{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Neural Network"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Basic Usage of TensofFlow\n",
    "- References\n",
    "  - https://www.tensorflow.org/versions/r0.11/get_started/basic_usage.html\n",
    "  - https://www.tensorflow.org/versions/r0.11/resources/dims_types.html\n",
    "- **TensorFlow** is a programming system in which you represent computations as graphs. \n",
    "- Nodes in the graph are called **operations**. \n",
    "  - An operation takes zero or more Tensors, performs some computation, and produces zero or more Tensors. \n",
    "- A **Tensor** is a typed multi-dimensional array. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "matrix1 - Tensor(\"Const:0\", shape=(1, 2), dtype=float32)\n",
      "matrix2 - Tensor(\"Const_1:0\", shape=(2, 1), dtype=float32)\n",
      "\n",
      "matrix3 - Tensor(\"MatMul:0\", shape=(1, 1), dtype=float32)\n",
      "matrix4 - Tensor(\"MatMul_1:0\", shape=(2, 2), dtype=float32)\n",
      "\n",
      "matrix5 - Tensor(\"Const_2:0\", shape=(2, 2), dtype=float32)\n",
      "matrix6 - Tensor(\"Const_3:0\", shape=(2,), dtype=float32)\n",
      "\n",
      "matrix7 - Tensor(\"add:0\", shape=(2, 2), dtype=float32)\n",
      "matrix8 - Tensor(\"mul:0\", shape=(2, 2), dtype=float32)\n",
      "matrix8 - Tensor(\"Const_4:0\", shape=(2, 2), dtype=float32)\n",
      "matrix9 - Tensor(\"ones:0\", shape=(2,), dtype=float32)\n",
      "matrix10 - Tensor(\"add_1:0\", shape=(2, 2), dtype=float32)\n",
      "\n",
      "matrix3_result -\n",
      "[[ 12.]]\n",
      "matrix4_result -\n",
      "[[ 6.  6.]\n",
      " [ 6.  6.]]\n",
      "matrix7_result -\n",
      "[[  11.  101.]\n",
      " [  12.  102.]]\n",
      "matrix10_result -\n",
      "[[ 2.  2.]\n",
      " [ 3.  3.]]\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "matrix1 = tf.constant([[3., 3.]])\n",
    "matrix2 = tf.constant([[2.],[2.]])\n",
    "print \"matrix1 -\", matrix1\n",
    "print \"matrix2 -\", matrix2\n",
    "print\n",
    "\n",
    "matrix3 = tf.matmul(matrix1, matrix2)\n",
    "print \"matrix3 -\", matrix3\n",
    "matrix4 = tf.matmul(matrix2, matrix1)\n",
    "print \"matrix4 -\", matrix4\n",
    "print\n",
    "\n",
    "matrix5 = tf.constant([[1., 1.], [2., 2.]])\n",
    "print \"matrix5 -\", matrix5\n",
    "matrix6 = tf.constant([10., 100.])\n",
    "print \"matrix6 -\", matrix6\n",
    "print\n",
    "\n",
    "\n",
    "\n",
    "matrix7 = matrix5 + matrix6\n",
    "print \"matrix7 -\", matrix7\n",
    "matrix8 = matrix5 * matrix6\n",
    "print \"matrix8 -\", matrix8\n",
    "\n",
    "matrix8 = tf.constant([[1., 1.], [2., 2.]])\n",
    "print \"matrix8 -\", matrix8\n",
    "matrix9 = tf.ones([2])\n",
    "print \"matrix9 -\", matrix9\n",
    "\n",
    "matrix10 = matrix8 + matrix9  #broadcast\n",
    "print \"matrix10 -\", matrix10\n",
    "\n",
    "print \n",
    "\n",
    "sess = tf.Session()\n",
    "matrix3_result = sess.run(matrix3)\n",
    "print \"matrix3_result -\\n\", matrix3_result\n",
    "\n",
    "matrix4_result = sess.run(matrix4)\n",
    "print \"matrix4_result -\\n\", matrix4_result\n",
    "\n",
    "matrix7_result = sess.run(matrix7)\n",
    "print \"matrix7_result -\\n\", matrix7_result\n",
    "\n",
    "matrix10_result = sess.run(matrix10)\n",
    "print \"matrix10_result -\\n\", matrix10_result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. MNIST handwritten digits image set\n",
    "- Note1: http://yann.lecun.com/exdb/mnist/\n",
    "- Note2: https://www.tensorflow.org/versions/r0.11/tutorials/mnist/beginners/index.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting MNIST_data/train-images-idx3-ubyte.gz\n",
      "Extracting MNIST_data/train-labels-idx1-ubyte.gz\n",
      "Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n",
      "Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "mnist = input_data.read_data_sets(\"MNIST_data/\", one_hot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Each image is 28 pixels by 28 pixels. We can interpret this as a big array of numbers:\n",
    "<img src=\"https://www.tensorflow.org/versions/r0.11/images/MNIST-Matrix.png\" width=\"50%\" />\n",
    "\n",
    "- flatten 1-D tensor of size 28x28 = 784.\n",
    "  - Each entry in the tensor is a pixel intensity between 0 and 1, for a particular pixel in a particular image.\n",
    "$$[0, 0, 0, ..., 0.6, 0.7, 0.7, 0.5, ... 0.8, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.9, 0.3, ..., 0.4, 0.4, 0.4, ... 0, 0, 0]$$ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<type 'numpy.ndarray'> (55000, 784)\n",
      "<type 'numpy.ndarray'> (55000, 10)\n"
     ]
    }
   ],
   "source": [
    "print type(mnist.train.images), mnist.train.images.shape\n",
    "print type(mnist.train.labels), mnist.train.labels.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Number of train images is 55000.\n",
    "- **mnist.train.images** is a tensor with a shape of [55000, 784]. \n",
    "<img src=\"https://www.tensorflow.org/versions/r0.11/images/mnist-train-xs.png\" width=\"50%\" />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- A one-hot vector is a vector which is 0 in most entries, and 1 in a single entry.\n",
    "- In this case, the $n$th digit will be represented as a vector which is 1 in the nth entry. \n",
    "  - For example, 3 would be $[0,0,0,1,0,0,0,0,0,0]$. \n",
    "- **mnist.train.labels** is a tensor with a shape of [55000, 10]. \n",
    "<img src=\"https://www.tensorflow.org/versions/r0.11/images/mnist-train-ys.png\" width=\"48%\" />"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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WtWtxfU/fj8TthNsE3CZgN+Ems2e/jSvPoapg0cByBc0KlksYVAr0XM2ZPXkP+7J67ObW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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119c98850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "fig = plt.figure(figsize=(20, 5))\n",
    "for i in range(5):\n",
    "    img = np.array(mnist.train.images[i])\n",
    "    img.shape = (28, 28)\n",
    "    plt.subplot(150 + (i+1))\n",
    "    plt.imshow(img)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Neural Network Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Input Layer to Output Layer\n",
    " - $i=1...784$\n",
    " - $j=1...10$\n",
    "$$ u_j = \\sum_i W_{ji} x_i + b_j $$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Presentation of Matrix and Vector\n",
    "  - Shape of ${\\bf W} = 10 \\times 784$\n",
    "  - Shape of ${\\bf x} = 784 \\times 1$\n",
    "  - Shape of ${\\bf b} = 10 \\times 1$\n",
    "  - Shape of ${\\bf u} = 10 \\times 1$\n",
    "$$ {\\bf u} = {\\bf Wx + b} $$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 784)\n",
      "[[ 0.          0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.38039219  0.37647063\n",
      "   0.3019608   0.46274513  0.2392157   0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.35294119  0.5411765\n",
      "   0.92156869  0.92156869  0.92156869  0.92156869  0.92156869  0.92156869\n",
      "   0.98431379  0.98431379  0.97254908  0.99607849  0.96078438  0.92156869\n",
      "   0.74509805  0.08235294  0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.\n",
      "   0.54901963  0.98431379  0.99607849  0.99607849  0.99607849  0.99607849\n",
      "   0.99607849  0.99607849  0.99607849  0.99607849  0.99607849  0.99607849\n",
      "   0.99607849  0.99607849  0.99607849  0.99607849  0.74117649  0.09019608\n",
      "   0.          0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.88627458  0.99607849  0.81568635\n",
      "   0.78039223  0.78039223  0.78039223  0.78039223  0.54509807  0.2392157\n",
      "   0.2392157   0.2392157   0.2392157   0.2392157   0.50196081  0.8705883\n",
      "   0.99607849  0.99607849  0.74117649  0.08235294  0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.\n",
      "   0.14901961  0.32156864  0.0509804   0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.          0.\n",
      "   0.13333334  0.83529419  0.99607849  0.99607849  0.45098042  0.          0.\n",
      "   0.          0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.          0.\n",
      "   0.          0.32941177  0.99607849  0.99607849  0.91764712  0.          0.\n",
      "   0.          0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.          0.\n",
      "   0.          0.32941177  0.99607849  0.99607849  0.91764712  0.          0.\n",
      "   0.          0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.          0.\n",
      "   0.41568631  0.6156863   0.99607849  0.99607849  0.95294124  0.20000002\n",
      "   0.          0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.09803922  0.45882356  0.89411771\n",
      "   0.89411771  0.89411771  0.99215692  0.99607849  0.99607849  0.99607849\n",
      "   0.99607849  0.94117653  0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.26666668  0.4666667   0.86274517\n",
      "   0.99607849  0.99607849  0.99607849  0.99607849  0.99607849  0.99607849\n",
      "   0.99607849  0.99607849  0.99607849  0.55686277  0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.14509805  0.73333335  0.99215692\n",
      "   0.99607849  0.99607849  0.99607849  0.87450987  0.80784321  0.80784321\n",
      "   0.29411766  0.26666668  0.84313732  0.99607849  0.99607849  0.45882356\n",
      "   0.          0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.44313729\n",
      "   0.8588236   0.99607849  0.94901967  0.89019614  0.45098042  0.34901962\n",
      "   0.12156864  0.          0.          0.          0.          0.7843138\n",
      "   0.99607849  0.9450981   0.16078432  0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.          0.\n",
      "   0.          0.66274512  0.99607849  0.6901961   0.24313727  0.          0.\n",
      "   0.          0.          0.          0.          0.          0.18823531\n",
      "   0.90588242  0.99607849  0.91764712  0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.07058824  0.48627454  0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.\n",
      "   0.32941177  0.99607849  0.99607849  0.65098041  0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.          0.\n",
      "   0.54509807  0.99607849  0.9333334   0.22352943  0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.\n",
      "   0.82352948  0.98039222  0.99607849  0.65882355  0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.          0.\n",
      "   0.94901967  0.99607849  0.93725497  0.22352943  0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.\n",
      "   0.34901962  0.98431379  0.9450981   0.33725491  0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.\n",
      "   0.01960784  0.80784321  0.96470594  0.6156863   0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.          0.\n",
      "   0.01568628  0.45882356  0.27058825  0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.          0.        ]]\n",
      "\n",
      "(1, 10)\n",
      "[[ 0.  0.  0.  0.  0.  0.  0.  1.  0.  0.]]\n"
     ]
    }
   ],
   "source": [
    "batch_images, batch_labels = mnist.train.next_batch(1)\n",
    "print batch_images.shape\n",
    "print batch_images\n",
    "print\n",
    "\n",
    "print batch_labels.shape\n",
    "print batch_labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-  Transposed Matrix Operation in Tensorflow\n",
    "  - Shape of ${\\bf x} = 1 \\times 784$\n",
    "  - Shape of ${\\bf W} = 784 \\times 10$\n",
    "  - Shape of ${\\bf b} = 1 \\times 10$\n",
    "  - Shape of ${\\bf u} = 1 \\times 10$\n",
    "$$ {\\bf u} = {\\bf xW + b} $$ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(100, 784)\n",
      "[[ 0.  0.  0. ...,  0.  0.  0.]\n",
      " [ 0.  0.  0. ...,  0.  0.  0.]\n",
      " [ 0.  0.  0. ...,  0.  0.  0.]\n",
      " ..., \n",
      " [ 0.  0.  0. ...,  0.  0.  0.]\n",
      " [ 0.  0.  0. ...,  0.  0.  0.]\n",
      " [ 0.  0.  0. ...,  0.  0.  0.]]\n",
      "\n",
      "(100, 10)\n",
      "[[ 0.  0.  0.  1.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  1.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  1.  0.  0.  0.]\n",
      " [ 0.  1.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.  0.  1.  0.]\n",
      " [ 0.  1.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 1.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  1.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.  0.  1.  0.]\n",
      " [ 1.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  1.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  1.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  1.  0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.  1.  0.  0.]\n",
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      " [ 0.  0.  0.  0.  0.  0.  0.  1.  0.  0.]\n",
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      " [ 0.  0.  0.  0.  0.  1.  0.  0.  0.  0.]\n",
