1592 lines (1591 with data), 416.6 kB

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "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": 4,
   "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": 5,
   "id": "89395fb0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# from importlib import reload\n",
    "\n",
    "# import utils\n",
    "# reload(utils)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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": 7,
   "id": "aae668e2",
   "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": 10,
   "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": "code",
   "execution_count": 11,
   "id": "2fb48161",
   "metadata": {},
   "outputs": [],
   "source": [
    "INPUT_DIM = len(tokenizer.word_index)+1\n",
    "EMBEDDING_DIM = 216\n",
    "NUM_CLASSES = len(label_to_index)\n",
    "MAX_LENGTH = train_sequences_padded.shape[1]\n",
    "LSTM_UNITS = 64\n",
    "DROPOUT = 0.2\n",
    "BATCH_SIZE = 32\n",
    "EPOCHS = 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e828b34f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((3038, 100), (3038, 100, 79))"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_sequences_padded.shape, train_labels.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08ff4d01",
   "metadata": {},
   "source": [
    "train_sequences_padded is a 2D NumPy array with shape (3038, 100), which represents the padded training sequences. It has 3038 rows, where each row represents a sequence of maximum length 100 with padded zeros.\n",
    "\n",
    "train_labels is a 3D NumPy array with shape (3038, 100, 79), which represents the one-hot encoded training labels. It has 3038 samples, 100 timesteps, and 79 classes (including the padding class)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47692a34",
   "metadata": {},
   "source": [
    "## Building Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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, 216)          1505304   \n",
      "                                                                 \n",
      " bidirectional (Bidirectiona  (None, 100, 128)         143872    \n",
      " l)                                                              \n",
      "                                                                 \n",
      " dense (Dense)               (None, 100, 79)           10191     \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 1,659,367\n",
      "Trainable params: 1,659,367\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=LSTM_UNITS, return_sequences=True)),\n",
    "    Dense(NUM_CLASSES, activation='softmax')\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 11:17:46.056238: 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 [==============================] - 24s 221ms/step - loss: 0.9078 - accuracy: 0.8874 - precision: 0.8346 - recall: 0.7405 - f1_score: 0.7834 - val_loss: 0.3780 - val_accuracy: 0.9129 - val_precision: 0.9896 - val_recall: 0.8872 - val_f1_score: 0.9356\n",
      "Epoch 2/20\n",
      "95/95 [==============================] - 15s 163ms/step - loss: 0.3853 - accuracy: 0.9065 - precision: 0.9963 - recall: 0.8789 - f1_score: 0.9339 - val_loss: 0.3665 - val_accuracy: 0.9129 - val_precision: 0.9868 - val_recall: 0.8928 - val_f1_score: 0.9374\n",
      "Epoch 3/20\n",
      "95/95 [==============================] - 17s 181ms/step - loss: 0.3640 - accuracy: 0.9080 - precision: 0.9961 - recall: 0.8872 - f1_score: 0.9384 - val_loss: 0.3618 - val_accuracy: 0.9150 - val_precision: 0.9840 - val_recall: 0.8985 - val_f1_score: 0.9393\n",
      "Epoch 4/20\n",
      "95/95 [==============================] - 14s 152ms/step - loss: 0.3408 - accuracy: 0.9136 - precision: 0.9955 - recall: 0.8945 - f1_score: 0.9422 - val_loss: 0.3536 - val_accuracy: 0.9179 - val_precision: 0.9818 - val_recall: 0.9014 - val_f1_score: 0.9399\n",
      "Epoch 5/20\n",
      "95/95 [==============================] - 16s 163ms/step - loss: 0.3044 - accuracy: 0.9221 - precision: 0.9951 - recall: 0.8983 - f1_score: 0.9442 - val_loss: 0.3316 - val_accuracy: 0.9240 - val_precision: 0.9816 - val_recall: 0.9040 - val_f1_score: 0.9412\n",
      "Epoch 6/20\n",
      "95/95 [==============================] - 17s 179ms/step - loss: 0.2613 - accuracy: 0.9340 - precision: 0.9942 - recall: 0.9049 - f1_score: 0.9475 - val_loss: 0.3181 - val_accuracy: 0.9302 - val_precision: 0.9789 - val_recall: 0.9075 - val_f1_score: 0.9418\n",
      "Epoch 7/20\n",
      "95/95 [==============================] - 17s 177ms/step - loss: 0.2216 - accuracy: 0.9438 - precision: 0.9928 - recall: 0.9130 - f1_score: 0.9512 - val_loss: 0.3114 - val_accuracy: 0.9328 - val_precision: 0.9752 - val_recall: 0.9132 - val_f1_score: 0.9432\n",
      "Epoch 8/20\n",
      "95/95 [==============================] - 17s 177ms/step - loss: 0.1861 - accuracy: 0.9530 - precision: 0.9908 - recall: 0.9242 - f1_score: 0.9563 - val_loss: 0.3041 - val_accuracy: 0.9358 - val_precision: 0.9709 - val_recall: 0.9198 - val_f1_score: 0.9447\n",
      "Epoch 9/20\n",
      "95/95 [==============================] - 17s 181ms/step - loss: 0.1566 - accuracy: 0.9608 - precision: 0.9898 - recall: 0.9358 - f1_score: 0.9620 - val_loss: 0.3032 - val_accuracy: 0.9378 - val_precision: 0.9673 - val_recall: 0.9241 - val_f1_score: 0.9452\n",
      "Epoch 10/20\n",
      "95/95 [==============================] - 16s 170ms/step - loss: 0.1336 - accuracy: 0.9664 - precision: 0.9890 - recall: 0.9458 - f1_score: 0.9669 - val_loss: 0.3085 - val_accuracy: 0.9384 - val_precision: 0.9644 - val_recall: 0.9269 - val_f1_score: 0.9453\n",
      "Epoch 11/20\n",
      "95/95 [==============================] - 17s 174ms/step - loss: 0.1164 - accuracy: 0.9701 - precision: 0.9892 - recall: 0.9537 - f1_score: 0.9711 - val_loss: 0.3149 - val_accuracy: 0.9394 - val_precision: 0.9616 - val_recall: 0.9298 - val_f1_score: 0.9454\n",
      "Epoch 12/20\n",
      "95/95 [==============================] - 16s 170ms/step - loss: 0.1028 - accuracy: 0.9731 - precision: 0.9893 - recall: 0.9599 - f1_score: 0.9744 - val_loss: 0.3211 - val_accuracy: 0.9394 - val_precision: 0.9597 - val_recall: 0.9313 - val_f1_score: 0.9453\n",
      "Epoch 13/20\n",
      "95/95 [==============================] - 16s 173ms/step - loss: 0.0912 - accuracy: 0.9759 - precision: 0.9900 - recall: 0.9645 - f1_score: 0.9771 - val_loss: 0.3223 - val_accuracy: 0.9391 - val_precision: 0.9586 - val_recall: 0.9314 - val_f1_score: 0.9448\n",
      "Epoch 14/20\n",
      "95/95 [==============================] - 17s 174ms/step - loss: 0.0812 - accuracy: 0.9786 - precision: 0.9908 - recall: 0.9685 - f1_score: 0.9796 - val_loss: 0.3348 - val_accuracy: 0.9399 - val_precision: 0.9569 - val_recall: 0.9330 - val_f1_score: 0.9448\n",
      "Epoch 15/20\n",
      "95/95 [==============================] - 16s 174ms/step - loss: 0.0731 - accuracy: 0.9807 - precision: 0.9915 - recall: 0.9718 - f1_score: 0.9815 - val_loss: 0.3455 - val_accuracy: 0.9401 - val_precision: 0.9554 - val_recall: 0.9335 - val_f1_score: 0.9443\n",
      "Epoch 16/20\n",
      "95/95 [==============================] - 17s 174ms/step - loss: 0.0657 - accuracy: 0.9827 - precision: 0.9921 - recall: 0.9746 - f1_score: 0.9833 - val_loss: 0.3530 - val_accuracy: 0.9398 - val_precision: 0.9551 - val_recall: 0.9336 - val_f1_score: 0.9442\n",
      "Epoch 17/20\n",
      "95/95 [==============================] - 17s 175ms/step - loss: 0.0595 - accuracy: 0.9842 - precision: 0.9927 - recall: 0.9771 - f1_score: 0.9849 - val_loss: 0.3645 - val_accuracy: 0.9398 - val_precision: 0.9540 - val_recall: 0.9344 - val_f1_score: 0.9441\n",
      "Epoch 18/20\n",
      "95/95 [==============================] - 17s 179ms/step - loss: 0.0539 - accuracy: 0.9859 - precision: 0.9934 - recall: 0.9791 - f1_score: 0.9862 - val_loss: 0.3704 - val_accuracy: 0.9402 - val_precision: 0.9532 - val_recall: 0.9348 - val_f1_score: 0.9439\n",
      "Epoch 19/20\n",
      "95/95 [==============================] - 17s 182ms/step - loss: 0.0488 - accuracy: 0.9875 - precision: 0.9940 - recall: 0.9814 - f1_score: 0.9877 - val_loss: 0.3789 - val_accuracy: 0.9398 - val_precision: 0.9525 - val_recall: 0.9349 - val_f1_score: 0.9436\n",
      "Epoch 20/20\n",
      "95/95 [==============================] - 17s 178ms/step - loss: 0.0439 - accuracy: 0.9891 - precision: 0.9946 - recall: 0.9832 - f1_score: 0.9888 - val_loss: 0.3874 - val_accuracy: 0.9397 - val_precision: 0.9520 - val_recall: 0.9354 - val_f1_score: 0.9436\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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b48ePN4qLi6u9brdu3Qyz2Wzs37//mN+LiIiINK6WejxmGIbx888/G8AxL7///rthGIaxa9cuY+TIkYa/v7/h7u5uDBgwwPj222+rvNY777xjnH322UZQUJDh5uZmdOjQwXjooYeMnJwcwzAMo7i42HjooYeM+Ph4w8fHx/Dy8jLi4+ONN99887g1ikjDMhnGUXNARESkVerduzeBgYEkJCQ4uhQREREREWkF1FNKRET466+/WLduHWPGjHF0KSIiIiIi0kpopJSISCu2adMm1qxZw8svv0xGRga7d++2r04jIiIiIiLSkDRSSkSkFZs3bx7jxo2jtLSUzz//XIGUiIiIiIg0Go2UEhERERERERGRRqeRUiIiIiIiIiIi0ugUSomIiIiIiIiISKNzdnQBTZHVauXAgQP4+PhgMpkcXY6IiIg4kGEY5OXlERERgdms3/OOR8dQIiIiArU/flIoVYMDBw4QFRXl6DJERESkCdm3bx9t27Z1dBlNmo6hREREpLITHT8plKqBj48PYPvyfH19HVyNiIiIOFJubi5RUVH24wM5Nh1DiYiICNT++EmhVA0qhpv7+vrqgEpEREQANB2tFnQMJSIiIpWd6PhJjRFERERERERERKTRKZQSEREREREREZFGp1BKREREREREREQanXpKnQKLxUJpaamjy2iWXFxccHJycnQZIiIiIiIi0kpZrVZKSkocXUazVF/n9AqlToJhGKSkpJCdne3oUpo1f39/wsPD1ThWREREREREGlVJSQl79uzBarU6upRmqz7O6RVKnYSKQCo0NBRPT0+FKnVkGAYFBQWkpaUB0KZNGwdXJCIiIiIiIq2FYRgcPHgQJycnoqKiMJvV2agu6vOcXqFUHVksFnsgFRQU5Ohymi0PDw8A0tLSCA0N1VQ+ERERERERaRRlZWUUFBQQERGBp6eno8tplurrnF5xYB1V9JDSH9xTV/Edqi+XiIiIiIiINBaLxQKAq6urgytp3urjnF6h1EnSlL1Tp+9QREREREREHEXnpKemPr4/hVIiIiIiIiIiItLoFErJSYmNjWXGjBmOLkNERERERERE6qipnNOr0Xkrcu6559KrV696+YP3559/4uXldepFiYiInISSMis70w6z5WAuWw7ksjczn/fH9tMw/BaqzGLlqW+3kJRVwBs39MHTVYewIiLS+rTEc3r9iy52hmFgsVhwdj7xH4uQkJBGqEhERARyi0r550CuPYDafCCXHWl5lFqMKvslZxfSNkALkbREzk5mFq47QE5hKfuyCukc7uPokkRERJqc5nhOr+l7rcTNN9/Mr7/+yquvvorJZMJkMvHhhx9iMpn4/vvv6du3L25ubixbtoxdu3ZxxRVXEBYWhre3N/3792fp0qVVXu/ooX4mk4n33nuPESNG4OnpSceOHfnmm28a+VOKiEhzZhgGB7ILWbolldcSdnDnJ2s464Wf6DnlB0a/u5In/7eFr9bsZ8vBXEotBj7uzgxsF8gtZ7TjpWvi8XF3cfRHkAYUHWgLHBMz8x1ciYiISONrqef0GilVDwzDoLDU0ujv6+HiVOtpCq+++irbt2+ne/fuTJ06FYDNmzcDMHHiRF566SXat29PQEAA+/btY9iwYTzzzDO4ubnx8ccfM3z4cLZt20Z0dPQx3+PJJ5/khRde4MUXX+T111/nhhtuIDExkcDAwFP/sCIi0qKUWazsSs9ny8EctlQaBXWooOYlhSP9PejSxpduEb50jfClaxtf2gZ4aLpeKxId5MnG5BySsgocXYqIiLQwjjqnh9qf17fUc3qFUvWgsNRC18lLGv19t0wdWuueCn5+fri6uuLp6Ul4eDgAW7duBWDq1KlceOGF9n0DAwOJj4+333/qqaf4+uuv+eabb7jnnnuO+R4333wz1113HQDPPvssr732GqtXr+biiy+u82cTEZGWI7+4jK0ptml3FQHU1pQ8Ssqs1fZ1MpvoGOpN1zbl4VN5AOXv6eqAyqUpibGPlFIoJSIi9ctR5/RQ+/P6lnpOr1BK6NevX5X7hw8fZsqUKSxatIiDBw9SVlZGYWEhSUlJx32dnj172m97eXnh6+tLWlpag9QsIiJNU1pe0ZHwqTyA2puZj2FU39fL1ckeOnWN8KVbhB+nhXrj7uLU+IVLkxcTZAulNFJKRESkquZ8Tq9Qqh54uDixZepQh7xvfTi64/5//vMffvzxR1566SVOO+00PDw8GDlyJCUlJcd9HReXqr08TCYTVmv1X8FFRKT5s1oN9mbm2wKog0dGQWUcLq5x/3Bf9yoBVNc2vkQHemI2a/qd1E5UoEIpERFpGI46p69471PVnM/pFUrVA5PJ1CyWJnZ1dcViOfE82T/++IObb76ZESNGALaUde/evQ1cnYiINFVFpRa2p+ZVmX73z8FcCkqq/5tiNkH7EO9Ko5986dLGl2BvNwdULi1JTJDtgHv/oQIsVgMnBZoiIlJPdE7vOE3/W5d6Exsby6pVq9i7dy/e3t7HTDw7duzIggULGD58OCaTiccff1wjnkREWonsghJ78FQRQu1MP4zFWn3+nZuzmbiK5uPl153DfZrFQZ00P+G+7rg6mSmxWDmQXWgfOSUiItJatMRzeh01tiL/+c9/GDt2LF27dqWwsJAPPvigxv2mT5/OLbfcwuDBgwkODubhhx8mNze3kasVEZGGZBgGB3KK2JRsW/1u8wHb6Kfk7MIa9w/wdKFbhF+V1e/aBXvh7GRu5MqltXIym2gb6MHu9Hz2ZRUolBIRkVanJZ7TmwyjptajrVtubi5+fn7k5OTg6+tb5bGioiL27NlDu3btcHd3d1CFLYO+SxGRxmEYBsnZhWxKzmFjcg4bk3PZnJxDZn7NfQWiAj3o1sbPPv2ua4Qv4b7utVquuCU63nGBVNXQ39W4D1bz87Z0pl3Vg+sGHHtJaxERkePRuWj9ON73WNtjAo2UEhERaUEMw2D/ocoBVA6bknM4VFBabV8ns4mOod50j/Sz94Dq0sYXPw+XGl5ZxPGiy0dHJWaq2bmIiEhLoFBKRESkmTIMg31Zhbbg6UCOPYjKriGAcjab6BTmQ49IP7q39aNHpB9x4T6419NKriKNIbq82XlSVr6DKxEREZH6oFBKRESkGTAMg6SsgiqjnzYl55JTWD2AcnGqFEBF2gKozgqgpAWI0UgpERGRFkWhlIiISBNjsRrsycjnn4O59tFPm5JzyC0qq7avi5OJuHBfukf6Vgmg3JwVQEnLExNkC6WSMgswDKPV9jkTERFpKRRKiYiIOFBOYSlbD9pWvvvnYB7/pOSyLSWP4rLqy/a6OpmJa+NjD596RPrRKcwHV2etgCetQ8WKe3nFZWQXlBLg5ergikRERORUKJQSERFpBFarQWJWQXn4dCSESs4urHF/DxcnOof70C3C1z4NTwGUtHbuLk6E+bqRmltMYlaBQikREZFmTqGUiIhIPcsrKmVbSh7/HMxly0Hb9baUPApLLTXuH+nvQZc2PnRp42u/RAd64mTW1CSRo8UEetlCqcx8ekX5O7ocEREROQUKpURERE6S1Wqw71CBbdpdxeinlFz2ZdU8+snN2UzncB+6hPvaQ6i4Nr74ebg0cuUizVd0kCer92axL0vNzkVERJo7hVIiIiK1VFBSxtrEbFbvyWTVniw2H8jlcHH15uMA4b7u1UY/xQZ54uyk6Xcip0Ir8ImIiLQcCqWk1mJjY3nggQd44IEHHF2KiEijyCko5a/ELFbvyWLVniw2JedQZjWq7OPqZKZjmHel8Mk2Ekq9bkQaRnT5CnyJGiklIiJSJ03xnF6hlIiISLn0vGL+3HskhNqakotRNYMiws+dge2DGNAukD7RAbQP8cJFo59EGk10+UipJI2UEhERafYUSomISKu1/1BBlRBqd3p+tX3ah3gxIDaQAe1sl7YBng6oVEQqxAR5AZCSW0RRqQV3FycHVyQiIiInS6FUK/Huu+8yZcoU9u/fj9l85Bf9K664gqCgIB599FEmTJjAypUryc/Pp0uXLkybNo0hQ4Y4sGoRkfpjGAa7M/JZvSfLfknOrtqQ3GSCuHBfBpYHUP1jAwnxcXNQxSJSkwBPF3zcnMkrLmP/oQJOC/VxdEkiIiINrqWe0zt8vsEbb7xBbGws7u7uDBw4kNWrVx9z39LSUqZOnUqHDh1wd3cnPj6exYsXV9nHYrHw+OOP065dOzw8POjQoQNPPfUUxtHzL+qTYUBJfuNf6vCZrrnmGjIzM/n555/t27Kysli8eDE33HADhw8fZtiwYSQkJPD3339z8cUXM3z4cJKSkhriGxMRaXBWq8GWA7l8+Mce7p6zhv7PLOWCl39l0oKNfP13MsnZhTiZTfSK8udfZ7fn/bH9WPf4RXx//1lMubwbw3q0USAl0gSZTCai1OxcRETqk6PO6etwXt9Sz+kdOlJq7ty5TJgwgbfffpuBAwcyY8YMhg4dyrZt2wgNDa22/2OPPcann37KrFmziIuLY8mSJYwYMYLly5fTu3dvAJ5//nneeustPvroI7p168Zff/3FuHHj8PPz47777muYD1JaAM9GNMxrH88jB8DVq1a7BgQEcMkll/DZZ59xwQUXADBv3jyCg4M577zzMJvNxMfH2/d/6qmn+Prrr/nmm2+45557GqR8EZH6ZLUabDmYy8rdmazcncnqPVnkFlVdGc/V2UzvKP/ykVBB9I72x8tNg4ZFmpuYIE+2HMxVKCUiIvXDUef0UOvz+pZ6Tu/QI/Hp06dz++23M27cOADefvttFi1axOzZs5k4cWK1/T/55BMeffRRhg0bBsBdd93F0qVLefnll/n0008BWL58OVdccQWXXnopYOsu//nnnx93BFZrccMNN3D77bfz5ptv4ubmxpw5c7j22msxm80cPnyYKVOmsGjRIg4ePEhZWRmFhYVNPlUVkdbLajXYlprHil22EGrVnixyCkur7OPt5kzfmAB7P6iebf1wc1b/GZHmrmIFviStwCciIq1ISzynd1goVVJSwpo1a5g0aZJ9m9lsZsiQIaxYsaLG5xQXF+Pu7l5lm4eHB8uWLbPfHzx4MO+++y7bt2+nU6dOrF+/nmXLljF9+vRj1lJcXExxcbH9fm5ubt0+jIunLd1sbC51a7Y7fPhwDMNg0aJF9O/fn99//51XXnkFgP/85z/8+OOPvPTSS5x22ml4eHgwcuRISkpKGqJyEZE6MwyDHWmH7SHUyt2ZHCqoHkL1jw1gUIcgTm8fRNc2vjhrZTyRFicm0PaLskIpERGpF446p69471pqief0DgulMjIysFgshIWFVdkeFhbG1q1ba3zO0KFDmT59OmeffTYdOnQgISGBBQsWYLFY7PtMnDiR3Nxc4uLicHJywmKx8Mwzz3DDDTccs5Zp06bx5JNPnvyHMZlqPY3Okdzd3bnqqquYM2cOO3fupHPnzvTp0weAP/74g5tvvpkRI0YAcPjwYfbu3evAakWktTMMg13ph1mxO4uV5UFUZn7Vf1Q9XZ3oFxvIoPZBDOoQRPcIhVAirUG0vadU9RUzRURE6kzn9A7TrBppvPrqq9x+++3ExcVhMpno0KED48aNY/bs2fZ9vvzyS+bMmcNnn31Gt27dWLduHQ888AARERGMHTu2xtedNGkSEyZMsN/Pzc0lKiqqwT+PI9xwww1cdtllbN68mRtvvNG+vWPHjixYsIDhw4djMpl4/PHHsVqtDqxURFobwzDYk5HPit2ZrNydxcrdmaTnFVfZx93FTP/YQE5vbxsJ1bOtHy4KoURanZjy6Xv7DhVitRqYzSYHVyQiItI4Wto5vcNCqeDgYJycnEhNTa2yPTU1lfDw8BqfExISwsKFCykqKiIzM5OIiAgmTpxI+/bt7fs89NBDTJw4kWuvvRaAHj16kJiYyLRp044ZSrm5ueHm1jpWWDr//PMJDAxk27ZtXH/99fbt06dP55ZbbmHw4MEEBwfz8MMP130ao4hIHRiGQVJWASt2ZZYHUZmk5lYNodyczfSNCWBQ+yBO7xBEfFt/XJ0VQom0dm383HE2mygps5KSW0SEv4ejSxIREWkULe2c3mGhlKurK3379iUhIYErr7wSAKvVSkJCwgk7w7u7uxMZGUlpaSnz589n1KhR9scKCgowm6uesDg5OTWLhLAxmM1mDhyoPlc2NjaWn376qcq28ePHV7nfHIb+iUjTdri4jF+2pfHT1jRW7srkQE5Rlcddncz0jvZnUIcgBrUPole0vxqTi0g1zk5m2gZ4sDezgMTMAoVSIiLSarS0c3qHTt+bMGECY8eOpV+/fgwYMIAZM2aQn59vX41vzJgxREZGMm3aNABWrVpFcnIyvXr1Ijk5mSlTpmC1Wvnvf/9rf83hw4fzzDPPEB0dTbdu3fj777/tiaGIiDS+jMPFLN2SypLNKfyxM5MSy5EfCVycTPSOCuD09oGc3iGIPtEBuLsohBKRE4sO8mJvZgH7sgoY1CHI0eWIiIjISXBoKDV69GjS09OZPHkyKSkp9OrVi8WLF9ubnyclJVUZ9VRUVMRjjz3G7t278fb2ZtiwYXzyySf4+/vb93n99dd5/PHHufvuu0lLSyMiIoJ//etfTJ48ubE/nohIq5WUWcAPW1JYsjmFvxIPYRhHHmsX7MVFXcM4q2MIfWMC8HBVCCUidRcdaBsdlZilZuciIiLNlcMbnd9zzz3HnK73yy+/VLl/zjnnsGXLluO+no+PDzNmzGDGjBn1VKGIiJyIYRhsOZjLD5ttI6K2puRVebxHpB9Du4UxtFs4p4V6YzKpKbGInJqYQNsqSYmZBQ6uRERERE6Ww0MpERFpnixWg7/2ZvHDllR+2JLCvqxC+2NOZhMD2wVyUdcwLuoWrn4vIg7yxhtv8OKLL5KSkkJ8fDyvv/46AwYMOOb+M2bM4K233iIpKYng4GBGjhzJtGnTcHd3b8Sqaye6fAW+pCyFUiIiIs2VQikREam1olILf+zM4IfNqSz9J5XM/BL7Y+4uZs7uGMJF3cK5IC6UAC9XB1YqInPnzmXChAm8/fbbDBw4kBkzZjB06FC2bdtGaGhotf0/++wzJk6cyOzZsxk8eDDbt2/n5ptvxmQyMX36dAd8guOLUSglIiLS7CmUOklaze/U6TsUaR5yi0r5eWsaSzan8Mu2dApKLPbH/DxcuKBLKEO7hXN2xxD1hxJpQqZPn87tt99uX0Dm7bffZtGiRcyePZuJEydW23/58uWcccYZ9uWlY2Njue6661i1alWj1l1bUQG2UCq7oJScwlL8PFwcXJGIiDQ3RuXGp1Jn9XFOr1CqjlxdXe1LMIaEhODq6qreKHVkGAYlJSWkp6djNptxddVoCpGmJi23iB/KV8xbuTuTUsuRf7Db+LlzUVdbf6j+7QJxcTIf55VExBFKSkpYs2YNkyZNsm8zm80MGTKEFStW1PicwYMH8+mnn7J69WoGDBjA7t27+e6777jpppuO+T7FxcUUFxfb7+fm5tbfhzgBLzdngr3dyDhcTFJmAT3a+jXae4uISPPm4uKCyWQiPT2dkJAQndPXUX2e0yuUqiOz2Uy7du04ePAgBw4ccHQ5zZqnpyfR0dFVVlgUEcc5mFPIog0H+W7jQdYmZVd57LRQb3uj8h6RfvqHW6SJy8jIwGKx2Fc0rhAWFsbWrVtrfM71119PRkYGZ555JoZhUFZWxp133skjjzxyzPeZNm0aTz75ZL3WXhcxQZ5kHC4mMStfoZSIiNSak5MTbdu2Zf/+/ezdu9fR5TRb9XFOr1DqJLi6uhIdHU1ZWRkWi+XET5BqnJyccHZ21omtiIOl5Rbx3caDfLvhIH8lHqryWO9ofy7qGs5F3cLoEOLtoApFpLH88ssvPPvss7z55psMHDiQnTt3cv/99/PUU0/x+OOP1/icSZMmMWHCBPv93NxcoqKiGqtkYgI9WZN4SCvwiYhInXl7e9OxY0dKS0sdXUqzVF/n9AqlTpLJZMLFxQUXF/UvEJHmJT2vmMWbbEHU6r1ZVJ5K3z82gMt6RnBx93DCfJvealsiUjvBwcE4OTmRmppaZXtqairh4eE1Pufxxx/npptu4rbbbgOgR48e5Ofnc8cdd/Doo4/W+Cuom5sbbm5u9f8BaqliBb59anYuIiInwcnJCScn9UR1JIVSIiKtQFZ+CYs3pfDthgOs3J2JtVIQ1Sfan0t7RnBpjzaE+ymIEmkJXF1d6du3LwkJCVx55ZWArRlpQkIC99xzT43PKSgoqBY8VRyoN9VGsNGBtlBKI6VERESaJ4VSIiItVHZBCUs2p/DthoMs35WJpVISFd/Wj8t6RnBJj3Dalq9gJSIty4QJExg7diz9+vVjwIABzJgxg/z8fPtqfGPGjCEyMpJp06YBMHz4cKZPn07v3r3t0/cef/xxhg8f3mR/RY4pHymVpJFSIiIizZJCKRGRFiS3qJQfNqeyaMMBft+RQVmlIKpbhC+XlY+IqpjyIiIt1+jRo0lPT2fy5MmkpKTQq1cvFi9ebG9+npSUVGVk1GOPPYbJZOKxxx4jOTmZkJAQhg8fzjPPPOOoj3BC0YFeABzIKaS4zIKbc9MMz0RERKRmJqOpjsd2oNzcXPz8/MjJycHX19fR5YiIHFdeUSkJ/6Tx7YYD/LY9gxKL1f5YXLgPl/Vsw6U9I2gX7OXAKkWaLx0X1F5jf1eGYdDtiSUUlFj46d/n0F6LMoiIiDQJtT0m0EgpEZFmKL+4jIStaSzacICft6VTUnYkiDot1JvLerbhsp5tOC3Ux4FViog0LJPJRHSgJ1tT8kjMKlAoJSIi0swolBIRaSaKyyz8vDWNb9Yf4KetaRSVHgmi2gd72UdEdQ5XECUirUdFKJWkZuciIiLNjkIpEZEmzDAM1iYdYv7aZBZtOEhOYan9sehAz/IRURF0aeODyWRyYKUiIo5R0excK/CJiIg0PwqlRESaoKTMAhb8vZ+v/06ucqIV7uvO5b0iGN4zgu6RvgqiRKTViw6y9ctLysp3cCUiIiJSVwqlRESaiJyCUhZtPMiCtfv5K/GQfbunqxMXdw/nqt5tGdQhCCezgigRkQoxgbaRUklZGiklIiLS3CiUEhFxoFKLlV+3pbPg7/0s/SfN3rDcZIIzTwtmRO9IhnYLx8tNf12LiNQkulIoZRiGRpCKiIg0IzrLERFpZIZhsDE5hwVrk/lm/QGy8kvsj3UO8+GqPpFc0SuScD93B1YpItI8RAZ44GQ2UVRqJS2vmDBf/d0pIiLSXCiUEhFpJMnZhSz8O5kFa/ezK/1I75Ngb1eu6BXJVX0i6dpGfaJEROrCxclMhL87+7IKScwsUCglIiLSjCiUEhFpQHlFpXy/KYWv1yazck8mhmHb7uZs5qJu4VzVJ5KzTgvG2cns2EJFRJqxmEAv9mUVkpRVwIB2gY4uR0RERGpJoZSISD0rs1hZtjODr/9OZsnmFIpKrfbHTm8fyFW923Jxj3B83V0cWKWISMsRVdFXKlMr8ImIiDQnCqVEROrJzrQ85v65j4XrDpCeV2zf3j7Ei6t6R3Jl70jaBng6sEIRkZYpJsj2d2uiVuATERFpVhRKiYicAsMw+GNnJu8t280v29Lt2wM8Xbg8PoIRfdoS39ZPfaJERBpQTPlIqcRMhVIiIiLNiUIpEZGTUFxm4f/WHWD2sj1sTckDwGSCC+LCGNWvLed2DsXVWX2iREQaQ3T5SKkkjZQSERFpVhRKiYjUQebhYuasSuLjFYlkHLZN0fN0dWJUvyhuHhxLbLCXgysUEWl9ostHSmXll5BXVIqPevaJiIg0CwqlRERqYUdqHrP/2MOCtckUl9kal7fxc+fmwbFc2z8aP0+dAImIOIqPuwuBXq5k5ZeQlFVAtwg/R5ckIiIitaBQSkTkGAzDYNnODN77fQ+/bj/SL6pnWz9uPbMdw3q0wcVJU/RERJqC6EBPWyiVqVBKRESkuVAoJSJylKJSC9+sP8D7v+9hW+qRflEXdQ3jtrPa0y8mQI3LRUSamJggT9bty9YKfCIiIs2IQikRkXKZh4v5dGUSn6zcS8bhEuBIv6hxZ8QSE6R+USIiTVXFCnxqdi4iItJ8KJQSkVavol/U/LXJlBzdL2pANH4e6hclItLURVWEUpkKpURERJoLhVIi0iodr1/UbWe155Lu4eoXJSLSjFSMZk3MyndwJSIiIlJbCqVEpFUpKrXwzboDvL+sar+ooV3DufWsduoXJSLSTMUE2UZKHcguotRi1Q8LIiIizYBCKRFpFXKLSvlg2V71ixIRaaFCfdxwczZTXGYl+VAhscH6e11ERKSpUyglIi3er9vTmTh/AwdzigCI8HPn5jNiGd1f/aJERFoKk8lEdKAnO9IOk5RVoFBKRESkGVAoJSItVl5RKc9+9w+fr94H2KZ2/PuizuoXJSLSQsUE2UKpRK3AJyIi0iwolBKRFmnZjgwenr+B5OxCAG4eHMt/L+6Mp6v+2hMRaamiA22jo5Iy1excRESkOdDZmYi0KIeLy5j23T/MWZUEQFSgBy+OjOf09kEOrkxERBpaRbPzxEyNlBIREWkOFEqJSIuxfFcG/523gf2HbKOjxgyK4eGL4/By0191IiKtQXR5KJWk6XsiIiLNgs7URKTZyy8u4/nFW/l4RSIAkf4evDiyJ4NPC3ZwZSIi0piiA4+EUoZhYDKZHFyRiIiIHI9CKRFp1lbtzuSheRvsv4rfMDCaScO64K3RUSIirU7bAA9MJigosZBxuIQQHzdHlyQiIiLHobM2EWmWCkssvLBkKx/8sReACD93nh/Zk7M6hji2MBERcRg3Zyci/DxIzi4kKStfoZSIiEgTp1BKRJqdP/dm8dBX69lb3sj22v5RPHppF3zcXRxcmYiIOFp0oCfJ2YUkZhbQNybQ0eWIiIjIcSiUEpFmo6jUwktLtvH+H3swDGjj585zV/fknE4aHSUiIjbRgZ6s2J2pZuciIiLNgEIpEWkW1iQe4qGv1rM7Ix+AUf3a8thlXfHV6CgREanEvgJfpkIpERGRpk6hlIg0aUWlFl75cTuzft+N1YAwXzeeu6on58WFOro0ERFpgmLKQ6lEjZQSERFp8hRKiUiT9XfSIf7z1Xp2pdtGR13VJ5InLuuGn6dGR4mISM1iAr0ASNRIKRERkSZPoZSINDnFZRZmLN3BO7/uwmpAiI8b00b0YEjXMEeXJiIiTVx0oG2kVMbhYvKLy/By0+GuiIhIU6V/pUWkSdmwP5v/fLWe7amHAbiyVwRTLu+Gv6ergysTEZHmwM/TBT8PF3IKS9l3qIC4cF9HlyQiIiLHYHZ0AW+88QaxsbG4u7szcOBAVq9efcx9S0tLmTp1Kh06dMDd3Z34+HgWL15cbb/k5GRuvPFGgoKC8PDwoEePHvz1118N+TFE5BQVl9lW1hvx5nK2px4m2NuVd27qy4xreyuQEhGROrH3ldIUPhERkSbNoaHU3LlzmTBhAk888QRr164lPj6eoUOHkpaWVuP+jz32GO+88w6vv/46W7Zs4c4772TEiBH8/fff9n0OHTrEGWecgYuLC99//z1btmzh5ZdfJiAgoLE+lojU0b6sAq6Y+Qczf96JxWowPD6CHx48h6Hdwh1dmoiINEMVU/i0Ap+IiEjTZjIMw3DUmw8cOJD+/fszc+ZMAKxWK1FRUdx7771MnDix2v4RERE8+uijjB8/3r7t6quvxsPDg08//RSAiRMn8scff/D777+fdF25ubn4+fmRk5ODr6+GfIs0pK0puYx5fzVpecUEebny9JXduaRHG0eXJSJip+OC2msq39WLS7byxs+7uPH0aJ6+sofD6hAREWmtantM4LCRUiUlJaxZs4YhQ4YcKcZsZsiQIaxYsaLG5xQXF+Pu7l5lm4eHB8uWLbPf/+abb+jXrx/XXHMNoaGh9O7dm1mzZjXMhxCRU7Im8RCj3l5BWl4xceE+fHf/WQqkRETklNlHSmUVOrgSEREROR6HhVIZGRlYLBbCwqquphUWFkZKSkqNzxk6dCjTp09nx44dWK1WfvzxRxYsWMDBgwft++zevZu33nqLjh07smTJEu666y7uu+8+Pvroo2PWUlxcTG5ubpWLiDSsX7enc+N7q8gtKqNvTABz7xhEmK/7iZ8oIiJyAtGBXgAkZeY7uBIRERE5Hoc3Oq+LV199lY4dOxIXF4erqyv33HMP48aNw2w+8jGsVit9+vTh2WefpXfv3txxxx3cfvvtvP3228d83WnTpuHn52e/REVFNcbHEWm1/rf+ALd99CeFpRbO6RTCJ7cOwM/TxdFliYhIC1HR6Hz/oULKLFYHVyMiIiLH4rBQKjg4GCcnJ1JTU6tsT01NJTy85ubGISEhLFy4kPz8fBITE9m6dSve3t60b9/evk+bNm3o2rVrled16dKFpKSkY9YyadIkcnJy7Jd9+/adwicTkeP5ZGUi933xN6UWW0PzWWP64enq7OiyRESkBQn3dcfVyUyZ1eBgTpGjyxEREZFjcFgo5erqSt++fUlISLBvs1qtJCQkMGjQoOM+193dncjISMrKypg/fz5XXHGF/bEzzjiDbdu2Vdl/+/btxMTEHPP13Nzc8PX1rXIRkfplGAavJ+zg8YWbMAy48fRoZozuhatzsxqwKSIizYDZbKJtoAcAiVqBT0REpMly6PCECRMmMHbsWPr168eAAQOYMWMG+fn5jBs3DoAxY8YQGRnJtGnTAFi1ahXJycn06tWL5ORkpkyZgtVq5b///a/9NR988EEGDx7Ms88+y6hRo1i9ejXvvvsu7777rkM+o4iA1Wrw9KJ/mP3HHgDuO/80HrywEyaTycGViYhISxUT6Mnu9HySshRKiYiINFUODaVGjx5Neno6kydPJiUlhV69erF48WJ78/OkpKQq/aKKiop47LHH2L17N97e3gwbNoxPPvkEf39/+z79+/fn66+/ZtKkSUydOpV27doxY8YMbrjhhsb+eCIClFmsPDx/I/PX7gdg8mVdueXMdg6uSkREWrqYIC8gncQsNTsXERFpqkyGYRiOLqKpyc3Nxc/Pj5ycHE3lEzkFRaUW7vnsb5b+k4qT2cSLI3tyVZ+2ji5LRKROdFxQe03pu5q9bA9Tv93CJd3DeevGvg6tRUREpLWp7TGBuguLSIPILSrl9o/+YtWeLNyczbxxfR+GdA1zdFkiItJKVKzAp55SIiIiTZdCKRGpdxmHixk7ezWbD+Ti4+bMe2P7MbB9kKPLEhGRViQ60BZK7csqwDAM9TEUERFpghRKiUi92n+ogJveX82ejHyCvFz56JYBdI/0c3RZIiLSykSVh1J5xWUcKigl0MvVwRWJiIjI0bQWu4jUmx2peYx8awV7MvKJ9PfgqzsHKZASERGHcHdxItzXHYDETDU7FxERaYoUSolIvVi3L5tr3llBSm4RHUO9mXfXINqHeDu6LBERacWiy/tKJWWpr5SIiEhTpFBKRE7Zsh0ZXD9rJdkFpcRH+fPlvwbRxs/D0WWJiEgrV9FXSs3ORUREmib1lBKRU/L9xoPc/8U6SixWzjwtmHdu6ouXm/5qERERx4sJ1EgpERGRpkxnjiJy0j5fncSjX2/EasCwHuG8MroXbs5Oji5LREQEqDR9TyOlREREmiSFUiJyUt76ZRfPL94KwHUDonj6yh44mbXctoiINB0xQV4AJGap0bmIiEhTpFBKROrEMAye+34r7/y2G4C7zu3Af4d2xmRSICUiIk1LRU+p1NxiikotuLtoNK+IiEhTokbnIlJrZRYrD8/fYA+kJl0Sx8MXxymQEhFpot544w1iY2Nxd3dn4MCBrF69+rj7Z2dnM378eNq0aYObmxudOnXiu+++a6Rq61+Apws+5X0O96mvlIiISJOjUEpEaqWo1ML4z9by5V/7MZvghat78q9zOji6LBEROYa5c+cyYcIEnnjiCdauXUt8fDxDhw4lLS2txv1LSkq48MIL2bt3L/PmzWPbtm3MmjWLyMjIRq68/phMJntfKa3AJyIi0vRo+p6InNDh4jLu+Pgvlu/KxNXJzGvX9ebi7uGOLktERI5j+vTp3H777YwbNw6At99+m0WLFjF79mwmTpxYbf/Zs2eTlZXF8uXLcXFxASA2NrYxS24QMUGebD6QS6JGSomIiDQ5GiklIsd1uLiMG95bxfJdmXi5OvHhuP4KpEREmriSkhLWrFnDkCFD7NvMZjNDhgxhxYoVNT7nm2++YdCgQYwfP56wsDC6d+/Os88+i8ViaayyG0R0oK3ZeVKmmp2LiIg0NRopJSLHVFxm4V+f/MX6fdkEeLrw4bgBxEf5O7osERE5gYyMDCwWC2FhYVW2h4WFsXXr1hqfs3v3bn766SduuOEGvvvuO3bu3Mndd99NaWkpTzzxRI3PKS4upri42H4/Nze3/j5EPalodq6RUiIiIk2PRkqJSI2sVoN/f7meP3baRkh9fMtABVIiIi2Y1WolNDSUd999l759+zJ69GgeffRR3n777WM+Z9q0afj5+dkvUVFRjVhx7cSU95RKUiglIiLS5CiUEpFqDMNg6rdb+HbDQVycTLx9U196tPVzdFkiIlJLwcHBODk5kZqaWmV7amoq4eE1T8Fu06YNnTp1wsnJyb6tS5cupKSkUFJSUuNzJk2aRE5Ojv2yb9+++vsQ9aRipNT+rEIsVsPB1YiIiEhlCqVEpJo3f9nFh8v3AvDSNfGc1THEsQWJiEiduLq60rdvXxISEuzbrFYrCQkJDBo0qMbnnHHGGezcuROr1Wrftn37dtq0aYOrq2uNz3Fzc8PX17fKpamJ8PfA2WyixGIlJbfI0eWIiIhIJQqlRKSKL//ax4tLtgEw+bKuXNGr+S4FLiLSmk2YMIFZs2bx0Ucf8c8//3DXXXeRn59vX41vzJgxTJo0yb7/XXfdRVZWFvfffz/bt29n0aJFPPvss4wfP95RH6FeOJlNtA3wACBRzc5FRESaFDU6FxG7pVtSmbRgIwB3nduBW85s5+CKRETkZI0ePZr09HQmT55MSkoKvXr1YvHixfbm50lJSZjNR36fjIqKYsmSJTz44IP07NmTyMhI7r//fh5++GFHfYR6Ex3kxd7MAvZlFUAHR1cjIiIiFRRKiQgAaxKzGP/ZWixWg5F92/LfoZ0dXZKIiJyie+65h3vuuafGx3755Zdq2wYNGsTKlSsbuKrGF1OxAl+mmp2LiIg0JZq+JyJsT83jlg//orjMyvlxoUy7qgcmk8nRZYmIiNSLihX4ErUCn4iISJOiUEqklTuQXcjY2avJKSyld7Q/b1zfBxcn/dUgIiItR8UKfEkaKSUiItKk6MxTpBXLLihhzOzVHMwp4rRQb2aP7Y+Hq9OJnygiItKMRFeMlFKjcxERkSZFoZRIK1VYYuGWD/9kZ9phwn3d+fiWAQR41bzkt4iISHNWMVIqt6iMnIJSB1cjIiIiFRRKibRCZRYr93y2lrVJ2fi6O/PxrQOI8PdwdFkiIiINwtPVmRAfNwASszRaSkREpKlQKCXSyhiGwaQFG0nYmoabs5nZN/enU5iPo8sSERFpUFqBT0REpOlRKCXSyry4ZBtfrdmPk9nEG9f3oV9soKNLEhERaXD2ZudagU9ERKTJUCgl0orMXraHN3/ZBcCzI7ozpGuYgysSERFpHBXNzrUCn4iISNNR51AqNjaWqVOnkpSU1BD1iEgD+Wb9AaZ+uwWAh4Z2ZnT/aAdXJCIi0nhiKlbgU08pERGRJqPOodQDDzzAggULaN++PRdeeCFffPEFxcXFDVGbiNST33ek8+8v1wFw8+BY7j63g2MLEhERaWTRgV6ARkqJiIg0JScVSq1bt47Vq1fTpUsX7r33Xtq0acM999zD2rVrG6JGETkFG/fncOcnayi1GFzasw2TL+uKyWRydFkiIiKNqqKn1MHcIorLLA6uRkREROAUekr16dOH1157jQMHDvDEE0/w3nvv0b9/f3r16sXs2bMxDKM+6xSRk7AnI5+bP1hNfomFM04LYvqoeMxmBVIiItL6BHu74unqhGHAvqxCR5cjIiIinEIoVVpaypdffsnll1/Ov//9b/r168d7773H1VdfzSOPPMINN9xQn3WKSB2l5RUxZvYqMvNL6Bbhy9s39sXN2cnRZYmIiDiEyWSyj5bapxX4REREmgTnuj5h7dq1fPDBB3z++eeYzWbGjBnDK6+8QlxcnH2fESNG0L9//3otVERqL6+olJtn/8m+rEJigjz5cNwAfNxdHF2WiIiIQ8UEebI1JY/ETDU7FxERaQrqHEr179+fCy+8kLfeeosrr7wSF5fqJ7rt2rXj2muvrZcCRaRuisss3PHxGrYczCXY25WPbxlAiI+bo8sSERFxuJggW7PzRI2UEhERaRLqHErt3r2bmJiY4+7j5eXFBx98cNJFicjJsVgNHpy7jhW7M/FydeLDcQPsB+AiIiKtXVT59D2twCciItI01LmnVFpaGqtWraq2fdWqVfz111/1UpSI1J1hGDz5v818tzEFFycT747pR/dIP0eXJSIi0mTEVIRSGiklIiLSJNQ5lBo/fjz79u2rtj05OZnx48fXS1EiUnczf9rJxysSMZngldG9OOO0YEeXJCIi0qTEBB0JpaxWrRQtIiLiaHUOpbZs2UKfPn2qbe/duzdbtmypl6JEpG6+WJ3Eyz9uB+CJy7pyWc8IB1ckIiLS9ET4e+BkNlFcZiUtr9jR5YiIiLR6dQ6l3NzcSE1Nrbb94MGDODvXuUWViJyiHzan8MjXGwEYf14Hbj6jnYMrEhERaZpcnMxE+LsDaAU+ERGRJqDOodRFF13EpEmTyMnJsW/Lzs7mkUce4cILL6zX4kTk+Nbvy+a+L/7GasCofm35z0WdHV2SiIhIkxYTqBX4REREmoo6D2166aWXOPvss4mJiaF3794ArFu3jrCwMD755JN6L1BEapacXchtH/9FUamVczqF8OyIHphMJkeXJSIi0qRFB3nCTtinUEpERMTh6hxKRUZGsmHDBubMmcP69evx8PBg3LhxXHfddbi4uDREjSJylLyiUm798E/S84qJC/dh5vW9cXaq88BHERGRVqdiBb7ETIVSIiIijnZSTaC8vLy444476rsWEamFMouVez//m60peQR7u/H+zf3xcVcgLCIiUhsVK/Bp+p6IiIjjnXRn8i1btpCUlERJSUmV7ZdffvkpFyUix/b0on/4ZVs67i5m3h/bj0h/D0eXJCIi0mxElY+USlKjcxEREYercyi1e/duRowYwcaNGzGZTBiGAWDvZWOxWOq3QhGx+/CPPXy4fC8AM0b3Ij7K36H1iIhI/du3bx8mk4m2bdsCsHr1aj777DO6du2qker1ICbI1uj8UEEpuUWl+Gq0sYiIiMPUuQnN/fffT7t27UhLS8PT05PNmzfz22+/0a9fP3755ZcGKFFEAH7amsrUb7cAMPGSOC7u3sbBFYmISEO4/vrr+fnnnwFISUnhwgsvZPXq1Tz66KNMnTrVwdU1f95uzgR5uQKQpL5SIiIiDlXnUGrFihVMnTqV4OBgzGYzZrOZM888k2nTpnHfffedVBFvvPEGsbGxuLu7M3DgQFavXn3MfUtLS5k6dSodOnTA3d2d+Ph4Fi9efMz9n3vuOUwmEw888MBJ1SbSFGw5kMu9n/2N1YDR/aL419ntHV2SiIg0kE2bNjFgwAAAvvzyS7p3787y5cuZM2cOH374oWOLayGiy/tKJamvlIiIiEPVOZSyWCz4+PgAEBwczIEDBwCIiYlh27ZtdS5g7ty5TJgwgSeeeIK1a9cSHx/P0KFDSUtLq3H/xx57jHfeeYfXX3+dLVu2cOeddzJixAj+/vvvavv++eefvPPOO/Ts2bPOdYk0FWm5Rdz60Z/kl1gY3CGIp67sbp8uKyIiLU9paSlubm4ALF261N6vMy4ujoMHDzqytBYjWivwiYiINAl1DqW6d+/O+vXrARg4cCAvvPACf/zxB1OnTqV9+7qP3pg+fTq3334748aNo2vXrrz99tt4enoye/bsGvf/5JNPeOSRRxg2bBjt27fnrrvuYtiwYbz88stV9jt8+DA33HADs2bNIiAgoM51iTQFBSVl3PrRXxzMKaJDiBdv3dAXV+c6/28rIiLNSLdu3Xj77bf5/fff+fHHH7n44osBOHDgAEFBQQ6urmWIqWh2nqVm5yIiIo5U50bnjz32GPn5tn/Ap06dymWXXcZZZ51FUFAQc+fOrdNrlZSUsGbNGiZNmmTfZjabGTJkCCtWrKjxOcXFxbi7u1fZ5uHhwbJly6psGz9+PJdeeilDhgzh6aefrlNdIk2B1WrwwBfr2JicQ6CXK7Nv7o+fp5qxSgtitYK1FCwlYCktv5SUbzuZ7ZUes5aC1QLO7uDsVulSft+p0m1nd3B2rXrfqdJ9s5Ojv6nqLKVQWgAlBbbr0gIoLYSS/KNuF0Jp+XVJQaXbFY8VQFkROHuAq1f5xbvS7aPvH+u2V9P8npqp559/nhEjRvDiiy8yduxY4uPjAfjmm2/s0/rk1ESXNzvX9D0RERHHqnMoNXToUPvt0047ja1bt5KVlUVAQECdpxRlZGRgsVgICwursj0sLIytW7ce8/2nT5/O2WefTYcOHUhISGDBggVVVv374osvWLt2LX/++Wet6iguLqa4uNh+Pzc3t06fQ6QhPL94Kz9sScXVycy7N/W1rxYk0mQZBpQchsNpcDi1/FLT7TTIz7AFR82B2eUYoZYbmJ2hIafTGgaUFVcKnsqDqKb43R0v2HLxtIVWZicwOdm+N7NzDdvKr03mo+47HdnXft+5+rb254KL+wlLberOPfdcMjIyyM3NrTLa+4477sDT09OBlbUcMUGaviciItIU1CmUKi0txcPDg3Xr1tG9e3f79sDAwHov7FheffVVbr/9duLi4jCZTHTo0IFx48bZp/vt27eP+++/nx9//LHaiKpjmTZtGk8++WRDli1SJ5+vTuKd33YD8OI1PekX23j/j0kdWMrgcArk7IfsfZCzDwoyG/59zc7g4mELSOzXnraTcWePqtcuHlW3ObvVPUQpK4H89OrB0uFU2+evvK30VE7wTLYRSk6u4OR85LbZ+QTbXcov5bfN5bdNZrAU20KdsqLy68q3K11bSqreN6xHyrKWQkmpLXBrakzm8v/2nuDqeeS2i8eRMMj+mAe4eFW9XfHnp6zINnqqJN/2OWu8fYz7RvmPQmWFtktBhuO+j//saBGhVGFhIYZh2AOpxMREvv76a7p06VLlx0E5eRU9pQ5kF1JSZtXUeBEREQepUyjl4uJCdHR0lVFJpyI4OBgnJydSU1OrbE9NTSU8PLzG54SEhLBw4UKKiorIzMwkIiKCiRMn2vtZrVmzhrS0NPr06WN/jsVi4bfffmPmzJkUFxfj5FR1isGkSZOYMGGC/X5ubi5RUVH18hlF6mrZjgweX7gJgAeGdOSKXpEOrqgVKz5sC5xyygMne/i033bJTT5yQt5smGoItDyO2uYGRblHwqbCrLq9has3eIeCd1gN1+W3PYNtYUmVkKmJTP8yDLCWHRVi1RBclRXb9mtozm6VwqajwqeTCRnrU8VIruOFWaX5ttFdhsX2fVmttmv7/Zq2WWyXKvscva38fuXnObk67ruoR1dccQVXXXUVd955J9nZ2QwcOBAXFxcyMjKYPn06d911l6NLbPZCfdxwdzFTVGolObuQdsEajSwiIuIIdZ6+9+ijj/LII4/wySefnPIIKVdXV/r27UtCQgJXXnklAFarlYSEBO65557jPtfd3Z3IyEhKS0uZP38+o0aNAuCCCy5g48aNVfYdN24ccXFxPPzww9UCKQA3Nzf7KjcijrQjNY+75qyhzGowonck91/Q0dEltVxWK+SnlQdNSUeCpsoBVOGhE7+O2Rl8I8E/GvzaglewbfRKQ7KU2UaklBYdua7oDVRaWH591DZ7eGYcmQZWWIf3NDsfP2SquPYKBTfvhvjUjcdkOjL6qrl/loZmMpWPyHMHLzXgri9r167llVdeAWDevHmEhYXx999/M3/+fCZPnqxQqh6YTCaiAz3ZnnqYpKwChVIiIiIOUudQaubMmezcuZOIiAhiYmLw8qr6j/jatWvr9HoTJkxg7Nix9OvXjwEDBjBjxgzy8/MZN24cAGPGjCEyMpJp06YBsGrVKpKTk+nVqxfJyclMmTIFq9XKf//7XwB8fHyqTC0E8PLyIigoqNp2kaYk43Axt3z0J3lFZfSPDeC5q3vUuU9bq2cYUJxrm0KXn2m7Lsgov59hu1QETrnJtpEvJ+LuB35R5Ze2tot/1JFt3qFNZ4TP8VhKKwVWBVUDraODrbIicPOtGjy5+4NZ01tEGkNBQQE+Pj4A/PDDD1x11VWYzWZOP/10EhMTHVxdyxEd6GULpTLzgRBHlyMiItIq1TmUqhjRVF9Gjx5Neno6kydPJiUlhV69erF48WJ78/OkpCTMlU6EioqKeOyxx9i9ezfe3t4MGzaMTz75BH9//3qtS6QxFZVauOPjv9iXVUh0oCfv3NQPN+dmEHQ0NEtpebBUHipV3K5yPwMKso7cr0sDaJMZfCIqBU3loZNf9JHb7r4N9/kaU8XIH1rI5xFpwU477TQWLlzIiBEjWLJkCQ8++CAAaWlp+Prq/+H6ombnIiIijmcyDMNwdBFNTW5uLn5+fuTk5OjgTxqcYRjc98U6/rf+AL7uziy4+wxOC20lU4YMA7ITIXE57FsNeSmVRjZlQnHOyb2uixd4BtmmE3kG2foXVdz3rRRA+bQpD2pERI6tsY8L5s2bx/XXX4/FYuH888/nxx9/BGwLs/z22298//33DV7DyWpOx1AfLd/LE99s5sKuYcwa08/R5YiIiLQotT0mqPNIKRGpX68s3cH/1h/A2Wzi7Zv6tuxAymqFjG2Q+AckrrCFUXkHjv8ckxk8AstDpWDwDKwUMpVfV1wq7rt4NM7nERFpACNHjuTMM8/k4MGDxMfH27dfcMEFjBgxwoGVtSzR5SOlkjRSSkRExGHqHEqZzebj9rmpr5X5RFqDBWv381rCDgCevaoHgzsEO7iiemYphYMbIGm5LYBKWlG9ebjZGSJ6Q/QgCOpw1MimYFtPp+bQs0lEpB6Fh4cTHh7O/v37AWjbti0DBgxwcFUtS0xgeSiVVYBhGOrjKCIi4gB1DqW+/vrrKvdLS0v5+++/+eijj3jyySfrrTCRlm7V7kwenr8BgLvO7cCoflEOrqgelBbC/r9s4VPiH7DvT9ty8JW5eELb/hAz2BZEte0Hrlr1SESkgtVq5emnn+bll1/m8OHDgG0hl3//+988+uijVXptyslrG+CJyQSFpRbSDxcT6uPu6JJERERanTqHUldccUW1bSNHjqRbt27MnTuXW2+9tV4KE2nJ9mbk869P11BqMbikezgPXdTZ0SWdnKIcSFplC6CSVkDy2uqNxt39beFTzCCIOQPaxKuPk4jIcTz66KO8//77PPfcc5xxxhkALFu2jClTplBUVMQzzzzj4ApbBldnMxF+HiRnF5KUWaBQSkRExAHqrafU6aefzh133FFfLyfSYmUXlHDLh3+SXVBKfJQ/00f1wmxuJlMG8lLLp+KtsF2nbAKOWivBp015CDXYdgnpAvpVX0Sk1j766CPee+89Lr/8cvu2nj17EhkZyd13361Qqh5FB3qSnF1IYmYB/WIDHV2OiIhIq1MvoVRhYSGvvfYakZGR9fFyIi1WSZmVOz9dw+6MfCL9PZg1pi8erk24X5JhwL5VsPEr2PUzZO2qvk9g+/KpeINto6EC2oH6coiInLSsrCzi4uKqbY+LiyMrK8sBFbVcMUGerNidSWKWmp2LiIg4Qp1DqYCAgCqNIA3DIC8vD09PTz799NN6LU6kJTEMg0e+3sjK3Vl4uznz/s39mu5UgfTtsGEubPwSspMqPWCCsG5H+kHFDAafcIeVKSLSEsXHxzNz5kxee+21KttnzpxJz549HVRVy1SxAt8+hVIiIiIOUedQ6pVXXqkSSpnNZkJCQhg4cCABAQH1WpxIS/LmL7uYt2Y/ZhPMvL43ceG+ji6pqrxU2DTfFkYdXHdku6s3dLkcul4O0aeDh/4/FxFpSC+88AKXXnopS5cuZdCgQQCsWLGCffv28d133zm4upYlJtC20EZiZv4J9hQREZGGUOdQ6uabb26AMkRatkUbDvLikm0APHl5N87tHOrgisoVH4at39qCqN2/gGG1bTc7w2lDoOco6HQJuHo6tEwRkdbknHPOYfv27bzxxhts3boVgKuuuoo77riDp59+mrPOOsvBFbYc0YG2f9+SNFJKRETEIeocSn3wwQd4e3tzzTXXVNn+1VdfUVBQwNixY+utOJGW4O+kQ0z4ch0At5zRjpsGxTq0HixlsPtnWxC1dRGUVjoQb9sfeo6GbiPAK9hxNYqItHIRERHVGpqvX7+e999/n3fffddBVbU8FdP3Mg6XcLi4DG+3elsDSERERGqhzv/yTps2jXfeeafa9tDQUO644w6FUiKV7D9UwO0f/0VxmZUL4kJ59NIujinEMCB