MNIST_Model2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## import library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers, models\n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get MNIST Data. \n",
    "### MNIST data loacted in tensorflow > keras > datasets > mnist \n",
    "### Split data to (train images, train labels) and (test images, test labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "mnist = keras.datasets.mnist\n",
    "(train_images, train_labels), (test_images, test_labels) = mnist.load_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### There are Total 60000 Train images and Train labels. (6000 images for single class)\n",
    "### Shape of single image is 28 x 28 (pixel)\n",
    "### "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of Train images : (60000, 28, 28)\n",
      "Shape of Train labels :  (60000,)\n",
      "\n",
      "Shape of Test images :  (10000, 28, 28)\n",
      "Shape of Test labels :  (10000,)\n"
     ]
    }
   ],
   "source": [
    "print('Shape of Train images :',train_images.shape)\n",
    "print('Shape of Train labels : ', train_labels.shape)\n",
    "print('\\nShape of Test images : ', test_images.shape)\n",
    "print(\"Shape of Test labels : \",test_labels.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train labels :  [5 0 4 ... 5 6 8]\n"
     ]
    }
   ],
   "source": [
    "print('Train labels : ',train_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot first train image. \n",
    "### when value is close to 0 : dark \n",
    "### when value is close to 255 : white"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0  51 159 253\n",
      "  159  50   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0  48 238 252 252\n",
      "  252 237   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0  54 227 253 252 239\n",
      "  233 252  57   6   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0  10  60 224 252 253 252 202\n",
      "   84 252 253 122   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0 163 252 252 252 253 252 252\n",
      "   96 189 253 167   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0  51 238 253 253 190 114 253 228\n",
      "   47  79 255 168   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0  48 238 252 252 179  12  75 121  21\n",
      "    0   0 253 243  50   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0  38 165 253 233 208  84   0   0   0   0\n",
      "    0   0 253 252 165   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   7 178 252 240  71  19  28   0   0   0   0\n",
      "    0   0 253 252 195   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0  57 252 252  63   0   0   0   0   0   0   0\n",
      "    0   0 253 252 195   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0 198 253 190   0   0   0   0   0   0   0   0\n",
      "    0   0 255 253 196   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0  76 246 252 112   0   0   0   0   0   0   0   0\n",
      "    0   0 253 252 148   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0  85 252 230  25   0   0   0   0   0   0   0   0\n",
      "    7 135 253 186  12   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0  85 252 223   0   0   0   0   0   0   0   0   7\n",
      "  131 252 225  71   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0  85 252 145   0   0   0   0   0   0   0  48 165\n",
      "  252 173   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0  86 253 225   0   0   0   0   0   0 114 238 253\n",
      "  162   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0  85 252 249 146  48  29  85 178 225 253 223 167\n",
      "   56   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0  85 252 252 252 229 215 252 252 252 196 130   0\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0  28 199 252 252 253 252 252 233 145   0   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0  25 128 252 253 252 141  37   0   0   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]]\n"
     ]
    }
   ],
   "source": [
    "print(train_images[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot First 10 Train images and Corresponding labels  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First 10 Train images in MNIST dataset\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Train labels match with Train label sequentialy\n",
      " [5 0 4 1 9 2 1 3 1 4]\n"
     ]
    }
   ],
   "source": [
    "print('First 10 Train images in MNIST dataset\\n')\n",
    "for i in range(10):\n",
    "    plt.subplot(1, 10, i+1)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.imshow(train_images[i])\n",
    "plt.show()\n",
    "print('\\nTrain labels match with Train label sequentialy\\n',train_labels[:10])\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Important\n",
    "### Change data shape (60000 x 28 x 28) to (60000 x 28 x 28 x 1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_images = tf.reshape(train_images, [-1, 28, 28, 1])\n",
    "test_images = tf.reshape(test_images, [-1, 28, 28, 1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Select one convolution model below \n",
    "## There are 3 example models. \n",
