Planar+data+classification+with+one+hidden+layer+v3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Planar data classification with one hidden layer\n",
    "\n",
    "Welcome to your week 3 programming assignment. It's time to build your first neural network, which will have a hidden layer. You will see a big difference between this model and the one you implemented using logistic regression. \n",
    "\n",
    "**You will learn how to:**\n",
    "- Implement a 2-class classification neural network with a single hidden layer\n",
    "- Use units with a non-linear activation function, such as tanh \n",
    "- Compute the cross entropy loss \n",
    "- Implement forward and backward propagation\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1 - Packages ##\n",
    "\n",
    "Let's first import all the packages that you will need during this assignment.\n",
    "- [numpy](www.numpy.org) is the fundamental package for scientific computing with Python.\n",
    "- [sklearn](http://scikit-learn.org/stable/) provides simple and efficient tools for data mining and data analysis. \n",
    "- [matplotlib](http://matplotlib.org) is a library for plotting graphs in Python.\n",
    "- testCases provides some test examples to assess the correctness of your functions\n",
    "- planar_utils provide various useful functions used in this assignment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.5/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n",
      "  warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n",
      "/opt/conda/lib/python3.5/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n",
      "  warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n"
     ]
    }
   ],
   "source": [
    "# Package imports\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from testCases import *\n",
    "import sklearn\n",
    "import sklearn.datasets\n",
    "import sklearn.linear_model\n",
    "from planar_utils import plot_decision_boundary, sigmoid, load_planar_dataset, load_extra_datasets\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "np.random.seed(1) # set a seed so that the results are consistent"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2 - Dataset ##\n",
    "\n",
    "First, let's get the dataset you will work on. The following code will load a \"flower\" 2-class dataset into variables `X` and `Y`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X, Y = load_planar_dataset()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualize the dataset using matplotlib. The data looks like a \"flower\" with some red (label y=0) and some blue (y=1) points. Your goal is to build a model to fit this data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgMAAAFdCAYAAACet25NAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nkgEhhBDCyf0/susIAB7VMQEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5cdd5e4c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the data:\n",
    "plt.scatter(X[0, :], X[1, :], c=Y, s=40, cmap=plt.cm.Spectral);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You have:\n",
    "    - a numpy-array (matrix) X that contains your features (x1, x2)\n",
    "    - a numpy-array (vector) Y that contains your labels (red:0, blue:1).\n",
    "\n",
    "Lets first get a better sense of what our data is like. \n",
    "\n",
    "**Exercise**: How many training examples do you have? In addition, what is the `shape` of the variables `X` and `Y`? \n",
    "\n",
    "**Hint**: How do you get the shape of a numpy array? [(help)](https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.shape.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The shape of X is: (2, 400)\n",
      "The shape of Y is: (1, 400)\n",
      "I have m = 400 training examples!\n"
     ]
    }
   ],
   "source": [
    "### START CODE HERE ### (≈ 3 lines of code)\n",
    "shape_X = X.shape\n",
    "shape_Y = Y.shape\n",
    "m = X.shape[1]  # training set size\n",
    "### END CODE HERE ###\n",
    "\n",
    "print ('The shape of X is: ' + str(shape_X))\n",
    "print ('The shape of Y is: ' + str(shape_Y))\n",
    "print ('I have m = %d training examples!' % (m))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "       \n",
    "<table style=\"width:20%\">\n",
    "  \n",
    "  <tr>\n",
    "    <td>**shape of X**</td>\n",
    "    <td> (2, 400) </td> \n",
    "  </tr>\n",
    "  \n",
    "  <tr>\n",
    "    <td>**shape of Y**</td>\n",
    "    <td>(1, 400) </td> \n",
    "  </tr>\n",
    "  \n",
    "    <tr>\n",
    "    <td>**m**</td>\n",
    "    <td> 400 </td> \n",
    "  </tr>\n",
    "  \n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3 - Simple Logistic Regression\n",
    "\n",
    "Before building a full neural network, lets first see how logistic regression performs on this problem. You can use sklearn's built-in functions to do that. Run the code below to train a logistic regression classifier on the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.5/site-packages/sklearn/utils/validation.py:515: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    }
   ],
   "source": [
    "# Train the logistic regression classifier\n",
    "clf = sklearn.linear_model.LogisticRegressionCV();\n",
    "clf.fit(X.T, Y.T);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can now plot the decision boundary of these models. Run the code below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of logistic regression: 47 % (percentage of correctly labelled datapoints)\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAhQAAAGHCAYAAADoYMuVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nhBBixCRQCCGEEGLE/h/96Dt9FJOxrwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5cdded64a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the decision boundary for logistic regression\n",
    "plot_decision_boundary(lambda x: clf.predict(x), X, Y)\n",
    "plt.title(\"Logistic Regression\")\n",
    "\n",
    "# Print accuracy\n",
    "LR_predictions = clf.predict(X.T)\n",
    "print ('Accuracy of logistic regression: %d ' % float((np.dot(Y,LR_predictions) + np.dot(1-Y,1-LR_predictions))/float(Y.size)*100) +\n",
    "       '% ' + \"(percentage of correctly labelled datapoints)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "\n",
    "<table style=\"width:20%\">\n",
    "  <tr>\n",
    "    <td>**Accuracy**</td>\n",
    "    <td> 47% </td> \n",
    "  </tr>\n",
    "  \n",
    "</table>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Interpretation**: The dataset is not linearly separable, so logistic regression doesn't perform well. Hopefully a neural network will do better. Let's try this now! "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4 - Neural Network model\n",
    "\n",
    "Logistic regression did not work well on the \"flower dataset\". You are going to train a Neural Network with a single hidden layer.\n",
    "\n",
    "**Here is our model**:\n",
    "<img src=\"images/classification_kiank.png\" style=\"width:600px;height:300px;\">\n",
    "\n",
    "**Mathematically**:\n",
    "\n",
    "For one example $x^{(i)}$:\n",
    "$$z^{[1] (i)} =  W^{[1]} x^{(i)} + b^{[1] (i)}\\tag{1}$$ \n",
    "$$a^{[1] (i)} = \\tanh(z^{[1] (i)})\\tag{2}$$\n",
    "$$z^{[2] (i)} = W^{[2]} a^{[1] (i)} + b^{[2] (i)}\\tag{3}$$\n",
    "$$\\hat{y}^{(i)} = a^{[2] (i)} = \\sigma(z^{ [2] (i)})\\tag{4}$$\n",
    "$$y^{(i)}_{prediction} = \\begin{cases} 1 & \\mbox{if } a^{[2](i)} > 0.5 \\\\ 0 & \\mbox{otherwise } \\end{cases}\\tag{5}$$\n",
    "\n",
    "Given the predictions on all the examples, you can also compute the cost $J$ as follows: \n",
    "$$J = - \\frac{1}{m} \\sum\\limits_{i = 0}^{m} \\large\\left(\\small y^{(i)}\\log\\left(a^{[2] (i)}\\right) + (1-y^{(i)})\\log\\left(1- a^{[2] (i)}\\right)  \\large  \\right) \\small \\tag{6}$$\n",
    "\n",
    "**Reminder**: The general methodology to build a Neural Network is to:\n",
    "    1. Define the neural network structure ( # of input units,  # of hidden units, etc). \n",
    "    2. Initialize the model's parameters\n",
    "    3. Loop:\n",
    "        - Implement forward propagation\n",
    "        - Compute loss\n",
    "        - Implement backward propagation to get the gradients\n",
    "        - Update parameters (gradient descent)\n",
    "\n",
    "You often build helper functions to compute steps 1-3 and then merge them into one function we call `nn_model()`. Once you've built `nn_model()` and learnt the right parameters, you can make predictions on new data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.1 - Defining the neural network structure ####\n",
    "\n",
    "**Exercise**: Define three variables:\n",
    "    - n_x: the size of the input layer\n",
    "    - n_h: the size of the hidden layer (set this to 4) \n",
    "    - n_y: the size of the output layer\n",
    "\n",
    "**Hint**: Use shapes of X and Y to find n_x and n_y. Also, hard code the hidden layer size to be 4."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: layer_sizes\n",
    "\n",
    "def layer_sizes(X, Y):\n",
    "    \"\"\"\n",
    "    Arguments:\n",
    "    X -- input dataset of shape (input size, number of examples)\n",
    "    Y -- labels of shape (output size, number of examples)\n",
    "    \n",
    "    Returns:\n",
    "    n_x -- the size of the input layer\n",
    "    n_h -- the size of the hidden layer\n",
    "    n_y -- the size of the output layer\n",
    "    \"\"\"\n",
    "    ### START CODE HERE ### (≈ 3 lines of code)\n",
    "    n_x = X.shape[0] # size of input layer\n",
    "    n_h = 4\n",
    "    n_y = Y.shape[0] # size of output layer\n",
    "    ### END CODE HERE ###\n",
    "    return (n_x, n_h, n_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The size of the input layer is: n_x = 5\n",
      "The size of the hidden layer is: n_h = 4\n",
      "The size of the output layer is: n_y = 2\n"
     ]
    }
   ],
   "source": [
    "X_assess, Y_assess = layer_sizes_test_case()\n",
    "(n_x, n_h, n_y) = layer_sizes(X_assess, Y_assess)\n",
    "print(\"The size of the input layer is: n_x = \" + str(n_x))\n",
    "print(\"The size of the hidden layer is: n_h = \" + str(n_h))\n",
    "print(\"The size of the output layer is: n_y = \" + str(n_y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output** (these are not the sizes you will use for your network, they are just used to assess the function you've just coded).\n",
    "\n",
    "<table style=\"width:20%\">\n",
    "  <tr>\n",
    "    <td>**n_x**</td>\n",
    "    <td> 5 </td> \n",
    "  </tr>\n",
    "  \n",
    "    <tr>\n",
    "    <td>**n_h**</td>\n",
    "    <td> 4 </td> \n",
    "  </tr>\n",
    "  \n",
    "    <tr>\n",
    "    <td>**n_y**</td>\n",
    "    <td> 2 </td> \n",
    "  </tr>\n",
    "  \n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2 - Initialize the model's parameters ####\n",
    "\n",
    "**Exercise**: Implement the function `initialize_parameters()`.\n",
    "\n",
    "**Instructions**:\n",
    "- Make sure your parameters' sizes are right. Refer to the neural network figure above if needed.\n",
    "- You will initialize the weights matrices with random values. \n",
    "    - Use: `np.random.randn(a,b) * 0.01` to randomly initialize a matrix of shape (a,b).\n",
    "- You will initialize the bias vectors as zeros. \n",
    "    - Use: `np.zeros((a,b))` to initialize a matrix of shape (a,b) with zeros."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: initialize_parameters\n",
    "\n",
    "def initialize_parameters(n_x, n_h, n_y):\n",
    "    \"\"\"\n",
    "    Argument:\n",
    "    n_x -- size of the input layer\n",
    "    n_h -- size of the hidden layer\n",
    "    n_y -- size of the output layer\n",
    "    \n",
    "    Returns:\n",
    "    params -- python dictionary containing your parameters:\n",
    "                    W1 -- weight matrix of shape (n_h, n_x)\n",
    "                    b1 -- bias vector of shape (n_h, 1)\n",
    "                    W2 -- weight matrix of shape (n_y, n_h)\n",
    "                    b2 -- bias vector of shape (n_y, 1)\n",
    "    \"\"\"\n",
    "    \n",
    "    np.random.seed(2) # we set up a seed so that your output matches ours although the initialization is random.\n",
    "    \n",
    "    ### START CODE HERE ### (≈ 4 lines of code)\n",
    "    W1 = np.random.randn(n_h,n_x) * 0.01\n",
    "    b1 = np.zeros((n_h,1))\n",
    "    W2 = np.random.randn(n_y,n_h) * 0.01\n",
    "    b2 = np.zeros((n_y,1))\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    assert (W1.shape == (n_h, n_x))\n",
    "    assert (b1.shape == (n_h, 1))\n",
    "    assert (W2.shape == (n_y, n_h))\n",
    "    assert (b2.shape == (n_y, 1))\n",
    "    \n",
    "    parameters = {\"W1\": W1,\n",
    "                  \"b1\": b1,\n",
    "                  \"W2\": W2,\n",
    "                  \"b2\": b2}\n",
    "    \n",
    "    return parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "W1 = [[-0.00416758 -0.00056267]\n",
      " [-0.02136196  0.01640271]\n",
      " [-0.01793436 -0.00841747]\n",
      " [ 0.00502881 -0.01245288]]\n",
      "b1 = [[ 0.]\n",
      " [ 0.]\n",
      " [ 0.]\n",
      " [ 0.]]\n",
      "W2 = [[-0.01057952 -0.00909008  0.00551454  0.02292208]]\n",
      "b2 = [[ 0.]]\n"
     ]
    }
   ],
   "source": [
    "n_x, n_h, n_y = initialize_parameters_test_case()\n",
    "\n",
    "parameters = initialize_parameters(n_x, n_h, n_y)\n",
    "print(\"W1 = \" + str(parameters[\"W1\"]))\n",
    "print(\"b1 = \" + str(parameters[\"b1\"]))\n",
    "print(\"W2 = \" + str(parameters[\"W2\"]))\n",
    "print(\"b2 = \" + str(parameters[\"b2\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "\n",
    "<table style=\"width:90%\">\n",
    "  <tr>\n",
    "    <td>**W1**</td>\n",
    "    <td> [[-0.00416758 -0.00056267]\n",
    " [-0.02136196  0.01640271]\n",
    " [-0.01793436 -0.00841747]\n",
    " [ 0.00502881 -0.01245288]] </td> \n",
    "  </tr>\n",
    "  \n",
    "  <tr>\n",
    "    <td>**b1**</td>\n",
    "    <td> [[ 0.]\n",
    " [ 0.]\n",
    " [ 0.]\n",
    " [ 0.]] </td> \n",
    "  </tr>\n",
    "  \n",
    "  <tr>\n",
    "    <td>**W2**</td>\n",
    "    <td> [[-0.01057952 -0.00909008  0.00551454  0.02292208]]</td> \n",
    "  </tr>\n",
    "  \n",
    "\n",
    "  <tr>\n",
    "    <td>**b2**</td>\n",
    "    <td> [[ 0.]] </td> \n",
    "  </tr>\n",
    "  \n",
    "</table>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.3 - The Loop ####\n",
    "\n",
    "**Question**: Implement `forward_propagation()`.\n",
    "\n",
    "**Instructions**:\n",
    "- Look above at the mathematical representation of your classifier.\n",
    "- You can use the function `sigmoid()`. It is built-in (imported) in the notebook.\n",
    "- You can use the function `np.tanh()`. It is part of the numpy library.\n",
    "- The steps you have to implement are:\n",
    "    1. Retrieve each parameter from the dictionary \"parameters\" (which is the output of `initialize_parameters()`) by using `parameters[\"..\"]`.\n",
    "    2. Implement Forward Propagation. Compute $Z^{[1]}, A^{[1]}, Z^{[2]}$ and $A^{[2]}$ (the vector of all your predictions on all the examples in the training set).\n",
    "- Values needed in the backpropagation are stored in \"`cache`\". The `cache` will be given as an input to the backpropagation function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: forward_propagation\n",
    "\n",
    "def forward_propagation(X, parameters):\n",
    "    \"\"\"\n",
    "    Argument:\n",
    "    X -- input data of size (n_x, m)\n",
    "    parameters -- python dictionary containing your parameters (output of initialization function)\n",
    "    \n",
    "    Returns:\n",
    "    A2 -- The sigmoid output of the second activation\n",
    "    cache -- a dictionary containing \"Z1\", \"A1\", \"Z2\" and \"A2\"\n",
    "    \"\"\"\n",
    "    # Retrieve each parameter from the dictionary \"parameters\"\n",
    "    ### START CODE HERE ### (≈ 4 lines of code)\n",
    "    W1 = parameters[\"W1\"]\n",
    "    b1 = parameters[\"b1\"]\n",
    "    W2 = parameters[\"W2\"]\n",
    "    b2 = parameters[\"b2\"]\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    # Implement Forward Propagation to calculate A2 (probabilities)\n",
