LSTM on GME (1m).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Implementation of LSTM on GME 1min data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. importing required packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import yfinance as yf\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from matplotlib.pylab import rcParams\n",
    "rcParams['figure.figsize']=20,10\n",
    "from keras.models import Sequential\n",
    "from keras.layers import LSTM,Dropout,Dense\n",
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. importing stock data using yahoo! finance API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Datetime'>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = yf.download('GME', start = '2021-01-20', interval = '1m', end = '2021-01-27')[['Close']]\n",
    "data.head()\n",
    "  \n",
    "data.plot(figsize = (10, 10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Datetime</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-01-26 10:55:00-05:00</th>\n",
       "      <td>89.730003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-26 10:56:00-05:00</th>\n",
       "      <td>88.650101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-26 10:57:00-05:00</th>\n",
       "      <td>89.040001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-26 10:58:00-05:00</th>\n",
       "      <td>88.095100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-26 10:59:00-05:00</th>\n",
       "      <td>87.610100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Close\n",
       "Datetime                            \n",
       "2021-01-26 10:55:00-05:00  89.730003\n",
       "2021-01-26 10:56:00-05:00  88.650101\n",
       "2021-01-26 10:57:00-05:00  89.040001\n",
       "2021-01-26 10:58:00-05:00  88.095100\n",
       "2021-01-26 10:59:00-05:00  87.610100"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. preparing dataset with 60:40 train-to-test ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "prop_train = 0.6\n",
    "\n",
    "cl = data.Close.astype('float32')\n",
    "train = cl[0:int(len(cl)*prop_train)]\n",
    "scl = MinMaxScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "scl.fit(train.values.reshape(-1,1))\n",
    "cl = scl.transform(cl.values.reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1812 60\n",
      "1087 60\n",
      "725 60\n",
      "1087\n",
      "725\n"
     ]
    }
   ],
   "source": [
    "def processData(data, lb):\n",
    "    X,Y = [],[]\n",
    "    for i in range(len(data)-lb-1):\n",
    "        X.append(data[i:(i+lb), 0])\n",
    "        Y.append(data[(i+lb), 0])\n",
    "    return np.array(X), np.array(Y)\n",
    " \n",
    " \n",
    "lb = 60\n",
    "X, y = processData(cl,lb)\n",
    "X_train,X_test = X[:int(X.shape[0]*prop_train)], X[int(X.shape[0]*prop_train):]\n",
    "y_train,y_test = y[:int(y.shape[0]*prop_train)], y[int(y.shape[0]*prop_train):]\n",
    "print(X.shape[0], X.shape[1])\n",
    "print(X_train.shape[0], X_train.shape[1])\n",
    "print(X_test.shape[0], X_test.shape[1])\n",
    "print(y_train.shape[0])\n",
    "print(y_test.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "60\n"
     ]
    }
   ],
   "source": [
    "print(X.shape[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. train the model, look back window = 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "34/34 [==============================] - 7s 143ms/step - loss: 0.0414 - val_loss: 3.1465\n",
      "Epoch 2/100\n",
      "34/34 [==============================] - 4s 127ms/step - loss: 0.0059 - val_loss: 2.8494\n",
      "Epoch 3/100\n",
      "34/34 [==============================] - 4s 128ms/step - loss: 0.0054 - val_loss: 3.2951\n",
      "Epoch 4/100\n",
      "34/34 [==============================] - 4s 130ms/step - loss: 0.0067 - val_loss: 3.9493\n",
      "Epoch 5/100\n",
      "34/34 [==============================] - 4s 132ms/step - loss: 0.0090 - val_loss: 4.2729\n",
      "Epoch 6/100\n",
      "34/34 [==============================] - 4s 125ms/step - loss: 0.0116 - val_loss: 4.6722\n",
      "Epoch 7/100\n",
      "34/34 [==============================] - 4s 128ms/step - loss: 0.0176 - val_loss: 5.0453\n",
      "Epoch 8/100\n",
      "34/34 [==============================] - 4s 133ms/step - loss: 0.0192 - val_loss: 5.5424\n",
      "Epoch 9/100\n",
      "34/34 [==============================] - 4s 127ms/step - loss: 0.0176 - val_loss: 5.7961\n",
      "Epoch 10/100\n",
      "34/34 [==============================] - 4s 128ms/step - loss: 0.0141 - val_loss: 5.0042\n",
      "Epoch 11/100\n",
      "34/34 [==============================] - 4s 128ms/step - loss: 0.0114 - val_loss: 4.0679\n",
      "Epoch 12/100\n",
      "34/34 [==============================] - 4s 133ms/step - loss: 0.0080 - val_loss: 3.0151\n",
      "Epoch 13/100\n",
      "34/34 [==============================] - 4s 128ms/step - loss: 0.0054 - val_loss: 2.1689\n",
      "Epoch 14/100\n",
      "34/34 [==============================] - 4s 130ms/step - loss: 0.0028 - val_loss: 1.6263\n",
      "Epoch 15/100\n",
      "34/34 [==============================] - 4s 130ms/step - loss: 0.0014 - val_loss: 1.5047\n",
      "Epoch 16/100\n",
      "34/34 [==============================] - 4s 131ms/step - loss: 0.0011 - val_loss: 1.5094\n",
      "Epoch 17/100\n",
      "34/34 [==============================] - 4s 125ms/step - loss: 0.0011 - val_loss: 1.4798\n",
      "Epoch 18/100\n",
      "34/34 [==============================] - 4s 130ms/step - loss: 0.0011 - val_loss: 1.4421\n",
      "Epoch 19/100\n",
      "34/34 [==============================] - 5s 137ms/step - loss: 0.0011 - val_loss: 1.4064\n",
      "Epoch 20/100\n",
      "34/34 [==============================] - 4s 131ms/step - loss: 0.0011 - val_loss: 1.3724\n",
      "Epoch 21/100\n",
      "34/34 [==============================] - 5s 135ms/step - loss: 0.0011 - val_loss: 1.3400\n",
      "Epoch 22/100\n",
      "34/34 [==============================] - 4s 128ms/step - loss: 0.0012 - val_loss: 1.3093\n",
      "Epoch 23/100\n",
      "34/34 [==============================] - 4s 131ms/step - loss: 0.0012 - val_loss: 1.2801\n",
      "Epoch 24/100\n",
      "34/34 [==============================] - 4s 129ms/step - loss: 0.0012 - val_loss: 1.2521\n",
      "Epoch 25/100\n",
      "34/34 [==============================] - 5s 134ms/step - loss: 0.0013 - val_loss: 1.2252\n",
      "Epoch 26/100\n",
      "34/34 [==============================] - 5s 137ms/step - loss: 0.0013 - val_loss: 1.1988\n",
      "Epoch 27/100\n",
      "34/34 [==============================] - 4s 133ms/step - loss: 0.0014 - val_loss: 1.1728\n",
      "Epoch 28/100\n",
      "34/34 [==============================] - 5s 135ms/step - loss: 0.0014 - val_loss: 1.1466\n",
      "Epoch 29/100\n",
      "34/34 [==============================] - 5s 138ms/step - loss: 0.0015 - val_loss: 1.1202\n",
      "Epoch 30/100\n",
      "34/34 [==============================] - 5s 135ms/step - loss: 0.0016 - val_loss: 1.0931\n",
      "Epoch 31/100\n",
      "34/34 [==============================] - 4s 131ms/step - loss: 0.0016 - val_loss: 1.0654\n",
      "Epoch 32/100\n",
      "34/34 [==============================] - 5s 134ms/step - loss: 0.0017 - val_loss: 1.0367\n",
      "Epoch 33/100\n",
      "34/34 [==============================] - 5s 134ms/step - loss: 0.0017 - val_loss: 1.0073\n",
      "Epoch 34/100\n",
      "34/34 [==============================] - 4s 124ms/step - loss: 0.0017 - val_loss: 0.9772\n",
      "Epoch 35/100\n",
      "34/34 [==============================] - 4s 127ms/step - loss: 0.0018 - val_loss: 0.9465\n",
      "Epoch 36/100\n",
      "34/34 [==============================] - 4s 126ms/step - loss: 0.0018 - val_loss: 0.9156\n",
      "Epoch 37/100\n",
      "34/34 [==============================] - 4s 128ms/step - loss: 0.0018 - val_loss: 0.8847\n",
      "Epoch 38/100\n",
      "34/34 [==============================] - 4s 128ms/step - loss: 0.0018 - val_loss: 0.8541\n",
      "Epoch 39/100\n",
      "34/34 [==============================] - 4s 129ms/step - loss: 0.0018 - val_loss: 0.8241\n",
      "Epoch 40/100\n",
      "34/34 [==============================] - 4s 126ms/step - loss: 0.0018 - val_loss: 0.7948\n",
      "Epoch 41/100\n",
      "34/34 [==============================] - 4s 127ms/step - loss: 0.0018 - val_loss: 0.7665\n",
      "Epoch 42/100\n",
      "34/34 [==============================] - 4s 127ms/step - loss: 0.0017 - val_loss: 0.7392\n",
      "Epoch 43/100\n",
      "34/34 [==============================] - 4s 128ms/step - loss: 0.0017 - val_loss: 0.7131\n",
      "Epoch 44/100\n",
      "34/34 [==============================] - 4s 126ms/step - loss: 0.0017 - val_loss: 0.6881\n",
      "Epoch 45/100\n",
      "34/34 [==============================] - 4s 125ms/step - loss: 0.0017 - val_loss: 0.6643\n",
      "Epoch 46/100\n",
      "34/34 [==============================] - 4s 128ms/step - loss: 0.0016 - val_loss: 0.6416\n",
      "Epoch 47/100\n",
      "34/34 [==============================] - 4s 126ms/step - loss: 0.0016 - val_loss: 0.6200\n",
      "Epoch 48/100\n",
      "34/34 [==============================] - 4s 132ms/step - loss: 0.0016 - val_loss: 0.5995\n",
      "Epoch 49/100\n",
      "34/34 [==============================] - 4s 129ms/step - loss: 0.0016 - val_loss: 0.5799\n",
      "Epoch 50/100\n",
      "34/34 [==============================] - 4s 130ms/step - loss: 0.0016 - val_loss: 0.5612\n",
      "Epoch 51/100\n",
      "34/34 [==============================] - 4s 124ms/step - loss: 0.0015 - val_loss: 0.5434\n",
      "Epoch 52/100\n",
      "34/34 [==============================] - 4s 124ms/step - loss: 0.0015 - val_loss: 0.5264\n",
      "Epoch 53/100\n",
      "34/34 [==============================] - 4s 127ms/step - loss: 0.0015 - val_loss: 0.5102\n",
      "Epoch 54/100\n",
      "34/34 [==============================] - 4s 125ms/step - loss: 0.0015 - val_loss: 0.4946\n",
      "Epoch 55/100\n",
      "34/34 [==============================] - 4s 127ms/step - loss: 0.0015 - val_loss: 0.4798\n",
      "Epoch 56/100\n",
      "34/34 [==============================] - 4s 125ms/step - loss: 0.0015 - val_loss: 0.4655\n",
      "Epoch 57/100\n",
      "34/34 [==============================] - 4s 129ms/step - loss: 0.0015 - val_loss: 0.4519\n",
      "Epoch 58/100\n",
      "34/34 [==============================] - 4s 128ms/step - loss: 0.0014 - val_loss: 0.4389\n",
      "Epoch 59/100\n",
      "34/34 [==============================] - 4s 126ms/step - loss: 0.0014 - val_loss: 0.4264\n",
      "Epoch 60/100\n",
      "34/34 [==============================] - 4s 126ms/step - loss: 0.0014 - val_loss: 0.4144\n",
      "Epoch 61/100\n",
      "34/34 [==============================] - 4s 125ms/step - loss: 0.0014 - val_loss: 0.4029\n",
      "Epoch 62/100\n",
      "34/34 [==============================] - 4s 123ms/step - loss: 0.0014 - val_loss: 0.3919\n",
      "Epoch 63/100\n",
      "34/34 [==============================] - 4s 126ms/step - loss: 0.0014 - val_loss: 0.3814\n",
      "Epoch 64/100\n",
      "34/34 [==============================] - 4s 127ms/step - loss: 0.0014 - val_loss: 0.3714\n",
      "Epoch 65/100\n",
      "34/34 [==============================] - 4s 125ms/step - loss: 0.0014 - val_loss: 0.3618\n",
      "Epoch 66/100\n",
      "34/34 [==============================] - 4s 123ms/step - loss: 0.0014 - val_loss: 0.3526\n",
      "Epoch 67/100\n",
      "34/34 [==============================] - 4s 127ms/step - loss: 0.0014 - val_loss: 0.3439\n",
      "Epoch 68/100\n",
      "34/34 [==============================] - 4s 122ms/step - loss: 0.0013 - val_loss: 0.3356\n",
      "Epoch 69/100\n",
      "34/34 [==============================] - 4s 121ms/step - loss: 0.0013 - val_loss: 0.3276\n",
      "Epoch 70/100\n",
      "34/34 [==============================] - 4s 123ms/step - loss: 0.0013 - val_loss: 0.3201\n",
      "Epoch 71/100\n",
      "34/34 [==============================] - 4s 127ms/step - loss: 0.0013 - val_loss: 0.3129\n",
      "Epoch 72/100\n",
      "34/34 [==============================] - 4s 125ms/step - loss: 0.0013 - val_loss: 0.3061\n",
      "Epoch 73/100\n",
      "34/34 [==============================] - 4s 126ms/step - loss: 0.0013 - val_loss: 0.2996\n",
      "Epoch 74/100\n",
      "34/34 [==============================] - 4s 130ms/step - loss: 0.0013 - val_loss: 0.2935\n",
      "Epoch 75/100\n",
      "34/34 [==============================] - 4s 129ms/step - loss: 0.0013 - val_loss: 0.2877\n",
      "Epoch 76/100\n",
      "34/34 [==============================] - 4s 129ms/step - loss: 0.0013 - val_loss: 0.2823\n",
