crypto_prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1b5267b0-107f-4f50-9158-470973fc4dbd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import tensorflow.keras as keras\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, LSTM\n",
    "from tensorflow.keras.metrics import MeanSquaredError"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "14200724-394d-4d7e-aef5-3c2dc94f8c23",
   "metadata": {},
   "outputs": [],
   "source": [
    "ethereum = pd.read_csv(\"Coin_Ethereum.csv\",index_col='SNo') #getting Etherium price history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "97505a89-8a1f-41c8-91d8-b1fe7cd812cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='SNo'>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ethereum.pop('Symbol')\n",
    "ethereum.pop('Name')\n",
    "ethereum.pop('Date')\n",
    "plt.figure(figsize=(16,9))\n",
    "sns.lineplot(data = ethereum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b2c281b9-2b46-4f5b-a9c5-55090d74b970",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_sequences(arr,start,end): #This function makes test and train sets\n",
    "    x_data = arr[start:end]\n",
    "    y_data = x_data.pop('Close')\n",
    "    \n",
    "    x_data = x_data.values\n",
    "    y_data = y_data.values\n",
    "    \n",
    "    x_data = x_data[:,:,np.newaxis]\n",
    "    return x_data,y_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f0a68d11-bc06-4c02-b3de-68acfcb147dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "def normolize(arr):#This function normalize the data\n",
    "    try:\n",
    "        for i in range(arr.shape[1]):\n",
    "            power = np.floor(np.log10(max(arr[:,i]))+1)\n",
    "            arr[:,i] = arr[:,i]/(10**power)\n",
    "    except:\n",
    "        for i in range(arr.shape[0]):\n",
    "            power = np.floor(np.log10(max(arr[:]))+1)\n",
    "            arr[:] = arr[:]/(10**power)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1c4eae38-c307-435a-a2b3-b4a92139b001",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lstm (LSTM)                  (None, 5, 128)            66560     \n",
      "_________________________________________________________________\n",
      "lstm_1 (LSTM)                (None, 64)                49408     \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 25)                1625      \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 1)                 26        \n",
      "=================================================================\n",
      "Total params: 117,619\n",
      "Trainable params: 117,619\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "def build_lstm ():\n",
    "    # Build an LSTM model\n",
    "    model = Sequential()\n",
    "    model.add(LSTM(128, return_sequences=True, input_shape= (5, 1)))\n",
    "    model.add(LSTM(64, return_sequences=False))\n",
    "    model.add(Dense(25))\n",
    "    model.add(Dense(1))\n",
    "    \n",
    "    # Compile the model\n",
    "    model.compile(optimizer='adam', loss='mean_squared_error', metrics=[MeanSquaredError()])\n",
    "    model.summary()\n",
    "    \n",
    "    return model\n",
    "\n",
    "model = build_lstm()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "47f812c1-8f72-4bdd-9d65-e2552e82a93a",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_x , train_y = create_sequences(ethereum,len(ethereum)-120,len(ethereum)-20)\n",
    "normolize(train_x)\n",
    "normolize(train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e29d1dbe-c49b-4e92-a33f-90b3d14a06e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_x , test_y = create_sequences(ethereum,len(ethereum)-20,len(ethereum)-1)\n",
    "normolize(test_x)\n",
    "normolize(test_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e81d7a6c-25cc-4702-bfba-c94bb100d1d4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/400\n",
      "4/4 [==============================] - 6s 23ms/step - loss: 0.0502 - mean_squared_error: 0.0502\n",
      "Epoch 2/400\n",
      "4/4 [==============================] - 0s 21ms/step - loss: 0.0080 - mean_squared_error: 0.0080\n",
      "Epoch 3/400\n",
      "4/4 [==============================] - 0s 23ms/step - loss: 0.0063 - mean_squared_error: 0.0063\n",
      "Epoch 4/400\n",
      "4/4 [==============================] - 0s 23ms/step - loss: 0.0035 - mean_squared_error: 0.0035\n",
      "Epoch 5/400\n",
      "4/4 [==============================] - 0s 22ms/step - loss: 0.0018 - mean_squared_error: 0.0018\n",
      "Epoch 6/400\n",
      "4/4 [==============================] - 0s 21ms/step - loss: 0.0034 - mean_squared_error: 0.0034\n",
      "Epoch 7/400\n",
      "4/4 [==============================] - 0s 22ms/step - loss: 0.0022 - mean_squared_error: 0.0022\n",
      "Epoch 8/400\n",
      "4/4 [==============================] - 0s 20ms/step - loss: 0.0013 - mean_squared_error: 0.0013\n",
      "Epoch 9/400\n",
      "4/4 [==============================] - 0s 21ms/step - loss: 0.0016 - mean_squared_error: 0.0016\n",
      "Epoch 10/400\n",
      "4/4 [==============================] - 0s 21ms/step - loss: 0.0013 - mean_squared_error: 0.0013\n",
      "Epoch 11/400\n",
      "4/4 [==============================] - 0s 21ms/step - loss: 0.0011 - mean_squared_error: 0.0011\n",
      "Epoch 12/400\n",
      "4/4 [==============================] - 0s 20ms/step - loss: 0.0011 - mean_squared_error: 0.0011\n",
      "Epoch 13/400\n",
      "4/4 [==============================] - 0s 20ms/step - loss: 0.0010 - mean_squared_error: 0.0010\n",
      "Epoch 14/400\n",
      "4/4 [==============================] - 0s 21ms/step - loss: 9.4889e-04 - mean_squared_error: 9.4889e-04\n",
      "Epoch 15/400\n",
      "4/4 [==============================] - 0s 20ms/step - loss: 9.6238e-04 - mean_squared_error: 9.6238e-04\n",
      "Epoch 16/400\n",
      "4/4 [==============================] - 0s 20ms/step - loss: 7.6187e-04 - mean_squared_error: 7.6187e-04\n",
      "Epoch 17/400\n",
      "4/4 [==============================] - 0s 20ms/step - loss: 8.1045e-04 - mean_squared_error: 8.1045e-04\n",
      "Epoch 18/400\n",
      "4/4 [==============================] - 0s 21ms/step - loss: 6.8437e-04 - mean_squared_error: 6.8437e-04\n",
      "Epoch 19/400\n",
      "4/4 [==============================] - 0s 20ms/step - loss: 6.9579e-04 - mean_squared_error: 6.9579e-04\n",
      "Epoch 20/400\n",
      "4/4 [==============================] - 0s 20ms/step - loss: 7.0982e-04 - mean_squared_error: 7.0982e-04\n",
      "Epoch 21/400\n",
      "4/4 [==============================] - 0s 22ms/step - loss: 5.1571e-04 - mean_squared_error: 5.1571e-04\n",
      "Epoch 22/400\n",
      "4/4 [==============================] - 0s 20ms/step - loss: 4.7860e-04 - mean_squared_error: 4.7860e-04\n",
      "Epoch 23/400\n",
      "4/4 [==============================] - 0s 21ms/step - loss: 4.6491e-04 - mean_squared_error: 4.6491e-04\n",
      "Epoch 24/400\n",
      "4/4 [==============================] - 0s 20ms/step - loss: 3.8726e-04 - mean_squared_error: 3.8726e-04\n",
      "Epoch 25/400\n",
      "4/4 [==============================] - 0s 20ms/step - loss: 4.0894e-04 - mean_squared_error: 4.0894e-04\n",
      "Epoch 26/400\n",
      "4/4 [==============================] - 0s 20ms/step - loss: 3.7932e-04 - mean_squared_error: 3.7932e-04\n",
      "Epoch 27/400\n",
      "4/4 [==============================] - 0s 22ms/step - loss: 3.6264e-04 - mean_squared_error: 3.6264e-04\n",
      "Epoch 28/400\n",
      "4/4 [==============================] - 0s 19ms/step - loss: 3.6726e-04 - mean_squared_error: 3.6726e-04\n",
      "Epoch 29/400\n",
      "4/4 [==============================] - 0s 22ms/step - loss: 3.6341e-04 - mean_squared_error: 3.6341e-04\n",
      "Epoch 30/400\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 3.7668e-04 - mean_squared_error: 3.7668e-04\n",
      "Epoch 31/400\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 3.8659e-04 - mean_squared_error: 3.8659e-04\n",
      "Epoch 32/400\n",
      "4/4 [==============================] - 0s 13ms/step - loss: 3.9712e-04 - mean_squared_error: 3.9712e-04\n",
      "Epoch 33/400\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 3.7917e-04 - mean_squared_error: 3.7917e-04\n",
      "Epoch 34/400\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 3.6215e-04 - mean_squared_error: 3.6215e-04\n",
      "Epoch 35/400\n",
      "4/4 [==============================] - 0s 22ms/step - loss: 3.6445e-04 - mean_squared_error: 3.6445e-04\n",
      "Epoch 36/400\n",
      "4/4 [==============================] - 0s 13ms/step - loss: 3.5715e-04 - mean_squared_error: 3.5715e-04\n",
      "Epoch 37/400\n",
      "4/4 [==============================] - 0s 13ms/step - loss: 3.6732e-04 - mean_squared_error: 3.6732e-04\n",
      "Epoch 38/400\n",
      "4/4 [==============================] - 0s 13ms/step - loss: 3.5986e-04 - mean_squared_error: 3.5986e-04\n",
      "Epoch 39/400\n",
      "4/4 [==============================] - 0s 13ms/step - loss: 3.5425e-04 - mean_squared_error: 3.5425e-04\n",
      "Epoch 40/400\n",
      "4/4 [==============================] - 0s 13ms/step - loss: 3.5666e-04 - mean_squared_error: 3.5666e-04\n",
      "Epoch 41/400\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 3.6343e-04 - mean_squared_error: 3.6343e-04\n",
      "Epoch 42/400\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 3.4673e-04 - mean_squared_error: 3.4673e-04\n",
      "Epoch 43/400\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 3.5385e-04 - mean_squared_error: 3.5385e-04\n",
      "Epoch 44/400\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 3.4436e-04 - mean_squared_error: 3.4436e-04\n",
      "Epoch 45/400\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 3.5795e-04 - mean_squared_error: 3.5795e-04\n",
      "Epoch 46/400\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 3.5380e-04 - mean_squared_error: 3.5380e-04\n",
      "Epoch 47/400\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 3.4284e-04 - mean_squared_error: 3.4284e-04\n",
      "Epoch 48/400\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 3.4918e-04 - mean_squared_error: 3.4918e-04\n",
      "Epoch 49/400\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 3.3344e-04 - mean_squared_error: 3.3344e-04\n",
      "Epoch 50/400\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 4.0419e-04 - mean_squared_error: 4.0419e-04\n",
      "Epoch 51/400\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 4.2977e-04 - mean_squared_error: 4.2977e-04\n",
      "Epoch 52/400\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 3.5716e-04 - mean_squared_error: 3.5716e-04\n",
      "Epoch 53/400\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 3.6506e-04 - mean_squared_error: 3.6506e-04\n",
      "Epoch 54/400\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 3.5946e-04 - mean_squared_error: 3.5946e-04\n",
      "Epoch 55/400\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 3.7206e-04 - mean_squared_error: 3.7206e-04\n",
      "Epoch 56/400\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 3.5756e-04 - mean_squared_error: 3.5756e-04\n",
      "Epoch 57/400\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 3.4601e-04 - mean_squared_error: 3.4601e-04\n",
      "Epoch 58/400\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 3.7247e-04 - mean_squared_error: 3.7247e-04\n",
      "Epoch 59/400\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 3.4008e-04 - mean_squared_error: 3.4008e-04\n",
      "Epoch 60/400\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 3.3980e-04 - mean_squared_error: 3.3980e-04\n",
      "Epoch 61/400\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 4.2422e-04 - mean_squared_error: 4.2422e-04\n",
      "Epoch 62/400\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 3.6867e-04 - mean_squared_error: 3.6867e-04\n",
      "Epoch 63/400\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 3.5300e-04 - mean_squared_error: 3.5300e-04\n",
      "Epoch 64/400\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 3.2813e-04 - mean_squared_error: 3.2813e-04\n",
      "Epoch 65/400\n",
      "4/4 [==============================] - 0s 19ms/step - loss: 3.4075e-04 - mean_squared_error: 3.4075e-04\n",
      "Epoch 66/400\n",
      "4/4 [==============================] - 0s 13ms/step - loss: 3.2437e-04 - mean_squared_error: 3.2437e-04\n",
      "Epoch 67/400\n",
      "4/4 [==============================] - 0s 13ms/step - loss: 3.1788e-04 - mean_squared_error: 3.1788e-04\n",
      "Epoch 68/400\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 3.2240e-04 - mean_squared_error: 3.2240e-04\n",
      "Epoch 69/400\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 3.1669e-04 - mean_squared_error: 3.1669e-04\n",
      "Epoch 70/400\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 3.1858e-04 - mean_squared_error: 3.1858e-04\n",
      "Epoch 71/400\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 3.1567e-04 - mean_squared_error: 3.1567e-04\n",
      "Epoch 72/400\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 3.1402e-04 - mean_squared_error: 3.1402e-04\n",
      "Epoch 73/400\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 3.4974e-04 - mean_squared_error: 3.4974e-04\n",
      "Epoch 74/400\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 4.0569e-04 - mean_squared_error: 4.0569e-04\n",
      "Epoch 75/400\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 3.7363e-04 - mean_squared_error: 3.7363e-04\n",
      "Epoch 76/400\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 3.4754e-04 - mean_squared_error: 3.4754e-04\n",
      "Epoch 77/400\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 4.4189e-04 - mean_squared_error: 4.4189e-04\n",
      "Epoch 78/400\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 3.5070e-04 - mean_squared_error: 3.5070e-04\n",
      "Epoch 79/400\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 3.7113e-04 - mean_squared_error: 3.7113e-04\n",
      "Epoch 80/400\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 3.7611e-04 - mean_squared_error: 3.7611e-04\n",
      "Epoch 81/400\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 3.1528e-04 - mean_squared_error: 3.1528e-04\n",
      "Epoch 82/400\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 3.0142e-04 - mean_squared_error: 3.0142e-04\n",
      "Epoch 83/400\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 2.9429e-04 - mean_squared_error: 2.9429e-04\n",
      "Epoch 84/400\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 3.3954e-04 - mean_squared_error: 3.3954e-04\n",
      "Epoch 85/400\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 2.9743e-04 - mean_squared_error: 2.9743e-04\n",
      "Epoch 86/400\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 3.1680e-04 - mean_squared_error: 3.1680e-04\n",
      "Epoch 87/400\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 2.8810e-04 - mean_squared_error: 2.8810e-04\n",
      "Epoch 88/400\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 2.9702e-04 - mean_squared_error: 2.9702e-04\n",
      "Epoch 89/400\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 3.0113e-04 - mean_squared_error: 3.0113e-04\n",
      "Epoch 90/400\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 3.0190e-04 - mean_squared_error: 3.0190e-04\n",
      "Epoch 91/400\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 3.0173e-04 - mean_squared_error: 3.0173e-04\n",
      "Epoch 92/400\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 2.7895e-04 - mean_squared_error: 2.7895e-04\n",
      "Epoch 93/400\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 3.0140e-04 - mean_squared_error: 3.0140e-04\n",
      "Epoch 94/400\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 3.2082e-04 - mean_squared_error: 3.2082e-04\n",
      "Epoch 95/400\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 3.3272e-04 - mean_squared_error: 3.3272e-04\n",
      "Epoch 96/400\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 2.7586e-04 - mean_squared_error: 2.7586e-04\n",
      "Epoch 97/400\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 3.4176e-04 - mean_squared_error: 3.4176e-04\n",
      "Epoch 98/400\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 3.0790e-04 - mean_squared_error: 3.0790e-04\n",
      "Epoch 99/400\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 3.0359e-04 - mean_squared_error: 3.0359e-04\n",
      "Epoch 100/400\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 2.9020e-04 - mean_squared_error: 2.9020e-04\n",
      "Epoch 101/400\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 2.7111e-04 - mean_squared_error: 2.7111e-04\n",
      "Epoch 102/400\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 3.2254e-04 - mean_squared_error: 3.2254e-04\n",
      "Epoch 103/400\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 3.0336e-04 - mean_squared_error: 3.0336e-04\n",
      "Epoch 104/400\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 2.7016e-04 - mean_squared_error: 2.7016e-04\n",
      "Epoch 105/400\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 2.8059e-04 - mean_squared_error: 2.8059e-04\n",
      "Epoch 106/400\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 3.8663e-04 - mean_squared_error: 3.8663e-04\n",
