CIFAR10_VGG16_Transfer_Learning_Classifier.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CIFAR10 Transfer Learning based Classifier\n",
    "\n",
    "This notebook outlines the steps to build a classifier to leverage concepts of Transfer Learning by utilizing a pretrained Deep-CNN. \n",
    "Particularly in this case based on VGG16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
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   "source": [
    "# Pandas and Numpy for data structures and util fucntions\n",
    "import scipy as sp\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from numpy.random import rand\n",
    "pd.options.display.max_colwidth = 600\n",
    "\n",
    "# Scikit Imports\n",
    "from sklearn import preprocessing\n",
    "from sklearn.metrics import roc_curve, auc, precision_recall_curve\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "import cnn_utils as utils\n",
    "from model_evaluation_utils import get_metrics\n",
    "\n",
    "# Matplot Imports\n",
    "import matplotlib.pyplot as plt\n",
    "params = {'legend.fontsize': 'x-large',\n",
    "          'figure.figsize': (15, 5),\n",
    "          'axes.labelsize': 'x-large',\n",
    "          'axes.titlesize':'x-large',\n",
    "          'xtick.labelsize':'x-large',\n",
    "          'ytick.labelsize':'x-large'}\n",
    "\n",
    "plt.rcParams.update(params)\n",
    "%matplotlib inline\n",
    "\n",
    "# pandas display data frames as tables\n",
    "from IPython.display import display, HTML\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1669,
     "status": "ok",
     "timestamp": 1531341784750,
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from keras import callbacks\n",
    "from keras import optimizers\n",
    "from keras.datasets import cifar10\n",
    "from keras.engine import Model\n",
    "from keras.applications import vgg16 as vgg\n",
    "from keras.layers import Dropout, Flatten, Dense, GlobalAveragePooling2D,BatchNormalization\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "from keras.utils import np_utils"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load and Prepare DataSet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "collapsed": true,
    "id": "fS8uGXn5dgRU"
   },
   "outputs": [],
   "source": [
    "BATCH_SIZE = 32\n",
    "EPOCHS = 40\n",
    "NUM_CLASSES = 10\n",
    "LEARNING_RATE = 1e-4\n",
    "MOMENTUM = 0.9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "collapsed": true,
    "id": "PoT9P1phLuyT"
   },
   "outputs": [],
   "source": [
    "(X_train, y_train), (X_test, y_test) = cifar10.load_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Split training dataset in train and validation sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "collapsed": true,
    "id": "khNrl8nHqesu"
   },
   "outputs": [],
   "source": [
    "X_train, X_val, y_train, y_val = train_test_split(X_train, \n",
    "                                                  y_train, \n",
    "                                                  test_size=0.15, \n",
    "                                                  stratify=np.array(y_train), \n",
    "                                                  random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Transform target variable/labels into one hot encoded form"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "collapsed": true,
    "id": "IJdZ6DLUqu-P"
   },
   "outputs": [],
   "source": [
    "Y_train = np_utils.to_categorical(y_train, NUM_CLASSES)\n",
    "Y_val = np_utils.to_categorical(y_val, NUM_CLASSES)\n",
    "Y_test = np_utils.to_categorical(y_test, NUM_CLASSES)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preprocessing\n",
    "\n",
    "Since we are about to use VGG16 as a feature extractor, the minimum size of an image it takes is 48x48.\n",
    "We utilize ```scipy`` to resize images to required dimensions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "collapsed": true,
    "id": "6udZF8zHbTaR"
   },
   "outputs": [],
   "source": [
    "X_train = np.array([sp.misc.imresize(x, \n",
    "                                     (48, 48)) for x in X_train])\n",
    "X_val = np.array([sp.misc.imresize(x, \n",
    "                                   (48, 48)) for x in X_val])\n",
    "X_test = np.array([sp.misc.imresize(x, \n",
    "                                    (48, 48)) for x in X_test])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prepare the Model\n",
    "\n",
    "* Load VGG16 without the top classification layer\n",
    "* Prepare a custom classifier\n",
    "* Stack both models on top of each other"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "collapsed": true,
    "id": "j2py7602Kxlq"
   },
   "outputs": [],
   "source": [
    "base_model = vgg.VGG16(weights='imagenet', \n",
    "                       include_top=False, \n",
    "                       input_shape=(48, 48, 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
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    "id": "iI9P8ni-L8H8"
   },
   "outputs": [],
   "source": [
    "# Extract the last layer from third block of vgg16 model\n",
    "last = base_model.get_layer('block3_pool').output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "collapsed": true,
    "id": "MI90lh6hL9ua"
   },
   "outputs": [],
   "source": [
    "# Add classification layers on top of it\n",
    "x = GlobalAveragePooling2D()(last)\n",
    "x= BatchNormalization()(x)\n",
    "x = Dense(256, activation='relu')(x)\n",
    "x = Dense(256, activation='relu')(x)\n",
    "x = Dropout(0.6)(x)\n",
    "pred = Dense(NUM_CLASSES, activation='softmax')(x)\n",
    "model = Model(base_model.input, pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since our objective is to only train the custom classifier, we freeze the layers of VGG16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "collapsed": true,
    "id": "fc3EhfLTMD4I"
   },
   "outputs": [],
   "source": [
    "for layer in base_model.layers:\n",
    "     layer.trainable = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
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    "colab_type": "code",
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    "id": "PRXPI3DCMIIK"
   },
   "outputs": [],
   "source": [
    "model.compile(loss='binary_crossentropy',\n",
    "              optimizer=optimizers.Adam(lr=LEARNING_RATE),\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
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      "startup": false,
      "wait_interval": 0
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     "base_uri": "https://localhost:8080/",
     "height": 714
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 814,
     "status": "ok",
     "timestamp": 1531341814064,
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         (None, 48, 48, 3)         0         \n",
      "_________________________________________________________________\n",
      "block1_conv1 (Conv2D)        (None, 48, 48, 64)        1792      \n",
      "_________________________________________________________________\n",
      "block1_conv2 (Conv2D)        (None, 48, 48, 64)        36928     \n",
      "_________________________________________________________________\n",
      "block1_pool (MaxPooling2D)   (None, 24, 24, 64)        0         \n",
      "_________________________________________________________________\n",
      "block2_conv1 (Conv2D)        (None, 24, 24, 128)       73856     \n",
      "_________________________________________________________________\n",
      "block2_conv2 (Conv2D)        (None, 24, 24, 128)       147584    \n",
      "_________________________________________________________________\n",
      "block2_pool (MaxPooling2D)   (None, 12, 12, 128)       0         \n",
      "_________________________________________________________________\n",
      "block3_conv1 (Conv2D)        (None, 12, 12, 256)       295168    \n",
      "_________________________________________________________________\n",
      "block3_conv2 (Conv2D)        (None, 12, 12, 256)       590080    \n",
      "_________________________________________________________________\n",
      "block3_conv3 (Conv2D)        (None, 12, 12, 256)       590080    \n",
      "_________________________________________________________________\n",
      "block3_pool (MaxPooling2D)   (None, 6, 6, 256)         0         \n",
      "_________________________________________________________________\n",
      "global_average_pooling2d_1 ( (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_1 (Batch (None, 256)               1024      \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 256)               65792     \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 256)               65792     \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 10)                2570      \n",
      "=================================================================\n",
      "Total params: 1,870,666\n",
      "Trainable params: 134,666\n",
      "Non-trainable params: 1,736,000\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Augmentation\n",
    "\n",
    "To help model generalize and overcome the limitations of a small dataset, we prepare augmented datasets using \n",
    "```keras ``` utilities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "collapsed": true,
    "id": "_Y-jNseQMNcf"
   },
   "outputs": [],
   "source": [
    "# prepare data augmentation configuration\n",
    "train_datagen = ImageDataGenerator(\n",
    "    rescale=1. / 255,\n",
    "    horizontal_flip=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "collapsed": true,
    "id": "mHPmLOf-N3SQ"
   },
   "outputs": [],
   "source": [
    "train_datagen.fit(X_train)\n",
    "train_generator = train_datagen.flow(X_train,\n",
    "                                     Y_train, \n",
    "                                     batch_size=BATCH_SIZE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "collapsed": true,
    "id": "JSIJycdbrBWK"
   },
   "outputs": [],
   "source": [
    "val_datagen = ImageDataGenerator(rescale=1. / 255,\n",
    "    horizontal_flip=False)\n",
    "\n",
    "val_datagen.fit(X_val)\n",
    "val_generator = val_datagen.flow(X_val,\n",
    "                                 Y_val,\n",
    "                                 batch_size=BATCH_SIZE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
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     "base_uri": "https://localhost:8080/",
     "height": 1397
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1291407,
     "status": "ok",
     "timestamp": 1531343114582,
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    "id": "upzna-SWcdVK",
    "outputId": "a3acb1f3-4ed8-45c6-d026-0e9707df282b",
    "scrolled": false
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/40\n",
      "1328/1328 [==============================] - 33s 25ms/step - loss: 0.2495 - acc: 0.9094 - val_loss: 0.1866 - val_acc: 0.9277\n",
      "Epoch 2/40\n",
      " 726/1328 [===============>..............] - ETA: 12s - loss: 0.2001 - acc: 0.92311328/1328 [==============================] - 32s 24ms/step - loss: 0.1955 - acc: 0.9248 - val_loss: 0.1640 - val_acc: 0.9369\n",
      "Epoch 3/40\n",
      "1119/1328 [========================>.....] - ETA: 4s - loss: 0.1774 - acc: 0.93121328/1328 [==============================] - 32s 24ms/step - loss: 0.1773 - acc: 0.9312 - val_loss: 0.1520 - val_acc: 0.9411\n",
      "Epoch 4/40\n",
      "1246/1328 [===========================>..] - ETA: 1s - loss: 0.1664 - acc: 0.93531328/1328 [==============================] - 32s 24ms/step - loss: 0.1662 - acc: 0.9354 - val_loss: 0.1444 - val_acc: 0.9441\n",
      "Epoch 5/40\n",
      "1317/1328 [============================>.] - ETA: 0s - loss: 0.1580 - acc: 0.93851328/1328 [==============================] - 32s 24ms/step - loss: 0.1579 - acc: 0.9385 - val_loss: 0.1396 - val_acc: 0.9458\n",
      "Epoch 6/40\n",
      "1328/1328 [==============================] - 32s 24ms/step - loss: 0.1529 - acc: 0.9408 - val_loss: 0.1353 - val_acc: 0.9469\n",
      "Epoch 7/40\n",
      "   4/1328 [..............................] - ETA: 26s - loss: 0.1634 - acc: 0.93441328/1328 [==============================] - 32s 24ms/step - loss: 0.1470 - acc: 0.9431 - val_loss: 0.1321 - val_acc: 0.9485\n",
      "Epoch 8/40\n",
      " 813/1328 [=================>............] - ETA: 11s - loss: 0.1450 - acc: 0.94361328/1328 [==============================] - 32s 24ms/step - loss: 0.1444 - acc: 0.9438 - val_loss: 0.1301 - val_acc: 0.9489\n",
