Z12_D_FashionMINT-Copy1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import keras\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Conv2D\n",
    "from keras.layers import MaxPooling2D\n",
    "from keras.layers import Flatten\n",
    "from keras.layers import Dense\n",
    "from keras.layers import Dropout\n",
    "from keras.layers import BatchNormalization\n",
    "from keras.callbacks import History\n",
    "from keras.callbacks import LearningRateScheduler\n",
    "from keras.optimizers import Adam\n",
    "\n",
    "# Clear the session\n",
    "keras.backend.clear_session()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "# Set up the data\n",
    "fashion_mnist = keras.datasets.fashion_mnist\n",
    "(X_train_full, y_train_full), (X_test, y_test) = fashion_mnist.load_data()\n",
    "X_valid, X_train = X_train_full[:5000] / 255., X_train_full[5000:] / 255.\n",
    "y_valid, y_train = y_train_full[:5000], y_train_full[5000:]\n",
    "X_test = X_test / 255.\n",
    "\n",
    "# Reshape the inputs to have one channel\n",
    "X_train = X_train.reshape(X_train.shape[0], 28, 28, 1)\n",
    "X_valid = X_valid.reshape(X_valid.shape[0], 28, 28, 1)\n",
    "X_test = X_test.reshape(X_test.shape[0], 28, 28, 1)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "history = History()\n",
    "\n",
    "def schedule(epoch, lr):\n",
    "    if epoch >= 40:\n",
    "        return 0.0001\n",
    "    return 0.001\n",
    "\n",
    "scheduler = LearningRateScheduler(schedule)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "1719/1719 [==============================] - 51s 29ms/step - loss: 0.4951 - accuracy: 0.8284 - val_loss: 0.2898 - val_accuracy: 0.8920 - lr: 0.0010\n",
      "Epoch 2/100\n",
      "1719/1719 [==============================] - 55s 32ms/step - loss: 0.3455 - accuracy: 0.8764 - val_loss: 0.2755 - val_accuracy: 0.9050 - lr: 0.0010\n",
      "Epoch 3/100\n",
      "1719/1719 [==============================] - 46s 27ms/step - loss: 0.3091 - accuracy: 0.8887 - val_loss: 0.2960 - val_accuracy: 0.9054 - lr: 0.0010\n",
      "Epoch 4/100\n",
      "1719/1719 [==============================] - 47s 27ms/step - loss: 0.2864 - accuracy: 0.8950 - val_loss: 0.2277 - val_accuracy: 0.9152 - lr: 0.0010\n",
      "Epoch 5/100\n",
      "1719/1719 [==============================] - 46s 27ms/step - loss: 0.2698 - accuracy: 0.9013 - val_loss: 0.2462 - val_accuracy: 0.9098 - lr: 0.0010\n",
      "Epoch 6/100\n",
      "1719/1719 [==============================] - 46s 27ms/step - loss: 0.2583 - accuracy: 0.9063 - val_loss: 0.2183 - val_accuracy: 0.9154 - lr: 0.0010\n",
      "Epoch 7/100\n",
      "1719/1719 [==============================] - 46s 27ms/step - loss: 0.2431 - accuracy: 0.9110 - val_loss: 0.2201 - val_accuracy: 0.9190 - lr: 0.0010\n",
      "Epoch 8/100\n",
      "1719/1719 [==============================] - 47s 27ms/step - loss: 0.2410 - accuracy: 0.9117 - val_loss: 0.3279 - val_accuracy: 0.8960 - lr: 0.0010\n",
      "Epoch 9/100\n",
      "1719/1719 [==============================] - 46s 27ms/step - loss: 0.2233 - accuracy: 0.9170 - val_loss: 0.2020 - val_accuracy: 0.9244 - lr: 0.0010\n",
      "Epoch 10/100\n",
      "1719/1719 [==============================] - 46s 27ms/step - loss: 0.2216 - accuracy: 0.9188 - val_loss: 0.2440 - val_accuracy: 0.9138 - lr: 0.0010\n",
      "Epoch 11/100\n",
      "1719/1719 [==============================] - 46s 27ms/step - loss: 0.2104 - accuracy: 0.9220 - val_loss: 0.2081 - val_accuracy: 0.9250 - lr: 0.0010\n",
      "Epoch 12/100\n",
      "1719/1719 [==============================] - 46s 27ms/step - loss: 0.2014 - accuracy: 0.9259 - val_loss: 0.2122 - val_accuracy: 0.9236 - lr: 0.0010\n",
      "Epoch 13/100\n",
      "1719/1719 [==============================] - 46s 27ms/step - loss: 0.2012 - accuracy: 0.9255 - val_loss: 0.2642 - val_accuracy: 0.9152 - lr: 0.0010\n",
      "Epoch 14/100\n",
      "1719/1719 [==============================] - 46s 27ms/step - loss: 0.1891 - accuracy: 0.9308 - val_loss: 0.1962 - val_accuracy: 0.9290 - lr: 0.0010\n",
      "Epoch 15/100\n",
      "1719/1719 [==============================] - 47s 27ms/step - loss: 0.1891 - accuracy: 0.9294 - val_loss: 0.2203 - val_accuracy: 0.9180 - lr: 0.0010\n",
      "Epoch 16/100\n",
      "1719/1719 [==============================] - 47s 27ms/step - loss: 0.1769 - accuracy: 0.9355 - val_loss: 0.1981 - val_accuracy: 0.9306 - lr: 0.0010\n",
      "Epoch 17/100\n",
      "1719/1719 [==============================] - 46s 27ms/step - loss: 0.1750 - accuracy: 0.9353 - val_loss: 0.1914 - val_accuracy: 0.9362 - lr: 0.0010\n",
      "Epoch 18/100\n",
      "1719/1719 [==============================] - 46s 27ms/step - loss: 0.1753 - accuracy: 0.9345 - val_loss: 0.1933 - val_accuracy: 0.9308 - lr: 0.0010\n",
      "Epoch 19/100\n",
      "1719/1719 [==============================] - 46s 27ms/step - loss: 0.1654 - accuracy: 0.9383 - val_loss: 0.1874 - val_accuracy: 0.9328 - lr: 0.0010\n",
      "Epoch 20/100\n",
      "1719/1719 [==============================] - 46s 27ms/step - loss: 0.1611 - accuracy: 0.9398 - val_loss: 0.1851 - val_accuracy: 0.9304 - lr: 0.0010\n",
      "Epoch 21/100\n",
      "1719/1719 [==============================] - 46s 27ms/step - loss: 0.1544 - accuracy: 0.9426 - val_loss: 0.1840 - val_accuracy: 0.9348 - lr: 0.0010\n",
      "Epoch 22/100\n",
      "1719/1719 [==============================] - 47s 27ms/step - loss: 0.1510 - accuracy: 0.9430 - val_loss: 0.3511 - val_accuracy: 0.8864 - lr: 0.0010\n",
      "Epoch 23/100\n",
      "1719/1719 [==============================] - 48s 28ms/step - loss: 0.1535 - accuracy: 0.9429 - val_loss: 0.1821 - val_accuracy: 0.9366 - lr: 0.0010\n",
      "Epoch 24/100\n",
      "1719/1719 [==============================] - 47s 27ms/step - loss: 0.1455 - accuracy: 0.9465 - val_loss: 0.1733 - val_accuracy: 0.9376 - lr: 0.0010\n",
      "Epoch 25/100\n",
      "1719/1719 [==============================] - 47s 27ms/step - loss: 0.1429 - accuracy: 0.9471 - val_loss: 0.2597 - val_accuracy: 0.9156 - lr: 0.0010\n",
      "Epoch 26/100\n",
      "1719/1719 [==============================] - 47s 27ms/step - loss: 0.1421 - accuracy: 0.9467 - val_loss: 0.1752 - val_accuracy: 0.9404 - lr: 0.0010\n",
      "Epoch 27/100\n",
      "1719/1719 [==============================] - 47s 27ms/step - loss: 0.1398 - accuracy: 0.9482 - val_loss: 0.1755 - val_accuracy: 0.9386 - lr: 0.0010\n",
      "Epoch 28/100\n",
      "1719/1719 [==============================] - 47s 27ms/step - loss: 0.1356 - accuracy: 0.9497 - val_loss: 0.1819 - val_accuracy: 0.9390 - lr: 0.0010\n",
      "Epoch 29/100\n",
      "1719/1719 [==============================] - 47s 27ms/step - loss: 0.1315 - accuracy: 0.9511 - val_loss: 0.1860 - val_accuracy: 0.9372 - lr: 0.0010\n",
      "Epoch 30/100\n",
      "1719/1719 [==============================] - 47s 27ms/step - loss: 0.1278 - accuracy: 0.9522 - val_loss: 0.1783 - val_accuracy: 0.9360 - lr: 0.0010\n",
      "Epoch 31/100\n",
      "1719/1719 [==============================] - 47s 27ms/step - loss: 0.1296 - accuracy: 0.9515 - val_loss: 0.2238 - val_accuracy: 0.9326 - lr: 0.0010\n",
      "Epoch 32/100\n",
      "1719/1719 [==============================] - 47s 27ms/step - loss: 0.1392 - accuracy: 0.9485 - val_loss: 0.1862 - val_accuracy: 0.9398 - lr: 0.0010\n",
      "Epoch 33/100\n",
      "1719/1719 [==============================] - 47s 27ms/step - loss: 0.1224 - accuracy: 0.9540 - val_loss: 0.2008 - val_accuracy: 0.9356 - lr: 0.0010\n",
      "Epoch 34/100\n",
