug29uh8s/8M2CT3Dbj5RyeMErTMp6AQMKdDsFpr+mlrLWVsDRXbD3Z9j9g9qGPXezy+NrwlL4e+mZrIqfzpcxXYkFHv/d4KB8LUII0REFPEDyxoMPPshdd92l/7u0tJRu3boFcUWeCfeii11JVR02RwDjnEECtWSvttLepFGDtgfJ3RI7zdgMebN6vNKCc02YxcsudhY1U+l1kwZQy+zKc9VGDV1He38c4XPVjQLfWpvTvzc6gppR16otMj0RGgO/f09t/73jazi0Xr737VFyGHZ8g23pF5xTuUa9bsz1MOU5NWsHEBoN3caofyY+CKVH4PB6NZtnCoHUYfx0KJR/fvIbYyPlZ78QQjQn4AFSamoqtbW1FBcXu2SR8vLySE1NbfYxVqsVq7Xztp8Ot2itud1/c6mV10WFmrGYXXtphFvMFFXWNWnUUKxnkFovsQu3mIkND9FL8sb2SnB7XaJzibS6/hdvbwap8Rtpj8RnQPZKadTQATX+vuoZpOJsNftgMMKQS707eNowtfPdpg9h0ZNw7VftXO0JqCBT7SK4bzGgMNRx9doeNzLmvOdbD1yj09U/Tgp3qXsBnfe3CiGEaBDwYQejR48mJCSEhQsX6tft2rWL7Oxsxo0bF+jlBIQ3TRq0DnaJkU0DQ+1Nb+OSPe0xcRFt/9LTZiHFhofQPyXK7XWJzqXJHiRvu9i1t8QOnDrZSaOGjqbx91Xfg7TrR/XvbqdARDs+SDnjfjCGqG/ws5Z6f5wT0aF1MPcc2PcLoED3cfw7dDbn1DzPwtTrPM/q0fC7onF1ghBCCJXHGaTy8nL27t2r/zsrK4tNmzYRHx9P9+7dKSwsJDs7myNHjgBq8ANq5ig1NZWYmBiuu+467rrrLuLj44mOjuZPf/oT48aNOy472EFDmVOFB+VJ2pDY5n6BaQFX4wyS1sq5uaCqsfTYULbnlHJyz3iMrcxMEp1bhNU3TRr0LnbtCpC0TnaSQepoqusaN2lwfJ93O+YY9W+5c6lb4nrA6Fmw9t+w+DnImNC+450oNn8GX/9J3QOWNhx+/y7E9+KdOQs5olRzkmM4uKeam7EnhBCigccZpHXr1jFy5EhGjhwJwF133cXIkSN59NFHAfj6668ZOXIk559/PgBXXHEFI0eO5I033tCP8be//Y0LLriASy65hAkTJpCamsq8efN88fV0SOGtDHdtSXMd7DTam97GxyvQs05tfyo4pqdae37+sDS31yQ6H1+V2GlzkKoaDxD1RJzMQuqoms0gVZdC1jL1ivYGSACn3almkQ4sh+xf23+841nxQfjmDph3gxoc9Z0Ms77XP2QorVZ/9pd6GSA1N2NPCCFEA48zSGeeeSaKorR4+6xZs5g1a1arxwgNDeW1115rcdjs8UYviXMzQLLbFVZnFQLN/wLTjpd5tJxzXlrCFSd357rTMpzmJrX9qeB1p2Vw3tA0usWHu7Um0Tk1ziAFbQ4SNJTYVeSr7Yatkd4fS/hU4+YbtfV2yFwE9jpI6AOJfdv/JDFdYMSVsOF9WPYSzPis/cc83hzbA0ueg63zQHF8TybcC2c+qHYFBGx2Ra8eKGl3BkkCJCGEaE7A9yCdiLQmDY1L4ppjsyvc8/lvfPObWqJ47uCUFo/35YbD7Mkv57/rD1FdZ9OP787GW7PJKMHRCSA8xOSyRSEsxMsudr7YgxQWB2GOrllFsg+pI2k8GLam3g67HfuP+k3x3RONv0Nt+LDnJ8jZ3ObdjxvlR9XMaXMfLhYfhDX/hv9cAq+drA7XVWxqGeI18+Gsh/XgCKC8uuH3iLcBUpEESEII0apO0ea7s3NuqqAoSqszhxbtzGfexsOYjAZe+P0wzhrQNEDSZtscKakGIK+0Wt9/ZDEbibLKt1WojEYDERazHjx7XWLniwAJ1CzS4UL1zWLq0LbvLwKi6RwkW0N5XZ9JzTzCSwm9YfDFsPW/sPwldU/N8ezASlj6gtpgQbGrZaZdRqutuCsL4OhuOLrD9TH9pqrtudOGN3vI0uqGoMjbAEmaNAghROvknXQAaHuQ6u0KNfV2/c1mc/bkqxPRfzc8nYtGdm32Po3LpgorajlSrAZLiREWGfoqXERYTXqA1N4mDe2agwTqHorD66WTXQfTOPC1VhyG0kNgNEO3k337ZKfdqQZI2+bDxL2Q2Me3x+8o1s6F7+9tKJUzhqiZ08bZU4MRuo2FfpOh/3mQ1L/Vw5a1M4PkOoRcAiQhhGiOBEgB4Dyss7LW1mqAdKS4CoAusWEtH6+ZDNH2IyWAe/uPxIlFfb2oGUZv23z7ZA4SSKOGDqpxF7u04o2OC8PBEuHbJ0sdomZJdv8AK/4G04+zvaiKAgufVDNkAEMugYl/hsgUtc158QGoLlFLTuN6QteTPWqh7pxBKq2uw25XPOpEWlxVh2MGuVsjIYQQ4kQkAVIAmIwGQkOMVNfZqaipb7WsQcsEpbcSIDXuTAaw9UgpIIP/RFPOJZftHRTb7hI7bbP/0V3tO47wqcbf1y5lm9QL3f00m+70u9UA6bdP1AYEMc1nyzulJc81BEdnPQyn39Mwq2jgBe0+vHMGSVGgrKaemLDWh4M7K6xQPyyJDjUTYpJtyEII0Rz56RggDbOQWm/UoGWQ0mNDWz5WcwHSYTWD5M4MJHFiiXAJkLz7TCTUov6oqK6ztdrFsk3avqO8rWBvR8tw4VM1jgBJex/fs3yTeqHHeP88Ybcx0PN0sNerpWjHi7VzYfEc9fLkv6gd6Hxc8ty4tbenrb496XYqhBAnKgmQAqSl2UV2u+ubTbdK7JrJAuzJLwckgySa0l57BgOEhnj3X14rC7UrUGtrR2CT0BdMVqgtl052HYiWQYqymomnlJTabPWG7n4c3n3yjerfG96H+hr/PU+gHFoHP9yvXj7zIRh3q1+epqzaNSDydB+SNGgQQoi2SYAUIFppU0VNQynLvZ//xvjnFum/4Mqq6/QBgGlu7kFKiVY/BbQ5Aq1EmYwuGtFKMsNCTF438HDeu1Rd244AyWSGlEHq5dwt3h9H+JTWfCMmPIQxRkf5Y9JACI/335P2Pw+iu0DlMdj+lf+eJxAqC+Gza9W5UQOnwRn3+e2pSqtdP2TzNECSGUhCCNE2CZACJLKZDNIvu46SU1LNjhx1/1COo213dKi52X1GGucAqXEbcMkgicacAyRvhZiMmB0bwdu9D0krs5MAqcOorleD3tgwCydpAVIPP+0/0pjMMHq2ennNv/37XP62eA6UHob43jD9nz4vq3PWOINUXOllBilcflcIIURLJEAKkHAtQHJqk1xeo/5i037BNew/ajl7BBATph7LajYyvo9r9yPZgyQa0wJqb1t8a3zWqCF1mPp37gk0KLSD0zJIseEhDDM6Ogx2HeP/Jx51rdr++tAayPnN/8/nD/k7G/ZRTXtZnXHkR2XtzCDpAZJ8mCaEEC2SAClAtOGuWgapzmbXW+sWO2ZSuNPBDqB3UiSzTu3JY9MG0zUu3OU2ySCJxrTXnrcd7DShvpqFJBmkDkdr3x4TamSwYb96ZfpI/z9xVAoM+p16ee1b/n8+f/jfn9VZRwMugIwJfn+6Uh/tQZIZSEII0TIJkAIkvFEXO+dSu+KqxhmkljvYARgMBh7/3WCuGtudtBjX+0oGSTTWkEFqX1d/n2WQUgarf5flQPnR9h1L+IQWIPUx5hJpqKYaKyT2C8yTj7lB/Xvz51BVHJjn9JU9C2Dvz2oW7JwnfXron7fnsWhnXpPrtQxSXLja2luaNAghhO9JgBQg2j6QSkeTBucyiSI9g+ReiZ2zxEgrJqchgfJLTzSmvfbC27EHCRoCpJr2BkjWKIjvpV7OkyxSR6AFvX3q9wCw29QbjO17vbit+ymQPBjqq2DTR4F5Tl+w1cFPf1Yvj70JEnr77NDlNfXc/OF6rntvHQcLK11u09p6d4tXqwekSYMQQvieBEgBopU3lTsyR+VOGaQSbQ9SSdstvhszGQ0kR6lZo9jwEBn8J5o4vW8Sw7vGcOno9g3j1Evs2hsgQcM+pCOb2n8s0W5aBqlHzW4AduC7N/ttMhjg5OvVy2vf6jzzsda9A8d2QXiCOu/Ih/JKq6mzKSgKfL7+kMtt2odrXePU3xOezkHSBsUmSMdTIYRokbybDhCtzKmytmmAVNRoD1JajPsBEkBKtFpmJzXlojmpMaF8ddtpXNLeAMms/rjwSYCkzdfZ+3P7jyXaTdsPmVaxA4AtSq/ALmDoZWCNgcJM2LsgsM/tjcpCWPwX9fLEP0NYrE8Pf7SsYS7Uf9cd1Mc4QEObb23/qScZpPKaeqdBsfL7QgghWiIBUoBENJqDVO5SYleH3a6QU+LeHqTGtH1IMhld+FOYr5o0gDoDByB7FVQca//xRLtU1dkwYSOudCcAv9kzArsAaySMvla9/Os/A/vc3ljyPFQVQfIgGDXT54d3DpCOlFSzbE/DXj2tSUM3RwbJkwDpx6251NsVeiVGNNm/KoQQooEESAESYXVt0tC4xO5YRQ11NgWjoSEj5C7t/onyiaDwI20PUrUvMkhxPSBtOCh22PV9+48n2qW6zkYfw2FMtmrKlDB21ycHfhEn3wgGI+xbDHnbAv/87jq2B9Y65jZNfkad5+Trpyivcfn3Z+sOAur3qdYxs8qbDNIXjnK9i0d18XpotBBCnAgkQAqQiEaDYhuX2GnldSnRoR7vIxraJQaAgan+nb8hTmw+62KnGTBN/XvHt745nvCKoihU1dkYYcwEYJvSk5p69fqAiu0OAx0tvztqFklR4If7wF4P/aZA77P88jRaBkn72b7+QBHQsP/IYGho5uNugHSoqJJV+woAuHBkF5+uVwghjjcSIAVIQ4DUtMSuuKqOw0VqeZ03ZQ8Xj+rCz3dN4JaJfXywUiGa1zAHyUeb6AdeoP697xeoKfPNMYXHaurtKAqMN24FYLV9AAC1tiA0SzjlFvXvzZ93zBbwa/4NmYvAHArnPuO3p9EDpK5qgFRYUYuiKJQ5yusiLWbiItQ236XVaol2W77ccBiAcb0SmszPE0II4UoCpADR9yA5SuzKnDJItfV29h0tBzxr8a0xGAz0SY5yafcthK/pJXb1PsogJQ2A+N5gq4Wt83xzTOGxmjo7Bux6gLTcpg7yrakPQoDU7WToMhpsNbDu7cA/f2vyd8CCR9TL5zwJif77QEorseufEgVAnU2hvKZeb9AQHRZCTJgaICmK6++Txspr6nl4/hb+9rPaofDiUZI9EkKItkiAFCCtZZAAth0pBTxr8S1EIOkldr5o0gBqndBJs9XLS/8KddW+Oa7wSFWdjYGGbBIMZSiWSDYq6hv/mrogBEgGQ0MWae1bUF/T+v0DpaIAPrkK6quhzyR1v5QfHXUESN3iw/T/d4UVtXoGKSrUjNVsIjRE/RXeWqvvv/64k//8mo1dgekj0qW8Tggh3CABUoBEWBrvQXL9hbY9Rw2QpLOQ6Ki0LnY+adKgGXM9RKVDycGOlzE4QVTX2TjNqA7sNfQYj8msNnup8VWm0FODpquviYp82PpFcNbgrK4KPr4CCvep+6QufF0N5PxIK7FLigzVB7qqAZL6+yMqVP19omWRiitbDpBWZxUCMOfiofz9ipEyK08IIdwgPykDJNzasMHdZldcmjQAZDumpXtTYidEIFh9OQdJExIGZz6gXl72AhQd8N2xhVuq6myc5iivo/dE/fsclBI7AFMInHyDevnX19UasmCpr4HProVDayA0Bmb8FyL92+HPblf0WUVJUVaXAEnLFEWHqoFRXLh6mzZLr7E6m51MR/n2aX0S/bpuIYQ4nkiAFCCR1oZWsJW19fongY1JgCQ6Kp/OQXI2Yoa6H6myAN46Gw6u9e3xRauqqyo42ajOP6LXRKyOkq7aYAVIoM4WMlkhdzMcXBOcNdjq4LOZsOd/YA6DKz6GpP5+f9riqjrqHU0XEiItxLWSQdKGvRZUNF+KmHWsgjqbQqTVTNc4+d0ihBDukgApQKxmI1oPhcpam55BMjdqrCB7kERH5fM23xqTGa6eBylDoeIozJ0EH14GWUuDmz04QdTtW0GooY4CYwIk9Q9+BgkgIgGG/l69vObN4Kxh0dOw+we1Y91Vn0DP8QF5Wq28Li48hBCTkQTnDJK+B0nNICVEqMPBtYxTYztz1e6Q/VIiZe6REEJ4QAKkADEYDHqjhvKaen0vUlpsw56j0BAjseEhQVmfEG3x6aDYxmK6wB9+gCGXAgbY8xO8Nw3enAA7ZZCsP1mzFgCwO/oUMBiwaAGSP77PnhjraISw/SsozQnsc2cthRV/Vy9f/C/odWbAnlrffxSlBj9aGV1hZS35peptWuaoIYPUfIC0K1fd29pfZuQJIYRHJEAKIK1RQ2WNTe9i1zW2YR5FemyYfMonOqxQvUmDnzIL1ii4dC7ctk5t3mAOU0usPrkSProcynL987wnMkWha/4SAI6lTQTAala/z0HNIAGkDYdup6hDWX99LXDPW1UM824CFLXUb9D0wD03DS2+EyPVAEkLggrLazlcrM7L0yoNtPsUlDdfYrfLkUEakBrlvwULIcRxSAKkAIpwNGoor6nX51Z0i28oqZPyOtGR+a3ErrHEPnD+i3DXdhh/BxhDYPeP8O75UJbn3+c+0RzbTUJdDjVKCIbeZwB0jBI7zel3qX+v/lfgGngsehrKjqgzuqbMCcxzOmkpg1RUWcsRLUBy7CfSyu/aKrHrLwGSEEJ4RAKkAGqYhVSv70FynmguLb5FR+bzOUhtCY+Hc56APy6HmG5QsBfe/x1UHAvM858Idv8IwCr7ILqnqt3ZGgKkIJfYAfQ9FzImqINjFz3l/+c7slGdvwRwwd/AEuH/53RYsvsod322iR2OsjgtO6R1sTvmlEHSKg8SHPc51kyJXXlNPYeK1PtLBkkIITwjAVIAaSV2R8tr9L3nzhkk6WAnOjK/zEFyR/IAmPmNOhvn6E74+Ep1No1oN9tONUBaaB9Jz0Q1GOgQXew0BgOc4wiMtnzu3w6Hdjt8exegqA0iep3hv+dqxt9/3s28DYeZt+Ew0JBB0gKkzPxyaurtGAyQ6vgwTd+D1EyJnVZelxJtJdaRhRJCCOEeCZACSCuxyyutBsBkNJAaLQGS6BwCVmLXnPgMuPYrCI1VZ9LMuxHsHSDD0ZlVFmI8tBqAjdaT9dk6HarEDiB9BAy/Sr389Z/U2UT+sONrOLIBLJFw7tNuP+zLjYdYtudou59+37EKl38nNcogaWXZKVGheiONxFa62G09XAJIgwYhhPCGBEgBpJXY5Tk6EUVazcRFNHStkz1IoiOzhjQMilWC0X47qR9c8RGYLOqb2c9n+e/N8okgcxEGxcZOezcsiT31qztMFztnk5+BiCQ4ugOWvej749tt8Mtf1MvjboWoVLcedqCggjs//Y1bP9zQrv8TxZW1FFfWuVyX2CiDpOniNM9IyyBV1dmorG2YrZd5tJwX/rcLgJN7xnm9LiGEOFFJgBRA4RYtQFIzSJFWs74BF2QPkujYtAySogQxu9BzPFz6dkOQ9OHvoaYsOGvp7Bz7jxY5lddBB8wggbof7by/qpeXvQiH1/v2+Js/g2O71AzluFvdf9ghNUtTWl1PaQvDv92R5cgexYWHEB9hwWCAjAT1exITFoLzuDznD9LCLSZCHR9cFJTX8vm6gzz21VZmvr2Gsup6RveI44YJvbxelxBCnKgkQAogrePQ7jz1DZ0WIEWHmomymqXETnRooY4ACaDGX62+3TFwGsz4XC2FylqizkuqKAjeejojWz3sUecfLbSN1N+MQwdq893YoAvVP/Z6+Hw2VJf45rj1tbDY0a3utDsgNMbth27PKdUv5zs++PKGFiD1S4niy1tO5ZMbTqF7gtqIwWQ0uOwhcs4gGQwGfVjsysxj3Pvfzby36gCHiqroEhvGm9eM1r+fQggh3CcBUgAN6aL+4tU6C0WGmrGYjXxx86l8ccupLm9AhehoQkxGQkzqR9nltd5/Wu4Tvc6EmV9DWLzaeezfE/27gf94c3A1VBdTaohmo9K32QxSwJtxtMVggGl/h9juUHwAvrpNbazQXpv+ox4vIhlOvrHNu+eXVbP+QCEA2480BEha6bQ39jsCpF5JEfRIiGBsrwSX253L7Bp/kKaV2f2yU90H1SsxgkcuGMSXt56qd8ITQgjhGQmQAmhk91iXf0c69iT1TYmiX4q0YRUdX6/ESADW7S8M8kqALqPhDz81vGF+e7JaftXcXpCKAija3/xtJyJHed1SRmDHSIZTgJQSrZb67jta0exDgyosFi59F4xmtcTyl2fad7y6aljiKN07/W632nr/38cbueT1VazKLGCbS4DUjgxSQSUAPROaf/54pwxS18YBkiN4WpGptr8/s38y152WQXKUlGwLIYS3JEAKoJToUJd9RpGh5iCuRgjPnTVQnZWzcEd+kFfikNQPbloGQy4FxQYLn4RPr4Z9iyFrKSx8Ct48A/7aC/4+HJ7tDt/cIc0ddv8EwA81IwDokdAwj+3kDHVT/9r9hcFpxtGWrqPVTBLAshdgw/veH2vdXHUobHRXOGl2m3evrK1n7f4iAP69bB/HnNpr55V5HyBpGSTnTJ4z5wySc4kdNMxCKnPsgRqYJh+2CSFEe0mAFGDDu8bql6OsEiCJzmWSI0BavCufOlsH2aMSFguXzoVpr6jNG3Z+C+9PV/cmLXsBcjap9zNZoKYU1r+jzlKqrQzmqoOncB8c24ViNLPMPpSYsBCiQhu6aQ7tEovVbKSgopbMjphFAhh5NUy4T7387V3elVfWlMOyl9TLZ9wL5rbL0X47WILNrgaNi3a6fkiQV+JdgKQoir4HqVcLAVKcc4DUQomdZlC6tPUWQoj2kgApwEY4ldlFSoAkOpkR3eKIj7BQWl3POscn6R3G6Jkw+0foNwWSBkBMNzWzdOEbcPdueOiI2iY8JBwyF8Jn1/pmD0tns/t/ABQnnkQpEU26Z1rMRr0ceE1WByilbMnEh2Dg78Bep34vyz2cRbT6dag8BnEZMGKGWw/ZkN30NW9wdJjzdA/Sgu15TPvHcv69bB/lNfUYDNAtPrzZ+2pldLHhIfq4CI02CwnAbDTQJznSo3UIIYRoSgKkABvRLVa/LCV2orMxGQ2c2T8JgIU78oK8mmZ0HQ1XfQq3roY7t6qZpRFXQlQKmEJgwPlwzZdgDoW9C2Dtv4O94sBz7D/Kij8NaH5A9ckZapOANVkduDugwQAX/hMS+6llcvNvdn+PWVURrPiHenniQ+prww3rD6gBkvOHWyMdP9PdLbErrqzluR93csP769hyuIS/fL8TgPSYsBYb9WgZpOZm5TlnkPokR0rXOiGE8AEJkAJsaJcYfaaFZJBEZzRpYArQtMSo0+h+CpzzpHp5waOQvzO46wmkmjLYvxyATWFjgebnr53cMx5A32/TYVmj4LIPwGRVA97Nn7r3uF/mQE0JJA2EIZe49RC7XdEzSDef2Vu/fmJ/tew0v40MkqIoPDx/Cyc/s5DXF2cCrue+V1LLDSIGpqr7ioY7fcCmSXDqVDcwTcrrhBDCFyRACrAIq1nvWBclGSTRCY3uoW7i319Q0TE38btjzA3Q+2yor4Z5N6izcE4AW5bNB3sd9rhebKtRA93mMkijesRiNho4XFzF1sM+mjfkL8kD4Mz71cs/3A9lbWQ29y6ENW+ql899GozuZVz2HauguLIOq9nIdadl0DMhnNjwECYNUs9jflk1dsf+pJKqOrYcKnH5/5FfVsN/fs2m1mZnYFo0r141ki9uPlX/oKylDnYAp/ZJ5Jd7zuTxaYOb3JbgtD9JGjQIIYRvSIAUBDec3ovh3WI5rW9SsJcihMe0N3R2BaqDOTC2PYxGmP4ahMVB7uaGQaHHuaPrvgJgT+xp5JSo89iayyCFW8z63LYL/rGcKS8vpbwmyLOvWnPq/0HqMKguhm/+r+VSu4oCmH+LennMDdB3kttPoWWPhneNJTTExJe3jOd/d07Q9/zU2RQOF1dx16ebOPmZn5n26nLmbzqsP764sg6AuPAQfrj9dC4Ylk56bBjPXTKMHgnhTBue3urzZyRGYDE3/ZWdKBkkIYTwOQmQguCS0V356tbxzdaTC9HRhVtM+sb0Dv2muS3RaQ3tole8DNm/BnU5fldfw5jqlQD8GjKWHEfXtbSY5n8O3Tu5P8O7xWIyGtiZW6bvv+mQTCFw4etqp8LdP6rtuxuz2+CL66A8V923pJVZummjI0Aa5cigxkVYSI4KJcRkJNGxD+jxr7cxb+NhaurVDw42HCjWH19SpQZIMWGu+53OH5bGknsncnJGvEfr0cRHWLCajZiMBgZJgCSEED4hAZIQwiMGg4EIi5pFqujMARLAoOkw/CpQ7PDVrVBXFewV+Y2y539EUUGOEs+Cil4cKVa/1vTY5geKju+TyFe3juesAeoemwMFHbTltyZ1CEx6Qr3805/hwCrX2395Bvb9onYx/P27YGm+Y1xLduSUATCkS9MgRBuuu2iXui9vTE81iDpY1NBKvrSFAKm9LGYjr189in/OGOWyH0kIIYT3JEASQnhMK7Pr1BkkzZQ5EJkKBXth8bPBXo3f1G/6DICvbePYcLBUz3KkNlNi56ynY4js/mOdYG7U2D9Cn0nq3rL3fwfr34WqYjVgWvaiep/f/QNSmu7laY2iKOzNLwfQ95A60wIkRVE7Pc4enwHAwUKnAKlaDZCifRwgAZw1IIXJg1N9flwhhDhRSYAkhPBYhFXd2H5cBEhhsXCBY2Doyn/AofVBXY5fVJdg2vsTAF/ZxlNZawMgMdLSZlvoHo7mAfs7egYJ1L1ll70PAy4AWy18czs81wNWvarefuaDMPRSjw+bU1JNeU09ZqOh2WYKKdENmZuTesQxJF3dv3WoqEpv1KCV2EWH+j5AEkII4VsSIAkhPKZlkDp9iZ1mwPlqu2fFpna1qykP9op8a8c3GG017LF3YbvSQ7+6rewRqM0BoJMESACWCLX198SHIaa74zpHO/AzH/DqkHsc2aOeLTRKSI5qOI/nDEohLTYUowFq6u0cLVPbf5dWqf9X/JFBEkII4VsSIAkhPBZxPJXYac57AaK7QGEm/PRgsFfjW5vV8rr5tvGAQb+6pQYNzno4SuwOFlZis7fe1v2VhXuY9o/lerYkaIxGOONeuHML3LUT7t0Dg37n9eH25Kn7j/o6OtY15hxonj0whRCTUT+32j6khhI7Ge8ghBAdnQRIQgiPRegZJFuQV+JD4fFw0RuAATa8Dyv+HuwV+UZpDmQtBeAr+6kuN6W7kUFKiwnDYjJSZ1P0xg7NURSFucuz2HK4hPUHCtu3Zl+KToOQ9nUM3ZOnZpD6NrP/CBqCyH4pkXrGrVu8+pzZjn1IpVJiJ4QQnYYESEIIj0XpGSTvMgU5JVW8/PNuvfyow8iYAJMeUy8veBRWvxnc9fjCtnmAwsHIoRxSkkmOatgvk+bGqAGT0aC/2T9Q0HKjhoOFVXrmKL+0g31f22l3vppB6pfSfAZpXK8Enr90GP+cMUq/rluclnlTg8qW2nwLIYToeCRAEkJ4rKHEzrsM0tvLs3j55z28v2q/D1flI6fdCaffo17+4T5Y8nzLg0c7A0d53eqIswA4rW+iflNzQ2KbozUmyGplH9KWwyX65Q4X+DZSXlPPlxsP6WVvrVEUhb1aBim5+QySwWDgspO60cfp9m7xDaWJ4N8udkIIIXzL4wBp6dKlTJs2jfT0dAwGA/Pnz3e5XVEUHn30UdLS0ggLC2PSpEns2bPH5T6FhYXMmDGD6OhoYmNjue666ygvP842RQtxHItoZ5OGfMcb6H3HOujG/7MebgiSfnkG/vdwcNfjrWN7IGcTGEwsMKjldaf0SsBsVPchpbs5rLqno2zsQCvfL+cAKb+DB0jvrdzPnZ/+xptLMtu8b25pNWU19ZiMBr18zh1a1k3fg6Q1aQiVPUhCCNHReRwgVVRUMHz4cF577bVmb3/++ed55ZVXeOONN1i9ejURERFMnjyZ6upq/T4zZsxg27ZtLFiwgG+//ZalS5dy4403ev9VCCECKtLR5tvbAKm4Uv00/VBRBx3MajDA2Y/A1OfVf696Fda/F9w1eWPrPPXv3meRWam+Ye8SG8b0EV3olRTB4PSmQ0+bo89CaqXEbqtLgFTd4v06gn1H1UBv25HSNu+725E96pkQ3mwHu5ZIiZ0QQnReHn+UNXXqVKZOndrsbYqi8PLLL/Pwww8zffp0AN5//31SUlKYP38+V1xxBTt27ODHH39k7dq1nHTSSQD84x//4LzzzuOFF14gPT29HV+OECIQ2tvFTnuzeKiwgw8fHXsTVJfCL0/Dd3dD0gDoPjbYq3Lfjm/UvwdfyNFMNauTHGXlxcuGoygKBoOhlQc30GYhHWihxE5RlE6VQdICOC1Qao3Wwa65AbGt6e4oscspqaLOZpcSOyGE6ER8ugcpKyuL3NxcJk2apF8XExPD2LFjWbVqFQCrVq0iNjZWD44AJk2ahNFoZPXq1c0et6amhtLSUpc/QojgifRRgFRQUUtlbQdvFT7hHhg0Hex18OWNUNvBgzpN0X7I2wIGI9W9ztXPuTazx93gCBr2IB0orMTeTKtv5wYN0LH2ID04bwsPztuiD2yFhiYSh4oqqalvfR9dTokaTGl7ityVFGXFajZiV9RMqfZ/RbrYCSFEx+fTACk3NxeAlJQUl+tTUlL023Jzc0lOTna53Ww2Ex8fr9+nsTlz5hATE6P/6datmy+XLYTwUHsHxRZX1uqXD3fUMjuNwQDTX4PormrQ8cszwV6Re3Z8q/7dYzxHbWr3NYvZ6NUcnvTYUCwmI7X1dg430+pbyx4lRFgANYOkdIDGFsWVtXy8JpuP12STebRhn2ueI4NkV1rvzAcN2TDn7n/uMBgMdI1Tyxp35JTqfT5kDpIQQnR8naKL3YMPPkhJSYn+5+DBg8FekhAntPZ0sbPbFZdsg7aJvUOzRsEFf1Mv//pPOLw+uOtxh1ZeN3AaR8vVN/lJkVaPMkcas8lIz0Q1g7I337Whjs2usHhXPgBn9E8CoLberjclCKbCioZAfNmeYwDU1Nv0PXAA+4623iAov1QNppI8DJCgocxOCyBDQ4xYzSaPjyOEECKwfBogpaamApCXl+dyfV5enn5bamoq+fn5LrfX19dTWFio36cxq9VKdHS0yx8hRPC0p4tdWU09zlVaHbZRQ2P9zoWhvwfFDos6eBapPB8OOkqWB5yvl5QlR3v+Jl+jtbh2DpDyS6s5/5VlfL7+EABn9k/Wu7R1hEYNRU6B0Iq9aoDUeEZTZhv7kI7qGST3WqI70/ZubTmkBkhSXieEEJ2DTwOkjIwMUlNTWbhwoX5daWkpq1evZty4cQCMGzeO4uJi1q9v+AR20aJF2O12xo7tRJufhTiBtafErqTSdfbMwY7eqMHZxD+DwQiZC+HIpmCvpmU7vwMUSB8FMV056ghWkiK9D5B6J6tles4B0tzlWezMLSMq1MyfzxvItGFpJEergURH2IfkXMr5675C6mz2JoFbW40a9ADJi+BS6/63+VAxIB3shBCis/A4QCovL2fTpk1s2rQJUBszbNq0iezsbAwGA3fccQdPP/00X3/9NVu2bOHaa68lPT2dCy+8EICBAwcyZcoUbrjhBtasWcOKFSu47bbbuOKKK6SDnRCdRISjzXeZFwFScVWty787TQYJID4DhlyiXl7xclCX0iq9vO4CoH1v8jV9tADJqSRNC5bumzKAGyb0wmAw6Ht1OkInO+cSu/Kaen47WExeowzSvmMtl9hV1dr017g3JXZaBqm02tGgQQIkIYToFDwOkNatW8fIkSMZOXIkAHfddRcjR47k0UcfBeC+++7jT3/6EzfeeCNjxoyhvLycH3/8kdDQhvKEDz/8kAEDBnD22Wdz3nnncdppp/Gvf/3LR1+SEMLfoqzqG73aejt1NrtHjy1ulEHqVAESwGl3qn9vmw8FbQ8aDbjqEshaql4e+DugoRObN2Vimj5JDRkkrQGDNui3t9MA1SQ9QAp+iV3j19ryvcf0PUW9HGvOdPp6GtO+htAQI1FWz5sr9Ehw7XwnQ2KFEKJz8Pin9ZlnntlqdyKDwcCTTz7Jk08+2eJ94uPj+eijjzx9aiFEB6FlkEAts4sNt7j92GJHg4a48BCKKus4WFTJnO93sO1IKW/NPInQkA6+iT1lMPQ9F/b8D9a9DZM72H6k3f9TW5In9ofEvgDsd8wvavyG3RO9kiIwGtQW7cfKa4kNDyHbUR6ZkdQQIOkZpNIOkEFylNhFh5opra5n+Z5jjMmIB2Bsr3iyCioora6noKKWxGbKD/Od9h9509yia1w4RgP6njvJIAkhROfQKbrYCSE6FrPJiNWs/vjwdBZSieNN65AuMYD6Kf+bS/exfO8x1mQV+nah/jLmevXvTR9CXfAzJS52fK3+7SivA9jvaGWd4ZTp8VRoiEmfBbQnv4zswkpsdoVwi4nU6IbMlJal0jrnBZO2B+ncwWoDoN8OFZPtOBfd4sNJj1HbcL+xOJPvNuc0+fBPb27hRXkdqG3VuzhafYPsQRJCiM5CAiQhhFcaGjV41upbK3tKjwkjLtz1DWNmGy2XO4w+k9S5SFVFDQFJR1BXBXt/Vi8PnAaoAay2B0nbE+MtrcwuM79cb26QkRjhkl3R9jl1hAxSUYX6WhveNYbkKCt1NoWlu48CkBIVqu+remt5Frd+tIGftrl2YNWbW3gZIAH0iG8459LFTgghOgcJkIQQXmmYhVTXxj1daTOQYsND6BrnWvLVeMZOh2U0wahr1cvr3w3qUlxk/gJ1lRDTDdJGAHDAUV4XH2Fpdwajj1MnuyxHc4PGWamOtAdJK7GLi7AwpqdaWqc1XUiJDuX2SX05f2gaI7rFAvDyz7uxO/Wg93ZIrDPnskYZEiuEEJ2DBEhCCK9EejksVtuDFBMewrThaaREW7l2XA+gEwVIAKOuUVt+H1gBR3cFezUqrXvdgAvAkdXZf0wtKWvP/iONcyc7LYPUy5FV0nSkLnZaiV1cuIWTesa53JYcbWVU9zhemzGKd2ePIdJqZmduGf/bnqvfRw+Qor1vbtHTKWsnJXZCCNE5SIAkhPCKt7OQtBK72DALN07oza8Pns2lo7sCnajEDiA6HfpNUS+vf8/vT1dVa+OBLzbzy8785u9gq4fdP6iXXfYfOUrh2lleB9AvRR0Wu/lQCVuPqMNPeyc1ziCpwURZdT3VdZ4Fz75WWKE1BGnIIGlSnDr6xYZbmD2+JwB/+X4nn67Npqy6Tg+Q2lVi55xBkhI7IYToFCRAEkJ4Retk53GTBsccpFjH/iODwUBvRxbiWHmty3DPDm/0bPXv3z7ye7OGJbvz+WTtQf76UwvZqgMr1D1R4QnQfZx+9f5jWge79gdIQ7rE0DMhnLLqerYeLgWalthFh5pJiFC7Gm7MLm73c3pLUZSGDFJECANSo4iwqK9Zq9nYpNzt+tN6ER9hIbuwkvu/2MLVb63WW4K3L0By2oMkGSQhhOgUJEASQnglot0ZpIY3ixFWM+kx6if6narMrs/Z6n6fqiLY/pVfn0qbZbS/oKL5UQtaeV3/89Q9Ug5aBqlnYvtL7ExGAzdM6OVyXeMAyWAwMHFAMgA/73BtehBI5TX11Dv2E8WFWzCbjIzqoZbZJUdbm7TtjgkP4Zs/ncZd5/QjLMTEb4dK2JVXpt6/HQFS9/iG8y4ldkII0TlIgCSE8Iq+B6nawwDJaQ+Ss95ODQA6jQA2a8h1ZDMqa21NW2jb7bDzO/Wyo3udxhctvp1dMqoriZFqhig5ykpUM2VjkwamALBge16rc/P8SetgFxZi0mdraWV2KS0MzO0SG8b/nd2X84amAaAtvT0DdsMsJoZ3iyXSatbbpAshhOjYJEASQnhFD5Bq3Q+QFEWhRMsgNRou26czBkgAI68BgwmyV0L+Tr89jXPb7AOOoEd3ZCOUHcFuiWTGolDu/uw3wLctvjWhISZmj88AoH9qVLP3mdAvEYvZSHZhJXuC9P0s0hs0NARwvxueTkZiBBeO7NLqYy8e1XC7yWjQSwa99emNp7D8/omSQRJCiE5CAiQhhFe8KbGrqrNRa7