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Deeplearning.ai week3 assignment 1
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"### <font color = \"darkblue\">Updates to Assignment</font>\n",
"\n",
"#### If you were working on the older version:\n",
"* Please click on the \"Coursera\" icon in the top right to open up the folder directory. \n",
"* Navigate to the folder: Week 3/ Planar data classification with one hidden layer. You can see your prior work in version 6b: \"Planar data classification with one hidden layer v6b.ipynb\"\n",
"\n",
"#### List of bug fixes and enhancements\n",
"* Clarifies that the classifier will learn to classify regions as either red or blue.\n",
"* compute_cost function fixes np.squeeze by casting it as a float.\n",
"* compute_cost instructions clarify the purpose of np.squeeze.\n",
"* compute_cost clarifies that \"parameters\" parameter is not needed, but is kept in the function definition until the auto-grader is also updated.\n",
"* nn_model removes extraction of parameter values, as the entire parameter dictionary is passed to the invoked functions."
]
},
{
"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](https://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": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Package imports\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from testCases_v2 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": 4,
"metadata": {
"collapsed": true
},
"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. In other words, we want the classifier to define regions as either red or blue."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
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toITNj86k/0cP1YFlNU9JkZVXnllCQb4Fa7kDo1FmztfxTJzaiYnXdMZsNnBg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\npZRyGQ3sSinlMhrYlVLKZTSwK6WUy/wfuoiIWc8YlM0AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f171cebd9b0>"
]
},
"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": 6,
"metadata": {},
"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",
"### 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": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"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": 8,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy of logistic regression: 47 % (percentage of correctly labelled datapoints)\n"
]
},
{
"data": {
"image/png": 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Jzqy/mf2Moa/1FtjFNNLL6Mew8yP0NRRPzL3KwwunSfkixMwMwVtUyxxbvsSp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\nBIVCoVDk+P8B2BAvSDnlqLAAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f16e972e2e8>"
]
},
"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",
"$$z^{[1] (i)} = W^{[1]} x^{(i)} + b^{[1]}\\tag{1}$$ \n",
"$$a^{[1] (i)} = \\tanh(z^{[1] (i)})\\tag{2}$$\n",
"$$z^{[2] (i)} = W^{[2]} a^{[1] (i)} + b^{[2]}\\tag{3}$$\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": 9,
"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",
" \n",
" ### START CODE HERE ### (≈ 3 lines of code)\n",
" ### END CODE HERE ###\n",
" return (n_x, n_h, n_y)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"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": 11,
"metadata": {
"collapsed": true
},
"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",
" ### 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": 12,
"metadata": {},
"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": 13,
"metadata": {
"collapsed": true
},
"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",
" ### END CODE HERE ###\n",
" \n",
" # Implement Forward Propagation to calculate A2 (probabilities)\n",