      "Epoch 107/400\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 4.0300e-04 - mean_squared_error: 4.0300e-04\n",
      "Epoch 108/400\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 3.2088e-04 - mean_squared_error: 3.2088e-04\n",
      "Epoch 109/400\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 3.3053e-04 - mean_squared_error: 3.3053e-04\n",
      "Epoch 110/400\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 2.8447e-04 - mean_squared_error: 2.8447e-04\n",
      "Epoch 111/400\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 2.7088e-04 - mean_squared_error: 2.7088e-04\n",
      "Epoch 112/400\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 2.7018e-04 - mean_squared_error: 2.7018e-04\n",
      "Epoch 113/400\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 2.6654e-04 - mean_squared_error: 2.6654e-04\n",
      "Epoch 114/400\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 2.5799e-04 - mean_squared_error: 2.5799e-04\n",
      "Epoch 115/400\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 2.5602e-04 - mean_squared_error: 2.5602e-04\n",
      "Epoch 116/400\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 2.6252e-04 - mean_squared_error: 2.6252e-04\n",
      "Epoch 117/400\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 2.7691e-04 - mean_squared_error: 2.7691e-04\n",
      "Epoch 118/400\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 2.8282e-04 - mean_squared_error: 2.8282e-04\n",
      "Epoch 119/400\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 2.5633e-04 - mean_squared_error: 2.5633e-04\n",
      "Epoch 120/400\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 2.6230e-04 - mean_squared_error: 2.6230e-04\n",
      "Epoch 121/400\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 2.6118e-04 - mean_squared_error: 2.6118e-04\n",
      "Epoch 122/400\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 3.1724e-04 - mean_squared_error: 3.1724e-04\n",
      "Epoch 123/400\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 2.4612e-04 - mean_squared_error: 2.4612e-04\n",
      "Epoch 124/400\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 2.5602e-04 - mean_squared_error: 2.5602e-04\n",
      "Epoch 125/400\n",
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      "4/4 [==============================] - ETA: 0s - loss: 1.2129e-04 - mean_squared_error: 1.2129e- - 0s 14ms/step - loss: 1.3571e-04 - mean_squared_error: 1.3571e-04\n",
      "Epoch 247/400\n",
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      "Epoch 269/400\n",
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      "Epoch 270/400\n",
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      "Epoch 275/400\n",
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      "Epoch 276/400\n",
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      "Epoch 277/400\n",
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      "Epoch 278/400\n",
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      "Epoch 279/400\n",
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      "Epoch 280/400\n",
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      "Epoch 281/400\n",
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      "Epoch 282/400\n",
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      "Epoch 283/400\n",
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      "Epoch 284/400\n",
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      "Epoch 285/400\n",
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      "Epoch 286/400\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 3.0880e-04 - mean_squared_error: 3.0880e-04\n",
      "Epoch 287/400\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 2.5528e-04 - mean_squared_error: 2.5528e-04\n",
      "Epoch 288/400\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 1.1887e-04 - mean_squared_error: 1.1887e-04\n",
      "Epoch 289/400\n",
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      "Epoch 290/400\n",
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      "Epoch 291/400\n",
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      "Epoch 292/400\n",
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      "Epoch 294/400\n",
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      "Epoch 295/400\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 4.4540e-04 - mean_squared_error: 4.4540e-04\n",
      "Epoch 296/400\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 3.7163e-04 - mean_squared_error: 3.7163e-04\n",
      "Epoch 297/400\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 1.8158e-04 - mean_squared_error: 1.8158e-04\n",
      "Epoch 298/400\n",
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      "Epoch 299/400\n",
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      "Epoch 300/400\n",
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      "Epoch 301/400\n",
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      "Epoch 302/400\n",
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      "Epoch 304/400\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 1.4169e-04 - mean_squared_error: 1.4169e-04\n",
      "Epoch 305/400\n",
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      "Epoch 307/400\n",
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      "Epoch 309/400\n",
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      "Epoch 310/400\n",
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      "Epoch 311/400\n",
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      "Epoch 315/400\n",
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      "Epoch 316/400\n",
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      "Epoch 317/400\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 1.6583e-04 - mean_squared_error: 1.6583e-04\n",
      "Epoch 318/400\n",
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      "Epoch 319/400\n",
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      "Epoch 320/400\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 1.1723e-04 - mean_squared_error: 1.1723e-04\n",
      "Epoch 321/400\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 1.3582e-04 - mean_squared_error: 1.3582e-04\n",
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      "Epoch 323/400\n",
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      "Epoch 324/400\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 2.0656e-04 - mean_squared_error: 2.0656e-04\n",
      "Epoch 325/400\n",
      "4/4 [==============================] - 0s 13ms/step - loss: 1.7695e-04 - mean_squared_error: 1.7695e-04\n",
      "Epoch 326/400\n",
      "4/4 [==============================] - 0s 13ms/step - loss: 1.7279e-04 - mean_squared_error: 1.7279e-04\n",
      "Epoch 327/400\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 1.3636e-04 - mean_squared_error: 1.3636e-04\n",
      "Epoch 328/400\n",
      "4/4 [==============================] - 0s 13ms/step - loss: 1.5823e-04 - mean_squared_error: 1.5823e-04\n",
      "Epoch 329/400\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 1.5069e-04 - mean_squared_error: 1.5069e-04\n",
      "Epoch 330/400\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 1.1866e-04 - mean_squared_error: 1.1866e-04\n",
      "Epoch 331/400\n",
      "4/4 [==============================] - 0s 13ms/step - loss: 1.6187e-04 - mean_squared_error: 1.6187e-04\n",
      "Epoch 332/400\n",
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      "Epoch 333/400\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 2.1058e-04 - mean_squared_error: 2.1058e-04\n",
      "Epoch 334/400\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 1.5408e-04 - mean_squared_error: 1.5408e-04\n",
      "Epoch 335/400\n",
      "4/4 [==============================] - 0s 13ms/step - loss: 1.2573e-04 - mean_squared_error: 1.2573e-04\n",
      "Epoch 336/400\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 1.4316e-04 - mean_squared_error: 1.4316e-04\n",
      "Epoch 337/400\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 1.1072e-04 - mean_squared_error: 1.1072e-04\n",
      "Epoch 338/400\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 1.1168e-04 - mean_squared_error: 1.1168e-04\n",
      "Epoch 339/400\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 1.1416e-04 - mean_squared_error: 1.1416e-04\n",
      "Epoch 340/400\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 1.1018e-04 - mean_squared_error: 1.1018e-04\n",
      "Epoch 341/400\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 1.1823e-04 - mean_squared_error: 1.1823e-04\n",
      "Epoch 342/400\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 1.1486e-04 - mean_squared_error: 1.1486e-04\n",
      "Epoch 343/400\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 1.2383e-04 - mean_squared_error: 1.2383e-04\n",
      "Epoch 344/400\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 2.0557e-04 - mean_squared_error: 2.0557e-04\n",
      "Epoch 345/400\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 1.6534e-04 - mean_squared_error: 1.6534e-04\n",
      "Epoch 346/400\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 1.3572e-04 - mean_squared_error: 1.3572e-04\n",
      "Epoch 347/400\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 1.0930e-04 - mean_squared_error: 1.0930e-04\n",
      "Epoch 348/400\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 1.2800e-04 - mean_squared_error: 1.2800e-04\n",
      "Epoch 349/400\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 1.1436e-04 - mean_squared_error: 1.1436e-04\n",
      "Epoch 350/400\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 1.1184e-04 - mean_squared_error: 1.1184e-04\n",
      "Epoch 351/400\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 1.1640e-04 - mean_squared_error: 1.1640e-04\n",
      "Epoch 352/400\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 1.1841e-04 - mean_squared_error: 1.1841e-04\n",
      "Epoch 353/400\n",
      "4/4 [==============================] - 0s 13ms/step - loss: 1.1487e-04 - mean_squared_error: 1.1487e-04\n",
      "Epoch 354/400\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 1.0894e-04 - mean_squared_error: 1.0894e-04\n",
      "Epoch 355/400\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 1.5793e-04 - mean_squared_error: 1.5793e-04\n",
      "Epoch 356/400\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 2.0063e-04 - mean_squared_error: 2.0063e-04\n",
      "Epoch 357/400\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 1.3133e-04 - mean_squared_error: 1.3133e-04\n",
      "Epoch 358/400\n",
      "4/4 [==============================] - 0s 13ms/step - loss: 2.5741e-04 - mean_squared_error: 2.5741e-04\n",
      "Epoch 359/400\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 1.6216e-04 - mean_squared_error: 1.6216e-04\n",
      "Epoch 360/400\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 1.2038e-04 - mean_squared_error: 1.2038e-04\n",
      "Epoch 361/400\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 1.4203e-04 - mean_squared_error: 1.4203e-04\n",
      "Epoch 362/400\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 1.3484e-04 - mean_squared_error: 1.3484e-04\n",
      "Epoch 363/400\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 1.1174e-04 - mean_squared_error: 1.1174e-04\n",
      "Epoch 364/400\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 1.1386e-04 - mean_squared_error: 1.1386e-04\n",
      "Epoch 365/400\n",
      "4/4 [==============================] - 0s 13ms/step - loss: 1.0837e-04 - mean_squared_error: 1.0837e-04\n",
      "Epoch 366/400\n",
      "4/4 [==============================] - 0s 13ms/step - loss: 1.2210e-04 - mean_squared_error: 1.2210e-04\n",
      "Epoch 367/400\n",
      "4/4 [==============================] - 0s 13ms/step - loss: 1.5858e-04 - mean_squared_error: 1.5858e-04\n",
      "Epoch 368/400\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 1.3284e-04 - mean_squared_error: 1.3284e-04\n",
      "Epoch 369/400\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 1.1108e-04 - mean_squared_error: 1.1108e-04\n",
      "Epoch 370/400\n",
      "4/4 [==============================] - 0s 13ms/step - loss: 1.8013e-04 - mean_squared_error: 1.8013e-04\n",
      "Epoch 371/400\n",
      "4/4 [==============================] - 0s 13ms/step - loss: 1.4955e-04 - mean_squared_error: 1.4955e-04\n",
      "Epoch 372/400\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 1.1339e-04 - mean_squared_error: 1.1339e-04\n",
      "Epoch 373/400\n",
      "4/4 [==============================] - 0s 13ms/step - loss: 1.4239e-04 - mean_squared_error: 1.4239e-04\n",
      "Epoch 374/400\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 1.2562e-04 - mean_squared_error: 1.2562e-04\n",
      "Epoch 375/400\n",
      "4/4 [==============================] - 0s 13ms/step - loss: 1.1455e-04 - mean_squared_error: 1.1455e-04\n",
      "Epoch 376/400\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 1.4304e-04 - mean_squared_error: 1.4304e-04\n",
      "Epoch 377/400\n",
      "4/4 [==============================] - 0s 13ms/step - loss: 2.3457e-04 - mean_squared_error: 2.3457e-04\n",
      "Epoch 378/400\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 2.5483e-04 - mean_squared_error: 2.5483e-04\n",
      "Epoch 379/400\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 1.5591e-04 - mean_squared_error: 1.5591e-04\n",
      "Epoch 380/400\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 2.1141e-04 - mean_squared_error: 2.1141e-04\n",
      "Epoch 381/400\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 1.6825e-04 - mean_squared_error: 1.6825e-04\n",
      "Epoch 382/400\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 1.9668e-04 - mean_squared_error: 1.9668e-04\n",
      "Epoch 383/400\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 1.8999e-04 - mean_squared_error: 1.8999e-04\n",
      "Epoch 384/400\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 1.2905e-04 - mean_squared_error: 1.2905e-04\n",
      "Epoch 385/400\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 1.5893e-04 - mean_squared_error: 1.5893e-04\n",
      "Epoch 386/400\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 1.2905e-04 - mean_squared_error: 1.2905e-04\n",
      "Epoch 387/400\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 1.3601e-04 - mean_squared_error: 1.3601e-04\n",
      "Epoch 388/400\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 1.2445e-04 - mean_squared_error: 1.2445e-04\n",
      "Epoch 389/400\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 1.3032e-04 - mean_squared_error: 1.3032e-04\n",
      "Epoch 390/400\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 1.0930e-04 - mean_squared_error: 1.0930e-04\n",
      "Epoch 391/400\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 1.1824e-04 - mean_squared_error: 1.1824e-04\n",
      "Epoch 392/400\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 1.0981e-04 - mean_squared_error: 1.0981e-04\n",
      "Epoch 393/400\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 1.1273e-04 - mean_squared_error: 1.1273e-04\n",
      "Epoch 394/400\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 1.1860e-04 - mean_squared_error: 1.1860e-04\n",
      "Epoch 395/400\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 1.1311e-04 - mean_squared_error: 1.1311e-04\n",
      "Epoch 396/400\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 1.2549e-04 - mean_squared_error: 1.2549e-04\n",
      "Epoch 397/400\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 1.1030e-04 - mean_squared_error: 1.1030e-04\n",
      "Epoch 398/400\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 1.0914e-04 - mean_squared_error: 1.0914e-04\n",
      "Epoch 399/400\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 1.2658e-04 - mean_squared_error: 1.2658e-04\n",
      "Epoch 400/400\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 1.1355e-04 - mean_squared_error: 1.1355e-04\n"
     ]
    }
   ],
   "source": [