      "Epoch 9/40\n",
      "1166/1328 [=========================>....] - ETA: 3s - loss: 0.1405 - acc: 0.94551328/1328 [==============================] - 32s 24ms/step - loss: 0.1407 - acc: 0.9454 - val_loss: 0.1274 - val_acc: 0.9502\n",
      "Epoch 10/40\n",
      "1254/1328 [===========================>..] - ETA: 1s - loss: 0.1381 - acc: 0.94651328/1328 [==============================] - 32s 24ms/step - loss: 0.1381 - acc: 0.9465 - val_loss: 0.1258 - val_acc: 0.9506\n",
      "Epoch 11/40\n",
      "1309/1328 [============================>.] - ETA: 0s - loss: 0.1350 - acc: 0.94741328/1328 [==============================] - 32s 24ms/step - loss: 0.1351 - acc: 0.9474 - val_loss: 0.1244 - val_acc: 0.9510\n",
      "Epoch 12/40\n",
      "1328/1328 [==============================] - 32s 24ms/step - loss: 0.1329 - acc: 0.9485 - val_loss: 0.1225 - val_acc: 0.9515\n",
      "Epoch 13/40\n",
      "1328/1328 [==============================] - 32s 24ms/step - loss: 0.1311 - acc: 0.9493 - val_loss: 0.1220 - val_acc: 0.9521\n",
      "Epoch 14/40\n",
      " 735/1328 [===============>..............] - ETA: 12s - loss: 0.1288 - acc: 0.95031328/1328 [==============================] - 32s 24ms/step - loss: 0.1291 - acc: 0.9502 - val_loss: 0.1205 - val_acc: 0.9528\n",
      "Epoch 15/40\n",
      "1121/1328 [========================>.....] - ETA: 4s - loss: 0.1264 - acc: 0.95051328/1328 [==============================] - 32s 24ms/step - loss: 0.1264 - acc: 0.9507 - val_loss: 0.1193 - val_acc: 0.9530\n",
      "Epoch 16/40\n",
      "1272/1328 [===========================>..] - ETA: 1s - loss: 0.1257 - acc: 0.95151328/1328 [==============================] - 32s 24ms/step - loss: 0.1257 - acc: 0.9515 - val_loss: 0.1189 - val_acc: 0.9531\n",
      "Epoch 17/40\n",
      "1316/1328 [============================>.] - ETA: 0s - loss: 0.1243 - acc: 0.95201328/1328 [==============================] - 32s 24ms/step - loss: 0.1242 - acc: 0.9520 - val_loss: 0.1188 - val_acc: 0.9524\n",
      "Epoch 18/40\n",
      "1328/1328 [==============================] - 32s 24ms/step - loss: 0.1227 - acc: 0.9521 - val_loss: 0.1174 - val_acc: 0.9533\n",
      "Epoch 19/40\n",
      "   4/1328 [..............................] - ETA: 28s - loss: 0.1148 - acc: 0.96021328/1328 [==============================] - 32s 24ms/step - loss: 0.1210 - acc: 0.9528 - val_loss: 0.1166 - val_acc: 0.9541\n",
      "Epoch 20/40\n",
      " 843/1328 [==================>...........] - ETA: 10s - loss: 0.1178 - acc: 0.95391328/1328 [==============================] - 32s 24ms/step - loss: 0.1186 - acc: 0.9538 - val_loss: 0.1168 - val_acc: 0.9537\n",
      "Epoch 21/40\n",
      "1172/1328 [=========================>....] - ETA: 3s - loss: 0.1170 - acc: 0.95461328/1328 [==============================] - 32s 24ms/step - loss: 0.1174 - acc: 0.9544 - val_loss: 0.1161 - val_acc: 0.9538\n",
      "Epoch 22/40\n",
      "1261/1328 [===========================>..] - ETA: 1s - loss: 0.1168 - acc: 0.95441328/1328 [==============================] - 32s 24ms/step - loss: 0.1170 - acc: 0.9544 - val_loss: 0.1160 - val_acc: 0.9544\n",
      "Epoch 23/40\n",
      "1315/1328 [============================>.] - ETA: 0s - loss: 0.1157 - acc: 0.95481328/1328 [==============================] - 32s 24ms/step - loss: 0.1156 - acc: 0.9549 - val_loss: 0.1146 - val_acc: 0.9546\n",
      "Epoch 24/40\n",
      "1328/1328 [==============================] - 32s 24ms/step - loss: 0.1147 - acc: 0.9558 - val_loss: 0.1142 - val_acc: 0.9546\n",
      "Epoch 25/40\n",
      "   7/1328 [..............................] - ETA: 29s - loss: 0.1127 - acc: 0.95981328/1328 [==============================] - 32s 24ms/step - loss: 0.1119 - acc: 0.9567 - val_loss: 0.1148 - val_acc: 0.9549\n",
      "Epoch 26/40\n",
      " 838/1328 [=================>............] - ETA: 10s - loss: 0.1103 - acc: 0.95751328/1328 [==============================] - 32s 24ms/step - loss: 0.1116 - acc: 0.9569 - val_loss: 0.1138 - val_acc: 0.9552\n",
      "Epoch 27/40\n",
      "1152/1328 [=========================>....] - ETA: 3s - loss: 0.1095 - acc: 0.95791328/1328 [==============================] - 32s 24ms/step - loss: 0.1097 - acc: 0.9578 - val_loss: 0.1137 - val_acc: 0.9552\n",
      "Epoch 28/40\n",
      "1289/1328 [============================>.] - ETA: 0s - loss: 0.1098 - acc: 0.95741328/1328 [==============================] - 32s 24ms/step - loss: 0.1098 - acc: 0.9574 - val_loss: 0.1138 - val_acc: 0.9551\n",
      "Epoch 29/40\n",
      "1328/1328 [==============================] - 32s 24ms/step - loss: 0.1087 - acc: 0.9580 - val_loss: 0.1136 - val_acc: 0.9554\n",
      "Epoch 30/40\n",
      "   1/1328 [..............................] - ETA: 32s - loss: 0.1197 - acc: 0.95311328/1328 [==============================] - 32s 24ms/step - loss: 0.1076 - acc: 0.9586 - val_loss: 0.1130 - val_acc: 0.9554\n",
      "Epoch 31/40\n",
      " 840/1328 [=================>............] - ETA: 10s - loss: 0.1044 - acc: 0.95981328/1328 [==============================] - 32s 24ms/step - loss: 0.1060 - acc: 0.9591 - val_loss: 0.1134 - val_acc: 0.9552\n",
      "Epoch 32/40\n",
      "1154/1328 [=========================>....] - ETA: 3s - loss: 0.1043 - acc: 0.95921328/1328 [==============================] - 32s 24ms/step - loss: 0.1049 - acc: 0.9591 - val_loss: 0.1134 - val_acc: 0.9557\n",
      "Epoch 33/40\n",
      "1282/1328 [===========================>..] - ETA: 0s - loss: 0.1045 - acc: 0.95951328/1328 [==============================] - 32s 24ms/step - loss: 0.1043 - acc: 0.9595 - val_loss: 0.1131 - val_acc: 0.9553\n",
      "Epoch 34/40\n",
      "1313/1328 [============================>.] - ETA: 0s - loss: 0.1043 - acc: 0.95951328/1328 [==============================] - 32s 24ms/step - loss: 0.1044 - acc: 0.9594 - val_loss: 0.1123 - val_acc: 0.9558\n",
      "Epoch 35/40\n",
      "1328/1328 [==============================] - 32s 24ms/step - loss: 0.1025 - acc: 0.9604 - val_loss: 0.1124 - val_acc: 0.9561\n",
      "Epoch 36/40\n",
      "   1/1328 [..............................] - ETA: 34s - loss: 0.0916 - acc: 0.95941328/1328 [==============================] - 32s 24ms/step - loss: 0.1019 - acc: 0.9605 - val_loss: 0.1123 - val_acc: 0.9560\n",
      "Epoch 37/40\n",
      " 850/1328 [==================>...........] - ETA: 10s - loss: 0.0999 - acc: 0.96141328/1328 [==============================] - 32s 24ms/step - loss: 0.1003 - acc: 0.9612 - val_loss: 0.1132 - val_acc: 0.9560\n",
      "Epoch 38/40\n",
      "1180/1328 [=========================>....] - ETA: 3s - loss: 0.0996 - acc: 0.96171328/1328 [==============================] - 32s 24ms/step - loss: 0.1000 - acc: 0.9615 - val_loss: 0.1122 - val_acc: 0.9560\n",
      "Epoch 39/40\n",
      "1277/1328 [===========================>..] - ETA: 1s - loss: 0.0995 - acc: 0.96121328/1328 [==============================] - 32s 24ms/step - loss: 0.0994 - acc: 0.9612 - val_loss: 0.1128 - val_acc: 0.9559\n",
      "Epoch 40/40\n",
      "1304/1328 [============================>.] - ETA: 0s - loss: 0.0981 - acc: 0.96201328/1328 [==============================] - 32s 24ms/step - loss: 0.0981 - acc: 0.9619 - val_loss: 0.1119 - val_acc: 0.9560\n"
     ]
    }
   ],
   "source": [
    "train_steps_per_epoch = X_train.shape[0] // BATCH_SIZE\n",
    "val_steps_per_epoch = X_val.shape[0] // BATCH_SIZE\n",
    "\n",
    "history = model.fit_generator(train_generator,\n",
    "                              steps_per_epoch=train_steps_per_epoch,\n",
    "                              validation_data=val_generator,\n",
    "                              validation_steps=val_steps_per_epoch,\n",
    "                              epochs=EPOCHS,\n",
    "                              verbose=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyze Model Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 378
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1720,
     "status": "ok",
     "timestamp": 1531343116483,
     "user": {
      "displayName": "Raghav Bali",
      "photoUrl": "//lh4.googleusercontent.com/-HPass-4Bl9U/AAAAAAAAAAI/AAAAAAAAKiI/A0BQ8MHwVME/s50-c-k-no/photo.jpg",
      "userId": "117317575176939780509"
     },
     "user_tz": -330
    },
    "id": "ucbrAJdAerCe",
    "outputId": "6a650099-386a-427f-c2f2-33e9ee54dd82"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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THku/3gn065VA/14J9E6LxesuHh8Oh1m6ptKdPlpESUU9AIEIL+NMOgeO70te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PHu3t6g+xpxEPcQ2QNREIRDlerqal54YSxpaRn06tXnYCdHEARBaASbzUao002o001CWPwO4YmJ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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff9da64a710>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 5))\n",
    "t = f.suptitle('Deep Neural Net Performance', fontsize=12)\n",
    "f.subplots_adjust(top=0.85, wspace=0.3)\n",
    "\n",
    "epochs = list(range(1,EPOCHS+1))\n",
    "ax1.plot(epochs, history.history['acc'], label='Train Accuracy')\n",
    "ax1.plot(epochs, history.history['val_acc'], label='Validation Accuracy')\n",
    "ax1.set_xticks(epochs)\n",
    "ax1.set_ylabel('Accuracy Value')\n",
    "ax1.set_xlabel('Epoch')\n",
    "ax1.set_title('Accuracy')\n",
    "l1 = ax1.legend(loc=\"best\")\n",
    "\n",
    "ax2.plot(epochs, history.history['loss'], label='Train Loss')\n",
    "ax2.plot(epochs, history.history['val_loss'], label='Validation Loss')\n",
    "ax2.set_xticks(epochs)\n",
    "ax2.set_ylabel('Loss Value')\n",
    "ax2.set_xlabel('Epoch')\n",
    "ax2.set_title('Loss')\n",
    "l2 = ax2.legend(loc=\"best\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "collapsed": true,
    "id": "6O7_wTjerlCJ"
   },
   "outputs": [],
   "source": [
    "predictions = model.predict(X_test/255.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "collapsed": true,
    "id": "-kXS6GcHrhP0"
   },
   "outputs": [],
   "source": [
    "test_labels = list(y_test.squeeze())\n",
    "predictions = list(predictions.argmax(axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 85
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 895,
     "status": "ok",
     "timestamp": 1531343127722,
     "user": {
      "displayName": "Raghav Bali",
      "photoUrl": "//lh4.googleusercontent.com/-HPass-4Bl9U/AAAAAAAAAAI/AAAAAAAAKiI/A0BQ8MHwVME/s50-c-k-no/photo.jpg",
      "userId": "117317575176939780509"
     },
     "user_tz": -330
    },
    "id": "lQ9T1O9vrdyI",
    "outputId": "603e0d65-ca22-4996-9dcf-3b5451c30df6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.764\n",
      "Precision: 0.764\n",
      "Recall: 0.764\n",
      "F1 Score: 0.7632\n"
     ]
    }
   ],
   "source": [
    "get_metrics(true_labels=y_test, \n",
    "                predicted_labels=predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "collapsed": true,
    "id": "uwIk8tnYmEGi"
   },
   "outputs": [],
   "source": [
    "label_dict = {0:'airplane',\n",
    "             1:'automobile',\n",
    "             2:'bird',\n",
    "             3:'cat',\n",
    "             4:'deer',\n",
    "             5:'dog',\n",
    "             6:'frog',\n",
    "             7:'horse',\n",
    "             8:'ship',\n",
    "             9:'truck'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 949
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 3795,
     "status": "ok",
     "timestamp": 1531343132615,
     "user": {
      "displayName": "Raghav Bali",
      "photoUrl": "//lh4.googleusercontent.com/-HPass-4Bl9U/AAAAAAAAAAI/AAAAAAAAKiI/A0BQ8MHwVME/s50-c-k-no/photo.jpg",
      "userId": "117317575176939780509"
     },
     "user_tz": -330
    },
    "id": "gFZVsFfqlvB8",