" ### START CODE HERE ### (≈ 4 lines of code)\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": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.262818640198 0.091999045227 -1.30766601287 0.212877681719\n"
]
}
],
"source": [
"X_assess, parameters = forward_propagation_test_case()\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:50%\">\n",
" <tr>\n",
" <td> 0.262818640198 0.091999045227 -1.30766601287 0.212877681719 </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",
"$$J = - \\frac{1}{m} \\sum\\limits_{i = 1}^{m} \\large{(} \\small y^{(i)}\\log\\left(a^{[2] (i)}\\right) + (1-y^{(i)})\\log\\left(1- a^{[2] (i)}\\right) \\large{)} \\small\\tag{13}$$\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",
"Note that if you use `np.multiply` followed by `np.sum` the end result will be a type `float`, whereas if you use `np.dot`, the result will be a 2D numpy array. We can use `np.squeeze()` to remove redundant dimensions (in the case of single float, this will be reduced to a zero-dimension array). We can cast the array as a type `float` using `float()`."
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": true
},
"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",
" [Note that the parameters argument is not used in this function, \n",
" but the auto-grader currently expects this parameter.\n",
" Future version of this notebook will fix both the notebook \n",
" and the auto-grader so that `parameters` is not needed.\n",
" For now, please include `parameters` in the function signature,\n",
" and also when invoking this function.]\n",
" \n",
" Returns:\n",
" cost -- cross-entropy cost given equation (13)\n",
" \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",
" ### END CODE HERE ###\n",
" \n",
" cost = float(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": 32,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"cost = 0.6930587610394646\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> 0.693058761... </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": 37,
"metadata": {
"collapsed": true
},
"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",
" ### END CODE HERE ###\n",
" \n",
" # Retrieve also A1 and A2 from dictionary \"cache\".\n",
" ### START CODE HERE ### (≈ 2 lines of code)\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",
" ### 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": 38,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"dW1 = [[ 0.00301023 -0.00747267]\n",
" [ 0.00257968 -0.00641288]\n",
" [-0.00156892 0.003893 ]\n",
" [-0.00652037 0.01618243]]\n",
"db1 = [[ 0.00176201]\n",
" [ 0.00150995]\n",
" [-0.00091736]\n",
" [-0.00381422]]\n",
"dW2 = [[ 0.00078841 0.01765429 -0.00084166 -0.01022527]]\n",
"db2 = [[-0.16655712]]\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> [[ 0.00301023 -0.00747267]\n",
" [ 0.00257968 -0.00641288]\n",
" [-0.00156892 0.003893 ]\n",
" [-0.00652037 0.01618243]] </td> \n",
" </tr>\n",
" \n",
" <tr>\n",
" <td>**db1**</td>\n",
" <td> [[ 0.00176201]\n",
" [ 0.00150995]\n",
" [-0.00091736]\n",
" [-0.00381422]] </td> \n",
" </tr>\n",
" \n",
" <tr>\n",
" <td>**dW2**</td>\n",
" <td> [[ 0.00078841 0.01765429 -0.00084166 -0.01022527]] </td> \n",
" </tr>\n",
" \n",
"\n",
" <tr>\n",
" <td>**db2**</td>\n",
" <td> [[-0.16655712]] </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": 41,
"metadata": {
"collapsed": true
},
"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",
" ### END CODE HERE ###\n",
" \n",
" # Retrieve each gradient from the dictionary \"grads\"\n",
" ### START CODE HERE ### (≈ 4 lines of code)\n",
" ## END CODE HERE ###\n",
" \n",
" # Update rule for each parameter\n",
" ### START CODE HERE ### (≈ 4 lines of code)\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": 42,
