examples/time_series/cases/9.ipynb

{
 "cells": [
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   "id": "467482e4",
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   "source": [
    "### Example 9\n",
    "\n",
    "Refer to [/examples/time_series/generate_data.ipynb](https://github.com/serhatsoyer/py4ML/blob/main/examples/time_series/generate_data.ipynb) for the artificial time-series dataset (used in this example) generation  \n",
    "\n",
    "|Case|Uncoupled All Sensors|Coupled All Sensors|Uncoupled Less Sensors|Coupled Less Sensors|\n",
    "|-|-|-|-|-|\n",
    "|Example Number|[2](https://github.com/serhatsoyer/py4ML/blob/main/examples/time_series/cases/2.ipynb)|[8](https://github.com/serhatsoyer/py4ML/blob/main/examples/time_series/cases/8.ipynb)|9|[11](https://github.com/serhatsoyer/py4ML/blob/main/examples/time_series/cases/11.ipynb)|\n",
    "|ID|0_1_0_3_128_1_0_1_0_0|1_1_0_3_128_1_0_1_0_0|0_1_0_2_128_1_0_1_0_0|1_1_0_2_128_1_0_1_0_0|\n",
    "|Total params|17,185|17,185|17,057|17,057|\n",
    "|Total time (min)|1.5|3.1|1.0|1.6|\n",
    "|Num of epochs|49|107|31|51|\n",
    "|Time per epoch (s)|1.79|1.76|1.92|1.85|\n",
    "|Train RMSE|0.53|0.57|1.60|1.42|\n",
    "|Test RMSE|0.58|0.81|1.68|1.58|\n",
    "\n",
    "- More difficult to do predictions when the sensors are coupled\n",
    "- As the number of sensors used in the input is decreased, the performance deteriorates as expected\n",
    "- The performance deteriorates less when the sensors are coupled as expected\n",
    "\n",
    "Previous example: [/examples/time_series/cases/8.ipynb](https://github.com/serhatsoyer/py4ML/blob/main/examples/time_series/cases/8.ipynb)  \n",
    "Next example: [/examples/time_series/cases/10.ipynb](https://github.com/serhatsoyer/py4ML/blob/main/examples/time_series/cases/10.ipynb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6a748aa8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.insert(0, '../') # To be able to reach 'functions.py'\n",
    "sys.path.insert(0, '../../../') # To be able to reach 'datasets' folder\n",
    "import functions as fn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f978cd32",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Do the settings in this cell and run all\n",
    "class Train_Sets:\n",
    "    vers = 0 # 0 or 1\n",
    "    within = 1 # 0 or 1\n",
    "    inter = 0 # 0 or 1\n",
    "    numOfSens = 2 # 1 or 2 or 3\n",
    "    batchSize = 128 # (2 ** 0) or ... or (2 ** 8)\n",
    "    useHalf = True # False or True\n",
    "    dense = False # False or True\n",
    "    shuffle = True # False or True\n",
    "    stateful = False # False or True\n",
    "    resetStates = False # False or True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2fb5b2fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ID: 0_1_0_2_128_1_0_1_0_0\n"
     ]
    }
   ],
   "source": [
    "Train_Sets, Paths = fn.init(Train_Sets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e3350324",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train shape:      (9216, 32, 2)\n",
      "y_train shape:      (9216,)\n",
      "X_val shape:        (3072, 32, 2)\n",
      "y_val shape:        (3072,)\n",
      "X_test shape:       (3072, 32, 2)\n",
      "y_test shape:       (3072,)\n",
      "Common data type:   <class 'numpy.float32'>\n"
     ]
    }
   ],
   "source": [
    "X_train, X_val, X_test, y_train, y_val, y_test = fn.split_data(Train_Sets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6ad3409e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Metal device set to: Apple M2\n",
      "\n",
      "systemMemory: 8.00 GB\n",
      "maxCacheSize: 2.67 GB\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-12-11 23:06:03.074957: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:306] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.\n",
      "2022-12-11 23:06:03.075228: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:272] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/128\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-12-11 23:06:03.895490: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n",
      "2022-12-11 23:06:04.888722: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2022-12-11 23:06:05.065859: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2022-12-11 23:06:06.315421: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2022-12-11 23:06:07.957691: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2022-12-11 23:06:08.589661: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "70/72 [============================>.] - ETA: 0s - loss: 19.7067 - root_mean_squared_error: 4.4392 - mean_absolute_error: 3.4451"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-12-11 23:06:10.776373: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2022-12-11 23:06:10.842738: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2022-12-11 23:06:10.906226: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "72/72 [==============================] - 7s 34ms/step - loss: 19.3252 - root_mean_squared_error: 4.3960 - mean_absolute_error: 3.4044 - val_loss: 5.9774 - val_root_mean_squared_error: 2.4449 - val_mean_absolute_error: 1.9112\n",
      "Epoch 2/128\n",
      "72/72 [==============================] - 2s 24ms/step - loss: 4.8861 - root_mean_squared_error: 2.2104 - mean_absolute_error: 1.7015 - val_loss: 4.2802 - val_root_mean_squared_error: 2.0689 - val_mean_absolute_error: 1.5466\n",
      "Epoch 3/128\n",
      "72/72 [==============================] - 2s 24ms/step - loss: 3.9163 - root_mean_squared_error: 1.9790 - mean_absolute_error: 1.4715 - val_loss: 3.7007 - val_root_mean_squared_error: 1.9237 - val_mean_absolute_error: 1.3430\n",
      "Epoch 4/128\n",
      "72/72 [==============================] - 2s 23ms/step - loss: 3.4394 - root_mean_squared_error: 1.8546 - mean_absolute_error: 1.3323 - val_loss: 3.3947 - val_root_mean_squared_error: 1.8425 - val_mean_absolute_error: 1.2634\n",
      "Epoch 5/128\n",
      "72/72 [==============================] - 2s 23ms/step - loss: 3.2873 - root_mean_squared_error: 1.8131 - mean_absolute_error: 1.2778 - val_loss: 3.4082 - val_root_mean_squared_error: 1.8461 - val_mean_absolute_error: 1.2599\n",
      "Epoch 6/128\n",
      "72/72 [==============================] - 2s 25ms/step - loss: 3.1188 - root_mean_squared_error: 1.7660 - mean_absolute_error: 1.2252 - val_loss: 3.2350 - val_root_mean_squared_error: 1.7986 - val_mean_absolute_error: 1.1568\n",
      "Epoch 7/128\n",
      "72/72 [==============================] - 2s 25ms/step - loss: 3.0422 - root_mean_squared_error: 1.7442 - mean_absolute_error: 1.1927 - val_loss: 3.1641 - val_root_mean_squared_error: 1.7788 - val_mean_absolute_error: 1.1747\n",
      "Epoch 8/128\n",
      "72/72 [==============================] - 2s 25ms/step - loss: 3.0816 - root_mean_squared_error: 1.7555 - mean_absolute_error: 1.2129 - val_loss: 3.1609 - val_root_mean_squared_error: 1.7779 - val_mean_absolute_error: 1.1713\n",
      "Epoch 9/128\n",
      "72/72 [==============================] - 2s 24ms/step - loss: 2.9719 - root_mean_squared_error: 1.7239 - mean_absolute_error: 1.1758 - val_loss: 3.0724 - val_root_mean_squared_error: 1.7528 - val_mean_absolute_error: 1.1485\n",
      "Epoch 10/128\n",
      "72/72 [==============================] - 2s 24ms/step - loss: 2.9442 - root_mean_squared_error: 1.7159 - mean_absolute_error: 1.1650 - val_loss: 3.0910 - val_root_mean_squared_error: 1.7581 - val_mean_absolute_error: 1.1333\n",
      "Epoch 11/128\n",
      "72/72 [==============================] - 2s 24ms/step - loss: 2.8919 - root_mean_squared_error: 1.7005 - mean_absolute_error: 1.1449 - val_loss: 3.1745 - val_root_mean_squared_error: 1.7817 - val_mean_absolute_error: 1.1587\n",
      "Epoch 12/128\n",
      "72/72 [==============================] - 2s 24ms/step - loss: 2.8696 - root_mean_squared_error: 1.6940 - mean_absolute_error: 1.1383 - val_loss: 3.0039 - val_root_mean_squared_error: 1.7332 - val_mean_absolute_error: 1.1198\n",
      "Epoch 13/128\n",
      "72/72 [==============================] - 2s 24ms/step - loss: 2.8975 - root_mean_squared_error: 1.7022 - mean_absolute_error: 1.1504 - val_loss: 3.0127 - val_root_mean_squared_error: 1.7357 - val_mean_absolute_error: 1.1375\n",
      "Epoch 14/128\n",
      "72/72 [==============================] - 2s 24ms/step - loss: 2.8649 - root_mean_squared_error: 1.6926 - mean_absolute_error: 1.1415 - val_loss: 3.0575 - val_root_mean_squared_error: 1.7486 - val_mean_absolute_error: 1.1156\n",
      "Epoch 15/128\n",
      "72/72 [==============================] - 2s 24ms/step - loss: 2.8233 - root_mean_squared_error: 1.6803 - mean_absolute_error: 1.1186 - val_loss: 3.0152 - val_root_mean_squared_error: 1.7364 - val_mean_absolute_error: 1.0980\n",
      "Epoch 16/128\n",
      "72/72 [==============================] - 2s 24ms/step - loss: 2.8028 - root_mean_squared_error: 1.6742 - mean_absolute_error: 1.1101 - val_loss: 3.0451 - val_root_mean_squared_error: 1.7450 - val_mean_absolute_error: 1.0732\n",
      "Epoch 17/128\n",
      "72/72 [==============================] - 2s 24ms/step - loss: 2.7851 - root_mean_squared_error: 1.6689 - mean_absolute_error: 1.1098 - val_loss: 2.9868 - val_root_mean_squared_error: 1.7282 - val_mean_absolute_error: 1.1150\n",
      "Epoch 18/128\n",
      "72/72 [==============================] - 2s 24ms/step - loss: 2.7795 - root_mean_squared_error: 1.6672 - mean_absolute_error: 1.1023 - val_loss: 2.9457 - val_root_mean_squared_error: 1.7163 - val_mean_absolute_error: 1.0809\n",
      "Epoch 19/128\n",
      "72/72 [==============================] - 2s 24ms/step - loss: 2.7352 - root_mean_squared_error: 1.6538 - mean_absolute_error: 1.0946 - val_loss: 3.0573 - val_root_mean_squared_error: 1.7485 - val_mean_absolute_error: 1.0674\n",
      "Epoch 20/128\n",
      "72/72 [==============================] - 2s 24ms/step - loss: 2.7558 - root_mean_squared_error: 1.6601 - mean_absolute_error: 1.0964 - val_loss: 3.0253 - val_root_mean_squared_error: 1.7393 - val_mean_absolute_error: 1.0777\n",
      "Epoch 21/128\n",
      "72/72 [==============================] - 2s 24ms/step - loss: 2.7679 - root_mean_squared_error: 1.6637 - mean_absolute_error: 1.1024 - val_loss: 2.9708 - val_root_mean_squared_error: 1.7236 - val_mean_absolute_error: 1.1025\n",
      "Epoch 22/128\n",
      "72/72 [==============================] - 2s 24ms/step - loss: 2.7341 - root_mean_squared_error: 1.6535 - mean_absolute_error: 1.0948 - val_loss: 3.0339 - val_root_mean_squared_error: 1.7418 - val_mean_absolute_error: 1.0571\n",
      "Epoch 23/128\n",
      "72/72 [==============================] - 2s 24ms/step - loss: 2.7241 - root_mean_squared_error: 1.6505 - mean_absolute_error: 1.0867 - val_loss: 2.9419 - val_root_mean_squared_error: 1.7152 - val_mean_absolute_error: 1.0927\n",
      "Epoch 24/128\n",
      "72/72 [==============================] - 2s 24ms/step - loss: 2.7088 - root_mean_squared_error: 1.6458 - mean_absolute_error: 1.0863 - val_loss: 3.0208 - val_root_mean_squared_error: 1.7380 - val_mean_absolute_error: 1.0975\n",
      "Epoch 25/128\n",
      "72/72 [==============================] - 2s 24ms/step - loss: 2.7096 - root_mean_squared_error: 1.6461 - mean_absolute_error: 1.0903 - val_loss: 3.0039 - val_root_mean_squared_error: 1.7332 - val_mean_absolute_error: 1.0654\n",
      "Epoch 26/128\n",