      " [ 1.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 1.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  1.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  1.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  1.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.  0.  1.  0.]\n",
      " [ 0.  0.  0.  1.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.  1.  0.  0.]\n",
      " [ 0.  1.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  1.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  1.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  1.]\n",
      " [ 0.  0.  1.  0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  1.  0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 1.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  1.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  1.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  1.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.  1.  0.  0.]]\n"
     ]
    }
   ],
   "source": [
    "batch_images, batch_labels = mnist.train.next_batch(100)\n",
    "print batch_images.shape\n",
    "print batch_images\n",
    "print\n",
    "\n",
    "print batch_labels.shape\n",
    "print batch_labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Mini Batch (ex. batch size = 100)  \n",
    "  - Shape of ${\\bf x} = 100 \\times 784$\n",
    "  - Shape of ${\\bf W} = 784 \\times 10$\n",
    "  - Shape of ${\\bf b} = 100 \\times 10$\n",
    "  - Shape of ${\\bf u} = 100 \\times 10$\n",
    "$$ {\\bf U} = {\\bf XW + B} $$  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x - (?, 784)\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "x = tf.placeholder(tf.float32, [None, 784])\n",
    "print \"x -\", x.get_shape()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- we also need to add a new placeholder to input the correct answers (ground truth):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y = tf.placeholder(tf.float32, [None, 10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- construct a single layer neural network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "W - (784, 10)\n",
      "b - (10,)\n"
     ]
    }
   ],
   "source": [
    "W = tf.Variable(tf.zeros([784, 10]))\n",
    "b = tf.Variable(tf.zeros([10]))\n",
    "print \"W -\", W.get_shape()\n",
    "print \"b -\", b.get_shape()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "u - (?, 10)\n"
     ]
    }
   ],
   "source": [
    "u = tf.matmul(x, W) + b\n",
    "print \"u -\", u.get_shape()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- softmax\n",
    "\n",
    "$$ {\\bf z} = softmax({\\bf u}) $$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Error functions\n",
    " - Squarred error\n",
    " - Using maximum likelihood estimation\n",
    " - Cross entropy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "error = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(u, y))\n",
    "total_loss = tf.train.GradientDescentOptimizer(0.5).minimize(error)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Suppose you have two tensors, where u contains computed scores for each class (for example, from u = W*x +b) and y contains one-hot encoded true labels.\n",
    "\n",
    "<pre>\n",
    "u  = ... # Predicted label, e.g. u = tf.matmul(X, W) + b\n",
    "y  = ... # True label, one-hot encoded\n",
    "</pre>\n",
    "If you interpret the scores in u as unnormalized log probabilities, then they are logits.\n",
    "\n",
    "Additionally, the total cross-entropy loss computed in this manner:\n",
    "\n",
    "<pre>\n",
    "z = tf.nn.softmax(u)\n",
    "total_loss = tf.reduce_mean(-tf.reduce_sum(y * tf.log(z), [1]))\n",
    "</pre>\n",
    "is essentially equivalent to the total cross-entropy loss computed with the function softmax_cross_entropy_with_logits():\n",
    "\n",
    "<pre>\n",
    "total_loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(u, y))\n",
    "</pre>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "init = tf.initialize_all_variables()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sess = tf.Session()\n",
    "sess.run(init)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "batch_size = 100\n",
    "total_batch = int(mnist.train.num_examples/batch_size)\n",
    "for i in range(total_batch):\n",
    "    batch_images, batch_labels = mnist.train.next_batch(batch_size)\n",
    "    sess.run(total_loss, feed_dict={x: batch_images, y: batch_labels})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<type 'numpy.ndarray'> (10000, 784)\n",
      "<type 'numpy.ndarray'> (10000, 10)\n"
     ]
    }
   ],
   "source": [
    "print type(mnist.test.images), mnist.test.images.shape\n",
    "print type(mnist.test.labels), mnist.test.labels.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[7 2 1 ..., 4 5 6]\n",
      "[7 2 1 ..., 4 5 6]\n",
      "0.9116\n",
      "884\n",
      "Error Index: 8, Prediction: 6, Ground Truth: 5\n",
      "Error Index: 33, Prediction: 6, Ground Truth: 4\n",
      "Error Index: 63, Prediction: 2, Ground Truth: 3\n",
      "Error Index: 66, Prediction: 7, Ground Truth: 6\n",
      "Error Index: 77, Prediction: 7, Ground Truth: 2\n"
     ]
    },
    {
     "data": {
      "image/png": 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MgZ6ygGKZ30fKoCdQy3aqNL1+rPSILRXFGurLOdCz+Vaz+Rb0Bow3KO9IocL7ltFvUP4M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ZRMyveYiR7lcncp8XQL+xusaR8peyayZ72C9RkRje83mH5BbxvarmTbK6oeosodeVSTFT1lAbMG\nlg04p1BjjRuXtOM5N+ML/jq+5a/DW0brsdZgncHa8f768dz+/fd68Ozx9RPPnouKcCcISwizRKgi\nQQfiaZP6rPBU2XOcKT+GnDWCiDoiew7KnjQRPQfPnonZeDYf6U9J00NCTGVcB8+eA+Fz8Ow5Nl0+\n5dDPwvMme5SAWsBB2fOqQHxeIf6hQowjOAXbg2fPZND8b3YI2+d5RUOtE3MNaw1nGjpRoNMMn9Z0\n8QV34i3f8if+wp8JbIE7ErfTLTyS7j174Bll6Akn/OGgi0g9cyzXI+dXHS/f7Hj71Z4v/909g7b0\nztF1ju7WYgpHFyP9NBP/8DldVvZ0ZsGdPOc2vuTWf8bN+A/s9EsYbkmHGCW4QLrviPM8ITgoe1ou\n4i2vwh2f+1v+wd1yqVvuHNz6xF1IyJDwCYaUiEL+3i/9hBP+GTgmeo7JnhKpffbsWXlml4LVGzj7\nKnH150j1wVKMPfp2RxG26O2G4tst+v/ZEnzEJ3Ap4cm54lNeMaiJ7KkXuYRrcQmr1zC/glVKzH2g\nMY6yNRTVOJE9JT/Q54WTN+AJWdkzsFzsuDjb8Opqw+dvNvzDlxvqW4eOEeUCeojoNqI3Id/efjr9\n8CHXQclDGZfhN13PCpG76pZTGdd8Aas1nJ2TosKFG0w/Z5g8e8o7jb4TudX5GuozoMitzuc1rNfQ\nDxrparxb0LbnXG9e8vX2Lf968yU+GlJqSakFWlISpOR/8j1HJQiFwjdPlT2OeJOIi0ScRWIViIU8\nKXueEY4KkO6p9mOy5zCDfL8Z1EHV8+DZ4ydlj5t2kNmzJ06ePTwTZc8BP3Dn/6CyB9B5TCdlzz8Z\nz5rskUS0cBRyRKuWQguKIqJLyzzuWOtb1nLLQrRUaUQHN7V7S/lGKsmt2escqgZpI2IMMDrSaAnj\niE89znckjg0STzLqE074xSFFrnHQMrdiVTIvApWEcwkzj1AG4RWiBXETkDODfOfg2sPGwz6Qhkh0\n6T7VlZj+6+lbSAFRgJGJgUgVA8p7MI6AxSsD4+TobKc2keG5dEmAx7Po8ayqEbFEBI10EmVAj5Gi\nDxTJowfQJh8CSz/N2ccHNiec8ARK57aqqkjoqYW5KhJKR1JmQ0gukfw0uizBu/doFEd2ySLvC5MW\nJC2Ihbi/ToWYNlc/HsAPjjIlZPComFDBIaNBBYWKivnMsdaGdRw5MyOLvaG+HlEzg7reIz60iNse\ntgO0hjRkBY+ICTW9Xi3z500UECXUhUZS4IKmMwV0GrMpKEXBt5tzrndnbLo1rZkxupIQBY/JnZN6\n+IQjxIT0EWUieggUrafcOao7S9EGpM0ZkCqBX0ripUSGhGlLbKtxrSIoSUyCdG/c/NtBiogWlkJ1\nFFqhi0RROnQ9MA87VsU7luqWlcytUYpoISRcUIxoWlkgVEEoNaYu6Gaa79IL3pUvuS7O2cgZewqG\nkHDOEtJxl7FDTv30exaHn7ON6DGg+0DROsrGEa0nCk+sInGVSFeJ2IMvBYWDwoH2IJ1AHNiCE0/7\n0SBJSdAFvphhC8eoI0Mh6YoCh0NEASF/yB9fu/kao68w4QWmv2C8W2O+m2PKiv59ZPwuYW4ibhcI\ngyS656LsOT5AkY/kOiklYjB42+OGCtMWDJWk1wKzTcQWRA/aQh1gnvK8OabE4CP16Kk6h94ZZDlC\nHGAzws7mbiyjz34N4Vn8IH8WnjfZIyKlcDSipxaJRnpqOdDIPTO5Yy4+MP//2XuTJTmSNFvv08km\n93CPwFRIZFVl9e1e80n4ClxRuOOOO24oQuG6NxThjkIRPgUfgEu+wxXe7szqBBARPtikMxdqHuFA\njpVMVBYi/Yj8UPMAEAAcbqaqR89/jrhnLY60YsLgESwMYg2sgPVHNSfoPfQW+gE4QtgjuCOzvGbk\nMa3g4ldxwQW/GpQskrvaPFazjM8ktIGMJU8D+V6SVCJaT/prIH8dyG8jeZ/IY9lEQiF6tCjmjeZs\nRIJTiUl4+uyo44z2I5K+sMB2ADdBsBB88TV4MmTPh4lDD4qGHCE1EGpwFVgNk4JRlEfdJfn5gr8R\nymTqLlF3kWYZ6y5Rt5E0JtL4OObTGBOG5b4Vi85muZZKkFpJbCWpU2U8va7K6WlaDDE/rrLl5aNR\noCJUPmCcp/KZymUqnzEu0648nbZ0wdH1jva9pZIO4R35fiB/PZDejqR7SxwWcjgXkkeo4iMizkpK\nkFWFoMW5jtB3DKZF0sHU8e/vVnxz1/Fuv2I/dEy2JsQT2fN38E+54LODSIADMWbkISPvM+p9QnUJ\naTOMgpQkyQBXC+tYCeZ9hdtVeGMIQhGjJM9/f+ZekmjkTCcFnfJ0eqQzOzrT0sqe1nxNq7+lVXe0\nsqcRFkUiCcUsW1AdznSMdceu6Wjajvfxmq/rZ7w119zrjkFJnAxk0UN2fJhs9/PaIWXMheiZInpY\nCLWdo9GONHlSDqQ6kLaJ/IdMEhCvBPUoMKNATwI1ghyX/7ML2fPZICtJbGtC2+E6mFeasWsw7RWV\niGQvHwqvHq791RpnrnHxGjdc4263OLXGuYb5NjB/nXBvE/4+EodQyJ4nAQkYEOY7Y84QvSXMI3bo\nmXXFJBRDBN9D2IMYwMzQ+LLs1gqmnBh9oJ88dW/RekYylrX63UL4HB1Myzycfj9z5NMme0hUODqR\nWEvPlZhYS8Na6jIhyPfUYkctehomjPDIc7KnAzbA9VJbYExQe1AzMEI4wrwjc0vZ5Qw8pmJcqPkL\nLvhVoWQhdlZ10WM/VE1eQe4sWQykqSLdKZLNpPtAeh9I7yLpXSTvEnlIRSFAUQNUsth71YuZYy3L\npmtSiV4EuoXsMYzINAAN+AH8BN5CXMieJ7PBOsVM648qL2RPBd7ArGGSMIhC7pzInpO9wVN6Sy74\nJNAm03SR1Tawug6stpHVdaC7CsR9qbQLxH0kEogukInUgu9UI0BrUdJwNpqwUYTt43VsNWHxRiip\nJ4/X5aMqF5pELJRJIXu0T7Szp5087RxoZ08zlQCHqvYY5dHBY3qPlh7jPaL35P1EfjeR3k7EnYXB\nk12x2dSLYazWYEwZtQGjwRmDZ4V1W/ywxYst3m+xhw3f7gzvdob3B81hMIzW4KPku4qei7LnggVx\nCdIaM+KYkbuEfJdRVUKmTHKSHCXJSNJakowibSRzW2ONwQtNiJpkFVn//TebikgjZq5k4FqNbLVi\naxTbStPGHq3fovU7lLxDyx4lLYpMFJpZtni9ZTBbVLVF1dfIdst9XPOu7nhbdex0x6AUTnjK+v3k\nuPKxSv/HIWJG+oSaA2Yhe5p7R6MsaQ6lGaeJpG0ki0xqIdxAtQezA70vwmWZlm45/wnf1At+VWQp\nSU2F34Dbaubrhmp7hd46rMgkq0izIllNmhV5ViSrCE2HNyt8XOP7FV6u8X5NONbYncS+jbh3Eb8L\nhF6S3RMie4QBahDNUjXQkHMihhFvj7ihYcYwRcXgikgn9sBC9rShdHc1Eoac6X2knQP10WGwyDDB\nOMJhgoOF3sEYwKWLsuepQJKohKMVno2YuBaCGyG4loJG9ih5i5b3KHFEiwl1ruypKMqeDfAMeAE8\nB46p9ChgIYwwH0HvEWJFzqegvdNux3FR9lxwwa8IKaHS0DWw6WDbwXZVRpXIciBzIM+GZCVpl4jC\nlw3jfSpEzy7BmB8WUmpR8zQKurPSMtOLxF4E2pOyJ41I3wMVhAHCouw5kT35qZC7JzXPctpCtYz5\nTNljwKoPyZ6TqPGkgr+QPRf8BJTJ1KtC8GxferYvPNuXns1zR3jnCe89wXgCnug9YfAkAh3QCugE\ntHIZBVRa4Nvil+GfG/xzjX9ucC8Mfq0JmMUEMy7eCHlJPDnRJZJEXhJSCuFT2cx6CKyHmVU/sx4m\n1sPMup9Q0pN1gBDIfSD7WMb3AXpH3lnyvSXuLHnwSB9JuTx3hCpET11BUz2OR2M40uHcNUdecPQv\nOA4vOOhn7PrMfQ+7PrMfMpODEE9uER8TPRfC5wIWsicjpow8LsqeOiFVRugSRpCEJBhNqAo5GoVi\nNjVWVPhgCLMi9vK3IXtEohGejUw8U4mXOvFCJ15WiSYMZLMj6R1Z7UjySBa2GNqKGq86ot4QzQti\n9YLYvCS1LziEhl2tuDeKnVYMSuFlINPzmKd03s71E3N7LmTPSdljzpU9ypFcJOVY2riuE7nNpBsI\no6B+J6gagdbFnkRaShraBZ8NspLEpiJcaezzBvMyoV8m5IuElJI4aOJoiKP+8FpUBF0TQ0MYaoKv\niceaUDX4o8DvwlKKODylNi4J6ELwiO6sVqQciaHHz3scLTZWjE4xTAJpIc2FDDUWVCg5lEnBISf2\nPtJNnhqHiTNqnqAeYJxgsDCeKXviU1mv/zSePtlDpBORKxF5JgMvZOSFjDRyJIsdiD1ZHEFMZDyw\nmHicK3ueAS+B18BuCdYLM8wjDD3oHZmGR1uu0wRxUfZccMGvinNlz6aDZ1fwfKngwR7Ic1uUPbMk\nzZlkPWmIpCEXRc8yPrRxUdo/GllInitdPN2Ngn2O7HKgy5YmzZg0InMP2VCivCZI52TPk5iF+VDZ\nU1EeiPUSvLKQPd582MZ1UvZcLMsu+BtQjNXTA9nz7I3lxRvHzR8svnP4yhJweO/wo8VrR8KxFkt3\ntVhKlqq1xLUVdmOwzyvc6wr7usJ+UeG2Bk+1JJ2kh5Byj8QvEbgl+eTkgFO+Vk+Z7cGzPcxsDz3b\nfc/2cGRb9eA9LkecjzifcMeIzQmXI3kKpMHDEBCjJy/KnkTplJGLsqepyiOtKyJFgqjo8wrrt+z9\nS96PX/CON7znFcNkGWbHYB3DbBmtI8TTmuNC8FzwXYiUEba0B8ljRlUZqTIqJ+TCkqZ2SZFqDa41\n+NYwyxoXK5zVhEGRdpL0G5A98qTsUZbnyvJaz7wxljdmppYT1gw4NWDVgJUDTlisSPhF2TOpLXP1\nnLn+gql5w9y+YfCKoQ4MVWA0gUGGRdkz8Wize5Kn/jyjcxkycvHreSB7OkcjLUlmkkykOpPaRJKZ\nLCBYQdWKovLLAmUFohcI9Snf0Qt+dShFagx+I3HPJfa1RL5R8EYipSYcDf5YEY6G8DAaotekpIhR\nkQZJPKryOinimIlDOeCIw0L2PDVlj2gWkueqlLwiZ0/0ewIrXGyYfcU0KYYeTCgEj/JnYy7nwPuU\nufeRjkAdHHqekdUEagQ7g7VgHTh/aeN6SjgpezosG2F5JiyvpOW1tFRyJMgBL3qCGAhMeOEJ58qe\nc7LnFfAFUKeyqZwtDCPsj6BbBNXZEvHjRIwLLrjgV4GSS35qXdQ8z67g1TX8YUseLPn+njy35NkU\nz567TLr3JJfILpN8JjsejV4pbVxGFrJnpQrRszVQK7iPiXUMdMlRhxkdJ0TsIRrIM2RbyJ7sIYcn\nRPacK3tOZE9bfio1EKtHz555UfZ4PlT2XNq4LvgZ0CZTd5H1QvY8f+N49ZeZl3+c8dWMY4k+Hmb8\nbsaZmYhjC2xEqa2EjSxjq0v7ybypmZ/XzK9r5j/VzH+umZ/VWCKOhCVjEVgEDoVHLjG3hfCJy3VA\n0g3w7D7w7H7m2X3Ps3bHs3rHM70j9Y5+zvRTZpgT/ZRhzvgpkVwCl0g+gkuI0/ViKHlS9jRV6Ua9\namHTQu8Nwq9w7pqDf8lb/4Z/81/xjX+DCz0u9PizMcTTzQY/lC14we8YEXC5tHEdMlJmFAkVElwr\nuFkiw43GXRnsTY29rrCpwlqD7w1hp4ntb6XsiTTSciWPPFNHXusjfzI9X1VH6jDRa0evPb1yDNLT\nC0cgFXWSbDnqDQfzgkP1BYfmKw7tV1ifcPWANwNOF7LIy3nx3vzY/+rnGeiclD3qpOw5eurG0UpH\n6jKxo5A9HeQuE9viE1trMKkYzqoe5D0Xsuczw6Oyp8Y9q1Cva/hzRf6qAlnjdxV+XxcPrF2Nbyt8\nVRMHQR6Lj2QeEmnMD6+TSyTnyE6TvCI5SXJPJNlULMoeTsqeNcgtiGtytsRwh6G8JOcAACAASURB\nVE+r4l00GUap6CV0GaoloMtkqJeqFNznxNpH2uiprcMIi5QTMJaD2TCXg9kQiqrn0sb1NPDo2TOy\nEQPP5MBLOfCFHKjExCwtk5yZhWUSxWMnfmzQvOWR7HlDeSLPHvoZ9gM0DeiK/JBYc8EFF3wynAya\nV0sb17M1vNrCm+dwN5HtmrxryFNFupOkrxPxm0BK6dHN4qPnuypelNTqUdlzbaBVmVsS6/TYxmX8\n0sYVNB8q+J5Sz9J5SsJJ2dMsJb9f2WMWsufcoPkpvSUXfDIok2lWidU2sH3peP7G8oe/zHzxTxOW\nEesm7DjhdhO2m7B6IjJzIyglz0YJnVZMbcO4aZieN0x/aBj/2DL9U2B6FZlJS7O1YEZikcwYHImA\n/OA8v1wLVn3mxXvPy/XMy7bnVbXjpbrlJe/x2XHv4T7AfQ/cZ/wOxK58gwedzUf3QVrauB6UPQ1s\nW7hewfvBIH2Hddccxpd8O37Jfxn+wn+e/gzclcr3S2LYqX/yoiK+4AeQFs+eqSh6JBkVMmrO5JSh\nEqQrRawU/spgX1SMXzTMvsYNFX6nCe8VsfltPHskafHsOfJc3fFa3/Inc8c/V3cYObMzcK/hXmWk\nhCDKVJSEZlItB73h1rzgffUFt/WfuW3/Be8s1HdQ3ZF1AjWB8JT+qV9wL+WTZ08syp4+UNWeWjsa\nYUlSkFpItSBtBfEZpGeCgKDKAmMF5ihQdyX9lwvZ81khS0lsasJVh3u+QnyxIv2pI/7ziiRb3F2N\nu2uw6xrXNri6waqaLBLZuSIiGBzcLXXrIAbKgZv+KG/8KUBQDJlrEC3IKxBbkM9IeSaGDSGtsKll\nzhVTUgyLgkedDkoktApWEjoJm5RZh0iXPHUqbVwyTZAWH918Ljv/fS1OnzTZQ8oIm5DHiLwLqG8d\nprUYPWNmi//aod565H1ADuXUjZzJEaItlhx2D/N7GJtinDgdii8rKaNraK4zV19kbnQmeEUMkhQU\nKUpiUA/XD34eOUJaxpyekMfHBRf8mjiP+36M/1ZZY1LChBnjDxiXqOyMmQ48n//KK/tXbtx7Or9H\nh5EUPXPK2Pxh9/35Y94LwygNUhiSNFhl6JXB6Jp/T8/4Nj7jTt5wkCsmoQsh/DBhnMu8n1D7xCkq\nSGiQBkRV5LZaEk2NFxVz0oxecZwkO1mMJQ8DDBNMtihl46KUTVniQgVzRxxWzPuO/q6jetsxppa3\ndw33h5bD0DDNDS4YUn4iJ1gX/CjEYocsRUaJhJYRIwNGepKMJBmJIhJFWnK08gNpG5dQvTmX7AQD\nxJCY58R8jMz3gWntsa3EGfBDLif+RBKBjCcvzVwCg0ShluQ5cVZ6mBH3B/LdkXA/Mt9ZxnvPcZeI\nx4ztIY4g5hKh3AYICbwqprfRKJJRRCOXUWG1pNcSYRRRSyYlOUbJ3aj