    "    ### START CODE HERE ### (≈ 4 lines of code)\n",
    "    Z1 = np.dot(W1,X)+b1\n",
    "    A1 = np.tanh(Z1)\n",
    "    Z2 = np.dot(W2,A1)+b2\n",
    "    A2 = sigmoid(Z2)\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    assert(A2.shape == (1, X.shape[1]))\n",
    "    \n",
    "    cache = {\"Z1\": Z1,\n",
    "             \"A1\": A1,\n",
    "             \"Z2\": Z2,\n",
    "             \"A2\": A2}\n",
    "    \n",
    "    return A2, cache"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.000499755777742 -0.000496963353232 0.000438187450959 0.500109546852\n"
     ]
    }
   ],
   "source": [
    "X_assess, parameters = forward_propagation_test_case()\n",
    "\n",
    "A2, cache = forward_propagation(X_assess, parameters)\n",
    "\n",
    "# Note: we use the mean here just to make sure that your output matches ours. \n",
    "print(np.mean(cache['Z1']) ,np.mean(cache['A1']),np.mean(cache['Z2']),np.mean(cache['A2']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "<table style=\"width:55%\">\n",
    "  <tr>\n",
    "    <td> -0.000499755777742 -0.000496963353232 0.000438187450959 0.500109546852 </td> \n",
    "  </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that you have computed $A^{[2]}$ (in the Python variable \"`A2`\"), which contains $a^{[2](i)}$ for every example, you can compute the cost function as follows:\n",
    "\n",
    "$$J = - \\frac{1}{m} \\sum\\limits_{i = 0}^{m} \\large{(} \\small y^{(i)}\\log\\left(a^{[2] (i)}\\right) + (1-y^{(i)})\\log\\left(1- a^{[2] (i)}\\right) \\large{)} \\small\\tag{13}$$\n",
    "\n",
    "**Exercise**: Implement `compute_cost()` to compute the value of the cost $J$.\n",
    "\n",
    "**Instructions**:\n",
    "- There are many ways to implement the cross-entropy loss. To help you, we give you how we would have implemented\n",
    "$- \\sum\\limits_{i=0}^{m}  y^{(i)}\\log(a^{[2](i)})$:\n",
    "```python\n",
    "logprobs = np.multiply(np.log(A2),Y)\n",
    "cost = - np.sum(logprobs)                # no need to use a for loop!\n",
    "```\n",
    "\n",
    "(you can use either `np.multiply()` and then `np.sum()` or directly `np.dot()`).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: compute_cost\n",
    "\n",
    "def compute_cost(A2, Y, parameters):\n",
    "    \"\"\"\n",
    "    Computes the cross-entropy cost given in equation (13)\n",
    "    \n",
    "    Arguments:\n",
    "    A2 -- The sigmoid output of the second activation, of shape (1, number of examples)\n",
    "    Y -- \"true\" labels vector of shape (1, number of examples)\n",
    "    parameters -- python dictionary containing your parameters W1, b1, W2 and b2\n",
    "    \n",
    "    Returns:\n",
    "    cost -- cross-entropy cost given equation (13)\n",
    "    \"\"\"\n",
    "    \n",
    "    m = Y.shape[1] # number of example\n",
    "\n",
    "    # Compute the cross-entropy cost\n",
    "    ### START CODE HERE ### (≈ 2 lines of code)\n",
    "    logprobs = Y*np.log(A2)+(1-Y)*(np.log(1-A2))\n",
    "    cost =  -1/m * np.sum(logprobs) \n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    cost = np.squeeze(cost)     # makes sure cost is the dimension we expect. \n",
    "                                # E.g., turns [[17]] into 17 \n",
    "    assert(isinstance(cost, float))\n",
    "    \n",
    "    return cost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cost = 0.692919893776\n"
     ]
    }
   ],
   "source": [
    "A2, Y_assess, parameters = compute_cost_test_case()\n",
    "\n",
    "print(\"cost = \" + str(compute_cost(A2, Y_assess, parameters)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "<table style=\"width:20%\">\n",
    "  <tr>\n",
    "    <td>**cost**</td>\n",
    "    <td> 0.692919893776 </td> \n",
    "  </tr>\n",
    "  \n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the cache computed during forward propagation, you can now implement backward propagation.\n",
    "\n",
    "**Question**: Implement the function `backward_propagation()`.\n",
    "\n",
    "**Instructions**:\n",
    "Backpropagation is usually the hardest (most mathematical) part in deep learning. To help you, here again is the slide from the lecture on backpropagation. You'll want to use the six equations on the right of this slide, since you are building a vectorized implementation.  \n",
    "\n",
    "<img src=\"images/grad_summary.png\" style=\"width:600px;height:300px;\">\n",
    "\n",
    "<!--\n",
    "$\\frac{\\partial \\mathcal{J} }{ \\partial z_{2}^{(i)} } = \\frac{1}{m} (a^{[2](i)} - y^{(i)})$\n",
    "\n",
    "$\\frac{\\partial \\mathcal{J} }{ \\partial W_2 } = \\frac{\\partial \\mathcal{J} }{ \\partial z_{2}^{(i)} } a^{[1] (i) T} $\n",
    "\n",
    "$\\frac{\\partial \\mathcal{J} }{ \\partial b_2 } = \\sum_i{\\frac{\\partial \\mathcal{J} }{ \\partial z_{2}^{(i)}}}$\n",
    "\n",
    "$\\frac{\\partial \\mathcal{J} }{ \\partial z_{1}^{(i)} } =  W_2^T \\frac{\\partial \\mathcal{J} }{ \\partial z_{2}^{(i)} } * ( 1 - a^{[1] (i) 2}) $\n",
    "\n",
    "$\\frac{\\partial \\mathcal{J} }{ \\partial W_1 } = \\frac{\\partial \\mathcal{J} }{ \\partial z_{1}^{(i)} }  X^T $\n",
    "\n",
    "$\\frac{\\partial \\mathcal{J} _i }{ \\partial b_1 } = \\sum_i{\\frac{\\partial \\mathcal{J} }{ \\partial z_{1}^{(i)}}}$\n",
    "\n",
    "- Note that $*$ denotes elementwise multiplication.\n",
    "- The notation you will use is common in deep learning coding:\n",
    "    - dW1 = $\\frac{\\partial \\mathcal{J} }{ \\partial W_1 }$\n",
    "    - db1 = $\\frac{\\partial \\mathcal{J} }{ \\partial b_1 }$\n",
    "    - dW2 = $\\frac{\\partial \\mathcal{J} }{ \\partial W_2 }$\n",
    "    - db2 = $\\frac{\\partial \\mathcal{J} }{ \\partial b_2 }$\n",
    "    \n",
    "!-->\n",
    "\n",
    "- Tips:\n",
    "    - To compute dZ1 you'll need to compute $g^{[1]'}(Z^{[1]})$. Since $g^{[1]}(.)$ is the tanh activation function, if $a = g^{[1]}(z)$ then $g^{[1]'}(z) = 1-a^2$. So you can compute \n",
    "    $g^{[1]'}(Z^{[1]})$ using `(1 - np.power(A1, 2))`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: backward_propagation\n",
    "\n",
    "def backward_propagation(parameters, cache, X, Y):\n",
    "    \"\"\"\n",
    "    Implement the backward propagation using the instructions above.\n",
    "    \n",
    "    Arguments:\n",
    "    parameters -- python dictionary containing our parameters \n",
    "    cache -- a dictionary containing \"Z1\", \"A1\", \"Z2\" and \"A2\".\n",
    "    X -- input data of shape (2, number of examples)\n",
    "    Y -- \"true\" labels vector of shape (1, number of examples)\n",
    "    \n",
    "    Returns:\n",
    "    grads -- python dictionary containing your gradients with respect to different parameters\n",
    "    \"\"\"\n",
    "    m = X.shape[1]\n",
    "    \n",
    "    # First, retrieve W1 and W2 from the dictionary \"parameters\".\n",
    "    ### START CODE HERE ### (≈ 2 lines of code)\n",
    "    W1 = parameters[\"W1\"]\n",
    "    W2 = parameters[\"W2\"]\n",
    "    ### END CODE HERE ###\n",
    "        \n",
    "    # Retrieve also A1 and A2 from dictionary \"cache\".\n",
    "    ### START CODE HERE ### (≈ 2 lines of code)\n",
    "    A1 = cache[\"A1\"]\n",
    "    A2 = cache[\"A2\"]\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    # Backward propagation: calculate dW1, db1, dW2, db2. \n",
    "    ### START CODE HERE ### (≈ 6 lines of code, corresponding to 6 equations on slide above)\n",
    "    dZ2= A2-Y\n",
    "    dW2 = 1/m *np.dot(dZ2,A1.T)\n",
    "    db2 = 1/m*np.sum(dZ2, axis =1, keepdims = True)\n",
    "    dZ1 = np.dot(W2.T,dZ2)*(1 - np.power(A1, 2))\n",
    "    dW1 = 1/m*np.dot(dZ1,X.T)\n",
    "    db1 = 1/m*np.sum(dZ1, axis =1, keepdims = True)\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    grads = {\"dW1\": dW1,\n",
    "             \"db1\": db1,\n",
    "             \"dW2\": dW2,\n",
    "             \"db2\": db2}\n",
    "    \n",
    "    return grads"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dW1 = [[ 0.01018708 -0.00708701]\n",
      " [ 0.00873447 -0.0060768 ]\n",
      " [-0.00530847  0.00369379]\n",
      " [-0.02206365  0.01535126]]\n",
      "db1 = [[-0.00069728]\n",
      " [-0.00060606]\n",
      " [ 0.000364  ]\n",
      " [ 0.00151207]]\n",
      "dW2 = [[ 0.00363613  0.03153604  0.01162914 -0.01318316]]\n",
      "db2 = [[ 0.06589489]]\n"
     ]
    }
   ],
   "source": [
    "parameters, cache, X_assess, Y_assess = backward_propagation_test_case()\n",
    "\n",
    "grads = backward_propagation(parameters, cache, X_assess, Y_assess)\n",
    "print (\"dW1 = \"+ str(grads[\"dW1\"]))\n",
    "print (\"db1 = \"+ str(grads[\"db1\"]))\n",
    "print (\"dW2 = \"+ str(grads[\"dW2\"]))\n",
    "print (\"db2 = \"+ str(grads[\"db2\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected output**:\n",
    "\n",
    "\n",
    "\n",
    "<table style=\"width:80%\">\n",
    "  <tr>\n",
    "    <td>**dW1**</td>\n",
    "    <td> [[ 0.01018708 -0.00708701]\n",
    " [ 0.00873447 -0.0060768 ]\n",
    " [-0.00530847  0.00369379]\n",
    " [-0.02206365  0.01535126]] </td> \n",
    "  </tr>\n",
    "  \n",
    "  <tr>\n",
    "    <td>**db1**</td>\n",
    "    <td>  [[-0.00069728]\n",
    " [-0.00060606]\n",
    " [ 0.000364  ]\n",
    " [ 0.00151207]] </td> \n",
    "  </tr>\n",
    "  \n",
    "  <tr>\n",
    "    <td>**dW2**</td>\n",
    "    <td> [[ 0.00363613  0.03153604  0.01162914 -0.01318316]] </td> \n",
    "  </tr>\n",
    "  \n",
    "\n",
    "  <tr>\n",
    "    <td>**db2**</td>\n",
    "    <td> [[ 0.06589489]] </td> \n",
    "  </tr>\n",
    "  \n",
    "</table>  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question**: Implement the update rule. Use gradient descent. You have to use (dW1, db1, dW2, db2) in order to update (W1, b1, W2, b2).\n",
    "\n",
    "**General gradient descent rule**: $ \\theta = \\theta - \\alpha \\frac{\\partial J }{ \\partial \\theta }$ where $\\alpha$ is the learning rate and $\\theta$ represents a parameter.\n",
    "\n",
    "**Illustration**: The gradient descent algorithm with a good learning rate (converging) and a bad learning rate (diverging). Images courtesy of Adam Harley.\n",
    "\n",
    "<img src=\"images/sgd.gif\" style=\"width:400;height:400;\"> <img src=\"images/sgd_bad.gif\" style=\"width:400;height:400;\">\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: update_parameters\n",
    "\n",
    "def update_parameters(parameters, grads, learning_rate = 1.2):\n",
    "    \"\"\"\n",
    "    Updates parameters using the gradient descent update rule given above\n",
    "    \n",
    "    Arguments:\n",
    "    parameters -- python dictionary containing your parameters \n",
    "    grads -- python dictionary containing your gradients \n",
    "    \n",
    "    Returns:\n",
    "    parameters -- python dictionary containing your updated parameters \n",
    "    \"\"\"\n",
    "    # Retrieve each parameter from the dictionary \"parameters\"\n",
    "    ### START CODE HERE ### (≈ 4 lines of code)\n",
    "    W1 = parameters[\"W1\"]\n",
    "    b1 = parameters[\"b1\"]\n",
    "    W2 = parameters[\"W2\"]\n",
    "    b2 = parameters[\"b2\"]\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    # Retrieve each gradient from the dictionary \"grads\"\n",
    "    ### START CODE HERE ### (≈ 4 lines of code)\n",
    "    dW1 = grads[\"dW1\"]\n",
    "    db1 = grads[\"db1\"]\n",
    "    dW2 = grads[\"dW2\"]\n",
    "    db2 = grads[\"db2\"]\n",
    "    ## END CODE HERE ###\n",
    "    \n",
    "    # Update rule for each parameter\n",
    "    ### START CODE HERE ### (≈ 4 lines of code)\n",
    "    W1 = W1 - learning_rate*dW1\n",
    "    b1 = b1 - learning_rate*db1\n",
    "    W2 = W2 - learning_rate*dW2\n",
    "    b2 = b2 - learning_rate*db2\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    parameters = {\"W1\": W1,\n",
    "                  \"b1\": b1,\n",
    "                  \"W2\": W2,\n",
    "                  \"b2\": b2}\n",
    "    \n",
    "    return parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "W1 = [[-0.00643025  0.01936718]\n",
      " [-0.02410458  0.03978052]\n",
      " [-0.01653973 -0.02096177]\n",
      " [ 0.01046864 -0.05990141]]\n",
      "b1 = [[ -1.02420756e-06]\n",
      " [  1.27373948e-05]\n",
      " [  8.32996807e-07]\n",
      " [ -3.20136836e-06]]\n",
      "W2 = [[-0.01041081 -0.04463285  0.01758031  0.04747113]]\n",
      "b2 = [[ 0.00010457]]\n"
     ]
    }
   ],
   "source": [
    "parameters, grads = update_parameters_test_case()\n",
    "parameters = update_parameters(parameters, grads)\n",
    "\n",
    "print(\"W1 = \" + str(parameters[\"W1\"]))\n",
    "print(\"b1 = \" + str(parameters[\"b1\"]))\n",
    "print(\"W2 = \" + str(parameters[\"W2\"]))\n",
    "print(\"b2 = \" + str(parameters[\"b2\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "\n",
    "\n",
    "<table style=\"width:80%\">\n",
    "  <tr>\n",
    "    <td>**W1**</td>\n",
    "    <td> [[-0.00643025  0.01936718]\n",
    " [-0.02410458  0.03978052]\n",
    " [-0.01653973 -0.02096177]\n",
    " [ 0.01046864 -0.05990141]]</td> \n",
    "  </tr>\n",
    "  \n",
    "  <tr>\n",
    "    <td>**b1**</td>\n",
    "    <td> [[ -1.02420756e-06]\n",
    " [  1.27373948e-05]\n",
    " [  8.32996807e-07]\n",
    " [ -3.20136836e-06]]</td> \n",
    "  </tr>\n",
    "  \n",
    "  <tr>\n",
    "    <td>**W2**</td>\n",
    "    <td> [[-0.01041081 -0.04463285  0.01758031  0.04747113]] </td> \n",
    "  </tr>\n",
    "  \n",
    "\n",
    "  <tr>\n",
    "    <td>**b2**</td>\n",
    "    <td> [[ 0.00010457]] </td> \n",
    "  </tr>\n",
    "  \n",
    "</table>  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.4 - Integrate parts 4.1, 4.2 and 4.3 in nn_model() ####\n",
    "\n",
    "**Question**: Build your neural network model in `nn_model()`.\n",
    "\n",
    "**Instructions**: The neural network model has to use the previous functions in the right order."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: nn_model\n",
    "\n",
    "def nn_model(X, Y, n_h, num_iterations = 10000, print_cost=False):\n",
    "    \"\"\"\n",
    "    Arguments:\n",
    "    X -- dataset of shape (2, number of examples)\n",
    "    Y -- labels of shape (1, number of examples)\n",
    "    n_h -- size of the hidden layer\n",
    "    num_iterations -- Number of iterations in gradient descent loop\n",
    "    print_cost -- if True, print the cost every 1000 iterations\n",
    "    \n",
    "    Returns:\n",
    "    parameters -- parameters learnt by the model. They can then be used to predict.\n",
    "    \"\"\"\n",
    "    \n",
    "    np.random.seed(3)\n",
    "    n_x = layer_sizes(X, Y)[0]\n",
    "    n_y = layer_sizes(X, Y)[2]\n",
    "    \n",
    "    # Initialize parameters, then retrieve W1, b1, W2, b2. Inputs: \"n_x, n_h, n_y\". Outputs = \"W1, b1, W2, b2, parameters\".\n",
    "    ### START CODE HERE ### (≈ 5 lines of code)\n",
    "    parameters = initialize_parameters(n_x, n_h, n_y)\n",
    "    W1 = parameters[\"W1\"]\n",
    "    b1 = parameters[\"b1\"]\n",
    "    W2 = parameters[\"W2\"]\n",
    "    b2 = parameters[\"b2\"]\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    # Loop (gradient descent)\n",
    "\n",
    "    for i in range(0, num_iterations):\n",
    "         \n",
    "        ### START CODE HERE ### (≈ 4 lines of code)\n",
    "        # Forward propagation. Inputs: \"X, parameters\". Outputs: \"A2, cache\".\n",
    "        A2, cache = forward_propagation(X, parameters)\n",
    "        \n",
    "        # Cost function. Inputs: \"A2, Y, parameters\". Outputs: \"cost\".\n",
    "        cost = compute_cost(A2, Y, parameters)\n",
    " \n",
    "        # Backpropagation. Inputs: \"parameters, cache, X, Y\". Outputs: \"grads\".\n",
    "        grads = backward_propagation(parameters, cache, X, Y)\n",
    " \n",
    "        # Gradient descent parameter update. Inputs: \"parameters, grads\". Outputs: \"parameters\".\n",
    "        parameters = update_parameters(parameters, grads)\n",
    "        \n",
    "        ### END CODE HERE ###\n",
    "        \n",
    "        # Print the cost every 1000 iterations\n",