MDriV24NohrVOJToP+U9XLG/zXrCGvtGGPk7avSOeYxbSofgQr9pfzxYZD5JZU+7TFt7MbTu/Fw+cP5JELBjV7e7jFzGl9EgE1ixQMzi2+NT0TI/jlnjO5+pQerT72lF4J+vDbxEgLRqOh1fu3JTTE1OQDASGEEB2XBEhCCK94MyhW238UYjIQbjG53KY1auh0GSSA0bPUvzd9pA5r9QPnAMklg6QosPNbAOZXj9SvXrO/0Kctvp1ZzEauP72X3tWuOVqZ3f+CFCA5t/j2lMlo0LNM7WnQIIQQonOSAEkI4ZWGQbHuZ5C0ACkmzNJkk7yWQTpcXBX09tAe630WxHSH6mK/NWtwLrHTGi8A6gymgr3UKGZ+sY/g3EFqYLImq4C1+wsB6JfcciDjL1oGaceRUpfhq4Gi7UGKDfcuczbr1J6M7hHH1WNbzzYJIYQ4/kiAJITwit7m24MmDcWNWnw7S4iwEGk1oyhwqMg/WRi/cW7WsO4dnx++oqaeMqdA1CWD5Ohet9w+lEpDGBc5Mh+rMgv4fksOAJOHpPh8TW1Jiw3FYIBam10vdwskbQ9SvJf7h1JjQvni5lO54uTuvlyWEEKITkACJCGEV6JCHSV2HjRpKNU62DWzH8ZgMNA1LgyAg0WVTW7v8EZerTZrOPgrHN3t00M7l9dBo1bfu74H4Cf7ScSGhTC2VwIAmUcryC+rITrUzGl9kny6HneEmIwkRarlabkl/p0R1RwtQJK9P0IIITwlAZIQwitae+cyTzJIlS0HSABd49S9MocKvQuQckuquevTTezNL/Pq8e0SnQZ9z1Evb/zAp4fOc5TXdYkNw2BQz3lRZZ06f+nIRgAW20YQH2EhPsJCv5RI/bGTB6diMQfnR32aY7bVkeLAZwS1Ert4L0vshBBCnLgkQBJCeCXaESBpWSF3VDn2FoU1atCg6RavZZC8e0P96dqDzNt4mGd/2OXV49tt5DXq3799DDb3z0tb8svUDEz3+HDSHN3Vso5VwP7lgEJZVC/yiSMhQs3YnJwRrz/2guHpPluHp9Ji1O9nbmnwMkhx7WzRLYQQ4sQjAZIQwivRYWqJXWl1ndvDQGvr1RbfVlPzP3q6OTJIB73MIBVUqJmWVZnH9OcKqH6TISIZKo7C7p98dlitxC4l2qrPMzpQUAH7lgBwOHYM0LDf5uQMtcwuLjyEU3sn+GwdnkrVM0jBCJCan7clhBBCtEUCJCGEV7QMUp1NobrOvWBEC1paKvnqFu8osfMyg6SV8FXU2lh/oMirY7SLKQSGX6Fe3vShzw6rldilRIfSM1E9R/sLKiFLDZD2RIwGGrIlUwanMuvUnjx7yTBCWghGAyE9Vg2QcksCW2KnKIr+nO0d8iqEEOLEIwGSEMIr4RYTJscAzdJq98rJtCGxLQdI7WvSUOxU7rdk91GvjtFuI2aof+9ZAJWFPjmkVqKWHB2qz4vavH07HNsNBiNbQoYCDcGAxWzk8d8NZvLgVJ88v7dSHSV2RxxNGupsdnJKqjjs5z1JBwoqKaqsw2Iy0tdpP5YQQgjhDgmQhBBeMRgMRDs62bm7D0kvsWspQHKU2BVX1lHmZtDlrMSpnfTSYAVIyQMgdSjY62D7fJ8cMt+pxO7CkV0ICzERl/eremPacA7XqJkab1ta+4vWpCG3pJqVmccY9vj/GDdnEeOfXcQCPw6Q3XhQzR4O6RKN1dz8fjchhBCiJRIgCSG8Fu3oRuduBqmmjRK7CKtZf5N/sNDzLINzBml7TqkeWATc0MvUvzd/7pPDOZfYJUZaufqU7ow3bQNAyTiDwnI1MEyI7LgB0hfrD+tNOgDW7fdNdq05Gw4UAzCye5zfnkMIIcTxSwIkIYTXGjrZudfqWy+xM7X8qX63dsxCatxGfNmeYx4fwyeGXgoYIHslFGe361CKojQ0aYhSA44bTs9gvHErANtCR1JY0b6hqP6SEt0wLHbhTjVjdHrfRMC/w4C1DNIoCZCEEEJ4QQIkIYTXnDvZuaOmrvUMEjTMQvK0k53NrujrOHtgMgDrs4PQqAEgOh16nqZe3vLfdh3qYGGVnnlLjlbbeCfXHibNUEiNYubHkp4UdNAAyXlYbHFlHUYDTB/RBYBDfhoGXFlbz44cdQ7WqB6xfnkOIYQQxzcJkIQQXvN0FlJbTRoAujoaNXiaYSitqkPrNn5GvyQAfjtY7NExfGroperf2770+hCfrzvIea8sA6BXUgShIY7M275fAFhv78f2Y3X6zB9tDlJHopXZAQxMi2ZgWhTgvwzSlkMl2OwKqdGh+hwmIYQQwhMSIAkhvKYHSNVultjVq3tQWguQtEYNnmYYtP1HkVYzJ/VUB6XuzC2j2mnfS0ANmAYGE+RuhsJ9Hj9cURT+PH8r5TX1DO8WyxtXj2640dHee4V9CBuyi7DZ1cgwLiLEJ0v3Jecg5eSMeD1DWFBRS2Vt66+bytp6lu05it3u3pwtgA3ZxYBkj4QQQnhPAiQhhNeivO1i18psHm0WUtaxCo/WUuzIosSEhZAeozYzsNkVth4u8eg4PhORABkT1Mvb5nv88Ko6m36+Prx+LP1S1MwLdhtkqVmllfbB+r6rSKu5Q3ZsS3XKIJ3cM56YsBC9++HhNrJIbyzO5Jq5a3hv1X63n2+TY//RyG6y/0gIIYR3JEASQnjN0y527pTYDU6Pxmw0kHm0gh05pW6vRcsgxYaHYDAYGNEtFoBNwSyzG3yh+rcX7b7LHFk5owEiLE6BT+5mqC5GsUZzMLS/fnVH23+k0YbFAozJUDN7XePcGwi83fH9X7gj3+3nO1CgZh5l/pEQQghvSYAkhPBawxwkd0vs2g6QEiOtnDs4BYD//HrA7bWUVDYESAAjusUA8NuhIGWQoKHMLuc3j8vstAAp0mrGYDA03LBPLa8z9BhPr5RY/eqOGiB1iVWDoV5JESQ6GjZ0jdP2mbVeRqkNyF13oJCaevdKJbUhtF1iZf+REEII70iAJITwmscZpDYGxWquHtsDgPkbD1Ne417wpZXYxYapgcIIR4mVVnIVFBEJkHG6etnDmUja1x0V2mhfkWP/Eb3OoI9TliShgwZI5wxKYdapPXlq+hD9uuYySJW19dQ7Moya3BJ1/lN1nZ3fDrYd6JZW1+mBZboESEIIIbwkAZIQwmuedrFra1CsZlzvBHolRVBRa+PLjYddbtt2pIT5Gw+jKK4b951L7ACGdlUzSAcLqygor3FrfX4xYob698b/gN3e+n2dlFdrAZK54cr6GjiwSr2ccQZ9khoCpI6aQbKYjTz+u8GM75OoX9eQQVIDpCPFVUx4fjEXv75Sv0+dzU5BRcP37dd9BW0+1xFH9ig2PIQIq7mNewshhBDNkwBJCOG1hgySp4NiW//RYzAYuOrk7gD8sCVHv95uV7jx/fXc8ekmFmxXB49uPlTMwcJKvVmBFiDFhIXQKzECaNjLEhQDp4E1BkqyIWux2w8rq25ovqA7tBbqqyAiGZIHuuyziY/smAFSc7RGHNow4Ce+2cax8ho2HyrRSyXzy2pwjoFXZTYESCVVdVTVNi250wKkdGnvLYQQoh0kQBJCeE0fFOthF7u2MkigtoQG2JVbpl+35XCJvsfkw9XZrD9QyPTXVnDt22ualNgBdHFkKvJLg5hBCgmDYZeplzd84PbDyhwldpHOGSTH/iMyJoDBQN/kKP2m+PDOEyA5Z5B+3p7HT9vy9NsOFKrdC/Mc+4+018r67CKq62xkHi3njL/+wtS/L23S/vtwsfoY7fsuhBBCeEMCJCGE1xrmINU1KXlrjrsldgB9kiMxGNR5OcccJXL/256r3750z1EenLcFRVFbgu/JLwcgJrxhz06SoynA0WCW2AGMulb9e+e3UNF2qRg4l9g57UFy2n8EkBJtJcqRYeqoJXbN0QKYwopaHpi32eW2/Y4udHklarAzJD2a5CgrtfV2XvzfLv74wXqKK+vYX1DZpAue1jZcGjQIIYRoDwmQhBBe00rs6mwK1XVt769xt0kDQLjFTHdHKdZuRxbpf45MQ6TVjKLA7rxy/f5aGV1smFOAFOUIkMqCHCClDYP0kWCrhbX/dushWpMGvcSupgwOr1cvZ6gBksFgYLijnXlPRzlhZxAdGkKM4/t0rLyWAalRnD8sDYD9jvlXWge71JhQZp7aE4B/L8vSA2GAnbmupZNHpIOdEEIIH5AASQjhtQiLCaOjA7U7nez0EjuTewNN+zuGo+7MLWPf0XL25JdjNhp49IJB+n1CTOoCtARWrFOpWYcJkABO/T/179VvQm3bQ3C1PUh6k4aspWCvh7ieENdDv98Lvx/OO7PGcFKPzjUYtVeSGtCNzYjn05vGMSgtGoD9Ba4BUkp0KLdO7MOrV40kNjwEq9mo33d3XpnLMbXyS+lgJ4QQoj2kzY8QwmsGg4HosBCKK+soraojJTq01fu7MyjWWf/UKP63PY/deWXUOR47rncCF43qwtsrsqistTFteBqv/ZKpPyY2vANmkAAGTYe4DCjKUvcinfLHVu+ut/nWMkjbvlT/7n+ey/1SY0JJjWn9vHdET184hLVZhVxxcndCQ0z0SFCzhQcaldilOl5TFwxLZ2L/ZCpq65m34TDbc0rZmesaIOkZJNmDJIQQoh0kgySEaBfnfUitsdkVbI5N9Z4ESKBmkL7adASAcwelEGIy8t3/nc7Cu8/glF4JLo9xKbHrKHuQAIwmGO/IIq38B9RVtXp3fVBsqFm9764f1BsGX+zPVQbM4PQYZo3PIDREzSb2TFAzSgcKmpbYaSKsZpKjQvXMonMDjzqbXW/skB7b+QJGIYQQHYcESEKIdtE72bXR6lsrrwP39iBBQ4nd5kPFbM8pxWIycsGwdABMRgMhJiOD02MaraeDZpAAhl8FUelQegiWvtDqXfUAyWqGPf+D2nKI6Q5dTwrESgNOyyAdK6+lrLqOPEfnweayklrgnHWsgpp6td13bkk1dkVtIZ8YYQ3QqoUQQhyPJEASQrSLu8NinQMkdzNIPRMjCDEZ0Lo5TxmSSlyjbm3xERbSHFmGsBCTnpEASHRkkEqq6vQ30kEVEgpTn1Mvr3gZ8ra3eFe9xC7UDFvnqVcOvhAMBv+uMUiiQkNIdMxy2n+sktxGJXbO0mJCiQo1U29X2HdUzTjpM5BiQzEaj89zJIQQIjAkQBJCtEtDiV3rGaQamxqgGAxgdvMNbIjJSO+khmGoV5zcrdn7DU5XN+077z8CdVis1sThWHmtW89Zb7NTWeve4FuvDJwG/c9XGy58dUuLpXZam+9YUw3s/km9csjxUV7Xkh6OMrsth0uoqlNfL83trzIYDE3K7KRBgxBCCF+RAEkI0S7uDott6GBnxOBBFkQrp+qZEM64RvuNNIMcZXYxYa4BktFo0LNIx9wss5vx1momPL+YAn/tWzIY4Ly/QmgsHNkI828Ge9MW6VoGqXvWp1BfBYn9IG2Ef9bUQWhldquz1FlRMWEhLhlBZ87700BafAshhPAdCZCEEO3ibpOGWg+GxDqbMjgVgwFuO6tvi4HVyT3jgYaN/s482YdUUVPP6qxCjpXX8JNj5pJfxHSBy/8DxhC1O92ip5rcpbS6Diu1JG+dq14x/vbjtrxOo33/Vu8rBJovr9MMcARIWqtvbcBs17hwfy5RCCHECUACJCFEu2hNEUqr2iix82BIrLOpQ9PY/fRULh3dtcX7jO+TwEfXj2XOxUOb3OZJJ7usYw3ziX7cluvROj2WcTr87h/q5eUvwfr39JsURaG8pp5LTMswV+ZBdFcYepl/19MBaMNutQ52ydEtN1von6qWVe5wDAje4wiU+qVEtvgYIYQQwh0SIAkh2kXb91NY0XoA4lxi56mQNh5jMBg4tU9ikwYO4FkGyTlAWrn3GCVtlA2224gr4Yz71cvf3gmbPgJFobLWRn+yud38hXrbqX8Cc9Ov7Xhzau8EeiU2ZAFP6hHf4n0HpqkZpJySagrKa9idVw5AP0dmSQghhPCWDIoVQrRLcpRaBqW1ZW6Jp0NifcWTAEnriAZQb1dYtDOPi0a2nLnyiTMfhKL9sPlTdT/ShvcxRnZjnuUrwg01KAl9MIy61r9r6CASI60suudMyqrrKKqoo1t8y/uJokJD6JEQzoGCSn7alkdVnQ2L2UiPeCmxE0II0T6SQRJCtEuKowwq31EW1RJv9yC1l2cZJDULoTV7+HGrn8vsQN1XNP01OOsRMFkhexVh2z8j3FDDKoZhuG4BWE6sN/1RoSF0Twhvs5mH1r1w/sbDAPRJisTsRYZSCCGEcCa/SYQQ7aK1Yc4vq8GuDSxqRq2+B6n5rmT+4skepH2OEruZ43oAsHT3sVa/Jp8xhcCEe+DWX+Hcpzl80v3cXnsL94c+CuEtl5md6LQhwWv2q00d+kt5nRBCCB+QAEkI0S6JkVYMBrUkraCi5VlDNR08g6QoClmOErtzB6cCUFVn8/8+JGfxveDUP7Gv/w18ZT+N8NCWmxQIGOTIIGn6pUiAJIQQov0kQBJCtEuIyajPGsprpcxO34MU4BIo5wBJUVrOBh0rr6Wsph6DAfqmROrNJ9zJPPlamWNIbFSobBNtzeAmAZJ0sBNCCNF+EiAJIdpN24eUV1rNpoPFvLJwD1W1Npf7BGsPkha8VdXZqGi0Jmf7jqr7j7rGhWE1m/TSPHcHzPpSuR4ghbRxzxNbclSoHgCDZJCEEEL4hgRIQoh20wZ65pXW8PjX23hpwW6e/WGHy32CFSBFWM1EWNR9T601ktBafGckqlkIPfMUjAxSjRogRVolg9QWLYsUYTHRJbblrndCCCGEuyRAEkK0W7IjQMopqWJnrjq48/1fD7DOsXkeoKZezd4EOkACSHE0kshtJUDSGjRoc3i0zJM73e98TcsgRUqJXZu0AKlvShRGY+td74QQQgh3+OWdSllZGXfccQc9evQgLCyMU089lbVr1+q3K4rCo48+SlpaGmFhYUyaNIk9e/b4YylCiADQMkhr9xdSXadmihQF7v9isx4Y6V3sgtCGuSHD1UqA5GjQ0CtJDZA8aQ/ua2XVamMI2YPUtmnD00mMtHDJqC7BXooQQojjhF/eqVx//fUsWLCADz74gC1btnDuuecyadIkDh9WZ1U8//zzvPLKK7zxxhusXr2aiIgIJk+eTHV163NUhBAdk7YHaf2BIkDNwiRGWsk8WsHHq7OB4JXYQUOAlFvSEOyUVdfx7A87OVCgBkZa5qtPUqMSu2BkkBwldlFSYtemAanRrHv4HK4Z1zPYSxFCCHGc8Pk7laqqKr744guef/55JkyYQJ8+fXj88cfp06cPr7/+Ooqi8PLLL/Pwww8zffp0hg0bxvvvv8+RI0eYP3++r5cjhAiAFEcAUmdTu8SN6B7LHZP6AvDqL3upqKlv6GIXxBI75wzSf37N5o0lmcz5fidFFbUcKqoCYHAXdbaOJ/OTfE32IAkhhBDB4/N3KvX19dhsNkJDQ12uDwsLY/ny5WRlZZGbm8ukSZP022JiYhg7diyrVq3y9XKEEAGgBUia/ilRXD6mGz0SwjlWXsvby7OcBsUGM4PUECDtzisD1LLArUdKAOiREE5MmNo5LqgZJH0PknSxE0IIIQLN5+9UoqKiGDduHE899RRHjhzBZrPxn//8h1WrVpGTk0Nubi4AKSkpLo9LSUnRb2uspqaG0tJSlz9CiI4jtXGAlBpFiMnIXef0A+Ct5VlBGxQLDQGcc5MGra13QUUt3/6WA8AQR/YIGpo0HAvKHCTZgySEEEIEi1/eqXzwwQcoikKXLl2wWq288sorXHnllRiN3j3dnDlziImJ0f9069bNxysWQrRHbHiIywDY/qnqPJrJg1MBKKmqo6CiFgCLyRTw9aU2KrFTFEVvygAwf5O6P3KoU4CkZZAKKmqpd5QHBkpJlQRIQgghRLD4JUDq3bs3S5Ysoby8nIMHD7JmzRrq6uro1asXqanqG6a8vDyXx+Tl5em3Nfbggw9SUlKi/zl48KA/li2E8JLBYCDZ0aghKtSsZ5RCQ0yEO2YQ5TnK24LZpCG/rAabXeFoWY2+zwfQs1vOAVJ8hAWjQe3GV+gI7gJBURRyHOcqLUbm+gghhBCB5td3KhEREaSlpVFUVMRPP/3E9OnTycjIIDU1lYULF+r3Ky0tZfXq1YwbN67Z41itVqKjo13+CCE6Fi0IGZAahcHQMI8mLtwCQF5Z8AKkxEg12LHZFQrKa8h0yh45G5LeECCZjAYSHGV2+QHch1RSVUdlrdoaPS0mtI17CyGEEMLX/PJO5aeffuLHH38kKyuLBQsWMHHiRAYMGMDs2bMxGAzccccdPP3003z99dds2bKFa6+9lvT0dC688EJ/LEcIEQDaPp9+KVEu18dFqI0GcoOYQTKbjHrJXG5pNZmO/Ucju8fq9+keH05MuGtThGB0sjtcrHbTS4y0EBoS+HJEIYQQ4kTnlwL3kpISHnzwQQ4dOkR8fDyXXHIJzzzzDCEh6puP++67j4qKCm688UaKi4s57bTT+PHHH5t0vhNCdB5n9Evi5x15TBrk2oBFyyDVBHFQLKgZrrzSGnJLqvX9R6O7x1FaVUfm0QqX8jpNYpQVcgLbyS6nWA0k02OlvE4IIYQIBr8ESJdddhmXXXZZi7cbDAaefPJJnnzySX88vRAiCC4b042LR3XB3CgAinUESJpgZJBAy3CVkOeUQeqVFIlNUcg8WsEpvROaPCYpCJ3sjpSoGSQprxNCCCGCQ1okCSF8pnFwBBDfqGwtWAGS1skut7SafcfUAKl3UgQXjkxnQr8kJvRNavKYYMxC0krsJIMkhBBCBIcESEIIv2qcQQrGoFho2CN1oKCSQ0VqENIrKZJwi5mJ/ZObfUwwAqQjjhK7LhIgCSGEEEERnHcqQogTRnxExyix07rs/bqvEEWB6FAziZGWVh8TjAApRzJIQgghRFBJgCSE8KvYxiV2wWrS4Cix0/YTDe8W69KOvDldYtXHbDtSqg9v9bcjxbIHSQghhAgmCZCEEH7VUTJIzhmZ4d1imXPx0DYfM7JbHP1SIimvqefdFfv9uDpVvc1ObqmU2AkhhBDBJAGSEMKv4jpIF7ueCeHcNrEP908ZwH//OI6uceFtPsZoNPCns/oCMHf5Pkqr/ZtFyiurwa5AiMlAoqODnhBCCCECSwIkIYRfNS6xC1aTBoPBwD2T+3Pzmb0J8aDM77yhafRJjqS0up4PVh3w4wob9h+lxoRiNLZe/ieEEEII/5AASQjhV01K7EymIK3EOyajgWtO6QHA2v2Ffn0uvcV3jJTXCSGEEMEiAZIQwq/CQkwuZXXBKrFrj+4Jajlefql/u9lJi28hhBAi+DrfOxUhRKdiMBiId9qH1BkDpGRHu+/8smq/Ps8RafEthBBCBF3ne6cihOh0nPchBWsPUnskR6kttwsqaqm32f32PJlHywHoGicBkhBCCBEsne+dihCi03Heh9QZM0gJERZMRgOKAsfKa/3yHPU2O5sOFgMwsnucX55DCCGEEG0zB3sBQojjn9bq22AAcyfszmY0GkiKtJJbWk1eabU+dBZAUZQ2B866Y2duGZW1NqKsZvomR7b7eEKI9rHZbNTVBWZAtBCidSEhIZgC2ORJAiQhhN9pJXYWk9EnwUQwJEerAVJ+WUOjhjs/3cTa/YX8cPvpRIWGtPLotq0/UATAyB5x0uJbiCBSFIXc3FyKi4uDvRQhhJPY2FhSU1MD8j5CAiQhhN9pJXadsbxOo+5DKtEbNdTU2/jmtyPU2xW2HyllbK+Edh1fC5BO6iHldUIEkxYcJScnEx4e3mk/1BHieKEoCpWVleTn5wOQlpbm9+eUAEkI4XexjhK7ztigQZMcrXayy3O0+t6TV069XQFwySp5SwuQRkuAJETQ2Gw2PThKSGjfhx5CCN8JC1ObF+Xn55OcnOz3crvO+25FCNFpxEc0lNh1Vlqr76OODNKOnFL9tvYGSLkl1RwursJogBHdYtt1LCGE97Q9R+Hh4UFeiRCiMe3/ZSD2BnbedytCiE5DyyB15hK7lGi1MYM2LHa7S4DUvvlIWvZoYFo0EVZJ7AsRbFJWJ0THE8j/l5333YoQotMY3jWW1OhQzuyfHOyleE3LIOU1k0E6Wtq+DNL2nBIAhkv2SAghhAg6CZCEEH4XH2Fh1YNn8fjvBgd7KV7ThsXml9agKAo7csr029wtsVMUhWvmruaif66gpt6mX6/ta+oSKwNihRAd16xZs7jwwgt9ftzHH3+cESNG+Py4nli4cCEDBw7EZrO1fefjwPbt2+natSsVFRXBXkqHJAGSECIgOnvJSoqjScOx8hoOFVVRUtVQA+1uiV1OSTXL9hxjY3Yx323OcXq8GiBpWSohhPDErFmzMBgMGAwGQkJCyMjI4L777qO6un3lv55avHixvg6DwUBKSgqXXHIJ+/bta/Vx99xzDwsXLgzQKpt333338fDDDzfZ/F9VVUV8fDyJiYnU1LS/IU9HMWjQIE455RReeumlYC+lQ5IASQgh3JAQacVoALsCy/ceAxq68rmbQdqbX65ffnflfhTF0QWvVH0Tkxwd2uzjhBCiLVOmTCEnJ4d9+/bxt7/9jTfffJPHHnssKGvZtWsXR44c4fPPP2fbtm1Mmzat2cyMoijU19cTGRkZ1K6By5cvJzMzk0suuaTJbV988QWDBw9mwIABzJ8/P/CL86PZs2fz+uuvU19fH+yldDgSIAkhhBtMRgOJkWqGZ/EudRbDuN7qL/Tiyjpq6m38siuffUfLWzyGc4C0+VAJGw8WA3BUMkhCiHayWq2kpqbSrVs3LrzwQiZNmsSCBQv02+12O3PmzCEjI4OwsDCGDx/Of//7X/12m83Gddddp9/ev39//v73v3u1luTkZNLS0pgwYQKPPvoo27dvZ+/evXqG6YcffmD06NFYrVaWL1/ebInd22+/zeDBg7FaraSlpXHbbbfptxUXF3P99deTlJREdHQ0Z511Fr/99pt++2+//cbEiROJiooiOjqa0aNHs27duhbX+8knn3DOOecQGtr0Q6q5c+dy9dVXc/XVVzN37twmtxsMBt58800uuOACwsPDGThwIKtWrWLv3r2ceeaZREREcOqpp5KZmak/JjMzk+nTp5OSkkJkZCRjxozh559/1m9vnInT/syaNUu/z+uvv07v3r2xWCz079+fDz74oMm63nrrLS666CLCw8Pp27cvX3/9tct9zjnnHAoLC1myZEmL5+ZEJQGSEEK4SZuFtHyPmkE6pVeC3rp86e5jzH5nLbd8uKHFx+91BE9mo1pu+O6K/dTZ7BRU1KrHlwBJiA5HURQqa+sD/kfLMHtj69atrFy5EovFol83Z84c3n//fd544w22bdvGnXfeydVXX62/Obbb7XTt2pXPP/+c7du38+ijj/LQQw/x2Weftev8afNramtr9eseeOABnn32WXbs2MGwYcOaPOb111/n1ltv5cYbb2TLli18/fXX9OnTR7/997//Pfn5+fzwww+sX7+eUaNGcfbZZ1NYWAjAjBkz6Nq1K2vXrmX9+vU88MADhISEtLjGZcuWcdJJJzW5PjMzk1WrVnHZZZdx2WWXsWzZMg4cONDkfk899RTXXnstmzZtYsCAAVx11VXcdNNNPPjgg6xbtw5FUVwCvPLycs477zwWLlzIxo0bmTJlCtOmTSM7OxuAU089lZycHP3PokWLCA0NZcKECQB8+eWX3H777dx9991s3bqVm266idmzZ/PLL7+4rOuJJ57gsssuY/PmzZx33nnMmDFDP0cAFouFESNGsGzZshbPzYlK+skKIYSb1EYNpVTU2rCYjJw9IJkPVh3gcHEVP2xR9xTtzS+n3mbH3MzMJy2DdPUpPXh35X5+2ZmvZ4/MRgNx4ZYmjxFCBFdVnY1Bj/4U8Ofd/uRkwi3uv0379ttviYyMpL6+npqaGoxGI6+++ioANTU1/OUvf+Hnn39m3LhxAPTq1Yvly5fz5ptvcsYZZxASEsITTzyhHy8jI4NVq1bx2Wefcdlll3n1NeTk5PDCCy/QpUsX+vfvz8qVKwF48sknOeecc1p83NNPP83dd9/N7bffrl83ZswYQC2HW7NmDfn5+Vit6odKL7zwAvPnz+e///0vN954I9nZ2dx7770MGDAAgL59+7a6zgMHDpCent7k+rfffpupU6cSF6cO8J48eTLvvPMOjz/+uMv9Zs+erZ+j+++/n3HjxvHII48wefJkAG6//XZmz56t33/48OEMHz5c//dTTz3Fl19+yddff81tt92GxWIhNTUVgIKCAq6//nr+8Ic/8Ic//EH/emfNmsUtt9wCwF133cWvv/7KCy+8wMSJE/Xjzpo1iyuvvBKAv/zlL7zyyiusWbOGKVOm6PdJT09vNug70UkGSQgh3JQao5ZfhFtMvDN7DH1TokhyZH1+cZTd1dsVckqa3xid6QiQpg1Pw2CAspp6duaq7cKToqwYjZ27kYUQIngmTpzIpk2bWL16NTNnzmT27Nn6npq9e/dSWVnJOeecQ2RkpP7n/fffdyn9eu211xg9ejRJSUlERkbyr3/9S89qeKJr165ERESQnp5ORUUFX3zxhUs2q7lsjSY/P58jR45w9tlnN3v7b7/9Rnl5OQkJCS5fS1ZWlv613HXXXVx//fVMmjSJZ5991uVrbE5VVVWT8jqbzcZ7773H1VdfrV939dVX8+6772K3213u65wFS0lJAWDo0KEu11VXV1Naqv68Ly8v55577mHgwIHExsYSGRnJjh07mpzruro6LrnkEnr06OFS7rhjxw7Gjx/vct/x48ezY8eOFtcVERFBdHQ0+fn5LvcJCwujsrKyhTNz4pIMkhBCuOmaU3pQWVPPH07LYFjXWKChLK6osqGr3cHCSrrF8TK3YQAAI19JREFUh7s8tqiiVi+lG5gWTXpMGIeLq1idpZY7SIMGITqmsBAT25+cHJTn9URERIRehvb2228zfPhw5s6dy3XXXUd5ufrhzHfffUeXLl1cHqdlYT755BPuueceXnzxRcaNG0dUVBR//etfWb16tcdrX7ZsGdHR0SQnJxMVFdXsWluileS1pLy8nLS0NBYvXtzkttjYWEBtG37VVVfx3Xff8cMPP/DYY4/xySefcNFFFzV7zMTERIqKilyu++mnnzh8+DCXX365y/U2m42FCxe6ZMCcy/e0jq3NXacFVvfccw8LFizghRdeoE+fPoSFhXHppZe6lCEC3HzzzRw8eJA1a9ZgNnv+lr1xWaHBYGgS3BUWFtK7d2+Pj328kwBJCCHcNDAtmpevGOlynbYvydnBoqafxmn7j7rEhhFuMdMzMZzDxVWs0QIk2X8kRIdkMBg8KnXrCIxGIw899BB33XUXV111FYMGDcJqtZKdnc0ZZ5zR7GNWrFjBqaeeqpdtAW1mXlqSkZGhByueioqKomfPnixcuNClXEwzatQocnNzMZvN9OzZs8Xj9OvXj379+nHnnXdy5ZVX8s4777QYII0cOZLt27e7XDd37lyuuOIK/vznP7tc/8wzzzB37txWSwTbsmLFCmbNmqWvp7y8nP3797vc56WXXuKzzz5j5cqVTTr8DRw4kBUrVjBz5kyXYw4aNMjjtWzdupVLL73U8y/iONe5/scLIUQHow2QdZZd2EyA5Civ650cCUDPhAhW7C1gy6ESx3EkQBJC+M7vf/977r33Xl577TXuuece7rnnHu68807sdjunnXYaJSUlrFixgujoaGbOnEnfvn15//33+emnn8jIyOCDDz5g7dq1ZGRkBHztjz/+OH/84x9JTk5m6tSplJWVsWLFCv70pz8xadIkxo0bx4UXXsjzzz9Pv379OHLkCN999x0XXXQRgwcP5t577+XSSy8lIyODQ4cOsXbt2mZbeGsmT57Me++9p//76NGjfPPNN3z99dcMGTLE5b7XXnstF110EYWFhcTHx3v19fXt25d58+Yxbdo0DAYDjzzyiEtm5+eff+a+++7jtddeIzExkdzcXEDNrsXExHDvvfdy2WWXMXLkSCZNmsQ333zDvHnzXDrhuWP//v0cPnyYSZMmefV1HM9kD5IQQrRDc4FNdmGVfnlDdhEPz9+itwbvk6QGSBmJaolJvV1xHEdK7IQQvmM2m7ntttt4/vnnqaio4KmnnuKRRx5hzpw5DBw4kClTpvDdd9/pAdBNN93ExRdfzOWXX87YsWMpKChwySYF0syZM3n55Zf55z//yeDBg7ngggvYs2cPoGb0vv/+eyZMmMDs2bPp168fV1xxBQcOHCAlJQWTyURBQQHXXnst/fr147LLLmPq1KkuDSgamzFjBtu2bWPXrl0AvP/++0RERDS7D+rss88mLCyM//znP15/fS+99BJxcXGceuqpTJs2jcmTJzNq1Cj99uXLl2Oz2fjjH/9IWlqa/kdrWnHhhRfy97//nRdeeIHBgwfz5ptv8s4773DmmWd6tI6PP/6Yc889lx49enj9tRyvDEp7+kgGSWlpKTExMZSUlBAdHR3s5QghTmCLdubxh3fV+Rp9kyPZk1/O8G6xfHWruoF25ttrWLL7qH7/v1w0lKvGdufn7Xlc/37DXI45Fw/lypO7B3bxQggX1dXVZGVlkZGR0exMHHH8uvfeeyktLeXNN98M9lICora2lr59+/LRRx81afjQUbX2/9PXsYFkkIQQoh2cMz/nDU0D4JBTid3BRuV2A9LUDcs9E103KUuJnRBCBM+f//xnevTo0aSJwfEqOzubhx56qNMER4Eme5CEEKIdnJs0nDc0jb8v3ENBRS3lNfWEh5g4VKyW2z194RAURWFkt1gAuseHYzSAo8KOFOliJ4QQQRMbG8tDDz0U7GUETJ8+fVyG7wpXEiAJIUQ7JEVauXhkFyxmI/1SIokND6G4so6DhZUkRFiorbdjNMDlY7oR4jQ81mI20iUujIOO/UqSQRJCCCE6BgmQhBCiHQwGAy9dPkL/d/f4cIorSzhYWElVnQ2A1OhQl+BI0zMhgoOFVRgNkBApAZIQQgjREcgeJCGE8CFtQGx2YSWHitTsUJe45gcfap3sEiKtmIyGwCxQCCGEEK2SDJIQQvhQd0eAdLCwkjqbusGoa1x4s/ftmaAGSFJeJ4QQQnQcEiAJIYQPaQHSvmMV+oyjri1kkMb3ScRiNjK+T2LA1ieEEEKI1kmAJIQQPjSsawwAG7OL9eu6xDYfIPVPjWLzY+cSGmIKxNKEEEII4QbZgySEED40IDWaKKuZ8pp6VmcVAi2X2AESHAkhhBAdjARIQgjhQyajgdE94wCorVcHDrbUpEEIITqbWbNmceGFF+r/PvPMM7njjjsCvo7FixdjMBgoLi726XH379+PwWBg06ZNPj2uJ2pra+nTpw8rV64M2hoC7ZRTTuGLL74I9jJ0EiAJIYSPjekZ7/Lv9FgZAiuE8J9Zs2ZhMBgwGAxYLBb69OnDk08+SX19vd+fe968eTz11FNu3ddfQU1LevbsqZ+XiIgIRo0axeeff97qY7p160ZOTg5DhgwJyBqb88Ybb5CRkcGpp57a5LabbroJk8nU5tfR2Tz88MM88MAD2O32YC8FkABJCCF8bmxGQ4CUHGXFapYyOiGEf02ZMoWcnBz27NnD3XffzeOPP85f//rXZu9bW1vrs+eNj48nKirKZ8fztSeffJKcnBw2btzImDFjuPzyy1vMzNTW1mIymUhNTcVsDs42fUVRePXVV7nuuuua3FZZWcknn3zCfffdx9tvvx2E1fnP1KlTKSsr44cffgj2UgAJkIQQwueGdo3BYlZ/vLbUwU4IIXzJarWSmppKjx49uPnmm5k0aRJff/010FAW98wzz5Cenk7//v0BOHjwIJdddhmxsbHEx8czffp09u/frx/TZrNx1113ERsbS0JCAvfddx+Korg8b+MSu5qaGu6//366deuG1WqlT58+zJ07l/379zNx4kQA4uLiMBgMzJo1CwC73c6cOXPIyMggLCyM4cOH89///tfleb7//nv69etHWFgYEydOdFlna6KiokhNTaVfv3689tprhIWF8c033wBqhumpp57i2muvJTo6mhtvvLHZErtt27ZxwQUXEB0dTVRUFKeffjqZmZn67W+99RYDBw4kNDSUAQMG8