1545 lines (1544 with data), 407.1 kB

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f8302cf8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import re\n",
    "import pickle\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6851f1a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Error loading punkt: <urlopen error [SSL:\n",
      "[nltk_data]     CERTIFICATE_VERIFY_FAILED] certificate verify failed:\n",
      "[nltk_data]     unable to get local issuer certificate (_ssl.c:1131)>\n",
      "[nltk_data] Error loading stopwords: <urlopen error [SSL:\n",
      "[nltk_data]     CERTIFICATE_VERIFY_FAILED] certificate verify failed:\n",
      "[nltk_data]     unable to get local issuer certificate (_ssl.c:1131)>\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "sys.path.append('../scripts')\n",
    "from utils import predict, predict_multi_line_text, load_data\n",
    "\n",
    "sys.path.append('../')\n",
    "from config import entity_to_acronyms, acronyms_to_entities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "89395fb0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# from importlib import reload\n",
    "\n",
    "# import utils\n",
    "# reload(utils)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "55b67971",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras.preprocessing.text import Tokenizer\n",
    "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
    "from tensorflow.keras.utils import to_categorical\n",
    "from tensorflow.keras.layers import Input, Embedding, Bidirectional, LSTM, Dense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e73cc064",
   "metadata": {},
   "outputs": [],
   "source": [
    "from config import entity_to_acronyms, acronyms_to_entities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5d959f70",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dir = '../data'\n",
    "model_dir = '../models'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f86da10e",
   "metadata": {},
   "outputs": [],
   "source": [
    "(train_sequences_padded, train_labels), (val_sequences_padded, val_labels), (test_sequences_padded, test_labels), label_to_index, index_to_label = load_data(data_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c7ad2170",
   "metadata": {},
   "outputs": [],
   "source": [
    "if train_sequences_padded.shape[1] != train_labels.shape[1]:\n",
    "    print('Sequence length mismatch')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "36c1529c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the tokenizer from file\n",
    "with open('../data/tokenizer.pickle', 'rb') as handle:\n",
    "    tokenizer = pickle.load(handle)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f666677",
   "metadata": {},
   "source": [
    "## Model parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "335a945b",
   "metadata": {},
   "outputs": [],
   "source": [
    "INPUT_DIM = len(tokenizer.word_index)+1\n",
    "EMBEDDING_DIM = 64\n",
    "NUM_CLASSES = len(label_to_index)\n",
    "MAX_LENGTH = train_sequences_padded.shape[1]\n",
    "\n",
    "LSTM1_UNITS = 64\n",
    "LSTM2_UNITS = 32\n",
    "DENSE_DIM = 64\n",
    "\n",
    "DROPOUT = 0.2\n",
    "BATCH_SIZE = 32\n",
    "EPOCHS = 20"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47692a34",
   "metadata": {},
   "source": [
    "## Building Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "770388cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras import backend as K\n",
    "\n",
    "def precision(y_true, y_pred):\n",
    "    true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))\n",
    "    predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))\n",
    "    _precision = true_positives / (predicted_positives + K.epsilon())\n",
    "    return _precision\n",
    "\n",
    "def recall(y_true, y_pred):\n",
    "    \"\"\"Compute recall metric\"\"\"\n",
    "    true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))\n",
    "    possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))\n",
    "    return true_positives / (possible_positives + K.epsilon())\n",
    "\n",
    "def f1_score(y_true, y_pred):\n",
    "    \"\"\"Compute f1-score metric\"\"\"\n",
    "    _precision = precision(y_true, y_pred)\n",
    "    _recall = recall(y_true, y_pred)\n",
    "    f1_score = 2 * ((_precision * _recall) / (_precision + _recall + K.epsilon()))\n",
    "    return f1_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d662fe53",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " embedding (Embedding)       (None, 100, 64)           446016    \n",
      "                                                                 \n",
      " bidirectional (Bidirectiona  (None, 100, 128)         66048     \n",
      " l)                                                              \n",
      "                                                                 \n",
      " bidirectional_1 (Bidirectio  (None, 100, 64)          41216     \n",
      " nal)                                                            \n",
      "                                                                 \n",
      " dense (Dense)               (None, 100, 64)           4160      \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 100, 79)           5135      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 562,575\n",
      "Trainable params: 562,575\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Define the model architecture\n",
    "model = tf.keras.models.Sequential([\n",
    "    Embedding(INPUT_DIM, EMBEDDING_DIM, input_length=MAX_LENGTH),\n",
    "    Bidirectional(LSTM(units=LSTM1_UNITS, return_sequences=True)),\n",
    "    Bidirectional(LSTM(units=LSTM2_UNITS, return_sequences=True)),\n",
    "    Dense(DENSE_DIM, activation='relu'),\n",
    "    Dense(NUM_CLASSES, activation='softmax')\n",
    "])\n",
    "\n",
    "\n",
    "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy', precision, recall, f1_score])\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b8db2244",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-04-09 12:36:29.315905: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "95/95 [==============================] - 9s 60ms/step - loss: 1.0527 - accuracy: 0.8896 - precision: 0.7680 - recall: 0.7164 - f1_score: 0.7407 - val_loss: 0.3995 - val_accuracy: 0.9129 - val_precision: 0.9827 - val_recall: 0.8837 - val_f1_score: 0.9305\n",
      "Epoch 2/20\n",
      "95/95 [==============================] - 5s 52ms/step - loss: 0.4036 - accuracy: 0.9065 - precision: 0.9900 - recall: 0.8747 - f1_score: 0.9287 - val_loss: 0.3741 - val_accuracy: 0.9129 - val_precision: 0.9839 - val_recall: 0.8887 - val_f1_score: 0.9339\n",
      "Epoch 3/20\n",
      "95/95 [==============================] - 5s 51ms/step - loss: 0.3806 - accuracy: 0.9064 - precision: 0.9948 - recall: 0.8789 - f1_score: 0.9332 - val_loss: 0.3704 - val_accuracy: 0.9129 - val_precision: 0.9880 - val_recall: 0.8883 - val_f1_score: 0.9354\n",
      "Epoch 4/20\n",
      "95/95 [==============================] - 5s 51ms/step - loss: 0.3675 - accuracy: 0.9071 - precision: 0.9957 - recall: 0.8826 - f1_score: 0.9357 - val_loss: 0.3721 - val_accuracy: 0.9128 - val_precision: 0.9812 - val_recall: 0.8965 - val_f1_score: 0.9369\n",
      "Epoch 5/20\n",
      "95/95 [==============================] - 5s 51ms/step - loss: 0.3518 - accuracy: 0.9102 - precision: 0.9947 - recall: 0.8911 - f1_score: 0.9400 - val_loss: 0.3688 - val_accuracy: 0.9149 - val_precision: 0.9835 - val_recall: 0.8998 - val_f1_score: 0.9398\n",
      "Epoch 6/20\n",
      "95/95 [==============================] - 5s 51ms/step - loss: 0.3366 - accuracy: 0.9134 - precision: 0.9945 - recall: 0.8971 - f1_score: 0.9432 - val_loss: 0.3715 - val_accuracy: 0.9154 - val_precision: 0.9831 - val_recall: 0.9011 - val_f1_score: 0.9403\n",
      "Epoch 7/20\n",
      "95/95 [==============================] - 5s 51ms/step - loss: 0.3254 - accuracy: 0.9157 - precision: 0.9948 - recall: 0.8985 - f1_score: 0.9442 - val_loss: 0.3756 - val_accuracy: 0.9177 - val_precision: 0.9807 - val_recall: 0.9031 - val_f1_score: 0.9403\n",
      "Epoch 8/20\n",
      "95/95 [==============================] - 5s 51ms/step - loss: 0.3138 - accuracy: 0.9185 - precision: 0.9950 - recall: 0.8994 - f1_score: 0.9447 - val_loss: 0.3765 - val_accuracy: 0.9186 - val_precision: 0.9804 - val_recall: 0.9033 - val_f1_score: 0.9402\n",
      "Epoch 9/20\n",
      "95/95 [==============================] - 5s 52ms/step - loss: 0.2996 - accuracy: 0.9220 - precision: 0.9952 - recall: 0.8998 - f1_score: 0.9450 - val_loss: 0.3751 - val_accuracy: 0.9206 - val_precision: 0.9805 - val_recall: 0.9040 - val_f1_score: 0.9407\n",
      "Epoch 10/20\n",
      "95/95 [==============================] - 5s 52ms/step - loss: 0.2816 - accuracy: 0.9264 - precision: 0.9950 - recall: 0.9010 - f1_score: 0.9456 - val_loss: 0.3696 - val_accuracy: 0.9218 - val_precision: 0.9796 - val_recall: 0.9052 - val_f1_score: 0.9409\n",
      "Epoch 11/20\n",
      "95/95 [==============================] - 5s 52ms/step - loss: 0.2543 - accuracy: 0.9320 - precision: 0.9938 - recall: 0.9072 - f1_score: 0.9485 - val_loss: 0.3650 - val_accuracy: 0.9254 - val_precision: 0.9790 - val_recall: 0.9081 - val_f1_score: 0.9422\n",
      "Epoch 12/20\n",
      "95/95 [==============================] - 5s 51ms/step - loss: 0.2274 - accuracy: 0.9383 - precision: 0.9922 - recall: 0.9136 - f1_score: 0.9512 - val_loss: 0.3568 - val_accuracy: 0.9286 - val_precision: 0.9781 - val_recall: 0.9106 - val_f1_score: 0.9431\n",
      "Epoch 13/20\n",
      "95/95 [==============================] - 5s 53ms/step - loss: 0.2056 - accuracy: 0.9436 - precision: 0.9914 - recall: 0.9191 - f1_score: 0.9539 - val_loss: 0.3602 - val_accuracy: 0.9294 - val_precision: 0.9731 - val_recall: 0.9144 - val_f1_score: 0.9428\n",
      "Epoch 14/20\n",
      "95/95 [==============================] - 5s 53ms/step - loss: 0.1881 - accuracy: 0.9487 - precision: 0.9905 - recall: 0.9249 - f1_score: 0.9566 - val_loss: 0.3658 - val_accuracy: 0.9299 - val_precision: 0.9715 - val_recall: 0.9157 - val_f1_score: 0.9428\n",
      "Epoch 15/20\n",
      "95/95 [==============================] - 5s 54ms/step - loss: 0.1729 - accuracy: 0.9527 - precision: 0.9905 - recall: 0.9291 - f1_score: 0.9588 - val_loss: 0.3732 - val_accuracy: 0.9312 - val_precision: 0.9694 - val_recall: 0.9182 - val_f1_score: 0.9431\n",
      "Epoch 16/20\n",
      "95/95 [==============================] - 5s 53ms/step - loss: 0.1584 - accuracy: 0.9566 - precision: 0.9904 - recall: 0.9342 - f1_score: 0.9615 - val_loss: 0.3923 - val_accuracy: 0.9324 - val_precision: 0.9650 - val_recall: 0.9210 - val_f1_score: 0.9425\n",
      "Epoch 17/20\n",
      "95/95 [==============================] - 5s 52ms/step - loss: 0.1457 - accuracy: 0.9602 - precision: 0.9894 - recall: 0.9395 - f1_score: 0.9638 - val_loss: 0.3939 - val_accuracy: 0.9330 - val_precision: 0.9648 - val_recall: 0.9224 - val_f1_score: 0.9431\n",
      "Epoch 18/20\n",
      "95/95 [==============================] - 5s 53ms/step - loss: 0.1336 - accuracy: 0.9635 - precision: 0.9898 - recall: 0.9449 - f1_score: 0.9668 - val_loss: 0.4057 - val_accuracy: 0.9330 - val_precision: 0.9625 - val_recall: 0.9236 - val_f1_score: 0.9426\n",
      "Epoch 19/20\n",
      "95/95 [==============================] - 5s 54ms/step - loss: 0.1226 - accuracy: 0.9661 - precision: 0.9892 - recall: 0.9505 - f1_score: 0.9695 - val_loss: 0.4244 - val_accuracy: 0.9341 - val_precision: 0.9586 - val_recall: 0.9261 - val_f1_score: 0.9421\n",
      "Epoch 20/20\n",
      "95/95 [==============================] - 5s 52ms/step - loss: 0.1111 - accuracy: 0.9692 - precision: 0.9891 - recall: 0.9558 - f1_score: 0.9722 - val_loss: 0.4360 - val_accuracy: 0.9348 - val_precision: 0.9579 - val_recall: 0.9272 - val_f1_score: 0.9423\n"
     ]
    }
   ],
   "source": [
    "# Train the model\n",
    "history = model.fit(\n",
    "    train_sequences_padded, \n",
    "    train_labels, \n",
    "    epochs=EPOCHS, \n",
    "    validation_data=(val_sequences_padded, val_labels)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c744592c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def plot_graphs(history):\n",
    "    fig, axs = plt.subplots(2, 2, figsize=(12, 8))\n",
    "    \n",
    "    axs[0, 0].plot(history.history['accuracy'])\n",
    "    axs[0, 0].plot(history.history['val_accuracy'])\n",
    "    axs[0, 0].set_title('Model Accuracy')\n",
    "    axs[0, 0].set_ylabel('Accuracy')\n",
    "    axs[0, 0].legend(['train', 'val'], loc='best')\n",
    "    \n",
    "    axs[0, 1].plot(history.history['loss'])\n",
    "    axs[0, 1].plot(history.history['val_loss'])\n",
    "    axs[0, 1].set_title('Model Loss')\n",
    "    axs[0, 1].set_ylabel('Loss')\n",
    "    axs[0, 1].legend(['train', 'val'], loc='best')\n",
    "    \n",
    "    axs[1, 0].plot(history.history['recall'])\n",
    "    axs[1, 0].plot(history.history['val_recall'])\n",
    "    axs[1, 0].set_title('Model Recall')\n",
    "    axs[1, 0].set_ylabel('Recall')\n",
    "    axs[1, 0].legend(['train', 'val'], loc='best')\n",
    "    \n",
    "    axs[1, 1].plot(history.history['f1_score'])\n",
    "    axs[1, 1].plot(history.history['val_f1_score'])\n",
    "    axs[1, 1].set_title('Model F1 Score')\n",
    "    axs[1, 1].set_ylabel('F1 Score')\n",
    "    axs[1, 1].legend(['train', 'val'], loc='best')\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4e14092b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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7djQmTpxoFBUVVXrfnj17Gmaz2Th06FC134uIiIg0rOZ6PmYYhvHdd98ZQLW3NWvWGIZhGPv27TOuu+46IyAgwPDw8DAGDRpkfPHFFxXe6/XXXzfOP/98o1WrVoa7u7vRqVMn4+9//7uRlZVlGIZhFBUVGX//+9+NmJgYw9fX1/D29jZiYmKMV1999ZQ1ikj9MhnGSWNARESkRerbty9BQUHExcU5uxQREREREWkBNKeUiIjwyy+/sHXrVsaOHevsUkREREREpIVQTykRkRZs27ZtbN68mRdeeIH09HTi4+Mdq9OIiIiIiIjUJ/WUEhFpwT7++GPGjx9PSUkJ77//vgIpERERERFpMOopJSIiIiIiIiIiDU49pUREREREREREpMEplBIRERERERERkQbn4uwCGiObzUZycjK+vr6YTCZnlyMiIiJOZBgGOTk5REREYDbr97xT0TmUiIiIQM3PnxRKVSE5OZmoqChnlyEiIiKNyMGDB2nTpo2zy2jUdA4lIiIiJ/qr8yeFUlXw9fUF7F+en5+fk6sRERERZ8rOziYqKspxfiDV0zmUiIiIQM3PnxRKVaG8u7mfn59OqERERARAw9FqQOdQIiIicqK/On/SxAgiIiIiIiIiItLgFEqJiIiIiIiIiEiDUyglIiIiIiIiIiINTnNKnQGr1UpJSYmzy2iSXF1dsVgszi5DREREREREWiibzUZxcbGzy2iS6uqaXqHUaTAMg5SUFDIzM51dSpMWEBBAeHi4Jo4VERERERGRBlVcXMz+/fux2WzOLqXJqotr+kYRSs2dO5fnn3+elJQUYmJiePnllxk0aFCVbUtKSpg5cybvvvsuSUlJdOvWjWeffZbLL7/c0aZ9+/YcOHCg0r733Xcfc+fOPeN6ywOp0NBQvLy8FKrUkmEY5Ofnk5aWBkDr1q2dXJGIiIiIiIi0FIZhcPjwYSwWC1FRUZjNmtmoNurymt7podSSJUuYPHky8+bNIzY2ltmzZzNs2DB27dpFaGhopfbTpk1j0aJFzJ8/n+joaFauXMno0aNZt24dffv2BWDTpk1YrVbHPtu2bePSSy/l+uuvP+N6rVarI5Bq1arVGb9fS+Xp6QlAWloaoaGhGsonIiIiIiIiDaK0tJT8/HwiIiLw8vJydjlNUl1d0zs9Dpw1axYTJkxg/Pjx9OjRg3nz5uHl5cWCBQuqbL9w4UIee+wxRowYQceOHbn33nsZMWIEL7zwgqNNSEgI4eHhjtsXX3xBp06duOCCC8643vI5pPQ/3DNX/h1qXi4RERERERFpKOWdWNzc3JxcSdNWF9f0Tg2liouL2bx5M0OHDnVsM5vNDB06lPXr11e5T1FRER4eHhW2eXp6snbt2mo/Y9GiRdxxxx3VDrMrKioiOzu7wu2vaMjemdN3KCIiIiIiIs6ia9IzUxffn1NDqfT0dKxWK2FhYRW2h4WFkZKSUuU+w4YNY9asWezZswebzcaqVatYtmwZhw8frrL98uXLyczM5Pbbb6+2jpkzZ+Lv7++4RUVFnfYxiYiIiIiIiIjIX3P68L3amjNnDl26dCE6Oho3NzcmTZrE+PHjq52Y7K233mL48OFERERU+55Tp04lKyvLcTt48GB9ld9stG/fntmzZzu7DBERERERERGppcZyTe/Uic6Dg4OxWCykpqZW2J6amkp4eHiV+4SEhLB8+XIKCwvJyMggIiKCKVOm0LFjx0ptDxw4wOrVq1m2bNkp63B3d8fd3f30D6SJuPDCC+nTp0+d/A9v06ZNeHt7n3lRIiIiZ+BYXjHvrEtgW1IWb44boG74zZTVZvDk53+SdKyAOWP64uPu9LV6REREGlxzvKZ3ak8pNzc3+vfvT1xcnGObzWYjLi6OwYMHn3JfDw8PIiMjKS0tZenSpYwaNapSm7fffpvQ0FCuuOKKOq+9OTIMg9LS0hq1DQkJ0WTvIiLiNMmZBTz1+XaGPPMtc+L2ELczjc0Hjjm7LKknFrOJT7cmE7czjeTMAmeXIyIi0ig1xWt6pw/fmzx5MvPnz+fdd99lx44d3HvvveTl5TF+/HgAxo4dy9SpUx3tN2zYwLJly4iPj2fNmjVcfvnl2Gw2Hn300Qrva7PZePvttxk3bhwuLvo17fbbb+eHH35gzpw5mEwmTCYT77zzDiaTia+//pr+/fvj7u7O2rVr2bdvH6NGjSIsLAwfHx8GDhzI6tWrK7zfyV39TCYTb775JqNHj8bLy4suXbrw2WefNfBRiohIc7c3LZe/f/QbFzz/HQt+2k9BiZWeEX7MvbkffdsGOrs8qUeRAfalp5OOKZQSEZGWp7le0zs9rbnxxhs5cuQI06dPJyUlhT59+rBixQrH5OeJiYkV5osqLCxk2rRpxMfH4+Pjw4gRI1i4cCEBAQEV3nf16tUkJiZyxx131PsxGIZBQYm13j/nZJ6ulhoPU5gzZw67d++mV69ePPXUUwD8+eefAEyZMoX//ve/dOzYkcDAQA4ePMiIESP4z3/+g7u7O//73/8YOXIku3btom3bttV+xpNPPslzzz3H888/z8svv8wtt9zCgQMHCAoKOvODFRGRFu23g5m89v0+Vm5PwTDs287uGMR9F3bmvC7BGrbXAkQEeLL9cDZJ6iklIiJ1zFnX9FDz6/rmek3v9FAKYNKkSUyaNKnK177//vsKzy+44AK2b9/+l+952WWXYZSftdazghIrPaavbJDPOtH2p4bh5Vaz/4T+/v64ubnh5eXlmK9r586dADz11FNceumljrZBQUHExMQ4nv/rX//ik08+4bPPPqv2vxPYk9sxY8YA8PTTT/PSSy+xceNGLr/88lofm4iIiGEY/LQ3g1e/38u6fRmO7Zf2COPeCzvRTz2jWpQ2gWU9pRRKiYhIHXPWNT3U/Lq+uV7TN4pQSpxrwIABFZ7n5uYyY8YMvvzySw4fPkxpaSkFBQUkJiae8n169+7teOzt7Y2fnx9paWn1UrOIiDRfVpvByj9TeO37ffyRlAWAi9nEqD6R3HNBR7qE+Tq5QnGGiAAPAM0pJSIicpKmfE2vUKoOeLpa2P7UMKd8bl04ecb9Rx55hFWrVvHf//6Xzp074+npyXXXXUdxcfEp38fV1bXCc5PJhM1mq5MaRUSk+SsutfHJr4d4/Yd44tPzAPBwNXPTwLbcdV4H2gQ6fzJOcZ7IAPt/f80pJSIidc1Z1/Tln32mmvI1vUKpOmAymWo8jM6Z3NzcsFr/epzsTz/9xO23387o0aMBe8qakJBQz9WJiEhLlVdUyvsbE3lzzX5SsgsB8PNw4fYh7Rk3pD2tfNydXKE0BuopJSIi9UXX9M7T+L91qTPt27dnw4YNJCQk4OPjU23i2aVLF5YtW8bIkSMxmUz885//VI8nERGpc0fzinlnXQLvrksgq6AEgDA/d+46tyNjYtvi467TFDkusmxOqZTsQkqsNlwtTl9EWkREpEE1x2t6/TVvQR555BEsFgs9evQgJCSk2vGks2bNIjAwkCFDhjBy5EiGDRtGv379GrhaERFprpIzC3jy8z8555lveSluD1kFJXQI9uaZa87ix0cvYsL5HRVISSXB3u64WczYDEgt61EnIiLSkjTHa3qT0VBL1DUh2dnZ+Pv7k5WVhZ+fX4XXCgsL2b9/Px06dMDDw8NJFTYP+i5FRFqWvWm5zPthH8t/TaLUZj/96BXpx30XdmZYz3As5r9eDtkZTnVeIBXV93d14fPfkZCRz5K7zya2Y6s6f38REWkZdC1aN071Pdb0nEA/Q4qIiEi92nowk9e+38s321Mp/ylscMdW3HdRJ87tHIzJ1DjDKGl8IgI8ScjIJzlL80qJiIg0BwqlREREpM4Vllj56o/DvLchkV8OHHNsv6xHGPde2Im+bQOdWJ00VZEB9nmltAKfiIhI86BQSkREROrM3rRc3t+YyMebDzkmL3cxmxjVJ5J7L+xI51BfJ1coTVlEeSiVqTmlREREmgNNdC4iIiJnpKjUyqdbk7jx9fUMnfUDb63dT1ZBCZEBnvzt0q78NOViXrghRoFUA/rxxx8ZOXIkERERmEwmli9f/pf7fP/99/Tr1w93d3c6d+7MO++8U+911lb5CnxJmeopJSIi0hyop5SIiIicloT0PN7fmMhHmw9xNK8YALMJLo4O45bYtpzfNaTRTl7e3OXl5RETE8Mdd9zBNddc85ft9+/fzxVXXME999zD4sWLiYuL46677qJ169YMGzasASqumfLhe8kKpURERJoFhVIiIiJSY8WlNlZtT+W9jQf4aW+GY3u4nwc3DozixoFRjiFW4jzDhw9n+PDhNW4/b948OnTowAsvvABA9+7dWbt2LS+++GKjDKWSjhVgGIYmyRcREWniFEqJiIjIX0rMyOf9TYl89Msh0nOLADCZ4MKuIdwc246LuoXgYtGsAE3V+vXrGTp0aIVtw4YN46GHHjrlfkVFRRQVFTmeZ2dn10d5DuH+9uWmC0qsZOaXEOjtVq+fJyIiIvVLoZSIiIhUqcRqI25HGu9tTGTNniMYhn17iK87Nw6w94qKCvJybpFSJ1JSUggLC6uwLSwsjOzsbAoKCvD0rLr328yZM3nyyScbokQAPFwthPi6cySniKTMAoVSIiIiTZxCKREREakgKbOADzYmsmTTQdJyjveCOa9LMLfEtuWS7mG4qleUAFOnTmXy5MmO59nZ2URFRdXrZ0YEeDpCqV6R/vX6WSIiIlK/FEpJjbVv356HHnroL7vyi4hI02O1GXy3094r6rtdaY5eUa283bh+QBRjBkXRrpW3c4uUehMeHk5qamqFbampqfj5+VXbSwrA3d0dd3f3+i6vgjYBnvx2MJOkY5rsXEREpDYa4zW9QikREZEWLCWrkA822XtFHc4qdGwf0qkVN8e25bIe4bi5qFdUczd48GC++uqrCttWrVrF4MGDnVRR9SIC7PNKaQU+ERGRpk+hlIiISAt0ICOPl+L2snxrElabvVtUoJcr1w+I4qaBUXQM8XFyhXImcnNz2bt3r+P5/v372bp1K0FBQbRt25apU6eSlJTE//73PwDuueceXnnlFR599FHuuOMOvv32Wz788EO+/PJLZx1CtRwr8CmUEhERafL002cL8cYbbxAREYHNZquwfdSoUdxxxx3s27ePUaNGERYWho+PDwMHDmT16tVOqlZEROrLwaP5/OPj37n4hR9YuuUQVpvBoA5BzLmpD+unXsJjI7orkGoGfvnlF/r27Uvfvn0BmDx5Mn379mX69OkAHD58mMTEREf7Dh068OWXX7Jq1SpiYmJ44YUXePPNNxk2bJhT6j+VCIVSIiLSAjXXa3r1lKoLhgEl+Q3/ua5e9vW4a+D666/n/vvv57vvvuOSSy4B4OjRo6xYsYKvvvqK3NxcRowYwX/+8x/c3d353//+x8iRI9m1axdt27atz6MQEZEGkJxZwNzv9vLhLwcpsdp7Rl3YLYSHhnalT1SAc4uTOnfhhRdilE8MVoV33nmnyn1+/fXXeqyqbkQG2kMpDd8TEZE646xreqjxdX1zvaZXKFUXSvLh6YiG/9zHksGtZpPOBgYGMnz4cN577z3H/4A//vhjgoODueiiizCbzcTExDja/+tf/+KTTz7hs88+Y9KkSfVSvoiI1L/U7EJe/W4v7288SLHV/svaeV2CeWhoV/q3C3RydSK1Vz58Lz23mMISKx6uFidXJCIiTZ6zrumhxtf1zfWaXsP3WpBbbrmFpUuXUlRkX9578eLF3HTTTZjNZnJzc3nkkUfo3r07AQEB+Pj4sGPHjgpd+0VEpOlIyynkqc+3c/5z3/Hu+gMUW23Edghiyd1ns/DOWAVS0mT5e7ri7WYPotRbSkREWpLmeE2vnlJ1wdXLnm4643NrYeTIkRiGwZdffsnAgQNZs2YNL774IgCPPPIIq1at4r///S+dO3fG09OT6667juLi4vqoXERE6klGbhGv/xjP/9YnUFhi7xk1oF0gky/rypBOwU6uTuTMmUwmIgI82ZOWS1JmgeZAExGRM+esa/ryz66h5nhNr1CqLphMNR5G50weHh5cc801LF68mL1799KtWzf69esHwE8//cTtt9/O6NGjAfuqPQkJCU6sVkREauNYXjHz18TzzroE8outAPSJCuBvl3Xl3M7BmGo4B6FIUxAZaA+l1FNKRETqhK7pnUahVAtzyy23cOWVV/Lnn39y6623OrZ36dKFZcuWMXLkSEwmE//85z8rzeovIiKNT1Z+CW+tjWfBTwnkFpUC0LuNPw9f2pULu4YojJJmybEC3zGFUiIi0rI0t2t6hVItzMUXX0xQUBC7du3i5ptvdmyfNWsWd9xxB0OGDCE4OJh//OMfZGdnO7FSERE5lezCEt5em8Cba+PJKbSHUd1b+zH50q4M7R6qMEqatfLJzpMyC51ciYiISMNqbtf0CqVaGLPZTHJy5bGy7du359tvv62wbeLEiRWeN4WufyIizV1uUSnvrkvgjR/jySooAaBbmC8PX9qFy3qEYzYrjJLmr01geSjlpOW7RUREnKS5XdM7ffW9uXPn0r59ezw8PIiNjWXjxo3Vti0pKeGpp56iU6dOeHh4EBMTw4oVKyq1S0pK4tZbb6VVq1Z4enpy1lln8csvv9TnYYiIiNSr/OJSXv9hH+c/9x3Pr9xFVkEJnUN9eOXmvnz94Hlc3qu1AilpMcqH7yWrp5SIiEiT5tSeUkuWLGHy5MnMmzeP2NhYZs+ezbBhw9i1axehoaGV2k+bNo1FixYxf/58oqOjWblyJaNHj2bdunX07dsXgGPHjnHOOedw0UUX8fXXXxMSEsKePXsIDNTS1yIi0vQUllhZ9PMB5v2wj/Rc++opHYK9eWhoF67sHYFFQZS0QOXD9w5nFWCzGQpkRUREmiinhlKzZs1iwoQJjB8/HoB58+bx5ZdfsmDBAqZMmVKp/cKFC3n88ccZMWIEAPfeey+rV6/mhRdeYNGiRQA8++yzREVF8fbbbzv269ChQwMcjYiISN0pKrXy/oZEXv1+H2k5RQC0DfLigUu6cHWfCFwsTu/sLOI0ob7uWMwmSqwGR3KLCPPzcHZJIiIichqcdkZbXFzM5s2bGTp06PFizGaGDh3K+vXrq9ynqKgID4+KJx2enp6sXbvW8fyzzz5jwIABXH/99YSGhtK3b1/mz59/ylqKiorIzs6ucBMREXEGm81g+a9JDJ31AzM+305aThGRAZ48e+1ZxP3tAq7r30aBlLR4LhYz4WVB1CGtwCciItJkOe2sNj09HavVSlhYWIXtYWFhpKSkVLnPsGHDmDVrFnv27MFms7Fq1SqWLVvG4cOHHW3i4+N57bXX6NKlCytXruTee+/lgQce4N133622lpkzZ+Lv7++4RUVF1c1BioiI1JBhGPyw+whXvryWh5Zs5eDRAkJ93fn31b347pELuXFgW1wVRok4RDrmlVIoJSIi0lQ1qdX35syZw4QJE4iOjsZkMtGpUyfGjx/PggULHG1sNhsDBgzg6aefBqBv375s27aNefPmMW7cuCrfd+rUqUyePNnxPDs7+y+DKZvNVgdH1LLpOxQRsfv9UCbPfL2TdfsyAPB1d+GeCztxxzkd8HSzOLk6kcYpMtATEiBJoZSIiJwmwzCcXUKTVhfX9E4LpYKDg7FYLKSmplbYnpqaSnh4eJX7hISEsHz5cgoLC8nIyCAiIoIpU6bQsWNHR5vWrVvTo0ePCvt1796dpUuXVluLu7s77u7uNarbzc3NsQRjSEgIbm5umEyaXLM2DMOguLiYI0eOYDabcXNzc3ZJIiJOkZCex/Pf7OLL3+09ft0sZsYObsfEizoT6K1/G0VOJSLAPnxPPaVERKS2XF1dMZlMHDlyhJCQEF3T11JdXtM7LZRyc3Ojf//+xMXFcfXVVwP2lC0uLo5Jkyadcl8PDw8iIyMpKSlh6dKl3HDDDY7XzjnnHHbt2lWh/e7du2nXrl2d1G02m+nQoQOHDx8mOTm5Tt6zpfLy8qJt27aYzRqOIiItS1pOIS/H7eX9jYmU2gxMJhjdN5LJl3alTaCXs8sTaRIiA+z/X0nSnFIiIlJLFouFNm3acOjQIRISEpxdTpNVF9f0Th2+N3nyZMaNG8eAAQMYNGgQs2fPJi8vz7Ea39ixY4mMjGTmzJkAbNiwgaSkJPr06UNSUhIzZszAZrPx6KOPOt7z4YcfZsiQITz99NPccMMNbNy4kTfeeIM33nijzup2c3Ojbdu2lJaWYrVa6+x9WxKLxYKLi4sSaRFpUXIKS5i/Zj9vroknv9j+9+OibiE8enk03Vv7Obk6kaalvKeUhu+JiMjp8PHxoUuXLpSUlDi7lCaprq7pnRpK3XjjjRw5coTp06eTkpJCnz59WLFihWPy88TExAqJW2FhIdOmTSM+Ph4fHx9GjBjBwoULCQgIcLQZOHAgn3zyCVOnTuWpp56iQ4cOzJ49m1tuuaVOazeZTLi6uuLq6lqn7ysiIs1PcamN9zYc4OVv95KRVwxATFQAUy6PZnCnVk6uTqRpahNon+hcoZSIiJwui8WCxaL5O53JZGhmr0qys7Px9/cnKysLPz/9ci0iIqfHZjP4/Pdk/vvNLg4etV84dwz25u/DunF5r3D1Fm0idF5Qcw35XeUXl9Jj+koAfp9xGX4e+qFQRESksajpOUGTWn1PRESkqViz5wjPfL2TP5OzAQjxdefhoV25fkAbXC2aS0/kTHm5uRDo5cqx/BKSMwvwC1coJSIi0tQolBIREalDfxzK4pkVO/hpbwYAvu4u3HNhJ8af0x4vN/3ZFalLEQGejlAqOly92ERERJoanR2LiIjUgYT0PP77zS6++P0wAG4WM7cNbsfEizoT5H36y+SKSPUiAzz5MzlbK/CJiIg0UQqlREREzsCRnCJe/nYP721IpNRmYDLB6D6RPHxpV6KCvJxdnkizFhFQPtl5oZMrERERkdOhUEpEROQ05BaVMv/HeOaviSe/2ArAhd1CeHRYND0iNIxIpCFoBT4REZGmTaGUiIhILRiGwfKtSfzny52k5xYBENPGn38Mj2ZIp2AnVyfSspT3lEpWKCUiItIkKZQSERGpoZ0p2Uxf/icbE44C0L6VF49eHs3wXuGYTCYnVyfS8kSWD9/TnFIiIiJNkkIpERGRv5BTWMLs1Xt4Z10CVpuBh6uZ+y/uwl3ndcDdxeLs8kRarPKeUqk5hZRYbbhazE6uSERERGpDoZSIiEg1DMPgs9+S+c+XO0jLsQ/Vu7xnOP8c2cPRQ0NEnCfYxw03FzPFpTZSsgq1uICIiEgTo1BKRESkCntSc/jnp9v4Of74UL0ZV/Xkwm6hTq5MRMqZTCYiAzzZn55HUmaBQikREZEmRqGUiIjICXKLSnkpbg8L1u6n1Gbg7mJm0kWdmXB+RzxcNVRPpLFxhFKaV0pERKTJUSglIiKCfajeF78f5t9fbic12z5U79IeYUy/sod6X4g0YhEBHgAkaQU+ERGRJkehlIiItHh703J54rNt/LQ3A4C2QV7MuKoHF0eHObkyEfkrkQH20DhZoZSIiEiTo1BKRERarLyiUl7+di9vrY2nxGrg5mLmvgs7cc8FnTRUT6SJUE8pERGRpkuhlIiItDiGYfD1thT+9cV2DmcVAnBxdCgzRvakbSsN1RNpSiID7SthKpQSERFpehRKiYhIixJ/JJcnPvuTNXvSAWgT6MmMkT0Z2kND9USaosgAeyiVnFmAYRiYTCYnVyQiIiI1pVBKRERahPziUuZ+t5c3fiwbqmcxc88FHbn3ws54ummonkhTFe7vgckEhSU2juYV08rH3dkliYiISA0plBIRkWbNMAxW/pnKv77Y7hjec0HXEJ68qiftg72dXJ2InCl3FwshPu6k5RSRlFmgUEpERKQJUSglIiLNVkJ6HjM+/5Pvdx0B7MN8po/swWU9wjTER6QZiQz0JC2niOTMAnq3CXB2OSIiIlJDCqVERKTZKSyx8up3e5n3QzzFVhuuFhN3n9+RiRd1xstNf/pEmpuIAE9+Tczk0DFNdi4iItKU6MxcRESale92pjH9s20cPGq/OD2vSzBPXtWTjiE+Tq5MROpLG8dk54VOrkRERERqQ6GUiIg0C4ezCnjq8+18vS0FgNb+Hky/sgeX9wrXUD2RZi6iLJRKysx3ciUiIiJSGwqlRESkSSu12nhnXQIvrtpNXrEVi9nEHee056GhXfF21585kZYgUj2lREREmiSdrYuISJO1JfEYj3+yjR2HswHo1zaA/4w+i+6t/ZxcmYg0pOM9pTSnlIiISFOiUEpERJqczPxinl2xiw82JWIY4O/pypTh0dw4IAqzWUP1RFqayEB7KHU0r5iCYiuebhYnVyQiIiI1oVBKRESaDMMwWLYliae/2kFGXjEA1/Zrw2Mjomnl4+7k6kTEWfw8XPBxdyG3qJSkzAI6h2phAxERkaZAoZSIiDQJe9NymLZ8Gz/HHwWgc6gP/766F2d3bOXkykTE2UwmE5EBnuxKzSFZoZSIiEiTYXZ2AQBz586lffv2eHh4EBsby8aNG6ttW1JSwlNPPUWnTp3w8PAgJiaGFStWVGgzY8YMTCZThVt0dHR9H4aIiNSDgmIrz6/cyfA5a/g5/igermYevbwbXz1wngIpEXEoH8KneaVERESaDqf3lFqyZAmTJ09m3rx5xMbGMnv2bIYNG8auXbsIDQ2t1H7atGksWrSI+fPnEx0dzcqVKxk9ejTr1q2jb9++jnY9e/Zk9erVjucuLk4/VBERqaXvdqYx/bNtHDxqv8i8ODqUJ6/qSVSQl5MrE5HGJiLAA4BkhVIiIiJNhtN7Ss2aNYsJEyYwfvx4evTowbx58/Dy8mLBggVVtl+4cCGPPfYYI0aMoGPHjtx7772MGDGCF154oUI7FxcXwsPDHbfg4OCGOBwREakDh7MKuHfRZsa/s4mDRwto7e/BvFv789a4AQqkRKRKkQH2fxuSjimUEhERaSqc2n2ouLiYzZs3M3XqVMc2s9nM0KFDWb9+fZX7FBUV4eHhUWGbp6cna9eurbBtz549RERE4OHhweDBg5k5cyZt27at9j2Lioocz7Ozs0/3kERE5AyUWm28sy6BF1ftJq/YisVs4o5z2vPQ0K54u6vHq4hUr7ynlIbviYiINB1O7SmVnp6O1WolLCyswvawsDBSUlKq3GfYsGHMmjWLPXv2YLPZWLVqFcuWLePw4cOONrGxsbzzzjusWLGC1157jf3793PeeeeRk5NT5XvOnDkTf39/xy0qKqruDlJERGpkS+IxRr7yE//+cgd5xVb6tQ3gi/vP5fEreiiQEpG/1EZzSomIiDQ5Te4sf86cOUyYMIHo6GhMJhOdOnVi/PjxFYb7DR8+3PG4d+/exMbG0q5dOz788EPuvPPOSu85depUJk+e7HienZ2tYEpEpIFk5Zfw7MqdvL8xEcMAf09XpgyP5sYBUZjNJmeXJyJNRESAPZRKySrEajOw6N8PERGRRs+poVRwcDAWi4XU1NQK21NTUwkPD69yn5CQEJYvX05hYSEZGRlEREQwZcoUOnbsWO3nBAQE0LVrV/bu3Vvl6+7u7ri7u5/+gYiISK0ZhsEnvybxny93kJFXDMC1/drw2IhoWvno32QRqZ1QXw9czCZKbQZpOYW09vd0dkkiIiLyF5w6fM/NzY3+/fsTFxfn2Gaz2YiLi2Pw4MGn3NfDw4PIyEhKS0tZunQpo0aNqrZtbm4u+/bto3Xr1nVWu4iInL69abmMmf8zkz/8jYy8YrqE+rDk7rN54YYYBVIiclosZhPh/lqBT0REpClx+up7kydPZv78+bz77rvs2LGDe++9l7y8PMaPHw/A2LFjK0yEvmHDBpYtW0Z8fDxr1qzh8ssvx2az8eijjzraPPLII/zwww8kJCSwbt06Ro8ejcViYcyYMQ1+fCIicpxhGLzx4z6Gz/mRn+OP4uFq5tHLu/HlA+cR27GVs8sTaXbmzp1L+/bt8fDwIDY2lo0bN56y/ezZs+nWrRuenp5ERUXx8MMPU1hY2EDVnrnIsiF8h7QCn4iISJPg9DmlbrzxRo4cOcL06dNJSUmhT58+rFixwjH5eWJiImbz8eyssLCQadOmER8fj4+PDyNGjGDhwoUEBAQ42hw6dIgxY8aQkZFBSEgI5557Lj///DMhISENfXgiIlImu7CERz78jW+224dsXxwdypNX9SQqyMvJlYk0T0uWLGHy5MnMmzeP2NhYZs+ezbBhw9i1axehoaGV2r/33ntMmTKFBQsWMGTIEHbv3s3tt9+OyWRi1qxZTjiC2isPpZIzm06QJiIi0pKZDMMwnF1EY5OdnY2/vz9ZWVn4+fk5uxwRkSZve3I29y3eTEJGPm4WM9NH9uCW2LaYTJqIWBq/pnpeEBsby8CBA3nllVcA+xQJUVFR3H///UyZMqVS+0mTJrFjx44K0yr87W9/Y8OGDaxdu7ZGn+ns7+qFb3bx8rd7ufXstvz76rMa/PNFRETErqbnBE4fviciIs3bR78cZPSrP5GQkU9kgCcf3TOYW89up0BKpB4VFxezefNmhg4d6thmNpsZOnQo69evr3KfIUOGsHnzZscQv/j4eL766itGjBhR7ecUFRWRnZ1d4eZMEeopJSIi0qQ4ffieiIg0T4UlVmZ89icfbDoIwIXdQnjxhj4Eers5uTKR5i89PR2r1eqYDqFcWFgYO3furHKfm2++mfT0dM4991wMw6C0tJR77rmHxx57rNrPmTlzJk8++WSd1n4myofvJWlOKRERkSZBPaVERKTOJWbkc+1r6/hg00FMJvjbpV1ZMG6gAimRRuz777/n6aef5tVXX2XLli0sW7aML7/8kn/961/V7jN16lSysrIct4MHDzZgxZWV95RKyixAM1SIiIg0fuopJSIidWrV9lQmf7iVnMJSgrzdeOmmvpzbJdjZZYm0KMHBwVgsFlJTUytsT01NJTw8vMp9/vnPf3Lbbbdx1113AXDWWWeRl5fH3XffzeOPP15h4Zly7u7uuLu71/0BnKbynlK5RaVkF5bi7+nq5IpERETkVNRTSkRE6kSp1cYzX+9kwv9+IaewlH5tA/jygXMVSIk4gZubG/37968wabnNZiMuLo7BgwdXuU9+fn6l4MlisQA0mV5Hnm4Wgsp6ZGoIn4iISOOnnlIiInLG0nIKeeD9X/k5/igA489pz9Th3XFz0W8fIs4yefJkxo0bx4ABAxg0aBCzZ88mLy+P8ePHAzB27FgiIyOZOXMmACNHjmTWrFn07duX2NhY9u7dyz//+U9GjhzpCKeagsgAT47mFZOcWUCPiKazWqKIiEhLpFBKRETOyMb9R5n03hbScorwdrPw3HUxXNG7tbPLEmnxbrzxRo4cOcL06dNJSUmhT58+rFixwjH5eWJiYoWeUdOmTcNkMjFt2jSSkpIICQlh5MiR/Oc//3HWIZyWiAAP/kjKIilTPaVEREQaO5PRVPpjN6Ds7Gz8/f3JysrCz0+/sImIVMUwDOaviefZFbuw2gy6hvnw2q396RTi4+zSROqUzgtqrjF8V099vp0FP+3n/87vyNQR3Z1Sg4iISEtX03MC9ZQSEZFayy4s4e8f/cbKP+2TKF/dJ4KnrzkLLzf9WRER54oI8ADgkHpKiYiINHq6ehARkVrZnpzNfYs3k5CRj5vFzPSRPbglti0mk8nZpYmI0CbQvgJfskIpERGRRk+hlIiI1NhHvxxk2vJtFJXaiAzw5NVb+hETFeDsskREHCIC7KGUVt8TERFp/BRKiYjIXyossTLjsz/5YNNBAC7sFsKLN/QhsGzpdRGRxiKyLJRKyymiqNSKu0vTWTlQRESkpVEoJSIip5SYkc+9izfzZ3I2JhNMHtqViRd1xmzWcD0RaXyCvN3wcDVTWGIjJauQdq28nV2SiIiIVEOhlIiIVGvV9lQmf7iVnMJSgrzdeOmmvpzbJdjZZYmIVMtkMhER4En8kTySMgsUSomIiDRiCqVERKSSUquNF1bt5rXv9wHQr20Ac2/pR2t/TydXJiLy1yLLQynNKyUiItKoKZQSEZEK0nIKeeD9X/k5/igA489pz9Th3XFzMTu5MhGRmimfVyo5s9DJlYiIiMipKJQSERGHLYnHuGfhZtJyivB2s/Dsdb25sneEs8sSEakVxwp8mflOrkRERERORaGUiIgA8NEvB3n8k20UW210CfXhtVv70znUx9lliYjUmnpKiYiINA0KpUREWrhSq42ZX+/krbX7ARjWM4xZN/TB211/IkSkaTreU0pzSomIiDRmuuIQEWnBsvJLmPT+FtbsSQfgwUu68OAlXTCbTU6uTETk9LUJPB5KGYaByaR/00RERBojhVIiIi3U3rQc7nr3FxIy8vF0tTDrhhiGn9Xa2WWJiJyxMD8PTCYoLrWRnltMiK+7s0sSERGRKiiUEhFpgeJ2pPLgB1vJLSolMsCT+WMH0CPCz9lliYjUCTcXM2G+HqRkF5KcWaBQSkREpJHS+t4iIi2IYRi8+v1e7vrfL+QWlTKoQxCfTTpHgZSINDsRAR6A5pUSERFpzNRTSkSkhSgotvKPpb/z2W/JANwS25YnRvbEzUW/T4hI8xMZ6MWWxEySFUqJiIg0WgqlRERagMNZBdz9v838kZSFi9nEjKt6cuvZ7ZxdlohIvSnvKXXomEIpERGRxkqhlIhIM7f5wFH+b+EW0nOLCPJ249Vb+nF2x1bOLktEpF61CbCvwKeeUiIiIo1XoxizMXfuXNq3b4+HhwexsbFs3Lix2rYlJSU89dRTdOrUCQ8PD2JiYlixYkW17Z955hlMJhMPPfRQPVQuItK4fbjpIGPe2EB6bhHR4b58OvEcBVIi0iJElIVSmlNKRESk8XJ6KLVkyRImT57ME088wZYtW4iJiWHYsGGkpaVV2X7atGm8/vrrvPzyy2zfvp177rmH0aNH8+uvv1Zqu2nTJl5//XV69+5d34chItKolFptPPn5nzy69HeKrTaG9wpn6b1DiArycnZpIiINIjJQPaVEREQaO6eHUrNmzWLChAmMHz+eHj16MG/ePLy8vFiwYEGV7RcuXMhjjz3GiBEj6NixI/feey8jRozghRdeqNAuNzeXW265hfnz5xMYGNgQhyIi0ihk5hdz+9ubePunBAAeGtqFuTf3w9tdI7ZFpOWILOspdSy/hPziUidXIyIiIlVxaihVXFzM5s2bGTp0qGOb2Wxm6NChrF+/vsp9ioqK8PDwqLDN09OTtWvXVtg2ceJErrjiigrvLSLS3O1JzWHU3J9YuzcdLzcL827tx0NDu2I2m5xdmohIg/L1cMXXwx7Gq7eUiIhI4+TUn83T09OxWq2EhYVV2B4WFsbOnTur3GfYsGHMmjWL888/n06dOhEXF8eyZcuwWq2ONh988AFbtmxh06ZNNaqjqKiIoqIix/Ps7OzTOBoREedavT2Vh5ZsJbeolDaBnswfO4Durf2cXZaIiNNEBniyMyWHQ8cK6Bzq6+xyRERE5CROH75XW3PmzKFLly5ER0fj5ubGpEmTGD9+PGaz/VAOHjzIgw8+yOLFiyv1qKrOzJkz8ff3d9yioqLq8xBEROqUYRjM/W4vExb+Qm5RKbEdgvhs0rkKpESkxYt0rMBX6ORKREREpCpODaWCg4OxWCykpqZW2J6amkp4eHiV+4SEhLB8+XLy8vI4cOAAO3fuxMfHh44dOwKwefNm0tLS6NevHy4uLri4uPDDDz/w0ksv4eLiUqFHVbmpU6eSlZXluB08eLDuD1ZEpB4UFFt54IOtPL9yF4YBt53djkV3xRLk7ebs0kREnK58svOkzHwnVyIiIiJVcerwPTc3N/r3709cXBxXX301ADabjbi4OCZNmnTKfT08PIiMjKSkpISlS5dyww03AHDJJZfwxx9/VGg7fvx4oqOj+cc//oHFYqn0Xu7u7ri7u9fNQYmINJDkzALuXvgL25KycTGbeHJUT26JbefsskREGo2Isp5SScc0p5SIiEhj5PSlmCZPnsy4ceMYMGAAgwYNYvbs2eTl5TF+/HgAxo4dS2RkJDNnzgRgw4YNJCUl0adPH5KSkpgxYwY2m41HH30UAF9fX3r16lXhM7y9vWnVqlWl7SIiTdXmA0f5v4VbSM8tIsjbjddu6Udsx1bOLktEpFHR8D0REZHGzemh1I033siRI0eYPn06KSkp9OnThxUrVjgmP09MTHTMFwVQWFjItGnTiI+Px8fHhxEjRrBw4UICAgKcdAQiIg3rw00HeXz5H5RYDaLDfZk/dgBRQV7OLktEpNFx9JTS6nsiIiKNkskwDMPZRTQ22dnZ+Pv7k5WVhZ+fJgoWkcbBZjP495c7WPDTfgCG9wrnhRti8HJz+u8LIs2azgtqrrF9V6nZhcQ+HYfFbGLXvy7HxdLk1vgRERFpkmp6TqArGRGRJsBqM/jH0t/5ePMhAB4e2pX7L+6M2WxycmUiIo1XiI87rhYTJVaD1Jwix3A+ERERaRz0c5GISCNXYrXx0JKtfLz5EBaziTk39eHBoV0USImI/AWz2URr//J5pTSET0REpLFRKCUi0ogVlVqZ9N4WPv8tGVeLibk392VUn0hnlyUi0mREBHgAWoFPRESkMdLwPRGRRqqwxMq9izbz3a4juLmYmXdrPy6ODnN2WSIiTUpkgBdwVJOdi4iINEIKpUREGqH84lIm/O8XftqbgYermTfHDuTcLsHOLktEpMmJLO8ppVBKRESk0an18L327dvz1FNPkZiYWB/1iIi0eDmFJdy+YBM/7c3A283Cu+MHKZASETlNkYGaU0pERKSxqnUo9dBDD7Fs2TI6duzIpZdeygcffEBRUVF91CYi0uJkFZRw21sb2ZhwFF8PFxbeFUtsx1bOLktEpMmKKFtxT3NKiYiIND6nFUpt3bqVjRs30r17d+6//35at27NpEmT2LJlS33UKCLSIhzNK+bm+T+z9WAmAV6uvD/hbPq1DXR2WSIiTVpkwPGeUoZhOLkaEREROdFpr77Xr18/XnrpJZKTk3niiSd48803GThwIH369GHBggX6oy8iUgtHcooY88bP/JmcTbCPGx/cfTa9Iv2dXZaISJNX3lMqr9hKVkGJk6sRERGRE512KFVSUsKHH37IVVddxd/+9jcGDBjAm2++ybXXXstjjz3GLbfcUpd1iog0WylZhdz4xnp2peYQ5ufOB3cPJjrcz9lliYg0Cx6uFoJ93ABNdi4iItLY1Hr1vS1btvD222/z/vvvYzabGTt2LC+++CLR0dGONqNHj2bgwIF1WqiISHN06Fg+N8/fQOLRfCIDPHlvQiztWnk7uywRkWYlIsCT9Nxiko4V0DNCvVBFREQai1qHUgMHDuTSSy/ltdde4+qrr8bV1bVSmw4dOnDTTTfVSYEiIs1VQnoeN8//meSsQtq18mLxXbG0CfRydlkiIs1OZIAnvx/K0gp8IiIijUytQ6n4+HjatWt3yjbe3t68/fbbp12UiEhztzcth5vnbyAtp4hOId4svutswv09nF2WSNNgGJCfAdnJkHO47D4FLpwCJpOzq5NGyLECn0IpERGRRqXWoVRaWhopKSnExsZW2L5hwwYsFgsDBgyos+JERJqj7cnZ3PbWBjLyiokO92XhnbGE+Lo7uyyRxqG0qCxoOgzZSccf5yQfv89JAWtx5X0H3Q3erRq+Zmn0jq/AV+jkSkREROREtQ6lJk6cyKOPPloplEpKSuLZZ59lw4YNdVaciEhz8/uhTG57ayNZBSX0ivRj4R2xBHq7ObsskfpnGFBw7KTeTSfelwVO+Rk1f0+vYPBrDb4R9nvDVn/1S5NW3lPqkHpKiYiINCq1DqW2b99Ov379Km3v27cv27dvr5OiRESao80HjnL7gk3kFJXSt20A74wfhL9n5Xn5RJq0wiw4shuO7Dx+y9hnD55Ka9hLxeJeMWzybQ1+ERXvfcPBRT0MpWbaBJb3lFIoJSIi0pjUOpRyd3cnNTWVjh07Vth++PBhXFxq/XYiIi3C+n0Z3PnuJvKLrQzqEMSC2wfi465/M6UJK8iEI7sqhk9HdtmH3J2KV6tTh01+EeAZqLmhpE6V95Q6klNEYYkVD1eLkysSEREROI1Q6rLLLmPq1Kl8+umn+Pvbl9TNzMzkscce49JLL63zAkVEmrofdx9hwv9+oajUxrmdg5k/dgCebrogkiai4Jg9bErbURZCld3nHK5+H98ICOkGod3t9626gH+kPXRS7yZxgkAvVzxdLRSUWEnJKqR9sLezSxIRERFOI5T673//y/nnn0+7du3o27cvAFu3biUsLIyFCxfWeYEiIk3Z6u2p3Ld4C8VWGxdHh/LqLf30C700TvlH7b2dHOFTWe+n3NTq9/FrUzF8Com233v4N1zdIjVgMpmICPBg35E8kjILFEqJiIg0ErUOpSIjI/n9999ZvHgxv/32G56enowfP54xY8bg6qq5UUREyn31x2EeeP9XSm0Gl/cM56UxfXFzMTu7LGlMrCWQmwa5KZCTau99lJtqX12u/N5aUjaUzQSOEW2mE7bV8B5O2lb23FoKGXsg70j1dfpHHQ+cQrvbHwd3BQ+/+vhWROpFZKCXI5QSERGRxuG0JjTx9vbm7rvvrutaRESajeW/JjH5w63YDLgqJoJZN8TgYlEg1WKUFh0PlU4MmHJSjgdQuSmQlw4Yzq72uIC2ZeFT2S20LHxy93V2ZSJnLDLAA4CkYwqlREREGovTnmV3+/btJCYmUlxcXGH7VVdddcZFiYg0ZUs2JTJl2R8YBlzfvw3PXNsbi1mTNjcbhmGfzDvlDziWUHXoVHCs5u9ndgGfMPvNN7zsvjX4lm0rn4PJMACjLMMyTnhedl+hTTX3ldpg7y0V2MHeC8pNQ5qk+YoM0Ap8IiIijU2tQ6n4+HhGjx7NH3/8gclkwig7qTWVDQWwWq11W6GISBPyv/UJTP/0TwBuPbstT13VC7MCqaartBjSd9sDqJQ/IOV3SN1Ws9DJ4gY+4fagyTes7HFZ4ORzwjavVmBWLzo57uDBg5hMJtq0aQPAxo0bee+99+jRo4d6qp+B8hX4NHxPRESk8ah1KPXggw/SoUMH4uLi6NChAxs3biQjI4O//e1v/Pe//62PGkVEGj3DMHj9x3ie+XonAHee24FpV3R3BPbSBBRk2gOnEwOotJ1gK6nc1uxiH97WqvMJvZrKA6iy3k6egcfnbhKphZtvvpm7776b2267jZSUFC699FJ69uzJ4sWLSUlJYfr06c4usUlSTykREZHGp9ah1Pr16/n2228JDg7GbDZjNps599xzmTlzJg888AC//vprfdQpItJolVhtPPHZn7y3IRGAiRd14pHLuimQaqwMAzIPlAVPJ4RQWYlVt3f3h/Czym697Pch0ceH1YnUsW3btjFo0CAAPvzwQ3r16sVPP/3EN998wz333FOrUGru3Lk8//zzpKSkEBMTw8svv+x476pkZmby+OOPs2zZMo4ePUq7du2YPXs2I0aMOOPjcrYIRyhViM1mqBeriIhII1DrUMpqteLra5/wNDg4mOTkZLp160a7du3YtWtXnRcoItKYZReWMHHxFtbsScdkgsdHdOfOczsokGosSosgbYc9dHL0gtoGRVlVtw9oC+G9IazX8SAqoK16PEmDKikpwd3dHnquXr3aMV9ndHQ0hw8frvH7LFmyhMmTJzNv3jxiY2OZPXs2w4YNY9euXYSGhlZqX1xczKWXXkpoaCgff/wxkZGRHDhwgICAgDo5LmcL9/fAbIJiq430vCJCfT2cXZKIiEiLV+tQqlevXvz222906NCB2NhYnnvuOdzc3HjjjTfo2LFjfdQoItIoHTqWzx3vbGJ3ai6erhZeGtOXS3uEObuslqsw2x46Hf7Nfkv5A9J3ga20cluzK4R2twdQ5b2fwnqBZ0CDly1ysp49ezJv3jyuuOIKVq1axb/+9S8AkpOTadWqVY3fZ9asWUyYMIHx48cDMG/ePL788ksWLFjAlClTKrVfsGABR48eZd26dbi6ugLQvn37Mz+gRsLVYibMz4PDWYUkHStQKCUiItII1Hpm1WnTpmGz2QB46qmn2L9/P+eddx5fffUVL7300mkVMXfuXNq3b4+HhwexsbFs3Lix2rYlJSU89dRTdOrUCQ8PD2JiYlixYkWFNq+99hq9e/fGz88PPz8/Bg8ezNdff31atYmIVGXrwUyunruO3am5hPq68+H/DVYg1ZDy0mFvHKyZBR+Og5f6wjNR8M4IWDkVfv8A0v60B1KegdDhfBg8Ca6eB/f8BI8lwz1r4Oq5cPa90P5cBVLSaDz77LO8/vrrXHjhhYwZM4aYmBgAPvvss1MOvTtRcXExmzdvZujQoY5tZrOZoUOHsn79+ir3+eyzzxg8eDATJ04kLCyMXr168fTTT59yEZuioiKys7Mr3BqzSE12LiIi0qjUuqfUsGHDHI87d+7Mzp07OXr0KIGBgac1XKW2XcunTZvGokWLmD9/PtHR0axcuZLRo0ezbt06+vbtC0CbNm145pln6NKlC4Zh8O677zJq1Ch+/fVXevbsWesaRURO9PUfh3n4w60UltiIDvdlwe0DHXOVSB0zDMhOKuv99HtZD6jf7duq4h9l7/3UOgZa97b3gPKL1PA7aVIuvPBC0tPTyc7OJjAw0LH97rvvxsvLq0bvkZ6ejtVqJSysYlgeFhbGzp07q9wnPj6eb7/9lltuuYWvvvqKvXv3ct9991FSUsITTzxR5T4zZ87kySefrOGROV9koCe/HDimyc5FREQaCZNhGEZNG5eUlODp6cnWrVvp1atXnRQQGxvLwIEDeeWVVwCw2WxERUVx//33V9m1PCIigscff5yJEyc6tl177bV4enqyaNGiaj8nKCiI559/njvvvPMva8rOzsbf35+srCz8/PxO46hEpDkyDIM3foxnZtkKexd2C+GVm/vh417rfF+qYrPBsf1weGvFACo/o+r2rTqfEEDF2B9713xok0hNNfR5QUFBAYZhOAKoAwcO8Mknn9C9e/cKPw6eSnJyMpGRkaxbt47Bgwc7tj/66KP88MMPbNiwodI+Xbt2pbCwkP3792OxWAD7EMDnn3++2rmsioqKKCoqcjzPzs4mKiqq0Z5DPbtiJ699v49xg9vx5Ki6OZcVERGRymp6/lSrKylXV1fatm17ym7ctVHetXzq1KmObX/VtbyoqAgPj4pzAHh6erJ27doq21utVj766CPy8vIqnJSJiNRGidXG9E+38f7GgwCMHdyO6Vf2wMVS61HQAmAttc/3VKEH1B9QnFO5rclyfP6n8h5QYb3Ao/Fd8IrUhVGjRnHNNddwzz33kJmZSWxsLK6urqSnpzNr1izuvffev3yP4OBgLBYLqampFbanpqYSHh5e5T6tW7fG1dXVEUgBdO/enZSUFIqLi3Fzc6u0j7u7u2NS9qbg+PC9QidXIiIiInAaw/cef/xxHnvsMRYuXEhQUNAZffjpdC0fNmwYs2bN4vzzz6dTp07ExcWxbNmySkHZH3/8weDBgyksLMTHx4dPPvmEHj16VPmeVf3KJyJS7uQV9v55RQ/Gn9NeK+ydqKTA3qPJcTtadsuoeCso2553BKzFld/H4l428fgJAVRoT3DVhMTScmzZsoUXX3wRgI8//piwsDB+/fVXli5dyvTp02sUSrm5udG/f3/i4uK4+uqrAXtv9Li4OCZNmlTlPueccw7vvfceNpsNs9keuO/evZvWrVtXGUg1RZpTSkREpHGpdSj1yiuvsHfvXiIiImjXrh3e3t4VXt+yZUudFVeVOXPmMGHCBKKjozGZTHTq1Inx48ezYMGCCu26devG1q1bycrK4uOPP2bcuHH88MMPVQZTTW0+BBFpOAeP2lfY25PWglbYKy2G/PQqQqWjJ4VLJ2wrya/957j5ls37dEIAFdwVLK51f0wiTUh+fj6+vr4AfPPNN1xzzTWYzWbOPvtsDhw4UOP3mTx5MuPGjWPAgAEMGjSI2bNnk5eX51iNb+zYsURGRjJz5kwA7r33Xl555RUefPBB7r//fvbs2cPTTz/NAw88UPcH6SSRgfZQSnNKiYiINA61DqXKf22rC6fTtTwkJITly5dTWFhIRkYGERERTJkyhY4dO1Zo5+bmRufOnQHo378/mzZtYs6cObz++uuV3nPq1KlMnjzZ8bx8PgQRadm2Hszkrnc3kZ5bTJifO2+NG0ivSH9nl3XmrCWQdQgyEyHzgP3+2IHjz3OqnjvmL5ldwasVeAWddN8KPE947BUE3sHg1wbMGv4ocrLOnTuzfPlyRo8ezcqVK3n44YcBSEtLq9U8TTfeeCNHjhxh+vTppKSk0KdPH1asWOHooZ6YmOjoEQUQFRXl+LzevXsTGRnJgw8+yD/+8Y+6PUAnKl+UIqughNyiUs0JKCIi4mS1/ktc3eorp+N0upaX8/DwIDIykpKSEpYuXcoNN9xwyvY2m63CEL0TNbX5EESk/n39x2EeWrKVolIb3Vv7seD2AbT2byIr7Nms9tXpMhMrB06ZifbXDNup38NkOSFEagVegSc9PzFsKguc3H21yp1IHZg+fTo333wzDz/8MBdffLFjTsxvvvnGsdJwTU2aNKnac6rvv/++0rbBgwfz888/17rmpsLH3QV/T1eyCkpIziyga5ivs0sSERFp0Zz+81Btu5Zv2LCBpKQk+vTpQ1JSEjNmzMBms/Hoo4863nPq1KkMHz6ctm3bkpOTw3vvvcf333/PypUrnXKMItJ0GIbB6z/G80zZCnsXR4fy0pi+jevXdJsNclNOCpwSjj/PTgJb6anfw8UDAtqW3drZ7wPbHX/u1UoBk4iTXHfddZx77rkcPnyYmJgYx/ZLLrmE0aNHO7Gy5iEiwJOsghKSjimUEhERcbZaX2WZzeZTTu5b25X5atu1vLCwkGnTphEfH4+Pjw8jRoxg4cKFBAQEONqkpaUxduxYDh8+jL+/P71792blypVceumltTtYEWlRSqw2/rl8Gx9ssq+wd/uQ9ky7onvjWGHPMGD3CvhpDiRtrnqS8BOZXSEg6qTQqf3x5z6hCp1EGrHw8HDCw8M5dOgQAG3atGHQoEFOrqp5iAzwZMfhbE12LiIi0gjUOpT65JNPKjwvKSnh119/5d133z3tycJr07X8ggsuYPv27ad8v7feeuu06hCRliurwL7C3tq96ZhN8M8rezD+nA7OLss+FG/7clgzC1K3Hd9usoB/ZFngVEVPJ99wMFuqfVsRabxsNhv//ve/eeGFF8jNzQXA19eXv/3tbzz++OMVfqyT2osMsK/mqVBKRETE+WodSo0aNarStuuuu46ePXuyZMkS7rzzzjopTESkoZy4wp6Xm4WXx/Tlku5OXmHPWgK/fwhrZ0HGXvs2Nx8YeCf0G2cPniyNaEihiNSZxx9/nLfeeotnnnmGc845B4C1a9cyY8YMCgsL+c9//uPkCps2rcAnIiLSeNTZFc3ZZ5/N3XffXVdvJyLSIH5NPMaE//3SeFbYKymErYtg7RzISrRv8wiA2Hsg9v/sk4qLSLP27rvv8uabb3LVVVc5tpWvhnffffcplDpD5SvwJR1TKCUiIuJsdRJKFRQU8NJLLxEZGVkXbyci0iC++uMwD5etsNejtR9vOXOFveI8+OVtWPeyfRJzAO8QGDzJ3jvKXZPxirQUR48eJTo6utL26Ohojh496oSKmpfIAPWUEhERaSxqHUoFBgZWmOjcMAxycnLw8vJi0aJFdVqciEh9MAyD137Yx3MrdgH2FfZeHtMXb2essFeYBRvnw8+vQn6GfZtfJJzzIPS9Ddy8Gr4mEXGqmJgYXnnlFV566aUK21955RV69+7tpKqaj/JQKiW7kBKrDdfGsJiFiIhIC1XrK7AXX3yxQihlNpsJCQkhNjaWwMDAOi1ORKSulVhtTPtkG0t+Ob7C3j+v7IHF3MAr0eVlwIbXYMMbUJRl3xbYHs59GGLGgIt7w9YjIo3Gc889xxVXXMHq1asZPHgwAOvXr+fgwYN89dVXTq6u6Qv2ccfNYqbYaiM1u5A2gQr/RUREnKXWodTtt99eD2WIiNS/rIIS7l20mXX7MjCbYPqVPbi9oVfYy0mxD9H7ZQGU5Nu3BXeD8x+Bntdo8nIR4YILLmD37t3MnTuXnTt3AnDNNddw99138+9//5vzzjvPyRU2bWazidYBHhzIyCfpWIFCKRERESeq9dXP22+/jY+PD9dff32F7R999BH5+fmMGzeuzooTEakr8UdyuXvhZvaWrbD3ys19uTi6AVfYy0yEn+bAloVgLbJvC+9tD6OiR4KWeBeRE0RERFSa0Py3337jrbfe4o033nBSVc1HZIAnBzLySc7SvFIiIiLOVOtQaubMmbz++uuVtoeGhnL33XcrlBKRRsVqM3j7p/08v3IXRaU2wv08eOv2AfSMaKAV9tL3wtoX4fcPwFZq3xYVC+c9Al0uBVMDDxsUERGtwCciItJI1DqUSkxMpEOHysNd2rVrR2JiYp0UJSJSF/an5/H3j37jlwPHADi3czD/vT6GcH+P+v/w1D9hzQvw5ydg2OzbOlwA5/8d2p+rMEpExInKJztPyix0ciUiIiItW61DqdDQUH7//Xfat29fYftvv/1Gq1at6qouEZHTdnLvKG83C49d0Z2bB7WtsFBDvUjaDD++ALu+PL6t6+X2nlFRA+v3s0VEpEaOh1LqKSUiIuJMtQ6lxowZwwMPPICvry/nn38+AD/88AMPPvggN910U50XKCJSG/vT83j049/YlGDvHXVO51Y8c01vooLqeSLb1O2wegbsWVm2wQQ9RsF5f4PWWsJdRP7aNddcc8rXMzMzG6aQFiAy0B5KJSuUEhERcapah1L/+te/SEhI4JJLLsHFxb67zWZj7NixPP3003VeoIhITdhsBm+vS+D5lTspLGnA3lFZSfD907D1PfswPZMFet8A506GkK7197ki0uz4+596rjt/f3/Gjh3bQNU0byfOKWUYRv33ohUREZEq1TqUcnNzY8mSJfz73/9m69ateHp6ctZZZ9GuXbv6qE9E5C8lpOfx94buHVWYBWtnw8+vQmnZnCTdr4JLnoDgzvX3uSLSbL399tvOLqHFaF02t2BBiZXM/BICvd2cXJGIiEjLVOtQqlyXLl3o0qVLXdYiIlIrNpvBO+sSeO6E3lFTR3Tnlth67B1VWgy/LIAfnoWCo/ZtbQfDpU9B1KD6+UwREalTHq4Wgn3cSc8tIimzQKGUiIiIk9Q6lLr22msZNGgQ//jHPypsf+6559i0aRMfffRRnRUnIlKdk3tHDenUimevrcfeUYYBfy6DuKfgWIJ9W6sucOmT0G2EVtMTEWliIgM9HaFUr8hTD50UERGR+lHrUOrHH39kxowZlbYPHz6cF154oS5qEhGp1sm9o7zcLDxW372jEtbCN/+E5C32596hcNFU6DsWLKfd4VRERJwoMsCD3w7a55USERER56j11VRubi5ubpW7OLu6upKdnV0nRYmIVCUhPY9HP/6djQn2YXP13jsqbYd9Rb3dK+zPXb3hnAdh8ERw96mfzxQRkQYRGaAV+ERERJyt1qHUWWedxZIlS5g+fXqF7R988AE9evSos8JERMrZbAbvrk/g2RXHe0dNHdGdWwa1xWyuh95R2cnw3dOwdfHxFfX63w4XTgGf0Lr/PBERaXCOFfgUSomIiDhNrUOpf/7zn1xzzTXs27ePiy++GIC4uDjee+89Pv744zovUERatgbtHVWYDT/NgfVzobTsIqX7yLIV9bSwg4hIcxKpUEpERMTpah1KjRw5kuXLl/P000/z8ccf4+npSUxMDN9++y1BQUH1UaOItEAN2juqtBg2v21fUS8/w74tKhYu/Re0ja3bzxIRkUYhQsP3REREnO60Zui94ooruOKKKwDIzs7m/fff55FHHmHz5s1YrdY6LVBEWp4DGXn8/ePf2bjf3jtqcMdWPHddPfSOMgzYvhxWPwnH9tu3teoMQ5+E6Cu0op6ISDPWJtAeSqXnFlNYYsXD1eLkikRERFqe01426scff+Stt95i6dKlREREcM011zB37ty6rE1EWhibzeB/6xN4dsUuCkqs9t5Rw6O5JbZd3feOSvgJVv0Tkjbbn3uHwIVTod9YsLjW7WeJiEij4+/pipebhfxiK8mZBXQM0QIWIiIiDa1WoVRKSgrvvPMOb731FtnZ2dxwww0UFRWxfPlyTXIuImfkQIZ97qgNZb2jzu4YxPPXxdR976i0nWUr6n1tf+7qDUPuhyGTwN23bj9LREQaLZPJRGSAJ3vScklSKCUiIuIUNQ6lRo4cyY8//sgVV1zB7Nmzufzyy7FYLMybN68+6xORZi6nsIQ3foxn/pr443NH1XXvqNw0SPkdtn8Kvy46YUW9cXDBFPANq5vPERGRJiWiLJTSvFIiIiLOUeNQ6uuvv+aBBx7g3nvvpUsXrUIlImemqNTK4p8TeeW7vRzNKwbqYO4omw0yE+Dw7/YQKuUP++PclIrtoq+0r6gX0vXMDkJERJq0yLJ5pZKOKZQSERFxhhqHUmvXruWtt96if//+dO/endtuu42bbrqpPmsTkWbIZjP49LckXvhmN4fKLgI6hXjz6OXRXNYjDFNNJxcvLYYjOyuGT6nboCi7isYmaNUJWveBgXdBu8F1djwiItJ0RZatwJeUWejkSkRERFqmGodSZ599NmeffTazZ89myZIlLFiwgMmTJ2Oz2Vi1ahVRUVH4+mo+FhGpmmEY/LD7CM+u2MWOw/bgKMzPnYeHduW6/m1wsZir37koB1K22QOo8l5QaTvAVlK5rcUNQntA694QXnYL6wnumitEREQqOh5K5Tu5EhERkZap1qvveXt7c8cdd3DHHXewa9cu3nrrLZ555hmmTJnCpZdeymeffVbrIubOncvzzz9PSkoKMTExvPzyywwaNKjKtiUlJcycOZN3332XpKQkunXrxrPPPsvll1/uaDNz5kyWLVvGzp078fT0ZMiQITz77LN069at1rWJyJnbejCTZ77ewc/x9knMfT1cuO/Cztw+pD2ebictwZ2TWtb76YQA6mh81W/s7l8WPp1lD59a94bgrlo9T0REaiSiLJRKVk8pERERp6h1KHWibt268dxzzzFz5kw+//xzFixYUOv3WLJkCZMnT2bevHnExsYye/Zshg0bxq5duwgNDa3Uftq0aSxatIj58+cTHR3NypUrGT16NOvWraNv374A/PDDD0ycOJGBAwdSWlrKY489xmWXXcb27dvx9vY+k0MWkVqIP5LLf7/ZxVd/2Od0cnMxc/uQ9tx3YScCvNzsjTITYfdK2Lsakn+F3NSq38wvsmL4FH4WBLSDmg73ExEROUn5nFKHswqw2Yy6W2BDREREasRkGIbhzAJiY2MZOHAgr7zyCgA2m42oqCjuv/9+pkyZUql9REQEjz/+OBMnTnRsu/baa/H09GTRokVVfsaRI0cIDQ3lhx9+4Pzzz//LmrKzs/H39ycrKws/P7/TPDKRlistu5A5cXv4YNNBrDYDkwmu7deGhy/tSqSfGyRtht0rYNcKSPvzpL1NENylYvgU3hu8g51yLCIiOi+ouab2XZVabXT75wqsNoMNj11CmJ+Hs0sSERFpFmp6TnBGPaXOVHFxMZs3b2bq1KmObWazmaFDh7J+/foq9ykqKsLDo+IJg6enJ2vXrq32c7KysgAICgqqg6pFpDo5hSW88WM8b67ZT0GJFYCh3UN59KI2dM3dCN+/bu8VlZ9+fCeTGaLOhq7DoN05ENYD3NSjUURE6p+LxUy4nwdJmQUcOlagUEpERKSBOTWUSk9Px2q1EhYWVmF7WFgYO3furHKfYcOGMWvWLM4//3w6depEXFwcy5Ytw2q1VtneZrPx0EMPcc4559CrV68q2xQVFVFUVOR4np1d1epdIlKdolIri35O5JVv93As3z75+LDIIh7rlEC7jDfh3bVgLT6+g7s/dL4Eul4OXS4FLwXGIiLiHJEBniRlFpCcWUD/doHOLkdERKRFcWoodTrmzJnDhAkTiI6OxmQy0alTJ8aPH1/tfFYTJ05k27Ztp+xJNXPmTJ588sn6Klmk2bLZDD79LYn/rtzN4cw8+pj28ne/Pxjp8Qe+Gbsh44TGQR2h6/CyHlFDNBm5iIg0ChEB9t5RSZkFTq5ERESk5XFqKBUcHIzFYiE1teLExqmpqYSHh1e5T0hICMuXL6ewsJCMjAwiIiKYMmUKHTt2rNR20qRJfPHFF/z444+0adOm2jqmTp3K5MmTHc+zs7OJioo6zaMSaf4Mw+D73Ud45etfCU37icmWLVzs8RuBZEMx9pvJAm3PtveG6nq5fZ4oTUouIiKNTPlk58kKpURERBqcU0MpNzc3+vfvT1xcHFdffTVgH24XFxfHpEmTTrmvh4cHkZGRlJSUsHTpUm644QbHa4ZhcP/99/PJJ5/w/fff06FDh1O+l7u7O+7u7md8PCItwfY/f2fjysV0OraW9807cHM7Yeisuz90GWrvEdX5Eg3LExGRRi8iwB5KJR1TKCUiItLQnD58b/LkyYwbN44BAwYwaNAgZs+eTV5eHuPHjwdg7NixREZGMnPmTAA2bNhAUlISffr0ISkpiRkzZmCz2Xj00Ucd7zlx4kTee+89Pv30U3x9fUlJsS9H7+/vj6enZ8MfpEhTZbNB9iFI30Pm9jjyt31Bj+ID9ACw2JtYAztiiR5h7w3V9mwNyxMRkSYlsjyUUk8pERGRBuf0UOrGG2/kyJEjTJ8+nZSUFPr06cOKFSsck58nJiZiNpsd7QsLC5k2bRrx8fH4+PgwYsQIFi5cSEBAgKPNa6+9BsCFF15Y4bPefvttbr/99vo+JJGmxTAgLx2O7oOMvSfc9sHReCgtBCCg7FZqmDng3Zvg/qPwj7kKS3BnJxYvIiJyZhRKiYiIOI/JMAzD2UU0NtnZ2fj7+5OVlYWfn5+zyxGpG0U59qCpPHA6MXwqyqp2t2LDQqIRxjajPalhF3LRyDF0bde2AQsXEXEunRfUXFP8rvKKSun5xEoAfp9xGX4e6vErIiJypmp6TuD0nlIiUodKi+BYQuUeTxn7IDflFDuawD+KooAO7CgKZXWaL9sKg4k3WpNiCmFozwjuOKcDV7fXHFEiItK8eLu7EODlSmZ+CcmZBfiFK5QSERFpKAqlRJqyo/GwbRkkrrcHUJmJYNiqb+8dAq06Q6tOENQJWnXGaNWJjVkB/G9TKiv/TKHUZu88GeLrzphBbbl5UFvC/T0a6IBEREQaXmSApyOUig5vGj28REREmgOFUiJNTXYy/PkJbFsKSZsrv+7maw+dysOnE0MozwBHs7yiUpb9msSilQfYlZrg2D6ofRC3DW7HsJ7huLmYK7+/iIhIMxMR4MmfydlagU9ERKSBKZQSaQry0mH7p/ZeUQd+AsqmgjOZocMFEH0FhPawB1A+oWAyVftWe9NyWfTzAT7efIjcolIAPF0tXN03krGD29G9tX4hFhGRluX4ZOeFTq5ERESkAZQU2EfdpO+BkG4Q2t1ppSiUEmmsCrNg55f2HlH7vgPDevy1toOh17XQY5Q9hPoLpVYbq3eksfDnBH7am+HY3jHYm1vPbse1/dvg76k5NEREpGXSCnwiItLs2GyQfcgePGXsg4w99ilf0vdC1kEcHR0umKJQSkTKFOfD7hX2IGrPKrAWHX+tdQz0ug56joaAqBq9XXpuEUs2HWTxzwdIzrL/+ms2wSXdwxg7uB3ndArGbK6+V5WIiEhLEBloD6WSFUqJiEhTU3DMHjRl7K0YPB3dB6Wn6AHs4Q+tuoBPSMPVWgWFUiLOVloM++LsQdTOr6Ak7/hrwd3grOug5zUQ3LlGb2cYBlsSM1m4PoGv/kih2Gqf+DzI240bB0ZxS2xb2gR61ceRiIiINEkR5T2lNKeUiIg0RuWrrKfvqRg8ZeyF/PTq9zO7QlBH+zQvwZ3tIVSrzhDcBbxanXLal4aiUErEGaylkLDGHkTt+Mw+VK9cQFt7j6he10JYzxr/Q1FQbOXz35J5d30CfyZnO7b3iQpg7OB2jDirNR6ulro+EhERkSavfPheak4hJVYbrhYt9CEiIk6Sl2FfXT1xPRzZVbbK+oFTr7LuG2Ff3Cq4LHRq1cUeQvm3BUvjjn0ad3UizYnNBoc22oOoPz+BvCPHX/MJtw/LO+s6iOxfq8R635FcPtiYyIe/HCKroAQANxczV8VEMHZwO3q3CajjAxEREWleWnm74eZiprjURkpWIVFB6lEsIiINJDsZDqyzL2h1YD0c2VF1Ozef472cWnWueHP3adia65BCKZH6YhhQnAfpu+0h1J+flE0oV8Yz0D5Rea/roN0QMNe8F9PhrAI+/y2Zz35LZlvS8V5RUUGe3BrbjhsGRBHo7VaXRyMiItJsmc0mIgM82Z+eR1JmgUIpERGpH4YBx/aXhVBlQdSxhMrtQqLti1u17l3W66kL+IQ1iuF2dU2hlEhNFedDfgYUHLXf55ffn3w7dvzxiROVA7j5QvQV9h5RHS8ES81XvDuWV8xX2w7z6dZkNiUcxShbLMHFbOL8riHcenZbLugaikUTl4uIiNRaRICHPZTSvFIiIlJXbDZI31XWC6osiMo5XLGNyQzhvaHdOfbOCm0Hg3cr59TrBAqlpOUyDPtEcTnJVYRMVTwuPc2TVFdv6DLUPkdUl8vA1bPGu+YVlbJqeyqf/ZbMj7uPUGozHK8N6hDEVTERjDirNUHqFSUiInJGyueVStIKfCIicrqspZDyuz18Slxvvy84WrGNxQ0i+tkDqHbnQNQg8PBzTr2NgEIpaVlsVji4AXZ8ATs/h8zE2u1vdgXvYPAMAq8g+4oFlW6BFZ+7etWqm2VxqY0fdh/h061JrN6RSmHJ8Qntekb4cVVMBCNjIhwrBYmIiFRn7ty5PP/886SkpBATE8PLL7/MoEGD/nK/Dz74gDFjxjBq1CiWL19e/4U2AuV/V5MVSomISE2VFkHSluM9oQ5ugOLcim1cvezBU3lPqMj+teqo0NwplJLmr7QI4n+wh1C7vq44wbiLBwR2KAuQTg6Zgipvd/Opl3G8VpvBhv0ZfLY1ma+3pTgmLAdo38qLq/pEclVMBJ1Dm+4EdiIi0rCWLFnC5MmTmTdvHrGxscyePZthw4axa9cuQkNDq90vISGBRx55hPPOO68Bq3U+9ZQSEZFTysuAjD320TYZe+DQZji0qfKULR7+9iF45T2hWsfUatqWlkahlDRPRTmwZxXs/AJ2fwPFOcdf8/CHrsOh+5XQ6WJw83ZKiYZh8PuhLD77LZnPf0smLef4P2Zhfu5c2TuCUX0iOCvSH1MznNBORETq16xZs5gwYQLjx48HYN68eXz55ZcsWLCAKVOmVLmP1Wrllltu4cknn2TNmjVkZmY2YMXOVR5KbU3MZNY3u7i4exi9I/0xa65GEZGWo7QIjsYfD57S99rvM/ZCwbGq9/EOPR5AtRsCoT3AbG7YupswhVLSfOSlw66v7EPz4r+vmFj7hNsnGO9+JbQ/z6lJ9d60XD77LZnPtiaRkJHv2O7v6cqIs8K5KiaSQR2CNGG5iIictuLiYjZv3szUqVMd28xmM0OHDmX9+vXV7vfUU08RGhrKnXfeyZo1a/7yc4qKiigqOv73Njs7+xStz9CXf7P3cO4xCiIH1PkJf/fWfvh6uJBTWMpL3+7lpW/3EuzjzsXRIVwcHca5XYLxcdeps4hIk2cY9snGTw6e0vfYV0s3bNXv6x8FrTrbV8ML62UPolp1apar4jUU/WWVpi0zsWx+qC/sE8md+A9IUEfoPhKiR9rH7ToxrU7OLODz35L57Ldk/kw+fsLu6WphaI8wRsVEcH7XENxclKiLiMiZS09Px2q1EhYWVmF7WFgYO3furHKftWvX8tZbb7F169Yaf87MmTN58sknz6TUminMhi0L7T84rX8FfCOgx1X2gCoqFsyWM/6IQG83fvz7RcTtTOPbnan8uDud9NwiPvzlEB/+cgg3i5nYjkFcEh3KJd3DiAryqoMDExGRelOUa+/hlLH3hABqD2Tsg5K86vdz9zsePLXqAsGd7fetOmkuqHqgUEqaFsOAIzuPT1R++LeKr4f3LguiroTQ7k5NrG02g5V/pvD2TwlsTDi+4oKL2cQFXUO4qk8EQ7uH4a1fXUVExMlycnK47bbbmD9/PsHBwTXeb+rUqUyePNnxPDs7m6ioqLov0MUDrlsA2z+1zw+Zkwwb5tlvPmH2v/09RkHbIWA5/b+rgd5uXNe/Ddf1b0NxqY1NCUeJ25FG3M5UDmTks2ZPOmv2pDPj8+10CfXhku5hXNI9lL5RAbhY9MOSiMgpGQYcS7CvTldSCNZisJXYV6yzlYC15PjzKl8rtd9bi48/ru61gmP23lDVMVkgsH1Z8HRiANUFvEPU86kBmQzDMP66WcuSnZ2Nv78/WVlZ+Pm13KUZGw2bDZK3wI7P7GHU0X0nvGiyj9uNvtI+PC+wndPKLGezGXyzPYXZq/ewM8U+l5XJBIPaBzGqTyTDe4UT6O3m5CpFRKSmmuJ5QXFxMV5eXnz88cdcffXVju3jxo0jMzOTTz/9tEL7rVu30rdvXyyW4z2ObDZ772Oz2cyuXbvo1KnTX35ug3xXpUWw77uygOpLKMw6/ppXsH2ofo9RdTpc3zAM4tPziNuRStyONH45cAyr7fgpdICXKxd2DeHi7mFc0CUEfy9NaCsigmHY52dKWGu/HfgJspMatgav4MrBU6vO9kDKRddk9amm5wQKpapQrydUu1fCD8/W7XtWywQm80k3U9ntpO0V2ppOuq+urcneXd5ktifNjsfmKraXPTebT3j8F9sBkn6BnV9WTLktbtDxQnsQ1W0E+IQ00Pd5aoZhsPLPVObE7WHHYfsQPV93F8af054xsW1p7a+uniIiTVFTDKUAYmNjGTRoEC+//DJgD5natm3LpEmTKk10XlhYyN69eytsmzZtGjk5OcyZM4euXbvi5vbXJ+8N/l2VFsP+H2H7cvtQ/hMnofUMtP9g1eNq6HBBnV58ZOWX8MOeI3y7I5Xvdh2psGquxWxiQLtALukeysXRYXQK8daCJSLSMhiGfXjcgbIQKuEnyE2p2MbsCuFn2Refsrjan1tcyu7dTnjsCmaXE9pU85rF7aR2Za+5+9qH23kGOue7EIVSZ6JeT6i2vgfL763b92wJ3Hygy2X2Xz87XwoejeeiwDAMvtmeypzVe9heFkb5uLtwxzntufPcjvq1VESkiWuqodSSJUsYN24cr7/+OoMGDWL27Nl8+OGH7Ny5k7CwMMaOHUtkZCQzZ86scv/bb7+dzMxMli9fXuPPdOp3ZS2xXwRt/xR2fA756cdfc/eH6BH2HlQdLwJXjzr72FKrjS2JmcTtTOXbHWnsScut8Hr7Vl5cHG0f5jewfZDmjxSR5qN8apXyXlAJP0FeWsU2Fjf74hTtz4X250CbQeCmOflagpqeE2gym4bW4XwY80H9f45hAIb93rAdv1W1rco2J2+voo3NWrbdWvbYan/NZj3erny7Y5vVPhzP8fjk7daK+wW0tfeI6nBBnZ5A1gXDMFi9I43Zq3c7Ji/3drMw/pwO3HVeBwK81B1URESc58Ybb+TIkSNMnz6dlJQU+vTpw4oVKxyTnycmJmJuTktWW1yh00X22xUvwIF1ZQHVZ5CbCr+9b7+5+UK3y+0BVeehZzxprYvFzKAOQQzqEMTU4d1JzMjn252pxO1MY0P8URIy8lnw034W/LQfH3cXzu8aTJ+oALqE+dItzJfW/h7qSSUiTYPNBmnbywKosiAqP6NiGxcPaDPQHkK1OwfaDNDk4HJK6ilVhab6i6g0DMMwiNuRxuy43WxLOh5G3X5Oe+46t6PmixIRaWZ0XlBzjfK7slnh4EZ7QLX9U/sk6eVcvaHrZfaAqstl4OZdpx+dW1TK2j3pxO1I5btdaaTnFldq4+vuQucwH7qF+dIlzJeuYT50DfMl1NddYVVDMoyTfkw94cfTWm2v4gdbc9mQI7Pl+BAjs0vZUKQThyNZTnjNVRMti/PZrJC6zd4D6kDZ7cRh0gAunhA1yD6PX/tz7Kueu7g7p15pVDR87ww0yhMqcTrDMPh2ZxqzV+/hjyT7pKpebhbGDWnPhPM6EqQwSkSkWdJ5Qc01+u/KZoOkzfY5qLZ/BlmJx19z8YQuQyGir33aADfvsvvyx97gfsJzV69ahQY2m8HvSVms2X2Enak57E7JYX96HqW2qk/F/T1d6Rrm4+hR1aUsrAr20cUe1hIoyrHfinPty74X5UBx2bZqn+dCUfYJz/PsK3UZVmcfUdVMlhPmyrFUnC+n/GZxtfdM8Qys+uYVVPG5R8AZrVApzZi1BHLT7BORH9xgD6IS11VcTALsYX7bWHsvqPbn2f/N1IThUgWFUmeg0Z9QSYMyDIPvdx1h9urd/HboeBg1dnB77j5fYZSISHOn84Kaa1LflWFA8q9lPaiW25cprxVTxcDKzds+se6JzyuEWuX3XvbeM9ZSSkuKyMjOIy0zh4zsXI5l55GZm0dufgGuWHGhFFdKccVqvzeV4uViEOgO/m7g62rg7WLgZbHhYpQviV5sf38XT/tnuXodD9HcvOwXlKfcXsXrrp41D+CsJVBSAKWFUJJvX/a9tKCG94Un7FtgD40qBUm59tcb2skL+JhPWqDHcW+uvAiQrbTsv03Z0vaVHpcADXhJ5u4HngFlQVVQ9YHWiaGWh796vzRFhmH//01uqv2Wk2IPnnLL7k98fvIwvHJuvtD2bHsvqPbnQeuYOlvZVJo3zSklcoYMw+D73UeYvXoPvx3MBMDT1cLYIe24+7yOtNIvlSIiIk2XyQSR/ey3oTMg5Q/7ir/Zh+xhSFHu8VDk5HsADHtPnOKc0y7BBQgru1V6oToGUFh2azCmE8KrsrDKbKk6SGrIXkcuHmVBoI/9vvzmeO5jD2BOfO7me8Jjn7JhcqcKmcpXlK7noXQ2mz2kspYcD7HKAytbiX0YleNxeahVFmwV50Nhpn1Y1Ym3/KMnPM+EorIeL0XZ9ltm4qkqqszibl9syN3Pfu/hf/yxu/8pXjvhuXrU1A1rKeQdOR42OUKnKp6XFtT8fc0u4B0KrXuX9YQ6B8Jj1LtO6pX+1yVyEsMw+HFPOi+u2s3WsjDKw9Xs6BmlbvMiIiLNjMlkvwhr3fuv29ps9ou8otwTgqry0OqE5+XDwyq9lm8POSyux5czP3nJc4vb8aFa5a9bXCkxLBzJN0jJtZKcZyUpq5RD2aWk5lkpwYUSXCjFgs0w4WEqxs9SQucAEx0DTLTzgUhvgyC3Uswl+fZeTMV5Zff5UFJW24nbHT2SDPvrJXm1+15dPO0L1VR5f6rXyu7Lh0y6+1YMk8qDp+bUW8NsBrN7/fZGspbah2IVHIOCo5VDrEpBVtmtMAswwFpkD0Lyjpx+DS4ex0Oqk0Ms71Dwi7DffFuDXyR4tbJ/N82ZteSE7/5o9Y/zy/575KVBXjq16l3n7gc+oeATbr/3Da/ieZi951xz/76l0XF6KDV37lyef/55UlJSiImJ4eWXX2bQoEFVti0pKWHmzJm8++67JCUl0a1bN5599lkuv/xyR5sff/yR559/ns2bN3P48GE++eQTrr766gY6GmnKDMNgzZ50Zq/ezZbETMAeRt12djvuPr8TIb4Ko0RERFo8s/n48LzKfZzqlSsQUXbrd8L23KJS9qTmsCc1l92pOexIyWbroSyyC0vhCPZbGU9XC70i/TgrMoDeXfw5q40/HVp5YzZX0RPIZq0+tLJZ7eGRq2fVgZKLuybqbmwsLuDdyn6rDZvV3rOqMLua+6zj99W1Ke9hWFrWuy4vrWafbXYtC6giwK81+EZUfuzbunH0wLKW2o+1QsB3ipCp4Jg9aDrd3pYmsz3I8w2zB0rlN0fgdMI2N6+6PVaROuTUUGrJkiVMnjyZefPmERsby+zZsxk2bBi7du0iNDS0Uvtp06axaNEi5s+fT3R0NCtXrmT06NGsW7eOvn37ApCXl0dMTAx33HEH11xzTUMfkjRBhmGwdm86s1fvYfMB+2oS7i5mbj27Hf93QUdCfT2cXKGIiIhI9XzcXejbNpC+bQMd2wzD4EBGPr8nZfHHoUx+P5TFtqQs8oqtbEo4xqaE4yto+bq70CvSn95t7CFVTJsA2gR6YjJbjg+Jk5bLbDk+x9TpqjbYKguyCrPs8xplH7ZPtJ1z2D7Xka3EviBB1l8MNfQKrtzLyq91xcfufvY5lk7sKejoLXjS40rb8u3BWoWQNq/iY2vl1TVrzlRxni+voOPzfZ04Wb1XkP1YfcPLepFZzuAzRRoHp050Hhsby8CBA3nllVcAsNlsREVFcf/99zNlypRK7SMiInj88ceZOHGiY9u1116Lp6cnixYtqtTeZDKdVk+pJjVJp5w2wzBYty+DF1ft5pcTwqhbYttxz4UKo0RExE7nBTWn76pxs9oM9qfn8vuhrLJbJn8mZ1NUaqvUNsDLlbPKgqrebQLo3cafcD8PTOr9JA3FWmKfiDunLKjKPgw5yZCdXPFxTcMgi9sZBkc15OZz0kTxQdU/PnEieQVM0sw0+onOi4uL2bx5M1OnTnVsM5vNDB06lPXr11e5T1FRER4eFYMCT09P1q5de0a1FBUVUVRU5HienZ19Ru8njZvNZrBqRyqvfr/PMYG5m4uZW2Lbcu8FnQj1UxglIiIizY/FbKJzqC+dQ325pl8bAEqtNvak5fLHoSx+O5TJH0lZ7DicTWZ+CWv2pLNmT7pj/2Af97KQyp+zIu03nTdJvbG4QkCU/VYdw7APh3OEVWW3nLLgqvxxYVblQKrCCpTeFR9X2uZlD5sqrFZ50oqV5StsapVCkVpxWiiVnp6O1WolLKziWPywsDB27txZ5T7Dhg1j1qxZnH/++XTq1Im4uDiWLVuG1Xpmq3zMnDmTJ5988ozeQxq/4lIbn25NYt4P+9h3xD5Rp7uLmTGD2nLvhZ0I00mViIiItDAuFjPdW/vRvbUfNwy0X/wXlVrZnZJrD6kOZfF7Uha7U3NIzy3i251pfLvz+HxAIb7u9Irw46xIf3pG+tMr0p8If/WokgZiMh2fJyv8rOrbFedBfkbZBPpe9ntN6C3SKDh9ovPamDNnDhMmTCA6OhqTyUSnTp0YP348CxYsOKP3nTp1KpMnT3Y8z87OJirqFIm8NCl5RaV8sOkgb66J53CWfRUZXw8Xxg5ux+1DOmgCcxEREZETuLtYOKtsfqlyBcVWth/OPj4/VXIWe9NyOZJTxHe7jvDdruOzqQd5u9Ezwo9eZb2pekX4ExXkqaBKnMexOIGINDZOC6WCg4OxWCykpqZW2J6amkp4eHiV+4SEhLB8+XIKCwvJyMggIiKCKVOm0LFjxzOqxd3dHXd3BRPNzbG8Yt5dn8A76xLIzC8B7L/m3XVuB26ObYuvRzNaRlhERESkHnm6WejfLpD+7Y5Pdp1fXMqOwzn8mZzFH4ey2JaczZ7UHI7mFVca+ufnYZ9MvVekvyOwqnbVPxERaTGcFkq5ubnRv39/4uLiHBOR22w24uLimDRp0in39fDwIDIykpKSEpYuXcoNN9zQABVLU5GcWcCba/bz/sZECkrsQzvbtfLi/87vxDX9IvFw1SSCIiIiImfKy82lUlBVWGJlV0oO25Kz2JaUzbakLHal5JBdWMq6fRms25fhaOvtZqFnhD89I+3D/3pF+tMx2BsXi4ZViYi0FE4dvjd58mTGjRvHgAEDGDRoELNnzyYvL4/x48cDMHbsWCIjI5k5cyYAGzZsICkpiT59+pCUlMSMGTOw2Ww8+uijjvfMzc1l7969juf79+9n69atBAUF0bZt24Y9QGlQe9Nyef2HfSzfmkSJ1b6oZM8IP+69sBPDe7XGol/iREREROqVh6uFmKgAYqICHNuKS23sScvhz6Rs/kiyD/3bcTibvGIrGxOOsjHh6An7m+nR2t6TqmuYLx2CvWkf7E1rPw/1qhIRaYacGkrdeOONHDlyhOnTp5OSkkKfPn1YsWKFY/LzxMREzCdMQFdYWMi0adOIj4/Hx8eHESNGsHDhQgICAhxtfvnlFy666CLH8/K5osaNG8c777zTIMclDWvrwUxe+34v32xPxbBnUZzdMYh7L+zM+V2CNX+BiIiIiBO5uZjtPaIi/B2TqZdabew7kse2spBqW1IWfyZnk19sZUtiJlsSMyu8h7uLmXatvGjXytseVLXypn2wFx2CvQnzVWAlItJUmQyj/DJeymVnZ+Pv709WVhZ+fn7OLkeqYBgGa/ak89r3+1gff7wb+GU9wrjnwk70axt4ir1FRERqTucFNafvSs6E1WaQkFEWVCVlse9IHgnpeSQezafUVv0li4er2R5StfKmXbAXHVrZe1d1CPYm1NddP1CKiDhBTc8JmtTqeyJWm8GKbSm89sNetiVlA+BiNnF130juuaAjnUN9nVyhiIiIiJwOi9lEpxAfOoX4MKpPpGN7qdVGcmYh+zPsIdX+9DwSyh4fPFZAYYmNnSk57EzJqfSeXm6Wst5VXo7gqn2wvZdViI8CKxERZ1MoJU1CUamVZVuSeP2HfSRk5APg6WrhpkFR3HVeRyIDPJ1coYiIiIjUBxeLmbatvGjbyosLuoZUeK3EaiPpWIEjsEpIz2N/Rj4J6XkcOpZPfrGVHYez2XE4u9L7+ri70D7Yi47BPnQI9qZjiLf9cYg3Pu66TBIRaQj611YatZzCEt7fmMiba/aTllMEgL+nK7cPac+4Ie0J8nZzcoUiIiIi4iyuFnNZzydv6FbxteJSG4eO5ZOQkcf+dHtQlZBhvyUdKyC3qLRshcDKgVWor3tZUOVDx7LAqkOwN1FBXrhqdUARkTqjUEoahfziUlKyCknJLiQ1u5CUrCIOHsvni9+SyS4sBSDcz4O7zuvAmEFt8davVyIiIiJyCm4uZnuoFOJT6bWiUisHj+azPz2f+CO57E/PI/5IHvHpeaTnFpGWY79t2H+0wn4uZhNtg7wcIVXHkOO9rDQcUESk9nRlL/XKMAyO5hU7wqbDWYWkloVPKdlFpGQVkJJV6AieqtIxxJt7LujE1X0icXPRL1MiIiIicmbcXSx0DvUtm480rMJrWQUlJKTnEZ+ey/4jeexLz2P/EftcVgUlVuLT7eHVyXzdXehQHlaVDQPsGOxN51AfPFwtDXRkIiJNi0KpZshqM9idmkOp1cBksk8aaTaZsJjBZCp7bDJVeM1sxn5f/poZLKbKr5lNOH4BKi61kZZzPGxKyToheMou7/VURHGprUZ1e7tZCPP3oLW/B2F+HoT7edC3bSCXRIdqmV8RERERaRD+nq7ERAUQExVQYbvNZpCaU+joUXViD6tDx/LJKSrl90NZ/H4oq8J+9gncvene2u+Emy+hvh4NeFQiIo2TQqlmaNaqXcz9bl+9vb/JZA+sTrU078mCfdwI86sYOIX7l93KHvt6uNZbzSIiIiIiZ8JsNtHa35PW/p6c0zm4wmtFpVYSM/LLwqo89qfbA6s9ablk5pewOzWX3am5fLo12bFPsI8b0eH2gKo8rOoc6qM5q0SkRVEo1cwUlVpZvCERgBBfd8wmsNrsw+hshoHVZmAYYC17brNh327Yt9eEYUBpWWM3i5lQP/dThk2hvh4adiciIiIizZa7i4UuYb50CfOtsN0wDFKyC9l5OIftZasA7jiczf70PNJzi1m7N521e9Md7V0tJjqH+tK9tS89TuhZpcV9RKS5UijVzHzzZyqZ+SW09vdg7T8uxlKLYW/24MoeUlUKrGwnBln2dq4WE0HebprQUURERESkCibT8d5VF0WHOrYXFFvZnZrjCKl2HLY/zikqdWxbRpKjfZife4Xhfz1a+9Ih2KdW5/oiIo2RQqlm5sNfDgJwff82tf4jZTKZsJjAgv64iYiIiIjUF083S6V5qwzD4NCxggoh1Y6UbA5k5JOaXURq9hG+33XE0d7dxUy3cF96/j97dx4eVXXwD/w7+5Jtkkx2QkIChJ1ggBQQcaGyWNwrgq8gWvzVitZSq1CRpa1Gqy8vVqloW0SlKta9LqBEQJFVBJQ9CVnIvmeSmcx+f3/cySRDFpKQZCaT7+d55pm7nHvn3BkSTr5zzrmxIZiYEIqJiaEYHKblF8ZE1K8wlPIjhTUmd/ffX06M93JtiIiIiIiosyQSCeLDtIgP0+L60dHu7Q0WO86WGnCqpLln1dnSepisDvfE6m8fap6+Y1JiKNISwjApMRSjYoIh5xxVROTDGEr5kf98XwhBAKYNDUd8mNbb1SEiIiIiossUqJIjLSEMaQlh7m1Op4D8ahNOlxhw/EItvs+vwY+Ftaiot+Dzn0rx+U+lAACtUobUeB0mJooh1YTBoQhU8U9AIvId/I3kJxxOAe8dKQQA3MFeUkREREREfksqlWCIPgBD9AGYOzYGAGC2iT2nvs+vxvd5Nfg+rxoGsx37cqqwL6dKPE4CjIwJdg33C8OkxDBEh6i9eSlENMAxlPITe7MrUVTbiBCNArNadPclIiIiIiL/p1bIMHlIGCYPEXtUOZ0CsisacDjPFVLlV+NCdSNOFhtwstiA1/fnAwDidBpMShRDqomJoRgeGQQpJ1Anoj7CUMpPvHtYnOD8lglxUCtkXq4NERERERF5k1QqwfCoIAyPCsJd6QkAgDKDGd/n1YhBVX41ThUbUFTbiKJjjfjoWDEAIFgtR5qrJ9XEhFCMj9fx7wsi6jUMpfxAtdGKL0+J48Y5dI+IiIiIiNoSFazGDeNicMM4cchfg8WOYwW1OJxXjSP5NfihoAYGsx27zlZgl+tOfwqZBBMTwnBTaizmjIlBiFbhzUsgIj/DUMoPfHi0CDaHgLFxIRgVG+zt6hARERERUT8QqJLjymF6XDlMDwCwO5w4XVLvnpfqcF41yust2H++CvvPV2H1xydxdUoEbkqNw3UjI9mDioguG0Opfk4QBPfQvTsmsZcUERERERF1j1wmxdhBIRg7KARLpg2BIAjIrzLh8xMl+ORYMc6U1uPLU2X48lQZAlVyzBodjZtSYzE1ORxymdTb1SeifoihVD937EItzpbVQyWX4sbxsd6uDhERERER+QmJRIJEfQB+c/VQ/ObqoThTasAnx4rx8bFiFNU24v0fCvH+D4XQByrxi3GxuDE1FhPidZBIOFE6EXUOQ6l+7t3vxV5SN4yNQYiG47uJiIiIiKh3jIgOxojZwXj0+hT8UFCDj48V47OfSlDZYMWWfXnYsi8Pg8O0uCk1FjelxmJoZJC3q0xEPo6hVD9mstrx3+MlADh0j4iIiIiI+oZUKhHvzpcYhtXzRmFvViU+PlaEL0+VoaDahBe/zsaLX2djVEwwbp4Qi3njYxETovF2tYnIBzGU6sc++7EEDRY7EsO1SB8S5u3qEBERERHRAKOQSXHNiEhcMyISJqsdO0+X45NjRdh9tgKnSgw4VWJAxhdnMDkxDDdPiMOcMdHQaZXerjYR+QiGUv3YNtcE57+cGM9x20RERERE5FVapRw3jo/FjeNjUWO04vMTJfj4WDEO5VbjoOux+uMTmDE8EjelxmLmyCholLyDH9FAxlCqn8oub8D3+TWQSSW4PW2Qt6tDRERERETkFhqgxF3pCbgrPQFFtY3473FxgvTTJQbsPF2GnafLEKCUYdboaNyYGosrh+p5Bz+iAYihVD/1H9cE59ekRCAqWO3l2hAREREREbUtTqfBr2ck49czknGurF68g9/xIlyobsQHR4vwwdEihAUoMXdsNG4cH4eJCaGQSjkShGggYCjVD9kcTrz/QyEA4I6JnOCciIiIiIj6h+FRQXh0Vgp+f/1w/FBQi0+OFeHTH0tQZbRi64ECbD1QgNgQNX4xPhbzxsViTFwwpyoh8mMMpfqhr8+Uo7LBCn2gCteMiPR2dYiIiIiIiLpEIpEgLSEUaQmhePIXo7AvpwqfHC/GjhOlKK4z49VvzuPVb84jSR+AX7jmqRoaGejtahNRD/OJQbsbN25EYmIi1Go10tPTcejQoXbL2mw2/OlPf0JycjLUajXGjx+P7du3X9Y5+5umCc5vS4uDguOuiYiIiIioH5PLpLhqeASe/+V4HF41E5v+Jw03jI2BSi7F+Uoj/paZhZnr92DuC99i054cFNaYvF1lIuohXk80tm3bhuXLl2PNmjX44YcfMH78eMyaNQvl5eVtll+1ahVeeeUVvPjiizh16hR+/etf45ZbbsHRo0e7fc7+pLTOjN1nxeuYz6F7RERERETkR9QKGWaPicbGu67AkSd/jg3zU3HtiEjIpRKcKjHgmS/O4Mpnd+G2l/fh9X15qKi3eLvKRHQZJIIgCN6sQHp6OiZNmoSXXnoJAOB0OhEfH4+HHnoIK1asaFU+NjYWTzzxBB588EH3tttuuw0ajQZbt27t1jkvZjAYEBISgrq6OgQHB/fEZfaYjbuy8dyOs5icGIZ3fz3F29UhIiLye77cLvA1fK+IqLfUGK344kQp/nu8GAdyq9D0V6xUAkwbqse8cbGYNSYaIRqFdytKRAA63ybw6pxSVqsVR44cwcqVK93bpFIpZs6cif3797d5jMVigVrtebc5jUaDvXv3XtY5LZbmhN1gMHT7mnqT0yngXddd9+6YxF5SREREREQ0MIQGKLEwfTAWpg9GmcGMT38swSfHi3H8Qi2+zarEt1mVWPXRCcxIicCN42Mxc2QUNEqZt6tNRJfg1VCqsrISDocDUVFRHtujoqJw5syZNo+ZNWsW1q9fj6uuugrJycnIzMzEBx98AIfD0e1zZmRkYN26dT1wRb3rYG418qtMCFTJMXdstLerQ0RERERE1OeigtW478ohuO/KIcivMuK/x4vxyfFinCtrwFenyvDVqTJolTLMHBmFG8fH4qrhEVDKvT5zDRG1od/9ZL7wwgsYNmwYRowYAaVSiWXLlmHJkiWQSrt/KStXrkRdXZ37ceHChR6scc/ZdrgAADBvfCy0St44kYiIiIiIBraE8AAsu3YYvvzdDOx45Co8eE0y4sM0MFkd+OR4MX71xveY9NROPP7ej/jyZCkaLHZvV5mIWvBqsqHX6yGTyVBWVuaxvaysDNHRbfcEioiIwEcffQSz2YyqqirExsZixYoVSEpK6vY5VSoVVCpVD1xR76lrtOGLE6UAgDs5dI+IiIiIiMhDSnQQ/hA9Ao9en4JjF2rxyfFifPZjCcrrLdj2/QVs+/4CFDIJJiaEYUZKBK5OiUBKVBAkEom3q040YHm1p5RSqURaWhoyMzPd25xOJzIzMzFlSseTeKvVasTFxcFut+P999/HTTfddNnn9GWfHCuCxe7EiOggjBsU4u3qEBERERER+SSJRIIJg0OxZt5o7F95Hd5amo5FUxIwOEwLm0PA/vNVeOaLM5i94VtMyfgaj7/3Iz7/qQR1jTZvV51owPH6GLDly5dj8eLFmDhxIiZPnowNGzbAaDRiyZIlAIBFixYhLi4OGRkZAICDBw+iqKgIqampKCoqwtq1a+F0OvHYY491+pz90bamCc4nxjPJJyIiIiIi6gSZVIKpyXpMTdYDAPIqjdh9thx7zlVg//kqlBrM7l5UMqkEVwzWYcbwCFydEolRMcGQSvm3F1Fv8nooNX/+fFRUVGD16tUoLS1Famoqtm/f7p6ovKCgwGO+KLPZjFWrVuH8+fMIDAzE3Llz8eabb0Kn03X6nP3NiaI6nCgyQCmT4pYJcd6uDhERERERUb+UqA/APfohuGfaEJhtDhzKrcaecxXYfbYcORVGHM6rweG8Gjz/5TnoA5W4algEZqREYPqwCIQFKL1dfSK/IxEEQfB2JXyNwWBASEgI6urqEBwc7O3qYPXHJ/DG/nzcMC4GGxde4e3qEBERDSi+1i7wZXyviKg/u1BtwjdZFdh9tgL7sithtDrc+yQSYPwgsRfVjJQIjB+kg4y9qIja1dk2Qb+7+95AY7Y58NHRIgCc4JyIiIi6ZuPGjUhMTIRarUZ6ejoOHTrUbtl//OMfmD59OkJDQxEaGoqZM2d2WJ6IyN/Eh2lxV3oC/rFoIo6uvh5vL/0Z/t+MJIyIDoIgAMcu1OKFzCzc+vd9SPvLV3jo7aN4/0ghyuvN3q46Ub/l9eF71LEdJ0thMNsRp9NgmmscNBEREdGlbNu2DcuXL8emTZuQnp6ODRs2YNasWTh79iwiIyNbld+9ezcWLFiAqVOnQq1W49lnn8X111+PkydPIi6O0wcQ0cCilEsxJTkcU5LDsXLOSJTWmfHNuQrsOVeBb7IqUGuy4b/Hi/Hf48UAgNGxwZg+LAKTEkNxxeBQhHKoH1GncPheG3yp6/nCfxzAvpwqPDJzGB6ZOdyrdSEiIhqIfKld0BXp6emYNGkSXnrpJQDi3Yjj4+Px0EMPYcWKFZc83uFwIDQ0FC+99BIWLVrUqdfsr+8VEVFX2B1OHLtQi91nxZDqp6K6VmWSIgKQNjgUaQniIzkikJOm04DS2TYBe0r5sPwqI/blVEEiAX45kUP3iIiIqHOsViuOHDmClStXurdJpVLMnDkT+/fv79Q5TCYTbDYbwsLC2i1jsVhgsVjc6waDofuVJiLqJ+QyKSYmhmFiYhgenZWCygYLvjlXgQPnq3AkvwY5FUacdz3+c6QQABCslmNCi5BqfLwOgSr+OU7EnwIf9p/vxV9g04dFIE6n8XJtiIiIqL+orKyEw+FodefhqKgonDlzplPnePzxxxEbG4uZM2e2WyYjIwPr1q27rLoSEfV3+kAVbr1iEG69YhAAoNZkxdGCWhzJr8GR/Bocu1ALg9mOPa7hfwAglQAjooPdIVVaQigGhWogkbA3FQ0sDKV8lMMp4D1Xqj6fvaSIiIioDz3zzDN45513sHv3bqjV6nbLrVy5EsuXL3evGwwGxMez3UJEA5tOq8Q1IyJxzQhx/j67w4kzpfXukOpIfg2KahtxqsSAUyUGvHkgHwAQEaRyD/m7IiEUY+KCoZLLvHkpRL2OoZSP+uZcBUoNZoRqFZg5qvVkpERERETt0ev1kMlkKCsr89heVlaG6OjoDo99/vnn8cwzz2Dnzp0YN25ch2VVKhVUKtVl15eIyJ/JZVKMiQvBmLgQLJ6aCAAorTPjh4LmkOpkcR0q6i3YfrIU20+WAgCUMinGDgoRQ6rBobgiQYfIoPa/KCDqjxhK+ahthy8AAG6ZMIjpOBEREXWJUqlEWloaMjMzcfPNNwMQJzrPzMzEsmXL2j3ur3/9K5566ins2LEDEydO7KPaEhENPNEhaswdG4O5Y2MAAGabAz8V1blDqh/ya1BltLrXm8TpNBgTF4wxsSHuoCsiiF8OUP/FUMoHVdRbsPO0+M3m/EnsAk9ERERdt3z5cixevBgTJ07E5MmTsWHDBhiNRixZsgQAsGjRIsTFxSEjIwMA8Oyzz2L16tV46623kJiYiNJS8Zv6wMBABAYGeu06iIgGArVChkmJYZiUKN5cQhAE5FeZxFCqQAypzpbVo6i2EUW1jdhxsrknbFSwCmNiQzA6LgRjYoMxdlAIooPVnJ+K+gWGUj7ow6OFsDsFpMbrkBId5O3qEBERUT80f/58VFRUYPXq1SgtLUVqaiq2b9/unvy8oKAAUqnUXf7ll1+G1WrF7bff7nGeNWvWYO3atX1ZdSKiAU8ikSBRH4BEfQBuSxMnUDeYbThVbMCJojrxUWxATkUDygwWlBnKkXmm3H18eIDSHVKNiQvB2LgQTqROPkkiCILg7Ur4GoPBgJCQENTV1SE4OLhPX1sQBMxcvwc5FUZk3DoWCyYP7tPXJyIiIk/ebBf0N3yviIj6ltFix+kSgzukOlFUh6zyBjicrf/MD1bL3UP+xrgCq8TwAEilDKqo53W2TcCeUj7mh4Ia5FQYoVHI8ItxMd6uDhEREREREfmoAJUcExPDMNE17A8Q56c6U1qPE0V1OFlchxNFBpwtrYfBbMe+nCrsy6lqPl4pw+jYEIx2zVM1dlAIkvQBkMukbb0cUY9jKOVjmiY4v2FcDILUCi/XhoiIiIiIiPoTtUKG1HgdUuN17m1WuxPnyurdIdWJ4jqcKjbAaHXgUF41DuVVu8uq5FKkRAdhVEwwRsUGY2RMMEZEB/HvU+oVDKV8SIPFjk9/LAHACc6JiIiIiIioZyjlUvewvfmTxG12hxM5FUbX0L86nCwy4GRxHYxWB34srMOPhXUe50gI14pBVYwYVI2KDUZMCCdUp8vDUMqHfHq8GCarA0kRAZiYEOrt6hAREREREZGfksvEHlEp0UHuydSdTgEF1SacKjHgVLEBp0oMOF1iQEmdGflVJuRXmfDFiVL3OXRaRaugamhkIBQc/kedxFDKh2z7Xhy6N39iPNNmIiIiIiIi6lNSafNd/+aObZ7juNpoxWlXUHW6RAyrssobUGuytZqnSimTYlhUoBhStRgCGKLh8D9qjaGUjzhXVo+jBbWQSyW49YpB3q4OEREREREREQAgLECJaUP1mDZU795mtjmQXd7g2auq2IB6ix0niw04WWzwOEecTuMOqFKigjAsKhCJ4QFQytmraiBjKOUjmiY4v3ZEJCKCVF6uDREREREREVH71AqZe56qJoIgoLCmsdXwv8KaRhTVio+vTpW5y8ukEiSGazEsUgyphkYGYlhkEJIiAqBWyLxxWdTHGEr5AKvdiQ+PFgEA7pzMCc6JiIiIiIio/5FIJIgP0yI+TItZo6Pd2+tMNpwuFYOqM6Xi0L/ssgbUW+zIqTAip8KI7SebzyOVAIPDtBjqCquGucKq5MgAaJWMMfwJP00fsPN0GaqNVkQFq3DVsAhvV4eIiIiIiIiox4RoFfhZUjh+lhTu3iYIAsoMFmSV1yOrrEEMqsrrca6sAXWNNuRVmZBXZcLO02Ue5xoUqhFDqqggV88qsYdVkJpzVvVHDKV8QNPQvdvTBkHOuxQQERERERGRn5NIJIgOUSM6RI3pLTpnCIKAygYrssrrkV3e4AqsxOXKBisKaxpRWNOIXWcrPM4XE6J2D/8bGhmI5IgADI0MRFiAkjcS82EMpbysuLYR32SJP0x3TOTQPSIiIiIiIhq4JBIJIoJUiAhSYWqy3mNftdEqBlXu3lXic3m9BSV1ZpTUmfFtVqXHMTqtAskRYkiVHBHoCqwCMShUw04hPoChlJf95/tCCALws6QwJIQHeLs6RERERERERD4pLECJyUPCMHlImMf2OpMN2RXNwwCzyhtwvqIBRbWNqDXZcCS/BkfyazyOUcqkSNRrXYFVc1iVFBGAABWjkr7Cd9qLnE4B/zkiDt27c9JgL9eGiIiIiIiIqP8J0SqQlhCGtATPsKrR6kBupRE5FQ3IqWhAdnkDciqMOF/RAIvdiXNlDThX1tDqfDEhanfvqqawKjkyEJFBKg4F7GEMpbxoX04VCmsaEaSWY/aY6EsfQERERERERESdolHKMCo2GKNigz22O50CimobXWGV0R1Yna8Q561qGgq4N9tzKGCgSu4eBpgUEYBEfQASwwOQEK7lROvdxFDKi7Z9L/aSujk1DmqFzMu1ISIiIiIiIvJ/UqkE8WFaxIdpcXWK575ak9UdVOVUNCCnXFwuqDahwWLH8cI6HC+sa3VOfaDSFVAFYIhe63pmYHUpDKW8pMZoxY4TpQCA+ZM4wTkRERERERGRt+m0SqQlKJGWEOqx3WJ3oKDK5O5VlVtpQn6VEXlVRlQ2WN2P7y+auwoAwgOUSHQFVEPCA5CgD3A9axE8wAMrr4dSGzduxHPPPYfS0lKMHz8eL774IiZPntxu+Q0bNuDll19GQUEB9Ho9br/9dmRkZECtVgMA6uvr8eSTT+LDDz9EeXk5JkyYgBdeeAGTJk3qq0vqlI+OFcHqcGJUTDDGxIV4uzpERERERERE1A6VXIZhUUEYFhXUal+92Yb8KhPyqozIqzQir8rkehYDqyqj+Lh4snVADKwSwrXuoYDis7g+EAIrr4ZS27Ztw/Lly7Fp0yakp6djw4YNmDVrFs6ePYvIyMhW5d966y2sWLECmzdvxtSpU3Hu3Dncc889kEgkWL9+PQDgV7/6FU6cOIE333wTsbGx2Lp1K2bOnIlTp04hLi6ury+xTYIgYNth1wTnk9lLioiIiIiIiKi/ClIrMCYupM0OJ+0HViZUNljcgdUPBbWtjg0PUGKIPgBJEQEYog90Lw8O0/rNFEASQRAEb714eno6Jk2ahJdeegkA4HQ6ER8fj4ceeggrVqxoVX7ZsmU4ffo0MjMz3dt+//vf4+DBg9i7dy8aGxsRFBSEjz/+GDfccIO7TFpaGubMmYO//OUvnaqXwWBASEgI6urqEBwcfOkDuujHwlrc+NJ3UMqlOPzHmQjR+n/6SURE1F/1drvAn/C9IiIi6ryWgVV+lQm5lUbkVxmRWykGVu2RSIA4nUYMqfQBrrBKDK1idRrIpN6/Q2Bn2wRe6ylltVpx5MgRrFy50r1NKpVi5syZ2L9/f5vHTJ06FVu3bsWhQ4cwefJknD9/Hp9//jnuvvtuAIDdbofD4XAP5Wui0Wiwd+/edutisVhgsTR/4AaD4XIu7ZKaeknNGRPNQIqIiIiIiIhoAOqoh1WDxY68SiPOVxqRW2FEbmUDciuNOF9hRL3FjsKaRhTWNOLbLM87BCrlUiSGazFEL/auStIHYEiEGF6FBSghkXg/sGrJa6FUZWUlHA4HoqKiPLZHRUXhzJkzbR6zcOFCVFZW4sorr4QgCLDb7fj1r3+NP/7xjwCAoKAgTJkyBX/+858xcuRIREVF4e2338b+/fsxdOjQduuSkZGBdevW9dzFdaDR6sAnx4oBAPMncugeEREREREREXkKVMnbDKwEQUCV0eoKqBpahFZibyur3YlzZQ04V9YAoMzj2GC1HEMiAt29q4boAzA2LgSJ+oA+vDJPXp/ovCt2796Np59+Gn//+9+Rnp6O7Oxs/Pa3v8Wf//xnPPnkkwCAN998E/feey/i4uIgk8lwxRVXYMGCBThy5Ei75125ciWWL1/uXjcYDIiP753A6POfSlBvsSM+TIOfJYX3ymsQERERERERkf+RSCTQB6qgD1RhUmKYxz6HU0BxbaMrqHL1rKoUA6ui2kYYzHYcv1CL4xdq3ccsmZaINfNG9/FVNPNaKKXX6yGTyVBW5pnclZWVITo6us1jnnzySdx999341a9+BQAYO3YsjEYj7r//fjzxxBOQSqVITk7Gnj17YDQaYTAYEBMTg/nz5yMpKanduqhUKqhUqp67uA6EByoxKTEUM4ZHQOoD4zyJiIiIiIiIqP+TSSWID9MiPkyLGcMjPPaZbQ7XvFWevatGx7YeOtiXvBZKKZVKpKWlITMzEzfffDMAcaLzzMxMLFu2rM1jTCYTpFKpxzaZTJxx/uL52gMCAhAQEICamhrs2LEDf/3rX3v+Irrh6pRIXJ0SCafTa/PLExEREREREdEAolbIkBIdhJToIG9XxYNXh+8tX74cixcvxsSJEzF58mRs2LABRqMRS5YsAQAsWrQIcXFxyMjIAADMmzcP69evx4QJE9zD95588knMmzfPHU7t2LEDgiAgJSUF2dnZ+MMf/oARI0a4z+kr2EuKiIiIiIiIiAYyr4ZS8+fPR0VFBVavXo3S0lKkpqZi+/bt7snPCwoKPHpGrVq1ChKJBKtWrUJRUREiIiIwb948PPXUU+4ydXV1WLlyJQoLCxEWFobbbrsNTz31FBQK3uWOiIiIiIiIiMhXSISLx70RDAYDQkJCUFdXh+DgYG9Xh4iIiLyI7YLO43tFREREQOfbBNJ29xAREREREREREfUShlJERERERERERNTnGEoREREREREREVGfYyhFRERERERERER9jqEUERERERERERH1OYZSRERERERERETU5xhKERERERERERFRn2MoRUREREREREREfY6hFBERERERERER9Tm5tyvgiwRBAAAYDAYv14SIiIi8rak90NQ+oPaxDUVERERA59tPDKXaUF9fDwCIj4/3ck2IiIjIV9TX1yMkJMTb1fBpbEMRERFRS5dqP0kEfu3XitPpRHFxMYKCgiCRSHr8/AaDAfHx8bhw4QKCg4N7/Py+aiBe90C8ZoDXzeseGHjdA+e6BUFAfX09YmNjIZVy5oOO9GYbaiD+2wN43bzugYHXPXCueyBeMzAwr7uz7Sf2lGqDVCrFoEGDev11goODB8w/yJYG4nUPxGsGeN0DDa97YBlo180eUp3TF22ogfZvrwmve2DhdQ8sA/G6B+I1AwPvujvTfuLXfURERERERERE1OcYShERERERERERUZ9jKOUFKpUKa9asgUql8nZV+tRAvO6BeM0Ar5vXPTDwugfWdZP3DdR/e7xuXvdAwOseONc9EK8ZGLjX3Rmc6JyIiIiIiIiIiPoce0oREREREREREVGfYyhFRERERERERER9jqEUERERERERERH1OYZSvWTjxo1ITEyEWq1Geno6Dh061GH5//znPxgxYgTUajXGjh2Lzz//vI9q2jMyMjIwadIkBAUFITIyEjfffDPOnj3b4TFbtmyBRCLxeKjV6j6qcc9Yu3Ztq2sYMWJEh8f0988aABITE1tdt0QiwYMPPthm+f74WX/zzTeYN28eYmNjIZFI8NFHH3nsFwQBq1evRkxMDDQaDWbOnImsrKxLnrervxv6WkfXbbPZ8Pjjj2Ps2LEICAhAbGwsFi1ahOLi4g7P2Z2fk752qc/7nnvuaXUNs2fPvuR5+/PnDaDNn3OJRILnnnuu3XP2h8+bfBfbT2w/tae/f9bAwGg/AWxDsQ31kcd+tqHYhroUhlK9YNu2bVi+fDnWrFmDH374AePHj8esWbNQXl7eZvl9+/ZhwYIFuO+++3D06FHcfPPNuPnmm3HixIk+rnn37dmzBw8++CAOHDiAr776CjabDddffz2MRmOHxwUHB6OkpMT9yM/P76Ma95zRo0d7XMPevXvbLesPnzUAHD582OOav/rqKwDAL3/5y3aP6W+ftdFoxPjx47Fx48Y29//1r3/F3/72N2zatAkHDx5EQEAAZs2aBbPZ3O45u/q7wRs6um6TyYQffvgBTz75JH744Qd88MEHOHv2LG688cZLnrcrPyfecKnPGwBmz57tcQ1vv/12h+fs7583AI/rLSkpwebNmyGRSHDbbbd1eF5f/7zJN7H9xPZTe/zhswYGRvsJYBuKbajW2IZiG6pDAvW4yZMnCw8++KB73eFwCLGxsUJGRkab5e+44w7hhhtu8NiWnp4u/L//9/96tZ69qby8XAAg7Nmzp90yr732mhASEtJ3leoFa9asEcaPH9/p8v74WQuCIPz2t78VkpOTBafT2eb+/v5ZAxA+/PBD97rT6RSio6OF5557zr2ttrZWUKlUwttvv93uebr6u8HbLr7uthw6dEgAIOTn57dbpqs/J97W1nUvXrxYuOmmm7p0Hn/8vG+66Sbh2muv7bBMf/u8yXew/cT2U3v88bMWBP9vPwkC21AdYRuqff74ebMN1T72lOphVqsVR44cwcyZM93bpFIpZs6cif3797d5zP79+z3KA8CsWbPaLd8f1NXVAQDCwsI6LNfQ0ICEhATEx8fjpptuwsmTJ/uiej0qKysLsbGxSEpKwl133YWCgoJ2y/rjZ221WrF161bce++9kEgk7Zbzh8+6SW5uLkpLSz0+y5CQEKSnp7f7WXbnd0N/UFdXB4lEAp1O12G5rvyc+Krdu3cjMjISKSkpeOCBB1BVVdVuWX/8vMvKyvDZZ5/hvvvuu2RZf/i8qW+x/SRi+6lt/vhZD8T2E8A2VEtsQ7XNHz9vtqE6xlCqh1VWVsLhcCAqKspje1RUFEpLS9s8prS0tEvlfZ3T6cQjjzyCadOmYcyYMe2WS0lJwebNm/Hxxx9j69atcDqdmDp1KgoLC/uwtpcnPT0dW7Zswfbt2/Hyyy8jNzcX06dPR319fZvl/e2zBoCPPvoItbW1uOeee9ot4w+fdUtNn1dXPsvu/G7wdWazGY8//jgWLFiA4ODgdst19efEF82ePRtvvPEGMjMz8eyzz2LPnj2YM2cOHA5Hm+X98fN+/fXXERQUhFtvvbXDcv7weVPfY/uJ7Se2n1rzh8/6YmxDidiGYhuqLf7weXeH3NsVIP/z4IMP4sSJE5cc/zplyhRMmTLFvT516lSMHDkSr7zyCv785z/3djV7xJw5c9zL48aNQ3p6OhISEvDuu+92Kgn3B//6178wZ84cxMbGtlvGHz5r8mSz2XDHHXdAEAS8/PLLHZb1h5+TO++80708duxYjBs3DsnJydi9ezeuu+46L9as72zevBl33XXXJSfZ9YfPm8gb2H4aWL8n2H4auNiGYhuqPf7weXcHe0r1ML1eD5lMhrKyMo/tZWVliI6ObvOY6OjoLpX3ZcuWLcOnn36KXbt2YdCgQV06VqFQYMKECcjOzu6l2vU+nU6H4cOHt3sN/vRZA0B+fj527tyJX/3qV106rr9/1k2fV1c+y+78bvBVTY2p/Px8fPXVVx1+w9eWS/2c9AdJSUnQ6/XtXoM/fd4A8O233+Ls2bNd/lkH/OPzpt7H9hPbT2w/XZo/fNZsQ7ENxTZU5/nD590ZDKV6mFKpRFpaGjIzM93bnE4nMjMzPb7paGnKlCke5QHgq6++are8LxIEAcuWLcOHH36Ir7/+GkOGDOnyORwOB3766SfExMT0Qg37RkNDA3Jyctq9Bn/4rFt67bXXEBkZiRtuuKFLx/X3z3rIkCGIjo72+CwNBgMOHjzY7mfZnd8NvqipMZWVlYWdO3ciPDy8y+e41M9Jf1BYWIiqqqp2r8FfPu8m//rXv5CWlobx48d3+Vh/+Lyp97H9xPYT20+X5g+fNdtQbEOxDdV5/vB5d4p351n3T++8846gUqmELVu2CKdOnRLuv/9+QafTCaWlpYIgCMLdd98trFixwl3+u+++E+RyufD8888Lp0+fFtasWSMoFArhp59+8tYldNkDDzwghISECLt37xZKSkrcD5PJ5C5z8XWvW7dO2LFjh5CTkyMcOXJEuPPOOwW1Wi2cPHnSG5fQLb///e+F3bt3C7m5ucJ3330nzJw5U9Dr9UJ5ebkgCP75WTdxOBzC4MGDhccff7zVPn/4rOvr64WjR48KR48eFQAI69evF44ePeq+Q8ozzzwj6HQ64eOPPxZ+/PFH4aabbhKGDBkiNDY2us9x7bXXCi+++KJ7/VK/G3xBR9dttVqFG2+8URg0aJBw7Ngxj591i8XiPsfF132pnxNf0NF119fXC48++qiwf/9+ITc3V9i5c6dwxRVXCMOGDRPMZrP7HP72eTepq6sTtFqt8PLLL7d5jv74eZNvYvuJ7Se2n/zjs2Ybim0otqFEbEN1DkOpXvLiiy8KgwcPFpRKpTB58mThwIED7n0zZswQFi9e7FH+3XffFYYPHy4olUph9OjRwmeffdbHNb48ANp8vPbaa+4yF1/3I4884n6PoqKihLlz5wo//PBD31f+MsyfP1+IiYkRlEqlEBcXJ8yfP1/Izs527/fHz7rJjh07BADC2bNnW+3zh896165dbf6bbroup9MpPPnkk0JUVJSgUqmE6667rtV7kZCQIKxZs8ZjW0e/G3xBR9edm5vb7s/6rl273Oe4+Lov9XPiCzq6bpPJJFx//fVCRESEoFAohISEBGHp0qWtGkb+9nk3eeWVVwSNRiPU1ta2eY7++HmT72L7ie0nQfDPz7qJv7efBIFtKLah2IZqwjZU50gEQRC628uKiIiIiIiIiIioOzinFBERERERERER9TmGUkRERERERERE1OcYShERERERERERUZ9jKEVERERERERERH2OoRQREREREREREfU5hlJERERERERERNTnGEoREREREREREVGfYyhFRERERERERER9jqEUERERERERERH1OYZSRERERERERETU5xhKERERERERERFRn2MoRUREREREREREfY6hFBERERERERER9TmGUkRERERERERE1OcYShERERERERERUZ9jKEVERERERERERH2OoRQREREREREREfU5hlJE5DckEgnWrl3b5ePy8vIgkUiwZcuWHq9TX0tMTMQ999zjXt+9ezckEgl2797ttToRERFR32O7iIj6A4ZSRNSjtmzZAolEAolEgr1797baLwgC4uPjIZFI8Itf/MILNey+poCn6SGTyRAZGYnbb78dp0+f9nb1iIiIyMcMpHZRy8edd97pLnfo0CH85je/QVpaGhQKBSQSSZdex2q14oUXXsCECRMQHBwMnU6H0aNH4/7778eZM2d6+rKIqI/JvV0BIvJParUab731Fq688kqP7Xv27EFhYSFUKpWXanb5Hn74YUyaNAk2mw0//vgjNm3ahN27d+PEiROIjo72dvWIiIjIxwyEdlFLiYmJ7uXPP/8c//znPzFu3DgkJSXh3LlzXTr/bbfdhi+++AILFizA0qVLYbPZcObMGXz66aeYOnUqRowY0ROXQURewlCKiHrF3Llz8Z///Ad/+9vfIJc3/6p56623kJaWhsrKSi/W7vJMnz4dt99+u3s9JSUFDzzwAN544w089thjXqwZERER+aKB1C662AMPPIDHH38cGo0Gy5Yt61IodfjwYXz66ad46qmn8Mc//tFj30svvYTa2truVrvLzGYzlEolpFIONiLqSfyJIqJesWDBAlRVVeGrr75yb7NarXjvvfewcOHCNo8xGo34/e9/j/j4eKhUKqSkpOD555+HIAge5SwWC373u98hIiICQUFBuPHGG1FYWNjmOYuKinDvvfciKioKKpUKo0ePxubNm3vuQiE2xgAgJyenW69tNpuxdu1aDB8+HGq1GjExMbj11ls9zvf8889j6tSpCA8Ph0ajQVpaGt57770evQ4iIiLqHQOpXXSxqKgoaDSabh3b1BaaNm1aq30ymQzh4eEe24qKinDfffchNjYWKpUKQ4YMwQMPPACr1eouc/78efzyl79EWFgYtFotfvazn+Gzzz7zOE/T0MR33nkHq1atQlxcHLRaLQwGAwDg4MGDmD17NkJCQqDVajFjxgx899133bpGooGOPaWIqFckJiZiypQpePvttzFnzhwAwBdffIG6ujrceeed+Nvf/uZRXhAE3Hjjjdi1axfuu+8+pKamYseOHfjDH/6AoqIi/N///Z+77K9+9Sts3boVCxcuxNSpU/H111/jhhtuaFWHsrIy/OxnP4NEIsGyZcsQERGBL774Avfddx8MBgMeeeSRHrnWvLw8AEBoaGiXX9vhcOAXv/gFMjMzceedd+K3v/0t6uvr8dVXX+HEiRNITk4GALzwwgu48cYbcdddd8FqteKdd97BL3/5S3z66adtXjsRERH5Dn9uF9XX17fq6RUWFtYjPYoSEhIAAP/+978xbdo0j15mFysuLsbkyZNRW1uL+++/HyNGjEBRURHee+89mEwmKJVKlJWVYerUqTCZTHj44YcRHh6O119/HTfeeCPee+893HLLLR7n/POf/wylUolHH30UFosFSqUSX3/9NebMmYO0tDSsWbMGUqkUr732Gq699lp8++23mDx58mVfN9GAIhAR9aDXXntNACAcPnxYeOmll4SgoCDBZDIJgiAIv/zlL4VrrrlGEARBSEhIEG644Qb3cR999JEAQPjLX/7icb7bb79dkEgkQnZ2tiAIgnDs2DEBgPCb3/zGo9zChQsFAMKaNWvc2+677z4hJiZGqKys9Ch75513CiEhIe565ebmCgCE1157rcNr27VrlwBA2Lx5s1BRUSEUFxcL27dvF4YOHSpIJBLh0KFDXX7tzZs3CwCE9evXt3o9p9PpXm4q38RqtQpjxowRrr32Wo/tCQkJwuLFi1vVedeuXR1eGxEREfW8gdAuauuRm5vb5jEPPvig0JU/QZ1OpzBjxgwBgBAVFSUsWLBA2Lhxo5Cfn9+q7KJFiwSpVCocPny4zfMIgiA88sgjAgDh22+/de+rr68XhgwZIiQmJgoOh8Pj2pKSkjzaYE6nUxg2bJgwa9asVu20IUOGCD//+c87fW1EJOLwPSLqNXfccQcaGxvx6aefor6+Hp9++mm7XdQ///xzyGQyPPzwwx7bf//730MQBHzxxRfucgBalbv42z1BEPD+++9j3rx5EAQBlZWV7sesWbNQV1eHH374oVvXde+99yIiIgKxsbGYPXs26urq8Oabb7on+ezKa7///vvQ6/V46KGHWr1Oy7vTtOz2XlNTg7q6OkyfPr3b10BERER9y1/bRatXr8ZXX33l8eipG79IJBLs2LEDf/nLXxAaGoq3334bDz74IBISEjB//nz3nFJOpxMfffQR5s2bh4kTJ7Z5HkB8vyZPnuwx4XxgYCDuv/9+5OXl4dSpUx7HLV682KMNduzYMWRlZWHhwoWoqqpyv4dGoxHXXXcdvvnmGzidzh65dqKBgsP3iKjXREREYObMmXjrrbdgMpngcDjanQgzPz8fsbGxCAoK8tg+cuRI9/6mZ6lU6h7W1iQlJcVjvaKiArW1tXj11Vfx6quvtvma5eXl3bqu1atXY/r06WhoaMCHH36Id955x6OLeldeOycnBykpKR12RweATz/9FH/5y19w7NgxWCwW9/au3laZiIiIvMNf20Vjx47FzJkzu3VsZ6hUKjzxxBN44oknUFJSgj179uCFF17Au+++C4VCga1bt6KiogIGgwFjxozp8Fz5+flIT09vtb3l+9ryHEOGDPEol5WVBUAMq9pTV1fnMaUDEXWMoRQR9aqFCxdi6dKlKC0txZw5c6DT6frkdZu+pfqf//mfdhsO48aN69a5Wza+br75ZphMJixduhRXXnkl4uPje/y1v/32W9x444246qqr8Pe//x0xMTFQKBR47bXX8NZbb3XrGoiIiKjv+WO7qC/FxMTgzjvvxG233YbRo0fj3XffxZYtW3rt9S6eoL3pfXzuueeQmpra5jGBgYG9Vh8if8RQioh61S233IL/9//+Hw4cOIBt27a1Wy4hIQE7d+5EfX29x7eCZ86cce9venY6ne4eRk3Onj3rcb6mO9A4HI5e/fYOAJ555hl8+OGHeOqpp7Bp06YuvXZycjIOHjwIm80GhULRZpn3338farUaO3bsgEqlcm9/7bXXevQ6iIiIqHcNhHZRX1AoFBg3bhyysrJQWVmJyMhIBAcH48SJEx0el5CQ0Oq9AVq/r+1p6pEWHBzsF+8jkS/gnFJE1KsCAwPx8ssvY+3atZg3b1675ebOnQuHw4GXXnrJY/v//d//QSKRuO9U0/R88V1qNmzY4LEuk8lw22234f3332+zgVJRUdGdy2lTcnIybrvtNmzZsgWlpaVdeu3bbrsNlZWVra4bgPuWzzKZDBKJBA6Hw70vLy8PH330UY9dAxEREfW+gdAu6klZWVkoKChotb22thb79+9HaGgoIiIiIJVKcfPNN+O///0vvv/++1blm9pUc+fOxaFDh7B//373PqPRiFdffRWJiYkYNWpUh/VJS0tDcnIynn/+eTQ0NLTa76vvI5EvY08pIup1HY27bzJv3jxcc801eOKJJ5CXl4fx48fjyy+/xMcff4xHHnnE/c1UamoqFixYgL///e+oq6vD1KlTkZmZiezs7FbnfOaZZ7Br1y6kp6dj6dKlGDVqFKqrq/HDDz9g586dqK6u7rFr/MMf/oB3330XGzZswDPPPNPp1160aBHeeOMNLF++HIcOHcL06dNhNBqxc+dO/OY3v8FNN92EG264AevXr8fs2bOxcOFClJeXY+PGjRg6dCh+/PHHHrsGIiIi6n0DoV3UUn5+Pt58800AcAdGf/nLXwCIPZPuvvvudo89fvw4Fi5ciDlz5mD69OkICwtDUVERXn/9dRQXF2PDhg2QyWQAgKeffhpffvklZsyYgfvvvx8jR45ESUkJ/vOf/2Dv3r3Q6XRYsWIF3n77bcyZMwcPP/wwwsLC8PrrryM3Nxfvv/++xxyhbZFKpfjnP/+JOXPmYPTo0ViyZAni4uJQVFSEXbt2ITg4GP/973974m0jGji8dds/IvJPLW993JGLb30sCOIteX/3u98JsbGxgkKhEIYNGyY899xzHrfcFQRBaGxsFB5++GEhPDxcCAgIEObNmydcuHCh1a2PBUEQysrKhAcffFCIj48XFAqFEB0dLVx33XXCq6++6i7T1Vsf/+c//2lz/9VXXy0EBwcLtbW1nX5tQRBvI/zEE08IQ4YMcZe7/fbbhZycHHeZf/3rX8KwYcMElUoljBgxQnjttdeENWvWtLqtckJCgrB48eJWdd61a1eH10ZEREQ9byC3iy4u19ZjxowZHR5bVlYmPPPMM8KMGTOEmJgYQS6XC6GhocK1114rvPfee63K5+fnC4sWLRIiIiIElUolJCUlCQ8++KBgsVjcZXJycoTbb79d0Ol0glqtFiZPnix8+umnXbq2o0ePCrfeeqsQHh4uqFQqISEhQbjjjjuEzMzMDq+HiFqTCIKrLyMREREREREREVEf4ZxSRERERERERETU5xhKERERERERERFRn2MoRUREREREREREfY6hFBERERERERER9TmGUkRERERERERE1OcYShERERERERERUZ9jKEVERERERERERH1O7u0K+CKn04ni4mIEBQVBIpF4uzpERETkRYIgoL6+HrGxsZBK+X1eR9iGIiIiIqDz7SeGUm0oLi5GfHy8t6tBREREPuTChQsYNGiQt6vh09iGIiIiopYu1X5iKNWGoKAgAOKbFxwc7OXaEBERkTcZDAbEx8e72wfUPrahiIiICOh8+4mhVBuaupsHBwezQUVEREQAwOFoncA2FBEREbV0qfYTJ0YgIiIiIiIiIqI+x1CKiIiIiIiIiIj6HEMpIiIiIiIiIiLqc5xT6jI4HA7YbDZvV6NfUigUkMlk3q4GERER9TGn0wmr1ertavRLbD8REZG/YSjVDYIgoLS0FLW1td6uSr+m0+kQHR3NiWOJiIgGCKvVitzcXDidTm9Xpd9i+4mIiPwJQ6luaAqkIiMjodVq2SjoIkEQYDKZUF5eDgCIiYnxco2IiIiotwmCgJKSEshkMsTHx0Mq5SwSXcH2ExER+SOGUl3kcDjcgVR4eLi3q9NvaTQaAEB5eTkiIyPZFZ2IiMjP2e12mEwmxMbGQqvVers6/RLbT0RE5G/4FVUXNc0hxcbU5Wt6DzkvFxERkf9zOBwAAKVS6eWa9G9sPxERkT9hKNVNHLJ3+fgeEhERDTz8///y8P0jIiJ/wlCKuiUxMREbNmzwdjWIiIiI+g22n4iIiDxxTqkB5Oqrr0ZqamqPNIYOHz6MgICAy68UERHRZao323CmtB6TEsO8XRXyQ2w/ERGRP7LYHbhQ3YggtRxRwWqv1YOhFLkJggCHwwG5/NL/LCIiIvqgRkRERK3ZHU4cL6zFt1mV2JtViaMXaiGVAMfXXA+tkk0b6ltsPxERka8y2xy4UG1CXpUJ+VVG5FYakV9lQl6VEcW1jXAKwB9mpeDBa4Z6rY4cvjdA3HPPPdizZw9eeOEFSCQSSCQSbNmyBRKJBF988QXS0tKgUqmwd+9e5OTk4KabbkJUVBQCAwMxadIk7Ny50+N8F3c/l0gk+Oc//4lbbrkFWq0Ww4YNwyeffNLHV0lERP5IEATkVhrx5v483P/G95jwp69w28v7sWFnFr7Pr4HDKSBOp0FRTaO3q0p+hu0nIiLydWabA+fK6vHlyVK8+k0O/vjhT7jrnwcw7ZmvMXL1dvz8/77B0je+x18+O41/HyzA3uxKFNaIgVSAUgaL3enV+vPrxB4gCAIabY4+f12NQtbpyS5feOEFnDt3DmPGjMGf/vQnAMDJkycBACtWrMDzzz+PpKQkhIaG4sKFC5g7dy6eeuopqFQqvPHGG5g3bx7Onj2LwYMHt/sa69atw1//+lc899xzePHFF3HXXXchPz8fYWEcTkFERF1Ta7Liu+wq7M2uwLdZYuOppRCNAlcO1ePKYXpcOVSP+DDeFbe/8Vb7Ceh8G4rtJyIi8gWNVgcKqk2unk5G5FWZkOdaLjGYIQjtHxuokiNRr0VCeACGhAcgIVyLRH0AEsMDoA9Uev0GGgylekCjzYFRq3f0+eue+tOsTg9TCAkJgVKphFarRXR0NADgzJkzAIA//elP+PnPf+4uGxYWhvHjx7vX//znP+PDDz/EJ598gmXLlrX7Gvfccw8WLFgAAHj66afxt7/9DYcOHcLs2bO7fG1ERDSwWO1OHMmvwd7sCuzNqsSPRXUeDSyFTIK0hFBMHxaBK4fqMSYuBDIp70LWn3mr/QR0vg3F9hMREfWVerMN+VUmFFSbkO8abpdXZURepQmlBnOHxwap5GLQpA9AYrgrgHIFUeEB3g+eOuITodTGjRvx3HPPobS0FOPHj8eLL76IyZMnt1nWZrMhIyMDr7/+OoqKipCSkoJnn33W4z/utWvXYt26dR7HpaSkuBsR5GnixIke6w0NDVi7di0+++wzlJSUwG63o7GxEQUFBR2eZ9y4ce7lgIAABAcHo7y8vFfqTERE/ZsgCMgqb3DNC1WBg7nVMFk9e80MiwzE9GERmD5Mj8lDwhCg8olmCxEAtp+IiKhrBEFAtdGKvCoTCqrFsEkMoMR5nqqM1g6PD1bLMUQfgIRwMXhKdC0P0QcgVKvw6eCpI15v3W3btg3Lly/Hpk2bkJ6ejg0bNmDWrFk4e/YsIiMjW5VftWoVtm7din/84x8YMWIEduzYgVtuuQX79u3DhAkT3OVGjx7tMY6/M5NPdpdGIcOpP83qtfN39Lo94eK7wDz66KP46quv8Pzzz2Po0KHQaDS4/fbbYbV2/EOiUCg81iUSCZxO745PJSIi31FRb8F32ZViEJVdgTKDxWO/PlDpGpIn9oaKDvHenWCo93mr/dT02peL7SciIrqY0ymgxGBGfpURBVWmVgFUg8Xe4fHhAUoMDtciMTwAg8O0SNSLy4nhAdD14+CpI14PpdavX4+lS5diyZIlAIBNmzbhs88+w+bNm7FixYpW5d9880088cQTmDt3LgDggQcewM6dO/G///u/2Lp1q7ucXC53d7PubRKJpF/c7UepVMLhuPTcDd999x3uuece3HLLLQDEb/7y8vJ6uXZERORvLHYHDufW4JsscV6o0yUGj/0quRSTh4Rh+jA9rhwagRHRQZBySN6AwfYTERH1Rw6ngAvVJuS6g6fm5ws1jbB2MHG4RALEBKubg6cWAVRCuBZBakW7x/orr7YErFYrjhw5gpUrV7q3SaVSzJw5E/v372/zGIvFArXa85tTjUaDvXv3emzLyspCbGws1Go1pkyZgoyMjHYnmbRYLLBYmr+tNRgMbZbr7xITE3Hw4EHk5eUhMDCw3W/hhg0bhg8++ADz5s2DRCLBk08+yW/siIioU/KrjNh9tgJ7zlVgf05Vq4msR8UEY/pwPaYPjcDExFCoe6jXL1FvYfuJiGhgMlntOF9hRE5FA7LLG5BT0YCcciNyK42wOtr//S6XShAfphV7OoVrMTi8aZ4nLQaFatn2uYhXQ6nKyko4HA5ERUV5bI+Kimp3/qdZs2Zh/fr1uOqqq5CcnIzMzEx88MEHHt9gpaenY8uWLUhJSUFJSQnWrVuH6dOn48SJEwgKCmp1zoyMjFZzUPmjRx99FIsXL8aoUaPQ2NiI1157rc1y69evx7333oupU6dCr9fj8ccf99ugjoiILo/JaseB81XY4wqi8qpMHvsjg1S4arg4L9S0oXroA1VeqilR97D9RETkvwRBQEWDBTnlnuHT+Qojimob2z1OJZciscWd7Jp6OiWGByAmRA25TNqHV9G/SQSho5sH9q7i4mLExcVh3759mDJlinv7Y489hj179uDgwYOtjqmoqMDSpUvx3//+FxKJBMnJyZg5cyY2b96Mxsa2/9HU1tYiISEB69evx3333ddqf1s9peLj41FXV4fg4GCPsmazGbm5uRgyZEirHlvUNXwviYj6n6YJyptCqEO51R7fFipkEkxMCMOMlAjMGC4Oyevv8x8YDAaEhIS02S4gTx29V/x/v2fwfSQi6jq7w4mCahNyKozNvZ4qGpBT3gCDuf15nsIDlEiOCERyZIDrORBDIwIRq9PwLsCX0Nn2k1d7Sun1eshkMpSVlXlsLysra3c+qIiICHz00Ucwm82oqqpCbGwsVqxYgaSkpHZfR6fTYfjw4cjOzm5zv0qlgkrFb26JiIjaYjDb8F1WJfacE4OokjrP2xLH6TS42hVCTR2qRyDvkkdERER9zOkUUNlgwYWaRhRUG5FT3hxA5VUZYXO03R9HKgHiw7Ri6BQRgKGRga7lQIQGKPv4KgYer7YalUol0tLSkJmZiZtvvhkA4HQ6kZmZiWXLlnV4rFqtRlxcHGw2G95//33ccccd7ZZtaGhATk4O7r777p6sPhERkV9yOgWcKjGIIdTZChwpqIHD2dyQU8ml+FlSOGYMj8CMlAgk6QP6fW8oIiIi8m0Op4AygxmFNY0oqjWhsLoRRbWNrvVGFNU0djjXk0YhQ1KE2OPJHTxFine24zxP3uP1rzKXL1+OxYsXY+LEiZg8eTI2bNgAo9HovhvfokWLEBcXh4yMDADAwYMHUVRUhNTUVBQVFWHt2rVwOp147LHH3Od89NFHMW/ePCQkJKC4uBhr1qyBTCbDggULvHKNREREvq7aaMW3WWII9U1WBSobPG9jnxQRgKuHR2JGSgTSh4Sx8UZEREQ9yuZworTOjAs1JhTVNIdNhTUmFNU2oqTWDLuz49mHpBIgJkSDQaEa91C75EixB1RsiIZ3+fVBXg+l5s+fj4qKCqxevRqlpaVITU3F9u3b3ZOfFxQUQCptniTMbDZj1apVOH/+PAIDAzF37ly8+eab0Ol07jKFhYVYsGABqqqqEBERgSuvvBIHDhxAREREX18eERGRT7I7nDh2oRbfZFViz9ly/FhUh5azTAYoZZg6VC/2hhoegfgwrfcqS0RERP2ewymguLYR+VUmd9BUWOMKnWoaUWow4xKZE+RSCWJ1YugUp9NgUKgWcaHN69Ehaig4yXi/4vVQCgCWLVvW7nC93bt3e6zPmDEDp06d6vB877zzTk9VjYiIyG8UVJnwTVYFvs2qwL7sKtRbPCf2HBkT7A6h0hJCoZSzUUdERESdZ3M4UVTTiLwqI/KrTMitNCLftXyhxtTuvE5NlHIpBuk0HkFTy+ApMkjNCcb9jE+EUkRERNTz6s027M+pwrdZlfgmqwL5VSaP/TqtAtNa9IaKCuadvIiIiKhjFrsDhTWNyKs0Iq/KhPyq5ufCmkaPeSgvppRJER+mQXyY1h04DQptDqH0ASoOsRtgGEoRERH5CYdTwE9Fdfj2nDgv1A8FtR4NQ7lUgisSQnHVMD2mD4vAmLgQfttIRERErZhtDhRUm5BXKfZyaur5lFdlRHFtY4fD7NQKKRLCApCo1yIxPAAJ4QFIDNciQR+A6GD2dCJPDKWIiIj6seLaRnybVYFvzlVib3Yl6hptHvuH6AMw3RVC/SwpDEFqhZdqSkRERL6i0epAcZ14x7riWvFRVGtGUa0JBVUmlBjMHnNNXkyrlCExXAye3KFTuHgnu8gg9naizmMoRURE1I+YrHYcPF+Nb7Iq8M25CuRUGD32B6nlmJasx/Thelw1jBOUExERDTSCIKCyweoKmhrbeDaj2mi95HmCVHIk6gOQEN7U40nrXo8IVEEiYfBEl4+hFHVaYmIiHnnkETzyyCPergoR0YDhdAo4VWIQ54U6V4Ej+TWwOpzu/VIJkBqvw/RhEbhquB7jB+kg511niHwG209E1NPMNgdK6sxiyFTTHDi5ez7VmWG1Oy95ngClDHGuycRjXY84nQaDXSFUqFbB4Il6HUMpIiIiH1NS14i9WeJwvL1Zlai66NvMOJ0GVw2PwFXD9JiarEeIlkPyiIiI/IUgCKgyWt13rWs5mXhRjQmVDZfu5SSRAFFBasTq1IgL1YrPOg1iQ8RJxWN1GgSr5QydyOsYShEREXlZg8WOAzlV2JtdiW+zWg/J0yplmJocjunDIjB9mB5D9AFsRBIREfVjTqeAsnoz8ipNKKhucRe7ShMKqk1osNg7PF6jkLnDpTidBnE6tUdvp+gQNRTsOU39AEOpAeLVV1/F2rVrUVhYCKm0+ZfTTTfdhPDwcDzxxBNYvnw5Dhw4AKPRiJEjRyIjIwMzZ870Yq2JiPyT3eHE8cJafJsl9oQ6dqEW9ha3sZFKgLGDdJg+VI9pQ/VISwiFUs6GJVFfY/uJiC6H3eFEca0Z+U2hU6X4XFAt9oCydDDETiIBYkM0SAjXuh7iZOKDQrUYFKpBiIZD68g/MJTqCYIA2Ex9/7oKrfjbqhN++ctf4qGHHsKuXbtw3XXXAQCqq6uxfft2fP7552hoaMDcuXPx1FNPQaVS4Y033sC8efNw9uxZDB48uDevgojI7wmCgPOVRuzNqsS3WZU4eL4K9Rd9A5oQrsWVQ/W4ciiH5NEA4a32E9DpNhTbT0R0KRa7A4U1je5eTvlVRuRXm5BfZcKFapPHl04Xk0kliA/VIME1iXjLu9gNCtVArZD14ZUQeQdDqZ5gMwFPx/b96/6xGFAGdKpoaGgo5syZg7feesvdqHrvvfeg1+txzTXXQCqVYvz48e7yf/7zn/Hhhx/ik08+wbJly3ql+kRE/qyqwYK92ZX4zjUvVHGd2WO/TqvAtGSxJ9T0YXreJY8GHm+1n4BOt6HYfiIiAGi0OpDv6t3UNLdTUwhVXNcIof3cCUq5FAlhWnfw1BQ6JYRrEavTcIgdDXgMpQaQu+66C0uXLsXf//53qFQq/Pvf/8add94JqVSKhoYGrF27Fp999hlKSkpgt9vR2NiIgoICb1ebiKhfMNscOJRbje+yxd5Qp0oMHvuVMinSEkJx5TAxhBodGwKZlN3uqfs2btyI5557DqWlpRg/fjxefPFFTJ48uc2yNpsNGRkZeP3111FUVISUlBQ8++yzmD17trvM2rVrsW7dOo/jUlJScObMmV69Dl/H9hPRwFBvtrkmFW8dPpUZLB0eG6CUtdnbKSFci+hgNaT8/56oXQyleoJCK37j5o3X7YJ58+ZBEAR89tlnmDRpEr799lv83//9HwDg0UcfxVdffYXnn38eQ4cOhUajwe233w6r9dJ3diAiGoicTgEniw3iHfKyK3A4r6bV7ZdHRAdh+jA9rhwWgcmJYdAo2Q2fesa2bduwfPlybNq0Cenp6diwYQNmzZqFs2fPIjIyslX5VatWYevWrfjHP/6BESNGYMeOHbjllluwb98+TJgwwV1u9OjR2Llzp3tdLu/FpqK32k9Nr91JbD8R+QdBEFBrsrlDp5bhU36VqdWdbi8WrJZjiD7AHToNbhE+6QOVnN+JqJsYSvUEiaTTw+i8Sa1W49Zbb8W///1vZGdnIyUlBVdccQUA4LvvvsM999yDW265BQDQ0NCAvLw8L9aWiMi3CIKA3Eojvsupwr7sSuw/X4Vak82jTHSw2t0TamqyHhFBKi/Vlvzd+vXrsXTpUixZsgQAsGnTJnz22WfYvHkzVqxY0ar8m2++iSeeeAJz584FADzwwAPYuXMn/vd//xdbt251l5PL5YiOju6bi2D7iYh6QY3RitwqI/Jck4qLz+K6wdzxHe30gcoWw+w8ez7ptMo+ugKigYWh1ABz11134Re/+AVOnjyJ//mf/3FvHzZsGD744APMmzcPEokETz75JJzO9u8GQUQ0EJTUNWJfdhW+y6nE/pwqlFw0L1SAUoYpyeHiBOXD9EiOCOQ3pdTrrFYrjhw5gpUrV7q3SaVSzJw5E/v372/zGIvFArVa7bFNo9Fg7969HtuysrIQGxsLtVqNKVOmICMjgxN2g+0nIl9Ta7K6A6fcFqFTXpUJdY22Do+NCVFjcJgrdNJ7hk+BKv55TNTX+FM3wFx77bUICwvD2bNnsXDhQvf29evX495778XUqVOh1+vx+OOPw2AwdHAmIiL/U2O0Yv/5KuzLqcS+7CqcrzR67FfKpLgiQYepyXpMGxqOcYN0nKCU+lxlZSUcDgeioqI8tkdFRbU7/9OsWbOwfv16XHXVVUhOTkZmZiY++OADOBwOd5n09HRs2bIFKSkpKCkpwbp16zB9+nScOHECQUFBbZ7XYrHAYmmea8Vf2w5sPxH1vbpGm7uXU26lOMSuKYC6uKfyxWJC1EgI12KIPgCJ4QFIdD0nhGt5RzsiH8NQaoCRSqUoLm49f0NiYiK+/vprj20PPvigxzq7oxORvzFa7DiUV439OVX4LlucnLzlHXSkEmBsXAimDtVjanI4JiZwXijqn1544QUsXboUI0aMgEQiQXJyMpYsWYLNmze7y8yZM8e9PG7cOKSnpyMhIQHvvvsu7rvvvjbPm5GR0WpydH/E9hNR7zBZ7ThfYcT5SldPp0ojcl1zPFVfYo6nqGAVEsMD3PM8DdFrkagPQEJYAP+vJupHGEoREdGAYbU7cbSgBt/lVGF/TiWOFtTC7vS8j/OwyEBMc4VQ6UnhCNEovFRborbp9XrIZDKUlZV5bC8rK2t3PqiIiAh89NFHMJvNqKqqQmxsLFasWIGkpKR2X0en02H48OHIzs5ut8zKlSuxfPly97rBYEB8fHwXr4iI/JnTKaC4rlEMnyoacL7SiPMVRuRUNLQaFn+xyCCVq5eTGDgNcfV6SgjXQqvkn7JE/oA/yURE5LccTgGnig34LqcS32VX4vu8GjTaHB5l4nQaTBsajmlD9ZiSHI7IIHU7ZyPyDUqlEmlpacjMzMTNN98MAHA6ncjMzMSyZcs6PFatViMuLg42mw3vv/8+7rjjjnbLNjQ0ICcnB3fffXe7ZVQqFVQqTuhPRECDxY5cV9h0vqIBOa7wKbeyAWZb+3OthQUokaQXezwluofbiXM9BXCOJyK/x59yIiLyG2abAyeK6nDsQi0O51XjwPnqVhOe6gOVmJIs9oSalqzH4PDO3xqeyFcsX74cixcvxsSJEzF58mRs2LABRqPRfTe+RYsWIS4uDhkZGQCAgwcPoqioCKmpqSgqKsLatWvhdDrx2GOPuc/56KOPYt68eUhISEBxcTHWrFkDmUyGBQsWeOUaicj3OJwCimsbXcGT0f18vrIBZQZLu8cpZBIkhAcgSR+ApIhAJEc0P/OudkQDG0MpIiLql5xOAecrG3C0oBbHC2tx7EItzpTUtxqOF6SSIz0pDFOT9Zg6NBwpUUG8Qx71e/Pnz0dFRQVWr16N0tJSpKamYvv27e7JzwsKCiCVNk/CbzabsWrVKpw/fx6BgYGYO3cu3nzzTeh0OneZwsJCLFiwAFVVVYiIiMCVV16JAwcOICIioq8vj4gukyAIsDkEWB1OWGwOWOxOWO3OFs+OVssty1jsjuZlmxOlhkbklIvzPVnt7fd60geqkBQRIIZO+kDXciAGhWog541BiKgNDKWIiKhfqKi34NiFWhy7UIPjF+pwvLAW9WZ7q3L6QBVS43WYMFiHqcnhGBsXwoawP3PYAEu9+AiJB6QD57NetmxZu8P1du/e7bE+Y8YMnDp1qsPzvfPOOz1VNSK/4nQ2hTstAxwHzLbWIY8Y4niGPA6nE3anAEeLh9397GxjmwCHQ3x2Ck3bnbA7mvc7BcG9bnM4W7yeK2xyOD1u3NGTlDIphugDkBTherjCp6SIQM7DSERdxlCqm4Te+i0/gPA9JKL2NFodOFFch2MFtThWWItjBbUoqm1sVU6tkGJcnA7j40OQGh+K1ME6xIao2RPK19mtgLWhOUxquXzxunvZ9Wytb163NgD2FpPkPp4PaHReuyzqHP7/f3n4/rXmdAowWu0wWR1osNhhtNhhtDjEZ2vzcoPFDpPVjgbXuslqh9nWcahksTtgc/T/91wpk0Ill0Ipb/ksg0ohFfc1PbfaJoNSLkVEkKsHlD4QcaEayKT8f5aIegZDqS5SKMT032QyQaPReLk2/ZvJZALQ/J4S0cDkdArIqWjA0Qu1OH7BNQyvtB6Oi4bhSSTinfHGD9IhdbAOqfE6pEQFsReUrxAEwFwL1OQDtfmu5wJxub6kRejUADjan3ek2+RqwGpkKOXDZDLxFu1Wq5VtqMvgz+0nQRBQ2WBFVlk9ssobUNVgcQdIYrjkCphcyw0WB0yuMKqvSCSA+qLgRiWXQeUKe1RymTv4USlkUMqkUMgkkEolkEslkLmfpR7rrfbLpBeVb/9YmVQClULmETq1rIdSJoV0IIRIggA4rOL/BdYG15cXrmWrEbA1ir1ppXJAqnA9ywCZosU2mbgsa7HfXVYOyOStj/eFL8KcTsBpB5w2sQex09787LQBjpb7HC2Wm/ZdtF8mF/9fbXoo1BetawC5CpBr+raHsiAAdov4hZT72ey5bnOtCw5ApvR8yJuWVeJnLFe1LjOAelz7CoZSXSSTyaDT6VBeXg4A0Gq1/Ea+iwRBgMlkQnl5OXQ6nbuRSkQDQ73ZhgPnq3HsQg2OXajFjxfqUG9pPQwvMkgchjc+XocJ8TqMHRSCILX//RHWr1gamoOmlqFTUxBlMXTtfHINoAoCVIGAMhBQBYvLqiDXumubezkIUAZddIxrXcZ/G75OLpdDq9WioqICCoXCY84rujR/az/VGK04V1aPc+UNOFdaj3OuIKraaO32OaUSIEAlR6BKjgCVHAFKmfjcYjlQJYdWKUeASlzXeIQ5MlfI1CJocodP4rpcKmHbvyfZreIXGo01rhCp3hUkGV29Y40tgqUGz3VLQ+v9ztbtiV7XFFhJ5YBEBrj/ebT4d+Lxb6at7V0o63S4QiR7c7AktD/PV6+TKjxDKrmqjRDronWZUgwQOwqV2gudev165O2HVu2FWnK1uE+uFvfJWzw81tVtlG9ab6O8zHUTAEEQQzanQ/ysPZadnts91puWHRctC57bg+OAsCG9/962g6FUN0RHRwOAO5ii7tHpdO73koj8lyAIOF9pxNeny/H1mXIczqtuNRm5RiHD2EEhmOAKoVLjdYjhMLy+Z7cAtReA2ry2QydT1aXPERAJhCYAugRAN1hcDh4EqIM9wyZlkPhNLA0YEokEMTExyM3NRX5+vrer02/1t/aTwWxDVlk9zpU1iCGUa7mivu0ekxIJMDhMi2GRQYgJUbuCJBm0yhZhkytQCnBt06pkCFTJoZJL+f+Gt9gtYrB08cNU3cb2aqCxVly2NvROfeQaQBngerj+35GrXX+Y21uEOi2Wna5eQu4eRm082tLRPm+StOwF1tT7S+Hq7aXw7BHWtK+pp5jTflFAZPZcd7a4s7HTBlhsQC90gr7EBbYIw1qGXq51qUz8LB0W8dluabFuFQNRh9XzWoDmz9PW9qv6pem/B65b7bWXZ2uwG5oaVZGRkbDZBtK/1p6jUCj6/Td8RNQ+i92Bg+er8fWZcuw6W478KpPH/iH6AExODHMHUMOjAjkMr6c57GLPJXNdx4/GalcQ5Rpmdylq3UWhU6K4HJogTjSu1Pb2lVE/plQqMWzYMFit3e8NM5D5cvvJaLEju7wBZ8vqPUKokrr2ezbE6TQYHhWI4dFBGB4ZhOFRQRgaGQiN0jev0eucDvGPaIdrmFXTH9QOW/s9Ipwte1W01buik/usxjaCptrmbTbjZVyYxPXFRUhzkNTUG7YpVGr53N4+lWtZEdA7X3o09S5xtgitWoVaTT2WBM/jmlfa2N7Fsu6wyTW00CNsajm0sBfbVQ67GO60F1rZG8UQyNbYxj6z+G+3qUdQm8GSuo19LXpiydXidfdECO10ip9de6FVh6GWxdWDy+K53Grd3KJnmLXjst2a4kDiGkoqdfXWk7ZYb1pub7sU0IZf/vt4GRhKXQaZTOazDQMior5WZjBj15lyZJ4px3fZlR5zfChlUqQnheGalEhcOyISifoAL9a0n3A6XMFR7aWDJfejRQhlre/e6yoCWvd0agqddIMBdUiPXiYNPFKpFGq12tvVoG5yOgXklNfjVGE5zpfUIL+8BgXltagy1EMJO5SwQQUblBI7hsKOUVIbIrUSDA6WIS5YhrhAKaICJIjQSqGCvfmPsDIrUGzx/EOw5R/oFw9zkki6sew6vs1lNG9rY7H1vo6Oa7EsCM3DrJoCJWfTst0zWGoZODkvWndY4RFS+CKJVPziQhPa/NCGea67H2HiHICaUPH/FWk/+JtKIhGDH/bybX4flH7QnpNKAakrIPMFTXOjNQVYQNtBkkTWYrl/9xDlTxQREXWLwyngeGEtdp0Rh+WdLPacTygySIVrR0TimhGRuHKoHgGqAfhfjtPp6q1U2/yNclvLTfNpuJdruz4/U3sUAWKDv9Uj2PWsA0IGNQdP2vB+37ghoh5ia4SpNAsF546jpuAEnBVZCDblIUEowTCJZw9YdPT3nB1AtetBPUcic03M7OoV4/FHaovlpl4SHr0lpJ7b2upV4X6Wiv+XtAqZdJ4hkyqYk0QTXS6JpHlOqQFiAP6FQERE3VXXaMO3WRX4+nQ5dp+r8JiQViIBxg/S4doRYm+o0bHB/jm3h6kaqM4FqnOA+tI2AqUWy+a6y598tN1Q6VIPnRg8cQJwIuqIIACGIqAyC0JlFhqKz8BUfAbKuvMIsZZCCwEjLj7mol/tTqkCEpkSkpaT8zZN2OsxGfCl9qlaTCSsFEORlsOWPIY2uere1HuozXJCO+UuPgae5S9rH1qTyV3X4xpqJVO6npuGXbVYbwqZWm1TtF5nAEREfoChFBERtUsQBGSXN+BrV2+o7/Nr4GgxSXmQWo6rhkfg2pRIzEiJgD7QT77VaawBqs4D1efF8Kkqp3m5sabr55OrXUMUdOI3y+7hDW0t65rLqkPEP9CIiC6XpQGoygIqs13PWXBWZkGoyobM3ghAzJqCXI8mdYIWhdJBaAgaAnnkMOgTxiB26FgoQuPdd46S+uMXEERE1CcYShERkQezzYED56vc80MV1jR67B8aGSgOy0uJxMTEUCj66wTljbWuwKmN8KnxEmNMgmKAsCTxFrrthkstlhWcP4eIepnNLN4h01QFNJQBVdlAZVZzEFVf3OqQpt/eNkGGAiES54UY5CMW5pAkBMaORNzQcRgzPAmjdbyBARER9Q6GUkREhPwqI3afrcCecxXYn1OFRluLScrlUkxJCncPy4sP60d/nJjrWvRyOu9adq2bqjo+NjBaDJ7Ck4CwZNey69kfJvYkIt9lt4rheFPI5H5Ut7Hseu7E3c8qhWCcF2Jw3hkjPguxqFYPRmTCCKQmRiAtIRTT40KgVvSDSaeJiMgvMJQiIhqAGq0OHMitwp6zFdh9thx5VZ4T1kYHq3GNK4SaNjQcWqWP/ndhtwJ1F4DafKAm/6LnvE4ET1EtAqcW4VNYknhraSKiy+V0isN+TZWAsVJ8dgdLNW0HT928e6ZTIodRFoIaSTDOWiNwzhGN885YnBdikCPEoF4SiJSoIFyREIq0waFYmBCKhHCtf87/R0RE/YKP/pVBREQ9SRAEnK80iiHUuQocPF8Fi715Am65VIKJiaG4OiUSM4ZHYER0kG/8keJ0AIZioLag7eDJUIxL3iI7INLVwykZCBviuawK6vhYIqKLOWxieNQUMBkrPddNVYCxqnlfY3X3bnggkQKaMAjacFiUOjRIQ1AjCUKFIxClVi0KzGrkGFW4YNaiGkGoEYJQDw1azkIeqJJjwhAdrhocit8lhCJ1sA7Bat78gIiIfAdDKSIiP2W02LE/pwq7z5Vjz7kKXKj2nBsqNkSNGSmRuDolAlOTwxHkjT9UBAEwVjSHTBcHT3WFgNPW8TnkGkA3GAhNAHQJrufB4nJYkngHOiKiSzFWAuWnAEOJZ6+mlgGTqVIcFtwd6hBAqwe04UCAHtCGAdpwODXhqJcGo8wRgGKLFnmNamQ3qJBVJ8WFGjNKisytb/Z2kbAAJZJCNRgUpkV8qBYJ4VqkxuswPCoIMqkPfMFARETUDoZSRER+QhAEZJU3uHpDleNwbg2sjuZv55UyKSYPCcPVKRGYMTwCQyMDe743lN0KmGvFScTNteKQlbaWTVWu3k8FgM3UwQkh3ho7JP6i4CmxOYAKiAB8oVcXEfUPVhNQcRooPw2UnQLKT4rPxvIunETiCpX0roCpKWhqf90qyPBTUR2OFtQgr8qIC1WNuJBtQmFNI6z2lj2pLK5HM41ChvgwDeJDtYgPcz1CNe7lQBWb9ERE1D/xfzAion6s3mzDd9lV2HOuHHvOVqC4zuyxPz5Mg6uHi72hpiR3cm4op0PsCeAOkVzPjTXNgZM7aKr1DJw6MdFuaxIgOLZ1L6emACo4FpBy0l0i6iKHXbypQfkp8VF2UnyuzkW7w36bAm+PQKll+OR61oRe8veSwWzDD/k1+P54DQ7l5eL4hVqPYdMtyaQSxOrUYugUqhUDKHf4pIU+UOkbQ6qJiIh6GEMpIqJ+RBAEnC6px55z4gTlR/JrYHc2/3Glkkvxs6Rwd2+oIfqAjv+QsRrFP9RKjgOlPwIlP4p/tDmsl1dRdQig1gEanfjHW6vlUCBkkPgHYMggQK66vNcjooFLEID6Elfw1CKAqjgLOCxtH6PVA1GjgMjRrudRQMSIy7rBQZnBjMN51TicW43DeTU4U2qA86LsKzxAibSEUAyLCkR8qBaDXcFTTIgacpm0269NRETUXzGUIiLycQ6ngMN51dh+ohQ7Tpai5KLeUEn6AFw1PAJXp0TgZ0nh7d/K21TdHDw1hVBV2e1PwKsIEMMjja5FqKRrDpUuXm4KnNQh7NlERL3DXCcOu7s4gDLXtl1eoRXDposDqMDIy6qGIAjIqWjA4bwaMYjKq241bx8AJIRrMTEhDJOHhGJiYhiSLvVFARER0QDDUIqIyAdZ7U58l1OJHSdK8dWpMlQZm3suaRQyTE1u6g0VicHhWs+DBUG8K13L3k+lPwJ1F9p+scAoIHocEDOu+Tl4ECBX9uIVEhF1gc0MvDQJqCtoe79ECoQPFQOnqNGu51GALhGQXn4PJJvDiRNFdfg+rwaH8qrxfV41akyeN2GQSoCRMcGYlBjmeoQiMlh92a9NRETkzxhKERH5iEarA3vOlWP7iVJkni5HvcXu3qfTKvDzkVGYPSYa04bqm3tDOZ1AZVbrAMpU1faLhCY2B08xqeJyUFSvXxsR0WVRqOGeByootrnHU1MApR/uKtMzGix2HC2ocQ/FO3qhBmabZ69SlVyKCYN17hBqwmCdd+5iSkRE1I8xlCIi8iKD2YavT4tB1O5z5R5/9EQGqTBrdDRmj4nG5MRQKMzVQG0+8NOXzQFU2QnA2tD6xBIZEJECxIxv0QtqrDi0joioP7rrP0BQtDhUuBcIgoCXvs7GjlOlOFXcej4onVbhMRRvTGwIlHLOA0VERHQ5GEoREfWxqgYLvjpVhu0nS/FddiVsDgGAAB0aMC3EgLmDrEgPbUAcKiCpKwB2FAC1BYDN1PYJ5Rqxt0DL4XeRowCFpk+vi4ioV0WO7NXTny2rx/9+dc69PihUg8mJYZiYKAZRSfpASKWcD4qIiKgnMZQiIuoDJbUm7DqWhR9P/oi64mzEogIzJJW4S1qBoaoqxKECSqcJsADIae8sEiAoBghP9uwBFT4MkPHXORHR5SiuFScqT4oIwL9/lY6YEAb7REREvY1/xRAR9RRLA1CTK/Zqqi2AoSQbVUXZQE0Bwu0lWChpxEIAuHjKkZbTlARGA7rBQGiC+Ox+JAAhgwC5qu+uh4hoACkzWAAAieEBDKSIiIj6CEMpIqKucNjFuz9VZgNVWUBVtjjReFUOUF/sUTTY9QAAuEZ8mJThkIUmQKVPvCh4SgBC4nt0ol4iIuq8MoMZABAVzPCfiIiorzCUIiK6mCCId6+rdIVOVVmuECobqD4POG3tHlotBKJAiEShEIFiREAZnojBySMxfsw4hMcNhVap7cMLISKizmrqKRUZxC8HiIiI+gpDKSIauGyNYg+ni4OnqizAXNfuYYJcDVNgAvIQi0OGcPxk1uO8EIvzQjTM8mBcNUyP2WNicMfISOi0yj68ICIi6q5yd08phlJERER9haEUEfkXpxOw1gNmA2AxiM/mOnHZVA1U5zT3gKorBCC0cyKJOJxOPxQIHwqbLhknLJH4vDgA72cLqC51uEsGKGW4ZkQkfjUmGlenRCJQxV+tRET9TVk9h+8RERH1Nf7lRES+w+kErA3NYZLHc11zuNRqX4vgyVKP9oOmNqh1gH4YEC6GT+7lsCQ0OBXYdaYc20+WYveBchitDgB2AECoVoGfj4rC7DHRmJqsh1oh6413hIiI+ki5a/gee0oRERH1HYZSRNQ+QQAEJ+B0AA6rONzN3ig+Nz1arZsBmwmwuZ7bXG/reLPYw0lwXrpenSFTAqpgQB3c/KzWAWFJLcKnYYA2DJBI3IdVG63YeboMO744gW+zK2G1N9cnOliN2WOiMWt0NCYlhkIuk/ZMXYmIyKvsDicqG1xzSrGnFBERUZ9hKEXUH9gtQEMZUF8GNJQC9aWuddeyubY5PBIcYpjkdIjbBEeLZafndo/1lse7lrvS46gnSeWAOuSiUKnFeqt9wYAqxHNfF+5iV1LXiC9PlmH7iVIczK2Cs8VlD9EHYNboaMweE41xcSGQSiXtn4iIiPqlKqMVTgGQSSUID2AoRURE1Fd8IpTauHEjnnvuOZSWlmL8+PF48cUXMXny5DbL2mw2ZGRk4PXXX0dRURFSUlLw7LPPYvbs2d0+J5HXWI2eAVNDGVBf0iJ8cj031ni7ps1kKkChaX7INRetqwGFVgyFFNqL1jVtbGta1wCqIFegpPHovdQbciuN2H6iFDtOluLYhVqPfaNigjF7jBhEDYsMhKSX60JERN5V5prkPCJQBRm/fCAiIuozXg+ltm3bhuXLl2PTpk1IT0/Hhg0bMGvWLJw9exaRkZGtyq9atQpbt27FP/7xD4wYMQI7duzALbfcgn379mHChAndOidRj7FbxV5LjbWuOZBqxUCpvcDJWt/5c8uUQGCU+AiKFh+B0UBQFKAJFXsXSWSAVApIpOKyRApIZS2WO7lPKruonFR8fbla3NfP2B1ONFjsKKxpxJenyrDjRCnOljW/9xIJkDY41D00Lz5M68XaEhFRXyszcOgeERGRN0gEQfDS+BxReno6Jk2ahJdeegkA4HQ6ER8fj4ceeggrVqxoVT42NhZPPPEEHnzwQfe22267DRqNBlu3bu3WOS9mMBgQEhKCuro6BAcH98RlUn9ia3SFSrXNz+a61tva2mczdf31FNrmoCkwCgiKEYOmpsAp0BVAaUJ7vfeQLxIEAUarA4ZGG+rNdtSbbTCYxWWD2X7RdvG5/qLt4gTlnuRSCaYkh2PW6GhcPyoKkZzYlojawXZB5/XX92rrgXys+ugEZo6Mwj8XT/R2dYiIiPq9zrYJvNpTymq14siRI1i5cqV7m1QqxcyZM7F///42j7FYLFCrPf941Gg02Lt372Wd02KxuNcNBkO3r4l8nM0MVJ4DKs4A5aeAinOAsaJFr6ZawGG51FkuTRUCaELEibU1uot6OMV4hlCqIJ8LmwRBgMXuhMnqgNFiR6PNAYvNCbvTCbtTgM3hhMMpwO4Ql+1OQXw4nOI2p7jf5nBtc5W1O52wOQQ4XM92p9O1XXD1ZnK4A6d6sw2GRhsaLHaPOZ4uR4BShmlD9Zg9JhrXjYhCiFbRMycmIqJ+rdw1fC+KPaWIiIj6lFdDqcrKSjgcDkRFRXlsj4qKwpkzZ9o8ZtasWVi/fj2uuuoqJCcnIzMzEx988AEcDke3z5mRkYF169b1wBWRz3DYgKocV/DkCqDKTwPV5zt3dzeJVAyU1CFiqNQULrmfQ9rY1rQ9pE+HuDVaHahrtMFktcNkdbgezcuNVjuMFy03XlSm5TFN+3oqCOopcqkEwRoFgtRyBKnlCFY3LStaLIvbgzXi9ovLKeW8Wx4REbXWNHwvir1miYiI+pTX55TqqhdeeAFLly7FiBEjIJFIkJycjCVLlmDz5s3dPufKlSuxfPly97rBYEB8fHxPVJd6m9MB1OSJgVP5aaDC9VyZBThtbR+j1gGRo4DIkeIjKLp1yOSF3kuCIKDeYket0YZqkxU1JitqTVZUG22oda3XGG3is8mGGqO4zWLvRMh2GVRyKbRKGVRyGeQyCRQyKeRSCWRS17JMAoVUCplU4t4v7pNALhX3y6USyGVSKFzPcldZuVTavE8mgVYpbxUoBbsCJbVCygnHiYioV5TVs6cUERGRN3g1lNLr9ZDJZCgrK/PYXlZWhujo6DaPiYiIwEcffQSz2YyqqirExsZixYoVSEpK6vY5VSoVVCo2QnyaIAB1F4DyFr2eKk4DFWcBu7ntY5SBYugUMcIVQrmeA6P6LHCy2B04WWxAVcOlQ6ZakxX2bnZPkkkl0Cpk0ChlCFDJoVHIEKCSQaOUQ6uQQauSQauUQauUu57bXtYoZQhosaxVynkXIiIi8nvl7onO2VOKiIioL3k1lFIqlUhLS0NmZiZuvvlmAOKk5JmZmVi2bFmHx6rVasTFxcFms+H999/HHXfccdnnJB9iaQBOfgBcOOQafnem/TvVydVARIoYOLUMoELivTJXU7XRil1nyrHzdBm+OVfR5iTbHdEoZAjVKqDTKhEWoIROq0CoVonQACVCXcs6rQJhAUr3cqBKzl5ERERE3VTe1FMqiKEUERFRX/L68L3ly5dj8eLFmDhxIiZPnowNGzbAaDRiyZIlAIBFixYhLi4OGRkZAICDBw+iqKgIqampKCoqwtq1a+F0OvHYY491+pzkwyrOAYf/CRx/G7BcNOG8VAHohzUPu4twPYcm9ukcTm3JrTTiq1Ol2HmqHN/nV3vMx6QPVCIuVOsZKGmV0LlCpjCtEjqtEqEB4n61wrvXQkRENJDYHE5UNlgBcPgeERFRX/N6KDV//nxUVFRg9erVKC0tRWpqKrZv3+6eqLygoABSafPkxGazGatWrcL58+cRGBiIuXPn4s0334ROp+v0OcnHOOzAuS+AQ/8Acvc0bw9LAsbcBkSNFgOo8GRA5ht3S3M4BRwtqMFXp8uw81QZciqMHvtHxQRj5qgo/HxkFMbEBbMXExERkY+qqBeH7ilkEoRqlV6uDRER0cAiEQTBx+6x5X0GgwEhISGoq6tDcHCwt6vjvxoqgB+2AN9vAQyF4jaJFBg+G5h0H5B0LSD1nbulGS12fJtViZ2ny/D1mXJUG63ufQqZBD9LCsfPR0XhupFRiNNpvFhTIiLqSWwXdF5/fK+OFtTglr/vQ2yIGvtWXuft6hAREfmFzrYJvN5TigYYQQAKDwOHXgVOftR8hzxNGJC2GEhbAoQmeLWKLZUZzNjp6g31XU4VrC3udBeiUeDaEZGYOTIKVw3XI0jtG724iIiIqPPKOMk5ERGR1zCUor5hNQEn3hOH6JX+2Lw9Lg2YtBQYfQug8H5jUBAEnCmtx85TZfjqdBl+LKzz2D84TIufj4rCzJFRmJgYCoXMd3pyERERUde5JznnfFJERER9jqEU9a6qHOD7zcDRrYC5VtwmUwFjbwcm/QqIu8Kr1QMAq92JQ7nV2Hm6DF+dKkNRbaN7n0QCTIjXueeHGhoZyPmhiIiI/EiZoSmU8v6XY0RERAMNQynqeU4HkPUVcPgfQPbO5u26BHGuqAl3A9ow79UPYo+oA+er8fahAuw6U456i929T62QYvqwCPx8ZBSuGRGJiCB+c0pEROSvmobvMZQiIiLqewylqOeYqoEf3hB7RtXmN28f+nNg8lJg6ExAKvNe/QA0Wh34+FgRtuzLw5nSevd2faAKM0dG4uejojBtqB5qhXfrSURERH2j3HX3vUh+CUVERNTnGErR5Sv6ATj8T+Cn9wCH2LCDOkTsETXxXiA82bv1A1BU24g39+fjncMFqDWJk6trFDLcekUcbksbhNRBOkilHJZHREQ00JRz+B4REZHXMJTyJw47UHIcyPsGKP0JkEgBqQKQuR4tl2VKQCpvZ7mdYy4+vugHcYhe0ZHmOkSPE3tFjbkdUGq9915AHKJ3MLcaW77Lw5enSuEUxO3xYRosnpKIX6bFI0TLO+YRERENZJxTioiIyHsYSvVnTidQ9hOQ+y2Q9y2Qvw+wGPq+HjIlMOpmMYwaNEmcHdyLzDZxiN5r33kO0Zs2NBz3TB2Ca0dEQsZeUURERAOexe5AjasHNe++R0RE1PcYSvUnggCUnxYDqNxvgLy9zXe0a6IOARKuBOIniWGRwyY+nDbAYXUt25uX3fsuLmf3XHZYXestljWhQOpdwBWLgcAIr7wlLRXVNmLrgXy8faj1EL3FUxMxPCrIyzUkIiIiX1LumuRcKZciRMPe00RERH2NoZQvEwSgKlsMoJpCKFOlZxllIJAwFUicDgyZLg6f8/Jk4n1JEAQcyq3Gln15+PJUGRyuMXqDQsUhendM5BA9IiIialt5vTh0LzJIBYmXe3oTERENRAylfIkgADV5zT2hcr8FGko9y8g1wOCfiQHUkBlATCogG3gfo9nmwCfHivHavjycLmkesjg1ORz3TE3EdSOjOESPiIiIOlTm6inF+aSIiIi8Y+ClGb6mrlAMn3K/EcOougue+2UqIH5yc0+ouDRAPnDnPCiubcSbB/LxzqEC9xwQaoUUt14xCIunJCIlmkP0iIiIqHOaJzkfuG0rIiIib2Io1dcaKoDcPa7Ht0BNrud+qRyIm+jqCXWVOHG4QuOduvoIQRBwOK8GW/blYsfJ5iF6cToNFk9NwB0T46HTKr1cSyIior61ceNGPPfccygtLcX48ePx4osvYvLkyW2WtdlsyMjIwOuvv46ioiKkpKTg2WefxezZs7t9Tn/Q1FMqMog9pYiIiLyBoVRfO/428NWTzesSKRA7wdUT6ipxaJ4ywHv18yFmmwOfHC/Glu/ycKrFEL0pSeG4Z1oiZnKIHhERDVDbtm3D8uXLsWnTJqSnp2PDhg2YNWsWzp49i8jIyFblV61aha1bt+If//gHRowYgR07duCWW27Bvn37MGHChG6d0x80zSnF4XtERETeIREEQfB2JXyNwWBASEgI6urqEBwc3LMnLz4GfPKQGEANuQoYPAVQ9/Br9EP1ZhvOlNbjVLFBfJQYcLasHla7E4A4RO+WCeJd9EZE8/0iIqK+06vtgm5KT0/HpEmT8NJLLwEAnE4n4uPj8dBDD2HFihWtysfGxuKJJ57Agw8+6N522223QaPRYOvWrd06Z1t88b3qyP/88yD2Zldi/R3jcesVg7xdHSIiIr/R2TYBe0r1tdhU4NffersWXiMIAsoMFpwqqcPJIjF8OlViQH6Vqc3ycToNFk1JwPxJHKJHREQEAFarFUeOHMHKlSvd26RSKWbOnIn9+/e3eYzFYoFa7dkbSKPRYO/evd0+pz9onlOKPaWIiIi8gaEU9Rq7w4nzlUZ3z6em52qjtc3yMSFqjIoJxqjYYPdzfKgWUg7RIyIicqusrITD4UBUVJTH9qioKJw5c6bNY2bNmoX169fjqquuQnJyMjIzM/HBBx/A4XB0+5yAGHZZLBb3usFgaLesL+JE50RERN7FUIp6RIPFjjMlnuHTmdLm4XctyaQSDI0I9AifRsYEIyyAPaGIiIh6wwsvvIClS5dixIgRkEgkSE5OxpIlS7B58+bLOm9GRgbWrVvXQ7XsW41WBwxmOwAgkj2liIiIvIKhFHVZrcmKYxdqcaKozh1C5bUz/C5AKcPIi3o/DY8Kgloh6+NaExER+Qe9Xg+ZTIaysjKP7WVlZYiOjm7zmIiICHz00Ucwm82oqqpCbGwsVqxYgaSkpG6fEwBWrlyJ5cuXu9cNBgPi4+O7e2l9qmmSc7VCiiAVm8RERETewP+BqUMWuwOnS+pxrKAGxwvrcOxCLXIrjW2WjQ5We4RPo2KCMTiMw++IiIh6klKpRFpaGjIzM3HzzTcDECclz8zMxLJlyzo8Vq1WIy4uDjabDe+//z7uuOOOyzqnSqWCStU/h76VGcRhh1HBakgkbKsQERF5A0MpchMEAflVJhy7UOt+nCo2wOpoPQRviD4A4waFYHRsMEbFhGBkTBDCA/tno5SIiKi/Wb58ORYvXoyJEydi8uTJ2LBhA4xGI5YsWQIAWLRoEeLi4pCRkQEAOHjwIIqKipCamoqioiKsXbsWTqcTjz32WKfP6W/c80kFcegeERGRtzCUGsBqjFYcK6zFsQIxgDpeWItak61VuVCtAqnxOoyP1yHV9eCd8IiIiLxn/vz5qKiowOrVq1FaWorU1FRs377dPVF5QUEBpFKpu7zZbMaqVatw/vx5BAYGYu7cuXjzzTeh0+k6fU5/0xRKRXKScyIiIq+RCIIgeLsSvsZgMCAkJAR1dXUIDg72dnV6hMXuwMliA4636AWV38Y8UEq5FKNjg93hU2q8DoPDtOzWTkREA5Y/tgt6S396rzI+P41XvjmP+64cgid/Mcrb1SEiIvIrnW0TsKeUn8qrNOKHghp3CHWqxACbo3X+mKQPEMOnwTqMH6TDyJhgKOXSNs5IRERE5D/cw/fYU4qIiMhrGEr5oS3f5WLtf0+12h4WoPToATV+kA4hWoUXakhERETkXS0nOiciIiLvYCjlh/ZmVwIAhkUGYvqwCKQO1mFCvA6DQjUchkdEREQEoKzeNacUJzonIiLyGoZSfqiwphEA8MQNI3F1SqSXa0NERETke8rdPaU4fI+IiMhbOHmQnxEEAUWuUGpQqMbLtSEiIiLyPQ0WOxosdgBAJIfvEREReQ1DKT9jaLSj3tXIitNpvVwbIiIiIt9T7prkPEApQ6CKAweIiIi8haGUn7lQYwIA6AOV0ChlXq4NERERke/hJOdERES+gaGUn2maTyoulL2kiIiIiNpS3jTJOeeTIiIi8iqGUn6mqJbzSRERERF1pJw9pYiIiHwCQyk/U+gavjdIx1CKiIiIqC1lrjmlGEoRERF5F0MpP1PIO+8RERERdaisXuwpFRnE4XtERETexFDKzzSHUpxTioiIiKgt7ClFRETkGxhK+Rn38D32lCIiIiJqUzlDKSIiIp/AUMqP1DXaUG+2AwDiGEoRERERtSIIAsrcE51z+B4REZE3MZTyI0WuoXthAUpolXIv14aIiIjI99Rb7Gi0OQAAkUHsKUVERORNDKX8CIfuEREREXWsaehekFoOjVLm5doQERENbAyl/AjvvEdERETUseahe+wlRURE5G0MpfwI77xHRERE1LHy+qZJzjmfFBERkbcxlPIjRbUcvkdERETUEXdPKc4nRURE5HUMpfxIU0+pOB1DKSIiIqK2lLnmlIrk8D0iIiKvYyjlRzh8j4iIiKhj5e45pTh8j4iIyNsYSvkJg9mGukYbACCOw/eIiIiI2tTUU4oTnRMREXkfQyk/UeTqJRWqVSBQJfdybYiIiKgtdrsdO3fuxCuvvIL6+noAQHFxMRoaGrxcs4GjjBOdExER+QymF36CQ/eIiIh8W35+PmbPno2CggJYLBb8/Oc/R1BQEJ599llYLBZs2rTJ21X0e4IguCc6j+RE50RERF7HnlJ+oqhGvPMeJzknIiLyTb/97W8xceJE1NTUQKNp/v/6lltuQWZmphdrNnDUNdpgtTsBABFB7ClFRETkbewp5Seae0oxlCIiIvJF3377Lfbt2welUumxPTExEUVFRV6q1cDS1EtKp1VArZB5uTZERETEnlJ+gqEUERGRb3M6nXA4HK22FxYWIigoyAs1GnjKm+aT4tA9IiIin8BQyk8U1orD9zinFBERkW+6/vrrsWHDBve6RCJBQ0MD1qxZg7lz53qvYgOIez4pTnJORETkEzh8z0+4e0qFsacUERGRL3r++ecxe/ZsjBo1CmazGQsXLkRWVhb0ej3efvttb1dvQCgzNN15jz2liIiIfIFP9JTauHEjEhMToVarkZ6ejkOHDnVYfsOGDUhJSYFGo0F8fDx+97vfwWw2u/evXbsWEonE4zFixIjevgyvabDYUWuyAeBE50RERL4qPj4ex48fxxNPPIHf/e53mDBhAp555hkcPXoUkZGR3q7egFDuDqXYU4qIiMgXeL2n1LZt27B8+XJs2rQJ6enp2LBhA2bNmoWzZ8+22UB76623sGLFCmzevBlTp07FuXPncM8990AikWD9+vXucqNHj8bOnTvd63K51y+11xS5ekmFaBQIUiu8XBsiIiK6mM1mw4gRI/Dpp5/irrvuwl133eXtKg1ITcP32FOKiIjIN3i9p9T69euxdOlSLFmyBKNGjcKmTZug1WqxefPmNsvv27cP06ZNw8KFC5GYmIjrr78eCxYsaNW7Si6XIzo62v3Q6/V9cTleUVjTNJ8Ue0kRERH5IoVC4dGrm7yjzDXReSQnOiciIvIJXg2lrFYrjhw5gpkzZ7q3SaVSzJw5E/v372/zmKlTp+LIkSPuEOr8+fP4/PPPW00QmpWVhdjYWCQlJeGuu+5CQUFBu/WwWCwwGAwej/6Ed94jIiLyfQ8++CCeffZZ2O12b1dlwCrnROdEREQ+pUtj2n788cdOlx03btwly1RWVsLhlsuomwAANPpJREFUcCAqKspje1RUFM6cOdPmMQsXLkRlZSWuvPJKCIIAu92OX//61/jjH//oLpOeno4tW7YgJSUFJSUlWLduHaZPn44TJ060ecvljIwMrFu3rtPX5muae0rxzntERES+6vDhw8jMzMSXX36JsWPHIiAgwGP/Bx984KWaDQxOp4Dyek50TkRE5Eu6FEqlpqZCIpFAEIQ29zftk0gkcDgcPVLBi+3evRtPP/00/v73vyM9PR3Z2dn47W9/iz//+c948sknAQBz5sxxlx83bhzS09ORkJCAd999F/fdd1+rc65cuRLLly93rxsMBsTHx/dK/XtDUS17ShEREfk6nU6H2267zdvVGLBqTFbYHGIbNiKQPaWIiIh8QZdCqdzc3B59cb1eD5lMhrKyMo/tZWVliI6ObvOYJ598EnfffTd+9atfAQDGjh0Lo9GI+++/H0888QSk0tYjEnU6HYYPH47s7Ow2z6lSqaBS9d/GSdPwPd55j4iIyHe99tpr3q7CgFZeLw7dCw9QQin3+rSqREREhC6GUgkJCT364kqlEmlpacjMzMTNN98MAHA6ncjMzMSyZcvaPMZkMrUKnmQyGQC024OroaEBOTk5uPvuu3uu8j6keU4pDt8jIiLydRUVFTh79iwAICUlBREREV6u0cBQZnBNcs6he0RERD6jS6HUJ5980umyN954Y6fKLV++HIsXL8bEiRMxefJkbNiwAUajEUuWLAEALFq0CHFxccjIyAAAzJs3D+vXr8eECRPcw/eefPJJzJs3zx1OPfroo5g3bx4SEhJQXFyMNWvWQCaTYcGCBV253H7BaLGj2mgFAMRx+B4REZHPMhqNeOihh/DGG2/A6XQCEL9YW7RoEV588UVotfxyqTc1TXIexUnOiYiIfEaXQqmm3kyX0pU5pebPn4+KigqsXr0apaWlSE1Nxfbt292TnxcUFHj0jFq1ahUkEglWrVqFoqIiREREYN68eXjqqafcZQoLC7FgwQJUVVUhIiICV155JQ4cOOCX30Q2zScVrJYjRKPwcm2IiIioPcuXL8eePXvw3//+F9OmTQMA7N27Fw8//DB+//vf4+WXX/ZyDf1bU0+pqCD2lCIiIvIVEqG9MW8DmMFgQEhICOrq6hAcHOzt6nRo15lyLNlyGKNigvH5b6d7uzpERER+p6faBXq9Hu+99x6uvvpqj+27du3CHXfcgYqKisusqff5chtq1Uc/YeuBAjx87VAsvz7F29UhIiLya51tE3CWx36usMYEgEP3iIiIfJ3JZHL3BG8pMjISJpPJCzUaWMpcw/c4pxQREZHv6NLwvYsZjUbs2bMHBQUFsFqtHvsefvjhy6oYdU7zJOcMpYiIiHzZlClTsGbNGrzxxhtQq8VgpLGxEevWrcOUKVO8XDv/V940fI+hFBERkc/odih19OhRzJ07FyaTCUajEWFhYaisrIRWq0VkZCRDqT7CO+8RERH1Dy+88AJmzZqFQYMGYfz48QCA48ePQ61WY8eOHV6unf9z95QK4kTnREREvqLbw/d+97vfYd68eaipqYFGo8GBAweQn5+PtLQ0PP/88z1ZR+pA0/A99pQiIiLybWPGjEFWVhYyMjKQmpqK1NRUPPPMM8jKysLo0aO9XT2/5nQKqGhouvsee0oRERH5im73lDp27BheeeUVSKVSyGQyWCwWJCUl4a9//SsWL16MW2+9tSfrSe1ouvseQykiIiLfp9VqsXTpUm9XY8CpMlrhcAqQSAB9oNLb1SEiIiKXbveUUigUkErFwyMjI1FQUAAACAkJwYULF3qmdtShRqsDlQ3iXF6DdBy+R0RE5MsyMjKwefPmVts3b96MZ5991gs1GjjKXPNJ6QNVkMt4nx8iIiJf0e3/lSdMmIDDhw8DAGbMmIHVq1fj3//+Nx555BGMGTOmxypI7SuqFYfuBankCNZc1pz1RERE1MteeeUVjBgxotX20aNHY9OmTV6o0cBRXt80yTnnkyIiIvIl3Q6lnn76acTExAAAnnrqKYSGhuKBBx5ARUUFXnnllR6rILXvgmuS87hQDSQSiZdrQ0RERB0pLS11t51aioiIQElJiRdqNHA0TXIeFcT5pIiIiHxJt7vXTJw40b0cGRmJ7du390iFqPN45z0iIqL+Iz4+Ht999x2GDBnisf27775DbGysl2o1MDQN34vkJOdEREQ+pduhVG5uLux2O4YNG+axPSsrCwqFAomJiZdbN7qEohpOck5ERNRfLF26FI888ghsNhuuvfZaAEBmZiYee+wx/P73v/dy7fybu6cUh+8RERH5lG6HUvfccw/uvffeVqHUwYMH8c9//hO7d+++3LrRJRTWiHNKMZQiIiLyfX/4wx9QVVWF3/zmN7BaxRuVqNVqPP7441i5cqWXa+ffyg1Nc0qxpxQREZEv6facUkePHsW0adNabf/Zz36GY8eOXU6dqJMK2VOKiIio35BIJHj22WdRUVGBAwcO4Pjx46iursbq1au9XTW/V+aa6DwyiD2liIiIfEm3QymJRIL6+vpW2+vq6uBwOC6rUtQ5nFOKiIio/wkMDMSkSZMQFBSEnJwcOJ1Ob1fJ75W7h++xpxQREZEv6XYoddVVVyEjI8MjgHI4HMjIyMCVV17ZI5Wj9pltDlQ2iA0s9pQiIiLyXZs3b8b69es9tt1///1ISkrC2LFjMWbMGFy4cMFLtfN/dofT3WaK5JxSREREPqXbodSzzz6Lr7/+GikpKViyZAmWLFmClJQUfPPNN3juued6so7UhqJasZdUoEqOEI3Cy7UhIiKi9rz66qsIDQ11r2/fvh2vvfYa3njjDRw+fBg6nQ7r1q3r8nk3btyIxMREqNVqpKen49ChQx2W37BhA1JSUqDRaBAfH4/f/e53MJvN7v1r166FRCLxeIwYMaLL9fI1VUYrnAIgk0oQHsBQioiIyJd0O5QaNWoUfvzxR9xxxx0oLy9HfX09Fi1ahDNnzmDMmDE9WUdqQ9PQvTidBhKJxMu1ISIiovZkZWVh4sSJ7vWPP/4YN910E+666y5cccUVePrpp5GZmdmlc27btg3Lly/HmjVr8MMPP2D8+PGYNWsWysvL2yz/1ltvYcWKFVizZg1Onz6Nf/3rX9i2bRv++Mc/epQbPXo0SkpK3I+9e/d2/YJ9TJlrkvOIQBVkUraZiIiIfEm3774HALGxsXj66ad7qi7UBbzzHhERUf/Q2NiI4OBg9/q+fftw3333udeTkpJQWlrapXOuX78eS5cuxZIl/7+9+w+uurrzP/66NyE3CeaHGpIQpES0xqUGsLHkC2jbkQwBnRS2O5Qf8rMChYGZ1rSuoPxYS5V23WXZ7SA4TkDcbiuyS9udhUExNWwVkN3EroViRgTFRHJDovltft37+f5B7oVrbpKbkNzP53PzfMzcGe/nns/hHA6JZ973fd6fFZKkPXv26PDhw9q7d682bNjQrf2JEyc0ffp0LVq0SJKUmZmphQsX6p133gloFx0drfT09H6Nxerc/npSZEkBAGA1A86UkqQ//vGPWrx4saZNm6bKykpJ0r/+679GxLdqVseT9wAAsIdx48aptLRUklRTU6OzZ88GPMG4qqpKSUlJIffX3t6u0tJS5eXl+a85nU7l5eXp5MmTQe+