    "        if print_cost and i % 1000 == 0:\n",
    "            print (\"Cost after iteration %i: %f\" %(i, cost))\n",
    "\n",
    "    return parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.5/site-packages/ipykernel/__main__.py:20: RuntimeWarning: divide by zero encountered in log\n",
      "/home/jovyan/work/Week 3/Planar data classification with one hidden layer/planar_utils.py:34: RuntimeWarning: overflow encountered in exp\n",
      "  s = 1/(1+np.exp(-x))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "W1 = [[-4.18494056  5.33220609]\n",
      " [-7.52989382  1.24306181]\n",
      " [-4.1929459   5.32632331]\n",
      " [ 7.52983719 -1.24309422]]\n",
      "b1 = [[ 2.32926819]\n",
      " [ 3.79458998]\n",
      " [ 2.33002577]\n",
      " [-3.79468846]]\n",
      "W2 = [[-6033.83672146 -6008.12980822 -6033.10095287  6008.06637269]]\n",
      "b2 = [[-52.66607724]]\n"
     ]
    }
   ],
   "source": [
    "X_assess, Y_assess = nn_model_test_case()\n",
    "\n",
    "parameters = nn_model(X_assess, Y_assess, 4, num_iterations=10000, print_cost=False)\n",
    "print(\"W1 = \" + str(parameters[\"W1\"]))\n",
    "print(\"b1 = \" + str(parameters[\"b1\"]))\n",
    "print(\"W2 = \" + str(parameters[\"W2\"]))\n",
    "print(\"b2 = \" + str(parameters[\"b2\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "\n",
    "<table style=\"width:90%\">\n",
    "  <tr>\n",
    "    <td>**W1**</td>\n",
    "    <td> [[-4.18494056  5.33220609]\n",
    " [-7.52989382  1.24306181]\n",
    " [-4.1929459   5.32632331]\n",
    " [ 7.52983719 -1.24309422]]</td> \n",
    "  </tr>\n",
    "  \n",
    "  <tr>\n",
    "    <td>**b1**</td>\n",
    "    <td> [[ 2.32926819]\n",
    " [ 3.79458998]\n",
    " [ 2.33002577]\n",
    " [-3.79468846]]</td> \n",
    "  </tr>\n",
    "  \n",
    "  <tr>\n",
    "    <td>**W2**</td>\n",
    "    <td> [[-6033.83672146 -6008.12980822 -6033.10095287  6008.06637269]] </td> \n",
    "  </tr>\n",
    "  \n",
    "\n",
    "  <tr>\n",
    "    <td>**b2**</td>\n",
    "    <td> [[-52.66607724]] </td> \n",
    "  </tr>\n",
    "  \n",
    "</table>  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.5 Predictions\n",
    "\n",
    "**Question**: Use your model to predict by building predict().\n",
    "Use forward propagation to predict results.\n",
    "\n",
    "**Reminder**: predictions = $y_{prediction} = \\mathbb 1 \\text{{activation > 0.5}} = \\begin{cases}\n",
    "      1 & \\text{if}\\ activation > 0.5 \\\\\n",
    "      0 & \\text{otherwise}\n",
    "    \\end{cases}$  \n",
    "    \n",
    "As an example, if you would like to set the entries of a matrix X to 0 and 1 based on a threshold you would do: ```X_new = (X > threshold)```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: predict\n",
    "\n",
    "def predict(parameters, X):\n",
    "    \"\"\"\n",
    "    Using the learned parameters, predicts a class for each example in X\n",
    "    \n",
    "    Arguments:\n",
    "    parameters -- python dictionary containing your parameters \n",
    "    X -- input data of size (n_x, m)\n",
    "    \n",
    "    Returns\n",
    "    predictions -- vector of predictions of our model (red: 0 / blue: 1)\n",
    "    \"\"\"\n",
    "    \n",
    "    # Computes probabilities using forward propagation, and classifies to 0/1 using 0.5 as the threshold.\n",
    "    ### START CODE HERE ### (≈ 2 lines of code)\n",
    "    A2, cache = forward_propagation(X, parameters)\n",
    "    predictions = (A2>0.5)\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    return predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predictions mean = 0.666666666667\n"
     ]
    }
   ],
   "source": [
    "parameters, X_assess = predict_test_case()\n",
    "\n",
    "predictions = predict(parameters, X_assess)\n",
    "print(\"predictions mean = \" + str(np.mean(predictions)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**: \n",
    "\n",
    "\n",
    "<table style=\"width:40%\">\n",
    "  <tr>\n",
    "    <td>**predictions mean**</td>\n",
    "    <td> 0.666666666667 </td> \n",
    "  </tr>\n",
    "  \n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is time to run the model and see how it performs on a planar dataset. Run the following code to test your model with a single hidden layer of $n_h$ hidden units."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cost after iteration 0: 0.693048\n",
      "Cost after iteration 1000: 0.288083\n",
      "Cost after iteration 2000: 0.254385\n",
      "Cost after iteration 3000: 0.233864\n",
      "Cost after iteration 4000: 0.226792\n",
      "Cost after iteration 5000: 0.222644\n",
      "Cost after iteration 6000: 0.219731\n",
      "Cost after iteration 7000: 0.217504\n",
      "Cost after iteration 8000: 0.219454\n",
      "Cost after iteration 9000: 0.218607\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f5c9c089c18>"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAhQAAAGHCAYAAADoYMuVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAAPYQAAD2EBqD+naQAAIABJREFUeJzs3Xd8G/X5wPHPo+G994jtOHH2TgiQEPbGZa8GKN20DS17\npj8KFEqhhTLK7oBCStgjbCgkkBBW9l6O7Xjvbcm2pO/vj5MdWbETx0uy/X2/Xn4lOp3unruT7p77\nrhOlFJqmaZqmaX1h8nUAmqZpmqYNfTqh0DRN0zStz3RCoWmapmlan+mEQtM0TdO0PtMJhaZpmqZp\nfaYTCk3TNE3T+kwnFJqmaZqm9ZlOKDRN0zRN6zOdUGiapmma1mc6odD6nYhkiIhLRK48zM+tEJHl\nAxXXcCYieSLyb1/H4UlEEkTkdRGpFBGniFwzgOtq/87d0IN57xIRVw+X6xKRP/TnMvubPx77/tLb\nc4nmGzqhGIZE5MfuH2H7n01EikTkIxH5nYiEDUIYvRnTXQGDflIWkeO99pdLRKpE5GsRuWyw4+kl\nfxxD/xHgVOBPwI+Aj3wbToeB+J4pfHcM/PHY9yefbp+IXO4+J9T7Mo6hwOLrALQBo4A7gDzACiQB\nJ2Cc5G8QkXOUUpsHZMVK5YtIMNB2mB89dSDiOQyPAGvc/48FLgWWiEikUuop34U1ZJ0IvK2UetjX\ngXi5B/izr4PQDq0P55J+ISKhwANAoy/WP9TohGJ4+0gptc7j9QMicgLwPvCOiExSSrUMxIqVUq29\n+IxjIGI5DKuUUm+2vxCRp4G9wGXAiEkoRCQQaFV9f3JgAlDXDyEB/ReXUsoFHPb3Uzt8ImIGTEqp\nXicEvTmX9KM7gHpgOXCuD+MYEnSVxwijlFqBcYeWAVzh+Z6ITHDXeVe5q0m+F5GzvZchIpEi8rCI\n5IqIXUQKROQ/IhLjfv+Aek8RSRSR59zz2kWkWETeFpF0j3lWiMjnXuuKF5F/iUipO6YN3vWpnvXn\nIvJLEdnjXsd3InJEH/ZVG1ADdEp0RMQsInd4rCdXRP4kIgFe83VZ/+5d5+1RRTVfRP4mIuUi0igi\nb4pIbBef/z/3fmwSkc9EZHIX80SLyIMisklEGkSkTkQ+EJHpXvO1V/dcKiL3ikgh0ATMdE+/totl\nz2//TFf7rX173C9/657X6fF+poi85v6eNbmrls7qYVzhXa3T67MH/Q5IF+0dRCTA/Z0uF5F693cz\ntZvlL3D/NmwisltErjpILFeIyBoRaXZv71IRGeU1zwr3cZokIsvd+6RQRG4+1LZ2s85DHnsRCXV/\nxw4oPRKRVBFxiMitHtMiReQREdnn3q+7ReQWERGPeTx/h9eKyB7ADkw6SKynishKEalxx7pDRP7U\nxTKvdL/uqnqy/W+v17LPFJEv3dtZLyLvdfVbOUhs44DrgBvwOgdoXdMlFCPTi8B9wGnAvwBEZAqw\nCijEKA5uAi4B3haRC5RS77jnC3XPN8H92fVAHHAOMAqo7madb2KcWB4D8jHuXk8F0oF97nk63XmK\nSBDwBTAG+DtG9c3FwPNiVEP83WsdlwNhwNPuZd0KvCEiY5RSTg4t3OMCHuNe3hTgZ17z/Qu4EngV\neBA4CrgdmAhc2IP1dHeH/XeM/XcXMBq4HngcWNg+g4jcA/weeA/4EJgNfIJRreVpDMYxeQ3IBRKB\nXwErRGSyUqrUa/47gBbgr0AAsAP4CmMfPOo17+UYd23vdLMdX2Akq0vcsb3gEX8C8DUQ5F5uNfBj\nYJmIXNj+PesmrkAOXbLQk+9AV+0d/oVREvVfd3wnYZTkeX8npwIfA+XAHzD2+13u13jN+3vgj8DL\nwD+AeOAa4AsRmaWUaq+TVxjftw8xficvAxcB94vIJqXUx4fYZm+HPPZKqSYReQu4VERu8Cr1aW83\ntMS9HcHAl0Ayxn4tAOZjnCeSMC64nn6GcayewTh2XZ4T3Bf3d4EN7D/OWe5ld2c7XjdCQDTwN6DM\nY9k/Ap7HaLdzCxAC/AZY6d73+zi0R4DPlFIfdZc8a16UUvpvmP1hnKCdwOyDzFMDrPF4/T+M5MDi\nNd8qYIfH67vdyz7nIMvOwGj0dqX7daT79Q2HiHs58LnH62vd6/qhxzQzxoWuDgj1Wl85EOEx79nu\nz591iPUe7/680/1v+18bcJvXvNPd7z3tNf0v7s8f7zHNBfyhi/XlAv/2Ol4ujCoqz/kewriAhrtf\nx2Hc8b3jNd+97s97LtPaxXrTARvw+y62fTcQ4DX/L93bNN5jmsW9n//Vg++hC3jMa9rD7mXO85gW\nCuQAOT2J6xDfuUN+B4A7AWcXx9Q71iXuz/7BY9pbGMl2qse0Ce7viucy093TbvVa5mT3Mb3NY9py\n93ou8zx+QDHwag+23fv71NNjf6p7vad5zbuBzr/D/8NIIMd4zXefe1tSvY5BDRDTg7jbf9/RPTiu\nVx5knncxzgcTPL5P1cBTXvPFu2N7ugexZWMkOO3LfA6oP9TnRvqfrvIYuRpxFx+LSDRGA7rXgEgR\niW3/w7jDHCciye7PXQBsVEotO4x12TBOPCeISNRhfO5MoFQp9XL7BGXcZT6GcRd6vNf8L6v9d30A\nKwHBuGPribuBU9x/lwBLgftE5Hce85yFcUfpXVT8kHtd2T1clzcFPOs1bSVGApXhfn0KxoXGu2Tm\nkQMW5lFnLSImMaqjmoGdGKUa3p5XB9ZVv4pxUr3cY9oZGA1WlxxsYw7iTOA7pdTXHrE2YWz76C6K\npLuK62B68x1oP6Zd7VfPIn0TRqneW0qpIo/4d2KUWni60P3Z17x+T+UYSdKJXvM3KqVe8lhmG/Dd\nIeLu0mEc+/8BJXgcX3cJzHSMUsx2F2HsxzqvbfkMI8E8ziuE15VS3ZVUeqp1/3u+Z9XJ4RCjSvEs\n4Mfu4wBGohQJvOwVrwK+5cB9771MK0aJx1Mey9R6QCcUI1cY0OD+fxbGye8eoMLr7y73PAnuf8cC\nWw5nRe4Lwq0YF5MyEflCRG4WkcRDfDQD4+Trbbs73gyv6QVe620/YUX3MNQtSqnP3X+vK6WuxKha\nuN+jKqT9jmmP17rKME6Q3jEdjgKv1zXuf9vjb1+297orPeYFQAzXi8gujKSgEuNiNg3jZOstz3uC\nUqoO4+7Ps+vs5UCRUqq344VkYFzYvG33eP+gcR1Cb74D7cc0x2u6d5zxQDBe+7+bebMwzq976Px7\nKseoGkvwmr+wi2XWHCLuLvX02Cvj1vu/wHnu6kUwjq8NeN1jkeMwEknvc8OnGBdp723J62Gor2CU\nNv4D47ywVEQu7mlyISJnYFQ73aeUetsrXsEo+fHe96diHMeDuQEjab6rh9uhuek2FCOQGI3NItl/\nYmxPLB/kwDutdl2dRHtMKfWoiCwDzgNOx6hbvl1ETlRKbezLsj10106iV3c/bp9hlDociVHH3a4v\nPQ3M3UzvKn6hd/G319//E6PIuhrjovkoXd9I2LpZzgvARSJyNEYieTZGu47B0l1c3RmI70BvmDD2\n9xl0PeaFdzfE/oz7cI79C8DNGL/LlzHa67yrlGrwmMeEkTw80E08u7xe9+iYKaXswHEiciLGb+wM\njK7an4nIae6Ep0sikolRSvaxUuoOr7dNGL/PK/BoV+Gh2waWIhKBsf+ewCitjcTY5jDjbckAmpVS\nFT3ZxpFGJxQj05UYP7j2gYbaW0e3KaU+7/ojHXKAqb1ZqVIqF6Oq4GERGQtsBG50x9OVfIy7Km+T\nPN4faO2/kfbBwPIxTljj8LgrdTc2jPKKqcY9DY/5rBiN23rK86TavuxxeNwFikgcB97JXohRD96p\nB4K7yulwToYfYdzhXo5RBB9M76s7wNiGCV1MH8xj6q39mI6lc4nYRK/5KjAuluO6WIb3vDkYF6I8\npVSfkvFe6PGxV0ptFZH1wOUiUoTR1uJqr+XlAGF9KJU6KPdylwM3icjtGG2CTgS6PBe5S1PexEiU\nuhp4rn3fV/TgfOYtGuO3fgtGqaq3XOBtjKpfzYuu8hhhROQkjLuWvcBLAO5sewXwKxFJ6uIzcR4v\n3wBmiEiP+2SLSLAYYwh4ysWocvGe7ukDIMmzhbUY/dp/5/7sFz2NoQ/au822l6J8gHGyus5rvhsx\nLv7ve0zL4cD65V/RfQnFofwP4+7qd17Tr+9iXided5MicjHQZVfI7rjbrCzFuHP8CbBZKXVYVV5e\nPgCOFJGjPOIKBa4CcpVS2/qw7N76EGNfeQ8Nfh0eCZ0yxq/4GKOKoKPrp4hMwmhb4elNjFKBO7ta\nobtdw0A53GP/Ikap4XUYyaP3iKavAvNExHsb27uT9ur77G675W0jRuwHOy88g1GldL67Ws7bxxiN\nSBeLyAE3zV7nM2/lGKU157v/bf9bjpFMnoseFK1buoRi+BLgLPfJzoLRdewkjDrEXIxeGp6N3a7G\naHi1WUT+gZFwJALzME5Es9zz/RWjkdZrIvIcsBajvvFs4Feq69E3x2MUY74KbMO4KF6AUfe69CDb\n8CzGBfh5McYSyMPoNjoPuNbdmK8/HefuIgdGN75zgWOBpUqpXQBKqU0i8h/gKvcJ8QuMbqNXAm8q\npTyTnH8CT4vI6xhFxjMwLjxdlRB0V7TdMV0pVSkiDwK3ich7GBfnWeyv3/b0HnCHGONdrMYo6bmc\nA9sJ9MQLGBfbEzDu3Prifoxi9Y9E5DGMu8yfYLRj8Mldn1Jqo4gsBRa57+JXAydjlFh4H5c7Mfb3\nKhF5EqOR7G8xqoOmeyxzr4j8H0aj3kyMu9oGjEaW52FcFP82QJt0uMf+JYxeSucBT6oDu1j/FaMb\n6nsi8jzGbz4UY3svwOji3JNGmN7+ICLHYSTh+Rjnm99gdCNf1dUHRCQbYxj31zHGSpnp8XajUuod\npVSDiPwG43u7TkRexvh9pGNUraziwOQRAKWUDTigwbmInA/MVUq924vtHDF0QjF8KYxeC2D0sKgG\nNmP8kJ73vhgrpba7L9p3YnRjbG+Rvt5jOSij//oC97TzMS6k5Rh3z54NyzyL6gswTlonY9RrOjDG\nObjYqzFVp88ppewicjzGRehKIAKjmuEnSqkXu/hcV3Wu3U3vaj7PO/9WjKRqMUbbEk8/xzg5/wTj\nJFyK8byKP3rN9w+Mk+3PMe4Av8RI6D7rIqbuYuw0XSn1exGxAb/GuMB/g5GkeI+ZcB9G3/vLMHqs\nrMVoDX//Yay7fZ3rRGQrRrH+Swebt4vlesdfLiLzMOrjf4sxHsUm4AdKKe8748Ntp3I43wHv1z/F\n+B5fjpFItredKaDzd3Kz+079bxi/gUKMhoEpeCQU7nkfEJGdGCVI7QOcFWCUAHhftHp0/A8yT2+P\nffsx+QSj0fQB1VlKKZv7wr8YI6H/EUYJwC73dnmWEvT09wbGOCYZGPs+DqN0ZAVwl1cbDs9lxrn/\nfyEHjvmS714mSqml7iqc24CbMEo8ijBump7rYXze+tJuakSQg7R70TRNA0BE1gFVSilfP29FGwAi\n8iYwVSk13texaEOXbkOhadpBuUuuZgL/8XUsWv9zjzGTjceIpprWG7qEQtO0LokxHPsRGP3yY4Cx\nhznIlObHRGQ0sAD4BTAH4/geMIS4pvWULqHQNK07F2E848IMLNTJxLBzPEapRDrG0NY6mdD6RJdQ\naJqmaZrWZ8O+hEJEQkRktoiE+DoWTdM0TRtKDucaOhK6jc7E/RhmEdnh62A0TdM0bQiZiPHMl2Mw\nxjXp1khIKEa7//2vL4PQNE3TtCFsNDqhMJ55sGTJEiZNmnSIWUee66+/nocf9n4St3a49H7sH3o/\n9g+9H/vPSN+X27dv54orroAePEV2JCQUdoBJkyYxe/ZsX8fidyIjI/V+6Qd6P/YPvR/7h96P/Ufv\nyw72Q80w7BtlapqmaZo28HRCoWmapmlan+mEQtM0TdO0PtMJxQi3cOFCX4cwLOj92D/0fuwfej/2\nH70ve27Yj5QpIrOBtWvXrtUNazRN0zTtMKxbt445c+YAzFFKrTvYvLqEQtM0TdO0PtMJhaZpmqZp\nfaYTCk3TNE3T+kwnFJqmaZqm9ZlOKDRN0zRN67ORMPT2gFg97Qe+DkHTNE3TOpm/+T2frVuXUGia\npmma1mc6odA0TdM0rc90QqFpmqZpWp/phELTNE3TtD7TCYWmaZqmaX2mEwpN0zRN0/pMJxSapmma\npvWZTig0TdM0TesznVBomqZpmtZnOqHQNE3TNK3PdEKhaZqmacPA4uxFPl2/fpaHpmmapg1h8zff\nyAm32Xwdhk4oNE3TNG0omnmmg7NM14AfJBOgqzx6zddFS5qmadrItTh7kZFM+BGdUPTB4uxFOrHQ\nNE3TBpW/Xnd0lUc/WJy9iBX3B7N62kO+DkXTNE0bpvw1kWinE4p+csJtNshexH3vP+nrUDRN07Rh\nxN8TiXa6yqOf6WoQTdM0rb8MpeuJLqEYILoaRNM0TeutoZRItNMJxQBqrwb5wPUYGz7Uu1rTNE07\nOH8ZU6I39FVuEJxlugay0e0rNE3TtC7525gSvaETikHUXoSlEwtN0zSt3VCs3uiKbpTpA4uzFxG0\n/AJfh6Fpmqb52HBJJmCIJxQicpuIuETkb76O5XDd8GDSsPoiaZp2ePJbGlndUE5hS5OvQ9F8YDj2\nCByyVR4iMhe4Ctjo61j6QleDaNrIUudo5e6CDXzbVNExbX5YAn9Im0m42erDyLTB8Mozl7FxWZSv\nwxgQQ7KEQkTCgCXAL4BaH4fTLxZnL2Lev6f7OgxN0wbYnQXr2dZUy1VM5kHm80sms7GxmnsLNvg6\nNG2ALc5eNGyTCRi6JRRPAO8qpT4XkTt8HUx/OfGNBZC9QJdWaNowlWdv4PumSn7NFI6URADmkYRL\nKf7VuJ2i1mZSA0J8HKXW34Zb1UZ3hlxCISI/BGYCR/g6loGiq0E0bXgqbG0GYDyd71LbXxe1NumE\nYhiZ9+/pxo3iCDGkqjxEZBTwCHC5UqrN1/EMtMXZi3jlmct8HYY2hNQ4WviyvpQ1jZU4lMvX4Whe\nRrmThV1eNbXtr1MDQgc9Jm1gLM5eNKKSCRh6JRRzgHhgnYiIe5oZOE5EfgsEKqVUVx+8/vrriYyM\n7DRt4cKFLFy4cCDj7bONy6LYqIfx9ltKKXbb66l02BkbGEFiQLDP4ni2fCf/rczF6U4kYq3B3JU6\ng9lhsT6JSTvQ6KBw5obGsaRpFy6lGE8UO6jhZXazICxBl04MA0O5emPp0qUsXbq007S6uroef166\nuf76JREJBTK8Jj8PbAfuV0pt7+Izs4G1a9euZfbs2f0Wywm3fdBvyzocehhv/1Hc2swd+9axw278\n4EzAGVGp3JwyjQCTeVBjebs6n78Wb2H6hPMYl3EC9pZ61m15iZqaPbwy7njirEGDGs9IUu9sY4et\nljCTlUnBkey/1+naq5V7eax0O55n3gBM/D3zaKaGRg9ssNqA8odkYsX9Z/Xr8tatW8ecOXMA5iil\n1h1s3iF1ZVJKNQHbPKeJSBNQ1VUyMRzpYbz9g1Mpbsz7Dnuri+uYzijCWE8lr9buIdRs5brkKV1+\nrtnp4PP6EgpajLrykyNTCDV3/zOsd7ZR3mYj0Rp80C6Fr1btY3TKkcycaAyYFhocw/FHXcsbH13D\n+zWF/Dghq28bPMJtaa5hSUUOW5tribYEcHZMGudHZ/B8xR7+W5lDq7tUKC0glLvTZjEhOLLL5dQ4\nWniydAfHksyppFFFCyFYeJ7tPFW2gyfGzBvMzdL6iT8kEv5gSCUU3Rg6RSz9SDfc9K1vGyvY19rE\nHRxBpkQAcDKjaFRtLKvex1UJEwjxShT22hu4Lvdbqp0txEkQlcrOM2U7eXj0kYz3ugDZXU4eKdnK\nRzWFtKGwYiI7ehTXJE8msIvSj+LWJmbEdE4aAqyhRIWnUtzW3M9bP7J831jJjXnfkUwIx5BMmbOZ\nx0q28UlNEdvsdWSTwTEkU0MLr7fu4brcb3llwomYgE3NNQSKiekhMVhNJlbWl+FAcRFZhImVVMIA\nOEtl8M/m7VQ7WoixBPp2g7UeG85jSvTGkE8olFIn+ToGX1qcvUgnFT5Q1NqEFVNHMtFuPJG8o5xU\nO1o6JRRKKe4sWE+w08KfmU0CwVRh5wnnZu7Yt46l40/A5FFU/ufCjaysL+M8xnTUsy+rycPucnJH\n2swD4kkPDKOschuTx57RMc3WUk91fQFRsaP7fweMEEopnijZzlgiuIlZWMRox75SFfOcfQdTieFC\nGQtAEiH8Vk3nFtdq/ly4ke8aK7ErJwAx5kAWj5pOq3JhQgikc1IY7D4V/z5/LdtttQSbLJwRlcrP\nE8cTpge78kuLsxfBMl9H4V+GVC8PrWvDcQhXfzcqIJQ2XOSozg2WdlJLkJiJtXa+y9xtr2dvSwOX\nkEWCGA03YyWIHzKOwrZmtjTXdMxb0trMZ/UlXMo4zpQMxkok2TKasxnNx3VFbGqqPiCey2JHU1C6\nge82vUhNfQHF5Vv47Ou/4kKxpDKHews36l4fvVDnbGN3Sz0nkNqRTADMJ4kATFi9TqHREkgkAXzZ\nUMYClcz9HM2dzCXVGcbt+WtJCwjBieILijo+U6gaWcIuzAi7bfUcRRLzXcm8Xb2PK3Z/id3lGLTt\n1Q5Nn2+7N+RLKLT9dDXI4DkyLJ6MgDD+0bqNH6pxjCKU9VTyAfmcH5NBsKnzT6veafRyjqNzL5A4\ngjq9D0bViAJmEAdAm3KxhF18RQkK+E3u18wNi+fOUTOIdhePnx6VSrWzhX/nr2BH7qcAhIXEc/LR\nN1PfVMLHm/9LgjWIqxInDMTuGLasIghgw9lpeisunChqaOk0vU61UEsLE4niMhnfMf1qNZWbWM2a\npirOi05nac1udqpagjDzNWWEYWE+SRTRxCpKOIfR3MQs7nOs5Ybc73hy7PzB2FztIEbamBK9oROK\nYWhx9iKWX7iKr3+2ydehDFtmER4aPZc/7FvPY3ZjP5sQzoxKZVHixAPmHx8UQYCY+EaVch5jOqZ/\nTSlmhEkebSgSrEbSkU8D0QSylN2slnJmT/4hqYkzqKrNY82WJVy4awWZ1hB+EJPOOTHpXBY3lgBM\nPFy6jZOPvpGUhOmICClMoaGxjDfzV/DzhPGYD9ELQdsv1Gzl6LB4Pm7cx0wVR7QE4lKKt9iLC0Ue\nDbyt9rLA3YbiVfYgyAEDVwWImQwVTlFrMydHJPNpbTHrVAUCZBDOrcwmQIxqkLfVXt4ljwUkk0QI\nG2015Lc0khEY5oM9oIH7Zu0NX0fh/3RCMUzpYbwHXnJACM+OnU9OSwOVbXbGBkUQ3033zAhLAJfE\nZvLfyhyqVQuTiGY3tXxJMRfGjCbW/bnKNjsr6koIN1lZ4tqJKMVKSpg+8UImZ50JQGR4CoEBYXz2\nzYO4WoSHSrawsbmaO0fNpNrZSnhQNKmJMzqtPy56LNv3fkx5m51kH42VMVRdlzyFq/d+zW3Or8lS\nkZRjowo71yZNpsrRwiuVe1lGHmDUIStgOzWcq1RHF9IW5SSPekZJAn8oXI+451PAGWR0JBMAZ5LB\n++SznkqaMEqutjTX6ITCB3TVxuHRCcUwp6tBBpaIkBUUQVZQxCHn/VXiBKIsAbxSmcsqRwmx5kB+\nGTeBy+OMRn1rGyu5Of972pQihkDqaOFRNgOQHN+5G2py/GQAjiGZY0nhX3XbuSh2NKMDw2io2ENt\nQxFR4akd8xdXbMYkJhblfMVTY48hSScVPTYqMJQXxx3HuzUFbLfVMskSQXZ0GhPdpUo/jMvkoeIt\nfFlfyhVMIBgLT7OVF9jJyWoUdpy8xV4c4mKnrR6A88hkJnHcyfcor45q7a83UkmDO6HQTyEdfDqZ\nOHw6oRghdGLheyYRFsaN4YexmdiVkyAxd9zBbmqq5oa87zAhZBFBAU2AMJYwcmigonoPcdH7q0rK\nq/cAEE8w44niNfawuqGcH8dnkRgQwopv/sasKT8kLCSevKJvyNm3knMYzSpnCU+WbueP6f03yNtI\nEGEJ4PL4sV2/Zw5gTWMVp5DG8ZJKvWrleFL4mlK+oBiAREsQfxk1l1vyvucI4jlbMgEYrcL5mH3M\nUHEEukspPiAfJ4pt1JBAMK0mB3aXk//VFXNEaBxRloDB2egRSicSvacTihFmsR7G2yeUUuyw11HW\naiMzKJyMwDDKWm18Vl9Mo6ON92oKySCc65lBiFixKwdPsoV9NCIIG7a/RkBAKKmJ06mqzeW7Dc+T\nKuGMV1G4UDhQWMVEoMnMYxlHcnXet3zx/d8BCBQL55LJOYwmADNv1+/FqZRuS9FPbC4HDa42Mgjn\nNbWHTyjA6S5lsCDMDYvjgYy5mEVwoBjD/vYylzOeB9nAzaxmuoplH40U0ogFwYmiAhviEu4uNB5t\nbsXEr5ImsDBuTJexaL0XtPwCbngwyddhDGk6oRiBTrjNBnr8ikHzbnUBj5dsw6acHRearKAIcu0N\nmBGsmGjCwc+ZTIgYRdtBYuEiNZa7+B4LQqolgK/WPdOxzFQJ51o1FQE+JJ9mHJwQkYRSivdrC6hu\nsxGKhWuZTqoKI1iMn3qQMuNAuYvVdULRH0JMFhIsQXzqKCCPBs4lk2NIpho7S9nNhqZqmlwOIsxW\nkq0hbGmr4nSVhogwViK5Uc3kz6xll7mGSSGRpKsQVjeWE4GVetqYTyLnMwYzwofs4/HS7WQEhjI/\nPNHXmz5sLM5eBA/6OoqhT49DMYLp/tQDb0nFHu4v3kSCCuFEUknGePjTHns980niYRZwNdMACKVz\nPXn7aweKe9Jm8dK447k+aTKRJivl7rEL/sB3vEUuP47PIjMonLer9/FCRQ5Hk0gTDqpp6UgmWpST\nLyjmiNDYTmMqaH1jEuGy+DHk08h8kjhHMomVIMZJFNdgDGb1cW0hAFcljWcbNfyHneSqejapKl5k\nJ0Fi5vlxx/Kn9CPYa29gFnFMJoYIrFgxsYIimnFwKVmMIYI3qvJ9vNXDgz4H9i9dQqGxOHsRf7up\nFPuJb/o6lGGlxtHCP8p2cQbpXCLGsNgupfg931BHK5czngAxk6kiCMbCcor4kRrf0a5iOUUIkGwN\nYUV9CdlKnaoJAAAgAElEQVRRaVwUl8nJUSksq97HpuYaks1R3BI9lSPCjDErXq3K5UgS+DmTacHF\ns2xjraoghkBWU0ozDsaYQlndUMbRYQmdRufUeu/86AweKdnGOK/uolESSKIKpqjVGP785MgUah2t\nPFu2ky9dRvuK0QFhPJ42j2hLIPtaGil12DiPMTzPDuw42U0dtbTwPvn8iAlkEkFOa+0BMWg9N/NM\nh/FcJK1f6YRCAzDqDnU1SL9a01iJA8UZpHdMM4mQqkJR0NFVMFDMnK8yeYndlNLEFBXDLmrZTDUC\nBLeZeal8L0sqcrg/4wiODIvnxwnjulxncWszx5KKiPArNYXPKeIrillLBS4UiQSzq6GBmxvWcGx4\nIvemz+5UWlHa2swb1fnsttUTZw3knOh0pofGDORuGhYsJhMp1mB2ttVwHCkd06uVnTJspAeEdky7\nMHY0P4hOY7e9nhCThUx3d9Av6kt5t7oAgDfIwYxwF3NJl3DalIuX2c2L7CSOIKYF6aeS9pYukRg4\nOqHQOtG9QfpP+92/g85DXicSwjoq2acaSJdwAE5iFJ9TRIE0UiRNtCkX8SqI3zOHCAnEphw8qTZz\nT8FG3pxwElZT11UWaQGh7Git4WRGYRETp5GGRQlL2MVVTOYojHr3dVTyZMNm3qsp4LyYDAC222q5\nZu+3iIKJRLOeGj6sLeK65MlcHJs5ULtp2Lg0bgwPl2wlRgUxnyRqaOE1cgg3WzktKrXTvIEmM1ND\njKRAKcWfizbxfm0hmYQzhWi2UcN5jOn4fljFxCUqi68ooRI7l8YZxyPX3sDm5hrCzVbmhyd0+eA4\nzaATiYGnEwqtS7oapO+ODIsnUMy8rXL5iZqISYQW5WQ3tZgQHmQDp6s0otzVEeU082D6kURbAvhp\nziquZhoRYgytHSwWLlZZ3OX8nnVNVRwVHt/lOhfGj+G+ok28rHZzLCnU0cKb7GUckRwt+1uwzyGe\naSqWT2qLOxKKh4q2EK+CuZlZhIgFl1IsZTePl2zn5MgU/RTMQ7gwJoMaRwtLK/fyvjLaOGQEhPFw\n2pHdPuDLoVz8pWgz79cW8lMmcqyk0Kqc/JoviKJz99AATARiZl5EAhOCIrmzYB3/qyvpeD/SZOWe\n9NnMcVd/afvpZGJw6IRC61Z7NYgexrt3ws1Wrk+ezAPFm9lBDaNVBNupphkHs0JiiLUGsawujzZc\njAuM4IGkuRwVHs9G98O/wr0aaYa7LzDtT7DsyllRo6hytPBC+R4+UUbxeZCYiVQHJgPhWKlwGnX7\nu211bLfXcRzJNNFGCBZMIpynMvmcQlbVl3FOTPoBy9D2ExF+mTiBS+PGsNNWR5jZwsSgyI42MV15\nuHgr79cWEkcQC0gG6GhXs4oSjlHJHSVdW6mmgTbOjU7nuYrdrKgr5adM5GiSqMbOi66d3Jq/hjcm\nnESkHqsC0InEYNMJhXZIehjv3js7Jp0xQeG8XZ1PfksTR1jiuDQukxnudgltqS5alYtQj0edTwiO\nJNRk4QtXMT9i/8O8VlCEFWF6SPf15yLClfFZXBiTwQ5bHaFmK1/Vl/Hfir1UKzsxYgzxXada2UAl\n54Sn81Z1Po8UbwVgJSWspITTVToXMxYLJgTBqVS369Q6izBbmduDUoKyVhvLavYxhghacHZKPM4n\nk4fZyL2sYZ5KohI7X1LE7JBYjgiN5e6C9ZxAKseK0V4jkRCuUlO4SX3FJ3VFI76KSo8p4Rs6odB6\nTLev6J0pIdFM6SYJsJoOfAR2kMnMLxLG82jpNiqVjUnEsIc61lHBj+OzOp4wejChZmtH0XeiNYj3\nawq4x7GGY1UyJoSVlBBoNjMrJIZb9q3hBFK5gDFYMfE/CniDvaQRRiV2FIqju6liGQ5qHC1801CB\nAo4Ki+t4rspA22arxQUcTwrPsYMtqoqpEgvAaCIIw0q9uYVXnLsJEjOpASGkB4Wy2VZDnXsgLU8R\nEkCMCqK8zT4o8fsrPaaE7+iEQjtsOrE4uCang+LWZuKsgT26+HflkrhMYqyBLK3Yy7stuaQEhHBr\n7DTOjk477GVFWwJ5eux8/lW2iy/qja6KCyIS+FnCeF6o2EM8QVzB+I6i9WxGs13V8BK7aMLBFXFj\nSQ4I6dV2+COby8ELFXv4qKaIWmcrbcrV8TQNM8JViRO4opthtvtThLtdRQqhTCGGR9nEHBVPGFa+\noxwxwV8z5vLnok3ktDSgWoSVLWW8Xb2PCJOVja4qjnFXkwAUqybKsZEVFN7dKoc1XSrhezqh0Hpt\nse5m2olDuXi2bCdvVOVjV05MwHERSdyaMo2IXtRpnxKZwimRKYeesQcSrMHcPmoGt3tNr2yzk0rY\nAeNRpBNOrtRzd+osTo5MZrhwKsWNed+zvbmWLCIpp6XjvVAJIE2F8lTZDsYEhTM/PGFAY5kZGkui\nJZiXHbu5iimspYKvKKECGyFmC/8YM5+lVbmUtNi409191KUU75LHO65c1lDO82o78zCqRJaRS4o1\nmBMihs/x6omOMSV0qYTP6YRC6xNdWrHfs2U7WVqZy1lkMINY9tHAW/W53O5Yy+OZRx+0cZ6vjAuO\n4LXGPJpVW8ew307lYhNVHBkWxylR/ZPQ+IvVDWVsbK7mBmbwlGwjOCCCmZMuIjwkntyib9iRv4JY\nAnm7Kn/AEwqzCPemz+aGvO+43fUNcRJEhbIRawnkscyjSQkM5dPaYo4ntaP7qEmEH6gMVlBEVmg4\nG22VfOkyenocERrLbanTR1TXUd3o0r/ohELrFyM9sWhyOnijKp+zSOcCMR7cNJZIolUQjzVvYqut\ntmPcAX9yfkwGb1bl81fXes5UGQRg5jMKKaOZO+NndMzX5nLxSV0RK+vLUCgWhCdyelQqAUPs4rWm\nsYpEgrHhxKbayD76RmKjRgOQFD+ZNoeN0uJ1lLXaBiWeySFRvD7hRD6tK6a4tZnRgWGcHJlCkHu/\n2l1Owrx6+5jFRIiykBEYxoMZcylsbSbcbCV+kNp++AOdSPgnPaC/1q8WZy9i5pkOX4cx6ErbmrEr\nJ9Pp3Lp/OkYju732Bl+EdUgJ1mAezTyakCAzT7OVx9hEfUAL92fM6UiAWl1Obsr/jvuKNlHe0EJl\nQysPFG/murzvaHF134XVHwWbzNhwkE89wYGRHclEu7Sk2dhVG6ODwwYtpjCzlfNjMrg6aRLZ0Wkd\nyQTAnLBYVlNCq0dX4Z2qhhKamRMaS4DJzJigcJ1MaH5Bl1Bo/e4s0zWQPbJKK2IsgZgQ8mkgy+Px\n1PkYiYQ/n/AnBkfyz6wFlLXaaFMuUgNCOlXPvF9byLqmKm5mFpPESDJ2q1r+0ryet6v3dYzaOBSc\nEpXCi5U57KORltYGbPY6goP2H6/qun2ImLgsduAbZfbELxLHc3XT19ytvudolUgtraymhOnB0SyI\nGFlPG9WJhP/TJRTagBlJT/KLtgRyYkQSb7OX9aoCp3KRq+p5ju2kWIN7NC6BryUGBDMqMPSAth7L\n60qYQmxHMgEwTqKYTiwrPEZq9GdNTgevV+XxYkUOk4Mj2UI1SsHKtU/S0FSOy+Vkb+Fqtud8zPHh\nCUwIiTz0QgfBpOAonhozn6zwcD41FbDNUsWl8Zk8NPrIEfPE2KDlF4yY88hQp0sotAE3Uobxvil1\nGr93rOXvzZs7pqVYQ/hLxtwhffJ3KEUAZtaocpZTRBV2UgnFjhMZAgNeVbTZWZT7LSVtzSREj6UJ\nY5yGQISKql289b+bAAEUx4Qn8odRM30ar7cJwZHcn3GEr8PwCT2mxNCiEwptUIyEYbwjzFYeyzyK\nHbY69tjrSbAGc0RYHGaPO36lFPtam1BKkR54YHdNfzQ/PIFnmneylgomEMVs4tlCFYU0cVKA/3dR\nfKp0B7Vi4tyT7iciLAmlXGzY8Sabdy3jTjWXcmwU0MB75HNyZNKI6iXhr+b9e7oxQq82pOiEQhtU\nw30YbxFhUkgUk0KiDnhvXWMVDxZvIb+1ETBKL65PmTLg3RP76oSIRJ4u28GZpHOxZAFwkRrLE2xm\nU1M1DuXy2xIYl1J8Xl/K1InnExFmDHokYmL6+HPZtfdT1jsqOE/GMJcEvqecXbZ6Tj/w0PnUdlst\nK+pKaVMujgqLZ25Y3JBIRHujY0yJN3wdidYbOqHQfGKkdTPNb2nkpvzvyFDhXMd0TAgftxVwe/4a\nnhl7DBOD/aPOvivbbXUo4HT2PxzMJMJpKo0HnOvZa29gvJ/G70LRppxYrZ1H+jSZLFjMAbQ5jEfL\nN6o2qmkhzo8azyqleKJsB0sr9xJJAFZMvFKVyzFhCfwpfU63j7AfqnQ7iaFveH0jtSFnpJxEXq/K\nI0hZuIGZTJc4pkos1zKdWIJ4pXKvr8M7qPbSh1Y6dxFtw9XpfX9kEROzQuPIyVuB09naMb2gdB1N\nLXVMJIpqZedfbMMkcFo/jUzaH75vqmRp5V4uIYuHOIYHmMfvmMY3jRW8UZ3n6/D6zSvPXDZizgPD\nnS6h0HxuJJRW5NgbmEg0AbK/ft4iJiarGHJs9T6M7NCODIsjWMy8pfbyMzUJs5hoUU7eJY/0gFAy\nAwdvzIbe+HXieH6b+y3vL/8/0kcdTVNzJbmFXwPCo2zCBYSaLNybNmfQHgzWEx/XFpFKKKeT1tHz\nZhbxzFHxfFhTxA/jxvg4wr5bnL0Ilvk6Cq2/6IRC8xvDObFIsAaxlVqUUp26ZebTQGKA/1zEuhJq\ntnJTylT+VLSRXdSRrsLYTR0OcfFQ6ly/HFLc09SQaJ4eM4//lO9hY85HhJut/DIhizmhsWy31RFu\ntrIgPLHTI+R9yaUU3zdWsrW5BifQQBsR7H8WTDSBFDj9c6C0nnrlmcvYuMzPGqtofeYfvyBN87A4\nexEr7g9m9bSHfB1KvzkvJoNP64pZwi7OVZmYED4gn73U8+sY/+8SeEb0KDKDwnmneh8lbc2cHTiK\n82NHkzoEnkL6cW0h/6nMJd9eT4wlmNMjk7ksbgwWMXX7WPnB1uZy8UVDKesaq/i6oZxyh50wLDTh\n4HpWcYxK5lKyMCOsoYIjw2J9HXKvzN98IyfcZtOlEsOUTig0v3TCbTbIXsQHrsfY8OHQ/5rODI3h\nxuQpPFayneUUAWBB+HXiBI4ZIiMeTgiO5JbUab4O47C8VZXPgyVbSEuaxbykWVTX5vNc/nIKW5v8\nZryJOkcr1+R+y56WehIIpp42wGijYkaIJ5jVlPI9ZYQTgE3auCLOP0byPByLsxfBbYPzjBTNN4b+\nmVob1obTMN4XxI7m5MgUvmmsQCnFkeHxxFgCfR3WsNXmcvGPij2MTT+WY2b90piYAdGR6Xy88Tmu\njBvL6KBw3waJMU5GaUszd3AEmRKBXTm4ka9IIpRrmU6EBFCrWniEjVSKnScy5/lF3D2lx5QYOfy3\nebameRguw3hHWgI4PSqVM6JH6WRigDQ7HbxVnc/vC9ZS57AzNu3YTu+PTTMubpuaa3wRXidOpfi4\nrohTSCNTIgDYRyM2nCxkHBFitJ2IkkAuJYtm5cCJ/49O2m5x9iKdTIwguoRCG1IWZy9ixjm1XPqr\nl3wdiuaHKtrs/Hbv1xS1NZOO0fuk2VbdaZ5mu/E6zGw94PODbUdzLa3KRRT7k0sbxtN6ozwaYhqv\njXkaXf7/NN/hkPxrh0+XUGhDzsZlUfqE5UdaXE5eq8rlNzmr+cWeVfyjbCe1jtZDf3AAPFKylcq2\nFiYRTTrhpBHGhu2vU99YasTa2sh3m14g3BzgFyOUPlexm0BMrKYEl/u5KJlEYEZYRecHr62ihEAx\n+fUgaKCTiZFMl1BoQ9Zw62a621bPh7WF1DlbmRoczWlRqX7TlbE7bS4XN+V9z4bmamYQSwgBLLXn\n8kltMc+OnU/0IFbrFLU08UVDOSJCTWwyhU0V1NsaCbLZefuzW4gITaDZVo0Z+HPabIJ8/MwOpRTf\nNVZyDEmspIT7WcdRKpEq7CgUy8ijTNmYQBQ7qOE7yvl5/DjC/aBkpSs6kdD8+2ylaT0wHJ5m+npV\nHg+XbCWSAGIJ4pPaYl6q3MsTY44mwRrs6/C69VldMeuaq7iFWUx0P968Qtm4u+17/luRw2+TJw9a\nLE+U7SAoIJwzj7uTsJA4lFJs3PkWm3a+TSAmUl0tnJSQxRlR/tN+xSJCjAriBmayjFxeYhehWDAB\nU4OjKXDU821bGWnWUG6Kn8p50emHXOZg02NKaO10QqENC+1PMx2KpRWlrc08WrKVkxnFpWRhERNl\nqpm/tK3n7yXbuSd9tq9D7NaqhjKyiOxIJraqat6TfFqU4rXqAiIsAR1jPgwkh3KxqqGcGZMuJiwk\nDjAe1DZt3NnsyPmIFoed3yROZE5Y3IDGcThEhBMjklleV8Q8krhd5uBSLj6mgNfI4dqUKUwMjjxg\nMDR/0fEgLz2mhOamEwptWBmK1SCf1ZVgxcRFjO248CZKCKeqNN6oz6HF5fTbR2oLgnL3OlivKnic\nLUSGppAQPIqWtgaeKdvFDlsd96XPGdA4XErhVC6sls6lOSaTBTFZSLGGMDvU/waDmh+ewIr6Ehar\nbwhWZqyYqaGFy+LGdLSV8MdkQldvaF3RjTK1YWkodTO1u5wEYMbq9XMMx4oThcPl8lFkh3ZsRCI5\n1LNZVfIaewkOiqK2sYjKmhzqG0oAxRf1paysLx3QOAJMZmaFxrEn7/NODwHLL/6OltZGbk2Z6ncX\n5k9ri7izcD2JKpTTSWcUYdTQwplRo1iUONHX4XVp3r+nD5nflTb4dEKhDWuLsxcRtPwCX4dxUHPC\n4migjXVUdExzKhdfUIQJ+GPhBtr8NKk4KTKZo0LjeZhNlNJEs72Go2f8lEvPfIJLznyCaePPAeCh\noq0DHsuvE8fT0FjEe5/fzrptr/LlmidYtfYpjotI8quqDjCqaP5esp05xHMHR3CRjOUWmU02GXxa\nW0St0ze9ZA5GjymhHYpOKLRh74YHk1icvYh5/57u61C6NCMkmgVhCTzDVp5T23lf5XE3a8ihnpnE\ns7qxnNeqc30dZpcsYuKBjCO4LmkyIiZSE2cwfvSJmExmLOYAZk68kPDQRCqcdmocLQMay9SQaJ7N\nnM8RViuleZ9jqtrB1YkTuCdtlt+VTuTYG6hytnAKaZg8YjuVNBwo1jZW+TC6zoZSaZ/mWzqh0EaM\nE99Y4JcnRhHh3vQ5pAWEsJpS3iKXJtpII4x1VBBHMB9WF/k6zG5ZTSYujsvEKmYiwpI6vSciRIan\nANDscvbL+pxKsctWxy5bHU7VedTIccER/DFtFu9MOJEXsxbww0FoENobVndMrXTeJy3u1xY/SYD8\n8fei+S/dKFMbcfyx4abVZKLe2UY8wdzBEQSL8dP8XBWyhF20OgIOsQTfmxoUwY7i75k16WIsZiNe\nW0s9JeVbCDVZSOqH7q+rG8p5qGgLpQ7jIVNJlmBuTJ3qF4NUHY7MwDDSA0JZ1prHWBVJsFhwKBdv\nspcQsXBkWLxP49OJhNYb/pe6H4KI3C4i34lIvYiUichbIjLe13FpQ8/i7EXMPNM/hjFucTmpdrZy\nKqM6kgmA40khGDPRVv9PKK5NmYzdXstHK+9hz76V7Mz9jI++vBuXcnJVwnjMfbzr3mWr4/b8NcQ5\ngrmFWdzCLOIcwdyev4Zdtrp+2orBISLcljqdQmnkZlbziNrIrXzN95RzS+pUQnw0oFnQ8gt0MqH1\n2pBLKIBjgb8DRwGnAFbgExHx39F/NL91lukavzmBCuDd9FK5/2aGxAx+QIcpKyiCxzKOxNJcwer1\n/+DbTf/BZa/l2sSJXBSX2eflv1aVSzSBXMN0Jko0EyWaa5hOFIG8VuWfbUwOZkZoDEvGHccFcRmE\nhZk5MSaJ57IWcGpUqk/iWZy9yBjPRdN6achVeSilzvJ8LSI/AcqBOcAqX8SkDX2+rgYJNJk5Kiye\nzxoLOUolEibG8MqfUYgdJ+fFZvgkrsM1KyyWtyacSKOzDRMQ0o/DRO+1NzKB6E5tIixiYqKKJtfe\n2G/rGUzJASH8Jsm3XUT9JaHWhr4hl1B0IQrjJq76UDNq2qEszl7E8gtX8fXPNg36un+bNIlFe7/m\ndtfXTFWxVGIjh3oujR1NVlDEoMfTFwPxJM+kgGBy7A2dRo5USpFHPeMCwvt9fcNdx0iXmtZPhnRC\nIcZZ5RFglVJqm6/j8QcuRxuFa96mfMvnOFqaiEybStq8SwiLH+3r0IaME99YANkLBr20IjMonOez\njuX16jw2N9WQYgnmF9HjODI0jvyWRqItgUT46YOhBsOFMaP5Xf03vMgufqCMEpv3yKeQJm6Nmebj\n6Drb2FTNG1V5FLc2kxEYxsVxmX71lFBdKqENhCGdUABPApOBY3wdiD9QysXWN++hJn8DmSlHExIT\nQ96+b9mw50ZmXHY/4UnjfB3ikOKLapDEgGCuTpoEGN0j/12+i3sKNtKsHJgRTolM5oaUqQNSAuDv\nZofFcmPyFB4v3c4KZXSjDRQTNyZNYXaY/wyr/V5NAX8u2kQKIYwhknX2aj6pK+be9NkcH+HbNgo6\nkdAG0pBNKETkceAs4FilVMmh5r/++uuJjOx8h7Bw4UIWLlw4QBEOvprc9VTnruXEo64nLWkWANPH\nn8P7K+8i78sXmXbJH/t9nc42O02V+7AEhBASO6rfl+8PfNW+4l/lu3ixYg+nkc4MYsmnkWV1uVQ5\n1vLI6KP8brCmwXBB7GiOiUhkU1MNZhHmhsX51eO8m5wOHi3eyjEk8VMmYRLBqVw8wRYeKtrCMeEJ\nPhsXYyglE0q5qN23mebKfQRGxBMz5ghM3fR8cTkdVO35Flt1EUFRScSNm4fJYu20rOaqQsRkJjg6\nZUT+bnpq6dKlLF26tNO0urqe96AakgmFO5k4FzheKbWvJ595+OGHmT3bf5/a2B+qc9cRGhLPqMSZ\nHdMslkDGpR/P2q0vo5QL6ceTWeH3b5H/1VIcLU0AhCeNY0L2DYTG+d8jlvvDYD4mvdnp4NXKXM4g\ng4tkLAATiCZOBfF402Z22OqYFDL8Hxld0Wbn2bIdLK8tpQ0XwSYLDa42AsTEqZEpHOFnD/xa31RF\ns3LyA0Z3jIBpFhPZKoM/OdeyvK5k0Htx+FMiUZ27jtKNn9DaVEN48jhS55xNUGRip3lam2rY8tpd\nNJTtQUxmlMtJUEQCUy+++4Bzi622hM2v3IGttoSAgDBaWxsJDI9j2iX3EBqXTuWeb8n537PY64xn\nyYTGjWbc6VcTOWryoG3zUNLVTfa6deuYM6dnD/cbcgmFiDwJLATOAZpEpP3bWKeUsvsuMt8zWQJw\nOFvcicP+p1O2OeyI2YrRMbF/lG7+lJzP/8mE0SczNv1Ymu01rN/xOpuWLmbuVc9iCQw55DKUUlTu\nXEXJxk9oa64lPHk8o+aeT2BEPA5bA9bQqG7vSnxlsB6TXtzWjE05mUnnZ1C0v95trx9SCUVZq40v\nG0pxKsW88AQyAsMO+Zk6Ryu/3PMVtc5WXCicKHDB+WSCgk9rC9hjr+fZscf45WiYXflz4WbGBUUw\nOmjgG5EGLb/Ar7qB5q9+mbyVLxIdmU50WDLFGz6heN37jDryfNKOuhBLYCgAO99/mLa6Sk6bfxuJ\ncZOoqS9g5bqn2frGPcy96plON0U7lv0VaWljzpSFREekERQYwap1z7DtrfsYf9a1bHvzXpLjpzJ5\n3o9wuhxs3rWMza/ewZyfPU5wVLKvdsWw5V9n6575NUavjhVe038KvDDo0fiRhIkLKPjmVbbsfp9p\n489GRKhvLGNH7v+In3hsvxb1FX77JmnJczhqxo87psVEZvDW/26ifNtyUmZl01xVQPGGj2ipLyck\nNo3kmWcSFGGMAKiUYts791O5cxXxMeOJC0+nYMc3lG7+FBEzLmcr1uAIUueeR/rRF/dryUp/GOhq\nkFhLICaggEay2F9VV4DRPTLOGjQg6x0IL1Xm8FTpDkwIgvD30u1cFJPBdclTDvqdfKUyl2pnC6FY\nOZ5kCmliG7W8Qz5nMIrfMJW/2jewqr6MEyL94+IwKzSWEDHznsrjp2p/lcf75BNJABaE/1Ts4c60\nWQMax+LsRfDggK7isNhqS8lb+SLTxp/DzIkXIiK0tjXz0cp72ff1qxStWUbWaYuoyV1Lde5ajpn1\nS5LijVKEmMh05s/4KR+uvIfafZuJzpgBQGN5HvUlOxExsXarUUwfHBjFlKyzWLP1JXK/eIHwsCRO\nOup6TCbjBispbhJvfHoDxeveZ+xJv/DNzhjGhlxCoZTyryuLHwlLHEv6vEvZ8PUr5BSuIiQomvKq\nXQRFJpB5/I8PvYAeUkrRVLWPqdNP6rz+kDgiwlNoqtxHxc6v2L7sAQKsocREpFOSu4yiNcuYfP7v\nCY5KZtdHj1K7bxMzJ15IevIcggIj3cM0b2ZCxkk0NFdQUbOb/C+XUFewlSkX/B9my/7RIttsDZRu\n/pSGkl1YQyJJmnaKTxqdLs5exAeux9jwYf/+lKItgRwXnsTbDXuJU0FMJYZCmniO7SRZgjnSz56e\n2Z0NTdU8UbqD00njHDKxYGI5RbxcvZuJwVGcGd19u5vP64sBuIVZPC87yVF1pCRMAzHxYfkmtlJL\nnApkS3ON3yQUoWYL1yRP5v7izeRQT5aKZBe1VGLnN0wlj3q+aRy4R7n7qnqjtamWlvoKgqISsQYf\n2MV554ePYhIzU8ed3ZFEBlhDmJJ1Fl+tfxZnWws7338IEAQTm3a9Q0VNDlOyziQ8NJGoCKOqo9Xj\noWklmz4CYMaE88jKOAF7Sx1rtixlw47XAbBVFxEflsbegq9IiB1HRFgyVksQSbH/z955BsZRXW34\nme1d0qr3XizJsiRXuWNswJjeQgshECA4IQkJIYSEEEIglBDSSAJ8IQktQAjVprj3bsuy1XvvdXel\n7TPfjxVrK+5YruzzyxrN3Ll3rZ1999xz3pOFtbvx1L4gX1HOOUER4Ogkz72NkKQCusp8ZaMpk75F\n1H/WRdUAACAASURBVMSFx7UFcbwIgoDGFEHvYD2ZHBAVTpcN63AXsoEOOvd+RlxkPnMnL0UuV+Jy\nj7Bq67OU/udRJMk7Oo6MstpP2Fv5X3zbMRKTc26mvPYTvKKbhOjJ2J0W2hr2sOm5a9CGxJIw4zpM\ncTns+/dDuEeGCDenMzhcTvueZaQtvIfYyVeM2zqPl0tl34Ml4x+teDB2Ig827eJ5ewkyBEQkIhUa\nnkma6g/xd7nsdLjtxKl0Z2XU4uOBZqLQcQNp/g+Si4inTOrjo/7mowqKEa+XLIKpYIA6aYhL5vyc\ncLNPNPYNNvLZhl8BXoIUZ5ct+eXmBNZbuii29dGGjVSCuIcckgUT+6U+NDL5sQc5Qc6Up4THOULN\nihfoqdjg22qVKYjKvZC0Rd9GNvr/4rT2MtS8H5lMPqazKuCPHISHpNAzUAdIxEROQq8109Kxm8a2\nbVwy++f0W3ypcoaIFP+1A3W7SYydTl7mVQDoNMHMn3Yf//ns+7652S20De+lrWsvACnxsyia9E36\nLc0YUiad0tflq0pAUJyHBCdMJDjh1NblxxReRt36fxBijCU1YS4jjgF2lr6OJIkMNOwBJCZnfw35\naAa+SqkjP+saVm19Fp3WjOj14HBZyEi6gLioAmoa11Hfupn+oQYQBK5c8BRajS/U39pZzJrtz6NH\nQ9Wnv0drjkcpybls4W/Ra82Iksiu0jepWv0yoelF/m2V0814b4MEKVT8LaWIfSMD1DkshCu1FBnD\nUQgybF43T7buY4O1Ewmfh/6ioBh+HDsRrezseVv3e5xEoztkayMaPeWeo7fojlfr6RxxsIdeYiJy\n/WICIDQ4ibjoQpo7drEo6MxYVR+NG8OS2WrrZjpRLCQOmSBQLQ2yjU5uCU4d13uNZ1RC9LjxOKwo\ndUEIxyF8Kpc9h6WphCm5NxNhTqejp5ySsvfxuJ2YkwtwjQzicQwDEqLooaJ+JbnpSwDweF1U1K8g\nLDiFSVnXsGrrM8yZfC/JcUUAFGbfwPL1j7J+1wsM2/sITZ2G/iA/Hae1h4i4sVFSlVKPQReGZbiT\niNAMZuTdhk4bSn3LZrbve5VBSxu24W4cNduQr36JxJk3odQGTNHGi7PnyRPgnCJu6lU4hrrYtfdt\ndo3uXyrUeiTRS17mVeyr+gClYmx7lS9+9no9eDx2slMXMznnRgA8Xif1rZtp6yohM3mhX0wAxEUV\nEGSMJdgUizkogZqmdUzJuQm91tffQibIKMi6lurGtfRWbyFuypX+ayXRy0hfCwgydKHxCIKA1+Wg\nv2E3ottJUMLEcRcgDy9ZyqQrBvnaPW+e9FiCIDBJb2aSfmwvj+83bKfJYeM2MkkjmEoGeHeoDhH4\n5Snenz8RJmiDecfWgE1y++3E3ZJICb1MPEZS6W3hqfywaScioDpMDo0gyIhWaolSnX1tfCbrQ7nB\nnMRb/TWsogWdpKAZGxO1IdwSnnLsAY6D8RUSLurX/4vOks/wuh0otUHETbuK+OnXHTF/aaS/jb7a\nbcwqvIfUeJ8VUGhwMsP2Pqor19BTsR6FQoPHY0dARlLcdPaUv01rZzHBpjhau/bidNlYVPQgzR27\n0WqCSYqd4R9fpdSRkbSA3WX/JjJ3IemLvj3m/jpzHB29ZUxIvch/zO4YwmLrQJDJmTt5KRq1Tyxk\nJF3AwFAz1U1riY2YRJAxmpqSlQw2lVDw9eeQn4XRvXORgKAI8KUQZHLSL1pK/PTrsLSVI1fr6dy/\nEnq6yExeSGn1x1TWr6Ag+3rAl3dRUb8SmSDH63XhFd3ERR344IsOy0GvDcXutCBJY1tkSZKEJHqR\nCTLSEuZQ3bgGt2dsQY9crkImUyB6nP5jvTVbfSVjlm4AdOZ4InIuoHX7f/G4hr9YCLGTLyd1wV3j\nmrRa8lEwJUuWsu4pLVsmPjdu4wK83FVFpWOIu8lmhuDL4o9Fj0yC14eqWRqVRcQ4tAofD642J/Je\nXxNPi3u4REpAiYzVtNKPg1vCjv5NfZohnBtDk3mrrwGhez99g42EBicBMGhto7VjN3eMQ9OxU4Eg\nCHwvOpt5QdGsHmrHJXq505jOfFPUuFSkjHeuROXy39FXs42c1MWEBafQ3r2fqvWvMtzdSNblDxxW\nVAz3NAIQG3EgGur2OHw5C+YMZhXejV4bSkdPGet3/onO3kpmFdzNztI36BtsJCluOjmpiwk2xdHS\nWYwoepGQEA6qRhNFD4JMQealPzjk/Rk37Roqlz/H9n2vkp44H7tzkOKKdxHkCvRas19MfIE5OAmp\nUeSC6d9HJlOQmjCHj9f9nK6ytcTkLx7HV/OrS0BQBDgpNEERaIIiAOguW4tH9KJVm5iYcQUlVe/T\nO9hAuDmNju5SegZqyc+6bjRpSmBgqImoMF9jJJlMzuScm9iw689UN64lI2kBBp0v8bCxbRuW4U6m\n5t3KsN3XsqW5cw+56UuQjYb361u34PHYCUnyeY1Y2iopf/9JYiImkp17B6LoobjyXRo3vkZ8dCFT\ncm5CrTJQ3biGPbveQWeOI6bgUsab+Q/ZYRwTN0e8Ht7sqQcgm7FRi2zMSMAeWx+XHCU34XQSrtTw\nQsoMnm8v4+8jFQCkq008Fz2NjGNYUQuCwH3R2cwxRfJIy14+3fAY8dGTEQQZrR27iVNpuTY06TSs\n4sshCAL5ejP5+vHrFHsqki5H+lrpqdxIUf6dpCfOAyA+uhClUktZxXJG+trIu/HxQ5ItNSbf+75v\nsIHYSF9OQnPHbtweB7MK7/a/f2MicsnLvJLdZW+zff9r6LVhDFlbUMjV6LQheL1uRNGD02WlqmEV\nE1J8EYcR+wCVDSsJy5h5WLEfmbsA18ggdZv/TVXDKgD0oQnEz7ieps3/xmLrwGQ4kKzb1lWCyRDt\nf2aEmOJ9OVhNJURPuhgQAqZXJ0lAUAQYN8IyiiivWE9LZzF5mVdRWrMci62TQWsbwcYYLpzxALGR\nebR07sY23MPeyv9i0EcQFzmJQUsrpTUfo9SFIEkiH6z+CXGReTicVrr7q0mOK8Kkj2L19t+hM8cz\nMNjMsvWPkhA9GYutk8b27URkz8cYlQb4TLeMhigumPYDf+JXR08ZVlsXcwq/jUKhBiA3/TJ6Bxro\nKF5+SgTFF4xX4mapfQDXaJPzeixjfCrq8DnaldsHzxpBAZCqMfHnlCKGPC48kohZoT6hB3e+PpS3\n0+fxXn8T6wZrkCS4IyyZa0OTvjIW5CfqKSF6PTitvSg1BhSao3t+WDtrAEiKmTbmeGLMNEprluHo\nb6N25d+YcMWDY35viErDGJXOtn2vMiv/TsLN6XT1ViCXK9FrxxqOBRliAImIvIV4nDZCnTFU162l\nunENAgKi5EUmU7Jz/+vUNm/EoA2jvWc/Cq2RlPm3H3Hu8dOuISZ/MbbueuQqLfrwZESPk679q1i1\n7Tnys67BoA2jrmUzzR27SI4twukaRq3SI0kSI/Z+PK19bHj2SmRyBeGZs0me/03UhvETgV8lAoIi\nwBgk0YvT2otcrUN5guY7YRkzCU2bwdrtzxMZNgFBJiM2ciIzJn3Tf44oehke6cPptqHQGFm7/XkQ\nBJAk1MYw8m54DLUpnPbiT+it2szwQBOCIKd3qIn3V/8YtS6EvGuexO2w0bL1HSpb1qHUBZO64C5i\nCy/z32e4p4nE8Fy/mABwOIcINsb6xcQXhAYn0V5/enrLnWzipnI09CxD4FUqkUlZpBFEBQO8TS1a\n5HgkadzmO5582WqMEa+HEdHDzWEp3Bo+vgmN5wIn4ikhSRLtez6mafNbuO0+gakPSyZq0iIicxYc\nNgFROZrLMmTrICzkQH6HxebraDAhZRGlVctJd353TLWYIAhkX/0wZe8+zootT40Zs6On1FfiO0pT\nx07UejNpF97lT/Z02vrpq9mGa7gft92G6HUjCDJctj5sbidxM64npuBSVPqj59rIVVqC4nIO/KzU\nkHfjk1R98ns27f7b6FxlqJQ6mtp30NZdwgXTf0hrZzHDI30Em2LJy1mM22OnonYlxU0lTL7jBf9r\nNdLfhsvWhy404Zhz+aoTEBQB/HTsW0HTxjdw2npBkBGaNo30RUtRG4/P3liQycm5+mG6ytbSU7kR\nuctEbfNG4qIKiY3IQxTd7K18D4fLQvol3yNq4iJsHdXYuutRGUMxJ0/2O2MmzryRxJk34hoZoqt0\nDU5LN1FhCURmz0c+moQXdN2jR5yL2hRG71DjmGNBxlga23dgd1rQqn3hW0mSaOvejz4s8ZjrkyQJ\n98ggMoX6pMtwH/6SbpsTdSGEydUMez24EPk9B9qsp2CiHgsTzqKulidDv8fJ8+1lrB912IxW6bkr\nIp2LT7N19Zniy2xvdOz9hNpVL45xyh3pbaRu9Us0rP8nGZd8j8icC8ZcE5KYh8YUwbZ9/2TelO9g\n1EfSN9jAnvL/EBWWTbg5HUn04nHYDvm715giKPzmHxlqLcMx2IE2JI76da+wftcLTEy/nGBjDE3t\nu6hr2Ujawm+PqRxRG8ynLCqoDYkm/5an2fXKd2HYxoXT7yfYFIfdMcS6nX9g5ZanEEUPBl04l879\nJQq5T+wmxc7gwzU/YefL95B99c9o2vQ6g82+95ggUxCVt4i0hfcg+4pExk4UQTpLv82MF4IgFAK7\nd+/ePa69POY/9Mm4jXU20FW2lsplvyUpdgYp8bMYHullX/VHCDoDk+/405d6A3ndTsr++zgDTcXo\ntKG4PXbc7hFS5t9B/PRrT8EqDtBbs5Wy935NTtoSctIvRfR62F32Fo3t2wk2xTEp8+rRHIq1NLRu\nIefaXxCWNv2I4/XV7qBh3T8Y7mv2ia2UKaQt+vYhfQgOZrinkZ6qLUiiB3PKZEyx2YcN9Z+osNhq\n7eYnTbvwIpGKiRSCUCCwjS50Sjmvps89JV4H44FXkuhx29HJFJiOErFwiyK