    "hist = model.fit(train_x, train_y, epochs=400)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "df9d85f8-c6b6-4a14-9589-8e2ab4cabc06",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = model.predict(test_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4ff2d933-9670-412b-a976-2326a535d97e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\dear user\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "c:\\users\\dear user\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Close', ylabel='Density'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1i0tnV7A4q2OE0r51yXEhZ5XWc1RBE7+rKOGKzUU835zBh6ZtpST5DXZUm3MChAOw9t7BsmjPcyyNJEnjTixnGPUBnwrD8IUgCDKB54MguBf4L+D+MAy/EwTB54HPA5+LYQ5JkiTtRFJPE/M3/oWE/i5WTnsHbWmTX/fa/hDuqMnj2s0FxAch75uyjaMLmoa189holJPYz6dmbObRhiz+WFnMZ5dP5+JJNRxb0LTjdZeCAOaeNFgarbsf4hNh4ZkjnluSpFiKWWEUhuFWYOv2z1uDIFgBlAOnA0dsv+zPwENYGEmSJEXmtWXRimkX05Fa9rrXVnQm8+uNJazrSGVJdivvnVJNXlLfCKaNjSCAw/JbWJjZwa83lvL7ihJebM7gI9O2kL6jXd2CAOadAv29sOFhSM2DGYePfHBJkmJkRNYwCoJgGrA38DRQvL1MAtjG4CNrkiRJisBwy6KBEG6rzuPqzYWkJ/TziembOXAY29KPNflJfVw6u5K7a3P4S2UxX1w5lc/M2kz5jhbEDgJYeAZ0NcHymyA1F0oXjXBiSZJiI+YPWwdBkAFcD3wiDMOW154LwzBkcH2jHY37QBAEzwVB8FxtbW2sY0qSJE04Qx9De/2yqKk3nu+sncQVm4tYktPKjxau5215468s+ocggBOKmvjynAra++P54oqpvNCc/joXx8HeF0POFHjxr9C4cUSzSpIUKzEtjIIgSGSwLLoiDMMbth+uDoKgdPv5UqBmR2PDMPxtGIZLwjBcUlhYGMuYkiRJE05ST/OQsqj9dcqil1vS+Nzy6SxvTeN9U7ZxyYwtZO7oEa1xaH5mJ9+ev5Hi5F6+t3YSN27NI9zRnzrjk2C/90FKFjx7ObTXjXhWSZJ2t5gVRkEQBMDvgRVhGP7oNaduAd61/fN3ATfHKoMkSZL+U0JfO/M2/Y2E/s7XLYv6Q7h6cwGXrZlMekI/l83fxLGFr7MI9DhWkNTHN+Zt4m25rVy9pYhfbCylb0d9WXIm7P9BCEN45jfQ0z7iWSVJ2p1iOcPoYOAdwFFBECzd/u8k4DvAsUEQrAGO2f5/SZIkjYD4/m7mbbqSpN5mVk25cIdlUVtfHN9ZO4kbtxVwRH4zl83byJTU7gjSjg7JcSEfm76Fc0trebQhmw89lU1X/w4uzCganGnU2QjP/QEGdnSRJEljQyx3SXsMeL2/QR0dq/eVJEnSjgUDfcypuJrUrmpWTzmftvQp/3FNVWcS3183idqeRD4wdStHFzRHkHT0CQI4p6yejIR+/lhZwnsey+Hyg5tJT/i3Z9TyZsCiC2Dp32DFLbD/+6IJLEnSWxTzRa8lSZI0CoQDzKq6nsyOTawvP53mzNn/cclzTRl8ceVUOvvj+OqcCsuiHTihqIkf7tfC03WJvP2RHJp6dvD30UlLYNqhsOFheOXvIx9SkqTdwMJIkiRpvAtDZmy5lbzWVWwqOYH6nD3//TQ3bM3n++smUZbSw7fnb2RuRmdEYUe/s6d28csDm1nelMAFD+dS07WDW+oFp0PudLjlo1C9fORDSpL0FlkYSZIkjXOTq++nsOklqgoPpzp//yHn+kL49aYSrtlSyCF5zXx9bgX5SX0RJR07ji/v4Q8HN1HRFsc7djTTKC4B9v2vwcWwr7kYupytJUkaWyyMJEmSxrGihucoq3+C6twlbC48bMi5zv44vrd2Eg/V53BOaR0fmbaVpLgd7RuvHTmkuJffHdzMhrZ43vVoDm29/1YapWTDuX+Gpk1w43/DwI62V5MkaXSyMJIkSRqnclpXM23rnTRmzGZj6QmDKzdv19gbz9dWTeGVlnQ+OHUr55bVvfa0humgol5+eWAzrzYl8N7Hd7B72tS3wfGXwao74LEfRpJRkqQ3w8JIkiRpHErr3MKsquvpSClh7aSzIfjXbV9VZxJfWjmNrd1JfHZWFUe5uPVbckxZDz/ar4Vn6hL58JPZ9Pz7RKL9PwB7ngsPXgYVT0WSUZKkXWVhJEmSNM4k9TQzt+Jq+uLTWDXlAgbik/55bm17Cl9ZNZXegYCvzdnE3tntESYdP06f0s239mnlgW3JXPJMFv2vfbIvCOCUH0POFLj+/dDZFFVMSZKGzcJIkiRpHInv72JuxZXEDfSyasqF9CZm/vPcstY0vrl6Mhnx/Xxz3iZmpHdHmHT8uWhGF5fu2crtVSl886WMoSeTM+Hs30PLZrjtksGt6SRJGsUsjCRJksaLcIDZlX8npbueNZPPpTOl6J+nnm9K59trJlGQ1MfX5lZQnNwbYdDx6wNzO3nv7A7+tDaNv65LHXpy0hI48lJYdgO8dFU0ASVJGiYLI0mSpHFi6ra7yW5fz8ayk2nJmPHP4080ZPLDdZOYnNrNV+dWkJfUF2HK8e/SRW0cU9rN15Zm8NCqmqEnD7kEph4Ct38a6tdFE1CSpGGwMJIkSRoHihqeo6ThWbbmH0ht7t7/PP5AXTY/21DGnIxOvjynkqyEf9/GS7tbfAA/PaCFudl9fOTKF1m1rfVfJ+Pi4azfQHwiXP9e6OuJLqgkSW/AwkiSJGmMy2rfwNStd9GUMYuK4mP+efze2hx+s6mUxVntfGF2JWnx/759l2IlPSHk9wc1k5YUz3v+9Cy1ra9ZLyp7Epz2M9jyIjz4rehCSpL0BiyMJEmSxrDk7gZmV15Hd3IeayedBcHg7d3dNTn8rqKEfbLb+PTMzSTHucjySCtNG+D379qP+vZuPvDX5+jqfc3srgWnwz7vhMd/ChVPRxdSkqTXYWEkSZI0RsX3dzG38mpCAlZNuYD++BQA7qrJ4Q+VJeyb3conZ2wm0bIoMntOyuYn5+/FixVNfO2WZUNPHn/Z4Gyjmz8MvZ3RBJQk6XVYGEmSJI1FYcjMqhtI7m5gzeRz6U7KA+DOmlz+WFnCEsuiUeOEPUr5nyNncvWzlVz7bOW/TiRnwmk/h/q1PpomSRp1LIwkSZLGoPLaR8htW8um0hNoTZ8GDJZFf6osZr+cVi6ZsZkE7/RGjU8eO5eDZ+Xz5Ztf5dXNzf86MfNI2Pe/4MlfQOUzkeWTJOnfeRshSZI0xmS3rqG89mFqcxZTk7svAPfXZv+zLPqEZdGoEx8X8LML9iYvPYkPX/ECzR29/zp57Dchqxxu8tE0SdLo4a2EJEnSGJLc08isqhvpSClhQ+lJEAQ8Wp/F5RUl7JXVxsenbyEhiDqldiQ/I5lfvH0ftjZ38qnrljIwsP1xwZSswV3T6tfAg5dFG1KSpO0sjCRJksaIuIFeZldeC8CayecSxiXyTGMGv9xYyvyMDj410zWLRrt9puTypZMXcN+KGn718Lp/nZh51PZH0/4PKp+NLJ8kSf9gYSRJkjQWhCHTttxOelc1ayedSXdSLkub0/nJhnJmpnfx2VmbSbIsGhPe+bapnLa4jB/es4qn19f/68Sx34TMssFd0/q6owsoSRIWRpIkSWNCUePzFDa/TFXhYTRnzmZ5ayo/WFfO5JRuPj+rktT4gagjapiCIODbZ+3JlLw0Lrlm6b/WM0rJglN/CnWr4bGfRJpRkiQLI0mSpFEuvaOKqdvuojFjFpsLD2d1WwrfXTuJouRevji7kowEy6KxJj05gZ9esDc1rd1cetMrhOH22WGzj4E9zoZHfwB1a6MNKUma0CyMJEmSRrGEvnbmVF5HT0IW68rPZGNnCt9ZO5nshH6+NLuSrMT+qCPqTVo8OYdLjp3D7S9v5e/PV/3rxPHfhoRUuP0SCH3MUJIUDQsjSZKk0SocYFbV9ST0d7Jm8rls6s3mf9dMJiVugC/NqSQvqS/qhHqL/vvwmRwwPY+v3rKMjXXtgwczi+GYr8KGR+Dla6INKEmasCyMJEmSRqnJ1Q+Q3b6RDWUnsz5uCv+7ZjJxwJfmVFCU3Bt1PO0G8XEBPz5/LxLj4/j41S/S27/98cJ93w2T9oO7L4WOhmhDSpImJAsjSZKkUSi3ZQVl9U9QnbsvK9P25X9XT6F3II4vzamgLMWyaDwpy0nl22ftyUtVzfz43tWDB+Pi4JSfQGcT3PuVKONJkiYoCyNJkqRRJqW7jpmbb6YttZxXC07ksjWTaeuL44uzK5iS2hN1PMXASXuWct6SSfzq4XU8v6lx8GDJHnDQR+DFv8KmJ6INKEmacCyMJEmSRpG4/h7mVF7LQJDAK2Xn8e1106npTuSzs6qYkd4ddTzF0JdPWUBZdiqfue4lunq3L2Z++OcgZwrc+gnosyyUJI0cCyNJkqTRIgyZseUWUrrrWVV+Nt+unM+6jhQ+PmMLCzI7o06nGMtMSeS7Zy9ifV07P7xn1eDBpHQ46QdQtwqe/nW0ASVJE4qFkSRJ0ihRUv8U+S3LqSg6istqD+bllgw+MHUb++W0RR1NI+SQ2QVcdMAUfvfYBp7ftH2x6znHw5wT4OHvQuu2aANKkiYMCyNJkqRRILN9I1Oq76Mhcx6XdZzJE41ZXFRew1EFzVFH0wi79KT52x9Ne/lfj6Ydfxn098C9X402nCRpwrAwkiRJilhibwuzK6+nKymPH8W/m7vr8jiluJ7Tit1OfSLKSE74z0fT8mfC2z4CL18NFU9HG1CSNCFYGEmSJEUoGOhnduXfiQt7+FPGe/nbtskcltfM28trCYKo0ykqO3w07dBPQWYZ3PFpGOiPNqAkadyzMJIkSYrQlOp7yOys4q6s8/nelsXsk93GB6dtJc6yaML7j0fTkjPguG/CtpfhhT9HHU+SNM5ZGEmSJEUkv+llShqe5eWMQ/hY9YnMzejkEzM2k2BZJAYfTfv2WXuyvq6dXz60bvDgHmfD1EPg/m9Ch48sSpJix8JIkiQpAqld1UzfchvVydO4sOF9lKd089mZVSTHhVFH0yhy2JxCTt+rjF89tJa1Na0QBHDid6GrCR78VtTxJEnjmIWRJEnSCIvv72JO5bX0xKVyftsnSU+AS2dXkp4wEHU0jUJfPmUBaUkJXHrDqwwMhFCyB+z3PnjuD7Dt1ajjSZLGKQsjSZKkkRSGzNx8E0k9zfxPz8doDHL44uxKchNdxFg7VpCRzKUnzeOZjQ1c93zl4MEjvgDJWXDPFyF0VpokafezMJIkSRpBZXWPkdu6mp9yIU/2z+MLsyspSemNOpZGufOWTGb/6XlcdsdK6tq6IS0Pjvg8rH8I1twbdTxJ0jhkYSRJkjRCstvWMqnmQe4LDuSX3Sfw2VlVTE/rjjqWxoAgCLjszD3o6Onjm7ctHzy45L2QNxPu+RL090UbUJI07lgYSZIkjYCkniZmVt3IpqCcj3W+n4/P2MqCzM6oY2kMmVWUyYeOmMXNS7fw8OpaSEiC474JdavghT9FHU+SNM5YGEmSJMVYMNDH7Mrr6OmHd3V9indMbWK/nLaoY2kM+vARM5lRkM5Xbn6Vrt5+mHsSTD0EHrwMupqjjidJGkcsjCRJkmJs2tY7yejaysd7PszBZXEcVeAv9npzUhLj+frpC9lU38Hlj6yHIIDjvwUdDfDoD6OOJ0kaRyyMJEmSYqiw8QWKml7k531nkFo4jdOKG6KOpDHu0NmFnLhHCb94aC1VjR1QthcsvhCe+hU0bow6niRpnLAwkiRJipH0zs1M2XInj/TvyYtZx/D28lqCIOpUGg++dMoCAgL+97YVgweO/jIE8XDf16MNJkkaNyyMJEmSYiChr4MpG69nW5jLH9P+i/dNq7Es0m5TnpPKR46axV3LtvHI6lrIKoODPw7LboDKZ6OOJ0kaByyMJEmSdrdwgOKNN5Hc3873Ez7Ie2a0EG9ZpN3sfYdOZ1p+Gl+7ZRndff1w8Mcgoxju/QqEYdTxJEljnIWRJEnSbpZW9SiTutfys7i3c8bsRJLi/OVdu19yQjxfPW0h6+va+cNjGyEpHQ7/HFQ8AavvjjqeJGmMszCSJEnajfprVrNny8PcGB7B4tnTSYsfiDqSxrEj5xZx7IJifv7AGrY2d8I+74S8mXDf12CgP+p4kqQxzMJIkiRpN9my6ln2rrmBpeEskmcfRnaiv7Ar9r5yygL6B0Iuu2MlxCfC0V+B2hXw0tVRR5MkjWEWRpIkSbtBbfVWuPrttJLG5qlnk58cdSJNFJPz0vjgYTO49aUtPL+pARacDuX7woPfgt7OqONJksYoCyNJkqS3qLmti4rLL6RwoI4XSy+gINO2SCPrg4fPpDgrmW/cupyBEDjm69CyGZ75bdTRJEljlIWRJEnSW9De3ccDv/wf9u17kY0HfoOc/KKoI2kCSk9O4LPHz+OlqmZuWroZph8Ks4+DR38InY1Rx5MkjUEWRpIkSW9SV28/v/v1Dziz4+9UzDif2Sd+JOpImsDO3LucRZOy+e5dK+no6YOjvwpdLfDYj6OOJkkagyyMJEmS3oTuvn6+9YdreX/Dj6jP25spF/1f1JE0wcXFBXzllAVUt3Tz64fXQ8kesPgCeOrX0FwVdTxJ0hhjYSRJkrSL+voH+PzfHub9m78MKdnkv/tqSEiKOpbEkml5nLKolN88vI7NTZ1w5KVACA9/N+pokqQxxsJIkiRpF/QPhHz62hc4c91XKI9vIu3iKyGzJOpY0j99/sR5hMD37loJOVNgyXvgxSugfl3U0SRJY4iFkSRJ0jANDIRcesMrzFv2Ew6Lf4X4U34Ik/eLOpY0xKTcND5w6AxuXrqF5zc1wqGfgoRkePCyqKNJksYQCyNJkqRhCMOQb9y2nPYXruW/E24dnLWx77uijiXt0IeOmElhZjKX3bGCML0QDvhvePV62PZq1NEkSWOEhZEkSdJOhGHId+5aydNPPsyPUi4nnHwgnOCaMBq90pMTuOSYOTy/qZG7l1XDwR+D5Cx48FtRR5MkjREWRpIkSTvxs/vXcv3DL3JVxk9ITM8lOO8vLnKtUe+8JZOYVZTBd+9aSW9S9mBptOoOqHw26miSpDHAwkiSJOkN/PaRdfzyvlf5e+7/kR22EFx4FWQWRx1L2qmE+Dg+f8I8NtS1c/UzFYOPpaUXwgPfjDqaJGkMsDCSJEl6HX9+YiOX3bGCKwr/wrTOZQRn/RbK9o46ljRsR88vYv/pefzkvjW0hsmDC2BveBjWPxx1NEnSKGdhJEmStAN/fHwDX71lGT8pvY8lrQ/AUV+GBadFHUvaJUEQcOlJ86lv7+G3j6yHfd8NWeWDs4zCMOp4kqRRzMJIkiTp3/zhsQ18/dblfGHqSs5o/CMsumBwZoY0Bu01OYdTFpVy+aPrqe4EDv8cVD0Lq++KOpokaRSzMJIkSXqN3z+2gW/ctpwPzGzmA/Xfh8kHwGk/gyCIOpr0pn32+Hn0D4T86J7VsNfbIXc6PHiZs4wkSa/LwkiSJGm73z26nm/etpyL54Z8oemrBBmFcP4VkJAcdTTpLZmSn8Y7DpzGdc9Xsqq2Ew7/LGx7eXDXNEmSdsDCSJIkicHd0P739hWcsyCNb7Z9naC/B97+d8gojDqatFt89KhZpCcn8P27V8Ge50HeDHjo284ykiTtkIWRJEma0MIw5Cf3reayO1Zyxh75fK/vewRNG+GCK6FwbtTxpN0mNz2JDx42g/tWVPN8VQsc9lnY9gqsvD3qaJKkUcjCSJIkTVhhGPKdO1fyk/vWcO4+Zfwo+TfEVTwBZ/wKph0cdTxpt3v3wdMpyEjmu3etItzzHMibCQ9/x1lGkqT/YGEkSZImpIGBkK/esozfPLKedxw4le/l3EjcshvgmK/DnudEHU+KifTkBD529Cye2dDAw2sbt69l9AqsvC3qaJKkUcbCSJIkTTj9AyGfu/5l/vLkJj542Ay+UfoEwRM/hf3eBwd/POp4UkxdsN8UJuel8r27VjGw8GzInwUPfRcGBqKOJkkaRSyMJEnShNLV28//XPEC1z1fxcePns3nJy8juPOzMPckOOG7EARRR5RiKikhjk8dO5flW1u4bVnt4FpG1c4ykiQNZWEkSZImjNauXt79x2e5a9k2vnzKAi6ZVkFw4wdh6kFwzh8gPiHqiNKIOG1xGfNKMvnhPavoXXDW9llG33GWkSTpnyyMJEnShFDb2s0Fv32KZzc28OPzF/PeqbVw7TugaD5ceBUkpkYdURoxcXEBnz1hLpvqO7jm+S1w+OegZhmsvDXqaJKkUcLCSJIkjXuVDR2c++snWFfbxuXvXMKZZS1wxbmQWQIX3wAp2VFHlEbckXOL2G9aLj+7fw2dc86A/NnOMpIk/ZOFkSRJGteWbWnm7F89QWNHL1e870COLO6Av545OKPoHTdBRlHUEaVIBEHAZ0+YR01rN399pnL7LKPlsOKWqKNJkkYBCyNJkjRuPbiqhvN+/STxcQHXfvBt7JvTAX85Hfq64B03Qu7UqCNKkdpvWh6HzSnk1w+vp232aVAwFx52xzRJkoWRJEkap658uoL3/fk5puanc9P/HMzctDb48ynQ0QDvuGFw7SJJfPLYOTS09/Dnpyrh8M9un2V0c9SxJEkRszCSJEnjysBAyPfuWsmlN77CIbMKuPa/30Zx0Ax/PhXaauDi66F836hjSqPGXpNzOHpeEb99ZD0tM08ZnGX0kLOMJGmiszCSJEnjRldvP5+4Zim/fGgdF+4/hd+/awkZvY2DZVHLFnj732Hy/lHHlEadS46dQ3NnL394ogKO+BzUroDlN0UdS5IUIQsjSZI0LlS3dHH+b5/ilpe28NkT5nLZmXuQ0NUIfzkNmirg7dfC1LdFHVMalfYoz+aEhSX8/tENNE07CQrnbV/LqD/qaJKkiFgYSZKkMe+lyiZO+7/HWFPdym/esS8fPmIWQXvtYFnUsB4uuhqmHRJ1TGlU+8Sxs2nr6ePyxzcN7phWu9JZRpI0gVkYSZKkMe3mpZs57zdPkhAXx/UfOojjF5ZAcxX88USoXwcXXgUzjog6pjTqzSvJ4uQ9S/nj4xupn3oiFM7fvpaRs4wkaSKyMJIkSWNS/0DId+9aycevXsriyTnc8pGDmV+aNTij6A8nDi5w/Y4bYeZRUUeVxoxPHDOHrt5+fvvoxsG1jOpWwbIbo44lSYqAhZEkSRpz6tu6eecfnuZX2xe3/tt7DyA/IxlqVg6WRT1t8K5bXLNI2kWzijI4fa9y/vzkRmomHw9FC+Dh77ljmiRNQBZGkiRpTHmhopFTfv4Yz25s5HtnL+LbZ+1JUkIcbFk6+BgaIbz7DijbO+qo0pj0saNn09sf8quHN8ChnxqcZbTy1qhjSZJGmIWRJEkaE8Iw5M9PbOT83zxJQnzADR86iPP2mzx4ct2D8OdTISkd3n0nFM2PNqw0hk0vSOesvcu54ukKtk06EfJnwSPfhzCMOpokaQRZGEmSpFGvpauXj129lK/esoxDZxdy20cOZY/y7MGTL14BV5wD2ZPhPXdD/sxow0rjwMeOns3AQMgv/jHLaNsrsPruqGNJkkZQzAqjIAj+EARBTRAEr77m2NeCINgcBMHS7f9OitX7S5Kk8eHFikZO/tmj3PHKVj5z/Fx+984lZKclDs52eOi7cPOHYerB8J47Ibs86rjSuDA5L43z9pvM1c9WUDXpZMiZ4iwjSZpgYjnD6E/ACTs4/uMwDPfa/u+OGL6/JEkawwYGQn750FrO/fWTDAzAtR88kP85chZxcQH098ItH4GHLoPFF8Hb/w4p2VFHlsaVjxw5i4CA/3t4ExxyCWx+DtY/FHUsSdIIiVlhFIbhI0BDrF5fkiSNX9UtXbzjD0/zvbtWccIeJdzx8UPZd2re4MnOJrjyPHjxb3D45+CMX0JCUqR5pfGoLCeVC/efzHXPV7FpyhmQWTY4y0iSNCEkRPCeHwmC4J3Ac8CnwjBsjCCDJI1qVz5dEXUEKRJhGPJSVRO3vrSVvoEBztq7nH2n5nL7y1sByG5dy2EvfJy0zi08u8c3WJ92JjxTGXHqoVxBafx7esPw/ya6rn9sfz8vzU4lAC75+wounfROlqz4DvfeeQO1eUuijhYzFx0wJeoIkjQqjPSi179i8D5qL2Ar8MPXuzAIgg8EQfBcEATP1dbWjlA8SZIUlbbuPq54uoJrn6uiMDOZjx45myXT8giCAIDJ2+7luCcvIqG/g/sP+APrJ58ZcWJp/MtKTeSA6Xm8WNHE0zmn0JmUxx7rLo86liRpBIxoYRSGYXUYhv1hGA4AlwP7v8G1vw3DcEkYhksKCwtHLqQkSRpxr2xu5if3rWZVdSsnLCzhA4fNoCAzGYAg7GfR6p9x6IufpDljNncddDV1uXtHnFiaOA6fW0RCfMA9a5pZOf1dlNY9QX7TK1HHkiTF2IgWRkEQlL7mv2cCr77etZIkafxr6ezliqc3cdUzFeSmJfGRI2dx2JxC4rbPKkrqaeLw5z/CHusuZ+2ks7nvgD/SmVIccWppYslITuBtMwp4uaqZJ3JOpzsxm4Xrfht1LElSjMVsDaMgCK4CjgAKgiCoAr4KHBEEwV5ACGwEPhir95ckSaPXQBjyzIYG7l62jf6BkOMWFHPo7ELi44J/XlNU/wwHvfQFknsaeGbBl1g75TwIgjd4VUmxcsjsAp5cX8e96zs4dNrFLFrzC3JaVtKUNS/qaJKkGIlZYRSG4YU7OPz7WL2fJEkaG7Y2d3LTi5upbOxkZmE6Z+xVTn5G8j/PBwO97Ln2Vyxc9zta06fy8L4/pzF7QYSJJWUkJ3Dg9HweW1vHEzPPYd6GP7Nw3eU8vvfrLkkqSRrjotglTZIkTUCdPf08sLKaJ9fXk5IYz7n7TmKvyTn/XNQaIL2jioNf+hwFTS+zbtKZPD//8/QlpEWYWtI/DM4yquee9Z0cMvVCFq77Ha+0raclY0bU0SRJMTCswigIghsYnB105/YFqyVJkoZlIAx5fmMj9yzfRkdPP0um5XL8ghLSkl9zGxKGzKi6kX1Wfh+Ax/b6PhWlJ0SUWNKOZKYM7pj25Pp6njz8fOZt/BsL113Ok4u/HXU0SVIMDHeG0S+BdwM/C4LgOuCPYRiuil0sSZI0Hmysa+e2l7ewpbmLqflpnLqojLKc1CHXpHdUcsCrX6ek/mmq85bw1J7/S3taeUSJJb2RQ2cX8vSGBu7e0MshU85j7oa/8sqsD9OWPjnqaJKk3WxYhVEYhvcB9wVBkA1cuP3zSuBy4G9hGPbGMKMkSRpjalq6uGd5Ncu3tpCdmsgF+01mz/LsIY+fBWE/czZeweI1/0dIHM8s/DJrJ58DwYhu4ippF2SlJrJkWh7PbKjnqcMuZM6mq1iw/vc8s+fXoo4mSdrNhr2GURAE+cDFwDuAF4ErgEOAdzG4G5okSZrgmjp6uH9FDS9UNJKUEMcx84s4ZFYhSQlDS6Dc5hXst+x/KWh+mc2Fh/PMwi/RmVoSUWpJu+LwOYU8u7GBOzfBQZPPZlbFdbw664N0pJZGHU2StBsNdw2jG4G5wF+BU8Mw3Lr91DVBEDwXq3CSJGlsaO3q5dE1dTy1vp4QOGhmPkfMLSI9eeitRkp3HYtW/x8zq26gOymXxxd/l02lJ8JrZh5JGt2yUxNZMjWX5zY28vRhb2dWxXUsWP8Hnlv4xaijSZJ2o+HOMLo8DMM7XnsgCILkMAy7wzBcEoNckiRpDGjp6uXR1bU8s7GBvv6QvafkcPT8YnLTkoZcF9ffw9xNV7DH2t8QP9DNyunv5NWZH6Q3MTOi5JLeisPnFPLcxkbuqEzgoPLTmVl1A6/O/ABdKYVRR5Mk7SbDLYz+F7jj3449Ceyze+NIkqSxoLmzl0dW1/LsxgYGwpDFk3I4Ym4RhZnJQy8MB5iy7R4Wr/4ZmR2VbC48nBfmf5rW9GmR5Ja0e+SkJbHP1Bye3djI04e+kxmbb2L+hj/x4vzPRB1NkrSbvGFhFARBCVAOpAZBsDfwj/niWUBajLNJkqRRZltzF4+treWlymZCQvaZksvhcwrJz/jPomjytvvYc+2vyGlbS1PGLB5c8mu2Fh4cTXBJu93hc4p4flMjt1WlcFDpicyuvI7lM99Hd1Ju1NEkSbvBzmYYHQ/8FzAJ+NFrjrcCl8YokyRJGkXCMGRtbRuPraljTU0bifEB+0/P45BZBeSmJ/37xUyqfoA91/6S3NbVNKdP5/HF36Oi9DjCID6aL0BSTOSlJ7H35Fye3dDAM4f8F9O23M7cjX/l5TkfizqaJGk3eMPCKAzDPwN/DoLg7DAMrx+hTJIkaRTo7utnaWUTT69vYFtLF5nJCRy3oJj9p+eRljT0FiK+v5Npm29j3sa/kd2+npb0aTy++DtUlJ5gUSSNY0fMLeSFikZu3ZLF24qPYc6mq1kx/d2uTyZJ48DOHkm7OAzDvwHTgiD45L+fD8PwRzsYJkmSxrDqli6e3tDAixWNdPcNUJqdwll7l7PX5BwS4uOGXJvaVc3simuYVXEdKb1NNGTN54lF32ZT6QmEccNdKlHSWJWfkcxek3N4ekM9zx70Hs6uvpfZFVezfOb7o44mSXqLdnYnl779Y0asg0iSpOj09A3w6pZmntvYyMb6duLjAvYsz+bA6XlMzksjeM2290HYT0ndE8youonJ1Q8QhP1UFR/FymnvoDZ3H3jNtZLGvyPmFrG0sombtxXwtsJDmLfhr6ya+nb6E1zyVJLGsp09kvab7R+/PjJxJEnSSAnDkMrGTp7f1MDLVc109w2Ql57E8QuK2XdaHhnJQ28TMts3MqPqJqZvvpW07hq6EnNYPfUCVk29iPa0yRF9FZKiVpiZzKJJ2Ty1voHnDngvp9W+m1mV17Nq+juijiZJeguGNVc8CILvAf8LdAJ3AYuAS7Y/riZJksaQ+rZullY18VJlM3Vt3STGB+xZnsO+U3OZlj90NlF6RxWTt93HlG33UtD8MgPEsbXwEJ6f9Hk2Fx3BQFxihF+JpNHiiLlFvFzVzA11kzggbwnzN/yJNVPOZyA+aeeDJUmj0nAXFzguDMPPBkFwJrAROAt4BLAwkiRpDGjp6uXVzc0srWyiqrGTAJhWkM6hswvYszyblMTtC1OHIVlt65lUfT9Ttt1LXssKAOqzFvDi3EvYUHYqXSmF0X0hkkal4qwU9ijP5sn19byw5L2cuPRDTN98M+umnBt1NEnSmzTcwugf150MXBeGYXPg+gSSJI1qDe09LNvSzLItLVQ0dABQmp3CiXuUsGd5Njlpg3/5T+hto6T6aUprH6e07nEyOrcAUJeziBfmfZrK4qNpT5sU2dchaWw4cm4Rr2xu5tqGWeyfvQcL1/+e9ZPOdAF8SRqjhvvd+7YgCFYy+Ejah4IgKAS6YhdLkiTtqjAMqW7tZtmWZpZvaWFr8+CP6rKcFI5dUMzC0iyKslJI6OugoPE5iqqeo6jheQqaXiYu7KM3Pp1tBQewfMZ72VJ4GB2pJRF/RZLGkpLsFBaUZvHkhnpe2Od9HPvSJ5i69U42lp8adTRJ0pswrMIoDMPPb1/HqDkMw/4gCNqB02MbTZIk7cxAGLK5sZNlW1pYtqWZ+vYeAmBKfhon7VnKwpJMyuMbyW9aSsGWlyl89QXyWpYTF/YzEMTTkLWAFdPfxZbCQ6jLWUzomkSS3oIj5xWx/MEW/t6ykCWZs1m47ndsLDsZgrioo0mSdtGuzA+dB0wLguC1Y/6ym/NIkqSdaOvuY21NK6ur21hT3Up7Tz9xAcwszOCE6fEclLaZ8q6nyW9+hfyKV0jrrgWgPy6J+uw9WD7jPdTkLqEudy/63PZa0m5UnpPK3OJMHl3XwEuL38sRr3yeydX3U1lybNTRJEm7aLi7pP0VmAksBfq3Hw6xMJI0UT33x5i+/MyKhpi+vsaWgRDWtKfyUks6S5vTWd+RAoQsSNjKB9NWs3/memZRQVbHVpLWtv1zXGdSHu2p5dTk7ktbWjkdySWEcYOLW6d3bia9c3NEX5E0ccysuC7qCCPu4pwUvlw9jfu3JHFAUh77rPgeSd2NMFbWQI3Pi91rL3l37F5bknaz4c4wWgIsCMMwjGUYSZI0qLE3npeaM1jaks6KlmTKB7ayIO5VPpq0lkUZm5jcX0Vi2A09EPYEdCYX0pw+g46UEtpTS+hIKaY/PjXqL0PSBDQno4s9Mtu5tbqAd0w+hDlbbyG7bS3NmbOjjiZJ2gXDLYxeBUqArTHMIknShNUXwuq2VF5tTqajuZ6C3goWBes5PX4jsxI3k7B9gm9/kEhHYjH1WYsGy6GUYjqTi9yFSNKoclZpPd9YPYVrew/hM4kPUV73GM0Zs8bOLCNJ0rALowJgeRAEzwDd/zgYhuFpMUklSdIEUN8dsKW+lb7WGnK7N7NfsIGLg0oSg35IhK64dLpSS6hJPZD2lBI6UkroSsrzFy5Jo96CjA7mpndwY3UxF5YdxMzqu8js2ERr+rSoo0mShmm4hdHXYhlCkqSJIOhupa1hK3FtWynoqWJxWEFK0AtAe3wq9cmT2JLxNrrTymhPLaUnIctySNKYFASDs4y+vXYy14dH8ImERymvfYyVFkaSNGYMqzAKw/DhIAimArPDMLwvCII0ID620SRJGtuSepoImiuhuYqS7g2UUAdAe5jMxrgpPJ9yMAlZRSRlFdGTlGs5JGlcWZzVzoy0Tq7fVsJ5JW9jWs19pHdspj2tPOpokqRhGO4uae8HPgDkMbhbWjnwa+Do2EWTJGlsiRvoIb1tEzRuIr9jLcUDNQA0hBm8zBweSTmcILuMotwcUhKCf/4Q7okusiTFzD9mGf1g3SRu4gg+Ev8Y5XWPsnrKBVFHkyQNw3AfSfsfYH/gaYAwDNcEQVAUs1SSJI0Rib1tpDSvJrVxNZN61pNIH11hIk+H87kl4Ui6s6YyKS+d0tR+pkYdVpJG2L7ZbUxJ7eLamnLOLjyAybUPk9pVTWdKcdTRJEk7MdzCqDsMw55g+1T5IAgSgDBmqSRJGsWSepqJa1hDVvNKpvZtII6QDQPFXMUxVKfNJi23hAXZvSyKH9g+oj/SvJIUlbgAziqp5ycbyrkt/ig+EPckZbWPsW7y2VFHkyTtxHALo4eDILgUSA2C4Fjgw8CtsYslSdLoEtffQ1C/mpzGl5nVtxaAFQNT+Ev8qdRmLqAsN5PZ6d3MDeA1G4pK0oR3QG4rZVu6uap6MqfnLaG0/kmquo+gOzk/6miSpDcw3MLo88B7gVeADwJ3AL+LVShJkkaDcCCkq2kzGfWvsLDnJVLpYeNAMX9JOJ2mnIXMyk9hflIf8wFLIknasbgAziit55cby7gr8Wj+K3iGsrrH2VB+WtTRJElvYLi7pA0EQXATcFMYhrWxjSRJUrQ2t8NAzQr26XicGWyhJUzlobgD2Za9mJKCAuYm/+MRs75Ic0rSWHFIXgt/31LAFTXTOClnb4oan2dz4eH0JGVHHU2S9DresDAKBhct+irwESBu+7F+4OdhGH4j9vEkSRoZTb3xLKvtYVLjs5wQPkZW0MGaYCo3Z1xIWvFM8pLjyANcj0iSdl18AGeU1PPbilLuLz6aC3me0von2FR6YtTRJEmvY2czjC4BDgb2C8NwA0AQBDOAXwVBcEkYhj+OdUBJkmKldyDg2aYMNtS2cXT3XXw87inCII6VKYtoL9oXMksp2b7hgyTprTksv4Xrtxbw19pZHJuzmKLGF9lScCi9iRlRR5Mk7cDOCqN3AMeGYVj3jwNhGK4PguBi4B7AwkiSNOZs6Uri/rpsauoaeU/ctVwS/yxd8cmszTmE9qIl9CZmRh1RksadxLiQU0sa+FNlMY8UHsXZTUspqX+SypJjo44mSdqBnRVGia8ti/4hDMPaIAgSY5RJkqTdri+Epxszua82h7C9js8k/J6jEpfSHaRQmX8YNfn705eQFnVMSRrXji5o4sat+fy5bh5HZC+kuPF5thQcQn9CatTRJEn/ZmeFUc+bPCdJ0qjQ0hvPfXU53FObQ3JvC19K/hMnJT9Bf1wKlQVHUp23H/3xKVHHlKQJISku5NTiBv62uYgnC47i1OZXKWl4ms1FR0QdTZL0b3ZWGC0OgqBlB8cDwLtrSdKoVdGZzB3VuTzWkEVK2MVX0q7mzPh7iQtCtuUdyJbCQ+iP9y/akjTSji1s5OZtefyxYQ8OzpxLScMzbM1/GwPxyVFHkyS9xhsWRmEYxo9UEEmSdoeVbancvC2fF5ozSInr44tZd3Jh740kD3RQl70HlUVH0ZOUE3VMSZqwUuJDTipu5JothTw34yiOa/0VxY3PsbXg4KijSZJeY2czjCRJGvXCEJa2pHPTtnxWtqWRGd/HR4te5r29V5LTWUFr2mTWlFxEe2pZ1FElScAJRY3cWp3H7xsWc0D6DErqn2Jb3v6EcS6TKkmjhYWRJGnMCkN4sSWda7cUsqEjhfzEXt4/qYJ3DNzKpIYn6Y9PZn3ZqdTm7AVBEHVcSdJ2afEDnFDYyA3bCnhx+pEcsfX3FDW+SHX+/lFHkyRtZ2EkSRpzwhBeaU3j2i2FrGlPpTiph/+eupVTkl9g9tbbSO5tpiZnLyqLj3HnM0kapU4sbuT2mjwub17CPmn3UFr/BDW5+xLGuSqGJI0GFkaSpDFlVVsqV20uZEVbGvmJvXxgylaOzKtles39lFQ/S2dSPsunvYvW9KlRR5UkvYGshH6OK2zktuo8Xp1+JAdt/QsFzS9Tm7t31NEkSVgYSZLGiG3diVxZVcjTTVnkJPTx7snbOLqgmdyuSmasv4nUnga25h9AZdFRroEhSWPEKcUN3FWTy+XNB7Ao5V7K6h6nNmcxBHFRR5OkCc/CSJI0qrX1xXHD1gLuqs0lIQg5p7SWU4sbSA36KK99mLK6x+lJzGLFtHfQkj496riSpF2Qk9jP0QVN3Fuby8pph7Nk29XktyynPnuPqKNJ0oRnYSRJGpUGQri3NodrthTS0R/HEfnNnFdWR15SH0k9Tcyu+jsZnVuoydmLipLj6Y9PjjqyJOlNOLWkgXvrcvlt28EsSL6fstrHqM9a6GYFkhQxCyNJ0qiztj2F31cUs74jlT0y23nnpBqmpnUDkNOyipmbbwZCVk8+l8as+dGGlSS9JQVJfRyR38QD9bmsmXIYi6uvJ7d1NY1Zc6OOJkkTmoWRJGnUaOuL48rNhTxQl0NOYh8fm76Zg3JbCQIIwn4mVT9AWf2TtKeUsmbyOXQn5UYdWZK0G5xe0sCDdTn8ruMwvp/4AGV1j9KYOcdZRpIUIQsjSVLkwhAeb8ziT5VFtPfFc1JRI+eU1ZEWPwBAUm8Lsyr/TmZnFdW5S9hUchxhnD/CJGm8KE7u5ZC8Fu6uy+e/Jx/CwppbyWpfT0vGzKijSdKE5d22JClSDT0JXF5RwgvNGcxO7+QDcyqZktr9z/MZHZXMrryW+IFe1kw6m4bshRGmlSTFypml9TzakMXvu4/kOwkPUV77mIWRJEXIwkiSFIkwhAfqs/lrZRH9YcA7J1VzYlEjca95+qCgcSnTt96+fRe0d9KVXBhdYElSTJWl9HBgbit31Bby35MOZk7NXWR0VNCWNiXqaJI0IcVFHUCSNPHU9STwrTWT+e2mUqandfH9BRs4ufg1ZVE4wJRt9zBzyy20pk3h1envsyySpAngzJJ6ugbi+WPP0fTGp1Fe+1jUkSRpwnKGkSRpRD3ZkMnlFSX0hQHvnbKNYwqahswqiu/vYlbV9eS0rWNb3n5UlBxHGMRHF1iSNGKmpnWzJLuVW2pLeF/Z25hZez9pnVvoSC2LOpokTTjOMJIkjYiO/jh+saGUn2wopzSlh+/N38BxhUPLoqTeZhZs+CNZbRvYUHoym0pPtCySpAnmrNJ62vvj+WvfMfTFJTvLSJIi4gwjSVLMrWpL5f82lFLbk8jZpXWcVVpHwr/tlJzaVc28TVcSN9DDqqlvpyVjejRhJUmRmpnexV5ZbVxfW867i/dnav2jpHbV0pnio8mSNJKcYSRJipmBEG7elsfXVg0uWPr1uRWcV/afZVFm+0YWbPgTAMun/5dlkSRNcGeW1tPal8BV4XH0B4mU1TnLSJJGmoWRJCkm2vri+P66SVy5uYj9c1v57oKNzM3o/I/r8pqXMW/TFfQmZrJs+nvoTCmOIK0kaTSZl9HJgox2rqmdwtbcJeQ3v0pyT2PUsSRpQrEwkiTtdmvbU/j8ium81JLOf02u5hPTt5AWP/Af1xXXP83squtpTy1j2bR305OUHUFaSdJodHZpPY29iVwXHE8YxFFa93