ZNm2aSktL/Uf8Lly4oCNHjuihhx4KaPfBBx8oIyND48eP1yOPPKJLly6FPC6r8mVKpVLkHAAAyxlwptR//Md/aMmSJXrkkUdUVlamtrar30LV19fr2Wef1ZEjRwZtkOiOJ+8BAGAPy5Yt07p163T27Fn94Q9/0N13362cnBz/5ydOnOhX6YOamhp5PB6lpaUFXE9LS9P7778f9J5FixappqZG999/vwzDUGdnp9asWRNwfC83N1cvvfSSsrKydPnyZT399NN64IEHdObMGSUkJATtt62tzb8HlKSGhoaQ5xEu1V1BKTKlAACwngFnSv3sZz/Tnj179OKLL2rEiGuFtqdPn66ysrJBGRx6xvE9AADs4W//9m+1atUqHTp0SLGxsTp48GDA52+//bYWLlw4pGMoKSnRs88+q+eff15lZWU6dOiQDh8+rG3btvnbzJ49W/PmzdPEiROVn5+vI0eOqK6uTq+++mqP/W7fvl1JSUn+19ixY4d0HgPhP76XQKYUAABWM+BMqfLycn3zm9/sdj0pKUl1dXU3MiaEoNJX6JygFAAAluZ0OvXTn/5UP/3pT4N+/uUgVV9SUlIUFRUlt9sdcN3tdvdYD2rz5s1asmSJVq5cKUnKzs5Wc3OzVq9eraeeekpOZ/fvKZOTk3XXXXfp/PnzPY5l48aNKiws9L9vaGiwXGDK3eg7vkemFAAAVjPgTKn09PSgm5S33npL48ePv6FBoXetHR5VN1791o/jewAADC8xMTHKyckJeGKf1+tVcXGxpk6dGvSelpaWboGnqKgoSZJhGEHvaWpq0ocffqjRo0f3OBaXy6XExMSAl9VUd2VKUVMKAADrGXBQatWqVfrhD3+od955Rw6HQ59++qn+7d/+TT/+8Y+1du3awRwjvuTTuqtZUvExUbo5fkQfrQEAQKQpLCzUiy++qP379+vcuXNau3atmpub/U/jW7p0qTZu3OhvX1BQoN27d+uVV17RxYsXdezYMW3evFkFBQX+4NRPfvITHT9+XB999JFOnDihv/7rv1ZUVNSQHy0catVdmVIc3wMAwHoGfHxvw4YN8nq9mjFjhlpaWvTNb35TLpdLjz/+uD81HEPj+ifvORwOk0cDAADCbf78+bpy5Yq2bNmiqqoqTZ48WUePHvUXP7906VJAZtSmTZvkcDi0adMmVVZWatSoUSooKNAzzzzjb1NRUaGFCxeqtrZWo0aN0v33369Tp05p1KhRYZ/fYOnweFXT1C6JQucAAFiRw+gpZztE7e3tOn/+vJqamjRhwgS98MILeu6551RVVTVYYwy7hoYGJSUlqb6+3pJp6L9+55Ke/O2f9eDdqdq7/BtmDwcAgIhm9X2BlVjt7+rTui807ed/0Igoh8q3zZbTyZd5AACEQ6h7gn4f32tra9PGjRt13333afr06Tpy5IgmTJigs2fPKisrS//8z/+sxx577IYGj95V1l198t6YZIqcAwAA9MTd0FXkPCGWgBQAABbU76DUli1btHv3bmVmZurixYuaN2+eVq9erX/6p3/SP/7jP+rixYt64oknhmKs6HL98T0AAGBvn3zyib7//e+bPYyI5PYXOefoHgAAVtTvoNTBgwf18ssv69///d/1+uuvy+PxqLOzU//3f/+nBQsW+ItlYuhcC0rx5D0AAOzus88+0/79+80eRkSiyDkAANbW70LnFRUVysnJkSTdc889crlceuyxxyi4HUYVn189vkemFAAA1vef//mfvX5+4cKFMI1k+PEd36PIOQAA1tTvoJTH41FMTMy1DqKjddNNNw3qoNCztk6PPxWdoBQAANY3d+5cORwO9fZsGb7cGxrXju+RKQUAgBX1OyhlGIaWL18ul+vqN06tra1as2aNRo4cGdDu0KFDgzNCBLhcd/Ubv9gRTt0yMqaP1gAAwGyjR4/W888/rzlz5gT9/E9/+pM/Cx2Dq7qxKyiVQKYUAABW1O+g1LJlywLeL168eNAGg75dX0+Kb1UBALC+nJwclZaW9hiU6iuLCgNX7T++R6YUAABW1O+g1L59+4ZiHAgR9aQAALCXxx9/XM3NzT1+fuedd+rNN98M44iGDzdBKQAALK3fQSmY61qmFEEpAADs4IEHHuj185EjR+pb3/pWmEYzfLR1evR5S4ckCp0DAGBVTrMHgP65likVb/JIAABAKC5cuMDxPBNUdxU5j4l2KiluhMmjAQAAwRCUspnKuquZUmOSyZQCAMAOvvrVr+rKlSv+9/Pnz5fb7TZxRMNDdaPv6J6LOpwAAFgUQSmb4fgeAAD28uUsqSNHjvRaYwqDw92VKZWWQD0pAACsiqCUjbR3elXVVbCT43sAAAA9o8g5AADWR1DKRi7XfyHDkFzRTqXcFGP2cAAAQAgcDke342McJxt6vkypVIqcAwBgWTx9z0auP7rHZhYAAHswDEPLly+Xy3U1ONLa2qo1a9Zo5MiRAe0OHTpkxvAilq+mVCrH9wAAsCyCUjZS2RWUGsPRPQAAbGPZsmUB7xcvXmzSSIYX39P30siUAgDAsghK2UjF5y2SKHIOAICd7Nu3z+whDEvUlAIAwPqoKWUjPHkPAAAgNNeCUmRKAQBgVQSlbORaUIrjewAAAD35ot2jhtZOSVIqmVIAAFgWQSkb4fgeAABA33xFzuNGRCnBRbUKAACsiqCUTXR4vKrqSkO/LZmgFAAAQE/c1xU554nFAABYF0Epm6iqb5XXkGKinUq5idoIAAAAPfHVk+LoHgAA1kZQyiY+8R3dS46T08k3fgAAAD3hyXsAANgDQSmb8BU5H0M9KQAAgF5dabx6fC81gexyAACsjKCUTfDkPQAAgNBcy5QiKAUAgJURlLIJnrwHAAAQmmuFzjm+BwCAlVkiKLVr1y5lZmYqNjZWubm5On36dK/td+7cqaysLMXFxWns2LF67LHH1NraekN9Wl2lP1OKoBQAAEBv3I1dhc4TCEoBAGBlpgelDhw4oMLCQm3dulVlZWWaNGmS8vPzVV1dHbT9r3/9a23YsEFbt27VuXPnVFRUpAMHDujJJ58ccJ92UEFQCgAAICTV/kwpju8BAGBlpgelduzYoVWrVmnFihWaMGGC9uzZo/j4eO3duzdo+xMnTmj69OlatGiRMjMzNXPmTC1cuDAgE6q/fVpdp8erqq7aCNSUAgAA6FlTW6ea2jolSakc3wMAwNJMDUq1t7ertLRUeXl5/mtOp1N5eXk6efJk0HumTZum0tJSfxDqwoULOnLkiB566KEB92l1l+tb5fEaiolyatRNfOMHAADQk+quL/JuckXrJle0yaMBAAC9MfX/1DU1NfJ4PEpLSwu4npaWpvfffz/oPYsWLVJNTY3uv/9+GYahzs5OrVmzxn98byB9trW1qa2tzf++oaHhRqY16HxH9zKSY+V0OkweDQAAgHX5ipyncnQPAADLM/34Xn+VlJTo2Wef1fPPP6+ysjIdOnRIhw8f1rZt2wbc5/bt25WUlOR/jR07dhBHfOMq63z1pDi6BwAA0JvqriLnaRQ5BwDA8kzNlEpJSVFUVJTcbnfAdbfbrfT09KD3bN68WUuWLNHKlSslSdnZ2Wpubtbq1av11FNPDajPjRs3qrCw0P++oaHBUoGpis9bJFHkHAAAoC8UOQcAwD5MzZSKiYlRTk6OiouL/de8Xq+Ki4s1derUoPe0tLTI6QwcdlRUlCTJMIwB9elyuZSYmBjwshKevAcAABAad1dNKYqcAwBgfaZXfywsLNSyZct03333acqUKdq5c6eam5u1YsUKSdLSpUs1ZswYbd++XZJUUFCgHTt26N5771Vubq7Onz+vzZs3q6CgwB+c6qtPu7mWKcXxPQAAgN64G7tqSiWQKQUAgNWZHpSaP3++rly5oi1btqiqqkqTJ0/W0aNH/YXKL126FJAZtWnTJjkcDm3atEmVlZUaNWqUCgoK9Mwzz4Tcp92QKQUAABAaX6ZUGplSAABYnsMwDMPsQVhNQ0ODkpKSVF9fb/pRvk6PV3dvPqpOr6GTGx/U6CQCUwAAhJOV9gVWZ4W/q28/96Y+qm3Rqz+Yqim332LKGAAAGO5C3RPY7ul7w427sU2dXkMjohxK5SkyAAAAPTIMQ24KnQMAYBsEpSyu4rOr9aQykuMU5XSYPBoAAADramzr1BcdHkniyzwAAGyAoJTFUU8KAAAgNNVd9aQSY6MVFxNl8mgAAEBfCEpZnD8olcyT9wAAAHpz7egeWVIAANgBQSmLq6y7enxvDJlSAAAAvapu5Ml7AADYCUEpi+P4HgAAQGh8mVKpCRQ5BwDADghKWdy1oBTH9wAAAHrj7qoplUqmFAAAtkBQysI8XkOf1pEpBQAAEIpqf00pMqUAALADglIW5m5oVafXULTTQW0EAACAPvgypdg3AQBgDwSlLKyyK0tqdHKsopwOk0cDAABgbW5/oXMypQAAsAOCUhZW8fnVJ+/dlkw9KQAAgN4YhnFdoXMypQAAsAOCUhZW8Rn1pAAAAEJR/0WH2ju9kqRUMqUAALAFglIWxpP3AAAAQuPLkro5foRc0VEmjwYAAISCoJSFVdR1Hd8jUwoAAKBX1Y0UOQcAwG4ISllYZVem1BiCUgAAAL3yZUqNSuDoHgAAdkFQyqK8XsP/9D0ypQAAAHrnbiBTCgAAuyEoZVHVjW3q8BiKcjqUzuYKAACgV9X+oBSZUgAA2AVBKYuq+PxqPanRSbGKjmKZAABAoF27dikzM1OxsbHKzc3V6dOne22/c+dOZWVlKS4uTmPHjtVjjz2m1tbWG+rTSnzH98iUAgDAPoh2WNS1J+9xdA8AAAQ6cOCACgsLtXXrVpWVlWnSpEnKz89XdXV10Pa//vWvtWHDBm3dulXnzp1TUVGRDhw4oCeffHLAfVqNu6vQeWoCQSkAAOyCoJRF+epJjUmON3kkAADAanbs2KFVq1ZpxYoVmjBhgvbs2aP4+Hjt3bs3aPsTJ05o+vTpWrRokTIzMzVz5kwtXLgwIBOqv31aTbU/U4rjewAA2AVBKYvyHd8jUwoAAFyvvb1dpaWlysvL819zOp3Ky8vTyZMng94zbdo0lZaW+oNQFy5c0JEjR/TQQw8NuE8r8XoNVTdS6BwAALuJNnsACI7jewAAIJiamhp5PB6lpaUFXE9LS9P7778f9J5FixappqZG999/vwzDUGdnp9asWeM/vjeQPiWpra1NbW1t/vcNDQ0DndYN+bylXR0eQ5I0KoFMKQAA7IJMKYu6FpTi+B4AALgxJSUlevbZZ/X888+rrKxMhw4d0uHDh7Vt27Yb6nf79u1KSkryv8aOHTtII+6f6sargbGUm2I0ggfEAABgG2RKWZDXa6iSTCkAABBESkqKoqKi5Ha7A6673W6lp6cHvWfz5s1asmSJVq5cKUnKzs5Wc3OzVq9eraeeempAfUrSxo0bVVhY6H/f0NBgSmDK3UCRcwAA7IivkizoSlOb2j1eOR1SehKbKwAAcE1MTIxycnJUXFzsv+b1elVcXKypU6cGvaelpUVOZ+C2LyoqSpJkGMaA+pQkl8ulxMTEgJcZfEXOUylyDgCArZApZUG+o3ujk+JIQQcAAN0UFhZq2bJluu+++zRlyhTt3LlTzc3NWrFihSRp6dKlGjNmjLZv3y5JKigo0I4dO3TvvfcqNzdX58+f1+bNm1VQUOAPTvXVp5X5MqXSyJQCAMBWCEpZkO/Je2M4ugcAAIKYP3++rly5oi1btqiqqkqTJ0/W0aNH/YXKL126FJAZtWnTJjkcDm3atEmVlZUaNWqUCgoK9Mwzz4Tcp5W5/U/eI1MKAAA7cRiGYZg9CKtpaGhQUlKS6uvrTUlD3/XmeT33Wrm++/Ux2vG9yWH/8wEAwDVm7wvsxKy/q1Uv/6+O/cWtn829R4v/37iw/bkAACC4UPcEnA2zIJ68BwAAELpq3/G9RI7vAQBgJwSlLMh3fO+2ZI7vAQAA9MXdVeic43sAANgLQSkLqqzzZUoRlAIAAOiNx2voSpMvKEWmFAAAdkJQymIMw1Alx/cAAABC8llzuzxeQ06HdOvIGLOHAwAA+oGglMVcaWpTW6dXToeUnsS3fQAAAL1xd9WTSrnJpegotrYAANgJ/+e2GF+R8/TEWMVEszwAAAC9qW68GpRKpZ4UAAC2Q9TDYnxBqTHUkwIAAOiTv8h5AhnmAADYDUEpi6GeFAAAQOh8x/dSKXIOAIDtEJSymIrPWyTx5D0AAIBQ+DOlOL4HAIDtEJSymAp/phRBKQAAgL5Ud2VKpZEpBQCA7RCUsphrmVIc3wMAAOiLu9EXlCJTCgAAuyEoZSGGYaiyrqvQeTKZUgAAAH3xHd9LpdA5AAC2Q1DKQmqb29Xa4ZXDIY1OZmMFAADQm06PV7VNvppS7J0AALAbglIW4qsnlZYQK1d0lMmjAQAAsLba5nZ5DSnK6dCtI2PMHg4AAOgnglIWwpP3AAAAQufuKnI+6iaXnE6HyaMBAAD9RVDKQnjyHgAAQOh89aQocg4AgD0RlLIQX6bUGIJSAAAAffJlSqVSTwoAAFsiKGUhlf5MqXiTRwIAAGB91V1BKTKlAACwJ4JSFsLxPQAAgND5j+8lkCkFAIAdEZSyCMMwrgtKkSkFAADQF3ejL1OKoBQAAHZEUMoiPmtu1xcdHklSRjIbKwAAgL74MqVSOb4HAIAtEZSyCF+WVGqCS67oKJNHAwAAYH1XyJQCAMDWCEpZRGUd9aQAAABC1eHxqqapXRJBKQAA7IqglEVUfN4iiXpSAAAAobjSePXo3ogoh26OH2HyaAAAwEBYIii1a9cuZWZmKjY2Vrm5uTp9+nSPbb/97W/L4XB0ez388MP+NsuXL+/2+axZs8IxlQHjyXsAAAChczdcPbqXmhArh8Nh8mgAAMBARJs9gAMHDqiwsFB79uxRbm6udu7cqfz8fJWXlys1NbVb+0OHDqm9vd3/vra2VpMmTdK8efMC2s2aNUv79u3zv3e5rF0AkyfvAQAAhI4i5wAA2J/pmVI7duzQqlWrtGLFCk2YMEF79uxRfHy89u7dG7T9LbfcovT0dP/r2LFjio+P7xaUcrlcAe1uvvnmcExnwHzH98aQKQUAANCnal+R8wTqSQEAYFemBqXa29tVWlqqvLw8/zWn06m8vDydPHkypD6Kioq0YMECjRw5MuB6SUmJUlNTlZWVpbVr16q2tnZQxz6YDMPg+B4AAEA/+I7vpZEpBQCAbZl6fK+mpkYej0dpaWkB19PS0vT+++/3ef/p06d15swZFRUVBVyfNWuWvvvd7+r222/Xhx9+qCeffFKzZ8/WyZMnFRUV1a2ftrY2tbW1+d83NDQMcEYDU9fSoZZ2jyRpTDJBKQAAgL5cO75HphQAAHZlek2pG1FUVKTs7GxNmTIl4PqCBQv8/52dna2JEyfqjjvuUElJiWbMmNGtn+3bt+vpp58e8vH2xJclNSrBpdgR3YNmAAAACHQtU4qgFAAAdmXq8b2UlBRFRUXJ7XYHXHe73UpPT+/13ubmZr3yyit69NFH+/xzxo8fr5SUFJ0/fz7o5xs3blR9fb3/9cknn4Q+iUHgqyfF0T0AAIDQXGm8minF8T0AAOzL1KBUTEyMcnJyVFxc7L/m9XpVXFysqVOn9nrvwYMH1dbWpsWLF/f551RUVKi2tlajR48O+rnL5VJiYmLAK5x8mVIc3QMAAAgNmVIAANif6U/fKyws1Isvvqj9+/fr3LlzWrt2rZqbm7VixQpJ0tKlS7Vx48Zu9xUVFWnu3Lm69dZbA643NTXp8ccf16lTp/TRRx+puLhYc+bM0Z133qn8/PywzKm/rmVKxZs8EgAAAOtr6/To85YOSTx9DwAAOzO9ptT8+fN15coVbdmyRVVVVZo8ebKOHj3qL35+6dIlOZ2BsbPy8nK99dZbev3117v1FxUVpffee0/79+9XXV2dMjIyNHPmTG3btk0ulzXTuyvrePIeAABAqKq7ipzHRDuVGGf6dhYAAAyQJf4vvn79eq1fvz7oZyUlJd2uZWVlyTCMoO3j4uL02muvDebwhpzv+B5BKQAAgL5VN/qO7rnkcDhMHg0AABgo04/vDXeGYVwXlOL4HgAAQF/cXZlSHN0DAMDeCEqZrP6LDjW1dUqi0DkAAEAoKHIOAEBkIChlMl+WVMpNMYqLiTJ5NAAAANbny5RKTbRmvVAAABAaglIm8wWlxnB0DwAAICTVZEoBABARCEqZrOLzFkkUOQcAAAhVdWNXTSkypQAAsDWCUibjyXsAAAD9468pRaFzAABsjaCUyfxBKYqcAwAAhMQXlErl+B4AALZGUMpk147vUVMKAACgL1+0e9TQevXJxRzfAwDA3ghKmayyjuN7AAAAoapuvJolFTciSje5ok0eDQAAuBEEpUxU/0WHGru+6RtDUAoAAKBP7oZrRc4dDofJowEAADeCoJSJfEf3bh0Zo/gYvukDAADoC/WkAACIHASlTMST9wAAAPrH/+Q9glIAANgeQSkT+YJSHN0DAAAITXVj1/G9BIqcAwBgdwSlTFTpz5TiyXsAAAChqCZTCgCAiEFQykS+mlIc3wMAAAiNr9B5aiKZUgAA2B1BKRNRUwoAAKB/3I1kSgEAECkISpnoWqYUx/cAAABCUd2VKUVQCgAA+yMoZZKG1g41tHZKksYkkykFAADQl6a2TjW1Xd0/pVLoHAAA2yMoZRJfkfOb40dopCva5NEAAABYn6/I+U2uaPZPAABEAIJSJqngyXsAAAD9QpFzAAAiC0Epk/DkPQAAcCN27dqlzMxMxcbGKjc3V6dPn+6x7be//W05HI5ur4cfftjfZvny5d0+nzVrVjimErJqX5HzBOpJAQAQCch7NglP3gMAAAN14MABFRYWas+ePcrNzdXOnTuVn5+v8vJypaamdmt/6NAhtbe3+9/X1tZq0qRJmjdvXkC7WbNmad++ff73Lpe1MpLcDb4n71lrXAAAYGDIlDKJL1OKIucAAKC/duzYoVWrVmnFihWaMGGC9uzZo/j4eO3duzdo+1tuuUXp6en+17FjxxQfH98tKOVyuQLa3XzzzeGYTsh48h4AAJGFoJRJKuuoKQUAAPqvvb1dpaWlysvL819zOp3Ky8vTyZMnQ+qjqKhICxYs0MiRIwOul5SUKDU1VVlZWVq7dq1qa2t77aetrU0NDQ0Br6HkbvTVlCIoBQBAJCAoZRL/8b1byJQCAAChq6mpkcfjUVpaWsD1tLQ0VVVV9Xn/6dOndebMGa1cuTLg+qxZs/Tyyy+ruLhYv/jFL3T8+HHNnj1bHo+nx762b9+upKQk/2vs2LEDm1SIOL4HAEBkoaaUCRpbO1TX0iGJ43sAACC8ioqKlJ2drSlTpgRcX7Bggf+/s7OzNXHiRN1xxx0qKSnRjBkzgva1ceNGFRYW+t83NDQMaWCq2h+UIlMKAIBIQKaUCXxH95LjRyghdoTJowEAAHaSkpKiqKgoud3ugOtut1vp6em93tvc3KxXXnlFjz76aJ9/zvjx45WSkqLz58/32MblcikxMTHgNVQMw5C7q6ZUagKZUgAARAKCUiao+OxqUIosKQAA0F8xMTHKyclRcXGx/5rX61VxcbGmTp3a670HDx5UW1ubFi9e3OefU1FRodraWo0ePfqGxzwYGts69UXH1aOEqQlkSgEAEAkISpnA9+S9224mKAUAAPqvsLBQL774ovbv369z585p7dq1am5u1ooVKyRJS5cu1caNG7vdV1RUpLlz5+rWW28NuN7U1KTHH39cp06d0kcffaTi4mLNmTNHd955p/Lz88Myp774ju4lxkYrLibK5NEAAIDBQE0pE/DkPQAAcCPmz5+vK1euaMuWLaqqqtLkyZN19OhRf/HzS5cuyekM/O6xvLxcb731ll5//fVu/UVFRem9997T/v37VVdXp4yMDM2cOVPbtm2Ty2WNo3K+o3vUkwIAIHIQlDKB/8l7ZEoBAIABWr9+vdavXx/0s5KSkm7XsrKyZBhG0PZxcXF67bXXBnN4g85NkXMAACIOx/dMcC0oRaYUAABAKKobu4qcJ1ojcwsAANw4glIm8NWUotA5AABAaMiUAgAg8hCUCrPmtk593tIhSRrD8T0AAICQVPtqSiWQKQUAQKQgKBVmviLnibHRSoobYfJoAAAA7IFMKQAAIg9BqTDzHd2jnhQAAEDo3I1Xg1KpBKUAAIgYBKXCjCfvAQAA9I9hGHJ3Hd9L5fgeAAARI9rsAQw3BRMzdHd6olzRxAMBAABCdWjtNLkbWjm+BwBABCEoFWY3j4zRlNtvMXsYAAAAtuFwOHTPmCTdMybJ7KEAAIBBRLoOAAAAAAAAwo6gFAAAAAAAAMKOoBQAAAAAAADCjqAUAAAAAAAAwo6gFAAAAAAAAMKOoBQAAAAAAADCjqAUAAAAAAAAwo6gFAAAAAAAAMKOoBQAAAAAAADCjqAUAAAAAAAAwo6gFAAAAAAAAMIu2uwBWJFhGJKkhoYGk0cCAADM5tsP+PYH6Bl7KAAAIIW+fyIoFURjY6MkaezYsSaPBAAAWEVjY6OSkpLMHoalsYcCAADX62v/5DD42q8br9erTz/9VAkJCXI4HIPef0NDg8aOHatPPvlEiYmJg96/VQ3HeQ/HOUvMm3kPD8x7+MzbMAw1NjYqIyNDTieVD3ozlHuo4fhvT2LezHt4YN7DZ97Dcc7S8Jx3qPsnMqWCcDqduu2224b8z0lMTBw2/yCvNxznPRznLDHv4YZ5Dy/Dbd5kSIUmHHuo4fZvz4d5Dy/Me3gZjvMejnOWht+8Q9k/8XUfAAAAAAAAwo6gFAAAAAAAAMKOoJQJXC6Xtm7dKpfLZfZQwmo4zns4zlli3sx7eGDew2veMN9w/bfHvJn3cMC8h8+8h+OcpeE771BQ6BwAAAAAAABhR6YUAAAAAAAAwo6gFAAAAAAAAMKOoBQAAAAAAADCjqAUAAAAAAAAwo6g1BDZtWuXMjMzFRsbq9zcXJ0+fbrX9gcPHtTdd9+t2NhYZWdn68iRI2Ea6eDYvn27vvGNbyghIUGpqamaO3euysvLe73npZdeksPhCHjFxsaGacSD4+/+7u+6zeHuu+/u9R67r7UkZWZmdpu3w+HQunXrgra341r/93//twoKCpSRkSGHw6Hf/e53AZ8bhqEtW7Zo9OjRiouLU15enj744IM+++3v74Zw623eHR0deuKJJ5Sdna2RI0cqIyNDS5cu1aefftprnwP5OQm3vtZ7+fLl3eYwa9asPvu183pLCvpz7nA49Nxzz/XYpx3WG9bF/on9U0/svtbS8Ng/Seyh2EP9LuBz9lDsofpCUGoIHDhwQIWFhdq6davKyso0adIk5efnq7q6Omj7EydOaOHChXr00Uf17rvvau7cuZo7d67OnDkT5pEP3PHjx7Vu3TqdOnVKx44dU0dHh2bOnKnm5uZe70tMTNTly5f9r48//jhMIx48X/va1wLm8NZbb/XYNhLWWpL+53/+J2DOx44dkyTNmzevx3vsttbNzc2aNGmSdu3aFfTzv//7v9e//Mu/aM+ePXrnnXc0cuRI5efnq7W1tcc++/u7wQy9zbulpUVlZWXavHmzysrKdOjQIZWXl+s73/lOn/325+fEDH2ttyTNmjUrYA6/+c1veu3T7ustKWC+ly9f1t69e+VwOPQ3f/M3vfZr9fWGNbF/Yv/Uk0hYa2l47J8k9lDsobpjD8UeqlcGBt2UKVOMdevW+d97PB4jIyPD2L59e9D23/ve94yHH3444Fpubq7xgx/8YEjHOZSqq6sNScbx48d7bLNv3z4jKSkpfIMaAlu3bjUmTZoUcvtIXGvDMIwf/vCHxh133GF4vd6gn9t9rSUZv/3tb/3vvV6vkZ6ebjz33HP+a3V1dYbL5TJ+85vf9NhPf383mO3L8w7m9OnThiTj448/7rFNf39OzBZs3suWLTPmzJnTr34icb3nzJljPPjgg722sdt6wzrYP7F/6kkkrrVhRP7+yTDYQ/WGPVTPInG92UP1jEypQdbe3q7S0lLl5eX5rzmdTuXl5enkyZNB7zl58mRAe0nKz8/vsb0d1NfXS5JuueWWXts1NTVp3LhxGjt2rObMmaOzZ8+GY3iD6oMPPlBGRobGjx+vRx55RJcuXeqxbSSudXt7u371q1/p+9//vhwOR4/tImGtfS5evKiqqqqAtUxKSlJubm6PazmQ3w12UF9fL4fDoeTk5F7b9efnxKpKSkqUmpqqrKwsrV27VrW1tT22jcT1drvdOnz4sB599NE+20bCeiO82D9dxf4puEhc6+G4f5LYQ12PPVRwkbje7KF6R1BqkNXU1Mjj8SgtLS3gelpamqqqqoLeU1VV1a/2Vuf1evWjH/1I06dP1z333NNju6ysLO3du1e///3v9atf/Uper1fTpk1TRUVFGEd7Y3Jzc/XSSy/p6NGj2r17ty5evKgHHnhAjY2NQdtH2lpL0u9+9zvV1dVp+fLlPbaJhLW+nm+9+rOWA/ndYHWtra164okntHDhQiUmJvbYrr8/J1Y0a9YsvfzyyyouLtYvfvELHT9+XLNnz5bH4wnaPhLXe//+/UpISNB3v/vdXttFwnoj/Ng/sX9i/9RdJKz1l7GHuoo9FHuoYCJhvQci2uwBIPKsW7dOZ86c6fP869SpUzV16lT/+2nTpumv/uqv9MILL2jbtm1DPcxBMXv2bP9/T5w4Ubm5uRo3bpxeffXVkCLhkaCoqEizZ89WRkZGj20iYa0RqKOjQ9/73vdkGIZ2797da9tI+DlZsGCB/7+zs7M1ceJE3XHHHSopKdGMGTNMHFn47N27V4888kifRXYjYb0BM7B/Gl6/J9g/DV/sodhD9SQS1nsgyJQaZCkpKYqKipLb7Q647na7lZ6eHvSe9PT0frW3svXr1+u//uu/9Oabb+q2227r170jRozQvffeq/Pnzw/R6IZecnKy7rrrrh7nEElrLUkff/yx3njjDa1cubJf99l9rX3r1Z+1HMjvBqvybaY+/vhjHTt2rNdv+ILp6+fEDsaPH6+UlJQe5xBJ6y1Jf/zjH1VeXt7vn3UpMtYbQ4/9E/sn9k99i4S1Zg/FHoo9VOgiYb1DQVBqkMXExCgnJ0fFxcX+a16vV8XFxQHfdFxv6tSpAe0l6dixYz22tyLDMLR+/Xr99re/1R/+8Afdfvvt/e7D4/Hoz3/+s0aPHj0EIwyPpqYmffjhhz3OIRLW+nr79u1TamqqHn744X7dZ/e1vv3225Wenh6wlg0NDXrnnXd6XMuB/G6wIt9m6oMPPtAbb7yhW2+9td999PVzYgcVFRWqra3tcQ6Rst4+RUVFysnJ0aRJk/p9bySsN4Ye+yf2T+yf+hYJa80eij0Ue6jQRcJ6h8TcOuuR6ZVXXjFcLpfx0ksvGX/5y1+M1atXG8nJyUZVVZVhGIaxZMkSY8OGDf72b7/9thEdHW38wz/8g3Hu3Dlj69atxogRI4w///nPZk2h39auXWskJSUZJSUlxuXLl/2vlpYWf5svz/vpp582XnvtNePDDz80SktLjQULFhixsbHG2bNnzZjCgPz4xz82SkpKjIsXLxpvv/22kZeXZ6SkpBjV1dWGYUTmWvt4PB7jK1/5ivHEE090+ywS1rqxsdF49913jXfffdeQZOzYscN49913/U9I+fnPf24kJycbv//974333nvPmDNnjnH77bcbX3zxhb+PBx980PjlL3/pf9/X7wYr6G3e7e3txne+8x3jtttuM/70pz8F/Ky3tbX5+/jyvPv6ObGC3ubd2Nho/OQnPzFOnjxpXLx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      "text/plain": [
       "<Figure size 1200x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the training history\n",
    "plot_graphs(history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8dd56fac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "28/28 [==============================] - 1s 13ms/step\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.00      0.00      0.00        15\n",
      "           2       0.00      0.00      0.00        37\n",
      "           3       0.91      0.81      0.85        36\n",
      "           4       0.00      0.00      0.00         4\n",
      "           6       0.40      0.45      0.43       467\n",
      "           7       0.59      0.54      0.57       100\n",
      "           8       0.00      0.00      0.00        16\n",
      "           9       0.00      0.00      0.00        56\n",
      "          10       0.33      0.53      0.40        97\n",
      "          11       0.25      0.21      0.23       478\n",
      "          12       0.48      0.52      0.50       684\n",
      "          13       0.26      0.15      0.19       236\n",
      "          14       0.36      0.23      0.28        65\n",
      "          15       0.17      0.02      0.04        45\n",
      "          16       0.00      0.00      0.00        16\n",
      "          17       0.00      0.00      0.00        11\n",
      "          19       0.40      0.11      0.17        55\n",
      "          20       0.40      0.31      0.35       465\n",
      "          21       0.00      0.00      0.00         2\n",
      "          22       0.31      0.40      0.35       143\n",
      "          23       0.46      0.53      0.49        49\n",
      "          24       0.00      0.00      0.00         6\n",
      "          25       0.00      0.00      0.00         2\n",
      "          26       0.00      0.00      0.00         2\n",
      "          27       0.00      0.00      0.00         3\n",
      "          28       1.00      0.38      0.55         8\n",
      "          29       0.00      0.00      0.00        11\n",
      "          30       0.50      0.01      0.03        74\n",
      "          31       0.80      0.88      0.84        32\n",
      "          32       0.00      0.00      0.00         7\n",
      "          33       0.34      0.37      0.36       558\n",
      "          34       0.00      0.00      0.00        12\n",
      "          35       0.00      0.00      0.00        10\n",
      "          36       0.29      0.18      0.23       163\n",
      "          37       0.00      0.00      0.00        10\n",
      "          38       0.00      0.00      0.00         4\n",
      "          40       0.00      0.00      0.00        11\n",
      "          41       0.00      0.00      0.00         7\n",
      "          42       0.91      0.89      0.90        71\n",
      "          43       0.00      0.00      0.00        11\n",
      "          45       0.52      0.51      0.51       505\n",
      "          46       0.00      0.00      0.00         1\n",
      "          47       0.00      0.00      0.00        10\n",
      "          48       0.00      0.00      0.00        15\n",
      "          49       0.42      0.57      0.48       161\n",
      "          50       0.20      0.22      0.21       460\n",
      "          51       0.63      0.54      0.58       924\n",
      "          52       0.33      0.24      0.28       199\n",
      "          53       0.38      0.40      0.39       107\n",
      "          54       0.16      0.14      0.15        59\n",
      "          55       0.29      0.05      0.09        78\n",
      "          56       0.25      0.08      0.12        13\n",
      "          58       0.29      0.22      0.25       188\n",
      "          59       0.40      0.28      0.33       385\n",
      "          60       0.00      0.00      0.00         2\n",
      "          61       0.37      0.29      0.32       119\n",
      "          62       0.59      0.48      0.53        48\n",
      "          63       0.00      0.00      0.00         9\n",
      "          64       0.00      0.00      0.00         4\n",
      "          65       0.00      0.00      0.00         2\n",
      "          66       0.00      0.00      0.00         1\n",
      "          67       0.00      0.00      0.00         3\n",
      "          68       0.00      0.00      0.00         9\n",
      "          69       0.00      0.00      0.00         9\n",
      "          70       0.00      0.00      0.00         1\n",
      "          71       0.24      0.17      0.20       354\n",
      "          72       0.00      0.00      0.00         3\n",
      "          73       0.00      0.00      0.00         6\n",
      "          74       0.29      0.20      0.24       127\n",
      "          75       0.00      0.00      0.00         8\n",
      "          76       0.00      0.00      0.00         7\n",
      "          78       0.98      0.99      0.98     79004\n",
      "\n",
      "    accuracy                           0.93     86900\n",
      "   macro avg       0.22      0.18      0.19     86900\n",
      "weighted avg       0.92      0.93      0.93     86900\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "\n",
    "# Get the model predictions\n",
    "y_pred = model.predict(test_sequences_padded)\n",
    "\n",
    "# Convert the predictions from one-hot encoded format to the label format\n",
    "y_pred_labels = np.argmax(y_pred, axis=2)\n",
    "test_labels_labels = np.argmax(test_labels, axis=2)\n",
    "\n",
    "# Print the classification report\n",
    "print(classification_report(test_labels_labels.reshape(-1), y_pred_labels.reshape(-1), zero_division=0))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "fe75e157",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 2000x2000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "report = classification_report(test_labels_labels.reshape(-1), y_pred_labels.reshape(-1), zero_division=0, output_dict=True)\n",
    "plt.subplots(figsize=(20, 20))\n",
