Keras walkthrough with Fashion MNIST.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Keras Walkthrough using Fashion MNIST\n",
    "This walkthrough builds and tests some deep learning models using Tensorflow/Keras. For this, we will use the [Fashion MNIST](https://research.zalando.com/welcome/mission/research-projects/fashion-mnist/) dataset, comprised of 28x28 pixel grayscale images of clothing in 10 categories: T-shirt/top, Trouser, Pullover, Dress, Coat, Sandal, Shirt, Sneaker, Bag, Ankle boot.\n",
    "\n",
    "Requirements: Python, Tensorflow (the GPU version, if your hardware supports it), numpy and matplotlib. The demo is constructed using Jupyter notebook, which allows us to run Python within separate cells and see the result, but the python commands here can also be put into a separate python file and run from the command line.\n",
    "\n",
    "**Getting started:** The following import statements allow us to bring in the code libraries we are going to use so that we can reference them. Numpy provides mathematical functions for managing arrays of data. We also use matplotlib, which allows us to display images and graphs. Keras is a machine learning framework packaged within Tensorflow. We need some of the functions for creating neural network models and layers, as well as the Fashion MNIST dataset that can be loaded directly through Keras."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['figure.dpi'] = 200  # make graphics appear larger\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.datasets import fashion_mnist\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Conv2D, MaxPool2D, Flatten, Dense, Reshape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's start by loading the dataset and printing out an example of each category. Labels in the dataset are represented by numeric digits 0 through 9, so we create a list to look up the corresponding text labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1200x800 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# load the data\n",
    "(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()\n",
    "class_names = ['T-shirt','Trouser','Pullover','Dress','Coat','Sandal','Shirt','Sneaker','Bag','Boot']\n",
    "\n",
    "# display one of each clothing category\n",
    "for i in range(10):\n",
    "    im = train_images[train_labels == i][0]\n",
    "    plt.subplot(1,10,i+1)\n",
    "    plt.imshow(im, 'gray')\n",
    "    plt.axis('off')\n",
    "    plt.title(class_names[i], size=6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use the Sequential() model constructor to define a network that takes a single input and passes the information sequentially through a list of layers. The network will accept an image and output a predicted score for each of the 10 possible class labels.\n",
    "\n",
    "Let's start out with a very simple model: a single fully-connected or \"dense\" layer. The *Dense()* layer learns a weighted connection between every input to every one of its outputs. We specify the number of outputs we want as the first parameter. We can specify an activation function to apply to the output of the layer. In this case, we use 'softmax' which normalizes the outputs to sum to 1, so that they are consistent with a probability distribution over the possible classes.\n",
    "\n",
    "Since we are working with images that are 2D, we also to reshape the input before we pass it to the dense layer, which expects a vector. The *Flatten()* layer reshapes the input into a vector. For the first layer, we need to specify the size of the input."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "255 0\n"
     ]
    }
   ],
   "source": [
    "print(np.max(train_images), np.min(train_images))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "flatten (Flatten)            (None, 784)               0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 10)                7850      \n",
      "=================================================================\n",
      "Total params: 7,850\n",
      "Trainable params: 7,850\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# define a simple network - really, this is one layer of neurons, since flatten just reshapes\n",
    "model1 = Sequential([\n",
    "    Flatten(input_shape=(28, 28)),\n",
    "    Dense(10, activation='softmax')\n",
    "])\n",
    "model1.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For fun, let's see how well the model does **before** we train it. We would expect it to do poorly, since the network weights are randomly assigned and unoptimized. The plot shows the probability assigned to the predicted label by the model and the label. In parentheses is the true label. The title is green if the model got it right."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1200x800 with 15 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot a random sample of test images, their predicted labels and ground truth\n",
    "def plot_results(model, images, labels, rows, cols):\n",
    "    pred = model.predict(images)\n",
    "    p = np.random.permutation(len(images))    \n",
    "    for i in range(rows*cols):\n",
    "        plt.subplot(rows, cols, i+1)\n",
    "        plt.imshow(images[p[i]], 'gray')\n",
    "        plt.axis('off')\n",
    "        j = p[i]\n",
    "        pred_label = np.argmax(pred[j])\n",
    "        pred_prob = np.round(100*pred[j][pred_label],1)\n",
    "        true_label = labels[j]\n",
    "        pred_class = class_names[pred_label]\n",
    "        true_class = class_names[true_label]\n",
    "        color = \"green\" if pred_label == true_label else \"red\"\n",
    "        plt.title(f\"{pred_prob}% {pred_class} ({true_class})\", size=4, color=color)\n",
    "plot_results(model1, test_images, test_labels, 3, 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In Keras, we first \"compile\" the model and then we train it using the \"fit\" method. When we compile the model, we specify some options for how the model will be optimized and what metrics we want to evaluate. In our case, we will use the Adam optimizer and the sparse_categorical_crossentropy loss function. We will use accuracy as an evaluation metric when we test the model. Let's train for 20 epochs - each epoch is one pass through all of the training data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 60000 samples\n",
      "Epoch 1/20\n",
      "60000/60000 - 5s - loss: 16.8087 - accuracy: 0.7497\n",
      "Epoch 2/20\n",
      "60000/60000 - 4s - loss: 12.1210 - accuracy: 0.7889\n",
      "Epoch 3/20\n",
      "60000/60000 - 4s - loss: 11.4113 - accuracy: 0.7956\n",
      "Epoch 4/20\n",
      "60000/60000 - 4s - loss: 10.6585 - accuracy: 0.7990\n",
      "Epoch 5/20\n",
      "60000/60000 - 4s - loss: 10.5759 - accuracy: 0.8018\n",
      "Epoch 6/20\n",
      "60000/60000 - 4s - loss: 10.0154 - accuracy: 0.8055\n",
      "Epoch 7/20\n",
      "60000/60000 - 4s - loss: 10.1004 - accuracy: 0.8048\n",
      "Epoch 8/20\n",
      "60000/60000 - 5s - loss: 10.1919 - accuracy: 0.8058\n",
      "Epoch 9/20\n",
      "60000/60000 - 4s - loss: 10.2717 - accuracy: 0.8062\n",
      "Epoch 10/20\n",
      "60000/60000 - 4s - loss: 10.0086 - accuracy: 0.8082\n",
      "Epoch 11/20\n",
      "60000/60000 - 4s - loss: 9.8911 - accuracy: 0.8080\n",
      "Epoch 12/20\n",
      "60000/60000 - 5s - loss: 9.7891 - accuracy: 0.8111\n",
      "Epoch 13/20\n",
      "60000/60000 - 4s - loss: 9.7349 - accuracy: 0.8096\n",
      "Epoch 14/20\n",
      "60000/60000 - 4s - loss: 10.1184 - accuracy: 0.8056\n",
      "Epoch 15/20\n",
      "60000/60000 - 4s - loss: 9.8990 - accuracy: 0.8122\n",
      "Epoch 16/20\n",
      "60000/60000 - 4s - loss: 9.8208 - accuracy: 0.8121\n",
      "Epoch 17/20\n",
      "60000/60000 - 5s - loss: 9.8622 - accuracy: 0.8103\n",
      "Epoch 18/20\n",
      "60000/60000 - 4s - loss: 9.3620 - accuracy: 0.8129\n",
      "Epoch 19/20\n",
      "60000/60000 - 4s - loss: 9.8673 - accuracy: 0.8104\n",
      "Epoch 20/20\n",
      "60000/60000 - 4s - loss: 9.7791 - accuracy: 0.8116\n"
     ]
    }
   ],
   "source": [
    "model1.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n",
    "model1.fit(train_images, train_labels, epochs=20, verbose=2);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Not great. We only achieved about 81% accuracy on the training data. Let's evaluate on the test data and plot some results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000/10000 [==============================] - 1s 88us/sample - loss: 12.1944 - accuracy: 0.7942\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1200x800 with 15 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# evaluate accuracy on the test data\n",