    "outputId": "bddc35fa-32bc-477c-aa7b-00e8decc847a"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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sHvF4EsmzH479vmJ5Tdrk6WVph8ORtF2eD2SZv3+SiE0nDSkXdI1jqteY7Jvb\nXUgUbaEZBl40RV73O/p23G2iilLtJpvDp4+qPw7vBM97uE/5q9c+T+3zHPjAB944vZmDgcxT+4PQ\nNUzu+dyc2F+PxoqM5qxrq2I7g4Fv770u26w8Pn7Gm7V3tusBjYGdjhy3TWPdeOyPocOxzBuzXOx6\ncXGfHKst18Vj08ra7d6xTpy8bVo+dUrmhqO+1HEylnJo4zxvDeew08+pv2o0/XSjjZZ8F9Gcn59F\nShOdCDJOo/Bfr7JMjjUZ0Xg8oblVKp+H9eW+j7978a/92TnbqK4IKoqiKIqiKIqizBj6IqgoiqIo\niqIoijJj7Jo0lOUqLP8IpSC8XZqx5IOlJFJm2RYvOYffRSyOiEjeRFKSwTBIrRPL0myjLbcuItlX\nTrK58diXakVtWcFttGQJeWnvnmn5yF2ulmNBjnX0VlkiB4AJLd+v9UVmsNAQaR2vFzdif8kbCctq\nqmUhLP9kmRsAtFvtyu9YJuhLkorK7QFfztlskuyPlr/rjgX4bSaUFivbZ0QSwhHZxSiQgrBsE5E8\nv6Qp8o9mR8ptkp41Wr4ckp/tJCVbpnOwlLkVSEPjGvkEyJa4X8izoL3QdyzF6JLchqVrbO8RfBuL\nI5aGsmREtmMpS5H79zWiOseJ3BdPMUsy0aLwFSIsWclSkrySFL7f71eW11ZElg0A6VgkfVFE1+ld\ni9zvLPVtlO9TXtRL9JStMxqS7JGacRz09Q1qewW4vchOCZlKg+TVRdCmx2N+jvwNjSdkn2lgXyx9\nYvvifp/7erY1V2eSGOdZzefUvqiOcSiHq5HHeePLtoKqn5tSq/aU26lL3aVsVsWtyGSVLcFzE55b\nhe4t7IaQJDRPbIq9jkbSB7OUOsv8hhFF1es74TnrPmep55hckprsxsCSU78j8PefyHX1eiRLb9I+\nmzS3nI7tj2ds4/Xyz4T7QpZJe3NePp9fmYL+zukAGX9OEnF/bPTrwq827EIzHtO7kCcN9euSNGgO\nsMNLeLoiqCiKoiiKoiiKMmPoi6CiKIqiKIqiKMqMsWvSUMaTdWwiDZ14Ef7kc5awTUgCFUoIx2OR\nPrEsBRQxLSU52iT3IzultIY8n4hUrNGmpWESYOTBtXCkpnZHpJWtDkX37LUry1kgebzz6B3T8hrJ\nCpoczbNLUti2v/yf0G8AKUmEfGko1YWkrEAgyfOCjvK9JCkvR5ULlry7JBVkyel2pKGhlEjZPv01\nif7VJznhGkVwBYCCnlObooPOL0m7blNk2U5PbKcZRLNlAVNW8DPnNsMRRP0uLGFpMe1SJ03MAuka\nRx/kdtomaStH8Ny0vZFMJKboT8a/AAAgAElEQVQyS0nYXjgya3n0aSlpyHZNkv40G2yHvowvz6T+\nE5L4jAYiMRoMpL8cDKW8supHDR1SW2g2yEYjkhHmJBNNg2sh+X0Ub0tvpwSsrcp4FrEMOfb7apZL\nsx2x1Cn2ZFPSvkYj/zny3xFH5KOxraCxeTAIIgrS3wWdtC66Xyhbq5O31QTK9P7YEDWQJWHc19RI\nS1EjudtQF2+f2pptslPtRls71hb2zsPzqRz0vDBJuV2Byv797vdZek/tjJ4Ly0dZDprE/hjI7Zzn\nSnVRQ8M595jGtBFJUNttknZuMmcfDnlMkT5qcYGk7C12e+C+w8dzDUu5TNcCdgcJ3DNi7vs46ifN\nB9i9IxibYhpDfQkp9VEkAY3YVSJ4xiwHHY54zsquKrJ9WJcGVSBOdta9QlcEFUVRFEVRFEVRZgx9\nEVQURVEURVEURZkx9EVQURRFURRFURRlxtg1H0FOTRD6fDGcaoA1vuwXOByJDnky5vQR/nFXVsXP\nZa5HKQvoHI2mvBuPJ4F/xFjOuRCJz1OrKbrgXk98iXrzS97+3TnxmWqSj2AeyXk6LdnmihaHpfUO\nhdbnZf+v3HzztHz61CnZiP10gmtp0/UXXihgCu/NaSEC/wj2qUjpnnt+I035fLwi9z4KHneT/A97\n5EvGbYQ17eFzrUtFopwbg4H4yPapPBj4aVVy0uEnLXl+DfLX63U5lUSLtve7oCb5CzTZ5y2WfdgP\nsUXnA3zf36Igf2GIfwO35Sjwr2C/RPZz89pcxDbC/hjeofzQ11RuxLI/1yVwnUU6kTrHMfsEkK8A\n1Z/7IQAoOuTHkVIYcPLP6PbERttdST2TkL0CQF7I859QuOsInCKH04j4/UWHfCw5XYyyfVbpGTUS\nSvWT+H63cSLP2Eu1w/6ttT8J+42a7YBTPvBYwXYzGIrvEwAMyOcobsj+dT5LYV9f6/NE20Q8nod+\ngQTHA8h43kDXHFHfFoXjzlbSoBS1f/ibnXXOiLP3s+U9gkwzwX1Wf8GdIp/wHI5jLvj3uNUUu1hb\nlfLqipSb1L/Pzcm4N9eTOSNQ72PLn7O9RcHkMq6Za/HYOKGxaZz6fsDsd+/FjOBYDrQNx6soQh/g\nmnryPBM18QMA/5rZr9CbM6bse+fbVeL5BbLDMfddXBd6fxj68Qf6fU6rwX0X+Xc3q/07AYBPv+E+\nnSO6IqgoiqIoiqIoijJj6IugoiiKoiiKoijKjLFr0tD5RVnOjvu0HAp/yZNlGp7szwv3zHKRvLLs\ntpP9k4RC3neqJYjj1E8fMaJQuOOhhMCP9ohUjWVPnY4v0eHUEmMKx8/pG5pteSRtks4szMv5AODA\nwf1UFznWiaPHqP4idzkdSL3mWV7mpdKQIi9Ne+k2ADRIEsfyXZbZxnSwyZiWyQNVS5Gx1IylAHFN\n2d+fv9tSFG5lS6yunJ6Wx5SWJQ2kICnJITjNSUS/M7EtdLtiI+3ARljKFZHcLYpZWkqpWwJpZwGx\n8WwidZ6MRaI28T730z+w4oT7iNSTHBeV5bDtcYjqgk7D27G0NGzYRVHd30XgcNOUliXy0wY0KcVL\ng1I+NJvyjNpteRbz89KPDdf89BEphQT3u+hqiUoUSPK6XX7+3XBzZRs0G9Ind9oknW6H0lvZbkRp\nRDKSd3HI9wmNG2mYAonKLP1uUF3YPMZB2iOWlCGrbjubuYqE49A6OR2LWx67IbA9AsB4SClVSLKa\n0LU0Gpyqxm/TfC82EX3W7LHZdjsIy+m8zzdseH7OP+MkidgIz2WLMIVWR/rePJe+dzJmaSjPLSkd\nU9CfeikUSKbtnZLaRWhSzSalKmrKuMlS6jUaH4Zjdg8AvIxMBV0/fR5TyoQOj4eZb2PpmCTjdJoB\nyS4nbOOhCxP1Cxm7akxYJkr1CsZgnh140syIx32ac1B/2Q9S5wxGPAlgdzSaM1O5CPorHlJDN5Jz\nRVcEFUVRFEVRFEVRZgx9EVQURVEURVEURZkxdi9qaE+kQpNMlkzjof9uOp7I8mpCEjSWHSLn0D5S\nDFdPE285nKSNtBybc8Sw1SBqD9UlG9OyL23GUfwQSFNHJOEcUjQ1XlruUtRRjhLlnQ9AjyR1Bw8e\nkGPR0vTyqeVpeaXvR3pkaQLLirx7QUvTzWYoyaGlbZKDJhxljVa28zFFjJr4crwxSQsmtHyeZ/Qs\nouqIVw5PDARlZ+ivSvvJKWplKGIqKGpXTJE+WyQdYzkgy6db1PaAIIpmIvvEcY/K1EYLX3qWTqSd\nD/sihx6TrHtE7Y0jc7q6SZ3bLW7z1fLzLGeJuneo4G+Sj5BdNXKKuBpIzzwNac6SV+kL0pRkPK1Q\nIsRRHaW/aFA0X7b37lyXyn4kuiH1H5knp2UpMNlrcC1NioDMZWX7LJF7Ra8nculux4+ky9KntTUZ\nd1g21R/J5ywfDWVfMdlnMuZ2xHbvy72ZqCaioWdHLIMOpF481nBEwJEnXSbZF2qipAIY0Rg8HEi5\nQ9L1JKLxfIvNtl7aust+CxzUN6q/mM2kucrZwSpNX+kXRFVui83y7Z+Qe9AczZnZpYKlzEDQyjxp\nsGzHEa35cwBoJOSe1Kbo9hxxmKL2I/UHvjbZTJv6hXZB0fXp/AvsGuSbKJo07W0OZZ8+zRP7tP8g\nmP8Nmywhpb4nk+3YtaUIgs5zpNGC5kA5R1Olvou3H40DWTzJdBOWgLLOk3S6YcBjlq2GU4VzRUdk\nRVEURVEURVGUGUNfBBVFURRFURRFUWaMXZOGDihq5oikTpM8jDJGiV5pnb3By9y0HMtRwsLIdfwn\nL3Oz/HRMcpcwghBHO+x1ZSk/ITnchI8VSFFYmsKSNE+ySsvcE1rkP3bH7d6x+DwxLS0fvOKKabmg\n9/xjx+709u+TTJUjF3Jyd5aITAIJXUJJihNKsB3lLBPkKFMSgXV11Y9IyN9xMuLJmK+Rn6UvReDr\n30zyopwdeS73n9tS1PCjU85Rgvd9Bw9Oy1dceWRaXtqzZ1ru9sh2gmPxb1NxTdLWPOVof3403MHg\n1LTcXxZpK0uxx9Su8iAqYrYgdYuiLm0n7Z8jg3F7DQ7lKdaZRsFJ7yniah5IdKhdj8ckeR1y38my\n8iVv/96C3PMWyUaLXOxy5CW0587TtzGWs6725b5GGUdilu2bbf+5pm2W84bPXNkOR648NC23SWLd\nagX3nsbHVoOj0rE0VNrUgCL1FWO/EY8pSm2Dxr0EFNGQJGyckB3wZVQ8pnBEwpQlVEFIQ05iz3D0\n3zxln4S8+nP40apZpoq6KORBEufcmx9UJ+FmNro0nH+2ekYvouVuS1gvIyYZSeozlhP695inLfML\nJPtMxK7ihOZZlOl8nJNME/64w+fk5svzNATtOiK76DW5X5HPW9RGGuH+MddfLqxHY8ICvXl0aZ6R\nFzJOA8AcfbdI876UxsYBjaErmX9fTwxlnxMsGc9lHz5jP/b7CHaJaSY1/QLZexRxBH7/9YrnsAXd\n/4LqXLDMNoyASn9m/nT8nNFZs6IoiqIoiqIoyoyhL4KKoiiKoiiKoigzxq5JQzlpLRNKP3JKSBuD\nl1Z5mba6HAfCCP6L5V0sFUu97JJ+3Roc0SeqloKw3CVMYOslhWYZFlgCRomvKWLbWpAQnqWhCcl9\nel2JJLe4sCDbj/z7ffq0SOjG9F2LpACsEJkgXIuW83dbIl/IE74ukgGRdCcLJTq1z1K22Syh/FZk\nOcrZw9E9PRlUw48K2FsQaeieffum5X0HpNyjiJQc+S+US/nSCilmFFl4SNLE06d9yfOpU8fku1PS\nxv3ooHLgMBpuFFP/EVW3JS/aISeszcLtSbJMspAorm6vUWAXE1SfZ0zRyPoD6SN6c76shpOBLyzI\ns2ADill6x0lyg76LpXuciDyjPiqh/ZOG/1wLkvznWXXfr5wdHNU2ov55PPbvb5by+EQRc1OORE1j\nE497sd+mWR7pycWpGSVNjnwXRJtOpV34dsSScEoOH4bO47pwX19TrpsbhH/zeM6RUSN/B78CuyD1\nPBf8WxTIXD1pqLJT3HrbTdMyS7Q5oTngtz+O5Nxs8rhBtscRLANXmYyieOd0noxl3pTQvRHIVDmO\n91yLIoDS20KHzt8N5oZNcjVq0P7NiXzeYtcD6pPQ992GmpPTUpdEtitaMj51JhQBP4jc3aJG36I+\npkFRQ2O6X8tB2NA1+nutIPk5zw1iHk/JtSroHzwFLdlfSu4dQ9A1BvtndC15GKL8HNEVQUVRFEVR\nFEVRlBlDXwQVRVEURVEURVFmjF2ThrIshOUfrZafYLrwIoCd3dLoRpkgSbJIAJHnNRGAAmkpf8eR\nRjkiYURL4QikZQ1eMk9Y5irbsBx0MOBommESdjl/RFKgNsn2Oi0pL5J8DwCWT8mS+2hIkeAomWiS\ncHL54F6QHKDT5Ey1Z/5tgaWBgJ+0dDSS699qlLXN5D/K9pmjhNUpR7mKg6ihJEFeWJJ2Nr8k+7Pc\nhROPF4EshSPAshyx35fIaHfecdu0fPvtUgaAr97x1Wn5FElDE+pjehS1dM9e3y44ypnXklna6fUj\nAifoBnw5MyvkOBJi6snjfIlNmootsLR6QMmvV05LH9Ht+bKaiRcBmSQ6rRrpnpe/PoieRnXzzbJG\nlh/0fTEffIdlLbPKiNwIUi+Srp8EniXL/TV5jqurNZF0WRYcPMeCZGiFF2VWijnJqULvCI4ImqZp\n5edeBE8EbgTc13NE0No2RTK5cGzwXA+iyjIT7u+PNdV24Eex3sGxKZTU18YHrXa1yIL7paPm+eGW\nW0QaOpmwq0wQ+ZzaCSeI56DaXlcd1UtDQRH1C5JA5mOypZHYnoyGjgUan1oUKZ+DPSeJHKsV+3PT\ndoui0LdJvs5RLyn6MM95i2CeW5AbAsvfeXBu0IHbwdrWEng+SxFQOek9S0ZDWT393R9JBNgRu20k\nZHs0z0Egiy/IRklZi4IkwzyHmBS+TfNcIQ0krOeKrggqiqIoiqIoiqLMGPoiqCiKoiiKoiiKMmPo\ni6CiKIqiKIqiKMqMsWs+gpwmgvXtoW9KOqIQ1xyenOJVZ+RXOJmwPt9XvvM52S8tacnnkwmFqZ8E\n4dwp7PnJk+J/NKQ6z+eyzeLSnLd/IyL/x6i6nkVR7Ssx3/WV3K1YHt2EfC3G5O/HbgBFcC18/0Z0\nzdmy+GItLIiPF/tuuWNzaHwOvV1d5lQEoY8gPxfPz4j8sur8OMO/1Udw51jcKykHPB+eyPcRXFhY\nmpbnKE1Ei3xHC/YJoHDPSehXR/4FnDLltq+I799nPvvZadl+/kZv/y/fcuu0zH68CwtiP4cPH5iW\n75Xcw9u/3ZB2GuXs40cpJ6jvaHh+tN6h2K3Q7+NoO/Ztmkx8H8HBQOo/pn5wbVVs9PavSroM9i0B\ngBXarkkpXvbsleeVsO3G7CflXwun1eh2qe+cl/vaTNgHOghJTn1sM6insj2OHZc0KqORPOvhcOBt\nx2Hrx+QnNCQ/oQGlJBmz715gn7mXqkc+H9P4srIqvjSh8xn7SWW0D/vHZmQgrZY/VrAPb206hLhm\nm8AfOff8DalMduu76QQG7h2ONvTGIB63Qj8+z0kRVVQnmfLtFvD7J+5rOO2MlxIm9KncUDdlJ1hZ\nFh9ubvtsk4A/J+JYEtGI5zZkI+xPHqZMoL6+ReNTg9pYk3wM9wbzsStast1iLrY8z2nGIjl/u+Ov\nJ3UL6d+TCfc90i9x2iFOhYbMb4c5xwUhX2f2lxtRWoxx6telSCR+QW+eyr290/LiWM7ZXfb97OMT\nnFpOjn1sRH76lP5iwLFHGsE6G83Zef7t+QvSNeZB55nV+CHvBLoiqCiKoiiKoiiKMmPoi6CiKIqi\nKIqiKMqMsWvS0LguHHuor9rC/g2SGhUFhakPpKGdDsnW2iTT5JD1JPNsNHwJHGgXXuYfU7ju/hqF\nc+/6qTC4nt4bOC1/Z7ksuReQpfik4S+ZN2n5nTUjWVotd0GwlNym1BJxzM2AZbpUryyUetHyP4Xl\nZSlRRpJTXspukbQO8GURK6siB/TkoJsEuOZw3yoN3TkSljbELJPseNstLYnUsNORZ5tTWx6TlCKh\ntlQ0/LbAj++Or5Ic9IZPTcv/+NFPTMtfuuUOb/+TKyJlaVL9e2SL/b7UZc+CSEQAoEGht/NFkqh1\nZP+Y2jufI4qCUPc1KWriotquWLbn6kn9Csntjt95clq2n/vStDwc+2kD5in9xwod6173um5aPnxI\nZLIRyVUagXyT7Z2lc02S5bfb0i46bb/v6/VEJt/q+O1H2R5Hj0tfOaZnPxz50lBuY1lKUmSvTGkd\n8uoyAF/NSJq0iKRiRUHtMJAc8pjCbiAZlTlFxSSQ0OVF9W/XeU3aJx4PWN4OABlJYL2UFVxllmCG\ncmnvr+pUEltOzFBUFr2TcDqnTtfvN5tNsTe+ryNKC5LRs9yQbqLu/Mo50WhS+gSWA4aqQW6zbK+T\n6vlwk8agbtCf7tuzZ1peoPQPHe7S+yIrn6PUMwCwlI/pO7HlHo1vfKxmHEgYKa3NKKUUNTXp0Fj+\nGQWvJBH1HxHJRhu0z4TadRGmVWnL/evtFzeGhQMHpS4NGZvaJ6VPBQDcLu8MxdETUmcpYkgpJlZI\nlj8MnnHR4FQW8m6RsDuVdytDWb5KQxVFURRFURRFUZQdQl8EFUVRFEVRFEVRZoxdk4b6EtB6aR8v\nh0ZFteSCpYFJQkvxib9MzJLEhJZpU0+WIu/GSdOXhiYRy05JckH1mgxpWXzgL7k3aDk4oshMHMEr\no8hQOagc+cvEBf/NEliOhEbbB0HG0KS6tJpyX6JY7t+Ylrw5Sqg7DUniSFrKEsB+X6QAHCmw1wwi\nwZF8IvWix/lRFKfni8K6VLcF5dxgKRJHpGy2/Wi4C4sSjYsjwGYkOSlAUUe95uo/L5ZrHTt2dFr+\n/Oe+MC1/8t8/My0fP0URCgGAon72SD61RnLQEUm5rzx4yNu9GbPNU/Q0alfdLl0AR9oMbITlahwJ\nMaZyTlLqydhv7/01kZmcOCFRim+9VeSw//H5m6fl0yt+xLNuT+7FKl1/k/q+BYr6yR1GI/GloWy/\nKUlW2HZbXZHRzC9JxFkAWFgQuVKnNw/l3DlxiiMSUoTZiT/u+JH3aKzJq8scyTYPIm36MnweNylS\nJUu9AimnF8m65pwsicoDm+LunctFzbjHks9xIJ2ejPk+1ciueGzdbGypmZtsR8JV1MQK5cigHIUX\n8KOg83VOUo4myhK88Jz8h4pDdwp2KShI2kgB6AEAOUmgc5JsFyQnbdIz3zMvfej+PeKaAQBXXSFy\n/8UORWvOZAwYHhfbjZf9yrRp3tommWiPooC3qd8vAgnjYCDjVn/t9LSc0tyQQ2fHEbt5eYdCg+bc\nMfUFXKYmjkkjmCe3KLppT86zsF/uX3PvldNycoDqCCCn8TGmMZH7rhFF9D61JvORfiDrHVMEVVL2\nepGQE/bGCcPBnkd0RVBRFEVRFEVRFGXG0BdBRVEURVEURVGUGWPXpKGjocgXWG6SB1FDIw6vxBJK\nToKecqRNWn6N/csraAk6o6VlPyAmRUcMQzuRGqNLsrmUo4/RsUJpaBLLenBEUT9jSr5ccKL5Bktn\nfNnYuJBjjygRNUsOmhFLYf0oflFBsk26T12Sd40adNxALcKSPi+CKkeio4TBI0q6GUp2myQzGJOc\nkKV1nJw+igK5EW0Xth9l++zZK5G1Ioqg22r3vO16CyIVbTQpmTOq2zVHs2XbAfxn60W9JVvkRxzK\ntxsctZSbWVEt5bj9ttu9/bttOV6HyxR9zW+X1BaDds3R3zwpvJ8JW44bJO/ur4qNHr1DZLI3f/nL\n0/Ktt8vneWAXoD7q85+7cVq+kiKF3uXqK6blxXm5xo1RBasTzzcp+nC7KxLh9pwvV5rfd0TOs7Qf\nyrnT74sEixOqZ0E78iJCFtVt0u9DqRwkHo+8dhBX7hNH1TJJt92Z68JNL47CsYLcK6gdssyzYFeP\nTD6fDMWeACClMSkmTVZUyCAa5RwtN/zdvNq9pc49YYPbi3fNJLmtkXDy9hv7TZKDkkuHPzepfsZA\nINlVaeiOERfVNhKFkmt6tl1KCD9PMvorD4mE8ciV0m8fPujL8PfMy1xvfFrGh5XbJNRlSlFDGxPf\nLpq5tJ+Y3XPIFtiss5EvpxwPJPLmiGSi4AjEbPsUKrOZ+P1Nk6Lls2sWt1eOWtpEME+maxuvSLTt\n0Ukpt5bEPWQxkNleQ/Oe7khkn8lYyulAXDJOrci9H636bisjmpyP6f0lpjlTQnOIJPDn8tzedtgF\nSlcEFUVRFEVRFEVRZgx9EVQURVEURVEURZkxdk0aypGFWO6RBNHqONn4hJJbsrQ0JVkIRwNtRr4c\n0o/FVS015KXZMLk9S9U40g9LZLyAY+F7ds7HJjlsjWSDFC4IFY/8t/9ddVi1RuLXZX5O5HwcHXSN\nEroXFEGUo1cBQE7nGVASTU4izgEhvSS/gVwoK6qTCddFhlUuDPOLkmydJdqNpp/MuEVJxdlePWm1\nl7CYn2twUtpngRLVH7n6qmn5Kipnt4n0BQBWSY7tSStilmDKx8OhL2vhvyeU9JYvhaXMzZZcbyjt\nBEdKrWnLfI/CNs7y95VlsctTpyQSG2JOUux35xy9kWWEfYpsxtfb7bC9B7IUksiwYSfUFlgmGgdS\n9EZT5E6t9gKUc6fgRMocOTmQ9hU1UmR4+8jHngw/GMOimvbqtRYvgmdYl5p6eeMx1SWQY3J0zA5H\nZKQxJKP5QH9FbPJk7kcNzWk+EdNUiKP1NWg+EIdRxCnydhxXy7ZYmjmZ+NEZ+e+6e8FkNCHg+RPg\nR0LOKPJ2mtZJhsNz6Ph6Plg9QQnZO+JSwVE/AWAPjXULc9I/LsxLef9+kYAeJDno3iXfVSNOpW0M\n7pC+fvX4ndNyukLRPFdpPAEwHsg+MbW5vEeuTewnFcgxE3J16rRJzgl/Djk9Fs2/mx1/bsER6b2+\nw1OZ0vw78/uLdERz2xPHp+U4vkXOuVdcJTr7RXILAHu7ZNcUdXSyKPdiuFfm0ifX5LmOA/lvymNw\nKhcwoT4ipY6Y5xbuAymG70nniq4IKoqiKIqiKIqizBj6IqgoiqIoiqIoijJj6IugoiiKoiiKoijK\njLFrPoKhb846oU8A6+1ZI8+aePYRbLMvWuAr4Wnk4+rQzRwaPQ99LeA5AE5pxKzXpf3hw74bXmh5\n2iZn38EJhR7OAg1/Lo/O86nwfBzJ3zD3ddyttmic+30Kf0v+R422+PUkTd/nh32ACvKFajTI3zJm\nP8r68Np5XWh91H28iQ+MsmN0evOVn0eBPj2h9u+VOV0LxXsu2B8osPeYfAyX9u6Zlq+59ppp2Zh7\nTMvDid+u+7d8VY7NdaS/Gtwug5QPdf57rNdvUblJoZ/TwAeCU6zknBaGbCGhfZpNvzvm87M/Efed\nLfKZClO8sF01qM5Jg3wByXZS8onMAxvz/KO81DfUD3nhrsPfGPnvnfVvmFXYR3BzD69q/8G6X4HZ\nJsOe1fNpZTtmh3DPN2bDKFhVrVrCMZjPz/6C3Q75ElE7bsVyj+5sBuPOhPxxaNzq0tjY6dEYSGH9\nAaDRIJ9YsgO2O993jxybAupuhW+faWU53K7wckGwbz71bWGqGWoNOpzuHHlf+roe+fsdOXDE2+6a\na66elpeWZLsupS3qdKUPnl8Qv8B2y3+WgxOSGmH1lJRPHxV/+mRZ5nnZadkGALI1+Y6PXKRSlziS\nNGOtjn9+HhObTU67xEEjaB8ejzekOaM5LKUjy/ldgLK09YPXiuFA7GQwkOskFz3MHTo8LXd6vo9i\nh1Jl9SAnWmqJjR2cl+dyeEnmTMsT395XObUa+fiOabsJz9NDQ+S4IPHOGqmuCCqKoiiKoiiKoswY\n+iKoKIqiKIqiKIoyY+yaNJTlEyxz2CAVI6kW78NSQ9ZV8DZZEM59NJZ146SQ5dzIk4rVvxvXycZY\n5pmTXCcrfPnGmCRRMZUbDZbTSb0asSyrp6m/FMx1brXoWuiS11ZE8rl2WsLPu0pTaHkKJz8ay5L1\n2kjKURyEziapaLcnMoH5OVkab3RlmT3yJLf+c8lYkpazxGVr6SOimjainBsNSgeQ0X3dckh5fmYF\n2w5LQ/0uKKI0JZyWYt9eSWVx/X3vzTt4+7cpXPXpE9TmyRbnSFK2d6+E7XZ/ixx1cXGRyiLX4XQ3\nhSeD8+vSpJQLUbO6X+E0Db2giS8sSt26XZGoJAnL4KRPG7JGBkC7Lffy4NUiReLQ43M9OW6e14Wz\n9yW7SU2o/MmY022E10sS/3wC5dzh9EZFTfoF9zdL9Gv2Yfv0bDh01aDzc12oT/ckYGGd69Ik8HHp\nWNymAGB5eVm2IxlVuy39w+KitOk5SomyfFzCxwPAsC+yzTa5QRy+SmylQ6H8EcjWUnLX6JPUq9+v\nTs8Spo+ok4rWir420f/yuOffY465TzLR2D9YFG1trFXOjuvvca9p+dAhSU1w5dWHve0WSVLYalNa\nEhoe2RQjdrUI2tGY0nml1C5zLp8WO8pWpAwAOc37mjQ3HQ2kjawlNGePfMl0pyvzQZ5DcAqyiNMR\n0dwydDvhd4MCYq+DsZRPrMi4d+dJfwyc0JjY6ZIsnsbGYZ9SaYz9tCxNGmtHI7HlPqXYGIyknLTE\ndpptv7/geXqTXFoSlnzT9HUy8PuLdEjvEztsoroiqCiKoiiKoiiKMmPoi6CiKIqiKIqiKMqMsWvS\nUJYvsKwhD5a5myTjYklWSlKlopAl282OlZNUtElLuA0+FktUgjB8XGeWdRRpXvl5KE2N6XanJAEt\nWPZJ2s7MizIaSmZpCZ2+YpnleCTlNZKrAABIwjqmqKtpJvtMOLJp5N/LmPYHyUo4kttcTBKBBssH\nw2isAssK0rEsheckgQulK55ESmUtO0bs6VLqJZBeW2QlEtkCS37jgqVqfhfUIClGm2Qliwsinbma\npFuTIGpoiyJvHj92QqiKkakAACAASURBVM5P8osOSTQOHT7o7X/11VdOy4fpuwU6P0f3rJW6BdeS\nNKr3KWokcYAfNfUgyYoOHTo2LQ9GLI/zZTWLi3PT8j3ved20fOURkSV1Sb7N/UUoDWUFbMzRULm/\nJclnGJWQJYbFJtJBZeskG+S369SPWxz5OkZ124020R1Fvh608hze52FfXXvsajljKJ8cj6W9ra1J\nuXVajttpit3MUfs+cpUfqXFtlaRyPJxRnzQiaepo4MvORmPZiSWgXN4samhttOu6wKrbCRSoEUB3\nlXvfQ6Jdz82RZLnnu9q0KApkQlHYo6LGpQDc7/rjRkqyyWIibTbJZKxosHtGy5cwckT9Bo11TRpf\nWPofBLDFgCSN8YTm2RSBk6Nl53Ss8TB055rUlOW4a6nY+CDz5xMTnlvT9U+4u6JOLc38i1kjm79j\nWebQX7xD5hZfuE3KXx3LtRwbBBHNqf6Tgt4fUN2P+m4nITs7huqKoKIoiqIoiqIoyoyhL4KKoiiK\noiiKoigzxq5JQxMvMS19HkiKOhTNK6Ut04HIL3iZPOMl10BqxZLCZoMTRMvSOEdHTHNfysHHzlJO\nbknn95K+BnLKSJaZGzGdc8LLxLTkD4qYFEb5SlhqJUvII5J3sSxlNPZlLQUtgWdcZvkrXX8eJAaO\nvIie8nma0nkKjjRJsrVALpQkIpNYoEiNGUluxxw9LggMmntJklV2tlN4yaM9FVkg+6N7znJsLmck\ncUlYCR2Fkmf5u0ERxNpko3somubVR0TKCfh2fXKfRMfMycZaJDHfu19kZABw6PCBafnAQYlE2Jvj\nyGgka2f5dyB5TpLqZPGenJL6lEaQ3H6BIskdvvLQtHzXUxLlbUwyoCyQmh04INdv7iHS0EMHRfLK\n9Uonm/wuyP0C9Wu+jJDsMDBDry1FmlB+J+gEUenW2SAVm1RLfgtUyzn5OYbyJF+qWHcsKm4iDfVk\nop7tsE3518JuFCP6bnVNjrW4KLY6Py+uBvsOiG0DQKMpUbVXSPa1RtFEh+mafE7jKQCMR9UJ3usj\neJ49W927qJPpbmH7EPWu2DnuckTGkNwztyCCLLWlguSNcYujyFN0zbrwvQhcosj2GzSf61F07Qa5\n8ABAVND5aQxLmhT1k92Rgv6mvyZ2kmZSnl9imSmNh3Qpa0GkzDWa5w9pDphFIgcd5pS0PhhDJ7Fc\ncxJTvfhe0hiYBf3dgObNt69IXW68QyKS/8dXRBp6PJN7tBIY0oBsbkwuWGnOfQc9u8CFgudG4bzp\nXNEVQUVRFEVRFEVRlBlDXwQVRVEURVEURVFmjF2ThvaasrTL0sx2p+1tN9+RZesRSUlGLUp2TjJJ\nliTFwTJxj6Q0XUqI3qiJ6JcFy68jWsMeTUiamnvi1mkpDyIQFV6ydF5OZ6kYJ/wl+WWQ6D6niJ5j\nilK2TMlB+7SsHi55szIkp98Diiiv3qYI9ZiyZE55dZHRUjrL1vh6u3O+FKG7INGkOPF8Rvv0Kclp\n2pfrAoCIKhBGK1S2T4MklLnXdv3t8rxO1sWf1ySdD6S83M78iJTVNsK2C/gJ0imHvJcku9cRKcl+\nkn8CwJ59IsecX+CotySXiatl2enEl4Jz1FCvXZJchSMTh9F02yRZYZnn3e9+7bTMSWobDV9yuZek\nsUeOSMTEbk+uy4vmSnVsBcmzxyTf5r4vjmS7JvWpYUL5mPovblfK9tm7R2T0LEMO5ZynlyVh8pCS\nSo9Z0kVmmNckqncfsByUP66WGoYf+z0Fa8Sr7SDsH3hMb1AbXViQe9HpSh8AanfjQI43oKjUy325\nL6tDTvDM/Y63ux+tukaauRvSUJ+osrxx+/r+Xdk+eUQRNPnzcDpF0tBJLLbcyGSs6vUkCnSnKZ9H\nkd+uGywh5BNRBNGY5nlJJ+irQX0/R/hOqhO/Tyb+fGy1L3LqAc3bODppN+bjyufhPDOnud14QhLO\nQvbxXRoCKTnNJ3MajyeJvGdEkXweBRbHUs1RLtfcL+T+DwrpbyZ0rCIOHjKq58McbZtdy7hegD9u\nNhvVbgHbRWfNiqIoiqIoiqIoM4a+CCqKoiiKoiiKoswY+iKoKIqiKIqiKIoyY+yaj+D+PeKb02yx\n9jXwHyHfnow0vp2WaHzbVGZJ/AZ/MQ7fy/p+CgHPivowFUGD/A0y0kvHdf4VgUS4SRrfVot9jkR7\nnKaiFx6Tpjub+P6GE9puMJDQ16MhhZPPOC2Fz4RCwHOaCA4F7HkxhqJ2hpwKcgqFm1HoYo6kmxcc\nit+//oT8orpzoolfWFyYllcDX6yUfDHP1SdDEZpkV9yWsixoC14k6+ow9DWuKhtSLrAm3z9ndVqY\nJPBFa7elzsUcfy6a+sUF+WLfAT99RKdT7QvIvkp8Tt4mif3u1Av3zCk2uI2y7YRpVahjmeuJzRzY\nL75/E+oXQn/JxSXxm5ojv8CGl3qGUkFE7H/lH6vblf3ZRjsd8Y/ozUmf3mr7Nt6gfp3Tgijbh599\ni2y1O+ff+yaFfV9dpZQJVB6SXxwPXBt8y/Mam95O2h72FeZMEp6/oL8LxxNYIDves1fsmOMM8Di3\nRn6AALBG/pKDoYy1o0m1n3yYCiOqNuNtUWzlj6iyeKaj1ewfpADSlBHnhazguQm19yA1AGfU4dAW\nMc3CIko5EKWcEsZPa5KRj14+lDae0z4TkF9h8BbA4wPXs0n9doPmrwjm7N2M/Nca1Ed1Iirz7jS2\nNv3KJE0aXwdky0Oap1KKCk7HBABxLLbQonlmu5ByNqA568ifZ8fgdw4ZAzttGfcaDbmYYsTp5/xj\njaktjCmWRkpp0nguzOcAgC75PnM6rZ1AVwQVRVEURVEURVFmDH0RVBRFURRFURRFmTF2TRq6tCiy\nJQ6LGgUahX5fZI8sxWAJaZNSUbCsJZQ/cHj3UcQhW+XzOGa5ii9h4hD0HL7VS7PghWP3r4VlLSwT\nZRkWS+NSCiU7HPvL/8PRsLI8Hsl2vH8omRyTbJNldyxb49pvJg1lqR6Hv53QOVgJkab+8j3LURM6\na6crS+4sDR2tiPQBAPIxS1trq6mcJU2W95GUIcv9cNVbUkWx8XKKkjDcsyczpnZZ0/7CNAUtSmGQ\nzMl3vXmWkUmKiIUFP5VJFLHkhM7vhV6vlnyG6Rv4O5bURZRiJo6rZSEAEENsqU3y+YV5kYisUT8a\n7s9yTg6776euYcmt1DGUhsaJ3L82XVd3bmlanidpaLsj99gdj6T0sf7+uBOsrsjYyPeXU6gAwNKS\nPKMmtVEvXUkufepoTP12YHe5l+qoRk94jjpDLxlT7B+r15Wxfu+SjAmLC9Le2Ib7a3JdK8G4sbom\nsrkhXXMeOIhMCdNH1HR8NcrvTSlqe9Fqt5Xwr7jmK57PcP8QBTL2ujmMcm6wnJhTsUQN//k1Y7Ff\nv/1QKpOhtN9JTHO71Jc8D04en5bHJP/OSUo+5JQFud8fN1sk4aTPfdcBkmyG7lwN6X+6lNqhMydt\nrt2ldEItKbezYM7cI6kkjefxssxHWCabTPz+qkGS+VaX3I5IzpmSNHTSl+MCAGiePk9pp/ZQf7M4\nJ/f4xHBFjpv60tBRRqnVaG6c5yy/lfPx+A0APXLvaLd3NgWTjsiKoiiKoiiKoigzhr4IKoqiKIqi\nKIqizBi7Jg31Ygt6a+HBdiwVqymz5KEoWPriS6XSieyTpn3aRz5PSELVDiLf8d8NbzmcIgKyzDHx\nl7lDuZXURZaQBxTJ7NTp09Py6tqKt8+EJZi0NM7HYsknyzTDetaVtxqBk+syHMnyd4OimXYgcoHJ\nxF9+5zqzHJTvN8udTjf9iEnjWI5XBLJFZfuwzNKPFBq0JSpHLDv02g9LymoiiyKUorGNc6ROkqg0\nfYkEyxaTRNrM4pKUF+ZJchH5MmWWTXLQ4ZhkdAXLt+m6kthvl7knt6LjehESSboV9hctitJG96zZ\nlfP0SCZaBJIuVtUVOcvEKWoo3WO29zjoq1iKHzfl/i0tXTEtLywdoHMH+zdZrqi/P+4EKyRt9KV9\nfps+sF8ianZ7IpVqJCIZTagdrq5Kv73WF7cDABiNWarIsjdpq7mnpvTbtBc50fuK7UMupktSUADY\ntySSrH17RRbdosY+HFBE7RWKDBpEDR17cneOls3tkyXtvn1FBdsOKsuok89ugKXj1X2gJ1gN6sJ9\nYrPBUX1lDG1RROW44fdVwxHds6F/n5Tt0+2JpJ6fURLIpzlyfn9Z5nprp5en5XQgn2d9mudO/Oe1\ncttXpuXRsswh85TdLmT7ImhLZMpoUNTOnKtM+7SCCJYNMtk0l7aYkBw2adE5SeYaypJbPAY2ONo1\nHQtywnHP7y8W9xyUY/XIbYTkqKBsBP3VoO0ncqN6dJ1XHZRxb3kodTy2dvO0fGrZl6IXGble0Fwh\n99zRpC6hXDynue1ksrM+UDoiK4qiKIqiKIqizBj6IqgoiqIoiqIoijJj7Jo0dG1N5CctWhZPgmTD\nI4qCORiITKW/JuW0LgFskFzSSzxJUqmUovnwPs1gybvTFklUi+RRSVId9bQIMsoPh1RnkkPyNS6v\nrFBZZAHDkb9knRXVEk4/6mK15DP8O/IS+1ZLWUKZqH+d1bqYuuOGCcnrJDosBeBnsSGioUYhPC8k\nXqRHkvOGiZW9v1mPWCcBrZccewnaqS/gctOTZfv9BcufOh1pM90uR+yVc6SpL1P2k1mzxq3G3kgv\nE8qSWb7i21tRU/Z296TlLNFpkdxmnpKHc4Lxjcer7hciL4Iry318WTxHB220FuhzkR02WyL/TGJf\nssttqa6PUc4Ojr7cZznf6SCpMj3jxQV5Rh2KPLdvrzzfthfd2pc3rdC4PaIo3BwIutjM1nlM4DqS\n3bdJtrVvzxKYPUsiB53jxPHk+jAkCeiA5gnjoW+fWVotY/eiqaJ6buH+rhnrWG7NF7mJpwWbBEcb\nj71IjdLXtUJJPM2hWA7aoXvENjgOxuCJF2FcQ2/vFE2KWum5GgRTloiTuNOQFlFUZ6Q1CcmXT3rH\nWjt+TLajqKFFym4PfBK/MgXL+um7jKSNY4rUHrf9MRieqxb3EVL/KOdxvto1CwASvlE0B/DadU/G\noKgrkk0AWDp0lWxH9eR3gcFpuUfjVT86/2RV+r+YxrRFkqAePrBvWj549NS0fIrczwBglfoivv9e\n9GWaT4wDFyqOIJvs8JRXZ9CKoiiKoiiKoigzhr4IKoqiKIqiKIqizBi7Jg3t92XJNU3rZX9rlBB2\nQBHMBgNaGmdpqBfBM0hcHssSNCfFHk/kuBMqh1KQhKILddoSDapFy/9e/TdJ4j7oU2Q2ukaW+LBk\nNAtkrqyr2Y6005OfcERGkgLkJBHJN8nUHnNS7RoJX6vJ98XfPx2TTJcioDYoshlLAzeThmoq3J0j\novsa+aEugw1ZDlpZ9APueVJmv11xBOC45plzU0yCHoz36VKUMM71HpHEItpUBsUJ7Tl6oHfGaSm0\nMZaG1oUV5ITyiP26cN1YMdMkXUiPJCqjkf9cWH7uPaLa+0+RSVt+9LUeSUBbnb20nUjkOeJZnPjS\ntdhLYK3S0J2A++fxhN0AQgmkjGl5JtJKloMuzIncl6MANoIIzRHZ1xq5agwo6iQytpuwztVySk50\nP9cTaeO+vdLuAGBhXsZdjry4NiCpFyeKpzlDOvKl06G7xJSi7o9wdKmOoOrZGo/N4e70XUI27Y+h\nUuYk0p2On2y605bnxPJ4PhYp+zBc82VrqRd5XCNv7xQJjVsRdeKBGtOLRB15UbFpbkaywZQkiywF\nBYAhRZtPh2IXCUfHp/44iX1ppxdtnyJMjyfVMs+iEUpDxS5SnkNStPEu3QCOxpkEx4rY9aOQOjfa\nlBx+/5VSPnx3b//eIfkOsdR/vErRVGlsH6/4UZLHbCccjbchfeeBJek7rzwoY+OdQ3HtAoCTY5Gg\nTqiPoO7S69MnE1+mmqV18vVzR1cEFUVRFEVRFEVRZgx9EVQURVEURVEURZkxdk0a6ic7lrXRYd+P\nlLN8QqLwcLTJiN5hG3wwiuwTN0J5EkkxsupL56iV/YEvnxhSgsgIsuzLcjRvmT2Q0PF1TrzE71RO\nWVbDUZ42idRYIxNlwmisXE8v6ibJB1hym8Vh4m3ZjiOWdSma03yPoqx2JFpdFC7/0/lzimzFqrUG\nPctm249oGJPstNCfNnaMghMbU/uLw6ih9F1B0cCKOjllXi2TdJtVn4cTnLc8yar/wNnGuczRy/yk\n8UHENLLFlKQwmSehrJHJFn6f4klsvGiqLLkmiU0eyr+rJe8JXVebpHuhlN2LRMiypIiloSQ34XsX\naG5ZItjtiRQmSSgqXo3cXDk/eDJLjhad+Q2h36do2ST7m5DrQfNqeb49SoLdCvraJkkQOfH8KkvV\nKDr2cOzLDCfcv4MlzjJW7Nsr8iqWggJ+2x/XRBTnOcR4RONpaB80WHASdz+dfHUEUbcPyflq3DPY\nbSJp+jbF7hLdbre6TBFA2yzXDoZ5nhvxfVmdyDMa0HNZ6Qdzm6HcM47AqpwbMUV3bSQsEw36Rx4H\n2hSRs0tjQiyS55XT8rxHgyAJOjX0BkuOI5IP03gah3NLauY5ja88Z+Xy2iQYg2l+l3O0eHLJAN2X\nDp0jjDZdFHKsFFL/iKJYdyi5+9wRP2ooKCF9Tu2/mMg9y8diC5OJfy8H9F06kGvp7hEbPbgofdS1\nhySC6NG1E96xbl+Vv1fIxnjOyvc+nNtESf37wLmio7WiKIqiKIqiKMqMoS+CiqIoiqIoiqIoM4a+\nCCqKoiiKoiiKoswYu+YjmFBY3HQi2uHhwA/fOqTwt10Ki9zrii7XSyVBIcyjOPBzIe1yEVcLcznc\nbRz4N+QFpXNgTXeNpj5012N/gyJ0WKjYhl/TQ00wp3moi8bupYUIfHaarFf3Um5QiOEgKnDdsRcX\n56flPXskPPn8vPgFZplsH2aiKNJq/4Ym+VBwSPO5RfFRAoA1CvG7GoTFVs6BOr+XwL+BsyRwVOMa\nd1U/fUEYwj1mnzXyHWiQP4/n27MJdOyCbJx95EJ/R3avyslfN+X0Ld59Yf/a0I/X+0tKefX1F0GK\nmIgdBjz7JbtmH5AwjDfE38JzNyBfDfY99Pxwg7QBdalcPN9jvi9BTep8l5Xtw+0lpbY6yfw0CUVG\n4+OY0/hI++r1xH+F2/TcvN/XLlHf2yZ/wU5H2k6PfJY4rQMAjChVUE7teGlBxnNOHxHmnxhTCogx\n+yJSmojJWK6XbTjM/sAtMq75g8fd0GenLqURj1tsU62W7//UIhtr0nct2qdBvvzsE8o+fQCwRvd8\n0KcUVDQ3GdF8ZhykiMg2SQ+lbB9vDsZ+ecHkin3xEi81CM1HO7L/aEXmVo1mEAuD2vKEYz5wjA2e\nZmbB3JJ968nHbpBKHZdpmrV2yp+zsy10OrJPtyv13BPJXL5JY0sM/76kGY8vZBfkp96akzlnmPZo\nQHE+hidPTsv9o5Jy49Qdd0r5hKR4AIDTy9J/jShmxmIsx+3FUpeFOerHuqF/s9yX8VhslMMPcGqo\ndse/llaX+5hNJufbQFcEFUVRFEVRFEVRZgx9EVQURVEURVEURZkxdk0aylKI0UhkDixrAIAxyRkW\nurIc3iOZ6IhkEiy5LDaokTi8Ock6SCbJS8utli+/aDQpDLcXrrs65UNRBHKLqFoOWifz3EyOx/vw\ndiy7Y1lCmD7CCw/P9wzVErgkkJ216f7v2y/hvvfulbC+LbqXy8vyXEMZSpHTRZMUmOWg/Fzm5kWK\nCgDduRXZ7rQvk1C2T1QjgQxlfkWouVrfjrfx5KCUliHz5ZBJUp1yokbJvSFnQu6lZuBwyyQ5paZc\nhPvnvD+nj+B61mhhN6Rv4A/qpKEUgj4MTx/z/myjVKZtkjBlA0l0OBUI2xvLwhueVM2XhiZ0LF//\nS0VPJlvdJpSdg8eXLOW0J/64VaQ8PpLUjJ7XncdFGspjo/fcAcxTOodWU8bjTku2m+uSC8fQl4YO\nSdqZUePpksyU00ENg9D4eUbyRnIj4BD6E5ozcHqWMOJ6k+TmCUvzaKxLmjxP8MfAZotkXGQvPDby\nPKcR3Eu2PS+NS87PVZ5dn1xgVihdBwAsr8nfLBtNqa/JN7NJVW6fH3jc448DmbGX9qrFMnxpC0ku\n7a9FbjfNQELI4zOPr+yCxakcQpkq23yzIcfqp/L58YHsf8udflvskDT6wB6p2xHqFzjtUJPTvAWp\nb7iPiMjtq9mWfihJZP987M8nxqdlbti/U/q407eLNPTobVI+vezL6ldI9TogaegwEmnoQiHXn7fl\nucSxP4aCZK9jslGep/eaIgtemPNT98wtynedrv/MzxVdEVQURVEURVEURZkx9EVQURRFURRFURRl\nxtg1aWiX5H2rqyLr6I/8CERjlkYMZTmWIxstr8ryb0bLx0mLoy8BEUUdSjgyEqrlVc0gGlOn4y/V\nrpOSLIeX4rPMj8xVeFrVGjldXaTGTSKW1UkB6o4F+MvRmScbY2mtfN5q+/dicY8sgR88cnBanqOo\nSdmEJLNrJBEKJCr8LFOKcjah+8qSpiTxm227Lc+lS/Jh5dzw5KCsqwraYlRUyxbrZJN5TdTMDXA7\n4X2KTSIBeqdkmTTZEv3+lQVtkevDUiqW3uXeOei+FIH8miMY829uHJmYKuxFCQUQo7q/4OviLaJA\nGhrxdqQ9Sr1t5Agt6u9aG6KGss3xPeM618uHlZ2nRRH9Upb058HvuxHJLjlyIdnEgKJ1Hz8pEiqW\nkAFAke+flucXZAzv9Uga2ZTj9rr+mDkm46Fg0V6kz/FQ5gPjYD7AUUDZJifkXpLTGMJ9U6ftt+km\njWmtDtWf6szbNIOon50WS0Ap8iHZIcvL1/q+hG6N5JwDknpmdI18vcOxjKGDkT+36I+qo7HmLD9l\nmwzcVtRazw9jaotNklw3AzkmS0N5rldQpEl2KYDnKhSGYae/2YXJkwnLJo3QpYDmyQX1JavUFRwd\nSIu56aTfFufaNO6SXR3hyNNkLzy3KLIwcjb1V0124ZL9M5JZ9ifHvf0LsrGCIwv3ZZ+Tp2Sb0wPf\nEga59AVjisI9WJNrPjGWaKTFHNn7yO87EXPfS3JQupY9izJ/3rvPd4GaW5S5bbsTyk7PDV0RVBRF\nURRFURRFmTH0RVBRFEVRFEVRFGXG2DVpKEekzGgpexwkZ+9T0tgGSyVJnjSZUDTPmKRdcRANiSMS\nNlhaWR2dL4y0yVLRCdXTi/jF6otAfpHXyOA8CWhcLa8KI47VSUPrjrVhf4rU5C3Hk+yOb193zo9S\ntLBXlrB7CyLBjem+jljiwjK1IAIqSxPSvFr2xzK9UMrA0UXng4iiyvbJOdFsTrKGoNvIWZoSsYSR\nP6diTdn9XS1S8vZhaWYo7aw5lhehj+yQIwwCvjQ5zTjxenX0Pc90AvuOvST2FImQoxc3qu3VHZDl\nsDXSUL4v/t6195KjJLNMlpNfh/JrjiTJ1xLpb4m7xp69e6blAUmg+qvBhiyr96L/UrJrGk8y6oM5\nITMAnDzF7YXkTW2JFt3tyViRB+1jTCYyoOTyy2MZ5/mcg1X/YjLPXYAkaHQtLYpg2puTNj2/KImf\nAaBD9Wx2qiWgfkTF0D5pDuNJOEXaOqT5y2pwLatr8veYop6mJJP1osFyv1P4dSkKloOyewfJDGn7\njX2DRvk9H7CrTkzPMorDhOA8qNGgQu5FE2pXqydPTcvLJ0SaCPhtJubnTH26Fzm6EcgMG2IXE2rz\nq2Svx/sy5z666vcRBxOZG0Zki+05nifK+ccZybrDSPsNkWb+/+y9eZhkSVnv/809a+19NtZhhABk\nE0Ev3HsBn/tzQVEBAVkEEVlkuYoKwqAgF1FGRQEX8KdsKiAicBFBBETBBUVBlD2YGWaGYRZ6umvp\nWnI/5/4Rp/L9RnRmdXVXdVdX5/fzPP10ZNZZ4sQ5b0ScjO/7vhWSQ0b7t82OOokcs0zS8lLX6lmn\nSUSd7D3vxXbRpUip3bJtN6C5UZ9k9V1qo1Y/jt48S9LOy+oXDctzFPX14DzJ7WfShPIkra3u7Kub\nRnEhhBBCCCGEmDD0IiiEEEIIIYQQE8auSUN7tOTNcsBIGghgnSKIVUhqVi1zNCSSg9K+WT9efueP\nJZKQcqTQVGrGsHRrXBljIhUWZ6Xz0Ldj5JybJYSPpZ6lkeVKZbTsCwAqJHPN+PwkS+DAgbysDQBz\n+2z5v0xR4vq0f3fACUzp3m0aAZVka1Qvlq+myVg52iFHLRXbg4Pesky0lHQbJYosmEeSpdFRL2NV\nUmwjY/82Rk+aJZKmKLosyW+iAKSUaJ6T7AJAjz5HUm5WybK0tMdRDNNIfKPllHWWZpZZjpnKvaiP\nozrnJGsp55tJ1+gvkQyQoyfbNtxHlErjjxX/rTRymzy5L4oiuvPMUdTOnMbNbpLEnaWhLAUu081v\nUARMHs663VjetLp6YljeTzKmOstESeqUJxK4Vp+ihnKE7b51Ni2KrrmyYucD4iiabN9zsyb75Gim\n8yQHnT8wHx2rTnLQUiTRJrcTsul2O+4rVikS9jpFPl9bNwna+joneo8joHY6tn/OEVBJJtvvUftz\nv1FNEkpXbLAe7eiSSEMhzgUZz4H6JMvOkztA86NoPO1ypEuLjn+CkqMvHU8iZdIzUxozH8x5npm4\nAeT0uUPjzom22egiReBcTdy5DlF0z/qc2V+d3Hb6ZdtnjSSvpWTO3CC3nzpF86XhFP2O2Vt3LZap\n1shtrERRd6sl60empqwfqCaBPjm/fcYuTPR9m/quEy2z934lng9wpP39NZunzpPkk2Wi5WoyBvN8\nGDuLVgSFEEIIIYQQYsLQi6AQQgghhBBCTBh6ERRCCCGEEEKICWPXfAS7XU6/QD4MiS8J+/zwPt2a\n6aCjEM+RX03s30DuBahmpjdmR5nI264U+yuyb1CcGmJMOPfEzyZOU2FsxS+wnPhalKIw4Ox7mI3c\nJ00fUY104eS/h/Vx5gAAIABJREFUR35C8wdN033gYBx6m1NG9DPy9ySNNX8fhbQ+yX/IypwKosK+\nYBQqvJT4cfI+JbkI7hgcCjnyw83i9ueQz5xaIeMQ1WQK7HqXhouOPka2NPpZSFM28PlL7BvFrmxc\n/5NSPpAtjbErTh3TbnXo+/HpYthXo16j8PrUD5UQ+/2wjzH7BfI1Rg2WdC/j+gj2qW00rB+o0vel\nJJUFn7Ocj/HdFOcUTpvEPoLpLRnQ816p8HNg/eYU+d90KMx6pxv7tc1OWZqI+VnzeTl00FJZVGk8\n7SV9xaBNoeIz61+6dC1t8rFbS1IusG/VLPmDH9hv/n+XXnaJfX/ogNUr8bnp52bHnb5dZw7yt+yZ\n/9LyYlyXb95qIfxbrdEpHwZRv5n4ELM/NTk9Rf680c0cb2tRK0cpI0b7FZ0UCmGTNDTizCnRHIgd\ny/IsdkaL5m08vtFY01m3Z7HbZj/S2EevRI5tleimj04zxinXAKBHdV7r23YnyB9+ja6lORv73s4d\nvnhYnj5yB6tz0+aQy5nZ0npneVien4ljWcxOW38zQ/6+eY1sJ6d0L9M0rweAltWzRXl1OpRyIs/p\nXSJ5+KtlTrlBf+D4Aw0aG6ntGvXE35F8ETmuxlSDUtfQdeVJR859Sb8fv5tsF60ICiGEEEIIIcSE\noRdBIYQQQgghhJgwdk0ayuGmm/XREhUAaLBcKRstz+LFZFZb5KksJbPl3D6t87IEsUZymVQCx1Kr\nOM0Ex1a3Yvmk1+zR+/My/bjyyYIN/swSsNFy0lSmyp/qVduuUrdHYj/JQadnpsBUSFpa4vODQ5Ub\nNQ7PncgBQSknyuCleFoK79jyfSVpC5bPVGu79khfcPS6LOOiUO+DVJZAqT3ouY5MJGOZ5egygHHZ\nCCJpYixr2iz9xOj9I/noSalMOLWD/Y1TQ3C/wOkm2u1Yij6g0OGRxL1KcjuSCKXS8SaFkmZjipSZ\ndP2pnJM7Qw4dXmNpaJ3kKlXuO1LtGMuP+Dq5wStjvhdng30kzZyiezeV9IHtlknKGiRLniYZVY1k\nTIO+pWzo53EqisOHTAZ24ACFhq/b+Vss7WzF+6+u27PTo7+xZLRPLiBZIoGqkrsEjynT03ZdMzN2\nXbN0jYl3BQZkH/UxLg3rKxayv9uKpaE9+tyg9A1TlMqCbbKf9Jsdvs4By1HtWJy+o0uS036e2vro\nD+U478uwWElcRRKHHIgdYsAuUDSGbtI/Rv04F2s0nyMbzyvj13Oi9BF03IzmaYM8lpZ2aXq2RjnX\neiUbK6bmDw7Ld7zUygBw4FKThvYah4flW9tkbzTW1WhuUSP5KwAc7tlYeXhg/crMHD2/GdlYL5kb\n0sXkJG2t0NjcoD5uPmnLcoNSsFHf2aM0Zytku/2S1bddi4+VT5GclPqiZpPeP7gfTl1wqP7IJA0V\nQgghhBBCCLEN9CIohBBCCCGEEBPG7klDSYLYbJgUYjqRhk437XO/Y0u4UQRPWibliJRZKZYgsjR0\nQPLEHCybsroMEllKLA3lSImjI4jmJ4XmOjWxtI4jMCbRDSMZGJ9ntJw0rQpLUWp1O9bUNN0LkoNW\nErlRJJulunBEz0h+ysvk/aQy+ejIavmAyiVbyk8jy3JE1PLJelxxhrAsie/FIJElVE6SEQaiAKCl\nMc9iIn+I7WpcpFDeI5FylEbbD9c/o/Jm0XijSG4s8aG6sEy2l0Rv65KsJSMpSq/MsmqqeyWuC8uc\nKxwBlCMLR/VPpCgsG40k6ywXGh3ZtZz0nSWWg+YUYZEkarGUPbVDSUV3miMUKbNPssF2IuNfIXlj\nk8bTuRmTlpYqJFHOTfLYrse2ftmlh4bl/fssameJIg+ut9fo3CbnAoDVVRvD+/RIDToUoZglXIl0\nO3IpoYi30xSRr0lR+HjcSaWhIKlbnWSmrY6dP6M5R2fdJLMA0CjbdgcPWYTtIxeZNK5EJ+10Yun4\nKsngWCa6Tudca5mtraxau2bd+L4MKGJxifqt0piI5BxpG0jnGjsrO5toIpcKchVIJmQs3S/Tc1lm\nSf+USRNr02a71WZs792y2W+FxsNq1AePnjMDwIDG3XUa0vKKnX92v8mfD97+8mj/qXmL9Nmq2LWs\nU2Tdcm7HKvWtvL5yPDrWgQV7/i9atv0vOWzPeKNsz3K5H0djbVBfVG+bXVX7dmHTAxqnEXcS0zQm\nz0zbvWhTv7TQs/ZbH9j2a2mU4oa1a5/+1GBpKElROWo7AAzoPlV2eDjVrFkIIYQQQgghJgy9CAoh\nhBBCCCHEhLF7IRZpabNC8o1GM04oOUWJyzOKTMZJN0HL312WEybL7yw7ZJlEjSLncaL1VKrFkqps\njDwsSmh/UnTM0RFBx8kZ4/qWxv6NqxlHHmTJZRKxjNqp0rA2LlNkpJxkLZwcHABykndyNFeWnLKE\njiNWleuJHI+iPg0oCmOPj0XbV066L/o942zAkS4jmWQS6bNMkWpZZlKK9ufjskw0tVGSc0Z14eTL\nJD8ujZalpvXPxthlpRR3gRwtM6Noahx9jZ83ll6l8m1+/vlvA3DCaYo4lrRFn/aP+it6/lkamiX3\nJY/sh6KRcSTCKHobHzeJxsoRRSnKWomHkJzKJ4VvjYTCY8ridNhPCZY5qXQ7kfGzPHKGJGX7D5i0\ndN+8fd+786VW7sUJ5Q8eMGno1JSNG/zoTtE5er1EdkZR/fqkDY36ASpz0nsAmJ6x8Wlu3qSp+w/a\nteynaKZzFMEzy2OpFSe0b7etvHbCpLRHj946LC8eOxrtf9mltx+Wv+VyS5x9+zvecVjuU3LvdjeW\nhq6tW9sukXz32KJJUAfHFofldZK2lXqJdJvHV+pfa9Q/1OrWdml0do5o2knqKc6c+LEeHXkaGD+H\n4e8bdbtn8/sPDMsrRy6O9uHnpzegaOscLXuTc5dIHknBspGxZLVutl+jRPEAUKrZ3/okuc5orO10\nrL860TLbO3o8jhqKW03aOfN1s7+L5r85LM/Tcz2XRP28CGYzF1etLQ5VrDxD43y5Hz/7rR5FPWa7\noGuskPx1hq+3HtelXWVptm3XJFl7neTupVI8Zy/TeQbJq8V20QxaCCGEEEIIISYMvQgKIYQQQggh\nxISxa9LQapOihg5syXt6bjrabmrF/hbliuyPlmP2Sf6RJ+unHFmN5ZzjInVuJi2NpF5jpKFpYlZO\n9s5sdk77Pl0L5s+jI/dFkagSOSXrSadIYjN/aP+wXCOZ7qAbR0TsUxRFDhrJkodIGstRIwdJu1L7\n8f5xsuzxErLNoqGJM4fbkiPoptF0k6ecvh8tAeWonemxBvRs8DMbPcssZUnsImf7j/9ANaQksdVU\n5kqSb7Lx6FmMonFylNHoUOiTrqbP11mh/oqTRCcRz7J89Hm4LbicJ9F4IzksSa47LJHLOOk9y+Lj\na+HIvKiyfIak/GP6t2InKvPvj410Q7FFBuwGwdLlSjysV6t2jxoNG09nZkzStW8fy7tsPOh1Y2lo\nbNNWrlPk70MUxbBajSWIg/7CsLx8gqJgUkTFKiXOnt9n0TgB4PBhi0h48SWWyJrrX6/zMzk6ojcQ\nR3Hsdk0C1iU5GNtXWpc73N4ktJfd7qJh+RDVsU3RUBtJ1NAqya17lMSe5WizHWu/Vpv640Ha71n/\nUqL7X6vaPhX6Pk9cRfLB6D5BbA92ydksCnyf2p/nOpELEZWn5k0KffDSy6JjtRaXh+XO0WPDcm/d\nIvjWaJw7aW5F0uL1Nj+XNAegZ7dUid25SjRu90ne2KHonOt9k4C2aDyqzsU21lu3eq70bJ+cwpku\nUSTi6UEyhjasnWdn7boO0GtGmfueZD5RY3cLdp3IeD5CbhcDlr5Hh0LGEY/JLnkKUuLnIJlPsBtI\npbKzr25aERRCCCGEEEKICUMvgkIIIYQQQggxYehFUAghhBBCCCEmjN3zEWyMPvX0IPYRbCyaljbr\nmcae9f1RaPcSv9vGel/2+elRuO0uhYWtkp9LNwmjzPuM9e0hHXia8oH13lH83ijE8GgfwzS6cA7O\nGUGh3dmXqMrhomPfuRqFj95/xPwC919k5bxk19tB7COYdcl/qkzlMSkycrBPZeLfQH5hrIPm+5qX\nRvubAfIRPFtwuw5I6z9I2r+UjfZrjX1q2Q+W7CVJM1Cp2d/q5KPKPmrsL5sn/izsbxpXcrS/XWmT\nn8L4ORuM8WNll4JeP36uuxQim/uOvMY2an1M7aT+xo7XjNLllEYVT4Lbv0/HHtBxOZz/ILP2TqJw\nR+1fqY7ue1Ae748FCj1eKrNPiXwEz5S1NfOZ4fQBeeoLl3M/Sv1rlDqFw5bz3rF9ra2t0Xa2z8ys\njSez5OeTZfGDtHCc/Je6Vv8+hbmv1+24B8lnHQAuuezIsHzRxeYj2KTxLCN/uTb7OCb9Vrtt5293\nuC2tAQ4esjD9Bw6Y7x8A3OlOt6ftrJ5N6rfYT7c/iI21TL7CJfJlbDTsHs3O2XWtr5utdBOffbZp\nTudTrfDcgP30Ewcm6pMr5dH9uTh9Ul/ADfLk+w7PbcmfPKvbdjXqg+vkh7v/IvNPBWIfweUupUjh\nVAhkI+lyEPvpr7XMfjqZPYvVGfIPrsU+gpWK/a07sHOut5aoLpYupVS2Z3n/fvNPBoA2DQ+tNfK/\no0p31sz3sbUep584RM98L6d6cton8n0sJX0n2w9PudmHvk9tudKitBjd+B2Hh/DmjLVlTrbboznD\nIEkHlfFYu8NZl7QiKIQQQgghhBAThl4EhRBCCCGEEGLC2DVpKEsdayQFYYkHANRI0ri+YmurLNus\nlUnWQseNJEyIpYosIWutm9wlJ3lXq2VLzgCwTp87FG6aZaqIQsGm0lBegmbZpDFOGspyGSAOOcsS\nvjJpuubmbJn9osss1DUAHDxispppiqVbadJSOoX7bdQSuRFJMOslK3e73K5dKpMsoZTI90iKM9W0\nuvSoXSPpRBL6mts8vefizFlcWByWByyHTOQTbL/8xGcsceHnmoq9XiJRKnEKA3uuajXTiLBcI1Xe\n9ElmUSd770fSGztnlshaM5KodUhW1iZ775DMs0390Np60l+QTGXQt/37Pa4/S/piG69Q6P1KnaQ4\nlHonatck3jT3Jdxf5hSqvkoysKxP0tBEesJh9Fk+U65y2HOSHaZpOWomZcpzu6/lUiy3E1tnYcnk\nVY2G3bt6LZbH5xQOnvvnE6v8vLLcmeRk7TUwrZY902yfBw7avefzdRK58xqNtWwvA5JX1eha5uY4\nrQUwNWXPEbtBdHpsn3RO7h8S++iSfff6ZhP79pvM89JLLxmWp6djt5X9s5SyomH9U5b1R5Z7LM0D\n0Grb9XdJJsseJbMzdr2teTt/uxW3a5vG1yzn849ONTM9FUuyq2Sfm8nNxenBtpjRuNMZxNLe1UWT\nTVY4tde0zeFmZ6xcoTlfoxHLKS+9wx2H5RmaDy817PlvLVsal16SdqjfIzkzjXWUeQb75+3DpYfs\n2QGANtlya9Ge686KpbIoV+m49ChmgzhdTTmjeT4NStWG2UKfxvAa4rnh1Jw1QG2aXCpo3ALNGSpp\nCqSSVS4nG+n27bgLa1bnbxy1+7icx+tsVeovpqgxswa9l1DbdZN1ui6l3OgOdla+rRVBIYQQQggh\nhJgw9CIohBBCCCGEEBPGrklDKyQdykmyUO3HEYgaTYpUVOXIc7TMS0vGM9O2/XwzlpnmuS37tlq0\nTE+RerokN8mSZeZa0/Zv9EkOSZH3OEpUKrHgiJi8/M/v46Ux4UTLqRyPIjU1SeZRp/LcYZO4HLjE\npKAAcNHtLNJUc4akRCTbbLepjklbllixQ820tmLL1yy/zXsUgTJLJWxWrtepLXJ6PFmrlrZrbUxE\nQ7Et2ussAyPJZjW2UXBUQgqjyfd/EJUp6idiiRM/G3well5V6NkvJ1KOSsZRZ+m4fbZRei4Tu2KJ\nWKtDEc/aVs/FJZO3HT1mEdpuu81kIQDQ7XDEQjtno2rPeLtDcp1erHNdbVPUT+pXKnQv9s2zxCs2\nDL4XHCGx37N6VTiab8/6gTR6WtSWJOctsWSUOrxKPb4vtSbJURsk71PQ0DPm+II9b82mNWSjkUhD\nqYNt0VjHEQFP0LiRsSS/E8udeyS7nJmbtz9wtG2ShK+sxtLSpeVl2s7Ow/1LvU51SZ5DtskyHboc\nhbml/igyqUQGnvH57ftq3ca6qRmLgDozHUvgeA7CkcN5eOO+ptuNpaFtktny/jWSE9bpvnbaVpf2\nWtxvdkkq2iNpHz8X83S/Dh2OJbex64mihu4UPOeLJkr92CWiyzJpkv31KAplmeavtSr11UlkXpCE\ncfqwRdmtkHvP6pLJSbvH4nGrv2422qT5GI81Rw7bOeZn4r5+fcEk64vHbhuWlxZNjjqzz563qWmO\n+BuPYTWaz3Fk3DZFCq3QmD3ViOsyN2vtNEPK7mad55N2/kEe952d3P62NLB5x40n7Pye5KA3LK4O\ny71KPGeehbXZiRNW51rD2rhPUthO4vfSo361Ny46+hmiFUEhhBBCCCGEmDD0IiiEEEIIIYQQE8bu\nRQ2tsFSL/pBkMq7WOKk0RbUjaWWNZEjzB0zysO+gJYMFgDyzyz121Ja/j7U5OiJJNJrxMjNHCZue\nsyXkNsnGOIl0mqy6XuUoiCxv49tACZ4p0WUviWBaJXlZk2SbMwdM/jF32CLyzRwiGQ+AmYMmM2k2\n6PwUzapMsrF6OZF6Vaz+Pbp+liH1WMbTs3MkcSLBq9ycZLfKsj+636lwpUIJuvXTxs7BUc4qFU42\nnshvSc6RkeyTo3NyuU8Sh1QmzPKHGkXWak6ZlKXaHF+XMj8n1LFwmetSSpInd3ujpXNLy6ZDu/nW\n48PyjTdZJLTFJetHAAAkC2PF8lTNPtQbtk1lLZaO9QaUgJcSSPO9aJDMkhMOA7EElxNmd1qUFJyM\nr98lyW8iS2HJe4faJW4/26aaJBmemrVra9C9nD0CcYYsLtkYVucozvX6qM0BxJEjqyTHbJKclKNg\n95NIlzlJnC+6xCJq9ijadpekx4vLJ6L9F5ZO0D4URTuK/G3ltVZy/jI9hxT1sxqNpxSJl4MVJwnl\nK9EYavv3Ocoqy9HacaTHfonGN6ozD/ws6U6TwLfbFOmTbJWlYnwvp2ic52iiQJyIutOx+zc/Z+P8\n4cPmHnLpJYei/WvkXpGlyebFGcOBakvcp6bSPo5Wv27P9cqylddadrBKZRMbL9l8bJYi8E4dvnhY\nnp+152J5EI8bpQU75z6KPD+zz565fbNkL/04ifvCgo2PN99067C8uGrS0H7ZjtuYJXeOk1TJ7CpE\nLhWr1o80aNypTCfzAR54eZpbYX8kigY6iMetEz37/I01O89Xj5kE9Eu32jj9TYrKXGvG96jUsP1v\nO05uN3TKXm59QjexQ44oyv3wTqBpsxBCCCGEEEJMGHoRFEIIIYQQQogJY/ekoVQekGQjS5bMS/no\n7KZlTkJOMomDh0wOefDiRHdEEQW7XVumPXHCpFJ9WmY+eCCOrHXkEjv2FEtDO7YUv07L+uUsXmZu\nkoyrTpHJcrrGNklh1ihh8OLN34yOxclpWeIzO2vnmNtnctDp+TgZLkvlWMKZk5ytTHep0YylKDP0\nuT81OkF3n+QqNbrGfjW+xywV5EhJkWqQ5T4nydbo+YnDxIltwLIkThaeJzbJSWejZMocQZRlmmTv\nOeL7leec0N3kE+sU9XJ61p7lci3pwkok5epRAlZOKE/y50qi3+Z6rlOS5hu+fsuw/JWrbxyWb/zG\n0WG534+vhQMDN6mec7MU4bFO8tlEFnP0uElNFynhcEbtfYil4CT3AeL256iE7bb1UX2KbNqhKMGD\nJKodR1OtstwmkquQNLQSS3Sa0yalaVBS8NkjD4A4M1okVeyQJKlcjpMyczRXLnPico6+y/0rR70E\nADa3hUWTZx2jCKZsXwsLJl8FgBVKYs+PGJ+HZcw8tgJAp0vj7jS5jdAYWCpxeXRUWwBokEsERyss\nV6yOPZJZrlXj/ZtkBxyds1qjJNR0/sFgfLTsqH+lboTPn1P/0kzkvwcPsOuH1XPfPM2HyFVmPpkP\ncJTgwUDrAztF1rXnl+W/pSTa9cyc3ZvFVYu0ecMtNu+76T+vHpZXKZpoliQu3zdnz9/ltz88LF9x\nB5sPX3zAyvOH4/0Pr5td5GWbg3Yr1m8vUnT448dviPb/yvU3D8vX3GD1b2e2zypFx1yjuWwtifo+\nM2XPaZNcH9gNok/ReBfXY/n1jSvU/jT/P14jW6aI9ic68Rh+G0UnvXHRytcuWfko9R0dSnSfJ/Ld\nJarb0oq1UST5rNj5GzNxSO1qjfsy7CiyeCGEEEIIIYSYMPQiKIQQQgghhBATxq5JQzk6UIWkDKlU\nK2epKGkpqpQAdf9Bkz/M7jc5Z2M6XpoddO08VYrcx2WOgDg1HS/N7j9okY7mDlASyigBr21TQSyn\nbFCizypFEGWl4zpJZ05QZNVSkph3vUWRGinp5xTJ5lgO2piKr6XdJvkLye5YdjY9bftPTcXXgij6\nnF1Lg5KDTpNMtcKS0VosN+p1rf04AimHF82z8UluWZI22OFEmxMN6Q+iNu6lSeA5aizJHDhxeyTn\ntXuZyrUyjjpLspoOPaO9HkfjTPoLOnY+Vo46nh4lS2fJ+A1f/8awfNPNJhO97ThJ3/IkYhlp72pk\ny6urZDvU93CUTwA4tmDS0C7J5ebnzMbWW3cfllMb5eOx/LpNUvDOul0jCwrzRBI4oLZkaSjfPi5X\nEmlonxqdZabizOmz1Iz6x5NUQywNHXOsEkehI1vPE2vp04385lGLDti45jo6mO1/9JhtAwBrJLfm\noHiDAdWMTtnuxvbd7Vuf0OrYM8bS1kqZo4vbPKGSRNWtte3YNaoX17FB9tmsxXWZoYiMs7MUBbFJ\nbUnzGZaaA3EUUo6g2mmTNJe2b1HS8VQaNk3jLkeAnZuj+cA0t1cyTlKblyH3ip2C51MsDU3dK0oV\nm5+dIAnh124014MvX2Nj0OKqjQd5IjO96IjNhwckk56aNflpk+a2ldnLov0P3s5kxoOaSb5blLi+\nW6b982RuCXNRyCo2Hz+6ZBFEW3077s23UtT+ZGyYpsjhszS+1VgmSRF722kU8XX7vESyz8bAbHGF\nIoK3E1l0m16RWtT3tWpWr8q8bVOne5ElMtO1trlHZBxBmOYJVZKr15LemuXEPLfYCbQiKIQQQggh\nhBAThl4EhRBCCCGEEGLC0IugEEIIIYQQQkwYu+YjyKGQS/Q+Wk7k6ewnxCGOp6ZMo7ufUkZwaPlS\nJdbRDmC6+Ix18CQrrpIOt9FM0j9M2ed6nZ1jqiO3qVXiEM31itWZQ1yz+1SFG6Bn2/eScM/lGvlS\nUSjcxjylqCAfyUrSFmvkb7C2bHrtPrU3+/l0e7F2uzsY7adVJu12tWnXOOhau5QSfXMkl6fzR46k\nROofwX6BvZ78j3YK9g9i7T770QHAIKM2H+NfVIpjpds2yb3kzfhZ7NDz2qGUB9U0ewQ5lrJfY+zq\nRP61ySPWJX/VVfLXvfWohfQ+vmg+DWuULibPYv8E9kvkNAsV8s9h944sSYuyurpKn6xexxbM16NF\nbdFLUj6wDxnbb7tt/hGtlvmalAbs0xk3DKdoqZNvFPtdlclXo1JLbmyF0ncoxcuOEIej38RnpDT2\ng30b3e9N/LHpGb3t6MKwHNkk3Xt+vsJnew56PUr7k432Eaz04t+quXtvkVNrno/2Wa+RL36tHncW\nHI6Ah6RqzZ7vBvvfJz6Cs1N2bL6u5rRVktNXrFIfBgCr5OfVpfQf7I/bJ5vsUpj86ZnYH3iKffMp\n7Hy9wfEPKJZBO+4roudC5rljDLqctoj932MfzVbbPh89bmlZvvZ18xG85huWimGF/E3LtTgWRp/6\ngn37LP3D/v02ntQatv/Bffuj/Stz5tdX69G8k2x0ilKkHGhy6hJgav7SYXl638XD8vU3XTss33Dz\nV4flm8hHcOWEzUUBoEZz0NkZ88M9cMTGwPqMPftdmmMDQJ/SxC2SX+CA2u/orXTOatyWU7PWFtNk\n7xUaA6fIrjgWRqcfp/HJcjtnnfqVMo2h1Sb3XfF8olpmH1/sKFoRFEIIIYQQQogJQy+CQgghhBBC\nCDFh7Jo0tE8h6AcUlrbVipdT10/Y0nalbNsd2G/L2XP7bWm6TnLOQSnWOOT0eUDLuSx3meH0C9Ox\n/KJeb9A+tLRP67Qs0Skn0sZSxc5TqdjfWA3apBDPgy6Fid+fpG+Yt7pk0yRlOWBL2SWSsnR7yTI1\nSRbKpM+rVe1YLA/rJ2kZOIx3j+rf6rZGbsNpBbJEGtane9HPTVaTg8Pl2qMaSe4QS+q63Ti1gThz\nOJUD22u3HYdB7w0oRDbLG8dIAEtkx+UkzQBLDXu5nXN91fqBtRXqEyrxs8BSqEjsRnaZ8e9fybPU\nYzlln6WStAvZRSRL7sfPXp9kpn2S1rItgOXqScoGTpMxT9LwbEwqjEFyLV2S8HK/yjJRlvnmJC0t\nlWIZGyvHpnOSn5OShkN6o5LWhULi57LRnYCfFx53UpUou16Uxsk+U422/SE5mN3HVsuekf7Anq8y\n21ryTPbHhNCP0ruQBC1LwsmzgrXb42NTCqoyybAr9kxXu3Ffw22Rkx6S+6QqNUutmqSPoLnG0opd\n/9SUSfDKlL6C7RkAWtSPRjZJ/Q6npuI61uqx2wpLa7kLbnco3Qb1Ab1e3DeXcw7Hv8O6swkmo3RY\n3B+vrsZj6M1HLQ3RDTdYeqIbbzFp6AqllQDJQdkdCgAqNXvmFmj+7K+7cVheXrN6Hdx3MNp/etrm\nkBnNu1gyXc3sWgbrJu0EgHWSudbJJel2l5lktFTh/sLa4lgiv66TPHIfpUKZ3TdDG9lceJCk0sip\nj2Bp+Ay5beUZpX6px6kw+H2gRtLMBXLbaK2QvbMstxm/Xk3Pm5x136y5s7FIe53SSnRa8TOSUQqt\nQbaz+m1QD8MPAAAgAElEQVStCAohhBBCCCHEhKEXQSGEEEIIIYSYMHZNGtptkZyMJCKDThzNiiUj\n01O2TLuPpKEsZxyQvKk3iI9VLnE0MIr6QxIPjtTTTKIxNWsmiepltkzLUscyySryRB7F0TV5ybtP\nkpGcltIbFIG0yUvhAMokr8OM1bM6Z3Usk66lP4glNjnJViu0ZF6henE0prway0XWKUrc+potjXN0\nxz5JX0ihE10vALQ6th1H/eRIdhWOmFRNozNSm+/wkvkkw3ec23WQRKcc0D1jKVOftotkbHwvE2lo\nlaShLHfr1kzK0qB+oFZLpKVl+9sgChpKkUrpytJIm9zfcF2aU2ZXLF/l60oj1rIcdBBJq1mSRuVE\nnlepUB81PTssz82ZrKRCfV+/n9gVRWxcXV0blldIytJes+/7ZIeNRHrG0Zs5GitHEx1QuZxIyft9\nkgNLerYjlEhiXKJnupREBuVPY6OLkuRzs7sT3Tq+9xQFfEwA0PB5tJoziuAZ7ZVGFY4+jT5RJEfl\nSLgnhcMcbYdl6ji4d+j14sqwvbUoOmFtzeyoSvaZXCQyjghKfUeX3WZ43KbTNztxdESWmZZpnsH9\nGbtNtNqJPJv6nmhuIbbF6qJFAOUoue31eKxoLVtf3Vq1uVWH3AuqPE/lKLGzsdtQNO8j14PlFo0B\nX79hWG42LBopAMzMmDT0wKHDw/IsRxMl+en6eux2tLxi17J8wuaDK+vWFguLJnldXbbxqJrYSIPn\npjTUd9vk6kARQDtZbKN1GkOrTftbvWr2emDOznHZRfE8+5KLbKzl7uOGW2huQ+N8t0xuE81YZjpF\nUUdnZ2ye0ieZdpUku8uteG7S7nHE5Vg2ul1k8UIIIYQQQggxYehFUAghhBBCCCEmjN2ThrZ5aZyk\nDIm8iZMosjyrOU1Rf0gJ0iOJRC+RQ3JCyFlaWm/PckS+0ecOn22ZuRdFxKTEuLR9GjSxRPKLCl1z\nn47Fibd5+b2eLP9z5MWcJKQcMYqlL4NEJpuxloYiNZVIElam9hokPxm0KILRwnFLLNynJXtuzAa3\nXZKQvM1JV+n+s7K2RNGgapUkYhonIe9LGrpTsOyPo4Gmic9zklINSMrCCZB7vdGRIkuJDKlC+g/+\nW4Vk2lxuNGKJVJ3CWPYHLFsElTkaaiKZpmepTklz52Zn6XuWY9L1JtFUoyigkQyO7C2JcsawTGaW\n5Dr79ln0sWqF7Sq+Fk5Iv7xsUekWF6zcWrOocj2SezeT5Nu9nklmytXRvx9yJDOOlggAlT5HANbv\njztCNMCwJCpu3xLGbcff2oO/aW76Ej+7NJ7xc8y5ybNEHMp1pmpGp6ykglI+/+gI3aUxkbt5DDyp\nLmOuk/uAzcbznCRd3QGN4eTewvJyLqf17HU5aqgdq9/v0PbWYKkcr1bnCK4kbe2Pjhy8muwfB3eV\ndHunOLFk9yWj6PidbhJNl2Sj7CLA0v+pho1H9WkaA6fj54qj21abNE+kadOx47cNy91kPtZo2LHv\nmN9xWN7ft3GnRgnV19ZtLggAiySHve02S9Z+9KjJQddobOpn9rzPzcXjeblkdeF5dofmmWttq387\niYY7RW4k9bIda6ZG0T3n7Ps7XBJHYL38Djbu8tyS59Pr63as9dy2zxvxfak2yK7IBQxdut/0jNRK\n8Tx3UCF3uuTdZrtoRBZCCCGEEEKICUMvgkIIIYQQQggxYeyaNJT1fKzkqCSyoRpJvVj+MchGR65j\niUMpicJXJ2nGvjmTelE+c6zQknWaDJcljeVI3kXNWOK6xNfCctiszRHDbGm8QrKzEsnkYslnLKfs\nrFq5BmsvXr5PEwl3c452SNdFcrZqz+SopUTZ1yWZCS/Zc0RIlrmyNC8N7MlRHFnqFj0jpMupJ0lH\n+5HsT9LQnYLlSiyLOEliRfePbYZlvixbHBdNFIjlT1V65qskAR2QXdVPkoaafKZKEplI5kqS0V4i\n0clZmkF9z8EDJotp0vcdsgOOxgnEibxZslolWUqJJSJJVEOW03K3ON2kaMa0O8u1AWBpyRL9Hj92\nnMomC2pRf5eTRGdqKm5XjjLMCYyrHFmZ5IH1RiKrp2hu5c20h2LrjFFQlkqbSCvHfV/i8Xj8/eE/\nlcfIMXlszBHbN3+Mxg3ef5Pfp8dJQMedP4riPb5ZorrEY/s4yWlSlzHHivvQpC2i8ZGTyNM8IZrb\nsMwztvUownKXpaV23HXqq1I5H8tJN2sncXqsl60fLVFy9m7SyP263acauQHMUnT8nJ+LMj8X8bHm\n5q1/PnhgfljmMWgwsPt/gtwGAKDTNXeB4ws3D8utro0njaZdV2M6dluqT9u1zO2z55KHx26f/bms\nyBFvAaBW48jZNO7Q2D5Nc5NWN5a5cjTeOieUp+j6++bYNSqWhq5SpOA++UeV6ZpnDtv3bbrGVj+e\nNK+vk+sE15Ei+PYom0KjGWctODhvEUxrtQPYSbQiKIQQQgghhBAThl4EhRBCCCGEEGLC0IugEEII\nIYQQQkwYu+YjWIt83kjHn/gfTZFOtkK6YE4NQdGaI31/rR77uVRZbzxLvmjk11afMR+jmdlYLwz2\nUSQ/o4x8e9jXopK4WvQ4rDP5BPQH5JdHYdfL5AtXrcahZNEhPwDyJWJfvmqV/PWSurTJx6BFYe9L\n5Ec5NW9a9dTtpEO6Zi6zw16Z7muNfQfL8WPHPn9l8p/KqV0if8GkYbnNy7vn9XrB0SW9/WATv77o\nb6TJz+i57I95XtNUFJxaIcvsuGwjParXUtPsFQDqlP5kfr9p6nNyqGLfO/anAYAeXQv7K87Nma/F\nvn123FnqI9bW4pDssS/l6JDu1cgnMvarq1MY7wP7zad5/362Szvu2or5dgDA4rFjw/LKkoXxbq2t\nDcu9Lqd7oTDcib8iu26skH9ErUo+yZRuI/WP5s/p38TeIUqVlPG4Vxq9TZJzgf82zhUtx3h/xTwb\n/bdKhf36bHv2EUw9JMelmdh6+gS6Zj7SmDZKneOjtshH++hxF8JN2e2mvtWUgilKj0Nh7inkfrsd\n93v9jM8vJ8Gd4uZF62ur1FciSa/T47kazXmr5Nc2WKEUIdRXl/N43OhRbIeMYkFwNrR6vTLyewDI\n6Zmp1jgFmc0TT6zY/DE7Yb6DANBs2vmnZmw8vugS83dsTNlJV1ftuirJBK40Zt7YJB/2CqVyyMux\n7ys7IDbIb539/