4t/lL/mpf8N5dsfcVY0i4\nNJHzng8TP0/PoSfy3Lng0yCfjWclcn6MZfcRbQNm9sRJkgdBPWmqucK4gA4RFRPit/ioZSDm4js0\nZ8SweA81CR0zpod6gtaV7owAZAmezBwS4xyp+oDee0TtSGomH5dN9cHDEMGmctP+1L0klp3mx6OC\nrD0xW0KYcXbA9ppZyRJWqSWxUiUh0JVks4TiKGsGuWYSHbMsc2sUajlAvuBzQU6ZbBOxj8R7j3/r\nEJ2CSpFlxu8ifheJO7+UI+8q8j7B0ZdI8MmB9eA9JA/59Jw/T4V7GntGoTKqDsjKIesJVfXIWqMq\nySbsWbme2o5oNyOsJ7qIs2CVQNUS2UhELaEW5EaSaskwt0y2xlqNtxDmRLIO4rmP7vn7+PuZN580\n2SMiZWJYyB7ZBpT06GhR1qK+8ahvA/I+IvqIsLnIq2P5bPgj2HuYTGEQVSj3YugLV6MraK9hreFm\nC26W+NngbbWMBj9XpFmXGzeeyj2+vpA9F1zwEU7kzlnk93Kts6GNiVWY6FxgNY900z2roWIzvmM7\nf821fcfa32PCQEqOifJ4P02XH5M9DsMgVgTZMasVB9XR6BVCd3yT1vw1rnkfVxzEiklUpdXzYcI4\n7+d/QhOHEIW9UQvZoyqQDVkroqlxsmJOhsErjrNkh0AKOI7QL2SP9RAWG6OUJc5XhGnF3G+R+y3q\ndoNcbRliy7tbxe1Oc+g146xxXpHzUznBuuDHUOjckoN1npOlCYSHvKximyweQtHLHecpe7SJ8pQQ\ngA1gp4Q9RNxtwFYSKwQ2gLvLizFzIOKXZq4KqBBoBApZdA+cnjsChZosHPaEfY87TEx7x3EfkIdM\nPsLcg1/W5dpDu0zr3khcZ/Bdhe8MeVURO0PsKuZsIGpCNIzJsI+aJhgaZ/h6fsk38wveuzW7oBlj\nxKcBuAcOFPXByONT7bKOuOCXQaSMjAntA8ZK8iRhKN4+zaCppgpja7SLyBAR6Tf4rGXKxH1KRB8o\nt4Ep63x9hGqE1hYxRM7FD8uTmUJkmCNN76mMQ4kZEaeiori1sHNloz1HCD9jDheqzInSgDIgq+Va\nkrQjMuPDiJtrrDKMSIYkiJUitIa4MkRnCMEQk6GXDUfWjHTMNLjFQD4/GQXH7wQR0hxJx0C884jW\nIqQgx0yWgXBwhKMlHg3pUJGPFRxNCf05BhhC6RrxoewNCRSi5/uMED9/SJ3QjcesZ8zKYNYasxbo\nVWJr71kNB5p+wAwzonekHPEOrJHITsNakdeadKWJa41fK/q+YzzWzL3G9YIgEzl6cKd07NP7+vsz\nlHzSZA8xI+aEPCbkbUQpj4oOPTu0d6h3Hvk2FCKoT2AzLKKbOIPviy3FLEBHkDNMAvzSvqvrTGPg\napO5STAPEjsY5r7GDg30DalvCLIpvYKn8nL5rF38fC644Ls4kT0fx35rdIIuJjZ+4tqNXNvE9Zi5\nHjLteEc7v6N172j8DhMHUvZMfGid/pi6U1BO0lZMcouU10hVKusNb2PFO2V4Lyv2smIShiB+iOx5\nQpPHSSt7yoPW9VKaqAvZM2XD4DWHLNkFgQDGGYb5UdkTFmVPThLna+K8Ih2vSbvnpNVzUv2cMTXc\n32bu93AYMuOccR7Sk/E/uuDHkR/Inu8nfB7JHnl23p1y2ZtZQGYglTuwCuCnjDtEfBVwQuB9xs0J\nv46UXC+/OPdo8kkmj0Y+kMwSsVRCoqyDfiAOA7YfmQaL6iP0GTEuoXwL92J82YAawGrJ3BnstoHr\nhvgwtsy2xk8141ijpgY11ihXo8aat+6Kd27DO3fF3ptC9uRh+ZePlN3uiex5OhuAC/7+ECmjYkL7\nSLIBJhBDRrWJeqioJ4+ZPdoHVEiI38LU9ET2WMret6fcYLLc+/oI9VgU+TmAyKAlODJ9SBzmQHMs\nHh4qTmDHksizn2HvoA8L2ZN+ego/kT26AdWcjZqkJiIDPhyxc8OcNZOXjAF8qwhrQ9jUBNsQQk3I\nNb1o6VkxihPZUxFRF7LnM0OOmTwn0jEQbl3xB4iZNEey8MTREJYUrjRo8qDJo4EhwRgL2TPFJSUq\nlA8yjvKhPz+qfBrPeqkSuvXUG0t9rWhuBPVNpL72bMcdq92B5n7A6AmRS5qdG0Dp0ruVtxXppiLe\nGPxNhbup6O87xvua6U7jpCDESJrPj3rPj3uf0Hr9Z+Bpkz0JxJyKskcFVPKo2aGOFhUsahdRu4jc\nRUR/1sYVyqThe7ASVFyiEAeYF7sKmrL3aRq4qsE2mXEvGfcGuW9g3xHNCi9WkFtwI3hdJJ+ZhVFy\nv/EbdMEF/4j4Ps+YEgGuc6BNlk2Yee5mXs6WV9PMy7G0csl5h7Q7pN8hY1H2zOQHLv+88aoo2UVZ\nXImOKK+J8iVRlXL6GbsAe5nZSTiIQvaGh5XnxyaOT2jiOCl7tC4OsqaCqiFrTRQ1HsOcdFH2BMle\nCEgl6GBe6vuUPXZe4YYtbv8c27zG6T8wpob+znPYe/reM84OHzw5n9KFLnjKKHf70sq1kD0nokcv\nxM8j0ZMXdc+i7FnIHlKJjw4ZdMiEKRGOiSADIRY5d+gToY0EPAH18N0zmlPbFogHwkcs1xmB8gGm\niTjNuHlimixMgThl1NJNJZY1ufbF2g/AGInsDGxrwosO+WJFfrkivlzhjh1515F2LYmObFtS6Ehj\ny85X7LxhFyr2oZA9IY/lD1ochz7cBDyNDcAFf3+I/NjGZezi6zMm1DHSDJZqchgbUC4iY0L+FmRP\n4kNlj+HB00Zk0D1UE2QHIoDOUEmwInMIiW4ONMJTJYuyM2KYigRwsEXh8zcpe2RRuqoGzApMB3oF\npiKrnsABHzucrZmDYbKSwQn8WuK3hjDWeNviQ4vPHT0tR9Ey0mJpcKIioEkXsufzQipkTzyGQvQk\nHsifLDRxVsRZk2ZFmhV51jCr0oM8R5hTGV0q/e8syc8fJDw/oTYundBNoLqytM8F3ctE98rTvbSs\n+x2r9kCjBnSeEc6T+oQXGWkEudPkjSE+rwmvGvyrGveypl91jFWNFRoXRWnjOnrKfHluuXAhe54W\nYmnmF4eEjAE5B9TRoW8tOjnUkJBDRI4JMSSEzYhzZY8Auyh66CHew3QN/gZYDrvb68zVM4g3YO4V\n8n1JCorVCi+vkGkDfl2knmI5k8wRkrvY7V9wwffiXNlzngZVofNMG0e2YeKFO/CF3fPldODL4QBj\nT5hGvBsIfsCHEZ88lu9m5J0/5r0wTGLFLG6Y5Ctm9YZJf8mkXjIqy6Aco3QM0jIKR3gIbM5n3+nB\n/vnv9zZ9Skj5SPZUFVQ11A1oQ0wljndOhiEoDkmyioIUiwL5vE7KnpQk1leM04qh3zLWLxj0a0bx\nR8ZUM99OTPuRaZiY5xHnR3K6pBn+PpC/08alCJgHWiY+tHKJs/PulB8P+xOLykeADJCmXHx+Qi7X\nh0i8DcUzA0lEfqQXUogzjzC1kDwnRyEVIzhHdA7nPDhHcAHrM5Uvah4dymgCmEXZo42CzhC3De7F\nCvlmA282xDdXzHdXuHqNZY2zK+xxjQtr7LhmCIkxJoaYGEI8a+M63wCcn1Q+jQ3ABX9/nJQ9hbDM\nqCmhB0XUgWaoqcdC9mgfi7Lnt1BcZpZEMR57NgHSouyZimePtEWFX2doJVgyuxBZTYEmeoy1qH4G\nM5YYLGuXk4lfqOwxHZgNVFdgGhIHYt4RQofzNTOGKUuGGvxW4W4MfmpwtsWHNS6tGURHT8VAzbwc\npBQS+uLZ8zkhLyoeDkB8JHrirSILRfIlSSt7SfKS7FTp8vC5EDw+l7YRl0pSVD4/VDwfn8azXqqE\naT31BrpnifVrz/pLy9WbkW5/T6uPNHnA+Bk5OGIVcQBaklpN3FT45w3udYv9ssW86TjWHaOsmaLG\nzYJ4TCRzOiA5Hco+QSX+z8CTJnuKZ09CxoicI+roUcajjUVli/IZ6TLSZ4Qv5m8fePacET1JQzAl\ncT1oyJvi2dNcw9UbkF+Cei+hM6S6wcsOm65Q7hqmTdk8kYtHT/Tg5+VrF1xwwXfxfdHfTVH2xMTW\nT7xwO97M7/hqesdfhnf4cWaYPYN1DN4zRI9Njil/SMV8TMs4UTGIFQd5zVG95CC/5KD+wlF/gddH\nvOrx8ogTPV70BHFqncgffacnNHEIUZ5PJ2VPXUFTk01FcA0uV8xBM3jN0UlaJ4iheM/H9OH4qOyp\nGaYV+/6ag37BXrxmH//ElGvC7oDfHwj9oXS6+kDK82/9Llzwd4B4IHvymbInovEfKXvSg7IHHpU9\np1EBUpST/jwlcsikKZEPiWwEyQiyEiROda4TKvXxj6fkE5kzxEiMEXc2qphpUvHoaVNR9JjTa4rk\nPLYGd90wv1wh31yRv7omfnWN/Y8tPVsGt6E/bhjUliFsGIYNLo34NOJyGX0a8Xmk7HTPqevz8YIL\n/naclD3CZ6RN5EmQTSApQTO0VJOnmj3ahWLQ/Fu2cTkeiZ6F9xSUyHLhyliF5a6QMOXMVUh0MdBY\nRyUWzx4xLtHLM4QlCSnEnxfFLNWjskevCtFTX0PVkcI90V/hQ4cLDbPXjEEyaoG9UbijwU01znXY\nsMblDQMdRwyj0EsqoL549nyOOLVxPbRzSYQRCCPJQpBjKdLpWpZIxrgkDSTOrj9eraaP6vOH1And\nBuqrRPvcs36t2P5Jsv2Lorm9x+QDlRvQw4TYOVIV8QKykcROEbYV7kWNft2i/7RGf7Wil205PLSL\nZ899IumTsif/QP0+8KTJHtLi3D9HJAGJR+FQ2GV8NHU8p11yLM99YcvrE5/6ELS8KV88GTTLN5nq\nXzKsJclovKixsWN0V6hxC8cbHlik5EqDvzJlMyXy2eftb3i4C35Pn9MLflcQPLZwGU5ED7SoPNPG\nxCZMvHB7Xs9v+fP47/xz9e9MY+Buhvuls8EuqpLpR/8osaRxrdjLa97LV9yqN9zqv7DTf6TkoN4u\nhox5eSicPHueKE5Ej1IlgrAyUC89q1VNzDUunDx7ikFzNf14V2pKAucrxnnF4bjlPS+4jV9wa//E\nnGvob4tZYS+Kl0KwJU7lgt8FHuiXnJA5oj6oc3NmEKLMkw9C9/N5cLkWi6qXh1/54S/5Por2RPmc\nX5+/Pv8zz3H62JvlN2igFXAFyEriVhXztkG/6JBfbMh/vib8y3Om6hlHe8PucM3u/Q07dcMuXLMb\nbniIV+eOR/n+AOx/ztt5wQU/GyKBiAk8KAtLVyMIqAdLNfkHZU8xaP6tyB6x9GzyQPRku6wWYqkP\ngjEVTCFzFSKruCh7okWFuUTnkfnQ+PZnmrZ+oOw5kT1bqK/I03uiX+NDWwyaZ8M0SwYB9oXC9gY7\n1ti5w/o1Nm0YWHNEMmbJlCVuUR5e2rg+MyxtW+WM6qJI/ilInTBtot5A+zyz/gNs/pi5+Scwqz3K\nHpDDgNzNiPeeVEWcgGgkvtOoTYV83qBed8g/rVD/6YpjrBhmw3zQuDuBb09kzwVPmuw5KT9P9kwn\nE/8jZQt5Oic7kTjnj/kTlxr5cNEXQiSOnrSf4XaEtUHUCikE5n2m/jbQfevw7ybSfQ/HPXraQtwj\n9AHR7RH1AbHZI+IR4lTcCbJeBOuakNXSsyvLaaZIjyebyzWU0/JSqoyU6xzFd5V/5xYjP4QT+3Xy\nw1Vn4yn6JHzP+L1NMpfTxgt+Kc48e8SZske0ZCZCqktLkNUMk+KgBfcC7AT9AONUFHjeP3qgS11K\nmbOAKQNCZ7xOWOUZlaXRI0YOSH+AuIP5AG4AP5UTwBguCXq/BKeH8em5cdpULILHh8CJ32cq5u8a\nwUvm0dDvGu7fR9pVpqolKWrmrxPTXWIaE1NOTE1kuk44m5AyImVCyYgUESXL1xSl3USFiArFZ0SF\niPJlo5rTyTR8GfOiQOO7JNDPOfvLGpKRhEoQKokzAlsJqkoyvWoZbyqG2tAHzfEg2f+HYCfh+E1k\n+MYxvZ+xuxE/aqI/neLsKS60E+VU8uLLc8GnQZKSqBTBKGKtiK0irhTxSnE3bNm3G471msm0OF0R\n5aeyHxCL1YH6MNIcBaohq0ASlpjLGsAFjRMSt6j5RC7nFA+/9bR2Damc3uKLqY+0SxQzPK7+PyZ6\nxA+WEQIjPZUaMEpgtKMyPbWpeRH+Cy/CX7lRt6zkHiMmMgGXBbNTTH3FdN8yvV0xdRsmfcOwumL/\nb5njt5npDuwh4+dMvvAFF3zmeAyuEw+hdafr9YvE6lmkbQJNjFR9xHwbkCqidj3yfkBZi9QJtZGo\n1xUydeSXHem6I1YtyTfkfUP+piFRc/f/GvZ/1fTvNdNe4UdJDBfSFH7HZE/F4zLq1JQRP/q95x1+\nJwSfiJMn7y353QhGIYRA+oTeeer3M93tSLo9Iu53qMOGZlwj9IjUA7IZyrUakHoANTPnBpslMxKb\nK+Zck3NDRqNEQIvFrFI8liATkiEkTUgGnwwha3IyZK8evRtPdfqH/tgEchJRnIQU9Vn5RSJxXixv\n3Af+AafJEy6L0wt+GU6LPl3IHlEBDYiWJBp8qrHeMFlNrxR7IblPJV2xH2A4I3vi8hGUGkwLVVtG\n05VRN+BSZIqePlnqOGFSjwwHiB3Y4yPZEy2kJdf1gr8dJ5PN0wP5JKvMPD6fToesl4Xu7wbBK6ah\n4riP3L3LaCMRQuPmBnsL863AjmCzYG7A3gi8zGjlMdpjtMOcXYNHzSWMoZpLklBlHdWUUT4S4yKy\njUu74clXKv9wg9SP3fFZCVIriStFWCn8SmJXCrNSTNuGYVvTV4Zj0BwOkoOA3Zjp3wb6bxzT2xm7\n04RBkPxp1XJc6hKvfsGnRRQKpyusqXB1hWsr7KrCrSver9bs2jV9s2KsWpz61GTPMufLahnL/J9l\nQ5KWKCZC7vGxKjr9LHCytG8+1PJaCIpTs0vFtTmHQvZEC2Iuju4fpBydH1CeDpxO7eSPKX1GCFbC\ns5I9nXaszJGVqVhVkk34mo3/Kxv1npXaY+RIFgGbYLKaoa8Y71qGds2gtwzphr7dcPwmcPxrZLyN\n2GMgzJEUT63iF1zweUIoUI1Ar0oVL/NyvdpkVteRrvE0yVEdLFo61OSQ04TajyjrUCeyJ1WobkVY\nd7jrDl+1eN/i9g2eBj/U7L7R7L5RHN8pxp3CjpLkL2QP/AyyRwjxvwP/NfBtzvm/Wr72PwH/HfB2\n+WX/Y875//pkf8tfiJ8ie06hdj+k7Dn9/g++X0ik0ZP2llyVSU+EhBw95jDT7EfSvkfsO/RuRXVY\n0U0dsrOo2iJXFtW5ZbTQBoasGFJFnyVDriB3hLwm5golHEY6alH6jUsVnwMXDS7V2FgjYk1ONTHW\nYPVjMuuw1Glj9WOKthpYfVTdMs4U47HTGlRQ5scZim7xVKfWi8tu7VPjc743fxzLQkuoR7JHFGVP\noinKnlAxzpojin0S3HlBdEXVM07Fd9GFR7JHLWRPvYHmCppNKXMF05To58BhmqnnET31yLAv0Xt+\n+FDZk/xF2fNLcGLPT/2wkkei55zs+b5D1s8QT/fe/PURvGQaDcddIXrAEHzNcPS4UeFHhRs1Litc\no3A3ithK6mqmNjPNaTQzVDNKzNDPqH6m6meafqYdMq0K6LlY5sVwVrkc/sf8YWLfKQvuJ+92LUit\nIm40/kbjrjV6qbFpGHXNYCqOQXHYS/ajYPcuM95Fxnee6d2M3Un8kEnu5EI7ntX3HUdd8EtxuTc/\nRJQSpwxT1TDVLWPbMq5apquW20PHrms51i2TabG6In6yYJGF7JE1yKaUah6us5hIoifSElKNzxqX\nCtmj1FJnwiAUiwfPkrqSfLn55en087Qg/j456XlIxLnkXVGRWUnHtXJcq55rnbk2mW0VafxbGv2O\nWr+jkYei7BGLsscqxr7meN/S6zXHtOFon9HXG4Z3juGdY7x12IMlTI4cPz5qfvq43JtPC0KBaqHa\nCKrrU0mqa8Gqjax0ojWeJk5Uxwk9T8jbERUdyjuU92gVURuJbmvUS0HUHUl3WNMy+ZZp1zINNdO7\nmuM7xfGdpH8vmfYSN8pFLXvBz1H2/B/A/wr8nx99/V9zzv/66/+Vfj38GNlT891upO9T9py/lkAK\niTgG0n4u04KPiMkjdzNmHElDj+gb9NBQDTVt3+LGGlVHlI7oVULdRPRNRD2LiA3sUsU+taisIFWE\n3DGlK2Ru0XKmkjO1mGmkpBHQyIgkM4eKKTSI2JFDS4wtPnQwmULM7Jd/6Ol/+dST9kNogTWwAbYf\njUMuP294JHoezFBOi9KziISn7Gnyj4PP9t78cYhHHbbQyynfx8qeiglDnxSHILm3ZS1nXVH1WLco\ne05tXKaQPc0VdM+gew6rZ9DcwLCP7A+e7mBpGDG2L21cU138tR7KXtq4finOyR7PI9FzWl+fmPen\nEy70RO/NXx/BK+YRjrtC9HgXmYfI4T7iU0XIFSFV+FwR6opQVSQUbT3S1QO+GYj1CHVRzhoxInYD\naqepdpJ2l1mbwCpLKgFBFT/WKBZiJz1+LE/a1HMe8mQB8kPIelH2bBXhucG/MriXFeqVYcoNo6vp\nnaH3msMk2TvBzmWmfcTtHHYvsLuMHwPRn+KGzuW4lzauXxmXe/MMSRZlz2RajvWavl1zXK05Xq25\nXdXs2opjXTFW9adt4zr1YMmq7BBVB2pVSjRkehJ7Ym4JucKjcUHgVFni5uUAQahyPiQNJaIvp0XG\nF8qBjTgne879Dr5P2XMKidAPoxGWlZy5lpaXeuaVtrw0ludmQpodQu+RaoeQe6QclzYumJ1i7Ct6\n3bJPKw52y76/4Wi2zLuZeT8y7ybmA/g5L8qe3xfZw+XefFIQCnQjMBtB/VzSvhQ0ryTNS0mnYOUS\nnfPUfqY6Dmh3RLkeqQOqymiTUVVCdxJdVWhjsKEjug7nOkbXchwbjq7UuBOMe8G0K+VGcSF7Fvwk\n2ZNz/r+FEF99z0/9w7+DP0b2OH5crv1xak9iWQD6RdkjyjWjR+wscjVirEFYg54rKmtoZ0OYDdEb\n9FagtUR3AnMj0K8F+guBeK6pU4dOGbIkpIo5dei4QbBCyREjNbWUtBJWMtJJj8wJHSqEb8ihW0zh\nVgi/hqEqvo7nRM8po/bHVq0Nhey5Bp59VAfKXHf6XhNg8vKmVHwnIoFPdfpzwQmf8735oxDwoOyR\npqh6ltO9REtIFdYbxqTpvWJvBa1ajNX9WfS3L20a8KGyp3sGV6/g6g+wegmH94mrytNlS20njFyU\nPZNZWrfc45guZM8vxmldfU70hOXnzlOkn4Bnz5O9Nz8BgpdMQzlF9x6mAY77TNNBrFti3ZKaplw3\npagN6/aA746k9gjtEdkeMO2RJA5wq1HvJVWTaXVgJRxbL6kTBAlelM7kkEr6bRBlTXDaAsKHnn0/\nhoc2ro3GvzCo1zXyyxrxZc00twy7iuHecJw0h71iv4PdfcYOkTA6/JAJY8SPjuRnyqR9Tj+d6vLc\n+TVwuTc/RBSLssc09PWKXbtht7pmd7XldqXZtYpjo5iMxilF/GQpsqc2a6V/eQAAIABJREFUrgpk\nC2oN+gr0hixaUjoQ0z0ht/hU45LGJoFLC9GjF75o8eSjBlR+7NUMHuSJ7Dnlt/9QFPMpJOKk6KmW\nMlTC0QnPtep5pQ680Qe+NEf+UB0IdsTrAa9GghrxYsKLgM2CyepC9uSWg12z6zfc391w1Df4sccN\nCj8K3JgJcyjem78zXO7NpwWpBKotZE/zXNB+Iem+lKzeSNooaHeJ9t7RzDPVscfcH5C7ParLqGuJ\nulboTqE3Cn2jMNcKceyI9x121zIMDftdw/2uZrersQO4sYjx7ViuY/jpv+fvAf9/PHv+eyHEfwP8\nP8D/kHP+h4uJ+DGy5yTR5nvG03WmPGFORI8Ackjk0ZN9JA8ejEQYhTISESUqKKooyUGRoyQFBUlh\nosFog1kZzDODeW2ovjLwRYNKxVwkJMmcKobYodIGka9QUmOUpJHQychKedbSoUgIZ8i+IboO79co\nv0G4DRyb7yp6LEV882PrxRPZswWeA6+Al8t4v7w7p9atnkfy54M/6LRkviTp/Ib4h783fxyn47mz\n/n1Zg1zauGKNTRVj0PQo9kgqQJ7FfZ9Hf8OjZ89J2XP1B7j+I6xfw72JrLOnszN1Py5kzwEmXfr8\nU4Dsl57/i2fPL8LH5M7p+pwj/r4D1qeHz/ze/PXhvSKPEr+QPkej0EaijSJdr8g3K/L1mlStyM2K\ndLNGbmvcakdc7WC1Q652mK6lWddEoeFKoptMpQMtjnWc2E6Cxi/UyWLl4WMhhd2SonVO9Jzolp9C\nXtq4wkajnhvcFxXizw38pWHaN4yiph/PDJq/Eey+zngbSD6TfCR5R/KS5E/+IOcb0PPrCz4hfpf3\nZjxT9vT1mn275XZ1w+36GXcrya6FvoaxElhdIs0/Dc7auNSJ7NmCvgYxk8MdKa+JuSPECh8XZc8S\nKCJyEQRLBfmc7IkJwnICpBaDZjFDVvxwFPN5G5dZqgRFGHFkJT3XcuCluuNL/Z6/VO/5srplMJ5R\newblGWQpJzwua2a32DXYlsNxzb3ZcmeecZA3JF/M2ZNPRB9IfiZ9ujf6c8Tv8t783CF08eyproqy\np3stWf9Jsf6LpJ0FtYrUk6eOE9VhQP/HAfX1HfJaolKNamu0rtFbg/miRn9ZI992JFrc2DKGlsOu\n4e7rhvff1ASXiT4TwzL6TPSXeRN+OdnzvwH/c845CyH+F+Bfgf/21/tr/Tp4CIARAodgRjAJwYB4\nTNlaxCkyf+i5/30EELBovlPhM85+PTza4pymiYdrAVVsMaqhaluqTUP1sqV60yL+nEnR4VNiioI+\nKupUYWKHSmuMSlQqUCtHKytWSnOlBCoLstNEV+FdjXUtxq5Q7gqxbx//8SeW62RU9FOePR0lM/aa\nR8LnC4qKZwZxSn89kUkCHkgeMVMmRbW44y2TZ2YJzT3fJ1+I+k+Ez+Le/A7Eww+Lw6JESAVSI07x\nWbJCZEOKBh8VNirGJDhGgUnffZidxyYrDaaBai1or6F7Llj9ATZvBCsP3Rip956qmlFiQIYjzKeT\nv++rC34U4mxYjDMFIBIQMiJmhMiFoctpiURaKvM4Pi18nvfmJ0YMkhg0djptqpYNljDwegP5CqoN\nbDYl5vhqi3zZkNZ3iPUaddVh1g31laFdK4IUZJUQOaCCo7ITzWjoDopuLEoAF0EHit+HFGdOc5kg\nHrWpp8/tw4fx4xMhFmVPI0lrRbjWyJcV4osK/twwv62ZjhXjt6WN67hXHL6V7P+zIMVMoZSe3gf9\nM8Tv9t5MQuKlZjY1Y1VauXbtlrvVM3Zd5tgkhioym4RXiSQ+0Rz4QRtXA3pVTPXMNVnMJDbEvCLQ\nlDauoHBe4DJlPQoI+f+x92ZZclxJ+t/vTj5GRA5IgGCzq1StFWgJ0hb0qA1oGVqIHvSmDWgD0h70\nrFPd/y6yACQQk4931MP1yIxMgig2iwMIxneOHbseICICQb+Df/aZmchdNwsINUSf51nykWQ9STkQ\np5zhTz3+nNUNPOsKKigplKBVjivdc6e3vNZv+JP+lj/rN+w07DSYpdSPU9ArcEIze8XkCvqxoqPh\nkFZsueLINUtveR6DpefU8x8ef9i5+bvE2Vle6EXZsxaUN4L6paD9F8H6z4L6kDBdoHhrMWGi6HrM\n2wP633eo0aDaiLqTKFmgWo26K1F/bkk0+EPNrGp6W3PYV2z/XvLu/yv4+Fn9/In+j4ufRPaklN6d\nXf7vwP/16b/xf5+N/7LYrwApEIVCFhpVFKiiQpkGXczoZBA2go3Zu/hw/XPcF+dvEYFgI7KP+K1H\nvHPQSJIRiFkS4hERt5hgqKNgHSPXwaHSmpXqWMlsjeqoZE8hjygilZ1wrie6I8ntkXaLchvssYR3\nZPtAJmd68h5iP/GlT4qdPZkYkjyU35FbkG8T8gPIA8gRpMuBE/QBUezA7BFmB8XizR7rFM5JrFVY\nl81ZhfNfaprXXxf7bfC7mZtCgF6qKp57LVGiohCGQgSM6ClIFGKkEFvacM/a/ycb95a139L6niJZ\nRFwy7JdOHOrcJFTKoLzBDQWHvcG9M3Sm4K0v+Pe/XfHt2w33H67YH9cMY4X3p5v/PJnzyT/gH9hP\nxaf0hqfP/Zj/1Ps9t18oR0osre3P7NTifhIJGzyTn+nDSOE7VDgg/Q5SCfEAqYc0QppzTt7P/h3/\nymVufo44vy8fwjD5j/wMs8lPTAcBRQIVIZaEdo9rjkztQN9M6NYj2wgC0huNf1Mwv6kY7lccd559\nF6nHCjcJrJU4J3BB4qLAIXEy4YqIKwK2iDgT8EVEFBGVAslGsCmnb9u4XEeET8gxoA4e895hVjNl\nISgFlB8Uxd9KzDuJ3hpU3yDtmhxROUVkntsfkUz+K5e5+dtAxUDhHc08spp65qHAHxTUoA4S0Qvi\nCG4WjE6g4j+7x/0ABPmJpFqsfvQJiBOEEdwE8whTgsGDURJdaHSj0WuNvnq0d67lvtqw1Vd0rJhC\njZv1P/z6YqkVIk1EFhFZBGThkcZRFx5dBESR14eJxHFK7AL0NqsEYwlqA1UF601uAja7yOg8nbMU\ndka7Eel6CCWPvYHP21F+Lg+of+UyNy/4QQjx9Byv5MNYXIOsPJKAGgNqa9HfBowMFN2B6tsd5Ycj\nZTdQuplSeqo6gVbEWJLGBrvbML25IpVXRK54/13Bh78V7N8Zhp1hHgzenc7rH1Ppfcn4Kz92bv5Y\nsufJ6i6EeJ1S+vty+T8D/++n//r/+CM/5ueFUAJRSURrkG2BWlXotsG0Dh009B76sPjFfFoq+P/X\n8am/FV0i9AGx84i3ErQgpayUyWSPwURBFSOraJnjSJEaajnSyOHBajlQygGZIpUfCO4Ifo/0Ldqt\nKHyL7QvYku0Dud5Oz2PrsR/CeXrWGdHDCPoA+h3oD6APCT2CcUv8wXSI+ohoO0RzfLS2o+8N/WDo\nhoK+L+gHQ5/EF0z2/IWnm8v/80t/4O9ybiJFDn+VBooCyiKPywIlFVWSrIi0aaBNEy1b2iSp3Qcq\n+y2VekNlt1R0FHHOD1wCtIRCglmsUNlLqRGhwY4Nft/SmQZJS5pavntb8+3bmnfbmt2xYZyq5f58\nTvScS7zhvB3r0zatP/Ug/DFi5jyh9KcQS8+jHOHs9fPP/echJOjisa190Ty2uZ9FYp4C/WSpx4ly\n6tHTARG2+WScjpnsYcon419E8fAXLnPzc8U52XN6KWWyZ1LQL0SPzInZyRaE+oitj0x1j65nZOVI\ndSRIgX+vmO8Lhvc1x/ee/S7yoROUY4OfFd5KnFN4r/BR4ZMiqASVg9ZB66F1pNYhWodKntQHUudJ\nfR7Te9KiUpNjRB0C+t5RFJJSCCqfqPYlxbcR81aidwWyrxF2g0g3PKY9u2f+j0j2/IXL3PxtoGKk\n8JbaTqzGjtArOApUGRBHQ+w1dlSMs6b0GhWXvKmfG4J89jzvCrtaDIg9eJNrbtkEk4dRgpYSWRSI\nukSuK8RNhXxRIl5UvJ8b7nXLjoZjaBnnCt9rkvj0vikNqCZh2ohuw2Ie3Sqa5DHRQwr4lJhi4jjD\nfspH7Jkcv1BV5qoEIGNiGCLd4KkHRzHM6GFExh5CQa6xcCJ7nvcG/q3xFy5z84IfxOksX+Tz+6Mv\n4CogqhGZBtRk0dsJI0eMHSmHI9WbPfX7I3U/ULuZSgbqCrxRTLFgHlvsbsNc3jKJW6b5Bbt3ig/f\nCg7vBP1OMveCYE8FV54HMz+XOfRL4S/82Ln5Y1qv/5/k2fNCCPEfwP8G/E9CiP+B/Kv+Ffhff+pX\n/UWhBKJSyI1GXheomwp97dA3AeMVaetIO0vaSZIAXCINP0/1++e3WLSPZA9aEBMEmxD7lMmeJNAx\nUifLKg6EdKRKFZWYKOVMJSYqOVGJ3J1Lkgi+I4Ua6St0qCl8TRVq/Ghy2tbhzE5kz6fSuEYeiZ7E\nA9HDEUwPxTZbeYBigMKlfP42I7IZEJsBcTUgr/vsrwa2u4rtvma3rzA6khJYqxgfC/5c8BPxu56b\nUuYNoiqhqaCpF1+hFdTRsgkz13HkJlquo+UmWop5i9L3SPkOxRYVO6S3CJHJHiOhVFApqPTiFVhp\nsL5hHq+Y99dYrpjtNfPhindbzf1Wc/9Bsz9qhlGfKXs+pbA5kTtPW7P+9HpVz5U35/55gui5/xTO\nFQPnleqe1yf45zdFIbJ8vmigunpsb19tYJaJ4eg5HCz1caSQPTrsEdMWUrWoeoas7MHyeUU2/+v4\nXc/NXx3P7/Gz18MMs4AhgVqq6PgZRkMoB1zZM5UDspxIpSOUESdg3iuGfUG3q9nvI+1e0HaKYvR4\npwmL+aAJUeOTRsiEqWb0esbczJjr7PX1jAiWtHOkrSPuLEhH8gnGsCh7Imrv0YWgkFD6RDUGyq6m\neBcx7yRqV6D6JtfV45bHDfa8xt2pyuAFvxQuc/Mp5KLsqe2IHxX0oMpAYSzxWOL6knEq6G1J4ctF\n2fMLkT0nZU/DYzfYq2WFKHK9IJdg9rnr5ihASkkqClLTkDYt6WZFetmSXrVsp5p7SnahpJtLpqHE\nGfNQVuAHfxOT0E3CXEWKm0h57SluJMW1pB49ZgyIKeLHyDhGujmxm3Pc4mSqhLKEosznkm4fOew9\n1cFSqgkdR8Q8kGX052TP56bs+fVwmZu/Q4hnZ/l6Oc/XFbQOUYHEosaI2k7o+Uix31PMR6pdR7Pr\naLqB1lla6WnqxGgkKZbMi7Kn44bD/JLD4SsO28TxPnJ4F+h3gbmPeHc64/7RlD0/Hj+mG9f/8pGX\n/49f4Lv8/FACWSnk2qBelOhXDv0qYF4ltFWk1UQsJUnkwmhxCD+LOvUUh394TEzkFLEhws4vRE9E\ndQHuAzFJRAqYZKniQExHZNoyU1AIu5jDPIwtgkSKJTIU6FBSxIIqlDSxJNi8YT90RD/ZxNPnvec4\nPTueKXo4AjsoJqg7qI5QdVCNOQhaAUrPyHpCbmbk3YS8mxGLf3vfUr/zGB1ICWar6Ifin/+RL/h9\nz00hHjeItoFNC+tsSjnqcGDtR16Ega/8npf+wKtwQJs9Qe6JaU+Ie4LvidISeEr2NAYaDa2BVsNB\nGnyoseMVR+442Dv23Uv25R37Y2R/TNl3iWFKOH9el+CHUrhOZM9jW9ZHwuen4PTQ+0N5xx9TEv0j\nssfz/VKzp885v/7nCR8hF7KnhfoqF8JuX+Q297NMHD942mKmliNF6NDzASF2ECtIEzBmn2wuhP07\n3qx/13PzV8c5yfns2guYU1bgpoXomUboNNFMuGJGmgnMTDAOZwKTEAy94tgXVF2k7gVVr6n6Aj0G\noi8I3jz6WBAwaJWoq4FmM1LfjjSvRsSrEfNqQPmJ+HYmlRIkRJ8QQyBJcZbGJTACjE+UQ6Q6eMrB\nUuwDZn9S9jRIt4Z0S96Qlxp3wKPS54JfEpe5+RQPyp5ZwQS6DxTGUasRd2iYuoZurNnPkcKJRdnz\nC+BE9pyUPRseakcmIMqle54HO8M0ZLInSUkwhtDU+PWacHNFeHmF//qK/VDxLii2s+LYa8aDwmn1\nj5U9OpM9xXWkehmoXkqqV57qpaDeecwuwC7gY2QaE8cJzBHUVSZ5VAl6A3KzpHMVsL+PtJWnVo4i\nzuh5QvY9eQ04kb6niOwfk+y5zM3fIaQAfSJ7Glg3+Sy/ahHljFAWmXrkGNHzjN4fMPKe0nVU40gz\njKzGiZWbWUvPqk5orZhDAVOD22/o7C0fjq+4v39NfwwM+5lhbxn2M/NgCe40b86DsxfS5xy/0Kr9\neUDIrOwRG418UaBe1+hvEvpfBXpWxFIihCD6RBo8Yu9I8p976Pmhx6boUpaBRxBzQnYBvw2IyhNS\nRDBj0kCdDshUYijxyaCER5+M8HAtSMho0FFTJI2PGh8NPmmil496UvvMf0q4dDpvO/K+cyRvvAXU\nDprpqbUemgTKOGTjUVcO+cIhXzvU1w75taOuHXpR9MxW0/cFO/3zqKcu+B1DnpE9qxquVnC9gZs1\nSk1UbmLjAneu57X7wDfuDd+4t0h1ZEoDUxyZ/MCkByZpmXhM46pUJnrWBtZF9kEaOt/gxiuO9gVv\nu695o/6Fd/I1wzQzjJZhmhnHmWGy+PCPch7PyR5zZgU/nex5nm517s8/Tz4bf+rg6nhKCJ3qhJz+\nDekj/qdBiJzGVbRZ2dPe5a5n66/A6sS+DKykpQ4j5dSjugOCJpM9pxSWdEpl+WMedv+4OFeynSl9\nfIL5nOjR0CtSoQjKYZUjaYdfxpOOuVHyrDFzwswCM2mKucDMNcomYiyJoSSFMtcFCAUxlRQqclX1\nbNYdVy96xOuO4psC8Y1CeYUoJVEKkstEj9g7EDykcWnh0T5RjIFi76nuJaW1lEPEDAI9FKjhXNkz\nkOfwaU5afvraccEFPw0qZWUPFtQYKLSlViMWw3xc0/eO/RhpZrGkcZW/zBc5J3tOjUJuyI1CRK7Z\nHzy4eWmtrHMaV5AKWxS4usGuN9ibG+zdC9zrFxz6gg9zYtdDd4CpSjhDVvJ/AtKAbuID2dN8I2m+\nETT/IqjfeEwRECnip1yzp5sT8phr9JRLrZ5iA+VLqF5CahLbMtIqTx2Xmj3diFDnZM/pzOH5vNK4\nLrjgE3gI3BbQ1rBZzvJXG9AD0vZIq7Kyx04Ye8DY95Show6WJlpWwbGJlo30bCpIUnGIJYwNdt7Q\nHW74oF7yd/k102iZhx479syDwg4Jb09Ez8fKLlwAXzjZgxKIWmZlz21AfZ1QfwbzF4WZJEFA8BGG\nkNO5SvmzKXvOx4IlZStF4pwQvQAtECZXkA3MCDQmKQQKg6JOiohEioQgIolIEZHkawHoJPOfJElK\ngrhcpyg+XvfxH9V+PDUDGHmalaKgCbDysAqw9o/jFaB1RNURtYnIFxH1dUD9OaL+FBeiR2AXRc9u\nV6H1H7EmwQVPIOUSDSgWZc8Kbq/g7hqtj9Tzlo2NvJgHvrbv+fP8Lf9m/4MkOo7Rc/COo/UcZ0eU\nnlkk1Jmyp9awMnBVwHUJQ9So0ODsFYdwx7vwNf8t/pn/Fv6E9x3Od48+dDh/yps/XxDOiyJ/TNmT\nW7P+9GX1RO6c20lqJ3hMF3uePvapRetEBp0/RJ+/9vOkcMFZGtei7GnvYPM1XH+TyZ4P0tMGSz2N\nFF2HNjVCVDmNi7AUZT63C/4YOD+cnXLvl3s6BJgdOAmTyMUflcz7pgwkEQly2RtPHoEKChkW7wtk\niKgQEUGQUr7nUqpJsXq4rmVkrA749QFxe6R4XdD+SSP+TaCsyDe4z6ne6eCgXKrBzzEre3zCDBFj\nBKURVEZQBUvhIsZJlC+QrkG4zaLsOSlcT6lbExey54JfG3JR9igbKCZLVIogJCEqhoNj10dWo6C2\nisKVqPgLnd8keRuteFT23AB3mZyJPvO9boD5CJOBQYBTkqkoGJuGabNmur1hevmS6etXdEfNofcc\nD57jB89YebzxJPEpifuSxtUmiqtIdRdp/iWw+u9g9RdoipBr9kyBsMtkj5wT8Qjrq7xMFKcCzS9h\n/Q3IK1irSJsC9ewouxldjUg1kM8L51HZP24a1wW/Q5yUPWX5SPbcXMHtNQKDOGyRs0aNEX2Y0Psj\n5vCeIh2pdKTRgdZE1jpwZSLXRcIGRekLhG2xbkPnb/ngXvLGf83sBoIvCU4RPATnCH7ij1Wr57+O\nL5vskQLM8uS3LuAaxJ2E1xpGQeog7RJxHYmNJxaeoDwsypmTsWT4io/dQJ946Yn3Kef4/wBOQY3P\n9X/IKYV6Qw64nHutFulqk6Wr6hrUHaivYBwUXVew35e8XzfUraeoI9IAafltlufQdFHc/XEgMjMj\nKo1oDWxKxE2FuGsotKUZBZvJcTP13I1bXuu/8438D4Ibqebc4QmdizWO4vEttchFmWuV07fWBq4M\nvHca5UqCa5jmDfv5lnf2Fd/Or4EdjzPPA1MuNixPnYHgaT2Rxaf06JKAJLP9hJo9p8dcTmuOyMSM\nQDx+bDqtRPlz0gPp86n3VSAkSSgQarleLJ1NusTZtTjLGksImXIHJB2ROiB1RKqIPP3ZWb1qZXJR\n5nID9Q20L2H9NViTaG2g7h3lfsZUI8r0CA45beuj8tsL/jj4gUNaCD/I+/1wA+hzYvR5bThFbvPz\nfWtlJJkC1RiKjaa5UaxfSuLXgmQFdJB2MUc7aoswCiEEMoAMCTmHB+pXi9N+blnCNqikkVQIVuRi\nJJJM8szkCIvmp9f7uuCCnwYZI9JF9Owf6zUu/OP2AJtO0Y4F1VxhvEOGX4iIP03bgu8RPklAHCAc\nwdfgiqzsmQU4IRmMYagqhral32wYbm8YXt7Rl5phOzHczwzNzFzO5N4gC5kilg8W5APE6bKUqAbM\nOlJeB+oXifZVZPV1oOxnzM4hak/QgTlFmBOhzx1qTcpEb2oE6kpgXgrMraEcFcUR9DaiGocsZsQD\n2ePO7EL2XPAZYjkCi/OxAAqJqCTUGtEYaEtYlYirmsI7ilHljrqzozyOufvWuy0lPWUjKGtBdbJC\nUBUGYyuYG/zYMA0rumHDbrjm3XhLSKcW66e9U/MYJLrgh/C5cgs/C2IAN0mmo6J7r6nWoIt8hxZW\nEL5VhA8loW8J/pqgJkIzoaWlSJYyzRTkcb4+eyhZ9onleS+rd1K2mJ6Nz77T73X5TjwKfyaeHku1\nBzXkluzqHlQNSoOM0L0VzAeIQeSCdbeClRNcl4IwJ6KFcGZxzhGcC75sSBEwyqJNjykVpkmYlUVv\nBu7Entv0hta9R6cD0Q2Mg2PXJcIRDj30Yy7QaD2Ej5T6eJINFUALT1XNtGXP1WrPHe85phVzMjxW\nMT+vat4Bw8JjPFX35HkvSfNEtIZkDXGxNBtS+EcEzA/9JhElI0oFlAxnPqvjQlCEKBevHq4/pewJ\nJpsvctOPYDShUATTwNJCGhsfxy6CB6kNspKoVURuHHIzIjcdRaVp9x31YaA0E0Y4VPCIeSG+BEQp\nCFIQNHgtcEbgjMZrRVCSoEROhxHn/9M+9ctccMHPhdMCcSpYbjkp3WJMOBdzgOJYUW4j+p1CrCpq\nWyPvS8ROIY8JOXqEm9BJPGjrYgInnupzOrL6YBL5WOoFxDPy9qN2wQW/Jk7CMrGIK5dDXuohbSEe\nyIHR89r5XwKUzMFgrc68BqMQLwKy9igCZvQU7z1lFaiip/j3DxTf7jHvOtRuRA4W4fIhxPqSzhak\nscD2Bd2+YPuhQKSa/9je8N3xmvvhhsPUMjqDj4lHgudE8lxSuC74zLDUY5SFyL4UyAJUIfLz3sqi\nVx2qAo1DTT1qu2MVDtwM/8l1eMON/sBN03F9M3OjIm3SaF3gdUGvC4Is6X3B+7Hgjf2Kv02veGuv\n2PmSPiRsGvOCRE8+q58K0Z5qXF3wKXzRZE+KAjcJpqOi/5AwlUBIQQwK4zXhXUn84Al9IHpPVIHQ\neCo10qYemTqK2GNSRxuhTRb5/NnkbOxj7hTglmem0zg+8kPnQfrfFU7HY8vTErQRUA7UmMke+T5H\n9+Xyet+BPWbiTZaC6hpWWnB9JbAduH6xLmX/vGnQBV8kpIgUylKZgbpM1LWjbgeq9ZFbDly7tzTD\nFp0OBDcwjo7dIRE7OA4wTAvZ43Lw/3tz8jwjyoM2gdJMrEzPlTnwwrxnMDXeSPLm0S3+NO7I0fbv\n6Xky2RMEoVeETmV/GgdFDD8uF/T5GqBEotCRQgeMiU/GMQqcl1inFi9xKGyUn1xLnC6wdYFtC1xj\nsE0eh6bIRW+HxU7j5MEnpDHoSqBXEX3tMLcT+lZRNJK27KjMSCEmjLeoKSBUhEgudi8EcSF1npM9\nXkmClI9kz8NPdSF8Lvi1cFocTvWscipjjAJnI9OYyR6zVchVRaxXtK7C3EuKXaLoPGacKJx5IHtO\n73qKNYoEQUAnFrJHghUCJxey55SdGcX3CerLFLjg10RcSqWFLLJMC1uZ1EL0HHLb8zQuIswvJYCu\nJJQmp5JXBuoij2uDuJ5R9YhmRE8TZjtSMFL1I+a7PfrbPfq+Q+1H1GARLuRyBcGQbIsdW/puhTm0\nmG1LjC3f7lv+fmx5P7Qc5obRG8ID2fO81sJlEbjg84GQoEqBXgl0KzAt6JXAtAJTQaEdpeoptKNg\noJx2FK6gjR1X87dswls2esum7djomasmYYKCVBNiS5da+rQC3yJcyzv7gu/mF7yzG3auoA8JF0cS\nW/K5PAdjH8meL2VR+uXwRZM9J2XPeMiFQ4WURK+wQ0CnkrgXxAPEXhC9ICqIrWBlOkTcUsQtbdxR\nRGiD4yYKZEr8UETORphiric5CxAhn+VCerp0/7yVMn4dnJQ9p8oCkKeXJ3fElQPIPUi9ZH9YkD30\nEeYoiHFpRWkEq2vBlRfMW5i3MO0SUuUKfGGGMP3Al7jgi0Eme2ZaE1mVlnU9sm4N67XhKh65Ht7S\n6qzsCXZkGGwme/pM9JzIHueysuc8HfB7yh4PqlyUPW3PdbtjbEsSx88oAAAgAElEQVRsa6CJ5M3j\nuQ3AvKRyZpynciYPfivwW4HbCrwS+CBwE4QfUfjr+dxPgBGJSkVqE6nKSF0mqiKPQxBMVjLNi0cy\nRckk5AOZ/DFMumVqVoybFdOVIl1pwlWDuFqR9h5Oplz+R1kPIiC1RteCYh0pri3FnaR4lahW0OiO\nWgxUfqaYLbr3SJlyFpgQuTuKlPk30RJnJM5onFEErYjqRPYI0keVPb+nlfGC3xeeK3vEw2spSZyF\naVB0B4XYVoQKrIbW1zT3iWbraY4TYuwpnH6q7CEHeFhK5tn0jOyR4FVuH/1I9vB96e/l9r/g18TC\nMXzsWBu7bKnjsVHilxJEP5E9bQmrCtZ19qsaUR+RVUSJCT14ijhQ9nuq+wP63RF136Hue9T+pOwJ\nJMCGAmtbGG+guyHtr6G5wfsN77aG+4PmfW/YT/qZsuec6DkvVH/BBb89hARZgVkJihtBeSMoriXl\njaAqoXaW2jsa11N7QT1C4wVt6Gi5Z8U9K72lVUdWzcyKSHSG2dbM84bJ3jDbG+b5mtne8MGueeda\n7u2K7bmyhx35CfTUYvq8e90Fn8IXTvaIJY3rpOhJ2DEx7BNKSNJsiFNOvYjekJQhNQZf7ClCSxsK\nCAITHW3ouQ4Cfb4On86Ny3gMYPxjBmFKuZmI+8h3+70t4+fKntP1gwjegTgpe8hEj+hB7qCrYa4h\nVgJZCcpKsKpztH94A7pOCC1IMRFmgex+b7/MBT8FUsRcm6ewXFWCm0ZwsxLcbAQr31Ef39OoD49p\nXKODYyZ7ZguTzd76jyh7nhE9SNDSU1UTq3XH9U2Ju9GkG4G+tjwWR5yfjMXSyvFE+Jz7ZBP2bcJW\nCacSNibslLCH+KOEaR+jNQqZaFWiLSJtlbLVeewDDJOkl4IBQR8lvc/jT5FLg7mmryNqo+FFQ7jT\nuLsG7q4R945UO1AWossyqT4XiFTGoCtJsYpUN47qJVSvPfVVpBUddRgo5wnTO1QZECrnqyaRU7SC\nkgQtF7JH4Yz6gTSu57/CZf5f8EvjFLo4jfNuFqPBOc04GsTRECuD05oRwypUbO4dYTcjjj1m3JOc\nQUXxcIg6KXvCsudLscQf5UL2KPBKEHVWTXyvDvm59PeCC34lpJgJnHOLfvETpGFR9Yx8Wc9V+kT2\nVHDVwk0L19mEABknVJKY0WOGgTLtqeI75H48symTPT7X97OhxNkVdrzBda+wh1fY8hWzvWG/i+yP\nkf2QOEyR0SVCPK0a8Zld9sELPh+clD1mLShvBfUrSfVKUn8lc8D26FgdLaujY310rKZ83caeuthT\nl4fFdzTFTF1GJqsJXU3fXdH3d+zdK3bhFfvxFXtbsveavVccvKYPERtH0oN29twuyp4fgy+a7ElL\npB0hCF7iRhj3YBqQqsjdOKhh8UnVpKaGsGUVDM6DCI4i9LS+4DoIzPla/GxcCJ7k7/v4/V45v9cl\n/ETunI9PKV1iUfaI9Ej0iH1mgvs7mG8hGh5r9twK0kagK5CKB6LHHRPyi74jLzhBykihPI0JbErP\nbR142XpergON61HlHqn2qFMa12CZF2WP8+DD4v1HlD3nwXtJTuOSgbKcWW163AtN+grkV4Hq1ekE\n68/s8fqR5HlqaUrMlWdWgTkE5ikwHQOz9rgfsfF8TMtSCdjoxNokNmVi0yQ2LaybhPNwlIIjgmMU\nHL3gqOAgBOETi0qpParWsKkJLyLutWb6uoHX11BZhLKkOIO1MMxgNEJYpJaYRdlTXTuaO0/ztaS5\ncdS+o54Hyn7C7C2q8DmNi1PNHkmUkqAUTquF7NF4vdTtWdK44pM0rvNf44ILfimcFocTHvSpxBhx\nViIGRTxWWN0wUlP4hjHUxO2M3PYU3Z5mquBZGtc5d3Naj54oexR4nfdCNI/CotNfOKl9Lrjg18QS\nxUuLxY/5+fG/+WLIHqWg1FnZc9XAizXcreFug5wccjiie4EePcUwUPY7quEdop8Ro0MMFhaf07jA\n+oLetvTjNUP/in7/Db36hn6+Y9hNjMeZoZ8Y54nRzfh4SkP5SPT4sh9e8Jkgkz0sZM9C8vxrtpWK\nXL+zXKmeK9dzdey5Hnuutj1NGijXA4UeKfVA2YwU65lykzhMmt7UeDZ0/gX3w9e88d/wZvyGziqG\n6BiDZ4ieMThsHHk8nzuentW/lEXpl8MX/WgdI9hJErzAjYJxL1FGoLQAU+WWMeWaVKyh3JDKNRRr\ndLrn2pPbL/sB43e0ruDGn5E9HzEtzoieJa3r9NrH8Htayk+x0FNT3JMSXQDCZaJHOBADCL00/NEw\neZiNIFwLVCmoboB/FahXYiF6IMxgjwndcCF7/iA41expzMSmnHhRT3y1mvh6M1NOA74a8KrHpx5v\nR+bREQ6RMOR5HdPiF3vAx9K4xKlA80S71qQ7UF97ij/PrL458n329tHkE5Ln8TqNkVE5xmCZJsd4\ndEzvHaOyuE9sPD9Q7osENAKuFdwUiesKruuUg43rnK62A3ZJsPOws1BLQcGntzltJDQNYXONfRGY\nXmvUvzaIP12T9JxP8XaCfoa9zi00UUgT0XV8UPY0LyOr15H2bqacOsp+oNxNmNaiyoCUWZaQ5KOy\nxyuJf0L2ZGXPYxoXz9K4Lrjg18D5fBcPlmLC2oowKuyhQrJC+SvUdMUUa+Sxxxz21McP+LFeyB7J\nSaRzXibsZEc+QvZokZuEnRM98ezrXHDBr4mlZk+aHhU88WT+TPFzXj/4S4A6V/Y0cLuCr67h9TVi\nNyBTieolZnQU7weK+x3V+7fgAsnHHHHykeQWj8CGTPbsxhu23St26ht24t84TF/hdwf84YAfDvjp\ngPcRH0/BJvj46eCCCz4DSFCVwKwWZc/rTPSs/3vFlfTcassL33N73HLLlttpy+12R5sGtPLoxqO1\nQ7cefevRd5EwKjQ13m3ohjvei6/5m/8z/z7+G4ML+Njh0xGXOnyy+DSS6Hia7nhuF3wKX/SjdYqC\nYAXBnqiJMysrWDXQrmF1DfIaymsormmRDPLILHd48Z4kWiQVRmiKED9+nwVylC8KZAKRBClBTOKh\nieIPZYA9faBcLMXHs2DioetX+o32gPPv/D2EXJ9IzPnyPGAfWvC3CeEj2kTqdcTcBapvPAwQDgn3\nAaYVmDIhP93I6IIvBJKIEZZajKxkz5XquJU9r1SHkRMjM0OcGd2MsxPz6Bj7RBw//b55zuV6XUFk\n80AioJSlKAfqJsLGoW4mqpfVp94NQVrmZ3zwgkQaAsPOMr6fGVvLUM2MZqaQFveJRK6PkTyncSvg\nVsILBbcGXpRwW8GLOreZbRzUNteSLDUYCWr5933vIfHURVa3xPIG3wzYlWXaRIobiX5h4Bhh52Gl\nEI2EUoIRSA21iTSFY1V71o1j3XpWG8dqM2FWR0zTY6oJYyxae4SMeX1IEp8ULmlmCgwGlQxzqhhT\nxZwKbDKElBtSX/CFQeQo4ENb1pMH0pK398Q/TID0cf+L4OMPUykpgo+ESeXWI7QQNmBviVFTD+9Z\nDSvmocbPBckrVOIJ2XMed7QsFcAEzBKcyjV7ogbM8uOcijQHHn+oCy74uSHEkmKrsuJSaawxzKYg\nhkAkkUIizok0JGKXSN3SUfaBlM+3qNKgda7RRjo7n6YcvPu1GUvxsE8HJAFNQOMwyWFIaAKKhBIg\nhUAImSORSiONQVQG0RjEqkBsCsR1SeU01Q6q6KlGS7UfqN8eqL7dElmaECAIYgkDCY1XhomGzm/Y\n2WveD7e8Vy+55zX78Ss4FtBJGGLOQXdDPqxcVAkX/CZ43noEhEiLLWOZr4saqgbqNTSbRHsN7S2s\n7gQb4bneT9zWHXdmx514z0v/jrvpHXWaEF5kMYASiAJoBGKtkKokHGumYs1RXfGBW974l/zn/JrZ\nzeRn9UCu0RMXf+BChP40fNFkz9Mk+Gc0S/K517efwPYwqZxkT8RzYPQDe++5D5LGFxR+Bf4WE933\n0rdOdkyKo1QcjeJoNMekOCRFlxSnc+25z4G89Njm/ZnX0RMCT8wv/rcifT6F0699+moCkClQREuK\nE/ie5Aw4SbABrCI4iQ2SKSj6KMlx0stD4BePkBBjQhwD8r1HrRy6sGgxo+cZ9d8s6o1Dbj3iGHNr\n739wz8eUC6TOMUfTdchTOgHzlJiPkelDYGock5FMQjDb9ORtn9+/z0lYeSJ7xsj8Hxb7d4+794RD\nIE6J5P/xxHz+GQ+rVMoKuY89NUqX64GVAeqlJkiQgF78SW4nno5F4UhxgGlPOtzDuzqnasWI/M6i\n3s+obka5GSVnVDOjNzNt5WlFoJ097dHT3nva2lN3M+JvH5Bv94hth+wm5GQRIRe4tb5ATC2hqxl3\nDcf7hnLdMOua797XvNtV7LqabqyYXUmIF3b3S4HUIMvMk6gHn1u1xigJIXerC17nrnU+Xz9ucP7p\nRhc/34egH4rBP0/AuFSjuuBzQFCKqSjpmpbdOnJ/I1nfGMqbhqK3COMQ0iGiQziHGB0kx2gS3iRE\nkSiKSGsiV0XghQmEWeAtBCvw86OPHytS+QtCEikWalVxpKSiSQaLpKPkIEBLgZAQlGHWBmEERpQU\nUVNaTzF0lMdA8aGnNO+5fv83rvd/4+b4jptxx/XcswqOCrDSYFWJVwWzKrGLn1XFtvoTh+qOXq2Z\nUoG1iTgMYA/QH2EcYJ7A2bzepYsi4YJfG5IcolBnY4kQgqK0FIXDlI6icBSLL9eB5pWkaSWNkDSD\npL6X1FpSionyzQ5zOGD8gNYTau2RXyVSlNiNwpU6BwEnjdtpHIo3wzX/ed/ydmfYdtCNltn3pLQl\nh0qO5O6403L9+Z4Hfg/4wsmeE55XbxVZlxosuBHmR6KH6PDsGcPIIXjug8CEEhFWOH+DTv4p0XPm\nB1nQq3Mz9KpkkCaTPGfG4gWRVeyIqUfFDhk7qtixioEyeJzNdVNPXrhM9ITP