{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Image Classification\n",
    "In this project, you'll classify images from the [CIFAR-10 dataset](https://www.cs.toronto.edu/~kriz/cifar.html).  The dataset consists of airplanes, dogs, cats, and other objects. You'll preprocess the images, then train a convolutional neural network on all the samples. The images need to be normalized and the labels need to be one-hot encoded.  You'll get to apply what you learned and build a convolutional, max pooling, dropout, and fully connected layers.  At the end, you'll get to see your neural network's predictions on the sample images.\n",
    "## Get the Data\n",
    "Run the following cell to download the [CIFAR-10 dataset for python](https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All files found!\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
    "\"\"\"\n",
    "from urllib.request import urlretrieve\n",
    "from os.path import isfile, isdir\n",
    "from tqdm import tqdm\n",
    "import problem_unittests as tests\n",
    "import tarfile\n",
    "\n",
    "cifar10_dataset_folder_path = 'cifar-10-batches-py'\n",
    "\n",
    "class DLProgress(tqdm):\n",
    "    last_block = 0\n",
    "\n",
    "    def hook(self, block_num=1, block_size=1, total_size=None):\n",
    "        self.total = total_size\n",
    "        self.update((block_num - self.last_block) * block_size)\n",
    "        self.last_block = block_num\n",
    "\n",
    "if not isfile('cifar-10-python.tar.gz'):\n",
    "    with DLProgress(unit='B', unit_scale=True, miniters=1, desc='CIFAR-10 Dataset') as pbar:\n",
    "        urlretrieve(\n",
    "            'https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz',\n",
    "            'cifar-10-python.tar.gz',\n",
    "            pbar.hook)\n",
    "\n",
    "if not isdir(cifar10_dataset_folder_path):\n",
    "    with tarfile.open('cifar-10-python.tar.gz') as tar:\n",
    "        tar.extractall()\n",
    "        tar.close()\n",
    "\n",
    "\n",
    "tests.test_folder_path(cifar10_dataset_folder_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Explore the Data\n",
    "The dataset is broken into batches to prevent your machine from running out of memory.  The CIFAR-10 dataset consists of 5 batches, named `data_batch_1`, `data_batch_2`, etc.. Each batch contains the labels and images that are one of the following:\n",
    "* airplane\n",
    "* automobile\n",
    "* bird\n",
    "* cat\n",
    "* deer\n",
    "* dog\n",
    "* frog\n",
    "* horse\n",
    "* ship\n",
    "* truck\n",
    "\n",
    "Understanding a dataset is part of making predictions on the data.  Play around with the code cell below by changing the `batch_id` and `sample_id`. The `batch_id` is the id for a batch (1-5). The `sample_id` is the id for a image and label pair in the batch.\n",
    "\n",
    "Ask yourself \"What are all possible labels?\", \"What is the range of values for the image data?\", \"Are the labels in order or random?\".  Answers to questions like these will help you preprocess the data and end up with better predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Stats of batch 1:\n",
      "Samples: 10000\n",
      "Label Counts: {0: 1005, 1: 974, 2: 1032, 3: 1016, 4: 999, 5: 937, 6: 1030, 7: 1001, 8: 1025, 9: 981}\n",
      "First 20 Labels: [6, 9, 9, 4, 1, 1, 2, 7, 8, 3, 4, 7, 7, 2, 9, 9, 9, 3, 2, 6]\n",
      "\n",
      "Example of Image 32:\n",
      "Image - Min Value: 0 Max Value: 255\n",
      "Image - Shape: (32, 32, 3)\n",
      "Label - Label Id: 1 Name: automobile\n"
     ]
    },
    {
     "data": {
      "image/png": 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35V58xaszl7vV66+kcguJB2QuNyjXpm0/nOlM4+t6rbU2M87ltifxv9ne/l7q1nQcf790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\nYYoeAAr7v3To37eZkHdPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f961c32c518>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 250,
       "width": 253
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "\n",
    "import helper\n",
    "import numpy as np\n",
    "\n",
    "# Explore the dataset\n",
    "batch_id = 1\n",
    "sample_id = 32\n",
    "helper.display_stats(cifar10_dataset_folder_path, batch_id, sample_id)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Implement Preprocess Functions\n",
    "### Normalize\n",
    "In the cell below, implement the `normalize` function to take in image data, `x`, and return it as a normalized Numpy array. The values should be in the range of 0 to 1, inclusive.  The return object should be the same shape as `x`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tests Passed\n"
     ]
    }
   ],
   "source": [
    "def normalize(x):\n",
    "    \"\"\"\n",
    "    Normalize a list of sample image data in the range of 0 to 1\n",
    "    : x: List of image data.  The image shape is (32, 32, 3)\n",
    "    : return: Numpy array of normalize data\n",
    "    \"\"\"\n",
    "    return (x - x.min()) / (x.max() - x.min()) # This is local normalization\n",
    "    # Note : I should divide by a global maximum like 255, \n",
    "    # so that the normalization does not depend on the image maximum\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
    "\"\"\"\n",
    "tests.test_normalize(normalize)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### One-hot encode\n",
    "Just like the previous code cell, you'll be implementing a function for preprocessing.  This time, you'll implement the `one_hot_encode` function. The input, `x`, are a list of labels.  Implement the function to return the list of labels as One-Hot encoded Numpy array.  The possible values for labels are 0 to 9. The one-hot encoding function should return the same encoding for each value between each call to `one_hot_encode`.  Make sure to save the map of encodings outside the function.\n",
    "\n",
    "Hint: Don't reinvent the wheel."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tests Passed\n"
     ]
    }
   ],
   "source": [
    "encodings = {}\n",
    "for i in range(10):\n",
    "    v = np.zeros(10)\n",
    "    v[i] = 1\n",
    "    encodings[i] = v\n",
    "\n",
    "def one_hot_encode(x): # I could use LabelBinariser to scale to large labels\n",
    "    \"\"\"\n",
    "    One hot encode a list of sample labels. Return a one-hot encoded vector for each label.\n",
    "    : x: List of sample Labels\n",
    "    : return: Numpy array of one-hot encoded labels\n",
    "    \"\"\"\n",
    "    assert np.max(x) < 10 and np.min(x) >= 0, 'label must be between 0 and 9'\n",
    "    encoded = np.array(list(map(lambda v: encodings[v],x)))\n",
    "    return encoded\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
    "\"\"\"\n",
    "tests.test_one_hot_encode(one_hot_encode)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Randomize Data\n",
    "As you saw from exploring the data above, the order of the samples are randomized.  It doesn't hurt to randomize it again, but you don't need to for this dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Preprocess all the data and save it\n",
    "Running the code cell below will preprocess all the CIFAR-10 data and save it to file. The code below also uses 10% of the training data for validation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL\n",
    "\"\"\"\n",
    "# Preprocess Training, Validation, and Testing Data\n",
    "helper.preprocess_and_save_data(cifar10_dataset_folder_path, normalize, one_hot_encode)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Check Point\n",
    "This is your first checkpoint.  If you ever decide to come back to this notebook or have to restart the notebook, you can start from here.  The preprocessed data has been saved to disk."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL\n",
    "\"\"\"\n",
    "import pickle\n",
    "import problem_unittests as tests\n",
    "import helper\n",
    "\n",
    "# Load the Preprocessed Validation data\n",
    "valid_features, valid_labels = pickle.load(open('preprocess_validation.p', mode='rb'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Build the network\n",
    "For the neural network, you'll build each layer into a function.  Most of the code you've seen has been outside of functions. To test your code more thoroughly, we require that you put each layer in a function.  This allows us to give you better feedback and test for simple mistakes using our unittests before you submit your project.\n",
    "\n",
    "If you're finding it hard to dedicate enough time for this course a week, we've provided a small shortcut to this part of the project. In the next couple of problems, you'll have the option to use [TensorFlow Layers](https://www.tensorflow.org/api_docs/python/tf/layers) or [TensorFlow Layers (contrib)](https://www.tensorflow.org/api_guides/python/contrib.layers) to build each layer, except \"Convolutional & Max Pooling\" layer.  TF Layers is similar to Keras's and TFLearn's abstraction to layers, so it's easy to pickup.\n",
    "\n",
    "If you would like to get the most of this course, try to solve all the problems without TF Layers.  Let's begin!\n",
    "### Input\n",
    "The neural network needs to read the image data, one-hot encoded labels, and dropout keep probability. Implement the following functions\n",
    "* Implement `neural_net_image_input`\n",
    " * Return a [TF Placeholder](https://www.tensorflow.org/api_docs/python/tf/placeholder)\n",
    " * Set the shape using `image_shape` with batch size set to `None`.\n",
    " * Name the TensorFlow placeholder \"x\" using the TensorFlow `name` parameter in the [TF Placeholder](https://www.tensorflow.org/api_docs/python/tf/placeholder).\n",
    "* Implement `neural_net_label_input`\n",
    " * Return a [TF Placeholder](https://www.tensorflow.org/api_docs/python/tf/placeholder)\n",
    " * Set the shape using `n_classes` with batch size set to `None`.\n",
    " * Name the TensorFlow placeholder \"y\" using the TensorFlow `name` parameter in the [TF Placeholder](https://www.tensorflow.org/api_docs/python/tf/placeholder).\n",
    "* Implement `neural_net_keep_prob_input`\n",
    " * Return a [TF Placeholder](https://www.tensorflow.org/api_docs/python/tf/placeholder) for dropout keep probability.\n",
    " * Name the TensorFlow placeholder \"keep_prob\" using the TensorFlow `name` parameter in the [TF Placeholder](https://www.tensorflow.org/api_docs/python/tf/placeholder).\n",
    "\n",
    "These names will be used at the end of the project to load your saved model.\n",
    "\n",
    "Note: `None` for shapes in TensorFlow allow for a dynamic size."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image Input Tests Passed.\n",
      "Label Input Tests Passed.\n",
      "Keep Prob Tests Passed.\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "def neural_net_image_input(image_shape):\n",
    "    \"\"\"\n",
    "    Return a Tensor for a bach of image input\n",
    "    : image_shape: Shape of the images\n",
    "    : return: Tensor for image input.\n",
    "    \"\"\"\n",
    "    # shape [None, 32, 32, 3]\n",
    "    return tf.placeholder(tf.float32, [None] + list(image_shape), name='x')\n",
    "\n",
    "\n",
    "\n",
    "def neural_net_label_input(n_classes):\n",
    "    \"\"\"\n",
    "    Return a Tensor for a batch of label input\n",
    "    : n_classes: Number of classes\n",
    "    : return: Tensor for label input.\n",
    "    \"\"\"\n",
    "    return tf.placeholder(tf.float32, [None, n_classes], name='y')\n",
    "\n",
    "\n",
    "def neural_net_keep_prob_input():\n",
    "    \"\"\"\n",
    "    Return a Tensor for keep probability\n",
    "    : return: Tensor for keep probability.\n",
    "    \"\"\"\n",
    "    # TODO: Implement Function\n",
    "    return tf.placeholder(tf.float32, name='keep_prob')\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
    "\"\"\"\n",
    "tf.reset_default_graph()\n",
    "tests.test_nn_image_inputs(neural_net_image_input)\n",
    "tests.test_nn_label_inputs(neural_net_label_input)\n",
    "tests.test_nn_keep_prob_inputs(neural_net_keep_prob_input)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Convolution and Max Pooling Layer\n",
    "Convolution layers have a lot of success with images. For this code cell, you should implement the function `conv2d_maxpool` to apply convolution then max pooling:\n",
    "* Create the weight and bias using `conv_ksize`, `conv_num_outputs` and the shape of `x_tensor`.\n",
    "* Apply a convolution to `x_tensor` using weight and `conv_strides`.\n",
    " * We recommend you use same padding, but you're welcome to use any padding.\n",
    "* Add bias\n",
    "* Add a nonlinear activation to the convolution.\n",
    "* Apply Max Pooling using `pool_ksize` and `pool_strides`.\n",
    " * We recommend you use same padding, but you're welcome to use any padding.\n",
    "\n",
    "Note: You **can't** use [TensorFlow Layers](https://www.tensorflow.org/api_docs/python/tf/layers) or [TensorFlow Layers (contrib)](https://www.tensorflow.org/api_guides/python/contrib.layers) for this layer.  You're free to use any TensorFlow package for all the other layers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tests Passed\n"
     ]
    }
   ],
   "source": [
    "def conv2d_maxpool(x_tensor, conv_num_outputs, conv_ksize, conv_strides, pool_ksize, pool_strides):\n",
    "    \"\"\"\n",
    "    Apply convolution then max pooling to x_tensor\n",
    "    :param x_tensor: TensorFlow Tensor\n",
    "    :param conv_num_outputs: Number of outputs for the convolutional layer\n",
    "    :param conv_strides: Stride 2-D Tuple for convolution\n",
    "    :param pool_ksize: kernal size 2-D Tuple for pool\n",
    "    :param pool_strides: Stride 2-D Tuple for pool\n",
    "    : return: A tensor that represents convolution and max pooling of x_tensor\n",
    "    \"\"\"\n",
    "    w_conv = tf.Variable(tf.truncated_normal((conv_ksize[0], # Kernel height\n",
    "                                                 conv_ksize[1], # Kernel width\n",
    "                                                 x_tensor.get_shape()[3].value, # Input channels\n",
    "                                                 conv_num_outputs), stddev=0.05)) # output depth\n",
    "    bias_conv = tf.Variable(tf.random_normal([conv_num_outputs], stddev=0.05))\n",
    "    \n",
    "    padding = 'VALID'\n",
    "    \n",
    "    \n",
    "    # DEBUG\n",
    "    #print('conv ksize: ', conv_ksize)\n",
    "    #print('conv strides: ',conv_strides)\n",
    "    #print('pool ksize: ', pool_ksize)\n",
    "    #print('pool strides: ',pool_strides)\n",
    "    #print('conv_depth: ', conv_num_outputs)\n",
    "    #print('x_tensor :',x_tensor)\n",
    "    #print('conv weights :', w_conv) \n",
    "    # DEBUG\n",
    "    \n",
    "    \n",
    "    # Apply conv to x_tensor\n",
    "    cv1 = tf.nn.conv2d(x_tensor,\n",
    "                       w_conv,\n",
    "                       [1, conv_strides[0], conv_strides[1], 1],\n",
    "                       padding=padding\n",
    "                      )\n",
    "    # add bias\n",
    "    cv1 = tf.nn.bias_add(cv1, bias_conv)\n",
    "    \n",
    "    # Apply relu\n",
    "    cv1 = tf.nn.relu(cv1)\n",
    "    \n",
    "    # Apply Maxpooling\n",
    "    cv1 = tf.nn.max_pool(cv1,\n",
    "                         ksize=[1,pool_ksize[0], pool_ksize[1], 1],\n",
    "                         strides=[1, pool_strides[0], pool_strides[1], 1],\n",
    "                         padding=padding\n",
    "                        )\n",
    "    \n",
    "     \n",
    "    \n",
    "    #print('conv layer :', cv1.get_shape())\n",
    "    return cv1 \n",
    "\n",
    "\n",
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
    "\"\"\"\n",
    "tests.test_con_pool(conv2d_maxpool)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Flatten Layer\n",