    "sns.heatmap(pd.DataFrame(report).iloc[:-1, :].T, annot=True, cmap='Blues', fmt='.2f')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "387f0c9d",
   "metadata": {},
   "source": [
    "In the case of NER task, all three metrics - precision, recall, and F1 score - are important.\n",
    "\n",
    "* Precision measures the proportion of predicted entities that are actually correct. In NER, precision means how many of the predicted named entities are actually true named entities.\n",
    "\n",
    "* Recall measures the proportion of actual entities that are correctly identified by the model. In NER, recall means how many true named entities are correctly identified by the model.\n",
    "\n",
    "* F1 score is the harmonic mean of precision and recall. It is a balanced metric that takes into account both precision and recall. F1 score is commonly used in NER evaluation as it takes into account both false positives and false negatives.\n",
    "\n",
    "A high precision score means that the model is making very few false predictions, while a high recall score means that the model is identifying a high proportion of the true named entities. A high F1 score indicates that the model is both precise and recallful."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ccbe2961",
   "metadata": {},
   "source": [
    "## Save the model as an .h5 file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0f6a25b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save(os.path.join(model_dir, 'model_2.h5'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10695d43",
   "metadata": {},
   "source": [
    "## Load the model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "16c6c34f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Register the custom metric function\n",
    "tf.keras.utils.get_custom_objects()[precision.__name__] = precision\n",
    "tf.keras.utils.get_custom_objects()[recall.__name__] = recall\n",
    "tf.keras.utils.get_custom_objects()[f1_score.__name__] = f1_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ff528d2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_2 = tf.keras.models.load_model(os.path.join(model_dir, 'model_2.h5'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "50dda26b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "28/28 [==============================] - 1s 14ms/step - loss: 0.4709 - accuracy: 0.9320 - precision: 0.9558 - recall: 0.9253 - f1_score: 0.9403\n",
      "Test loss: 0.470919132232666\n",
      "Test accuracy: 0.9319677948951721\n",
      "Test precision: 0.9558389782905579\n",
      "Test recall: 0.9253436923027039\n",
      "Test f1_score: 0.940338671207428\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model on the test set\n",
    "test_loss, test_accuracy, test_precision, test_recall, test_f1_score = model_2.evaluate(test_sequences_padded, test_labels)\n",
    "\n",
    "# Print the test loss and accuracy\n",
    "print('Test loss:', test_loss)\n",
    "print('Test accuracy:', test_accuracy)\n",
    "print('Test precision:', test_precision)\n",
    "print('Test recall:', test_recall)\n",
    "print('Test f1_score:', test_f1_score)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8602ba7c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-04-16 13:35:47.732562: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 1s 649ms/step\n",
      "Predicted Named Entities:\n",
      "patient: O\n",
      "underwent: O\n",
      "electrophysiologic: Diagnostic_procedure\n",
      "study: Diagnostic_procedure\n",
      "mapping: Diagnostic_procedure\n",
      "accessory: Biological_structure\n",
      "pathway: Biological_structure\n",
      "followed: Sign_symptom\n",
      "radiofrequency: Therapeutic_procedure\n",
      "ablation: O\n",
      "interruption: O\n",
      "pathway: O\n",
      "using: O\n",
      "heat: O\n",
      "generated: Therapeutic_procedure\n",
      "electromagnetic: Therapeutic_procedure\n",
      "waves: O\n",
      "tip: O\n",
      "ablation: O\n",
      "catheter: O\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<span class=\"tex2jax_ignore\"><div class=\"entities\" style=\"line-height: 2.5; direction: ltr\">The patient underwent an \n",
       "<mark class=\"entity\" style=\"background: #c5b4e3; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    electrophysiologic\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Diagnostic_procedure</span>\n",
       "</mark>\n",
       " \n",
       "<mark class=\"entity\" style=\"background: #c5b4e3; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
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       "</mark>\n",
       " with \n",
       "<mark class=\"entity\" style=\"background: #c5b4e3; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    mapping\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Diagnostic_procedure</span>\n",
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       "    accessory\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Biological_structure</span>\n",
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       "    pathway\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Biological_structure</span>\n",
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       ", \n",
       "<mark class=\"entity\" style=\"background: #bde0fe; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
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       "</mark>\n",
       " by \n",
       "<mark class=\"entity\" style=\"background: #e6ccb2; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    radiofrequency\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Therapeutic_procedure</span>\n",
       "</mark>\n",
       " ablation (interruption of the pathway using the heat \n",
       "<mark class=\"entity\" style=\"background: #e6ccb2; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
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       "<mark class=\"entity\" style=\"background: #e6ccb2; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    electromagnetic\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Therapeutic_procedure</span>\n",
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       " waves at the tip of an ablation catheter).</div></span>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predict(\n",
    "    \"The patient underwent an electrophysiologic study with mapping of the accessory pathway, followed by radiofrequency ablation (interruption of the pathway using the heat generated by electromagnetic waves at the tip of an ablation catheter).\",\n",
    "    model_2, \n",
    "    index_to_label,\n",
    "    acronyms_to_entities, \n",
    "    MAX_LENGTH\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1f5e8611",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 1s 633ms/step\n",
      "Predicted Named Entities:\n",
      "57: Age\n",
      "year: Age\n",
      "old: Age\n",
      "man: Sex\n",
      "presented: Nonbiological_location\n",
      "emergency: Nonbiological_location\n",
      "department: History\n",
      "2: Date\n",
      "day: O\n",
      "history: Sign_symptom\n",
      "worsening: Sign_symptom\n",
      "shortness: Biological_structure\n",
      "breath: Sign_symptom\n",
      "chest: O\n",
      "pain: Nonbiological_location\n",
      "reported: History\n",
      "recent: History\n",
      "travel: History\n",
      "sick: History\n",
      "contacts: History\n",
      "medical: Nonbiological_location\n",
      "history: Nonbiological_location\n",
      "significant: History\n",
      "hypertension: Nonbiological_location\n",
      "dyslipidemia: Nonbiological_location\n",
      "type: Nonbiological_location\n",
      "2: Biological_structure\n",
      "diabetes: Diagnostic_procedure\n",
      "mellitus: Diagnostic_procedure\n",
      "examination: Lab_value\n",
      "tachycardic: Detailed_description\n",
      "tachypneic: Detailed_description\n",
      "oxygen: Biological_structure\n",
      "saturation: Diagnostic_procedure\n",
      "88: Detailed_description\n",
      "room: O\n",
      "air: Biological_structure\n",
      "chest: Sign_symptom\n",
      "radiography: O\n",
      "revealed: Biological_structure\n",
      "bilateral: Biological_structure\n",
      "opacities: Biological_structure\n",
      "consistent: O\n",
      "pulmonary: Detailed_description\n",
      "edema: Distance\n",
      "patient: Therapeutic_procedure\n",
      "admitted: Medication\n",
      "intensive: Medication\n",
      "care: Medication\n",
      "unit: Medication\n",
      "management: Dosage\n",
      "acute: Dosage\n",
      "decompensated: History\n",
      "heart: Date\n",
      "failure: Nonbiological_location\n",
      "started: History\n",
      "intravenous: History\n",
      "diuretics: Duration\n",
      "inotropic: O\n",
      "support: O\n",
      "dobutamine: O\n",
      "next: O\n",
      "several: O\n",
      "days: O\n",
      "symptoms: O\n",
      "improved: O\n",
      "discharged: O\n",
      "home: O\n",
      "instructions: O\n",
      "follow: O\n",
      "primary: O\n",
      "care: O\n",
      "provider: O\n",
      "1: O\n",
      "week: O\n"
     ]
    },
    {
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       "<span class=\"tex2jax_ignore\"><div class=\"entities\" style=\"line-height: 2.5; direction: ltr\">A \n",
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       "    57\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Age</span>\n",
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       "<mark class=\"entity\" style=\"background: #f6c3d0; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    old\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Age</span>\n",
       "</mark>\n",
       " \n",
       "<mark class=\"entity\" style=\"background: #c5b4e3; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    man\n",
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       "    presented\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Nonbiological_location</span>\n",
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       " to the \n",
       "<mark class=\"entity\" style=\"background: #f7a399; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    emergency\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Nonbiological_location</span>\n",
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       "<mark class=\"entity\" style=\"background: #e2f0cb; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
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       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">History</span>\n",
       "</mark>\n",
       " with a \n",
       "<mark class=\"entity\" style=\"background: #f1f0d2; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    2\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Date</span>\n",
       "</mark>\n",
       "-day \n",
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       "    history\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Sign_symptom</span>\n",
       "</mark>\n",
       " of \n",
       "<mark class=\"entity\" style=\"background: #bde0fe; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    worsening\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Sign_symptom</span>\n",
       "</mark>\n",
       " \n",
       "<mark class=\"entity\" style=\"background: #9ddfd3; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    shortness\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Biological_structure</span>\n",
       "</mark>\n",
       " of \n",
       "<mark class=\"entity\" style=\"background: #bde0fe; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    breath\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Sign_symptom</span>\n",
       "</mark>\n",
       " and chest \n",
       "<mark class=\"entity\" style=\"background: #f7a399; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    pain\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Nonbiological_location</span>\n",
       "</mark>\n",
       ". He \n",
       "<mark class=\"entity\" style=\"background: #e2f0cb; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    reported\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">History</span>\n",
       "</mark>\n",
       " no \n",
       "<mark class=\"entity\" style=\"background: #e2f0cb; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    recent\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">History</span>\n",
       "</mark>\n",
       " \n",
       "<mark class=\"entity\" style=\"background: #e2f0cb; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    travel\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">History</span>\n",
       "</mark>\n",
       " or \n",
       "<mark class=\"entity\" style=\"background: #e2f0cb; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    sick\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">History</span>\n",
       "</mark>\n",
       " \n",
       "<mark class=\"entity\" style=\"background: #e2f0cb; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    contacts\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">History</span>\n",
       "</mark>\n",
       ". His \n",
       "<mark class=\"entity\" style=\"background: #f7a399; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    medical\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Nonbiological_location</span>\n",
       "</mark>\n",
       " \n",
       "<mark class=\"entity\" style=\"background: #f7a399; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    history\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Nonbiological_location</span>\n",
       "</mark>\n",
       " was \n",
       "<mark class=\"entity\" style=\"background: #e2f0cb; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    significant\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">History</span>\n",
       "</mark>\n",
       " for \n",
       "<mark class=\"entity\" style=\"background: #f7a399; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    hypertension\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Nonbiological_location</span>\n",
       "</mark>\n",
       ", \n",
       "<mark class=\"entity\" style=\"background: #f7a399; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    dyslipidemia\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Nonbiological_location</span>\n",
       "</mark>\n",
       ", and \n",
       "<mark class=\"entity\" style=\"background: #f7a399; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    type\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Nonbiological_location</span>\n",
       "</mark>\n",
       " \n",
       "<mark class=\"entity\" style=\"background: #9ddfd3; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    2\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Biological_structure</span>\n",
       "</mark>\n",
       " \n",
       "<mark class=\"entity\" style=\"background: #c5b4e3; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    diabetes\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Diagnostic_procedure</span>\n",
       "</mark>\n",
       " \n",
       "<mark class=\"entity\" style=\"background: #c5b4e3; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    mellitus\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Diagnostic_procedure</span>\n",
       "</mark>\n",
       ". On \n",
       "<mark class=\"entity\" style=\"background: #f4b3c2; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    examination\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Lab_value</span>\n",
       "</mark>\n",
       ", he was \n",
       "<mark class=\"entity\" style=\"background: #ffb347; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    tachycardic\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Detailed_description</span>\n",
       "</mark>\n",
       " and \n",
       "<mark class=\"entity\" style=\"background: #ffb347; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    tachypneic\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Detailed_description</span>\n",
       "</mark>\n",
       ", with \n",
       "<mark class=\"entity\" style=\"background: #9ddfd3; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    oxygen\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Biological_structure</span>\n",
       "</mark>\n",
       " \n",
       "<mark class=\"entity\" style=\"background: #c5b4e3; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    saturation\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Diagnostic_procedure</span>\n",
       "</mark>\n",
       " of \n",
       "<mark class=\"entity\" style=\"background: #ffb347; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    88\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Detailed_description</span>\n",
       "</mark>\n",
       "% on room \n",
       "<mark class=\"entity\" style=\"background: #9ddfd3; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    air\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Biological_structure</span>\n",
       "</mark>\n",
       ". \n",
       "<mark class=\"entity\" style=\"background: #bde0fe; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    Chest\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Sign_symptom</span>\n",
       "</mark>\n",
       " radiography \n",
       "<mark class=\"entity\" style=\"background: #9ddfd3; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    revealed\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Biological_structure</span>\n",
       "</mark>\n",
       " \n",
       "<mark class=\"entity\" style=\"background: #9ddfd3; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    bilateral\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Biological_structure</span>\n",
       "</mark>\n",
       " \n",
       "<mark class=\"entity\" style=\"background: #9ddfd3; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    opacities\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Biological_structure</span>\n",
       "</mark>\n",
       " consistent with \n",
       "<mark class=\"entity\" style=\"background: #ffb347; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    pulmonary\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Detailed_description</span>\n",
       "</mark>\n",
       " \n",
       "<mark class=\"entity\" style=\"background: #bde0fe; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    edema\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Distance</span>\n",
       "</mark>\n",
       ". The \n",
       "<mark class=\"entity\" style=\"background: #e6ccb2; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    patient\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Therapeutic_procedure</span>\n",
       "</mark>\n",
       " was \n",
       "<mark class=\"entity\" style=\"background: #f9d5e5; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    admitted\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Medication</span>\n",
       "</mark>\n",
       " to the \n",
       "<mark class=\"entity\" style=\"background: #f9d5e5; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    intensive\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Medication</span>\n",
       "</mark>\n",
       " \n",
       "<mark class=\"entity\" style=\"background: #f9d5e5; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    care\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Medication</span>\n",
       "</mark>\n",
       " \n",
       "<mark class=\"entity\" style=\"background: #f9d5e5; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    unit\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Medication</span>\n",
       "</mark>\n",
       " for \n",
       "<mark class=\"entity\" style=\"background: #b9e8d8; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    management\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Dosage</span>\n",