7v07N+2lPj1UQyBGYorUmradc3st/KxdfPF7yYps9p0rGxAvocdqyOn4momcxuO\nEzIuhdOZohFZCCGEEEIIISYMvQgKIYQQQgghxISxa0I6lkGx5GFQiWUJ9QZLOK3YJ2koR4xtNGw5\ntVKLw6+y7LFGcsQSyS4bFFaWpZUAUOLzdyjcc8ZyMtsmyXKAAV1oRmGdWVrKupAKSU/KlaQuVOZj\nccj+OA50LHdpcyjpNVuaL5F8NkorkezfpbDYkTSUlrZrtJRfpaX4cilumArdF5b2ZSShYznhST9f\n8HXqp40dg2VFGUkzI8kugEG/N7Kc8fckweTv0xQtHD56EMkp6Zwkg1pZjuUTzWmTjExNc+hukmLQ\n+VP5Bss2We81NWX9whxJRLi8vBynj2Ab4YNxG1VIIlRN5EIzJFPft8/OMz8/YxuRrIVTQQDACUof\nwXLQftckPjn1XWC5ejeWsbXI/NdW7VgzFOp8hruxpL/IKJVPKduq9E6cK85EDpiNkROy5DJLBKCR\nvZdHy0mZzVNZcGoHOmx59CAQy0TT7VjOOTo0/8lVHF3neJ/xYf7jz6PPM648GKSpZkgex+krSKrf\nIXk+fw8Ag03qKc6cWxdMrs8pF+onpeehZ5Flf/TIdkna26MxBFk8brTWbTu+543G6FRsJymhydWm\nOWXzMU7N1aI541Iy7h06dHBYvujiI8Py4SMmDeX5N5tht5Om/+LUVPYtz+2zErkjleP9a1XbqVbj\n+bSddJ2kmatrNjYCQH3F7OqyutW/Pm3nb86wmxjdo258rC4Nol1KS8Hzd3bBSLLfRf1Sr5ekotkm\nmjYLIYQQQgghxIShF0EhhBBCCCGEmDBKkgEIIYQQQgghxGShFUEhhBBCCCGEmDD0IiiEEEIIIYQQ\nE4ZeBIUQQgghhBBiwtCLoBBCCCGEEEJMGHoRFEIIIYQQQogJQy+CQgghhBBCCDFh6EVQCCGEEEII\nISYMvQgKIYQQQgghxIShF0EhhBBCCCGEmDD0IiiEEEIIIYQQE4ZeBIUQQgghhBBiwtCLoBBCCCGE\nEEJMGHoRFEIIIYQQQogJQy+CQgghhBBCCDFh6EVQCCGEEEIIISYMvQgKIYQQQgghxIShF0EhhBBC\nCCGEmDD0IiiEEEIIIYQQE4ZeBIUQQgghhBBiwtCLoBBCCCGEEEJMGHoRFEIIIYQQQogJQy+CQggh\nhBBCCDFh6EVQCCGEEEIIISYMvQgKIYQQQgghxIShF0EhhBBCCCGEmDD0IiiEEEIIIYQQE4ZeBIUQ\nQgghhBBiwtCLoBBCCCGEEEJMGHoRFEIIIYQQQogJQy+CQgghhBBCCDFh6EVQCCGEEEIIISYMvQgK\nIYQQQgghxIShF0EhhBBCCCGEmDD0IiiEEEIIIYQQE4ZeBIUQQgghhBBiwtCLoBBCCCGEEEJMGHoR\nFEIIIYQQQogJQy+CQgghhBBCCDFh6EVQCCGEEEIIISYMvQgKIYQQQgghxIShF0EhhBBCCCGEmDD0\nIiiEEEIIIYQQE4ZeBIUQQgghhBBiwtCLoBBCCCGEEEJMGHoRFEIIIYQQQogJQy+CQgghhBBCCDFh\n6EVQCCGEEEIIISYMvQgKIYQQQgghxIShF0EhhBBCCCGEmDD0IiiEEEIIIYQQE4ZeBPcIzrncOfeY\nXTr3W51zH9iNcwtxtthNmzrbOOc+7pz7vU3+/hHn3GuK8sudc184d7UTYnc5U9svxsJ3b/L3P3LO\nvXd7tRNCbGajzrl7OOe+5Jxbd87d4VzX7UKjutsVON9xzt0VgAfwae/9d5zGfo8E8FXv/ZfOWuXs\nXPcDcJn3/q/P9rmE2C57waa2y/luk97779ntOojJ40K3fe/9M3a7DkJshz1io88EkAPY773vnoPz\nXdBoRfDUPBPA/wVwb+fcfU9jv18BcM+zU6WT+EkA33+OziXEdtkLNrVdZJNCnMwk2L4Qe5m9YKP7\nAVynl8CdQSuCm+CcqwN4KoAnI/z68EwAz6W/PwbAywFcDuBrAK703n/AOecB3A3AO5xzTwPwHADX\nAXig9/7TtO9feO9Lxef7AHgNgG8rDv9xAM/x3t86ol6/BOBR3vtvd879EcKkM3POPdV7P+uc+ziA\nzwB4MIA57/29nHM5gMd6799dHOMBAP4dwOXe++udcwcBvA7ADwDIAHwQwPO89ysjzv8MAL8K4L97\n768+zWYVE8xesKni8x0B/C6CDdURbOU53vuvFn8fa08AfhEn22QdYaB8LIBLAXwVwEu99+8v9v84\ngH8CcCcAjwRwHMDTAdwFwMsAzAL4Q+/9L1A7jj1eQbXoHx4HYBnA73rvf5PO9wXv/fNGtMWDAFwF\n4L4A+gDeC+Bnvfdr6bZCbJU9ZPvfB+DXANwVQBfAh4t9l2ifKwE8H8Eu34owVubOubcCOOy9f4Rz\n7qkAXgXgZxHs6WIAfwvgx7z3y2fShkKcTfaCjTrn3gXgR4rv2wAcgE8AeBOAJyC8IP6Ac+5ShDnt\nQwHMAPgkgJ/x3n+52Pf+AP4EwBUAPg3gtQDeDeCI9/7Ymbfi3kMrgpvzIwB6AD4K4I8BPMk5Nw0M\nH6I/BfBiAPsQOvp3O+fu7L13xf5P9N5vdVXg3QA+jzBYXFH8/5ujNvTev3Jj0CqkKP8A4A+897O0\n2RMBvATAvbd4/jcBOAzgWwDcvfj36nQj59z3FPV6hF4CxRlw3ttUwRuLet4R4UVrCcFGTskYm3wF\nwgve9xfX9kfFtV1Buz4dYVJ5pKj3HwO4B4JNPgfAC51z7jSO9wSEAfIiAP8bwK875753s7oXg+ff\nAPiLoh73R3ghvGor1y7EJpz3tu+cqxX7vqGox90R7OAltMtDAKwh9A2PRrDN7xpTj4MIP67eF8GO\n74ww+RXifOS8t1Hv/eOKenzIe9/03t9QbPbjAH4UwCOKz+8FUEMYQy8DcBTA+51zZedcA8CHAPwr\ngEMAXgjg17dY7wsOrQhuzrMAvM17P3DOfQhAG+FBewvCQ/dJ7/1GEJW3O+d6CEZ0JtwfQM973wOw\n6Jz7IICnbKPuX/De//1WNnTOHQLwQwAe5r1fKL57GoDbJdvdC8CfIRj7v22jbmJy2Ss29cMA4L1v\nAUARAOLNZ1gPIPyy+gve+68Un3/POfdzCAPvbxTf/bv3/mPF+T6EMKC9wnvfds69r9hmw39jK8f7\ngvf+bUX5L51zn0KYlH54k3o+AcBN3vuNQDNfd869EsA7EF4mhThT9oLtNwFMAVjx3mcAbnPOfW9R\n3uCY9/53ivKHnXNHESRxfzfieHUALytWAJedc28A8EoATzuzyxLirLIXbHQcf++9/zwAFJLW/wbg\nrjSnfQmAGwA8AOEF8SIAr/TerwP4V+fc2wD88jbOv2fRi+AYnHN3R1hSfi4AeO/7zrm3A3gGglFc\ngbD0PcR7/65tnPKhAH65OG8dQAXATds43nWn3mTI5Qirw8N9vPdfBPBF2uYiBLnoB8/XABji/GaP\n2dR9AVxVBH1pIthH7Uwq4Zw7AOAAgNSJ/hqEa97gRiqvA2h57xcBwHu/XiwGNk/jeF9M/n4tgNuf\norp3C1V27eT7qnPuiPf+tlPsL8RJ7BXb996vOOdeBuBPnHMvQlgZ+TMAn6XN0rG1hdBHjKLnveft\nrwNwwDk3tfEjkxDnA3vFRjeB63YXAF3v/TUbX3jvv+6c6yJcRw/AAOHFcINPbePcexpJQ8fzrOL/\nTznnVp1zqwgG8qBiZSzD9tqvslFwzt0NYRn7/QBu571vAvilbRwbCL4NWzo/wrUAm1/PAxD8G37U\nOffA7VRMTCx7wqacc/sQ5JFfAnBFse+Tt3ruETQ2+VtO5Sz5W/p5u8crIfzCuxktAP9USG74X1Uv\ngWIb7AnbBwDv/a8iyD5fjyAN/Xfn3LNpk3zkjqMpOedK/PkMjiHEuWDP2OgYeM7bgNkaU0KwvTKA\nvvd+s/FyYtCL4Aicc02EJeqXALgf/bsXgP9A+IXkWgQnVd7vWYXBpGz88jdN3/Ev9/dHMJKrKDgL\n+yvtBO1Nzn89ghEMr8c5d2/n3DNpmw97738SwXfi7c65mR2un7iA2WM2dQ8EH4jf9N4fH7PvZvaU\nchTACshf1zlXLs5zJn62Wz3e3ZP9vgXxquMorgbwrYWv1Max9xWrkEKcNnvM9uGcO+y9v9V7/0fe\n+x9CCBzz7FPtN4YqQgCoDS5HkJae6gcZIc4Ze81Gt8C1AGrkU7/x8llDGOOOAmg45y6jfbacKuNC\nQ9LQ0TwWQerx+jS6l3PuDxEigT0UwPOcc48H8B4Ef57XIhgOECaKdy1WF24DsAjgR5xzn0TwJ3g8\nHfY6BKN4kHPuswB+AsGp/IBzbrrQMG9GC8Dlzrn9CBPEUXgAj3TO/TlC8IvhS573fqHwgXqZc+5z\nCMvmv4NgTH9YbDYo/n8xgP9VXKtyJomtspds6usIP4z8D+fcLQAeheBvAOfc7bz3N2ETeypIbfKP\nAfy8c+5jCC9jz0cIJPHnm7baCLz3mXNuK8f7NufcoxF+df0+AA/Eqf383oEQEfgq59zLEe7ZGxF+\nRX3k6dZVCOwh23chYu5HnHOPAPCPAOaK43/1DK+9B+ClzrnnA5gH8FMIKyFCnE/sGRvdIp9GCERz\nlQvRe8sIwW3+C+HFdgbACQBXOudeiPCj6uNHH+rCRyuCo3kWgHeNCfH8DgQ98/0QJoivQAjN/nKE\ncPLXFtu9HsHx9MOFo/lPIUyklgH8FsJkCwDgvf9U8d37EQzkEoSQ74uINcwAQihd59xn6Ks3A/gf\nxb6Hx1zT8xGMcRFhsviq5O8/gbAyeDWArxTH+rn0IMUvmU8C8GQXEogKsRX2jE15728G8IJi/1sR\nIgI+GmFg+WLxK+Kp7Cm1yV9AkFZ/DMA3EYK2PNR7/41NW208WzneWxCC3iwgtN3zTxXkyYcQ+Y8A\n8CCEX02/gBAxVcEtxJmyl2z/XwC8COHHj1WE8XAA4KQ0K1ukg2Cjn0P4YfV6hAiFQpxP7Bkb3QqF\n5POHEBa7rgHwZQTp6Pd573Pv/SrCmP4IhFRNr0BIxwRMoES0lOeSqgshhBBC7BTFSsTv+TitkxDi\nPMA5VwFQ8t73i88/BuD/995PnNuTpKFCCCGEEEKISeGLAD5eyLYPAPhpAB/YfJcLE0lDhRBCCCGE\nEJPC4xCC33wTwH8i+AE/d1drtEtIGiqEEEIIIYQQE4ZWBIUQQgghhBBiwtCLIOGcu94594Ki/EdF\nSoVzcd6HOedy59y4iJ9n+/zD695km19yzn28yFd2znDOvdY593/P5TnFzjGpNnW2Ka7tMZv8ve2c\ne1RR/rhz7vdO8/g/5pz7yrnOFyp7v3DYi7Zf1HlshFDnnC98ik7nmBo7xZ7gfLVZ59zjnHPfdM59\n8SzX4+7OuaNjciNu57h3Lo57XuYq3BPBYpxz1wO4HSyXXR8hJOxrvfdvPRvn9N5vOUdekUbhq977\nL52Nupzt42/h/A8G8PMA7l3kMKsBeDVCaPp9AD4D4H977788Zv/7AvgNAA8ovnongJ/z3ndom/8O\n4E8BrHvv2QhfBOCzzrnneO9fv8OXNrFMuk1tl/O9ft775pnu65y7C4DfB/Aw7/2ac64E4JcQEg5f\njJBS4gXe+0+O2f/OCP3D/0TITfVBAM8pUlNsbPN8AD8L4AiC0/7PFMeTvZ9lZPtnjvfenXorQ2On\n2Alks7gSwF8ipLk4KzjnqgDeBeCV3vsv0PfPAPAaAG/13m+aRqZoh5cBuCtCft/f9t6/0Xu/8YL9\n5865e3nv187WdZwJe2lF8ErvfbOY4BxEyGHyB865x+5utQCE/CP33MPHPxWvAvBGylH2CoSk8t8N\n4A4ISTo/6JxrpDu6kFD7wwid1uUA7gvgPqC8a8Wk8B0IuV4iigHvlQB+2Tk3vYPXJCbbprbL+V6/\n7fDLAD7ivf9s8fmZAJ6D4Fx/MYB3I9j7kXTHIiT3BxDGlnsBuAtCDqo30zZPR3gJfBRCjsV3AXiF\nc64sez9nyPbPDRo7xU4xyTa7H+FF82wGNXkyQrv+wcYXzrn3AHgqRuQ2THHOfStCTuGrEMa15wF4\nnXPuu4tN3gagDeDZO1rrHWBPrAimeO+7AN7nnHsfgMcWN+BhADyAH0N4G78FIeny0xA63BsR3vT/\nFACcc1MA3oCQdHIdlkwSxd/fCuCw9/4RxefHIBje5QC+hmCUH3DOeQB3A/AO59zTvPff75y7FMDv\nIPwiPgvgnwA8z3t/TXGs+wP4QwD3QPh1/S3JuT8C4Ive+58dc/wcIdn7zwD4EMLD9/cAjnjvjxXH\neEFxzjsXn+8N4HcRfllcAPAG732aBHvj/G8DcAXCgHVXAA9BMAYU8pZnFce+uvjuFxGShz4cwPuS\nwz0Y4Vf/F3rv1wGcKOr2Eefci7z3vWK7b0cwnDuNqNKfA3gdgMeDJpRi55gkmyo+Pwyhw74HQuf8\n18Xx1oq/jbWnTer3OgAPBTAD4JMIq1xfLvbPEQaaZwP4NoRVsMciJJd+EoA1hITvf1Fsv+nxCm7n\nnPs7AN+JkKj6+d77j9L5Huu9fzcSnHNPA/B8BBu/DSHX2auLvx0q6vPdtMuzAfwuvRj+tnPuucV2\nr00PD+BbATzGe39bccznArjFOXeJ9/5WAC8G8Cve+/8o9vnN4t8GsvdzyATa/s8jRAe8BCGZ9JsB\nvJwmmdPFGPjDCH3DL3jv31Lsez0KeymuqYlge88AUCva4MXe+7xYzdPYKXacSbJZ59ytCD9AvqpY\nnftehCT0P4Xww8prvPdXuSC7/C0A90ZYOX0/wph5ojjmUxB+RJkH8F6EKKFPoFX0nwbwpqJtN/g8\ngB8F8LenuicIfcAnvPfvKj7/nXPunQh9zUcLRcAfICgEXr2F450z9tKK4CgqCEvkQLj51yHILW5B\n+AX7eQAeA2AO4RfoNzrnNiQWVyJMsh4A4O4A7o/wsJ1E8dD+KcIEZh/CBPLdzrk7k1Tkid777y/K\nfwmghTApuhTBAN9bHKsM4D0APo3wq8FTkYSs9d5/z8agNeb4APBEBCN7zinaCMWvgR8C8AmEgeUR\nAH7BOfeEEdu+omiTRxSDz/cC+Jr3/rpikysQcq5sTOLgvW8jTGwfOOL0Jfq3wQKCMV5R7P/ajQn3\nKLz3AwD/gHhyKs4OF7xNFQPgXwL4C4Rn+f4ItvTirTTQmPq9F2EieA8AlwE4CuD9LvYL+mmEQfpy\nhEnoPwD4RwAXIQxav0PbbvV4LwJwqNj/fc65g5vV3Tn3Awgvb89DsMHHA3iJc+5Hik3+F4AugH8u\ntm8iPAf/kRzqMxhv70A8tiwDyADczzl3OwS7LzvnPuucW3LOfcw5d/eNjWXvu8Yk2P6DAfwqgEd5\n76cBfB/CRJnH1p8C8EcIdvUGAL/vnBuXFP7hCD+mXFoc67kIP/gAGjvF2eeCt1nv/SUIK3JX+lia\n/cMIq5C/7oJv4ccQftC9pLim+6P4obKo/x8jyDYPI9jEz9H1XYyw4h698HnvX+6LhPNb4IE49Tj5\ndwDu4Jw7LYn52WZPrggWk5OHI/yS8WiEhm4g6HH7xTbPQvgV+/PFbn/tnPsAwkP3aYRf49/kvf9a\nsf2VAJ4+5pQ/DuCT3vuNZJNvd871APTSDYsH7oEAftB7v1x89wIAC4UBVgDcGeGXmRaALzvn3oSg\nQT4d3ue9v7E4/qm2/V6EAejXCrnI55xzjwawmNT9KQht8GDv/fHi6/sA+BxttiEHi/ZFGKBGOfr+\nM6RWgrQAACAASURBVMKvrr/hnHsxgGkE6VmGMNBulc8h3DNxFpgkm/Let5xzd0DwqckA3FSsrI2a\njJ2S4pf//wbgrt77heK7lyAMXg8A8G/Fpn++MSl0zv0LAOe9f2fx+a8A/FQx4bxii8d7p/f+34u/\n/yrCL40PQzHgjuFZAN7mvf+H4vO/OufeAuAnEAbn+4QmGv4qehDhpW6UvV8+4vi++Perxa+3PYRf\nmnsI9n77YrunIExQlgC8HsAHnHP3pPPK3s8Rk2T7CBKzHMAJAPDef9E5d6eiH9jgb7z3nyjO9WcA\nXlqc4ws4mSXv/W8V5U865/4awCMB/Ak0doqzxITZ7DjevjFPdc49CcGufr2w5eucc7+F8CPO04u2\nusF7/6Zi3zc5554Ms7t7I/zg8jmcOUdwatv+IoL93hthnDwv2Esvgq9yzr2yKHcRGvEp3vsPOuce\nCODmZEn3bgBeWaxwbVAG8DdF+fYArt34g/d+oViCHsUVCL+0gLZ/15ht71b8f0PygpYhPPw5gK43\nnwEgPByny3Wn3mTIFQBu8eRg7r3/+2SbhyC8MD7be389fX8IwM30eUM+w79Sjvq8cZ4l59wjEJbs\nv4Hwy9AvIqxEnNSJbMIx2EAqdoZJtqlHAfh559wVCANTFUG+cibcpTj/NRtfeO+/7pzrIlznxovb\njbTPOoCbks9AkJpt9XhfpL+vOueOwl60xnE3AA93QR66QQk2KB1CGLw2OF177zvnfhhBjnZ1caxf\nQ/iFukf7vdp7fy0AOOd+DqFv+A7YPZC9n10m1fY/hqCO8c65fwTwUYSVDrZFrlur+H9c8KV0Mncd\nwo8xgMZOsbNMqs2Og+tzFwBfSX7QuQZBlnoxwqrk15L9PwXgB4ryIQQ56YkzqMcGOU5h24U8dAHn\nmT3upRfBK33hxzKGbvK5hRBd6y2jNkb49aSSfDdOKptt8reUVrH9TCHLiHDOPXHEsc5Eoptebwpf\n21bq/xCE5fyXOefes/FLTgE76B4t/j8EgDuNwwD+ddSBvfefAvA/Nj475+5U1O8bo7YfwygjE9tj\nIm3KOfddCD4JTwfwDu992zn3BgQZ5jjS62IaGP1slhDbTpb8Pf283eOVEHyaNqOFoAz45U224XMs\nIAyQ6QrEYcT2P8R77xFkcgAAFyIlvh7B3jf2WaDtb3LO9REksFwH2fvZYyJtv/gx9NHOuXsirKb8\nCIBfcs49zHv/6WKz0wlIkV5zaqMaO8VOMZE2uwl8vScFWyLy4vhp+6TjZ+63F4zmKLY2Tp539rjX\nfQQ342oA9+MvnHN3dCGqHRB+Abwj/e0ijNFHI/xqEv204Zx7lhuda+RqhHa9D21bciGk+sZ5q845\nnvTc+5RXszkbv1pyZLArqHwtQmCJoZ+Dc+77nXMPp21eheB3eBvCpG2D44gf7usQfmHc0JnDhVxj\n34oQ0CLCOddwzj3ZhSAUGzwcwLXe+5vT7TfhSFE3sXtcKDb1nQBu9N6/ufDRAULAhQ1OZU8p1wKo\nOfr50zl3NwQfv6s32W+7x7s7/X0OwdeQVx1HMeoeXuacqxcfI3svJs7/hdjeSwjSn3HpIx7nnGPZ\n6Ibf4WcRJrDLCAFzNra/PcKPkhyZTfZ+fnFB2L5zruqc2++9/5L3/iqEVejPIEiVz4S7JJ8vh9mg\nxk6xm1wQNrtFrgVwTxf70N8LYYXvaPEvdWXgnH7Hizrt20Yd/g1k2wXfCbLton4HcZ7Z44X8Ivj7\nAJ7mnPvuovN/EEKH/4PF3z8I4CddSPQ4h/AiNO7X9DcD+A7n3OOdczUXEjW/FjZhbAO4q3Nunw95\nVD4O4DXFBKuJ4GPwL0X5UwgP3Uucc1OFIT31FNcyPP6Yv1+L4DD8WOdcxTn3UADfQ3//EMKD9yvO\nuRnn3D2Ka+IBZlBoy58E4JEuaK6BEDVpaKTF0vsbAFzpnLtr0XZXFXX4WwBwzr2qWGEBwgTwZQB+\nrRjY7oPg5zAyYukm3Bvb02+L7XOh2NR1AC5yzt3NOXfAOfdrCL/QXVIMkqeyp6h+CP4WnwdwlXNu\nn3PuAIJN/BdOdh7fCls93hOcc/cuXuKuRBj0Usl3yu8D+AHn3BOKdr8nguP8RtCpzwO4G70Ybuzz\nXOfct7sQaOdFCL/Abvg3Ps85937a/pkAfsc5N+fCCsZvIeS7ahV9zBsAvMg5dz/n3DxCBLXPA/h3\nOobs/fziQrH9FwL4hAu5MoEQafMyhAiCZ8Lh4vmvuxCI5uEwH12NnWI3uVBsdiu8E+EF60WFLd4V\nwWf+rYXdfQxhXHtC8fcfR/gBZoMN/9/TeiF1zn3FObcxN/hDAA92zj2xsNfvQfDf/D3a5Z4Iq7Dn\nlT1eyC+Cf4Lgm/ImACsA3grgpd77jRDNVyK8wf8ngK8gTEKuPfkwQOFs+yiEULXLCCF0H7vh44Kw\ngvbLCDl/gBAZ8Hhx3FtR+N9579vFCsQPIsg9NkJXRx27c+4jzjl2nE2Pn9bvGMJD/8Kifs8FhWMv\ndOPfhfAr/jGEF8PXee/fNuJYX0WILvX7xSTuwwCucPZrDop2+ADCLx3fRNCE/yDJAC5FCFeMYqn9\nMQi/Di0gdD6vJqddOOfazrk2QtLqe258ds49pPh7uWjDj466fnHOuFBs6j0IufA+gzBZuxUhUuBB\nAP96KntK61c84z+EsKp1DUJOry6A7zsTqclpHO81RT2Wiu0fTSuc4479CYSAMf8H4R5+ECGa2uuK\nTf4W4SXvwbTPmxFe5v4SwRn+hwA8nOTjhxGvjDwNwafqZoTn4a8Q7u8GLwXwZwA+gtD28wB+YMO/\nQ/Z+XnKh2P5vI/zw8c/OuRZCJO33ILygnQmfQHiZvAXB9+p3vfd/VvxNY6fYTS4Umz0lPgRO/EGE\nselYUY/3IIzh8N7/I4AXILwc34qgAHoTCnmoD2mN/gvA/0d1eAjZ10MQgrltfB5uhjB+bbhEPArA\nSxB+lP19AE/33v8zbf9dCGqk8yZQDACU8vxs5mcUFwLOuU8gTJBftEvnfzzCRPVyH1JaCCHOEs65\nPwbQ9N7/6C6dX/YuzntckmdtzDYaO4U4D3DONTwFTHTOvRHAZb5IeeGc+wmEF927eMvRuZPnLyEE\nxXnzKXw9zzkX8oqg2DmuBPAMF2u6zwmFRO2lAP6PBjIhzgmvAPB9LqTFOKfI3sUFhsZOIXYZF9JF\nrTrnnuKcKzvnvh1htf2vaLM/RVC8POssVeNJAKZw5uqDs4ZeBMUp8d5/EkFS83YXO+OeC34dwFe9\n968/5ZZCiG1TyH2eC+CdzrnpU22/w8jexQWDxk4hdp9COvp4BKnoCoJs9HUIfn0b2/QBPA4hcv63\njjrOmUK+8j/qvV/byWPvBJKGCiGEEEIIIcSEoRVBIYQQQgghhJgw9CIohBBCCCGEEBNGdbdOvJpn\nQ01qieWpiVS1jNKwXMJoxm1TQnysSAY7ThFbonfj5IR5lg3Lg0F/WO4P+HsrZ1kceCgfDGy7/mDk\nPmvr6yPLKMfv7DfddOuwfM1XLeLv1VdbvulLLrHcoPe9b5we5Tu+w/JnHziwf1iu0HlyaqTS2NY/\nR/C9K22tLuVyeZcrvbfps5Xk9rwiG0Tblcd8ytkut3jPzivG9hGjN8rzLN6sVBmW2d6XTiwOy//y\nL58Ylj/+iY9E+1/tvzgs9zoWsbpWtW67XqkNy3P7jkT7X3J7y+jw7Q/6n/b9pcMcwti/78CwfODg\nvNW3G8eWOP5Ny1P/hf/41LB8/bVfGZZv/sb1w/LCsl0jAKy37HidbndY/ugnrt6DD8b5wZ2uumb4\n8FXo+zKS57Bs7Z2X7HnNcrPVQW5pI/OcpwXp7aHnfYytg+ygWorrUoXZQa082nb61Nfnpbh3yaPe\nhsp5aeT3+WbjVqlP21kblakuFapLOekPylxP+j6LphnU3ul8ZEzfUYrGXfo+mr/EfTC3OZdzejIy\nKudJrx1PwWz/r//Kd8o+t8HU3P6Ro0g6HvLncnn0+gxvw3PZLEvHnVOPu5uNx3zsrZQ3u5bTHffT\naxlXr+h8iB7e6G/cF26lLpttE/U8bK9R2bYqJX0Xz1vzzP6WjbmXWTlti9HXv7y2um0b1YqgEEII\nIYQQQkwYehEUQgghhBBCiAlj16ShFV4+jiRk8fIn/23cW2tpTPlkbSfLYqzM0sx+r2PlvklHAKDX\nM6lnt2Pbdbq2Xa9r2/T7tg0AZH37G0vFMpLaHVs4PiwfP75gVU+u5cTy6rC8vGjbcb3W10yOtbS0\nHO2/3jKp2fy8nb9aMfnIrstBmb0oLdzjRPbG7Z9IV0r5aAuMnp+9ePu2VOfxG7HMhG38xAmzxQWy\n96WlWE7J/U8+TqdKp88SuVi/3x1ZZulXucJSFjtYj+SbALC+Zv1Np9Maedzo/Ek/zirtSkW/P+4E\n1bKNJzVq7zri56BUsr5+QM9RL5JQGixIypMpQs7yJnCZjkUHy5J+e0Cf+THI89HjYSoNzRIR7Ki6\nxJJRK55kqSW7Nr7mjKWVdDGVRPo9TqqWjVGq5YlkN/pc5rbkbbgxSY6XJzZE7VeK+oFx8tnxkt+9\n2Vmfn2xVJrkV2eJWI/xv5ZybSTtP9xznirEy1+hT0kb5mH3G1n+z6xotBR+/dVoXKo6R2WaRXHzs\n7juORmQhhBBCCCGEmDD0IiiEEEIIIYQQE8buSUMjOah9n8oRxwg+xm6Tj5F/AkDGElCK+tnpmLxp\nnSJ1tkg+Gf5mn9stk0e123Sstkkze4k0NB+YlCej8+eZnf+WW2+x8i1W7vViuU9GqtVuj45FkUk7\nFGlweWkp2n9lZWVYPrB/37DcqFv0uD0Z6VHsGIkAlIqpJW4mOdr86wuLNHqalbm/Ob5wbFj+5lGL\n/nvsuH0PxNE1K6UxejOwxD2OUtzrWR/VIzlnTp1HqcLRHq3Ybtv2ALBCctY2RQDtkZQ+i6LJppEr\nrVyuTMTDcNZplOw+NKh9p0qpNJSeA7pH7YykyyR7HFCkzwyN6FgZ3cicpJVRRDySYw2SMSQnWTnH\ndOZHus+RTU+ShrIclKNgjo4czpE9y6m4iqXbuUXfjSJwUnudLC0lifW4Di7bmtSMDYRlthnLb0ss\nDU3ucW42Xc7GzJSiy0/6qtFditgm42SaW51bbVUOerpsdtydlKluty7jGFvHHW+u0e8T4yKVbhbB\nlGWqsTSU+yGWn46PMrzTaEVQCCGEEEIIISYMvQgKIYQQQgghxIShF0EhhBBCCCGEmDB2zUcwegPN\nx+tqS5ELy2h/FtbO9slHrtuJQ6Cvkv/f+rr5Tayt2vcrJ8x3bvlE7CN4Yo3CsdN5BgPy0+Foz1ns\nJ1MlpwgulwZWlxJ9Pz3dHJaXjseh5Xtd9l2w23jo0IFheWp6iraJ23WNfH7W18xHcKppPoLVqvlN\ncPh3INV1nwufn9ML3QsA5ZN82cQZk4+2vS2zzUdku+Gut82Ya843CffMDMhHcJF8BBcWrby6eiLa\np9M1/7tG1Ww8r3B4eypncX/X71q/0u1aH8dpHthGBtx3tuO+r039ZU7+SLGvQxSEHzFxUgKxffbD\n+vAaRo8tAFCv2n1tsU8n+Zazz9mA0lL0y7GPYI/88galOpVtrMgpBRHKnO4hTj/Ri3z0bJt+Xh65\nPZD4KGK0kyGneShl7DsX+9CiRKmS+LhUmUrZ9q/ksX1V89E+giWe3VTJ3y9JPzEg/8M+p7Mqk61z\nu1J9s8SESlH6DbvOCtldifvQ9ACb+TaJM6ZMPrGbp4+wctyn2vc8n+RjldN0TltIGbHZ9qc7vm7m\n48d/G9cWUVyPLB03oorZsUqjbR/lxK8ui31pRx92dF1O+pyPHnf5+zheSZrYgm8m3TNy0OX+7uSW\nOHvzHs2UhRBCCCGEEGLC0IugEEIIIYQQQkwY54c0dLMlcyrzoi1LrTjU+crK2rC8dGINzPKKyWLa\nHZNPdLpW7lL6h6XVeFl5eZ2WbTNrOlZZZJHCIl5mnqqT7LNm9a+RFKZWs+POzE4Py4N+XJe1Ndu/\n1bFF5A6lmSiTNLbbNmkYAKydOD4sLy/O2PlJgjY3b5LReo3CawOb3rOzTqpEUDT6s09p7If4fpyl\ne7EXUpmkVeT0LYskAb3xG9cPywsLtw3LvV4sPeN0NzlJXqJI7yRL6ffj/Tvt1WF5ZXmRvqe+gDpi\nPhb3rwDQp7pxXzTo23Y9KveT/qpHMsRuL5HoiTPi4GBhWM5hMk0kcs4qyR67AxvfKh0bH6ug72v2\n3JbK5p4AAF0aq7q5nadLaSZ6mdUlJ/koAORlGkdYlkxCqJzKafoITtnAlsASyBpJOOu5XUs9i1Oi\nVCLZKMlkSVpZza1daok0tEZpOriNy5RWo1yhtqjE92VQs7+1Ye3SIjtcJ6lcJ6c5R9rRcmqJKK0F\np6iIdoj3HyOzFdvjTFJG8GapS85WjruZ1HEr+7OEM5KpZpxWZjzbGalTmWucYsWOXCHJObs95dkm\nckz+PpLfji6Hz9nIv3Gatakp6yNnZ+aG5XQMXFuz/md9zcbgQXYOJlCnQCuCQgghhBBCCDFh6EVQ\nCCGEEEIIISaMXZOGnskCaEZR7VoU1e7YMZPI3HZsycoLJo0CUmknR+2xIr8Zr/ViOeTqgJbcB6Oj\ngWWbRECqVG3/qWg5nqIhVey4jaZJSfYdiG9VXjH5SmvJ2mKNJGC8rN3tJlEA1yzi3IkFk4nGkTat\nXjMzM2BqtDS+1chYp82YdkUSjQlj5BOSjG6PbLQqY0eb9STpCktWdvA848651ed1nFgqy/hY8VYc\nBfTmW74xLF9/w7XD8vHjJhntJ3JM7j/Gl237QSINbbdJJr9kNr6+bv1iRlHVchoOOIIoEEtD+ySr\n73bt+3bHZHSddlIXlqlLGrojzPVtrOti1spJpM7IjYHkSjnd0xo9u9O0ey0RgfXJKtskoWyTtLRF\nktE8t8jVAICyfc7oeetHUStJelxKoo5Gfb/tU6eIuVN9e76nejbO1XtxVN56n6SxXCYJaY2loYif\n6TpLQ2kMrJD8tVojaW1zf7R/VrfPvbq5YSwPrP2Wu3b9Kz0rdxG3S59uUxZF2KY5C43t2SYKPAlD\nzw6bRdcsjZsPjpvb7CC74XaxWVtERPOB0ZFSuYkSZSgy+mLcOTeLWsqf+V7MzFp/e/HFR4blSy6+\nbFheXY1d02699eiw3KGxst+Nx/3xKGqoEEIIIYQQQogdQi+CQgghhBBCCDFhnBfS0E3yyUdyK04C\nfxvJQa/9ui25Hl+2JdeVVryU2h1YFM563d6BmzVKSE+ykCxZlo+SznLiSpKMjksuCUS5ZVGvUAJZ\nlk39P/beY0m2LEvPW0e7DHllZmXJRgFVRoKEGUkzmpFTDjjigO9JNgcY4BFIA2GNbnSjukSiUlwV\nN5SHq6M5yE5f39rhJ+pm3CjeAmL9o+0eR5+tPPa3/tUgmS9cA5PcOo7lY8VPsi2Wzzd6/2lGN1KL\nufLWyo0iM+/f6jL1Ei5HJydPzP7HJye78nSm2GiS/Onl6ztRBCbWbbFkD0fCKLVYTNzDQer/B5Ti\nsagEFpHinYUJq4dk2rLBMe/Yh2jGByAr98FaPsRV7dZ1DbgBG6yktVjJ+3N1BP3DH363K7/69utd\nebFQlLttLDJJ7LQFit6gLaTJcJJpJphfr2525c1GkZUamGaHPqIN8M3tWl3O1hstb9ZwYt7oPuu1\nRdGJvTp69jDq4GLZ4qluIzusb+oYf9MxsB6h7sRw94Rz9Chw7YwQuhCjwsVgE+MG7pyhI2Gn54QJ\npkmI3gO5pBupiEiLcZfnH7dav2cbbV/TpZbHtWLYIiIFcdBW62sC5DUTuHuLxaXzmO6cuBc+swTj\n9OxQqCx+ptcy/+muXCXHu/J1pe/rYq3Xcmmbl1yhUa3gQNokwIKj/c9OxCKkrofTUBL4uzQUavOh\nDqR3JmXfs/+tMZBj2geMjx+akP6DksgPm+GavsTsb3K+2+vlGDrkAMrrCp8dt0tTbVfPnmvb/dWv\n/8Wu/PLl57vyq1dvzLFugIrGGGujZn+oR9gm/5xt1FcEXS6Xy+VyuVwul+uRyX8Iulwul8vlcrlc\nLtcj0ydDQymzknxrlVq/WAF7fH+t+MnVRnGTdatuPnVsccgGLEoBrCOJFbGpDeY5jIYaF0WTAJRo\nqN2/AH2TJbrdYqVo5gaYZo918cNDRTFFRGIgmDFQSTqNjiZ6v9nIIjZ0+1svFavpRLGv80t1pVut\nrANSWem7ePHi5a48maorXJrur163aYP9SUsrOMNWRF4Ley+8tzTTc/5nkIP8L1o3wPsmhbalOM+C\nLYlHwvEPqGRs2shA4xGLctDBNqJLGHe4CzMd+NCZJOwW9zLebQYZ0e/pqEm0stzahNWvXr3alb/6\n6qtdeXGtToYV3MP6W1jK3ss3ZaI7tx+FfsMk8AZ/MSj2/vsKP/O6UiTFLkaKsdHVWUSkqtGuG3cN\nfRCNDnbFJtFxb5tYp84t0PkaY1CLkIgOY2XLBPRBpUpNX013Ty1HHAMzOwYkKcMriHqxre13uvzu\n/Bj3iHButE2lCw0VSRbq1pu0Gk4iIhK32l5jYLa8x4zhHEFfhegSE5JgLpnH3dq+Jr3W6096xT4L\nOIgexvpeR6Lta15oWUTkINPP7xp95jfYZkucLrbX0vV4L74+8GfRXW7VH4KADiGXt5Og/2lX7LvQ\nUjtu7rcOv2t/8xdsFxPt5Faccgc/AMxng4NqHzOCg31R2L4vQ+hEjr6IoVIJtlks2GLsvPfwUPvb\n42PFt/m74P17dedeLKxLMd9LhmupKu0HWvSptyewjoa6XC6Xy+VyuVwul+uB5D8EXS6Xy+VyuVwu\nl+uR6S8CDbWyS8PElTZIZLzY6NJwDXwiHmsC16IPsJRa8Ys8VgQygTNYZVx77O9kujmZy4y507C7\nYgEUJ430Xm6uFcG8WerSMhPQH871HkVEkkSXw+kOOgWaOZ4wsa3F+dYrvf+m0v3p1HkNR8CbpSbp\nFbFoaIIExienirBOJoqrEO0Lnaj4jokWlgs9Z4nrTQok6RWRYq6upSO8/8nEbuf6YbrBMxfRZxnk\nq5YeLpZbOPtut1pHCtS/nJhpYttog/cfI5l0gu0yXECcBm3U4Cf7ncWISW7KwH6v34+ydGgXNVCO\nLXDQmxttxyIib1693pXfvtHyZqP7tHh2oeMZUZIWrooN8JHUuKKF3TmehUFudQuYQEqDhPQV2reI\nSA2ck225GGt/M58rOlPVNvl2ib6XLsmu+6sfK55UI6F8KRYbbCJtbx2dr1Fu0KYaYIJVb3HlFHUv\nBl4YJUiKzCTMqR13IoYL4PwcQhOGZAQJ5WO06RROtNlW+6p4jbEKDrldZ+tdi8+RaH03ZGea7C2L\niPQdQ0X+9P/U+9K2qQ64dY0+QYBYx4WObbORIqOjkXXxnmTqYhjF2lcnrT7va1Bnldj32jApd//n\nQ9Aem4YwzY9N4n5XEvQPTtY+fHB+2JWGkM8wnGvozhgqNXSs0PnahGrEfJZanky1vp8C2RQROcC8\neTbV8mSi41aG8IbXr3WcFhF5+1Yx8+Pjo105xz4XCKG6vkLYRxX2N9oAc8yHtglC0zBOSujY7Gio\ny+VyuVwul8vlcrkeSv5D0OVyuVwul8vlcrkemfyHoMvlcrlcLpfL5XI9Mv1FxAga3jngm/m5j/Vy\n41w5+slMed0Z2N0st0x/g9ie5kaZ/PJGv696ph+w8Q15BhtuAXCPmIoIv62L3P7OziNlgbsGsUWL\ny115sXiv+xd6LX2nKRpERPJcueiDA30WPXjrnBh3B/ZYRBY3am1bIBaRNtiLK91muWS8mMgGsWC0\nzX/+4rNd+eT0qZ5jpNe73Vr7+NUa8YqIm0gQZxRXev3r3lpfj2bKfo/BhP/yr34mrvtrY+JA0UYb\nG+uyWWlMDuPiri403vXk9HRXPgRrPx5rDIyIyBbvORJYPyMmdoR0IdOxtYvOR9pmGd7UIo6iRL26\nWdqUD23LmD18X2udY/xcidik9craRS8QM7hZqy11j1i82KR/COJ2cC0t4phbWNJLRBvsICYZ99xg\nH8ZaZEwnUOuzqFvb3hlPlaBjmcz0XfTJfFfuYtvfdEgvYALCXPdWk2jb6YTtwLYJxnRyRIx6xo3u\nT+kSBXGnbJOsr5FoPYwYjxrE1SWIEewR/9cjBjfBdaVi989bHR9ypmbAuJWNtH9J5Me7ctfYOPeq\n0nG3QznqtX0zlUYXBfHMMWME9XveP1vkrf+6dxx3EQ+M/qVCu2uQIiOe2NjmHN3o0wwpnBBbnXb6\njq4aezUlrqWOPjLGzLUTY0eZNunO9A0DMX4mLI/z4j7sT6O95WigNt66kggxvrL/+k38eTSc9sic\n0QQAso/Rc4S+GpjyC+0E0lw/PHumcYE//uILs//xofYFM84TxzpnZrxeHIyhZYUUM2jvF5faX6wx\nf6UvwnRi5zYjpD3LkfIiSzGXZkqaMEYQcdwfG2MaylcEXS6Xy+VyuVwul+uRyX8Iulwul8vlcrlc\nLtcj018EGnrXMmfd0DadKIUu507GWi6muvw6GluspK10aXoBOnGzIJoKy3os34qI5FibrmENWyPn\nRN/qb+txET5eLCHD4ppIWblVu+s01XtJeotapVjCbnCdMdCbmJhbYNm+2QB/MZepz+ISVrhtb7E3\nppPY4JpXwAQXSIsxmam1fNNY9IFpQWiFPMd9TXBf29KiocsLvc4e1+xo6MepXOo7b9b6zprKYklv\nXn27K3/7tZavLvT9P3vxYlc+faZW59O5WqKLiJRo71G0Hw0l1nEUpFWZIZVIDJSCNY4pKlYbey8N\n0jkQE623+3FQttemsuhZA7wyK7ReF2PtI+oWfVSA0UU1kBv8yy4Dyp2jjyuC/i4bEatX/GS1ebsr\nn51/ievXe1wsrY121aGNRbDqz7TtFnjK895i9YIUP+Op///xIWQIMoyhSTicmvG131MS6Qze7F23\nIwAAIABJREFUNWDzLjZ0gOldDHYGtDBL7LtmFe+B+FeWIdsVkwCXHvfaXosYKR8w1kYCJCsDvtra\ndELpFu2oxDU32qZJZ7UBtmZBt35vOcFzaQKEjm26AgJbddq/bWu95gbTtXhrU4TEQK/bMcZqIHSj\nDG0woA9Nxggntx9QRDg/bI+h+TC/N6nMgvwNFs0cmlsPX8xQXeZlxXelfDBpMoCWyv6+I7krHRTG\nuiRjSIK28cMDnUMwBEnEYrNDY3tiUiDZ+cTRkWKnqyVDmJByDvNX3nua2vl/RgSV/SjKKfrLKMzT\n9QEpau4rH5FdLpfL5XK5XC6X65HJfwi6XC6Xy+VyuVwu1yPTJ0NDh5yR2tZyCZutIo0VUSksLY9y\n4pgRvrfnoNveNsHSMFCUDOU0s/vnBZy9EkUxSiAfNdwF88TeS10pcnKzUNRqs1GkrDGOjEBBeuu0\nSce3BFhJkgDJAnpTB66hJdDWBs+163Sf6wWQ1drimGmq17+CI+J6TTRUnZUOD9U1MskUeRUR6Xif\nwG+LBGggHJfaAHdY3Og5Vxvrdui6v67O3uzKLdz6lkuLCf/+d7/bld+9OduVt2utY0Q+l3CNHc8t\nTlkbFEXrQoZ6PSoU5QjR0IO5OlfmheKk1q0Q56ttu2jRZhq4e1ZbRdKqjbbjqsT1t/q9iEgELG16\noPX36FSxlmyk59uWtjuu8MxJhaTg68ZoF5ORbVfFSI+X5HovVzeK7/6nr/VYHRx7ry/fmWNt6otd\nue4UM22BpEWp3m8esGcHwNyn8wB5cd1LLbBkAVKVpBbLNf/tJZ4IbKozztf73f1EROKITplEwPa7\n3wbG2VLAHbPHWJNwf1xjFuCUI9S9DIgySeQODqZ9pKgYQyVERDLRvxWRtsm8w3hETjJw0+yM6yrK\nREOBffVix/ASx64SRT03seJoGznR80Vwg+2DNlQD1UsQBpJovxWhDWaBA2pO1O8ObND1wzQ0z/3Q\nfSwlut+l1mKiIt2Aa609FtFEe346hZIzTYAtJpibta2dG1p3VDiCYp8UdqAZ+qussOFYdPFMMt3n\n6ETbyGSic4C2tc/7ZqHjcwmEs8F8hHPmceBC/uyphrGcReroT3fQNIW7Ou6xKOx4bFFRvmPcI51B\nA6y+74njPmwb9RVBl8vlcrlcLpfL5Xpk8h+CLpfL5XK5XC6Xy/XI9OnQUOJZcLRcB4nLLy8UO9yU\nXJrGcjLxixIJnjdBsuiV4k0JMM3jkS7tjoFVJIlFvVLyWbkuLW9aXD/QzipIVr281vOfv1P0ar3B\nOYmAody1ARrawGkTiEmMB8vV47azy/c1MLAK11zjuGWl5c3WJhEnPlICoWOy7g0wzRKOhPOZdYoc\nY2k/zRTtqzo9/w1dVkOnx1qP3QeYguv++s3f/rtdOYXjVYi7bJbaTkYjRZzmcyaRf7orJ7luUwaW\nY+2A+2BtMGd9/9vKuuGeX6lTaQbXWbqJERmVMIHtUNJWoFTJSPGRAq5mTWXr3gSY6pPnz3fl0UTP\nUaK/qltbr+mqyK7HOI7hum4hPkR0gNisNue78pdfarvq4KpWb2zfx890XCM51wE1DOsIsTiTXN51\nb63ffb0rt9Mnu3JiaWlJgFLzvTR4j0TyEyYhD/5XTLfAyPyN32s9GAcOiCM6HyJ0opR2bzkLQyI6\n1Nday1u0vQ7jkdTaptJSx18RkRguv3QnnUy03WbEuwI71h5O3jmRMDp3o39bbywGv8S8px1pX1kl\niqNtgYm2kfabSW7RULorNglc0CPt67oabTJonwk+Zw+crPpxa38S9jv3GHLdRAffm7423J+fgCBi\nbBuN4LSfWRwzA4IZwcU9TvaPjZxLilg0lf3FqNBz0h2TyOT80M4NY7iFp0BIj588xVZ6jnIduoDr\ntfXA3/NM2+56pWPwyYmi2CIiL56/3JUvLrT/+O1vNRzmyy/t/X+vEA01SeTxzM1zTfjug/rCj62j\noS6Xy+VyuVwul8vl+gj5D0GXy+VyuVwul8vlemT6dAnlsZ69vFYXwrevrVvdxULxyiZVNKIHXrbd\nqoNlDrQziyya2W0VG0uAnExHcPcz1FiQ4BkYVopl9gqJLm9iRVTeLS2Kslmoo+JioY6afBajQhG2\n6XiKTQIsxTigAY2s4GwK1KxtLULHZeZtqdvdLBXFXa5X2CZY/geaUAEHrYHqVThuC8RgEzh7zpFU\n/PCITnZwdgUKvF1bTJUIa1M7dvZQujrXtpgDh0zzwA0LyMP0ULGq6Uyxpgx1uQN61gb4A123elZS\nJslFW6gaWy83pdaZFHVxgjY2McikbeMp3ReZ3JUIJlw7cyAeo0ngeIZ+IQbKlb7XurxaaT9QVdr2\nvpPeS2TcE9HGidbeIkloLYejAp3bAtnu4KDa1ra/6IjYdHSexDa4FqJLIiItnr97Ej6MZhGcn4He\nNmLdMZmXmG8lJfkMN88UY1gSvi2DS2uxp/MdXUPF9sc5Qhxy0esfA4PuUdfHiT1/hzGtSrTurrFd\nXmhlnxwqJzvrbJ2cRDrujNEPTGfahxVjHY/pWihice0M+xOtjNAmboKwl4ul3st5jf6l0f61afS4\na7TBNvgXfg+ETlBO4M6YEtML9m/xLhtvoA+mJNmPT99OAt/vL6P+dLJ/DAjRUO6f5zrWTCZInH6g\n7aLIApdhOsgiXMC6k2IMDsYKJm7vkQUggiN/gnF2gnHz9IlFM6NIr63rkB1gpNffNHrcbWfnA1cI\nLVtl+C1R8xlpG5/NDsz+z58rps3nfHysc5tr/H6hg2roQEpZTFTv0WC2QQJ5zqGjyNFQl8vlcrlc\nLpfL5XJ9hPyHoMvlcrlcLpfL5XI9MvkPQZfL5XK5XC6Xy+V6ZPpkMYIdeNcFGNvX33xrtrtaKn8c\nM80A+NsaMYKHU2WX87HlaJtStxPEn6WILxiDXY57y05HkbL7U1hM970+xlGsx12eB/FqjTLKNWzj\nM1jGThCLNQUHLZ2NZerBYUsPa/dGj9siHqOtbFwdtd5qrMLFpcZRXiNNQ1Pb+Ao+2QRpPcpSz8PY\nwwZ2t6u1taZfrdRWu0bs47w62pWLHO+7CuOPcF39h1k0u/60tkjZwXQhRRBXN55pPT1AmojpASzR\nEXhS3xEvFgfxCrvzM9YAcRN1bY9QIf6NdSlFyogC54+CWDbzudf7jMHrR4iViFI97nhsffvzCeKL\nYCVdo8I2eAJNEN8gvbafHjHNHeKpGpQlqPu0Ho+6/dbjHeIrWvQpbZCGxYRX4TyMC+xwX10Qj2XD\nCr2NPoReHmqfuIIF+9I2T+kR09ox/gfjDjMjmNjBoH10wmPJ3nKK+JWks+NO3GifMoq1fhdIKZIx\nRjCIIe5SxEKOtL52Y+03Dmcaj3xyoG3wKNMUGyJ2rGecVI545hztNg36pixHyg32CQMxtOt1kOpm\noff/9SWeC3wRkhXGZljjV/1+y3oRkSxT/4QCngMF7jeP7XtlGp3e1wceTEkwVn6vML0O+1v2nT3T\nP/SMWb/jpGh/eaF1dDbX/uIQsbNpcI2M6ytLpIIQjoGIg41tbDyvre6RIqVjvCHmzGN4DEy17n4n\nxM81+9MmZRnqdR54Bqz1nCuMpxH8P54+1dRORWHPP51yni/YTvuF01Od5/B9x0E+pwqeBQcHGou4\nxfy7RJq27lbMP8oPHMfrLd7lcrlcLpfL5XK5Hpn8h6DL5XK5XC6Xy+VyPTL9RaChXDK9WVmL5bfv\nFFUsmze7cpQAH8mBWb5Uu9dRqku2IiKLG102Ljd63KhXFONwDPv4WLEKEZE8U7vp+VyxxQmQsDHQ\nTmksFrPZ6HmubxRTzUSvfwab+QLL18QCRKy9e1MTIcPpsRTftNZSnLbEm1KPdXmjmO7NmukfzO4G\nlYthkZw1WqXqhrgD0NCNRUMXN2rxS0z16ZFud3ygdr2jQtEdEZEczz8u7Dtz3V8t8Oka6UfiABeb\nHwLhRbvoM30X61LfJZHNLoRDYZEOR3uJmSbBoKG2jVVIJRKBUalx3Ab3FQU+7IauZPYFIKAmdQpw\nlai2xyoKxT8OTvVZdMBaaBFdby06tt3ChrtnmZbi5PgCNJQ3w3fGYvAuVcH/CHGd3If9CO2t2zZk\nV/q9Rdf99euf/3hX/nat7+frrUW9GqRrafAee5RTpJxgyog4qFN1xPQuwKCwTQzMM+ptnc4w1s5i\noFKp7lPgWsogjCBBfR9Pdaz94kcvduXnzxRPP5rr2BAFOF4Cvir+gDoZol45cHPidex3WNfLA4tb\nHxzp/R+fKIL24lox0TcXCJu51PLV2iJwJZ7zIRz4n5wAk0VqnyK175Voq5PbDyemBuhM/2jrAusM\n/8a+PsV0PcKcK47tC4uRPoTpI5iyocackWkZRERapOBaLrQuEnskcpymYZo1jE8IXUhxnaORopXE\nLG8w//xuO62zWa5tmc9lOtU6PkPIlojIaqFz6yVSo8Ux02roPlFQ+W8Wej2vXr/alTeYp56caoP7\n/LPPd+U0tT+v1mu9lqMjnSdx3OZcOMwLwrCnPn7YRuorgi6Xy+VyuVwul8v1yOQ/BF0ul8vlcrlc\nLpfrkemToaHffqPLrG/fvt2V35+9N9u9Axradoo/5DnwkymcOm90WZzORCIi19eKY5ZbLBN3QCuB\nd2WxxSmnYxwPy7ZcMidicXBgl6kP8Xk+U3eiqNbrmsEx7QCOa0Vnr2WL8zSwfCOqZfBNscv3LfAd\nUnc1XAQ3cDBqAzaUmExK9AjOg1UFF0J8n2FZXURkA3fSjkv+uP6nueIDT7AULyIyPlU3uGRu/+a6\nv7Zwmi3w/PPEtqsMaG4PhHJL9zHgLqwjbWcRGdYT4qAxndRw3BB5Jg5NMrLY6nWVW72X0NWt4/5w\n8YwT9DfA44yzaIDYxHA8o5Pf8RPFR2L2IwGe9vYN2+LlrtzgWcRg2vqAuTQGqGzk9g/cA8cNMFPg\nPxHszLh3QpfSgFwJr8318TqdaT1ugCFVgbvlFTDd0hDCCIPA+EBkMgreWyxE1YCw4fsRxpZpbtvE\nYaTt8OlIr/lQb0VyoKHLyI47HF8PDxSv+sUXL/W4GA/GQNDaJsSgiZvDSdeErWgfyH5LRCRBJU8H\n3AKNG2Tg3F2vFDuL4bZ9ChPG+XPFyz8H5nm5VMRWRGSBMI4+0b+lpc6t+vOzXXkbIOG8N3Of//X/\nIq77K3QH/V5R0EEOuYsa92XO51DHsgDNzEd01ATK3TNUCONGEHbEgTPPeSytmKOxoqF5Zl1D2a44\nPhPTPjjUej0a69hY2yZmnhMx187Mv/X6p2M75/5nf/UvduWy1HYR45E9f6ZY+Xhsw44S9Ksvnmsf\nsz3UY+W53v8XX3whQ1oAM+X7PjvTdvnqlf4uuoGbvogYq1De80PIVwRdLpfL5XK5XC6X65HJfwi6\nXC6Xy+VyuVwu1yPTJ0ND//Eff7srv8Ny6Lt3b812lxfqtJNmcCDCNm2uy8cruO6Ey8xXC2CmcE1K\nIiZn19/Go9Qu62dwEe3o7ITley5lZ5l9vKPRCGVFVrZM9AocdFypY1paWRSkKXRpOU2AApFaI7YW\nW3yASdkjIcoCN1EgnCFWw6XpNNXzx0BriaY2cG1MS/tcSriWZlgybya6TF8A03s6gzOriMyeqDts\nfPxCXA8jOoXmqNdJVtgNUf9KJI7fVEiAjPdvEpc31v2uAiLV1/gbMOMGLsN0JRMR6ZhpNSa6BZQG\niAkTRouIJHARbYF6JilwTKKxEdpeY3GfHiRYJ4qPzEeKyJyc/kivMXAsK+G02l0CXdvinmOgZxI8\nC2G/NoSQdnvLUehEF+9HQ2NihMCV+iR