    "model1.evaluate(test_images, test_labels)\n",
    "\n",
    "# plot a random sample of test images, their predicted labels and ground truth\n",
    "plot_results(model1, test_images, test_labels, 3, 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can do better. Let's try adding another dense layer to our network. For this layer, let's use the rectiffied linear (ReLU) activation function. The ReLU outputs 0 for negative values, but passes through any non-negative values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "flatten_1 (Flatten)          (None, 784)               0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 256)               200960    \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 10)                2570      \n",
      "=================================================================\n",
      "Total params: 203,530\n",
      "Trainable params: 203,530\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# define network with two fully connected layers\n",
    "model2 = Sequential([\n",
    "    Flatten(input_shape=(28, 28)),\n",
    "    Dense(256, activation='relu'),\n",
    "    Dense(10, activation='softmax')\n",
    "])\n",
    "model2.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 60000 samples\n",
      "Epoch 1/20\n",
      "60000/60000 - 5s - loss: 3.2893 - accuracy: 0.7248\n",
      "Epoch 2/20\n",
      "60000/60000 - 5s - loss: 0.5893 - accuracy: 0.7947\n",
      "Epoch 3/20\n",
      "60000/60000 - 5s - loss: 0.5515 - accuracy: 0.8058\n",
      "Epoch 4/20\n",
      "60000/60000 - 5s - loss: 0.5080 - accuracy: 0.8299\n",
      "Epoch 5/20\n",
      "60000/60000 - 5s - loss: 0.4922 - accuracy: 0.8366\n",
      "Epoch 6/20\n",
      "60000/60000 - 5s - loss: 0.4814 - accuracy: 0.8383\n",
      "Epoch 7/20\n",
      "60000/60000 - 5s - loss: 0.4668 - accuracy: 0.8436\n",
      "Epoch 8/20\n",
      "60000/60000 - 5s - loss: 0.4574 - accuracy: 0.8467\n",
      "Epoch 9/20\n",
      "60000/60000 - 5s - loss: 0.4501 - accuracy: 0.8491\n",
      "Epoch 10/20\n",
      "60000/60000 - 5s - loss: 0.4509 - accuracy: 0.8492\n",
      "Epoch 11/20\n",
      "60000/60000 - 5s - loss: 0.4442 - accuracy: 0.8499\n",
      "Epoch 12/20\n",
      "60000/60000 - 5s - loss: 0.4407 - accuracy: 0.8517\n",
      "Epoch 13/20\n",
      "60000/60000 - 5s - loss: 0.4404 - accuracy: 0.8519\n",
      "Epoch 14/20\n",
      "60000/60000 - 5s - loss: 0.4347 - accuracy: 0.8542\n",
      "Epoch 15/20\n",
      "60000/60000 - 5s - loss: 0.4364 - accuracy: 0.8501\n",
      "Epoch 16/20\n",
      "60000/60000 - 5s - loss: 0.4384 - accuracy: 0.8519\n",
      "Epoch 17/20\n",
      "60000/60000 - 5s - loss: 0.4272 - accuracy: 0.8543\n",
      "Epoch 18/20\n",
      "60000/60000 - 5s - loss: 0.4333 - accuracy: 0.8545\n",
      "Epoch 19/20\n",
      "60000/60000 - 5s - loss: 0.4220 - accuracy: 0.8567\n",
      "Epoch 20/20\n",
      "60000/60000 - 5s - loss: 0.4188 - accuracy: 0.8583\n"
     ]
    }
   ],
   "source": [
    "model2.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n",
    "model2.fit(train_images, train_labels, epochs=20, verbose=2);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are up to 85% on the training set now. But we need a more powerful model to do even better. We will now add two new types of layers: \n",
    "\n",
    "*Conv2D()* layers apply a learned set of filters (kernels) at each 2D spatial location. Each of these layers will return an output value for each kernel and each spatial location. The first parameter is the number of kernels. The second is the size of each kernel. The third is a stride by which to sub-sample the output. Setting padding to 'same' means that kernels will overlap the borders of the image so that the we retain the same spatial output shape.\n",
    "\n",
    "*MaxPool2D()* layers compute the maximum input value within each 2D neighborhood. The first parameter is the size of the neighborhood to pool. The second is a stride by which to sub-sample the output. Here we downsample by 2.\n",
    "\n",
    "Our convolutional network should do much better. It has the advantage that fewer parameters are needed for convolutional layers, and takes into account that images have spatial redundancy. Unlike our previous model, we don't want to flatten the input until we reach the Dense layers. Instead, we actually need to reshape our 28x28 images to 28x28x1 since Conv2D expects a dimension for color. We could do this outside the model, but in this case I used a *Reshape()* layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "reshape (Reshape)            (None, 28, 28, 1)         0         \n",
      "_________________________________________________________________\n",
      "conv2d (Conv2D)              (None, 28, 28, 32)        320       \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 28, 28, 32)        9248      \n",
      "_________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D) (None, 14, 14, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 14, 14, 64)        18496     \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 14, 14, 64)        36928     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 7, 7, 64)          0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 3136)              0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 256)               803072    \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 10)                2570      \n",
      "=================================================================\n",
      "Total params: 870,634\n",
      "Trainable params: 870,634\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# define a convolutional network\n",
    "model3 = Sequential([\n",
    "    Reshape((28, 28, 1), input_shape=(28, 28)),\n",
    "    Conv2D(32, 3, 1, padding='same', activation='relu'),\n",
    "    Conv2D(32, 3, 1, padding='same', activation='relu'),\n",
    "    MaxPool2D(2, 2),\n",
    "    Conv2D(64, 3, 1, padding='same', activation='relu'),\n",
    "    Conv2D(64, 3, 1, padding='same', activation='relu'),\n",
    "    MaxPool2D(2, 2),\n",
    "    Flatten(),\n",
    "    Dense(256, activation='relu'),\n",
    "    Dense(10, activation='softmax')\n",
    "])\n",
    "model3.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This time, we will also tell Keras to use our test set for validation. This evaluates the accuracy metric on the test set at each epoch, but it only trains the model on the training set. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/20\n",
      "60000/60000 - 10s - loss: 0.3702 - accuracy: 0.8648 - val_loss: 0.2887 - val_accuracy: 0.8939\n",
      "Epoch 2/20\n",
      "60000/60000 - 9s - loss: 0.2285 - accuracy: 0.9147 - val_loss: 0.2406 - val_accuracy: 0.9103\n",
      "Epoch 3/20\n",
      "60000/60000 - 9s - loss: 0.1814 - accuracy: 0.9319 - val_loss: 0.2207 - val_accuracy: 0.9207\n",
      "Epoch 4/20\n",
      "60000/60000 - 9s - loss: 0.1476 - accuracy: 0.9450 - val_loss: 0.2388 - val_accuracy: 0.9137\n",
      "Epoch 5/20\n",
      "60000/60000 - 9s - loss: 0.1189 - accuracy: 0.9556 - val_loss: 0.2160 - val_accuracy: 0.9272\n",
      "Epoch 6/20\n",
      "60000/60000 - 9s - loss: 0.0933 - accuracy: 0.9650 - val_loss: 0.2574 - val_accuracy: 0.9289\n",
      "Epoch 7/20\n",
      "60000/60000 - 9s - loss: 0.0723 - accuracy: 0.9728 - val_loss: 0.2914 - val_accuracy: 0.9249\n",
      "Epoch 8/20\n",
      "60000/60000 - 9s - loss: 0.0584 - accuracy: 0.9782 - val_loss: 0.3130 - val_accuracy: 0.9265\n",
      "Epoch 9/20\n",
      "60000/60000 - 9s - loss: 0.0464 - accuracy: 0.9827 - val_loss: 0.3480 - val_accuracy: 0.9221\n",
      "Epoch 10/20\n",
      "60000/60000 - 9s - loss: 0.0429 - accuracy: 0.9846 - val_loss: 0.3591 - val_accuracy: 0.9259\n",
      "Epoch 11/20\n",
      "60000/60000 - 10s - loss: 0.0372 - accuracy: 0.9866 - val_loss: 0.4240 - val_accuracy: 0.9289\n",
      "Epoch 12/20\n",
      "60000/60000 - 9s - loss: 0.0347 - accuracy: 0.9875 - val_loss: 0.4267 - val_accuracy: 0.9231\n",
      "Epoch 13/20\n",
      "60000/60000 - 9s - loss: 0.0319 - accuracy: 0.9883 - val_loss: 0.4873 - val_accuracy: 0.9218\n",
      "Epoch 14/20\n"
     ]
    }
   ],
   "source": [
    "model3.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n",