7L4/J3rOH2ZVCsho\nUWFEBYP0EmUTWEdwBucLJm/og8Ekk1vbX8ieLx4igJgi8hBRHzyqdGhhMc6i7Yz+zqPeeOSHgOwW\nsucfnIsiOX1yTjl4JgXgl4KpY2I+BuwHjzWSOQlmD7b/9Jt+rF6PIJHmiP3OM3/ncO89/hAJY+ST\nBXS+997fnzfE5bc5lQ9a6kVLD9plsqdZxIXIXHYgnPbsx337wQvjSWmEaQ+HTPSQEmKaMR8s5oOj\n6PLvXiiHqS3FlaMuPbUINDZQHwL1faAWgWJrSX/bEd/uiduOeByJkyP5SEwC60r81DJ2G9TuCvl+\ng2yumHTDu3vN/V6z7xT9pLFOE9Nlrn8pEBp0DcUqd+0wLZgVFCuBDwpnC9xcYK3JY1sQZpNzFK1d\nNrnFUvosyZ6PkTcfI3h+SMF3wQW/BbxUzEVJ10R2G0l7Y6heNsi7DdVxRMkRlUaUHdHjiFIJlRyj\nBt+AaBNFG2nayNUqMjUB2wnmTmAXm3tIQfxmZI9ipExHYjIEJJHAkQYjSoQoibJkVgVaF6BLtJQ0\nUbCynnbsWB16VkawkoL19g2r3Xesu3eshx0rO7AOjhpIqsCaBmdWTGZFX6wYzIrerDjolxz0Hb1c\nM8UCZyMhjrmI5djB2D8jey4rwwW/NhQ5j1g/8UIKinKgaaFdOdqVp1lNtKuBemUp1oJiJSiEoBgk\nxTtBOYssUNgfKA5HtOvRekZtHComUhLMlWEoC4ZU0o8FAyX9VPJuuOG7Dy1vTmTPZLGuW8gexyPZ\n86VVhv9t8Acge84X07OKMw/KHgVWgIg5QTlYPB1jGNlHjwkCYokLK/p4i4o/1KMSpqJiNBVTUTMW\nFVNZMxY1U1k9EjySJ2NJIMYtKmypwxYVFFWIrMNE62GaFlNZHp9SPht/zjh/gM3KHkcRJoyXFC5R\nOI+cZ6yrmHxFFyqqWFFEUOkS6f9DICbElJDHgHwfUMKjvUWPM9pZ1L3PtvWILiBszKqXTyAsyp4p\nPtKFp9fclHDHiDMeh8C5hBsTbvfpDeRE7pyPBYnkEu4+q3rsfcAfAnGMP7ot7ceIHpHyMvRQ/OOU\nC2JBBtAeyhOfJEDKzN1EyCu5OvPLWBgHaYD5kGvyxJRbme17qsFR9Z6qd9TOU0lPVTsq4SmrQEmk\ntIHyGKlkoLQRXTrc2y7bhw57HHGTxYWITxLrCsLYEvtr4v4FsbkjlC+YVMv2fWK7S+yOiX5MzC4R\nP9U3/oLfFaQGU0OxybXZytvF3wic08xjwTRWyLGCsSKMFYz1ssGN2Z82uXCaAJ8ffgzhw0euL7jg\nt0JQiqks6WvJbl1Q3jSoO0t87airjiIeMe5IMWrMEQrlKIBJg28S4iphbhLtdeT6JhKuIsNWMG4F\n41YidRap+Imct/grQhJROEpGBAaBzGnXaaZmA6wJcs0sDb3SGNUizBotA02cubIzN8PEjZm5kTM3\ncaY5vKfa31Mf76nGHbXtqYKlJit7MC2+vGaqbujKW/bVDYfymj6t6VnTpzVTMjgbiXbMhQXnDuYz\nZY8PzwoOXnDBL42T7FsDJVAsvkQKQVEk2pXj+haubjxXNyPXN0ea1YhSIKXI6ZADyFmgtlBGR2E7\nzNxj3IA2i7KnjMQomTEcRcUuNeymmt1cs6Ph/XjN/aHl/aFg2yW6aVH28IF8AD61V78oe34O/AHI\nHvj+kSsuRT0suEXRkzz4GdyIZ2SIIyZ6SBIXC/q4ZhcjMsUfDONZ02Jlm33dMrcrbNNimzbzSyee\nST56hUeHt9S+InqFDIHKT2y8Zu1g6HOOtBBL4WefH/I+RzxPmoOs7DHRUgVB7RO1C9R2xtiR0bZ0\nrmXvI3UAExUyFb/Rt7/gV8Wi7BHHgBQe5R1qsOj9jPEz+hBQ+4g8nCt7Pv3oFMnEjlzI1wDYBFOC\nMCX8MRIIeAd+jFmNc/+PGqU/tls/jSGBT/hDwO8j/hAIh0gcf1wa1wlPFD2PH/fQMl4sSj5mkDGn\nccWlAJgi1+ypFMTT3v39YM0D2ZMmnQ+Wk4VDjyi2tD7QhEDrF5Oepg40ZaAoI0ZEzBwxx0RhI+YY\nkTIwbkem7ci4HRm7CTE5QgjEheyZp5a5u2bevWQuXjOr14yqpbt3HHeWrrP0o2W2jpgsl+J6Xwak\nBl0Liiuo7gTNV9B8JWheCWarGDuD7CroWkLX4LoWuhaGAbR5SvRY+1v/c57g+ax+Huv5r7x2wQW/\nNsKDsqegXCfkdSLdJdzrRF3sqVxFNWqqY6IqHbUaqRKMOuHrTPYUd5HmVeTqqwB3gfJN7tiYG21I\n/ATq+OvXnZJEDBaTBgwCQ0Ani2GgxBJEwgpDr1ZUyqB1i9A3GDFRh8CV7bkbOr4Se17FPa/snrLb\now97THdAjweM7dHBoYFeFaAz2TPWrzg2r9g2X7Gt7phcVqpPLpvzkegGcHOu0eNGcFNe3y7Kngt+\nE5yUPQVQP1hW9jja1cjVDdy98tx9NfLyqyOrtiNNiTixFHJPpAnimCijp5AThRoxasrKntKhVCIE\nxTxrurniw9zwdlrzbl7xdl6xHTYchpb9YDj0OY0rK3sE+RD8vL36qfrtBT8FfwCy53li0XKdfK7e\nSsqKHm8zq6I0HsuYRkgemwR9KtmlFXVUCOLTtzrzIa7xcoMvNvhmg19v8Jsr/Hr9kFpxInpOZI8W\njtpXbFwuDqncROmPrJ3mZgatMtGTluL/1i7pKZ8pzmuRwGPNnjomViGw8jMrZyhtwdE59j6wClBF\nhUkF8vLg94eACCmncYmIch41OvTeYe5ndLSoMaKGiBwjYlxys35EzR4PmegRi8pHgIkQp5iLqLpE\nHBJhH4l1IJafYk4fP1A8G6cIcYzEMRLGtPgfr+x5fK/HTzope8RS8PwhjUvlP9PLfidT3qqrpehr\nOhE8xfe9xEEcYEqIySLoEewQVKxVZKMjaxUfx3VkpSJKJDQJZRPKJfQxv0aIdJ2j6yz66BCdJU4O\n+/+z9ybZkR3Zut5n1Sm9ABAMBjMzMu/NO4I3Aw1BPfU1hdd50hTUVFctqacpaKmhpjpaSz01n95S\n3kwWAcDhfiqr1bDjgEcwgsyCZJKZ/nPttc1BwOGOcDtm57d//zsmctK4UDPOPeNww1C9ZlS/Zsxv\nmdSG5X5iOYwsw4SdR5yfSOnc4++KXzqEBt1BvRO0n0H/K8H2rWDzVrAsCnWsyE8N8anDH3fIQ+l+\nianKCcYl0aN+fgrPT5E2fwm5cyV9rvh7ICqFrSqGTqK2inSr8K8V0xeKXnV0k6I7ZfpHT1cv9PJE\nl8VaxpVhnzCvM92vE/w2Yb6ImCYjtSx7UyuwJ4E0P/17O5dxNQhaEk12NMy0uaIiYoVhkj1HmV7I\nHnOLzie6NLJ3gc/EwK/SO976r3g7fYWaRxhnxDjDPIObEbEoDfWzsueWpXvNsPkNh81b3nVf4OeE\nmxM+RbxLpYxrnmGJpe1stBCXcr9x9ey54u+CS2VPC/RAjxCSqp7pt5qbW3j9JvDF24Vfvz2y64/4\n+4y/T3ib8VN+fmxCpNp4zMajNw7deNQmIDeZHCTuyXA61jwsPV/NW/5w3PPvTzc8zR2zbZidYbYw\nO4fzIymf94SXrdU9133i34Z/ArIHvkX0kNeNZYbsi8OpkIVVkZJAYs4BT2DMEk2NzgpN8/7TffD0\nOe1J8pZsbkntDWlzS769Jd3cfMwPi6ygEo69VywuEf2C9Cca98DWa27m9xU91pXOyOJnquy5xPlP\nJHPCZEcTA5tg2XvJ3klap3nyiUcPfVA0scKkBnVd/P45ENcyrhCRc0Q9ebR2aG3R2aJDRkWQISND\nRoQ/g+yhEDxnRY+kEKOS9STCJ/KUySqRtSAryOr7mdNvf0d5LTlmcoAciqKntKj9627lzmVczx5g\n5/VtvW7IXMq4VFzJK1EUPUlTruIVl4rc57H0YZWOu2JEbxU4jbCKmz5z07HmzL7K3LSZfZsRFqTL\nCAfCZqQr3faSy9RLRNkESyLahF0iMhbPHusrxqXnMNxwkJ/xlH/FIfyOUW6JT0/EpwPx9ERYBNFH\n0ve1V7viFwN59uzZQ/uZKGTPvwj2vxdMsyI/VMTHBt/32HaLMjegbsoP5lxOM7wrJV0/V/nqio9V\ncn+shOtK7lzxc8DZoFm2NXlX4W5rps8ajl/UbETN9pTZHgKbfmFbn/BKEzMEDfFC2SN+naj+JdL9\nLr1M20VgT4n5QSD1T/9pV0QqPC2JDY4NExs0GxQamETPSdzxIBON0hjdg75Dh0yb7tm7wOs08Gv/\njn+Z/sDv9X8BawlLIC6BsESCDYQQiICWVSF7mhuW9nNOm9/wuPtX3m1+S5QjKc0kN5HyVPI0wzQX\n5+rs17z2tL+WMV/xk0Lwouy5JHu2SCGp6iP9xrC/E3z2xvPrtzO/+7cT+/bArBKLTcwPiXlKLO8S\n83+JqJCoXydMSpgmoXVCbxPy80T2Aospyp7c8eWy4/873PKfv7rjNDeEKAhJEmMmREdIgZwnXjbC\nl3GdK38L/knInktcED85fbQMMPFslUGZHGaN70HaQd6XNu7qFswdVHfQ3r5vgH5B+hjpGMMTS/WI\nDztS2CBCh/Y1pq3QZHRcT9iXjNClHd734dz+FglCipdSTQQpC3KW5CTIWZCyJGfx3HZPivStfP5z\n5XUO5vXx980/SUInqFOkidBF2ATYeMEmGLpY06SWKjl0jogr2fPPgVzaDackSEGQpCQISZAKsiLm\nTEyZlFMpa898r+T5vDycv01cBIHV7CZ/u+PkJZvzEe7no79VlKKu/JwlWYlCIp2LvbJYJamizBfE\nd84Xp8EpsAIWMnOCKUBtMxVAEC89nlP5HfI8r+Va23XuBa0z6IwMApVBh4RZPNXkqWdBPUGXMluV\n2DeZO5m4qzKv+sTNJsPASl4V2S4D5AHCBC6AiyUvsZhGV+eOJ1kTfM0ydwxqy4Eb3oVXjGILg4Ah\nwuSLP4vXq9Txil8U5NpPXVyOBbIF1WWqPtNsoNsmNrvMbg/KRILPOK9YrEEvNbLpoN5AFcAsoGdQ\nppA/f/OJxuXkvggBQqTnlrJCZqTICJkQJkDroXYIY0HPCDmDGOnERMNMhaX0EgkI0veSPmdct6lX\n/D2REDipEaoiqQ6nW+aqY6ha5srh64FYP0Fdo2pD1SiaBnKXEX3GbBJqG6h3nrT3xL0l7xRxqwi7\nhN9K3CYxbxSuK8asOQlSWte+VPadxcsAvi2PX8fnUw95ESojWOeszKsE9mKG54yKARMCtRO0VtDP\nsB0FbmrolxOdm+iCpUmBOidqKWjIdNGzSRM7f+JGPPJKfsNr8UdSiNggsEHgssAqgaglSSvoG2LX\n4doNS7Njqm8Y6lccq1elHFWK9RBZgA/Fo2ceea7PJl7E9cpwxU8LIV4OQoV4yV0N2zqxbQP7znHT\nW263M3e7iZtmYGoik0xUMWKWhDpGxH1ChIxpJGpXTiCjlrhOsdzUjH7Dcdhw0Fse2PGN2/PVcMOf\nHm8ZF8O3ux2dvfp+SYXQ33ND8fzli2tfXvNZeHL++o+If0Ky50dETKVl1rzAMIExoGRh7z/ZMccT\n5ICVjknCURkeTc83/R0EzykFTj4wLIF5CngTSOKl9OFjHzOhQNRiDYmsBaIqjxMaFyq8r3DerLnC\ne4OSicpYamOptEUbS2UctbFkn4oC1b2oUdOar7jiL0VSEldXzHXHUEcONWxqQ1u3mLQwW7+GY7Ee\nbz3Z+u8kfCSgBag1NC9j8eHcuxx/2Lb8PZboE69fCKJSBKkJShOkIihdTkRRxKRfWk2nNUe9kj8f\nxygyTmZmmRll4igyh5S5dwmTRdk8hjVHuebVc0ysrs45FW+eGCEkjkFyjJKjVDzViqOUJfcStbeY\nnaXeWfqdxW8dcWehd8RU1ITRlm5fgfKUPsCYi6d90qXMtDXlEpcl+CYxm8goAnX0aGsRYoFsYLaw\n+MISnc0pr54FvywoCZUqzuCVgkqDKVm8CqidQ2uHcY7m4OClSrUAACAASURBVGj/aOmyI02J+UlT\nH2rMoUUdHPIQ4JDhWMhFply8GH8QxfZ6qiL0e1kogakcpvaYtcWsqQKmdmgTkVoiVUZqh9QTUp0Q\n6oE+DdyJP3DLV9yIB1oGFJa4vtDzLdzHzj++51JyxRU/OnLI5CmSngLhnUN2ElEJyJlwWEj3C3J2\nGBFousjmdWIfIL+OpN6TpCUtC+lBk2pJdIn4lSYfFSooTK2obzXtW8WuVjircVbjrcY5jbMKbw3B\nS94/tb8cAzIjVEKYBFVANAHRerQIyCYg64g0CalTIYJE8bELS8YfBcs96C4jjCCTGWaP++NM/vIJ\nff+O9qljN2leucyNf6AXf8CIb0Ac8GJkFI6DKB5HrtLYRuOUxkqNkxonFcd6z1i1LJXGVomQLWk+\ngn+A0wDDae26NfPSi/5M7ly+75/zDewV/4gQZGrlqPRIrRK1ttR6oNYHNl3mTfclr8VXbO0D1dOR\n9NXMohxDFXH/ngjvEgwZ7TKtBNUBSaG7mtjUjFWNNzUnVWGoecob/sCer9jzkPcM3GDZkej4bvrh\nkhj9MP+ccHlD8cENhrxg0qR8GZ89g9NZ3XeRf0RcyZ4fEjGWVlnzUjbDZx8CH97/PFz69qhAbEZs\n4xibzKmpeGx7ts0tmcjkLfNimSbLfLJ4bUmyLBiXm8jL+1MpBbKRyI1EbUqW25IDNXHusEuHn3um\npWOaO6alwyjPphkQ7YBpBnQz0LYDm8aTlowfwA/nvJbMOK7r1RV/MZKS+KZi3nSctoLHjabZtJjt\nlirOuNOMGyb8UMYByD58Z2tzKQrZUwmo5PsZ/UGYi/HH5qbiu8keCVZrrKmwugZTE01F1jVBVCuh\nWshU7yt8MDhfkeKnvUjqnFliZEiJY4xsUlp9rhI6rnVbUa5ZvTzm3Kt9lYenAMGDD4xCM2TNoAyD\n0gy1YcQwoDHbgWZ7ot8MLNsBvx1I2widI4a1E/Zc1EYOcGlV9chC9mQFWkIrCgcgJSw6MerISXjq\n6NDOItMCycBi13KyAD4Wcvx67fhlQQmodTkG7Kr3QmwdajugdaRyifppoWWgH0fSAtOppjq16NMG\nfXKIY0ScII/AkEuH1Usvxr8aq1RdVCDqNZc6R6kFVTfR9DPtBto+0G4CbW+pjEXlVDzD8oROR1R6\nQOeOJoz0fEkvvqJnJXuEJa3qno/dxn2K5LkSP1f81Mhh9ZU7BuQ7jzdFOZddxi8z+ckWsgdP20f6\nzzJ7A3mbSJuwkj2a9CiICdIxwqCQJ40JmqpStHea3mh2e8001CVOgmnQTIMmhYrgNS+z5UyAwFnV\nI+SqXjcRWcdC9nQBhUc1AVlFpImFEFoV7jlCnMGdQN1npBFkkUleMNiA/WYiff2EetfSPml2U+KV\nc+zjEz1/ohJfg3jEi4ERx0FkYqNxVYVta1xT45qX8SnvGVPHnBUuJUJaSPMJYg3jBOMI8wRuuiB7\nzge0HyoZrrjiJ4TIVMqzqRKb2rGpBra1ZlNrdl3kpnvHjfyGrbunejqS1cTsPahEepfI9wkxZIzP\nKJmp20wSktQ1hHaDqzec9IYktyQ2HNjwZe4pq2bPQI+lvyB7PnbSKnmvFS2OsjE4r7Q/F5xf64c3\nF7ooLqQsG2Ol1rxuklmbQz0rJ9bxj6z0u5I9PyRSKuaSy3JhOLma7Vx+pi+zjoTdgN05JrMqe9qe\nbndL1AK3TLhpwp5GXCNwJpHk+y1pP5wqSoFqBGor0XcKdatQdwp9q7CixZ525GGHG/ZMpx3HYc9x\n2FFri9g8Um0e6LYGvYF249ltJtKYWB4y9lEgNZAzyf1cm+Ne8XNHUhLXVCw7wXBrONw1mDuPvPVU\nYSQ9nkgPJ5LRpAzJB/K4fOdzKsCIYlzcSGgv8rOnzYdxJn3UR+I7KkmiFEy1YqpqRN2S6g5frVm0\nONew2AZrz7llsQ0xfvqSa0JisIHORlobaF2kCyWrICCpi9CFbUlqrf+3kNZFI1jQDpxjNoZZ18ym\nZtYVk66ZTcWsa5rNA/3mgW3/yLJR+E0i9Qu0xUPSzbAYmGUxul4S2FiU+Hk1htZrtAaMzExkjkRa\nAlX06OgQdiV7rC3KR+uvyp5fKqSE2kBfw66BfQu7FvYtop6QJmLUUsieg6UdT3TfPBIWQTO1VNMG\nM82oySLHUNQ880r0zJTWeT7/jcoeUZQ8ogbRgGifQ2gwraLdwfY2sL1Z2NxGtreW1gSMdRg7YewJ\nYw3GGYytMMwY8Q7DPRWPGE5oLHHdnF0WZeT3X8mfKxa84oofDTlm0hyJTxGhffmc+kQaIyHPZG+R\n/kLZYxL7PWSTSMaTpCNaSXrMpCEStUMmjU6aOmvaWrMxmu1ec/KG42Pi+CB4qjVSQYoKO59N5c7k\nx2VJ16pUX5U90iRkVcge2XqUCKg2IOuAqBJCJ4QqJV05lpbv7lgUPZCJXuDHzOQ97nEiPz6hD5r2\nKbObHNGP9HGg5x4j7gvZw8gkHAcgNsXQ2m0a3K7D7bvnfJr3jHPLPGvclAjzUpQ9syx+Y7Mt9wB2\n+UDZ8yHRc137rvhpIchU2tFXmds2c9dlbtvEXZfZd56uPdCKA619KmSPn5hPniQi8pTXSBifi1il\ng6gVU98wN1um6o5J3zLLOyZuecob3lFzn2seqTnlGktNft58X/qbXI4tReY7r1//uRE98OJ/dGma\nuXZGERqkKk2ftCph1pwChLnEuVw9x2LN8CPuh69kzw+JGMvNjFxW57qV6Jnmj+/6BGSdCGnAGsfU\nw0kZHpue+iYTak0cT4STJvaCWCei8cTVvPJDLvSclQLdCPRWYu4U+o1Gv9GYNxpoUYct6XCHO7xi\nql9x1K944BVtNVHd9PR7Q74R6BtPezOxuxHEp9JWV+pSbxgd+LGo0q73a1f8pchyVfZsDadXGfMm\nId9k0ptE40fEVw3S6PKZdgE5LQgpvvNmSYpC9tQSOgm9WkOuB/sNZa/ZXETNR4n5ZwLoEwhKoBuN\naCtS0+HbDaLdkNstQW6wc8cy90U1N/drdARfffI5tYtUY6AePDWeKgTq5KldQHpR2JWkS87mZSwd\npGXt8rGAmkEtIBdcW2Nli5UNrm6xbYttG1zb0vdfsusbxk6z9AnXWWJ/ItcQpzK/lwomBSMwJljC\num6JleipSzMlXUOt4BQTmxBpQyjKnmCRYYFgysbXu6J0DPE7VVpX/EyhZFH29DXcdPBqA696eLVB\nCIMKC9qfCtkzWtpwovcPeKto7IZ62WPsjFoc0sZC7rhcuu25/KLs+VvJnmdlTwtyA6IHuUEaMC10\nu8D21cLNa8Ht55Hbzy29makHSTUI6kFQDbLkIJFiAY5kjmRxJIuBTFH2fMpG8krsXPFzwaWyBzJp\nJXriY8BXC9lYpPEYE2i7yMasyp6QSD6QgiUumXSKRO+JwaJbTdVq2sbQt5ptq9m3hlFV3H8lqGqN\nlDUpgV278ZVF9yyfPc+QVMarV4/UK9lTR2QTkZ1HCY9sAmot4xK6fK8Qq7JnAX9kfW8CP2XsEywh\n4E4zeXhCD5l2cOzHAekeadJCxxEjjsARJ0ZGHEpkAhJXGdymxb3a4D7b4l5vcZ9tOR22jI8ty6PG\nhoSfVmXPYe0k6PyaXVnvnsmejxE91zXwip8OQlCUPbXjtnV8vnG82TjebB233YIUI1KOSDsg3Ug6\nTczC4XOicpnaF/9Y7TO1zFQd+ErhuobQbBmrOx71Gx7VGx7FGw5sOGbJEcURtRY/S9IzSfIxqb2m\nED0D7xM9Dt7rrP33xvmO+9wC93xzsXZIkaW7N+YiKlMOZb15n+iJP75s4kr2/JCIqZRxnTuLWFfa\nNmrz/rHe5dgkgnHY3jHGzFFVVG2P3htc38BRw0FAl6DxxchSyo+Xb62hpEDXErNTVK8U5guNeWuo\nfmtItMh3W3J/h28+Z9JvOPIFD+ENfT3Q31bYV5A/8+hXE+1nT+xeScLaaSFnQXIZPwisydcd7RV/\nFUoZl2beKswrifyVIr1VuLeS1g4Yo4stug+YccEcBoz4HrKHC7JHwUbBTsFOr2RPDXSUBgTdxfjs\nv64vxufHn4BXAtFrUlfj+pa53yC7Pam/Iagdbtwyj1vGcccwbp/DuvqTz6nmgNYejUMHh1k8Ojm0\n8wgnCsGTDeT1BOH8WFiQU3FPlhPIsWQxEWRLqHuC6ghNT9h0hF1P2HbsuoabTjO2iaVb8O2J1Bqo\nIA7gmqLsGRWcgGOCOaxVO3I9tKig7aBtIejMYclsbKKNq7LHWcTZjPlZuhoKMX5V9vzycC7j2qxk\nz2cb+GIPb3YIL1DHI/qoMGOiOS60Tye64z3Oauqwo/K3mDCjvEP4ACGXQ/4P428lAsUF2SN6kDuQ\n+1XZE2j3C9tXA7e/gs9+HXn9m4WdGWkeE81jLCESTYg0cwThsGLGigXLjGPBYrGkT97GXRU9V/xs\nEDJpisUP1CXkUIge2UrCbibtFuTeUVW+KHtuEvsd5FMiHQPxmElLJB496WkhDpr6laF9ZdhozVIb\nllvD8plh6iuqRiNVTUoddoHxqFD6fDN0nhHnWVOIH3Eu41JrGVcVUU1R9Cjx6TKuFMsheabcM/kp\nIw+gWkGIAWsn8pLR1tHaAbm0VL7BJE/LQiWKrNCzMAlHIhNQuKoqZM/dBverPe43N/hf7zl92TDq\nijkq3JgIyZLmDIdlNbqLJYc1pw91f1ei54q/D87Knk01cttOfL4Z+c1+5Lc3I6+6GW8tzto1L3hn\nma1HhEgvKKVboqi4W5XpNLhWcupqYrtjrF9xb77gS/mWP/FbDrlnJrOQmHNmeT4iybxsuC8UMc/j\niRdp/Zno+Y7T178bzmVcZyK7LSFqkKbc+2sDVVWIntqUg9n3iB5XBCI/8i7hSvb8kIjxRdHjXPkH\nleKljeyH/5br/VvoM/a2dN45SoNqNex7ln2PPgjUQ0J1Hl0vKG1QQj5binyL6OFF2VNtJdWdpvpC\nU//OUP2+IogW1e/I9R1Of86Uf8MxvOV+eYtvn7i5FbjXnvxmQn/xRPemZvdGEr4Wq6In40eBecyo\nT4sUrrjiO3Eu45p3NeKuIn1R435bM/9bRbucaIHGB9pxoX0cELVBy+++GEpRPGRqUciIrYK9htsP\nr8UbSrfJc3ystOucPwGvIW0Uflsxb1vMZoPc7snbO7y6xZ5umE97xtMNx9Oe4+mG4/GGxTaffE4x\nBiQWGSxitkjpEMkinQW71qLlc1/1aiV9KmAGMZRguBiP5KojpQ1Zbkn1lrzZkG63pLsNN63iVZOY\nmoWlGfDtA7HRoCE+gV/JnknBUcAhwbxe4pSAZlX2tC3s+1Kp9SCKz1D7bNDskNNSWo0lW7qUZF8W\nuXwle35xUBdlXDctvN4Wsuc3N4gpIfM9etQvZVx/OtF/+YD1hibdUqUBnWdUssi0th7O+SJzWdXx\nV+Js0vUh2XOL1FC1C+1uYPvKcPuF4PVvI7/6V8ttNdJ+ZelqRyssXXC0s6VVlojnROREZCByIhCI\nRHLxE7v47R+WcX1sfMUVPyVyyDAnos+kUSBUMbgTCvzrmfyFRVYOsw+0fSqePV8A3yRi8sQhkqwn\nPgrinyTxG0HrKpw2+L3BVQZ/a3BvDfOrphA9scMukfEIT/cKpc+L8Bln356Xeuni2XOp7All7ytC\nIXvqiFgNmsVq0JxDUfZED2Lk2TJDqEzKnhQTKTp0Gmijoo6KPmpUTqU8bC3C9AQSkQUI+YLsebXB\n/2qP+5c73L/ecTKSMQrmUWDvV8+eyZZD2ZxermWX42+VbV3XvSt+egiKZ09fTdx2T3y+OfD25onf\n3x143Q2cniKDT5xc5PQUcU+J5SkSXUK1UDdAmzFt2fftWpg3Ct2/KHse9Bf8Uf6O/8zvOdATsy+B\nI1ByxlPuVutPxPnm8kz0zPz8yJ4Py7jONxh9KR+XFahV+l5VUFfQVJDm8uNnoics5YJ1JXt+Qci5\nED5/SWmhgThq3KyZrcIEjUyaLDROBioZqISlkjOVGKiEQYr3PxTiI1nIdcEzICuBrAWyEUgpkZ1C\ndhrZVci+LrFpkI1D9hWqN6heo1qFaiW6FeQGVHXRGfd7PE3e+7OI0r2o+MkKooSgFFEqopQkIUvr\n6nM73yv+4ZERBKmxyiBMQ65aQtNgm5YOiW0munoi1BOpGsnViNAjUn+6/VulipmwXyNcNKwSguc2\nroUVzevBQl6VPBf5mfT59IYsqopQNfi6xdcdrumwTc/S9Cxqw+w3TG5bwu4Zqz1Dtcfm9tN/FOtB\nWZAWsC8+PN6WN/P8wi4In+fHl3XPFzJ530PYQt4CW5BbUFswW6Zqx1RvmJtuLe2q8a0hGIVvM64G\nW2UWBbPITBmmVMi0VkOqBaIFtRFUe8hGUgkwMaF9RM0emSz4BexZmut4kW/8zS2XrviJIUS5/ss6\nI9uM3CTkPiHvElsT2Bw8nbS0wVJPM9VhQn89oMMWxbyKuC2SS9PSs53x5ZHFWV73/Ju//VrWVsxi\nbdH88rhsaiW5tFl/zpFOCXY6sNOeXeVKNAu7dmFnJrpqptULnZrpmOnyQhdnfIyliUaCkEr1mToL\nWwUvnTfky1hIAcogtFrbA2aEWruKJFfibKh+Jj+vN4FX/BhIkFPxw/rwExZUJGwzwUu80Pi6Juw6\nwmcW4RIcElImREioKZIPnvx1JmwD1a3BLwYfDEEYvDFUbWJoOk7tzL5ZODWWU+vYNw5XexAfmq+W\nkE1GVQapNEooVJKoIFAWWrHQuZHGT9RhQUePTBFBLhyxp/Qn4ENaJSFIgP9W34UPryiXdtEOidUG\nVzfYrsNtt9jbG9zrV5wOkamPLE3AqUjIkeQCzNf17Iq/B759ByhkQsqMXNfE87jSge1mYdtP7LqB\nfXtkXx+4re650QNQSNNlBjlAOoB7Vyrw3Rb8pohus6JUKq1ahowkJIULhtlVDEvNsWo4TQ1iEQiX\nEF6iQkanhHiu1b706Snm7ZmyVpf9gS/fKz40av4Af+uy+TEZ7tlqRYiXkOdx+aUiZYgJkRIiRcS5\nyxahvCcRQEiyUCAjyPL+kkxkkUgiFzN58eOv/Fey5++MDCQv8YvGjRXzoULdV8hdRZoT7Tcz8XEg\nHxvEVKGcJkX5/LOXcV5qYoJgi5EWDxE2gVxJohB4NSMPJ+rDI7uxxgWFUIm68/TViS/Ev3Njv6Y9\nPiLlgA+WYUz4d5npj5n5G3CHTBjLXvX7UEgeSVACpyROS5ZKICvFYupyMqQ1QalC+lzZnn8K5JTJ\nSyKeEuEh4r8MiKqw/cJH+JMiPbTE8YYQAk4plq5FqU/XtjpRYhYwilWNkuE+UDatSyoX3LLzLfJq\nn1aCZ83PkUB/+vLrleFx3PN46nnsGx77isde8bgRHGViGD3zYFnGGTcY4ijIQy6ux5/CGODRwdHB\n5EoZaHCQHWVRPMuNlouxoSyAI0X6Oq3/fzU/ia6QLbOCQZT3JiNkT9odiWEkCIczsVjiacPc1Ng6\n40zC60xQ6+K0XmmSFqRGEnqB3wncrWS5E+S6wmqNQxJiJtpAmh1ZLryY7jle2i1db25/GXi5JisS\nNQs1JxoSNY6agZoHbvI9d/w7r/iGLY/UDIDDk3FkAnk9O88XTjfx4ndoCnHZ8mLc87H653IoIKuA\nriOqDug6oJpYsk7o4NB+QIeM8h4dRnR4oiVx57/mbvqa/eEr2q/vUeZIYGTRC+IbS/7Gkd8F0n0k\nHBL+VCokx7k0lPNr0zu5XjLQklxLcq1eolGkWkHuIFWIKAtTFB2kEfxTMcUKQ2klFN2qeLveMF7x\n0yIIzSRankTkXkl6VVOrHqFvqJTFaIfWFqMsRjmMsmjlCCGT50Q8JsJ9xLUCqwXL6Eh/nDH3I/1U\ncZcksQK1i+zDCbDl5k2shxqihGhB9hahZqQbEccT8qseqXqqZNn84U/0X71j8/BEO4xU1iLijzNf\nUpaEpHGpYoktS+yZw5bF7zkFzxQ9S3S45AnJXVzPrrjip8KHbq0vY2MCVe0xdaBqAlVdHneN4/N2\n4qZdaFuLkp7oAtMpcxxheITpBMsEzpamrimvJZKxNFJdXFkLlSyHPzYn5miJ/oSwD1TLhs1UcTsI\nqqVFvnOoR4s8OdRskc6hskMgV6Pm8+Hl2bi5QjAhxNNH41kNflENmS8f/xV/RvFhY5Z1nJUgakXQ\nmqDV8zhqRc4RaS3KZtTikXZC2Rpli+o+J0MOhuwrsjRkYUhUxOTwdsT5AR9GfBzxyeHX4rYfC1ey\n5++NLIhBERaDHWrUoUW8a8htQxwz8euB9NDDsUFOFcYqcnqfEPlwiYkRxJIRp0S+j+RKkBAEn3Fm\nQcwnmrlmP0tESDTKsu9GajVxx5fcuq/oTo9IP+AGy+k+4R8y05ewfJ2xj2Wfmv6MtutZCKKUBKnw\nWmGNYjESKsNiaqyp8MoQVpVPFley558BOUJaciF77s9EjyR7WUwh32nCQ4cfEzYoFtVR97dIEz75\nnFMuMSQ4ZugTbBJsMggXQIXCtOdQfGNCABtAJ9BxzWklehKoT2/eglIcj1uO7YZj23LsKo6t5tjC\nSUamOTBOlmWe8JMgzJk8RfgOg2aWCE8ejr4QP4tf7yw9ZRG/NBa6NLY7y1xnXjoYWCCU1o5Oli9d\nED0ES45PJDERjMW3EYtgMYapaVmqiK0iTkeCikQJWSQQmawlsZaEXuL2CnsnWV4rclNjhcZFgfeZ\nOEfScK5HlutrOjvwXqo6rvh54ttn4IpEi2VDYotly4kNFVvMudEqG75iwyMNIwL3LNr2K+GT1v/y\nez2sLmvfO94nei43tetYgKocZmOpNo5qe86Rqo7Uk6WeM9XkqOeRaq6op4qWSO8e2Iz39I8PtPoB\nlQ9EN2LVDI+O9OiIjx7/GHGHhB0yaVqb7Kwb4BwL2VMBQglSq0kbQ9qa5yw2BhE2MNewyHLyP1tY\nRvAH8DPEsZA9ya4Kn+sN4xU/LTyaWbQcpeSdbDBqA9rijKXXI70e6NRIpwd6NSBVopYOETN5zqSn\niG8EVpbzlPkA6XHGPA5sRklMGV0F+r1lVO1K9Kyn9ZfjGthMCDWAb+HYgmwRrkUFR/PlNzRfvqN+\nONCcRqrFIdOPRPagCNlgU80SW8bQM/odk79hCpYpLMWzJwlCTqR8ViJcccVPheeWPB+ERFeWpk/0\nW0+3CXRbS79d2HQLr8XIjZhphUUJT3CR0WdkhOEE46ksUW61oDpX24fVknaxL0RPyuBiZPYLwZ4Q\n8z31WLFp4bbztLbBPDj0o8ecPHr2GO/QySNWc+N8cXCZVw8FIWakGJByQMoT4nk8rC/og+rI/Ddw\nPeet9RriwrszVRJXV9i6wp2jSri6ImWBOS3ok8MMEnOSaKEwXiKSIiVNioYUNEkaEpqUNS4lZu+Y\nvGMOjiWWA93At1WXPyS+l+wRQrwF/hfgDWUH9j/lnP9HIcQt8L8C/wL8v8B/k3N++hFf6z8mMiSv\n8LNBDQ3iqSW3HdH0hBOkr4/w0KGOLWaqCE6Tozj/6POHI708XZmBS4JTIlWRJCD4jJ4SqZ4R6UiT\nJCIlmmTZqxHbPWHEQifu6ewDrX9ADie8sJxkwj/Bcp9ZHsAdIIyl9fr3fTiTkESpCErjtMZqzVxp\ncmVYqgqnK5zWBKXXcq4r2fPn4hc9NyOkJRGPEW8iiED2kjgJYk6Ek8afOuygqULHpG6oOoesPr25\nqyMMAboIbYA2QxtLCLluLNNZ7bJ2y5h9IYF0BHUROq4qoE+8fKkYm46x7hjrlrGuGWvF2MAkEov1\nLItlsQJnM3GJZOtKV6pPwa5vYIyF7LErIZUD35a7XuazLN5exKqgSWe505noWWuE7UQWR6IZCa3F\n+YhDsGjN3DQsdcCagNeBoEsJZha5lGRqQWwkodf4ncLdaZbPFbmpsEnjrSTMiXAKJOPI8qxm8hdx\nVvb8Y+IXPTffw/uqGk2iYWHHwh28Fy1HKu7XeKRiQODWT2deyZ5EfI/sOTvenMmempfPxYdliS8h\nAFXNVP1MczvT3gmau0x752nbQHu0tE+W7ihoj4JOCNoATQwY/0Q1HjH6iMlPKHskDANWLqSjJ548\n4RRwx4g9JaoT5PnlkvGhskdoSWw1cV8R72q4reGuJt/VsPSIpxqeFDylckJiz8qe5YXsiWuL5vxz\nay/7j4d/nLn5w6AoeyRPssbIDCrhVWLSiRv9xI1+5EY/EpVCqnI4KBUr2ZOIT4KgIi5m5iWzbDNp\nnjGzop8zOgX6ynK3n3BtXQ5cpF9zKGSPDKAh1TVZ1WRXk59qsq3JhxrhPfr+gLo/oB8O6NOIsvbH\nI3uyJGSNSzVzbJnihiFsOfkbFj9ho2KJAptyKePKP343nX8GXOfmX4JLz5j3w5hE23u2N5n9XWB3\nt7C/m9jvRvbLyN7OdNYiF0+wkWlJpAWmEaapKHv86oN1JnvOyh65NsVKaW2s6hKzXQjzCVlX1LVg\nU3vu6gnvK+qjpzoGqlOgngOVD1Q5IJHk9fXmNc5jiUXJGSknlJxRakLKGSUnyPk9oue5+vlvUPY8\nW/xd2AaJGmKjmLtmjZa5S8wdLK0iJkH94KkfEtVDoiZRhUg9JWQUxKSJURG9IqKJKwE0ZcHgMyef\n0QFIGZ9+/JqWP0fZE4D/mHP+v4UQG+D/EkL8b8B/C/zvOef/QQjxn4D/HvjvfsTX+g+JjCAFWZQ9\np5rctES9wYktvhXw7oB67DHHhnqqiE6Tk7z4+Recz0JzhLxk8imWs1OfkVNCHRKilQgjaapEYxyY\nEcwTtO+Q0SP9EeVOSHcsyh5viS4Rhow7gTuCPWX8WO6Z/yxlj5B4pfBKY02FNoZcVauyxxRlj1Kk\nq7LnL8Uvdm7mlJ+VPYhIDoE4CsIBgpB4p7FOoV2HXtt96w7Ed+ztKg+1g8pBnaGKmTpDHQBsOT2P\nS/HAMUs5otBLIXvkGuqD/AkkIVlMw2LqElWFNYrZzlTBLAAAIABJREFUCKxIeO9xXuBcxvlI8I7k\nFojfYTLn11KzJRYn5CWW45Qcef8U58NcaptfFDMXpErM4FJ5L6uip7TYMiTzRGxHwtbiQ1H2zNow\n1w1L7XCVwBsIKq81xqV8JmtBbBS+V/i9wd5pzGtD7mqs17hJEIZMbCOpcmSxrG/wfHN/6dnzD6vs\n+cXOzYKPl08pIi2OHZ5XOL7A82bNFQOZI3Ba8wBYHHmlHjORvJI98QNlz/l47ZLoqfjkZ16AqgbM\nRtHcCrrPE/0bz+YLSb9JbN4FNveeTR3YCs8mBDazp4mW5CfyOJHTCHYinSbCw0jCEuZImAJujugp\nYuaEnvIzT5zOFjtnsgeQWhBahdhViFcNfNGS37TwpkMMPXxTgZGInMBaOJ2VPb6YNaZlNS+/+lj9\nRPiFz80fFgHNLBRPUoNSOKWYtOKgFa/1O2bVEJRC6kytLVEN5WQ/Qp4zUSV8BDtnlqfM3GVyXor+\nNAd6FnI1kk27mkjGdb2N70UGYjakbIjOkKwhHsrjtEQ4DXAcyMehjBcL8ccpfShlXOa5jGsMPaew\n4+hvcEHjgsCnjEtxLeP6Mw0sr/g+XOfmn42PedxVCKHRlaPtJdubzO3rwKsvLK/ejNzdnmieJprD\nTHuwKF/KuMZjxh7L8rSsW2O3rGVcZ2VPBLdymimtwngP0UameSGYE6KCyng2ZiKZAzlominSTJF2\nSjRzpPGRNkckgox6L0CRkUgR0MKipEUpi1YvOZ89S879PQQvX/tr/4xmJXpaEGuXXtFC6BTjtmfY\nBoZNZtzCsFWM24oYI83G0VaWVjia4GgnSyMdMidiUoQoCShiloRYHg9ZcwgGFQ05akI0LNkgsubP\nNsL9K/C9ZE/O+Uvgy3U8CCH+H+At8F8D/9X6bf8z8H/wTz/5/gpkiEEhFkMeaqLpcGzQcYdrJOpx\nizl01McGN1UEp0jpRdlz8TQvW/OUyUvZVEsPckqIg0C2Ed1n6k2k3ljq7Ui1qambmrqrST7iw0yw\nC35Y8KcFP1imUyLMEJa85hLJ8+eRPVKuyh6DNhWqqoh1vXr2VHitn82ar549fz5+yXPzXMYFiewj\naRTEA8gm47VBigolKqSoS1YVqjvf/H0c2pZuXJpcqrLEWpkVKDdVcS6lE+ocU8nCryeN/v3xd5A9\nGYHXeiUqSy6EJgQRCdETYyaESIiOGDU5akjfcTGPuRA+PoG7GOczjSs/kc/eJx+JmMCtpVvewqLA\nlMjdkbidCLPDrWTPog1T07BUAmsEXkNQiSjjakonSEasZVwat9O4uwr72hD7Cjtr3FHgD5nYFGUP\ncubFVeys6PnH9jj4Jc/N9/G+c+G5jGvHyGeM/IqB3zLylhHFjGXBrbnE2bOH1bMnf6Ds+bCMC16I\nntXk8FuKNoUQoGpN1Qua20z/uWf71rL7rWS3S+x7y76e2IuJfZjYLxP740S9LFjnWJLFWocdHIu2\nWO3wORB8wrmEei9yESCsll+i8NOo1dYraoloNWJv4LOG9KsO+XaD+G0Phx50jUhrjcvRgRjBy3Is\nmt2LEfvVs+cnwT/O3Pxh4IVmkg2IFi8bRtXwpBpa3TDrjqgVUhdFz1YNJGWKsmf17EkxE+aEeyp+\njEsdqepM1XhMs1A1hqo2VI3B1KqwpHItk77IKUGYNGFSxEURpstI+MUSFkuYl5IXh08/EtnDi7Jn\nSS1T7BnClid/QwiirO0xEpMjZk3KV7Lnh8B1bv4lOO8BL1uY14DBVAttL9ndZu4+D3z+G8sXv534\n7LMB+eWMUAvSFx+d4CLTMcN9IXN8eMnPyh7WrWQohSMhFqJHK0gqsaiFoEAoT60msj6gVIuKks4l\nOpfXnOh9oktpJXvkuZXCxViiiBgR0NKjVSihS5Dys+0mCbL4G5fNtZERdSF46EGsnXrD1nDcB55u\nEscbON4ojvuK400kBOgrS8dI70e6aaI/jHRqQhHwWRCifMlSELzkiRqdOnLq8KljSR0mdfzY3cb+\nIs8eIcS/Av8B+D+BNznnr6BMUCHE5z/4q/snQfIKvxjCUCNEiww9wu6oKoUZNtSnnvbU4KeKaDU5\nvq/sOS92zzTJWiKDF4gplTabqngLNNtA85mleTWxlZJto9hpybZTBJsYhsjJRYZjJL6L+PvE6V0m\nuEyKPEde8/e+NyFIUhGkwmmD0hXC1ISqYalqrK5elD3XMq6/Gr+4uRlXg2YfSdPZIC0jVEJUqtw8\nNT00G0SzRTQbRLMB9elLltSFKhSxSE0FZS8pPOUGS4wgpovxGpy9A9zLmNVL4JMQ5bO9fmaTkCQp\nV1f9RM6elCM5OXIWpCzJWbAyJh9HZl3I8sWYF0O6j/bdu/zhM6FyoXGNofzOIEDIi65BgrQ7EW9H\nwmLx/oXsKWVc4CrwOhFVJMlwoeyRq0HzWdljUJ/XxE2FHTTuURLuV2WPORs0f2gnf+Gu9w+OX9zc\nfMaHLSoEmriWcR15xYFf8cjveOT3HAD73J78tBI57rnR6sfKuC4Nms+b1vMp5fkzcilTvxgLUJXA\nbBLNjaf7fGH7G83N7wU3t5m7ynInTtyFJ+6WJ26PT7wyTxgxcfKRo02ccuKYIj4nQkq4lMr1ImVk\nymsu5I5eiR2TX5r4yXWslCjt6XYVaSV74u828G9buO8QqQar4Bihtgghi9ourEq3HC7ytYzrp8Qv\nd27+cCjKng4vt4xyi1ZbtN5i9JagTSF6dCF6XqkDUelV2VM8e+Is1lLNVBzjlEDfBPSNYHMj2RrJ\nppJs94Lu/2fv3X0kS/L9vk/EOXEe+aiq7p7X7t5LDvQXXJ8ODRmyZcgQDQly5EggIJOObDkE6MiR\nQIeeQOdSjgABAmnQkCGRwJUod3l57+5MT3dVPs4zXj8ZcbIyq6Z7tnd2eqa6Oz7ADxGZWVWZnZ2/\nPBHf+D02OrWxuzfu59GBfa1ws8Y5lQ4NXivcK8XcC1OITCEwhcC4zEOM7+XI4FygOaVx9X7D0V2x\ndzeIE6IPSLBInFIh1iz2/ORk3/xDPE7jOok9Ncb058ieLzxf/Hrm11/3fPnlEVfMOGfxR4srHM4F\n/D7iXidBJ0Twcp6LnCN7TqlbWqVaN1qB6EBQE0E5tBqoVUGpClpVUKHYBEm1M6MkC7CRFMeTRB61\nrFrVYlAqwSih1BGjBVNGTJlGZAl2X4Le5dQ168e+jfqh2KPWwBbUFty14e5F5O6F4u55wd2LiuZ5\nQ/Ui4Dxs1cTWdmyGPdv9gU2zZ6v3aLHLWa3CKoVTqa+YVdCyArnBcc0kN/QSMVKg+IFOvT8B7yz2\nLCF1/xz4h4vi+vi9/YH3+l9ezL9eLAPcf3DFKhh12pDFAnxBNAVuKvCzRoJGaUVZKyqBql4U16Vj\n66Uhp/Xj9zdUpQ3IomSWDTQrWM2wXdRcP6d8Td1B3IO7hfnVUjrk0V+734Iu+8g3WdlqqApCUWFj\nTXQNdlyhu5ZD39JNDcNcMTuDCwXxhzbDHwS/Xezn44P0TQHxAl4uNn2LWFFXsAbWCqQEXUO1ArVZ\nvpXfwuN/9kW45/dakj8QSsxb7Mfm4V8KL78wwsOeshfEzuL6wNTB0BuOXcO+h7ovmIY61RxyDbOf\nmGLDJBNeeaBKXQWkwscKFytsqAih5hAb+lgxSsksainIe6o59EvzW7JvvguX3T0e1coRSxnAuEA9\nT7Rjx7rfc3V8RewcYUjh35NdMgfj6eMXETwKS8GEoaPiQMPuB6I5T4vZkxOX9/MCRassKzWzKizr\nYmZdJtuUI5uyZ1N0bPSBjdqxVXdsuaOKAxLSwtWFVCarCmBCulyK0ulUX2lEa6RQiNIUoqiiwkTS\neHE7Fi222GKLLa7cYMsN1qxxZsVcNsyFwSmFV0IUT4xziuKLp39XuBg/fvHzzfyW7Ju/DEE00Rd4\nZ1BzjRpbVL9GdVvWw5bttOXaXnEdrngm1+zVDYfywDEKvUQGiUwizBJxUQgxIj5SRKjE0yBsFFxr\nYV2Q2jbfd7s5z2MAJ0sdkBncAO4AfpcKxvZaUWpQWqVU4gqcLi6usqcCpxcrVFki8RYR9zxPUedR\n6zfavNkw1ysm3TCHmmk2TF3BdKdSm89OpbafSx+Ep3Cpf3/8luybT5D7jVcBeik4oxooG3RbUTYl\nplY0VaStHetqZmNGJuNRxhNNREqFLwsmUzGXBQFFEL2MKo1o4h97WRJBiU9JZgqKItllZaECEFl6\nvF6cbwpgSqGuhMpEahOpKrkf7yN7LgWfB4HilyuKZS4pWu9siohGJF3ji01Mto4UbaBoIkUd8bWj\naGvUuoGrBrm2xOeO8JnH+8j2znJ1NXG1Gtg2R7bmwJXeoZmxslTPlHNFzXSMvKYFagoMFSUN6R3/\nMdf+3/KuvvlOYo9SqiQ53j8Tkb9c7v5WKfWliHyrlPoKePn2v/D33+nFfLKcEiC9Tf3u9AiqR3lN\nIQNVOdGsZ1Yrx0YC1yJslwtiWOw09xM/WONR4vI0I0wddBXoIjmYn+F4C8Mexn5pvee/X1njMq5A\nSMEWRQ1lBUWV5sUyl88L4toQVI0fV8TXG6Tc4Ic1L/+65vXvKvavavp9xTwavHsKm8I/ha95eHH5\nV+/12T5833yDMBL9+cM8l6n0v15UC/0DoY6XjagsaUN1/8EdL2zg3Lnq1Ar8VEfm02kJ7l3B1Nd0\nO83uZYWpG3ThcKNj/mvL/HuHfWWZDw47WuboCHhqV1D3Jc2uoH5ZUNcFtSqJq5KX/6Hh9pua/euG\n4Vhhx5IYnoqA+zXZN9+Ftxd+jAH8PGD7hmlX0bclh1KzE4X0cHwJwy1Mh7RZO6X6FngqRhoOrHmN\noyZQoojIG4TA8yfmzWlcWuBq3nPV79neHWhf7qnrPUbv0LcH5Hd7wu877MuR8W6m7zzlFKkCDGqp\nk14lSXe1BNzVWuMKgy2qJS3zNBoIJYXVyeaC0mqKWVPYgqgqnG9wQ4O7q3HfNnjT4MSwvy3Y/63m\n+J1mvIO5F4I9pbA9jsT7lPma7Ju/ED7C6JHDDK9HaIuUAx0F+2qi/0a4u63ZdNfU9nM0AWtqpsIx\nlTaNhWMuLVI4SuPQbYQmENqI05HZBsZ9hEmQAmQ50xSdBB8plkzGO5BDyroWl/azZQlVq3C1xlWa\nqtK4qsBUmrLWS3O+xzECMSWGeEHNgp4FbU9z0FZwZclcVUu3nRp7MT/efEX3/Dljs2aOJb7zxJcD\nuDv4/QFednA7wGGG0af38KPla7JvPkEKnfKojIHSQFmDacE0xE1DKA3eF9hOMb8UxjIwHB3zd8J8\nUMy+xDYl/rkQvBC24KLBSomLJVZKbCxxYvA/MnKtJq22j6Tz29XFWMhFkMKjeW08be1oa0tbO1a1\nu7+tlp9NByYXQk/k/jvg/H0g9yKKpcLKYlRYqbFUBFXQVBNNPdNUixUzTZhgBmUjhQsUPlBGTyWO\nSiwaTYWjUh6jAqWKlCqm7Yo+nz1H0kGS5yT2nHccl4nsP46veVfffNfInn8K/DsR+ScX9/0L4L8E\n/gfgvwD+8g2/l3kXYkzpFu7UpngAMVArCjNgqpHGWFbGsa0C10bYCrgO7MmOy/72DzT0iDEV3ZpH\nGI/nvXNYtKZ+D8Ph3Hov+IsskgX1aF6WUDVg1smq1TJfwXSlmVYVk2qYphXT7ZbJXTG+3nL7Tcnt\nNyWHVyX9vmQeSoL/0MWen50P1Dcv03keLZTELx2zxkXogZSb6H5Y7Jnlzd2904MkcWd6w/yx0POJ\niD22ZOo13c5QNYIuIjFGxkPEfeNx3wbsa487etwUsMETVKByiqqH6k5R1VBpReUhNprbbwyvvzEc\nXhv6g2GeSmL4ZH36A/XNy1oA1QOTKPi5w3YNY1PTG8NRNDsLaoL+VRJ75sPSWdyxVOTxGCYajqy5\nJSxB3AVLa49Hz37msgvdea4F1rZj03Ws746s6o5Gd5ThiN508F2Pf9VhvxsY7yzl0aNmwQRwZTJM\nWievlrXyXGrGqkKbFjEt1rQ40zKaluBrVF9CX6D68oEFXeBdge8Lwl2BL0t8LPBTQbcvOHyr6V4q\nhp3C9uDtabV6GXr4OL0x8575QH3zPeBi6kq5t0gzpqP4CMwBuxvp74TdbU3dXaHmSJCKwVwTm5HY\nDMuYTJqRsp7QKlXoCsrj8cwWRitEJcQila57bOJBHxcbl0ZdQGGAAtxa49YlblVi1iVmVVKuS1QJ\nOsUfXIzLfIroXij6iO7kPAfGqkBtGsJ6jazXuPWKfrOmX685NC/om2eMzYo5FrguEPwAhzv4rkt2\nO8Jxhsl95GLPz072zXehUFAV0JQpGr6poU7pGnFbE0qD8yX2qJgKYfSe/tbheo3tC2zQSUB9XuBr\nTfhsqVEVaqYHY4OLf1TFl3sM0ADtG0Z9GZWzlAY52aqaWbcjm3Zk05xH344ouShvcH/5FCRy4fty\nEb8TUUAvKwZZL+N57qTiSh3Zqi4ZR0QpTPAU1qOsoH2k9AETPCY6KiwFmko5DI5SBYyKlEoolaSg\nqyUaIgoElaIW3yT2/Fw7jndpvf73gH8A/JVS6t8sr+kfkZzuf1FK/VfAvwf+s/f5Qj9ahCXWfRF7\nmEFGiAUqKoqiP0f2bDybTeB6I1wpmO6S6SWzJXrwww9HlF5G9oxLRE/wqXZrcDB2SeiZLiJ7LsWe\ny6ie0zydvEC9heYa6uvzeCgLfGGIqmYcVxzshsP+mr265vBac3itON5q+r1iHjXBP5UogKfPh++b\nl5ubeL7vJPZ4nb4ZT0KPn5Nk/jYuO5CnirAXzmB/wE5doi519o9/05UiewzdTqO1IgbNPGq6W4W/\njfhbSeMh4qaID6nSirGBso+YXaTUAeMiZojESji81uxfFxxuNcNBY6fiCUX2/Hx82L55iuxZEtlp\n7i0Gwc8rbN8wlhU9JUdXsBsVeoZpD+MepuNZ7DlF9pglsicuqVkFlooeQX3vAOHMm9PJNNDakbYf\nae4GWj1S+wEzDujViNyNhN3IvBspdjOq88QpUvl0joIB0iEoZQttC3Oj0XVFrFtsvUWaLa7eMtZb\nZtsiO4PsDXFfIcYQVYV4Q1CK4CKhj4TblMYS5kg4RsZO07/WDLeK8Q7sEtnz8HvmMsIn8775sH3z\nPXAR2aNKhURJRf17h+0n+k7YdRW6vyLYiokr9makXB0pNx3l9phsk8ZipdGTg8kSJ42bFfMkFFMg\nuLTxCfpiXOZEMGNqlGlGMG5JqDagC4XdFpjrEnNdUV4bymuDua7QFRQEitT35mLUFH2g2EWKnUpj\nGSlEUVihbEvCtmG+2SA319jrG4aba/Y31xy5oo9bxrDGxhLfBeJhSN00d8PZjnMSynyutfVTkH3z\nj+Ak9rQlrCpY17BuYbVCmoZQVnhXMB8Vk4uMx8BQOxwVXhQOg28Mrqnwzyu8VFi/YgwrBp+sDysG\nv2aO1Y96iSVpBVE9GmtSbc1TqbrLsnXiYVMPXK+OXK06rldH7OqIX3XIukCfKkbfiz7c54EVBDSB\nYhF7T6MSoZcVB7liH6/ZyxUHuWYfr5ljzQt3x3N3i3UVOIVxnpUfKWePcoJ2kSKkyB4TU2RPQFNh\nMcqnYtIqUmqhWJYpSs5X9yDgVDqPfpPY83Nc+d+lG9e/5u1lov/jn/blfIrIObIHBzKlYw6vUBGK\n1YApR+q1Zf3MsX0RuH4RuS6gXz8Sesal0O0PcIrssWO6fRJ6pj7lTNspPWanpfWeP+dTPhZ6TnaK\n7Gm3sHoG7WewegHtC/BW01tDtA3TuGJvt3xnr3k1P2M4CMNRGI/CcBDmIYUTZt6Nj8M3L/+/I6nS\nmk+tiN1S0Cq6lNLlqpSj/DZOsZKPv0nTgxfmHt2+1Nc/oTQuWzL1Bl0YYjDYyTAcKvbrknCE0EHo\nVBqnpVifEgrnKHpLqR2FdxSDo9g7pAwMR2E4wHBMNo8QPsF18Ifvm5diT7vYCgkRP6+Y+4ZJKnpX\nchg0uwMUDmwPdjiPwaXrh74Xe4ql4aqlYqBlB28Re9L4uPNcmiuBys5UnaVWM1WYqcYZc5jRtUW6\nGd9bbDejekvsHG4WqqVLnylTzTqzhXID5RbsWhNXBtuuGNot0j7DrW4Y22cM0xr/qia8qvGmxtPg\nfU2YagKB6Cyhn4nREueZeJyJryzzCPNRMx0V8xHmbknjklMS9Jss8z758H3zJ8ZHZPCowiJRUHNA\neofczVgHnRWUrfG2YpyvOIjw2ghte8fqesfq2R2rZy2r54bimaLcCGo3IztN2CmcE7QNqL3G9QG/\n9AvwKok8p7kCGgfNclku/BJfWELRKKorjXteYl8YzIsa86Km/KxGN4oSR4lfxoISh0FR7hXFq0DZ\nLH9HhNIpygFUUzBtG7pnW+TzZ9jPP2P47DP2n7+gG1Ma8thVzF2K7IndAJ2HboJuhn6C/lNI4/r5\nyL75R6D1WezZGtjWcNXCdkWkIYjB+QJ7VMxHYcQzKIdflYS1TuOqwa9bwqol1Cus2