      "1719/1719 [==============================] - 47s 27ms/step - loss: 0.1211 - accuracy: 0.9545 - val_loss: 0.1838 - val_accuracy: 0.9356 - lr: 0.0010\n",
      "Epoch 35/100\n",
      "1719/1719 [==============================] - 47s 27ms/step - loss: 0.1215 - accuracy: 0.9543 - val_loss: 0.1810 - val_accuracy: 0.9380 - lr: 0.0010\n",
      "Epoch 36/100\n",
      "1719/1719 [==============================] - 47s 27ms/step - loss: 0.1181 - accuracy: 0.9566 - val_loss: 0.2063 - val_accuracy: 0.9342 - lr: 0.0010\n",
      "Epoch 37/100\n",
      "1719/1719 [==============================] - 47s 27ms/step - loss: 0.1168 - accuracy: 0.9569 - val_loss: 0.1876 - val_accuracy: 0.9392 - lr: 0.0010\n",
      "Epoch 38/100\n",
      "1719/1719 [==============================] - 47s 27ms/step - loss: 0.1170 - accuracy: 0.9566 - val_loss: 0.1881 - val_accuracy: 0.9370 - lr: 0.0010\n",
      "Epoch 39/100\n",
      "1719/1719 [==============================] - 47s 27ms/step - loss: 0.1116 - accuracy: 0.9588 - val_loss: 0.1765 - val_accuracy: 0.9424 - lr: 0.0010\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7fe2b0effa90>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "model = Sequential()\n",
    "\n",
    "# Start with convolutional layers\n",
    "model.add(Conv2D(32, (3, 3), padding='same', activation='relu', input_shape=(28, 28, 1)))\n",
    "model.add(BatchNormalization())\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "model.add(Dropout(0.25))\n",
    "\n",
    "model.add(Conv2D(64, (3, 3), padding='same', activation='relu'))\n",
    "model.add(BatchNormalization())\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "model.add(Dropout(0.25))\n",
    "\n",
    "# Then fully connected layers\n",
    "model.add(Flatten())\n",
    "model.add(Dense(512, activation=\"relu\"))\n",
    "model.add(BatchNormalization()),\n",
    "model.add(Dropout(0.5)),\n",
    "model.add(Dense(10, activation=\"softmax\"))\n",
    "\n",
    "model.compile(loss=\"sparse_categorical_crossentropy\",\n",
    "              optimizer=Adam(learning_rate=0.001),\n",
    "              metrics=[\"accuracy\"])\n",
    "\n",
    "early_stopping = keras.callbacks.EarlyStopping(patience=15, monitor=\"val_loss\")\n",
    "\n",
    "model.fit(X_train, y_train, epochs=100,\n",
    "                    validation_data=(X_valid, y_valid),\n",
    "                    callbacks=[history, early_stopping, scheduler])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1719/1719 [==============================] - 11s 6ms/step - loss: 0.0405 - accuracy: 0.9883\n",
      "Train accuracy: 0.9883090853691101\n",
      "157/157 [==============================] - 1s 6ms/step - loss: 0.1765 - accuracy: 0.9424\n",
      "Validation accuracy: 0.9423999786376953\n",
      "313/313 [==============================] - 2s 6ms/step - loss: 0.2204 - accuracy: 0.9323\n",
      "Test accuracy: 0.9322999715805054\n"
     ]
    }
   ],
   "source": [
    "train_loss, train_acc = model.evaluate(X_train, y_train)\n",
    "print('Train accuracy:', train_acc)\n",
    "\n",
    "# Evaluate on validation data\n",
    "valid_loss, valid_acc = model.evaluate(X_valid, y_valid)\n",
    "print('Validation accuracy:', valid_acc)\n",
    "\n",
    "# Evaluate on test data\n",
    "test_loss, test_acc = model.evaluate(X_test, y_test)\n",
    "print('Test accuracy:', test_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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002zfvt1xX2hoKKGhoYDZpstqteLnV7OaeHjrn62IiEhNdKZ87VQeW+HNzc1lzZo19O3btzAYHx/69u3L33//XeJzcnJyTktEgoODWbZsmeP777//nu7duzNkyBBiYmLo0qULH3300RljycnJITU1tdiXMwzDIDM3v9K/nPldpV69eo6viIgILBaL4/tt27YRFhbGL7/8Qrdu3QgMDGTZsmXs3r2b6667jtjYWEJDQ+nRowcLFy4sdt1TSxosFgsff/wxgwYNIiQkhJYtW/L999879fMUERERcSWPLeElJydjtVqJjY0tdn9sbCzbtm0r8Tn9+vVjypQpXHTRRcTFxbFo0SLmzp1bbLzvnj17eO+995gwYQJPPPEEq1at4v777ycgIIDRo0eXeN0XX3yRZ599ttzvJSvPStunfy3388try3P9CAlw3R/hY489xquvvkqLFi2oXbs2Bw8epH///vz73/8mMDCQzz//nIEDB7J9+3aaNGlS6nWeffZZXnnlFf7zn//w1ltvcdNNN7F//37q1KnjslhFREREysrjm9ac8cYbb9CyZUtat25NQEAA48ePZ8yYMcX6p9psNrp27crkyZPp0qULd9xxB+PGjeP9998v9bqPP/44KSkpjq+DBw9Wxtupcp577jmuuOIK4uLiqFOnDp06deLOO++kffv2tGzZkueff564uLizrtjeeuutjBgxgnPOOYfJkyeTnp5+xs2IIiIiIu7ksRXeqKgofH19SUhIKHZ/QkIC9erVK/E50dHRzJs3j+zsbI4dO0aDBg147LHHaNGiheOc+vXr07Zt22LPa9OmDd98802psQQGBhIYGFju9xLs78uW5/qV+/kVeV1X6t69e7Hv09PTmTRpEj/99BNHjhwhPz+frKwsDhw4cMbrdOzY0XFcq1YtwsPDS92IKCIiItXD+oMn2ZOUzg1dG3k6lNN4LOENCAigW7duLFq0iOuvvx4wV2cXLVrE+PHjz/jcoKAgGjZsSF5eHt988w1Dhw51PNanT59im7EAduzYQdOmTV3+HuwsFotLSws8pVatWsW+/9e//sWCBQt49dVXOeeccwgODubGG28kNzf3jNfx9/cv9r3FYsFms7k8XhEREfEswzBYsfc4by/exbJdydQK8OXSVjHUrhXg6dCK8WiWNmHCBEaPHk337t3p2bMnU6dOJSMjgzFjxgAwatQoGjZsyIsvvgjAihUriI+Pp3PnzsTHxzNp0iRsNhuPPvqo45oPPfQQvXv3ZvLkyQwdOpSVK1fy4Ycf8uGHH3rkPXqzP//8k1tvvZVBgwYB5orvvn37PBuUiIiIeJxhGPy2I4l3Fu9i9f4TAPj5WLi6Q33yrFVvkcujCe+wYcNISkri6aef5ujRo3Tu3Jn58+c7NrIdOHCgWH1udnY2EydOZM+ePYSGhtK/f3+++OILIiMjHef06NGDb7/9lscff5znnnuO5s2bM3XqVG666abKfnter2XLlsydO5eBAwdisVh46qmntFIrIiJSg9lsBv/bcpS3l+xic7zZ1SrAz4eh3Rtx50VxNK4T4uEIS+bxz+HHjx9fagnDb7/9Vuz7iy++mC1btpz1mgMGDGDAgAGuCK9GmzJlCrfddhu9e/cmKiqK//u//3O6ZZuIiIh4v3yrjR83HuGdJbvYmZgOmHuJbj6vCeMubEFMeNXuX+/RwRNVVXUcPCFnpz9bERGR4nLyrcxdG897v+3mwPFMAMKC/Li1dzPG9GlOHQ/W6jozeMLjK7wiIiIiUnHZeVZOZuZxMiuXrFwrEcH+RIYEEB7kh5+vc51os3KtzFp1gA+X7uFISjYAdWoFMPaC5txyflPCg/zPcoWqRQmviIiISBVjsxnsTkonKT2HlMw8TmblOZLZkxkFt5l5pGTlcSLTPM7JL32fTViQH7VDAogM8XckwpHB/tQO8Sei4DgyxPxaufcEH/+xh2MZZlem2PBA7rgojhE9G3ttVyrvjFpERESkGrLZDOb/c5Q3F+1k29E0p5/v52MhMsSfQD9fUrPzSMvOByAtO5+07HwOHC/7tRrXCeaui+O4sVsjAv1c2/u/sinhFREREfEwq83g501HeGvxTnYkmJvCgvx9aFQ7pMjqa+FKbERIALVD/IkMLrpq609ooB8Wi8Vx3TyrjdSsIivEBavBJ7PySMnM5WRWHicK7k8pOCc82I/b+jTn2k4NnC6FqKqU8IqIiIh4iNVm8MOGw7y1eCe7kzIAs/xgTJ/m3NanGZEhFdsU5u/rQ93QQOqGln+ibHWghFdERESkkuVbbXy3/jDvLNnFnmQz0Q0P8mPsBS24tU8zIoK9a1NYVaeEV0RERKSS5FltfLsunneW7GL/MbPNV2SIP+MubMGo85sS5mXdD7yFEl4RERERN8vNt/HN2kO8s2QXh05kAWabr3EXtuCW85sSGqiUzJ300xUREREpwmYzOJmVR1JajvmVno1hQJC/L0H+PgT5+RJoP/b3Nb/8fAgO8CXIzxcfn8JNYzn5VuasPsR7v+0m/qSZ6EaFBnDHRS24+bymXtvmy9vopyxldskll9C5c2emTp0KQLNmzXjwwQd58MEHS32OxWLh22+/5frrr6/Qa7vqOiIiUnNl51lJSsshMS2HpLTsIgltDomp5m1SWg7J6TnkWcs/iDbA14fAgmQ4J89KakFrsOiwQO66OI6RPZsQHODdbb68jRLeGmLgwIHk5eUxf/780x77448/uOiii9iwYQMdO3Ys8zVXrVpFrVq1XBkmkyZNYt68eaxfv77Y/UeOHKF27doufS0REakZTmbmMu7z1azad8Kp59UO8Sc6LJCo0EB8fSxk51nJzrOZt/lFjvOsxRLkXKuNXKvN0QM3NjyQuy+OY3jPJgT5K9H1BCW8NcTYsWMZPHgwhw4dolGjRsUe+/TTT+nevbtTyS5AdHS0K0M8o3r16lXaa4mISPWRZ7Vxz/S1jmQ30M+HmPBAokMDiQ4zv2LCgszjIvdFhQYS4Ff2HrRWm+FIfrPzCxNhq82gVb0wrx/c4O2qRzdhTzMMyM2o/C+j7B+3DBgwgOjoaKZNm1bs/vT0dObMmcP111/PiBEjaNiwISEhIXTo0IGZM2ee8ZrNmjVzlDcA7Ny5k4suuoigoCDatm3LggULTnvO//3f/3HuuecSEhJCixYteOqpp8jLywNg2rRpPPvss2zYsAGLxYLFYnHEa7FYmDdvnuM6mzZt4rLLLiM4OJi6detyxx13kJ6e7nj81ltv5frrr+fVV1+lfv361K1bl3vvvdfxWiIiUv0ZhsEz3//DX7uPUSvAlx/vu4Btz1/FH49extx7+vDBLd154foO3H95S0b0bELftrF0ahxJg8hgp5JdAF8fC7UC/agbGkjDyGDiokNp1yCCjo0ilexWAVrhdYW8TJjcoPJf94nDEFC2kgI/Pz9GjRrFtGnTePLJJx1TWObMmYPVauXmm29mzpw5/N///R/h4eH89NNP3HLLLcTFxdGzZ8+zXt9ms3HDDTcQGxvLihUrSElJKbG2NywsjGnTptGgQQM2bdrEuHHjCAsL49FHH2XYsGFs3ryZ+fPns3DhQgAiIiJOu0ZGRgb9+vXj/PPPZ9WqVSQmJnL77bczfvz4Ygn9kiVLqF+/PkuWLGHXrl0MGzaMzp07M27cuDL9zERExLt9+uc+Zqw4gMUCbwzvQvuGp/+bIjWDVnhrkNtuu43du3fz+++/O+779NNPGTx4ME2bNuVf//oXnTt3pkWLFtx3331cddVVfPXVV2W69sKFC9m2bRuff/45nTp14qKLLmLy5MmnnTdx4kR69+5Ns2bNGDhwIP/6178crxEcHExoaCh+fn7Uq1ePevXqERwcfNo1ZsyYQXZ2Np9//jnt27fnsssu4+233+aLL74gISHBcV7t2rV5++23ad26NQMGDOCaa65h0aJFzv7YRETECy3ZnsgLP20B4PGrW9O3bayHIxJP0gqvK/iHmKutnnhdJ7Ru3ZrevXvzySefcMkll7Br1y7++OMPnnvuOaxWK5MnT+arr74iPj6e3NxccnJyCAkp22ts3bqVxo0b06BB4Ur3+eeff9p5s2fP5s0332T37t2kp6eTn59PeHi4U+9j69atdOrUqdiGuT59+mCz2di+fTuxseb/1Nq1a4evb+HHSPXr12fTpk1OvZaIiJwuPSef3YnpNIgMJio0wPGpYVWxIyGN+2asw2bA0O6NGHdhC0+HJB6mhNcVLJYylxZ42tixY7nvvvt45513+PTTT4mLi+Piiy/m5Zdf5o033mDq1Kl06NCBWrVq8eCDD5Kbm+uy1/7777+56aabePbZZ+nXrx8RERHMmjWL1157zWWvUZS/f/FpNRaLBZvN5pbXEhGpztKy81i97wTL9x5j+Z7jbI5PwWoz95HUDvGnZUwYLWNDaRkTyrmxYZwTG0p0aKBHEuFj6TmM/WwV6Tn59Gpehxeu71DlEnKpfEp4a5ihQ4fywAMPMGPGDD7//HPuvvtuLBYLf/75J9dddx0333wzYNbk7tixg7Zt25bpum3atOHgwYMcOXKE+vXrA7B8+fJi5/z11180bdqUJ5980nHf/v37i50TEBCA1Wo962tNmzaNjIwMxyrvn3/+iY+PD61atSpTvCIiUrrU7DxW7zvOij3HWb7nGJviU7Cdsk+6Tq0ATmTmciIzj5X7jrNy3/Fij0eG+NMyJpSWsWGORLhlTCjRYe5LhHPyrdz5xRoOHs+iad0Q3r+5m9Obz6R6UsJbw4SGhjJs2DAef/xxUlNTufXWWwFo2bIlX3/9NX/99Re1a9dmypQpJCQklDnh7du3L+eeey6jR4/mP//5D6mpqcUSW/trHDhwgFmzZtGjRw9++uknvv3222LnNGvWjL1797J+/XoaNWpEWFgYgYGBxc656aabeOaZZxg9ejSTJk0iKSmJ++67j1tuucVRziAiImWXkmUmuMv3HGPFXnMF99QEt0mdEM5rUYfzWtSlV4u6NIwMJivXyu6kdHYlprMjIY2diensTEhj//FMTmbmsWrfidN630YE+9O+YTgTrjiXbk3ruOw9GIbB43M3sXr/CcKC/Pjv6O7UrhXgsuuLd1PCWwONHTuW//73v/Tv399Rcztx4kT27NlDv379CAkJ4Y477uD6668nJSWlTNf08fHh22+/ZezYsfTs2ZNmzZrx5ptvctVVVznOufbaa3nooYcYP348OTk5XHPNNTz11FNMmjTJcc7gwYOZO3cul156KSdPnuTTTz91JOV2ISEh/PrrrzzwwAP06NGDkJAQBg8ezJQpUyr8sxERqQly822s2nec37Yn8veeY2w5nHpagtusbgi9mtflvLg69GpelwaRp28iDg7wpX3DiNO6H2TnnZIIJ6SzMzGd/ccySMnK489dx/hr99/ccl5THunXirAg/9Ou7az3f9/D3LXx+PpYeGdkV86JCavwNaX6sBiGE81ca4jU1FQiIiJISUk5bUNVdnY2e/fupXnz5gQFBXkoQnEH/dmKSHWWnJ7Db9uTWLwtgaU7kknPyS/2ePOoWpzXwkxue7WoQ/2I0xPcisrOs7InKYNP/9zLnDWHAKgfEcQL17fn8jbl/4Tu13+OcteXazAMeO66dow6v5mLIpaq7Ez52qm0wisiIlINGYbBliOpLN6ayKJtiWw4dLLYvKKo0AAuaRXDhS2j6NW8LvUi3P+LfpC/L20bhPOfIZ24rnNDnvh2EweOZzL2s9Vc07E+kwa2Izos8OwXKmJzfAoPzlqPYcCo85sq2ZUSKeEVERGpJrJyrfy5K5nF2xNZvDWRo6nZxR5v3zCcy1rFcFmbWDo2jMDHx3PdCy5oGcWvD17E1IU7+HjZXn7aeIRlO5N58po2DOnWqEwb2xJTsxn3+Wqy8qxc2DKKpweUbd+J1DxKeEVERLzY4ZNZLNqWyOKtCfy1+xg5+YXtF4P9fbmgZRSXtY7h0lYxlbKK64zgAF8e79+GgZ0a8H/fbOSfw6k8+vVG5q2L58UbOtC0buktP7PzrIz7Yg1HUrKJi67F2yO74uerjgxSMiW8IiIiXmZvcga/bD7C/M1H2Xio+ObihpHBXN4mhstax3Bei7oE+fuWcpWqo33DCL67tw//XbaXKQt28NfuY1z5+lIeuuJcbr+g+WmJrGEY/GvOBjYcPElkiD//Hd2DiOCKb3yT6ksJbzlpr1/1oz9TEamqDMNgR0K6I8nddjTN8ZjFAt2a1ObyNrFc3iaGljGhXjlowc/XhzsvjuOq9vV44ttN/LnrGC/9so0fNhzm5cEdi3WCeGPRTn7ceAQ/Hwvv39yNZlHeMfxJPEcJr5Ps07syMzMJDnb9DlbxnMzMTOD0CW0iIp5gGAab4lP4ZfNRft18lD3JGY7H/HwsnB9Xl6vb1+eKtrFOb/SqyprWrcWXY3vx9ZpDvPDTVv45nMq1by/j9gtb8FDfc1m4NYGpC3cC8O9B7TmvRV0PRyzeQAmvk3x9fYmMjCQxMREwe8J642/SUsgwDDIzM0lMTCQyMhJf36r/8Z+IVE82m8HaAyf4ZfNR5m8+SvzJLMdjAX4+XNQyiqva16dvmxgiQ6rvUAWLxcKQ7o25pFUMz/24hR82HObDpXv4edMRktJyABh3YXOG9Wji4UjFWyjhLYd69eoBOJJeqR4iIyMdf7YiIpXFajNYsfcYv2w6yq//HCWxIKEDc9PZZa1j6Ne+Hpe1jiE0sGb9sx0dFshbI7pwfecGTJy3mUMnzF8ALm8dw2NXt/FwdOJNatZ/OS5isVioX78+MTEx5OXleToccQF/f3+t7IpIpbHZDFbvP8GPGw/z86ajJKcXJrlhQX70bRPLVe3rcfG50V6x6czdLm8TS68WdXlr8U6S0nJ47rr2+HqwpZp4HyW8FeDr66skSUREysQwDNYeOFmQ5B4hIbUwyY0M8adf23pc1aEefeKiCPBTe61ThQb68bhWdaWclPCKiIi4iX3j2Y8bj/DTxiPFanLDgvy4sm09BnSqzwXnROGvHrIibqOEV0REaiTDMEhKzyH+RBaHTmQRfzKL+BNZpGTlUadWANFhgUSFBhAVGmh+FXwf6HfmT/YMw2DrkTR+3HiYnzYdYf+xTMdjtQJ8uaJtLNd0bMBF50ad9Voi4hpKeEVEpFqy2gwSUrOJP5nFoROZpyW2h05mkVtkKllZhQf5FSS/gUSHBhZLjA+fzOLHTUfYk1TYQizY35fL2sQwsGN9LmkVo5pcEQ9QwisiItXKN2sO8faSXRw8nkm+7cwDZSwWqBceRMPIYBrVDqZh7WBqhwRwPCOX5PQcktJySE43j5PTc8izGqRm55OanV8sqT1VgJ8Pl7aKZkDHBlzeJoaQAP1zK+JJ+i9QRESqhZx8K5O+38LMlQcc9/n5WKgfaU9oQ2gYaSa1jWoH0ygyhHoRQWXeIGYYBilZeQWJcC5J6Tkkp+UUSYxzCPTzpV/7WPq2iSUsSENsRKoKJbwiIuL14k9mcc+Xa9hwKAWLBR64vCVDuzcmNjzIZe2rLBYLkSEBRIYEcE6MSy4pIpVECa+IiHi1pTuSeGDWOk5k5hEZ4s/UYZ25pJUyUhEppIRXRES8ks1m8PaSXby+cAeGAR0aRvDuTV1pXCfE06GJSBWjhFdERLxOSmYeD321nsXbzBHvI3o25pmB7dQBQURKpIRXRES8yj+HU7j7y7UcOJ5JgJ8PL1zfnqHdG3s6LBGpwpTwioiI15iz+iAT520mJ99Go9rBvH9zN9o3jPB0WCJSxSnhFRGRKu/UlmOXtorm9WGdiQwJ8HBkIuINlPCKiEiVduhEJvdMX8vGgpZjD/U9l/GXnoOPi9qNiUj1p4RXRESqrKU7krh/1jpOFrQce2N4Fy4+N9rTYYmIl1HCKyIiLmEY5tjdk5m5nMjM40RmLiczc8mzGlgwBzeYtwVfWLAULNIWe6zg/n8Op/Dub7vVckxEKkwJr4iInNHB45lsPZLKiaKJbEZewffmfSczczmZmUe+zXD564/o2YRnBrZVyzERKTclvCIiUqL0nHymLtjBp3/tw+pEIhsS4EvtkAAiQ/ypHRKAv68FAzAMCm7Na5nfG+Zt0WMAA3x9LAzp3ogbujZyx9sTEVeb/wRENITz7sHx8U0VoYRXRESKMQyDHzYe4d8/bSEhNQeAtvXDiQ0PpHZIALVrBVA7xJ/IkICC783E1p7kaiVWpAZa+REsf8c8btoHGnT2aDinUsIrIiIOuxLTefq7zfy1+xgATeuGMOnadlzaKsbDkYlIlbXjf/DLo+bx5c9UuWQXlPCKiAiQmZvPW4t38fEfe8izGgT6+XDPJedw58UttGIrIqU7uhm+HgOGDTrfDBc85OmISqSEV0SkBjMMg1//OcpzP2zhcEo2AJe1jmHSwHY0qauOCCJyBmlHYcYwyE2HZhfCgNerXO2unY+nAwB45513aNasGUFBQfTq1YuVK1eWem5eXh7PPfcccXFxBAUF0alTJ+bPn1/q+S+99BIWi4UHH3zQDZGLiHivvckZjP50FXd9uZbDKdk0jAzmo1Hd+eTWHkp2ReTMcjNh5nBIPQR1W8KwL8Cv6k4+9PgK7+zZs5kwYQLvv/8+vXr1YurUqfTr14/t27cTE3N6zdjEiRP58ssv+eijj2jdujW//vorgwYN4q+//qJLly7Fzl21ahUffPABHTt2rKy3IyJS5WXnWXl3yS7e/30PuVYbAb4+3HlxC+655ByCA1S+UG6GAda8Kv2PvohL2Gzw7R1weB0E14GbvoLg2p6O6ow8vsI7ZcoUxo0bx5gxY2jbti3vv/8+ISEhfPLJJyWe/8UXX/DEE0/Qv39/WrRowd13303//v157bXXip2Xnp7OTTfdxEcffUTt2lX7D0FEpLIs3JJA3ym/8+biXeRabVx0bjS/PnQRD1/ZSsluRc0aCZPrw+xbYOdCsFk9HZGIeyyaBFt/AN8AGD4D6rTwdERn5dGENzc3lzVr1tC3b1/HfT4+PvTt25e///67xOfk5OQQFBRU7L7g4GCWLVtW7L57772Xa665pti1S5OTk0NqamqxLxGR6mRXYhq3f7aK2z9fzaETWdSPCOK9m7ry2ZgeNI+q5enwvF9eFuyYD7Z82Po9TB8MUzvCkhfh5AFPR1d95OdAtv6N9qg1n8Gfb5jH170DTc/3bDxl5NGShuTkZKxWK7GxscXuj42NZdu2bSU+p1+/fkyZMoWLLrqIuLg4Fi1axNy5c7FaC3+TnjVrFmvXrmXVqlVliuPFF1/k2WefLf8bERGpgvYmZ/DzpiP8uPEIW4+YSYKfj4XbL2zB/ZefQ0iAx6vaqo/ELeYu9eA60HEYbJhp1jb+/hL8/jLEXQZdR0Gr/ip5KK+8bPjvFZC0HW74ANoN8nRENc+e3+CnCebxJY9Dx6EeDccZXvd/uzfeeINx48bRunVrLBYLcXFxjBkzxlECcfDgQR544AEWLFhw2kpwaR5//HEmTJjg+D41NZXGjRu7JX4REXfafyyDnzYd4aeNR/jncOFKmJ+PhUtaRfPY1a05JybMgxFWU0c3m7f1O8HVL0HfSbDtR1j7GexdCrsXmV8hUdBpuJn8RrfyaMhe549X4ehG83jOGMg8Bj1u92xMVVVeNhzfDTFtXdc1IWk7zB5lforRYQhc/H+uuW4l8WjCGxUVha+vLwkJCcXuT0hIoF69eiU+Jzo6mnnz5pGdnc2xY8do0KABjz32GC1amPUja9asITExka5duzqeY7VaWbp0KW+//TY5OTn4+havUwsMDCQwMNDF705EpHIcOJZpJrmbDrM5vjDJ9fWx0DuuLgM61ufKtvWoXUsri25zdJN5W6+DeesfBB1uNL+O74F1X8K66ZB+FP5+2/xqfJ6Z+La7HgLOUlZis0H2SchIKvKVbN5mHoPo1uZrVfGNQ+WW8A8se908bnYh7PsDfnoY0pPgkseqbCssj8hOhc8GwpH15i9gFz4MrQeCTwWqWDOSYfoQyEkx/95e+7bX/cwthn2ouYf06tWLnj178tZbbwFgs9lo0qQJ48eP57HHHjvr8/Py8mjTpg1Dhw5l8uTJpKWlsX///mLnjBkzhtatW/N///d/tG/f/qzXTE1NJSIigpSUFMLDw8v3xkRE3Ojg8Ux+3nSEnzYdYeOhFMf99iT3mg71ubJdPeooya0c/+0HB5fDDR+V/jGvNR92LYC1n8OOX8EoKMULCDOT1YbdIDO5MJFNTyyS1CabK2tn4hsIba+DrrdA0wsqluBUJTYr/PdKiF8NrQfAsC/NMpHfXjQf73E7XP0K+GjTJXnZMP1G8xeCoqLOhQsmmH/PfP2dv+bn18LBFVC7Gdy+GGrVdVnIFeFMvubxhHf27NmMHj2aDz74gJ49ezJ16lS++uortm3bRmxsLKNGjaJhw4a8+KL5F3vFihXEx8fTuXNn4uPjmTRpEnv37mXt2rVERkaW+BqXXHIJnTt3ZurUqWWKSQmviFRFJzNz+Wr1QX7adJQNB0867vexwPlxdbmmQwP6tYulbqg+sapUNhu81ARy0+Ce5RDT5uzPST0CG2aYye+JfWV/raAIqBVd8BUFtWIgMAx2LoDEfwrPq93cTHw73wRhJX9i6jWWvw/z/w8Cw+HeFRDewLx/5Ufw8yOAAW2vhxs+BL8a/Hffmg9zRpulNAFhMOxz2P83rPwAsgt+KY5oAn3uhy43g3/w2a9pGPDNWNj8jfl3b+xCiD7Xve/DCc7kax6v4R02bBhJSUk8/fTTHD16lM6dOzN//nzHRrYDBw7gU+S31OzsbCZOnMiePXsIDQ2lf//+fPHFF6UmuyIi1cHm+BTu+Hy1YxqajwXOa1GX/h3qc1X7ekQpyfWck/vMZNc30GzAXxbh9c2Pmvs8ZK7GbZhpruiGxhQmssUS24Lb0hK6vpPg8Fozgd70NZzYC4ueg8X/hnOvMksnzukLvh7/Z985Jw+Y7wPM92hPdgF6joOQujD3DtgyD7JOwPDp5i8AVUVuJuxaCE17m39+7mIY8OMDZrLrGwgjZkLzC83Nkr3vg9X/hb/fgZQD8PO/4PdX4Px7ocfYM/+8lkw2k10fP3NlvQolu87y+ApvVaQVXhGpSn7ceJh/zdlAdp6NZnVDGHthC65qV4/oMCW5JTIMyM8u2wqWK2z5Hr66Bep3hjt/r5zXPJOcdDMBXPu5+TG0XVh9c8W3y81Qp7nHwiszwzDrRnctgCbnw60/l1ymsec3mHWTOd62fie46RsIja70cIvJzzU3LC79D6QnmD/74dPNshV3WPC02SrM4gNDv4A2A04/Jy/LrCX/8w1IOWjeFxQBve4yv0LqFD9//UyYd5d5fN075t+bKsarShqqIiW8IlIV2GwGUxbs4O0luwC4+Nxo3hzRhYhgJ2vwqrvsFIhfC4dWw6FVZq1n5jEYMq1yWlct/jcsfcVMCK57x/2v54zEbbDuC1g/A7KOF97f/GJz1bf1AHODXVW06Wvz43TfALjrzzOvLh5eB1/eaNY612kBt3xr1ptWNms+bJxttqOz91+2+Jr12n5B5mavjkNc+5rLpsLCZ8zjsiSm1jzY+JW5CfDYTvM+/1rQfQycP9789GHfn/D5dWDLM2t/+z7j2phdRAlvBSnhFRFPS8vO46HZG1i41exic+dFLXj0qtb4+njXzmiXs+ZD0taC5Ha1mdwmbQdK+Kes7fUw9DP3xzRzBGz/2dw41etO979eeeTnmDGu/Rx2L8Hx8wquDS37QfOLoMXFENHIo2E6ZByDd3qYv7hcOhEufuTsz0neBV8MMj+2D60HN38D9c6+Ud0lbDbY+p1ZApC8w7wvNBYuegTa3QDf3WMOJgEzgbzsKddsKlz7OXx/n3l8xfNmfW6ZY7aa09L+eLWwy4hvgNlHetuPZolI2+vgxmlVdgOkEt4KUsIrIp60/1gGt3+2mp2J6QT4+fDy4A4M6lJFEpHKlna0yMrtGnMlNy/j9PNqN4OG3aFRD/Nj3V8eMTfoPLTJ/TG+3t78iPjWn6FZH/e/XkWd2A/rp5sfb6fGF3+sTpyZ+Da/CJpd5Lnd+N/eZdY1x7SFO34v+7CO1CPw5WBzA19gBIycZdbPuothmBsGFz9f2CM4uDZc8BD0GAcBIeZ9NqtZi/znVPP7VteYwzMqUm+89Qf4apQ58KTPg3BFOQdoGYZZZ7z0VbPTiF3DbnDrT5VXGlQOSngrSAmviHjKn7uSuWf6WlKy8ogND+SDW7rTuXGkp8OqfMd2w4xhhR+5FhUQBg27mslto+5molu0ZjM71eyagAH/2uXees6sE/ByM/P4sQNmTaS3sFlh3zKzBnbv72ZZgGErfk69Dmb5Q/OLzcQxMNT9ce1ebK7UYoGxC6BxD+een3USZg6HA3+bZQQ3fgqt+7s+zn3LYNHzhUliQJi5Eez8e0r/e7Bhtrkia80xk/kRM8tXerF3qZnYW3Ohyy1w7Vuu6Yu770+zxjc33SwJCo2p+DXdSAlvBSnhFZEzyk6BVR+bu99j27nkkoZhMO2vfbzw01asNoPOjSP54JZuxIZX0fpKd7O3orL4mIlBw26FCW7UuWfvufp2T0jeDiO/gnP7uS/OvX/AZwMgsgk8WAmrye6UdRL2/2Umv3t+N0tHivLxM3+5sK8AN+rh+jZguRnw7vlwcr+5kerql8t3nbwscxrbjl/Mv0MD3zTbtLlC/BpY/IKZmIOZVPccZ3bcKMuK+KHVMGukuZktpC4M/RyaXVD21z+8DqYNMJPS1gNgyGfe133DRbyqLZmIiFex5sNXo2HPEvjzTbh9EUSdU6FL5uRbeWreZr5afQiAwV0b8e9B7Qnyr8GN9NMLJnD2uB36/8f55zfsaia88Wvcm/AmFIwUrtfRfa9RWYIjzZVQ+2poWoLZMs2+AnzygLmaeXC5OfjBv5Y55ez88a6r8Vwy2Ux2IxrDZRPLfx3/YLON1g8PwPov4fvx5oa2Pg+WfyU0YQss+bdZ3wrmLwDdboUL/2Vu9CqrRt1h3BIz6T2y3twc1v9Vc9PY2STvNFd2c9PNiXOD/1tjk11n6ackIjXWX7uTOZaeS2iQH+FBfoQG+hMa5EdooPlV4gax/000k10wR73OGAq3Lzy9pU8ZJaZlc/eXa1mz/wQ+FniifxvGXtAci5eN7XS5jETztrwfqTbsZtaAxq91XUwlOXWkcHUSFls4HhnMARl7fjeT371LzQlwC54yjwe9X/E+s/FrYfm75vE1UyreT9fXD65724zrz6mwcJKZvAdFOn+tnLSCFV3DXDHuOBwu+b/yd4KIaAhjfoHv7oV/5sKPD0LiVug3ufQENiXeLPXIPGa2wBs+o+p22KiClPCKSI00b108D85ef8ZzQgJ8CbMnwEH+DMhfwLgT7wHwY5NHuTjxC8KO7yZn+gj8R3+HT4Bz//hsOpTCHV+s5khKNuFBfrw1sisXn+vh/qFVRXqSeRsaW77nN+xq3savMTfluOsXCPtGpdhK6gbgSbWbQbdm0G20+TNd8ynMf9zsk/v+BeZY5eYXlu/a1jz44X6zhrj9jXDula6J2WIxN3PViob/PWkmvBXR9jq49EmIblXx2AJC4MZPzJKdJS+YE9GSt5u1s8G1i5+bebygA8VBqHuO2YEiSCWXzlDCKyI1zq7ENB6fa67Mtakfjo8F0nPyScvOJy07jzyrubUhM9dKZq6VBHLobtnG6IA3wQKv5d3IWzs6c66lLl8HTCI8fjnzXhjCx1GPEBcTRlx0KOfEhBIXHUqzqBAC/U4vTfhufTyPfr2RnHwbcdG1+GhUd1pEV8KGoPKw5puJw9nqZl3JXtJQq5wrvLHtwcff7D17cr97erLm5xa0RKN6rvCeicUC3W+Dxr1gzq1mK67PBsLFj8LF/+f835W/3zZXy4Nrw1UvuT7e3uOhcU84sqH812hynuv/nC0Ws+VaTGuYe6eZkH90GYyYXdh3OCcdpt9oJsPhDeGWee6d2lZNKeEVkRolMzefe6avJSvPSu+4unwxttdppQs5+VbSs/MdSXDusf20/2k8ATlW9sVeSXDrx7gzO5/9yfWYfOT/eCHjOa73WcrOhHq8c/j6YtfysUCTOiGOBDguOpRdSel8uHQPAJe1jmHq8M6EB1XRYRJJ281pV0ERZnuoyurHmWFf4S1nwusXaCYnh9eaq7zuSHiTd5i75AMjzE1rNVFsO7jjN/j5UbNW9veXze4Fgz8uPgb4TI7tht8Kktx+L7qvq0bjnuZXVdRmIIxtBjNHwvE98PHl5upv84tg9s3m3+HgOuZAjcjGno7WKynhFZEawzAMJs7bzI6EdKLDAnljeJcS63QD/XwJDPWlbmiguWv8+3sg5zjU60iz2z7jHntvTQC6YV0RCr/8i0f8v6JTpy78Sh92J6WzOzGdtJx89h3LZN+xTBZuTSz2OnddHMcj/VpV3WESRzYW1Awmm99nHa+clSXDgPQK1vCCWdZweK1ZG9p+sGtiK8qxYa29+0omvEFALbj+HbN7w48Pwf4/4b0+Zl3v2TYMGoa5sSw/G1pcCp2GV07MVVG9DjBusTmm+sDf5v6Aeh3NjW3+teCmr11TSlFDKeEVkRpjzupDzF0bj48F3hrRheiws7RUstnMBvgJm8wawBEzCxvJF+Hbaxyc2AvL3+HK7ZO48tYfoXEfDMMgKS2HXQXJ7+6kDHYnpZOalcdtFzTnus4N3fROXeDQavjyBrMFm13a0cpJeLNOmCNNwfy5l1fDbmb7OHdtXLNvWKsJ9btl0XGo+TOfc6tZ2zxjqNnB4fJnSh8cse5LsxOEXzAMeL1m/+IA5ur2qO/hpwnmSOgj683SnOHToVE3T0fn1ZTwikiNsPVIKk99Z67IPXxlK85rUYZ+mUtfga3fm+M2h00/89jVK583k97tP5ujZsctwlK7GTHhQcSEB9E7zotq7vYtM4c+5KabNZrpieZ7S08AKiG5s6/uBkVWrM9rw4IE4ch6sw7Z1e2bqnOHhvKqG2d2LfnfU+YmrL/fNnv73vgJ1Gle/Ny0BHMjGcBlT57+eE3lF2AOkqjXAVZ/av5s4i71dFRer2oORxYRcaH0nHzunb6WnHwbF58bzd0Xx539SVu+g99eNI8HvA5Nep35fB9fc5d6vY5mCcD0oWYjf2+zc2Fhn8/mF8HNcwvrX9MTz/hUl3G0JCtnhwa7ui3N6Vd5meaGH1cyjCIJr1Z4i/ELhP6vmL8kBkWaZSUfXASb5xY/75dHzU8Q6neGXnd7ItKqy2KBXnfCvcvN+l6pMCW8IlKtGYbB43M3sSc5g/oRQbw+rDM+Z6uZPbLRLGUAOO9e6HJz2V4sMBRGzoawBmaCNWe02W7JW2z9wRzJmp8NLfuZU8oCQyGsnvl4+tHKicMV9btgbrBr0Nk8jl9TsWudKu2IWdNs8YXoNq69dnXRZgDctcz8lCAnFb4eY9br5mXBtp9hyzzz53ftWxqeIG6nhFdEqrXpKw7ww4bD+PlYeHtkF+rUKqWW0C490SxJyMuEuMvhiuece8HwBjBylrnJZM9v8NPD5mpgReVlwdrPYcHTkPBPxa93qo1zzAlytjyz1+iwL81pVVCYeFbWCq+rEl4o3o/Xleyru1Hnqvn/mUQ2hlt/ggsfBiywZprZduunh83He98H9avBlDqp8pTwiki1tTk+hed+2ALAo1e1olvTs0xDy8+B2bdA6iGzufuNn5Rv5al+J7jxv+ZEprWfwV9vlSP6AqmHYdFzMKUtfH8f/PmGuQP+67FmOydXWPMZzB0HhhU6jYTBnxTfZGQvLUirrBXeCvbgLcpex+vqjWv2gROq3z07X3+4/Gm4Za75Z5q4BdIOQ+3m5mhikUqghFdEqqXU7Dzumb6WXKuNvm1iGHdhizM/wTDMndEHl5t9VUfMguDI8gfQ6mpzTCiYq7Jbf3Du+QdXwde3wdQO8Mdr5sfnEU3g3KsAAzZ/DW/3MJPglEPlj3P5e+aEKwzoPhaue+f0JN+e8FZaDW8Fe/AWZU94E/4xV8ld5WiRlmRSNnGXmSUO51wBgeHm3zX7pwgibqaiGRGpdgzD4NE5GzlwPJNGtYN5bUhnLGdrd7TifbNFksUHhnwCUS0rHkivu+DYLrM11jfj4LZfoEGX0s+35pmb5Za/W/wj+KZ9zGu16m8mo0c2wOJ/w85fzTKHDbPMZPXCCc4liUtfhcXPm8e974Mrni+5LZQj4U0o+7Urwv46rkh4wxuaq4oZiWYZgqsGD6hDQ/mExcLNX4PNWrmT+6TG0wqviFQ70/7ax/x/juLva+GdkV2JCDnLFLNdi+DXJ8zjK1+Ac/q6JhCLBa562bxefhbMGF7yamxGMiz9j7ma+81YM9n1DYDON8GdS2HMz9D22sKV1/qd4Kav4Lb/QdMLzGlfK96DNzqZ5Q9ZJ84cl2GY59mT3UseLz3ZBQ8kvC7q0gDme3KUNbiojjcn3ZyGBRCrhLdclOxKJVPCKyLVyvqDJ5n881YAnuzfhk6NI8/8hORd5u5xwwadb4bz7nFtQL5+cOOnENPW7HIwYxjkpJmPJfwD340363MXv2Du/A+NhUufhIe2wPXvmsltaZr0glt/NMeNNuhqbrT74zUz8V36qpmYncowYP7j5nlgJrqXPHbmhv9hBYlnTirkZpbv5+AMe8JbkaETRbl641riFsCA0HruG4MrIi6lkgYR8biTmbkcTc3m3Jiws7cMO8t17p2+ljyrQf8O9Rjdu9mZn5B10mzDlZ1itk4aMMU9k56Cws12ZR9dbo6jnTnCvH/fH4XnNOhi9iJtN6j0qVQlsVjM2sgWl5pDLxa/YCZki583yzQufBi6jTE7Cdis8OODZhkEwDWvQY/bz/4ageHgF2S2K8tIhIBmZY/PWTaba2t4oUjC66KNaypnEPE6SnhFxKMSUrO54d2/iD+ZRXRYIH3bxNC3TSx9zokiyL/sH3vabAYPf7WB+JNZNK0bwkuDO565btdmM8sHju006zyHfVmxqV5nE9nE3Ag37ZrCRNfia5Yq9LrbrC2tSLJtsUDra8xNbZvnwm+TzY/d5z9mdom4+FFzgtqmOWad8nXvQOeRZb92aAycPGBOx7IPonCHrONmtwhw3Qpvg4KE9/hus9wjuHbFrqeBEyJeRwmviHhMZm4+Yz9bRfxJc/d8UloOM1ceZObKgwT5+3Bhy2iuaBPLpa1jiA47czL60R97WLQtkQA/H94Z2ZXwoLPU7e78H+xaCH7BMGKm61YTz6RRNxgyDZb8G8653FxdPdO44vLw8YWOQ6Dd9bB+Ovz+CqTGmw3/AXz8YPDH5kqyM0LrmQmvu+t47eUMwXXMdlauEFLHbIF1Yi8cXmeuiFdEgr1Dg1Z4RbyFEl4R8QirzeD+mevZHJ9KnVoBfHXneRw+mc3CrQks3JLA4ZRsFmxJYMGWBCwW6Nw4kr5tYrmibSwtY0KLrd6u3necV341R8c+M7At7RtGnD2ADTPN2+5jzlwn62qtrjK/3M3XH7rdCh2Hw+pPzJrd3Awz4S7P6zuGT7g74bV3aHDBhrWiGnYzE974NRVLeG3WwsEf2rAm4jWU8IqIR/z7p60s3JpAgJ8PH43qxjkxYZwTE8ZF50bz7LXt2HIklUVbE1m4NYGNh1JYd+Ak6w6c5D+/bqdJnRAubxPDFW1iOScmlPEz1mG1GVzXuQEjezY5+4tnnYTtv5jHnYa79X16nH8QnH8P9Bhrbmor78f5jvHCbk54HfW7Lt4M1rCr2bs4fl3FrnN8r/lz9AuGunGuiU1E3E4Jr4hUus//3scnf+4F4LUhnU6bgGaxWGjXIIJ2DSK4//KWHE3JZtG2BBZtTWTZrmQOHM/k0z/38emf+/CxgM2AuOhaTB7U4ez9dgG2zANrjtk5oV4NGWvqF1ixGuXKak3mzhVegPjVZqeK8tZL2yesxbZVay0RL6KEV0Qq1ZJtiUz63vxI+JF+rRjYqYG5apZ2BJr2LvE59SKCuKlXU27q1ZTM3Hz+2JnMwi0JLN6WyLGMXIL8fXj3pm7UCizj/9I2zDZvOw5zT1eG6she0pBWSTW8rhgrXFS9juYmwfQEc1xzRMPyXUcdGkS8khJeEak0/xxOYfyMtdgMGNq9EfdcEmd2S/j8Oji5H8YuOOskrJAAP/q1q0e/dvWw2gw2xacQGexPs6haZQvi+F448BdggY5DK/6maorQSippcAydcHHCGxBirugnbILDa8uf8No3rMWqQ4OIN9HgCRGpFEdTshk7bTUZuVZ6x9Xl3/byg4MrzGQXYMUHTl3T18dC58aRZU92ATZ+Zd62uATCGzj1ejWaY9NaontfJ8NNCS+4ZgCFY4W3hpTCiFQTSnhFxO0ycsz2Y0dTszknJpT3bu6Gv2/B/37++bbwxC3fuTehMgzYOMs8ru6b1VzNXlObkWiuyruLu1Z4oeIDKDKSzdIbMGt4RcRrKOEVEbcy24+t45/DqdStFcCnt/YgIrigv6rNBlu/N48Dw8GWB2s+c18wh1aZwxj8a0HrAe57nerInoDa8s3hEO7irhpeKNy4dnhd+ZJ2++punRYQGOa6uETE7ZTwiohbPf/jFhZtSyTQz4ePRnencZ2QwgcPrjBXzALDod9k877Vn4A13z3BbChY3W0zEAJD3fMa1ZWvP4TUNY/dVcdrs0Jmsnns6i4NANFtzHZiOalwbJfzz9fACRGvpYRXRNzm0z/3Mu2vfQC8PqwzXZuc0gPWXs7Qqr+5gSwkCtIOw/afXB9Mfg5s/sY8VjlD+diT0LSj7rl+5jEwbIClMLl2JV+/wiEjh8tR1mBf4dXACRGvo4RXRNxi4ZYEnv9xCwCPXd2a/h3qFz/BZjNrdsEcc+sXaE4GA1j5kesD2vk/yD4JYfWh+UWuv35N4OjF66Y6a/vKca0oMzl1B0c/3nJsXDuqFV4Rb6WEV0RcbnN8CvfNXIfNgOE9GnPnRS1OP+ngckg/apYzxF1q3td9DFh8YN8fkLjNtUHZyxk6DtXAgPJy9/AJd9bv2pV341peNiSb46upp5ZkIt5GCa+IuNSRlCzGfraKrDwrF5wTxfPXty95+tk/88zb1tcUTgCLaGSWNwCs+th1QWUehx2/mscdVc5Qbo7WZG5OeN3RocHOnvAe3Qj5uWV/XtI2c8NecG0IL2cPXxHxGCW8IuJwMjOXWSsPMHftIZbtTGZnQhopmXkYhlGm56fn5HPbtNUkpObQMiaUd2/uWth+rKii5Qxtry/+WM9x5u2GmZCdWv43U9Tmb8wOEPU6qp1URYS5efiEO3vw2tVubiat1lxI/Kfszys6cELT+US8jiatiQgAuxLTuW3aKg4czzztsQA/H2LCAokNDyImLND8sh8X3EaFBvLo1xvYeiSVqNBAPrm1B+FB/iW/WEnlDHbNL4aocyF5B2ycXZgAV4S9nKHTiIpfqyZzbFrz4hVeiwUadIXdi8w63gZdyvY8DZwQ8WpKeEWEv3Ylc9eXa0jNzqdBRBDNo2uRmJpDYloOKVl55ObbOHQii0Mnss56rSB/Hz4+tf3YqUoqZ7CzWKDH7fDLo+bmtR63V2xFLXkXxK8Giy90uLH815HqUcML5