    "Implement the `flatten` function to change the dimension of `x_tensor` from a 4-D tensor to a 2-D tensor.  The output should be the shape (*Batch Size*, *Flattened Image Size*). You can use [TensorFlow Layers](https://www.tensorflow.org/api_docs/python/tf/layers) or [TensorFlow Layers (contrib)](https://www.tensorflow.org/api_guides/python/contrib.layers) for this layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tests Passed\n"
     ]
    }
   ],
   "source": [
    "\n",
    "def flatten(x_tensor):\n",
    "    \"\"\"\n",
    "    Flatten x_tensor to (Batch Size, Flattened Image Size)\n",
    "    : x_tensor: A tensor of size (Batch Size, ...), where ... are the image dimensions.\n",
    "    : return: A tensor of size (Batch Size, Flattened Image Size).\n",
    "    \"\"\"\n",
    "    flat_size = np.multiply.reduce(x_tensor.get_shape()[1:].as_list())\n",
    "    return tf.reshape(x_tensor, [-1, flat_size])\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
    "\"\"\"\n",
    "tests.test_flatten(flatten)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Fully-Connected Layer\n",
    "Implement the `fully_conn` function to apply a fully connected layer to `x_tensor` with the shape (*Batch Size*, *num_outputs*). You can use [TensorFlow Layers](https://www.tensorflow.org/api_docs/python/tf/layers) or [TensorFlow Layers (contrib)](https://www.tensorflow.org/api_guides/python/contrib.layers) for this layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tests Passed\n"
     ]
    }
   ],
   "source": [
    "def fully_conn(x_tensor, num_outputs):\n",
    "    \"\"\"\n",
    "    Apply a fully connected layer to x_tensor using weight and bias\n",
    "    : x_tensor: A 2-D tensor where the first dimension is batch size.\n",
    "    : num_outputs: The number of output that the new tensor should be.\n",
    "    : return: A 2-D tensor where the second dimension is num_outputs.\n",
    "    \"\"\"\n",
    "    full_w = tf.Variable(tf.truncated_normal((x_tensor.get_shape()[1].value, num_outputs), stddev=0.05))\n",
    "    full_b = tf.Variable(tf.random_normal([num_outputs], stddev=0.05))\n",
    "    \n",
    "    full = tf.add(tf.matmul(x_tensor, full_w), full_b)\n",
    "    full = tf.nn.relu(full)\n",
    "    return full\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
    "\"\"\"\n",
    "tests.test_fully_conn(fully_conn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Output Layer\n",
    "Implement the `output` function to apply a fully connected layer to `x_tensor` with the shape (*Batch Size*, *num_outputs*). You can use [TensorFlow Layers](https://www.tensorflow.org/api_docs/python/tf/layers) or [TensorFlow Layers (contrib)](https://www.tensorflow.org/api_guides/python/contrib.layers) for this layer.\n",
    "\n",
    "Note: Activation, softmax, or cross entropy shouldn't be applied to this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tests Passed\n"
     ]
    }
   ],
   "source": [
    "def output(x_tensor, num_outputs):\n",
    "    \"\"\"\n",
    "    Apply a output layer to x_tensor using weight and bias\n",
    "    : x_tensor: A 2-D tensor where the first dimension is batch size.\n",
    "    : num_outputs: The number of output that the new tensor should be.\n",
    "    : return: A 2-D tensor where the second dimension is num_outputs.\n",
    "    \"\"\"\n",
    "    out_w = tf.Variable(tf.truncated_normal((x_tensor.get_shape()[1].value, num_outputs)))\n",
    "    out_b = tf.Variable(tf.zeros(num_outputs))\n",
    "    \n",
    "    out = tf.add(tf.matmul(x_tensor, out_w), out_b)\n",
    "    return out\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
    "\"\"\"\n",
    "tests.test_output(output)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Create Convolutional Model\n",
    "Implement the function `conv_net` to create a convolutional neural network model. The function takes in a batch of images, `x`, and outputs logits.  Use the layers you created above to create this model:\n",
    "\n",
    "* Apply 1, 2, or 3 Convolution and Max Pool layers\n",
    "* Apply a Flatten Layer\n",
    "* Apply 1, 2, or 3 Fully Connected Layers\n",
    "* Apply an Output Layer\n",
    "* Return the output\n",
    "* Apply [TensorFlow's Dropout](https://www.tensorflow.org/api_docs/python/tf/nn/dropout) to one or more layers in the model using `keep_prob`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------\n",
      "Tensor(\"x:0\", shape=(?, 32, 32, 3), dtype=float32)\n",
      "Tensor(\"MaxPool:0\", shape=(?, 15, 15, 60), dtype=float32)\n",
      "Tensor(\"MaxPool_1:0\", shape=(?, 6, 6, 120), dtype=float32)\n",
      "Tensor(\"MaxPool_2:0\", shape=(?, 2, 2, 400), dtype=float32)\n",
      "Tensor(\"Reshape:0\", shape=(?, 1600), dtype=float32)\n",
      "Tensor(\"dropout/mul:0\", shape=(?, 600), dtype=float32)\n",
      "Tensor(\"dropout_1/mul:0\", shape=(?, 60), dtype=float32)\n",
      "Tensor(\"Add_2:0\", shape=(?, 10), dtype=float32)\n",
      "--------------\n",
      "Tensor(\"Placeholder:0\", shape=(?, 32, 32, 3), dtype=float32)\n",
      "Tensor(\"MaxPool_3:0\", shape=(?, 15, 15, 60), dtype=float32)\n",
      "Tensor(\"MaxPool_4:0\", shape=(?, 6, 6, 120), dtype=float32)\n",
      "Tensor(\"MaxPool_5:0\", shape=(?, 2, 2, 400), dtype=float32)\n",
      "Tensor(\"Reshape_4:0\", shape=(?, 1600), dtype=float32)\n",
      "Tensor(\"dropout_2/mul:0\", shape=(?, 600), dtype=float32)\n",
      "Tensor(\"dropout_3/mul:0\", shape=(?, 60), dtype=float32)\n",
      "Tensor(\"Add_5:0\", shape=(?, 10), dtype=float32)\n",
      "Neural Network Built!\n"
     ]
    }
   ],
   "source": [
    "def conv_net(x, keep_prob):\n",
    "    \"\"\"\n",
    "    Create a convolutional neural network model\n",
    "    : x: Placeholder tensor that holds image data.\n",
    "    : keep_prob: Placeholder tensor that hold dropout keep probability.\n",
    "    : return: Tensor that represents logits\n",
    "    \"\"\"\n",
    "    \n",
    "    \n",
    "    conv1_hyper_params = {\n",
    "        'conv_num_outputs': 60,\n",
    "        'conv_ksize': (3,3),\n",
    "        'conv_strides': (1,1),\n",
    "        'pool_ksize': (2,2),\n",
    "        'pool_strides': (2,2)\n",
    "    }\n",
    "    \n",
    "    conv2_hyper_params = {\n",
    "        'conv_num_outputs': 120,\n",
    "        'conv_ksize': (3,3),\n",
    "        'conv_strides': (1,1),\n",
    "        'pool_ksize': (2,2),\n",
    "        'pool_strides': (2,2)\n",
    "    }\n",
    "    \n",
    "    conv3_hyper_params = {\n",
    "        'conv_num_outputs': 400,\n",
    "        'conv_ksize': (3,3),\n",
    "        'conv_strides': (1,1),\n",
    "        'pool_ksize': (2,2),\n",
    "        'pool_strides': (2,2)\n",
    "    }\n",
    "    \n",
    "   \n",
    "    conv1 = conv2d_maxpool(x, **conv1_hyper_params)  \n",
    "    conv2 = conv2d_maxpool(conv1, **conv2_hyper_params)\n",
    "    conv3 = conv2d_maxpool(conv2, **conv3_hyper_params)\n",
    "\n",
    "    \n",
    "    \n",
    "    \n",
    "    flat = flatten(conv3)\n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    full1 = fully_conn(flat, 600)\n",
    "    full1 = tf.nn.dropout(full1, keep_prob)\n",
    "    \n",
    "    \n",
    "    full2 = fully_conn(full1, 60)\n",
    "    full2 = tf.nn.dropout(full2, keep_prob)\n",
    "\n",
    "\n",
    "    \n",
    "    \n",
    "    \n",
    "    # TODO: Apply an Output Layer\n",
    "    #    Set this to the number of classes\n",
    "    # Function Definition from Above:\n",
    "    #   output(x_tensor, num_outputs)\n",
    "    out = output(full2, 10)\n",
    "    \n",
    "    \n",
    "    # DEBUG\n",
    "    print(x)\n",
    "    print(conv1)\n",
    "    print(conv2)\n",
    "    print(conv3)\n",
    "    print(flat)\n",
    "    print(full1)\n",
    "    print(full2)\n",
    "    print(out)\n",
    "    # DEBUG\n",
    "    \n",
    "    \n",
    "    return out\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
    "\"\"\"\n",
    "\n",
    "##############################\n",
    "## Build the Neural Network ##\n",
    "##############################\n",
    "\n",
    "# Remove previous weights, bias, inputs, etc..\n",
    "tf.reset_default_graph()\n",
    "\n",
    "# Inputs\n",
    "x = neural_net_image_input((32, 32, 3))\n",
    "y = neural_net_label_input(10)\n",
    "keep_prob = neural_net_keep_prob_input()\n",
    "\n",
    "# Model\n",
    "logits = conv_net(x, keep_prob)\n",
    "\n",
    "# Name logits Tensor, so that is can be loaded from disk after training\n",
    "logits = tf.identity(logits, name='logits')\n",
    "\n",
    "# Loss and Optimizer\n",
    "cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=y))\n",
    "optimizer = tf.train.AdamOptimizer().minimize(cost)\n",
    "\n",
    "# Accuracy\n",
    "correct_pred = tf.equal(tf.argmax(logits, 1), tf.argmax(y, 1))\n",
    "accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32), name='accuracy')\n",
    "\n",
    "tests.test_conv_net(conv_net)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Train the Neural Network\n",
    "### Single Optimization\n",
    "Implement the function `train_neural_network` to do a single optimization.  The optimization should use `optimizer` to optimize in `session` with a `feed_dict` of the following:\n",
    "* `x` for image input\n",
    "* `y` for labels\n",
    "* `keep_prob` for keep probability for dropout\n",
    "\n",
    "This function will be called for each batch, so `tf.global_variables_initializer()` has already been called.\n",
    "\n",
    "Note: Nothing needs to be returned. This function is only optimizing the neural network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tests Passed\n"
     ]
    }
   ],
   "source": [
    "def train_neural_network(session, optimizer, keep_probability, feature_batch, label_batch):\n",
    "    \"\"\"\n",
    "    Optimize the session on a batch of images and labels\n",
    "    : session: Current TensorFlow session\n",
    "    : optimizer: TensorFlow optimizer function\n",
    "    : keep_probability: keep probability\n",
    "    : feature_batch: Batch of Numpy image data\n",
    "    : label_batch: Batch of Numpy label data\n",
    "    \"\"\"\n",
    "    session.run([optimizer], feed_dict={x: feature_batch,\n",
    "                                        y: label_batch,\n",
    "                                        keep_prob: keep_probability\n",
    "                                       })\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
    "\"\"\"\n",
    "tests.test_train_nn(train_neural_network)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Show Stats\n",
    "Implement the function `print_stats` to print loss and validation accuracy.  Use the global variables `valid_features` and `valid_labels` to calculate validation accuracy.  Use a keep probability of `1.0` to calculate the loss and validation accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def print_stats(session, feature_batch, label_batch, cost, accuracy):\n",
    "    \"\"\"\n",
    "    Print information about loss and validation accuracy\n",
    "    : session: Current TensorFlow session\n",
    "    : feature_batch: Batch of Numpy image data\n",
    "    : label_batch: Batch of Numpy label data\n",
    "    : cost: TensorFlow cost function\n",
    "    : accuracy: TensorFlow accuracy function\n",
    "    \"\"\"\n",
    "    loss_feed = {\n",
    "        x: feature_batch,\n",
    "        y: label_batch,\n",
    "        keep_prob: 1.\n",
    "    }\n",
    "    \n",
    "    valid_feed = {\n",
    "        x: valid_features,\n",
    "        y: valid_labels,\n",
    "        keep_prob: 1.\n",
    "    }\n",
    "    \n",
    "    loss = session.run(cost, loss_feed)\n",
    "    accuracy = session.run(accuracy, valid_feed)\n",
    "    \n",
    "    print('Loss: {:>10.4f} - Valid Accuracy: {:.2f}%'.format(loss,accuracy*100))\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Hyperparameters\n",
    "Tune the following parameters:\n",
    "* Set `epochs` to the number of iterations until the network stops learning or start overfitting\n",
    "* Set `batch_size` to the highest number that your machine has memory for.  Most people set them to common sizes of memory:\n",
    " * 64\n",
    " * 128\n",
    " * 256\n",
    " * ...\n",
    "* Set `keep_probability` to the probability of keeping a node using dropout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# TODO: Tune Parameters\n",
    "epochs = 9\n",
    "batch_size = 512\n",
    "keep_probability = 0.75"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Train on a Single CIFAR-10 Batch\n",
    "Instead of training the neural network on all the CIFAR-10 batches of data, let's use a single batch. This should save time while you iterate on the model to get a better accuracy.  Once the final validation accuracy is 50% or greater, run the model on all the data in the next section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL\n",
    "\"\"\"\n",
    "print('Checking the Training on a Single Batch...')\n",
    "with tf.Session() as sess:\n",
    "    # Initializing the variables\n",
    "    sess.run(tf.global_variables_initializer())\n",
    "    \n",
    "    # Training cycle\n",
    "    for epoch in range(epochs):\n",
    "        batch_i = 1\n",
    "        for batch_features, batch_labels in helper.load_preprocess_training_batch(batch_i, batch_size):\n",
    "            train_neural_network(sess, optimizer, keep_probability, batch_features, batch_labels)\n",
    "        print('Epoch {:>2}, CIFAR-10 Batch {}:  '.format(epoch + 1, batch_i), end='')\n",
    "        print_stats(sess, batch_features, batch_labels, cost, accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Fully Train the Model\n",
    "Now that you got a good accuracy with a single CIFAR-10 batch, try it with all five batches."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training...\n",
      "Epoch  1, CIFAR-10 Batch 1:  Loss:     2.2161 - Valid Accuracy: 18.40%\n",
      "Epoch  1, CIFAR-10 Batch 2:  Loss:     2.0625 - Valid Accuracy: 25.98%\n",
      "Epoch  1, CIFAR-10 Batch 3:  Loss:     1.7185 - Valid Accuracy: 34.68%\n",
      "Epoch  1, CIFAR-10 Batch 4:  Loss:     1.6533 - Valid Accuracy: 39.72%\n",
      "Epoch  1, CIFAR-10 Batch 5:  Loss:     1.5159 - Valid Accuracy: 43.98%\n",
      "Epoch  2, CIFAR-10 Batch 1:  Loss:     1.6357 - Valid Accuracy: 47.40%\n",
      "Epoch  2, CIFAR-10 Batch 2:  Loss:     1.4579 - Valid Accuracy: 48.64%\n",
      "Epoch  2, CIFAR-10 Batch 3:  Loss:     1.2390 - Valid Accuracy: 51.38%\n",
      "Epoch  2, CIFAR-10 Batch 4:  Loss:     1.2468 - Valid Accuracy: 53.06%\n",
      "Epoch  2, CIFAR-10 Batch 5:  Loss:     1.2385 - Valid Accuracy: 53.86%\n",
      "Epoch  3, CIFAR-10 Batch 1:  Loss:     1.3218 - Valid Accuracy: 55.72%\n",
      "Epoch  3, CIFAR-10 Batch 2:  Loss:     1.1992 - Valid Accuracy: 54.00%\n",
      "Epoch  3, CIFAR-10 Batch 3:  Loss:     1.0670 - Valid Accuracy: 57.56%\n",
      "Epoch  3, CIFAR-10 Batch 4:  Loss:     1.0798 - Valid Accuracy: 57.62%\n",
      "Epoch  3, CIFAR-10 Batch 5:  Loss:     1.0199 - Valid Accuracy: 59.20%\n",
      "Epoch  4, CIFAR-10 Batch 1:  Loss:     1.0873 - Valid Accuracy: 61.30%\n",
      "Epoch  4, CIFAR-10 Batch 2:  Loss:     1.0366 - Valid Accuracy: 61.46%\n",
      "Epoch  4, CIFAR-10 Batch 3:  Loss:     0.9007 - Valid Accuracy: 61.18%\n",
      "Epoch  4, CIFAR-10 Batch 4:  Loss:     0.8702 - Valid Accuracy: 63.08%\n",
      "Epoch  4, CIFAR-10 Batch 5:  Loss:     0.8193 - Valid Accuracy: 65.02%\n",
      "Epoch  5, CIFAR-10 Batch 1:  Loss:     0.9168 - Valid Accuracy: 63.96%\n",
      "Epoch  5, CIFAR-10 Batch 2:  Loss:     0.8561 - Valid Accuracy: 65.00%\n",
      "Epoch  5, CIFAR-10 Batch 3:  Loss:     0.7567 - Valid Accuracy: 65.08%\n",
      "Epoch  5, CIFAR-10 Batch 4:  Loss:     0.7559 - Valid Accuracy: 65.62%\n",
      "Epoch  5, CIFAR-10 Batch 5:  Loss:     0.7026 - Valid Accuracy: 66.76%\n",
      "Epoch  6, CIFAR-10 Batch 1:  Loss:     0.8156 - Valid Accuracy: 67.12%\n",
      "Epoch  6, CIFAR-10 Batch 2:  Loss:     0.7452 - Valid Accuracy: 67.18%\n",
      "Epoch  6, CIFAR-10 Batch 3:  Loss:     0.6952 - Valid Accuracy: 65.44%\n",
      "Epoch  6, CIFAR-10 Batch 4:  Loss:     0.6694 - Valid Accuracy: 66.68%\n",