4Fjj2AC3l+oE6GOs7aYFk14lFQ5srbS9Jh4TwZrDQYhIT\nEbbn7FGnONZxnzlIsaPAxfkYqNlBou17giaVoB4ngfssXUwPp2hHwET5PcfgtrHtg+2ACF5nkntr\nua5tX2USf7OMa+Sxyq0dw1cLhqpoZzEFQvf0WO8rB463XNvwivMrndtcoXx2oaE2Z+cXu/L10rqx\nLvGZ4778b46Gfoy6IPRhSEQ9SY126GvpGsm2F87t6FRKxJ/XQuI0DrKTJwipmkx0rpXn++evnP+J\nWJw6w9+OjtT5nQnVuX8bIM+8N7qTVjVDTTDQRnpcEZGf/fwXeiw81xZzmwxz6SzAXCdwJP3Vr361\nK6/hZsx+4Rkci8NwqtBF9HvN8SzoRNzf2P07ExLiaKjL5XK5XC6Xy+VyuT5C/kPQ5XK5XC6Xy+Vy\nuR6ZPhka+rd/9x925fVSHfFubq7Mdk0PpyIgL1m+H2spkZS5bBSFEBFZr9WFh86DMFaSgg6cAWIT\nRft/N9PBKII7H3GR77ajMxm+H0iQvUE5aey1bKe6T4WEmBkRrpguUxbVotMR72sL7G6FZfowf+UI\nS9i1QWP3J5ondta1FrGJZL9jm8FsifnZSzFJQHv/38aDaTLRdzybqLPXBGijiEiF5MZlp85Y24pJ\n0IkGAqsOEuvGdK1da/1fItHsAuhTU1tEKgKixqS3tMbNiLZ2tmKnQF7o+LaFgzCdOiPRe68S6wpI\nF026ClZoO1PgXtNDdSUTEfnsx9oWI+A+MBmTutHn0nX2WXRstGhLRNda4Kxttx89Cj8TRe+Y2Ljl\nsUKMDtjoYBJ71w/R85PDXTleaTtaVzaJ+3WnGFPWAT1KtK53Jok83OmCTOsFBssxkKoD9BXPjhQn\nO5kG4REIw1icvdmVtysN6diwTQcccQ2UfIE+gYmo2VcVGOfCrPHG0RH33PT7xyCOmSKB0yPbF8aw\nDPvMD/R9fbeP7n+90PunIyNdDImx5wGONyn08zEcyUsgbL8DJvoPv/m92f/yWp9fFdQf1/1l+1H2\nr32wnZY70w8DGeVEl6h/b/taTq/o4p0C+cyJjwbz2hhzKI6VdO3kvZRl4IYLVJLzzrzQfmE6VRyS\nKOtmY+8lHnCi5hzy8lLn+dXW7v/TL36yK4/hTrpEf/P1N/9pV05SdfQWEfnxTzR04/kLxT5vkJz+\nGvOR+UznRmmA7B7MdZ5unfLhro5n195KKP/nC6/wWbPL5XK5XC6Xy+VyPTL5D0GXy+VyuVwul8vl\nemTyH4Iul8vlcrlcLpfL9cj0yWIE17CQ3yJGrAo4/gYsMMOJGGbSIk1CDYvo0C768kLtmmlTD1da\nSZ4oRzwOrGTDOLudaD0NeLdrbf4Ka5uvZcYILqv98VNRbe2eI1HeOso0piAt8Nu+339dIpZdb/Cc\nNht9LrS0jpPhmKEW12ntfvdbd2epZad5LBMzZgMptRjYeLcbTTHSZQv85Ym4PkKIY6jwzJeLpd0M\njs810q/UrBd8x6iXbAci1m6dbbSt97edcmPtpmtYSa8TrRf5SNv1bK6W7FlmU5FIzC4RFt0V4qZS\nxlPh2msbK8F4W9Z/toscfUwS/FuO97bVW5GuRr/UajkWG3vHMJQW/WiPOMwejz/C7tEd8QlNh3eB\n99Xi+/5WKovhvsh1Pz05OdmV05HWtTS1bWKKePo2Qmx4quNGg3dCZ/Istf3+eKR1dz7W+KFDxAge\njbU8KWxfLxgT3yK26RrXuEZ/ngS7bzeMH2RsDfoE9FUjpoII4pFpp9+alBF63CS8AO6PfZpuf9xr\nZtLu2PnEGLH9DeYwjMWyqXoYL2Y7C9r0Hx9p//bTn/5sV94iFcdiZeMAm/6bXfni6lpcD6PJRNsY\n0wk0wbhnU5MwRpdpurTtcS4aBf1+j3rNWsLY7hipY5Iw/QS2m0w05o0xsoyvXa8xOP3TFel16jUz\nXnaMON7xWMfgqrJt9OBQ6/IBYmzfIxVKXeucr2ntGDw/1PYzQ/xejHDHyYXG4c4QxycicoC2NDvU\nvyXZ/rQWE8RBJkGcPcdQxjRfIY3MCmN+d2uY1PMwlchDyFcEXS6Xy+VyuVwul+uRyX8Iulwul8vl\ncrlcLtcj0ydDQ0dztY9tY10Wj0q7zExr3B6W5LTYbYk04fsysEF+/55pKtT+NcNTOBw/25WPZnqN\nInY5vjdOvkx/QDTVIowNlv+JArRYA64aLa+4TF7be5kUugQ+5bGy/a+UWIKISEeEj6hdqccy+IJd\nsTf3z+2SBChABkShgo1wcI0JmDiioTGeJT2Rm7XFZKvr8125q/jMfy6u+4u4U4X2cnUTMAuxYh4d\nLNGZpoCICytPG+JaAxbJ3I71JUxzQMx5i7q8Wmm/slkrXpbmNuUC0490xl2e59yfFiJMF9PWei3l\nVs9DeOWyRLqbje37mBqjKZf4Xs+fxIoeJaltV1GEPqPXs3atHrcHzkqUtA+wdvZdVbW/j2iRFiRK\nArQUr6kXR0MfQnz2KTrow8I+3/wYSBn63iTDGIBdiEMWAcY/GStTNQcCOkP6ghHTFgXW9HWr5y+B\nWqF5SVIQgTO7SwKreTOeot/YNlrvC46bAWvFsdrgdOy38Czi4GI2JtQDx0L9NhR0FIREAMkrkBoi\nxjkZNsJQkzDFQ4u/zeaKun3+I7W/H80Vrds09lkkOc7/1TfiehgVSBVk+81gbtjsT7mQZFou0C4S\nxiQE6XhMyhO0F4YhsGImsU1FMhoR29TyEBpa13asYNomthmimePJeO/28wM7537yRMN7jo8VhW9M\nminFKcO0HOOJtqWjY20XI4Ps6rN//uK52X8OVJT3wvcaid7XKEFatSCtxnal1/nq1atd+Qz5oFZb\n3SYJ1uk47+gfOLzCVwRdLpfL5XK5XC6X65HJfwi6XC6Xy+VyuVwu1yPTJ0NDP3v2clc+O9dlznIV\nuGOOdZl7XGg5w5J5FMOtjjhbZbGvs7OrXfkarj3TsT6Gutbl54A6M0vDEfAP4htETEJ8ox5wETTX\nDIaqoUtYbF9ViosbAQupK+BlWEpub5mawW0QxyLqFgE/aAKErwfmwOfUwTmwrHhSfUZNa1GEgi6K\nuNAxzj+Ogd/eKAoqItKWWmc685z+J3HdXz3qz9VC28vFxZnZrgT22wN/igfcMWPjxBc0MhAPdJdN\njbsm6j7QFRHr4LWp0C/guDcrxSy74H9hxHdGcDPrgJJkqeIi2UiRqvnMOo7FR9qXdGjvdaXIyLs3\nr3flyzP7XK/hjBZFei9Frs+1mOg56OgoIpKhv+yAhq6X6lK2vtE+0eCngXtbtamxj+IrJdA7uobG\nmX2vdB3uw47VdS/963/zb3blBM83CZw+OW6lqDtJSndK1CkgYKOcrpUiI7h7joGdFdg/R3hAmoeu\nvEDlUMU2pY7VW9SpEA2NeP6Rnr+H5e0WyGgB5G5UWNfOHGNFjDGcaF2E/inE2DmGbzu9/rokwrm/\nDxIR6VrOVfajgTX6UJKlq5V1bt4AKWu64115RHdG3P+vf/kLsz/R9TpwP3bdX5wDMhwoREMt6gfX\nzYiYtW6TGeTTzqcmeOcTIJh0HV0hvGZU2DY6R9hWPOhaqt/zfN/tr+PgAVDPn/9cQ3UYwrFYaNiJ\nBA6ovE+e5xBuotfXOp6VW+uYzPCs4yO9ruPjp7vyT7/4XM+XB3NTuCHHsNhO8F6ySPep4Gr89vUb\nc6zf/P3f78r/+A//sCufvdNxv2emg8BGnHXE0VCXy+VyuVwul8vlcn2U/Iegy+VyuVwul8vlcj0y\nfTI09Pzt212Zrj0nh8dmu4NTuOJFiiwwieTllSJUXFguA9eeFRJfbpEsPQfG1Hb7l+hFbjugfa8e\nZ22BedaB02cFJIyOptta94mB1UyB6KRji53lcGljgu1NC6zGOJ7ZV83E70RRUiA+sUFkrCKDL8h+\n0c0Kz+4U7lEiIn8Fp6Z/+aPPduWfHOvy/wTPRUqL/HZ4zreTcLruqx7Jp0vgt1eXF2a76wVcu1Cv\nshxtl/XP8F5h0lV9gURRiIiYcm5xr4z1F3WGSaI36AcCwzXTRjfAZ9ZLReQ2cCBdTdWJbDrTsojI\nBGipSRwPJI8Ja0M3TSbQjYVYkN4Xca/RxD6LFMm8o0iRk6tLRVHeR0gk/U7LmwANZX+53er7q8r9\nDrCBEZ3EKVF+R0MfQv/u7/69fohRd4JhiliuSURNd0KMFXm6H/MUEcmAK3E7hhSwnKQWLY2RrFpQ\nbtHuO4ynaeBamg7g5sRZZ0C0D+Y61symdtwZw6lzhD5lhCTuOd00A051u2GbgMMv8K4xHBjTwNU3\nThH6weT0KNN5kPOMt2c2POLyUh3R13BLPj4BJgqMfjyy7+VHL+GW2AUW4a57a4ME4ewfbQJ56zwf\nkQHGHIwhSGyv06lFM+muOcOYxPpLB9A8GENZTzkec9xlEvgQDeVnoqFTjJV08aaMU72IbIF6cj7P\nYz05Pd2VOX6LiEyBxo5Hep/zqc5Nxvnwz6CI/Sp+LvE866XOE97CDfQPv/+DOdZv/uE/7sqvv9Ht\n1kRjGcMV2/mAdUj3hPIul8vlcrlcLpfL5foI+Q9Bl8vlcrlcLpfL5Xpk+mRo6PvX3+7K0wNFNp48\nOTLb/QzIQl0qDnp2pkvrZ++AjBKxCPAmkzQWP4ENfdIPJIP9bq9dicv0Zsm/YeJlizBugTRusMy9\n3upy+BRJQ2czXWKfH1tkNoqwTI2Enhskpe6NE1mYzFavmYlqTaJSFOPA6c8m9d7PYzKJ+BS4wC8+\ne2G2+1c//+mu/C9/og5Oc+wT49k3XZCMtWUScmdDH0pRrPgQDfOIQYmIrJfaLolj0rGr6+hgKSgH\nSZ6JRkRE2uBeBjQydA0lMjIJUDC9FqDcIb5dAuFM9uOoN9fA0oHVTAPkmclxmUx3MtHnmgFPe/rS\ntovTp8CFiGKjLSfAiLLAFTGBAxr7uyhV5HRb6rO8vNQ+qQ3IE/akdP3shegStmnCvpfok7fRh9DZ\n+ftdeV1rm1wG6Hwf7XebMzgatuekIAnDIxhGwMTxMTFk3T4Kxh3uT0yUOFqCATkLcEqLiCN0AttN\nDLam5RANnU2AdWOfOb/HPnlmccoa84sKqF+EserZU52/zKYWHSfizZASg4a2+93Fr641mTE+AAAg\nAElEQVTptCjy9p2ioj1dmVE+7NlWbfs8gtNj9sUX4noYcaxk2+MYJHJ7fvW9hgDAxCQ3t/WSqCgd\nPImAEu0McUzObdleeR6OsyEyzbFuGtT570Xkm9dSV3Zux+fEMu/LOvjbeyEaTqy9bxBChfcSBeEZ\n0tBNGF8jOfzinfbDX/7297vyb/6joqAiIv/4m9/syu/hFFrCaZQ0aDiX5fnDZ/6x8hVBl8vlcrlc\nLpfL5Xpk8h+CLpfL5XK5XC6Xy/XI9MnQ0FO4WdEBs0gt3lQABYl6OgISTdTtLe5il99PTnSZui6B\nLSKhfBIT8QiWieF8yWVbYiGbtS4Zrzc2ueUGS8AbOI7drIlw6vJ7n+mS+dOnFhubYmm8xtL+H79W\n5PYMCaoX19ZlTICJLBaaVLrD8yOu0AVL0VHM5w+XN6BAB3Cs+q9+oclE/5sgme0vv1CnUJJEa56f\n7zLAKvpeMZfWHc8eTGms9S+O9iOTIiLHJ+pSFg1ggw0QC6JPTR0ghMQ8UK8rvNdyo8els6eIyPW1\nuucRZZmMtS7O0HYOjyyKTpxzAqwl6rViNsDwKmB46014LdquipG6lBFXG8O9rBjZ55qlROeQ/BvP\nP4E9Z9oF+Haz3xWyTxSXmR9r23taarsqxhZFXy70udY3isZu13qP5UbLdWOfRd9qX9h3nrD6IfTL\nX/5yV/7mNVzo3rwy2xHzZfuiYy4RbQwN0gZjYNLq5xpliYgw7sepRPaFW/wwDSV7j4wbqo5NROiS\nADPNjVMq5iAFHRWBcae2fRJxJw5aFNqmX754uSs/e/LE7P8E7o5z9E8joHJFvv9aphOL3B0caJ8W\nox/oga/SXbsKkfithpTUpZ23uO4vInwGjw/RPoZBRPvr9QhjCDHPEDNdwxWb2KUJm0DYzXJp++rV\narV3H5Z5/psbiykzVIrYKa+f+CiTwy+QzF5E5PRUE7+/eIF5IiaKYxy3bWy9ntHRFNkJ1jc6njFS\n4VbUAvs7YKvnQLFf/fHrXfn11+q8/eWXX5pD/QGfF3hmfH8cp01HLCJRxDm3u4a6XC6Xy+VyuVwu\nl+sj5D8EXS6Xy+VyuVwul+uRyX8Iulwul8vlcrlcLtcj0yeLEZwdqYU5Y/GSW+gr4w1gIY9YwhS2\nzg3A/VFib+8niEXrO2WJs0g55skI1uyxZa8FcWo1GPu+YYyg8tXLlWWv60r3oYU6ed+0ANM91zid\nY7DSIiIrxCJ++UeNC/ybv/v7XflmudyV4yDWg3bZ1voa7yKlJbj9n0FMlpnvhXEXY42pGD/Vcv/U\nPtfrI73Oy1afUYvYvwbPPuTAWxMjGL4z1301yjTuZDrR9np6Yu3pR2Ntf6zzl1eIPUWKkgiBQmnQ\n4GPR+tOiyiHbibG3Dm3Qm1LPw5iYBrF827Xy+YwVEBFZwC5+iniF2YHGMRQjbTspLOylsVb5jI8o\nGZ9TaYzcttRnNypsDFKaJSgjFnAgRjBObHy1se5n3AmCw/iM0hS2+9MwXY2ep8pgyT2Bvf5GY0ia\nSt+9iEi5vtD9t54+4iH0P/73/8Ou/He/UavysrVt4nxxvSszPreLGS9IC3XG2Q+/K5uKgjGG+7+/\nc58PKN86P665v+M6v1cYVxOb9DRMa8EYQ46BHxazw3jeV+/f7crzIL3MwVT7V6asmM/0exNLhf4o\njAvr8F6Tbn+/U5aYs7TWZp/pXvognYDr/ooHYlRD+3/GhnHexXjVY6QQY4qIIrfjBo/FeD3WGcYO\nVpWdT3Hamqb7fyLUmDOG6ScOEGf/9OnTvd8zlQvnkkWu8X4iNn5wBs+JHvFz9KWIxPZ9TBnRYtyl\nlwTbQheknGtLpqPT7a7PdDy7vtDyYoH4+SqIhce1TJHWYovnX+Na+mg4jtRjBF0ul8vlcrlcLpfL\n9VHyH4Iul8vlcrlcLpfL9cj0ydDQpCBSBWws8G/lcjaXg6MoHShzid3+zn1xqHhbAtYshp153OnS\nbhSH1wLkolLUjFuVJa3l7ZI7U1vkuLYcS/tz4B/Hx2o3nQVL5q+/VMva//vf/s2u/G//5m/1XoC7\nPD1Vq2oRkR999lyvC6vMxGKIBXTBe6F9r+A5xcamXg+8ivW5vGnfm2OtaDUPG/IuwbI8vm86xRK+\n+wzU7Zb/r+u+ylPFTw5miqX0wf+P5keKObx+/XpXPr9EygFi0djX2CWLCImZGGhEwlQiaEcBBWcw\nlR71pES6ls1S919c2PqSwaJ9AnzlEGj2MWzg5+hT4tgiOhHaT9IAjcY1xriXNMC9og7oGayvhbgO\ncNBWLIrStHo84qgGKyNe1/C5Bmk9kLpHgJBKjn4UWM54ZN+rRESJLFrsup/+u//2X+3KMXDEmyBt\nUf373+/Ka6QwaoFddkwFwbwSt5DL/dgm26T5PkT18Tci4r3ZZD+y+t12+My62w+EBJjNg/Hc3DKO\nhbrfC/qTYP/I9GT7x52zK+0DQxyQ4+sY4zuR0UPgdKdI03OEfkdE5OgAoTa5Hne01brAVBppgJZ1\nzf70H66PUw6knykXcqQ2EhHJ0v2p0YiGnmLcOTwAkp/ZcYfpH9j+ttvt3u9DsWqYOSD2CXFQiqkp\niIayzFQsdY35fzC3mACZznCfjAgZz/VZ5sGcn2ho1+gY2GD+3qCNVGs7t6w3xEb1/m8uFbdfLhBq\ngv41CrDag2PFXEfAXC/QR1xeIqTigfHPu+Qrgi6Xy+VyuVwul8v1yOQ/BF0ul8vlcrlcLpfrkemT\noaEXF+e78nykS76z6dRsR2cuLk3T6Wi9We/KXP7OcosnYZVYxiPipMTOtBwiEkQQ6RoaA01NgKbO\n54p4iIhMJ7qEPCKCNtFzvnz2DGXFN1+/VvcxEZG/+ffqDvr/onx2ocvMOVwH88y+6i8+VwfVHNeS\nwbkwA2ISkgDEcYnlRLF+X9f6wL/+UpHbm61dfj86VyxmdqLXUkyBBmZ0DbVYA9+TUy0Ppx7tgi5X\n+di6U+ZoS+/fK/ZLlKoF2kjXz7i2+AP3iWK68mGjAbc/EZEcbT5BHa1KoKFAtusmQEHK/S57q7X2\nKxs49h6dKoaTFRbfjjO2Ja3XT44Usz04UvTrs2eK/oiIzKZ6vHykzzxDW85zxTS7AI9bwcH49ds3\nu/LVpbr0Xl8p4vL+jCivfS4kiRo88k2p52iB2P/ohd6XiEgf4dnEFoty3U/PTrW+/PqXv9yVqwDr\nXaO//fadjiPrtb6vKMe4R/xTgr6Wn4fQygF8NPxs8LR+/zZ3uoYOHMv0O90w8tj1+8cNHsvAn719\nrhY13Y+p0pk0SgMMHu04QrlLdZ8m0mNt4JY9qm373GI+skb/hN1lCzx+nNk2SIPCLnQrdN1bc6C9\nRCanKIsEYx2+Z9gQ0eCXL1/gWED1ReSrr77alYmJLuEiT4VtbARX7Cnm42wXG+DnIfJMR1Ge8+hI\n0UiipQ3mc8cn1q16MoZDNcbT7RZO/2Ni1fZZZAhXqDY6By1rbQsV0NCuDmNNMOeHo+gGvznY3mq0\n0XFwLU9HL/U86Efp8nx2rg6kDEUTudt19mPlLd7lcrlcLpfL5XK5Hpn8h6DL5XK5XC6Xy+VyPTJ9\nMjSUCZ77DL9He7scen6uqNkGS7tXcNfZYvmbSRyrKliyZiLnHM5MCZKow92yyK27XXat56lLXdrN\nkLg+AorS1dbFj85cGTCRly8UAT09PdXtsWL/D7/5nTnW7//4za68WOoydWScC4nSWie5EkiXIEF1\nHCmCliS65B7dSoCq+7RY5mdC9+0Gy+yXwPFqiyKsLnX/02f6tydP4Vp5CHw1MLzKcC1DCVBdP1w1\nnSrx+rPCoqEJ2y+4FiY5NviJcQu0aGcLp0sx7n3YH7uExloxHGwjwzvRqVO3oXuZiEiaap1PkCye\nLppLJOgmCpIGTnApntOYCaOB0maJouCHM4uWHh0qWk4UhLc1m2kbiQP0bDqGk+SNIifv32pfcPbu\n7a78zVevdmU6uYmIxMl+jK0FepPCsa1u7IupgQBvPaH8g6gAKvXsiTry/fO/su/uLcIFWjSer79V\nXNg4aH4oGsrvTfMGpnnHhvEA9nkHDRpstx/HHERGQwdSEwaC8BBDrHKfABvDn+hQznYwGmmfMB7Z\n/mHCvyEkYwLEfFxwf0XN5mMbQjND/zId6f4juJHmwEGJqouIRDHR4GFHSdcP0wHCg4hZjka2ryfC\naR05tZKtlph/AgWOIouZEmE2mDKT27M/D8ZgzvVSbFfDhZv7MLm9iMh8pjhnAadUXn9Dl1ocaz63\nbrh0DbVlOFRjnJ2MgrlJxxAuOIBi3BbMOfok6CNS/Zxi4jla6m8DZkCIMJ/ogufK/qdAu2ZdyDHn\n2AbhGZxnd46Gulwul8vlcrlcLpfrY+Q/BF0ul8vlcrlcLpfrkemTcXREKJmIuA1wyvdAl8pSHYjW\na3XgbOH81wJpKku7zLuBix5ptjzFki+MfqII+KSI1K0iNkWsaOoES8MjOHXGQdLODm5KxHo+/0wd\nPOmaenmtKOzf/6NFQ1+9PduVeziVFoW+0jRmQnu7zLxe67HTfIa/ALPM9Fqi2GJnROrortjATYlO\njZHo/nWQT3p7BXxhq9c/7/VlpHAazCJ7LbOxogmTzGIKrvurxIsiOhTimEQ7SiDfFcrERJkoPrqV\nNJVOo1ovOiLXdKmNbBuPgXbQ9bRjEneccjRh3ReZHahr2ajQ+neD/mYNl7GrCzj0BWhmDjezGsls\nN0d6zqrW/dvW9n1tp+cvK7istbgXJh8eW5cyOpO1wFJWcHy7uNQ+7eKKjmXmUAYly+ksDMdnYi1N\nY9/LFs6Vy5W9T9f9xPYxQR/4+YuXZrtf//Kf78o3NzqmnV8o4rza6jsxOGXgIBmi3Pr9n95GRO7E\nunff34Mctmiofh/jym4dt0d77XifemGJwensBacJEVBg4Gj387mOoYchQge3xwncIUdoR0w0nqPM\nbUSsC/kYZeLqCZ1CEzv1IwLbBe6orvuL74yJ31l3RESaiqEzmM+ir7+60jnn4SXcNINxh/vQYbrI\nWS/VzbRt7PtuMIfu2Regv2G7mE/tGHpypO6mhweKeq6WOu7QuZuYamB8LTmu+QDYKJsy7z/IJy9x\nDwSWYSPYJknQlkJHeoQxlVt9TqOV3kuK9h7hXujSKyJys9I5xNGxzjOKVM8/xXykarR/FhFpGCoT\nDtAfKV8RdLlcLpfL5XK5XK5HJv8h6HK5XC6Xy+VyuVyPTP5D0OVyuVwul8vlcrkemT5ZjOAI6RsS\nwPuMmREROT9/vSszDiBDLN78WFMupGvdn5a8IiLblbLXfYSYuZZ8vD6Ssrb7Xy7e7cpTXP+zE2WX\nR0ewvw1iIMZj8PqIP/rsuaaPWIA9/v2Xf9yVv36lz0FEZA3+OM9gRYxnmYGJzoOcC32HmBDE4iWJ\nXuNkotbHScChJ/hIy/6qQlyWiTVgXFfwYPCxQbznFjFT255xDzYWKoVFcZ5aW2bX/VUhVqFrGaNm\n402bVnn/izNN97JYKBPP+Jw8Rbxn0AMx9iBivKDJS6HlNggq6BBL17OagalnfOsT2O6LiPz4Jz/b\nlQ+PNdbh62++3pVfvdG2WF6c78p1EN8sqV5AU2m9XCOO9psz7VPqwLZ9MtGYIsaNMCaX1tNpEDfU\n4QFc4128u9Byhf8Fzp9qKos8OBat6xmHSXv7An1y29pAYMZ0L5dX4vp4bWAvTmv4Ioj/+mdf/GhX\nvkR9fY/y77/W+t2Y9D7B/4oZgDcQ7xf1d8QI8tADQYKMbQ1TSfSDKScYd6zfx7zI+FZw805M/zBh\nmgbE7M9nNmUDbfPnKE8n2ianSBExye3YNEEbG5u4QMQ8sYxBl/FmIiIZxkCmrOD3HLTDCCPGHzFV\njuvjtLjRfo+x7exPReycaIxY7wL+EzF8GtaY5y4WNpZsfqDzthzxoqNc628x0nIZxLLRS6PcIM4f\ncfpMKxHey+mJxr+9fPZiVz7Ptd8fb5CKIkYcY2HjaFnPc8TRmvQXzF4V2ZrNj1lEzwvEbuJZ9LXd\nv8FYW1+rR0mDGM8tU6Yh1vPq0r6X9+fa344xZ5/k+vye4rfMemvH0Baf+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jIAACAASURB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+hAvzhub/X5w2o/f+RZsB4r3/2mY//sp7v+C9/1gz27cA+DoAT2n+/lMAbQAPB3AtgGMA/sI5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=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ffa6744b908>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "utils.plot_predictions(model=model,dataset=X_test/255.,\n",
    "                       dataset_labels=Y_test,\n",
    "                       label_dict=label_dict,\n",
    "                       batch_size=16,\n",
    "                       grid_height=4,\n",
    "                       grid_width=4)"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "default_view": {},
   "name": "Copy of cifar10_vgg16_july12.ipynb",
   "provenance": [
    {
     "file_id": "1tlQq2thZWPDNqaMbfBJ9_TOo0YTqv7IL",
     "timestamp": 1531320536438
    }
   ],
   "version": "0.3.2",
   "views": {}
  },
  "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": "3.5.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}