"metadata": {
"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": 45,
"metadata": {
"collapsed": true
},
"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\n",
" ### START CODE HERE ### (≈ 1 line of code)\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",
" ### 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": 46,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Cost after iteration 0: 0.692739\n",
"Cost after iteration 1000: 0.000218\n",
"Cost after iteration 2000: 0.000107\n",
"Cost after iteration 3000: 0.000071\n",
"Cost after iteration 4000: 0.000053\n",
"Cost after iteration 5000: 0.000042\n",
"Cost after iteration 6000: 0.000035\n",
"Cost after iteration 7000: 0.000030\n",
"Cost after iteration 8000: 0.000026\n",
"Cost after iteration 9000: 0.000023\n",
"W1 = [[-0.65848169 1.21866811]\n",
" [-0.76204273 1.39377573]\n",
" [ 0.5792005 -1.10397703]\n",
" [ 0.76773391 -1.41477129]]\n",
"b1 = [[ 0.287592 ]\n",
" [ 0.3511264 ]\n",
" [-0.2431246 ]\n",
" [-0.35772805]]\n",
"W2 = [[-2.45566237 -3.27042274 2.00784958 3.36773273]]\n",
"b2 = [[ 0.20459656]]\n"
]
}
],
"source": [
"X_assess, Y_assess = nn_model_test_case()\n",
"parameters = nn_model(X_assess, Y_assess, 4, num_iterations=10000, print_cost=True)\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",
"\n",
"<tr> \n",
" <td> \n",
" **cost after iteration 0**\n",
" </td>\n",
" <td> \n",
" 0.692739\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr> \n",
" <td> \n",
" <center> $\\vdots$ </center>\n",
" </td>\n",
" <td> \n",
" <center> $\\vdots$ </center>\n",
" </td>\n",
"</tr>\n",
"\n",
" <tr>\n",
" <td>**W1**</td>\n",
" <td> [[-0.65848169 1.21866811]\n",
" [-0.76204273 1.39377573]\n",
" [ 0.5792005 -1.10397703]\n",
" [ 0.76773391 -1.41477129]]</td> \n",
" </tr>\n",
" \n",
" <tr>\n",
" <td>**b1**</td>\n",
" <td> [[ 0.287592 ]\n",
" [ 0.3511264 ]\n",
" [-0.2431246 ]\n",
" [-0.35772805]] </td> \n",
" </tr>\n",
" \n",
" <tr>\n",
" <td>**W2**</td>\n",
" <td> [[-2.45566237 -3.27042274 2.00784958 3.36773273]] </td> \n",
" </tr>\n",
" \n",
"\n",
" <tr>\n",
" <td>**b2**</td>\n",
" <td> [[ 0.20459656]] </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": 59,
"metadata": {
"collapsed": true
},
"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",
" ### END CODE HERE ###\n",
" \n",
" return predictions"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"scrolled": true
},
"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": 61,
"metadata": {
"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: 0.219471\n",
"Cost after iteration 9000: 0.218612\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x7f16e962ea90>"
]
},
"execution_count": 61,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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X4tNtwqCBqXyWw8McqSzt/EXuc0pFr00oV6uK+WtOc3vj+9UlF5TCccBzFfGkgWX11+JW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\nAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f16e912cfd0>"
]
},
"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": 62,
"metadata": {},
"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": 63,
"metadata": {
"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",
"Accuracy for 50 hidden units: 90.25 %\n"
]
},
{
"data": {