312+mW5TIzrgcgzaM\n+tbNNHfs5lfxBVwYFHMaZ336OZyYkCQJJPGI5ZyS6GXrn27F7bCQmXQhuRmXI0kiJZXvU9eykejw\nXDp7y5ly51/QhcaPudbaWcv+t3+O22FFpdTjcg8TZIhm4cwH2VvxHq39ZUxf+s/jKiX1OGzUrn6J\n7vL1SKIHtd5MfNENxBRedlpzFEb6Wtn5f/cwf9r3SYg+0Iuib7CR5et/gUKlZ0LiAn/i+Bd8vukJ\n+gYbkQRQK/VMybmJEFMCrV3FFFe8S3ThEtIuvPu0reNEWffU+Iq0g3p5TJYkac/Rzg1EKAIgSRLN\nW94mLqqQOZPv9b/pw81pLFv3CL3VW4mYMPeEx5Ur1Uz82q8YaChmsLkEmVJDRNbc09KVNCy9iOS5\n36B80xuU1S4HfL1O4ou+Rn/dDtbt+AMAKl0ImYt/cFQxMdBUQul7jxMdls3kKd/F4bJQVvsJJW8+\nxOQ7XjhstKJhw2s0b30LpVKHXK6keevbhGfNJeuyHwESgkzhf51PtOlYkTGC19Pn8kJnBcW2fuok\nCwIwxxjJ/TE5Z62Y+GyglZe6quny2BGAIkMED8TkEnmYss+N1i4aHRaWzHuM0GBfJUd89GTWbnuO\nV3rqzltBcTgh4XU5aNz0Op37VuJx2jBGppEw6ybC0meMOc9tt+B2WDAHJTIt7zb/39fMgjvp6a9B\nrTKgUvmqsVLm3zHmWmNUGtPu/SfF//o+jsFOkuOKiI+eQnH5u9S3biZt0b3HJSYAFBoDWUt+SPqi\ne/E4hlEZQo772vHE7bACHJLP8UWyqFylZdg+1gtFkkRGHAMEG+PoHaxj9vQfEhU2AYAgYzRuj4Oy\nvZ+SNPvWcTULPF8ICIoASF43I/0tFBRcMuYbhDkoEb0+nMHm/bhHhpBEL+aUKehC47B0VNNdtga3\n3UpQXDaROQv8WxEHIwgyzCmTMaccX7e68SSh6Aai8hYx0LgXQSbHnFyIQmMgafYt2AfaEd0OdGEJ\nx4y+NG95i9DgJC4segDZaA5DbEQeH6x+kK6yNWNyNwAGGvfSvPUt8rOuJSd9CTJBRn3rVjbveZH+\n2h14PQ6UGqOvT0nRDQiCzN907HirQRLUBp5OnIokSQx6XagEOfqzrJHawawd6uDxthKmEMGNpDOA\nk+W2Ru5r2MZr6XNR/88HzvKBFnSaEL+YAN+efULMNLZ078Mpeg+55lzmSNsbkiRR9t7jWForyEq+\nEKM+ksb27ZS99zjZVz1MeOYs/7lylQ6ZTEFYSOqY97EgyAgLScE20otJH43T1n/YeylUGgpue566\nNS/TVLaOhtatqI1hpF+0lOj8E//WK1dpD/tMOF0YwpOQK7XUt2z2lxsD1LdsBgQicubRsOMDEmOm\nERdVgCR5Ka1ZhnW4m+jEXHoH64kMzRozZnR4DvuqPsBp7UWhPj+7Kp8MZ+8TKMBpQ5ArUKj1DFrb\nxhx3umzY7QMM7/0UQSZDEGTUrXkZU+wELG0V6LShqFUGasrX0bD+XyTOvoXoSZcgV6qPcKfTj0of\ncsiesSAI6MzHvw9v7ahhUvoVfjEBYNRHYA5OwtpRfcj5XaWrMRljmJhxhf/Bbh3t35AYNZmosAl0\nD9RQu/E1nJYeMi65z3/tiVaDCIJAiOLseb2PxD+7a5mImXs50BAsXQrmEfd2Vg91cOlBVSkdrhG2\n23oQBAUu9zAqpd7/uyFbBzq50p+cej5wtFyJweZ9DDTtZcH0HxIXlQ9AeuJ81mz/HY0bXhtTUilX\nqlGZwmnv3o8oev0JyV6vm87eSiLDMmlq30nSpCNHGxVqHZmLv0/awm/jdQ4ft2Pm2YhcpSWh6AYq\nNvwLu8tCbHguPQN11DStI3rSxSTN/jq2zjrW7vg9Ok0I3tHS1bzMq/B4nIBE70A94eYDicA9/TUI\nMkWgCuQInD/vygBfGkGQETXpYiobVtHYtgNREhmx97Op+CVE0UtG0gXceMlfuGnx38hJW4KlrYKc\ntCUUZl/PoKUVpUKLTmGkbvVL7PnHfTitvWd6SeOKShfEkK19zDGv6ME20uvPkD8Yt92KQRvmf9A7\nXcOU1X3KxPTLmT35HtIS5zIz/04KJlxH574VuA7zjfHhJUvPWKOn8UaUJGqdFgoIRxAERElitdTK\ny0IFCkHBv3pqqRqtSABYMdiGChkySWRL8d/9ZmfNHbuprF/B5cGxh/SEOBd5+8Wbj/l/bGktR6Uy\n+H0ewCciU+JnMdLfgmc0rP8FGZfch22kl7U7/kBXXxWdvRWs2f48ducQ3X01KDRGovIWHnNucqUa\nlcF8zoqJL4ifcT3pFy2l09rA5uKXaewuJnH2LaRftBSZQsnErz2OOXUaI45BggzRzCz4Fgq5mqrG\nNShUOjYVv0h7dykOp5WapvWUVH9IVO6FxyzF/aoSiFAEACBp9q2M9LWwYdefkcmViF4PMpkcrTaE\n6Xnf8H84KhRqlAoNWcmL+GD1j0mKncaMSXegVKgZtLSxctsz1K56kZyrf3aGVzR+RE26mPqNrxMd\nlk1SXBEej51dZW/hdNmImnjow9kUN4Hmzf9mxN6PTmumf6gJr9dFSvysMeelxM+muOJdhlorCM+a\ndcg4ML423mcKmSAQIlfR7vW5k/6dCrbRRULUZDL1kbR07OSe+q38Pmka+XozVq+bINTcQBovdhTz\nbscelHIVLq8DELgjIuPMLugkmbn/Rz7Ds4+Ofa5Co8fjceByj6BWHYjUjNj7EWQKZP8TnQpJnET2\n1Q9T+/kLfL7pCcD3hUGSRJThMWRftPSEy8HPZQRBIKZgCdH5lyJ5PQhyxSHbQTlX/4yG9f+ko/gT\nuov/D0GmIDLnAmKnXEXVst+yausz/vPDM2eTuvDsTcg80wQERQDA940k99pHsXZUY2mrQKEx0FW6\nGqNL6et/4XXR3lNG70A9MpmS1q5iRElkau6tKEcfasGmWHLTlrCz9E08zpHzJmkpbto12Lrq2LTn\nRbbtexWv6AIEMi65D33YofuoMZMuoWPPcj7Z9DgTki/C6fR9ixy29xFkPJBMODzii+TIVEfvI3Aq\nbbxPF1eYE3izpx6DpGQrnczM/xZpib7Qe37WNazY9AQvdFXxckoRuboQ/t3XgAkVzzGTXXQz7HWz\ng270atk5bWb18JKl8JAd0ethuLsBkBgZ6KC3ahOi1405ZQpRuQuRj/5NhGfNoX7tK2zf/ypFebej\nVGrpG2yktPYTwrNmH3Z7MTxjJmFp07F11yNJEgq1HoVKi+orHKYXBAFBcfi/G5lcQeqCb5E462ac\n1l5UhhC/6Cr85p+wdlThtPZhiEhGG3J+JgOPFwFBEcCPIAiYYjIxxWQCPkOXzt0f09S2k+37/onD\ndSC8WlG/AoVcOeZbE4BOEwKSiNft8AsKx1AXg037kCnVmFOmnHNCQyZXkH3VT7F0VDPYVIJcpSU8\nY+YRH9BKXRCTbnmG+nWvsKfiHSTRiyDI2FX2FgsM0Rh0YYzYfd1ZZXIV5qT8o95fkiSs7VVMuKIT\nXeEi/rh7xTlnEXx7eBqNDisfWBtQKDSkJMz2/04uV5KRfCGbi1/G5nUz2xRJliaI3ztKWEgcZjSU\n0U8rNp6NmnoGV/HlOXhro6dyE3WrXsQ57NvqEgQZel04Bl0YdatepLPkcybd/BQKtR6VPoTMS++n\ncvnvaOncg04TgtXWiT4skdQFdx3xfoJMjjEq/Yi/D3AoCrXukERL3zMx6whXBPhfAoIiwBGJLVhC\n+55lbNj9AlFh2UydeAtadTC1zevZXfYWgC/JK9ZXcilJErXNG9GFxKLS+yy061a9RNueZYDP70Su\n1JK55P4x2ennCqboDEzRxxdu1wZHkXPVw4heD5Iosu/tnzHUXsl7K3+EUR+ObTQ6kbLgrqPuUzut\nvZS99wTWzgPJn0vicsi++mf8du0bJ7eg04hKJufJxCk8317GB4PtiF73mHC92+NAAOSCgEKQ8Xzy\ndP7WWcnngy04JC9ZmiCeiZxKkTHizC3iS3KwmBhqLaf8o6eJjyogp2ApouRlX9UHvqRJcwaXzv0l\nn21+gpbt75E89+sARGTPwxQ3ge6ydbhGhoiLySQsoyhgrhTgrCMgKAIcEbUpjMjcBXSVrGDe1O+i\nUvoiCzlpl9I32Ehj2w427f4bXX1VBBliaOrYQVdvJdlXPoQgCLTtWU7bnmVMzrmRjKQFOF1WdpX9\nm4qPnsHwrb9+JcKHMrkC5DDpxido3PQmHSWfYR3pQxscTeqCOwk9iv+FJEmUf/AU3qFeLix6gPCQ\ndOpaNrK38j3K33+Sh295mrXXbmLrHfuOOMbZxvWhSfy3v5GSqg8ozL4eQZD5mkDVfUqRMRLtaOMm\nk1zJg7ET+VFMLh5JPCdLRA+XcNm66wOCDNHMm3qfv2ooPCSV/678IXUtGwkNTiI5ZgbtVZv9ggJ8\njpQJRTectrmfT9i6G+ip3ITocRKSlE9IcuERW7IHODkCgiLAUZG8HkzGaL+Y+IJwcxpNbTvISV9C\nbdN67E4LgiCQueSHhGfNAaBjzyckxkwlJ83XGlipUDO78B7eXfEDOvatIGXe7ad7OWcMmUJFyvzb\nj9ro6H8Z7q7H0l7BBdPvx6SPZtXWZ+gdqANgqLWU8g+fYp73AWRLZn8pG2+L181GSyd20UuhPpSU\n05CsF6fW8+3ILP5au5zW9h3o9ZF091Vikin4fvKMQ86XCwJy4dwSE0er3LD3NBMfnjOmBFkuVxEZ\nmkVXXxWVDauICc9F8rpPx1TPexo3vUnT5jdQq40o5Gpad76POXkyOdc8guwIORUBvjwBQRHgqOjC\nEuncv9JfsfAF7d37CQmKp2DCdRRMuA6H08Y7ny1lqLWczpIVuGx9OC29mDPHdjBUyFUEGWLOu9LS\nU4HD0g2A2ZTIyq1PI4peLph+P8HGWFo6drO7/B0aTP8i9YI7eXDh7Qy1lPKNXcsp0If6v+kfidVD\n7TzZug+n5EWGgBeJi4NieTguD8Up/vZ2a3gqeboQ3ulroHSgFjUgAn/sLOfWsFTyxrHd9+nmWGWg\n6uBIevprkCTJnwcjSiJ9gw0YtGH0W5poaN9GWO780zDbcxev20HH3k/prd6KJIqEpk8jpuCyMflZ\nQ63lNG1+g7zMq8jLuAJBkNPWVcK6nX+kZed7JBZ97Qyu4PwkEPcJcFQicy9AqTGwattzNHfspqe/\njq17X6Gtq4SctAPueYrR/dzOks/QOkWSQ/ORCTJau/ZycL8Yu2OQvsEG9OFJp3sp5xy6UF+CWHn9\nZ1hsHcydspT4qAKM+giy0xaTm76EjuJPaNuzjG0v3Ebpfx/jx027uLplE58MtB5x3FbnMI+17CVP\nCuV3zOavzON2slg51M5bvQ2nZW0Wr5sNlk56PA4UunDCY4vYJwosbdjK54Ntxx7gLON4PCUAYgov\no2+wgZ2lbzBs78c63M2WPS9jG+k9kEujVBM//bpTPONzF6/bScmbD1G39u94+joRLEM0bniDva//\nGI9z2H9eV9ka9PpwJmVehWzU6j4uKp+k2Bl07199Bldw/hKIUAQ4KkqN0dcKePnz/v4XMpkCoz6K\nxJgDGfclVR8AvhLAvMyrAIgIzWDdjj+wYdcLZCZfiNNlo6TqA+RqLdETF53+xZxj6MyxhKZNp6p+\nNXKZakxraYDI0Cz2V39E7cq/kpowh4npVwAi+6o+4onWLVz6WB49z7ZTbR8iWKEiWxuMIAh8MtiK\nGjl3MAHV6HbCXGKolYZ4r6+RK0Lij9q462Rxil6eaNkLgpyU2OnMKrzb75Wwfuef+W1HCfEqPRO0\nQaelmsUrSZTbB7CLXnK0wehPINnxRDwlAEJTp5I05+tUbnydyvoVACjkaqLCsujsrSAkqZDMS39w\n3B1+v4o0b3sHW1cdMmRo1UFYhjsBCXt/K227PyZx5o0AeBzD6DXmQ/Il9Foznr7SMzDz85+AoAhw\nTPThSRR84/fY+9vwuuw4bX2Uf/AbPlz7MHGRkxiwtNLZUwYITEi9xH9dQvRkslMXU1H/OU3tvRi+\nrAAAIABJREFUOwAwRWeSt/ghlLrzo8X2qSbrsgfY/59fYmkro2+wcUxPgu7+al95oD6Smfnf8n/4\nziq8i+6BWhb+eDWOwU4kSQQgSWPi4ZhcetwOItH6xcQXxKFns6eDJZWrmGuK5IGY3FNi6717uA+L\n5AEgN/2yUTEhUVqznPbu/Xi8bu6q30yKJohH4/JI05jGfQ5fsHe4j1+1lNDlsQOgEeTcEZHOLeGp\nx7jygKfEiZI480aC4rKpXP47nJYePF4nvdZmUhfcRdzUq054vK8aHXuWY9BFcNGsh3ziwONkc/FL\ntHQU01u52S8oguJzqKvahMXWgckQDYDH66KxfQem+JwzuYTzloCgCHBcCILg7xJqJJ38W5+ldft7\nNHbtR2UwE52/mI69n+L1ulEqDhg1hZvTKK/7lIk3/AptSCza4KgztYRzEoVaR/7Nv2HHi3exYfdf\nmDbxVoKNcbR07Ka0ZjkKtYFIc8Yh7n9KuYrhwTYKs79GUuw06po3UVrzMXfXb0EuyPAi0iRZSBR8\nH9aSJLGHHqLRcwGxfGxp4EeuHfxf6uxxt7l2iV7/v6XRcuLa5g0UV/yHCSkXk5YwhxHHAMXlb/P9\nxh28lT4P4ykokexxO3igcScJkpE7yUaPgrVSG3/pqiRCqWFR8OH7vbz94s2UfHSo5fqJEJyQx/Rv\n/4OR3iY8LjuGiGTkyqMbnAUAh6UHt8PKjMm3oh/N6VIo1EzJvYWm9p1jtjwicxbQtvNDPt30BFnJ\nC1EpddQ2b2DY0U9G0cNnagnnNQFBEeBLYYrOIPuqh/w/u0eG6Nq/ir2V745adctwuUfYX7MMY2Qa\n5uTT3230fEGQycm78deUf/AbVm/97RcHicxZgOh20tlWiSiJ/soBj8fJoLWdrJRF5KQtprWrhL2V\n7xEVlk1qwmxG7H2U1X7Ck55ibpLSMKNmPe1UM8T3yWOSEEacpOdpRzHbbT3j7v2Qrw9FgYAgyNlX\n9SFzpiylvPZTEqKnMnXiLQCEBCUQbIrn/ZU/5PPBNq4LTRrXOQB8PNCMJMF95KETfI/Cm8mgS7Lz\nVm/DYQXFw0uWHvf2xrEQBCGQS3SCiG4HABr12KiVRuXrraGPSPIfU6h15N/yNPXr/8X+ymWIHjch\niflMuuIHGCKPHYEKcOIEBEWAcUGpCyJ14T1Uf/5n2nvKCDHG0dlXiSTAxMt/faand86jDYmh8PY/\nYuuqxWUbQB+RjMYUjqW9ir2vP8D6nX9iYvplSJLE3qr3kSQvEWafCde+qg+IDM1k0cwf+3MV5HI1\nu0rf4FWqADCi5B5ymCSEAZBBMHoUVNst4y4oghUqbg5L5bXeWprad9Czoo4RRx9ZKReNOU+vNRNs\niKbJaRvX+39Bi3OYBAx+MfEFmQTzmatpzLHzpVHbuYJjqIuOks8Y6W9DGxJD9KSL0QZHow2JQW0I\npbpxLVFh2f7IXE3TegASZ908ZhyVwUzWkvvJvPQHIInnfLOzs52AoAgwbsTkL8YQkUJHyWfYrH1E\nFV5KTOESNKZzz93wbEQQhEPslE0xmUy44ifUrvwbLRseA0ClC0Gh0tHVV0V89GR6B+qYnnc7giDD\nK3pYt+MPtHWVYNCFI3pdjDiHCEPLNA78Pw3gZAQPcuCvnRV4JYnLQ+JJHCevChERJTJuIp0axxA7\nBQU9AzVkJi/wn+NwWhka7iIm4tRYSMer9WykC7vkQXuQqKhmkNhRS/mAkDh1iB437Xs/oad8PaLH\nRXBSPnFTr2akv5XSdx9DLsgJDU6mq2EvbTs/JOfaRzAnF5I873Yqlz/His0W4qIK6B9qpKF1G5G5\nCzFGpR32XoIgwDnmZ3IuEhAUAcaVg3uBBDg9hGfNJjR9BtaOahgVHc3b3qFy85toVEaUCh22EZ+n\nRVX9Stq7S1kw/X5iI/MBicqGVezc/zofUM+VUgq9OPgH5cgQeLG7GnE0z+HffQ1M14fxTNLUk/aq\nWDHYzmyimSfEMo9YEqRm3mrZTJAhZjSHYpDd+19HjcAlR8hlOFkuC4nnjZ56/iTt4xIpgQoGKKGP\nTkZYGpQVEBOnEEn0UvrfxxhsKiEuqgC11kBzyQq6y9YhyGREBKdywfQfoFRocHucrN/5R6qXP8+0\ne/9BZO4C5GodLdveZW/1+6gNoaQuuJPYyVec6WV95QkIigBnLbauOpq2vMVQ837kKi0ROReQMOMG\nfydGAGtXHbbOWlSGUMzJBV/ZkKZMriAoLtv/c2LR1/DYbZQUf4gkeqisX0lUeDb1rVtIjJlCXFTB\n6JkCWcmLqG5cy8fWJj6lGQ8SKgQkJJIwcRXJlNHPPvrYPtzLL5qLeTLx5HJi7KIXEwdKUxcSTx9O\nVlW8S3HFfwAIU2p5NnHKKak0AYhQank2cSqPtuzhD2IpAjJCQ5LRDHfw1+5qssrXEZE9/5Tc+6tO\nb/UWBhqLWVj0IDERuQDkO67lozU/xekepqBgqT+5W6lQUzDhOpavfxRLWznBCXmEpc8gLP1QZ9UA\nZ5aAoAhwVmLtrGXvGw+i14SQnbgQu3OIuh3vM9RSyqSbfoPX7aTiw6fob9jtv0ZjiiDn2kcwRKQc\nZeSvBoJMTtrCu0kouoGhtkpatv6b1Vt/i0ymJDJ0bPdEQRDQa81IJhPhWXO5rWoHz7WXYhXdXEsq\nf6UUDyLZmBGA9dZO/t5VzZ2Rx9co7XAU6s1ss3ZxsZSAWpAjEwRmSlGspIVrzYnMMUVRoDefdCRE\nlCTe62/ivb4mutx2UjVGbglPZZ7JV21UaAglSq1HkmtZOPOnaDVBeL1uNu/9P6o/+xPm1Kko1Ppj\n3CXAidJZuga1ykhD61ZsIz0kx81EpwkmOjyXpvbtKBVjrf6/+Nnrdp2J6QY4TgJOmQHOSho3vY5R\nG8bl8x5nUtbVzJh0OxfOeICh1jJ6a7ZRt/pFrK3lzJv6XW69/BWWzPsVWkFL6X8eQwz0QfCj0gcT\nnjGDgtueZ+INj6MNi6exfTsu94j/HOtwN529FYSlFxE35QrW3PJrLKKbENR8QhMGlDxNEd8VJvI4\n07mCJF7pqeHNnjqcB5WAngjfjMhgUHDyK3ayXGrkLamGZ9hDutrEd6ImMNUQNi4W4H/oKOf5jjIi\nXTqukJKR7AIPN+/mo/5mADpddspHBpiYeTVajc8bRS5XMiX7RrxuB321O056DgHG0rrzA/rrfK/r\noLWNbSX/ZPn6XzDiGESjNiAIMiobVo5x2K1qWIlMoSYobsKZmnaA4yAQoQhwVjLYVEJ+xtUoDgp3\nR4VlYdRH0rb7IyxtleSlX07fYCN7K/6LBISHpDHQspH+up2EZcw8rvsc3FPhfEYQZJiTC9EERVL8\n6v0sW/8o6Qlz8XidVDetQ20MI3rSxf7zNYZQ+m199OPkNjIxCqrRcQQulRL5lGZe6Krkvf4m/pg8\ngxiV7ki3PizpWhN/TSni713VfDLchEYm5/LgBO6ISB+3zqJ1dgvv9jeiRMYOuunDwZUkE4Sal7qq\nWBwcx4Rfp8ENa1Apx0YhfM3wBES3c1zmEsCHfaCdujX/x4TUi5mcfSMymZxBaxsrNj/F9pJ/0tVf\njSkum+rGNQzZOogKzaKrv5rOnjJS5t8RiBad5QQERYCzErlCjdM1tlxQlETcbjuu9iok0UNt80Zc\nbhtJsTMQBIGG1u0IgpzhvlbCjjH+YPN+mja9wVBrGQq1nojcBSTNvnVMc6HzEZ05lvxbf0vjxtco\nqfkQmUxBWOZskuZ+HYXG4D8vYc6t1Hz6ByRA+T+BTBkCCgQuII697j5+07qPP6Wc+H52hjaIp5Om\nHvvEL4FHEvlp827UyFhIPGY0bKWT5ynhBtLY6u3kR9MWo9+ZhMb4PlWNq4kOz/bbNFc3rgEgOHHS\nKZnfV5Xuig0oFBoKJlyPbFQ4BhtjmZByEcUV76IJiiL7yp9iaSundecHVLSsRRMSTfaVD/m7GAc4\newkIigBnJcbYLKoaV5McV4Q5KAFJEimtXobDZQF8OQJ25yCXz3+CIKPPVjcnbQkfrfkp9v4jN8YC\nn5jY9/bPMAclMiXnZkYc/VTtXYG1rZL8W5897xM79WEJ5Fz9s6OeE513EY7BLlq3vs1KWpgqRfit\nujfQjh0vs4ghERMvj5TT5bITqdKejukfF1us3bS5R3iIQjIEn6vlPCmGp9nDetoBkKu0CDI5yfO/\nScXHz/Dppl8TF5FPv6WJ5vZdxBQuQRsSfSaXccYQPW4s7RVIoogpNmvcXDy9bicKhRq5bKzzqUZt\nBCQm3fo0Kn0wYRkzjzvKGODsISAoApyVROYuYKB+N8vWPUJYcDJ2p4Vhey+p8XOoa9mIUmMiKijF\nLyYAjPoIEmKm0NvbctSxGze+jjkokcWzH/F/S4qLKuDzTU/QV7t9zIPM7bAiCPLzPnJxOJLnfp3g\nxEmUvvMIPxW3UyiF0cUIpfQznxjiBQP20Z4cVtFNJCcvKMZrC2rfcD/haPxiAkAmCBRJUbxKFcaI\nVLQhMQBEZM9DoTHQsv1dyho/R20MJf2ie4nOX3zS8zgX6a3eQs3nf8Y1MgSAQq0ndcFdROWdfEO/\nkMQ8Wra9Q2vXXuJHK41E0UNN03pMMVloDIGmaOcyAUER4KwkNHU6cpWOIG04RkMUocHJJMZOp6x2\nOWpDKJqQWDzOQzO+PR4XMvmR/6wlSWSotYxpE7/uFxMAkaGZGAyRDDaXEpYxk8Hm/dSv/TvWzhpA\nwJwymdQL70ZnPjWeCGcrIYl5FN7+Bxo2vsa6mu2EoeEOJjATX5XENjoxyZTEq7783rYkSXww0Mzb\nPQ20uoeJVeq4ISyZa8yJxyUuJEniv/1NvN/XRL/HSbLGSIRSgw03TsmL+iBDo34cyBBIv/QHY8Yw\np0zGnBKwh7d111P+4VPEReSTN/VKZDIFZbXLqfr096iDIgg5yS2g4MR8zMmTWb/zz6TGz8agC6Ox\nfQeD1jbyLnl8nFYR4EwRqPIIcFYiV6pJv2gpfUNNdPfX4HQPs3HPX2nvKSP94u8SkT2X9p5SOnrK\n/Nd09VXR2rWXsKPutQooVFrsjoExR71eFy6XDYVah7Wjhn1v/xy1S2J24T1Mz7sNV1czJW8+hHv0\nW9tXCX14ErnXPEJY9jx6cNKEla108heplHW0c/toIqVHElk11M4vWvbw8+bdLB9owS2Kxxz/le4a\nftteSrRbz61kEuc28ruOMl7urj7mtcNeDzfXrOf5jjIaXTYsopuGESsrh9px4OVtanBJvkqUWmmI\nVbQRPvFCjJGB0uIvcFh6sA90IEki7buXodUEM2/qdwgNTiLEFMesgrsJCUqgbffJNzERBIGca35O\nfNH1tAyUsa92GZjDmHTTbwhOyBuH1QQ4kwQiFAHOWiKy56E1x9K+ZxmDg50EZ84gpvAyDOFJiF43\nvVWbWbnlacLN6QiCjO6+KoLjJxKTf8kRxxQEgYicC6gsXU1cVCHh5lS8Xjd7yt/B5RohIns+DRtf\nxagL5+JZDyOX+d4i8dGTeW/Vj+go+ZyEohtO10twVpFx6f2og6PYuGc5qx2t6IKiyCj6HnvzLkJc\n0k/xkgfYbOsmBRNyBJ607GP5QCvPJ007YuWGxevmjd46LiWR6wRfw6YLiCVc0vJmTz03hCYTrFAd\n9lqPJPL1mvV0eRz+Y0aUOPAShoYBuch6bwdbhR6MgopeaRhTZBppC+4a/xfnHMTaWUvN5y9g7fQJ\nN21wDCAhR8ay9Y+iVQeRnjiPxJhpRJgzaO2vGZf7yhQqkmbfQtLsW8ZlvABnDwFBEeCsxhiV5mvs\n8z/I5Epyr/slPZUb6K3eiiRJZE6/n4jseciO0eo6ee5tWNur+HTjY5iMMTicFlyuYdIW3o0uNA5b\nezVp0dP9YgJApwkm0pyJpaNq3Nd4riCTK0ie83WSZt+K5PUgyBX+LYmVT++hytbN95jIIC42Cp2E\noGXfSD8vdVVxX3T2YcestA/ilES6GeE70ga8iEwklHnE8DEi5fZBZh6hOdkfO8rp9ji4iXSmE0kP\ndt6kmk5G6MUBXphwxU+w97fhdtjIjssmLH3GOZ9063HYsLRXIlNqCIqd8KXW47T2su+thzFqwpg7\n5Tso5GoqGlbS0b0ftcpITMREhqztbNj1AjlpjXT3V6OJiDkFqwlwPhEQFAHOWWRyBZE5C4jMWXDs\nkw9CoTGQ//Xf0lu9jaHWUoLVeiKy56MPSwB8nVMttq4x10iSiHWkG2PMVyuH4nAIgoCgGCvaeio2\nkSmEsFvqZTMdxEXkE2WIxNW2k3f6m7ggKJpcXcihY0m+fdcqBrmQOFTI2Eg7f6EUAIPs8I8oryTx\n2WAbFxLHIiEeABMq7pVy+Qlb/edpQ2KImDB3nFZ+ZpEkieZt79C85S1Ejy9/SGOKIPOyHxEcn3tC\nY7UXfwpekYtmPoR6NP8lJmIiH6/9GUZ9JDMm3Q7A/uqP/VboeYu/PX6LCXBeEsihCPCVRCZXEjFh\nDumL7iV57m1+MQEQmbeI5o5d1DStRxS9uD1Odpe/jW24m8iJx5/p7rB0j+5NS8c++RxH8roRJZHN\ndFCUfwcLZvyQqbm3cOXCZwg2xvFC5+EjO1UOCwICP2MK1wgpXCYk8ShTUSJDJ5MfVoQA2EUPw6KH\nNILGHA8TtASN9gjRGsIxnEe5El1la2jc8CpZSRdy1YXPsnjOLzApzZT+51Gc1r4TGsvWXUdkaIZf\nTADIZHLiogoYtB6okpqQejGCICdiwryTTsgMcP4TiFAECPA/xOQvxtpRzda9f2dX2ZuIohev6CFl\n/h0ExR7b+tfSXkXlx89iH+wAfO3E0y75DuHpRad66meMkJTJ1DbvQ63QkppwICKgkKvITFnE1r1/\nZ9jrQf8/FTh7h/vIJoQI4UDJqU5QMk2KpFTWi2x0S6XJaaPGbsGsUJOvN6OTKQiVqyn3DjCNSP+1\nHdIwg7gQEMi88kG/UdX5QNvOD4mLKmBKzk2jRyJZMP37vLviB3TuX0nizBuPeyy1IZTBjl2Ikojs\noNdowNKMVnNAxEmSCAKY4g6/ZRUgwMEEBEWAAP+DIJOTteSHxE6+gv6G3cjkKsIzZ6IJijzmtfah\nLva+8SCS6CHYGItcrqJvsIGK959EeeOTBCdMPA0rOP1E5y+mdft/8TjtSKIH5AcSKb1eNyAgP0wJ\nqF6upB37IceHcBGsUOEUvTzeWsJaS4f/d3FqA7+JLyRozi1sWPcKRknpz6F4m1rkgpy8W57FFJt5\nStZ6prAPtJOZMdZZVKXUE2xKwD7QfkJjRU+6hI6Sz9le8i8KJlyHXK6isn4F7d37mZ53O+DbYtlX\n9SGSJBGaNn28lhHgPCYgKAIEOALGqDSMUWkndE3d6peQRA9zJt9LcpwvItHVV8WKzU9Rs/KvTL3z\nL8c1juj10LL9XTr3foZzuB9DRCoJM28g7CyNcijUOnKue5Ti137I/pplTMq8GkEQsDuGKK//DHPq\nFH51+X0APLnc9xrYRQ8zDRGsGmpnjdTKfGIRgBL6KKaHpcFZ/Kmzgk22XmYW3EVCdCGD1na2lfyT\nu7oqmHLlD/A4h/lsx/ss9zYBYDDHU3jtI+elX4g2JIbO3kpy0i71H3O5hxmwNBOfc2LW58bodNIv\n/g51q16kpmktCAJIIMgU7Kn4D119FQxY2xiytJI87xtoTOHjvZwA5yEBQREgwDhiaaskNDjZLybA\nZ5qVGDOVls7i4x6navnv6KncRFrCXIKTYmnp2EPZe78m67IHiMy5wH/eSF8Lwz1NqI1hGGMyj2kE\nJXo9CDLZKdkKMMVkkjj7FvZteoPG9h2YdBF09lUgU2mZcFCp5o/m3YjutQdZb+nEi4RRpuR1sZpP\naUaJjE5GmGEI59LgOK6sXkNOxlWkJfi8RSLM6cyZfC8fr32Y/rpdJM+9jfhp12DraUSpNY3JhRlv\nJNHLQGMxTls/hshUjJGpp+xehyNu2lVULnuOnaVvkJm0AIfTyp6K/4AgI+oEcnu+ICZ/MeEZM+mr\n3YEoeghJKkAQBNp2f8xAZw2qmGTyFt9LSFL+KVhNgPORc1JQCILwHeABIAooAe6TJGnnmZ1VgAC+\nZM//7VwJoFbqQXZ8H+K27nq6K9Yzs+Au/wdpVvIi1u/8M40bXiViwly8bieVy56jr3ab/zpDRAo5\n1/z8sFszQ61lNGx4laGWUt8WTtZsUuZ/E5XB/CVXeniSZt1MUFwOXftXMTwyRGz6dcTkL0al9+3L\nix4X+9/8CTLrAJeTSANWqsQBZIBWJSNTG8SlqlhmGiOwet24RC9hIWMTK4ONsSgUahxD3YCvaudE\nqxxEUcQ51IVcY0ClNQLgsvUz0LQPW1ctglxJcMJE34epJNFe8hmNG17F4zjQsM6cPJkJVz502mzZ\nI7IvwGnto3rLW1TUfQ6AxhRJ7vW/RG38cpbVSl3QIZbaqQu+ddJzDfDV5IQEhSAIk4DLgX7gHUmS\neg/6nQn4vSRJd4zvFA+Zw9eA54C7gR3A/cDngiBkHDyfAAHOBGGZM2nf/TGD1jaCjb6wu91poaFt\nK6a4Yyd0Agw2lyKTKUg5KMohCAJpiXNp3vYcDksPjRtfZ6ixhFmF9xAbmUf/YBPb9v2T0ncfY/Id\nfx4TgbC0V1Hy74cJMcUzPe8bOF3DVNSuoKS9isLb/4hcdfjGTy5bP52lq3EMdqILjSMydyFKrRHR\n66GvZhuW9kqUOhMR2fPRmA54RYQkTiI4YSK27kacQ124R4aQRBGntQdrRw3DA+38hAJeoQILbjIJ\nQY7APlcvra4RAF7pqSFNZUSv19HeU0pMxIHck67+ajweJwONxQy1lKKPSMYQlcZIbxOCTE5YetFR\ntzxqV79EZ/GneL2jpZdBkRij0ump3gyShFymRKnU0rLtHUyx2XgcVkb6WpDLfHkhOk0IE1IvZl/1\nh9SuepGsJfcf1//rySIIAgkzriem4FIs7dXIlWpMMZnnvK9GgPOH4xYUgiBcBHwM1ABG4FeCIFwv\nSdLa0VO0wDeAUyoo8AmIFyVJenV0Xt8Gloze95lTfO8AAY5K0uxb6a3YxCfrf0lqwhwUchW1zRvx\nIpF+8X3HNYZCrUUUvThdNrSaA82t7I5BAESvm57KDUzJvpHU+FkAxETkMiv/W3y++UkGm/ePKfFr\n3vIWJkMUi+c84jfrSoyZwodrf0pX+VpiDtMEa7B5P6Xv/hJEEZMxmq79q2je8jYTrvwpdatfZLi3\nCb02FIfLSuOG18i87EdEZs/HbbdSt/olusvXI41aXgMIgsxXMYBApKBng9TB/7N33gFyVWUb/507\nve7ubO81u9n03iAhCVUCfIAfSFcBUWnSRMFPRBFQqVIVRUSFiCAl9FBDei+b3WR7721mdmen3/P9\nMZtNNo0kJCEk8/tv7tx7z7kzd8595pz3fd5uEUCVKlvoHtqHwX1i0dEbCDAQDFJW9QFajYHs1Cl0\nOWtZs+UfkT7WbURKdWiWRqsxIoWk9vMXyJl95V6zHqo++QvN694iLWkchTnz8PndrNv6Mp3lKwBJ\nYc58Jo++FK1GT0tHCZ+v+SNCKJw1+5ckxhXg6mth6fpnqaj7lLEjzmVj2esUnHYdWsOh1zI5WLQG\nC47ciUetvShRDpSDmaG4F3hYSvkLEVmo/SmwaFBUfHBEercbQggdMBl4YMc2KaUUQnwMHJvRalFO\nKLQGM5OveYrapf+idvsypBomNm8SeXO/jzFm746Pu5MwYiZVuj+xpuRfzJr4A3RaA+7+drZULCIu\nZxIyFECqYRIdwwNGEx0jAPA5W3FrDajhILbUQlxNpYzJPWuY82eMLY2E2DxcTaV7CAo1HGL72w+R\nEJPL3Kk3YdBb8fqcfLL6UUrf+C2KCt+a/SsSHfkEg15Wbfk75e88Qkx6MWX/vY+Brgb0GgPTJ3yf\n5PiRdPRUsGrTC6SFNJilhkrZy2q8JMQVMGn0d6hpXE5F3afE2bNw9jWhqiGcBFCEgk5CEXGUlb/N\nlvI3AVAUHUIo5KbPIC4mi/Wl/2bG+O9RkHUKqgxTUrGIkqX/xJ5ePExYSTVM68b3SIjL59QZtyGE\nQiDoZW3Jv0iKH4Grr5mpYy5HM+i0mp4cER3VjctJGvxsY+3pzBj/fd5f+msEClINEfA4j6qgiBLl\nWOVgBMVo4EqIPMSBPwghmoDXhBCXAEcjhiEB0ADtu21vB46vHLEo31h0JjuFZ1wPZ1x/SMdrjVaK\nzr6VbW8/RMvim7GYE3G6GzHaEhlx5g1ojRYUjY7WzjIS4nYGBrZ2RQql1X3xTwIDkdkMRShIBB7v\ncOMjVap4BrqI1RXu0b6rsQR/fzdTJv8Eg94KgMkYy4SiC/l09WOMK/5fEh2RdnU6E9PHXUV98xqq\nPn6Ovs5aAGaOu5Kc9EiqYXbaVECyZO1T3Mp4SulBo+iYP+M2AGoal5GWOJaWzhLGjDiXgqw5eH29\nrCtdiNvVwA/kaATQygCPiM1YbGkM+JzMnHgti5c/SHryeApzIm6pChomjPw29a3raCv5aJig8Pd1\nIcNBstOmDi0JDXi7CatBDDoLJkPskJjYgdWcSHCXWiEAsbaIBXVbdxlavRmDLQGAkH+AmiUv0F36\nOWo4iDkpj4IzbsCWcnSDN6Mcecaf5zxqbX3nhy8f3AG/O/vL9zlCHIyg8AOxu26QUr4shFCBV4Db\nD2fHDje33norMTHDXfUuvfRSLr300n0cESXK14fOEktsxmj62yoZCPaRMvZ08uf/YCgAMGXcGWze\n/CYaRUvaYAzFurKFCKEhd0BwARN5nwY2y270KFQ1LCUzZSLpyRNQ1RCbtr+O1+/CHg7u0XY4EPGF\nMBmG/170OjMgsZiGBwDqtGZ0WhN9bVXEChNO6SXRMVyo7HgdQiUWPbqYLPQ6Mx09lYTCAdyeNnLS\npzNp1EUA2K3JzJ9+G/9dfAsraOUMkUUBMYSkipSSWFsaGkWL399HQuzwoE0hBHZLMp7ffXCiAAAg\nAElEQVQB97DtGr0ZhIKzb6dng9kUh6JoURQdzr4melwNOGIimSKqVKlpWoHZONyts6F1PQDN7VvI\nmnUJGl1kNmjjCzcRdHUwm1Rs6FneWs+mF3/C2EsfPKb8R95TnzhqbW16/xsZ9//lvPt1d+DIsHDh\nQhYuXDhsm8t14BWWD+bb3gTMA9bvulFK+e/BJZAXD+Jch0oXEAZ2D2NPBtr2d+Bjjz3GpEmTjlS/\nokQ5KMIBH67mbQhFISZ9FMoutTE6y5ez/a0HyVCszAgn0BDqp3TLYhacpuf6Oy4AIHDTufz+5328\nvfA/rCuNDABpWYk4mxRukePYQjeb6eYaiimhmy2yh09XP4bFEEso7Mcf8pKLDaWvdMgXYgfdQR8X\nCIWqhiWMKzp/aHtt82qEUKhpXEZuxsyhFNXWzq34g/3kK4JWGQl0bOsqw2Y5ZejYtq4yACpw4iSA\n1t1IIOjFqI9kWPQPdA3zVwAwGWOwW5Jp799pfJWMmR6/C7enDa/fTUJcHo1tG5g46iK0g2ZaXr+b\n9s5SrnRkc81u13aZzkRN4zJS4ovIzZiFqqpYTPE0tK7HbHTw0YrfU5x3JiZjDFUNX9DtjMy4rN36\nEmlJY+nurWFLxdtohMJl8bn8wOVG8+4zvNZdx1JXG79gMvkiIsTOlJn8ijW0v3ovz4w8da/3QUfQ\nyzu9jTT4PWToLZwTl0mK3rTXfQ8Xm76ZyX1RjgJ7+5O9YcMGJk+efEDHH8yd9Syw1yo7UsqFg6Li\niNYFllIGhRDrgVOBRQCD7Z4KHD3ZHWWfjD/PefBTdCcYi3oaeKptGx41BIAehREmO9ckFTLFmsBF\n5Z8xTsZzY3hsxHpawlvU8sKj73Luu14SdZGsjJ9i45oR86j395OkM/FkaxlO1Y5BaFgl28jHzkki\nlc2ym3TM/A+5lPl7MaBhGkksoYXNTV0wcnj/4nVGLonP4aXtr+PsaybJUUhr51Ya2zYSo+ho6dzK\nxyt+R07GTPo8HWyvXgwIvp9YwC+bNpKImfUlLwGQkjCS9u4K1pX8i1iMLBbNZKVMGQx4fJyJxRfj\niMnB1ddMe/d2inJ3Pni9fjcuTzteEuiRPspx0oMXnz+Momj5aPmD5GWeTF3LGj5Yeh8j884gHA6w\nrep9LELhfMeenhSP5EzlqqplLN/4F1Zt/jthNQRItEJhwNcDwKbt/wUkacKKHoVicwzVdZ+xrfpD\n9ELDKbZEbk0dQ7zOMHTeD51NpGMZEhMARqHlJJnKolAtQVVFt1va8CZPN3fUrQUJWdhYRjsLu2r4\nQ/YUJlsTDvX2IiRVOoM+7Bodli+pvBslyuHkgAWFlPIN4A0hxLxdMjt2ff9lIYTtsPZu7zwK/H1Q\nWOxIGzUDfz8KbQ+x+7+6KIMcp1OBB8JAOIRE7ncQX9ffxe9bShhDHNtxYkXHaBzUefu4rX4Nl8Tn\n0h7yciVFQ3UsAE4nk7eoZW1/F2fHZQxtd2gNOLSRB1u63sxmmgjKMAHC2AaLZI3Fwd/ZjhkdF4tI\nIKdLBlhNO/PsKXvt54+TR5KsM/FKVynrW9aSZbDyi/TxpOtM3Fy3mq7uClq7tqFBIYzKWTFpzItN\n47ZwgEdaS9GEFVZuen6XMwqCaJBSJTdjJiNzT2PZxud4f+mvh96va16NzZJMQdYcBny9rCv9N0II\nVsl2Vg2GTc2yJnFBfDbPtZdT1dfKhrJXAOh1N7Ji418AmG5N4ie504nX7ZkOm6o38+7IU3mlq5ZV\n/Z3EafVck1TIsr52nmsvZy7pTCWJOAy8IWtpx8Pd6eNJ0hnpDvmJ1eox7aUCqirBQwhVymHfWz8R\n2/GADKPbpRZjWErua9xMlrRxE+MwCy1eGeIpWcJ9TZt5rWge2oMwHwtLyTJ3Gy931VDt68Mrw2gR\nzI9J5dbU0di1+i8/SZQoX5FDmfv6QAjxBHC3lDIIIIRIAF4ATgb+fBj7twdSyv8MtvcbIksdm4Az\npZSdR7LdKFH2RbnXxS8bNtAajEzN2xQtN6WO4lu7PPh38GpXLVlY6SFAHnZuZwI6oUFKyX+o4tXu\nOgD8hIcdt+O1bj9OmOc5svhvdx3PsJUULCyhmQ7pZQbJLKGF37OBaTIJI1rW0I5Oo3Blwt6txYUQ\nfDs+h2/H5+zx3l/zT+alrmpKPb3EavScG5/FOXGREuIXxucwzZrIYmczNf4+Sr1OOoI+QDJj/FWs\n3foSve5GJoy8kAtPe4TO3ir6BzpZvuE5IFIuu6RiEQCmmBTGX/57tAYrF3/0VzL1FjINFtb0d2IS\nGkDFLDSk6E2cbEtmnj2VDIMFs2b/w5pe0XBlUgFXJu289kyDhVpfHx+6mvmCFsJI9ELhnowJZAxm\ncKTq921gdXpMGk/6tvEhDZwps1CEoE66WUoLNo0W824ipMzbS1vIy/cpxiwi75mElm/LfH4bWkfJ\nQC8TLQdmVhVQw9xZv461nogNzxzSmEwirXh4x1VHc2CAP+fN+lIX1ShRviqHIijmAf8AThdCXAbk\nAs8DFcBR8WiVUj4DRKcIonzttPoH+GH1ClQkM0kmHiOr1Xbub96MVghOjx1usNQY8JCBjRW0cTPj\n0ImIKZEQgnNkDh/SSJLWyLuheopkHGahJSxVXqcao9Aww7bv1NNsg5X7sybzYPMWNoe7UYDfsJZT\nSKOIWJrxsIp2HBoDp8ekcXlC/iGt148w2bk3c98+CJVeF2/21NMdDqAoOsYVXcCW8jfITJtMr7uR\nsqr3iLWlk5U2FZMhho1lr6IoWs6Y9XN0WhMllYuoa15NxsyLsadF1mPeueReHnj3GVb0dfCz+rXk\nE8OljKBT+ljib6ZM6+S65C+3Ht8XWqFwT+ZELkvMZ6OnG7OiZbY9BfsBLhlcnJDLK921vBqq5lOa\nsEk9dfShAD9PHbtHv3yqCoCF3bJKBodknzpcUO6PV7vrWO/pwoKWySRyBYV8SANLaSWASpnXyb+7\narg0MZptEuXIctCCQkq5QggxAfgTsAFQgF8CfxhMJ40S5YTh6fZtBFG5mXFMEJF17wUym/tYz9Nt\n2/cQFJl6CzUB995OxY5Hzlmx6bzWXc+dcgUjZAxN9NOLn7vSxmP7kgfcSfZk3rCeysaBbnqCfjZ4\nulne10ZQVZlhTeSG1GLS9/NP+6uy3NXG/zVFapZoFD0F2XNITxrDlvI36HU1MGnUxfQPdPLFuqcH\nr1gihML8GbeTFB/JBJkz5QZCoQCt698mbfyZQ+e+e8H1tDx+KYXEcgcTh5YWRss4HvdsYZ2nm6lf\nIfYAoMBop8BoP+jjFCF4acQpPNC8haXuNrrxk6ozcWNKMXNjUvfYf5QpFpPQ8Jls5nJ2ZsR8SjMG\noWGMOW6PY/bFYmcz40lgI11MJIHnKGUjXcwihRTMrKGDp9u3k220MssWiWf3q2H6w0FitPqDWlqJ\nEmV/HGq4byEwBWgC0oh4QJgBz2HqV5QoR4Rd17j9apil7nYaAx4y9Rbm2JPRH6SN8dYBJ7HoGc/O\n6Wmd0DBXpvGvUAUhqQ4bsC9OyOXm/tVY0PIB9YyWcUNLHu9QjwbB/8bn8D+ObN7sqafa18ccfTLn\nxWVRaIrZWxf2QKcoTLNGqkOetZdllyPFJ64WftW4CYDRBWdTUfc5ZmMsCXEFxNmzWLX575w86Trm\nTb+VirpPWV/6CmE1gCMmm/Sk4WmVSfGFtFa+NWxbOOCl0u/mGoqHxSmMJZ4Y9Gw6DILiq2DWaPlt\n1iSklIQHAz33hUWj5ZrkQp5q20a7HKCIWCpwUUI3P0oq+lLhuCsDaphMDGgRbKWHdXRyHaOYISLx\nMWfKLB5lE8+2lTPRHM+z7dt5t7cJnwzj0Bi4PDGP78TnRpdEonxlDlpQCCF+DvwaeI6IW2YB8E9g\nixDiCinlysPbxShRvhqqlPynu5ZXu+poC3nJ0Jk5PTaNd3ub6Aj5sKPDTZAEjYHTYtOwa3TMtCUd\n0APcrGjoIBh5gLBzQPYQREGgMHyQnmxN4K70cTzeUkqldHEnKxkr42mgjwb6+XHyyKFgwh+l7JZ+\ncQzzUmc1z7RvR1G0aBUdpVXvYTLEUtO0klH5ZzF32k18uupR3l9639AxZkcmPlc7TncT/kD/kImW\nlJKWjhI0uwVVCo0WRdHiUgPDtnsJ0U+Qj92tLOnrZLTJziXxueQaj0aM+J4IIYbdC/vi0oQ8ErVG\nXumqZXGgkXS9mXsSJnBm7MGVXp9kcbDc2clkEvmCVoxomLZLZr0iBHNkGn/yl/KLhg1s8nRzJllk\nYWNLuIsn27bxhbuNer+H/nCQseY4rkkuPOAYjihRdnAoMxQ/Ac6XUr4/+HqrEGIaETvszwHDvg6M\nEuVIMxAO8XZvI8vc7WiE4BR7CvX+fl7rqWMWKZxJFmXBHl7srCIFM/cznVRhoUV6eDK8hVe769Cj\n8FxHBefGZXJn2thh/4Z353/jc3iktZRF1HK+zEMRgjY5wEc0MdYUu9djz4nL5LSYNBY7m1nqbqcj\n6CFbb+EnjuL9xkgcqyxxtfFM+3aKck9j0qiL0Wr01LesY+m6Z/AH+njvi1+TnzWbjJTJeOo+QjFa\nKTr7FuJyJrLqj5cQ9nv5eOVDTCy+CKPBRkXdp7R1lRGbNW5YO4pGR2LxbN4vW8EY6SBL2AjKMA+y\ngTCSkDUDsy2dz9s28FHNCv6YPZUMgwWdULAeo+mTp8WmcVps2lc6x5WJBSxxt1OtutGj4COMjzDm\nXYb3PoIIYLVn+OzFBJlACT2UDbiYRzoOjKweaOPm2tU8ljONKV/jjE+Ubx6HIijG7l7VczDb46dC\niHcOT7eiRDl4POEQN9aspNrfx1jiCaDyiGcrAOeTx7kiB4AsaWMtnVxKIakiEsGfJixcJgt5jM3c\nwQTq6OefveWMNcexYDCDYW9c4MjmE1cr7wzUs4xW4qWRGtxYFR2/3k/golHRcJ4ji/P24pXwTUFK\nyes99TzWWopBb2XqmMtQBrMZctKn0dJRQkvHFgx6K+u2LkRRtKSMP52c2VeiM0XiFPSxyfS3V+N2\nNfLxyuG1/dImn7dHm3nzrqWktYp7e9aSKmx0Sx8BgkwefcmQMVZozGUsXvpbbqlfi08NIYAUrYmT\n7ElcFJ87lLVxvJBhsPCnvJk831HJCncbKpGMoStkIVqh0CEH+IAGCgx2Kv1uprBTtJbQTS9+7mAC\no0SklP2pMp3fs5G/tFdEBUWUg+JQgjL3WSJcSrnkq3UnSpRD57XuOmr9/fySKWQNWqK8I+t4nRpO\nZmdgXB+RKfMUhgcnJg++9hBinkhnk+zknZ7G/QoKIQRP583kY2cL/+muxS/DXGnL54qEAixfkr74\nTcYVCnBnw3pKvU70ehtWc+KQmNiBzZKEL9BPyNWAojUw+ftPDCsrPtDTTH97NeeTi5SSt6jDpLej\n11tw9bfSuPIV4rLHD9mNA+gtsUz8/hN0li/D3bwdS08z4aYyRuaePrSPVqNnZP6ZLNvwZ75LESrw\nXqie13saeK2nngsd2fwkddRxFYyYY7RxX1bECXhRTwN/aClhE50kSBN19JGkNXJhfDa/bymhHS/p\nRERVOU7iMFDMziBQjVCYJVP4h7d8jxig/eEKBRhQQyTpTGii8RgnJMfviBflhMGrhlje18FbPfWM\nJG5ITADkE4mD6MVP3OBqXBY2FARr6eAsds4QrKUdDYJMIscnYaYq3HtAfTgcU9ffJB5sKaEy4EVK\nlezUKVQ2LMHd347dGlm7V9Uwdc2rkWoYY2o2xefegTFmuGN+V+VK9ELLKBnHA2xgXNH5jCs6H0Uo\ndPZU8dHKP1C//GXy51877DhFqyd59HySR8+nae0buJpKB0uj70QdfD2ZJKxCx3gZz52sxIKW13vq\nKfH08lTejGN2KeSrcJ4ji7HmON5zNuEMBbjAlMmZsRnohcJf2yt4Mbyd6+QoEoQJLyEGCBJERc/O\ngGQXAYxCs0cM0N5oD3h5uGUrK/s7kECy1sgPkov26sMS5fjm+JHoUU5IVvd1csH2T/lV40baQz5K\n6OY5WUpo8IFSSAwGFBZSgXuwzoSKxIyW16ji37KSDbKThbKSN6hhLunECD1+GWYTnYw2x+6v+ROS\nrqCPZe42igsWAJCcUIzFlMDi5Q9QVv0BNY3LWbz8QXrdDaRMOIsJl/9hDzEBgKoiEKyhA7M+ZkhM\nACQ6ChiRdQodpZ/vty/xI2aghkNsrXqXHVnrgeAAZVXvUUgcVhERDA5hJB87o3FwF5No8Hu4YPsn\nPNi0mSb/8Zeclmu0cUNKMb/IGM+F8TlYNFp0isID2ZPpVAb4GSu5WS5lCS34UXmVaoKDv5l62ccn\nNHF6TNp+44cgkil1U+0qyvtdXEkRtzCOrJCd3zZv5lNX69G41CjHENEZiijHPGEpWepu462eBkKo\nzLQmcZ4jC78a5u6G9YyQsVxJIbEYWEkb/6CcJEycTx6N9BNApQY3t7OcZGmmnQFMaJhKMh/TyGIa\n0SAwoSUFE0tlC5/SjEeEuCwhaga0O7+ecS6y/BNSE0dT17yS8rpPOH3mHWzY9irrS/+NlCpCKBR9\n6xZSxp2+z/M48qdS+8WL1OLGqLcNiYkdGA12wkHvPo6OYIpNJfuky9iy/KVIrRFrCs3tW5DhALew\nsxhgWKp04iUbGyNELKfJDBbLRpY5O1jibufP+bPINli/2gfzDWCMOY7/jpzPZ642OoNe8o12WgMD\n/LGtjNW0E4ueJjzkG2wHlGX0qauV5uAA9zGd9MF4pLEyHh8hXuyoZP5ePDiiHL9EBUWUY5p6fz83\n1KzEFQ6gAg4MbPRs56+DWRhhKbmOUVgG/4nOJo162ccHNNAs+9lINymYicNALW4ysTKfdMYSz8tU\nYBAa/l4wG78M80zbdl7ur0QC480OfpEy/WtLPfw6aA4M8Fp3HVVeNwk6A//jyGaCxTH0/it/vozN\ni2Ix+T0oWj0tHVuYNu4qPln5MB8uf5DUpDHEWFNx9jWTM+eq/YoJAGtSLmkTF1C98V3od9PVW01C\nXETAhcMBappWEHMAZb9zTr4Me1oRbVsW0+txYk4twNVYQj19pEsLflT+SzVOAkOxNA4MBFH5DdP4\njbqOFzoq9+v+eTxhUrTD6sEATLMl8oGzib5wkKvNI5hnTzkgT5Zyn4tUzENiAiJxRRNlIv/wl+9R\n2yTK8U1UUEQ5ZglLyW01q/GEQ6Rg5mbGkyRM9Eo/T8ktvN3TSLww7mFfnIWNAM14DAFsQS0GVcPF\nFPAnSllNO5U4WUglWiG4P2vyUNT/IznT8KohVMlxHVC5N7YO9HJL7Wq0UmEkcZTgZLGrhVtSR3FR\nfC53L7h+sL4vaA0WUid8iy3rFzEWycwJ11Be+zG1TSvRGKyMveg3OPIOrNxx5oyLcdZtwuts5cPl\nD1KYPRdF0dLQvhGPt5sJJ/30gM7jyJs81KZUw5S/+ygvlH3OS1QQRiKBKykkS9hQpWQl7RQQg1Xo\nmSVT+Kyv+VA+tuOGbIOVHyYfvO9JvNZADz68MoRJ7PzNNOMhTqOPiokTjBNr1IzyjWK9p4u2sA+A\niyggSUTqTsQJA5fJQu5nPe1ygFY8Q+mfAJvpIldv5fkRs9k24OTWujXcr64nFRMaBG4CXODI4rtJ\nI4Yqde5gb5Ukj3eklDzSvJVUaeEOJmAUWqSULKSSJ9rKWf3tu9m9VmXe3KsRQqF043uEQ36EUEgo\nOpnCM29AazywpQMpVcpeuxe9q4fvyEKWhlsor/0EVUbqWMRkjEFvdXzJWfZEKBpGnvtT0qdeQPP6\nt+ks+xytqtKDn89kEytpo4Y+bmM8ECm8pjuOMj6OJmfFZvB8eyXPs40rZCE2dKylgy9o4TJH3iGd\nM6iqVPndGISGXIM16uD5DeLEGz2jHNPsKIpkVDSDVSojxDPcNTGBQXGh0fN4eDPnyVwcGFlBKxvp\n4p6kyMOi2BzLvwvn8oGziQa/h3P0mZwdl0GcNuq/toP2oJcKv5sbGINx8F+mEILzZC4fyya6q9eS\nOu6MYccoGi35868l+6TL8Lna0Vsd6M0HZg2+A2dDCX2dtdzJRHrxU4Wb3LSZ5GXOon+gmy2Vb7Hl\n379g8tVPohxCNoYtpYCRC26l4LTrqF3yIh9s+YhwOEASJm5lHKOEgxbpYSmtLIiNZiQcCok6I/dl\nTeTexk3cLpejRSGIyhxbMt9L3Hsl2/3xTm8DT7Zuo18NAZCuM3NP5oSDqm0S5esjKiiiHHGklJR6\nnazu60QrFObak3GrQcoGXMRq9cyxJ9McGOCp1m1DJZinWhJYMLjOq0NhJW1cxM4BaiVtCODBrCn8\npaOc5z3bAHBoDNyRPIYzd3lAxGr1XJJwaP+WTgR21LXU7Jb0pdmRMqgOT8ncFa3BjDUp95Da9XQ1\nAJFMnP8T68hMnsTsKT8eej/Rkc87n/+SroqVJBXPOaQ2In20MOKM68k/7UdULn6ats0fsFDUYKeR\ncnoxx6XzvcQRez3Wr4Z5saOKt3obcIcDFJti+V7SCGZ9Ax1NjxSz7Sm8NfJUlrjb6A+HGG9xMPJL\nbOurfG5e6KhkXV8XKhKViEV+AJUxxHEeeXgJ8UawhptqVvFY7nTW9HfiDAUYbY7ltJg0DAdZdyfK\nkScqKKIcUUJS5b6mTXzsasWKjhAqz3WUAxGhEETFqmgJSRWHNHElRYDkE08TDw9spchop9rXx/s0\n4JIBiomjGhdLaGGqJYExljj+mDuDzqAPTzhIhsFyXBkWHQ3SdCay9VYWB5sYIx1Dn9+HNCAQxB1g\nPMTBEA76adsUce+vwEWb7Oek1OHtOGKysZgT6W+v+kqCYgeKolB45o0kjJhBx7YldAV9ZCflozXZ\nuE1nwJE3hYc/Xzi0v5SSG2pWUuFzM5d0kjGx1tvBnfVruT9rMqfYU75yn44XrBrdfg3gdqXS6+ZH\nNSuwSx0aFMKEmUc6ZrQspZVK3BjRUCBiyJd2fsIybqyNeIjEYWRRbwP/6qzmqdwZQ3VvohwbRAVF\nlCPKmz0NfOpq5VqKmUEKYVTepp53qONWxpOAkRfUbZTj4lbGEy8iA8R0mczP5SpGmWIxCy0bvT2s\nop0VtKEgmGZN4KHsaUPtJOqMJEYHl0NCCIH9/J9R+uq9/EJZyzg1jnrhoYpesmZdgtGeeNjbbNnw\nDt6uBuIx8ALbMAgtvX1Nw/bxBfrw+nrRWw4+jmJfCCGIz59KfP5U6lf8m/plLw2ZYgmh4fyik3gi\nLMgyWHnP2cQ2n4vrGcMUEZmRmC8zeJzNPNW6jTmDpcA3DfSwzevEoTEwx56C+QQL6D1Ynu+oIE4a\nmE8GL1PBr5lGhojE3ZwmM/kVa3ibOn7MGHyEUZHMJo3LGYFOaGiW/Twc2MQTbWX8OnPSl7QW5WgS\nvfOjHFHe621kIonMEpF0PQUNF8hc1g6Kg6tFMdfK0dzBckrpYQ4Rt0mz0DFWxlPhdfNcwUl0B32s\n6evEqGiYak3Eqj3+HA6/Du5ecD0ADmDCVY/StOYNVrdVobdnM2rij0konHVE2u0q/YwpJHEheTxD\nCZ3Sx/aaj0iIzSMrbSpen5NVm19AqmHi8qcc/vYrV1G39J+MHXEuo0csQEqVzeVvsn37Yi4FUsae\ngbWzFgMaJrFTUClCMFum8WxwK7fWraZkwIlfhtEgCCGxtZbxQNZkJlmjlTr3xbr+Ls4mm2pc5GEf\nEhMABqFhukxmCZGsm1WD7rXfoQCdiCxxpAsrZ8hM3nDV4E8PR5c+jiGigiLKEcUVCpLLcLdJIQSJ\n0kQ/QQBi0WNCixP/sP068ZI0GDwZrzPyLceBTalG+XKMn13IbQ8Pn7K3pRRQfN6BpWl+VdRQADMa\nkoSJX8mpVODiFbWKL9Y9jSI0qDKMYXB4ctZtwuI4vEGTrZveJ8FRwMRRFw1tmzrmclo7t6JR9LRv\n/ZhOrQFQGSCEdZfU5BK6AWj2+JhLOvX0sY1eTiODFtXDXQ3reL3o1BMu9fhAMSoa+sNBjGjoI4iU\nclgmh4sAejS0ywGW04oeDUaGi4ZYDISQBKWKgaigOFaILjZHOaKMNseykU6CMjy0rVf6KcdJPpGK\nk5W4GCBED35CUiUkVRbLBqpwDQVmRjl83L3g+iExERhwUfnRs6x44jKWP3YRpW8+MBQseSSJyZ/C\nGtGFS/oRQlAkYrmWYhRgvIzjWop5lFnEKib8fd2Hvf2AuwuHfXilVyEEjphstFoDeZkng5SoSBZS\ngX/w/m2U/ayijXHE8xumcbEo4KdiIueRw2c0823y8aghPndHbaf3xWmxaSyllRzsdODlfRpQB23T\ny2UvK2ilCx93sYo2BhggxCZ21qRUpWQpLRQY7FhOwDTvY5notxHliHJlYgHX9S3nQbmBuTIdH2E+\npAENgiRMfCwbeYc64jR6vgi3sI4OAAYIcbEjJxr4dhjZsbyxg3DAy+aXf0aor5fCrFPQaY1UNS5l\n0z/vYOJVj2KOP3JiLnPahXSVLeEe7zpOkskEUVlOKylYuJZRmISWJtlPrzpASmLOYW/fkpxLc20J\nYTWEZvChFAr5ae0sJTttGjHWVGoalyGJTLtvoguHNNJMpO7H6WQOM206gywWUUcz/ZjQ0hPy763Z\nKMDVSYVs7O/hRf92YtDzGtUspgGL1NHKADZFh0cNohcaTo9JpSXg5U8DpcyWqSRhZh3t1ODm9ylT\nox4VxxhRQRHliDLCZOeJ3Ok821bO3we2owC5BhtdQcmzaikKglPsydyeNobukJ+l7nYEMNueTL7R\n/nV3/7hgwrdCnK3cvMf2tq2f4O1p5tx59xNri5QVH5l3Bos+u5uGla8w8pzbj1if9NZ48s+4nvoV\nC/mopwVFKASDYXKx0Ug/ndLLG6Iesz3liMRxZEy9gA3blvLxyocYnf8tVBmmtPJdQiEfI3NPZ+3W\nl7Ak5pI0eh61n7+AV4YHxYQCqAQZnkobGEy+7cDLACGKviRt8kTGrtHxXP4sPnhGOu8AACAASURB\nVHa1sMHTjVcNoUXBqtEx1ZrAbHvysEwtvxrmxc4q3ulpxB0OMMocx6NJ05lqTfgaryLK3ogKiihH\nnLFmB8/kzcSrhtAg0CsaQlKlI+jDqtFhHzQtitMaKIiKiMPK7rMSu+Ks30JSfNGQmADQ60zkpk+n\nqn7NEeuTlCoV7z9JW8liTMZY9DozXm8v1pQRrOlpYXlgAwCOzPGMPfsWlCMQgGtNzmfM/95L1UfP\n8unqRwGwmOKZOOpiSireoqVjC8Xn/Zyk4tkkj5lPb91GBBCbM4mSl3/O2z31FMlYTEKLKiX/pRot\ngs9oZrQplimW6MNufxgUDQviMg8o1dSgaLguuYjrkouOQs+ifBWigiLKQVPv7x8qIhWvM5CuNyMQ\nZBgszLen7jNtbldba61QSNObj1aXTzj2JyR2oNEb8fndewTFef1utAbTEetbe8kntJUsZuaEqynI\nivhLVDcuZ8XGv1B41s3YUgvRGW0Y7Ef2oezIncjUH/yZ3rqN1H7xD/rbKllb8i/05jgKz7qZpOLZ\nAOjNMSSPmjt03Ihv3UzJK7/gjtBKimQMdbhxEkABTopJ4tbU0XutYSGlpNLnpifkp8BoJyGa5nzY\nWd3XyX+6a2nye8gwWPhOQi7TrIc/7TnK3okKiigHxYb+bm6vX4NZahlJHGVeF5/RhgUtA4T4U9t2\nHsuZzghTdKbh6+JAxARA0qi5lGz9hO01HzEy7zSEUGjtLKOueTVZJ11yxPpXu+RFEh0jGJE9d2hb\nQdZsqhuX0br5Q1LHn3nE2t4dIQSO3Ek4cifhc3cQ9nsxOdJR9pOhYU8fyeRrnqVl4zs0dNRhtCUw\npnAmj5au3GdmR5Pfwz2NGyj3uQFQEJwbl8FtaWOiRmyHiTd76nmoZSt52BlNPBVBJ7f2r+FnaWM5\nz5H15SeI8pWJCoooB4yUkodbtpIjbdzGBPRCg5SSf1HBUlq5hyn8PbydXzZu4OURp0QrDR4CUkpW\n9HXwkasFrxpisiWBBXGZB5SCeKBCYgdxORNJn3wua9f/i7KaD9BqjbjcTcRmjSNj6gWHegn7xdPV\nQNDrwurYs7Kl1RRPV9u6PWZMjhZG+4HbaRtjksibe/WwbffnTwXggXefGbY9JFVurVtDOCi5hfGk\nYWY9nbzWW41No+fHKXt+FqqUvNvbyNu9jfSGAowyx3B5Qj6FJ3Bshl8Ns7KvA3c4yGhz7LAYq4Fw\niKdbtzOHVL7LSIQQSCn5G9t4um0bZ8SmYxz0q/CpYZa42+gIeskz2JhhS0Kzn/vNq4b40NnMZk8P\nVo2OM2PTo7VF9kFUUEQ5YBoCHuoD/dzCOPSDJjNCCP5H5vIZzbQwwCWM4PeBjZR5ndEf3UEipeSh\nlhLe6m0kCytWdDzVt403eup5Jm/mPgua7c1TAiDk66d+5X/o2rYUNRwgLnciWbMuxeyIxEwIIcg/\n9YckjpxN5/alqKEg6blXkTBiBuIImQX1d9QgpUpT2yZ8fjdGQ+Sh4Av00dC6jnDIh7enGZC0lXxE\nwOPEmlxAythT0Ros+z/5McLdC67n89+ZWDH2EQBW9HXQEhzgXqaSJWwAnEkWfTLI6931XJ00Yg9z\npodaSljU28h44hlDPJtdXVznWsHjudOYYDnxTLM2ebr5RcMGnOHA0La5thTuyZyAQdFQMtDLgAxx\nJllDYlQIwZkyi+VqG1sHepliTWCb18lP69bSGw5gHpxVzTPYeDRn2l6ddntDfm6oWUVDoJ987DgJ\n8HpPPdckFXJ10t7rv5zIRAVFlANGDuaKKwxX8zteq0gSB6uAukIBohwcGzzdvNXbyFUUMVdEHvqt\n0sODgfX8raOS29PG7HHM3Quuh4f3PFc46GfjP+/A72onL2MWRr2N6poVbKpex8SrHsUUF3EkFUIQ\nkzGamIzRR/TadmAYdJBUhIb3vriXwpz5gKC89hNCgw+Lju1LqV/+EnqdBZsliZrSz2la/RrjL/vd\nUL+Pdeb+3AsLruc99Qle+ocHE5ohMbGDQmJ5T9bjDAVI1u+MWanyuVnU28iVFDJPRFJ3vy3z+QMb\neKp1G38tOPmoXsvXTV84yJ3168hUrdzJJBIwsoZ2Xuwr5y8dFdyYUox2UETsnn2z47VWCEJS5a76\n9cSFjfyMSSQJM9XSxTP+rTzYtIVHc6ft0faf28rpCfi5j+mkCQuqlCyiluc7KphjT44Gke9GdPEu\nygGTZbCSrjPzIQ2EBusfSCl5j3o0CMYQP2SVG02bO3g+d7eRhIlT2PnQTBUWTiaNz1zDjZLuXnD9\nPpc4/P09rPnztQz0NBIOB6htWgVCcM4pv0EndNSveOWIXsf+iMkcjTEmGYkkEPSycdtrbCj7D16f\nE6vUojdYaVixkLyMWVx0xh9ZMOdeLjjtIXSqQuWHT39t/T5UzlZuZsmsb+MlTIPsG/ZeJU7MQkus\nVj9s+6q+ToxomL3LfaATCvPJYJvPdcKJ9U9cLXjVMNcxmhRhRisUZolUTiODRT2RsWic2UGcRs+b\n1A6NTUGpsoha4jUGxpjjWNPfSWfIx3cZSZKIBITnixguII/Vnk46gt5h7Uop+djVwjzSSROR2TFF\nCM4hBxs6PnG1HN0P4htAdIYiyl6RUvKhs5l3htZwY7k0IY9b00bz8/p1/B+rGSUd1OKmnj6mkcQi\nallCMxc6sqMR7IM0BwZ4tbuWsgEnsRo95zgymW1L3muMQFCq6FH2eM+AhuDgILkvT4kdSKmy8R+3\nIoJBZk64hlhbBo1t6ympeBtF0ZKbPpOqulWH9yIPAjUYQKMz4g91kZEyAYGgqW0jIOkjgCNzIj1V\na5gy5jI0g+nEVnMCYwvPY+Wm5wkOuNCZv1li1ZE/DbM9iWf6yrhcFpCGhfV08AENXBKfu8dyh1YI\nwkjCyGED9A6vi/2t9x+PdAR9xKAnVgxf8svChkcN4VPDWDU6fpY+lv9r2MDPWEmetFONCw9B7k+f\njFYo9A4KsRSGZ5elDr52hgIk6YZnN/mlioXhacsaBCa0+NThsyFRooIiym741TC9IT8vdlaxqLeR\n0TjIxc4XgXY+cDYz3ZrAj1JGUjHgpNrvRkgVW0jLGrWDOI2ea+MLuSKx4Ou+jGOCCq+LG2tXoVEF\no4mnjQHu6l/P5Ql5XJ9SvMf+062JvN3bSJnsYZSIVNj0yCDLaWWGLfGAgi57azfg7+vijJPuJiUh\nEuyX6MgnHA6yrfpDMlMnofkaxV7r5g8Y6Gni7Dm/Ii4mEnnv6mvm3c/vIWn0fAJ9XSiKBr12+MBu\n1EeWC8KhAN+0snCKRsuYS+5n2xsP8Fjn5sg2BAviMvbqrTDHnsJTbdtYRC3/K/MRQuCWARbTyFRL\nAlbN8E9ASkmp10mD30OG3sxYc9xx5SBZYLTTi58G2Tds2WgzXSRrTUP227PtKbxQMJs3eupp8nuY\nb0jlQkc2ucbIMSMHZ03X08EMdsYcraMDs9CSuVuMjhCCyZZ4lnlamSvThoqTldJDB16mRgvA7UFU\nUEQBIKiq/Kl9O2/1NOAdrFtwGSOYSzpPU0I/QdKxUN8/wMr+bZxsS+ZvBSejFQqqlHjVMCZFE83s\n2IUnWsuIUw3cxWRMIvJTe1fW8VJXDQviMsk2WIftP9uezCRzPI8PbGaKTMKGnnV00G/Q4rroXg7E\ntcPdUo6i6EiOH/6gSk8ex7aaD6ltWkXGjG8frks8aLorV5HkKGRL+Zs0tW8CBBnJE0hJHEV/bwta\nsx1VDVHduJwR2acAg0ZYdZ+iCA0G2zdzEDfFpTHx+0/S315NwNOLNSmXn3/xn73um6Y386PkkTzb\nvp1NdJEkTWzHiUmj4ZbUUcP27Q76+Hn9esp8zqFtI40x/C57yl6DDL+JzLYlk6W38GRgC/8j80jC\nxGraWUU7dySOGRJPvSE/r3bX8omrlYCqohcKYeTQefKNdmbbknmxbzttcoAc7JTQzWc0c01i4TCf\nnB1cl1zEjbUruVeuZZpMphcfK2lnsiWe6dYDzwo6UYgKiihAJKr8A2cTk0iklQE68TKXdJbRyha6\nuYVxjBMRo6GNspOn+kr4wNnMOXGRmgYnQmVFvxrmC3cbtf5+0vTm/Zp49YeDbBzo4fuMHBITEKn5\n8A71LHO3k504XFBohcLDOVN5tbuOxc5m6lU3huJTGDHjYkxxqQfUR4M9CVUN4upvGeaA2e2sAwR6\nm4PMaV+foFDDQbp6qjGbHEwe9R0kUF77CV6fE1NiJo7cyfRUrWXVpr/R3rWNWHsGjS3r6HTWYInP\nQnyDPRuEENhSds7e7Zhx2j3NFOCKxHzGmGN5p7eJ3pCfy015nO/IIn43kXBv4yZafV5uYzxFxFKB\ni+d9Zdxcu4qXjpPUbZ2i8Mfc6fyuqYS/ebYBYFd03JRUzPmD/hKecIjra1bSGwgwjwxMaFjW38qP\nq1fwXP5JQ7MU92ZO5Om2bbzX24hPhonV6Lk+YSSXJuQNa7M1MIA7HCTfaOPZvFm82FHJ554mrBod\nV8Xlc3lC/gm39HQgHP9PgShfSnvAy3vOJuIxspZIQJhKJGtjDe2MJX5ITABMFImMknF87GzhnAOw\nzj2WcYcCDKhhknTG/Q6+TX4Pt9StpjXoJQ4DTvw807qN+7Mm0Rz00hX0UWSKYZo18UsHmv29a1A0\nXJGYT9n3Hjmo6wh63VR/8hfay5YghMLSdc8wa+IPiLWl09C2ni0VbyGEwqSrHkdr+PocSoVGh6Lo\nOHvOrzDoI4IqP/NkXv/4djQ6E8mj59O04j8Ij4uu5g00Na9BJyMiQigKbSUfkTzm1G+0sNidfQmL\nCZb4/aaI1vv72TDQzY8ZwxgR2W80Dq6URTwZKOGuhnX8LmvKcbH8kaQz8WjuNLqCPtzhIOl687DY\nk/edTTQFBriPaaQOBlDOk+ncI9fwj84qfpU5EYiUTr89bQw3phTjDgeJ0+qHjMWqfW4WdtWwoq8D\nVzgIgFXRclViAfdnTT4uPscjTVRQnICEpMqnrlaW93UggESdEUmkwufPmEgsBu5iFe9Rj58wsezp\nf2BGh0+N/OjCUrK6v5Mqn5skrZFTYlL2On14tJBSsnmgl89crQSlygxbIifZkoc96NsCAzzcspVV\n/Z1IwIDCJGs8d6SNJUW/p+30fU2bUYNwH9NJFxa6pY9n1BJ+UreGMBIrOvoJUmS080jONOK0BiaY\nHXw80MQUmTQ0S/ExjfgIc7I9ea99n1VyeyTl8GCuVw2z8R+3Efa4mFT8bUCwpfxN3l1yz9A+ikbH\nmIt+jc789aa5qQEvWamThsQEgNFgIzNlEt2hLrQGM+OvfIiqxc/SXbM2cgwKhcQQ6Oyh/L3HcdZv\noWjBbcfdAH/3guv3OluxL9oCkfskh+HpqDlEvuNlfR2s93QzxZpAWEqqB1068432Yb8FTzjI2v4u\nJDDZmjBUW+dYJEFn3GvA90ZPN4XEDIkJAKPQMlUmsc7Tscf+BkVD4i6C5ENnE79t2owVHZnY8OFC\nAMWqg2fat2NWtFwQn31Erul4IiooTjACapg769ex1tNFHnYkko9oQRApyVwkImZU58kcFlGHFS0t\neOiRPhwi8kPukl620MUVtny6gz5uq1tDlb9vyH77idYyHsqZyuivwdhKSsmjraW83lNPAkYMaHir\nt4Eplnj+kD0Vg6LBq4a4sWYV/pDKlRQRi4GltLCyv5MrKpfw1/yTyDHuHKQb/P1s9fZyA2NIHxyw\n4oWRK2QR97GOHzKaaSRRiYtnfCU82lLKfVmTuCl1FDfVrORuuYqxMp52BqjExaXxuXvET8DgP9WD\nFBMA1Z8+j9fZyrdm/5JER8RspzjvTD5Yeh8ubweZs75DxuRzUXZLT/w60JpsePp79tju8XajjYk8\nCI0xyYy56F5KXvkl1JUxghjW0oGKRCBoL/2MpNHzcOROOtrdP+Lsbxlkd3IMVgSwlR7msXN5ayvd\nAMRjYIm7jYBUeaR5K22hyL2VojVxQ2ox481xLHe388e2bfgG46b0QuH6lJFcFJ97mK/syGJRtPTg\nZ7vsxYFhKC3URWAoaHNf9IeD/KF5K9NI5mqK0QoFjwzyMJvowMt0knipq5rzHVnHnYg93EQFxQnG\n272NrPd0czsTGD2YSbBOdvAMW0lgp/I/X+SRL2N4ki0A3MtaTpapqEhW0oZDZ+BCRw4PNG2my+/n\nbiZTIGLolF7+opZxV/16/ls0H51ydKemV/V38npPPZdTyDzSUYSgVPbwR89mXu2u44rEfD5yttAW\n8vIAM0geHHjGy3geYiM10s2f2sv5XfaUoXO6Bg2Xdph27WDHa0FkfbyQWBbIHF5xV9EXDjLSFMML\nBbN5ZTBtNF6r57txk5hrH+5qOfNv45j330MzK5JqOFK10xA7JCYANBotI/PPYPmGP5M+ccExISYA\nksfMZ/s7j1BZ/zkFWXOQQFX953R0l1N80s+G9pNqmN76TWRjYb3Sw6TiS0hJHEVHdyUbyl6h6qNn\nmXbdX76+CzkA1HCQkLcPrcm+39oge+PuBdcz/jwn3/nhy/vcJ1lv4vSYNP7tqiQgw4wkjgqcvE4N\nU0iM/BEI+rmrfh0jieMqRtJHgJdCFfyyMVLRVUEwghiuoRgNCu/KOh5vLSNDb2ambe+zaMcaQVXF\nFQrQiZc/sBGAUTKOk0llDe1cHVu43+NX9nXgk2EuomBo+cMidJwjc3iaEmaQzOpgB141vM+YqSgR\nop/OCcYnrlbGET8kJgAmk4gRDatoY5ZMGVLhehRCSG5MLmZZXzvLfC3ohYbTY9L4bmIBYamyor+D\n7zKSAhFJyUoUJr4nR/J/4dWs6u9gtn1PS+gjyUfOFjKwMJ/0oesYLRxMlUksdjZzRWI+5T4XaViG\nxAREBMEkmUgFTpb3dRCWcmhaOM9gwyg0rJbtZO0yvbyadgSQy85lhFTMqEj6wkFsGh0ZBsteHS53\ncPeC6+G/B3+dAY8TNRwgHAwQDvw/e/cd3lZ9NXD8e7VlWZL3jvceibN3SMJMQpmFNtBJW0rTlpdC\nR5rSSedbWjre0tLSQssIFGhKGAECGSRkObETx47jGe+9bdna9/1DjhNnD9uS7d/neXgefH0lHV05\n0tFvnDOITeHAZreg1Zwa8u3rb0ahVI9J++8rFZa5nO6aQvYe/gcFx/8DyFitPUTOuInQ9KWnThzq\nxVBDH3OzPkN64nUABJnjUKt0fFTwVwY6G4bLiPsSt8tJ9e4XaCp4C6fNglpnJGr2x4hb9MnLKml+\nZHMAR84o432m70RPp3igm387KpDx1EiYRzi5BPNniol06QlAy4NMx43MD8lDiYJ7SCEA7fCi63os\n5Eoh3CunUkYPv28qmTAJxZPNJezrb+PjJDGdEOro4yXKeZpjzPIL5pMhFx5tsQ/VeNEx8rXRD/1c\nSz8mhXq4F4hwfiKhmGLsbhfmM3byS5JEtGygmC6e4Ajz5XDasbKVOsJUOv7UUoI0tJRwACfBKi0h\nah1V1j5kzi4UEz70zb3zEiv6uWSZXpcdSYbtvc0cH+wmUKVldWAMseeYGriQAbcTE5qzhiaNaKh2\n9VJt7cPictKBFZvsQiudepNoxIIeFYNDBYROMijVrA1J5Jm2cvplB1kEUUUv71NPOoGESqdGLg7Q\nSpBSS9hFtuxdbiOv4efXUUf5e0/SXesZOZJOxi/L7DvyD+ZP/xxajT+NbUcprtyCPih6zPpyXAlJ\nUpC66n+InHET7RWeAlshKQswRqaNeM0kSYEpOoPehmNEheWMuI+TPw+01/pkQlHx3pM0F71PZuKN\nhAal0NJ+nON7XsJptZB83f2XfX+nl/E+vGXkW7ZOoeS38fO4v/IjHG43uYRgxcVTHGOhfyhdTjvp\nBKKSFOyWm2hhYHgdEMBsOZRfU8CbVJNLCJIkkSKb2W1vwim7x7QTarN9kN19LbhlmYXGsLPqQFwK\ni8vJ6121rCGeVZJnjUM0Bsyyhl9zmM+EJaNVKIerZ57r+cw2hKAA3qeejxEPeJqzfUA9/qjZTzOf\nDk6eFDtmxppIKKaY+cZQXrKeoF0eJGTog7BVHqCWfq43R1Jp7efvthJ0kpIZhiD297dxM3GsxvOP\n9W1q+FtrGal6M7MNwRgVag66W0klYPgxDtEGQIbeTKW1lyprH+Fq/VkFd2RZ5pWOal5or6TdaUOJ\nhIxMHEbasPJCeyUbomewKjDmkp/fLEMw/9dXQrM8QMTQCMSA7CSPVhQy3Fvx4fC5z1DCPXIqBlTs\np4VdNKFDyeJzdB/8QlgK/koVG9ur2OVswqhQE67UccLRw2b5BNH4k08be2nmobDM874RX2kiAZ6d\nHEdeXI9WoWPxzC+hURsorf6AxtajKJUa6poPU9f0IGq1Hpu9HwkF/pEXHu71BkmSMEWnY4o+u8vm\n6eKX3Evhy9+jo7sKk/+pb8vt3VUAaM2+VwfA1ttO09GtzM1aS0aSpw17bORstBojRwr+S+yiT6C5\nwkqfqxUPwpqz11fEaA38I3kpL3VUcbCvHT+FigcDM7gtMI4f1xdQbu1FlmVO0Es0huFkAoaKN8lh\nvEgZsizjRqaYThy46Xc5zyoLPlqea6vgry2lKJCQkPh98zHWhiTy1fD0y1qn0OIYxCa7yWTkeq10\nApGAI5ZOXu2oZndfKzIy8/1DWReRPqJTaYRGz9qQRF5or6JC7iYOE4dpp55+AG40R/H5UNEI7FKI\nhGKKuSs4gXe7G/iJI4/5cgQyMvtoIVyt4+GoHExKNQMuJxqFgkdr80nAyB1S0vDt7yCJYrmTzZ21\nLDKGcU9oIk+1lGKX3cwgmFr6eYda5htCeKqllH39bcO3TdIa+UXcHKI1ng/6F9ureLLlOEuIpJ5+\nerHzbWYRJulxyG7+xXF+1VDIAmPoeTttnmlN4DQ2ddbwc/shlsqR6FDyEc30YUfrUvIAWSRj5i1q\n2EEDebSiRIETNyoklBLnbCctSRKfDEnk7uAELG4nfgoVVreLPzUfY0t3DTbZTZhKxyOhWdwedO7V\n4FeTTAA0Fb6H02rhlut/gp/Ok8BFR+Ty1o4f0N3XgCy70OsCsdn7kCQFCoUarX/QRe51fDisfbQc\n/QBLWzVaYwgR069HZ77wkHpgfC7mmGwOFD2PWqUnIiSD1s5y9hf+C1NUOv5hiRe8vTf0tVSA7CYu\namSjqfjoeRw+/iqW1hNo4nOv6jHOtXAzQqPnocgsOKNcyR1B8Xy9dx/PUYoG5TlH5poZwICacnp4\nh1raGMRfocJ/jNYLHOxv5y8tpawmjpuJQ4HEB9Szsb2SdL2Z68yX3gAuRK1DjUQlvaSc9qWmil5k\n4OWOE+jdKu4mCQUS2/sb+ErVXv6RtISY00ZEvhKeTrzWyKaOGvY5mghWa7nXP4k1gTHnXEAtnJtI\nKKaYAJWGpxIX8Vx7Jbt7WpAkuMU0jU+FJg1vFzu58KjNPkgUZw9DRmGgfaiRzqdDktBICv7VWsGH\n7kbUSKwwR2J3uzjQ18EDZJFDMNX08S/bcb5Tnce/UpbhkN0811bJtcRwOwl8nV18ijTChkZN1JKC\nT8gp7KOFHb3N5/2QPpNBqeLJxIX8vbWMbd2ebaPZfoG0Wgb5MhnMlkI9cZNGuKznJSoIV+vQKhQs\nMIZxV3D8WfX8T6eQJIxD18mgVPHt6On8T2QWFrcTs1JzzhoULz91D0c2B5x1/HLIskxfYxkhgUnD\nyYQnHgWxUXPoLm1AkhT46QJIibuGgcFuKmp3EJ65/KoedzRY2ms5+uJ6HIN9xCpMNMsW6va9QsZt\n6wlJWXjB22be9l2KN/2Ubft/O3zMGJ5M5q3f9ckV9xo/z2vTa2nGT3/qW3Nvv6e5m9pwdX8Hpzuz\nTfq5zPIP5puR2fyx+Ri2oWH/ZznOvXIqfqg4RBs7aMCFzC/Jx2/oI2FtSOKYTXds7qwlBgN3kjj8\nGq4ijqNyB2901l5WQmFSqrkhIJrN3ScwympmEEItfTxHKWaFBpvbyfeYg0nyjLQskiP5nnsfL3VU\n8c2oU1NpkiSxOjCG1ZcxGiqcTSQUU1CwWsdDkVmebzTn8H53I8+1VVBh60NFHyGynpuJQykpsMku\niunkGr1nsaUMVFr76HE70KPEjcx7PZ5tqPeQyjzJ8y00g0A+J6fzK3sBhy2dBKg09LkdzCUMO25k\nwHTG2g49SlQoGHA5h/sV1Nj6iVT7kWsIOu+cZqBKyzejcobfMLb3NHHA0k4aI9/MpxPCS1SwPno6\ns66iLr9WoTyrwROcVlNi8xXfNbLbRd2B12g4uBm7pQutxojb7URx2la47t56tGoDNkc//QOdlFZv\nw2brJXHFF/EL9n7hsfK3f0eg1cW3WEigrMUmu/irfIyiN35D4NeeR6k5/3oTjSGA3Ht/TV9zOYMd\n9egCIjBFZ/hkMgFgjErDEDyN/UefY/mcr2E2RtHVW0de8UaM4ckYQka3lsHJ9RUX2mZ6e3Ac1wVE\nkdffTqGlk/921nKQVjQoGMSFv6SiX3YCYMfJ3cEJfHoM+/F0Om1EYjjrNYzEQLWj57Lv7xtRWfS7\nHPy9r2T4WJLWSJCkwWDVDicTAH6Silw5hEJL15U/AeG8REIhjPDfzhp+3VjEDIL5NKnU0M9mTlBK\nFyvlGN6lFqvk4hNDK6ff6KpjS3c9nyOdxUTgRuZ5ythFE4mMLKKUhGfuuMkxQOzQcGMLA6RgJhI/\nPqSJmXLocKKwnxZsuEjVm/hK1V6ODp56E0jUGvnfuDlEai5e9fHkYq9SupjNqXn3UrqQ8PROGG1X\nWlPiTJXb/k5D/mZS45ZjiA2hoOQV9h55htlZn0St1FFeu5OaxjySpi2msm43xoQc9AHhhGWtxD80\n/uqfyFWy9rTQ01TKWrIIHOoWqZWUrJWT+bZjLx2VeYRlLL3gfUiShCkyFZMPrgc5kyRJZNy2gaP/\n/j6vb1uPVmvEZutDZ45g+q3fGbNE6GL1K4xKNSvNkaw0R/LZsGR29DZjN81WswAAIABJREFUcTnJ\nNQSRqTNTaeunx2UnWWcas3UTJ6X7BfDGQC0DshO/oYJvDtnFUTqY63f5ib1OUnJncDzTtAbsspul\nxnBmGoLZUHuIeuvZ/wY7sA6PMgqjSyQUwjCH283TLWUsJoL7OPUtMFb253nKOE43KVoTT0TNG55X\nfKuzjhmEsEzyDFMqgbvkJHbTRDGdI7ZUFuEpaJSgNRKi1rHAP5T/9p8gBn/uJIk/cZSfc4g5chhN\nWNhDMytNkbzUfoLaQQsPMYNMAqmkh2dsx/luzSGeSV5y0TfpZJ2JWX7BPDdQilOWScbMMTp5hUqu\nMUacszLmlbqamhJnsvd30ljwJjPTP05O6scA0GvN7D3yDJW1u1EolLjdTlLiluNyOVDr/Mm8db1P\nfXt3DVVz7MQ6YiGwEc+Hlstx9UmXrzGExDLv/r/RXraXwe4m/IKiCU5ZgGIcPsQ2rFnH9jt3s/e+\nwvOeE6jSnjWFmKIfvwqqHw+KY3NnLb9y53OTHIsSia3U0SvZz+qpcTG9TjvfrMmjeLAbDQrsuNna\n3civ4uawOjCG9X2HeE+u41qikZD4iCaK6GRD4PQxenZTm0gohGF1dgtdLjtLiBzxobSESJ6njK+G\ne5ronP67bpedrDNK//pLGoJkLa9zAqUskUMwJ+jlNSqZrg8kY6iN8Pro6XzjxH4esx/EhBoZqKWP\neqmfEJWWLwalstwUwdrynXyRDKYP9StII5BPy2n8xnaY4sFusi+hIudjsbP4UV0BT1mKh48tM4az\nIWb03liutKbE+fQ1VyC7XSTEnFpnkBy3DLMxii27fkJEcAYp8ctp76qkuOZtklZ+yaeSCcdgH1Xb\n/g7Av6nk31QyWw7lPjLYTRMgERCbc+E7maAUKg1hmdd45bFXvLYE1iy5rDLe4ylC48cfExbwRGMx\nfxs8BkCazsQTkfNG7L64FI83FlEzaOERcskkkFYG+bvrGN+pOcirqSu4KyielzrLeYtqFEj0YGdV\nQDSrAsRaibEwYRIKSZLigO8DK4EIoAF4AfiZLMsOb8Y2WfgNzct3M7J+RDc2ACI1fmd9YGX7BZDf\n08YdciKaoZXjPbKdPuyk6ExsslbxCpUALPYPY0PMjOH7CFXreDZlKXv7Wqmw9hGm1rHijA6ehy2e\nUY34M6ZPTvYvaHVYL+m5Bag0/C5hPjW2fhrtA8Rq/Yd3m1ytq929cT4qvec5Wgba8fc71ZzN4fQ8\n58a2ozS2HUWp1hG3+F6i59w6JnFcqeOv/xJr7TG+SAbJBHCcLl6inO9zgE6sROWuRh9waV1Uhct3\nOWW8x1ua3sxfkhbR5bThluWzuqheil6Xg+29zXyS5OFCfeH4cZ+cyQbXPvb2t/FQVBarAmPY2duM\njMxiYzhZ+gCfSrwnkwmTUADpeKocfwmoBLKBpwE/4NtejGvSiNDoma4P5PXBEyTKJkIlPQOygxcp\nx6hQs9B49r7/e0KT2NHbzK/kfJbL0Z4hR+owKNX8On4uCiTq7P2EqfSEn2NqQSUpWGqKOG9FzVit\nASUShXSM2HFydKhfQeJlbumK0/qP6jawsUomAExRafgFRpNX/CLL5z6Iv18IfZY2Dh7biCEkjoxb\nv4vT2ochNN6rHUTPxdJeS2fNYR4ga3hhbhh6kD27DGIXrSV+yT1ejnJq8OXE4lK3g59Lt9OGG/ms\nnWhh6FEi0T6UeKfpzaTpr6z2h3B5JkxCIcvyu8C7px2qliTpceABREIxar4bM50HT+xnvXMvMfjT\nygBI8LNps89ZejZZZ+IPCQv4U1MJzwweRwIW+ofx9cgMgobeLAJUV14LIWioYuamripcsptMgqig\nh/9SxWL/sBFNvMbTWCYSJ0mSgoxbv0Phy4/yn62PYPALxjLQgcYQyPQ7H8MQ4v0dHOcz2NkAeAoM\nne7kTpuAadmTqgX5RHAp20wnkgi1HpNCTYG7nUxOvccU0oELmTSdSCLG24RJKM4jADi7daFwxWK1\n/ryQcg1bexqotPYRqvbMN4ZeYEgy2y+QPyctwuJyopSkUa95/3BkFkokNndV8xpVKJC4zhzJNy/Q\nI2Os6LbfwcOPj09/Ell201K8A4fVAshYBtpR6Uykf+ybGHxgB8eF6AI9UxnldI/YWVOOZ1ugLmB0\nrqHb6aC9bA+W9hq0plDCMpahuoISzlPFpWwznSg0CiX3hCbyl5ZS3LLMTEKox8KbVDPTL4gcL3Q7\nnuombEIhSVIy8DXgYW/HMtkYlCpuu8RCUmfebixoFEq+FZ3DlyPSabIPEKbWXdVQ6ZUYrinx+Pg9\nZsPB16nP20Ru+h0kxS5lYLCDvKKNHNv0c+Y98DRqL43OXAr/0HgCYrL5V0M5sgwpmCmhi5ekSkIS\n56EfhYTC2tNK4YvrGextwSzp6ZWtVG//B1l3/RhzTOYoPIuxIcsyTUfeofHQm1h7mvELjiVm3h0X\n3T47mnx5GuRyfCrEUwHzhbZKtrsbUCNxrTmKb0RliXUSXiDJsuzdACTpF8B3LnCKDGTIslx22m2i\ngR3ANlmWv3yR+58FHFq2bBlm88ghsLVr17J27dorintPzs1XdDth4hmP6Y1z2f/nzxNlTmXJrFMN\npQat3by69Rskrfwi0bNv8Upcl8pu6eb467+kq+7o8LHg+Fmk3frtq06GZFkm769fwt7Tikt24Sep\nWSCHUYOFer3MvK/+c1y2aV6Jqp3PUrfvFWKj5hIamExjWxFNrUdJvu4Bomd/bNzjudg204nA4XbT\n5rRiVqox+OjrPl4WHX3zim+7ceNGNm7cOOJYT08PH374IcBsWZbzL3R7XxiheBx45iLnVJ38H0mS\nooBtwO6LJROne+KJJ5g1a9aVRShMSaNZU+JyyW4X1t5WwhNHJq56XQBGQziDXU1eietyaAwBTL/n\nl1jaqhnsbsYvKHrUKndWbf87g91NJMUuJTpsOu1dVeyseo9sORDbYDudJ/IJSZ4/Ko81muz9ndQf\n+A8z0u9gRtptAGQm3cS+I89wYtfzREy/AaV6fEfffH2b6aVQKxRjUqBuqjnXl+z8/Hxmz559Sbf3\nekIhy3IHDC3Zv4ihkYltQB5w31jGJUxto11T4nJJCiV6cwRN7SWkxC0fPm4Z7KSvv4UwH2zbfT6G\n0PhRXfPhsg/SVLCFzKRVzMn2vPnFR8/H5B/OviPPAuAc7B21xxtNPfXFyG4XqXErho9JkkRq/ArK\na3bQ31qFOTrDK7FNlmkQwXsmzDLroZGJHUANnl0dYZIkhUuSdOGWhYJwGTasWee1KY4zRc+7ner6\nvRwqfpmevkYaW4vYtv8JVDp/wrNWXPwOJilLRx0up5WEmAUjjsdHnyoAZoq6cGt0b1EMLW622vtw\nuZ309rdgs/djtfUBoBzFqq1XasOadei23+HtMIQJyOsjFJfheiBx6L+6oWMSnjUWo7utQJiSfCWR\nOClq5hocAz0c3/8axRVvAWAIjmX6HT+d0jsZ1EMFv/osrQQHJAwf77O0AGCOyfaJpmjnEhiXi1pv\nYmfeH7HZ+4dbzatVevyCYka9ediVevjxCLiEMt6CcLoJk1DIsvxP4J/ejkOYfHwtkThJkiTil9xL\nzJxb6WupRKU14B+eNOVXr+sDIjHHZJFf8gpmYzSBphgsgx3sP/IsKo0fOXf/2NshnpdCpSYkfSlN\nBW+RErecuKh59PY3k1/yCjZLF4ef/xY6cxhRs272iZ0qk2F9hTB+JkxCIQijbTxrSlwNlc6fwLgZ\n3g7jvNxOB21lH9HfXIHaEEB45gq0xitvB38p0tY8zNGXvscb2zeg1wcyaO1GrfUn5xOPobyCMs7j\nRXa76CzfR9K0pSzM9SwDk2UXLqcNFVrc3e10t9XQWrKT1JseJHLGjV6O2EOsrxAuhUgohCknd5WT\n1YoHx7WmxGRl6++kcOMGBjrr8PcPZ3Cwi5pdz5Nxy3cISV148Tu4QvqACOZ88S+0l+/F0laDzhxG\naPpSnytBfibHQA+2/g6mZXp2nLllN3sOP4NKpcHptGI2RuF2O7E7Bih/70mfe04isRAuRCQUwpTi\nq9MbE1XF1j8jD/Rx8/KfEmSOxe4YYE/B05S88WsWfPWfY1p8S6FSE5axDLyzKeKKqHT+KFQaunvr\niY2cTVdPDYPWTnRaE6uX/RizMRK37Kbg2L8prniblqIPvFKb4mI2TJJqm8LomjC7PAThauSucopk\nYpQ5bRY6yveRk/IxgsyxAGjUfsyf/lncLjvtpXvG9PFddiu1+14h/5/f4NAzX+fEh//C4aPbRU9S\nqDSEZ62kqOJtapsO4XI7AYmMxBsxGz3lyhWSgtz0O1AptXTXFXk34AvwpR1Rgm8QIxTCpCfe9EaX\nLMt0VB6g6ci7yLIbg37kegmd1ohKqcFp7R+zGFwOG4UvbaC/pZJpkbNRqlXU5r1OW8kuZn76cdR+\nvtsYKmnlF7F2t7DjwO/xfKeT0WpGdsBVKNSolBrcTrtXYrwcYhpEOEkkFMKkJRKJ0SfLMuXv/h9N\nR94hwBSLSqmlsm43MREzh3ef1DYdwum0YY7JGrM4Worep7e5jNVLf0hIYCLg2Ub6xo7vU7njWZDd\nWFqr0BpDiJy5muCkuWMWy+VSavTkfOIx+hpLaS/fR/3+Vymr3k5S7FKUCs9bcn1zAVZ7H8FDTdYm\ngg1r1vG2+w8c3iI+VqYq8coLk5JIJsZGd20hTUfeYcGMz5Mav4Kquo/Ynf8UH+x7nLioefT0NXK8\n+n2CEudijEobszg6Kg4QGZI1nEwAGA1hhAUl03h0Kwa/EKJCs+jsrKXo1R+RsOyzxC68e8ziuVyS\nJGGKTsc/PJHG/Dfp6q3lze2PkhCzkP6BdqrqdgMSYVkrvR3qZVmteBDWiNGKqUokFMKkIhKJsdVe\nuht/Q9hwOfDEaYtRKNQcOPovGluPotYZiZ5zC3GL7x3TehmSpMAtu0Yck2WZ9q4qwoNTuW7ht1EO\nNYk6VPwyx3Y/T3jOdWj9g8YspiuhUGmIXfQJTux8FqfLRlHFWygVKmQJguJnYYpI9naIV0RMg0xN\nYlGmMCnott8xoZMJt9NB7d5/k/fX+9n7x3sp3vQz+porvB3WWdwuF0qlZkSyEB89j7T4a1Fq/Fj4\n4EYSl9835g2uQlIX0dJ+nKa24uFjDc2HsTssZCWvGU4mAHJSb0Z2u+iqOjSmMV2pafM/TtLKL+FQ\nuHE6rThlJ5G5q8i8fYO3Q7tqooz31CJGKIQJb8OadRO6poQsuyne9FO6qgtIjF6EITSI6sYDHH7+\nW8xY+wtM0b7TlyIocTbHCt+lrimf8JA0NGoDNruFirpdBCfNHbcqnmFZy2k9tpOte/6XiNAMlAo1\nDS2eNuky8ohzZXnoZx8tMCpJEjFzbyN69sdwDPai0hpQqDTeDmvUiDLeU4dIKIQJayKPSJyuq/oI\nnVUHWTHvIaZFegoe5aR+jLd3PcaJD//JjLW/8HKEpxhC41HrzWw/8DsA9FozTrcTlAoyl9w7bnEo\nlGqy7/ohLUXbaC/7CLfLReKK+2gpfJeiireIDM1CpdQgyzKFpa8jKVQEJfrOwsxzkRRKNIZAb4cx\nZkQZ78lPJBTChDNc6XKS6KouwE8fTEzEzOFjSqWGlNhrOHD0X8huF5LC+/3vnNZ+Cl/agEZSk5vz\nKTRqA2XV22jvqiR99XfwG+eW6gqlmsgZN44oT22MSOboKz/gP+9/k6iQLDp7a+nurSNp5RfRGALG\nNT7h3MT6islLrKEQJpQNa9ZNqmQCQKnS4HTZzlpkaHcMoFCqwUeagTUf3Yrd0sWNizeQkXgDSdMW\nc+Pi72I2RtNa9IG3wwMgIDaH2Z/7A0EZi2h3d6KOjCPz9kcJz77W26EJZxDrKyYfMUIhTAgTeXpj\nsKuJhvw3sbSeQGsKJTL3JszRp+pFh6YvpWbPRgpL/0tu+h1IkoLe/maOn9hKaPpSJMk38v7exjJC\nA5Px9wsZPqZQqIiNnM3x2u1ejGwkv+BppNzwVfqayqn44CmObfopAKboDJKuvR9TZKqXIxROOrm+\nQoxWTA4ioRB83kROJnrqj3H0399HpVATEZxOx4mjHC56n5Qbv0ZU7ioADKFxxC/9NEd3PUdV/V4M\n+iDaOsvRmcNJuOZz3n0Cp1H7megeKMYtu1GcluT0WVp9rjLlYFcjRzZ+F5NfGItn3g+SREnVuxRu\n/C6zPveHcZ+eES5MTINMDiKhEHzWRE4k4GRVyT8RaIzh+oXfRq3SIctu9h35J5Xv/5XQtCWo9Z7m\nWXGLPklA3AxairbhtPaTNHMF4dnX+lSnyYic62jMf5NDRRvJzfg4SqWa6vq9VDfsI2H5570d3gj1\nB19HrdRw0+INqNV6AGIjZ7Ppg2/RcPB1Um6Y2H9bk9WGNevY8Us9e3J+4+1QhCsgEgrB5yw6+gjL\n1w96O4yrNtjViKW9mnnzv4FapQM8BZlyM+6kvGY7nVUHCc9aMXy+OTpjxFSIrzFGpJB07f2UbHua\nsprtKBQqHI4BQtOXEjPnVm+HN0J/UznRoTnDyQSAWqUjOnQ6rU3lXozs3NxOB/V5m2gt3obTNoB5\nWjaxC+/GEBrv7dDG3fL1gyDKeE9I4tUSfMqGNetgEiQTALLbs8jyZH+Gk5RDOzZO/n4iiZlzKyEp\nC2kr3Y3baScwPhdjZNq41Z+4VGo/Mz2dTWcd7+lvQh3kW1szZdlN0Ws/obv2CAlRC/ALCuREzX4K\nKh4h55M/x9HfgbWnBb/gWAITZvrMmpqxJsp4TzwioRB8wkSf3jgXv+AY9OYIjlW+S0RIJoqhRKK4\nYguSQklgwiwvR3hldOYwps3z7dX5ETNupPg/j1FYupnM5JuQgGOV79DeVUHW8ke9Hd4InVWH6KrO\nZ+WCR4gJnwFATuqtvLH9exzduAGX04pCocbtdqBU6wlMnE3sgo9jjEjxcuTjQ6yvmDhEQiF43WRM\nJsAzvZF03Zcp3vRTNm/fQFRYDp09NbR2lBK35F6f6ysxmQQnzyd24d0c3vtvCstfR0LC5bIzbcFd\nBKcsAMDtctJZlYe1uxl90DSCEmZ6pd5H14kC/A1hRIdNHz6mUqpxOq3464NJiF7I4eOvYfKPJDQo\nmebaEgrKHibj1vWEpi0e93i9ZcOadcy4pZtPfPlFb4cinIdIKASvmayJxOmCk+eRe++vqT+widq2\nY2iNoWQu3TClPgi8QZIkEpZ9lvDs6+ioOAB4XouTuzsGOhso+vcPGOxpRqnU4HLZMQTHkn33j9GZ\nwsY1VoVKjdNlQ0ZGGqoP3tJ+HKu9l6VzvsKOA38kMWYRi2d9ydMUze1iZ94fqXjvzwQnz0ehnDpv\n40c2B3BEbDP1WVPnL1HwGVMhkTidKSqNzNvWezuMKya7XbQe20lr8TZctgHM8TOInvUxNBNghMUv\nKBq/ebePOCbLMsc2/Ry1W8HKa35CcEA8bZ0VfHjoSY5v/jW5n/r1uMYYmr6Euv2vUlz+FtkpNyNJ\nEh09NQDY7BYczgFyUm8ZXjuhUCjJTvkYdbt+TE/dUQLjZ17o7iclMQ3im0RCIYyrqZZMAFh7W+mp\nK/LMfyfMGvNOnKNJlmWOv/k4rSUfki4FYZbVHG7eRGvhVmZ8+rfozOP7bX409DWWYmmv5vpF3yE4\nIB6A0KBk5mStZWfeHxnoqMMveNq4xWOMSEGp0VNQ8gqVtbvQ6wNpaS8FoK3T03H2zDWvJxfBlr37\nf8z+7O9R6fzHLV5fIhIL3yISCmFcTMVEQpbdlG35A81H34ehDphqnZG0mx8hOMk3GlXJspuO8v1D\nuzYcBCXOJjxrxXC3y67qw7SWfMj9ZLKACJCgW7bx44FDVO96jvSbH/HyM7h8dksnAAHGmBHHT/5s\n6+8c14RClmVc9kEykm7C4RjA7hhkbs49NLcdo/TE+ygVagrL3mDxzC96pjxkN0Xlb6LVGLH3dVJ3\n4D8kLPvMuMXrizaIaRCfIBIKYUxNlpoSl8vtcnLkxe/Q23icWZmfIDV+BVZbD3lFL3Js08+Ze/9T\n6ExhOAZ6sA/0oDOHj/vIhSy7Of7G47SW7GSaZEKHgrKyj2gu2ELO2p+j0vrRUbGPEIWB+e7w4dsF\nSFqukSPYUrZ3XOMdLYawRECirvkQqfErh4/XNR9CUqgwhMSNazySJGEInoZloJ3l8071qYkMzaKu\nOR9QUFW3m/bOCsKC02jpOE7/QBvL5qyjoeUojaV7pnxCAWK0wheIhEIYM5OppsTlqvnoRfqayomP\nXkB2yhoANGo/ls1exyvv/Q+N+W9i7W6hvWwPsuxGpfEjet7txC365LjVGego30dryc5Tow/ACXr5\nZWsB9XmbiF9yL8jyOW8rISFz7t/5On1ABGGZ13Cg6AUGbb2EBibT3F5CccXbRObe5JWupDHz76T0\n7d+x78izJMcuxTLYScHx19Aagpj52d9R9J/HsHU209VbS0hgEktnP0BIYBJNbccmZD2TsSQSC+8R\nCYUw6qbi9MbpZLeLxvy3kJAICUwc8Tu1Wo/JP5LmwveRXC7mZn+KQFMMdc35HNv9IpKkIG7RJ8cl\nzrbju5kmmYaTCYAEycR8OYzDx3YSv+ReglMWcLTgLfJoZR6eUYoe2c4OqZng1EXjEudYSFv1P6j1\nRoqOvIXLaUOp1hMz7zbil3rnm3549nU4rf1UffQSZdXbADBFpjF9zQ/QGoOJyl1F2Tt/ID56PkqF\nmj5LKxqNP9WN+wmbcYNXYvZ1ooz3+BMJhTCqpnoyAeC09uO09WM0hNPQUkhG4o3Di+gGrN109dQi