       "</mark>\n",
       " of \n",
       "<mark class=\"entity\" style=\"background: #b9e8d8; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    acute\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Dosage</span>\n",
       "</mark>\n",
       " \n",
       "<mark class=\"entity\" style=\"background: #e2f0cb; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    decompensated\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">History</span>\n",
       "</mark>\n",
       " \n",
       "<mark class=\"entity\" style=\"background: #f1f0d2; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    heart\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Date</span>\n",
       "</mark>\n",
       " \n",
       "<mark class=\"entity\" style=\"background: #f7a399; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    failure\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Nonbiological_location</span>\n",
       "</mark>\n",
       ". He was \n",
       "<mark class=\"entity\" style=\"background: #e2f0cb; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    started\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">History</span>\n",
       "</mark>\n",
       " on \n",
       "<mark class=\"entity\" style=\"background: #e2f0cb; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    intravenous\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">History</span>\n",
       "</mark>\n",
       " \n",
       "<mark class=\"entity\" style=\"background: #ffdfba; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    diuretics\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Duration</span>\n",
       "</mark>\n",
       " and inotropic support with dobutamine. Over the next several days, his symptoms improved and he was discharged to home with instructions to follow up with his primary care provider in 1 week.</div></span>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predict(\n",
    "    \"A 57-year-old man presented to the emergency department with a 2-day history of worsening shortness of breath and chest pain. He reported no recent travel or sick contacts. His medical history was significant for hypertension, dyslipidemia, and type 2 diabetes mellitus. On examination, he was tachycardic and tachypneic, with oxygen saturation of 88% on room air. Chest radiography revealed bilateral opacities consistent with pulmonary edema. The patient was admitted to the intensive care unit for management of acute decompensated heart failure. He was started on intravenous diuretics and inotropic support with dobutamine. Over the next several days, his symptoms improved and he was discharged to home with instructions to follow up with his primary care provider in 1 week.\",\n",
    "    model_2, \n",
    "    index_to_label,\n",
    "    acronyms_to_entities, \n",
    "    MAX_LENGTH\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "df05127c",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 19ms/step\n",
      "Predicted Named Entities:\n",
      "57: Age\n",
      "year: Age\n",
      "old: Age\n",
      "man: Sex\n",
      "presented: Nonbiological_location\n",
      "emergency: Nonbiological_location\n",
      "department: History\n",
      "2: Date\n",
      "day: O\n",
      "history: Sign_symptom\n",
      "worsening: Sign_symptom\n",
      "shortness: Biological_structure\n",
      "breath: Sign_symptom\n",
      "chest: O\n",
      "pain: O\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "reported: Clinical_event\n",
      "recent: Nonbiological_location\n",
      "travel: Date\n",
      "sick: O\n",
      "contacts: O\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "medical: O\n",
      "history: History\n",
      "significant: History\n",
      "hypertension: History\n",
      "dyslipidemia: History\n",
      "type: History\n",
      "2: History\n",
      "diabetes: History\n",
      "mellitus: Nonbiological_location\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "examination: Diagnostic_procedure\n",
      "tachycardic: Sign_symptom\n",
      "tachypneic: Diagnostic_procedure\n",
      "oxygen: Diagnostic_procedure\n",
      "saturation: Lab_value\n",
      "88: Detailed_description\n",
      "room: Sign_symptom\n",
      "air: O\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "chest: Biological_structure\n",
      "radiography: Diagnostic_procedure\n",
      "revealed: Detailed_description\n",
      "bilateral: O\n",
      "opacities: Biological_structure\n",
      "consistent: Sign_symptom\n",
      "pulmonary: O\n",
      "edema: O\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "patient: O\n",
      "admitted: Biological_structure\n",
      "intensive: Biological_structure\n",
      "care: Biological_structure\n",
      "unit: O\n",
      "management: Sign_symptom\n",
      "acute: Distance\n",
      "decompensated: Distance\n",
      "heart: O\n",
      "failure: O\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "started: Medication\n",
      "intravenous: Medication\n",
      "diuretics: Medication\n",
      "inotropic: Medication\n",
      "support: O\n",
      "dobutamine: O\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "next: History\n",
      "several: History\n",
      "days: History\n",
      "symptoms: History\n",
      "improved: Nonbiological_location\n",
      "discharged: Nonbiological_location\n",
      "home: History\n",
      "instructions: Duration\n",
      "follow: O\n",
      "primary: O\n",
      "care: O\n",
      "provider: O\n",
      "1: O\n",
      "week: O\n",
      "\n",
      "\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<span class=\"tex2jax_ignore\"><div class=\"entities\" style=\"line-height: 2.5; direction: ltr\">A \n",
       "<mark class=\"entity\" style=\"background: #f6c3d0; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    57\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Age</span>\n",
       "</mark>\n",
       "-\n",
       "<mark class=\"entity\" style=\"background: #f6c3d0; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    year\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Age</span>\n",
       "</mark>\n",
       "-\n",
       "<mark class=\"entity\" style=\"background: #f6c3d0; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    old\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Age</span>\n",
       "</mark>\n",
       " \n",
       "<mark class=\"entity\" style=\"background: #c5b4e3; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    man\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Sex</span>\n",
       "</mark>\n",
       " \n",
       "<mark class=\"entity\" style=\"background: #f7a399; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    presented\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Nonbiological_location</span>\n",
       "</mark>\n",
       " to the \n",
       "<mark class=\"entity\" style=\"background: #f7a399; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    emergency\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Nonbiological_location</span>\n",
       "</mark>\n",
       " \n",
       "<mark class=\"entity\" style=\"background: #e2f0cb; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    department\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">History</span>\n",
       "</mark>\n",
       " with a \n",
       "<mark class=\"entity\" style=\"background: #f1f0d2; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    2\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Date</span>\n",
       "</mark>\n",
       "-day \n",
       "<mark class=\"entity\" style=\"background: #bde0fe; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    history\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Sign_symptom</span>\n",
       "</mark>\n",
       " of \n",
       "<mark class=\"entity\" style=\"background: #bde0fe; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    worsening\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Sign_symptom</span>\n",
       "</mark>\n",
       " \n",
       "<mark class=\"entity\" style=\"background: #9ddfd3; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    shortness\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Biological_structure</span>\n",
       "</mark>\n",
       " of \n",
       "<mark class=\"entity\" style=\"background: #bde0fe; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    breath\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Sign_symptom</span>\n",
       "</mark>\n",
       " and chest pain. He \n",
       "<mark class=\"entity\" style=\"background: #77c5d5; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    reported\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Clinical_event</span>\n",
       "</mark>\n",
       " no \n",
       "<mark class=\"entity\" style=\"background: #f7a399; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    recent\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Nonbiological_location</span>\n",
       "</mark>\n",
       " \n",
       "<mark class=\"entity\" style=\"background: #f1f0d2; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    travel\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Date</span>\n",
       "</mark>\n",
       " or sick contacts. His medical \n",
       "<mark class=\"entity\" style=\"background: #e2f0cb; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    history\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">History</span>\n",
       "</mark>\n",
       " was \n",
       "<mark class=\"entity\" style=\"background: #e2f0cb; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    significant\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">History</span>\n",
       "</mark>\n",
       " for \n",
       "<mark class=\"entity\" style=\"background: #e2f0cb; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    hypertension\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">History</span>\n",
       "</mark>\n",
       ", \n",
       "<mark class=\"entity\" style=\"background: #e2f0cb; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    dyslipidemia\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">History</span>\n",
       "</mark>\n",
       ", and \n",
       "<mark class=\"entity\" style=\"background: #e2f0cb; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    type\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">History</span>\n",
       "</mark>\n",
       " \n",
       "<mark class=\"entity\" style=\"background: #e2f0cb; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    2\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">History</span>\n",
       "</mark>\n",
       " \n",
       "<mark class=\"entity\" style=\"background: #e2f0cb; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    diabetes\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">History</span>\n",
       "</mark>\n",
       " \n",
       "<mark class=\"entity\" style=\"background: #f7a399; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    mellitus\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Nonbiological_location</span>\n",
       "</mark>\n",
       ". On \n",
       "<mark class=\"entity\" style=\"background: #c5b4e3; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    examination\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Diagnostic_procedure</span>\n",
       "</mark>\n",
       ", he was \n",
       "<mark class=\"entity\" style=\"background: #bde0fe; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    tachycardic\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Sign_symptom</span>\n",
       "</mark>\n",
       " and \n",
       "<mark class=\"entity\" style=\"background: #c5b4e3; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    tachypneic\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Diagnostic_procedure</span>\n",
       "</mark>\n",
       ", with \n",
       "<mark class=\"entity\" style=\"background: #c5b4e3; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    oxygen\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Diagnostic_procedure</span>\n",
       "</mark>\n",
       " \n",
       "<mark class=\"entity\" style=\"background: #f4b3c2; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    saturation\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Lab_value</span>\n",
       "</mark>\n",
       " of \n",
       "<mark class=\"entity\" style=\"background: #ffb347; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    88\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Detailed_description</span>\n",
       "</mark>\n",
       "% on \n",
       "<mark class=\"entity\" style=\"background: #bde0fe; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    room\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Sign_symptom</span>\n",
       "</mark>\n",
       " air. \n",
       "<mark class=\"entity\" style=\"background: #9ddfd3; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    Chest\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Biological_structure</span>\n",
       "</mark>\n",
       " \n",
       "<mark class=\"entity\" style=\"background: #c5b4e3; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    radiography\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Diagnostic_procedure</span>\n",
       "</mark>\n",
       " \n",
       "<mark class=\"entity\" style=\"background: #ffb347; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    revealed\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Detailed_description</span>\n",
       "</mark>\n",
       " bilateral \n",
       "<mark class=\"entity\" style=\"background: #9ddfd3; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    opacities\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Biological_structure</span>\n",
       "</mark>\n",
       " \n",
       "<mark class=\"entity\" style=\"background: #bde0fe; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    consistent\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Sign_symptom</span>\n",
       "</mark>\n",
       " with pulmonary edema. The patient was \n",
       "<mark class=\"entity\" style=\"background: #9ddfd3; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    admitted\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Biological_structure</span>\n",
       "</mark>\n",
       " to the \n",
       "<mark class=\"entity\" style=\"background: #9ddfd3; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    intensive\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Biological_structure</span>\n",
       "</mark>\n",
       " \n",
       "<mark class=\"entity\" style=\"background: #9ddfd3; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    care\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Biological_structure</span>\n",
       "</mark>\n",
       " unit for \n",
       "<mark class=\"entity\" style=\"background: #bde0fe; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    management\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Sign_symptom</span>\n",
       "</mark>\n",
       " of \n",
       "<mark class=\"entity\" style=\"background: #bde0fe; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    acute\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Distance</span>\n",
       "</mark>\n",
       " \n",
       "<mark class=\"entity\" style=\"background: #bde0fe; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    decompensated\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Distance</span>\n",
       "</mark>\n",
       " heart failure. He was \n",
       "<mark class=\"entity\" style=\"background: #f9d5e5; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    started\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Medication</span>\n",
       "</mark>\n",
       " on \n",
       "<mark class=\"entity\" style=\"background: #f9d5e5; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    intravenous\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Medication</span>\n",
       "</mark>\n",
       " \n",
       "<mark class=\"entity\" style=\"background: #f9d5e5; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    diuretics\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Medication</span>\n",
       "</mark>\n",
       " and \n",
       "<mark class=\"entity\" style=\"background: #f9d5e5; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    inotropic\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Medication</span>\n",
       "</mark>\n",
       " support with dobutamine. Over the \n",
       "<mark class=\"entity\" style=\"background: #e2f0cb; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    next\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">History</span>\n",
       "</mark>\n",
       " \n",
       "<mark class=\"entity\" style=\"background: #e2f0cb; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    several\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">History</span>\n",
       "</mark>\n",
       " \n",
       "<mark class=\"entity\" style=\"background: #e2f0cb; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    days\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">History</span>\n",
       "</mark>\n",
       ", his \n",
       "<mark class=\"entity\" style=\"background: #e2f0cb; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    symptoms\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">History</span>\n",
       "</mark>\n",
       " \n",
       "<mark class=\"entity\" style=\"background: #f7a399; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    improved\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Nonbiological_location</span>\n",
       "</mark>\n",
       " and he was \n",
       "<mark class=\"entity\" style=\"background: #f7a399; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    discharged\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Nonbiological_location</span>\n",
       "</mark>\n",
       " to \n",
       "<mark class=\"entity\" style=\"background: #e2f0cb; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    home\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">History</span>\n",
       "</mark>\n",
       " with \n",
       "<mark class=\"entity\" style=\"background: #ffdfba; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    instructions\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Duration</span>\n",
       "</mark>\n",
       " to follow up with his primary care provider in 1 week.</div></span>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predict_multi_line_text(\n",
    "    \n",
    "    \"A 57-year-old man presented to the emergency department with a 2-day history of worsening shortness of breath and chest pain. He reported no recent travel or sick contacts. His medical history was significant for hypertension, dyslipidemia, and type 2 diabetes mellitus. On examination, he was tachycardic and tachypneic, with oxygen saturation of 88% on room air. Chest radiography revealed bilateral opacities consistent with pulmonary edema. The patient was admitted to the intensive care unit for management of acute decompensated heart failure. He was started on intravenous diuretics and inotropic support with dobutamine. Over the next several days, his symptoms improved and he was discharged to home with instructions to follow up with his primary care provider in 1 week.\",\n",
    "    model_2, \n",
    "    index_to_label,\n",
    "    acronyms_to_entities, \n",
    "    MAX_LENGTH\n",
    "    \n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7589ef46",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
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   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
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