    "model3.fit(train_images/255., train_labels, epochs=20, verbose=2, validation_data=(test_images/255., test_labels));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wow! 98% accuracy on the training set! But notice it only gets 91% accuracy on the test set. This indicates that there is some overfitting to the training set, but this is still pretty good for this dataset. With additional effort there are some tricks we could do to reduce overfitting, such as by augmenting the training dataset or adding *Dropout()* layers. But for now, let's just show the improvement in our predicted labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA8oAAAKLCAYAAAAuHHFJAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAB7CAAAewgFu0HU+AACX/klEQVR4nO3dd7xcVbn/8e8CEnIS0nuhhNAJEDoqXZryUyyIeikXvahcUUQsWNlsRC9cC4qKWCiKYAFBuChKEZTeFBCQThJCGum9QNbvj7WP2Zz9PIfZYZJT8nm/Xr7wPLOyZ8/MWnvtNXvmOyHGKAAAAAAAkGzQ0TsAAAAAAEBnwkIZAAAAAIASFsoAAAAAAJSwUAYAAAAAoISFMgAAAAAAJSyUAQAAAAAoYaEMAAAAAEAJC2UAAAAAAEpYKAMAAAAAUMJCGQAAAACAEhbKAAAAAACUsFAGAAAAAKCEhTIAAAAAACUslAEAAAAAKGGhDAAAAABACQtlAAAAAABKWCgDAAAAAFDCQhkAAAAAgBIWygAAAAAAlGzU0TsAAAAAAGsq5OECSe+UtLmknWIWHyvdNkzSLySNk7Rc0skxi3cWt/WWdLGkPSWtkvSFmMVrnPvYW9KPJfWW9KKk42IWp4U89JB0laSxkp6TdEzM4ishD70k3STpqJjFuc42b5e0maQFSuuyyZI+FLM44w08Hdb9XCPpmzGL94Q8nCXp45KmFjevlPSpmMW738D2R0i6TtJbYhZfeaP721mss4VyF+7A+0s6T1KLpJ6S5kp6T7M7cOn+oqS+MYuLmrzd70i6P2bx1yEP/SX9UNIEped0A0nfiFm8spn3WbrvyyQ9GLP4gzewjSDpb5JOiFl8oVn71pV14TF1u5o8KYQ8nCjp7pjFp9tpUx4DJ0r6rqSJrTdLOjNm8bo3sA8TJG0Ts/jb4m/6bEkX7q/MAW/8vi8TcwBjoEGMAXcbXX4MrGVXS/pfSXcat50r6d6YxSNCHvaUdHXIw7hiQfdZSctjFrcKeRgr6Z6Qh9vajofi+b9C0kkxi7eHPHxW0nckfVDS4ZLmxCy+K+ThEklHSLpB0pmSfuCNrZJTYxZvKO7nwmKfPrdGz4Ih5GEvSQNiFu8plX8Rs/jZ4vZjJH1P6RizRmIWp4c83CfpOEmXvYHd7VTW5Uevr5a0r6RJxm2tHXhrSR+SdEXIQ+si/t8dWKkjXhjyMLDtBkod+LSYxW0k3ajUgaXVHXgXSfOUOrD0Oh242IdrJX08ZnFCzOIOkj4saXGtR74OlZ63cm20pLdJ+k1ROkfSDKWJemdJb5L0wDrbyZpCHjaKWYySzpeUdfT+dCJdbkyVnFqMqfFKi9XPNvB423OipG28G40xIEm3FPswQdJHJP3sDe7DBEnHtP5Bn63ocv2VOaBz6EZzAGNgHWAMrJ9iFv8WszjFufkYpTdGFLP4gNJrv29x2/tLt72g9GbEUcY29lAah7cXf/9Y0ruKN6FWKr05peK/K0IedpK0beub540o+m5fpTejFPIwIuThtpCHh0IeHg95uKAY5wp56B/y8LuQhydDHv4S8nB5yMO3nE1/TOnY4BnYep/Ftn8Z8vBgyMOjIQ83FG/ktd729ZCHZ0Me7gt5+GbIw4Ol7VypdD7VbayzK8oxi3+TpJAH6+ZjlN7lVMziAyEPrR34dqUOfGJx2wshD60d+LI227A68MwGOvCX2tntvsX/ppUex1Ot/7+4MnafpDdLGiXp5pjFk4vb+ipNULtI6iXpbkmfjFlcGfJwutI7UBsV+/bJmMX7yndcDIRzJW1XtB2tdAVsmNI7uj+OWbywaBuVJtJ3KB3o274L9WFJVxcHWSldzftr698xiwslLSy2dWJxf3MkjVd6Z/uYmMXni9uPl/QJST2Kf3NKzOJjxfN5oaQ+xeO9PGbxf9o+oSEP+0m6SNJ/SnpE0tckHVw8pieV3kWfV7z7ukBp8bOppB0l/Z+ki0Ie+hb7vF7romPqNUqTwpRS7fNK/WOVpEeVTtDmhzxsIukCSXsVTa+KWcxDHk4q9vWCkIdzJH0pZvGPbe6q7Rhoa5BeO0nsIen7Sv15maRPxyzeVdx2vKTPS4pKV2w+WjwfZ0vqF/LwsNIJ78miz/5bF+2vzAHMAU3DGGAMFP9+vR0DHSHkYbCkDWIWXy6VJyr1ARX/neTcVvaadjGLC0MeFkoaKelmSUeHPDwi6V5Jf1F6o+pDDe5m6/nLaKWx9omiPk/SO2IWF4U8bKj00eb3Kr3pdqakuTGL2xVvnP1d0u+c7R8oqe0i+oSQh0Mk9ZfUT+nNtFanxSzOkqSQhy8U9/WJkId3SPp/SmN6abEfZQ9J2jXkoU/MYqd9M62ODg/zWpsdWOkA1tqBFxYdeL5SB/6OpE+1t2/FO6wXSnom5OGPIQ9fDXloe9VqnFIHHC/p8JCHNxX1b0v6W8ziXkodaiOt7viXxyzuGbO4q6RTlT5SVdZL0q+VJrN3Kx2kr5T0mZjFPZXe+Tw55GG30r/ZOGbxwJhF66MaBypNUK2+K+nLxbtFPwh5+H9t2u+t9NGunSTdIukMSQp5eIukD0jaP2ZxN0lf0ep3qCZKOqSo7y7pmGKx8W8hDx9UWugcGbP4oNJEtihmca/iqt7jkvLSP9lX0tExiztKUsziSkmPSXqL8RhR6MxjquSCYkE5TWl8/LDY97cpTSxvKfrfYknfKP7NV5VOJHZW6qPvCnl4X8zizyQ9qNVXqdsukqXqGJCkQ0IeHg55eFppcjm92Ieekq6RdFZxpeF0pY9p9Ql5GC/pm5KOKG67W9JPYhZnKk0krVepTy6eM/rs6+jM/ZU5gDlgXWAMMAbW9zGwDrR9k7ztu1Wxndtedzsxi6tiFj8Ss7hLzOLHlPr57yVtFPJwZUhXfg9uZ7unFq//MKVPcFxS1DeQdF4xbv+h9GbYhOK2gyRdWtz/3OL+PGMkTW9T+0VxvjJW6Y26a0IeWorbji3Gxj8lndTmPn8bs7g4ZnGVpJ+XN1j0z3lKx5xuobOEea31DqzSRwFCHk5TqQNL2ljSD2MW/1LZYBZPC3k4X6lzvFXSP0IeDo/Fd4ck/Tpm8VVJS4sT/3GS7pH0Lkn7hDx8pmjXImlF8f93DXn4sqTBkl6RtEPIQ8+Yxdbb/yTpd63vRIY87KD0TuKvS+9E95W0g9I7SNLqQWV5zQCJWbwt5GEzSQcovQv845CH38csnlI0uTNmsXWyvUfSJ4v/f5TSZHdfaT+GFguLFqWPg01QuhK4qdLAav1IxoeU3jU+KGZxXlF7l9IVuKOLv3sqfXeq1W9j9TtK04vHg/Z12jFVODVm8YbiisFZSv33vZIOkXRFqY/8SOlkScVtnyrue3HIwy+K2lXt7H8ra5K4JWbx6GL/x0u6pTjpGixpRczin4vHemfIw0ylBfoekm6IWXyp2MaFkr5SPA4Pffb1ddr+yhzAHLCOMAYYA9L6PQbWipjF2SEPCnkYWnozanOlfBQV/91CUvk26w331naS/v2Jidd82qKoby7p7Upfb/i5pJ8oXWm9V6kPt7evMeThN1r9dbTTlcbI3jGLy0L6nn2v1rtSdbx7lij1T/MrFjGLt4aUVzA+5GFjpYX+m2MWXw55eKfShYBG77OX0tXmbqHDF8pdoQMXB8vLJF0W8rBY6Z2X1gliWanpq1r9nAZJ74rFR3VK999T6erVgTGLD4U89FN6d7enVk8gt0o6LOThB8U7wkHSrOLdJk97oRetA6T8mBYrPY9/DHm4QSnMo3WCaO8xXRKzeKbaCHn4htJ3PnaNKSDkGq0ezFL6eNH+Su84tz53Qeljtd5iynpM3WoArg1dYUyV9rXtpGAdhGMDt72eyhhosx+PhTxMVnqX/ilnu9HYh0bunz7bjq7QX5kDmAPWJsYAY2B9HwPrwFVKr+1ZIYV5jdDq16D1thNDCvM6QNLJxjYektQr5OHAmL7e8DFJvy+uopZ9T+nrWqtCHvoonSesUvpIfiPeqnQeIqXvDk8vFsnDJb1Pq79nf5vSx/fvDnkYoPQmjhnyp/Q1tu20OuX6NUIedpG0idKnIvZR+sj/nGKsfqzU9DZJecjDd5XGyPFttjNc6Y0v8366og7/6HWhtZOqnQ6sUge+3tjGvztw8ffrdmClTut24JCHTUIe3hZWf3G+RdL2eu27fZ7rJX0hFKESIQ8DQx62UjrA9VD6bqO0+l3Ksq8V//7mkL538JSkJSEPJ5T2bauQh0EN7Ie0eoC0/tvDwmuDQHZv8DH9n9J3GjYttrNBWP2xooGSphSTw7aSDm3zb/+ulPZ5achD623XSzo9pERPhTz0Dnlod2Gl9Pw/0sC+ru865ZhylCeFmyV9oDjBk9L3f28p3faRkIdQTD7HlW5boPQ9G89rxkBbIQ9jJG0t6Wml74htHIqPSYU8vFnp41D/VDp5e3tIP4Mgpcn01pi+5+btA3329XXK/socUMEcsPYwBl6LMeDrrmPgDQl5+GHIwxSlq+23hDw8W7r5DElvDnl4RukNn+Pj6p8w+qaklqL9n5W+cz6n2ObJIQ9nS//+VMZxkr4X0le2jpT0mdJ9KOThPyQ9ErP4eFE6V+mj9g8q9WnPBSF9FexRSUcrLYBV/Ns3h/RJjUu0+pxHSrkow0IenlD6WsJdSm84Wa5WCrMrO6G4z0eU3jA7vnij7kZJzyqdC/1Z0sOt/yBm8fqi9ojSovm5Nvd5hKRro58H0+WssyvKIQ8/VHq3Y4RSB14UU4KjlDrw5UUHXqFqB76k6MCr1KYDSxoVs3hm8c7NcUohBy2SXlLq0OV9sDrwT5XexbQ6cFA6Ef5eyMNSpQP7n1R8n/J1nKb0cwoPhzysUvq4zRkxi8+GPJwp6f6QrmBZk51iFs8PeVik9D2iI5QCKs4PKY5+Q6V3lo9tYD+kNEBO0OqPJe0k6dvFxLdK6d3m45x/W96nv4U8fEnSdSGFCvSQ9AelA8A5Sq/hsUrvSFkf33oi5OEISX8IKRzgXKX0xvtCCuKQ0nP2eNt/K0khD1sU23nMun1900XHVKvW4IoNlD4K9J+SFLN4Y0iBKPcUfeJRpd/6U7G97ystWKUU5tUaJPETpT79OdlhXm3HgFR8R1lpnG9U/LtHisf13mIfW8O83ldcfXg85OGLkm4K6WN3rWFeUlpEf7aYdO6JWTyZPrtaF+2vzAGv3SfmgDeAMcAYWN/HwNpUfGz+FOe2GZIOc25brBSYZ912UZu/71H66L23D1e2+fv+9toXbQ5s57ZJWh1g2tZiSR8srjb3U3pj7edO28uUzqvOiun7xWcpfe3Nus9XVH0+vlz6/1+PWfxCyMMGSr8WUv7JqZPUzVKvQ+w+i344is78gNLvJHrR+Z1eyMO5kp6JWWwb+gG0q6PGAH0WnQFzANZ3jAF0NyH9ZNONSm8atSjlu5zdTvtDlD7G/YbeZAl5uFbpKx4tSp+SODlmcUFIH7t+a9s3C7q6Dv+OMta+4l3mjyl17C47QSh95+HSjt4JdD0dOAbos+hwzAFY3zEG0N3E9Gsbu9dof8vrt2poO+926jOUPgLerXBFGQAAAACAks4S5gUAAAAAQKfAQhkAAAAAgBIWygAAAAAAlLBQBgAAAACghIUyAAAAAAAlLJQBAAAAAChhoQwAAAAAQAkLZQAAAAAASlgoAwAAAABQwkIZAAAAAIASFsoAAAAAAJSwUAYAAAAAoGSjZm8whBCbvU2gjhhj6Kj7pv+jo3Vk/5cYA+h4zAFYnzEHYH3XzDHAFWUAAAAAAEpYKAMAAAAAUMJCGQAAAACAEhbKAAAAAACUsFAGAAAAAKCEhTIAAAAAACUslAEAAAAAKGGhDAAAAABACQtlAAAAAABKWCgDAAAAAFDCQhkAAAAAgBIWygAAAAAAlLBQBgAAAACghIUyAAAAAAAlLJQBAAAAAChhoQwAAAAAQAkLZQAAAAAASlgoAwAAAABQwkIZAAAAAIASFsoAAAAAAJSwUAYAAAAAoISFMgAAAAAAJRt19A4AWD9ttFH18PPKK6+YbXfccUezPnDgQLO+YMGCSm3VqlVm20022cSsDx482KzffPPNldqKFSvMtj169DDrK1euNOsAAHRHIYRKLcZYaxvHHXecWbe2M3HixFrbXrx4sVlftmxZQ/fXXt07/7DOg7xzEu/8yNu2ZdGiRbW20atXr4brvXv3bng/JGm33XYz69brcOmll9badjNxRRkAAAAAgBIWygAAAAAAlLBQBgAAAACghIUyAAAAAAAlLJQBAAAAACgh9RpAh6iTdvnpT3/arL/1rW816/Pnz6/Udtlll1r7YSV0eu3vueces+2b3vQms37XXXeZ9f79+1dqt9xyi9n29NNPN+sAAHSUDTawr8FZycpHHnmk2fZTn/qUWe/Zs2fD+7FkyRKz3rdvX7P+6quvmvWlS5dWal4CtbcNj7Ud6/7a492n9esaXor1gAEDzPrQoUMbbu+9Nt6vfMyYMcOsW0ngBx54oNn217/+tVlvJq4oAwAAAABQwkIZAAAAAIASFsoAAAAAAJSwUAYAAAAAoIQwLwCd3tNPP23WJ0yYYNatwK1f/epXZtv/+I//MOtemNcf//jHSm3RokVm28cee8yse4EaVnDGtGnTzLYAAHQ2VmiXZM/X5557rtl2p512MusPPfSQWbfmayscsz3efg8ePLjhbXjhoHVCQ71zj7oBYtbj6dGjh9nWCtBqj3WfXgiZt+3Ro0eb9ZdffrlSmzVrltn2jDPO8HaxabiiDAAAAABACQtlAAAAAABKWCgDAAAAAFDCQhkAAAAAgBIWygAAAAAAlJB63YGsJDgv1a4ZevfubdZ33XXXWu1vvvnmSm2DDez3XLwUQY+X9mfxUgTRNdTpGxtuuKFZ9/qdley48cYb19qG1xcXL17c0P1J/mP0+u7y5csrtSeeeMJsCwBAV3HRRRdVarvvvrvZ1pvz+/bta9bnzJlTqbW0tJhtvZToOonV3v55vPMJ6/zDa+v9WsbKlSsbvk9vv5txPu1tw1vXeOdH1tpj3333NdvOmzevsZ17A7iiDAAAAABACQtlAAAAAABKWCgDAAAAAFDCQhkAAAAAgBIWygAAAAAAlJB63YHqJFz379/frE+YMMGsjxgxolKbMmWK2dZLEZw4cWJD+ybZCd6StGLFioa3IZFkDdvIkSPNupdkvWTJkkrtqKOOMtt6CZiLFi0y6wsWLKjU/vKXv5ht+/XrZ9athE7J7v+TJk0y2wKSdMwxx5j1j3zkI5XaZz7zGbPto48+atab8SsE3tzlJcXPmjWrUmvWryqgPqsP1J2nvX7UjPl+xx13NOsf//jHK7VTTjml1rbX5n5727bqXv9fm7+S8kZsueWWZn3+/PmVmneOeOSRR5p1a26XpKVLl1Zq3jHGe+6981ir7qVHe8ekOsdS69cvJP98wkvDtrbjPcY6id/t1S3eY/eeQ6tPjBo1ymx7zz33mPXDDjuswb17fVxRBgAAAACghIUyAAAAAAAlLJQBAAAAAChhoQwAAAAAQAkLZQAAAAAASki97mS8FN93vvOdZn3lypVm3Uqk85LxvBS8J5980qzX2Y9m2Hbbbc369OnT19p9onmakRy65557mnUvsdpKwLz88svNtr/61a/M+hFHHGHWP/CBD5h1S910VCvF1Hos6FhrMw33ggsuMOv77LOPWf/HP/7R8L586UtfMtt6fboZj+e8884z697jsX7JwUuS9dKA0TxWH6j7vNdJJz/ooIPM+sEHH2zWvfMXKz29T58+ZtvFixeb9WaM8zppx57Omm7t2XXXXc36n/70p4a3MWzYMLNu/eqEZPcx7/ygZ8+eZt1LybbSmb0x4L3e3hiwzvm9Xwrwfi3DO/9uaWmp1Orut6fOccFLt/bu03pOvF/n8ea/ZmKWAQAAAACghIUyAAAAAAAlLJQBAAAAAChhoQwAAAAAQAlhXp3MFltsYdanTJli1sePH2/WrS/9Dxw40GzrBVnUUTf0xQvgGD16dKU2ZswYs+1LL71U6z7RdQ0dOtSsT5w40axvueWWldqPfvQjs+1NN91Uq77TTjtVagMGDDDbegEeXsiIFW7hjRWsG1ZASZ1wIkl629veZtYvueSSSm3+/PlmWy+wxQs/mjFjRqW244471trGbbfdZtaHDx9eqV100UVm23Hjxpn12bNnm3UrzOvhhx8229Z9HdAcdZ93L0g0y7JKzTuWrlixwqx7x9J+/fpVarvssovZ9u677zbrzehfdc+NrPZvfetbzba33nrrGu3T2uada951110Nb8ML7aoTyOb1Oy9EygvzsnjBVV5/9PqBdVxftGiR2XbevHlm3RszViiWN46858Sr1wn08x67d25jvfZPPPGE2fZf//pXw/uxpriiDAAAAABACQtlAAAAAABKWCgDAAAAAFDCQhkAAAAAgBIWygAAAAAAlBCnWvAS3Ky0trophh4rke+AAw4w27a0tJj1ZcuWmXUr2c57jF7Sds+ePRvedl1e2p2VpuqlCHrJgOhc6owXL8XdS3XccMMNzbrVNy6//HKz7R577GHW77vvPrO+zTbbVGpemqfXz700VSt1s2/fvmZbrBkvxdPrS6+88krD2373u99t1r/0pS+Z9cmTJ1dqVlq1JN1xxx1mfeTIkWZ97NixldrChQvNtuedd55Z9+YGq/8uWbLEbOul13r9+utf/3qlduSRR5ptuyuvj9bhHXfrptvWSX5+xzveYdaPPfZYs26l+D733HNm25dfftmsX3nllWbd+pWDL37xi2ZbL7H9D3/4g1lvhr333tusW/v4wAMPmG07a+q1N7a9pHvLsGHDzLp3nLHOE70+baVBS/6x3pobvP3wzrOHDBli1l988cVKzTvf32677cy696s4lrrHhTq8OdT7xQavP1ivvfcLDPfff3+De7fmuKIMAAAAAEAJC2UAAAAAAEpYKAMAAAAAUMJCGQAAAACAEhbKAAAAAACUdNvUay/BzUt8q5Ps6G3bS+Y94ogjzPrMmTMrNS+Nz0pHlfyUvt69e1dqAwcONNv269fPrP/Xf/2XWZ8zZ06ldvvtt5ttZ82aZdYHDx5s1seNG9fQ/aF7Ov300836xIkTzbqXKm31fy8hdNddd621Lw899JBZr6NOEvhb3vIWs/7ggw++4f3oLqy0cC99dPny5Wa9Trr1KaecYtY//OEPm/X58+ebdWsf+/TpY7b1UlO947o1p3njyPtlAe+56t+/f6VWNzXV+9UCa0xbr6/kp6l2dc1KrK6zba9+6KGHVmonnXSS2dZ7TR955BGzbiW5b7311mbbSy+91KxfdtllZt06f/GS4//jP/7DrJ9wwglm3Ura9vZ78803N+ve8du6Ty85vrPyfhnFOw5adtttN7PuPRfW8c47J/eOa955qXVM8lKvvXMS71x4k002qdS8OcA73nn3afF+Kcdbe3gJ3NZr7P2qgvUYJWn06NFm3UrP3n333c226wJXlAEAAAAAKGGhDAAAAABACQtlAAAAAABKWCgDAAAAAFDCQhkAAAAAgJJukXptJT7WSZWV/FTN4cOHV2peiuGwYcPM+jPPPGPWrSQ4L6XvmmuuMev77befWbdSTL2kPy+p0koOluzUvPe85z1mWy9N3Esu7NmzZ6U2ZswYs+0WW2xh1tExrKRCyU/UffOb31ypve997zPbPvzww7X2xUpwPPzww2ttw0tZvPjiiys1KwVY8tM/6yQyH3300Wbb733ve2a9K6mb4usdT5qRfjxixAiz/uUvf7lSGzVqlNn2hRdeMOsjR4406y+99FKl5qWSvu1tbzPrdeY67xcOvDE6b948s27to/caeEm3dcaMN166Eu/4WKet9zp59To222wzs37kkUdWatY8LUmf+9znzPpNN91k1vfZZ59K7aijjjLb7rHHHmb93nvvbXjbZ555ptnWGuOS9NRTT5l1bx8t3hjyfj3BOi/05i7vnK6jecfvOsdpLynZez4t3jlsr169zLr3CyvWePTSoL3XxLvPpUuXVmre8+clbXtzhrXf3rHCOyf37tN6PIMGDTLbesf6uXPnmnXLDjvs0HDbZuv6sw8AAAAAAE3EQhkAAAAAgBIWygAAAAAAlLBQBgAAAACgpEPDvLwvrNcN4rLae+EfXgjFm970JrNuBXdNmjTJbOsFuXihBBYv+GTs2LFm3ftivhUe8/LLL5ttvSAA7wv41pf4vRAEL3ihb9++Zv2VV16p1Lznzwv5wtq10Ub2YcN67SQ/mObSSy+t1LyAk7phPlbQxvHHH2+2vfzyy826F6Jl9emBAweabb3x6R37rGC9XXbZxWzbWdUJ6PLCuerOAePGjavU9t13X7Pt9ttvb9YHDx5s1q3X2wtps0LkvG1I0jbbbFOpPfnkk2ZbL1jIG19Dhw6t1LxAmT59+pj1OoE13jziPa/Tp08369ZY2mmnncy2Dz74oFnvjOoEbtUN57LGlvX6S9KBBx5o1r2wrGnTplVqXsCPdx5wxBFHmPX777+/UnvLW95itj3llFPM+uOPP27WzzvvvErtjDPOMNsedthhZt07Vrz44ouVmjdHeedRXoCYxQuI8o4JHW3y5Mlm3QvNtXjhihMnTjTrVnCXd07ivVZeSKMVuuiNAW/cefOitSbxjule2K8VCCbZj9ML3KoTNijZ++3NLzNnzjTr3utgzf/e6+6FKzcTV5QBAAAAAChhoQwAAAAAQAkLZQAAAAAASlgoAwAAAABQwkIZAAAAAICSpqdeeylmVrpp3WRTj5UEt8UWW5htvRQ8L8HOSor2kvG8xDev3q9fv0pt4cKFZlsvkfL5558367fcckulduihh5ptrUQ/yU/gHjJkSKXmPd/PPfecWZ8yZYpZt9JXvUQ/L+0S9dVJoPfGkOfmm28261aSp5eOaI0VyU/lffrppyu173znO2bbSy65xKx7CdzWMcFKXZakxYsXm3UvxdQ6fnpJ/V66Zkfzjut1jvcTJkww6+985zvN+ujRoys1L5naO554+2cd771+N2vWLLPu/WrB1KlTKzUvCdzrY54FCxZUal5ivdcfvefK6pNecrb3Cwde/7Xmo2adK6wL3rHUerxesq+XTLvZZpuZdeu59xJyvXO0uXPnmnVrO1bCsCRdf/31Zt0bt5/61Kcqtb/85S9mW+u8Q5L2339/s27NJY8++qjZ9otf/KJZt86jJGnTTTet1Lwx5PHmUavunUPW/TWIjmal/HvHTOuXWyRp5cqVZt1KF/eOX95x0Eubt+YA75jkzTvePG4dH705wOsH3vrFepzeY/fu06tbr4M157R3n17StvW6efOIt/Zopq41ygAAAAAAWMtYKAMAAAAAUMJCGQAAAACAEhbKAAAAAACUsFAGAAAAAKCk6anXXkKaxUs8GzZsmFn3km832WSTSs1Lu/PSGq3EPMlOwZs3b57ZtkePHmbdSuCVpDe/+c2Vmpdqd/XVV5v1z33uc2bdem6952TZsmVmfeLEiWb9d7/7XaXmJVKefvrpZv3ee+816y+88EKl5qVJeumiXV2dBGrJTr70Uga9RMY6qbLeOLT6heSPOStV2kt79fqoN16sx+MlrX//+98361tvvbVZHz58eKXm9efNN9/crHsJmNZ+e8cVb/86q4MOOqhSO+SQQ8y23mP20qat1E8vEXbw4MHeLpqsFNrZs2ebbb39nj59ulm30r132GGHWtuu80sT3i8ZeOPIm6Ot9OXHHnvMbHvxxRebdS9l+W9/+1ul5qUVe/N2R/LG9mGHHVap7bTTTmZb71hlnetIdh/1UpW919Q6rkl2ovakSZPMtt6524knnmjWL7vsskrtJz/5idn2q1/9qln//Oc/b9ZPPfXUSu3kk082215xxRW1tv2FL3yhUnvxxRfNtl6SsndeM23atErNGyvemDvnnHPM+rpy//33m/UjjzyyUnvwwQfNtpMnTzbrG2+8sVm3+rv3qxPeccNL1LZeK2981Znbve14bb0+4627rMfvzRfeeadX944jdXhjw7pP7zmxEuibjSvKAAAAAACUsFAGAAAAAKCEhTIAAAAAACUslAEAAAAAKGGhDAAAAABASdNTr71Eta222qrhbXjJjl6Sr1X3tuGle86YMcOsW4l0XgLvCSecYNYHDhxo1q3tWGmikp9I+cc//tGsWyl41157rdm2Lus1njp1qtn2a1/7mlnffvvtzfrzzz9fqXkpglbSbWdl9VEvqbBOArW3HS/duq7jjjuuUvOSQJ988kmzfsMNN5h1K/HVO37UTfG2krZnzZpltrVSXSU/Hdk6Vnhj3EuGrJM86T1GL2m+o+21115m3epLXnKol5Tu1a3+4aWjeunRXmKvlTQ6c+ZMs+2IESPMupemavF+bcBKg5bs9HjJTua2fmlBstO3Jemqq64y682aSxrlPa/eLzl0JG9s//KXv1xr99mMBNpmzUcW7zmxUuw/+clP1tq2d+z94Q9/2FBNkgYMGGDWvbT1Y445prGdW095Kf8LFiyo1LxzQW9e9vqSNZd448I7n/BSmK379LbtzS91fqWlzi+ctLcvVt17jHVZxwtvv716nfMjbxvePN9MXFEGAAAAAKCEhTIAAAAAACUslAEAAAAAKGGhDAAAAABASdPDvPbdd1+zbn3Rvn///mZb70v8db6E7n3x+8EHHzTrU6ZMaXjbnssuu8ysH3TQQWb95z//eaXmhZ6NGzfOrP/hD38w61//+tcrNS/86q677jLrXoCK9SV+70v5VqCMJD388MNm3Qrr8V53L1StM/JCi9YWL5zH64sHH3ywWbdeay/gx7PbbruZ9b59+1ZqXsiGF5Dh1a0AOC9MY9tttzXrI0eONOuDBg2q1LyQDS8gp06Ihdd3OmuYnRXIJ0mLFy+u1LzQRW9u8J43K4hr0qRJZlvv2PP444+b9fvuu69S8/rGKaecYtb//Oc/m/Xf/OY3Zn1tufPOO5uyHSsUc+jQoWZbLxTPCzizxqkXxOeFc3ak0aNHm3UrFM47Jnlj3tqGZB9nvLbe814nnMg7TnvBOt7x0doXr794z4l3fjBq1CizbvGeK2+cW89VM0I4ve14z2uzQjvXlX/961+V2oEHHmi2rRMAJ9nzYd3QrjphXnXCuSS/fzQrXKvR+/Se1zrb8NQ9D/KORVb7uiGczcQVZQAAAAAASlgoAwAAAABQwkIZAAAAAIASFsoAAAAAAJSwUAYAAAAAoKTpqdcLFiww688880yl5qWY1U2Hs5LtFi5cWGsbXvqql9Zm8dJeP/3pT5v1AQMGVGrf/va3zbZWWqAkbbbZZmZ98uTJldrXvvY1s62XeDx9+nSzbiXb1Xme2mMlWXuvzcCBA5tyn+uC9Ri8hHgvsdp7rbfccstKzUt7fOmll8z6zTffbNat19W6P0kaPny4We/Xr59Zt5Kpvf326l7qrfV8v/jii2ZbLznUS162xoXX/70UyDrpmt7+PfDAA2a9o3kJxaeeemql5iWHemPeO66vTdY8NXXqVLPthz/84VrbtuYAb7x4aa9eGrD1XHlzrvdLE964s/q7Nxa9ceSNgXnz5lVq3nOyaNEis96Rpk2bZtatlH8vzdg7nli/CuGpe07jnXfVSXiuexz0+qOl7txQp62X+F33uaqzba9ujeelS5eabefMmdPwfnQGjz32WKV29NFHm229seE9b9b5hFWT/JTzOn3MS2z2+rp3nLbmQO9YWvfXU+qclzcjDdsbF17dm/+t9t55UN20+TXBFWUAAAAAAEpYKAMAAAAAUMJCGQAAAACAEhbKAAAAAACUsFAGAAAAAKCk6anXDz/8sFm30j29VMYlS5aY9fnz5ze8H17anZfu6SWqWamMXuqet9+/+c1vzPrhhx9u1i3vec97zLqXGHj++edXalbqpiTtvPPOZv3RRx8161b66siRI822gwcPNuseK8HO2+9777231rbXhX322ces//SnP63UvORcr597ib9//vOfK7Xrr7/ebDt27FizvtNOO5n17bffvlLzXlNvzHnjxUqS9JIXvWOF195KzvUSQr1teMmYVlqvl97q1escbx566CGzbZ3jYWflJVZ6fd1LULb6h3fc8PqplzTqvVaWusm81pzhJZt6/dR7DgcNGlSpeWPR66fWrxB49+m19caul+RrPd/WeJakmTNnmvWOtHjx4lp1oLvxjnfWse33v/+92fYDH/iAWfeOg9b48pKzvcR97zhoHau8+aIZSenesbRuGrZ1n3V/iaNOcnbddOs6CeHe+diNN95o1v/nf/7HrK8JrigDAAAAAFDCQhkAAAAAgBIWygAAAAAAlLBQBgAAAACghIUyAAAAAAAloU4SW0MbDKG5GwRqijHaEXvrwIknnmj2/89//vOVWp3EP0lasGCBWbcSGadNm2a29dIUBw4caNathGAvNbh3795m3UtqtFIWvVRe7zjltd92220rNS/1+vnnnzfrQ4YMMevWc7ho0SKzrZcY6dV32223Su2AAw4w2z722GNmvSP7v8QcgI7XkWOA/o+O1tFzwAYbbGCOgTrrjR/96Edm/aMf/ahZf+KJJ6z9MNt6v9zx8ssvm/URI0ZUat65x6xZs8y6d95k/SqAl5DvnRt6KeNW2nSdhGzJT8O22nttvfrKlSvN+uabb16p3XTTTWbbD37wg97+NW0McEUZAAAAAIASFsoAAAAAAJSwUAYAAAAAoISFMgAAAAAAJYR5odvpjEEuVmDDSSedZG5jn332MevbbLONWR8wYECl5oUnLFy40KwvX77crHvBDxYvUMILvejRo0eltvHGG5ttrXAuSXr44YfN+l/+8pdK7eKLLzbbPvnkk2b9+9//vlkfP358pTZ79myzrfecDBs2zKw/+OCDldqpp55qtvV0dJALcwA6WmecA4B1pbPOAVa4lhds6fnP//xPs/7Zz362UvMCtLx1jxc8ap2XeOdMVriqtw3JP0ewWOdM7d2nde7lndN554we6zn0tt2nTx+z7oWtWedvJ554YuM7J8K8AAAAAABYa1goAwAAAABQwkIZAAAAAIASFsoAAAAAAJSwUAYAAAAAoITUa3Q73TXxdOTIkWb9iCOOqNTe9773mW133313sz5o0CCzXifZ0DuWrFixwqzPnDmzUrvmmmvMthdccIFZnzZtmllvhgULFpj1JUuWVGrPPPOM2XannXYy695+b7/99g3una+zJp4C60p3nQOARqyPc8BPf/rTSu3www83206aNMmse+cqLS0tlZqXqO0lOffu3dusW6nX3i+FbLbZZrXu03o8zz//vNnWO69buXKlWV+2bFmlNnr0aLOt9WsekvSjH/3IrN9yyy1mvQ5SrwEAAAAAWEtYKAMAAAAAUMJCGQAAAACAEhbKAAAAAACUsFAGAAAAAKCE1Gt0O50x8dRKJVy1atVa359G7bDDDmZ91KhRldqYMWPMtlYKoiQ9/fTTZv3vf/97g3tXXzOe7w984ANmvW/fvpWal5Ddo0cPs37DDTeY9Xnz5jW2c+1YHxNPgbLOOAcA68r6OAeccMIJldrJJ59stn3xxRfN+qJFi8z6sGHDKjXvl0KGDh1q1r1kaitt+pvf/KbZ9uKLLzbr1jmJJC1evLhSGzBggNnWWwvOnTvXrK9r3rmUl9a9atUqUq8BAAAAAFgbWCgDAAAAAFDCQhkAAAAAgBIWygAAAAAAlLBQBgAAAACghNRrdDsknmJ9tj4mngJlzAFYnzEHYH3XzDHAFWUAAAAAAEpYKAMAAAAAUMJCGQAAAACAEhbKAAAAAACUsFAGAAAAAKCEhTIAAAAAACUslAEAAAAAKGGhDAAAAABACQtlAAAAAABKWCgDAAAAAFDCQhkAAAAAgBIWygAAAAAAlLBQBgAAAACghIUyAAAAAAAlLJQBAAAAAChhoQwAAAAAQEmIMXb0PgAAAAAA0GlwRRkAAAAAgBIWygAAAAAAlLBQBgAAAACghIUyAAAAAAAlLJQBAAAAAChhoQwAAAAAQAkLZQAAAAAASlgoAwAAAABQwkIZAAAAAIASFsoAAAAAAJSwUAYAAAAAoISFMgAAAAAAJSyUAQAAAAAoYaEMAAAAAEAJC2UAAAAAAEpYKAMAAAAAUMJCGQAAAACAEhbKAAAAAACUsFAGAAAAAKCEhTIAAAAAACUslAEAAAAAKGGhDAAAAABACQtlAAAAAABKWCgDAAAAAFDCQhkAAAAAgBIWygAAAAAAlLBQBgAAAACgZKOO3gEAAN6okIcLJL1T0uaSdopZfKx02zBJv5A0TtJySSfHLN5Z3NZb0sWS9pS0StIXYhavce5jb0k/ltRb0ouSjotZnBby0EPSVZLGSnpO0jExi6+EPPSSdJOko2IW5zrbvF3SZpIWKM3JkyV9KGZxxht4Oqz7uUbSN2MW7wl5OEvSxyVNLW5eKelTMYt3v4Htj5B0naS3xCy+8kb3FwDqYA5oH3PAmuk0C+Uu3MH3l3SepBZJPSXNlfSeZnbwkIeJkv5f+Tkx2mwh6cGYxSHO7RMkfT1m8cji75OVBkmUtLGkh2IWj23iPre7P6V2Jyo9tqO76yBbW7rqmCm2e4qk/5a0YVF6WNIZMYuT1/C5eJekqTGL97fT5lRJvWMWzy3+HifpXEl7SFqkNFFcGLP4szXchwGSPhqz+L+l2reVxsGv1mSbqOVqSf8r6U7jtnMl3RuzeETIw56Srg55GFccZz4raXnM4lYhD2Ml3RPycFvb/hvyECRdIemkmMXbQx4+K+k7kj4o6XBJc2IW3xXycImkIyTdIOlMST9obywUTo1ZvKG4nwuLffrcGj0LhpCHvSQNiFm8p1T+RcziZ4vbj5H0PaVjwhqJWZwe8nCfpOMkXfYGdne91xWP7SEPf5Q0qvhzF0n/LPZhYczifg085hNVnAsYt+0h6dPeOUrIw4GSesYs3tTO9t8p6e2Sflb8T5IGSeonaWLx9xUxi99sYF9vl/St1jHb5razJT0es/gb59+eJunKmMWZxd/vUHrcH3u9+8XrYg5wMAesuc700eurJe0raZJxW2sH31rShyRdEfLQusj/dwdX6qgXhjwMbLuBUgc/LWZxG0k3KnVwaXUH30XSPKUOLr1OBy/24VpJH49ZnBCzuIOkD0taXOuRrxvfUFrQt046n5V0YPGYt5f07Q7cN0lpkElqHWR4fV1uzBTbzZVe4yNiFreXtIPSicuIRh+44V2S9mrnPlskfVrSD4q/RyhNpjfFLI6NWdxJ0iF6Y28eDpD0+Ta18ySdFfLQmY613VLM4t9iFqc4Nx8j6YdFuwckzVAaO5L0/tJtL0j6m6SjjG3soTRubi/+/rGkdxULi5VKCw4V/10R8rCTpG1jFn/b6GMoxmhfpTdcFfIwIuThtpCHh0IeHg95uKAYlwp56B/y8LuQhydDHv4S8nB5yMO3nE1/TGksewa23mex