      "Epoch 77/100\n",
      "34/34 [==============================] - 4s 127ms/step - loss: 0.0013 - val_loss: 0.2771\n",
      "Epoch 78/100\n",
      "34/34 [==============================] - 4s 125ms/step - loss: 0.0013 - val_loss: 0.2723\n",
      "Epoch 79/100\n",
      "34/34 [==============================] - 4s 123ms/step - loss: 0.0013 - val_loss: 0.2677\n",
      "Epoch 80/100\n",
      "34/34 [==============================] - 4s 123ms/step - loss: 0.0013 - val_loss: 0.2635\n",
      "Epoch 81/100\n",
      "34/34 [==============================] - 4s 126ms/step - loss: 0.0012 - val_loss: 0.2595\n",
      "Epoch 82/100\n",
      "34/34 [==============================] - 5s 135ms/step - loss: 0.0012 - val_loss: 0.2558\n",
      "Epoch 83/100\n",
      "34/34 [==============================] - 4s 128ms/step - loss: 0.0012 - val_loss: 0.2524\n",
      "Epoch 84/100\n",
      "34/34 [==============================] - 4s 126ms/step - loss: 0.0012 - val_loss: 0.2492\n",
      "Epoch 85/100\n",
      "34/34 [==============================] - 4s 124ms/step - loss: 0.0012 - val_loss: 0.2462\n",
      "Epoch 86/100\n",
      "34/34 [==============================] - 4s 130ms/step - loss: 0.0012 - val_loss: 0.2436\n",
      "Epoch 87/100\n",
      "34/34 [==============================] - 4s 128ms/step - loss: 0.0012 - val_loss: 0.2410\n",
      "Epoch 88/100\n",
      "34/34 [==============================] - 4s 126ms/step - loss: 0.0012 - val_loss: 0.2390\n",
      "Epoch 89/100\n",
      "34/34 [==============================] - 4s 128ms/step - loss: 0.0012 - val_loss: 0.2366\n",
      "Epoch 90/100\n",
      "34/34 [==============================] - 4s 129ms/step - loss: 0.0012 - val_loss: 0.2360\n",
      "Epoch 91/100\n",
      "34/34 [==============================] - 4s 127ms/step - loss: 0.0012 - val_loss: 0.2328\n",
      "Epoch 92/100\n",
      "34/34 [==============================] - 4s 125ms/step - loss: 0.0012 - val_loss: 0.2475\n",
      "Epoch 93/100\n",
      "34/34 [==============================] - 4s 132ms/step - loss: 0.0012 - val_loss: 0.2690\n",
      "Epoch 94/100\n",
      "34/34 [==============================] - 4s 127ms/step - loss: 0.0011 - val_loss: 0.3313\n",
      "Epoch 95/100\n",
      "34/34 [==============================] - 4s 127ms/step - loss: 0.0013 - val_loss: 0.2907\n",
      "Epoch 96/100\n",
      "34/34 [==============================] - 4s 126ms/step - loss: 0.0016 - val_loss: 0.3236\n",
      "Epoch 97/100\n",
      "34/34 [==============================] - 4s 129ms/step - loss: 0.0014 - val_loss: 0.4245\n",
      "Epoch 98/100\n",
      "34/34 [==============================] - 4s 128ms/step - loss: 0.0013 - val_loss: 0.3792\n",
      "Epoch 99/100\n",
      "34/34 [==============================] - 4s 126ms/step - loss: 0.0019 - val_loss: 0.5575\n",
      "Epoch 100/100\n",
      "34/34 [==============================] - 4s 125ms/step - loss: 0.0013 - val_loss: 0.4191\n",
      "Model: \"sequential_3\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lstm_3 (LSTM)                (None, 256)               264192    \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 1)                 257       \n",
      "=================================================================\n",
      "Total params: 264,449\n",
      "Trainable params: 264,449\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "#Build the model\n",
    "model = Sequential()\n",
    "model.add(LSTM(256,input_shape=(lb,1)))\n",
    "model.add(Dense(1))\n",
    "model.compile(optimizer='adam',loss='mse')\n",
    " \n",
    "#Reshape data for (Sample,Timestep,Features) \n",
    "X_train = X_train.reshape((X_train.shape[0],X_train.shape[1],1))\n",
    "X_test = X_test.reshape((X_test.shape[0],X_test.shape[1],1))\n",
    " \n",
    "#Fit model with history to check for overfitting\n",
    "history = model.fit(X_train,y_train,epochs=100,validation_data=(X_test,y_test),shuffle=False)\n",
    " \n",
    "model.summary() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'validation on testing data')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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//vfjOBPzmWPEZedFREREZGg9kRgAiQn6cCYiInLSevAzcGTTxB6zZiVc+dVR7RqLxXjwwQe54oorAHj++efZvHkz8+bN49Zbb6W0tJQXXniBcDjMeeedx2WXXcZLL73Ejh072LRpE0ePHmXZsmX8wz/8Q5/jNjY28t73vpe1a9cyb948WlpaKC8v5wMf+ABFRUV88pOfBODtb387H/vYxzj//PM5ePAgl19+Odu2beNLX/oS559/Pv/+7//O/fffz6233johPxoFQiIiIiLjEHJbxjRDSEREJDf19PRw6qmnArZC6KabbuLpp5/mrLPOYt68eQD87W9/Y+PGjan5QO3t7ezatYu1a9dyww034PV6mT59OpdccsmA4z/77LO8+tWvTh2rvLx80PN4+OGH+8wc6ujooLOzk7Vr13L33XcD8LrXvY4pU6ZMyPetQEhERERkHEKaISQiIjIxRlnJM9GSM4T6KywsTN13HIfvfe97XH755X32eeCBBzDGDHt8x3FG3AcgkUjwzDPPkJ+fP+C50bx+rDRDSERERGQckkOlY1p2XkRE5IR1+eWX86Mf/YhoNArAzp076e7u5tWvfjW/+93viMfj1NfX8+ijjw547bnnnsvjjz/Ovn37AGhpaQGguLiYzs7O1H6XXXYZ3//+91OPkyHVq1/9an7zm98A8OCDD9La2joh35MCIREREZFx6ImqQkhEROREd/PNN7Ns2TJOP/10VqxYwfvf/35isRjXXHMNCxcuZOXKlXzwgx/kwgsvHPDaqqoqbr31Vt785jezevVq3va2twHw+te/nnvuuSc1VPq73/0u69atY9WqVSxbtiy12tkXvvAF1q5dy+mnn87f/vY3Zs+ePSHfk5mo6dTjsWbNGic5eVtEREQkl9z1Yi2fvHMDM6fk8+S/DJwbICIiIkPbtm0bS5cuPd6ncUIY7GdpjHnRcZw1g+2vCiERERGRcUitMqYKIREREckhCoRERERExiG1ypgCIREREckhCoRERERExkGrjImIiEguUiAkIiIiMg7JodKqEBIREcnOZJhtnOuy+RkqEBIREREZh5A7Q0gVQiIiImOXl5dHc3OzQqFxcByH5uZm8vLyxvQ63zE6HxEREZGTQnqGUOI4n4mIiEjumTlzJrW1tTQ2Nh7vU8lpeXl5zJw5c0yvUSAkIiIiMg49biCkPEhERGTs/H4/8+bNO96ncVJSy5iIiIjIOKhCSERERHKRAiERERGRcUhVCDmQ0BwhERERyREKhERERETGIRSNpe7HNRBTREREcoQCIREREZFxSLaMgVYaExERkdyhQEhERERkHHoUCImIiEgOUiAkIiIiMg6ZFUIxBUIiIiKSIxQIiYiIiIxDTzROQcALqEJIREREcocCIREREZEsxRMOkViCoqAP0NLzIiIikjsUCImIiIhkKRSxK4wV59lASBVCIiIikisUCImIiIhkKTlQuijPD0AsrkBIREREcoMCIREREZEsJQdKl7gVQglHgZCIiIjkBgVCIiIiIllKBkLJljGtMiYiIiK5QoGQiIiISJa63RlCJW7LmGYIiYiISK5QICQiIiKSpdbuCACVRUFAM4REREQkdygQEhEREclSa8gGQlXFNhBShZCIiIjkCgVCIiIiIllq6Y4C6UAolkgcz9MRERERGTUFQiIiIiJZag1FyPN7KApqlTERERHJLQqERERERLLU3BWhvCCAz2MAzRASERGR3KFASERERCRLraEI5UUBPG4gpBlCIiIikisUCImIiIhkqaU7wpTMCiEFQiIiIpIjFAiJiIiIZKk1FKG8MIBXFUIiIiKSYxQIiYiIiGQpXSFkP1IpEBIREZFcoUBIREREJAuRWILO3lifCiG1jImIiEiuUCAkIiIikoW2UASAKYUBfF61jImIiEhuUSAkIiIikoUWNxCqKAzgMckKocTxPCURERGRUVMgJCIiIpKFlm63QihjlTFVCImIiEiuUCAkIiIikoXW7iiAZgiJiIhITvId7xMQERERyUUt3WEAphT6U5VBCQVCIiIikiNUISQiIiKShRa3QmhKgSqEREREJPcoEBIRERHJQmsoQnGeD7/Xg9dohpCIiIjkFgVCIiIiIlk41BKipiQPAJ/HfqRShZCIiIjkCgVCIiIiImPkOA4battYNbMMAK83WSGkZedFREQkNygQEhERERmjw209NHVFOHVWKUDGsvPH86xERERERk+BkIiIiMgYbTjUDsDqWWUAqaHSqhASERGRXKFASERERGSMNtS2EfB5WFJTApAaKq0ZQiIiIpIrFAiJiIiIjNHLh9pYPr2EgM9+lPJ4DMZolTERERHJHQqERERERMYgkXDYVNvOanegdJLPY1QhJCIiIjlDgZCIiIjIGLT1ROmJxplbUdBnu9djVCEkIiIiOUOBkIiIiMgYtHSHASgvCvbZ7vN4FAiJiIhIzlAgJCIiIjIGzV0RACoKA322q0JIREREcokCIREREZExaOm2gdCUgr6BkJ0hpGXnRUREJDcoEBIREREZg5aQWyFU1DcQ8qhCSERERHKIAiERERGRMWjpGqZCKK5ASERERHLDiIGQMeY2Y0yDMWbzIM990hjjGGMqM7Z91hiz2xizwxhz+USfsIiIiMjx1NwdoTjoI+Dr+zFKM4REREQkl4ymQugXwBX9NxpjZgGvBQ5mbFsGXA8sd1/zQ2OMd0LOVERERGQSaOmOUN6vXQxshVDcUSAkIiIiuWHEQMhxnLVAyyBPfQv4NJD5yeeNwO8cxwk7jrMP2A2cNREnKiIiIjIZtHRHKC8cGAh5PYaYKoREREQkR2Q1Q8gY8wbgsOM4G/o9NQM4lPG41t0mIiIickJo6Y5QXjBYhZCHuGYIiYiISI4YcyBkjCkAPgf8+2BPD7Jt0E9Gxpj3GWPWGWPWNTY2jvU0RERERI6pn6zdy+6GTtp7ovzvX3fQ2RsFhq4Q8qhCSERERHKIL4vXzAfmARuMMQAzgfXGmLOwFUGzMvadCdQNdhDHcW4FbgVYs2aNPj2JiIjIpNEdjvHlB7Zxx7pDLJtewh9frmNxTTFXr5o2/AyhROI4nK2IiIjI2I05EHIcZxMwNfnYGLMfWOM4TpMx5k/Ab40x3wSmAwuB5yfoXEVEREReEd2RGAC7GrrY1dAFwO6GLrrCMSLxBBVDzBBSx5iIiIjkihEDIWPM7cBFQKUxphb4guM4PxtsX8dxthhj7gC2AjHgw47jxCfwfEVERESOuVDYfnyZX1VIeWGAurZedjd20dpt28amDDpDSBVCIiIikjtGDIQcx7lhhOfn9nv8ZeDL4zstERERkeMnFLGB0CcvW8wVK2q46Zfr2NPQRXN3GICKQVrGvB5DTCVCIiIikiOyWmVMRERE5ETWE7UtYwVBH8YYFkwtYm9TN42dNhAqLwwOeI3Pa4hrqLSIiIjkCAVCIiIiIv0kK4QKAl4AFlQVEYkl2FjbDjDosvMek15l7JHtR/nBo7tfobMVERERGTsFQiIiIiL9JAOhfL8NhOZPLQLg9ucPUprvp7p0kAohT7pC6L4N9fz48T2v0NmKiIiIjJ0CIREREZF+egapEAJo7o7w3gvmEfR5B7zG6/GkAqFIPEFHb4xwTGtriIiIyOSkQEhERESkn3TLmF1/o7TAT2VRkLICP+8+b96gr8msEIrG7WpjTV2RV+BsRURERMZuxFXGRERERE42oYgdKp0fSFcCffryxUwpDFAUHPzjk9driLnLzkfd1caaOsPMKMs/xmcrIiIiMnYKhERERET66T9UGuC6M2cN+xqvGaxCKHyMzlBERERkfNQyJiIiItJPKBIn4PXg947+o5LPk15lTIGQiIiITHYKhERERET66YnE+rSLjYbXY0ikAiG3ZUwzhERERGSSUiAkIiIi0k8oEu/TLjYaPu/ACqHGTlUIiYiIyOSkQEhERESkn1A0nlWFUGrZ+ZgbCKllTERERCYpBUIiIiIi/fRkUyHk8QycIaQKIREREZmkFAiJiIiI9BOKxCjwj20xVk+fVcaSM4QUCImIiMjkpEBIREREpJ+eyNhbxuwMIVsZlF5lTEOlRUREZHJSICQiIiLSTzZDpe0qY/Z+MhBq74kSjsUn+vRERERExk2BkIiIiEg/NhAaW8uYz5OuEIrEEgR99mNWs6qEREREZBJSICQiIiLSTygSy65CyIFEwiEad5hWmgdojpCIiIhMTgqERERERPrJpmXM5zEAxB2HaDzBKVVFANz1Yi2O40z4OYqIiIiMx9hqoUVEREROcPGEQziWGPNQaY8bCEXjCWIJh5UzSplXWcjPntzH4ppi3nH2nGNxuiIiIiJZUYWQiIiISIaeqB0CnW2FUG/UzhEK+Dx87qqlnFJZyCPbGib2JEVERETGSYGQiIiISIZQJAZA/hiHSns99mNVrxso+b0Gj8dQWuAn4q46JiIiIjJZKBASERERydATcSuE/NlVCIUiyUDIk7qNKhASERGRSUaBkIiIiEiGZKBTGBxbIJRcZr4rbCuMkoFQwOshElMgJCIiIpOLAiERERGRDNm2jOW5FUWdvVHABkFgZwlF41plTERERCYXBUIiIiIiGZIVQmMdKp2sEOrsdSuEfLaFzO81qhASERGRSUeBkIiIiEiGZCCUP8YZQskKoa7efi1jPq9mCImIiMiko0BIREREJENPthVCfvuxqsNtGUsPlTZaZUxEREQmHQVCIiIiIhnSLWPZzhCyFUIBDZUWERGRSUyBkIiIiEiGbneVsIIxrjKW5+sbCPn7DJVWICQiIiKTiwIhERERkQztPVE8BorGXCGUHCqdbBlLDpXWKmMiIiIy+SgQEhEREcnQ3hOlNN+Px2PG9Lr+LWN+X3KGkFrGREREZPJRICQiIiKSIRkIjVUqEArbCqFARstYJJ7AcVQlJCIiIpOHAiERERGRDNkGQkG3ImjAsvNu61gsoUBIREREJg8FQiIiIiIZ2nuilIynQigVCKVnCAFqGxMREZFJRYGQiIiISIZsK4S8HoPfa+gMD1xlDNBKYyIiIjKpKBASERERydDeE6WsYOyBENil55OrjAUyhkqDKoRERERkclEgJCIiIuJyHCfrCiGAoN9Lb9QGP/0rhCKqEBIREZFJRIGQiIiIiKs7EieecLIOhPL86Y9WyRlCydXGonENlRYREZHJQ4GQiIiIiKu9x7Z7ZR8IeVP3kxVCahkTERGRyUiBkIiIiIirPTTeQCizQkhDpUVERGTyUiAkIiIi4mrriQBQmh8Y+4sbd/KR0I8wJPB6DF5Pctl5e6sZQiIiIjKZKBASERERcXWMp2Vsx/1c0XM/FXSmQiBIzxBSy5iIiIhMJgqERERERFypGULZLDvf3QSAj1iqXQzUMiYiIiKTkwIhEREREde4hkp3NQDgM/FUVRCkZwkpEBIREZHJxHe8T0BERERksmjvieLzGAoD3pF37q+7EQA/cWKDVAipZUxEREQmE1UIiYiIiLjaQlFK8/0YY0beub9Uy1gcvy/9+tSy83FnQs5RREREZCKoQkhERETE1d4TzXrJ+XSFUIy4J6NCSEOlRUREZBJShZCIiIic1B7ZfpQfP74HsIFQSTaBUCKRCoR8xDVUWkRERCY9BUIiIiJyUrvjhVq+/tcdtHRH6Mi2Qqi3DZw4MFjLmL2vQEhEREQmEwVCIiIiclJrCUWIJxxuf/4g2490Mq+ycOwHcauDwA6VzqwQ8muotIiIiExCCoRERETkpOA4Do/taMBx+g53bu2OAPDNh3YSjiV457lzxn5wd8l5AK/p1zKWGiqtQEhEREQmDwVCIiIiclJ4fl8L7/75Czy7t6XP9taQDYTiCYdLl05lflXR2A/er0IokFkh5N6PxrTKmIiIiEweCoRERETkpHC4rQeAox29qW2JhENrKMprl1VTnOfjQxcvyO7g7pLzAD5iqblBAF6PwesxROLx7I4tIiIicgxo2XkRERE5KRztCAPQ1BVObevsjRFPOJxzSgU/edea7A+eUSHkI47j7XvNLeD1EI2rQkhEREQmDwVCIiIiclJIVga1uDODwA6UBigvzGJlsUzd6RlCfuI4vr6BkN9rNFRaREREJhW1jImIiMhJobHTVgb1CYTc+1MKAuM7eHcTeIOArRAK9K8Q8nk1VFpEREQmFQVCIiIiclJIVgg1ZwRCyRXGygvHGwg1Qsk0APym7wwhgIDXEFWFkIiIiEwiCoRERETkpHC0c+iWsXFXCHXWQ8lMwFYI+ftVCPl9HlUIiYiIyKSiQEhEREROeI7jpIZKt0x0hVAiDh11UD4PAC+JAYGQHSqtQEhEREQmDw2VFhERkRNeR0+MSCyBx0BzV5hQJMa2+k5aQhECPg8FAW/2B+88AokYlJ8CgJ8Y/gFDpT1EYlplTERERCYPVQiJiIjICS/ZLnZKVREdvTF+/tR+3nrL02w53EF5QQBjzAhHGEb7IXvrVgjZlrG+x1PLmIiIiEw2IwZCxpjbjDENxpjNGdv+xxiz3Riz0RhzjzGmLOO5zxpjdhtjdhhjLj9G5y0iIiIyasmB0sumlQDw5K4mHAee2dvMlPEOlG5LBkLJCqGBM4SCXo+GSouIiMikMpoKoV8AV/Tb9hCwwnGcVcBO4LMAxphlwPXAcvc1PzTGjKMGW0RERGT8kvODlrqB0IsHWwGIJxzKC/3jO3iyQmiKrRA6bWYhFyys6rOL32c0Q0hEREQmlREDIcdx1gIt/bb9zXGcmPvwWWCme/+NwO8cxwk7jrMP2A2cNYHnKyIiIjJmDW7L2JJpxQBEMqp1xr3CWPshyJ8CeSWA4bWLKzhjzpQ+uwS8ahkTERGRyWUiZgj9A/Cge38GcCjjuVp3m4iIiMhx09ARpjjPx8yy/NS202eXAeNcYQygvRZKZ9n7Xj/EowN2sUOlFQiJiIjI5DGuQMgY8zkgBvwmuWmQ3QZdUsMY8z5jzDpjzLrGxsbxnIaIiIjIsI529FJdktcn/LnxVXOBCagQajuUDoQ8frviWD8aKi0iIiKTTdaBkDHmRuBq4B2O4yRDn1pgVsZuM4G6wV7vOM6tjuOscRxnTVVV1WC7iIiIiEwIGwgFKSsIYAz4vYYrVtTwwYvmc9XKadkf2HFsy1hZMhDyDVohFPR6NENIREREJpWsAiFjzBXAvwBvcBwnlPHUn4DrjTFBY8w8YCHw/PhPU0RERCR7DZ1hphbn4fUYphQEOKWyiKDPy79csYTFNcXZH7i3DSJdUOqOU/T6Bq8Q8nqIxgYtmhYRERE5Lnwj7WCMuR24CKg0xtQCX8CuKhYEHjLGADzrOM4HHMfZYoy5A9iKbSX7sOM48WN18iIiIiIjcRyHho4wU0uCACyuLmZRddFEHBg2323v92kZG2SGkM+kWsYisQSxRIKCwIgfw0RERESOmRE/iTiOc8Mgm382zP5fBr48npMSERERmShtoSiReILq4jwA/u/msyfmwE99Gx7+IsxYAwteY7d5/RB3K4Q6j8DPLoP/dzcBr5eoO1T6vx/cxnN7W3jgny6YmPMQERERyYIuTYmIiMgJ7ai75Hx1iQ2EvJ7B1sDIwp5HoXoF3PQ38HjtNo8vXSHUvAfaDkDDVvy+hakKoUe3N9DZO7CtTEREROSVNBHLzouIiIhMWkc7wgCplrEJ07oPqpakwyDou+x8zAZRRLoIeu0qY0c7etnfHNIS9CIiInLcKRASERGRE1pDh1sh5LaMTYhYBNprofyUvtszl52P2SCKSDd+rwfHgWf2NAMQ1opjIiIicpwpEBIREZETWkPnOCqEmvfA/qcGbm87CE4Cyuf13e71DVoh5PfZj1xP7m6ym2IJHEerjomIiMjxo0BIRERETmhHO3opzfeT5/eOvHN/j38N7niXXVEsU8teeztohVAyEHIrhMJdBLz2I9fanY2pXaNxBUIiIiJy/CgQEhERkRPa0Y5ephZnOT+ouxFCTfY2U+s+ezsgEBqsQqibmlLbrtbQGWaaez+itjERERE5jrTKmIiIiJzQGjrDqRXGxizU7B5kG+x4EIprYNHltkLIXwiFVX339/ohEbf3UzOEurhq5TSe+ewlJBx4aMsRvvjnrXaw9ATPuRYREREZLQVCIiIickJq6OzlgY311LX1cN6CyuwOEmq1t0c2wWP/DTWr3EBon60OMv2WsPf40pVBGRVCANNK8wEI+GzrmlYaExERkeNJgZCIiIickH7+1H5+9NgegOwrhHpa7O2G2yHSBY3b7Dyhlr0wdcnA/fssO5+uEMoUcAdMKxASERGR40kzhEREROSEtOFQG9NL81g5o5Tz5mdRIRSLpMOco5vtbU8rdB6BtgMwZd7A1/RZdr7H3roVQkl+r60qisTjYz8nERERkQmiCiERERE54SQSDptq23nDqdP58jUrsztIsjrIG4R42LaDJWKw8fcQj8C01QNf02fZ+cErhIJuhVBYFUIiIiJyHKlCSERERE44e5u66QzHWD2rLPuDhNxAaOYae7v09fb2hZ/Z27kXDHxNn2Xn3RlC4cFbxrTsvIiIiBxPCoRERETkhLOxtg2A1TPLsj9IskLolIvt7eq3Q14ZtB+EykVQXD3wNV4/xJMtY8kKob4tYwGvhkqLiIjI8aeWMRERETnhbDjURkHAy4KpRdkfJFkhtOhy+1/NSpi6FA4+M3h1ELhtZf0qhPoHQhoqLSIiIpOAKoRERETkhOI4DusOtLJiRilejxn5BUNJVggVlMO0VXaJ+Sp3ZbF5QwRC3syh0hkzhJx0e1gqENJQaRERETmOFAiJiIjICeWxnY1sqevgqhU14ztQskIovzy9bdZZ4C8YvkIo3q9CCAeiodQuAa8qhEREROT4UyAkIiIiOasnEmdLXXvqcSye4Cv3b2NuRQFvP3vOOA/eAr48CBSkt626Hj62BQqHWMbeM0iFEECoGQ48DaQrhLTKmIiIiBxPCoREREQkZ/3+hYO87rtPcv/GegB+9NgedjV08Zkrl6aCl6yFWvtWBwF4PLaFbCjewSqEgGdvgZ9fBaGW1LLzqhASERGR40lDpUVERCRnNXdHAPjEnS+z7kALv3x6P288dTqXLx9kBbCx6mkZPvwZzGDLzgMc2Qg4EOkm4LMVR5G4AiERERE5flQhJCIiIjmrszdGnt/D0mkl/OqZA8ytKOS/3rQCY8YxTDop1AL5U8b2Gq8fnAQkErZlLK/Mbm/YZm/jEfyaISQiIiKTgCqEREREJGd1hWOUFwS450PnpQKWcbeKJYWaoXrZ2F7jcT9aJaIQ7bWzhnrbINRkt8cjWnZeREREJgVVCImIiEjO6uqNUZRnQ5iAzzNxYRDYlrH+M4RG4vXb23jUtowVVPR9PhbWKmMiIiIyKSgQEhERkZzVFY5RFDwGBc+JBPS0ZjdDCGyFUCw8MBCKR/B7bTtbVDOERERE5DhSICQiIiI5qzMcoyjPP/EH7mm1s4AKq8b2umSFUCI+ZIWQMYaAz0NYgZCIiIgcRwqEREREJGd1h2MUH4sKoc46e1s8bWyvS84QikcgPliFUBiAoNejljERERE5rhQIiYiISM7q6o1RGPRO/IE7j9jbbAOhSLe9zStJbwOIRQA770iBkIiIiBxPCoREREQkpyQSDn/fdhTHcdwZQsegZazDrRAqGWMglGwZC3faW18+BArTz8cVCImIiMjkoEBIREREcsrjOxu56ZfreHZviw2E8o5Fy1i9vS2qGdvrUhVCXfbWF4RAMRi3iikzENIMIRERETmOFAiJiIhITtl0uB2AvU02dDk2M4TqoaASfIGxvS5VIZQMhPJshVDZbPs4ZmcIBTRDSERERI4zBUIiIiKSU7bWdQCwv8nO6TkmFUId9WNvF4P0svOZFUIXfhou/Bf72B0qPVzLWEt3hNd/70l2Hu0c+9cXERERGSUFQiIiIpJTttTbCqH9zSEAio7VKmNjHSgNg8wQyoOVb4XFV9jH7lBpv3folrF9TV1sOtzOb549MPavLyIiIjJKCoREREQkZ7T3RDnU0gMc4wqhziPZBUIDZgjl2Vtv0N5mVAiFh6gQSm6/f1M9Mc0ZEhERkWNEgZCIiIjkjO31Han7B1pshdCEzBDqaoCfvhbaa20VT3cjlEwf+3EGzBAK9r2NRwEIDtMylgyEmroiPLO3eeznICIiIjIKCoREREQkZ2x1A6Gl00pSgcqEVAjVb4Da5+HAM9B11G4rHuMKYzDIDCG3QsjjtSuNZQyVjg5R/ZMZFN23oX7s5yAiIiIyCgqEREREJGdsq++gsijA0mnFqW0TMkOop9Xeth9MLzlfnEWFULJlLDVDKJh+zhsY1VDpZIXQjLL81LwkERERkYmmQEhERERyRkt3hOqSPCqL0kHLhAZCbYego87ez6ZCyNtvhpA/P/2cL5AaKh3wDT1UOhkULakpZl9jN47jjP08REREREagQEhERERyRmdvjKKgj8qiQGpb4UQEQqEWe9t+CDoO2/slM8Z+HM8QM4TADpaOp1vGhq4QigOwuKaY7kicxs7w2M9DREREZAQKhERERCRndIVjFOf5qCi0QUue34PfOwEfZ1IVQgehYRsUVEJB+diP4x1ihhDYcCizQmiIQCi5fXGNbYvb566mJiIiIjKRFAiJiIhIzugKuxVCxTYQKgr6x36QJ78FW+7pu63HrRBqOwRHN0P1cjBm7MdOzRAarEIoAPGRA6FwqmWsBEgHQo7jcOe6Q1z5nSfY3dA19nMTERERyaBASERERHJGV2+MojwfFYW2Zax4rCuMxWPw+Nfh5dv7bk9WCMV6oH4jVK/I7gRTFULJodIZFUL9hkqHR5ghNLeygIDPkwqEfvrEPj5110a21XfwyPaj2Z2fiIiIiGsCmu5FREREXhmd4RhFQT9VqQqhMX6UadgK0RB0N9rHsbANakItYDzgJMCJ2wqhbKSWnXfbvLzpWUd9hkq7M4Qcx8H0q0QKx+J4DAR9XuZWFLDXDYTWH2xlbkUB0bjDpsMd2Z2fiIiIiEsVQiIiIpITwrE4kViC4jwf5W6F0JgDodoX7G2oyYZB31gCL//WVghVLkrvl20g5M0YKu3L69t21m+oNEA0PnAFsUgsQdDnBWBeZWGqQqixM0xNaR4rZ5SyqbYtu/MTERERcSkQEhERkZzQHbarbxUGvPi9HsoK/BSNtWWsdp17sGborLezg+rW20CoZpV9znihakl2J5mcIRTr6Ts/CAYMlQYGXXo+Ekuknp9XWcTB5hDxhENTV5iq4jxWzixlf3OI9p5oducoIiIiggIhERERyRFdvTEAivJsFc5ps8pY6q7ENWrJCqFoN7Tss/eb90BvO5TPg0AxVC4Ef97QxxiON2PIta/fMfrNEAIGHSwdjiUIus/PrSggEk9Q19ZDY2eYqqIgK2eUArDlcHt25ygiIiKCZgiJiIhIjugKu4GQ2yb28/ecNbYD9LRC8y6YMg9a98HRLXb7kY2AA/nlMOM0qFyc/Ul6A5A/xc4QWnT5wOfi/SqEBgmEMiuEZk4pAGB3QxfdkTiVxYFUILTpcDuvWlCZ/bmKiIjISU2BkIiIiOSEZCA05pXFko5ssrcLL4Pnf2yXlwcINdvbgnJ45x/Hd5IeL/zTRhv+9K8y6jdUGiA6SMtYZoXQjCn5ALx8qA2AqqIgUwoDzJySz9N7mnn/hfPHd74iIiJy0lLLmIiIiOSErrCdmTPmQdJJHXX2dsbp9vbI5r7P508Bj8f+Nx55JYO3nCWHSh96gQWH7wVs+NNfOJYg4A6VnlZqj5MKhNzV1a4/cxaP72zkL5uPjO9cRURE5KSlQEhERERyQmdqhtA4A6HqFfa2cXvf5/OnZHlmo5SsEHrhJ6x++Uvk0zvEDKF4qkIoz++lqjjIBndVscoiGwi9/8L5LJtWwr/du5neaPzYnreIiIickBQIiYiISE5ItYxlWyHUWQ/BUpgyxz5ORCGvLP38sQ6EkhVCve14nChne7aPuMoYwIyyfNpCtjpqqlsh5Pd6ePd5c2nqCtPYGT625y0iIiInJAVCIiIikhO6JqJCqGQaBIpsOAMwcw1g7P1jHggFIB6F3g4AzvdsGnGVMUjPETIGygsDqe2FAftz6FGFkIiIiGRBgZCIiIjkhK5wDI+BfL83uwN01kPxNJusFLqrc5XNhpLpgIG80gk710H5AhCzFUIA53k2D7nKWGYgNLPMBkLlBQF83vT2/IC9r5YxERERyYYCIREREZnU/rL5CJd84zGauyMUBn0YY7I7UEe9G/6QDoSKaqBsDuSX2RXCjqWMljEHw1LPIRIdA4dCh2Pxvi1jboVQcqB0Up4bjPVEJi4Q2nm0k/f/et2EHlNEREQmJwVCIiIiMqltqG1jb2M3mw+3Zz8/KBGHrqO2QgigwA2Eiqtt21jV0ok52eH43HavUBORqasA8BzdMGC3SDxB0JcOp2aUDR4IJSulJrJl7O71h/nrlqOsO9AyYccUERGRyUmBkIiIiExqrd0RALbVd2Q/P6irAZy4nSEE6Qqh4mlw6Zfg3fdPwJmOIDm3KNaLZ8psAHpCXQN2C0cTBLwDK4SSK4wl5QdsIDQRLWOdvXZo9Qv7bRC0/kDbuI8pIiIik5sCIREREZnUmt1AKBp3KMp6hTF3yfniZMtYlb0tqgaPx/53rHnTA6F9xdUA9PaEBuwWiScI+vuuMgZQWRTos1+eb2IqhA639XDqfzzEPS/VstFd3n79wVZauyNsresY17FFRERk8lIgJCIiIpNaskIIoCjPP3CHeAx+djnseWSQ56Lwu3fAht/Zx4NVCL1SfOlAxxRNBYYIhGJ9K4SK8/x87qqlXLtmVp/9khVCPZGBg6nH4nBrD/GEwxf+uIVo3GFGWT4vHWzlY3e8zPW3PkMi4Yzr+CIiIjI5ZXmZTUREROSV0ZIRCA06Q6inBQ49C/vWwvxL+j536DnYfl/6cbJCaNX1dlUxt1LnFeHNaPkqshVKkXDPgN3Csb4VQgDvffUpA/bLm6AZQsl2sY7eGMbAe86by3/dv43HdjQCtoJoVnnBuL6GiIiITD6qEBIREZFJrSWUUSE0WCAUcefwdNQPfG7XQ+n7Hl+6VaxkGqz5hwk8y1HwZQZCNoiK9guEYvEE8YRDwDvyimfJodLjnSHU4QZCXo9hcXUxFy22PyOPu5jbzqOd4zq+iIiITE4KhERERGTSisUTtIWiqYHKhYMFQmE3EErOCcq0+2GY/SqYuswuOf9KzAoaijej3a3QtozF+gVCkbht/+pfITQYv9fg9ZhxLxHf0RMD4JvXreY/37SCUyqLWDathH++dBEAOxQIiYiInJDUMiYiIiKTVluPrV45a94UHth0ZPBVxlIVQv0CoY46OLoZLv0iLH0DhI7zUuqZLWOFFSQwxKK9fXaJxGwglDlDaCjGGPL93gEtY3sbu/B7PaNu8+pwf8ZXrKhJLXf/wD9dAMDvnj/IziMTEwh96s4NXL68hkuXvYJteiIiIjIkVQiJiIjIpJUcKH32vAo8BioKAwN3inTb2456cDIGIO94wN4ueC1UzIdZZx7jsx1BxlBp8sqIewIQCxONp4dCh2OjrxACO0eofyD00dtf4l/v2TTq0+oMx8jze1JhUKZFNcXsPNo16mMNpakrzJ0v1vLwtqPjPpaIiIhMDFUIiYiIyKSVXHJ+wdQi7vrgq1hSUzxwp7BbwRLthnCHHRYdi8BT34Fpp0L18lfuhIeTWSEULCHhCRIkSmsowtTiPGBsFUIA+QEPvRktY7F4gp1HO1MtdqPR0ROlZLDV24BF1cU8vaeZWDyBb5TnNJgt7vL1TV3hrI8hIiIiE2vEd3ZjzG3GmAZjzOaMbeXGmIeMMbvc2ykZz33WGLPbGLPDGHP5sTpxEREROfElK4SmFAQ4ffYUCgKDtYx1p+8fegFuvQjuvBHaDsIlnwdjXpmTHUlyqLS/ELw+HF+QIJE+q6iFYzbcCfq9NuiKDFyWPlP/lrEDLSGicYf69l5CkRibD7fT7raEDaWjN0pJ/tCBUCSW4EDL8Ocxks2H2wFo7IqMsKeIiIi8UkZzqecXwBX9tn0G+LvjOAuBv7uPMcYsA64Hlruv+aExZuRlMkREREQGkawQqigapFUsKZLR0rTuNqh7ybaLzToHFrzmGJ/hGCSHSueVuo+DBE20XyCUUSH0+3fC/R8f9pD9A6FdGQOgt9V38OYfPs2PH98z7DE6emIUDzabCVhcbSuydoxzjtCWOhsINXWqQkhERGSyGDEQchxnLdB/CuMbgV+6938JvClj++8cxwk7jrMP2A2cNTGnKiIiIiebZIVQWcHgFSxA30Bo90NQPA0+8iLccPvkqQ6CdMuYGwgZfx5BYoMGQkG/B9proWnXsIfM83v7rDK2K2Pez59eriMST7B9hDCno3folrGF1UV4PYatbstXtjYftq9v7ArjZM55EhERkeMm22bwasdx6gHc26nu9hnAoYz9at1tIiIiImPW3B2hOOgbdOBxSrgLkgXJ8QjMOhsqF0BB+StzkqOVHCqdVwKAx59nZwhlBELJGUJBrweiPRBqGvaQ+QEvvRkVQjsbuqh0q6n+tMGuurarYfhAqLM3NmTLWJ7fy4KqolSFTzbaQ1EOtoQoLwwQiSXoDMeyPpaIiIhMnIleZWywy3CDXgYyxrzPGLPOGLOusbFxgk9DRERETgStoQhT+q8s5jhw8Ln0imKRbhuyFFTYx7POfmVPcrT6VQh5A/kEiaTa4qBfhVC0G0L9i7T7GqxlbOWMUmpK8mgN2dlBta09faqI+rNDpYdeZ2T59JLUUOhsbKm3YdKFi6oAtY2JiIhMFtkGQkeNMdMA3NsGd3stMCtjv5lA3WAHcBznVsdx1jiOs6aqqirL0xAREZETWUt3hPL+gdCh5+G2y2D3w/ZxpAsCRVA83T6ePUkDIV/fQMjjC1Lg7dsyll5lzGsrhMIdEBs6QMkMhGLxBHsbu1lUXcy8ykIAivN8OA7saeziub12tbBMjuMMO1QaYNn0Eho6wzRmGeQ8t7cFj4FLltiC8iYNlhYREZkUsg2E/gTc6N6/EfhjxvbrjTFBY8w8YCHw/PhOUURERE5WgwZCyTaqXQ/Z22QgVDIN/AVQs+qVPcnRSg6VDtqWMXxBCj2xPhVCqUDI40Cs124MNQ95yLyAl56Ifc2h1h4i8QQLphYx1w2E3niqDclue3Ifb7v1WR7d0bcquzeaIBp3hhwqDTYQAthan12V0GM7Gzl1VhkLphYBWnpeRERkshjNsvO3A88Ai40xtcaYm4CvAq81xuwCXus+xnGcLcAdwFbgL8CHHccZukZZREREJszRjt4TbmDvkfZephYH+25MLjO/5xF7G+6CYBGceTO85gvp4GWy6dcyhi+PoInT1ZueqZNcdj6PjCqa7qHnCOX70zOE6tt6AJg5pYBT3EDomtNm4PUY7n7pMABH2nv6vL6z17aVDTVUGmD5NHu+2cwRau4Ks7G2jYsWT6WyyH7/CoREREQmh6EvB7kcx7lhiKcGXcfVcZwvA18ez0mJiIjI2Bxp7+X8rz3CG06dzv++dTUezyRaXStLLd12vs78qqK+TyRXFWveBW0H3QqhQlh0+St/kmPhC8IZ70mfpy9InokQiqQDodRQaac3/bphKoSSLWOO49DoBi1VxUHedNoMEo7DabOmMLeigD2NNkTr367VkQyEhmkZKy3wM3NKflZzhNbuasRx4KLFVZQXBvAYsm49ExERkYk10UOlRURE5DhYf7CVWMLh7vWH+fpfdxzv05kQuxts8LOgun8g1J2+v+cR+zjQb5/JyBh4/bdh9jn2sS+PAFG6w+li6vAYA6E8v4d4wiEad1JBS1VxkKriIO+/cD4ej2Hh1GL75Tymz7wigPYeG0YNN1QaYF5lIbUtoSGfjyccdvRb3j4ci3P3+sNUFgVYMb0Ur8dQXhhQhZCIiMgkoUBIRETkBLChtg2/13D2vHIe29Ew8gtyQDIQWjh1iECoqBr2P2VbxnIhEOrPFyDgDF4hFMgMhDJbxqK98JfPplYfy/N7AeiJxmnsChPwegaEO9efNYsPXzyfORUFNHf3DWNGUyEEMLU4b9jKnvs31XPFd9ayp9H+mfVG41z342d5YlcT773glFTFWmVRkMbOdCiVSDhsPpz9kvYiIrksFk9Q2zp02C5yrCkQEhEROQFsONTGsmklzJiST2fGTJpctquhk3y/l+ml+X2fiHSDLx8qF6VbxoK5GAjl4SdKKJJZIWTvBxIZ4UsoIxCqfxme/SFsuQeA/IANhHqjcRo7w1QVBzGmb7vgRYun8qnLl1BRGKQ5o2VsU207de7coZEqhKqKgzR2hYecUbW9vgPHgRf22aDq+X0tbDjUxleuWcn7L5yf2q+yKNinQui3zx/k6u89OaC6SETkZHDL43u45BuPq3JSjhsFQiIiIjkunnDYfLiDVTPLKA766AqfGIHQ7oYuFkwtGjgPKdJtZwaVzoL22vQMoVzjC+JLRPoEQskKIV8844pxZoVQsn2s7iXAzhAC6InEaeqKUNl/AHeGiqJAakWzWDzBW295mv+8bysw/FBpsIFQNO7QFooO+vz+Zlu1tf5gKwAvHWzDGLh69bQ++1UW9W0Zu3t9LQDP728Z9uuLiJwIdhzp5Ond9t/0RMLh9+sOEYkleGr30IsHiBxLCoRERERy3N7GLrrCMVbPKqMozwZCJ8JqY8lAaIBUIDQTOusgHoFA8St/guPly8PvROiORFN/XuFYgqDPg4kNMUPIbRWj7mUgIxBKVggVBYb8cuWFgdQMofr2XsKxBL1RG0CN3DJmg6bGrjCPbm9IVRYl7WuyAdb6g20AvHyolQVVRQOCplnlBdS393KguZuDzaHU/usPtA779UVETgTffWQXH/zNehIJh3UHWjnUYv8tXbtTgZAcHwqEREREctyGWjuD5dRZpRQGfcQTTuoX/VzV2Rulvr13iEDInRlUOhMc9/vMxQohrw1v/E4s9ecVjiUI+DzpOUkFFX0DoR43EGrYCtEe8gL9AqFhK4SCtIYixBMOhzMCnYDXQ9A3/EfC5HEPt/bw3l+t4weP7k495zgO+5u6CXg97G7ooi0U4eVDbZw6q2zAcd55zhyCPg9feWAbt79wEIDVM0t5UYGQiJwE2kIR2nuibK3v4A8v1lIY8PKaJVN5YlfjCXEhR3KPAiEREZEct7+pG4+BUyqLKA7aWTC53jaWnCkzYoVQUo7OEAIIEqXbHSydrBAi6gY2pbMGbxlz4nBkc6pCqDsco6U7TFXRMIFQYQDHgdZQhNpWe/wrV9SwsLpowNyh/pKB0IsH7Gp2W+vTS9Af7QjTE41z6bKpAPxpQx2toSinzi4bcJypJXl86KL5/HXLUX702B5evaiK162axsGWEA2dvQP2FxE5kXS4Kzs+tPUo922s46qV07h8eQ0NnWF2Hu06zmcnJ6PhJwiKiIjIpBdLOPg8HjweQ1FeOhDKrBZ5dm8zp84qS61KNdn9/Kn9FAa8nD2vfOCTyUCobHZ6W06uMmb/fAJE6XHnCPVG43ZQdNSdIVQ2Gw48lX5NqAW8QYiHoe4l8qcvBKCurYeEwwgVQrYiqbkrwmE3EPrW204l4B35+mCyZey5fTaQ2l7fSTzh4PUY9jXZaqY3njqDv245ynf/bquHTps1ZdBj3XzBKXSGYyypKeby5TVsc8Ol9QfauGJFzYjnIiKSq5IrO/547R56owluOHs2NSX24sADm+pZXJOD7c+S01QhJCIikuMSjoPHfUcvCtqZLV0ZK40d7ejl+luf5a4Xa4/H6Y3Z9iMd3L+pnvecN4+ygkFm4iQDoZIZ6W05GQgNrBDqDscoDPj6BkI9rZBwB0/3tELFfCiqhsPrUquMHWyx+1cOWyFkn2vuClPbGmJqcZA8v3fg0O5BFAV95Pk9bDhk2xN7ovHUIOnk7fLpJXzlmhVEYnFK8/0sqh78zyTP7+WzVy7lmtNmUhDwsXx6KQGfh6f3aIaGiJxYfvXMfh7ZfjT1uKPHBkK90QRLp5Vw2qwyppfl89pl1dz21D7ahxjcL3KsKBASERHJcfGEg9dt+SlyW8Y6w+kPlUc7bCvO7obcKEf/hVsddPMF8wbfIbmqWKDAztiB3G4ZM1G6wzbwCUXiFAZ9fVvGnAT87DI4+JxtGSuogPmXwPb7KUjYIOigO5h0VBVC3REOt/Uwc0r+qE/VGENVcZBIPD2bKlnZs6+pm4DPw/TSfN525mzWfvpi7vvo+fhGUXkENiC6fHkN97x0mO4cb3UUEcn0kyf2cuvavYCdt9bRG2NuRQEAbz97dqpd9+OvXURnb4xbn9hz3M5VTk4KhERERHJcPOGkqjyKky1jGRVCzV12Zalka89kt7+5myXTSgavDoJ0hRCk5wjl4lBpn/3+gkQJJSuEIjEKAl77PfryYMWb4ewPwJFNsPkPtmUsfwqc9V6IdFG68y4ADrkVQsMGQoXJlrEwta09zJxS0HeHSCgdRA1iarENsFbPLMXnMWytSwdCcysKUn8HywoCzCovGPI4g7nx3Dl09sa49+XDY3qdiMhkFo05bKnrwHEcQpE48YTDtWtm8d9vXsl1a9Jz8JZOK+HixVXcv7H+OJ6tnIwUCImIiOS4eMLB5+lbIZQ5VLqpKwzkTiDU1BWhcpjl0/sGQrPsbQ63jAWIEnJnCIXCcbdlrAf8BVA0Fa78GlQugtb9boVQOcw4A2asofDln1FNSyoQGq5lrKwggDF26fj69h5m9K8Quusf4I8fHvL1yYHVi2uKWTC1iK31HYRjcZ7b28yKGaXj+EHAGXOmsHx6Cb9+5sC4jiMiMpnEEgk6e2McaulJzQ+qKAxww1mzCfr6zvSrKc2j230vEHmlKBASERHJcXHHDvcF+gyVTmruthVCta0hIrHJvxx9U1d4YLDhOPD3/4TGHRDrSQdAqQqhXAyE7Pc4oEIo6A6V9mdU2UyZYwOhnlbIdwdtX/AJTOteng5+lHN61lIQ8Np2syF4PYbyggDb6zuJxp2BLWOt++DoFnv/DzfDjgf7PJ2sPppXWcSy6SVsrG3noa1H6eiN8fpV07P/OWBb0i5ePJWdRztJJLT0soicGKJx++/Z5rr21ApjJfn+QffN83vpVSAkrzAFQiIiIjkukXDw9J8h1KdlzFYIJRw41Bp65U9wDKLxBG2h6MDWp446eOJ/4aVf28fJCqFpqyGvFPLLXtHznBCDzBDqM1Q6kBkIzYXmXXa5+eTcpCVXYf7xJfB4ubrqKB++eMGIX7K8MMD6g60AzCjrFwj1dtifc28HbLoTtt3X5+mpqUCokLeeMZOW7gif+cMmphT4OX9hZRY/gL5K8/0kHOiKaI6QiJwYou7ctc2H22l3B0qX5A0eCOX7vfREJzYQ2lbf0WeotUh/CoRERERyXHL5b4Cgz4PPY/oM521yZwgB7Guc3G1jyXlHAyqEOursbZNd0jwVCK26Hj62NVVtk1O8yRlCkYwKobitEIqEwJ8R2EyZa4dLg20ZSyqfhzdYxJWLS0cVCF1z+gza3F9KBsz5CXfY/xq328dtfdu3akptgLVgaiGvml/J1aum0RWOceXKafhHOUB6OKUF9pckrbIjIieKmFshtKWuI7XCWEn+4JWcBQEvsYSTCpEmwvcf2c0n7tgwYceTE8/QdcUiIiKSE+KOY6/wPPNDzKq3UZTnGzBDaE5FAQeaQ6klwo+VUCTGwZYQS2pKsnp9ct7RgECo0w2EmpOBkNsi5vHk5gpj0GfZ+VAkTjSeIBJLpCuE/BmDsqfMTd/PL+97HH9Bepn6EXzoogW8Zkk1mw63c0plxvETcbt6G8DBZ+1t636IReCx/4ZXfZTXr55OWUGABVOLAfi31y2joSPMO8+ZM4ZvemilbhtFe0+UWRNyRBGR48dxHKKJ0VcI5fntTKGeaHxCQnaAw209tIaitIUiQy/UICc1VQiJiIjkuHjCYZqnBf76Wdh6D0VB34BVxhZUFVGa7z/mg6V//cwB3vj9pwjHsit7b3QDoarifh9ckxVCrfvsbS6uKtafW9VU5EsQisRTg6ULAskZQv0qhJKSLWNJgYJhVwfrb3FNMW89Y2ZquWMAwp3p+4ees7cdh2HfWnjym7D9PvL8Xl67rDq1W01pHnd84FyWTssu/OsvMxASEcl18YSD40BZgZ/m7kjqgsxQM4TyAzYQmsg5QvXt9r0hVxaVGEpjZ5jVX/obcz9zP1/44+bjfTonFAVCIiIiOS6ecAgYt8Q83ElR0Ednn6HSYSqKAsytLDzmHwoPtoQIxxKpmThj1dQ5RIVQh7scecL9vvxjW9Z8UnIrhIq9MbrDsVTbWGHQXWUsc4ZQ6SzADXAK+lcI5dsWs/EId6TvJyuEnATseMDebzs4vuOPggIhETmRxNwB+QuqbBXrpsPtAJTkDd6kk59RITQRovEEDe576rGuDj7WaltDtPdECfg8PLu35XifzglFgZCIiEiOSzgO/lQg1EVxXrpCyHEcmrsiVBYFWVxdxLb6Dhzn2K3idLTDfvjMnGE0Fk1DzhCq7/s4F1cV688NhIp8tjooGaKlK4QyAiF/HhRPs/fzp/Q9zhhaxoaUWSEUakrf336/vW099svBKxASkRNJchbQgqn2/Wrz4XYKA158Q7SDTXQgdLSjl+Tb/b6myb2gxEiSFbSLq4s52BI6pp9jTjYKhERERHJcPJEZCNkKoeQMoY6eGLGEQ0VRkFUzy2gNRalttSXkd647xMX/+xhHO3on7FwaO+2xurNcKaqpK0y+f5Dl05MtY0knRMuYbYsr8trqoFSFUMDnDpXuVwU1ZS4YD+SV9d3uzx9Ty9igejv6Pq5cZG+7jtjbiaoQivZA++FBn8oMhD5/72b+9Z5NE/M1RUSOg+SS83MrCzHGXvAYql0MIM9tGeuZoJax+vb0e/v+HG8ZSwVCNcX0RON9FsuQ8VEgJCIikuPiCfB63KtlkS6K8vypQKipO9mCFWD1zDIANtS2AfDnjfXsa+rmo799idgErWqSrhDKsmWsK0xl//lBYFvG8krTj0+IQMhWCBV6Y30rhIJeG5z0D4Qq5kNRtR2knck/yAyhPY/At1fZoOel38BdNw1/LsmWMeMee+ZZqVXQgAErjmXt7/8JP7l40KcKAl58HkN7T5Rn