jUkSRpQrEwkiTtNmEId9fk8NVVUwiBr8/dxIlFjQTBf15YVvsI07bdTUPmPFZMvZj+hNQoIkuSRqmFmR3MSe/gitrpVOfsTWHTSyT2tkQdS5ImDAsjSdJu0TcAv60o4Q+VJeyZ2cF35m9gdnrXf14YhkyueYDJNQ9Rm72INZPPIYxLHPnAkqRRLQgGd0yr60nkpvjjCcIBSuuejDqWJE0YFkaSpLesqTeeb6yewgN1OZxZUsdnZ1WRmfCfj6ARhkzddjdldY9Tnbsv68tPh8AfRZKkHdsrq53paV38uXY2tdmLKGp8noS+9qhjSdKE4F26JOktWd+ezKUrprGhI4WPT9/MBeV1xP37I2gA4QDTt9xGScMzbM0/gI2lJ/Gfz6pJkvQvQQBnldSxrTuJ25KOJy7so6T+6ahjSdKEYGEkSXrTnmjI5CurpgLwjXmbOCivdccXhgPM3HwzRU0vUlV4KBXFx1kWSZKGZUlOG5NSuvlD7XzqsxZQ3PAs8f07eORZkrRbWRhJknZZGMJN2/L46YZyZqZ38e35G5me1v06Fw8wY/MtFDS/QmXRUWwuOtKySJI0bHEBnFVaR1VXMvckH0fCQDfFDc9EHUuSxj0LI0nSLukP4fKKYq7aXMTBec18aXYl2Yn9O744DJmx5VYKm1+msuhIthQeMrJhJUnjwttyWylN7uG3dYtozJhNSf3TxPX3RB1LksY1CyNJ0rB19sfxvbWTuL8ulzNL6vjItK0kxoU7vjgMmb71dgqbXqKq8HC2FB46smElSeNGXABnlNSzsTOFB1OPJbG/k6LG56OOJUnjmoWRJGlYGnoS+NqqKbzcks77p2x9/cWtAcKQaVvvpKjxBTYXHMLmwsNGNKskafw5JL+ZwqReftmwL81p0yitf5JgoC/qWJI0blkYSZJ2amtXIl9ZNZVt3Yl8dlYVxxQ2v/7FYcjUbXdT3PgcW/IPoso1iyRJu0FCAKeX1LO2PZXH0o8hqa+NwqalUceSpHHLwkiS9IY2diTz1VVT6RoI+OqcCvbObn/D68trH6Kk4Rm25h9AZfHRlkWSpN3miPxmchN7+UXDAbSmllNa9wRB+Drr6EmS3hILI0nS61rRmsrXVk0hIQj5+twKZqS/zk5o25XUP8Wk2kepydmbiuLjLIskSbtVYlzIacUNLG9P5+mMo0npbSK/+dWoY0nSuGRhJEnaoRea0/nWmsnkJvbxjXmbKE95491oCppeYuq2e2jIms+GspMtiyRJMXF0YRPZCX38rOlg2lOKKat9HMKBqGNJ0rhjYSRJ+g+PN2Tyg7WTmJzazdfnVlCQ9MaLiua0rGLG5ltoTp/O2vIzIfDHiyQpNpLjQk4ubuDl1kxeyDyS1J468lpWRh1LksYd7+glSUM8Wp/FzzeUMSejky/PqSQr8Y3Xhshs38jsqr/TnlrK6snnE8YljFBSSdJEdVxhE+nx/fy8+XA6kwoor30EwjDqWJI0rlgYSZL+6cG6bH6xsZSFmR18flYlafFvPMU/rXMbcyuupispj1VTLmIgPmmEkkqSJrLU+AFOKmrgmZZslmYfRVp3DbmtzjKSpN3JwkiSBMD9tdn8elMpe2Z28NlZVaTEv/FfapN6mphbcSV98SmsnPp2+hLSRiipJElwQlEjqXH9/Kz1CDqTCphU87CzjCRpN7IwkiRxT20Ov60oZa+sNj4zq4rkuDe+4Y7v72RexZXEDfSyaspF9CZmjVBSSZIGZSQMcEJRI081ZfNyzpHOMpKk3czCSJImuHtqc/h9RQn7ZLfx6ZmbSdpJWRQM9DGn4hqSexpZPeV8OlOKRiipJElDnVTUSFJcyC+cZSRJu52FkSRNYA/WZf+zLPrUjCoSd1IWEYbM3HwTWR0VrCs/ndb0aSOSU5KkHclK7OeYgiYea8xhWe4RzjKSpN3IwkiSJqjHGrL4zaYSFmW1ccmMzSQM4yfClOp7yW9ZzqbiY2jI3iP2ISVJ2olTS+qJD0J+1XYEnUn5zjKSpN3EwkiSJqCnGzP5xYZS5md0DOsxNIDi+mcprX+KbXn7sy3/bSOQUpKknctN7OeogmYeashlZa5rGUnS7mJhJEkTzAvN6fx0Qxmz0jv53DAWuAbIbl3L1G130Zg5h00lx0EQjEBSSZKG57SSegjhN+2HO8tIknYTCyNJmkBebU3jR+vKmZraxRdmV5ESv/Ob6dSuGmZVXU9HShFry8+CwB8dkqTRpSCpj8Pym7m/PpfVea5lJEm7g3f9kjRBrG9P5vtryylO7uXS2ZWkxQ/sdExCXztzKq5mIC6R1VMuYCA+aQSSSpK0684oracvDPhdx2HOMpKk3cDCSJImgC1diXx77WQyEwb44uxKMhN2XhYFA33MqbiGpL42Vk85n57E7BFIKknSm1OS3MsheS3cU5fP2nxnGUnSW2VhJEnjXENPAt9aMwWAL86uIC+pb+eDwpAZW24hs7OKdeVn0J5aHuOUkiS9dWeU1NMzEPDHjkOdZSRJb5GFkSSNY219cXxrzWTa++L4wuxKSlN6hzWurO4xCppfpbLoSBqyF8Q4pSRJu8ek1B72z2nlztp81ucf7iwjSXoLLIwkaZzq6g/4ztrJVHcn8plZm5mR1j2scTktq5hc8yB12XuwpeCQGKeUJGn3Oqu0ns6BeP7SNTjLqLzmEWcZSdKbYGEkSePQQAg/31DG2vYUPjZ9CwszO4Y1LrWrhlmbb6QtpYz1ZadCEMQ4qSRJu9e0tG72zW7l9poCNuQfTnp3tbOMJOlNsDCSpHEmDOGPlcU815zJuydXs39u27DGxfd1MqfyGvrjElkz5TzCuMQYJ5UkKTbOLK2nrT+eK7sPcZaRJL1JMSuMgiD4QxAENUEQvPqaY3lBENwbBMGa7R9zY/X+kjRR3VKdxz21uZxaXM/xRU3DGxQOMLvq7yT1trBm8nn0JGbFNKMkSbE0O72LRZnt3FpTwKaCw5xlJElvQixnGP0JOOHfjn0euD8Mw9nA/dv/L0naTR5vyOTKzUUclNvCReW1wx43Zdu9ZLdvYEPpybSlTY5hQkmSRsZZpXU09yVwTY+zjCTpzYhZYRSG4SNAw78dPh348/bP/wycEav3l6SJZnlrKr/cWMr8jA4+PG0rccNcfqig6SVKG55ma94B1OXuFdOMkiSNlPmZnczP6ODm6gIqnGUkSbtspNcwKg7DcOv2z7cBxSP8/pI0Lm3uSuIH6yZRnNzLp2dWkRg3vL+gpnVuY/qW22lOn0ZFybExTilJ0sg6q7SOht5E/t57MJ1J+UyqeRjCgahjSdKYENmi12EYhsDr/kYTBMEHgiB4LgiC52prh/9YhSRNNK19cXx37SQSgpDPz6oiI2F4N8Lx/Z3MrryWvvhU1k46GwL3QZAkjS97ZnYwK62Tm6oLqCg8nLTuGvKbl0UdS5LGhJH+7aA6CIJSgO0fa17vwjAMfxuG4ZIwDJcUFhaOWEBJGkv6BuBH6ybR0JPAp2dWUZTcO7yBYcjMqhtJ6mthzeRz6UtIj21QSZIiEASDs4xqe5K4ue9A2pOLmVT7MEHYH3U0SRr1RrowugV41/bP3wXcPMLvL0njRhjC7ypKWN6WxoembWVORtewx5bVPkpu21o2lZxAW9qkGKaUJCla+2S3MzW1ixu3FVJZdAQpPQ0UNL0UdSxJGvViVhgFQXAV8CQwNwiCqiAI3gt8Bzg2CII1wDHb/y9JehNurc7jwfoczimt4+C81mGPy25dy6Tah6jNXkRN7r4xTChJUvQGZxnVs7U7mTt696UttYzy2kcIBvqijiZJo1pCrF44DMMLX+fU0bF6T0maKJ5tyuDKzYUclNvCOaV1wx6X3NPIrM030JFSzMaykwfvoiVJGuf2z2llUko3N2wr5IQpR7Gg4m8UNT5Pdf4BUUeTpFHLFU4laYzZ1JHMzzeUMTOtiw9N2zrszicY6GV25d8hhDWTz2UgLjG2QSVJGiXiAji7tI6qrmTu6llES9pUymsfI26gJ+pokjRqWRhJ0hjS0hfP99eVkxbfz6dnVZEU97qbTQ4VhkzbeifpXVtZN+kMupPyYhtUkqRR5sDcViandHHd1kI2FR1FYn87xfXPRB1LkkYtCyNJGiP6QvjJ+jKaehP49MzN5CYOf4eXwsYXKWpaSlXhYTRlzolhSkmSRqe4AM4tq2NrdzJ3di2kMWMWZfVPEN8//E0jJGkisTCSpDHir5VFLGtN5wNTtzErffg3t+mdm5m27U6aMmayufCwGCaUJGl02z+njWmpXVy/pYBNhUeS0N9FSf2TUceSpFHJwkiSxoAH6rK5qzaPU4rrOSy/ZdjjEvo6mF15HT0JmawtPwsCv+1LkiauIIDzyuqo7knizo551GfNp7T+aRL62qOOJkmjjr85SNIot6otld9VlLAoq42LymuHPzAcYFbV9ST2tbNm8rn0J6TGLqQkSWPEPtltzEzr5IatBWwqOIK4gV5K656IOpYkjToWRpI0ijX2xvOjdeUUJPXy8elbiB/mjmgAk2oeJrt9AxtKT6YjtTR2ISVJGkP+McuotieRO9pmU5e9JyUNz5LY2xp1NEkaVSyMJGmU6huAH68rp3Mgjk/PrCIjYWDYY7Pa1lNW9yg1OXtRl7tX7EJKkjQGLc5qZ056BzdszWdjwREQDlBe+2jUsSRpVLEwkqRR6i9VxaxqT+NDU7cyJbVn2OMS+tqYuflGOpML2VR6YgwTSpI0NgUBnF9WR0NvIre3TKc2d28Km14gqacp6miSNGpYGEnSKPRIfRZ31+ZySnE9b8vbhSnyYcjMqptI6O9m7aSzGYhLjF1ISZLGsIWZHSzI6OCmrflsyD8MiGNS7cNRx5KkUcPCSJJGmVcbE/jtphIWZLTv2iLXQGnd4+S0r2djyQl0phTFKKEkSWNfEMC5ZbU09SVwW9NUqvOWUND0MrRVRx1NkkYFCyNJGkWaegL++8lsMhP6+cSMXVvkOqOjksk1D1KftZDa3L1jF1KSpHFiQWYne2a2c8u2PDbkHjI4M3fVnVHHkqRRwcJIkkaJgRA+8UwWNV1xfHLmZrIT+4c9Nr6vk1lVN9CdmMOGslMG/2wqSZJ26ryyWlr6Eri1cRLb8g6ArUuhuSrqWJIUOQsjSRolfrUqjYe2JfPlxW3MTu8a/sAwZMaWW0jsa2Xt5LPpj0+OXUhJksaZORld7J3Vxq3b8lmfexAkpsKqO6KOJUmRszCSpFHgqdpEfvhqOqdO7uLiGZ27NLa44VnyWldRWXQ07allMUooSdL4dW5ZHW398dxaXwYzj4aa5VC/LupYkhQpCyNJilhtV8BHn85iWkY/396ndZeeJkvr3MqU6ntpzJjNtvwDYxdSkqRxbGZ6F0uyW7mtOo/m8sMhORtW3gphGHU0SYqMhZEkRag/hI8/nU1LTxy/fFszGYnDvzGN6+9mVtX19MWnsb78dNctkiTpLTi3rI6O/nh+vz4H5p4AjRuh+pWoY0lSZCyMJClCP12ezhO1SXxz71bmZQ9/kWvCkOlb7yClp5G1k86iLyEtdiElSZoApqV1c0BOC39Yk0pj4QGQUQQrb4eBXfj5LEnjiIWRJEXk0epEfr4ijbOndnLutF1Y5BooaHqJguZX2Fx4GK3pU2OUUJKkieXcsjra+wJ+vSYT5p4CbdVQ9WzUsSQpEhZGkhSBbZ1xfOKZbGZn9fPNvXdt3aKU7lqmbb2T5vRpbC48NHYhJUmaYCan9nDGlG7+tDaNbdmLIWcqrL4T+nuijiZJI87CSJJGWN8AfPSpLDr74JcHNpOWMPyxwUAvsyuvZyAukXXlZ0Lgt3FJknanSxa0MRDCT1dkwPzToKsZNjwSdSxJGnH+piFJI+wHy9J5tj6Jy/ZtZVbWrq2LMHXbPaR117Cu/Ax6EzNjlFCSpIlrSsYAF83o5NqNKaxPmgNFC2HtfdDTHnU0SRpRFkaSNIIe2JrEr1elc+H0Ts6Y0r1LY/Oal1Pc+Dxb8t9Gc+asGCWUJEkfmd9OclzID5elw7yToa97sDSSpAnEwkiSRkhVexyXPJPFgpxevrpX6y6NTe5pZPqWW2lLLaeq+KgYJZQkSQCFKSHvm9PJ7VUpvNI/BSYtgY2PQkdD1NEkacRYGEnSCOgZgI88nU1/CL88sIWU+OGPDcJ+ZlXdAMDaSWcRBrswWJIkvSnvm9NBbtIA33s1HeaeBASw6vaoY0nSiLEwkqQR8P1XMljakMh3l7QyLWPX1i2aVP0AGZ2b2VB2Kt1JuTFKKEmSXisrMeR/5rXzaHUyT7QWwYzDYfPz0FQZdTRJGhEWRpIUYw9tS+LyNWlcPKODkyft2rpF2a1rKKt/kurcfWnIXhCjhJIkaUcuntlJaWo/330lg3DG0ZCUDituhjCMOpokxZyFkSTFUE1XHJ9+Nou5WX18aXHbLo1N7G1l5uabaU8uZlPJcTFKKEmSXk9KPFyyoJ2XGhO5uzYb5pwA9WuhZnnU0SQp5iyMJClGBkL41DNZtPUF/PyA5l1at4hwgJmbbyRuoJe1k88mjEuMWU5JkvT6zpraxczMPn7wagZ9kw6C9MLBWUYDu/aIuSSNNRZGkhQjl69O49GaJL6yuJU52bt2U1le+yjZ7RvZWHoiXckFMUooSZJ2JiEOPrNHO2tbE7ihKh3mnwptNVD5VNTRJCmmLIwkKQaWNiTw/VfTObG8iwund+3a4Pq1lNc+Ql32ntTlLI5NQEmSNGzHl3WzOLeXnyxLp6tgT8ibAavuhL5d/BkvSWOIhZEk7WatvQEfezqb4tQBvrNvK0GwC4N72uCFv9KVlMuG0pPYtcGSJCkWggA+t2cbWzrj+dv6NJh/+uDP7HUPRB1NkmLGwkiSdqMwhC+9kMnmjjh+un8L2Um7sItKGMLSK6G3jbWTzmEgPjl2QSVJ0i45qKiXQ4t6+MXKdFozpkHZ3rDuQehsijqaJMWEhZEk7UbXb0rh5soUPrGgnSUFvbs2eMNDg7uuzD+DjtSSmOSTJElv3mf2aKOxJ47LV6fBvFOAEFbeFnUsSYoJCyNJ2k3Wt8bzlRczOLCwhw/P69i1wU0VsOI2KFkE0w6JTUBJkvSWLMrr4+RJXfxudSq1cQUw4wjY/Bw0boo6miTtdhZGkrQbdPfDR5/OIikefrJ/C/G7svRQbye88GdIyYJFF7hukSRJo9gnF7bTPRDwi5XpMOsYSM6E5TcOPlouSeOIhZEk7QbfezWDZU2JfH9JCyWpA8MfGIbw8jXQ2Qh7vxOS0mIXUpIkvWUzM/s5b1oXV6xLpaIrDeaeDI0bYcuLUUeTpN3KwkiS3qIHtybx+zVp/NesDo4t69m1wRVPwtalMPdEyJsek3ySJGn3+viCduLj4PvLMmDy/pA1CVbcAv27eB8gSaOYhZEkvQU1XXF8+rks5mX38vk923ZtcMsWWHYjFMyFmUfHJqAkSdrtSlIHeP/sDm6tTGFpYxIsPAO6mmD9QxEnk6Tdx8JIkt6kgRA+/Wwm7X0B/3dACynxuzC4r3tw3aLEFNj7Ygj8dixJ0ljywbkdFCQPcNnLGYR5s6BkMay9D7qao44mSbuFv6FI0pv0p7WpPFKdzJcWtTIrq3/XBi+7EdpqYK+LBxfLlCRJY0pGYsjHF7TzTF0S925NgvmnQtgPK2+POpok7RYWRpL0Jqxoiuc7r2RwTGk3b5/RtWuDNz8PlU8N7qxSODc2ASVJUsxdML2TmZl9fOeVDHpTC2D64VD1DDRVRB1Nkt4yCyNJ2kVdvf184plsshIH+O6SFoJgFwa318LL10LudJhzQswySpKk2EuMg8/v2cb61gSu3pAKs46D5Cx49XoId2HXVEkahSyMJGkXfefOlaxqSeCH+7WSnxwOf2B/3+C6RXHxsM87Bz9KkqQx7ZjSHg4o6OEny9NpJRXmnwJNm6DquaijSdJbYmEkSbvgwVU1/OmJjbxnVgeHl+zi1rkrb4HmKlh8EaTmxiagJEkaUUEAX1zURn13HL9ZlQblSyBnKqy8FXo7o44nSW+ahZEkDVNdWzefue5l5pVk8tk923Zt8LZXYcMjMO0wKNkjNgElSVIkFuX1cfrkLn63Jo0tnQmwx9nQ3QZr7ok6miS9aRZGkjQMYRjy2b+/TEtXLz+9YG9SduVpss5GeOlKyJoE80+LWUZJkhSdz+zRxkAI3381A3KmwOQDYMPD0Lot6miS9KZYGEnSMPztqU08sLKGS0+cx9ySzOEPHOiHF/4y+HGfd0F8QuxCSpKkyExKH+B9szu4sSKFpQ0JMO9kiE+C5TdCuAtrHkrSKGFhJEk7saa6lf+9fQWHzynkXQdN27XBq++Cxg2w57mQURiTfJIkaXT48LwOCpL7+eZLGYRJmTD3RKhdBdWvRB1NknaZhZEkvYHuvn4+dvVSMpIT+P65iwiCYPiDa1fB2vtg8v4waUnsQkqSpFEhIzHkM3u083x9ErdVJcPUQyCzBJbdBP27uFmGJEXMwkiS3sAP7l7Fiq0tfO+cRRRlpgx/YHcrLP0bZBTBwrNjF1CSJI0q50zrYkFOL995JYOuMB4WngWdDbDugaijSdIusTCSpNfx6JpaLn90A+84cCpHzy8e/sBwYLAs6u0aXLcoITl2ISVJ0qgSH8CXFrWxuSOe369Og4I5ULb34Kzj+nVRx5OkYbMwkqQdaGjv4VPXvsSsogwuPWn+rg1e98Dg42gLz4CsspjkkyRJo9dBRb0cV9bNL1elUdMZBwvOgLh4uPOzLoAtacywMJKkfxOGIZ+7/mWaOnr56QV7kZoUP/zBDRtg1R1QuhdMOShmGSVJ0uh26aI2evoDfrAsHVKyYc6Jg7OMVtwadTRJGhYLI0n6N1c/W8m9y6v57AlzWViWPfyBPR3w4l8gNRcWnQ+7skC2JEkaV6Zl9PNfszq5bmMKrzQmwLRDoXgPuOvz0N0WdTxJ2ikLI0l6jXW1bXzj1uUcMquA9xw8ffgDwxBeuhK6WmDvd0JiauxCSpKkMeGjC9rJTw756ouZhEE8nPxDaNkMj3wv6miStFMWRpK0XU/fAJ+4einJiXH88LzFxMXtwgyhDQ9D9asw/1TInRq7kJIkaczISgz57B5tvNCQyE0VyTDlQNj7YnjyF1CzIup4kvSGLIwkabsf3buaVzY3852zFlGclTL8gY2bBtcjKN4Dph8eu4CSJGnMOWdaF4tye/n2Kxm0dffBMV+HpAy4/dMugC1pVLMwkiTgiXV1/OaRdVy4/2RO2KNk+AN7O+CFP0NKFiy+0HWLJEnSEHEBfG2vVmq64vnFg2shvQCO+RpsegxeujrqeJL0uiyMJE14TR09fOral5iWn86XT1kw/IFhOHij19UE+7wLktJjllGSJI1d++T3cfbUTn7/6AY21LUP3jdM2g/u+SK010cdT5J2yMJI0oQWhiFfuOEValu7+ekFe5GWlDD8wRsfgW0vw7xTIXdazDJKkqSx73N7tJOUEMf/3rYc4uLg1J9CVzPc++Woo0nSDlkYSZrQrn2ukjtf3canjpvLokk5wx/YVAHLb4GihTDjiFjFkyRJ40RR6gAfPWoW96+s4cFVNVC8EA76GCy9AjY8EnU8SfoPFkaSJqz1tW187ZblHDQznw8eNmP4AzubBtctSs6EvS5y3SJJkjQs7z54OjMK0vnGrcvp7uuHwz8LudPh1k9Ab1fU8SRpCAsjSRNST98AH796KcmJcfzovL2Iixtm6ROGcMtHobPRdYskSdIuSUqI46unLWRDXTu/e3QDJKbCKT+GhnXw6A+jjidJQ1gYSZqQfnTval7Z3Mx3zlpESXbK8Ac++ztYcQvMOwXypscuoCRJGpcOn1PIiXuU8PMH1lDZ0AEzj4RF58NjP4aalVHHk6R/sjCSNOE8sa6O3zyyjgv3n8wJe5QMf+CWpXD3pTD7ONctkiRJb9qXT1lAQMA3bls+eOD4yyA5A279OAwMRBtOkrazMJI0oTS29/DJa15iekE6Xz5lwfAHdjXDdf8F6YVwxq8h8NunJEl6c8pyUvnY0bO5d3k1D6yshvQCOO5bUPkUvPCnqONJEmBhJGkCCcOQz9/wMvXt3fzsgr1JS0oY7kC45WODO6Od8wdIz49tUEmSNO6995DpzCrK4Ku3LKOrt39wI43ph8M9X4HmqqjjSZKFkaSJ4+pnK7l7WTWfPm4ue5RnD3/gs7+D5TfB0V+GKQfGLJ8kSZo4khLi+MbpC6ls6OSXD60b3HX1tJ9B2A+3XTL4BytJipCFkaQJYV1tG9+4dTkHz8rn/YfOGP7Aqufhri8Mrlt00MdjF1CSJE04B80s4LTFZfz64XVsrGuH3Glw9FdgzT3w8rVRx5M0wVkYSRr3evoG+PjVL5KcGMcPz92LuLhgeAPb6+Had0JWKZz5G4jzW6YkSdq9vnTyfJLi4/jKLcsIwxD2/wBM2h/u+hy01UQdT9IE5m8/ksa9H96zilc3t/DdsxdRkp0yvEED/XDD+6C9Fs77C6TlxTakJEmakIqyUvjUcXN4ZHUtt768FeLi4fRfQE8H3PHpqONJmsAsjCSNa4+vreM3j6znogOmcPzCkuEPfPi7sO4BOOn7ULZ37AJKkqQJ751vm8aiSdl849blNHf0QuEcOOJzsPxmWH5L1PEkTVAWRpLGrcb2Hj557VJmFqbz5ZMXDH/g6nsGC6O93g77vDN2ASVJkoD4uIDLztyThvZuvnPXysGDB30MShbB7Z+CjoZoA0qakCyMJI1LYRjyuetfpqG9h59esDepSfHDG9i4CW54PxTvCSf9YHDHEkmSpBjbozyb9xw8naueqeC5jQ0Qnzj4aFpnA9z1+ajjSZqALIwkjUtXPVPJPcur+ezx89ijPHt4g3q7Bhe5DkM478+QlBbbkJIkSa9xybFzKM9J5dIbX6GnbwBKF8Ghn4aXr4EVt0YdT9IEY2EkadxZW9PGN25bxqGzC3jvIdOHP/Cuz8PWpXDmryB/ZszySZIk7Uh6cgLfOH0hq6vbuPzR9YMHD/v04KNpt34C2usizSdpYrEwkjSudPf187GrXiQ1MZ4fnLuYuLhhPlK29Ep4/o9wyCUw7+TYhpQkSXodR88v5qQ9S/jZ/WvYWNc++Gjamb+B7ha47RODM6ElaQRYGEkaV35w9yqWb23he+cspjgrZXiDtr0Ct10C0w6FI78U24CSJEk78dVTF5IUH8elN75CGIZQvACO/OLgY2mv/D3qeJImCAsjSePGo2tqufzRDbz9gCkcu6B4eIO6mgfXLUrNhXP+APEJsQ0pSZK0E8VZKXz+pHk8sa6ea56tHDx40Edh0v5wx6egZWu0ASVNCBZGksaFmtYuLrlmKbOLMvjSyQuGNygM4aYPQ1MFnPsnyCiKaUZJkqThunC/KRw4I49v3b6Cbc1dEBcPZ/4a+nrglo/6aJqkmLMwkjTmDQyEfPKal2jt6uP/LtqH1KT44Q18/Kew8jY49psw5cDYhpQkSdoFcXEB3zlrEb0DA3zpplcHH03LnwnHfgPW3gsv/DnqiJLGOQsjSWPerx5ex2Nr6/j6aQuZW5I5vEFr74P7vw4Lz4QDPxTbgJIkSW/CtIJ0PnXsXO5bUc1tL29/DG2/98H0w+GuL0D9umgDShrXLIwkjWnPbWzgR/eu5tTFZZy/3+ThDapfB39/DxQthNN/AcEwd1KTJEkaYe8+eBqLJ2XztVuW0dDeA3FxcMavID4Jrn8f9PdGHVHSOGVhJGnMamzv4WNXvcik3FQuO3MPguEUP92tcPVFEMTDBVdAUnrsg0qSJL1JCfFxfO+cxbR09fKNW5cNHswuh1N/CltegIe/G21ASeOWhZGkMSkMQz7z95epbevm5xfuTWZK4s4HDQzADR+EujVw3p8hd2rsg0qSJL1Fc0sy+fARs7hp6RbuX1E9eHDhGbDXxfDoD2HTk5HmkzQ+WRhJGpP++PhG7ltRzRdOnM+iSTnDG/Twd2HV7XD8ZTD9sJjmkyRJ2p3+58hZzCvJ5PM3vEJTR8/gwRO/AzlT4YYPQFdztAEljTsWRpLGnFeqmvn2nSs4Zn4x7z542vAGrbgVHv4O7PV2OOCDMc0nSZK0uyUlxPGDcxfT2N7DV2/Z/mhaciacdTm0bIbbPx1tQEnjjoWRpDGltauXj1z1AgUZyXz/nEXDW7eoZgXc+N9Qvi+c/CMXuZYkSWPSHuXZfPSo2dy8dAt3vrJ917TJ+8Hhn4VXroWXr4s2oKRxxcJI0pgRhiFfvPFVqho7+dmFe5ObnrTzQR0NcNWFg4tbn38FJKbEPqgkSVKMfPjImexZns0Xb3qVurbuwYOHfhomHwC3fxIa1kcbUNK4EUlhFATBxiAIXgmCYGkQBM9FkUHS2HPtc5Xc8tIWLjlmNvtNy9v5gP4+uP69g9O0z/8bZJXGPqQkSVIMJcbH8cPzFtPW1ceXbnyVMAwhPgHO/t3gLOq/vwf6eqKOKWkciHKG0ZFhGO4VhuGSCDNIGiNWV7fy1VuWccisAj50xKzhDbr/a7DuATj5hzB5/5jmkyRJGilzijP51HFzuGvZNm5eumXwYM4UOO3/YMuLcN/XIs0naXzwkTRJo15nTz8fufIFMpIT+NH5i4mPG8YaRC9fC0/8HPZ7P+zzztiHlCRJGkHvO3QG+07N5Ss3v8q25q7BgwtOg/0/AE/9AlbdGW1ASWNeVIVRCNwTBMHzQRB8IKIMksaAwXWLXmFNTRs/Pn8vijKHsQbRlhfhlo/C1EPghG/HPqQkSdIIi48L+MG5i+ntD/nM319iYCAcPHHsN6FkT7jpQ9BcFW1ISWNaVIXRIWEY7gOcCPxPEASH/fsFQRB8IAiC54IgeK62tnbkE0oaFa58poIbXtzMJcfM4dDZhTsf0LoNrn47pBfCeX+G+MTYh5QkSYrA9IJ0vnTKfB5dU8cfn9g4eDAxBc75E/T3wvXvG1zTUZLehEgKozAMN2//WAPcCPzH4iJhGP42DMMlYRguKSwcxi+Jksadlyqb+PotyzlibiEfOXIY6xb1dAzuiNbZBBdcCekFMc8oSZIUpYv2n8Ix84v57l0rWbmtZfBgwSw45cdQ8SQ85GxrSW/OiBdGQRCkB0GQ+Y/PgeOAV0c6h6TRrbG9hw9f8QKFmcn8+Ly9iNvZukUDA3DTfw8+jnb276B00cgElSRJilAQBHz37D3JSknk41ctpau3f/DEovNg74vh0R/A6nuiDSlpTIpihlEx8FgQBC8BzwC3h2F4VwQ5JI1S/QMhH79mKbWt3fzq4n3ITU/a+aCHLoPlN8Nx34R5J8U+pCRJ0iiRn5HM989dxKrqVr5316p/nTjx+1C8J9zwfmjcFF1ASWPSiBdGYRiuD8Nw8fZ/C8Mw/NZIZ5A0uv38gTU8srqWr522kEWTcnY+4KVr4JHvD+6G9raPxDyfJEnSaHPk3CLe9bap/OHxDTyyevsasElpcP5fIAzh2ndCb1e0ISWNKVEtei1JO/Tw6lp+ev8azt5nEhfuP3nnAyqegls+AtMOhZN+CMFOHl2TJEkap75w0nxmF2Xw6eteoqG9Z/Bg3gw489ewdSnc9blI80kaWyyMJI0aVY0dfPzqF5lbnMn/nrEHwc7Kn/p1cPVFkD0ZzvsLJAzj0TVJkqRxKiUxnp9csBdNHb18+rqXCMNw8MS8k+CQT8Lzf4IXr4g0o6Sxw8JI0qjQ3dfP/1zxAv39Ib+6eF9Sk+LfeEB7PVxx7uAU67dfB2l5IxNUkiRpFFtYls2lJ83jgZU1/P6xDf86ceQXYfphcPsnYdsr0QWUNGZYGEkaFb5523JeqmrmB+ctZnpB+htf3Ns1OLOouQouvBryZ45MSEmSpDHgXQdN47gFxXz3rpW8VNk0eDA+Ac7+A6TmwjUXQ0dDpBkljX4WRpIid8MLVfztqQo+ePgMjl9Y8sYXDwzAjR+EyqfgrN/AlANGJqQkSdIYEQQB3ztnEUWZKXzkqhdo6eodPJFROPgYf8sW+Pt7oL8v2qCSRjULI0mRWrmthUtvfIUDpufxmePm7nzAfV+F5TfBsd+EhWfGPJ8kSdJYlJOWxM8u3IstTV184fpX/rWe0eT94eQfwfoHB++rJOl1WBhJikxzZy8f+tsLZKUk8vOL9iYhfiffkp79HTzxM9jvfXDQR0cmpCRJ0hi179Q8PnXcHG5/ZStXPlPxrxP7vAP2/wA8+X/w0tXRBZQ0qlkYSYpE/0DIx69+karGDn7x9n0oykx54wErboM7PgNzToATvgs720FNkiRJ/PdhMzl0dgHfuHU5y7Y0/+vE8ZfBtEPhlo/B5heiCyhp1LIwkhSJH9yziodW1fK10xay37Sd7HC26Um4/r1Qtjec84fBRRslSZK0U3FxAT8+fy9y0hL50N9eoLlz+3pG8Ylw7p8hoxiufju0VkcbVNKoY2EkacTd+tIWfvXQOi46YApvP2DqG19cswKuOh+yJ8FF10HSTnZQkyRJ0hAFGcn88u37sKWpk09du5SBge3rGaXnwwVXQFcTXPsO6OuONKek0cXCSNKIWralmc/8/SX2m5bL105d+MYXN1fB386GhFS4+IbBmxpJkiTtsn2n5vHFk+dz34oafvXwun+dKF0EZ/wSKp+GWz4K/1gcW9KEZ2EkacQ0tPfwgb88T05qEr98+74kJbzBt6COBvjrWdDdChdfD7k7mYkkSZKkN/RfB03j1MVl/PCeVTy+tu5fJxaeCUd9CV6+Bh75fnQBJY0qFkaSRkRv/wAfvuJ5atu6+e0796UwM/n1L+7pgKsugMYNcMGVULLHyAWVJEkap4Ig4Dtn7cnMwgw+dtWLbG3u/NfJQz8Niy+EB78Fr/w9upCSRg0LI0kxF4YhX7n5VZ5a38B3z96TRZNyXv/ivp7BZ+grn4GzLofph45YTkmSpPEuPTmBX128L129/Xz4ihfo7usfPBEEcOpPYcpBcNOHoeLpaINKipyFkaSY+8PjG7nqmUr+58iZnLn3pNe/cKAfbng/rL1v8IZl4RkjllGSJGmimFWUwffPXcyLFU185aZlhP9YtygheXAR7OxyuPoiaNwYaU5J0bIwkhRTD66s4Vu3L+f4hcV86ti5r39hGMJtn4DlN8Fx/wv7vmukIkqSJE04J+1ZykePmsU1z1Xylyc3/etEWh5cdC0M9MH/t3ff4VFX+R7H32cmmfRKSCAQIFSpUoJItStY1wa4KkUEbOs2t7p33dW79+7eLe7aOwIKigiCigVQEQSkSYdIDQQIJUAqaTPn/vELQiBRSjKT8nk9zzwz+f3ODN/hOc9kfp+c8uZQOHYkcEWKSEApMBKRGvPt/jx+MvUbLmgSzZPDuuNymcobWgtz/wtWTXLmz/f7iX8LFREREWmAfn5le67smMjjH2xk8baTFsFOaAfD3oDD2+GtO6G0KHBFikjAKDASkRqRnV/MmInLCfO4eWVkGuGeoKobL/wnLH4aeo91dugQERERkRrnchmeHNad1gkRPPjmKnYfLjxxMnUg3PwCZHzlLBng8wauUBEJCAVGIlLtikq9jJ+8kgO5xbw8Io3k2LCqGy99Hj57AroNgyH/5yy4KCIiIiJ+ERUazMsj0vD6LGMnraCguOzEya63wTX/A5tmw0e/cUaFi0iDocBIRKqVz2f55TtrWJFxhH8N7U73lNiqGy9/BT7+LXS8AW56Flz6SBIRERHxt1YJETzz4558uz+PX0xbjc93UjDU90Ho+xAsfxkW/StwRYqI3+nqTESq1d8+2cyHa/fxuyEXcF23plU3XDUJPvwltB8Ct74G7mD/FSkiIiIiFQxq35hHr+vEJxv287dPNlc8edUT0PV2mP84rJ4SmAJFxO++Z1EREZGz88bSDF5csJ27Lm7BuEGtq264eirMfhjaXglDJ0KQx39FioiIiEil7unfip2HCnhxwXZaxkfw4z4tnBMuF9z0HBQchFkPQVg8dBgc2GJFpMZphJGIVIvPNx/gj7PWc/kFifzphs6YqtYiWjcdZj0AqYOc3TeCQvxbqIiIiIhUyhjDYzd04tIOjfmvWev58tuDJ04GeWDoZGjaDaaNgB1fBq5QEfELBUYict7W78nhwSmr6Ng0mqfv6EGQu4qPlg0zYcY4aNEX7pgKwd+zGLaIiIiI+F2Q28XTd/SgXWIkD7y5ivSsvBMnQ6PhrhkQ3xqm3gGZKwJXqIjUOAVGInJeMrILGDVhObFhwbw2qjcRIVXMdF03HaaPgea94cdvgyfCv4WKiIiIyBmJCnW+14V73Nzz+nIO5BWdOBkeDyPeg4gEeONWyFofsDpFpGYpMBKRc3Ywr5gRry2jzOdj0pg+JEWHVt5wzdswY6wzsuiudyEkyr+FioiIiMhZSY4N47VRvTlcUMI9ry8nv7jsxMmoJjBiFgSHw+SbIXtb4AoVkRqjwEhEzkleUSmjJizjQG4xE0b1pm1iZOUNv3kTZo6HVgPgzmkQUkU7EREREalVujSL4dk7e7BpXx73TV5JSZnvxMm4Vk5oZL0w6SY4ujtgdYpIzVBgJCJnrbjMy/jJK9mclcdzd/WkR4u4yhuunAizHoQ2l8GPp2kamoiIiEgdc/kFSfz1lq4s2nqIR95Zg89nT5xs3B7unglFufD6dQqNROoZBUYicla8PsvP317N4m3Z/P22blzWIbHyhstehvcfhrZXwnAtcC0iIiJSV92elsJvBl/A7DV7eeLDjVh7UmjU9EIYMROOHYWJ10NOZsDqFJHqpcBIRM6YtZY/vLeeOeuyePTajtzSs3lljeDLf8CcR6DDtTD8TQiuYm0jEREREakT7rukNff0T2XCVzt5YcH2iieb9XJGGhUecUYaKTQSqRcUGInIGbHW8pcPNzF12S7uv7QNYwe1rqwRzP0jfPYEdBsGQydBUIj/ixURERGRamWM4Q/XdeTGC5P528ebmbb8lOlnzY+HRofh9eshZ09gChWRaqPASETOyL/nbeGVRTsY2bclv76mw+kNfF744Gew+CnofS/86AVwB/u9ThERERGpGS6X4R+3X8jAdgn8dsZaZq/ZW7FB815w93tQmK2RRiL1gAIjEflBL325jf/M38JtvZrz2A2dMcZUbOAthRljYeXrMPCXcO0/wKWPFxEREZH6xhPk4qW700hrFc/P317NpxuyKjb4bqRRNkwYAoe3V/5CIlLr6YpORL7XG0sz+J85m7mua1P+dms3XK5TwqLifJg6HNa/C1f+Ga74I5waKImIiIhIvRHmcfPaqN50bRbDQ1O+YcG3Bys2aJ4GI2c73xNfGwIHNgemUBE5LwqMRKRK76zYzX/NWs/lFyTy5LDuuE8Ni/IPOrthbPscbngKBvwsIHWKiIiIiH9FhgQxcfRFtE2MZNykFSzZll2xQXIPGD3HeTxhCOxd7fcaReT8KDASkUpNW7GbX7+7lgFtE3juzp54gk75uMjeBq9e5fzFaPgU6DUyMIWKiIiISEDEhAczecxFtIgPZ8zE5azMOFyxQWJHuOcj8ETCxBsgY0lgChWRc6LASEROM23Fbn5THha9PCKN0GB3xQZ7VsGrV0NRDox8HzoMDkyhIiIiIhJQjSJDePPePiRFhzLi1WUs33lKaBTf2gmNIhNh8s2wdV5gChWRs6bASEQq+MGwaMtcZ6tUTziM+RRSegemUBERERGpFRKjQ3lr3MUkxYQy8rVlLN1+yvS0mOYw+iNo1BamDIM1bwWmUBE5KwqMROQ7PxgWff0STBkKjVrDmLmQ0C4whYqIiIhIrZJUHholx4YxasIyvtp6qGKDyEQY/SG07Aczx8OiJ8HawBQrImdEgZGIAM5uaFWGRd4ymPMr+OhX0O4aGP0xRDUJXLEiIiIiUuskRjmhUcv4CO55ffnpu6eFxsCd06HLrTDvT/DRb8DnDUitIvLDFBiJCC8u2MYf3lvPZR0STw+LinJh6nBY9hL0fQiGvwkhkYErVkRERERqrYTIEKaOu5jWjSMZO3EF8zbur9ggKARuecX5XrnsRZg+GkqLAlOsiHwvBUYiDZi1ln9+ms7/frSZ67o15YW7elUMi47ugteugW2fwfX/hmv+Ai53la8nIiIiIhIf4WHKvX24oGkU499YybsrMys2cLmc75VX/zdsnAWTfwQF2ZW+logEjgIjkQbK57P8+f2NPP3ZVoalpfDU8B54gk76SNj5Fbx0GeTsgbumQ9rowBUrIiIiInVKXISHKWMvpk9qPL98Zw2vLtpxeqN+P4HbXoO938DLl8GBzf4vVESqpMBIpAEq8/r47Yy1vL54J/f0T+Wvt3bF7TLOSWth2csw6UYIi4N750GbywNbsIiIiIjUOZEhQUwY3ZvBnZvwxAcb+ccn6dhTF7ruciuM+hBKj8GrV8HWeYEpVkROo8BIpIE5VuLlvjdWMm1FJg9f0Y7/ur4jxpSHRWXFMPshmPMItL0Sxs6Hxu0DW7CIiIiI1FkhQW6evbMnw3un8MznW/nDe+vx+k4JjZqnwdjPILYlvHm7szOviARcUKALEBH/OVxQwj2vL2dN5lGeuKkzd/dtdeJk7j54+y7YswIG/Rou/Z0zv1xERERE5Dy4XYb/vaUrcREenv9iG/tzi3nqju6Ee066HI1NgXs+hhljnZ15D26GwX+FIE/gChdp4HQ1KNJA7D5cyK3PL2bTvlyev7NXxbBox5fw4iDnF/OwN+DyRxUWiYiIiEi1Mcbwm8EX8OcbO/PZ5v0Me3EpB/JO2R0tJNL5Ltr/p7DiVZh4vfNHTREJCF0RijQA6/fkcPNzizlcUMKb9/ZhcJcmzgmfDxb8HSbdVL5e0XzoeENgixURERGRemtkv1a8PCKNrQfyufnZxaRn5VVs4HLDVY/DbRMgaz28dAlkLAlMsSINnAIjkXpu7sb9DH1xCR63Yfp9fUlrFe+cKMiGN2+Dz/8butzmzBtPvCCwxYqIiIhIvXdFxyTeua8vpV4ftz2/mEVbDp3eqMstzuYrnghnpNGyl53NWUTEbxQYidRT1lpeWLCNcZNX0DYxkpkP9qddUpRzctfX8OJA2LkIrv833PKSMwRYRERERMQPujSL4b0H+9MsLoyRE5YxcfHO03dQS+oEYz93NmOZ8wjMHA/F+YEpWKQBUmAkUg8Vl3n51fS1/PWjzVzbpSlvj+tLUnQo+LzOFLQJQ8AdDGM+hbTRcHyXNBERERERP0mODeOd+/pyWYfGPDZ7A795dy3FZd6KjcJiYfhUuOxRWDsNXrrUmaomIjVOgZFIPXO4oIS7X1nG9JWZ/PSKdjx9Rw/CPG44ugtev96Zgtb5Zhi3AJK7B7pcEREREWnAokKDeenuNH5yeVumrchk+EtL2Z97ymLYLhdc8msYORuK8+Dly2H5q5qiJlLDFBiJ1CPr9+Rw4zOLWJ15lKfu6MHPr2qPy2Vg3XR4fgBkrYObX4RbX3H+WiMiIiIiEmAul+GXV3fg+Tt7kp6Vxw1PL2LVriOnN0wdBPctgtSB8OEv4J1RUJTj93pFGgoFRiL1xLQVu7nl+cX4fJZ3xvflxguTnV+gM++Hd8dA4/Zw30K4cLimoImIiIhIrTOka1NmPNCPkGAXw15cwutf7Th9XaPIxvDjd5yd1DZ/AC8MgIzFgSlYpJ5TYCRSxxWXefn9zHX8evpa0lrG8f5PBnBhSixsnQ/P9YW1b8GgX8HojyA+NdDlioiIiIhU6YIm0bz/0AAGtWvMn97fyANvriK3qLRiI5cL+v8URn8Mxg0TroW5f4Sy4sAULVJPKTASqcP2Hj3G0BeXMuXrXYy/pDWT7rmIRsEl8P5P4Y1bnG1Ix8yDy//gLHItIiIiIlLLxYZ7eHlEGr+/9gI+3bif659axPo9lUw9S+ntTFHrNRK++g+8dJkWxBapRgqMROqoTzdkce1TC9m6P4/n7+zJ74Z0JChjITzXD1ZOhH4Pw/iF0LxXoEsVERERETkrLpdh3KA2TBt/MaVeH7c8t5iJi3eePkUtJBJu+A/8eBoUHHR2UVv0JHjLAlK3SH2iwEikjikq9fLYrPWMm7ySZrFhvP+TAQxpEwKzHoRJN0KQB+75BK5+AoJDA12uiIiIiMg569UynjkPD2RAuwQem72B0a8v58Cpu6gBtL8GHlgKHYbAvD/BK1c4G76IyDlTYCRSh2w9kM/Nzy1m4pIM7umfyoz7+9J63xx4pjesnurM5R6/EFr0CXSpIiIiIiLVIi7Cw6sj03j8ps4s2ZbNNf/+ko/XZ53eMKIRDJ0Et02A3D3OaKP5j0NpJQGTiPwgBUYidYC1lilf7+KGpxexP7eIV0em8cf+oYS8dTvMGAtxLWH8Ame3CE94oMsVEREREalWxhhG9G3Fhw8PpHlcOPe9sZJfvbOG/OKyUxtCl1vgwWXQ9XZY+M/yndSWBKZwkTpMgZFILZeVU8TICcv5/cx19GwZy0cPpHHF/tedHdB2L4chf4cxc6FJ10CXKiIiIiJSo9omRvLu/f146LK2vLsqk2ue/JIF3x48vWF4PNz8Atz1rrN72oTBzhIOBYf8X7RIHaXASKSWstYy85tMrn5yAct3HObxGzsxue9+kiYNgi/+B9oPhoeWQZ9x4HIHulwREREREb/wBLl45JoOvHNfP0KDXYx8bRm/mLaaIwUlpzdueyU8sMRZumHNW/B0T1j2Mvi8/i9cpI5RYCRSCx3IK+K+N1by87fX0D4pinl3JzFi689wvTMCQqJg5AcwdCJEJwe6VBERERGRgOjVMo4PHx7ITy5vy+zVe7nqyQV8uHZf5TupXfU43L8Yml4Icx5x1jfavSwgdYvUFQqMRGoRn8/y5tcZXPHPBXyefpAnrmzMtJR3aTb1Cti7Gq79B4z/ElIHBrpUEREREZGACw1288urOzD7oQE0jQnjwSmruHfiCnZlF57euHEHGDHbWRS74BC8ehW8OxaO7vJ/4SJ1QFCgCxARR3pWHr+fuY6VGUe4tFUE/2qxkPhlL0DpMeg1Ci571Nn5QUREREREKuiUHM3MB/ox4audPDnvW658cgH3X9KG+y9tQ2jwScs3HF8Uu93VsOhfsORZ2DgLLr4fBv4CQmMC9yZEahlz2nC9WigtLc2uWLEi0GWI1IhjJV6e+XwLLy7YTmyI4YWu6fTa/jwmPws63gBXPAYJ7QJdppxqxYQaffmvdxyu0dcXERGRyvVJja+5F08bXXOvLd/JyiniL3M28f6avaTEh/HH6ztzZcdEjDGnN87JhM/+G9ZMhfBGcOnvoOdICPL4v3CRADDGrLTWplV2TlPSRALEWsvsNXu5/J9f8PznW3g8dQNfx/6etLWPYeJawj2fwrA3FBaJiIiIiJyFJjGhPH1HD6aM7UNokJuxk1Yw4rVlbNybe3rjmObObmrjFkBiJ2d9o2fSYPUU8Jb5v3iRWkQjjEQCYF1mDn9+fwOrMrIZ32gNDwfNICxnGyR1hct+Bx2udYbLSu2lEUYiIiL1kkYY1S+lXh+TlmTw1Pwt5BaVckuP5jxyTXuaxoSd3tha2DoPPnsC9q2BRm2dEUedbwGXxlpI/fR9I4y0hpGIH2XlFPGvuenMWJnB0LCVvJrwATH5W52/ZgydDBdcr19GIiIiIiLVJNjtYsyAVG7r2Zxnv9jK61/t5IO1exkzIJXxl7QhJiz4RGNjoN1V0PZK2PwBfPYXeHcMLPwnDPoVdLoJXO6q/zGRekaBkYgfHCko4fkF25i6+FtuNl+wPPoT4or3QGgHGDwBOv1IQZGIiIiISA2JCQ/m99d25O6LW/KPT9N57ottvLE0g3sHtmZ0/1ZEhZ4SHHW8wRn1v2EmfPFXmD4a4tvAgJ9Bt+Fa40gaBE1JE6lBBcVlvLZoB1O/XMdN3k+4P+RTor2HoVmaswtD+yEKiuoqTUkTERGplzQlrWHYsDeHf8/bwtyN+4kND2bswNaM7NeKyJBKxlT4vLDpfWekUdZaiG4GfR+CniMgJNL/xYtUo++bkqbASKQG5BeXMXlJBnO/XMhNJR8wLHghobYI2lwBA34OrQZojaK6ToGRiIhIvaTAqGFZl5nDv+d9y/zNB4gLD2ZUv1RG9G1JXEQlI4ishW3zYeG/IOMrCI1xdlS7aBzEpvi/eJFqoMBIxE9yCkuZ8NU2vv1qFkO9H3Kpew0+lwdX19vg4vug6YWBLlGqiwIjERGRekmBUcO0evdRnvlsC/M2HSAs2M2w3incOzCV5nHhlT9h9zJY+hxsnO383PF6uPgBSOmjPwxLnaJFr0VqWFZOEdMWrKJ05RvcYueR6tpPaWRjuPhRXL1GQWRioEsUEREREZEqdE+J5ZWRvfl2fx4vfbmdN5ZmMHlpBtd3a8ro/ql0T4mt+ISUi5zb0d2w/GVY+TpsnAVNukKv0dD1dgiNDsRbEak2GmEkch7W7DrMok/fJTXjHa50rcBjvBQ2uYjwfmOdhay1GF79pRFGIiIi9ZJGGAnAvpxjvLpwB28t301+cRkXNo9hRN9WXNetKaHBleyUVlIAa95yviPuXwfBEdD1Vic8Su6hUUdSa2lKmkg1Kinz8dXXS8hePJk+efNIcR2k0B2Nt+twovrfC407BLpE8QcFRiIiIvWSAiM5WV5RKTO/2cPExTvZdrCARhEehvZOYVhaCq0SIk5/grWwZxWsfA3Wz4DSQkjqChcOc0YdRTXx/5sQ+R4KjESqwe7MXWyaO5GmGbPoyha8uNgX34dG/UcS1u1mCA4NdIniTwqMRERE6iUFRlIZay2Lt2Xz+uKdzN+0H591+srQtBSu7dqUME8lo46KcmDtNFgzFfasBOOCNpfDhXdAh2vBU8X6SCJ+pMBI5BwdO3qA9AVTcW2cRaeibwgyPnZ7WlPc6XZSLxuFOyY50CVKoCgwEhERqZcUGMkP2Z9bxPSVmbyzYjc7swuJCgnium5NubF7Mn1SG+F2VTL97OC3sPYtWPM25GY6U9baXwOdb4Z2V0FwmP/fiAgKjETOijdnHzsWT8e7/j3a5K8iyPjINE3IajaYVpeOIKFtr0CXKLWBAiMREZF6SYGRnClrLct2HObtFbv5eH0WhSVekqJDuL5bMjdemEy35jGYU9cu8vkgYxGsfxc2vQ+F2U541GEwdLrJGYEUEhWYNyQNkgIjke9jLb6s9exdNgOT/hHNCjcBsMsmsbXxVTTqM5SuPQficrsCXKjUKgqMRERE6iUFRnIujpV4mb95P7NW7+WL9AOUei0p8WFc06kJV3duQq+WcaePPPKWOeHRhpknwiO3B1IHQYch0H4IxDQLzBuSBkOBkcipjh2hbOsXHFrzEaEZXxBbuh+A1b627Gg0kPieN9Gnz0BCPUEBLlRqLQVGIiIi9ZICIzlfOYWlfLxhHx+tz2Lx1mxKvD4SIj1c2TGJKzom0a9NIyJCTrnO8JbB7qWQ/hGkz4HD253jTbpCmyuckUctLoagEP+/IanXFBiJlB6DzBUUpH9OUfo84o6sw4WPXBvG13QhK+kSGve8gf7dOxMVGhzoaqUuUGAkIiJSLykwkuqUV1TKF+kH+WRDFl+kHyS/uAyP20Xv1DgubZ/IpR0a0zYxsuLUNWvh0BYnONryKez+GnxlEBwOrQZA68sgdSAkdgaXZkHI+VFgJA1PcT7sWUHZ9oUUfPslEQe/IciW4rWGNbYNK4N6UNryUtr0uJRBF1Sxq4HI91FgJCIiUi8pMJKaUlzmZcXOI3yRfoAv0g+y5UA+AEnRIfRt3Yi+bRrRr00CKfGn7J5WnAc7F8G2z5xb9lbneGgstOwPrfo790ldwK0ZEnJ2vi8wUm+Sus9aZ8hm5nLKMr6meMdSwo5sxoUPYw07bCrL7TUcaNSbhI6X0L9La+5Njj59AToREREREZEaEhLkpn/bBPq3TeDR62DP0WMsSD/I4m2HWLT1EO+t3gtAs9gwereKo1ereHq1iKNDkyjcHYY46xoB5GTCzq9g50InSEr/0DkeHAHNekLz3pByETS/CCIaBejdSn2gEUZSt1gLRzNg72rs3tWU7F6J2bcGT2kOAPk2jG98bVhl23Eo9kIi2w2gd4cWXJTaiMhT5wmLnA+NMBIREamXNMJIAsFay5YD+SzZls2SbdmsyDjCofxiAKJCgujeIpYuzWLokhxD5+RoWsSH4zq+iHbOHshYDJnLYPcy2L/emcIGEJMCTbo5ayE16QpNuznH9MdzKacRRlL3WAv5++HAJjiwCXtgE6X7NuDKTieo1Bm66cXNFl8Ka3092WTaUJjYk6S23enVKoERLeKIi/AE+E2IiIiIiIj8MGMM7ZOiaJ8Uxch+rbDWsvvwMVbuOsyKnUdYtesoL3+5nTKfM+AjKiSIjsnR3wVIXZoNpk3nWwlyu6CkEPZ+A5nLIWsdZK111kOifLBIaGx5gNTNCZCadIVGbbWgtpxGgZEElrcMcjPh8A7I3ordv5HSrI2Yg5sJLjn6XbOjRJHua066ry9bbApHYjsT2eJCOrdoTNdmMdyaHE1IkNYhEhERERGRus8YQ4tG4bRoFM7NPZoDzhpI32bls2FvDhv25rJ+bw5TlmVQVOoDICTIRfukKNo0jqB140RSE4bSuvVoWidEEkYR7N/ohEdZa50gacWrUFZU/g+6ILalExwltHPujz+OaqoRSQ2UAiOpeceOwpGdJ912UJa9A2/2DoLz9+Cy3u+a5tlw0m1ztvh6km5T2O1uAYkdaZqcQsfkGLo0jeK2JtGnb0MpIiIiIiJSj4UEuenaPIauzWO+O+b1WbYfzGfD3lw27M1hc1Yey3ce+W49pOOSY0JJbRxB64Q+tG58OS3bhdMs2kNz3x4iDm+CQ99C9hZnQe2Mr6C08MSTgyOgURsnQIpPhZjmzrS2mObOLSTKX/8F4me66pZz5y2DggOQuw/y9kJeFuTuxZu7j5Ije7C5+wgqyMJTllfhaYeJYpcvkV22GbtsD3bbJI5FpuBKaEtcUgtaN46kdeNILk2IoEl06Im5uSIiIiIiIvIdt8vQLimKdklR/KhHs++OHyvxsuNQATsOFbD9YD7by+/f+2YPecVlFV4jKjSaZrEDSY69muQmoSR3CKFNaB6t7F4al2QSVbCD4CPbYe8q2DT7xPpIx4XGVAyQYppDdDOIaAyRiRCRCOHx4NKMkLomIIGRMWYw8B/ADbxirf1rIOqQk1gLJQVQlAOF2RVutuAQZXkHKc07hLfgEKYwG3dRNiHFR3Dhq/AyZbjZb2M5YOPIsnHst6nssQkc8iRTHNkCV3xL4uISaBYXRmpCBNckRNCiUbimk4mIiIiIiFSTMI+bTsnRdEqOrnDcWsvB/GJ2Hz7G3qMnbnuOFrH36DFW7TrC0cLS8tYuoAXQgnDP5SREhpDYyE3rsAJSPUdJcWXTlEM08h4gung/Ydm7CMlYiqv46OkFGZcTIEUkQuQp92FxEBbr3IeW34fFQXCYpsIFmN8DI2OMG3gWuArIBJYbY2Zbazf6u5Y6zedzhgkev5UUQukxKC2AkkJsaSFlRfmUHCugtCgfb1EevmO52KIcTHEOpjgPd0kuQaV5BJfm4ynLx4W30n/KWkMeERyxURwmiiM2imzbjAPEctTViMLQRErDm0BUEzzRSTSJCyc5JoymsaG0jw2jaUwo4R4NZhMREREREQkkYwyJUaEkRoXSq2VcpW0KisvYl3OMzCPHOJhXzKH8Eg7lF393W5MD8/ODOFwYjbWppz0/gmMku47SMrSAFE8+yUG5JLlySTA5xBYeJSY/i6jSjYSXHSbIV1JlrdbtgVAnTDJhsc7UN08khERCSPSJx57Ik85FQXA4BIdCUKgTOgWFnri5XNX1X9kgBOIq/iJgq7V2O4Ax5i3gJqB+B0YlhfD1C+AtBW8xeEugrMS5r/RY+a2s+MT50qITAdHxxcmqYIDg8huAzxryCSOXcPJsGLlEkGfDyKMRuTacQlcEJUFReIOjKA2JwxvWCBvWCBMZT3BEPFHhYcRHeEiI9JAUEUKnSA+NIkII82hkkIiIiIiISH0RERJE28Qo2iZ+/9pEZV4fhwtLOJRXQs6xUnKOOfdHC0vLfy7lwLFStpYfyy0qpbDES2FxGQUlXsASyTFiKCDWFBBtCogln5jj92UFxBTnE5tbQLyrgEhziAiKiDBFRNhCwijCdXzntzPlDikPk8KquA8FdzC4Pc7NFXTi8cnHg0Kg/8Pn/p9cRwQiMGoG7D7p50ygTwDq8K+yIpj/Z+excZd3suMdL+Skxyd1wOAwZ0ieO9j5OSjMOeYJL09NwyE4jPQjPr7Ylg+ecIwnHFdIBEEh4bhDIgkOjSA4NBJPWAThIcGEedyEe4KIDXaT7HET5nETGRJEaLCCHxERERERETkzQW7Xd6OVzpbPZykq81JQ7KWwpOzE/UmBUmFJGTnFXvYWl1FU6qXE66O41Ofcl3kpLS3Dlh7DVZpPUGkB7rJCgsvycXmLcHuLcHuLCfIVM+biJrSMdjkDMMqOVXFfBMV5UHDQWav3u4EdJac8LnbegFuBUUAZY8YB48p/zDfGpAeyHqmVEoBDgS5C5Aypv0pdoz4rdYn6q9QR94D6q9Q9dbrPPlFTL/zHerO+UsuqTgQiMNoDpJz0c/PyYxVYa18CXvJXUVL3GGNWWGvTAl2HyJlQf5W6Rn1W6hL1V6lL1F+lrlGfbbgCseLTcqCdMSbVGOMBhgOzA1CHiIiIiIiIiIhUwu8jjKy1ZcaYh4BPADfwmrV2g7/rEBERERERERGRygVkDSNr7RxgTiD+balXNGVR6hL1V6lr1GelLlF/lbpE/VXqGvXZBspYe5bb0ImIiIiIiIiISL0WiDWMRERERERERESkFlNgJLWOMWawMSbdGLPVGPPbSs4PMsasMsaUGWNuO+VcC2PMp8aYTcaYjcaYVn4rXBqs8+yz/2eM2VDeZ58yxtSb/TmldjqD/vqL8s/PtcaY+caYliedG2mM2VJ+G+nfyqWhOtc+a4zpboxZUv4Zu9YYM8z/1UtDcz6fseXno40xmcaYZ/xXtTRU5/mdQNddDYACI6lVjDFu4FlgCNAJuMMY0+mUZruAUcCUSl5iEvB3a21H4CLgQM1VK3J+fdYY0w/oD3QDugC9gUtquGRpwM6wv34DpFlruwHTgf8rf2488BjQB+fz9TFjTJy/apeG6Xz6LFAIjLDWdgYGA/82xsT6pXBpkM6zvx73BPBlTdcqUg39VdddDYACI6ltLgK2Wmu3W2tLgLeAm05uYK3daa1dC/hOPl7+ARdkrZ1b3i7fWlvop7ql4TrnPgtYIBTwACFAMLC/5kuWBuxM+uvnJ312LgWalz++BphrrT1srT0CzMW5CBepSefcZ62131prt5Q/3otzMdPYb5VLQ3Q+n7EYY3oBScCnfqpXGrZz7q+67mo4FBhJbdMM2H3Sz5nlx85Ee+CoMWaGMeYbY8zfy5NzkZp0zn3WWrsE+BzYV377xFq7qdorFDnhbPvrGOCjc3yuSHU4nz77HWPMRTjh/LZqrU6konPur8YYF/BP4JEaq06kovP5fNV1VwMRFOgCRKpREDAQ6IEzBehtnGlArwawJpEqGWPaAh058dfFucaYgdbahQEsSwQAY8xdQBqaJil1RFV91hjTFJgMjLTWnjrSUyQgKumvDwBzrLWZWs5QaptK+quuuxoIjTCS2mYPkHLSz83Lj52JTGB1+bDKMuA9oGf1lidymvPpszcDS8uH8ebj/NWmbzXXJ3KyM+qvxpgrgUeBG621xWfzXJFqdj59FmNMNPAh8Ki1dmkN1ypyPv21L/CQMWYn8A9ghDHmrzVbrjRw59Nfdd3VQCgwktpmOdDOGJNqjPEAw4HZZ/HcWGPM8fUJLgc21kCNIic7nz67C7jEGBNkjAnG+auNpqRJTfrB/mqM6QG8iPPF8OQFLD8BrjbGxJUvdn11+TGRmnTOfba8/UxgkrV2uh9rlobrnPurtfZOa20La20rnGlpk6y1p+1aJVKNzuc7ga67GggFRlKrlCfUD+FchGwCpllrNxhjHjfG3AhgjOltjMkEbgdeNMZsKH+uF+cX7HxjzDrAAC8H4n1Iw3E+fRZnt4ltwDpgDbDGWvu+39+ENBhn0l+BvwORwDvGmNXGmNnlzz2Ms3vP8vLb4+XHRGrM+fRZYCgwCBhVfny1Maa7n9+CNCDn2V9F/Oo8vxPouquBMNbaQNcgIiIiIiIiIiK1iEYYiYiIiIiIiIhIBQqMRERERERERESkAgVGIiIiIiIiIiJSgQIjERERERERERGpQIGRiIiIiIiIiIhUoMBIRERE5HsYY5oYY94yxmwzxqw0xswxxrQ3xqwPdG0iIiIiNSUo0AWIiIiI1FbGGAPMBCZaa4eXH7sQSApoYSIiIiI1TCOMRERERKp2GVBqrX3h+AFr7Rpg9/GfjTGhxpgJxph1xphvjDGXlR/vbIxZZoxZbYxZa4xpV378rpOOv2iMcfv7TYmIiIj8EAVGIiIiIlXrAqz8gTYPAtZa2xW4A5hojAkF7gP+Y63tDqQBmcaYjsAwoH/5cS9wZw3VLiIiInLONCVNRERE5PwMAJ4GsNZuNsZkAO2BJcCjxpjmwAxr7RZjzBVAL2C5M9uNMOBAYMoWERERqZoCIxEREZGqbQBuO5cnWmunGGO+Bq4D5hhjxgMGZz2k31VjjSIiIiLVTlPSRERERKr2GRBijBl3/IAxphuQclKbhZRPKzPGtAdaAOnGmNbAdmvtU8AsoBswH7jNGJNY3j7eGNPSL+9ERERE5CwoMBIRERGpgrXWAjcDVxpjthljNgD/C2Sd1Ow5wGWMWQe8DYyy1hYDQ4H1xpjVOGshTbLWbgT+AHxqjFkLzAWa+u0NiYiIiJwh43wPEhERERERERERcWiEkYiIiIiIiIiIVKDASEREREREREREKlBgJCIiIiIiIiIiFSgwEhERERERERGRChQYiYiIiIiIiIhIBQqMRERERERERESkAgVGIiIiIiIiIiJSgQIjERERERERERGp4P8BWQK4ijoMp24AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "sns.distplot(a=ethereum[len(ethereum)-20:len(ethereum)-1]['Close'],label='Real Close price of Etherium')\n",
    "sns.distplot(a=predictions,label='Predicted price of Etherium')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ea0eca45-ae0d-478a-a1de-78f4e0814cfb",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_plot = np.zeros((19,2))\n",
    "data_plot[:,0] = np.array(ethereum[len(ethereum)-20:len(ethereum)-1]['Close'])\n",
    "predictions = predictions.reshape(19)\n",
    "data_plot[:,1] = predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "07c3e1d6-6351-4695-a07c-a7371f16f373",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_plot = pd.DataFrame(data_plot, columns = ['Real Price', 'Predicted Price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "30fff38b-513e-4fe3-9388-35295ec64cbe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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APihtyxmAgFBncro7CTb84L7gREREREREfIwS1AoI8rczumscc5IPsO1gdtlunvNv+OEOyNrvnuBERERERER8jBLUCrqqWxwBdhsfLyrDKirA+f+F4nyY/i/3BCYiIiIiIuJjlKBWUO3wQC7uUJdvVuwmM7ew9DfWagx974cN38Om6W6LT0RERERExFcoQXWB63vFk1/k4Iulu8p2Y4+7oHYL+Pk+KCjjFmEREREREZFKRgmqCzSPqUavJpFM+D2FwuIytI/xC4ALX3Vu9T24yX0BioiIiIiI+AAlqC4ytnc8aUcLmPbHvrLd2KAr3P0H1O/knsBERERERER8hBJUF+nbtDaNa4fywcLtmKZZtpsDQiDvMMx7AUqK3ROgiIiIiIiIl1OC6iI2m8H1veJZt+coy3ZklH2AlEUw73lY8pbrgxMREREREfEBSlBdaESH+tQI8efDhWVsOQPQ/HxIGOpMUg+nuDw2ERERERERb6cE1YWCA+xc1TWOXzemsTM9p2w3GwYMfREMG/z8f1DWbcIiIiIiIiI+Tgmqi43pHoefzeDjRSllv7l6fRjwGGydBeu+dXlsIiIiIiIi3kwJqotFVQviwrZ1+ToplSN5RWUfoMuNUC8R0ta7PjgREREREREvpgTVDa7vFU9uYQmTlu8q+802O1w3DQY94frAREREREREvJgSVDdoXa863RpF8MmiFIpLHGUfwC/QeQZ11Rewc7HrAxQREREREfFCSlDdZGyvRuw9ks/0dfvLN0BxPvz2H5h6JxTluzY4ERERERERL6QE1U0GNo+iYa2Q8rWcAfAPhgv+B+lbYOF/XRuciIiIiIiIF1KC6iY2m8H1veJZnZrJip2HyzdIk4HQZiQs+C8c3OTaAEVERERERLyMElQ3uqRjfaoF+fFReVdRAQY/B4Fh8ONd4CjHeVYREREREREfoQTVjUID/biiawOmr9tHakZu+QYJqw3nPgsF2ZB7yLUBioiIiIiIeBElqG52TfeGGIbBp4tTyj9I+yvhprkQFuWyuERERERERLyNElQ3q1sjmPPb1GHS8lSy8ovKN4hhgN0fDmyE38a7NkAREREREREvoQTVA8b2iieroJivk3ZXbKCNP8HcZ2HzTNcEJiIiIiIi4kWUoHpAu9gaJMbV5JPFOyhxmOUfqOddULs5/Px/zjOpIiIiIiIilYgSVA+5oXc8qRl5/Lphf/kH8QuAC1+FI6kw9znXBSciIiIiIuIFlKB6yDktY4iNCObDirScAWjQDRKvh6Vvw95VrglORERERETECyhB9RC7zeDaHvEsTznMmtTMig028AmIaAQZFUx2RUREREREvIgSVA8amVifsEC/iq+iBteAcUuh9QiXxCUiIiIiIidzOEzmbTpAYbHD6lCqFCWoHhQe5M/lnWOZ9sc+9h3Jq9hgdj8ozIFfH4fDO10ToIiIiIiIAPDp7ylc+/Fy3pi71epQqhQlqB52TY+GOEyTTxe7IKnMOwzLP3RW9TUrUB1YRERERET+lJqRy/gZm7DbDD5euIMjuUVWh1RlKEH1sNiIEIa0juHLpTvJKSiu2GDV68OAR2Hrr7D+O9cEKCIiIiJShZmmyUPf/YHNgA/GJJJVUMwHC7dbHVaVoQTVAmN7xXM0v5hvV+6u+GBdboK6HWD6v5wrqiIiIiIiUm6TV+xm4dZDPDi0Bf2bR3F+mzp8vCiFzNxCq0OrEpSgWqBjg5q0j63Bx4tScDgquDXXZnf2Rs3NgF+fcE2AIiIiIiJV0IGj+Tzz0wa6xEcwuksDAO4a1JScwmLeX6BVVE9QgmoBwzAY2yueHYdymJN8oOID1mkH3cdBYTY4Sio+noiIiIhIFfT4D+spKHbwwog22GwGAM2iwzm/TR0+WZRCRo5WUd1NCapFzmsdQ93qQa7bzz7oabj0I+eKqoiIiIiIlMn0P/YxY/1+7jmnGY1qh5303F0Dm5JbVKJVVA9QgmoRP7uNa3s2ZMn2DNbvPVLxAW3H/ig3/gRL3qn4eCIiIiIiVURmbiGP/bCe1vWqcUOv+H883zQ6nAvb1uXTxSmkZxdYEGHVoQTVQqM6NyAkwM6HC3e4btAN38Ovj8HBTa4bU0RERESkEvv3TxvJzC1k/CXt8LOfOkW6c2BT8otKeE+rqG6lBNVC1YP9GZkYy49r9nLgaL5rBh38PPiHwI93g8PhmjFFRERERCqp3zYf5NuVu7mlb2Na1q122uuaRIUxrF1dJizeySGtorqNElSLXdezIcUOkwm/73TNgGG14dxnYNdiWPWZa8YUEREREamEsguKefi7P2hcO5TbBzQ56/V3DmxKQXEJ783XKqq7KEG1WFytUM5pEc0XS3eSX+SiCrwdroK4Xs6tvtkuqBIsIiIiIlIJvTRzE3uP5DH+0rYE+Z+92Gij2mFc3L4eE35P4WCWVlHdQQmqFxjbK57DuUV8t3KPawY0DLjwFQivC1n7XTOmiIiIiEglkpSSwae/p3BN94Z0ioso9X13DGxKUYnJu79tc2N0VVepElTDMIYYhrHJMIythmE8eIrn7zUMY4NhGGsNw5htGEbcscfjDMNYaRjGasMw1huGcYurv4HKoEt8BK3rVePDhdtxOEzXDBrZFMb9DnXaumY8EREREZFKIr+ohAe+XUvd6sHcPzihTPfGR4Zycft6fL50JweyXFRHRv501gTVMAw78CZwHtASuMIwjJZ/u2wVkGiaZlvgG2D8scf3Ad1N02wPdAUeNAyjrotirzQMw+CGXo3YdjCH37YcdOXAcHgnfH8bFOa4blwRERERER/2+pwtbD+Yw/Mj2hAa6Ffm++8c2ISiEpN35uksqquVZgW1C7DVNM3tpmkWAhOBi068wDTNuaZp5h77cglQ/9jjhaZpHt+cHVjK+aqkoW3qEF0tkI9c2XIG4OheWP05zHnGteOKiIiIiPig9XuP8M5v27m0U336NKt9+gtzM+Cb6yH/yD+eiqsVyogO9fhi6U7SXNWNQ4DSJYz1gNQTvt597LHTGQtMP/6FYRixhmGsPTbGf0zT3Pv3GwzDuMkwjCTDMJIOHnThCqIPCfCzMaZ7QxZsOcSm/VmuGziuOySOhSVvwfIPXTeuiIiIiIiPKS5x8MA3a4kIDeCx8/++KfRvFr0CydPAsINpwo75zo/H3DGgKSUOk7fn6SyqK7l0RdMwjKuARODF44+Zppl6bOtvE+AawzCi/36faZrvmaaZaJpmYu3aZ3gXo5Ib3bUBQf42Plzo4q0C5/0Hmg6Gn/8P/vjGtWOLiIiIiPiI9xZsZ/3eo/z7olZUD/E//YVZ+2Hpe9ByGASGwZZf4dML4aMhsPN3ABrUCuGSjvX5ctku9h/RKqqrlCZB3QPEnvB1/WOPncQwjEHAI8CwE7b1/unYyuk6oHf5Qq38aoQEcGmn+ny/eq9rm//a/WHkpxDXA6bcDGnrXTe2iIiIiIgP2HYwm1dmbeG81jEMaV3nzBcv+C+UFELffzm/btwfLvgfHE6Bj4fAl6MgbT23D2iCw2Hy1rytbo+/qihNgrocaGoYRrxhGAHA5cDUEy8wDKMD8C7O5PTACY/XNwwj+NjnNYFewCZXBV8ZXdcznsJiB58v2enagf2D4YqJMOQFiDrLdgYRERERkUrE4TB58Nu1BPnZeOqiVme+ODMVVnwMHa6CWo2dj9n9IfF6uHMVDHzCuYr6dk9iDy/hssT6TFyWyt7MPPd/I1XAWRNU0zSLgduBmcBG4GvTNNcbhvG0YRjDjl32IhAGTD7WUuZ4AtsCWGoYxhrgN+Al0zT/cPl3UYk0rh3GgOZRfPb7TvKLSlw7eFA16HKjs7rv5pmwZ6VrxxcRERER8UJfLN3J8pTDPHZBS6LCg8588ZqJzo997v/ncwEh0PteuGs19H8E4npxW/8mDDSW8cms5S6PuyoyTNNFfTddJDEx0UxKSrI6DEst2nqI0R8sZfwlbRnZOfbsN5RVSRG82RXyDsP1M6B22Xo/iYiIiIj4ij2ZeZz739/oGFeTCdd3wTCMM99gmnBgI0SXctdhXiaF45tR6DBwdL+dav3vhsDwCsddmRmGscI0zcRTPae2L16oR+NaNI8J56NFO3DLGwh2fxg9GWx+MOFiZ69UEREREZFKxjRNHv7uD0zgueFtzp6cHt7p3G1Y2uQUILgGh6+exSKzLdWWvASvtoel70JxYUVCr7KUoHohwzAY2yue5P1ZLNqa7p5JajWGq6dAUQ5MuAiy0twzj4iIiIiIRaas2sNvmw/ywOAEYiNCznzxgY3wWntY/WWZ54lu1JYFHf/LJcX/Jj8iAaY/AL8+Xr6gqzglqF5qWPu6RIYF8oGrW86cKKY1jP4GstNg4pUn9XUSEREREfFlB7MKePqnDXSKq8nV3Rue/Ya5z4F/KDQbUq75buvfhD/MpjxV83m46jvodovziS2zYPMveq1dSkpQvVSgn50x3eOYt+kgWw9kuW+i2C5w+ZdwztPO7QwiIiIiIpXAkz+uJ7eghP9c0ga77Syvc/euho1TofttEBJRrvnqVA/m8i6xTF6xh9SI7lCzofOJJW/Cl5fBJ+dD6rJyjV2VKEH1YqO7NiDAz8ZHi1LcO1Hj/tCwJzgczobExS7swSoiIiIi4mEz1+/n57X7uHNgE5pElaJg0dxnIagGdB9XoXnH9WuCzWbwxpwT+qJeMQmGvgSHtsCH58BXV8KB5ArNU5kpQfVitcICGdGhHt+t3E1GjgcOWe9cBNPvh2/HQkmx++cTEREREXGxI3lFPPb9OlrUqcbNfRuf/YZdS2HLL9DrbgiqXqG5Y6oHcWWXBnyzcje70nOdD/oFOFs