conformal_prediction_GBTC.ipynb

{
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conformal Prediction with LSTM base model and Random Forest Regressor for GBTC ETF\n",
    "Conformal prediction is a relatively new framework for quantifying uncertainty in the predictions made by\n",
    "arbitrary prediction algorithms. Fundamentally, it does so by converting an algorithm’s predictions into\n",
    "prediction sets, which have strong finite-sample coverage properties. A new framework was recently developped for time series forcasting which doesn't require exchangeability of the data. [More details and citations on this in our final report]\n",
    "\n",
    "We use conformal prediction to predict an interval around the prediction with (1-alpha) confidence level. This enables us to build confidence in our prediction and define a probability threshold for our trading decision.\n",
    "\n",
    "Conformal prediction can be used in addition to any machine learning model. In this project, we applied the method to several regressors. This notebook first uses a LSTM - extending the LSTM model previously developped as a point value predictor. The prediction performance is then compared to a Random Forest Regressor.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data cleaning and preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The class Preprocessing defined in the Data_preprocessing.py python file defines the main methods for the data cleaning, handling missing values, preprocessing the historical data to feed into the models.\n",
    "\n",
    "In this project, a prediction is made using the data from the 3 previous days and the open of the current day. Historical data from GBTC value and other crypto assets was gathered from Yahoo Finance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from Data_preprocessing import Preprocessing\n",
    "preprocessing = Preprocessing(timestep=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         Date  Open  High   Low  Close  Adj Close   Volume  Open_ETCG  \\\n",
      "0  2019-11-22  8.63  9.29  8.53   9.27       9.27  4922600       9.10   \n",
      "1  2019-11-25  9.15  9.45  8.92   9.04       9.04  3050000       9.06   \n",
      "2  2019-11-26  8.95  9.03  8.91   9.00       9.00  1446100       9.20   \n",
      "3  2019-11-27  9.04  9.74  9.04   9.58       9.58  3452200       9.07   \n",
      "4  2019-11-29  9.77  9.89  9.55   9.57       9.57  2272600       9.55   \n",
      "\n",
      "   High_ETCG  Low_ETCG  Close_ETCG  Adj Close_ETCG  Volume_ETCG  \n",
      "0       9.35      8.61       9.053           9.053        28200  \n",
      "1       9.25      8.83       9.100           9.100        22300  \n",
      "2       9.28      9.10       9.160           9.160         4100  \n",
      "3       9.90      9.07       9.502           9.502        26300  \n",
      "4       9.89      9.55       9.890           9.890         1400  \n"
     ]
    }
   ],
   "source": [
    "x, y, xscaler, yscaler = preprocessing._load_and_preprocess_data('GBTC',  ['ETCG'])\n",
    "x_train, x_test, y_train, y_test, x_last = preprocessing._chunking(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LSTM Neural Network\n",
    "The model and its main methods are defined in the LSTM class inside the Conformal_LSTM.py python file. Methods are implemented to build, train the model and then predict the high for today or for a time interval for back testing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from conformal_LSTM import LSTM_Predictor\n",
    "LSTM = LSTM_Predictor(xscaler, yscaler, x_last)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For time series conformal prediction we use the mapie library available at: https://github.com/scikit-learn-contrib/MAPIE/tree/master. The library is compatible with scikit-learn model so our Tensorflow object is wrapped using scikeras.wrapper. We use the funtion MapieTimeSeriesRegressor with method='EnbPI' for our prediction intervals.\n",
    "\n",
    "The model is trained for 100 epochs using loss MSE and Adam optimizer.\n",
    "\n",
    "We use the BlockBootstrap sampling method instead of the traditional bootstrap strategy for training the model since the former is more suited for time series data. Here, we choose to perform 10 resamplings with 10 blocks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\meuni\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\layers\\rnn\\rnn.py:204: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(**kwargs)\n"
     ]
    },
    {
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
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       "\u001b[1mModel: \"sequential\"\u001b[0m\n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ lstm (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>)          │         <span style=\"color: #00af00; text-decoration-color: #00af00\">1,200</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ lstm_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>)          │         <span style=\"color: #00af00; text-decoration-color: #00af00\">1,200</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ lstm_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>)           │           <span style=\"color: #00af00; text-decoration-color: #00af00\">456</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ lstm_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>)              │           <span style=\"color: #00af00; text-decoration-color: #00af00\">120</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │             <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
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       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ lstm (\u001b[38;5;33mLSTM\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m12\u001b[0m)          │         \u001b[38;5;34m1,200\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ lstm_1 (\u001b[38;5;33mLSTM\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m12\u001b[0m)          │         \u001b[38;5;34m1,200\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ lstm_2 (\u001b[38;5;33mLSTM\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m6\u001b[0m)           │           \u001b[38;5;34m456\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ lstm_3 (\u001b[38;5;33mLSTM\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m)              │           \u001b[38;5;34m120\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │             \u001b[38;5;34m4\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">2,980</span> (11.64 KB)\n",
       "</pre>\n"
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       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m2,980\u001b[0m (11.64 KB)\n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">2,980</span> (11.64 KB)\n",
       "</pre>\n"
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      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m2,980\u001b[0m (11.64 KB)\n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
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       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
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     "name": "stdout",
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     "text": [
      "None\n"
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     "name": "stderr",
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     "text": [
      "C:\\Users\\meuni\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\sklearn\\utils\\validation.py:1300: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\meuni\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\saving\\saving_lib.py:415: UserWarning: Skipping variable loading for optimizer 'adam', because it has 30 variables whereas the saved optimizer has 2 variables. \n",
      "  saveable.load_own_variables(weights_store.get(inner_path))\n"
     ]
    },
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     "text": [
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 212ms/step - loss: 0.9794 - val_loss: 0.8462\n",
      "Epoch 2/100\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.9957 - val_loss: 0.8372\n",
      "Epoch 3/100\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - loss: 1.0120 - val_loss: 0.8255\n",
      "Epoch 4/100\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 1.0155 - val_loss: 0.8088\n",
      "Epoch 5/100\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - loss: 0.9828 - val_loss: 0.7845\n",
      "Epoch 6/100\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.9025 - val_loss: 0.7494\n",
      "Epoch 7/100\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 0.8957 - val_loss: 0.7020\n",
      "Epoch 8/100\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.8562 - val_loss: 0.6421\n",
      "Epoch 9/100\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.7652 - val_loss: 0.5726\n",
      "Epoch 10/100\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.6477 - val_loss: 0.4999\n",
      "Epoch 11/100\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.5782 - val_loss: 0.4296\n",
      "Epoch 12/100\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.5104 - val_loss: 0.3668\n",
      "Epoch 13/100\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.4187 - val_loss: 0.3138\n",
      "Epoch 14/100\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.3570 - val_loss: 0.2699\n",
      "Epoch 15/100\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.3082 - val_loss: 0.2349\n",
      "Epoch 16/100\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.2828 - val_loss: 0.2080\n",
      "Epoch 17/100\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.2340 - val_loss: 0.1874\n",
      "Epoch 18/100\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.1991 - val_loss: 0.1705\n",
      "Epoch 19/100\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.1911 - val_loss: 0.1569\n",
      "Epoch 20/100\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.1792 - val_loss: 0.1420\n",
      "Epoch 21/100\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.1722 - val_loss: 0.1283\n",
      "Epoch 22/100\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.1561 - val_loss: 0.1187\n",
      "Epoch 23/100\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.1553 - val_loss: 0.1147\n",
      "Epoch 24/100\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.1354 - val_loss: 0.1113\n",
      "Epoch 25/100\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 0.1312 - val_loss: 0.1077\n",
      "Epoch 26/100\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step - loss: 0.1207 - val_loss: 0.1027\n",
      "Epoch 27/100\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step - loss: 0.1136 - val_loss: 0.0978\n",
      "Epoch 28/100\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 0.1253 - val_loss: 0.0931\n",
      "Epoch 29/100\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step - loss: 0.1194 - val_loss: 0.0888\n",
      "Epoch 30/100\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.0953 - val_loss: 0.0832\n",
      "Epoch 31/100\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.1024 - val_loss: 0.0780\n",
      "Epoch 32/100\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - loss: 0.1020 - val_loss: 0.0733\n",
      "Epoch 33/100\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0931 - val_loss: 0.0680\n",
      "Epoch 34/100\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0808 - val_loss: 0.0633\n",
      "Epoch 35/100\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step - loss: 0.0804 - val_loss: 0.0590\n",
      "Epoch 36/100\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step - loss: 0.0696 - val_loss: 0.0561\n",
      "Epoch 37/100\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0666 - val_loss: 0.0524\n",
      "Epoch 38/100\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.0681 - val_loss: 0.0508\n",
      "Epoch 39/100\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.0549 - val_loss: 0.0503\n",
      "Epoch 40/100\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.0558 - val_loss: 0.0469\n",
      "Epoch 41/100\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 0.0483 - val_loss: 0.0468\n",
      "Epoch 42/100\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step - loss: 0.0515 - val_loss: 0.0468\n",
      "Epoch 43/100\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0484 - val_loss: 0.0442\n",
      "Epoch 44/100\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0480 - val_loss: 0.0446\n",
      "Epoch 45/100\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 0.0496 - val_loss: 0.0445\n",
      "Epoch 46/100\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0474 - val_loss: 0.0425\n",
      "Epoch 47/100\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.0401 - val_loss: 0.0444\n",
      "Epoch 48/100\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step - loss: 0.0448 - val_loss: 0.0434\n",
      "Epoch 49/100\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0410 - val_loss: 0.0416\n",
      "Epoch 50/100\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 0.0378 - val_loss: 0.0431\n",
      "Epoch 51/100\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 0.0403 - val_loss: 0.0423\n",
      "Epoch 52/100\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - loss: 0.0401 - val_loss: 0.0415\n",
      "Epoch 53/100\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step - loss: 0.0364 - val_loss: 0.0424\n",
      "Epoch 54/100\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0385 - val_loss: 0.0414\n",
      "Epoch 55/100\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.0365 - val_loss: 0.0419\n",
      "Epoch 56/100\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0356 - val_loss: 0.0407\n",
      "Epoch 57/100\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.0369 - val_loss: 0.0413\n",
      "Epoch 58/100\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0371 - val_loss: 0.0411\n",
      "Epoch 59/100\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0321 - val_loss: 0.0404\n",
      "Epoch 60/100\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.0315 - val_loss: 0.0421\n",
      "Epoch 61/100\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.0351 - val_loss: 0.0398\n",
      "Epoch 62/100\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.0313 - val_loss: 0.0406\n",
      "Epoch 63/100\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.0324 - val_loss: 0.0403\n",
      "Epoch 64/100\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.0315 - val_loss: 0.0401\n",
      "Epoch 65/100\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0308 - val_loss: 0.0401\n",
      "Epoch 66/100\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.0321 - val_loss: 0.0395\n",
      "Epoch 67/100\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.0304 - val_loss: 0.0389\n",
      "Epoch 68/100\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.0277 - val_loss: 0.0396\n",
      "Epoch 69/100\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.0286 - val_loss: 0.0396\n",
      "Epoch 70/100\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.0305 - val_loss: 0.0387\n",
      "Epoch 71/100\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.0318 - val_loss: 0.0382\n",
      "Epoch 72/100\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0301 - val_loss: 0.0392\n",
      "Epoch 73/100\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.0268 - val_loss: 0.0366\n",
      "Epoch 74/100\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.0249 - val_loss: 0.0391\n",
      "Epoch 75/100\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.0259 - val_loss: 0.0367\n",
      "Epoch 76/100\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.0300 - val_loss: 0.0379\n",
      "Epoch 77/100\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0278 - val_loss: 0.0372\n",
      "Epoch 78/100\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step - loss: 0.0285 - val_loss: 0.0362\n",
      "Epoch 79/100\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0284 - val_loss: 0.0368\n",
      "Epoch 80/100\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0274 - val_loss: 0.0357\n",
      "Epoch 81/100\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0278 - val_loss: 0.0363\n",
      "Epoch 82/100\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - loss: 0.0271 - val_loss: 0.0354\n",
      "Epoch 83/100\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.0250 - val_loss: 0.0355\n",
      "Epoch 84/100\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0279 - val_loss: 0.0360\n",
      "Epoch 85/100\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.0285 - val_loss: 0.0349\n",
      "Epoch 86/100\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 0.0262 - val_loss: 0.0348\n",
      "Epoch 87/100\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0236 - val_loss: 0.0351\n",
      "Epoch 88/100\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0248 - val_loss: 0.0349\n",
      "Epoch 89/100\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 0.0240 - val_loss: 0.0354\n",
      "Epoch 90/100\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0241 - val_loss: 0.0340\n",
      "Epoch 91/100\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0233 - val_loss: 0.0344\n",
      "Epoch 92/100\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0248 - val_loss: 0.0340\n",
      "Epoch 93/100\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0269 - val_loss: 0.0333\n",