      "72/72 [==============================] - 2s 25ms/step - loss: 2.6651 - root_mean_squared_error: 1.6325 - mean_absolute_error: 1.0709 - val_loss: 3.0328 - val_root_mean_squared_error: 1.7415 - val_mean_absolute_error: 1.1348\n",
      "Epoch 27/128\n",
      "72/72 [==============================] - 2s 25ms/step - loss: 2.6987 - root_mean_squared_error: 1.6428 - mean_absolute_error: 1.0901 - val_loss: 2.9660 - val_root_mean_squared_error: 1.7222 - val_mean_absolute_error: 1.1022\n",
      "Epoch 28/128\n",
      "72/72 [==============================] - 2s 24ms/step - loss: 2.7039 - root_mean_squared_error: 1.6444 - mean_absolute_error: 1.0908 - val_loss: 3.0528 - val_root_mean_squared_error: 1.7472 - val_mean_absolute_error: 1.0602\n",
      "Epoch 29/128\n",
      "72/72 [==============================] - 2s 24ms/step - loss: 2.6581 - root_mean_squared_error: 1.6304 - mean_absolute_error: 1.0722 - val_loss: 3.0254 - val_root_mean_squared_error: 1.7394 - val_mean_absolute_error: 1.0542\n",
      "Epoch 30/128\n",
      "72/72 [==============================] - 2s 24ms/step - loss: 2.6789 - root_mean_squared_error: 1.6367 - mean_absolute_error: 1.0738 - val_loss: 3.0602 - val_root_mean_squared_error: 1.7493 - val_mean_absolute_error: 1.0649\n",
      "Epoch 31/128\n",
      "70/72 [============================>.] - ETA: 0s - loss: 2.6741 - root_mean_squared_error: 1.6353 - mean_absolute_error: 1.0767Restoring model weights from the end of the best epoch: 23.\n",
      "72/72 [==============================] - 2s 25ms/step - loss: 2.6865 - root_mean_squared_error: 1.6391 - mean_absolute_error: 1.0792 - val_loss: 3.1211 - val_root_mean_squared_error: 1.7667 - val_mean_absolute_error: 1.1555\n",
      "Epoch 31: early stopping\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as lstm_cell_layer_call_fn, lstm_cell_layer_call_and_return_conditional_losses, lstm_cell_1_layer_call_fn, lstm_cell_1_layer_call_and_return_conditional_losses while saving (showing 4 of 4). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: /Users/serhatsoyer/Repos/py4ML/datasets/time_series/cases/0_1_0_2_128_1_0_1_0_0/model/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: /Users/serhatsoyer/Repos/py4ML/datasets/time_series/cases/0_1_0_2_128_1_0_1_0_0/model/assets\n"
     ]
    }
   ],
   "source": [
    "model, df, train_time = fn.get_model(Train_Sets, X_train, X_val, y_train, y_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1c649667",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"0_1_0_2_128_1_0_1_0_0\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " lstm1 (LSTM)                (128, 32, 32)             4480      \n",
      "                                                                 \n",
      " lstm2 (LSTM)                (128, 32)                 8320      \n",
      "                                                                 \n",
      " dense3 (Dense)              (128, 32)                 1056      \n",
      "                                                                 \n",
      " dense4 (Dense)              (128, 32)                 1056      \n",
      "                                                                 \n",
      " dense5 (Dense)              (128, 32)                 1056      \n",
      "                                                                 \n",
      " drop0.0625 (Dropout)        (128, 32)                 0         \n",
      "                                                                 \n",
      " dense6 (Dense)              (128, 32)                 1056      \n",
      "                                                                 \n",
      " out (Dense)                 (128, 1)                  33        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 17,057\n",
      "Trainable params: 17,057\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6a3689ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total training time:          1.0 min\n",
      "Num of epochs:                31\n",
      "Best val RMSE epoch:          23\n",
      "Training time per epoch:      1.92 sec\n"
     ]
    }
   ],
   "source": [
    "fn.train_info(df, train_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9939f067",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fn.plot_learn_curve(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fc576e3a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-12-11 23:07:10.638637: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2022-12-11 23:07:10.696824: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2022-12-11 23:07:10.775486: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y_train_pred shape: (9216,)\n",
      "y_val_pred shape:   (3072,)\n",
      "y_test_pred shape:  (3072,)\n",
      "Common data type:   <class 'numpy.float32'>\n"
     ]
    }
   ],
   "source": [
    "y_train_pred, y_val_pred, y_test_pred = fn.get_predictions(Train_Sets, model, X_train, X_val, X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8c28b462",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mse_train:     2.56\n",
      "mse_val:       2.94\n",
      "mse_test:      2.81\n",
      "rmse_train:    1.60\n",
      "rmse_val:      1.72\n",
      "rmse_test:     1.68\n",
      "mae_train:     1.03\n",
      "mae_val:       1.09\n",
      "mae_test:      1.08\n"
     ]
    },
    {
     "data": {
      "image/png": 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+PvZk6aa/Eb15NjUFoSgqeJ6DxWLUSqYA2iyrPrVgOebOvRCKoiI93QGjkUw5KYqKYDCM+novOI7D+TMeSSrj05XXv2VQ4nLZMH/+5Rg8OAu8wMPnC2LTXi9Wbw8gZ9xEGGzxm5SNCCFDrsZAwYNBqfqY16Ij53ZPBUmdeY16qzzOenw6SW8EPieddBIcDgeys7MRDAYBkM2/oqICjY2NWL16tRb4fPXVV9Dr9Zg7dy727NmDzMxMTJs2DT///DM+/vhjACTwefnll7F48eKYx/J6vfB3Y0p04cKFKC8vx+rVq+HxeHDVVVfh9ttvx6RJk7Bp06a4f2Oz2VBaWor3338fjz32GHQ6HebNm4cTTjgBAwYMgCRJsFgs+Nvf/oYNGzbg/PPPRzAY7NXAh/WsJM/48YV497274fMFEAiEYLdbkJeXDkHgIUkyVBXQ6QQ0NDTBU+/r1deuO9/Hvrqybvn6tsRsNsBmMye9kfdX6OY5ceIQCAIPk8kAjuOgqiqCwRAkScb69eT5tbfB5uWlY+jQXHAcQN4iFaoK7f3atGkPJh1zW5vvH32/J08ejnv+ehHq6rxtvv6LX1wJVVVRWnoQb775NU488TcxQYnRaIDDYQbHcagPctjWYMKORhO8UvzxcwOnYIhDxDBbE8SqA/D5AjHPs6ysFk6nBXZ7cud2T14AdPQc7c1gnvX49GM2bNiAwYMHY8aMGXj99dcBADNmzMD+/fuxZ88e7Tin04kTTjgBJ554Ir766isAwL59+7Bu3bpW9+n1elFVVdWj6549e3bMv++55x6cc845OPvssxMGPsOHD4fb7cZ9992HAwcOAADmzZuHLVu2YNCgQdizZw/8fj9uuOEGAMDxxx+PlJSUnnwaMbCelY6xceMe7NhRhlGj8lBWVousLBcEgUcoRNyb9XodAgERpaUHe+214zgO48cX4oknr4LLbUdJSfPnoDPvY6JN4713v0VJSVWPBkJpaY6Y0k1LOlO6SYbeDvTGjRuMMWPyYbWawPMcwmEZqqqC4ziYzUaoqooJEwqhqoDPF4DFYoTfT8T3aDnL7xcxZkwBnE6rFvTQNdPMo6KocDgsGD++EBs2FMd9TtHvt81mhtNpgcViQmVlPbze2AtHh8OCnJxUzHvwUgAkUHvu+esRCkmQZQW1tY3Q6/Wwu13Y4jFhm8eEqmD8YIeHigGmALJRi7G5OnBQIAgCAtku7NrVHPjU1XkxcuQgNDWJSZ3bPV0q7eg52lN9WF2BBT59xNKlS3HVVVdpgc+sWbOwZMkSTJkyRTvG5/PB6/Xi3HPPxffff49QqHV0nSx79+7FK6+8gnnz5nV16Rocx8Fut6Ouri7hMTt27EB1dTWuvvpqPPLIIxAEAVdffTV++eUXlJaWdttaOkt//FD2Z1RVxeOPvYNFi25Afn4mzGY9JEkGx3HQ6QQoioLKynoArV+7nthc6aY1btxguN12KIqCoUNzW21ayb6P8TYNt9uOE08cialTR6GxMQCfL9Bj5bOamkaEQhKMRn3cq2mjUY9QSEJNTfeJ0vVFeTA93YmUFCt4ntOCZoCcX+Ewef4pKaQU5HRaAaiQJBmKokKnE7TskF4vQFVVhEJhCIIAnudAChkEjgMGD87CBx/eiy1bSls9p5bvt98vwm43w2IxIC8vHaWl1dp5NGBAKjIzXQCAUEiGJMnQ6QQ4HBYAQHlVA6r5VNQImfh4lwlq1DqiyTGHUeQMIN/cBLOeA6AHz3MQRRmqKsNkMmiBnsVihM1mhsGgR1WVJ+79RZ/bGzfu6bYLOXpBMWnSMIDj8MP3O7BhQ3GHz9G+CubbggU+fcRrr72GRx99FHl5eVBVFccffzwuvvjimMBHlmVceeWVWLx4Ma677jps2LABa9euxZtvvoktW7bE3N/jjz+Ohx56KOa2s846C2vXrgUAFBcXo6amdZqxK9x2222wWq34z3/+k/AYn8+HKVOm4P3338e9994LANi5cydOO+00yHLfjWFSuvqhbG8z7y/NiMmQ7FpXr/4Z1133PJ548iqMGVMAjiNX1oGAGAk4yNVq9GvXE5vr1KmjsezV2UhPd4LnyRU+x/Gw200wmzOwd+9BbdNK5ss1XvbPbjcjK8sFnucBkDKezxfosUbjlhm1lrjddmzZUoqNG/fE3B793tXWkr6WY44ZGrNhJcp2dGd2INlzKDXVDkEgTcgtEQQePM9rWRxBADiOh05Hpp3CYRnhsKQFQOT8kyFJYfA8B0Egx6oqCTxUVdWa7ulzWrNmSxtZQhFmsxE8zyMriwQ6WVkuOBzN7uY6XeQxAOzz6fGrx4RibyrCCg80J2s0XAYJRc4gilKCcBpIKS4cVrVAjq4bAHieg91uQW5uGkwmfeT14JCW5oAoSq2yUNHndnddyE2dOhqPP3ElRo4cBL1ep73uW7eW4q45yzp0jvZFMN8eLPDpI2pra7FixQpcccUV4DgOK1asQG1t65Povffew4oVK3DCCSfg2GOPxfTp03HnnXfimmuuwbJly7TjnnzySbzyyisxf1tWVqb9/8knn9yt67/44ovxwAMP4JxzzkF1dXXC40wmE5YsWYL//e9/uOSSSyAIAm6//XZ8/PHHOProo7Uep76iKx/K9jbzQ6nRtqNrXb36Z1x37XP44MN7IYph+P2iVoqg0NcuvyATt916brem3jmOw+NPXImsLBdUVYUsK9rGAXDQ63UYMCAV27b5Y9bS1pdrvE0jupTH85zWwN1TZdDojFpubirq6rwx/RzhsIQvPt+kXd3T0kZ0qcZmM0Gn40FLMXTDmnPnKzGvc3eXeTtyDtXWeiHLCniehyw3m2XqdGRKq3mNAFpkTvR6klkUBB6qSo4xGAQEgwoURYVe3/zcSWYI8PtF1NQ0Ii8vAy8sugF1dV7k5WUgPd0JRVExdGiOFrRXVtYjLy8DgsDDYjEgPz8jkkkiKIqK2hAJdnY2GNEktR4/BwCLIGO4U8SIlCDSTTKa22k5cBwiAQWnPU+jUa89n5wct5b94nnyXIxGfassFBB7bndHdmXq1NFYtuwWZGW7I9k0CRxH3puxYwfjlWW3YOHC91GQnxH3HPV6A3j8sXcAkH6g9HQnysvrkJ+f0aFgvidhgU8fsmTJEvzjH/8AANx4440JjxNFEZ9//jk+//xzzJ8/H4sXL8a8efNiAp+amhoUF/dOKeYPf/gDXn75ZVx44YX44osv2jz20ksvRX5+Po499ljty/PSSy9FfX09zjnnHLz11lu9seSEdPYKu70r5UX//BTXXTv9kBhJ7uxV/4YNxdiypRSjRuXFDSjoazdjxnFJba4Aks6OjR9fiJEj87QvZgCRqSA+8jcczGYDLBYT/P5gUl+uLTcNi8UIk8mAcFiGTseD48iVt16nAyD2WBmUZtRoEOF228HzvHblf8vsc3DDjWdix44yrFy5QTvPSLO5SbtCV1UVkqRAp+MxduxgLHt1Nq6YuVB7L6MDvZZj4EDHyrwdPYeqqxvg8TQhJcUKvV4HVW3OfERDg5eWGAx6hMMSFEXVMkT0b8l/aa8P2ewFgcfQoTmwWExIS3NAUVTtfZYkGWazEXl5GSgtPQivN4DS0oPIznbBZrNAEFTIsgpvmMe2eiO2N5pQJ8bfOvWcgkJHCEelBDHAGkbs0+HinM+xz4/+Lwl2SHAkSTJCIQl6vU7LQsmyrL1fLpdNO7fHjRvcpewKDYbTM1JiPluqCm0N6elOnHbaeFx//Qu4c8752jkaCklaOREAPvl0nhYE8zwPm80Eg0GHysr6uIFSb2bCWeDTh3z66acwGIh+w8qVK5P+u19//RXnnntuD62qbS6++GIsWbIEl1xyiTZV1hYWiwWKosSc1PTftHzQl7R3hR3vQ5nMlfLcuRdCkuReaZhuWeYASCkhmdJaV676k3nt3nv3W8y9+8J2U+9/+tNpmHH+cUlnnCZNHg69XkA43NwfEg7LMBq5yEZCNhSXywKXy5rUl2vL7J9OJ0Cn46HX6xC99xYMzkRFRR2qqxt6rDdh9eqfsWbNFowbNxjTpo3BjTeeCYNR3yqoOP74EZAkGVVVHuTkuCMlnuaAQRB4iGJY27Ci38u0NAdsNhNcLiuMxtipqsrKejQ1BZN6fp05hzZu3IPNm/diwoQhMJn0MJuNiBPfxA16yO1Afb0PZrMRVqsRAAedTgdFUUADDNrc7PH4tAwODY4kSdYCCUHgEQ6TTT0rywWvNwCvNwCdToBgNGNDmYIS0Y4a2YKW2SeQR8MgWwhHpYgYbBehb+NrjT4fGpwneHqRzA+nTY4ZjXrk5LjB86SUO3RojnZ/ohjGypUboKpqpy/kKOPGDcaoUXngONJw3hJZliEIPEaNyoPH48Pp0+9vdbEyZcqouEGwTifAZDJogWd0oNTbF4F9v/McwSiKghEjRmDEiBGRD2wsbrcbX3zxBS677DKMGjUK+fn5uOCCC3DnnXfi/fffjznWbrcjMzMz5sdut2u///zzz9vMKiXDxRdfjFdffRW33XYbvv/+e+1xHI7mL8Zzzz0X27Y1e8Z89tlncLlceO6551BUVISjjjoKS5cuhSRJWL16tXbciBEjMGbMGLjdbjidTowZMwZjxozp0nqThV5hb9lSCpvNjOxsN2w2M7ZsKcX1cbId7dXR/X4RKSlWNDXFL+NFX0l3lalTR+OTT+fh3ffuxr9fvwNfff04vvr6Mfz79dvx7nt345NP52Hq1NEJ/74jPQHxaO+1Kympajf1brOZce+9F2H06Hz4fAFUVNTF9NHEXX9kA42OYxRFgShKMZ8lo9GQ8H1sCd003G7yuXE4LFqvSTSCwCM3Nw05Oe4e7U2gG9m0k8fCYNTjwIEaBAKhSD9VCKIowWw2wm43o6AgE1arSQv66N/T3idZlsFxwKhRedp7mV+QCYfDArPZCFlWEA6TySSa/aBX8e09v86cQzRorqtr1Jrio6eyksFuN6Oysh6yrEQ20nCk5wda0FNeXguXywaDQRcT+Oh0QqTHkDRI03KS2WyEyWxCLe/CJmUwXt6djp/8WaiRrWgZ9GSawpiS5cWfhtfivLxGDHe2HfTEwkFVW3/nN78+5P1QVRW5uanIznZDEEiQ1tzXRM6DYDCM666djqlTR2uvq88bQG5uKsxmA3ieZD9zc1PbvQAgWU99pL+q9TFEu4uDwaBHWpoDqqpiw4ZirFq1UcsKRgfB0efr3r2VaGz0Y++eKsy66hmcP+MRnD79/j7JfLOMTx/TltaAz+fDDz/8gNmzZ6OwsBB6vR779+/H4sWL8cgjj8QcO3/+fMyfPz/mtmiRxMLCQqSlpXVprddeey30ej2ef/55PP/889rt0YKLTqcTRUVF2u927NiBs88+G/fffz++++47KIqCjRs3Yvr06aisrNSO+/jjj2NEGOl4fKIrvu4m+gq7vVJLe3V0RVG1L6Z4dNcUQ3R5IRAIwWo1an0uVqsJXm/7Tbjd0RPQ1ms3fnxhu6l3i4WML3dkDP2HH3ZGrtKFmKkgEvwoMBjI1f/NN72I119fm9SG2jKDFe85qyppTOV5DunpKfjyi80Jr567o7E9UVBht1uQk+OKbFDkXKP9ITT4IQ9FNknSA9W8YXEchxkzjtPOVVVtDpZo9iM724W1a7e223vR1jlksRih1+tgsRiRnu6M+d3q1T/jqaeWY+HCayIbemwg2xZkoksHWZbh8wWhqiqamoKwWk2w283gOGDv3qrI+WXS/ia6lETOEVIqMxoNqAjosM1jwk4uA6KBB8TWj+vQyxjhDKIoRYTLmNxwBn1foiHvEx9zDAlYabaSh07HaWP5pAmcizpeRWVlPaqrGwAg5nMSr1SabHaFZD3DUFVzVIO5qn2X0TJiKBSOGxAnEwRn57hRXd3Qp1OyLPDpZWiAkIho4b5QKIS7774bd999d5t/U1BQ0O7jJnNMe0ydOrXdY5YtWxbTewRA609qi+5YX1ehVy/t0V5DNL16bNmvQIlXZ+/oJtmyvDB0aG4rPR2Xy4Zdu8rbLFd118RFoteuvdQ7GQ9WUVVVH/d+E/WZbNhQjK1b92Hs2MFaHwQt79Bpny1bSpMOeih001j49DWRxunmTYsGPRSe57Bu3c52tWG64mOUKKjIynJFBSz0PGv+/+bfqVHnYvOGRTeo8vI6ZGe7tCCCqifTjfm9d7+N+/yi15qW5mh1DtFpOJPJELk/DvfffwlCISnm+ZfsrYLH04RgMITc3DSt0dlkiq99Q6HrHDQoA01NAcx74A2sW7cLqal25Odn4rbbzoXDYUFKilXbvFt+HjmOQ4Okw/YaI7Y1mOANx29SNnAyUsMHUWD0YvxgZ6RxPBaaraKffVpKUxQSSAoCyWpFN27HrgVQFIDj1Kj3oPm9NBh0UWUycltKik0LfPx+EWPHFODSS0/E9u0HoNcLuOfuVwEkX/YGyOe1rKwWGRkpEakAAFAjz0OONJSrCctl/XF0PR6HZeBz/fXX44477kB2dja2bt2KW265Bd98801fL4txmNDeZm6xGOHxNMFiMaK+3tfq9y3r7J3ZJFs2pppM+pjRYElq1gNpq0m1qz0B7dFeH5AYDGsNqPFI9EWpqirm3PkKli27BekZzkiwQzcgBRUVDZhz5yud6qFavfpnvPXW1xg5Mg/hMJlooRoxNJsiywo4jkNTU+u0QHf6GL333retgoro95uWskipSIjJLJASF5100sVsWKecMhYGgw4VFXUIhSTNckQQyMbt9xP15OgsXFtrtVpNsNlM2Lu3Cna7WeupCYdlcJyAYDCEvPyMVs+fBt5+v6iNkYfDEiRJgk7XenuiwS3NRlgsRpjNBjy14Gps3boPc+58BS+++Cl2767AgoVXIyODZJmiM8dNEoedDSZs8xhxMIG4oMCpGGwXMdjsw2CnBB4cBMGZsC+xOdNG/ksbzOk0lCTJkGUFer2A5qxObCBG3y/aq0aDWBpMRT8WsfYwRPSQbDCZSA/NSy/fBEVR4PeL8PmC2vdIstmVKVNGkRH+qICaSAqQMpuiKKio8CQsl/XH0fV4HHY9Pn/4wx/w9NNP4+GHH8a4cePw9ddf45NPPsHAgQP7emmMw4Rk6uiPPvp2UnV2ukl2qL8FsVdWNqspUuKKTYXT7IcohmEw6BKWbrrSE5AMbfUBzX/oTfh8Qc1jqSVtfVGuXv0zrrjiaaxduxW1tY1obAygtrYRa9duxZVXPN2l3oHSkoPac5YkBaIYhiiGEQqR/9IM07591ZgwYQhuuPFM3HDDGZg4cWjCHoeyslrY7GbMueuCmE2vrXPgttvO1ZqoKTSjpSgqFAWRUWdD3AwjHYPmeQ7V1Q3aexm9QXm9fuzaVYbi4gqUlFShuLgCBw5Uw+cLtHrdE62ViJlaUFCQidzcVM3ChPbvlJfXxX3+mzbtRXl5HbKy3Kiv90FRFOj1OoTDSqtSsSTJCARCkaCTbMiiGIIohrVR62Wv3oKnFlyNx5+4Enl5GaCfibAC/FpvwHslDry0IxVrK21xgh4VAywhnJLTiD8Pr8WZA70YkaZC4BDpA+r4dkkbzMNhOfK86Fg7F7esJ0lKTGkp6p5a3a9OJyAnxw2LxaAFXjxPbrdaTVBVtd3vkZb3OeeuC6DTCdhdXBmZ7ovNlPl8wTY/Wy375FridtuxY0dZr46ux+Ow8+r6/vvvsWHDBs0CASBTUMuXL2+3ZAT0vFcX4/Ah0VX6E4+/C4/Hh2nTxuDc845FTo47bianKx4248cXYsXHD8BiMcJo1GtpaVVVIEmKpmtTXFwBVVXb9XfqDc2heKUcgIy9Jso4JePj0xMikTzPo7LqNbhcNgSDra9cTSYDfL4Adu0qx8iReZEreWgq1uXldXH7HJL1MaLj5e6IuJ7TYYHNbkZdnRc8z6OwMEsrZ9HJpLYgPR4lmpYPfdyOvO7tna8FBVkR8T2ztnkHg+EYFe3o5+90WjHnrgswZkwBUlPJRknLbYIgaM3IsiyjttaLuromDByQCquN9OwEg+EWjex6bZPmOA6KCuxr0mObx4TiRiMkNX7pOc0YxogUEcOdImz6xA3HiVAUBRwX2wRPy1UtIRk4RRNjjC1fkcBHURRIkhzpfWuepErU70gvbABoWRZqHUNL3cl4YcXz4LJYTLBZjQAHhMNEGqE9nzgaHNPzteWkZzKDBp3liPTq0uv1mDBhAh577LGY21etWoXjjjuuj1bFOFyJ19SbkmLTtC1oAFFRXof//vc7fBFphKVfPl1RWU1JscFmM8FkMkCSJK3MwXE8DAYi7Ob3B+H3i9oXX1tXWatX/4y1a3/BxRefoOmZvPnm13GnDTtLoj6gtkphoVBrwT5Ky4Dns882dZsWiKIoePTRt/Hoo1dEtHwkLZgkvRvkynzs2MFaQ7CqQpseGjAgFRaLEaIYgs8nwu8nE37t+Ri1dLoHVIwcmYdFiz7GyJF52nklyyp4nuj0mM3NI+wAYjZceh5Q77ToUlNHZRzaO18rK+uQluaEzxdAdXUjwmGplagl0XMxY+bMk3D66RMijfkiRNEEs9kAQSBZmHBYRnFxBVZ8tA4jf0Oed3qaHSazQXu9o8/N5oktDgeDOmzzGLGjwYSAHD8gtOpkjHCKKEoJIs3UNQX56ExQs5RA4mPp+Hx5eT3S00nmlmhFCaioqENTUxA6nRAZWaclMS5hMGUw0NesuVycbKk7mnj9OeQ7JBhZO4fsbHe7/Tldaa7uLQ6rwCctLQ06na6VWWdVVRWysrLi/o3BYIDRaNT+HT0CzmC0R/RmPnXqaLzwwvWtejvy8jNwzTWn4qefdsdsJJ1tBOQ4DnfOOT+izyJAr9fHnRoxGvUoKMhEba03Rkm1ZcaFasWcd96xyI7KTs28YlqvfFHFF+zjIn01fIxgX3eqYreXKVq4YDkAYO7cC2OE9urrfaitbUR+fiaiRd4AaMGRTido/SWqSkpdBw7UQpKkhD5GLZ3u6UZnMOjxhwt/iyuueBoejw9paQ7k52fi7rsvRE5uKppVioHokgiZ2EJkPLm12nRHN6hkzlee5xASJYRC4ZgeDxrQmc168DyPP197OjgOOHjQo6ljByP9Xs39MRw++mgdbr99CcaNG4zTThuPO+ecD7PZCEmKDcibFD02VVuwvcGI+lD8bc3AKxjqEDEiRUSuJZwwOOkK0dmxeNBG61BIRk1NA0QxjLy89EhPjIja2kY4nVakpjq0EX/aUAy0ziRFNx9HvybRpW6fL5BUQ3F39ud0ZEq2LzisAh9Kyxc3elyzJXPnzsUDDzzQC6siLF26FFdeeSUAQJIklJeXY8WKFbj77rvh8Xi04/bu3Yv8/HxcfPHFrdSNf/nlF4wcORJXXnmlNkE1duxYzJ8/H8cccwwcDgcqKyvxww8/4MYbb0RtbS3y8vJQUlISd02TJ0/GDz/8kNT6s7Ky8NRTT2HChAkYOnQo/v73v7dybU/EFVdcgVtvvRXDhg2Dx+PBO++8g7/85S/a70899VTMmzcPI0eORDAYxFdffYXbb7894br7E50RcevsFw298q6oqEN+fqYmWte6WZJkfm64/gUAsUqqoZCkTYQMHJiG1FSHtt6KinqEw+EOqUx3tdzUSrDv/86EwYAeU8VONnBauGA5nnn6g5hM2M6d5Xj/g79GShPN2QKSDYqdDKJTVxaLEQUFGfB6g1i/fndcH6OWTvfk78ljGIx63Dnn/JhyhdNpwfyH/ghBAGjXQvOUV7M9Au37AFpnETuyQSVzvvr9IirK65AXZU8QHdABQDAY0qaUcnJSQabNJG1qjkxB8cjIcOKhh/+IW25ejKOPGQqHw4xwWIbZTEbQgzKHYq8JO31WVAYNcd9nHiry7SGMcAZRYA8hzkBWt9NWQKWqJHgxGEgfjiRJaGoSYTIpCIUkjBw5CIIgQJZJyZracpD7jW6sBwBi8EqnzKh2ET2WTpclG7B096BDslOyfcFhFfjU1NRAkqRW2Z2MjIxWWSDKo48+igULFmj/ttvtMR5XPcEnn3yCq666CjqdDkcddRSWLFmClJQUXHrppTHH7du3D1dddVVM4DNp0iRkZWXB52ueFkpPT8fnn3+ODz/8EKeddho8Hg8KCgrw+9//HhaLJcYDbNq0adi6dWvM48TzCEuE0WhEdXU1Hn744aQDHgCYPXs2brvtNtxxxx344YcfYDKZMHhws6BZQUEB3n//fSxYsACXXXYZnE4nFi5ciPfeew/jx49P+nH6is6UrTr7RUOvvHmeZBbolXL0eLNeL6C62oNQSMaQIdm47bZzWzmOjx1LXn+y0RJZfpNJj0GD0lBaWp20ynR39QdRwb5HHr0CBoO+S6rYjY3+hGO8tAfB5bbD5wvA42mCoigJAydFUfD662u1f5966rhIM3asVhNReI7fYEz9w0wmPZ584t0Ype0dO8owfnwhzGY9FEWJ2cB0OgGBgIiDBz2tzp8vvtiMW2afoynhkl4TkiFqfro0G0ACNFEMIzMjBaedRj5T9LVJZoPatGkvKsrrMGx4Lior61uVsej5+sTj7+KFF67XSmg0oKNloEAgBJPJoDXjAhyMRj0Updlzjf5Mnjwc3373pPY6yiqHkiYyfl7iNUBJ4ICebQ5jREoQwxwiTLreyzJET3e1hPY9qSq5KMnISIHPF8D69buxauVG3HTzWQCIInVDQxPcbjsGDEjTAmxZVrUMEO0Doq9bOCzBYNBDUUjQTM+bZEvddH0dLX8eqhxWgU84HMb69etxyimnYPny5drtp5xySiulY0ooFEIo1PrqpScRRVELxMrKyvDWW29pWaBo/v3vf2P27NkYMGAADhw4AACYNWsW/v3vf2PmzJnacccddxwcDgeuueYazfG8pKREU0bmOA6/+U0eACAjw4LVqw92+uQtLS3FLbfcoq0lGVJSUvDQQw/h7LPPxpdffqnd/uuvv2r/P378eAiCgL/+9a/a2v72t7/h/fffh06ngyRJre63P9GZslVnv2jolbfZ3Gwz0CyABvA8Kbl4PH6kpFjxpz+f1ioT5XLZtA3ZHOmboFovtEdl2zZ/u/0B3e3unYwqdl6eE6WlBxO+zieccBQ++PBeAIjbUP74E1diUF4GAKLOHG3T4HBYWgV6LbNZtbXeiOK0SRtxJ/YHcewMuFhbAoNBj6ef+bPmWxQKSQgGQ3A4zJFSGtAsPkjKF9TbqOX5s3HjHmzZUooTTxwJjuMjZRDyd81lL1XbAInooRtmswG3zD4Hf752etIBKg1uCwYTtWdHRDSTZgejz9foEtqoUXkRSwla5uHgctlaNWPTYIDeTko1vPYaHmjS4dd6I4p9JoSU+Gkbl0GKNCkH4TR0X29ae9D3iioqt3UcMWblUF/fhHnz3sD3323Hpk178fEnD8Bg0KOysl6zdqipIZ+nIUNyIAg6qKocCRxFVFYS3avCwmwAZEovK8ulNTnLsoL6el+HJzMPhf6c7uCwG2dfsGABrrnmGlx11VUoKirCggULMGjQICxatKivlxaXgoICTJ8+PaYxjVJVVYWVK1fiiiuuAACYzWZcdNFFWLJkScxxlZWV0Ov1MeKHFGppsGgRsat46qmrE9oY5OUR08cTTzyxO56aximnnAKe55Gbm4tff/0V+/fvx1tvvYUBAwZox/z000+QZRlXXXUVeJ6Hw+HAH//4R6xatarfBz1AbBkgHonSzR21ywCaM0V0ZLXll60gCDETLzk57phAgppvUjVfSnMAxcFsJkq7bY3CtyzvJTO63R5dUcWmonkGgx6iGIbH0wSqHr3on2Sk909/Pg1jxhREaabE2jSIYjjGXiHaEmTpK7fg3ffuxsOPzERZWS1UlWR5SPat7edINx2djkdR0QDo9aSRVVVVFBZmQxCEmECL6PPwEVNKtzYePGRoDsaPL9QC3scfewfVBxs0JWKSVWkOuGSZBE603GSxGBEMhrB/f3VSsgn0NaAj7DU1jdizpxLBYAhmswGDB2ciLc2JLVtKccP1L6ChoQmnnjoODQ1NOOP0B/D2f76JCWoEgUs4gRZ7mnCoFQV8U2XFyzvdeLfUhW2NllZBj1lQMNbtx6WD6zFzSD2OSff3atADkEnKgwc97Q4C0P4lnU6A0ajD9m0HsGFDMcaOLcCYMfmw2UwoLMxGfn4mCguzNT+u/furIcvEPPbAgRoUF1dAkmQ4HBZUVNRhy5YSTVNKkkgmqKkpCCri2dEpqtWrf8bp0+/H+TMewVVXPt2n1hI9xWGV8QGA//znP0hNTcV9992H7Oxs/PLLLzjjjDOwb9++vl6axllnnQWv1wtBEGA2mwEgYdloyZIleOqpp/Dwww/jggsuQHFxMTZv3hxzzA8//ICHH34Yr7/+OhYtWoQff/wRX375Jfbs+QmPPnox7A4LGhtJZ35W1pnIyjoTp5x8F0QxDFlW4HQ6oSgKwuEwtm/fDr/f363Pd/DgweB5HnfffTduvvlmNDQ04KGHHsJnn32G0aNHIxwOo7S0FKeeeirefvtt/POf/4ROp8O3336LM844o1vX0lN0pT6e7ERVdObhvXe/RUF+RiRTQGwbaEOwopDNzu22o7y8Djk57phAgurAtNT+odDsT1qaAz5fIGF/QEfKe9Q5uqt9JG2pYtOSiqIoSEtzxGRhnE4LXlh0Q+SKm48ZUY+2aXC7bWhqEpGW5mgzmyXLSqSc5kiqSTY6+ON5DpmZLlitZuh0vFYCIe8fHW8m2TieJ4Gq3W5GOCzhnnv+0CqLdeWVz+Dvz/4ZhYVZMSPdza+LO9IEr0M4LKGsrDYmQG2rnBmvdy0QCKGx0Q+LxYjMTBf27KnEk0+8p00zWixGKIqK2tpGZGakRJ5jYqf1aHxhHjsajNjmMaEmgQO6jlMxxCFiREoQA1s5oPc+dFKNXowMHJge95yIHl1XFBUvvHA9rrvueUycOASpqY7IedisQE6D8X37qtHYGEBJSRXS0hzIznbHZGGie7Q6alKciP7cn9MdHHaBDwC88MILeOGFF/p6GQlZvXo1rr/+elgsFlxzzTUYNmwYnn322bjHrlixAv/85z/xu9/9DrNmzWqV7aH89a9/xYIFC3DSSSdh8uTJuO666zBoUA48DZ+jpGQ3DAZyxb67+GMEg7XIzHRh544yzJr1jLbBlpeXY8SIEd3+fHmeh8FgwE033YTPPvsMAHDJJZegsrISU6dOxapVq5CZmYmXXnoJy5YtwxtvvAG73Y4HH3wQ77zzDk455ZRuX1N3EROMvPctCgoyOlwfj9cj03KiKt4x1dUNKCk5iMGDsyIZHCLwVl/vhcNhhtcbwOLFKzF37oUxgQTp91AjY7hkg40eAKCTJ3q9DpmZLmzYUNwlefpp08bgkUevSKoHqLOq2DSLRcsN0f9PN6GhQ3O0SSeqphsNGQE2QhQlDB2ag0svm9Jms3p1dQMcDgv0eqFDWS26BovFAEEQtNFsnY5kC2nWhno2CQK07E1FRV2rRu/TThsfaZRVoNdzkccgLuWCQNy8OY5DIEAyPV5vIGY98cqZ9LyefGwRRo3Kixvc+v0iKivrMHhwFl5++S9wplhhMOi0wJpOtZH7A+IF2QAgyhx2NxqxvcGE/U36uMdRB/QRThGFjo6YgfYOVqsJWVkuiGIY4XAYgkDkJeKLHqqoqvJoZVWHg1z8UlFMIDYYJxlbL6679jmoqhr34uFwDlJ6gsMy8OnvNDU1obiYnKg333wzvvzyS9x///247777Wh0ryzJee+01zJs3D5MmTYpbzqLU1dXhnXfewTvvvIP//OclfP31t9DphgLYrR0TCnkhig2org5g4CALHI6eb1SrqKgAENvTU1NTg5qaGgwaNAgAcOONN6KxsRFz5szRvnQXL34M//73p5g8eTK+//77Hl1jZ6aSEgUjDQ1+pKc7k6qPJ9MjAyDuMbm5qfD5AvjHPz7C7373G+TkuLXnEn01OGPGcTGBhN8vakqy5OpT0YIEmmlQFNJkKgbDCUfh28rOWCxGWCxGGAx63HjjmTAY9e32ANH34IvPN6FoeG7CAHLFinX4w4W/RV5eBg4e9EAUw5rgHyHW6ygamjEiZSFV84aipRi9XoDLZcOjjxH9nmAwFDH15GEwCAiHJPiaRNTVeZGe5kRjox+BQAg5OW6oqqpteIloPqeaR9BJEEaePxVAbLl+VYX2GtTUNKKsrBYFBZl48MHL4PUGohqG6fPk42ZYOI5MmEU3JrfsH4o+r+12MxwOC6xWIyoq6lsFTaIYhstl0zKIVFG5vThQVoFSnwHbPUYUe42QE4gLZpiIuOAwZxDWXmxS7gwmkx4lJVUQxTAGDcrQXovoCTvabJ6V5cKBAzUYNYr0XgYCoUijcmxZn+rxlJfXYcOG4sOisbg/wAKffsC8efPwySef4IUXXtCChGiWLFmCO+64A2+++WbMyHtbpKSYoSg+QI1vvNebZnH/+9//AADDhw/XJuZcLhfS0tJQWloKALBYLJBlOeZL12QifQ1PP/1nzJ3r77Eac2emkhIFLLm5qfB5A3j0kbdRUlLVZhCV7Ag8gDaPGTkyD5OOuQ1jxxa0SnePGze41ZSNKIbR0ECyJlxEkZWO2EZfoYZCYcx/6E0ArUfhqUJ1y+xMtDklDUTsdhOqqjxab068EsuUKaNi3gMaiNCJpVBI0h5jxozjYDKTwMpmM8HvFxEKSVHeVfFLeNGvO0ACCfI3fEx5SBB4WK1EHdhqNWn/T6GTSZIkIxgMa31C9CdRo3NzJodAX2van9N8jAKeF6L+rtkRfkBuKgBSEjQa9TCZDKSklJmiBXXRnlHRmM0GFBZmQZIUrZnb6w3E9J+1PK8VRYHdbobFYkJBQSZqahrh8wW1bFlmZkqrEf5EQY+qApUBHbY3mLCjwYhgAnFBh17GiJQghjtFuJN0QO8P6HQ67TuVvgY000XKmYqmz0TMW/mIxhJQUVGPQYPSWpnFCkSrAMv/+x0LeroRFvj0A9auXYutW7fi7rvvjtG1oWzfvh2pqakJe2/OPPNMXHzxxXjzzTexc+dOcByH00+/EEZjLmpqvog5VqczQaezwGw2QJb0UFUi4CiKInJycvDFF19g5syZWLduXcL1jhkzBgBgs9mQnp6OMWPGIBQKYdu2bQCAc889F48++qhWNtu1axeWL1+OZ555Bn/+85/R2NiIRx99FNu3b9cmz1asWIHZs2fjP/95CYpahsZGEWbT0QiFGjFwkKFTE0LJ0JmppGQClhnnH9euRHwyPTL0ijDeMUQZmOjtjB9fCEVRMGZMQSshwh07yrSSSPSkRlNTUCsJCQIHSVIQCoXg8TTBZNJj48Y92L2rIuHr88IL12PRPz9FQT4p74liWOuzAUhpRqcjSscDBqRBkuRIeYRsuLTE8qc/n4bbbj231WO43XaEwxKe+8cKNDT6yUi+nRxTW0s2/sxMUl546KE3cc01p2Hs2MGRzYbq2cQnWuCto9CsiSwrKCur1WwtzGajpujc8rHJGLOqmWsC8csgLYMICg2kBJ2AQYPSkZXljmjhADk5qdpr3l7JjUggECsC2k/mcFiwZUspNm8uwddfP4aMzBQEg2Hk52fCYNBrOjGCwCM72x0zedZeUzcA1IsCtjcYsd1jQkMCB3SjoGBYpG8n2ywl1TfV3+B5oktEAlCgpRFpOCxrbQWkh8eAUCgc+V0YpaXVrcxiRTEMv1/EF19sjvOIjM7CAp9+woIFC7B06VI8/vjj2uh6NHV1dQn/9tdff4Xf78dTTz2FgQMHQhRF7Nq1C79s/RCpqQ0xxxYNvyDm36tWXaKJJOr1ehQVFcFisbS51k2bNmn/P3HiRFx22WUoKSlBQUEBAMDpdKKoqCjmb2bOnImFCxdixYoVUBQFa9euxfTp07WJrTVr1mDLL8tx1IgTwfNj4EqR4PNVYMfO9xAM1ielJ9NReJ7HQw//EalpDlRW1mvlGo7j4PUG4HLZ4j5mV6wmokmmR4bK0dNjLBYjHA6LpiRMSwyffT4fsqzA4SDvXUshwoL8DFx//Quah9h55x0Lk4lkDASBhyiGUVPTAK+XqLzW1/vwxOPv4s4557cZ4J122nhcf/0LuHPO+TjhhKOg1+sgSXLEYVqnZXloKY02bJaWHkRTUxButx1/+lPrcfvox5h28lgARAiv5TElJVXIzU3Feecdh5cWr8Sz/7hOK/O0Tdd3VkHgI+PC/kiJimSRmieroh6N46AosjaiTZt96e/aXGmccpXR2PzVTTNCyQYLJPhSwXECCgoycfBgA6qrPdhd/KLWmOtoIxFMsxhtPZ5f4rCzgejtVAXadkAvcorIt4XQjt1Yr0PKwLKWdWkLeo7zPOnNoedB82tEtLVEUdFKylarSeudo1lTr9evlW0lSYbLZeuQaCAjOaIlPhk4vExK+9IsrqPEM8iLpqW5Y1eZOnU0Hn74j5h49FBtqoY2F0YL0cmyjNmzX8KL//xU+9tTTx2Hpa/cgoqKurhj1dTT5qorn8aqVRu79JzpGHMwSEqTFktzCYmI1skRjY/Ypkg63VVaWg2v16+JmNGyF82u0OZJk4ko39bWerF58148/tg7aGhoSvo94TgOH3x4r3aFmpubCqvVqGU16CYgiqTxMxAgo7n0+dXVedt9DaKPid4cqAnrBec/ileWzcaIEQMBtF1ySTQZ1hHo/dTUNGL//mqMGpUXJR6oalkeajBJr+Cp5EFHGqLbXgdtUO/cc6D9XF0lrAB7vEZs8xhR6jNAjRtcqhhgIX07QxwijELfbz+KosLj8SElxaaVSwnEIDWRREU0VJ8nGAxDVdXIdB19TZvVtKmTPP1sXh/Vw3cofE/3d45Ik1JGLIeSGFVnfas6Aw0IU9PICCmRfRdgsRi1x6JNpkajHvfeezF27SzXXq/u8rRJdgTe7bZpZpjUhqK5VEMalEOhcGSyS40ERGQiJCvLBa+3WYhw/kOXtzuafMbpD0BRFJx66rhW7wkNOHQ6IqtvtRC9Hxp41dV5YTIZYDIZEArJMBhoz03z9Bht2MzISMG+fdWtxu2jaZn1iu4hag4mQgiHZaSm2nHzTS/i/Q/+qgVy8ZWUlchr2LlgIfa+VOj1JACrq/NBFMORXg5FK+2lpTmRk+OOCNf5kJ7uhCwrmm1DR7I18en486CPyXHoUtCjqMCBJj22NZiwu9GAcAJxwVSjpPXt2DvhgN6TqKoKh8OiNZpHG4PqkvS4oBdA9HNAVJV5bUSdKlETdWUFW7aUYs6dr2jfKYfK9/ThAgt8DnP6u1kcJZlgAiACbl15DtH9OZWV9bBaTZEmwmZPHLqpcxz9AtPHlLy6y9MmGeXmJx5/F489fkVk7XSDbB47J/ejaBsoae7lIvL/MsxmAzIynGhqEmG1mjB4cBZqahparYWOJufkuDF2bAE2bCiOeU90OhJEkRHs5gyGqqq47/5LsOyVL6KOFbTfSxINfriYEpBOJ0AUw1j84krMvZuM29OeGxow0Ped9kG43Xath6il3onZDOQXZGLXznIcOFCDoUNzEmZUeJ6PZGMUAHyXgg5FIaalgwuywPFc3CxgTU0DQiEpYkZJe6qa+6A6G3jQ59+Z9XflOasqUB0UtCblJil+Kciqk1EUcUBP76IDek9D/O7UyGeMj7yHsX1M8dzRacbM7w/CYjFp7wfPEwd26rpOJwh37SrHM898gMUvroz5/jpUvqcPF1jgcwRwKIhRtRVM2O1m5OdnQFEQV8CtI0T35wQCZCKHTFg069jQ4IGUZEKoqqqP6dnpTk+b9rJyDQ1NSE934sCBGqSnO2GxGCPBgxrTO0NozqoIAukp4HkeOTmpmtNzICAmnVWj78mECUNgtZJMD/VWopuuqgLDhuXittvORXV1A3JzU1Ff74OqqtDphBiRRNIDo4MsE0PGh+a/hcWLV+KaP52KUaPyI8eQ+w4GwxELCaJiDQAnnjgSPN/SxFPVMmDXXHMqnA4L7A4L9u2rRlqaA2azsZWbdXSDadcyLaSsSMUjZUVNGLiHw+T5vLR4JWZeMQ1ZWS4YjfouZVvo+x5vQ+4JGkNEXHB7gwm1CcQF9dQB3Skitx+ICyZD80UPFzk3VO08pyVLqvRdUeEBzwOCoINeT5zP//7MRzj1tHE44YSjtEnBQIBOzZFsalYW0U377W/nJFR4PhS+pw8XWODD6BckCiaoUR8AlJfXoL7e2yUvKFpS0+v1GDAgPWbzocJ9JMVNxkoT+SStXv0zrr/+Bcx/6HIMHpwFnufg94tx09PtaQS1dbVHy00VFXXw+0UMGZKt9SQBNBPWrPbb7B0lgAYo5KqTPEer1aRpwbSkZYlOVVU88fi7eP+Dv0Y0f1TEBj1kHTqdAJvdjIZGP3y+IDIyUgBAs1Cg9yVJira+rVtLsXjxSkyZMgpZmSnaexAOE3FFi4WMXldW1uPxx97B0KE5mDp1FIBmF+qWStUjR+ahqSmIkhLig1dT04iRIwdFsiyxz5VMnAla1iq+0Fzb0FJoYWE2ZFnGnj1VcLvtKCurjelB8vtFZGW5oKrANX86DTabGWazoVOPGX8dJDNJsmt6SJKU0DC1owRlDrsajNjeYESZv20H9CKniMF2sVcc0LuTZgkBPuKKTkpTzZlTJdKsrMY4zG/e3HzxtXDhcvzpz6fh3nsvhtGoR1UV+d4wmw1wuWyoqWnEPfe81q6tBaN3YIEPo98QL/uRkmKFLCsoKTkIr5eM8ycjtZ+ImppG8DyPvLx08Dyn6WpQHRW6WQSDIZSX18Hr9UfGTmN7dqZOHY0755yPnBw3sUmQFVSU1+GJx9/F2rW/4NJLT0ReXgasVhMmHj20XY2gRFd70eUmv19EIEC+TKnQmaKoUTYH5N806Gn+PflCLy09iPz8TGRnu+IGPvFKdB6PDz5fAFQNmbxGiFickEkmi8UAr9ePgQPTUFvjhdVqhF6vizpWjagTq1FZI/JaP/7ElUjPSNE2FRLIkSBJUUjguWbNFuj1AhobAxEDx+Zx31Co2ZOLXoFT0tKcMBoN2utL31uyqRHbClEMo6KiDoWF2do5kAyqCoTDUiTYILpDTqcFOoHHqFF54LhmMT/6XL1eP3y+AGprG5Gd7UZWlivpx2sL2o/i94eg05EJPWJ8au3U/UkKUOIzYLvHhD0+A5QE4oLZ5jCKIg7o5n4uLtgWscEIbUxuzviQMpUKb6MfDz/0Fnbvrmh1AaOqKl7856fYtbOc9eocArDAh9GviM5+TJ48HPf89aKEEz/Jjo1Hs2nTXggCD51O0PyayGQUr2msyLKCHTuaJQVaBgQttX+opkxefgbeePMOGAw6WK0mUAVeRVFRXe1BeXldh7NVLUuAlZX1yMtL14TOKLQHinpRUaLdnL3eACoq6pCbmxqjetxWiY4KCJaV1SI/PwOyrGhBBCmlkQxOVpYLPM/DYjaivLwOAwemab0stHdHlhUEAqLW4Pvgg5dizBgigRAOywiFJO29UVUVFRX1SE93Yty4wRHhvIAWhEWP9GdkpGh9FdFZjvR0hxZ4Ac0BGy0RhsMygsEQbrv1ZdTUNOKPM0/ClVdMgz1iIRBNSxXkQEAEz3OR90FBVZUHLpctagJIhaqSjZOMQ6uorm5AIBCC3W6G223v1hIVNf/0+UQ4HOYOZ5NUFSj367GtwYhdDUaICZqUUwwSRjhFDE8JIqWXzUApyXh+dez+mjM+JGCnZW9a+ibN+bKi4Pvvd7T5XcN6dQ4NDrGk5KHP0qVLoapqXC+x554jXixLly5t9btjjz0WkiThk08+afU76qoe72fSpEnduv4TTjgBH3zwAcrKyqCqKs4555yk/u53v/sdfvrpJwQCARQXF+Paa69tdczNN9+M7du3o6mpCcuXr8a0aZcC4NuZ+InvHJ6IsWMLIMuylr6ObjJtbhYmJSGz2RDRaWkOCNpyJJckGWlpDjgcFm3MmjZHZmSkID3dGeNc/tDDf8Rpp43X3LbjQUuAPm8AubmpkCQJ+/bVaNNOxJRT1aaIKIqioqamAbt2lWPXrnLNaqCuzovGxgD27atu5Qa/YMFy6PVCzHpoxokGcDRooFkOukbaL2OKjLgDHEIhCYFACLIsQxTDKC6uwK5d5air88Jg0OHCP5yg9ezQjUGWFYhiGBzHwe22ae8vDQDdbjsEQUBamkPL5pD3jqw3K8sVURo2Qq9vbljVBAAjthREnZlM8VVXN2D9+t245eYXceedSxEMhmPeP9qYGgiI2jrp86eqzVlZLthsJhgMOiiKgtpaL0pKqnDgQK2WVSRrs2Dw4Gxtoqu7oHpJDZ6mDgU9daKA/1VZsGSXG2+XpOCXenOroIc6oF9cUI8rhtRjUoa/14Ke+PFCd8kANJeH6+ubogKq5kA52sNOEARs2rQ3ifsl2dtVqzYym4l+SqcyPhaLBXfddRemTZuGjIyMVh+0wsLCblnc4cq+fftw8cUXY/bs2QgGiWu60WjEJZdcolk4tGTWrFl49tlncc0112DgwIHYv39/q2OmTZuGrVu3xtxWW9t66qgrWK1WbN68GUuXLsV7772X1N/k5+fj448/xuLFi3H55Zfj+OOPx/PPP4/q6mrtPi699FI89thjmDVrFr799lsMGzYM//73azAYq2E0rkUgEGrVN5Hs2Hg006aNQUqKDQC1CiClFVlW4PeLUFXiuZORkQKfL9AqTZ1YvJBDVhbxyopukibNkeT/s7JcqKrywG43w2434+ijh+K1f92GpqZgm83aLUuABoMO9XVeTTvkwIEa+P0hWCwGWK0mZGe7wXGAwWCI8WQCmqekXln6Oerrfait9SIvLwMzzj8Oc+de2Koct2bNFi3jRNWJWzc4N2cudDoB6emOyOsgaGKG0QrJNCvictkgy7IWVEVDDUObmkTU1DQ294D98wYUFGRGqQnrtKwODYQHDEiLnBPxrRvIbdBKgBs37tH6sFJT7fD5AqipaYxkn3gtqNTpBBQUZIHjyCRXIBCKUaqmj2MyGZCVZUBKihW1tV4tu2SxGFFQkKE5stPzprtoaPAhO9vd7nFNYdqkbMTBYHyNGh2notBBxAUH2UIQ+qhJua6uEZKkwGo1aTIK7Y2YdyQjJMsKFEXBW29+hT/OPAkOhyUqC9d8AUntSOjEI+PQplOBz0svvYQTTzwRr732GioqKlhE20E2bNiAwYMHY8aMGXj99dcBADNmzMD+/fuxZ0/rEWiLxYI//OEPOProo5GVlYUrr7wS8+fPb3VcbW0tqqqqOr2uZIw6P/30U3z66acJ7iE+1113Hfbt24fZs2cDIBYcEydOxO23364FPsceeyz+97//4Y033gAAlJaW4rXX/o2rrroAWVlurbcjeupHkmSsX787aVXTqVNH48Ybz4Qg8JHJCymiryFAUdTI1bkEt9uOhx96C99/v6PVa5BIb8jttkVtaFxMcADQXhsBOTnuSNaCHFtf70MoFG63/NUyhZ6W5sDDj8yMERf0+0X4/SJSUqwRt3J9jCFl9HTc3ZHpuOrqBmRlpkCn1yW07KBN57QHhq6dXh3TjSJaiVhVY41Ao+0h3G47ysvJ6HzLniUK7fkpL6/T3t/Vq3/GRx+uw823/F6bKKNTNwA0pWiLxRjRzWnbswsgwddJ08bgjjtmYPjwXFitJqSkWGE2G1BWVou6Op/2vmdmpmibLn0P28JkMiAnx625sdPXjb5G3c2AAekJg4KQDBR7jdjmIQ7o8cQFOagYaA1jREoQhfYQDH0gLhg9LUhkD3RoamrCzz+X4Mkn3sP8hy7DqFH5mpcbQKcwo2UJEr+20cG6JMmoqWlEOCxj565yBIMhVFc3IifHpQXnikK+a8gFi6lXvA0ZPU+nAp/TTz8dZ555Jr799tvuXs8Rw9KlS3HVVVdpgc+sWbOwZMkSTJkypdWxF110EXbs2IGdO3fiX//6F5599tm4gU9b5OXloaSkBFOmTMHatWtb/b4zRp3Jcuyxx2LVqlUxt61cuRJXX301dDodJEnCN998g8svvxxHH3001q1bh4KCApx++un437drccop9oh2i6RNXNhsJsiyjJUrNyQVeNMSlcGoR1NTEGazsZWxZFaWC35/EFu2lOKFFz6Je7+J9IaMUSaTdNScCKFFN7g2b5jUHDMclpJu1o5ugG5LXLC+3gejUQ+9XgeLxYhgMNTGdFw+BIFHcXGF9nxaruf06fdrGaeJE4fC5bIiWvS9udQVveGQ/zcYSIaJBjK0dEj1e4JBL7KzXa3MGam6bbTeCcdx+N2Jv4GqAqIY0jYwWnprtscgK0imf8ZiMWLFivsQDstoaGhCdTXp17JYiLVGdXUDXC67ZuhKSXYMnUoLAN1b2opHy6BHUSMO6A1GFDcaIbXhgF7kFDHcKcLax+KC0dOCsizj3r++iu++Ixcg48YNRna2GyUlVTEZYJ1OwMCBaZq1hKI0Tw8mun9RJBc9qal2rF27FT98vwOhkIRwOIzt2w+0yi6TAQddh7LLjP5Lp3p86uvr2/SOYrTPa6+9ht/+9rfIy8vDoEGDcPzxx+Nf//pX3GOvvvpq7XeffvopbDYbpk2b1uq4b7/9Fl6vN+aHliHJB3p7XKNT2qw7enQ+fD7SAOvzBbSr/qlTR3fpuWZlZbXKRFVVVUGv1yMtjWzGb731Fu6991588803CIVC2LNnD9asWQO9viSmqZWO6fp8AXi9AZx22vikNpToElVlZT0URdHui5akrFYjwmGpTQ2e6F6TaMSItkz0CCwJfmI1ZKj4niAICAbDMaWo6Gbt9ogOwOx2M4YOzUFhYXZkassduaKVYLOZkZdHJrlkWUFxcWXEdbu5HKCqatwJo+j1rF79M06ffj9u+ssiVB9sQFlZDaqqGrTx39Y090mQhlEy0bVlSymuv+55vPTSKlSU18HptKCqyoNgIARBECJ9VyQDt3nzXixevDLmPczJcUcarKG9xiSTRIMgcntNTSOamoLa39L+pJgVRt5jGiBmZ7sxbFiOFnQZDDrk5qbCYmk9Dp8s9DRqDpR6NvhRVeKAvqbCisU7UvH+Pid2NJhaBT12vYyj0/z4Y2EdLi30YHxaoM+DHgp9n7ZsKcXzz3+i9cm0zLb6/SIaG/2a9AV9jySJ9JQRaYTW9y2KEuhFCc/zeO/db7FhQ3HM55reN/18ut127NhRxjyzDhM6lfG599578eCDD+KKK65AIBBo/w8YraitrcWKFStwxRVXgOM4rFixIm4/zrBhw3DMMcdgxowZAIh31FtvvYVZs2bhiy9indcvuugizSGdQkc1y8vLNbf0aJJxGu8Oc9CWfxu96QLAiSeeiHvuuQc33HADfvjhBwwZMgQvvPAcjKYKlJV9H7fHx2w2JD3VFf2lGQiEUFp6ELm5qTECd6pKPLHaex7x9IZokzPPc9oXLlUtpo2/9L2gG2tlZX3MfXfElqNZXLAQVquplZox1R2hJRYqxhgNVVgOh4mFRHRZLHo9kycP18qfb775NW6ZfQ5GjcqLOH3HV+2NzsbIsoz773sdn3++CRs37sGUKaPw8ScPoGBwJhwOC5xOa0ymhmb3vv5qa0z5dciQbABAMCjCZCJBKhWbbFbd5SJX8o6YgJj+ruUaW8LzHEym9r2ZkqXlQ/RU0qchxGO7x4TtDUbUh+J/rRt5BUOdRFwwxxLusbV0FlreUhQyJTfnzldivjfaUncvK6vDkCHZmkI4+Sw2ZyRJFqnZl0xVVfj9ZCChpKQq4ee6M6KkjP5PpwKf2267DYWFhaiqqkJJSQnC4djNYsKECd2yuMOdJUuW4B//+AcA4MYbb4x7zNVXXw29Xo+ysjLtNrJZhZGSkgKPx6Pdvn//fhQXd6zxrrucxtuisrISWVlZMbdlZGQgHA5rwd78+fPx2muv4eWXXwYA/PLLL5g8eSTuuPMB7NnzNQC0atTtSKAQ70uTjE0rCIWoiB0Ht9ve7qh5IrXl3bvLtYkdOu0jSbKmaeP3h2A2G2I0gqLpSLN2S3FBWk6iI9Z0ozca9aioqIfVaiQj93npmnEpnVyix1qtJlitJgBAU1MQFosRKSlW3PPXiwBA6wnKy8uAIAgJJm6aocGP1xvA559vwoYNxTFSAOQ9kZGT49bKNNFZmL/cdDZmXX0KgsGwFjSmpFjR0NAEg0EfI6pI3MbJTk4bqknJrLWOz+FCQOKws9GI7R4TKhI4oPOcigJbCCNSgsi3hfqduKCqNksM0AuQ9euL8dd7Xmv1+WtL3d3r9WvnLJnmaz6fqAFxaWk1ZFluZW5LP2+Hkrcho2t0KvBZvnx5Ny/jyOTTTz+FwUC+mFeuXNnq94IgYObMmbj11ltb9ci8++67uOyyy/Dcc891aQ29YQ763Xff4eyzz4657dRTT8VPP/0ESSIlIovF0krVtKHBB6iA0WhAIBAb9AAdCxRafmnSaRyS9iYbbSAQQklJVVJZrkR6HbfMPgdz516oacyoqoraWi9ee+1LrFq5Effffwny8jNaBT1A8h5fFCIuGNQEGKmoH73iDYWIgq8gNJffdDpBMy71+0UEg+GIizqHAQPSYhqVaTYo+up31Kg8CIKAioo6OB3WuJo3tFRBZP7JOtLSHHGziwMGpEfEEJtFJEn/BQngHA4LTCYJpaXVCIfpuehERUUdHA4LzGbae0MFGxUt40Y3PCKa2M92/E4iRRzQtzcYUeI1QElQOsu1ECXloU4Rpn7ggA7E9rpR5/rogITaOpyQwNahvaxMZWU9XvznSpx/wXGamrrNZgLP81qwH01ubmqrzxvT4Tky6FTg8+CDD3b3Oo5IFEXRyk/xPuhnnXUWXC4XXn75ZTQ2xm7u77zzDq6++uqYwCc1NRWZmZkxx3k8HoiiiJycHHzxxReYOXMm1q1bp/2+o07jVqsVQ4YM0X5fUFCAMWPGoK6uThuxf+SRR5Cbm4srriDmmosWLcL//d//4amnnsLixYtx7LHH4uqrr8Yll1yi3c+HH36IW2+9FRs3btRKXbNm/R+qDu6A221DWVnrwKcjgUL0l2ZeXgbMZqOWFYi2PQCSz3LFU1teuGA5/v7Mh5gz53wUDslG8e4KPP74u5rYYCgkdVs6nYgLKti1qwwmk0Fr9MzNpd5cJHiRJEUbRZdlOWbaq7GxCY5I8EI3pOiyEJmsIWUyarOgqiocDivKymox1JrTqtGXBiI0mwSQ86xldpFOnkVnxUiWKrYRWBB4ZGe7UFFRh4qKegwYkIqMDCd2764Az/MoKMjUSnvRfR1tleIOJRQVKGsi4oK7G40IJRAXdBslYgrqDMLRR+KCLZFlGfX1TfjXv1Zj8uQiFBXlwm63ACDl9IqKem2SMhlbh2SyMgsXLtcCl/yCTNx227lwOMyQJCmpz1siFXXG4UOXlJvHjx+PESNGQFVV/Prrr9i0aVM3LevIwev1Jvzd1Vdfjc8//7xV0AOQjM8999yDcePGaY3mLXt+AODiiy/GW2+9Bb1ej6KiIlgslpjfd9RpfOLEiVizZo32+4ULFwIAXnnlFVx11VUAgOzsbAwaNEg7pqSkBGeccQYWLlyIG2+8EeXl5bjppptidIAeeughqKqKhx56CLm5uaiursaHH36ITz99A0899cduCRTol+YTT16FMWNs2qYYDpPAjl4RdjTLFd2Hkl+QiRkzjouZjvvdib/RvpS7M51eW0vOHbfbrjVjOhyWqOmm5rFdovicoT1nvV4Hs1lFRkaKNhZOn0s0dBJLlhWYzcZIeRCw201wp9ojjxOrQNzcv0UmmrzeADZu3INTThkbk120Wk1aIEWmcJBwDTabCQUFmZr9Bc/zUY2oIRgMeq2Bmao/d8UAtD9QHRSw3UMc0H0JHNAt1AHdKSLdJPWrvh1FUfHTT7txz92kbEU/J9OmjcF55x2L7Bw37HYTQiFdh87/9rIyLQMXZiPBaEnzTGoHSE9Px5tvvokpU6bA4/GA4zg4nU6sXr0aF198MWpqatq/k36K3W5HY2MjHA5Hm0HJ4QTtu7DZzXGDi+s7aATaWRLpCHX3qP2ECUPw6coHYTYbtECAagNVVtZr01Dnz3ik3Su/6LXZbCY4HBYoiory8jptZNzttsPnDcT0DUU/VxrApKbaE6bWW742KSk23DnnfJxwwlERY0oZwWAYHo8PWVmuSBOngEBAxK5d5QCIjk9ubipMJgM89T6EJRlutw08LyAclrRMD52ea87YcFAUGeGwAqORGI9SnzMaXAmCoG26LbVqDh704NJL/oaGhia8+97dUFUVLpcNZjPtx0iu6ZfIGSjQ60lT9vPPr8DHK35CeroDTz/z55jxZrqOQ62vx0vFBT0m1LThgD7EHkJRShAD+6kDuqKo+HVrKcaNuyVuBicZzbDupLcfj9E3JLt/dyrj8+yzz8LhcGDkyJHYvn07AGDEiBFYtmwZ/v73v+PSSy/t3KoZfUJ/aOprL7jpzrq702mFyaTXVIxpY6XZbEBeXjqamsSkhBFpwOhyE7VfKnrGcRyys10IhSTIsgyvNwC32x7TN0SvSqdOHY2HH5nZZlBHX5tRo/I0pWmTyQBRJMJqtF/JYjHAaEzR7DhIMBRCeroTTU1BeL1EAmDLllI8OO8NFBZmYd6Dl8PhMGu9Qc2ChLElL2IaqmgZFTKaL8QNWKKDDVVVkZJiw+NPXInJk25HdXUDxo4drJWlqPhcMuh0AsLhZhfyU04Zh22/7seMGcfBYjFFtHzIsYqigOMOjYyPKHPY3WjAtgYTDjTpEW/knYOKfFsIRSnEAV3fz55a9GeRBsP/+teahGWr3i4nsfIVI5pOZXw8Hg9OPvlk/PTTTzG3H3300Vi1ahVcrtaaIIcKR2LGh9JXV0UtTT+jM04tMyVdheM4fPLpPEycOAQWixE8z0dNN5EpqGAwhHN+/xC+/HJzm/fzw49PYdSofACI6RWiDbV0YoUqHMuyitm3LMaLL36a8Hk7nVakpNjQ5Atg1qy/Q1VVLFt2C9IzUrSxeGr8GQ5L2Lv3IACiw0P7fKIFBaOzMJIko6rKgyuveBqrV/+M8eML8eFH9yE11aG9BhTiUN3sMC6KdFyf18a9aaYmUWaFBDeSFtj87cn3cP4Fx2Pw4CxNoI7n+Q66olNtJFUL1hRFhc8XgN1ujunp6c8ZH5k6oDcYscdrhJxAXDDLHEaRM4hhThGWfuCA3jKbJ0kSOI7XlMqpjlJtbSPOPGMeCzYYvUqPZnyI4mzrKaBwOHzYTE8cifTFVVFv6QhRaINtRUWdNuFkMhm0q9RAQERTUxAej09bX7xg8E9/Pi3GWZz0swiRjbx5ExBFOaLSTHzB7r33IuzaVY41a7bEPG+73YwBA9K0tTidFrz51p2orW1EVkSQMByWtcBHVUmfzoABqdi2jTSV5+amRjR7Yt3ZyVo46HQ62GwmbdPauHEPtmwpxYknjoxkU5ptIyRJhtEYOxJMgjs+SvyQBiGtN206VUYyRyr0eh1uv2OG1tMjCBx0On2HA5PoDBR5vQFBINYTLQUK+1vQo6pARUCH7R4TdjYaEZTjf1c6DTKKnEEUOUW4jPHEIXsfNcqstb7eh4wMJwAgHFagqrLWVyXLCny+ADZvLmFif4x+S6cCny+//BLPPPMMLrnkElRUVAAAcnJysHDhwrgNtgxGInpDRyialkKGXm8gRhgxGAwhO9uNtDRHwvLbE4+/iz/96TTwPI9gkEzC0TIR7X9pvg3a/0uSDINRjzl3XQCvN6AZf6alObRyFRUhFAQeLpcNbrctKoiA5j9FAxqz2YC0NCeyslJaNfPSx4++Snc6bXjzrTtx8UVP4MsvN+Pxx97BUSMGICvbDYNBB1kmpQkS4JByEVW5VlUVoVBYK6ORzY5YTbQse3EctNITDUCae284dDUmIVmvWIftZCwq+oI6UdD6dhrC8ZuUTYKC4ZGJrCxz7zcph0JhrbcrPiR7R93ta2oaYbOZNT824mkVgiiGUVvrZWJ/jH5NpwKf//u//8P777+PkpIS7N+/H6qqYtCgQdiyZQsuv/zy7l4j4zCmN3SEook3vh8tjEg8eSQyBnvrua3Kb6NG5WHJ0ptgtZpinMWjJ4qaUbUvfzISLuLgQQ/GjCnAK8tmIz3dGemVIbsc7XlRI3YMOp0aKTc1byDk/pqnV3ieQ2amU/P/MhoNUcfRZuVm3RSO4+By2fCvf9+Gyy97Cl9+uRkLn/4A9913cUypSFFU7N5dDq83gPz8TNTVebUSXn4+ma4yGgVQZ3uyztjJrkSbd3ds6s2P1fbmGi2M15s0SRx2Npiw3WNEVQIHdIFTUWgXUZQiIq8PHNBp5o5aN0iSAp0u9v2h9iCAqpVYd+4owz33vAaO4zD/ocs1zRy/X+w2fz8GoyfpVOBz4MABTJgwASeffDKKiorAcRx+/fVXlu1hdJiO6gh1lWTH92fMOC5h+W3w4CxYrcZWzuI0cGke54ZWkqI6QcSfzA6329YqQ2PQjE5VLZMBkE2+ZYBFMj/NKsehkBQzEh6deSL3Ea3NwyE93Yk337oTjz36Dq699jQEAiHU1Hhh0PPQ6QXo9XrwPIc33vgK1107HfbIxB/JCKkwmZrLVLSpuy9oK8ACkjMq7S7CClDcSMQFS32GuA7oiDigFzmDGOIIwdhHDuj0HKI9WqIY1jzVAA6yTM5X6ldlsRiRmenCzp2xAoOrj/+ZTUsxDjk61dx8OHMkNzf3BbTZOFEgQtVVT59+f7d9obY3vr/gqeWYe/eF8PkCcYMxl8uGvLwMVFV5kJpqB8/zmrO4Xi9o5QLq/B4MhlBZWQ+vN4CjjhoEs9nQ5gg3zVJEBz6iGNbKUDqd0KohmAgWNvf/kN6a+A+gqtDKFrIsIyRK2H+gppUlCH3tn3j8Xdw553yt5Od22zvUkHw4o6jAviY9tntMKPYaEVbiv+ZpJgkjnEEMd4qw9aIZKA1QaO8lKVdKoI72ADlP9+6tAs9zSEtzwmIxoLExgMrKuj6TtmAwOkO3Nzf/5S9/wYsvvghRFPGXv/ylzWOfffbZ5FfKOKLpC3PA9sb39XohbvmN9gKFQjJkWYHJZEBpaXWkQVqvWUaQrAzZTKihKv17s9kQ9dzjBz806KHNuoLAacaKAAe9vnWfCG0aDgbDMBp1aMsFnKo6k2Zl4nlVWJit6RhRIUfaX+Xx+HD69PsxbtxgZGSk4OUlN8V1c+9OaPDXUkm6P6CqwMGgDts9RuxoNMEvxW9StulkFKWQvp00U+83KauqCp9PhNVqhCQp2rlGG9T9flHzdnOlWNHkF/HDDzuwcuUGnHbaeCb4xzhsSTrjs2fPHkycOBF1dXXYsydxt76qqigsLOyu9fU6LOPTM7Q3Kt/dIoVdWdP48YV49727