9zbz7N7mifmaIiLHQfJ9tSjoo6bE/ns/1EBpmPgKobo2+75QU5KX8y1jyQsmS2rsogaHWnO74mkyUSAkIiKS4+KJRMYMoQ6Kgl463Zax5DLuFYVBFtcUE/B62FjbTiSW4IV9LcyvKuT5/S3c89LgVRtjkUg4qaHQoXFUCA1oF3Mc6KyHGWekt50ILWMeHxgPBZ54nxlCRQEvRLv7zhACuOTf4IbfDTzOYC1jL/2fDXGadto5QFvusSuJDSVZIZQcXj1lDpTNtvenLrc//1h47N9jfzsfhK6jEB1YlWaMoTTfT3tPlCMdvTR0TMDXExE5TqLuDCG/x2FhmZ0BN9SS85ARCE1QhdARt0Lo3PkV7Gvqzuk2q+QFk+QKpodaFAhNFAVCIiIiOS7ugM+4H/TclrHkDJ9mN6CpKAoQ8HlYOr2EDYfaePlQGz3ROJ+6fAml+X5eOtQ27vNo7o4Qdz8AZz1DqDMyMBAKNUM80i8QOgEqhIwBXx75HrdCKLnKmC9hBzpnrjIGUFwD008deBx/ft9AKBaBXQ/Z+637bTDkxKGrYehzSVYIVS21t6WzoMxdTn7J6+xt26ExfXsDtOyDlr32fuacogylBX6OtPfS2RujKxxLVbqJiOSaaMxeqJlX/yC3NL2LANHhK4QCE1shVN/eS3HQx4oZpXT2xmjpzt02q+QFk0U19mLQwWYFQhNFgZCIiEiOSyT6DpUuCvrpicaJxRM0ZQRCAKtnlrLpcDtP7mrEGDj3lAqWTSthi7v6yXg0dKarPsbVMjbUCmPVK8DjB+NNLdme83xBik0Pc3q2EXLDjyKPWxnjH2Xo1b9C6MBT6YCn7UB6/k9n3cDXJiX3n7rE3pbNhqoldoD1KRemjzUeex5J3+9uHHSX0nw/O46kB1wfaZ+4+VYiIq+kWMK+LxeHj1AQ76SE0LAzhCa6QqiurYdpZXmcUmnfS3K5bSw5Q6i8IMDU4mCflrGmrjCXf2st2+o7hnq5DEOBkIiISI4bMFTaXdK2OxynsSuCMfZDFMDqmWWEInF++uQ+VkwvpbTAz4oZJWw70plaESVbyeVtYWxDpR3HYfPhdn759H5aQhGqivrNEEquMFY6y660FSiy1TUnAm+Qs9se4EfhfyHWZatmCnBX2epfITSU/kOldzwAvnwIlsKRTdDrhn0ddfDwl+DJbw08Rm+HDdpmnmlnG1UuhIv+BW7+e7qNbEICIffPrXvw+UCl+X4Ot6W/l4kceC4i8kpKDpX2Ofb9MN/0DrnkPKQrhHonsEJoWmk+08rs/KIj7bnbhtsdiRH0efB5PcwqL+BgRsvYk7ua2HG0k+f3aTn6bAz9N1JERERyQtxx8GYEQsXuCl2d4SgNHb1UFgVTy9y+4dTp7G/u5pHtDVx/1iwAlk8vJRJLsKexK9Wfn42GjrFXCDmOw7tue54ndtkwxOsxrHSHX6ckK4RKptm2qcQJ1EbkC+LF/qwSoTYA8nF/jv2HSg/FX2Bb6uIx8Ppg3xMw93zblrXvifR+HfWw4XbbCnb+x/oeI9wJeSWw6Ar4xA7IL7Pb80rt7CGPf3wrjcWjsG+tPa/9TwzdMtbv6rkqhEQkVyUvsnhxV5AkPLoKoQkLhHpYMaMkVXWbrBjORaFwPLX66Ozygj7hzwv77X3NFcqOAiEREZEcF0846RlCka5UhVBnb8y9QpiX2tfv9fCJyxbzicsWp7Ytn25DoC2HO8YZCNkPm8aMfobQ03uaeWJXEx+4cD7vftVcKosCqfAqpe2gDSSKqu1KW/Hc/VA7gC/9ZxPt7SLg8+NPuCFI/6HSQ0nuF+sB8qB5Fyy+ws7sqXspvV/jdjscOjjIn3G4w243Jh0GJXm8UDrTziPKVu06+zVWXWcDoe7RBUJHOxUIiUhu6l8hVOqNMK106MrPvFTL2PhX/WzvidLUFWF6aT5TCgJ4THqmYC7qjsRSgdms8gLuffkwkViCgM/Duv2twNhWHlu7s5HHdzby2SuXDPzMcZI5ub97ERGRE0D/lrGpbsvVkY5ejrT3Ul2SN8yr4ZSqIvL8HjbXDT5H6D/v28q7bnueRGL4FUqOdvZSVuCnKOAbdcvYT57YS2VRkH++dCE1pXmDfzBr3WdXvfJ44fKvwPW3j+rYOaG4ml5fKQChrg4KA14Id9nnRruSWrK1LNoDzXtsBVXVUvszS8org32P2/u9bQOP0dsxeFCUNHMN7Pxbun1vKG0HITLInIo9j9gl7Ze+3oZ7I1QIFef5KM7zcVQVQiKSo5LLzvvcKtDvvXURbz1jph38/9R37W0Gr8cQ8HkmpELo79uOAnD+wkq8HkN5YYBGd9XRWDzBjbc9z0Nb7T65sPqYrRCygdCSmmIcB77/6G7aQ1F2HLVz5w619Ax3iJRILMFn/rCRnz25jy8/sI09jV20h6LH7NwnOwVCIiIiOS7hOPiSgRAOcwrCXOt9jEPN3Rzp6FshNBivx7B0WglbDg8cyBhPOPxhfS1rdzbym+cP8v5fr+PnT+0jFk/w0yf2sr8p/ct/Q0eY6uI8CoLeUVUI7W7o4rEdjdx47pzUldFBteyDKfPs/YJyKJ0x4rFzxvW/5dnTvg5Ae0c7BQGfbd8CCI42EHIrhCLdtgoIoGpxepWwYClMXQrNu+3jnlbo/wtAsmVsKBd91ral/f0/7OOtf4I/fqTvPok43HIBPPHNga/f84hdJS5/ChRUjFghVFOSR01JHkc0Q0hEclTMvYjidWzYMDUYI+Dz2CrJhz6fDukz5Pu9fWYIHe3oZefRzlS4NFoPbDrCtNI8Vrst2JVFwVTL2Av7W3l8ZyP/cd8W1u5sZOm//4Ubb3ue5/YOPtttMghF4/b9EbhieQ3XnjGT7/59Fx/+7XoAFlcXj7pC6I51h6hr7+XseeX8/Kn9vOYbj/PWW54mHJuYVr1co0BIREQkx8UTDj7Sv+BX7r2X//HfSue+F2nviVIzQiAEcMbsKbxc2zZgmOXLh9poC0XJ93v5/L2b+euWo3z5/m188s4N/Nf923jbrc+kQqGjnWGmlgQpDProjsT584Y6fvCoDSHaQhG+8/AuPnr7S3T22g/Hj++0K0295YyZQ5+Y49hWpfJ5Y/mR5I5gMVU1NuA62tRir4BG3Aqh4Sp2MmVWCDVuBwxULkoHQlNmQ8n09P7xSHpVsvW/gt+9A8Ltw3+9ivlwzgdgw29tldCOB+Dl39i5RUmt+2310dHN6W1/+ij89FKoWw/zX2O3FVZCqN8vHn/5LOx+ODVfo6Y0j5rSPI505G6Lg4ic3CLJGUKO+74acf/d7bEtTqn5eBny/d7UKmP3vnSYs7/ydy771lp+8fT+UX/drnCMtbsauWJFDR6PHeRfWRRMtYz9besRwFbU3PTLF6goDLKtvoO33fosn7xzA796Zv+kG+gfCsdSFUIej+Grb1nFu86dw5O7m/B5DFevmkZnb2zESp9EwuFHj+3htNll/N/NZ/P1t67iY5cuYldDF7c8tveV+FYmHQVCIiIiOc7OEEoHOcatEtlfZ8vBR6oQAjhvQSWRWCLVi9/YGeahrUd5bEcDHgM/fMfpzKss5JvXraY038+9L9dx6dKpROMOb//Js/xtyxF2Hulk5pR8CgM+usMx7lh3iP/56w7W7mzkdd99km89vJP7N9bxod+sJxpP8MK+FmaV5zO9bJjVtELNdvZM+Snj+AlNbrOnVgDgiYXcCiG3UmvULWNuhVAyEJoy184VSraMlc2xq7Nl6mmzt1vuhe33Qcv+4SuEAOZdZG/bDkLnEXAS0N2Qfr5hm71NViJFe2DjHVD7gt13gRsI9a8QatoFz/4QXvoNZRkVQtUleWoZE5GcFXNnCHncGUKpsD8VCA1swc0PeFMtY7saOvF6DLPK83lqd/rfzMbOMB29gwcf8YTDDx7dTSSW4MoV6X/3K4oCNHVFcByHv205yiVLpnLqrDIMhlvfdQaPf+pi3nPeXP70ch3//sctXPrNx/nThrrx/ggmTHckTr4/Pf7Y6zH8xxtX8Pv3ncP3334aC6vt++VIVUJb6zs43NbDO86eg9/r4bo1s/inSxfy+tXT+cGju6lvH13b2YlEQ6VFRERyXJ+h0gBNOwFobm0B5o44QwjgrHnl+DyGp/Y0cf7CSn7x9D5+8Oge8vweTp89hYuXTOXiJVMBKC8M8If1h/naW1ayr6mbt//kOd736xeZXprHRy9ZyMfveJlQOJ76wPqeX7yAAX7/vnM42BLiU3dt5Na1e3lhfwsXLq4a/sRa9tnbKSdohRBQXGJnCBWYsL0CmpwhFCwe3QFSgVAIGrZD1RL7uHSWndszZS6UuG12Hp+dMdTTalvvjm6x2yOdI3+94hp721kPXUfT95PVR41uINS631YO7X8KYr1wza02bJp5pn2+sLLvsOvt99nbhq2UnpGuEEo4Do1dYeIJB697lVtEJFfEUhVCbniTnK+WnOPWWQ+HXoAHPw297XDl18nzB1OBUHNXhIrCAOcvqOS+jfUkEg5xx+FNP3iKM+ZM4bs3nAZAR2+Uzt4YNSV5vPdX63hkewNXr5rGmjlTUueSbBnbVt/J4bYe/vE1C7hkSTUNnb0sn27fg77w+uV8/nXL2NPYxaf/sJFP3rmBVy+spKwgcOx/WCMIRdIVQpnOPsVeUNl82M5APNQSYsWM0iGPk6xMvnBR388en7xsEX/eUMc9Lx3mQxctmKjTzgmqEBIREclx8T4zhIDGHQAUusuXp1Y16TwKbYcGPUZh0Meps8p42r0KubuhC4+B3mgiFQQlXbR4Kt+74TQKAj6WTy/l1zedxaVLp/Krm85meplbIRSJ0dQVpjTfTzzh8MnLF3P2KRVcu2YWFyys5IeP7qa5O8LZ88oH/6Z6Wm270eF19vGJ2jIGqVXCCghnzBAyECgc3euTgVC4w1bnTHUDIX8evO03cM6H0qFNzSp729tmq3S6jqSPM1KLWrLKqPNIOhDKvMLd4M4vSsSg7QDsftiuorbsDbD4SruCGUBBJXQ3w5//GW6/Abb+0W5v2kVZ0P49rnZnCMUTDvuaBhlSLSIyySVbxlIVQslW3WSFZmc9bPsTHNloB+2v/yX5fk+qZaypK0JFUZAz55bT2Rtjx9FO7ttYx+G2ntRS6wCf/cMmLv7fx/jo7et5ZHsDn796Gd+74bRUuxjYQCgUifPXLfbf/EuWVFNVHEyFQUkej2FhdTFfuWYlkViCu16sPQY/mbELRdIzhAYzq9y+D2ZWCD25q4mvPri9z4IYj+1oYMWMEqqKg31eP6eikDPnTuHu9YfZVNvOPS9Nju/7laAKIRERkRyX6DdDiB77QbHA2HkBNckKofs+Zlt8bn540OO8akEl339kF+09UfY1dXPJkmpuOn8ep88pG/brr5pZxk9vPDP1uDDoo6M3Skt3hA9fvIDLl9eklrYH+Ifz5/HELhs8nTl3iEDowNN2vk1eKWDS83BORH4b/OQTtquMRbpstY4ZZVVMcobQ0S2QiELl4vRzS66ytx1u6f+ss+08n55WOLLJbvPl2yXrR2oZKyi3K4S1HUzPAOrMCIQat9uh0T2tdrWz3Q/D3PPT55dUWGlnFr38W4i7M4KqV8DRzcynjvdeMI/LllXTGoqS5/fwlh89zc/fcyanz56CiEiuSLaMeRPJCqFky1ibve2oB28AKhbYf5s3301x1fvpSlYIdYepLAqk3idf2N/C7563F3Xq23tp6OilJN/PI9sbSCQcHth0hDedOp1/OG8upt/7R6W7+ujjOxuZUZY/IBDpb+m0Es6YM4XfPneQm86fN+B4r7RQOGbfH4dQmu+nJM+XWmmsNxrnk3du4EhHL6X5furbe+iNxll/sI0PXjh/0GO8+fSZfPbuTbzlR08TiSdYPbOMU6pG2bqdw1QhJCIikuNshdDA1TEKscvA5yc/RDXvtu08QzjnlHISDrywr4X9zSHmVxVy7vwKgr5hVgAbRGHQS31bLwkHphYHWTGjtM+HyQsXVjG/qpDKoiDzKoeogulyZ9P0ttt2J//IbW85yxcgYXxuy5g7Q2i084MgHbg07bK3ZbMH7jPjdLj8v+GMd9vHPa3pdrGVb7W3I7WMGWOrhI5sTG9LBkLxmG1VXOwGULv+Cs270oOkMxVUuK8J2+e9QbjoMwD4mrbzudctY2pJHotrinngHy8gGk9wz/qBw1dFRCazWKJfhVByqHSqZazOvi+Xz4dFl0Okk5XxbRkVQmEqCgPMnJJPTUke33xoJ1vrO7jWXYhhQ207T+1uoica53s3nMZXrlnJl69ZOWh4U1kUdF/T1ucCzXDeftZs9jZ18+KB1ix/AhMjkXDcVcaG/ywyu6KAlw+1kUg4/PqZAxzp6GVxdTFf+8t2fv3sAe59qY54whmyVf2qldMoCHiZVZ6P32v49bMHjsW3M+moQkhERCTHJRLgxRmwvYDedHWQ40D7IVuyHo+C1z9g/9Uzy/AYeGBTPZFYYuiwZgSFAV9qud3kh9BMHo/hB+84nY6e2NBXHbsyhhWfyO1iLsdfQH7UDYS6RjHPJ1OyZazVnbeUnPWTyeOFcz+UXtK+pw0atkJRDSy5Gl76tV2efiTFNVC/If2480j6a8cjtiJo232w7ud2XtHyawYeo7DS3gaK4Ibf2TlD/nxbfdSwpc+up1QVUVkUpCscG3gcEZFJLDJgqLTb/pqsEAo1Q28HLLwM5l0I3gCnh5/ngehCwJ0hVBTEGMMFCyv548t1/OMlC3jfhfO5+6XDbKxto7EzTFHQxyVLpw578Sb5Xuw4DGgTG0oyONlQ284at0opkXBYu6uR5q4Ir1s1jTz/2C4YZaM3FsdxoCDoc3+GJtVqnemd58zhX/6wiS/8aQv3vnSYCxZW8o3rVvNv92zmhrNms2BqES8eaO0zWylTab6fB//pAiqKgvzbPZu4a10tn7xssX1fPoGd2N+diIjISSCWSODNnCHkKjS96SXne1rT8wu6jkLpwKXeC4M+FlUX8+Bm+0t+tqXSBRkfnoYqS19SM8IVyu4GyCuDWWfB7HOzOo9c4gkUUhKOECsKQnMXBMfws09+MG4ZJhBK7VsExuu2jG2G6uVwykVwzodh/sUjf63iGqh93t73BtIVQskh0VVL7BL1deth+VugZNrAYxS4gdApF4EvYP8DqFoMR7cO2L0o6KOzV4GQiOSW5FBp079lLFkhBLbNt2K+/Td/3qs5f88f+UfTSqjrNEKROBVuq9d/vHEFn71qKeWF9vGi6mKe3N3EoZYQFy6uGrGSt7I4PRh6tBVClUVBKouCbK/v4Ok9TXzyjg2EYwmauyMAfPUv2/n9+8455m1V3WFbMVUY8MKd77Hvedf+YsB+162ZxR9fruPXzx5gwdQivnLNSqYW53Hru9ak9knOGhrKnAp7Ieyd587l3pfr+NOGOm44a5Cq2xOIWsZERERyXDwBPgYGQlWBGHPdDze0ZwyT7jwyYN+k02ZPSa1wkn2FkJcSujEkRpxTMKSuBiiqhnfcCRd8PLtj5BATKODKRSW889w5topnLBVCPrdlLNQEgeLhh1EbY+f8dDfYmT81K2w73hVfSVfuDCdz+frq5XYGRjwKa//Htj3UrLS/3ACc9f7Bj1E2CzC2MinT1GWw/wm47Urb/tbdDA9/iZKgoXOIJZZFRCar1LLzif5DpVttdWZShbuq1dXfYmfpebwp8RBde54F0pU9+QFvKgwCOHVWKS8dbKOjN8a7zhlhxl7bIWp+fjbzjA3wl88YXSAEsHRaMduPdPLnDfW09US5ZMlUvvW21fzm5rPp7I3ykyf2jfpY2Uq20BUEfHbBgsPrB93PGMM3rzuVT7x2EXd/6FUjhj/DOX12GT9+5xlcc9qMrI+RKxQIiYiI5LjEEDOErlxczMcvW2QfZK4uljkIuJ/TZpUBUBz0pYZQjlWZt5engx/ldZ7nBm0ZG5XuRiiaOvJ+J4pAAQUmbMvvk0OlR8vrs9U6AMXVI++fP8UudZyIQvXKsZ1nqvrIwLTV9u/Sutvs/KDL/su2Ip76djj3I7a6azBls+HDz8Pq6/tuP+PdMPscOPg0HHjKziF68pssMYfUMiYiOSeSqhAapGWsell6x2QgVDab52e+C4Cuzg6AId+Hr1o5jYVTi7j9vWenll4f0uF1mLYDnB3cT0VhIN1KPgpLaorZebSTZ/Y0cfa8cv7n2tVcc9pMzltQyRtWT+felw7TcYwD++6I/fkVBr0Q7rIXuGKRQfetKc3jo69ZSEnewLb4sTDGcPnymlekJe54UyAkIiKS4+IJJz1DyF2xirxSCulNfyhqz1hCddgKoTIATqkqzHpVkcrYEYpML3N9zdn33nc1QOHggx9PSP7C9C8L4U5b6TOm17tVQkXDtIsl5ZdB0w57v2bF2L5OskKooAJKZ9nWh7X/A3MvsEvLA8y/BC7/8vCrpFUtGvj83PPgbb+x93va7FV0oMLXo0BIRHJOskLIJNzwItJtKyqj3bYiEmwbb1E6yPe6FZ7JQKiicPCLKhcsrOKhj1/IGXOGWKkzU6sdjjw/r2vAIg8jWVJTQjiWYH9ziHP6BU/vPGcuPdE4fzjGS9OH3EAoP+Cz749OYtgFMmRsFAiJiIjkuETCwZusEMovA+OB8lPS8wrAXlHz5dn5McMEQvOriijN9zN/avYzAcridknyisDAqqVROwkrhIja5XLH3DIG6cHSo60QgvRyx2ORrBAqrkmHQ92NcP4/Dx8AjVag0A6j7m2zK8wB5d4eujRDSERyTCyRwJh+FULJgdJlc2y7b/kpff7t9AXtv+UdyUAoy0rdPtzw5M0LPfznG8d2EWDJtPR7Uf9KpJUzSzljzhR+9Nge2nuOXZVQaoaQ3wMRd2GElj3H7OudbBQIiYiI5LhYwsHruDOE8srs1ca80nTFCdhAqHSWfW6YQMjjMfzfTWfz6cuXZH0+JdEmAKZkGwhFe+3S6ydVhVCBnS/hOG4gNMZALhkIjapCyA2EqpYMutrcsJIhUFF1OhyqXDT48vLZMMb+He5pS/3iVOYJaai0iOScSDyB3+uxKzCCGwi5S7jnT7GVktNW9XmNL89WCHV2DV8hNCZttkKoItHC7IqxzdVZMLUIr8dQGPCyYpBh1F98/XKausJ8/S/bAahv72H9wYldpj7kzhAq9ERtdRBA8+4J/RonMwVCIiIiOS7uOHg9bstYxSkwdaktQ88MhNoO2WG+xTV9ZwiFO+HWi2Dv46lNK2eWplcny0JRpBGAMl+WVwy73SXnT6oKIbdlLNYLTvzYVgjlldnbmjHOD4J0CFRUba9sA5z74YmpDkrKL7PVQe5KPKUmRCSeIBwbR8WZyDG0pa6d//fT51LDb0XAtoz5PQbiGUOlkyuM5ZfBO++FK77W5zXJQCjU1UlhwEt+YAJm2LgtY8NdDBpK0Odl+fQSzp1fic87MDpYObOU95w3j988d5DHdzby7tte4L2/XDfeM+4j2TJWZHrSG5tVITRRtOy8iIhIjkskHHyO+4vIG75nW4Hu+1i/lrFaOy/Gl5f+cAiw/0m7ZPhzP4ZTLpyQ8ynoPQpASTIQat0PU+aO/gBdbiBUeBIFQskKobBbDh8Ya4WQO0OoeJBl3vtLVghVLx/b1wBbeVY62762fB58dH06GJooeWX2lyaPrV4qcmyw2dUbI1h04g/4lNzz2I5GntzdxO6GLlbOLD3epyOTRCyesCFKatn5jAqhvDIoGDj/J5hnw/3eUBcV2S7KkCkRh7aD9v4wC0oM56c3riHoHfrf3k9etphHtzdw0y9eIJawF6fae6KU5vetQN1wqI1dDV289YyZY/r63clVxpxQeqNaxiaMKoRERERyXNxx8Bq3jNpfaKtNAhlDiqO9tuqmdPbACqF9a+3trr9BqGVCzifYYwOdIk8EatfBd1bDkU2jP0AyECo6iVrGAoUQyQiEgqNfFhjIGCo9mhlCZfa2eowDpcFWAn10HZzzIfu4Yv7EVgeBPb+ettSV9FQgpMHSMkkdarG/qB5u6xlhTzmZROJO35YxnPT7b/Lf4X7yA356nADh3u6JmR/UWW8DqWCprRBynDEfYmpxHqUFQ7cX5we8fOttpwKkVjBL/j+R6fN/3My//GEj7aGxVQ+H3H/7Cxz3/6+8UlUITSAFQiIiIjkskXBwHPAk++o9bvFvZiDUcdjels60FSQ9LRAL2217H4eSGfYD47Y/Tcg5BXpshVBlMJ5e3Wws/f7dJ3OFkJ0bkfUMoeJRzBCadRbMOANmnD62r5HkC4LnGH6ETFYIuTOEChK20k1zhGSyOqhASAYRiyfwe92WseR7c7v7fpxs3e0nP+ClhwDeWA+VE1EhlFyNa9ZZtiU52bI2wVbPKuPRT17Ere86A0j/P5G0sbaNjbXtxBMOj+w4OqZjJyuEgnH3M8201fZzTaRf6HTvh+CBT2f3DZzEFAiJiIjksLh7tc9LMhBy39oDRTZgSMTtQGlwAyE3MGg9AF2N0LAFzrzJrja16a4JOSePewW03B/LCKXGUKreZWcQnVwzhAoAB7rtQO4xzxAKJIdKj6JCaMYZ8N5Hxv41Xil5pX0qhPLiCoRkcksFQq0KhCQtlnDweY2tEEoGQMmLJENUCJ02awreYCErpgZ4/6snoB032SI++xx7O5b34jGaVV7AvEo7A6l/IPTb5w6S7/dSWRTgoa1jC4R6IjEKAl48UffzxMwz7e3RLemdEnHY+ic48HTW53+yUiAkIiKSw+Juv77XJOyS8kkB+6GMaMgOlAY7VHr6aXZZ+p9eCnfeaLfPuwgWXwmHnksvfT6Y5ByC4cQidhny5NdOzjHqrBv5tY4DLXuhcZsNBXwTcHU0VyQrfJJDP7OZIeTLsz+3XJccKu1WCAVjtmpKLWMyGUXjCercyqA6VQhJhtQqY4loenZbx2H77/sQKzzmB7yUFpdwxrQga+YOnDE0Zq377Xt+MkTJco7QaBXn+SkvDPQJhEKRGH/aUMfrV0/jtctqeHxH45gWCWjviVKc50u3VC+41N4ezAh/mnbaJelH81lD+lAgJCIiksMSmRVCnoy1IpKBUKTbvSJpoHi6XVnqA0/CgkvsB9Npq+1/cy+wVzFrXxj8C+19DL69Eg6/OPwJdWVc+Yv2ZARCw6xu0rgD/vKv8N3T7H+b/2DnHZ1MkoFQ8uc31hlCy66B8/554uf5HA95ZXaltbhta/RH7S8BXeEsV60TOYbq2npwc/msW8aSwb6cWGLxBH5jIBFLVwS11w7ZLpbiz09fnInH4LYrYedfszuJhq1QOgvK3PfULFYaG6tZ5QUcbE4HQg9tPUooEuetZ8zisuXVdEfifOGPW1Krh42kvr2XmtJ8G/iArWguPwUOPJPeqdZd2SzUnG6Jl1FRICQiIpLDUhVCThw8mRVCboVJpNu2jBVPA587oLJ6OVz7C/inDfD+teD12XJy47GrjqUOHoW732/Lsg8+Z7ft/Jv9QLntz/ZxuLPvMOrUwMxy+7XDbiDUUW8/2CYHRmf6v7fCCz+FyoXwum/A2++AG347vh9Mrgn0D4TG2M618FK4+LMTe07HS79WCm/EVgipZUwmo2QlxPyqwjFXCL2wv4Wzvvww8//1AW5dqyG5J5pY3CHP61bCJEOglj0wdcnwL0zOlANoP2grYZ67ZewnEO2BPY/Yippku3jyPTrcBQ3bodN9zznwNNRvHPvXGMSc8oI+FUJ/3lBPTUkea+ZM4cKFVdx8/jx+v+4Qb/3RM7SFIsMcyapr62F6aV7fVTjnvAoOPgMJt13+cMZS96MJvaK98PT34Zbz04trZDFw+0SgQEhERCSHJT8LeRiiZSzSZQOh0hGWec0rhZpVsP+p9LaWvbDxd/DS/0H9Brtt72Pw0Bfg9//Pfpi840b4ycW2VQygwy3Xrpjfr0KoHp6/Fb61wr4uqaPefuB97ZfgHXfCmTfDosvTVzNPFn73zyv5QXasQ6VPJJlXz4un4Qm3AwqEZHJK/uJ77vwKmrsj9ERG1wrjOA5feWAbHmNYVF3E/z17EOck/YX0RBVNOOR53T/TzKB7ydXDvzCzQig5FHrv42NfCXTPozZYWnq1PWZemX2PiUXgO6vgh2fDjy+w79u/uRZ+8boJWb1rdnkBh9t6iMUTtIeiPL6zgatXTcPjMXg8hn+7ehm33Xgmuxu6eNdtzxOJJYY8luM4boWQGwgZj/1eZr/Kzpl78NPwh/faQMtnVzgbVSD06H/B3z4HLfvgrn+AP/8z/M/8k3L1MgVCIiIiOSw5VNpDol+FUEbLWNsoAiGAuefblrFor32cnBl04Kl0IFT7Amy9195/6POw5+/2A+sGt6KndZ+9LU8GQu4QyM56OPSsbQO675/TSVbdS/Z2+mlj+bZPPKkKoQb3A2/B8T2f4ynzF6eyOZhIF6Xe8KhnQyQSDtvqbVXR+oOtfOfhXQBsq+/g43e8zHW3PEN7j9rPZGIcbA4R8Ho4fbadEXO4rQfHcXh0ewPR+NC/6D61u5mXDrbxkUsW8L5Xz+dgS4j1B1tfqdOWV0A0liDP069CCANLXjf8C/0FGYGQOxTaicP2+/rulxj67xdg988rtS3hACXTbfjTUWtbq5a90Val/uqN6Ys3v77GtnA/8U3bppZFSDm7vIB4wgY5D207SjTu8PrV0/vsc/GSqXz9ravYWNvOYzsGqRx2dfTGCEXiTC/Nt1VNwWLbGj3nXLvDCz+BTXfYGULzX2O3jea9Yvff4ZSL4eaH7XFf/Dn0dsBj/z3m7zfXKRASERHJYTH3A6GXIVrGwp12VlDZrJEPNu/VNrDZ83f7uM39IFq/0X6AXHiZ/VAa64XqFbDrb+AN2vtr/xc23w2P/w9MPx1KZ0C0O13iHQ3Zfv/8KbbM+/fvgKNbof5lG4DUrJyYH0iuSlYIdR2BQPGJMQsoW5kVQlPmAPA5/+28a+ONo3r5X7cc4crvPMHjOxv5jz9v5VsP7+RQS4iP/f5l/ryhjuf3t7DhUNvEn7eclA62hJhZns+schvi1rX1sP5gG+/5xQv8/Kl9Q77uB4/upqYkj2vXzOSKFTXk+T185YHtvOH7T3LdLc9w57pDr9S3IMdILJEgz7ihTXKo9KyzR15B05+fbhlr3W/nA5bNgS33pPd59hb434XptuxYxFbzRnth6x/ha/Ng4+9h0RXpAdZlc+xFm2TIdOZ7Yf4lNkw55SK44Xc2QFr3M/j7l+C318Hv3p5+Hx+l5P8Le5u6eXJXI5VFAVbNHLjgwdWrplFRGODelw8Peaz6dhuMTStzK4QCbjv1lHlw2v+Dy78C1//WtsWveY99rn+FULzfBYDuZjtbad4FMHUpvOteuPE+eNVH7GqrR7e6xxnbami5SoGQiIhIDhuxZaxlnx0WXTqKQGj+JXbw9PO32sepVcXcK4RnvQ98+XbZ8td+yW5b8Ra44r/tB7C73gMF5XDD7fYKp5OAnowr3t0NcPYH4eLP2aqjX73BlnlXLk6f78kqWSHUdig96+FklblSWpkNhC4wL1Mca7FLC4/g5do2AD77h4287AY/tz9/kO1HOnn3q+YCsK+peyLPWE5iO452Mq+ikOll+YCtEHrxgG3t+fWzB/oMjH5wUz2fuGMDmw+388zeZt5z3lyCPi9FQR9XLK/hxQOtJByHllCEz9y9iS117cfle5KJEY075HndN+nCKgiWwuq3jfzCzJaxtgO2hXr5Nem2scad8NC/Q6gpvfT6+l/CHz8Mj38N/v4ftpJm5XXwqn9MH7f8FBswJS/2lM2GS/7Nvl+/+lMw9zz4wBPwuSPwr/Xw2v+EHQ/Acz8e0/e9cmYpAZ+HR7c38PSeZs6dX4kZ5CKHz+vh6lXTeHhbAx29g1dt1rfZiuVppXl2qHRyvp4x8MYfwLkfthVXn9huZyV5A+nWdYAjm+G/Z8GOB+0Hpsad9vMHpCunZp9jw6FX/aM9/qNftvt9eyVs+N2Yvvdc5Bt5FxEREZms4iOtMtbozusZTSDk9cOZN8Ej/2lX/mo7CCUzbBtTIgoz18C1P7fHmroMLv0irHirrT76+DaoWw/TToXi6vTX72qwVUTuilFMWw2Lr4A558EvrrJL1K9++4T8LHJaskXMidsy/pNZZsuYWyE0zXFbCiJdfQOjQWyr78RjoK69l5I8H4VBHz990lZqvO3M2fz2uYMKhGRC1LX1sLexm7efNZvq4iB+r2F3Qxd1bT0YA4daenhsRwOvWVoNwE+e2Mv6g22s3dVIvt/L9WemZ6V98Q3LufFVczl1VhntPVEu/eZaPn3XRv744fPweQdew9/X1M17f7WORMLhhrNm895Xn/KKfd8yOtF4gryAO/ssUAif2Da6duDModKtB2wwvvxN8NS37YIOG2637/fxMBzdBDNOh6e+Y/d/8pv29tpf2BApU/k8e9zaF+wFpJIZ9t/Yzx4GT8bfMWPsRYrz/hH2PgrP/8SGJV4/PPwFWPpGmHnGkKdfFPRx4aIq7lh3iFAkzqvmVwy575tOm8EvnznAXzYf4bo1Az+n1LcnA6F8WyE03Hw9Y+wFlcwKoUf+E2I9sPZ/4PB6WPt1qFhof8b9W9ULyuHcj8BjX7EX03x56Ta0E5gqhERERHJYwr367BlqlbHGHfZ2NDOEAM54tw1w1v3cBkKVi+yHzbI5tuR98ZVQs8J+eDz/Y+lWtKIqOwy62P7ig99eLae7wQ6YTkq2hs15Fcw6x94/2ecHQd8KqVXXHb/zmAwCxbaNEAYOF4+MHORsrevgDauns3RaCe+94BQuWTKVSCzB7PIC5lcVMqeikP3NCoRk/J7c1QTABQur8Hk9nLegkr9sPsL6g61ctWIa1SVBvvnQTnoicZq6wrx0qI3iPB+NnWHefPoMSgv8qWOVFQQ4bfYUjDGUFQT416uWsKWug3UHBs4VOtQS4vpbn6GlO8KUwgBffmAbm2ptNVF7KMqd6w4NO6hXXhmxuEMgOUPIG7D/zo+mHbj/UOkpc+zFlilzbSvXwWdsZW5eqa2A2XSnXTzi6m/boKNyESx9w8DjTplnb/c+btu6ve5FJM8wkcC5H7atzFvutkHLU9+Bv/2brbbZtza9oEQ/r1s5jZA7YH24QOjUWWVMK83j8R2NgB0i/a/3bOL7j9jZb/XtPXgMTC0OpmcIDad4up1Z+NR34YFPwc6/2Lb2wy/CE/9rq5ybd9nWPa9/4OvP+aD9rNOwBS76jP1sc4JTICQiIpLDku0ItmUs4209GTA0uL3wo5khBFBYaYdL73nEBkJls+F134Q33zq2E0vOxOlpTQdC+eV2qCXYD8UX/Yu9yjn3vLEd+0SUvGo87VSoXHhcT+W483jsLzrBEvt3JtMIgVBDZy9NXWFWzSzjwX+6gI++ZiEXLrIf6C9ZMhVjDPOqClUhJBNi7a5GphYHWVRtA/irV03ncFsPRzvCnDWvnP9600q21nfwz79/ib9vO4rjwC3/7wxuOn8eH7lkwbDHvnRZNV6P4cldTTiOkwp4wrE4H/rNenoicW5/7zn8/D1nUhz0ccvjewjH4tz8qxf41F0b+cD/vUhvdHQrnsmxEU0kCHrcYM47hsYcf4Gt/ulpg54WGwQZA8veZIdB16yy83OqV8LRzfYCTtVSe0HnnffAdb/ue4EoqdwNhNoPpdpxRzT/NVCxwLZOJT9PHHwa7nk//PL1cOeNg4ZClyydSsDrYUZZPrPLh66KMsZw7vwKntnbTCLh8Mun9/Pb5w7yi6cP4DgOdW29TC3Os1Vy4c70xa6hFNfY8Oehz9v295IZ8M577Wy6QBG8/3F7MWr19YO/Pq8Ervw6LH09nPXe0f2McpxaxkRERHJYLDMQymwZ8wbs4942mLFmxDabPuZdAA9/0d4vm20rgsYqWSEEdnZCXpmtDsq8Ojr/EvjMQc0PAvvzmnkWnP3+430mk0NemZ0XlNk+BiMON91Wb59fOq0kte2ChVVctbKGt59tq43mVRTyl81HiMYT+AdpxREZjXjC4cndTbxmSXVqPsply6sJ3O0hEk9w2uwyVs0s49+vXsaX/ryVx3Y0Ul0S5FXzKzhvQeWIxy/J83PqrDKe2NVIJJ7g7vWH+b+bz+Ina/ex6XA7P3nXGhbX2GqJd5wzhx+v3cOuhk52Hu3i2jNmctf6Wq695Rm+ff2pzK8a4ZdoOSai8YxVxjyDVKMMJfn+mWz5ToY3q2+w7WJX/a8NfGpWwLrb7JzA13zBvr/OPmfo45bOsq1iTnz0gZAx9iLRlnvSgZAvz67sNXW5nTH04Kfg9d/p87KSPD8fuGg+5QX+QecHZTr3lAruXn+Yv2w5wlce2E5lUYCmrjDbj3RypKPHDpQG2zIcLBn2WJRMt21xwRL4pw02XPPnwXW/tN971WK46a/DH2PVdSdVpa7eBUVERHJYwhmiZcwYG7R4g/CmH47toPNenb4/2g+N/WUGQoFCOO+fBg87FAZZxsDND8HKtx7vM5kc8ssgv3RgkDlChdDWOrvc/LKMQCg/4OWH7ziDRdX2l+e5lYXEEw6HWkITespyclh/sJX2UJSNtW20haK8elE63CnJ8/PqRVXk+72pUPI9583jc1ctJRxLcOnS6hF/Oc50/oJKNh5u5+dP7aOpK8zrvvskf1hfyz+9ZiGvXVad2u+m8+exfHoJ5YUB/uetq/ifa1dz6zvXUNsa4u0/eRYni6XDZfxicYcAGS1jo5V8/2zYZm/dWWpMXQKf3Amzz7aPq1fYMAgGzgsajC+Qbh+fMob39umnQ287bL8fCqfa9/OalfAPD8LZH4D1v4aWvQNe9vHXLuLd580b8fDnui1ln75rI36v4RfvOQuAJ3Y1Ut/Wa5ech5FnCEF6UYYzbrQzgfxumHTKRfZilwygCiEREZEcFs+cIWT6lYivvsGWllctHttBa1bbq2vhjoEzXEYrM+gJFMMFH8/uOHJymnOerRAKFIHx0O0rozDaQry3k0EaIVK21Xcwoyy/z2yW/uZV2r+b+5q6OUWVEzIGTV1hrr3lGd5y+gzKC4P4PIaLFmUsIX7gGf7ztTM5cMG8PtVn7331KayZO4UFU8f29+3Viyr5zt93YYzh5+9ewzce2sG7zp07YPhuVXGQ+z7a95fd1y6r5pOXL+Zz92zmcFsPM6eMYpixTKho3CHgzbJlDNIzAMvmDr5fsnp3+unpdrCRlM9zVy4bQyA043R7e/AZmHchXPyvcNFn7YWM8/7Ztqw9+S14w/dGf8wMM6cUMKeigAPNIT56yQJWzChlwdQi7nmpjtq2Hi5ZMhUcxw2ERpghNGMNlMy0QZWMiiqEREREclgyEDL9W8YArvwanPaOsR/U67NDnyH7QCizQmikK3oi/V3+Zbjyq/YXjlMu5tDMqwFoax84YDfTkY5eZkzJH3afzEBIZCwe3HyEeMLhzxvquW9jHefOr0iHj7Ew/PL1TNv2c845ZeAQ3dNmT6E4bwxtQ8DqmWVUlwR5z6vmcvGSqdz30QsGXYlpKIvdqridR4dvtZysNh9up6Gz93ifRtai8QRB3FXGsqkQat5t27MKygffr2opFFXDmveM/tjJwdJjeW+vWmqHMYNdYRTS7d8l0+D0d8LLv7UroIGdP/jTS6F5z6i/xMWLp1JeGODmC+xqeRcsrGRbfQdl+X7b7hvpBpyRZwjNPQ8+vmX0C2nI+AIhY8zHjDFbjDGbjTG3G2PyjDHlxpiHjDG73NspE3WyIiIi0lfflrEJvM6z+nq7CkdR9cj7DsafWSGktjAZh3feTcep7wOgtW34QCgUiVEUHP5K/JQCP2UFfnY3dE3YKcrJ4f6NdZTk+eiJxqlt7eGy5TXpJ1v3QyIKHXUT9vV8Xg+Pf+pi/uWKJVm9fqEbCO04knt/1w80d/PmHz3N1x7cMerXbD7czlXfeYIj7QNDpIPNId75s+c42vHKBUyxeIKAcSuExjRDyK0QatljW6CGajP058EndsDp7xr9sZOLFoy2ogjsRaJpq+z96mUDn7/4c3ZBhN+/E176DTz2Nbu0/YGnBz9ePDpg02euXMJDH3s1pfn253TDWbO5fHk1d33gVbaSM+L+HR6pQkjGLOtPjsaYGcA/Amscx1kBeIHrgc8Af3ccZyHwd/exiIiIHAN9hkr3bxkbj+XXwE1/yz5k6jNDSBVCMj7Tquyclq729mH3C0Xi5AeG///AGMPqmWWsPzh8uCSSqaGjl+f2tfDu8+alKm9euzQjME/OUOlqmNCvm+f34vGMfu5QptJ8P9NK83KuQshxHL74py1EYgk21raN+jX/8eetbK3v4G9bjwx4/jfPH+CJXU387Ml9E3y2Q4smHAImOUNoLC1j7vtn6wG7jPpwxjCTCoDTb7SrbhXXjLhrH9PdtrGpgwRCBeXw7vvglAvhz/9kB18DtNcO3PfwevjKDNj/VJ/NeX4vFUXB1ONF1cX8+J1rmF3hhmPJBQUUCE248V5K9AH5xhgfUADUAW8Efuk+/0vgTeP8GiIiIjKERGKIodLHmwIhmUA1VbYFp7urbdj9QuE4hSMEQgBnzp3CzqNdtIUGLpcsMpjfPn8Qx4HXr5rG5163lE+8dhE1pXnpHZLtMRMcCI3XoupidhzJrUDojy/X8eiORmaU5bOnsYueSJzucGzY4dgPb2vg+f0teAys3dkE2JDo+X0tdIdj/OllW7l1+3MH6QrHBj3GvqZufvjYbr77910TMog7Gk8QMNm0jLkhiBO3LVkTKVgE8y8e++uWv8nOdqtePvjz/ny49pd2WLUvaFeKbD80cL9HvwLxMOx8cGxfv7vR3g7VPidZy3qotOM4h40x/wscBHqAvzmO8zdjTLXjOPXuPvXGmKnDHkhERESyNuwMoeMps01MM4RknPz+AGEC9HZ3DLtfdyRGQWDk/w/OmGN/qVh/sJVLlmTZFimviK5wjP/30+cI+jzMqywkEk/wr1ctpTKjmuBYq2/v4ceP7+WqlTUsrC5mYXUxr15U1XenZIVQ9+QKhBbXFPPM3mZi8QQ+7+QfH7ulrp3P3L2Rs+aW8+7z5vKh36zn0R0NfOKODXz5mhW8+fSBs2F6o3H+6/6tnFJVyJlzyrlvYx1toQj/du9m7ttYz5yKAurbe7np/Hn87Ml93LnuEO/pt/pVbzTOW370NC3dNiReOaOUi5dk/2tsPOHgOOAnm5axjAsqxRMcCGVr9jnwngeG3ye/DG56yIY3f/zIwECodh3sfshWM+97Ymxfv809VmmWcw1lSONpGZuCrQaaB0wHCo0x/28Mr3+fMWadMWZdY2NjtqchIiJyUosnZwglBlll7HjyBsC4HzM0Q0gmQNiTT7Rn6EoHx3HoicQpGEWF0KmzyvB5DC/sV9vYZPe9v+/i5UNtdPTGeHhbA/e8dJhfPr0/q2M9v6+FLXXDtx0O5n/+uoO44/DZK5cOvVOLWyHU3WhXyJskFlUXE4klONASOt6nMir/+9cdFAX9/OAdp3PqrDIAvv6X7fRE49y5bpAWJOA7f9/FgeYQ//XGFVy8ZCrdkTiv//6TPLCpnrecPpNDLSEKA14+edliVs0s5Z6XDpNIOPz62QMcbusB4MHN9bR0R/jZjWuYUZbPdx8ZX5VQNG6DoHSFUJaBUMkILWOTTUG5Xdm0dGbflrFEHP7yGSiogHM+CEc2Qk/b6I/bftDealj0hBtPTHwpsM9xnEbHcaLA3cCrgKPGmGkA7u2gMbnjOLc6jrPGcZw1VVVVg+0iIiIiI0i4Fx9thdAkuvprTHqwdEA9/zJ+UW8Bid6hA6FIPEEs4VA4wlBpgPyAl+UzSnlxkEDo8/du5sFN9eM6Vxm/A83d3PbkPn725D6uWzOTB//pAtb926VctKiK379wKPUL92i1hSLc9IsX+MIft5BIONz8y3U8vPXoiK9r6Y7w5w11vP2s2cwqH2bp9mSFkJOAUMuYzo1QC3Q12qW1J1hy3tHmw2MPwo6HzXUdXLS4iqriINNK8ygvDLC/2YZZz+5rpqHfUOgj7b3cunYvbz1jJq9aUMm58yvwegyHW3v43g2n843rVnPbu8/kG9edSn7Ay1Urp7Gxtp3fPHeAz9+7mff/eh2RWILfPneQuRUFXLx4Kh+8aD4vHWzjmT3NWX8fyb+ffpIzhLIYKg1jn/UzWSQDoeSHlGe+bwdNX/E1WHyl/f/k6e/Bg//iriA2grZDUFAJgWH+H5SsjOeT40HgHGNMgTHGAK8BtgF/Am5097kR+OP4TlFERESGEnM/bBknPrlaxiB9lVMtYzIBHH8h3liI3ujg1RehsN0+bIVQey3c/X4ItXDmnCm8XNtGKJKeJ9LRG+XXzx7g4W3/v727jm/jPh84/jmhmRlix3aYmZmaFFJm5rVru3Vr13Zb13b9rdsKW2FlSiFlSJNCkgaaNMyc2LFjZmZblu73x1emxEnsxInt+Hm/Xn7JOp1Od/LZlh490LnKfrqj2xds5akl+4nwdeXhJlO2rh0TRW5ZDSva+DN699cjlNXUsSejhP1Zpfx8IIf3W5FptGhnBja7ztWjTzDuva5GnVuBzv0sdwaaUjdCxrYTP8DB7+GF/vBcHLw3r/ENdDvpH+ZFqLcLX2/PaNftngn55TXkldXQN0QFsTRNY0CYFwDXj+2BrsMX29LJK6tpuM8Pe7KwO3R+MzUWUI20/zyvH69eN5zzB6tyq6l9gjhvoAqszHVe/n3JAdwtRvZmlHL56+vZklzENaN7YDBoXD4iArNRY01C/ikfS51dBfdM9RlCp1wy1sUyhOr59AB7rcqYq62AVc9An/Nh0OUQPhJMLrD2Odj0usocOpmSNPA5we+gOGWnHBDSdX0T8CWwHdjj3NabwD+BWZqmJQCznNeFEEIIcQY0GzvfmUrGoPFFrZSMiXagWT1wo5r0opZLXyptxwkI1VbCFzdD3iE4sAR2fwo//olpfYOorXOwtsmbvn0ZqkdRWfWxY5HF2VNeU0dCbjm/nRbHqj9ObdYvaFqfQIK9rCza2foAR2ZxFe+tS8bf3UJNnaOh5GxjUgEllY0/60PZZdzxwVZ+2puFw6Gj6zqfbUljcIQ3fUO8jv8ARckq46HHWHW9vo/QN3fDdw8cu36dM6CRvA4+u16N8h59F6Suh5R1qjF1OzWnNho0rhgZyZqEPNI6edlYffPrfqGNz/WwSB/MRo3fzexN3xBPnl16iNH/+Jn9mep39fs9WfQL9SI2sPGDh1sn9uS8gS333onyd6d/qBe1dgf3TIvjrskxlNfUccmwcK4Zo/rTuJiNxAV5ciDrxD3L6hVV1FJV2zxQbXMcnSF0Ck2lof2bSp8t9aVdJWmQugHqqmDUbc7sYRcYei30u1Cd99s/UH+bT6Q4DbwlIHQmnFZuua7rf9N1va+u6wN1Xb9B1/UaXdcLdF2foet6L+dlG3MmhRBCCNFa9VUTmu7oXFPGoDEQZJaAkDh9JldPPLRqrn5zE39fsv+Y2yudk4OOaSqduAL2fQN7vlB9KwD2fM6Yui14uZhYvj+HOruDapudPRnFgMoUEh2n/o348CgftKPGapuMBkZG+bG/lW/Wk/MruOL1DQC8cNVQAL7dmYHZqFHn0Fl1qDHw8umWVJbvz+Huj7bzu8928uW2dA5ml3HFyJO8Ea2fMNZjnLosz4PSTCg6Ajl7oKJJ6VHhEXgmEhJ+ht2fqb+PNy2GmU+o8trNb8Jb0+HFobDt/VYd48lcNUrt/+dbW5j61InU/9zrM4QA7poSy/f3TyLAw8rzVw7hL+f3QwN+2ptFZnEV21KKuGBw24Imlw4Px9vVzLWje/DovH6s/MNU/nPVULxcGrN4+oV6HnOOZRRXsWR3ZrNldXYHF77yK499s6fZcpszQ8hMfQ+hNmTwGs2NH/B0lqbSbdU0IJS0WgXE6n8/AC74D1z1Ecz5P/DvBb++cPxt6bozQ0gaSp8JnajZgBBCCCHaqmHKWGctGTO7d67eRqLLcvf0xt9iw2Z3sPnIsZ83Vjg/oXe3GtUbiM1vQeYOiF+qVkjfqgJC0ZPAIxjTvq+Y1jeIFQdymPfSWq55ayO70lWfldKqlsdSi7Ojvt/NgDDvFm/vE+JJamFls3K/43nmxwOUVdv45M6xTO4VQJCnFZtdZ86AEAI9rSxv0kdoQ2IB42L8+cOs3ny3K5OHvtzN+Fh/rhx5kka2B5eorI7oSep6eQ6krG+8PeXXxu8Tlqmx29veg4TlEDddBc8tbjBgPhz4Dkoz1HjvxQ/Ajo9O/Nhlzsc6Qf+hcB9XxvT0axb86iy2pxaRVaIaOx/MLiPQ04p/k4wwd6uJ3s4+SAPCvLl9Ugwjo/xY7mwwDjBvUNuCJrdO6MmGR6fj6378rJ3+oV7kldWwP7OUBz7dwapDuVz/9iZ+u3AHh3PLG9Zbk5BHelEVy/ZlU1PXmCVU5/y0xsQplIxpmjqfXP3UCPeuqD6bp9gZEIoc03L/H6MZRt+hSisztre8rYo8qKuWgNAZIq/QhBBCiC6svmRM0+2NU706C7Ob9A8S7cbk4kkPd52JcQEtBgLql7maTaovxQ9/hC9vU2+6Qb3hyD0I4SMgeCDkxzOrfzBFlTbic8rZkVrc0GS4rEYyhDrSvsxSAjysBHm2/Ga4T4gnug7xOeUt3l6v2mZnTXw+Fw0NY2C4N5qmMayHDwBjY/yZ2S+Y1YdyqbbZyS+v4WB2GZN6B3DfjF786by+zBkQzFs3jsRqOkH2ZWkW7P4cht2gJkKZXFTJWMo6lfFj8YAjaxrXT1qtLg9+D2WZ0GtO421Dr1OX434Lt/4EPSfD93+EHGdGXO1RJV+Jq+C18fDeXPVV2qQZenEa2BobMPcJ9iQ5v7JhclZVrf245Zdni92hc9M7m7nlvS3Y7A4OZpc2yw46npn9gziQVcr/Vh1mWp9Aega0LQvVYNCOzSQ8Sn9n2dojX+9m0c5MbnlvS0PJXdMg4mdb0jBoKiC9vkkT6voMIdOplIyB+kClq00Ya8rFW53/mTsgew/ETD3+ukOuVh8ebXlHXXfY1f0O/6yuN4ycl5KxM6GTvXIUQgghRFvUNcsQ6mQlY2Y36R8k2o/FA2rLcLUYj+nXAY1Npf0rEmDpn1WD38JEKM9WmRs1peCwQcggCOgN+QlM6x3AnAHBvHbdcLxdzdTUOTBokiHU0fZmlDAgzOuYcrF69UGDgycpG9uYVECVzc6MfsENy4b38AVgTE8/Zg8IpqLWzobEgoY38+NjAwD4zdRY3rhh5Mmn1m1+A3S7GqWtaeARpPr/pKyHHmNUmUziKpWpVp4HR9ZC2DDAmdHTa1bjtqLGw20/w4y/qb/nl74NJguse1GVpf0rGvZ/pxpPr/4XfHgJuAfCrKfUG+iVf1fb2fExvDQMPr4c7Opcjg5wp7ymjvzyWkCNap/74to2T2trT0fyyymrqeNgdhlPLt5HfE55QyDmRGY6f561dQ7+ckH/M7Jv9X2MdqeXMKlXAHdNieHFq4cxOMKbZfuzAdUEe8WBXG4cF427xciyfY2BItsxGUJt/P9sdu265WKgfhd8o2Df1+p6zLTjr+viDYOvgL1fOXu+3QRvToWPLlPB0PqR89JU+ozoZLnlQgghhGgLR2cuGes/X/XREKI9WNyhtgI3i7GhgXRTFc4MIe/ifeoN+tUL4dt7IG0jTH0E3l+rVgwdAtUlYKvEvSaXN24YCcDO9GLe+CWJQRE+7EkvxuHQMRg0ftiTxcHsMn4zJRbXE00wE+2i2mbncG45M/oFHXedSF833CxGDjqbEB/PigO5uFmMjIvxb1h2/dgoYgI96BXsSQ9/N/VGfn8OoOPpYmJQeMtlase17xvoNRv8eqrr7kEqIyLvIAy+Sv1dPrwcFl6pgje1ZTD+flj9jHoj7HHUcUaOavzeMxh6n6fKzPxiVKnZry+oQOfqf8Dgq+GCF9TvRlkObHpNleBse18FRJPXqky5ib8n2plFk1xQQaCnla3JhZRV13EouwxPFxM2u05ckAebkgqIDfJo1sj7TNntLNEcHOHNRxtTMWgwLtb/JPeCmEAPJvUKYHgP32bNpNuTr7uFUG8XskqquXFcNLP6qyBUUl45zy+PJ7e0mlWHcqlz6Fw1KpK8shqW78/hbxf2x8VsbJwyptepcrHjBDePa9y9jX14uqqLXobElep3IGzYidftf7E6b3ctVA2m+18M+7+FI7+A3ZmxKRlCZ0Qne+UohBBCiLZo1kOos00ZG3ZdR++BOJdYPMBei7vJQWULGUL1WUMudc6sEfcAuPQNlTnRY7x68223qTfW9WPB8+Mb3nTdPTkWV7MRs9HArrRiKmrr8HQx89baJHakFrNkVyZv3DCCXsEnL2kRpy4+p4w6h37c/kGgSn56B3s2TKU62pbkQh79eg+ZxVVMjAvAxdz4t9Hdamp4c281GZnaN4gluzOprXMwq38wRkMr3rivekaVfN3wtZowNvK2xts8guHQ9ypDst9FKqjj4gVGKyy+H9Cg5xRVutiarJHe56nm0xteUYGF+hKcvhfAJa83Bhom/g62vqveVA+/Cc5/XmXKbX4Dtr1Hv4sWAnAkv4JhkT7szVTBmF3pxXy8MZXDueVcMDiUr3dkcP6gUP533fCT79tp2p1egqvZyMI7xpKQU0ZckAeeLq3rtfPhbWPO8N7BkAgfoJhpfQIbls0eEMLzy+NZui+bzclFBHla6RviyTWje/D9niz+/M1enrticMOUMRP2tpeLAYy5q30OoiOFD1dfrRE9EazesPwJQIfpf4GsXarc0sVH/f129Tlz+9qNSUBICCGE6MLs9Y1EHZ2wZEyI9uTsR+VtqKW2zkGd3YHJ2Nj9oL6ptNVWqoKjVi/1JsI3Wq0QMxVqytTvSUBvtSwvHmKnAyoj4Hcze/PZFlWeUFatAkJphVUM7+FDWlEVl762nteuG8HEXgFn5ZC7o70ZKqA38AQBIVBlY0v3ZaPr+jGlZYt2ZpBeVMnwHr7cPCH6hNuZ3T+Y73dnERfkwVPzB558B+11sPUd1eh2+wK1LKJJVo9XmOrndvm7EBCnlo24WV16BkPeIXD3V1+tETdDZVjUlMLUx2DjqyqwOfdfzbNOPILg4lfVOT78RnXb3H/ByFvhtXEEFe3EZBjGkfwKDueVU21TAYuf9mazP6sUT6uJr3dk4OduYcXBHCpq6k5eLnea6ksDPawmhjlL+Y6h61Bb0X796LL3gFtAq8a5P33JQKpt9mZ/Z3oHe9An2JMvt6WTXlTFlN6BaJrGxF4B3D+jFy+tSGB9Yj5+zobVRt3Wtglj3ZXRDL1nq2mQwYMgoBf0nAR7vwFHHQy+sqP38JwlPYSEEEKILqyxZKwTjp0Xoj05+1F5GWsAjikbqx87b64tVp8kH12iccmbqowMVOmOi7fKEDpKfYZCabWNqlrVbHh63yC+vXcCYd6u3PTeZj7f0rnHd3dl+zJL8HQxEennesL1BoR5UVRpY39WaUNT6HobkwoZF+PPwjvGNvQEOp45A0J4aE4fPrxtdMOb+BNKXKmCQQDrX3aWwwxtvH3yH+GWn6DP3GPvGztd9RpqCxdviJqgvh9yFVz2Nly5oOVyooGXwoibGs99TYOgvuAXgyH/ID383EjOr2B3msoOivZ3Y21CPgCf3DmWD28bzavXDafa5mDxrkxeWZlAXlnNsY/TDursDvZlljIo4iQleqv+AS8ObiwbOhldh+V/UxlcxzxoLbx/viqja4UADysRvs0nY2maxhUjI9iVXkJBRS0T4hrPr9/N6MVfzu/HuFh/iivV/roZ9VPLEOqO+p6vLgdeoi57TlEllnVVbf+9Ea0m4UohhBCiC6tvKo1e1/lKxoRoT86AkKdBvUGtqrXj1aS8pNJmx2zUMFYXg2sL2QZml8bvNc3ZWPrYgFD9Nkur6hqmMEX6uRHu48qXvxnHnR9s47Fv9jBvcCgeZziDojvam1l6wobS9S4aGs6/lx7ihWXxFFfZyC2rZu3D08krq+FwbjlXjGhd/xUXs5F7p8W1fgd3f6rOL49g1ScobJhqAFzPM0R9tafJD6mG077RjRlvbRHYF3IPEB3gzpH8Cvw9ivG0mrhoaDgvrUgg0s+14Tl3OHRCvFx49Js96Dp4u1m4YWxU+x4PkJhXQZXNzuATBYQqCmDD/8BWAYVJcHgFVObDjMePf5/938K6/0LUxMYAQ73ktap/WOJKNYGt6d+ENrh4WDj//PEgdQ69WUDIYNC4fVIMoD6syS2rwWf1j20bOd+d9ZkH0/4MI25R16MnqcuYaRDUr+P26xwnGUJCCCFEF9Y4dl4yhMQ5zqJKRjw1NUr76D5ClTV1apR0VWHLAaGjBfRR5Tv1ZZdOXq4mPKjEkbWb9KIqgIYsAU8XM3dOjqHOobM3o+R0j0gcpc7u4GBWacv9g8rzVLmPk7ermVsn9GTFwVy2pRSRVlhFdkk1m46oaWFjYlpRklVVrHoAtVbyOjiwGAZept68QvNysTOl5yTVGP1UBfWHwkRifU2kFFSyNbmIgeHeDOvhA8D0PkENATiDQeOyEeFYjAYMGmQWV7XDARzrgHNCXIs/64JEeGsGfHa9CgYB5O6Hbe/B5rfUlLWWVBXBj87nKWMr1NWopt97v4KSdDj0g7rNVgkpv7a8jR0fw/P9YM1zKmjUggAPK/MGhTI4wpsQ75aDSgaDpm6zS8lYq5msMOVhcPNT1z2D4aJXYO6/O3a/znESEBJCCCG6sPqm0qqHkLzoFOcwZ0DIDWfJWG3z0fAVtXbcLUb1ptDV7+Tb6zEWKnJV1kATni5m7jYtZuSKq0krLAcg0rcxA6Q+o2FXWvGpHkm7WbovmynPruLqNzew+UhhR+/OKbM7dO7+cBtvrEmips7BwPAWRo9/eQu8MRlWPq0CBRtf59aJPYn2d2toEr07vZiNSQW4W4wMDDvO+HK7TTWrBVjxJLwz55igYItyD8LCq1SGztRHG0vCeoxr+wGfbUF9QXcwxCWXKpudg9llTOsbyKhoPybE+XPVqB7NVn9wVh+2/GUmYT6uZJ2hgFBiXjkGDaL83Y69MWGZCuikblDTKjUDpG2B/ATVS6kg4dj7VBXBh5dCZQFMfBDqqmHLO/DFzfDlrfDqeBUcipsJJleIX6bu9+Mj8NHl6nt7Haz+J9SWw8q/q6/jeO6KIXx2Zyt+9g6blIydjuE3QGDvjt6Lc5oEhIQQQogurHHKmJSMiXOcs2TMXVNvUKuOyhCqqrWrsfBVRa3LEBp0heoltO6lZou9XEz011Iw2avIzi/EajIQ6Nk4gtvfw0qknyu70otP73jawTtrj1BRU0daYRW3vLeZHalFHb1LpyQht4yf9mXz7NJDQJOsEVs1JP0C6dtU4M4rAtY8q7J0Vj+Dt9nBqj9O5aWrh2E0aOxKL2ZNfD6jevo1awTczNrn4Y0pKmMkazeUZ0Npxsl3cs2/VanhDd+qCXaRo+H2FWo8dmcX1B+Ayb753Dguio9uG8Mdk2LwsJr4+Pax9D8qeGY0aHi5mAnzdiWzpHmWTG2dg4x2CBIl5VUQ6eeG1dTC/63svep386FEuPQtNRlwzxeAM3CXvvXY+3xzN+Tshas+grH3qGUrn1bT3m75Edx8VbBo4OUQMwXif4LaStjxIRxers6F/d9CSSpc8gYMvV5lI5Wkt7j/FpNB/b05GbtNSsZEpyYBISGEEKILczRMGXOAQf6ti3OYmyoBijr4NlMMu7CV5ja7uaLWORWpqrh1ASGzC4y+S70ZzNnXsNjTxUwvTQUICgoKiPB1PaafzZAIH3aldWzJWHpRJZuTC7l5fDTf3DMeX3cLj32zt0P36VTtSC0GwGI04GI2EBOggn9seBk+uAg+vESNpP7Nryogc9XHUF0Mh35E0zRcLUZ6B3vy+dZ0UgsruWBwWMsPZKuCzW8COmTuVCWD0KwUDVBBp4ztjddL0mHft2p6l3d44/KIkV3j765fLBjMeJUe5qn5A5nYK+CkPZoAwnxcjikZe3LxPua9uLZhoEFTOaXVrD6Ue8zyliTmlTf+nI/Z0B4IHqgmsZmsqn9MhXO7JhfI2AbrX1EjyQHSNqsAz7THoM954BEI/nGq3GzApar/0s3fq/40Ay5WQbziFPj+DyobCGDdi/DLv1Rvsd7nOUv0dLXsdEjJmOjkusBfMCGEEEIcT2NTaSkZE+c4n0i48CVcShJZYPkXY76ZAKVZDTdX1tjxNOuqpKQ1ASGAUbepco7tH6iyy9RNWOyVRBrUFKmSkoJjpgwBDI30IaO46oxNYGqNRTszAZg/NJwgLxfOHxRKYm55YxlpF7I9pQhfNzOvXT+cP53XtzG7J36pGhFuq4Axd6mJW7HTVLmWVzjs/LhhG0MivMkrq8HFbOC8gUc1dXbYVfnQiqdUlghAwlI1wQiaB4TyD6vMki9vVQEkaAwijb7zzDwBZ5rJogIkh39WvZhaKdTHlZzS6oZzKresmi+2plNSZaOgovaY9f/7cwI3v7eFhJyyE27X4dBJLqggJrCFUfJ2G+QegJBBjcucGU54R0LkGNUTaNmfYe0LavnqZ9R50vTnU1/KN/xG530jVH8asysMulxlHe1aqO43+CrY+6XqJzX33yrI5xOpekUdWKxKCvd+rQJPoM6Xo/sLVRVB0uoWDlZKxkTnJgEhIYQQogtr+JTWISVjohsYcRPpN23iGds1GPQ6Ve7jVGmrI8DkfJPm1ooeQvXr9T4P9nwJv74A786Gbe833FxaXNzi+PMhkT5Ax/URqqip47MtaYyM8iXSTwWsovzdqbU7yCo5Mz1fzoSM4iqKKmrZnlrEsB6+zOgXzC0TeqobKwtVadCo2+AP8apvTz2DUb2JP/yzWg8YHOEDwHkDQo6d/nb4Z/j+Qdj4KoQOVRkz+xc5b9Qge3fjuglL1WXRERVAStkAG15VmSa+7T9t66wZf58KtLw2TgUv6lWXHLeHUpi3Cza7Tn65Cny+vy6ZWrtq6NzSebY+UY2wf2114gl3JbOkimqbg5jAJhlC9Y2i8xPAXntUQMg5YSp0CISPUNlhAGmbVHAmcaU6PkuT7Y25G6Y8okr7jmY0N55P/eer+/r3gqsXqoBjvcjR6rkqSIRF98I3d6nspNcnwuY3Gterq1X9pT6YDyVHlR/aa6VkTHRqEhASQgghujD12lyXKWOi23D18GGP7gwa1FY2LK+ssRNgdE4kam2GEMCQq9Uo61X/UNfXvdh4W20ZkS1kCMU5MxtSCyuPue1M03WdR77eQ3pRJQ/Oamy2Gh2g9jOl4Ozv06m69b0tzP/fOhLzKhjunHjVIHEloEPcLFU6dHRpVsxU0B2QuQOAcbH+uJgNXDumhaBN0mpVanTz93DNpxA6WAVCAKInNs8Qil+qxrQPv1EFkN6bq7JLzn+unY66gwy7Dq5cABV5jU21i1Ph2V7qmOGY6V1hPioYmllchcOh88nmVHo6y7yyjuotlF5USUpBJYGeVhbtyuTNNYlsP05Pq6Q89XsaE+DMENr4GvynP9RWNP4sWsoQChumfl4Aw65X08KW/w3Q1O9xUyEDYdqjqu9TSwZepsbXT3hAPdZ9W6HXrObrhA1Xl9veU49VmASfXuc8iNWN6y3/qwpOASStar4Ne50KQAnRSUlASAghhOjC7LqOob7RppSMiW7A1WKkSnc2ebY1Bj8qauvwN9QHhHxav8G4WY1TyYIHNfYqATypUg13s/fCyyMbRpR7uZoxaFBUeWzZzJm2IamAxbsyeXBWb8bHBTQsj/ZXb9STCyrO+j6diqpaO/G5ZQ1BteE9jgriJSxXP5fw4S1vIGyounQGhHoGuLP/yfMY3bOF7LCk1WqqXPRE8ApV/WlAlQvFTFE/1+oSqC6FlHXQew5c8F+4YgH0PV9ljrQlyNhZhQ5Rl4VJ6jJxFdhrIH0LJP8Kz0Q03gaEetcHhKpJzCunqNLG1aMiAY6ZPrY+UZXiPX/FEII9rfzjh4Nc+up6bl+wlfKa5hMBk/JU357Y+gyhHR9DWRbs/lz1DzJaVcZOvYDecMF/YMQtEDsdfr8PZj7lPIYV6mfreVSZ4MkYjDDpDyfO+grqr8q9ti1Q1z3D1H66+kLqRpUZVFerbh9yLXgEOwOZTThsEhASnZq8chRCCCG6MIdDx6I5py1p8jmPOPe5mY1U4gwI1TYGPypr7fhop5AhZLLArKegpkz1qFl0D1WaG656JX6mGhVg2PiRGnW98TWY+y+MBg1vV3OHBIS2JausixvHRzdbHuLlgtVkIDlfPQdrE/J4dVUid06OYWqfwFY1ET6b4nPK0HWY1ieQQ9llDWV4DVLXQ89Jx898dPFWfXGcASEAg+GoYzz4vZpWlbsfBl/ZuDxksLoM7AshziBJ+lYoz1Xlt73PU4874GL1da7wDFOZUvVBn+Rf1WX+IXW8tgrYuVA9LwcWE37+W4AqDyurtgEwq38wzy+LPyZDaP3hfAI8rEzqFcC6R6ZTVGnj0y2pPLf0EE8v2U+Yjys/7MnizskxbE0pwsNqUtP7CpNUEAgNNvxPNXkOHdK8EbOmwchbG697R6jLoAGQu0+VfZ0JJosKHmZuVwGq6X+G9S+rXkXf3KXOPc0AdVUqiIiusq2aDnmQkjHRyUlASAghhOjC7LqO2VCfISQlY+LcZzIaqDO6qCvOpr+6rjsDQs6JQW3N5hh+g7qsLATNSKprX/pUbmdIkFGNxc7cqW7f8ZGaZOTija+7haJK2+kfUBvtzighJtAdL5fmbzINBo0ofzeSnSVj//05gW0pRWxIKuBP5/XllgnRrE3IZ2a/oLMaHFq0MwOz0cC8QaHNlh/MLgXgiYsG0MPPrfk+1VZCcRoMve7EGw8bBinrW74tLx4+vbaxt1rPKY23hTgzhAL7QNQ48AyFnx5R/WJCBqnGxecigwF8e0JBkuobVB8QyjvUWC628xOoq4bKfLymPoqbxUhGcRXl1XX4uVvoGeBOiLdLs3H0qw/l8vOBXKb3bTy3/Nwt3DM1jpIqG2/8ogJQAR5WHvxclauNjvZT6x5YrDYy8feqj5fFU2UDtUbPSSog1O/C039ujid8uAoIRY2HAZeor8pCQIPkNY0No6PGq+DPrk/gwCKIGA0eQVIyJjo9CQgJcY7YlVZMckEFPfzcGHZ02rUQ4pxld+iYNSkZE92M2Q10VEYDUFPnwO7Q8dTrA0KtbCp9NDc/uOQNVuzR6ZNwBwP8nUGKrJ2qbCU/XjWO7TGeENeZFLUwaelM25NewtiYlo8vyt+dlIIKDmWXsS2liIfP68Oe9BJeWH6IZfuz2ZFazLs3j2R63+Czsq81dXYe/XoPlbV2bpvYk0fnNk4QO5BVhpvFSKSv27EBqoLDgK6e8xMJGwZ7voCyHPXmO2evyujQNDVBSjOqYI+9prFcCtSyiQ9CvwvA6gnz/wcfXaoyOW749twOrvvFqKycwiQoy1QZVIVJKtvOxRtK0xtW1RJXcr17HiFpO/moaizDe/iiaRqh3i5kl1RRUmnjnz8d5JPNqfQN8eSPs/sc83APzurNwawy+oV68dCcPmxKUqVl/cO81AoHFqtG35P/CCVpqndTfcDuZCb9UfX9qc8YOhPChqnLqAmNy9z81Hl2eGVjpppHEMRMU+fQFzer9QxmdS4GnuQ8FqIDyStHIbqQjUkFJOVVEBfk0axGXtd1rnt7E+U1dZiNGqsfmka4z7FTUYQQ5x67Q8dqcH6yK1PGRDehmd2hloam0pW1qmzSy1GqSjisXqe+8cFXUJl1iNp4IzHeusoaKUqGGX+DinxVyrTpNZ4xL+Nh1ydO+1jaIre0muzS6oaJWkeL9ndjTXweH21MwWI0cPWoHlwzCramFLEzrRiDBluTi85aQGhrchGVtXbG9PTjnV+PEJ9Txv+uG46Xi5mD2aX0CfE8tswLVOANVAbPidQ3/c3aqSZBLX0UrvtS9ZnZ9akKFlzyuuoN1DTIo2kw82+N1+NmwLznVGZZa4MRXZVfT9V3J3mtuj78Jlj7HJRmwOSHYfObatJW9l7Y+xV/qN7LkcognqoZxBUjVP+gMB9XNh8p5IHPdrA2IZ/bJ/bkj3P64GI+9n+Q1WRkwa2Nk76a9r3CXqey78bdqyaEXfZ2247FIxDiZrb1GWibPvNUuVqf85ovH3ylaiatGWHotY37c89Gdf6WZ6vMqwOLIXjAmd1HIU6DBISE6CJ0XefW97dQWWvH29XMrr/NbrituNJGeU0d14/twWdb0nh11WH+75JBJ9iaEOJcYXfomKRkTHQzmsVVBYRs9QEh1bTW3VEGLj7HTqRqoxvHRaFt88Jdr4Is50jy0CEqcAAQv5SohVcy2f4jcMFpPVZb7E5Xk7EGR3g3v6E0CzyCiA5wp6bOwYcbU7hseAR+7qqc5ePbx5BfXsM/fzx43MlPZ8Kqg7lYjAbeu2UUS3Zl8aevd7NgXTK/nR7Hwewy5g5sXkbGr/9RZUxhw1Vgzz/uxA8QOlhli313v5oUB5CxXd23LAvm/ksFeVpTQjj6jlM7yK7GP1aVhG16A7wiVJbUWucEtYhRMOJmlQHz8xOw6XWsQKihGIARUep5DPV2IdsZnLxrcgwPn9f31PalOEU1XT5ZJlhHcvNruYRt3L2qR1XaRlUuVi8gTn3Vm/uvM7+PQpwG6T4pRBdRWWunstaOxWSgrNqGrusNt2WXqjrucTEBXDUqks+3ppHRZPrDgaxSijug8aUQ4sxz6E1LxiQgJLoHF6sVG+aGgFC1TWUIudhL22UaVJCXC2Y3T6gpV9kn0Fg6AtB7DoXWCHrZDjX7f3w8CTll5JfXtOqx1x3O5/vdWS3eti21CIPWpNwGoCQdXhwCC69kaIgLRoPGTeOiePrixkyX3sGejI8NYHgPX3allVBnd7Sw9fa36lAuY2L8cLOYuHJUJLGBHuxKLyGntIbiShv9Qj2b3+HgD3D4Zzj0A/hGg8l64gewuMONi9SkqIA+4B2pGhQnrlSTqnrNOWPH1mX5xajL3P2qd1bTYEzIQPAOB7OryrIC0Ix466W8fvUARkU7A0I+rtgdOnaHfmxQry3yE9RlQK8Tr9cZGYxw2Vsw+CrVhFyILkoCQkJ0EYXOPgVh3i44dKht8mKuPiAU4m3lnqnqU4lXVx0GoKKmjkteXccLy+PP8h4LIc4Gu0PHoknJmOheXC1GqjWXhpKxapv6HXCxlbZt5PyJWL3U5LGsXSrQ4Na8b0+ez2CGaglU1dYdZwNKZW0dl766nueWHjrpQy7fn8NN727m3oXb+WpbYy8Xm93Bje9u5rXViYyI8sXN0iTJ/8Bi1SPn8AoGLL+OA7/rxZPzB+JqOfbvwfAoX6psdg5ml510X07XwexSEvMqmNYnqGHZgDAv9mWWsDOtqOF6A4cDcvap73P2tj5rJHI03LYMfrNOBe1y9kHaZvW92aW9DufcUR8Q0gww7AYVVPOOVH23PJsEd3pOUdlCEx4A4LweekOvpzAPE1cYV9PH28HA8KPKM3Udtr7rbLx8EgXOgNDJMsE6K58ecOmbx/xtEKIrkYCQEF1E/WjbMGdvoOraJgGhkvqAkCthPq7NsoRWH8qj2uZgR2rxWd9nIcSZJyVjojtyrw8IOTOEaupUhpDJUaNKiNqD1RNqSlX/oBYyGMoDhhKkFVOakwyoMe/f786itLr55LEf92RTVlPH4dzyEz5cYUUt9y7czoBwb8bH+vPwV7tZdSgXgD0ZJayJz+O2iT1568aRze+4f5Eav33F+5Afj+XNSVCc2uJj1Jf8bEtpv7KxNfF5TH9uNU98t69hWWZxFbe9vxU/dwvnD24MMgwM8yarpJrFu7NwsxgZFO7TuKHiZGeTcGdPobaWEWmamhBWmKTGgUeOPvl9uiOvCDC5qqwW73C1LGaq6rfUtLm32QUufBGinc2Uy7LVpcPOiB2P8qz5TX4bvPvYhuBpm2HJ72HT6yffl4LDKhAlARUhOowEhIToIuozhEK9nQEh54tfUAEhTYMgT5VaXZ8l9OLP8fy0T/0DP5hd2vCCWQhx7rA7dCwGmTImuhc3i4lKrGoyEo0ZQia99uRlRq1l8YDacijNBM+wY26uDVENjWtTNgPw+892cu/C7cx+YU1DTyOAz7emATSMgz9aTmk1FTV1rDucT22dgycu7M8bN4ygb4gn93y0nV1pxWx3BnDumhyDj5tzzHVloepvlLoR+l8EAy6GG76BuipI39LiY4V5uxDq7cKa+LxTekqacjh0nl92iBvf3Ux6URUfb0oht0x9QPXo13soqbLxwa2jCfZqzNKpzwj6cU8Wo3v6YTEZVPPi7+5XQRxQY73h5A2lWxLsLJNz2KDH2FM+tnOawQDXfwnnP9+4bP4rKtOlJfXnflmmutzwP3wSFwEwKbiFdgSJK9Tlwe9Pvi/5h7tmuZgQ5xAJCAnRRRRXqk8cw3zUC6uq2sbgTk5pNf7uVszOUa5hPq7cOqEnn29N56e9WQR7WbHZdQ5mnfkUcSHE2WXXdUw4A0JSMia6CVeLkSrdAjbVL6++h5DRUQumdioTsnqqoEtZNngdGxAyhw2iWjdjyNiKw6FTWFHLkEgfskurWb4/B4DUgko2HSkk0NNKfnkN5TXNy8t0XefSV9fzp692sz4xH08XE4MjfPB0MfP+LaPxdjU3NIIO93ElqElwhXfnwBuTAB36z1fLAvupy4KkFg9J0zQuHxHBykO5pBRUnPJTU22zc9+nO3h55WGuGhnJ4vsmUufQ+WhjKgeySvklPo+7p8QwMLx58+sBYeq6Q4fxsf5q4fYPYPsCWPO8KmOa+TfoNbuxh01bNJ3mFCEZQscVPbHFc7pFXs4Mr1JnX6uD36tyPM9QfGz5x65/2BkQytkLuQchfhk4jvOBZEEC+EtASIiOJAEhIbqIhh5CzpKxKluTDKHSakK8m38i+sc5fRgZ5YvNrnPfdPXPdndGyVnaWyHE2eJw6Fjqx85LyZjoJtwsRip0a5Om0up3wOCoBaOlfR7E6gklaYDe4ptnH0939uo9cc3bSXltHQ4dzh8UQriPK19vzwDg+z3qTfRdk1XflpSCCkoqbTgcKoibXlRFRnEVP+3NZvn+XMbG+GN0jmEP9LRy9ehINh4pYN3hgoZyLwCqitRo695zYe6zEOic8mRxA69wKExsXDdrV0OvJYDrx0ZhMmg8uXg/N7yzia3Jrej10oTDoXPjO5v5YU8Wj83ryz8vG0SfEE+m9Qnigw3J/G3RPtwsRq4fG3XMfb3dzET4qtcx42Od48dT1qvL3H3gF6uaSV/3ResDFk359ACrt+qT4xHY9vuLY7n4qCBrWZbqqZWxFWKmqX5D9VlD9SoLIXM7DLlGXX93Diy8AhZeBdWlalnmDrBVq+vlOWrqmRCiw0hASIguoqiyVk0WKVvHP01vNg8IlVQT4uWqGvnVqcCR2Wjg9RtG8Od5/bh6VCT+7hZ2pxV30N4LIc4Uu45MGRPdjqvFSIXD0qRkzJkhZK9p3wwh3Rls9Qo/5mZfNwvpegCWyhxKnFm8Pm4W5g8NY21CHnllNSzbn83gCO+G4MfmI4WMeeZnljgDRTud/5frHDr55TVMqM+acbp4aDi6DiVVNob38Gm8IXuvuhx9O4y5s3nvF78YKHAGhI6shTcmw8IroU5NOQt2N3H+wBBWHsxlbUI+X21vbF7dGhnFVWxOLuShOX24c3JsQw+Zh8/rg7+7hc3JhVw1KrKxtA1UltXW90DXGRLhg5+7hf6hXlBVrDJJPILVeiEDj33AttA0NTlrxM2ntx3RSNOcwZ8sFbxz1KmeQ15hKmuoshB++beayJe0Sv3OjLxVle/VlKmfReJK+OQa2PERvDkVPr4cEpap7UvJmBAdSgJCQnQRhRW1+LhZCMlezdWm1Y0v9miSIbTuRXhpqPoHDAR4WLljcgwmo4HBEd7skQwhIc45docDk0wZE92Mm9lEuW5Fb2gqrX4HNHtN+/UQsjYZid5Ctoq3q5kC3RtrTRElVc6AkKuZS4eF49DhHz8cYEdqMbP7BxPlrxpdv7cumWqbg32Z6v/xzrRirCYDo6NVU90JcQHNHiM6wJ0hkT6AmhDWIHuPugwZfOx++8WoDKG6Wvj+D6ppb/Ja+PY3KrPo1bH8w+NT/nnpICbGBbD5SMsZQttSChuyk5tKcfZCGhbp22x53xAvlv9+Ct/cM54/nde3+Z3WvwxLfgd7v+LP5/fjw9tGYzBokLYJ0GHOP1Qz8MgxLe5Lm8z5v4bJWKKdeIaqoF7SahVwjRzjDAhlwoHvYNX/wRc3wbLHVfA0bDhc9g7cvlw1pr74NUj5FRbdq5qFp6yHr24DF28IH3nShxdCnDnSfVKILqK40oavmxmrTb2IdE9bDcNGUm2zU1xpI8TLRaXplmbApjdg8h+b3X9guDdrEvKpqbNjNcmbRiHOFXYpGRPdkJvFSBUW9NpKNBozhDR7OzeVrtdCQMhkNFBh8sHiqKS0VH0QE5exiJhlL3PD6A/5cLMqG5s9IAR3q4lATyuphSqYkl6keh/tTCtmYLg3j87ry+JdWcQFeRzzOLdOiOa11Yn0C20y3rs+q8Yj6Jj18Y+FygL1IVH+Ibj2C1WO9fMTkLIByjJxM1q4+sJ/U1hZy79/OkRBeQ3+Ho3P28HsUi57bQN+7hZunRBNmI8rFwwOw2IykOzsPVQf5GrKYNAY1sP3mOUkLFeXyx8n7LdzCfNx9hZKWQcGM/Q9H3pOVsEr0fl4hULGdnVe9RirJpB5hkJNCWRsU+sc/hksnnDL92A0QVCToOCQq9S5uPcruPE7yDuopvf1ny8TxoToYBIQEqKLKKyoxc/dgrlWBYR8M9YAfySnVE30CPZygcPOMbPrX4bRd6hPXpziAt3QHDZSCyrpFex59OaFEF2U3QEuDSVj8m9ddA+qqXSTKWN19QGhM5AhZHIF1xaCHECNxRdqobJETfT0KU+AkjQeG25nTaIbRk2jlzPIE+3vRl6ZKttKL6zEZnewN6OE68dGMayHb8uBFGD+0HDmD3WWrNmqwV4L2bsbJ2odzc/Zk2X9SxA6FHrPViPFS7Ng8xvgEwV5B6C2klHOzKRtKUXMHhDSsIkPNqRgNRno4efGc8viAfhxbzavXjeclIIKLCaD+iCqNYqSVTCg/3zYvwj+OwhG3wlTH1GZIuHDweyqvkTn5BkKRUfU92N/oy7rg6SHV6om0yNvVU29Q4e0vI0Zj8P0v6oStPpG1UKIDiclY0J0EUWVtfi6WTDXFAPgl7cJbNVkl6iAUKi3KxSlQMQoqC6Bb+8Bh6Ph/mMzPmCV5Q8k5sqkMSHOJQ5dl5Ix0e24W41U4kJtdTmbkgqcTaV1tLpqMLZzQMgrrHmPnibqXFXPH1upGuPualf/Y11ztvPFXeNYcOvohh47A7xrmWrYSYSvK2lFVRzMKqOmzsGwpr2BQJXmpGxQwZ+jLb4fXhkFeYcgZFDL+13fpLemFIZeq77XNDjvn3DXGlWepTsgZy+DI7yxmAxsadJYuqTKxjfbM5g/NIxv7hnP7idm87cL+7N8fw7//ukgKQWVRPm5qZKv1qjPDpr+OFz7Ofj2hF//q0rasvdIyVBX4OkM4AT0hqHXN19Wmq6m2w2/EcJHnHg7x/k9EkJ0HAkICdFFFFaogJCxpphs3ReToxpSN5DtzBAKcbFBVSH0mQfnPQMHl8DKvzfcPzD5OyINeeSktzyKVgjRNdkdOub6gJBB/q2L7mFopC/eXt646DUs3plOjc2Ou8mZKdfeGUInmnblpnr+1JWpgJDF5vzQJX0LQV4uRPo5y6p0nbsK/sk7lue4aog/hRW1rEvMdx6LT/NtLn4A3jsP/h0De79uflvqRijPVllCxwsI+UarS4MJBl7WuNxgUNkbYcPU9cwdWE1GhkR4syW5CIB9mSXcvmALVTY7N46LRtM0vFzM3DKhJzP7BfPDnmySCyqI8nc//nPSVFk27PpUBYH8Y6H3HJXBXFcF8T9CXfXxj0N0Hn5qSh6znlLlYNC80XpQv7O/T0KIdiGvHIXoAnRdVz2E3C0YqovY7nBOZCjLakg/D9Zz1TLfKJXOO/xGWPdfNW62OBVD/kEAajL3dcARCCHOFLtDxyQlY6Kb6RngzlXjewNQW1NFTZ0DL1OdurE9p