sa13YvMOt4eZXyOY8Oa6ndFvJESXpHqJj8X1nxqfvwcd9lZG+R/teogT3y7iXybQdcmkXw4qjtRoYVJaHaelaS0HBLTsh1JcEJqNolp5nFiwfHxjFz8fS1MHdaFzo0jS3/BM5Uz2HUaAYueMzcJ7fujYl0V7JPVzrncvSuHNUFldWlwRw/eopzduGYYRVZ41aFBxBsp4RWpbpa/CwufMY99A8xksdXVcO7VEFG42cZmM3jl1+28//tuAAZ2asB/buxIkH/xDgZB/r40rhNy5hVbIDffRr7NRkjAWf634ihniDi9nMHxouHQcRis/i+s/LD8Ca/NZpZFgHk9qRj7Lww5KZCXBf7Brr1+RlLB60S79rqnalCQ8CZtg5y0s09NSzlovmcff4hq5d7YRMQttGlNxJ3++bbw4/vKYLOak8rAbNxvzYVdC+Gnh+H1tvDBRfDbS2QfWMu909c4kt37L2/Jm8M7n5bsOiPAz+fsyS4UDpto3f/0coai7LW7236GlPjyBXXgbzh5wKwVbn1N+a4hhYIiwC/IPHZHWYOjhtfNK7xhsRDeCDDgyIazn29f3Y1uDX4Bbg1NRNxDK7wi7pJ5HL4eC4YVYlZCdCWsDO1aBCf3m4nJg5vNlantP8P2X+DgSvMf9yMbCPrtRZ4y6nCBf1ea9bmRPpc2rZyd5zYrbPnePG436MznxrSBphfA/mVmicZlE51/PXs5Q9trXb8aWRNZLOYq78kDZnJau5nrrm3NNyetgftreMEsa0g9ZG5ca3bBmc/VwAkRr6cVXhF3SdpuJrsAKz6onNdc/V/ztvPNEBBiJtkXPARj/wf/2smRS17jd59eZBqBNLAc5ybfhfRZfhe80gJm3wLrZ0LGMffFd6BIOUOLUsoZiup5u3m75jPneqaC+ZG7fXVd3Rlcx13jhTOTAcPcXBhSx7XXLokzE9eObjRvtWFNxGsp4RVxl6Qik8I2zISsE+59vRP7C4crdL/ttId/ize4YkljRmc+wKDQLzk64AvzvLD6kJsOW7+HeXfBa63cV4axpeC6ra8p20fDrQeY8WUkmvE5Y/vPkJMKEU2gSW+nQ5VSuKtTQ9GxwpUxCc+xcW3d2c9N0AqviLdTwiviLknbC4/zMmHt5+59vTXTAKOgj+05xR764u993DZtFek5+ZzXog6z772Eet2vhQGvw4StcMdvcPH/QUw7c0DDL49CboZr4ytWznB92Z7j6w/dxpjHKz907vU22DerDQUf/a/OZdzVqSHdvmGtkjpp1O8MWCDlQOFrlyQ7BU7sM49jtcIr4q30r4CIuyQXJLyNe5m3Kz8y6xTdIT8X1n1hHve43XG31Wbw3A9beOq7f7AZMLhrIz6/rReRIUVWVy0Ws/n+pU+YiW/tZuZq219vuzZGZ8sZ7LqNBh8/OLgCjmws23PSE83NegCdNErYpRwJr4tLGhwrvJWU8AaFmwNOAA6foT1ZQsE0tvBGlVNqISJuoYRXxF3sK7yXPml2TEg5CNt/cs9rbf3ebOkUVh9a9QcgIyefO79YzSd/7gXgkX6teHVIRwL8zvCfvV8AXF7Q0uzPN1y7iudsOYNdWD1oc615vOqjsj1n8zdm/XTDbhDV0qkw5SzC3LTCm1FJHRqKKksdrwZOiFQLSnhF3CE7FVILWmnV71j4sfzy99zzevZWZF1Hg68fR1KyGPL+3yzcmkignw/vjOzKvZeeg6UsnRjaDTL7lOZlwG8vuSY+m7Vw2MTZujOUxN6ibOOcstVCb5hp3mqzmuu5a9OaoyWZm3vwFuWo4z1TwmvfsKb6XRFvpoRXxB2Sd5q3obEQXNssM/DxM/vCHl7v2tdK2AL7/zR3t3cbzcq9x7n+nT/ZciSVqNAAZt5xHtd0rF/261kscOUL5vGaaZC0o+IxHlhufmQdFAEtLnH++U3ON+uL87Ng/Ywzn5u41Wy/5uMH7W4oV7hyBm6r4fXECm+RiWuGUfI59g1rqt8V8WpKeEXcwV6/a68RDK9fuLK54n3XvlbB6q6tVX9e+jOVYR/+TUJqDi1jQvn2nj50bVLb+Ws262OWRhhWWPRsxWO0lzO0crKcwc5iKWxRtvIjc4JaaTYU9N5t2Q9q1XX+teTM7AlpRuKZ/xycVdk1vGAmsb4BkHW8cGNaUdZ88xdK0AqviJdTwiviDvaWZNGtC+/rdbd5u/kb162O5aQ7EryJh3rx/u+7MQwY2r0R397b56zjgM+o7ySw+MC2H2H/X+W/TkXLGew6DDU3vJ3YC7sXl/5am+aYx9qs5h61CkoObPlmougqGZXcpQHMSX/2lduSNq4d2wXWHAgIhdrNKy8uEXE5Jbwi7mAvAyg6Xa1RN2jUwxz3a6+5rSDbxjmQm8Zeox4zk5tRp1YA79/cjVdu7ERoYAUHKUa3gq6jzOP/PVX6R75nU9FyBrvAUOg80jwubfPavj/M2umgSDi3X/lfS0rnF2BuwgTX9uK1X6syE14osnGthITXPlI4tp1a24l4uSrxX/A777xDs2bNCAoKolevXqxcubLUc/Py8njuueeIi4sjKCiITp06MX/+/GLnvPjii/To0YOwsDBiYmK4/vrr2b59eylXFHEDxwrvKeOEe91l3q76GPJzKvQSR05mcvB/bwLwZf7lXNwqlvkPXshV7etV6LrFXPIE+NeC+NWFq7TO+udb87b1gPKVMxRlb7m249eSP4K2lzO0v8FcvRP3cPXwifzcws2IlVnDC8XreE+VYE94Vb8r4u08nvDOnj2bCRMm8Mwzz7B27Vo6depEv379SEws+SPfiRMn8sEHH/DWW2+xZcsW7rrrLgYNGsS6dYXTcn7//Xfuvfdeli9fzoIFC8jLy+PKK68kI8PFjfRFSpKXBSf3m8dRpyS8ba+DsAbmx7eb55b7JX7YcJhHpv6Xpnl7yDb8adXvLj69tQcxYUEVCLwEYbHQ+z7zeOEk58f72qyFE9LaXl/xeKLOKejha5y+Sp6bUTjYoqPKGdzKvgqb5qKE117O4ONnrs5XJvsK75H1p/fJtq/wqn5XxOt5POGdMmUK48aNY8yYMbRt25b333+fkJAQPvmk5I98v/jiC5544gn69+9PixYtuPvuu+nfvz+vvfaa45z58+dz66230q5dOzp16sS0adM4cOAAa9aUYWa6SEUd2wWGzfyH+9SPZ339CzdfrXjP6TKBlKw8Hpy1jvtmrmOQ1fxkI6/NIIZe1LFsLcfKo/d95kaiE3thzafOPffA364pZyjK3qJs7efmLxd2W380W6nVbg6Ne7rmtaRkoQWfIrhqhdfeg7dWdOWXDtRtCQFh5jTEouPAoUgP3o6VG5OIuJxHE97c3FzWrFlD3759Hff5+PjQt29f/v777xKfk5OTQ1BQ8VWs4OBgli1bVurrpKSkAFCnTslTcnJyckhNTS32JVJu9oET0a3M7gKn6jYG/ILM1lkHlpf5sn/tTubqqUuZt/4wdS2pXOdnlv6EXXCXK6IuXWAoXPq4efzbS+ao1bL6Z55564pyBrtzr4KIxuZH4EVXyTcWlDN0Gl7yz11cx/6LnKs2XzpaklVy/S6YCXaDzuZx0Y1raQlmIm7xgZg2lR+XiLiURxPe5ORkrFYrsbHFa7ZiY2M5erTkpub9+vVjypQp7Ny5E5vNxoIFC5g7dy5Hjhwp8XybzcaDDz5Inz59aN++5DqsF198kYiICMdX48aNK/bGpGYrmvCWJKQOdBxqHi9/96yXy8m38u+ftnDTxys4nJJN07ohfHfBfvyMXKjfubAG0Z26jDJXwrKOw7KpZXtO0XKGinRnOJWPL3S/zTy2b15LPQJ7fjOPOw5z3WtJyVw9Xtie8FZmS7KiSpq4Zi9nqHsOBFSg24mIVAkeL2lw1htvvEHLli1p3bo1AQEBjB8/njFjxuBTysdg9957L5s3b2bWrFmlXvPxxx8nJSXF8XXw4EF3hS+eYs2HlPjKeS1HD95SEl4obFG27Uc4eaDU07YdTeW6t//koz/2Yhgwomdjfr6vD412Ffx97jG2clYzff3gioJ+vMvfLdvPsmg5Q/OLXRtP11Fm/9TD6+DQGrMVmWGDxudBHbWPcrswe0mDq1Z47R0aKnnDml1JG9e0YU2kWvFowhsVFYWvry8JCcXrwBISEqhXr+Sd5tHR0cybN4+MjAz279/Ptm3bCA0NpUWLFqedO378eH788UeWLFlCo0aNSo0jMDCQ8PDwYl9SjRzfCx9dAq+3hb1/uP/1HCu8rUs/J7atmQQaNnOQwinSc/J577fdXPvWn2w7mkbdWgF8NKo7L97QkVqHlpr1tIER0H6wm95ECVr1hya9IT8blvz77Oc7yhkGuq6cwa5WVOEUtZUfFnZnUO/dyuHYtOaiFV5HD95KHCtclH2FN+GfwrpwbVgTqVY8mvAGBATQrVs3Fi1a5LjPZrOxaNEizj///DM+NygoiIYNG5Kfn88333zDdddd53jMMAzGjx/Pt99+y+LFi2neXCs+NdbOBfDhJYX/eJU2sMBVrHlwbLd5HH3umc89r2CVd+1nkJuBYRis2X+cR7/eQM9/L+Tl+dvItdro2yaGXx+6iCvaFqx+rfqvedt5BATUcs/7KInFAlc+bx6vn1G4oackxYZNXO+eeOyb1zbNgcR/wDfQfa8lxYVWsxXe8IbmaxtWOLLRvM+xYU0Jr0h1UMHO9BU3YcIERo8eTffu3enZsydTp04lIyODMWPGADBq1CgaNmzIiy++CMCKFSuIj4+nc+fOxMfHM2nSJGw2G48++qjjmvfeey8zZszgu+++IywszFEPHBERQXBwcOW/Sal8NhssfcXcZIVhfqyenWJ+BO5Ox/eCLc/sXRte+qcKgDn6tnZzOLGXP795i0lHzmdnYrrj4bjoWtx5cRxDujUq7MBw8iDsKOg73X2sm97EGTTqbrYX2zIPFj4DN39T8nkH/jY3/ARFur6cwa5hN7OG+ch68/tWV0FwOcYoi/PsK7w5KeaKqH8F/7+aXrDCW8tDK7wWCzToCjt+MTeu1e8Ix3aajynhFakWPF7DO2zYMF599VWefvppOnfuzPr165k/f75jI9uBAweKbUjLzs5m4sSJtG3blkGDBtGwYUOWLVtGZGSk45z33nuPlJQULrnkEurXr+/4mj17dmW/PfGEzOMwcxj89iJgmMMKRhaMmz2yvvwTw8rCUb/b8oztlWw2gz92H2OO3zUAxG79jF2JqQT5+3Bjt0Z8fdf5LJxwMUO7Ny7ebmztZ2YZRLMLz76C7C59nwEff9i1EHYvKfkcVw6bKI3FAj3vKPy+0wj3vI6cLijCXFEH17Qm8/QKLxTfuJa4xfzvLCTKszGJiMt4fIUXzFrb8ePHl/jYb7/9Vuz7iy++mC1btpzxeoY7Exqp2o5sgNm3mIMf/IJgwFTzo//8HDNJyzphbhKr3dQ9r++YsFZy/e6RlCzmrD7E7FUHiT+ZRSiduSowmHN8DvPfC9Lo3ncI4UH+JV87P9fsPQvmZjVPqdPCfP0V78OCp6D50uLJvc1aOADC3SUG7W+AP98wBxac0/fs54trWCzmUJKTB8yyhtrNKna9DA+2JbNr2MW8jV9bvH5XLe5EqoUqkfCKuMT6GfDjQ+amqsimMOxL86NJMMfMxrY1E+Ij692Y8O4wb4usvuZZbSzamsjsVQf4fUcStoLfx8KC/BjUpQ05eSMI2/wJl52cC0EjS7/2th/NlbDQWHPl1JMuerSgjncTbPqq+GaxyihnsPMPhnv+BiyVP7Cgpgu1J7wVXOHNyy7s7ezJhLdBQaeG47sLN7fWU4cGkepCCa94v/wcmP84rC7YzNXySrjhw9PrOet3NhPew+vMEb/uUGSF1zAM3v99D/9dtofk9MKRvL2a12F4z8Zc3b4+Qf6+cPw+2Pwp7FpgJsyllSrYR+l2HW1ObPOkWnXhgodg0bOw6Hmzrte/YCBMZZQzFOXj6/7XkNPZP+qvaKcGe4cG34DKHytcVEgd89OL43tg6w/mfZqwJlJtaElEvFtKPHzavyDZtcAlj8OI2SVvXnJMU1rvnlhsNkgu2OgS1Yq3Fu/i5fnbSE7PJSo0kLsujmPxwxcz+87zGdSlkZnsgvmPbKurzeOVH5R87aTtsO8Pc+pTt9Huid9Z591t7m5PPVQYd7FyBhcOm5CqxzF8ooKdGooOnfB0+YB9ldeaY96qB69ItaGEV7zXnt/hg4sgfrW5MnTTHLjksdI/2m5QUKPnro1rKQcgPwt8A/hmrx9TFpjlDU/0b83fj1/GY1e3pkV0aMnP7VUwHnj9TMg6efrj9tXdc6+GiLN0f6gs/sFw6ZPm8dLXzM2C+/8qLGdo4eZyBvEsR8JbwZIGR/2uhzo0FGXfuAbmpryolp6LRURcSgmveB/DMMfbfnE9ZCabG0vu+A1aXnHm58W0Lb5xzdUK6nczwprxf9+aGyvvujiOOy6Kw9/3LP+pNb/IjC8vA9Z9Ufyx3AyzXhY8u1mtJJ2Gm6tgOSmw9FWzXRlAmwGeL7sQ97LX21Y04a0KHRrsio7pjmmjv8Mi1YgSXvEu2anw1S1mD1jDBp1GwtgFZRsna9+4Bu7px1tQv/vHibrk2wwGdmrAo/3OMF64KIulcJV3xYfmKGS7TV9DTqrZs7fFpS4OuoJ8fAtHDq/8EDYX9OVtq3KGas8xXriiCa+He/AWVa8jWApKjbRhTaRaUcIr3uPYbvjoUnNDiY8/DHgdrn/Xuab39Tubt/ZhBS6Uedhc1d1mrU/P5nV4dUhHfHycqEnsOBSC65ilEdt/Nu8zDFj1sXnc/baq2Ykg7nJocYk5cCPrhMoZagrHeOFqtMIbEFL4S3GsBk6IVCdV8F9PqfIykuHd3jD3DnOTUmXIOgHTh8CxXeZGqdvmmwmgs5tc7HW8Lt64lpadx/5ta83jsHP46JbuBPo52T3APxi6mxMGWfG+eRu/Bo5uNOsJu9zswohdyGKBK54DCv4sVM5QM9gT1IxEc8NmeVWFHrxFXfI4tOpv/gIqItWGEl5x3vJ3IfEf2Dgbfn3S/a9nzYc5Y8z+mOGNYNwSc8Rtedg7Nbhw41qe1cbdX6yhYf5BAMYNuoqIkHImfD1uN4co7P/TbKG2qqDVWvsbzLZJVVX9TubUMx8/6Habp6ORylCrIEG15Zu/kJZXehVLeFtfAyNmVu3/3kTEaUp4xTnZqbDy48LvV7wHKz9y72sueAr2LAH/EPMforAKfPRZbOPa/gqHZhgGj8/dxI7duwi3ZGJYfKjXogK1f+ENCnsE//YS/DPXPO5exTarleTql+HxeGjU7eznivfzCzBLcADSK9CLt2hbMhERN1HCK85Z/Ym5Iz+qFVw20bzvl0dhx//c83prvzBXlAEGvV84Oa28/AIhtp157IKyhqkLd/L1mkOc6xMPgKV2c/M1KqLX3ebt9p/NqXH1OpR/RbsyWSyFwyekZnDFxjX74ImqUMMrItWWEl4pu7zswuTzggfhwn9B55vMbglfj4Gjm137egeWm6OCAS5+zHXT0YqWNVTAV6sP8sYic9DEg50KahijW1fomgA07gENiyS4PW73fEN+kZI4WpOVc/hEXpbZgQSqRh9eEam2lPBK2W2YYa7khDeC9jeaSdiAqdDsQshNhxnDKj5m1O7kQZh9s7nzv821cPH/uea6UNipoQKtyZbuSOKJuZsAuPfSOLrXKlilKm0ssLPOK1jlDQyHDkNcc00RV6voeGF7ouwXZP5dFxFxEyW8UjbWfPjzDfO4931m/R6Yt0M/h7rnmCNmZw6H3MyKvVZuBswaYX7UGdvBLGVwZTuuoiOGy7FxbcvhVO6ZvpZ8m8F1nRvwrytbmaN/wTUrvADtbjA7HwyZBgG1XHNNEVer6HjhqjRWWESqNSW8UjZbv4MT+8xNKl1vKf5YSB0Y+ZX52OF18O0d5W9TZLPBvLvh6CYIiYIRM1yf8MW0Bd8AyD7p9Ma1wyezGDNtJek5+ZzXog6v3NgRi8UCyQUJb5SLVnh9fKDPA3DO5a65nog7VHS8cFVrSSYi1ZYSXjk7w4A/XjePe91VcgJaNw6GTzcTya0/wKJJ5Xutpf+BLd+ZnRSGfQmRTcoddqn8As2kF5zauJaSlceYT1eRkJpDy5hQPrD32s08XrjxxlUJr4g3qGjC6xg6oYRXRNxLCa+c3a5FkLAJ/GtBz3Gln9e0N1z7tnn85xuw5jPnXmfLd/DbZPN4wBRoen754i0LR1lD2ep4c/Nt3P3lGrYnpBETFsi023oSEVzQa9dezhDRBAJDXR+rSFUVVtGE196hQQmviLiXEl45u2UFq7vdx5y9GXunYYUbzH6aAHt+K9trHNkI395lHve6G7qOKleoZebEiGHDMHjsm438tfsYtQJ8+eTWHjSMLDLOOGmbeeuqDWsi3sKxaa2CK7zqwSsibubn6QCkiju4EvYvM0sMzrunbM+55HE4ths2fw2zR8HtCyC6VennpyfBrJGQlwktLoUrX3BN7GdSdMSwYRTbMJOdZ2XfsQx2J2awJymddQdPsnhbIr4+Ft65qSvtG0YUv1byDvPWVRvWRLyFfWU2J8VsMeYffObzT6UaXhGpJEp45czsq7udhkNEw7I9x2KB696BlENwcDlMHwLjFkOtqNPPzc+Fr26BlINQJw6GfAq+7v9racS0Ad8ALNkn+XbJX2zMiGRPUga7k9KJP5lVYvOGyYPac0mrEv5htq/wqn5XapqgSPANBGuO2XGhdlPnnl/VxgqLSLWlhFdKl7jVnPaFxewY4Az/IHMT28eXm90dZo2EUd8Xn8RlGGbZw4G/zR6cI2ZBcG1XvgOHIylZzF0bz67EdHYnpbMnKYMZRkM6+uxlwcL5/Gw7r9j54UF+xMWE0iIqlLiYWvRqXoduTUsp50jSCq/UUBaLWdaQcsAsTyh3wqspayLiXkp4pXTLppq3bQZCVEvnn18rCkbOgY/7wsEV8N29MPjjwvKBFR/Aui/A4gM3fuK2Gth9yRkM+/BvElJzit2/2b85HdnLgKgEGrZqTlx0KC2iQ2kRXYu6tQLMdmNnk5Nm9h8G1fBKzRRWJOF1lqMPr6asiYh7KeGVkp08AJvmmMcXPFT+60SfC8O+gC9vMGt668bBpU/A7sXw6+PmOVc8By2vqHjMJTh4PJORHy0nITWHuOhaDO7WyFy1ja5Fs/0J8PNi+kcl0P+atuV7AXv9bmis21anRaq08rYmy0mHvIzi1xARcRMlvFKyv94GwwotLoGGXSt2rRYXw4DX4fv74PeXzQ1wf78Fhg06jYDzx7sk5FPFn8xi+IfLOZySTVx0LWbdcT7RYYGFJ1gL3lcJG9fKLMnFAydEvI29/tbZTg32DWv+IWrnJyJup7ZkcrqMZFj7uXlckdXdorqOgj4PmsdLXoDsFGjUAwZMdctI0aMp2Yz4cDnxJ7NoHlWLmePOK57sQvGJayf2le+FXD1SWMTbhNYzb51d4VUPXhGpREp45XQr3of8LLN1V/OLXXfdy5+BNteax+ENYdj04pvYXCQxNZsRHy3nwPFMmtQJYca4XsSEl/A6fgGFE9fK0I+3RI6E9wxt10SqM3vCaq/HLSv14BWRSqSEV4rLSYOVH5rHF0xw7eqrjw/c8BFc9y7c9mvhlCYXSkrLYcRHy9mbnEHDyGBmjOtF/Ygz9AYt2o+3PJKV8EoN56jhPerc89SDV0QqkRJeKW7NNLPcoG5LaD3A9df3D4IuN0FkY5df+nhGLjd/vILdSRnUjwhi1h3n0ah2yJmf5OSI4WLysgpLIaKU8EoN5Rgv7OwKrxJeEak8SnilUH4O/P2OedznAXNF1kuczMzlpo9XsD0hjZiwQGaOO4/Gdc6S7EKREcMbKHHaxJkc22VuvAuK1D/aUnMV7dJgs5X9eY6WZPpvR0Tcz3syGnG/DbMg7QiENYCOwzwdTZmlZOVxy39XsvVIKlGhgcy84zyaRdUq25MrsnGtaP2uGzbeiXgFe8Jqy4esE2V/nlZ4RaQSKeEVk80Kf75hHvceb27o8gJp2XmM/mQlm+JTqFsrgJnjehEX7USLI78AiG1nHju7cU0b1kTM/4aCC6YQOtOpQTW8IlKJlPCKaev3cHy3OTyh62hPR1MmGTn53PrpKtYfPElkiD9f3t6LlrFhzl/IXtbgbB2vfcOa6nelpivPxjV7cqyhEyJSCZTwilm7uux187jnnV7RBD4zN58x01axZv8JwoP8+HJsL9rUDy/fxRwb19Y79zz14BUxObtxzTAK+/BqrLCIVAIlvAJ7lpibtvxDoOcdno7mrLLzrNz+2WpW7j1OWKAfX4ztRfuGEeW/oL01mTMb16x5cGy3eRytKWtSwzk7Xjgnzez1DSppEJFKoYRX4I8p5m3X0VCrrmdjOYvsPCvjPl/NX7uPUSvAl2m39aRT48iKXTS6jfMb147vBVse+NeC8EYVe30Rb+fseOGMgtXdgFAIKOMGUxGRClDCW9MdWg37/gAfP3OzWhWWk2/lnulr+WNnMsH+vnw6pifdmtau+IWLblwrax2vo363pVe1bxNxC2fHCzvqd7W6KyKVQ/9S13T22t2OwyCi6q5UWm0GD81ez+JtiQT5+/DJrT3o2byO617A0Y93fdnOT9pm3qp+V8T5kgb14BWRSqaEtyZL2g7bfjSP+zzg2VjOwDAMnvpuMz9vOkqArw8fjerO+XEuLr1wdsRw0g7zVvW7IoUrtc4mvFrhFZFK4ufpAKQS5edARrJZP5eRBKv+a97fekCV7iU7ZcEOZqw4gMUCU4d35sKWbtjVbe/UcGS9uXHtbIMktMIrUijMyZIG9eAVkUqmhNfb2axmtwB7EnvaV7K5mpKRDDkpJV/jgocqN2YnfPrnXt5avAuAF65vT/8O9d3zQo6NaylwYi/UaVH6uTYbJO80j9WDV6Qwcc1Ogbxs8A868/nqwSsilUwJr7ebfiPsXlz28338zL6XtaLM2xaXQqPu7ouvAuati+fZH7YA8PAV53JTr6buezH7xrXD68yyhjMlvCkHzJZKvgFQu5n7YhLxFkGR4BsI1hwzma19lv9W1YNXRCqZEl5vlryrINm1mAlarWgIjS5IaEv6ijInqZ3t4/oqYMn2RP41ZwMAt/ZuxvjLznH/izboYia8R9ZD+xtKP89ev1v3HPDVf0IiWCzmam3KAfMTpbMmvFrhFZHKpX+tvdmmr8zbcy6Hm7/xbCwutGb/ce7+cg35NoPrOzfg6QFtsVRGku4YMbz+zOc56ndVziDiEBpTkPCWYbywvQ+vanhFpJKoS4O3MgzYNMc87jDUs7G40PajaYz5dBXZeTYuaRXNf4Z0wsenklakT924VhpHD14lvCIOZW1NZhjq0iAilU4Jr7eKXwvH94BfMLS+xtPRuMTB45mM+mQFqdn5dG0Sybs3dcXftxL/ip66ca00SQUJr1Z4RQqF2RPexDOfl51i1vqC+vCKSKVRwuut7OUMrftDYKhnY3GB5PQcbvnvChJSczg3NpRPbu1BSEAlV9z4BUBse/O4tLIGwyjSg1cJr4iDfYU37SwlDfZyhsCIs3dzEBFxESW83siaD5vnmsfVoJwhLTuP0Z+sZN+xTBpGBvP5bb2IDAnwTDD2sobSRgynHTXbu1l8zE1rImIKLeMKr2PDmjo0iEjlUcLrjfb+bjZuD65jbljzYtl5VsZ9vpp/DqdSt1YAX97ei3oRHlz1OduIYXv9bu3m4BdYGRGJeIey1vA66nfVoUFEKo8SXm9k36zWbhD4+ns2lgqw2gwemLWO5XuOExrox2e39aR5VC3PBuXYuLah5I1rjvpdTVgTKcbZhFc9eEWkEjmd8DZr1oznnnuOAwcOuCMeOZu8LNj6g3ncYYhnY6kAwzB48ttN/PpPAgG+Pnw4qhvtG0Z4OqyCjWuBpW9ccyS851ZuXCJVXdFNazZb6edlaIVXRCqf0wnvgw8+yNy5c2nRogVXXHEFs2bNIicnxx2xSUm2/wK56RDRBBr38nQ05fafX7cza9VBfCzw5ogu9I6L8nRIJvvENSi5jlcrvCIls6/Y2vIg+2Tp56mGV0Q8oFwJ7/r161m5ciVt2rThvvvuo379+owfP561a9e6I0YpytF790bw8c6KlI//2MO7v+0GYPKgDlzVvp6HIzqFY+Pa+tMfc/Tg1QqvSDF+geYkRzhzpwb7WGGt8IpIJSp3xtS1a1fefPNNDh8+zDPPPMPHH39Mjx496Ny5M5988gnGmRr3S/lkHoedC8xjLy1nmL/5KC/8tBWAR69qxfCeTTwcUQlK27iWebywpZISXpHThRb88nqmOl77Y+rBKyKVqNwJb15eHl999RXXXnstDz/8MN27d+fjjz9m8ODBPPHEE9x0001lvtY777xDs2bNCAoKolevXqxcufKMr/vcc88RFxdHUFAQnTp1Yv78+RW6ptfY8p35cWFse4ht6+lonHbweCaPfL0BgFt7N+Pui+M8HFEpGnQxbw+fsnHNXs4Q0bha9D4WcTn75LQzJbwaKywiHuB0wrt27dpiZQzt2rVj8+bNLFu2jDFjxvDUU0+xcOFCvv322zJdb/bs2UyYMIFnnnmGtWvX0qlTJ/r160diYsm9HCdOnMgHH3zAW2+9xZYtW7jrrrsYNGgQ69atK/c1vYajnMH7VnfzrDbun7WOtOx8ujSJ5Mlr2mCxVNLIYGfFFGxcy0kxp9nZJW0zbzVwQqRkZ+vUoLHCIuIhTie8PXr0YOfOnbz33nvEx8fz6quv0rp18Q08zZs3Z/jw4WW63pQpUxg3bhxjxoyhbdu2vP/++4SEhPDJJ5+UeP4XX3zBE088Qf/+/WnRogV33303/fv357XXXiv3Nb3CyYOw/0/zuP1gz8ZSDq/+bzvrDpwkPMiPN4d3qdyRwc7y9S/cuFa0rCG5YMJalBJekRKdbbxw1gnzUypQWzIRqVROZx179uxh/vz5DBkyBH//knvA1qpVi08//fSs18rNzWXNmjX07du3MCAfH/r27cvff/9d4nNycnIICio+mCA4OJhly5ZV6JqpqanFvqqczd+Yt037QGRjz8bipN+2J/LB7+ZK6Ss3dqRxnRAPR1QGjrKG9YX3aYVX5MzONl7YnggHRWpwi4hUKqcT3sTERFasWHHa/StWrGD16tVOXSs5ORmr1UpsbPHdurGxsRw9WvL/MPv168eUKVPYuXMnNpuNBQsWMHfuXI4cOVLua7744otEREQ4vho3roIJpZeWMySkZvPwV2bd7i3nNeWq9vU9HFEZOQZQrC+8L6lghVcJr0jJzrZpTT14RcRDnE547733Xg4ePHja/fHx8dx7770uCepM3njjDVq2bEnr1q0JCAhg/PjxjBkzBp8KtOh6/PHHSUlJcXyV9P48KmELJGwGH39oe52noykzq83gwVnrOZaRS5v64Tx5TRtPh1R29k4N9o1rOWmQesi8Tx0aRErm2LRWSkmD6ndFxEOczhK3bNlC165dT7u/S5cubNmyxalrRUVF4evrS0JC8dWAhIQE6tUruTdrdHQ08+bNIyMjg/3797Nt2zZCQ0Np0aJFua8ZGBhIeHh4sa8qxb662/IKCKnj2Vic8M6SXfy95xghAb68PbILQf6+ng6p7E7duGav360V41V/BiKVyrFp7SwlDUp4RaSSOZ3wBgYGnpZMAhw5cgQ/Pz+nrhUQEEC3bt1YtGiR4z6bzcaiRYs4//zzz/jcoKAgGjZsSH5+Pt988w3XXXddha9ZJdlssOlr89iLyhlW7DnG1IVmkvjC9e2Ji/ayNl6+/lCvvXl8ZH2RCWsqZxAplX3TWnYK5GWf/rh68IqIhzid8F555ZWOEgC7kydP8sQTT3DFFVc4HcCECRP46KOP+Oyzz9i6dSt33303GRkZjBkzBoBRo0bx+OOPO85fsWIFc+fOZc+ePfzxxx9cddVV2Gw2Hn300TJf06scXAEpByAgFFpd7eloyuR4Ri4PzFqPzYDBXRtxQ9dGng6pfBxlDeuU8IqURVAk+AaYxxkllDU4evCqQ4OIVC7nlmSBV199lYsuuoimTZvSpYu5k339+vXExsbyxRdfOB3AsGHDSEpK4umnn+bo0aN07tyZ+fPnOzadHThwoFh9bnZ2NhMnTmTPnj2EhobSv39/vvjiCyIjI8t8Ta9iL2doMxD8gz0bSxkYhsEjczZwNDWbFtG1eO66dp4OqfyKjhgOKFihjm5d2tkiYrGYZQ0pByEtASJPmaRoX+HVpjURqWQWoxwzgDMyMpg+fTobNmwgODiYjh07MmLEiFLblHmb1NRUIiIiSElJ8Ww9rzUPXj0Xso7DzXPhnMs9F0sZffzHHl74aSsBfj58d28f2tSvYvXQzjiyET64EAIjzLrdE3th9A/Q/CJPRyZSdX10OcSvhmHToc2A4o+9fyEc3Qgj58C5V3omPhGpNpzJ15xe4QWzz+4dd9xRruDECbsWmclurRhofrGnozmrDQdP8vJ8s1ftUwPaeneyC8U3ruUUlPBo6ITImZ1p45o2rYmIh5Qr4QWzW8OBAwfIzc0tdv+1115b4aCkgL2cof1g8C33H1WlSM3OY/zMteRZDa5uX4+bezU5+5OqOvvGtfg15vdBkfqHWuRsSmtNZrMVqeHVf0ciUrmczqL27NnDoEGD2LRpExaLBXtFhMViAcBqtbo2wpoqJx22/2weV/HuDIZh8PjcTRw8nkWj2sG8NLij4++D16vfuTDhjW5l1iiKSOnCShk+kXUcjIJ/HzRWWEQqmdNdGh544AGaN29OYmIiISEh/PPPPyxdupTu3bvz22+/uSHEGmrbT5CXCXVaQMPT+x5XJTNXHuSnjUfw87Hw1oguRARXj1puoHDjGqhDg0hZ2Fdv005JeO0rvsF1zE9PREQqkdMrvH///TeLFy8mKioKHx8ffHx8uOCCC3jxxRe5//77WbdunTvirHk2fWXedhhapVcVtx1N5dkf/gHgkX6t6NKktocjcrEGXQqPVb8rcnaOGt5TE151aBARz3F6hddqtRIWFgaYU80OHz4MQNOmTdm+fbtro6up0pNg9xLzuAqXM2Tm5jN+xjpy8m1c0iqacRe28HRIrhfd2ty4Zj8WkTMLtZc0nFLDqx68IuJBTq/wtm/fng0bNtC8eXN69erFK6+8QkBAAB9++KFjvK9U0D/fmrVuDbpA1DmejqZUk77/h12J6cSEBfLakE74+FTdlehy8/WH3veZA0Ca9vZ0NCJVn2PTWgIYRuEnVFrhFREPcjrhnThxIhkZGQA899xzDBgwgAsvvJC6desye/ZslwdYIxUtZ6iivlsfz1erD2GxwNThnakbGujpkNzn8qc8HYGI97AnvLY8yDph9rCGwhVfjRUWEQ9wOuHt16+f4/icc85h27ZtHD9+nNq1a1efnfmedHwvHFoFFh9of4OnoynRvuQMnpi7CYD7LmtJ77goD0ckIlWGXyAE1zaT3fSE0xNetSQTEQ9wqoY3Ly8PPz8/Nm/eXOz+OnXqKNl1lU1fm7fNLyps71OF5ObbuH/WOjJyrfRsXof7L6u6JRci4iH2soW0IsMnMpTwiojnOJXw+vv706RJE/XadRfDqPLlDK/+bzsbD6UQGeLPG8M74+fr9L5HEanuHJ0aimxc0wqviHiQ09nKk08+yRNPPMHx48fdEU/NdnQjJO8wuwK0GejpaE7z+44kPly6B4BXBnekfkSwhyMSkSqppPHCquEVEQ9yuob37bffZteuXTRo0ICmTZtSq1atYo+vXbvWZcHVOBsLVndbXQVB4Z6N5RSJadk8/NV6AEad35Qr21W9cgsRqSJOHS9ss0JmcsFj6tIgIpXP6YT3+uuvd0MYgs0Km78xj6tYOYPNZvDwVxtITs+ldb0wnujfxtMhiUhVdup44cxjYNgAC4TU9VhYIlJzOZ3wPvPMM+6IQ/b/CWlHICgCWl7h6WiK+eiPPfyxM5kgfx/eGtGFIH9fT4ckIlXZqZvWHOUMUeDr9D87IiIVph1HVYW9nKHtdWZbnypiw8GT/OdXc4LeMwPb0TI2zMMRiUiVd2pJg32lV/W7IuIhTv+q7ePjc8YWZOrgUA552bDle/O4CpUzpGXncf+sdeTbDK7pUJ/hPRp7OiQR8Qahp5Q0OMYKK+EVEc9wOuH99ttvi32fl5fHunXr+Oyzz3j22WddFliNsvN/kJMCYQ2gaR9PRwOAYRg8NW8z+49l0jAymMk3dFCvZREpG3tim33S/IXeMVZYCa+IeIbTCe9111132n033ngj7dq1Y/bs2YwdO9YlgdUom+aYtx0Gg0/VqDKZuzaeeesP4+tj4c0RnYkI9vd0SCLiLYJrg28AWHPNgRPqwSsiHuay7Oq8885j0aJFrrpczRLeAIIiq0w5w56kdJ76zpym91DflnRrWsfDEYmIV7FYig+fUA9eEfEwlyS8WVlZvPnmmzRs2NAVl6t5rn4Z/rUT6nXwdCTk5Fu5f9Y6MnOtnNeiDndfotHBIlIO9tXctKNFxgqrB6+IeIbTJQ21a9cuVstpGAZpaWmEhITw5ZdfujS4GsUvwNMRAPCf+dvZHJ9K7RB/pg7rgq+P6nZFpByKblxzlDREey4eEanRnE54X3/99WIJr4+PD9HR0fTq1YvatWu7NDipXEu2J/Lxsr0A/OfGTtSLCPJwRCLitRytyYomvFrhFRHPcDrhvfXWW90QhnhaYmo2//pqAwC39m5G37b6h0lEKsCe3KbGm5PWQDW8IuIxTtfwfvrpp8yZM+e0++fMmcNnn33mkqCkctlsBhO+2sCxjFza1A/nsatbezokEfF2YQUJb8IWwACLL4RoA6yIeIbTCe+LL75IVFTUaffHxMQwefJklwQlleuDpXtYtiuZYH9fjQ4WEdewr/AmbjFva0WBj/7fIiKe4XTCe+DAAZo3b37a/U2bNuXAgQMuCUoqz7oDJ3jtf+bo4EnXtuWcmFAPRyQi1YI94c3PLvhe5Qwi4jlOJ7wxMTFs3LjxtPs3bNhA3bp1XRKUVI7UoqODO9ZnaHeNDhYRFzl1g5rqd0XEg5xOeEeMGMH999/PkiVLsFqtWK1WFi9ezAMPPMDw4cPdEaO4ydPzNnPweBaNagczeZBGB4uIC526oqsODSLiQU53aXj++efZt28fl19+OX5+5tNtNhujRo1SDa8X2ZWYxrz1h/GxwBvDu2h0sIi4ll+gOWI464T5vXrwiogHOZ3wBgQEMHv2bF544QXWr19PcHAwHTp0oGnTpu6IT9zk6zXxAFzWOoZuTdU/WUTcIDS2SMKrFV4R8RynE167li1b0rJlS1fGIpXEajP4dt0hAG7s1sjD0YhItRUaA0nbzGPV8IqIBzldwzt48GBefvnl0+5/5ZVXGDJkiEuCEvf6Y2cSCak51A7x57LWWnURETexjxcGlTSIiEc5nfAuXbqU/v37n3b/1VdfzdKlS10SlLjXN2vNcoZrOzUgwM/pvwIiImVTdOOaShpExIOcznbS09MJCAg47X5/f39SU1NdEpS4T0pWHr/+cxSAG7upDZmIuFHRJFclDSLiQU4nvB06dGD27Nmn3T9r1izatm3rkqDEfX7aeITcfButYsNo3zDc0+GISHUWVlDS4ONndmwQEfEQpzetPfXUU9xwww3s3r2byy67DIBFixYxY8YMvv76a5cHKK719ZqDAAzu1lB9d0XEvewlDbWiwUflUyLiOU4nvAMHDmTevHlMnjyZr7/+muDgYDp16sTixYupU6eOO2IUF9mdlM7aAyfx9bFwfeeGng5HRKq7xr0g7nKIu9TTkYhIDVeutmTXXHMN11xzDQCpqanMnDmTf/3rX6xZswar1erSAMV15q41W5FdfG40MeFBHo5GRKo9/2C4Za6noxARcb6G127p0qWMHj2aBg0a8Nprr3HZZZexfPlyV8YmLmS1Gcwt6M4wuKt674qIiEjN4dQK79GjR5k2bRr//e9/SU1NZejQoeTk5DBv3jxtWKvi/tqdzJGUbCKC/bm8jXZLi4iISM1R5hXegQMH0qpVKzZu3MjUqVM5fPgwb731ljtjExf6Zo1ZzjCwU32C/H09HI2IiIhI5SnzCu8vv/zC/fffz913362Rwl4mNTuP+eq9KyIiIjVUmVd4ly1bRlpaGt26daNXr168/fbbJCcnuzM2cZGfNx4hO8/GOTGhdGoU4elwRERERCpVmRPe8847j48++ogjR45w5513MmvWLBo0aIDNZmPBggWkpaW5M06pgG8KujMM7tpIvXdFRESkxnG6S0OtWrW47bbbWLZsGZs2beLhhx/mpZdeIiYmhmuvvdYdMUoF7EvOYNW+E/hYYFAX9d4VERGRmqdCo29atWrFK6+8wqFDh5g5c6arYhIXsq/uXtgymnoR6r0rIiIiNY9LZj36+vpy/fXX8/3337vicuIitqK9d7up966IiIjUTBpuXo0t33OM+JNZhAX5cWXbWE+HIyIiIuIRSnirsa8dvXcbqPeuiIiI1FhKeKup9Jx8ftls9t7VKGERERGpyZTwVlM/bzpCVp6VFlG16Nok0tPhiIiIiHiMEt5qyj5KeHA39d4VERGRmk0JbzV04FgmK/Yex2KBG7qq966IiIjUbB5PeN955x2aNWtGUFAQvXr1YuXKlWc8f+rUqbRq1Yrg4GAaN27MQw89RHZ2tuNxq9XKU089RfPmzQkODiYuLo7nn38ewzDc/VaqDHvv3QvOiaJ+RLCHoxERERHxLD9Pvvjs2bOZMGEC77//Pr169WLq1Kn069eP7du3ExMTc9r5M2bM4LHHHuOTTz6hd+/e7Nixg1tvvRWLxcKUKVMAePnll3nvvff47LPPaNeuHatXr2bMmDFERERw//33V/ZbrHQ2m8HcdYWjhEVERERqOo8mvFOmTGHcuHGMGTMGgPfff5+ffvqJTz75hMcee+y08//66y/69OnDyJEjAWjWrBkjRoxgxYoVxc657rrruOaaaxznzJw584wrxzk5OeTk5Di+T01Ndcn784SV+45z8HgWoYF+9GtXz9PhiIiIiHicx0oacnNzWbNmDX379i0MxseHvn378vfff5f4nN69e7NmzRpH8rpnzx5+/vln+vfvX+ycRYsWsWPHDgA2bNjAsmXLuPrqq0uN5cUXXyQiIsLx1bhxY1e8RY+w994d0LE+wQHqvSsiIiLisRXe5ORkrFYrsbHFJ4DFxsaybdu2Ep8zcuRIkpOTueCCCzAMg/z8fO666y6eeOIJxzmPPfYYqamptG7dGl9fX6xWK//+97+56aabSo3l8ccfZ8KECY7vU1NTvTLpzcjJ5+dNRwCNEhYRERGx8/imNWf89ttvTJ48mXfffZe1a9cyd+5cfvrpJ55//nnHOV999RXTp09nxowZrF27ls8++4xXX32Vzz77rNTrBgYGEh4eXuzLG83ffJTMXCtN64bQvWltT4cjIiIiUiV4bIU3KioKX19fEhISit2fkJBAvXol154+9dRT3HLLLdx+++0AdOjQgYyMDO644w6efPJJfHx8eOSRR3jssccYPny445z9+/fz4osvMnr0aPe+KQ+zlzPc2FW9d0VERETsPLbCGxAQQLdu3Vi0aJHjPpvNxqJFizj//PNLfE5mZiY+PsVD9vU161TtbcdKO8dms7ky/Crn0IlM/t5zDIBB6r0rIiIi4uDRLg0TJkxg9OjRdO/enZ49ezJ16lQyMjIcXRtGjRpFw4YNefHFFwEYOHAgU6ZMoUuXLvTq1Ytdu3bx1FNPMXDgQEfiO3DgQP7973/TpEkT2rVrx7p165gyZQq33Xabx95nZZi7Nh6A3nF1aVQ7xMPRiIiIiFQdHk14hw0bRlJSEk8//TRHjx6lc+fOzJ8/37GR7cCBA8VWaydOnIjFYmHixInEx8cTHR3tSHDt3nrrLZ566inuueceEhMTadCgAXfeeSdPP/10pb+/ymIYhmPYxI3arCYiIiJSjMWoSSPIyig1NZWIiAhSUlK8YgPbqn3HGfL+39QK8GXVxL6EBHj09xgRERERt3MmX/OqLg1Ssq9Xm6u7/TvUV7IrIiIicgolvF4uK9fKTwW9d1XOICIiInI6Jbxe7td/jpKek0/jOsH0aFbH0+GIiIiIVDlKeL2cfbPa4K6N8PFR710RERGRUynh9XKb41MA6Nsm9ixnioiIiNRMSni9WG6+jROZeQA0iAz2cDQiIiIiVZMSXi+WlJ4DgL+vhchgfw9HIyIiIlI1KeH1YklpZsIbFRqo+l0RERGRUijh9WKJqdkAxIQFejgSERERkapLCa8Xs5c0RCvhFRERESmVEl4vlphqT3iDPByJiIiISNWlhNeL2Vd4VdIgIiIiUjolvF6scIVXCa+IiIhIaZTwejGt8IqIiIicnRJeL5ZU0KVBK7wiIiIipVPC66UMwyhc4Q3XpjURERGR0ijh9VInM/PIsxoARIUGeDgaERERkapLCa+XSiyYshYZ4k+gn6+HoxERERGpupTwein7WOHoUNXvioiIiJyJEl4vlZhWMFY4XAmviIiIyJko4fVSWuEVERERKRslvF7KXsOrDg0iIiIiZ6aE10vZV3g1dEJERETkzJTweil7Da+GToiIiIicmRJeL+Wo4VXCKyIiInJGSni9VKJKGkRERETKRAmvF8rOs5KWnQ9AdJg2rYmIiIiciRJeL2QvZwjw8yE8yM/D0YiIiIhUbUp4vZBj6ERYIBaLxcPRiIiIiFRtSni9kDasiYiIiJSdEl4vpA1rIiIiImWnhNcLFQ6d0IY1ERERkbNRwuuFElNV0iAiIiJSVkp4vVBSukoaRERERMpKCa8X0lhhERERkbJTwuuF7CUNquEVEREROTslvF7GajM4lpELaIVXREREpCyU8HqZ4xm5WG0GFgvUDQ3wdDgiIiIiVZ4SXi9jb0lWJyQAf1/98YmIiIicjTImL6MNayIiIiLOUcLrZRxDJ8K1YU1ERESkLJTwehn7WOHoUK3wioiIiJSFEl4vU7jCq4RXREREpCyU8HqZJK3wioiIiDhFCa+X0QqviIiIiHOU8HoZR5cGrfCKiIiIlIkSXi+TqC4NIiIiIk5RwutFMnLyycy1AurDKyIiIlJWSni9iH11NyTAl9BAPw9HIyIiIuIdlPB6EceGNa3uioiIiJSZEl4vorHCIiIiIs5TwutFCld4tWFNREREpKyU8HoRx1hhrfCKiIiIlJnHE9533nmHZs2aERQURK9evVi5cuUZz586dSqtWrUiODiYxo0b89BDD5GdnV3snPj4eG6++Wbq1q1LcHAwHTp0YPXq1e58G5UiSQmviIiIiNM8utV/9uzZTJgwgffff59evXoxdepU+vXrx/bt24mJiTnt/BkzZvDYY4/xySef0Lt3b3bs2MGtt96KxWJhypQpAJw4cYI+ffpw6aWX8ssvvxAdHc3OnTupXbt2Zb89l9MKr4iIiIjzPJrwTpkyhXHjxjFmzBgA3n//fX766Sc++eQTHnvssdPO/+uvv+jTpw8jR44EoFmzZowYMYIVK1Y4znn55Zdp3Lgxn376qeO+5s2bu/mdVI7EVHMlW10aRERERMrOYyUNubm5rFmzhr59+xYG4+ND3759+fvvv0t8Tu/evVmzZo2j7GHPnj38/PPP9O/f33HO999/T/fu3RkyZAgxMTF06dKFjz766Iyx5OTkkJqaWuyrKkpO1wqviIiIiLM8lvAmJydjtVqJjY0tdn9sbCxHjx4t8TkjR47kueee44ILLsDf35+4uDguueQSnnjiCcc5e/bs4b333qNly5b8+uuv3H333dx///189tlnpcby4osvEhER4fhq3Lixa96kC+VbbRzLyAXUpUFERETEGR7ftOaM3377jcmTJ/Puu++ydu1a5s6dy08//cTzzz/vOMdms9G1a1cmT55Mly5duOOOOxg3bhzvv/9+qdd9/PHHSUlJcXwdPHiwMt6OU45l5GIY4OtjoU6tAE+HIyIiIuI1PFbDGxUVha+vLwkJCcXuT0hIoF69eiU+56mnnuKWW27h9ttvB6BDhw5kZGRwxx138OSTT+Lj40P9+vVp27Ztsee1adOGb775ptRYAgMDCQys2mUCialmOUPdWgH4+lg8HI2IiIiI9/DYCm9AQADdunVj0aJFjvtsNhuLFi3i/PPPL/E5mZmZ+PgUD9nX1xcAwzAA6NOnD9u3by92zo4dO2jatKkrw690SekFG9bCq3ZiLiIiIlLVeLRLw4QJExg9ejTdu3enZ8+eTJ06lYyMDEfXhlGjRtGwYUNefPFFAAYOHMiUKVPo0qULvXr1YteuXTz11FMMHDjQkfg+9NBD9O7dm8mTJzN06FBWrlzJhx9+yIcffuix9+kK9hXe6FAlvCIiIiLO8GjCO2zYMJKSknj66ac5evQonTt3Zv78+Y6NbAcOHCi2ojtx4kQsFgsTJ04kPj6e6OhoBg4cyL///W/HOT169ODbb7/l8ccf57nnnqN58+ZMnTqVm266qdLfnytprLCIiIhI+VgMey2AOKSmphIREUFKSgrh4eGeDgeAp+Zt5ovl+xl/6Tn8q18rT4cjIiIi4lHO5Gte1aWhJnOs8KqGV0RERMQpSni9RGKauWlNNbwiIiIizlHC6yUStcIrIiIiUi5KeL2AYRiOkoboUG1aExEREXGGEl4vkJqdT06+DdAKr4iIiIizlPB6AfvqbliQH0H+vh6ORkRERMS7KOH1Ao4Na2Fa3RURERFxlhJeL1A4dEIJr4iIiIizlPB6AceGNU1ZExEREXGaEl4voBVeERERkfJTwusFEh0rvEp4RURERJylhNcL2DetaYVXRERExHlKeL1AklZ4RURERMpNCa8XcIwV1qY1EREREacp4a3icvKtnMzMA1TSICIiIlIeSniruOT0XAD8fS1Ehvh7OBoRERER76OEt4pz1O+GBmKxWDwcjYiIiIj3UcJbxSWmaqywiIiISEUo4a3iktI1ZU1ERESkIpTwVnGJqWpJJiIiIlIRSniruESNFRYRERGpECW8VZyGToiIiIhUjBLeKi5JY4VFREREKkQJbxVnX+GNCdemNREREZHyUMJbhRmGUaRLg1Z4RURERMpDCW8VdjIzjzyrAUBUaICHoxERERHxTkp4qzB7h4bIEH8C/Xw9HI2IiIiId1LCW4UlqSWZiIiISIUp4a3CEtM0VlhERESkopTwVmGFK7zq0CAiIiJSXkp4q7BEDZ0QERERqTAlvFWYxgqLiIiIVJwS3iosSTW8IiIiIhWmhLcKU0mDiIiISMUp4a3CtGlNREREpOKU8FZR2XlW0rLzAa3wioiIiFSEEt4qyr66G+jnQ3iQn4ejEREREfFeSnirqKJDJywWi4ejEREREfFeSnirKI0VFhEREXENJbxVlDo0iIiIiLiGEt4qKjFVHRpEREREXEEJbxWlkgYRERER11DCW0UlasqaiIiIiEso4a2iktILVnjDlfCKiIiIVIQS3irKXsMbHaoaXhEREZGKUMJbBVltBscycgGt8IqIiIhUlBLeKuh4Ri5Wm4HFAnVrBXg6HBERERGvpoS3CrJ3aKhbKwA/X/0RiYiIiFSEsqkqyN6hISpU5QwiIiIiFaWEtwpy9OAN14Y1ERERkYpSwlsFJWrohIiIiIjLKOGtguwrvBo6ISIiIlJxSnirII0VFhEREXGdKpHwvvPOOzRr1oygoCB69erFypUrz3j+1KlTadWqFcHBwTRu3JiHHnqI7OzsEs996aWXsFgsPPjgg26I3D00VlhERETEdTye8M6ePZsJEybwzDPP/H979x4cVXn/cfyzCcmShCTkArkACTcbQUhUIjFFbWsyJKGTEcUWJaOBWhhqYFDG1kKJgVEbR2eorRfsWLF/lIvFEWtbL21TgakFoWEi2IH8JHUG2hBC6JDL0iSQfX5/0Gy7EmCrmzyHw/s1szPJ2bPZ7375/vHhmeec1f79+5WXl6eSkhK1trYOeP7mzZv1/e9/XzU1NTp06JBeeeUVvfbaa1q9evUF5+7bt08//elPlZubO9gfI6z+s8LLRWsAAABflPXAu379ei1evFiLFi3S1KlT9dJLLyk2NlYbN24c8Pw///nPmjVrlhYsWKDx48dr9uzZuvfeey9YFe7q6lJFRYVefvllJSUlDcVHCZtW9vACAACEjdXA29vbq/r6ehUXFweORUREqLi4WLt37x7wNV/+8pdVX18fCLh/+9vf9Pbbb2vOnDlB51VVVenrX/960N++mJ6eHnV0dAQ9bPH1nNOZ3j5J7OEFAAAIh2E237ytrU19fX1KS0sLOp6WlqbDhw8P+JoFCxaora1Nt9xyi4wxOnfunJYuXRq0pWHr1q3av3+/9u3bF1IdtbW1Wrdu3ef/IGHUv7obGx2pOK/Vfx4AAABXsL6l4X+1Y8cO/fCHP9SLL76o/fv364033tBvf/tbPf7445KkY8eOacWKFdq0aZOGDw9tD+yqVavU3t4eeBw7dmwwP8IlcYcGAACA8LK6hJiamqrIyEidOHEi6PiJEyeUnp4+4Guqq6t133336dvf/rYkafr06fL5fFqyZIl+8IMfqL6+Xq2trbrxxhsDr+nr69OuXbv0/PPPq6enR5GRkUF/0+v1yut1RsDkDg0AAADhZXWFNzo6WjNmzFBdXV3gmN/vV11dnQoLCwd8zZkzZxQREVx2f4A1xqioqEgHDx5UQ0ND4JGfn6+Kigo1NDRcEHadprWDOzQAAACEk/VNoitXrlRlZaXy8/M1c+ZMPfvss/L5fFq0aJEk6f7779eYMWNUW1srSSovL9f69et1ww03qKCgQEeOHFF1dbXKy8sVGRmp+Ph4TZs2Leg94uLilJKScsFxJzrZxR0aAAAAwsl64J0/f75Onjypxx57TC0tLbr++uv17rvvBi5kO3r0aNCK7po1a+TxeLRmzRr94x//0KhRo1ReXq4nn3zS1kcIq/4VXgIvAABAeHiMMcZ2EU7T0dGhxMREtbe3KyEhYUjf+/6Ne7Xr/07qmbtz9Y38cUP63gAAAFeK/yWvXXF3aXC71g4uWgMAAAgnAq/DtHVx0RoAAEA4EXgd5FyfX6d8vZJY4QUAAAgXAq+DnPL1yhgpMsKj5Lho2+UAAAC4AoHXQfrv0JASF63ICI/lagAAANyBwOsg/d+yNjqB7QwAAADhQuB1kJOdXLAGAAAQbgReB2n9d+AdNYIVXgAAgHAh8DpIYIWXLQ0AAABhQ+B1kP49vNySDAAAIHwIvA7ynz28BF4AAIBwIfA6SGAPL4EXAAAgbAi8DmGM4S4NAAAAg4DA6xAd3efUc84viRVeAACAcCLwOkT/6m788GEaHhVpuRoAAAD3IPA6ROBb1ljdBQAACCsCr0Oc5II1AACAQUHgdQguWAMAABgcBF6H4JZkAAAAg4PA6xB86QQAAMDgIPA6BF8rDAAAMDgIvA7BHl4AAIDBQeB1CPbwAgAADA4CrwP0nOvT6TNnJbGHFwAAINwIvA7Q1tUrSYqK9GhkbJTlagAAANyFwOsArR3/vmBthFcej8dyNQAAAO5C4HWAwLesJXDBGgAAQLgReB0gcMHaCPbvAgAAhBuB1wECtyRLIPACAACEG4HXAVjhBQAAGDwEXgdghRcAAGDwDLNdAKRJo+J0smukspJjbZcCAADgOgReB1g1Z4rtEgAAAFyLLQ0AAABwNQIvAAAAXI3ACwAAAFcj8AIAAMDVCLwAAABwNQIvAAAAXI3ACwAAAFcj8AIAAMDVCLwAAABwNQIvAAAAXI3ACwAAAFcj8AIAAMDVCLwAAABwNQIvAAAAXI3ACwAAAFcj8AIAAMDVCLwAAABwNQIvAAAAXG2Y7QKcyBgjSero6LBcCQAAAAbSn9P6c9ulEHgH0NnZKUkaN26c5UoAAABwKZ2dnUpMTLzkOR4TSiy+yvj9fjU3Nys+Pl4ej2fQ36+jo0Pjxo3TsWPHlJCQMOjvd6WiT6GjV6GhT6GhT6GhT6GhT6GhT5dnjFFnZ6cyMzMVEXHpXbqs8A4gIiJCY8eOHfL3TUhIYKhDQJ9CR69CQ59CQ59CQ59CQ59CQ58u7XIru/24aA0AAACuRuAFAACAqxF4HcDr9aqmpkZer9d2KY5Gn0JHr0JDn0JDn0JDn0JDn0JDn8KLi9YAAADgaqzwAgAAwNUIvAAAAHA1Ai8AAABcjcALAAAAVyPwOsALL7yg8ePHa/jw4SooKNDevXttl+Qoa9eulcfjCXpce+21tsuybteuXSovL1dmZqY8Ho/efPPNoOeNMXrssceUkZGhmJgYFRcX65NPPrFTrGWX69XChQsvmLHS0lI7xVpSW1urm266SfHx8Ro9erTmzp2rxsbGoHO6u7tVVVWllJQUjRgxQvPmzdOJEycsVWxHKH366le/esE8LV261FLF9mzYsEG5ubmBL04oLCzUO++8E3ieeTrvcn1insKDwGvZa6+9ppUrV6qmpkb79+9XXl6eSkpK1Nraars0R7nuuut0/PjxwONPf/qT7ZKs8/l8ysvL0wsvvDDg808//bR+8pOf6KWXXtKHH36ouLg4lZSUqLu7e4grte9yvZKk0tLSoBnbsmXLEFZo386dO1VVVaU9e/bo97//vc6ePavZs2fL5/MFznn44Yf161//Wtu2bdPOnTvV3Nysu+66y2LVQy+UPknS4sWLg+bp6aeftlSxPWPHjtVTTz2l+vp6/eUvf9Htt9+uO+64Q3/9618lMU/9LtcniXkKCwOrZs6caaqqqgK/9/X1mczMTFNbW2uxKmepqakxeXl5tstwNElm+/btgd/9fr9JT083zzzzTODY6dOnjdfrNVu2bLFQoXN8tlfGGFNZWWnuuOMOK/U4VWtrq5Fkdu7caYw5Pz9RUVFm27ZtgXMOHTpkJJndu3fbKtO6z/bJGGO+8pWvmBUrVtgrysGSkpLMz372M+bpMvr7ZAzzFC6s8FrU29ur+vp6FRcXB45FRESouLhYu3fvtliZ83zyySfKzMzUxIkTVVFRoaNHj9ouydE+/fRTtbS0BM1WYmKiCgoKmK2L2LFjh0aPHq2cnBx95zvf0alTp2yXZFV7e7skKTk5WZJUX1+vs2fPBs3Utddeq6ysrKt6pj7bp36bNm1Samqqpk2bplWrVunMmTM2ynOMvr4+bd26VT6fT4WFhczTRXy2T/2Ypy9umO0CrmZtbW3q6+tTWlpa0PG0tDQdPnzYUlXOU1BQoJ///OfKycnR8ePHtW7dOt166636+OOPFR8fb7s8R2ppaZGkAWer/zn8R2lpqe666y5NmDBBTU1NWr16tcrKyrR7925FRkbaLm/I+f1+PfTQQ5o1a5amTZsm6fxMRUdHa+TIkUHnXs0zNVCfJGnBggXKzs5WZmamDhw4oEcffVSNjY164403LFZrx8GDB1VYWKju7m6NGDFC27dv19SpU9XQ0MA8/ZeL9UlinsKFwAvHKysrC/ycm5urgoICZWdn65e//KUeeOABi5XBLe65557Az9OnT1dubq4mTZqkHTt2qKioyGJldlRVVenjjz9mr/xlXKxPS5YsCfw8ffp0ZWRkqKioSE1NTZo0adJQl2lVTk6OGhoa1N7ertdff12VlZXauXOn7bIc52J9mjp1KvMUJmxpsCg1NVWRkZEXXJV64sQJpaenW6rK+UaOHKkvfelLOnLkiO1SHKt/fpitz2fixIlKTU29Kmds2bJl+s1vfqP3339fY8eODRxPT09Xb2+vTp8+HXT+1TpTF+vTQAoKCiTpqpyn6OhoTZ48WTNmzFBtba3y8vL04x//mHn6jIv1aSBX8zx9EQRei6KjozVjxgzV1dUFjvn9ftXV1QXt3UGwrq4uNTU1KSMjw3YpjjVhwgSlp6cHzVZHR4c+/PBDZisEf//733Xq1KmrasaMMVq2bJm2b9+uP/7xj5owYULQ8zNmzFBUVFTQTDU2Nuro0aNX1Uxdrk8DaWhokKSrap4uxu/3q6enh3m6jP4+DYR5+nzY0mDZypUrVVlZqfz8fM2cOVPPPvusfD6fFi1aZLs0x3jkkUdUXl6u7OxsNTc3q6amRpGRkbr33nttl2ZVV1dX0P/wP/30UzU0NCg5OVlZWVl66KGH9MQTT+iaa67RhAkTVF1drczMTM2dO9de0ZZcqlfJyclat26d5s2bp/T0dDU1Nel73/ueJk+erJKSEotVD62qqipt3rxZv/rVrxQfHx/YR5mYmKiYmBglJibqgQce0MqVK5WcnKyEhAQtX75chYWFuvnmmy1XP3Qu16empiZt3rxZc+bMUUpKig4cOKCHH35Yt912m3Jzcy1XP7RWrVqlsrIyZWVlqbOzU5s3b9aOHTv03nvvMU//5VJ9Yp7CyPZtImDMc889Z7Kyskx0dLSZOXOm2bNnj+2SHGX+/PkmIyPDREdHmzFjxpj58+ebI0eO2C7Luvfff99IuuBRWVlpjDl/a7Lq6mqTlpZmvF6vKSoqMo2NjXaLtuRSvTpz5oyZPXu2GTVqlImKijLZ2dlm8eLFpqWlxXbZQ2qg/kgyr776auCcf/3rX+bBBx80SUlJJjY21tx5553m+PHj9oq24HJ9Onr0qLnttttMcnKy8Xq9ZvLkyea73/2uaW9vt1u4Bd/61rdMdna2iY6ONqNGjTJFRUXmd7/7XeB55um8S/WJeQofjzHGDGXABgAAAIYSe3gBAADgagReAAAAuBqBFwAAAK5G4AUAAICrEXgBAADgagReAAAAuBqBFwAAAK5G4AUAAICrEXgBABfl8Xj05ptv2i4DAL4QAi8AONTChQvl8XgueJSWltouDQCuKMNsFwAAuLjS0lK9+uqrQce8Xq+lagDgysQKLwA4mNfrVXp6etAjKSlJ0vntBhs2bFBZWZliYmI0ceJEvf7660GvP3jwoG6//XbFxMQoJSVFS5YsUVdXV9A5Gzdu1HXXXSev16uMjAwtW7Ys6Pm2tjbdeeedio2N1TXXXKO33nprcD80AIQZgRcArmDV1dWaN2+ePvroI1VUVOiee+7RoUOHJEk+n08lJSVKSkrSvn37tG3bNv3hD38ICrQbNmxQVVWVlixZooMHD+qtt97S5MmTg95j3bp1+uY3v6kDBw5ozpw5qqio0D//+c8h/ZwA8EV4jDHGdhEAgAstXLhQv/jFLzR8+PCg46tXr9bq1avl8Xi0dOlSbdiwIfDczTffrBtvvFEvvviiXn75ZT366KM6duyY4uLiJElvv/22ysvL1dzcrLS0NI0ZM0aLFi3SE088MWANHo9Ha9as0eOPPy7pfIgeMWKE3nnnHfYSA7hisIcXABzsa1/7WlCglaTk5OTAz4WFhUHPFRYWqqGhQZJ06NAh5eXlBcKuJM2aNUt+v1+NjY3yeDxqbm5WUVHRJWvIzc0N/BwXF6eEhAS1trZ+3o8EAEOOwAsADhYXF3fBFoNwiYmJCem8qKiooN89Ho/8fv9glAQAg4I9vABwBduzZ88Fv0+ZMkWSNGXKFH300Ufy+XyB5z/44ANFREQoJydH8fHxGj9+vOrq6oa0ZgAYaqzwAoCD9fT0qKWlJejYsGHDlJqaKknatm2b8vPzdcstt2jTpk3au3evXnnlFUlSRUWFampqVFlZqbVr1+rkyZNavny57rvvPqWlpUmS1q5dq6VLl2r06NEqKytTZ2enPvjgAy1fvnxoPygADCICLwA42LvvvquMjIygYzk5OTp8+LCk83dQ2Lp1qx588EFlZGRoy5Ytmjp1qiQpNjZW7733nlasWKGbbrpJsbGxmjdvntavXx/4W5WVleru7taPfvQjPfLII0pNTdXdd989dB8QAIYAd2kAgCuUx+PR9u3bNXfuXNulAICjsYcXAAAArkbgBQAAgKuxhxcArlDsSAOA0LDCCwAAAFcj8AIAAMDVCLwAAABwNQIvAAAAXI3ACwAAAFcj8AIAAMDVCLwAAABwNQIvAAAAXO3/AVtXd+ADpGlYAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Summarize history for accuracy\n",
    "plt.figure(figsize=[8,6])\n",
    "plt.plot(history.history['accuracy'])\n",
    "plt.plot(history.history['val_accuracy'])\n",
    "plt.title('Model Accuracy')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Train', 'Validation'], loc='upper left')\n",
    "plt.show()\n",
    "\n",
    "# Summarize history for loss\n",
    "plt.figure(figsize=[8,6])\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('Model Loss')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Train', 'Validation'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
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