fashion_mnist_indra_adagrad.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "#importing necessary libraries\n",
    "import keras\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Conv2D, MaxPooling2D, Dense, Flatten, Dropout, LeakyReLU, ReLU\n",
    "from keras.optimizers import Adam, Adagrad\n",
    "\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import the Fashion MNIST dataset\n",
    "fashion_mnist = keras.datasets.fashion_mnist\n",
    "(x_train, y_train), (x_test, y_test) = fashion_mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#The class labels\n",
    "class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',\n",
    "               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Mean subtraction and normalization on the input data\n",
    "\n",
    "mean_train = np.mean(x_train)\n",
    "std_train = np.std(x_train)\n",
    "\n",
    "mean_test = np.mean(x_test)\n",
    "std_test = np.std(x_test)\n",
    "\n",
    "x_train_norm = (x_train - mean_train)/std_train\n",
    "x_test_norm = (x_test - mean_test)/std_test\n",
    "\n",
    "# x_train_norm = x_train / 255\n",
    "# x_test_norm = x_test / 255\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 25 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Checking the first 25 images\n",
    "plt.figure(figsize=(10,10))\n",
    "for i in range(25):\n",
    "    plt.subplot(5,5,i+1)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.grid(False)\n",
    "    plt.imshow(x_train_norm[i], cmap=plt.cm.binary)\n",
    "    plt.xlabel(class_names[y_train[i]])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Splitting the training data to train and verification data\n",
    "x_train_norm, x_validate, y_train, y_validate = train_test_split(\n",
    "    x_train_norm, y_train, test_size=0.2, random_state=12345,\n",
    ")\n",
    "\n",
    "#Reshaping the data\n",
    "img_rows = 28\n",
    "img_cols = 28\n",
    "batch_size = 512\n",
    "img_shape = (img_rows, img_cols, 1)\n",
    "\n",
    "x_train_norm = x_train_norm.reshape(x_train_norm.shape[0], *img_shape)\n",
    "x_test_norm = x_test_norm.reshape(x_test_norm.shape[0], *img_shape)\n",
    "x_validate = x_validate.reshape(x_validate.shape[0], *img_shape)\n",
    "\n",
    "# # One-hot encode the y\n",
    "# y_train = tf.keras.utils.to_categorical(y_train, 10)\n",
    "# y_validate = tf.keras.utils.to_categorical(y_validate, 10)\n",
    "# y_test = tf.keras.utils.to_categorical(y_test, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:\n",
      "The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
      "For more information, please see:\n",
      "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
      "  * https://github.com/tensorflow/addons\n",
      "  * https://github.com/tensorflow/io (for I/O related ops)\n",
      "If you depend on functionality not listed there, please file an issue.\n",
      "\n",
      "WARNING:tensorflow:From F:\\Programs\\Anaconda\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
      "\n",
      "WARNING:tensorflow:From F:\\Programs\\Anaconda\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:3976: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n",
      "\n",
      "WARNING:tensorflow:From F:\\Programs\\Anaconda\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:131: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
      "\n",
      "WARNING:tensorflow:From F:\\Programs\\Anaconda\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:133: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n",
      "\n",
      "WARNING:tensorflow:From F:\\Programs\\Anaconda\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n",
      "WARNING:tensorflow:From F:\\Programs\\Anaconda\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Building the model\n",
    "\n",
    "name = 'Xavier_2Layer'\n",
    "modelXavier2L = Sequential([\n",
    "    Conv2D(32, kernel_size=3, activation='relu', input_shape=img_shape, \n",
    "           kernel_initializer=tf.contrib.layers.xavier_initializer(), name='Conv2D_Layer1'),\n",
    "    MaxPooling2D(pool_size=2, name='MaxPool'),\n",
    "    Dropout(0.2, name='Dropout-1'),\n",
    "    Conv2D(64, kernel_size=3, activation='relu', name='Conv2D_Layer2'),\n",
    "    Dropout(0.3, name='Dropout-2'),\n",
    "    Flatten(name='flatten'),\n",
    "    Dense(64, activation='relu', name='Dense'),\n",
    "    Dense(10, activation='softmax', name='Output')\n",
    "], name=name)\n",
    "\n",
    "name = 'He_2Layer'\n",
    "modelHe2L = Sequential([\n",
    "    Conv2D(32, kernel_size=3, activation='relu', input_shape=img_shape, kernel_initializer=tf.keras.initializers.he_normal(), name='Conv2D_Layer1'),\n",
    "    MaxPooling2D(pool_size=2, name='MaxPool'),\n",
    "    Dropout(0.2, name='Dropout-1'),\n",
    "    Conv2D(64, kernel_size=3, activation='relu', name='Conv2D_Layer2'),\n",
    "    Dropout(0.3, name='Dropout-2'),\n",
    "    Flatten(name='flatten'),\n",
    "    Dense(64, activation='relu', name='Dense'),\n",
    "    Dense(10, activation='softmax', name='Output')\n",
    "], name=name)\n",
    "\n",
    "name='He_3Layer'\n",
    "modelHe3L = Sequential([\n",
    "    Conv2D(32, kernel_size=3, activation='relu', input_shape=img_shape, kernel_initializer=tf.keras.initializers.he_normal(), name='Conv2D_Layer1'),\n",
    "    MaxPooling2D(pool_size=2, name='MaxPool'),\n",
    "    Dropout(0.25, name='Dropout-1'),\n",
    "    Conv2D(64, kernel_size=3, activation='relu', name='Conv2D_Layer2'),\n",
    "    Dropout(0.25, name='Dropout-2'),\n",
    "    Conv2D(128, kernel_size=3, activation='relu', name='Conv2D_Layer3'),\n",
    "    Dropout(0.4, name='Dropout-3'),\n",
    "    Flatten(name='flatten'),\n",
    "    Dense(128, activation='relu', name='Dense'),\n",
    "    Dropout(0.4, name='Dropout'),\n",
    "    Dense(10, activation='softmax', name='Output')\n",
    "], name=name)\n",
    "\n",
    "\n",
    "name = 'Xavier_2Layer_LeakyRELU'\n",
    "modelXavier2LLeakyRELU = Sequential([\n",
    "    Conv2D(32, kernel_size=3, input_shape=img_shape, kernel_initializer=tf.contrib.layers.xavier_initializer(), name='Conv2D_Layer1'),\n",
    "    LeakyReLU(),\n",
    "    MaxPooling2D(pool_size=2, name='MaxPool'),\n",
    "    Dropout(0.2, name='Dropout-1'),\n",
    "    Conv2D(64, kernel_size=3, name='Conv2D_Layer2'),\n",
    "    LeakyReLU(),\n",
    "    Dropout(0.3, name='Dropout-2'),\n",
    "    Flatten(name='flatten'),\n",
    "    Dense(64, name='Dense'),\n",
    "    LeakyReLU(),\n",
    "    Dense(10, activation='softmax', name='Output')\n",
    "], name=name)\n",
    "\n",
    "name = 'He_2Layer_L2'\n",
    "modelHe2LL2 = Sequential([\n",
    "    Conv2D(32, kernel_size=3, input_shape=img_shape, kernel_initializer=tf.keras.initializers.he_normal(), \n",
    "           activity_regularizer=tf.keras.regularizers.l2(10e-3), name='Conv2D_Layer1'),\n",
    "    ReLU(),\n",
    "    MaxPooling2D(pool_size=2, name='MaxPool'),\n",
    "    Conv2D(64, kernel_size=3, activity_regularizer=tf.keras.regularizers.l2(10e-3), name='Conv2D_Layer2'),\n",
    "    ReLU(),\n",
    "    Flatten(name='flatten'),\n",
    "    Dense(64, activation='relu', name='Dense'),\n",
    "    Dense(10, activation='softmax', name='Output')\n",
    "], name=name)\n",
    "\n",
    "cnn_models = [modelXavier2L, modelHe2L, modelHe3L, modelXavier2LLeakyRELU, modelHe2LL2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From F:\\Programs\\Anaconda\\lib\\site-packages\\keras\\optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
      "\n",
      "WARNING:tensorflow:From F:\\Programs\\Anaconda\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:3341: The name tf.log is deprecated. Please use tf.math.log instead.\n",
      "\n",
      "WARNING:tensorflow:From F:\\Programs\\Anaconda\\lib\\site-packages\\tensorflow\\python\\ops\\math_grad.py:1250: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
      "Train on 48000 samples, validate on 12000 samples\n",
      "Epoch 1/10\n",
      "48000/48000 [==============================] - 21s 428us/step - loss: 0.7843 - acc: 0.7500 - val_loss: 0.4508 - val_acc: 0.8341\n",
      "Epoch 2/10\n",
      "48000/48000 [==============================] - 19s 393us/step - loss: 0.4028 - acc: 0.8547 - val_loss: 0.3503 - val_acc: 0.8820\n",
      "Epoch 3/10\n",
      "48000/48000 [==============================] - 19s 396us/step - loss: 0.3616 - acc: 0.8708 - val_loss: 0.3288 - val_acc: 0.8855\n",
      "Epoch 4/10\n",
      "48000/48000 [==============================] - 19s 404us/step - loss: 0.3350 - acc: 0.8790 - val_loss: 0.2996 - val_acc: 0.8973\n",
      "Epoch 5/10\n",
      "48000/48000 [==============================] - 19s 400us/step - loss: 0.3153 - acc: 0.8872 - val_loss: 0.2887 - val_acc: 0.9001\n",
      "Epoch 6/10\n",
      "48000/48000 [==============================] - 19s 398us/step - loss: 0.3015 - acc: 0.8916 - val_loss: 0.2770 - val_acc: 0.9049\n",
      "Epoch 7/10\n",
      "48000/48000 [==============================] - 19s 400us/step - loss: 0.2911 - acc: 0.8941 - val_loss: 0.2671 - val_acc: 0.9071\n",
      "Epoch 8/10\n",
      "48000/48000 [==============================] - 20s 414us/step - loss: 0.2802 - acc: 0.8993 - val_loss: 0.2651 - val_acc: 0.9071\n",
      "Epoch 9/10\n",
      "48000/48000 [==============================] - 20s 422us/step - loss: 0.2706 - acc: 0.9022 - val_loss: 0.2556 - val_acc: 0.9104\n",
      "Epoch 10/10\n",
      "48000/48000 [==============================] - 20s 411us/step - loss: 0.2654 - acc: 0.9035 - val_loss: 0.2518 - val_acc: 0.9131\n",
      "Train on 48000 samples, validate on 12000 samples\n",
      "Epoch 1/10\n",
      "48000/48000 [==============================] - 20s 425us/step - loss: 1.0622 - acc: 0.7271 - val_loss: 0.4363 - val_acc: 0.8388\n",
      "Epoch 2/10\n",
      "48000/48000 [==============================] - 19s 402us/step - loss: 0.4196 - acc: 0.8479 - val_loss: 0.3446 - val_acc: 0.8793\n",
      "Epoch 3/10\n",
      "48000/48000 [==============================] - 20s 411us/step - loss: 0.3744 - acc: 0.8628 - val_loss: 0.3272 - val_acc: 0.8868\n",
      "Epoch 4/10\n",
      "48000/48000 [==============================] - 20s 424us/step - loss: 0.3454 - acc: 0.8732 - val_loss: 0.3277 - val_acc: 0.8776\n",
      "Epoch 5/10\n",
      "48000/48000 [==============================] - 20s 420us/step - loss: 0.3243 - acc: 0.8811 - val_loss: 0.2896 - val_acc: 0.8970\n",
      "Epoch 6/10\n",
      "48000/48000 [==============================] - 20s 417us/step - loss: 0.3118 - acc: 0.8857 - val_loss: 0.2846 - val_acc: 0.8974\n",
      "Epoch 7/10\n",
      "48000/48000 [==============================] - 20s 415us/step - loss: 0.2984 - acc: 0.8904 - val_loss: 0.2723 - val_acc: 0.9019\n",
      "Epoch 8/10\n",
      "48000/48000 [==============================] - 20s 418us/step - loss: 0.2861 - acc: 0.8946 - val_loss: 0.2741 - val_acc: 0.8997\n",
      "Epoch 9/10\n",
      "48000/48000 [==============================] - 20s 419us/step - loss: 0.2774 - acc: 0.8979 - val_loss: 0.2639 - val_acc: 0.9052\n",
      "Epoch 10/10\n",
      "48000/48000 [==============================] - 20s 418us/step - loss: 0.2705 - acc: 0.8993 - val_loss: 0.2595 - val_acc: 0.9062\n",
      "Train on 48000 samples, validate on 12000 samples\n",
      "Epoch 1/10\n",
      "48000/48000 [==============================] - 44s 909us/step - loss: 0.9925 - acc: 0.6826 - val_loss: 0.4311 - val_acc: 0.8484\n",
      "Epoch 2/10\n",
      "48000/48000 [==============================] - 42s 867us/step - loss: 0.4772 - acc: 0.8257 - val_loss: 0.3484 - val_acc: 0.8747\n",
      "Epoch 3/10\n",
      "48000/48000 [==============================] - 43s 902us/step - loss: 0.4138 - acc: 0.8507 - val_loss: 0.3196 - val_acc: 0.8825\n",
      "Epoch 4/10\n",
      "48000/48000 [==============================] - 43s 898us/step - loss: 0.3816 - acc: 0.8620 - val_loss: 0.2938 - val_acc: 0.8922\n",
      "Epoch 5/10\n",
      "48000/48000 [==============================] - 43s 906us/step - loss: 0.3591 - acc: 0.8692 - val_loss: 0.2811 - val_acc: 0.8967\n",
      "Epoch 6/10\n",
      "48000/48000 [==============================] - 43s 901us/step - loss: 0.3378 - acc: 0.8781 - val_loss: 0.2649 - val_acc: 0.9033\n",
      "Epoch 7/10\n",
      "48000/48000 [==============================] - 44s 908us/step - loss: 0.3236 - acc: 0.8815 - val_loss: 0.2640 - val_acc: 0.9034\n",
      "Epoch 8/10\n",
      "48000/48000 [==============================] - 43s 889us/step - loss: 0.3147 - acc: 0.8870 - val_loss: 0.2542 - val_acc: 0.9063\n",
      "Epoch 9/10\n",
      "48000/48000 [==============================] - 43s 902us/step - loss: 0.3074 - acc: 0.8889 - val_loss: 0.2488 - val_acc: 0.9102\n",
      "Epoch 10/10\n",
      "48000/48000 [==============================] - 43s 897us/step - loss: 0.2957 - acc: 0.8926 - val_loss: 0.2446 - val_acc: 0.9117\n",
      "Train on 48000 samples, validate on 12000 samples\n",
      "Epoch 1/10\n",
      "48000/48000 [==============================] - 21s 446us/step - loss: 0.7447 - acc: 0.7695 - val_loss: 0.4582 - val_acc: 0.8296\n",
      "Epoch 2/10\n",
      "48000/48000 [==============================] - 20s 424us/step - loss: 0.4076 - acc: 0.8533 - val_loss: 0.3587 - val_acc: 0.8731\n",
      "Epoch 3/10\n",
      "48000/48000 [==============================] - 21s 435us/step - loss: 0.3683 - acc: 0.8683 - val_loss: 0.3312 - val_acc: 0.8880\n",
      "Epoch 4/10\n",
      "48000/48000 [==============================] - 21s 434us/step - loss: 0.3422 - acc: 0.8778 - val_loss: 0.3201 - val_acc: 0.8843\n",
      "Epoch 5/10\n",
      "48000/48000 [==============================] - 21s 433us/step - loss: 0.3262 - acc: 0.8838 - val_loss: 0.2969 - val_acc: 0.8957\n",
      "Epoch 6/10\n",
      "48000/48000 [==============================] - 21s 445us/step - loss: 0.3110 - acc: 0.8885 - val_loss: 0.3248 - val_acc: 0.8818\n",
      "Epoch 7/10\n",
      "48000/48000 [==============================] - 21s 439us/step - loss: 0.3001 - acc: 0.8916 - val_loss: 0.2848 - val_acc: 0.8977\n",
      "Epoch 8/10\n",
      "48000/48000 [==============================] - 21s 436us/step - loss: 0.2930 - acc: 0.8952 - val_loss: 0.2777 - val_acc: 0.9009\n",
      "Epoch 9/10\n",
      "48000/48000 [==============================] - 21s 435us/step - loss: 0.2871 - acc: 0.8971 - val_loss: 0.2738 - val_acc: 0.9012\n",
      "Epoch 10/10\n",
      "48000/48000 [==============================] - 21s 431us/step - loss: 0.2775 - acc: 0.8997 - val_loss: 0.2601 - val_acc: 0.9093\n",
      "Train on 48000 samples, validate on 12000 samples\n",
      "Epoch 1/10\n",
      "48000/48000 [==============================] - 22s 463us/step - loss: 87655.3169 - acc: 0.7184 - val_loss: 49807.5036 - val_acc: 0.8239\n",
      "Epoch 2/10\n",
      "48000/48000 [==============================] - 23s 471us/step - loss: 40013.1882 - acc: 0.8401 - val_loss: 32136.4601 - val_acc: 0.8387\n",
      "Epoch 3/10\n",
      "48000/48000 [==============================] - 22s 469us/step - loss: 28428.3845 - acc: 0.8498 - val_loss: 24715.7708 - val_acc: 0.8506\n",
      "Epoch 4/10\n",
      "48000/48000 [==============================] - 23s 470us/step - loss: 22752.0201 - acc: 0.8540 - val_loss: 20467.4445 - val_acc: 0.8552\n",
      "Epoch 5/10\n",
      "48000/48000 [==============================] - 23s 469us/step - loss: 19238.7330 - acc: 0.8570 - val_loss: 17629.6644 - val_acc: 0.8554\n",
      "Epoch 6/10\n",
      "48000/48000 [==============================] - 23s 474us/step - loss: 16785.4247 - acc: 0.8588 - val_loss: 15558.8014 - val_acc: 0.8558\n",
      "Epoch 7/10\n",
      "48000/48000 [==============================] - 23s 470us/step - loss: 14943.2158 - acc: 0.8596 - val_loss: 13959.0485 - val_acc: 0.8555\n",
      "Epoch 8/10\n",
      "48000/48000 [==============================] - 22s 467us/step - loss: 13490.8983 - acc: 0.8612 - val_loss: 12673.5378 - val_acc: 0.8553\n",
      "Epoch 9/10\n",
      "48000/48000 [==============================] - 23s 472us/step - loss: 12306.8185 - acc: 0.8625 - val_loss: 11610.1083 - val_acc: 0.8559\n",
      "Epoch 10/10\n",
      "48000/48000 [==============================] - 23s 471us/step - loss: 11315.6877 - acc: 0.8627 - val_loss: 10711.0025 - val_acc: 0.8579\n"
     ]
    }
   ],
   "source": [
    "# train the models and save results to a dict\n",
    "\n",
    "history_dict = {}\n",
    "\n",
    "for model in cnn_models:\n",
    "    model.compile(\n",
    "        loss='sparse_categorical_crossentropy',\n",
    "        optimizer=Adagrad(),\n",
    "        metrics=['accuracy']\n",
    "    )\n",
    "    \n",
    "    history = model.fit(\n",