4xBixPG6rn4qB4+ekU+VpMBY61ztHb6luYrHvqB0PwNGHEw0L0YgCW7MwnwsBLu45wS6rCry7RN0HOKmrj15a2w4VW1japiKE6B/hdD9CTVc6clZlfwj4Pe54F7wLG3e4WBexBk7gRgQJg3CTll6LrOvR9vJzGvgn9cMoiB4d7N7jaldwAZxVUczi0n2t8NilNVXxmAwyvgw0vhf2Mbx4vn7IeXhqsx4+PubcwOqS8p2vGRupSAUOfXew7cvQ76zG1c1rTsSwJCQnRZEhASoguoqLVTa3cQ6GJHq6siW3c24KutIK+8BovRgEelc2ysT7S6nPV3cPOHJQ/CwR8atmUqOHR2d14IcUY1yxCSkjHRnZhVlkpddTnVNjueJufvwVnMEPL0U2+K7WW5+LiZVdAGVL+VcpU1hK6rhs5W1RQ6ypBPnJaOIXMHQyN9VEnZ5jfhhX6q2W5VEQy4GG5cBP0ugKWPwrK/Nk4WG34D3Lyk5YbS9W5cBPP/1/JtmgZhQ1WgBogJdKei1k5SfgXJBZXcNrEn147pcczdJvYKBMChQ1SAuzqmd+fAxtfUWPucfao30a5P1R3WPg+aAe7drLKC6vn3UkG7wz+D0aLKkETnZjCqAGVTFnewOoOGEhASosuSgJAQXUCRc+xrkElNJclB9SzAVkVeWQ2Bnla04jS1zMf5Is7VR/ULyNgKSx8D32jKTX74ViSi6/pZPgIhxJlib9pDSKaMie7EosqxHDUVVNvseJlUY+l2CwjVN5L2iTzuKgH+gdTqRmpKcvFxtagefvUlW6kb1GXBYciPV1kyQLA9iydMC3jW/IbqH6TrsOVtKM9RQRRQY7vNrnDFApXxu/4l2P2Zuq2lUfNH845QrwOOJ2y4avR86Eemp77CQ6ZPWb4/B4DYwGMnnYFqih3u49rwPYVJqnTtp0fAOxLu3aR6yGx5S92272sYeQsExDXfkNGkGmLrDgjsCybLyY9HdE5eYSrQeYIsOiFE5yYBISE6uTfXJPL09/sBCDCqcbUFRvUpXX1AKMDTqtLIzW7N08MHXQ6XvgVGM/S/mDKvOHrqqeSU1pztwxBCnCEOKRkT3ZVZBYTCKg8yvOB7PIzOgFB7NZX2ClNNkAdffdxVwnzdKMQLQ1UB3q5mqC6G2OngFaEyaGorGpsqD7kGzG64VaQxwJBKmFbAsEgfyNqpAkYAe75U+x88QF03GGHG39Tv9o6PwCPkxJlBrTX6TggaAJ9cTcSBt7jd+CMr9qoPlnoFtxwQ0jSNCXHqA6lof3coToOoiep4r/5YBaBG36mO5d3z1D6Pvaflxw91BrVaE9wSnVdQPzXMRJpFC9FlyStHITq5jzamklqoAkF+hnIAyk0+2BwWzLZK8spqiPB1UxPGfKKO/ac8+EpV+212g/wH6VXwGTtzSwnxbqceC0KIDtUsQ0iTz3lEN+IMCF1S/ik9S1PY7P2SWt5eGUKg/n+eQJiPC4W6F76U4uNqgOxS1Z/nktdgwYXw06NQkqbKonyjwDcaLW0TvppqPj0o0AAbPlelUz2nwOHlqqeO0dz4IO4BqnfLgcWNgZTT5e4PNy+G5Y+jY8C6/X2q0/dgNsYSVd8IG2DNs5C2RQV8jGZuHBeN0WAg3M0BlfkQNx0m/aFx/f4Xw86FKrtp6HXHHy9eHwiS/kFd28WvqkwvIUSXJa8chejEKmvrSC2sVH0JgACTCgzVmL2p1axgqyS/XJWMUZzaWC52NBdvMJpxCR+Iu1ZDfnri2ToEIcQZZnfomJCSMdENOUvGoh2pmLDjbXSOaW+vptKtEOzlQiGeBGilhFhUeTeuPqrh88Tfw/YFkLgS4map23yiGnr3AHjW5sOBJdBrduOI+PDhxz5Q/ajv9gyguPrCRS+jTf4jAMMMCfQMcMdkNKgytrpa1dA6YanKdtr2PgP1BJ65dBCG0vq+hVHNt2l2gZu+g2s/g/4XHf+xe05WfQ6P1xhbdA1mV9VLSAjRZUmGkBCdWEKOygj6v4sHERfkgX/GVwDUmn2otVlxra2ksKKWQA8LHEqBqHEn3J5npGoIqOcdAE68rhCia7A7dMzG+oCQ/FsX3YizqbQZNV3M35lFezZ70piNBipMvkTWHSLI4gxIuTgb7U5/XGXv7vu6MdOovr9QvcIkKElVfYJ6zVIj4ftecOwDxc2E8ffDkGvb/yC8Iygx+jPUnkhBkCfsXwRL/wIT7oeqQlVatuEVta7FA27+Hsqdk029j99f6YT8Y+HhpPbZfyGEEKdMXjkK0YnF56iU8n6hnsQEesDhIgBqLV7UVFmprSrHoUO4Sw3UlB77Sd1RTKHqk0X3Ahk9L8S5wu7QMZqcPYRkypjoTixuza76UB8QOrsl0bVWP/zsZQQ6Bz80BIQMBrjkDRhzN/QYo5b5Ov9PG8zgsEHqenXdr6eaanbn6pYfxGiC2X8/MwegaeR5D2JY/gFSgjwgZb0KUv3wR5XFc8sPqn9RcH/47n5YeJUKFsHxM5OFEEJ0CVIyJkQnFp9ThsVkIMrfmY5bWQgmFwwWd6pwoba6AoBIzTna9mQvzFy8SDNEEFAmASEhuoLM4ir++u1eymvqjruOQ5eSMdFNmV2bXfXWnAGh9moq3Uq6WwCeWhWBWola4OLTeKPJ0hgMgsYMofqM3uR16tIv5kzv5gnVhAynpyGHft42KEpWPY0ABlyiSuDG/1Y1j573HJRnw85P1DoewR2520IIIU6TBISE6MQO5ZQTF+iB0eBsFF1VBK6+uFqMVGPB7gwIBTvUqNiGTx5PIM21Dz2qDp2pXRZCtKN//HCADzem8PHGlOOuY3fI2HnRTZmb9y7x1uszhM5uQMjkqSZ/BtmcfXXqM4RaUp/JGzFKrZe1U13363nmdrAVIgZOBGCCWyoUHlE9jS55EyY/3HzFmCkqAytnjxptb5C3EkII0ZXJX3EhOrGEnDL6hHiq5o6Vhc6AkB+uZiNVWLDXqoCQX222ukMrUrfzvAbgrxeSlnyYOz/YSkml7UweghDiFO1JL2HJ7iwsJgPv/HqEmjp7i+vZHTrGhiljEhAS3chRJWOeuiqzPtsBIau3ypLxr0pWC1x9jr9yQC8YfBUMvAw8w8BRp5o7u/qe8f08Ee/oYQB4Fh1QGUJ+PWHIVeB5VAaQ2RWiJ6nvpVxMCCG6PAkICdFJlVTZyCqppnewJxxcAs/1gvQtKkPIbKRCt0Kt6lfgUZ0OVu9WvaCsCFCjXvdvXc2y/Tl8uiX1jB6HEOLUvLr6MD5uZv571VByy2r4dHMauq5zIKsUu0NvWM+u65hwBoskQ0h0JyZVMubQVRatp6PUufzsBoS8I/sD4Fe4Uy04UYaQ0QyXvgnBAxpHsndwuRgAbn7gFQ6HV4C9BnxPkLHUyzkx7VQbSgshhOg0JCAkRCeVlKdS3+OCPODwz+pTxPIccPXBajZS4bCg1VXhYTVhLk0D31Z+UhcymDrdQE3qVgA+2pTS7M2lEKLj1dkd/JqQz7xBocwdGMK4GH+eXLyPq97cyNwX1/LiioSGdR0OMGrO32GZMia6E4MBu9GFI3oIAO72+oDQ2W0qPWLYCBxGK9bCgypLz+LRujt6hqnLzhAQAgge2Njk+uhpaE3VB4ROtI4QQoguQQJCQnRSqYWVAET5u0HKBjWRBMBNlYxVOCwY66oI9LRCcepJJ4zV8/fxJl6PxLdkPy5mA2mFVfwSn3umDkMIcQr2ZJRQVlPH+Fh/NE3j7ZtGMj42gJ2pxfQO9uCtNUnklqoR13ZHk6bSUjImupm82Mv4xD4dALf6gJDx7I2dB9CMZgxBfdUVF2/QtNbdsTNlCAGEDATd+bfkRD2N/GLg2s9h5K1nZ7+EEEKcMRIQEqKTSi1QAaFIaxXkH1IjXr17QGA/XC0Gyh1mTI5qAt0tbQoIBXu5kKSHEK7ncPmICHxdYMX+7DN5KEKINlqfWADAuBh/ANytJhbcOprNf57BmzeMxGZ3NGQJ2XVpKi26r9zJ/+Bbu2qI7FrXMRlCAAQNUJcnKhc7mmcnCwgFO49BM568HKz3HFVmJoQQokuTgJAQnVRqYSVBnlZcs7eoBXGz4IGdMO6ehh5CFr2a/j41YKts1YQxgGAvK1m6P6FaIQNCvVhi+CPDU94+cwcihGiz9Yn59A3xxN/D2QulNBOjvQYfNwvRAe5cPCycb3dkUFxZS2mVDYuhvmRMAkKie3GzmKhE/Z5Y65xj389yhhAAwaqP0AkbSh+tPhAU2Lfdd+eUBA9Sl94RqteREEKIc54EhITopFIKK+nh5wYp68FohfDhDW/2XMxGqnQLFuoY5KoyCVo77SPAw0oW/rhqtQx0LybckUn/sg1n6jCEEG1UbbOzNbmI8bEBakHWbnhpOHx3X8M6Fw4Jo6LWzl++3UtNnYM+Qc7x21IyJroZd6uauglgtlepYFBHjEIPcgaE2pIhFDMV7loLYUPPxB61nV+Myq46UbmYEEKIc4oEhIToZP636jDbU4tIK6ykh78bZGxXLxabTE1xMRupcn4i2seYqRa2MiBkNhqosAQBEFe5S13ak9BrytvvIIQQp2zVwVxq6hxM7RMIlYXw6XVQVwX7voZS9fs+PtYfHzczS3ZnEe7jSg8fZ0aEZAiJbsbdakLHQJXu/B3oiHIxUA2ZAVx8Wn8fTYPQwWdkd06J0QRjfwODr+7oPRFCCHGWSEBIiE6k2mbn2aWHeG7pIbJLq1WGUHEK+MU2W8/VbKTa+YloqMPZENojuNWPU+uuJpu4Zm4CwKzZKT68sR2OQAhxur7clk6wl5UJcQGw+3MoSYXL3gGHHba+C7qOefcnPBy2B4BLhoVjaBg7L1PGRPfiZlZB0PqysQ4pFwPwCAKvCDW6vSub+QQMvaaj90IIIcRZIq8chehEMourgMaGstG+ZijLUvX8TbhajFTq6sWvjy0b0MDVt9WPM6hff9gMpKxrWFaVuA7fATNP7wCEEKclt6ya1fF53Dk5BqNBg4RlENAbBl0Oe7+C9a9A0mpI38LVRgvvu73CFSMjYI9MGRPdk8lowGoyOLNmyzouQ0jT4LalYPXqmMcXQgghToFkCAnRiaQXVTW7HutSrkbAHh0QalIyZixNV5M+2lAqcut5Y1QmQdER7FYfDjgiMaVvPv0DaAff7sjgold+xe7QO3pXhDjrFu3IxO7QuWx4BNRWQPKv0Gu2unHuv2HgpVCRD+N+i8FhZ9mo7UT5u4OjTq3TEb1ThOhg7lZTw4ckmDooQwjU/2oXCQgJIYToOiRDSIhOJMOZIWQxGaitcxBpKFQ3HBUQUj2EnC96i9PALaBtD2QwqnG3JWngG8W2igCuKNgIuq4+5exAi3dlsju9hIPZpQwIa0NzTiG6OF3X+XJbOsN6+BAX5AGHfgJ7DcQ5M/d8IuHiVxvvUF0MW9+DiQ9C9u5W9xET4lzjZjFSZasPCHVQhpAQQgjRBclHiUJ0gN3pxRRV1B6zPL2oEqNB4/8it/GZyz/wrs1WN3hHNlvPxWygur5fQlkmuLcxIAQNfQ6Mfj3JMEdjtVdAeW7bt9OO7A6dzckqCLblSGGH7osQZ9vejFIO5ZRx+QhnADhhKZjdIWp8y3eY8DsVMNrytioj6zPvbO2qEJ2Ku8XUkDXbdACDEEIIIU5MAkJCnGUF5TVc9tp6Xl55+JjbMoqqCPV24WLveMawF62+jOsEPYTQHeDm3/Yd8VKNpfGNpsrTmVlQmNT27bSjQ9lllFWr0pctyUUtrqPrOp9sTiW1oPJs7poQZ9yX29KwmAxcMDgMaith79fQa9bx3+AG9IIe4+HX/0BdNfQ9/+zusBCdhJu1yf9EowSEhBBCiNaSgJAQZ9niXZnY7Dp7M0qOuS29qIpwH1fMZRlqwf7vVLDH4tZsvZ4B7kzo1yRr6FQCQt7OSSi+Uei+zilmhYlt304Tuq4Tn1OGrh/b/8fh0NmWUsj6xHzKa+pavP8WZ3bQyChftiQXtridvRmlPPr1Hi5/fT0HskrJL6/h95/t5Pllh6i22U9r/4XoKDa7g+92ZTK7fzDermbVQLq6GEbdfuI7Dr8BHDY16rrHcTKJhDjHeVhNjVPGJENICCGEaDXpISTEWfbNzkwA9meV4nDoGAyNPXsyiqsYHxsAKalqQUUuhA45ZhtWk5E/XTgMXnQuOKWSMWfWkW807iFB2JKN6HmHOZ12nF9sTefhr3Zz77RYHprTt9lty/Znc/dH2wG4ZnQPnrl00DH333ykkHAfVy4b7I/vT4/z8sJS0q2xzOgXzOz+wWiaxvrEfECVl819cS2uZiN1Dgc2u86Pe7NZdO8EauscVNfZCfV2PY2jEeLs+fVwPkWVNi4eGq56eW15CwL7QfTEE9+x/3z46RGVHWSUf+mie3KzGKnSJSAkhBBCtJW8ehTiLErKK2dXWjG9gjxIyC0nrahSTQgCauscZJdWE+WlqUBQvaP6BzUwN8kaamtTaYDIUaqxdPAgphuNpG8IwJx8gIiT3/O4Pt6Ugsmg8b9ViUT4unHN6MYmt7/E5+NpNTEg3IsNzqBOU2mFlayJz2NW/2AmWROIMG7hyMFv+Ml4PZ9vTeeKERE8c+kg1icWEBfkwce3j+GbHRkk5JRzx+SeZBRVcduCrfx9yX7WJxaQV1bDs1cMVuU3XUC1zY7RoGE2SuLmmRCfU8bB7DIuGnLi8+Hn/TlU1NYxf2j4WdozZcmuLDxdTEzqHQAp6yFrF5z/wsmbvFvc4a414Op7dnZUiE7I3SIZQkIIIcSpkICQEGfRqkN5APxhdh/u/mgb+zNLGwJCWSVV6Dr0cilWK7v6QVXhMf2DGjQNCJ1KhlD4CPjDQQCGu+lsMoUTmt9YMmazO9oUnNifWcqu9BL+cn4/vtyWzqKdGc0CQusT8xkT48+Ynn783w8HyC2tJshLTYOpqKnjjg+2omlw34xeROz8FoBbooq445ZZvLzyMC+uSMDu0NmSXMjlIyII9nLh7skxDW+Y+4Z4cfWoSD7dkobVZKB3sCe/XbiDH/Zk8ejcfkT6uR2zz52Fw6Ez/5V1DIrw5rkrjs0IE6cnMa+cq97YQFGlDYdD5+JhLQd7qm12/vDFLkqqbOxILWZK70BG9fTDw9r4r9Lh0EkrqiTI04VPt6Ty1fZ0DueW89p1I5jWN6jN+/bJ5lRSCipZtj+bOQNCsJqMsPY5cA+Code2biO+0W1+XCHOJW5WI1U4p4tJDyEhhBCi1SQgJMRZtCe9mGAvK1P7BPKA6WuG//QkDFKNo+NzygGIMjizZ/pdCNsXnCBDqEk51Kn0EGrCYNAwB8YSkL2EwvIaDmaXccv7W3jnplFM7NW6YNPnW1VD3MtHRJBZXM3Hm1KoqbNjNRlJL6okpaCSm8ZFMyJKZTJsTi7kgsFhOBw6D36+k/icMt6/ZTQ9A9zhyFoAXPJ2g0Hj97N649D1hkbc42P9YcnvIfcg3LwEDEYA/nReX1ILK7lxXBTT+wbz+i+JvLY6kZ8P5PLAjF7cOy2uYX8ra+tws3SOP4HrEws4lFNGelElT188EBezsaN36Zxhd+jc+v4WjAaNoZE+PPr1HgaGexEX5AnA4dxywnxccLOYWLwrk5IqG5N6BfD++mTeX59MDz83Hpnbl6LKWhwOnS+3Z7Arrbhh+8N6+ODlYubllQltDggt3pXJo1/vabh+weBQyNgGiSth5pPNf8eFEMcV6OGCbnYFHRk7L4QQQrTBab0b0jTNB3gbGIj6N3wrcAj4DIgGkoErdV1veVyQEN3MnowSBoX74GIr4W7TElwrqqE8l8dX5PLBhhQMGoRrzoDQkGtg92ct9hACVBDEaFVjp08lQ+go4bED8Mj5gkU7D3Co3IWaOgf3LtzO4t9OpIf/ybNrtiQXMqanHz5uFkZH+/LuuiPszShhRJQf6xMLABgf50+vlE+51pLElqRILhgcxhtrkli6L4e/XtCfyb0DoaYMMneAVziUZqjJZ/6xPDCjFxsSC9ieWsSYaD/44TuozIcdH8KIm2HPl/gmr2XhtX9teD7un9GLK0ZG8Ndv9/Ls0kNcODiMpPxynvnhIAm5ZTxz6SCuGtXjBEd1dnyyORWDBhW1dn5NyGdm/+CO3qVzxobEAlIKKnnl2mGMjvZj1n/W8Jdv9/LJHWNZdSiXOz7YxvhYfz64dTQfb0olLsiDD24dTUZxFYeyy/jzN3u55+PtDdsL8rTy2Ly+lFTZGBbpy4x+QSxYn8wTi/ezNbkQP3cLSXkVDI7wbsiAq1dnd/Dg57s4lF2Gze7gSEEFo6J9efbyIezLLGVK70BY9CRYvWDUbWf7qRKiy7p9Uk9q9L6wDikZE0IIIdrgdD8efxH4Sdf1yzVNswBuwGPACl3X/6lp2iPAI8CfTvNxhOjyyqptJOVXqN4km97AlWoAcg9v46ONDs4fFMqDs3vjvet5MJggcjQ8knriF7cWN6iqObUeQkcJie4P6yH18F521cUR4etKUUUtr/1ymGcuHXzC++q6zpH8CkZF+0FNOTNXX8w/TKFsSurNiCg/1ibk4+9uoY9XHdpPf+IfBgc7966D+Wv4fGsaE+L8uXVCtNpYygbQ7TD2Hlj2Z1j7PCQsxzT8Rt66+rccKLTjW3lEBYPMbrD8cdi5ENI2qfvHL4XrvlT9kba9S+io23lsXj9+PpDL6vhc3l57BB2dEVG+PPL1HlzMxrPeL6ap/PIalu7L5oaxUXyzI4Of9mV3yoDQtpQifv/ZTp65dBAT4k7/fDtbFu3MwMNqYma/YFzMRv44pw9//XYv93+6k+X7s/F2NbM2IZ8b393MzrRi/nZhfzRNI8LHlQhfN0ZG+bE/q5QofzeMBg0fN7Mq62riipGRPL88nstf39CwzMfNzJMXDWDuwFAsJlV6+f76ZL7blcmkXgF4WE3MHxrOTeOj8HGzEB3gDnYbHFwCfeaB1fOsPk9CdGXuVhPuPj7qigSEhBBCiFY75e6lmqZ5AZOBdwB0Xa/Vdb0YmA8scK62ALj49HZRiHPDvsxSdB0GhXnA5jcoDhwJwLc/LcOhwx/n9CE20AOKU1V2jMF48he29X2E3PxOe/+0wD4AuKevYXd6CZN7B/JgwEbKk7ac9L45pTVU1tqJDXSH1c9gKjjItaZVBOx6nfSiSn7ck8UFg0PREleC7uBwwAyG1u3mwJ4tHMmvYFqfIDRNg7paWPdfdVzDb1Sp/zs/VmO11z6H78ezGe9VACm/qge+7G3VP8VRBzP+Bnf+oiY0fXU7fHs3rHwaPr6Cnl46PfzceHNNEqmFldw/vRcf3jaG4T18eeK7fZRU2U77+TtVvybkU+fQuWJkJHP7+JC5bx355TU89MUuLnz5V9IKKwHYm1HCMz8eoLbOAah+Nz/tzW64fiYVVdRy38LtpBZW8scvdlFa3XHPV1vUP0dzBoSoMrzaCq4zr2ZcuJmf9mYxpqc/Pz0wiRFRvqxNyOfm8dFcPzYKEpbD830gbTPebmbGxfoT5uNKsJfLMcEgAHejg5fnBXDP+BCevXwwH942mkhfNx74dCcjnl7O/1YdZvn+HJ5fFs+MvkF8cOtoXrt+BA/M7IWPm3Oun67DkV/UqPn+88/uEyXEucCi+vFJQEgIIYRovdPJEIoB8oD3NE0bAmwDHgCCdV3PAtB1PUvTtLZ32RTiHGB36Bg0VKAD9YYeYKglHaqK8J57F4XfHsG/Ip6xMVeq3jkAJWng08oyJrOrKi9pjxfAvtEkBc3mupyvWVA7hmGhMVy26yX2OyKpqrkJV2dj3S3Jhdz6/hZWPDiloSQmKU/1PxpgzoKNr8KIm9l7+AiXFL3H3Z/Pw6Bp3D01Fla+Aq5+mOc8CR+vYO2yr4FJjI3xh9IslRGUsg4ufQtcvCBksCofu2kxVBbCV7fB2zMhsC94hqlMir7nNz+O+a/Ax5dD3gEYcCns/xbtxSH8230696VNw2LyY85AFSB48qIBXPjKr9z14VaS8ip4dF5fLhl2OnPW2m5HahHuFiP9Qr14iPfx1T9h7nPexFf7YDEZuPh/6/jD7D68sDye/PIaAG4aF809H29nZ1oxd0+J5Y+ze5NbVkOot0vD+dYe6vs7/bA3G3R4+uKBPL5oL08v2c+/L+/8za+X78+hrKaO+UPDIP8wfHoNhvx4Pup1HrV3ftxwTr9940iOFFQwvIevCsys/ieU58BnN8DE36k3mkOva+hVRW2lKlU0u0JlAfzyLFNtFUyNGAUXLgdNY2yMP2sT8li4KY1nlx4CINzHlSfnDzj2Z7TjY/jpUfAMAYsnxE4/i8+SEOeI+g9IpKm0EEII0WqnExAyAcOB+3Rd36Rp2ouo8rBW0TTtTuBOgB49Or6HhxDtIa2wEqNBI9jLhcn/XsVVoyK5f0YvAHanlxDm7YJv3jYAtKjxGEMH0i8tmRvHRUPmTvj+QcjZDwMva90Dml1Pu6F0U1XTn6bmkyk8YvqEvm69MWBnoCGZQ9t+pM/4CwH4fncWZdV1JOSWNwaE8isAiC3fCroDJj9EUI8DmL/5BVLWceXoSwj1tKrMi7gZRMUNJFsLIqJkG96u0+mvpcCLM8FeC1Mfg8FXqh2a8w+VMREySF2/YyW8NR3SN8OgK1oeyd1rFkz5E5RkwEUvw8hbYes7jD7wLaut3/G/iGfxqsmFr37HwMA+/GbAGF7dW4in1cTflxxgep9gvN3M7facnsz21GKGRPpgzD9IQPynoOlMcmxl7LjbuGl8NPct3MFj3+zBw2piVv9g3lyTxDtrj2A2GhjT04831ySy8mAO8TnlxAV5cN2YHlw1KrJdGmbvSi/m252ZXDQkjJsnRDO8hy+ZxVW8ujqRuYNCmdYniMziKlIKKhkb43fSYNSe9BJWHMzhzskxZ7yht8Oh879Vh+kZ4K5K3D65B8pzYeStGLe+i+u212H8bwHw3fMuvumbIfxN1dQ5YyuMuh12fQo/Of+t7f5cXVYWqOBkeXbjg/WZBx5BsO19FdCMnojZaGB632Cm9w1mY1IBpVU2pvUNUpP78g/D4Z+hxxjn7/0fVDAo/xAMvgrM0hRXiDazOANCkiEkhBBCtNrpvCJPB9J1XXc27uBLVEAoR9O0UGd2UCiQ29KddV1/E3gTYOTIkfpp7IcQncat72/B193CU/MHkFFcxRu/JHLjONUjZFd6MYMjfCB1PXj3AO8IvKOH4ZW1nv79A+DbxyH3APjHQu85rXtAi0e7TlTp06sXnzGZiw2rcak6AEC57oL7tlfBGRBad1g1vc4uqW64X1JeBa5mI15lieDiDV7hBPUPQF/swp965hA8p6/K9KnMh7hZoGkUBI5mXM5qxkT7YDi0RJWF/XYrBDROAiNyVPMd9OkBV34AH1wMvWYf/0CmPdb4fc9J0HMStpwE9DemcavLL7A7HxKWQeIqHrJ8wGVXLqAmZAQXvLyW//wczxMXDTit57G1qmrtHMgq5a4pMbDy7+rn6eLDY/7JGOcPBGDxfRP5blcGUf7u9A3x5MHPdhHp58oNY6PxcTdz3n/WUFRp4/cze7M6PpcnF+/nw40pfPfbic3GpZ+K5ftzMBo0/j5/YEOQ7PfBOyn2y+RPX1r5ywX9+b/v95NTWsPAcC96B3syb2Boiz2Q3lqTxDM/HsChw+pDebx38yh83S2ntX8n8vOBHA5ml/H8FUMwlmepAMzE38P0v6rA0M9/g4hRqkxr1f+pO4WPgEM/gosPzHoKpv8F6mrg0A+w9M/g2xP8YtR5OP4+da7bqlS/L1sVHFgMK/4Orj6Qn6CCRBf8h7Ex/Rp3zFYNn10HeQcbl4UOVVlwRUeOP1VQCHFi5vqSMQmoCiGEEK11yu8WdF3P1jQtTdO0PrquHwJmAPudXzcB/3ReLmqXPRWikzucW05Cbjlmo8ZG51Stilo7761L5vqxUaQUVHLd6EjYvAFipqo7BQ9Ec9ggfSsc+A6GXQ/nP9/6B536aLseg8looCJkDO45S1VJjNWbL2yzuKngK6gqJsfmQkKuKg+rzdoHlW7g5kdSfjk9A9zR8g5BYD+VuWN2Qesxjt5l28DVDBteVgGP3iqQ4zdwJr65S7gwpAhS1kPwwObBoOOJGg8PJ6pttYE1uBfWAXPxSFwB1ckQNgyuWID2wXxif7oB7tnAlSMjWbg5ld817e3SCgk5ZRRW1DImRmVrrU3I44MNKfzfJQNxNRspqrAR6ed6TAbNnowS6hw6I8LdYfNyNVlKM2Dc/KaatlacinHZX7hk2l8gYgQ47Lwet0kFDJ2T3366vQ9mF3dcPX15YGYvVh7M4fYFW3n82728cNXQNj1HR1u2P4cxPf0aM6YSV2FedBd/N7uz1/AS93+yA183M4/O7cv3e7L45VAei3Zm8sSF/TmYXcaQCB8uHxFBfnkNzy49xLQ+QVwwJJQ/fbmHf/10kH9e1tis/Mc9WTz6zR5un9iT2yfFqJ4/p0jXVXZQlL+bKhdb94LKXBt2vTo3578Cr0+Gd51BxYGXQUUeLHUGEi96xdmPxPkGc+StMPxmMJyg7Z7ZFUbdAb/8UzV57zkZktfCm1NVCVifeSrzbfnjKhh0yRugGcGvp5okaDQff6KgEOLkGjKEzlygWQghhDjXnG7O/n3Ax84JY0nALahG1Z9rmnYbkApccZqPIUSXsHx/DgA2u86CDSn4uJm5KiSb2nXL2RD0MADj/UqhIheixqk7hQ8HNPj8RqirVn1K2