      "Epoch 94/100\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.0224 - val_loss: 0.0333\n",
      "Epoch 95/100\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.0246 - val_loss: 0.0345\n",
      "Epoch 96/100\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.0240 - val_loss: 0.0334\n",
      "Epoch 97/100\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0230 - val_loss: 0.0339\n",
      "Epoch 98/100\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.0239 - val_loss: 0.0337\n",
      "Epoch 99/100\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0258 - val_loss: 0.0342\n",
      "Epoch 100/100\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0244 - val_loss: 0.0325\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\meuni\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\saving\\saving_lib.py:415: UserWarning: Skipping variable loading for optimizer 'adam', because it has 30 variables whereas the saved optimizer has 2 variables. \n",
      "  saveable.load_own_variables(weights_store.get(inner_path))\n",
      "C:\\Users\\meuni\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\mapie\\utils.py:542: UserWarning: WARNING: at least one point of training set belongs to every resamplings.\n",
      "Increase the number of resamplings\n",
      "  warnings.warn(\n",
      "C:\\Users\\meuni\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\mapie\\aggregation_functions.py:116: RuntimeWarning: All-NaN slice encountered\n",
      "  return np.nanmedian(X, axis=1)\n"
     ]
    }
   ],
   "source": [
    "LSTM._create(x_train.shape)\n",
    "LSTM._train(x_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The function defining the trading strategy is a method from the preprocessing class. It also includes a method to plot the predictions from our different models with their prediction intervals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 886ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1s/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 904ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 926ms/step\n",
      "WARNING:tensorflow:5 out of the last 5 calls to <function TensorFlowTrainer.make_predict_function.<locals>.one_step_on_data_distributed at 0x000001F3AB28AA20> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 729ms/step\n",
      "WARNING:tensorflow:6 out of the last 6 calls to <function TensorFlowTrainer.make_predict_function.<locals>.one_step_on_data_distributed at 0x000001F3AA9F5E40> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 887ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 768ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 800ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 748ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 758ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 908ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\meuni\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\mapie\\estimator\\estimator.py:297: RuntimeWarning: All-NaN slice encountered\n",
      "  return phi2D(A=x, B=k, fun=lambda x: np.nanmedian(x, axis=1))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step  \n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step  \n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step  \n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step  \n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step  \n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step  \n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step  \n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step  \n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step  \n",
      "Predicted High: 56.5090217590332\n",
      "Predicted 60.0% lower bound: 56.11291278316803\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\meuni\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\mapie\\estimator\\estimator.py:297: RuntimeWarning: All-NaN slice encountered\n",
      "  return phi2D(A=x, B=k, fun=lambda x: np.nanmedian(x, axis=1))\n"
     ]
    }
   ],
   "source": [
    "probability_threshold = 0.60  # Probability threshold (e.g., 60%) for trading strategy\n",
    "alpha = 2 * (1 - probability_threshold)\n",
    "\n",
    "lstm_point_pred, lstm_point_lb, lstm_test_pred, lstm_test_interval = LSTM._predict(x_test=x_test, alpha = alpha)\n",
    "\n",
    "print(f\"Predicted High: {lstm_point_pred}\")\n",
    "print(f\"Predicted {probability_threshold*100}% lower bound: {lstm_point_lb}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Today's open is: 55.650001525878906\n",
      "Do not enter trade\n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "todays_open = json.load(open('./data/todays/GBTC.json'))['open']\n",
    "print(f\"Today's open is: {todays_open}\")\n",
    "\n",
    "trade = preprocessing._enter_trade(lstm_point_lb, todays_open)\n",
    "if trade:\n",
    "    print(\"Enter trade\")\n",
    "else:\n",
    "    print(\"Do not enter trade\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We compare our trading strategy based on our prediction and trading at random."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "With our strategy we trade: 0.32142857142857145\n",
      "Among these trades we lose:0.3888888888888889\n",
      "and win: 0.6111111111111112\n",
      "At random we trade: 0.49994642857142857\n",
      "Among these trades we lose:0.5335214487266493\n",
      "and win: 0.46647855127335075\n"
     ]
    }
   ],
   "source": [
    "from random import randint\n",
    "from statistics import mean\n",
    "\n",
    "# our trading strategy on test set\n",
    "open_test = x_test[:,0,0]\n",
    "loss=0\n",
    "win =0\n",
    "entered = 0\n",
    "for i in range(len(open_test)):\n",
    "    trade = preprocessing._enter_trade(lstm_test_interval[i,0, 0], open_test[i])\n",
    "    if trade and open_test[i]*1.01> y_test[i]:\n",
    "        loss+=1\n",
    "        entered+=1\n",
    "    elif trade:\n",
    "        win+=1\n",
    "        entered+=1\n",
    "print(f\"With our strategy we trade: {entered/len(open_test)}\")\n",
    "print(f\"Among these trades we lose:{loss/entered}\")\n",
    "print(f\"and win: {win/entered}\")\n",
    "\n",
    "# at random\n",
    "loss=[0]*1000\n",
    "win = [0]*1000\n",
    "entered = [0]*1000\n",
    "for k in range(1000):\n",
    "    for i in range(len(open_test)):\n",
    "        trade = randint(0,1)\n",
    "        if trade and open_test[i]*1.01> y_test[i]:\n",
    "            loss[k]+=1\n",
    "            entered[k]+=1\n",
    "        elif trade:\n",
    "            win[k]+=1\n",
    "            entered[k]+=1\n",
    "print(f\"At random we trade: {mean(entered)/len(open_test)}\")\n",
    "print(f\"Among these trades we lose:{mean(loss)/mean(entered)}\")\n",
    "print(f\"and win: {mean(win)/mean(entered)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We evaluate the model by calculating the loss on unseen data. To visualize the confidence improvement made using conformal prediction, we plot the prediction for a testing time interval."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "preprocessing.plot_with_interval(y_test, lstm_test_pred, lstm_test_interval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The high of the day was outside of the one-sided predicted interval 44.642857142857146% of the tested days\n"
     ]
    }
   ],
   "source": [
    "mean_error = preprocessing.performance_conformal(y_test, lstm_test_interval)\n",
    "print(f\"The high of the day was outside of the one-sided predicted interval {mean_error*100}% of the tested days\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When using the EnbPI method, to improve the fit of the prediction interval, we can update the residuals during the prediction, each time new observations are available so that the deterioration of predictions, or the increase of noise level, can be dynamically taken into account. It can be done with MapieTimeSeriesRegressor through the partial_fit class method called at every step.\n",
    "\n",
    "Implementing this could be a way forward for this project."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest Regressor\n",
    "The model and its main methods are defined in the RFRegressor class inside the Conformal_RF.py python file. Methods are implemented to build, train the model and then predict the high for today or for a time interval for back testing.\n",
    "\n",
    "For this model, partial fit was implemented.\n",
    "\n",
    "To feed the data into the RF regressor it must be a 2D array so x data is reshaped."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from conformal_RF import RFRegressor\n",
    "RF = RFRegressor(xscaler, yscaler, x_last)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To tune the Random Forest Regressor we perform a hyperparameter random search when calling the method _params_fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<bound method BaseEstimator.get_params of RandomForestRegressor(max_depth=9, n_estimators=65, random_state=59)>\n"
     ]
    }
   ],
   "source": [
    "RF._params_fit(x_train, y_train)\n",
    "RF._train(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1055, 3, 12)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_point_pred, rf_point_lb, rf_test_pred, rf_test_interval = RF._predict(x_test)\n",
    "rf_test_pred_pf, rf_test_interval_pf = RF._partial_fit(x_test, y_test, gap=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted High: 56.63671271956447\n",
      "Predicted 60.0% lower bound: 56.32359775804714\n",
      "Today's open is: 55.650001525878906\n",
      "Enter trade\n"
     ]
    }
   ],
   "source": [
    "print(f\"Predicted High: {rf_point_pred}\")\n",
    "print(f\"Predicted {probability_threshold*100}% lower bound: {rf_point_lb}\")\n",
    "print(f\"Today's open is: {todays_open}\")\n",
    "\n",
    "trade = preprocessing._enter_trade(rf_point_lb, todays_open)\n",
    "if trade:\n",
    "    print(\"Enter trade\")\n",
    "else:\n",
    "    print(\"Do not enter trade\")"
   ]
  },
{
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "With our strategy we trade: 0.39285714285714285\n",
      "Among these trades we lose:0.09090909090909091\n",
      "and win: 0.9090909090909091\n"
     ]
    }
   ],
   "source": [
    "# our trading strategy on test set\n",
    "open_test = x_test[:,0,0]\n",
    "loss=0\n",
    "win =0\n",
    "entered = 0\n",
    "for i in range(len(open_test)):\n",
    "    trade = preprocessing._enter_trade(rf_test_interval[i,0, 0], open_test[i])\n",
    "    if trade and open_test[i]*1.01> y_test[i]:\n",
    "        loss+=1\n",
    "        entered+=1\n",
    "    elif trade:\n",
    "        win+=1\n",
    "        entered+=1\n",
    "print(f\"With our strategy we trade: {entered/len(open_test)}\")\n",
    "print(f\"Among these trades we lose:{loss/entered}\")\n",
    "print(f\"and win: {win/entered}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We evaluate the model on the same data as we did for the LSTM."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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LSqI004xBMyIiIpos4ymRrASf34fF+y3e944A3nHIO/C3v/wNDY0NCIVCQ+7T3NKM5//9PN5z9HsAWL3CXvrPS3jHIe8Ycv8VK1bAMAz86+l/2eWZToVMN13Xh13X8uXLoWkannvuObs8s6enB5s2bcIBBxwwqp+t4JBDDsEhhxyCyy67DKtWrcIdd9wxqUEzDgIgIiKiaUFOdpfcllieSURERDRqn/r0p1BXX4czTjsDzz7zLLZt3Yann3oaX/2fr2LXrl0AgAu/fCGu+ck1uP/v92PTG5tw0ZcuQiwaG/Y15y+Yj0//16fxhc9/Affddx+2bt2K1atX4+6777Yenz8fgiDgH//4B7q7u5FMDj5+W7JkCU455RR8/vOfxzPPPINXXnkFZ599NubOnYtTTjllVD/b1q1bcdlll2Ht2rXYvn07Hn30UWzevHnS+5oxaEZERETTAnuaEREREY2fz+fDo088itbWVpz5iTNxyEGH4ILzL0Amk7Ezzy66+CKcedaZOP+z5+P9R78fwWAQHz3loyO+7s//9+c49bRT8cUvfhHLli3D5z//efT39wMA5s6di6uuugrf/OY30dTUhC996UtDvsZtt92GQw89FCeffDJWrVoF0zTxf//3f4NKMkf62d544w2cfvrp2H///XH++efjwgsvxBe+8IUx/IbGTjCH6/Y2S8TjcYTDYcRisWHTE2eavem9eKXrFTQHmiu9FCIiorJZdOd58Ha/ad+OLzwaO0/+cQVXRERERDOdkTNg9phoW9AGt8dd6eXMSKqhwiW64JGnrr/ZRGUyGWzduhULFy6Ex1O67rHEiZhpRkRERNOCkujCm4qCLzU14O5ggD3NiIiIiKiiOAiAiIiIKk7QspAzUdzYWI+nfF486/XguASDZkRERERUOcw0IyIioopT8kMA3sr3tdAEAVGNPc2IiIiIqHIYNCMiIqKKk5Od0AHsUopJ8P1aunILIiIiIqKqV/Gg2e7du3H22Wejrq4OXq8XBx10EF544QX7cdM0ceWVV6KlpQVerxfHH388Nm/eXMEVExERUbkpiS50yhI0QbDvS+o5wNAruCoiIiIiqmYVDZr19fXh3e9+NxRFwUMPPYTXXnsN11xzDWpqaux9fvKTn+D666/HTTfdhOeeew5+vx8nnngiMplMBVdORERE5aQku7BTLm21GpdESLn+Cq2IiIiIiKpdRQcB/PjHP0Zraytuu+02+76FCxfa26Zp4rrrrsMVV1yBU045BQDw+9//Hk1NTbjvvvvwqU99asrXTEREROWnJLuwUxkQNBNFiNkEdM/Io8CJiIiIiCZDRTPN7r//fhx22GH4xCc+gcbGRhxyyCG45ZZb7Me3bt2Kjo4OHH/88fZ94XAYRxxxBNauXTvka2azWcTj8ZI/RERENL0pyc7BmWaiCCnLYQBEREREVBkVDZq9/fbbuPHGG7FkyRI88sgjuOCCC/CVr3wFv/vd7wAAHR0dAICmpqaS5zU1NdmPDXT11VcjHA7bf1pbWyf3hyAiIqIJUxJd2JmfnFlgBc0SFVoREREREVW7ipZnGoaBww47DD/84Q8BAIcccgg2bNiAm266Ceeee+64XvOyyy7DJZdcYt+Ox+MMnBEREU1zcn8XdtX6Su4rlGcSERERTQZVV6GbUzd0SBIkKJKy7x1p2qho0KylpQUHHHBAyX3Lly/H3/72NwBAc3MzAKCzsxMtLS32Pp2dnXjHO94x5Gu63W643e7JWTARERGVnaBmIGXi2KmU9i6LSyzPJCIiosmh6io29mxESk1N2Xv6FB8OrDtwTIGzjo4O/OTqn+Dhhx7Gnt170NDYgJUHr8SXvvIlvPs978bitsW46JKLcOnXLx303Kt/cDVuvuFmbN62GYpS/mCd4Jh6HgqFsGLFCvy///f/cOyxxwIAzjvvPESjUdx3331lf++pUtHyzHe/+93YtGlTyX1vvvkm5s+fD8AaCtDc3IzHH3/cfjwej+O5557DqlWrpnStRERENDmUZCeiooikWHpYkhAFlmcSERHRpNBNHSk1BUVU4FN8k/5HERWk1NSYMtu2b9uOdx/xbjy1+in88Ec/xPMvPY+//+PveO9734uLv3IxXC4XPvXpT+EPv/vDoOeapok//v6P+PTZnx5VwGz7tu3wKb597jfQbbfdhvb2djz77LOor6/HySefjLfffnvMrzNdVTRodvHFF2PdunX44Q9/iLfeegt33HEHfv3rX+PCCy8EYEUt/+d//gff//73cf/992P9+vU455xzMGfOHHzsYx+r5NKJiIioTKx+ZoOT3+OiCCnHTDMiIiKaPIqkwC25J/3PeMoyL/ryRRAEAU+veRofO+1jWLL/Ehxw4AH4ysVfwepnVgMAzv3Mudj85maseWZNyXP/9fS/sPXtrTj3M+NrfTVakUgEzc3NWLFiBW688Uak02k89thjQ+7717/+FQcddBC8Xi/q6upw/PHHo7+/f1LXN1EVDZodfvjhuPfee/HnP//ZTuO77rrrcNZZZ9n7fP3rX8eXv/xlnH/++Tj88MORTCbx8MMPw+PxVHDlREREVC5Kf9egyZlAoacZg2ZERERUfXp7e/HYI4/hCxd8AX6/f9DjkUgEALDioBU49LBD8bvbf1fy+B9u/wOOXHUkli5bOhXLBQB4vV4AQC6XG/RYe3s7zjzzTHz2s5/F66+/jtWrV+O0006DaZpTtr7xqGhPMwA4+eSTcfLJJw/7uCAI+N73vofvfe97U7gqIiIimiojZppl4xVYEREREVFlbXlrC0zTxP5L99/nvud+5lx86xvfwjXXXYNAIIBEIoF777kXP7v2Z1OwUksqlcIVV1wBSZLw3ve+d9Dj7e3t0DQNp512mt2S66CDDpqy9Y1XRTPNiIiIiORkZ0mmmVt0AQASogiBPc2IiIioCo0lA+uMT50BXdfxt79YQxX/evdfIYoiPn7Gx0d83qEHH4qGSAMaIg049OBDAcC+3RBpwCknn7LP9z7zzDMRCAQQDAbxt7/9DbfeeitWrlw5aL+DDz4Yxx13HA466CB84hOfwC233IK+vr5R/4yVUvFMMyIiIqpuSrIbuxyZZm2h+dgc3QxTEJDOMGhGRERE1We/JftBEAS8uenNfe4bCoVw6mmn4g+/+wPO/cy5+MPv/oDTPn4aAoHAiM+79/57oaoqAGDPnj048bgTse6FdfbjHu++22Jde+21OP744xEOh9HQ0DDsfpIk4bHHHsOaNWvw6KOP4n//939x+eWX47nnnsPChQv3+T6VwkwzIiIiqijFkWkWdoVQ562zH+vnIAAiIiKqQrW1tTj+A8fj5htvHrJZfjQaLbl97mfPxZpn1+D/Hvw/rFu7Dud95rx9vkfb/DYs3m8xFu+3GG1tbQBg316832LMnTt3n6/R3NyM/fbbb8SAWYEgCHj3u9+Nq666Ci+99BJcLhfuvffefT6vkhg0IyIioorSkl3ozgfNGv1NCCjFq6L92vSeqEREREQ0Wa67/jrouo5jjjoG991zH97a/BbeeP0N3PC/N+D9R7+/ZN/3HP0eLN5vMT7/mc9j6bKlOPKoIyu06qE999xz+OEPf4gXXngBO3bswD333IPu7m4sX7680ksbEcsziYiIqGLEXD/ajax9u8nXBL9SnBDVr2UQqsTCiIiIqCqoujpt32fhooVY8+81+MnVP8E3v/5NdLR3oL6hHoe88xD84pe/KNlXEAScc945+M4V38Gl37i0XMsum1AohKeffhrXXXcd4vE45s+fj2uuuQYnnXRSpZc2IgbNiIiIqGLkZHfJ5MwmXxPcktu+nTA1hHUVpqRUYnlEREQ0S0mCBJ/iQ0pNQTWmJnDmU3yQBGlMz2lpacG111+La6+/dp/7fu0bX8PXvvG1ca1t/oL5SKmpMT1nX8MKbr/9dnt7+fLlePjhh8eztIpi0IyIiIgqRhkwObPZ34ycnrNvxyURYi4J3VtTieURERHRLKVICg6sOxC6qU/Ze0qCBIUXAmcUBs2IiIioYpREV2mmmb8Jvele+3ZcFCFlGTQjIiKi8lMkBQoYxKLhMWhGREREFaMkO7FLLi3PzOrFHmdxUYSUSVRiaURERERU5Tg9k4iIiCpGcfQ084guhFyhkumZcVGEmGPQjIiIiIimHoNmREREVDFCsgN78plmTb5GCIJQMj0zkS/PJCIiIhoXATBh7rNpPc0u5fr7ZtCMiIiIKqanvwuaIAAAmgJzAKAkaGb1NGOmGREREY2PIAkwYSKTzlR6KTSFcjlrsJQkjW1a6UDsaUZERESVYZroyEYBhAFY/cwAwCt7IQAwYU3PZNCMiIiIxksQBQheAd3d3QAAj9cDIX/BjkZHMzSYojljIkiGYaC7uxs+nw+yPLFFz5AfmYiIiGYbMdeP3UJxzHuT3wqaiYIIv+RBUs9YPc1YnklEREQTIIUl6NDR2dUJIf8/Gj3d1CGJEhRx5kwaFUURbW1tEw6QMmhGREREFTHU5MwCv+y1g2bsaUZEREQTIQgC5IgMM2TC1E0rnZ1GLZ6Jo8XXgrZwW6WXMmoulwuiOPGOZAyaERERUUUoiS57ciZQzDQDgIDiR2e2DwlRhJiJV2J5RERENMsIogBBZJbZWBmaAcklwePxVHopU46DAIiIiKgilGQnduYzzSQIqPPU2Y/5XAEAgC4IyOTY04yIiIiIph6DZkRERFQRkiPTrNEVgiQWpxv5XCF7u59BMyIiIiKqAAbNiIiIqCL6k3uQyveaaPY2ljzmz2eaAUC/2j+l6yIiIiIiAhg0IyIiogrp7O+0txuCc0se8yt+ezuppadsTUREREREBQyaERERUUV0ZPvs7abgvJLHAkox0yypZ6ZsTUREREREBQyaERER0dQzTbRrSftmk7+55OGSTDMYELTslC2NiIiIiAhg0IyIiIgqQMomsFMqjnxv8jWVPO4MmsVFEVKWwwCIiIiIaGoxaEZERERTTk50Yqcs27cb/QMGATiDZpIIMZsEEREREdFUYtCMiIiIppyS7MJOxQqa1YkuuCV3yePMNCMiIiKiSmPQjIiIiKacGt+NXkkCADQr4UGPDw6aMdOMiIiIiKYWg2ZEREQ05fbGd9jbzd76QY87p2cy04yIiIiIKoFBMyIiIppynf0d9nZjcO6gx32Kz95OiCJEBs2IiIiIaIoxaEZERERTrj3ba283RBYOelwURPhFFwCWZxIRERFVitzfAzmbBEyj0kupCHnfuxARERGVV7vaD0gCAKAh1DbkPn7Zi/5cDnFJhJRj0IyIiIhoqrX+32VY2rEBpiACV3QDUnWFkZhpRkRERFPLNLHHzNk3m/xNQ+7ml60SzbgoQkzHpmRpRERERFQkZeIAAEPxVV3ADGDQjIiIiKaYlIlip2xNzvSbAoJKcMj9/C5rGIAmCMjm2NOMiIiIaKpJ2XzQzBOq8Eoqg0EzIiIimlJCvB0d+aDZHNEDQRCG3M/nKgbTUgyaEREREU0t04SUsY7BGDQjIiIimgJ9fVug5wNlLa7hD8D87rC93c+eZkRERERTSsz1QzB1AIDuOC6rJgyaERER0ZTqju2wt5s99cPu51f89nZS65/UNRERERFRqUI/M4CZZkRERERTojPZbm83BlqG3c8ZNOvXMpO6JiIiIiIqVehnBgC6h5lmRERERJOuI9tjbzdEFgy7X0mmmZEDTHMyl0VEREREDsw0Y9CMiIiIpli7WuxPVl+7/7D7BZSAvR0XAFFNT+q6iIiIiKjIGTRjphnNHIYGGHqlV0FERDQuu80cAEA2TdSE5g27nzPTLC6KEDkMgIiIiGjKOMszDTczzWgmiLcj/Oezsejluyu9EiIiojEzDR27RavMco4hQBSGPxRxBs0SomiPPCciqjbu7s2offmukqwPIqLJxkwzQK70AmgMcv3ALe+HkmjHgl0vYPv8Veiff2SlV0VERDRqidgOZEQrUDZXdI+4b0mmmSRCyjJoRkTVR9BVzL//EiipHrj7tqP9/V+v9JKIqEqwpxkzzWYWlx844gsAAAEm5j16FeREZ4UXRURENHp7e960t1uUkQ++/C6WZxIRyclOKClrgIpn7+YKr4aIqgmnZzJoNvMcdRFyi48FAMiZGFof/jagaxVeFBER0eh0x7bZ283euhH39culQTNmmhFRNVISXfa2lI5VcCVEVG2Yacag2cwjikh8+KdIBxoAAL6ODWha86sKL4qIiGh0uvrb7e1G/5wR95VECV5RAcCgGRFVL8VRWSJnGDQjoqkjZRL4h9+HPwcDeKpnI/QqHEjIoNkMZHojWP++S2HkTyTqX74LwbdWV3ZRREREo9CR6bG3G8Pz97l/QPIAKATNWJ5JRNVHSXbY21I2ARisMiGiqSFl4/hDOIgf1tfi5y9fD0EQKr2kKceg2QyVaFiCzqO/Yt+e+/gP4IruquCKiIiI9q09Z2WLCaaJ2rol+9zfL3sBWIMABE6NI6Iq5CzPBMAJmkQ0ZaRMHDFRAgAEXcERp57PVtX3E88ivQedhtiS4wEAUq4f8x66HIKWrfCqiIiIhrfHtL6nGnUdQnDk8kygOEFTFQRoLM8koiqkDBj8xaAZ0fjJiU40r74GwS1PVXop059pQsrEEZWssFHQFazwgiqDQbOZTBCw59hvIBtpAwB4925G89PXVnhRREREQ0upKUQFEwAwTwdMxbPP5/gdB2ipLE8Uiaj6KMnSoJmcjlZmIUSzQOO/f4u69X/DvEe+w8Ea+yBoGeiGin6RQTOawQyXHzs/9AMYshsAULvxfoRff6jCqyIiIhqsM1kcAjBHcI3qOT53cbx5v8pMMyKqMqYJJdGJB/w+XNJYj82KAonDAIjGzRXdAQAQ9Ry8HRsqvJrpzZllBjBoVhHf/e53IQhCyZ9ly5bZj2cyGVx44YWoq6tDIBDA6aefjs7OzhFesTpl6xZjz/u+Zt+es/oncPdsqeCKiIiIBtvbV/xuahnlgZcz0yyp9pd9TURE05mYSyKrpfHd+jo85vfhxpowg2ZEE+DM1PR1bqzcQmYAKRNHNN/PDACCCoNmFXHggQeivb3d/vPMM8/Yj1188cV44IEH8Je//AVPPfUU9uzZg9NOO62Cq52+Yss/hL4DPgIAELUsWv/vcog5nlwQEdH00R3bZm+3uGtH9Ry/K2Bv92vpci+JiGhaUxJd2K7IyInWxLpdsszyTKIJcJZkejsYNBuJnIkx0wyAXPEFyDKam5sH3R+LxXDrrbfijjvuwLHHHgsAuO2227B8+XKsW7cORx555FQvddprf+8l8HS9Ae/ezXBHd2DOEz/GrhOvAqpwLCwREU0/nck99nZjoGVUzykMAgCApM5hN0RUXZREB7bIxVO2qCRyEADReBl6Saamt/M1wDSAKpwIORpSNo6YyKBZxf/r2Lx5M+bMmYNFixbhrLPOwo4dVo3xiy++CFVVcfzxx9v7Llu2DG1tbVi7du2wr5fNZhGPx0v+VAtTdmPXST+A7rJOMMKb/4ma9fdUeFVERESWzkyPvd0Qmj+q5ziDZgkjZx3cEhFVCSXZiZ2KYt/uE0VImWjlFkQ0g0nZBASYxdu5frj7tldwRdPbwJ5mIVeogqupnIoGzY444gjcfvvtePjhh3HjjTdi69atOProo5FIJNDR0QGXy4VIJFLynKamJnR0dAz7mldffTXC4bD9p7W1dZJ/iuklF5mH3cddbt9u/tf18HS+PvS+eg4Pvv0gHtv+GEzTHHIfIiKicmnPWReyQroOT3h0388lQTNRZOsBIqoqSqILO5RipllGFKEyaEY0LtIQpc0s0RzeoJ5mVZppVtHyzJNOOsneXrlyJY444gjMnz8fd999N7xe77he87LLLsMll1xi347H41UXOEvs9z70vOOTqHv5LoiGitaHr8CWT94Gw1OMDO9N78U1L1yDrbGtAIA5/jk4sP7ASi2ZiIhmOc3Q0G1a5ZWtmgY12Diq5wWUYk+zhChCyiZhuKvzoI2Iqo+S7MAOufSULcmgGdG4DNUP0NuxEdEDTp76xcwAUjbBnmaYBuWZTpFIBPvvvz/eeustNDc3I5fLIRqNluzT2dk5ZA+0ArfbjVAoVPKnGnUe9UWkmq0gmCvejrn//L5d0vJ6z+v41r++ZQfMAGB7nGmpREQ0ebpSXSgUVraqGrTA6IJmzkyzuChCyiYmYXVERNOTkujETqU0aBbPJSu0GqKZbajJsz5mmg3LyjRzBM04PbPykskktmzZgpaWFhx66KFQFAWPP/64/fimTZuwY8cOrFq1qoKrnBlMScHOD34fmicMAAhtfQa1L/4Jj257FN9f933Ec6W93voyfZVYJhERVYnOVKe9PRcyTMk1queVBM0kEWKWJ4tEVD20ZBe6BmSaxVV+DhKNh5wefM7r7n0bYi5VgdVMf1KGgwCACgfNLr30Ujz11FPYtm0b1qxZg1NPPRWSJOHMM89EOBzG5z73OVxyySV48skn8eKLL+Izn/kMVq1axcmZo6QFm7D7A9+BCQE5ADdtvhu/3fBb6KYOAFgQWmDv2+NozkxERFRuncl2e7tFDoywZ6nSTDOBmWZEVD0MHR3Z3kF3x/UsYOgVWBDRzCali5lmqq8OACCYBjxdb1RqSdOalI0jKhV7mgVcoz9+m00qGjTbtWsXzjzzTCxduhRnnHEG6urqsG7dOjQ0NAAArr32Wpx88sk4/fTTccwxx6C5uRn33MNpkGORnH8kNh36KXy2pQn3BIsnHh9e9GFcuepK+zYzzYiIaDJ1x4ptAJo9taN+nizK8AhWlgXLM4momsipXuyUBp+u9Un8LCQaD9nRDzC54Ch729exoQKrmf6c5ZkeyQNFVPbxjNmpooMA7rzzzhEf93g8+NWvfoVf/epXU7Si2Wdz32b8vP819HncAAC3YeAyI4xlyz4NiBL8ih/9aj8zzYiIaFJ1JffY243+Fru/2Wj4JRcympYPmrEsiYiqg5LsLJmcWdAniZDSUejeyNQvimgGc07PTCx8N2peewAA4O18rUIrmt6kTBxRv9VOw5n5X22mVU8zKq/VO1fjqrVXoS9rZZE16wZ+196J03duRMO/fwsAqM1f7e/L9ME0zUotlYiIZrnOdDcAwGWYCAfnjum5AcmaqB0X2dOMiKqHkujEDnlwZkdUFCFl4kM8g4hG4gyapVpWQs+XG3o7NgI8Fx5EyMYRz2eaMWhGs4pmaLh9w+246ZWboBkaAGBZ7TL85OCv4ADVurbf8Pzt8G9fZwfNVENFQmWaNxERlZ9hGujID6CZp2kwgk1jen7hQC0ritAcpRVERLPZUJMzAaBPkkrKzIhodOT89ExTEKF7Qkg3HwAAUFI9UBIdlVzatCNoWfQbKgxBAAD4FF+FV1Q5DJrNMvFcHFc/dzUe3vawfd8H5n8AVxx5BVwL3oOuVecDAASYmPfoVajPX70HgN704EajREREExXNRJHLF2S2ahrUQOOYnu93NJ5NZQePiycimo2c5ZlesThxuI+ZZkTjIuWnZ+qeECCISDWtsB9jiWYpZz8zgJlmNEtsi23Dt/71LWzs2QgAkAQJ5688H5896LOQResLd+87z0JiwbsBWJH2hTv/Yz+/N8OgGRERlV9nqtPeblVVqGPMNPO5QvZ2v8ryTCKqDka8HR2ydQw/z99iD0WJ5nuaEdHYyPnpmZq3BgDsTDMA8HIYQAlrciaDZgCDZrPGmj1rcOWzV2Jvei8AIOKO4MpVV+LYtmNLdxRE7D7h28iFWgAAc2Pt9kMMmhER0WTo6C+WPMzTdGj++jE93+8J29v9OQbNiKg6dKW67O2m4FyEZKs8yirPZNYt0VgIagailgEA6PnjinTTgfbjvo6NFVnXdCVl4ogx0wwAg2YznmEauOP1O3D9f65HzsgBABZHFuOH7/khltYuHfI5uieEnR/8PgxRQZOm2/czaEZERJPBmWk2R/TClMY2stzvzDTTUmVbFxHRdLYnWzw2b/K3IOQKArAGAQjMNCMaE2cfQC0/eVb3hpGNtAIAPN1vQtDVCqxsepIycUQlyb7NoBnNSP1qP37y75/g/i332/e9d9578Z1V30Gtt3bE52aalqP7XZ9BI4NmREQ0yTr7i1nNze6aMT/feaCW1NJlWRMR0XQmaFnsMbL27WZ/MwJuKzvGEASkedxONCZSupidqeeDZgCQbrJKNEU9B/fezVO9rGlLyibQx0wzAAyazVgd/R24/JnL8XL3ywAAURBx7oHn4r8P/m+4JNfIT85LNy1Hs86gGRERTa6uxB4AgGCaaPA3j/n5JUGzfFY1EdFspiS77CEAgBU0C3qKFx3iWQ4CIBqLwhCAPbKEn2kdeK79OQBAurk4DIAlmkVSJoYYe5oBYNBsRnpm9zO49j/X2j1igkoQlx9xOU5aeBKE/EjY0VADTQgZBjyGNdGM0zOJiGgydKa7AQDNug5hjEMAgAFBM1MDDK1sayMimo6UROfgoJm72N8xkU1UYllEM1ahD+BNkTAeyO7B9f+5HtFMFKnmYl8zbyeDZgWcnlkk73sXmi4M08DNr96MG16+wb5vfmg+vnrYV9Hoaxzz62mBRggAGnUdO0QRPZmeMq6WiIgISOaSSOpW491WVYMaGPv3lfNALS6KkLL90L3hEZ5BRDSzKclO7JSt/o8BQUFACSCY72kGAHEthUClFkc0AxUmzm7LB6N1U8ere19FpOUoGJILop6Dl5lmtkE9zeTqDZox02wGyWgZPLrtUfv24c2H46qjrhpXwAwADJcPujtoDwNIa2mk2SuGiIjKyDkEoFUbX9AsoBRPDa2gGTMsiGh2M+PtaJetE9YWdw0EQSgJmsWMDGDowz2diAaQ80GzHkcg6NXuVwFJRrpxGQDAHdttl3FWOyk7YHqmi0EzmgF8ig+/eP8vEHFH8F/L/wtnLT0LHtkzoddUA41oZF8zIiKaJM6g2TxVgzrB8sy4KEJk0IyIZrme+E4Y+bYrzX7rczPkmCQcFUVI2WRF1kY0E0lDBM3Wd6+HYRr2MAAA8Ha8NtVLm5ac5ZmKKMMljq5v+mzEoNkM0xZqw4OnPYjzV54PSZKgTbCvixpoRJNWfA0GzYiIqJw6+x2ZZqo6rkwzn+Kzt+MSTxSJaPbrcFxwaAy1AUBJpllUEiFlYoOeR0RDkzMxZAQB/Y7sqVguhh3xHaXDANjXDEChPNP6XQWU4Jh6p882DJrNQCFXCCF3CBF3BPHcxCbnqMHm0kwzDgMgIqIyKsk00w1o/roxv4ZLcsGVP2SJiyKkHDPNiGh225OL2tuNoVYApZlmfSKDZkRjIaX70CMNDn+82v1q6TAA9jUDAIjZOKKilZXnDNhXIwbNZihRENHka0JWzcI0zXG/jhpoRLPG8kwiIpoczkyzFiUEiOObQRSQ3AAK5ZnMNCOiWcw0sVtL2Teb/S0AgKC7eOLaJ0kMmhGNgZSOlZRmFrzS/Qq0QCNUfz0AwNv5GmAaU728aUfNJpATreyygKu6x44waDaDRTwReBXvhJr3q8FGNDJoRkREk6Qrn2kW0XV4xlGaWRCQrB6eCQ4CIKJZTsomsEsqlkI1+5sBWENRCvdGRdFubE5E+yano0MGzTb1bUJGzyLdZGWbSbl+uPu2T/XyphVBV5HQs/Zt50CmasSg2Qzmlb1o8DUgMYEyFTXQhCYOAiAiokmQ03PozVhTqNpUDWpg7EMACvyy1dcsLYowmF1BRLOYkujADsXKyvVBtMsyRUFEMJ912ytJkDITa9NCVDUMHVI2XlKe6c7/W9IMDa/3vs4STQcxm7D7mQGlpeHViEGzGa7eWw8BwrgHAqjBRtTpOqR8iSeDZkREVC7dqW6YsL5f5mka1EDDuF/LOeo8xaAZEc1miXbska2g2RzZX9KAOyRbn4VRSbSnARLRyKRsEoJplGSaHdFyhL39averSDNoZnNOzgRYnsmg2QwXdoURcoWQzI2vv4sWaIQEoD6fbcZBAEREVC4dqQ57u1XVoE0k08zRhLZ/gkNwiIims76+bdDzgbIWd03JY4WG3P2iCCPdN+VrI5qJpPy/lR6xGDR795x3Q8gXPL/a/SrSjctgClZ4xMegGaKOACMHAdCMJokSmgPN4+5rZkouaN4aNOX7msVysXFnrRERETk5hwC0ahrU4Ph7mvlcYXu7n9MziWgW60rssLcLQwAKgu7iZ2Eyw6AZ0WjI+Qx1Z3lmS6AF+9XsBwDYndyNbq0fmbrFAAB379sQc6nBL1QlpOyATDP2NKOZrsZdA688/oEAapB9zYiIqPy6Ul32dusEe5r5PI6gmdo/oXUREU1n7alue7sx1FbyWNBTzDxLZFmqTjQahVJmZ3lm2B3GyvqV9u313evtEk3BNODpemNK1zidWJlmLM8sYNBsFvApPtR56sZdoqkGGtGkFbPL+njVioiIyqAz5cw0U6FOYHqmszwzqWUmtC4ioulsTy5qb9fX7lfyWMATsbfj6viO/YmqjTwgaOaRPHBLbhzceLC9zyvdr5T0NfN1bJjSNU4nciaOmMjyzAIGzWaJel89DNOAbuj73nkANdiERkemWU+mp5xLIyKiKlUoz/QaBupMAZqvdtyv5bzK2W9kR9iTiGhm260Xq0eaB2SaOafYxTVm3RKNxsBMs3C+zHlxeDF8+encG/ZuQKJxuf0cb+drU7vIaUTKDsg0Y3kmzQYRdwRBVxDJcVxxUgNNdk8zgJlmREQ0cYZpoCttlWfO1TRo/nrAcdVyrPxKcXpmUmfQjIhmKUPDLtEAAHhM6xjfyZnxEdezgGlM5eqIZiQpE0UOQCIfCCoEzSRRwor6FQCApJrEm6IOPX+RztuxETDNiqy30gZOz3QG66sRg2azhCzKaPY1I6WOvWGhGmgszTRLM9OMiIgmpjfTaw+WsSZnjr80EygNmiUEE4LGwBkRzT5Csgu7ZRkAME9QIOSnaBY4g2ZRUYSYZYkm0b7I6Sh6B/QzK1jZUOxr9sre9Ug3HwAAUFI9UBLFKeDVRMrEEMsHGEUI8Cm+Cq+oshg0m0VqPDVwS25kxtjrRQ02lfQ04yAAIiKaqEGTM8sYNIuLIsQcy5KIaPaJ9bwFLR8omyMPLolyZnz0SqI9FZCIhielY4OGABQ4g2avdr+KVNMK+3a1lmhKmTj68tUBfsUPUajusFF1//SzTMAVQK2nFolcYkzPG5hpxqAZERFNVMkQgAlOzgQGB82k7Ni+64iIZoKu6Nv2drOnbtDjAzPNJAbNiPZJzkTR4+jRFXYVg2aNvkY0+5sBAJv7NqOnYbH9mLdKhwFI2YSdaRao8iEAAINms06jrxG6ocMYQ38DzV8HF0TU5ANnDJoREdFEDco0C5Y300xiSRIRzUJdiV32drO/ZdDjzkyzqCRBSjNoRrQvUjo6bKYZAKyst7LNdFPHC0oxROLr2Dg1C5xmjEwcSbEQNKvuIQAAg2azTsQdQcAVGNtAAFGG5q+3hwH0ZfrGFHQjIiIaaFCmmX9iQTOX6IICq2SJmWZENFu1p7vt7Ybw/EGPuyU3lPwpXK/ETDOi0ZAypeWZAwdsHNx4sL39cmwLspFWAICn+00Iujola5xO+h2Va0GFmWYMms0yiqRYAwFyYxsI4CzR1E0d8Wx8MpZHRERVohA0k0wTLWXINBMEAQHRBQCISyJEBs2IaBbaoxaPwRtq9x/0uCAICMteAFZ5ppyOTtXSiGYkQctCUtOl5ZkDMs0OqDsAkmAF1V7tfhXpJmsYgKjn4N67eeoWOx0YGhJ62r4ZZHkmg2azUY23BoqkIKuPfrKYGuAwACIiKg/TNO3yzGZNhwJAm2BPMwAISG4ALM8kotlrj24N9HIbJkI1C4fcJyRb5ep9kgSRQTOiEUn5fyMjlWd6ZS/2r7GC1B2pDrxdv8B+rNpKNKVsElGx+LtieSaDZrNSUAmixl0zpoEAarCJwwCIiKgskmoSKc3KeG7VVBiiDM1XM+HX9eezK1KiCJMlSUQ0yximgV2i1SJlngGIkjLkfsH8SawmCMhk+qZsfUQzkTyKoBlQOkVzrasYJvF2VlnQLBNH1JGVx0wzBs1mJUEQ0OhrhKqro+5NpgYb7Z5mAINmREQ0fiVDAFQNWqABKMO48oBjGECaJ4pENMv0JfZAFazejXMF17D7BT3FixDJND8LiUYiZaIAYJdnukQXPJJn0H7OoNmL6U4YkvVv0Ft1mWZxRMXiMVtAYaYZg2azVI2nBgElgH61f1T7W+WZxaBZT6ZnspZGRESzXMkQAE2DGphYP7MCn+PArZ+9N4loltnb84a9PWeE5tsBT629ncjxs5BoJAMzzcLuMIR8cNppYXih3fR+Q89GJBqXAgDcsd12iWc1YKbZYOMKmm3ZsgVXXHEFzjzzTHR1dQEAHnroIWzcWF1R2OnMJbnQ6G8cQ9CsEU16sadZH6/gExHROA2anFmGfmYA4HeH7O3+HHuaEdHs0hXdZm83e+qG3S/oidjbcZWfhUQjkdJRqACijqDZUERBxEENBwEA0loa/6lrtR+rpmwzKRNHzNnTjJlmYw+aPfXUUzjooIPw3HPP4Z577kEyaX1Qv/LKK/jOd75T9gXS+NV56iALMnJ6bp/7qsEBmWZpZpoREdH4lJRnljPTzDEivp8nikQ0y3Qmd9vbTYE5w+7nzPyI5/tHEtHQ5HQUffvoZ1bgLNFc5ymWSPuqqK+ZlC3NNAu5QiPsXR3GHDT75je/ie9///t47LHH4HIV/0M69thjsW7durIujiYm6Aoi7A6PaiCA7o3AJ8jwGVYPNPY0IyKi8XJmms1TNWjlCpo5siuSPFEkolmmPb3X3m4MLxh2P+dJbFzPAqY5mcsimtGkTMzuZwaMHDQ7qP4ge/uFXPF8uNoyzUp6mnF65tiDZuvXr8epp5466P7Gxkbs3bt3iGdQpYiCiGZ/M3J6Dua+vkwFEVqgwc4268307vs5REREQyhkmtXqOvymCTVYnqCZ33HgltIyZXlNIqLpYo9q9SdTTBPh2sXD7ufMNOsTBYgsVycalpyO7nNyZkGdtw7zAvMAAJsT29ETqAcAeDtfA0Y5YG+mY0+zwcYcNItEImhvbx90/0svvYS5c+eWZVFUPhF3BD7ZN6reZmqwCY26FTTL6lmkeBWfiIjGKKfn0Je1+mK2qlavzHL1NHP21Uga2bK8JhHRdGCYBvbkP9fmqRqMUMuw+zozzaKSCCkTm/T1Ec1U0sCgmWv4oBlQLNE0YeKZhgXWa+T64e7bPmlrnE6s6ZnW78sjuSGLcoVXVHljDpp96lOfwje+8Q10dHRAEAQYhoFnn30Wl156Kc4555zJWCNNgEf2oNHXiP7cKIJmgUY0acVhACzRJCKisRo4ORNA+TLNFL+9nTA0liQR0awRzUSRzQ/0a9VNGCOURJVmmomQ0wyaEQ1nYHlmxNEfdSjOvmZrvB57u1pKNKVMArH87yvIIQAAxhE0++EPf4hly5ahtbUVyWQSBxxwAI455hgcddRRuOKKKyZjjTRBdd46iKIIzdBG3E8NNqFJLw4D6E0zaEZERGNTMgRA1WBILuiOXmQTURI0EwFBY7YZEc0OHf3FSp65ohsQhGH3dfYY6pMkZpoRjUBO9426PBMAltcthyIqAIDntSgKl+eqJWgmZmKI5XuaBViaCWAcQTOXy4VbbrkFW7ZswT/+8Q/88Y9/xBtvvIE//OEPkBz/MdL0EXaHEXFHEM/FR9xPDZRO0GSmGRERjVVpppkKNdAw4snfWPgUn70dF0VI2X0PuiEimgm6Ylvt7TnKyNPqZFFGQLBO6qOSCImZZkRDMw1ImfiYgmZuyY1ltcsAAN1qHFsVa/ihr0qCZulcAnr+uC3o5uRMABh3gWpbWxva2trKuRaaJKIgosnXhL1798J0mxCGOXmxyjMZNCMiovEbmGmmhcvTzwwo7WlWCJppgYayvT4RUaV0R7fZ283e+n3uH5K9SKqqVZ7JTDOiIUmZBATTGPX0zIKVDSuxfu96AMDT9fOwqP1tuHvfhphLwXD59vHsmS2RSwCwgmUBlmcCGEfQ7LOf/eyIj//2t78d92Jo8kQ8EXgVL9JauuRKvZM1CIA9zYiIaPycmWbzVA1qoDz9zADr6q8EATpMxEURYpYT44hoduhI7rG3mwJz9rl/UPEDahxxSYLJlipEQ5IyUQCwM81kUYZP3nfQa2XDSvzp9T8BANb4fTgPgGAa8HS9gdS8d07SaqcB00BCTaMQNOPkTMuYg2Z9fX0lt1VVxYYNGxCNRnHssceWbWFUXl7ZiwZfA3Yldg0fNGOmGRERTVAhaOY1DNQZBvaWMWgmCAKCgoKomWN5JhHNKu2ZHgCAbJqoDS/Avi4JhFwhIGX1Qetn0IxoSHI6CqAYNAu7wsNWXTm1BdsQdocRy8bwkpGCCkAB4OvYMKuDZmI2iR6p+PthppllzEGze++9d9B9hmHgggsuwOLFi8uyKJoc9d567E7shmZoQ46ONdxBhCQ3ZNOEJgjoSfdUYJVERDRTGaaB7lQ3AKs0U0D5JmcWBCQXoloOcUmElGOmGRHNfKZpYo9qXQSYq2kwQs37fE7QUywxS2SicE3a6ohmLikdhQ4gmm9sP5rSTMC6SLeyfiX+tftfyJgaXva4cXgmC2/na5O42sqTsnF7cibATLOCMQ8CGPJFRBGXXHIJrr322nK8HE2SsCuMkCuE5HAnGYIAw5Ft1sdMMyIiGoO96b3QTes7pFWzyv3VQPl6mgGAX7LGvydFEUiPPOCGiGgmiGVjyMAAYF1wUIP7/tz0e+rs7cQ+hn0RVSs5HUWfKMLIZ5eNNmgGWCWaBc8ErHJFb8dGwDSHe8qMJ2XiiIrFoQnOSb3VrCxBMwDYsmULNE3b945UMZIooTnQjLSWHnYfNdBo9zVLqEnk9NxULY+IiGa4gUMAAJS1pxkABBwtBlJZXtwhopmvI9Vhb7epGrRRfG6GvLX2dkJl1i3RUKRMbEyTM50OajjI3n42YGVcKakeKImO4Z4y41lBM0emmcJMM2Ac5ZmXXHJJyW3TNNHe3o4HH3wQ55577rgX8qMf/QiXXXYZLrroIlx33XUAgEwmg69+9au48847kc1mceKJJ+KGG25AU1N5r1pXkxp3DbyyNRDAK3sHPa4Gm9DU/bZ9uzfTi2b/vlPEiYiInEMACplmozn5Gwu/o79GOhMb/xhwIqJpoqO/eBI+V3DBlPZdbOksm4qNcEGcqJpJ6b5xB80i7ggWhBZgW3wbNgkaekURtYYBb+drUEMtk7HcimN55tDGnGn20ksvlfx59dVXAQDXXHONHewaq+effx4333wzVq5cWXL/xRdfjAceeAB/+ctf8NRTT2HPnj047bTTxvUeZPEpPtR56oYt0VQDTWjUOQyAiIjGzplpNk/VYMhu6J5QWd/D5ziA689xEAARzXydyXZ7u8U1us/MkqCZkZ3VJWNE4yWnY+hxBIHCrtEHzYDSbLN1Xqs9hLdjQ3kWNw3JmURJphnLMy1jvkD75JNPlnUByWQSZ511Fm655RZ8//vft++PxWK49dZbcccdd9hTOW+77TYsX74c69atw5FHHlnWdVSTel899vTvgW7okBw1y0A+04wTNImIaBxKM81UqIG5wCimVI2F33GVuD+XwNgOf4mIpp+u+A57u9nbMKrnhBzBtZggQFRTMFz+sq+NaCaTMjH0OjLNIu7ImJ6/smElHtjyAADgWa8HH+pPwTeLhwFImTiijt8XM80sZetpNl4XXnghPvzhD+P4448vuf/FF1+Eqqol9y9btgxtbW1Yu3btsK+XzWYRj8dL/lCpiDuCoCuI5BD9D9RAI5ocvel6OcKaiIhGqRA0k00TLZpe9n5mAOD11tjb/Wp/2V+fiGiqdST3AABE00RDcO6onuM8me2VREjp6GQsjWhGkydQngkAy2qWwSVa5dJr/H6YADxdmyDoajmXOW1I2WJPM1kQ4ckPX6p2o8o0O+SQQyCM8krxf/7zn1G/+Z133on//Oc/eP755wc91tHRAZfLhUgkUnJ/U1MTOjqGb7539dVX46qrrhr1GqqRLMpo9jVjU9+mQR8caqARTSzPJCKiMTJN0y7PnKNpkFH+IQDAgEwzLVP21ycimkqmaaI9P9SkRdOB4Oh6JTmDZlFJhJSJQQ2PLuBGVC2kTAw9Lkd55hiDZoqk4IC6A/By98vYKwp4S1GwRM3BvXczMk0HlHu5FWdlmlm/r4DsH3UMaLYbVdDsYx/7WNnfeOfOnbjooovw2GOPweMpXwTzsssuKxlWEI/H0draWrbXny1qPDVwS25ktAw8cvH3z/JMIiIaj3gujoxuBbEKkzO14CQEzRyDAJI6g2ZENLMlcgmkDCtrZb6qIhcc3cAzn+yDBAE6TERFCXI6NpnLJJqRpHQUPd7iccNYg2aAVaL5cvfLAIA1Xg+WqCp8Ha/N2qBZLJ9pFmQ/M9uogmbf+c53yv7GL774Irq6uvDOd77Tvk/XdTz99NP45S9/iUceeQS5XA7RaLQk26yzsxPNzcNPc3S73XC73WVf72wTcAVQ66lFd7q7JGhmKl5EZB8E04QpCOjJ9FRwlURENFM4+5nNy5f5T0qmmVLs2ZM0Z2d5BBFVj45UsYKmVdOgBUYXNBMEAWHRhV4ja5VnZhg0I3IStCwkNY0eyQqUSYJUcgwxWgc3HGxvr/F6cG48AW/nBgAfL9dSpw01G0PGk880G0eAcbaqWE+z4447DuvXr8fLL79s/znssMNw1lln2duKouDxxx+3n7Np0ybs2LEDq1atqtSyZ5VGXyN0Q4dhGiX3C4Em1OnWfX2ZvkosjYiqgaFD0LKVXgWViXNyZiHTbLKDZglTAwZ8hxERzSQd/cWgWZuqQR1lphkAhGQfACAqihDZ04yoRKHPX2F6ZsgVgiiMPfwxJzAHtZ5aAMCLHg8yggBvx8ayrXM6SWaL/eCDo5zkWw3GPD1T13Vce+21uPvuu7Fjxw7kcrmSx3t7R1fOFwwGsWLFipL7/H4/6urq7Ps/97nP4ZJLLkFtbS1CoRC+/OUvY9WqVZycWSYRdwQBVwBJNVkygUcNNqIxtwV7ZQnRTN+QUzaJiCZC0FUsuutzcEV3YPtHf47UvHfu+0k0rZVOziwEzUZ/8jdazqBZXBQhqmlOjCOiGct5wWGebkDz1Yywd6mQ4gdyfciKIjRe6CYqIWeiMAB7euZ4SjMBK6tzZcNKrN65GllRwH/cbhwV2w0pHYXujZRtvdNBQu0HYB1TBVieaRtzqPWqq67Cz3/+c3zyk59ELBbDJZdcgtNOOw2iKOK73/1uWRd37bXX4uSTT8bpp5+OY445Bs3NzbjnnnvK+h7VTJEUNPuakcqlSu5XA8W+ZgZMxLJM9yai8nK1v4pL5BhOaqlH739uq/RyqAyGzDQbZ08z0zQHZUEXDAyaSdnEuN6DiGg6cGaazXWFgREyYQzTgGma9u2Au3jRO5FiSxUiJykdRUwUoeeb2Q8Mmg13nDGUkhJNn9XaaNZlm5km4nravhlUgiPsXF3GHDT705/+hFtuuQVf/epXIcsyzjzzTPzmN7/BlVdeiXXr1k1oMatXr8Z1111n3/Z4PPjVr36F3t5e9Pf345577hmxnxmNXY23BrIoI6cXMwbVQCMaHRM02deMiMrt7e4N+JfPiz2KjN9kd0LMJiu9JJqggT3NdMUHYxxXKVNqCh39HSVBOCev7EUh9zkuivxvh4hmtI7kHgCAYJpo8I2cnduebEc0G7VvB93FrLRklplmRE5yOoYeqVgt5QyaRbNR7EnsgaqPrjfqivoVEGAF39Z4raCZr3N2Bc1ENYWYY1jmwEwz0zQhiNU5TXPMQbOOjg4cdNBBAIBAIIBYzMpCOvnkk/Hggw+Wd3U06fyyHx7Zg6xe7CukBpvQ7Jigyb5mRFRuexO77e01Hjcybz1WwdVQORSCZg26Dq9pQgs0AGMYVZ7Vs+hIdiCn59AabIUiKtAMbdB+giDAL1jdJZhpRkQzXWd+EECzrkMcoaQ9p+fgltwlx+xBb529Hc/xs5DISUr32f3MgNKgWVbNotnfjL3pvaPKOAu6glgUXgQA2OxyoVsSZ12mmXNyJmD9zE6GacCX76NYbcYcNJs3bx7a29sBAIsXL8ajjz4KAHj++ec5tXIGkkQJEVcEGS1j36cGm9CkF09UmGlGROXWk+62tw1BwFPbHqngamiiMlrGLuVvVa2rtqMdAqAZGrpT3Yhn45gbnIuVDSuxKLIIbsld8t3kFBRdABg0I6KZLZlLIqlZ5VD7GgKQ1tLwKT4IEOwSzYCnmGlm9SIiogI5U5ppFnFH7G0TJup99WjwNqC7v7uk7Hk4KxtX2ttrvF54O1+bVcOIpEwcUWnooJlu6JBFGV7ZW4mlVdyYg2annnqqPdHyy1/+Mr797W9jyZIlOOecc/DZz3627AukyRd0B0uu5quBRjQ6Ms16M6Mb7kBENFrdA3olPqz2wFTTw+xN011Jaabdz2zkMiPDNNCX6cPe1F7UeGqwsmElltYsRdAVhCIqCLqCJRkVTn7JKo1IigKQYdCMiGam0smZ6oifmxktg4ASgEtyIWdYbVWCjp5mMY3foUROUjpaWp7psjLNcnoOiqQg4o5gv5r9EHAFRpUksrLeGTTzQMr1w923vfwLrxApE0fUkWkWUIrlmRk9A7fkrtpMs1FPz/zlL3+Js88+Gz/60Y/s+z75yU+ira0Na9euxZIlS/CRj3xkUhZJk8sreyEKIgzTgCiI0AIN9iAAAOhNM2hGROXVradKLtvsliW8/ca9WHzQpyu3KBq3kiEA9uTM4TPNErkEEtkEIp4I9ovshwZvw6ApzSF3CB2pjiGfH5C9QA4wBQEZXtghohnK+RnXpmrDThw2TROmaaLeW49+tR9ZLQu35EbIVQyaxY0cYJpjKosnms2soNng8sxCAMgjeSCJEpbULMFrPa8hno0j5AhED7SkZgk8kgcZPYN1Xg8MWMMAsrULJ/tHmRJSNo6Y41jMmWmW1bIIu8NQJKUSS6u4UWeaXX755ZgzZw7OOussPPHEE/b9q1atwiWXXMKA2QzmU3wlPRJMyYVax5cwM82IqKxME53m4F5VT+xYPfVrobJwZprZkzOHCJpltAw6kh0wDAPLapfh4IaD0exvHhQwA2BfzRyqZMI5QTOViU50+UREFTHwgsNwmWYZPQOP7EHAFUDYHUZGt0rXnSe1fQIgMmObyDawPLMQNMtqWURcEfvYo8ZTg8WRxchqWaTU1PCvJ8o4sP5AAECvJOENlzKr+ppJmTj6HEFG5yCAnJ4rKW+tNqMOmnV0dOCmm27Cnj17cMIJJ2DhwoX4f//v/2Hnzp2TuT6aAm7JDb/iL+kdowQaEdStGu1e9jQjojKSsgl05L+Um0wRYcP6rPmXEUNyQNkmzQwlQbMhMs1UXUVnfyf61X60hlrxjsZ3oDXUCpfkGvY1vbJ3UNPrAr+jZCDF/2aIaIYqLc8cPmiWUlMIuULwyl4EXUEY+e9NZ9AsKomQMvw8JCoYVJ6ZD5rppo6gu7TJfZOvCQvCCxDLxpDTc8O+5sENB9vba7xe+GZZ0KwwCEBAsTyzcPHSp1RnaSYwhqCZ1+vFOeecgyeffBKbN2/Gf/3Xf+HWW2/FwoUL8cEPfhB/+ctfoKqjG9lK00/YHS75gFADxWEAveneUTVHJCIajVzfNiTyQbP5ShAnu5qt+wUB/954VyWXRuNUki2RzzTTgk0wTAM96R70ZfrQ5GvCwQ0HY/+a/Ud14OWVvfBK3iGDZj5H+UQqlyzDT0BENPWc5ZktohuGyz/kfjk9h1pvLQDrs1ESJeiGXpppJomQ0tFJXS/RTCKno+iVCkEgwQo4mwYECIN6cwmCgNZgK+YF56En3QPd0Id6SaxsKPY1W+v1wN37NsTc8NlpM4mULQ4C8EseiIK1nTNycEmuqu1nBoxjEAAALFq0CN/73vewdetWPPTQQ6irq8N5552HuXPnlnt9NEX8ih8mioExNdho9zVTTQ1JlSclRFQe0b637e1mdx1OXXK6ffuJ9jWVWBJNUCHTzG8KiOQzIPYqHnT1dyGgBLCifgWW1y0vGfe+L4IgIOwOD51p5onY2/38fiKiGaqQadaoaZCH6WemGRpkUbazPnxysa2KS3LBK1iZNH2ixEwzogLTgJSJ25lmIXcIoiAio1n9zLzK4CmQkihhUXgRGnwN6E4NPVGzydeERp+VSf8fjxtpmPB0vTG5P8sUsQYBWL+voKMNRlbLwiN74JE9lVpaxY0raFYgCAJkWYYgWKOPmWk2c/lkHxRRgapbf4dqoAmNOidoElH59cZ32Nst/mYsPfi/cGDW+ux5y8xga3RLpZZG41DIJgOAuboBAYCm+CC4glhetxwHNRyEBl+DfcVyLAKugF2G5OT11NjbSU6MI6IZqF/tRyJnTf+1hgAMPTwlraXhlb12L0dFUkraqoRENwCrPFNm0IwIACBlk4Cp20GzwuTMrJ6FX/HDLbmHfJ5LcmFJZAlC7hD2pvcOelwQBDvbTBMEvOBxw9c5O0o0zUzMrgRxZrFm9AzC7vC4juNmi3H95Dt37sT3vvc9LFq0CCeccAL27NmDW265Be3t7eVeH02RQu+YQmNRNdjECZpENCm6k8XvipbwfMAdxGneVvu+1W/8tRLLonGKZ+PQTev7olm1ssKMUAsObjwYcwJzoIjjn7RUKEPSjNLBESWZZo5+nEREM4WzrH2+pkENNg+5X1pLo8ZTA1mU7fsi7ojdViWcL5mKiSLA8kwiAICU7kNCFKDmp8kWMt1H09Dep/iwJLIEiqggNkTf1JX1xRLNNV7vrBkG4OwRG3BUBhiGUTKptxqNOmiWy+Vw55134gMf+AAWLlyIW265BZ/+9Kfx5ptv4oknnsBZZ50Fj6d6U/ZmOkmUEHKF7DIYNdBo9zQDgB4OAyCiMtnryFxtqd0fAHDSsk/Ck88oenbvqyM2YaXpxfn90JLPOHfVLCxLGr9P9sEjeQaVaDqnZyaMweWbRETTnbOfWauqDjsEwDAMO0umoNAX0jRNBPNlm4YgIJ0anBlDVI2k9NCTM02YJccQw4l4Itgvsh9yem7QRM0V9SvsrKs1Xo8VNJsF/b8Lma9AMWimGzpEQYRXHlzOWk1GHTRrbm7Geeedh1AohAceeADbt2/H97//fSxatGgy10dTKOQOQcsHytRgExodmWZ9mb5KLYuIZplurdiDqjk/ujt4wKk4IWWV2SWhY92edRVZG41doTQTQDFDOVSeHqeFMqSsNnzQLGlqA59GRDTtOTPNhivPLPQtC7gCJff7ZB9ckguqoSLoGIySZDsVIgCAnO4bFDTL6TkoojLqAFCjrxGLwosQz8ZLLuYWMtEAYKtLwd5sFEqyc7iXmTESWjE4WOihmNWzVg84Bs1G54orrsDOnTvx17/+FSeddBJEsXprWmcrn+yDKIgwTAOarw5NejFizkwzIiqXbqN44NEcnm9t+Otwqr94EWb12w9O9bJonJw9P5oLGcplCpoB1tXenFGaeViSaWbqwDBTroiIpqvCEAAAaNO0ITPNUmoKQVdw0NS6QlPujJZB0NHjMcGeZjPGUE3mqXykTAw9jnhFxB0ZcwBIEATMC84bcqKmc4qmnW02k5kmEo4esYWeZhk9A7/LD5fkqtTKpoVRR74uueQSNDQ0TOZaqMK8shcuyWWVwYgSahz13hwEQETloKkZdIrWgWLEFOwSEwA4bNnH0ZYv73stsR1dqa6KrJHGxvn90FzINAuXL2jmlb2AWXqC4QyaxSURYq6/bO9HRDQVSsszNahDTM/M6lnUemoh5PsyFYiCiLA7jIyeQcBbZ98fz8Unb8FUNs/ufhaffeSz+PUrv2bwbJLI6eigTLOsnkXYFYYkSiM8s5QoiFgUXoRGXyO608WJmoODZhvKt/gKELQs4igOXioEzVRNRY27ZrinVQ2mi5HNLbnhU3x2GYw/0AB3vsdQb5qZZkQ0cVpsB7ryBzEtYmnPK2H5yTg1UQx+PLbtsSldG42PMxO5uczlmYCVBe2W3SXZZl7Zi8IpZFwUIWUTQz+ZiGiaKmSa1Ws6vCagBUqTEwzTgAhxUGlmQVAJwjAMBLy19n3O8iqavh58+0GktTSe2PkEntn9TKWXMytJA4JmIVcIqq4i5B57Q3tFUrBfZD+