      "Epoch  6, CIFAR-10 Batch 5:  Loss:     0.6272 - Valid Accuracy: 67.82%\n",
      "Epoch  7, CIFAR-10 Batch 1:  Loss:     0.7157 - Valid Accuracy: 68.12%\n",
      "Epoch  7, CIFAR-10 Batch 2:  Loss:     0.6450 - Valid Accuracy: 67.92%\n",
      "Epoch  7, CIFAR-10 Batch 3:  Loss:     0.6158 - Valid Accuracy: 66.10%\n",
      "Epoch  7, CIFAR-10 Batch 4:  Loss:     0.5492 - Valid Accuracy: 68.74%\n",
      "Epoch  7, CIFAR-10 Batch 5:  Loss:     0.5193 - Valid Accuracy: 68.12%\n",
      "Epoch  8, CIFAR-10 Batch 1:  Loss:     0.6347 - Valid Accuracy: 69.08%\n",
      "Epoch  8, CIFAR-10 Batch 2:  Loss:     0.5660 - Valid Accuracy: 69.54%\n",
      "Epoch  8, CIFAR-10 Batch 3:  Loss:     0.4913 - Valid Accuracy: 67.54%\n",
      "Epoch  8, CIFAR-10 Batch 4:  Loss:     0.4737 - Valid Accuracy: 69.96%\n",
      "Epoch  8, CIFAR-10 Batch 5:  Loss:     0.4385 - Valid Accuracy: 70.38%\n",
      "Epoch  9, CIFAR-10 Batch 1:  Loss:     0.5353 - Valid Accuracy: 69.56%\n",
      "Epoch  9, CIFAR-10 Batch 2:  Loss:     0.4946 - Valid Accuracy: 69.56%\n",
      "Epoch  9, CIFAR-10 Batch 3:  Loss:     0.4304 - Valid Accuracy: 68.02%\n",
      "Epoch  9, CIFAR-10 Batch 4:  Loss:     0.4035 - Valid Accuracy: 70.64%\n",
      "Epoch  9, CIFAR-10 Batch 5:  Loss:     0.3885 - Valid Accuracy: 70.56%\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL\n",
    "\"\"\"\n",
    "save_model_path = './image_classification'\n",
    "\n",
    "print('Training...')\n",
    "with tf.Session() as sess:\n",
    "    # Initializing the variables\n",
    "    sess.run(tf.global_variables_initializer())\n",
    "    \n",
    "    # Training cycle\n",
    "    for epoch in range(epochs):\n",
    "        # Loop over all batches\n",
    "        n_batches = 5\n",
    "        for batch_i in range(1, n_batches + 1):\n",
    "            for batch_features, batch_labels in helper.load_preprocess_training_batch(batch_i, batch_size):\n",
    "                train_neural_network(sess, optimizer, keep_probability, batch_features, batch_labels)\n",
    "            print('Epoch {:>2}, CIFAR-10 Batch {}:  '.format(epoch + 1, batch_i), end='')\n",
    "            print_stats(sess, batch_features, batch_labels, cost, accuracy)\n",
    "            \n",
    "    # Save Model\n",
    "    saver = tf.train.Saver()\n",
    "    save_path = saver.save(sess, save_model_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Checkpoint\n",
    "The model has been saved to disk.\n",
    "## Test Model\n",
    "Test your model against the test dataset.  This will be your final accuracy. You should have an accuracy greater than 50%. If you don't, keep tweaking the model architecture and parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing Accuracy: 0.7007892191410064\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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z2wQcTfP15MtZvpMNwd2ngKJEpWVDzUREthsKtjcIMzvIzE7s5stgizINeE/J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6Cd9vEhM/vnwC8QKtPPBX402+TZwErcxwsHQTvt421xmPk/3durFBEREdmewnamyK77y1ot4l09\nn7Nxfp0zp0/HyvbZELbPT2kpYiguaJs2Ds9uYVDZpihp3bqLFYSg3VYhaJfGpDQKg8aNuoWmhbZ1\nmjSfOgvc7nE+dRm6h5eAjZyydUZtS9PUNPWcpg5LgdXzmvk8/OxtzXw6ZWP9HNONdebTDebzaVwD\nPL4fm4J2vB2bs/XeJ7NF1TrKW6GZFaFu7aEJmocXEoeODxufOW6xWh+vF33L4zh/ETmMvhB4MOED\n/eqtNnL3O83szcCTlzz8pLQZi6Hky7wOeDlwdXxOHrbTPj7u7m/aZh+vQWFbRERE9pnCdqZX2R5U\na1tvaRpnOpuyfi6E7VOnTnP27DnOn58ym9W4FWAlTqxsNzVNrCrThur2qAxDwts2rO4VGrGlgFqG\nIeQxcBuON6Gu3bahtVjbOk0scVsRzpEuBIdGam6GeagQF61jdQjuRQHg0La0TQjKtA1tPWM+3aCe\nzboh5G3TdmPp8+Hjae56F7SLIm4W36tsWa60RFeK0V2WzuZm9zuLp8nbvqhkeziP1GytW6PbQxAX\nkUPpc7Lbd+yw7R+zPGynudwfcPe7t3qyu8/N7F3A32bz/O/PJvwF8yc7nMOfAVPCUHIRERGRfaGw\nnenWiM46bBdGqCQ3LbW3nF9f5+zZM5w6dYrTp0+zfv48s9mcto2FVguDodumDRXk6Yz5xpS2DoG7\nNMMKoy1DVTuFz7TMWGmOeUvbON62zOYNG/PQdK11x1to3SkKY1RVWFUyKlKH7kVjsbaNXxC0LW1T\n09Zz2nqGNzXmoeN5FTuGW2qIlpqi5e8JWffxbkg9XRW7F7TTA8Qp2CFphxhtZJXrGKrbrPlv2sgX\ngdviEmFOWg5sMczcPR+uLiKHzIOz23ftsO3HttmH7+L5AH+z5LgAD4rXJ7d7sru3ZnYv8NBdHGsH\nLfCQXWxXxouIyG6dOOgTELmkTpw4wYkTO/85n81ml+Fs9ofCdiYP2yFoh6WsUgfytpmzvr7OmbNn\nOXXqNKdPn2F9/TyzWd3NpyYOdW5S2J5NmU2nNPUcvKU0KIsiFG4LsLLtOp67W1yLuqVtG5q6YTat\n2diYsz6twzbx/1VVSWlOURpV4VAs1qhu3PFYiW+ahrau42Ue1vFu2/DPPAuLceEtNHUXthfDwNMQ\n8aJ7X/pLfmXz2vM3cMlS2P1gHB+MU7iXPWSxE3w+dHxR2Y7rcGvOtshhlX/kd/qg7vS92W4+6Pux\nj320bbYXERGRJW6//XZuu+22gz6NfaWwnenaeBVxGHlhsUocwvZ8Og2V7TNnF5Xt9Q1m85rWLc5x\nDoG3bRqaumY+C0O0m/k8hNzCqEoLFVsPQTXcjlXcNlXSa+r5nNl0zvnzM86dn/XmkdNWeGnYuKQq\nqjCk2kIQbR08NkGrmxpPQbueE0rwIeeWZiGjty3ehrBNDNtOnLtOVtHuVf4XIwD67143Vnzxc6pa\nxxS96FYeviQI3drTpmlIvXVzulPrNPN0HV+rK2yLHFL3ZLcfSugEvpWtysD3EP722E21OW1zz+D+\nVK3ettRs4RvFB223zW6ZGddcc82O25VlWNZRRGSvrrvuuoM+BZFL4tZbb+Xmm2/ecbubbrqJkyev\njC+2FbZ7rHczNPtqQ1O09XXWz50N3cfPhqZoaa5202Rdty1E1DQP29sYJuP85dIsrLVdhmeUluZh\nt/8/e28ba1uWneU9Y8651trnVjcd2SbqahoiggChCMWWsJDNR5SYH41QjAjEfCjIYENaYEVEgBRF\nOIE2EXFiJSAESA1SwETkR0CxYwwhgShxLEv5aBARYCchIRF0UwQTt7vqnrP3XmvOMfJjzPWxz723\nqrqruupWezyldffX2nOvvW7VqfOud4x3oKqoNbDm86kxSjKmIrQxbz3kSYRhyJyGxJRhSEbrfeVq\nsn0mpl2YQsoepJZScqGNMCRjfmViuZxYrk/Q1pjnmeTN3V76vX4trxvv/dpyM1t7P3W95Hstj7/R\n3bfCuB+WvzWlG+Esvfzcw9F6eJqJX1A4LLML/SAIXjKOyeFfD/zIm+z79S94/m8D3wD8bBH56hf1\nbfcRYV+H/3T4249e/jv4nO+vfYvj/YX43O13fAXvYx/7GJ/97Gff6TJBEARB8FOOV199lVdfffUt\n9xvHD07ESnrrXX7qsHcQy6EP2ViWmfPDA2+8/gXeeP11nj59ysP9uYvtxUdkbS6vHNxbvCxbFbAu\nnqFkGIswDsI0wJCNQZRMJVNJNBKNLMqQhdOQeGUqPJkKr2ybi+2xuGjOKGINa76xim2sl8UnTzwf\nM6dp4MndwIeeTHz4yYkPf+gJP+3DH+LJ3YlxHDax7aeil6cLXWTLTRr59lwX4Wm9XZ9bTfEu1m84\nOPXpuFZa1+gXB9YkdPx+upn3HYI7CF5C/jruKgP85hftJCI/gxfM2Qb+2robPtLrRfyrwEcevWfl\nv+23XyMiv/JN1vjWN3ktCIIgCILgSyLE9gvYBCJGXWbO54c+6ut1nr5x353tC8tSac3nUd8IbtZU\nbe+fFrM+axtKFoYMU/FtzEZJSrYuslESSkYZE5yGtAvtU+GV08CTsXA3JqYCJZkHq6m62O7zvb2u\n3T835T7HeyhMU+HJaeRDT0Y+9IqL7Q9/6BWePDkxDi62DwO2/Oukfk5S8n7254rjPSH9RkDL4QLE\nwQjfQ9ZWx34X16vQ3sR9So/23bcgCF4uzGzGx3UJ8LUi8nsf7yMiGfhTPH/GNsD3Af+wr/H7RORx\n0jgi8jOB7+kPH3h2RNj34injAH9ERL76OWt8A/A7iRCIIAiCIAjeZaKM/E0QvPd5dbafvvE690+f\ncn544HK5ssy1+9VpKx/3ULG0qfU1PkzkOFKs74ph4sJarPXNy8cRkJy8BLy74cdxZCUnShZKEnLy\nWdyAi3v1tWEPMPNUcSNnYSiZofiosNM0cHcaWZ6cOE0T0zhSSqGU3MeJHWdrp+5Y7yL6sbPsqej7\ncz60awsYZ6sEX594BrsZn32c0U3iOeXpQRC8pHwX8C3Ax4H/UES+Dp9n/Y+Bnwf8Hnwe92d4Tim5\nmVUR+deBv4g71z8iIt+Du9UN+CXAv4X3Yxvwe8zsJx6t8ZqIfAr4Q8DPBf66iHx3/8wJ+ATwu4HP\nAR8Cvob46RIEQRAEwbtEiO0Da+o2Ij1oTGnNQ84u57ML7fOZ63Wmro72NouamwZnWR3anDwIJx1n\nVXvwl4eo+abNA83A3eEiBcuQ1WjmU7LWsdKCC/CchLyVWu9p4dYDyWQT3InUg8XWSwIJc/GdXHyP\nY2GaRqZp5DSNLEs92vvbBYTbJPLnnMPtXK4xaGkbS+YuPz2xnRuxbYfU8Wd+1ZVVdEtX63vyehAE\nLydm9rqIfAL4q3jf9G/s27YL8GeAH+YFfdtm9pdF5LcAn8bF8Hf17bhGBb7TzP7kC9b4bhH5WcAn\ngZ8J/IlHu/xjvBT9+/rjy9v7hkEQBEEQBG9OiO0DNy5tL/9W9dCwy+XMw9N7zg8utpdaaard5V3f\n6wFpIrel0Jsg7oKbnhbeemJ5a9VHdKm6A55cpIsfhs/X7hv91kvSDyPK1kC3m3BwH53lcW2yueqH\nCDdKhqEkxmFgGgemcWSaJual0XXxXkrepfSWHP7cCm7ZAsjp/eJ+V3qCuD9PTxr3U30ruh+vt330\n6miHsx0EHwjM7EdF5J/DHehfA/ws4A08QO1Pmtl/LiLfyjEy49k1/lMR+SHg38T7u38W/mPsH+Iu\n9x8zs7/zFsfxO0XkLwPfAfwi4AnwWeAvAd/THfCf1nf/wjv5zkEQBEEQBCshtg9IWudJ+4gps9ad\n7evubD88cL1eWZZGa0rK7hZvZePb7S60c17d573sWtV8NFftQrtvOWeSJErOPahsHXHl+6s2VF3E\nrs629zSzd+DflGhLD0lbC96NJD5OKz1ytsdx6M72xDw3tH+2PiOMj5HgPPMr8rEfe3uml81v5vXh\nAoKIeMic2bO/bct+Z1+zzwZ/y9G6QRC835jZTwL/dt+e9/r34r3Vb7bG38fLvd/Jcfwg8IPPe60H\ntX0E/wn1d9/J5wRBEARBEKyE2D6Q0zrz1GdNt6osy8I8L1znmct13gLR1lnQz91WwX1wtNMWFOaf\nYGY0NVQBEikV8tBd8JJJ2Ud9+SgwO4jcPSl9K1EvmdGgIjSAqlg1ai9Nz0nJyRiSULbyc7ysPPln\n5Zy5uzvxkY98mKbGKx960h11d9ZrbbTWqNUdeB9Tpj5mbBPO4BcH2MX+Vh7uI8G6ZO/nWfp1Adsq\nA5KkR63ccnt/ezHxjMoPgiD40vhNh/v/4/t2FEEQBEEQfEURYvtAzi62VX0+dmuNulTmZWG+zlyu\nV+Zloba2h3w9b+N2pFVex1elXTqa+XQutd7XnBM5Qc7Jy8N7Gbn0Zm3TW2G5rZ0zpRQGYBJQfM3a\nFFMvUx+KJ4UPSRiyC+6UvO05pbxtp9OJj3zEKEPpI80MNd/m68J1Xphnv+BQW+tl8M33URfeuopv\n9Z53NiGum9C2rS59F91+jcIFtDwS22vlOce3RSl5EARvAxF5Avw0M/tHL3j964Dv7A8/Y2Y/9p4d\nXBAEQRAEX9GE2D6wim1MqeZie1kWlnnmOs9cr1fmzdnmRlw/drWPpeQp5X0EFmuJuov6pmy93Hkb\nq7WOtFJkE6nHyvBdyJeSGUqhCah4a3RTY54N6z3hlhKZxJASw8HZNmN33nPh7u5EGQqvvPLES9u7\n0FY1zucL5/OFh/PVe9bnxc/N4r3r2tSd+qbbY1Fxwa2Kba70HhB3uPSwnRtB9nL4LsptLT+/yU8T\nHqnyIAiC5/HTgR8Tke8H/grwv+PjwD4G/Erg24A7/FrlOypVD4IgCIIgOBJi+0DuPdsNF9rz9crl\ncuFyufp2nVmWhabt4M4+drV31odrC3VrRm0NscTSlKUZVSGLUHJGctmWMVnLrqWP617Vph3WP4hu\ng5b9t8XVvc5iZIwiPtt7LImSfda3G8Me6JaSl5HnnDmdpk0De6m4u9bnhxMP5wtPzheu15m5i+15\nqZ6o3pTalFabu9697LytJeereNfugh8db/VS8/Wfja6xxeTgZN+W1AdBELwNTsCvB37Dc14zXHz/\nNjP7kff0qIIgCIIg+IomxPaBtPZTq1L7uK+H+/s+V/vSg9G6sw1s/dM3PdscQsDYHOzalLlWzteK\n5MRlaVyrUpsxJiGRKCn1tuReaq09SK0L1F2HWv80O4zachFdzMX2mIUpCxSYSmIqmXHI5B5UZija\n3XlJe++3O+XZk81Nt/7su2nkyZPT7mrXSq1eZl9rY6nez11rrwZYmie2r+Xm27Y739tzun9Os9ZT\n13uA+eF87vXkz44OC4IgeAGfw+d9fwIfMfbTga8CHoD/Bx9N9sfM7B+8XwcYBEEQBMFXJiG2D6S1\nzLtpn639wMPTp5zPD1y6o7uKbYwtefwZod1ndq0OrPUe6uvSeJgXUspcZxfbqkYqUESQlFmFsD3q\nfX7cs33Ek8UhC1j2vuwhw5iBLExFmIbMNBTAUG00pa/Ze79L8dFfp4HTNJJzwrYQNGVZ6ratYWmt\nKrW1TVgvS2OePVBu7sFydRXl621rh8fret4DXltDmnTnm718fK8256i8n5NdHgRBcIOZVeAv9C0I\ngiAIguA9I8T2gU3TaWOZZ84PZ+7v73l4OHdnexXbhlkPQjuEoW2szvTqbJuXj89L43xdSFmZl8Zc\nXVQWBUU8tczWkDEPULMe1mZq2wQteTRGiz5HO/dDGBKMWRiLIOrl42NJjENGVVlMt+MCurMN4zjw\n5O6OV56cGIaC6Zo87ret+eYjyPxCQWvK3EX4vFSu88L1Om/bUusjoV635+br3EPXFpa6kJbKIkJr\nbSudN7NHxeKrxA6hHQRBEARBEATBy0uI7QOqDYBaF+brhfPDPQ/391zOZ+Z57o7uY5f50Wgq2V1u\nNe29zM1d7evC0/NCLl5WXtVF8tJgqcbcDDv0PrfasNawptDaJrSTQFHBUoPcIGj0WAAAACAASURB\nVClNoZlvS1MAhpJJwDAUck6A+MixNfhtqVshelnHiPWS8pQSiiF9LJdID21LHvyWLW1l8sMw9HRy\n3UT3ur6XmB9vK3Vpm/O99n/P88z14Iav5eY+g1xp2jaRbz24zaKMPAiCIAiCIAiCl5QQ2wfUh15T\nl4Xr9cr54YHz/T3Xy2UX2+q9zntA2uF27TE+zNKuqiy1cV28X/v+spDLOqLLSCKUBqUZpWoXrI15\n0S621QW3Kgkjd7E9qCC5kXJDUqMZVIW6BpCJUEqm5MQw5J54fnTZF+rStrT0lPMmuNdxZZiB+Fou\ntL2nPBkHN182l9xD4Bq1ukheg9LWEvFa9xLyZak3Dvj1OnOZ/daPrT5yxT2QrbbWx4ypn8QgCIIg\nCIIgCIKXkBDbB1rbne3r9cL54aE72xeWeaG1ijawdcbWUVnT78samOYCtKmytMZlqTxcF8p5oYzr\naDAhZ1ia9U1ZqnKdlevsoWPWmo/Oaurp4n1rBql0sZ3bJrSrD+52QV6yi/ks7mzL6ra3TcQOZSCl\nXWjnnMjZx4F5UnhC0J5aroitCejrfO70jPD2YDXro810c6lrrZv4npeF66WL7Ou8Jb6fL9c+Zs0d\n7+vcX7v2OHKB2nz2drNQ20EQBEEQBEEQvJyE2D7Q2gLgI6362K9zLyFflrr1Ulu3sLeAtHWu9s1q\nXuash1Jo7YFjal4+vpacKz4be6nGUpW5qSeVVwPdNxXzfdfRYNdGozJroqrR1KjNyDkxlUQaMqlH\nrKsq1djc5VbdIRaBnBPDUBhKpvRS8pQE04QlxSxtCelm60WC3EV57vPEvbwcVtffbpLUm9qeRt68\npHyelq1v+3r10Wqb0z3PXK9eZn6++Izv83ThOi+9VH2h1vae/bsRBEEQBEEQBEHwxRBi+8CyuNie\n59l7iC9X5kMCua5Cu8+nFkm34Wiy+9xmHm62jrQCIyV6UriXZIskUk4IsvVaL2vPtvomeKm5pMSa\nwK3A0qDNjUudkUsPK+vbOGQ+dBrc1U6ZZi7yMfU54X3mNeAzukthHIbe2+1udZKEJQNy/z6KmXSB\nLo82//K3GXH+2EeXJxBFJHdX3LecEiVnxnFgmgbuuoheDmnm13nhfL7ugvty5XKZuVyvzPPyZf43\nIgiCIAiCIAiC4EsjxPaBWo/O9sz1eu0J5IsHox16tdcU8lVhrmXUO+voLk/vFjOy9LFcJblY7yXY\niM/TXqqXkS9Nqao0M3wY2D6WTHvKeVXzEDVT1BZUvWxd1XhyGsji477MPIHcWu0p617KbT2cLaXE\n0AXvMBRKSS7uuygGd6tVfe72+l1TSofveyu0fR+21/06QULELxyoucguRRmHwV33Vrc+73ro1Z6X\nyvl84eF85Xy+cP9w4eHhzP3Dmcvl+i7+7QdBEARBEARBELx7hNg+sDrby9Kd7XV81dJTyG11tffb\nTXRzlNpdaJvuzrZ5r3VJ3dneAsdctKqBriJb97Rtkz2MbBX7BixqXJfGZTHmpe/rH40qnIZMbYP3\njbdGqxWty+bSm9kWhFZKYRwHSjk42wehvX6+ah93djim3dleOT42P0fmlyfMDHrumpkxwJYovpXY\nqyePr+Xuy1I5X66cz1cezlfu7s+8MY2UUhiG4cvzL0IQBEEQBEEQBME7JMT2gWt3Sq+Xaw/o6snY\n1fu1nd3JlmPdOF7kvcnutV+7VtqyYFoRU4oYZdXYyUW3mQdrW3+qJJDsGWxFIGMU8dLxKtDUQ9Uu\nc+Ph2jjP6iO7UqLkQzhba7S60OpCXRbqMvvcaoGhlE1ku6PdhXZXyutULRf5h/Ff6fD9H5fRP8K7\n2A0TQcwAweRwjtaJ2f2YhO7gS4LMzYWMlBJDKd5bPhRO08g5nO0gCIIgCIIgCF5SQmwfuF672L7O\nzNferz17EJe708dRX3sJudPFZP/TTNHWaK1S64K1hpgnig+uKjfB3RQwTxgXgZK9U1qSkMXvF9zW\ndtcbajMui3J/qdxfGtOYOQ2FnAsGvXy90qptQnuZZ++XzoVSMuMwbL3apXjgmfQRYasQBjZX3YXv\nrdt9vPjwfMTPh/itW/WryL49hevFi5TWnnghZb+IMJRCnSamaeQ0jTx5cmK+zl/i33QQBEEQBEEQ\nBMGXlxDbB+YutudrHz81+/gpF9u9TFtue7VhldnrPQ9Rs4OzXeuCbs42m7MtWbDU3V/Fx3mJ+Wsi\nJPO/oIyRcaGdxPu2l+Zl5A/XyhvnBbWRnDJjryX3EvZKrUqtM8viWymFkgtDKUzTeONse6L48Yzs\nD/z5BOghEO3ZMvLdr/YHYodnZX316Go/+rQEYgkBcva9rJQtDX25m3hyPXHtCfFBEARBEARBEAQv\nIyG2D6zibZ0H3Zo72maKrRLSdg/bVs1oiupeWi7iJeXbiKvrwvVy5fxw5v7+HgPyOFFGSIVNnSbZ\nPHPSWkKtLq7NfDTYdTEus3FZGteqLM3QPm46JaGURM4Cpu5qC9TFw8dM1dfOiWEYXGhv5eO38W7b\nl+3sgvpYRv5sCbnxiC2ofS9HX8vGn0WOVy72t2c5lLXTR48l6hijv4IgCIIgCIIgeDkJsX2gNVet\nNwFlB5G9s4aRGa6GEyTFbdneqWzQVKlLZb5eub9/4Auvv8HpJ+64Lo3Tk1c4PYHxBObS2lPAD/9g\nXmKuDbQZD7Nyf2k8vSoPl8ZcfRxZyolSEmPJnIbMmL1ku9XG3MvZTRUQUsq9V9v7tXPxWdybJ7+N\nN7NtXvb2VVdeWDL+5hz7v+0ZVf7MSd6esn6FY/27yL3Wfu0fD4IgCIIgCIIgeNkIsX2gNXdKtWl3\ntJ8VmnbzxypRFVHpZdjW0898nWVZSClxf//A66+/QRlHlmZ8SPFZ2zkjKXtvsuTjMC3UvLR8qcay\nGPfXLrYvlYdZWaoL4m18V0mcxsyQIZkHo8217kZx74EuJW/OdilrKFoXtGtvdf9mz4piv5jAo1L6\nt8tRcL+I9bzTE9bNdrEtmJ/nlMHSF/35QRAEQRAEQRAE7wUhtg/szrZ6j3Z3t3dPFTaRfXzeegCY\n7OLPzGja48Nl4eHhwvDGPZIHd7JzoUyTb2UNQzu66F4eXitcq5eOP1wb99fK/blyqT6KzJCeQp4Y\nSmIqmSE3pHlAWrVKTqmP80rknCmll5APAzklJMmeVmb2jL9sB8X9WCMfM9ofVYA/W2Le11kF95tz\nFNrHSgL6RY1wtYMgCIIgCIIgeHkJsX1AdXW2myeJV6XVhjbDdHd598yvgwgF7+22xD5n20PStDWW\nZeFyuVIezgzTienJzOm6MM7Vs8PwoG4f2aVoU+alcr4sPFwWzpeZp5fK/aVyvi7URhfQmZyFkqCI\nkWlkPEgtpUQi+5ivoTCUgbu7O6Y+pzrntI8w41YMHx8fvvUthzjxx68+Twy/lcg29tJ9v5ZxLCmw\nm57v/awHQRC8PLz22mt8/OMff78PIwjedT760Y/ymc985v0+jCAIgg8UIbYP2FpGrm0X3Dcl5YcC\n68dCG/Oy5u7EYi7QFaU1ZVkql8tMyhfG04W7y5W7eeG0tG1+dcnJ962NpTYuc+X+MvP0YeH+vHC+\nVh6ulcu1YuDOdIahi+0sRkJJGClBskQWGIaRafLtdDoxTWN/b96P/1Daffxux77t25O1O+E3vEBk\nr+ve9m0f6wX2UvG9bJxNZO8fu/d8i7xYuAdBELwfqCqf+9zn3u/DCIIgCILgJSDE9oGjs91ac1e7\nKtoTv20LD2M3VW1PJV/d7K383BQUmrhLLZcrJpnh7glPLjPX68K8VFKCkgXTRGuNuTauc+XhUnl6\nXnjjvPDGw8J1rn1rXnaeMmnYxfbmbIsP6RIRcioM48B0mnjSXe1xnPqor3RTov3mzvazvFXv9ePX\nXiS0fbFdcN/cP3zWfr+/IZztIAi+RETknwH+7/7wt5jZn333Vv8Z795SQfC+8xqg7/dBBEEQfCAJ\nsX3DMRjME8lVdU8mXwPTgOPsaLnRfes+24ww31PV08n73O26VGpt1NpoJWGqWO8Vd3dbmZuP9lqa\nURVa39SMbOKudRKmkpiyMGZh6FuSRBaj5OSO9jQxTdOWQC5JDgdtN9L1sZP8Io6i+a16qJ8ntN9O\n7/bNee/VBbKd8HC2gyB4x7zLP0gS8Nl3d8kgeF/5OBDVGkEQBF8KIbaPeLv1Vsq8Cu21jPymVXgr\no+6P+4xo6w73qgd9FnV/n7mgtqa9VL32LaGa+2caTXdxrSaYJJAMooj4mLAsQknCWIRTEaYhMZa+\nDYncy9JLTpymiXGaGKeRUjI5ZWAVuo8vIqzHevv75/NE8WNn+x2FlgmICSYHd/sZoU0/3qPgDoIg\nCIIgCIIgePkIsX1E9m0X3KvDfewdlqOx/Qy2mtprxfl6x6w72Mee8OZivmkPYfOS9daM2nz0lyFI\nSi60xZO4kwglC1MWTiW5u90F9zQUSk7k4uFo02nq5eMjKaV+XM+Zdf0CQf28+296Gp9TPr7ePu4N\n355D+mivg+B+9LnPOttBEARfMvFDJAiCIAiCLysxqPiAYr7ZcdtFsB2c61vhuSZ6yzMFibvwXNdw\nV9t6SrmLbN3WVO3uthm69ohL6rO4s4/uyonSXeypJE5D4rS62iUzlMw4DpymkdNpYhw8fTzlDOLz\nu2tTlqbUprT1+9FN5ee4yo/vH9PW364Ifys2UX74HfjZY3j2OIIg+OAjIt8oIn9KRP43EfmCiLwh\nIj8mIt8nIr9ZRD78aP+PisjvEJE/LyL/h4g8FZGLiHxWRL5fRL5FXlBuIyIK/L31IfBnREQfbf/u\nl/krB0EQBEHwFU442wdqrQDuNtvzRORtb/NNSJfIvh0s8pvhWVtwmm+CJ2rLLnPZRfnqjicf7VUS\nAmSBMcNUhFdOA3enzN2YmMbEUDK5ZEoplGFgGArDMGCSmKux6IJanyPeZ4oPRRi6UF+/13594Ha+\ntd08t38v39+Q1K/eiGyV9btw3idx22Fu+fH2ccn484S235cehB7GVBB80BGRE/CfAL+hP3X8sfnz\n+vargT8AfFd/T8KbSJ8X3vAq8M19+3YR+TVm9vBon2NT0OPPDIIgCIIgeFcIsX1g6WK7tuaCtMvC\n7R+zzbwWA1uFsqRNaK9l3nKQmauQvHVjDREj4Wu44NZd2Bpg0hPFoRTv0bYM1rxP+8mp8GQq3E2J\naciMQ6bkQi6FYSiM40gZCkuji+3qTnZVamsIcDcVnkyZLEJK62+ea7z6ennhMGO8W9+r+w0+zxuU\nRMLSKrQPZ2ArG3/+eX9G0N/cu70IsT63tswHQfDBpTvPPwD8Cvw/9L8L/AngM8ADLpy/EfiWx2/F\n45H/O+CvAH8L+HHgw8A/C/x24Bv6un8c+K2P3v8LgY8B/03/3O8E/stH+/zjd/r9giAIgiD4qU2I\n7QOrs12191HbOuri4DyvolNufetNXh+E96EBnLUC2kw3ASuY73ojtB852ymRSAxJEBOSCWKJuwKv\n3GWeTJnTmJmGfONsD8PAMA6UUpi1MbfG/aWyLG2b450E1JScRsYh+bcQIT1KZX8streLBnro45bU\n3fqj5b39sT1hj+T0MZztRnTb+vnPKxk/7hMEwQeYf4NdaP8XwG8ys+Xw+t8E/ivg3xGRV9cnzayJ\nyM83s7/Hs/ww8L0i8vuB3w/8ZhH598zs/zq8/0dF5P7wns+Z2Y++e18rCIIgCIIgxPYNu5Y79mgf\ny6Zv+7S3KnKvB38mbscd8P7+vp9I8rFcScjJb1Mvv5ajCPdubbIAyZO6sySKZIoIp4KXjw+JIQs5\nCym5WDY8WK0ZaIPz3Hh6Xnj9YWGeK0sfOZYT5ARjydxN/q9CEv9DDj3mR/HPoa987dfOLZNyIueE\nmSed+7Gkg0jfqz23SvrtdB9t8xcHsUV/dhB85dBd7d+L/yj4LPCtj4T2DWb22qPHzxPaR/4g8B3A\nV+Ml5X/4HR1wEARBEATBF0mI7QNrWpwcNN3ep2y7uN5f2UrKn4+/T7pIFHERmlMiJRenOSdyHw8m\nfd9VbKfDPGkRY8zCmDNjTpwKnAZhKEJJLsoPoefUZlANE3e0X3+Y+ck3Zq5LpdZKq42ShXFI3E2F\n2gYEV/2rIb2J6oPgpvd8t6Y9Sd3IuVF6eJuZl5Wvott7q/fjOhSF7441HG5fcCYfhaHJM655EAQf\nML4WH+BrwJ96Tl/126YL94/iZeTD+jQu4r8a+Off2aEGQRAEQRB88YTYPnBT8GyPHNj1Dzvs+WKV\n3cW5bH3Pbn4LSdLmAueUKEnc3V7F9ia01d1uWXu78ZnaQ+JuEKYCp2yM2d3p1R0HPMVcDa1KA+6v\njdcfFj7/9Mq8LLTqI8fG3vd9vRupbRX33aTvolpVaYe0dAxaU2p1h7w1peRMy0opigiUkvtYr+eI\n535St7J64LaK4Jj6vr9l79tmC0eLnu0g+EDzdYf7P/ylLCAi/xrwbcAvBu5esJsBX/OlrB8EQRAE\nQfBOCLF9ZG0/vnniRm0f/WywvW/7RjBa1+GyB6sZLrYl+YzsnDxl3F1uIQskccGbxUjiItrw+4Yx\nZGEqwjS4sz0ko4h2oe77qJqPE0NBG4sZ95fK00vl6Xk5iO3KNCQu18p1adS2rpOw5N9Q1WjNXGz3\neeOmPi5sWSrLUqm1Ufo876ZKSolh0GdE9o2LzVFY2zMi+7l/NTevhdAOgq8AjgL4tRfu9RxEZAK+\nD/gEt2mKL+JFQvzLgAL/9NvYL/ctCF52vqj/PIMgCL5kXnvtNV577a1/5szz/B4czbtDiO0bDsL5\nJoTrOb/DdUW9pXI/ShvfRfchZOxQRC3sgjoL5ORp40MWhgJT849RpM/bNooYCSWty6hhyba52c2U\npBWr0MTQZCwqPFwr86I0M5qy3zZjacbSlLlqT1I3kgpJfF01o6mhraHNXe5lqVznynVeqEtjHAfq\nYIxqlC66jyXfx+9/FN3rLPNnz9vuft8K7J6VLt4P/oIRukEQfPD4YgMZvpNdaP/3eIL53wD+kZmd\n151E5IeAX8Z73nPy4+/txwVBEATBVwCf/vSn+dSnPvV+H8a7SojtAzfC2o4isSM3L68WrZdMc0zN\nVtRALIGpS+tNOdoWVp6gC24X2iUnBjWfe61gYqgKzVz4ZlEyghiIAskFqaqniqMNmtEwKkpFuXax\nfa1K1R6cptDUqOq93bUac9Ue2gZq66iuPQzNR4a5kz3Plct14XxdWJZGbcakfizj4KXlx3OyC+dn\nL0aY7WXv276698ivYlsO6e5r+FqI7SD4QPNPDvc/ho/9ert8O/4T+IfN7JveZL+v4osX8u8IEeFr\nvuatq9Zzz7kIgg8KH/3oR9/vQwiC4CucT37yk3zzN3/zW+73iU98gh//8Q/Ghe0Q2zc8drYPv6Md\nArnWUnLrDvVaFg23zqzPxko3pdKrSu+h372k3NPES3JXe1SoJfWycHopt1EEEkYy7wc37Xo/4b3V\ngGJUNWZTZlWuTTivzrZy2NzdrqosTVmqUrJQsqBdKB9Tx1tTHxu2LFyvC+fLwsPF082brhckhGla\nxfbtWT2K6+c52fs4sS64/S+in9NVVCefBS4eMBdiOwg+0PyNw/1fDvzQ23mTiHwVHoZmwJ9/k/1e\nAX7+myz1ZRHhH/vYx/jsZz/75Vg6CIIgCL6iefXVV3n11Vffcr9xHN+Do3l3CLF9ZC1T7iO0ZE0J\nX1O+t17tfutP7ilqsgtuLx+X2/LyHjhmus7U7g537+EuJdMwSlGGngJezdzhNqNVY67uXCexLVgt\ndTdaTVCEaonFMrMlFk3M1VjqYVY2LvfVhKowN+O6qDvr2QV+SlvHOnRnXq2XkbfGXBvXpXKZG5KV\nlJVUjNpcxOtz3Wzdysb1ELr2OIn89jdgL21f+7Q90T35/PEQ20HwQeZ/Bf4B8DOB3yYi/9HbTCQ/\n/n/rlTfZ77f3fV8kqi+H+9Pb+NwgCIIgCIIvihDbR3IXb2kPMlvDx4AtWfzN2v88LXsX3Kwl0l1o\nq7YuNF1w773biZwzBWNQoZZGU8GSh5RhSq2NpVWsNujvS/2igNIFN9AssZCpJJplGolmCevTvBHB\nxHPPm8JSXWwPRRlLckH86Dt6ubp5EnnTLrYbl6WRSiMVo1Rj6a75WpK/Cm419fC2FwjuzcW+qSaQ\n/SgEpPdtr0I7nO0g+OBiZiYi3wP8UXwE2J8Vkd/4vFnb62ivPmv7x4GfBD4C/EYR+cOP3yMiXw98\nF2/uXv9/wIyPCvs578Z3CoIgCIIgOBJi+0jy4Vmbs53Y3G04Cu0XhN+uCeQmh9lhsoWkreXY2tO9\n175mSULO7mwrxlCgqtGyoc1LxzGlLQvzdWGZZ7TpwX1fxbbn4DYSDRfZKgXJA5IHyLI51ZB8X4Wl\nO9vT4CXouyu97rs623bjbF+WxmVu5KKUQRkazzrbPMfZVr0JRnvOmdyOEti+I6uzfRDcQRB8oPnj\nwL8M/ArgXwH+loj8CeAzwANeLv4NwG8A/hzwXV2k/zngO/D52T8iIv8x3vP9EeBXAb8DeAP4HC8o\nJTezJiL/C/BLgG8Tkb8J/E1gFe4/YWaff/e/chAEQRAEP1UIsX1A8lpGnpDs5crpWEfOrbO9jtta\ng8BEZC8nPwjutRfZev+zNqW25rOql0qrg/c5K/u+qyhtjbpUlrlyucxcL1cu5yu1qju/sk7m3kux\nVRIqGZUEqZDHiTxAJm0J42sp+VFsz1VpzcvIbRPm/k2tp6I39VLxpXlJ+6UqpSpl8W2unm7u6/TN\nuqt/ENqqytH5Pia6czzDPqC8f9fdyd8D04Ig+KDShfOvBr4X+HXAzwX+yPN2ffT49wHfCHwt8IuA\n/+zR6/8E+LXAH+TN+7b/feAHgK9+zhp/AHfHgyAIgiAIviRCbB+Qngwr2QO4tm11t+329z3Depu2\n9bHbvbe4v4rJts8qorWZj866zDw8XHh6f3ZNmQRJmdq0O8aVy7XycJ55OC88nGcul32rrfX1wRc4\njNpKCVLGUkLyQFYjq5BNQFIXwJAQmkHtgrs22xLLfdxYwrqm3Xq8Tajqvd7LKtSrkRZFrpUnV+/j\nvi7uftsmtHW/2NBF9+p6byJ7S4CnC+yjnBY/s7IdUGjtIPgKwMwuwK8XkX8B+K3AL8Ud7Yo70z+K\nB6H9xcN7XheRXwL8buBbcJFe8R7wHwT+qJn9w95q8ijt8uaz/7KIfBPwu4CvB346XlYeBEEQBEHw\njgmxfWAX25mUszvc6VFv8Na37X7vjdd9zE/zHfrzu6tt6r3X12sX208feuhXJqVCbcp1aVznyvm6\n8PBw5f5+5v7hyvk6c7ksXK4zteqe7o33ia8mvDvzGVIilYGsUCyRSUgq3rPdj95ndBtzW/ut2YLW\ndiEvGImG0EyohgvuBkuD66JIUpDG+Vq5zJV5qSy19dFnB6d+7d3eZnEfxbYdLlh0US1pP/E3FQZB\nEHwlYWY/xNtMJO/7X4A/1LcX7fMvvtufGwRBEARB8HYJsX1Asgu7lJO723kfMbXN0d72tm1+ttzM\nuTqIwp5avotNF9x1aVy62J5OZyRlUi6kXGhqzF2sXi4LD/cX7u8vvPH0yvm6cOnbUtvmUJsZKXm+\nm5vaiZQzqSRyGSgmKIkshVSsf17GWOduG0vV7mxbd7a7P+/qHRNBLbnYVnFXW2FRI1UD8eneD9fG\ntTvbS23bBQmRQzl5F9s8EtvW768XA5C0S/5HQjtM7SAIgiAIgiAIXmZCbB9Ij5zt3G9T9lFT3pO9\nJ3UfBd9tCTk3r6x299qbXFvler3y9OGBVBJNvYd7Xnxmda0+9/p6XXj6MPP0/srThytLf14Bk4RJ\n73820OYjwQQjZaMMRiG7YG0VqQssV9QaIhlJGWuNgpKtIlo5FeNugPOYXeRuieLGZfHy9oercp6V\ny6zMS2NeFKjb6LHLvHBZFubFLxgk8dR0SRwS2X3rEee349F6Sf5awq8pezK8umD3hPLbPvogCIIg\nCIIgCIKXjRDbByStznYX2SWTS+5znddU8u74rs3Mj0dk9T8ea8E1ksxMabVyuVzJ5R4DlrqWjc+Y\nCa2ZO9yLcv8wc997trfSbklIXodSG8ahD9qUbOa91kkQbbRWkTpDzog2V76SaTWTtEIrWK1MBe6G\nxN1UDsfvn3OelYer8jA3ztfW+7JdcFsX2s3gPC9c5sp1qcy1krvjnk22/u3W2nas6H7cezq59KqC\njCTrFzoMET2UlYfgDoIgCIIgCILg5SXE9oHHPdvrJjmRJPXk755FdkjDti3Iay9wPuZ8cRDaZkqt\n7mwjeI/2vHC+ztyfr16ybS7ml2o8XCrnS+XhspBKIedMLgmR1Gd4Kyou0LUZrTWKdnc4Q86CtEqr\ni9eYS3VXnOxitlWsFnRZOA3Cw1i4Ow37yLO+nZfGeXZn++GqNyFoal6OnptxuS5cZ9/mZaBkgbyW\n4e+jz1Qb9B72dVsvGCBC0kzK5tUGqv59JeHDzUJoB0EQBEEQBEHwchNi+4AkF9sp9X7tkslr3/Y6\ne9vM+5hXhb21Z9ttkBrr810XHkqymzaWZdlGb9Vm1OYusUgGXFjWBufr7iRnhCKJ3EdxVRKLQMVo\nCKq46EaRJqSWoCl5XkgKuakLbUsofgGhDoWlFOahMGaYhsRp6hcdkiDJhe3DtXF/bTxcFg9BWxpz\n7/MWMUS9fF0xWp/H3VrzxPMeeGZdZG9l5KpY0728vDvf7mx7L3zSfr5JgPT7spXyB0EQBEEQBEEQ\nvIyE2D6Q1jLy3i+c0+OQtHUE2GplH1Kzn2F9zrYE8+OYK+1haa3P206L91enpCAZkUxTXLT2ILTW\nQ8lohkpPBTehkgF1AWqCKaCCVahiSK3ItYFcoY/wMgSRxFgyQy4MJZNpDFkYh0xV666+p5q/cV64\nP8/cn2fO15l5btTmiegpJYahcJoGpnHwtdIeKqdqNNzZtuaCeysh7yUArzkOtAAAIABJREFUxhog\n50FpzbqRLT0Ers/59lA6tjT1IAiCIAiCIAiCl5EQ2wd2sZ17ovc6a1u2Wdu34vpRv7bRy6/lpsrZ\n+otr4vbq5DZVpClSG5IqIKRk3qcs0ExoTd0pBmpPDm/NaMmo6q5xEyORSAhpFaUK2iBjmC7uILd1\nDJdscW5DzpSc/DYZ45AZx0IzSGVASkFy4el54el55v4yc74uLIuLbcVT3IehcDqNTJOL7ZTcznex\n3U9Od65NG+Yx6n1Oec9KWy9C0M9TM0z8YkNTtvR1PQjuIAiCIAiCIAiCl5EQ2wfWgDTp4VxrObnP\n206ehL31a3cRLc/3tTcO5eObq22GmCKt0dZeZFncJc5GSj7KS0m01gW2waz7thjb3GsFCo1iiWJC\nwdAGVSCp0ZZKm2d0nnuZ9l72XlLyLSdKFsZxYJxGVDIyNNIwIsVcbF+8r/x6Xdyt1h5mtjnb4yNn\n+xAK15PHj8Fo+/lhE9xegt4FtymtzwGv6qX2rb9VLZztIAiCIAiCIAheXkJsH0hdNmdJlFIYx5Fx\nnBiGxceASULFtsTxFbN9lvSeqA1HJW7rtjrcalhytzYdnG5EQXqRtEAqiSELDD4Gq0pGU6HhZeTN\nxIWsrY66z6Y2A2ueX67Lgs4z7Xpxwd/7nkWEmgRLCdXEvKzhZpVhXkgmJDJimbk2lkWp1XuuDUF6\nuf1QMtOYuZsypyExZEgomDvYxu1MbfoFC6FXApjQBEy8X9voDra5o11Vqc3HnvkM8C62Q20HQRAE\nQRAEQfCSEmL7wDolO6VEyS62p2lkGAZKL402QNQ28Sy7jN5E9zq+qtvg2/pubt/2bUvqTrcatHUs\nl/aRV5BLYpRMSoXFEhcyRqapuNvb+5vpReRJEs0UVbqD3rClovOMzddtdBgikBKmXWyLuNju27BU\nEplEI1ljXhpLa9TmYlu6819KZhgy05C4GzOnQSgJEoZp69/Vx3pt4ea9HH/vg0+IGajPDjdWod0d\nbVWW5lszc6GtnkseBEEQBEEQBEHwMhJi+8Aqi1dnexgnxmliGC7ubCdBe7z4Lqxld6Ifie5tRVl7\njHfn++hwr8nciCDqQjt1cZpKIpWBUgaumkia0JaoQDWjolQUcalNdpmLmPla2rBlgcXFNrDOBAPN\nWIKmLnznZdnc7TIvZMskConGUt3V9tFdRk6QkpBLZhxe4Gz3Put1vrb3vicX2KmLbPEjR9NNeb5u\nJeXuatfWXGyr9bne66zzIAiCIAiCIAiCl48Q2wfWnu1UCmUYGKaRcZooY3FnOydSL28W6ZXRxyC0\nrb58LSvfVt6e3fawvaRc16jxVWz3MVjS15AkSMmk5kIVSx4whu1r4SHpltz1NRTxWLV+2/qtC2sk\nQTrMBced4mb0/mhDk5JaI4mL3XVGtuEXHFLuznbJjCUxFWHIkEXBBDe2dQs+83OMX3xgPyEm64UI\nPwbtFybUDuXkfdO+ma0XPIIgCIIgCIIgCF4+QmwfSMVPRx4KeRoo00g5jZRh8OdyQhWS6ZbvJcjm\nyB77s2+c7QNrMvmNC26KqkCDJgmRhqaMJE8rT7k3Kq+92WYkvFQ79zLtjJHEIPW+b2sYC6SKtEZW\nI5mL9ZSyzxRPGSNh0udu5wFJxZ+X5Eeots3Gti7uRcRd7ZQoOVOyMCRhSEYWP05VfNxXH33mX/5w\n0cFkE9jQeviZ9sRx25zr9TOPFwW2R2FsB0EQBEEQBEHwkhJi+0AaynabR3e2yzRSxoFcCjn77Gsx\nPE1cgb0TmZvZ2siN3j56sDdhaazNx54qLqJoUrQ1JKv3cquS3OL18nC64DYjYRSMJIqI94BjikkF\nKmILoo1kUEzIpG2OOJJRyZ56TiaXguSMSUa3A++l6MfgN+kl5Dkx9K1kKAmyeNK6trWXfJ9Jbj4N\nbCv/1nUUmvWk8bVMXA9ufT+v61Lrcs+/lBEEQRAEQRAEQfByEGL7gKxiexwoozvbQxfbZcjknMjN\naAlQkC0A7Si0cUEpfkdeKAl3oWl4fzVmiCRaau48NyU39URv3ceHrc62YWRTxJTUBbeHsilIc8Ft\nC6JKNqOQKGSyZHLKQKaRqRSEvDvb0p3ttWReG9pDzowexbaOCyvp1tnu/eKmHmbmfdmpX3sQjt3t\na3l5011o3zrb2+m8uX3LcWtBEARBEARBEATvMyG2j/RycMmZPI4MdxPjkzvGuxPjNDGOI2oLzSqp\n2aHE+biG/7GOtULWkVy38vBRTrkLbmQX4KqYNlQb0hrWKqKJbIlBE9o8pbuZ0lBSF9xiBrIem2Io\nKoZKQgXUkn94M0QUo6ef95Fhx/JsA5+NbWB9PreXkLvQHkpmGgrTUBiHzDgUhuLhaEJfT/YgNOjB\nZv3CQdtGnhm1J5231vr3WueL2zOOtx9ceNtBEARBEARBELy8hNg+YKmL7ZK9jPx0Ynpyx3Q3MZ4m\nhmnYwsOSaC+1lkeOq9uuchDZu9Dend2j0F5Lzve08h6QZs1d5VaRWkimFE2MJliD1AV36m736h2b\nx4xtYtsAFUFTpvlAa3fIUZ9tnRJJ3C2X9djxfbT3VLtA9u+Qeq/2WEoX23kT3EMGSIipH08fMyY9\nkV17otvR0a6qLrJXwd3FtnaxXXtiu6215ax931+ufxOCIAiCIAiCIAjeGSG2Dzwjtu8mxsuJ8XRi\nOI2M00ityrIoSVoXtrBL56PQZttgdblf9MFsM7lXsW3WUM3I6mynipiQV7GtkNRI5iPAVmENvdxb\nDMVDzVSkb8mPp5elJ18EkV7CvnVIyzbezEy3iwDr91id7XHInA7O9jQUSnYX3CytX2xz+L0cXjeh\n7X3abRvtVdWd7aZK0zWFnD2BXB+f7yAIgiAIgiAIgpeTENs3dLGdEnkYGKaJ6e7EeJoYp5FhGihz\nJefaRfTek23P1dJduh6c7Vu6Wyy2WbXrKLBtRnYX2qaZZIkBA0kkEZYEC5DwMd3N3AEWlKS6lZNL\nE9CEWfI54QbWPFE9dzd+DU2TlCAlduHfXeVDGfm2+V4kgbwllAsm6fDd9u9tpn4RoE86a2q9UkBv\ntqZ6M/JrHW92PIe2BtAFQRAEQRAEQRC8hITYfg5JErlkhtHnbA/TxDCNDONIKQspp8MM7eMwKttG\nWtE7sB+z9i7vdKEtu7i1bRSYIqmRtIE2coJJhJJgMGFWuDYhqVEryGLU6mO/BCV3Zzu7gd3ncyut\nmfds96PMyZPIUylIyohkrM/C1l7ivTnbqfdfm2HNS9y1lT77GkQSKclWUr+O99pKyFWxnrzeg9b9\nQoHavjXbhLbPNN/LBATZytlDawdBEARBEARB8LISYvtIV29JhJwLMkI7nRh7Cfk4DeQhk5OL7cfd\n19siq839gsbiTag/Lh+nz7UW9XWlYdo3qySg5IRkaAhZBUmCNNy9NkNrQ5qXk29y1NYebEHVS9BN\n/RCzz/FysZ0LKZXubKdeym5oa5uzvKaLm3UhXpu/rn0GuPTRYtnd9zVxXM3HmAkJE93Et5r1FHJo\nzYV2bWsiuTvcKYEkd85tc8lDbAdBEARBEARB8PISYvvAJptTokjBUqJNS3e3vYx8GMomJG+EtB1X\ncCt309KP12cvhubQ+Q2gpiQVFLd8VRtqDdPqCeBJKQVUusDuDrSJB4qlxUvPk7nYTvi+irhbbeL9\n0LqODxOkO9s5+5xtkbytq+pp4FtwWu9HX4V9WxZaHdCmu7udEjkXck40VRf/Kj4DPO2hci64ZSsr\n3257MNo6BgzJZPaqgHVO93obBMHLhYh8K/Cn8R9yP9vM/v77fEjvGa+99hof//jHAfjoRz/KZz7z\nmff5iIIgCIIgeL8IsX0gySqBU2+3doe7DIVxGplOJ8bpwjAWcknklLZyaFt7rtfFekL4WhoucuuD\nv9ngqnWetvW0bqnVg8lyomgm4eO9MkrG/xKrNpJWRBs0JYmRxWdit35su7u892nnUijDyDidKMNA\nznk71v1Ifbr22n+uaszzDAvoLExZmbIyZqW+cuLuNHG68+MVxMW3QNZMUUPLem0i7eXhKSFVQLxv\nXLR5croqKfXXU9qqBsLZDoLgZURV+dznPvd+H0YQBEEQBC8BIbYPJEl+R/bgspwzwzAwjhOnHpY2\nDAOlZFL2kmzZhPbquNIFYX9mFdxdlJvd5IZx45Fv76UHpbnoNKCUjJn2fuxExihAMSP3MWE0v03J\nyAg59UC03gu+XgRIgovtXBiGkWE6UcaRlMtW577PtGYNWnchrsq8LCytMssqtI0h99RySZRhYBwA\nEXfQJVGyYZb3IoCj0E4J0i66pQpQMWwLb0tJtjnh1md2B0EQvHwkQN/vgwiCIAiC4H0mxPaBJAev\nWTJglFwoZejOdk8lHws5Z0/ehi3hey0dBzZVbUehvcnxF+EusvWZ2fQSbqORzGitefiZGCLqbjHW\nxXYl9TFhqJIwSvaebNuSvRWx9ddA7692Z9uT18swknJG0mEK+NqrDT2gzEvd27zQrhekLS60k28p\nJYZx4O7utH1bSf6+nP3Xz218lyR3so8O93ofnxSezJAutN3x3svQNUaABUHw0pGAV4Fwt4MgCILg\npzohtp+LbMFlktylnU4Td6884XT3wDRNjGPhWgpW1YPCDhOgbwzhVXBjN/3b6+eIHHLSDp3c9Pew\njt4Cn0HdGq02UgZreFn4apevY70OYnQ9njWrTQCSkCVRhsIwDAyTp66vZeTbCDR6n/aaLt6Vtzal\n1sp8nbHlyv2YmAoMGXJOlJIZStnc85S9ZF3V+7Ctp6/7vO51Bvm++QUMJZui1vvje7+59mC11jxQ\nLQiCIAiCIAiC4GUkxPZzsb10WoQyFKbTiScf+hB3rzxwujsxTSOX8YrRaGrQXCua3a5jq9S1TfIC\nhyrq3kHN0VXf3rUKaE/vbk1ZamNeqo/7UtCeQt7nbmEpYWq05EJeBZqsItd7x1Pvix6HgWEcGcap\nO9vDPtZs3Tclt6RX5948nbzWyrIstHnm4ZwYMqQ+czuJ32+tUkqmlEJxVe091/0MCJB7cvnx+xuG\nWqZp8xFoXWibGq0JtSq1wlK/DH/1QRAEXxq9DykuAgbBB4XXXnuNT3/603zyk5/k1Vdffb8PJwiC\nt0lrbb2b3s/jeDu89Af4XrLPuT44wSLef3zXxfaTJ967PY0MgwelpZT2gma58af3cnIOv4L1hm1h\nTfd2YezzqbcFurjVzRFurVFrY1kWlqXSqo8Fw7w421PJEyqZJplFCguFSsYLy710O2UXwMM4+HZw\ntlPKq/zvx+T7y1ra3eduu9iuzPPC+Xzl6dMzX3j9ns//5FM+/5Nv8BOff53Pf/4LfOELb/DGG/fc\n35+5nK/M80KtPlIMhJwzYykMJTMMHkZXSiaX3Pvi01bW7mnlPhqsVr/wsNTtP7YgCN4jROSfEpHv\nFpEfE5EHEfl/ReSvisiv+yLWyCLy7SLyl0TkcyJyEZEfF5EfEpHfJSLT21zn14rIXxCRvy8iZxH5\nCRH5n0TkO0XkI2/yvj8tIioif68//qiI/Aci8rdF5PX+2i9/u98HyF/EvkEQvAS89tprfOpTn+K1\n1157vw8lCIIvgoPYfun/3xvO9gE7NCiLdRNWhFwK43SCV5S7J3dMdxPTNDKOA7UZKTUXo+tIKnm0\nXhfx27yro+rufdBb6fZqVK/J5mv4Wk8mr7WxpEpSf4/1lPB+ZQBLsglrRbw8fV+ozxDvYnsYu7Pt\nGyljWyJ7d7azIJK3edioz9VutVHr4mJb3H1vrQLmc7ExVCvTNHE6eb/7UAb/3DJQCpTBhX3Juc/V\nNoolmgq5z9VOKdGUrYR8n8Wt1BoOUhC814jILwD+Gt6YvP5HOAH/EvBNIvKngf/hLdb4OcAPAL/g\nsAbAVwG/FPhlwO8UkV9lZv/nC9b4GuD7gW98tMYI/CLg64HvEJFfbWb/81sczy8GfrB//kr8gAmC\nIAiC4B0RYvuAWk+PNTYn1/C50alk8jhQptGd4B6WtlQll4pI7ZpXbkrJzWPFsaTuUlu67d1+PANs\n7bvW7lVLAlEQObjKiYx4iFvyBY7Ra3Yo+V7jxJIIKXfHuIvtXDI5Z3eut1JuX0v69157vKu2npKu\niFbQtl0AWJoitaEpkeZKvizIcKUlYZorp+vM6Xz1VPdh6OnuI9M0ME0jYNRWadU3bc2T07Gtp90d\nbajVhXZr2t3xIAjeK0Tkw8B//f+zd+dhlmVlne+/71p7nxOZVQUoRVFV0NricNUGGykKQdQSpy5E\nUbH1wn1UJpVufXyc29ZuQdDmtmNre+2WFkFAvXgRcGjBgdYLqCiTtO3YLcplKiChoIbMiDhnr/Xe\nP9bawzkZkRmRGRkZWfH71HM40469d5wkMvK337XeBVxP+dF8KfBi4APAJwHfATwFePA59nE98EfA\ndcAW8F8o4fwdwNXAFwHfCnwi8Goze5i737W2j5P1az4Z6IBfAl4N/APQAp9Tz+X+wKvM7NPd/V27\nnNLVwMspIf2HKBcSzgAPAVTuEhERkQumsD2Rcgnb/Vxqw2rgNSxGYtvS9MOuT8yYbczYXnbErViX\npVptjtbP/XZKIzM3x8POw9X7rOzU17OvLpEVAp4TKQW6UCrXFsv8ayYXBvp1w3LtZg7QhDJEPZrR\n9IG7aUrgjv1Q7dIZfBjvbkYZ1R7LMVIHSyd7qkG7VLhzTiy6QLZMZwnbTtjmAo+BJc68iWw0gY02\nMp/NmNXbxsaclOeAE2Nt/tZ1pG5JrsPj+5EB9XpFCdvJSV3p0p7VIE3ksD0TeCDlb4nvdfcfmbz3\nZ2b2q8BvUQLzbn6OErTfAXyuu79z7f3X1f28HngQ8K+A71/b5ocpQfvDwOe7+9vW3v9jM/tl4A2U\nCwPPBb52l/O5FrgLeLS7/8Xk9bec43sQEREROS/N2Z5IOdVbnSddAzFDZbshzme0G5NlwPo1t22c\nf107jA0dxT2PS4CNj3cZo9gH7X7bnIfqeE6Z3JV5211K5JzLOTIG7b6xmmcv30fKw7reMYYyhDw2\nY9humlrZrkt+9Utw0XcLD7RNpAlGMMc8l/W8Vyrbia0uc3qZuWvRccfWkg+f2eZDd29y+52nuf2O\nu7j9w3fw4Q/fwUfuuJM777yLu+6+mzNnNtne3qbrOrrlkq5b1sp2h+cM5LrmuQ9ztZeTynbfnV1E\nLj0za4GnUv66+fO1oA2Auyfg6cByl338E+BxdR/fvEPQ7vfzNuBnKH91PWVtH/etx3Dg3+4QtPt9\nvBP4wbqPrzKzE7t8aw788FrQFhEREbloCtsTvnLr52+Xpa9CjIRa2Z7N58xOzJmfmNPOW2ITCSGM\n84z7jt/WN0GjZu/S8KwP3vRhnvKvwVA7iJfK+qSDuedJs7SMp1SCd841jOdh20D5Qw3mZR3u4LRN\nYDZvObFR5k/PZuUCQQxxOMfV9F/21Tdti8GIARpzGstEy8RaLbcYyCGysMAmkbtz4K5k3NHBHUvn\nzmXmrkXi7u2O09tLzmwv2dxesLVy22axLEG7VLQz1DXBjfGzS6k0iuuXMOs7n4vIobiJcU7zi3bb\nyN3fA/zuLm9/Wb0/A/z2eY7Xz/u+0cweOHn9nwEb9fHLz7OP19f7lnL+u/nl8+xHREREZN80jHw3\n1rctK0O4yxxmo5nVyvbJDeYn5szObJeu5LGss+V1SHhZastqOHT6TmUlbOchdFNnSJcluQy3gFsu\nzc36yd39WPPsEGrAzglPAbfaIM0zwfMQsvv8HMyYzyInNmacPDEv3cYt1q7kdQ3rWk0fDuZ15Drl\nFs1oDHIoYbuJEKMR20hILV1o6WLDIrR01pBp6HJkmSKde5kL744lCF2miXnoZL69vaBtI5DrN5qH\ndcPHmeheh5L7MLQ8BAPXtSKRQ/SQyeM3nWfbN1Iq2OseXu+vAlLfG2MPrgfevbYPgPftcx87udvd\n37HXnYiIiIjslcL2RB83x3+61SHVFuvQ6lDmbG/MmZ3YYHZyg/buTZo2EksL7jEzUqqwwcYp2WM1\nOw9VbjxAXw0PobyXS8XW+07kUCvPZQ1tD7k0XcuZnFMJzDljOBGvTdNKvg+xhu0TM05etVE6i9fu\n3hYCZiWwevYa0Ccd2bGhehyD01jZdxucphnDtoeGpbVshYZgDR0NS29Y5oCTSxWaTMxOTJmmS7Q1\nbG81kdgEYqBWy/vD9xciGC4G9MP6S1XbCEe+2b/IPcq0U/cHzrPt+3d5/bp6v9+GCyd32Md+93Ny\nl9c/ss9zOQ8H3gvAYrHgrW99645b3XDDDVrXV0REZOK2227b01J8y+WOs9WOJIXtqb5JmY3PrV8T\nuya72DZDk7R2PqNpa5OxEIaR2J7BzQmWyWtFlxK0A8P6233HcKNWto1s43DyodEaw47xbKW7eT+M\n3Pph107ox1gDZk4MxqyJzOcNJ07MgVA6enel0/l0mDYrxypfH2wauMHrEPIYjdAEQhtxGjprWNCA\nNSQinQdSDpR3yxjOWYY2OV3tqL7oOprlkmY70DYBomHNOOy+DCPvRwT4UNkuVXdjHxUtEbl4K+sm\n7GPbqf4S2T8AX3qO7db9ww77WAAP28c+3r3L65eg8UP5eE6dOsVNN+08ev1Zz3oWP/ADP3DwhxYR\nEblCPe95z+PZz3725T6NA6WwfRyc65+ih5JX1w+ikCxyBbp98vj+wI7rX1fX7fL6hyZf/7fu/XqL\n+9LvYwbc7u67VdEP21D57y8Exhj5qI/6qB03ft7znsfzn//8wzkzEdnRYrEA4NZbb2U2m13msxGR\nlBL3u9/9zrvdqVOn+ocffa7tjgKF7YmveugjlQJFRHb2PyaPb6aslb2bm3d5/c+AL6YM6X40YwOz\n/fizyeMvAl5yAfu4FIbfH/1Ioa7rpv8gEJEjSj+nIlesI5/dbBw+LCIisjMzmwG3AfcB3ubuO46P\nNrMHAG+nVJ4d+Lh+iS8zezileZoDr3b3L7mA87iRMqy8Af4ceHhdcmy/+3kh8GTgHe7+oP1+/Q77\nOw3MKZ07zjenXURERC7cdZQ+ztvuftXlPplzUWVbRETOy90XNaB+B/BQM/sud/+x6TZmFoGfo7Rp\n2Gkfbzaz36VUpB9rZs9x92fudkwz+1jgUe7+0sk+3lvP4xuBTwN+zsy+YbfAbWb3Ax7v7j+/r294\nn476L3sRERE5fKpsi4jInpjZvYC/APp1r/9v4MWUSu4nAd9JWc/6zZSh5CuV7bqPGyhLh91AGf71\nRuAFlCr1FnBfSoh+LPAY4JXu/tVr53EV8MfAg+s+/gb4WeAtwN2U6vs/Ab6w7ufP3f0Ra/s40Mq2\niIiIyDpVtkVEZE/c/U4zuxX4Pcq61U+qt2ET4Bcoc7F3nLft7reZ2aOAl9VtbgYesdOm9f6OHfZx\n2sxuAX4JuBX434Cf3M8+RERERC61cLlPQERErhzu/leUqvGPAP+TUo0+Bfw+8CR3fzp1FUR2WSLM\n3d/l7o8EvgJ4KfD3wGnKcl4foDRf+3HgFnf/hl328RF3fxzw+cAL67ncBSwpHcvfCPwMpSHbF+32\n7ex2jiIiIiIXS8PIRURERERERA6YKtsiIiIiIiIiB0xhW0REREREROSAKWyLiIiIiIiIHDCFbRER\nEREREZEDprAtIiIiIiIicsAUtkVE5Ngzs48xsx83s782s7vN7ENm9kYz+y4zO3GAx3msmb3CzN5l\nZlv1/hV1/XIR2adL+bNrZk82s7zH29cd1Pckck9lZvczs8eZ2bPN7FVmdmryM/SCS3TMJ5nZ75jZ\nbWa2aWbvMLOXmNkjL8Xxzjq+lv4SEZHjzMy+FHgJcC/OXnfbKGt4P87d334RxzDg54Cn1Zemx7F6\n/3Pu/owLPYbIcXOpf3bN7MnAC3fY906e6u4vvpDjiBwXZpbXXpr+bL3I3Z/GATGzDeDlwGPZ+e+H\nDDzH3Z9zUMfciSrbIiJybJnZpwMvBa4B7gK+D/hM4PMp4diBTwT+q5lddRGHei4laDvwFuBJwCPq\n/Vvr619vZj90EccQOTYO8We390XAQ85x+7UDOIbIceD19k7gdxkvOB+0FzIG7d8Hvpzye/fpwN9R\ncvCzzOzrL9HxAVW2RUTkGDOz1wKfDSyBz3b3N669/53Aj1J+WT/7Qq6Am9knAn8JROBNwC3uvj15\n/wTwWuDh9Tw+9WKq6CLHwSH97E4r2x/n7u+86BMXOcbM7FmU34NvcvdTZvaxwD9QfsYOrLJtZo8B\n/lvd728AT/BJ6DWz+1IufH8M8GHgQe5+x0Ece50q2yIiciyZ2c2Uf6w78Pz1f6xXPwH8NeXK+7ea\nWbyAQ3070NTH3zIN2gDuvgl8S33aAN92AccQOTYO8WdXRA6Quz/b3V/l7qcu8aG+u94n4Jt9rbrs\n7h8Cvqc+vQ9wyarbCtsiInJcffnk8S/stEH9Bd3Pw7wP8JgLOM7jKaHgb9z9Tbsc50+Bv6UEgy+7\ngGOIHCeH9bMrIlcYM7sa+DzK793fc/f37rLpK4A76+OvuFTno7AtIiLH1WfV+9OU4WS7ee3k8aP3\ncwAz+zjgxh32c67jPKAOrRORnV3yn10RuWLdDMzq411/77r7EvgTykXum82s2W3bi6GwLSIix9Wn\nUK58/527r3dInfqbta/Zj0/dZT8HfRyR4+QwfnbX/YKZvcfMtutyRW8wsx80sxvP/6Uicogu5Pdu\nA3zCpTgZhW0RETl2zGwOXFufvvtc27r7RygVNIB/tM9DPXDy+JzHAd41ebzf44gcC4f4s7vuFuB6\nyj/KP5rS1fjfAH9nZt94kfsWkYNzpH7vXpJyuYiIyBF3zeTx3XvY/jRwErj6Eh7n9OTxfo8jclwc\n1s9u7+2UtXr/hPEf5g8CvhL458AG8J/NLLv78y/wGCJycI7U712FbREROY42Jo8Xe9h+mzKv68Ql\nPM60S/l+jyNyXBzWzy7AK9z9RTu8/hbgZWb2xcArKf+e/g9m9htqBahTAAAgAElEQVTu/oELOI6I\nHJwj9XtXw8hFROQ42po8nu261WhOmSO6eQmPM5883u9xRI6Lw/rZxd3vOs/7rwKeQwnzJ4Gn7/cY\nInLgjtTvXYVtERE5jqb/iN7L0LGr6v1ehq1e6HGumjze73FEjovD+tndq/9CCfNQ5nWLyOV1pH7v\nKmyLiMix4+7bwAfr0weea1szuw/jL+R3nWvbHUybs5zzOKw2Z9nvcUSOhUP82d3r+ZwCPlSfPuBS\nHENE9uVI/d5V2BYRkePqrynDPz/BzM71+/CT175mP/5ql/0c9HFEjpPD+NndDz//JiJySC7k924H\n/N2lOBmFbREROa7+sN5fBdx0ju2mQ0P/aD8HcPd/AN67w3528jn1/j3u/v/t5zgix8wl/9ndKzO7\nlnEpsveea1sRORRvYmyMtuvvXTNrgUdSLpa9yd27S3EyCtsiInJc/drk8VN32sDMDPi6+vQjwB9c\nwHF+nVKF+2Qze8Qux3kk5Qq7r52XiJztsH529+IZlJ9vgNdeomOIyB65+93Af6P8XH6Bmd24y6Zf\nCdyrPn7FpTofhW0RETmW3P1NwOspv5CfbmafscNm3wV8CiUE/6S7p+mbZnaLmeV6e8Euh/pJyhA1\ngJ82s+myJNTn/7E+7YCfuqBvSOSYOIyfXTP7WDN76LnOw8y+BPj++nQLeOH+vxsR2Q8ze/LkZ/eZ\nu2z2Y/W+AX5mfbpJHZHy7+vTjwA/f2nOVutsi4jI8fatlOGlJ4DfM7PnUipgJ4AnAd9Qt/tb4CfO\nsZ9d52y6+/8ysx8D/jVwM/BHZvbDwNuBjwe+B/j0uo8fcfe3X9R3JHI8XOqf3X8M/IGZvQH4TeBt\nwAcoAf9BwFdRKmNW9/Gd7n7bRXw/Ivd4ZvZo4BMmL107efwJZvbk6fa7rHM/vL3rG+5/YGYvBZ4I\nfBnl74ifpEz1+DTg+4CPqfv4Hne/Y1/fyD4obIuIyLHl7m8zs68GfpEynOy565tQ/rH+OHc/fRGH\n+jfA/YCnAQ8FXrp2DAee7+7fv8PXisiaQ/rZdcqczked4/3TwLe5+yWrjIncg3w98OQdXjfgs+qt\n58C5wvb5PA24Bvhi4HOBx6ztOwHPcffnX8QxzkthW0REjjV3/y0z+zRKpexxlKVCFpTOpP8P8DPu\nvnWuXazd73QMB77BzF4OfCOlwn0tZQmjNwE/6+6/e7Hfi8hxcol/dt8CfA0laD8cuIHyM9sAHwb+\nkjIv9Pnu/sEdvl5EdrbX7v3n2u68+6g/+19qZk8EngL8U+A+wPuB11H+fvjTPZ7LBbPy+19ERERE\nREREDooapImIiIiIiIgcMIVtERERERERkQOmsC0iIiIiIiJywBS2RURERERERA6YwraIiIiIiIjI\nAVPYFhERERERETlgCtsiIiIiIiIiB0xhW0REREREROSAKWyLiIiIiIiIHDCF7SuImb3QzHK9fd0B\n7fPJk32+4CD2KSIiIiIictwpbF+Z/ArZp4iIiIiIyLGksC0iIiIiIiJywBS2BcaqtqrbIiIiIiIi\nB6C53Ccgl5e7vwh40eU+DxERERERkXsSVbZFREREREREDpjCtoiIiIiIiMgBU9g+ZGb2QDN7ppm9\n1szeZ2ZbZnanmf29mf2pmb3AzJ5oZvfd4/5Omtk3mdnrJ/t7p5n9spl95h6+/rxLf5nZLZNtfn/y\n+hPM7NfN7B1mtmlmt5nZ75jZ15qZ7f1TERERERERuWfRnO1DZGbPAH4COFFf6huStcBVwD8Gbgae\nAvwh8Dnn2d8nA68APpnV5mYPBJ4IPNHMnu3uz97D6e2lOZrX414N/CLw+LWvvT/whfX2L8zsy939\n1B72KyIiIiIico+isH1IzOzLgf9MCaYO3Am8AXg30AH3Bj4JeDAw28MuHwC8BrgB+DDweuB9wLXA\n59X9ATzTzP7K3V92YN8M/AIlaGfgjcBfAXPgMykXDAAeBbzGzB7t7ncf4LFFRERERESOPIXtw/NM\nxgrwTwP/2t231jcys5PAY4Gb9rC/GfDDwHOm+zKz+wC/SgndDjwXOKiw/Zn1uG8Hvtrd/2zt/J8G\n/CdKtf7BwI8C//KAji0iIiIiInJF0JztQ2BmVwEPrU/f5e7ftlPQBnD3M+7+cnf/vnPtkhJ4n+vu\n37e+L3f/CPB/AKfrtg8ys5sv+hspZsDdwBeuB+167BcA31yPa8A3mNnHHdCxRURERERErggK24fj\nXpPHHzqgfZ4CfnC3N939A8BvTV56xAEd14Efd/d3nOPYPw+8pT414OsP6NgiIiIiIiJXBIXtw/FB\nYIsSPB+8ly7h5+HAb7r74jzbTSvPH3uRx4Ry/gAv2cO2L548fswBHFtEREREROSKobB9CNx9Cfxa\nfdoCv29mLzKzLzWze5/jS8/lf+xhm2kV/UKPs+6D7v73e9juDfXeGIfQi4iIiIiIHAsK24fn24H/\nSalKt8DXAr8OfMjM/ruZ/V9m9mVmNt/j/u7YwzbLyeN2X2e7Mwfeucdtp9vN63JhIiIiIiIix4LC\n9iFx9/cDDwd+CHg/4xJgBjwE+CbglcB7zOxfmdn5/mz2si72pXBmj9udXnt+zUGfiIiIiIiIyFGl\nsH2I3P1ud38WZY3sRwHfTRlefooxfH8U8O8pS3cdRSf3uN1Va8/vOugTEREREREROaoUti8DL97o\n7j/h7l/p7vcHPhv4jclmX2ZmT7hMp7gbAz5mj9v+o8njbXe/+xKcj4iIiIiIyJGksH1EuPsfu/tX\nAK+ZvPz4y3U+53DtHtfNflS9d+Btl/B8REREREREjhyF7aPnNyeP73/ZzmJn/Tzxr93DttNt/uAS\nnIuIiIiIiMiRpbB9CMzsajPbazfw6fDrD1yK87lIBnynme26breZPQW4uT514OcP4bxERERERESO\nDIXtw3ET8A4ze5aZfcpOG5hZMLP/HfiWycuvPpSz259t4GrgNWb26etvmtlTgZ9lbPj2/D2uyy0i\nIiIiInKP0VzuEzhGbgCeBTzLzN5Hmcf8PqCjDBe/CbixbuvA69z9pZfjRM/jDcDtwBOAN5vZnwB/\nDcwp87QfNNn2Lykd10VERERERI4Vhe3DsQksGT/v+wO3rm3TV4IBXgY8/XBO7YI8BZgBjwMeydgM\nDcbv4U+Ar3B3LfklIiIiIiLHjsL2IXD3N5rZdcAXAJ8FfDrw8cB9gQjcCbydElB/0d3ffK7dMQba\nPR1+7f5Ctxk3Lst4Pd7MvhJ4MvBplAsIHwH+nPI9vGQf5ygiIiIiInKPYu77yW1yHJnZLZSO4g68\n1t0/7zKfkoiIiIiIyJGmBmkiIiIiIiIiB0xhW0REREREROSAKWyLiIiIiIiIHDCFbREREREREZED\nprAt+2GX+wRERERERESuBFr6S/ZqX8uDiYiIiIiIHGda+ktERERERETkgGkYuYiIiIiIiMgBU9gW\nEREREREROWAK2yIiIiIiIiIHTGFbRERERERE5IApbIuIiIiIiIgcMC39JSIicpHM7DQwBzLwgct8\nOiIiIvdk11GKxtvuftXlPplz0dJfE7/2ylc6wLLrWC6XLLoli8WCza1Ntra22NzaInWJLiVSlwkh\nsLFxgo35BhsbG8xmM+azGbPZjBjjsF93p1t2pK6j6zq2zpzh9N13cfr03WyeOcNisc32cpvFYhtw\nsPJ1MRpN09K2LW3bEGMghkgMgRBs5dxDCMQYiTEOj0Ooz2ODhXIfQiCEiIWAmYE7/f8HzAwzI/Tv\nhQAWsBCgvofV93o2fl3/uueyz3Ir3z++uv91PjkPDEIMhBiwYLg7OWfcMzlnck7Dc4Bv/Lqn21k7\nFBE5RGbWAfG8G4qIiMhBSe5+pIvHR/rkDl0NixYCoYlEnMadmXsJmzGSUiZ1mZwyFgLz2Zz5bM5s\nNqdtW5q2pWnaISTiAE6IjgMNRjufM88JN4hNZLGcM18uWC6XOLkEYJxgRtNEmqahaWrIjoEY1gIv\n1ABe3g8h1lDdB+vxFmp4tiE0l0AMvhKazUrQHUN2/7hsWy4I1Of922Vv5X2cmrTrQwcCWHlsNnw0\nQA3blMBtGO4ZdzBCPbf+ePWAgK4TicgR4lAuKt54442X+1xEZA8WiwWnTp3ifve7H7PZ7HKfjojs\n0Xvf+96+SHfk04DC9oTVimsAYq3sWoyEpqGZtcy6jpQcz05OjlmgbVraZkbbtsRYgnGMETPIuVRk\n8UzAwCIhJjwY1gTaecvG8gRdt6y3Ds+pBM+cASfGUCvVYaho7xS2LVgN0qVy3D8uoTqChXqzIWgb\ntvL/UGOsPjOtVk+r6Ob0IbvkXuujb03bjpPPqmyX42TwUN5nsk8fK+HZczlvD2NW95UzLDc33FXQ\nFpEj43bgumuvvZZ3v/vdl/tcRGQP3vrWt3LTTTfx27/92zzsYQ+73KcjInt03XXXcerUKSi/e480\nhe2pPmybQQyYR0LONLOWNs/L0OWSnfFcqq4xRGJsiKEh9EE3hlKhTRkskVMmRMdiBhpC09DOW3Le\nIKeOlBKp3ueUyjDplKBWgEMdqh3r/mOwWhkew6atDeceQ3MADCeUcForw4YN1WnqXR+bhypy6PfZ\nXzbqh3n78Ppq3C0xuiTkPIZtpoE547VavfqVJWi7ZzzXYzuTQD35vrDyvStsi4iIiIjIEaWwPWV1\nLrGVOGgwGf5dq7OZUlXNJfQFK0Ozg4VJNbhUtd1LYC6jp72GXwhEykefgYznMiw950xOXQncKeE5\nlfNxL18frAb6SQDuM+swlNvG4FyDaaZUgd1LJdv66vDKUHDrdzN+HGM2L8G3nMzqUPP1oeg+7Irp\nDm3Yn41PhvP3lf+MYSD6ZCfjrQ/aqmyLiIiIiMhRpbA94ZN7P+u5lSBY02dpUBYmxeE+ipbgmXEy\niVz/d5zDDGZebpTAilkdJu5kC2RLZEtlKLl7aQRWJjAP4Xc8R68Buh/hbTXwGkao5w1u1k8oZJj7\n3A8BnzQ3W/9Ehop2H8z7CnMd0m4W6tz02uAsOH3tPNQh5z4E+ckccCYh3R1zI5iRcyihvDZxI4Ra\nwbbxe6nDzbPCtogcMR/84Ad54AMfeLlPQ0T2YLFYAHDrrbdqzvYV4vrrr+fNb37z5T4NkT1T2J7I\nk8dnh23oK6vTOc82DL72IZoCJQz2/3lmGo0DJaaPwX0My24Rt0S2jKdErvO33TNuDlZbkJkPc6Gd\nYSr1StDuwy1eLxYMleUyNn34HnYI26XgPM677oeW993H++HyfdjO9cKA5VqBx+v51op/bY42Njmr\nc7lr53LzUq0OVi8qDB3TQ7lQMPRcK5X67IGsDmkiVywz6//K/QF3f84F7uMW4A/q089199cdyMld\nBHfnPe95z+U+DRHZhzr3U0TkwClsT/gO9+uvMQ2olCoyMAyj7iN3H7L7/7yvbPcDuWulOVggUKq6\n0UIJ1paHwJ1zLpXulMtz8/J+nR/d/y/1XMYZ2ZP5zdMc3YfsvqGajQF6Oojc3Mlel/Eqr4zDx0MY\nbiEEcs6lTp1rZbvONS/Zuu983g/R74ePewnLVi4m9GG7r+CHlco29YJBHcWvyrbIPcVBXTE7Ylfe\nHnC5T0BE9mQBnALuB6iyfbTdxmpZTOTKoLA9EWogHIZmTxqD9Y2+wjRo16+zOsw695XmfhmrvodX\nmMyTdh/CZz8Me6iN13nI2Y2c+znWZSg4oQxMzySSlzDvDtnrMO6hWdlwJCyPPdB8cq5hqIDnla+b\nKiPDc5177kO1PJhhwQnBCcmx/oLAsBb2alO0Ycj8dDJ3nZTdV7azT7uXZ8yMLmdCrlnba1M6h5Sc\nlCET1B9NRGC9xcNlZ4C6kYtcGd4K3AT8NqBu5EfbAwGNGpIrj8L2RAh92C7/u1LZNmrzLptO0WZt\n9HWputZqs/cBM0zCuUEg1Ip2Ca/9gTKQcwnauTZh6+cpY4HsiQQkLzf3XCu95QSHkd4+bZJWT6x/\n2Fed65xx70vG00HwQ1jul+6q1XIra3+XCndZZ9xgGOqe+8CMDfuwOvTdyCtzxBn2v37L5VgRQiqj\n0Iflv3I9llMuQvTVchE5ltz9tUC83OchIiIishOF7Yk+bMNq87Hp8z5oD4F7Gra9b5A2Du4mlPA7\nnR8dJv+ZjzO+yV7DNqQauKedw0sYdzp3kvtQ1e4r28CwP6Z3Nt5DH7QBJgG5n//sk7ZoPs6Vni67\nNXRdp1TG+4p29jzMDx+7h5fq93QYeh+6V9bhrmm63BtWg/ZQ2e7L5f3xCWdf6RARERERETkiFLYn\npsPIh2A3lLBrss4+DIHup2H3FeSUM11KdLk0Nst4vadWhEslO9QqdqI0BsupVoZzJnWJrkukLpFz\nP/S6LM+19MQyd3S5I3ltnoYPAZV6ymMEHUdW2lr4Lm/XCwO1ojzMO1+Zh251OPgkaLMa6Fcr0+WN\nYWj6sK53GJYYs8k+pk3l+nK4BcNCJIRICNP3bVxPPIxHERE5AtLlPgER2a8bgGfVexG5UsQ4DGo7\n8r97FbYnbOWRD03NxnnYQC7zjMk1WFo/AdlYdIlFt2S5XNLlMpg81aHewfqGZKUhmvVV6wypy6RU\nQnbXdSyXHd2yI+U8NCcD6HIieaLzNM5zruc22nnq4rj+9uo2Ow/l9n5keV/LZ9pFfIi5a2G7/xzG\nz3DsuF5ydhias/XBuRdqs7Vyi8QYCU1DjE0ZDdAHbKvd272vmIvI5WZmNwDfCnwh8PHASeB24APA\nXwC/A7zc3e8+xz5uBr4D+CxKt6IPAr8PPNfd/2aXr7mFc3QjN7MXAk8G3uHuDzKzG4HvAh5HmQB4\nGngj8NPu/jsX8K1P1b+QdBFQ5MpxA/ADl/skRGSfJmH7yIcBhe2JsVJaG6T1c4X7QeR9d+7sePLJ\ne6Xx2XKR2F4s2VosWKauBO065DuEMFS3rYZtauOv5bIrAb3rWCw6Fssli8WSLqWVhmOlA3e/nNjq\nhYDxrHfgu1WBfZhbDZNmZXkSuOuc8Z3X4Z7uaRwNML1oYTZWuYdO5rW6Pc7/NmJsaJpIbBqapq03\nJzblByrGQIw1cA/zzs/95ykil56ZfTbwm8C9WL3ad796ezDwRErL31ftso9vAn6S1fnXNwBfAzzB\nzG519z88x2mct0Gamd1Uj3/t5OUN4IuBLzazH3f37z7ffkRERET2SmF7Yshu3gftPtD6+Dg7OXkd\n+j1+lTssFomtrSVntrdZLJckz0PgLt3Hy23ovOaQsrPYXrJYLNheLNleLFgslmxvL1h2XRmG3q9H\nTV9vH+dYT+dbD2F75Z+dtvKa+dlv9Q9yHcredxcft7O1f8nuVj1f2enQFK6vqoehsh3GtbrNCMFo\n21m5zWa0baadOW0LbRtoGmhaowGC1znnob8MICKXi5nNgJcC1wB3Av8J+H8pFe0Z8HHAZwJfcY7d\n3Ao8AvjvwE9RKuEn6td8a338EjP7RHfvLvBUTwIvq+f5fwKvBraBzwC+F7gR+A4ze6e7//QFHkNE\nRERkhcL2xPbmaYBhHvO0eViuAddzaVLmaZzTXOY1G8utTRabm2xvnmF7sSDlMn875Vwq2mth2702\nPKtDx5ddrXAvO/Kyw1MaW3EPa3RPQqaPz8vc8XH493Rhsn498Olrg8nw7GxGKm3PyJm1id6s7HEc\nYL7aJc5s7TmUpcb6+dah3IdQQnaoobttAm1rtI3RNNAGp7FMQyICMUOTx0Ht5JXvQkQuj0dTKtAO\nPMndX732/huBXzGzb6cE3p08EvivwBPWwvQfmdntwA8BH0MZ+v3rF3ie11EW1P18d/+jyetvNrNX\nAH9KGVb+78zsl939Qxd4HBEREZGBwvbE9pkxbA+NwxiHVA9DunNZomtoNz6E7S0WZ86wfeY024tt\nll0qw8lTGodP17A9zInOTpcSKeV6X245dZDq4tLl4DXI9iZBm1qJH7qlT2vK5b8wvMYkRNfh3KGc\nVzKI+BC4xynaNgR8W7lNAvd0TPcknI9rktfj1MAdzAjRiPUCRAnaoYTtaMQIMTjRMhEI7sScxw9O\nVW2Ro+D6yePX77aRu2dgp/naBmwCT9ulav0fgWcCLfDZXHjYduBn14J2f263mdl3Ar8CXEWZ4/0T\nF3gcERERkYHC9sT25umV5bvG+dJ9s7RxOayyBna/dFcJ3MvNLRabZ9g+fZrN7a1arS5zr+nna1sf\nU8eu5tm9dCKv86XLvOk8DhEfKtsl0w592SYBeCVwe+3+3Yfcep7B60Tn/ual6hyCEZpASpA8E7ye\n36SyHWpUDzVgh3rcQN8TbXX7XrBJdbs2RyuVbYixBPAYwxi0W6OJEIITzQlkgjvmmZD7Ift5aF4n\nIpfVbZPHTwX2OwTbgd9z9w/u+Kb73Wb2v4BPBR50Yac4+IVzvPdK4CPAvYEvQGFbREREDoDC9sRy\n80x54GvV7PLiMJLbPeDe13f7GGqw3MK6JTF3NDlRxppnzFONvZkST+v++ntzcqjHCo67lTW0mYT9\n2ui73IYB45xd4R0jtlGbsvWretew7Wa4UYa1N5FQG5PlnMkpk3PC65Btq2HZvIbenDGc4BlzJ+AM\nH0Wdm+2M3ctC3xBupSFawIIRQyTEQAyBpgnEOoQ8RgjmhJBLSA/TRmswrmJ+5BsQitzT/SHw95Qg\n/FNm9jWU4Po64E3uvtzDPnbsND5xO+VvmGsu4jwXwJ/v9qa7d2b2Z8BjgIdcxHEofy9dt4ftIqv9\n4EREZHe3nX8TueLddttt3Hbb+f+sF4vFIZzNwVDYnkj9nO36fGw8xtD5u0+Wfdg2DLcAbsS8YG4J\nb6D1SGogJSPlOCko2/QI9TjjwOg8fe7Qj1Z3jGSQQplb7cZkHrczLq/Vr2kdx6XG6r1ZqUs74FYq\n0bFpatiODMVy9zLH253gZQg3qcNSwroOywnzXO+9BOwauL1W7vvwPZxP/7h+EKFWuYcmaX3H8WCE\nUIbMB8uEem2ifG3f9dx377wuIoemhtQvAX4V+BTg4cDN9e1NM3sd8GLgV+pQ8p2cOc9h+q+7mGR6\n+zmO33t/vf/oizhOderidyEiInLMPO95z+PZz3725T6NA6WwPdFtnf1vPp/8zzRsr9xqwIy5Y2aJ\n0BjZAtmNnAM+LFXlw/Bv61ue07cas/qqjaGbPliX+y4EOjO6YOQhbJd53dNltILFcgsRs4iFCKE0\naMv1O8neh+0StJvYDGuBRzMCELIT3QnZYbGA5QIWSywtS/Duq/fTsE0539LB3MaQTaj5e3JRYHoL\nVi8MUIN2WUd7Mr18+ieiIeQiR4S7/42ZPQT40nr7HOATKMtq/bN6+w4ze+xuw8UP4zT3sM2B9Fw0\nM6699trzbleWNFRlW0RkP66//vrzbyRXrGc84xk8/vGPP+92t956K6dOXRkXthW2J9LWZnlg64tY\njdXt6YJW/ZJY/Tzs6CUkziJ4Lck6kT5cl7Bd69TWB25KZZxQQnUN3Jka2IORLJAtsIyBZSihu5Ro\nylBucKKFMv/ZAiFEQmgIoSlBO/a3QHbGdboNmliCdrkPNCGWmxkxZWJ2Ysr41tZwY7nAcgepg9zV\nkM1Q2S63MFyEsMlnNum5xlgB7+9seM/INaSf3RTurD8cEbmsvAwD+o16w8zuT1nS65uBm4CHAc8D\nvvIyneJ9zczcz3mVrh/7ffvFHOjGG2/k3e9+98XsQkRE5Fi64YYbuOGGG8673Ww2O4SzORgK2xND\njcF9mIJcnk9j3qQDt083Ymh4NoTP6Rt90LY6BLp/bJBxskEyo7NAZ4GlGcsQWFqgC8bSQq1yl3vD\naCzSAI0ZVqvTbdMQYwnZITZYjOQY8abcRzNiPblg0IRAa4Em1KAd62OMJmea5DQp08VAwulSR84d\neBnK3g/nNrOVj2Lokj48r59D/3g6kn7SyXx1ebIdVtI+uwebiBwx7v5+4EVm9kvAn1DC9peY2dzd\nty/DKc2Afwq8bac3zSwCD6X8jfQXh3heIiIicg+msD0RfRIGB+Mc674mclYEHMeac1YaHO69zpPO\npSGa+SRkB7oatrdDYCsEtmNkywLbwdiqr/fLdwUCLcY8BOZ13rPNZjTtjPlsRmxaLEasVrRT05Cb\nCG1ThplT5kxHjJkZLeVWAncogRtoU6bJTpMSC5xF6rDFFqkL5GzktMMSXDbpjr72EYyb7vT5jfO8\np5/yyhiDyXR3DSIXOfrqnO7XUsJ2A9yHcW70YXsyu4Rt4AnAR1H+annNoZ2RiIiI3KMpbE800xGG\nRpmIbL6W7Nabm42Bsx8abf0w9EnF1s1xy3jI5CF4U8O20xl0ZmxF43QMnG4iZ0LgTDA2zThjgdaN\nJhutGxsEcmwIMdLGiG1s0GxsMN/YILbtELS9iVjbkNoGbxtCiDQWaKwE9pkz3JpQAncMRksJ223O\nNCmz1ZWg7WcaiAaJ1cp2/d9pRXvt8sP4ytBsbnzdbTI8/6xPe9y7iBwdZvZZwG3u/vZd3m+BW+rT\nu7l8ncMM+Jdm9jJ3/+OVN8yuB360Pj0DvOiwT05ERETumRS2p9ab1ZqNWXvHqqzXUNmv+dxXZ0tr\nsDolu4RqnAxkjAR0GB0lYC8t0llDFxoWIbIdItsW2apV7k0zNkNZ1zt6IHiZVz1vGjZi5GTTcmI2\nY2M2Y9a0NP36WU0J26Fp6GqlO1gdIg60Dk2dl20pYyFgMQxLbQXK0PomWF0v23Ey2Se3nLEwfMf1\nAsXYrb1vmmZYHRngqyG7Hy3Qd1iv6397/fzHu/L1CtwiR8rnA99vZq8HfouyvNYp4ATwScC/oFS1\nHXj+HjqC7+ZiB7N8gBKkX2Nm/wF4FbANfAbwvcCN9Rj/9jI2cRMREZF7GIXtCZ+u2zwMV14Pd77D\nw76ruE++bhwI3XcXTwadGx1lTvYCo/NAZw3JGhINCyJLAst632Gk2o08WEMTGjYsclVsuLppuVfb\ncE3TMo+x3KhLdQ0XAiB4JmTDukTwTOMQMyVkLxPeJXLXkYREWBoAACAASURBVGIJ2zRl/escQmn0\nZobnREqJLnV13nYi5UT2hOUSxvuGaEM1eqjs2+rnuEOPov5axbRpWv/13q+xbasD+BW7RY4Eo3Qg\nv2WH9/qhP78GfN9FHuNinAH+OfBqSrj+3sl7/Tn+lLv/1EUeR0RERGSgsD2RPQEMwe7ctZQd/u3n\n7DjsvHQX74M2LCyw7YGFlVCdKUE7W8OiBu1FH7QJJErYttjSNi0nYstVTcvVbcu92pZ7NW1tlOY0\nQBiGtZdmbMEdy5k6ZZyYnJic0CVssYTFkrzosCaQmhq220huW7xtoGnItTFaSh1dTvV5wnNdnquu\ng903NivfeV+HPnt4+I4f7mTYPbYaur3+eUw30bxtkcvuhykN0L4QeBSlQtx39X4f8Ebgxe7+6os8\nzg4NIvb03riR+1vN7GHAdwGPAx4AnAbeRAnav3uR5ygiIiKyQmF7op9/XDLzZAz4WXaekdx/sdtq\ney93L8t4YXTYULkuVeyI00xuhnuA3K9PXYdyG8yisWGRk03DVW3L1e2Mq9uWa9oWSx2WEyEl8Dys\n6+04pFpZtoClTFhmQueEZQeLBXl7CdsLaALeRnIbsVlDN5+T8pzsTuo6uq4bKtueE+4Z9zwE6tB/\nLr7a5KzMY9/ts5x+dj5+ad/S3cZ9mq2ONFBlW+Tycvct4Hfqbb9fG/a43WPO8d5rmSwksYd9vQf4\n9noTERERuaQUticmGZGVKDcUWVeXpRqe+1p/clt7YmXdbTeDEDALhBCIFnGLwxeYO5adkDPRYN45\nJwy2DRYB7j3L3DvD1RhXYZzEOAHMcfJyiS8X5OWiNGar3ceJAa9Tyh3w5JAc65zcJXy5hGWtbteK\ntrURTy2WOyx3eJqxvb3FYrFg0XV0Kddx3z5WtIcGZ4zlZx+r0+N7qx/Sjhl8Mnx8/bXppgrbIiIi\nIiJyVClsT4x1llqXttXAXYQaHAN9066VQne/L5sETwMs1Mc1bFsftsvj4EbAidlpPNN6ZunOwpwl\nzgLnXgmuwbjGAlfbJGy7021v0W1vkra2SsatjdEIAXfHs5NzCfOeIafSFM27brjRBKyN0EZSmkFa\n4rkjp47trS0Wi22WXUdKaViLPNTvz+p87SESr62fPVywmMzjHkeK20pyHsYMTLu5Tz7b1f2IiIiI\niIgcPQrbE75j2bQPy2MwdAJ92C6NyHcIfn3DsL5KG0IN2jaE7WCRpq53HYHG++W2OuYp0eXEkkzn\nmaVnrgautsDVoeEqC5wENoC5Z9jeIp05g585jbsTmgZvYgnbOeOp3nItcWcgO54SnhI5p2EY+RC2\nc4d7wnNia3uLxXLBsuvIOZema/1nVr/PYemuySLbKx/p5ALEdHm06TZjn7SzVjM/6+MVERERERE5\nqhS2J8xC/6AER2OH+z5oT8P22Q22+8DYz1UONVAP77sTyORhj+X9YAmzRAyptE1LmS4nmpyYLxfM\nQgnoljp82dA1DYsmstzaZLm1Rbe9VcL2MmAhQAh0OZOyk3Pu19RiMrAbC04MAdoGm5VbnM1ompYY\nmnL2IWCxIcQWGicQiGSCl0sPVpf1stVlyFc+kH6+tVE+r7E4vdMw/Ok87/WmcwrbIiIiIiJytCls\nT4QwxuFhWPQ0+JnVaDzel6Dta2F7fT5yCdJGnZdNJrqRzMnWF4ZrXdgyFjIRJ+EET4TcEXJittwu\ngTw7LBZ0TWS7CeQY6JYLlosF3XKBe98HvOw8uZPrzSyQQxnOHkLAQiQ05T7OWsJ8RpjPiG0N201L\niA1N09I0M5rZjGwQyOXcyFgdnm7upWLeN5rr19Veq3KPQ+/7udmrfw7m4wUO69fsXu/y7grcIrIn\ne+pWLiIiInLQFLYn+sp2H/JWGn8NodtqnTrUzuFeuo3n9b5odlYYtFoFjl5CdnbDbTJX3CDjuGU8\nOskyMWciiehL2g6a7IRlwmOki4ZHYxmMnOq616l0CSeDZ4bm4JnarywEQozkJmIWCSEQmkCctTTz\nOc18g2Y+J7azEsRDxCwQm5bYtjTtjGTUqnbGPGEpQ38LDnlS6t9hLvbwZD1kT94qM+PrRQ1zzA0f\nArf+3Swi5+fuTwWeernPQ0RERI4nhe2JMOmm3f8HKwOux//MsL6QCwQb02Ot1zJNk6FfWKxvLAZk\ncg3staFaMIIZXkN/zEaXM9YFAkaTnZgTwcC6jhwgl6ngtXQzHmOo5WSvVeLJ+O5ycIglaLfzGe3G\nCdqNDWYbJ2jnG4S2rUG3DHLPKZdbzuRlLCE7d5gnfNmVOnwN2f1yX/1nMC6oNp7CdHT4auYel/ga\nPvezhqabQreIiIiIiBxpCttT01BnPswr9uGNfsy3M0xOHoLtGDFZe1R24isjofsvy5SKusWINbFs\nG0rn8pwz5AbSArpYG6kZ0a3m5VpVd69zqkul2mp3cyOUCwI5k3Mi9eX32qwthIZZu8GJE1dx8ppr\nmM1L4G7nG8Smpe+e7gSaGGliJMbIcrGFdwvoluRuQc5O7jK5dj0fKtv9R9R/HEM2nqRsn6xIvtIp\nzctnMwnVPv16ERERERGRI0xhe2o9DLI+n9jHDuM2SZLuY+V4SJZ13nc/N3ltBHRfdPZQ6twhlOZj\nFiOEiIVQgmtq8K7BYyR6Jrr3A9ixGkizQ7SAxZbQNsTQ0IRItEjA6JZLUtfRLZfldK1U0s0is3bO\nyZNXc80192E236i3ObFpcStrg7sZMQZCLEPQF9sNaXuLtNgi4XRdIls3LDG2srD38MlNBolPPoPh\ncytXNcYlumGt69z6vHgREREREZGjS2F7Yr0HF9TwuLIRtfo8mZPt06C9Xt228ZWhGG5DMXwIyqEh\nNDNC02KhwWKDJ8e7iC8DHq0MIfdEzAlI5DpnPGcnWMCahjib0zYtTWxpY0Mk0C0WLLe3MQI5ZxIl\npIfQMJuVyva9rrk3840TzGZzZvM5oWlxM7IF3CDEWG5NpNmMLGNgac4iJ/JyiWF49jJfvL+SMHwM\nNhlePv14HR8Gi68n7fHPQERERERE5EqjsD2RvZ+BvcMazzUsDstKD92wvXbG9nEZsP5LamO1vu5d\n+pCXxmgJalguy2f5pBoczAgWIII3Dd62kJzYLbDOyXRljWy8NA3DCBZpY8tstsFsNqdtZ8yaGSEE\nts9s4QS6ZHhK4LlUoUubs+G8+qp3+cb6qnkmOWWIONQ53JCzk7pEWixJy46UurJmd05rncLHrujj\nXPbpe/1rxjSOT0edD13N16kduYiIiIiIHFEK2xPZpzH57CQ3xG0vQXdom+aTOcX1Nm1YlmESaK10\nIsfItdFXngRuq/uNdXi5Nw3M+vWrM+SO3DmeM7kvxRtEi7TNjPlsY6VCHUMEIl02bOlgJRTnnMke\nyG711gdqr6G7zAXvHDp3upxrM7iyFFdOmW6ZWNawnbuOnDvIeejebitz3Mcmc6uf6fTRpAK+0sXc\nzwrcZkraIiIiIiJydClsT/Rhe+ijbeOzscrqQ4V2UgSu71GW3KrrbmdKE7Oy7JbVgD0+9jpPOXgJ\nz+QM7kNlO1iEpsFyWXs65w7vQl0zO9dCuGOhbN/Elnk7Z2PjBPONE8w3NgixocvG9tKx7QS+wL3D\nvSPXynYJ214arkFpDkcJ28mdLjnJawM0K1X6Utnu6BYdabkkdx2eujJ/va5RTgilpt/PXe/Z+HlZ\n/7nVz3SlB1r9/PtLAN53VjdjOgBdRERERETkqFHYnvC+Qk0N2r7SQrtEweGlEgz7lbnLEHIf7vNa\n2C5htlS13cegDeCpw1MkdwEPEQ9LCBEPGer8bKzsLZNI5GHIu1N7tdXKeJnDnUkp0aWEmdG5k4KR\nY4M3XkJwarAY6DC2lkvuPrNJlzMpZ1JOhKYhAZ0bHZC6Ug0flvfKdSmwlIYlwcr89owTSij2XL/P\nWu7vq92TT3Tl0doI8/6jLpXtWnMf5sWvDjsXERERERE5ShS2J8bKdo151oc6mLbQ9kkntT7uTWcf\nT5e5nr7Th+Hyso1HSkvycmh5Rl8DtxDKvOiuo+sSKW2R84LkS9zTUOXFjC53LBZbxM1IlxPbi23i\n9ibEhq1Fx1bXsYyQrQEaIkYIxhK4a2uTrbRkY2POiY05Gxtz2tkMiw3E0qyt6zpSl/A+VNfbtPu4\n0y9vVj6ADKVaP+kTZ0PXcRsvbpwnM/cV/L6aPa7krcq2iIiIiIgcTQrbEz5NfbZagV2pwU7KsG42\nqcCWLXOdY9wPue4DeKk+51r1LcewnKHzukZ1wjxjnjDvsBBLhbrrSCmRum1SXpC9w0kYgVD/N6Ul\ni8UWBAjdEtuuHc1jpCPQeWAZasfyGAmxwYBFt2Bzc5N015KN+YwTNXDPNzZo53Pa2QbtbF4q1ymV\nBmsp187jPqmo9x9NH6Anw7z78rsZVm/Tz3o9bJdieD+OfHXJr2H4uK/+qYiIiIiIiBwlCtsTOZeh\n2SGEsfo6jdmTBmpA389rWIu7X8prqGzXwF2+ogTtfmmsoat5PUbOCVLASODlFmKgS6kE7ZRIaUnO\nS5J3YE4Yvh661MFym4zDYkEOgRzqsPTZHNoNaDeIs4bYzojtDNzZvGvBmc1NTt91JxvzGSdPzDm5\nscHJEyfYOHkVJ06OjcvIeahoj99s/3H45BrEZCxAvbiAGxbCnv4cVq959IF7NVhPh5SLiIiIiIgc\nNQrbO/Da5Ku/71+DPuSNQ6b7Rmd9o69+bnFpkOaTbFqr132DtbC6/nYJpmU5LbclmQxmpJzJuYTt\nvqNYaJs6KjtgGGYBYiBjWHayl3nUySBbxCxisQxRD5SLA25l2Pz2csFdm2e44847ufqqk4QYmM/n\neAhDJRqzSbAu59+EwKxt8fmMpSeWuSOHBe51ma++uZz1/dVXh3zv1k18+pnbLpXw6Z+HiIiIiIjI\nUaSwvYsdg3YdK93PvS41Xx+WusIngdsZ1qfO/Zzjur41/bxlxsq0u+MZMgnD6VJXun57Cds5Z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zp4OI/JfAS9rTV95EaAPg7m8A/p+2hlfdreN3Op1Op9N5uOmR7QOW3wLNI1JttmyNaq3n9MH+\n1Z3ZQjTP5pxvJzabic12QlRZr9asV04RZajG6M66ZWaDI1ZJGu3B8pg4SgmrhXkbEeG92HasOjIo\nQxoYx5Gj1ZrVasW4GhmGTGn9sPFIGV9CyJqENA6kVYzV8XET25djHF1mXF9iWB+T08gqr7DxiHme\n2ZSJbdmyLTOr1Yqhie2ccwTLATVv6faR6L305jY1TASbC7M75yXW1oLVUceuSlIhLZ9vru1LNHsJ\nYAuOmmNG62Aek3RD8s6CiIzA+xKR2uUm4uHNxI8gouH3i09p23cSIvdW/CPgKw4+85232O+7Logi\nv/ng8T+94FjLfu8jIo/ccPPibq7XgR9w91+/2QTu7iLybURa/nOAjwb+/QXHfCr8ibb9+YsyDxo/\nAnwW8FIR0VvcGOh0Op1Op9O5Y7rYPmD5zap6tLUqNUR2KZVaC6WWXZ2wA9WNrRlTNba