iJmSjsegXLTNdfCf5+C7N0QNZHC2rEYsr6E9K2sK+sLgJVaLtl+OxhvhLn/4kh+BQPDvCD9QPOGuDFT4OcnIHEl7PsWJj0Irr4AhA6ZDSthnmGjCoiNuKn1O3mqE5h6z4E9n6uyn6mPgW8U3LgIXh0LS37HYzYDM7U8vtrWh9smxZxwU9U2O4dzyxkQ5sXvPttJUl4Fax6ehovZwB+/2EVOaQ3xOWUUV9ooqbIR7uPKn8/vx7xBoQ3b2J5aBMAIczLYa1Swy9UPNryiGmIf/F71lErfBlcugCNr4NcXYPencNvPsPlNvFb+HYIHwG3LoSiZ6TGh3D+jF//9OQGHrvP4hQPwa5KJ80t8Hs8tPcQLVw4hLsiDxbuz+O/yeHr4uzEkwocdacX4upkJ83HlcG4514/pAav/BRv/p/rr+PbEWJrB171/5P2QPzO5dyC9gz25a0os5TV1XPn6Bv66aB8mg8bHm1L5dmcGPQPcqXM4ePzC/kT5u7P5SBFfb0/n4fP6klJQQa9gT57+/gB2u85zy+L5dEsaT140gBn9Tm3a2o60Ynall/DU/AGYsKvgZvQkld0D6hy8cgH88m8YfoMK1uTHq8yzib9Xy452omBQvYm/g4Be6jyzekJZDqx6Go6sVVlGPzwEdVUw8jYYcvUpHZsQ4jjqPyQwuXbsfgghhBBdyGkFhHRd3wmMbOGmGaezXSG6omX7s4kJdCc3P58BhRsoi72Q23iXIH7k2mXjsBi96FOzT60cOVZd+seqSVmL7lUZMmHDOu4AnKw+oRDQW71BDh1MqPsE7JkambtX8XO1L37uFmZb43GprITiVGrq7KQVVnJNPwskFKmGz/XqM6E+vEQ1yx3328bbvMMhbhbGja+o3kE9xp35g4ubqbIydDv0mauW+UbB1Edg+eN4ATON8OS6T9AnPnbCnjiPL9rL51vT+duF/dmXWQrAKysTqKy1k1tWw1/O78cLy+MZGunD7P7BfLk9nXs+3s41oyN5av5Askuq+WB9Mr2CPPDO3642GjlWBSt6zYZNr6smqVd9rDJXPrxYrRM2HDK3w8vDVbAodIjqe/PJ1apHk3sA94/5Da4Tx/Ds+izWJOTzxEUDuGhIGEt2Z/LApzuxO3TeXnuEAeFePL5oH31DPNmbUcIv8Xn0CfYkMbecjOJMTAaNWf2D4b0PVKDKKwzmPQv7vsG05lluj5sOwddBTTlsfRePARfzwW2jWbovmwsGhfH9niye+G4f6xMLmDcohCh/d8hP4Hc+v7LebuHSV9eRXFCJj5uZ4kobH98+Bk2DJ7/bzx0fbOWj28Yw/hRG3C9Yn4yH1cSlwyNg8xtQlAxznmm+UvhwuPbTxuuBfeDB/S33pWotsysMurzxumew6mOl67DvGxXgG3hZ47knhGg/fjEqw/boQQNCCCGEOK4z29VTiG4it6yanWnFPDizN/5bPuXa6k/42H8MgSmqT8jw4uVUh9+COWupmgrWNGgy6HKIGAkG8+m9GW1P0ROdAaEhXNq/D4dXRlO69xd+qBrPb6bGMmzvh2q9siwScspx6DDYRWVINTu20KEw/39Qkq6O0c2v+eOMvRsOL1ffR40/44eFqw9ET4DCI42NqgHG3gO1FRA3k+LP7+HmkvfZcvgWRvcKa3EzyfkVfLU9A4AnF+/HxWxgau8gFmxIAeCuKTHcPimGG0aFYHVRZQzXjY3iP8vjeXV1IpuOFFJUUYtDh7duGgm/vA5+seARqB7gui9U42HdrgIVsdNgz5dQkKB64HxxC6T8qsqOBl8FH12meiL1mg12G4aVT3GXwcS8+W9z31Zv7v9kB8n5FbzxSyJDI30I83Hlu12ZLD+Qw/hYfz68bQx1DgfVtY6G8rCSShul1TbCqw5BaTpc/BoMvVbtn38vSN8Ci++H6hKI/0n14vnlXwT0ms11rr5gfJJrx/QgLtCdfy89pJqr7/kSvrqNYGCJqyc3FD7ENaOnsSY+n5FRfqr585E1fH19FPM/TObehduZ3DuQcB9Xzh8cyoAw74afwb7MEnoGuFNYUcu+zFJm9w9G0zS2JBfy/e4sbhgXhYetUE0Mi5vZuiDMmfr90zQYeKn6EkKcGZqmmsELIYQQotUkICREO/jlUB66DtP7BhK6aQ0A4xzb0IqOAHCpcS01PX6vSqPCRxxbfuIbfZb3+CTiZqmJSRGjcDEbMUSNo/+Rrxgb5c2DM3tRsm2LWq8smy3JhQAMMKkACUFNGuhqmurbcjyxM1Q2kq6rBrxnw8Wvg62y+Zt/o7mhEbXrvKfx+ewq1q94h9G9/triJl5amYDZqPHIeX35vx8OcOHgMB4aAU+k/okjM99k3Jh+kHcI69szYfiNMPFBzHu+4OEBI+gVNJiFm9PoF+rFb6bEMiDUC1I3qrKlppr2U7K4Ny+pu3IB2G2NPTMufRMOr1DBRYNRBZO+vIXIVQ/wxe2/cNM3Rl5YHo+n1cRL1wyjsLyWX3cdpMZm5k/njcJo0DDWlGF12IBAcDjwdjGq4NCKJaAZoPd5jY9vsqjm3p9eB0udfaxmPw3J6yBrJxSlqOCVZmR0YRJfXvoWBHvCV8+qTLjzn8f1qzv5wvwfzOffhn7JIBw6kL0HFlyIu3sg7899j7tXubIjtZjvd2fxxpokFt07gYHh3ry8IoHnl8fjajZSa3dgd+i8c9NIauocPPDpDiL93Lhrciysf1oF+s77V+cJtgohhBBCCNFJSEBIiFaottmpqKnD36Plcbar4/MI8rTSv+4AWq0KjEQf+QyA0pgLiElawgVeCZCzDyb94azt9ynrMxcePKBGYQOxw6djSF7I67OtmIsSCbBlkqd7E1Cew7Yj+YT7uOJdlqimM3m0oe+LpsE1n6pJTmeLd/gJb7b2nUOmax/GZH1MYdkf8fNs3o8iMa+cb3dkcNvEntw+LpRJCf/Eb8jtBB36CmxphGR9DPZR8PWdUFuu+gHt+AiqiwG4JGIUl8x/AUKdDZXTNkNVoRpB3lpGs/qq5x4AQ65qvB4QpwI2b07B/OYk3h16Ew8bpnP+2AGEe1kJ3/cmG13/jyT3ofQLuxg+v0lNyAKVjZSxXf1MgvpCcSpETTg2u8vFG25eooKcteWqPHD8feq2HR+pMkiLpyqBe2+uyo7JOwiXvAk9xmK6/B14ZyZsfQet8AhGTVOPZfUGsysRX17AkphpcP2L5JtDmP7cal5akcCIKF+eXx7P+YND8XOz4GY18v3uLJ5deojM4ioGhHmz4NbReGtVsG0BDLi4dc3KhRBCCCGE6GYkICTESRzOLefOD7ZSXGVj9UNT8XIxN7u9zu5gbXwecwaEoO1+TzW07DkZQ8JSALzOfwr9jV8YsuMJNekoYlQLj9LJaFpDMAjAED0BNCM+m54Hew12g4VPa6dxn/Yth5OTGRUXpzJDgge0PRPDP7Z99/10aRpM+B0xP/+G5Us/YtbldzS7+aUVCVhNRu6aEou28RX6pn0Gqw5CSYbKpNnzOdgq1PNxxQLY+xXk7oerF6qAyKp/wFvT1Ehznyj4/kHwDGuegdMe/HrCrctg7XO4bHmVl1w+BvObsOUILH8cq3cP+pVsgnX/hf3fqklaZjfV/Dtmqgrs5e4DowVG3Hz8x4looY3csOvVsfnHquymxb+DPV+o4xxwiVoncpRq9PzzE+r3ot70v8KIW2Dru7D+ZVh4JQFXL+ThYXX8dUMWy/bncOGQMP571VCMBnWuRfm589g3e7CYDLxw5RC8Xc3w6ytQU9q8b5UQQgghhBCigQSEhDiBmjo7V72xAbuuU1xpY8G6ZO6b0avZOrvSiymtrmNqb39Y+h30nQeRYyBhKXiEgH8s2shbYP1L6g7hIzrgSE6TVxic90/48SEADo54il0bSgAwV2QzIbwPHNypmjOfA8LGX0X2qqcI3/cGdRffismkRqDH55Tx3a5M7pocS4CjANY8D17hkLVL3XHW32H5X1UD4amPquyU+qlrmqb6Fw28FL67XwVCQAVOblx0ZkrmgvqqpuUTfw/f3A2f36CCVnEzVbPj/wyElX8H/ziY95wqN5vzf+3z2D0nNX5/+bvqefAIbj4SevJD8MFa1cMpdrp63sbcDVYPmPKQChp9dBm8PJzrgVkufnwa9gj3XDkXY1UhLFK9n66MGMMct/c50vsWYnymqoyn/d+qwFb48PY5HiGEEEIIIc4xEhAS4gQ2JBZQUFHLOzeNZOGmVN7+9Qg3T4jGs0mW0IoDuRgNGlNcE9U4834XNvYECh2iLsfcDRtfBe9IVd7TFY2+AyryoK4ae78byVn/EQDBWhETjPsBHWKmdew+theDkYLBdzFgxxNsXLOYsdMvBuCZHw7gYTVx1+QY+OlecNTBTYvhs+uhPEf9nNHVRK760eVHZ0y5+qpyrqTVKiMnbCiYWi5FbDfBA+CGb+Cd2VCeCxf8VwX5ep8Hh75XpV4G45l7fE1TwbGjxUyB3+9TQTVNg16zjrp9Ktz4neotZPUkcM1zPFD1KpTMhgUXqefcIwhT8lr8XXzwT34LVteqYNDkh2HCA2fumIQQQgghhOjiJCAkxAmsOJCLq9nIhGhPeqduIDPpY35d9w/mzpgBqN5Cn21JY0rvQDySvlHlNXEzVdmYV7h6wwuqb82Mx8Hi0YFHc5o0Dab/GYCQ0mpydF8A+riXE1qwUfWLOYeyMfqedxeFO/6DZeNLMP1i1sTnsepQHn+e1w/fgh2qNGzSH1RZ1LWfQXWpyn5pTRBC01SvnrPJPQDuWKmmgvlEqmVTHgarJwy++uzuS1PeESe+PXqC+gIM7gGw8Ep4c6oqM7v1RwgZogJD5dnw1nRY96IKyjrPVSGEEEIIIUTLJCAkxHHous6KAzlM7BWAy6oniNz8BmEGA/u3/QucAaFvd2RQUFHL7ROjYckSldFg9VQbeGAXGJr8ip1D2Qr+HlaKDd7YdY1r+1vQkpapEiGj+eR37iKMVjcSY29gVOIrZOxdyxsbzYT7uHLj6BB4/0bwDIWJD6qVfXp07M62lquP+qoXNhQufaODduYU9JqtenClb1HNyOvLL73D1VfcTEj6BWY91bH7KYQQQgghRBdgOPkqQnRP+7NKySypZnYfP9UQd8AlLA+5g0GVm7Alb0TXdd7+9Qj9Q70YZ0mC4hToe0HjBozmc3bUtdGgccGQSGpcAggv3AJFR1Qw7BwTNvsBCnUPapY/zcakQi4ZFo515d9UCdP5L6heN+Ls0TRVbnfTYjUJ72gXvwa3LQW/mLO/b0IIIYQQQnQxEhAS4jgW7czEaNCY5RavxoIPugLz+N+Qp3tRtvxfrI7P43BuOXdM7on26wuqN8zASzt6t8+aF64aiptfOKRtVJlQ9dOjziHhwUF843YFMSUbGaof5JLAdNj8pppc1XdeR+9e9+QVBj0nt3ybR1DXbNouhBBCCCFEB5CAkBAtqLbZ+XxrGrP7B+Nz5HvVHyd2BmP7RLLIMRmvzDUs/GUXwV5WLgjMh/ifYOy9jeVi3YVXmLrsM/fMTMnqBCqH3Eqp7sqtbmuJyVkGRquaICaEEEIIIYQQXZgEhIRoweJdmRRX2lS/mAPO8hSzC+5WE+VxF2DS6/BM/pmbxkdjXvccWL1hzJ0dvdtnn2eIuhx+U8fuxxk0bVAUyxyjmMFmtIPfO/tESamYEEIIIYQQomuTgJAQTVTV2vnrt3t5cvF+4oI8GFu+EqqKYNh1Devcd/1VlFtDuNZzBzf0rFQBozF3gYt3B+55B4mdAb3mQOz0jt6TM2ZAmBeBY6/GxV4OJWnQ9/yO3iUhhBBCCCGEOG0SEBKiib9/v5+PNqUwu38wr183HG3T6xA0AHpOaVjHaDTgMewyRtq24/nDPWqU/NjfdOBed6B+F8B1n4PB2NF7csZomsaU865UPaLQWm5mLIQQQgghhBBdjIydFwI1Yv6Lbeks3JTKXZNjeHRoNWx8FHL2wkUvHzstbPxvIe8gJK2GyQ+Bm1+H7Lc4S4xmGHsPFCads72ShBBCCCGEEN2LBIREl5BVUoWr2YiPm6Vdt2uzO/h0SxpL92bz6+F8RkX78ofhwLsXgO6AAZfCoCuPvaNXGNzwNdiqwWRt130SndSUhzt6D4QQQgghhBCi3UhASHR6hRW1zHtxLT383fn2nvFoR2frnIY31yTx7NJDhPu48ujcvtw20IjpkyvB5AJ3r22conU8Zpd22xchhBBCCCGEEOJskR5ColPTdZ2nl+ynqNLGkbQMDq75AnT9pPezO3SW7cumsrbuhNv+als6Y6J9+XXUOu7KeAzT6+NU4+Ar3jt5MEgIIYQQQgghhOiiJENIdFoL1ifz758OUlFr546J0UzZ9lv6rdoOkcEkuA8nvaiKaX1b7ufy2urDPLcsnkHh3rx900iCvVyw2R0AmI0qDrorvYSk/AoeGVKFtvY58IuFfhfC9L+AT4+zdpxCCCGEEEIIIcTZJgEh0Wl9tDGFcG8rT8bFM9qyCaO+nTrdQNZ3/8fVZQ9RVFnLl78Zz/Aevs3uty+zhBdXJDCyhw9zct5g0X/ep3j0g3y4KY2y6jp83Mz0DvakqtaOxWRgauVSVSJ2x0pw9emYgxVCCCGEEEIIIc4iCQiJTimjuIqE3HLeGJPHuB2qma8jejJfFvXi6uJ36K0fwtfDQfh7D1B79etY+swEIKe0mjs/2Iavm4UPhuzBbfki0OH79SmM7/EI5/nm45m7hfhyT3aVejB9QH8s+7+GfhdJMEgIIYQQQgghRLchASHRKa0+lAvA2NoNYPWCO1dj8I3mwvISyl76hgXG59DQMOuFFH5+J7/M+I6vD1QQn1NGeXUdiy9w4PbTX6DXHGw9JjBv5RPMy7sJLaMMgJkAGnDI+YDDru+IwxRCCCGEEEIIITqEBIREh0ktqCTUx6Whp09Tqw/lEeltwSt1BfSaBf6xALh7+cFvfoaPL4eKfDYM/TcjdzyK34+/oczrIe723MnsPlWEL38fAnrDJa9jdvOD2Cmw8u8QOQbG3gNVhVCSDgWJYK+BnpPP8tELIYQQQgghhBAdRwJCokNsSCzgurc3Mq13IG/dMByDqfFUTCusZN3hfO7vXYR2OA/6zGt+Z/9YuPtXqClnnGcwB802Jm/9K5Mrb0arsEMBEDoErvsS3PzUfcKGwvVfNW7D6qEaR0eNP+PHKoQQQgghhBBCdDYSEBJnXW5ZNfd/ugNvq4FLk/5M8XPZ+P12JXgEkldWw03vbsZkgGtNq8FggriZx27E4q6+gL7n/xZ694EdH8KoOyBqAhiOzToSQgghhBBCCCGEIgEhcVbZHToPfLKTsmob60b8gv/OzdRVGaj44Er2zfqY+77YT0llDauGrsFr76cw5jeta/bca5b6EkIIIYQQQgghxElJGoU4aw5klfLIV7vZkFTAB8MT8d/5GrXDbuGvpt/jnrudvQseJNaUz7aI/xC693UYcTPM+UdH77YQQgghhBBCCHHOkQwhcVb8mpDPDe9uoi8pfBS1l9F7v4Cek7Fc8CyTY/JZ8NlubjX9yC32X9GKDHDx6zDkatC0jt51IYQQQgghhBDinCMBIXFWvLQygThPO99r/8SQVwnRE+Dy98BoZu6gUHLD30L//EI0gwmueB98ozt6l4UQQgghhBBCiHOWBITEGbc1uZDNRwr5pt8aDEdK4K61EDq42TpBfr5w5y+gGSQrSAghhBBCCCGEOMMkICTOiOySag7nlhPibeXhr3Yz0K2IoRkLYcAlxwSDGhiMZ3cnhRBCCCGEEEKIbkoCQt3AluRCyqptjIz2w8vFTFWtne2pRSTmlVNeVUOcN0zuE4SLh+9pPU5BeQ3PLj1Eenoybrk7GKAdYaJhL3/S/JnumoCGCab9pZ2OSgghhBBCCCGEEKdKAkLnuN3pxVz71kbu4FvGmr4l03cgL1fPI7EEHjV/Qj8tBRfNhv07je1aX2y+scT2G07A+BvBI7DVj1NZW8etC7biyNrDQsvTeJrL0DUDOR79GWw/gtk9EK78EALizuDRCiGEEEIIIYQQojU0Xdc7eh8YOXKkvnXr1o7ejXNOeU0d57+0ltE1G3m27p8cNPfHUlNIjCEbHQ27dxT0nUdqnR85OZlE5K/DWpVDkFaMw2DBcNFLMPQa6uwOjuRXsD+rlJS0VGoy9+NXdYQwSxUhvh5keQzkywNVhJXs4HGPb7BYXOGytyFkILh4g65LXyAhhBBCCCGEEOIs0zRtm67rI1u8TQJC566/fLuHVZu284vHnzEFxFJ30w8s2ZPDjPwP8LSXwKynwOrZ7D45pdU88voX3Fn+GuMM+9hoHktqjRtRZBKnZeCvlZ34QcOGwaVvSyaQEEIIIYQQQgjRwSQgdI6pqKnj5ZWHudY/gR4u1TD4imPW2XykkKvfWMfKgOeJrk2Au38Fv56t2n5eWQ0vLt3L+MT/MMy2Ay8qqPKKxhjUD8/IgZiC+0JgH+xuQeTm52HJ3o6f2Ybm0wPCR0g2kBBCCCGEEEII0QmcKCAkPYS6mJIqG7e8t5mBGZ8RYf4A0KEiF8bdy/7MUhKSjhBbsJLPd5Sy0G0V0eU7YP6rrQ4GAQR6Wnn68hHARw3L3FtYzwiEhoZDaPjpHpYQQgghhBBCCCHOIgkInSG1dQ5Meh2G6iLwDG5xndzSah7/Zhc3Vy9giCkV1/5zYdQdYLIcd7t//mYPfhmreMq8gFWMwmg0MXnpY3y4JYu0nHz+YPoCq1bHc4COGc5/AYZdd4aOUgghhBBCCCGEEF2RBITa0cakAqy2UgKTF/Pcpgr+YPmGyLoUuPMXCOyDw17HroxSDPE/Ysw/xL6kVG6rPcgo7SDJeijRaY9B4irypz9LTUku4eV7oNds8OkBwMqDOWzYfZA1nu+C30C8Zn/ITe9t5UVDDTcUvgxmKOt5Hltif8PQUFc8fALAP7aDnxUhhBBCCCGEEEJ0NtJDqB3d8tpyfp/1EIMNRwAo1V2xWixk6gFYjTr+NRnkOLzoYcgDoAoLRjd/KsY9yAXr4phZ+SN/M7yFgcafSY3ZiyNBswiqTmJpYQgz2UyAoRztzlUQPICf9mbz92938GnEF0T27AeT/gAGQ4ccvxBCCCGEEEIIIToPaSp9Nug6dQvmY0hZx7exTzGhbyR3Laskonwvr1heJhdf1mijmOhXQu2ga3H0u4ggXy/cLCpJK6Wgghd/TiCibBdDremklBtZlOrK0+Z3idMyOKhHMsCQiu4Xh+Wy1yB8eJOH1tGkkbMQQgghhBBCCCGakIDQ2ZK6ESoLoO/5APyakM8XW1N5YlAeHtEjMbj5YTS0LnDjcOgs259DhI8L7mbYnVXB7N6+uLq6yhQvIYQQQgghhBBCnJQEhIQQQgghhBBCCCG6mRMFhKTZjBBCCCGEEEIIIUQ3IwEhIYQQQgghhBBCiG5GAkJCCCGEEEIIIYQQ3YwEhIQQQgghhBBCCCG6GQkICSGEEEIIIYQQQnQzEhASQgghhBBCCCGE6GYkICSEEEIIIYQQQgjRzUhASAghhBBCCCGEEKKbkYCQEEIIIYQQQgghRDcjASEhhBBCCCGEEEKIbkYCQkIIIYQQQgghhBDdjASEhBBCCCGEEEIIIboZCQgJIYQQQgghhBBCdDMSEBJCCCGEEEIIIYToZiQgJIQQQgghhBBCCNHNSEBICCGEEEIIIYQQopuRgJAQQgghhBBCCCFENyMBISGEEEIIIYQQQohuRtN1vaP3AU3T8oCUjt6PdhIA5Hf0ToguQ84X0RZyvoi2kPNFtJWcM6It5HwRbSHni2gLOV/aV5Su64Et3dApAkLnEk3Ttuq6PrKj90N0DXK+iLaQ80W0hZwvoq3knBFtIeeLaAs5X0RbyPly9kjJmBBCCCGEEEIIIUQ3IwEhIYQQQgghhBBCiG5GAkLt782O3gHRpcj5ItpCzhfRFnK+iLaSc0a0hZwvoi3kfBFtIefLWSI9hIQQQgghhBBCCCG6GckQEkIIIYQQQgghhOhmJCDUTjRNO0/TtEOaph3WNO2Rjt4f0Tlomvaupmm5mqbtbbLMT9O05ZqmJTgvfZvc9qjzHDqkadqcjtlr0VE0TYvUNG2VpmkHNE3bp2naA87lcs6IY2ia5qJp2mZN03Y5z5cnncvlfBEt0jTNqGnaDk3Tljivy7kijkvTtGRN0/ZomrZT07StzmVyzogWaZrmo2nal5qmHXS+jhkn54toiaZpfZx/V+q/SjVN+52cLx1DAkLtQNM0I/A/YC7QH7hG07T+HbtXopN4HzjvqGWPACt0Xe8FrHBex3nOXA0McN7nVee5JbqPOuAPuq73A8YC9zrPCzlnREtqgOm6rg8BhgLnaZo2FjlfxPE9ABxocl3OFXEy03RdH9pk/LOcM+J4XgR+0nW9LzAE9bdGzhdxDF3XDzn/rgwFRgCVwDfI+dIhJCDUPkYDh3VdT9J1vRb4FJjfwfskOgFd19cAhUctng8scH6/ALi4yfJPdV2v0XX9CHAYdW6JbkLX9Sxd17c7vy9DvZgKR84Z0QJdKXdeNTu/dOR8ES3QNC0COB94u8liOVdEW8k5I46haZoXMBl4B0DX9Vpd14uR80Wc3AwgUdf1FOR86RASEGof4UBak+vpzmVCtCRY1/UsH8fCCwAAAw9JREFUUAEAIMi5XM4j0UDTtGhgGLAJOWfEcThLgHYCucByXdflfBHH81/gYcDRZJmcK+JEdGCZpmnbNE2707lMzhnRkhggD3jPWZb6tqZp7sj5Ik7uauAT5/dyvnQACQi1D62FZTK+TbSVnEcCAE3TPICvgN/pul56olVbWCbnTDei67rdmXIdAYzWNG3gCVaX86Wb0jTtAiBX1/Vtrb1LC8vkXOl+Jui6PhzVEuFeTdMmn2BdOWe6NxMwHHhN1/VhQAXOcp/jkPNFoGmaBbgI+OJkq7awTM6XdiIBofaRDkQ2uR4BZHbQvojOL0fTtFAA52Wuc7mcRwJN08yoYNDHuq5/7Vws54w4IWdq/mpUbb2cL+JoE4CLNE1LRpW1T9c07SPkXBEnoOt6pvMyF9XfYzRyzoiWpQPpzixVgC9RASI5X8SJzAW267qe47wu50sHkIBQ+9gC9NI0racz0nk18F0H75PovL4DbnJ+fxOwqMnyqzVNs2qa1hPoBWzugP0THUTTNA1Vf39A1/UXmtwk54w4hqZpgZqm+Ti/dwVmAgeR80UcRdf1R3Vdj9B1PRr1GmWlruvXI+eKOA5N09w1TfOs/x6YDexFzhnRAl3Xs4E0TdP6OBfNAPYj54s4sWtoLBcDOV86hKmjd+BcoOt6naZpvwWWAkbgXV3X93XwbolOQNO0T4CpQICmaenA34B/Ap9rmnYbkApcAaDr+j5N0z5H/QOtA+7Vdd3eITsuOsoE4AZgj7MvDMBjyDkjWhYKLHBO2jAAn+u6vkTTtA3I+SJaR/62iOMJBr5Rn1NgAhbquv6TpmlbkHNGtOw+4GPnh+NJwC04/zfJ+SKOpmmaGzALuKvJYvmf1AE0XZfyOyGEEEIIIYQQQojuRErGhBBCCCGEEEIIIboZCQgJIYQQQgghhBBCdDMSEBJCCCGEEEIIIYToZiQgJIQQQgghhBBCCNHNSEBICCGEEEIIIYQQopuRgJAQQgghhBBCCCFENyMBISGEEEIIIYQQQohuRgJCQgghhBBCCCGEEN3M/wMrhTvzj/WXNwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,8))\n",
    "Xt = model.predict(X_test)\n",
    "plt.plot(scl.inverse_transform(y_test.reshape(-1,1)), label=\"Actual\")\n",
    "plt.plot(scl.inverse_transform(Xt), label=\"Predicted\")\n",
    "plt.legend()\n",
    "plt.title(\"validation on testing data\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Full Prediction')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = X.reshape((X.shape[0],X.shape[1],1))\n",
    "\n",
    "plt.figure(figsize=(20,8))\n",
    "Xp = model.predict(X)\n",
    "plt.plot(scl.inverse_transform(Xp), label=\"Predicted\")\n",
    "plt.legend()\n",
    "plt.title(\"Full Prediction\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. credit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Source: \n",
    "> https://datamahadev.com/how-can-you-use-lstm-for-stock-market-prediction/<br>\n",
    "> https://predictivehacks.com/predict-stock-prices-with-lstm/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}