    "## 3, 5, 7 layer each \n",
    "## MODEL 1 : 3 Layers with 1 Convolution layer  \n",
    "## MODEL 2 : 5 Layers with 2 Convolution layer \n",
    "## MODEL 3 : 7 Layers with 4 Convolution layer "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def select_model(model_number):\n",
    "    if model_number == 1:\n",
    "        model = keras.models.Sequential([\n",
    "                    keras.layers.Conv2D(32, (3,3), activation = 'relu', input_shape = (28, 28,1)),  # layer 1 \n",
    "                    keras.layers.MaxPool2D((2,2)),                                                  # layer 2 \n",
    "                    keras.layers.Flatten(),\n",
    "                    keras.layers.Dense(10, activation = 'softmax')])                                # layer 3\n",
    "\n",
    "    if model_number == 2:\n",
    "        model = keras.models.Sequential([\n",
    "                    keras.layers.Conv2D(32, (3,3), activation = 'relu', input_shape=(28,28,1)),     # layer 1 \n",
    "                    keras.layers.MaxPool2D((2,2)),                                                  # layer 2\n",
    "                    keras.layers.Conv2D(64, (3,3), activation = 'relu'),                            # layer 3 \n",
    "                    keras.layers.MaxPool2D((2,2)),                                                  # layer 4\n",
    "                    keras.layers.Flatten(),\n",
    "                    keras.layers.Dense(10, activation = 'softmax')])                                # layer 5\n",
    "                    \n",
    "    if model_number == 3: \n",
    "        model = keras.models.Sequential([\n",
    "                    keras.layers.Conv2D(32, (3,3), activation = 'relu', input_shape = (28, 28,1)),  # layer 1\n",
    "                    keras.layers.MaxPool2D((2,2)),                                                  # layer 2\n",
    "                    keras.layers.Conv2D(64, (3,3), activation = 'relu'),                            # layer 3\n",
    "                    keras.layers.Conv2D(64, (3,3), activation = 'relu'),                            # layer 4\n",
    "                    keras.layers.MaxPool2D((2,2)),                                                  # layer 5\n",
    "                    keras.layers.Conv2D(128, (3,3), activation = 'relu'),                           # layer 6\n",
    "                    keras.layers.Flatten(),\n",
    "                    keras.layers.Dense(10, activation = 'softmax')])                                # layer 7\n",
    "    \n",
    "    return model \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = select_model(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## If you want to see information of model, model.summary() will help\n",
    "### summary() is also built in function "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d (Conv2D)              (None, 26, 26, 32)        320       \n",
      "_________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D) (None, 13, 13, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 11, 11, 64)        18496     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 5, 5, 64)          0         \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 1600)              0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 10)                16010     \n",
      "=================================================================\n",
      "Total params: 34,826\n",
      "Trainable params: 34,826\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Components in training step \n",
    "### Optimizer, Loss function, accuracy metrics "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(\n",
    "    optimizer = 'adam',\n",
    "    loss = 'sparse_categorical_crossentropy',\n",
    "    metrics = ['accuracy']\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training Step \n",
    "## Training for 5 epochs. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 60000 samples\n",
      "Epoch 1/5\n",
      "60000/60000 [==============================] - 23s 387us/sample - loss: 0.3974 - accuracy: 0.9432\n",
      "Epoch 2/5\n",
      "60000/60000 [==============================] - 22s 373us/sample - loss: 0.0689 - accuracy: 0.9799\n",
      "Epoch 3/5\n",
      "60000/60000 [==============================] - 23s 375us/sample - loss: 0.0567 - accuracy: 0.9826\n",
      "Epoch 4/5\n",
      "60000/60000 [==============================] - 23s 378us/sample - loss: 0.0495 - accuracy: 0.9846\n",
      "Epoch 5/5\n",
      "60000/60000 [==============================] - 22s 374us/sample - loss: 0.0419 - accuracy: 0.9872\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7f831df27e50>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(train_images, train_labels,  epochs = 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test Step \n",
    "## Perform Test with Test data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000/1 - 1s - loss: 0.0304 - accuracy: 0.9849\n",
      "\n",
      "Test loss :  0.060800487249649086\n",
      "Test accuracy : 0.9849\n"
     ]
    }
   ],
   "source": [
    "test_loss, accuracy = model.evaluate(test_images, test_labels, verbose = 2)\n",