1cr4JsX2+\n2aIpc1xhRrE8sKqVlRkTguKIG+oCHiZkQ0oMg1LmmY2Cm1HrjFXHaxOarfb76LrI9kgeMiaESVmN\nfGtfIttJSWMmr1cRib50zHi8iO1LLbJ9zLg6QgdHakVqpcwTZ9tzzrYZnTYh7MeRNAzkIZNFya0m\nXWU5J6dqoZYKWhGHKspkznkpUc8tzUBNhJwTg6adIZ2K4Kr7FmEWhmjaUsjV2q2Og5TzzsOLiBwD\nf5Go+X0xcf/nVjz3GVnUrflw4q+Nn7yoHtjd3y4ibyWi8R9+wXy/cMF773w39rtC1Lwv3O31/ocL\n3oPrxfVLuHti+2Pa9j9v7vF3wkCI/t989w5pRDn67VhuWb6ncfV+L6DT6XQ676VcvXqVq1dv/+/M\nNE233edBoYvtA6Ym5qpDcSgIM0rB4rEvdcbexLawddg6TAhbUbaamNJA0sykiYSSTDiZDTYT9fQa\nqykzJGFIQkqKSkI0IaKcnW05PT/jZJ7Y1oo1wWkQEeN5pm4F2yTkfITVgGdlLlHnXUphs90yTxOl\nlHACr45YiNbkrZWXKqoJzRkdBtJqFQ7jTWwjkOqMVmZiQgAAIABJREFUzhqiuRjTdsLcmZKiAokw\nOpvOzpjOzpnOzpinmdrWMW1nTq5d4+TsjNNr17AWKBIcVWEYBsacGYYcAj5n8pBiXYuTO5BNSAYp\nsuP3kXTtYvthpbWc+iHgA7mhvf2Nu7bt0TOwrIt4TtveidP1rxPn9ZwL9jm74L1DUXmn+92o+u72\nem83z2/c5Nh3g+fz1A0enH0K/bvJO57exzudTqfTeQh53etex2tf+9r7vYy7ShfbB2zbr57VhXkn\ntmEWZUaZRcNlvDloF2BCmQS2wKyZOcE8KKaJpAMqGXHB58J8tuXcPUS2QhYnqaDS+lKrsp0Km21h\nM81MteIuLGXKxSrTvGUjhTk5NiRqFoo4dTFXK5XzzTnb7USZC1YMr4ZUj+Gg3oSsKppS9Mseh2jl\nVQtSFfOKTinkrkOdC7Ua22lujY7adbDK5toZm2vX2F67xrSdmKeZMs1stxOnZ2ecXjvj2tk5ZnV3\ns0JVGMeRcRwYh+g/Pq5GVusVQ47IedJEFmVAGRDctV2vds261n6Y+Q5C4BnwLcD/Dfws8A53nwEk\nrO2XqOyD8m25E+H3oKwV7t56bzfPvTrn5SbCjxJt0u6Ux9/dA4oIz33u7RMpUkqk9J4Y2Q4effTR\n+72ETqfT6byX8epXv5pXvOIVt93v5S9/Oe94x3vGje0utg+YmtguwOzCRKSBT01oT2i00Wr1xBXY\nokzAJFAVahaqR//sKQ2IJNyVeaqc+4aTeSKJoxjqhoo31/KINBeDYs5sUC1cuy2StJmssJoLG4NZ\nKpYTlsJELPpZO27G+fmGqUW2rVa8xmTRFzsEtxCGa5pSi2yPEdkughTFakG1tT4zIp3eZ6ob1S16\niVuk15+dnnJ+eo2z01O2mw3zdmLazmw3W66dne9GiO2og09JGMcVq9XIarXi6PgoRimsxxVjSoya\nGVPCJOMt3XwpRhfRpfi985AhIv8Z8PHEH8O/6e5fdotdL4qQLlHd25lEXnqKy7sVvw08StSV3473\nJc7tt+/Ssd8d7vZ6bzfPYd713Tzv32rHft4d1GzfFV74whfytre97Zk4VKfT6XQ671W84AUv4AUv\neMFt9xvH8RlYzd2hi+1DhvaDk+ZyLRoGXV4Rq6hZxLTdWq9nJzcjNTcnLfXVxcM5PGWGlMmaEHGq\nONaM0XAQs6jdFmvCtmCiGEpFqKq4aLymyiTGRozsxrYWyrxl3g5sUwrp4GHb/eRcODNnUqXkgZoS\nVZUKFPeIkJfCdp7YThPb7cT5dovWCvMMc2E6O+Pk5JSTJ084ffKEqRZmq8y1Ur1SLQR3qYVrpyec\nnZxy7fSE7fmGeZqZt23ezZbzzYbzzRYz2/XW1pQYV2G8tloVjs2ZEIokZhNWObPOkdJfk8Q1EBhE\nyCJkjdc6DyUvPnh8kQHYx1zw3knbPvs2x/rQ27x/pynKbyF6SX/0ReZbIvI8ov55+cz94m6v96W3\nOd7h+3fzvH8K+DDgQ0Xk/d39V+7i3J1Op9PpdDoX0sX2AUeXrwBQVMlNJM+qWGtJVUWuE9rmHgKW\nEIWLmZlXRyxMwBJKgva5imN4LW3MuNUwXWvzmghVmtgWDZGsIbwnMUQMxDgbMhsVzsw5mQvicUxx\nOHF4Yhg4Oz5mKhUZM4wD5ER2J88Ten4GKWMGZa5MmwmfZ2yzoW63bK6dcfLkEzz5xJOcPPkkxSrF\njFIrhrde2E5149rJCaenJ1w7OWHaTrua7dL6hU9zYS6lpeCHOlETUAM1XA2dDZ0MHQ1PhuGYWji+\n48wYoziDOFmdQSB1rf2wcvj31kWR59dc8N4vte0VEfkQd//FG3cQkQH4U7dZy6ZtV7fZ7weJnt/P\nBj6DaDN1M/4crVCjfeZ+cTfXK8Cnisj7tpZj178Z6f6vbE9/h+iHfbf4HuC/bY//CvBFd3HuTqfT\n6XQ6nQvpYvuA9ZUQ2zVlch7IOeqJfcgwZHwYiDrlRXAT7a1EMAQWsW0O1ZA2qJFybVawWqh1ps4z\ntUzUMlNrpGNbLRitv7SEwC4qFFXmpJgYFaOKkZNypsqJG0fzjDoxLH77fzIPnB1fYguQot+YJ4n0\n9XmGszPMoE6V+Xxie7qhbDbM52F2dn56ypNP7MV2NaOYUc1wodV7RyOu09MTTk9izPO8S103M8zY\ntVHz1tYLEdQFT01Yq6GTo2MI7uW1aiHmhya4t+oMTWgP6uQuth9WDoXxq7iJc7WIvAZ4BbeOPP/r\ng8f/Ezev5/0Gon/0RSyWmb/7Nvu9Hvgywnjr60Tkx27sXS0iHwF8SXv6NuCf3WbOe8ndXK8TNyNe\nJyKfcZMo+ZcQDuQOfPNSc3+X+E6ilv/3Al8oIj/l7v/oVjuLyIuBD3L3772La+h0Op1Op/OQ0sX2\nAUeXHwGg5IE8rsirEVut0PUaXa+QozW472qjHUATotEsO0R2pHL7XPFpxqcZm2dKiVHrzDxPlGli\nnsM1fJ4nbJ7wecIBkxDxVZU5CXMKsT1RmcSYMETgFGFtxsoKyZtbt8PscD4MnB8fM2UFb/23qQgG\n84T5EtGe2QwbzoYzprMzNqenbE5Pufbkkzz5xJMhuJ88ab3GQzSjSh4SaUigcHpywsnJCScnT1JL\niV+Z3YFwPZd2jaLOOkZC8eK4OqaOlohuy2x4Nmoyao7IdsbJEoZyg8CoMLTRefhw958SkbcQraa+\nUESeA/xjQvg+BnwuEZH+t8Af4iaC293fJCI/DvwB4AtEZAV8G/AE8CHAq4FPIoy1Pv6C5fwY8HnA\n80Xk6wnjtifae/OStuzuvykifxn4e8D7AT8hIl/dPp+JKPIXE33CDfiCi1pu3WvuwXrfSNz8+FER\n+QbihsnziZsln932+VX2Pbvv1nmYiHw28XO8DPwDEflM4uf088Dc1vFRwH8FfBzwt4EutjudTqfT\n6Txtutg+YPHbylmR1UBar7H1EaxHWI0wjpRSqF4ozVk7q5KSkvIAFkJbmiD1VvrtCjYoZhmzEasr\nai3UUnbbUktEuaFFtcMJfSuwxdkCG69srbLxirlHS6wmsPEwbKvAlBJzFso6U+ua2Qp4xbzgolSJ\ntmTnkjkVWEtlVbeUsmUqE3OZ2FjlTOA8J+ZxaOXgEc1XFTwnyClaeFXjWBTJA9bahoEgIkhOSMpo\nSq2HtuAqSMrthsaKPK44unTM+tJxbNcrxlVmtUqMYw639BzbnBI5KTkpSbvafoj5XOD/Bd4H+Kw2\nFhx4M/CZXNwU+POAHybE1ivZpzIvc/xt4Ge4WGz/EyIy+0HAX2pj4a0cRLzd/e+LyLOAv9GO+Q03\nzOVEYsrnu/sbLjjmM8JdXu/fA15GiOt/cpN5fg34NHc/4S7j7m8RkY8nUuE/BPgU4mbBu+zaxhM3\nea/T6XQ6nU7nKdPF9gGL2I52WAN+tMaPjrBxaGMEPHpI1xDbknOI7TFHzbRF7TRawmMtzLOJS+0R\nGd/VfS8O4jWcuq1iCFUjsj3jnFfj3Gpsa2VTC+cljMq8GlajtVeFaAGGMw2ZeZUprClewAruhWoz\nxWHrwuBCNmEwGKww1EotE6Wlts+1MOMh3Jvjn7f/iSrkhKQQ2yOK5oFhXON4OJhLFFXrkJEhI8OA\niYS7ugikRF6tSOOKtLiRXzpmfXzMej2yGhLjoIxj6wWeEzpksoY5WhYldTPyhxZ3f7OIfCQhdP8o\n8ELC9Ow/Em3AvsndJ7mgP5y7/7yIfDTwpcAfIwzBniCisH/X3d8gIq9kL8JuNsc1Efm4to5PJczC\nlh7NN4uof7WIfC/w54FPbus24FeANwD/+21MvG65lnux391cr7v/WRH5AeALiLTxy8AvA98NfI27\nXyRyb3c+tzuPt4jIhwGfA/xJ4L8Ankf87fxbRJT73wLf7e5vuuA4nU6n0+l0OneMuN+pme57P9/6\nHd/iAHLpMvLIs5Arz4JLlyhDbmPg/OyMs2tnnJ9dwx2OLx1zdBxDm0GZmiOlItOETDMyFyKoK6i2\niO8yAGk9q8XDeGypA5/duDbPnM1ltz2bCtfmynaemebCtGzFmdTZijEpTNmZklPUUZuROrWtocXR\nZTvVMCabDL92hp2e4Sdn+PkGm2ZsmvBpRpb/REiqDJoYkpJVoVYoFa81hLYKkpogHwdkNSDjgDVH\n9NrEto4jaRVie3181K7jEevVwJiVVVbG3KLjTdwnFbJE89ylQ+1f+8P/dZfdnU7nviIibwNe9KIX\nvai3/up0Op1O5x7y2GOP8fjjjwM87u6P3e/1XESPbB/gJaLV2npSJ6GlPStoilplTWQVkggOJIQk\nShYFDMxwM6gFSoEyI2UOAaqK+lLHLAiKqqIi0fMampFZ1DXP7qQ8MAyFoVTWpXI8Vy7NhW0b0zyz\nnWc2GFsxNhiTGlOCKTmzGmYZtwG3Oc6xRDS8zBUk/NTFCz5kfMz4eohzGRKyGqDUdnMg1ppUySmF\n4FaN/t3uJPM4r5QgaQjk1aHYlqVqHNeEjkMI7mFkfXTE+mjN0dERqzGzSsIqCWNSJDfhnlM4vEt8\ncdOtfpCdTqfT6XQ6nU6nc5/pYvuAst3Gg3G1i0rrqqCaIDnSnMJNM6QBd2elyggkM6zM1KlQ5xmm\nCdlO6LSNeTSi2tqcvEW0Rbo1BKQqqdUyRyQ3o6oMAj5kNGdWZlwyp5gz10qZK3MpTKWytcLWY7vx\nypYYYaqmzJKYNVHFqMmxGgZkrgVPBctRz21JsZzhaIVY3DwQM5JEjXQSZdTEKiVWmhg1kdxJDtmJ\nGwlZkdSi0WOO6PY4RAo54Z3mqmjOpDygeWC1WrFer1ivVqyGzEqFlQqjHkS28z6ynZvo7nQ6nU6n\n0+l0Op0HkS62D6jTFA+2TWjPM8wFTRk1j5JrUVzD9MvdGSUxOmQz5rlg2w1lu8U2W3S7RbZbZJ7R\nyK6OzlehuHfR4mEx/dKoFddxRN2QYSCnSJ8eUmoF4KEwzZ1ajFKNWivbMrMpM9s6sy0z53Vm08a5\nKOeaOffKnIzZYHaPz2oI7ZoLVcP0zIcMpSAttV0JI7iI6i9iO7PWENwDQnYYmvu4ZkUXsT00sT1k\nQHaFlYK0mu+EamY1DqzGkfU4hpCXJrYXk7UhoTlFVF0lxHb3R+t0Op1Op9PpdDoPKF1sH1CnAoDM\nMz4XfCohtrMh5qiH25lqIqUR3EiayKJkh1orzAXbbqmbDb7dINstOk2tFtubUfdBv2lRSAnPGVIC\nW+E4SESJU0rknGAc43FzPxfAzLEa7bimeWI7TWznGOfTls205bwkTt04xTh1Y+vOFmdymMwouVDm\nEtucqU0cuxVUBNG4STCk3Oq0E2tNrDVz1AT3gDAiDCIkTWjSMDRLTWznjAwJQXYWRlH/rZE+L8oq\nZ8Y8sBpyiHlgJXuxrW2ORWwPKiTtoe1Op9PpdDqdTqfzYNLF9gHjsAYgpRFFkQo+O2RDZycNjngi\n68hqjIrhNCTSEG7ZaTSGCqODSYhyaVFwwXdjwZvpmKqiRBsvaa3D3Jxq3nKuY18DTCPlXERwBU8R\n5bYUojSXAcqKVNasSuG4Fo69csWNczO2bkzmTGZMtTJNM/M0M+fCnDKzZqaUsVoiAq8huMc8MC6C\nWHMT3ImVJAYRMku0uZ1PE92SQnRrjusl7fSVJYU+6tbHlBlyZlhqwQUGCQG/S0lPrWZciPGMfjs6\nnU6n0+l0Op1O587pYvuAVRPb6Ih4ggJMhmRHS7h3ZxTVERmbu3jWnRAcBmcyYSWCNcEZQjEjbs11\n3Fr7r/bw4Phu8RoW0Wq3tl/o7eZSrlRVRBVvbY0cwYeE1oHBjFwNLFqDuRmTVSY3JjO2tYbIrsY0\nF7Z5YsoT22lmSpltymxzotQaTXFSCO7VMMYYR1baos+SWKk2kzjISJi9LaZvqtcNQZAI2qNCZAWo\n7gzXsqa2lWaEFoMU54tqpOPjbXQ6nU6n0+l0Op3Og0kX2wcskW1PI0bCK/hsUAyZnVSIyGuLvmpS\nSLoTpMVhhTJrq+tuQ1MCN8RqKGoz3MDNMTOqH2zdsd17bbQAt4pQm4iV3Gq4o6cY4kM4gju7kT1c\nwqtZjFqZSmUqJYT2PLPNWzZ5Ypu2nKfMRjPnKTFbxVXwFGM9rlmvVqzHdUS4RVmJMhACOImgxBoV\naaI4nNrjtXBbX9qOq0irUw+hnZroTk2oi4Zwl4NzRBTwqGdvo9PpdDqdTqfT6XQeRLrYvo6IFIuD\nNoErBqk6qTo6G+qGuqLJI425tQeL/xKqAykDHqJTJKEph9j2GhFnM6zux2yVUg232szXWjR8NxLk\nDJpBE64pYrvNMM2R0N2iiC/R8tbDm0hNz2YkM1KuDKWyypX1UJiGkWmamFZrttOW7bxmOx0xW8FF\ncBVMhdU4Mo4rVuMqbjaIMoi2aHYT2cLumNqOrzQRjqAePcXVnSQSRmcabdM0NaGtUcO9d5OL4SJ4\nE9vSNHbvEN/pdDqdTqfT6XQeVLrYPsCtAiBuIQyJKGz21nu71BCzBlYdSY5YQrJDSqiHuE4Kkls0\nVzOah4jCekXcsFqppVJroZSK14KVilRBhwHaCBfvAR1GbBha/+qEkCJ13MAkRjsDBJgRsjip1VGr\nEmtpwjVpQrORbWAsI2W9otZKmWfmeaLMM6VWXMBxTGAYBnIeyMMQrukHad47oS0hsCP3PbayiGyI\nftwWNyySRzR8aeEVqehhGCca4pom9l0Ua5Ftd8fVI92+0+l0Op1Op9PpdB5Qutg+wGsT2xbRV6WJ\nQHNSNXSuIfIqmEautrqjnglpHWI7+mgnkiaSDehQEa9hceYWInueKfMMqWAlkWSmikDeC21ftk10\nRzq14pLAwXCqQw3/8hgtGJyQXYb7gDAkibTylEgeNxHEwd1wizsIVsp+VMNxvKW2a8o7h/GlJlsO\nI9ptG35u8Tn3aB2Gt2wBq3Eda41rCvvRhHbSiGq7tJ7msghvxURjbuDA2LzT6XQ6nU6n0+l0Hji6\n2D7AdmK7gFXUjOSGmiGlIlrCvEwMtELKiDvukTwtKqjKrp1V1hZFdkOwxU8crWVXz02aMU2YamzH\nARsHPIfYtpyxFAZsyN4azKBFy5sgFqhElFsEaopE8iRxfyAEqhLdrlvbLdineAtQDWpFmrFaGLSF\n2KaJ4N2WfdvvRXiLxIHMLT7Thizp41VJUkkISg3R705yR30R3ouwlja/YFIQKYiWOGermBq+q9n+\nXc/UV6TT6XQ6nU6n0+l07ogutg+wugVAZsXygM0hdINI0RZNuyirpmZ45o6aY7rUFkeEGXEsrNYQ\ncUQMkRaZ1YQOkFUhZVIdGcZKTYk6JGrKGBKR62pYqYuzWJs3XLlTE6vVoeBUAVcBj/pmA2YcNyjN\nQE3dScY+cr9EqNt76pH37UnbmTcn9Fa/HRH0dh5LS7P22RZybynkcbzcUsSlDW1D3JvzuoEoYlEs\nv6s7F41rpm9H5CqiV1HOMY1UdF/S1fk99/qr0el0Op1Op9PpdDpPiS62D6hlAkBUd0LbUojWRWwj\nIbZpYls8DLvM9jXLJoKKYwpJHFMPjaweH0UQDQduTZnkzmgRQS4izApFhIJSDKQatRSkObJJCnO0\ntAhoc+Ym6HHH0OjBDRiKeTidS3XEDKmG1rhBkFvddbTb0oM67CWdO7ZFoAjUFkF3nJZoHtfgYOgu\nYh29suVAbC9CWxFogtktLqCYgrXzSOFdHhHzdyD600h6M/BOtEXz94nkr3lmvyidTqfT6XQ6nU6n\ncxu62D7AmtiuqmjJ2Bzp3QsCUS8tipNIyVDbO5f7wZ4moCkCzJpCo6u3lG1delDHNj4Rqdmz+64v\n9lJXLWYwe9SEq6KtbrmyRLUjioyH/C0C5kTiuliryzbMHOaKlAJz3YntQZWhteAaUmqtuIiU8WbK\nNhGCfhKnYlQXjLhBsKSzixnqTm6tx4bm0Z6VcGwXRcV2gtud6CdeLWzfdV/fHRnki9h+O6o/DfrD\nuPzG3n/tnn8jOp1Op9PpdDqdTufdo4vtA9yjZtutUOuM1AlmRYgocDVrke2ESMLT0NKuQZfy4Rbd\nFiVCwAqmEsI7CZ7AVcNlWyNCHTXUshPcS3q3h7oHa720TZCmS12a6RgtStw+rxJttaooFaU24R9R\nbos096VBl1uI9hZZNjOqVVJVJCU8Z9wT7plZYRaYgSreItsxryBomzUjSwc1Es3gjJZ67tEz3Jb0\ncXe81XebGVIrhZbK3mYVEUyehdljGC/G5dGmsn0nuhnv7fei0+l0Op1Op9PpdJ4qXWxfRyhm94pZ\noZYpjLp8Sb+uiCZUMyKZlIxykDYN+7pkaWXTro4qYQvehsvebVtErxPbixBXFVLS5nimqEsYsZlA\nDTtx8ehhnSQhEqnqSaCoUJrgLi7X1VzHFB411xKp2GYhm6tb9LAWQBNmmWoZGypFNeZNYc7GQRr3\ncnNAm9AWb87k7IU2Lfofwnovsr09RwxKwVt0XJdWYKpUno/5h2N+CffTiOv7sga62O50Op1Op9Pp\ndDoPHF1sX8eB2K5zizoTBdm1IKlE32wdUM2Qoh93aVpxL7Sjt7U0ozRXx5O2yHa8by2OvRfaEcUl\nJ8gZzbnNpYgriuKu4ccmEdVViU7g3kzCo291E9sqFITEPp08BLc353PFDhzHi9WowfYwdTNVig1U\nHyhYGLdlpWqC3dojup7acbJoRNvb+S2V7ossDwd1p5oj1kT3EuGukeZuEnXwWZSUEskT1Z6P+SWq\nfSDu887lfB/a7nQ6nU6n0+l0Op0Hiy62D9i1kvKKWwnDrtqaR2ukWmsyUoSym4hdGnrJXhzLIoC9\nCW7HTfG6F9v4XrLqgdiO0uX9PLo4m9vSwiuMyWSndttxvfW6JqLd6rKLfEf9tkaNNQkTx8SpGqng\nIXKlCe2IdBdgtkoxpZhiKlGn3eqpl7ZhS/L7roUYe/GtNMdxmjZuAjvady29uGmp7HHsiseMqeKl\n4poxuUzlCrXVeS814l1sdzqdTqfT6XQ6nQeVLravYxHbFsNqaz+1tNIyxA1ZirG94K6YKbU2cax7\nkWwA0gQ3hrmECN5J1BCrJtpag+0du0XCOM087Xpde3LcmvtZ2JuHc7do6/HtUQtu0gzO2BuROXhL\nRTcSYfwtuCRMDfNKdaO4UakUEXLOlJSZNWGtx7d53BhItKx4h+wwANmdoT0ePEYmUuydiF57Nay5\novuBWJZl64QQr4aXism8u6nhy15L7+5ukdbpdDqdTqfT6XQeULrYPsSXNPIQ1bGt4LoT2EJtwru2\ntO6KSaFWQZv9uKpGPTbsxbaDWJiVReq47hPJxaMtWBPatgh8aNHfENyY42kvtuMzGvXbJk1wE2Hm\n5uqtsjilS6ubDiszF8GTtvZdhtGEthtlEduqzCmRd2I7sViXZYTcxHQI7bYlWpHt+nm3dmBOGLCx\ntPlqr+8j/DspDUtauVTwlpLeasB3orz9NDqdTqfT6XQ6nU7nQaSL7etYIqUGTWS3ousQ3F6bDVhF\nPOSeeQmhK+HujSYgRcsqJ4TwgSaURWyje9GtUcO8r+WWXT20e9ovSz0M0pJHkXbL3xYF1Waepq2A\nW2NjKtf3yPJQ4q5tK61ftjilCe5CuIJn1RDcsjinR914QprIln0Um/Ap06XfdtiOxzWgtR+rrUZ8\nJ7pbRXdLg18i1r5Etr1QbV/vvchxPXBw73Q6ndshIi8Dfqg9/SR3/5F7dayrV6/y2GOP3avp7xqP\nPvoob3zjG+/3MjqdTqfTea+mi+0DFvEm0dw5HsPOU1tZekqHeDSrIZA9csZNErqkmi9T2PVie5n/\nOrEtirT08yVZWtvnpTWdFqFF2Nu6zHF1MNu1EAu3ccXVdqJbFnvxVmsdRt6LCA6RvdSVhxW5tzXG\n++LNTZ143yWM2AaEQZrgNidbpJCHyLZwTPeoZl/miVZlIZYj3T4M09Rpae1LO7CW9l6ibrtWo5pR\nzEgiSMqklCKToNPpdO6ce157YmY8/vjj9/ownU6n0+l03gPoYvsAbXXSLfzMrnmV+z5iuxPbLcXZ\n9u2tRAxzRaQ2wd7muFFsL9Zi0rpkawxEUaKF1zJEFnEac4FGWrZopJWLNBEc0Wc7eByiW3dCO6Lx\nvosis7ilH7QCS9LOWVsauhoJBbe2RiOLMIjuotnJjGxOqhHRdrMWvW657O38dyZqraZdZW+qZjuD\n8XArr6VSSqWWipeZMs9M80zShI4jwziS8nBvvxCdzkOGiLwSeD3xl80Hufuv3OclvYfyovu9gAu4\nys6fpNPpdDqdzj2li+0DUijaELKtTljcr4tse9hqtzbTstOTboRRGYu5Wes5LTcLpMjiGb6LarMY\nq7H0y5Zd32xXgdqcvd0jFTzsx3frdFF8EdpNbNPEsdDcypelNHOxuEHQWpO1vHXR6MMtYbhOcqhY\nuzFgiAhZhFFgFGUQSOZoNVINgW016rPdF5UPqMR8zX1dVVGR3fOlD7cDtVSmuUW1txO23VC2W6bN\nhiFnbL2Gauiq/8LY6XQeNBR42/1exAU8BvTIe6fT6XQ6zwRdbB/g1tpUiS2Z1y3KHU2pFndxb4I3\n2lA1Z2wlWn6hrQ6ZRW/T/L8OaPnlreBaXKPNmO4jvgak1uYruYdZm+7TzZfo+xJVp9VUsxPZrV3Z\nEq33ln7e1rFzAhcQjfWqth0WkzUVJEULsagrjzrprEpuUfCMoNWa2I5aazGLCHcT26JxfHVHzVCL\nGxepGbjpbk0sfcBItZJKQec5xjSh84RYRVRalL27kXc6nU6n0+l0Op0Hky62DyilxINdDnUTiwam\njlSDZCFitRmbJd+JxYhUJyJ1XFoPbA6Eti8V0c0dPMLNjocjOY5VdkLf3VCrWC2UkvZiWxPI3qHb\nAUkZTQlJaRclj+1y8BYFX1pmHfaoXlK9dS+OQ9CGy/kNOe24WGvHpVTaTYrFEG1ngNbS7qXVcQsh\ntNs5yU7sN9d1a8Zo7nipME1oKWSrjB614qj05BhVAAAgAElEQVSQBLJXpE743MV2p9N5YFjubN7f\nVXQ6nTvm6tWrvO51r+PVr341L3jBC+73cjqdzh1Sa10e6v1cx53wwC/wmaTOJUZZRt09Lq1uuE4z\ndZ6o84yVCSszVgteS/TlrnXvtG0tyttqvXftrpob92Ky5laxg1HLTC0TZZ6YW/r0dH7OdnPOdrNh\nszlns9mwOd/EdrNh2m6Z55lSClYqtVastFENqxVvI143rFS8lhC3pcBcrxtysJUb3vO5YtNMnQo2\nx7WKY9Wd27iYIbWitZJKRUtFS0FK2W2ZZphmfNri2y222eKbLTLPaJnJtTK4sxLnKAkr9b3Ynjb4\ntLnfX5tO5z0eEXmZhJvi65eXgLeKiN0wPrHt/63t+X9qzx8Vka8RkbeIyJM37PuyGz9/wTqW/f6X\n2+z3B0XkH4rIz4nIEyJyIiI/KyLfLSKfKyJX3o1r8GwR+Xft+FsR+dNPcYru2NjpvIdx9epVXvva\n13L16tX7vZROp/MUOBDbD/y/vT2yfcDuB2cR1RYnor5Gq2d2RA08ta23Xs/72ukIaMvOjVx2PcAi\n3hG10hIRXJbIdES2fYlTmyJmoJWDiUCXyHVqtdmtHZYI2ZzsTnbw5Nc5nKu0NmVLw+9dm63mRt5W\nt6x/l/qdBKmtcFxbqy1t6esuLF7ju5Njaf3F7saCthsNy1asol4P6s/DfdyrYTXqvb3GTQutNW5g\nEO7uKcX6hRpZBnW+91+KTufh4TAN5vD5je9f91hEPhb4XuA5t9j3otcuWse7ICJr4FuA/+Ym+35o\nG38C+HLgf73D4yEiLwB+AHgxcAZ8prv/izv9fKfT6XQ6nc7N6GL7gJTi5kikYIeo3ddHt/rqg1Ru\nVW3Z14ugXHo/18g93/Xv2h9jn1G+vBgC0rWJXz9wXDPd1Y2H63htLuMpTNOWdeki+MN6zc1aCrs2\n4a+7x3tn8ji6NrEtrRbd2PfdNo12YpaWfuBRK22yzzwv3kzcaGXeLU1dm7O4uaHNLE28IlapVluk\nf29AF/23HWtbLDIExA3BSS2N3H0xneupmp3OXeTfAy8BPh34CuIP2KcS1tWH/NINzy8D30k0JvgK\n4AcJsfqSm3z2aSHxl+v3AJ/S1veLwDcBb2zHfAHwB4HPeorzfjDwL4EPBJ4A/ri7/+hdW3in0+l0\nOp2Hli62D8ipXY7FIVzS4hq2E7aiGk7aeiBgIVKmgV0za1H2oe7dGzc3S5Noz+Xaor1iuCtgLLLS\nkdZLWzFRfDEJU0XS0kqslV7XqOkWZC+6NUFrudXeiaVIiGSREMZmRrUagls1ener7M9Z2zpazbV7\ntB9rfmo7we14JAhYxT1agYm1SLXVaCW2OL0vrcjaye4d3x0Isa3NZM3joO29Lrg7nbuBu58DPyMi\nLz14+RfvoPXXc4ET4OPd/S0Hr//E3V4j8D+wF9rfBXyOux+mt7wJ+BfAX2+R6tsiIi8B3gA8CvwG\n8HJ3f/NdXXWn0+l0Op2Hli62D0jaItuqLaodYnsxNUNC4KYD4blEpqP314I3R3PdGaUdRrd3T5wW\nsV6i2ctjiZruZn7mTbxWEaootbX2ElUkJcQSLALawbXiuwMemKq1dlvahLguqehNJFcr1FqptWBN\nRPuuIXYzXtOI9EfUOlLfXcBaq7Lom+170d1q0v1AaLuVdnOipZzjbfWHBuP7PmVNZyMSKfdm7Rpf\nd807nc59wIGvuUFo33VaVPuL2/HeBrzyBqF9/aLcbxtVF5GPI9Lfnw38MvBH3P0/3p0VdzqdTqfT\n6XSxfR27KPVue/gmraaaXa1xqL4lgXrpXO1460uNWNRJSxPNbUJbRDSCH0a/dZ9aHrHiqGe2Fsmt\nCIUaDuCqaE4IhuIYUC0c0/W6GwRNkGsMby3CUktNt4M2ZbWEwVotM9XtoFZcoc3hKdGy0Vv9dLT9\nXlLdXfY3B5yowT4U2dQmvL3GVWtz7MdBBsDBtd9t292HRex3Op37zv/5DBzjI4kG0Q78Q3c/ezqT\nicinEenvR8DPEUL71572KjudTqfT6XQO6GL7Ovap0SJLSvf+NZBwDl9aZ4m2VO3WMzuad0UsVuyg\n/Ze0NPDFEO1gLOneANbabUkz1hN2wt6spXnjFPcWcfd2VKG6R5p2Kbs67UUoa81IykjyEMxi2C7q\n7rtk9VrCaX2eZ8xqax3WzNFaazFyZmlpJiI7gSzahPKuZ3ZL9bbFobzuhXZLI4/1Xy+2l9NeblDs\nXlhahLX6bmvGap1O575y6u5vfQaO81EHj//N05zrM4HPBwYi3f2PuvtvPc05O51Op9PpdN6FLrYP\nWNKSQ+PK3qhs8esOd65Wp2w7QStocxNfRPRBVHhn770X14dCG29zKBHNbnXMHGSeh3Bd6qljuAqJ\n6GPtCmZCXRKyF7Hd6ro9jXuXcA2ncteWJo4T8W3H5pkyTZRpi1ndm6KJYrniOYf5Woo1q+5T0veC\nm10PbzeD5i4uBxFtb2JbW/RbuUFotweH6ftLTXrUgjdDtV6z3encb975DB3nuQePn67x2he17Qb4\nk3dfaDvw/DvYL3F/Opb0FkedTqfTeTC5evXqHbXim+f3nI5EXWzfhKVFF0s0+0DWhXlXRF1FLKLQ\nTfDGZ+WG9PBFVB9u9WCfvch2DCXE/OItvhiFuTtm0S+7Wm1RcMWSolWwnbkY+2h0q9V2D7GqaATK\nlX29eGvg5Xj0FJ9bH/FamwlcmKO5W7sZ4UTrM2FXD+5NGS8O64dtvXYp5Ae12153Ne6Kh7t5i3Af\nlmwvbc12RnMee0Rq/b38BnQ6nTuk3n6Xu87T/dP/ncBnAGvgn4rIp7n76dNf1iHvuLvTdTqdTqfz\nEPC6172O1772tfd7GXeVLrY795+4p9HpdN47OXQy1FvtJCLHF8zxmwePX0i0/Xp3+bvAjwNfC3wc\n8H0i8sfc/drTmBMO+owvvh+34073uxe8/e1v57HHHrtvx+90HgSmaQLg5S9/OeM43ufVdDqdWivP\ne97zbrvfO96xu6n9nIv2exDoYvuAV/21r+ySr9PpPMzci5yRk4PH73PBfh96wXs/efD4E4F//XQW\n5O5fJyIZ+CrgE4DvbYL7/GlMu/v3w+/QUOJO97sXmBmPP/74fTt+p/MgcfCLe6fTec/igdduXWx3\nOp1OZ2Fz8Hh1l+Z868HjjwG++xb7fc4Fc7wZ+FXg/YA/JyJf93Qdyd39ayTcKL8CeBnwz0Xkj7v7\n5jYfvRVb4poZ8Pans7ZOp9PpdDoX8nwiW257vxdyO7rY7nQ6nc7CoSvJB/P00rUBcPd3isj/B/w+\n4PNE5Gvd/TpjNRH5Q8Bf4BaRdXd3Efla4O8QLcC+XUT+9M16bbee3I/eSa9td//KFuH+cuCTge9p\ngnt6amcJ7n7pqX6m8/+zd+9htm1nXee/7xhzzlVVe++TaCfh5MJFBBtUUEmCwRsEsI0gEAERWiUS\nLlFpLjbY0CCGgA9PNyrS0tiGi4FAt1GQBrXTEaFDBB+ICchFQW4ikOQoh0iIOWdXrTnHePuPd8y5\nZtXZl9r71N5V59Tvk2dmrao1a81Rtfc6tX/rHeMdIiIiT243XT8nIiKXzr9hV93+SjP7SDN7XzP7\n7e2422r332237wb8kJn9aTP7vWb24Wb2NcC/AN7MraeDfX07D6LB2U+Z2eea2R9oz/UiM3sFsW/2\nZ552YO7+FcBXtg8/EvgeM+tP/62JiIiI3Jgq2yIiAoC7v8vM/g7wV4APAr73xCkfBvzLu3jqbwT+\nGPBi4P2Bf7C+LPCTRID+T7cYm5vZxwHfCnwi8L7A197o1DsdnLu/vE0p/5I2zu82sxffqHIuIiIi\nclqqbIuIyMLdv5ioDP8g8HZgIgJsPXkqpwy2Hp3APpHY4/pNwLva8RNEwH2Bu992nbO7H7r7nyam\nfH8b8B+AR4F3Aj9DbOv1KUSn8cd8+a3G6+5/Ffjqds6LgO9sU8xFRERE7oqdZzdUERERERERkScj\nVbZFREREREREzpjCtoiIiIiIiMgZU9gWEREREREROWMK2yIiIiIiIiJnTGFbRERERERE5IwpbIuI\niIiIiIicMYVtERG59MzsPczsb5nZz5jZu8zs7Wb2r83sC81s/wyv88fN7LvM7FfN7LDdfpeZveis\nriFymdzL166ZvcTM6imPTz2r70nkycrMnm5mH21mrzCz15rZw6vX0N+/R9f8FDP752b2kJldN7P/\naGbfZmYvuBfXe8z1tc+2iIhcZmb2McC3AQ8AJ38pGvBzwEe7+y8+jmsY8I3AS9un1texdvuN7v6y\nu72GyGVzr1+7ZvYS4FU3eO4b+TR3f/XdXEfksjCzeuJT69fWt7r7SzkjZrYH/GPgj3Pj/z5U4Cvc\n/SvO6po3osq2iIhcWmb2+4DXANeA/wp8CfAHgI8gwrED7wv8MzO78jgu9VVE0HbgR4FPAT643f5Y\n+/xnmNlffxzXELk07uNrd/bfAR9wi+O7z+AaIpeBt+NXgO9l94bzWXsVu6D9/wEvJn7vfjrwC0QO\nfrmZfcY9uj6gyraIiFxiZvYG4A8DI/CH3f1fn3j8C4C/QfyyfsXdvANuZu8L/DsgA28CPtTdj1aP\n7wNvAJ7XxvE7H08VXeQyuE+v3XVl+7e5+6887oGLXGJm9nLi9+Cb3P1hM3tP4JeI19iZVbbN7IXA\n97fn/SfAx/sq9JrZf0O88f0ewG8A7+3uv3kW1z5JlW0REbmUzOz5xD/WHfimk/9Yb74G+BninffP\nM7N8F5f6y0DX7n/OOmgDuPt14HPahx3w+XdxDZFL4z6+dkXkDLn7K9z9te7+8D2+1F9ptwX4bD9R\nXXb3twNf1D58KnDPqtsK2yIiclm9eHX/W250QvsFPa/DfCrwwru4zscSoeDfu/ubbnKdNwI/SwSD\nj7uLa4hcJvfrtSsiTzBmdhX4cOL37r9w97fd5NTvAt7Z7v/JezUehW0REbms/lC7fYSYTnYzb1jd\n/4N3cgEz+23As27wPLe6zrPb1DoRubF7/toVkSes5wNDu3/T37vuPgI/QrzJ/Xwz62527uOhsC0i\nIpfV+xPvfP+Cu5/skLr27098zZ34nTd5nrO+jshlcj9euyd9i5m91cyO2nZFP2xmX2lmz7r9l4rI\nfXQ3v3c74H3uxWAUtkVE5NIxsw3wtPbhW251rru/g6igAbz7HV7qOav7t7wO8Kur+3d6HZFL4T6+\ndk/6UOBB4h/lv5XoavylwC+Y2Wc9zucWkbNzoX7v3pNyuYiIyAV3bXX/Xac4/xHgALh6D6/zyOr+\nnV5H5LK4X6/d2S8Se/X+CLt/mL838AnAJwJ7wP9hZtXdv+kuryEiZ+dC/d5V2BYRkctob3V/e4rz\nj4h1Xfv38DrrLuV3eh2Ry+J+vXYBvsvdv/UGn/9R4DvM7KOA/5v49/TfNrN/4u6/dhfXEZGzc6F+\n72oauYiIXEaHq/vDTc/a2RBrRK/fw+tsVvfv9Doil8X9eu3i7v/1No+/FvgKIswfAJ9+p9cQkTN3\noX7vKmyLiMhltP5H9Gmmjl1pt6eZtnq317myun+n1xG5LO7Xa/e0voEI8xDrukXkfF2o37sK2yIi\ncum4+xHw6+3D59zqXDN7KrtfyL96q3NvYN2c5ZbX4Xhzlju9jsilcB9fu6cdz8PA29uHz74X1xCR\nO3Khfu8qbIuIyGX1M8T0z/cxs1v9Pny/E19zJ376Js9z1tcRuUzux2v3TvjtTxGR++Rufu9OwC/c\ni8EobIuIyGX1Q+32CvDcW5y3nhr6r+7kAu7+S8DbbvA8N/JH2u1b3f2X7+Q6IpfMPX/tnpaZPY3d\nVmRvu9W5InJfvIldY7Sb/t41sx54AfFm2ZvcfboXg1HYFhGRy+q7V/c/7UYnmJkBn9o+fAfw+ru4\nzvcQVbj3M7MPvsl1XkC8w+4nxiUij3W/Xrun8TLi9Q3whnt0DRE5JXd/F/D9xOvyI83sWTc59ROA\nB9r977pX41HYFhGRS8nd3wT8IPEL+dPN7Pff4LQvBN6fCMFf6+5l/aCZfaiZ1Xb8/Ztc6muJKWoA\nX2dm621JaB//nfbhBPxvd/UNiVwS9+O1a2bvaWa/91bjMLM/AXxZ+/AQeNWdfzcicifM7CWr1+5f\nu8lpf7PddsDXn1xu0mak/C/tw3cA33xvRqt9tkVE5HL7PGJ66T7wL8zsq4gK2D7wKcBntvN+Fvia\nWzzPTddsuvvPm9nfBL4YeD7wr8zsfwV+EfjtwBcBv689x1e7+y8+ru9I5HK416/d9wJeb2Y/DPxT\n4MeBXyMC/nsDf4qojFl7ji9w94cex/cj8qRnZn8QeJ/Vp562uv8+ZvaS9fk32ed+efimD7i/3sxe\nA3wy8HHEfyO+lljq8YHAlwDv0Z7ji9z9N+/oG7kDCtsiInJpufuPm9knAd9OTCf7qpOnEP9Y/2h3\nf+RxXOpLgacDLwV+L/CaE9dw4Jvc/ctu8LUicsJ9eu06sabzQ27x+CPA57v7PauMiTyJfAbwkht8\n3oA/1I6ZA7cK27fzUuAa8FHAhwEvPPHcBfgKd/+mx3GN21LYFhGRS83d/x8z+0CiUvbRxFYhW6Iz\n6T8Cvt7dD2/1FCdub3QNBz7TzP4x8FlEhftpxBZGbwL+nrt/7+P9XkQuk3v82v1R4M8SQft5wDOJ\n12wH/Abw74h1od/k7r9+g68XkRs7bff+W5132+dor/2PMbNPBv488HuApwL/GfiXxH8f3njKsdw1\ni9//IiIiIiIiInJW1CBNRERERERE5IwpbIuIiIiIiIicMYVtERERERERkTOmsC0iIiIiIiJyxhS2\nRURERERERM6YwraIiIiIiIjIGVPYFhERERERETljCtsiIiIiIiIiZ0xhW0REREREROSMKWzfZ2ZW\n21HO8DlftXreTz2r573Lsbx8NZa/dp5jEREREREROS8K2+fDn2DPezcu0lhERERERETuK4Xt82Hn\nPQARERERERG5dxS2nzwcVZNFREREREQuhO68ByCPn7t/GvBp5z0OERERERERCapsi4iIiIiIiJwx\nhW0RERERERGRM6awfQGY2fPM7BvN7GfN7F1m9nYze6OZfbGZXTvF1992668bbcllZntm9ulm9s/N\n7JfN7Kg9/oE3eY4Xmtn/aWb/0cyum9nbzOxfmtlfNLP9x/dTEBERERERefLQmu1zZmZfDvxV4o2P\nucHZPvD8dny2mf0pd/+RUzzdaRqkebvu+wHfCfzOE1/7mOcwswx8A8fXhTvwbsCDwB9q4/z4U1xf\nRERERETkSU9h+3zMgfdzgL/WPv554I3AFvgA4Hnt3GcD/6+Zfai7/+QZXf9pwOuAdweuAz8E/DJw\nFXjBDc7/NuCT2QXxdwCvB94OvAfwYcD7A68F/skZjVFEREREROQJS2H7fP0NIux+uru/Zv2AmX0I\n8A+B5wAPAN9mZh/k7uUMrvsXgAx8B/DZ7v72E9fOq/t/juNB++uAL3L3o9U57wZ8O/ARwF86g/GJ\niIiIiIg8oWnN9vkxoAdecjJoA7j7DwMvAo7aub8b+HNndO0M/HN3/+STQbtduwCYmQFfyS5ov8rd\nP38dtNv5/xn4GOCn2vckIiIiIiJyqSlsnx8HftDdv/OmJ7j/NPD1q0995hlc19rt55/i3D9GTBM3\nogL/V252orsfAl/Qzj3N2nEREREREZEnLYXt8/XqU5zzre3WgOefQddvB37S3X/uFOe+cPU1r3X3\n37jlE7t/H/BWdoFeRERERETkUlLYPh9zGP3h253o7j8FvKt9mIEbbst1h370lOf9vtX92461eeMd\njkVERERERORJR2H7fP3KKc97y+r+08/gug+f8rz1tU471tOeJyIiIiIi8qSlsH2+Hj3leY+s7l87\ng+teP+V5V1f372asIiIiIiIil5LC9vk6OOV5V1b3/+u9GMhNvGt1/27GKiIiIiIicikpbJ+v9zjl\nec9e3f/1ezGQm1hPNz/tWN/9XgxERERERETkiURh+3zMW2O94HYnmtnvZjd1vAA/ca8GdQP/ZnX/\ntmNtfv+9GIiIiIiIiMgTicL2+fpzpzjnJe3WgTe5+2nXW5+F17dbAz7KzJ56q5PN7COA56B9tkVE\nRERE5JJT2D4/BnyomX38TU8we3/gs9mF12+8HwNb+V7gV9v9A+Crb3aimW2AvzV/eI/HJSIiIiIi\ncqEpbJ8fB7bAt5nZJ5980Mw+BHgdsCHC678Fvv2+DtC9Al82Dwn4dDP72y1Yr8f6IPDPiD3Aj+7n\nGEVERERERC4ihe3z9T8B+8D/ZWY/a2avNrNvNrM3Av+KaDZmRAfyl7j7dL8H6O6vBv4R8eaAAZ8H\nvM3MvtPMXmlmrwV+CfgI4D8Af/d+j1FEREREROSi6c57AJeUAe7uX2dmTwO+FHgf4H1X58xTx98K\nfJK7//h9HuPanyH22Z7Xj/8WYD393YGfbp/7lPs7NBERERERkYtHle37z1cH7v5y4A8ArwJ+HngE\neAfwo8CXAL/L3X/kDp73dufc+YDdi7u/lKhe/0NiHfcR8J+AHwI+F/hgd/+5x3MdERERERGRJwtz\nVy4SEREREREROUuqbIuIiIiIiIicMYVtERERERERkTOmsC0iIiIiIiJyxhS2RURERERERM6YwraI\niIiIiIjIGVPYFhERERERETljCtsiIiIiIiIiZ0xhW0REREREROSMKWyLiIiIiIiInDGFbRERERER\nEZEzprAtIiIiIiIicsa68x6AiIjIE52ZPQJsgAr82jkPR0RE5MnsGUTR+Mjdr5z3YG7F3P28x3Bh\nvOyT/mj7YRgpJcwMs0TKOT5Ou/vxseGwHJBwiyN1PV2/Tzfs0fUbsARksEStE2U8okxbyrSlli0+\nHVHLFvOK1Yq5Y15jOO3PyFZXsvXt/GfYbt0dJ/7FB5ByxlKOW1t9bzf4GZiBAWbzo9Y+mbDliM/F\nz+fEE6z+Os1/t/xGD7ZrHLvO6uvWx/Hnbz+D1edf/rWvvtG3IiJy35jZBOTzHoeIiMglUtz9QheP\nL/Tg7rc52JnF/V0QnIMtEYJph89BtIXG3GPtSN1A6jbkbsDyQK1OdagljlKNUhPVE04G6/EE1AlS\nwWrBMfAa16/rWB9h2uZb97jnMVb3OCPONGAXrM0Mr/VE0N09Ngdo8/aDWJ6hXdt2P4v40cyj4FgA\n3v1Md2Oax728edCSvZktOXx+3J1V2N593fL1c+gWEbkYHOK/Z8961rPOeywicgrb7ZaHH36Ypz/9\n6QzDcN7DEZFTetvb3jbntgsfBhS2TzgZsm0VNnchux3mreKbIWVSv08e9sjDPtYNWOqx1IF1lHFi\nmgrjNFEK1JLwmnDv4q+JGaREImF1xG1X2XZ38NIC9fHKrh0Lur5Utefs7RjuTvKMJz9RlY7vc77d\nVfNPBu72hO17P8Z2IX+prB/7gbbg7/P3sQvQzNX1do1dgX53nrc3Enah/cRziIhcDP8FeMbTnvY0\n3vKWt5z3WETkFH7sx36M5z73ubzuda/jgz7og857OCJySs94xjN4+OGHIX73XmgK2yvLtOc5SHur\nvN4odHvF3DAMTxlyTzfs0e9fo9u/RsoDTlStqxu1HDH5EYdToRbwanjN4I5hpBbavdWrzQuY4RXc\na4TtE0FzHhPrILoE7t1Ec2tfYzXtqvCsK9lxuHuMYwnd8fzukJK1n4kvZe3VxO/VuGZzgF4F5uq4\n11XFep6Gvq6Os5zLsbB9stpdb1RIFxERERERuRAUtlfm8GZzwrRVFdkdo0YAr0AyYt53VITJHbnf\noxsOGPauQh6YilMKTFNlLMbhCNePCqVUqBX3igHZIKdETgYUrK3tXhZQL2GzLoF7/txuOnddhe15\n8jeAxXsGMd+8Be3d9O15XboliyJ1MhKOWd1VuC0Cv1drU92Xhd3xhkAb2zLN+8T66zk0V3e8Vmpt\nU+N3dfn57FUlfFfFrieD9iqEi4iIiIiIXEQK2zfijhtLiKwe66JrncNqwathGXIeWhO1ecp4ppKZ\nJufwcMv1wy2Hh1sOD4+Wo5SJWiZqLUBl6IyhTwyd0VulxzESmTlwt1tSqwHH9PKYhN3CZwvQj60u\n27GPvVXr52nmrf6MFbAUz12hNUKLqruZkRySGblWlry+TP9ehWDWzdXWj0H1ilePW39MzF5NQ2+h\ne/WcJxumKWyLiIiIiMhFprB9A60IjFvFPSq3tRpQY9o4EXRjhbVjlsgtbLt1VDLj5Dx6feSd73yE\nd73rUbbbLdvtyHa7ZZomSpkoZQScvU3H/qaj7PV4qqQEXWohm8TcEdzmsZFYmqUtiflkRXstJpwv\nq69XGdXbtPB5Svr8vVvadSWPynYU9X3pQL47fx2Il6vPgftEVdo9KtuP6VTufuv78/Osqt/K2iIX\ni5m9HHg54O5+V525zewHgD8C/IC7f/gZDu9W13zc4579+q//Os95znPOZmAick9tt1sAXvSiF51b\ng7QHH3yQN7/5zedybRG59xS2byLWTc/rh2P6tFdbL1kGy219dyLljrSqbI/TyKPXt/zmOx/hHe94\nJ9M0xTFOTNPIOI1MU4TtKwcbat2AGal3ug6GOWzPxzz1m5gT7vO2YMzV7CX6zoM7fsxBfM7A7f/W\nFeWl+Vtqzdna9cwgW6v223ry94lKs+8y9q7qzbFmZ3U1Hf5Y07Ple1lGt/7DOBbqd9+ziDwJPaFf\n3O7OW9/61vMehojcgdZoSUTkzClsH7Nr0nWyQOyrLa7co0o8ryVuvbwYx4nqR9TpEa4fTWy3W7xW\nckqkvqPPGR8Gjo6O4HplHLeUaWQcE+OY4xYYqUzmpEwMxBKkrk1vn9dsz+PxZd10e0cgEvEqaNsS\n1tNjvts5OK/i9u4xB6cu670dX4VtX4XfOTjvLI3PfDclfL0Wez1FvP1Qj79VMDdxW/4v7sRbCjf4\nAxKRJ5P2H5knqmef9wBE5FS2wMPA08ZrtyMAACAASURBVIH7Xdl+CKi3PUtEntgUtm/kZNBepkzv\nAuD8L8Hq4NUppTKWLdsjGJk4Givjdksy2Bt6UkrL8eijj+Je2G4PmWqllhLV7m1iNJjMGRMki728\no0N4XgVUB1Ks3bYWslkH8TmWpnZvVSWH1Tmtcs/x6d+OLVX9+f+X/b5PHsemdq9+fLa61FLBXq21\nXk8jv3FS321P5uvn9HjjQ1lb5EnJ3V943mN4fAzQ1l8iTww/BjwXeB1wv7f+eg6gWTAiT3YK2zfV\nAuCyNdXxTy+bgLWp0bU6R9OW69PE9emQqUSGTMDeZqDve4a+px8G+i6xPTrkEUvUUihToYwTY06M\nyeIokFO0RMNap/J5CnadK9opppOvQnZ0TW8V7fa169slEK+q0stU7t0+Yqwy8hK0ndqmrx8P27tA\nfrOf4i6J+y1D9u59DpvfLjA78eaHtbXbmkouIiIiIiIXl8L2ym7/6dZ1vM2+nnk7Jyq/8aC7USuU\nUplGZ7uFwyOnuJFTJqVMlzNdTnQ50edElxI5tevg0JqGlVKpNVEqVE8Uj47npI6UiS2zagU7vgUY\nPncn9zarfA7brbJtq/vzdzI3NaMu07uX0Mzueecp2/PWXtXnHbx31z8ZtP3kvfVjj0nl8898/ZnW\nAb3t9w2267e2DM9Zdz0XETln5bwHICJ36plEb8RnnvdAROQO5Lz0Mr3wv3sVtldSimnWyxbX5sva\n4zBPzU6QYh21W6a6QfW2dtvnxc5RhS4w1UoZtxwSz/vIo49y+OijUApdivDd5UTOiZRyhMycSV0i\nd5m+S/RdptYSlfBS8FqWxmNzOJ4L01EMnoP2air5rh85u7Bcl6nkuxA+NzCruBe8xrptq21Kt6/O\nn697sj/b2vzzYA7VuwnnZrsu6fMds3nqfDr++DHa+EvkojOzpwD/I/AJwHsSCyR/AvgGd3/NTb7m\nB7hJN3Ize0/gl9qHf97dX21mHw98BvB7gGcAP3iDr3sW8KXAi4BnAf8FeDPwd9z9+8/gW4Vl8aXe\nBBR54ngm8OXnPQgRuUOrsH3hGx8obK/Mf3CxtbUv+0nPRWRvYTtZxixD6nHSsm67Vto07lZlrqXt\n0+2M48i43TKOI9vtEUfbLV5LVLpzIqccjdRSxnLGUofljtz3dEPHZuiZykSZRpgmainRCb1VnJdx\nt+/lsWE7Phvfxcl117tp4jGlvLbp6rGfeE0l/iobWPV23flNhVXkNVaty1abeQPHk7izBO15XXb7\nkt12Y/ORjn+t775eRC4uM3sv4PuA92b3Ij4APgz4MDN7MfDf+3prheDc/K279TlmZq8G/uytzjez\nPwz8U+CB1XkPAn8C+Bgz+/JTfUMiIiIid0hhe2UdtlnCdoTopYBsGazDrGv7arfp3rV1J6cFxtam\nvLpTp8LhI4/wyCOP8Ogjj1BqxVLsnT1PMc85RdjOmZR6LHdYHsj9QL/ZMOxtsHELaaTaFlJZpna7\n1xZSl/7jwDwFO63C766TeNzWdn8XsmnrsqOinajFoqLNKueyW7d9sr68vlbcnava6x3Ao/y+VLCX\nvbt325vFzAI79lxO/JnEmnUeu+ZbRC6Sf0hUs/8u8I+B3wQ+EPgi4HcAf4roDvQFN/ja07yb9pfb\n870B+HvAzwFPBd5reRKzdyeC9jViqtkrT4zli4mylja5FRERkTOnsL3Sde3H0Srb88xraz3ISoWc\nelLuyWkgdQPW7ZP6Pazbox5NFArbMkVFu8S072mMbcCmMfbWrtWxZFhKYDCVhE0TloyhdpAyud+j\n39tj2N9js7/PZn+PPG6Xo0zHw3Z07F6tll5mhZ9YdN7C9Ry2I2hX8LjvXjCfm5A5kHfrpa11Aa/z\nll5zZ/HV05/kLIF71y7Nl3XZbRrB8bXxy1rsk1PfbZWvVd0WucAMeB7wKe7+j1af/zEz+w7gh4hp\n359rZt/s7j99F9f4AOBb3P2ltzjna9hVtP/MLcbyvLu4voiIiMgtKWyvHA/bQGqV6rmQWyHlga7b\nkLsNXbdHHvbJwwG536fYIWM9xLaF6jWmfY8j43bcBe1SqO5ExdnxAkwROh3Y7G0gdeRhj2H/KsP+\nPpuDffYODhjHLakdtYVtr3OztLk67W1KexxzM7FdEt59M0bFvMTXeoljXntdHUs5Ppyn05e476sG\nbVEFh5t0SGvT8NssgVXlPx6z5TjZMs1bt3c79tldLT0eV2Vb5IJy4J+eCLfxgPsjZvZZwBuJJhh/\nAfjcO3x+A34D+JybnmD2bsCL72AsIiIiImdKYXuly+3HkXbHkiNbATj3Eba7fp9+OKDbHNBvDuiG\nA46KcX1bwI7a3tsl1mgfHTGOW0qZKLUsQdW8hUyLEFtxSnVImW7YY9i7wubgCpuDA/auHJDHMSrb\n221rklZXgbsule5SorM5U6HWFobna7aqts1VbZ8icLfmZ0v1O7X12PM0cGsB1wxPsf2Y10r1CMRL\no7aTBefl4111e95lbFmvbce7q7UV6Mt1jz/dPBFeUVvkgvuWmz3g7m8ys38H/C7gI+/iuecA/cgt\nznkhkNu5px2LiIiIyJlR2F6Z12z7qum4z6XdFIm4Hzb0wz7DcIV+c0A/7NMNB3TDPv31Lak9R62F\nWqNzeFmq2ZAsUalLM7Go9rbAmRK56+mHPTb7V9g7uMZm/4Bh/4B+bx/LI5YHUjdSpmkXtJewHZXm\nUgrTWLBcKGV9jkd5uk0bdwpWWQL/Lm2n5f5SczaPT0ME81TbDHE71hH9mGXv7uUC7TnnT7U12ra0\nlDtht+XX+hlO1z9JRM7Zm27z+L8mAu7vMLPO3ac7fP6fvM3jH3AXYxERERE5MwrbN2LrI2Ep01nC\nLbPZ7LPZu8Jmc5V+2Cd1m1i73bYNi7AbFezqEaotxxZezgBmMcWbaJ5GSvSbgWFvj2Gz4crVa1y5\n9gBXrj2Fg6sPsNls6Ie4RibGYCmT8zyN3FdBOzqK11LJfSFPE2VqYb/EGnIvE14L1SeijXqCmmLN\n9jxfnNTWctsSjedp3cePhFPxXY5ub054m1p+ozXV65bpS/vx9qEdC+K2vl1x9+WNChG5sH7tNo//\n53ZrwG8BHr7D5/+N2zz+W+9iLGegEjuQ3U5uh4hcTg+d9wBELpyHHnqIhx66/Wtju93eh9GcDYXt\ntWO9xJZNn0k5Y7knpZ7N3gH7B1fY279K3+/j1vbaJsfa4yVsT23PbUg5kemw1m28lEqplakWLCX6\nYcPe/j77B1e4cu0aV689wNVrT+HK1Qfo+o6+60i5j4XkKZNyt2z9NQftuSOaA7UU8hRhe5oKZZyY\npokyTRRLeGvSFtPOE3jC5tsWtOcNzOb9uXcBexW8DZwU4drmZmnzmmpfFbbbc62r1KuQjR3vPr50\nJ7f5+hz72l1PNoVtkQvsdi/Qx9vhsNzB89/rsZxwp+8biIiIyCtf+Upe8YpXnPcwzpTC9o3MDcGI\nfZ5T7sjdQO43bPb22du/wsGVa3T9HqVCqcZYoglYdafUeR/sVtlORraOlCHPa7nLhLfGaP0wsLd/\nwMHVq1y5+gBXrj7A1WsPcOXqtQjoqW2PVTOWKp7LMi18PmLccVtLJU/TErjHPMJ2izNS3aKBGpXq\n5VjYjqC9O2ih21ht9jWvM5+r2u3n5b67nXfsBua+4zwmcBu7UA3Y/D2eOOBExXtFUVvkQns3Ymuv\nm5nLv87tq9R347/cxVgeNzPjaU972m3PyzkvS5dE5PJ68MEHz3sIIhfGy172Mj72Yz/2tue96EUv\n4uGHnxhvbCtsr5S5NZclsEzKidTHGuphs08/7LO3v88wDHQ5kXCmGuujx3GCumXIzpW9jmybaPLd\nwu04FcaxME4FKwlKwrqOlBJXrl7h2rWrPPDUp3Dt2lUODvYY+p6uyy10tj2zDTwZeIqtxVp1utYT\nLcPMyOy2Ltt9OkXwdfBqsS1YzTiJWlK8sUCaW7G3SnfBPWG1xrRzq1jrfE6btr5U1ZetyLxNb2cJ\n2mZpHlobTCslLTPJTwbt1edOVLc1e1zkCeH53DrgPr/d/vxdrNc+jZ+6i7E8bs961rN4y1veclZP\nJyIicmk885nP5JnPfOZtzxuG4T6M5mwobK/UpWYbe2Bb15O7DcNmn739A/b2r9D3EYRzapXfMlK2\nR4xHR1CPGHLl6n7Ppk9ABktUN64fbrl+uMUPt9gUQTt7pes6rly9wgMPPMBTn/oUHrh2lYP9PYah\noztR7Z0b68ay6EqpTq01Oo7P/1vmbne7rbVaAzazHOHfjVpTHIwR3i2D5XYbY4/IPmFkqDUCt/ku\nbFvbr9trm0J+PGzPZW9r3ebmcM1yu5qUvgTrGwdv2FW35ynqInKhvQT47hs9YGbPA3438R+B77tH\n1389MdU8nXIsIiIiImdKYXulLiVg6FIm5Z6u3zBs9nZTx3NPTh05JcpUqdOWaXud7eGjWBkZOufq\nQUfxHrMeSz2VRH7XdbDEWKIp2rwfTT8MXLlyhQceuMZveepTuHr1Kgf7+1HZzmlZt7wLopAs9pie\n136XWqnEtPLqvgvZKaawz5Vls9KCtpGLUUrCyVRPeI2Kdk2JZAlsiuZwZKA1U6uV1DYdj4Dcwva8\nL9oqbLNUtuegPbcyX3cor0sn8yjc2+42GWkVwCFC+Dxl/tjW4SJy0RjwsWb2ie7+ncceMLsCvLJ9\nWFf3z5S7/ycz+x7g428zlm/gsZsWioiIiDxuCtsrlmL9XM49XT8s08eHYY++39B3PUbCqzOVkXEc\nGY8OGY8eZTx8hForGWfoDCxjqYPc45452o7kwxwh0lNrlpbYbPbY399n/2CfK1cO2NvfYxgiaKc0\nV3Zj+ndaBVDHSamSaiXPVW6vFK9YtTgKlKXpmEdluxq1GF5STA8fIww7CbMcR4rbqMxPQG7TyGs8\nD3UJ2mYVKNHNnLbXd50DN7v1355O/FPWd83VqEsjtfkNhfX0+WVdd3vMUdIWueAceDPwD8zsw4Dv\nBN4JfCDwRcB/28753939397k68/CFwB/FLh2g7H8HuCLgfdpYz2zqeQiIiIioLB9TN9v4nazx2bv\ngGH/CpvNPn2/if2xi1PLljIWylQYt1sOrz/K4eGjbA8fhdah21pVGS9Qy9IhvJToCu4YfU70fc9m\ns2HYDBGwu46cMymlZYFyskRK8bl0rGu34ymT29TxUgvFo8N5adt8za16LRvJnUSGIfYLN9oa7ZxI\nY2oV8LZe21qTtBa4zcoubKdYs22UJWjb/M9i97Z92NKrbVeVXv3T2dePGHFd3z06N1Vbfaq9wTB/\naLv16SJyUX0S8P3AXwT+0onHnAi9X3CTrz2TKrO7/7KZfSzwPUTg/ksnxuLAK9r1FLZFRETkTCls\nr8xhexj22NuLNdrDZj/CrmVqqYxHI9ujI7ZHW7ZHh+24zrg9JOXYk9tyalOeC/g8dbtEIC4T1sLz\nEraHgb4f6LqeLueoaMOuk3lO5NztppIzV3jbHXOmUmIrsZpINjEdWxMd2T8ZsRG2tzXZlkljTNmO\nInNMK3fmNdtTVOiZYn12inXb5qVNCy9t6XXbesxrC/K7yrNhmM/j3QXxec7mLnjvpocvH8152nbP\ntUTsuQO6iFw0sXFBBN3nAl8I/EngPYER+Angle7+mts9x1089tiT3d9gZr8L+J+BjwKeSXQ/fxPw\nde7+fWb28jt9XhEREZHbUdhe6YcI25tNhO2Dg6sMmz1qiY7ftRS22y2Hj17n+qOPsj28zjgeMY1H\nlGlL7ju6viNZFwG2Ziq7sF3LxFQKnaVd2N7bMAwb+r6n76OybWmecm3L3txd1y1BG1rGTlGRNjNS\nmUhlwspqe622sDkBbtZmcsc67GSZlHIL2lFJrx7rt6snIKaPR6DOkFrIrhXzCeYtvZzjQbu2cbvv\nxjs3Rt/1TFvdsVanXq3Hnv9v+T52Td7mh5YvF5ELw91fQVSK549/E/iydpz2OV54i8d+mfgP052O\n663A/3CLx4+NW0REROQsKGyvbDZ7AHTdQEptbfY4Lcc4jWwPj9geHVKmkVomvBa8FmotpJqotZK8\n4qVSLR6bHLwUvDrmMTW8y5m+Hxj6ga5tARZ9xRyrTqkOpbTO4FHRtSVlRvfulNq67mSMZWJsYX6a\nxmXMpRRww2tMH3ePSdgpQddZVLEtY6mPPcMLTDFjPLYGw2LHL0qb3l3i+nNDNNqYKvjcsbzOFfCY\nVm4kUnuuXSXbjpWQ5v25lz3Dl0Ru7Wdw3GpHMxERERERkQtHYXtls9kHIHcDyTJeK9M4sj3acnR0\nxPboiGkcmbZbpnFs1eqC13riiPRZKVSm6Bbezouga3S5Y+gHhmFD1/XRvKwFW6rvwnpp/b5bBdna\nlO1d2I5ma9MStifGaaLMYXuKnW9sXgfdpnSnDJ1FI7eUnNzBWGAcwacoRddqEbqZp4CXVk2umMeb\nABHkd2OOoF3a4cuV5+B+/GDZmsy9rcJu88h9fQ4VmPfp3pWzTZVtERERERG5oBS2V+awbSmTLCrb\ntU5sj444fPRRrl+/HqG5tGp2mfA6UesNArc7lUJxo9RELbWFZciWVmF7oMurynZ13CrVKqnEuu+5\n4k1ta6NrbJWVUibnCNxjmZim0m5bNX4ble1EbOe17NdtRk5EVTwncu3IbtgWMKfi1MmXKeB1DtaL\n1oGc1Krl4GUO22UVtmusFXdbmp6xrCCf9w6PyvVuCrnHdPS56/hyvu9q3cuMcqVtERERERG5mBS2\nV1LaLQWsrUIdXcePGMct0/aoBemYQu21LPeP9dZpHbmjCO3Hp493HX3X0fc9w9Az9AM5d4BF0K61\nrYWesOrkVCmlkHNue13HVOsErbIdDdWmsgvapZS2B3ih1tqG5C1wR9O1XX6ukJzkLE3Bqzu1Em8S\neEwn303ZjoZsNn+/qeJlohJbiVEttvwqjnnd9U5j3tgrxRT0OXxbwmzZ4XzVAM12neDMwSq1vWGw\nfF5EREREROSCUtheqbW0W6fWSvUI29O0xWt05MZqbOnlFafg1AifLQDavHWW7aZuG5BzYug6NgNs\nhg2bIdZr932/VLVjfbXjyfGUsFRJbbp4ntuPt+p2gti6qzVIm6bC1NZse/Ul+y8dvD324W6lajDH\nzYldrqOKPE0T2zGOcSytaZnhSz+i9v21ZmXz/tlewVpFfmlu1t5kiNv4WcbPou5+LtammC8V7l3Y\n9tZ53VfVeDt2zOFdRERERETk4lHYXillardRTS6ltIZjW2qdWDqBMQftaBY2z3RegmBKRAU3RYMw\nM3LK9B1sPLG3GdgMcfRdT06xHnmaCp6cmjwCaHu+1I7I9fO6bdq+2xE5Y5wRuCGu16XcwnGrsHtE\n6wjFJRqSWdsNDBinMZrAbSfGqbY3DXLbd9vijYa2Pzetq7mRYgq5FZyp/WxqbHtWLar61aFNKZ9r\n2/HexC48A6u12sfvz9XsJaCf/DoREREREZELRmF7pZSy3I7jGFOy27TsWieshWyWoB2V7sjF0RXc\nUtpVt2sLyxg5Jfo+4ea7yvYQlW0sGoRNJaZ9V0tUa+uc51nT83Zabd23+W5itsFqvXYhp8zQ96Qu\nkTrDo7V4C9xTW0s+Rehuod6NaK42TmzHkbHEmu7odp5bpd7b95vb0UXgThVLBUsT1XZvRuCtyl6B\nEmvBY/V1W7Xdfm5pCdvHO5TDvEbbVv3UorqusC0iIiIiIheZwvbK4eEhENPJa43KdvXSmpRFuDaL\nSnZKhqdEIjOvP87dhtxt6LoNlRzrlmPnLTpzqsWU7a41NYsGZHU3XducakY1KC1sp6W6HdVpqkOp\nu82oWzqdpmmpbCcrbV9wp5vKqv+3U7xQfKS00E1q3caSxZsLtbaZ6h5ryFvAT8nIOWGpi+7lVkhW\nY0/xusxMx8m4dbjNtxOUEWcieXRjt7b92O6NhPnb2UXtqMKv9t1u5/ry80ht+rmIiIiIiMjFo7C9\ncnh0Pe64416PHfP08eh0NofERMqZlLrWGXyzBO5aEzZVfHIqNcJ2TNwm50xu07/jWtHArDpL2K4Y\nbgnSrqUYddf1e14PDcR+4G3Lr2maSJaopYXtXEhm5BTBvfjEVCemuqV6gZSwZJASUyktbHvbaoy4\ncq10XSblhFlHSpCS06VKomBdhO3kRiVRLeOkCN6MEbyZMC8kjy7lsXbbI+u3Bebe3kBYT3mftwKL\nR+adwqLyryZpIiIiIiJyUSlsrxwdHS73o/I6dxdfBW5j+XxKidz1dP1A1w2kvCHnDSlvKNXwbaHa\nRPV5nXSUf7suOogbc2D2pWO3W6JQo7JtEGnbSWbUudlYqRG451BanXEaW3V7wswouVKmSpc7upzI\nOdHlRKkTYx0ZS4RtSwnyvFe3U9q2ZV7nrbfijYeUOvCMWTR0y9nJyclWMTeqR3yOKfARtiuZyhan\no/oWq1NsCZYieEft3iPQt322ff1GR921b4s/FD+2blthW0RERERELiqF7RWvc6iD3VZec/hrU6rb\nOmNSInUd/bCh3+wzDPukPJBSC9vFIU24jVQfY+9sKsULqT2/1xod0C2qyzmlmDpOhNe5+fi83Ze3\nanUt817etC7jcZTqTDWmmNcKxSpdLvRdpvcOyJRao5laKRQvWHKsVkiJMrVdzIj151SWKeVxtG5q\nc5szA0uxPZnlulpP3sbthlmmWoelDqsTqU6YTyQvJJxkEcvdK7UdXmPtuntpHeLbn8FqezWf9xQT\nERERERG5gBS2V7quB1qQax21vU2pjvXL0Rxs3t/aug3dZp/NwVX29q9E0E4DKQ9Mo+PpiMoRxROV\niVLHtqd0BO1SJso0kvuenHty32Ot4XnradbCNEDBS40GavPWXuw6erul3c5g7ZwJY5wmau3xtka6\neEwVLzXCObW2TmWV6tFlPKdo8FaZA7fvptbXipe2TVdKS6BOqV8CNzUq4+YWITv1UMsqaE9kKsmc\nbJDNW9iOfctrLdQyUetEKdMSur2WFsZdYVtERERERC40he2VLkfYrrWsKrqV2gJnqU5Osb81KZP6\ngW4vwvbB1QcwG7A0kNLAOBaqZYonSjWKQyrRtRv31oBtpJSO3HexD/fQxzbeU