5rS2I2jQfCjKOPBbYwTYiqsc1ENTBMfWJiIg4gJ+HC/6eLmQXlLIvq4AubZrY1HoREZEWrs6hVFJSEu3atau2PSYmhqSkpBqeIdI6lVms3Pv532QcLqFrG19eu643TuZGXpUuazds+MoWRlVeOc8zGLpfbRsVFdlHq+WJiEirFRPoSXZBDomZCqVEREQaW51DqdDQUDZs2EBsbGyV7evXrycoKKi+6hJp9t78ZRd/J2Xj4+7Mu2P64tVYUwLyM2Dz17Ygav+fR7Y7e0DcpbYgqsN54OTSOPWIiIg0YVGBnqzfn0NSlpqdi4iINLY6nyVfd9113Hffffj4+HD22WcD8Ouvv3L//fdz7bXX1nuBIs3R+n3ZvJqwA4CnruhO24AGbhReUgDbv4cNX8LOpWAts203maH9ubYgKu5ScPNp2DpEROSkXXXVVcd9PDs7u3EKaWViyvtKJWaq2bmIiEhjq3Mo9dRTT7F3714uuOACnJ1tT7darYwZM4Znn3223gsUaW4KSsp4cO46LFaDy3q24YpeEQ3zRkU5tgBq2/ewbTGU5B15rE28LYjqfjX4hDfM+4uISL3y8/M74eNjxoxppGpaj5hAL0Ar8ImIiDhCnUMpV1dX5s6dy9NPP826devw8PCgR48exMTENER9Is3OM4v+YXdGPuG+7jxzZQ9M9dmv6dBeWwC17TtI/OPIiCgAv2hbw/KeoyCkc/29p4iINIoPPvjA0SW0ShUr8CmUEhERaXwn3eSmY8eOdOzYsT5rEWn2ftqaypxVtob/L4+Kx8/zFPs2Wa2QvMY2NW/b95C2perjwZ2g8yXQeRi0HQBm86m9n4iISCtTMX0v+VAhZRYrzk76t1RERKSx1DmUuvrqqxkwYAAPP/xwle0vvPACf/75J1999VW9FSfSnGQeLua/8zYCcOuZ7TjjtOCTe6GSfNj9iy2E2r4E8tOOPGZyguhB5UHUJRDU4dQLFxERacXCfNxxdTZTUmblQHaRfeSUiIiINLw6h1K//fYbU6ZMqbb9kksu4eWXX66PmkSaHcMwmLhgIxmHi+kc5sNDQ+s4fS73IGxfbLvs/gXKio485uoDHYfYRkOdNgQ8A+u1dhERkdbMbDYRFeDBrvR8ErPyFUqJiIg0ojqHUocPH8bV1bXadhcXF3Jzc+ulKJHm5su/9vHjllRcncy8MroX7i5Ox3+CYUDqpvIm5d/DgbVVH/eLPjIaKuYMcK7+/5yIiIjUj5ggL3al56uvlIiISCOrcyjVo0cP5s6dy+TJk6ts/+KLL+jatWu9FSbSXCRm5vPk/2y9nv59USe6RvjWvGNZMexdZhsNte17yNlX9fHIftD5YtuIqNCuUJ8N0kVEROSYogPLm51nKpQSERFpTHUOpR5//HGuuuoqdu3axfnnnw9AQkICn332GfPmzav3AkWasjKLlQfnrqOgxMLAdoHcdlb7qjuUFsGW/7OtlrczAUryjjzm7AEdzoNOF0OnoeAT3rjFi4hIi/fGG2/w4osvkpKSQnx8PK+//joDBgw44fO++OILrrvuOq644goWLlzY8IU6WEUolahQSkREpFHVOZQaPnw4Cxcu5Nlnn2XevHl4eHgQHx/PTz/9RGCget1I6/LWL7tYm5SNj5szL4+Kx8lcaXRTxg746mbbNL0K3mG2EKrzJdDuHHBV3woREWkYc+fOZcKECbz99tsMHDiQGTNmMHToULZt20ZoaOgxn7d3717+85//cNZZZzVitY5VsQJfoqbviYiINKqTWvP20ksv5Y8//iA/P5/du3czatQo/vOf/xAfH1/f9Yk0WRv2Z/Nqwg4Apl7ZjbYBlQKmDV/CO+fYAinPYDj7IbjtJ5iwFS5/zRZKKZASEZEGNH36dG6//XbGjRtH165defvtt/H09GT27NnHfI7FYuGGG27gySefpH379sfcr6WpCKWSMvMxDMPB1YiIiLQeJxVKgW0VvrFjxxIREcHLL7/M+eefz8qVK+uzNpEmq7DEwgNz11FmNbi0Zxuu7BVpe6CkAL65FxbcDqX5EHsW3PUHnP8YtO0L5pP+X05ERKTWSkpKWLNmDUOGDLFvM5vNDBkyhBUrVhzzeVOnTiU0NJRbb721McpsMip+WMovsZCVX+LgakRERFqPOk3fS0lJ4cMPP+T9998nNzeXUaNGUVxczMKFC9XkXFqVZ7/7h93p+YT7uvPMld0xmUyQvh2+GgtpWwATnPMwnPNfMJ9gJT4REZF6lpGRgcViISwsrMr2sLAwtm7dWuNzli1bxvvvv8+6detq/T7FxcUUFxfb7zfXlZjdXZwI93UnJbeIxKwCgrzdHF2SiIhIq1DrYRvDhw+nc+fObNiwgRkzZnDgwAFef/31hqxNpEn6eVsan6xMBOCla+Lx93SF9V/Au+faAimvUBizEM6bpEBKRESahby8PG666SZmzZpFcHBwrZ83bdo0/Pz87JeoqKgGrLJhRQdpBT4REZHGVuuRUt9//z333Xcfd911Fx07dmzImkSarKz8Ev47bwMA486I5cwYT1g4HtZ9atuh3dlw1XvgE3acVxEREWlYwcHBODk5kZqaWmV7amoq4eHVV3vdtWsXe/fuZfjw4fZtVqsVAGdnZ7Zt20aHDh2qPW/SpElMmDDBfj83N7fZBlMxgZ6s3pOlFfhEREQaUa1HSi1btoy8vDz69u3LwIEDmTlzJhkZGQ1Zm0iTYhgGkxZsID2vmI6h3kzsC8w63xZImcxw7iNw00IFUiIi4nCurq707duXhIQE+zar1UpCQgKDBg2qtn9cXBwbN25k3bp19svll1/Oeeedx7p1644ZNLm5ueHr61vl0lzZm51rBT4REWkNLKVwaC8cTndoGbUeKXX66adz+umnM2PGDObOncvs2bOZMGECVquVH3/8kaioKHx8fBqyVhGH+mrNfpZsTsXFycSHvXfg9sG1UFoA3mFw9Xu2UVIiIiJNxIQJExg7diz9+vVjwIABzJgxg/z8fMaNGwfAmDFjiIyMZNq0abi7u9O9e/cqz/f39weotr2lig7yAmBnWh6lFisuTlqcREREmrGiHMjeBzn7IWdf+WX/kW15BwEDzn8czv6Pw8qsU6NzAC8vL2655RZuueUWtm3bxvvvv89zzz3HxIkTufDCC/nmm28aok4Rh0rKLODJbzbjQRFft/2ayF//Z3ug/blw1SzwDnVofSIiIkcbPXo06enpTJ48mZSUFHr16sXixYvtzc+TkpIwa1VYu9jykVLr9+fQZ+qPnN05hCFdQjm3UygBXq4Ork5ERKQSq8UWKuXsLw+ako7crgifimux+IiTq22ghQOZDMMwTvVFLBYL//vf/5g9e3aLCKVyc3Px8/MjJyenWQ9Dl/phsRqMfmcFOUkb+cBzJm0tSUem6501Qc3MRURaOB0X1F5z/q6sVoOnFm3hm3UHyMwvsW83m6BfTCAXdAnlgi5hdAjxsq26KyIi0lBK8mse5VQx0ik3GQzLiV/HIxD82oJ/tO3ary34RZVf2oJXCDTQD1S1PSaol1CqpWnOB1RS/974aQd7E95lqvOHeJhKwDscRr4PsWc6ujQREWkEOi6ovQb9rmadD4YVQuIguJPtOqQzBMTW6w9EFqvBun3ZJPyTSsI/aWxLzavyeGyQJxd0CeOCLqH0jw3UND8REakbw4CCLMhJKg+e9lUa7bTPtq0w68SvY3YG34iqIZN/VKXgqS24ejX85zkGhVKnQAefUmHTnmR2zP4XI5x+t23ocD6MeBe8QxxbmIiINBodF9Reg31XllJ4JhysZdUfc3KFoI4Q0qlSYNUZgk4DZ7dTfut9WQX8tDWNpf+ksnJ3JqWWI4fOPu7OnNs5lCFdQjmnUwj+nprmJyLS6lVMrbMHTvuO3K64rs2UOTffY4RN5bd9wpv0rB2FUqdAB58CULR/I6mzryXGuh8rZkznP4bpzAcbbHijiIg0TTouqL0G+66sVsjcCRnbIH0rpG+3XWfsgLLCmp9jMkNAu/IRVZ0guLMtrAruBG7eJ1XG4eIyft+eztJ/0vh5WxpZlab5OZlN9I0JYIh9mt/JvYeIiDRhVisUHoKCDNsUOnvj8IrAKQlyD9T8I8rRvMOOBE3+UeAXXX5dft/dr+E/TwNSKHUKdPDZyhkGrP2Y0m//g4tRQiqBeF73IT6dz3F0ZSIi4gA6Lqi9Rv+urFbbCUD69qMCq21QnHPs5/lFVZoCWCmw8gys9VvbpvkdYuk/aST8k8r21MNVHm8X7MUFcbaAqn9sAM6a5ici0vRYLbaQKT/DFjTlZ0B+OhRkVr9dkGG7bVhP/LpmZ/CNLO/lVHm0U5Rtm28kuLg3/OdzIIVSp0AHn61YcR58+yBs/AqAXyzxuF3zLoN6xjm4MBERcRQdF9Rek/muDAMOpx4JqTK22YKq9G2Qn3bs53mF2E4UPPxtv1C7+5ffrnTt7ldpW4DtvtmJfVkFLC3vQ7VqT9Vpfr7l0/wuKF/Nz8/TpQE/vIhIK1eSb+vPVCVoOsbtwqzahUxHc/e3TZ+zj3KKqtRMPKrJT61rDLU9JnBuxJpEmraUjfDVzZC5kzLMvFQ6iuIB9/CEAikREZHmxWSynRD4hEP7c6s+VpAFGdurB1Y5+2y/iOen1/39XH2I8vBnnLs/4zz8Ke3mS0qJO7tyndh8yExKiTs5G734eqMXH5u8iYxqz8CeXbmoR1tCfE6975WISKtUkGX7+ztjW6W/z7fbRtDWlbu/7YcJr2DwDKp0O9h2Xfm2ZxA46ceF+qJQSsQwYM2H8P3DYCkmyymE2wvuJiekL98O6+ro6kRERKQ+eQZC9Om2S2XFhyFzBxxOg8JsKMqBouzy29k1byspn7JXkme75OwDwAWIKr+cW7GhshQoO2gmdUkAW13DcA2KJrTtaXiHxlZasrut7STJZKr/70BEpLkwDFvT8PRt5T8obDsSRB3vRwR3P/AKrSFQOjpkCrH9u6CQyWEUSknrVpQL3z4Am+YDcCD0bIYl3cBhsy8LR/fC3aV1D7kUERFpNdy8IaJ33Z5jKS0PqnLKg6pDxw+xCrMpy8/CdDgFZ8qIJBNKMyFlC6TU8Pqu3rbphPagKgr8Kt33jayXFQZFRBzOaoHsxEoLWWw/EkQV5x77eb5tbT0BKxayCOls6xPoFdR4tcspUSglrdeBdTDvFsjaBSYnss94lKG/dyUPKw9d2Inukc17tQMRERFpYE4uR35xryVnsDVoz08jbf9ONm7ezL692zGy99HGlEWEKYMIUybBplzbSKyM8hEBx+IdVim4Km+mGxALsWc0+5WbRKQFKsq1rVhXZcrdNtsKq2VFNT/HZIbA9uWLUtTPaqrSdCiUktbHaoWVb8LSKWAtBd+2WK6ezW3fWcgrPkT/2ADuPKeDo6sUERGRlspsBp9wQruEc0GXMwFIySliyeYUPth4kD/3ZuFilBBhyqSNKZN+/vkMDi6kq2cOviWpkFO+DHlZoa2h++FUOLD2qPdwgdgzIe5S6DzMNsJKRKShWC226XS5yZB70DblLvdA9euSw8d+DSc3CO5YfXXUoA4aFdqCafW9GjSZlWOk/h1Og4V3wc6ltvtxl8Hlr/PGqixeXLINbzdnvr//LKICPR1bp4iINBk6Lqg9fVf1Iz2vmB+2pLB4UwrLd2VisR45XD8t1Jth3cO5pHs4cX6lmHL22wKq3GRbT6uc/bbFWzJ3Vn3RNr2OBFRh3dSrSkRqr6TgqHCpIng6cCSAyksBw1K713Pzs4VPlafchXQG/5hWv2JdS1LbYwKFUjXQAVULtTMBvr7TthS0sztcPA36jmPTgVyufOMPyqwGL10Tz8i+bR1dqYiINCE6Lqg9fVf171B+CT9uSeX7TQdZtjODUsuRQ/fYIE8u6dGGS7qH0yPSD1PloCljB2xdBNu+g32rgUqH/P7R0PlSiBsG0YPBSZMnRFq14jzb3xmZOyFzF+Tut4VNuQdswVNRTu1ex2S2TSn2aQO+EbZLxe3K15py1yoolDoFOqBqYcpK4KepsPx12/3QrjByNoR2oajUwmWvL2Nn2mEu6R7Omzf0qXpAJyIirZ6OC2pP31XDyiks5aetqXy3MYVft6dTUma1Pxbp78El3cO5pEcbekf5YzZXOp45nAbbF8PW72D3z1X7trj7Q6ehthFUp10Abj6N94FEpPFYymyNxDN3lgdQO2wBVMYOOFzTSgtHcfEC3zZHhUyR5dsibNdeoQq5xU6h1CnQAVULkrnL1sz84Drb/f63wUVPg4sHAFO+2cyHy/cS6uPGkgfOJsDL1XG1iohIk6TjgtrTd9V4DheX8fPWNL7fdJCft6ZTWHpk2kwbP3duP6s91w+Mrr6ScEk+7PrZNoJq2/dQmHXkMSdXaHeObQRV52HgE95In0ZE6oVhQEFm1eApY6ftOmuPrZ/usXiFQtBpEHwa+EXbQibfiCOBk5uvpv1KnSiUOgU6oGoBDAPWfwGL/g2l+eARAJfPhC6X2Xf5v3XJ3P/FOgA+umUA53QKcVCxIiLSlOm4oPb0XTlGYYmFX7en8d3GFH7amsbh4jIAwn3duef80xjVLwpXZ3P1J1otsG+VbZrf1kVwaE/VxyP7lQdUl9r6veiEVKRpKC2CrN3lo512HgmeMnZAUfaxn+fsbguegjpAUEdbX6egjrb7Hv6NVb20EgqlToEOqJq5olxYNAE2fmW7H3MmXPVulVVn/rf+APd/8TdWA249sx2PX9bVQcWKiEhTp+OC2tN35XhFpRYWrE3m9Z92cDDHNk2vbYAH913Qkat6R+LsVEM4BbYf9NK3HulDlbym6uOB7W2jp+IuhaiBakYs0hjKiiFjO6RugbTNkPYPpG+D7CSq9Ik7ml9U+ainjuUhVPlt37a21T9FGoFCqVOgA6pmbP9fMP9WOLQXTE5w7iQ4a0KVA6dFGw5y3xd/Y7EajOrXlueu6lm174KIiEglOi6oPX1XTUdRqYUvVifxxi+7SM8rBqBdsBf3X9CR4fEROJ3o2Cf3IGz/3taHas+vYCk58phnEHS4ACJ6QXhPCO+hURYip8JqhZykI+FT6hZI22IbBWUtq/k5br41B0+BHcBVK4mL4zWrUOqNN97gxRdfJCUlhfj4eF5//XUGDBhQ476lpaVMmzaNjz76iOTkZDp37szzzz/PxRdfbN9n2rRpLFiwgK1bt+Lh4cHgwYN5/vnn6dy5c63q0QFVM2S1wvJX4aenbX9x+0XD1e9B9MAquy3edJDxn9kCqav7tOXFkQqkRETk+HRcUHv6rpqewhILn65M5K1fd5GVbwuWOoZ68+CFnbi4W3jtjoOK82yrGG/7DrYvqXl6kH8MtOkJ4fHl1z1tPak05U+kqoIsSN1sC50qrtP+gZLDNe/v5gdhXW2LNYV1hZA425Q771D9/yVNWrMJpebOncuYMWN4++23GThwIDNmzOCrr75i27ZthIaGVtv/4Ycf5tNPP2XWrFnExcWxZMkSJkyYwPLly+nduzcAF198Mddeey39+/enrKyMRx55hE2bNrFlyxa8vLxOWJMOqJqZvBT4+l+w+xfb/W4j4LIZ1X6x+2FzCnfPWUuZ1eCq3pG8eE38iX8lFBGRVk/HBbWn76rpOlxcxkfL9/LOr7vILbKNvOjSxpd/X9iJC7qE1n71YUspJK2AxOWQshEObrCN8KiJV4gtnKoIqdrEQ0A7TR+S1qG00DYltmLUU9oW2+1jrXRndrH1bqsIn0K72a59IxU+SbPUbEKpgQMH0r9/f2bOnAmA1WolKiqKe++9l4kTJ1bbPyIigkcffZTx48fbt1199dV4eHjw6aef1vge6enphIaG8uuvv3L22WefsCYdUDUj25fAwrtsq0y4eMIlL0DvG6v9xb10Syp3zVlDqcXgil4RTB/VS4GUiIjUio4Lak/fVdOXU1jK+8v2MHvZHntD9PgofyZc2ImzOwbXPpyqrCDLFlClbLCFVCkbbH1wDGv1fV19ILx71bAqJA6ctQKyNDCrxTajwlJqu664bz3q/jEfL99mOep+5cfzM46MfsraXfP/A2AbWRjWDUK7lIdQ3WzT75xcGvc7EWlAtT0mcG7EmqopKSlhzZo1TJo0yb7NbDYzZMgQVqxYUeNziouLcXd3r7LNw8ODZcuWHfN9cnJyAAgMDDzmaxYXF9vv5+bm1voziIOUFcOPT8Cqt2z3w3vA1bMhpFO1XX/emsbdc9ZSajG4rGcbXtYIKREREWml/DxcmHBhJ8YNjuXd33fz4R97Wb8vm7GzV9M/NoAJF3ZmUIegur2oZyC0P8d2qVBSYDsxP7j+SFiVtgVK8mwjrZIqHeubXWwn55Wn/4V1Bzfv+vnQ0jIZhm0qaX4G5KfbLofTqt6vfCnKafwaPQLLw6dKo59C48DNp/FrEWmiHBpKZWRkYLFYCAsLq7I9LCyMrVu31vicoUOHMn36dM4++2w6dOhAQkICCxYswGKx1Li/1WrlgQce4IwzzqB79+417jNt2jSefPLJU/sw0njSt8O8WyB1o+3+6XfDkCng7FZt11+3p/OvT9ZQYrEyrEc4M0b3OvaqMyIiIiKtRICXKw9fHMctZ7Tj7V938cnKRP7ce4jrZq1kcIcg/n1RJ/rG1PyDbq24ekLbfrZLBUuZbQRV5RFVKRtsYUHFbSpmPpjAp42tb453WKXrMPAJq7rN9cTtOaSZKCupFCRVhEtpVe9XDp6spaf2fiYzmJ2PfXGqfN/JFqBWvu901H03P1voVDH6yTtMU+9ETsChodTJePXVV7n99tuJi4vDZDLRoUMHxo0bx+zZs2vcf/z48WzatOm4I6kmTZrEhAkT7Pdzc3OJioqq99rlFBkG/P0JfP8wlBbYVn658i3oNLTG3X/fkc7tH/9FicXKxd3CefXa3gqkRERERCoJ8XHj8cu6cvtZ7Xnj55188WcSy3dlsvytFZzbOYR/X9iZHm396ufNnJxto0XCukL8tbZthgHZiUdCqorrvIOQd8B2ORFX7/KAKrzmEMs71NZ03TPYVoM0rrLi8jApFQ6XX+en2cKlw2lHHjvZ0UxuvuAVbOthVvniHVp1u2cQOLkeFTzp3EDE0Rz6t3JwcDBOTk6kpqZW2Z6amkp4eHiNzwkJCWHhwoUUFRWRmZlJREQEEydOpH379tX2veeee/j222/57bffaNu27THrcHNzw82t+igbaUIKs+F/98OWhbb77c+FEe/YDjBq8MfODG776C9Kyqxc2DWM167rjYsCKREREZEahfu589SV3fnXOe15PWEn89bu55dt6fyyLZ2Luobx4IWd6NKmAfqEmUwQEGu7dL38yPb8DMhOKg8uUitdpxy5nZcKZYW2VcuyDtt6+Bz/zWwhReXQyifcNiLLJxx8ImzX3mHqcXUi9hFNlcOlSrft91PrHjSZnCqFS8E1BExHhU0u7id+TRFpshwaSrm6utK3b18SEhK48sorAdt0u4SEBO65557jPtfd3Z3IyEhKS0uZP38+o0aNsj9mGAb33nsvX3/9Nb/88gvt2rVryI8hDS1pFcy/zbayi9kZzn8cBt93zF82lu/K4NaP/qS4zMoFcaG8cX0fXJ0VSImIiIicSNsAT54f2ZO7zu3Aawk7+HpdMj9sSeWHLalc2rMNDw7pyGmhjdAPxyvYdjkew7AFUvaQKqWGECv1yCgcw3pkaljq8V8az2BbWOXb5qjgqlKA5RVsm7LV3FV8jwVZUJgFhYfKbx86si0/48j3eDjV9lhdmJ1tYZJ3+aXy7cr3vULA3V8jmERaEYevvjd37lzGjh3LO++8w4ABA5gxYwZffvklW7duJSwsjDFjxhAZGcm0adMAWLVqFcnJyfTq1Yvk5GSmTJnCnj17WLt2Lf7+/gDcfffdfPbZZ/zf//0fnTt3tr+Xn58fHh4eJ6xJK8c0EVYL/P4y/PIcGBbbL2hXz4a2fY/5lFW7M7n5gz8pLLVwXucQ3r6pL27OLeBgQUREHEbHBbWn76rl2ZmWxytLd7Bow0EAzCa4slck9w/pSExQM+rlZLXYVmu2B1VpthArL6V8qmCl27XtU2Ryqj7ayrdN1QDLK/RIXyKTky3Esl83QK+hshJbYFSYdSRQqgiYKt8+etvJ9GYyO1caxXSMwKnitkeAeiuJtDLNYvU9gNGjR5Oens7kyZNJSUmhV69eLF682N78PCkpCXOlpLyoqIjHHnuM3bt34+3tzbBhw/jkk0/sgRTAW2/ZVmQ799xzq7zXBx98wM0339zQH0lOVUEWbPk/WPsxHFhr29ZjFFz6Mrgf+w/zn3uzGPehLZA6u1MIb92oQEpERETkVJwW6sMb1/dh/Lm5vLJ0Oz9uSWXB38n8b8MBbj+rPfecfxqerg4/pTgxs9ORoIQex97ParWFNXkHjgqsDkLuwSP389NsP5rWtu9VjUzlzbMrB1bmSsGVcw3bKvavtM1aCgXlAVPJ4ZOsBXBys62k6BFYfh1Q6X5QpWmP5WGTR4BGNInIKXP4SKmmSL/yOUBRLmz7DjbNh10/gbXMtt3V2xZGVTTDPIY1iVmMeX81+SUWzuoYzKwx/XB3USAlIiKnTscFtafvquXbsD+bF5ds4/cdGQBE+nsweXhXLuoahqk1jYSxlNmmsuUdrHSpFGJVBFiFWQ4ozgQe/pXCpUrXHgHgGVDzYy4eGs0kIvWm2YyUklastBC2L7EFUTt+gLKiI4+F9YDuV9nCKN+I477M2qRDjJ39J/klFgZ3COLdmxRIiYiIiDSEnm39+fiWAfy4JZUn/7eF5OxC/vXJGs6PC2XK8G5EB3k6usTG4eRsm6rn2+b4+1mtthFV1jLbFELDUn5tPca28n3t2yy21zjeNrNz1dFN7n4to9eViLQKCqWkcZWVwO6fbUHU1kVVhxgHnQbdR9rCqJDOx36NStbvy2bs+6s5XFzG6e0DeX9sfzxc9Y+wiIiISEMxmUxc1C2cMzsGM/Onncz6fTc/bU3jj50Z3H3uafzrnPb6gbCC2QyYwcnF0ZWIiDRJCqWk4VktsHcZbJoHW76Bouwjj/lF2UKo7ldDeM86DRneuD+Hm95fRV5xGQNiA5l9swIpERERkcbi6erMfy+O46o+bZn8f5tYviuTV5Zu5+u/9zPl8m6c2znU0SWKiEgTp1BKGoZhwP4/YeM82LLQtspJBa9Q6DYCeoyEyH4n1SBxU3ION76/ityiMvrFBPDBuP7No8mmiIiISAtzWqg3c24byP82HOTpb7ewN7OAmz/4k0u6h/P4ZV2J8D/x6tciItI66Sxe6o9hQMpG24ioTV9DTtKRx9z9oevltul5sWee0jz3LQdyufH9VeQUltIn2p8PbxmAl5v+KIuIiIg4islk4vL4CM7rHMKMpTv4cPlevt+Uwq/b07n/go7ccmY7XJy0UpuIiFSlM3k5denbbT2iNs2HzB1Htrt6Q9yltql57c8DZ9dTfqutKbnc8N5KsgtK6RXlz0e3DMBbgZSIiIhIk+Dj7sLjl3VlZN+2PL5wE38lHmLa91uZt2Y/T13ZndPbBzm6RBERaUJ0Ni91ZxiQsQO2LbIFUSkbjzzm5AadhtqCqI4XgWv9rcCyPTWPG2at4lBBKfFt/fj41gH4uKtppIiIiEhT06WNL1/+axDz1+5n2vdb2ZF2mGvfXcmI3pFMGhZHqI+7o0sUEZEmQKGUnJhhQOZO2Pu7rWH53mVVe0SZnaHD+bYgqvMwcPet9xJ2pOZx/ayVZOaX0D3Sl49vGYivAikRERGRJstsNnFNvygu7BrGi0u28dnqJL7+O5mlW1L5z9DO3Hh6DE7m2i9yIyIiLY9CKanOMCBz11EhVErVfZzcIPp0W8PyrleAZ2CDlbMz7TDXzVpFxuESurbx5dNbB+LnqUBKREREpDnw93TlmRE9GNUviscWbmJjcg5PfLOZL//ax1NXdqdPdICjSxQREQdRKCW2ECprd9UQKu9g1X2c3CBqgK1JeeyZtlXzXBp+2PXu9MNcP2slGYeLiQv3Yc5tA/H3PPXeVCIiIiLSuOKj/Fk4/gw+W53Ei4u3svlALle9uZzrBkTx36FxBHjpGE9EpLVRKNUa2UOoZZVCqANV93FyhbaVQqi2/RslhKpsb0Y+181aSVpeMZ3DbIGUDlZEREREmi8ns4mbTo/hku7hPFfeAP3z1ftYvCmFhy+OY1S/KMya0ici0moolGoNDAMO7akaQuUmV93HydUWPFUJoTwcUy/w6/Z0HvpqPWl5xXQM9WbO7QMJ8nZzWD0iIiIiUn+Cvd146Zp4RveP4vGFm9iaksfEBRuZ+9c+nr6yO90i/BxdooiINAKFUo2ttAhK8hv+fYqyIXF5pRBqf9XHzS5VQ6ioAQ4NoSocLi7jmUVb+Hz1PgA6hXkz57bTCVYgJSIiItLi9I8N5H/3nslHy/fyyo/b+Tspm+GvL2PMoFgmXNRJC9uIiLRwCqUa2+YFsPCuxn9fswu07VceQp1lC6RcPRu/juNYviuDh77aQHJ2IQA3D47l4Yvj8HB1cnBlIiIiItJQXJzM3HZWey7rGcHTi7bw7YaDfLh8L/9bf4AHL+zEtf2jcHYyO7pMERFpAAqlWiqzC0T2tYVQ7c6y9YdqYiFUhYKSMl5YvI0Pl+8FoG2ABy+OjGdQhyDHFiYiIiIijSbcz52Z1/fh2v4ZTP5mE7vT83ls4SY+Wr6XRy7twrmdQjCZ1G9KRKQlMRmGYTi6iKYmNzcXPz8/cnJy8PX1rd8XNwzbpaGZTLZLE/fX3iz+89V69mYWAHD9wGgeGdYFbzflpSIi0jQ06HFBC6PvSupLqcXKZ6uSmLF0O4cKSgE4q2MwjwzrQpc2+rMlItLU1faYQGf+ja2ZhEUNrajUwvQftzPr990YBrTxc+f5q3tydqcQR5cmIiIiIg7m4mRm7OBYruwdyRs/7+TDP/by+44MLn3td0b1i2LCRZ0I9WnclaFFRKT+aXK2NLp1+7K59LXfefc3WyA1sm9bFj9wtgIpEREREanCz8OFR4Z1YemEc7i0RxusBnzx5z7OffEXXkvYQWGJxdEliojIKVAoJY2muMzCi0u2cvVby9mVnk+IjxvvjenHS9fE4+ehlVVEREREpGbRQZ68cUMf5t81iF5R/hSU2Ebdn/fSL8xfsx+rVR1JRESaI4VS0ig2H8jhipl/8MbPu7BYDS6Pj+CHB85mSNcwR5cmIiIiIs1E35hAvr57MK9d15tIfw9Scov491frufyNZazYleno8kREpI7UU0oaVKnFylu/7OK1hB2UWQ0CvVx55sruXNKjjaNLExEREZFmyGQycXl8BBd1DePD5Xt546edbErO5bpZK7mwaxiTLomjfYi3o8sUEZFaUCglDWZ7ah7//nI9G5NzABjaLYxnRvQg2NvNwZWJiIiISHPn7uLEned04Jq+bZmxdAefrU7ixy2p/Lw1jRtPj+H+CzoS4OXq6DJFROQ4NH1P6p3FavD2r7u47LVlbEzOwc/DhVev7cXbN/ZVICUiIiIi9SrI242nruzOkgfO4vy4UMqsBh8u38s5L/7MrN92U1ymZugiIk2VRkpJvdqdfph/f7Wev5OyATivcwjPXd2TMF8t2SsiIiIiDee0UB9m39yfP3Zm8PSif/jnYC7PfPcPn6xMZOIlcVzSPRyTyeToMkVEpBKFUlIvrFaDD5bv5YXFWykus+Lt5szky7pyTb+2+sdfRERERBrNGacF8+29ZzJ/7X5eWrKNpKwC7p6zln4xATx6aRd6Rwc4ukQRESmnUEpOWVJmAf+Zt57Ve7IAOPO0YJ4f2ZNIfw8HVyYiIiIirZGT2cSoflFc2qMN7/62m3d/281fiYcY8eZyLo+P4L8Xd6ZtgKejyxQRafUUSslJMwyDOauSePa7fygoseDp6sSkYV24cWC0RkeJiIiIiMN5uTnz4IWduG5ANC/9sI35a/fzzfoDLN6cwi1ntOPu8zrg6+7i6DJFRFotk2EYhqOLaGpyc3Px8/MjJycHX19fR5fT5BSVWtiWksdLP2zj9x0ZAAxoF8hLI+OJDtIvTiIi0rLouKD29F1JU7f5QA7PLPqH5bsyAfB1d+a2s9pz8xmxCqdEROpRbY8JFErVQAdUNoZhkJJbxD8Hc/nnYB5bU/LYejCX3Rn5WKy2PzZuzmb+e3Ec4wbHYjZrdJSIiLQ8Oi6oPX1X0hwYhkHCP2k8v3grO9IOAwqnRETqW22PCTR9TwAoLLGwPTWPfw7msjXlyHVOYWmN+/t7utAvJoBJw7rQIcS7kasVERERETk5JpOJIV3DOD8ulO82HeTVpTvYkXaY6T9u573fdyucEhFpRAqlWhnDMNh/qNA+6umflFy2HsxjT2Y+NY2ZczKb6BDiRVy4L13a+BLXxocu4b6E+bqpb5SIiIiINFtms4nLekYwrHsbhVMiIg6iUKoFO1xcxraUPLaWB0//HMxlW0oeecVlNe4f5OVqC57CfYhr40uXNj6cFuqNm7NTI1cuIiIi9eGNN97gxRdfJCUlhfj4eF5//XUGDBhQ476zZs3i448/ZtOmTQD07duXZ5999pj7i7QUCqdERBxHoVQL9Mu2NKZ8s5m9mQU1Pu7iZOK0UB+6hPsQ18bHPgoqxMetkSsVERGRhjJ37lwmTJjA22+/zcCBA5kxYwZDhw5l27ZthIaGVtv/l19+4brrrmPw4MG4u7vz/PPPc9FFF7F582YiIyMd8AlEGpfCKRGRxqdG5zVozk06DcPgwld+Y2d508ZQH7cq0+7i2vjQPtgbV2ezgysVERFpHprrccHAgQPp378/M2fOBMBqtRIVFcW9997LxIkTT/h8i8VCQEAAM2fOZMyYMbV6z+b6XYnUxGo1qoRToIboIiK1pUbnrdRvOzLYmXYYbzdnfpxwNm38PBxdkoiIiDSykpIS1qxZw6RJk+zbzGYzQ4YMYcWKFbV6jYKCAkpLSwkMDDzmPsXFxRQXF9vv5+bmnnzRIk2MRk6JiDQ8DZdpYWYv2wPANf3aKpASERFppTIyMrBYLISFhVXZHhYWRkpKSq1e4+GHHyYiIoIhQ4Ycc59p06bh5+dnv0RFRZ1S3SJNUUU4teSBs5l5fW86hnqTW1TG9B+3c+ZzP/Fawg5yi2pesVpERI5PoVQLsjMtj1+3p2Mywc2DYx1djoiIiDRTzz33HF988QVff/017u7ux9xv0qRJ5OTk2C/79u1rxCpFGpfCKRGR+qfpey3IB3/sBWBIlzBigrwcW4yIiIg4THBwME5OTqSmplbZnpqaSnh4+HGf+9JLL/Hcc8+xdOlSevbsedx93dzccHPTQinSumhan4hI/dFIqRYiu6CE+Wv3A3DLGe0cXI2IiIg4kqurK3379iUhIcG+zWq1kpCQwKBBg475vBdeeIGnnnqKxYsX069fv8YoVaTZ0sgpEZFTp5FSLcTnq/dRVGqlaxtfTm9/7IakIiIi0jpMmDCBsWPH0q9fPwYMGMCMGTPIz89n3LhxAIwZM4bIyEimTZsGwPPPP8/kyZP57LPPiI2Ntfee8vb2xtvb22GfQ6SpO9HIqdH9o7hhYAyxwZrJICJyNIVSLUCpxcpHy/cCcMuZ7TCZTI4tSERERBxu9OjRpKenM3nyZFJSUujVqxeLFy+2Nz9PSkrCbD4yaP6tt96ipKSEkSNHVnmdJ554gilTpjRm6SLN0rHCqVm/72HW73s4p1MIN50ew3lxoTiZdbwuIgJgMgzDcHQRTU1ubi5+fn7k5OTg6+vr6HJO6Jv1B7jv878J9nblj4nn4+bs5OiSREREWozmdlzgSPquRI6wWg1+2Z7GJysS+WV7OhVnXZH+Hlw/MJrR/aMI9lZPNhFpmWp7TKCRUi3A7GV7ALjx9BgFUiIiIiIiTYDZbOL8uDDOjwsjKbOAOasSmfvXPpKzC3lxyTZeXbqDYT3CuWlQDH2iAzTbQURaJYVSzdzapEOs25eNq5OZGwbGOLocERERERE5SnSQJ5OGdeHBCzuxaMNBPlmZyLp92Sxcd4CF6w7QtY0vNw2K4YpeEXi66hRNRFoPrb7XzFWMkrqiVwQhPhr+KyIiIiLSVLm7OHF137YsHH8G/7vnTEb1a4ubs5ktB3OZtGAjA59NYMo3m9mVftjRpYqINAqFUs3YgexCvt9kWxln3BntHFyNiIiIiIjUVo+2frwwMp5Vj1zAY5d2ITbIk7yiMj5cvpcLXv6VG95byeJNBymzWB1dqohIg9HY0GbsoxV7sVgNBrUPomuEmomKiIiIiDQ3/p6u3HZWe245ox3LdmbwycpEEv5J5Y+dmfyxM5NwX3euHxjNtf2jCPV1d3S5IiL1SqFUM1VQUsbnq5IAuOVMjZISEREREWnOzGYTZ3cK4exOISRnF/LZqkS+WL2PlNwipv+4ndcSdjC0ezhjTo9hQLtANUYXkRahSUzfe+ONN4iNjcXd3Z2BAweyevXqY+5bWlrK1KlT6dChA+7u7sTHx7N48eJTes3maP7aZHKLyogJ8uT8uFBHlyMiIiIiIvUk0t+Dh4bGsXzS+bx6bS/6xwZQZjVYtOEgo99dydAZv/HJir3kFZU6ulQRkVPi8FBq7ty5TJgwgSeeeIK1a9cSHx/P0KFDSUtLq3H/xx57jHfeeYfXX3+dLVu2cOeddzJixAj+/vvvk37N5sZqNfjgD1uD85sHx+Jk1q8kIiIiIiItjZuzE1f0iuSrOwfz3X1ncf3AaDxdndieepjH/28zpz+bwKQFG/ljZ4Z6T4lIs2QyDMNwZAEDBw6kf//+zJw5EwCr1UpUVBT33nsvEydOrLZ/REQEjz76KOPHj7dvu/rqq/Hw8ODTTz89qdc8Wm5uLn5+fuTk5ODr2/R6Nf28LY1xH/yJj5szKx65AG83zcIUERFpKE39uKAp0Xcl0vByi0r5em0yn6xMZGfakVX6AjxduKhrOBf3COeMDsG4Ojt8/IGItGK1PSZwaJpRUlLCmjVrmDRpkn2b2WxmyJAhrFixosbnFBcX4+5etcGfh4cHy5YtO6XXLC4utt/Pzc096c/UGGYvs42SGt0/SoGUiIiIiEgr4uvuwtjBsYwZFMOK3Zl8s+4AP2xJJSu/hLl/7WPuX/vwcXdmSJcwLu4ezjmdQnB3cXJ02SIiNXJoopGRkYHFYiEsLKzK9rCwMLZu3Vrjc4YOHcr06dM5++yz6dChAwkJCSxYsACLxXLSrzlt2jSefPLJevhEDW97ah6/78jAbOL/2bvz8KbKvH3gd5Jm65Kke5tSugEtlFWWDiCuKIuDy7gCCqKD7zigo+gojAioo4yv/hgcdWScecFtHFd0HEFQKqjIOiwiIFigG93XpE2a/fz+yNKmTUtb2iRt78915UpytjyngfJwn+/zHCyckhro5hARERERUQCIRCJMyYjBlIwY/PFGBw4U1GLb8XJsO16OygYzPjlSgk+OlCBUJsGVmXGYOTIBV2bF8aI2EQWVPvcb6aWXXsLixYuRlZUFkUiEjIwMLFq0CBs3buz2MVesWIFly5Z53uv1eiQnJ/dEc3ucey6pa0ckIDkqNMCtISIiIiKiQAuRiD0B1Zo52ThSXIcvfizHF8fLUVLfhC0/lmHLj2WQhYhx2dBYzBqZgOnD46EOlQa66UQ0wAU0lIqJiYFEIkFFRYXX8oqKCiQkJPjcJzY2Fp9++ilMJhNqamqg1WqxfPlypKend/uYcrkccrm8B86od9UaLNh8uAQAcM+laQFuDRERERERBRuxWITxKVEYnxKFJ64bjh9LdPjCVUGVX23Ajp8qsOOnCoSIRZgyJAazRibg2hHxiA4P/v8PEVH/E9DZ72QyGcaPH4/c3FzPMofDgdzcXEyePLnDfRUKBZKSkmCz2fDxxx/jhhtuuOhjBrt/HSiC2ebAyCQVJqZGBro5REREREQUxEQiEUYP0uDxmVn4+pHLse2hafjd1UORGR8Bm0PAtz9XYcXmHzHx2R244/W9eHNPAcp1pkA3m4gGkIAP31u2bBkWLlyICRMmYNKkSVi/fj0MBgMWLVoEAFiwYAGSkpKwdu1aAMD+/ftRUlKCsWPHoqSkBGvWrIHD4cBjjz3W6WP2RRabA2/tLQAA3HtpGkQiUWAbREREREREfYZIJEJWggpZCSo8fM0wnKtq9FRQ/Viiw75ztdh3rharPzuBSwZrMGtkImaO5JQhRNS7Ah5K3X777aiqqsKqVatQXl6OsWPHYtu2bZ6JyouKiiAWNxd0mUwmrFy5EufOnUN4eDhmz56Nt99+GxqNptPH7Iu+OF6GCr0ZsRFyXDdKG+jmEBERERFRH5YeG44lVw7BkiuHoLjWiO0nnHNQHSqsw+GiehwuqsezW3/CyCQVrsqKxy/SojBucCSUMt7Jj4h6jkgQBCHQjQg2er0earUaOp0OKpUq0M2BIAi44dXvcey8Do9cMwwPXD000E0iIiIaMIKtXxDM+LMi6vsq9CZnQPVjOfbn18DR4n+LUolzOOCktChMSovChJRIRCg4WToRtdXZPkHAK6Xowg4V1uHYeR1kIWLMyxkc6OYQEREREVE/Fa9SYMHkVCyYnIqaRjN2/FSBPWdrsP9cLcr1JhwqrMOhwjq8tussxCJghFaFSanRyEmPwsTUKESFyQJ9CkTUhzCU6gM2fp8PALhpbBLvikFERERERH4RHS7H7RMH4/aJgyEIAs7XNWF/fi0O5Ndgf34tCmuMOF6ix/ESvef/LMPiw12VVNHISYtCvEoR4LMgomDGUCrIna8zYtvxcgDAPZemBbg1REREREQ0EIlEIiRHhSI5KhS3jB8EACjXmXCgwBVSnatFXmUjfq5wPt7ZVwQASI0O9QqpBkUqedMmIvJgKBXk3tpbCIcAXDokBpkJEYFuDhEREREREQAgQa3A9WO0uH6M80ZMNY1mHCyow4H8WhwoqMHJUj0KaowoqDHig/+eBwAkqhWYlBaFnLRoTEqLQkZsGEMqogGMoVQQM5ht+NcB5xWGey5NDWxjiIiIiIiIOhAdLsfMkQmYOTIBAKA3WXGooM4z5O/YeR3KdCb8+2gp/n201LlPmAyT0qIwJlmDbK0K2Vo156UiGkAYSgWxjw+fR4PJhrSYMFwxLC7QzSEiIiIiIuo0lUKKK7PicGWW8/8yRosNR4rqPSHVkaJ61Bgs+OJ4Ob5wTVkCOKupsrUqjNCqXUGVCkkaDvsj6o8YSgUph0PApu8LAACLpqZCLOYvYCIiIiIi6rtCZSGYOiQGU4fEAADMNjt+PK/DgYJanCjR40SpDgU1RpTpTCjTmbDjp0rPvppQKUYkqjzVVNlaFdJjwyHh/5OI+jSGUkFq5+lK5FcboFKE4OZLBgW6OURERERERD1KHiLBhNQoTEiN8ixrMFnxU1kDjpfocKLUGVSdqWxEvdGKPWdrsOdsjWdbhVSMrATvoCozIQIKqSQQp0NE3cBQKki5b6k6d9JghMn5NRERERERUf8XoZC67tbXHFSZbXb8XN6IE6XNQdVPZQ1ostpxtLgeR4vrPdtKxCIMiQ13BlVJatcwQBVUCmkAzoaILoRpRxA6Va7H92dqIBGLsGBKaqCbQ0REREREFDDyEAlGDVJj1CC1Z5ndISC/2oATpTqcLNV7wqo6oxWnKxpwuqIBm4+UeLZPjlJiWFwEhsSHY2hcBIbGhSMjLhzhLAAgCij+DQxCm3YXAABmZicgSaMMbGOIiIiIiIiCjEQswpC4cAyJC8cNY5MAAIIgoExn8gRUJ0r1OFGiQ6nOhOLaJhTXNiH3VKXXcZI0SmTEhWOo+xEfjiGxEVCHsrKKyB8YSgWZmkYzPjnqTPTvuTQ1sI0hIiIiIiLqI0QiEbQaJbQaJa4ZEe9ZXmew4KcyPc5UNSKvohF5lQ04U2lAdaMZJfVNKKlvwrc/V3kdKy5CjiGuoGpIfIQntIoOl/v7tIj6NYZSQeaf+4tgsTkwZpAalwyODHRziIiIiIiI+rTIMBmmDInBFNdd/9zqDBZPUHWm0h1WNaJMZ0JlgxmVDWavidUBIDJUiqGeYYDOoYBD4sIRr5JDJOKdAIm6iqFUEDHb7Hh7XyEA4J5L0/hLjYiIiIiIqJdEhskwMSwKE1vc/Q9w3gHwbJUBeRUNrrDKGVoV1xlRZ7TiQEEtDhTUeu0TIQ9Belw40qJDkRIdhtQY53NadBg0oVL+346oHQylgsiWY2WoajAjXiXH7FGJgW4OERERERHRgBOhkGJssgZjkzVey5ssdpytcgZU7sqqvMpGFNYY0WC24YfievzQ4k6AbipFCFJjwpxhlTu0cj3HhMsYWNGAxlAqSAiCgP/bnQ8AWDA5FVKJOMAtIiIiIiIiIjelTIKRSWqMTFJ7LTfb7CisMeJsZSMKaoworDGgoMaAwhojynQm6E02HDuvw7HzujbHDJNJvCqr3GFVWkwY4iI4JJD6P4ZSQeJgQR1OlOohDxFj3qTBgW4OERERERERdYI8RIJh8REYFh/RZp3JakdRrREF1c6Qyh1WFdQYUFLfBIPFjpNlepws07fZVyEVIzU6DCnRoa7nMAyKVLomc1cgVMb/zlPfxz/FQWKjq0rqV5cMQmSYLMCtISIiIiIiooulkLYfWJltdhTXNrkqq4xez+frmmCyOnCqvAGnyht8HlutlCJRrYBWo/R6TlQ7Q6sEtQLyEElvnyLRRWEoFQSKa4348mQ5AOCeqamBbQwRERERERH1OnmIBEPiwjEkLrzNOqvdgZK6Jq/KqoJqZ3VVWb0JDWYbdE1W6Jqs7YZWABATLkOiWtkmvNJqnOFVXIQcIZw6hgKIoVQQeGNPARwCcNmwWAz1kaATERERERHRwCGViJEaE4bUmDCf6xtMVpTpTCitb0KZzoSy+iaUtnhfWt8Es82B6kYLqhst+LGk7XxWACAWAfEqhbPCSqOEVq1AglqJeJUcCSoF4lUKxKnkrLiiXsNQKsAaTFa8f7AYAKukiIiIiIiI6MIiFFJEKKQ+hwUCzhtp1RmtzaGVrgml9c7nsnoTSnVNKNeZYHMIrvUmoKi+3c+LCpMhXqVAgkqOeFdYlaBWIN71PkGlQFQY7yRIXcdQKsA+OnQejWYbMmLDcNnQ2EA3h4iIiIiIiPo4kUiEqDAZosJkbe4W6OZwCKhuNKO0VaVVhd6ECr0J5XoTKvRmWGwO1BosqDVY8FNZ+58pk4gRGyFHgtoZUsW5qq2c4ZXCE14pZay6omYMpQLI7hDwxp4CAMCiqWkQi5kqExERERERUe8Ti0WIUykQp1JgbLLG5zaCIKDeaEW5K6Sq1JtQrjO7AqvmR3WjBRa7AyX1TSipb+rwcyMUIYgNlyMmXI7ocJnnOTpcjljXs3tZhDyE1Vf9HEOpAPr6VCUKa4xQK6X41SVJgW4OERERERERkYdIJEJkmAyRYTIMT1S1u53F5kBlg7OyqkJvQrnOhIoGEyp0zRVX5ToTmqx2NJhsaDDZcK7acMHPl4WIERPmDqqcz9HhMsS2DLTCnOuiwmSctL0PYigVQBt35wMA5k4ajFAZvwoiIiIiIiLqe2QhYgyKDMWgyNB2txEEAQ1mGypdlVXVjWbUuJ6rGy2oaTQ7lxksqG4ww2Cxw2JzOIcV6kydakdkqNQZXIXJEB0uQ2SoM6yKDG31PkyGqFAZhxIGASYhAXKiVIe952ogEYuwcEpKoJtDRERERERE1GtEIhFUCilUCimGxF14+yaLHTUG78CqZZhVYzCjusH5XGuwwCEAdUYr6oxWnOlkm5RSiSukknoCqyhXYBUZ1jbQ0oRKIWU1Vo9iKBUgm74vAADMHpWIRLUysI0hIiIiIiIiCiJKmQSDZB1XX7nZHQLqjJYWlVdm1Butngnaa40W1Lle1xmdz1a7gCarvVPzYLWkUoR4qq00Sik0rrBKo3Q9h7qWKZtfR8hDOId0OxhKBUBVgxmfHS0FANwzNTWwjSEiIiIiIiLqwyRiEWJcE6RnIuKC2wuCgEazDXUGq8/AqrbVe2cFlgWCAOhNNuhNNhTUGDvdPrEIUCud1VjqUCk0Xq/bhlnudQMhzGIoFQD/3F8Ii92BcYM1GDc4MtDNISIiIiIiIhowRCIRIhRSRCikGBx94UoswFmNpW+yosYVWNUbLahvsjqfjVbv10arZ73RYvcaWtgV7jBLrZRC7Qqs1O4KLNcytVLqqcpybud8lof0jfmyGEr5mdlmxzv7CgEA90xNC3BriIiIiIiIiOhCJOLmOxF2hdlmh84TWjkrrpzvnRVY9UYrdE0W1Bmc2+iMzuVN1lZhVhcqswAgVCbxBFruwMpdlaVu8X5YfDiGxl+4uqy3MJTys//8UIbqRgu0agVmjUwIdHOIiIiIiIiIqJfIQySIU0kQp1J0aT+T1Q59kxW6puZAq95ogc69zNi8TueqynKvEwTAaLHDaLGj7AJ3Lvyfy9KxYvbwiznFi8JQys/iVXKMG6zBjOwEhHDWfiIiIiIiIiJqRSGVQCHtepjlcAhoMNlcgVXz0EKdO7zyCrOsSI8N66Uz6ByGUn42bWgspg2Nhd0hBLopRERERERERNSPiMUi5/C8UCkGo3PzZQUSS3UCRNLPZ9AnIiIiIiIiIuoIQykiIiIiIiIiIvI7hlJEREREREREROR3DKWIiIiIiIiIiMjvGEoREREREREREZHfMZQiIiIiIiIiIiK/YyhFRERERERERER+x1CKiIiIiIiIiIj8jqEUERERERERERH5HUMpIiIiIiIiIiLyO4ZSRERERERERETkdwyliIiIiIiIiIjI70IC3YBgJAgCAECv1we4JURERBRo7v6Au39A7WMfioiIiIDO958YSvnQ0NAAAEhOTg5wS4iIiChYNDQ0QK1WB7oZQY19KCIiImrpQv0nkcDLfm04HA6UlpYiIiICIpGox4+v1+uRnJyM4uJiqFSqHj9+sBqI5z0QzxngefO8Bwae98A5b0EQ0NDQAK1WC7GYMx90pDf7UAPxzx7A8+Z5Dww874Fz3gPxnIGBed6d7T+xUsoHsViMQYMG9frnqFSqAfMHsqWBeN4D8ZwBnvdAw/MeWAbaebNCqnP80YcaaH/23HjeAwvPe2AZiOc9EM8ZGHjn3Zn+Ey/3ERERERERERGR3zGUIiIiIiIiIiIiv2MoFQByuRyrV6+GXC4PdFP8aiCe90A8Z4DnzfMeGHjeA+u8KfAG6p89njfPeyDgeQ+c8x6I5wwM3PPuDE50TkREREREREREfsdKKSIiIiIiIiIi8juGUkRERERERERE5HcMpYiIiIiIiIiIyO8YSvWSV199FampqVAoFMjJycGBAwc63P7DDz9EVlYWFAoFRo0aha1bt/qppT1j7dq1mDhxIiIiIhAXF4cbb7wRp0+f7nCfN954AyKRyOuhUCj81OKesWbNmjbnkJWV1eE+ff27BoDU1NQ25y0SibBkyRKf2/fF7/rbb7/FnDlzoNVqIRKJ8Omnn3qtFwQBq1atQmJiIpRKJaZPn468vLwLHrervxv8raPztlqtePzxxzFq1CiEhYVBq9ViwYIFKC0t7fCY3fl74m8X+r7vvvvuNucwc+bMCx63L3/fAHz+PReJRHjhhRfaPWZf+L4peLH/xP5Te/r6dw0MjP4TwD4U+1Cfeq1nH4p9qAthKNUL3n//fSxbtgyrV6/G4cOHMWbMGMyYMQOVlZU+t9+zZw/mzp2Le++9F0eOHMGNN96IG2+8EcePH/dzy7vvm2++wZIlS7Bv3z589dVXsFqtuPbaa2EwGDrcT6VSoayszPMoLCz0U4t7TnZ2ttc57N69u91t+8N3DQAHDx70OuevvvoKAHDrrbe2u09f+64NBgPGjBmDV1991ef6//3f/8Vf/vIXbNiwAfv370dYWBhmzJgBk8nU7jG7+rshEDo6b6PRiMOHD+PJJ5/E4cOHsXnzZpw+fRrXX3/9BY/blb8ngXCh7xsAZs6c6XUO//rXvzo8Zl//vgF4nW9ZWRk2btwIkUiEm2++ucPjBvv3TcGJ/Sf2n9rTH75rYGD0nwD2odiHaot9KPahOiRQj5s0aZKwZMkSz3u73S5otVph7dq1Pre/7bbbhOuuu85rWU5OjvA///M/vdrO3lRZWSkAEL755pt2t9m0aZOgVqv916hesHr1amHMmDGd3r4/fteCIAi/+93vhIyMDMHhcPhc39e/awDCJ5984nnvcDiEhIQE4YUXXvAsq6+vF+RyufCvf/2r3eN09XdDoLU+b18OHDggABAKCwvb3aarf08Czdd5L1y4ULjhhhu6dJz++H3fcMMNwlVXXdXhNn3t+6bgwf4T+0/t6Y/ftSD0//6TILAP1RH2odrXH79v9qHax0qpHmaxWHDo0CFMnz7ds0wsFmP69OnYu3evz3327t3rtT0AzJgxo93t+wKdTgcAiIqK6nC7xsZGpKSkIDk5GTfccANOnDjhj+b1qLy8PGi1WqSnp2P+/PkoKipqd9v++F1bLBa88847uOeeeyASidrdrj981275+fkoLy/3+i7VajVycnLa/S6787uhL9DpdBCJRNBoNB1u15W/J8Fq165diIuLQ2ZmJu6//37U1NS0u21//L4rKiqwZcsW3HvvvRfctj983+Rf7D85sf/kW3/8rgdi/wlgH6ol9qF864/fN/tQHWMo1cOqq6tht9sRHx/vtTw+Ph7l5eU+9ykvL+/S9sHO4XDgoYcewtSpUzFy5Mh2t8vMzMTGjRvx73//G++88w4cDgemTJmC8+fP+7G1FycnJwdvvPEGtm3bhtdeew35+fmYNm0aGhoafG7f375rAPj0009RX1+Pu+++u91t+sN33ZL7++rKd9md3w3BzmQy4fHHH8fcuXOhUqna3a6rf0+C0cyZM/HWW28hNzcXzz//PL755hvMmjULdrvd5/b98ft+8803ERERgV/96lcdbtcfvm/yP/af2H9i/6mt/vBdt8Y+lBP7UOxD+dIfvu/uCAl0A6j/WbJkCY4fP37B8a+TJ0/G5MmTPe+nTJmC4cOH429/+xueeeaZ3m5mj5g1a5bn9ejRo5GTk4OUlBR88MEHnUrC+4P/+7//w6xZs6DVatvdpj981+TNarXitttugyAIeO211zrctj/8Pbnjjjs8r0eNGoXRo0cjIyMDu3btwtVXXx3AlvnPxo0bMX/+/AtOstsfvm+iQGD/aWD9nmD/aeBiH4p9qPb0h++7O1gp1cNiYmIgkUhQUVHhtbyiogIJCQk+90lISOjS9sFs6dKl+Pzzz7Fz504MGjSoS/tKpVKMGzcOZ86c6aXW9T6NRoNhw4a1ew796bsGgMLCQuzYsQO//vWvu7RfX/+u3d9XV77L7vxuCFbuzlRhYSG++uqrDq/w+XKhvyd9QXp6OmJiYto9h/70fQPAd999h9OnT3f57zrQP75v6n3sP7H/xP7ThfWH75p9KPah2IfqvP7wfXcGQ6keJpPJMH78eOTm5nqWORwO5Obmel3paGny5Mle2wPAV1991e72wUgQBCxduhSffPIJvv76a6SlpXX5GHa7HT/++CMSExN7oYX+0djYiLNnz7Z7Dv3hu25p06ZNiIuLw3XXXdel/fr6d52WloaEhASv71Kv12P//v3tfpfd+d0QjNydqby8POzYsQPR0dFdPsaF/p70BefPn0dNTU2759Bfvm+3//u//8P48eMxZsyYLu/bH75v6n3sP7H/xP7ThfWH75p9KPah2IfqvP7wfXdKYOdZ75/ee+89QS6XC2+88YZw8uRJ4b777hM0Go1QXl4uCIIg3HXXXcLy5cs923///fdCSEiI8OKLLwo//fSTsHr1akEqlQo//vhjoE6hy+6//35BrVYLu3btEsrKyjwPo9Ho2ab1eT/11FPC9u3bhbNnzwqHDh0S7rjjDkGhUAgnTpwIxCl0yyOPPCLs2rVLyM/PF77//nth+vTpQkxMjFBZWSkIQv/8rt3sdrswePBg4fHHH2+zrj981w0NDcKRI0eEI0eOCACEdevWCUeOHPHcIeVPf/qToNFohH//+9/CsWPHhBtuuEFIS0sTmpqaPMe46qqrhJdfftnz/kK/G4JBR+dtsViE66+/Xhg0aJBw9OhRr7/rZrPZc4zW532hvyfBoKPzbmhoEB599FFh7969Qn5+vrBjxw7hkksuEYYOHSqYTCbPMfrb9+2m0+mE0NBQ4bXXXvN5jL74fVNwYv+J/Sf2n/rHd80+FPtQ7EM5sQ/VOQylesnLL78sDB48WJDJZMKkSZOEffv2edZdfvnlwsKFC722/+CDD4Rhw4YJMplMyM7OFrZs2eLnFl8cAD4fmzZt8mzT+rwfeughz88oPj5emD17tnD48GH/N/4i3H777UJiYqIgk8mEpKQk4fbbbxfOnDnjWd8fv2u37du3CwCE06dPt1nXH77rnTt3+vwz7T4vh8MhPPnkk0J8fLwgl8uFq6++us3PIiUlRVi9erXXso5+NwSDjs47Pz+/3b/rO3fu9Byj9Xlf6O9JMOjovI1Go3DttdcKsbGxglQqFVJSUoTFixe36Rj1t+/b7W9/+5ugVCqF+vp6n8foi983BS/2n9h/EoT++V279ff+kyCwD8U+FPtQbuxDdY5IEAShu1VWRERERERERERE3cE5pYiIiIiIiIiIyO8YShERERERERERkd8xlCIiIiIiIiIiIr9jKEVERERERERERH7HUIqIiIiIiIiIiPyOoRQREREREREREfkdQykiIiIiIiIiIvI7hlJEREREREREROR3DKWIiIiIiIiIiMjvGEoREREREREREZHfMZQiIiIiIiIiIiK/YyhFRERERERERER+x1CKiIiIiIiIiIj8jqEUERERERERERH5HUMpIiIiIiIiIiLyO4ZSRERERERERETkdwyliIiIiIiIiIjI7xhKEVG/IRKJsGbNmi7vV1BQAJFIhDfeeKPH2+RvqampuPvuuz3vd+3aBZFIhF27dgWsTUREROR/7BcRUV/AUIqIetQbb7wBkUgEkUiE3bt3t1kvCAKSk5MhEonwy1/+MgAt7D53wON+SCQSxMXF4ZZbbsFPP/0U6OYRERFRkBlI/aKWjzvuuMOz3YEDB/Db3/4W48ePh1QqhUgk6tLnWCwWvPTSSxg3bhxUKhU0Gg2ys7Nx33334dSpUz19WkTkZyGBbgAR9U8KhQLvvvsuLr30Uq/l33zzDc6fPw+5XB6gll28Bx98EBMnToTVasWxY8ewYcMG7Nq1C8ePH0dCQkKgm0dERERBZiD0i1pKTU31vN66dSv+8Y9/YPTo0UhPT8fPP//cpePffPPN+OKLLzB37lwsXrwYVqsVp06dwueff44pU6YgKyurJ06DiAKEoRQR9YrZs2fjww8/xF/+8heEhDT/qnn33Xcxfvx4VFdXB7B1F2fatGm45ZZbPO8zMzNx//3346233sJjjz0WwJYRERFRMBpI/aLW7r//fjz++ONQKpVYunRpl0KpgwcP4vPPP8ezzz6LP/zhD17rXnnlFdTX13e32V1mMpkgk8kgFnOwEVFP4t8oIuoVc+fORU1NDb766ivPMovFgo8++gjz5s3zuY/BYMAjjzyC5ORkyOVyZGZm4sUXX4QgCF7bmc1mPPzww4iNjUVERASuv/56nD9/3ucxS0pKcM899yA+Ph5yuRzZ2dnYuHFjz50onJ0xADh79my3PttkMmHNmjUYNmwYFAoFEhMT8atf/crreC+++CKmTJmC6OhoKJVKjB8/Hh999FGPngcRERH1joHUL2otPj4eSqWyW/u6+0JTp05ts04ikSA6OtprWUlJCe69915otVrI5XKkpaXh/vvvh8Vi8Wxz7tw53HrrrYiKikJoaCh+8YtfYMuWLV7HcQ9NfO+997By5UokJSUhNDQUer0eALB//37MnDkTarUaoaGhuPzyy/H999936xyJBjpWShFRr0hNTcXkyZPxr3/9C7NmzQIAfPHFF9DpdLjjjjvwl7/8xWt7QRBw/fXXY+fOnbj33nsxduxYbN++Hb///e9RUlKCP//5z55tf/3rX+Odd97BvHnzMGXKFHz99de47rrr2rShoqICv/jFLyASibB06VLExsbiiy++wL333gu9Xo+HHnqoR861oKAAABAZGdnlz7bb7fjlL3+J3Nxc3HHHHfjd736HhoYGfPXVVzh+/DgyMjIAAC+99BKuv/56zJ8/HxaLBe+99x5uvfVWfP755z7PnYiIiIJHf+4XNTQ0tKn0ioqK6pGKopSUFADAP//5T0ydOtWryqy10tJSTJo0CfX19bjvvvuQlZWFkpISfPTRRzAajZDJZKioqMCUKVNgNBrx4IMPIjo6Gm+++Sauv/56fPTRR7jpppu8jvnMM89AJpPh0Ucfhdlshkwmw9dff41Zs2Zh/PjxWL16NcRiMTZt2oSrrroK3333HSZNmnTR5000oAhERD1o06ZNAgDh4MGDwiuvvCJEREQIRqNREARBuPXWW4Urr7xSEARBSElJEa677jrPfp9++qkAQPjjH//odbxbbrlFEIlEwpkzZwRBEISjR48KAITf/va3XtvNmzdPACCsXr3as+zee+8VEhMTherqaq9t77jjDkGtVnvalZ+fLwAQNm3a1OG57dy5UwAgbNy4UaiqqhJKS0uFbdu2CUOGDBFEIpFw4MCBLn/2xo0bBQDCunXr2nyew+HwvHZv72axWISRI0cKV111ldfylJQUYeHChW3avHPnzg7PjYiIiHreQOgX+Xrk5+f73GfJkiVCV/4L6nA4hMsvv1wAIMTHxwtz584VXn31VaGwsLDNtgsWLBDEYrFw8OBBn8cRBEF46KGHBADCd99951nX0NAgpKWlCampqYLdbvc6t/T0dK8+mMPhEIYOHSrMmDGjTT8tLS1NuOaaazp9bkTkxOF7RNRrbrvtNjQ1NeHzzz9HQ0MDPv/883ZL1Ldu3QqJRIIHH3zQa/kjjzwCQRDwxRdfeLYD0Ga71lf3BEHAxx9/jDlz5kAQBFRXV3seM2bMgE6nw+HDh7t1Xvfccw9iY2Oh1Woxc+ZM6HQ6vP32255JPrvy2R9//DFiYmLwwAMPtPmclnenaVn2XldXB51Oh2nTpnX7HIiIiMi/+mu/aNWqVfjqq6+8Hj114xeRSITt27fjj3/8IyIjI/Gvf/0LS5YsQUpKCm6//XbPnFIOhwOffvop5syZgwkTJvg8DuD8eU2aNMlrwvnw8HDcd999KCgowMmTJ732W7hwoVcf7OjRo8jLy8O8efNQU1Pj+RkaDAZcffXV+Pbbb+FwOHrk3IkGCg7fI6JeExsbi+nTp+Pdd9+F0WiE3W5vdyLMwsJCaLVaREREeC0fPny4Z737WSwWe4a1uWVmZnq9r6qqQn19PV5//XW8/vrrPj+zsrKyW+e1atUqTJs2DY2Njfjkk0/w3nvveZWod+Wzz549i8zMzA7L0QHg888/xx//+EccPXoUZrPZs7yrt1UmIiKiwOiv/aJRo0Zh+vTp3dq3M+RyOZ544gk88cQTKCsrwzfffIOXXnoJH3zwAaRSKd555x1UVVVBr9dj5MiRHR6rsLAQOTk5bZa3/Lm2PEZaWprXdnl5eQCcYVV7dDqd15QORNQxhlJE1KvmzZuHxYsXo7y8HLNmzYJGo/HL57qvUt15553tdhxGjx7drWO37HzdeOONMBqNWLx4MS699FIkJyf3+Gd/9913uP7663HZZZfhr3/9KxITEyGVSrFp0ya8++673ToHIiIi8r/+2C/yp8TERNxxxx24+eabkZ2djQ8++ABvvPFGr31e6wna3T/HF154AWPHjvW5T3h4eK+1h6g/YihFRL3qpptuwv/8z/9g3759eP/999vdLiUlBTt27EBDQ4PXVcFTp0551rufHQ6Hp8LI7fTp017Hc9+Bxm639+rVOwD405/+hE8++QTPPvssNmzY0KXPzsjIwP79+2G1WiGVSn1u8/HHH0OhUGD79u2Qy+We5Zs2berR8yAiIqLeNRD6Rf4glUoxevRo5OXlobq6GnFxcVCpVDh+/HiH+6WkpLT52QBtf67tcVekqVSqfvFzJAoGnFOKiHpVeHg4XnvtNaxZswZz5sxpd7vZs2fDbrfjlVde8Vr+5z//GSKRyHOnGvdz67vUrF+/3uu9RCLBzTffjI8//thnB6Wqqqo7p+NTRkYGbr75ZrzxxhsoLy/v0mfffPPNqK6ubnPeADy3fJZIJBCJRLDb7Z51BQUF+PTTT3vsHIiIiKj3DYR+UU/Ky8tDUVFRm+X19fXYu3cvIiMjERsbC7FYjBtvvBH/+c9/8N///rfN9u4+1ezZs3HgwAHs3bvXs85gMOD1119HamoqRowY0WF7xo8fj4yMDLz44otobGxssz5Yf45EwYyVUkTU6zoad+82Z84cXHnllXjiiSdQUFCAMWPG4Msvv8S///1vPPTQQ54rU2PHjsXcuXPx17/+FTqdDlOmTEFubi7OnDnT5ph/+tOfsHPnTuTk5GDx4sUYMWIEamtrcfjwYezYsQO1tbU9do6///3v8cEHH2D9+vX405/+1OnPXrBgAd566y0sW7YMBw4cwLRp02AwGLBjxw789re/xQ033IDrrrsO69atw8yZMzFv3jxUVlbi1VdfxZAhQ3Ds2LEeOwciIiLqfQOhX9RSYWEh3n77bQDwBEZ//OMfATgrk+6666529/3hhx8wb948zJo1C9OmTUNUVBRKSkrw5ptvorS0FOvXr4dEIgEAPPfcc/jyyy9x+eWX47777sPw4cNRVlaGDz/8ELt374ZGo8Hy5cvxr3/9C7NmzcKDDz6IqKgovPnmm8jPz8fHH3/sNUeoL2KxGP/4xz8wa9YsZGdnY9GiRUhKSkJJSQl27twJlUqF//znPz3xYyMaOAJ12z8i6p9a3vq4I61vfSwIzlvyPvzww4JWqxWkUqkwdOhQ4YUXXvC65a4gCEJTU5Pw4IMPCtHR0UJYWJgwZ84cobi4uM2tjwVBECoqKoQlS5YIycnJglQqFRISEoSrr75aeP311z3bdPXWxx9++KHP9VdccYWgUqmE+vr6Tn+2IDhvI/zEE08IaWlpnu1uueUW4ezZs55t/u///k8YOnSoIJfLhaysLGHTpk3C6tWr29xWOSUlRVi4cGGbNu/cubPDcyMiIqKeN5D7Ra238/W4/PLLO9y3oqJC+NOf/iRcfvnlQmJiohASEiJERkYKV111lfDRRx+12b6wsFBYsGCBEBsbK8jlciE9PV1YsmSJYDabPducPXtWuOWWWwSNRiMoFAph0qRJwueff96lczty5Ijwq1/9SoiOjhbkcrmQkpIi3HbbbUJubm6H50NEbYkEwVXLSERERERERERE5CecU4qIiIiIiIiIiPyOoRQREREREREREfkdQykiIiIiIiIiIvI7hlJEREREREREROR3DKWIiIiIiIiIiMjvGEoREREREREREZHfMZQiIiIiIiIiIiK/Cwl0A4KRw+FAaWkpIiIiIBKJAt0cIiIiCiBBENDQ0ACtVguxmNfzOsI+FBEREQGd7z8xlPKhtLQUycnJgW4GERERBZHi4mIMGjQo0M0IauxDERERUUsX6j8xlPIhIiICgPOHp1KpAtwaIiIiCiS9Xo/k5GRP/4Daxz4UERERAZ3vPzGU8sFdbq5SqdihIiIiIgDgcLROYB+KiIiIWrpQ/4kTIxARERERERERkd8xlCIiIiIiIiIiIr9jKEVERERERERERH7HOaUugt1uh9VqDXQz+iSpVAqJRBLoZhAREZGfORwOWCyWQDejT2L/iYiI+huGUt0gCALKy8tRX18f6Kb0aRqNBgkJCZw4loiIaICwWCzIz8+Hw+EIdFP6LPafiIioP2Eo1Q3uQCouLg6hoaHsFHSRIAgwGo2orKwEACQmJga4RURERNTbBEFAWVkZJBIJkpOTIRZzFomuYP+JiIj6I4ZSXWS32z2BVHR0dKCb02cplUoAQGVlJeLi4liKTkRE1M/ZbDYYjUZotVqEhoYGujl9EvtPRETU3/ASVRe555BiZ+riuX+GnJeLiIio/7Pb7QAAmUwW4Jb0bew/ERFRf8JQqps4ZO/i8WdIREQ08PDf/4vDnx8REfUnDKWoW1JTU7F+/fpAN4OIiIioz2D/iYiIyBvnlBpArrjiCowdO7ZHOkMHDx5EWFjYxTeKiIjoIljtDtQaLKhqMCNbq2IVCfU49p+IiKi/sdkd0JtsqDdaEK4IQVyEImBtYShFHoIgwG63IyTkwn8sYmNj/dAiIiIaiKx2B2oaLahuNKOq0YzqBvezc1l1oxlVDc7nOmPzvDo/rrkWEQppAFtOAxH7T0REFChWuwO6JivqjRbUG63Oh+u9c7kVdS1e1zc5t2sw2TzHeGj6UDw0fVjAzoGh1ABx991345tvvsE333yDl156CQCwadMmLFq0CFu3bsXKlSvx448/4ssvv0RycjKWLVuGffv2wWAwYPjw4Vi7di2mT5/uOV5qaioeeughPPTQQwCc8xv8/e9/x5YtW7B9+3YkJSXh//2//4frr78+EKdLRERBpnXQ5A6V3EGT532roKkzxCIgKkwOvcnGUIp6FPtPRETkLyarHdWNZtQ0WlBrcPaPWgZJrV/rmqxoNNsufOAORChCIAg9dALdxFCqBwiCgCar3e+fq5RKOj1M4aWXXsLPP/+MkSNH4umnnwYAnDhxAgCwfPlyvPjii0hPT0dkZCSKi4sxe/ZsPPvss5DL5XjrrbcwZ84cnD59GoMHD273M5566in87//+L1544QW8/PLLmD9/PgoLCxEVFXXxJ0tEREHLZLWjTGdCma4JZfXO51KdCeU6E0rrm1ChN3UraIoOlyMmXI6YcBliI+SIdb+PkCE2XIGYCBliwuWIDJVBIuawvb4mUP0noPN9KPafiIiou2x2B+qMVtQYzJ4LczWNlhbvm1/XNJphsHTv30SRCFAppNCESqFRSqEOlSGyxWuN0rUuVAq1Uta8nVKKEEngpxlnKNUDmqx2jFi13e+fe/LpGQiVde4rVKvVkMlkCA0NRUJCAgDg1KlTAICnn34a11xzjWfbqKgojBkzxvP+mWeewSeffILPPvsMS5cubfcz7r77bsydOxcA8Nxzz+Evf/kLDhw4gJkzZ3b53IiIKDhYbA5U6J3hUpnOhFJP8OQKoXQm1BosnTqWRCxCVJjMGS5FuMKmcDliI9zhk5xB0wASqP4T0Pk+FPtPRETkZncI0DVZUWswo9bgfK5utLQKmsyoMThDpvoma5erkGQSMWLCZYgOlyMqTIaoMBnUyubASRPqCpVahE0RCmmf7jMxlCJMmDDB631jYyPWrFmDLVu2oKysDDabDU1NTSgqKurwOKNHj/a8DgsLg0qlQmVlZa+0mYiILp7F5kBVoxll9c7KpjJX8OQOm0rrTahuNHfqWEqpBIkaBRLVCiSqldCqFUjUKJGgdi6LdQVN4j7caSJqif0nIqK+SxAEGCx21DZaUGu0oM7gHDJXa2j/fXdCJpEIiAqVITpchugwOaLDnRffosOcwZPzffO6cHnIgLtpC0OpHqCUSnDy6RkB+dye0PouMI8++ii++uorvPjiixgyZAiUSiVuueUWWCwdXwmXSr3n8RCJRHA4HD3SRiIi6jyT1Y6qBjMqG0yo0JtRqTehssHsfN1gcq0zd7rCSRYidoZMaqUzdNK4gieNAgkq57NaKR1wnSi6OIHqP7k/+2Kx/0REFDwEQYDeZPP0f9zzMtUaLKgzNj/XNDqf6wxWWOzd+12rUoQgKkyGyDCZZ5oBd6gUHS5HTIvAiZXfF8ZQqgeIRKJOD6MLJJlMBrv9wuNUv//+e9x999246aabADiv/BUUFPRy64iI6EKMFhsq9WZUuEKmSlfHq1Lf/FyhN0Fv6vykl1KJyFXNpGyuctK0fK9AVJiMgRP1OPafiIjoQtw3SnGHTe4La77em21dD5nkIWJEuwIm93C5yNDm163fa0KlkAbBPEz9SfD3BKjHpKamYv/+/SgoKEB4eHi7V+GGDh2KzZs3Y86cORCJRHjyySd5xY6IqJdZbA6U6ZpQXNuE83VGnK9zPpe7Ayi9uUt3WJGHiBGnkiMuQoF413NshBzxKgXiIuSedZGhrHAi6gj7T0REPUsQBDSaba0CJu+gyf2oNVq6NGQuQh6CWJXcM0QuMkyGqFDns/d7KaLD5FDKemb0EXUfQ6kB5NFHH8XChQsxYsQINDU1YdOmTT63W7duHe655x5MmTIFMTExePzxx6HX6/3cWiKi/sVmd6BMZ0KxJ3Bqwvla7/DJ0YlOV6hM4gyVIhSeYClOJfcET+51KuXAm5OAqDew/0RE1HlGiw0VrsrtCn1zFXdFg6vSW++cWqArd1+ViEWeO/HGRTjnqYxTyT135o1TyREb7rz4xpCp7xEJQlen6ur/9Ho91Go1dDodVCqV1zqTyYT8/HykpaVBoVAEqIX9A3+WRNSf2B0CKvQmFNc2h07OAMqI4tomlOtNsF8gdVJIxRgUGYpBkUrXIxSJakWL4EmBcDmvJ/lbR/0C8sY+VO/jz5GIAsE9X2WFK1RyBk0tQidXANXQharucHmIM1hyPeI8zwqvwInzMvVNne0/sWdLRETUSY1mGwqqDSisMaKgxoCiGiPO1ztDp9L6JtguEDrJJGIMilQiKVKJ5KhQT/CU7HqOCefcTUREROQ/7gnCS+ubPI+KltVNOmf4VG+0dvqYoTIJElTNF9TcUwe0fB2nkveJeQWp9/FPARERUQsGsw0FNc7gKb/a4Amh8msMqGowd7ivVCKCVqNEcotqp5bhU2y4HGJe6SMiIiI/sdodqNCbUFpvQml9E0pcj+YQytTpOStlIWLEq+SIj3CFS57QybkszvU6XM4pBKjzGEoREdGAY7TYUFDtrHYqqHEGT+73lRcInqLDZEiJDkVqTBhSosKQHOWqdopSIi5CwfJyIiIi8gtBEKBvsjWHTDp34GRqUfXUuTkrI0Ol0GqUnrvvxreocnIHT2olb45CPY+hFBER9UtNFrsncMqvMaCw2lntVFB94eApyhU8pUWHITXG9YgORUp0GNRKqZ/OgIiIiAY6k9XZn8mvcvZnztc1oaSuudLJYLnwhOEyiRiJGgW0aiW0GiWSNApoNcoWDwWH0lHA8E8eERH1aXUGC/IqG3HG9cirbMDZykaU6kwd7hcZKnWFTa5HTKjrmcETERER+Y/F5kBxndF5Ia3Fo6DacMH+DOC8mKZ1hU5JkUoktQqcYsI4fQAFL4ZSREQU9ARBQIXejLzKhhbhUyPOVjaixmBpdz9NqNQVOjmH26W1CKHUoQyeiIiIyD/sDgGl9U3OqqdWwVNxXVOHd+hVKUKQFhuO9JgwJLtumOIJndRKKGUSP54JUc9iKEVEREHD7hBQXGt0Bk9VjcircD6frWzscBLOJI0SQ+LCMTQuHENcj4zYcESGyfzYeiIiIhrIBEFAVYMZ51xhU8vwqbDWCIvN0e6+SqkEqTFhSI9xVm+nxYQjzXVBLTKUczlR/8VQioiI/M5ss6Og2ug15O5MZSPOVRva7bBJxCKkRIdiSGw4hsa7wqfYCGTEhXEeBCIiIvIbm92Bwtrmfoz7ca6qscM5nmQSMQZHO6cLSI91Vm67g6d4lZzBEw1I7MUTEVGvMtvsOF3egGPndfjxvA7HSnT4uaKh3TJ1eYgY6bHhbSqfUqPDIAsR+7n1RERENFCZrHacqzLgTJU7eHJeRCuoNsJi930RTSwCBkWGesKmNNcNU9JjwqDVKHmXXqJWGEpRp6WmpuKhhx7CQw89FOimEFGQstodOF3egB9LdDh2XofjJTqcKtfDam8bQEXIQ5AR1zZ8GhQZyg4bEfUb7D8RBb8GkxVnqwzIq2jwTBuQV9mI4loj2pvqSSmVICMuDEPjIjzTBgyJC8PgKF5EI+oKhlJERNQtNrsDZ6oavSqgfirT+xx+FxkqxahBGoxOUmPUIDVGJqmhVStYpk5ERER+U9No9pq38qzruVzf/h3uVIoQDI2P8EwfkOG6mKZVK3lHO6IewFCKiIguyO4QcM4dQJU4HydKdTBZ2wZQEYoQjB6kxqgkjetZjUGRSgZQRERE5BcOh4D8GgOOl+hwslSP46U6/FTWgNoO7tgbFyH3VG0PjQv3VHPHhnOuJ6LexFBqgHj99dexZs0anD9/HmJxcznpDTfcgOjoaDzxxBNYtmwZ9u3bB4PBgOHDh2Pt2rWYPn16AFtNRIHgcAgoqDF4huD9eF6H46U6GH1M3BkuD8HIJBVGD9JgVJIaowepMTgqlJ03IuoX2H8iCn5WuwN5FY04XuoKoFyV2+1NOD4oUuk1bcAQ1/A7tVLq55YTEcBQqmcIAmA1+v9zpaFAJ//jd+utt+KBBx7Azp07cfXVVwMAamtrsW3bNmzduhWNjY2YPXs2nn32Wcjlcrz11luYM2cOTp8+jcGDB/fmWRBRgDVZ7DhaXI9DhbU4WFCHw0V1aDDZ2mwXKpMgW6tqroAapEZadBhL14moewLVfwI63Ydi/4kouJisdvxUpsfxUj1OlupwvESP0+UNPicdV0jFGJ6oQrZWhZFaNbK1agyJC4dSJglAy4moPQyleoLVCDyn9f/n/qEUkIV1atPIyEjMmjUL7777rqdT9dFHHyEmJgZXXnklxGIxxowZ49n+mWeewSeffILPPvsMS5cu7ZXmE1FgVDea8d+COvy3oBb/LazD8RIdbK1m8ZSHiJGt9a6ASo8N5wTkRNRzAtV/Ajrdh2L/iShw9Carp/LJPQTvbJXB5917IxQhzeFTkvM5LSYMIRJOOE4U7BhKDSDz58/H4sWL8de//hVyuRz//Oc/cccdd0AsFqOxsRFr1qzBli1bUFZWBpvNhqamJhQVFQW62UR0EQRBwLlqgzOAKqjDfwvrkF9taLNdvEqOCalRmJgSiQmpUchKiGBHjogI7D8R+UNNoxnHS/U4UarDiRJnAFVY47uSMiZchmytGiOTVM5nrRrJUZy7kqivYijVE6ShzitugfjcLpgzZw4EQcCWLVswceJEfPfdd/jzn/8MAHj00Ufx1Vdf4cUXX8SQIUOgVCpxyy23wGJpfzJAIgo+Zpsdx0v0niqoQ4V1bSb1FImAYXERmJAaiYmpURifEsmJyInI/wLVf3J/diex/0TU88p1JuzPr8G+c7U4kF+Ds1VtL5gBQJJG6ayASlJ7nuMiOPE4UX/CUKoniESdHkYXSAqFAr/61a/wz3/+E2fOnEFmZiYuueQSAMD333+Pu+++GzfddBMAoLGxEQUFBQFsLRF1hs5oxaEiVxVUQR1+OF8Ps817XgV5iBhjkjWYmBqJCSlRuGRwJNShnMyTiAKM/SeiAeN8nRH7z9Vif34N9ufX+qyCSo8JQ7Y7fNI6nyPDZAFoLRH5E0OpAWb+/Pn45S9/iRMnTuDOO+/0LB86dCg2b96MOXPmQCQS4cknn4TD0XbCQCIKrDJdE/adq8FB15xQP1c0ttkmKkyGCSmRmJDqHIo3UquGLIRD8YiIuov9J6LOEwQBRbXOEGpffg32n6tFSX2T1zZiETBCq0JOWjRy0qIwKS0KmlAGUEQDEUOpAeaqq65CVFQUTp8+jXnz5nmWr1u3Dvfccw+mTJmCmJgYPP7449Dr9QFsKREBzo7d6YoGfHmiAl+eLMfxkrZ/L9NjwjA+xTUULzUS6TFhLGsnIupB7D8RtU8QBJytMjiroM7V4kB+Lcr1Jq9tJGIRRiWpkZMehV+kRWN8aiRUClZtExFDqQFHLBajtLTt/A2pqan4+uuvvZYtWbLE6z3L0Yn8w2Z34L+FdfjqpDOIKq5tvrooEgGjB2kwyVUFNT4lEjHh8gC2loio/2P/iaiZwyEgr7LRE0Ltz69FdaPZaxupRIQxgzTISY9CTlo0xqdEIkzO/3oSUVv8zUBEFASaLHZ8m1eFr05WIPenCtQZrZ518hAxpg2NwbUjEnDV8DiGUEREROQ3doeAn8r02J/vnJT8QH6tVz8FcPZVxg3WOIfjpUdhXHIklDJJgFpMRH0JQykiogCpaTQj91QlvjpZge/yqmCyNs9DogmV4qqsOFw7IgGXDYtBqIy/romIiMg/6gwWfJtXha9PVeKbn6tQ3yqEUkolGJ8SiZy0KOSkR2NMshryEIZQRNR1/F8OEZEfFdYYnMPyTlTgv4W1cAjN6wZFKnHNiHhcOyIBE1MjESLh5ORERETU+wRBwMkyPXaddgZRR4rqvPoo4fIQTEiN9FRCjUpSQ8p+ChH1AIZSRES9SBAEHC/R48uT5fjyRAVOVzR4rc/WqjxB1PDECE5QTkRERH5hMNvw/Zlq7DxdiZ2nqtpMTp6VEIErs+JwVVYcxiVreLGMiHoFQykioh5mtTuw/1wtvjxZjq9OVqBM19zJk4hFyEmLwrUj4jF9RDwGRYYGsKVEREQ0kORXG7DzVCV2nq7E/nO1sNibpw5QSiWYOiQaV2bF4crMOGg1ygC2lIgGCoZS3SQIwoU3og7xZ0j9ic3uwK7TVfjPsVJ8faoSDSabZ12oTILLh8Xi2ux4XJkZB02oLIAtJSIKLP77f3H486OuMNvsOJBfi52nqrDzdCXyqw1e6wdHheKqrDhcmRWHnLQoKKScF4qI/IuhVBdJpVIAgNFohFLJqwcXw2g0Amj+mRL1RUU1Rrz/3yJ8+N/zqGxovh1yTLgM04fH45oR8Zg6JIadPCIa8CQS5+9Bi8XCPtRFYP+JLqRcZ8Ku05X4+lQlvj9TDYPF7lkXIhZhUloUrsqKwxWZcciIDePUAUQUUEERSr366qt44YUXUF5ejjFjxuDll1/GpEmTfG5rtVqxdu1avPnmmygpKUFmZiaef/55zJw507PNmjVr8NRTT3ntl5mZiVOnTl10WyUSCTQaDSorKwEAoaGh/EXeRYIgwGg0orKyEhqNxtNJJeorzDY7vjxRgfcPFmP3mWrP8qgwGW4al4TZoxIwNjkSEjF/NxARuYWEhCA0NBRVVVWQSqUQizk/TVew/0TtsTsEHC2ux85TziDqZJnea31shBxXZsbiqqw4TB0SgwgFA00iCh4BD6Xef/99LFu2DBs2bEBOTg7Wr1+PGTNm4PTp04iLi2uz/cqVK/HOO+/g73//O7KysrB9+3bcdNNN2LNnD8aNG+fZLjs7Gzt27PC8DwnpuVNNSEgAAE8wRd2j0Wg8P0uivuBMZQPeO1CMzUdKUGuweJZPGxqDOyYOxjUj4iEL4X+yiIh8EYlESExMRH5+PgoLCwPdnD6L/ScCAIvNge/PVmPLsTLk/lSBOqPVs04kAsYM0jiH5WXGIVurgpgXyogoSImEAA9Mz8nJwcSJE/HKK68AABwOB5KTk/HAAw9g+fLlbbbXarV44oknsGTJEs+ym2++GUqlEu+88w4AZ6XUp59+iqNHj3arTXq9Hmq1GjqdDiqVqt3t7HY7rFZru+upfVKplFf4qE9ostix5ccyvHegCP8trPMsj1fJcduEZNw2IRnJUZysnKg/62y/gDr3s3I4HLBYLD7XUcfYfxrYbHYH9pytwZZjZdh2ohy6pub/h6gUIbhsWCyuzIzD5ZmxiAmXB7ClRESd7z8FtFLKYrHg0KFDWLFihWeZWCzG9OnTsXfvXp/7mM1mKBQKr2VKpRK7d+/2WpaXlwetVguFQoHJkydj7dq1GDx4cLvHNJub54LR6/U+t2tNIpGwY0DUTx0v0eG9g0X495FSNJidk5ZLxCJcmRmHuZOScfmwWN4amYioG8RicZu+HBH5ZncI2H+uBv85VobtJ8q9KrVjwuWYPSoBs0clYkJKJPslRNQnBTSUqq6uht1uR3x8vNfy+Pj4dud/mjFjBtatW4fLLrsMGRkZyM3NxebNm2G3N0/gl5OTgzfeeAOZmZkoKyvDU089hWnTpuH48eOIiIhoc8y1a9e2mYOKiAYevcmKz46W4r2DRThe0hxOJ0cpccfEwbhl/CDEq/gfKSIiIuo9doeAgwW12HKsDF8cL0N1Y3MQFRUmw6yRCbhudCJy0qI5fyUR9XkBn1Oqq1566SUsXrwYWVlZEIlEyMjIwKJFi7Bx40bPNrNmzfK8Hj16NHJycpCSkoIPPvgA9957b5tjrlixAsuWLfO81+v1SE5O7t0TIaKgIAgCDhfV4V8HirHlWBmarM6AWyYR49rseMydNBiT06M5FwMRERH1GofD2R/5/FgZtv5Y5nVHX02oFDOzE/DL0Vr8Ij2KFVFE1K8ENJSKiYmBRCJBRUWF1/KKiop2J3CMjY3Fp59+CpPJhJqaGmi1Wixfvhzp6entfo5Go8GwYcNw5swZn+vlcjnkco67JhpIag0WbD58Hu8fLEZeZaNn+ZC4cNwxMRm/umQQosJkAWwhERER9WeCIOBIcT22uIKoMp3Js06lCMGMbGdF1NQhMZAyiCKifiqgoZRMJsP48eORm5uLG2+8EYBz8svc3FwsXbq0w30VCgWSkpJgtVrx8ccf47bbbmt328bGRpw9exZ33XVXTzafiPoYh0PA3nM1+NeBInx5ogIWuwMAoJCK8cvRWsydlIxLBkdCJGJVFBEREfU8QRDwY4kOnx8rw5ZjZSipb/KsC5eH4NoR8fjlmERcOiSWd/QlogEh4MP3li1bhoULF2LChAmYNGkS1q9fD4PBgEWLFgEAFixYgKSkJKxduxYAsH//fpSUlGDs2LEoKSnBmjVr4HA48Nhjj3mO+eijj2LOnDlISUlBaWkpVq9eDYlEgrlz5wbkHIkosMw2Oz44WIy/f5ePolqjZ/nIJBXumDgY14/VQqWQBrCFRERE1F8JgoATpXps+dEZRLXsi4TJJJg+Ih7XjUrEZcNioZDyJkpENLAEPJS6/fbbUVVVhVWrVqG8vBxjx47Ftm3bPJOfFxUVQSxuvkpgMpmwcuVKnDt3DuHh4Zg9ezbefvttaDQazzbnz5/H3LlzUVNTg9jYWFx66aXYt28fYmNj/X16RBRAZpsdH/73PP668wxKXSXxEfIQ3DBOizsmDsbIJHWAW0hERET9kTuI2na8HFt+LEN+tcGzTimV4KrhcZgzOhFXZMYxiCKiAU0kCIIQ6EYEG71eD7VaDZ1OB5VKFejmEFEXWWwOfHioGK9+3RxGxavk+O0VQ3DrhEEIlQU8jyeiPoT9gs7jz4oGMpPVjr1na7Djpwrk/lSJcn3zHFHyEDGuyorDdaMTcVVWHPsiRNTvdbZPwN+GRNRvWO0OfHToPF75+oxnjoa4CDl+e0UG7pg0mFciiYiIqEdVN5rx9alK7DhZge/yqj138QWAUJkE04bGYPaoRFw9PB7hcv7Xi4ioNf5mJKI+z2p3YPPh83j56zM4X+cMo2JdYdRchlFERETUQwRBQF5lI746WYHcnypwpLgeLcedJKgUmD4iDlcPj8fk9Gj2QYiILoChFBH1WVa7A58cLsHLO/NQXOsMo2LC5bj/igzMz2EYRURERBfPanfgQH4tdvxUgR0/VXj6HG6jktS4engcpg+PR7ZWxbv4EhF1AUMpIupzbHYHPjlSgpe/PuO5g01MuAy/uTwD83NSoJQxjCIiIqLu0xmt2PVzJb46WYFvfq5Cg8nmWScLEWNqRjSmj4jH1VnxSFArAthSIqK+jaEUEfUZNrsDnx4txctf56GwxhlGRYc5w6g7f8EwiogGlldffRUvvPACysvLMWbMGLz88suYNGmSz22tVivWrl2LN998EyUlJcjMzMTzzz+PmTNnerZZs2YNnnrqKa/9MjMzcerUqV49D6JgUVBt8FRDHSyog93RPC4vJlyGq7Kcw/KmDY3hROVERD2Ev02JKOjZ7A589kMpXv76jOeWylFhMvzPZem4a3IKO4ZENOC8//77WLZsGTZs2ICcnBysX78eM2bMwOnTpxEXF9dm+5UrV+Kdd97B3//+d2RlZWH79u246aabsGfPHowbN86zXXZ2Nnbs2OF5HxLC36/Uf9kdAo4U1eGrnyqw42QFzlYZvNZnxkc4h+WNiMfYQRqIxRyWR0TU00SC0HJqPgJ4O2OiYGF3CPjPD6X4S24ezrnCqMhQKf7n8gzc9YsUhPEuNkTkB8HYL8jJycHEiRPxyiuvAAAcDgeSk5PxwAMPYPny5W2212q1eOKJJ7BkyRLPsptvvhlKpRLvvPMOAGel1KeffoqjR492u13B+LMicjOYbfjhfD2OFNXjcGEdDhXVod5o9awPEYuQkx6F6cPjMX14PJKjQgPYWiKivq2zfQL+j46Igo7dIeDzY6V4KTcP51xXLTWhUtx3WToWTk5lGEVEA5rFYsGhQ4ewYsUKzzKxWIzp06dj7969Pvcxm81QKLznvVEqldi9e7fXsry8PGi1WigUCkyePBlr167F4MGDe/4kiHqZIAjIrzbgcFE9jhTV4XBRPU6X6+FodTlerZTiysxYXD08HpdnxkKlkAamwUREAxT/Z0dEQcPuELDlxzL8JTcPZyobATjDqMXT0rFwSirCGUYREaG6uhp2ux3x8fFey+Pj49ud/2nGjBlYt24dLrvsMmRkZCA3NxebN2+G3W73bJOTk4M33ngDmZmZKCsrw1NPPYVp06bh+PHjiIiI8Hlcs9kMs9nsea/X63vgDIm6rtFsww/FzgqoI8XOIKquRRWUW5JGiXGDNRg3OBKXDNZgVJIaIRJxAFpMREQAQykiCgKCIGDrj+VYv+Nn5LnCKLVSisXT0rBwSioieNWSiOiivPTSS1i8eDGysrIgEomQkZGBRYsWYePGjZ5tZs2a5Xk9evRo5OTkICUlBR988AHuvfden8ddu3Ztm8nRiXqbIAg4V23A4cI6TyXU6YoGtJ6URBYixugkNS5JicS4ZA0uSYlEvIp3yiMiCiYMpYgooI6dr8eaz07gcFE9AEClCMGvp6Xj7qmpLKEn6mkOO2C3AnYL4LA5XzusgOBwPQTnM9DqvdDBe+EC613Hdticn2u3Nn+uV1tc69p77bWP+7UFsNuaXy/4N6BQB+7n6ycxMTGQSCSoqKjwWl5RUYGEhASf+8TGxuLTTz+FyWRCTU0NtFotli9fjvT09HY/R6PRYNiwYThz5ky726xYsQLLli3zvNfr9UhOTu7iGRF1rMFkxdFi11xQRXU4UlQPXZPvKqhLUpwVUJcMjsTwRBVkIayCIiIKZgyliCggqhrMeGH7KXx46DwEAQiVSbB4WjrunZbGMIr6F0EAbGbAagSsTa7nlq+bml9bfKyzm53Bi93iCl/ae91ecNMi/EE/v7eJzXzhbfoBmUyG8ePHIzc3FzfeeCMA50Tnubm5WLp0aYf7KhQKJCUlwWq14uOPP8Ztt93W7raNjY04e/Ys7rrrrna3kcvlkMvl3ToPIl/MNjvyKhpxolSHo8X1OFxYj58r21ZByUPEGDNI4zUUL45VUEREfQ5DKSLyK4vNgTf3FOAvuXloMNsAADeNS8LjM7OQoGZnktAc4tjNzmeb2Rmo2EytXltabdNqe7ulbRUPhFbP8LG8vW19PNutbYMla1PbcClowyARIJECIgkgEgEisXOZyPWAa5nXutbvO7mtRAqIpYBEBkhCvF9LZK730hbbuR8XWufaXyIF5APnbm/Lli3DwoULMWHCBEyaNAnr16+HwWDAokWLAAALFixAUlIS1q5dCwDYv38/SkpKMHbsWJSUlGDNmjVwOBx47LHHPMd89NFHMWfOHKSkpKC0tBSrV6+GRCLB3LlzA3KO1P81mm34qUyPEyU6nCjV40SpHnmVDbDa2/7OTI5S4pLBzcPwhieqIOVcUEREfR5DKSLym52nK/HM5yc9d9QblaTGmuuzMT4lMsAtox5jswDGGh+PWu/3TXXth0x2S6DPovdIZIBUCUhDfTy3XuZ6hMjbCXN8hDRtXvvYp2WII5YE+idC3XT77bejqqoKq1atQnl5OcaOHYtt27Z5Jj8vKiqCWNz8H3aTyYSVK1fi3LlzCA8Px+zZs/H2229Do9F4tjl//jzmzp2LmpoaxMbG4tJLL8W+ffsQGxvr79Ojfqim0ewJnk6U6nCyVI/8GkObCijAOa/kyCQVRiVpcMlgDcYO1iAugheuiIj6I5Eg+PqnYGDT6/VQq9XQ6XRQqQbOVVei3pJfbcAfPz+J3FOVAICYcBkem5GFW8YPglgsCnDrqF12G2Cq71zI5F5m7oU7b0nkQIgCCJG5XrseElk7y+WuZTI0V/K0qOoBWlT2+Hj22tbXM5rfi6WALLRFqOQrWGqxTMJrQX0R+wWdx58VCYKAUp0Jx13VTydLnc9lOpPP7RNUCoxMUmGEVo1srQrZWhWSNEqIROwftMtua76wYzM1D88WOqrwvUAV8AUrhB3OeQkFByDYAYej+bVnnd25vdd2LV5faJ3n31aJq9LW/XC/FzUv87mNyHtZm23Err6CwrsP4fVezgs2RD2ks30C9o6JqNc0mm14+es8bNydD6tdQIhYhEVTU/HA1UM5b1RrDoczAGqqaw58mmqbwx7P6zrA1tT+UDQIrpFi7a3raL8Wy8x6oKke3Rp2JpIAoVFAaLTr0fK166GMdAY1PkOmFq8l0uYgiYiIgordISC/2oATpTpPBdSJUj3qjW0nIQeAtJgwjNCqMLJFABUd3s/mJHM4nP+WGyqBxkrnv99WkytEMjWHSdam5lDJZnb+297yvdd6k3cI5bAF+iz7N3GId//Ec8FLfuFQS9KN/m1X+zme9rX8bHnHy3xevJOzn0VBgaEUEfU4h0PA5iMleH7bKVQ1OCcevnxYLJ785QgMiQsPcOv8wG5zdkh9hkq1Ld63CJ+a6prvehZslJGtQiUfIVPLdXI1IOY8H0RE/YHJakeZzoTS+iaU1Deh1PU4U9mIn8oa0GS1t9knRCzC0PgIZGtVGKlVITtJjeGJKoTL++h/PexWwFDtDJoMVUBjVXPo5F7uXmaodlb++It72HabKl+0ei/uoAJY3MF+rmdP1ZHE+W98ywomsaTF69bLfezTcrlnH1e/wWFvcedWdyWV+y6u7vcdrb/Avq3noHQHfS0vwjlszofV4K9vMYBEPsIrWYvqtHbmkWx3nsmW+3RivkmvP3vitp/pfrSsZve5rtXntq6y8/wZFTW/7syfz5bVdl5/zt3PId4PSUjbZV4PSfNUCuKQFuc/sPXRfxmIKFgdLa7Hms9O4GhxPQAgNToUT/5yBK7Kius/pfiC4Ox01p4Fas4ANWddr88CumLApOv+sWURQKgrBFJGNQdAntdRzmFg7Q4rg+91QPudUV/rFCrn5yo0HHJGRNRPCYKAGoMFJXVNLUInZwBVqnMuq27seJ4/pVSC4YkRyHZVP41MUmNofDjkIUE8BEoQnDehMFS3CJUqm0OlRnf45Hpuqu36Zyg0QHic899SaWhzJbB7rkD3+5BW7ztaL1W4liual3Go2cUTBGcI5Z7f0mZqG1zZW4VYLefDbPne7rtKsAcb6wzYfN4QpmVb3TeDaXljGFcbvUJTobkSb2DcwDb4iKXth1bu8AtAcz/d9Rrw0Zf3sV2bfXzsP+5OYNLiXjm9zuD/NIioR1TqTXh+22l8fPg8ACBMJsEDVw/Foqmpwd0x7UhTHVBzznf41Jm5kxSa5uohZVSL15HtLI9yXp0iIiLqASar3VXZ1KrSSedcVlLfBIvtwlW6oTIJtBoltBolkjRKJGkUSI4KRbZWjbSYMEgCPT+k1eR7nkNPlbKPORFtvue4apdIDITGOIOmsBggLM71Orb52f06NIb/nvclIlHzTUL62WhSn7xCrZahWougy1Nl5qo888xLhlbvW68XmvcFOt4GQqvPablv63Utl3e0zsdyz3xnLSrmfM5t5mvblnOh2X3Mq2Z3Bpp2q3Odu8rO0eK93ep83+73cYH1/pBxVUA/nqEUEV0Ui82BTd/n4y+5eTBYnFdebr5kEB6fmYk4VR+4U465Eag95wydas86Qyj3a2NNBzuKAHUyEJ0ORA8BojKA6AxAk+LsrLLCiIiI/MThEHCuuhGHC+txuKgOJ0r1KK1vQo3hwnczFYmA+AgFtBqFJ3TSeh4KJGmUUCulvVvt7HA0V2tYjc6QyWp0Vh5f6AYbxpruD7OSyJzhUpg7bIoDwl3hkue1K3AKjWJVEvUPYonzJi0IDXRLBhaHo0VgZWsVWtl8P+w2+J4D1vUa6MQ6H9t5nlyvI1N7+my7hP9jIqJu+/pUBZ75/CfkVzs7g2OSNVgzZwTGDY4McMtasRiBugJn+OSpenKFT43lHe8bnuAMnaLTXcHTEGf4FJnmLKMnIiLyswaTFT8U63C4qA6Hi+pwpKgeuibfV9pDZRKvoCmpVfgUr1JAFtKJeQDdE3i7AyGTzjk5t7XFw/PeFSq1DplsJt/bd7VqyReRpJPzH7ZYLgvjfC5E5B9iMSCWAWAVZWsMpYioy85WNeKZz09i1+kqAEBMuBzLZ2XhV+OSIA5UCb+5AajNdwVP7ofrfUNpx/uGRjdXOkVnNIdPUemAfABMzE5EREFLEAQU1BhxqNAZQB0urMPPFQ1wtLo5qkIqxuhBGlwyOBJjk9UYHBWGJI0SKmVI2yonQQAsjYCxEqioAQytK5B8PPx1Qw6x1DWvkgJQqDsfMinUDJiIiPoghlJE1Gl6kxUv5+Zh0/cFsDkESCUi3DM1DUuvGoIIRTdugdtVTXUtwqZWAZShsuN95WogKq250slT9ZTunOOJiIgoCBgttuYqqMI6HCmuR61rGJ4IDshgQzgsSFNLME6rxNgEGbLjFUhTixHiMAG2fMBiAM7XAaerfQx3cy2zX3hon09ytTMIUqidlUYhCmeI5H6EKC/wXuGc+FuqaJ4A3P0+RMmh70REAwx/6xPRBTkcAj46dB7/u/2U5y48V2XFYeV1w5Ee24OVRILg7Ch7VTu1eDTVdbx/aIwzeIpKb/tQRvIKKhER+Z9JB+hLAV0JoC8BGsqdVUo2MwRrE4xGA+r1DWg0NMJkbITNYkIELLgWVlwPC+QiKxRyCxQiK2RoMUTPDCDf9eiuEIXz386WVUhhMT4qk1zLlJGcwJuIiHoUQyki6tCB/Fr8cctJHDuvAwCkx4ThyTkjcGVm3MUdWBCcQdP5g85HyWHnHE8XuqtdeEKLsCnN+7VCfXFtIiIi6ixBcP6bpStxhk76867nkhbLSgFLQ7uHEAEIcz08OjG9k3NnsbOyKETuqkCSt3gf2na4W1hM22WysAt/DhERUS9iKEVEPhVUG/CnL05h2wnnRODh8hD87uqhWDgltXMTorbWVA+UHALO/9cVQv23nconEaAe5AyZIltXPaWxA01ERL1PEJornPSuCqeW1U7u95bGTh3OHKJCpSga+RY1SuyRaIQSJshgFqSwimWIUquQGB2JQXGRSI2PRpRa1Spkah06KQFxCCuAiYioz2MoRURedEYr/vJ1Ht7aWwCrXYBYBNwxaTAenj4MsRHyzh3EbgMqTzqDJ3cIVf1z2+0kckA7Fhg0EUgaD8RnA5oU3tWOiIh6jyAApvpWIVNpq2qnzgdOUGicF1NUWkCVBKMyHqeNKhysVSK3JAQ/NoTBiOZ/16LCZJiYGonxKZGYPDgSI5PUUEglvXKqREREwY6hFBEBACw2B97ZV4i/fJ2HeqNzzorLh8XiD7OHIzMhouOdG8pdw/BcIVTpYeetn1uLSgeSJjhDqEETgPiRnJuCiIh6jiA4q3A9QVPLoXQtnn39G+WLMhJQuQMnLaBOAlRJrveDAFUizGIFDhXU4bsz1fgurwonSvUQWtwZTxYixtTUSEwbGotLh8RgRKIqcHeqJSIiCjIMpYgGOEEQ8OXJCvzpi1PIrzYAADLjI/CH64bj8mGxbXewmoCyH5qH4J3/L6ArbrudXOWsfho0obkSKiyml8+GiIj6LUFw3j1OX9LOkDpXhZOtqXPHC432VDe1fXa9loX6aIaAnysa8d1PVfgu7wT259fAZHV4bZOVEIFpQ2Nw6dBYTEqNglLGSigiIiJfGEoRDWDHS3R45vOT2J9fCwCICZfhkWszcev4QQiRuOaNshiAvK+Awu+dAVT5j4DD6n0gkRiIG+EMoNyVUDHDAHE35p4iIiJqzeEA1g4CrIbObR8a46psalHl1DJsUmmd8zJ1UmWDCd+fqcZ3edXYnVeNygaz1/rYCDmmDYnBtGExmDokBnERHIZORETUGQyliAagMl0TXth+Gp8cKYEgAPIQMX49LQ33XzEE4fIQwNwInNwGnPwUyNvR9qpzWFzzELxBEwDtOEB+gSF+RERE3SUWA0qNM5QKi2sOmdQ+qpwiEi96bkKT1Y4D+bXYfaYa3/5chVPl3nfQU0jFmJQWjcuGxuDSoTHIjI+AiJOOExERdRlDKaIBxGC24W/fnMXr353zDDW4cawWv5+ZhSSlDTj9iTOIOrMDsJmad4xMBYbNBJInOSuhNIN5xx8iIvKvX+cCoVHOu8/1kvcOFOHzY2U4UFALi817SN7IJBUuHRKLaUNjMD4lkpOTExER9QCGUkQDgN0h4KNDxXjxy59R5RpyMDE1EquuScaoxj3AFy84gyh7i+EIUenAiBuB7BuBhNEMoYiIKLBUib16+DOVjVi++UfP+0S1ApcOicG0YbGYmhGN6PDeC8OIiIgGKoZSRP3c7rxq/HHLSc/Qg+woB9Zml2CU7i2I3v0asFuaN44e0hxExY9kEEVERANGUa1zvqrU6FD8Y+FEZMSGcUgeERFRL2MoRdRPnalswHNbT+HrU5VQwYA7FUdwX/SPSK7bB9HBFhOVxwxzBlEjbgDisxlEERHRgFRa7xy2PiQuAkPiwgPcGiIiooGBoRRRP1PTaMb6HXnYcuAkrhYdxBuyA5gmOQ6JYANqXBvFZjUHUXHDGUQREdGAV6Zz3tRDq+Gd84iIiPyFoRRRP2Gy2vGvb44i/7sPMN2xB6ukJyAV2Z0rBQCxw53D8kbcCMRlBbClREREwadM56yUSlQrA9wSIiKigYOhFFEfJzRW4tiOd2E69gnush9DiMgBuG8IFJftCqJuAGIzA9lMIiKioFZW7w6lWClFRETkLwyliPoahx04/1+YT21H4/EvEK0/iTHudSKgXpUJ1fhbIc6+EYgZGsCGEhER9R3u4XsMpYiIiPyHoRRRX9BQAZzNBfK+gu1MLkLMOsgBuG9OfUJIgy5tNi6ZeTc0CcMC2VIiIqI+RxAEz/A9rYbD94iIiPyFoRRRMLLbgPMHgTNfAXlfAeXHPKtCAOiEUHznGI2TYZOQPPF6zJ48Ftmh0sC1l4iIqA+rM1phtjkAAPEqVkoRERH5C0MpomDRUA6c2eEMoc7tBEw6r9XHHGnY5RiD3RgH7YipmPuLdPw+LQoi3jmPiIjoopTWO4fuxYTLIQsRB7g1REREAwdDKaJAsVuB4gPOaqgzO4DyH71WN4gi8LVtFHbZx+A7x2iERiViXs5g/HX8IMSEy9s5KBEREXVV89A9VkkRERH5E0MpIn/Sl7aohvoGMDdXQwkQoSxsOP5jGIFt5lH4QciASCzB9OFxWJeTgkuHxEAsZlUUERFRTyvnJOdEREQBwVCKqDeZG4HSI84g6swOoOK412ohNBql0VPw78bh+EdZOmpNKgDOTvFDkwbj9onJnNuCiIiol5W6KqUS1ZzknIiIyJ8YShH1BJsFqDkDVJ50Pipcz/WFrTYUAUnjoRt0Bf5jyMbLp8JQkWdzrhEBVw6LxfycFFyRGYsQCee0ICIi8oeyelZKERERBQJDKaKucDicQVPlT0DlCedzxUmgJg9w2HzvE54ApF8BR8bV2C2MwhtHG7Hz20oIAgDYEBMuxx0Tk3H7xGQkR4X682yIiIgILSqlNKyUIiIi8ieGUkS+CAJgqAIqTngHUJWnAKvB9z5yFRA3HIgb4XzEjwBih6PCHob3Dxbjva1FKNWd82w+dUg05uek4JoR8ZCyKoqIiChgyt0TnbNSioiIyK8YShGZ9EDVKe9hd5UnAWON7+0lMiA2szl8ihvhDKPUg5xj8ADUGSzYfqIcW3LPYM/ZGtgdAgAgMlSKW8YPwtxJg5EeG+6vMyQiIqJ2OByCJ5RKYChFRETkVwylqP8TBMBQ7Rx2V1cA1OW7nl3vdcXt7CgCotKdFU/u4Cku27lM0vavTq0riNr6Y5lXEAUAE1MjMT8nBTNHJkAhlfTGWRIREVE31BgssNgdEInAm4sQERH5GUMp6h+sTUB9kStsKmgOnNyP9obcuUUkNgdP8dnO55hMQNbxHE81jWZsO1GOL34sx95z3kHUiEQVrhudiFkjE1gVRUREFKTKdM5JzuMi5BxOT0RE5GdBEUq9+uqreOGFF1BeXo4xY8bg5ZdfxqRJk3xua7VasXbtWrz55psoKSlBZmYmnn/+ecycObPbx6Q+wOEAGst9B051Bc51HRIBqiQgMgWITG1+aFKAmKFAaFSnm1LdaMa2486KqH3natAih0K2VoXZoxIxe1Qi0mLCunaORERE5Hdl7knO1ZzknIiIyN8CHkq9//77WLZsGTZs2ICcnBysX78eM2bMwOnTpxEXF9dm+5UrV+Kdd97B3//+d2RlZWH79u246aabsGfPHowbN65bx6QgYbc5h9jVngNqzgK1Z70rn+zmjveXq7xDJ00KEJnmep0MhMi73bTKBhO2n6jA1mNl2J/vHUSNSlK7gqgEpEQziCIiIupLyuqdlVKJnE+KiIjI70SCIAgX3qz35OTkYOLEiXjllVcAAA6HA8nJyXjggQewfPnyNttrtVo88cQTWLJkiWfZzTffDKVSiXfeeadbx2xNr9dDrVZDp9NBpVL1xGmSm8PunMOp5qx3+FRz1hlIOWzt7yuSOMMlTatqJ/dDGemZaLwnVDaYsO14ObYcK8OBglq0/JsyepAriBqZiMHRHQ/xIyKivo39gs7riz+rtVt/wt++PYd7pqZh1ZwRgW4OERFRv9DZPkFAK6UsFgsOHTqEFStWeJaJxWJMnz4de/fu9bmP2WyGQuF9JUupVGL37t0XdUyzubkKR6/Xd/ucCM6hdg2lQM2ZtuFTXQFgt7S/b4jCOZF4VDoQneF8dodOqkE+JxjvSZV6E744Xo4tP5bhYKsgaow7iBqViOQoBlFERET9gXv4nlbDSikiIiJ/C2goVV1dDbvdjvj4eK/l8fHxOHXqlM99ZsyYgXXr1uGyyy5DRkYGcnNzsXnzZtjt9m4fc+3atXjqqad64IwGEEEAGsqbq5w8z+ecD5up/X0lMuewOnfoFJ0BRGU4nyO0gNi/k4xW6E344scybP2xHAcLvYOosckaXDcqEbNGJWBQJIMoIiKi/sY90XkCh+8RERH5XcDnlOqql156CYsXL0ZWVhZEIhEyMjKwaNEibNy4sdvHXLFiBZYtW+Z5r9frkZyc3BPN7V9MeuDcLiDvS+BMrrMaqj3iEGd1kztsahk+qQcBYom/Wu3FZnfgTFUjTpTocbxUh6PF9ThaXO8VRI0b7A6iEpGk4aSnRERE/VlpPSc6JyIiCpSAhlIxMTGQSCSoqKjwWl5RUYGEhASf+8TGxuLTTz+FyWRCTU0NtFotli9fjvT09G4fUy6XQy7v/iTY/ZYgABUngDNfAXk7gOJ93nM+iSSAZrB3pVNUBhCdDqgH9/pQuwsxWe04Xd6A46U6nCjV40SJDqfKG2C2Odpse8lgjWdonpZBFBER0YBgdwio0HP4HhERUaAENDWQyWQYP348cnNzceONNwJwTkqem5uLpUuXdrivQqFAUlISrFYrPv74Y9x2220XfUxCczWUO4hqXQ0VPQQYcg0wdDqQMhWQBkeA02Cy4mSpHidKnRVQJ0v1yKtshN3Rdh7/cHkIRiSqMEKrQrZWhalDYhhEERERDUA1jWbYHAIkYhHiIhhKERER+VvAh+8tW7YMCxcuxIQJEzBp0iSsX78eBoMBixYtAgAsWLAASUlJWLt2LQBg//79KCkpwdixY1FSUoI1a9bA4XDgscce6/QxqQVBACpPAnlfOR+tq6FClEDaNGDotcCQ6UBUWuDa6lLTaPaET+4KqIIao89to8JkyNaqkK1VY2SS8zklKhRicc/dpY+IiIj6plLXJOdxEXJI2DcgIiLyu4CHUrfffjuqqqqwatUqlJeXY+zYsdi2bZtnovKioiKIW0x8bTKZsHLlSpw7dw7h4eGYPXs23n77bWg0mk4fc8BrWQ11JhfQl3ivD5JqKEEQUKYz4XiJK3xyhVDuu+S0plUrkJ2k9gqhElQKiETsZBIREVFbZfXOSc4TOck5ERFRQIgEQWg7vmmA0+v1UKvV0Ol0UKlUgW7OxWtZDXVmB1C013c1lDuIikoPXFsB/FzRgM2HS/DZ0RLPFczW0mPCMEKrwsgWIVRUmMzPLSUiooGg3/ULelFf+1lt3J2Ppz8/ietGJ+LVeZcEujlERET9Rmf7BAGvlKJeYtID+d80B1Gtq6GiMoCh1zgfQTA3VGWDCZ8dLcXmwyU4Wab3LA8RizA0PgLZWhVGalXITlJjeKIK4XL+0SUiIqKLU6ZzVUqpWClFREQUCPyffX/R8k55Z3KDvhoKAIwWG748UYHNR0qwO68K7jnJpRIRrsyMw03jknBlVhwUUklgG0pERET9krsiO5E3PCEiIgoIhlJ9WVM9cG6nsxLqTC7QUOa93l0NNeQaIDXw1VCA89bLe8/WYPOR89h+vBwGi92z7pLBGtx0ySD8clQiIjkUj4iIiHqZe04pLeeUIiIiCgiGUn2JwwGU/+AMofJ2AOcPAkJzqOOshrrMGURlXAVEZwSura38VKbHp0dK8OnRElTozZ7lg6NCcdO4JNw0LgmpMWEBbCERERENNOWslCIiIgoohlLBzlDjrIbK+wo4mwsYqrzXx2S6qqGuBgZPAaTBc6WvQm/Cv4+WYPPhEpwqb/AsVyul+OXoRPzqkiRcMjiSd8cjIiIiv7M7BFQ0OC+U8e57REREgcFQKtg47EDpkeYJyksOAWhxg0RZOJB+hTOEGjId0AwOVEt9Mpht2H6iHJ8cKcH3Z6q95om6KisON40bhCuzYiEP4TxRREREFDiVDSbYHQJCxCLEhMsD3RwiIqIBiaFUMGisdM4JdWYHcPZroKnWe338SFcIdQ2QnAOEBNd8S3aHgO/PVOOTIyXYdrwcTdbmIYUTUiJx0yVJuG5UIjShwdVuIiIiGrjKXEP34lUKSMSs2iYiIgoEhlKBYLc554M646qGKvvBe71cDWRc4QyhhlwNqLQBaWZHBEHAyTI9Pjlcgs9+KEVlQ/M8UanRobhp3CDcNC4Jg6NDA9hKIiIiIt/K6l3zSXHoHhERUcAwlPK3w28B21cCZp338sSxzuF4Q6YDgyYCkuD8aposdnz2Qwne2luIE6V6z3JNqBRzRmtx0yVJGJes4TxRREREFNTKdM4773GScyIiosAJzuSjPwuLcwZSykgg4+rmO+WFxwW6ZR0qqDbg7X2F+PC/xdCbbAAAmUSMq4fH4aZxSbgiMw6yEHGAW0lERETUOaWuSiktK6WIiIgChqGUv6VdBvw6F9COA8TBPdm33SFg56lKvLWvEN/+3HzXv8FRobjzF4Nx6/hkRIZxnigiIiLqe8r1rkophlJEREQBw1DK32ShwKAJgW5Fh2oNFrx/sBj/3F+I83XODptIBFwxLBYLJqfi8mGxEHNCUCIiIurD3JVSCWoO3yMiIgoUhlLkcbS4Hm/tLcDnx8pgsTkAOOeKum1CMubnDEZKdFiAW0hERNS32Ww27Nq1C2fPnsW8efMQERGB0tJSqFQqhIeHB7p5A4p7TimthpVSREREgcJQaoAzWe347IdSvLOvEMfON0++PipJjbsmp+D6MVoopME9zJCIiKgvKCwsxMyZM1FUVASz2YxrrrkGEREReP7552E2m7Fhw4ZAN3HAsNodnjsHJ7JSioiIKGAYSg1QRTVGvLO/EB/8txj1RisA58TlvxydiLsmp2As76BHRETUo373u99hwoQJ+OGHHxAdHe1ZftNNN2Hx4sUBbNnAU9lghiAAUokI0Zwfk4iIKGAYSg0gDoeAb36uwlt7C7Dr5yoIgnN5kkaJO3+RgtsmDEJ0uDywjSQiIuqnvvvuO+zZswcymXcIkpqaipKSkgC1amAqq3cO3UtQKzhPJhERUQCJA90A6n31Rgte//YsrnhxFxa9cRA7TzsDqcuGxeIfCybg28euxP1XZDCQIiIi6kUOhwN2u73N8vPnzyMiIqLLx3v11VeRmpoKhUKBnJwcHDhwoN1trVYrnn76aWRkZEChUGDMmDHYtm3bRR2zLyvVOSc559A9IiKiwGKlVD/243kd3tpbgM9+KIXZNXG5ShGCWyck485fpCAthhOXExER+cu1116L9evX4/XXXwcAiEQiNDY2YvXq1Zg9e3aXjvX+++9j2bJl2LBhA3JycrB+/XrMmDEDp0+fRlxcXJvtV65ciXfeeQd///vfkZWVhe3bt+Omm27Cnj17MG7cuG4dsy8rd09yruYk50RERIEkEgT3IC5y0+v1UKvV0Ol0UKlUgW5Ol/1UpseKzT/iaHG9Z9mIRBUWTE7BDWOToJRx4nIiIqLO6ql+QXFxMWbOnAlBEJCXl4cJEyYgLy8PMTEx+Pbbb7sU/OTk5GDixIl45ZVXADirsJKTk/HAAw9g+fLlbbbXarV44oknsGTJEs+ym2++GUqlEu+88063julLX+lDrfnsBN7YU4DfXJ6B5bOyAt0cIiKifqezfQJWSvVDr397DkeL6yGViHDdKOfE5ZcMjuTE5URERAGUnJyMH374Ae+//z5++OEHNDY24t5778X8+fOhVHZ+GJnFYsGhQ4ewYsUKzzKxWIzp06dj7969Pvcxm81QKLyrgpRKJXbv3t3tY7qPazabPe/1en2nzyOQytyVUhpWShEREQUSQ6l+6FxVIwDgz7ePxS9HawPcGiIiIrJarcjKysLnn3+O+fPnY/78+d0+VnV1Nex2O+Lj472Wx8fH49SpUz73mTFjBtatW4fLLrsMGRkZyM3NxebNmz1zXHXnmACwdu1aPPXUU90+l0Ap45xSREREQYETnfczgiAgv9oAABgSFx7g1hAREREASKVSmEymgH3+Sy+9hKFDhyIrKwsymQxLly7FokWLIBZfXFdwxYoV0Ol0nkdxcXEPtbh3NYdSrJQiIiIKJIZS/Uyd0Qq9yQYASIniROZERETBYsmSJXj++edhs9ku6jgxMTGQSCSoqKjwWl5RUYGEhASf+8TGxuLTTz+FwWBAYWEhTp06hfDwcKSnp3f7mAAgl8uhUqm8HsHOYnOgutE55JChFBERUWB1afjesWPHOr3t6NGju9wYungFNc4qqUS1ghOaExERBZGDBw8iNzcXX375JUaNGoWwMO+LR5s3b+7UcWQyGcaPH4/c3FzceOONAJyTkufm5mLp0qUd7qtQKJCUlASr1YqPP/4Yt91220Ufs6+p0JsgCIA8RIyoMFmgm0NERDSgdSmUGjt2LEQiEdq7YZ97nUgk8sxRQP5V4Bq6lxrNKikiIqJgotFocPPNN/fIsZYtW4aFCxdiwoQJmDRpEtavXw+DwYBFixYBABYsWICkpCSsXbsWALB//36UlJRg7NixKCkpwZo1a+BwOPDYY491+pj9Rcuhe7wJDBERUWB1KZTKz8/vrXZQD/GEUjEMpYiIiILJpk2beuxYt99+O6qqqrBq1SqUl5dj7Nix2LZtm2ei8qKiIq/5okwmE1auXIlz584hPDwcs2fPxttvvw2NRtPpY/YX7jvvJXDoHhERUcB1KZRKSUnprXZQD8mvMQIA0mJCA9wSIiIi8qWqqgqnT58GAGRmZiI2NrZbx1m6dGm7Q+t27drl9f7yyy/HyZMnL+qY/UVpvbNSSss77xEREQVcl0Kpzz77rNPbXn/99V1uDF08d6VUCofvERERBRWDwYAHHngAb731FhwOBwBAIpFgwYIFePnllxEaygtK/uCulErUsFKKiIgo0LoUSrknvrwQzikVGIIgeEKpNA7fIyIiCirLli3DN998g//85z+YOnUqAGD37t148MEH8cgjj+C1114LcAsHBvecUgmslCIiIgq4LoVS7qt6FJxqDBY0mG0QiYDBUbzaSkREFEw+/vhjfPTRR7jiiis8y2bPng2lUonbbruNoZSfuCultJxTioiIKODEF96E+gp3lZRWrYRCKglwa4iIiKglo9Hoc9LwuLg4GI3GALRoYCqrd999j5VSREREgdalSqnWDAYDvvnmGxQVFcFisXite/DBBy+qYdR1Ba5JzlM5yTkREVHQmTx5MlavXo233noLCoWzSqepqQlPPfUUJk+eHODWDQwmqx01BmefVcs5pYiIiAKu26HUkSNHMHv2bBiNRhgMBkRFRaG6uhqhoaGIi4tjKBUA7kqpVE5yTkREFHReeuklzJgxA4MGDcKYMWMAAD/88AMUCgW2b98e4NYNDBV6Z5WUQiqGWikNcGuIiIio28P3Hn74YcyZMwd1dXVQKpXYt28fCgsLMX78eLz44os92UbqpPwaTnJOREQUrEaOHIm8vDysXbsWY8eOxdixY/GnP/0JeXl5yM7ODnTzBoRS19A9rVoJkUgU4NYQERFRtyuljh49ir/97W8Qi8WQSCQwm81IT0/H//7v/2LhwoX41a9+1ZPtpE5gpRQREVFwCw0NxeLFiwPdjAHLPcl5IofuERERBYVuV0pJpVKIxc7d4+LiUFRUBABQq9UoLi7umdZRpwmC0BxKsVKKiIgo6KxduxYbN25ss3zjxo14/vnnA9CigadMx0nOiYiIgkm3Q6lx48bh4MGDAIDLL78cq1atwj//+U889NBDGDlyZI81kDqnqtEMg8UOsQhIjmJHi4iIKNj87W9/Q1ZWVpvl2dnZ2LBhQwBaNPB4KqXUrJQiIiIKBt0OpZ577jkkJiYCAJ599llERkbi/vvvR1VVFf72t7/1WAOpcwqqnXfe02qUkIdIAtwaIiIiaq28vNzTd2opNjYWZWVlAWjRwFNWz0opIiKiYNLtOaUmTJjgeR0XF4dt27b1SIOoe9xD9zjJORERUXBKTk7G999/j7S0NK/l33//PbRabYBaNbCUuofvcU4pIiKioNDtUCo/Px82mw1Dhw71Wp6XlwepVIrU1NSLbRt1gfvOe5zknIiIKDgtXrwYDz30EKxWK6666ioAQG5uLh577DE88sgjAW7dwFDO4XtERERBpduh1N1334177rmnTSi1f/9+/OMf/8CuXbsutm3UBYU1nOSciIgomP3+979HTU0Nfvvb38JisQAAFAoFHn/8caxYsSLArev/mix21BmtADh8j4iIKFh0e06pI0eOYOrUqW2W/+IXv8DRo0cvpk3UDfmuOaXSYkID3BIiIiLyRSQS4fnnn0dVVRX27duHH374AbW1tVi1alWgmzYguCc5D5NJoFJ0+7osERER9aBuh1IikQgNDQ1tlut0Otjt9otqFHWNIAjNlVIcvkdERBTUwsPDMXHiRERERODs2bNwOByBbtKAUO6ZT0oJkUgU4NYQERERcBGh1GWXXYa1a9d6BVB2ux1r167FpZde2iONo86pbDDDaLFDIhZhUCQrpYiIiILJxo0bsW7dOq9l9913H9LT0zFq1CiMHDkSxcXFAWrdwOGZ5JzzSREREQWNbodSzz//PL7++mtkZmZi0aJFWLRoETIzM/Htt9/ihRde6Mk20gXku+68l6RRQhbS7a+UiIiIesHrr7+OyMhIz/tt27Zh06ZNeOutt3Dw4EFoNBo89dRTAWzhwFBWz0nOiYiIgk23E4wRI0bg2LFjuO2221BZWYmGhgYsWLAAp06dwsiRI3uyjXQBBdWc5JyIiChY5eXlYcKECZ73//73v3HDDTdg/vz5uOSSS/Dcc88hNzc3gC0cGJorpTjJORERUbC4qFketVotnnvuuZ5qC3VTvms+qbRoDt0jIiIKNk1NTVCpVJ73e/bswb333ut5n56ejvLy8kA0bUAp17FSioiIKNhc1Fiv7777DnfeeSemTJmCkpISAMDbb7+N3bt390jjqHNYKUVERBS8UlJScOjQIQBAdXU1Tpw44XUH4/LycqjV6kA1b8AoazHROREREQWHbodSH3/8MWbMmAGlUonDhw/DbDYDcN59j9VT/lVQbQTAUIqIiCgYLVy4EEuWLMEzzzyDW2+9FVlZWRg/frxn/Z49ezj1gR+UuuaU0rJSioiIKGh0O5T64x//iA0bNuDvf/87pFKpZ/nUqVNx+PDhHmkcXZjDIaCw1j18j6EUERFRsHnsscewePFibN68GQqFAh9++KHX+u+//x5z584NUOsGBoPZBr3JBoCVUkRERMGk23NKnT59Gpdddlmb5Wq1GvX19RfTJuqCigYTTFYHQsQiDIpkJ4uIiCjYiMViPP3003j66ad9rm8dUlHPcw/di5CHIFx+UVOqEhERUQ/qdqVUQkICzpw502b57t27kZ6e3qVjvfrqq0hNTYVCoUBOTg4OHDjQ4fbr169HZmYmlEolkpOT8fDDD8NkMnnWr1mzBiKRyOuRlZXVpTb1Ffmu+aSSo0IRIrmoKcKIiIiI+qUy9yTnGg7dIyIiCibdvlS0ePFi/O53v8PGjRshEolQWlqKvXv34pFHHsGqVas6fZz3338fy5Ytw4YNG5CTk4P169djxowZOH36NOLi4tps/+6772L58uXYuHEjpkyZgp9//hl33303RCIR1q1b59kuOzsbO3bsaD7RkP55VcwznxTvvEdERETkU1m9a5JzNavKiYiIgkm3k5rly5fD4XDg6quvhtFoxGWXXQa5XI7f//73+PWvf93p46xbtw6LFy/GokWLAAAbNmzAli1bsHHjRixfvrzN9nv27MHUqVMxb948AEBqairmzp2L/fv3e59YSAgSEhK6e3p9RkGNs1IqhfNJEREREfnkufMeJzknIiIKKt0e7yUSifDEE0+gtrYWx48fx759+1BVVQW1Wo20tLROHcNiseDQoUOYPn16c4PEYkyfPh179+71uc+UKVNw6NAhzxC/c+fOYevWrZg9e7bXdnl5edBqtUhPT8f8+fNRVFTUbjvMZjP0er3Xo69wD99L4533iIiIiHzyDN9jpRQREVFQ6XIoZTabsWLFCkyYMAFTp07F1q1bMWLECJw4cQKZmZl46aWX8PDDD3fqWNXV1bDb7YiPj/daHh8fj/Lycp/7zJs3D08//TQuvfRSSKVSZGRk4IorrsAf/vAHzzY5OTl44403sG3bNrz22mvIz8/HtGnT0NDQ4POYa9euhVqt9jySk5M7+dMIvAJXKJXKUIqIiIjIp1J3pRTnlCIiIgoqXQ6lVq1ahddeew2pqanIz8/Hrbfeivvuuw9//vOf8f/+3/9Dfn4+Hn/88d5oKwBg165deO655/DXv/4Vhw8fxubNm7FlyxY888wznm1mzZqFW2+9FaNHj8aMGTOwdetW1NfX44MPPvB5zBUrVkCn03kexcXFvdb+nuRwCCisdc4plcbhe0RERH1ScXEx7rnnnkA3o18rd1VKaVkpRUREFFS6PKfUhx9+iLfeegvXX389jh8/jtGjR8Nms+GHH36ASCTq0rFiYmIgkUhQUVHhtbyioqLd+aCefPJJ3HXXXZ55q0aNGgWDwYD77rsPTzzxBMTitjmbRqPBsGHDfN4tEADkcjnkcnmX2h4MSnVNsNgckEpE0PLKHxERUZ9UW1uLN998Exs3bgx0U/ot90TnCZxTioiIKKh0OZQ6f/48xo8fDwAYOXIk5HI5Hn744S4HUgAgk8kwfvx45Obm4sYbbwQAOBwO5ObmYunSpT73MRqNbYIniUQCABAEwec+jY2NOHv2LO66664utzGYFdY4q6SSo0IRIun29GBERETUiz777LMO1587d85PLRmYGkxWNJhtAMCLeEREREGmy6GU3W6HTCZrPkBICMLDw7vdgGXLlmHhwoWYMGECJk2ahPXr18NgMHjuxrdgwQIkJSVh7dq1AIA5c+Zg3bp1GDduHHJycnDmzBk8+eSTmDNnjiecevTRRzFnzhykpKSgtLQUq1evhkQiwdy5c7vdzmDkmeScjSM0BgAAJ6ZJREFUQ/eIiIiC1o033giRSNTuxTMA3bq4R53jvvOeWilFqKzbN54mIiKiXtDlf5kFQcDdd9/tGe5mMpnwm9/8BmFh3sHI5s2bO3W822+/HVVVVVi1ahXKy8sxduxYbNu2zTP5eVFRkVdl1MqVKyESibBy5UqUlJQgNjYWc+bMwbPPPuvZ5vz585g7dy5qamoQGxuLSy+9FPv27UNsbGxXTzeocZJzIiKi4JeYmIi//vWvuOGGG3yuP3r0qKcKnXqeO5RK5NA9IiKioNPlUGrhwoVe7++8886LbsTSpUvbHa63a9cur/chISFYvXo1Vq9e3e7x3nvvvYtuU19QUOMKpaJDA9wSIiIias/48eNx6NChdkOpC1VR0cUpq3dOcs5QioiIKPh0OZTatGlTb7SDuiGflVJERERB7/e//z0MBkO764cMGYKdO3f6sUUDS6m7UkrDO+8REREFGw6s76PsDgHFtc4rf6mcU4qIiChoTZs2rcP1YWFhuPzyy/3UmoHHXSmlZaUUERFR0OEt2/qo0vomWOwOyCRiaHnlj4iIKGidO3eOw/MCqFzvrJRKULO/REREFGwYSvVR7qF7g6NDIRHzjj1ERETBaujQoaiqqvK8v/3221FRURHAFg0spayUIiIiCloMpfqoQs8k5xy6R0REFMxaV0lt3bq1wzmmqOcIgtB89z1WlhMREQUdhlJ9VH61EQCQFsM77xERERH5ojfZYLTYAQAJKlZKERERBRuGUn1UQQ3vvEdERNQXiEQiiESiNsuo95XpnEP3IkOlUMokAW4NERERtca77/VRBa45pdI4fI+IiCioCYKAu+++G3K5HABgMpnwm9/8BmFh3v+Gb968ORDN69fK6l1D9zjJORERUVBiKNUH2ewOFNU6h++lsFKKiIgoqC1cuNDr/Z133hmglgw8pa5KKa2GQ/eIiIiCEUOpPqikvgk2hwB5iBiJnB+BiIgoqG3atCnQTRiwyl2TnCfwzntERERBiXNK9UH5rqF7KdGhEIs5JwURERGRL6UcvkdERBTUGEr1Qe75pFI5nxQRERFRu8o4fI+IiCioMZTqgwpqnPNJpXE+KSIiIqJ2eYbvqVgpRUREFIwYSvVBBTWuSimGUkREREQ+CYLAic6JiIiCHEOpPojD94iIiIg6Vm+0wmR1AOBE50RERMGKoVQfY7U7UFznvOrH4XtEREREvrmrpGLCZZCHSALcGiIiIvKFoVQfc76uCXaHAIVUjHiVPNDNISIiIgpKnvmkWCVFREQUtBhK9TEth+6JRKIAt4aIiIgoOJW6QqlENSc5JyIiClYMpfqYfM4nRURERHRBZfWuSc5ZKUVERBS0GEr1MbzzHhEREdGFNQ/fY6UUERFRsGIo1ce4K6XSYkID3BIiIiKi4OWe6FyrYaUUERFRsGIo1cd4KqU4fI+IiIioXWWcU4qIiCjoMZTqQyw2B0rqnFf90jh8j4iIiMgnQRBahFKslCIiIgpWDKX6kOI6IxwCECaTIDZCHujmEBEREQWlWoMFFpsDIhEQr2IoRUREFKwYSvUhBa75pFKiwyASiQLcGiIiIqLg5K6SigmXQxbC7i4REVGw4r/SfYh7kvNUTnJORERE1K7Setck5xy6R0REFNQYSvUhnOSciIiI6MI4yTkREVHfwFCqDymoNgIAUjnJORER0YD36quvIjU1FQqFAjk5OThw4ECH269fvx6ZmZlQKpVITk7Gww8/DJPJ5Fm/Zs0aiEQir0dWVlZvn0avcIdSCayUIiIiCmohgW4AdZ57+B7vvEdERDSwvf/++1i2bBk2bNiAnJwcrF+/HjNmzMDp06cRFxfXZvt3330Xy5cvx8aNGzFlyhT8/PPPuPvuuyESibBu3TrPdtnZ2dixY4fnfUhI3+wqlulcw/c0DKWIiIiCGSul+giT1Y5SVweLw/eIiIgGtnXr1mHx4sVYtGgRRowYgQ0bNiA0NBQbN270uf2ePXswdepUzJs3D6mpqbj22msxd+7cNtVVISEhSEhI8DxiYmL8cTo9rqyew/eIiIj6AoZSfcT5OiMEAQiXhyAmXBbo5hAREVGAWCwWHDp0CNOnT/csE4vFmD59Ovbu3etznylTpuDQoUOeEOrcuXPYunUrZs+e7bVdXl4etFot0tPTMX/+fBQVFfXeifSiMr3zQl4ih+8REREFtb5Zkz0A5XvmkwqFSCQKcGuIiIgoUKqrq2G32xEfH++1PD4+HqdOnfK5z7x581BdXY1LL70UgiDAZrPhN7/5Df7whz94tsnJycEbb7yBzMxMlJWV4amnnsK0adNw/PhxRERE+Dyu2WyG2Wz2vNfr9T1whhfH4RBQ7p7oXMNKKSIiomDGSqk+oqCad94jIiKi7tm1axeee+45/PWvf8Xhw4exefNmbNmyBc8884xnm1mzZuHWW2/F6NGjMWPGDGzduhX19fX44IMP2j3u2rVroVarPY/k5GR/nE6Hqg1mWO0CxCIgPkIe6OYQERFRB1gp1Ufk13CScyIiIgJiYmIgkUhQUVHhtbyiogIJCQk+93nyySdx11134de//jUAYNSoUTAYDLjvvvvwxBNPQCxue51So9Fg2LBhOHPmTLttWbFiBZYtW+Z5r9frAx5MueeTiotQIETC669ERETBjP9S9xHuSqkUVkoRERENaDKZDOPHj0dubq5nmcPhQG5uLiZPnuxzH6PR2CZ4kkgkAABBEHzu09jYiLNnzyIxMbHdtsjlcqhUKq9HoJW5hu4lcD4pIiKioMdKqT7CHUqlxYQGuCVEREQUaMuWLcPChQsxYcIETJo0CevXr4fBYMCiRYsAAAsWLEBSUhLWrl0LAJgzZw7WrVuHcePGIScnB2fOnMGTTz6JOXPmeMKpRx99FHPmzEFKSgpKS0uxevVqSCQSzJ07N2Dn2R1lrrsVazUMpYiIiIIdQ6k+wGS1o9R11Y9zShEREdHtt9+OqqoqrFq1CuXl5Rg7diy2bdvmmfy8qKjIqzJq5cqVEIlEWLlyJUpKShAbG4s5c+bg2Wef9Wxz/vx5zJ07FzU1NYiNjcWll16Kffv2ITY21u/ndzHclVKJak5yTkREFOwYSvUBhTXOO+9FKEIQFSYLcGuIiIgoGCxduhRLly71uW7Xrl1e70NCQrB69WqsXr263eO99//bu/vYqsv7/+Ovc3pz2iJthUJvoNLqJk6tleFoQJct2lDQVPhtUW6cIBOc/CCZdlsUBTpDtHPuS8gMilkKaoyKJqjJMDjsLJuKsl+L8WZIRFtQewOt397Q0pb2XL8/2nP0SHvOaWnP5/M5fT6Sk9DPuT5Xr4urp3nn3et6f158cTSHZ5lvk1LslAIAwO6oKeUANU3fFjl3uVwWjwYAAMC+6lv6j++xUwoAAPsjKeUAxweevMfRPQAAgOD8O6WoKQUAgO2RlHKAWl9SKo2kFAAAwFD6vEYNbf1JqSx2SgEAYHskpRyghifvAQAAhNR0ult9XqMYt0tTJnqsHg4AAAiBpJQD1Db1FzqfwfE9AACAIdUN1JNKn+hRjJs6nAAA2B1JKZs709Pn34aeS1IKAABgSN/Wk+LoHgAATkBSyuZ89aRSEuN04YR4i0cDAABgX76kVEYKRc4BAHACklI2V9tEkXMAAIBw1A8c38siKQUAgCOQlLK5moGdUrmTKXIOAAAQjP/4Hk/eAwDAEUhK2Rw7pQAAAMJT1zqwUyqVnVIAADgBSSmbq23uf/JeLkkpAACAoBr8NaXYKQUAgBOQlLI5/04pnrwHAAAwpN4+rxoHnlhMTSkAAJyBpJSNdXT36mR7tySSUgAAAMGcbO+W10ixbpfSLvBYPRwAABAGWySltm/frpycHCUkJKigoECHDh0K2n7btm2aOXOmEhMTlZ2drXvvvVddXV3n1acd1Q4UOb8wKU4pSXEWjwYAAMC+6gfqSaUnJ8jtdlk8GgAAEA7Lk1K7d+9WSUmJSktLVV1drfz8fBUVFenkyZODtn/++ed1//33q7S0VEeOHFF5ebl2796tBx54YMR92lVtU389KYqcAwAABOd78h5FzgEAcA7Lk1Jbt27VmjVrtGrVKl1++eXasWOHkpKStHPnzkHbv/vuu7r22mu1fPly5eTkaP78+Vq2bFnATqjh9mlXvp1SuRzdAwAACKq+pT8plUmRcwAAHMPSpFRPT4+qqqpUWFjov+Z2u1VYWKiDBw8Oes+8efNUVVXlT0J98cUXev3113XjjTeOuM/u7m61tbUFvOygxlfknJ1SAAAAQdUNHN/LZKcUAACOEWvlN29qalJfX5/S09MDrqenp+vTTz8d9J7ly5erqalJ1113nYwx6u3t1d133+0/vjeSPsvKyvTQQw+NwoxGVy1JKQAAgLA0DBzfy0wmKQUAgFNYfnxvuCorK/XII4/oiSeeUHV1tfbs2aO9e/dqy5YtI+5zw4YNam1t9b++/PLLURzxyNU299eU4vgeAABAcHW+pFQqx/cAAHAKS3dKpaWlKSYmRo2NjQHXGxsblZGRMeg9mzZt0u23367Vq1dLkvLy8tTR0aG77rpLDz744Ij69Hg88njs9ejg9q6zajrdLUnKSUuyeDQAAAD2Vt/Sf3wvi5pSAAA4hqU7peLj4zV79mxVVFT4r3m9XlVUVGju3LmD3tPZ2Sm3O3DYMTExkiRjzIj6tKPjA7uk0i6I18SEOItHAwAAYF89vV6dGvhjXkYKx/cAAHAKS3dKSVJJSYlWrlypa665RnPmzNG2bdvU0dGhVatWSZJWrFihadOmqaysTJJUXFysrVu3atasWSooKNCxY8e0adMmFRcX+5NTofp0Al+R8xkc3QMAAAjqZHuXjJHiY9yaPCHe6uEAAIAwWZ6UWrJkiU6dOqXNmzeroaFBV199tfbt2+cvVH7ixImAnVEbN26Uy+XSxo0b9fXXX2vKlCkqLi7Www8/HHafTuAvck5SCgAAIKj6gXpSGSkJcrtdFo8GAACEy2WMMVYPwm7a2tqUkpKi1tZWJScnWzKGkpc+0J7qr/X7+Zdq/fU/tGQMAADAHnGBU1j1f/XaB1/rty9+oILcSdr9G+eUawAAIFqFGxM47ul744V/p1QaO6UAAACCafA9eY96UgAAOApJKZuqHSh0zvE9AACA4HzH9zJTefIeAABOQlLKhlrPnNU3HT2S2CkFAAAQSl3LGUlSFjulAABwFJJSNnS8uf/o3pSJHl3gsbwWPQAAgK19W+icnVIAADgJSSkbqhmoJ5XL0T0AAICQ6qkpBQCAI5GUsqHapoF6UmlJFo8EAADA3rp7+9R0uluSlEVNKQAAHIWklA3VDhzfm8FOKQAAgKAaW/sTUp5Yty5MirN4NAAAYDhIStmQ//geRc4BAACCqm/tL3KemZIgl8tl8WgAAMBwkJSyId9OqRx2SgEAAAT1bT0pju4BAOA0JKVspqWzRy2dZyVRUwoAACCUOt9OqVSKnAMA4DQkpWzGd3QvPdmjpPhYi0cDAABgb/UtPHkPAACnIillMxzdAwAACB/H9wAAcC6SUjZT29QpiSLnAAAA4fAVOs/i+B4AAI5DUspm/DulSEoBAACExE4pAACci6SUzdQ2+Y7vUeQcAAAgmK6zffqmo0cSNaUAAHAiklI2YozxFzpnpxQAAEBwDQO7pBLjYpSSGGfxaAAAwHCRlLKR/+08q7auXknSjEkkpQAAAIKpG6gnlZmaIJfLZfFoAADAcJGUshHfLqnMlAQlxsdYPBoAAAB7q2/p3ymVRT0pAAAciaSUjXxbT4pdUgAAAKE0tPUnpTKoJwUAgCORlLIRnrwHAAAQvrqW/uN7WSSlAABwJJJSNlLb3ClJyk3jyXsAAACh1A8UOs9M5fgeAABORFLKRji+BwAAED7fTimO7wEA4EwkpWzCGONPSuVyfA8AACAkX00pCp0DAOBMJKVsormjR+3dvXK5pOxJHN8DAAAI5kxPn1o6z0qSMlPZKQUAgBORlLIJ3y6prJREJcTFWDwaAAAAe6tr7T+6d4EnVskJcRaPBgAAjARJKZuo8dWTosg5AABASA0DRc6pJwUAgHORlLKJ2maKnAMAAITLV+Q8k6QUAACORVLKJmqbOiVR5BwAACAc9a0UOQcAwOlIStmE//geO6UAAABCqh+oKcXxPQAAnIuklA0YY3Tcd3yPnVIAAAAh+XdK8eQ9AAAci6SUDZw63a2Onj65XVL2JLagAwAAhFLf0p+UyuT4HgAAjkVSygZ89aSyUhPliY2xeDQAAAD2VzdwfI+dUgAAOBdJKRuoHagnRZFzAACA0E5396q9q1eSlMFOKQAAHIuklA3UNFPkHAAAIFwNA7ukJibE6gJPrMWjAQAAI0VSygZ8O6Uocg4AABBa3UA9qSx2SQEA4GgkpWygxn98L8nikQAAANhf/cBOqYwU6kkBAOBkJKUsZozR8eb+Qucc3wMAAAitvnVgpxRFzgEAcDSSUhZrbOvWmbN9inG7lD2JnVIAAACh1A8c38vk+B4AAI5GUspitQNFzqdfmKi4GJYDAACEZ/v27crJyVFCQoIKCgp06NChoO23bdummTNnKjExUdnZ2br33nvV1dV1Xn1apW7g+F4mx/cAAHA0siAW8xc55+geAAAI0+7du1VSUqLS0lJVV1crPz9fRUVFOnny5KDtn3/+ed1///0qLS3VkSNHVF5ert27d+uBBx4YcZ9W8h3fY6cUAADORlLKYjXNvqQUR/cAAEB4tm7dqjVr1mjVqlW6/PLLtWPHDiUlJWnnzp2Dtn/33Xd17bXXavny5crJydH8+fO1bNmygJ1Qw+3TSg2+pBQ1pQAAcDSSUhbz75RKY6cUAAAIraenR1VVVSosLPRfc7vdKiws1MGDBwe9Z968eaqqqvInob744gu9/vrruvHGG0fcp1Xaus7qdHevJI7vAQDgdLFWD2C8q20aePIeSSkAABCGpqYm9fX1KT09PeB6enq6Pv3000HvWb58uZqamnTdddfJGKPe3l7dfffd/uN7I+lTkrq7u9Xd3e3/uq2tbaTTCpuvyHlKYpyS4gllAQBwMnZKWcjrNf5C57nUlAIAAGOksrJSjzzyiJ544glVV1drz5492rt3r7Zs2XJe/ZaVlSklJcX/ys7OHqURD62eIucAAEQN/rxkoYa2LnX3ehXrdmn6hRTqBAAAoaWlpSkmJkaNjY0B1xsbG5WRkTHoPZs2bdLtt9+u1atXS5Ly8vLU0dGhu+66Sw8++OCI+pSkDRs2qKSkxP91W1vbmCemfEXOs1KJnQAAcDp2SlnIV08qe1KSYmNYCgAAEFp8fLxmz56tiooK/zWv16uKigrNnTt30Hs6OzvldgfGGjExMZIkY8yI+pQkj8ej5OTkgNdYq29hpxQAANGCnVIWqm0eqCfFk/cAAMAwlJSUaOXKlbrmmms0Z84cbdu2TR0dHVq1apUkacWKFZo2bZrKysokScXFxdq6datmzZqlgoICHTt2TJs2bVJxcbE/ORWqT7uo8z15j6QUAACOR1LKQr56UhQ5BwAAw7FkyRKdOnVKmzdvVkNDg66++mrt27fPX6j8xIkTATujNm7cKJfLpY0bN+rrr7/WlClTVFxcrIcffjjsPu2iwZ+U4vgeAABO5zLGGKsHYTdtbW1KSUlRa2vrmG5DX/Ps/9P+/zbqoZuv0Mp5OWP2fQAAwMhFKi6IBpH4v7r+fyr1xakOPb+mQPMuSRuT7wEAAM5PuDEBhYws5KspxU4pAACA0Iwxqm9hpxQAANGCpJRFvF6j49/015TKnUxSCgAAIJS2M706c7ZPEjWlAACIBrZISm3fvl05OTlKSEhQQUGBDh06NGTbn//853K5XOe8brrpJn+bO+6445z3FyxYEImphK2u9Yx6er2Ki3EpK5WgCgAAIJS61v4n702aEK+EuBiLRwMAAM6X5YXOd+/erZKSEu3YsUMFBQXatm2bioqKdPToUU2dOvWc9nv27FFPT4//6+bmZuXn5+uWW24JaLdgwQLt2rXL/7XH4xm7SYxAbVP/LqnsSUmKjbFFbhAAAMDW6geSUuySAgAgOlieDdm6davWrFmjVatW6fLLL9eOHTuUlJSknTt3Dtp+0qRJysjI8L/279+vpKSkc5JSHo8noN2FF14YiemErWbgyXsc3QMAAAhPnb+eFEkpAACigaVJqZ6eHlVVVamwsNB/ze12q7CwUAcPHgyrj/Lyci1dulQTJgQmdyorKzV16lTNnDlTa9euVXNz85B9dHd3q62tLeA11ihyDgAAMDwNrRQ5BwAgmlialGpqalJfX5/S09MDrqenp6uhoSHk/YcOHdLHH3+s1atXB1xfsGCBnn32WVVUVOjRRx/VgQMHtHDhQvX19Q3aT1lZmVJSUvyv7OzskU8qTMebSUoBAAAMh6+mVCb1OAEAiAqW15Q6H+Xl5crLy9OcOXMCri9dutT/77y8PF111VW65JJLVFlZqRtuuOGcfjZs2KCSkhL/121tbWOemKpp4vgeAADAcNRzfA8AgKhi6U6ptLQ0xcTEqLGxMeB6Y2OjMjIygt7b0dGhF198UXfeeWfI73PxxRcrLS1Nx44dG/R9j8ej5OTkgNdY6vMafflN/1/6ZkxOGtPvBQAAEC0a2ji+BwBANLE0KRUfH6/Zs2eroqLCf83r9aqiokJz584Neu/LL7+s7u5u/epXvwr5fb766is1NzcrMzPzvMc8Gupazqinz6v4GLeyUgmqAAAAQjHGqK6l/496WSSlAACICpY/fa+kpER/+9vf9Mwzz+jIkSNau3atOjo6tGrVKknSihUrtGHDhnPuKy8v1+LFizV58uSA66dPn9Yf/vAHvffee6qtrVVFRYUWLVqkH/zgByoqKorInELxHd27aHKSYtwui0cDAABgf//beVbdvV5JUnqKx+LRAACA0WB5TaklS5bo1KlT2rx5sxoaGnT11Vdr3759/uLnJ06ckNsdmDs7evSo3n77bf3jH/84p7+YmBh9+OGHeuaZZ9TS0qKsrCzNnz9fW7ZskcdjjwCm1lfknHpSAAAAYfHtkkq7IF6e2BiLRwMAAEaD5UkpSVq/fr3Wr18/6HuVlZXnXJs5c6aMMYO2T0xM1BtvvDGawxt1/iLnadSTAgAACEdDK/WkAACINpYf3xuPageSUjlp7JQCAAAIR31r/04pnrwHAED0ICllgdrmTklSLsf3AAAAwlLn3ylFUgoAgGhBUirCevu8+vKb/qQUO6UAAADCUz9QUyqTJxcDABA1SEpF2NctZ9TrNfLEupWRzF/6AAAAwlHPTikAAKIOSakI8xU5nzE5SW63y+LRAAAAOIMvKZXFTikAAKIGSakI8xc5p54UAABAWLxe43/6HjvNAQCIHrFWD2C8+T+zpuvyrBR5YskHAgAAhGvP/52n+tYuZXB8DwCAqEFSKsJSkuI0J3eS1cMAAABwDLfbpSunpejKaSlWDwUAAIwitusAAAAAAAAg4khKAQAAAAAAIOJISgEAAAAAACDiSEoBAAAAAAAg4khKAQAAAAAAIOJISgEAAAAAACDiSEoBAAAAAAAg4khKAQAAAAAAIOJISgEAAAAAACDiSEoBAAAAAAAg4khKAQAAAAAAIOJirR6AHRljJEltbW0WjwQAAFjNFw/44gMMjRgKAABI4cdPJKUG0d7eLknKzs62eCQAAMAu2tvblZKSYvUwbI0YCgAAfFeo+Mll+LPfObxer+rq6jRx4kS5XK5R77+trU3Z2dn68ssvlZycPOr929V4nPd4nLPEvJn3+MC8x8+8jTFqb29XVlaW3G4qHwQzljHUePzZk5g38x4fmPf4mfd4nLM0PucdbvzETqlBuN1uTZ8+fcy/T3Jy8rj5gfyu8Tjv8ThniXmPN8x7fBlv82aHVHgiEUONt589H+Y9vjDv8WU8zns8zlkaf/MOJ37iz30AAAAAAACIOJJSAAAAAAAAiDiSUhbweDwqLS2Vx+OxeigRNR7nPR7nLDFv5j0+MO/xNW9Yb7z+7DFv5j0eMO/xM+/xOGdp/M47HBQ6BwAAAAAAQMSxUwoAAAAAAAARR1IKAAAAAAAAEUdSCgAAAAAAABFHUgoAAAAAAAARR1JqjGzfvl05OTlKSEhQQUGBDh06FLT9yy+/rMsuu0wJCQnKy8vT66+/HqGRjo6ysjL95Cc/0cSJEzV16lQtXrxYR48eDXrP008/LZfLFfBKSEiI0IhHxx//+Mdz5nDZZZcFvcfpay1JOTk558zb5XJp3bp1g7Z34lr/61//UnFxsbKysuRyufTqq68GvG+M0ebNm5WZmanExEQVFhbqs88+C9nvcH83RFqweZ89e1b33Xef8vLyNGHCBGVlZWnFihWqq6sL2udIPieRFmq977jjjnPmsGDBgpD9Onm9JQ36OXe5XHrssceG7NMJ6w37In4ifhqK09daGh/xk0QMRQz1asD7xFDEUKGQlBoDu3fvVklJiUpLS1VdXa38/HwVFRXp5MmTg7Z/9913tWzZMt155506fPiwFi9erMWLF+vjjz+O8MhH7sCBA1q3bp3ee+897d+/X2fPntX8+fPV0dER9L7k5GTV19f7X8ePH4/QiEfPFVdcETCHt99+e8i20bDWkvSf//wnYM779++XJN1yyy1D3uO0te7o6FB+fr62b98+6Pt//vOf9de//lU7duzQ+++/rwkTJqioqEhdXV1D9jnc3w1WCDbvzs5OVVdXa9OmTaqurtaePXt09OhR3XzzzSH7Hc7nxAqh1luSFixYEDCHF154IWifTl9vSQHzra+v186dO+VyufTLX/4yaL92X2/YE/ET8dNQomGtpfERP0nEUMRQ5yKGIoYKymDUzZkzx6xbt87/dV9fn8nKyjJlZWWDtr/11lvNTTfdFHCtoKDA/OY3vxnTcY6lkydPGknmwIEDQ7bZtWuXSUlJidygxkBpaanJz88Pu300rrUxxvz2t781l1xyifF6vYO+7/S1lmReeeUV/9der9dkZGSYxx57zH+tpaXFeDwe88ILLwzZz3B/N1jt+/MezKFDh4wkc/z48SHbDPdzYrXB5r1y5UqzaNGiYfUTjeu9aNEic/311wdt47T1hn0QPxE/DSUa19qY6I+fjCGGCoYYamjRuN7EUENjp9Qo6+npUVVVlQoLC/3X3G63CgsLdfDgwUHvOXjwYEB7SSoqKhqyvRO0trZKkiZNmhS03enTpzVjxgxlZ2dr0aJF+uSTTyIxvFH12WefKSsrSxdffLFuu+02nThxYsi20bjWPT09eu655/TrX/9aLpdryHbRsNY+NTU1amhoCFjLlJQUFRQUDLmWI/nd4AStra1yuVxKTU0N2m44nxO7qqys1NSpUzVz5kytXbtWzc3NQ7aNxvVubGzU3r17deedd4ZsGw3rjcgifupH/DS4aFzr8Rg/ScRQ30UMNbhoXG9iqOBISo2ypqYm9fX1KT09PeB6enq6GhoaBr2noaFhWO3tzuv16p577tG1116rK6+8csh2M2fO1M6dO/Xaa6/pueeek9fr1bx58/TVV19FcLTnp6CgQE8//bT27dunJ598UjU1NfrpT3+q9vb2QdtH21pL0quvvqqWlhbdcccdQ7aJhrX+Lt96DWctR/K7we66urp03333admyZUpOTh6y3XA/J3a0YMECPfvss6qoqNCjjz6qAwcOaOHCherr6xu0fTSu9zPPPKOJEyfqF7/4RdB20bDeiDziJ+In4qdzRcNafx8xVD9iKGKowUTDeo9ErNUDQPRZt26dPv7445DnX+fOnau5c+f6v543b55+9KMf6amnntKWLVvGepijYuHChf5/X3XVVSooKNCMGTP00ksvhZUJjwbl5eVauHChsrKyhmwTDWuNQGfPntWtt94qY4yefPLJoG2j4XOydOlS/7/z8vJ01VVX6ZJLLlFlZaVuuOEGC0cWOTt37tRtt90WsshuNKw3YAXip/H1e4L4afwihiKGGko0rPdIsFNqlKWlpSkmJkaNjY0B1xsbG5WRkTHoPRkZGcNqb2fr16/X3//+d7311luaPn36sO6Ni4vTrFmzdOzYsTEa3dhLTU3VpZdeOuQcommtJen48eN68803tXr16mHd5/S19q3XcNZyJL8b7MoXTB0/flz79+8P+he+wYT6nDjBxRdfrLS0tCHnEE3rLUn//ve/dfTo0WF/1qXoWG+MPeIn4ifip9CiYa2JoYihiKHCFw3rHQ6SUqMsPj5es2fPVkVFhf+a1+tVRUVFwF86vmvu3LkB7SVp//79Q7a3I2OM1q9fr1deeUX//Oc/lZubO+w++vr69NFHHykzM3MMRhgZp0+f1ueffz7kHKJhrb9r165dmjp1qm666aZh3ef0tc7NzVVGRkbAWra1ten9998fci1H8rvBjnzB1GeffaY333xTkydPHnYfoT4nTvDVV1+publ5yDlEy3r7lJeXa/bs2crPzx/2vdGw3hh7xE/ET8RPoUXDWhNDEUMRQ4UvGtY7LNbWWY9OL774ovF4PObpp582//3vf81dd91lUlNTTUNDgzHGmNtvv93cf//9/vbvvPOOiY2NNX/5y1/MkSNHTGlpqYmLizMfffSRVVMYtrVr15qUlBRTWVlp6uvr/a/Ozk5/m+/P+6GHHjJvvPGG+fzzz01VVZVZunSpSUhIMJ988okVUxiR3/3ud6aystLU1NSYd955xxQWFpq0tDRz8uRJY0x0rrVPX1+fueiii8x99913znvRsNbt7e3m8OHD5vDhw0aS2bp1qzl8+LD/CSl/+tOfTGpqqnnttdfMhx9+aBYtWmRyc3PNmTNn/H1cf/315vHHH/d/Hep3gx0Em3dPT4+5+eabzfTp080HH3wQ8Fnv7u729/H9eYf6nNhBsHm3t7eb3//+9+bgwYOmpqbGvPnmm+bHP/6x+eEPf2i6urr8fUTbevu0traapKQk8+STTw7ahxPXG/ZE/ET8RPwUHWtNDEUMRQzVjxgqPCSlxsjjjz9uLrroIhMfH2/mzJlj3nvvPf97P/vZz8zKlSsD2r/00kvm0ksvNfHx8eaKK64we/fujfCIz4+kQV+7du3yt/n+vO+55x7//1F6erq58cYbTXV1deQHfx6WLFliMjMzTXx8vJk2bZpZsmSJOXbsmP/9aFxrnzfeeMNIMkePHj3nvWhY67feemvQn2nfvLxer9m0aZNJT083Ho/H3HDDDef8X8yYMcOUlpYGXAv2u8EOgs27pqZmyM/6W2+95e/j+/MO9Tmxg2Dz7uzsNPPnzzdTpkwxcXFxZsaMGWbNmjXnBEbRtt4+Tz31lElMTDQtLS2D9uHE9YZ9ET8RPxkTnWvtE+3xkzHEUMRQxFA+xFDhcRljzEh3WQEAAAAAAAAjQU0pAAAAAAAARBxJKQAAAAAAAEQcSSkAAAAAAABEHEkpAAAAAAAARBxJKQAAAAAAAEQcSSkAAAAAAABEHEkpAAAAAAAARBxJKQAAAAAAAEQcSSkAAAAAAABEHEkpAAAAAAAARBxJKQAAAAAAAEQcSSkAAAAAAABE3P8H8w18z/Cq+KQAAAAASUVORK5CYII=",
      "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 [==============================] - 2s 40ms/step\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.00      0.00      0.00        15\n",
      "           2       0.51      0.49      0.50        37\n",
      "           3       0.76      0.86      0.81        36\n",
      "           4       0.00      0.00      0.00         4\n",
      "           6       0.45      0.46      0.46       467\n",
      "           7       0.66      0.63      0.64       100\n",
      "           8       1.00      0.12      0.22        16\n",
      "           9       0.50      0.14      0.22        56\n",
      "          10       0.44      0.49      0.46        97\n",
      "          11       0.31      0.24      0.27       478\n",
      "          12       0.52      0.51      0.52       684\n",
      "          13       0.38      0.26      0.31       236\n",
      "          14       0.47      0.31      0.37        65\n",
      "          15       0.32      0.29      0.30        45\n",
      "          16       0.00      0.00      0.00        16\n",
      "          17       0.60      0.27      0.37        11\n",
      "          19       0.22      0.11      0.15        55\n",
      "          20       0.37      0.34      0.35       465\n",
      "          21       0.00      0.00      0.00         2\n",
      "          22       0.52      0.45      0.48       143\n",
      "          23       0.60      0.53      0.57        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       0.67      0.43      0.52        74\n",
      "          31       0.83      0.91      0.87        32\n",
      "          32       0.67      0.29      0.40         7\n",
      "          33       0.42      0.43      0.43       558\n",
      "          34       0.00      0.00      0.00        12\n",
      "          35       0.00      0.00      0.00        10\n",
      "          36       0.34      0.26      0.29       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      0.90      0.91        71\n",
      "          43       0.24      0.36      0.29        11\n",
      "          45       0.53      0.52      0.53       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       0.52      0.55      0.53       161\n",
      "          50       0.24      0.16      0.19       460\n",
      "          51       0.59      0.54      0.57       924\n",
      "          52       0.38      0.25      0.30       199\n",
      "          53       0.52      0.46      0.49       107\n",
      "          54       0.29      0.29      0.29        59\n",
      "          55       0.46      0.17      0.25        78\n",
      "          56       0.40      0.15      0.22        13\n",
      "          58       0.35      0.36      0.36       188\n",
      "          59       0.39      0.26      0.31       385\n",
      "          60       0.00      0.00      0.00         2\n",
      "          61       0.60      0.37      0.46       119\n",
      "          62       0.65      0.54      0.59        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       1.00      0.33      0.50         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       0.33      0.30      0.32       354\n",
      "          72       0.00      0.00      0.00         3\n",
      "          73       0.00      0.00      0.00         6\n",
      "          74       0.38      0.18      0.24       127\n",
      "          75       0.30      0.38      0.33         8\n",
      "          76       0.20      0.14      0.17         7\n",
      "          78       0.98      0.99      0.98     79004\n",
      "\n",
      "    accuracy                           0.94     86900\n",
      "   macro avg       0.32      0.24      0.26     86900\n",
      "weighted avg       0.93      0.94      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": "code",
   "execution_count": 16,
   "id": "2745ce74",
   "metadata": {},
   "outputs": [],
   "source": [
    "# def clean_word(word):\n",
    "    \n",
    "#     # remove non-alphanumeric characters and extra whitespaces\n",
    "#     word = re.sub(r'[^\\w\\s]','',word)\n",
    "#     word = re.sub(r'\\s+',' ',word)\n",
    "    \n",
    "#     # convert to lowercase\n",
    "#     word = word.lower()\n",
    "    \n",
    "#     if word not in STOP_WORDS:\n",
    "#         return word\n",
    "    \n",
    "#     return ''\n",
    "\n",
    "# def tokenize_text(text):\n",
    "#     # Tokenize the text into a list of words\n",
    "#     tokens = []\n",
    "#     for sentence in text.split('\\n'):\n",
    "#         for word in sentence.split():\n",
    "#             word = clean_word(word)\n",
    "#             if word.strip():\n",
    "#                 tokens.append(word)\n",
    "#     return tokens\n",
    "\n",
    "# def predict(text):\n",
    "    \n",
    "#     sentences = re.split(r' *[\\.\\?!][\\'\"\\)\\]]* *', text)\n",
    "#     sent_tokens = []\n",
    "#     sequences = []\n",
    "#     for sentence in sentences:\n",
    "#         tokens = tokenize_text(sentence)\n",
    "#         sequence = tokenizer.texts_to_sequences([' '.join(token for token in tokens)])\n",
    "#         sequences.append(sequence[0])\n",
    "#         sent_tokens.append(tokens)\n",
    "        \n",
    "#     padded_sequence = pad_sequences(sequences, maxlen=MAX_LENGTH, padding='post')\n",
    "\n",
    "#     # tokens = re.findall(r'\\b\\w+\\b', text)\n",
    "# #     tokens = tokenize_text(text)\n",
    "# #     sequence = tokenizer.texts_to_sequences([' '.join(token for token in tokens)])\n",
    "# #     padded_sequence = pad_sequences(sequence, maxlen=MAX_LENGTH, padding='post')\n",
    "\n",
    "#     # Make the prediction\n",
    "#     prediction = model.predict(np.array(padded_sequence))\n",
    "\n",
    "#     # Decode the prediction\n",
    "#     predicted_labels = np.argmax(prediction, axis=-1)\n",
    "\n",
    "#     predicted_labels = [[\n",
    "#         index_to_label[i] for i in sent_predicted_labels]for sent_predicted_labels in predicted_labels]\n",
    "\n",
    "# #     # Print the predicted named entities\n",
    "#     print(\"Predicted Named Entities:\")\n",
    "#     for i in range(len(sent_tokens)):\n",
    "#         for j in range(len(sent_tokens[i])):\n",
    "#             if predicted_labels[i][j] == 'O':\n",
    "#                 print(f\"{sent_tokens[i][j]}: {predicted_labels[i][j]}\")\n",
    "#             else:\n",
    "#                 print(f\"{sent_tokens[i][j]}: {acronyms_to_entities[ predicted_labels[i][j][2:]]}\")\n",
    "#         print(\"\\n\\n\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05963a19",
   "metadata": {},
   "source": [
    "## Save the model as an .h5 file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "955f026a",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save(os.path.join(model_dir, 'model_1.h5'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4407206c",
   "metadata": {},
   "source": [
    "## Load the model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "44b70c5a",
   "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": 17,
   "id": "51a930ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_1 = tf.keras.models.load_model(os.path.join(model_dir, 'model_1.h5'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "add60e79",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "28/28 [==============================] - 0s 16ms/step - loss: 0.4219 - accuracy: 0.9355 - precision: 0.9491 - recall: 0.9317 - f1_score: 0.9403\n",
      "Test loss: 0.4219094216823578\n",
      "Test accuracy: 0.9355235695838928\n",
      "Test precision: 0.9491192102432251\n",
      "Test recall: 0.9317142367362976\n",
      "Test f1_score: 0.9403346180915833\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model on the test set\n",
    "test_loss, test_accuracy, test_precision, test_recall, test_f1_score = model_1.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": 24,
   "id": "071fbea0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 14ms/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: Biological_structure\n",
      "radiofrequency: O\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",
       "    study\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",
       "    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",
       "</mark>\n",
       " of 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",
       "    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",
       "</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",
       "    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",
       "</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",
       "    followed\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",
       " by radiofrequency 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",
       "    generated\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",
       " 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",
       "    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",
       "</mark>\n",
       " 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_1, \n",
    "    index_to_label,\n",
    "    acronyms_to_entities, \n",
    "    MAX_LENGTH\n",
    ")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1f5e8611",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-04-16 06:09:30.892629: 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 [==============================] - 0s 363ms/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: Duration\n",
      "2: Duration\n",
      "day: O\n",
      "history: Sign_symptom\n",
      "worsening: Sign_symptom\n",
      "shortness: Biological_structure\n",
      "breath: Sign_symptom\n",
      "chest: History\n",
      "pain: History\n",
      "reported: History\n",
      "recent: History\n",
      "travel: History\n",
      "sick: History\n",
      "contacts: History\n",
      "medical: History\n",
      "history: History\n",
      "significant: History\n",
      "hypertension: History\n",
      "dyslipidemia: History\n",
      "type: History\n",
      "2: Sign_symptom\n",
      "diabetes: History\n",
      "mellitus: Diagnostic_procedure\n",
      "examination: O\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: Distance\n",
      "chest: Distance\n",
      "radiography: O\n",
      "revealed: Distance\n",
      "bilateral: O\n",
      "opacities: O\n",
      "consistent: O\n",
      "pulmonary: Detailed_description\n",
      "edema: Distance\n",
      "patient: Distance\n",
      "admitted: Distance\n",
      "intensive: Medication\n",
      "care: Therapeutic_procedure\n",
      "unit: Medication\n",
      "management: Dosage\n",
      "acute: Dosage\n",
      "decompensated: Dosage\n",
      "heart: Clinical_event\n",
      "failure: Other_entity\n",
      "started: Nonbiological_location\n",
      "intravenous: Date\n",
      "diuretics: Date\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"
     ]
    },
    {
     "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",
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       "-\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",
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       " \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",
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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",
       "</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: #ffdfba; 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\">Duration</span>\n",
       "</mark>\n",
       " with a \n",
       "<mark class=\"entity\" style=\"background: #ffdfba; 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\">Duration</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 \n",
       "<mark class=\"entity\" style=\"background: #e2f0cb; 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\">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",
       "    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\">History</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: #e2f0cb; 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\">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",
       "    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: #bde0fe; 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\">Sign_symptom</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: #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 examination, 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: #bde0fe; 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\">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",
       "    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\">Distance</span>\n",
       "</mark>\n",
       " radiography \n",
       "<mark class=\"entity\" style=\"background: #bde0fe; 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\">Distance</span>\n",
       "</mark>\n",
       " bilateral opacities 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: #bde0fe; 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\">Distance</span>\n",
       "</mark>\n",
       " 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",
       "    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\">Distance</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: #e6ccb2; 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\">Therapeutic_procedure</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: #b9e8d8; 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\">Dosage</span>\n",
       "</mark>\n",
       " \n",
       "<mark class=\"entity\" style=\"background: #77c5d5; 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\">Clinical_event</span>\n",
       "</mark>\n",
       " \n",
       "<mark class=\"entity\" style=\"background: #d5f5e3; 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\">Other_entity</span>\n",
       "</mark>\n",
       ". 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",
       "    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\">Nonbiological_location</span>\n",
       "</mark>\n",
       " on \n",
       "<mark class=\"entity\" style=\"background: #f1f0d2; 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\">Date</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",
       "    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\">Date</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_1, \n",
    "    index_to_label,\n",
    "    acronyms_to_entities, \n",
    "    MAX_LENGTH\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "df05127c",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 15ms/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: Duration\n",
      "2: Duration\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: O\n",
      "recent: History\n",
      "travel: History\n",
      "sick: O\n",
      "contacts: O\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "medical: O\n",
      "history: O\n",
      "significant: History\n",
      "hypertension: History\n",
      "dyslipidemia: History\n",
      "type: History\n",
      "2: History\n",
      "diabetes: History\n",
      "mellitus: History\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "examination: Diagnostic_procedure\n",
      "tachycardic: Sign_symptom\n",
      "tachypneic: Diagnostic_procedure\n",
      "oxygen: Diagnostic_procedure\n",
      "saturation: O\n",
      "88: Detailed_description\n",
      "room: Detailed_description\n",
      "air: O\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "chest: Biological_structure\n",
      "radiography: Diagnostic_procedure\n",
      "revealed: Detailed_description\n",
      "bilateral: O\n",
      "opacities: Distance\n",
      "consistent: Distance\n",
      "pulmonary: O\n",
      "edema: O\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "patient: O\n",
      "admitted: Biological_structure\n",
      "intensive: Nonbiological_location\n",
      "care: Nonbiological_location\n",
      "unit: Detailed_description\n",
      "management: Detailed_description\n",
      "acute: Distance\n",
      "decompensated: Distance\n",
      "heart: O\n",
      "failure: O\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "started: Administration\n",
      "intravenous: Medication\n",
      "diuretics: Medication\n",
      "inotropic: Medication\n",
      "support: O\n",
      "dobutamine: O\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "next: Age\n",
      "several: Age\n",
      "days: Nonbiological_location\n",
      "symptoms: Clinical_event\n",
      "improved: History\n",
      "discharged: Nonbiological_location\n",
      "home: Date\n",
      "instructions: Date\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: #ffdfba; 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\">Duration</span>\n",
       "</mark>\n",
       " with a \n",
       "<mark class=\"entity\" style=\"background: #ffdfba; 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\">Duration</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 reported 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 sick contacts. His medical history 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: #e2f0cb; 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\">History</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",
       " saturation 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: #ffb347; 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\">Detailed_description</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: #bde0fe; 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\">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",
       "    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\">Distance</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: #f7a399; 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\">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",
       "    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\">Nonbiological_location</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",
       "    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\">Detailed_description</span>\n",
       "</mark>\n",
       " for \n",
       "<mark class=\"entity\" style=\"background: #ffb347; 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\">Detailed_description</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: #f7a399; 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\">Administration</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: #f6c3d0; 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\">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",
       "    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\">Age</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",
       "    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\">Nonbiological_location</span>\n",
       "</mark>\n",
       ", his \n",
       "<mark class=\"entity\" style=\"background: #77c5d5; 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\">Clinical_event</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",
       "    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\">History</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: #f1f0d2; 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\">Date</span>\n",
       "</mark>\n",
       " with \n",
       "<mark class=\"entity\" style=\"background: #f1f0d2; 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\">Date</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_1, \n",
    "    tokenizer, \n",
    "    index_to_label,\n",
    "    acronyms_to_entities, \n",
    "    MAX_LENGTH\n",
    "    \n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ee5fc391",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
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