93rkKBjwKKQtg8rXa8nsaSlC93PW94skvcvDlUg9U2o3vDUNegI0/wo93OldURURERER8yHM/byQ9p5AXL22Lv70U6U5kU+j3EHS5ySXz39qvMX42g9fnbDn5icAwZ2/VO1fDiPecx+sObYWZj0BuhkvmrgyUoHq5ZtHh9GlWmwm/76Sw2AMJY7dbnf9AV38Bvzyid3ZERERExGcs2nqISUmp3Ni7Ea3rlXI1NCQC+j0IAaEuiSG6WhBXdm3Ad6v2kHIo558XhNaCOm2dn6fMhyVvwavtYP6LUHiK66sYJag+YGyveA5kFfDT2r2embDvv6DbOOc/lqQPPTOniIiIiEgF5BYW8+B3a4mPDOXuQU3PfoNpwpejYMWnLo/l1r7HV1G3nvnCxOvh1t+hYW+Y84yzh+qy96GkyOUx+QolqD6gT9NImkaF8eHCHZieWNE0DDj3WRj4BLQa4f75REREREQq6OVfNpOakccLI9oQ5G8/+w2bZ8LmGYDrX19HVQviqm5xTFm1mx2nWkU96eLmcMWXcP0vUKsJ/PIoZB9weUy+QgmqDzAMg+t7xbN+71GWbPfQ/nSbDXrf69zycHQfbPjBM/OKiIiIiJTRyl2H+WjRDq7q1oCujWqd/QaHA+Y+AzXjof1ot8R0S9/GBPjZeH32lrNfDNCgK1w3DW5ZBNXrQVE+fHk5bJ1VpY7dKUH1EcM71CMiNIAPF+7w/ORzn3FWGtsw1fNzi4iIiM8ocZhk5VfdrYlijYLiEv71zVrqVAviX0Oal+6mjT/A/j+ctVfs/m6Jq3Z4IFd3i+P71XvYdjC7dDcZBkQ2cX6euRMObIDPL4FPL4TdSW6J09soQfURQf52ruragNnJaWffJuBqQ/4D9To5289sm+vZuUVERMRn/PunDfR7cR55hSVWhyJVyJtzt7HlQDbPDm9DeFApkk1HCcx9Dmo3hzaXujW2m/s2JtDPXvpV1BPVToDbk+C8F+FgMnwwECZd5az8W4kpQfUhV3WPw99m4+NFHl5FDQyD0ZOhVlOYOBpSl3l2fhEREfF6Ww9k89mSnaTnFDLtj31WhyNVxMZ9R3lr7laGd6hH/+ZRpbspNx3CoqH/w2ArxVnVCogMC2RM9zimrtnL1gOlXEU9kV8AdL3J2UO138POxaKM7c7nKum2XyWoPiQqPIhh7esyOWk3R3I9vH0muCZcPQXCouCLS+HIHs/OLyIiIl5t/Ixkgvxs1KsRzNdJqVaHI1VAcYmDf327lurB/jx2QcvS3xgWBdf8CC2GuS+4E9zUpxFB/nZeK88q6nGB4dDvX3D3H9D0HOdjk6+FXx6rdD1UlaD6mOt7xpNXVMJXy3d5fvLwaBjzA/S+D6rV9fz8IiIi4pWSUjL4ZUMat/RtzJVdG7B0R8ap+z+KuNDHi1JYu/sITw5rRURoQOlu2jQdUhY5z3oahnsDPKZWWCBjujfkx7V72ZKWVbHBQiKccZcUgX8wLH7d2ZpmwctQmOuSeK2mBNXHtKxbjR6Na/Hp4hSKShyeD6BmHPS80/kPY8uvcNRDvVlFRETEK5mmyXPTNhIVHsjY3vFc2qk+NgOtoopbpRzK4aVfNjGoRTQXtK1TupuK8uGne2H2Ux7fHntTn0aE+Nt5tSKrqCey+8Pwd+DWRRDXHWY/Da91gNVfumZ8CylB9UFje8Wz70i+tec78o/CdzfChIshJ926OERERMRSM9ensXJXJvec04yQAD+iqwXRLyGKb1fuptiKN9Ol0nM4TP717VoC/Gw8O7w1RmlXQpM+gqy9MOBRj62eHhcRGsC1PRvy8x/72FzRVdQTRbeCKyfBdTOgZkM4nOK6sS2iBNUH9U+IolFkKB8t3IFp1eHooGow6gtn+evPRzgTVhEREalSikocjJ+RTJOoMC7rVP/Px0cmxpJ2tID5Ww5aGJ1UVhOXp7J0RwaPDG1BdLWg0t1UkA0L/wvxfSG+j3sDPI0bezciNMCPV2e5aBX1RHHd4foZ0Od+14/tYUpQfZDNZnBdr3jW7D7Cip2HrQukYU8YOQHS1sFXV0BRnnWxiIiIiMdNXJ7K9kM5/GtIc/zsf72sHNgiisiwACYt1zZfca19R/J4ftpGujeqxajOsaW/cdm7kHMQBjzmvuDOokZIANcdW0VN3u+GxR3DcFtPV09SguqjLulYj+rB/ny40MMtZ/6u2WAY/i7sXATf32ptLCIiIuIx2QXFvDprM10aRjCoxcntPfztNoZ3qMfsjQc4mFVgUYRS2ZimyaNT1lHkcPDCJW1Kv7XX4XCezWw2BGI7uzfIs7ihVyPCA920ilpJKEH1USEBfozu2oCZ6/eTmmFxxa42l8Kw16DrLdbGISIiIh7z/vztHMou5KGhzU+ZKIzqHEuxw+T7VWpNJ64xdc1eZicf4L5zE4irFVr6G202uGkenP9ft8VWWtVD/LmuVzzT1+1nw14dkTsVJag+bEz3htgMg48XpVgdCnQcAw26gaME1k6utI2DRUREBA5k5fP+gu0MbRNDhwY1T3lNk6hwOjaowaSkVOtqZkilkZ5dwFM/bqBdbA2u6xlf+hvzDjsLegaGQ/V67guwDMb2iic8yI9XZ2+2OhSvpATVh8VUD+KCtnX4OimVo/lFVofjtH4KfHeDs3y3iIiIVEqvztpCYbGD+wc3P+N1ozrHsvVANit3ZXomMKm0nv5pA1n5RYy/pC12Wxkq8M5/CV7vAHmZboutrKoH+zO2Vzwz16exfu8Rq8PxOkpQfdzYXo3ILijma28pQtD6Eki8Hhb+Dxa+YnU0IiIi4mJbD2QzcXkqo7s2ID7yzNssz29bl5AAu/e8ThGfNHtjGj+s3stt/ZuQEBNe+huP7oPlH0DCUAiu4bb4yuP6XvFUC/LjFZ1F/QclqD6uTf3qdImP4ONFKd7Ra8wwYOhL0PpSmPUEJH1sdUQiIiLiQuNnJBPkZ+OOgU3Pem1YoB/nt6nDT2v3klNQ7IHopLLJyi/ikSnrSIgOZ1y/JmW7ecFL4CiGvv9yT3AVUC3Inxt6N+LXDWn8sVurqCdSgloJjO0Vz57MPH7ZkGZ1KE42Owx/B5oOhp/ugU0zrI5IREREXCApJYNfNqRxS9/GRIYFluqeUZ1jySks4ec/9rk5OqmMnp+ezIGsfP5zaVsC/MqQuhzeCSs+hQ5XQ0QZzqx60HU9G1I92J9XZuks6omUoFYCg1pE0yAixPqWMyey+8PIT6HbrdCgq9XRiIiISAWZpslz0zYSFR7I2N6lf8HfKa4mjWqHapuvlNnv29L5cukuru8ZT/vYGmW7ef54MGzQ5363xOYK4UH+3Ng7ntnJB1iTmml1OF5DCWolYLcZXNezISt2HmbVrsNWh/MX/2AY8jwE14QjeyB1mdURiYiISDnNXJ/Gyl2Z3HNOM0IC/Ep9n2EYjEyMJWnnYbYdzHZjhFKZ5BWW8NB3a2kQEcL/nZtQ9gG63ATnv+w1lXtP55oeDakRolXUEylBrSQuS4wlPMjPu1ZRT/TjnfDZCNi7yupIREREpIyKShyMn5FMk6gwLutUv8z3j+hYD7vN4OskraJK6bwyazMp6bm8cEkbggPsZbvZNKFOO+h4tXuCcyHnKmoj5m466F0LTRZSglpJhAX6cUWXBkxft589mXlWh/NPF77mXEn9/BI4qHeIREREfMnE5alsP5TDg0Oa42cv+8vHqPAgBjSP4tsVeyjyhqKO4tXW7s7k/QXbuaJLLD0aR5bt5rT18H5/OJDsnuDc4JoeDakZ4q+KvscoQa1ErunREIAJi1MsjeOUqteDMd+DYYcJFzkProuIiIjXyy4o5tVZm+nSMIKBLaLKPc7IxFgOZRcwN/mAC6OTyqaw2MED36yldnggD57XouwDzH0O0rdBWPn/rnpaWKAfN/VpzG+bD7Jip1ZRlaBWIvVqBHNe6xi+XLbLO0u512oMV0+Bohz47GLIr1wltVenZvL8tI04HKbVoYiIiLjM+/O3cyi7kIeGNscwjHKP0z+hNrXDA/k6abcLo5PK5p3ftpG8P4tnLm5D9WD/st28ZyUk/wTdb4eQCPcE6CZjuscRERqgs6goQa10xvaKJyu/mMneesYjpjWM/gbaj4bAalZH4zLJ+48y5sOlvDt/O6mHc60OR0RExCUOZOXz/oLtDG0TQ4cGNSs0lp/dxiUd6zN30wEOHM13UYRSmWxJy+L1OVu4oG0dzmkZXfYB5jwDwRHOLhI+JjTQj5v7NGLBlkOs2JlhdTiWUoJayXRoUJOODWrw8eIUSrx1JS+2C/S5DwwDts+DwhyrI6qQ1Ixcxny4jOJjv9/J+7MsjkhERMQ1Xp21hcJiB/cPbu6S8UYm1qfEYfLtyj0uGU8qjxKHyQPfriUs0I8nh7Uq+wA7F8O22dDrbgjyzUWQq7vHERkWwP9+rdpnUZWgVkJjezViZ3ouszemWR3KmR3dC19cBhNHQ3GB1dGUy6HsAsZ8tIz8ohK+uMHZ73WzElQREakEth7IZuLyVEZ3bUB8ZKhLxmxUO4zODWsyOSkV0/TSN9LFEp8uTmHVrkyeuLAVkWGBZR8g/yjU7Qidb3R9cB4SEuDHzX0as3DrIZanVN1VVCWoldDgVtHUqxHMB97acua4anXhgldg+1z49gYo8cJzs2eQlV/EtR8vY9+RPD66tjMdGtSkQUQIm9KUoIqIiO8bPyOZID8bdwxs6tJxRybGsv1QDkkqBiPHpGbk8uLMTfRPqM1F7euWb5CEIXDjHAgIcW1wHnZVtzgiwwL5369V9yyqEtRKyM9u47qeDVm2I4N1e7y8EFGH0TD4edg4FX68Cxy+UXo+v6iEmz9bwcZ9Wbw1uiOJDZ0H8ZtFh7NJK6giIuLjklIy+GVDGrf0bVy+1awzOL9tHUID7Exa7qX1MsSjTNPkoe/+wGbAs8PblL0Ql2nCgv/C0X3O42M+LjjAzi19G7F4WzpLt6dbHY4llKBWUiM7xxIaYOdDb19FBeg+Dvo+CKs/h9lPWR3NWZU4TO6ZtJrF29J58dK2DGj+1yH+5jHh7DiUQ0FxiYURioiIlJ9pmjw3bSNR4YGM7R3v8vFDAvy4sF1dfl67j6z8IpePL75lctJuFm49xINDW1C3RnDZB9g03fn6ceuvrg/OIld1i6N2eCD/q6IVfZWgVlLVgvwZ1bkBP67Zy/4jPlApr9+D0Ps+SBhqdSRnZJomj/2wjunr9vPo+S0Y0bH+Sc83iwmn2GGy/aBvF34SEZGqa+b6NFbuyuSec5oREuDnljlGdo4lr6iEn9fuc8v44hvSjubz75830CU+gtFdGpR9AIcD5j4LEY2g3ZWuD9AiQf52bu3bmCXbM/h9W9VbRVWCWold17MhDtNkwu8pVodydoYBAx+DBl3BUQLb5lod0Sn979fNfLl0F7f2a8wNvRv94/mE6HAANuscqoiI+KCiEgfjZyTTJCqMyzrVP/sN5dQhtgZNo8KY5K1t8cTtTNPkse/XUVjs4IURbbDZyrE9d8MUSFsH/R4Gu3veTLHKlV0bEHVsFbWqFRRTglqJxUaEcG7LGL5ctou8Qh/acrr0HfjsYlj9pdWRnOSTRTt4bc5WRibW54HBCae8Jj4yFH+7oXOoIiLikyYuT2X7oRweHNIcP7v7XiYahsGozrGs2pXJFr2pWyVNX7efXzakcc85zWhUO6zsA5QUw9znIKoltL7E9QFaLMjfzrh+jVm2o+qtoipBreTG9o4nM7eIb1futjqU0ut8AzTqB9+Pg4X/cx5+t9gPq/fw5I8bOKdlNM+d4QB/gJ+NRpFhSlBFRMTnZBcU8+qszXRpGMHAFlFun+/iDvXwsxkqllQFHc4p5PEf1tG6XjVu6FXOc867l0HGduj/MNgqZ0pzeZcGxFQLqnKrqJXzT1P+lBhXk3b1q/P6nC2+8w6lXyBcMRFaj4BZT8L3t1raJ/W3zQf5v6/X0CU+gtev6HDWd5QTYsLVakZERHzO+/O3cyi7kIeGNi97JdVyiAwLZFCLaKas2kNhsW9U8RfX+PfPG8jMLWL8Je3Kv1If1wPuWAHNL3BtcF4kyN/OuP6NWZ5ymEVbq84qqhLUSs4wDJ4b0QaHCSPeXszCLYesDql0/IPhkg+dZwrWfOXcwmGBVbsOc+vnK2gaHc4H1yQS5G8/6z0JMeHsPpxHdoFv9XUVEZGq60BWPu8v2M75berQoUFNj807qnMs6TmFzElO89icYq15mw7w3co93NK3MS3rVivfIIe2OmuWRDSqFK1lzmRU51jqVK9aq6hKUKuAVnWr8/1tPalbPZhrP17GxGW7rA6pdAwD+v0LrpgEve91PlbiuaRv64Esrv9kOZFhgXx6fWeqBfmX6r5mKpQkIiI+5tVZWygsdnD/aWosuEufZrWJqRakbb5VRHZBMY9MWUfj2qHcMbBJ+QYpyoNPzocfbndtcF4q0M/OuP5NWLHzMAt8ZaGpgpSgVhH1agTzza3d6d64Fg9+9wf/mZGMw+Ej78IkDIGg6nBkN7zZBZKnuX3KvZl5jPlwGXabjc/GdiEqPKjU9zaPOZag6hyqiIj4gK0Hspm4PJXRXRvQMDLUo3PbbQaXdKrHb5sP+kZbPKmQF2cks/dIHuMvbUug39l3pZ3S8g8gez90GO3a4LzYyMT61K1Cq6hKUKuQ8CB/Pr62M1d2bcDb87Zxx1eryC/yoeq+hh2CqsHEK2HhK24rnnQ4p5AxHy0jK7+YT6/vTFytsv2wrlcjmJAAO8lKUEVExAeMn5FMkJ+NOwY2tWT+kYmxOEx8q6CjlNmCLQeZsGQn13RvSKe4iPINUpDlLKDZqD807OXaAL1YoJ+d2wY0YdWuTH7bfNDqcNxOCWoV42e38ezFrXlkaAumrdvH5e8t4VC2dQWIyqRaHbh2GrS6GGY94azy6+LiSTkFxVz3yXJ2ZeTy/jWJtKpbvcxj2GwGzaLDtcVXRES8XlJKBr9sSOOWvo2JDAu0JIa4WqF0axTB10mpvrO7S8ok5VAOt3+5imZR4RXbRr7kHchNhwGPuS44H3FZp1jq1Qjmf7O2VPpVVCWoVZBhGNzYpxFvj+5E8v6jXPzmIt+p8BsQApd+DP0egjVfwoSLXXYutbDYwa1frGTt7kxev6ID3RrVKvdYCdHhajUjIiJezTRNnpu2kajwQMb2LmerDxcZmRjLzvRclu7IsDQOcb2s/CJunJCEYcD7YxIJDfQr30B5h2Hx65AwFOp3cm2QPiDAz8btA5qwJjWTeZsq9yqqEtQqbEjrGCbd1J38Igcj3l7Moq0+cvDaMKDfg85EtcUFYC/nf3QncDhM7pu8hvmbD/L8iDYMbhVTofGaxYSTnlPoO6vTIiJS5cxcn8bKXZncc04zQgIq/rO0Is5rXYfwQD8mJ6lYUmXicJjcM2k12w/l8NaVHWlQK6T8gwVWg6EvVsnV0+Mu7VSf+jWDK/1ZVCWoVVy72Bp8f1sP6lYP5pqPljFpuY9U+AVnn9Tutzk/X/Y+bJpRrmFM0+TpnzYwdc1eHhiSwKjODSocmgoliYiINysqcTB+RjJNosK4rFN9q8MhOMDOsPZ1mbZuH0fzi6wOR1zkv79uZtbGAzx2fgt6NIks/0CmCTY7tBsF0S1dF6CP8bfbuGNAE9buPsKc5ANWh+M2SlCF+jVDmHyswu+/vvWxCr/g3OK75iv46nJY9FqZiye9OXcrnyxOYWyveG7t29glIR1vNaNCSSIi4o0mLk9l+6EcHhzSHD+7d7wcHNU5lvwiB1NX77U6FHGBn9bu5Y25WxmVGMs1PRpWbLBfH4Pvb3NbgUxfMqJjfRpEhPBKJT6L6h3/I4nlqgX585GvVvi1+8E1PzmLJ/36mLMvVimLJ325dBcv/bKZ4R3q8cjQFhguavZcOzyQWqEBKpQkIiJeJ7ugmFdnbaZLwwgGtoiyOpw/talXneYx4Xytbb4+b92eI9w3eQ2d4mry9MWtKvb66uheWPoeYDqPeVVx/nbnWdQ/9hxh1sbKuYqqBFX+5H+swu/DQ5szbd0+rnjfhyr8nlg8afXnMOEiyDnzmdrpf+zj0e//oH9CbcZf2habzbX/6TWLDtcKqoiIeJ3352/nUHYhDw1t7rI3Zl3BMAxGJsaydvcRNu47anU4Uk6Hsgu4aUISNUMCeOeqTuXvd3rc/BfBdEDff7kmwEpgRId6xNUK4ZVKehZVCaqcxDAMburTmLdHd2TjvqMMf2sRWw/4SJJ1YvGkI3ugMOe0ly7edoi7Jq6mQ4OavDW6E/5u2N6UEBPOlrQs39ouLSIildqBrHzeX7Cd89vUoUODmlaH8w/DO9QjwG7TKqqPKix2MO7zlaTnFPLe1YnUDq9g66LDKbByAnQcAzXjXBJjZeBnt3HHgKas33uUXzakWR2OyylBlVMa0roOk27qTl6hg+Fv+VCFX3AWT7ojyfkfWUEWbJ930tPr9hzhpgkraBgZwofXJBIcUMF39k4jISacnMIS9mTmuWV8ERGRsnp11hYKix0V60XpRjVDAzinZTRTVu2hoNhHjhrJn578cT3LUjIYf2lb2tQvey/5f5j3H7D5QZ/7Kz5WJXNx+7rER4byyqwtlW4xRAmqnNbxCr91qgdxzUfL+Hq5D72b6XfsHbvf/uPslbr4dTBNdhzK4ZqPllE92J8J13elRkiA20I4XihJ/VBFRMQbbD2QzcTlqYzu2oCGkaFWh3NaIzvHkplbxKwNlfN8XWX12ZKdfLl0F7f2a8xF7etVfEDThKBq0O1WqFan4uNVMn7HKvpu3HeUmev3Wx2OSylBlTOqXzOEb27tQffGtXjg27WM97UKv/0ehpbD4JdHyfvmFq77YCEmMGFsF2KqB7l16mbRYQBsUqEkERHxAuNnJBPsb+eOgU2tDuWMejWJpG71ICZpm6/PWLI9naemrmdA8yjuO9dFq/OGAef9BwY96ZrxKqFh7erSqBKuoipBlbM6XuH3ii4NeGveNu6Y6EMVfgNC4NJPyO9xH8HrJ/JS3uN8dnkjGtcOc/vU4UH+1KsRrBVUERGxXFJKBr9sSOPmPo2IDKvguUA3s9sMLk2MZcGWgzom4wNSM3IZ98VKZ9Gey9tjd0XRybT1sPxDKC6s+FiVmJ/dxl2DmrIpLYvp6yrPKqoSVCkVf7uN54Y7K/z+vNa3KvzmFZtctW0gdxffQQe/HbTa+ZnH5k6ICVerGRERsZRpmjw3bSNR4YGM7R1vdTilclmn+pgmfJO02+pQ5AxyCoq5cUISRSUO3h+TSLUgf9cMPPvfMPspKMp1zXiV2AVt69IkKoxXZ2+uNKuoSlCl1E6s8Lthr29U+C0qcXD7lytZseswg0aOw37jHOe2X4DcDLfPnxATzraD2RSVONw+l4iIyKnMXL+flbsyufecZoQE+FkdTqnERoTQs0ktJq9IrTQvuisbh8Pkvslr2JyWxRtXdqSRq3an7U6CzdOhx50QXMM1Y1ZidpvBnQObsjktm5//2Gd1OC6hBFXK7Lw2dZh0c3fyCksY/tZiFntphV/TNHnw2z+YnXyApy9qzQVt60JMa/ALgAMb4dV28PubzkP4bpIQHU5RibM4k4iIiKcVlTgYP2MTTaLCuLRTfavDKZORibHsPpzH79vTrQ5FTuGNuVuZvm4/D53Xgr7Nartu4Dn/hpBI6HqL68as5M5vU4emUWG8OnsLJZXgDZ1SJaiGYQwxDGOTYRhbDcN48BTP32sYxgbDMNYahjHbMIy4Y4+3Nwzjd8Mw1h97bpSrvwGxRvvYGkwZ15OYakGM+WiZV/Yre2F6Mt+u3M09g5pxdbe/9c6q0QAa9YOZD8OPd7rtjIMq+YqIiJUmLk9l+6EcHhzSHD839Px2p8GtYqgW5MckV3URKMiGrbMh6WOY9SR8cz18MAgmjnbN+FXIzPX7+e+vmxnRoR43uHLb+I4FzvaAve+FQPfXC6ks7DaDuwY1ZeuBbH5au9fqcCrsrPs8DMOwA28C5wC7geWGYUw1TXPDCZetAhJN08w1DONWYDwwCsgFxpimucUwjLrACsMwZpqmmenqb0Q8LzYihG/H9eC2L1bywDdr2Zmew/+dk4DNFYfjK+jd37bx7vztjOkex50Dm/zzgoBQuOxTmPcczH8R0rfDyAkQWsulcTSOCsVuM3QOVUREPC67oJhXZ22mS8MIBraIsjqcMgvyt3Nxh3pMXJ7Kkdwiqoec5XxjTjrsWw2Zu4792un82KgfDHgUju6Bz0c4r7X5QfVY5xvWtRo7H3M4wOZbSbwVNu3P4t5Jq2kXW4PnRrTBMFz4um/dtxBeBxKvd92YVcTQ1nVIiN7Ka7O3cEHbuq4pVmWR0hxE6AJsNU1zO4BhGBOBi4A/E1TTNOeecP0S4Kpjj28+4Zq9hmEcAGoDmRWOXLzC8Qq/j32/jjfnbmNnei4vXdaOIH+7ZTFNTkrl+enJXNC2Dk9e2Or0/3HabM4fWJEJ8MNt8Mn5cMtCsLvufE6gn534yFCStYIqIiIe9v787RzKLuT9Mc1dm0R40MjEWCb8vpMf1uxhTKtjR3T+noB2vQXaXArb58K3Y503npiAhh7bflozHq6b7nwsvA7YTnitkroMptwMV30LEY08/436iMM5hdwwYTmhgX68d3Un17/eu+B/cORe8A927bhVgO3YKuq4L1by45q9XNzBBb1oLVKaV+L1gBP3VuwGup7h+rHA9L8/aBhGFyAA2FaWAMX7+dttPD+iDQ0jQ3lhejJ7M/N4f0witSwoYz9rQxoPfvcHvZtG8t+R7Uu3mtv2MoiId76z6sLk9LiEmHDW7Tni8nFFRERO50BWPu8v2M75berQoUFNq8MpnYwdkLHthAR0F60zdzE06homLU9lTNYvsPg157UnJqB+x15vxPc9fQIKzhoUcT1OPXeNBpC131k99rKP3fc9+rCiEge3fbmStCMFTLy5G9HVXNhP3jSdbz5Et3T+WUi5DGkVQ/OY8GOrqHV8blv/cS59NW4YxlVAItD3b4/XAT4DrjFN8x/lTA3DuAm4CaBBA/2l9EWGYXBL38Y0iAjhnkmrGf7WYj66tjNNojx3fmDZjgxu+3IlretW4+2rOhHgV4Z/lPUTcf7VBWY+AtXqQrdxzibRFZQQHc60P/aRW1jsM9UTRUTEt706awuFxQ7uH5xgdSh/ObTlr1XPP3+lwnXTwO4PP93jXAWFkxLQ81rW4o55R9k8cDjNrjvv9AloWG3nr/IIj4Hut8P88c6P9TtV7HuthJ79eSOLt6Xz0mXt6OjqNz2Sf4JJV8GYH5xbsqVcbDaDuwc15ZbPVzJ1zV5GdPStwmjHlebV8h4g9oSv6x977CSGYQwCHgH6mqZZcMLj1YCfgUdM01xyqglM03wPeA8gMTHR90tPVWFD29ShTvUgbpyQxIi3FvHO1Z3o0TjS7fNu3HeUsZ8up17NYD66tjNhgeVMBEuKnT88f38DDibD0Jed77hWQLPocEwTtqRl0y62RoXGEhEROZutB7KZuDyVq7o2oGFkqGcnP7IHtsz8KwG1+cGI95zPfTQYco9V5LX5Q/X6zmSzIAtCIqD/I9D3gX8koH1yiwhYOIsvtgbw1EUd3Rd7zzsh6SP49XG49ieXvEldWUxavotPFqcwtle866tBO0pgzrNQqynE9XLt2FXQuS1juKZ73J+FOn1RaV7FLweaGoYRjzMxvRy48sQLDMPoALwLDDFN88AJjwcAU4AJpml+47Koxat1aFCTKeN6cv0nyxnz4TKeH9GGyxJjz35jOaVm5DLmo2WEBvgx4fouFdtabPeDyybA3GdhwUsuKZ7UPOZYJd+0LCWoIiLiduNnJBPsb+eOgU09N2lRvnP18fc3oTj/rwQ0utVf11z8DgSGH0tAY/65Ahrb+ZRDVw/xZ0irGL5fvZeHhrZwX52LwHDo9yBMuw+2/ALNBrtnHh+zYmcGj36/jt5NI3novOaun2Ddd3BwI1z6kVuOWlU1NpvBUxe1tjqMCjnrHkjTNIuB24GZwEbga9M01xuG8bRhGMOOXfYiEAZMNgxjtWEYU489PhLoA1x77PHVhmG0d/l3IV4nNiKEb27tQddGEdz/zVpemrnJLY22D2YVcPWHSyksdjBhbBfq1wyp+KA2Gwx8DEa8D7uXwwcDIL38R6djI0II8rep1YyIiLhdUkoGv2xI4+Y+jYj0RC2I473E7f6w5VdoMQxuWw6PpsFdq+HyL/66ttm5ENcdqtf7Z3J6FqM6x3Ikr4iZ6/e7LvZT6XQtNL/AWe1f2JuZx82fraRejWDeuKKj6880lhQ7OypEt4aWw107tvisUr1NYZrmNGDa3x57/ITPB53mvs+BzysSoPiu6sH+fHJdFx6dso435m4lJT3HpRV+s/KLuPbjZaQdLeDzG7q6fitD25HOin+/Pu7celROdptBs+hwtZoRERG3Mk2T56ZtJCo8kLGu7E156slg03SY/RRc8iHEtIaxv7it+mr3RrWoXzOYr5NSuai9G6uT2v1PTqqrsPyiEm7+bAX5RSVMvKnr2dv8lMearyBjO1z+lVr8yJ/0N0Hcyt9u44VL2vCvIc35ae0+Rn+wlPTsgrPfeBb5RSXcNGEFm/Zn8dZVHekU56YKhbGdncUbgmvC0X3Osylm2VeCm0WHq9WMiIi41cz1+1m5K5N7z2nm3qJ8e1bCJxfAxCuc5wcLs52Pu7E1iM1mcFmnWBZtTSc1I9dt8/zp0Fb4fhwUemAuL2SaJg98s5Z1e4/wyqj2NIly03nG5ufDkBcg4Tz3jC8+SQmquJ1hGNzarzFvje7Iuj1HGP7WYrYeyC73eCUOk7snrub37c5Kcv0T3Nx8/HiRhOXvOysM/nQ3lBSVaYiE6HAOZhWQkVPo+vhERKTKKypxMH7GJppEhbm+iM1xWWnw7Y3wfn/nmcGhL8G436FBN/fM9zeXJtbHMGDyit3unyznIKz+Apa85f65vNA7v21n6pq93HduAoNaRrtnEtN07lDrdqsKUslJlKCKxwxtU4evbupGbmExI95axO/b0ss8hmmaPPr9Omas38/jF7T0bBPi/o9Cr3thxSfw2XDIzSj1rQnHCiVpm6+IiLjDxOWpbD+Uw4NDmrv+nODxnUOGzdkGpte9cOcq6HKjc0ush9SrEUyvJpF8k5RKiRvqWpwkrrvzLOrCVyDnkHvn8jJzktMYPzOZC9rWYVy/xu6ZpDAX3u4J66e4Z3zxaUpQxaM6HqvwG1UtiDEfLeWbMr4L+t9fN/PVsl3c1r8x1/dy8/mav7PZYNATMPxdSF0K7w+Ag5tKdevxBFWFkkRExNWyC4p5ddZmusRHMLCFC3cVlRTB0nfh7R5QkO3sMXr3H86fhUHVXTdPGYzqHMveI/ks2uqBpHHgE1CUC7+Nd/9cXmLrgSzu+mo1LetU48VL22G4a2Vz+ftwYD2EuWl1VnyaElTxuNiIEL69tQdd4iO4b/IaXv5lE2YpznV+vGgHr8/ZyuWdY7nvXAsbj7e7HK792XnmJvmnUt0SFR5IjRB/NmkFVUREXOz9+ds5lF3IQ+c1d01CYZqw8Ud4sytMfwBCIyHvsPM5N54zLY1zWkZTM8SfSUmp7p+sdjPodA0kfVihav6+4kheETdOWEGgv433xiQSHOCmdj75R50r040HQlwP98whPk0JqljieIXfUYmxvD5nK3dOXE1+Uclpr/9h9R6e+nEDg1tF88zFrd33jl5pxXaBWxc7tzkBpK0/Y/Ekw3BW8tUKqoiIuNKBrHzeX7Cd89vUoUMDFxQM3LcWPj4PJl3l3L575WQYMxVquK+feVkE+tm5uEM9fl2fxmFP1HXo+6CzUOK+Ne6fy0IlDpM7v1rF7sO5vH1VJ+rVcOMbEUvehrwMGPCo++YQn6YEVSxzvMLvA0MS+HHN3tNW+J236QD/9/UaujWK4NXLO7j+bE15hUU5D/XvXQXv9HYWUDpD8aSE6HA2788q1WqxiIhIabw6awuFxQ7uH1zBnUUOh/Nj/hHnauEFr8Ati5y9S61+U/hvRibGUljiYMqqPe6fLDwa7l4HrUe4fy4L/WdGMr9tPsjTF7Wmc8Pyt9Y7q9wM+P0N5/neeh3dN4/4NC95pS9VlWEYjOvXhDev7Mgfxyr8bjv4V4XflbsOc+vnK0mICef9MYku66HqUjHtoOedsOJj+HwE5GWe8rKEmHCyCorZdyTfs/GJiEiltPVANhOXpzK6awMaRoaWb5C8wzDzEfhypHMnUHxvuHstJF4Hdje2qqmAFnWq0bZ+db5OSvXMm77+Qc6iPqs+L1erOW/33crdvDd/O2O6x3FFlwbunSzvMNRpB/0fdu884tOUoIpXOL9tHSbe1I2cgmJGvLWY37elsyUti+s/WU5UtUA+ua4L4UGeqxRYJjYbDHoSLn4Hdv4OHw2GzH+ejVGhJBERcaXxM5IJ9rdzx8CmZb+5uAB+fxNebe/8GB4NJce2zFp8zrQ0RibGkrw/iz/2HPHMhOu+gR9ug03TPTOfh6xOzeTB7/6gW6MIHrugpfsnrNUYrv0Jolu5fy7xWUpQxWt0bFCT72/rSWRYAGM+WsoV7y/B327js+u7Ujs80Orwzq79FXD1d3B0H3xxqbN5+QmaHWtyrUJJIiJSUUkpGfyyIY2b+zQiMqyMPyOTp8GbXWDmw85tlrcshIveBD8f+Fl7zLD2dQn0szFpuQeKJQG0uwJqNYVZT0BJsWfmdLMDR/O5+bMkosIDeWt0J/zdeYTKNGHGQ85jUSJnoQRVvEpsRAjfjetJl/gICosdfHpdFxrUCrE6rNKL7wNjZzrP7tjsJ20Fqh7iT53qQVpBFRGRCjFNk+embSQqPJCxvcvQcu34OdNDm8E/FK76Fq6eAjGt3ROoG1UL8mdomzpMXb2XvMLTF1l0Gbu/c7fUoc2w+nP3z+dm+UUl3PTZCrLyi3l/TCIRoQHunXDVZ7DkLedOM5GzUIIqXqd6sD+fj+3K0ocH0bJuNavDKbuoFs4G3w4HfHM9rPj0z6dUyVdERCpq5vr9rNyVyb3nNCMkoBTnRNO3wddjYO4zzq+7jYNbFkCTQe4N1M1GJsaSVVDMjPX7PDNh8/MhthvMfQ4KczwzpxuYpskjU9axOjWT/45sR4s6bn6tlbHDuXoa3we63uLeuaRSUIIqXskwDPf13/KU4nwoOAo/3glzngHTpHlMOFsPZlNc4rA6OhER8UFFJQ7Gz9hEk6gwLu1U/8wX52bA9Aed/Uy3zIJA51ET/AKcu3x8XLdGEcTVCvHcNl/DgHOehuw0Z59YH/XRohS+XbmbuwY2ZUjrOu6dzFECU24BwwYXveWs2yFyFt5Znk2kMggIgSsmws/3wvwXITOVhNh/UVjsICU9lyZRYVZHKCIiPmbi8lS2H8rhgzGJp2+7Zpqw+HWY/xIUZkGHq51VU8NjPBusmxmGwWWd6vPSL5vZmZ5DXK1yVjIuiwZd4eYFUKet++dygwVbDvLsz86+8neVp7hWWS1+DVKXwPB3vaaXrng/vY0h4k52f7jwNWcz6rUTOXfVbYSRy2YVShIRkTLKLijm1Vmb6RIfwcAWUf+8wOFwJqeGAalLncnUrYth2GuVLjk97tJOsdgMmJy023OTHk9O07d5bk4XSDmUw+1frqJZdDj/Hdkem80D/W2rxzrfIGk7yv1zSaWhBFXE3QwD+twPw98l2N9OseGnc6giIlJm78/fzqHsQh46rzmG8bfkImURfDAQNs9wfn3JhzB6srMuQiUWUz2Ivs1q882K3ZQ4PNijdOl7zkrIh7Z6bs4KyMov4oYJSRgGvD8mkdBAN2+iPF4kss2lcNEbztdCIqWkBFXEU9pdjv2aqdStVZPMXeth31qrIxIRER9xICuf9xds5/w2dejQoOZfTxzaAhNHwydDIWv/X4mBf5A1gVpgVOdY9h/NZ/7mg56btNXF4BcEs5/y3Jzl5HCY3DNpNTsO5fDWlR2JjfBAd4RfH4epd/xVOVqkDJSginiSzUZCTDgj9rwIH58HW2dZHZGIiPiAV2dtobDYwf2DE5wP5B+Fn+9zFkDaPg8GPAZ3rIDmQy2N0woDmkdTKzTAc8WSAMKioMedsHEqpC7z3Lzl8PKvm5i18QCPX9CSHk0i3T9hyiLnGWjDpqJIUi76WyPiYc2iw7kl71YcNRrCFyNh5QSrQxIRES+29UA2E5enMrprAxpGBDsf9AuEbXOg07Vw5yroc5+zOF8VFOBnY3iHeszamEZ6doHnJu5+G4RFwy+PndT33Jv8uGYvb87dxuWdYxnTPc79E+Yfhe9vgZoN4dxn3T+fVEpKUEU8LCEmnH1mBMnnTYJGfZ1bYOY867U/3ERExFrjZyQT4m9wX53V8GZnyNzlTFBvXQwX/Ne5mlfFjewcS7HDZMqqPZ6bNDAM+j3krFK7aZrn5i2ldXuOcP83a0iMq8nTF7X+57lld5jxEBzZ7azaG6huBVI+SlBFPCwhxtmHbmMGcOXX0P4qmD8eUhZaG5iIiHidpJQMspLnMDv8ScKn3QYBoc5VKqhS50zPpll0OO1jazBpeSqmJ9/w7XA1nDceGvXz3JylcCi7gJsmJBEREsDbV3UiwM8DL/k3z4TVn0Ove5wVpEXKSQmqiIfFRYQQ4GdjU1qWsw3NRW/ANT9CfG/nBcWF1gYoIiJewczYgfnlKL4KeJZIWzaMeB9unAcxra0OzSuN6hzLlgPZrE7N9Nykdj/oerPzjYOSYs/NewaFxQ5u/XwFGbmFvDcmkdrhgZ6ZuEF36Pug85dIBShBFfEwP7uNplFhf7WaMQyI7+P8fNXn8F4/OOLBLUoiIuKVlq9aRVj+PlYn3I3tjiRoO1JFZ87ggrZ1CPa383WSB4slHbf6K3ijExRY20bONE2emLqe5SmHGX9pO1rXq+6JSSEnHYKqQf+HwC/A/XNKpab/5UQskBAdfupeqNXqOs8WfTAI9q/zfGAiIuIV0o7mM+73cO6u8SatRz4B/sFWh+T1woP8Ob9tHX5cs4/cQg+vZkY2hcMpsPgNz877N58v2clXy3Zxa7/GDGtX1zOTrv4CXu8IBzd7Zj6p9JSgiligWUw4+4/mcyS36OQnGg+A6481Wf9oiLNCo4iIVCnFuZmse/saqhfs543RHfGz6+VaaY1MjCW7oJif1+7z7MT1E6Hlxc72Kllpnp37mN+3pfPUjxsY0DyK+85N8Mykh1Ng+oMQ0wZqNfHMnFLp6X88EQscL5S0+cApVlFjWsMNs6BGA/jiMlj3nYejExERKy377DEG5s3g8QG1aRodbnU4PqVzw5o0igxlctJuz08+8HEoKYDfXvD41KkZuYz7YgVxtUJ45fL22G0eqNjrKIEptzqPKl38lrafi8vob5KIBRKOveBIPtU2X4Dq9eD66dD8fIhq6cHIRETESguWLafT3i9ZVXMIffsPsTocn2MYBpclxrIsJYPtB7M9O3mtxpB4Paz41KPbXXMKirlxQhIlDpMPrulMtSB/z0z8+xuwazGc9x/nm+oiLqIEVcQCdaoHER7kx+bTJagAQdVh5ASIag5F+bDwFSgpOv31IiLi03al55I37VFMw07Lq1+yOhyfdUnHethtBl9bsYra5wFocSHY7B6ZzuEwuW/yGjanZfHGlR2Jjwz1yLzkZsC8/0DzC6DdFZ6ZU6oMJagiFjAM4/SFkk5ly0yY9YRzy+/x/nciIlJp5BeV8MYnn3IuSyjodieBEbFWh+SzoqoF0T+hNt+u3E1xicOzk4fVhpGfOldTPeD1OVuZvm4/Dw9tQZ9mtT0yJwAhEXDtj3Dhq84tviIupARVxCLNYsLZlJZVuobiLS+Ci96ElAXw8VA4utf9AYqIiMc89eMGAjI2kRsWR/UB91odjs8bmRjLwawC5m06aE0A23+DH25ztmBxkxnr9vO/WZsZ0bEeY3vFu22ef0hZ6Dx/Wq8ThEZ6bl6pMpSgilikeUw4R/KKOJBVULobOlwFV34Nh3c429CkrXdvgCIi4hHfrtjNV8t2Ua33LYTcnQQBIVaH5PP6N48iMiyQSVb0RAVnddtVn8PGH90y/OyNadz79WraxdbgueFtMDy1irnzd/jkAme1YhE3UYIqYpFmZyuUdCpNBsJ108F06IeDiEglkLz/KM99v4zHoxZy74CG4BdgdUiVgr/dxiUd6zEn+QAHsvI9H0D70VC7Ocx60qX1I7YfzOa6j5cx9tMk6tYI5r2rOxHk75nzrhRkwZSboWYcdL7BM3NKlaQEVcQixyv5nrFQ0qnUaQs3zIbz/+v8OifdxZGJiIgnZBcUM+7zldzm/yPXH30Lv4PaGeNKlyXGUuIwmbJyj+cnt/vBoKcgYxus+KTCw2UXFPPC9GQGvzKf5SmHefT8Fky/qzfR1YIqHmtpzXgIjqTC8HchMMxz80qVowRVxCI1QwOICg8s2wrqcdXrObeAZeyANzrBb+Pdes5FRERcyzRN/vXtWorSU7iWn6DtKOeZPnGZJlFhJMbVZFJSaunqPbhas8EQ1wvmveBcfSwH0zSZsmo3A16axzu/bePi9vWYc19fbujdCH+7B1/GJ0+DVZ9Bz7ugQTfPzStVkhJUEQslxISzOa18P7QAqFYPmg2Buc/C1DvUhkZExEdM+H0nP6/dx8f1f8Jms8PAJ6wOqVIa2TmW7QdzWLHzsOcnNww452nwC4L0bWW+fd2eI1z6zu/cM2kNdaoHMWVcD168rB1R4R5cNT1u7USIbgP9Hvb83FLl+FkdgEhVlhAdzudLd1LiMLHbylHgwC8ALn4bqsfC/PHO6r4jP4XAcNcHKyIiLrFq12Ge+XkDt8Sn0WTfr9DvIefOGHG589vU4amp65m0PJXEhhGeD6B+J7hrNdj9S31LRk4hL87cxMTlu6gVGsD4S9tyacf62MrzOsFVLv0YctN1Rlo8QiuoIhZqFhNOfpGDXRm55R/EMGDAIzDsddg+Dz69EEqKXRajiIi4zuGcQm77YiXR1YK4J+YP506YHndaHValFRroxwVt6/LzH/vILrDoZ6PdH47sgdVfnfGy4hIHny5Ood+Lc5mclMr1PeOZc18/RibGWpecbvwJdieBzQ5hUdbEIFWOVlBFLHS8UNKm/VnER4ZWbLCOY6BaXTi6z1mcQUREvIrDYXL3pNUcyi7k21t7EFivv3Pni9rKuNXIzrFMSkrl57V7GdW5gTVB/P4mLH0b6nWE2gn/fHpbOk/9uJ7k/Vn0ahLJExe2pGm0xbuhDu+EKbdAvQ4wZqrzDXERD9AKqoiFmkaHYRhU7BzqiZoMgo5XOz9f9JqzUbiIiHiFN+du5bfNB/n30Ia0cWx0vuDX1l6369igBo1rhzJpuUU9UQF6/x8EhDnbzpxgT2Yet325kiveX0J2QTHvXNWJz8Z2sT45dTjg+3HOz4e9oeRUPEoJqoiFQgL8aBARwqbyVPI9k6I8WP0lfH4JrJnk2rFFRKTMFm45xH9nbebi9nUZmf8NfDQYDm21OqwqwTAMRnWOZeWuTLYecPHP29IKrQW97oZN0yBlEflFJbw2ewsDX57H7I1p3HtOM2bd25chrWMwvCEZXPIm7FwI573g7Hsq4kFKUEUslhAdziZXraAe5x8M189wloKfchPMf1FtaERELLL/SD53TVxFk9phPDegBsbvb0CbyyCyidWhVRkjOtbHz2bwddJu64LoeitmeF0yf3iQQS/P47+/bmZg82hm3duXOwc2Jcjfbl1sJ0rbALOfhoTzof1oq6ORKkgJqojFEmLC2XEoh4LiEtcOHFwDrvoW2oyEOc/Aj3epeJKIiIcVlTi4/cuV5BWV8PZVHQmZ/2/AgEFPWh1alRIZFsjAFlF8t3I3RSUOS2LYmlnC+35XEJaxjnZ+O/nyxq68Oboj9Wt62RnkrL1QsyFc+Kq29oolVElFxGLNosMpcZhsO5BDy7rVXDu4XyCMeA9qNIA1EyEvQ1X4REQ8aPyMZJJ2Hua1KzrQJH8DrPsW+v4Lqte3OrQqZ2RiLDPXpzF74wGGtI7x2LxH84t4ddYWPl2cQlhAZ2r1+p5XB/TGz+6l60RNBsG4/s7KvSIW8NJ/GSJVR/MYZyEElxVK+jvDgIGPwa2LnMlp3mHI2u+euURE5E8z1u3n/QU7GNM9jmHt6sKClyC8DvS8y+rQqqS+zWoTFR7I5CTPFEtyOEy+TkplwEvz+GjRDi5LjGX2fQO45Jy++OGAAxs9Ekep7VoK398G+UeVnIqltIIqYrGGkaH42w2SXV0o6e+Cazg/fnczHNgAo7+BqObunVNEpIramZ7D/ZPX0K5+dR45v4XzwUs+gPRtEFDBtmJSLn52G5d2qs87v20j7Wg+0dWC3DbX6tRMnpi6njWpmXRsUIOPr+1Cm/rV/7rgp7tg03S4czUEuXj3VHkUZDtrVpimtvWK5bSCKmIxf7uNxrXD3LeC+nf9H4KSQvjwXNixwDNziohUIflFJdz6+UpsNoM3R3ck0JEPOekQVN3ZB1MsMzIxFocJ36xwT7Gkg1kF3D95DRe/uYh9mXn8b1Q7vr21x8nJKUDiWMhNh0WvuiWOMvvlEWff0+HvQKDFLW6kylOCKuIFEmLCXd9q5nTqdoCxv0J4DHw2HNZO9sy8IiJVxJNT17Nh31FeGdXeWQBn0avwegfIOWR1aFVew8hQusRHMDkpFdOF1e2LShx8sGA7A16ax/er93Bz30bMua8fwzvUP3XbmHodofWl8PubcHSvy+Iol80zYcUn0PNOiOthbSwiKEEV8QrNosPZk5lHVn6RZyasGQdjZ0JsV/juRsjY7pl5RUQquclJqUxcnspt/RvTv3kUZKY6E9QmgyA00urwBBiVGEtKei7LdmS4ZLwFWw5y3qsLeObnjXRqWJOZd/fhofNaEBZ4lpN0Ax8DRzHMfc4lcZRLTjr8cDtEt4b+j1gXh8gJlKCKeIG/CiVle27S4Jow4l04/2UIrH7260VE5Iw27jvKYz+so3ujWtwzqJnzwdlPOT+qrYzXGNqmDmGBfkyqYLGk1Ixcbv4sias/XEZRiYMPr0nk42s706h2WOkGqNkQutwEq7+AQ1sqFEu5+QVCiwtg+LvOz0W8gIokiXiBZtHOBHXT/iw6xdX03MTV60PnsZ6bT0SkksrKL2LcFyupFuTPq1e0d7YQSV0Gf0yGPvc7232JVwgOsHNhu7pMWbWbJ4e1olqQf5nuzyss4e15W3ln/nbshsH9gxO4oXc8gX7lqHzb5z7ndt+IxmW/t6JKiiAwDC74n+fnFjkDraCKeIF6NYIJDbB7rlDSifatgfkveX5eEZFKwjRN/vXtWnZl5PL6FR2ICg9yVkOd+QiExUDPu60OUf5mVOdY8osc/LRmX6nvMU2Tn9buZeDL83htzlbOax3DnPv6clv/JuVLTgFCIqDNpWCzQXFB+cYoj8xUeK0jbJ3luTlFSkkrqCJewGYzaBYTTvL+o56ffNdSmPNvaHOZ82yqiIiUyceLUpj2x34eOq85XRvVcj5oGDD0Rcg95FylEq/Srn51EqLDmZSUypVdz766nbz/KE9OXc+S7Rm0qFONVy7vQJf4CNcF9MtjsHMRjJ3lTFbdyeGA72+FvAyo1cS9c4mUg1ZQRbxEQrSzkq8rqwqWSsOezo87F3t2XhGRSmDFzsM8N20j57SM5qY+jZwPFhc6k4C67Z3FkcTrGIbByM6xrEnNPGMV/czcQp74YR1DX11A8v4snrm4NT/d0cu1ySlAVAvYswI2THHtuKey9G1IWQBDnneegxXxMkpQRbxEQkw4h3OLOJRd6NmJa7eAoBqwc6Fn5xUR8XEZOYXc/uVK6tQI4qXL2v3VTmTBy/DBQCjMtTZAOaPhHerhbzeYtPyfxZJKHCZfLt1F/5fm8dmSnYzuGse8+/pxVbc47LZTtI2pqLajnJV0Zz3l3q2+BzY652h2HnS42n3ziFSAElQRL5FwQqEkj7LZnH3PtIIqIlJqJQ6TuyauIj2nkLdHd6J68LFCO0d2O9vK1IyDgBBrg5QziggN4JyW0UxZtZvCYsefjyelZDDsjYU8POUPmkaH89Mdvfn3xa2pERLgvmBsdjjnKcjcCUkfuWcO03S2lAkMh2GvObehi3ghJagiXqLZsVYzm6wolBTXw9kL9Wjpi0WIiFRlb8zZyoIth3hqWCta1zuhVdesp8B0wKCnrAtOSm1kYiyHc4uYtTGNtKP53DNpNZe+8zvp2YW8dkUHJt3UjZZ1q3kmmMYDoVE/+G085B9x/fiG4Wwtd8n7EBbl+vFFXERFkkS8RGRYIJFhAWyyolBSwlDwDwb/IM/PLSLiYxZsOcgrszczokM9Lu8c+9cTu5Pgj6+h9/+p6JyP6N20NnWqB/GfGckczCqguMTk9v5NGNe/MSEBHn6ZbBhwztOw+ivnaqcrZaZCeB3nuWgRL6cEVcSLNIsOZ1NatucnrtXY+UtERM5o35E87pq4mqZRYTwzvPVf505NE2Y8CGHR0Osea4OUUrPbDEZ1juWVWVs4p2U0j57fgrhaodYFVKed85crFebAhGFQtwNc6qbtwyIupARVxIskxIQzaXkqDoeJzR1FGM5kz0rY9Tt0v82z84qI+IiiEge3fbGSgqIS3r6q08krbKYJ7a+EoOrOM37iM27r34Tz29ShabQX/bklfew8enPuvys+1i+PQsYOGPZ6xccS8QCdQRXxIgnR4eQWlrD7cJ7nJ98+F2Y+DDnpnp9bRMQHvDA9mZW7MnnhkrY0rv233qY2GyReD60vsSY4KTd/u827klOA9K3w+xuQtr5i42z51Vl0qftt0LCXa2ITcTMlqCJexNpCScd+cO1SNV8Rkb+b/sc+Ply4g2t7NOTCdnVPfnLBy/Dz/4GjxJrgpPLp/X/OlfhZT5Z/jNwM+OE2iGoJAx5zWWgi7qYEVcSLNDv2Du5mKxLUuh3AL0jtZkRE/mbHoRzu/2Yt7WNr8PDQFic/eXQvzH8Jcg45W4WIuEJIhDNJ3fIL7JhfvjGWf+hMUoe/qyKI4lOUoIp4kbBAP+rXDCbZ071QAfwCoH5n2LnI83OLiHip/KISbv18BX52gzdHdyTA728vnWY/7Vw5PUdtZcTFutwM1WPh18fB4Tj79X/X+/9g7Eyo09b1sYm4kRJUES/TPCaczVYkqABxPWH/H+7pvyYi4oMe/2Edm9KyeGVUe+rVCD75yT0rYM1XzvN9NRtaEp9UYv5BMOBRsPlBXkbp7zuyG3YtdZ6LrtfJffGJuEmpElTDMIYYhrHJMIythmE8eIrn7zUMY4NhGGsNw5htGEbcCc/NMAwj0zCMn1wZuEhl1Sw6nG0HsyksLse7pRXVchicNx7wcAVhEREv9PXyVL5O2s3t/ZvQLyHq5CdNE2Y8BKFR0PteawKUyq/NSBj7K4RGlu56hwO+HwdfXKY3m8VnnTVBNQzDDrwJnAe0BK4wDKPl3y5bBSSaptkW+AYYf8JzLwJXuyZckcovISacYofJjkM5np88uhV0uRGCqnl+bhERL7Jh71Ee+2EdPRrX4u5Bzf55QVEeRDaFQU+orYy4j80GhgG7V8CGH85+/bJ3YcdvcO7TzpZHIj6oNCuoXYCtpmluN02zEJgIXHTiBaZpzjVNM/fYl0uA+ic8NxuwaL+iiO9JsLKSLzh/CC7/0Jq5RUS8wNH8IsZ9sYIaIf68dkUH7KfqSx0QAhe9CR2u8nyAUvXMfRam3gl5h09/zcFNzqq/zYZAx2s8FpqIq5UmQa0HpJ7w9e5jj53OWGB6WYIwDOMmwzCSDMNIOnjwYFluFal0GkWG4Wcz2LT/qDUBJP8I0x+AQgtWcEVELGaaJg9MXkvq4TzeuLIjkWGB/7xo2fuwZqJzm6+IJwx60rlld+H/Tv18SRF8dxP4h8CFrzlXXUV8lEuLJBmGcRWQiHNbb6mZpvmeaZqJpmkm1q5d25UhificAD8bjWqHsml/tjUBxPUCRzHsXm7N/CIiFvpw4Q5mrN/Pg0Oa07lhxD8vOLrPWVU1+SclAeI5ddpCu8thyTuQmfrP5w9tgcxdcOGrEB7t+fhEXKg0CeoeIPaEr+sfe+wkhmEMAh4BhpmmWeCa8ESqpmbR4WxKs2gFNbYLGDZIUbsZEalaklIyeGF6Mue2jOaG3vGnvmj208438c552rPBifR/xPlx7nP/fC66Jdy12lnsUMTHlSZBXQ40NQwj3jCMAOByYOqJFxiG0QF4F2dyesD1YYpULQnR4aRm5JFTUOz5yYOqQZ12sHOx5+cWEbFIenYBt3+5ino1g3nxsnYYp1od3bMS1nwJ3W6FiEaeD1Kqthqx0PVm2DwD8jKdjxXmwC+POs+mqiiSVBJnTVBN0ywGbgdmAhuBr03TXG8YxtOGYRx/m+ZFIAyYbBjGasMw/kxgDcNYAEwGBhqGsdswjMEu/y5EKpnjhZK2HLBqm29P5xbfonxr5hcR8aASh8ndk1aTkVvIW6M7Uj3Y/58X/dlWpjb0vs/zQYoA9Lkf7lwJwTWcX//6OCx+HfavszQsEVfyK81FpmlOA6b97bHHT/h80Bnu7V3u6ESqqD8r+e4/SvvYGp4PoPUIZ9N5s8Tzc4uIeNhrs7ewYMshXhjRhlZ1T7MKlXMICo7CgEfVikusc/zvXl4mrP0aln8A3W6DeL3clsqjVAmqiHhWbM0Qgv3t1hVKqtfJ+UtEpJL7bfNBXpuzhUs61mdU59jTXxhWG25eoMJIYj3ThAkXwb7VULs5DHz8rLeI+BKXVvEVEdew2QyaRYdZVygJIHU5rPrCuvlFRNxsb2Yed09cRUJ0OM9c3PrU504B1n0HBzaC3Q9sds8GKfJ3hgF97oPQKBj+LvgHWR2RiEspQRXxUs2iw61bQQVYOwmm3e/srSYiUskUFju47cuVFJWYvDW6I8EBp0k8s/bDD7fD3Gc9G6DImbS4EP5vE9Rtb3UkIi6nBFXESyXEhHMou4D0bIu6NsX1gKIc2LfWmvlFRNzo+ekbWbUrk/9c0pZGtcNOf+Hsf0NJIQx6ynPBiZSGTS/jpXLS32wRL/VnoaS0LGsCiOvp/LhT/VBFpHL5ee0+Pl6UwnU9G3J+2zqnv3DvKlj9BXS7BWo19lyAIiJVmBJUES+VEO1MUDfvtyhBDY+GWk3UD1VEKpVtB7N54Js1dGhQg4fOa3H6C00TZjwMIbWcrT1ERMQjlKCKeKna4YHUDPG3bgUVnNt8dy0Gh9rNiIjvyyssYdznKwnws/HmlR0J8DvDy6D0rbBvzbG2MqdpPSMiIi6nNjMiXsowjGOFkixMUNteDtFtnIWSVLlSRHyYaZo8+v06Nh/I4pPrulC3RvCZb4hsCneugtBIzwQoIiKAVlBFvFrzmHA2p2VjmqY1ATTsCV1vUgl7EfF5k5N28+3K3dwxoCl9m9U+88U7f4fCHOdRB705JyLiUUpQRbxYs5hwsguK2ZOZZ10Qu5bCH99YN7+ISAWlHc3n6Z820L1RLe4a2PTMF2elwReXwfQHPBOciIicRAmqiBf7s1CSledQkz6CGQ86C4aIiPigp3/aQGGJg+dHtMFuM8588dxnoDgfet3rmeBEROQkSlBFvFiz461m9mdbF0TDnpBzEA5tsS4GEZFy+m3zQX5eu4/b+zehYWTomS/etxZWfgZdb1ZbGRERiyhBFfFi1YL8qVs9iE37j1oXxJ/9UBdaF4OISDnkF5Xw+A/raBQZys19G535YtOEGQ9BSITayoiIWEgJqoiXS4gJZ1OahSuoEY0gLEb9UEXE57w1dys703N55uLWBPqdpdhR6lLnG3H9H4HgGh6JT0RE/kltZkS8XLOYcBZtTaeoxIG/3YL3lAzD2Q81ZZFzhcE4y/ktEREvsO1gNm//to2L29elR5NStIpp0A2u/Rliu7k/OBEROS2toIp4uYTocApLHOxMz7EuiA6jodfd4CixLgYRkVIyTZPHvl9HkL+dR85vefYbMnc5PzbsBXa9dy8iYiUlqCJeLsEbCiU1GeQsGqIXbiLiA35YvZfF29J5YEhzaocHnvni7APwVg+Y9x/PBCciImekBFXEyzWuHYbNwNpCSQC7lsDGn6yNQUTkLI7kFvHMzxtoF1uDK7s0OPsNc56B4jxofYn7gxMRkbPScoiIlwvyt9MwMpRNVvZCBVj8OqStgxYXWBuHiMgZvPhLMhk5hXxyXZez9zzdtxZWToBu4yCyiWcCFBGRM9IKqogPaB4Tzqb9FieocT3gcAoc2WNtHCIip7Fq12G+WLqLa3o0pHW96me+2DRh5sMQXBP6qq2MiIi3UIIq4gOaRYezMyOXvEILixQd74e663frYhAROY3iEgePTFlHVHgg957T7Ow3bJ4BKQug/7EkVUREvIISVBEf0DwmHNOErQcsLJQU0wYCwmHnIutiEBE5jQm/72TDvqM8cWErwoP8z35Do35w3ovQ6Tq3xyYiIqWnBFXEBzSLdlbyTbayUJLN7uwTmKIEVUS8y/4j+bz8yyb6JdTmvNYxZ7+huAD8g6HrTapOLiLiZZSgiviAuFqhBPrZ2Gx1oaRO10Lnsc6zWyIiXuLpn9ZT7DB5elhrDOMshZGyD8IrbeCPbzwTnIiIlIneNhTxAXabQdPoMJKtLpSkCr4i4mXmbjrAtD/2c9+5zWhQK6QUNzwLuekQ09b9wYmISJlpBVXERzSLDrd+BRVg52LYMsvqKEREyC8q4fEf1tG4dig39ml09hv2r4OVn0LnG6F2KQopiYiIxylBFfERzWPCSTtaQGZuobWBzHseZj9pbQwiIsAbc7aSmpHHvy9uTaCf/cwXH28rE1Qd+j7gmQBFRKTMlKCK+IjjhZKs74fay7kKkZdpbRwiUqVtPZDFu/O3MaJDPXo0jjz7DZumwY7foN/DEBLh/gBFRKRclKCK+IiEGGeCavk237gegAm7llgbh4hUWaZp8uj36wj2t/Pw+S1Kd1P1WOhxJySqrYyIiDdTkSQRHxFTLYhqQX7WF0qqnwj2AGc/1IQh1sYiIlXSlFV7WLI9g2eHtyYyLLB0N9Vp6/wlIiJeTSuoIj7CMAwSYrygUJJ/MNTr5ExQRUQ8LDO3kGd/3kiHBjW4onOD0t209D21lRER8RFKUEV8SEJMOJv2Z2Fa3Ye08w3Q7gprYxCRKmn8zE1k5hXx7MVtsNnO0vMUoCgP5j4Dm6a7PzgREakwbfEV8SEJ0eEczS9m/9F86lQPti6QNpdaN7eIVFkrdh7my6W7uKFXPC3rVivdTRt/gvwj0PFq9wYnIiIuoRVUER/iNZV8AVIWwY75VkchIlVEcYmDR79fR0y1IO4+pww9TFdNgBpx0LCP+4ITERGXUYIq4kO8ppIvwC+PwtznrY5CRKqITxansHHfUZ4c1pKwwFJuAMvY4XwjrcNVYNNLHhERX6D/rUV8SI2QAKKrBVpfyRegYU/YkwRF+VZHIiKV3N7MPP7762YGNI9icKuY0t+47hvAgPZXui02ERFxLSWoIj4mIaaad6ygxvWEkkLYs8LqSESkknv6xw04TJOnhrXCMEpRGOm4nvfA2F+hen33BSciIi6lBFXExyREh7ElLZsSh8WVfBt0Awy1mxERt5qTnMaM9fu5Y0BTYiNCSn+jaYLdD2I7uy84ERFxOSWoIj6mWXQ4BcUOdqbnWBtIcE2IbqUEVUTcJq+whMd/WE+TqDBu7N2obDd/dyNMvdM9gYmIiNsoQRXxMc1jnK0VvGKbb9eboeVFVkchIpXU63O2sPtwHs9c3JoAvzK8ZMk5BOu/h4BQt8UmIiLuoT6oIj6mSVQYhgHJ+7MY0rqOtcF0HGPt/CJSaW1Jy+K9+du5pGN9ujWqVbab104CRxF0UO9TERFfoxVUER8THGAnLiLEO1ZQAVIWwq6lVkchIpWIaZo88v06QgP9eHho87LeDCs/g3qJEN3SPQGKiIjbKEEV8UEJMeHe0WoG4Me7YcFLVkchIpXItyv3sGxHBg+e15xaYYFlu3nPCji4ETpq9VRExBcpQRXxQQnR4aQcyiG/qMTqUJz9UHctAYcXxCIiPu9wTiHPTdtIxwY1GJUYW/YBMrZDWAy0GuH64ERExO2UoIr4oISYajhM2HYw2+pQnP1QC45C2jqrIxGRSmD8zGSO5BXx7PA22Gxl6Hl6XNuRcM96CKrm+uBERMTtlKCK+KCEmDAANnnDNt+4Hs6POxdbG4eI+LwVOzP4alkqY3vF06JOORLM9G1QkO3sfyoiIj5JCaqID4qrFUqA3cYmbyiUVL0+1IhzFksSESmnohIHj0xZR93qQdw1sGn5BvnhdvhkqGsDExERj9JbjCI+yN9uo3FUmHesoAJ0vx38yljIRETkBJ8sSiF5fxbvXt2J0MByvDw5tBV2LYZBT7o8NhER8RwlqCI+KiE6jGU7MqwOw6nrTVZHICI+bE9mHv+btZlBLaI4t2V0+QZZ9RkYdmh3pWuDExERj9IWXxEflRBTjb1H8jmaX2R1KM6+gymLYO8qqyMRER/01NT1OEyTJy5shWGUozBSSTGs+QqaDYbwcia4IiLiFZSgivio44WSNnvLNt9vx8Li162OQkR8zKwNafyyIY27BjYjNiKkfINs+QWy06CDep+KiPg6JagiPqpZdDiAdxRKMgxnu5mdi52rqSIipZBbWMwTU9fTLDqMG3rHl3+g8BjocBU0Pdd1wYmIiCWUoIr4qHo1ggkL9POeQklxPSBrHxzeYXUkIuIjXpu9lT2ZeTxzcRv87RV4SVKvI1z0ptrLiIhUAkpQRXyUYRg0i/aiSr5xPZ0f1Q9VREphc1oWHyzYzmWd6tMlPqL8A22YCsk/uy4wERGxlBJUER+WEFONzWlZmN6wrbZ2AoTUchZLEhE5A4fD5JEpfxAW5MdDQ1uUfyDThNlPweI3XBeciIhYSgmqiA9LiA7jcG4RB7MKrA7FeQ61xx3QsJfVkYiIl/tm5W6Wpxzm4fNaEBEaUP6Bdv0O6Vuho4ojiYhUFjqsIeLDmsX8VSgpqlqQxdEAve6xOgIR8XKHcwp5ftpGEuNqcmmn+hUbbOVnEBAOLS9yTXAiImI5raCK+LCE45V8veUcqqPEeQY1bb3VkYiIl3phejJZ+cU8M7w1Nls5ep4el38UNnwPrUdAQKjL4hMREWspQRXxYbXCAokMC/SeBBXgy8th6btWRyEiXmh5SgaTklIZ2zue5jHVKjbY+ilQlAsdx7gmOBER8Qra4ivi45rHhLPZG3qhAtjs0KCbKvmKyD8UlTh4dMo66tUI5q6BTSs+YNuRzsJs9TpVfCwREfEaWkEV8XHNosPZnJaNw+EFlXzB2Q81fQtkH7A6EhHxIh8t3MGmtCyeHNaKkAAXvD/uHwwtLnAWaBMRkUpDCaqIj0uICSOvqITUw7lWh+J0vIqvVlFF5Jjdh3N5ZdYWzmkZzTktoys+4Ox/w9Q7nW1mRESkUlGCKuLjEo6d4/Kac6h12oF/COxUP1QRcXpy6gbnx2GtKj5YcQEkfQT5mVo9FRGphJSgivi4plFhgBclqHZ/6H67zoWJCAC/rN/PrI1p3D2oKfVqBFd8wE3TIC8DOqg4kohIZVSqBNUwjCGGYWwyDGOrYRgPnuL5ew3D2GAYxlrDMGYbhhF3wnPXGIax5diva1wZvIhAaKAfsRHBbPKWQkkAAx6BdpdbHYWIWCynoJgnp64nITqc63vFu2bQlZ9BtfrQuL9rxhMREa9y1gTVMAw78CZwHtASuMIwjJZ/u2wVkGiaZlvgG2D8sXsjgCeArkAX4AnDMGq6LnwRAUiIruY9K6gAJcWw83c4tNXqSETEQq/N3sLeI/k8O7w1/nYXbNo6shu2zYH2VzqrhouISKVTmp8WXYCtpmluN02zEJgIXHTiBaZpzjVN83iFliVA/WOfDwZ+NU0zwzTNw8CvwBDXhC4ixyXEhLHjUA4FxSVWh+LkKIYJF8GKj62OREQskrz/KB8u3MGoxFgSG0a4ZtA9K8AvEDqMds14IiLidUqToNYDUk/4evexx05nLDC9LPcahnGTYRhJhmEkHTx4sBQhiciJEmKqUeww2XEox+pQnPyDoH6iKvmKVFEOh8mjU9YRHuTHg+c1d93ALS+C+7dBzYauG1NERLyKS4skGYZxFZAIvFiW+0zTfM80zUTTNBNr167typBEqoSE6HDAiwolgbMf6r41UOBFMYmIR0xekUrSzsM8PLQFNUMDXDNo9kEoLoTAMNeMJyIiXqk0CeoeIPaEr+sfe+wkhmEMAh4BhpmmWVCWe0WkYuIjQ/GzGd6XoJolkLrM6khExIMycgp5fnoyXRpGcGmn+me/obRmPAhvd1fvUxGRSq40CepyoKlhGPGGYQQAlwNTT7zAMIwOwLs4k9MDJzw1EzjXMIyax4ojnXvsMRFxoQA/G41rh3lXglq/Cxh29UMVqWKen7aR7PxinhneGsNVfUrzDsPGH6FRf/U+FRGp5PzOdoFpmsWGYdyOM7G0Ax+ZprneMIyngSTTNKfi3NIbBkw+9sNol2maw0zTzDAM4984k1yAp03TzHDLdyJSxTWLCWfVrsNWh/GXwDDoditE/b3ot4hUVku3pzN5xW5u7deYZseOHrjE2slQUgAdr3bdmCIi4pXOmqACmKY5DZj2t8ceP+HzQWe49yPgo/IGKCKl0zwmnB/X7CW7oJiwwFL903a/wc9aHYGIeEhhsYNHv19HvRrB3DmgqWsHXzUBYtpCnXauHVdERLyOl7yKFZGKOr5asTkti44NvKTdcEkx7EmC8JgqX3Uzef9RcgtL8LfZ8LMb+Ntt+NsN/I59PPlxG3abtjGKb/lw4Q62HMjmw2sSCQ5wYY/Svath/x8w9CXXjSkiIl5LCapIJXG8ku/m/V6UoBZmw0dDoN+Dzl9V1MIth7jqw6VluscwwN92chLrZ7Ph7/dXMuv82oa/zTgpufWznSIBttuc19vPfK2fzSDAz3ltkL+Nnk0iCfJ3YbIhlVJqRi6vzt7M4FbRDGwR7eLRTWg6GNpc6uJxRUTEGylBFakk6tcMJiTATrI3FUoKrgExbap0oSSHw+T56RupXzOYf1/cmuISk+ISB0UOk6JiB8UOB0XHHysxKXI4/rym8NjHYodJYYnD+XmJ+Y97i449nl1c7Hy+xOF87Nh1RQ7zz3sLjz1e4ihdJdT7BydwW/8mbv5dEl9mmiZPTl2PzTB44sJWrp+gbgcY/bXrxxUREa+kBFWkkrDZDJpGh7M5zYsSVIC4nrDiE2f/Qj8X9UP0IT+u3cv6vUd5ZVR7+idEWR3OnxyOE5Nh5+fHE92iY8nyQ9+tZcqqPYzr19h11Vil0vllQxqzkw/wyNAW1K0R7NrBdy2BkkJo2FvVe0VEqojStJkRER/R3CsT1B5QnAf7VlsdiccVFJfw4sxNtKxTjWHt6lodzklsNoNAPzuhgX5UD/EnMiyQOtWDiY0IoVHtMBJiwrmkU322Hshm/d6jVocrXiqnoJgnp66neUw41/Zs6PoJ5jwDP9yu3qciIlWIElSRSqRZTDiHsgs5lF1gdSh/ievh/Jiy0No4LPDl0l3sPpzHg+c1x+aDRY+Gtq6Dn81g6pq9VociXuqVWZvZdySfZ4e3wd/u4pcUGTsgZQF0uApserkiIlJV6H98kUrkxEJJXiM0EhKvh1qNrY7Eo7Lyi3h9zlZ6NqlF76aRVodTLjVDA+iXUJupq/eW+syqVB0b9x3lo0UpXNEllk5xbijMtvoLwID2V7p+bBER8VpKUEUqkYQYZ4LqVYWSAC74H7S8yOooPOq9+dvJyCnkwSEtfPr85kXt67H/aD5Ld6RbHYp4EYfD5JEpf1A92J9/DWnuhglKYPWX0GQgVK/v+vFFRMRrKUEVqUQiwwKICA3wvnOoJUWwaykcrRpbRQ8czeeDBTu4sF1d2tSvbnU4FTKoRTShAXZ+WFU1/uykdCYlpbJyVyaPDG1BjRA3FD/bNgeO7oEOV7t+bBER8WpKUEUqEcMwaBYdxiZvS1BzDsJH58L6KVZH4hGvzN5CscPB/ecmWB1KhQUH2BncKoZp6/ZRUFxidTjiBYpKHPzv1810aRjBiI713DNJbBe48FVIGOqe8UVExGspQRWpZJrHVGPz/iwc3nRmsFpdqBkPOxdbHYnbbTuYzaTlqYzuGkeDWiFWh+MSF3WoR1Z+MXOTD1odiniBX9ancSCrgFvd2X4oqDp0urZKtqYSEanqlKCKVDLNosPJKSxhT2ae1aGcLK6nM0F1OKyOxK1enLGJID8btw9oYnUoLtOzcS0iwwL4YfUeq0MRL/DZkhRiI4Lp06y2eyZY8QlMewBKit0zvoiIeDUlqCKVzPFCSV53DjWuB+RlwKFNVkfiNit3HWbG+v3c3LcxkWGBVofjMn52Gxe0rcvs5AMczS+yOhyx0Ja0LJZsz2B01zjs7midZJqw5G3YswLsfq4fX0REvJ4SVJFKpll0GOCFlXwreT9U0zR5YVoykWGBjO0Vb3U4LndR+7oUFjuYsW6/1aGIhT5fspMAPxsjE2PdM8HuJDiYDB1VHElEpKpSgipSyYQH+VOvRrD3raDWbAhtR0E1NxVVsdic5AMsS8ng7kFNCQ2sfCs/7WNrEFcrRNt8q7CcgmK+XbmHC9rUISLUTWdDV00A/xBoNcI944uIiNdTgipSCSXEhLPJ21ZQDQNGvAfNK19VzhKHyX9mJNMoMpRRnd20smQxwzC4qF1dFm9L58DRfKvDEQtMWbWH7IJiru4e554JCrJh3XfQajgEVXPPHCIi4vWUoIpUQs2iw9l2MJuiEi8rSFRSBKnLIOeQ1ZG41Lcrd7M5LZv7Byfgb6+8/60Oa18P04Spa9QTtaoxTZPPl+ykdb1qtI+t4Z5JUpdCUa56n4qIVHGV95WUSBXWPCacohKTlEM5Vodysowd8OE5kPyz1ZG4TH5RCf/7dTPtYmswpHWM1eG4VZOoMFrXq8YPq5WgVjVJOw+TvD+Lq7vFua+1TJOBcM8GaNDNPeOLiIhPUIIqUgk1i3ZW8vW6QkmRTSG0dqXqh/rJ4hT2HcnnofOau++FuxXyMp1vJJgn99O9uH09/thzhG0Hs62JSyzx2e87CQ/yY1g7N50hL8xxtqCqVsd5HEBERKosJagilVDjqFDsNsP7CiUZhrOabyVJUDNzC3lr7lYGNI+iW6NaVodTcaYJqcvh+3HwcnPYNtf5Z1ZcAJmpAFzYri6GgVZRq5CDWQVMX7ePyzrFEhxgd88kv/0H3ujk/LsmIiJVmhJUkUoo0M9OfGSo962gAsT1hCO7IHOX1ZFU2FvztpFVUMwDQxKsDqViivJh2fvwTi/4cBBs+AHaXQ4drnI+v+ozeK0D/Px/RJvpdG9Uix9W78H82+qqVE5fJ6VSVGIyulsD90xQUgSrv4LaLcCv8vQPFhGR8lGCKlJJJUSHe98KKvzVD9XHV1H3ZObxyeIULulYn+YxPlhx1DSdZ4IBbHb4bbzz4wWvwP8lw4WvQN32zuebDnYmqys+hdfa85T/p+Sl72HN7iMWBS+eUlzi4IslO+nVJJLGtcPcM8mWXyDnwF9viIiISJWmBFWkkkqICWdXRi65hcVWh3KyqFbQYhiE+PaW2P/+shmAe85pZnEkZZR/FJZ/AO/2hjc6Oysq2/3hloVw83xIvA4Cw0++p0asM2G9YwW0u5wmOycxP/BuFi1eYMm3IJ4zJ/kAe4/ku6+1DMDKzyAsGpqe6745RETEZyhBFamkmkWHY5qwJc3LitnYbDDqM2h6jtWRlFvy/qN8t2o31/VoSL0awVaHUzp7V8PUO5xnS3/+P+dj5/0H/I/FHx599jFqxsGw1zHuWMGsiCv4eHMgxSUOWPU5ZB90W+hinc+W7KRO9SAGNo9yzwRZ+50rqO2uALufe+YQERGfogRVpJJKiHGugm3yxm2+JUXOYjx5mVZHUi7/mZ5MeKAft/ZrbHUoZ1aQ9VfRmeXvwx/fQOsRcOMcuHkBdB4LAaFlHzciHr+BD3Mop4gVa/+AqXfCq23h18chJ92134NYZsehHBZsOcSVXRrg567+vtkHoG4H9T4VEZE/KUEVqaQaRIQQ5G9jkzcWStq31lmMZ9scqyMps9+3pTN300HG9W9CjZAAq8M5tb2r4ce7nKul6751PjbgMefZ0ovegHqdKtzKo19CFOFBfkzaYsJtS6H5BbDoNWeiOuspyM2o+PchlvpiyU78bAajusS6b5I6beHG2RDZxH1ziIiIT1GCKlJJ2W0GTaO8tFBSnXbgH+pzhZJM0+SFGcnUqR7EtT0aWh3OyQqyYcUn8N7/t3ff8VFc5/7HP2dVQRISCCEhIdEMoiPRi8G4YVyFr+24ALYTlzTnprebep3kl+4bJ3GKWxKDcY/B3eBe6EVUUw2SkECAQBR1ac/vj1FsjCUQaEczK33frxevlXZmznnk8ezus+fMc6bB/efBuidgyExIG+5sT0iD2MSQdRcbFcGlw9J4deM+qrr0g2secBLVgZfAe/8HS+8LWV/S9qpqG3hq9R5mDEujR0KsO50c3A4laz+11q6IiHRsSlBF2rHstAR/jqBGRELWeCh43+tIzsjLG/exrqicr188kNgol9aDPFN1Vc7j7vecUdP6Grj0t85o6cz7Pk5QXTAzJ4OK2gZe31LqPJGSDdc+DF9aChO/7Dy34gF485dhO527o3p+XQlHquqYM8HF4kjv3gP/vPLj/4dFRERQgirSrmWnJrD/WA2HK2q9DuXTek+C/ZvDZipoXUOQ3766lYGp8Vwzqpe3wdRWwJpH4IEL4JnbnecGXAy3vQZfXALj74ROSa6HMb