7V+GPDwY8vBoyMMNxeKs9bavhzw8G/JwX8jDN0MeHixt50pJH2n08cLV5Y7tMYtvL86DJhSlNxd/v+4i+fXELD7YziJ5I0kHSjrsdTbzdUnnFttq3c8zJd3S+ncji+QG9vVMa5Ec8rBBMQ+cJmlYqf3/SdqzeOMWbwBzAHPA2tBprijHLP5NkkIerJuPUXp3UzGLD4Q8tHbw25U6+InFbS+EPLR28MvabMPq4DMb6OBfame3+xb/m1Z6HE+1/v+Qh9OV3mnaqLiPT8Ys3lfcFiV9QdJ7lA6aZ8csXlrctp+kCyUtlXS/pFDa5jeVJoUekuYrvbP1TDv7qJCHzSTt2PocS9q0+LcLin2Okv5eav9LSdspXSGfLOnDMYszi3dtvyvpbklvKR7Xf8YsPlj8u1OUFiPTJP21tL2NJP1B0mClK+8PS/pIzOISY3evVFq0X9beY0LXHDMhD32UFpO7t05oRf+7tdTm+KJNVLrS8dGYxZeK7V8oqY+kXpIuj1n8n5CHtytdfTkk5OEkpZO5tleFj5Z0Z8ziouLvUyTdEbP409YGMYtzJF1U7MPw4v9vpTT+LohZ/ElxmzcGL5I0IOThYUmvxCzuUYybFyS9VdLN3vOCtSfkYbCkDWIWXy6VJyp91E3Ffyc5t5W9pl3M4sKQh4WSRiq9tkeHPDwi6V5Jf1FafHyowd28IOThHEmjlY6fnyjq8yS9I2ZxUcjDhkqfuHmv0kLqTElzYxa3KxZDf5f0O2f7B0pqewJ1QsjDIZL6K11VO7x022kxi7MkKeThC8V9faL16pfSFcOlxX6UPSRp15CHPjGLnfEN4y6hKx7bGxXyMFTphH2k0jH+oZjF1nHSN+ThV5LGK10tPyZm8fni3ONbMYt7hOLTapIukHSopGsknSxpg6I/XxOzeHab+9xP0ryYxYk19/UdSgvsVUrnO1+OWbyuuHm/kIczlK6i3xyzeHLxby5T+hTRD0L6eOs4pTlrK6XxOUrpauYySSfGLD4s6bdKF1m+XGf/0BjmAEnMAWus01/lWJsdXFK5gy8sOvh8pQ7+HUmfam/findWL5T0TMjDH0MevhrysE2pyeUxi3vGLO4q6VSlj0SVLYtZ3Fvp40AXhDxsFPKwsaRfKy2q91J6Z6v8eM4rtjlB0o8knd/ePhYOUBq4rf6s1MFfDHn4dcjDJ9q863xacZK/s9JVtzNLt+0o6ZLi3ebvK00iCnnYWekg/5biHeRBpX/zqqT/iFncQ2kCXKD0sW/LvwdZA48Lhs48ZpT6z4qYxSecfR8v6ZtKV5t3VnpT5iel/TwkZnE3SbtLOibkYY+YxT9Kul7pasEEY5EspUmi/N2b3SXdY7RrdYGkJ4srzQdL+mpIH12S/DF4stLJ2ISir7e6W2mhjI4T2/zddgUS27ntdbcTs7gqZvEjMYu7xPQRyk9I+r2kjUIerize9T+4ne2eWvSnYUqfUrqkqG8g6bxinP1DaYEzobjtIEmXFvc/t7g/zxhJ09vUflH01bFKi69rQvrkhSQdW1xN+Kekk9rc529jFhfHLK6S9PPyBmMWVyqd2I1sZ1+whjr5sb1Rx0maGLO4U3GM/0zptr2VPi6+k6RbJJ3hbGOwpGdjFveLWTxf6U3K1v58ttH+QL32+N+oc5Q+2j5B0s4qXQBQWgAfqHROc3jIw5ucbRxUbGPnmMVc6TuhRxf7+nDRhjli7WMOYA5YI51+oVzotB08ZvE0pQPlbyVtI+kfIQ+tH+fYNeThryEPjykdyHcIeehZ+udXFNv4l6RXlD56uq2kJa3v9BYf2Zhf+jeHhTzcU2zzTK3uvO15zQApruTup7RAv1vpqvajIQ+ti1tvgEjSU61XkJUWGq0fFzpQ0h/i6u9m/6T0b4KkT4c8/EPSo5KO9Pa7Ow6yDtJpx4yxzbKDJN0Qs/hS8feFkg4O6aNGLZJ+VvTLe5W+vzehnW2VWZNEew7R6o9izVS6atF6IlN3DE4v7h8dIGZxtvTvK1mtNlf6tIyK/27h3Fb2mnYhD5VPFBX1zZWOrT+S9DWlY+GJSm8svt6+Rkm/0eqPu56utCjYu1hUXKn0aQopjcX2xlLZEqXx493vrcV2xxfz1yckva1YsJxe8z57Kb0Ri7WjMx/bG3GvpCNCHr4d0veGy1ed7oxZbF3Al88v2lomqU7uQ93jf6tbJX035OHzknaOWZxXuu3XMYuvxiwuVfqUnLevNxRzSHuYI9Yi5gBJzAFrrNMvlLtCB49ZnBSzeFnM4vGSLle60tVT6SMQp8csjpe0v1IHKy+Ul5X+/6tKH+1xJ7biI9QXSDq22OYHtLrztqcyQGIWY8ziP2IWL1BaACySdODrDBBvn9Xefkv6D6Wr2vsX2/zW6+x3txpk61onHzNPSNo45GEHZ/fbHoTL//8bSt8r2rX4RMPtaqz/S9Ux8JAk7wqAdd+SFNdwDNKfO95VSh+3V0hBLq3fUW9721ilY9X1xjYektSr+BiolL7z9fvizb2y7ykFD61S+shlVProZqOfknmrpNav8AyUND1mcVnxdYD3ldrdJuk/i/0eIPs7da0eVfo6jSnkYRdJmyhdgRyo9KmfOcU8Vg4Zuk3S+0Ieeof0fcvj22xnuNKbvlOFpuvkx3ZTyMMXQh4eLv53eExhQhOU8kjeK+mB4iOlkn9+0dbiYkHRqHYXCaV9vbvYz/skKWbxdKWPzi6R9PNiwdyq0X1d5NTLmCPWPuYA5oA10ukXyoVO2cFDHjYJeXhbWP3F+halYKznlA58PZS+YylJn2zwsT4pqSWkNG2FPByt9P0BFf9dIWl6cZ+fsDdR8ZoBEvKwXfFR6VabShoq6Xm1P0Dac5ukt4fVX/j/r9JtAyXNjun7HH1VfIfK0h0HWQfplGMmpu8If0vST0MeWhNSFfLw9pBSWG9V6ketwV4nS7q1OCkaKGlKTAms2yp9P63VAq0eJ5a2k8SFkg4Iefj394dCHgaFlEgqpY/9fbSoD5X0bqWPILY3BhdI6h1WB+u02l7SI+3sG5og5OGHIQ9TlK7M3BLy8Gzp5jMkvTnk4Rml72seH1cn639T6Zj7rNLXUk6J6fvqCnk4OaQUWxX9+zhJ3wt5eFrpkzHlj40q5OE/JD0Ss/h4UTpX6Y2VB5UWGp4LihP0R5W+T/+frfVivx9W+ijeLaV/c7akYSEPTyhdZbhLr/30UdnVkt7WpnZCcZ+PKH187vjiI703SnpWaS76s9LVMhXPwfVF7RGlY/5zbe7zCEnX1lzEoJ5OeWz3xCy2fiVmQszin4v9WlR8Wu6TSp/E26TR7TnqHv+9fW0NINtbSudKMYuPxyz+QOnNgn3e4H56+8oc0QTMAcwBa0OnCfMKefih0rshI5Q6+KKYkhul1MEvLzr4ClU7+CVFB1+lNh1c0qiYUghXhTwcJ+miYkH7ktqkKzsd/KdKV4GtDh6UTuS/F/KwVGlh/CdJPyxO5s+UdH/Iw2TZk1VFzOLykIcPKqVVLlW6aja5uO2fIQ9XSXq8qDUaDnSnpM1CHgYVz01vSecXi5GlxeP4QsziwyF9nPQ4pQEyRemj2Yc72y3v96MhD9+QdHfIw3Sl8K5Wv5B0VDGYX5J0h1JggaXbDbK1pYuOGSl9XPllSTcVVxKi0ndvvhCz+GLIwxeL26QizKv4d+cUj+lYpXc9/1La5uWSLgt5eJ/sMK+rlSaZMyUppp9B2VfSucU4XagUYvPDov2pxeN+VOkNxa/H4qenvDEYszgn5OEKSf8MeVgcU/BMUHp3+BvOc4EmiVk8RcUiwbhthpxU3JgCR97v3HZRm7/vUQox8fbhyjZ/399e+6LNge3cNkl+mvtiSR8srjT0UzrO/9xpe5nST56cFdN3y86SdJZzn6+o+nyUQ4a+HrP4heJqws/02u/6n6RulnjaEbrwsb0RB0o6PeThVaWfB/xczOL8YAeXNepaSccXi4lKmJeKn+kJedgwZvHVGtv9n5ByZ1YoXVX+7zeyk4ULJF0a8rBEq8O8jpAfwoQGMQcwB6wNIbIeWS+E9JGhGJvw8wdrU8jDHUqJ2E929L6gewnpdz6zmH4aYl3d5xFKH9M+/nUbAzUUn965UWmx0aL0G7BWkFFr+0OUPsL3mNemwfu9Vuljuy1KKasnxywuKD4N9Na2J4pAZxDSb9PeFrN4VUfvS1nIwxClT1LtGbO4oqP3B10Hc8C6wUJ5PRFSmvZ/xSxe2NH74umugwydQ8jDVpK2i1m8YR3e5/uVfoaKrxIAQAcpFqRHxCz+sqP3paz4ytGGMYtrksoNYC1joQwAAAAAQElXCfMCAAAAAGCdYKEMAAAAAEAJC2UAAAAAAEpYKAMAAAAAUMJCGQAAAACAEhbKAAAAAACUsFAGAAAAAKCEhTIAAAAAACUslAEAAAAAKGGhDAAAAABACQtlAAAAAABKWCgDAAAAAFCyUbM3GEKIzd4mUEeMMXTUfa/P/b9nz55mffDgwWZ98eLFlVr//v3NtgsXLjTr8+bNa2znJIVgd4sYu9dL1pH9X+p+Y8Dq1ytWrKi1jUGDBpn1fffdt1Lr1auX2dYbRytXrjTrc+fOrdQeeOABs+3kyZPNumfDDTes1F599dVa21ib1rc5wDq2rc3jmvX6S9KVV15p1pcuXWrWH3nkkUpt4403NttuttlmZn306NFm/eyzz67UHnroIbNtd8McgPVdM8cAV5QBAAAAAChhoQwAAAAAQAkLZQAAAAAASlgoAwAAAABQwkIZAAAAAICS0OxkRNLu0NHWt8TTZthoIzsA/5VXXqnUttpqK7PtFVdcYdZbWlrM+qJFiyq1Pn36mG379etn1j/84Q+b9dtuu61Sq/MYuzIST9fMBhvY7xuvWrWqUvP66Sc+8Qmz7s2zf/7znyu1Z599tuH9aG/b48ePr9Te9ra3mW29tPkLLrjArNdNyV7XmAN8o0aNMutvectbzPquu+5aqU2cONFse8ABB5j1d77znWbdSs/2fj3BS9r2kqwvvvjiSm3EiBFm26efftqsW/OIJE2dOtWsdxbMAVjfkXoNAAAAAMBawkIZAAAAAIASFsoAAAAAAJSwUAYAAAAAoMROtwGwXqkTaDV69Giz7gUCvfzyy2Z9+fLlldrixYsbbitJffv2NeuWuqFdIdhZEM0OQMS65YUCvfrqq2Z9woQJldo+++xjtv2///s/s/7EE080tnNN9OCDDzZUk6ShQ4ea9fe85z1mff78+ZXar3/96xp7x/hqT93nxmp/2mmnmW2HDBli1hcuXGjWn3vuuUptyZIlZtvzzz/frL/00ktmff/996/UvMfuBW794he/MOubbLJJpeYF4nkBZ5/85CfN+sqVKyu1b3zjG2bbZcuWmXUAXQNXlAEAAAAAKGGhDAAAAABACQtlAAAAAABKWCgDAAAAAFDCQhkAAAAAgJLQ7ITJEAKRlehQMUY7NnMd6G7930ol/frXv262tVJGJT9tevbs2ZXaBhvY791tvPHGZn3FihVm/Qc/+EGldt1115ltu5uO7P9S5xkDG21k/6iD1x933HFHs24lXF988cW19qVnz55m3Ura9ubkOonHkj+WLHUT4U855ZRKbfLkyWZbLwm8R48eZt1KFK5rfZsDDjvssEpt9913N9v+61//qrVtq+96yfFeqvSkSZPM+qJFiyo1ry/269fPrI8dO9ast7S0VGpe3/L220vCt44Vzz77rNn28ssvN+trE3MA1nfNHANcUQYAAAAAoISFMgAAAAAAJSyUAQAAAAAoYaEMAAAAAEAJC2UAAAAAAEpIvUa3s74lntbhJfueffbZZn3TTTet1JYvX262Xbp0qVn3jjFW6u2yZcvMtl6yr5ec26dPn0pt5syZZturr77arHdEWmkzrI+Jp1b/8Ppd3759zXqe52b99NNPb3g/vKRtLz232fPvmvJSjL39tpxxxhlm3RtHU6dONet1XkvP+jYHfOYzn6nUvOfX46VNW326bn/2Ut8bvb/2ePvtzRkWr/97rF9bGD9+vNn2a1/7mln35rpmWB/nAKCM1GsAAAAAANYSFsoAAAAAAJSwUAYAAAAAoISFMgAAAAAAJSyUAQAAAAAoqRcvCKBL+9a3vmXWBw0aZNbnz59fqa1atcps6yWbWgmhkp1u2tLSUmsbK1euNOuzZs2q1KwkbEk67bTTzLqXGnvrrbdWahtsYL/n6D1X6Dj//d//bdYvuOCChrdRNyXa6x91UqWbwUsC9vqplypvjbuLL77YbHvsscea9e9973tmvRmp193VmDFjzPqAAQMqtenTp5ttlyxZYta919pKZ/b6v1f3jtMWL8W6LmvMef287rjt3bt3w/uxxx57mPU777yz4W10Z1/5ylfM+g477GDWvf77u9/9rlK78cYbzbbeuYD3elv36fUl71h1wgknmPVRo0ZVat6vk1x//fVm/U9/+pNZHzp0aKXmPX8e73FadW8cedvo37+/WZ88eXKDe7ducEUZAAAAAIASFsoAAAAAAJSwUAYAAAAAoISFMgAAAAAAJYR5Ad3Q+PHjzfqwYcPM+rx588y6FW7hBQJ5gS1eyNeiRYsa3rYV/CVJG2+8sVm3AmiWLl1aaxvvfe97zboV5kVo17pRJzStb9++ZtvFixeb9YkTJ5p1K6CobgiX1z/qBA41Q91QLG9MW/ttBei1d5+bbrqpWX/xxRcrtbrhad3VZpttZtatgJ6RI0eaba3nV/L7nXXsrRPOJflBYdZ9emPc6wNe/7L20dsPb97xtj169OhKzZrPJGnzzTc36+tjmNeQIUMqta997Wtm269+9atm3Tt+W/10//33N9sOHz7crHuvocWbX6xgPck/x3rkkUcqNW987bTTTmZ9++23N+tW6JzX1z0DBw4065tsskmltsUWW5htn3jiCbPuvZZ33XVXpfbb3/7WbFv38awJrigDAAAAAFDCQhkAAAAAgBIWygAAAAAAlLBQBgAAAACghIUyAAAAAAAlpF4D3dB+++1n1r0E6paWFrPupRJavETR5cuXm3UrqdVLWPQST71tW4/TS862ErIlabvttmt42ytWrDDborm8fmCl5+67775m22efffYN32fdtGUvPbduCnVnUSet+29/+5tZ33nnnc26lcq8LpJNu4IxY8aY9blz51ZqI0aMMNtaSbiSnzZtHWO9Y6m3DetYL0m9evVqeBt1x5w1H3nz38KFC826l+K74447Vmo33nij2Xbw4MHOHq5/rNf2T3/6k9n2nHPOMeveHGClvI8aNcps+/LLL5t1r19bfck797DGoiQtWLDArFvHTSv1WfLPVbz9tureGPCSwL1jjpXiPX36dLOtNwe8853vNOsTJkyo1LzU63Uxh3JFGQAAAACAEhbKAAAAAACUsFAGAAAAAKCEhTIAAAAAACUslAEAAAAAKCH1GuiGdtttN7PuJYr26dPHrFspi17yopds6qXhbrzxxpWal267dOlSs/7KK680fJ/W/Ul+euUmm2xi1vfYY49K7e677zbbornqpB9vs802Zv33v/99rfv0+tj6zBvTlscff9ysv//97zfrf/jDHyo1XoNk2LBhZt1K2t10001rbePpp5826/369avUvARqb3zWSeVtFiup2JsDnnnmGbP+7ne/26zPnz+/oZpkpzFL/lxcZ2x1NVbiunec9vqpl2Rt9SVvbvfqHqsveenx3rmUxxpfY8eONdvWPYexztW8/uX1x5deesmsP//885Wad5621157mXXv10K8VPKOwhVlAAAAAABKWCgDAAAAAFDCQhkAAAAAgBIWygAAAAAAlLBQBgAAAACghNTr11EnZdVr2xEphnX2O8a4zvejMz1X3ZGXeLpy5Uqz3rNnT7NuvR5ef+nVq5dZ99Kwrfv0UhDrpDpKdvKklVwp+c+J56CDDqrUSL1eN7z+YSWU33777WbbSZMm1bpPjklVdZ4Tb3xNnDjRrG+++eaVWt3XrKsbMmSIWfeSaa3E3759+5ptrdR+yU6xlfzjusUbn978suGGG1ZqXnKu99i9bVvzlNdvBwwYYNYnTJhg1q+55ppKzUvw9p4T7zWeOXOmWe8OrDRn7/nx+oHVZyT79fZeE+/800vXt/alzi9utMdKrJ82bZrZ1uvrXhq29fi987E5c+Y0vA1JGjRoUKW25ZZbmm2txyj5r/2zzz5r1jsKV5QBAAAAAChhoQwAAAAAQAkLZQAAAAAASlgoAwAAAABQQpjX66gTdFU3FMsKybBCaST/i/Z1ggOaFdplhSnUDY/66Ec/atZ/9rOfVWovvvii2bZOYNn6ZujQoWbdC6BoaWkx61aQg/e8e2Err776asN1L4DGC6vwwi288K862/Ceq5122qnhbWPNeIEtXl+y+s0zzzzTlH2x+vvaDEBcX/z4xz8261aY1/pm/PjxZn2zzTYz6//6178qtXHjxplt58+fb9a98XLAAQdUagsWLDDbenOAd55inR80a2xZQUH9+vUz2y5evNisL1q0yKwPHjy4Uhs4cKDZ1js38oLCbrrpJrPeHey8886V2j//+U+zrfUcS9KYMWPMuhXsuXDhQrOtF2jl9T0v0MrinTd4rH5ad/+8czJrjeH1U+959c6lrOfWO9/3zt+8fXnkkUfMekfhijIAAAAAACUslAEAAAAAKGGhDAAAAABACQtlAAAAAABKWCgDAAAAAFBC6vXraEbi6ahRo8y6l/hm8VIjvTRsax+9REov9XerrbYy61Yyt5d2N2vWLLP+j3/8w6yPHTu2UpsyZYrZluRZX//+/c367NmzzbqX6milD3upjl5CqJdgbPVHL+3R67tLly4161Yf9fbDG1tWiqYkbbfddmYd9fXo0cOse8cTz6GHHlqp/eIXv1ijfWqrznHG66feNrz2dbZRJ/3f24Y3Nrz2Vt0bR942hg8fbta/9KUvVWof+9jHzLbd1d13323Wp0+fbtat4/eBBx5otrV+WULyf3HDSuX1eP3Zm1+sFPu6v6rgsfqjtx/ePGL1RUnabbfdKrWnnnrKbOuliVtJ5d2dlWT92GOPmW233nprs+6df1ivt/crGl7d6+tW//D6jHce5LH6pJVWLTUnVd5bM3jrES+x2qp7Cdle2rw3z48YMaJS68gkbK4oAwAAAABQwkIZAAAAAIASFsoAAAAAAJSwUAYAAAAAoISFMgAAAAAAJR2ael0nrVNauynHddPkLOPHjzfrXqKolWDpJe166bBeurF1n16q5Y477mjWp06datat58p7jC0tLWb9hhtuMOvW8026dfu23HLLSs1LMJwxY4ZZ7927t1m3kg3nzZtntvX6gMdKe/SSJL0USG9cWP3IO954SaheuubQoUPNOtpn9Y+66dYf+MAHzPppp51WqVnJtJL017/+1ax7qZrPP/98YzunevOFZKf+doS6+90MM2fONOubb755pXbWWWeZbb16V+el7z755JMNb+P444+vdZ/HHnusWbeOg9584f1qgffLGtbx23vs3jmad1y3tu0l/m6//fZm/dJLLzXrt956q1lH+6zXav78+WZb73zV62MW77hW51c+vPbeuYdX9+6zTluv7j1Oayx5bb3zHe/xWOPRW494ydne49l5550rNW9+9n4JoJm4ogwAAAAAQAkLZQAAAAAASlgoAwAAAABQwkIZAAAAAIASFsoAAAAAAJR0aOp13TTjuinZde7TS4Kzkt3qpsZ5yW5WIp3X1kvD9gwZMqRS89LrZs+ebdbHjBlj1l9++eVK7bnnnjPbeimTdV77ZiSSd2d1Upi958zrG1Yqr5dg6CX4eomn1nju06eP2dZLQq2zL16f8xJc586da9atpOZNN93UbPviiy+a9fWR9Zpsu+22Zlsvgfeiiy4y66ecckqlNnz4cLPt0UcfbdZPP/10s37fffdVag8++KDZdsqUKWbdO35bx1Iv1bXufGkdN71x7o27YcOGmfVBgwZVamPHjjXbeq/xuHHjzLp1jPIST9c33lxo9Y1mnV9ZScDesbHu8dtLNm50PyRp+fLlZt36pQRv/vPmKDSXd/yxeH3JO8+uc45c99zRGnden6m7beuc3xvnHm9sWPvojUVvv73n1drvxYsXm229190b/9avtowePdpsS+o1AAAAAADrGAtlAAAAAABKWCgDAAAAAFDCQhkAAAAAgJIODfOqq244hcX6krjkf5F9/vz5DW/76aefNuu77rprw/fpBVN4vCAiK5hj0qRJZlsvhMn70v+6VidobX00atSoN7wNLyDDCq7ynnerreSHxFj9zguzs4JZJH+8WI/H60deIJjHCs7Ye++9zbaEebXv/PPPN+tXXXWVWfeCRaxQEK+fevUXXnjBrFuhYEceeaTZ1utjdYIovbbe/OeNuzrjy7tPr71VX7hwodl28ODBZt0LhLKCwo4//niz7bXXXmvWu6u1GWDpBe4sXbq0UqsbzlVnXNTt/16QkcUb+3XPuyx193t91Ldv30rNO+56YV7ea2idqzcjYNfj9bu6gcEWb7893uOxnivvXKrufVq8x+jdp/caW/N8nSC4ZmOlAQAAAABACQtlAAAAAABKWCgDAAAAAFDCQhkAAAAAgBIWygAAAAAAlDQ99dpLgvNS2Sx1k9P69etXqXlpat42RowYYdZvuukms27x9nvx4sVmfYsttqjUnn/+ebPtgAEDzLqVIihJs2bNqtS23XZbs+2QIUPM+h133GHWrRTTl19+2WzrJek1IwlybSaAdiXW67dkyRKzrZcc6PUvq+96KdE9e/Z09tBmvX5101S9xF9r297Y9+p1Uoa9cYj2PfLII2b90ksvNes77LCDWV+0aFGl1tLSYrb15iiv/1rp0d4x3fuVBC+t20rb9caAl6rrjV3ruOCN3bqJvdZz4s2h3thYsGCBWR85cmSl5r2WSKzXr+4c67W3tu39eog3v9T55YNm/ZqFdZz2zgubkXqN11fnl168NYPX96zX2zsP8o6DdX4pwWvrnfN69bXZ96zH6c1z3hxVJw3bG1/e+Zv3qz1W3ZoX1hWuKAMAAAAAUMJCGQAAAACAEhbKAAAAAACUsFAGAAAAAKCEhTIAAAAAACVNT722Ugzbq9fhpXtaKXhz584123rpzDNmzDDrm2++eaU2c+ZMs+0+++xj1j/84Q+bdSup9Ytf/KLZ1kr2lvyUOStd0EsLvPbaa826l1RnpbIuXbrUbOsl5tVJPL3rrrvMtocccohZX98MGjSoUrP6luSnQHqJp1aKqZcS7SVQe6mOVhqu148WLlxY6z6tx+P1Ra+fe8csazvDhw8322K1Aw44oFL705/+VGsbc+bMMetWYqf3+nlJm14attV/hw0bZrbddNNNzXqdNNU6aeuSnzRspcbWTZitk4Tsbds75ni/WmA9zrqJ+mge63jn9Tnv+O2lXlu8cehtw+u7Vj/yxiH9q7m23HJLs26Nee/189L/vXnc6pP9+/c323p9zDvGWvtYp0+3xzpueo+xzjFTsp9Dbwx466s6c4O3f17KuNfeOt+zfm1nXeGKMgAAAAAAJSyUAQAAAAAoYaEMAAAAAEAJC2UAAAAAAEpYKAMAAAAAUNL01OsxY8aY9cMPP7xS8xKbx48fb9a/853vmPWpU6dWalairuSnknpJ1v/7v//b8P7Nnj3brHus1DwrGVaSnnzySbPupcZZSb6PPfaY2fb88883616Sr5VY7aXuWWm0kp9oOGTIkErtueeeM9sisV5rLzWxpaXFrHup0laSpLdt77X20oettEcvYdG7T6+9lWLqJYF7++0dQ6x9GTVqlNkWq1kJpHfffXetbYwYMcKsW/3UOzZ6Sc4ea3x5yaHetr19qfNrEF5Sqzc2rGOyl1bs8fbbmru85GBvbvCSUK1k81133dXbRTSJ14+s19rr/81IpvbGRN1tW33XS2D3flUEa8Y7zljn/N5xwDveecnqXvs6vH5t7aPXti7vV0Qs3rHUY40l71xv3rx5Zt37lQhrfeC97nXXB9ZzO3ToULOtd07bTFxRBgAAAACghIUyAAAAAAAlLJQBAAAAAChhoQwAAAAAQEnTw7y23HJLs/6pT32qUrv11lvNtl4o1t57723WJ02aVKl5X5D3wkkGDBhg1q0Asfnz55ttrZAryQ/JsL747oWhec+rt20rgMYLMrvuuuvMuhea4D2HddouXrzYrFvPiRf61ozwhu7ACiLxAlG8wJ2nnnrKrFt92ttGnX4u2QEZ3mvqhUHMnTvXrFvBUV6Y10svvWTW64SJeIEkWM0K8POOpZ7NNtus4bZef6wb5mW197bhBbx4x0GrfZ2AuvZY+1h3G1bgnmQ/Hu/59sZRnbH+29/+1ttFNInXp63XqU4IXXu8gC5L3fAkq697IaLemMOa8QKWrNfEO254xyrvvNQ6z/D6l9d/vXOVOv3U442vZpw7ePOL1a9Hjx5ttvXOyesG8tZp69Wt59sLV10XQapcUQYAAAAAoISFMgAAAAAAJSyUAQAAAAAoYaEMAAAAAEAJC2UAAAAAAEqaHvX3t7/9zazvtttulZqXaufVvWTq3r17V2peCpyX4OalHlqJfF5Kn3ef1v5Jdnrf888/b7adOHFiw/sn2Wly3mP0EgC9VFLrcXrb8JL+vPZW2p33mtVNr+2urNfDSxP0ks+99GgrKXrIkCFmW+8+vZRKqw94qZMDBw4063WOIV5q6uDBg826l7RtPd9eojZWs8Zr3fTcoUOHmvU6ibjefXrbsI6bzUjg9bbjpaDWTWq1nm9vvvAej7ff1nHa27a3397xwtrviy++2GyL5vH6gFWv88sXUr3zqzr70d6+1BmjdRJ88fq8c3Xr9fbOPaxf85D819s6Dnp9wDtWNcPa7KfN4O2Hlypt/YKItx3vefW24Y27adOmVWovvPCC2Xb48OFmvZm4ogwAAAAAQAkLZQAAAAAASlgoAwAAAABQwkIZAAAAAIASFsoAAAAAAJQ0PfXaUyc9esmSJWZ93rx5zdwloFvwUnItdRKoJTu90hufddOErf32kqYXLFhg1r32VtK8d/zwUry9VHWr7qV8YjUrvd5LbF6+fLlZr5PCbNUkvz96+2LV66ZH10mV9lL+vV8hqJM27aUP132urPbe/nlj1LvPXr16VWpeMj2ax3v9LF5yrnfM9F7rOunD3ra95FxrbNXto1gzXoLy9OnTKzXvl17Gjh1r1r1jmHVe4h3T66a2W9vxtlF3bHjHe0vdhOw67esmgVuvg/faePU6SeCzZs0y286ePdvbxabhijIAAAAAACUslAEAAAAAKGGhDAAAAABACQtlAAAAAABKWCgDAAAAAFCyzlKvAawdw4YNq9S8FE8v9dNLh1y0aFHD2+jXr59ZnzNnjllvaWmp1LxUYy/Z20tNtB6PlwDpJSx7yZ3Wc+KlOmI1K91z5MiRZtuJEyeadS9x3VI3fdTr13XSo+uOLytp1EsI7tOnT8P7J9n93dt2M1KMvcded+ySIN8xvL5h9QEvIbfucdDqA16fqzunWeOizmOU/PnIS9pG4vWDTTfdtFI74IADzLb9+/c3697xxHoN65wfSNLixYvN+ty5cys1r2/UTcOuo05KtNe+GfvhbccbF17d2+/Ro0dXat4vH1jnkc3GFWUAAAAAAEpYKAMAAAAAUMJCGQAAAACAEhbKAAAAAACUkEADdHH3339/pfbhD3/YbOsFa82fP9+sDxkypFKbN2+e2dYLwvDCVixeqIoXDlIn4GjLLbc02953331mfdtttzXrVqjETTfdZLbFao8//nilduihh5ptvTCvO+64w6wfc8wxlZoXtuIFuXihWF57ixdOUicsyAuxmT59ulnv3bu3WbfC9awgOskfo3XCvLyAJy9AyQvlsULfvCAXrH1W//de6169epl1rw9Y/d/ri959eu3rBIV5+10n+Mjb9vpo2rRpZv3FF1+s1LzXdfLkyWZ9xowZZt069nqv3yabbGLWn376abP+0EMPVWre8cvjzQ3NCIbriDAvy9ocA3WD+H7zm9807b65ogwAAAAAQAkLZQAAAAAASlgoAwAAAABQwkIZAAAAAIASFsoAAAAAAJSQeg10cVOnTq3UvMTmH/zgB2bdS01897vfXal5qYleavDy5cvNusVLwPRSr7321uPZfPPNzbaXX365WR85cqRZP/jggys1K80Tr2UlLnt9ady4cWb9ueeeM+ujRo2q1LyU6LrJ1NY+etuomx5tpa96yatXXXWVWR8+fLhZP+644yo1L6nVex3qJI1649y7z5UrV5r1gQMHVmpeuj2ax+vTVh/w+rPX/73X2tpO3XTbOnNGnfmivbql7n53Z/fee2+teldU57xmbfPOvbqTjhxHXFEGAAAAAKCEhTIAAAAAACUslAEAAAAAKGGhDAAAAABACQtlAAAAAABKSL0GurgHH3ywUvv73/9utv3hD39o1q10a0maN29epWalF0v104StupdK2qtXL7O+ePFis26lQHoplTNmzDDr3/zmN836pptuWql5ScVo33XXXWfWP/jBD5r1pUuXmvWzzjqrUvvSl75ktrX6tOSn1vbu3btS8/qjtw3PwoULKzUvnf2kk04y614ivJVk7aVbe2O0zuNpaWkx694YtdKtJWnatGmV2ne/+92G9wNrpmfPnmZ9xYoVDW/D64vNSAju0aOHWa+TVu/tn6dO6jWA7omjAAAAAAAAJSyUAQAAAAAoYaEMAAAAAEAJC2UAAAAAAEpYKAMAAAAAUBK8xMA13mAIzd0gUFOMsV70bBN1t/5/zz33VGr9+/c323pp2F5yrpVAaqVVS9KSJUvM+oYbbmjWrURdK2FYkvbee2+z3lV1ZP+X1u4Y2GGHHcy6lU77la98xWy7++67m3Wvj1nb9uZNL7XdS9u16l7SrjeOvPbWvnip13Xv0xunlo033tisP/roo2b9C1/4QqVWN1V+fZsDrNep7rnd8ccfb9a947rFS872ks8t3n57qdd1k+YtQ4cONeuXXHKJWffGeWfRnecAoBHNHANcUQYAAAAAoISFMgAAAAAAJSyUAQAAAAAoYaEMAAAAAECJnTACoEvzQq7qhPBI0ogRIyq1LbbYwmw7adIksz579myzbu2jF3rkhRCNHDnSrI8ZM6ZSmzp1qtm2Lmu/6z6vSOr20yeeeKLhbXuvd69evcy615csw4cPN+vz588365tssolZt0KBvPAkLxTLGxuWlStXmnUvbGnWrFlmfd68eZWaF8514403mvUnn3zSrKNjbL311mbd6zOWZcuWmXVvnPfp06dS88a+t20v/MsaL978YoU/StKgQYPM+syZMys1LxCvzvgE0PlwRRkAAAAAgBIWygAAAAAAlLBQBgAAAACghIUyAAAAAAAlLJQBAAAAACgJXmLgGm8whOZuEKgpxhg66r67W//v0aNHpfaVr3zFbNu3b1+zvummm5r10aNHV2peOurzzz9v1r0k1Ntvv71S+/nPf2627W46sv9LXXcM9O7d26zvvPPOlZqXnusl3w4dOtSsb7bZZpXa/fffb7a96667zHp3Yz2HdZOD17c5IITqw617bve2t73NrFsp1HW37Y0XK/Xde62txyjZc5QkLVy4sFKbO3eu2dZLvX7ggQfM+vLlyxvev2afYzeCOQDru2aOAa4oAwAAAABQwkIZAAAAAIASFsoAAAAAAJSwUAYAAAAAoISFMgAAAAAAJU1PvQYAAAAAoCvjijIAAAAAACUslAEAAAAAKGGhDAAAAABACQtlAAAAAABKWCgDAAAAAFDCQhkAAAAAgBIWygAAAAAAlLBQBgAAAACghIUyAAAAAAAlLJQBAAAAAChhoQwAAAAAQAkLZQAAAAAASlgoAwAAAABQwkIZAAAAAIASFsoAAAAAAJSwUAYAAAAAoISFMgAAAAAAJSyUAQAAAAAoYaEMAAAAAEAJC2UAAAAAAEpYKAMAAAAAUMJCGQAAAACAEhbKAAAAAACUsFAGAAAAAKCEhTIAAAAAACUslAEAAAAAKGGhDAAAAABAyUYdvQMAAAAAsKZCHi6Q9E5Jm0vaKWbxsdJtwyT9QtI4ScslnRyzeGdxW29JF0vaU9IqSV+IWbzGuY+9Jf1YUm9JL0o6LmZxWshDD0lXSRor6TlJx8QsvhLy0EvSTZKOilmc62zzdkmbSVqgtC6bLOlDMYsz3sDTYd3PNZK+GbN4T8jDWZI+LmlqcfNKSZ+KWbz7DWx/hKTrJL0lZvGVN7q/nUW3WSiHPBwh6RxJPSUtkfSxmMVHitv2lPRdSX2VBsHpMYt/cbbzOUn/qXS1/Smlzjov5GGApGslDZF0R8zix4v2Q5UGx6ExiyudbU6UtKz438aS/iHpIzGLi9/A4z1N0pUxizPbabO2B8WBknrGLN5U/N1L0l2SDo5ZnL+m20VjuuqkUGz3FEn/LWnDovSwpDNiFiev4XPxLklTYxbvb6fNqZJ6xyyeW/w9TtK5kvaQtEhpTFwYs/izNdyHAZI+GrP4v6XatyU9GLP4qzXZ5vqqK/btkIc/ShpV/LmLpH8W+7AwZnG/Bh7ziZL+X8zi0cZte0j6dMzisc6/PVClY7HT5p2S3i7pZ8X/JGmQpH6SJhZ/XxGz+M0G9vV2Sd+KWbzBuO1sSY/HLP7G+benqTR3hTy8Q+lxf+z17rer64r9utjm/pLOk9SidI41V9J7mn0iX7q/KKlvzOKiJm/3O5Luj1n8dchDf0k/lDRB6TndQNI3YhavbOZ9lu77MqW54AdvYBtB0t8knRCz+EKz9q0buVrS/0q607jtXEn3xiweUawJrg55GFcs6D4raXnM4lYhD2Ml3RPycFvb8VA8/1dIOilm8faQh89K+o6kD0o6XNKcmMV3hTxcIukISTdIOlPSD9o7Hyqc2no8DXm4sNinz63Rs2AIedhL0oCYxXtK5V/ELH62uP0YSd9TOsaskZjF6SEP90k6TtJlb2B3O5Vu8dHrkIeBkn4p6fiYxZ0lnaHUmVs79rWSvlLc9gFJPw95aDG2c6ikEyS9KWZxB6WT968XNx8n6baYxZ0kbRfyML6of0dp0jIXySVHxyxOkLSD0onJiWv2aP/tNEnDvBvbGRQTiv34ptKgeCMOlHRY6x8xi8uUnvdPv8HtojFXS9pX0iTjttZJYWtJH5J0RchD6xtj/54UlA7uFxZj6DVKk8JpMYvbSLpRqb9LqyeFXSTNU5oUpAYmhZCHXGk8HRGzuL3SmPiZpBGNPnDDuyTt1c59tij1yx8Uf49QmkxvilkcW4zrQ/TG3jwcIOnzbWrnSTor5KFbHGvXoS7Xt2MW3146vkrSm4u/X3eR/HpiFh9sZ5G8kdocix1fl3Rusa3W/TxT0i2tfzeySG5gX8+0FskhDxsU4+A0leaumMX/k7Rn8cZVd9fl+nWxD9dK+njRR3aQ9GFJa/xG/9pWet7KtdGS3iaptW+eI2mG0hsWO0t6k6QH1tlO1hTysFHMYpR0vqSso/enM4pZ/FvM4hTn5mOU3hhRzOIDSq/9vsVt7y/d9oLSmxFHGdvYQ2kc3l78/WNJ7yrehFqp9OaUiv+uCHnYSdK2MYu/bfQxFH23r9KbUQp5GBHycFvIw0MhD4+HPFxQjHOFPPQPefhdyMOTIQ9/CXm4POThW86mP6ZiXeQY2HqfxbZ/GfLwYMjDoyEPNxRv5LXe9vWQh2dDHu4LefhmyMODpe1cKekjjT7erqC7XFEeJ2lmzOK/JClm8a8hD5uHPOym9BGGQTGLtxW3PRnyME/pgNn2HdldlK4WLyz+vkHSbZJOUTEIiol+Y6VBcISkuTGL99bY140l9dHqQbCh0sl066R1m6TPxCyuCHkYLukiSVtJCpIuiFn8ScjDmUpXLq4OeVgm6cSYxYfb3E/dQXGEpG8o9Ym5kv47ZvGJ4rbPK11lXyXpUaUr02MlnSxpg5CHQyRdE7N4tqRfKU02Z9V4TrAGYhb/JkkhD9bNxyi9RopZfCDkoXVSuF1pUjixuO2FkIfWSeGyNtuwJoWZDUwKX/L2OeShj9JicvfWCa2Y/G8ttTm+aBOVroh8NGbxpWL7FyqNn16SLo9Z/J+Qh7crXaU5JOThJKWTvrZXhY+WdGfpCsUpSmP9p60NYhbnKI03eWOvuO2bSguTHpLmK727/EzRfkDIw8OSXolZ3CNmcWbIwwuS3irpZu95wWt1xb7dqJA+hXSFpJFKffyhmMUPFTf3DXn4laTxSlcVj4lZfL64YvytmMU9Qh62kPSgpAskHao0j1nH4vJ97idpXszixJr7+g6lBfYqpbnhyzGL1xU37xfycIbSXHRzzOLJxb+5TMWVs5A+yTROacxuJel3sueu3yotvr5cZ/+6mi7ar/sW/5tWehxPtf7/4tMF90l6s6p9oa/SQn0XpWP23ZI+GbO4MuThdKUrcRsV+/bJmMX7yndcLAjOlbRd0Xa00qcDhyld2f5xzOKFRduo9IbCO5TOQdpejfuwpKuL+UZKH3X9a+vfxXnfwmJbJxb3N0dtxmJx+/GSPqE0ByyUdErM4mPeHNX2CS3G40VK51WPSPqapIOLx/Sk0qcJ5hVjaYGkbSRtKmlHSf8n6aKQh76lc1W0I+RhsKQNYhZfLpUnKvUBFf+d5NxW9pp2MYsLQx4WKh3Lb5Z0dMjDI5LulfQXpTeqPmRsx3JByMM5Sn18mlL/ktKbWu+IWVxUrBeuk/RepTfdzlRag2xXvHH2d6VjrOVASW0X0ScUc0Z/pQt4h5duOy1mcZYkhTx8obivTxRzwv9TGtNLi/0oe0jSriEPfeIb+NRsZ9JdrnI8I2loyMM+khTy8G5Jm0jaonihZ4Q8vLe4bW+lg84WxnYelHRoyMPw4gB9nNKJyyClE5utlD42fYukl5Qm9UYn9quLE+gZSidHre8wfVTS7sX/JiidVHyquO0CSU8WV7sOlvTVkIe9ipOgqSquUhuLZCkNirYfqz4h5OHh4sT9G5K+JP37I1+/lPSfxTurP2ndv5CHtykN9LcU+7FY6eNJDysd6FuvUp8tSTGL05Qm4O0afF7QZGtzUlA6KWidFBYWk8J8pUnhO1rddz07SlrR+iaMse/jlT7tcETRF+9W6o+t+3lIzOJuSuPlmJCHPWIW/yjpeqWrZROMRbJUHQ+7S7rHaNfKHHvFbefFLO5ZXJH7kdI7/FJarMwr9mGP0rbuVloo4w3q5H27UcdJmhiz2Hol6zOl2/ZW+oTSTkrzzBnONgZLejZmcb+YxfNlHIvbOFDV+aAR5yidtE+QtLOkv5ZuG1dsd7ykw0Me3uRs46BiGzvHLOay5671eox05n5dXGm+UNIzIQ9/DHn4asjDNm2aeX3h25L+FrO4l9KJ9UZavQC4vDiO7irpVKWPlpf1kvRrpUX9u5UWq1cqXUjYU+kK8MnFBZFWG8csHhizaH1k9UC9dgx8V9KXi6tmPwh5+H9t2ptjMeThLUqfTNy/mIu+otUXJSbKmKPKGw15+KDS/HJkzOKDSgv6RTGLexXj7HFJeemf7Ks0XnaUpJg+vfiYpLcYjxG+2Obvtu9WxXZue93txCyuiln8SMziLjF9jeQTkn4vaaOQhyuLK78Ht7PdU4vXf5jSJzguKeobSDqvGLf/UHozbEJx20GSLi3uf25xf54xkqa3qbXOGWOV3qi7Jqz+tO2xxdj4p6ST2tznb2MWF8csrpL08/IGi/45T+mY0y10i4VyTN+Hfa+kc0MeHlI6ID6h9C6llN55PSnk4e9KV0PvLN1W3s7tSgf2PyidRLe+g7oyZnFRzOLRxSDIlN5lP0/SViEPVxX/26Wd3Wz96PVgSS8U/1ZKH/e8OGZxeUzflfhpUWu9rfXjIDOVrhw0ejJRZ1DsLenhmMV/Fvd1haQxIQ8ji324ImZxXrGNH5X2zzO9uH90nM48KbTdZtlBkm6IWXyp+PtCSQcXb1y1SPpZceC+V+l7fhPa2VaZNR7a097YOyzk4Z6Qh8eU3mV9vX1gPDRXZ+7bjbhX0hEhD98O6XvD5Xfd74xZbF3o3KO0ALEsU/r0TqPq9v9Wt0r6bvGpop1L84Ak/Tpm8dWYxaVKX1Py9vWG2E6WRoEx0on7dcziaUqL4N8qXWj4R8jDvqUmXl94l6TPFRcJ/iFpP0lbF7ftGvLw1+I4epGkHUIeepa2+Sel85JPFifk2yq90frrYnt3K13p3qH0by6R7zVjIKZPGW6mdAyfJ+nHIQ8/LLX3xuJRSov++4r9+L7ShZqeev056kNKb0wcFFd/uuNdko4rLmI8rHQle8vSv/ltrH5Xm/FSQ8zibOnfn+ZptbnSJ05V/HcL57ay17QrPjHxmk9bFPXNlfIgfqT0aYGfKH0i5PsN7GtU+npA66dMT1daN+xdvLF6pdKbSFIa2+2dT5UtUeqf3v3eWmx3fDG2PyHpbcUbRafXvM9eSlebu4Xu8tHr1o80HShJIQ8bKx1IWj+K/ajSR61V3P4vpYW0tZ2LtPrjl/tImtL24y3FlaWhMYs3hDzcIel4pc5zmaQDXmc/Xwl5+J3SVbPPyO500fn/1t+e1kHhfZ/u1pBCPMY7+9B6X6+3f5ZuNUi6mpjF2SEPCnkYWrpCYU0K5dv+aGyq7qRwhNK7iz9R+vjNvUonNmVPSNo45GEH56py2/5W/v/fUPpExq7FOLpGqw/er6ftJPGQ0hWJ8+3mlfuWpBjysJnS1YC9YvpI7M5KV2baw3hokk7et03Fx9Y+UPx5Rszin0MeJii9GfNeSeeEPOxa3L6s9E9flT9HL46rP0LaiHZPkkr7erfSFbzlMYt7xyyeHvKwo9IbWD8Pebgirg6ra3RfGwlkWq/HSFfo18Wi8TJJl4U8LFZ6s701NMnrC0HSu2LxkeXS/fdU+ojogTGLD4U89FO6yt1T0oqi2a1Kb0r+oDgHC5JmxdUZAJb2+lplDMT00dA/SvpjyMMNSqFmpzTwmC6JWTyz7R2EPLzeHPWIpP2Vzrtan7ug9P1vbx6xHtN6PV7W0FVKr+1ZIYV5teaUlG87MaQwrwOUPiHW1kOSeoU8HFhcWPuYpN/HakbR95TCF1eF9HWzqPT1lT4N7utblcKEpfQ1yekxi8tC+krY+7T6e/a3KX18/+6QwkSPUvUrpa0eVfoKw1TrxuJC3yZKn4rYR+kj/3OKsVoOWrxNUh7y8F2lMXJ8m+0Ml/SKdz9dUbe4oixJxdXPVl+V9JeYxWeL20aU2n1E6R18L/V6ZPHf3pLOVkrQK9/eo6idXpTKg2CTBnf3YK0eBDcrDc6eIX2J/7+UPuaj4r8fLe53qNLHj1r3e4HS9wo8rYPC1GZQ3CNpQsjD9sVtH1B6g2B6sX8fKCZcFfvTun+VfQjpOxRbKn00CB2n9cCvdiYFlSaF641t/HtSKP5+3UlBrx0PlUmheGf8W5J+GvLQmhCskIe3h/S1iFslvb00Zk+WdGuxKBio1C9fCXnYVun7ma3qjocLJR0Q8vDv7w+FPAwKKZFX8sdef6UTuenFVe5PlLa5QCnHoO2CYXulEyQ0R6fs256YxdavBEwoFsljlT5q+VtJn1S6Qtfo3OF5Q/NBaV9bA8j2lqSQh+1iFh+PKan3R0onUG+Uta+MkU7ar0MeNgl5eFtYHSDUovR6PdfAY7pe0hdaj4khDwNDHrZSWuj1UMqgkNI4aOtrxb+/OaTvXz4laUnIwwmlfdsqpK/GNeI1YyDk4bDw2kC03Rt8TP+n9DW2TYvtbBBWf7y6vTlKSt8hfaekS0MKj1XxGE8vzjkV8tC7eHOqPYwXQ8jDD0Mepihdbb8l5OHZ0s1nSHpzyMMzSm/4HB9X/4TRNyW1FO3/rPSd8znFNk8OKclfxXg5TtL3Qh6elnSkXvvVGYU8/IekR2IWHy9K5yq9uf6gUp/2XFB8quBRpUyV/2ytF/v9sNInJm4p/ZuzJQ0LeXhC6UrzXUpvOFmuVumCYaH165iPKL1hdnzxRt2Nkp5V+r78n5U+JaLiObi+qD2itGh+rs19HiHp2ppv5HZq3eaKsqSvFR8X2Ehp4fdfpds+FvJwrNI7d/+S9O7WF7EYAFOLK8mSdFNIgV09JV2uIiW35HOSfh5X/yzCmVr9zm57Ue6t4SU9lBanre9W/UTpIz1/L/6+XWlgSOl7OxcVA2cDSV+Pq3/+5gKlg+0S2WFerYOi/IZA6xf3W6/atQ6K1nCKK4qF7jyld4sVs3hjSAEV94QUltEa5iWl71EcXwzg1gCZfSXdF/l5qLUupI+JHaV0QnVLyMOimFJRpTQpXF5MCitUnRQuKSaFVWozKUgaFVNy7aqQh+OU+mCL0vfyj2uzD9ak8FOl8eNNCmcqXRm5qehvUeljeV+IWXwx5OGLxW1SEeZV/Ltzisd0rNIYKvfty5WudLxPdpjX1UqTzJlS+i59cbw4N6RwvIVKX8do/eidO/ZCHq5S+h7ZZJUCumIW54Q8XCHpnyEPi2MKXgpK7w5/w3kuYOjCfbsRByqdGL+q9PNon4tZnB/sgKdGWcfishsknRnysGHM4qs1tvs/IX0fdYXSFbn/fiM7WbDmriPkh9B0G120Xwel85XvhTwsVTqH+ZNWHyvbc5rS18weDnlYpXSMPSNm8dniuHt/yMNk2Yt+xSyeH/KwSOlYf4RSUNf5If0sz4ZK84iZBm+4WulXTVo/nr2TpG8Xx+hVSlfdj3P+bXmf/hby8CVJ1xXzVw+lr+s9qPbnqNZ//0RI4al/COnTJucqpVjfV5xjSek5e7ztv5WkkML8FEs/LYYkZvEUrf5EQNvbZsj5ZYDikwXvd267qM3f9yh99N7bhyvb/H1/e+2LNge2c9sk+b/osVjSB4urzf2U3lj7udP2MqXz+LNi+n7xWXJCd4vjTtvno5zH9PWYxS8Ua6Wf6bV5Lyepm6Veh9h9Fv0oCekK8D1K32tYZ8lzIQ+/lvSzmMVbXrcxsA6F9Du3WUw/DbGu7vMIScfGLB7/uo2BtSik3+a8LWbxqo7el7KQhyFKnyTZM2Zxxeu1B9ZEcVL/gNLvRXs/IdTphTycK+mZmMW24WdYz4QUxHuj0ptGLUp5QlaYY2v7Q5Q+xv2G3mQJebhW6SseLUoX+U6OWVwQ0seu39r2zYKurjtdUUZJTLH1pyn93MQ6eecxpO88384iGZ3UqWrg46dN1l9+cjGwLp2p1QExnck4pZ8jZJGMtaa42v4xpRP8LrtQVvru56UdvRPoeDGFJO5eo31Tzs1jFt/t1GcofQS8W+GKMgAAAAAAJd0mzAsAAAAAgGZgoQwAAAAAQAkLZQAAAAAASlgoAwAAAABQwkIZAAAAAIASFsoAAAAAAJSwUAYAAAAAoISFMgAAAAAAJSyUAQAAAAAoYaEMAAAAAEAJC2UAAAAAAEpYKAMAAAAAUMJCGQAAAACAko2avcEQQmz2NoE6Yoyho+67s/f/DTaw3xuL0d5tr2655pprzPrAgQPN+syZMxu+vy233NKs53lu1v/whz+YdUsIdnfx6tY+1nme1raO7P9S5x8DzbDhhhua9VdffXUd7wkszAFYnzEHYH3XzDHAFWUAAAAAAEpYKAMAAAAAUMJCGQAAAACAEhbKAAAAAACUhGaH0PAlfnS07hrk4oVLbbRRNZNv5cqVa2s39OMf/9isH3DAAWb95ZdfNus9evSo1FpaWsy2m222mVnv37+/WT/iiCMqtZtuusls2wzWayBJr7zyylq7Tw9BLokXXGf1O0lavnz5G77PzTff3KxfeOGFZn3ixImV2pAhQ8y2w4YNM+sHHXRQYzvXDu+58uod0a/r6K5zANAI5gCs7wjzAgAAAABgLWGhDAAAAABACQtlAAAAAABKWCgDAAAAAFBCmBe6HYJcfF4g0CmnnGLWTzrppEpt8eLFZtu6xxIrtMgLIZs3b16turXtpUuXmm2vvPJKs/6LX/zCrL/00ktmvbNYH4NcrNCpVatWrevd0H333WfW99prL7O+YMGCSm3ZsmVmW6+v33///Wb9+OOPN+vN0Fmebw9zANZn6+McAJQR5gUAAAAAwFrCQhkAAAAAgBIWygAAAAAAlLBQBgAAAACghIUyAAAAAAAlpF6j21nfEk+PPvroSu2d73yn2Xbs2LFmvW/fvmZ9yZIlldr8+fPNtqNHjzbr/fr1M+vPP/98pTZr1iyz7bhx48y6ty8LFy6s1KykXkkaNGiQWX/llVfM+uTJkyu1yy+/3Gx70003mfW1icTT9u2xxx5m/aijjjLr+++/f6U2cOBAs+3IkSPNupdkbSWoe+Nozpw5Zt1rb83tjz32mNn2+uuvN+s/+9nPzLo1vjqT9W0OAMqYA7C+I/UaAAAAAIC1hIUyAAAAAAAlLJQBAAAAAChhoQwAAAAAQAkLZQAAAAAASki9RrfTXRNPt9hiC7N+9913V2ovvvii2fbVV1816xtuuKFZt5KfZ8+ebbbdZJNNzPqYMWPM+q233lqpbb/99mZbj5d6bendu7dZ79Gjh1lfvny5WR81alSlttFGG5ltd9llF7O+YsUKs94M62Pi6cYbb1yp/fGPfzTbbrPNNmbdmwutMeO9fi0tLWa9V69eZt3ab6/feX3MS6BeuXJlw9vo2bOnWV+6dKlZ/+53v1upff/73zfbdoTuOgcAjVgf5wCgjNRrAAAAAADWEhbKAAAAAACUsFAGAAAAAKCEhTIAAAAAACUslAEAAAAAKCH1uov78Y9/bNb/9a9/VWpWUmlns8EG1fduvKRWL3m2uyaefu1rXzPrH//4xyu1559/3my7ZMkSs96nTx+z3r9//0pt8eLFZttZs2aZ9UGDBpn1EKovk5di7SUBeyneVt1L5fb6l5eGbe33lltuabY999xzzfq3v/1ts94M62Pi6dVXX12p7bvvvmbbqVOnmnWvH1hWrVpl1r0+49WtZGqrf0l+Mr1Xt9KwvcfonQd47a0E+T333NNs+/LLL5v1tam7zgFAI7rDHOAdB+usWSZMmGDW3/SmN5l177huHe+8XzJYtmyZWffOsaxfFlm0aJHZ1jsPsrYh2fvo7Yf36wne8209J9454GOPPWbWvV9VeOqpp8x6HaReAwAAAACwlrBQBgAAAACghIUyAAAAAAAlLJQBAAAAACghzKsDbbzxxpWa92X9vn37mvWHHnrIrA8YMKBSe9e73mW2vfvuu8269WV9yQ+E6iy6a5DLww8/bNZHjBhRqXkBOl5YxeTJk836sGHDKjWvX3ghX1ZgkST17Nmz4f3z6rNnzzbrVrjF8OHDzbZeKNyQIUPMuvU4R44cabb1ntf99tvPrDdDdwhyqeuf//xnpWYFA0p+SIzX3guMs3j91AvFsvbFO75a80V792mF13lBM942vJAY67jwi1/8wmx7xhlnmPW1qbvOAUAjusMcUDfM67TTTqvU/uu//stsO3fuXLPuBSNa5+XesdGbL6zzHck+9nphXt59eo/Hau+dv3lhXl7glrXGGDNmjNl29OjRZn3BggVm3XoOv//975ttf/e735l1wrwAAAAAAFhLWCgDAAAAAFDCQhkAAAAAgBIWygAAAAAAlLBQBgAAAACghNTrDmSl+nmvxze+8Q2z/tGPftSsWyl4XnK2lwbsee655yq1sWPHmm29VOK///3vZt16/HPmzDHbHnvssd42umXi6dNPP23WvaRGS//+/c36xIkTzbqV9tivXz+zrZdg6KUJWymQXjKkl8g4f/58s26lDFvp4JKf+OuNxT59+lRqXjLk+PHjzbr3nDRDd0g89ey4445m/U9/+lOl5r0mXvKzl0zdDHVeb2//WlpazLrXf6dPn16pWWnV7W3D22/rGOAltW6//fZmfW3qrnMA0IiuNAfUTbf20v+feOKJSs37FZk6v2Qg2XOJt39e3ftVEOv8rUePHmbbwYMHm3XvOG0d173nxJtfvHMsaz1Rd8719tv6xQbrvEuSJkyYYNbnzp1L6jUAAAAAAGsDC2UAAAAAAEpYKAMAAAAAUMJCGQAAAACAEhbKAAAAAACUrL2YT/ybl0psJe95bd/+9rc3vA1J6tWrV8PbnjFjhln3DBo0qFLzEk+9FOO99tqr4fvz0p67q9GjR5t1L/Fw6tSplZqVGij5yYFjxowx688//3yl5iWZe/f5yiuvmHUrkdHrz3XSESU7YdLb70033dSseynx1n7PmjXLbOslenrpwzNnzjTrSA499FCzbiWaT5kyxWzr9bE6yc9eW0+dX5fwjtPeOKqTGls33dpLQrW2vdVWW5ltAY+VNO/18/WZN8a7A+/Y4x2n9957b7M+ZMiQSs369RdJ6t27t1n3fvlg5cqVlZp3nPZeK69uJUKvWLHCbOsdv