    "print('\\nTest loss : ', test_loss)\n",
    "print('Test accuracy :', accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Before prediction, change test image's type to float 32. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_images = tf.cast(test_images, tf.float32)\n",
    "pred = model.predict(test_images)\n",
    "Number = [0,1,2,3,4,5,6,7,8,9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction :  (10000, 10)\n",
      "Test labels :  (10000,)\n"
     ]
    }
   ],
   "source": [
    "print('Prediction : ', pred.shape)\n",
    "print('Test labels : ', test_labels.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Functions for plot images, probability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_image(i, predictions_array, true_label, img):\n",
    "  predictions_array, true_label, img = predictions_array[i], true_label[i], img[i]\n",
    "  plt.grid(False)\n",
    "  plt.xticks([])\n",
    "  plt.yticks([])\n",
    "\n",
    "  plt.imshow(img, cmap=plt.cm.binary)\n",
    "\n",
    "  predicted_label = np.argmax(predictions_array)\n",
    "  if predicted_label == true_label:\n",
    "    color = 'blue'\n",
    "  else:\n",
    "    color = 'red'\n",
    "\n",
    "  plt.xlabel(\"{} {:2.0f}% ({})\".format(Number[predicted_label],\n",
    "                                100*np.max(predictions_array),\n",
    "                                Number[true_label]),\n",
    "                                color=color)\n",
    "\n",
    "def plot_value_array(i, predictions_array, true_label):\n",
    "  predictions_array, true_label = predictions_array[i], true_label[i]\n",
    "  plt.grid(False)\n",
    "  plt.xticks([])\n",
    "  plt.yticks([])\n",
    "  thisplot = plt.bar(range(10), predictions_array, color=\"#777777\")\n",
    "  plt.ylim([0, 1])\n",
    "  predicted_label = np.argmax(predictions_array)\n",
    "  plt.xticks(Number)\n",
    "\n",
    "  thisplot[predicted_label].set_color('red')\n",
    "  thisplot[true_label].set_color('blue')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "(train_images, train_labels), (test_images, test_labels) = mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = 1\n",
    "plt.figure(figsize=(6,3))\n",
    "plt.subplot(1,2,1)\n",
    "plot_image(i, pred, test_labels, test_images)\n",
    "plt.subplot(1,2,2)\n",
    "plot_value_array(i, pred,  test_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 30 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_rows = 5\n",
    "num_cols = 3\n",
    "num_images = num_rows*num_cols\n",
    "plt.figure(figsize=(2*2*num_cols, 2*num_rows))\n",
    "for i in range(num_images):\n",
    "  plt.subplot(num_rows, 2*num_cols, 2*i+1)\n",
    "  plot_image(i, pred, test_labels, test_images)\n",
    "  plt.subplot(num_rows, 2*num_cols, 2*i+2)\n",
    "  plot_value_array(i, pred, test_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot images and probability that model predicted wrong"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def error_mnist(prediction_array, true_label):\n",
    "    error_index = []\n",
    "    \n",
    "    for i in range(true_label.shape[0]):\n",
    "        if np.argmax(prediction_array[i]) != true_label[i]:\n",
    "            error_index.append(i)\n",
    "    return error_index\n",
    "\n",
    "# change num_cols, num_rows if you want to see more result.  \n",
    "def plot_error(index, prediction_array, true_label):\n",
    "    num_cols = 5\n",
    "    num_rows = 5\n",
    "    plt.figure(figsize=(2*2*num_cols, 2*num_rows))\n",
    "\n",
    "    assert len(index) < num_cols * num_rows\n",
    "    for i in range(len(index)):\n",
    "        plt.subplot(num_rows, 2*num_cols, 2*i+1)\n",
    "        idx = index[i]\n",
    "        plt.imshow(test_images[idx])\n",
    "        plt.subplot(num_rows, 2*num_cols, 2*i+2)\n",
    "        plt.bar(range(10), prediction_array[idx])\n",
    "        plt.xticks(Number)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Find index of wrong prediction\n",
    "## Plot first 10 wrong predicted images and probability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[18, 184, 259, 290, 321, 340, 404, 412, 646, 659]\n"
     ]
    }
   ],
   "source": [
    "index = error_mnist(pred, test_labels)\n",
    "index_slice = index[:10]\n",
    "print(index[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1fMVZ32PdxYeY+KMH/2nVa5dhr48VCWu9n+C1f9r97MT3/s+ET/3Sy6r22XkHmbi2fH6j20BpaxRSIBdl//7W8cE57p8P7e2a//TeEBP3nBbZz5GDTnN+tMvPsNe4nFdTbeLCWc5akzuXLIvo3kQRGoUUyMXazZut4/WHha+b5/rVqL41eCK18B7nZ+COmV+Y+OXN3a16rWetMPHOGDyXCIhgpI+qfgNgQ9Dp0wG87I9fBnBGjNtFUdqEDchBHnIlD/D9IvkmfK9bsPsBPARge4gy8hDmone58y1DMtARXVGJlQ1f6HMigM9VdYO/o+dzACfFrbEUNeYikTcwF4m8gblI5G1NXdOno6quAgD/5w7hKorIMBEpE5GyGlSHq0YxVo0qZMP6a08FgM7uEyIyCEBXVf2wvnu5X8PKysrYN5aiwVz0gOB8y0YOqlEVqupuIvKDiIwRka7+c50BLHfV2SVXA5iLnsZcJPIG5iKRNzAXiTwi7lu2q+pIACMBoK201waqN6hsUbFz0COya1beeah1vO+ZzjDYOa/sHdE9zvrmOhPPP+65sPU2u0akF4StlTTm319EMgD8C8BlDV7keg1LS0ujfg0pOWKdi5GS4i4mzqgRq2zyZGc7zL/89h0Tn9l6lX0T12DhE2Y7Q98zxP4yxvV/OmQbxmzpZB13+C5kh0xcFaAIAH5U1f1F5Fr4/ur1GwASonrI14e5mB4SlYurb7Xf+87Je9R1ZG/ZPmd4oYn/dtSbJg6eznV9xZEm/v7fzjTJNst3oCmWnOe049njXjLxDbsttuo9dsvxJu4zrEmPItpFonJx4a2trOO9WjpTuo6bc6ZVtue9M01c35QS98+2o7o85CrJteoNffx3Ji76LPrpzLGyTldjCzZBRBYBeF5VR4SqJyLnAHgHwAGqWpbQRlLCJOtn1HiqOW5/6/iL8//hOnL+D/jXm/YAqG4rvJOnlD6aOtJnjYgUAYD/89rYNYliIQs52G6PNOgCWPNN2gAYAOArEVkG4GAAY7mYc8phLnpAcL5tRxWy7JF2aCVZgNOZ8xyAwE8DFQC6uqoG5yqlBuYikTcwFz0usA5eLvIAoB986072C67HdSdTHnORyCOa2ukzFkBgh5lLAbwfm+ZQrLRFPqqwBVW6FfCNJBgC3+sGAFDVTapaoKrFqloMYAqAwfwrSsphLnqAO9/qtA5rsByFvpE9RrVanbCDAQRWsf4UwAkiku/f2eIE/zlKLcxFIm9gLnpcYB28DGRCVXeA606mK+YikUc0OL1LREYDOBpAgYhUwLfLzAgAb4vIlQB+BnBuPBvp1mama5cDZ7Q31pW2t+q1zx1o4m9utncfyctw3eNPExBLI8543cR/W3ihVdbhvUUmro3zehwZkoG+WoKZmAgA/QHcr6rlInIfgDJVHVv/HchrvJaLkXLvtNPrd+HrvTzmtyb+4J/LrbI9Wzv58tkAZ+pJS7GnqNSEGRD8ynWDrePMid+Fb0gTuPNNoeiEYuRJOyzWcrRFPgqlE5ZjEQD0F5Hv4Vvs8DIAUNUNInI/gMD2gfepavBiiOQhqZKLVR3shAjOF7cFpz4T8vy31fY1FVc7g9Lyf4h+h8o+zgYm+OsZl5v4mKfsqZqzTnncxL8ZdrtVVjAy8nas09VYgFkAMEBEhnNKSWrzci5m7OssHzD+yCeCSp0pWOu32tOxirY7/RvbznJ2rVu7n/132gmXOFO6OmQ69/i8yh5l2unrTSb2ypyZwDp4NTDTQisAHOSu4153UkTuCHcvERkGYBgAdOvWLU4tpoZ4ORcTKsN5z1x+gj2ts2Om8/vn/BpnV9kuE7bFv13U7DXY6aOqQ8MUHRvmPHlEgRShAEUYr2Nmq+oDAKCq94Sqq6pHJ7Rx1GjMRW8L5JtbL3G26d1TBmKZzi9X1V2mUKrqiwBejHsjKSaYi6kpMKVkEI7AZIwrh29KyVhVneOuxyklqYO5mFa47mQKYy4SeVtTp3cRERERpYzAlJJcyQN8v2BySglREnDdSSKixGKnDxEREaW9wJQSlwoAnd0n3FNK6ruXe3vhyjhP1yZKN4F18OpQCxFpBa47SUQUV3Hfsj3WOr/urA9yQN1NJu40dq5VT7vtYeLKOnu0Z14cu7oGt97oxH9+0ipb8yfnrxqnjfi9iYtem23Vq928OU6tI/Iu+fZ7E288zC57/7/OGl1/LJjR6HsvPd2eV73nl42+BVGzMa1aTHz37fb+6Dk/TIvbc/NmO50nwWsJHZLl5PCG0p1WWcHIqB7LKSUUF8vOzDdxtxa5YesNKFxtHW/7uqOJR/V42MTudXt8Qt/z+BxrBA3Wvu7sCzDy7rNN3HpM8mYvBtbB+x7fAr5NDV7kupOUDlbe4SxNNfvCx8LWu+BpZ226zv/jFu0UfynX6UNERETUWI2cUgIAe8A3pYQjDIhirECKkKftsFk39Aqc47qTRETxweldRERElPYCU0qqdCsACDilhIiImrl1uhpbsAkiskhEhgeXi8hlIlIpIrP8H1clo50UnZQb6VO7br2JOz7hDIerDa640Zlmdcn/2ftEbznrVxN3budsZfnhXu/HppFhdMx01hKY9n/O9p2PXLeXVW/CsENN7J7yQpTOMvv3NfH7n70RVOqe0uVM+9j320utWm1znXVXJ+77lonnnfuUVa90mTM1dI9HOayWaEa1E1/y/vUm3vO9KQlrQ+2ipSYOnlY2wbWF+6cnPmqV3YSg+aBhBKaUzMREAOgP4H5OKaFke7V4Qj2l4aeFRerCNmtNvP3+D0z8WO8zrHpd/sb3QqJodTihImzZ+1sLTNz1iVkmrotri+oX2NUyF3nYis39AEwPtaslgLdU9cZktJFiI+U6fYiIiIiaokCKUIAijNcxs1X1AYBTSoiIqHkK7GpZgx1Q1R0iEtjVMrjTh1IcO32IiIiIiIiageLhH4UtWzbi1AS2hJItsKtlDXYETlUAOChE1bNF5EgACwDcpqrLgyuIyDAAwwCgW7ducWoxNVWz6PRp99qUoGMnlhbOP8Hpu58c9h51XTs4B2pv1JFREXq71rkjulrHXx/rrOJe5JrqdXv7eVa9qX8vNvGvfxpkP+vrmWHbmO74JpV+1tzkTGU8/BJnCleN2hM2L1h8mok3PNLdxF3ft3cSymjd2sQDX7rMxN8d+oL9YO61Q2S58w7XlK53EzelK5y88nXJbgJRUtxbua+JK3fkmXh4x8+teu4dwR7Z2NvE1XUtrXp37e78wf7Kts7Uk15XPWvVe2S083PUzmU/N7bZRM1W7dH7mXjc3s+ZOHja1l9GXWjiLts8PZ0y+KfkDwCMVtVqEbkWwMsAfrPLRdzV0tO4kDMRERERERFRMxLBrpZQ1fWqGlj57zkA+yeoeRRD7PQhIiIiIiIiakYCu1rWoRYi0gpBu1oCgIgUuQ4HA5ibyDZSbLDTh4iIiIiIiKgZyZAM9EUJtmEL4OvMeTuwq6WIDPZXu1lEykXkewA3A7gsSc2lKDSLNX1adOlsHW/dp5OJsz6ebuLaNWsRVj1lu2wX79fncvuaS065zcQn//0rEwev6fNWr3EmPvs+e62a6qPCN5Eo1Uwf/oSJ3ev4XPHTSVa9Hec4M6NzKu11fNzqtm517lfROmy9Nievdg4eC1uNqNloO2uNiXcmsR0BNXu0S3YTiKLS421nXaoj5l1vla06xnm/K/oy0yrbbZzzR/TaXzaZ+LmZh1r17u/gbPk89p7jTNxm4hKr3n4XOktvvHrrIyY+Ottu7zU3OD8r97qTa/oQhZO5m/3+tPNuJ9dbipPP5y05warX5UFvruNTIEXI03bYrBt6Bc65d7VU1bsA3JWUxlHMNItOHyIiIiJqGm6kQERElLo4vYuIiIiIiIiIKA2l7UifXy45xMS3/d+bVtlxuc6Wlaf86Q4T54/6Nq5tck8l+3qqs5170aSNVr2hbZxh9v8s/o9VdunQ35m47ejkb6tL1BgLnj0g6IyzTfsn2wpMvPkMe7h7bWVlTNuxbuoeJm6NJfXUJKJEkawsE28aviVsvdOmXGcdF+OHuLWJqKlq5ywwcZs5dlmbNxFWuCUDvtvY1T7hmt5lXR/0frnHo87xNSc6W0b/b593rHr/PvN5Ez98Z//wDSRq5pbdYOfHzL2ddQJW7aw28cLRfa16HeDN6V3UPHCkDxERERERERFRGmKnDxERERERERFRGkrb6V072oiJ3dO5AKBdhrNlwcQHHjfxiWvs3RWyPpmOeKldv8HEo2483So7Z9TTJu7Rwt5eoffNzhjhNaPj1DiiOMluvz1s2fB3nWHnPSubNtXSvVPfRcdODFsva0PYIqK0UfxRlXW88WIn//Iz7PeWRVcUmXjPJ53rdq5eg0SpPbCfiSeVPG+V/bzTaVPxvxLWJCLP2PRc0PSufzb+HjX/6eAc7GOXlWT9YuJfzz/YxG3e4lICRDLImdL1wbCHgkqdqcmHv+ssG7LnU5zORd7BkT5ERERERERERGmInT5pbJ2uxmQdBwADRGR4cLmI3C4ic0TkBxGZICLdE99KovQQyLdJ+gmW6bxdyn/SBQDQP1S+iUitiMzyf4xNYLOJiIiIiCiNsdMnTakq5mMmSnA4AJQDGCoi/YKqzQRQqqr7ABgDIHi8IhFFwJ1vh+BErMZybNHNVp022A0A5obJtypVLfF/DE5cy4mIiIiIKJ2l7Zo+HVzzKA8rusMqm335kybOcPd7JakLrLIkyzrOFAlTE5g4r7eJ+7i2uw62CRuQgzzkSh6gUABvAjgdgFkUSFW/dF0yBcBFjWw6UYNa9HAGkB3ezd4evaU4W7MXTQm3UW14GSV2P+ais9uZ+L0CZ8DMN9tzrHr5i2oa/az6WPkGoKN2RSVWIg9tTZ320gFQ1PkPmW8UdzLJ3tL5+BlXmbjsgNesMvf74ktnO2uHPPraGVa9bg+VmVhrdjS+TS1b2SdKnC1tzx35adjrTpnirLlXPIVbtBO5bd/N+QE2t556hdOddXtW1G6zyjpnOldu6uncr030zQtpna7GFmyCiCwC8LyqjnCXi8jtAK4CsBNAJYArVPWnODWHaFeu38d++qOTE11aZIWqDQDo/mFsf74kihWO9ElT1ahCNqxfdCsAdA5THQCuBPBJqAIRGSYiZSJSVllZGcNWEqWH4HzLRg6qUVXPFbvkW7Y/x6aIyBnhLmIuEhFRqguMjs1FHgD0A0ejExHFVYOdPiLSVUS+FJG5IlIuIrf4z7cXkc9FZKH/c378m0tR0lAnReQiAKUA/hHyItWRqlqqqqWFhYXxbB/Vg7mYHsLkWzdVLQVwAYBHRaRXqGuZi97AXExdXOsuvTAXU1NgdGwGMqGqO+CMRjdU9UtVDQxHmgKgS6LbSZFjLhJ5WyTTu3YC+J2qficibQDMEJHPAVwGYIKqjvD/4DQcwB/i19Sm6/XoAuv44mOON/GrxZ+b+NpHxlj1/nLZb03cfUSdiXVGeZPa8fO9h5r46nPHmfiidnZfSwbsrXSbIgs52G6PNOgCYGVwPRE5DsD/AThKVaujfjDFU0rmom5zvg9XbGtnldWoM6Vr2+7OVC97Mpa9FbvbKW/Y27Jf3naxiX9wzTy59/dXWfVafzS13jY3VnC+bUcVsnb5KgD4Rsrvkm+qutL/eYmIfAVgEIDFoW5AnpCSuVj4qPM9uf+t9uzCT/d7zsSXt13uxNc/YdXb/wDnul/Xt47ouR2+bmniqrN+scpmHDDKxD/scP4/2OfZW6163SfYU1GaIjC6YBCOwGSMC6x1N1ZV57iqBUYXbBOR6+AbXXB+1A+neEnJXGyK/Bn26M7Xf3W2X3/07qdMfGm/6616ve5wtlyv+36uiU/77mqr3ncHvB6TdkYiMDq2BuaNugLAQfVcUu9odADDAKBbt26xbCY1Ttrl4vqrDjbxrEMfD1tvn/9daeLi8eGX3iBKpgZH+qjqKlX9zh//CmAufNOETgfwsr/aywDCTkmgxGuLfFRhC6p0KwAIgCEArF2BRGQQgGcBDFbVtYlvJTUGc9G73PlWp3VYg+UoRJFVZ7NuBIDuCMo3EckXkSx/XADgMLjW3iLvYS6mpqC1t9xr3RkcXZBamItphaPRU8zx/+IAACAASURBVBhzkcjbGrWQs4gUw/cX6KkAOqrqKsCX6CLSIcw1pgc+u97l5SiWMiQDfbUEMzERAPoDuF9Vy0XkPgBlqjoWvjfQPADviG+xsp+5c1BqYC56izvfFIpOKEaetMNiLUdb5KNQOmERfgSATOyab3sDeFZE6uDriB8RNPKAPIy5mDrCrHXH0QVpgrmYOjgaPb0xF4m8J+JOHxHJA/AfALeq6mapZ4cpN1UdCWAkALSV9iF78eOtdt1663jzb53ppP+ZUmDi3+RWWPXOPnSUiWved4ad16DxuwwBQK6EG/IXfjrX8NUHWMf97l5l4p0NPK9AilCAIozXMbNV9QEAUNV7AuWqelwDtyAPSrVcrF3jDCL7ed1Au7CPEz5/96MmPrv/LVa1i461p3EFDGu3zDqucX1V5797s4l7/WcK4i2Qb269pL+J95MjMV7HfO9fu8dQ1ckAgv5hKBWkWi5mfvWdiYu+sssuPexGE6++0/ndamLpi1a9GUG7fkXkpPBF32x3dvO6ZeQ1Ju7298mhqsdDQ6MLjgp5kes1LC0tTcrPNuRItVxsitoF9ozfB98+18RTr3jExK+d9ZRV7w9fXWfi1t/MM3FuVuN334uVwOjYDGRCRFrBNxr9Ancd12j0kzgaPXWkUy5u2jOyej3vd3Kprp56bpXXHmLiwme+bUSriJomot27RKQlfAn8uqq+6z+9RkSK/OVFAPgfMlGcMReJvIG5mHqaMLpgMEcXeB9zMfVkSAb6ogTbsAXwTQN6OzAaXUQCI87do9FnicjYcPcjb2AuEnlXJLt3CYAXAMxV1UdcRWMBXOqPLwXwfuybR0QBzEUib2AupiaudZd+mIupq0CKkId2UNVe7tHo/uUHoKrHqWpHVS3xf3D5AQ9jLhJ5WyTTuw4DcDGAH0Vklv/cHwGMAPC2iFwJ4GcA54a5nohig7lI5A3MxRTEte7SEnORyBuYi0Qe1mCnj6r+D76/iIVybGybkxi1Gzea+KW+3U38t1uHWvW6n7nExDd2nmDiY3K2x7Q9N6443Dqe8HWJifs+tcIq27ni55g+m1JHOuRi7aI8+8ShTtinpfOl/Xhe+K0x3b7Zbm+Lfs9dzha0fb501j9o2ipcRKGlQy4Gk0mzTFw0yTl/3sHXWPVWDa8xcaTr+3xW5Wztfuu7l1tlPcdsMXHnafFfx4dr3aWXdMzFSHW/x1kH5OKjzjTxf/f82Kr3xbPPmPi2Vc665f8qeieOraPmJh1zsahkdcjz/d6+yTruPa/MxBm5ziLUqy8rsepdccNHJn7r3khX/yGKjUbt3kVERJRKiod/FLZs2YhTE9gSIiIiIqLEi2ghZyIiIiIiIiIiSi0c6eOyx6P20PJqZwdpPNbjFBM/sps9RWX+Nc7Q9Y4TnX60dfvZ92+7yBn12GHKZhPL/KVWvV7bnCG7DW3LTpRKetxlb0tZusY1RNa1QWebk+0htWd1caaevPycs/9z/qIaq17eR87W7JzSRRQDU36wDovOcOLfYv9G364nuDUtUaztvMqZUrLnH6+2yhad8JyJ/1U0NWFtIkp1f+3935DnNd/+2bPuoAEmPv7ZiSY+q81DVr2TX7/TxD3fn+HcL6pWEkWGI32IiIiIiIiIiNIQO32IiIiIiIiIiNIQO32IiIiIiIiIiNIQ1/SJ0M6lP4Ut63Nt6PPtXg9/Pw0TEzUnwetoGY/Zh5+irXMN4r+tMxERUaqoXbjExHs/0MMq651xlYkXHvd82Hsc9eM5Jt5janUMW0eUmi6fdLmJ5/xmpInnHv+MXfF4J8xwjafY++sbrWq9/uisacff/SjRONKHiIiIiIiIiCgNsdOHiIiIiIiIiCgNcXoXURwUD/8obNmyEacmsCVERETUXNQuWmod977UOT4F+4W9rjWWhC0jao72+mOliR/9uJ+Jb20/x6p3f6WTV58+friJ+4wpt+rVxrqBRI3AkT5ERERERERERGmInT5ERERERERERGmI07uIiChi9U1dBDh9kYiIiFLfzuUVJv5iYGsnxgFhr2kPZ4cuTuciL+FIHyIiIiIiIiKiNMROHyIiIiIiIiKiNMROHyIiIiIiIiKiNMROnzS2Tldjso4DgAEiMjy4XESyROQtEVkkIlNFpDjRbSRKF4F8m6SfYJnO26W8TmsBoGeofBORu/zn54vIiQlrNFEzw/dFIm9Yp6uxBZvgzzXmIlGSMBebB3b6pClVxXzMRAkOB4ByAENFpF9QtSsBbFTVPQH8C8DfE9xMorTgzrdDcCJWYzm26GarzgosA4Cdwfnmz8shAPoDOAnAv0UkM5HtJ2oO+L5I5A2BXMxFHgD0A3ORKCmYi80Hd+9KU5uwATnIQ67kAQoF8CaA0wHMcVU7HcCf/fEYAE+KiKiqJra19e8IxN2AyOusfAPQUbuiEiuRh7amTiVWAsB6/6HJN/jy8E1VrQawVEQWATgQcG0BQURRS7X3RaJ0FcjFGuyAqu4QEeYiURIwF5sPSeTrJSKVALYCWJewh4ZXgOS3I55tyAfQFsBPALoDuB3AQap6Y6CCiMwGcJKqVviPF/vrWG0SkWEAhvkP+wKYX89zG/qaIvmavVTHK23prqqFDVwfMeZizNvgzjcAaA8gD8DPrjr9AWxT1d0BJ9/geyOdoqqv+c+/AOATVR0T/BDmoifawlxM3TbwfZG5GBZzMaFtCOQiVLVQRC4Gc7EpdbzSFuZi6raBuZhabWmoTvhcVNWEfgAoS/QzvdqOeLYBwLkAnncdXwzgiaA65QC6uI4XA9g9nl9TJF+zl+p4qS1x+B5Jeg54pR3RtiGafAPwFICLXOdfAHB2vL8mr31vp1p7Y/nhhRzwSjv4vuj9Ol5qSxy+R5KeA15pB3PR+3W81JY4fI8kPQe80g7mYnLreKkt0Xw/cE2f9FUBoKvruAvgm18Sqo6ItADQDsCGhLSOKL1Ek2+RXEtE0eP7IpE3MBeJvIG52Eyw0yd9TQfQW0R6iEgr+BaKHRtUZyyAS/3xOQC+UH8XIhE1SjT5NhbAEP/uCD0A9AYwLUHtJmpO+L5I5A3MRSJvYC42E8lYyHlkEp4ZihfaEbc2qOpOEbkRwKcAMgG8qKrlInIffMPCxsI3jeRV/8KxG+BL9Gg19DVF8jV7qY6X2hJrXsgBwBvtiKoN0eSbv97b8C2atxPADaq+/d2jlGrf26nW3ljyQg4A3mgH3xe9X8dLbYk1L+QA4I12MBe9X8dLbYk1L+QA4I12MBeTW8dLbYm0zi4SupAzERERERERERElBqd3ERERERERERGlIXb6EBERERERERGloYR2+ojISSIyX0QWicjwBD73RRFZKyKzXefai8jnIrLQ/zk/zm3oKiJfishcESkXkVuS0Y54aei1DfUahKgT8t8oqE62iEwTke/9df4S5l6ZIjJTRD4MU75MRH4UkVkiUhamzm4iMkZE5vnbdEhQeV//9YGPzSJya1Cd2/ztnC0io0UkO8RzbvGXlwdfHy/MReYic5G5yFyML+YiczHC5zIX44y5yFyM8LnMxThjLiYxF5uyz3tTPuBbHGoxgJ4AWgH4HkC/BD37SAD7AZjtOvcQgOH+eDiAv8e5DUUA9vPHbQAsANAv0e1I1msb6jWI9N8oqI4AyPPHLQFMBXBwiHvdDuANAB+GedYyAAUNfF0vA7jKH7cCsFsD/warAXR3nesMYCmAHP/x2wAuC7puAIDZAHLhW1h9PIDeyX694vhs5mKSX1vmInMx3PcBczGxry1zkbkY7vuAuZjY15a5yFwM933AXEzsa8tcjF8uJnKkz4EAFqnqElXdAeBNAKcn4sGq+g18q427nQ7fiwT/5zPi3IZVqvqdP/4VwFz4XuSEtiNOGnxtw7wGCKoT7t/IXUdVdYv/sKX/w1qNXES6ADgVwPNN/YJEpC18//G84H/uDlX9pZ5LjgWwWFV/CjrfAkCOiLSAL1FXBpXvDWCKqm5T1Z0AvgZwZlPbHSHmInORuchcZC7GF3ORuRgR5mLcMReZixFhLsYdczGJuZjITp/OAJa7jisQ9AIlWEdVXQX4vnkAdEjUg0WkGMAg+Hodk9aOGIr5axv0bxRclikiswCsBfC5qgbXeRTA7wHU1fMIBfCZiMwQkWEhynsCqATwkn/Y3/Mi0rqe+w0BMNp6gOoKAP8E8DOAVQA2qepnQdfNBnCkiOwuIrkATgHQtZ7nxAJz0Y+52DDmYlwxF/2Yiw1jLsYVc9GPudgw5mJcMRf9mIsNYy5GLpGdPhLiXLPbL15E8gD8B8Ctqro52e2JkZi+tg39G6lqraqWAOgC4EARGeC69rcA1qrqjAYec5iq7gfgZAA3iMiRQeUt4Bte+LSqDgKwFb7hlKHa2wrAYADvBJ3Ph68HuweATgBai8hFQV/LXAB/B/A5gHHwDXXc2UDbo8VcBHMxopsxF5mLCcBcjOBmzEXmYgIwFyO4GXORuZgAzMUIbsZcbFQuJrLTpwJ2j1QX7Dp0KZHWiEgRAPg/r433A0WkJXzfnK+r6rvJakccxOy1DfNvFJJ/+NxXAE5ynT4MwGARWQbfsMHfiMhrIa5d6f+8FsB/4Rty6FYBoMLVKzwGvqQO5WQA36nqmqDzxwFYqqqVqloD4F0Ah4Zoywuqup+qHgnfkMaFYZ4TK8xF5mKDmIvMRTAXo8FcZC5Gg7kYO8xF5mI0mIuxw1xMYi4mstNnOoDeItLD39M1BMDYBD4/2FgAl/rjSwG8H8+HiYjAN99vrqo+kqx2xElMXtt6/o3cdQpFZDd/nANfoswLlKvqXaraRVWL/e34QlUvCrpHaxFpE4gBnADfsDm47rMawHIR6es/dSyAOWGaPhRBQ/X8fgZwsIjk+r+2Y+Gbdxr8NXXwf+4G4Kww94ol5iJzsV7MReYimIvRYi4yF6PBXIwd5iJzMRrMxdhhLiYzFzWxq3afAt8K24sB/F8CnzsavjlyNfD1yF0JYHcAE+DrJZsAoH2c23A4fEPYfgAwy/9xSqLbkazXNtRrEOm/UVCdfQDM9NeZDeCeetp0NEKsxg7f/Mvv/R/l4b4XAZQAKPM/6z