5fMqldYliwtuSTG3oMhs7dnJ/3bYC3f+VM/X37N04VYfE1ay2PLNtNdmoC4/p2c6eT6qOweQEMvwaiO7vTh4iIhCUlqCLt2EA/F0rqPdl5DJNpvo+vLGLXwQq+O2MQEYHW3b951vZtdCrw/n6QU5G3tgL6TXO2BSIgc2yr7y09ExEBw5Uj0nl7237KK5v5EuSqPzoFmXqfC2/+wklU3/kdBBvaLE45M+v2HGFj8VFmT+yNcev/p43PQF0l5N7sTvsiIhK2lKCKtGOD/pOg+nGab8ZoGHDJ2VWRbWMVNfXc+9p2xvXpxgVuLbfRnP8kcnXV8I/LnDUjsy+Dz74CX1oG4+5o23hOMjM3g7oGy0sb9jW/U88RcON8uPMtyBwPH77lJNSg6Z0+NHdpAXHREVydm+FeJ2vnQo8hkDHKvT5ERCQsadExkXasR0IMiZ2i/DmCGhkDs570OooWefDdXRw8XsP9N492b0TpZKWbYfU/YMuLTvGhmAS4YR6kDvt4+qwPDE3vQv+UOBbkF3PT+KxT75yeCzc98XFSWrAEnpgDk78KY2/XVE8fOFxRy/PrS7h+TCbxMS59RCjdDMWr4ZJftumIv4iIhAeNoIq0Y8YYslMT2ObHEVRw1kPds8qZqupTB4/XcP87O5kxNI1RWV3d7ayuCvLnw0PT4a8TYfW/nKnQNY3nr+9UXyWn4Pw/lpeTwYpdhygub+FoaFQn5zEmwSnitPhHcO9Ip/KvRlQ99dTqImrrg8x2szhSfCpc9FMYcb17fYiISNhSgirSzmWnJbC19BjWj0s5FLwPD17o6/tQ//zGDqrrg3x7RrZ7nfzn3LzwdVjwRadw1PRfOJV4r3kAuqS713cI5OU48T2/ruQ0e54kbTjcvMCZrtxjELz6P3BvjjO6Jm0uGLTMW1bIuL7dyG68PcAVcclw7tedRxERkZMoQRVp5wamJXCsup69R6q9DuXTeo2DQKRvl5spKKvg0eUFXD82k/4p8aFtvK4K1j0OD89wpvGCszTLLS/AXSth0l2+Gy1tTu/kOHKzkliwtvgsG5gItzwPt77o3JvcfaDz/J7VzrI50ibe3n6AwkOV3DzRxdHT7YvhjZ9DbaV7fYiISFhTgirSzg3ycyXf6M6QPsq3I6i/W7SNyECAr104IHSNHtgKr3zfqcT77Ofh+H6wQWdb2nDoOyUs78vLG5nOln3HWleQq8+5TjGlmARn3dRH8uCPo2DVw1Dvw6WS2pl5SwtISYhh+pA09zpZ9lfIf8y5B11ERKQJSlBF2rmBPXxcyRec9VCL1/huRGX9nnKeX1fC7VP60qNLbGga3fEa3DcOVjwA/S9wRg2/shqGXBWa9j10xch0IgKGhflnOYp6sthEuP4R6NLTmfr8p9HO2q8NdaFpXz6h6FAlb2zdz41jM4mOdOmjQXkR7HwDcmd9XMVZRETkJEpQRdq5xM5RpHWJ9W+hpN6TIVgHe1Z6HclHrLX86uUtdIuL5s6p/VrfYOlm57HPFOfe0m98ANf9wyl6FIajpU3pHh/Dued0Z2F+CcFgCO53NsZJ4m9bDLOegfgUZ+3XF7/R+rblU+avKCRgDDeerhJza+TPByzk3OReHyIiEvaUoIp0ANlpCWzxa4KaNR7OuQgioryO5CPvbD/Ikp1lfOWCc0iIbWVcy/8Of50EH77tTGucdJeTbLVDeTnpFJdXsbrwcOgaNQYGXAS3vw43PQnjv+A8v/s9Z6poQ33o+uqgqusaeGJlERcN7kHPxE7udBIMQv486HsedO3jTh8iItIuKEEV6QCy0xLYceA49Q1Br0P5tNhEmP2MM9XXB4JBZ/Q0s1un06/reTobnoaXvwODLndGitu56UPTiI0KhG6a74mMgYGXQOpQ5/f8+bDgC/CX8bD+SQg2hL7PDuLljXs5VFHLnAl93OukZC2UF8Kom93rQ0RE2gUlqCIdQHZqArX1QQoO+es+z4801DdWbPW+EM5z60r4YO9RvjU9m5jIVtwnt+N1ePYLTmJ6zYMQERm6IH0qPiaSi4ek8eL6vdS5/WVI3n1w/TyIjIV/3wF/meB8IaBE9YzNXVpAv5Q4Jp/j4rIvvUbDl1fCoCvc60NERNoFJagiHcB/1jT0baGkba/AgxdAyRpPw6ipb+B3i7YyLKMLV45oxdqje1bDE3MgZRDc+BhEuTRt0ofyRqZzuLKOd7YdcLcjY2DwlfD5d+G6f4GJgEU/VBGlM7Sx+AhrCsuZPb43xq37oRvqnbV+UwZCVIgKjomISLulBFWkAzinRzwB4+MENWui8+jxeqjzlhWy53AV35sxmECgFR/WAwHoMRhmP+1MYe5Apg5MIalzFAvzS9qmw0AAhs6ELy6Bz77kJEA2BEWaOohHlxcQGxXgmtG93Otk1UPwl4lQFcJ7k0VEpN1SgirSAcRGRdAnOc6/CWpcMqQMht3eJahHq+v48xvbmTKgO+cO6H52jRw/4IzgpefC7a9BgovrSfpUdGSAy4f3ZPHmUipq2rCAUSAA3frBnlXw5zFwcEfb9R2mjlTVsWBtCTNzMkjs5FKRMmthzVyIjIZOXd3pQ0RE2hUlqCIdxMDUBLaV+jRBBadIUtFyz6qy/v3tnRyurOO7MwadXQOVh+BfVzj3nUK7WT7mbOTlZFBV18CizfvavvOkLDhcACsfbPu+w8wzq/dQVdfA7Am93etkbz6UboDcOe71ISIi7YoSVJEOIjstgd1lFVTX+bSITO9JUHsc9q1r8673Hanmofd2kZeTzrCMs5iSW1sB86+HQ7tg9K0hjy/cjOndlYykTm03zfdE8T2cKb/5j0LN8bbvP0xYa5m3rIDcrKSz+3++pdbMhYgYGH6te32IiEi7ogRVpIPITksgaGHHfp9+aO9zLvS/wFkvsY3d+/o2GoKWb03PPvODG+rgyVugeBVc+xD0nRL6AMNMIGC4Kiedd7cf5ODxmrYPYOwdUHMUNjzZ9n2HiSU7y/jwYAU3T3Rx9LSuyqmsPOQqTe8VEZEWU4Iq0kEMTPV5Jd+ENJjzLGSObdNud+w/zhMri5g9oTeZ3Tqf2cHBICz8MuxYDFf8wakqKwDk5aTTELS8uH5v23eeOQ7SRsCKB1UwqRlzlxbQLS6aS4f1dK+T8iLnutb0XhEROQNKUEU6iD7JnYmODLDVz/ehNtRD8Zo2HUX9zStb6BwdyV3nn3PmBzfUwPFSuOBHMPqW0AcXxgaldWFQWgIL84vbvnNjYNwdTuGkyrK279/n9h6pYvEHpXxmTCaxUa1Y6/d0UgbCl5dD36nu9SEiIu2OElSRDiIyIsA5KfH+HUEF2Pg0PHA+7N/cJt2tLjjEos2lfOG8fiTHx5zZwdVHnfVNZz0DU77pToBhLi8ngzWF5RSWVbZ95zmznDVS486yInM79tiKIoLWMmt8lnudHCuFsp3OlwUduGCYiIicOSWoIh3IoLQwqOQLULDE9a6stfzypS2kJMTwuXP7ntnBq/8Ffx7rFEWKiNQH8GZcOdKZPurJKGogwjkvpZud5X8EgNr6II+tKOT87B5nPqX9TKy437lGKg6614eIiLRLSlBFOpCBaQnsPVLNkco6r0NpWlIWJGZCgfvrob72wX5WFRzmaxcNoHN0ZMsP/OAFeOFrkDoUumS4Fl970KtrZ8b16caC/GKsF/eCHi2Bv07SkjMnWLR5HweO1TDHzaVlgg2QP98peqYRbBEROUNKUEU6kOzGQknb9vt8FLXgfVeL29Q3BPn1K1vo1z2O68dktvzA3e/D05+D9FFw/VyIjHYtxvYiLzednQcq2FRytO0775IOAy6G1f+A+tq279+H5i4tILNbJ84bmOJeJzteh2MlMErFkURE5MwpQRXpQLLTnAR1i5/vQ+09CSoOQNkO17p4Zs0eduw/zndmZBMZ0cKXwX0b4LEboGsfmPUURMe5Fl97ctmwnkQGjDfTfMFZcuZ4KWx53pv+fWRb6TGW7zrE7PG9CQRcnJa+9hHo3B0GXupeHyIi0m4pQRXpQHomxpIQE8k2PyeofaZAv/Ohzp3COlW1Dfzf4u3kZiVxydC0lh9YtAJiusCcf0Pnbq7E1h51jYtmWnYKz60roSHowTTfcy5yvlRY8UDb9+0z85YVEB0Z4LozmTVwpo4fgK0vw8gbNMNARETOihJUkQ7EGMPAtAR/LzWT3B9uXgA9R7rS/D+W7GLf0Wq+N2MQpiXFjRoa79cdext8eRkk9nIlrvYsLyeD0qM1LN/lwZIvgYAzilq41BkF76CO19Tz7zXFXDGiJ93iXEwcjYHJX4VRN7vXh4iItGtKUEU6mOy0BLbuO+ZN0ZqWaqiHvetCfh/q4Ypa/vrWTi4c1IPx/ZJPf0D1EXjwwo+L7MQkhDSejuKiwanERUewcG2JNwHkzoJp34f4Mxgxb2eeXVvM8Zp6d4sjgVMU6cIfQ0q2u/2IiEi7pQRVpIPJTk3gSFUd+4/VeB1K81b/A/4+FcoLQ9rsfW/uoKKmnu/MGHT6neuq4fFZULoJkvqENI6OplN0BJcMTeOljXupqW/wIICuMO17EO9iYSAfs9Yyb2kBwzK6kJOZ5F5HJfnw/r1Q4+MZGiIi4ntKUEU6mP8UStrq5/tQXVgPdc/hSh5ZWsA1o3p99N+gWcEGeOY22P0uzPwbDLgoZHF0VHm5GRyrrufNLR6tSRpsgLd/C2sf9aZ/D63cfZitpce4eUKflk1rP+uOHoS3fuVqBW4REWn/lKCKdDADU8MgQU0Z7Ix6FbwXsibvWbQNY+DrFw889Y7Wwgtfhy0vwIxfwYjrQhZDRza5fzLd46O9q+YbiIAdi+Gd30Iw6E0MHpm7rIAusZFcOTLdvU5qjsOmZ2Ho1RDbxb1+RESk3VOCKtLBdIuLJiUhxt+FkgIByJoUshHUzSVHeTa/mFsn9yE9qdOpdz5aAltehCnfhAlfDEn/ApERAa4Ykc7rW/ZztLrOmyDG3gGHd8HON7zp3wP7j1Xzysa9XDcmk07REe51tHkB1B6HXK19KiIiraMEVaQDGtRYKMnXek+CQx/C0b2tbuo3r26hS2wUXzrvnFPvaC0kZsAXl8AFP2p1v/JJeTnp1NYHeWXjPm8CGJIHcT1gZcdZcuaJFUXUNVhmjc9yt6M1cyF5AGRNcLcfERFp95SginRAA1MT2L7/mDfrUrZU36nQbxpUl7eqmSU7D/LW1gN8+fz+JHaOan7H9U/B/M9AbQUkpDrLZUhI5WQm0Tu5s3fTfCOjYfQtsO1VOLzbmxjaUH1DkPkrCpkyoDv9UuLd6+jQh1C0DHJn67oREZFWa1GCaoyZYYzZaozZYYz5XhPbpxpj1hhj6o0x15607dfGmI2N/64PVeAicvay0xKorgtSdKjS61Ca13ME3LwQegw+6yastfzq5S2kJ8Zy88Q+ze+4/TVY8AWoqwLj4jTIDs4YQ97IdJbsLKP0aLU3QYz+LJiAk6S2c69v2c/eI9XMdntpma594Y43NL1XRERC4rQJqjEmArgPuBQYAtxojBly0m6FwK3A/JOOvRwYBeQA44FvGWNUPUHEY9mNhZK2+H2ab7ABSjef9eEvbtjL+j1H+Mb0bGKjmkk896yCJ+dAjyFww3yIij3r/uT0rsrJwFp4fp1Ha6ImZsBX82H8573pvw3NW1ZAemIsFw7q4V4n1jqjphmjIa4FawuLiIicRktGUMcBO6y1H1pra4HHgbwTd7DW7rbWrgdOLo04BHjHWltvra0A1gMzQhC3iLTCgNR4jIFtfi6UBLDkT/DXiVBx8IwPrWsI8ttXtzIoLYGrczOa3unAVnj0WohPhdnPqPpoGzinRzzDMrqwMN+jBBUgKctJrKoOexeDyz48cJx3tx/kpvFZREa4eDfPlhfhwYvhiEfTtkVEpN1pybtWBlB0wu97Gp9riXXADGNMZ2NMd+B8IPPMQhSRUOscHUlWt87+L5SUNdF5LFx6xoc+tqKQgrJKvjtjEBGBZu6Le/9eiIiGOc9CvIujTPIJM3My2FB8hJ0HjnsXxL/vhEfy2u2anY8uLyQqwvCZsS6/5a6dC+WFzpc8IiIiIeBqkSRr7SLgJWAJ8BiwFGg4eT9jzJ3GmFXGmFUHDni0iLtIBzMwNcHfS80ApOdCZOwZLzdzvKaeP76+nfF9uzEtO6X5Ha/4A3zuFejWt3Vxyhm5cmQ6xuDtKGrWeNi7zpni3c5U1Tbw1KoiZgzrSY8EF6esH90L2xdBzo0QEelePyIi0qG0JEEt5pOjnr0an2sRa+0vrLU51tqLAQNsa2Kf+621Y6y1Y1JSTvFhUkRCZlBaArsOVlBT/6nvjPwjMhp6jYXd753RYQ+++yEHj9fy/csGY06uKlpbAY/Pgn0bnPa79QthwNISqV1imdgvmYX5xVivRjBHXA/RCe1yyZnn1hVztLqeOW4XR1o3H2xQxZFERCSkWpKgrgQGGGP6GmOigRuA51rSuDEmwhiT3PjzCGAEsOhsgxWR0BmYmkBD0LJzf4XXoZxan3OdZLL6SIt2P3Cshvvf+ZDLhqeRk5n0yY0NdfDkzbD1JWdaonhmZk4GBWWV5BeVexNATIIz8rfpWTjefmbuWGt5ZGkB2akJjO3T1c2OYO086D0Zkvu714+IiHQ4p01QrbX1wF3Aq8AHwJPW2k3GmLuNMVcBGGPGGmP2ANcBfzfGbGo8PAp41xizGbgfmN3Ynoh4LDvNqeTr+0JJfac6/1pYKOlPb2ynpj7It6Znf3JDMAgLvgQ7XoMr74VBl7sQrLTUjOFpREcGvJ3mO/YOaKiFNf/yLoYQyy8qZ1PJUeZM7P3p2QOhdLQY6ms1eioiIiHXoptGrLUv4dxLeuJzPz7h55U4U39PPq4ap5KviPhM3+5xREUY/y8103sS3NKiSRvsOljB/OWF3Dguk34p8R9vsBZe/R/Y8CRc+BMYdbNLwUpLdYmN4oLsHrywvoQfXj7Y3UqzzUkZCLmz21WBn7nLCoiPiWRmc5WrQyWxF3xtvTPFV0REJIQ8+EQgIn4QFRGgf0q8/0dQwVkP9cCnbl//lN8t2kp0ZID/vnDAJzeUrIHlf4UJX4Jzv+5SkHKmZuamc/B4Le/vLPMuiLz7YFT7GAU8VFHLC+v38l+jMoiPcbFoUdlOOLwbAhEQEeVePyIi0iEpQRXpwAamJvh/qRmAt37prIda2/z9suuKynlx/V5un9Lv05VLM0bDZ1+G6b8AN6c9yhmZlt2DhNhIFuZ7vIZmeSGs+oe3MYTAk6uKqK0PMtvN4kjWwgtfg4dnOFN8RUREQkwJqkgHlp2WQHF5FQ+/t4v8onJq6306XS9zAgTrYc/KJjdba/nlyx+QHBfNnVNPqMr7wfOw7G/Oz70nQUAveX4SGxXBpcPSeHXjPqpqPawmveEpJ+k6sNW7GFqpIWh5dHkB4/t2Y2BqgnsdffA87HoHpnzTqYItIiISYvq0JtKBTctOoWdiLHe/sJmZ973PsJ++yn/95X3ufn4zz68rYc/hSu+WATlR5jgwAdj9fpOb3952gGUfHuK/Lxzw8dTGXe/C07fBxqed6r3iSzNzMqiobeC1D0q9C2LULRARDSsf9C6GVnpn2wGKDlUxZ6KLo6d1VbDoB9BjKIz+rHv9iIhIh6aVtUU6sKHpiSz9/oXsPVLF2sJy1hYeJr+onEeXF/Dw+7sA6B4fQ25WErlZSeRkJjGyVxJxbt7f1pTYLtBzJBQs+dSmhqDlVy9vIatbZ24cl+U8uXcdPHYjdOsLNz2p++R8bHy/ZFK7xLAwv4QrR6Z7E0Rcdxj6X5D/GFz4Y2cJmjAzd1kBKQkxTB+S5l4nS/7sTIe+5XmI0McHERFxh95hRISeiZ3oObwTlw3vCUBdQ5Ate4+RX3TYSVyLylm82RnhChjn3tXcrCRyM7uSm5VE/5R4AgGX7+3sPRlWPAB11RD18T2mC/OL2bLvGH+8MZfoyIBTwGXeNRCbCLP/DZ27uRuXtEpEwHDliHT+tXQ35ZW1JHX2aNrouDtg/eOw7nHn5zBSdKiSN7fu5ysXDHCuATfUVsDSP8Pgq5xln0RERFyiBFVEPiUqIsDwXokM75XInInOc4crasnfU87awnLyGwsSPbaiCICEmEhGZn48ypqTmURyfExog+p3PuxbD5UHnSUugOq6Bn6/aBvDMxK5YnhPp4DLs19wqv7e+iwkurzUhoTEzNwMHnxvFy9u2Mus8S5OUT2VjNGQngsrH4Kxt4dVMa1HlxcSMIYbx2W610l0HNz5pjMVWkRExEVKUEWkRbrGRXN+dg/Oz+4BQDBo2VVW8dHU4LWF5fzlrZ00BJ17VnsndyYnM4nczCRys7oyuGeX1o3uDLjI+XeCecsKKC6v4jfXjvh4BPfqv0F1ubPGpYSFoeld6J8Sx8L8Eu8SVGPgst9BbFJYJafVdQ08sbKQiwen0jOxkzudlBc6a8V263f6fUVERFpJCaqInJVAwNA/JZ7+KfFcO9oZ0aysrWfDniPkFzkjrcs+LGNhfgkA0ZEBhqV3ITerq5O4ZiWRkdQJcybJQLDBWX8xuT9Hqur485s7mDowhcm94+H1u53Kosn9XfhrxU3GGPJyMrhn8TaKy6vISHIp0TqdXmO86bcVXtqwl8OVde4VRwo2wOM3Qaeuzr2nIiIiLlOCKiIh0zk6kvH9khnfL/mj504uwDRvWQEPvecUYEpJiPkoWc3N7MqIXomnLsC06Eew+p/wvQL+9vZOyivr+O70/vDMbbDlRciaCAMudvmvFDfk5aRzz+JtPJdfwhenefglQ+FyeOHrMOupsJgiPndZAf1S4pjUP/n0O5+NNY/Avg1wzUPutC8iInISJagi4qqzK8DUtXFq8EkFmDLHwrL7OLhjBQ+/d5SZI3sydM1PYcsLcOlvlJyGsd7JceRmJbEwv9jbBDUhDfZvhtX/gAt+6F0cLbCx+AhrC8v58RVDzmwmQktVHYY3fgZZk2DYNaFvX0REpAlKUEWkTbWsAFMJj60oBD5ZgGl8ygDOBZa/+QLWTuXuhGdh1SMw9dsw/vPe/VESEnkj0/np85vZuu8Y2WkeLfXStTcMnOGM1E/9NkSGuNhXCM1bVkCnqAiuaZxiH3Jv/RoqD8Glvwqr+3JFRCS8KUEVEc81VYDpw4MVjfeyflyA6U9By+vRPYkpXsrPszPpsuqPMPpWOP8H3v4BEhJXjEznZy9+wIL8Yr47Y5B3gYy7A+a9DJufgxHXeRfHKRyprGNBfjFX52aQ2MmFdX4P7YIV9zvXV8+RoW9fRESkGUpQRcR3AgHDOT3iOafHpwsw1SyewOTSxdTMnA0fRITdkiDSvO7xMZx7Tneeyy/h29Oz3V9btzn9zodu/Z0EzacJ6tNr9lBdF2T2BJeKI3XtA9f9E3pPcqd9ERGRZri0oreISGj9pwDTkHPz6JSVS1JErTOtNxDhdWgSQnk56RSXV7G68LB3QQQCzhcfNcegqty7OJoRDFrmLStgVFYSQ9MTQ99B9RHnS58hV0Fc99C3LyIicgpKUEUkvAy9Gm59QR+c26npQ9OIjQqwYG2xt4GMuwO+tBQ6JXkbRxOW7Cxj18EKd5aWqa+Bv58Hr/1v6NsWERFpASWoIiLiG/ExkVw8JI0XN+yltj7oXSARUc4o4oFtvhtFnbtsN93iorl0WM/QN770Pji8C/pODX3bIiIiLaAEVUREfCVvZDrllXW8u/2At4Ec2gX3jXXWAvWJvUeqWLy5lOvHZhIbFeLp7Uf3wju/g0FXQP/zQ9u2iIhICylBFRERX5k6MIWkzlEsyC/xNpBufZ01QFc9BMEGb2NpNH95IRa4aVxW6Bt/7acQrIfpPw992yIiIi2kBFVERHwlOjLA5cN7snjzPipq6r0NZtwdcHg37HjN2ziA2vogj60o4oLsHmR26xzaxotWwPrHYdJdTmIuIiLiESWoIiLiO3k5GVTXBVm0eZ+3gQy+EuLTYMUD3sYBvLppHweP1zDbjeJIKYPgvO/Bud8IfdsiIiJnQAmqiIj4zpjeXclI6sSCtR5P842IgjGfhR2LoWynp6HMXVZAZrdOnDcgJbQNBxsgtguc/32IiQ9t2yIiImdICaqIiPhOIGC4Kied93Yc5ODxGm+DGX0rTPkmxCR4FsLWfcdYsesQs8f3JhAwoWu4+gjcNw42PRu6NkVERFpBCaqIiPhSXk46DUHLi+v3ehtIQhpc+GOI7+FZCPOWFRAdGeAzYzJD2/Dbv3FGhrv2CW27IiIiZ0kJqoiI+NKgtC4MSktgQX6x16FAQx289wfYtKDNuz5WXce/1+zhyhHpdI2LDl3DB7fD8r9B7mxIzw1duyIiIq2gBFVERHwrLyeDtYXlFJZVehtIIBI2POWsE2ptm3a9YG0xFbUNzAl1caRXvg9RneHCn4S2XRERkVZQgioiIr515cieACz0ehTVGBh7O5RugKLlbdattZa5ywoYnpHIyF6JoWt426tO4afzvgvxIS66JCIi0gpKUEVExLd6de3MuD7dWJBfjG3jkctPGfEZiEls0yVnVuw6xLbS48yZ0BtjQlgcKbEX5MyCcXeGrk0REZEQUIIqIiK+lpebzs4DFWwqOeptINFxkDsLNi+EY6Vt0uXcZQUkdoriypHpoW04dSjM/AtEhvCeVhERkRBQgioiIr522bCeRAaM99N8wZnmG6yDrS+63tX+Y9W8snEf143uRafoiNA0emwf/PMK2LcxNO2JiIiEmBJUERHxta5x0UzLTuG5dSU0BD2e5pvcH+5aBWM+53pXj68ooj5omTUhhMWRXvtfKFwGUZ1C16aIiEgIKUEVERHfy8vJoPRoDct3lXkdCnQf4DzWHHeti/qGIPOXFzJlQHf6do8LTaN7VsG6+TDxS06iLSIi4kNKUEVExPcuGpxKXHQEC9eWeB2K4+nPwWM3uNb8ax/sZ9/RauaEavQ0GISXvwPxqTD126FpU0RExAVKUEVExPc6RUdwydA0Xtq4l+q6Bq/DgbQRsPtdKN3sSvPzlhWQnhjLBYN6hKbB9Y9D8Wq46KcQkxCaNkVERFygBFVERMJCXm4Gx6rreWvrAa9Dgdw5EBEDK0O/5MzOA8d5b8dBbhqfRWREiN6mI6Jh4AwY4d6or4iISCgoQRURkbAwuX8y3eOj/VHNNy4Zhl8L656A6iMhbXresgKiIgzXj80KXaPDr4WbnoCA3vZFRMTf9E4lIiJhITIiwBUj0nl9y36OVtd5HY6z5ExdBeQ/FrImK2vreXr1Hi4d1pOUhJjWN1i2E174BlQean1bIiIibUAJqoiIhI28nHRq64O8snGf16FAxigYfBVEdw5Zk8/ll3Csup45E0NUHOnVH8D6J6ChNjTtiYiIuEwJqoiIhI2czCR6J3f2xzRfgOvnwqibQ9KUtZZHlhYwKC2BMb27tr7B7a/Btpedqr0Jaa1vT0REpA0oQRURkbBhjCFvZDpLdpZRerTa63AcR0tg/ZOtbmZtUTmb9x5l9oTeGGNa11h9LbzyPejWDyZ8sdWxiYiItBUlqCIiElauysnAWnh+nU/WRF3xADz7eSgvalUz85YWEB8TyczcjBDEdD+UbYdLfgmRIbiXVUREpI0oQRURkbByTo94hmV0YWG+TxLUMZ9zHlc9fNZNlB2v4YX1e7lmVAbxMZGtj+loCQyYDgMvaX1bIiIibUgJqoiIhJ2ZORlsKD7CzgPHvQ4FkjIh+zJY8y+oO7tpx0+u2kNtQ5DZE0JUHGnG/4MbHoPWThUWERFpY0pQRUQk7Fw5Mh1jYOFanxRLGncHVJbBpmfP+NCGoOXR5QVM6NeNAakJrYujJB/y50MwCBEhGIkVERFpY0pQRUQk7KR2iWViv2QWrivBWut1OND3POg+EFY+cMaHvr1tP3sOVzFnQp/WxWAtvPwdWPxjqPXByLKIiMhZUIIqIiJhaWZOBgVlleQXlXsdijOV9vJ7IO++Fh9ypLKOVzft4/8WbyclIYbpQ1NbF8OGp6BoOVz4E4jt0rq2REREPKL5PyIiEpZmDE/jhws3sjC/hNysEKwb2lp9p5xyc2VtPSt3H2bJzoMs2VHGxpIjWAuxUQHuvmoYURGt+M645rgzcpqeCzmzzr4dERERjylBFRGRsNQlNooLsnvwwvoSfnj5YCJbk+CFSsFSWPQDuOkpamKSWFtYzpKdZSzdeZD8onLqGixREYbcrK589cIBTOrfnZGZicRERrSu3/fugWN74TOPQMAH/x1ERETOkhJUEREJWzNz03ll0z7e31nGeQNTPI2lviHI9vIAg4tX8/jff8FPDl1MTX2QgIHhGYncdm4/Jp+TzOjeXekcHcK332ADfPgWjLgeMseFrl0REREPKEEVEZGwNS27BwmxkSxcW9zmCWowaNlaeowlO8tYsuMgy3cd4nhNPY9FDeH8Y8+zbdytTDwnlXF9u5HYKcq9QAIR8LlFUFfpXh8iIiJtRAmqiIiErdioCC4dlsaL6/dSVdtAp+hWTpU9BWstuw5WNE7ZLWPph2UcqqgFoG/3OK7KSWdS/2SG1n+TLs/dxo+ziyF7hGvxALBnNUR3hh6DIUKFkUREJPwpQRURkbA2MyeDJ1ft4bUPSrlyZHpI2y4ur2LJjoMs3VnGkp1l7DtaDUDPxFimZacwqX93JvVPJj2p08cHNcyEN38EK+6H7EtDGs8nNNTBwi9BsB6+vFL3noqISLugBFVERMLa+H7JpHaJYWF+casT1IPHaz5KRpfsPEhBmTNttltcNBP7JzOpfzKT+nenT3JnjDFNNxIRCWM+BxuehNoKiI5rVUzNWvkQHNgCN8xXcioiIu2GElQREQlrEQHDlSPS+eeS3RyuqKVrXHSLjz1SVcfyD8s+mra7tfQYAAkxkYzvl8wtE/sw6ZxkBvZIIBBoJiFtyuT/hinfdC9xrDgIb/0/6Hc+ZF/mTh8iIiIeUIIqIiJhb2ZuBg++t4uXNu5l1vjeze534lqkS3eWsbH4CMHGtUjH9ulGXm46k/t3Z2h6l9YtWxMZ4zyW7YSEtNCPor7xc2d09tJfQ3MjuSIiImFICaqIiIS9oeld6J8Sx8K1JZ9IUGvqG8hvXIt0yclrkWZ25SsXDGBS/2RyspJavxbpyfZvgb9MgCvucab8hkrVYdj4bxh3J6Rkh65dERERH1CCKiIiYc8YQ15OBvcs3sZrm0vZtv8YS3eWsXL3IarrPrkW6aT+yYzpE+K1SJuSkg1pw2DFgzD6s6Eb6ezUFe5aAVGdTr+viIhImFGCKiIi7UJeTjr3LN7G7Y+sAiA7NYEbxmYxqX8y4/slu7sWaVOMcUY5n/sKFCyBPpNb3+a+DdC1rzNtWEREpB1SgioiIu1C7+Q47r0hh4AxTOiXTEpCjNchwbBrYdGPYOUDrU9Qayth/g2QOgRmPRWa+ERERHxGCaqIiLQbeTkZXofwSdGdIXc2LP8bHC2BLq1YBuf9P8DRPXDNAyELT0RExG+UoIqIiLhp7G1gAhBoxVvu4QJ4/14Ydg30nhS62ERERHxGCaqIiIibuvWD6T9rXRuLfggYuPjukIQkIiLiVy6tIC4iIiIfqa+FpffB9sVnfuy+jfDBczDlm5DYK/SxiYiI+IhGUEVERNwWiIQVDzjVdwdcfGbHpg2DW56HXmPdiU1ERMRHWjSCaoyZYYzZaozZYYz5XhPbpxpj1hhj6o0x15607TfGmE3GmA+MMX80JlQLwYmIiISJQMC5F7VwqbNUTEsd2QPWQt+pWvdUREQ6hNMmqMaYCOA+4FJgCHCjMWbISbsVArcC8086dhIwGRgBDAPGAue1OmoREZFwkzMLIjs5I6ktUXkI/joZ3vi5u3GJiIj4SEtGUMcBO6y1H1pra4HHgbwTd7DW7rbWrgeCJx1rgVggGogBooDSVkctIiISbjp3g+HXwoanoKr89Pu/+QuoOeZU7hUREekgWpKgZgBFJ/y+p/G507LWLgXeBPY2/nvVWvvByfsZY+40xqwyxqw6cOBAS5oWEREJP+PugLpK2PLiqffbtxFWPexMC049edKSiIhI++VqFV9jzDnAYKAXTlJ7gTFmysn7WWvvt9aOsdaOSUlJcTMkERER7/QcCZ9/F3Juan4fa+GV70FsEkz7fpuFJiIi4gctSVCLgcwTfu/V+FxLXA0ss9Yet9YeB14GJp5ZiCIiIu1IzxFgDNRVN719y4uw+1244AfOtGAREZEOpCUJ6kpggDGmrzEmGrgBeK6F7RcC5xljIo0xUTgFkj41xVdERKRDefIWeOqWpredcxFc/nsY/dm2jUlERMQHTpugWmvrgbuAV3GSyyettZuMMXcbY64CMMaMNcbsAa4D/m6M2dR4+NPATmADsA5YZ6193oW/Q0REJHx0HwDbXoXDuz/5fG0lRMXC2NshEOFJaCIiIl4y1lqvY/iEMWPG2FWrVnkdhoiIiHuOFMMfhsPEL8P0nznPlRfB36fCFf8HQ2d6Gp6IiIibjDGrrbVjmtrmapEkERERaUJiBgy6HNbOhboq57nFP3Yq/GaM8jY2ERERDylBFRER8cK4O6DqMGx8Bna/D5v+DZO/BklZXkcmIiLiGSWoIiIiXugzBfpNg2A9vPxd6NILJn/V66hEREQ8pQRVRETEC8bAzQvBBqF0g3MvanRnr6MSERHxlBJUERERL/U9D6Z8C4Ze7XUkIiIinov0OgAREZEOLbk/XPgjr6MQERHxBY2gioiIiIiIiC8oQRURERERERFfUIIqIiIiIiIivqAEVURERERERHxBCaqIiIiIiIj4ghJUERERERER8QUlqCIiIiIiIuILSlBFRERERETEF5SgioiIiIiIiC8oQRURERERERFfUIIqIiIiIiIivqAEVURERERERHxBCaqIiIiIiIj4ghJUERERERER8QUlqCIiIiIiIuILSlBFRERERETEF5SgioiIiIiIiC8oQRURERERERFfUIIqIiIiIiIivqAEVURERERERHxBCaqIiIiIiIj4grHWeh3DJxhjDgAFXsdxGt2Bg14HIa2icxjedP7Cn85h+NM5DH86h+FN5y/8deRz2Ntam9LUBt8lqOHAGLPKWjvG6zjk7Okchjedv/Cncxj+dA7Dn85heNP5C386h03TFF8RERERERHxBSWoIiIiIiIi4gtKUM/O/V4HIK2mcxjedP7Cn85h+NM5DH86h+FN5y/86Rw2QfegioiIiIiIiC9oBFVERERERER8QQnqKRhjZhhjthpjdhhjvtfE9hhjzBON25cbY/p4EKY0wxiTaYx50xiz2RizyRjz1Sb2mWaMOWKMyW/892MvYpWmGWN2G2M2NJ6bVU1sN8aYPzZeg+uNMaO8iFOaZozJPuHayjfGHDXGfO2kfXQN+owx5mFjzH5jzMYTnutmjFlsjNne+Ni1mWNvadxnuzHmlraLWk7UzDn8rTFmS+Nr5bPGmKRmjj3l6664r5nz91NjTPEJr5WXNXPsKT+7Stto5hw+ccL5222MyW/m2A5/DWqKbzOMMRHANuBiYA+wErjRWrv5hH2+BIyw1n7BGHMDcLW19npPApZPMcb0BHpaa9cYYxKA1cDMk87hNOBb1torvIlSTsUYsxsYY61tco2wxjforwCXAeOBe62149suQmmpxtfUYmC8tbbghOenoWvQV4wxU4HjwCPW2mGNz/0GOGSt/VXjh96u1trvnnRcN2AVMAawOK+5o621h9v0D5DmzuF04A1rbb0x5tcAJ5/Dxv12c4rXXXFfM+fvp8Bxa+3vTnHcaT+7Stto6hyetP33wBFr7d1NbNtNB78GNYLavHHADmvth9baWuBxIO+kffKAfzX+/DRwoTHGtGGMcgrW2r3W2jWNPx8DPgAyvI1KQiwP58XfWmuXAUmNX0yI/1wI7DwxORV/sta+Axw66ekT3+/+Bcxs4tBLgMXW2kONSeliYIZbcUrzmjqH1tpF1tr6xl+XAb3aPDBpkWauwZZoyWdXaQOnOoeNucJngMfaNKgwogS1eRlA0Qm/7+HTyc1H+zS+6B8BktskOjkjjdOvc4HlTWyeaIxZZ4x52RgztG0jk9OwwCJjzGpjzJ1NbG/JdSr+cAPNvxnrGvS/VGvt3saf9wGpTeyj6zF8fA54uZltp3vdFe/c1ThF++FmptnrGgwPU4BSa+32ZrZ3+GtQCaq0e8aYeOAZ4GvW2qMnbV4D9LbWjgT+BCxo4/Dk1M611o4CLgW+3DhlRsKMMSYauAp4qonNugbDjHXuDdL9QWHKGPMDoB54tJld9LrrT38F+gM5wF7g955GI61xI6cePe3w16AS1OYVA5kn/N6r8bkm9zHGRAKJQFmbRCctYoyJwklOH7XW/vvk7dbao9ba440/vwREGWO6t3GY0gxrbXHj437gWZzpSydqyXUq3rsUWGOtLT15g67BsFH6n+nzjY/7m9hH16PPGWNuBa4AZtlmipC04HVXPGCtLbXWNlhrg8ADNH1edA36XGO+8F/AE83to2tQCeqprAQGGGP6Nn77fwPw3En7PAf8p0rhtTjFB/Stsk80zvF/CPjAWntPM/uk/ee+YWPMOJxrQl8y+IAxJq6xuBXGmDhgOrDxpN2eA242jgk4BQf2In7T7LfFugbDxonvd7cAC5vY51VgujGma+P0w+mNz4kPGGNmAN8BrrLWVjazT0ted8UDJ9VXuJqmz0tLPruKty4Ctlhr9zS1UdegI9LrAPyqscrdXThvrhHAw9baTcaYu4FV1trncJKfucaYHTg3Qt/gXcTShMnAHGDDCaW8/wfIArDW/g3ni4UvGmPqgSrgBn3J4BupwLONuUskMN9a+4ox5gvw0fl7CaeC7w6gEvisR7FKMxrfYC8GPn/CcyeeQ12DPmOMeQyYBnQ3xuwBfgL8CnjSGHMbUIBT4ANjzBjgC9ba2621h4wxP8P5kAxwt7X2bAq9SCs1cw6/D8QAixtfV5c1rkKQDjxorb2MZl53PfgTOrRmzt80Y0wOzvT63TS+pp54/pr77Nr2f4E0dQ6ttQ/RRD0GXYOfpmVmRERERERExBc0xVdERERERER8QQmqiIiIiIiI+IISVBEREREREfEFJagiIiIiIiLiC0pQRURERERExBeUoIqIiIiIiIgvKEEVERERERERX1CCKiIiIiIiIr7w/wGKptUnSVjjDwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,9))\n",
    "sns.lineplot(data = data_plot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7d64231a-0a81-4618-a791-bd5f9a94c619",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}