2WiRhW7hWd3qKXs\n1mvju87cAGax3hso7ngtUOfNsxxLtP28K6V4HK0J2VwvntdDp5QwT5T2wHJGrdRSqMnwmqFaa7CW\nI1S3hm3ewna0He+xHPuSJy8kn0gUMpWcoGuH17IE7lomahnj5zO12zJSy4SVQqHgpbQ3RURERERE\nRC4ehe2VvhsAKC0Ix/TlBOZ4tWgCRgRt63pyv6HfHERl++oDmPUtcPeko4lSjbHANFVyqaRclm2s\n3CulRqiEgZyMvu/w4kxu5DYNnFalLiXuz1XdeZ9qVuuX513AS419wusUwd7MSDm6ibtXpvactfrS\nDM1xUk7kbKSUwPLSkdwsOoh7nSvblZpSq7wbRo711Jn2RkLFWtCf12QbTvJCJpqkZXP6bHQZ+mRt\nqn6E8lpGpmlLmY7a7ZZpyhG8pxGfrM04UNgWEREREZGLSWF75crBA0AL23WMad51ik7fpTDViW4Y\n6IaBfrNhc3CFzcE1+v2rdJsrmHXEj7TDaiL1E90w0Y2FvlSmqdC1BmZmUbEutUS1uBa8TC28xlZb\nqSbKvLa71CVgx17esf2VpbaG2gyfK9u0SrxHQB/LRBrnjappFWRiu7G51TcObkvDNSyul1Pb2CxK\n7pTJW3OzEp3IPWNW2/rrpWd724W87QNumYRDasGcGluZdQlrR1o1S/Na6MuWUrbUJWzvbqftlmmM\n4C0iIiIiInIRKWyvXLnyFIBYK9zWC091ZJzDdpnILWh3S9h+gGHvGv3eVdxTbHnlieRGHgrdVOin\naErWTRPdOMb05zn0trXJXqYI3J6A3d7atux5Xdue29FZfF3VnruYt77ey1Ty0qbBpzLBGKHdkrXp\n4nOjs3n5d2t+VqFajUZn8z7fmVVVvVBqIlGYKOCZnAxiufdSx64YZdknfO6rlpgv7cmg76DPWN/F\n2u3kpAR4wcuI121MJ29Bu0xHTNsjxnZM4/b+/gURERERERE5JYXtlbmyvYTtdmyniW2ZGKeRvNnQ\n7+3R7W3Y7Edle9i/Srd3JZqazdPNq5H7QjcUuha0+3Fk6ruYEj6H7XVluxYAjEyyRLJWd65OKQXL\nGUsWzdlSop0c07nbVmGVqGpXorJda4Vpiup4LtHcLWdyew6bK9stfEdvuHmLs9S2KDOqtyr8VPFk\nGAUo4B2eE3TxfHPvttrWkPv8pkFb6x6VecNzhr7Hho409ORsdO1IFKgTXkeoI2U6Wqrc49Eh26Pr\nHB1eZ9oe3c+/HiIiIiIiIqemsL2SW4O0lDKZLtZVe6Grhb4WplrIw4Zub0O32aPf22fYu0K/2Sd3\nA1SnFmK9cq6kLpqf9dNAGUbqOFCmkWmcWlfxts1V60o+bY/wPGBmdJbxbJScmHIiW2oBvO0zDUv3\n8dq25por4vP67Wh+1jqTe6VWI+dMro5noopszJO9I0SbY9VIyemykTPknJmI6eWVglcoU9uaq0x0\nXaZ6xr2jeGwrNpboeG7Wtktzo0sezdU8YSQyidz+P8Ye4T4arqWYou4dljOp9HjfY6mLgVsiZ/31\nFRERERGRi0lpZcU9qrw2d+Q2pzcoOBucgpOHgbzZkIcNebNHt9mj6wZS7vC2XzRO7JudE13u8L6n\n9gN1E120R9syTlvGsbQtwEam8YhtNlJvpC7TdbE2u5ZMKZXSzVuO7aaO19bALCrYc+M0W7YDm7cL\ndyqlVZzdHU/RFC2lZVX1sn/4vA68i5RNSom+y1ALXqJpW4zZYtxmlL6j1DjcK1OJde7FW6W+zVgv\nOdMRU82pKfb3LhWfCpXU1nonugTZjEQip/grmlKCmon56oAbyfL9/OshIiIiIiJyagrbK3PYTim6\ncucukbLFftIptrmyfiAPG9IwIwQBZgAAIABJREFUkPoB6was60mpo3qJtc41uo6nlOi6DLWjDgNe\nS3usrX2eRqYS1eFp3GIGvWVS7umSkS1TS0ctldp1bT9smwcblea5yVqtwByYl8nhQHTttta9O9UI\n26k6lvKuqzm2BPm0VM69dUnPeE2UCZI5k5elMl/d6WtPaYfjTLXEmLziNm8IHuvIK7EnONWhOJ4q\n1eo8KZ3eoLrRpUSX2h7eObU6eN+mz8d4c1LYFhERERGRi0lhe2XeSiqRMMvknOn6jHWZ1HVx2w9Y\nH0GbroeU8Xak6lRrnbyTkXKCrotgXArUgtVoVFamkXEbnce9TNTJmMzJ3YANlS4BOVFKopRM6R1f\nqtNtL+3WAM3bQmnDyClR2nTzZLGymnnbLl82zm7ryuep5zCH7WSJmtr2Xx5TwHOOinpKYObgsc58\nnCZKra27eaW2HblLm35fl328PdaTO9EkrTV6o5bYxgtwcpv0DjWnaNpmsV4cIyruVsnueBfV9bR6\nS0FEREREROQiUdheGVvDrVqjo7h7pnpHpieb02Wg9dqGtve012i17dGpzDy2wZrDorfQm1Km6zq8\nL/RTzzQNlGGCtr91MrBaY1utdiQqfQLvowJdKpS2PzY2dxGPEE6XIxgnI3nsaU2rpHtK0U3cK20j\nL5Y55msObr50QC+lME0T2+3INE2UUpdO6hik1LYOm6e114jbtTVmK7772Nn9bHKbNB7V6kIlU8iY\nt6niOdaP1wwlQTYnWyWbwzRSxy1lHKna+kvkQjKzlwCvIv4j89vc/VfOeUj3zUMPPcRznvOc8x7G\nffPggw/y5je/+byHISIiciEpbK9sx0MAskfYrp6odPTmWDacDF4jVLd6bCTUGm3A5xBOm/ENJDNI\nsV2X5w66Su1jCnmd+vjaZavrCh6BO3khU+kz0UwsJcbiTFNlLDVicsvKrRk5KRm1xp7V1AkvXYTt\nWiNwe43q+Px1fjxsO8QbCEZ0H29hexxHpql1THdv14uu4jELIL46Ktzeppe3Tutz1dsr5pWMUyzi\ndaWjrsI2NeE1U4tRExSDbNCtw3YZ8WmLT2PsSy4icoHUWnnrW9963sMQERGRC0Bhe2U7RmU7V8Nr\nhG2nw7KRSwLvYNnJOgJ3BNY5/FasBfEEuzXW1tYXZ8d6p5ZCmQZKmWIaeAumXj3WdM+B2wpdiiZl\nHYk0VszKY4I2gKfUquweYbvE3t2UaXnMPS3rvKOD+foZ5ueLaeZzZXscJ8xSdEyv80Rx2h7fCUtt\nG7O2Nnxey13mddu1TSmvheQRrYsVCl0L3JlKBs94StSaKGYRtIml8tkqHRG4rRYoYxxtqzQRkYvl\n2ec9gPvgIdq7zCIiInITCtsr26NHgKgQT52RR6OfYjur4oVKJZdCVyrZwboKKeEWa7YjaLbAWdox\nf87nWGtYznR9z8Y35GStIhwV6M3eHptNz9DnaK5mGbcOrKPLhTxOpJQYcyFNlZQKU4kp7O4J2nRt\n7ztq6eP+PNXcve2X7dR5SngbE7B0MYfopJ5zjq24YkF4a/rWtulKleTpMVuOWWrz2y2q+tWif3g1\n6IifWyre3jTIVE+UmiJotzcWzIyyvGnhMY2cqGzvpsdPEbxFRC6UBLzlvAdxHzwHUAVfRETkVhS2\nV44OI2ybQcoRurttZiwjY4ntuvq9A7q9Ql8quZ8g91juIHXRbbutqV7CdikRbEs0MqseW4vlvseS\n0Q89USWPannfb+g3ewybgdx1WOohx223jT2tcy5sx4kuF7ajkaxE2G5T2s0zXnu8FtLcvLx1MV+6\niLc3BdZ9y213l2QRtufAba0dmbcu675c73hlvFbIsQkaBcfN8GpUS+TkEbhrwSbi88WY0rzlWNp1\nSF9N15+Ddm4/p+Rz9V9VFRERERERuZgUtleODt8Vd6x13TbIXWKYtmynLdtxyzAVhlIZqtNtKqkr\nWDeQslOqU9wplQjYtVKLL0Gbeb1zSnRpwPouqrfJo0GaQdd15K6n6wZy15Nyj+UhtgPLEbRTmqLy\nvB3jjQFj15ncLarZtcN9iK7iy3T2mE5eS21dxH2J2sZqDXfrQh77jbcO5URl21vQXnK5+7Gg7waV\n2trIReM4t0T0VYumb6mW2GPbYgK+m1NTbtPS2z7ac0O32iraxHrv1O7PVW8REREREZGLSGF7Zdxe\nB9q6ZWKP6JQTUy1MZWKcRjbVKR49yfvqpL6Seyf10Sm81LafdGn7YM9Bu9aluVq2Nk07Zbpssad3\narc5k1MmpUzOPbkbSN1A7oaoMucpupcnWwJ6MtrWXqltiTU3b4s9wy0lYt+utFtXXcoSmq1917Tt\nxPDd18+V79QC8xzIj39dbdcHzKmk2F/b5nXhrVt7jW3D4vzYWXveNsxSbLc2h+1aWkO2WkjzdPI5\nbP//7L17sGX5Vd/3Wev32/vc7hGY0gPPiDGUsXkZkwgb8X6YR4hAQRhwbCuFLYQAVUIRx0DiOOY1\ngnLFYBtiCsdCNiAoKCoYAYoNGMcBIRwcJB7BicDmIYIk2tHwEGhm+p69f7+18sf67XPPNH17umf6\nNer16frVOffeffbe53SfPve711rfr4yLEym2k+SOICLvAfz3wGcC7wO8E/hF4JXu/k+vcx8F+Dzg\ns4HnAc8a+3kT8BrgH7n7/jr28znAi4EPB54DXAZ+BfhfgW92998/53HfDrwE+A13f18RuR/468AL\ngfcGngH8OXf/yet5PkmSJEmSJFeSYvsIYbhbb/nQbhhKXxeWUYVFtjlqpZtQuo9FiHCPiC4bedpm\nUdkOpRqC27e2bAkZKSJoEWpVaomIsFoqtU6UoxVZ17GKCrWcrUN7eLezPGyiFT6+EQZkcU4dG6Zp\nIZr98cL5WHAfjNhk0+MHkSuRFRbbm+AGaMykRxSajlbviEkzU7zHdQfr0W7fh3O5IMgQ0QfTuWGs\nZm7jaxsXF84c35Mkub2IyAcB/xvwAGcOizvgk4BPHiL2mgJVRP4E8Frgg472AfBM4GOBjwP+KxF5\nobv/6jn7eDbwg8BHX7GPGfgw4PnAF4vIZ7r7zzzB+XwE8M/G8Tfyal6SJEmSJE+JFNtHCJvh1iY4\nDXOhDaHdzc7ENooNsV07FANzoTt0l3D77iG4o4X8rLJNEUSieh3V4KhUl1qYamWeJqY6MU1R2S51\npkxzVLTHKkcV8VqE3ju9R1xX0TMhqhrmY4hGO7cZZoqVUL0hWofYdgPXs4sCx9XurTq9vVYHMe74\ncEFzE8R1VKIFoQwhH/u3LoRButPEaN1oDutwUFcckbjQYRh4x62Bh7s5Hu3jIpuZ2238x5EkCSLy\nbsC/AO4nxOj3At8JvB14f+BLiWr1n77GPu4H/jXwnsAp8K2EOP8Nopr8qcBfA94P+BER+TPu/s4r\n9nFxPOYDgQZ8N/AjwJuBCfj4cS5/FPhhEflQd3/LOaf0DOD7CZH+dcSFhMeADyEst5MkSZIkSZ4U\nKbaP2Crbh/lnswj6co8Z49aOG5oxF+oQ2dWF7kJHMJfomDYLwe2Pr2zjiqpjJSrGIn6obE9TYZ4r\nu3linuYxvz1RpolyXNU+3EItQmud3hqtR3s5B7FtIbLHMje8R8QW1kce+IgqY8v5FtxltIb7qIDH\nToVwPD97PiGyMcGHA3kVoYigElsrIf77Cqs6q3RWOou3MbdtKDEbrtuFDbd4za0d2smxqJJvIeap\ntZPktvNVhA21A3/T3b/+6Gc/LyL/FPjnhGA+j1cRQvs3iDbt37zi5z859vN64H2B/w74yiu2+TuE\n0P494JPd/Reu+Pn/ISLfA/w0cWHgbwN/5ZzzeTbRvv4x7v5/H33/Z6/xHJIkSZIkSZ6QFNvHjCip\ng9C2cNU+jECr09YFLadj/lmGGZrTm2GiMa/MFWL1cZVtQ7ogptAL1gq2VtpSWU8rbbfDTnZ4O4Hd\nCfN8QhFBaqGoM1cBSrRTK9Si1FrordNbpfVwKp/Xxjx1ltbZLMWMETPWG94VNz06rz7Ot+FD3Now\nKGO0m49AbR4vtn17xog6RZSiergYEIJ7xHlpmMGV4qMyr5QaEWelVrRWSq2YOcschnTrOkUreY9z\n8iG2Bck+8iS5jYjIBLyU+F/gF68Q2gC4exeRlwG/TlSYr9zHBxMz0Q588VWE9rafXxCRbyGE9udx\nJLZF5FnAy8Y+vuIqQnvbx2+KyNcC/xD4z0Xki9z98tU2Bf7OFUI7SZIkSZLkKZNi+wi7Qmz7aKF2\n2eaVHVlXRPbDjEzo3emtU1uLFnMtuJTYj40WbNuq22EI5gK2Cr3AUoSlalS1q7K/cIH15CLtwgXs\nwkXsYhsu5QV1qCrIpIf28akW5qlEC3nr9N5Z186ydvZrY21GJ6rvxmY8pkNwC9bbiCZzujW6rfS+\n4L2dmbpZH0ZnZyJ7a7MHRsVdUVWqlpg3LyG6o1qtqAhdJ1QJoV0LdapMfaK1mTJFBb9OE4azLCG2\nl3XBeo9z6+Pv5yC0U2wnyW3kzxIzzQ68+ryN3P1tIvJjhKi+ks8ct48BP/oEx/tJQmw/V0QedPct\nvPo/BU7GeXz/E+zj9eN2Guf/U+ds9z1PsJ8kSZIkSZIbJsX2Ee5DzI3YqcjF9jGuHMK5ywIoYTDu\n1B4Ct7eGaEVKRbSwVYE3szG3jo/qsYnRxFklZpSLEqsIy4WL9Iv3Yesz8B5Ce6oV382IFqoOF/Ma\nbedzd1o3+sj17t1YW4jt3Sa2fTilO0NcK9aV3oTenL42uhurdNwXet/jbY1Kv51VvQ+z3FeK7VqR\naaJKZTpU26N6fRDbWtAeRnClKrVXuk3x2lljmifqNDPNM+7Oft3E9krvjdYbvTfMGUJb8RTbSXI7\n+ZCj+294gm1/hquL7Q8bt/cBXa6/O+V+YBPbH3b0/f9wg/u4Go+4+29c706SJEmSJEmulxTbR2yV\nbTPHfLh7b95gbP5gK+4S23THeqf0Tl9XtEzRDl1qCELnkENtZkNs94i/Gm7bgoVB2Yi06vvL+Loc\nxK5KiNdpKujI3dY6oaJUjXloVcWLhIg2Z66FuRrrVFmb0SwywJv5ENjQm9ERVnNWMaDRbUH6Au0U\n1vXx5ztEN8OojDHLjQA6ox552GW7GFCg1JjhFi2oVESHuZsVzCZsVPrNOtM8H5YLzENoL+tC7xG9\n1noj5H3YqSF6B/6VJMk9y7FT99ufYNv/75zvv+e4vVGn74tX2ceN7ufiOd9/xw2eyxNgPP4Uz6OM\n9XQlveOSJEmSm8ulS5e4dOmJP1+WZbkNZ3NzSLF9RO8h5czDebz7VtkWHBtmXgreRrQVh8xo741S\nCt4qWgoieia22arlo43c+2gpH8JV/CC61TpqhreV3lZaa6zryn7ZU6cddZ6p8w4pFYhILxFlqN7w\nABegRhv3VM8q382MhcbaO24rre2x9ZS+XKbtL9P2p/TlFFtPsbYeMrFxQ90Y9m8ghg3PcAfKME4L\nO7SCaAW10MJHYeCiBUVRr2cmdMP1fJompnlmnidcJNrKW2NqjW49/j6sx3S46MERPkmS28ZxCfmJ\nRO555eZNXb4Z+IxrbHclb77KPhbgz9zAPt56zvf7Od9/Cjx883eZJEmSJO/ivPKVr+Shhx6606dx\nU0mxfUQ7Etvmm9gGJL6PgHgfDtwR64V16A1vK14KWgqlFGSrbG+/B26t6b6J7s3wK6rcsbGNfTXa\nsrAuC21dWZY9p6eXmU8uMO1OmE9OqNMcVe5SKWVCtVI0hK6qUlCoIcJb66y903pHO7gYq61422Pr\nZWx5jHb6GH3d05c9tu7xvkZGtoe1muKoGCpR1bbxBxgye/iOSwHpiDoHK3IVXHVkizOczeHMeS7E\n9jzPTPMEItTemcY5n/1d2JnQlpKV7SS5vfzu0f0/Clw1/3pwXmn3d44e/+/cxyzKjbHtYwZ+193P\nq6LfEUSEZz/72U+4XRmfFU937r//vO78JEmSJLkxXv7yl/OiF73oCbd7wQtewMMPPz0ubKfYPqI/\nTmxH/NSxXo7RwIaZgxjeO14aroqWgmtBi+JFEdGDg7c4R5nVNpy+R6TVqHaz1YrXhb4sLKen7E9P\nD0L7scce4+TiBXYXLrK7cJF5d8I076jTjmnaMdUZqfPBDbwUpZYJEaWVxtqE1oBVaGLsbYW2xw+V\n7RDbti5Y22O9DQ/zqLqrejQ9io/KtoX4PWRqDzFNjYsTYkjxx4ttKRQtqIY7uRxeU5jnaVS3p3Au\nNxvdBSHp3ccrJApaQerBiC5JktvCvz26/3wiK/s8nn/O938e+HSipftjODMwuxF+/uj+pwLf9ST2\ncct47nOfy1vfel4RPUmSJEmS83jggQd44IEHnnC7eZ5vw9ncHFJsH1GmHcBIknYqW6/kUdSUy1Hm\ntCA6Kr6AuCEW20TreWwT3eSb2N7ap9th/jlmtgEcdQ/3b+v0trLs9yBCd2ftK/t1YV5OmXYnIbKP\n1nwQ3rsQrtNM0cK6rod1+tgjnD72aKzLj7KcXmbd72ltofc2TOI8XMNH7nWRMvK8o1gu4uPSgEci\n2jRRphmdZso0U+eJMk+UqY6W8gnRKUzS5PFiO9rsHdGCFEXGz3TLNvMQ+S4SrvBSDmIbTbGdJLeR\nnyVyrd+DyKz+pqttJCLvxfk52z8E/K1x/2/w5MT2jwAr8fn1pSLyPb65WyZJkiRJktxFpNg+YpqH\nf86WKhU9z8Eh+oqtzMoIoYpNpMetAeJHQntscRDaHGLAthZtGbnRqmNJPNLd6G1h2UM3Y21jdvv0\nMnXehXv3FNXteTphN0Uu9zzv2M0n7OYdtdaI0Rrr8mOP8Ogjj/DYo49wevlR1uWUdTmltxWzBliM\nWW9Z2SVupzKixoqGBpZ4noggtSI1Wtpl2lHqjE4TWisyhLaUGTkI7aiFh9COtnR5XLVbDq9rEYl2\ncR1LyhDvKbaT5Hbi7ouIfDvwpcDzROTL3f3vHm8jIgV4FVfJ2B77eOOIBftU4NNE5BXu/lXnHVNE\n3gf4KHf/3qN9/NY4jy8C/iPgVSLyhecJbhF5DvAid/8nN/SEkyRJkiRJniIpto+Y5gsAiMrZEuEw\nU+1jTruPHO4tDouzSCxGZFjo8rMquI9Wcj+YpoXQBsI0TIQyjjcegVunrdDMoC3oOlH2Fa2VUqcw\nTKszdZrZzRdiTRc4ObnAerLQTy4w1Yn9fs+y37Pfn3L58qM89ugjXH7sUU5PH6O3JarabRlGbY4o\noxW9MFVlqoWpKPO4LUVjXLrEa0SJfHFKCVFdd0iZzpbGrQ4zt83QLZzOBTcbr7WGeJcQ3OrRFbDF\nqUX1u0ZlW6cRsZYkyW3kFcBfBB4Evl5EPhT4TsKd/P2BLyPyrN/I+a3kLyWiwx4AvkJEPhX4NuAX\ngVPgWYSI/jTgE4EfAL73in18GfBRwJ8GPg/4SBH5R0T1/RGi+v7BwH8y9vOLQIrtJEmSJEluKym2\nj9gq21oELYqWIbZHFdrd8N5itT4SsEa12zziwEakVySGHbuEH/mlbUVdonJeNI5Xturt5uANMRfd\nDeuC9AZrtFtrmajTQqkztc6sc6PtOm22mCn3OKpNxrI/Zb8/ZX+6D+E9jNd6b5iF4N8uLMQplIgb\nqzWE9lSZSxmiu1CqjtcoxLaP83ZVKBPotiJ3XLf88bAnPzi1u/iI6vZ4nUcr/cFCbZipiUjEhukQ\n9VpGlTsN0pLkduLufyAiLwD+JZFb/eKxDpsA30G0h19VbLv7JRH5KOD7xjbPBz78apuO29+/yj4e\nFZFPAL4beAHwAVy9rf3cfSRJkiRJktxqUmwfMZ/cB4TYLpvYVkJc21gyjLrcYobYdUxlD2PyIbrd\nN7GtQzDqYUVx28bEs5/NMKtEe7SEoHQpCGCHcXEZLeqOeacPJ3QRxaxjfTice5iXmRs+zN7co/Vb\nVanTRLUdroztO24tKtolWtlLiYp2LSG4iyp1GMDpqG5vgtu3CwQq+MEpPNq8QyTLwQgtjOI2A+Ko\npMc4/NZSPi5gHPr3JTK+6diIW3NxXIYzeZIktxV3f5OIfDAxc/1ZwHsD7yQM1L7V3f8XEXkJZ3ED\nV9vHW4hq9IuAvwR8BOFQPhG5178C/DTwWnf/qXP28Q7ghSLy54DPBT6WqJafAH8A/BrwM8A/B37s\nvKdz3jkmSZIkSZI8VVJsHzHvtsq2UqpEBVccawvWV6wLxoi98h6O25FsHQ7aCtqdblGxPYht0UM0\nl5Z4ye0oY1pGhZvNAOwgtvUwu2wSaxPquIwZ61DiUaWOmCwbLe5mm2P4kdGbFrROTO7hnD7mxx0L\nU7QhoOuIpamlUktBRc+WKrq9PkVGJR5QcFEcHbPqo/VbNdrNN0fxg6DeMsbHGbqNLoFj+/fRbk50\nGLg5HcPombOdJHeIIXT/5lhX+/mrgVdfx35eC7z2KZ7LTwA/8SQe91KipT1JkiRJkuSWkGL7iK2y\nXYbYrlURMXor2Kr0lVEx7vRRbg4B6qgITZ2mBW2OKWdxWFIopVLrRK3TwZ18qzy7+5lj+VYZ1oJF\nsPc4u7jtQ5jaEKWOQBd6j0q3jzzvQ1Xb7LB/Rjt2qROoUGzzW4+ILy0SFxlG5bponHfRwlahx6NS\nXapSJqUUYVixh9j24R/nsF1o2FrUh3fciDobLuSHynZcuHD3w5z75up+JrihY3QXOkL3rfqdJEmS\nJEmSJElyd5Fi+4hpDrEdOdFOH0K1r4a1Tm9ttDT7IZpKxQ4j1upKcYeqqG/Z02EKVmo5VItF5NBC\n/jjjNA6yFxsp152j9uqRae0Mx+4tPgu2AvfhVlWGeFa6CVIUejiHqypYifqz+IgvG+Pi5cwVXYah\nmWs5aomPeC6KQBm3Q2hHK/iYFx9969E6v7WRj/l2iVZ7sDHzbphvbfR6dmFgE9rj2FE1F8yhj5Uk\nSZIkSZIkSXI3kmL7iDoM0swaZiu9L/Rm9NaxtdPXhnpH8TA1YwjqkbPtOirNUnCPqncI1tGSrZuT\n9xCSQyxvVe3wWYuKrSEhJs2GIB/HkM1uTY8E6RY/tpmJyWHuvFRFe8xZU+IxWsrmQzZax2Nt0dai\njNiyyPgyZDiJj7W1hRfBi4TI3gT3KG3LENZymFk/E9pbjJp5OK6HG7lyaChXPTNIkzgh326HQ7n5\nqO4nSZIkSZIkSZLchaTYPmKrbLe2py/O2hba2ulrx1qjr40qxqThnq2iRznZUXVmVHIZpmgqJVYp\n0ZatY+Z5rG2WeZur7gbNoA2HcxfC5dxCAOtQoT7E+lk5e3ypo6qtZ63wa1ekh9gOTzEdFwKEWmOV\nukVuRbXbPYr4ZhKj1CPXWqRGZXsYop21kI92cGwI7ugAEJeDybgeCe24MTDDeh+J5IoKuMvBiRzR\nw3IpY6TbMaB7qu0kSZIkSZIkSe5OUmwfoWUXd7rhLJgrvQsjWjsqqRpzylOFqpGrPerQw/U7KrbR\nOl0O1W2VgmqhSERYhdCOfO0tPQwcNQntatGKLT2c0cQkTNIYreYiw4hMR2TX5vq9ieYt+zvcxs17\nGKgxKusaFwC0CjoNsY1HCz2HExpnJcNZvI4Vol1kE9tnIn0EjkWVfXMdN0e2/HHzcWt471hv9NbB\nR+yZa8R2bw7uMMzj4vUSlxDpYmeG5UmSJEmSJEmSJHcZKbaPOHO3LmdLC6ITpTSUzjwJu1m4sFOm\n4sjI4GZkax+qtgexeHR7aKk+irWCQ8t1aHmhilJQKlDNqNZpbiG0HTrbbLdEW7XoIZ6rKIBhvbGu\np0Bn2a8s+5V1WXGtEVMtilMiRsyc8B+zsTbH8M1N/Sz6O8zgYmZ9i8YOcS9HAjvMzdwl8tCGO3o3\nw3rMwvdu9N5pvdF7x7tiKnSVMJObdrgLVUocZMs+d6EgdFG0bxFiSZIkSZIkSZIkdxcpto9wytnt\nyIqOtumKM4MY01w4uVC4eKEyF8D72SJmqkNoj9uDszYjQxrchgO5jbzubXLbQ2wXjSFqF6W6Uc3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f29bt6u7hiXgOweSpIkSZKnQortI3z8YiHCYebZ4VDVFmAqylSUuSi4hegsjhaBIrg4HcdE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\nrGwnSZIkSZIkS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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdb244f6400>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 445,
       "width": 493
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL\n",
    "\"\"\"\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "\n",
    "import tensorflow as tf\n",
    "import pickle\n",
    "import helper\n",
    "import random\n",
    "\n",
    "# Set batch size if not already set\n",
    "try:\n",
    "    if batch_size:\n",
    "        pass\n",
    "except NameError:\n",
    "    batch_size = 64\n",
    "\n",
    "save_model_path = './image_classification'\n",
    "n_samples = 4\n",
    "top_n_predictions = 3\n",
    "\n",
    "def test_model():\n",
    "    \"\"\"\n",
    "    Test the saved model against the test dataset\n",
    "    \"\"\"\n",
    "\n",
    "    test_features, test_labels = pickle.load(open('preprocess_training.p', mode='rb'))\n",
    "    loaded_graph = tf.Graph()\n",
    "\n",
    "    with tf.Session(graph=loaded_graph) as sess:\n",
    "        # Load model\n",
    "        loader = tf.train.import_meta_graph(save_model_path + '.meta')\n",
    "        loader.restore(sess, save_model_path)\n",
    "\n",
    "        # Get Tensors from loaded model\n",
    "        loaded_x = loaded_graph.get_tensor_by_name('x:0')\n",
    "        loaded_y = loaded_graph.get_tensor_by_name('y:0')\n",
    "        loaded_keep_prob = loaded_graph.get_tensor_by_name('keep_prob:0')\n",
    "        loaded_logits = loaded_graph.get_tensor_by_name('logits:0')\n",
    "        loaded_acc = loaded_graph.get_tensor_by_name('accuracy:0')\n",
    "        \n",
    "        # Get accuracy in batches for memory limitations\n",
    "        test_batch_acc_total = 0\n",
    "        test_batch_count = 0\n",
    "        \n",
    "        for train_feature_batch, train_label_batch in helper.batch_features_labels(test_features, test_labels, batch_size):\n",
    "            test_batch_acc_total += sess.run(\n",
    "                loaded_acc,\n",
    "                feed_dict={loaded_x: train_feature_batch, loaded_y: train_label_batch, loaded_keep_prob: 1.0})\n",
    "            test_batch_count += 1\n",
    "\n",
    "        print('Testing Accuracy: {}\\n'.format(test_batch_acc_total/test_batch_count))\n",
    "\n",
    "        # Print Random Samples\n",
    "        random_test_features, random_test_labels = tuple(zip(*random.sample(list(zip(test_features, test_labels)), n_samples)))\n",
    "        random_test_predictions = sess.run(\n",
    "            tf.nn.top_k(tf.nn.softmax(loaded_logits), top_n_predictions),\n",
    "            feed_dict={loaded_x: random_test_features, loaded_y: random_test_labels, loaded_keep_prob: 1.0})\n",
    "        helper.display_image_predictions(random_test_features, random_test_labels, random_test_predictions)\n",
    "\n",
    "\n",
    "test_model()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Why 50-70% Accuracy?\n",
    "You might be wondering why you can't get an accuracy any higher. First things first, 50% isn't bad for a simple CNN.  Pure guessing would get you 10% accuracy. However, you might notice people are getting scores [well above 70%](http://rodrigob.github.io/are_we_there_yet/build/classification_datasets_results.html#43494641522d3130).  That's because we haven't taught you all there is to know about neural networks. We still need to cover a few more techniques.\n",
    "## Submitting This Project\n",
    "When submitting this project, make sure to run all the cells before saving the notebook.  Save the notebook file as \"dlnd_image_classification.ipynb\" and save it as a HTML file under \"File\" -> \"Download as\".  Include the \"helper.py\" and \"problem_unittests.py\" files in your submission."
   ]
  }
 ],
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