M9//lO/rba2lttuu420tDRCQ0Pp0aMHc+bMaXG969evJzMzk/PPP7/JbZ9//jmDBg3igQceYOnSpRw8eNDldu37/Je//IWUlBRiY2P1TOK9995LfHw8Xbt25Z133nF53P3330+/fv0IDw+nV69ePPLII9TV1em3O2finP9otmzZwllnnUVYWBgJCQnceOONlJeXN1nXCy+8QFpaGgkJCdx6660uz2EymTjvvPP45JNPWjw3gSRd7IQQwsesZhMjusWyJquw1QYNQohOQFGgrjLwzxsSDgbvB0iHhYVRUFCg/3vhwoVER0ezYMECAOrq6pg8eTLjxo1j2bJlmM1mnn76aaZMmcLmzZuxWCy8+OKLvPvuu7z99tsMHDiQF198kS+//JKzzjqrxee99tprWbVqFa+88grDhw8nKyuLY8eO0a1bN7744gsuueQSdu3aRXR0NGFh6gdIc+bM4T//+Q9vvPEGffv2ZenSpVx99dUkJSVxxhlncPDgQS6++GJuvfVWbrzxRtatW8fdd9/t8Tkxm82EhIS4ZNBeeOEFHn30UR577LFmH3P48GEmTJjAmWeeyaJFi4iOjmbFihV6+eKHH37Io48+yquvvsrIkSPZuHEjN9xwAxEREcycOZNXXnmFr7/+ms8++4zu3btz8ODBJoGNs2XLltGvX79ms3Jz587l6quvJiYmhqlTp/Luu+/yyCOPuNxn0aJFdO3alaVLl7JixQquu+46Vq5cyYQJE1i9ejWffvopN910E+eccw5du3YF1CDy3XffJT09nS1btnDDDTcQFRXFfffdB8DatWux2WyAGjRfeumlhISEAFBRUaG/jtauXUt+fj7XX389t912G++++66+rl9++YW0tDR++eUX9u7dy+WXX86IESO44YYb9PucfPLJPPvssy2em0CSAEkIIfzg3EEprMkqZES32GAvRQjRHnWV8Jf0wD/vQ0fAEuHxwxRFYeHChfz000/86U9/0q+PiIjgrbfewmKxAPCf//wHu93OW2+9pWcD3nnnHWJjY1m8eDHnnnsuL7/8Mg8++CAXX3wxoO6N+emnn1p87t27d/PZZ5+xYMECJk2aBECvXr302+Pj1fLj5ORkYmNjATXj9Je//IWff/6ZcePG6Y9Zvnw5b775JmeccQavv/46vXv35sUXXwSgf//+bNmyheeee87t81JbW8uLL75ISUmJS4B31llnuQRbjTNTr732GjExMXzyySd6UNCvXz/99scee4wXX3xRP0cZGRls376dN998k5kzZ5KdnU3fvn057bTTMBgM9OjRo9V1HjhwgPT0pq+3PXv28OuvvzJv3jwArr76au666y4efvhhl2xOfHw8r7zyCkajkf79+/P8889TWVnJQw89BMCDDz7Is88+y/Lly7niiisAdf+PpmfPntxzzz16KR9AUlKSfvvtt99OTk4Oa9euBeCjjz6iurqa999/n4gI9fX66quvMm3aNJ577jlSUlIANWv46quvYjKZGDBgAOeffz4LFy50CZDS09M5ePAgdrsdozG4RW4SIAkhhB/8YXwGY3rGMzg9OthLEUKcAL799lsiIyOpq6vDbrdz1VVX8fjjj+u3Dx06VA+OAH777Tf27t3bJFNRXV1NZmYmJSUl5OTkMHbsWP02s9nMSSed1KTMTrNp0yZMJhNnnHGG2+veu3cvlZWVnHPOOS7X19bWMnLkSAB27Njhsg5AD6bacv/99/Pwww9TXV1NZGQkzz77rEv52kknndTq4zdt2sTpp5+uB0fOKioqyMzM5LrrrnN5o19fX09MjDoTb9asWZxzzjn079+fKVOmcMEFF3Duuee2+HxVVVWEhjZt7PP2228zefJkEhPVweLnnXce1113HYsWLeLss8/W7zd48GCX4CIlJcWl4YTJZCIhIYH8/Hz9uk8//ZRXXnmFzMxMysvLqa+vJzq66e+uf/3rX8ydO5eVK1fqQdOOHTsYPny4HhwBjB8/Hrvdzq5du/QAafDgwZhMDftx09LS2LJli8vxw8LCsNvt1NTU6NnFYJEASQgh/MBoNDBcskdCdH4h4Wo2JxjP64GJEyfy+uuvY7FYSE9Pb9JkwPkNLEB5eTmjR4/mww8/bHIs54yBJ7x5U6vtVfnuu+/o0qWLy21Wq9WrdTi79957mTVrFpGRkaSkpLhkW6DpeWmsta9JW/u///3vJgGcFgyMGjWKrKwsfvjhB37++Wcuu+wyJk2a1GSPlSYxMbFJ4GCz2XjvvffIzc11+b7abDbefvttlwCpcSBnMBiavU7rFrdq1SpmzJjBE088weTJk/VsmZat0/zyyy/86U9/4uOPP2bYsGEtnpOWtLYGTWFhIREREUEPjkACJCGEEEKIlhkMXpW6BVpERAR9+vRx+/6jRo3i008/JTk5udlsAaif8q9evZoJEyYAamZk/fr1jBo1qtn7Dx06FLvdzpIlS/QSO2daBkvbzwIwaNAgrFYr2dnZLWaeBg4cqDec0Pz6669tf5GoAYcn56WxYcOG8d5771FXV9fkTX5KSgrp6ens27ePGTNmtHiM6OhoLr/8ci6//HIuvfRSpkyZQmFhoV5y6GzkyJG8/vrrKIqiB3Pff/89ZWVlbNy40SULs3XrVmbPnk1xcbFesuiplStX0qNHD/785z/r1x04cMDlPnv37uXSSy/loYce0ksJNQMHDuTdd9+loqJCDzZXrFihl/h5YuvWrXrWMNiki50QQgghxAlmxowZJCYmMn36dJYtW0ZWVhaLFy/m//7v/zh06BCg7jd59tlnmT9/Pjt37uSWW25pdYZRz549mTlzJn/4wx+YP3++fszPPvsMgB49emAwGPj22285evQo5eXlREVFcc8993DnnXfy3nvvkZmZyYYNG/jHP/7Be++9B8Af//hH9uzZw7333suuXbv46KOPXBoA+NNtt91GaWkpV1xxBevWrWPPnj188MEH7Nq1C4AnnniCOXPm8Morr7B79262bNnCO++8w0svvQTASy+9xMcff8zOnTvZvXs3n3/+OampqS0GNBMnTqS8vJxt27bp182dO5fzzz+f4cOHM2TIEP2P1oGwuSygu/r27Ut2djaffPIJmZmZvPLKK3z55Zf67VVVVUybNo2RI0dy4403kpubq/8B9XUUGhrKzJkz2bp1q55puuaaa/TyOnctW7as1fLDQJIASQghhBDiBBMeHs7SpUvp3r07F198MQMHDuS6666jurpazyjdfffdXHPNNcycOZNx48YRFRXFRRdd1OpxX3/9dS699FJuueUWBgwYwA033EBFRQUAXbp04YknnuCBBx4gJSWF2267DYCnnnqKRx55hDlz5jBw4ECmTJnCd999R0ZGBgDdu3fniy++YP78+QwfPpw33niDv/zlL348Ow0SEhJYtGgR5eXlnHHGGYwePZp///vfejbp+uuv56233uKdd95h6NChnHHGGbz77rv62qOionj++ec56aSTGDNmDPv37+f7779vsQlBQkICF110kR705OXl8d1333HJJZc0ua/RaOSiiy5i7ty5Xn99v/vd77jzzju57bbbGDFiBCtXrnTpjJeXl8fOnTtZuHAh6enppKWl6X9AfR399NNPFBYWMmbMGC699FLOPvtsXn31VY/WcfjwYVauXMns2bO9/lp8yaC0tNOuAystLSUmJoaSkpIW08JCCCGEEJ6orq4mKyuLjIyMZjfKCxEImzdv5pxzziEzM5PIyMhgLycg7r//foqKivjXv/7V4n1a+//p69jA5xkkm83GI488og/76t27N0899ZRLxxNFUXj00UdJS0sjLCyMSZMmsWfPHl8vRQghhBBCiE5l2LBhPPfcc2RlZQV7KQGTnJzMU089Fexl6HzepOG5557j9ddf57333mPw4MGsW7eO2bNnExMTw//93/8B8Pzzz/PKK6/w3nvvkZGRwSOPPMLkyZPZvn27fGIjhBBCCCFOaLNmzQr2EgLKm8G//uTzAGnlypVMnz5d7zHfs2dPPv74Y9asWQOo2aOXX36Zhx9+mOnTpwPw/vvvk5KSwvz58/WhVUIIIYQQQggRaD4vsTv11FNZuHAhu3fvBtRBZMuXL2fq1KkAZGVlkZub69L+MSYmhrFjx7Jq1SpfL0cIIYQQQggh3ObzDNIDDzxAaWkpAwYMwGQyYbPZeOaZZ/T+8FpbwMat/1JSUvTbGqupqaGmpkb/d2lpqa+XLYQQQgghhBC+zyB99tlnfPjhh3z00Uds2LCB9957jxdeeEHvZe+NOXPmEBMTo//p1q2bD1cshBBCCNHAbrcHewlCiEYC+f/S5xmke++9lwceeEDfSzR06FAOHDjAnDlzmDlzJqmpqYDaV13roa79e8SIEc0e88EHH+Suu+7S/11aWipBkhBCCCF8ymKxYDQaOXLkCElJSVgsFgwGQ7CXJcQJTVEUamtrOXr0KEajEYvF4vfn9HmAVFlZ2WT4lclk0qO+jIwMUlNTWbhwoR4QlZaWsnr1am6++eZmj2m1WrFarb5eqhBCCCGEzmg0kpGRQU5ODkeOHAn2coQQTsLDw+nevXuLQ3Z9yecB0rRp03jmmWfo3r07gwcPZuPGjbz00kv84Q9/AMBgMHDHHXfw9NNP07dvX73Nd3p6OhdeeKGvlyOEEEII4TaLxUL37t2pr6/HZrMFezlCCNRki9lsDlhG1+cB0j/+8Q8eeeQRbrnlFvLz80lPT+emm27i0Ucf1e9z3333UVFRwY033khxcTGnnXYaP/74o8xAEkIIIUTQGQwGQkJCCAkJCfZShBBBYFAURQn2IjxVWlpKTEwMJSUlREdHB3s5QgghhBBCiCDxdWzg/yI+IYQQQgghhOgkJEASQgghhBBCCAef70EKBK0qUAbGCiGEEEIIcWLTYgJf7RzqlAFSWVkZgMxCEkIIIYQQQgBqjBATE9Pu43TKJg12u50jR44QFRUV9AFu2tDagwcPSsOIAJDzHVhyvgNLzndgyfkOLDnfgSXnO7DkfAdW4/OtKAplZWWkp6f7ZE5Sp8wgGY1GunbtGuxluIiOjpb/EAEk5zuw5HwHlpzvwJLzHVhyvgNLzndgyfkOLOfz7YvMkUaaNAghhBBCCCGEgwRIQgghhBBCCOEgAVI7Wa1WHnvsMaxWa7CXckKQ8x1Ycr4DS853YMn5Diw534El5zuw5HwHlr/Pd6ds0iCEEEIIIYQQ/iAZJCGEEEIIIYRwkABJCCGEEEIIIRwkQBJCCCGEEEIIBwmQhBBCCCGEEMJBAqR2eu211+jZsyehoaGMHTuWNWvWBHtJx4XHH38cg8Hg8mfAgAH67dXV1dx6660kJCQQGRnJJZdcQl5eXhBX3LksXbqUadOmkZ6ejsFgYP78+S63K4rCo48+SlpaGmFhYUyaNIk9e/a43KewsJAZM2YQHR1NbGws1113HeXl5QH8KjqPts73rFmzmrzep0yZ4nIfOd/umTNnDmPGjCEqKork5GQuvPBCdu3a5XIfd35+ZGdnc/755xMeHk5ycjL33nsv9fX1gfxSOgV3zveZZ57Z5PX9xz/+0eU+cr7d8/rrrzNs2DB9OOa4ceP44Ycf9Nvlte1bbZ1veW3717PPPovBYOCOO+7QrwvUa1wCpHb49NNPueuuu3jsscfYsGEDw4cPZ/LkyeTn5wd7aceFwYMHk5OTo/9Zvny5ftudd97JN998w+eff86SJUs4cuQIF198cRBX27lUVFQwfPhwXnvttWZvf/7553nllVd44403WL16NREREUyePJnq6mr9PjNmzGDbtm0sWLCAb7/9lqVLl3LjjTcG6kvoVNo63wBTpkxxeb1//PHHLrfL+XbPkiVLuPXWW/n1119ZsGABdXV1nHvuuVRUVOj3aevnh81m4/zzz6e2tpaVK1fy3nvv8e677/Loo48G40vq0Nw53wA33HCDy+v7+eef12+T8+2+rl278uyzz7J+/XrWrVvHWWedxfTp09m2bRsgr21fa+t8g7y2/WXt2rW8+eabDBs2zOX6gL3GFeG1k08+Wbn11lv1f9tsNiU9PV2ZM2dOEFd1fHjssceU4cOHN3tbcXGxEhISonz++ef6dTt27FAAZdWqVQFa4fEDUL788kv933a7XUlNTVX++te/6tcVFxcrVqtV+fjjjxVFUZTt27crgLJ27Vr9Pj/88INiMBiUw4cPB2ztnVHj860oijJz5kxl+vTpLT5Gzrf38vPzFUBZsmSJoiju/fz4/vvvFaPRqOTm5ur3ef3115Xo6GilpqYmsF9AJ9P4fCuKopxxxhnK7bff3uJj5Hy3T1xcnPLWW2/JaztAtPOtKPLa9peysjKlb9++yoIFC1zOcSBf45JB8lJtbS3r169n0qRJ+nVGo5FJkyaxatWqIK7s+LFnzx7S09Pp1asXM2bMIDs7G4D169dTV1fncu4HDBhA9+7d5dz7QFZWFrm5uS7nNyYmhrFjx+rnd9WqVcTGxnLSSSfp95k0aRJGo5HVq1cHfM3Hg8WLF5OcnEz//v25+eabKSgo0G+T8+29kpISAOLj4wH3fn6sWrWKoUOHkpKSot9n8uTJlJaWunxyLJpqfL41H374IYmJiQwZMoQHH3yQyspK/TY5396x2Wx88sknVFRUMG7cOHlt+1nj862R17bv3XrrrZx//vkur2UI7M9vczu/hhPWsWPHsNlsLt8AgJSUFHbu3BmkVR0/xo4dy7vvvkv//v3JycnhiSee4PTTT2fr1q3k5uZisViIjY11eUxKSgq5ubnBWfBxRDuHzb22tdtyc3NJTk52ud1sNhMfHy/fAy9MmTKFiy++mIyMDDIzM3nooYeYOnUqq1atwmQyyfn2kt1u54477mD8+PEMGTIEwK2fH7m5uc2+/rXbRPOaO98AV111FT169CA9PZ3Nmzdz//33s2vXLubNmwfI+fbUli1bGDduHNXV1URGRvLll18yaNAgNm3aJK9tP2jpfIO8tv3hk08+YcOGDaxdu7bJbYH8+S0BkuiQpk6dql8eNmwYY8eOpUePHnz22WeEhYUFcWVC+N4VV1yhXx46dCjDhg2jd+/eLF68mLPPPjuIK+vcbr31VrZu3eqyf1H4T0vn23mv3NChQ0lLS+Pss88mMzOT3r17B3qZnV7//v3ZtGkTJSUl/Pe//2XmzJksWbIk2Ms6brV0vgcNGiSvbR87ePAgt99+OwsWLCA0NDSoa5ESOy8lJiZiMpmadM7Iy8sjNTU1SKs6fsXGxtKvXz/27t1LamoqtbW1FBcXu9xHzr1vaOewtdd2ampqk2Yk9fX1FBYWyvfAB3r16kViYiJ79+4F5Hx747bbbuPbb7/ll19+oWvXrvr17vz8SE1Nbfb1r90mmmrpfDdn7NixAC6vbznf7rNYLPTp04fRo0czZ84chg8fzt///nd5bftJS+e7OfLabp/169eTn5/PqFGjMJvNmM1mlixZwiuvvILZbCYlJSVgr3EJkLxksVgYPXo0Cxcu1K+z2+0sXLjQpTZV+EZ5eTmZmZmkpaUxevRoQkJCXM79rl27yM7OlnPvAxkZGaSmprqc39LSUlavXq2f33HjxlFcXMz69ev1+yxatAi73a7/ghDeO3ToEAUFBaSlpQFyvj2hKAq33XYbX375JYsWLSIjI8Pldnd+fowbN44tW7a4BKULFiwgOjpaL60RqrbOd3M2bdoE4PL6lvPtPbvdTk1Njby2A0Q7382R13b7nH322WzZsoVNmzbpf0466SRmzJihXw7Ya9wX3SZOVJ988olitVqVd999V9m+fbty4403KrGxsS6dM4R37r77bmXx4sVKVlaWsmLFCmXSpElKYmKikp+fryiKovzxj39UunfvrixatEhZt26dMm7cOGXcuHFBXnXnUVZWpmzcuFHZuHGjAigvvfSSsnHjRuXAgQOKoijKs88+q8TGxipfffWVsnnzZmX69OlKRkaGUlVVpR9jypQpysiRI5XVq1cry5cvV/r27atceeWVwfqSOrTWzndZWZlyzz33KKtWrVKysrKUn3/+WRk1apTSt29fpbq6Wj+GnG/33HzzzUpMTIyyePFiJScnR/9TWVmp36etnx/19fXKkCFDlHPPPVfZtGmT8uOPPypJSUnKgw8+GIwvqUNr63zv3btXefLJJ5V169YpWVlZyldffaX06tVLmTBhgn4MOd/ue+CBB5QlS5YoWVlZyubNm5UHHnhAMRgMyv/+9z9FUeS17WutnW95bQdG406BgXqNS4DUTv/4xz+U7t27KxaLRTn55JOVX3/9NdhLOi5cfvnlSlpammKxWJQuXbool19+ubJ371799qqqKuWWW25R4uLilPDwcOWiiy5ScnJygrjizuWXX35RgCZ/Zs6cqSiK2ur7kUceUVJSUhSr1aqcffbZyq5du1yOUVBQoFx55ZVKZGSkEh0drcyePVspKysLwlfT8bV2visrK5Vzzz1XSUpKUkJCQpQePXooN9xwQ5MPWuR8u6e58wwo77zzjn4fd35+7N+/X5k6daoSFhamJCYmKnfffbdSV1cX4K+m42vrfGdnZysTJkxQ4uPjFavVqvTp00e59957lZKSEpfjyPl2zx/+8AelR48eisViUZKSkpSzzz5bD44URV7bvtba+ZbXdmA0DpAC9Ro3KIqieJwDE0IIIYQQQojjkOxBEkIIIYQQQggHCZCEEEIIIYQQwkECJCGEEEIIIYRwkABJCCGEEEIIIRwkQBJCCCGEEEIIBwmQhBBCCCGEEMJBAiQhhBBCCCGEcJAASQghhBBCCCEcJEASQgghhBBCCAcJkIQQQgghhBDCQQIkIYQQQgghhHCQAEkIIYQQQgghHP4fsnOC0lPgC3EAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the predictions\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(y_test_actual[:, 0], label=\"Real Prices (Amazon)\")\n",
    "plt.plot(y_pred_actual[:, 0], label=\"Predicted Prices (Amazon)\")\n",
    "plt.legend()\n",
    "plt.title(\"Amazon Stock Price Prediction\")  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Evaulating the Model\n",
    "F1 score and accuracy are not suitable methods for regression models.For regression models, metrics like MSE, MAPE, and RMSE provide better insights."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.15\n",
      "Precision: 0.51\n",
      "Recall: 0.57\n",
      "F1 Score: 0.54\n"
     ]
    }
   ],
   "source": [
    "# Plot the predictions\n",
    "y_test_class = (y_test_actual[1:] > y_test_actual[:-1]).astype(int)                                                                                                                                                                                                                                                                                                                   \n",
    "y_pred_class = (y_pred_actual[1:] > y_pred_actual[:-1]).astype(int)\n",
    "\n",
    "# Classification metrics\n",
    "accuracy = accuracy_score(y_test_class, y_pred_class)\n",
    "precision = precision_score(y_test_class, y_pred_class, average='weighted', zero_division=0)\n",
    "recall = recall_score(y_test_class, y_pred_class, average='micro', zero_division=0)\n",
    "f1 = f1_score(y_test_class, y_pred_class,average='micro', zero_division=0)\n",
    "\n",
    "# Print the classification metrics\n",
    "print(f\"Accuracy: {accuracy:.2f}\")\n",
    "print(f\"Precision: {precision:.2f}\")\n",
    "print(f\"Recall: {recall:.2f}\")\n",
    "print(f\"F1 Score: {f1:.2f}\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}