"image/png": 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sL3jN5S+GAecuxshvBqyvdu7EhuF8Oi/GQRAm0EmNmtaqet+zCS0LJi44+J5G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\nIoUascUK9jpujhRqdTdXPeySw2JnmHzX3nXzzo8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aipPZq1xIPtDcxGgpP6XNH2xsVSMj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YAAAg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\nYPkHqVSG9oj7Dk8Xv92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TcBxNteJhmtHbu7RaDreE7wcpSrF4+FjjjctXr9zEN8zgLKGB\nNkzqyTSlS5exrO3zH/RDEqRLh9cN5YYv9sSmE6uwVt1jarGAofX+LGt5OMHWVGZfbrnt2omP0+7b\n6SvYHU3uP57p+ii/9WlRyyD3nFMzBaGTaENR7uD3tTScILvT7GatFJntKulKiJv9h3VzEU3g5kou\nzuThWjvPJX6vyMalLNUurMict5sTCYObbd3sU634kXTz/nY0x/B9cF2ItYj9gtpXn5XZJwI3q0Nu\nNk3qqSyV6WlMq9CBUT8cmayJUk5TrxGlOPct/LrR0ClWd5laDLbw2nNzKZ9ge/KQm7ce3c2F0YMs\nEcvxg+drQbboiJtDkEA24rgJi62pDKOr5f1fhEahFU1B7B4Pc1g/fJ9MoR6a0rxXa1cZSnSlbufN\nz+d58xzrc5RSJFNHX4fWmvVVh52toOmEIoj3Zq9HpwmSaSncFimk7boFDuVNNtddKrk8vtVsVK0U\n1uURqEYnkDWMYOZ39f6BMJWCRFJdWOOPM80Ca43p+OjTNG7Rmsl7u5jHZmszhTpuzED5OvhMQjqw\nnmoojf/ujqaOpFRqQ7V8PK3AODaevcdRGqaWitQyMTanM6eqzxeEfsVJtnFz5fzdrAiavoSlNBsa\nRtcqLOe601n8oty8tuIEtZmNq8wIutlrkULaTgfDeZOtDZdybhjfavaaVhCfGYFqdALZVm5OJtW5\nBbLnuq3OGd08ca/Y5MLsTh3XMjD23Rz+Oz9xKI3/7o6mKI4eLAz5j+Dm6cVdqpkYW9PZIKtqgJBA\ntgco55NUcnGSFRdtBK25xx80pwyfFk17oRoaVKuDsaeDE+xDAZPyfCzHx42bQeqW1iSqLpbjYydM\nnOT5fs32AotONJwobLtsbzZWPBsTcb4P9xZtbjyeiMTs7+i4xcry0ZlQpYLmSO22pInHDUbHLXKF\nTQzXaQpmDQOm9G6nhv3QDOctkimDwraL60B2yCA3ZHb8szhrGlOqZDP6oITRaKpWS8fYuJxtKc2Y\n7QWro8cuNzTk14+u/PqcvQ5EAXbMaOpu7psGtZRFstLcqG4vA+P44xw+ZiTLDtN3d1l+LB+pRjSC\ncNEcd7Ph+oytdMnNrr9fD7x/e8/H7BM372y5wQQz7J/Buz4sL9pcj4ibx8YtVu6HuHnIbDvJHE8Y\njI6Z5AqbrLVw84QfUTcnG252ITdkkh06n1Xy81yFTRVtxlZKRxqebrZzc721m0fOy83xEDdbBvWk\nRaL6cG5OlYPyhUFzswSyPYI2Daq5oLPZ8EYlNNX4tBz/8p9pHOpQnZzWjK6Ug30oG/vZlIYTJKou\nsUP1BPWUxdqVITjnHP6FuXm+4H+aN754Pl9j19WsPgjpLAQ4tsauaxLJ7h8choYtHFuzue7ubyOU\nzhpMz5y8f+HEVIwPVO5x13uW2qH0YtP3GLULTNU2Oj38hyKRMC6kpT88XFv/WM1lfLl4ZKUkWXaY\nulvgwY1wqSif0MYNEP7bPKswNbTc03Ljco6pxV0s5+B3WsnFg/qcE8ajAMP3B7q5hCDsccTN682N\nX878eDykmw11EMSGuLmYT5IqO0d+87VUY9uODrj5PINZ19WsrYS72bY1tq1JJLrv5tywiW0H+9Tv\nuTmTNZhuU0+6x8R0nA9Wl1j0nqF+xM0u4/UdJutbnR7+Q5FIGkxeOj83n/duALGay/j9o25OneRm\nrTvu5lorN8/kmFwsBE2gGpSHEmR266dzs+eTKjn7x6RBQALZHsSJm8HKbOsa87b4BAXmx1Maj9zG\nVChfH/nxB7V2qf0ffn6tQma3HtymMQWZ26kDR39ciapLfqPCznluRN/gZeMTMHc+M8A7W+Gi3KNW\n9SPTcGJsIsbImIVdDzoMW7HTS3woDX/5wW/zlbFnuJOZwdSaJ4q3+fDWn/TunhvnxMM2kwirY1dA\nzPaZubXNyvXhplRcO2miQ2wZdiKrVbA1x57c4rYH+uik1OH76cZ9WnVp9S2DBzeGidcaqzNJCzdu\n4q+UyBbqR373YeNRPqGNqQRhkHHiFr7ioYNZzclu9kyFEermgzr4kbVyk5uHGrX2hx83WXUY3qye\nbU/6U3Keq7Pbm6dwcwTSi5VSjE/GGB2zsG2NFVNn2gFgOKX5Kw9+m9fHnuFu5jKm9nmyeIfv3Xpr\nINzciT4UQ1vVlm6+/M42q9fC3GwRFsmem5uNxrl0CJ5l8OBGft/N9aSF1+haninUTwyYlQ6yvS5u\nT4XuI4FsD1LJxRlZM1B+c+rDcfYWfWj811cHKY/59QrZwsEsz94PTivYuJTFsj1GNqr7IiyOJCmM\nN358WpPbqYW2HT+OoYPank4EsnsszM3z2o//Pq//1Ncf+jGqlfYzA7Wax3CEfjKG0VxHdFoyXo0f\nXPuDcx5R7/Kos8CxFnWsiiAdf/x+idVrw8euVGxezjK+fNBUrd35bzUbp5wP6mlMxyO7XSNe9zBc\nH8Pz0IYRdEjd2+pjMt1+2w6lsFMx7EM+3ZnIkKi5xOoHQWrYLLAm6JCeu7WFbxgURxKU8gcn0oIw\niFRycUbWFapNB/E9Qt2cibExnSW/0drNm5eyxOoe+c1jbh47cHN2p356N+/UOhLI7nEeAW212t7N\n9ZpHlE5nDfNR3Fzlh9a+cs4jijad3JPdssPPkxVguZqxByXWrja7eeNSlvH7p3RzLrHfWM60PXI7\ntWAvWM/HcAM3W42yvHo6cLN3RjdvT6aDLsn2UTc3BdZAomwzs13FNwx2R5PB2PrYzdH55QunRylW\nrg8zslIiXQo6uoZ9RTWwMZPFNxWZgo3ha8pDcarZoCHE9nSW7ekssZrL8EaFeN3DiZsUxlPYqSDt\noTSSxHQ1nqmOph/p8BPclkNu04ntvPjoqy/A3AsPLcxEQlEpt74+CjU4wvkTNgucqDjk1yvEqy6u\nBaWRNOXhBL4VPh9aS8eI19zQlRhFkJVgeH5TTU41G+fBjTyZnRqW62PHTfKbzTPIe7fdw4uZFDow\nMaRNxcq1YRKVYKUmUQlWQg7PKO+dgCcrex0UPUbWKsTqHtvT2XMfkyD0DEbw+xldLZNq52YF65ez\naEOR3bVRvqY8lKCajZ3KzbUsFEeTmK6PZxpH3Kz8s7r5EV/zKXmUdON4XFFt5+aBWK/sT866Cpuo\nOOTXKsRrLq6lKI2kTnCzFbg55LrAYy7K89HH3ZwL3JzdqWG6Pk7cZLilmw/ShL242ZFFG20arFw/\nnZtT+/0vPEZXy8RqHjvTnVtI6jYSyHaJVNFmeLOC6frUkzF2JtK4idN3evMsg40rQ0HzhopzpB0/\nBLO725MZqrlglqiebp0v7ySt4LHCUAovLG3VULgxI3QPrePpFxqoZi4uX/9hV2fzYxbbW+HpksF2\nL73/c3k3c4U38++haia4Ulnhg9vfJOMNUhLKAa3SiPf2T97TWtyFkfUKI+sVKtkYm5dzTftRFkeS\n5HZq6FYpgW2mdN14c1Ca3zz6mWxOZ1qK+txRCsNvBN+HLt4bvhMPfveHjzeGDsoKPNOgNJJkdLVM\numSjCVapticvcPyC8AikinWGN6qYnk891XBzu9WTY3gxk/UT3Lw1laG25+Y2bjzZzc3j0ga4lkHM\nPerm/Q6nxy47fBLeaR52dXZ0zAq6FYfQD27WwDuZWb6ef4qamWC28oAPbH+TjHfy9i69zFlLeRKV\n427WJ7t5NEWuUG/tZlrr2T0WlCpg6LibL7VuGnXuqKDsoK2bbf/IdYaGoZ0anqWoDCcZWS2RLjsH\nbp7KXNz4O4T6/9l77yDJ7vvA7/N7oXOePJtmI0AkkogECTCLAgmFO+pI6WifJZ/vqCNOJ9uqctUZ\nrrtzlctXkk6us1QWbckqlixKdwqmLJFikBggiCBBEoSIRIDA7mLD7OTQufvln/94PT3d0697ZnZn\ndie8T9XW7r5Or193v8/7/X7fMKhJ814jPXFW3vfzv3mrd+OGSRWb5BcbbbmtxczPnchtazDbSXuW\nynJwNIXycGLXC7HEahYjG8IiJbRNqUhf2lIRzAXkCHaiWi6Z1SaxpuPPPA/FN62oqLgeiitxdKVv\n2MR2hdlsuMxctXA7nCkE5AsqIzep2NBu8f3cHbyYfwuO4h9XIT2insXHpv+axAEXZid/8juf4MXP\n5/rePnqlRLzZp1cv/oVf0MWlanuMTFeIbAgzlvh57XOn+r/mRjTLJV6zkELQSEdu+iBwdLpCvN7b\nv9cT/nuJmv2Pj1RFV5sACTi6wuypvVlJ8elfe/x5KeX9t3o/9jMHxc3plSa55QA3T+W2NZjtJNqw\n/YgFy8HR/FXV3XZzvGb1pCwEudlTBPNTuYHtOrSWm6NNByuiUtkhN//tr8b59t3/25bfU6PuMjNt\n4W1085DGyNjNG4zvBs/l7+Kl3G0dbnaJuTYfm/4Kcc+8xXu388Se+ii/8hvj237c2OUSMWOAm9MR\nlo+ke25TbZfRqxV02+t1c1T1iz5tkVvu5qtl4o3enHFP+APvyCA3K6LdVQHWrk2UvkWvbjVbdfP+\nnsbaj0h/BqlzhlYAeH414pWAH+FWMBM6C1PZze+4gxipCAvHM2SXm+iWixXTKA/HcVWFVCtHwIpr\n1LLRnrCNTjTTZeJKuV0aXTf9E8XSkTRGqnfwKDw/r2FtxQchKI7EqeV7k+effPyJbQkznlA5c3sc\nw/CoV/0TQiqt7pkiTxtpNlxWVxwcW5JMqeQLGmpAcQlT0Xkh/xZcZf0nL4WCJXRezJ7j4dXrzy3e\nLzhC5dfe+fPE/i+brN6glosGTq5E+4gS/MqEibqN4ng9AnN1hcUTGcYvl1Edr32xiBAsT24v5NaJ\nqFT7FIO4GfRr8YEQuJqCNINzghXomfkW+K1BDlslxZB9hie7BrGwwc2T1+/m+Zvs5uYGN5stN3sd\nbjbjGvVN3KybDuNXyn64Mr6bEzWLpaMZjGTv4FF4kqHZanvFRwpBcTTRzu3v5L3/ugnbCDdOJFXO\n3h7HaLrUa/5q8152c6PhUmy5OZVWyRW0wPZ4pqLzYu52XGXdRVKoWAq8nDvLg6uv3Mzd3nWefPwJ\n+I3ubcKTJCsm0YaNE1GpZa/TzTUrMIXH1VUWprLBbp7YX25WvH5uBkcT6GaffHhAer1u1hyPWN0O\nvNbeL4QD2ZuMZnt9S3rHmsGV+YQrUV0PR1N2vEz+jWLFdZaO9QqtMrz14hH5pXp7EAv+sRASCgt1\nZpN6z0zR0FyNeM1aT3SXkvxCA81yUTyJFVWpZ2NtQW9XmACxmEJsjwpyjXLRYWFuvWedaTiUiw4n\nTsd6KiWuammE5/XUiPcUldn42E3a41vH//TYv2DicpnCgt8ew8OvZrh4LNPTokZuUtlB4q84BM3E\neqrC7MkcyapFpGnj6OrA/J29SiMTIWIG5fxKSsNxPz+2v097tx3CSooh+wvd7hO6ih9mH3jbQXfz\nYqM9iIUON8/XmD2d77n/0GyVeN3ucnNhvu4Xvmm7uXvw/OTjT/DWnyrxs7/4n7a0T7G4Six+favj\nN4tS0WZxzulyc2nVYep0rGeieVnPIjwXlO735CoqM/Ex4GAMZPutwiqu1z3AxA/fDXKzp4A6oO6X\n72aJF/D16HFzpOXmfRZWW09H0c1Gj5slgtJwglijsj03e/4ElbGPy1vsr0/wAOCq/csSeAImLxY5\n9voKE5dKxGomhfkaRy+sMnGpxLHzq6SXGzd1f28GsYYTeEw02+uZfVJcz5912/BDVYBM0SRdtigs\nNjl6oYhudl98PPn4E8Se+ujO7vwtwvMki/PdjdelBMeF4nJvSGhtpoonAn7u0iPtDKiisc+JPfVR\nnnz8CbIrzbYowf++KBKGZ2uwIb2ikY4MrFCIEDgDwuRRBPVslOJ4iupQfN8NYgFquRh2RPVnrWm1\nBRGwMp7CjussHUkFHqN2COPG7YofkhwSsldxNaWvm2WHm8cvlYjVLApzHW6+sEp65eC5Odou6NaN\nZns9URuK4/nRKoFuNkiXTQqLDY5eKKJtCH988fO56259ttfw3ez0uNl1YTWgjVBtuoIXdCkuPdL2\nwXDzk48/0TeUOLPcRLV73Tw0F+Dm1GA3S+HXbulLp5sL8X03iAW/AKsT5OYJ383Lk9t0s+C6Uxr3\nCvvvU9znSFWhno60v4Tt7fhyWCuiEjFdRq/V/L5Rkvaf/HKTo+dXiVcOTt6EGxBuA4AAb8NqrOLK\nvieyjbPGI9eqPff5ld8YPxDCtMzeo2DrEYpD48zL7hA4x5HIpQqZ1SWE230BoXoe95Re39V9vRU8\n/Jl7uuSZqPZOfoA/MaJtKFhWHE3hKSLwe+YJKI7E99zqy04jFb8y+up4ino6QjUfY24q187tM1JR\nlscTdB65NVG6avex87cpN7WoTEjIdvFUhUaqj5utdTdHTZfRa9VuN3uQX2py5Pwq8Zp1S/Z/N+h3\noS+FPznViep623BzJfB+Tz7+BH/yO5+4rn3dK5hmb1EhW4+yOjTOgtdd1M+2JWK5TKq8Euzm8hu7\nvLe7y5/8zic2vd5KVq3AgYjqeKgbCpYVx5J4SvCAzC9wmtiTuZ47iVQEcyfW3VxpuXktbaeZjrIy\nFuxmL8jNmkIzIE1gPxGGFt8CVsZTQJ1k1WznkQgpey601076G7eprmR4rsaSKjBuYjXg3aJSiHUV\nvwL/pFTPRHsGDO3iEZsUKRO0VnQD8iVgZ5u13wpUdf0QSODybW9l+szdrfBhhfN2mQ/Pf5O4a+LY\nEiHgzuee4rX73k1xeAIhPRTP5S2vfZfx1MotfS87zZOPPwGf694mB8htY5VDqQpmT+XILdVJVP0Q\ndtkqclQeTuzrXJJtIfzZ67X+eBtp5OJ4ukp2uek3bo+38vAUhcJCrd1+pJGKsDqePPAXGCH7n5WJ\nFIX5Gsmqte5mT/ZcaAt6w/QEoLmS4Zlq3xzS/UalECO3FODmgL6U9qAolQ4E+EV3XIkMmMR+8fM5\nXryBVj23mo1uvnTb25k+cyeK5yEVhfN2icfmvkncs9puvvt7X+fV+95NaWjdzXe8+h1G06u39L3c\nCE8+/gR8fvP7yf7rGD3elqrC3Mk8uaV6O71MCr9gU3k4cSCuh7eEMtjN9XwcV/fbBa25uTScQCp0\ntQZrpCOsju1/N4cD2VuBIliZTLHqJlE8D08RHDtf