zK5Lb3bcvRb\nOrfh4K6YQvOjXuJlU/iShxUxVQBZlA9xF6OFq/ZIt75j2Oywmx1+XRE3BXotaBWXmkByL/SoZSwJ\njwTfQIlHIfRxzS5ecyvPeR1f3NsYVgz9t9i+QgaN6T1tHAnTEcKMtovY4wNl8BixF2KPoyKlcZ1T\nuQRR53XMaVfxQ5E9T0LsyfwMnGr2iL0XetCACEUcMGaJ7Hnu2HwVuP6VcF09FHrcCPP+h/fCsET2\nLHVnQ0jRO+WQwthPj3mXFuneJTFo2YIDD0We0/K7NEnsaTZJ7Nl8Dpuvkg27gt3OEFzDOK3Z7za8\n3F3z+90z7BRwk8dOATunMfiPvtpd5p6LQoqXsWIiS72dRegJE+gyVXj7oW5tl3VOL8cffPBNtTM+\njc+fd6kQcwwtdmwYDi1V02Dqhjhr4qwRm8ZoNTHolJLjJvQwof0y7iZ0PSN6xk4x+fO9RWL4lAvP\nfohcRvZUpKieNbAhLmKPlYbRVfSj4Xgs2FeKIiwBefMy2qVmD2exB4Ryiehp2OGpHzzrQ6Hn8t6H\npgRK6yl6Txk85ego956i9ejCI9YT5nRdibPHzZ5pjtQ6yVaYFNFTLtes9hm4K4VdVwzrlmKzRdbP\ncOvPGDef0Q1X2FWLNQ1WtdjQYqeGuWuJ1hF9h/Q9MvXEY4cUPVIK3kbcpPGTwk1Js05pXCcFVB6N\nmczPzCmyRyRVLO8d3M3QFMzUKKkJ1IxSc6ChoaYxFTer77i5esX1i5bwhaH4QtF+ESivPfr3GgpF\ntII/BNTskb1C3y0bHcXSoeY8V8BmcfyC9M2jVIrsoVVUW419XmK+qDBf1ZRftpRfNcSVSifsOAzF\nMldpU3mb/NyUqZteaQUzRMoKQlvSbRvK5xviF8+wv/6c/ldfsf/VVwy3mukljF6YD7LU7PHwsk89\npx9bFnsyPzeFgkpDa2BTwU0Nzxq4XiFjjR8r3FRgR8U0CuMYGLwjfF4TP1eE2hCbhvh8Tfh8g7/a\nYu0Nk7thsDcc7Q17e8PO3jCG1Y96iaeVxGPTkJrdnpJZ7MP5s3bHePUKu13jtzWyLVBbKLeeQi3F\njGWp0bO0C1OwCL7+Qvz1hKXJex9X7MMVr+ILvo1fLvYVndvg7ipkpzA7zyqMXE8HfCggCMpGtD+J\nPe4+jSsuYo9RHsNZ6CkURHVuSfKHavZksedTIoYlmsGeN7Mq5RsWcaAqxyT2PHNsvwpc/13hZlkj\nnyJ65n06qfyDkT1hiX7z4KaLzlmnffZFkax40XYPHi7EL/salSXUbYrsWT+H7Rdw9Wu4+jO4LQtK\na4j7lMa1f73lu99d87tvnxGDTSehD8YHZdUzHz2PBR+WtMaYvjHDssG7T4L9AylB92r/hT14rjc9\n+Lbx48bZkhDq1B2v2CTTG7ReI1KClMhipzkKlOvBD6ihRy3t7JXqgZEYXLJol9ERg5BbSn9IPE7j\nSlE9sEFiSGKPa5nGml6XHLSm0UvD9KU96+PukMVyulZil6oaxX12/ZtSg8/ztyR4CSgrKC+oMaK1\noApBFRGlUqK8D5EYBXdqox5i6mKp0gFFu0T2tDdw/Rm4Z5r+ynDYthTbLXJ1g9t+xnj1K7ruhrFa\nMakVU1gzTium44qxXhH8BG6HTDsIO4gFEiKEmRgCEtX5fQmy+IM6/0MymV8SH9OCcA5QOESrtJHU\nCmuuCFXFaGoKc42urijMDWW94fPVhvG6JbwwFF8p2l9H5Dcz5bMZrUGcEA4BtCdaiz8o+O6cbe0A\nq863tQZMavhRGYgmNd8sDegWqm2BeVZivjCYX9eYP2sxf7YibqCipGKmQlOjlnQRwXyXhB4jglmE\nHrNXmArmpqTZNhTPt8iXz3C/+YLh7/ya/Z//OdPvPNZP2MOEjTO+m4gvJ/j3czolDTEtpuPFmMn8\nnFymcW0quG7geQvPV8S7huAN/rhE9twJw62nHj3iBak18twQ6wZ5vkb+/IrwxQ12fsE0v6CfX3Cc\nX7CbX3A7v6D3mx/1Et90Tb+/z/GwhObFeFy9Yr5e4a8q4rVGXwvltaO+mii1B1I0z+NCzGYReQzl\nIv46AhotZ7HndXzBt+FL/ib+GX8bfsNhvk5NzArPKkxcjwfGoiWEAmXTOqNwgSL4B2lccanZcyrQ\nfF+zZ0lNVUu22bvU7Pk5Ynqz2POLs/w3yyL4XAZ/OSE6h/fgQskca6a4ZpBrKhRWB3wZkSqg24hZ\nB5ptRIWLwlWPqpU/qHrO9z9ommVfTQp3V5wEobQAUDotAlShYZnHG4XfglvDXMNkwOhUbX0IDZ1r\n6MaGrq84HiuO+4rjnTm/KAIPw/QznyYXX3cS8j7oPSNREWJqrfmw45EhVb00KaJKXYxKLYvbkDYI\n4pfRLd9fp2+VguTX2Z8/Ds5LNvXo//T+1sWhwGPXVQgafy8jPV7cvG1RyNJZVhUqHWToZa5Pv39+\nLafbgiIqTShKnFraKcc0GqMIRohFhEJQKqK+uI3NAAAgAElEQVSVUCI4ToUbW3paBjlZQy8tk7SM\n0jJKk4yWkTbt81yd0kydAVuAK8Bpvtfd4NGrzWR+cSJvFSti7Yh1gEagTmfnqAplWsrYUsWWWlqa\n2NJKy1paGmnRMaK8oN3SBWsS9CAw6LPQczFaUi+GUEGol+7KaincXIIWQ09LR/LNXlZ00tJLiwj4\nJXHjbBpPQSUlRjxGAgZ/NgkMp7+1+Hovzb2/z8HinMfPGjdGQu+JRwu7FJ34/aLq2Z8zvwQX0eiS\nak6KhNQ10xbMc8U4NHTHNfX+Ct338Owa6a6R4Rrma8RdI/EaJzcMckUvW3rZMMiagRUD6Zr3Xl76\nW8ZCWurYUEmLiS0mJCtDSyF+WR/II2NJ33SL2FNSLqMS4Ta2D+wuNtzGlkNseBZWXPsVN27Ntd1w\nPW+5nq5g9vRDS9+19IeGflPRr0qGdikDvS+Jx4owRsIMzuvUvQzPQcFeYK8UB+CgUleyjjW9XDGx\nxtLiqJaGFe93rZzFnifBfRknkuiRYt+jCLPV9H3N3d2G9SpQmQKlGm7aI+67GXec8X4imhlzNbP9\ncmK1CmBTONwpPO40hnAuQ/vYFFDqJPIUKo2n27HSxLogVCWxLgl1Gn1dMl0DN+AaxSBw6KF5lfKv\n//qba373cs2ru5r9sWSYwHnPuYrupcaZL5qZzM9H5BxcOnEWXCOQonmQRQCKZRJ8YOmLO4CMyRhA\nHrex/7Q6m318BM5bsYm0VNAo3VNUI1U10VSWdeXZVpEbA+VS/P+UwnU5/7HoSqGbZMUy6lahq8fL\nPO7nUTTWVXhfY12VzKdR64Kx8fSF5xg8u8mzPXi2OPzc8N1xw3ebFS93Fa/Xmv0m0q0t0zAyfQP2\nm4j7LhB2nthZmKcUIusPS5GrpWd0FqszHwPxomaeHpZrgAY8odsz3/X01cxeBUwEbQ3udY36D4L6\nnUK/KlD7EjUadKgR5gdV89zFXAOjQB/gGGDnYKtgC+ixZDi2jLctg2kYVcsQGoa5gRUYCgwlFSWG\niooqbfpuPeXvA+U3gfKVp9wHzBAoXeDVdM03xzXf3la8rjWHIjDECTd3+G8c4W9HwncTcWeJvUfs\nm1K+s9iT+YUIEayHcYbjCGWV/NMLYTdhD8LY1/TTNaX/Ao3H6zXiNzCskd0G+W4D9RrRG/xhw962\nHF3JYIXZWrztEashzD/9679UeueH89DumPue4Thx3HiqjaA3BXFTUeji0RX/LPykel0niSelcRV4\ntAjfxIbvYsldFI7RMcYRH4+IFeZXI93ryO3rktXrNWb3DLqJO9sy7CvGumIsagapGG3FOFZEFM3f\nlLTfNDSvHM3B0YyOxnuCRDqBI4oORacURw2dKA7U7GVFx5pBVlhWBKnv1zHviyz2PAlOF5CHJ+ES\nwVpF19fc7bZUVRJ6fLjipu0puo6i69C+ozBHzBU0yqE24bwXGxYjHbyHcN6SnexeZloEnkpDrdNY\nLTXAQqtxa4PdVNh1DeuKsKkI65qpVrgKhgoKUZR9KtZZ7BXfvLrim1drvrur2XUFw3gp9lwWyv10\nCuNmMk+DUxcyy7kmoiz3LZE+Up7nsVjCNuZF3Jkezu9boD0WcDMfFqfr0emzMcNy8qSLnrIaqFcz\n7cqyXnm2beRmJRR+Kc58sjF1n7i/yPwIVAXFWlFuNeWVpthqyitFsdZJ2EEvy7zUYFVQ+FjiphVx\nXmOnFcPJ5jUxlvTNzFHPrMPMarSsmVm5mTAY7totd23LbWu4azW7VaRvLeM0YV8J9nXAv/KEnUW6\nCZlGsDYJPb5PYk+0yylr/uxnPnAkps9zmMCXKbJTBBGLPx6Yq55ez5jg0VYRh5Jp3aC+VahvNOpV\nidob1Fij/Yzg7q86j1skKIFe4Bhh7WGlUqWwlYAeC6ZDzWRqZrW0hJ5rpr5GGpZTfIO53+Q5DJ7y\nECi+C5SvIsWrQLFPnSQLG9iNV7w6rnhVG15pxT5GhnnG9j3ulSN8OxNezsSdRXoHLq9RM0+IEMG6\nJPaUI+gCRIONhOOEPcLUN3TzNcoHRAyzeg6+QYYG9i1SN6AaxLeEuyULwxUMPjI5i3M90UUIw3t4\n/TwM8buwUB+xx46hHalWDr0S4qrAr2q0SiUBHh7zsDR+OBdnLi/mCuF1bHkdS+4idOKZ4oSPB6IL\nTLuJ4z5yuzOY3Rr2z3GdsHUb5rpkKgpmKZhswTQWzMcCQVO9jNTfCvVroToI9ShUPhIjDGgGFINS\ny6gZRNFRcBRDJ4YRgxWDVybll79HstjzJDgtrhXn2hZCFMU8K7qupjIF0OLcFcPouWlHVvGOdbhj\nFQxrA+2VZ70eKEeQA8TjcjjPkmkxLe3fgEnOBaROF1+9RPLUGtoiWbOMbqUZrwzFTQ03LeGmhZsW\nf90SRRG9WjI6FLEH2Suih9f7Lbf7Nbf7U2SP4O/FHn/x7PlCmsn8vFxu6E8RPcsVWE5VuU7l9Ja5\n8DBk8HtB+Zct7H+ubOTMT8tJ/v++EKh0T1mPVKuZ9sqxvvJcbSM3V6AsTIdkqkgZTN7e7w//eBRo\nk4Sd8pmmel5gXmjMi4LyRt/X/BHO84hGB8PUXxH6K+xwzdBfceivOPTXOGc4FiNNMdKEgWYaad1A\n048EozjWW451y6ExHGvFoY70tWO0I24f8DuH21v8fiJ2BuYKnEsiTxghTlnsyXw8SEjdOtRE6pS5\nFHyME747MumeMszoORAHsPuSrm1Qtxp1W6JuDepQowa7dAbxD64OjyPLjxGaCE1IZeFbSbeVLrBH\ng9UGGyrsbLBdhd0ZpOJBF57ycuwDehdT2/V9RO8jxRDRNtJNG/aHFTtdsYuKvQ0M/Yzbdfi9J946\nwq1LkT2DR9zj6PN8bcv8goRwjuxRZRILHDA6wjBhRxjHGjVdE73ByYZBzSnVeDDIbimK5Q0yVoR1\nyehLxlCmwuTB4n0k+jkd9P3kr5/vq76LharH1h1jM1HUntgIrimY6ip144IHQs9pfFPb9WLZU+9j\nw0FKDgLH6JhkxMuR6D1zN9F1kepYQrfGdcLQGVZ+whZgJelqbgB7VNhbEKUwtwXmVmPuCqqDxowF\nxmuiaCalmdBMKObTXClGFIMIY4QRmJHUj+anf4cfkMWeJ8FJ7LmcB0Q0s9V0fY1SLc4rxlFxOGiu\nVjMvqhXPKwMVtJXDrAa2dUFjITZJ6IncX5sRnSSWkqXWrSR/O2ULKsAoqBeBZ10m25Qwt5riqoQX\nDeGzFfazNfLZhvD5mnnQzAeFPZLGHuwhtZo99m2yoabri0dpXI8v+VnsyWR+Pk5iz6nKyumoZea+\nDLvoh3NFChGUJYJHHl+pLwqEPWyHlvlgOF2DHOfUvvT50LqnrEaq1URzZVk/92yfBa6fCWpaIskX\noSe4FN3zp6ArRbFWmBtN9YWm+qqk/lWB+ay4L/J8Kvh8GvE1+nBFPDzHHl8wHJ5zOLzgtn7ONNSY\n2FGFxWyXbscOUZHBtIymZTAVo9EMJjIay+wh9J7QF4S+JAwlsS9hLsEtzRWihTgvYo//U/8TMplf\nHolLR8zlOyD6lNIRKoIemeOItjNxCNiDYliVNFUNxwJ1rFBdDUePGn3qOEt4cIW4NCVgBKqQOnFV\nAiamyHKFxusSH0rcVOL7Ar8rcesSSi5Kvj/a7E0R1Qu6j+jTOKQ6QqNq6PWaPlb0s6bvA8Nuwq0L\nQh+JnScePXL0yIM0rk+zoUPmiRGXyB41p+JWTmAK0FuCDTgLo62J1uD8hplIpSPiNQw6VUT3Chk0\n7DWxVtgozEGW0eLijITU8OCnf/18X/FdloyhmLHVyGAmonG4SphMQVfV6AsfPAk9p7l6cOwTH8T8\npppcJYMIg3hGGfGiiNEyj5bjGGEyuHHNMBn244Y6OrwEnAv4IeKPAX8X8KuAoCi7mvJYUXQV5bGm\nGCtKXyFSYtE4pbGqWMZ0e5aAjZZJO2y0WCxeHILlfX6XZLHnSfA4DzhF+cRYYOeajhrva4axZn9o\naOqaq5VlvjZwDc2142Y9YK4ObG80m5BKbESWlOspRZlHDZMCvaiI8aJCOJzTuGq9FHgv4crAtoSx\n1XBlCM9r7Jcril9t4ast/ldXjK8L+hJ6p+j3ir6H7pVi+FYxzYbJGmZrmOaS2Z7EnunRv/dS8Mpk\nMu+fk9hzKfSconlUOilSy3g/Z4laOFV8f9zK/rFP59PPD4/LyJ6HQqAqhkXsWSJ7ngeuPo/cfA4M\nSehhieixY+qsc+r0+GNQp8ieG031RUHzm4Lm75TUvzqf5T82cQ3q7op495z57guG9gsO1ZfcFl/S\nlQ3ltKeYDhRuv8z3lFONBIctKlxhsIXBFgpXRGxhCTEQ54JodbJZE22x1DOInAuVL40WcmRP5mNA\nlpo9p4gePaf2r67AB8c8O2JvsZVnNGAqQ1kCc0RNAeYIU0TNEXzyicurwuPYzzIuScOSoswLBWUA\ngiIGTZg0sdOEuiBWmlDpVLT9YlN3SvBUCNoLapbUuW+W1F1nFpQTXDTYWDHbCtsr5n3AVjO2FuIs\nyByQKSJzTKN7U6fY7OOZX4hTzZ5o06V68tBbKCdCNNhgiLHBB8McDaUYCpVq+jBI6sI3CLKLUAlS\nBny0eLGE6PBi8dES5RSp+hNz+gK43P4tY9COuXDEwuFKz1SAKTSmqIDk7485iT3fL9ycftZSYaVM\n8ejisDLiCcQ4MbsUFeVcyeAMO7ehdqmLX7CWODpCZYnGLqMDFHpenW1aRr8CqfBoglrWJUrjVUHQ\nBUEsnh4XO7zq8fR41SPyJ+S7vwNZ7HkSnC53p1QKAIVIZLYN3teM03Zpi7yl0Fuu1h71JbTacrMe\niGaPuarZflFwA4TTIcwEsVu6HCztcVHp8VN8zcXZPeVSp+cU2bMt4VkF1UoTrkrs85rxixb96zX8\n+TXhz2+Y24LOwW6v2Ili1yt23yn2/0ERgiJGTYhpjBFiPEUA5BOSTOaX4+SDpyD6xz2RlocfzE88\nFnLedOL5vV/KfBBc1ux5WL/nHNkz02wt62ee7eeRm19JSt+VlK1hB5iOqaHbn1J3UFdQrBTmpqD6\nvKD+dUn7taH+88tkjfI+S99TEOwK/WpL2DzHtp8z1L/mWP6aO37DnhWKO5S9RYUNalqhjjXqUIKd\niGo5E1QKUZqoAlEJIgqiSm3UIyBpft6tXqxWJQucmY8EiUsal0/RPWoxFN4KUUesErSOKA1alyil\nF/0/RQSoKOfb/PDVQUlqWawAFc6dYVEgs1oCTNViab48zOOUjuSL6e+lTaVczFMaRrSa2C8d+3Qg\n6pmoHRKXaIbIObIhvukVZzK/EGGJunMCc2BRMkGXBLUlYnCqRqkNWm1RaovSq3SB9h7Gy/LoKbJE\n6BGJCDMilsiA0C9p+++Bt2wBo0rfK57ItHTMVKpAK80P+eDjSJ/Lnz2nfEPEEQlEmREK5ljjpGaI\nNVpqitigpUaJRvQIekTUhOgR0ROoMT1D3KLiFhWvIF6hQpoLDaILRIo0qmXUBSIjwg7hjsgOCQrB\nA++hLtIFWex5Ujy+DCpilNQV05+a1qbWyNFr9uuKfd9wGBsOU8vBthz8Cq0dUSuiUYRGEdeaeK0I\ns2JuhZGIJeIlIku4W0mkUIDWxELjdMGsNYPWlGj6uOEYrzmGK45hS+c3dH5F5xqOtuA4Kw4THAbF\nvlfsO8XuAG9e6Z82mJlM5pclb0wzb+JUQw4epBbj8BLT+jJqxmjoQ80hrBDvOCyddI4CnaSCq51c\nVqL7vjz4prbrp3kUQ5CSGA0hlARvCK7EOfNQ4Lmo1jG6ls639L5JHXt8xRAqxlgxhgp8Bb4GW8Nc\nw9TA2KSTj7e+F5nMp8jipW/Q8SU8XsWdvFfzJ/E2Neh9LBkf/M3TujSvTTMfACJJjCUsQurps+sR\nXSG6AR2Xkos6derS5l74JISU7nEfXXOZV3VqtDFf2M/4T+NNuR5/4vfK957h/H6lqGBz8TwFKTSi\nuPi5yyJDJem77vRzJ6vSqCrOTU4e2f3vn7rdnsItcoHmT5zLOhojpw98lMhsJ46j5/agWNUVplyj\nuOHKlMRDQbQlUpbEbZHknLYg2IjF4hazS2HVCksRNcEbBl8RfMXoDTtfUbuKYWzZH9YcblfsqxV7\nteIQKvaT5vAtHP8Whu8U007hhnQYlMlkMpkPlccJF+CDMFnNYTC8PrSsmjWldkQR4ujoXsFxB8cj\nHCc4euh4u9hzQr1hVEDjSpq+pNmVNC9LmrqgKUqq6e1pXLNr+G5XcnsH+51n2M3Yu55wt4P9BN0B\n+iNMA9g5nXL+qArSmUwmk8n8ErxJEjlFYTtSp9SBFAKnUo0f5hQNFJd6i3LZBsuS9pgj54Ybn0rd\nxYvmJPdVbFnmEw+7R1+maAfSezWR5JQl8kjmRdzR5zEu4g8jxENKuZHTe+3hPR+6ZrHnSXNy5JPY\no+/vjzEyuZFuSGKPKStgjfPXrOsa8VWyskK2Bmkr5HmVivgwoNQAqS44Sg1UjKi5IAwt47BiHFrU\n0MLQovyKYazoDhVdVdEpQx8qusnQdYrutaL/RjG8hGkHrk/fJZlMJpP50Pi+yHO6P0QYF7Hn1aGh\nLDfEGJmdIs6B4Q6GHfQdDCP0LpUHeFPS7uX8bWJPZQuqvqC+K6iqgloXVL7AHL9foPlUltX6iruj\n4e6oOBw8/XFiPnTEYwVdDUOXbBqT2BPccqqZyWQymcyHxOX1+iREOJClK08EiCkVU8YUkhdPTTYu\nxvt95qW48anUnjulqhecq9gKac992XX2cQmSy2CMy/7WFfdNTUSnCCq9jMyknPd++f/4ebp3ZrHn\nSXNZOPUURpc+lPeRPUPAlAqocH7NMN/Qth4xDZgmje15XmpHpY7UHKjUcZkXVApiX2LvttjdYnGL\nnbZYt2UaCgajGJVi9IpxUoxHxXinGA+K8RbGW8W8A9crwntK8cxkMpnM++YyBue0uNGEKIy24NBX\nlLpBSEJPNxnEBqZjqtUzdTBNqWbk9OivfT9Z+cyDuYCxGtMXmJ3G6AITNGbUlHfFfb+Nx+3XXTAc\nh5LjAMc+MAwTduiIvYbRJJFnHtPolsie99FtJJPJZDKZ98blNRrOV9BlzyhLHdjol5o+VRIW3mT3\nUSozDyN7PoVr42Vkz+VtzTny6TLS6bHYc1rlnIIzSu472d4LPQrU8vfiuAg901IP6f2njmax58nz\n+AOYCmXGKMx25Dh6QGN9RT+t2XVCtRG4WqOuVtCuYLuCqxXqak1dWTbqjo26ZatqlCqolVApj99V\njKsrxvIZx3hDNz3jqG7o/LN0CKoCc/DYKTB3HnvnmdcBO4A9gu3AHlMaV47syWQymQ+Zx7KMpDSu\nWXPQBpEW6zXdWHJ3rBEfsSO4MRVodmPqDGvl+3/pbTwWfgqnKXtNoRWl1xSjpjwo9EovfTYue++k\n2z4WjLNhnGGcPeM0Y+eOMMelVfoMzoK1aR5yGlcmk8lkPiQeX7NOdfZkSc86NfzxSVBQIyiToljl\nspPqpZ2iV072KYg9l1FRXMzT3vr8PpzGx2LPqWX6aX8+c04FWzrZxlNxe71EUtkl1csultO4PnEu\nP4CXYWYFUWByMwwe5xX9VLGroDEl5VWBkg00W1SxQW22qM83qC83rNczz9UKpyuUKqkUKOWp1YS8\nrgnlFUN8xm76jNeHz3mtP+O1+4zZRoIf8dOEP06EesJXI76aCFbwkyLM4GcI86lmz/stOJXJZDKZ\n98FlHM45uSpF9miiGKxTdFNJbWpqk6JjgoNgU8OP4JKOEn6k2AOgrUL3Cu0VelTog0LXCl3BqbHq\nqRHr6XYUjfMlziuc91g/4Vwgegtep8KUwS+2zHMaVyaTyWQ+KH4gTlYuhZ6lOLDS3HeLvB/vW0py\nFjMuhaCPXeyBc2TUSbTRnNOyHgtil2ujN+/P70Wey8R0URc/Gy7S505RVVns+cQ5OZ3nsmJ3jDDb\niPNCPym0rtDKoHSLHit0e416fo0ur1Dba/QX16i/e8X19YTTNUqV1Bq2yoEeqVSH3zaEcMU4PWe3\n/4KX9Zf8Tn/F7/1XWOuQ6YjoxZRGdECUTa36TmLxhX0/SD+TyWQyHwbfT7wKASYpmJ2mUwatU1tU\nreV+/SjxPCJvXsK86b43XiksKK9gXCKgNSitvvfD8igmKIpCBKIERKbURlaGtOASORuX80wmk8lk\nPiTe1FH1QuhBLQ9fig9vO355U8L1p3BtPIk4ge9XEPyh9yNc/M7jXqKXv3K5PrmssQRv/v/76cli\nzwfBmz8MUZZ6y/ct4BaqCj0Y1FCihxI9FKhBoweNLjVrXbJWJStdsVI1rW5pVcswNBymhsNUc7CG\ngzccfckxaly4bA/3lg92JpPJZD5aBAjx8ru/+IGf/qmekD8hpf3NTVwzmUwmk/k4+XkEhI+LHyNu\nfTiCWBZ7PkaiIFNAHR1yOxPbAV1qooDfzExqoNOORkWM0ihlCKplel3z3d+U3L2E411g7GacHRA5\nknI5B1IhqlOruLyAzmQymUwmk8lkMplM5qmRxZ6PkSAwB6SzcDtBqYgCygb8yjGrkV5ZjIoopQmq\nYlYr5p3h7lvD3beK7i4ydhY390g0JHFnXOxTa8uXyWQymUwmk8lkMpnMh0MWez5GoqSet0eHlFOq\nR2Aj9A5f+xTZoyyoSEAzK0OvWlxXcrwtOd4pjneBqbNLZI8mCTunlnw5sieTyWQymUwmk8lkMpmn\nShZ7PkaCIHOAo01Czxygd8huIpjIxIhSlkDEKkVPxUG1+Klg7AzjUTF2KY3LzwqRy1Z07sKy2JPJ\nZDKZTCaTyWQymcxTI4s9HyNRYArIkrolvUN2Baoq8EVkUp6AYyYyKE2JwaCIXuHmEjcr3Bxw1uJs\nBHE8rJQZ+ROrZmYymUwmk8lkMplMJpN5T2Sx52MkCMwebECUAg0olbrOKggIFkEhKDSKCoVZOtCq\npW1uWFqqW0TepQVdJpPJZDKZTCaTyWQymadAFns+ViK8qf3e9++5bKP7mNy+L5PJZDKZTCaTyWQy\nmQ8N/Uu/gEwmk8lkMplMJpPJZDKZzE/HHxR7lFJ/ppT6P5RS/69S6q+UUv/tcv9/r5T6G6XU/73Y\nf/L+X24mkzmRfTOTeZpk38xknibZNzOZp0n2zUzm/fAuaVwe+O9E5N8qpTbA/6WU+t+Xx/6xiPzj\n9/fyMpnMD5B9M5N5mmTfzGSeJtk3M5mnSfbNTOY98AfFHhH5BvhmmXdKqf8P+M3y8NuKvWQymfdM\n9s1M5mmSfTOTeZpk38xknibZNzOZ98MfVbNHKfU18BfA/7nc9d8opf6tUup/Vkpd/8SvLZPJvCPZ\nNzOZp0n2zUzmaZJ9M5N5mmTfzGR+Ot5Z7FlC6v458A9FpAP+R+A/EpG/ICmxObwuk/kFyL6ZyTxN\nsm9mMk+T7JuZzNMk+2Ym89PyTq3XlVIlyfH+mYj8JYCIfHfxI/8T8L++/S/8y4v514tlMh8jv13s\n5yH7ZibzrvyW7JuZzFPkt2TfzGSeIr8l+2Ym8xT5Le/qm+8k9gD/FPh3IvJPTncopb5a8isB/lPg\n/3n7r//9d3yaTOZD52seXlz+1ft+wuybmcw78TXZNzOZp8jXZN/MZJ4iX5N9M5N5inzNu/rmHxR7\nlFJ/D/gHwF8ppf4NIMA/Av5zpdRfAJEkLf3XP/blZjKZP57sm5nM0yT7ZibzNMm+mck8TbJvZjLv\nh3fpxvWvgeIND/1vP/3LyWQy70r2zUzmaZJ9M5N5mmTfzGSeJtk3M5n3wx/VjSuTyWQymUwmk8lk\nMplMJvO0yWJPJpPJZDKZTCaTyWQymcxHRBZ7MplMJpPJZDKZTCaTyWQ+IrLYk8lkMplMJpPJZDKZ\nTCbzEZHFnsz/3969g8hZhWEcf14RCw3oWiSBLN6wlqBoEztBFpuIjenUwspbabrUWgg2Nl4gEcRC\n0KQzipWFupisiZdcQF41atYgFqYRkddiTtazs99M5Zz38M3/B4ObEfM9+7n/FIeZCQAAAAAAGBEO\newAAAAAAAEaEwx4AAAAAAIAR4bAHAAAAAABgRDjsAQAAAAAAGJHGhz3e9nJzefaAwrMHFJ49oOLZ\nAwrPHtCQZw+oePaAwrMHFJ49oOLZAwrPHtCQZw+oePaAwrMHFJ49oOLZAwrPHtCQZw8oPHtAxbMH\nFJ49oOLZAwrPHtCQZw8oPHtAxbMHFJ49oPDsARVf6O/OYU86zx5QePaAimcPKDx7QEOePaDi2QMK\nzx5QePaAimcPKDx7QEOePaDi2QMKzx5QePaAimcPKDx7QEOePaDw7AEVzx5QePaAimcPKDx7QEOe\nPaDw7AEVzx5QePaAwrMHVHyhvztv4wIAAAAAABgRDnsAAAAAAABGxCJisRcwW+wFgM5FhGVvGEKb\nWHa0CfSJNoE+0SbQp1ltLvywBwAAAAAAAO3wNi4AAAAAAIAR4bAHAAAAAABgRJod9pjZmpmdM7ML\nZvZiq+sO7HAz+8rMTpvZF42v/aaZbZrZmeq5FTM7aWbnzexDM7s5accRM7tkZqfKY63BjlUz+8TM\nvjGzs2b2fHk+455Mb3muPN/8vrRGm7Q5sKOLNpe5S4k2y7Vpc/sO2uwAbdLmwA7aTNZLl2VLSpu9\ndDlnC202brPJZ/aY2XWSLkh6SNIvktYlHYqIcwu/+M4t30u6LyL+SLj2g5KuSjoWEfeU516S9HtE\nvFz+YFqJiMMJO45I+jMiXlnktad27JW0NyI2zGyXpC8lHZT0lNrfk1lbHlfj+9ISbW5dmza37+ii\nzWXtUqLN6tq0uX0HbSajza1r0+b2HbSZqKcuy56UNnvpcs4W2mzcZqtX9jwg6WJE/BARf0t6V5Nv\nLoMp6e1rEfGppOnoD0o6Wr4+KunRpB3S5N40ExGXI2KjfH1V0neSVpVzT4a27Cv/usu/eeB/Qpui\nzYEdXbS5xF1KtCmJNgd20GY+2hRtDiADQ4wAAAKfSURBVOygzVw9dSkltdlLl3O2SLTZtM1WP4T7\nJP1U/fqS/vvmWgtJH5nZupk9nbShtjsiNqXJD4Gk3YlbnjWzDTN7o9VL/K4xszsk7Zf0maQ9mfek\n2vJ5eSrtvjRAm7PRpvppc8m6lGhzHtoUbSaizdloU7SZpKcupb7a7KlLiTabtrmMH9B8ICLulfSI\npGfKS8x6svj31Q17TdJdEbFf0mVJLV9et0vSe5JeKCed0/eg2T0Z2JJ2X5YQbQ5b+jbpMh1tDqNN\n2sxGm8Nokzaz9dxmVpcSbTZvs9Vhz8+Sbqt+vVqeay4ifi3/vCLpfU1e9pdp08z2SFvv5fstY0RE\nXInY+gCn1yXd3+K6Zna9Jj/wb0fE8fJ0yj0Z2pJ1Xxqizdlos4M2l7RLiTbnoU3azESbs9EmbWbp\npkupuza76FKizYw2Wx32rEu628xuN7MbJB2SdKLRtbeY2Y3lNE1mdpOkhyV93XqGtr8v74SkJ8vX\nT0g6Pv0ftNhRfsiveUzt7stbkr6NiFer57LuyY4tifelFdqsZog2a720uYxdSrS5bYZos0abuWiz\nmiHarNFmni66lLpos5cud2yhzYQ2I6LJQ9KapPOSLko63Oq6UxvulLQh6bSks613SHpHk0+I/0vS\nj5p8CviKpI/LvTkp6ZakHccknSn35wNN3se46B0HJP1T/T85VX5Obk24J7O2NL8vrR+0SZsDO7po\nc5m7LN8/bdLm9A7a7OBBm7Q5sIM2kx89dFl2pLXZS5dzttBm4zab/NXrAAAAAAAAaGMZP6AZAAAA\nAABgtDjsAQAAAAAAGBEOewAAAAAAAEaEwx4AAAAAAIAR4bAHAAAAAABgRDjsAQAAAAAAGBEOewAA\nAAAAAEaEwx4AAAAAAIAR+RdmSskKbNDOFQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12853d890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "batch_x, batch_y = mnist.test.next_batch(10000)\n",