71Ebet8b8mSJWbbAQMGmHXvV04sQ4cONeve+sB77a1fUPHSzt/97nc3uHdrjivKAAAAAACUsFAGAAAAAKCEhTIAAAAAACUslAEAAAAAKGGhDAAAAABACanX60CdVL8jjzyy1ranT59u1r0UY4uV6CdJPXr0MOtWwp6XAOjVraQ/yU4xfvLJJ8223ZWXwmyljUvSpEmTKrWFCxfW2vby5cvNutWPvG3PmTPHrFsJ7JLdv5YuXWq29fqRl+Bopa3XSW+UpIEDB5p1K8HRe/68tOMJEyaY9ZtuuqmxnVtP7bnnnmbdSsn0jrveOPJS0a200rWZQuv1dY+3L1tuuWWl5o0v71jvjV0rldVK35ek3Xff3aw/9NBDZh1dV51f+JDqJVz/7Gc/M+tbb721WT/ggAMa3vba5B2HvHFrPVd1UvO7Gq9veA4++GCzPm/evErNO1eZP3++WffSo63n3+vr3uvt/QKMtY91z3f69+9v1q399p4T79dvvDFqnQd55/VeYrV33mS195LKvV9yaSauKAMAAAAAUMJCGQAAAACAEhbKAAAAAACUsFAGAAAAAKCEMK91wAvLsowePdqse1/u98JZnn766UrNC70YMWKEWffCmawv1a9atcps6wUb1Am4sIJjurOxY8fWam+FM3jBVV4/8sJCrNd1o43sw4YX/OO91nWCXFpaWsy6F0BhhUH07dvXbOvtn/ecWPvi7Ye3jbqvMZLBgwebdSuIywuo8kJVvFAZazten/GOg15wj9U/mhUUZu2L1x+9x+7tizWneceFHXbYwawT5tV11Qmias9///d/V2oXXnih2dYLLvWCjKxxW+dcrFm8Y0J3Vuf4OGzYMLPtOeecY9a33357s26dr3oBjQMGDDDrXr+uc67ina96AWLWfOTNUV4o1pQpU8z68OHDKzXvvN4K55KaM9a9tkOHDjXrM2fOrNTqPifNxBVlAAAAAABKWCgDAAAAAFDCQhkAAAAAgBIWygAAAAAAlLBQBgAAAACghNTrJvIShb3ENysZ8GMf+5jZ1kt8PPDAA836vffeW6l95CMfMdv+4Ac/aHj/JDs51Uue8xIf66QvT5061WzbXXnJvl5irfc61eElClp1L0mybhr2smXLKjUvMXLJkiVm3XvsXv+yeMnxXt+1xvPIkSPNtnPnzjXrm2++eYN7hzIvIbVPnz6Vmtc3vP7o9RmrHzQrmbrOdrzxX2cb3lzkHb8HDhzY8H1623jPe95j1i+//HKzjs7P64very3ccccdZn3nnXeu1CZNmmS29ead5cuXm/Vf/vKXldrJJ59stvWO083gHYfe/va3m/VvfOMblVrdRPmO5s2d48ePr9S+9a1vmW29X6nwEqut44/1Cy2Sf6z3jqXWa1jnl0IkPznbuk/vfOKuu+4y69/+9rfN+h/+8IdKzXtevf3znivrufV+acI7r/O2bT3f3i+fDBkyxKw3E1eUAQAAAAAoYaEMAAAAAEAJC2UAAAAAAEpYKAMAAAAAUMJCGQAAAACAki6Veu2lB1opc3USm9urW9vxtu2lxnk+9alPVWpeyqiV6ipJ3/3ud836hAkTKrWf/OQnZlsv8dh7Tqy04roJgF46npWk522ju9pxxx3NupfMaSUHegm0Hi8dcs6cOZWa93p4acJe4qHVZzbZZBOzrZdM7aVUWimhdfu510etbXtpzN5+e+3Rvr///e9m/dBDD63U6qawe+1XrlxZqXn9zqvXSdT21L1PKw3Ye4ze+Pf67+TJkxuqSdLNN99s1rsr7/XwjjPN2LalTn+uyxpvknTTTTeZ9SlTppj1p556qlLzfuFg0003NeuLFi0y68ccc0yltuWWW5ptvf3+6Ec/atb/9a9/VWrjxo0z244aNcqse/ttzTveca+z+sAHPmDWt9lmm0rNS3j25l/vmGQl+nvnTF7ys/eaWGPG27+6deu4sHDhQrPtVlttZdZ//OMfm/VnnnmmUvPOx+r+koM3Ti1173PWrFmVmpduvy6S37miDAAAAABACQtlAAAAAABKWCgDAAAAAFDCQhkAAAAAgBIWygAAAAAAlHSp1GsvIdRKTmtWUrKVVOelr9X1pS99qVJ74oknzLZeWuMBBxxg1q+77rpK7ZBDDjHbzp4926zXSRRuVhKylWhoJXh3Z2PGjDHr3utkJep6CYt33323Wd9nn33M+owZMyo1L929bnLu4sWLK7UhQ4aYbb2kVi9JtqWlpVKbP3++2XbEiBFm/emnnzbrVorp4MGDzbYTJ040617qJtp30kknveFt7LzzzmbdS5Z95JFHKjWrf0l+unWdX0Somz5a59cgvOOxlY4qSfvvv3+tfYF/TLL6hpXUuybbttRNt/ZSm++4445KzUsqtlKsJX++t37Nw/vlgwULFph1b66z2nvP94EHHmjWvfnI+mUK7xzIS/z2ko2t+dJ7vr1fQ+loV111lVm3nn8vQd07J/nrX/9q1ocPH16pecdS73js/VpInTWGd77jpURbv8bh9VNvHHlzgHWO5c1Rnjpzl9fW278BAwaYdWvc9evXz2zrHReaiSvKAAAAAACUsFAGAAAAAKCEhTIAAAAAACUslAEAAAAAKOlSYV7eF/OtuvflcY/35fk6wV1eGMZvfvObhu9z4MCBZtuTTz7ZrF9//fVmfc8996zUZs2aZbatG3hghWd4gRre8+oFKy1atKhSGz9+vNm2u9p0003NuvdcWgEvXsCBFyzijRer7gXKeOExXsiXFUzh9bm627b6tBey4QVxWSE2kh2+4YVSWP1ZkoYOHWrWsfZ5Y8A7DtYJP6kTtlR3G97+eccFK3TR27bXf+vw9q8Zz0l3UDe4643aYostzPrRRx9t1r/4xS+adSt48M4776y1L14Qj9Vn6gaAeudoc+fOrdS23357s613/vLss8+adWu/vblrzpw5Zt3rD9bjtwIkJWnrrbc26x2tTl/3nuN9993XrHshWtZx2gs7817vOsdeL/jLe+zeOYy133UCGtu7Ty8Yz1InhLC9+7RYc5Hkvw7WuZp13iVJ06ZNa3g/1hRXlAEAAAAAKGGhDAAAAABACQtlAAAAAABKWCgDAAAAAFDCQhkAAAAAgJJ1lnptpbh5KZlespuXyrY2UzWt5L3TTz/dbLvffvuZ9SFDhpj18847r1Lz0iEvuugisz516lSzbj2HXkJw3QRAazsvv/yy2XbmzJkN759X33LLLc22XlpxV1c39dZKdvQSOL3kc8/ChQsbuj/JT0H09qWlpaVS8xJM66Y9zpgxo1Lz0sS959Xr09bj8fbDSoyV/DGHNWMlc3qvidd/n3rqKbNu9T2vz9RljfW1uW0vTbVOsned+0P73v3ud5t171zCO56MHj26UvOOMVYatCRdd911Zt1K6/X2w6svW7bMrFu8pF4vZdibG6xx5M1/3jbq/GKD96sK3i+ZeEnA1i8leM+ft+2OVicB/x//+IfZ9thjjzXr3jmCdbxfsmSJ2dY7z/ZeQ2u/vRR2L93aOw+q86s9dY/TVr/29tt7zeokcHt92ntOvH2xfrXFO7Z4v/DSTFxRBgAAAACghIUyAAAAAAAlLJQBAAAAAChhoQwAAAAAQAkLZQAAAAAASpqeeu0lLXqpZ+vazjvvbNa//e1vm/VDDjmkUvPScC+//HKz7qXJWal+f/nLX8y2Y8aMMete6nWvXr3MusVKWZSkoUOHmvWJEyc2VJP8/e7Zs6dZt1IHvT7lpRh3dV5/qZNW6G3DSrFuj5VM7aXbWm0lP6XSejzeccLbtvd4rH7kpTd6+zdnzhyzbvHSNadMmWLWt95664a3jddnvbZe6rX3etf5FYa6ydRe+zqJp14yr6dOCnUzUq/rJN2uj2655ZZKbdq0aWZb7xzDS5V98cUXKzVv3uzfv79Z935dwkqJ9+Zvr+71Det4741brx9548Vq7+1f3fNWq+6l6Xu8x1nnmFBnjuqsbrjhBrN+9tlnm/XFixebdescYfr06Q23ler9uobX1uszXntrTHt9w9tGM47f3n57fc+qe+ns3uPx0set+qBBg8y2Xn9oJq4oAwAAAABQwkIZAAAAAIASFsoAAAAAAJSwUAYAAAAAoISFMgAAAAAAJU1PvfaS0wYOHFipnXvuuWbbzTff3Kz37dvXrA8YMKBS22yzzcy2M2bMqLXtL37xiw3v36GHHmrWr7rqKrNuJUK///3vN9t66X1e2p2XMmexXhvJf64ee+yxSm2rrbYy21rpw5KfMrnJJptUal7a5ejRo816V+e9pl7dei6feeYZs+38+fNr7YuVYrjxxhubbb30dK+9lTzpHT+8/uxt20qHnD17ttl2m222MeteoraV1j148GCzbe/evRveP3SsV155xazXSZVvRsJzsxK16/DSlFHfcccdZ9b33HPPSu3ZZ58123rJ7F7CqzU3eMcebx7x+oBV99p6v7bhtbf2xft1B28OqJP46x13vfv06pa6Y9/bb+s5rDOHdgZ1ngsvuXvu3Llm3UuEt/qHl0TunX96fcxq771+XrK6175O8nud83rvPr39q7MNyU69rvNrO942JGnBggWVmjc/e7840kxcUQYAAAAAoISFMgAAAAAAJSyUAQAAAAAoYaEMAAAAAEDJOkvwuP766ys1L3DLC93xWAEXL7zwgtnWCz7xwiZ23nnnSu2ll14y244fP96sb7/99mZ98uTJlZr35XbvS/LeF+2tMAVv215wiPc67LTTTg3v37x588y6F6phhU15j7FuP+nqvD46aNCgSs0LffHC1erwgjDqBnFZ7b2gCS9Yy3s8VvCDFUzWHm+8WNv2wlb69+9v1r3nCmufFzTj1a1+UKdvtNfeOg42Y/883vw3adKkhreB9o0bN86s9+vXr1LbYYcdzLZeiJQ3b1r9rk44neSfB1hhOV6f845rdereY/TmBm8+ss4b6oyV9u6zzvlVXdZ2rHAjSdpxxx2bcp/N5s2HVh/z1gFWsKtkh+BK9pjxxpHXH70APOu8tG6Ionf+ZvVf75zJGxt1jgvePFL3Pq3x5Z13emHJ3jHHOq/z+pQXdNxMXFEGAAAAAKCEhTIAAAAAACUslAEAAAAAKGGhDAAAAABACQtlAAAAAABKmp56PXLkyIbbekl+Xoqhl8pmpcl5abhWQrDkp6+99a1vrdS8BLdnnnnGrLe0tJh1K8XNS06sm4RqpTV6iX7efntJw3XSHYcMGWLWvdRBK6Vv0003Ndt6SdtdndcXvdTEPn36VGpe2qM3hurwUsi9hNA6SY1eH/USq60kWW/bdR+7lxpr9V3veOMlY3rjFmumzvNZ91hq1esmnnqssVR3216/trbjPUZvTKO+c845x6xb5ztHHXWU2Xbfffc167xOjbPmQG9u9Z5Xb+6yzoG8+a9uQr513uWdL911111mvaN55x+W9773vWZ90aJFZv2JJ54w629605sqtZkzZ5pt6/4axeDBgxtuWzeZ2uKd63nqbNvrj16qdJ251TsnrzvvWGsm79eGJk6c2NjOvQFcUQYAAAAAoISFMgAAAAAAJSyUAQAAAAAoYaEMAAAAAEAJC2UAAAAAAEqannr9kY98xKzXSTEbMGCAWR84cGDD++Gl53rJbh4rmXDu3LlmW2//vAQ7ax+9RLpmpOAtWbLEbOulrFpJf5KdVOclFE6dOtXbRdP8+fMrNS/Rr06yYlfiJSh7fdrqG/PmzTPb1u3/1mvtpT3WTWS1Xtc6ieqS36et56pu6rXXv7y0UkvdxFOsfV6CutevrfHlHZPqJlY3Iz3b20adbdcdd3Xub31LePeOM+eff35DtfZsueWWZt3qo8OGDTPbjh492qx7ScAWa56WpDlz5ph17xc0rPbeuYS3bW88o+PUGfNHHnmkWZ8yZYpZ//KXv2zWjz322ErN+5WbGTNmmHXvHGbx4sWVmjeHe499bc753n3W+eWDtbkfXiK8x2rv/XrQww8/XGvba4IrygAAAAAAlLBQBgAAAACghIUyAAAAAAAlLJQBAAAAAChhoQwAAAAAQEnTU69/+tOfmvW99967UvMS6bwETi8l20qqq5s227dv34br3ra9tGLPrFmzKrXp06ebbV944QWzPnPmTLO+YMGCSs3bPy9Ru0ePHmbdSof10gKXL19u1r0UPCtN1nqeJD/ZubvyUpit19XrR95r6rHae4m/dfuRlVhtpUtKflq3l3hqjdu6yfHescLax0022cRs6z322bNn19oXtK9OkqeXquuNr7pJ8RZv/6x63SRs79hrjdM66ajofJ5//vmG2z799NNrcU+A5nj7299u1uv8yo0kvf/976/U7rjjDrOtdw6z+eabm3Xr+OjN7d6x1Fs31Pk1jq56nJ44caJZ9+Zc69zw17/+tdnWWwM1E1eUAQAAAAAoYaEMAAAAAEAJC2UAAAAAAEpYKAMAAAAAUNL0MK9p06aZ9SOPPLJSGzdunNn2sMMOM+v77ruvWX/zm99cqXmBLUOHDjXrXnsruMf78r33RXsvRMsKAPICzoYNG2bWvSCXlpaWSs0LE/BCjuoE5Hj74d2nF3707LPPVmrbbbed2faee+5pcO+6Fi9Eqk4AmhW41p6FCxeadet1rRsI5o2LXr16VWpeWM3IkSPNujcurDCIwYMHe7to8l4HK0DMe4xe2JgXYoG1zzv2eOPLUjcUq077Osddye+nlrpBfACwNnlhTHVDmu67775KzTumjx8/3qx7c4N1fGxWMGLv3r3f8DY81naaMUdJ9lziBV9623755ZfN+kMPPVSp1T2nbSauKAMAAAAAUMJCGQAAAACAEhbKAAAAAACUsFAGAAAAAKCEhTIAAAAAACUdGnX53HPPmfUf/ehHteoWL+1ut912M+teeu4BBxxQqXkJz15itZXAK0n9+/ev1J555hmzrXefXjKglT7nPUYvmbpfv35mff78+ZXawIEDzbZe6q9Xt5LtLrjgArPtT3/6U7P+k5/8xKx3FV4C+6hRo8y69Xp46YMer72VbOilIHrpu16irpWePXr0aLOt11+858oaL9bzJEnz5s0z6x4rIXzBggVmW2/MTZ06tdZ9on3WMcz7dQIvtb1O8rOX4ukdS9dm6rXH2k7d/a6jWUmtALA2PfbYYx29C+hCuKIMAAAAAEAJC2UAAAAAAEpYKAMAAAAAUMJCGQAAAACAEhbKAAAAAACUdGjq9dq0YsUKs37vvffW2s7111/fjN0BGjZ79myz7iXTWmmzXkr0tGnTzLqXEt+nT5+G29ZN/LX20Usk9hJ1vbR1K2neSzUeMGCAWZ8yZYpZ79u3b6Xm7bf32K00cawbvXr1qlW30rOblfBsbafutr1xZ/Uxr9/VTckHAGB9wBVlAAAAAABKWCgDAAAAAFDCQhkAAAAAgBIWygAAAAAAlLBQBgAAAACgpNumXgNd1cSJE836m970JrO+ePHiSm348OFm2/POO8+s//a3vzXrVuq1l8rrpe9uuOGGZn3RokUNb9vbhpVALdnJ3MuXLzfbel588UWz/p3vfKdS81LGrddG8lP5sWbqJEV7CeUeKynae/28fuqln1tjxmvr8ZKsre1427aSvQEAWN9xRRkAAAAAgBIWygAAAAAAlLBQBgAAAACghIUyAAAAAAAlhHkBncyoUaPM+oABA8y6FRi122671bpPL4zKq3cWc+fOXef3udVWW1Vq8+fPN9t6oU+DBw9u6j6hcVbQmyT179/frFuhc7179zbbeqFiXn3lypVmvc42vDAvq/3SpUvNtl5/9I5FU6dObXj/AADoqriiDAAAAABACQtlAAAAAABKWCgDAAAAAFDCQhkAAAAAgBIWygAAAAAAlJB6DXQyZ599tlm/9tprzbqVej1z5sym7MuGG27YlO2sLTFGs77BBtX3AL22r776aq373G+//So1L3m4paXFrE+cOLHWfaJ93mtrmTNnjll/9tlnzbrVl7w087qp114Cdx1e37P06NHDrHvp2wsWLFijfQIAoDvgijIAAAAAACUslAEAAAAAKGGhDAAAAABACQtlAAAAAABKWCgDAAAAAFAS6qSFAgAAAADQ3XFFGQAAAACAEhbKAAAAAACUsFAGAAAAAKCEhTIAAAAAACUslAEAAAAAKGGhDAAAAABACQtlAAAAAABKWCgDAAAAAFDCQhkAAAAAgBIWygAAAAAAlLBQBgAAAACghIUyAAAAAAAlLJQBAAAAAChhoQwAAAAAQAkLZQAAAAAASlgoAwAAAABQwkIZAAAAAIASFsoAAAAAAJSwUAYAAAAAoOT/AzTzB32ca5YyAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1200x800 with 15 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot a random sample of test images, their predicted labels and ground truth\n",
    "plot_results(model3, train_images, train_labels, 3, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1200x800 with 15 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_results(model3, test_images, test_labels, 3, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}