3N5TSMgtNZg/AD/cWi6GZnmkS0Z7kNpM6hTH\nAvpetQo7baz63DckbJPXvpHedrcSPaIQjQmMpmR54gTTp+/CUzVoXUssKzm+OvZOfmr2KfSIQErQ\nHYt7vvs1rEgUR48Sa1TJ5xQ4IAOzQU3Vq7loT5G1tYqFblCuq6awOpFmdWKXdvaAYCQjgRcPa+1H\ngH0vyZDDg1QEK5NpVl3pDzwEHL1Q2tZzKBKySw2M5M0t8LQbVPMxNNslVTK73Lwa1CdTERRHEl3n\n2UFu3qz1636dbI5EFCJRgWlIlianuHb6DqSq4bbdnOfrYw/zE3NPE4m23GxbvPU7AW7eh8Xxthvx\nVs33TpZIwIxpgWk5rq5cd+G1w4SRigROuh9EN4cD2VuIVAVua/rOUwRqv2pkfdCsAVnv+wkhKI0l\nqQzH0SwXV1MHtgOo5eM4HbNNG8NPoBVGoYiBFRnX2K/CPHI8yrUrJtOn78DTNhYsUlmMDlFX4yRp\nkh/SKK74eTsRyyRimSgKFIb39ymgqKeZiY/zhTseIf6JeY43HTwhqOWilIYT7RX9Wj5GrOl0hf25\nqsLSdVYJD9kCB0SSIYePTjdLxS+Ish36FY7adwhBcSxFeTixNTcX4rgRlcxyEy0gNBQ60g+2mJ7x\n5ONP8CXvt3jhy/vHVUePR7l21WT69J09bvaEynxshIYaJYFJvqBSXHV73Dy0D93cOYjVTYdY3QYP\n4nWLmNHh5pH1EOBqPka0YXe1e3M1ZduV/kO2wQFz8/77pRxEhKA8FPdnpTo2rw1r+33luoqneNKv\nxmY42LqyaVn9vYinKljxre1z52xTarVBYbHZ1RsLYGkLJ0LNdElU/Xzjf/vBX+Rr/zG1rd52txJN\nE0ydjvFsMrgUvIKHqUZIuk2GRzX0CKwuu7iuJJFQGB7TiUT213dkDQl8a/jtvJw7BxLyS36hFQGo\nUpIuGuiW688+gl9m/0gazXSIGg6upmAkeitih4SEhLQRgnIhTna5Gbp5i25upiI019y80qCw1Ovm\nrQxSdNMhXrVACH4q/S9xHlf3zWSzpvtu/nait+0QgMDDVCIkXJPhMR09IlhdWXfzyJiOvs/c3B7E\nSklhoU6ybHalxnW6WbPc9V7sQrB8NINuOkQMB1dTMRJa6OaQLRMOZPcI1UKMVNlAt7yuwggSvxz5\nxpwcT+DPahFQvlxAbrnJwokMdvTgfwrtnTUAACAASURBVMS1QgJXU8gt+bPAdlRlZSyJHR+co5Re\nbpBbabZPttmVJj/5zy2q+yzceKoxyyt6Em9D+X4hJVnLL6ohhCCX18nl93/eFsD/8p5/xshMNbCA\nE/jhfbG6jWa5OB0XlU5UwzkEv4mQkJCdoTIUJ1kxr8/Njsf4lV43z5/I7vtKoVuhNpTA0xQ/j97x\nsKIqq+NJ7NhgD2WWG2Q73bzcoDSS4MnHn+Cpn3mGZ//p/uh7frw5x2uRBJ7o/qxV6ZG1a0DLzQWd\nXGF/unljKHG8ZrcLoQWhSIgHuNmOaofiejVk5wm/NXsFIfwCCBs3b/h7LWR2+UgKs1VMIrvU8Mvh\nt+6jSJBSMjRXY34qt/v7vgdoZmI0M8Gzn0Folktupdl1shWtvONmSt9X4cZvK/2IC+njmET9purS\nQ5Mejy59H3XTLOH9xds+7PAR5ZcZvlbpK8o2wu+P5oRtX64LtZUbp9kuRkKnkYluORwwJOTAIETX\nILa9ecPfa1VBlyfT7f6XuQFuXpja/zm0W6GRjdHIbt3NuumQDXLzYoNGUud9n3sEHn9kX7j53tJr\nvJk6hqXouIqGkB5qy83KAXBzUD5sqmRs6mYp/L7ioZuvD9V2SRUNNMfDSOiBhVEPE+FAdg8hRW+V\nYuie7RWAIiVWx8xVsmoFSjZiuAjX23dhTDeDtWq0GxHA+JUK81NZnIh6XcWgbMujXvdQFUEyraDs\n8gkm7pl8bPqveSVzhmuJcdJOg7vLbzBq7t+Kh0F0SnNLR1SCs8/Cs/YK0YbN6HQFpN+jLVG1yK40\nmZvKhueTkEPHVt0sPInVsdKa6OPmqOEgPBlODAUQH+DmiQ1u/ttfjW8rFciyPBo30c0J1+Bj01/h\nh9mzXIuPkXbq3FN6gxFre8U99xo32sJQyLCv+PUSrduMXqsgpP+bSFQtMqt+lMdhdXM4kN1D1DJR\nUgNCMtaQQpAqGcQatj/bO7BIVCjK7SAAxZOMTleYPZUDIfi3H/xFFE/yb77xe6gMrvqxtGBTXGt6\nLvznO3oiQjyxuyftmGdxX/GHnL72Gq+kTvO3I28jKSzurl7geHN+V197t3n4M/f4s/Ad1DNR4jW7\n77fbA8x4GKoUhOJ4pFebxBo2TkSlWohjxTqOk5QMz9a6zkOKBByP7EqTUlDF0pCQA0w9E/XDJTe7\no6DbzZu0iQsJoE9uZJCbP/jf11De/0n+zTf+74HRR1JKlhZsSqtu+8l8N0eJJ3b34j/uWdy3+gqn\n6q/ySvoMT428nSQWb62e52hzYVdfe6dZi4gaRD0TJV7fzM16uBobgOJ4ZFabRFturhTi2D1urva4\nWbM9MqsG5VZKw2EjvMrbQ5RGkkQNB930T7Zrs5I9JwRPdoXerDU77ryfBIyEFtinLcTvn5VdbvSd\n+VUdj1jVIr/cRLNdEPC7Zz/Oh2af4XT9WuBz1mtuuzIwANL/HK5dsThzewyxi8ULpJRcnvZ45v7H\nMOIpPE2jCMwlRrm39Br3ll7btdfeTYJ6woL/+RkJjVjD6fl9yNbtqxNh1cONqLbLxKUSwvNXWqXh\nkqhYLB9Jtxuqr/Vf3ogi/dnfcCAbctgojiaIGA66tZmb2Zqbk3q4GtuHrbg5XrPaYdsI+J2zP8t/\n9z/ksT75fwY/Z92j1KoMDLTdPHPV5PRtu+/mS9MezzzwYcxYssPNY9xffIW3lV/ftdfeSba6CtvI\nRDDKA9ycibA6Hrp5I6rtMvFmCSE73FxtublVPE2zPJSAhStFQrJiHtqB7OFch96jSFUwfyLLwvEM\nxbEkK+PJnjnGNSl25Y903OYJ8BS/fPlKeCHfFyeiUhxJ9J/DFTC0UEe3XBQJiufPBv/NxLv4dx/4\npN/nTNFxO07V5WKHKDfQqO9uq6Rq2eXS8CmMuC/KNVxV5/n8nTSVm9ePTkqJt81WUht58vEnBotT\nCBaPZajmou2iKx7+9391NM7KkXR4oRhAfr7uf5db/xf4/x6aq7Z7yw06bhsb1IeEHAakqjA/lWXx\n2A26WYCjh24ehBNRKQ1v4ua5Orrltd2sepLf/PVV/t0Hg91cKjqBbpYSmo3ddXOl5HJp5Ex7ELuG\nq2o8V7gLQ7l5RZ6u183bCiVec3M2yM0JViZDNwdRmPejoLrcLKEw2+nm/o8/zMc0XJHdawiBFdex\nWhV3PU2hMFdD8SQCMKMaEdPpma0UgKMKKsN+j9VmMmwtshm1QhzF9citGL15TJL2Md+4PbdY59O3\n/WMito0iJW8pX+Ch1ZeQfUaxngczVy1SaZXhMW1XWt5Uyi7Ldx3r6VkH4AmFz079NCfqMzy6/DwJ\n19zx1wdwXcnCrE216oKEaEwwPhkhtsW2DQCxpz7Kr/zG+NbuLATF8RTVQrzdH7aRiuCGIUvBSEmi\