tYyP3W7Rghs6zaSqgKfeB0VVUVfnBc/zMJsNsNnMpOFZVSHohJhAbsCANDgcRDm7vVFranJKFImb+yx0Ol1UOYtMelFBP4C4y+t0ia0a6GQO7degt5FSGWlkpr5GPM8hO9uNq658GqtWbYxoDv0RxxwzvMebb6kMQMuSTF8GQA0hHjsaTNjmSeyAbuAVDHWQvp0BfeiA3tjox/wH38LatVvw79dvR35+hlb+JPIEYZSX18LhsKCk5CAenPcGqqsbtM8AE/xjHIoku3+zUlcLWODT/SQb1PSXL9vo8ltjYwB5eela8zA1BFUUBdXVjaisrEd6urPV8wLQ6jkrsoL8gsykNvDoTIffHwTP85GJmubyhCTJWhBDJqg47XcdsWtQVTLVI8uKZta6a1eZFsidP+MROJ1WLFp0AzIyXZrgIX2tuhtVVdHUJCIYFGG1mmA2G7Xb6enQW707QeqA3mBCuT+BAzpU5NtDGOEMosDetw7oiqJg2bIv8ec//QOKomDq1NFY9upsZGW5NC0n0ndGguWKijpN14nBONTpUR2fe++9F3/7298QCARibjeZTLjjjjswf/78ztwt4zAkkTjhqFF5WLTohhhxwv6irqqV3/55A/LzM8DznNYXodeTke/S0oNwOCyoq/Ph+uuej2s30VJt+rjjRuDpZ/6MtspQFHplLkkK9u2rgaIoGDQoHXq9DuGwHKO5Q4MgGhRF93NEN1q3jFFoOYlO9ZBsULOJqctlQ2nJQWRkpJCpL4cFwWAIDoe5RzMvHEfG9E0mfaTcRbI/oZAEVUWklNdzSAqw12fAdo8Je30GKAnEBXMsYQx3BjHMIcLcL8QFiWTCCb89Ciee+BtNK0qnE1BcXBmTtSRlVBVVVR6sWbOlr5fOYPQqncr4SJKE7OxsVFdXx9zudrtx8OBB6HSHbuMjy/h0HzRzMnp0fsx0FKUnGpe7kz9fOx0LF14DQYj282puVI7OiCQTsE2YMATf/O+JpBuDZVlBU1NQ89pKT3di0KB0yLLSqv/IZDJoWRBZVhAOyxG/Lj7u1BXtH4rOnET3Ffn9IgSBh88XgMGgh8NhRjjcLFTXW0Rnlnqy30dVgTK/Hts9RuxswwHdZZAwIkXEcGcQzn7igE5RVaCsrAY6nYAtW0pxz92v4p1358Y06UdrVqmq2qHzl8Ho7/Roxoeq3LZkzJgxmlM4g9Hb4oTJkmxJrWRvFTyeJng8TVqpK3ryiWoMTZ48PKny3IYNxdiypQTjxhUmVapRFBWVlfXav5uagpq4IW38peWv6PuLHmunQRBtiG62s0CcbBCnaQqZTERGwOGwwmAgitTRSs691WsTCklQFFXL/nQ3tUEB2xqIA7o3gbigRadguCOIohQRGX3kgK6qgMfjg98vamKX0VCdJkdEgmH48FxMmjy8VZN+9PnL81y3amQxGIcKHQp86urqQA0Wd+7cGfMFLwgCbDYbFi1a1O2LZBya9LY4YTJ0pImaagwpCsm8tMTttiMlxYp7/noRALTbkK2qKubc+QrefOvOpJ5zVRXJLFH8fhGBgAiLxaSV3KIbmwHS40H9r4hJqhIZaRa0jAkQmzWJDpKoIrJer0uo4tubDcZGo16TBeiuTJOPOqA3GFGdQFxQx6kY4hBRlBLEoD52QFcUBRs37sFLi1dh7t0X4sCBGuTmpmmj6tQ8luO4SOYv4n8WaVrvLY0sBuNQoUOBzy233AKO47BkyRLcf//9aGho0H4XCoVQUlKC77//vtsXyTg06W1xwvboSL8R0LbYIfXWkmUlZnIr0X1RVq/+Gff+9V945u9/hl6v06aV6Ag8z/PadFV00EOprm7EgAEGqKqqBT3RZqGhkAy9HjHeSRSS+eETBi66vuzKbYOONGonIiRz2O0lfTv72nBAH2QLY4QziMF2EYbeq+i1gjYi/7KlFC++uBKLF6/EKaeMhcGgg8fTFAl2lBh/MpqJo+rjP/ywMymxTuapxTjS6FDg8+qrrwIA9u7di2+//RaSJLXzF4zDkWRLRcmqJPfGF29nzFDb0hjKz88AAJSUHNSCumSNVRcvXokZ5x+HSZOGQadrFjxUVaCpSYwIHCqtBAUBEnA1NQUjTuzN4+ik1EGu/sNh6hZPfJWIwKICg14A10/9rHoCWQX2+QzY5iEO6FKCJuVMcxhFThHDnEFY+7hJmSp219Q04P77XsfixSu1cyg6AxkMxqqGA80tCFarCRs37sGGDcW9rpHFYBwKdHmc3WQyQa+PTRf3VVPw3r17kZ+fH3PbY489hrlz5yZ9H6y5uW06qrfTnkpybynBttTmaUlbjcotnzMApKTYUFlZH7d/KZmm5+jXxe8XtQkri8VIsjRxdICyslyw2y3w+QJobPQjNzc10ufDgVQ9qN2AHDP6DvSdZ1Vvo6pAZUCH7Q0m7GwwIpDAAd2hjzigp4hw9xMHdEpjox/nz3gUX365Oeb2eDIL0arhVGahtLQ65nPVFxpZDEZf0KPNzWazGU888QT+8Ic/IDU1tfWd9uFU17333ovFixdr//b5fH22lsONjpaKgLZVkp94/F00NDTh1FPH9bhuT1f6jVav/jlmNH3I0Bzcc88fUF9PPljRkzJ+v9hu7xLHcWhoaMJLL63Cuecdi5wctxZQlZfX4aXFqwCouOZPpyEnxw0A2ii3zxfAnj2VsFiMWnlMklRtzF2SFFgsBq0fJhgMobbWq42nH67UiwK2NxC9nYZQYgf0YZG+new+cEBPBllWcNecZa2CHiA2A+lwmFFZWQ+Xyw6zWQ+DQYCiKNiypRRz7nwl5nPY8vxlgoSMI51ORShPPvkkpk6dihtuuAGvvvoqbrzxRuTm5uLaa6/FXXfd1d1r7BBerxdVVVV9uobDkc6UiiirV/+MtWt/wcUXn4C8vAyUlh5EVZUnxv+pp69CO9pvFK+cR7M39L5cLjvcbrumjUItI+rrvQl7l+JdfTc1BSGKAux2M/LzM7Fg4TUAiKVAKCShvLwOK1duwGWXTYHPR/p+/H4xptxBndNLS6sxaFAaBIGHKIZRVeWBy2WD3W6JWUdbXmGHCn6Jw85IsFMZSOyAPtguosgpIt8WQje0C/UoO3eW4cUXP034+5YXEn5/EH5/EOXldVi8eCUWv7gybkDTXzSyGIz+QKdKXaWlpZg5cybWrl2LhoYGjB8/HsXFxbj88stxySWX4Mwzz+yBpbbP3r17YTQaYTAYsH//frz99tt48sknEQ7Hv8qPByt1xac7S0U8z8FmM0MUw6ioiDVC9HkDCRuDu0JHzFCnTBnVZmmA4zj88ONTGDt2sNaESktJgiCA5zls2rQHk465rZUtR8uMmctlx8CBaQCA6uoGuFw2CIIAjiP9OuXldTAa9VBkBSazEaWlVVpDq91uiSl36HQCGhv9cLvtWr8PtXuIR19bQHSGsALs8Rqx3UMc0JUEYpADLCGMSBExxCH2iQN6R6G2JPfc/SqefPK9do/vLyrnDEZ/okdLXW63G3v37gUANDY2wu12o7i4GN988w1eeOGFzq24G3jmmWewYcMG1NfX45hjjsGjjz6KgoIC/OlPf0r4NwaDAUajUfu33W7vjaUecnS2VBRvsx82LBcmkyEyeUQsEpJtDO4syZqhTpkyKma9PM/D6bTgmGOGYenSmzFr1t9jgjLSjNr8/9GMH1+I1FQ7amu94DgOTzx5FVxuO0pKmjOSJEghjcnp6c6oPhzSnJybm4Y9eyrhdttgsRhjMlZer18zSTWb9ZG1WgEQCwo6NZaIZi+s5oMkSQHxBOvDkaYWKCpwoEmPbQ0mFDcaEooLpholFEUc0B39TFywLRRFQSAQQlNTEF980brEFQ+WwWEwOk+nAp89e/YgPz8f+/btw6+//oo//OEPWLduHc4++2x4PJ5uXeD999+PBx54oM1jJk6ciPXr1+Ppp5/WbtuyZQvq6+vx7rvvYs6cOQmFFefOndvu/TM6N5oerzxmsRih15MASqcTkJXl0tzAgZ4VNWzPlX3Nmi345NN5sDssaGhoQl5eBsxmoxYYOBwWvPveXNw15xWkpztx4EANXC4bTCaDVuoKBIjWzsiRefj4kwdgMOhhMumgqsRVXZZlDB2ai8rKes0eQpJIM7IgCJoZKQ36dDoe+fkZqKiog81mQmamKyZw8nr98Hr9yM/PhMEgQJZVWCwkkE+mmTn6mGadn77PAqkqUB0UsD0iLtgkxQ/ErDoZRU7St5NmlA+p8h3NuBElbgmbN5ew0XIGoxfoVKnrlltugSzLePbZZzFlyhSsWLECgiBAp9Ph1ltvxd///vduW2BqairS0tLaPKakpASi2Hr0NycnB2VlZZg0aRJ+/PHHuH8bL+NTVlbGSl0t6EipiG7a8cpjDocF+fmZCIcl8DwHnudRXFyhjW63dATvqefSliu7qqrIyXFDr9dp48X07zgOmo5KaelBKIqqNTfrdDwMBj0yM1M0DR2qQUM3Oer5pSgKamoakZGRAlmWYTDoNTXmlmuVZTLaLkkSgsEweJ5rlbEKh0lQWlPTiAED0mG1GpPWv6Hu7OGwHAmaVM0VvbcnwRpDVFzQhFoxsQP6EIeIEU4RuX0sLpgsLTNrVHCQnlvBYAjn/P6huE3NDAYjOXq01BWdWVmzZg2KioowceJEFBcX4+efu7c3o7a2FrW1rTfaZBg3bhwAoKKiIuExoVAIoVDrDAYjlmRLRdHlqXjlMeoRRMssgsDFlFV6Q9QwUZmArtdqJYFMdNBD/47jONjtJoTDspb9EgQBmZnEADK6vETFBaN7aXQ6AcFgCHq9Dqmpdk1okP5N6+CHGIeazXrU1gYxf/6bmDHjOAwfnovMjBQoioo9eyqxfPn3uPmW30MUw6isrMfgwZltPn+6HkUhPUrV1Y1wuaxQVbpB916/SFDmsLvRiG0eI8r8hrjHUAf0IqeIwXaxTx3QOwLN4FHTWXo+0SCIBj86nYAhQ7JZ4MNg9ALdMne+f/9+7N+/vzvuqtNMnjwZkydPxurVq9HQ0ICjjz4aCxcuxPvvv9/naztcaK9UlMjyIbo8RqaRQjCbjZBlWWsOpvSlmiwNtsxmkgFs2WNE1ZFVlbiZZ2SkoK7Oh7y8dAgCD1lWY0otZLNrFkPkOE5rgFZVBUajQbvfaOjf0WwPsaYwwONpQmnJQfz3v99i5sxpGDw4i2TIctw499zJmvdSe2PrsXYVJPAcMCA1sk5yOwn8ei74kRSgJMoBXU4gLphlDmNESv9xQE8G8roRPaV9+6qRnu6A2WyMBJVEmZuc9wpkmZz7BoMe9957EXbtKmfaOgxGD9PpS7ujjz4aU6ZMQUZGBng+9vLrtttu6461dYhx48bh+eefR1FREYxGI0pLS/Hmm2/iiSeeQCDQWvo/EUfKVFdXpkKS/VtaHhs3bjDq632azo3dbkZeXgb0eh38fhG7dpX1iahhvPX+uG4Bxo4dHNeIl5aoFIW4tHu9fqSlkYbkUEiCXt+swhxd2mhLPLC9ySrix0RKbPX1RJPK4bBAUVTU1DTC7xc14UO32w6djoeiqJGMlK7VZ7M9IcPo9XT31JeqAuV+PbY1GLGrwQgxQZNyikEifTvOIFKMh06TMhAdGHPw+0Xs2HEAAwemQ1VVeL1BZGWlwGDQIxRqzoJyHGlkb2z0Y+PGPTHlYgaDkTw9WuqaO3cuHnroIezYsQNVVVWtygF9wcaNG3Hsscf2yWMfanRVyTXZiZIpU0bB7bYhJcUKt9sOWVYQDIqoq/Nptgt+v4jsbHebmaPeQlVVLH5xJZ79x7Va+S3auZyUnRRwHHFKX7bsS8yefQ44TtDG9On9RNNWoNFWYEEzSwCZtqqsrMfAgWngOA56PY/sbHdkDJo2QguRx6OP2fq+2+vZaZkN6g7qRAHbPaRvpzGBA7pZUDDcGUSRU0RmPxUXBKjbPcliklJtc/AWHfSoKpEnMBr18PtFVJTXYcjQnIjQZazVj04nIBAQcfCgp8ca+xkMRjOdCnxuvvlmzJo1C8uWLevu9TB6mM6oL3f1cQ4cqIXbTRRmrVYzzGYjNm/ei7vmLIPH4+tXWiSLF6/ENX86FePGFUYFCVQhWdbKFFu2lOKbr3/FDTecCaORbF6KQoIOEjB0/XlEb/6hUDjivm2MCWpo+Sw6SKEllb6kKcxhR6MJ2z1GHGzDAb3QQcQFB9lCEPo42KG9VaFQGDzPayawFElSUFfXiM2bS+B225CXl4FgMITsbHckAOLAcWQiz+8PoaamUWv6f+Lxd7Hs1dlwuWwQRZLFomVGRSFBbXuK3wwGo3voVOCjKAr+97//dfdaGD1MV9SXu/o4tbWN2vST221HXZ0Pq1f/3OHH6WiJLtHxiW5XVRVz7nwFy169BVlZxC5CkhSoqhJpQuZQWenBypUb8LenZsHpJIrIzQ7rqhaIdFepSFUBvV4Hq9UUcVCn942YLBN9rL4SJQzJQLGXZHb2+fRQEzigD7SGUZQSxBB7CIZ+JC7I8xwCfhHbth9AWpoDVosRPM+j6qAHy//7Hb76aiuqqxuwceMeTe/JZjfjwIEaLfhRVZIVrKlpQG5uqtb0v3r1z5g//00sXHhNVDaRyB9UVtbD6w1oruo92djPYDA62eNzxx13ICcnB7Nnz+6BJfUth3OPT1fUl/vD43TGILXl8eXlddjycwlGj85HdsQjK979TJ06Go8/cSVGjhyk9e2EwzK2bi3FG298heuum45BgzKg1wtoDkSaP07dHXyoqor6+ia4XLao8frmybG+Kg0pKlDqM2BHgxG7GxM7oKebwhjhFDHMKcKm7799O6GQhBN+OweKorQbXEefXzabKSIFQHp7fL5Aq3MqUc8bJZ4kBIPBSJ4e7fH529/+hhUrVmD37t349ddfW1lCnH/++Z25W0YP0xWjzr5+nI6W6Foer9frkJPjxoABqZg8eTgUhXhhVVTUIxwOt7qf1at/xqRjbsP48YWYNGkYwHH44fsd2LhxDz7+5AFkZKRErtw5LQABAFXlYgKTaOhoM/ldxyIVjuOQkmKJCnbI5FC8x+lpVBWoCuqw3WPEzgYT/Akc0O16WWtSTjX1Lwf0eFCbj2R72Fqaf9bWki/a1FR73IApWhLCbjejrs4LnufalIRgMBjdT6cCn2effRZTp07F6tWrUVtbyz6ohwidUV/uD4/T0RIdz/N46OE/IjXNgcrKeuh0AgYNSm8l6Gc06jFoUBpKS6vj3o+qqli/fjfWr9+t/c348YUYMyYfFosxRnOnucwEABxEMQSDQQ9FUbTSRldVkWkPUfTj9SYNIR7bPSZsbzCiPhT/q8PIKxjqJOKCOZZwv21SjockSfD7RaSmJm9b01HriI5KQjAYjO6nU4HPzJkzcf755+Pjjz/u7vUwepCNG/dgx46yhOrL3aWh092PM27cYAwfnova2viBUrTNhdNpxcMP/xETjx4KVVVhtZq0nhs6jUPLUqqqQhCabTOSsctIT3ciJcUa0fRJPCVVX+9DZmZKUs8vWXR9oNoXkDjsaiR9O+X+xA7oBbYQilKCyLeFDhlxwWiCwTCqqurBcVyP99i0zBT1l8Z+BuNIoVOBT11dHYqL2bjloUZH1Zc7q/XTGZXntki2dDZt2hhcc82pSE1zaDYM1AiVZoKiMyUGA7ndbjdh4MB01NV5YTDo2izBEbVlQWsujgfHASkpNsgyGXcOhyWtT+hQQFKAPV4DtjeYUOIzQEnQt5NrCWF4pG/H1I+alIHosmJzJi5eLxRRuvZozci9JZ7JTEYZjL6jU9/GDzzwAObNm4errrqqQ+KAjL4n2VR7V7V+upLSbxlw1dZ6kyqdnXfesbA7LKisrNcyPQDdBLlWjuPRk1AZGU643TZIkoL8/MR2D7W1XsiyAoOh7Y+O0ahHQ0MTrFYTOI6DJEnQ6fpv8KOqwAG/Hts9RuxqNCZ0QHdHHNCL+rkDOseR0iAJeBTIMiIZPuIPJ0kKqqs9qKysh9Goj5nAYpkXBuPwplNTXRs2bEBhYSE4jkNJSUmr5uYJEyZ01/p6ncN5qiuatrI5iRqJ3W47fN5Ah7R+Opo1ShRwud025OamJjRILSk5iJwcN3y+ADiOR15eOgwGHSRJjpS32hbvo0sKh8PYV1qd8DmOH1+Iz794WBtjbwufLwCfL4jUVHu/zfjUBAVs8xAHdF8CB3QLdUB3ikg39b24oCzLEZ0iTrMRAVr3PUUbw5aUHIQsExNWu92CnTvLkNPGVB+DwTj06NGpruXLl3d2XYx+QqJUe3do/XS2RNbW5JYsyZBlJWHpbPl/v8Odcy5ASooNJpMegkA8kWhDc3T2J97GTUshFRX1sNnNCZ/jxo17UF3dkFTgY7OZYbOZ2z2ut/GGIw7oHhNqEjig63kVQ+wiilKCGNjPHNB5XtACHpLVoVNusbpJJNOmoKTkILxePwDA5bJh/frdOOP0BzB2bAHrsWEwjkA6Ffg8+OCD3b0ORj+hI43E8QKnzpbIkgm4yspqUVfni1s6Gzo0Bw4HCTIkSYYkydDphFbqu2RcObZRuHmMmei4NDUFEz5HVVXx9tvfYO7cC5N7QfsJosxhdyPp29nfpEe86TIOKvJsIRSliCi0i9D30ybl6MBVlmVNWJL8m3ip6fUCFEVFWVkNmpqIOGB0f5miKKzHhsE4Qumf+XdGn9EVDZ6u2GEkE3Clpztx/XXPQ1XVmCt1AJhz1wVQFDViJEqu3GkAZDDoNZXjlvYOxIpCgaIokd4PuV2dofvu/Tduvvn3EcG6WLrb2LMryApQ2mTANo8Re7zGhA7omeYwRjiDGOYUYennDujU1Vyn4zVzz3BY1oQkAfI+Nzb6sWtXOdLTnf3GC47BYPQPkg58amtrMWzYMNTW1qKurq7NtHBqamq3LI7R+3RWg6erJbJkA67UVDtWrdoY87vx4wsxfHguKirqkJXlgl5Penti1ZTJOLtOp4s0GlNtHR56PQdF4dHUJMLvFzXrgNpaL8aPL4wrTrf4xZW4+ZbfA+iaMGF3o6pARUCH7R4TdjYaEUwgLujUyyhKIU3KLmP/EBdsT4Ga2oKEwxJCIUS0lHjwPHE+b2oKwGQywOcLYNZVf8fq1T+zkXEGg9GKpAOf2bNna81Cs2fPZl8ghymd1eBJlLGh/lx+v9hmiawrooc0aKqoqIMohpGV5YLJZNB6PwIBESaTEaIoaY7nJCjiI2PupEm2srJee45lZbV4+JGZETsCcyS7Q9SeJUmGIAiQZQWCwHdZmLA7qBcFbI/07TQkcEA3CQqGOUjfTnY/cECP15ScKGNGAx5FUaHTCSguroDVakJ2NmloF8UwwmEZv/yyOyarw8pZDAajJUkHPq+++qr2/8yV/fClsxo8LTM2drslEoDoo5pPgWnTxsTdjLoiehgdNHm9AXi9AS3golNdeXkZqK5ugCTJyMpywWw2QqeDpr5MFJZ55OamascMGJCGQCAEq9WoNUnb7RbwPBcxpIxdBzUo7S38EoedDUZsazChKpBYXHCwXcSIFBF51hCEftK3I8sy/P4QjEY9dDpSrgJIxkev12sBKYBI71U4RiVbpxPg8wVQUVGHe+5+FTU1jSyrw2AwkqJTPT6SJCE7OxvV1dUxt7vdbhw8eLBf65Uw2qczGjzRwYdOp0NeHrGIkCQZiqJGhAQF3Hjjmfjpp92t7qMroofxgqZo88esLBdkWUZDQxMURdUCI7vdDJfLBr1eB51OgN1mxqbNe+F22zBgQBoOHKjB0KG5EAQeoZAEnudhMjUHGDQxQQOe3gh6wkrEAd1jRKnPENcBHVAxwEr6doY4QjD2M3FBRVFRW+vD3j2VKBicCYvFBKNRj3BYiryGqnYc/Xd00EP6fGQtGH7jja9YsMNgMJKmUzo+siwjKyurVeCTnZ2N4uJiWCztj/r2V44UHZ9k6MhYOnWeHjUqDxaLCRYL6ZOh6PU6BAKkh6YtB+pkpsLirWvKlFFYtOgG2CLmj9FBE7WqqKlpjFtGc7lssNnMuO3Wl7BzZzne/+CvEEWSYcjNTYWiKAA4GI26hD08PdnUrKjA/iY9tntM2O01IJxAXDDNKKEoJYjhThH2fuaArqpESFBVgRee/xjLln2B9HQnliy9GV5vEIMGpYHneciyDL1ep2XUOA6Rfq3mCS5RDGuZves7oCnFYDAOb3pEx+cvf/kLAPIlf80118Dn82m/EwQBv/vd77B9+/ZOLpnR3+iIrD7N2Cx95WZYrUZIEi1dkLKEoiiorKyHJMkYPjwXl1zyu7jlifZ8jBIFRu+99y1eemkVzjvvWGTnuLUsVUnJQby//Hucc+5k5OdntCqjud02pKU5UVJShaqDDXhh0Q1IT3dGJsQQyfaoMSPT8ejuoEdVgeqgDtsajNjRYIQ/gbigTSdjuJP07aT3Ywd08vLw8Pn8eP/9H7BhQzHGjy9EKCQhHA6jtLRaK42SIIn8nSTJqKiog8tlh9lsAAA0NQXZhBaDweg0Hcr47NlD+ivy8vJw4MAByHLzF20oFEJJSQnuu+8+/Pjjj92+0N6CZXy6xh13zMCD8y8DUdUlZYlgMITKynp4vQE4HBbk5aWjweOHrCgdUs2NNy7vctmRk+PWRph9viAaGppwYH8tBg5MhcNphcGgA8/zsNlM2locDguys91a7w7V8QkGiau6JMngeU5Tfe6tRuCGEI8dDcQBvS6BuKCBVzDUIaIoRUSupX+JC8aDBMEcFEWBzxdEXW0jrrvueaxZs0XLEtKAlPZmGQw6ZGe7oCiAx+NDKCShorwO//3vd/jii82sl4fBYLQi2f27U6WuL7/8EjNmzIDH4+nCEvsnLPDpGuPHF+Ld9+6GFFFaliRZ67ex283Iz8+EIPAoLT2IhoampK0waClt9Oh8bVzebrdovUQAER9UVRVms1FrWg4ERNTWeqGqKlwuG3Q6Mo1ltydWVJYkGRwHiKIEk8nQ4707QZnDrgYjtjcYUeY3xD2Gh4p8ewgjnEEU2A8dB3RZViDLCoLBcCT49WtmoKdPv7/NEqXXG8CCp5ajpKSKNS4zGIx26VHLipNOOinm3zzPY9SoUSgtLT0sgyFGYlr222zatDfhdFZWlisyjRNEfT0pkyar8xNvXD4ry6U1Hut0AsxmI2RZjvw9CVYsFiMsFlNE0Zf0vUQ3KNOSVnSpimr8mM0COnFdkBSSApT4DNjmMWFvGw7o2eYwRqQEMdQhwtyPxQWj3dCJXpKAgwfr4fUGteDXYjHC4bDESBt0xcyWwWAwOkOnAp+FCxdiy5YtWLJkCXiex1dffYVjjz0Wfr8fZ511FtauXdvd62T0QxL126xcuQEF+Rkx01lOpxVWqwmSJGt6OdG0Z4XRclzeYjHCZNJrvURUT0dRFOj1Om0cmpapon9HDC7jBz3RdLc+j6oCZX49tjcYsavBCDFBk7LLIKEoRUSRMwhnP3ZApyiKGtE2It5YHk8T0tIc8Hj8aGoKwm43Y+jQnBhtJUDVpA3a6+tiMBiM7qRTgc+FF16If/3rXwCAs88+G/n5+SgqKsLMmTPx8MMP47e//W23LpLR/0hkTzFu3GCMPGog/vOfbzDyN3naVTyZ2FFQWlqtGUZGa+0Eg6E2bSJaChzSZuPoUfLmTA8X1ZNDjCybNWFiN9PeUFquDQrYHunb8SYQFzQLCoY7gyhKEZHZDxzQ26LlBBt9TZuaRFRW1kcm6WQoCikp5uVlaNYSqholbfB/zdIGHWmkZzAYjK7QqcAnNTUVlZWVAIAzzjgDb7/9Nnbt2oWXX34ZN910U7cukNH/iGdPYbebNcVknU7Addefga+/3opHH3kbJSVVSEtz4OFHZiIcDsccSzMA4TAxB6XKzO2V0KglBfXeIkFQW9kRrlWQ05PBhS/MR8QFjagOxhcX1HEqhjhIZmeQrf83KVNkmfiacRzRS6IyAYqiwO22o77eh9LSg8jNTYXFYtTKkRSeJ/Yger2uzfImg8Fg9ASdCnyqqqpw1FFHoaKiAtOnT8cNN9wAALBYLDGTXozDk5b9Ni2v6hWFWDmMH1+IgvwMXHfd83jjja8w84ppmDChEFarKSYDwHEczGYjOI5DSootpoRmtZqgyAr27KnEO+98G1NCCwbDsFqNWjaH53kYDLHlIxIUQfs9LXPR33UnIZnDbq8B2z3EAT2euCAHFYNsYRQ5gyi0izDETwD1O2jJMByW4an3ae/HqaeNw/DhuUhJscb05gDA0qU3w5ru1MqRRPE6WtpAarO8yWAwGD1BpwKfpUuX4j//+Q8qKiqgqio+++wzAMCkSZOYjs8RQMt+m+gmY4BskoLAoa7OC7vdrF3VP/H4u3j/g79Cr9chFAprGRtBEBAOSwgGw3js8SvgdFiQmuaAXq+LjKJzmJg6FKNG5+PVV79EYWE2hg/PBQBtoisZiIN39yKrwD6fAdsbjChuNEJK0KScYQqjyCliuDMIq/7Qy26oqorq6kYseOq/MePkCxcuT9ib89xzK/Dg/MvBcZzWdxUIhLTpLp7n2ixvMhgMRk/QqcBn3rx5+OWXXzBw4EC8/fbbCIWIGq4sy3j00Ue7dYGM/kd0vw3HcTCZDJrXEgCt30aS5JimZY/HB58vCEVRYTDoIAhczGYoSRJGjsxDKET6hajlhSSRAMlkMmDmzJNw7jkPw+PxYdE/b8SQIdmwWEzQ9eJ8t6oCVQEdtjcYsaPBhEACB3S7XsaISN+Ou584oHeWioo6XDFzYVyrkUTZmi++2IwbbjwzrrQB0LbxLIPBYPQUHdotVqxYAYeDXJ29++67sFgsMerNH330ER577LHuXSGj30G9sdxuu9ZkHN2jIQgCgsEw/H4RohiGwaBDWpoDaWkOKIqCXbvKUFxcgZKSKhQXV2DXrrJIBoCHXi9olgVET4fTNHloAzURSCSZp8bGADiuddNyT+AJ8fj+oAXLdrvw5l4XNtVZWgU9Rl7BKFcAF+Z7MGtoHY7L9B9yQQ99LVUVkCSiwzP3rlc6PFpOzxOLxYjGRn9M0AMQ49kdO8riGs8yGAxGT9GhjM9pp50Go9Go/XvOnDl444030NDQQO5Mp8Pw4cO7d4WMfke0oajLZQPQ3EsT3cMBtL6qp5milpsgAJjNhkhZRADP8zAaeVCNTToyraoqBg/Owh9nTkV2tguCIPSowGBA4rCz0YjtHhMqEjig85yKwbYQilKCyLcdOuKCbSHLCsJhGQaDDvX1Przxxtcdvo+uGM8yGAxGT9GhwKf1VMwhMobC6HaihedOOOEoGI36yGZJghw6sk4dtOlVfSJxQwCw2cxQFAU6nRDxayJ6LwBtXOYgywosFiOuu+506PWdqtS2S1gB9kQ5oCsJtHxyLSGMSBExxCHC1M8c0DtLOCxBlhVwHAeDQRcpX7/dzsRcYphAIYPB6G/0zM7BOCKgwnN/+9ssXH/DGdDrdeA4HbKyXNoGV1vbqF3VcxyH9977FiNHDkJ+fiaqqupjMgB+vwir1RhRTo4NJGgjNPXc6m4UFTjQpMf2BhN2NSZ2QHcbJRQ5gyhyinAcAuKCyaAoKrxePziOi0zcCVBVFfX1Pjz66NtYuGB5l+6fCRQyGIz+RIcCH1VVW31ZsS+vI5spU0bhrLOOht8vwmCQI8rIHMxmA/R6Aa++ug5r1myJGVE3mfSwWIwYPDgbfr8YUf3lYTCQKS6AZhNVzaWb9hHRslZ7qsvJQBzQBc0UtCmBA7pVJ2sTWekmuV+LC3YEVVUjTccK7pqzDC+9tAoXX3wC8vIyUFp6EG+++XWnMz3xHouNrDMYjP5Ah0tdr7zyCkSR9GeYTCYsWrQITU1NABDT/8M4/IkWMtyzhwhapqU5kJ7uhF6vg16vw3XXnYETThip+XTV1jaitrYRRqMeGRkp5I4iE2DBYAgOB9GC0usFrW+IBNwKOK57mme8YR7bPUZsazAldEDX8wqGOkIocgYxwHroiAvGg16wULkBvV6H6uoGeL0BuN12bNhQjMWLV0JVVbz+OrObYTAYhzcdCnyWLVsW829qWxHNq6++2rUVMQ4Z4gkZUk2fcFiGLJNMzujRBeB5DsXFlQgEiPRBIBBCaelBjBqVDwDYsqUEFosRiqJqhqI0+0NtKaIzLc0WFckRlDnsbjRim6dtB/Q8WwhFKSIG20XoD8Em5VBIipQcyb+bmoIoL6vDoLx0rfFclhX4/SLsEUd01mDMYDCOJDoU+MyaNaun1sE4BElGyFCna/bIyspyaU3PAPHqokagbrcNkkSaow0GPcJhCaIY1hqYW/pDAc3lr0TlLuqAvqPBhD1eA+QE4oJZ5jBGOIMY6hRh6ccO6O0R2xBOXNLLyurg9fpRWlqNrCwXrFbiYK/TCazBmMFgHJGw5mZGp0lOyJBYHUiSrPX20FF2nU6I2EhwGDAgXfs7QeDB82RKrD1l5pbBj6oCFQEdtnlM2NmGA7rTIKHIKWKEM4gU46HZpEzFH2l/FMdx0OmICraqEtNQSZLA8xwkSYLfH0RDgw/P/WNFjPoyg8FgHEmwwIfRaahA3ahRefB6A3GFDKmAIQmAuMjEFsFo1Gtml7QEQ01HBYGDICR3enIchzpRwHaPEdsbTGhM4IBuEhQMdxJT0Cxz/3ZAbw9VVbF/fw2qq4mGVn5+JqqrG/D8cx/h++93wum04s4557MRcgaDwWgBC3wYnSYZIcPy8jpkZaVo/TvUsBIAUlJsWvOyLJOsCz1Gp9PHPA6972iawhx2NJqw3WPEwQQO6AKnotAuYkSKiEG2EIRDNNhRFCXigcZDVYGDBz2orW2E2WyA221HXZ0X/3fjopigZvXqn9kIOYPBYLSAzAwfAtx9990488wzMXbsWIRCIbhcrlbHDBw4EM899xxOOukkBAIBvP7667j99tsRDoeTfhy73Y7GxkY4HA54vd7ufAqHLXRU/YQTjoLBoI9MaIU1M0q73YzCwmzIsoLdu8shimE4nVbk5WUAAFRVgapyEeE8aCPx8QjJQLGXKCnvS+CADqgYaA1jREoQhfYQjIeouKCiqFAUBXV1Ps22o6amEYGAiPR0JwwGHUIhCTt2lLFMDoPBOOJJdv8+ZDI+BoMBb7/9Nr777jtcffXVrX7P8zxWrFiB6upq/Pa3v0VqaiqWLVsGjuNw00039cGKjxyoQN2f/nwa7r33YhiNek2c0Gw2wOGwoKKiDlVVHqSnO5GV5YLNZgbP81AUMqYuCFzE96v1/SsqsM+nx7YGU5sO6OmaA7oIm/7Q7NsBENUXJeHssx5CXV1jTNYGAMvkMBgMRic5ZDI+lCuuuAJPP/10q4zP9OnT8dFHH2HgwIGoqKgAAFx00UV45ZVXkJGRkXT2hmV8uka0UGHLjER0cGS1GmGxGKGq0DR7SMlLhU4XKecEddjmadsB3aaTUZRC+nbSTIeWGWhbqCrQ0NCEU07+KxP+YzAYjCQ47DI+7XHsscfil19+0YIeAFi5ciVMJhMmTJiANWvWxP07g8EQI7xot9t7eqmHNS3tCWprycmXmmrHhAlDcOWV02A2G1BeXo/c3FTY7SYAzZNZTYqA7dUmbPMY4QnFPz0NvIJhDhFFKSJyLeFDukk5muasDbkekWUFaWmOvlwSg8FgHHYcNoFPVlYWqqqqYm7zeDwQRRFZWVkJ/27u3Ll44IEHenh1RxbUnmDq1NF4+JGZGD48FykpVpjNBuh0OiiKArvdHClzcQhIHHY1mrCtLQd0qCiwEwf0gkPYAV1RFCiKGjPdRm5vTrzSQI4avjIYDAaj++jTwOf+++9vN+iYOHEi1q9fn9T9xetzaDli3ZJHH30UCxYs0P5tt9tRVlaW1OMxYuE4Tsv05Bdk4rbbzkVqqgMmkwEmk0Hb0AWBh6QApU1mbKs0ocSb2AE9xxxGUUoQwxwiTIewuGAzHAKBIDiOh8Vi1JqWtd9GeZSVl9dpPT0MBoPB6B76NPD5xz/+gTfffLPNY0pKSpK6r8rKSkyaNCnmtpSUFBgMhlaZoGhCoRBCoVBSj8FITMvenpQUK3Q64vLdrL4MHPDrsd1jxK5GI0IJxAVdBgkjUogpqPMwcUBXVWhWHKoKmM16NDT44HTaNKFHQNVMQVVV1fyzGAwGg9F99GngU1tbi9ra2m65r++++w733HMPsrKyUFlJDDNPPfVUBIPBpDNGjMREZ3NaThJNnToaixbdALvDgnBYhk7gtJF0juNQExSwzWPCjgYjfAkc0C2CguHOIIpSRGSYDm1xwXhQfyw64j9qVD6sVhN27y5Hbm4qjEZS4iPZHxVbtpRi8Ysr+3bRDAaDcRhyyPT4DBw4EG63G4MGDYIgCBgzZgwAYPfu3WhqasKqVavw66+/4rXXXsMdd9wBt9uNv/3tb1i8eDGbzuoi7U1qzbnrAmRmuWA2G6GLNN94wzx21BuxzWNCbQIHdB2nYohDxIiUIAYe4g7oLaEj6aqqQlFUVFTU4uDBBu33FRV1yM1NhdttR2npQfA8D7PZAKvVhPp6H+bc+QrL9jAYDEYPcMiMsy9duhRXXnllq9unTJmCtWvXAiDB0fPPP99KwLAjpSw2zh5LdDantrYRohiG0aiH221HOCxh+X+/x5VXnQybzYyQwmF3owHbPCYc8OuBOH07HFQMsoUwwimi0HFoOqAng6KQKTUinsmhuLhC8ygDiI9ZXl4mSkqqNLNXJkbIYDAYnSfZ/fuQCXx6Cxb4NMNxHD75dB5Gj87HgQM12u12u0Vz+pYUBWVBC7Z5jNjjNSZ0QM8whTEiRcQwZxDWw6JJuW1CoTA4jviQ+f0h7NoV2zBvNhtgs5lxwfmPQlVVJkbIYDAYXeSI0/FhdD/jxg3G8OG5qK1tHqm22y0YNCgdNZIJ6ypN2OU1IZhAXNChlzEiJYjhThFu4+EjLtgWtEn5wIE6DBjgBs8LqKysb3Wc223Hli2l2LChmAU6DAaD0YuwwOcwpK1G5I7cx+Rji2C3m7USjZ8zwWvKxzf7bAkd0I28gmFO0reTfYg7oCcD7eEh2jx8pLylQKfjsWVLKbKyXHA4zJAkKaZM6PUG8Phj77Cgh8FgMHoZFvgcZrTViJxs3wi9j1Gj8qCzWNHkdmMbb0JN2AjEyR4KnIoCWwgjUoLIt4UgHAZ9O7Q5mfy/CqouHQpJ+P/27jy+iWr9H/gnaZMudKNA6SKUHQrIcssmAmURcEMWRdQLtshXEX6CorLYixb0IiqCoIiAYkXvReR6ZVEviEBBLAUsoJRFgVK2bnTfkiZp8/z+qI1Nm3Rv0+Xzfr3Oi2bmzMwzmTR9OHPmnBs3bqNNG48/R53+q5XHYChEZmYu1qzehYMHf8OZM1cxcuSdpuvh6ekKvb4AMTHX2Y+HiMhG2MenlMbcx6e8jsi5OVrMmfMRMjNzy20JGjWqDz786P8h07ENLuU4Ix1uVmdA93M2oIebFt08DI12BvTSCguN0OsLoFYXzR/2++/xeGrmOgwc2AVQKHDi+B84fTrWlNDceac/1GoV9HqD1YSmNlrgiIiofOzcXE2NNfGx1hG5WMeObSECaPLy4dzCEcZCI65eTcLSpf/CoUO/QaFUouvgAVi0djHynL1RAMvNNp4OBQhwL+q346oqRGGhscz0C42FTmdAfr4BLi6OUCqVf86IXggRQMSI27ezEBK81mrLDBMaIqKGg52bmxlLHZGLubo6wcXFCfb2dnB2VsPOzg5KpQKBnl2x5T/LsXX/DYh3J7i1boUsC/tuYV+I7m75CGipQ5s/Z0A3GgUFBYWmUZkbm2txyUhLL/rFcHV1RocOXgAEubm6cltvSiqek4yIiBqPxvlXi8ooHgtGpzOYljk7O8De3g6+vp6ws1NCqVRArVYhM1+BP7IccSnXCZkGFVx7ty2zP3sUoqu7Hj09dPBrYYACMOuorFQqoFQ2vo+PSNE8WAqlAkqlAg4OKri5OeHG9dtYvWYXrsUls/WGiKgJa3x/ucii1NRs6PUFcHBQwd7eHt7eLeHoqIJSqYCdnR10RgUupDvgQqYDErVqi/swFhbCR61Be/tMDOroCFXjvINllYggPT0HLVo4wsFBBR8fT3Y2JiJqZpj4NBFnzlzFH3/EIzCwC1q0cICdnRI6QyFu5Djhcl4LXMtVw2hlcMG2jnq0t8/EkpAwbNk0G926+NVz9PVDo9HBYCjE0aMX8I/Qz9GqlStbd4iImhkmPk2EiOCdt/+LXbuX4rbBCeeS1IjTOFmdAd1dVYAe7kX9dtxVhUhLy8GSlyaga9emmfQYjQKdzmAaP+fUqSu2DomIiGyAiU8T0bZTB3QY+wD+faMtNEaVxTqOf86AHuChQ1uzGdCL+v7cd/+ARjXgoEjRfFgFBUYcj7oIf38vtHBxhJOTA9Rqe9OTWiJAVpYGp07F8pYWEVEzx8SnganKI9KurVsh8IHxCJxwL3y7dwUAaIzmdewUgk6uOvT00KG9ix52VhIbV1dHKBpT1oOi98poFISH/4hnZ39oNnijs7MDjEZBcnIGtv37MA4c+I23tIiIiIlPQ1KZUZcdnJ1x5z1BGPDQ/eg8oD+UdmV7ICsguKOFAT3c89GpRT4cVUXDNZWX2DSWpEej0SE7WwNjoRECwFhoxOZN+wAAERFncfhwDMfWISIiqziAYSl1MYBhZVpxyht1OTtbi2Uf/ASHjj3RM2gYVI4OFo/jrsxHnzYF6O6mg7N9oenYjSSnKZdGo0NqajZSUv4aacjPrxViYq7jvnvDmNwQETVzHMCwgahMK45CocDiJY/A1c3ZNOqyALidr8bZZDekqzpj0LN9Le7fSWFAi+xb8FdnoXdHd7i5OUOpVKB4bqnieaYaavIjxqJJsUrHp9cXjaqs1xf8OQmoEbm5WtPYO5zok4iIqoOJTx2y1opz553+2LhxLp59dgMiIs6ajbqsVTggWdkGKXZtkK8smgSzdJucSlEIL0lHG8NttFHlo20XD3h4eCMlJQstWjiisBCwt1dCqWzYs4WKAHpDgakTstEosLMrmuE8PT3XNL4OAE70SUREtYKJTx2x1IoDAFqtHvHxafDza4XFSx7B4cMx8PH3QVxhK1wpbIEcBxeL+1NC0LIwAwEe+ZCUG1D+mQ3lFwIZGbnw9HRFixZOyM83wNnZwdRnp/jJp4bCPJ6in7VaPZKSMpCTo0GHDm1x/fptPDv7Q5w+HWtqzWHfHSIiqg1MfOpIeXNnAUBKWi6cOwbgxX99BO9evXFGYwdL84K6GbPhp8xAQEsD1Eoj0tNzoC3VBFRQUDRZqJOTComJGXBxKXpCS6RhdVoWgem2lUJRlASmpGQhKysPDg4q+Pm1Qnp6DhYtDC8zzg7nxSIiotrAxKeOWJo7SwBkKt2QrPRCuniiUGcH3z53lNnWyaiFV2EKvApT4Ag9/PxaISEhHb6+nmb7K6bR6JCfr0OLFk7w8nL/s48PGly/nuJ4zp6Nw5df/oTx4/+G7t39OHUEERHVGyY+daTk3Fmp+Xa4bdcGyXZtYFBYnicrPzsLAR56dHPVwJiVCn2BAQ5OKnh6tkJOjhYff/wDXnllKhwcVNBq9QD+moS0oKAQGo0Orq7OcHS0vH9bKb4bpVAAubn5WLQwHJs374OIYO17u3n7ioiI6hUTnzpSPHfWnXf642yyO5LsLcyArjDC2y4byuRY/Pjl98h0c0LbyXfB19cTavVfnXjfefu/yMrKQ2pqNtq3b4P09Jw/JyFV/3lLS6BS2f2ZZDScPj0GQ9ETWWq1PfLzDZgy+U0cPPiraT1vXxERUX1j4lNHRARvv/U1Nm6ci56eTkj6s6uPAoJ2LfTo6qKBffotODso4TPEBw8NfQbZ2Vrk5mqRmJCOnTujcPDgb/DwcMGixQ+je3c/uLg4wd3dGa1aucFoNEKvL5p2wt7evkRnZtskPUajmG6x/dWPRwF7eyW0Wj1ee+3fZkkPERGRLTDxqUMREWfx7LMbsGjJI/Bt745OHkZ099BDdPlISsoAIPD2bmV67Nze3g65uVr4d/DC//3fOBQWCuY//yBcXJyQkZGL69dvo0ePO+DkpIZSqYRKZQ+j0Qij0YjintH12dgjIigoMOLKlQR89NH/0LWLLwYN7gZfH0/TlBFXryZh6dJ/4dCh3+ovMCIiIis4cnMpdTVy8+OPj8CaNf+HnFwtMjJyAQBdu/rC2dkBen0BlEoFlEolYmMTodHo0LGjN1xdnaBUFs1HJSIwGIr6DBUUFEKlsoNOV4Dr12+jY8e29da3x2AoQE5OPvR6PVQqFTLSc0zjERWfK/vtEBFRfePIzQ2IiCA1NRuFRiOysvIAFHVMdnRUw2AomlqiaPA+Bezt7eDq6gxXV0eoVHbQ6wtQUFAIpVIBR0c17OyUKCw0Qq8vhL29HTw8WsDBwfJs7LUZv05XAJ3OgOysPKjU9tDrCxETc6PMU1jst0NERA0ZE596UvIpL61WD3t7O1PHZABQKhWmW0d+fn/d/ipebzQKCguNsLMrusWVn6+HUqlEmzbuddaZuWjKi6Kf7ewU+Oc/t+NwRAxbc4iIqNFi4lNPSj7lFR+fhoKCQtMoxnZ2StjbK6HXF8LFxRFOTioUFhpNyU/RejtT5+Hi1p+iaR7+So7KS4AsjeBsbTsRoLDQCEBgNAry8/UoKCjEuHF/w3trdjPZISKiRqthT+bUhBQ/5ZWbo4WfXyvTBKJOTmqo1fZQKpVwdFShXbvWUKnsoVLZwWg0wt7ezrS+eD9AUfKjUChQWGgsd4RmEYFGo0NaWg4uXryFr7YfhcHwV9JV3Oqk0xn+vIVWAIOhADdv3kZcXDJiYxNx+XICkpIy0L27H/r371Rv7xkREVFtY4tPPSp+ymvxkkcwcGDXcvvmFCUkxj8n8Czq4PzXur8eH1ep7P5snVHirzF8FKafCwqMyM/XIyMjF/Oe2wiVyg5BI3sjM1ODFs5qQAHk5upgb69Ehw5t/+w4bY+CAiOyszWmY+p0Bnh6uqJ1azd2YCYiokaLiU89i4g4i59+Oo/bKf8yJTPFt7BKUyiKkxyjKaFRKIpuQ6WmZqN1azeICG7eTIXBUAhv75ZwdlabZjgXAXJyNDh1KtbUCflvf+sMvb4ARmMhbqdkmY7l7OwAkaLZ0Yv6GhWaxeLgoIJeX4AOHdpi777l6N7dD2q1PfT6AvzxRzynmiAiokaBiY8NTJs2DK6uTqbH0o1GKTP+TtFtqKIEpmh+rqLWneJbU56erqb+QWq1PdLTc5CTozFNY+Hl5Y7Y2KQys5yX7mtUrGi+LwNcXByRm6uFRqMzi8fT0xXx8Wl46aVJcHVzRlpaNnQ6AxwcVLjzTn9s3DjX7LF2IiKihoh9fGzA39+rxBNdCrOkR+Sv+a2KW11UKvsSrTgCg6EQBkMBCguL1nt7t4Svryfc3YsebXd1dcLt21mmWc5L3oYq3deoaDBEBZyc1H/O8l6IgoJCs+V+fq2Qm6MFALi6OePWrVRotXoYjQKtVo/4+DS4uDph8ZJHGsx0GURERJYw8bGB69dv/3lbSfHn7SyFqRR1Wi6ql5KSBb2+ALm5+dDrDTAajdDpDCUSGQXy8w1QKBTw9vZEp07e8Pf3gkplj02b9lltfSnuaxQTcx0uLk7w8fGEi4sTTp26glde+RynTsWaLY+JuY7Va3ahTRt3pKVlW9xnenoOOz8TEVGDx5GbS6mLkZtLUyqVSEvfBjc3Z6t1CguNSE3Jxq34VHi1cYevX6s/W2SMUCoVsLOzQ9Gl+ytRSkpKh15fCGdnB+TmaCu89WStk7Kl5WPH9kP4Zy8gMTHdrKP1X+ekgI+PJ2aGrMX+/Wdq+A4RERFVDUdubsBEBElJGeUmPkBRx+TFiz7DgAFd8Pob06FQKKBS2UNE/hwEUWnqYKxS2UOrNSA7W4OMjFz4+bXC4iWP4PDhGKtPXFkbZdnS8tIDMJZW3Pk5NdVyixAREVFDwFtdNtC/fyc4OqqRnJyBggJjmfWFhUYYDAVYvWYXIiLO4uDB35CUlIFbt1Jx7VrR2Drx8amwt7eDwVBYYtTnv57Equ1bT8Wdoj09XS2u9/R0xR9/xOPMmau1cjwiIqK6wMTHBlq3doNabY+EhHT89lsc4uKSkRCfhvj4NFy+XDRgYGZmHq7FJQP4K+lwdnZAdrYGGo3ObMoLOzs75OcbzJ7E0ukMUKvt0bq1W63EXF6naD+/VsjJ0eLtt77meD5ERNSgMfGxgZK3jQBBenoOEpMykJSUgezsPBiNRrPbRpaSjqJBCwG12h5GoxFJSRlmx6iLW0/WOkXHxFzHHD7KTkREjUCj6eMTGhqKBx54AP369YNer0fLli3L1LHU2vDss89i06ZN9RFipVkbS6eYp6crYmKum902Kjnqc/HggYWFhVAqlbh+PQU5OZoK91EbIiLO4vDhGI7cTEREjVKjSXzUajX+85//ICoqCrNmzbJaLyQkBPv27TO9zsrKslrXVopbcDZunAs/v1ZIT88xDQbo6elq9bZR6aSjQ8e2eOmlSXBzc0JBQUGl9lFb8VvqFE1ERNTQNZrEZ9myZQCA4ODgcutlZmYiOTm5HiKqmdItOJ6ertDrCxATc73c6R9KJx2XLyVUeR9ERETNVaMbxyc4OBhr1661eqvr1q1bcHR0RFxcHLZs2YLNmzeX2+qhVqvh4OBgeu3q6or4+Pg6HcenpNqY8JOThhIRUXPXLMfxWbp0KQ4ePAitVosxY8Zg9erVaN26NVasWGF1m1deecXUmmQLtXHbiLeeiIiIKk9sVcLCwqQigYGBZtsEBwdLRkZGpfb/4osvSmZmZrl11Gq1uLq6moqvr6+IiLi6utrsfWFhYWFhYWGpWnF1da3U32+btvisX78e27dvL7fOtWvXqr3/48ePw93dHV5eXrh9+7bFOnq9Hnp92ZGIXV0tD9RHREREDU9l/27bNPFJS0tDWlrZx7lrS//+/aHVapGZmVnpbYrfuPj4+DqKioiIiOqKq6tr0+jj065dO3h6eqJ9+/aws7ND3759AQBXrlxBXl4eHnzwQXh7eyMqKgparRajRo3CihUrsHnzZostOtYkJCTAz8+v0h2biztDV2Ubsg1eq8aB16nx4LVqPJrLtXJ1dUVCQkKF9Wx+X64yJTw83GIfoKCgIAEg48ePl9OnT0t2drbk5ubK2bNnZf78+WJnZ9cg7imy2L7wWjWOwuvUeAqvVeMpvFZ/lUb3OHtDU9nH58j2eK0aB16nxoPXqvHgtfoL5+oiIiKiZoOJTw3pdDosW7YMOp2u4spkU7xWjQOvU+PBa9V48Fr9hbe6iIiIqNlgiw8RERE1G0x8iIiIqNlg4kNERETNBhMfIiIiajaY+NRAaGgoIiMjkZeXh4yMDIt1RKRMmT17dj1HSpW5Vu3atcOePXuQm5uLlJQUrFu3DiqVqp4jpdLi4uLK/A6tXLnS1mE1e3PmzMHVq1eh1WoRHR2NYcOG2TokKiUsLKzM705iYqKtw7K5RjNlRUOkVqvxn//8B1FRUZg1a5bVeiEhIdi3b5/pdVZWVn2ERyVUdK2USiW+//57pKSkYNiwYWjVqhW2bt0KhUKB+fPn2yBiKunVV1/Fxx9/bHqdm5trw2jo0Ucfxdq1azF37lxERkZi9uzZ2Lt3L3r27ImbN2/aOjwq4dy5c7jnnntMrwsLC20YTcNh8+GjG3sJDg6WjIwMi+tERCZOnGjzGFnKv1b33nuvFBQUiI+Pj2nZtGnTRKvVcoh3G5e4uDh5/vnnbR4Hy1/l+PHjsmHDBrNlFy5ckDfffNPmsbH8VcLCwuTMmTM2j6OhFd7qqgfr169HSkoKTp48idmzZ0OhUNg6JCrlrrvuwrlz58yagX/44Qc4OjoiMDDQhpERACxevBipqak4c+YMQkNDeQvShlQqFQIDA7F//36z5fv378fQoUNtFBVZ07VrV8THx+Pq1av48ssv0bFjR1uHZHO81VXHli5dioMHD0Kr1WLMmDFYvXo1WrdujRUrVtg6NCrB29sbycnJZssyMzOh0+ng7e1to6gIANatW4fTp08jIyMDgwYNwsqVK9GxY0c8/fTTtg6tWWrdujXs7e3L/L4kJyfzd6WBOXHiBJ588klcunQJbdu2xdKlS3Hs2DH06tUL6enptg7Ppmze7NSQSlhYmMVZ4EsKDAw026a8W12ly4svviiZmZk2P8+mUGrzWm3atEn27dtXZrlOp5Np06bZ/FybWqnOtSsuU6ZMERERT09Pm59Hcyw+Pj4iIjJkyBCz5aGhoXLx4kWbx8divTg7O0tiYqIsWLDA5rHYsrDFp5T169dj+/bt5da5du1atfd//PhxuLu7w8vLC7dv3672fqh2r1VSUhIGDx5stszDwwNqtbrM/2yp5mpy7Y4fPw4A6NKlC06ePFnboVEFUlNTUVBQUKZ1x8vLi78rDZxGo0FMTAy6du1q61BsiolPKWlpaUhLS6uz/ffv3x9arRaZmZl1dozmojavVVRUFP7xj3/A29sbSUlJAIBx48YhPz8fp06dqpVj0F9qcu369+8PAHws10YMBgNOnTqFsWPHYteuXablY8eOxe7du20XGFVIrVYjICAAR48etXUoNsXEpwbatWsHT09PtG/fHnZ2dujbty8A4MqVK8jLy8ODDz4Ib29vREVFQavVYtSoUVixYgU2b94MvV5v4+ibl4qu1f79+3HhwgV88cUXWLhwITw9PfHuu+/i448/Rk5Ojo2jb76GDBmCIUOGICIiAllZWRg4cCDee+897N69m49N29CaNWvwxRdfIDo6GlFRUXjmmWfQvn17bNy40dahUQmrVq3Ct99+ixs3bsDLywtLly6Fm5sbtm7dauvQbM7m99saawkPD7fYNyEoKEgAyPjx4+X06dOSnZ0tubm5cvbsWZk/f77Y2dnZPPbmViq6VgCkXbt28u2330peXp6kpqbK+++/L2q12uaxN+fSv39/iYqKkoyMDNFoNHLx4kUJCwsTJycnm8fW3MucOXMkLi5O8vPzJTo6WoYPH27zmFjMy5dffinx8fGi0+nk1q1b8vXXX0tAQIDN47J1Ufz5AxEREVGTx3F8iIiIqNlg4kNERETNBhMfIiIiajaY+BAREVGzwcSHiIiImg0mPkRERNRsMPEhIiKiZoOJDxERETUbTHyIqFkKCwvDmTNnbB0GACA8PBw7d+60dRhEzQITHyKqkbZt22Lt2rW4fPkytFotkpKScPToUcyePRtOTk62Dq9awsLCICLlFn9//yrv19/fHyJimiuOiOofJyklomrr2LEjIiMjkZmZidDQUMTExMDe3h7dunXDU089hYSEBHz77bcWt7W3t0dBQUE9R1w57777rtmEm7/88gs2b96Mjz/+2LQsJSXF9LNKpYLBYKjXGImo+mw+YRgLC0vjLHv37pUbN26Is7NzhXVFRGbPni27du2S3NxcWbZsmQCQZ599Vq5cuSI6nU5+//13mT59umkbf39/ERHp27evaZm7u7vZBLNBQUEiIjJ69Gj55ZdfJC8vTyIjI6Vbt25mx1+8eLEkJSVJdna2fPLJJ7Jy5Uo5c+ZMpc4zLi5Onn/+edPr8PBw2blzpyxZskTi4+MlLi7OdI4TJ0402zYjI0OCg4NN60uKiIgw299LL70kCQkJkpqaKuvXrxd7e3ubX2MWlqZWeKuLiKrF09MT48aNw4cffgiNRlOpbZYvX47du3fjzjvvxKeffopJkyZh3bp1WL16NXr37o1NmzYhPDwcI0eOrHI8K1aswEsvvYQBAwagoKAAn376qWnd1KlTsXz5cvzjH//AgAEDkJiYiLlz51b5GCWNGTMGAQEBGDt2LB588MFKbTNw4EDTtt7e3pgyZYpp3ahRo9C5c2eMGjUKwcHBCAkJQUhISI1iJCLLbJ59sbCwNL4yaNAgERGZNGmS2fKUlBTJycmRnJwceeutt0zLRUTWrFljVvfnn3+WTZs2mS376quv5LvvvhOg6i0+xXXuu+8+ERFxcHAQABIZGSkbNmwwO05UVFSNWnwSExNFpVKZ1auoxcfS+RTvLy4uTpRKpdn78OWXX9r8OrOwNLXCFh8iqhERMXs9aNAg9OvXD+fPn4eDg4PZuujoaLPXAQEBiIyMNFsWGRmJgICAKsdx9uxZ08+JiYkAAC8vL9NxoqKizOqXfl1VMTExtdqv5/z58zAajabXiYmJpviJqPYw8SGiarly5QqMRiN69OhhtjwuLg6xsbHQarVltsnLyyuzrHTipFAoTMuKEwGFQmFar1KpLMZTMgkp3l6prLuvOEvnYjQazWIFrMdbWukkSkTqNH6i5oq/VURULenp6fjxxx/x3HPPwdnZuVr7uHjxIoYNG2a2bOjQobh48SKAv56c8vHxMa3v169ftY4zZMgQs2WlX9eGlJQUs1i7dOmCFi1amF7r9XoAgJ2dXa0fm4gqh4+zE1G1zZ07F5GRkYiOjsayZctw9uxZGI1GDBw4ED169MCpU6fK3X7VqlXYsWMHTp8+jYMHD2LChAmYMmUK7rnnHgBAfn4+oqKisGTJEly7dg2tW7fGP//5zyrHuW7dOmzduhXR0dH4+eef8fe//x29evXC1atXq3Xe1hw6dAjPPfccjh8/DqVSibffftuU7ADA7du3odFocO+99+LWrVvIz89HdnZ2rcZAROVjiw8RVdvVq1fRv39/HDhwACtXrsRvv/2G6OhozJs3D++++y5effXVcrffvXs3nn/+eSxcuBDnz5/H7NmzMXPmTBw5csRU56mnnoJKpUJ0dDTWrVuHpUuXVjnOHTt24PXXX8fbb7+NU6dOwd/fHx999FGV91ORl156CTdv3sRPP/2Ebdu24d133zV74q2wsBDz58/H7NmzkZCQgN27d9d6DERUPgWKejkTERERNXls8SEiIqJmg4kPERERNRtMfIiIiKjZYOJDREREzQYTHyIiImo2mPgQERFRs8HEh4iIiJoNJj5ERETUbDDxISIiomaDiQ8RERE1G0x8iIiIqNlg4kNERETNBhMfoiYoODgYImK1BAUF2TpEi8LCwszizMvLw82bN7Fv3z4899xzcHFxKbNNeHg44uLiqnQcHx8fhIWFoW/fvlXaztKxRAQffPBBlfZTkTlz5iA4OLjMcn9/f4iIxXVEVDn2tg6AiOpOSEgIfv/99zLLL1y4YINoKm/8+PHIysqCWq2Gr68vxowZg3feeQcLFy7EhAkTcPbsWVPdN954A+vWravS/n19fbFs2TJcu3YNv/32W6W3q86xqmPu3LlITU3F1q1bzZYnJiZiyJAhiI2NrfMYiJoqJj5ETdi5c+dw6tSpKm/n6OiI/Pz8Si+vCicnJ2i12nLrnDp1CmlpaabXX331FdavX48jR45gz5496NatG/R6PQDg6tWrNYqnMopjro9jlUev1+PEiRM2jYGoseOtLqJmrvhWzezZs3HhwgXodDoEBwebbpeNHTsWW7Zswe3bt6HVauHg4ACFQoGFCxfi4sWLyM/PR3JyMrZu3Qo/Pz+zfUdERCAmJgbDhw9HZGQk8vLy8Omnn1YrzrNnz2LFihXw9/fHtGnTTMst3X565JFHcPz4cWRmZiIvLw+xsbHYsmULACAoKAjR0dEAgM8++8x0Wy0sLMy0v5ycHPTu3Rs//PADsrOzcfDgQavHKvbMM8/gjz/+QH5+Ps6fP28WI/DXbbzSit9nf39/AEBcXBx69+6NkSNHmmIrPqa1W1133303Dhw4gOzsbOTl5SEyMhL333+/xeOMHDkSGzZsQEpKClJTU/Hf//4XPj4+5bzzRE0LEx+iJszOzq5MUSrL/tpPmjQJc+bMweuvv47x48fj6NGjpnWffvopDAYDZsyYgYcffhgGgwEfffQR3nnnHfz444946KGH8Oqrr+Lee+/FsWPH0KpVK7N9+/j44F//+he2bduG+++/Hxs2bKj2+ezZswcAMGLECKt1hgwZgq+++gpXr17FY489hgceeACvv/467O2LGrhPnz6NkJAQAEW3roYMGYIhQ4bgk08+Me1DrVZjz549OHToECZOnGhKiqx56KGHMH/+fLz22mt45JFHcP36dWzfvh0PP/xwlc9x8uTJiI2NxenTp02xTZ482Wr9ESNG4NChQ3B3d8esWbPw+OOPIycnB99++y0effTRMvU/+eQTGAwGPPHEE1i0aBFGjhyJf/3rX1WOk6gxExYWlqZVgoODxRqDwWBWV0QkIyNDPDw8LO7js88+M1vevXt3ERFZv3692fKBAweKiMg///lP07KIiAgRERk1alSl4g4LCxMRkVatWllc7+DgICIi33//vWlZeHi4xMXFmV6/+OKLIiLi5uZm9TiBgYEiIhIcHFxmXXh4uIiIhISEWFxX8ljF719eXp54eXmZlimVSrlw4YJcunSpzLlZu1b+/v6mZTExMRIREVGmrr+/f5m4jx07JklJSdKiRQuz4589e1Zu3LhR5jilr9vLL78sIiJt27a1+eeWhaU+Clt8iJqwGTNmYMCAAWZl8ODBZeodOnQImZmZFvfx3//+1+z1qFGjABTdJirpl19+wYULFzBmzBiz5enp6YiIiKj+SZSgUCgqrPPLL78AAHbs2IGpU6fC19e3Wscqfd7lOXjwIG7fvm16bTQa8dVXX6Fr165lbv/VJmdnZwwePBhff/018vLyzI7/xRdfoF27dujevbvZNsWtZsWKO4oX32ojauqY+BA1YRcvXsSpU6fMyunTp8vUS0xMtLqP0uuKb2VZ2iYhIaHMra7y9l1VxX+cExISrNY5evQoJk6cCHt7e3z++eeIj49HTEwMHnvssUofJy8vDzk5OZWun5SUZHVZ6fejNrVs2RJKpdLqtbB0/JKdxgFAp9MBKOrATdQcMPEhIoudbq2tK/7DaalDrK+vL1JTUyu976p66KGHAACHDx8ut96ePXtwzz33wN3dHUFBQbh16xa+/PJLDBkypFLHqWrM3t7eVpcVv1/FT8Op1Wqzeq1bt67SsUrKyMhAYWGh1WsBoMz1IGrumPgQUZUcOnQIADB9+nSz5QMGDEDPnj1NT0DVtj59+iA0NBRxcXHYsWNHpbbR6/X46aefsHjxYgBA//79AdR+K8eYMWPg5eVleq1UKjFt2jRcuXIF8fHxAIBr166ZzqOkCRMmlNmfTqerVGwajQYnTpzAlClT4OjoaFquUCgwffp03Lx5E5cuXarOKRE1WRzHh6gJ6927t+lpppJiY2Or3RJw6dIlbNq0CfPmzYPRaMTevXvRoUMHvPHGG7hx4wbee++9moaNwMBAZGVlQaVSmQYwnDFjBm7fvo0JEybAYDBY3Xb58uW44447cPDgQdy6dQseHh54/vnnodfrceTIEQBF56/RaPD3v/8dFy9eRG5uLhISEqp9Wy41NRWHDh3CG2+8gby8PMydOxcBAQFmj7T/73//Q1paGrZs2YLXXnsNBQUFCAkJQbt27crsr/jW3KOPPoqrV68iPz8f586ds3jsV155BT/++CMiIiLw7rvvQq/XY+7cuejduzcef/zxap0PUVNn8x7WLCwstVvKe6pLRGTWrFmmuiIiH3zwgdV9BAYGllmnUChk4cKF8vvvv4tOp5Pbt2/L559/Ln5+fmb1IiIiJCYmptJxFz/5VEyr1Up8fLzs27dP5s2bJy4uLmW2Kf2k1f333y/ff/+93Lx5U/Lz8yUpKUm+++47ufvuu822mzZtmly4cEF0Op2IiISFhZn2l5OTYzE+a091ffDBB/Lss8/K5cuXRafTyYULF+Txxx8vs/2AAQPk559/lpycHLl586aEhYXJU089Veaprvbt28u+ffskKytLRMR0TEtPdQGQu+++Ww4cOCA5OTmSl5cnx44dkwceeKBS1zMoKEhERIKCgmz+uWVhqY+i+PMHIiIioiaPfXyIiIio2WDiQ0RERM0GEx8iIiJqNpj4EBERUbPBxIeIiIiaDSY+RERE1GxwAEMLfH19qzRPDxEREdmeq6truXP5AUx8yvD19TUNMU9ERESNi5+fX7nJDxOfUopbevz8/NjqQ0RE1Ei4uroiPj6+wr/dTHysyMnJYeJDRETUxNSoc3P//v3Ru3dv0+uHHnoIO3fuxIoVK6BSqWocHBEREVFtqlHis2nTJnTr1g0A0LFjR2zfvh0ajQZTp07FO++8UysBEjVVKpUKgwYNKlP4nwYiorpTo1td3bp1w6+//goAmDp1Kn766Sf8/e9/x9ChQ7F9+3YsWLCgNmIkapL69++Pee+uREJmummZr4cnPnj5FZw8edKGkRERNV01SnwUCgWUyqJGo3vuuQffffcdAODmzZto3bp1zaMjauISMtNxLSXZ1mEQETUbNbrVFR0djaVLl2L69OkICgrC999/D6DotldyMr/MiYiIqGGpUeLzwgsv4G9/+xvWr1+PFStWIDY2FgDwyCOP4NixY7USIBEREVFtqdGtrpiYGPTp06fM8oULF6KwsLAmuyYiIiKqdXUyjo9Op6uL3RIRERHVSLUSn6tXr0JETK87d+5cawERERER1ZVqJT4hISG1HAYRERFR3atW4vPTTz/VdhxEREREda7GfXwUCgW6dOkCLy8v05g+xY4ePVrT3RMRERHVmholPoMHD8a2bdvg7+8PhUJhtk5EYG/POVCJiIio4ahRZrJx40ZER0fjgQceQGJiolmHZyKqOZVKhf79+5stO3PmDAwGg40iIiJq3GqU+HTt2hWPPPKIaeBCIqoZpUKBXr16mV736tULo5+agYSMNACcy4uIqKZqlPicOHECXbp0YeJDVAmlW2969eoFRak6bd1bosPcp9EjMR4AcGe7DohNT+F8XkREtaRGic8HH3yA1atXw9vbGzExMWWa32NiYmoUHFFTUno29uKkBinm9ZKyM02Jjo+HZ32HSUTUpNUo8fnvf/8LAPj0009Ny0QECoWCnZuJLCg5GzuTGiKi+lejzKRjx461FQcRERFRnatR4nPjxo3aioOIiIiozikrrmJuwoQJpltYEyZMKLdU1pIlS3Dy5ElkZ2cjOTkZO3fuRLdu3crUCwsLQ3x8PDQaDSIiItCzZ0+z9Wq1Gu+//z5SUlKQm5uL3bt3w8/Pr6qnSERERE1UlVt8du3aBW9vb6SkpGDXrl1W61Wlj09QUBA+/PBD/PLLL7C3t8eKFSuwf/9+9OzZExqNBgCwaNEivPjiiwgJCcGlS5ewdOlS/Pjjj+jevTtyc3MBAGvXrsWECRPw2GOPIS0tDatXr8Z3332HwMBAGI3Gqp4qERERNTFVTnzs7Ows/lwT9913n9nrmTNnIiUlBYGBgaZpL1544QWsWLECO3fuBAAEBwcjOTkZTzzxBDZv3gw3NzfMmjULM2bMwMGDBwEA06dPx82bN3HPPfdg//79tRIrERERNV5VvtUFALNmzarTjs3u7u4AgPT0osd+O3bsCB8fH7PkRa/X48iRIxg6dCgAIDAwEGq12qxOYmIizp07Z6pDREREzVu1OjevW7cOjo6OiI+PR0REBCIiInDo0CHcvHmzVoJas2YNjh49ivPnzwMAvL29AQDJyeaDuCUnJ8Pf399UR6fTITMzs0yd4u0tUavVcHBwML12dXWtjVMgIiKiBqhaiY+HhweGDBmCoKAgjBo1Ch9++CEcHR1x/fp1HDp0yJQMJSYmVnnf69evR58+fTBs2LAy60rPBVY8XlB5KqrzyiuvYNmyZVWOk4iIiBqfat3qKigowM8//4wVK1bgnnvugYeHB0aOHInPPvsMnTp1wubNm6v1qPv777+Phx56CKNGjUJ8fLxpeVJSEgCUabnx8vIytQIlJSXBwcEBHh4eVutYsnLlSri5uZkKnwIjIiJquqqV+JRmZ2dnumXk4OAAe3t7xMXFVWkfH3zwAaZMmYLRo0fj2rVrZuvi4uKQmJiIsWPHmpapVCoEBQXh2LFjAIBTp05Br9eb1fH29kbv3r1NdSzR6/XIyckxK0QNVfEkpoMGDTIrKpXK1qERETUK1brV5eDggKFDh2LkyJEYPXo0BgwYgKtXr+Knn37C+vXrceTIkSrd5vrwww/xxBNPYOLEicjJyUHbtm0BAFlZWcjPzwdQ9Kh6aGgoLl++jMuXLyM0NBQajQbbtm0DAGRnZ2PLli1YvXo10tLSkJ6ejnfffRcxMTE4cOBAdU6TqMEpPYkpwBnbiYiqolqJT2ZmJpKTk7Fnzx6sW7cOR44cQUpKSsUbWjF37lwAwJEjR8yWh4SEYOvWrQCAd955B05OTtiwYQNatmyJEydOYNy4caYxfABgwYIFKCgowI4dO+Dk5ISDBw8iJCSEY/hQk1JyElMiIqqaaiU+v/32G/r164egoCCICIxGIw4fPmx6/LyqFApFpeotX74cy5cvt7pep9Nh/vz5mD9/frXiIKpNKpUK/fv3N73u1asXKvdJJyKiulKtxGfIkCFwdnbG8OHDMWrUKCxatAhffvklLl26hMOHD+PIkSM1bgUiauz69++Pee+uREJm0X8I7mzXAbHpKQB/LYiIbKbak5RqNBr88MMP+OGHHwAALi4uGD58OMaOHYuPP/4YLi4u7HBJzV5CZrrptpSPh2edHKO4w3NJZ86cgcFgqJPjERE1ZjWanR0ouk01cOBAjBw5EqNGjcLdd9+NFi1a4Pr167URHxFVoHSHZ3Z2JiKyrlqJz4ABAzBq1CiMHDkSw4YNg4uLC27duoXDhw9j/vz5iIiIYOJDVI/Y4ZmIqHKqlficOHECiYmJOHz4MF588UUcPnwYsbGxtR0bERERUa2qVuITEBCAS5cu1XYsRERERHWqWokPkx4ic6UfXQf4+DoRUUNU487NRFT20XWAj68TETVETHyIaknJR9eBunt8nYiIqq/Kk5S6urrWRRxEREREda7KiU9GRgbatGkDADh48CDc3d1rPSgiIiKiulDlW125ublo1aoVUlJSMHLkSI7OTM0S5+EiImqcqpz4HDhwABEREbh48SIAYOfOndDr9RbrjhkzpmbRETVQnIeLiKhxqnLiM336dAQHB6Nz584ICgrC+fPnodFo6iI2ogatPubhIiKi2lXlxCc/Px+bNm0CUDR1xeLFi5GVlVXrgRERERHVtho9zj569OjaioOIiIiozlX5qa7SZsyYgbNnz0Kr1UKr1eK3337D9OnTayM2IiIiolpVoxafBQsW4I033sD69esRGRkJhUKBu+++Gxs3bkTr1q2xdu3aWgqTiIiIqOZqlPjMmzcPc+bMwRdffGFatmfPHpw/fx7Lli1j4kNEREQNSo0SHx8fHxw7dqzM8mPHjsHHx6cmuyaialIqFOjVq5fZsjNnzsBgMNgoIiKihqNGic+VK1fw6KOPYuXKlWbLp02bhsuXL9coMCKqnrbuLdFh7tPokRgPAPD18MQHL7+CkydP2jgyIiLbq1HiExYWhq+++gojRoxAZGQkRATDhg3DmDFj8Oijj9ZWjERURUnZmWYTphIRUZEaPdX1zTffYPDgwUhNTcWkSZMwZcoUpKamYtCgQdi1a1cthUhERERUO2rU4gMAp0+fxowZM2ojFiIiIqI6VeNxfIiIiIgaiwaT+AwfPhx79uxBfHw8RAQTJ04sUycsLAzx8fHQaDSIiIhAz549zdar1Wq8//77SElJQW5uLnbv3g0/P7/6OgUiIiJq4BpM4tOiRQv89ttveO655yyuX7RoEV588UU899xzGDhwIJKSkvDjjz/CxcXFVGft2rWYPHkyHnvsMQwbNgwuLi747rvvoFQ2mNOkRkqlUmHQoEGm0qtXLyhsHRQREVVZjfv41JZ9+/Zh3759Vte/8MILWLFiBXbu3AkACA4ORnJyMp544gls3rwZbm5umDVrFmbMmIGDBw8CKJpJ/ubNm7jnnnuwf//+ejkPapr69++Pee+uREJmOgDgznYdEJueAqTYODAiIqqSRtEU0rFjR/j4+JglL3q9HkeOHMHQoUMBAIGBgVCr1WZ1EhMTce7cOVMdS9RqNVxdXc0KkSUJmem4lpKMaynJSM3JtnU4RERUDXWS+MyZMwevvvpqre3P29sbAJCcbD4uSXJysmmdt7c3dDodMjMzrdax5JVXXkF2drapxMfH11rcRERE1LDUSeLz8MMPIyQkpNb3KyJmrxUKRZllpVVUZ+XKlXBzczMVdoYmIiJquuqkj88999xTq/tLSkoCUNSqU/wzAHh5eZlagZKSkuDg4AAPDw+zVh8vLy+L84kV0+v10Ov1tRovERERNUyNoo9PXFwcEhMTMXbsWNMylUqFoKAgU1Jz6tQp6PV6szre3t7o3bt3uYkPkSV8iouIqGmqcYuPr68v7r77bnh5eZV5bPyDDz6o9H5atGiBLl26mF537NgRffv2RXp6Om7evIm1a9ciNDQUly9fxuXLlxEaGgqNRoNt27YBALKzs7FlyxasXr0aaWlpSE9Px7vvvouYmBgcOHCgpqdJzQyf4iIiappqlPiEhIRg48aN0Ov1SEtLM+tLIyJVSnwGDBiAw4cPm16/9957AIDPPvsMM2fOxDvvvAMnJyds2LABLVu2xIkTJzBu3Djk5uaatlmwYAEKCgqwY8cOODk54eDBgwgJCYHRaKzJaVIzVfwUFwD4eHjaOBoiIqoNNUp8Xn/9dbz++utYuXJlhZ2MK3LkyBEoFOXfTFi+fDmWL19udb1Op8P8+fMxf/78GsVCRERETVON+vg4Oztj+/btNU56iIiIiOpDjRKfLVu2YOrUqbUVCxEREVGdqtGtrldeeQXfffcd7r33XsTExMBgMJitf+mll2oUHBHVnFKhQK9evcyWnTlzpszvKxFRc1CjxCc0NBTjx4/HH3/8AQBlOjcTke21dW+JDnOfRo/EolHJfT088cHLr+DkyZM2joyIqP7VKPF58cUX8dRTT2Hr1q21FQ9RvVOpVOjfv7/ZsqY2bk9SdqbpCTUiouasRomPTqdDZGRkbcVCZBOlx+wBOG4PEVFTVaPEZ926dZg3bx6ef/752oqHyCZKjtkDNO1xeyz1+QHY74eImocaJT6DBg3C6NGj8eCDD+L8+fNlvjQffvjhGgVHRLWvdJ8fgP1+iKj5qFHik5mZiW+++aa2YiGiesI+P0TUXNUo8XnqqadqKw6ielO6M3NT68hMRETW1XiSUqLGhhOQlsWxfoiouahR4nP16tVyx+vp3LlzTXZPVCsstfAkcgJSMxzrh4iaixolPmvXrjV7XfwH5t5778WqVatqsmuiWsMWnsphvx8iag5qlPi8//77FpfPnTsXAwYMqMmuiWpVAlt4iIgIddTHZ+/evVi5ciU7P1O9aw6jMBMRUfXVSeLzyCOPID09veKKRDVkqf/O6KdmICEjzbSMt7aIiKhYjRKf06dPm3VuVigU8Pb2Rps2bTB37twaB0dUEWv9d5rLKMx1pfRTXvb2RV8VBQUFZvX45BcRNTY1Snx27dpl9tpoNCIlJQWHDx82zdhOVNfYf6f2lX7K6852HZBXYMDVKoz2bOm2IxMlIrK1GiU+r7/+em3FQVQpHHyw/pR8ysvHwxNZBl2Vnvoq3RrHR+SJqCHgAIbUqPDR9IajotthpcdLIiJqCKqV+BQWFpY7cCEAiAhUKlW1gqLmqbK3Rnhrq2Go6HZY6aSUs8ITUUNQrcRn8uTJVtcNHToU8+bNg0LBGxBUNaVbc+5o2QoHPv0C58+fN9Xhra2GpbzbYaWTUs4KT0QNQbUSnz179pRZ1r17d6xcuRITJkzAv//9b7z66qs1Do6atspMJXF/qT+UvLXVuJUeHboyT4+xRYiIalON+/j4+Phg+fLlCA4Oxg8//IB+/fqZ/Q+dyJrK9Ncp/YeSt7aalopul1lqEarolqil9aXr1Bc+2UbU8FQ78XFzc0NoaCjmzZuHX3/9FWPGjMHPP/9cm7FV25w5c7Bw4UL4+Pjg/PnzeOGFFxpMbM1FZf/4sL8OlXe7zFK/oNKDVJZOjkon1JbqlFb681pb4xbxyTaihqdaic/ChQuxePFiJCUl4fHHH7d468tWHn30UaxduxZz585FZGQkZs+ejb1796Jnz564efOmrcNrlCqTxFRmBOXSfXbYX4cqYqlfUOlBKksnR5aeJrOUQJX8/FpqfayNcYv4ZBtRw1OtxOett96CVqvFlStXEBwcjODgYIv1Hn744RoFVx0vvvgitmzZgi1btgAAFixYgPHjx2POnDkIDQ2t93jqW2Wa1qt6q6CySUzJOtZGUL6/1G0N9tehilR0u9PS7bLSn6vSdSx9fkv3Lys9blFlHt+vi+lSKmqNstQ6VVFfKUvfAVXtX1WdW4oN6TYkNV/VSnw+//zzCh9ntwWVSoXAwEC89dZbZsv379+PoUOH2iiqInWRkAAVf/laezKqvDo1SWIqum1V+rYGUW2ozOeqdJ2qJuGVfXy/vCStOp25K2qNstQ6VVFfKUu3Ayvapjr/IapMcljRNtVJ9CqzjaX3uqTqtHRXZn1lYqtqItiQ+5I1xNiqlfjMnDmztuOoFa1bt4a9vT2Sk82blZOTk+Ht7W1xG7VaDQcHB9NrV1dXs39ry4ABA/Da+rVIz8sFAHi2cMGO9Rvx+++/m+r06NEDjz73rNU6pdcDgH8rL+iMBUjKSDe9vpmZDnVOFgCgjZs7/i90kWl9ZeqUXm+vVOIOj1Yo8NGZ9uHt7oG8ggKo//ylLV3H290DrgUFZbYpuayqrxvyNoy14R7XWp3qfH5Lb2OvtLO6D0v76eXXHkNL/a6V/P219r2gsrMr97glX1taprKzQ2BgIFq0aGFxn5XdpuT3T+nvCcDyd0l530+V3aa819XdxtJ7XZKl79uKvpOr+51d0WegIhXFYUuWYnv9uRcQHR1d68eq7N9tBYCG13RTTT4+PkhISMBdd92F48ePm5aHhoZixowZCAgIKLNNWFgYli1bVo9REhERUV3x8/NDQkKC1fVNasqK1NRUFBQUlGnd8fLyKtMKVGzlypVYs2aN2TJPT0+kp6dbrN8QuLq6Ij4+Hn5+fsjJybF1OPWquZ47z5vn3Vw013PnedfOebu6upab9ABNLPExGAw4deoUxo4dazZz/NixY7F7926L2+j1euj1erNljeVDl5OT02hirW3N9dx53s1Lcz1voPmeO8+75vupSJNKfABgzZo1+OKLLxAdHY2oqCg888wzaN++PTZu3Gjr0IiIiMjGmlzis2PHDrRq1QqvvfYafHx8cO7cOdx///24ceOGrUMjIiKiBkBYGldRq9USFhYmarXa5rHw3HnePG+eN8+d592YzrtJPdVFREREVB6lrQMgIiIiqi9MfIiIiKjZYOJDREREzQYTHyIiImo2mPg0EnFxcRARs7Jy5coKtwsLC0N8fDw0Gg0iIiLQs2fPeoi2dvj7++OTTz7B1atXodFocOXKFSxbtgwqlarc7cLDw8u8V1FRUfUUdfXNmTMHV69ehVarRXR0NIYNG1Zu/REjRiA6OhparRaxsbGYPXt2PUVaO5YsWYKTJ08iOzsbycnJ2LlzJ7p161buNkFBQWWurYige/fu9RR1zYWFhZWJPzExsdxtGvu1Lmbpe0xEsH79eov1G+v1Hj58OPbs2YP4+HiICCZOnFimTnW+m6dMmYLz588jPz8f58+fx6RJk+og+uor77zt7e3x1ltv4ezZs8jNzUV8fDy2bt0KHx+fcvcZHBxs8TNQco7N6rD542wsFZe4uDhZunSptG3b1lRatGhR7jaLFi2SrKwsmTx5svTq1Uu+/PJLiY+PFxcXF5ufT2XK+PHj5dNPP5WxY8dKx44dZcKECZKUlCSrVq0qd7vw8HD53//+Z/ZetWzZ0ubnU1559NFHRafTyaxZs6RHjx7y3nvvSU5OjrRr185i/Q4dOkhubq6899570qNHD5k1a5bodDqZMmWKzc+lsmXv3r0SHBwsPXv2lD59+si3334r165dE2dnZ6vbBAUFiYhI165dza6vUqm0+flUtoSFhUlMTIxZ/K1bt7Zavylc6+LSunVrs/MeM2aMiIgEBQU1qet97733yhtvvCGTJ08WEZGJEyeara/Od/OQIUPEYDDIkiVLpHv37rJkyRLR6/UyaNAgm59vZc7bzc1N9u/fL1OnTpVu3brJ4MGDJSoqSn755Zdy9xkcHCyZmZlm179t27Y1jdX2bxZLxSUuLk6ef/75Km2TkJAgixYtMr1Wq9WSkZEhzzzzjM3Pp7rl5ZdfltjY2HLrhIeHy86dO20ea1XK8ePHZcOGDWbLLly4IG+++abF+m+99ZZcuHDBbNlHH30kx44ds/m5VLe0bt1aRESGDx9utU7xH0J3d3ebx1vdEhYWJmfOnKl0/aZ4rYvLe++9J5cvX27S19tS4lOd7+bt27fL//73P7Nle/fulW3bttn8HCt73qXLgAEDRESs/gcPKEp8MjIyajU23upqRBYvXozU1FScOXMGoaGh5d7y6dixI3x8fLB//37TMr1ejyNHjmDo0KH1EW6dcHd3r9QEsiNHjkRycjL++OMPbN68GW3atKmH6KpHpVIhMDDQ7FoBwP79+61eq7vuuqtM/R9++AEDBgyAvX3jHJDd3d0dACp1fc+cOYOEhAQcOHAAI0eOrOPIal/Xrl0RHx+Pq1ev4ssvv0THjh2t1m2K1xoo+txPnz4dn376aYV1G/v1Lqm6383WPgeN/fvcaDQiMzOz3HouLi64du0abt68iW+//Rb9+vWr0XGZ+DQS69atw2OPPYZRo0Zh/fr1eOGFF7Bhwwar9YtnqC89K31ycnKZ2esbi06dOmHevHkVzru2d+9e/P3vf8fo0aPx0ksvYeDAgTh06BDUanU9RVo1rVu3hr29fZWulbe3t8X6KpUKrVu3rrNY69KaNWtw9OhRnD9/3mqdxMREPP3003j44YcxZcoU/PHHHzh48CCGDx9ej5HWzIkTJ/Dkk09i/PjxePrpp+Ht7Y1jx47B09PTYv2meK0BYNKkSfDw8MBnn31mtU5TuN6lVfe72drnoLF+nzs4OOCtt97Ctm3byp1Y9Pfff0dISAgeeughPP7448jPz0dkZCS6dOlSo+PbvEmsuZawsDCpSGBgoMVtp0yZIiIinp6eFtffddddIiLi7e1ttnzz5s2yd+/eRnfePj4+cunSJfn444+rfDxvb2/R6XQyefJkm19zS8XHx0dERIYMGWK2PDQ0VC5evGhxmz/++EOWLFlitmzo0KEiIrVx/7vey/r16yUuLk78/PyqvO2ePXtk9+7dNj+H6hZnZ2dJTEyUBQsWNItrXVz27dsne/bsafLXu/Qtn+p+N+t0OnnsscfMlj3xxBOi1Wptfo6VOe+Sxd7eXnbu3CmnTp0SV1fXKu1XoVDImTNnZN26ddWOrfG2kzYB69evx/bt28utc+3aNYvLjx8/DgDo0qULTp48WWZ9UlISgKL/JRT/DABeXl5l/tdQ36p63j4+PoiIiEBUVBSeeeaZKh8vKSkJ169fR9euXau8bX1ITU1FQUFBmf+5lXetkpKSLNY3GAxIS0urs1jrwvvvv4+HHnoII0aMQHx8fJW3P378OKZPn14HkdUPjUaDmJgYq5/PpnSti7Vv3x733HMPpkyZUuVtG/v1ru53s7XPga2/z6vK3t4eO3bsQMeOHTF69OhyW3ssERH88ssvNfo+Z+JjQ2lpadX+4urfvz8AWH0MNi4uDomJiRg7dix+/fVXAEX31IOCgrB48eJqHbO2VOW8fX19ERERgVOnTmHmzJkQkSofz9PTE+3atavwkWFbMRgMOHXqFMaOHYtdu3aZlo8dOxa7d++2uE1UVBQmTJhgtmzcuHGIjo5GQUFBXYZbqz744ANMnjwZI0eOtJrkV6R///4N9tpWhlqtRkBAAI4ePWpxfVO51iXNnDkTt2/fxvfff1/lbRv79a7ud3NUVBTGjh2LtWvXmpaNGzcOx44dq+OIa09x0tO1a1eMGjWqUv35LOnXrx9iYmJqFIvNm8RYyi9DhgyRF154Qfr27SsdOnSQqVOnyq1bt2TXrl1m9S5evCiTJk0yvV60aJFkZGTIpEmTpFevXvLvf/+7UT3OXnx768CBA+Lr62v1UcaS592iRQtZtWqVDBkyRPz9/SUoKEgiIyPl5s2bDfq8ix9nnzlzpvTo0UPWrFkjOTk50r59ewEgb775pmzdutVUv/gR59WrV0uPHj1k5syZje4R5w8//FAyMjJkxIgRZtfW0dHRVKf0eT///PMyceJE6dKli/Ts2VPefPNNEZEGexvTUlm1apWMGDFCOnToIIMGDZI9e/ZIVlZWk77WJYtCoZBr167JypUry6xrKte7RYsW0rdvX+nbt6+IiOn7u/jppcp8N2/dutXsqc677rpLDAaDLFq0SLp37y6LFi1qcI+zl3fednZ2smvXLrlx44b06dPH7HdepVJZPe/XXntNxo0bJx07dpS+ffvKli1bRK/Xy8CBA2sSq+3fLJbyS//+/SUqKkoyMjJEo9HIxYsXJSwsTJycnMzqiYgEBwebLQsLC5OEhATRarVy+PBh6dWrl83Pp7IlODjYah8ga+ft6Ogo+/btk+TkZNHpdHLt2jUJDw+XO+64w+bnU1GZM2eOxMXFSX5+vkRHR5s91h0eHi4RERFm9UeMGCGnTp2S/Px8uXr1qsyePdvm51CVYk3Jz3Dp8164cKFcvnxZNBqNpKWlyU8//ST33Xefzc+lKqV4zBadTie3bt2Sr7/+WgICApr0tS5Zxo4dKyJFY/OUXtdUrnfxY/ilhYeHm+pU9N0cERFhVh+APPzww3Lx4kXR6XRy4cKFBpcAlnfe/v7+Vn/nS47jVPq816xZI9euXZP8/HxJTk6Wffv2lekPWdWi+PMHIiIioiaPj7MTERFRs8HEh4iIiJoNJj5ERETUbDDxISIiomaDiQ8RERE1G0x8iIiIqNlg4kNERETNBhMfIiIiajaY+BA1AGFhYThz5ozpdXh4OHbu3FmjfdbGPho7T09PJCcnw9/fv06PExcXh+eff75Oj2FNcHAwMjIybHJsW2nTpg1u374NX19fW4dCjZTNh7lmYWmIJTw83DSkul6vl9jYWFm1apU4OzvX+rHCwsLkzJkzptdubm7i7u5eqW2Lh4Lv27ev2fKq7KMmxZpp06bZ/BquWrVKPvnkkzLvlSWDBw+ucH/BwcGSkZFRZnnr1q3LTCFTFyUuLk6ef/55s2WOjo7Spk0bm7/XxcXV1VVef/11OXfunGg0GklNTZWTJ0/KwoULxcPDw+I2ERERVq+LiEhcXFyZbVavXi0ff/yxzc+XpfEVzs5OVI69e/di5syZUKlUGD58OD755BO0aNECc+fOLVPX3t6+1mbLzs7ObhD7qKyQkBDs27fPbFlmZqbFukqlEiICETFbrlKpYDAYqnxsa9s5Ojpi1qxZuP/++8usGzNmDM6fP2+2LC0trcrHLpaamlrtbWsqPz8f+fn5Njt+SS1btsTPP/8MNzc3vPrqqzh16hT0ej26dOmCJ554Ak888QQ2bNhQZrspU6ZArVYDANq1a4dffvnF7BoVFhaW2SY8PBwnT57EwoULrX7WiKyxefbFwtIQS3h4uOzcudNs2ebNmyUhIUGAv1ppZs6cKbGxsVJYWChAUUvLpk2bJDk5WbKysuTgwYPSp08fs/0sXrxYkpKSJDs7Wz755BNZuXKlWYtP6WMrFApZtGiRXL58WfLz8+X69esSGhoqQNkWl+JJHkvvQ61Wy7p16yQ5OVm0Wq0cPXpUBgwYYFpfPMHg6NGj5ZdffpG8vDyJjIyUbt26lfs+iYhMnDjR6vriVpIHHnhAzp8/LwaDQTp06CBxcXHyj3/8Q8LDwyUzM1M+++wzASBTpkyRc+fOSX5+vsTFxcmLL75otj9r25UukydPltu3b5sts9Y6VrL06dNHDh06JNnZ2ZKVlSXR0dESGBhocQLGsLAwU0wlW2JERJ555hn59ttvJS8vTy5cuCBDhgyRzp07S0REhOTm5sqxY8ekU6dOpm06deoku3btkqSkJMnJyZGTJ0/KmDFjTOsttYqUfH9LnsOzzz4rV65cEZ1OJ7///rtMnz69zDWbNWuWfPPNN5KXlyeXLl2SCRMm1Ph35qOPPpKcnBzx9fWt9j4qc42Ky9WrV2XmzJn1+r3A0iSKzQNgYWmQxVLis27dOklJSRGgKPHJycmRvXv3Sr9+/eTOO+8UAHL06FHZvXu3BAYGSpcuXWTVqlWSkpIiLVu2FAAydepUyc/Pl1mzZkm3bt3kjTfekKysrHITn7feekvS0tLkySeflE6dOsndd98ts2bNEgAyYMAAU8LStm1b03FK72Pt2rVy69YtuffeeyUgIEDCw8MlLS3NVL/4D3tUVJSMGDFCAgIC5MiRI/Lzzz+X+z5VJvHR6XTy888/y1133SXdunUTZ2dniYuLk8zMTHnppZekc+fO0rlzZ/nb3/4mBQUFsnTpUunatasEBwdLXl6e2YztlrazdNz33ntP/ve//5ktq8wf1ZiYGPn888+le/fu0qVLF3nkkUekT58+olKpZP78+ZKZmSlt27aVtm3bSosWLUwxlU58bt68KVOnTpWuXbvKN998I1evXpUDBw7IuHHjpEePHnLs2DGz+Pr06SPPPPOM9O7dW7p06SJvvPGGaDQaadeunQCQli1byo0bN2Tp0qWm4xe/vyUTn0mTJolOp5M5c+ZI165dZcGCBWIwGGTkyJFm8d24cUMee+wx6dy5s6xdu1ays7NNn4XqFIVCIenp6fLRRx/V6PeuKonP9u3b5dNPP7X5dwVLoys2D4CFpUGW0onDwIEDJSUlRbZv3y5AUeKj0+mkdevWpjqjRo2SzMxMUavVZvu6fPmyPP300wJAIiMjZcOGDWbro6KirCY+Li4uotVqTYlO6WLtD0XJfTg7O4tOp5PHH3/ctN7e3l5u3bolL7/8sgDmLT7Fde677z4REXFwcLD6PomIaDQaycnJMSsdO3YUoOgPs4iUafWKi4uTb775xmzZv/71L/nhhx/Mlr399tty7ty5crezVHbu3GnWv6fke5WXl1cmXqVSKQAkKytLnnzySYv7tNbHx1Li8/rrr5teDx48WETErHVi2rRpotFoyj2Hc+fOyf/7f//P6nEsxfTzzz/Lpk2bzOp89dVX8t1331mNz9nZWQoLC2X8+PHV/n3x8vISEZEXXnjBbHl0dLTpPd62bVuF+6lK4rN69Wo5dOhQtWNmaZ6FT3URlePBBx9ETk4OtFotoqKi8NNPP2HevHmm9devXzfr3xEYGAgXFxekpaUhJyfHVDp27IjOnTsDAAICAhAVFWV2nNKvSwoICICjoyMOHjxY7fPo3Lkz1Go1IiMjTcsKCgpw8uRJBAQEmNU9e/as6efExEQAgJeXV7n7X7BgAfr162dWbt68aVqv0+nM9lssOjra7HVAQIBZjAAQGRmJrl27QqlUWt3OEicnJ6t9X6ZNm1YmXqPRCABYs2YNPvnkE/z4449YvHgxOnXqVOGxLCl5vsnJyQCAmJgYs2VOTk5wdXUFADg7O+Ptt9/G+fPnkZGRgZycHPTo0QPt27ev0nGtvYflXWeNRoOcnByr1/mVV14x+zy3a9fO6vGlVN+tyZMno1+/fvjhhx/g5ORUpXOpiFarhbOzc63uk5o+dm4mKkdERATmzJkDg8GAhISEMp2X8/LyzF4rlUokJiZi5MiRZfZV3Q6YWq22WtuVpFAoAJT9o6RQKMosK9lRuHhdyaTDkqSkJMTGxlpdb+0cSr9/luIpjr287SxJTU1Fy5YtLa67efOm1XiXL1+Obdu24YEHHsB9992H5cuX47HHHsOuXbsqPGZJlt7H8t7bVatWYfz48Xj55Zdx5coVaLVafP3116ZOv1VR1etcvI2167xx40bs2LHD9DohIaFMnZSUFGRkZKBHjx5my4sT4JycHHh4eFT6HCrD09MTKSkptbpPavrY4kNUjry8PMTGxuLGjRuVemLr9OnT8Pb2RkFBAWJjY81K8VNDFy9exJAhQ8y2K/26pMuXL0Oj0WDMmDEW1+v1egCAnZ2d1X1cuXIFOp0Ow4YNMy2zt7fHgAEDcPHixQrPq75cuHDBLEYAGDp0KC5dumRqkamsM2fOoGfPntWK4/Lly1i7di3Gjx+Pb775BjNnzgRQ9F6X9z7XxPDhw/HZZ59h165dOHfuHJKSktChQwezOpU5/sWLFy2+hzW5zhkZGWafZUtPWYkIduzYgenTp9fb+Dq9e/c2G/+KqDKY+BDVogMHDiAqKgq7du3CuHHj4O/vj7vuugtvvPEGAgMDAQDr1q3DU089hZkzZ6Jr165YtmwZevXqZXWfOp0Ob7/9Nt555x3MmDEDnTp1wuDBg/HUU08BAG7fvg2NRoN7770XXl5ecHNzK7MPjUaDjz76yNSqEBAQgI8//hjOzs7YsmVLjc/bw8MDbdu2NSvVuQWxevVqjBkzBkuXLkXXrl3x5JNP4rnnnsO7775b5X398MMP6NWrl8VWhlatWpWJ18HBAY6Ojvjggw8QFBSE9u3bY+jQoRg4cKApabh27RpcXV0xevRotGrVqlZv3Vy5cgVTpkxB37590adPH2zbtq1MC8y1a9cwYsQI+Pr6olWrVhb3s2rVKoSEhGD27Nno0qULFixYgClTplTrPayq0NBQxMfH48SJE5g5cybuvPNOdOrUCZMmTcJdd91lljC9+eab2Lp1a6X2W3wNSiZUTk5OCAwMxP79+2v9PKjps3lHIxaWhlgsPdVVspQedLC4uLi4yLp16+TWrVui0+nk+vXr8sUXX8gdd9xhqvPKK6/I7du3JTs7W8LDw+Wtt96q8HH20NBQiYuLE51OJ9euXZMlS5aY1s+aNUuuX78uBQUFVh9nd3BwkHXr1snt27fLfZy95KCHffv2FRERf39/q++DNYsXLxag8h2Ci0vx4+zF5/nSSy9VajtL5dixY/LMM8+YXpc3gOG0adNEpVLJtm3b5Pr165Kfny+3bt2S999/36xz94YNGyQlJUVEyn+cveSTbpY67JZ+v/39/eXgwYOSl5cn169fl7lz50pERIS89957pm0GDx4sv/76q2i1WhGp2ePspZ/Ey8jIMHt6rrrFzc1NVqxYIRcuXBCtVit5eXny66+/yvLly82eGgsPDzd9VkuW8t6rkp/Dxx57TC5evFhv3wcsTarYPAAWFhaWOin33XefnD9/XhQKhc1jYandcuLECbOnFFlYKlvYuZmImqy9e/eia9eu8PPzw61bt2wdDtWSNm3a4Ouvv8aXX35p61CoEVKgKAMiIiIiavLYuZmIiIiaDSY+RERE1Gww8SEiIqJmg4kPERERNRtMfIiIiKjZYOJDREREzQYTHyIiImo2mPgQERFRs8HEh4iIiJqN/w8fSoO7A5LuKQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fn.evaluate_model(y_train, y_val, y_test, y_train_pred, y_val_pred, y_test_pred)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "1a17012b",
   "metadata": {},
   "source": [
    "Previous example: [/examples/time_series/cases/8.ipynb](https://github.com/serhatsoyer/py4ML/blob/main/examples/time_series/cases/8.ipynb)  \n",
    "Next example: [/examples/time_series/cases/10.ipynb](https://github.com/serhatsoyer/py4ML/blob/main/examples/time_series/cases/10.ipynb)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.10.6 ('study')",
   "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": "3.10.8 (main, Nov 24 2022, 08:08:27) [Clang 14.0.6 ]"
  },
  "vscode": {
   "interpreter": {
    "hash": "29d06b973d1ddb34db3279b24f9b5152402e688db937648b736e855ec4de60c0"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}