EXWF0p7sBAIsji+HPZ4Cu83rgnuETNKVsHFGpGBoKKAGYpgkTZskF7mrFoBnZBEGwo/AAoAWa0KhbJ0AMmhFROcR6NkPPXzFvGTiJJzwPH/UvhJi/ivfMztUwTAM0vTl7mjUWyjPD88r2+h7ZYw0DcPQ1EwURAVgHw1bQLDnc04mIpp20lran8rVpKjR/HUxJGbSPR/bYvYUG8ilWW5WAErTvizvKq2h6Mk0T7f3FKeJ/fO2P6FeZLV1uciY2KGgmYvwN7X2KD0tqlsAluhDNRCEKIlY0HAQAiEsStkS3QtDVsqy9EqRMHFFHOWvQFYRqqFCk0feAm80YNKMShagyAKiBJrupc0rPIKNlKrk0IpoForHt9naLd3DJf+Pyj+HotPVZ06cm8HLXy1O1NBqnQk+zGlOEu1BlEppTttd3liE5BUTrBJOZZkQ00ziHAAxXmpnW0gi7w8P2EvLKXrglN9yy274vauRmxRS/2SyeiyPtCG7GcjHc9cZdFVzR7GRlmhVDHR7ZA7c8sYb2YXcYS2qWQDM09Kv9WFlfLNFc55bg3rt5QmuuJCkTR9QRZAy4AsjoGXhkD4NmGGPQLB6PwzAGX/XXdR3xOGvoZwOv7IUsylB1FWqw0c40A9jXjIgmxjRNxBwnCi3B1sE7Lf8ITk0Us4b+uf2fU7E0GifDNBDNRgEAzXr++MATAVz7Huc+FkFXsKQBLwAEJOtEMSGKQIZBMyKaOZxDAOarQw8BUPWRewm5JNegBt0xARB4kXtac/7dFzy2/TG8HX27AquZvaR0FL2OIJBbdMOv+OGW3CM8a98KEzUT2QSW1S2z71/j9cDTu3VCr11JUiZWmmmmBJHVsgi7wxAF5lmN+jdw77334rDDDkMmM/iDOJPJ4PDDD8cDDzxQ1sXR1PPKXqsMRs+WZJoBDJoR0cTkjBximT77dnNk0eCd6pfgGH8bavMB+1e6X0Y8y4sy01U0E7VLaJty+eODMpZmFhQu6DgDZwHJ6olnCAJy/H4iohmkM+XINBticqaqq1BEZdh+ZgURVwQeR3/QPkmEzAma05rz777JZ/29mzBx6/pb2ZKijORMsadZUAlCMzWE3eFBQzXGY25wLlpDrRAhYq4rAgB41e1GrnfbhF+7UuTMgJ5mrgA0Q0NIGXsPuNlo1EGzG2+8EV//+tfh8w1uBOf3+/GNb3wDv/zlL8u6OJp6sigj6A4io2egBpvQVOhPAwbNiGhisloWvWoxi6yl/oAh91OWfQQfzQ8E0E0DT+58ckrWR2NXaIgLAM1avpdHGYcAFPhkH9yS2+65CZRO0Eyl+4Z6GhHRtOTMNmpTVaiBxpLHU1oKfsVf8jk3FL/LD4/ssU/o+iQRUppBs+nM+Xd/7oHnotnfDADYEtuCx3c8XqllzTpiOmaXZ4bdYZimuc9/T6N+bUHEwvBCNPubsX/YugCsCwI2xbaU5fUrQcrGEctnmrkFGbIoQxTG3wNuthl10GzDhg143/veN+zjxxxzDNavX1+ONVGFhd1haLoGNdDITDMiKpuMnsFevdjHo8WR1l5i+UdwarIYXFu9czVHsk9TzmbGzVr+IksZ+5kVKJICv+Iv6a1ZEjTL8SSRiGYOZ+CkVdWgBptLHs9oGdR6a/dZFuWVvXDLbgQFq8djVJQgZaJlXy+Vj/Pv/qinf4mv+5fat+9840675QGNn6BlkdXSyOSDQEFXELIolzUApIgKFkcWY0njO+z7djkuJM401iAAKzMvJPuQ0TLwSB54FQbNgDEEzfr6+qBp2rCPq6qKvj5e6Z0NvLIXgiBA9YTRYBb/E+lNM2hGROOn6xo6TCsbSTaBOl/90Du2HIxFvjl4R8bKKmrvb8fm6MxtrjqbdaecmWb5iyzh8meaAUDEHUFOz9m3/a7ixLgUBwEQ0QxSKNGr1XUETbOkPNM0TZimidDACdND8MpeeGQPQqLV16xPEiFNcXlmVs/in9v/ic19/J4ejULQTDRNtHa8gROe+wM+mkwBAPrVftzx2p8qubxZQRowOTPoCk5KQ3uv7MU7W95l396tJWbsIA4xHUMsn5kXVPzI6ln4FN+Ee8DNFqMOmi1YsAAvvPDCsI+/8MILmD9/flkWRZVVmMaTM1TUuiP2/T2ZnsotiohmNN3Q4dGy6Mh/ITcL8vBX0AUBWH4yTnMMBHhs22NTsUwao8LkTABoKgyOCZW/pxlgjXsHYGcd+jwR+7F+lUEzIpoZsnrWrt5oVa2EBGd5ZmFi3WhKyURBRNgVRkC2+pqlRBFGamovcv9jyz/wm/W/wffXfR+xLLN+R2Kaph00a9F0FEY4XNLTi2B+mM7Tu/+FPc9cAznROcyr0L7I6b6SyZk+xYeQKwRZlMv+XivqV9jbO0QrY2smymbj0PL93gIuawjASINIqs2og2annXYaLr/8cnR2Dv4H3NHRgSuuuAKnn356WRdHleGRrEh8Rs8g7G+ElD9B6XOcHBERjUVWz0JKdiKRP4hpkfZxMrD8IzixPwVffmLzvzv+XVKaR5WnG3rJCZJdnjlJmWY+2QeX5IJqWNmKPk/xYK5fTU3KexIRlVunY4p0m6rBkFzQvcXPs7SaRsAVGHVWTMgVgk8u9pxOpqb2eP3NvjcBWN/zhW0aWlJNIqVZ31etmgrMeSew6kuo9dTgf/qi9n6/6noWC28/DW1/vwTBt56EoKsVWvHMJKVLM838sh8h9+Q0tA+7w4jky6O3KTJcsV2T8j6TLeE4nvN7IjBh2hcraQxBs29+85sIBoNYsmQJvvjFL+IXv/gFfvGLX+CCCy7A/vvvj0AggG9+85uTuVaaIoIgIOKJIKNlYASaUJfPHuhNM9OMiMYno2WQjRf7X7V4akd+QusR8Hnr8MF+6+Ayq2fx7O5nJ3OJNEYZPYNYvpeYABR7YE7CIAAA8MgeeGSPHTz1Of4bSmrp4Z5GRDStlPQz0/JDABwT/XJ6DnWeulG/nlf2IuAoV09M8WAUZ5n+lr6Z2wh9KpQOgNCA1ncBJ/4AwiVv4KMnXIvlphV82eJy4U/hAII71qHtocux/22noOlf18Pdu7VSS59R5HS0JGgWcocmtaH9fLd1PNIly9D7ZubfUSJXzNj3uYJwSS4OAXAYddAsGAzi2Wefxdlnn4277roLF198MS6++GLcddddOPvss/HMM88gGAzu+4VoRvDLfsAE1GCj3acmpiahGcP3tSMiGk5OzyGV6rJvN/uaR9gbgCgByz6MUx0lmk/seGKylkfjkNbSdqZZnSlCKTwwCYMAgHwZkjuMjJ4PmrmLxxxJx1RNIqLprNDPDADmq1pJPzPN0CAKIoKu0Z9TeRUvgs6LCFPYSN4wjZIpyuw/OrJBQbPaxdYN2QXPyjPwPyf8EgKsAOoNNRF05AM/cjqK+pfvxH5/OgsL//J5RDY+ADHXP+XrnymkTGnQrNZTO7lBs0CxLUVX78z8N5DQihn7hVZNDJoVjTpotmjRImiahhtuuAF79+5FZ2cnOjo60NPTgxtuuAE1Nax5nU28iheyKCPrb0BjPtPMBNCX4bAHIhqbQg+qXseJQkt4FD0wl38EB2dzWJSzyhK2xLZgd2L3pKyRxi6jZhDNT2mz+5n56oBJnLQUVIIw8iW7zn4/SYOlK0Q0MwwMnDiDZmktDZ/iG1U/swK35EbEX7wQFZ/CHo/RbNQumQeAbbFtnHY9AufE6TZNA+oWlzx+1Nyj8KGFHwIAZAQBV654H2JLjoch2pel4OvYiLlPXI39f/tRzHn8h/C2r5+xzecni1WeWQxzNHgb4JE8k/Z+C2r3t7f3xLZP2vtMpoRjur1bciPiikASpRGeUV1GHTTbtm0b9PxBsSAIaGhoQGNjIwRHOjHNHl7ZC4/kQdJXUyy5AezGpUREo6UaKlySC92OEu85tUtHeEbewmMguEOlAwG2cyDAdKAbOpJqEgasAFZzNt9vLjw5QwAKfIoPoiBCN/SSk8qEySxoIpoZBpdnlgbNajw1Y25Y3hRqs7fjU9jj0VmaCQD9Wj+6HFnlVGpPco+93aaqQN1+g/b5+ru+jrArDABYG9+MBw45FW9+9n60H/M/yDiCbJKaRs1r/8Civ34Bi/90FsKv/9/k/wAzxMDyzHnBeZMas5jfeLC9vWcG9gAXtCziKJ7ve2Uvgm5WEDqNOmhG1UURFQTcASS9ITTpxZMRBs2IaKwymjUJrDPnaBrfcOC+nyi7gSUfwMnJfsj5q6hr9qyBbuj7eCJNtoyeQdrRR8weAhCZ3CnahZKBrJ5FQAnY9ycEk42SiWhGKJRnhnUdYcMsyTQzDMMOmIxFg784fTOmT93QnKECZBwGMLxCpplgmphniENeaKr11OKid15k375t421Iu7zoPfgMbDnz99hyxm/Qu+Jj0F3FC0eevm2Y98/vw9W7bdJ/hplgYHnmHP/ktI0omF9TDH6266kZl20pZROIOrLKAsroB5FUizFdxnjkkUcQDo/8Qf7Rj350Qgui6SPsCmOvpwaNzDQjognI6BnM9c5Fu54GROtKX/NoyjMBYPlHULfhr3hfKo1/+n2I5+J4vuN5HDnnyElcMe1LWksjrQ8RNKuZ3KCZS3LB7/Ijnosj4o7Y98dFEWIuWTKBjohousnpOfTks67bVOtzsxA0y+pZuCQXAq7AsM8fTpOvGHiLm7kyrHR0hgqaberbhKPnHT1la5hJCpl5TboOd+0iq3/rED6+/8dx31v34dW9r6Ir1YX73roPZyw9AxAEZJoOQHvTAeh4z1cQeutJ1L18J7x7rT5anp63katdMFU/zrRlZZpZuUFe2YuwZ+yB6LFoc2R67hRNaKkeKP76SX3PcpIycfQ5ylkj7ggnZw4wpqDZueeeO+LjgiDYJZw083llLzS3Dw2O/0wYNCOisTIMAyHJiw5BByAjYgqj/zLe73hAcuPURBL/9FvPeXzH4wyaVZiqq0g5SoDsnmaTnGkGABFXBD2pHogeEQGISMJAXBIhZRk0I6LprSvVBRNWFkpr/mJDoTwzrabhV/zwyWM/WXUGzaIwIagZmMrk9XAqcPboKtgW2zbp7zsTRTNRpPLN1uc7hwAMQRAEfPeo7+ITD3wCuqnj/i334z1z34M5gWLGlKl4EFt+EkxJQusj3wUAKInBfx/VSEpH0RO0ApJhV3hc/6bGwit70Sy40WFmsV2RYfRuAWZY0CwmOnrA+RrgltwVXNH0M6byzI6ODhiGMewfBsxmF5/ig0f2osYTse/rcfQkIiLaF93QIQoi3OkoOvOp8i3iGL6I3QFgv+Pw7nQGjfkTjI17N/KzqIJ0Q4ckSIg6JrRNVXkmAPhdfvukMyBYF3XioggpM3XNr4mIxmPQ9EQAWsAqrcxoGdR56sbVe6nOW2dv90kSpExshL3Lx5lpFnZb2Ty7EruQ06cu222m2BHfYW+3quqgIQADLalZgk8v+zQAa6rqbRtuG7LsTw222NuuOINmAKCmo0jlg0Dj6RE4HvPzE2xjkoTM3rcm/f3KScrGEXVkmjmDs2QZddCMDf+rj0fywCN5EHR+EQ9o+ElENJKsnoVbcqO/bwv0/PdIszLGNPllJ0MCcErSGq9uwMATO54o80pptNJaGl7Zi950MfO4uVDGP8nlmYB1RVeRFOT0HAL5AGxcFCFk45P+3kREE9HpmCLdpqnQ3EEYLh8M04AgCONuvu1TfHDnT+v6JBHyFAXN9uabnodcIRzWdBgAqyXD1tjWKXn/mWRncqe9PdTkzKF86ZAvodFnBVXX712Pde3rBu2jhopBMyXRMejxqmMaiGr99s1679RkfC0ItNrbsfhOqDOoz6qVaWZd2JYgoMHbUOEVTT+jDprNtIZ2NHGCICDijsDw1SGSzyLsZXYHEY1BRs/A7/Kju3eLfV/LWL+Ml54ECBJOTRQPgp7e9TS/lyoko2cQcofsDAPBBOrt8sy2EZ5ZHh7JA4/sQUbPICBbQTNdEJDL9E36exMRTcTATDM12AygeDHCOeBkrCKiCwAQFSVI6ckPmqXUlJ1xPDcwFwfWFQf8bO7dPOnvP5OYplkyabRN1YacnDmQT/HhW+/6ln37dxt/V9IaAQA0Xy0Myfq7V5hpBimbRK8jwjFVQbP5jqnwPalOuxR3JpAyxUyzgOQumU5OllEHzc4991x4vZyiUG38Lj8yvjo05bMI+nJxGKZR4VUR0Uyhaipq3DVoj71t39cSnDu2F/HVAgveg1ZNw+FpaypYd7obG/duLOdSaZRUXUXEHUFHyjr5azBMKAAQaAKUyT9OkEQJYVcYWS2LgKNPSTrNnptENL0NDprl+5lpaYTdYbjywY/xqMmf6EYlEWJ68i8i7Eruss8J5gbm4sD6YtBsZ3InzxccsnoWfY4LO6MNmgHAcfOPw9FzrcEK0WwUf33zr6U7CKIdfFUSHUCVX1CU0qWTMwuZepNtftM77O2ubBRpNT38ztOMlIkjmi9nDcpeTs4cwqiDZrfddhuCwdKUYdM08cQTT+DBBx9EXx+v8M5GPsUHNdiIxnwWgQYD8RxLYIho30zThAkTPsWH9uQe+/7myL5LEgZZ/hEAwKmJpH3XP3f8c8JrpLEp9DNziS67r1yzmu9dMwX9zApCrhB0U4ffETRLTVE5EhHReBXKM4O6gYhh2P3MVN26wDQRtUoIAKAJArJTUBnSkSwGAAdmmu1K7BqUEVXNUmqqZJjaPNFtXWgapW8d8S27MfvD2x7G9vj2ksfVkBU0k9T0lPWzm64kx+RMoHRIxmRaULfM3t6lJSEK4owJHGuZKNL5oFnIFYI0zFTXajbqoFksFsO5556Lgw46CJ///OcRj8dx9NFH4/jjj8dHPvIRLF++HK+++upkrpUqwCt7oQfnoKnQ5BkouVJCRDQc1VChSAq8shft+b4nANBSv3zsL7bswwCAE1JpBPMXUf/T+R/05/pHeBKVW6GEqF/rt5vx25Mzp6CfWYFX9kKAAJ+rWMqUylb3iQIRTW+aodkleq2aCgGAGmyCqqtQRAV+18RKoiKOwV3J1N7hdywDzdBKhsHMDc5F2B3GvMA8AFamWZx9Jm05PWcHTBs1Dd6aRcAY+oXPC87D+SvPBwAYpoFb199aEpDJOYYBVHuJppwpzTSbqvLMOYE5kPPHp9sFHX4TMyZw3J+J2ts1+YEGVGrUQbOvfvWrWLt2LT71qU9h/fr1+OAHPwhd17F27Vo899xzWL58OS6//PLJXCtVgCIqkCPziydF4ARNIhqdjJ6BR/bAK3vRoRYnG7Y4rsaNWmgOMPcweEwTH0pYr5Uzcnhq11PlWi6NQkbPIOwO282fARQvqkxBP7MCr+KFR/LA7ei70Z9LjvAMIqLK6kp12RcbCpMzc8EmpLU0fIoPfnliQbMaT3FwVyIzueXq/Wp/SeXJ3IDVdmFF/QoAVpCoo7+DvUdhBRgzWsb+fVmlmWPPuD/vwPMwP2RdnHqz702s3rnafsw5DMCVqO6gmZSOlQTNnJNlJ5MsypgnWWWNOxQZ9en4jOlrlnQEuCM+DgEYyqiDZg899BBuueUWXH755fjb3/6GdevW4eqrr8YRRxyBww8/HD/+8Y/x/PPPT+ZaqUJ89UvsnmYAStKLiYiGk9WyCLvDEAUR7aZVwiebQL1/nP0lhijRZNBsaqm6irA7XNKXx56cOYXlmW7JDZ/ig+LINOtXGTQjounL+bnZmr/YoAWsoFmtu3bCJVE1ju/WuCNzZDKk1TQSueLFsEKGmbNEs72/HWlt5vR1miwZLYOkVvx+mq+Nvp+Zk0ty4Yojr7Bv/+n1P9mBuFy+PBMAlHh1T9CU030VCZoBwHy39V4ZUYQWs/r6zYTAcUIr/luu8zePsGf1GnXQrLOzE/vvvz8AYO7cufB4PGhtLY5WbWtrQ3d393BPpxnM661DnajYtxk0I6LR0AwNISUEZGJoF60yhGZBhiiM+qunVD5odkBOxVJTBgBsj2/Htti2ciyX9kEzNMiiDL/iHxA0y2eaTWF5JgDUuGsgO4JmSZ6cEdE0NnAIAADkAo0wTANhT3jCr1/jL2YbxSYx81Y3dIiCWFJ5MqdrM9C3vWQYQHt/+4zJtJlMKS1VUqbXqqpA7Th6uwI4suVInLTwJABWtt8dr98BAFBZnmmTMrGSnmZ1nikMmgWLsZGuvi3wSB5k9eyUvf94JRz/fU7l72smGfWZi2EYkBxRW0mSIDhqsYUx1GXTzOKVvYi4IvbtXpZnEtE+GKYBURDhlb1I9ryJRP4ApkWaQPlJ3WKg8QAIAE7t7bLvfnT7oxNcLY1GRsvAK3nhU3x2bxYAaNanPtMMsAbVuFwh+3ZSz0zp+xMRjYXzc3O+qsIURCQ8QXhlL/zKxEozASASKAZO4trk9ftMaSn4ZJ89Qble8sFzxxnALe/Hct8cCLDOCXckdkDTtZFeqioYhlGScDCWyZlD+dphX7P/e1m9czXe7HuT5ZkOcjqK3nzWpl/2wiN7puy959cttbd3J7Yj4ArMiL5mCUdgr8YzsYEks5U8lp1/85vfIBCwrupqmobbb78d9fVWc71EIjHSU2kG88pe1PobAVhfjn39nSM/gYiqXkbLwCN54FN82NH9mn1/y0S/jJedDHS9hpOTKfy8vgE5U8dz7c/hMwd+Boqk7Pv5NG4ZPYMmbxMUUSnJmGjSdEAQgfC8KV2PV/Yi5Gx8reem9P1plHQNAkyY/PdJVa6kPFPVoPnrkdZzCLlD8MreCb9+jbfYwDs+idktaTWNBl+D3dtyrp5vSJ/qQWDXi1gQXoCtsa14O/Y2BEGwjgemMHAxnWT1LFySyx4AAYy/p1lBg68BXz7ky/jRv38EAPj1q7/Gj99zNQxRgWiozDRLFwcB1E1xU/sFTYcAr98OANjW34H3eOtL/u6nJV1DzCwGt8PuiWe9zkajDpq1tbXhlltusW83NzfjD3/4w6B9aPYRBAH+YCu8iT1IiyJ6M8w0I6KRZfUsAkoALsmF9t437fubfS0jPGsUln8EePonCBsGjjO9eAhJ9Kv9WLtnLY5pPWaCq6aRFPqZAcWTP8k00aDrQLgNmOKgiFf2IuKO2LeTpjql70/7psR2Y9Hd/x9MUcbbn7odmp9lH1S9OvMXnX2GgTrDQDrYhJyeK1s5VI27eFEqYWp2xnc5GaYBwzSQc1ykmJNzBOi2PYMVdSuwNbbVnrCpiErVBs3SahpBVxB7knvs+1qVAOCbWDDnk0s/ifveug9v9L6BXYldeGjbI1gaaoY7uhNKogMwzTFN55xNtHQfEn7rv/s679Q2tZ/vKE/ersYRdAWhiIo1IXeaXjiSsnFEHeWszuMqKhr1J+m2bduwdevWff6h2UmumW8PA+jJxmZEU0OiqbR652pc++K12J3cXemlTAtZLWsfwLfHd9j3t4QneHGl+SB7SuOpndvsu5/c+eTEXpdGVOhn5lN8AIplRg26DgmY8n5mACCJElocPXwSMKZ8DTSymtf+ATkTg5LqQWjL6kovh6hiNENDd9rKOGlTNQgAcv4GiIKIgBIY+cmjFHFk3kZFIJctfxVQRsvAK3sRzUbt++alipP3sO2Zkr5me9N7Z0RPp8mS0TOo9dRiR2I7AKBB0+CrHX9pZoEsyrjiyCvsUti/vPkX7AlaASJJTUPKxEd6+qwWz8bs7Tpv/ZS+d6OvEd78KfJ2QUMAEvyKf1r39pMyccREBs32pbyXH2jWkiJtaMr3rcmYGqfhEDn8p/M/uOmVm/Bc+3O46427Kr2cacGEaQdY2h19XFpq95/YCwsCsPyjAIAjUinMdVmZT2/0vmFfxafyc/Yzy+pZuz9LcXJmZTLNmx1TnuICIGjVe3I2Hfl2v2Rvu/q2V3AlRJW1N70Xuml9XhYmZ6b8dfDJPvhdE+9nBpSe7PaJIoz+8peF9av9qPHUoCtV7Cs6V3WUxndtxIH+YjP0rbGtdqZNtTFMAyJECIKAnvx3ZqumjXsIwEAHNxyM0/c/HYCV3f9zV/HvQanivmZRxyTtqZycCVjVWfMl69h3tyxD730bdd46pNXpe94sZROIOvrWO4PvVDTqoFk6ncY//vEP+/Zll12GSy65xP7zta99DZkMm/DOVq6aRWjSivXOnKBJZInn4rj51Zvt27sSuyq4mukhp+fgklzFoJnjinSL4wr0uOWnaIoATtGtkg8TJv65/Z8Tf20aUlpLI+wOQxEVdPUXT5bs74UpHgJQEHAF4DOtK+1xUYQ0CZkVND6CmoG3s9jP0M2gGVWx0smZVgAp6Ysg4olAEctTtiWLMkKC1XknKokw+/eW5XULTNOEZmio8dSUZNXP1Uqb/S+NdUESrJPwN/veREAJoH8SBxNMV2ktDY/sQV+mz75v/gSHAAx00SEX2Vn9q4043lasv/9q7WsmaFlEHa0aKjEJcr7Hym7TBQG72v+DsDsMURBhmNMzG17KxBF1ZJqFHAOWqGjUQbPf/e53uPnm4onhL3/5S6xZswYvvfQSXnrpJfzxj3/EjTfeOCmLpMqTa+bbmWYA0MO+ZkQwTRO/Xf9bxByp4F3prmn7xThVMnoGbskNj2QFtNr1Ylp6c2ThxN9g3rsAfyMA4GM7X7PLE57d82zV/+4ni2ZoxX5mqeLJnz05swLlmYDV1yyQPzmLSyLEbHIfz6Cp4uvYANEonkwzaEbVrDRoZv27yHjryl4KVSNa37t9ogQ5U96LCFk9C4/sQcgVGjFo5t35byyOWNlUW6JbEFACyGjVl1iRUlOocdegvb8YwJroEICBIp4Izlx+pn37TZcLAOCq0qCZlInZQwCAqc80A4D5wWLm/Y7uDQgq1oTc6TpFU8xEEcv3NAuKbsjimOZEVo1RB83+9Kc/4fzzzy+574477sCTTz6JJ598Ej/96U9x9913l32BNE0E56BBK56MOq+aEFWrNXvWYF37upL7NEOr+kzMrJZFxBWBJEqArqFDsAIrYVOws88mRBSBZR8GALRk+3FUyLpq25vpxUtdL430TBoHzdCgiIr9d+c8+SuWZ1YmaOaRPQiK1klCXBQhVnEfl+nGt/vlkttKsgtirvqyTYiAYh9IoFieqYVaEHCVp59ZQY1ilXomJBFyNlHWssiUmkLQZQUACln1IhzfAwXbnsGK+hUAAN3U0ZnuhAABujFgv1lON3REPBHscPR1bVXVsgbNAGC/SDFzbXsh0yzRMdzus1t/T2nQrAKZZgvqltvb22JvQ5EU1Hhqpm9fs1SfnWkWKccx+iw16qDZW2+9hYMOOsi+7fF4IDpS+d71rnfhtddeG+qpNBvILjTJxX9IPWlmmlF160334rcbfmvfnuOfY287e31UI93UEXQHre34LnTmD2BaRHf53mT5yfbmqbnihKgndjxRvvcgAFY/M4/sgT9/MuY8+bPLMyuUaQYAoXxGoyYI0DLRiq2DSvn3DA5gu/p2DLEn0eznvNgwP59pJkXmwyeX9yQ14gra22o2WtYm/Fk9iwZvAwRBwJ5+axpkM2TYxaWF3pZdG3FgoPidsDW2ddo3Qy+3nJ6DIinwK37sSBQ/99rK2NPMfk1nZpNsBc2qNdPM7O+ufKZZ8zvt7e35zPyIOwLDMKblID093Yu4HTQL7mPv6jXqoFk0GkU2W/zg7e7uxoIFC+zbhmGUPD4aN954I1auXIlQKIRQKIRVq1bhoYcesh/PZDK48MILUVdXh0AggNNPPx2dnWz0XCkNjrG9fQyaURUzTRM3v3oz+lUra2LVnFU4cs6R9uPO0eLVxjANCBDsE4Hurteg58eeNytl7JOw4BggXy74/refh1+2AjpboluqelLXZEhraURcETtlf1CmmeQCAs3DPX3ShZRiE+1UauZ8N/l2v4TGdb+GXOa+Q9OBoGXh7dg46H6WaFK1KgyqcZvW1GFdciESWQhBEPbxzLEp9LcCADWXREYvT1lkoVdp0BVEMpe021LMzQcA4Q7bQ3oA4MBcsSn9G71voN5TP23L0yZDWktbQx4Uf0mmWZu7DnCXN7uwNVgcvLAjX55ZrT3NkOpBj1QMb1Skp1nDCnt7u2plvwddQXhkz7Q8Pk2me2DmP4fCnJw5rFEHzebNm4cNGzYM+/irr76KefPmjenN582bhx/96Ed48cUX8cILL+DYY4/FKaecgo0brQOtiy++GA888AD+8pe/4KmnnsKePXtw2mmnjek9qHxaQsW/377+Kk37pYrbHt+OvenKnmQ+vuNxvNL9CgDrAPXj+38ci4zilS3nAVK1yWhWPzOv4gUAdPS8YT/W4gi8T5jsAvY/EQDgysZwgK8FABDNRrEzvrN870NWPzNP2L5dEjTTNSDcapXMVoizaW16hrQOELMJtD3wNTQ8fzuanv1VpZdTdt7O1yDq1kmz6iuetDBoRtXIMA07Q7dN1SAAyAQaEHCXP6ujxltvb2dzSWi6NsLeo5dSUwgoAfgVf0k/szmZfMl13SJgwdH2/Uu63rIHHGzs2Wh/h1RL39GMmkGdtw6iIGJn3Prcq9N0+GvLNwSgwKf47MSGHUo+aJZoB6ZhVtNkE9K9Fc80C3siqMkPKNomaICWg0/xIeAKTMvAcdoxQCniqa3gSqa3UR/lfuhDH8KVV1455ITMdDqNq666Ch/+8IfH9OYf+chH8KEPfQhLlizB/vvvjx/84AcIBAJYt24dYrEYbr31Vvz85z/Hsccei0MPPRS33XYb1qxZg3Xr1u37xansakMLIOc/gHsrHLSg6vRy18v4xtPfwEVPXIQXO1+syBo6+jvwh9f+YN/+7EGfRX3H6zj6X8UT355MD3J6bqinz3pZPQu/4odbskox22Nv24/NCc4t75vlp2gCwHLHVe3dyd3T8mreTDSwnxlQDJrJpok63ahoaSYA1HiKmRUpx1CO6SywfR2k/MGzr/3VCq+m/Py7rdJMFcCP5i/DL2rC0AG4GDSjKrQ3vRe6afXzalPzweRAY9n7mQFAja/R3k7k4hAEoSyBqoyWQb23HqIgYleyOCV8rpbvmVa7GGg7EhCsU0vX9rXYv2Z/AMC22DaIEOGVvUhr6QmvZbozTAMQrOyilJpCd354WpumWsHFSdAWsko0e0UgKQiQ1DTE9Mz4PiwX0zThSsftoJlXdJWnj+44zJes9+2SZaT2bgIA1Hvrp92xqWZoSKvFAUoRXxkvbs8yow6afetb30Jvby+WLl2Kn/70p/j73/+Ov//97/jJT36CpUuXoq+vD9/61rfGvRBd13HnnXeiv78fq1atwosvvghVVXH88cfb+yxbtgxtbW1Yu3btsK+TzWYRj8dL/lB5iJF5aMw3++zJ8fdKU+/fHf8GYPXMuu7F6/Baz9T2UTRMAze+fKP9pXdc23FYntNw8OM/xtxcBkI+qBzLxhCt0t5KOT1XMg2s3VGq2hwpbx8P7HccIFv9rA7ofMu+uy/TV7W//3JLa2l4ZE9J351CxkSjplsHERUaAlAQdlwZ7c+Vd1rcZAlufdbedsXbZ12DfF8+aHZPMIC7Mzvxm0gYT/q8zDSjqjTU5EwzNNe+uFROkWCLvR3PJeGW3BO+iKcZGkRBtLN6dyeKmWbzCn0t6/YDvBGgeaV1u3MDVuQb1JswsSW2BTXuGrutxWxWyLgPKAHsTBQz31tVzfo9TYKSvmb5YQBa39vD7T4r5YwcPLl+uzyz1h3exzMmz3xPMfi0o/0FAFYQVRGVsg7nmKiMlkG/I5AdcQTdqdSog2ZNTU1Ys2YNli9fjm9+85s49dRTceqpp+Kyyy7DAQccgGeeeQZNTU1jXsD69esRCATgdrvx3//937j33ntxwAEHoKOjAy6XC5FIZNA6OjqGLw28+uqrEQ6H7T+tra3D7ktjFJqLRt0KmsX1TNVm0lDlbI8XT7hUQ8VPn/8p3o5O3UHBg28/iE191hWjRl8jPt54BA557AeQcv1wm7D/fXSnuyEKYlX+GzFh2g3jAaDdkZXaUr+svG/m8gP7WRdWDkgU32dP/x5IgjTtruhNmGmgce1NaH76OghT9N9WWksj4i72M0traUSzUQD50kyg4plmQUf5Rb/jium0pWsIbi+9+OfumT0nN4KuwtdhtfN4LBSx79/odsEV3QkY5SkXI5opnEGz1nxmllyzYFLeqzZQzOiOaf3wyt4J9zVLaSm7vAxASXnmXDU/EbMwEXLBe+zHDnS0rdiwdwNqPDXTthl6OaW1NEKuEDyyp2QIwPzJDJqFnEEzqyzW7Ns2Ke81XWW1LKRMArF8plkl+pkVzHf8fWzrtr4P/bJ/2g3EyOgZ9Ds+HyKOzH0qNaYmJAsXLsTDDz+M7u5urFu3DuvWrUN3dzcefvhhLFo0vnTTpUuX4uWXX8Zzzz2HCy64AOeee+6EpnBedtlliMVi9p+dO9nbpmzCrcVJabCyOYimimEag3pVpbU0rv731SUHcJNlR3wH7tp0FwBAgIAvLD0Thzz6fbhSvfY+8/JXkGPZGAJKoOqynXJ6DoqowCt77fs61GJWakvdAeV/02XWFM35qgafYB0obe7bjCZ/06z7/Qff/hcaXvg96l65G+E3HpmS9zQMA2HH1dpCM2sAaMpnHlc60yzkK/bwmQlZDL72VyFlSzPiPHvfGmbvmcfT+TpELYukIOBFV/Ew8y1FgWhoVTvVjaqXc+JwIdNMnqSLDRFvMfO2T0sj4okgq03sAlJaTaPB12BfPCkJmhXOCwoTIR19zQ6MFSeJb+zZOK2boZdTTs/ZvbRKhgBMwuRM+7UdmWbb85lmwVR0WvbQmiwZPYOUWvxuratg1tT82uX29vbYVgCAJEqo89Yho5ZnOEc5aLqGhOMirLN/LZUaV+fe2tpavOtd78K73vUu1NZOrGGcy+XCfvvth0MPPRRXX301Dj74YPziF79Ac3MzcrkcotFoyf6dnZ1obh5+Spfb7bancRb+UJmE56Ipn0kDAL2Z3hF2Jiqvjv4O5Azrg/3AugOxrNbKWkrkEvjBuh+gO9U9ae+tGRpuePkGaPkMiQ/PPxEf+Nev4Y/l+3rU7Qes/GSxTAFWb4VqyzbL6llrCIAjaNZuWj+/bAL1/kk4gNn/RECUIQJYlrM+n9r72+GVvZBFGRlt+hycTJRzGqGn+81Jfz/N0CCLcmk/s9SAyZlAxTPNQv7iMUFiBvTLCW59xt4ufDp4emZP0My/+z8AgGe9Hjhzyra4rOwH9jWjajNUeaarZnKCJ87pmVEjC7/sn1BmV6EfmnPgSiFo5spPAgVQ7NXl6Gu2aNcr8EhWC4WNezfCp/gQcoVmxMWN8Sr0AS1k3DszzVpVDahdOCnvOz9U/B7eIVtBs5pMAvEqaqejGzrijhYNdZNxzDlK81sOtbe3p4tB85ArBAjTYyCGaZqAqSNmFs9TIpyeOazKjbsahmEYyGazOPTQQ6EoCh5//HH7sU2bNmHHjh1YtWpVBVdYxQJNaNSLX7wMmtFUcl6tWxRehPMOPM8+SOjN9OIHz/3ALhsrt7+9+Tdsi28DAMwLzMOXt76MSLdVpolAE3D2PcC8w9HqzMTM9qHR11hVGZlZPYuwKwxJzJdkZGLYI1oThJogQxQm4SvHV2tf2T4gVTw43JXYhUZfI+LZ2XPA6N1bDJS5p6DsYsh+Zs5Ms0J5ZqUzzRwHxonpnsFgmghufQYmgIsaG3Dkglb8w++De++WSq+sbApDAJ7ylTZg3iXLSAsC3L3bKrAqosopfG66INgXn6VJ+tx0DkbpEwx4BRGyKNsX/cYqpabgk3120Mw0TTtoNkc3rRNJby3gzb+vNwI0HwQAkDs3Ylm+r9mu5C5EM1HUe+tn9cXElJaCR/YgoFilrCWZZr5mQC5/HzsAaA0W2xEVyjP9qR4oojKrLh4ORzd0yKKMqKNFQyXLM9uaDra3tzuy30KuEHyyb1pkAGb1LAK6jqhYLKNm0Gx4FQ2aXXbZZXj66aexbds2rF+/HpdddhlWr16Ns846C+FwGJ/73OdwySWX4Mknn8SLL76Iz3zmM1i1ahWOPPLISi67eokSmhxXmnrSPRVcDFUbZz+zQ5sORYO3AeevPB8tfqvpbUd/B65+7uqyX8Hc3LcZ9711HwBAEiR8W/ViznZrIAFcQeCsv1qZNrWL7PJMwLoS2+JvmZ29tYah6ipC7uJnRLJnMxL5hqwt8iROMFpulWgekC0eiL/e+7r1+xel2XHAaJol2WVT0VA9nS/tKZTkAKUZE82aDih+wFe5A1MACDnKR5Pm9GmwOxRX33a4Y7vwituFJ/xeqIKAP4eC8PRsAWZDnx9dg7djAzQAT/tL/82bgoCtisxhAFRVDNOwyzPnGkLxxCs0Z1Lez6/4IcO6WNUnSvCqaXgkz7i/B1NaCjWeGrgkl/Wa2T57AubcXP41B/bpcpRorpCLxwSv9byGgCsAl+SatYGzjJpBnafOvni4I3/BtVbXEZykfmYA4FN8aPBazecLgwCUWDsafY2IzZCp0hOR1bNwiwp6Hf3C6ryVOzbxKj40G9a/w62CDjPfy1CRFNR4aqZFX7OMnoFfzSEmFcNBDJoNr6JBs66uLpxzzjlYunQpjjvuODz//PN45JFHcMIJJwAArr32Wpx88sk4/fTTccwxx6C5uRn33HNPJZdc9Zo8xd4xPY6MA6LJ5gyaHdFyBJbWLkWjtxFffMcX7atJ2+Pb8ZN//6RsQaqsnsUNL98AE9bJ7H95F+CoN6zsV1NUgE/9EWjJT4qqXVRSnrkruQthdxiNvsZZ11trKIZp2CPlCzq6i/0pW9wTK+UfUb6v2QG54kH4az2vIeQKWQeMmZl/wCineiCno/ZtpX/vpE9cNAwDYVdpf4vS8kzNChgLwqSuY1+cZUNJc3o3mQ9us6Zm3hcI2PdtdikQcv1QEsMPOZopvN1vQFLTeMXtRiyfZSqg+N/HFpfCoBlVld5ML1TDOmFuVa3/N321gGtyLiQJgoAawco06pNEKJkEAu7AuIYBmKYJ3dBLsteckzPtfmZ1A0pNHcMADkgXv6c29GxAQAkgqARnZYmmaZowzGIf0LSWRld+GJJVmjk5JbkFhWyzXklCUhCA6A40e5sgCdKsDVIWZLQMQgbQ44hsVDLTDADmy1aJbkISEXUcD0fckWkxECOrZRExTURFBs1Go6JBs1tvvRXbtm1DNptFV1cX/vnPf9oBMwDweDz41a9+hd7eXvT39+Oee+4ZsZ8ZTb6mYHEqT29yTwVXQtVma9xqpBlQApgbmIuwO4wlNUvQ4G3Alw/5sn3ivKlvE37+ws/HXYrg9OfX/4z2fqtp9VJXHS7a+KT9mHDqTcCi9xV3DreiVS+eHO5M7IQgCFWTbZbVs3DLA/qZ9RYzo5r9k/jZHWwGGpZjgarBa1h9Il7rec3+/cvSzO9t5unePOi+yewNperqoH5mQGl5ZrOmV7w0E0BpdiNMaNNonPtAwbefQVoQ8Eig+HtNiyJ2yTLcPTO/RLNYmln8HHjvvPfa228pivXf7WzIqiMaBedn5oKMlV0ihOZN6nvW5PuIRUUJZv9ehF1h6I6exKOV0TPwyl4EXUH7PucQgDkDhwAUtK0C8sHyFZ3Fz7WNezdCEATUe+snPJxgOsrqWXhkj93PbGeiOLyqbRInZxaU9DVTZCCXQNg0Ue+tn7T2JdNFTs+hxtDRIxVLDSuZaQYACzwN9vb29uft7ekyEMM0Tfi0LKL5TDOPIMEjeyq6puls2vU0o+mtIbzA3u