\nyy5Wzn+YmIhcAMJD0pFlN+UHNjFt3h3DaxhG22B3Ey3F23EM9GDpbEAhO/kLRTiRySaIRUSgQ4nb\naQMgMH4moWlL+EvpbnbSTICspkDqQNYbSFty75jEOB4UKg3J1z1AwrLPYbd0ofEP8upiWUmSiJl7\nO5G5qxlor0Wp9RvRwEyp8UOSFJTX7ECvDeBY5RYUkhK1IcDni4x5kyjjPb7EFRZGhUgkTlHp/NHo\nzRj9QmlsK2JPwdOkxi9n0NZLQcmrnFyguffw35k//bPERs0BICZiFiVV72HtbRuTbpgtRdsoffsJ\nVCotOm0Alv5mLMg4zKGolDoKOsvYJjXSJdsISL4e8HzQZdzybYKK5tBctI0G+wCh8YuJnn0rWmPw\nqMc43pQaHXpNpLfDGKZUazFGjqyA6bD2Ufr2E0yLmM2imV9Ao/ajs6eW9/f+Gr/wxAmxfdfbRBnv\n8SESCuGqiETibJJCSdTcW6n+8Dmiw2dQ15xPZd0uADRqf5bM+jJGQziFZa+z8+CfWL3shwQHxNPV\nU4OkUKEZg1bgtr4OSrf8nsSYxcyf/hkaWgvZmfdHls35GvHR8wDo6D7Bll2PISmVZC/4+IjnEzH9\neiKmXz/qcQkX1166B7fLzvzpn0Gj9nSfDTLHkpP6MQ4WvYjTNuBTXWl9mVhfMbamRpcZYUyIZOL8\nYhfcxbR5d9DUfgy7wwKAQR/E3av+j4SYhYQEJrJ87oPodWaOV22lrimfI2WvE5ax9JJrCljaaijf\n+meOvvIDKj74GwOdDec9t+34LiQk5ubci0qlpabxAMEBicPJBEBwQAIJMQvRGIPHvVqkr5HdLuTz\nLEgdb05rPyqlBp3WOOK4QR+MLLtx2Qe8FNnEJd67xoYYoRAu28tP3cORzeO/En4ikSQFiSvuY9qC\nu+hvqaTo1R8THT4DxWk7OBQKJRHBGVTVf0Rl3S4Cpk0n+fqvAGDtbcPtsKEPjDxnf4m20o8o2fwr\ntBojIQEJtDVso6ngbbLu/AFBCWdXTnTaBlCrdahVnnbeLpfTU3La7RzRDVWt1IHbPdqXY8LoPFFA\n7a7n6GkqRaXWE5ZzLQnLPoNKa/BaTKaYTJxOG7VNh4iL8tQvkWWZqro96EzhYsrjConRitEnEgrh\nkg3XlNjs7UgmDrXeiC4gArfLTktHGbIsDy/QdMtuWjpK0RiDybxlPcaoNAY66jj6yg/pbSgBQGcM\nJWH55wjLXD58n26nnfJ3/khMeC7L5nwVpUKF02Vn2/4nKH/nD8z78tNnJSHmaVnUfPQC9S2HMftH\n0j/QRldvLS+88QWiwrKZlfkJ9FoTJxr3EZx1zbhdH1/SeSKfon//gETJzC2k0emw8n7Bu/Q3lpL7\n6d+cM7Eb6Gygq7oApUpLcMp81HrTJT9eb2MpNR9tpKeuCJXWj7DslcQu+ATWniZsfR0YQuPRmUIx\nRaUTlDiH3flP0dZZgdkYRU1THo0thaTf/MiUaWc+VkRiMXpEQiFckqlcU+JqnZzf7ulrYE/B02Sn\n3IyMm8LS17EMthM7cy2m6HQcg70UblyPXunP0tnr0Gr8KaveTskbv0ap9Sc4ybN4s7u2EIe1j5np\ndw6PLqiUGmak3ca7u39GX3M5pqj0ETEExE4nMH4mO/P+D4WkQKsxMif7HhSSiuMntvL2hz9GqdSg\n0GiJnX/X+F4gH1H74XMkSWbWyzNRDCV9OXIwv2zOp6PiACGpC4fPlWU3Fe/9mcbDbyNJSmTZjeI9\nNSk3fpWInOsu+Dhup52qnc/SeOgNTIYIpiffzIC1i4q812nKfxun3TNFhqQgPHM5qTd9nczbNlC9\n+wXKC9/DYe3DEBJPxi3rCctYOmbXY6rZsGYd2+/czd77Cr0dyoQlEgrhgsRc49VT600EJ8+nr6aI\nmqa84QWaCoUaSake3vbXXLgVp3WA669/DD+dZ0opMjSLd3b/jLp9rwwnFO6hQkZK5chtjqqhn90u\n51kxSJJE1h2PUvjS97C0VLH6mh+h13q+TSdNW8ym97+FwhTA9LseQ2sKGYOr4Ntkt4ue5jJuJW04\nmQBIlQIIkvzoqS8ekVA0FrxN4+EtzM3+FKnxy3E4rRwqfpnSLb/HGJGMITQeALfTQWfVQewD3Z7j\nIfEUvvx9ehuOYTZGs2bZj5CRqa7fhyWkg/qWAlLiVpKTejP1zYcmmR8uAAAXfElEQVQ5eGwjSo2e\nlBvWkbTiPhKXfx7Z7UShVI/3JZoSVry2BNYsEaMVV0gkFMJ5iWRi9KTcsI7CjRsY6GpApzVjc/Qj\nS5B1+4bhEYz+thMEByQMJxMwVJ8gfAZHq94ePhYQk4VCpeVY5Rbm5XwaSZKQZZmSyndR64yYIlPP\nGYNSrUOh0hAdNmM4mQBPsa3YqDk09FVOyWQCAEmBSq2j02Edcdgmu7DgwHTGGormw+8QGzWHjCRP\nUSmlUsOC3M/T0FZIU+FWkq/9Er2Nxyn+z8+GeodIgIxfSBwD7TVIkoKU2GXY7P2899Ev6LW0EGCK\nQa3SUV6znbCgZNITr8PusFBY+AYJ13wOldYPSZKQRDIx5sQ0yJURCYVwFpFIjD6tMYTZ9/2J9rKP\n6GuuQOMfTHjWcjSGwFPn+AfTajmIy2VHqTxV2Kqztxat/6maDyqdPwnLPkPptr/R2VtLWGAKzR0l\ndHRVkbbqoQsWxVLp/OnvbTrreP9AOyr91OxYCZ7ELSx7Je8f3kqOHEyKFIBNdvEy5dhl54g1LOBp\nIBYYN3Lxq1Khwuwfhb2/A5fDStGrP8GsD2XR/G9hMkRQ23SIXYeeJDw4g86eaqz2PvKKXsDhsnHL\nip8TYIrG6bKz/8iz7D38d6LCsgkPTsN9/DXs/R1ia6gXiMTi8oiEQhgmdm+MLYVKTVjm8rM+nE6K\nmH4D9Xmb+KjgaeZkrUWjMVBevZ2ahgMkX3f/iHNj5t6GLiCCxkNvUNV6EH1wDDnX/5SghJn0NVfQ\nUrQNx2AvpphMIrJWotToPI+Rcz1Fr/2Y4ootZCTegCRJVNbuprG1kNRV/zPWl8CnJSz7LP2NZfyi\nJZ8QyUAfduyyk+Qbv4o+cGTxK//wROqaC5ieesvwoshBaw9tneW420voqSvCMdjDkkWPYvL3lCyP\nj57H4ZJXkSRIiFnI8ar3cbqszM76JAEmTyEzlVLD3Jx7OdGwj+rGAzgcgyiUGjT+E7+I2ES2Yc06\nfvvNZqwr/uPtUHya5Ct7rceKJEmzgEOHDh1i1qxZo3a/e3JuHrX78gViVMI3tJZ8SNmW3+NyWDk5\nTB6Zu4qUG9Zd0mr+uv2vUbXjHygValxuByChVOuYsfbnGCNTPdsNt/+d+rxNaDT+SJICm62X8Oxr\nSVv90JTfMSC7XbSX76On/hhqnT9hmcvPSiYAumqOUPjyo0SH5ZAWf61naqLsdQYGO8nNuJP2rhNU\nN+xjRvodzEi7bfh2+wv/SemJbaxc8DB5hc/TN9CCXmtGpdIxLWIW2Slr0Gr8eXnLOsKCUmlsLyZi\n+g2k3LAOl32QpsKtdFYdRKFQEpq+lLDMa865+0QYO74+WrHo6Jujen/5+fnMnj0bYLYsy/kXOlck\nFFdosiQUuu138PDjERc/URgXbpeTtuMf0lb6EUq1jpi5t2OMSL6k2w50NpD3t/tRKNQY/UJJiV+O\ny+WgpOo9HG4787/yj+Ftjf2tVbSXejqdBifPwxiZNrydVbg07WV7qNr+Dwa7PVNI/n6hrJz/DQJM\nMQAcLN5IadUH3HXTH9GoPfU/9hQ8TVX9XtxuF6qhqamkaUtAljnRsBetxkhO6i3sKfgbIBGevZLU\nG7+G2+XgyAvrsbTXEBmahdNlo7WjlOCkeWTd8ahIKrzAVxMLbyYUYspjilr4j+meFc2PezuSqcXW\n1071rhdoL9sDspugpLnEL/0U+sAobH0dHH35USwdteh0AdhsvXRW5pF15w8ImJZ90ftuK/kQSVKi\n0xhZveyHqIc+xBKnLWLT1m/RWPD2cCdT/7BE/MMSL3R3wjl0niig9qMX6W08jlpvIjznWjT+QVR+\n8DduWvIofvpTa2KSpy3lWMUWCkv/y7TIWdQ05lFR+yHxSz9DT30RvbVF3LLiFxgNoQBkJt/E5m0b\n2Hv47xgjUkm+8Wv4h8aiUKqp2fsyg10N3Lz8JwSapgFQ33yYbft/S9vxXeedRhPGjpgGOZtIKKag\nDWvWwWvejmLqcQz2cvj5b4HdRkbcShQKFeU1Oymo/iazPvs7yt97EvdAH6uX/YiQwEQGBrvYlf8X\njm36OfO/8sxwN0y7pYuaPS/RUboH2e0iKHkecYvvweWwopAUxEXNGU4mwFOiOTI0k576Ym899Umh\nozKPotd+QmhgEnOy76Hf0krZwTfQh8QCMGDtGpFQWKydAByr3MKxyi0oNQbil32G2AV3c/i5/UyL\nmDWcTAAYDeHEROTS0FGCtaeFgn8+iKRQoTUG47JaSIiaP5xMAMRE5BISlExb6UciofCShx+PgDXr\nfHa0YrxN7QnTKeblp+4RayW8qPHwFhyWblYv/SEzMz7OjLTbuPman6Bwu6nZs5HOqoPkpt1BSKBn\n5MBPH8jCGZ/HMdhDZ9VBwNPX4fDz36a9aAdJEfNJjVlGT3keBc89giEsAbfswjLYOeJxZVmmf7AD\ntc54VkzCpave+S/Cg9O4ccn3yEi8gbk5n+KaOV+lv6UCtd5MXtELDFp7ALAMdrDvyLMj6kVo/EyY\nhqaWJKUah9N61mM4nFZcNgvRgWlo1H4oJAUhukjcTjsKxdnf/5QKNfIULpXuKzasWSfeWxEJxZSx\nYc06sYPDy7prCokMzcbf71StB53GSGzEHHpqiwAZo2FkUy5/v1CQJBwDng+qxsNbsPW2sWbZD5mT\nvZZZmXdx8zWPgcNOb2MZusAoapsOUd2wH1mWcbtdHKvcQk9fA+HZK8fz6U4qTtsA/W1VJMcuG9GP\nJTo8F63WRFDyXLosjby69SH+u209r733MJaBdiJDMrlh8Xe5buG3MakCKHr1x/Q1VRCcMp+G1iM0\ntZ0aNWpsLaKxtYiI0CzszkF0GhN3XP9bVi54mNS4lZyo34dlsGP4/LbOSlrajxOcPA/BN2xYsw7d\n9ju8HYbXiCmPSU5kzb5DqdFh7W0/67jV1otab8RpM1HdsJ+IkIzh39U05oEsY4pKA6Cr+jBRYdkY\nDeHD5+h1ZmIjZ9NUc5hZn3mCguce4cODf0KnfR637MJu7ydm7u0EJswe+yc5SSmUaiSFikFr14jj\nTqcVp9OKISSOpBVfoKV4O9buZtwNx5D6+1kx7yEUQwsmw4NTeW3rw+T/y7M9V6X1Z+ueXxESmAzI\ntHdVAhLZyTezde8vWTDjc8MFyLJTVlPblMfrH6wnPmbBcLMwU1Qa4VkrxvNSCBdxchpkKpbxnpAJ\nhSRJGuAAMB3IlWV5ar1ql0DUlPA94ZkrOPb6L6io+ZCkWE8PhvrmAupa8km+9su4XU7Ktj+Nw2ll\nWsQsOntqKKl6l4DY6Vh7WgFQqrXY+jrPum+73YJSrUOl9WPOF56kp+4oHZV5SAoVoWmLL3mniHBu\nCpWa0PSlFFe+Q1TYdILMsThddvKKX8QtuwhLX4ZabyJmzq0A5D39FaKC00dsw1UqNUSEZNLRXU12\nyhpKqrbS47DiCjCj0vqROHMlVdv+Rt/g0Gt9Wml1vS6Am5Y+yn+2fova9kI0hiDilt5D9KxbLljI\nTPCek2W833b/gcNbJuRH7WWbqM/yf4F6IMfbgfiiDWvWiY6gPigkbREROdex5/DTHCl/HaWkore/\nieCkeUTmrkJSKFGo1NTve5UT9XtQqvWo/QPpri2ku9aTM+sDoxjsauRE/T7io+cjSRKNrUepa84n\nceUXAU/Vx4DY6QTETvfm0510klZ+gcKWSt7c8ShmUwwDg504nFZSb/r6cMlyl8NG9a7nsHY1UtFR\nS3NbMdkpN5MStxyQ6eg+QWhgMilx15AQvYD/bluP1j+QtNXfAKCzfB9Hy94gwBhD6Yn3iY+aO1w1\ntaHlCLLsJOfun4gdOhPIasWDsMZ3t5mOpgmXUEiStAq4HrgTWO3lcHyKqCnh2yRJQeqqhwjLWkl7\n2R5kt5vY5PsJSpwz/E02etbNRM1cjdNqoeydP9Bz4jBLZj1AZFg27V2V7DvyLGq9iV2HnqSw7HUU\nCiVdPbUExs8iKlf8cxhLGkMgsz73e9qO76a38ThmvZHwrJX4BUUPn1Oy+X/pPpFPRuKNBJljqWvO\nZ9+RZxi0dWMZ6KDP0sKSWZ6qpyqVlmkRM6lvKB2+fdqahzmy8btYeuqRkPjvB+uJi5pDT38zDS2H\niZh+g0gmJqipUMZ7QiUUkiSFA38FbgFEL+0hoqbExCFJEoFxMwiMm3GBcxS4nTbay/ayYMZnSZy2\nCIBpETNRSAo+2PcbEld+iYH2GmS3m8wVnyIkZYEobjQOFCoN4dkrz7nAta+lko6KfSyZ/QCJMZ7X\nLCFmISqljsLS15FlmQUzPk9oUMqp21haUfl51knIspv6vE1Ye5o9PyMzYO2ksvkAWnMYqTc9SMT0\n68fhWQpjacOadZN2GmSiPaNngCdlWS6QJCnO28H4AlFTYnLyrJmQCQ0a2Tn05M9aQyDT5t52jlsK\n3tLXeBxJUhAfNX/E8YSYhVTU7gRg0NaDy2VHkpSU1+ygsbWQtNUPAdCY/xYNhzZzF0ksI4pe7LxE\nJcesPeR+5rfoTKFnPaYwMU3WaRCvJxSSJP0C+M4FTpGBDOAmwB/41cmbXs7jfOMb38BsNo84tnbt\nWtauXXs5d+MzFh19hOXrxSDNZKUPiECSFDS3HyNwqJQzQHP7Mc/vg2POd1PBS1Q6E7LsxjLYMWL7\nb/9AKyARPedWjhz8D8UVbyNJChyOASJn3ER49rUANB3czHzCWTX0XcmAmgfkTB5276X56FbiF9/j\njacljCFfmwbZuHEjGzduHHGsp6fnkm/v9YQCz0D9Mxc55wSwAlgI2M7oOXBQkqQXZFn+/IXu4Ikn\nnhjVXh7etGHNOhDJxKSm8Q8iLPMa8kteQSEpiQz1rKHIK34Rc0w2xvAkb4conCE4eS5qnZG9R55h\n6awH0OvMdHRXc7h0E0GJc0i+9ktEzVw9tH7GRXDSXPxPex2tfW0kMHLgVS+piJD8sA3t8hEmJ1+Z\nBjnXl+zTenlclNcTClmWO4COi50nSdLXge+ddigKeBe4G88W0klP1JSYWlJu+Bqy283+o8+B7KmG\nGJQwm/SbH/FyZMK5KNU6Mm77LsWvPcar7z2ETmdmcLATv6BppN70NQD8gqKJXXDXOW9vCInjaGsX\n18vThhu1dcs26uU+4kPFDO9kNxmmQbyeUFwqWZbrT/9ZkiQLnmmPKlmWG70T1fgQuzemJqVGR8Yt\n3yZx+X0MdDWgM4Wds5W24DsC42Ywf90ztJV8iK2vA/+wRIJTFqBQXvytNmbBxyl+/Zc8w3GukT1r\nKDZJ1Si1BsKzrxuH6AVf4GvTIJdjwiQU5zG5e68z9Mcldm9MaVpTyHCdA8H3qXVGomauuezbhaYv\nJWWwjwM7/8lu2yEATKGJ5Kx5GLVe9GGZajasWceMW7r5xJdf9HYol2zCJhSyLNcAk3afnFh0KQhT\nT9TM1UTkXI+lvRqlWo8+KJoz1owJU8iRzQEcWbOOHb/UsyfnN94O56ImbEIxWeWucnrm0kQyIQhT\nkkKlxhiRcvEThSlj+frBCdEmXSQUPkQsuhQEQRDOx9fXV4j25T5CJBOCIAjCpfDVzwsxQuFlvvqH\nIQiCIPguXxytEAmFl4hEQhAEQbhavpRYiIRinImaEoIgCMJo2zC0G8SbxBqKcbRhzTqRTAiCIAhj\nwtulBsQIxTgQNSUEQRCEyU4kFGNI1JQQBEEQpgqRUIwRsehSEARBmErEGooxIJIJQRAEYaoRIxSj\nSCQSgiAIwlQlEopRIBIJQRAEYaoTUx5XSSQTgiAIgiBGKK6YSCQEQRAE4RQxQiEIgiAIwlUTCYUg\nCIIgCFdNJBSCIAiCIFw1kVAIgiAIgnDVREIhCIIgCMJVEwmFIAiCIAhXTSQUgiAIgiBcNZFQCIIg\nCIJw1URCIQiCIAjCVROVMq/Qjl+u9nYIgiAIguAzxAiFIAiCIAhXTSQUgiAIgiBcNZFQCIIgCIJw\n1URCIQiCIAjCVRMJhSAIgiAIV00kFIIgCIIgXDWRUAiCIAiCcNVEQjHFbdy40dshTAriOo4OcR1H\nh7iOo0dcy0snEoopTvxjGR3iOo4OcR1Hh7iOo0dcy0snEgpBEARBEK6aSCgEQRAEQbhqIqEQBEEQ\nBOGqTYXmYDqAkpISb8fhk3p6esjPz/d2GBOeuI6jQ1zH0SGu4+iZ6tfytM9O3cXOlWRZHttovEyS\npHuAF7wdhyAIgiBMYPfKsvzihU6YCglFMHAjUA1YvRuNIAiCIEwoOiAeeFeW5Y4LnTjpEwpBEARB\nEMaeWJQpCIIgCMJVEwmFIAiCIAhXTSQUgiAIgiBcNZFQCIIgCIJw1URCIYwgSZJGkqTDkiS5JUma\n7u14JhJJkuIkSXpakqQqSZIGJEkqlyTpR5Ikqb0d20QgSdJX/7+9+w3Zq67jOP7+MIaowwbNlBlI\nY6wQQSMlDNSCIopMZEGm+WBCscA/GCkrCEHxT1YjtAYJ5VAntQcp+UAcpnsgakvReuCYDzY2xZhO\nodGU0vntwTmzede2a577vn/3ufd+wfXg+nFf8OFwca7P+f1+5z5JdiR5O8kzSc5tnWlMkvwwyZYk\ne5PsTvJgkhWtc41dkjX9+XBt6yxznYVCU90BvAJ4+8/R+xQQ4DvAGcB1wGrglpahxiDJN4GfAzcC\nnwb+CjyaZEnTYONyPnAX8Fngi8BCYFOS45umGrG+1H6X7vuoI/C2Ub0vyVeAnwErgReBs6vqb21T\njVuSHwCrq2p56yxzWZJngD9X1bX9+wAvA3dW1R1Nw41UX8ZeAy6oqidb5xmbJIuA54DvAT8Gnq+q\n77dNNbc5QyEAkpwC3A18G3i7cZz5ZDHwZusQc1m/JPQZ4E8Hxqq70nkMOK9VrnlgMd1Mo9+/D+dX\nwMNV9XjrIGNxLDzLQ5O5B1hXVc8nOb11mPkgyXLgKsCrmsNbAiwAdk8Z3w18cvbjjF8/w/ML4Mmq\nerF1nrFJcilwNnBO6yxj4gzFPJbktn4z0aFe+5OsSHINsAj4yYGPNow950x6HKd85jTgEeD3VfXb\nNsl1DFtHt4/n0tZBxibJx+nK2OVV9U7rPGPiHop5rH+OyUeP8Gc7gI3A16aMLwDeBTZU1aoZiDca\nEx7H7VX1bv/3S4EngKeO9WM3iX7J4y1gZVX98aDx9cBHquqSVtnGKMkvgYuA86tqV+s8Y5PkYuAP\nwH7+e3G1gG75aD9wXPnD+X9ZKHSgkZ900NBS4FG6zZlbqurVJsFGqJ+ZeBz4C3CFJ57JHGJT5i66\nTZk/bRpuRPoycTFwYVVtb51njJKcCExd9l0PbAVur6qt//MhAe6hEFBVrxz8Psk+uma+3TIxuX5m\nYjPdrM8NwMe630Woqqn7A/RBa4H1SZ4DttDdcnsC3YlcE0iyDvgW8HVgX7/RGuAfVeWTlidUVfvo\n7nJ7X39OfMMycXgWCh2KV9ZH70vAsv71cj8WumO5oFWoMaiqjf1tjjcBpwAvAF+uqtfbJhuV1XTf\ntc1TxlcB9856mvnF8+EEXPKQJEmDeZeHJEkazEIhSZIGs1BIkqTBLBSSJGkwC4UkSRrMQiFJkgaz\nUEiSpMEsFJIkaTALhSRJGsxCIampJKcm2ZBkW/8o+LWtM0k6ehYKSa0dB7wG3Ez3DA9JI2ShkDSj\nkixJ8vckaw4a+1ySfyX5QlXtrKrrqup+YG/DqJIG8GmjkmZUVe1JciXwUJJNwEt0T7+8s6qeaJtO\n0nSxUEiacVX1SJK7gQeAZ4F/Aj9qm0rSdHLJQ9JsuZ7uIuYbwGVV9U7jPJKmkYVC0mxZDiylO+98\nonEWSdPMJQ9JMy7JQuA+4HfANuA3Sc6sqj1tk0maLhYKSbPhVuAk4GrgLeCrwD3ARQBJzgICLAJO\n7t//u6q2tokr6WilqlpnkDSPJbkQ2AR8vqqe7sdOp/ufE2uq6tdJ3gOmnox2VtWy2U0r6cOyUEiS\npMHclClJkgazUEiSpMEsFJIkaTALhSRJGsxCIUmSBrNQSJKkwSwUkiRpMAuFJEkazEIhSZIGs1BI\nkqTBLBSSJGkwC4UkSRrsP6YLrDp0SdAgAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5c9c0d03c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Build a model with a n_h-dimensional hidden layer\n",
    "parameters = nn_model(X, Y, n_h = 4, num_iterations = 10000, print_cost=True)\n",
    "\n",
    "# Plot the decision boundary\n",
    "plot_decision_boundary(lambda x: predict(parameters, x.T), X, Y)\n",
    "plt.title(\"Decision Boundary for hidden layer size \" + str(4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "\n",
    "<table style=\"width:40%\">\n",
    "  <tr>\n",
    "    <td>**Cost after iteration 9000**</td>\n",
    "    <td> 0.218607 </td> \n",
    "  </tr>\n",
    "  \n",
    "</table>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 90%\n"
     ]
    }
   ],
   "source": [
    "# Print accuracy\n",
    "predictions = predict(parameters, X)\n",
    "print ('Accuracy: %d' % float((np.dot(Y,predictions.T) + np.dot(1-Y,1-predictions.T))/float(Y.size)*100) + '%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**: \n",
    "\n",
    "<table style=\"width:15%\">\n",
    "  <tr>\n",
    "    <td>**Accuracy**</td>\n",
    "    <td> 90% </td> \n",
    "  </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Accuracy is really high compared to Logistic Regression. The model has learnt the leaf patterns of the flower! Neural networks are able to learn even highly non-linear decision boundaries, unlike logistic regression. \n",
    "\n",
    "Now, let's try out several hidden layer sizes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.6 - Tuning hidden layer size (optional/ungraded exercise) ###\n",
    "\n",
    "Run the following code. It may take 1-2 minutes. You will observe different behaviors of the model for various hidden layer sizes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy for 1 hidden units: 67.5 %\n",
      "Accuracy for 2 hidden units: 67.25 %\n",
      "Accuracy for 3 hidden units: 90.75 %\n",
      "Accuracy for 4 hidden units: 90.5 %\n",
      "Accuracy for 5 hidden units: 91.25 %\n",
      "Accuracy for 20 hidden units: 90.0 %\n"
     ]
    }
   ],
   "source": [
    "# This may take about 2 minutes to run\n",
    "\n",
    "plt.figure(figsize=(16, 32))\n",
    "hidden_layer_sizes = [1, 2, 3, 4, 5, 20, 50]\n",
    "for i, n_h in enumerate(hidden_layer_sizes):\n",
    "    plt.subplot(5, 2, i+1)\n",
    "    plt.title('Hidden Layer of size %d' % n_h)\n",
    "    parameters = nn_model(X, Y, n_h, num_iterations = 5000)\n",
    "    plot_decision_boundary(lambda x: predict(parameters, x.T), X, Y)\n",
    "    predictions = predict(parameters, X)\n",
    "    accuracy = float((np.dot(Y,predictions.T) + np.dot(1-Y,1-predictions.T))/float(Y.size)*100)\n",
    "    print (\"Accuracy for {} hidden units: {} %\".format(n_h, accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Interpretation**:\n",
    "- The larger models (with more hidden units) are able to fit the training set better, until eventually the largest models overfit the data. \n",
    "- The best hidden layer size seems to be around n_h = 5. Indeed, a value around here seems to  fits the data well without also incurring noticable overfitting.\n",
    "- You will also learn later about regularization, which lets you use very large models (such as n_h = 50) without much overfitting. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Optional questions**:\n",
    "\n",
    "**Note**: Remember to submit the assignment but clicking the blue \"Submit Assignment\" button at the upper-right. \n",
    "\n",
    "Some optional/ungraded questions that you can explore if you wish: \n",
    "- What happens when you change the tanh activation for a sigmoid activation or a ReLU activation?\n",
    "- Play with the learning_rate. What happens?\n",
    "- What if we change the dataset? (See part 5 below!)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='blue'>\n",
    "**You've learnt to:**\n",
    "- Build a complete neural network with a hidden layer\n",
    "- Make a good use of a non-linear unit\n",
    "- Implemented forward propagation and backpropagation, and trained a neural network\n",
    "- See the impact of varying the hidden layer size, including overfitting."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nice work! "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5) Performance on other datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "If you want, you can rerun the whole notebook (minus the dataset part) for each of the following datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# Datasets\n",
    "noisy_circles, noisy_moons, blobs, gaussian_quantiles, no_structure = load_extra_datasets()\n",
    "\n",
    "datasets = {\"noisy_circles\": noisy_circles,\n",
    "            \"noisy_moons\": noisy_moons,\n",
    "            \"blobs\": blobs,\n",
    "            \"gaussian_quantiles\": gaussian_quantiles}\n",
    "\n",
    "### START CODE HERE ### (choose your dataset)\n",
    "dataset = \"noisy_moons\"\n",
    "### END CODE HERE ###\n",
    "\n",
    "X, Y = datasets[dataset]\n",
    "X, Y = X.T, Y.reshape(1, Y.shape[0])\n",
    "\n",
    "# make blobs binary\n",
    "if dataset == \"blobs\":\n",
    "    Y = Y%2\n",
    "\n",
    "# Visualize the data\n",
    "plt.scatter(X[0, :], X[1, :], c=Y, s=40, cmap=plt.cm.Spectral);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Congrats on finishing this Programming Assignment!\n",
    "\n",
    "Reference:\n",
    "- http://scs.ryerson.ca/~aharley/neural-networks/\n",
    "- http://cs231n.github.io/neural-networks-case-study/"
   ]
  }
 ],
 "metadata": {
  "coursera": {
   "course_slug": "neural-networks-deep-learning",
   "graded_item_id": "wRuwL",
   "launcher_item_id": "NI888"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}