    "        x_train_norm, y_train,\n",
    "        batch_size=batch_size,\n",
    "        epochs=10, verbose=1,\n",
    "        validation_data=(x_validate, y_validate)\n",
    "    )\n",
    "    \n",
    "    history_dict[model.name] = history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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EICnl1h9RNQs4EHZeBIxqVWYTMB1tyGAqYBFCpBymbtZh5LsRuBEgJyfnR4ipUHRfAkFJlcNDZYO3SaFXHmZf42zbk7NEGbBZokiLj+ayYVlNgXl90uKwxbVvqdUad01Tl/7mis1sqdyCM6RdkqKSGGIbwqWnXMoQ2xAGWQcRa4yN6O/wowiEuq7Du+8bx+3tpWEFBSTmaF58boGm6Bu9eksmdEaXsk7X7OGHCDoc+MrK8ZeV4msow19TGubFl+EvLSVQc2jnqC7GhDExBkOKkagcgdEcwBDlxWh0YDDUYTQ50BklQhQ2VwpoPwOxQFZuc5Be2iAtcC8xt9OGJBSRI1I9Af8SQpiAucB/pJS17azX1l9M6/6uu4FnhBDXAEuBg4C/nXW1i1K+ALwA2nBAO2VTKLqMYFBS6/K19Njb8N4r7R6qHV7aSn4XY9Rjs0Rhs0RxijWOkfnJ2OKisVpM2OK069bQvvVY/dHwB/3sqtnVpPA3VWyisEFTGHqh59SkU7m096UMtQ1lqE1LK9tl0e5SgqMirPs+TNHX7G2edgZaN3lKH+gzrqWiT8rXEst0msiSYEODptDb6J73l5XiKy3TVqFrhT4pCUN6Osa0NGKGDsGYloYhLR1jehqG9HQMqWno445igPlch/Y4eB3a75E28EdNxVN0TyJiBEgpxwoh+gLXAWuFEKuBV6SUC49StQjoFXaeDRS3arsYmAYghIgDpksp64QQRcC5rep+3ZH3UCiOJ1JK6l3+Ft3vla32jYq9yu7F34ZmNxl02OKisFqiyE4yc3pOErY4UwuF3ngcGxW50b5KV2ULhb+9antTVrqU6BSG2oYyre80htqGMiBlQNcs9+p1QvWe5i77ql3N3fmh2QWAljQmuTekngb9L2k5Vh/mZR9P/DU1+EtK8IUpdH9paZP37isv1+aqhyMEBqsVQ3o6prw8zKNGY0hLxZiejiEtTdunpkZmGVpjDCRkaZvihCaigYGhQL/LgKeAejRv/V4p5XuHKW9ACwwch+bhrwGulFJuCytjBaqllEEhxMNAQEp5fygwcB0wPFR0PVpgYPWRZFSBgYrjjcsb4H9bSli7r7qVF+/FGwgeUt6gE00K3BpS6OFKPVy5W6IMx9ejdtXi87vYWf09m6q2sql6B5urd3DQVa7JKvScZsllaEJvhljyGWrJJdOUiJABkAEINm5+bZPB0PERrsnwOq2vhddrda3x3NOgKf36opbvEp+tBeRZQ0o+pa82Vp/Qq8tSunp27aL8iSexf/llyxsGA4ZUG8a0dAzpaWH7MC/eZkMYjV0it6Jnc9wDA4UQQ4BrgYuBhcClUsr1QohMYAXQphEgpfQLIW4FvkCbIviylHKbEOJBYK2U8iM0b//vQgiJNhxwS6hutRDir2iGA8CDRzMAFIrjyfbieuatKeT9DQdpcPtJjjWRFh+NzRJF79Q4TZE3KvSQN2+LiyIhxtglC88EZZASRwn76vaxr2Qde7f9l+/d5Ww3mfCExrhT/X6Gerxc4fYwxOOhv9dHtNzLcet0Ezot0l3otb1OH9qOcM0Yo43TNyn7PpryN3WDmIMQ3qKDVD7zDHUffoguNpaUm35NzMCBGNLStC0lRQWwKbqEiPQECCGWAnOA+VJKV6t7V0spX+/wQyKE6glQRBKHx88nm4v5z+oDbDpQi8mg46JB6VwxMoeR+cndIuubw+dgX90+9tbv1fZ1e9lXv4/C+sIWKWuTvAH66Wz0teUwNDaToeYs0qOTj00h6wxaAJvOEHYtrMzRrnWD3yuS+KuqqPz3v6l9ax4IQdJVV5Fyw0wMSUldLZriJKIzpgheBLiklIHQA3VAtJTS2Z0MAIUiUmwpquOtNYV8tLEYu8dP39Q47r9kANOGZ5Fo7vz84o1e/d46TdHvq9/XdFwe6soHbWpeVlwWefF5jE7sR96uJZyy8wdSSvLw7AwQtFdimTiclJm/JGbgwE5/jxOFgN1O9cuvUD13LkGPh8Rp07DecjPG9PSuFk2haEGkjIBFwHjAHjo3AwuAggi1r1B0OQ1uHx9tKuat1YVsPVhPtFHHxYMzuXJUL4bnJHWK1x/u1Tcq+b31eymsL8QTaF5S12KykB+fz+jM0eQn5JMXn0defB458TmYEAQW/D/q5v6bur1m3FWJuI0OLBPGY0hLp/add2j47HNiCwpIuWEm5tGju0WPRk8g6PFQ89ZbVP3r3wRqa7FMmoTt9tuJOiW/q0VTKNokUkZAtJSy0QBASmkXQpygCbAVJxNSSjYeqGXe6gN8tKkYly/AaekWHpwykCnDskiIiXygViAY0Mbq6/e16L5vy6vPjssmLyGPgowC8hI0RZ+fkE9y9KFDETIYxPHJG1S8+CgNuz3IYBxR/fqSdtPPSbjkYvSJWmIb6803UTNvHtWvvUbhtdcRPWgQKTfcgGX8ODVufRik30/dhx9R8cwz+EtKiC0owDZ7NjGDB3W1aArFEYlUTMC3wG1SyvWh8zOAZ6SUYzrceIRRMQGK9lDn8vHBhoO8tbqQ70obMJv0XDokkytG5TA0OyEinrHda2/utq9vOVZ/iFcf8ubzE/LJj88nLyGPXpZe7VpG1ltURN38t6n975v4a5zooiQJ48eSeP1dRA8YcNh6QY+Hug8+pOrll/DtL8SUm0vy9deRcNll6E7AJVV/DFJKGhYtouKJJ/Hu2UP04MGk3jWb2DHd7tOnOIk5UkxApIyAM4F5NM/xzwB+LqVc1+HGI4wyAhSHQ0rJuv01/Gd1IZ9uKcHtCzI4K4ErRuZw6dAMLNHH7vU3evXh3nxjgF6Fq6KpnF7oybZkN3Xb5yfkN3n2bXn1RyPodtOwcCG1776Hc+VKAGLT3SSefwZxtz+DLrH9Y9MyEKBh4UKq5ryIe9s2DDYbyb/6JYkzZqCPizsmuU4kHCtXUf7YY7g3b8Z0yinY7rwDy4QJauhE0e047kZA6CFGoB9aboDvpJTdcqUJZQQoWlPj8PLehoPMW13IrnI7cVEGpgzL5IqROQzKOrZlan1BH2tL17Jo/yLWl6+nsL4Qb7B50Z14U3zzGH1Cs2ffy9ILo75jQwtSStxbt1L77rvU/+9Tgg0NGFPMJGaWkjA0CeMVT2nLtHagfefKlVTNmYNj+Qp0FgtJM2aQ/MurMdhsHZK9J+Hauo2Kxx7DsXw5howMbLfeSsKUyQhDVy/FolC0TWcZAYPQFvtpSlclpXwtIo1HEGUEKEBTaKv2VvPW6kI+21qK1x9kWK9ErhyZw8VDMo4p25434GVF8QoWFS5i8YHF1HnqiDHEMCJtBL0Te7fw7JOiIh9A6K+upu6jj6h79z08u3YhoqOJHzOYhLh1mM0HEaNnwfn3QVTkvHbX1m1UvfgiDQsWIAwGEqZOJeW6azHl5kbsGd0Nzw97qXjqKRo+/xx9YiIpv55F0hVXnJSr0il6Fp0xHPBntKQ+A4BPgQuBZVLKyzvceIRRRsDJTZXdw7vri5i3+gA/VDqwRBuYdnoWM0bm0D+j/fnQXX4Xyw4uY+H+hSwtWorD58BitHBOr3MYnzueszLPItpw/HLNS78f+zffUPfe+zQsXgx+P9FDh5B4ySTiDcvR73xby5A35RnIGX3c5PDu30/Vy69Q9957yEAAy8QLSJk584SaXugrLaXy2eeofe89RFQUKddcQ/J1157UQyGKnkVnGAFbgKHABinlUCFEGvCilPLSDjceYZQRcPIRDEpW/FDFf1YXsmBbKb6AZERuEleMzOGiwRnEmNoX8W732llStIRF+xex7OAy3AE3iVGJnJ9zPuNzxjM6Y3SHu/SPhueHvdS9/x51H3yIv6ICfXIyCVOmkDhtKlG+HfC/32iLvYy9E87+XacteuMrL6fm9depeWseQbv9hJheGKitpXLOHGreeBMZDJI0YwbWX8/CkJLS1aIpFMdEZxgBq6WUI4UQ64DzgAZgq5Sy27kDygg4eShvcDN/neb1F1Y7STQbmXZ6NleM7EXfNEu72qh117L4wGIWFS5iRfEKfEEfthgb5+ecz4TcCZyRdgYG3fEdCw7YHTR88Tm1776Ha/160OuJO+ccEqdPI+7ssxHuavj0btjxkba065RnIWPocZXpsLI2NFD73/9S9eqrBCoqiR44UJteOGF8j5leGHQ6qX7tNapeepmg3U7ClClYb70VU7ZaTEfRM+kMI+A54F5gBvAbtKRBG6WU13a48QijjIATm0BQ8s2uCuatPsCiHWX4g5LRpyRzxcgcJg5Mb9eSuZWuSr4q/IqF+xeypnQNARkgMzaTcbnjmJA7gaG2oejE8V0/XkqJa/16at99j/rPP0c6nZjy80m8fDoJkydrgXhSwqa34PM/aEu/nvt7KLgdjnNvRHsIejzUffgh1S+9jHf//h4xvVB6vdS88w6Vz/+LQGUlcePGYbvjdqJPPbWrRVMoOsRxNQKE1teXLaU8EDrPA+KllJs71PBxQhkBPYvGiHTv/v0YMzIwZmVhzMxEZ26Zi6q0zs3baw/w3zUHOFjrIjnWxOVnZPPzM3vR23b0sdtSRymL9i9i4f6FbCjfgESSF5/H+NzxjM8Zz4CUAZ3Sre0rK6fuww+pe/ddvPv3ozObib/4IhKmTSNm2LBmGWoPwCd3wu5F0GsUTH4GbN1PWWnTCxdRNWcO7m3b0NuspPzqVyT+/OfoLe3rjTneyGCQ+v/9j4onn8JXVIR5xAhsv7kL8+mnd7VoCkVE6IyegHVSyjM63FAnoIyAnoGUEse3y6l85hlcGzcecl+fmIghK4taSwrbgmbWuqMojUkmq18+488fyrgRvYkyHNnrP1B/gIWFC1m0fxFbKrcA0DepLxNyJjA+dzx9Evt0iuKXXi8NX39N3bvvYf/mGwgGMY8YQcL06cRPvKClwRMMwtqXYNEDWk/A+D/DmTdoi/Z0Y5qnF76IY/lydHFxJF0xg+Rf/rLLphdKKbEvWULF40/g2bmTqP79tUQ/Y8f22DgGhaItOsMIeBaYK6Vcc9TCXYwyAro3rZW/wZaCddJA4gb3whc3GJ9DR9UPhezavIu6vYUk1leS5qrFFGiZlkJnsTT1GjTvMymLlyzxb+fzqmXsrP0egIEpA5s8/ryEvE57V/f331P37nvUffQRgZoaDKmpJEydSuLUyzDltSFH5W746FYoXAGnnAeXPglJPW9KnmvrNqpeepGGL0LTCy+7jJTrr+vU6YXOdesof+xxXOvWYczJwXbH7cRfeCGimxtTCsWPoTOMgO3AqcB+wIGWMEhKKYd0uPEIo4yA7omUEsc3S6l84p+4tu/GEG/AOqCBhF416MIc+lJDFh+5h7EoeAbm3gXMGJXP+afZ0NXW4Csu1raDB/EdDO2Li3EXHUC43C2e5zXpCKQmY8ntTVxOPqZWBoPeaj0u3mCgvp76Tz+l9t33cG/ZAkYjlvPPJ3H6NGILCtpOOBPww4qnYfHftWj/iX+HYVf2+GV3m6YXvv8+0u/HckFoeuGg4xdP7N65k4rHHse+ZAkGmw3rLTeTOH06wtj1cRQKxfGiM4yANk14KeX+DjceYZQR0I0I+JAH1+P49L9UvvM1roMuDGY/1gF2TEPTKUk5k++ih7CWAaz9oYJhrhVcYtrAmWxFL/1gToFTJ0G/C7VMeKZYQFtWd3PFZhbtX8SiwkUcbCgi3qPnHH1/xhr6MciXiqmiLmQwaIZDsK6uhWgiKqpFDIIxKwtjVmbTscFma3e0uwwGca5eTe2779GwYAHS4yHq1FNJvHw68ZdeeuS15Uu3wIe3QslGOO0SuPhRsJxYy9H6Kyqofu11at56KzS9cAwpN9wQ0emF3gMHqHjqaeo/+QSdxULKDTNJvuoqdDExEWlfoejOdIYRkNPWdSllYYcbjzDKCOg6gj4PtXtW4961BEPht0Rt20LNpihcVSaEGYoGZvJB5ihWikFUoqXrFQLSLNEMykpgxpm9OLefDYPPrgXE7fwUdi0Adx0BQzTr885kYUISXzqLKHdXYtAZGJMxhgm5Ezi317kkRR9e2Qbs9ha9B009CqF9oLq6ZQWjEWN6eqvhhrB9ehr+sjJqP/iAuvfex3fwIDqLhYRLLyFh2nSiBx4l0NDvgaWPwN+1y8IAACAASURBVLLHISYJLnoEBlzW473/I9H29MKZWj7+Hzm90F9RQeXz/6LmnXcQej3JV19Nyszr0SccWzpohaIn01nJgiTaMEA0kA/sVHkCTi6cXj/FtW6Ka10U17oora5HX7qR1KrV9HZsZEDgO2Lw4CiN4sDWFKgCu9nM4uET2TnqYtKs8WQlxpCZGE1mQgyZiTGkJ0Rj1Lc9TusL+lh9cDkLt/+HxWVrqZZeooJBxrrcjI9K55w+l2DpPxVs/TqsPINOJ76SkpbDDWGGgr+8vGUFnU4L3JOS2IIxJEybjmX8OHTR7Ujec2ANfHgLVO6EoVfAxL+BOblD8vckWk8vNObmkHLd9SRcNqXdKXoDDQ1UvfQS1a++hvT5SLx8OtabbsaYlnqcpVcouh+dsnZAqwcOB2ZJKWdFvPEOooyAH0cwKKmwezgYUvDa5m5x7nA6GSr2MEq3g9G67Zyh20WM0BbPKTLmU9HQB936akyFJZCaRsINN5L2s+nojyH3uifgYfnB5U15+hu8DZgNZs7JPofxueMYa0jBvGcx7PwfFG/QKiWfAv0u0rZeo0Af+eQ+Qa8Xf0lJk1HgPXgQXVQ08Zdc0v4kM14HfPUQrHwe4rPg0ieg74SIy9pTkIEADYu+1KYXbt2K3mYl+Ze/JGnGjMNOLwy63dS8+R+qXniBQF0d8RdfjO32207oNQ0UiqPR6UZA6KHrpZTDj0vjHUAZAW3j8PgprnWFlHqzN3+w1kVxnYvSOje+QMu/laQoyflxhYw17GBIYBu5zq0Ygh4AvNaBGE4Zi8gbi6PYSOWLr2nR/hkZWGfNImHa1HYnjXH4HCw7uIxF+xextGgpTr+TeFM85/Y6lwm5ExiTOYYofRuGRH0x7PxMGzbYuxQCXohJhlMnagZB7/MjuqhOh/jha/jodqjdDyOuh/EPQHT71zI4kZFS4ly1iqoX5rSYXph09dUYUzXPXvr91L73HpXPPoe/rIzYs39C6p13Ej1gQBdLr1B0PZ0xHHBX2KkOGA6kSCkndrjxCHOyGwHflzWw+LvykIJ3Nyn6OlfLKXZ6nSA9Plrrmk/UuuazLTr6+7+jV8MGkspXYSheB343ICB9EOT9BHLPgtwCZExSy6l+x6D8fQEfmyo2sap0FatKVrGlYgt+6Sc5OllL15szgTMzzsSoO4aIbk8D7P5SMwi+/wLctaCPglPOCfUSXNg1AXeuWlh4H6x/DZJ7w+SnIe+szpejh+DaFlq98IsFCL2ehKlTiRk2jKoXXsC7bx8xw4Zhu2s2sSNHdrWoCkW3obNWEWzED+wD3pVSutuu0aLuJOBJQI+26NA/Wt3PAV4FEkNl7pFSfiqEMAIvohkcBuA1KeXfj/a8k9kIqLR7GPfoEupcPuKjDWQmxoTG4Bu36KbzVEuU5tUXrYF9y2Dft9pxwAMIyBgCuWMhbyzkjtGC12hjnn87lH9QBtlZvZNVJatYWbqS9WXrcfld6ISOgSkDGZUxioLMAoanDkevi0D++YAPCldqBsF3/9O8b4CsMzSD4LSLwXba8Q/C++5T+N9dYC+Dgtvg3D+AUUWrtwfv/v1UvfIKde+9j/R6ierbB9vs2cSdd55K9KNQtKJLhgPagxBCD3wPTACKgDXAFVLK7WFlXkBbnfB5IcQA4FMpZZ4Q4kpgspRyhhDCDGwHzpVS7jvSM09mI2D2fzfyyeZiPr5tLKelt9HV7HWGKf1lcHCt1oUudJA+RFP4eWMhZwzEJLaoeizKX0rJgYYDrCxZyaqSVawuXU2tpxaAUxJOYVTGKEZnjGZE+gjiTce5S1xKKN+hxRB89ykUr9euJ+WHDIKLoNfoyMYROCrhs9/B1nchdaC23G9Wtxs56xH4Kyrw7PkB85kjeswCRQpFZ3MkIyAiXzYhxELgp1LK2tB5EjCvHcMBI4HdUsofQvXmAVPQFHojEmjUBAlAcdj1WCGEAYgBvEB9BF7nhGTZrkre33CQ28/v02wAeJ1wYBXs/1ZT+kVrIejTlH7GMBg1S+vizxkN0W1PqWpL+ac/8MAhyr/SVcmqEq17f2XJSkocJQCkmdM4O/tsRmeMZlTGKFLNnRy9LQSkDdC2s38L9SXw/WeaQbBmDqx8Vuvl6DtRMwh6nw9RPzLnvZSwZb5mAHga4Nx7YexsMHTPBXV6AgabrcvSDisUJwKRGg7YKKUc1uraBinlEVfgEEJcDkySUs4MnV8NjJJS3hpWJgNYACQBscB4KeW60HDA68A4wAzMllK+cJjn3AjcCJCTk3PG/v3dLofRccXtCzDpiaUAfPELG1E73tO69w+uCyl9PWQOC3Xtjw0p/SN74Efz/O1eO2vL1jZ5+7trdwMQb4pnVMYoRqWPYlTGKHLjc7tv963HDnu+1IILv/8cXDWgN0H+OZpBcOqFEJ/RvrbqDmpd/99/DlkjNO8/tf/xlV+hUCjohJ4AICCEyGlMDhTKINge66Ktr3/relegrUvwqBBiDPC6EGIQWi9CAMhEMxC+EUIsauxVaNGgZhy8ANpwQHtf6kThucW72Vfl5J2fZxH16kXgtUPm6TDmFk3x9xrV7kj0w3n+5ikXs7luByu2/otVpavYVrmNgAwQrY9meNpwLu19KaMyRnFa0mmRGdfvDKLiYMAUbQv44cBKrYdg5//gk9nAbMgcrhkE/S7WlHprg0ZKWDcXFt6vxSJM/BuM+jX0lN9AoVCc0ETKCPgjsEwIsSR0fjYhz/soFAG9ws6zae7ub+R6YBKAlHKFECIasAJXAp9LKX1AuRDiW2AEcIgRcDKzu9zO80v2MG1oKmeu+y3IINy6BlJ6H1M7hyj/9HSCd9/IN8PNrKz8kg3v/hN3wI1e6BlkHcT1g69ndMZohtqGYtKfAN3dekNzTMTEh6HiOy2ocOdn2tz+rx6CxFwtqLDfRVrcRF2hNu1v3zfasMrkp7ScBQqFQtFNiFhgoBDCCoxG8+5XSCkr21HHgBYYOA44iBYYeKWUcltYmc+A/0op5woh+gNfAlnA74DTgOvQhgPWADOklJuP9MyTKTBQSsmMF1ayo6SelSOXYV79FFz+MgyafkxthCt/rzWeVRdk82Z+CdXBBgD6JPZpGtMfkTaCOFM3mXvfWTSUhvIRfKbN9w94IDpRS/2rN8IFf4XhvzqhU/4qFIruS2cEBk4FvpJSfhI6TxRCXCal/OBI9aSUfiHErcAXaNP/XpZSbhNCPAislVJ+BPwGmCOEmI02VHCNlFKGli9+BdiKZni8cjQD4GRj/roiVu2t5pWfNGgGwPBftdsAkFJy8KtPKX/6aWK+2091gp75k3R8PdhBaoKdczLGa2P7GaOwxliP85t0cyzpMOJabfPY4YfF2rCBEHDeHyGhnRkDFT0Cn89HUVERbvdRZ0ArFJ1KdHQ02dnZGI9hVcwuDQzsCk6WnoBqh5dxj37NGcle5rjuRJhT4IbFYDIftk69t541JavZu/ADst/+ltxCN5Xx8PnZsXgnjuXMnALGZIwh25LdfYP5FIrjzN69e7FYLKSkpKj/Hyi6DVJKqqqqaGhoID8/v8W9zggMbGuFl8gnaFe0m799ugO728uT0f9G1DbALz86xADwBDxsKN+gTd0rXol+7Vamf+PnrIPQkBTF3lkTyb/iev6aNhCdaHsRH4XiZMPtdpOXl6cMAEW3QghBSkoKFRUVx1QvUop6rRDiMeBZtC7724B1EWpbcYys/KGK+euKmNt3ObEHlsIlT2jz4IHC+kIW7F/AyuKVbCjfgDfg4fR9Oq5daSJ7n59gagq2P99Mv+mXtzu3v0JxsqEMAEV35Mf8XUbKCLgNuA/4L9r4/ALglgi1rTgGPP4Af3x/C5MS9nNO0b+0NejPuAaApUVLuXvJ3bj8Lvolnsqtvp9wxie7MG7/AUOGFesDvz2mhX0UCoVC0bOJiBEgpXQA90SiLUXH+PeSH6ioKOOTlGcQMVlw6ZMgBO98/w4PrXyIfomn8k/z1QRffAvXxi+0JD9tZPhTKBQKxYlPRAZ6hRA2IcQjQohPhRBfNW6RaFvRfvZWOnhm8S5etb5BjKsMLn8FGZ3AU+uf4sEVD1KQMYZHFqbhuPX3+MrKSH/gAXp/8TlJM36uDACFogdx4MAB8vPzqa6uBqCmpob8/HyONRtqcXExl19+eUTkOe+88+jfvz8DBw7kySefbLp3zTXXMH/+/A4/Q3F8iFS015vAd0A+8Be0VQTXRKhtRTuQUnLfB1u5Sv8lp9uXwrj78WUM5Y/L/sicLXOY3nc6f604G+fCL7HefLNS/gpFD6ZXr17cdNNN3HOP1gF7zz33cOONN5Kbm3tM7WRmZh6Tgg4EAm1eNxgMPProo+zYsYOVK1fy7LPPsn379jbLHi+klASDwU595olApGICUqSULwkh7pBSLgGWhGUPVHQCH24spnLPel6NeQ3yx9FwxjXM/vImVpWs4tZht3JNwkT23jKN2LFjsd52qwpsUigiwF8+3sb24siuWzYgM54/XzrwqOVmz57NGWecwRNPPMGyZct4+umnsdvtTJkyhZqaGnw+Hw899BBTpkzh97//Pbm5udx8880APPDAA1gsFqZPn84ll1zC1q1bCQQC3HPPPXz99dd4PB5uueUWZs2axddff81f/vIXMjIy2LhxY5vKPSMjg4wMbR0Ni8VC//79OXjwIAMGDGhT9sPJed9992G1WrnjjjsA+OMf/0haWhq33347jzzyCG+//TYej4epU6fyl7/8hX379nHhhRdy3nnnsWLFCj744INjNoROdiJlBPhC+xIhxMVoqX+zI9S24ijUOr3885P1vGV+Fl1MEqUTH+LmBdeyt3YvD499mEvzLmb/VVcjjEYyHn5IGQAKxQmA0WjkkUceYdKkSSxYsACTyYROp+P9998nPj6eyspKRo8ezeTJk5kxYwZ33nlnkxHw9ttv8/nnn7fwnF966SUSEhJYs2YNHo+Hs846iwsuuACA1atXs3Xr1kPmn7fFvn372LBhA6NGjTpsmejo6DblvP7665k2bRp33HEHwWCQefPmsXr1ahYsWMCuXbtYvXo1UkomT57M0qVLycnJYefOnbzyyis899xzHfxFT04iZQQ8JIRIQMvu9zTa0r+zI9S24ij8v8+/43bvi2Tri9h1wbPctOQ2HD4Hz45/loLMAirnzMG1YQOZjzyCMS2tq8VVKE4Y2uOxH08+++wzMjIy2Lp1KxMmTEBKyb333svSpUvR6XQcPHiQsrIyTj/9dMrLyykuLqaiooKkpCRycnLYt29fU1sLFixg8+bNTcMDdXV17Nq1C5PJxMiRI9tlANjtdqZPn84TTzxBfPzhFyU7nJx5eXmkpKSwYcOGJrlTUlJYsGABCxYs4PTTT296zq5du8jJySE3N5fRo0d37Ic8iYnU7IBPQod1wHmRaFPRPtbuq8axdh4/M33NyjOvYvbWZzAbzLw66VX6JffDvXMnFU89jWXiROIvubirxVUoFBFi48aNLFy4kJUrVzJ27FhmzJjBF198QUVFBevWrcNoNJKXl9eU3vjyyy9n/vz5lJaWMmPGjEPak1Ly9NNPM3HixBbXv/76a2JjY48qj8/nY/r06fziF79g2rRpRyz75ptvHlbOmTNnMnfuXEpLS7nuuuuaZPvDH/7ArFmzWrSzb9++dsmmODwqDVwPxhcI8uz8Bfzd9DIf9hrMTdXLSY9N582L36Rfcj+CXi/Fv/s9+oQE0h/4sxoGUChOEKSU3HTTTTzxxBPk5OTw29/+lrvvvpu6ujpSU1MxGo0sXry4xWyBGTNmMG/ePObPn9/mjICJEyfy/PPP4/Npo7vff/89Doej3fJcf/319O/fn7vuuuuo5Y8k59SpU/n8889Zs2ZNk0EyceJEXn75Zex2OwAHDx6kvLy8XbIpjoxK7duDeWnJTu6s/zuvW+N41lDHyNSRPH7e48SbtG64ymeexbNzJ9nPP4chKamLpVUoFJFizpw55OTkMGHCBABuvvlm5s6dy4033si8efMYMWIEw4YN47TTTmuqM3DgQBoaGsjKymoK4gtn5syZ7Nu3j+HDhyOlxGaz8cEHR1wDrolvv/2W119/ncGDBzNsmLaMzN/+9jcuuugiAGbNmsWdd94JaDMbPv74Yy699NI25TSZTJx33nkkJiai1+sBuOCCC9ixYwdjxowBIC4ujjfeeKPpvuLHE7GlhHsKJ8oCQoVVThY+eT1FthXMj7dw8SkX82DBg5j02pQ/54YN7P/FVSRMm0rmQw91sbQKxYnDjh076N+/f1eLccISDAYZPnw477zzDn379u1qcXocbf19dsZSwlHAdCAvvE0p5YORaF/REiklb837F4UZa1hqtjBz8ExuO/22pkV+gk4nxffcgzE9nbR7VCJHhULRM9i+fTuXXHIJU6dOVQZAJxGp4YAP0YIC1wGeCLWpOAwfLF/CGuMcdpliuO/Me/jZgF+0uF/+z0fxFR4g59W56OPiukhKhUJxolFVVcW4ceMOuf7ll1+SkpLS4fYHDBjADz/80OF2FO0nUkZAtpRyUoTaUhyBLWXf8fSO22kw6nn8jHs4v5UBYF/2LTX/+Q/J11xD7MiRXSSlQqE4EUlJSWHjxo1dLYYigkRqdsByIcTgCLWlOAwbyjcw87MrCej8/CN9BucPbmkABOrqKPnjHzH17o1t9p1dJKVCoVAoegqR6gkYC1wjhNiLNhwgACmlHBKh9k96Fu5fyO+X/I4Mn4s7PKcxbtIfDylT+vDD+KuqyHv2WXRRUV0gpUKhUCh6EpEyAi6MUDuKNnh9++s8suYRBrj9/KlMknvHy9Bqzn/9Fwuo/+hjrLfeSsygrs1iplAoFIqeQaQyBu4XQgwFfhK69I2UclMk2j6ZCcogj6x5hDd2vEGBz8wjJbvYOu5tLAnJLcr5Kyoo/fOfiR40COusG7tIWoVCoVD0NCISEyCEuANtOeHU0PaGEOK2SLR9suIJeLh7yd28seMNfho3kOeKvuN/ybMYM7ZlZK6UkpL7/0zQ6STz//0DYTR2kcQKhaKziGs162fu3Lnceuutx9zOm2++yZAhQxgyZAgFBQVs2tTsu7V+huLEJFKBgdcDo6SU90sp7wdGAze0p6IQYpIQYqcQYrcQ4pBJ7UKIHCHEYiHEBiHEZiHERWH3hgghVgghtgkhtgghoiP0Pl1KrbuWGxbcwML9C/lt3yu5d8tClsgz+MlVfzok9W/de+9jX7yY1N/cRVTv3l0ksUKh6Ink5+ezZMkSNm/ezH333ceNN3Z+T6Lf7+/0ZyqaiVRMgAACYeeB0LUjVxJCDzwLTACKgDVCiI+klOELVv8JeFtK+bwQYgDwKZAnhDAAbwBXSyk3CSFSaF7SuMdS1FDETYtuothezD8LHmLsh/dRIeM5cPYjnJ/ccqEMb9FByv72N8wjR5J09dVdJLFCcRLz2T1QuiWybaYPhgv/8aOrV1RU8Otf/5rCwkIAnnjiCc4666w2yxYUFDQdjx49mqKioiO2/fHHH/PQQw/h9XpJSUnhzTffxGaz0a9fP5YvX47NZiMYDHLqqaeycuVKpJRtyvLAAw9QXFzMvn37sFqt/Oc///nR76voGJEyAl4BVgkh3g+dXwa81I56I4HdUsofAIQQ84ApQLgRINGWJgZIAIpDxxcAmxtjD6SUVR16g27Atspt3PzlzfiDfuZMeIHB3zyHzn6AP1se5u/nnt6irAwGKbn3XgAy//43hE6tBaVQnCy4XK6mHP0A1dXVTJ48GYA77riD2bNnM3bsWAoLC5k4cSI7duw4apsvvfQSF1545BjvsWPHsnLlSoQQvPjii/zf//0fjz76KFdddRVvvvkmd955J4sWLWLo0KFYrVauvPLKw8qybt06li1bRkxMTAd+CUVHiVRg4GNCiK/RpgoK4Fop5YZ2VM0CDoSdFwGjWpV5AFgQijGIBcaHrp8KSCHEF4ANmCel/L8f/RJdzNKipdy95G6SopJ4ftLznPLDCtg2n8f8l3Plz67AqG+p5Gtefx3n6tVkPPwwxqysLpJaoTjJ6YDH3hFiYmJaJO2ZO3cujWuiLFq0iO3bm/2o+vp6GhoasFgsh21v8eLFvPTSSyxbtuyIzy0qKuLnP/85JSUleL1e8vPzAbjuuuuYMmUKd955Jy+//DLXXnvtEWUBmDx5sjIAugEdMgKEEPFSynohRDKwL7Q13kuWUlYfrYk2rrVe0egKYK6U8lEhxBjgdSHEIDTZxwJnAk7gy9AiCV+2IeeNwI0AOTk57Xq3zuSd79/hoZUP0S+pH8+Nfw6rvZrg/37D6mB/as64jeE5LVcA9OzZQ/mjjxF33nkkTJvaRVIrFIruSDAYZMWKFe1WsJs3b2bmzJl89tlnR039e9ttt3HXXXcxefJkvv76ax544AFAWxkwLS2Nr776ilWrVvHmm28eVZbY2NhDrik6n472ITcO5KwD1oZtjedHowjoFXaeTXN3fyPXA28DSClXANGANVR3iZSyUkrpRIsVGN7WQ6SUL0gpR0gpR9hstva8V6cgpeSp9U/x4IoHKcgsYO6kuVgNccj519AQMPKgaTZ3T2o551/6fBT//h50sbFk/PXBQwIFFQrFyc0FF1zAM88803R+pDS/hYWFTJs2jddff51TTz31qG3X1dWRFep5fPXVV1vcmzlzJldddRU/+9nPWiwB3F5ZFBpSSjpzdd8OGQFSyktC+3wp5SlhW76U8pR2NLEG6CuEyBdCmIAZwEetyhQC4wCEEP3RjIAK4AtgiBDCHAoSPIeWsQTdGl/Ax73L7mXOljlM7zudp89/GrPRDAv+hCjbxp2eWdx06U9IiGk55a/y3y/g3rqV9AcewGC1dpH0CoWiu/LUU0+xdu1ahgwZwoABA/jXv/512LIPPvggVVVV3HzzzQwbNowRI5pXm3U6nWRnZzdtjz32GA888AA//elP+clPfoK11fdn8uTJ2O32pqGAY5XlREJKSTAoCfiD+L0BvG4/HqcPl92Ls96DvdZDQ5WbugoXtWVOqkscVB20U3mggYrCBlz2zotxF5GwOIQQX0opxx3t2mHqXgQ8AeiBl6WUDwshHgTWSik/Cs0ImAPEoQ0V/E5KuSBU9yrgD6Hrn0opf3e0540YMUI2jp11FQ3eBmZ/PZtVJau4ddit3DjkRs2j3/Ex/Pcq5spL+Cr3Dl699swWnr5ry1b2XXEF8RddSNb/9djwB4WiR9PWeu0KWLt2LbNnz+abb77palEihgxqyvzQPU3nhytzyMB2K4ROoNOJVnvtuinGgCn6x43Wt/X3GRoqH9FW+Y7GBEQDZsAqhEiieYw/HshsTxtSyk/RuvLDr90fdrwdaHN+i5TyDbRpgj2GUkcpN395M3tr9/Lw2IeZ3FuL6KW2ED68hf1Rp/JPxww+nTKohQEQdLspvuceDCkppP/pT10kvUKhUBzKP/7xD55//vmmWIDuRFtKWjs+kpLX7nMUJ7m1IjcYWyv1tvZave4ylNvR2QGzgDvRFP46mo2AerT5/4owvq/5npsW3YTD5+DZ8c9SkBmaoxvwwbsz8fv9/NLxa266oD85KeYWdSueeBLvnj30eulF9PHxbbSuONHRxgq1PcGW51JqHzQpNS9FSs0TaeyWbDyWreu1UbapTNMHE4Lh5zLs49nYRpj30/Shla08prDnBcPalkFJMPROLZ4TlARbv1f4h7rpHtqHVQiECO3Dzml13vgBbiorAJ1AJwS0bqepfPPekO7HUecBQh+85v8B0SraWYQftr4Zun44XRB243BtHlq0rZvw6qtzefqZp1tcLigo4Omnng4r1vwObbUBaN6tBEnobyZ07Tez7+auO+8GCR6XH6RsutdW+aYe6PB7jfo29PcZOgRC503XZNi95r/ftsrL8IKHQQiB0Dcr6qMpcqGj+bibKPKO0CEjQEr5JPCkEOI2KeXTR61wErOyZCWzF8/GbDDz6qRX6Zfcr/nm13+HA6v4q/EuTLY+3PCTluEUjtWrqX71VZKuvJK4wyT9UHQdwaDEXu2mpsxJbalT25c5cNb7mhWYRPu4hSvhsHtNiriVcmt9/0SiSbE2fmTDFbQuzMsKV+Ktuk0bFfWhxhBNyqTF79zqd23x36St3zxkmIT/9mdelYSj1tNVP9uPYvIFP2XyBT895Hp1saMLpGkDIZpsKRE6bz5uNqAadW6zgUdIEYtmQ6hV+ZYKvNkbP5EUeUeIVJ6Ap0PT9gagBe41Xn8tEu33dD7e8zH3L7+fvPg8nh//POmx6c039yyGbx5jo/VSXi0awTu/GIzJ0ByvGbDbKfnDvRhzepF692+6QHpFI16XP6TgndSUOqgNHdeWuQj4g03loswGEtPMJKebW3mRzV7noZ5m87VGb1SnQ/s4HtYrFU0fwWYPuFW5sDJN7TV5u22Xbf440vLDGaacG4+br7d9rj037Lz1u/YQwntLvtv5HbZelpY2WZi32eQBh12QhF2UtCzbunyru206sm21cdjCR3CGGz3nI7Qffqm1Ug5X2k33wrR5+8r3nL+DE5GIGAFCiD8D56IZAZ+iLS28DDipjQApJS9ueZGnNjzFyPSRPH7e48Sbwrry7eXw/iw8iX24qngaM87sxZl5LVcILPvHP/CVlJD75hvozGYUx5dgUNJQ5W5S8I3KvqbMibPO21RO6ATxKdEkpZvp1T+ZxDQzSemxJKaZibEY1YftBKPJmCLMyGlZooskUyg6RqTSBl8ODAU2SCmvFUKkAS9GqO0eiT/o5+FVDzP/+/lcfMrF/LXgrxj1YdP9gkF4/9dIVy2/i7+fqBgL91x4Wos2Gr5aTN38d0m58UbMp5+OInJ4XH5qS7Vu+5pSZ5Oirytv26vPGRBS9GmxJKabSbDFoDd0NM2GQqFQdC2RMgJcUsqgEMIvhIgHyoH25Ak4IXH6nPx26W9ZWrSUW6u8/QAAIABJREFUmYNncvvptx/qGS5/CvZ8yaoBf+LD9Uk8/vP+JJpNTbf9NTWU3H8/Uaedhu3WWzr5DU4MNK/e1ULJ14aOnfWtvHprNElpZnIGppCUZg559mai45RXr1AoTlwiZQSsFUIkos3nXwfYgdX/n707D6uq2h8//l5MgghqgMYgQzkBiqikpOa9ZIpeDVK0MM2vqWFOqd36iZVdLOtWNqBmg3OZN69DDt1S0RTNnBVUxAE1RMABxQFUxrN+f5zjEYyZA4dhvZ7nPLL3Xnvtzz4q+7P3XoOB6q5Vrt27xoTfJnAq/RQz/GfwfJvn/1ro4kHY/j5ZrQbwSnx7urdswnO+D8b/l1JyOWIm+bdu4bp4EcLC4q91KHrZd3P17+ofNMwr4q7e2kx7oW/34ELfpLm6q1dqn0aNGpGZmalfvj93QMHR+cpiw4YNzJgxAxMTE8zMzIiMjKRHjx4kJiYyYMAA4uLiDB26UsMYqmHgeN2P3wghNgO2Uspjhqi7Nvnz1p+M2zaO9Kx05gbM5W8t/vbXQvduwtpRYOPEDE0Y2Xl3ef+hMQFu/+8XMrZsweGfr2PZps1f66iHNPkabhd8V1/ggn/vobv6xg5WNGneEDdvO5o8+uCu3qqRSqYUpaBevXoRFBSEEIJjx47x/PPPc+rUqWqNIS8vDzMzQ92PKuVV2cGCihyr//42KeWRytRfm8RcjWHS9kmYClOWBC6hnX27vxaSEn6eDLdSOPLMj6z+OZOpz7TmMYdG+iK5V65w+f33serYEbtRo6rxDGqe/FwN52KvEr/7EpfO3UST96DJsvau3hq3doUf39vaq7t6pfp8fOBjTqUb9qLZ9pG2TOsyrcL7p6Wl8eqrr5KUlARAZGQk3YvpWtyo0YPfPXfu3Cn11dfChQtZsGABOTk5tGzZkuXLl5Ofn4+Pjw9nzpzB3Nyc27dv4+PjQ0JCAklJSUyYMIG0tDQaNmzIwoULadu2LSNHjuSRRx4hJiaGTp068dlnn1X4fJXKqWz6df9vzhLwA46ibSbrA+xHO8tfnReVGMX036fj2MiRr3t9TQvbFkUXPLwM4teTG/AvJv9hzmMODXj17w+aTkgpufTW28jcXJw++jdCNwlHfZN+6Q7xu1M5ve8yWXdysbW3xOfvLjR1tNZe8NVdvVLP3bt3D19fX/1yeno6QUHa0UcnT57M1KlT6dGjB0lJSQQGBnLy5Mli61q3bh3Tp0/n6tWr/PLLLyUed9CgQbzyyisAvPPOOyxevJhJkybx97//nV9++YXnnnuOlStXEhISgrm5OWFhYXzzzTe0atWK/fv3M378eLZv3w7AmTNn2LZtm36yIcU4KjtYUACAEGIlECalPK5bbge8Ufnwar7vT3zPp4c+xcfBh3lPz6OpZdOiC16Jh83h8FgAX9zty8X0P/nxFX8amD34D3Bz5Uru/PEHj0b8Cws3t2o6g5ohLyefc0eucmJ3KpfO3sLERODh64B3Dydc2jZFmKjGeUrNU5k79sqwsrIqNCPf/TYBANu2bSM+/sFcardv3yYjIwMbG5si6xo4cCADBw5k165dzJgxg23bthV73Li4ON555x1u3rxJZmYmgYGBgHYGwU8++YTnnnuOpUuXsnDhQjIzM9mzZw9DhjwYpCg7+8EgS0OGDFEJQA1gqBcxbe8nAABSyjghhG9JO9R2Gqlh9sHZ/HDyB55xfYZ/P/VvLM0siy6ccxfWvAwNbDn31OcsWHiKkE4uPPn4g7m7cy5c4Mons7Hu0YMmL7xQTWdhfNdTMjmxO5Uz+y+TfTePxg5WPDnocdr6O9LQVt3tK0p5aTQa9u7di5WVVbn269mzJ+fOnePatWvFlhk5ciTr16+nQ4cOLFu2jOjoaAC6d+9OYmIiO3fuJD8/n3bt2nH79m2aNGlS7PTB1tbW5YpPqRqGenl6UgixSAjxdyHE34QQC4Hinz/VAbmaXI5fO85wz+F8+rdPi08AADZPg7RTaJ77lmmbL2Njacbb/R/M8iTz80mdFo4wN8fxg1l1vktabnY+J/eksubjQ6x8/wAnfk/B1duO56Z2ZNh7/nTq46YSAEWpoD59+hTqJVDcRRjg7Nmz+rH4jxw5Qk5ODnZ2dsWWz8jIwNHRkdzc3L9MFjRixAiGDh2qn0rY1tYWDw8PVq9eDWhfdx49erTC56VUDUM9CXgZGAdM1i3vAr42UN01UgPTBizqs6jkiz/A8TVw5HvoMZVVN1py6MJxPhnswyPWDy5y1xcv4V5sLE6zZ2PevHkVR248aUkZxO9O5cyBy+Rk5dP00YZ0H9ySNv6Pqnf8imIgc+fOZcKECfj4+JCXl0fPnj355ptviiy7du1avv/+e8zNzbGysuK///2v/ibk9OnTuLi46Mt+8cUXvP/++3Tt2hU3Nzfat29PRkaGfvuwYcN45513GDp0qH7dihUrGDduHLNmzSI3N5fQ0FA6dOhQRWeuVISQxQ4qXTf5+fnJ++/Oqlz6n/DNU9DMk2tD1tErcg9tHrXhv2H++v9oWadP8+fgIdj06oXzF5/XuacAOVl5JBy8QvzuVK5eyMDU3ISWnZrh9ZQTjo83rnPnq9R9Rc3XrsCaNWvYsGEDy5cvN3Yo9VpR/z6FEIellH5Fla9sF8FVUsrnhRDHKWLKCSmlT2Xqr9XycmDNKDAxgZBFfLD5LHdz8vhw4IMxATQ5OaT+v2mYNm7Mo/96t85cEKWUXL2QQfzvKZw5dJW87HwecbLmqRda0brLo1ham5deiaIotcakSZPYtGkTv/76q7FDUcqpsq8D7j/+H1DZQOqc32ZC6hF4/nv+uG7Nupg4Jj3dkpbNHrTQvfblfLJPn8bl668wa1pMr4JaJPteHmf2Xyb+j1SuXczEzMKEln7N8e7hRHMP2zqT5ChKbbN06VLmzJlTaF337t2ZP3++QeqfN0/NJF9bVbaL4CXdnxcME04dcSYK9n4JfqPJajWAd+b8jrtdQyYEtNQXuXskhuuLFtFkyGBsAgKMGGzlSCm58udtTvyewtlDV8nL1WDfohF/G9qaVl0epYGVGglMUYzt5Zdf1jfYU5SCKvs6IIOiZ8IWgJRS2haxrW67fQnWvwrNvCHwA76KPsef1+7ww+iuWJpr+8Rq7t4ldXo45o6ONJsWbuSAKybrTi6n918mfncq6al3MG9gSmv/R/Hu4YSDq42661cURakFKvskoOjRJ+orTT789Ark3oMhSzl7I59vos8R7OtEj1b2+mJXP/2U3KSLuH63DNNGtaevrJSSS2dvcWJ3CucOp5Gfp6GZmw0Bw9vS0q8ZFpbqrl9RFKU2MehvbSFEM7RDCAMgpUwyZP013u+fQ+LvEDwfad+atxfsw9LchHf6e+mLZO7+gxv/+ZFHRo7EuksXIwZbdvcyczi9T3vXf+PyXSwsTfHs7ohXDyccWqg8UFEUpbYySBIghAhCO4+AE3AVcEM7WJB3GfbtC8wBTIFFUsqPHtruCnwHNNGVCZdS/vrQ9nggQkr5qSHOp0Iu7IHoD6H9EPAdxtojKez/M50PB7bHwaYBAPm3bnHp7bexaPk4DlOnGC3UspBSknLmJvG/p3AuNg1NnuTRx2x5eoQnLTs3w7yBGu5TURSltjPUiIHvA/7AGSmlB9AL+KO0nYQQpsB8oB/gBQwVQng9VOwdYJWUsiMQCnz10PYvgE2VC7+S7qbD2jHQxA36f0763Vw++CWezm5NCX3iwWRCl2d9QN716zh99DEmDRoYMeDi3b2dw5EtF1jx7j42fBFDUnw67Z5yJnRGF0L+nx+e3RxVAqAoRnbx4kU8PDxIT08H4MaNG3h4eHDhQvnaaKempjJ48GCDxBMQEICnpyfe3t6FeiKMHDmSNWvWVPoYleHu7l7icMhlER0dzYABZe8IFxERgbOzM76+vnh5efHjjz/qt40cORIPDw98fX3x9fWlW7dugHYOiIkTJ/6lroKzPZZUriIM9TogV0p5XQhhIoQwkVLuEEJ8XIb9ugBnpZTnQT8RUTDaO/v7JHC/gWFjIPX+BiHEc8B54I4hTqJCpIQNEyDzKoyOAktb/r36KBlZeXw4sD0muolvbm/ewu2ff8Z+0kSs2pX6gKRaSY3k4ql04nen8mfsNTQaiVOrJjwxwIPHOzpgZqEu+opSk7Ro0YJx48YRHh7OggULCA8PJywsDLdyTjzm5ORUrgt0fn5+kZP+mJmZ8dlnn9GpUycyMjLo3LkzvXv3xsvr4Xu6qiOlREqJiUnNmUp86tSpvPHGGyQkJNC5c2cGDx6Mubl2nJTZs2cbJAGrLEMlATeFEI3QDhe8QghxFcgrw37OwMUCy8lA14fKRABRQohJgDXwDIAQwhqYBvTGmDMWHlgAp3+FwA/BuRP7zl9n9eFkXv3b47R5VPu+PC8tjcsREVi2a4d9WJjRQn3YnZvZnNxziZN7Url9LQtLa3PaP+2Cdw8nmj5aexosKoqxXP7wQ7JPnjJonQ082/LoW2+VWm7q1Kl07tyZyMhIdu/ezbx588jMzCQ4OJgbN26Qm5vLrFmzCA4OZtq0abi5uTF+/HhAe5dqY2NDSEgIAwYMIC4ujvz8fMLDw4mOjiY7O5sJEyYwduxYoqOjmTlzJo6OjsTGxhaaofA+R0dHHB0dAbCxscHT05OUlJRik4Di4pwxYwb29vZMnqwdgubtt9+mefPmvPbaa8yePZtVq1aRnZ3NwIEDmTlzJomJifTr14+AgAD27t3L+vXry5wI3blzh0mTJnH8+HHy8vKIiIggODiYxMREXnrpJe7c0d5bfvnll/o79fsOHjxIWFgYa9euJTAwkD179uDg4IBGo6F169bs27evUPlWrVrRsGFDbty4QbNmzcoUX3UxVBIQDNwDpgLD0N6xv1eG/YrqR/Zwl8OhwDIp5WdCiCeB5bqpimcCX0gpM0vrjiaECAPCAFxdXcsQVhldOgpR70CrQPAfT3ZePm+vO45LUysm92qlPRkpufTuv9Dcu4fTxx8hzI07Wp5GI0k6cZ343akkHr+O1Eic2zTFP/hxHvN1wNS85mTRiqIUz9zcnNmzZ9O3b1+ioqKwsLDAxMSEdevWYWtry7Vr1/D39ycoKIjQ0FCmTJmiTwJWrVrF5s2b0Wg0+voWL15M48aNOXjwINnZ2XTv3p0+ffoAcODAAeLi4vDw8Cg1rsTERGJiYuja9eH7uQcsLS2LjHP06NEMGjSIyZMno9FoWLlyJQcOHCAqKoqEhAQOHDiAlJKgoCB27dqFq6srp0+fZunSpXz11cNvikv2wQcf8PTTT7NkyRJu3rxJly5deOaZZ2jWrBlbt27F0tKShIQEhg4dSsGh5vfs2cOkSZPYsGEDrq6uDB8+nBUrVjBlyhS2bdtGhw4dsLe3L3SsI0eO0KpVq0IJwJtvvsmsWbMA8Pb2/suETNXFUElAGLBaSpmMthFfWSUDLQosu1Dgcb/OaKAvgJRyrxDCErBH+8RgsBDiE7SNBjVCiCwp5ZcP7Y+UcgGwALRzB5QjvuLl3IXVL0NDO3juaxCCBTvPcy7tDktffgIr3SP0Wz/9ROaOHTR/azoNHn/cIIeuiPx8DWf2X+bQpgvcTruHlY05HXu3wLO7E02aNTRaXIpSm5Xljr0qbdq0CUdHR+Li4ujduzdSSt566y127dqFiYkJKSkpXLlyhY4dO3L16lVSU1NJS0ujadOmuLq6kpiYqK8rKiqKY8eO6V8P3Lp1i4SEBCwsLOjSpUuZEoDMzExCQkKIjIzE1rb4YWKKi9Pd3R07OztiYmL0cdvZ2REVFUVUVBQdO3bUHychIQFXV1fc3Nzw9/cv93cXFRXFxo0b+fRTbXvyrKwskpKScHJyYuLEicTGxmJqasqZM2f0+5w8eZKwsDCioqJwcnICYNSoUQQHBzNlyhSWLFlSaFCmL774goULF3L+/Hk2b95c6PiVfR1gqLFYDJUE2AJbhBDpwEpgjZTyShn2Owi0EkJ4ACloG/69+FCZJLQNDZcJITzRdkFMk1I+db+AECICyCwqAagy5lbQbRLYtQRrOxKv3WHejrP0b+9IQBtttpeTnMKVD/9Nw65daTp8eLWFVlB+vobT+y5zeFMit69l4eBqQ+Ar7fDoYI+pmbrrV5TaKjY2lq1bt7Jv3z569OhBaGgoW7ZsIS0tjcOHD2Nubo67uztZWVkADB48mDVr1nD58mVCQ0P/Up+Uknnz5hEYGFhofXR0NNbWpb8ezM3NJSQkhGHDhjFo0KASy65YsaLYOMeMGcOyZcu4fPkyo0aN0sc2ffp0xo4dW6iexMTEMsVWFCkla9eupU2bNoXWR0RE0Lx5c44ePYpGo8HS8sFMsY6OjmRlZRETE6NPAlq0aEHz5s3Zvn07+/fvL3RHf79NwE8//cSIESM4d+5cofrKysrKipycHCwstLOtpqen/+VpQ0UZ5CogpZwppfQGJqDtJrhTCLGtDPvlAROBLWi7FK6SUp4QQryn63YI8E/gFSHEUeBHYKSsCVMfCgF+L4PHU0gpeWd9HA1MTXj3We07MKnRcGn6dACcPvwAUc2NVfLzNMTvTmXFu/vYsfwUltbm9B/vw5DpfrTs3EwlAIpSi0kpGTduHJGRkbi6uvLmm2/yxhtvcOvWLZo1a4a5uTk7duwo1FsgNDSUlStXsmbNmiLvQAMDA/n666/Jzc0F4MyZM/r34mWJZ/To0Xh6evL666+XWr6kOAcOHMjmzZs5ePCgPiEJDAxkyZIlZGZmApCSksLVq1fLFFtxAgMDmTdvHvcvJzExMfrYHB0dMTExYfny5eTn5+v3adKkCb/88gtvvfUW0dHR+vVjxoxh+PDhPP/880U2nBw0aBB+fn589115HpQ/8Le//Y0ffvgBgHv37rFq1SoCDDTcvKGHeLsKXAauA2Vq/aDr8//rQ+veLfBzPNC9lDoiyhuoIW08msrus9d4L9ib5rbaLC/9+++5e/Agjh98gLmzc7XFkp+n4dTeSxzedIGM9CyaudnQM7Q1bu3s1FC+ilJHLFy4EFdXV3r37g3A+PHjWbZsGWFhYaxcuRI/Pz98fX1p27atfh9vb28yMjJwdnbWN+IraMyYMSQmJtKpUyeklDg4OLB+/foyxfPHH3+wfPly2rdvj6+vLwAffvgh//jHPwAYO3YsU6Zox0Zp0aIFP//8M88++2yRcVpYWBAQEECTJk30F9Q+ffpw8uRJnnzySUDbZe6HH34o8oJbHB8fH33Pgeeff54PPviAKVOm4OPjg5QSd3d3/ve//zF+/HhCQkJYvXo1AQEBf3nS0Lx5c37++Wf69evHkiVL6Nq1K0FBQaXOz/Duu+/y4osv8sorrwCF2wSAtt0FaLv/Ffze9+3bx5w5cxg7dixz585FSsmIESPo2bNnmc+9JMIQN9VCiHHAC4ADsAb4r+7iXeP4+fnJgo08KuvW3Vx6fR6Nc9OG/DSuG6YmguyzZ/lzUAjWPXrgMv/Larn45udpOLnnEoc3J5KZnk1zD1ue6O+Bq/cj6uKvKAZU1HztiuFoNBo6derE6tWradWqlbHDKZNDhw4xdepUfv/9d2OHUuS/TyHEYSmlX1HlDfUkwA2YIqWMNVB9tcZHm09x424u341qh6mJQObmkjotHBNraxzfm1nlF+D8XA0n96RyePMFMm9oL/4Bw9rSwktd/BVFqV3i4+MZMGAAAwcOrDUJwEcffcTXX39ttNb9lWWQJEBKWTunwqukwxfS+fFAEmN6eODt1BiAa98uIOvECZznzMHMQA03ipKfqyH+j1SObNFe/B99rDEBL7Wlhae6+CuKUjWuX79Or169/rL+t99+w87OrtL1e3l5cf78+Qrv37VrV7Kzswutu/+aoqqEh4cTHl57L4Fq2rcKys3X8NZPcTg1tmRq79YA3Dsex7Wvv8Y26FlsA/tUyXHzcvOJ332JI1sucOdmNo6PN+bpEZ64tG2qLv6KolQpOzs7YmNr7gPf/fv3GzuEWkclARUkgCF+LjzmYI11AzM0WVmkhodjZm/Po++8Y/DjaS/+qRzZfIE7t3JwbNmYXiM9cWmjLv6KoihKxagkoILMTE0Y89Rj+uW0LyLJOXeOFosXYVrCIBnllZeTz4nfUzkSdYG7t3JwatWEZ172wlld/BVFUZRKUkmAAdzZf4D077+n6Ysv0qh7ib0Zy0x/8d9ygbu3tRf/PqO8cW7T1CD1K4qiKIoaMaaS8jMzuTR9OhaurjR745+Vri83J5/YbUl8/85edq9OoKljQ557vSMD/9lJJQCKogCGm1p2xYoV+Pj44OPjQ7du3Th69Gixx6huxU3dO2zYMNq0aUO7du0YNWqUfnAjpWLUk4BKuvLvf5N7+TLu/1mBScOKj8Gfm51P3K4UYqIucC8jF+c2TenyijdOrdSFX1GUquHh4cHOnTtp2rQpmzZtIiwsrNob1+Xl5WFmVvZL0bBhw/Sj57344ossWrSIcePGVVV4dZ5KAiohY/sObq39CbuxY7HSjZJVXrnZ+RzfmUzs1iTuZeTi0rYpTwzwwKllEwNHqyiKof2+6gzXLmYatE77Fo146vnWFd4/LS2NV199laSkJAAiIyPpXsxryoJT5Pr7+5OcnFxi3T///DOzZs0iJycHOzs7VqxYgYODA23atClyOl0pZZGxREREkJqaSmJiIvb29vznP/8p8/ndH4UQoEuXLqXGrJRMJQEVpMnK4tK/3qVB27Y4TBhf7v1zsvKI25lC7Dbtxb+FZ1Oe6O+Bo7r4K4pSinv37umH5wXthDJBQdrpViZPnszUqVPp0aMHSUlJBAYGcvLkyVLrXLx4Mf369SuxTI8ePdi3bx9CCBYtWsQnn3zCZ599Vux0ui+++GKxsRw+fJjdu3djZWVVoe8gNzeX5cuXM2fOnArtr2ipJKCCTCwtcZ49G9OmjyB0MzuVxf2Lf8zWJLIyc3H1eoQnBnjw6GONqzBaRVGqQmXu2CvDysqqUH/9ZcuW6ee837ZtG/HxD0Ztv337NhkZGdjY2BRb344dO1i8eDG7d+8u8bjJycm88MILXLp0iZycHP30wsVNp1tcLABBQUEVTgBAO19Cz549eeqpp0ovrBRLJQGVYF2OOaxzsvI4Hp1M7NaLZN3JxdX7EZ7ory7+iqIYlkajYe/evWW+wB47dowxY8awadOmUkf9mzRpEq+//jpBQUFER0cTEREBFD+dbkmxVHQKYICZM2eSlpbGt99+W+E6FC3VO6CK5dzL49CmRL5/ew/71p+nuYctIdM68+wkX5UAKIpicH369OHLL7/UL5c0wl9SUhKDBg1i+fLltG5d+lONW7du4aybFfXhaXGLmk63PLGU1aJFi9iyZQs//vijflZApeLUk4AqknMvj2M7LhK77SLZd/Nwa2/HE//woLmH4QYSUhRFedjcuXOZMGECPj4+5OXl0bNnT7755psiy7733ntcv36d8eO17ZrMzMz0rxXu3r2Li4uLvuzrr79OREQEQ4YMwdnZGX9/f/7880/99qKm0y1PLEX57bffCsWwevVqXn31Vdzc3PTTCg8aNIh33323uCqUUhhkKuHaxNBTCT8s+14ex7Zf5Ohv2ou/e3s7nhjgQTM3dfFXlLpATSVctJo0nW59ZqyphOu97Lu5HN2ezLHtuou/jz1P9HdXF39FUeq82j6dbn2mkoBKyr6by9HfLnJ0ezI59/Lw6GDPE/09cHAtviWuoihKdVq6dOlfutJ1796d+fPnG6T+ykynu2XLFqZNm1ZonYeHB+vWrTNEaEop1OuACsrP03BoUyLHfrtITlY+j/k64NffHYcW6uKvKHWZeh2g1GTqdUA1MTEVXDh+HRfPR3iivzv2Luriryj1hZRSzeKp1DgVualXSUAFCSEY9EYnzCxMjR2KoijVyNLSkuvXr2NnZ6cSAaXGkFJy/fp1LC0ty7WfSgIqQSUAilL/uLi4kJycTFpamrFDUZRCLC0tC3WpLAujJwFCiL7AHMAUWCSl/Oih7a7Ad0ATXZlwKeWvQojewEeABZADvCml3F6twSuKUu+Ym5vrh8tVlNrOqEmAEMIUmA/0BpKBg0KIjVLK+ALF3gFWSSm/FkJ4Ab8C7sA14FkpZaoQoh2wBXCu1hNQFEVRlFrM2GMudgHOSinPSylzgJVA8ENlJHC/s31jIBVAShkjpUzVrT8BWAohGlRDzIqiKIpSJxg7CXAGLhZYTuavd/MRwHAhRDLapwCTiqgnBIiRUmYXdRAhRJgQ4pAQ4pB6j6coiqIoWsZuE1BU09qH+zgMBZZJKT8TQjwJLBdCtJNSagCEEN7Ax0Cf4g4ipVwALNCVTxNCXDBI9Fr2aF9NKFVLfc/VQ33P1UN9z9VHfdfgVtwGYycByUCLAssu6B73FzAa6AsgpdwrhLBE+5d6VQjhAqwDRkgpz5XlgFJKh0pHXYAQ4lBxgzAohqO+5+qhvufqob7n6qO+65IZ+3XAQaCVEMJDCGEBhAIbHyqTBPQCEEJ4ApZAmhCiCfALMF1K+Uc1xqwoiqIodYJRkwApZR4wEW3L/pNoewGcEEK8J4QI0hX7J/CKEOIo8CMwUmqHRZoItARmCCFidZ9mRjgNRVEURamV6t3cAYYmhAjTtTlQqpD6nquH+p6rh/qeq4/6rkumkgBFURRFqaeM3SZAURRFURQjUUlABQkh+gohTgshzgohKjaRtlIqIUQLIcQOIcRJIcQJIcRkY8dUlwkhTIUQMUKI/xk7lrpKCNFECLFGCHFK9+/6SWPHVBcJIabqfmfECSF+1PUsUx6ikoAKKDDccT/ACxiqG9JYMbw84J9SSk/AH5igvusqNRltI12l6swBNksp2wIdUN+3wQkhnIHXAD8pZTu0886EGjeqmkklARVTluGOFQOQUl6SUh7R/ZyB9hemmiOiCujG3egPLDJ2LHWVEMIW6AksBpBS5kh2FQQ7AAAgAElEQVQpbxo3qjrLDLASQpgBDfnrGDQKKgmoqLIMd6wYmBDCHegI7DduJHVWJPD/AI2xA6nDHgPSgKW61y6LhBDWxg6qrpFSpgCfoh1n5hJwS0oZZdyoaiaVBFRMWYY7VgxICNEIWAtMkVLeNnY8dY0QYgBwVUp52Nix1HFmQCfgayllR+AOoNoUGZgQoinap7MegBNgLYQYbtyoaiaVBFRMWYY7VgxECGGONgFYIaX8ydjx1FHdgSAhRCLa11tPCyF+MG5IdVIykCylvP80aw3apEAxrGeAP6WUaVLKXOAnoJuRY6qRVBJQMWUZ7lgxACGEQPv+9KSU8nNjx1NXSSmnSyldpJTuaP89b5dSqjsnA5NSXgYuCiHa6Fb1AuKNGFJdlQT4CyEa6n6H9EI1wCySsScQqpWklHlCiPvDHZsCS6SUJ4wcVl3VHXgJOC6EiNWte0tK+asRY1KUypgErNDdQJwHXjZyPHWOlHK/EGINcARtD6MYdDPJKoWpEQMVRVEUpZ5SrwMURVEUpZ5SSYCiKIqi1FMqCVAURVGUekolAYqiKIpST6kkQFEURVHqKZUEKIqiKEo9pZIARVEURamnVBKgKIqiKPWUSgIURVEUpZ5SSYCiKIqi1FMqCVAURVGUekolAYqiKIpST6kkQFEURVHqKZUEKIqiKEo9ZWbsAKqbvb29dHd3N3YYiqIoilItDh8+fE1K6VDUtnqXBLi7u3Po0CFjh6EoiqIo1UIIcaG4bVX6OkAIkSiEOC6EiBVCHNKte0QIsVUIkaD7s6luvRBCzBVCnBVCHBNCdCpQz//pyicIIf6vwPrOuvrP6vYVVXk+iqIoilKXVEebgAAppa+U0k+3HA78JqVsBfymWwboB7TSfcKAr0GbNAD/AroCXYB/3U8cdGXCCuzXt+pPR1EURVHqBmM0DAwGvtP9/B3wXIH130utfUATIYQjEAhslVKmSylvAFuBvrpttlLKvVJKCXxfoC5FURRFUUpR1W0CJBAlhJDAt1LKBUBzKeUlACnlJSFEM11ZZ+BigX2TdetKWp9cxPpqc+L3FOxcGvGoR+PqPKyiKEaUm5tLcnIyWVlZxg5FUQqxtLTExcUFc3PzMu9T1UlAdyllqu5Cv1UIcaqEskW9z5cVWP/XioUIQ/vaAFdX15IjLqPcnHyObLlA9t08Bv6zE3bOjQxSr6IoNVtycjI2Nja4u7ujmiEpNYWUkuvXr5OcnIyHh0eZ96vS1wFSylTdn1eBdWjf6V/RPcpH9+dVXfFkoEWB3V2A1FLWuxSxvqg4Fkgp/aSUfg4ORfaSKDdzC1OCp3TEzNyEDXNiuXnlrkHqVRSlZsvKysLOzk4lAEqNIoTAzs6u3E+oqiwJEEJYCyFs7v8M9AHigI3A/Rb+/wds0P28ERih6yXgD9zSvTbYAvQRQjTVNQjsA2zRbcsQQvjregWMKFBXtbC1tyJockdkvmTDnBgy0tXjQUWpD1QCoNREFfl3WZVPApoDu4UQR4EDwC9Sys3AR0BvIUQC0Fu3DPArcB44CywExgNIKdOB94GDus97unUA44BFun3OAZuq8HyK9IiTNc++1oGcu3lsnBPL3ds51R2CoiiKolRIlSUBUsrzUsoOuo+3lPID3frrUspeUspWuj/TdeullHKClPJxKWV7KeWhAnUtkVK21H2WFlh/SErZTrfPRF0vgWrXzM2W/hM7kJmexca5sWTdyTVGGIqi1BMXL17Ew8OD9HTt/dCNGzfw8PDgwoVix4QpUmpqKoMHDzZIPAEBAXh6euLt7c2cOXP020aOHMmaNWsqfQylaqi5AwzEqWUT+r3anhuX7vDL/KPkZOUZOyRFUeqoFi1aMG7cOMLDtcOshIeHExYWhpubW7nqcXJyKtcFOj8/v8j1ZmZmfPbZZ5w8eZJ9+/Yxf/584uPjyxVLZUkp0Wg01XrMuqDeDRtclVy97egz2pstC+PY9M1x+k/wwczc1NhhKYpSRWb+fIL41NsGrdPLyZZ/PetdarmpU6fSuXNnIiMj2b17N/PmzSMzM5Pg4GBu3LhBbm4us2bNIjg4mGnTpuHm5sb48eMBiIiIwMbGhpCQEAYMGEBcXBz5+fmEh4cTHR1NdnY2EyZMYOzYsURHRzNz5kwcHR2JjY0t8uLu6OiIo6MjADY2Nnh6epKSkoKXl1eRsRcX54wZM7C3t2fy5MkAvP322zRv3pzXXnuN2bNns2rVKrKzsxk4cCAzZ84kMTGRfv36ERAQwN69e1m/fn25E6H6TiUBBvZ4p2Y8PcKT3747SdSiEwSGtcPUVD1wURTFsMzNzZk9ezZ9+/YlKioKCwsLTExMWLduHba2tly7dg1/f3+CgoIIDQ1lypQp+iRg1apVbN68udCd8+LFi2ncuDEHDx4kOzub7t2706dPHwAOHDhAXFxcmbqeJSYmEhMTQ9euXYstY2lpWWSco0ePZtCgQUyePBmNRsPKlSs5cOAAUVFRJCQkcODAAaSUBAUFsWvXLlxdXTl9+jRLly7lq6++quQ3Wj+pJKAKtH3SkZysPH7/bwLbvzvJMyO9ECaqNbGi1DVluWOvSps2bcLR0ZG4uDh69+6NlJK33nqLXbt2YWJiQkpKCleuXKFjx45cvXqV1NRU0tLSaNq0Ka6uriQmJurrioqK4tixY/rXA7du3SIhIQELCwu6dOlSpgQgMzOTkJAQIiMjsbW1LbZccXG6u7tjZ2dHTEyMPm47OzuioqKIioqiY8eO+uMkJCTg6uqKm5sb/v7+lfsi6zGVBFQRn4AW5NzLZ//G85hbmvG3oa1VtyJFUQwmNjaWrVu3sm/fPnr06EFoaChbtmwhLS2Nw4cPY25ujru7u77f+ODBg1mzZg2XL18mNDT0L/VJKZk3bx6BgYGF1kdHR2NtbV1qPLm5uYSEhDBs2DAGDRpUYtkVK1YUG+eYMWNYtmwZly9fZtSoUfrYpk+fztixYwvVk5iYWKbYlOKp59RVqHM/Nzr2ceXErhT2rT9n7HAURakjpJSMGzeOyMhIXF1defPNN3njjTe4desWzZo1w9zcnB07dhTqLRAaGsrKlStZs2ZNkT0CAgMD+frrr8nN1fZuOnPmDHfu3ClzPKNHj8bT05PXX3+91PIlxTlw4EA2b97MwYMH9QlJYGAgS5YsITMzE4CUlBSuXr1aZN1K+agnAVVICMGTAx8nJyufI1uSsLAyo3Nfd2OHpShKLbdw4UJcXV3p3bs3AOPHj2fZsmWEhYWxcuVK/Pz88PX1pW3btvp9vL29ycjIwNnZWd+Ir6AxY8aQmJhIp06dkFLi4ODA+vXryxTPH3/8wfLly2nfvj2+vr4AfPjhh/zjH/8AYOzYsUyZMgXQ9mz4+eefefbZZ4uM08LCgoCAAJo0aYKpqbZhdZ8+fTh58iRPPvkkAI0aNeKHH37Qb1cqThipa73R+Pn5yUOHDpVe0ICkRrJ1aTwJB6/w1Aut8QlwKX0nRVFqpJMnT+Lp6WnsMOosjUZDp06dWL16Na1atTJ2OLVOUf8+hRCHpZR+RZVXrwOqgTAR9BrpibuPPb//9wyn9l4ydkiKoig1Tnx8PC1btqRXr14qAagm6nVANTE1NSHwFW9+mX+M7d+fxNzSlMc7Nit9R0VRlBri+vXr9OrV6y/rf/vtN+zs7Cpdv5eXF+fPn690PUrZqSSgGpmZm9Lv1fZsnBNL1KIT9J9giqtX5f/jKIqiVAc7OztiY2ONHYZiQOp1QDWzsDRjwMQONHW0ZtPXx0k9e9PYISmKoij1lEoCjMDS2pyg13xp9Iglv3x5lLSkDGOHpCiKotRDKgkwkoa2FgRN9sWioRkb58aSfqls/XEVRVEUxVBUEmBENo9YEjy5I8JEsDEyhtvX7hk7JEVRFKUeUUmAkTVp3pCg13zJy9WwITKGOzezjR2Soig1XKNGjQotL1u2jIkTJ5a7nhUrVuDj44OPjw/dunXj6NGjxR5DqZtUElAD2Ls0YsCkDtzNyGXDnFjuZeYYOyRFUeoBDw8Pdu7cybFjx5gxYwZhYWHVHkNeXl61H1N5QHURrCEe9WhM//E+/G/eUX6ee5TnpnbEwkr99ShKjbYpHC4fN2ydj7aHfh9VePe0tDReffVVkpKSAIiMjKR79+5Flu3WrZv+Z39/f5KTk0us++eff2bWrFnk5ORgZ2fHihUrcHBwoE2bNuzZswcHBwc0Gg2tW7dm3759SCmLjCUiIoLU1FQSExOxt7fnP//5T4XPV6mcKn8SIIQwFULECCH+p1v2EELsF0IkCCH+K4Sw0K1voFs+q9vuXqCO6br1p4UQgQXW99WtOyuECK/qc6lqLm2aEhjWjuvJmfzy1TFyc/KNHZKiKDXQvXv38PX11X/effdd/bbJkyczdepUDh48yNq1axkzZkyZ6ly8eDH9+vUrsUyPHj3Yt28fMTExhIaG8sknn2BiYsLw4cNZsWIFANu2baNDhw7Y29uXGMvhw4fZsGGDSgCMrDpuNScDJ4H7k0t/DHwhpVwphPgGGA18rfvzhpSypRAiVFfuBSGEFxAKeANOwDYhRGtdXfOB3kAycFAIsVFKGV8N51RlPHzseeZlL6KWnGDzt3H8Y1x7TM3UWxtFqZEqccdeGVZWVoUG7Vm2bBn350TZtm0b8fEPfg3evn2bjIwMbGxsiq1vx44dLF68mN27d5d43OTkZF544QUuXbpETk4OHh4eAIwaNYrg4GCmTJnCkiVLePnll0uMBSAoKAgrK6tynrliaFV6dRFCuAD9gUW6ZQE8DazRFfkOeE73c7BuGd32XrrywcBKKWW2lPJP4CzQRfc5K6U8L6XMAVbqytZ6rZ5ozt9fbEPSietsXRKPRlO/JnlSFKXiNBoNe/fuJTY2ltjYWFJSUkpMAI4dO8aYMWPYsGFDqUP/Tpo0iYkTJ3L8+HG+/fZbsrKyAO3MgM2bN2f79u3s379f/0ShpFisra0NdMZKZVT1LWYk8P8AjW7ZDrgppbzfEiQZcNb97AxcBNBtv6Urr1//0D7Fra8TvJ9ypltIS84ducqOH04hVSKgKEoZ9OnThy+//FK/XNIwv0lJSQwaNIjly5fTunXrYsvdd+vWLZydtb9mv/vuu0LbxowZw/Dhw3n++ecLTQFc1lgU46iyJEAIMQC4KqU8XHB1EUVlKdvKu76oWMKEEIeEEIfS0tJKiLpm6djbFb9/uHNqzyV2r0mgvk37rChK+c2dO5dDhw7h4+ODl5cX33zzTbFl33vvPa5fv8748ePx9fXFz+/BbLN3797FxcVF//n888+JiIhgyJAhPPXUU9jb2xeqKygoiMzMTP2rgPLGohiHqKoLixDi38BLQB5gibZNwDogEHhUSpknhHgSiJBSBgohtuh+3iuEMAMuAw5AOICU8t+6ercAEbrDREgpA3XrpxcsVxw/Pz95/91ZbSClZPfqBI5tT8avvztdn33M2CEpSr1W1HztChw6dIipU6fy+++/GzuUeq2of59CiMNSSr+iylfZkwAp5XQppYuU0h1tw77tUsphwA5gsK7Y/wEbdD9v1C2j275dajOUjUCorveAB9AKOAAcBFrpehtY6I6xsarOx1iEEPQY3Iq23Rw59EsiMVuTjB2SoihKIR999BEhISH8+98l3oMpNZAxOqJPA1YKIWYBMcBi3frFwHIhxFkgHe1FHSnlCSHEKiAe7VOFCVLKfAAhxERgC2AKLJFSnqjWM6kmwkQQMLwtuVn57Fl7FgtLU7yfqjPNHxRFqWJLly5lzpw5hdZ1796d+fPnG6T+8PBwwsNrfS/teqnKXgfUVLXtdUBB+Xkafv36OEnx1+kzyptWTzQ3dkiKUu+o1wFKTVZjXgcohmdqZkLfse1wfLwx25bGk3jsmrFDUhRFUWqxUpMAIYS1EMJE93NrIUSQEMK86kNTimJuYcqACR2wb9GIzQviSD59w9ghKYqiKLVUWZ4E7AIshRDOwG/Ay8CyqgxKKZmFlRnPTvLF1sGKX786xuU/bxk7JEVRFKUWKksSIKSUd4FBwDwp5UDAq2rDUkpj2cic4Mm+WNmY8795R7mWnGnskBRFUZRapkxJgK4//zDgF906Nb1dDWDdpAHBUzpiZmHKxrmx3Lxy19ghKYpSDRo1alRoedmyZUycOLHc9WzYsAEfHx/9QEH35w5ITEykXbt2BolVqdnKkgRMAaYD63Td9R5D29dfqQFs7a0ImuyL1Eg2zIkhIz3L2CEpilJL9OrVi6NHjxIbG8uSJUvKPOOgIeXl5ZVeSKkypd7RSyl3AjsBdA0Er0kpX6vqwJSye8TRmqDXfFn/+RE2zoll4D870dDWwthhKUqd9/GBjzmVfsqgdbZ9pC3Tukyr8P5paWm8+uqrJCVpBxaLjIyke/fuRZYt+EThzp07aOdsK97ChQtZsGABOTk5tGzZkuXLl5Ofn4+Pjw9nzpzB3Nyc27dv4+PjQ0JCAklJSUyYMIG0tDQaNmzIwoULadu2LSNHjuSRRx4hJiaGTp068dlnn1X4fJXKKUvvgP8IIWyFENZoB+w5LYR4s+pDU8rDwdWG/hM7kJmexca5sWTdyTV2SIqiVJF79+7h6+ur/7z77rv6bZMnT2bq1KkcPHiQtWvXlnp3v27dOtq2bUv//v1ZsmRJiWUHDRrEwYMHOXr0KJ6enixevBgbGxv+/ve/88sv2rfFK1euJCQkBHNzc8LCwpg3bx6HDx/m008/Zfz48fq6zpw5w7Zt21QCYGRlebfvJaW8LYQYBvyKdsS/w8DsKo1MKTenlk3oN649v3x1jP99eZSgyb5YWKrmG4pSVSpzx14ZVlZWhWbkW7ZsGfcHQdu2bRvx8fH6bbdv3yYjI6PY6YQHDhzIwIED2bVrFzNmzGDbtm3FHjcuLo533nmHmzdvkpmZSWBgIKCdQfCTTz7hueeeY+nSpSxcuJDMzEz27NnDkCFD9PtnZ2frfx4yZIh+tkHFeMpyhTDXjQvwHPCllDJXCFG/hhmsRVy97Ogz2pstC+LY9M1x+k/wwcxc/UdTlPpCo9Gwd+9erKysyrVfz549OXfuHNeuFT8I2ciRI1m/fj0dOnRg2bJlREdHA9ohiBMTE9m5cyf5+fm0a9eO27dv06RJk2KnD7a2ti5XfErVKEvDwG+BRMAa2CWEcANuV2VQSuU83rEZT4/wJPnUDbYsPEF+vsbYISmKUk369OnDl19+qV8u7iIMcPbsWf0U5UeOHCEnJwc7O7tiy2dkZODo6Ehubi4rVqwotG3EiBEMHTpUP5Wwra0tHh4erF69GtDOiHr06NEKn5dSNUpNAqSUc6WUzlLKf0itC0BANcSmVELbJx156oXWJB67xvbvTiI16uGNotQHc+fO5dChQ/j4+ODl5cU333xTbNm1a9fSrl07fH19mTBhAv/973/1jQNPnz6Ni4uL/rN69Wref/99unbtSu/evWnbtm2huoYNG8aNGzcYOnSoft2KFStYvHgxHTp0wNvbmw0bNqDULKVOICSEaAz8C+ipW7UTeE9KWSuHqavNEwhVxKFNiezfcB7vns78bWjrUlv/KopSMjWBUNHWrFnDhg0bWL58ubFDqdfKO4FQWdoELAHigOd1yy8BS9GOIKjUcJ37upGblceRLUlYWJry5MDHVSKgKIpBTZo0iU2bNvHrr78aOxSlnMqSBDwupQwpsDxTCFH8SyalRhFC4P/c4+TcyycmKgkLKzP8+rkbOyxFUarR0qVLmTNnTqF13bt3Z/78+Qapf968eQapR6l+ZUkC7gkhekgpdwMIIboD96o2LMWQhBD0DG1NTnYe+zecJzcrj4693bBspCaDVJT64OWXX9Y32FOUgsqSBIwDvtO1DRBAOjCyKoNSDE+YCHqN8EQgOLIliWPbk2nzpCMdnnah6aOqq46iKEp9VJZhg2OBDkIIW92y6h5YS5mYmvDMy150DHTl2G8XObXnEid2peDe3o4Oz7ji3LqJai+gKIpSjxSbBAghXi9mPQBSys9LqlgIYQnsAhrojrNGSvkvIYQHsBJ4BDgCvCSlzBFCNAC+BzoD14EXpJSJurqmA6OBfOA1KeUW3fq+wBzAFFgkpfyobKddv9k5NSLgJU+6Bj9O3K4U4nYms+GLGOxbNMK3Vwta+jXH1KwsQ0goiqIotVlJv+ltSvmUJht4WkrZAfAF+goh/IGPgS+klK2AG2gv7uj+vCGlbAl8oSuHEMILCAW8gb7AV0IIUyGEKTAf6Ad4AUN1ZZUyamhrQZcBHoz4sBsBL7UlP0+ybdlJvn97D4c3J6r5BxRFUeq4YpMAKeXMkj6lVawbWChTt2iu+0jgaWCNbv13aIcjBgjWLaPb3ktoHzsEAyullNlSyj+Bs0AX3eeslPK8lDIH7dOF4HKcu6JjZm6KV3cnhr7bhQGTOmDnZM2+9ef5bvof7PzxNDev3DV2iIqiFHDx4kU8PDxIT08H4MaNG3h4eHDhwoVy1ZOamsrgwYMNEk9AQACenp54e3sX6okwcuRI1qxZU8LeVc/d3b3E4ZDLIjo6mgEDBpS5fEREBM7Ozvj6+uLl5cWPP/6o3zZy5Eg8PDz0E0B169YN0M4BMXHixL/UVXC2x5LKVUSVzi6ju1s/DLREe9d+Drgppbw/gXQy4Kz72Rm4CCClzBNC3ALsdOv3Fai24D4XH1rftQpOo94QQuDmbYebtx3XUzKJ/e0i8X+kErcrBff29vg+0wKnVqrdgKIYW4sWLRg3bhzh4eEsWLCA8PBwwsLCcHNzK1c9Tk5O5bpA5+fnFznpj5mZGZ999hmdOnUiIyODzp0707t3b7y8qu/hrJQSKSUmJjXnVebUqVN54403SEhIoHPnzgwePBhzc22vrNmzZxskAausKk0CpJT5gK8QogmwDihqmK37QxYWdWWRJawv6m+6yOEPhRBhQBiAq6trKVErAHbOjeg1whP/4MeI25lC3K4U1n9+DQdXGzr0akFLv2aYmtac/2yKYgyXP/yQ7JOnDFpnA8+2PPrWW6WWmzp1Kp07dyYyMpLdu3czb948MjMzCQ4O5saNG+Tm5jJr1iyCg4OZNm0abm5u+ql8IyIisLGxISQkhAEDBhAXF0d+fj7h4eFER0eTnZ3NhAkTGDt2LNHR0cycORNHR0diY2MLzVB4n6OjI46OjgDY2Njg6elJSkpKsUlAcXHOmDEDe3t7Jk+eDMDbb79N8+bNee2115g9ezarVq0iOzubgQMHMnPmTBITE+nXrx8BAQHs3buX9evXlzkRunPnDpMmTeL48ePk5eURERFBcHAwiYmJvPTSS9y5cweAL7/8Un+nft/BgwcJCwtj7dq1BAYGsmfPHhwcHNBoNLRu3Zp9+/YVKt+qVSsaNmzIjRs3aNasWZniqy7VMs+slPKmECIa8AeaCCHMdE8DXIBUXbFkoAWQLIQwAxqj7Y54f/19Bfcpbv3Dx18ALADtsMGGOKf6wrpxA7oGPUbnvm6c3n+Zo79dZNvSePauO4dPgAtePZywtFbjDShKdTM3N2f27Nn07duXqKgoLCwsMDExYd26ddja2nLt2jX8/f0JCgoiNDSUKVOm6JOAVatWsXnzZjSaB5OLLV68mMaNG3Pw4EGys7Pp3r07ffr0AeDAgQPExcXh4eFRalyJiYnExMTQtWvxD2YtLS2LjHP06NEMGjSIyZMno9FoWLlyJQcOHCAqKoqEhAQOHDiAlJKgoCB27dqFq6srp0+fZunSpXz11Vfl+v4++OADnn76aZYsWcLNmzfp0qULzzzzDM2aNWPr1q1YWlqSkJDA0KFDKTjU/J49e5g0aRIbNmzA1dWV4cOHs2LFCqZMmcK2bdvo0KED9vb2hY515MgRWrVqVSgBePPNN5k1axYA3t7ef5mQqbqUmgToWu2HAO4Fy0sp3ytlPwcgV5cAWAHPoG3stwMYjPYd/v8B92eU2Khb3qvbvl1KKYUQG4H/CCE+B5yAVsABtE8IWul6G6SgbTz4YtlOWykvMwtTvJ9yxqu7ExdOXOfobxfZu+4cB39NxPNJR3yedqFJs4bGDlNRqlVZ7tir0qZNm3B0dCQuLo7evXsjpeStt95i165dmJiYkJKSwpUrV+jYsSNXr14lNTWVtLQ0mjZtiqurK4mJifq6oqKiOHbsmP71wK1bt0hISMDCwoIuXbqUKQHIzMwkJCSEyMhIbG1tiy1XXJzu7u7Y2dkRExOjj9vOzo6oqCiioqLo2LGj/jgJCQm4urri5uaGv79/ub+7qKgoNm7cyKeffgpAVlYWSUlJODk5MXHiRGJjYzE1NeXMmTP6fU6ePElYWBhRUVE4OTkBMGrUKIKDg5kyZQpLliwpNCjTF198wcKFCzl//jybN28udPzKvg4w1GvZsjwJ2ADcQvtuP7scdTuiHWTIFO2j+1VSyv8JIeKBlUKIWUAMsFhXfjGwXAhxFu0TgFAAKeUJIcQqIB7IAyboXjMghJgIbEHbRXCJlPJEOeJTKkCYCNzb2+Pe3p5ryRkc3XaRE7+ncHxnMh4+9vg+44pjy8aq3YCiVLHY2Fi2bt3Kvn376NGjB6GhoWzZsoW0tDQOHz6Mubk57u7uZGVlATB48GDWrFnD5cuXCQ0N/Ut9UkrmzZtHYGBgofXR0dFYW5c+oFhubi4hISEMGzaMQYNKnlpmxYoVxcY5ZswYli1bxuXLlxk1apQ+tunTpzN27NhC9SQmJpYptqJIKVm7di1t2rQptD4iIoLmzZtz9OhRNBoNlpaW+m2Ojo5kZWURExOjTwJatGhB8+bN2b59O/v37y90R3+/TcBPP/3EiBEjOHfuXKH6ysrKyoqcnBwsLCwASE9P/8vThooqy0tdFynlC1LKT6SUn93/lLaTlPKYlLKjlNJHStnu/pMDXWv+LlLKllLKIVLKbN36LN1yS9328wXq+kBK+biUso2UclOB9bk0sCQAAB5vSURBVL9KKVvrtn1QgfNXKsHexYZeI70Y8WE3Ovd1I/XsTdZ9doQ1Hx3izMHL5OdrSq9EUZRyk1Iybtw4IiMjcXV1/f/t3Xt0lPW97/H3d5JMZnK/cku4eQFEiFFTDYJUaivVw8LLUmsBa1U23uoR2PXodlW3Wtc+7J5dV6n1UC1aEem2anW1nqq0olisggKiKLdiQQgghCTknkyS+Z4/nieTBIZkCEmGZL6vtbJmnt8888x3ZmVlPvk9v+f349577+XHP/4xlZWVDBo0iISEBN59990OVwvccMMNvPjii7zyyith/wOdPn06S5YsoanJuTR4x44dofPikdRz6623ctZZZ7FwYdgpZjrorM6rr76at956i48//jgUSKZPn86zzz5LTY1zwdm+ffs4dOhQRLUdz/Tp03niiSdoXUn3k08+CdU2dOhQPB4Py5cvp6WlJfScjIwM/vznP/PAAw+wevXqUPvcuXOZM2cO119/fdiBk9dccw1FRUUsW7bsmMci8c1vfpMXXngBgPr6el566SWmTZvWrWMdLZIQ8IGITOyRVzMDUnJ6IsVXns5N/3sy35w1lkBDC399Zgsv/ORDNv7lKxrrbL4BY3rSb37zG0aMGMF3vvMdAO688062bdtGYWEh69evp6ioiBUrVjBu3LjQc84++2yqq6vJy8sLDeJrb+7cuYwfP57zzjuPCRMmcNttt9Hc3HzMfuH8/e9/Z/ny5bzzzjuhy97aryh42223kZ+fT35+PpMmTWL27NnHrdPr9TJt2rQOX6iXXXYZs2bNYtKkSUycOJFrr72W6urqE/rMCgoKQjUsXLiQBx98kKamJgoKCpgwYQIPPvhg6LNctmwZxcXF7Nix45iehsGDB/P6669z1113sW7dOgBmzpxJTU1Np+szPPTQQzz++OOhcRj33ntv6LMqLCwkEAgAzuV/rXXm5+dTUlLC4sWLefXVVyksLKS4uJjrrruOqVOnntD7Px5pTUHH3cHpvj8D2IVzOkBwpgEo6JEK+lhRUZG2H+Rhep4Gla8+L2PTqj3s236E+MQ4xl80lIJvDSc91x/t8ow5KeHWazc9JxgMct555/Hyyy9z5plnRruciKxfv54FCxawZs2aaJcS9vdTRDaoalG4/SMZE3B5TxRmYod4hFEFOYwqyKF0TzWfrtrL5+/tY/PqEkYX5lJ46XCGnG7jBowxHW3ZsoUZM2Zw9dVX95sAsGjRIpYsWRK10f0nq8ueAAAROQe42N1co6qf9mpVvch6AqKjpqKRze+V8MXf9tFY18ygUWkUfns4p5+bi8fmGzD9SCz3BJSVlXHppZce075q1Sqys7OjUFFHF154IY2NHcevL1++nIkTY+eMdo/3BIjIPcC/AK+6TS+IyNOq+sTJFmtiR0pmIpOuOp2iy0ex7cMDfLpqL39Z+gUpWYkUTBvO+CnDSPT3ybQVxphuys7OZtOmTdEu47haz9GbyEXyV/dW4EJVrQUQkf/EuZbfQoA5YQmJcUy8JJ8JU/PYvfkwm97eywd/2MnHf97F+IuGUfCtfNJybNyAMcb0hUhCgOAs4duqhfBT+RoTMfEIo8/JZfQ5uRz6qopPV+1l8+oSPnt3L6edm0vht0cw5LT0aJdpjDEDWiQh4LfAOhF5zd2+irYJfow5aYNGpvGdW85m0tWns3l1CV+s2c+XG0sZPDqNwm+P4LTCHBs3YIwxvaDLEKCqj7vz/k/B6QG4WVU/6e3CTOxJyfQx6eozOP/yUWz78Gs+fWcvK3/zOalZPs6aPJT8cVkMGpVqCxcZY0wPOe5fUxFJc2+zgN3AC8By4Cu3zZhe4fXFUzAtn9mPFHP57RNJyUrko9d38er/2cDShWt4/YlNbFz5FQd3VxEM2npQJvb01PryK1asoKCggIKCAi666CI+/bTtwq+jX6OvrV69mhkzZhzTPnv2bMaOHcuECRO45ZZbQjMcmu7prCfgd8AMnDUD2v+lFXf7tF6syxg8HuG0wlxOK8ylvjrAvh1H2Lejgn3bK/jwtS8B8PriGDYmk7wxGeSNzSQnLwXx2JAVYyIxevRo3nvvPTIzM3nzzTeZN29en4+wb25uJj4+8iuDZs+eHZpCd9asWSxdupQ77rijt8ob8I77yavqDPe266WjjOll/lQvZ5w/iDPOd5birK1sdAPBEfZtr2D3Z4cBSEyOJ29MpvMzNoOsock2KZHpNWte2sHhvTU9esyc4SlcfP2Ybj+/tLSU22+/nT179gDwi1/8gsmTJ4fd96KLLgrdLy4upqSkpNNjv/766zz22GMEAgGys7NZsWIFubm5jB07lg8++IDc3FyCwSBjxoxh7dq1qGrYWh5++GH279/P7t27ycnJ4Xe/+13E7++KK64I3b/gggu6rNl0LpJ5Alap6qVdtRnTl5LTExnzjSGM+cYQAKrLG0K9BCXbK/jnJ6UA+NO8Ti/BmEzyx2aSPshvocD0e/X19RQWFoa2y8vLmTlzJgD33HMPCxYsYMqUKezZs4fp06ezdevWLo/5zDPPcPnlnU8QO2XKFNauXYuIsHTpUn72s5/x85//nDlz5rBixQrmz5/P22+/zTnnnENOTg6zZs06bi0bNmzg/fffx+/v3iXBTU1NLF++nMWLF3fr+cZx3BAgIj4gCcgRkUzaLgtMA4b1QW3GRCw1y8e44qGMKx6KqlJ1uC0U7Ntewc71zopjyRmJ5I1tCwU2J4E5GSfzH/vJ8Pv9HSbtee6552idCfXtt99my5Ytoceqqqqorq4mNTX1uMd79913eeaZZ3j//fc7fd2SkhK+973vceDAAQKBAKNHOx3Ft9xyC1deeSXz58/n2WefDS2kc7xawFl0p7sBAJyFfqZOncrFF1/c9c7muDrrCbgNmI/zhb+BthBQBTzZy3UZ020iQnqun/RcP+MnD0NVOXKwzhlTsL2CvVvK2bHuIACp2T7yxmaS744pSMk88bW+jTmVBINBPvzww4i/YD/77DPmzp3Lm2++2eXUv3fffTcLFy5k5syZrF69mocffhiA4cOHM3jwYN555x3WrVsXmke/s1qOXp3vRDzyyCOUlpby1FNPdfsYxtHZmIDFwGIRudumCDb9mYiQOSSZzCHJTJiah6pSfqDW7SU4wq5NpWz74AAA6YP8bijIJG9sJklp3ihXb8yJueyyy/jVr37FvffeC8CmTZs6nDpob8+ePVxzzTUsX76cMWO67tWorKwkLy8PgGXLlnV4bO7cucyZM4cbb7yxwxLAkdYSqaVLl7Jy5UpWrVqFx2OXC5+sSOYJeEJEJgDjAV+79ud7szBjeouIkD0shexhKRRMG44GlcMlNaHTBzs/PsiWNfsByByaHOolyBuTiS8lIcrVG9O5X/7yl9x1110UFBTQ3NzM1KlT+fWvfx1230cffZSysjLuvPNOAOLj40OnFerq6sjPzw/tu3DhQh5++GGuu+468vLyKC4uZteuXaHHZ86cyc033xw6FXCitYSzatWqDjW8/PLL3H777YwcOZJJkyYBcM011/DQQw9FfEzTUZerCIrIvwOX4ISAN3CWFn5fVa/t9ep6ga0iaLoSbAlSurcmNJ5g/5eVNDc6M2dn56e4vQQZDDszg8QkCwWxJpZXEezM+vXrWbBgAWvWrIl2KTGtx1cRBK4FzgE+UdWbRWQwsPSkKzXmFOWJ8zB4VBqDR6Vx3vSRtLQEObS7mn3byynZfoTP1+zj03f2IgK5I1LdyxEzGXpGOl6frYRoYs+iRYtYsmRJaCyA6T8i6Qn4SFUvEJENwDSgGvhcVc/u4nnDgeeBIUAQeFpVF7uzDf4eGIUzE+H1qlohznVbi4ErgDrgh6q60T3WTcBP3EM/pqrL3PbzgecAP04vxT3axRuyngBzspqbWji4q4oSt6fg4K4qgi2KxyMMGtUWCoacnk6CNy7a5Zoe1h97An77298ecynd5MmTefLJ6I/xXrlyJffdd1+HttGjR/Paa68d5xmmMyfaExBJCPi/wAPADcC/AjXAJlW9uYvnDQWGqupGEUnFucLgKuCHQLmqLhKR+4FMVb1PRK4A7sYJARcCi1X1Qjc0rAeKcGYq3ACc7waHj4B7gLU4IeCXqvpmZ3VZCDA9rSnQwtdfVoZCwaGvqtGggkB6jp+sYclkDUsme1gKWcOSyRicRFy8DWjqr7Zu3cq4ceNsvglzylFVtm3b1rOnA1T1Tvfur0XkLSBNVT+L4HkHgAPu/WoR2QrkAVfijDEAWAasBu5z2593/5NfKyIZbpC4BPirqpa7b+avwHfdRY3SVPVDt/15nJDRaQgwpqcleOMYflYWw89yltQINDSz/x9HOPRVNeX7ayjfX8vuzWVOMMCZDjl9cBJZQ5PJzksma6gTEtJz/bZaYj/g8/koKysjOzvbgoA5ZagqZWVl+HwndplzZ5MFndfZY61d9ZEQkVHAucA6YLAbEFDVAyIyyN0tD9jb7mklbltn7SVh2sO9/jxgHsCIESMiLduYbvH64hk1MYdRE3NCbS1NQSoO1lF+oIbyfbWUH6ildG81X35yKLQyR1y8h4whSWS7PQdZw1LIHpZMapbP1kM4heTn51NSUkJpaWm0SzGmA5/P1+Fqikh01hPw89bj4nTFf4ozYVABzpf5lEheQERSgD8A81W1qpPkHO4B7Ub7sY2qTwNPg3M6oKuajelpcQkecvJTyMlPgW+0tTcFWqg4UEv5/lrK9ju3+/9xhB0fHQztE58YR9aQpA7BIGtYMskZifafaBQkJCSEZsozpr/rbLKgaQAi8iIwT1U3u9sTgB9HcnARScAJACtU9VW3+aCIDHV7AYYCh9z2EmB4u6fnA/vd9kuOal/ttueH2d+YfiPBG8egkWkMGpnWob2xvpmKA7WU7auh3A0Je74oZ9uHX4f28frjnVMJeclOMBjqhASb4MgYE6lIrmca1xoAAFT1cxHpcsond7T/M8BWVX283UN/Am4CFrm3f2zX/iM3dFwIVLpBYSXwH+76BQCXAf+mquUiUi0ixTg9Ez8AbGZDMyAk+uMZclo6Q05L79BeXxOg3O0xKN/vnFb4cuMhtqxpDu3jS0lwewtSQoMSs4Ym40u2OQ2MMR1FEgK2ishS4AWc7vY5QNdLUsFk4EZgs4i0rnTxAM6X/0siciuwB7jOfewNnCsDduJcIngzgPtl/1PgY3e/R1sHCQJ30HaJ4JvYoEAzwPlTvOSN8ZI3JjPUpqrUVbUPBzWU7a9l29oDNDW0hPZLTveSlZcSGojYGg5sbgNjYlcklwj6cL5sp7pNfwOWqGpDL9fWK+wSQRMrVJWaisYOpxTK99dScaCW5qZgaL/UbJ97CaOzvkJqto/UbB8pGYl2tYIxA8BJzRMw0FgIMLEuGFSqDtd3OKVQvr+Giq/rCLa0/T0Qj5CSmUiaGwpSs3ykZvtD2ymZFhKM6Q+6NU+AiLykqteLyGbCjLpX1YIerNEY00c8HiFjUBIZg5I4rTA31N7SEqT6cAPV5Q1UlzVQVVZPdZlzf+/WCmorGzv8JRCPkJzhJS3bH+o9cAKCn9QsHylZicRZSDDmlNbZycB73NsZfVGIMSa64uI8ZAxOImNwUtjHW5qD1FQ0UOUGg/ZBYd/2CmqOHBUSBJIzEt1w0BYUWsNCSqbPZk40Jso6u0SwdUKfr/quHGPMqSou3kN6bhLpuZ2FhEaqy+qdoFDeFhb2/aOC2o8a6XD2USAlI9E9zeDrGBaynJ+4BAsJxvSmzk4HVBN+8h0BVFXTwjxmjIlRTkjwk57rD/t4S0uQ2opGtwehgWq3F6GqrIEDOyv5x/pDoamVARBITvM6pxdCpxrawkJKViLxCbZAkzEno7OegNS+LMQYM7DFxXlIy/GTluMPO793sCVIzZHGdqcaWnsT6jm4q5KdG44KCUBSupfULB9JaV6S0xNJSvcedT8Rf1qCjU0w5jgivkDYneM/tDKBqu7plYqMMTHJE+chLdtPWnb4noRgS5DaykCHHoRqNyhUltZzYGclDbVNxz5RwJ+SQFKal6T0RJLTvKGAkJTuJbndfZszwcSaLn/jRWQmzjoCw3Cm+B2JM1nQ2b1bmjHGtPHEeUJjBTgz/D4tTUHqqgPUVQaorWykripAnXtbWxmgripAxYFa6qoCHS6HbBWfGOf2JHjbQkO7+629DL6UBDy2qJMZACKJvT8FioG3VfVcEZkGfL93yzLGmBMXl9AuKHRCg0pjXXOHoFBb5YSH1tBQtq+WvVsrCNQ3H/N88Qj+1ATntEOoZ6HjaYjW8BDvtXEL5tQVSQhoUtUyEfGIiEdV3xWR/+z1yowxppeIR/ClJDjrLIRdgLxNU6CF+lBPQuNRvQxO70Lp3mrqqwKEm3vN649v611oDQ1pXvypCfiSE/ClePG7tST6423ZaNOnIgkBR9zlgP8GrBCRQ8Cx0dgYYwagBG8cCe6Axs4Eg0pDTRN1VY1OYGh/GsINEId2V1FbFaC5sSXsMUQgMTkhFAqckOBuJ3vxpcS3hYZkCw7m5EUSAq4E6oEFwGwgHXi0N4syxpj+xuOR0H/5Ofmd7xtoaKahtomGGuenvqYp7HbV4QYO7a6ivraJYHP4Kd5F6BAYfB1ChLctRFhwMGFEEgLmAS+ragmwrJfrMcaYAc/ri8friz/ulRBHU1WaGluckFDrhoTWn9B2wA0O9RzcXUVDTVPYwY/gng5Jjm/X0+B1tlPahYbWUOFue/3xOCvEm4EkkhCQBqwUkXLgReAVVT3Yu2UZY4xpJSJtwaGL0xKt2geH+naBwdkOdNg+cqgutN1ZcEj0x+NNiifRH09iUutPQqjdl9T6eELoca8/Hl9Sgs3+eIrqMgSo6iPAIyJSAHwPeE9ESlT1271enTHGmG7pdnBoaDkqNARC2431zTTWOT+B+iZqjzQ62/XNtLRbnjqcuARPh/DgbRcUnPZ2weGoNq8/3i7J7CUnMjPGIeBroAwY1DvlGGOMiRYRwet3vnSPN/3z8TQ3tbjhoC0oNNY3EahrpqGumYAbFhrrmmisa6ahJsCRQ3Wh9qNngzya1xd3TC9Da1AIhYbk9r0UCaH3kpAYZyHiOCKZLOgOnB6AXOAV4F9UdUtvF2aMMab/iE+IIz49juT0xBN+buupi7YQ0RTqYWisdW4DdW67GzKqDjeEQkagIfzVFu0lJMY5QcINBV5/vNtTEkeCPx5vqC2OBF986L7XF0+Ce+v1xxEX7xlQYyMi6QkYCcxX1U29XYwxxpjY0/7URXcEW4IEGlqOCQ+B+mYCDe5tYwtN9U5gcNpaqK+uI1Dvbje0dNkbAeCJk7ZQ4AaDDkHB1xYwjm5rv53gOzVOcUQyJuD+7hxYRJ4FZgCHVHWC25YF/B4YBewGrlfVCnFi1WLgCqAO+KGqbnSfcxPwE/ewj6nqMrf9fOA5wA+8AdyjGm6qDmOMMQOZJ86DL9mDLzmh28dQVZqbgjQ1tLSFB/d+U2PHtiY3VATcUFFfHaCytCUUNo43D8TR4lt7J9oFA68vjvFThjFqYk6338uJ6M3VMp4DfgU8367tfmCVqi4Skfvd7fuAy3FmAz8TuBBYAlzohoZ/B4pwljXeICJ/UtUKd595wFqcEPBd4M1efD/GGGMGKBFxJobyOutHnIxgS9AJDseEiJZQz8SxbS00NTZTddjpzegrvRYCVPVvIjLqqOYrgUvc+8uA1Tgh4Ergefc/+bUikiEiQ919/6qq5QAi8lfguyKyGkhT1Q/d9ueBq7AQYIwxJso8cR4SkzwkJnW/Z6Kv9PWFm4NV9QCAe9t6lUEesLfdfiVuW2ftJWHawxKReSKyXkTWl5aWnvSbMMYYYwaCU2X2hnCjI7Qb7WGp6tOqWqSqRbm5ud0s0RhjjBlY+joEHHS7+XFvD7ntJcDwdvvlA/u7aM8P026MMcaYCPV1CPgTcJN7/ybgj+3afyCOYqDSPV2wErhMRDJFJBO4DFjpPlYtIsXulQU/aHcsY4wxxkSg1wYGish/4wzsyxGREpxR/ouAl0TkVmAPcJ27+xs4lwfuxLlE8GYAVS0XkZ8CH7v7Pdo6SBC4g7ZLBN/EBgUaY4wxJ0Ri7dL6oqIiXb9+fbTLMMYYY/qEiGxQ1aJwj50qAwONMcYY08csBBhjjDExykKAMcYYE6MsBBhjjDExykKAMcYYE6MsBBhjjDExykKAMcYYE6MsBBhjjDExykKAMcYYE6MsBBhjjDExykKAMcYYE6MsBBhjjDExykKAMcYYE6MsBBhjjDExykKAMcYYE6MsBBhjjDExykKAMcYYE6MsBBhjjDExqt+HABH5rohsF5GdInJ/tOsxxhhj+ot+HQJEJA54ErgcGA98X0TGR7cqY4wxpn+Ij3YBJ+kCYKeq/hNARF4ErgS29MWLv3LtuSTUN/XFSxljjIkRwamTufq+p/rktfp7CMgD9rbbLgEuPHonEZkHzAMYMWJEj724rzqAvy7YY8czxhhjyquP9Nlr9fcQIGHa9JgG1aeBpwGKioqOeby7Zqz8oqcOZYwxxvS5fj0mAOc//+HttvOB/VGqxRhjjOlX+nsI+Bg4U0RGi4gXuAH4U5RrMsYYY/qFfn06QFWbReRHwEogDnhWVa2P3hhjjIlAvw4BAKr6BvBGtOswxhhj+pv+fjrAGGOMMd0kqj02WL5fEJFS4KsePGQOcLgHj2fCs8+5b9jn3Dfsc+479lnDSFXNDfdAzIWAniYi61W1KNp1DHT2OfcN+5z7hn3Ofcc+687Z6QBjjDEmRlkIMMYYY2KUhYCT93S0C4gR9jn3Dfuc+4Z9zn3HPutO2JgAY4wxJkZZT4AxxhgToywEdJOIfFdEtovIThG5P9r1DFQiMlxE3hWRrSLyhYjcE+2aBjIRiRORT0Tk/0W7loFKRDJE5BUR2eb+Xk+Kdk0DkYgscP9mfC4i/y0ivmjXdCqyENANIhIHPAlcDowHvi8i46Nb1YDVDPyrqp4FFAN32Wfdq+4Btka7iAFuMfCWqo4DzsE+7x4nInnA/wSKVHUCzrTyN0S3qlOThYDuuQDYqar/VNUA8CJwZZRrGpBU9YCqbnTvV+P8wcyLblUDk4jkA/8DWBrtWgYqEUkDpgLPAKhqQFX7bvH42BIP+EUkHkjCVpgNy0JA9+QBe9ttl2BfTL1OREYB5wLrolvJgPUL4H8BwWgXMoCdBpQCv3VPuywVkeRoFzXQqOo+4L+APcABoFJV/xLdqk5NFgK6R8K02WUWvUhEUoA/APNVtSra9Qw0IjIDOKSqG6JdywAXD5wHLFHVc4FawMYU9TARycTpnR0NDAOSRWROdKs6NVkI6J4SYHi77Xysq6nXiEgCTgBYoaqvRrueAWoyMFNEduOc3vqWiLwQ3ZIGpBKgRFVbe7NewQkFpmd9G9ilqqWq2gS8ClwU5ZpOSRYCuudj4EwRGS0iXpwBJ3+Kck0DkogIzvnTrar6eLTrGahU9d9UNV9VR+H8Pr+jqvafUw9T1a+BvSIy1m26FNgSxZIGqj1AsYgkuX9DLsUGYIYVH+0C+iNVbRaRHwErcUadPquqX0S5rIFqMnAjsFlENrltD6jqG1GsyZiTcTewwv0H4p/AzVGuZ8BR1XUi8gqwEecKo0+wmQPDshkDjTHGmBhlpwOMMcaYGGUhwBhjjIlRFgKMMcaYGGUhwBhjjIlRFgKMMcaYGGUhwBgTVSJyia1aaEx0WAgwxhhjYpSFAGNMRERkjoh8JCKbROQpEYkTkRoR+bmIbBSRVSKS6+5bKCJrReQzEXnNncsdETlDRN4WkU/d55zuHj5FRF4RkW0issKd5Q0RWSQiW9zj/FeU3roxA5aFAGNMl0TkLOB7wGRVLQRagNlAMrBRVc8D3gP+3X3K88B9qloAbG7XvgJ4UlXPwZnL/YDbfi4wHxiPs9LeZBHJAq4GznaP81jvvktjYo+FAGNMJC4Fzgc+dqdvvhTnyzoI/N7d5wVgioikAxmq+p7bvgyYKiKpQJ6qvgagqg2qWufu85GqlqhqENgEjAKqgAZgqYhcA7Tua4zpIRYCjDGREGCZqha6P2NV9eEw+3U2D3m4JbhbNba73wLEq2ozcAHOCpJXAW+dYM3GmC5YCDDGRGIVcK2IDAIQkSwRGYnzN+Rad59ZwPuqWglUiMjFbvuNwHuqWgWUiMhV7jESRSTpeC8oIilAurtY1HygsDfemDGxzFYRNMZ0SVW3iMhPgL+IiAdoAu4CaoGzRWQDUIkzbgDgJuDX7pd8+5XybgSeEpFH3WNc18nLpgJ/FBEfTi/Cgh5+W8bEPFtF0BjTbSJSo6op0a7DGNM9djrAGGOMiVHWE2CMMcbEKOsJMMYYY2KUhQBjjDEmRlkIMMYYY2KUhQBjjDEmRlkIMMYYY2KUhQBjjDEmRv1/TJIGyk+w/YQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the accuracy and loss\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(2, figsize=(8, 6))\n",
    "\n",
    "for history in history_dict:\n",
    "    val_acc = history_dict[history].history['val_acc']\n",
    "    val_loss = history_dict[history].history['val_loss']\n",
    "    ax1.plot(val_acc, label=history)\n",
    "    ax2.plot(val_loss, label=history)\n",
    "    \n",
    "ax1.set_ylabel('validation accuracy')\n",
    "ax2.set_ylabel('validation loss')\n",
    "ax2.set_xlabel('epochs')\n",
    "ax1.legend()\n",
    "ax2.legend()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}