    "prediction = sess.run(tf.argmax(u, 1), feed_dict={x:batch_x})\n",
    "ground_truth = sess.run(tf.argmax(y, 1), feed_dict={y:batch_y})\n",
    "\n",
    "print prediction\n",
    "print ground_truth\n",
    "\n",
    "sum = 0\n",
    "diff_index_list = []\n",
    "for i in range(10000):\n",
    "    if (prediction[i] == ground_truth[i]):\n",
    "        sum = sum + 1\n",
    "    else:\n",
    "        diff_index_list.append(i)\n",
    "        #print \"%d - %d: %s\" % (diff_a[i], diff_b[i], diff_a[i] == diff_b[i])\n",
    "\n",
    "print sum / 10000.0\n",
    "print len(diff_index_list)\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "fig = plt.figure(figsize=(20, 5))\n",
    "for i in range(5):\n",
    "    j = diff_index_list[i]\n",
    "    print \"Error Index: %s, Prediction: %s, Ground Truth: %s\" % (j, prediction[j], ground_truth[j])\n",
    "    img = np.array(mnist.test.images[j])\n",
    "    img.shape = (28, 28)\n",
    "    plt.subplot(150 + (i+1))\n",
    "    plt.imshow(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "prediction_and_ground_truth = tf.equal(tf.argmax(u, 1), tf.argmax(y, 1))\n",
    "accuracy = tf.reduce_mean(tf.cast(prediction_and_ground_truth, tf.float32))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9116\n"
     ]
    }
   ],
   "source": [
    "print(sess.run(accuracy, feed_dict={x: mnist.test.images, y: mnist.test.labels}))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Single Layer Neural Network - All in one"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total batch: 550\n",
      "Epoch 0 Finished - Accuracy: 0.912200\n",
      "Epoch 1 Finished - Accuracy: 0.917600\n",
      "Epoch 2 Finished - Accuracy: 0.919600\n",
      "Epoch 3 Finished - Accuracy: 0.922700\n",
      "Epoch 4 Finished - Accuracy: 0.922400\n",
      "Epoch 5 Finished - Accuracy: 0.923200\n",
      "Epoch 6 Finished - Accuracy: 0.921100\n",
      "Epoch 7 Finished - Accuracy: 0.921700\n",
      "Epoch 8 Finished - Accuracy: 0.922900\n",
      "Epoch 9 Finished - Accuracy: 0.921200\n",
      "Epoch 10 Finished - Accuracy: 0.926000\n",
      "Epoch 11 Finished - Accuracy: 0.923100\n",
      "Epoch 12 Finished - Accuracy: 0.926600\n",
      "Epoch 13 Finished - Accuracy: 0.925500\n",
      "Epoch 14 Finished - Accuracy: 0.922900\n",
      "Epoch 15 Finished - Accuracy: 0.922300\n",
      "Epoch 16 Finished - Accuracy: 0.916700\n",
      "Epoch 17 Finished - Accuracy: 0.923100\n",
      "Epoch 18 Finished - Accuracy: 0.924400\n",
      "Epoch 19 Finished - Accuracy: 0.925600\n",
      "Epoch 20 Finished - Accuracy: 0.925500\n",
      "Epoch 21 Finished - Accuracy: 0.924500\n",
      "Epoch 22 Finished - Accuracy: 0.922600\n",
      "Epoch 23 Finished - Accuracy: 0.925100\n",
      "Epoch 24 Finished - Accuracy: 0.924500\n",
      "Epoch 25 Finished - Accuracy: 0.924900\n",
      "Epoch 26 Finished - Accuracy: 0.921500\n",
      "Epoch 27 Finished - Accuracy: 0.924200\n",
      "Epoch 28 Finished - Accuracy: 0.923700\n",
      "Epoch 29 Finished - Accuracy: 0.919600\n",
      "Epoch 30 Finished - Accuracy: 0.923600\n",
      "Epoch 31 Finished - Accuracy: 0.923600\n",
      "Epoch 32 Finished - Accuracy: 0.923000\n",
      "Epoch 33 Finished - Accuracy: 0.923700\n",
      "Epoch 34 Finished - Accuracy: 0.924800\n",
      "Epoch 35 Finished - Accuracy: 0.923000\n",
      "Epoch 36 Finished - Accuracy: 0.925400\n",
      "Epoch 37 Finished - Accuracy: 0.922000\n",
      "Epoch 38 Finished - Accuracy: 0.920100\n",
      "Epoch 39 Finished - Accuracy: 0.924100\n",
      "Optimization Finished!\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "\n",
    "# Parameters\n",
    "training_epochs = 40\n",
    "learning_rate = 0.001\n",
    "batch_size = 100\n",
    "\n",
    "# Network Parameters\n",
    "n_input = 784 # MNIST data input (img shape: 28*28)\n",
    "n_classes = 10 # MNIST total classes (0-9 digits)\n",
    "\n",
    "# tf Graph input\n",
    "x = tf.placeholder(tf.float32, [None, n_input])\n",
    "y = tf.placeholder(tf.float32, [None, n_classes])\n",
    "\n",
    "# Construct model\n",
    "W = tf.Variable(tf.zeros([n_input, n_classes]))\n",
    "b = tf.Variable(tf.zeros([n_classes]))\n",
    "u = tf.matmul(x, W) + b\n",
    "\n",
    "# Define loss and target loss function\n",
    "#z = tf.nn.softmax(u)\n",
    "#error = tf.reduce_mean(-tf.reduce_sum(z_ * tf.log(z), reduction_indices=[1]))\n",
    "error = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(u, y))\n",
    "total_loss = tf.train.GradientDescentOptimizer(0.5).minimize(error)\n",
    "\n",
    "# Initializing the variables\n",
    "init = tf.initialize_all_variables()\n",
    "\n",
    "# Calculate accuracy with a Test model\n",
    "prediction_ground_truth = tf.equal(tf.argmax(u, 1), tf.argmax(y, 1))\n",
    "accuracy = tf.reduce_mean(tf.cast(prediction_ground_truth, tf.float32))\n",
    "    \n",
    "# Launch the tensorflow graph\n",
    "with tf.Session() as sess:\n",
    "    sess.run(init)\n",
    "    total_batch = int(mnist.train.num_examples / batch_size)\n",
    "    print \"Total batch: %d\" % total_batch\n",
    "    \n",
    "    # Training cycle\n",
    "    for epoch in range(training_epochs):\n",
    "        # Loop over all batches\n",
    "        for i in range(total_batch):\n",
    "            batch_images, batch_labels = mnist.train.next_batch(batch_size)\n",
    "            sess.run(total_loss, feed_dict={x: batch_images, y: batch_labels})\n",
    "        print \"Epoch %d Finished - Accuracy: %f\" % (epoch, accuracy.eval({x: mnist.test.images, y: mnist.test.labels}))\n",
    "    \n",
    "    print(\"Optimization Finished!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. Multi Layer Neural Network - All in one"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total batch: 550\n",
      "Epoch 0 Finished - Accuracy: 0.822300\n",
      "Epoch 1 Finished - Accuracy: 0.855200\n",
      "Epoch 2 Finished - Accuracy: 0.870800\n",
      "Epoch 3 Finished - Accuracy: 0.881200\n",
      "Epoch 4 Finished - Accuracy: 0.887600\n",
      "Epoch 5 Finished - Accuracy: 0.892100\n",
      "Epoch 6 Finished - Accuracy: 0.898000\n",
      "Epoch 7 Finished - Accuracy: 0.901100\n",
      "Epoch 8 Finished - Accuracy: 0.905600\n",
      "Epoch 9 Finished - Accuracy: 0.908900\n",
      "Epoch 10 Finished - Accuracy: 0.910900\n",
      "Epoch 11 Finished - Accuracy: 0.910400\n",
      "Epoch 12 Finished - Accuracy: 0.912600\n",
      "Epoch 13 Finished - Accuracy: 0.914200\n",
      "Epoch 14 Finished - Accuracy: 0.914200\n",
      "Epoch 15 Finished - Accuracy: 0.916800\n",
      "Epoch 16 Finished - Accuracy: 0.917300\n",
      "Epoch 17 Finished - Accuracy: 0.918700\n",
      "Epoch 18 Finished - Accuracy: 0.917500\n",
      "Epoch 19 Finished - Accuracy: 0.919200\n",
      "Epoch 20 Finished - Accuracy: 0.920600\n",
      "Epoch 21 Finished - Accuracy: 0.921000\n",
      "Epoch 22 Finished - Accuracy: 0.919700\n",
      "Epoch 23 Finished - Accuracy: 0.921100\n",
      "Epoch 24 Finished - Accuracy: 0.922400\n",
      "Epoch 25 Finished - Accuracy: 0.923000\n",
      "Epoch 26 Finished - Accuracy: 0.923200\n",
      "Epoch 27 Finished - Accuracy: 0.922200\n",
      "Epoch 28 Finished - Accuracy: 0.922900\n",
      "Epoch 29 Finished - Accuracy: 0.923100\n",
      "Epoch 30 Finished - Accuracy: 0.924400\n",
      "Epoch 31 Finished - Accuracy: 0.925000\n",
      "Epoch 32 Finished - Accuracy: 0.924200\n",
      "Epoch 33 Finished - Accuracy: 0.923600\n",
      "Epoch 34 Finished - Accuracy: 0.924000\n",
      "Epoch 35 Finished - Accuracy: 0.924600\n",
      "Epoch 36 Finished - Accuracy: 0.926500\n",
      "Epoch 37 Finished - Accuracy: 0.925400\n",
      "Epoch 38 Finished - Accuracy: 0.927300\n",
      "Epoch 39 Finished - Accuracy: 0.925700\n",
      "Optimization Finished!\n"
     ]
    }
   ],
   "source": [
    "# Parameters\n",
    "training_epochs = 40\n",
    "learning_rate = 0.001\n",
    "batch_size = 100\n",
    "display_step = 1\n",
    "\n",
    "# Network Parameters\n",
    "n_input = 784 # MNIST data input (img shape: 28*28)\n",
    "n_hidden_1 = 256 # 1st layer number of features\n",
    "n_hidden_2 = 256 # 2nd layer number of features\n",
    "n_classes = 10 # MNIST total classes (0-9 digits)\n",
    "\n",
    "# tf Graph input\n",
    "x = tf.placeholder(\"float\", [None, n_input])\n",
    "y = tf.placeholder(\"float\", [None, n_classes])\n",
    "\n",
    "# Store layers weight & bias\n",
    "weights = {\n",
    "    'W1': tf.Variable(tf.random_normal([n_input, n_hidden_1])),\n",
    "    'W2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])),\n",
    "    'out': tf.Variable(tf.random_normal([n_hidden_2, n_classes]))\n",
    "}\n",
    "biases = {\n",
    "    'b1': tf.Variable(tf.random_normal([n_hidden_1])),\n",
    "    'b2': tf.Variable(tf.random_normal([n_hidden_2])),\n",
    "    'out': tf.Variable(tf.random_normal([n_classes]))\n",
    "}\n",
    "\n",
    "# Create model\n",
    "def multilayer_perceptron(x, weights, biases):\n",
    "    # Hidden layer with RELU activation\n",
    "    u_2 = tf.add(tf.matmul(x, weights['W1']), biases['b1'])\n",
    "    z_2 = tf.nn.relu(u_2)\n",
    "    # Hidden layer with RELU activation\n",
    "    u_3 = tf.add(tf.matmul(z_2, weights['W2']), biases['b2'])\n",
    "    z_3 = tf.nn.relu(u_3)\n",
    "    # Output layer with linear activation\n",
    "    u_4 = tf.add(tf.matmul(z_3, weights['out']), biases['out'])\n",
    "    return u_4\n",
    "\n",
    "# Construct model\n",
    "pred = multilayer_perceptron(x, weights, biases)\n",
    "\n",
    "# Define loss and target loss function\n",
    "# pred = tf.nn.softmax(pred)\n",
    "# error = tf.reduce_mean(-tf.reduce_sum(y * tf.log(pred), reduction_indices=[1]))\n",
    "error = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y))\n",
    "total_loss = tf.train.GradientDescentOptimizer(learning_rate).minimize(error)\n",
    "\n",
    "# Initializing the variables\n",
    "init = tf.initialize_all_variables()\n",
    "\n",
    "# Calculate accuracy with a Test model    \n",
    "prediction_ground_truth = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))\n",
    "accuracy = tf.reduce_mean(tf.cast(prediction_ground_truth, tf.float32))\n",
    "    \n",
    "# Launch the graph\n",
    "with tf.Session() as sess:\n",
    "    sess.run(init)\n",
    "    total_batch = int(mnist.train.num_examples/batch_size)\n",
    "    print \"Total batch: %d\" % total_batch\n",
    "\n",
    "    # Training cycle\n",
    "    for epoch in range(training_epochs):\n",
    "            \n",
    "        # Loop over all batches\n",
    "        for i in range(total_batch):\n",
    "            batch_images, batch_labels = mnist.train.next_batch(batch_size)\n",
    "            sess.run(total_loss, feed_dict={x: batch_images, y: batch_labels})\n",
    "        print \"Epoch %d Finished - Accuracy: %f\" % (epoch, accuracy.eval({x: mnist.test.images, y: mnist.test.labels}))\n",
    "    \n",
    "    print(\"Optimization Finished!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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