7NXz0hmmw96R67N9j+NftDyGe21HnrrMCZrwFfeedX7IDNK92v4Jcv/XJCjTbX712Ph7c9DABwCRJ+snUj7CK1D/wAOOjjpU+QZNSEW+HLB212JawhAdWSbZbVsvAr/pIv3HbHAWOLY/z2pJh3KCQAy/LZZruTuxHLxhB2h9Hka5rx2WaevYODZu7eyQuapXWrn5k/f6W0oJBpppgmagyj4kMAAFgTW/MnaHFRRG6a/luT0lH4OtbjCZ8XSbH08OtNlwL3EH/HM41v98sAgNX5oJkAAV9Y+QX78S0uF+RsAlK6eno9UnUrXHgDgLZ8phnCrcPsXR41+aBNThSQSnZYx0YCxpzZ0q/2I+KOlFwM25XcZW/P1QYMASjwRuws/PkdryOQ/x7Z0LMBgHWhQxKlslzcnE5SWgp+xW//vpwT31s1ddKDZm2O46xCXzMhusO6aGli1v2+Cwr/Xfty6dKgWaUzzUIL7O1te4uDnLyyFwFXoKJ9zQqDnjxqxu5pFpYYMBsJg2Y0JkqkDXX5L8k+R2NDGrucnsPPX/g5fvzvH0+LhpDTlWmaeKPnDfv2kpolJY83+hqxf83+aPI14cuHfBmKaB0orGtfh9+s/8240p9Tago3vXyTfft/evuwKB+MMY+8EDjqS0M+T6hdbPc125PcDc3QqibbLKtn7ZKEAmf/q5ba/Sd3AXMPAwAckC1mGb3WY6XDN/ubZ3y22VCZZpNZ5pbRMqjx1BSHOuQV/k6bNM06gJgGmWYAEMqHtOOiCG2a9tsMbFsLwTTw96B/0GObXC6Ina9XYFVlZGjw7XkF22UZW/OTMlc2rMSB9QfawyTeyp/Imd1vDPsyRLOJM9OstdD3NDy5mWYRR8l6X2I3vLIXbsk95mMQTddQ6yltreDMNLP7uA5VdpjvaybCxAHeJgBAV6oL3aluBJQA/Ip/1h37qpqKuv+fvfMOj+Qss/2p6qrOuSW1WmrlmdHknJzHAQMGJ1hslrCkZcGYtFxYYHfv7t5dWO4GYMlg4sJewAbjbIIDM07MjO3xeJInSaM0yp1zV7p/VHVVtdSSWurc07/n8ePSqKr6U0td9dX53vccg0te2FUnZ3Yx7Hxxsch0WlSimZSgieAIHHoHnAZn3VabMTwDrUYLQzpeVZVmXU3r5O3h0JC8LVdbVvCZIMkmodPooE1F5UozOzV/btJAoSGaNVge1na0SCXeQS5Zt6sW5eDwxGEcnjyMV6ZfweODj1d6OFVLKB3CrKqqsd/ZP2+fVlMr+mx98Jq9uHvr3dAQ4k3z6ZGn8fPTP1/2a/7k5E/gS4oP3rtSDN4ZDAIAuA23g7jxCwsf6OqTEzRZgZPNfy+FajNBEOSWhAwTqgmap3lDaQfg3QUAWJfODgMAIFeb1fL7n7PSrISiGc/zWV5hgCgmR6Q499ZMhUEVVJoBgJUUTarDJAkyUZ2JqZah5zCp0eCgXlzNVbc8ndXSMPuHavohUj9zFhomLleZAcB1ndeBIAj02cWH6os0hThBADPnCqoEbtCgVlD7QHaymUqz9gX2Lg4OldAVjE3BQBnEMIBl+JqluBRoDZ11nQLy9DQDsnzNNqg6Q0/5TkFDauAyuJBgEnmPp9rheA4EQcBCK++XWjTr5ADYSltxr27PHKYV0YwkSHhMHnA8B45ffptutZNkk9BTetDJMHySAKQlNHKCaaXobt0pbw8nprO+Z6bNoEkaTIXsJFJcCmbajFTCD1YSee1z5nwNsmmIZg2Wh60dbukmySN7Ba3B8rgQuiBvn/CdqOsqpJXCcAxSbAqBlNLK0z/yCnDwu8Cc9h6vxYteWy+6rd344OYPyubTjww8gofOP5T3a744+SKeGXsGAGDiBXxhehokgHTX5dDcfg9ALnLZnBsGILVoEgSBNnNb3VabMRwDWkNntXAAwASnGP167KVdYUXLOoA2ZSVoZirNAMBj9oDW1Gb0OpmOQxsU2zwSzWvASylmusBQSV4v42c2VwTNCgHI+OPYS9x2mydWqS04TRLgk6GqE2QILg3z8CE8ajZBkCao71r3Lvkzc0arhSE8jkh0uurGni8mqTVT7We2z7sPAGTRDAAu0DRcMX8jgbvBJUFmnkyBgCez2FDiSjOHQQnt8idmQBAE7Dr7snzEYkwMVq113n0gU2lm5AXYeR4wuwGdZf4JVL5m6wNKi2qmRdOmtYEgiJq93s0lwSZgpIwwaZX3aySkLGx1mNoADZXr0KKRCQIAgFGVaAYATr0TNq0NoXRtW1XkIsklYdVZQSb88EuVZi7aLFf8VYo29xZQUrfLMJvdnSVXW1YoRZPhGdj0NgTjiphnVwV+NJhPQzRrsDxsHXCrzEQzlTQNlgcv8LKxPQAMhYYwHZuuyxWgQvAn/HAb3bKZKgFg1fPfBn73WeAr64HHPg1I5tkEQaDD2oFuazf6Hf14z4b3yOf5xelf4InhJ5Z8vVAqhO8f+7789ed8frSxHFLN/aD//JcApV38BM5epV0B2SawVq215qudFiLFpaDTZIcAgGMxCfHv2SYQ8wzliw6pAdq3o4dhoFeFAWSo5fdf5xsAISW4Jpv7kZaEKm1oDOCKX+2b8TPLtNRlULfbZhZPqqY9UzVWPh2tuqQw48VXQDJxPGRWHqhuXXWr3G5+kaYQg4D2VKxmxSTTxVcQIgkc0esAiK1CPbYeAMAqu+Llc15Lwx6dBgECCbZ+Kk0aNJiLIAjydbOVoCE3jpXY08xucsvbQckP1qQ1yUng+ZBm02gyNGUJDxzPYTwmhoC1s4woiS2UCGmwA62bAAAbp7PDAADxnmygDDVdXasmzsZh19tlixAAGAkPAQBsHAeba/UCRxYPI21Es0E0nx+WWuEzohlFUmi3tCPNputGqMzA8zystBVsdBoBaWHbVQUpkBSlhVcQxzNC8OBVVWWZasskU/6FXEEQQICAkTIiqJpv2CvsAVftNESzBsvD6IKbV26gs/FGGMBKmInPZL13SS6JSDqCmfhMxSOIq4VoOgodpUO7uR0DQXHC1UnoYMy8P0wcePH7wDd2AL94BzD0HEgQ6LZ1o8PSgS1NW3DHmjvk8/3o+I/w/MXnF3w9QRDwg+M/QDgttnbti8VxazSGlKUVeOevQehtCx4ro2rPBJRKM0AU9Tzm+vQ2S3JJWLVWUKSyispFLmKKEh8RPKSuPANp3wEKwJq0ODEZjYzKv09ArDbTarQ196Cubs1MNq9BSmqJJHgO2vDFhQ5bMQv5mc1LzjQ4AH11lPNbVW0YXDq2rDakcmC58Dxe1WkxJHl97W7djXZzO9Y4FK+/c1oaHfEwKJJCNB2t1FBXBs/BOP4qnjcYwEkP2dd0XCM/cKsrzQZoGlrfBXjNXgSTwbp7gGvQIMN4bBxpXhTwvaq5M6ylbc90mtvk7UBKrCwyUAZQBJWXrUrGJHxua+ZMYkY+vj2zQLiYT5fka9bOsrBLCxsnfSchCAJoDQ2nwYkYE1v4+BqC53nYVUJNkk1iMjEDIONntoC4WGQy1WZ+jQZRggBCyuKtS++CRWuRbRbqAY7nQBIkjLQRwdi0XMnt0jctcWR56NaIc5MkSWB6+njW96xaK0Cg7PfANJ8WPeAoA0KqhWSbsXnhgxo0RLMGy4Qg4Fb1PNerqWQpCSQD0BAazEg30wxT8SlYtBbZS+tShhd4RNIRdFo64U/55QfgNZlJGqEB5MolATjzGPCTNwH3XAPN8V+j1+JFu6Udez178abeN0l7Cfj20W/jyNSRnK/57MVn8eKkGAnt4Dj846wfrM6C2Nv/B7p8W9CsXnhVxYLqlClAqnYy1Wa102KwHAurLls8mZ05JfsktNJlEla8UhiAytdMHSKRef9rLUlTHQKQaFoti2YAoPMPFf31cvmZAdmVZq0sVzVVZgCyxsuxiWW1IZUcQYB58Bk8PKfKDAD6HYpH41mtFkbfILosXYimozXlGaqfPQ9NOprlZ5ZpzQTmV5ohNAKvwQm7zl5318MGlzaCIOC0/zS+fuTr+JsDfyP/e6d6/mJpLekY7BZFlAswogBvpI3QaXR5WRTEmBhMtGmeJ1RWCAC7SAhABsnXjACwgRSvf/6kX76XOHQOCIJQ84vFaU4UIcxa5f1Sv1cdbPlEM7Wv2QhNiZVm0vtLa2i0W9qRYBI1/55nUHc6+FS+YS5VtWUl6TK0yNtD4y9lfc+qtcJIGctebZlkk9Br9DBQBgRV7br2KnnPqpWGaNZg2bSovBJiSX/VtcFUMzEmBo7nQJP0vDL5V6ZfwSrHKpAga6/KoMj4Ej40GZrQam7F2cBZ+d/XRCQfs5b1wF+fBK7/R8DiUQ6ceBV44K9Af30HVp94BF7KhOs7rse1HdcCADiBw1df/ipe82Wn1M0mZvHjEz+Wv/6HWT8cBIWp274JR9tO5I2GQpvFC1KajKjbMwHISZoUWdtJjmp4gQdJkPP9zGYVscpjKNOKn5ygmdvXDBBDI2qt2kxdaZZq6kPKqRLNihwGkPGnm+tjA2S347s5tmpCAADAokpuTTGxqnogoGbOQojN4Hcm8T01UAbc0HkDAGRVmp3R0sDUCXjMHrhNbvgT/oqMdyVoRw+DAfCcQbwOWGgLtrm3yd93G93yA/iA1DakC4yi29YNjufqrvq2waVHkk3iyeEn8dlnPot/euGf8ML4C+AEcRVNQ2jwxrBU9WxtE+0ESohDlRoY5MR7HU3SovF3Hp+1BJOAy+CaV22sFoLaMv5srlVYkC6Vr1lMqfo+6RNbNC1aC/SUvqbux7mIM3GYaFOWpcFwWLk3dzLs4u9TEem0qhI0aRpIhQHVwoRL74KJNiHK1MdzRpJLwkgbodVo4UsqXscus2eRo8pHl61b3h6eMx+lNTQcekfZfc0yafcEQSCgqvS0G5yLHNWgIZo1WDZus7KCFY3PVl0bTLWS5tKIpCLosfXAn5r/MHR0+ihMtAm99l7EmNgl+xCRZJMgQKDT2gmapHHWr4hm/WnpPXFvAIxO4KpPAZ84Brzl+4Bni3KSyASoP34Ra//7Ldj20v/gjqYd2OvZC0A0v/z3F/9dDmLgBR7fe/V78qTtzdEYrk+kcOHGf0LLmpuWbSRKu1bJyYJj4ZF537dqrWgxtiCcqs6Ev+WSWWGd6381ERyUtz3m0raiyFg9gNW7qGgmV5ulaqTajGehnz0PAEjZvOC1JqRVYpU2MP9vrBASXAIGyjDv9wlUeaWZ6iExzkRBkfm1IZUD6uwf8EejAREp1evGrhtlj7+MpxkgVpph6gQoQoMuaxe0Gm1We3G1wgs8dGMv4RW9Tv4Zr/RemeXtQxAEeqUwkPFMgubsWbj0LnjMHgQSgaoSOhs0yJfx6Dh+cuInuOvJu/CD4z/ISky0aC14Q/cb8I87P4udEamLoMQhAADgUBl6B3jlfmjT28Dwi6f1ZdrdbLr5lhR5J2dmMDhkX7MNfuXYE7NiGICe0sOus1fMDL1YJNkkXHpX1nxRvWjawTCLV+QVkU6LSjSjssMAAPE9bzO31c3iPMMycOjEv3efqmrKVSWtht2u9fL2kORxp8aus4Pn+bLe/3iel1uvQ5wiWOf6zDdQaIhmDZaNW6Wah5PB6mqDqVJ4gYcv4UO7pR3t5nYMqgSFFmnikebTODZzDB6TBx2WDvgT/ksuGEAQBASSAbSb2+GUItPPBM7I3+/PtN25NygHUVpg8x3AXx0A3vs4sPbNyKxsEkwCTScexBW/+QT+z+ggtlu6AYgpR1869CWMR8fxxPATOD4r+gy0sCw+7/Pj7BUfgWPz26HVLGH8nwtVgmaYic4TZzLVZhpSUxfVZkk2CQNlgE6T7Vs2oVqR9tjLM1kEAHh3oJdhoOXFCcgp/6l5u3hMHug0uppY3dYGx0BK1bzJZlFgSdk7IUh/48WuNEuySdh19nkVBoAimul4XkxNq5LkTACwqiugmTj0Gn1VfL5CqRBaxo7MCwDIYNFa0C6Jyue0NPi4D4hOw6K1oMfWg3g6XrFI+nwJJvxwTp1esDUzg7pFc4CmgdmzIAhxgcSsNdeOkN3gkofjObw4+SK+ePCL+NT+T+F3Q7/Lup+ssq/C+za8D/97z//G29a8DXuNqoWjMohmam+tAMEDrHgPMVAGECAWfUCPs3EYKeM8PzMg23JC9jRz9Cw+GMnXbGNKeVbIVJoBgMvgAsNW9zVuMXiBB0EQsMxJEB1RLZp2gcruiigh2ZVm80UzAGgyNMFAGerCT06AICeW+lTia+YZotJ0enbI28Oq9tEMFq0FOkpXtkIJlmehITVidwjPI6gS1TPiY4PcNESzBsvG6OiGhRNNCwNMWC4/b7AwM/EZNBma0GvrhYbUyMb2APC2iLLac2jiEEiCRLe1Gy3GlksuGCCYCsKqtcJrUSaVmfZMC0Epce2tG+cfTBBA9xXA2/8f8LGXgd0fAqQWMwIC2kZfwnePP4strCg2hNNhfOHgF/D/Tv5MPsW/zPgxs+VO0Ls+AJdhhSkyzt7sMIA5vmZAfVWbpbgU7Hr7vIq8iaQSdNHqWlu+AXl3gYYisA6Hh+etqFq0FrQaW2viIV0/q1RaJptE0Uyg9WCsoieOLjAk+5UUg4X8zABgMi6lwHGcKNk5uov2uoViMym+IVEmBrM2vzakUpLm0uDDE4j7z+NPBj0AoN3cjh3uHVn7ZVo04ySJixQFTIsPlK2mVrSZ2+BL+qr2PpDiUtD5BqFJRWTRTENocEX7FfP27bMp4vl5rSiaAeKDfJe1Cyku1bB7aFDVhFIhPHDuAXz86Y/jyy99WV5wA8T2x2u81+Bzuz+Hu7fejau9V2Nry1ZsbdkKjzrluMQhAACg1Whhkh7xAqQGSIhta0ZKbGNb7NqYYBJwGpxZlaIZ5nmaWdsB7RLJ2JKvWQvHoUkKBcqEAQCKaFANixwrIcGK1dlz/d9G1O2ZZi9AlueRO6vSbAHRzEgb0WpsRSRV24EAaS4NWkOLAhCbgl9QPmeuKkmCbHFvgUFKdB9m54uUBsoAi9ZSNl+zjAeckTICqTCCpDJ3t1dB4mg10xDNGiwfqxct0gRghokCQvmTP2qJQDIAI2VEn71PrlwalKqnDDyPWyPKRTRjRE9raKyyr4JFa4E/WTu+NoWQ5tJIc2l0Wbugp8QHzFAqJFe3rBYoyJd2dw7RTI2rD7jp34FPnQRe98/yJNUgCPj2xRH0S+17/qQfaekme2c4gu7uaxDe8yF0mAuIg3f1wcuoRLPIfNGsrqrNBMBEzfe/mlSlM3ma18/7fslonx8G8Jr/tXm7uU1u6DS6qo+7N6hCADKiGQCkpNZITToGKl6c8JDMBDSXn1k0HZVXpd0Z8bqa2jNNirF2mI3Dplu6DamUCIIAX8KHvunzeNRsBC+Jyrf03QKSyJ56qX3NzmppYEoUzUiCRJe1C2aqOquwBEFAIBFAV2AUF2gKo5JX2Xb39pxtHlmVZloaUHn1tRhb4DF5asrHrcGlgSAIOOM/g28c+QY+8uRHcO+Ze7MCm9xGN96x9h344pVfxO2rbsc65zpsbNqILS1b5MRmhFQpx2WoNAMABynON4MaEpDuEQbKAL1Gv6Boxgs8eIHPau9UkxHN7BwHkyAAzkWSMzNIvmYEgI2MeO+IpCNy+2Kmqq1WWzQTbAI2nW1eZ8JISOwosXA8bM7y+JkBoiDWJFVeD0vX5LmiGQC0mFpqzt91LkkuKYcAIO6DT6PcW1e88F1kCA2FLkGs3L9IcGDY7IUhgiDQZGgq2yJfik3BTJtBa2ggEUBIJeba9I32zMVoiGYNlo+tXX5oSoEHJ3C1/+BfIuJMHBzPoc/eJ5e6p7k0RqLjAIAehoGH49DFiA93x2aPyQ/xRloU2ggQdeM9sBj+hB8ekwfNKh8CdQhAf1wSYUzNgLll7uG5MTiAKz4BfOJV4K0/BNu6GVZewHcnp+X3HAA6GQbvt6zF+cvvQpe9W7yZrBRVeyaQWzQDlGqzWktyVJMp8zbS81eaJwRxAkAJQJMxz99XMfBsAUhqUV8zQKk2q3bPKP2MqtKseQ3SXBpxJo60U+1rVpwWzUyr7dxQB2Cun5n0911N7Zmq60aYS8o/Q6UqtAKpAKxaK5pGXsJDZqUC4ea+m+ft2+9UEjTPaLWyaAaI94FuW7e8qFBNhNIhmGkzXJOvLdmaCQB9qjbt8zQN+M4DkgUBSZDotHbCQBnqogK3Qe2T4lJ4euRpfP7Zz+MfX/hHPD/+vNxZQYDA9pbt+MzOz+Bv9/wt9nj2wGPyYFPzJmxu3oxWU2t2pVZINQ+wFbAotwwcGmnxkSTBxcXKb4IgYNfZF3xAz9wDcrVmMhyDqZgYBqP4meUhBhkccnfA+rAiNmZ8zQiCQLOxuWatXhiOmdfWlubSmEiI73kny4BQLXiVg0y1mV+jQZQgcopmFq0FLcaWmq42S7Ep2HV2cSEq7oNPo9hKVEulGQB0UeIcgCMIjE2/Ou/7ZtoMmqTL4sOa5tJKRVkiIIrqADQgYKHnf+4bKDREswbLx+aFm1NaMpNssuJtMNVImksjnAqjx9aTJQQNhYfAS8mZfWkWoPTYlRBFR5ZncXT6qLyvy+C6JIIBwukwDJQBndbOrCqMrOTMuPQgpfYzyxcNDWz6M1AfegbBd94LdO7F9yZnsSqdRhPL4f/wdpy+8mPosHUX7oNg60CHqmN5boJmBjlJU1O7SZrq2Orsb4QxIf0a3dDk9McqGVoj4N6AdUtUmgFi+1tVV5sJgpycyRrsmCQJ3PXkXbjrybtwzmSXd9P5h4rycgk2sbCfWVwRzdwcB5hbAVpflNctBlbVCmmYZ5asqCglSTYJlmPRbWzFa+N/wgWt+PC8070THZb5D8zzK81OZH2/xdgitmkmqqdNk+EYpNgUuqydoEYP4YBaNOvYl/OYFmOL/DA+oKUBNpn1MGeiTei2dSPOxKsmxKHBpUeaS+Pnr/0cH3nyI7jn2D1Z5t0W2oKb+27Gf17zn3jvxveiw9KBJkMTNjdvxqamTWgxtoAiqfknDatFs/IE49ildkGBIBBSvb5ZawbP5+4OiTExOPSOeR6lgLhwkkl9l/3MFgsBUCP5mm1YwNfMorWAJumq92+cC8MxYiqpNrs1cyw6Jr9XnQxbthCADPN8zXKIZoA4ByIJsuoWZPKFEzjFTiI2K4tmFAhYdbltJipBl8Etbw9PvDTv+2baDBNtKrnHXGb+YKCl+3UigKA037OR2mUHn11qNESzBstHb0OLoDxUpfnqWwGvNHON/9WoQwB6GQbY8nbsSSoTiUOTh7L295g88Fq8dRsMwPEcYukYOq2d89rCzvhVIQApaTK1VGvmYhAE7KvfAPZtP8bY7d/CN5uuxq9dVyJx3d/BaW2H11yEtglSA6+pTf5yoUozQEyqcRvdNVttluSSsOgs8x4SYv7zCEuTl9YcrZslp30nVqUZ0NIEIVelGSA+PLQaW6u2soWKzYJKBAGIrZnPjT8nC+hP8crfTNHCAAQs7Gc2NznTUT2tmUD2uMNgKyaa8QIvhplY2tE8eRIPGZTPxi19t+Q8psPSIQvPZ7Q0MHMGUD08EgSBLmsXbDobAslAaX+APPElfPCYPGiJ+RFIBnBUJz5k99h6sh7Y1BAEIbdoTlKUWAGhatEExFY3t8kNX6I4LccNGiyXp0eexsMDD2c9wPbZ+3DXlrvw5X1fxg2dN0Cn0aHF0ILNzZuxsWkjmgxNiy8OZVWalak9U6ssJASl7gZAbNHUkJqcwjTHcwu2ZmaFAGRa9PMVgyRfsw2qCnC1aGamzTDTZsRyeD5VM3E2DhNtmjd3HQ0ri6WdDJtfRV4R6bIq9+cRml5QNLNqrWgyNFVl+/9S8AIPAoSyaKtqz3RqDPNsECpJtypAb9g3fxFXQ2rgMriQZEq7gJ7m56TdqyrNbJr5HQYNsqmev6gGNYVbdTOOpCLyikoDkYzxf4+tZ95EaiCkhAD0MSyw5y7sTCgXyoyvWQaSINFj7anbYAB/0o9mYzNaVZ5EGTLJmQSAVUwRRDOJVlMrvF1X4uyOd+DkzndB0NvQbS2wLVOF1dknh2WMhRcXNFpNrTVbbcZxXE6RZXJGmQy3VSKNRwoDWCNVmw2FhhZcwWs1tUJP6auy2kyvEhSSzWvkdhYAGGCVlu1iiGZpLg1KQ+X0MwOAqfiUvN3KslXVmgmIxtd66dIYhgBCEBZtQyoV/qQfDp0DXdYupM88ht+axPfTQNK4sfvGnMeQBInVdrF9Z4ymEeMZsXVRhZ7So8fWUxV2CJnK4A5LB8jhF/Cs0SB7ti3UmplB3aI5oAoDyKAhNei0dkKn0V0StgQNqg/1dfbK9ivxxSu/iL/f+/dY61gLhmfgMXmwpXkL1rvWw2Vw5fdwnhHNaBOgt5dm4HNwqKrm/VFl0cNIGaHTzE/rS7JJ6Cjdggsn80IAgPwrzTpFXzMXz8PDi9eKU75T8kIwSZBoMjRV/Nq2XJJsEk6Dc97fwLA6BIBl83+fioS6onmEooBUGJAW4NQQBAGP2QNBEGquyi/JSn5mUtUUH5uFX1qsdeVoL64kXU1Kh8zQAs8EVq0VIErrEZ5iU+KCouQbzcRnEZc8zexzgiwazKchmjVYEW7JZBIAQtEJ0CTdqDaTUBv/5ypxHwgoD0N9Rg/QshZN9m6skh7wT/pOIpLO9higNbTsi1ZPwQBxJi6aXVu65lUrsTwrp4x2EXoYMmLhStozc9BmapMf4LosXbAXcSJLuFbJk8rJ+PSihuS1Wm3GCzxAILefmV8Re3KJoSXHK4YBrJNWtQUIOO0/nXPXaq4206tCAELOnqzKy6HYBFgp4r4YolnGyybX7xPIrjRzs1xVhQBksBLihDlMEkAqDJPWtGAbUilIsAlAEFeVdQSNPw4/hYi0ivu6ztctKEgCwBqn0qJ5ThUGoMald6HD3IFAMlCx8B2WZxFLx9Bl6xJbkoafz/Izu6bjmkWPzwoDoOeLZoD48NBp6UQkHanL6uoG1UvG9B8Q24Xfu+G9MNEmJJgE2i3t2NK8BWuda3MmRi9yUiUIwOYVk77LgF01Tw+qKjdpDQ0zbZ7nIRZn4rBoLTk9LYFs0ayNZQGCzD9B2eiUfc02JsQFrASbyGp9teqsIEDUzGdeEAQIgpBTZByJKJVdnYQOMJbXXyu70ix3gmYGu84Ol8GFULq25qApLgUTbZKfs0LRCXDSZ8tZicXaRejy7JS3hxPTOffJfPZKGcyQ5JKw6WyyyBuMKouh9ipqZ61WGqJZgxXhNivtZ/7oxZyrVpciuYz/5zIoTci0vID2pnXiP/Zdj10J8f3jBR5Hpo7MO85Em2SRpx5W4HmBRygVgtfizSlYjYRH5L+pNRn/DEIDNPfP23clEAQBr8WL9a71aLO0LX3AcnD2yKIZBx6TqlXeXNRitVmaSyupRXOYCCuTM88CrVolxdkH6O3ZCZo5SuIzuE3uqqw2U1eavaqjs8TXmcQMZuziajIdnQaZLqytJcEm4NTNXzHPkNWeybFV154JAFZCrBQNkySQDMFAGUCRVFn8sXiBRzAZRIe1Q0ztmjiKhyjl93Xrmrcueny2r5k2p2hGEAQ6rB1w6p0Va1/0JXxiC6XRDQgC0kPP4wWDuGpt19mxpXnLoserK83OzUnQVNNqbkWToamuFokaVD/jsXFEGHHRssvSBZZn0WXpwtaWreh39sOmsy3f9yfuBzIPwmVqzQQAp1lZsAqkstu67Tr7vIXuFJdCs6F5wZ/vYkQRzdpZVgw0oOYvDC+I5GumDulRV/WZtaKvU62kaCa5JPSUHuYcFTqjIVWlmaWzbEJp1mtKLCWakQQJj8kDjudqyksyzaWzAhh80Ql526USjKsBW/N6OCQv8GEu91xNq9HCqXeW1NeM47msZ9NgYkbetleZ0FiNNESzBiuiRdWfPR2bhE1ru+RFM4ZjEEqF5hn/Z+3DMxiOiROPHoaBJlM11XcddicVwWSur1kGl8GFXlt9BAMEk0HYdfYFfcQyrZkA0B+RJnxNa5Y3SVuCTEtAVspVMXD1wcsok4+FwgAy1GK1WVJKKNRr5pvBT6hM4z2qCpqyQZJA+44lEzQzVGu1WUY04zVaPBu5MO/7Jy1K+w0dyD0hzhdBEOaZGavJiGYGnoeVF6qz0kxacU6SJNLxWRjp3G1IpcCX8KHJ0IQOsyhkTp/6jSwmtdFW7Gzdudjh6HeoEzRzV5oB4sS6x9YDkiDLLvJG01FoNVp0WjvFyuDZc3iJDyMmtXdc1X5VbhN0FVmVZloamD2Tcz+apNFl7arIz9ng0oQX+Kwgpp2tO7G1ZStWOVYtuAiaFyHV/b9MIQAAYFctBgbmVBFlWtoydh9pTvQ6WuweMK/SbLkth7KvWe4wAJqk0aRvqpnPe5yJw6q1yq1uakZC4v3azPNwOMubnAmIHQBNknA0Qknz2wVEMwBw6p2w6+xVNwdaDF7gsyrjfVJaKQC4TO5ch1QOSosuyQt8mhAQX2CR066zg+f5ktjwcDwHDaHJWugOJpRFKbuhetJGq5WGaNZgRdgdvdDy4od6KumDVWcFy9XOCkWx4QUes4lZeC3eecb/akbDo2CltppehgFapEqznquwM82CkC6Uc33N1LSZ2+C1eOFL+GqmjH0uaS4truBau6DVaHPuk5WcmZIExdbC/czKgrMPHaxSZaI20F0Ij7m2kjQzUd+5VqUnVavanqbitNMuG+9OrE4zoJYIA8jQaq4ubzMiFYU2KP7dxJzdGM4hvE7alUqC5OQxhNPhFU220lwatIZesH1QEATZ08zNciCA6qw0UxnZhqOTYqpZjjakYhNNR6EhNFm+iI8N/172+bq59+YlfY9WO5QHq4UqzTI49A50WboQToXLdg/geA6RVASdlk7YdJKn6fBz2G/IvzUTEFtMM8cP0DQQ9wGx3FVzDr0DXosXoVSoYu2oDeoflmcxm5jFVGwqK6jp+s7rF22pzpuwIjbBNj89t1Q4VAuSgTnVK0bKCK1GizQvLizFmbhsxr8QmXlMC8tCJ2D5iZCSr5m6AlwtmgGATUpBroXPe5pLw2mYn7bOcAzGpRa8DoYF0VR+0QxQqs18lEYMXVlENNOQGrSZ25DiUjXz3ms12iwByKead7os5ROn86WLUoT3kZnjOfexaC3QUaVZ6EtxKeip7LT7YCoob9uMLUV/zXqjIZo1WBGEzQu3JJJNSabABIiauNiWgsWM/9VkhwAwQMt68QudBfb23ehPi0LLGf8ZBJPBnOfIBAO4jW7MxmdrLhhAEAT4E354zB55JSwXWcmZmUlWkfzMSo7NCy+niEmLJWhmsGqtcBvdC/7eqw1BEBZ8oBhXtVe0OnrLNaRsvLugBbBa+kxdCF9YVBAz0aaqqDbjBR7hVBixsRdBSAErTNtWnA+KXohqn8TzqktNRyoBUiAxGZ1EMBVc1rU4ySZhpIwL+pmF02HZZ6OVY8U2aWv52ozyxar6ewxLlXE2vU1+MCwFLM8imo6iy6r4IgrBUTwkKH9Ht65/55LnsWgt8oLLWS0NPjwGJBZOymyztKHJ2FS2Nk1f0ocmYxM8Zo/8b8KF53BA8jOjCA2uaLtiyfMQBIE+m/iwPU1Rov+cL3eLJgB4zV449c5Gm2aDopNkk5iOTcOf8MOmtWFT0ybZi4omaWwo1oJPBZIzAcBhVHmacdmLcZmH58yCQpJNosnQtKC4H2fi8mewfbkhABmMTsC9ETZeQCejzHXVtgPl8HUqBizPgiKpnCLjxehF8NK9u5Nhyh4CkEGdYjxKU4uKZoDYyWLT2RBOV3+1WYpLzbMH8anG7TJ5ch1WUbqNSvXb0MTLOfcxUAZYtJaSLN4muSRMtCmrUCGoes/s5gr4D9cYDdGswcqwedEiRU5HBPGGVyp1vNoJJoMwUAb02ntzGv+ryRjbA0AfKwBOlaCgatEUIOClqZcWPE8mGMCsNdfcw0QkHYGJNqHT0rmoN0imPdNC0GiVvACKkZxZFkgNOlQG+KOR/FrnPGYPaA1d9dVmmQljpsUjC57DJCFOqq0CUZyV+pXQvgMA5FVtXuCzqhdzkak2K6WnxEJkPLGmYlMgCRKrkspDwysWl5xQ/ObeN8v/fjatiCq28CS2urdifdN6aEktpmJT8Cf9eVUiJdkkHDpHfn5mLCe2GGkWb8OrBGpD5nBcbNXILOiUanHBl/Ch2diMNpXP58lX/xsDWnFiul3Xgg5rftUlmWqzOEniIqUBphaujqRJGt3Wbmg12pJ7XMaZODSEBl3WLqWVXRBw7uILGJf8cna17lq0tUtNPmEAGTLtoBBQ9dfFBtWPIAiIMTFMxiYRY2JoNbViS8sWbG7eDA2pka0UNrg2LDmfyxt1e6a1fBUwDr3iURSYs3BAEiSsOiuSXBIsz4pfL5CaCQDj0XF5u12a+y+70gxQtWiK40lxqax5sU6jg0PnqMg9eDkk2AQMlCHn/CYrBKACyZkZ1GEAwzS9pGhGkzTaze1IpBNVvxif4lKwa+1ZRQo+1WKtqwpbDbtsyvPe8AIeuwRBoMnQVJJn6TSbhl1nz/q3EKt8zuxV5gNXjTREswYrw9oON6c8jIVSIZhoU8nbYKqNOBMHy7Pos/ctOuHIMKhKzuw1ewGNyktr1fXYnVAeCg5PHl70XLUYDMDyLOJMHJ3WzgWrWgBRiJyOi+XtawQKsrRWK5VmAFrtfdBIE4+x0FBex9RKtVmSTUKv0ecMAeAi45iSYr89ZPH855aN0Qk4e7N8zea2gszFRJvgMXsQSUUW3a+YsDwLf8KP6dg0dBodNrg2YFvLNjiCyoPWn6BMBq/rvE6Okz8XGQWXWTWcPQedRoc2cxu2Nm/FxqaNMFEmzCZmMZuYXdTglxd4WHQLe/ZkWjOB6k3OBACLKv0pLFVgzW1DKibhdBg6jQ49tp4sL68Hh38nb9/ad0ve51P7mi3VogmIXoiZlMlSGTirA1vUD+HwD2K/oEy4r+nYl/c51WEA57U0MJPb1yyDS+9Cm7kNgWSg6h/mGlQnmQreydgkGI5Bt6UbW1u2Yq1zLZx6MQBF7We2zb2teC8eUrdnlq/SzKK1QCN9XAIED3DZKd5WrRUczyHOxmGkjYuK3uMxlWjGrLDSDJgnmgHZYQCAKPaVytepWMSZOJx6Z04PR7WHbSfDrkxcLAKZeQIAjFJLV5oBothk0VrkMIxqheGY7DmLIMDHK8+fVSmaNSvPL8PhhRPPzbQZNEkX9Z4uCAIECPOeu9QVqLkC2Rpk0xDNGqwMrRFuKILPdHwaNt2lFQaQMf7vtnajJc9e8AG/uLpACQI6mtZnf7N1C3aQJlloOTyROwxAjcvgQo+1B9F0dF4SUjXiS/jgNknJa4ugrgjqj0uCoMEJWKqv5HohKNcqeKQ2hrHoxbwngB6zB1qNtqqrKlJcCmadOWeAwuzMKbBSBaGHLsA8uRh4d2FdnmEAGVqN5ak2YzgGs4lZ+BI+mGkzNjRtwJaWLfLvH5MZzwsCh8LiSjxFUNjh3iGLK0kuiRFXj7ib7zwgtczTGhqtplZsbt6MjU0bYdfaRWEuPj3vOrGUnxmQIzmzSkUzq0rUCUteHXpKD71GX/QFHYZjEEvH0GPryTIJT8d9+C0rVgDqBQE3bn5/3ufMTtCkgenFRTNA9Lj0mDzwxUvTphlIBuDQOeYHtgw/L7dmAsC+ZYhm2ZVm2gUTNDMQBIEOSwesWuu8FMAGDRZDvShBEiT6Hf3Y1rINfQ5xoVNd7X5kWkkt396yvXiDULdnlrHSjCRI2AhxAStIaua1exsoAzSEBtF0FM3G5kVDPNQWE+0sC5DUyu4DXZcDILBhEV8zi9YCPaWv2ucJQRDAC7zi7TgHtSDSSZkB/dIL6qUgu9KMAlIhIBFc9BidRgeP2YNYOla1oiUv8CBBZi/aJkPwaRRJw6WvPtGs06OEAQ0nZxbcz0ybYaSNRW3RZHhmftq9ICCoWkycW4XWYD4N0azBinGrKqumYhMw0aaqvcgWG7Xxv9eS38ohy7MYktKHuhkG9NyqKZKEuedauTJmIDSIWVUazEK0W9rRYe2o+mCAGBODVqNFh6VjUd83YE5yZiwobrg3lD22uyBcveiQRLMol8wy3FwMq9YKt6m6q80YjoFNm3vCODGjCFOthtwpsmWjfSfWMGk5DOA1f+6SeDVG2giP2VMyb7M0l8ZMfAbBZBAOnQObmjZhc/NmtJpaFRGSY4Fp8X2cdPVgSGr32NS8CSbahDWqRNKzDkmA5hkgmL16SZEUWowt2NS8CZuaN6HZ0IxwKoyp2JQsyibYBIyUMWfVYIZ57ZlVGAIAAFZVe0E4JabFkQQJm86GJFc8EVoQBPiSPnhMnnkLAPuPfA9hafJ+g64N5kUq+ObS71QnaC5daQaIBs5d1i4YaANCqeKm7ybZJHiBR5dtfmDL7ODTOK4T/22VqX3RAJy5zKs0W6Q9M4Oe0qPb1g2WY2tigahBZUlzaUzHRb+yzKLE9pbt6LB2LFjlrq402+roB8IT86qzVkQmCMDUDNDzkxZLiUOq9g5oSCCebeVhoAzQU3qQWLw1E8hOzmxnpYWTlbToS75m61NpOfjq5Gz2dc5IG2HVWqu2RTNjqL5QaMJIUEm67rRW7l6ZVWkmtdHnU23WZGiCkTJW9fuvo3TZn+O4TxbNSFSnAKR3rZEX0i9w8QWflzWkBk2GJiSY4vn6JbnkvBAApGMIqR6pFhKBGyhUnylJg5qhxdAMcGIZ8lRgEIbO60CRFFJcKmcFSr0gCAJ8CV9exv9qLkYvIi2IolZvWhUCoGbV9dh98Qkc14sTnRcnX8Qbe9646HkzwQAJNoHZ+CyajE2LeoVVAl7gEUlFsMq+Kq8Lc1ZypmTkXjN+ZhmcvfAyLCDdo8YiY9ntTYvQamrFVGwKcSaeM868kgiCABBYUGSZDCkTRo/K56kieHdCJwB9aQZndFoMBgfF1tIl3tNWYysmohOIpqOLthEvhzSXRigVgobQoNnQjFZTKxz6BXzEfOcBSdQ65GoHGFEM2+PZAyC7je+M3ojXZ76YPZuzZYYkSDQZmuDSuxBMBTEZm8RMQhTuePBwW92Lpjuq2zNbq7g902pURNpwWmkvsWgt4Hm+aEE14VQYJsqU8/r/0PDv5e1bV922rPN6zV7ZBPuslhY9zXgeIBdf3zRrzei2duM1/2vQU/qi3H8FQUAgGUCntXP+qr0g4NmpFyGYxXFd2/36HGdYGJfBBYfOgUAqIHqaTQ0DTHJJQaHZ0AyP2YOLkYtoNjZX3X2uQeVJsklEUhFoSA1aDC1wm9xw6BxLztPiTByvST5DvaZ22L+2HchUeuhtouBlbAJMmf8W+NroyhaTOAaITIjbZWzNzGDXGAA+jgRJIhGZgKFlrfw9rUYr39+WLZoV4tPVfSWMU8fRyzAY0GpxLnBONnbP4DK4MBWfqspwsTgTlwMLcjEqzYGMPA+XszLJmYBoN9FkaMJsYhbDlHRPCI4Ans2LHmekjWg1t+JC8ELR5j/FJMEmYKEt2Z6DcR98ki2Ig9Tm/VxWVmg9ugQNJgBECAHBVHDBZwKr1goQkP0GCyXJJtFmbss+VyKAoEZ5nxqi2dI0RLMGK8Zt9sgGp1OhIRgoA8xac8XT58qBkTbmZfyvJisEgGGBlnXzd+q9Frt/n8IPpS8PTx5eUjQDxHasVfZVSHPpqg0GcOqdWWbZi5FJziRBYBWTEc1qx88MAODsg5dVPAnGomPY1Lwpr0Mz1Wbj0fGqXO3LlI/nYiKiTK499golZ2ZwbwQ0OqxPp3FGpwUncDgTOIMtzVsWPcxIG9FmbsNIeKRoKV4USaHN3IZWYytsOtviD/xyayZwUKsBpI/AXs9eANkVSWehqoSYPQv0L3y9IAgCDr0Ddp0dbek2TMWn4E/4YdUt/sA0FVN7mrHVW2mmCt8Iq0yBDZQBRtpYtKRJiqTQbeue9xmYiU7iecYHEARaWQ67t7x3WefVkBqstq/GsdljGKVpxNg4TMGh7MCYBXCb3AilQ1lVgYVi19nRYemY/7caHMZ+IglA/Pmv6bpu2efus/fhpamXMEtpECIAm39gyWs8QRDotHQinA5X7X2uQWWhSRpeqxduo3te++VinJg9AVYQ79fbWCiCGQAkQ+J/vvO5D56LwaGIaDoLkBF+ytiamcGpNQOMeN0LRcYwV+Zx6BzQa/TzKknnkhHNNIIg+lq6Vi26/6J0Xwkc+g42pNIY0GrBCizO+s9mzY+sWivMtLls6cDLgQABl96V82+L4RmMJ8TWu06GBdFUOdEMADotnaINBKVBjCBgUodSLEKLoQUzsZmqfP+B+QKPEJ2BXxKAXJoKhU/lQRdtxUGIC3rD08fh6Lw6534WrQUWrQWBZHHsCDSEZn53SMKPoLQgZyY0dV3sUiwaolmDFdNi6wZColn9VGwcFElhnXOdPPGoZyiCWvYKjFo06+VJwNY5fyerB1stvaCECFiCwOHxP+V9fhNtwsamjVnx3dWETqMDrVn6oszyrPxedZF66DMlzK01Vmlm86JD1S2rNofNh15bL1pN1RkBPc9PQsVEUmkp9rhyCMPlhNICni1YFzqFB6Quudd8ry0pmgHiZLOYZrIaQpN/kujkMQCAAOBQWkmB3NwkrhC3mdpgoUWz3jNplXgws3SbGyCKDzadDTadDXHz0tWMk3FRiDHxPCyCUL2VZma1aKaInVatFRubNsoJpIVCgMjZmvPY0XvASQ9SN+s8ILXLX6Vf7RBFMwA4p6WxdepkXqIZSZBYZV+V98JEPmhJbc6/jdTgAfzJIP67k9RjU1N+iwFqMqIZAJynaeyYPZvXwoiRNmJT06aqvc81qCwrmZsBwCvTr8jb24JiCBEIjZjCHJsB4j4g3wXhREDyD5tzPbbll6JbTOxaGyCtu/mj45g7o/CYPcjnsnhRWgxrZTnxwTGPa9KCZHzNUmk8LN2XT/hOZIlmZq0ZG5s2gkf1VZoBC1faT0QnwEpj7mArFwKQodPaKXv1jdAU1uXRnglI73/zRnBCdVq+GDTZ7384Og5Guve6tBX20l2ELmMrEBdFs6GpI9i6gGim1WixwbWhqM/TRmrOdTERQFBqabVVMrSrhmiIZg1WTJNzNchhATxBYDqTVFaFpbzVwoBf8enqs3Yu2HJjXHU9Nl+4F0f0eoxEL2IyNpm3eGKgDDDMW0usLYbDw3LS3ZpMShNBAs1rFzmqCiE18BrdAMSfZSy8PNGMIqm8ElmrjYl0WL6zeJpztCCXG+8urJ85Kn+ZTxgAIFb9VOz9lyrNBmkKM4z4oLbDvUMWnQmCwGrHahyZPoLJpA8hkoSN5/PyhprLUtdsQRDk6qVWlgU0OsC8eJBHpbAaVZ5mKoNbgiCyzPpLgSAIBbVmZsiqItRqRdFs3c15HVuua8ahwd8iId2/rm7ZvqL2kawwAC2NHUuEAaiph/tcg+pCLZptn5W8Idt3AH/5hLITmwJis0B8VhTSYj5JUMv19SwwN9U8z89xMbHrnfJ2MDY97/v5VJeEUiE5TVGuni+kPdPoBNwbsCGo3K/m+poBWDTNs1oZiSiiVBfDFFaRVwQ6Lcri/DBN5y2aAch/ka8K8KmCKlx52qBUgi5bLxAX73XDvsVTo0v9PM3HfQhL93EHVTu/60rSEM0arBjK1oEmjsM0RWGKqf+WzEIZ9J8GIJa3dzUtsqLedx12n/opjujFlfzDk4dxS98t5RhiVXBGJS72R6QqGtcqgK69hySvrRdgxd/7WGiwwqMpD5NCCoAGlAA0LZGSWha8O9B/6NvQCAI4gshbNKsYgiBXmh20KSJQpjUzwxrHGnkF+ay9Dbv8Y6JoJghFDcwIpoJyipnoZ9axpMdWpVALRmGhvJVIp/yncJ4V74Nbkyl0bXz7is4zL0EzjzCAcnMgcBKQFqb3rb59RefICgOg8wsDaNCgFHA8h6PSwkoTZVaEoe4rs3ekdICtXfwvH5ikIqhZPICl/JXjTpXPYyCPYKlczPMzAwqvoOq+Ev2HT4ISBLAEMS9Bs1YZCSuiVCfDAs6eCo5GrDTLMEpR88KC6gWfykLCVekAqkXobt4ITIiLa8OR/AXMUhCJToGX5oq2BUItGmRTnTPfBrWBrV30NgDg41ONdolF4HgOg9LEo4NhoV3M1L7zcuxmlJL0wxOHSz28qiIrOTMptVjVmp+ZhMW1GnZO/Iwstz2zJklFMCHdVdzQVIcZa/tO6AUBvZI33kBwoGqj7AGIptFxsXL3oEVZMZ0rmmUlLdqkSWIyKD6gFRF1CICbq94QAEBMWNRKrUZhlLet5KETP5O3b6VbAPPKJu5q0SzfBM1yIgRGsF8jPjhrBeCyjtztJUsxt9KsIZo1qBTng+dl79BtpBHyksNc0Wy50HrR/L9tW0UEMwCwq7oUVuqPNB4dl7fbGRbQaAsPNei+EnpBwCop6GkwNIi42keuRhkJK6JUh85Z8cXe7EozKq/0zFrEpxKEXVVqawIAba3b5TT3oeTKROxiEVTN7eyNEIC8aIhmDVaOpU18iIJoiTAbr+wFoJoZj40jJfWm9zFM7hCADLQem907oeXFC+vhi88vGE1cj+ROzqxN0QyuXnnVeioVQJpLL3FAbRP3nUdIMmNtneufUCnsnYCpBetS4nvPCizOBfJvBSs7UmsmC+AlQhT3nHonVjuyDYWzEzRVE/Miiw9qY/nWKg4ByGCVpjVhAgBXHn/NNJfG4yNPAgB0PI/X9715xeeyaC1oM4m+ZOe0NHj/IJCunjCQ107fj2lKbFLYbfCsuIXEoXfAKbWOndfSwOw5MSm0QYMyk6nYBYBtYcn4nKSAjj0VGlHxcFiUqrgAE1lkz4WZV2nm7AUKXRDrugIAsEG6L/MCj9NSN0YtMxJUOgo6rZW/V2ZVmtGUGGiRCFZuQCXClwrK2y5L+VNq84Vy9cEr2c6McPGKpsMG48oCq72KW1qriYZo1mDlUFq0EIp5oLoioUE2g6obaS/DAC2Lez3pVr8O21LiA/NEchZj0bFF968nzvrFh34rQcuiLNzLN5quCpzKDVJA9optPTIxo1TFeHRVchMmCMC7E+vTimBZ1S2aUmvmSZ0WUUlo3926e55v1CrHKvnfzkIlxpZUNKvuSjMAsBKioBMmyfyNuwvkmbFnEOLF6/X18QQs624r6HxrnGK1WYwkcZEigenqeZg8MPKUvL3Pu7IqswyZajO/RoMAlwTCF5c4okGD4vPKlMrPzCf9DbbvAHS137LksCrhA8EVJnGPqfyi2otlbm90Au6N2JBWqr5PzJ4o/LwVZjR8AQBg4Hk0O9cssXfpMdEmNBlEm4dhSvKvyzNBs5bwqQRhl7X8gRt5ozWhWxAF5xSRnUxeboKqBGqboWmRPRtkaIhmDQrCrfKQmYo0JrwLMRBSkjP7oF3aSLvveuxKJuUvX5x8sVRDqyoCyQCmE6JZbT9opU2iVivNnL1igpJEvbdoTvgVwcZjrKISee9OeUUbqHbRTKw0O6RXUgvntmYCohl6pvXifMoP+a8szwTNvIejEs3cLFf9lWakFgCQIEkwZap+fujsr+XtW2EGmvsX2Xtpsn3NtMBU9TxM7o8q7UfXbHhXQefK8jVrtGg2qBCZSjMDSWNNZnGl0NbMKsGh9jTjk4vsuTDzKs1cBSRnqum+EhtV9+Va9zVjeRZjCbF6p4NhQTStXuKI8pCZJ/goDWIEUZctmn5Wae11mVoqOJKl6dIqrZBDvtcqNo5gKiRv26v8PasWGqJZg4JoMbjk7enA+QqOpLoZUF0Y+yxdSxt1N/djD6Gsch66+EKphlZVZLVmxqXkKZ2tcP+MSmHzwquyVqr3isEJlQluaxW0Jsi078SaNANSanOuBdHsoFFpe9vjyd0mlPE1Swus6FcCFF14UFcQt3Js9VeaaRSxMRIpfWXnbGIWz46L1+cWlsWenjcWHMSgbr2tpjCAqaljOCX9ma0TaLTauws6X5avGS21aDZoUEYmohPyNW4zaYacJVknopldb5e3Ayv0Hc6IZjqeRxPHF6fSDAC6r8SqNCNbkdS6aDYRmwArtdt1smxhCaNFRN2iOVKnvmY+VVq2S+9aZM/K06VaUB6ZOlqxcQRV6b4Oc1vFxlFLNESzBgXhNit+CVOhocoNpMoZ9IntNYQgiOkpS0EQ2NC1DwbJ4+XFiT9dEr5mWcmZUcm01r2hqGmAZYXUoEOV5DMarr/JipoJlcDicVbHKisAoG0bjALQI4UBnAueq05/uVQE8A8iQRA4qhMrprxmL7wLeHRk+ZqZ7OJGkYWHmmvPpJXo9HAZWh8eG3wMHMRr8y3RGDRrbyr4nPMqzaarQ+Q9cPLn8vY11sI/3/Mrzc4ssneDBsUny88sJrVzk3Rd+JkBYkWyQZo6Bglh2T6PgiDIthJtLCdW/xdLDOq6AjSAfqm6bzg8jHC6PC31pWA0rHQSdDCsmPpeBajDAEaoOhTN2BR8hPhHTgiiX2Y1021XKjWHfJWzXgixSru2zbiy4KJLjYZo1qAg3Kpqkqk692taKYIgYEBaqfOyLPSLJWeqoFe9DtuTot/DTDqMC5JXQj2jTs6U2yRa83u/qhWv6jMyFhhYZM/aZzKleCR4mhf37SsreivQsg7rU6JoxvIszgWrsKpFqih6Ra8DI+nEC1WZAXOSFq2SJ0VoBEgXL4UsI5pZOB5G2iR60VQxlizRbLqkryUIAh46/6D89S0pEui6vODzdlg6YKDEcIczWlpsz6yCRZMDkwfl7X29hYuDjUqzBpXmlWnFz2xbYELcaN8BaE0LHFF72CWfR7+GFBOWl4Ev6UOSE9s62zNWE8USg4xOoGUDNqj8Rl+rYLtaoYxEFDGqi+XEEKIqILvSjK4/0Szuh08KoLITGlAkVeEBLU5Xk/JMM1xBy5Ygp7Rr23X2io2jlmiIZg0KosWlrDZPJ30VHEn1MhmbREIQH9b70kuHAMj07sPupGKS+uJE/fuaZVINSRDokwz0a9bPTKLFtVaOmB6LDC+xd20zofKVaLVXR2uCTPuO6g8DyLRmqv3M2ub7mWXItGcCwBmtEsoCX3HEB17g5dYld6Y1s8qrPq0qn81worT3pNP+0zgXFG0JNidT6Om9DtDQSxy1NBpSIwtKozSNeDIIRCYKPm8hJNgEDqXF97OZ5bBu3VsKPqdNZ5NNqgcanmYNKkBGNCNBYHNmvlUnrZkZ7KR4bwiRJPjYzBJ7ZzMvBIA2AhZP8QbXfWWW32hJwgD8F4CBp0u+8DCi6iTo1DcV5V5QDLIqzeqwPVOIzcCnEeUMF6lbYu/K0+zeIncRDacq99wcFJR27YZolh8N0axBQejtPbBLCYdT6dASe1+aqEMAxOTMdfkdaHRij6Vb/vLQ6IEij6y6YHgG56UH0G7SAH1mgpNnZV61olFFTI8lZuq3zZbnMCHZ0VsEwKytsuQx7y6sS1X5iraUnHnQoIhmu1t3L7i72+iWRaKzUJk8F6liJ5AMgJF8cFprIAQAAKyq1NawKh2qFDw08JC8fWs0Bqx5Y9HOnd2iWXlfs4MDv0NK0kuvIcwgdZainDfTohnQaOCLzwCJYFHO26DBUoTTYXmhrl9jgjlzb64z0cxJif6YHEEgssyEWnXit5dhAWdvcRdOSh0GcOZ3wLd2Az+7HTj0veKeew4jKk/eDltPSV9rOdS7p1kschEpUhLN6OqvECVcveiWngkucgkw3Mq8BguCSSCo+hjbdLaF920g0xDNGhSGrR0trCiaTfMp8JIJZgOFgaAqOZNcXnvT2p7XwcKJ7+lLUy/V9fs7FBqSH9D7M1VmIPIXGasVZ6/c1pDgGfjqtCKTi0xgihJL5NsI/RJ7VwDvTqxLp0FUcxjA5HEESRKnteIK9VrnWjj1C18vCIKQq81muAT80sSxWBU7k3G1n1n1hwAAgFV1fQ2ngiV7HYZj8NjgYwAALS/gDfEksPqGop2/2hI09597UN7e17ytaOddbVeq1cVqs0aLZoPy8Or0qxAkP8JtcalKmqSBjoUXKmoRO60I3IHw8trB5iVnOouUnJmh6wr0MIxceXNytoii2enHgHvfBWT8Sw/fU9JqsxHJ11nH82hxFZagXExMtEk2xx+haLFFN1k/RQ6+kCICOukaEH/0NnQJ4lyNIyoUEJYIIChV52lByHYQDRanIZo1KAyzG25J1GEgIJAMVHhA1cfgjDIJ6FtmoqBm1euwIylWkAS4hLwqWo9kJWdGpAoRZ2/te4u4+uBlFfNddbtDPeGbOQVWWoH2aItThVJUmtfCSJvkFb6zgbOVWeFbCI4Fpk7hsF4HQXof97QubUY9L2kRAGaKY6ieFQLA1UilmdTuBwDhdKRkr/PM2DMISqLc9fE4rB2XAYbiGRCrW28rXWnGCzwO+EXRTs/z2LP61qKdOysMgG60aDYoH1l+ZiHJ/7DO/MwAwKGqIgmqrun5ME80K3YipMkFTcsGrJOsE8Zj4/AXo0L4tUeA+/4CUCeG+geAiy8Xfu4ccDyHseQsAKCDZUE2VUcIQIYu6dljltIgRhBAsHJeWsXGF1WsC1xVHgKQoUsl7g37K3DPSwQQkhZZ7QQNosptN6qFhmjWoDBIDVpIpapkOl5a4+VaZMCvtIH1NG9e3sHendjNKhezFycOF2tYVUdWCEBSWvWtcT8zAIC1HR2csro5WkHjz1IyMav8nbfqmxbZs0KQGqBtmzw5Z3gmq3W64vjOAVwqqzVzMT+zDFlhADpptbBI1Tpq0cxdRcbGi2E1ueXtMBNdZM/CULdm3hKNAWveUNTzr3YoFViiaFa5ysiTsyfhE8TPzd5kCvqeq4p27qwwgIavWYMyohbNtmb8zIr4t10tOAwueTuwzDm6ugpGFM1KIAbN8TUruNrs1EPAr94L8JngAlXS77F7Czv3AkzFp8AIYtdNJ8MCzurydO2wdMjbo3XWoulTpba7jC0VHEn+dJna5O3hqaNlf30h7keQlMITNFXYGVKlNESzBgXjVlWVTIXr50JcDARBwGBMiutmWBhbNy3vBBoau5sUoe3Q0BPFHF5VcVa12tKfllYHa9zPDABAauBViUhj9SqaBQflbY+5vYIjWQTvTqxPVWkYgBQCcEgSzSiSwvaW7UsellWRZJFWWX3nAZ4reEhTqslozbRnmlvl7TCbKMlr+BI+PDv2LACghWVxWSIJ9BfPzwwQAw3apIn1Wa0W/OwZgE0vcVRp2D/4W3l7n7YZKJKfGQD02pV2r/ON9swGZYLhGByfFa+57aRerKQF6s7PDADshmZ5O5CYXdaxFyNipZmZ52HlhdKIQXNFs0J8zU4+APzqfYpgtuXPgb98AqAkYeDE/UAJKsyHw0rIUydTInGxALpUXS7DVJ2JZqq/aZfq/l/NdKkqrIcqUGmWiE0jTYoFGXbJ87DB0jREswYF41YJAtP+xoRXzUxiBhFenAyIIQB5JmeqWL3qJjls4WX/CXBFeBiuRjLtmTZSi5bMBLYeKs0AeFXpRWN1+hmZULVxeOxF9j0pFu07qzdBc/IYximNGAkPYEvzFhjppSczffY+aAhxxfBMpj2TSxVlUpzVnlkrQQCqFdywKlK9mDx+4XGwgvhQ9uZoDJqmNcVvW4JSRRgjSYyTQsWqsPYPK4s1V7cVV1Swaq1okaoDztM0hNnTRT1/gwa5eM3/GlKcWF0mV5mRNOCtLz8zAHCYlbTLQCp/LyuO5+R7QDvDggBKcp1D1xXFCQM4cT/w6w8AUsUXtr4TuPVbYtt8ZlEj7gPOP1XggOej7iDo4AFYq2vhsMOqrjSj60s0U3mXuizeyg1kGXQ1KwUUw9HyL6SHIkrAh60a7VSqlIZo1qBg3Kob8mToQgVHUn1khQAwDNC8fHNQcvUN2CVN6iI8g9OB+nuo8Cf9mEmIUej9Ag25IbW1DirNAHQ4lRa6sTr9jEwklCh7j2ttBUeyCN6dWQma1SWaHcchvVImv8eztJ8ZAOg0OnRbuwEAA0IK8hp6EQSWqZhSaeamrUWtMCoVVpWvWJgvjWfdQ+fnpGYWucoswxrVdeOMVlsRX7Px6DjOJsQH542pFJr7ihd2kCHTohnWaDAbGqlYRV2DSwd1a+b2sBTO490JaOuv6sKhEnCC6XDex03Fp+TFgXaWBbQWwNS8xFErwORCh3O1HHp1cub48s9x/NfA/X+pCGbb3g3c8k3RlgEANr9d2bcELZojqkqzLkMLQFbX43WXRVVpRlNAcHiRvWsLn8qGwWXrrtxAloGtZQMcUnHAcIlTvnMRVHUR2HX2sr9+rVJdn+oGNUmL6iI1pYqnbgAMBs7L232UFdCZl38SRzd2k8pxh0efKcbQqoozfpWfWSImbmgtgK36PZTywdi0Bk7pBjkWX54Rb60wwSiT8dbmKq0QtLTCbPWiixHFlDP+M3Jia0URBGDiGP6k8jO7zHNZ3odnxBUWAgalSrViiGaTMdFg18ZxMNRAlRkAGCgDKCkhLQx2ib2Xz2n/adl/cVMyhV6GBdaUSDTLStCkK5KgeWDsgLx9TTwJdC7ts7dcssIAKBII1OfCQoPq4cjUEXl7W6bSrA5bMwHAYVXmUX42nvdx80MAeoESGYaT3VfJVeAzSd/y/JFfvRf4zQeBTLr89vcAN389W7hadT1glLzdzjxe9PTIEZ8yh+209xT13MWgU/U3MFJvnmacYsPgslRXhd+COHvQJYVSTQspxJn8P5fFIBBXWlrtNRKeUA00RLMGBeN2Kiab08v0S6h3BmaOydu91pXfSHerHqAPDxW/tLzSZCdnSqu+7vVVt1q3Ylx96MjcINkYkmxp2sYqySQvPnhoBKBZZcZedah8zdJ8GoMqL7aKER6HkPDLfmZGyogNTfkLjzkTNAsUzXiBlx9cWlmuJvzMAIAgCFilqU0YwhJ7L595VWYGJ9BRmpau7N9rZSrNDgw9KW9fa/QCetsie6+M7DAAbSMMoEFJEQQBR2eOAgAsBCV2AQBAd/2FAACA3ahUhwWX0bKuTvouWQhAhu4rsSGVkr88MZvnAsHRXwAPfEgRzHa+H3jzf82fO2poYONbxW02KaZrFpGR8BAAQMsLcDurr9LeRJvg0oui4QhVZ+2ZgrLw6TJUYQhVLoxOdPGKAK32xCsHoVRA3rYZSlA9WqfUyRNpg0picfTBwIs3rKlllH5fCgyoKqh6W5aZnKmip/8WNLGSr1nofHVUxxQRtWjWn/GcqhM/MwCAsxdeVql6Ua/g1gWpKCaku4kbGmgyLRHVSHsVhgFMHsc5moZfI75vu1p3gSbpvA/PCgPQasWNmcKEB1/CB1ZqdWnlaiM5M4MV4vsYIQmATS2xd/4wHIPHBh8DANCCgDfEYsCa1ystQEWmw9IBvZRsdVZLA9Pl/VuNMTEcnn4ZgBgEsaZzX0leJ6vSjG4kaDYoLcPhYfillqitaU58ENJoAe+uio6rVNh0NhDS+kFQyL/6djymdI54S50I2XXF8sMAXvl/wIN3AZnFkV1/CbzpKwsvtm6+U9l+9ZcrH+sceIHHaFIsGPCyLMim1UscURky1WazlAaxVAhI1sHzmiDAB/H50yoQoDX5z5sqTbfWLm8Pq7qSykFQ5W3oMNVGeEI10BDNGhQMYe+AWxJ0pvn6q6BZKYIgYECadLSwLCytW1Z8LqLnKuySVuES4AqP5K4yMu2ZGhDKqm89iWZWL7ycUvWiXsGtB+L+8whJgk+rpso9Ybw7sa7awgAmj8tVZkD+fmYZ1BVJZ4xSK3eBwkN2CABbEyEAGayS4BglSXCJ4vmFPHPxGQSkFdprY3HYeAFY84ainX8uGlKD1Q7xAWyUohCPTgIxX8leby4vjL8ARhJOr4knQJSofa3PpjyMD2jpggXfBg0WI8vPLCJVXLTXp58ZICYxWyWnWD8h5J2snEnOBIB2litNCEAGUxM2qny3Tk4fW2RnAEd+Bjx0N2TBbPeHgJv+c/H20fYdivA39BwQKs48bDo+jbR0nexkmNK+TwXQqQqkGqUpIFQHSe7JEHwaUcpwEVSFB7M8ulShRUNL/b0XmaDKB85u8SyyZwM1FRXNvvSlL2HXrl2wWCxoaWnBbbfdhjNnzmTtk0wmcffdd8PlcsFsNuOtb30rpqamFjhjg4pgdMHNizeuGHhE09ElDrg08Cf9CEkta30MA7SsW/nJdBbsMSipMIcHf1fo8KoGhmcwEBIDE7o1Rugy2pJ708IH1RokiQ6t4hswFq6f0ngAmJxRRFyPrsr9ETxbsI7h5S9f879WwcFITB7DQZVottezPN+oJkMTnHonAOCMVpo4JvwFCSyTKu89N8sB9u4Vn6vcWDXKexmJTBTtvA+ce0Devi0aE6tTVl1ftPPnIuNrJhAEzmlpYLp8Cyb7R/fL2/viSaArf5+95WDWmtFqFFu6B2gawuyZJY5o0GDlqEWzran69jPL4JAEhaCGzNvPS10R38aWuNIMgKfzKtkc/eTsMQjCAu31L/8EePijkAWzPXcBb/y3BQWz2cQsJqIT4vflajNBDA8oAiOq+VxnqdtYC6DLqgoDoOrD1ywevoiEVFnoIvVL7F1ddKlsCUYC5V0oCrGKD5ytmu1UqoyKimYHDhzA3XffjYMHD+KJJ54AwzC48cYbEYvF5H3++q//Go888gh+9atf4cCBAxgfH8db3vKWCo66wTwIAi2kTv5yOtYQNQFgMKR4JfUxHFBgyfbuzmvk7cNj9RMGcCF0ASwvtgysYVQroIWIjFWIVxWFPeqvr4fCCf85edtjrPIbMG2AtWUDOuQwgNPy31+lYCaP4SW9eA116V1ZHk/5QBCELK74wWNWWnlFAeKDOjmzlautSjOLSjQLR4sjmk3FpvDsxWcBAG6WxeWJpPigXeJE0flhAOURzTiew7NSCICB57HL2gcYSieI9znEv/mIhsS0f0AMx2jQoARkRDMKBDZmWgLrXTSTrolRkkQ6kl8Y0VhUrMRychyMglDyCiqi5yrZOiHIxrPaQ2Ve+hHwyCeUr/feDbzhS1mCGcdzODp9FN945Ru445E7cO191+LG+28UW+s3v0059ti9RbnODEcUP6pOgSpNwmgR6LB2yNujdH34mvlCQ/K2i15B0FoF6WxRCgOGo+XtPgnyim1FIwggfyoqmv3ud7/De9/7XmzYsAFbtmzBT37yE4yMjODll0UPjVAohB/+8If4yle+guuuuw47duzAj3/8Y7zwwgs4ePBgznOmUimEw+Gs/xqUHrfWKm9PVoOxdhUwoBISerUOgNItsvfSeNfeBo/ki3U0NoY0l17iiNpAnZzZnwkBsHcBeusCR9QmXocihIwFByo4kuIzoTIx9dhqQFxRhQEkuRQuhCqY1pcM40R8HHFptXSPZw+IFSSUqcWVMxlfswJaNLPbMznA1rHI3tWFVTV5DhdpEefhgYfBS2bTt0ViomtaiVIz1cz7vZYpQfP47HEEJN+TKxJJ6EosKqyyqcIAiDSQ54N9gwbLwZfwYUgybd/ACtALglgxWqIwj2rBrrJNCOZR6Z7m0piJzwAA2hlWFMyNzpKNDwDQdYUiYiJHGMDh7wOP/rXy9eUfA17/RYAgEEwG8djgY/jcs5/Dvvv24d2/fTfuOXZPViX5V17+CtI2L9Ah2R9MnyrK9XQ0qMwfOozukiWMFkqXqv11uE4SNH1hpcXUpa2tZwZ9U7/8THch5V+4srIEBFXhCXadvWyvW+tUladZKCRO0JxO8cL88ssvg2EY3HDDDfI+a9euRWdnJ/70pz/lPMeXvvQl2Gw2+b+OjtqZ6NcyLVIqCwBM+xt+JAAwMH1U3u4rIDkzA+HZil2MeFFNQcCrqsj0WuZcQBEX1ySkKlP3xgqNpnQ0N62HTgrMGM21glrDTKgEFo+jOlsTsvDuyvI1q2iL5tTJglozM6jDAM7ICZrnFth7adTtma06J0DXTuuDVatUf4WlB79C4AUeD5xXt2ZKFgT9pfMzy7DGqYhm58pYafbH0T/K29fEE0DXFSV9vflhAPVVjdugOjiqmpdti0qL6t5dAG2ozIDKhEN1TQyovMoWYjw6DkFqf2wvQ2smAMDUhA36FvnLk1NKGy0O3QM8/mn5S+HyT+C17e/A947dg3c9/i5cc981+Nyzn8Njg48hmApmndZCiz/7dHxabLHffIfyzSIEAoyoFn67bL0Fn69UZIIAAGCEpoBgeRMbS4EvosylXarn0JrA2YsuqeMhIrDz/m5LBptCUEoGIQXAoi1ttXw9UTWiGc/z+OQnP4krrrgCGzeKD8yTk5PQarWw2+1Z+7rdbkxO5l6F/PznP49QKCT/NzpaB0aHNYDbrBgaPnXxGYRS+Xkm1DOD/tPydm/LykMAZEgSe2zKg/Hhsw8Wfs4q4ExAVWmWllY/WutPNCObVolmugAupoJy1Uo9MKmKr/Y010CAQzUlaE4eL45opg4DKEKl2ZSqrbHFWjvJmYCYFpchnCjcOP+lyZcwGhHnEnsTCXhZThT2y5AoatVa4TGJRr1ntVoI06fzNvIuhAOjYmsmIQi4qgyimboleUBLFyT4Lod6ug43WBq1n9m2S8TPDAAcqmqSQB4t62o/s3aWLZu5/YY25f53cvyQuHHwu8BvP4MoQeAJowH/sOFqXB98Dnc8die+efSbeHXm1azPsYk24XVdr8M/X/7PePptT+P7r/++/L0fnPgB0mvfDGTSqY//uuDracbTjBIEtDatLehcpcREm2RhaYSqk/bM+LS87TJWZ1vsgpia0MUpVYnD4TKJmIkgglLit43QgCSqRgqqeqomauLuu+/GiRMn8NxzzxV0Hp1OB52usDa4Bsun294HzIq/u/2BU7jpNzfhI1s/gjv67wBNVjYCeCo2hefHn8fu1t1ZvlKlZiAmTkxcLAe7Z2tRzrm7743Aa98CAByePIy7i3LWypJpz3SQOjRLJrB1lZyZwdkHL8tiUEsjBR6ziVm0GFuWPq4GmGBjgPQxb62FSjNXH9YTSmXBqdnKiWbxiVdwTLpndRpa4DGvLMmo19YLiqTA8izO6iQRbmbl1TqT0kOTk+Ogs9dAy60Kq15pIwoXYfX2N+d/I2+/JSJVw/aXvjUzQ7+jHxOxCURJEuNIo91/AWgq3edsNDIqh7NsTqXhaloLmEq7ij+/0qz0Fev/euhfcd+Z++DUO9Fj65H/67X1osfWA7fRvaJW6QbVS1YIQPISEs0Myuc3qBIaFmKeaFaOSjMALX03oOXgU5imKJyKDOH8/n/Bc0e+h2dbW3BErwNLEEB8aN5xfbY+XO29Gld5r8LW5q2gNcpzR7OxGdd4r8GBsQOYjE3ioYln8bbVNwJnHgOik8CFA0DfdSsaLy/wGE2K1cxehoWmac0SR1SWTmsnfEkfZikN4r4R1HperC+pLIq5VMUbNQFBoFvrACCa8g8FB7C1ZWvpXzcRQEjyvbWR2tK/Xh1RFaLZRz/6UTz66KN45pln4PUqokZrayvS6TSCwWBWtdnU1BRaW1srMNIGC9HTvBmfPhTAtxw2JEgS4XQY//fw/8W9Z+7FZ3Z+Bld5ryr7mMYiY/jRiR/hwfMPguEZtJvb8djtj0EjKeylJJgMwscnAWSSM9cX5byt625D57H/wghN41hyBgk2AQNVu20Fs4lZ+aa3RqAhP57UYXsmrO3o4BTPgtHIaH2IZjyPcbAAKFgEMQmv6iEI2Np3oj15ChdpCqf9p8DxXFmuDXN5eeYoWK34l7+3feXVPLSGRq+tF2cDZzFEaZAGoA2OAExi2a1HHM9hJilWD7pZrqZCAADAqnpADKcK8zUNpUJ4YugJAICNF3BdPA6AALa9q6DzLofVjtXYP7YfgFhF2D51oqSiWabKDAD2xRPAmtJWmQGAkTaizejGeHwKg1oawswZlFKuGgoN4RenfwEAmEnMYCYxg8OTh7PHRBmzRLTM/zusHRVfDGywfBJsQq4q7uYAJ88DGh3grW8/MwCwq+YagaR/yf2zRDOGK1ulGbquwPoDaUxTFKICi9uH7wNc843K9Ro99nj24Kr2q3Cl90q0m9sXPe2Ht3wYB6Rgkx8c+wFu2/hB0GceE7957L4Vi2Yz8RkkBXGxt7OM4uJK6bR0ysLxCBvF2mS4pv2DfWmlq8llrT07pi5zG5AWF6iGZ04Aa95a8tdkYjOISh66dk3t2G5UAxUVzQRBwMc+9jE88MAD2L9/P3p6sn2fduzYAZqm8dRTT+GtbxX/kM6cOYORkRFcdllpos8brAzCuxPvSZN4w9gEvu6w4WGL+OB8IXQBH3nqI7ii/Qp8ZudnslaTS8WF0AX84PgP8NjgY+AEpez6YvQijs8eL4uSr07O7GV5wFm4pxkAwNKKXTBiBAxYAnhl5Blc3vv64py7ApxVxSzLfma0EXB0V2ZApYQk4dXaAIgtqGPhUexw76jsmIoAF53AFCWKTR6ihqp823di/WtHcZGmkOBSGA4Po9deZj8SjsGh5BSgNQEA9rQXVvHQ7+jH2cBZsITY4rYuzQC+gWW3O88kZsBBbHdxs6wYzFFDWFVtGmEmUtC5Hr/wONK82Mr75kgUOgFA/01lvUap/erOamlcN3US2HBbSV5LEAQ8PPCw/PW+eALoLr1oBgB9jjUYj08hSpKY8p9DKZdG1T+jXqNHkkvO2yfOxnHSdxInfdk+chRBwWvxKmKavRc9VrFKrSYWDS5RTsyeACuIxtvb45IvoXdXTfk1rhSHqoI5kAwuub9aNPOWsT0T5mZspGzYD2bet7xmr1xNttO9E3oq/9/bxqaNuLL9Sjx38TmMx8bxMMXirTobkAoBrz0CvOnL8n14OYxElBbHToYBXNXraQbM8TWjKKwNjQL62u3s8DNRedtl667cQFZIt2M1MCWJZoHy+IGHoooPnJ1e/t/8pUxFRbO7774bP//5z/HQQw/BYrHIPmU2mw0GgwE2mw0f+MAH8KlPfQpOpxNWqxUf+9jHcNlll2Hv3pX5vjQoEUYn8PZfwP3r9+GLs1N4eziKf3M58apeLP18/uLzODh+EHf234m7ttwFu95e9CGcDZzF9499H78f+r1sYAoAJEHKfgf7R/eXRTQbUPmZ9elcQBErWHY3b8b9QTFh9sVzD9a2aKYKjeiPzIobLeuL+n5VE16TB2DFSdaY7zVg9W2VHVAR8M2cElsmAHjoGjIU9e7C+qPfxhMmsUHhpO9k+UWz2XM4qBMrVggAu1sLq3jod/bjkcFHAIgVSevSjGiovkzRbCquJE62cjVYaWZW5JYwEy/oXA+cUwIAbo9IE/Q9HyronMtFnaB5VqstaRjA8dnjcjDGxlQKqxim5H5mGVbZV+HZi88CAM6n/WhNRQBd8a8pvMDj0cFHAQAagsTj134blMDjQngEFyKjuBC7iMHYRVyIT+Ji0p81nwAAVmAxFB4SUxjn2Ob+eefr8bfX/mfRx9ygcLL8zC6h1kwAcKisSQJ5LCRclMICCEEQE/7KWEH1xtY9+J/ZA4iSJHYmk7i67Spcdc0/osvaVVC79Ie3fBjPXRRtZL5/6ie4Zf3NoF/5HyAdBU4/Dmx+27LPmfG6BIBOQi+mjFYx2WEAkq9ZDduh+Lik7M7uclS3YJkLT/NGUJO/BUsQGIouHdBRDIIqT0NbLc3Zq4CKimbf+c53AAD79u3L+vcf//jHeO973wsA+OpXvwqSJPHWt74VqVQKr3/96/Htb3+7zCNtkBfdVwAfeha4/wPYNPQsfjYxid+ZjPhKiweTYMAJHH5++ud4dPDRovqdnZg9gXuO3ZOV9gWIBsrvWvcuvKHnDbj1wVshQMCBsQP45I5PFvyaSzE4qUzO+oqcprN7ze3AYVE0OzzzalHPXW6yKs0yaYY1fANfig57HzArimaj/vIYXZeaiVklebLV0FTBkSyT9u1Yn84OA7i57+ayDsE/dhBndOLCwlqtq+DFhNWO1fJ2IQmak6o01NZarDRTVVWEucSKz3PKdypLQOpnGKB5HdBzdcFjXA6dlk65GuqslgamTpTste49c6+8fUc4CjT1A+bytJGrK9EHaBpXzp4D2rcX/XVenHwRE5Ln6OWxGJq/Lya0OwDMfbUkQWCYonBBS+MCTWGQpnGBpjFEU0iR8w2UfzHye7wv9DF4bLX1mbkUODKtJI5vv8REM7tNEUuC7NILCZlKMzfHgTY1l7WFr3PzO/HUT34OgQB01/5v4OpPL31QHmxp3oLL2y7HC+Mv4GL0Ih7tvw63Z7557N4ViWbDgfPKuE3VbxvUaZmboFnbYQA+QaxINPMCdCuoFKw0lGsVvAyLIS2NkVQAvMCX3Jhf7WlYigKWeqbi7ZlLodfr8a1vfQvf+ta3yjCiBgVjcQPvfhD44xdBPPcVvDEWx76hQfy0tQs/NNFIcCnZ7+yXp3+Jz+z6DK72ruwB5MjUEdxz7B48P/581r879U78xfq/wJ39d8qtEpuaN+HYzDGcD57HaGQUHZbS9r4PqMSgoiRnqmha9Xr0Pv85DNIUTrJhRFMRmEuwGl8OMsmZFEj0ZZIz69HPTKK9eSMwK4q7Y9GxCo+mOEwElVZkzxK+IlWF0Yl1pnZk2mVfq0CC5uHRZ+Ttvc2FXyfUCZpnC0jQzBLNOAGw1tDvFYBVZQgc5tOL7Lk4vzmnBADcngkA2PMhoMzm8BpSg1X2VTjhO4ERikI8NAJjCaqwQqkQfj/0ewCAhePxhlgcWFueKjMgO0HzfCZBswSi2UPnH5K3b4nGFt1XLwjoZxhRMFXBAZigNLKI9qzRgENSCu5TR3+Ad13zL0Ufd4OVw/Ecjk0fAwA4eaCDZSU/s10VHll5cKqqbwNLXBPjTBwBKRG7rRI+Xd1XQvuBJwCCBLw7i3rqu7bchRfGXwAAfH98P262dYAKjQIDTwPR6WUvEIz6lK6SziIvkJcCtWg2XA+iGSEAIOASajSwxdmDbobBkBQQNhWbWnEYVL6EVInidn1pA37qjaoIAmhQZ2go4IZ/BDr2AA/8FQzJED40MYTbdEZ8fcM+PBwQV8mHwkO4+6m7cUXbFfjMrvz8zgRBwKHJQ/jeq9/DS1MvZX2vxdCC9218H9665q2KOb4gAEPPYV8ijWPSfgdGD+Bd60tr4pxJzrRzHJyeIk/6aT12a10YFELgCAJHzj2Mqze+s7ivUQYYjpG937o1RsgZLnVcaaZvWoMWlsU0RWEstbQZby0wqSop99iL5N1XJhxtu+AJPIMJisJp36myrPKpORhSBK29PYW3WbsMLjQZmjCbmMUZrRYCAGKmMNHMrXeK1/Qawqg1QyMI4AgCYcnDaLkk2SQeH3wcAKDnBbwxGgP0dmDzHUUcaf70O/txwncCAkHgvJbG5unXgI7iGpg/eP5BpDixAufWaBQGQShbayYA9NiU68dAiRI0Y0wMT448CUAUBq+Nx4HefYDNC5A0oNECGlr6Tyv9m/prCtBoodHQ8GpoeDVaXE3SuHL0Odw2JrbyPjX2R5QvJqJBPpwPnkdEakvcnoiLIRMduy8JPzNADLWgBYAhgIAw3y9MjXpBz8uwgLsC5vZFvrZl2NqyFXs9e3Fw4iBGI6N4vG83bjkyCggccOJ+YO9dyzrfiNSeSQkCPE3rSjHkomLWmuHU2uFPBzFK1bZolkpFECVFscxVq8Es5lZ0KdbbGAoPlVw0C0ohTwBgV/m/Nlia2poJN6gt+t8AfOgZ4L73ABNH4U7F8cUjj+PPN92Kf9NzODoryljPjz+Pgw8fxB39d+AjWz6Ss1xUEAQ8e/FZfO/Y93Bs5ljW99rN7Xj/xvfjtlW3QauRpBeeF+Okn/sqcPFl7KNpfN0rXoj2j+0vqWgWSUcwzYstQb0MA8JdnORMNbtb9+CXE38AABweeLwmRbPB0CBYXnygXcOo7hp1LJrB2QevJJr5+BTiTBxGurZDvycSM/K2x7W2giNZAd6dWD/5JCYoCjEuieHwcNaDe0kRBBxkQwBFghYEbOu6tiin7Xf0YzYxi5CGxLRGA7fvnHg9zNFKthBTEeWhqbXWYtwBEAQBq0AgQABhVRjMcnhi+An5IfvGWAwWQQC2/8WKzKKLwdzW281TJ4r6YMkLPO47c5/89R2RKEBoytqKaqSNaDe6cTE+hQEtDWHmdNETNJ8YfgIJVrw/vzEWg05rBd7+84J/r71dV6D7Z7/CEE3hCBOELzYNl6kO0pHrhEvZzwwQr4kOEJiGgCCBRe8JGT8zAGhny5icWSY+vOXDODhxEABwT2oMN0F6GD5277JEM0EQMJIUvXjbWRZU05oljqgOumzd8M8cxQxFIR4cRq3OQH2BAXnbRS4vIbxqIEl06RwAxGvScOgCLmsrbdBhMK0kittroKW4mijfknqDSxNHN/D+3wM73y//08bjD+GnI0P4923/C63SB5YTOPzi9C9w0wM34X9O/Q8YXlwJ4wUeTww/gTsevQN3P3V3lmDWbe3GF674Ah65/RHc0X+HKJixKeDIT4Fv7QbufRdwUfT+WsUwaGdEgeblyZcRSReWqLYY6uTMPo4QV7CLzK71b5e3DwfPFP385UDtZ9YflaqubB2AwV6ZAZUDazu8rNKWXg8tmhOqG7CnqcYET+9OrE9l+5qVi9GJl3CREm/BWwmjUh1bIGucysT9jJYG2CQQGl3kiPlMSavnhCDAXe5whCJhlaY3YQJixfEyeeC8EgDwlkhMbBXa/cFiDW/ZzGu9LXIYwMGJg3IS3J5EEj0MC2x7Z9n8zDKskn7OOEliwl/8SjN1auYt0Riw9R1FEUIJnQnX60WBmScI7H/1RwWfs0HxuNRFMwBwEGI1jl9DQkgEFtxPnZzZXon2zBKzw71DDt0Zjk/gt+3SYt/4K8AyKrNnE7NISJXMHUwZE0YLpEMVBjBaJvP5UuALDsnbTrp2U4u7VbYmw7Oln4OGVImjNkvtLYpWkoZo1qD00HrgzV8Fbr8HkKpqiKkTeONj/xuPrLsLH936UfmBMZKO4N9e/De85aG34McnfozbH7odn9r/KZxWpVGusq/Cf1z9H3jw1gdx66pbxTCBZBh4/mvA17YAD38M8KnMr61eEACuiYury6zAzvNBKyaDqoten76pJP439vad6JfEl9NCEqHo5BJHVB9n/IrY1x+XRMx6rjIDAJKEl1YMdcfCyxMzqpFJPgkA0AgCmktcVl503BuxTlWI9JrvtYX3LTKHzj8mb++1Fk+Yyu1rtrwwgMm4WD3o4njQNZhIBQBWQiykj5IE+PTivlVzGQmP4MXJFwEA3WkG21MpYO2bAHvnEkeWDnWlmRgGUFzRTF1ldmc4IrYiXv03RX2NfOhzqHzNouMAt7L22lyMRcayfq+bU2lg5weKdv7rV90ibz81/IeinbdB4WREM70ArE2nRT+z9uL6ZVU7do3YisoSBGKR8QX3myea1YgYtBw+vOXD8vY9Ji3kacCxe3Pun4vMIgMAdDEs4KyNe2WXVQkpGeZiQKp0RQSlxKdKLnXpbBUcSWF0qe55Q4HSB4QFWSUcyV6DnQSVpCGaNSgfW+4EPvg04JIm/6kw9L96Hz40OYJHb3kQt/QpE86h8BC+8vJXsqq2Nrg24GvXfg3333I/3tDzBmhIDRCZAp78J+CrG4En/gGIKFG66LoSeOevgY+/AujtsmgGAPtH95fsxxyYfFne7i2VMShBYLdJrGATCAIvnfxFaV6nhFxqyZkZvEa3vD06U7oUvLKQjmNCuou4QYmfyVpCQ2O9Q6nMOlXGNNpDKk/GPZ69RTuvWjRTEjTzr0ZleAYzjFg96OZYwFGbKYAWUhQMBYJAZJEHxFyoq8xuj0bFFsE9H15w/3Jg09ngMYmi9FmtFsLUqRVV0OViMjYp3xObWRb74gmxOtxe2sCcXKjDAAZoAggOF+3cjww+Im/fEo2B6LkGaC5eS9WGLe+BmxUfvw+mphFNhpc4okE5mIxNymmpm5NJ0MAl5WeWwUEpFZWB8MJeVmrRzFtDYtBy2NW6CzvcOwAAQ0wQvzdL782x+8TW1TwYUb2HHZSpYq37y6XTqk7QpIFgbS7e+mLK856rhg3tm5vWwyD9zQ3HSl/5pw4CsesdJX+9eqIhmjUoLy3rgL/6I7DhLcq//embaLnvffji5rvxizf9Alubt2Ydsq1lG757w3fxizf9Atd1XicadfsGgEc+CfzXJtG3LBWS9iaAtW8G/vIp4H2PAatfB1BaYN2bsSuZhEm6MD079qzsp1VsBlQtJX3ubSV5DQDY7b1G3j488seSvU6pyCRnOkk9mjhpknIJiGYdtm55e8xfm621GeK+8whqRKGsVVObzhgu7164WfFa8Jr/NHghvwlzIfACj0NxsTXXzPPY0PuGop2729YtVt8COLOCBM3Z+CwyUkwrywH22hTNrKp213A0f9GM5Vk5XVEjCGILn3tjWQ3xF2KNJPBGSRLjXBQIFae9+/5z94OTvN/eGomBpo3AVf+rKOdeLlkJmjQNzBTnGikIAh4+L7ZmEoKAm6MxYNdfFuXcGUiDHdfRTQAAhiDw7ImfFvX8DVaGujVzq9yaeVWFRlM5HFqlyj0QXvjakRHNKEFAs7GlZsSg5XLXFsW/7HvNHvAAEBoBRg/mdfyoaq7faawdbyh1guZIDSdo+mKKn67L5F5kz+qGcPWhW7IPGk+HwXCLB3UUSkgVjmTX2Uv6WvVGQzRrUH50FuDPfgS88T/EZCpAvEl972psDM/ip2/8Kb58zZfx52v/HD96/Y/w32/4b1zRfgUIghA9B+57D/DNncDLPwakpC+QNLDt3cBHXwTe/v/mx1RvuB00gCukarNwOoyj00dL8uMNxsVWSQvHo9mzoySvAQA7Nr0TpFRpcDhavNX4cjCbmIU/KfqYrQGtmD27N1VsTOXC26wEQ4xFanOFL8PkzHF521OrN9/2HVgn+ZpFuSTGIqX3mTsXOIeA1BCyM8WAalq9xBH5Q5GULDwM0xSSBLGs9szJuNLqLYpmlWtJLAQrpYi44dhU3sc9f/F5zEjhFtfEE6Kgv+dDJWmzXy5rVFWRxfI1Y3gG95+9H4AoEr41EhV/3jJ7mWXosfWAlO4I57XFS9A8Mn1E9pDck0yi1egG+m8qyrnV3NB9o7z95MAji+zZoFwcmToib29PXZp+ZgDgUN2jg7Hclh6CIOCidA9sY1loXKty7lcP7G7djW0t4sL2INL4g0m6Z7z6y7yOH1Ytenbaa6eFVS2aDdeyaJZUEuhdtWYNosbZiy5GFMo4CKX1OuYYBAnxudEkEKA1NZo6WiEaolmDykAQwJ6/At7/O8AqGeXHZoCf3Q7i2f/EjZ034G/3/C12te4Sp88DTwP/fQtwzz7g1INAphpEawEu/xjwyWPArd8EFnr47LkGMDiwL6G0aB4YO1D0HyvOxDGelZxZusopi60T6wXxgnee5OGbKZ+JeaGcVa3QrUnExQ1KX5dtAHNxNW+SS7HHVMmTtciEXxFjPMYaXenz7sL6dHnDAA6qKkP3apuAIre1ZsQVniCWXa0zqXqYauUBmGvz92rVWuTtcGw67+PuP3e/vP2WSBQwOIBNbyvq2FbKvJCHqcLbu/eP7pdFwn3xBFopE3D5xws+70rRU3p4DaJgd4GmwS/DmHsxsgIAIjFg5/sATfED5Ldv+yDsnCiIP5sYR4pNFv01GiyPTKUZKQBbkilxrtFeugXNasVhaJK3A/Hcc49QKoQYK87J2tj6bM3MQBBElrfZ9xx2sdrs5IMAs/TndlRa9NQIAtqaa6dLwqw1w0mL98dRiipqC3w58aVD8rbLWpuLewAAazu6OMVqYThcwt9HMoSQRpR+7ETx73/1TkM0a1BZvDuBDz8LrLpB/Frggae/APz8DiA6A5y4H7jnGuBntwMXVCKXqQW4/h+Avz4B3PgFwLqEmaGGBta+GVfFk3J1Vil8zS6ELsjbfYIGMDcX/TXU7LYpIuGLJ35e0tcqJpnWTADoD0uTt+a1JXmIqTYIV59orgvgIhcHx3NLHFG9TKg8PTy1OmmxebGBVNpPTs0W12A9FwdHlWvZXmfxJ9v9TnUYAA3EZ4G4f5EjFCajik+IW+cAyNqcJlhVrUjhhC+vY2YTs3hm7BkAQAvL4opEEtjxXoCujjj7UlSa3XtGMb6+IxwVF6GMzoLPWwh90t9vgiRxsQjhHAk2gd9f+D0AwMTzuD7JANv/ouDz5oKyuLFPI5pSJwjgT6fyq1ppUBoi6QjOBcXFndXpNMyCAHh3XXJ+ZgBgNynVo4Fk7vtBVggAw9VlCICayzyXYUvzFgDAeZrCU0aDaPdybvEgD0EQMJKcBSCKi3RT8bwRy0GXRSxWmKEoxAMXlti7OvGxSsCPy95duYEUCqlBl1bxFhsOle73IcT9CElzOpvk+9ogf2pzNtygvjA6gXf8Crj274FMo975J4Av9wO/fj8woTLndvSISZyfPC56rhjs+b/Ohtth53lslcrzh8JDGAoNFeunAAAMTCtj7dWXVjADgN09SivI4fEXSv56xSJLNEtL7RLujRUaTZmxtqNDSj5lIGA6nn8VTLUxoa5KchavxbCsEATWNyltwaemXl5k58JhOAYvB8Q04GaWRW978UIAMmSHASwvQXMqpKxyttZwy4NVb5e3wws8IM7l4YGHZW+vW6MxUISmqOmKhdJl6YJOowNQnATNC6ELODRxCADQyTDYS5qAvXctcVTpWaUSfQciwwUHHjw98jRi0gPWjbE4jGvfDFhK50F0g/daefups78p2es0WJpjM8dk/7G6xwAAVLxJREFUn8ptmdbMnqsrOKLK4VAl5QVSwZz7qFvDvCwLOOtbNJtbbfZdu02sNlsiRdOf9CMmeUN1MrX3PnWovCNHS1nZVEJ8vPh5NvA8jNbyh9YUk26zV94e8p0u2etEIuPgJKsJO1Udi4G1REM0a1AdkCRwzWeAdz8AGKUSckFVgePZAvzZj4GPvSymeq1klbDnasDgFJPBJIrdoqlOzuwrg8fBtnVvAyU9ULyYnAJqpGopk5xJgURPWjK9bL1ERDOShFeVYjW2SIpVtTOZUsQIT1PttCfMpanjMjRL1X+ngmchFCmVMBfHZo8hIU229yRTIDxbiv4a6kozJUEzvza3yfCQvN1qrc0QAACwqtK0wqnQInuKCIKAB86pUjMjMWDdmyuSILkQGlIj+9WNUBTi/oG82ogW4r4z98nbd4SjIK/8a9FztMKo753nwYjWDQWQCXYAxNRM7PpgQedbir3bPwSj1IK/P3qhZKFDDZbmyLTKzyx56fqZAYDDojyYB5lozn2yKs1YFqhjT7MMV7RdgU3SwtlZnRZ/NBqAs79ftDp7JKJKzmQ5wNFd6mEWlS5VINVwojYXbn2C+Ozg5PmaD6voVC06DwfOl+x1ghHl822javs9qwQN0axBddF3rdiumUk26t0HvPtB4K8OABvfUpj3j4YG1t2Ma1SiWbFbNAcDSjVHX8vWop47F0a9DZuk1rIhisTUhepP0UxzaVwIiuXHPRoT5ALhSyA5M4PXoFQhjqnM9GuNCVV5vKdWK80AwLsT6yXxNsKlSmrEenBCSebam0gBLesX2Xtl2HQ2uCWPubNarZiGOZufr1nG04wQBDS5aqvlRI3VqHzGwunwkvsfmT6CIUkw3J1IooNlgT0fXvygCpARRAWCwHmazPv3OpcEm8BD58QqKB3P41aYi54muVLUCZoDBYYBTMYm5c9cO8Niu6UH6Lq84DEuhs7RhasgmooHCeDI2YeXOKJBqVAnZ267hP3MAMChSkL2c4mc+1yMqEWz2hODVkKuajOBZ4CTDyx4zIiqOquLsgBUbbW6ZSVo8kkglVtErVYYjkFYakxyCZqqCOopBFvTWjglL8zhWP5p38tFHQDi0NlK9jr1SkM0a1B9WNuA9z4K/O048BcPiUJasS6IG25HD8OiU0oqeWX6FYTyqELIlwEpec7I82ht31O08y7GLpdSoXX4zP2L7FkdDIYGwUqVNv2sqjKu5dIRzTpUFTyjRfDsqQg8jwmIv0eLIJrL1ixt27A+VZ4wgEPjimi2x+ABtMZF9l45GXEloiExQWnybs/MVA82cxxoR09JxlYOrCr/njATW2RPkd+cU9robo9EgdZNQOdlJRlbIWT7mq28RfN3F36HiGT4/fpYHParPl013m3dtm45QXOALkw0e3TwUQiibIxbo1GQuz9Ylges69sUYe7J135R8tdrMB+GZ3BcWpTysCxaOQ7o2A1QugqPrDLYTUpLcpBP59wnq9LM4L5kvN+uar8KG1ziHPS0Tov9RgNw7L4F9x+ZVVroOmvQxqBT5UE7QlNAqLaS3H2JWXnbRdZBAqSzR07QnGajiDPxkrxMUBUAYtc5FtmzQS4aolmD6qUU5bbdV4EwuuRqM07g8OzFZ4ty6iSbxBinTs5cV5TzLsWeNbfK24dV0erVSqY1EwDWRKTyd4sHMLkWOKL+8LqU9rmxIvvqlQs+OoVJSqz8bCVq/CFEZ8F6g/JA8dr0sZK8TIyJ4fis+BDXnWbQ6i5+a2YGtbhyRqvNS3hgOAY+Tmz3a2U5wF6j4Q4ArKoHxDCbu6oiQyQdwR+GRONnC8fjhngC2HNXVa5ez/u9rlA0u+/4j+TtOwUzsK00xvgrQafRoVOqxh2kKXDTK6umEwQBD597UP765hSAzXcWYYRLc9W2D4GW2ryfCp2VfbUalI/TvtNIStezbXJr5lUVHFFloTU0LLz4NxkUcrcMX5TaDg08D2cNL5osl/nVZlYIowcBf25T9hG/ck1S+4PVClmVZhQFBGvLJsSnsjVxaapjsacgnL3oYpTPZKkSNENJJRTJZrh0nrmKRUM0a3BpoaGAdTfjWrWv2WhxfM2GQhcgSM9YvYIW0Jen9HVL7+uhlSyYXuTDQHLpVqRKckY12eiPS2O9VEIAJNrdW0FID1RjteonMXMKrCQqeOjK+yAVyjr3Vnn71OSLJXmNl6deBit5Ne5JJsVqphKRHQZAA4GhJf2vpuJT8rabq+3WHKulXd4O84v/3L+98Fv54fpNsRj0Biew8a0lHd9KmV9pdmLZ5zg5cwInIkMAgHWpNDZd+bmqay/qc4jt3imSxMUVJtoenz2OCxHx4WNHIgnvxjvK5tlmblmPvbxYATFN8Dg59FRZXreBgtrPrCGaidghLnT5CcwL2OAFHuMxMT25nWVBXAJ+Zmqu8V6DdU5xsfuUTodnDXrg+K9y7jsSFSuzSEGAt7n25q9mrRlOSqxyH6FrTzTzqwKLXHUw/4StE12ssrBSKtEsmAzK23Zjy8I7NshJQzRrcOmx4XZsTaZg4cQL1PMXnwfDMwWfNisEQOVZVWp0Gh22ap0AgIsUhbHTDy1xRGVRJ2euybTEXUJ+ZgCgbV4rihIARplIhUezMiZmlRZGj6GpgiMpDi0dV8AltQufCg2WJAzgT+N/krcvS5RWNFvjVIsrWkDgAf/gosdMqvwu3AIJGGq3fN9kagYp/Q7DS1zf1a2Zb4lEgR3vq9q2JJvOhlajWEV3jtZCWEGl2b0v/Ze8fYdgBLHl7cUaXtHocyn3hPMrfIB4WBUAcGs0VnbPthtadsnbTx7/WVlfu2QMPQ88/3UgvXTLc6U5On1U3hb9zAxA+/bKDagKcEitbGENCTbuy/rebGIWaSm0op3lLokQADUEQeBDWz4kf/0dhw3CsV/OExcFQcColMjsYTnQTbXp/dkp3UdmMqEyNYRP5b3n0tkrN5BioaHQpbXLXw6pApmKSVDl72o3u0vyGvVMQzRrcOnRdSVoYxOuTIjVZhEmgiNFaGscmFTOUY7kTDW73Dvl7ReP/hDgqjOtSxAEnPWLbWJOjR5NUsLYpVZpBksbvNKqUhAcIunaE84mgooA4zG3L7JnbUB4d2F9WhRxQ3wK4yUwYz00eQiAuDq9s8SVZl2WLug1ovBzNs8EzSmpygAAWrW2qmxPzBeS1MAiPetE1EnMczjjP4OTPlF4WpdKYx3DA7s+UI4hrpiMIBrRkJhI+YFo/tWqoWQIv50S/w7NPI+bLvtcYQE7JWKVQxUGwASB9PI8XtJcGr8dfBSA2Gp2o2sL0FIey4QM+7b+pSzcPuU/VtJU3rIweRz475uBJ/43cH9pE0gLRRAEudLMwvFYxTCXtJ9ZBoeqlS0Uyhaj1X5mbQwLuMo7j60Gruu4Tq7SPqHT4fn4ReBi9vNBMBVEREpu7GSZmhUXO63d8vZosHSJjaXAF1XmKq46WLQFgC6LktQ97F+ZJcFSBFXhXTZL7c/by01DNGtw6aGhgPW3YF+RUzTVyZm97m0Fn2857FmrtBJ9j53C7OOfLOvr58tsYhaBVAAA0C+o2oEusUozkCQ6NIoB/MVwbZXGA8CkaoLtsdWB90lzP9aptObXihzQMJuYxTnpGrE+nYbN6AbMpSuP15AaOYVwhKIQJ4glRbNJv3INazXW/iqkVeqXDxMLixUPnFcS0t4SiQLrbxXDaKqY7Nbb5fmaPfKnLyEpaaG38HoYN76t2MMrCuqFp/NaGvAt76Fu/+h+hKWgg+vjCZh2lV/kcXp3YzsnTrOHCQ4D44fLPoaiwfNgHv1rfLLZgX0d7Xh69Gng7O8rPaoFGYmMwC9VA21OpcSmxEu8NRMA7JTiFRwIZ6dEj0WUr9tZFnBeeqLZvGozuw3Cq7/M2kfdOtfJCoDNW7bxFZNOp3IfGVHN52oBX0IxtHfVSZthp6o7YDhQmsq/oMrf1d4QzZZNQzRrcGmy4XZckUiAklZ+94/uL3gVeCAh+gHpeR5t3r2FjnBZbPbswTqzaOx5kaZw98QfEP/TN8s6hnxQt2b2J6XKAY0WaFpdoRFVDq9eMeEcLZHxfCkZV3mxeVTBBjULqcF6VaLUqfFDRT394QnlgXlPiVszM2QSNAWCwDnt0imEk6rqwVbVqmetYiHECqoIAfA5qm9TXAqPDDwCANDxPG6KxYA9H563X7Wx0gRNgWNx79Dj8td37Pg4QFbnNLDb2g1NAQmaD52+V96+haWBdTcXdXz5cr1LCft46tiPFtmzyjn6P/iv+Dk8ZTLCR2nwN80unPjDZ5b0SawUr0y/Im9vl/3MrqzQaKoHh84qbwci2UKJutLMy/GAowuXItd3Xo9Vtl4AwDG9Dn869yDAKS3+oyrRrENrq8pK3XzosvfK28OSwFwr+JIBedtVJ+KP3rUaHlacpwzFJ0pSmRxSpeY66qRCr5xU52ypQYNS03UFbIYmeTI1Fh3DYGhxv5/FSHNpjErJmT0MC01zedtANKQG33zjj9FKixOiUzodPnX0a2DOVNdKcFZyZkgSXZr7AU0dREYvE69FWZ0cm1mZ0XUlmVB5I3hq0Ag3FxvcigfRKZVHYTE4OHFQ3t5bJtFsuQmak1GlJdXtqP0qAyspVrPyBIFYbH4L49MjTyMs/R2/Lp6A1b1FbOGqctR+dcupNDt88MsYIsWJ+E6eRt+W6knMnItWo0WnXpzUX6BpcDOn8z52NjGL56fEMA83y2L3pndX7B5z/ab3yNtPTb9UkTEUTMyH/c/8H/zUpgguKZLExw0Mpg58qYIDWxi1aLYtlfEz21HBEVUHDp3iUxlUeVgCwEWViNZuaL4k52UAQBIkPrT1Lvnr7xgICOeVII8RVTBJl6m6q5IXo8OqLIyNIg2kohUczfLwqeafLtViZ03j7EUXI4qzES4pd+UUkyBEqwpaAAxUHaSOlpmGaNbg0oTUAOtuwTVFatEcDl4Al0nOJHSA1rj4ASWgxdiC7970U1gIcaLzvFGPf376YxCmTi1xZPlQJ2euSUmrv5ean5lEh0rQGCtAsK0Uk7z4+9MIAprMrRUeTXFwd10FpxTQcCo6XLSVPkEQZNFMywvYmkqXXTQ7q6WB2XMAzy+4/1RiFoD4O21uKq/wXwqsGsW/KByd71E3LwBgz4drwset09IJnfSznaPzTNDkGNx76n/kL+9c/66q/1lXSS2aaZLA6Ez+KaGPnX8YHMTP7i3RBDQ731+S8eWDp/d6bJCKHF9DGhdrcYHkic/h76zKZ6lFEl5mKAofu3AvEtP5C5rlIuNTSwkCNqbSQOeeqkuIrQTq6pJAfDbre+Mq8/F2Wy8uZW7suhF9BrHt76heh0NHfyB/b8SvLD512mvTzwwQfU8zDFMUEBqt4GiWh0/y5tLyAszW2myPnYezF12MUhFf9ARNnkNQsqqwgwRR5ff/aqQhmjW4dNlwe5av2YGxAys+1cCE0srVZ6icF1CfvQ/feN090EptLQ8adfjWA3cCsdkljiwPmUoziiDRK62oXHJ+ZhLe1q3y9uicFd+qh0lgQqpYaQEFiqQqPKDiQHh3YZ2U6Brg05iKTxXlvKORUUxIJvvbUinoBQFo3VyUcy/GvIokJg6EF/YumWLFleZmjoPGWfs+dVaVb2BYZRwMiP49GSGzk2GwU2MFNr6lrONbKRRJyX51wzSF+OyZJcNfZl78Hv5Ii59ZF0hcv/NjJR9nofQ1K8LyQJ4LC4Ig4KHXfi5/fXPLrsp61BEErrevlb986uj3KzeWFcAMPY/PTB1AWCO2oN3QdiV+dduDaJc+W69pafzd7z4AXlhYjC83/qRfTp9bn0rDIAiN1kwJu0mZnwbmtORdjIiiiZXjYKnRRMhiQRIkPrT9k/LX3wkeh5AIAQBGJO83QhDQ3lK7i75mrRlOUhTDR2gKCNaOaOaXFm1dPAfCVCdthvZOdDNKaFHRRbNkCCHJjsFG1Mecvdw0RLMGly5dl6NT50RvWhRvjk4flY1jl8ugKjmzt8zJmXPZ4dmJL13xRWS8r79nAH51720Am6rouNJcGhdCFwAAvRoz5ML/S1Q0s7dsgkmq+hlLhyo8muUR9w0gKD1EeTR1VOJtcWM9ofw8J2eOF+W081ozaRNQBlHKqrWiTWofOaulwQMLtmimuBT8UiJYK8sB9tr3s7HSiul1eE575oPnH5S3b4/EQOx8f00l62WqCAWCwAApLG6UzyRx/yvfASutLL+l6ybQNdB61acSfc8nZgB+4RTUDKf9p3FO8hfdnEyhZ89HSza+fLl+/Tvk7acm/lTBkSwTjsU3n/w4XtWLn4t22oL/c82/wal34ps33gMTL04ynhDC+PZTn6rkSLM4On1U3t6WqWhvhAAAABwWRUAOquYdLM9iUpr/trPcJRkCMJcbe29Cj0a8hxzRa/HSS98CAIykxPepleOga1674PG1QKYFfoaiEPfXRoImy7MISInYLo4DDM4Kj6hIUDp0ae3yl8UWzRLRSSQl0cxO1s5cp5poiGYNLl1IDbD+VrlFU4CAZ8eeXdGpBlRxzX2t5U3OzMWNq27G32xWDK2/QAZx4DfvAioYeT8QHAAn3ej6WdWqtLv0bWrVCGFtRwcrvh8TQhosv3ilSDUxqWqValV5pNQD623Kw8Kp0WeKcs55opl7Q9nMgzPVZnGSxEVKI7Zo5mAqplTVuaEBdOayjK+UWFWm12FV2hbHc3hQSs0kBQG3xJNABVv4VkK/KvnsjI4Gphdu+2Nf+iF+pRWvuSSAt+38eKmHVxRW2ZTWpwGKAIJLpww/fPzH8vatMAE9V5dkbMuht/9W9Ej3vFf4GGZDRa4gKBHP/vHv8CNarLylBOA/rv82rFrxM7WqZQv+Y/U7QUpziu9dfAqPnXtgwXOVkyw/s2QKoI1A2/YKjqh6cKh8rPyM4mE1GZuUW5q9LAu4Lu32TED0Cv6rfkXw/s7gAwilQghLi0udDAu4arc9EwA6zUpr46iv+tqscxFMBSEFY8MlkICmfqqmulVex8P+3HO1lRJSpeXaG35mK6IhmjW4tNlwW1FaNAfjYhUDLQjweq8oytAK5V3b78Z7u94IQDTC/nTsFI49+fmKjScrOTPiEzdMLYC5uUIjqjAkCS+pBwCwBDAZme+5VK1Mqjw9PHUS951hXfseefulycM46TuJodAQZuIziDPxZfuc8QKPw5NicqaF47EuXR4/swz9DkVcOavVArNncu43FVEmVK2UpeTjKgdWnU3eDieUKuIXxl/AlHTNvjqeQMvaWwFLbfnyZfnV0YuEAaRjOHD4a5imxAeLq5u3w2P2lGOIBdNl7QIlWQ2cz3jyLQLDMXhs5EkAotfNGza9rzp820gSN1hEMV4gCPzxyPcqPKClmZo6hr8beVT++pOr78Qm99asfa664nP4NJQqj3944Z9wbKbySdBq0WxrMgV0NPzMMthtSgVxkFOST9XJmW0s26g0k3jj9o+gWypwfRFJPHD8J/L3OnkA5srZsRSDToci+o2oPO2KyeiFp/DHA/8HTDpWlPP5Ej5520XW1+fa41gNSppjDoUGinruoMqiwk7Xxxyv3NSPPNugwUrovAxbaDvsHIegRoPnLz6HNJeGVpP/hZjhGQzxCYAAuhkWVBV5Qfz1Nf8XU4+O4bf+40iSJD46+jB+dmQ1urZ/oOxjUSdnro5KbQGXaGtmBq/OAXCi39zY1Cvw2mojBWhCVTbusdTGmPOlreta2M7/DCGNBkfi43j7o2/P+j5JkDBSRhhpI0y0CWbaLG5TJpjo7P+MtBEJNoFQSvx7351MQgOUVzRTVyRptbh+AeFhclYJDGk1uEo+rnJg1SsP9OFUUN5+QBUAcHs0BtzyYdQaWcmoOnph0ezQd3GfVqnsvXPLB0s9tKJBa2h06VwYSM1iiKbBzpwGtebGBfd/dugPCEhVINcm07Buf8+C+5ab6/v/DN8/+h8AgKfGDuBtFR7PYrA8i7/5w4cQ0Ijr6vtoF/7i8r+bvyNB4F1v/B4GfvUm3G8xIQ0eH3/qbvzy5l+h1VQZETrJJnHSJ34WutMMXDzf8DNTYTW5oREEcASBAJ+W/10tmrWzAmCvr/v6StFoKHyweS/+zi9Wi3/rlFLJ2qm1V4coXwBdTRsBaR1tuEgermpmpo7h7fs/jjBJYs/QY/janU/ApLctfeAi+FQewC6q/KFrpYRyrULH1B9wQUtjJDYJXuBBEsWpb1Kn5dpVVfgN8qdRadbg0obUQLP+VlwtVZvF2QRenHxxWacYDQyCle6bfaShqlY0SYLEF276CXbrxQlsQKPBXUe+DN/QykMPVspZVXVSf1qarF3iolmHqV3eHi2Sf1Y5yJjaA4DHWdvtCXMh2rbg8mR6we/zAo8oE8V0fBoXQhdwfPY4Dk0cwtOjT+ORwUfwyzO/xA9P/BBff+Xr+L+H/y++duRr8rF7EtLKfhlCADKoK83OaOkFPc0mA0qLudtcQeP0ImJVJcWFU2JEvS/hwx9H/wgAcLEcrnKsA7w7KzK+QrDpbHAbxSqHc7QWQi7RLBHEyMFv4AWj2IrhNbpxedvl5RxmwfTZRO8/hiAwovKqysXDryom+7e07AIKfDgrJus3vB2tnCheHuJCiFRJOE8uvrP/szjCi617rRyPL9z0kwWT1gj3Ovzd6j/HLuna5ksF8dGnPoo4Ey/beNWcmD0hWx00/MzmQxAE7FKxdACKR+CYqnWr3dBUNvuAWuCmvZ9BhxRclRSU90w9f6tVOhyKt+ooEy76+b/1x08jLPloHUICH7j/TSv2js7gCytt+i66zsQfZy+6pL+1lMBm2WYUSlCVlmvX1YkPXJlpiGYNGmy4XfY1A4D9o/uXdfjgRcXYt9dQfa1qWo0WX73t11hFig9Oo7QGdz/5EcT9xS39XQxBEOT2zCaNQVz9BcpacVONeB1KC8RYoDZMWAFgIqVMejzNtZselRPagL+jO/BZXwDvC4ZxZziCNxNWXNu8DXta92CDawO6rd1oMbTApDKaXwqtAFydSAAECbSsK+EPkI3X4oVB8q84o9UC0SkgEZy332RYSc5qtdeHn43VqLR+h5kIAODRwUfBSg8+t0ajoPfcVZGxFYNMFWFEQ2IiNj7/9/qnb+JXWqWd+G3r3lG0VetysUqdoBlY+J4VTARwICx+v4nlcPllnyn52JYDQWlxvUH0q2EJAs8cvafCI8rNn0afwfdH/gAA0AgC/mPNu2Gzdy96DL3vc/hKnJKFhTOBM/j8s5+vSKLm0Zmj8rbiZ1Z5n9lqwiHWOyNICLLP7cWgUoHstdZ+CEwxoZrX4IPE/OrrLufqCoymuHSqOgWGwQJFaqEEgLNDT+OBZLbtyEk2hPc8eDsm5qRZLwefKgHcpbOv+DxVibMX3YzibzxUxJbZkEqstNVJN0G5qa3ZU4MGpaBjLy7X2OQ+8gOjf1yWb9HAlJKc2eeozpuoVWfDd279NdyC+JE/SZP49IN/BjZZntTGmcQMglJ71BqoKvEu9UqzFqXiaCy28klEuZlklSoCT5X+zReC7cYv4l2w4VOBIP7eF8CXBk/g64cfwg9mQ/jl1s/gkdsfwVN3PIWD7ziIV//iVRx8x0E89ban8PBtD+OXb/olfnDjD/C1a7+Gf73yX/H3e/4e/2vbJ/DjKZ+YSta0BtCWr6WAJEisln5HF2kKUYLI6Q01pTLKb22q7USwDFaz0iIWlvzofnP6XvnfbucNwPrbKjCy4jDP12xaabFFbBbJg9/BAxZR2KVJCretuq3MIyycviblHnE+Mbngfo8f+ZZc8f1m0gqqbWuJR7Z8rl91i7z91NAfKjiS3MzEZ/C5/f9LNtn+GG/F1sv/ZukDdRbYb/wivjk1A7O0IPb06NP4xivfKOFoc3NENR/blkwBnXurqvq/GnBIPlBJkkRCShW+qAqnaHP15zzuUubNG9+NdiY7rMnbXPuLvhatBU5CdGoapSkgOLrEEfnzlef+AbxUofo2zoAWVnz/hlJ+vPvROzEYGlzRebPaM1XV5HWBoxtdLCN/eSF0oWinDqosKuym6ivwqAUaolmDBiQJ8/rbsCspthdMxKey/LeWYiCorH73uas3oanV2onv3vh9WKTF32c1LP7l17dA4LjFDywCZ/yqEICEJLiQlCggXMK0enbK6WOjBZaslw2ex4TkG2QRALOuDg1Fuy4HPv4K8OavAlYlzQjn/gD84Drg/70NGHsZgChKmWgTWowt6LH1YEPTBuzx7MF1ndfh5r6bcefaO/Helr3YnJBWcCtQXTk/DGD+9W0yHQQAUIIAVx08DACAVdVmGuaSeHXmVQxGxYeCHYkkure9r6YfqLNab+f6mj33VfxBKyCkEatKbux+PZz62mvJWGVXJWiCAWK+nPs9NPiIvH3L+neXfFwrYfvm98IhtWg+l55FMhWp8IgUOJ7D5//4Sfh5cR50ZTyJ973xuwCZ52PChtvR670C/zk9K9/TfnD8B3hk4JElDiwevMDjqNTC6+Q4dLFsw88sB3aNkpwXCA4BAMYTonjWxHLQu+pvIaxQ6I1vw1+FlbRRN8tC31y+ivFS0kmLbezTFIW4rziJjc+f/AWe58RFeQ/H47N3Poaftlwvtx5OpQJ4z+PvxsnZhVOfF8KnWuBzmWsrwGdJtEasohRbgV+d/ZXcbl4owbRyv7HXiQVHuWmIZg0aAMD621bcojkoTTYoQUBnZ3UkZy7Eqrbd+NrefwItTWp/w/nx3YfescRRhaNOzlwTFt8vNK0BKF3JX7uaoW0d8EgPUWNCcom9qwM+No1JSnwQbyVqV3BYEkoL7Hy/JJ79F2DrUL6XQzxblEmVX13FRbPcvmaTUpJaC8uBdNRHe47F0gZCutZF+DR+c+Y++XtviSWBHe+r1NCKQnalmUo0C48Dh7+Pey1m+ftv73/73MNrgg5rh5ygOaClgRwPdecvHsQpXlyMWcfwWL3jL8s6xnzR6Ey4Vieu8CdIAi+8+qMKj0jhnmP34NCsmHzZwrL4YvetID3L8F4kCOCm/8QVKQ5/4w/I//yPL/yjLGSVmvPB84hIbdhbkynxr6bhZzYPB61cFwLhMSTZJGZYcVGnnWUBV335lBYFkws3uy9Db1oUfXYnUoCrPhJGO42K8DQ6c6Lg83E8h/98+Svy159ouw46UzPab/ov/ITuw7qU6BkbTIfx/t+/H4cmDi3r/D5VxZTLXPu+cnPZbO3BpqTox3g+eB6/Pvvropw3yCqtt3b1YnCDvGmIZg0aAEDHHuwjlIqZAyNP5XUYx3O4IK3MdrIc6BrwONi17q34134lVezbkVP4zVOfLelrqiv31kgVfXDXmRfWSiBJeCXhKUIAoURgiQMqj2/mJBip5N5TbyasuaC0wM73AR87srB49j9/Boy9tPA5Ki2aORcPA0iwCYQIUVxqhaZuxGxSa4RZEs2mBAa/G/odAMDM83hd942AxV3J4RVMp7UTOinp+axWq4hmz/wHXtPwOKYXf49rHGuwpXlLpYZZEDRJo0cyLR6iaTBTp+bt8/Dhr8rbtzZvr+q/3+u73yBvPzX4WAVHovDi5Iv47qvfAQCQgoB/iwLO6/5x+SdqXgNcdjfeEY7ijrAoXjE8g0/88RMYj44vcXDhqMW57Q0/swVxaJX7djA6jvGY8rsRRbP6EIOKDb31z/GjiSl8fWoG/xDjAWPtVe7motPWLW+PBAqvNHvwpa/jvLQIvJHh8cZrvyR+Q0Oh6Y7/wQ+TRuyQgkPibBx3PXkXnhrO75kLAHxpMbCAEgRYLfUn/pDO3qyFh28d/ZacwF4IQU5ZmLdbOxbZs8FCNESzBg0AgCTRvvY2rJZSHY/7X8NsYul0q4uB80jLyZnG/FsZKswbLvsMPtOsVMX98+hjeOZI6YyJM8mZNKFBj1Sefan7mWXw0kop9thkHlVLFWZi9jV523MpmYkuJp6dfwL4wfULi2eTx5Rtd/lFs9Uq37lc7ZlTUosOALg19RXhbpUMmqZJAQlevPbcFI3BsPcjlRxWUaBICn1S++IITSExcwrwDwJHfop7Lcoi0J39dy6YflgLrLKIlY8sQWB46pWs77FMEo8GRLGQEgTctLe0C0CFsnfbB2GSfL/2J8bBsJWtMPYlfPjsgb8BD1Fc/kgwhJ03fAlYadv91Z8BYW3H53wBOS3Yn/Tjo09/FDGmeCbjuTgyrfIzS0l+Zhq6pK9ZizhUbdr+2CQuRhRj9XYOgKXRupWTNW+AS2fHtfEE9G3Va8WyXDpdiofpSKwwcTvOxPHN1/5b/vrTq94GUqdUNsLggOUd9+G7gRT2xcTqYIZn8KkDn8ID5x7I6zV8kqeuk+NAmOrM0wwAnL3Ymkrjpqh4vQymgvjuq98t+LQhyVaFEARY9I6Cz3cpUhtP+A0alIMNt2OfqkXzmbFnljxkYPQ5ebvXWFtVC3/xxu/g3XoxOYcjCHz62NdxYrD45sQpLiUnwPRpzJCnsI1KMwCA16QqjZ96tYIjyY+JoGLe2loHkevLZrnimSAolWYWD2BunnfKUmOiTeiwiOM8p6XB+S8AbFr+/uS0UgnXWmeTKWuOac5bjN1A+47yD6YEZFpveYLAgJAGHvooIgKHx82i+GmkjHhT75sqOcSC6VMl9J6fUwnxpxe/gRmNKAheTdrgqPL7itboxNWU+BkLkwRePvHzio2FF3j87XN/i5mkuEC4N5HAXzp3AutvXflJdWbg9f8KGsCXp2fRJVmmngucw2ef+Sw4vnQeqq9IgqqO58UWsEZrZk7sKvP0YGIWF1XJye16Z80s/pYd2gC84z7gik8Ab/rPSo+maHS2KNWYI8ncnpH58uPn/wWzED/j16cF7Ljy8/N3aloN/dt+hK/O+HFLRPSJ4wUe//DCP+DHJ3686Pl5gUdAEOcuLo4HTHW4cOsU08v/2h+EQUq6/cXpX2AwuLLghAxBKTXcKhDQkJrCxniJ0rgyNmiQwbsL1xDKisj+oSeWPGRg6qi8Xa3JmQtCEPj0W3+D10NMV0sQBO4+8L8wOju//aUQBoID4KSL9RpWFUHfqDQDAHTYe+XtsWUEUFSKyciYvO2x91RwJBVGLZ7d/DXApkS3K+LZW4FTDwHJoPjvFWjNzJARVxIkiVENIVYkSUz6lb87t7G+jHWtRHalSX8qjfW77q7QaIqP2tfsjJYGhp/HI2YTEtKD7819N8NEmyo1vKKwyq081A3EsyshHj53v7x9S/+flW1MhXBdx3Xy9pNnf1OxcfzoxI/wwvgLAEQD+C/5otC86T9Ff7JCWH8r0LsPNp7HNybGYZE+gwfGDuBrR75W6LBzMhmblNsMN6XS4uJcQzTLiUNlnh5IBnDRf1r+ur1hEL44HbuA1/2zLGzUA50O5WcZ4VZeDToVm8RPhsSWc0oQ8MmNH1y4VX7VDaBu/CL+ZdaPd4XC8j9/5eWv4CsvfwWCZKswl2AqiIzs7uI4wFiHolnHboCk0cpxeF9AbNPkBA7//tK/r/ycgoDg/2/vvsOjKtP/j7/PZCa9d9IogQDSuyAiAooNwVVBZJUtfi0LLojCb/Wron51USxrw7JNV9fuLrrqqqugsCKCNCkiHROEFALpPXN+f5wwkwQILclkJp/XdeXyzJkzM3fwycyZ+zzPfde9rUcq9XPa9C8ncoTNRp9ulxNd101y5YFvqDjB0ondhTtd210SvW/mgs0ewO+v/BeDa6x300M2uPmjn3Oorg15c6jfOTOjpG6dfnAMhPnWl/PTlRLrTh7uq5eQaqsO1Otc1EGt6a3k2aBfwK1rj5E8+xzecdcP9GTS7KjkSr0lmtmFe13biRG+0QTgiHBbw2YVV1T7YfSa5JlgWkD9enXb/f0xgbfD3Rd/Jnef7IGomld6/Q6ataVQbX0uF+1fz1KnNVMhygmjBnrHkttzB96Ef92XwqWle3G24Oyr41mXs45n1z8LWMt1Hs47SOyIWc1Tz8ow4OJHweagc3UNj2fn4GdYXzde2vLSSS/DOhX165kNqKwERwgk9W/21/EFUfUSY4erCvmpXgf45HqfE9I+hPmHEW1af5+ZNhOqyk7reZ5dfjcVdbVRp1T50WnoCS5OnX0LtgHXMe9QAbceKnDtfmnzS9y/8v5jzkrNL3fPhItxmuAfetQxXi88CUbdAcAvCgtJrCsxseKnFSe1AupYasoPUexn/T+OsGnJ+ulS0kykHlvvn7m6aFaYNazOXt3k8bvqEgg206RT2qgWj68l+IfG89Slr9K12vqAyqSame/9jLLqE39wmqZJSVUJewv38m32t3y852Ne/f5Vnlj7BHf99y5u/M+NPLP+Gdfx3YvrPvASep351WwfkdJhsGt73xlOjW8NB6rcBUk7xGm2oEtTybMjPJk0i66XNAtoWNcspzTbtZ0Y7VuJ0HB7oGvb32lyWc9rfarOUeNk6JrAAHb5W4nCgfEDG9zvrVLDUvGv66C50+GAQ9aX/E+/WUiVzdp/SXRvHI622wCgvpDwZIbXzWrPsxls+qF1Z5sdrjjM3OVzXTPAbyooYlhQEoyc3XwvEpcBI2YCMLyshDtxzwh54JsHWJtzZvU7D5YfZEnmEp5c+yS/+vRX3Pv1va77BlSonllTouqVFCioLmVfyQHAOo9NjNdnenuUWlfLNNdup+wYHYpPZFv+D7yfY3XBDKt1cvOQ28HP3vSDDAMufQIjbQQ3FhZxz8FD1OXc+MeOf3DHsjuoqq1q8JD8eufIMbZA3/0eMXIOxPUkyDSZk+e+UP3ot49SXVt9yk9XWG8JdpQtsIkjpSknGNEi7UzyYM4zgzlyHfTLPZ8yKuXYyTCn6WSPWQGGQWqtk4BI752hEZ7Yj+dHPsy0Fb8j1+7HpppC5v1rCjPPf5S8sjwOlh/kYPlB8srrbZflkV+RT3lN+YlfALBh0L3qSBOAtl13pjVFxGQQXuukyM/GvtrTu8LXmg44K8HPhs00idNSjqMdSZ71uxa+ex2WPw6FmWAPgo7nnPDhLeXI8kw40gzAfWKcXXnItZ3ogUYFLSncHgI1Vs2mseUVRAy9ycMRNa+IgAgSghPIKcthu78/b4f51iwzAD+bH539I9lWdZhMh52q3O/xj+rE+/kbwN86jb18yCzPBnmKxiadw7L9Vg3RJVvfpN9ZV7fK6zpNJ3evuJvcMms2+ZDyCm4uKIRpf7VqNjWnUXNh4ztQtI8pu9eya9hk3sj9hhpnDbO/mM3rl77uqrXYlMraSrbmb2Vj3kY2HtzIxryNHCg9cMxjg5xO+ldUQqeRzfu7+JDIet0SD9dWsN+0zjsSa2pxxHh/kl1OXcfAGL4rs2btZuVsoHuHk++2bJomjy2/k7oJUdxYG0Rkv5+f3IPt/jDlVfjj+UwuzCTc6eTO+DhqMPk883N+s+Q3PHX+U64SA/ll7gZtMXbfalrUgN0fLn8G/nIBF5WW8UZlFesD/NlbtJc3fniD63tdf0pPV1jkXsUS4fDhf7cWpqSZSH02G8O7XoZ/9kdU2QyWZS7FNM1jdh47cGg75XX7u/iFeP0Vj8Qel/P8oV1M3/ZnSmw2lpXsZdkHZ34i77A5iA+OZ2pgKlF7frR2qp6Zm81GCna+x8kBw0l1dQUORxu9ElRdTrbNuhQYjx92mz5Cjqt+8mzPMojsCKHxHgsnOTSZUEcIJdWlbPd3wEH3sunsmlKwgcM0iarXScsXpAfGQYX1vjM5bqhHGjG0tIyoDHLKcij2s/FpiPXlIjowmgs6XuDhyJpPemgq2w4dptYw2Ju9Dv+iLL6rS5h1s4XQs8MwD0d4akYPvAXbT5/iNAyWFG7nNqcToxUKsL+y5RXXEp/o2loezsvH76yJ0G1c87+Yfwhc9Ht42/qCN++Hlfx41tl8nb2KgsoCbl1yK3+/5O+E1ltiZZomWcVZruTYxryNbDu8jRpnTZMv1SGkA30qKrn6wFbCTBM6e+fM/9YQGBRNkNNp1bc0qyisWyqcXFPTPMtzxeukhaZAmfU5mZn/Pacy3/yrff/lmyKrVE1ydQ3XnvPQqTWTCImFqW/AXy7kotJSwrJzuK1DEuVmDasOrOKGT2/guXHPERUYRX6Ju6ZljH/4KUTphVKHwLCbMVY9z/87mM/UpA6YBrzw3Qtcln4Z0fW64J5IQYn7IkOk4zQ7I4uSZiKNBfe5mmF7/8F/g4PIrSnh+0Pf0yvm6CTPrsz/urbTfaR4dsaI23gqfxs3539F9QmSgGH+YcQFxREXFEdMUAxxQXHEBsUSGxxLbFCs63a4f7iVdFx8i/vBSpo1kOII43tnIU7D4ED2etJSh3s6pGMqP7yHw35W150kP12tOil2f+jm+eSFYRhkRHVnXe46DtjtFObuJMI0wTDIoRYwSHAa2Oz+J3wub3JJt0lUfvwfYkyDwT+/y9PhtIju0d3570/W59GRq/2Tuk7C3893/l92jT0LDm0EYFf+Vnb8+B+OtGKemH75MS9stWVRMV0ZTCCrqSTTD3bs+YyM9PEt+prf5X3XoBD/grx84m2BMH5By71oz8shfQzsWoq9MItHbZOYFt6JvUV72VW4i7nL53LdWde5EmSbDm6ioLKgyacMsgfRK6YXfeP60je2L33i+hAfFAeP94CKSqvO0SnMlGl3DIMo06AcyLW5C64nOw0I9a4u8NI80qK6Qu4KADILfzzpx9U4a3j86/tct2cbUfj3nHDqAST2hp/9Ed6axjnlFfzpp338JrUzRbXlbM7fzPRPpvPHC/5IfslProfE+Fin72Maczf88BG9CjOZWFLCe2GhFFcXs2j9Iu4Zfs9JP01BmbtOdWRAREtE2i4oaSbSWPIgRpuBHEmJLdv9yTGTZrvrdc7s4gN1Y44Yeukinn37av6Zv4FQ0yS2ppY400Zs90uJHTCd2LBkYoNiCfA7xfoxOZut/xo2iOvZ/IF7sdSgeCi1aoVl5axrs0mz7NxNru3EgEjPBSKnJSMqg3W56wDYblQxpPgApUBxXV2oxFP9m/YCjrMmMjkkDgIjIeEsT4fTIhrXLTMwuDqjdZb7tZb0xMGw/U0AdhzczAdBDsCOnwmXDrjRs8GdpjEJQ1mda51pLNn8aosmzQorC5m7bC41pjVj64aCQkaUV8CFD0JEcou9rqspwHNng7Oa8JUvsGj6YqZ+/TuKqor46qev+Oqnr5p8ii4RXegT24e+cX3pF9eP9Mj0o2c5H9wBJXW1GVXP7ISi8GM/zgb7kv0jvH7FhJyetLg+UDf5PLMsu+mD61m8/R/sqrBqbvWtqGT82KdOfwz1vAzG3ANL/49+lVW8/NMBburYibzKAvYU7uG6j68jxeFO+MQE+d6s8aMEhMKEJ+HvP2PW4QL+ExJCmc3g3R3vMrn75AaNgJpSWG9Za8QpzFCThtQIQKQxw2BUpwtdN7/c/fExD9tV6O44lO6FnTOPy+bHiCn/4LFzH+a+CgczCwqZUniYsav/Tr+3biD5p42nnjCrrYG8urbmMd2grS4/9JCUejVG9uX/cPwDPezAj8tc2x2CdUXa2zTstOiAvG3k1Ev+Jzp89ApkxxE+mzCDhvXqAEYmjyQlLMVD0bSMrjHuCy3vBTnItltJkxER6cQGxXoqrDMytv//uLaXHNrUxJFnxjRN7llxj6sO2MCKCmYcLoT4s2DYzS32ui6xXeGc31rbzmrSlj/BH0Y/gd04+rp9ZEAk5yafy4z+M3jxghdZMXUF7096nwdHPuj6knhUwqy2BrbU68ipemYnFHmMc7jkEN9YMSGnLq1eQ6rM6qKTekxpdSnPrn3CdXtuQBpG+vlnFsi5t0PvqwDoVl7EKzkFpIVaSf3s0mzWFLjLSsS0l/HadSz0m0psrZMbCwoAqzblwm8XYppm04+tU1Dhrlsb6aWfl22BkmYix5DYdxo9K62uLVsrcsgpzTnqmN11nTMN06Rz2nmtGl+LMwzoOxlmroHhM8GwluRR8CO8cQ28NhkO7T7558vfAUe64Ghp5lFSY9xf6PfV63LTpmz9gJU7P3Ld7BCv5S/epmGnRasZQPbBra59CcGeq7kmpy8tPA1/m3sp5pTuUzwYTctIDk0moK6DZp7dnTSZ2PcGT4V0xhI7DKCP0/pdttmcZGV93SKv89rW1/gi6wsAImudPJKbby0zufSJ1puRde7tcKRr466lDD2czeOjH+ec5HOY2mMqvx/5ez664iOWT1nOc+Oe4+Z+NzMiaQThx6tbVFkC3/8LFt8Mj3WDLx5y39fp3Jb/fbxclN/RTR+SIzp7IBJpC8KCY4h2WgmYTLPqBEdb/rrxTxyqsZpIXFBaRv8xD535TEXDgInPQtJAAFIOZ/K3wlq6N5pN7WeaRIa1o0ZU438PwbFcV1hMSrXVUG119mqWZi49qYcfrnInQiNDdMH7dClpJnIsyQMZ7XR/CVnWaLaZaZrsMisBSHJCUEsub/CkwHAY/xDcsqLhieiOT2HR2bD0Iag6iY6POVvc20qaHSUlcYBrO6s8t4kjPeTAdyz590xejrS+wNgwGJIx0cNByanqGtkVoy7xsM3fAQe3k1PgTn4nnkQnO2l77DY7gxOtmQKdwjsxMtn3Ztr42fzoYm+YQAkzHIzu5Pl6gWdiTKz74sPS7/7SrM9tmibvbn+Xx9c+7tr3UN5BEmtrof806NiKZQD8Q+CierXTPrmTMYnDeGHcC9w17C4mpE8gLTyt6dp0xTmw9mXrot3CLvD2dfDdG1DunkVBXE/o0L+lfgufEeUIPWpfcpzOzdqzVMP6zpPrZ1BW/2/qGLJLs3lly98AsJsmt4X3hrRmasbiCIJrXoewDgDEZq7mr7VxDIx3nydH19ZiC2lHM6aCo+HiR/AH7jhU4Nr92JrHqKo9cZKzsKrYtd2uko3NTEkzkWMxDM5LHe26+eX2fza4OydvK6V1dYDS/UJaMzLPiO8J0z+Aq/7q+iCjthKWL4RFw2Drh9DUNOEj9cwAEvu0bKxeKCG+D/a6f799NSUejqaR4mx2vj2Vu6LdJ9lzBs2hS2QXDwYlpyPYEUzHUGvZ3k6HPzUHfyC7XmHdxCh1TvNWD418iDuH3skLF7yAn83P0+G0iPTQhhenLk4bd+qlAtqYcX1/5dpekre+2Z63rLqMu766i/tX3u/qPPmLgiJGlVdY9f0ueKDZXuuk9bgMutZ16Sz6CZY/2vTxpgl52+C/T8Cfx8Hj3eGDWdZFu9pK93H+oXDWRLjij3DD5+Cncs0nEtWoGLi/0yQuvq+HopG2oGO98gxZ+9c0eewza/5ARV19xKlFxaSOub95gwnvANe8BnarlEv4d2/wQsRgxgcmYzNNriwuheB2lDQD6H0lZFzEmLJyhpZXALCvZB+vfv/qCR9aUOOe3BAZ5qOTPFqBkmYix3FWv18QX2N9KKwq2UtZtftNZ3fWcte2r3TOPCHDsN60Z66Bc2bBkboihZnw1jR47So4uPPYj9VMsybZ7f4kmdbb8T6jFrO2xsMR1akup/DNa/htiJOyuhbil3Qcz/W9pns4MDldGXW1oapsBpmHd5Jd7i4Qm6CZBl4rNiiWa3teS3Ko754Qp8c0rEt3ea9pHoqk+XTqOIp0p/XeusGoIi9n4xk/587DO7nmo2v4cPeHrn1TqvyYdbjAujHuPvDELA3DgIsXwpGurl8/C3nbGx7jrIUfV8J/7oZnBsGiobDkftj3LVDvwlxoIgz6JUx7F+bthsmvQL8pVuFsOaHIRsXAk2pqsMX6TkMrOXWp9erUZuVtPu5x3+d/zwd7/w1AeG0tN8WPgKT+zR9Q8iCYuMh1M+iz+Tx28DArf9zHjIJCCI5p/tdsywwDLn0Cwz+MefmHsdVdaP/jxj+SV5bX5EMLnO6LDJHhaS0api9T0kzkOIzkgYyqtRJDVZh8s/c/rvt25Xzn2u5ykt1LfEZAqHWV+paV0GW0e//Oz+H54fD5/VBV2vAxR5JmgREQ7rtf6s5ESt2MxVKbjcN5W05wdCswTWoX38L/q9lHlsOqe9Mzsiv3jfy/ppfQSJtWvxnAtupCsisLXLcTY323WL54v65po1zbnUKS6BvrGzNjxkZaiWzTMPhi/Z/O6Lne3/k+Uz+ayp7CPQCEOEJ4NGE0d/+0x6pjljwIBnrwokdMunXRDcBZDR/PtUo8/PARvDcDHsuAly6Cr5+BQ7saPjaup1Ub7YalMGer1VWu2wVg9+7Zhp4Q3ah+ZbITawmYtFsdw9017X48vP2Yx5imyeOrH3Glr28qKCJizPyWC6rPVXDuHXUv7oS8Hwg+sqqlPS3PPCIiGS64j+7V1VxVbK1KKasp4+n1Tzf5sEKnVQctyGniH3icOpFyQkqaiRyPYXB+B3fNj2Xfv+Xabtg5czDtUlwGXPceXP03CK/r1FZbBV89Ac8OhS3vWcsryg5ZSzEAEnqrpflxpNRrn70ve60HI6mzbCFPH1jKimCrYHCUI4wnxy4iyH50AWHxHvU7LW7zd5CDdTIVYEJkYJSnwhI5oQEJAwmtq8V0XZ9f+0zyfmyvn7u2l2R/c1rPUV5Tzr0r7uXuFXdTUWst3cmI7MabqZO4aM071kGGzSr+b/Pwqf/IORBRN9th95fwSEd481rY8Hcoc898xbBBx3Pgwofgt+thxjcw9l5IGeT538HLNS4GnuwI07lZO5cW08O1nVWvbEN9y/ctZ3XuOgBSq6uZ2vFiiO9xzGObzfn/ay3tbiyonZ6vDPoVpI1gxuFCwmqdgHWxZEv+8S+2FxjWcZEn12xTjsOjnzrLly9nwoQJJCUlYRgG7733XoP7TdPk3nvvpUOHDgQFBTFu3Dh27NjhmWClXRra/9cEOq03m2UFW3Ga1vbuCveJXZeOo4752HbBMKDXJJi52joRttV14iraB+9Mh1cnNWwFn9DbE1F6hdRwdxH2fXnfezASYPM/+eTbJ/lrpFXjwg8bj495iqRQFRD1dg1mmvn7k13XiTDRcPhMEkJ8U0RABP+8/J+8fNHLXJ1xtafDaTY9u15CknVqwWrKKTy855Qev6dwD9d+dC2Ld7o/a6+M7s9ru3fQackCqCm3dg75n5ZZRnWq/IPh4ofdt+sXsnYEW1+QJz0Pd+yEX/4bRsyEaNXQbE5RjeoaJde7aCftU2pif9f2j/W+4xxR7azm8W8Xum7fVlCM4/y7Wj4wmw2ueLHh94fAiNbr/NvW2Gxw+dNEGw5uLigEwMTkkdWPYB6jtrTpdFJYd2oXafhmvdPW4tGkWWlpKf369WPRokXHvH/hwoU8/fTTvPDCC6xatYqQkBDGjx9PRUVFK0cq7VVgylDOrrX+TPKpZXPWV5hOJ7uw1ocnOiEktJ3UNGuKfwiMmw+/+QbSx7r37/4SPprjvq16ZseVUi+ZkVW413OB/LSWbR/dyj2x7qUac4fOY0jiEM/FJM0mITiBcD9rtuB3AQGU1s3YSDhGNzWRtqZDaAcGJQzyqQSvYbMxJqwrADWGwfJ1L5z0Yz/a/RFTPpzCzgKrnmiQzcHvKwK4b+2/CCzMdB/YZ7JVy6yt6H4J9L3G2g6Jh4HXw9S3rPpk17wG/a+FkHZWs6gVRTWqa5Qc3tFDkUhbER7bg6jaWgAya8uOuv+f2//JnmLrPWVARQXjMq6C6M5HHdciAkJh6hvu4v8d+jV9vK+L7QbnzWNqUTGdqqzVAutz1/PJ3k+OOrSkLI+aus/LyLoOqXJ6PJo0u/jii3nwwQe54oorjrrPNE2efPJJ7r77biZOnEjfvn155ZVX2L9//1Ez0kRajGEwOm6g6+aXm/5Gfu4miuq+aLaLzpmnIrYr/PwfMOU19/KL+jTT7LhSEtzttPeV5XgmiMKfOPzmtfw2JpyKujE+KX0S1/a41jPxSLMzDIOMSKtLZrGf+xQgMbAd1gcRaSPGdXfPnFuyb3kTR1oqayt5YOUD/O6/v6O8biZZumnnjcwfmXCg3oqM9LFw4zK48k/WDK+2wjDcs8lu3waXPwPdLwKHlv+3hvCINIx6s1KS21ttXjmaPYA0p5VcyTWcrvcVgJKqEp7b8Kzr9h0FpRjnzWvd+CLT4MYv4cIHGzQIaLfOmYUjoTdzDx127Xpi7RMN/r8BFBS5L55E+gW2Wni+qM0WBdizZw/Z2dmMGzfOtS8iIoJhw4axcuXK4z6usrKSoqKiBj8iZ2JUv1+6tpcdXM+urK9ct7sEd/BESG2bYUDPy2DGKhg1D/zqivQGREB8T8/G1oalxLuvnO2rLmz9AKpKqXljCneEONnvsJbs9Ynpxd3D7/apWR0C3eP6HLUvQUtvRTymf8+riXZaSYwVzmLKS45/4SSzKJOf//vnvLP9Hde+y4tLeP3HPaRX13VeThkC0z+E6/7ZNpZkHovNBqFxqk/mAX6BEUQ46yXN6i3Nk/Yrrd5EgKzDO13bf9n8Fw7VNQ26qKSUvv1+YRWlb22RqTDiViuB1t75OeDyZxhVUcU5ZVaiLLs0m5e3vNzgsMJid326CIcmepyJNvtJlZ2dDUBCQsNilQkJCa77jmXBggVERES4flJTU497rMjJiOt4Hn3qzkO3U81Xez9z3Zce3cIFML2ZfzCM+V/4zUqrkOe0t9vWle42JjQwnCjTSk5lUQO1Na334k4nLL6Jx6uyWB1kXYmKCYjiD+c/RYCfOpP5mu7RRyevEyNbaZmFiBzFz+7g/GDri2CFzeDrdS8e87j/7P0Pkz+czA+HfgAgwOnkgbx8Hjx4yOoqF9cDrnkdfv0ZdD631eIXL2MYdKiroxdRW0tkQjtf7iYApAW6l0RnZq8H4EDJAV7d8goADtNkVnEVjLzNI/FJI8kD4ezfMO/QYfzqZo6+tOmvZJe68yQFxQdc25GOsFYP0Ze02aTZ6brzzjspLCx0/WRlZXk6JPF2hsF5Ue5aXO+UuJc+pHdop50zT0VMOpw3D9LO9nQkbV6KzVqakutno/Lw7tZ74S8e4l9ZS/l7hNWK2m748YcxT5HQqMOW+IaM6Iyj9iXG6AKAiCeN6zbRtb3kx88b3FdVW8WCVQu4fdntlFaXAtCpqprX9udwRUkpRkSqtdzxlq+hx6XqhCgndKsjiQEVFfyu0h8juJ12IpQGOoa6Z49l5m8F4On1T1PptJp1TCssJmXITdYMUWkbzv9fuoSmcE1RCQDltRU8ue5J190FZbmu7ciAyFYOzre02aRZYqJVXD0np+EU9ZycHNd9xxIQEEB4eHiDH5EzNbr3NNd2ab2lBJ3TdCVXmk9KoFV83zQM9u9f0zov+t1bbFn1NPfHuAv/33X2/zIgfkATDxJv1jWyK340/FKdENXVQ9GICMCwPtMJrVsy92VNPtXlBQDsK97H9H9P4/UfXncde3FJKW/uz6a7IwIuegRuXWsVz7epO5qcnHMnvMgr3aZz2RWvejoUaSPS6p0HZBbuZcvBLXy4+0MAImtr+Z8K01oeKW2HfzBMeIpbCgqIrGvk8NHuj9iQuwGAgnJ3J9SIoOhjPYOcpDabNOvcuTOJiYksWbLEta+oqIhVq1YxfPhwD0Ym7VFG10tJdDbcF+eEiJB4zwQkPiklNMW1nXVwS8u/YNZqDn74W2YlxFJls5IoV2dczdUZV5/ggeLNAvwC6NSo8H9iiLoAi3iSwxHIqEDr77DYZuPbDX9h6e6PmfzeRDbXLcd0mCb3HDzEI4VVhJx3J8zaAGffDHYto5dTFN0FxtwNiUfXuJT2KTXO3azrx7JsHlvzmOv2zQWFhA+/FYIiPRCZNKnLaCL6TWPGYXc95EdWP4LTdFJQ4W4UEBWs76xnwqNJs5KSEjZs2MCGDRsAq/j/hg0byMzMxDAMZs+ezYMPPsi//vUvNm3axPXXX09SUhKTJk3yZNjSDhk2G6PDGs7E6GIP9VA04qtSo7q5tvcV7GrZFyvIpPrNa7k9Npwcu1X4f0DcAO4cemfLvq60CRmR7vezIGyE+2tWtoinje18kWv7gR/+xqz/zqO4bmlUSnU1f8/OZ3Kv6RizNsDo/wcBqlEjIs0jPLYnUXWzldZXHWRNjrXioWN1NZNrAmHYLZ4MT5py4YNcZYbQtcr6vNicv5kPd39IQZW7IWKkSq6cEY8mzdasWcOAAQMYMMBaBjRnzhwGDBjAvffeC8C8efO49dZbufHGGxkyZAglJSV88sknBAaqZaq0vtE9rmxwOz1Y3eakeaUk9HVtZ5UcaOLIM1RZDK9fwyOBNayrez+ND47nifOfwOHnaLnXlTYjo8MQ13ZCYIw6pIq0ASP73UBAXUHnn2zu7oYXlJbxdux5nHXzarjo9xASe7ynEBE5PREppNV14K2tt/u2QwU4zp0DAZos0GYFRWG/5FHm5btnlj255gkOVLlnn0WoS/oZsXvyxUePHo1pmse93zAMHnjgAR544IFWjErk2Ib0uJrgNY9QVvfdMj26u2cDEp+TGu/uYLWvqqBlXsRZC/+4gXfL9/JWrNUpyd/mz1PnP0VskL6ItRfd63X+TYxK92AkInJEcFAkwx3RfFljffGxmyZ3OJK59qqnMOLVrENEWpA9gDQcfFdv18CKCsb4RcHgX3ssLDlJZ01k+Ma3GX3oG74MCSavIp+8endHhqcc96FyYm22pplIW+NvD2BEaEfX7S4dz/NgNOKL4kMScNRdR9hnVkJtTfO/yOf3seHHpTxUr/D/PcPvoXds7yYeJL6md2xvguxWt9aeMT09HI2IHPGrIXMJMqET/rwyZD7Tpn2qhJmItIo0R8NSDXPzCzDOmwsOrfLyCpc8xtySWuyNJiXZTZOQsOTjPEhOhkdnmol4m/8ZtYCNn99Et/BODOg0ztPhiI+xGTaSbQHsNSv5ye6HWZCJEdOl+V5g/d/JXbWI25ISqalbjjet5zQmdZ3UfK8hXiEqMIpFYxex6eAmrsq4ytPhiEidARkTWJF+EXabXcumRaRVdQ5OhMqdAFxSUkrvkCQYcJ2Ho5KTFt6BtLH3cd1X83kp0p0AjXA6MfyDPRiY91PSTOQUnBXfh8+nrtCJrLSYFP9I9lbmUG6zkZ/zHbHNlTTbu4LKD2ZzW0IsB+1+AAxNHMrtg29vnucXrzMkcQhDEoec+EARaVWqLSkinjA6pg9jdmykwjCYm38YJv4e9H7kXQZO58ZNb/N+7V4O+Vnn+1GmAfrueka0PFPkFClhJi0pNcRdqHNf7qbmedJDezDf+jkPRYexMTAAgKSQJB4971EcNp0MiYiIiLR3AVGdeSr3IC/m5BEbkwF9rvZ0SHKqDIPQCc/w28JS165IQ/OkzpT+BUVE2pCUqHQ4tB6ArEPb6X+yD6ypgooCKC+AisJ62wWw+k+8aa9icZhVxyzQL5Anz3+S6MDo4z+fiIiIiLQfnUeBzW41jbrg/8Dm5+mI5HTEpDNp8Cw+2/wcK4MCudyI8HREXk9JMxGRNiQltjfseheAfYd3wPrXGiXBjrNdU37c5/w2MICFifGu2/ePuF/F30VERETELSYdZn4LNZUQr/NEb+Y34rc8v3sZFXuXE3TZXE+H4/WUNBMRaUNS4/u4tveV58L7vznqGBMoNwzKbAYlho1Sm42SwABKbTZK6+0rtRmU2Gx8GhLsKvz/y16/5JIul7TWryMiIiIi3iK6GRtQief42TGuW0xQdRkEhHo6Gq+npJmISBuSHJbi2v4qOIibE+IoqUuAldpsdQkxA+dp1NYbkTSCWQNnNWe4IiIiIiLS1thsSpg1EyXNRETakGBHMDEBUeRXHuaQnx8rgoOa5XnTI9JZOGohfqpPISIiIiIiclKUNBMRaWN+lnEVf9r0pwb7guxBhDhCCHWEEuIIcf2EOkIJdgQT6ggl1D+UYHswof6hRx2bGpaK3aa3fBERERERkZNlmKZpejqIllRUVERERASFhYWEh4d7OhwRkZOSW5aL03QS7Agm2B6shJeIiIiIiEgzOJU8kb6FiYi0QfHB8Sc+SERERERERFqMzdMBiIiIiIiIiIiItDVKmomIiIiIiIiIiDSipJmIiIiIiIiIiEgjSpqJiIiIiIiIiIg0oqSZiIiIiIiIiIhII0qaiYiIiIiIiIiINKKkmYiIiIiIiIiISCNKmomIiIiIiIiIiDSipJmIiIiIiIiIiEgjSpqJiIiIiIiIiIg0oqSZiIiIiIiIiIhII0qaiYiIiIiIiIiINKKkmYiIiIiIiIiISCN2TwfQ0kzTBKCoqMjDkYiIiIiIiIiIiCcdyQ8dyRc1xeeTZsXFxQCkpqZ6OBIREREREREREWkLiouLiYiIaPIYwzyZ1JoXczqd7N+/n7CwMAzD8HQ4zaKoqIjU1FSysrIIDw/3dDgiLUrjXdobjXlpbzTmpb3RmJf2RONd2iLTNCkuLiYpKQmbremqZT4/08xms5GSkuLpMFpEeHi43nik3dB4l/ZGY17aG415aW805qU90XiXtuZEM8yOUCMAERERERERERGRRpQ0ExERERERERERaURJMy8UEBDA/PnzCQgI8HQoIi1O413aG415aW805qW90ZiX9kTjXbydzzcCEBEREREREREROVWaaSYiIiIiIiIiItKIkmYiIiIiIiIiIiKNKGkmIiIiIiIiIiLSiJJmIiIiIiIiIiIijShpJiIiIiIiIiIi0oiSZl5m0aJFdOrUicDAQIYNG8bq1as9HZJIs1i+fDkTJkwgKSkJwzB47733Gtxvmib33nsvHTp0ICgoiHHjxrFjxw7PBCtyhhYsWMCQIUMICwsjPj6eSZMmsW3btgbHVFRUMGPGDGJiYggNDeXKK68kJyfHQxGLnJnnn3+evn37Eh4eTnh4OMOHD+fjjz923a/xLr7u4YcfxjAMZs+e7dqncS++5L777sMwjAY/PXr0cN2v8S7eSkkzL/LWW28xZ84c5s+fz7p16+jXrx/jx48nNzfX06GJnLHS0lL69evHokWLjnn/woULefrpp3nhhRdYtWoVISEhjB8/noqKilaOVOTMLVu2jBkzZvDNN9/w2WefUV1dzYUXXkhpaanrmNtuu40PPviAd955h2XLlrF//35+9rOfeTBqkdOXkpLCww8/zNq1a1mzZg1jxoxh4sSJbNmyBdB4F9/27bff8uKLL9K3b98G+zXuxdf06tWLAwcOuH6++uor130a7+K1TPEaQ4cONWfMmOG6XVtbayYlJZkLFizwYFQizQ8wFy9e7LrtdDrNxMRE89FHH3XtKygoMAMCAsw33njDAxGKNK/c3FwTMJctW2aapjW+HQ6H+c4777iO2bp1qwmYK1eu9FSYIs0qKirK/POf/6zxLj6tuLjY7Natm/nZZ5+Z5513njlr1izTNPU+L75n/vz5Zr9+/Y55n8a7eDPNNPMSVVVVrF27lnHjxrn22Ww2xo0bx8qVKz0YmUjL27NnD9nZ2Q3Gf0REBMOGDdP4F59QWFgIQHR0NABr166lurq6wZjv0aMHaWlpGvPi9Wpra3nzzTcpLS1l+PDhGu/i02bMmMGll17aYHyD3ufFN+3YsYOkpCS6dOnCtGnTyMzMBDTexbvZPR2AnJyDBw9SW1tLQkJCg/0JCQn88MMPHopKpHVkZ2cDHHP8H7lPxFs5nU5mz57NOeecQ+/evQFrzPv7+xMZGdngWI158WabNm1i+PDhVFRUEBoayuLFiznrrLPYsGGDxrv4pDfffJN169bx7bffHnWf3ufF1wwbNoyXX36Z7t27c+DAAe6//37OPfdcNm/erPEuXk1JMxEREQ+aMWMGmzdvblD3Q8QXde/enQ0bNlBYWMi7777L9OnTWbZsmafDEmkRWVlZzJo1i88++4zAwEBPhyPS4i6++GLXdt++fRk2bBgdO3bk7bffJigoyIORiZwZLc/0ErGxsfj5+R3VYSQnJ4fExEQPRSXSOo6McY1/8TUzZ87kww8/5IsvviAlJcW1PzExkaqqKgoKChocrzEv3szf35+uXbsyaNAgFixYQL9+/Xjqqac03sUnrV27ltzcXAYOHIjdbsdut7Ns2TKefvpp7HY7CQkJGvfi0yIjI8nIyGDnzp16nxevpqSZl/D392fQoEEsWbLEtc/pdLJkyRKGDx/uwchEWl7nzp1JTExsMP6LiopYtWqVxr94JdM0mTlzJosXL2bp0qV07ty5wf2DBg3C4XA0GPPbtm0jMzNTY158htPppLKyUuNdfNLYsWPZtGkTGzZscP0MHjyYadOmubY17sWXlZSUsGvXLjp06KD3efFqWp7pRebMmcP06dMZPHgwQ4cO5cknn6S0tJRf/vKXng5N5IyVlJSwc+dO1+09e/awYcMGoqOjSUtLY/bs2Tz44IN069aNzp07c88995CUlMSkSZM8F7TIaZoxYwavv/4677//PmFhYa56HhEREQQFBREREcGvf/1r5syZQ3R0NOHh4dx6660MHz6cs88+28PRi5y6O++8k4svvpi0tDSKi4t5/fXX+fLLL/n000813sUnhYWFuepUHhESEkJMTIxrv8a9+JI77riDCRMm0LFjR/bv38/8+fPx8/Nj6tSpep8Xr6akmReZMmUKeXl53HvvvWRnZ9O/f38++eSTo4qji3ijNWvWcP7557tuz5kzB4Dp06fz8ssvM2/ePEpLS7nxxhspKChg5MiRfPLJJ6oTIl7p+eefB2D06NEN9r/00kv84he/AOAPf/gDNpuNK6+8ksrKSsaPH89zzz3XypGKNI/c3Fyuv/56Dhw4QEREBH379uXTTz/lggsuADTepX3SuBdfsm/fPqZOnUp+fj5xcXGMHDmSb775hri4OEDjXbyXYZqm6ekgRERERERERERE2hLVNBMREREREREREWlESTMREREREREREZFGlDQTERERERERERFpREkzERERERERERGRRpQ0ExERERERERERaURJMxERERERERERkUaUNBMREREREREREWlESTMREREREREREZFGlDQTERERERERERFpREkzERERERERERGRRpQ0ExERERERERERaeT/A8V7LuMv+Gm4AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "preprocessing.plot_with_interval(y_test, rf_test_pred, rf_test_interval)\n",
    "preprocessing.plot_with_interval(y_test, rf_test_pred_pf, rf_test_interval_pf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Partial fit doesn't seem to work really well in our case. Updated residuals don't help with the reliability of the model. Increasing the number of resampling and the block size in our bootstrapping can help a little bit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The high of the day was outside of the one-sided predicted interval 48.214285714285715% of the tested days when not using partial fit\n"
     ]
    }
   ],
   "source": [
    "rf_mean_error = preprocessing.performance_conformal(y_test, rf_test_interval)\n",
    "print(f\"The high of the day was outside of the one-sided predicted interval {rf_mean_error*100}% of the tested days when not using partial fit\")"
   ]
  }
 ],
 "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": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}