0A+SHq5AJYD6BdmHv8Bb7/ZGYDeBVAVog6E+H7D+J7AMd64fWK43OZi0l+bZmLzMVw3wfMxcS+tsxF5mK47wPmYmJfW+YiczHc9wFzMbGvLXMxfrko/psQEREREREREVEaSeT0LkogEXlRRNaKyOww5SIij4vIIhH5QUTCzT8kIiIiIiIiohQUVaePiJwkIvP9HQchV6qmpBkFe8GqYCcD6O3/GAbg6QS0ieKEuUjkDcxFIm9gLhJ5A3ORKPmaPL1LRDLhm5N3PHxz7qYDGKqq4RYvQivJ0mzUt3U9xVIdalGFLahD3TpVLXSXicizAL5S1dH+4/kAjlbVVfXds6CgQIuLi+PWZgptxowZu7yGAczF1PErNoZ9HRuLuZgczMX0wFxMfczF1LcdW7FDq0Nt49wkzMXkYC6mPuZieqgvF1tEcd8DASxS1SUAICJvwrfHfNgkzkZrHCTHRvFIaowq3YpZmISt2PxTiOLOAJa7jiv853bp9BGRYfCNBkK3bt1QVlYWj+ZSPUQk1GsYwFxMEeN1TH2vY6MUFxczF5OAuZgemIupj7mY+qbqhJjej7mYHMzF1MdcTA/15WI007vCdRoEP3yYiJSJSFkNqqN4HMVYqN7ckMO+VHWkqpaqamlhYUz+MEqxxVwk8gbmoset09WYrOMAYEC4aQYicp6IzBGRchF5I7EtpBhhLhJ5A3ORyAOi6fSJqNPA3WHQEllRPI5irAJAV9dxFwArk9QWig5zkcgbmIsepqqYj5koweGAb/vVoSLSz11HRHoDuAvAYaraH8CtiW8pxQBzkcgbmItEHhBNpw87DVLbWACX+HfxOhjApobW8yHPYi4SeQNz0cM2YQNykIdcyQN8v3QEphm4XQ3gKVXdCACqujaxraQYYS4SeQNzkcgDolnTZzqA3iLSA8AKAEMAXBCTVlHUftSp2IhK+IdI7iMiVwJoCQCq+gyAjwGcAmARgG0ALk9WWyNVPPyjsGXLRpyawJZ4DnORmi2P/b/AXPSwalQhGznuUxUADgqq1gcARGQSgEwAf1b1zQfzGo9973sNc5EiwjyKO+YiRYS5GF9N7vRR1Z0iciOAT+H7wehFVS2PWcsoKgPF+Tl2vI75QVVfcJerb9u2GxLdLoo95iKRNzAXU1LwNIMWAHoDOBq+v0hPFJEBqvqLu1LwBgfkLcxFIm9gLhJ5QzQjfaCqH8M3YoSIkoi5SOQNzEXvykIOtqPKfSrUNIMKAFNUtQbAUhGZD18n0HR3JVUdCWAkAJSWlobcBIGSi7lI5A3MRaLki2ZNHyIiIqKU0Bb5qMIWVOlWwLe46BD41rdzew/AMQAgIgXwTfdaksh2EhEREcUSO32IiIgo7WVIBvqiBDMxEQD6A3hbVctF5D4RGeyv9imA9SIyB8CXAO5U1fVJajIRERFR1KKa3tVcyf79reMOTy438cz/DjBxp4cmJ6xNREREVL8CKUIBijBex8xW1QcAQFXvCZT717u73f9B5Fk/vzPQxHMPe9XER88+w6qXdcKyRDWJiIg8iiN9iIiIiIiIiIjSEDt9iIiIiIiIiJqZdboaW7AJIrJIRIaHqiMi54nIHBEpF5E3Et1Gih6nd0XIPaXrgtc/tcrOb7PKxB9eNcvEIx/qGf+GEREREVGzUlcnJq7RWue8SqjqRES7UFXMx0zkIg9bsbkfgOkiMlZV5wTqiEhvAHcBOExVN4pIh6Q1mJqMI32IiIiIiIiImpFN2IAc5CEDmVDVHQDeBHB6ULWrATylqhsBQFXXJrqdFD12+hARERERERE1I9WoQjZy3KcqAHQOqtYHQB8RmSQiU0TkpFD3EpFhIlImImWVlZVxajE1FTt9iIjibJ2uxmQdBwADOF+aiIiIiDxKg45bAOgN4GgAQwE8LyK77XKR6khVLVXV0sLCwvi3khqFa/rU4+d7DzXxXy583cSnt14X9poPN+zrOvo1Hs0iSnvL7z7UOq4duMXEtw2cYOJrd1vRpPvvP+M8E+e8nG/i1mOmNul+9QnMlx6EIzAZ48oBDOV8aSIiIqL0krl3bxMPfGOhVfZgh++ceuKMu+jxwdVWvT7XTI9T63aVhRxsR5X7VBcAK4OqVQCYoqo1AJaKyHz4OoES11CKGkf6EBHFUWC+dK7kAb6/nnC+NBERERElVVvkowpbUIdaiEgrAEMAjA2q9h6AYwBARArgm+61JLEtpWix04eIKI44X5qIiJqjwNTmSfoJlum8Xcq3YxtEZJb/Y4GI/BIoE5FaV1nwL6FEFAMZkoG+KME2bAGAuQDeVtVyEblPRAb7q30KYL2IzAHwJYA7VXV9kppMTcTpXS7SspV1vLPvNhOf2XqDieuCrnt+k7M1+5pL3LMyOL2LqD4//9mZxnXFOZ+a+Ob8x6x6LZAZ8votdTus41/qdoas1z7Dzu1p+71p4n8W9zXxV5/uYdWr+zVuOVzffOkuACaKyABV/cW6SHUkgJEAUFpaGnwPIktmR3uWYMF72038Qrcvw143rdrZ8vnql260yrreP9m5f38nd6D2t+OWPs50/8p9nB81JMLv2uBdp93XFf/HnmJdO2dBZDclooRxT23ORi6mYQIKtBPypK2pk41c7NDtJQAgIjcBGOS6RZWqliS42UQNqj7lAOs4+w5nNtR+7Z3OzXsLZ1n13L8/lu9w3o97ja6NbQMbqUCKkKftsFk39AqcU9V7XLECuN3/QSmKI32IiOKoEfOl31fVGlVdCiAwX5qIiCjluKc2Z0gGOqIrKnd567MMBTA6Qc0jImpW2OlDRBRHgfnSVboVAAScL01ERGkueGpzNnJQbf8BxBCR7gB6APjCdTrbP515ioicEe45nPZMRNQwTu9y+fn3pdbx7KPcU0zC94/9sKWLiWsXLI51s4hSWkZ2tonnP7yvVTZl8D9MvHuG88PhsOXHWPUmfbqPiXPWOOfzF1Rb9VqOnxGyDQsfP8g+PvtpE2/a6Vpvpzb2Q2wzJAN9tQQzMREA+gO4PzBfGkCZqo6Fb770Cf750rXgfGlqohZdnfcjfcWejPxcN2fnu+Bpym6lWU4881p7quXoC53lqPZoMTfsPTq12GTivVu2dD23vic7MoLec93XHbPwJquszRwQUWobAmCMqrrfhLup6koR6QngCxH5UVV3+SGb054pXtzvp0sv6Wbi94b9w6rXo0U2Gsv9vrh4iP3reJ+vGn07ogax04eIKM4KpAgFKMJ4HTNbVR8AOF+aiIjSV/DU5u2oQpa9qYHbEAA3uE+o6kr/5yUi8hV86/3wL6tERE3A6V1ERERERBQz7qnNdVqHNViOQhTtUk9E+gLIB/Ct61y+iGT54wIAhwHgmD4ioibiSB8iIiIiIooZ99RmhaITipEn7bBYy9EW+SiUToGqQwG86R/xGrA3gGdFpA6+P1CPUFV2+hARNVGz7/TJ3LOHiS84/wurzL2mQEtxtoyuCZoxvOCeASZuhekxbiFRavvpd/uZeOEZT1pli107rJ9z47UmzvnAXpune91kNFZG69Ymvu4348PWGzfyMBMXbvs2bD2iVLDhCGcNgq/6PBHz+1/YZpWJ61+fJ7Oesoa9srmzdfzkU2eZuNP/frLKdoKIvCgwtdmtl/S3jlX1z8HXqepkAAPj2TZqnjILdreOdY9CEy89L98qu+QM5/fC93Z/31XS+DV8iJKN07uIiIiIiIiIiNIQO32IiIiIiIiIiNJQs5/eta13gYnv3P1Hq8w9cN09peuEOWdZ9bK/cq6LbDNaouZjZ5vwO6jO2dHRxDnvT4vpcysvcLZ5vz1/olW2sc7ZUWS3hTti+lyiZMqftcHE/d62tzafc17sp3tFa2q1s23ttS9eb+Kuf7WndHaAc8zpXEREFKmMNm1MXPNWrlX28V6vR33/k+aeaeKtO1qZeOK+b0V9b6JY4UgfIiIiIiIiIqI01GCnj4i8KCJrRWS261x7EflcRBb6P+fXdw8iih5zkcgbmIupa52uxmQdBwADRGR4uHoico6IqIiUJq511FjMRSJvYC4SeVskI31GATgp6NxwABNUtTeACf5jIoqvUWAuEnnBKDAXU46qYj5mogSHA0A5gKEi0i+4noi0AXAzgKkJbiI13igwF4m8YBSYi0Se1eCaPqr6jYgUB50+HcDR/vhlAF8B+EMM25UwrTY563nMrLb7wAZlhV6h52+93rWO7xl0hYnl2+9j2DoiR6rmYocyJ4+mn2ev7/On2YNN3Alzon5WZr8+Jv7LH14KW++EB+8wceF4btNOjePlXKyds8DEe95mlx1WfrOJ97pirlX2QvfP49amPp9e48RXlIWt1xWTw5bFwiZsQA7ykCt5gEIBvAnf6xb8n8/9AB4CcEfwPchbvJyLseZ+fwOAgZ1WJqklRLvyci5mtHbW8fl4r/eivt9tKw+1jrMu3G7iTad1dgr2jfpRRDHT1DV9OqrqKgDwf+4QrqKIDBORMhEpq0F1Ex9HRGEwF4m8gbnocdWoQjZy3KcqAHR2nxCRQQC6quqH9d3L/RpWVlbGvrEUDeYikTcwF4k8Iu67d6nqSAAjAaCttA+/jQ8RxRVzkcgbmIueYv79RSQDwL8AXNbgRa7XsLS0NKVfw+LhH4UtWzbi1AS2JPGYi0TewFwkiq+mdvqsEZEiVV0lIkUA1sayUYkkk53pWBdPudIqm33UcyGvCZ72tfh6Z8DUnpwpQonl+VxsPcZZFuP+qadbZV3WLzNx6MmUjbPi+AITn5SzzcSTgqZuFn3ws4m5/TPFiOdzcffnnTeo74rs4em4Nn7Tuz78jbNV/Jmjr7XKegxN3JToLORgO6rcp7oAcM+RaQNgAICvRAQA9gAwVkQGq2r4eWnkNZ7PxaZYfkqBdfxdz9Em7vP+dSYumJ5p1cvB0vg2jCg8z+fiftMuNvF3B74att4Jc84y8eZ3OlllBWuc99bsc/Ji2Dqi2Gnq9K6xAC71x5cCeD82zSGiRmIuEnkDc9Hj2iIfVdiCKt0KAAJgCHyvGwBAVTepaoGqFqtqMYApANjhk3qYi0TewFxMAet0NbZgE0RkEXe1TF+RbNk+GsC3APqKSIWIXAlgBIDjRWQhgOP9x0QUR8xFIm9gLqamDMlAX5RgJiYCQH8Ab6tquYjcJyKDG7icPIi5SOQNzMXUFNjVMhd5ANAP3NUybUWye9fQMEXHxrgtSdf7L1vsE19Edt1Th7xh4oePuMDEGRNnxqJZRADSIxd3Lq+I6f2W3X+IdTzt8odNvL7OmTB2+4P2Jjy7V3AeJjVdyuaib8oSAOCq88cl7LF9WrYy8fMHvWyV/WGoM92r7egpcW9LgRShAEUYr2Nmq+oDAKCq94Sqq6pHx71BFJWUzcUY6/vCVhPrjPIktoSaq1TJxU+2tbGODyxypvufcfhZwdWNnI2/mLjVLz+Frfduf/cUsezGNzDBArta1mAHVHWHiHBXyzTV1OldREQUoXW6GpN1HAAM4NBZIiIiIko27mrZfLDTh4gojgJDZ0twOACUg0NniYiIiMibQu1q+bsGL1IdqaqlqlpaWFgYz/ZRE7DTh4gojgJDZ3MlD/C9kQaGzgYLDJ3dnsDmERERxUVglOsk/QTLdN4u5TWohohUisgs/8dVgTIRuVREFvo/Lt3lYiKKWiN3tVwG4GD4drXkiPQU09Qt29NS7fxF1vFeH19v4qWnOtu316hVDce6tobe8eJbJn6qd58Yt7Bx1ulqLMAswD+lRFWtBdRE5DIA/wCwwn/qSVV9PrGtJGqcFp2drTL/PsTeXjNPsky8/5tO/vZ6Pnlr+IQZOnuQ+4R76KyIhJ0vLSLDAAwDgG7dusWhtdRc3JA/PynPPSirxjq+9E8f3Jj3HAAAGvlJREFUmPi/o/mXQSK3ddc469bNuu1Jq2zQwzeZuGjG5IS1KVKBUa6DcASykYtpmIAC7YQ8aRtc9S1VvdF9QkTaA7gXQCl8fyyZISJjVXVjYlpP6aRu868mfuTmC62y2mxn/EPu0qYNtF51+6HOPST+a9PFUmBXywxkQkRawberpVmgVlU3ASgIHIvIVwDu4K6WqYcjfdJUpFNK4HuzLfF/sMOHKDE4dJaIiNKWe5RrhmSgI7qi0hpAUK8TAXyuqhv8HT2fAzgpbo0laqYCu1puwxYAmAvuapm2ONInTVlTStSaUhK8GjsRxVEjh84CwB7wDZ0dzL+kEBFRKgoe5ZqNHGzChlBVzxaRIwEsAHCbqi6HbyHZ5a46uywuG8ARsETRKZAi5Gk7bNYNvQLnuKtl+mGnTz32/uMyEw+a5UwVef8PD1n1OmY6U0qOyllv4ltH7W/V6/WSs4V0xtfx3c49kiklfqHebC18QyUvmTeio4lPy91slR0350wT97rTG+shB4bOVulWABBw6Cwlizpzk0uevcUq+uGaJyK6xT/WOwNGXx/t7MRb/NrPVr1DPnKmS99d4KzlETw9+sp2znWP3+0sddX1r96brkKUaCOHP2biOmQmsSXx0QItAaBYVatF5FoALwP4DXzvlcE0xDmo6kgAIwGgtLQ0ZB1q3uq2OctwZH0yPeb37/TUDBNvu63WeZa0DHvNN9tbmbjvTbOsMn4TUzxwelfzEvz/yAfwvdnuA2A8fG+2u17EKSVETRYYOjsTEwGgPzh0loiI0lzwKNftqEKW/cdICDKgqtX+w+cABP5aWgGgq6tq8AhZIiJqBHb6pKkIppRAVdeHebMlohgqkCIcKicBwGxVfQDwDZ1V1bHBdVX1aI7yISKiVOYe5VqndViD5ShEkVWnDnXuw8HwrSkCAJ8COEFE8kUkH8AJ/nNERNQEnN5Vj9rKShN3eMqJLzn9Iqve033eMHHPls5QvrnHP2PV+98R2SZ+5OiTTbxzeUX0jQ3S0JQSABCRIlVd5T90v9kSecrSvzk7mIw+/HET/3ODvTZ57mXOzkA7lQNkicLp8fRC63jffZwdkbvm/2LiVR/ZU3q7vv2TibtUOFOwdgbd/9tTzNIAqJlabuKgX/KIKE1lSAb6qm+Uq0LRCcXIk3ZYrOVoi3wUSqfAlu3l8P0XsgHAZQCgqhtE5H4Agbk496lqyAWBiBJNDyuxjrf8yVlqIK+eKV1uteqMu9CaHbFpGFE92OmTptxvtvBNKbk/MKUEQJl/hMHN/ukl1pstEREREVE0CqQIBUGje3pJfxNnIQfVWtU/+DoAUNUXAbwY1wYSETUT7PRJY4E32/E6xppSEihX1bsA3JW0BhIRERERERFR3HBNHyIiIiIiIiKiNMSRPk2QdcIy6/jc4XeYeMZNjyGcI7OdOZsPZ7cKW4+ouas9ej/r+JXznzTx/q2cbWuvfeo4q17him/j2zCiNOFesw4Aup5TGbJeEew154LX7glnZ8WKpjSLqNnL7OOsh5Ut7ve08Fu2S0vXz5QlfSN6TofHf7KO/zfNWSPvncGPB1c35u1wpmtN2tzbKjs139l6+vtt3U3crsU2q96rD54KAKj9aEpEbSXykm17ZFnHXw18x8SXLjvRxC8Xj09Ym4gawpE+RERERERERERpiJ0+RERERERERERpiNO7YqDg+5qGKxFRvTKys0188/NvWGUHZImJ9516sYm7vvqDVc/aDFqcazILCqx6vx7R08Qdb1ts4p82tbfqrV/sHPe+hcPQieJpv1PmmLjyr0lsCFESVT3lTKLs0zL8UgDVB20x8U9tSk38w7AnInrOtGqxjqd06GHioVOuDntdztTWJt7j0clW2a0jLjdx93HVJt62h/11rC/xPXvnFxE1lchTMqvVOu7zyTUmzlvg+l6/Jfz0rttedHKsCyaHrUcUKxzpQ0RERERERESUhjjSh4iIjOLhH4UtWzbi1AS2hIiIiIiIopW2nT5VZxwYtiznvWlR3Xvl7w+1jv901esmzqhn8FRLCb/zAlFzlNGmjYl/ebuDiU/KsYe6Dpp+oYk7nzPPxNK7h1Vv9UnOPfJPXWniCf3fDduG+9YNNPGmP3a1ytpP5JQuomi53/tqNHy9l7pPMPFvsX88m0TkGZsvONg6vqP7G2Fq2mYf/pJzcLgT/qWyxKo39pUjQl6fu9aaEI0er0X/ftdzeOgdNNsEH7/p+1ypW6N+JlGiZX9o/x7Z6uJ9TPzdLc9GdI/282pj2iaihnB6FxERETUL63Q1Jus4ABggIsODy0XkdhGZIyI/iMgEEem+612IiIiIUgc7fYiIiCjtqSrmYyZKfMMiygEMFZF+QdVmAihV1X0AjAHwUIKbSURERBRT7PQhIiKitLcJG5CDPOT+f3v3HiVHWeZx/PvkOgmThISEMJIrkGACsgmbRW6LLuCCiIFdZQUPHFjxgC54YHFX43FVdL0E2SMiuGAW5LCoYIxcclRQRERWDSEkwdy4JBCSGEImoLmYQGYyz/5R1VVdne7pzkx3dXXP73POnLzV9XbX01P9SydV9b5lrQAO3Aecl9/H3R93993h4iJgXLpVioiIiFRX08zp0/+o5Nwed9x8U9S+au2FiXXbDj2potfsOOfPUftrxz4QtU9tSY5ZbrH415g/Qvo/249P9Ft6wZS438sbKqpBpJltu7ctai867r6o/ULHm4l+h9wW3yL2hTtmRu2HTr810e+4QfFt3zd0xreznfl08vazbV+NM9tvzfq4vXNZpaWLNB6Lb9G846J3luw26slNUbtz46aS/SrV4fHcBV10ddOztt5iDy0MyX9oE1D6FwGXAw8XW2FmVwBXAEyYMKFaJUof9PqxyVunzz7oTxU97+XO+HvyI5+8LmoPX7410a9trW4HLVJN9tfHJJZHj9hVomfS3624IGoPf+LFqN3d7D4DJsZzTXrr0MS6fauer2i75WzzLexiO2a2FrjD3efmrzez64CPAp1AO/ARd3+lKhuX1OhKHxEREemrik4tbWYXA7OAG4s+yX2eu89y91ljxoypZX0iIiI1kRv2PJRWgOlo2HPTKnulj5mNB/4XOIzgQpZ57n6zmY0CfghMAtYD/+TulZ2eEJEDpiw2rm2+hRdYDuHksTqL0tiUxcY0mCG8yZ78h8YBmwv7mdmZwGeBd7n7WymVJz2gLIpkg7LYmHLDnjvYi7vvNbPcsOfVuT7u/njeUxYBF6ddZ86kOT8tuW793PelWEnjqWR4VyfwSXdfambDgGfM7FHgMuAxd58b3gFjDvDp2pVaRv/k7dAnDhgUtX/29gcT67q+cOCXl+ffir2r4Nf2UkdH1L7gtn+La/hR8t+S+1566YC3K5KnMbJ4AG6ctqDo41MHtiSWf/ndeUX7dTEwsTxt3r9E7Qk/2xm12xavKFlDrQeb5M6izORv+R2P5CaPXejuq/O65c6i7DazjxOcRflQjUuTnmvILG6aEw9tXnr1zSX7vXPuNVF77C2VDe/qP+WIxPLay8fmLT1T0WtMe+zKqD2FpRU950AMZyR72MWe4DbRBlwIfDi/j5nNBL4DnO3uW/d/FcmYhsziG/8cZ3HA1J0l+73/+dlRe3fHoMS6rv85NGoftOCpqJ2lG0HnTng4zuFMZpK9PbF+L29iZqspcsLDzPYBuS/vDe4+G8myhsxiT8yZf29i+ZSWjhI9k85oi4dj3fOFU6P22N8nh3juHR4v/+mY+F+p33zvPYl+N10df30N+vmSimoolBv23MHe3EM9HvYs2VZ2eJe7v+ruS8P2TmANcDjBUcC7w253A+fXqkgRURYblSaPbT7KYmPqZ/04mhks40mAY4D57r7KzL5kZrn/UN4ItAI/MrPlZrawXvVKecpiduXfLe8kzmILG9nlOxJ9+tEfSg8b2ePuM8IfHfDJOGWxqfRo2LOZXWFmS8xsSXt7ey3rkx44oImczWwSMBN4Chjr7q9CEHQzO7TEc6LJDlsYWqyLiBwgZbFxaPLY5qYsNpbR1sZo2vilL1jp7l8BcPfP59a7+5n1q056Q1nMloITHoz18bSzmVaGR30GMJCCEx51GzYi1aMsNo5qDnt293nAPIBZs2YVPXAk9VPxQR8zawV+DFzr7jvMrNxTgOQHYLiNqt0HYF/ygtb8IVdHDRzc65d/ePewqH3T+vck1nXedljUPvz++C4Jnb3eqsj+Mp/FAv0PHhG1X727LbHulJb8YR+l38dje+IMX/n4ZVF7+vV/TPSb8MeGuUtJubMo7yr6JH2hZkqjZXH3lL3lOwEf+/hDUfuBWyqcpHhe8o57K6Z+K2+psntGtLzQUr6TSBGNlsVFX/521O4q+Dp4x28vi9qTL18ftYfsLBwG9nINKquewhMeLQxhO29095TCEx4tZraE4J/Tc939wWJP0smQbGm0LPbEdTdemVh+6nO3luiZ9B+j/xC3PxC3185OHkN5Ynd8t+cbf/H+qP3p5R9I9Ju8JJ42pKfDOnPDnvvRHzMbhIY9N62K/iVmZgMJAvx9d78/fPg1M2sL17cB+hCI1Jiy2Hh6cBZltiaPzT5lUSQblMXGV2LYyAR3n0XwH9BvmtmRxZ6rO+llh7LYeHLDnnezC4IheRr23KTKHvSx4BDtncAad/9G3qqFwKVh+1LgocLnikj1KIuNqcTksYkvzLyzKLN1FiX7lEWRbFAWs6vwhMeb7GFwcqgzUPqEh7tvDv98Cfg1wXAhyShlsXGNtjZaGYG7H5k/7NndF4btM919rObYamyVXOlzCnAJcHp4dG+5mZ0DzAXeY2YvAu8Jl0WkdpTFBqTJY5uSsiiSDcpiRuWf8OjyLl5jI2NIDvHeF0yEsN8JDzMbaWaDw/Zogv28GskyZVEkw8rO6ePu/0fpyTbOqG45PbdvbXJs85XXXhu122eUfptfuCi+7d71z56bWGer43l8JlwfzxUyiFcS/QqXRWqhUbI4YPLExLLdFc8jsmTKDwp6x2/nkvXxW1j+yLREr0m3vxi1p7Y/HbUbZd4sTR7bXBoli/uxeJqEft2c83lHy8ao/a3PJW40x4iTX4vavzluft6awtuyx68/0PpH7Y6CmRqO+c1HovbkLzfMnFySEY2axb+65ep44YTtiXVHXhPf9aZzv3l8Gkc/68fRHpzwcJy3MYlWG8E6X8VwRjLG3sZbwZVAuRMeEN+afRrwHTPrIvjLZK6766BPhjVqFntiaHtX+U4H4HMbkxfOrHl4arwwJt7WEde+nujX+Xq3c2SJJBzQ3btERERERETKyZ3wyHekHRO1hzKMHf7G2MLnufvvgHfUvEARkT6isltqiIiIiIiIiIhIQ2naK32GPLg4ak8oepPHwF3Xx0NRJrKiliWJNK1+LfGtls/+yfLEuk+MjIc/ruvYnVj3/nmfitoTbogzO74zOcyjp7eiFJE8Hl9530Xpy9NnDY7byz52c2Jd/rCw7l4j3/xdo6L2p3/1ocS6aZ96Lmor59JXHD639FDGRhm2LNJXDdm6N7F87nPxMOi/dAyK2iM+lkzzg799oOjrPbPqiMTy1K/Gfz9suebkeMXApv1vu6RAV/qIiIiIiIiIiDQhHfQREREREREREWlCuk5MRERERERERKRKJs35acl16+e+L8VKdNBHRKrAO+Nxy49te3ti3fZ9Q6L2z77y7sS68T+Mxy0X3MlZRKps6u1vRe27TpyUWPfBYS9E7WH9BnGgtncl5zi4f2d8y9kHLjs9rmHx4kQ/zeMjIiKNpN+Ty5IP5N2QfueD06J266YNiW7Tnrg8at9/8u1R+8j7Ss/kddjN8b+TNd+X9IaGd4mIiIiIiIiINCEd9BERERERERERaUIa3iUivZY/vGvPu15LrHuS+Hbuw1iUWk0iUmDxiqj5wPQxiVVf//a5UXvN+bdW9HLT538iag97KXkOaewt+bekXoGIiEizO+z8NVG7cNqCIz+8PGr/OydG7f4srXVZIrrSR0RERERERESkGemgj4iIiIiIiIhIE9LwLhERkT5uylVPRe3ZV/1NRc85SsM1RaQPyNJtl0VEekJX+oiIiIiIiIiINCEd9BERERERERERaUIa3iV9ii7RFRERERERkb5CV/o0sW2+hd/5IwDHmtmcwvVmNtjMfmhma83sKTOblHaNIn2BsiiSDcqiSHpyefutP8x6f26/9Y5TKm9m9pnw8efN7KwUyxbpU7b5FnaxnTBv+l5sUjro06TcnedZxgxOBVgFXGRm0wu6XQ78yd2PAm4Cbki5TJGmpyyKZIOyKJKe/LydxFlsYSO7fEeiTwdvQZG8hbm8EDgGOBv4bzPrn+obEOkDcjkdSivAdPS92LQ0vKtJbecNhtDKUGsNTqXAfcB5wOq8bucB14ftBcCtZmbu7ulWK9K8lEWRbFAWRdKTyBsw1sfTzmZaGR716aQD4O5wMcobQQ7vc/e3gJfNbC1wAvD7FN+CSNPL5bSDvbj7XjNr6O/FZpzGo1rvydLcX2bWDvwF2JbaRksbTf3rqGUNI4HhwCvAROA64J3ufnWug5mtBM52903h8rqwT6ImM7sCuCJcPBp4vpvtlntPlbznLPXJSi0T3X1MmedXTFlMtQZlUVksSVlMtQZlUVksSVmseg35eQMYBbQCG/L6zAAmFeaN4D+Yi9z9e+HjdwIPu/uCwo0oi5moRVls3BpyOcXdx5jZJeh7Mcu1lOtTOovunuoPsCTtbWa1jlrWAFwA3JG3fAlwS0GfVcC4vOV1wCG1fE+VvOcs9clSLTX4jNQ9A1mpQ1nMfp8s1VKDz0jdM5CVOpTF7PfJUi01+IzUPQNZqaO3NfQmb8C3gYvzHr8T+ECt31PWPtuNVm81f7KQgazUoe/F+vbJUi29+TxoTp/mtQkYn7c8Dthcqo+ZDQBGAG+kUp1I36EsimSDsiiSnt7krZLnikjv6Xuxj9BBn+b1NDDFzCab2SCCCfEWFvRZCFwatj8I/MrDQ4giUjXKokg2KIsi6elN3hYCF4Z3DZoMTAEWp1S3SF+i78U+oh4TOc+rwzaLyUIdNavB3TvN7Grg50B/4LvuvsrMvkRwWdhCgstl7wknyHuDIOi9Ve49VfKes9QnS7VUWxYyANmoQ1nMfp8s1VJtWcgAZKMOZTH7fbJUS7VlIQOQjTp6VUNv8hb2m08wmWwncJW77+tNPaFG+2w3Wr3VlIUMQDbq0PdifftkqZZK++wn1YmcRUREREREREQkHRreJSIiIiIiIiLShHTQR0RERERERESkCaV60MfMzjaz581srZnNSXG73zWzrWa2Mu+xUWb2qJm9GP45ssY1jDezx81sjZmtMrNr6lFHrZTbt8X2QZE+RX9HBX1azGyxmT0b9vliidfqb2bLzOwnJdavN7MVZrbczJaU6HOwmS0ws+fCmk4qWH90+Pzczw4zu7agz7+Gda40s3vNrKXIdq4J168qfH6tKIvKorKoLCqLtaUsKosVbldZrDFlUVmscLvKYo0pi3XMYk/u896TH4LJodYBRwCDgGeB6Slt+zTgeGBl3mNfB+aE7TnADTWuoQ04PmwPA14ApqddR732bbF9UOnvqKCPAa1heyDwFHBikde6DvgB8JMS21oPjC7zvu4GPhq2BwEHl/kdbAEm5j12OPAyMCRcng9cVvC8Y4GVwFCCidV/CUyp9/6q4baVxTrvW2VRWSz1OVAW0923yqKyWOpzoCymu2+VRWWx1OdAWUx33yqLtctimlf6nACsdfeX3H0vcB9wXhobdvffEMw2nu88gp1E+Of5Na7hVXdfGrZ3AmsIdnKqddRI2X1bYh9Q0KfU7yi/j7v7rnBxYPiTmI3czMYB7wPu6OkbMrPhBH/x3Blud6+7/7mbp5wBrHP3VwoeHwAMMbMBBEHdXLB+GrDI3Xe7eyfwBPAPPa27QsqisqgsKovKYm0pi8piRZTFmlMWlcWKKIs1pyzWMYtpHvQ5HNiYt7yJgh2UsrHu/ioEHx7g0LQ2bGaTgJkERx3rVkcVVX3fFvyOCtf1N7PlwFbgUXcv7PNN4FNAVzebcOAXZvaMmV1RZP0RQDtwV3jZ3x1mdlA3r3chcG9iA+5/BP4L2AC8Cmx3918UPG8lcJqZHWJmQ4FzgPHdbKcalMWQslieslhTymJIWSxPWawpZTGkLJanLNaUshhSFstTFiuX5kEfK/JYn7tfvJm1Aj8GrnX3HfWup0qqum/L/Y7cfZ+7zwDGASeY2bF5zz0X2Oruz5TZzCnufjzwXuAqMzutYP0AgssLb3P3mcBfCC6nLFbvIGA28KOCx0cSHMGeDLwNOMjMLi54L2uAG4BHgUcILnXsLFN7bymLKIsVvZiyqCymQFms4MWURWUxBcpiBS+mLCqLKVAWK3gxZfGAspjmQZ9NJI9IjWP/S5fS9JqZtQGEf26t9QbNbCDBh/P77n5/veqogart2xK/o6LCy+d+DZyd9/ApwGwzW09w2eDpZva9Is/dHP65FXiA4JLDfJuATXlHhRcQhLqY9wJL3f21gsfPBF5293Z37wDuB04uUsud7n68u59GcEnjiyW2Uy3KorJYlrKoLKIs9oayqCz2hrJYPcqistgbymL1KIt1zGKaB32eBqaY2eTwSNeFwMIUt19oIXBp2L4UeKiWGzMzIxjvt8bdv1GvOmqkKvu2m99Rfp8xZnZw2B5CEJTncuvd/TPuPs7dJ4V1/MrdLy54jYPMbFiuDfw9wWVz5L3OFmCjmR0dPnQGsLpE6RdRcKleaANwopkNDd/bGQTjTgvf06HhnxOAfyzxWtWkLCqL3VIWlUWUxd5SFpXF3lAWq0dZVBZ7Q1msHmWxnln0dGftPodghu11wGdT3O69BGPkOgiOyF0OHAI8RnCU7DFgVI1rOJXgErY/AMvDn3PSrqNe+7bYPqj0d1TQ5zhgWdhnJfD5bmp6N0VmYycYf/ls+LOq1GcRmAEsCbf1IDCySJ+hwOvAiBKv8UWCv2RWAvcAg4v0eZLgL4hngTOysL9quF1lsc77VllUFkt9DpTFdPetsqgslvocKIvp7ltlUVks9TlQFtPdt8pi7bJo4YuIiIiIiIiIiEgTSXN4l4iIiIiIiIiIpEQHfUREREREREREmpAO+oiIiIiIiIiINCEd9BERERERERERaUI66CMiIiIiIiIi0oR00EdEREREREREpAnpoI+IiIiIiIiISBP6f2kSS60cTVAIAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x720 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_error(index_slice, pred, test_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "DONE"
   ]
  }
 ],
 "metadata": {
  "file_extension": ".py",
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  },
  "mimetype": "text/x-python",
  "name": "python",
  "npconvert_exporter": "python",
  "pygments_lexer": "ipython3",
  "version": 3
 },
 "nbformat": 4,
 "nbformat_minor": 2
}