nT8iX8PyVWCei4moKdlT1c+w77uMJqOaiN2tvQ0L2FkJgJvR2ISdPVRiaryE63Ww5PT1nBWBrCtWh\nGLauYoRu3pTqkO/m7Gqvm5Eg6OPm+TqfPvdzqK5E9xzuWHNzn7Gq5/kRU6m0ysiYtistbypll+Wp\nY12D2PbrC5U/mPoHnKxf45Glvyfu7S03X3curBAUJ1JUh+KtFkr+Srurh24OREri9d4VbGhN1Dge\nrq7i6ip2RCVihm7uJBzI7nGaqQgzZ/JotoenCoSEIxeDCwXYUZVqPrinaEgw1aEEyZpfCl5phRtJ\nAbVMhFTVYmNKrADirZAZT6h4Al7JneON9BRj+XlGX3mJVKm3yJKUUK241OsuJ8/E0LSdvZARCkSM\nhm9mZYOchK/9K8kjrETz/NzVL+1KxcRrV0wMQ7abBZqG5Oplk5NnYuj65u/3ycefgN/Y/uuu5XmG\nDCbSSlkIQuBXXF1j6UiasasVVGf9B9BIRajlt159NCTkINNMR7iW6nCzJ5l8sxR4Xyd087apDCdI\n1Gw0u9vN9UyEZNVio8IEEG+uDwYcRePllptHc3OMvvIyqXJ/NzfqLlO74GZFgYjRHOjmS8kjrETy\nfHz6yzvuZikl1y633NzCNCTTLTdrfdx8owWdoOXmofB7vxkRw+l7m8DvVLLG0pE041crfohxa/Gk\nkY5Qyx1eN4cD2f2AEF2J8UZCJ1q3u+LCPQGV8GJ+20jFD+dOlg0SVQtXU6jmYzi6Srpi9d6f3rwo\nKRRMLcbV/HGuvesod33/KQoLM8Gv50FxxWFkbGfDiXJ5jaOXf8TS5Em8jbLs2E9DjXI1McFUY3ZH\nX99oepgdg9j2a3pQWrUZGesf2rwTwgzZArJ/9VVPEXgdFQ9dXWX2VI5Yw0F1PMyYdij6XoaEbItO\nN6t+gbmNuYGhm68PqQjmp7IkSwaJ2rqbXU0huQ03G1qMq4UTXHvkGHc/9w3yi8Hu8zworToMj+6s\nm7N5jaOXXmN5/PgAN6s0tBjTiXFONOZ29PUNQ2KavSd9KaG42nstspWCTiE7zyA3d1YjdiMqM6dz\nxBo2qiMx49qhL5wV5sjuQ5Ym/R6yfj6swBN+MQojdfPyIA8SUhHU8nEWj2dZmUxjxXU8TaGSj+F1\nmNHbbKJWKHiqxhtvfxd6NvizkBKazZ3PyUmmVI5R5OxLz6I6NrjBq2+OUClFMjv++rYl+xbINo3g\nGeZN82BDrhvF8YgYTlfRJiumBubRSKA8HHChLQRGUqeejYaD2JCQLbB8JI2R6HCzAsXRpB9OHLJt\npCKoFbrd7Ooq1dwNuDk9wM27kC+bTCkckyuceeW7KAPc7KJQ0nfDzV5gJLuUYJnd7/fJx58IB7G7\nzJqbRZebtb5uLo30c3PEd/MhH8RCuCK7L5GqwuKxDKrjoTie34/rECd67xalkQRmXCddbKK4kkY6\nQrTpDCwtD9CMJPj6oz/L2NULnH35uygbEnQikZ3/rKSUNBqSidJFRmcvc/XMXUyfuasnZ1aTLnmr\nvOOvH42JntVY8FPBNrY3CGqnE3IdSEnEdFFtDyvm588gJYX5OqmK2Z7hrWajFMeSIARLR9J+f1j8\n26TwIzyqYchwSMgN46kKi8czqK3K36Gbd4fSaAIzoZEpGghX0shEiDY2d3MjmuQb7/lZxq6e58zL\n30XZUNciGt2dz6rRkEyWzjM28yZXztzNtdN39rhZxaOwG26OKoGFroSAWMvNoZN3mC43a7i6AlIy\nNFcjWbWQApBQzcUojSZ8N0+mGb3W6+bDHDK8VcKB7D7G1RRcLVxU3zWEoJmOtNuSAOiGQ6xRbhWc\n6PMwwFNUFo6dRiA599J3NtxDcv61Jp4Hui4Ym9RJpm5sVs00JLbj20r1XE5ceJmF42cwFAUU/7kV\nzyXhNDnW2Pm+spGoQjKlUK95yFaEsavp6NIhm18/zfRrpxOyPRTHY3S6gm657QFrPRvFUwTJiuk3\nS29dvKTKJo6uUB1KYCZ0Zs7kSVQsVNfDiOuYCS0sPhMSsoO4uuJfvIbsDkLQTEdpptcL3OhJ381B\n4ZnthwGuojJ//AzC8zj7yve6bndlt5vHJ3USN+hmoylx1tzsupw4/zILx85gKmo7Z1bxXFJOg6O7\n0PM9GlNIJBUa9W43R6RDLq+FTt5hFMdjbLqC1nKz0ppMlggSVavLzemSgasrVAtxzKTOzOk8yaqJ\n4kqMhI4ZD928FcKBbAh4kljT8cvWhxe1A7FjGgsnsuQW60RbuVB9G3+rGrPHz3Hm1edQHBc9IlAV\nKBfXV2htW3LtisXkMZ105vp/jo7jV3Fcc7jiedz7zS9y/s4HWZk4jiLgZG2ad668sCuFngAmj0ZY\nXnJ4LXGCC2+5H0ePoEmXaul1/uXTj/H+J41ded3DyPBsdb1yYevjTJZMRECejSIhu2pQHfJ7zHmq\nEhZtCgnZD3iSWNNGChFe1G6CHdNYOJ4lt7QFNysas1O3cea17yNcj0hEIBRJZYObp69YHDmuk0rv\nnJtVz+Xeb36RC3c9yMr4MRQBp2pXeefKCwNXk2+EyWMRVpYcXk2d5OJt9+LoEaIJlc+l4n6Mcfi9\n2jFGZqroG9ycKpmB30dFQma12S5W6WlKWBTuOggHsoeceM1ieLba+p9AAktH0+0WA5rlkl5tEjFc\nrJhGZSh26EuoWzGNxeNZkJLcYoN0yfBn2QLu66kKf/ypT/GFn3yWZz/1Mpcv9hapAJidtjl1Tt1S\ndd8gYvHe8KGIaXDXD/6O4RmNwvDO5Wh5rsQwPFRNEI2urzoIRVA9NcX50XfgKP6pxUble8N387VP\nrsLw4WzWvdMojkes2VuqX4G+fYwVd3cmL0JCQnaHeLXlZgGbujmuUSmEbrbiW3ezq6r85yeeQAjJ\n//jF3+PyxeDWNzNXbU6fU/tW992MeICbo2aTu/7+aUbGNPJDO+dm15WYhoemCSIdblYUQfnUKS6M\nPth2s2FA1mwigWro5h1BcTyiRh8393tM6OYbJox9OcSotsvwTBXFo/VHonqS0emKP0vZdJi4VCJd\nMokZDumSweSlEvqAUuGHCiEojSWZOZPHjGmBJyopBHYswmNfey9/eNv7Bz7d1Utm3160m6FpgvyQ\n1jWxKoS/PZffufmqlWWbC68bzFy1uHLR5PJFA8de3+fv5+9qi3INf9bR6D/KCtkWsXpv64nNMGPh\nnGVIyH5BtV2GZ6socoObr1UQniTStLvdXDSYvFRGN0M3A11utmJqsJsV381WLMYfnX3PwKe7ITfr\ngnxB7XWzLsjmdtDNSzYXW26+vOZmp8PNhWA3Z0M37xihm28N4UD2EJOsBM9ACgmJqkVhoYbSMZsp\nAOFBfrF+0/ZxP+CpCqvjSeSGekdr1aTXDNZIJQee4xxbMn3ZwjSur3LiyJjOxNEI8YRCNCooDGuc\nOB1FUXcmbKhec1lecJDSb1UgpZ+bOzO9/j2q6cnAxwpPIrxQljdKrG4zNF8PXGGQQCPlV0yVHds8\ngV/sKSQkZF+QLJvBxfM8SFRNhubrAW6W5BcaN3M39zyeqrAyntrUzc3kYDfbtuTaZQvTvD43Dwe5\n+dTOublWdVleDHDz1XU3F6PBFZEVTw7MKw7ZGrGaNdDN9dDNu0Y4FXCIUdz+J7BkxSRi9JaJF0Cs\nsfVZ37X2H509Kg8idkxjfipLdqlBtOng6iql4XhXS6T548cxo1Giptk3F6bZ8LjypsmxqQjxxPbD\nxNIZlXRm58PLpJTMzwaHRZuGxLI8Hvxpj+rVOLGAFXtPFYHl5UO2R27Rv4DdiARcFVbHU6iOR3al\niW46WDGN8lAcJxqe6kNC9guKK/s6IlG20M1gN0cb9jZe4xC5+USW3HKDSNPB0VXKG9w8e3IKKxIh\nYll9j3uj4XHlosmxqWhPJf7NEELsqpsXBrjZtjz+3T/8JcYvlYgGfG9cVfhVdENuiPxio7+bNUFx\nPEXF8ciuNNBNt+XmRNjabgcIr24OMc1kxA/53IAvRCewwfhW0SyX4dkakdagxoqpLE+mD3TPKzuq\nsXy0fx84qSj81c//l/zk7/8BEat/mwApYWHWZurM3jlWlZKL0+caSQj4P97xMywrE0RHbUanK10n\ndE9AcSQRFpTYASJWcA9CgNmpHFJT8DSF5SPpm7hXISEhO4mR0skUg90ca9p93byVM6zv5mp7otqK\naSxPpg62m2MaS1ty82fR7cFuXpyzOHF67xTLKxcdnD5rC1o6wm8+/DEASqNJRq71urkUunlH0Ae6\nOdvh5p3vFXzYOdhTcSEDMRP95zGEADsi+obbDMqTFZ5k/ErZb/qML9eI4TJ+pXzow0vruRx/+ktP\nUE+n8QbIwzTldefk7AYry/0/b1PRKA4P+/9O6Cwey2DGNDwBVkRlZSJF/bD1QpPSb9VUs9orHzuB\n06fdlqcIZNiKKyTkQGAk+hcAEnKwm7WAVbf2Y9tudjvc7IRuBmr5PH/6S5+ikUoOdLNh7C03r670\n/7zrTUlpeAgAI6mzeDSDGVPbbl6eDN28U/RrhempAqkd3EmivUC4InuYEQIzphILCCH2hMDVVITV\nO4CRCmi2i90nST1etfycyM6XwpdoompSz3acONeEcIhmBD1N469+4Z/wtm8+w20vvBQ8s77HxiRu\nn8p6EnjhkXfh6usXXmZCZ34qe5P2bO/R3UdOIKSkUohTHo7f8Pe8PBSnsFDvmVUvD8UO1W8oJORA\nIwRWVA0MBfVUgacqCAJuU/yVoX7hiomq2dfN8apFI7vel/UwtmVxdZ0v/MJ/xb1/9wxnX3o50M2K\n4ocK7xVcp7+bn3/Po3ja+nWamdSZT+Zu0p7tPVTb77+u2R1uHopT3oGqzaXhfm4O2+nsNuFA9pBT\nGk0GhoKWRuIort9ftifuX/phtP3QbDcw91ZI0Gx/Bkw3HArzdaKGgxRQy0YpjSYPTR6lGY/z3Q/9\nGM1Uiru+8z30jtggISCXV29IlpbpsThv02x4KKqgMKSSK2hbek4pJStLNpWyCwgyWZV4XKFe6529\nNONxXn3gvuvez4PIyLXOPnL+DyGz2sSOqjQy0YGP3Yx6LoaQktxSE0VKpIById7uQxcSEnIwKI4F\nu7k4kkCzPaLNZk9InZBgD8i502xvgJv9gXGk5ebImptzUYojSTgsbk4kePaxD9FMJrjjuee73Oxo\nGqO5G1uNNU2PpZabVVWQ346bPcnKsk2l5ILw3RyLCxr13n0yEgneePvbbmhfDxojMxV0a4ObV5pY\nUY1mOjLwsZtRz0YRniS3vOZmQbkQoxr2bN91woHsIWctFDS/2EA3HVxNoTQcp5GNoTgemVUDKddn\ncD3hh6gMyqexYhpS0CNMKfzbVNtl/GoZ4bVmgyWkyiaa7bF07HDlD7z8jodIliucevU1PE1FdVwu\n33aO2m8/xMd/6U+QUmI0JbWqg6IKMhkVPTJ4uda2/IJRXmvc6XmSpQUHy5KMTQw+WTuO5M3zBrI9\nZpWsLDnoEVCSGk7D9U/SgKtpPPuhDx66WftBqLZLxAzoIychXTRueCALUMvHqeViKJ7EU0R4/ENC\nDiBmwg8FzS/V0U0XR1coDydoZKIojke6aCC9bjc3b9DNmuUydqXcHjwLCalSy80DckwPIi++650k\nqzVOvvYj3A43/6fHPsQXxW/zgy+pGE2PWtVFVQXprLZpH3jL8rga4GbbloyOD3azbXtcOm92dMpZ\nd7Mb1xBGt5u//diHQjd0oFnu+gRzB76bmzc8kEUIaoU4tXzo5ptNOJAN6RsK6mkK81NZ8ot1YnUb\nqQh/5XSTMAwjqWNHVHTLbQvRE+BEVJpJndxioz2IXUOREGvYaJZ7oItObEQqCs9++Mf5wbsfJV0q\nUs3mMFJJ+DK88JFP8a9+9zepVlxfXgJWFh3Gj+hksv1/uqvLTluU7deRUC66DI9IVC345Op5Hm++\nYQa2lGt4Gs8//G6G5+YZmZ2jms/x0jseYunokRt49/ufWN3yJ4EsF0dTqGX6y3An83EQAm+HWjeE\nhITsTfqFgrbdvFAn1vDdXM3F/PSFAawNdLUNbrYjKkZSJ7/Q6BnkKtJv+6VaLu4hc/O3PvIYz7/n\nUdKlEtVcDiPpt0r5iPxXPCq/xOnLP2q7efkG3FxadRkakah9zumet3EQu07D03juPe9lbGaW4bk5\nKvk8Lz/8EEtHJq/3rR8IYjWL/NLW3Kz2SZ26LkI333TCgWzIQJyIuv2ZWCFYOJElu9zwe9VKP+yi\nPORXxwtasfIf18rvOUSyXMNIJjCS3RMERy5dZqWhoEuXaqbAwtFTSCEozV3h/lSlbw+6ZjN4wCQE\nmJZHok/hgblrdt++6LrjkCmW+NbjH976mzrgROs2I9eq7QtC3fbIrfRWGgXwgGbqBmd8Q0JCQlo4\nEXX7EUxCMH88S3al4feqFVDPRP0cwQFulkKg24drILuGkUy2B7BrHL34JscuXERKqGYLLBw5BUJQ\nnrvMfekqSp8wbKPR382WKYkngh83Oz3YzelymWd+InTzGrG6xcjMFt0soBG6eV8TDmRDdgWpCEqj\nSUqjvc2erZhGtOH0lsyW/sxwiM/JV19Dt22unLmbK+feiqf4R2zuxDlqxTd5f+WFwMcJRUBATUvP\nA13vU1nPk9Sq/VcMPSGoZQ5GaJnwJLnFOqmKiZD+KsXqaHLbF2n5pd6+cf2aoXuqoBIWfQgJCbnF\nSHUTNzd7B7NCytDNHZz64avots3lc2/l6pm7/V68EmZPnKOxeoH3VF8KfmBQgjJrbu63GisD61O0\nbxeCeuZgtFsTriS/VCdZNhG03DyWxNW3993LBfR07edmVxVUC2Ee635mj9VGDTkMVPMxULrbB2wl\n9/aw4SmCZjzFldve6lceVBRQFDxN583CaRajhZ7HSCmxjGDpafogWTKwCaEnBG/e+ZbreRt7CykZ\nvVohVTZRPP+6Il6zmbhSRmwz9HdQ37iNVPMx/2InJCQkZI9SKcSQCj1ubqYi2x5MHGSkotBIprl6\n9m7fzWLdzeeHz7Ic6Q0Hl1JiW8HPp0dA6+Nm1x2caukJwaW33H49b2NvISVj02WSZRNFdrj5chmx\nzdDf0M2Hi/DTC7npuLrK/IkMRkLzV6sUQTUfY2lya7OKuulQmKkydqlEdrGO4uxg7uEe4s0772Rp\n8kRgSJGrKFxK9Oan2pbsG4I0CFWFZrw391m2/nz9Yz+DmbjxEvW3mojhEDG7K3GvtZ9Ilc1tPZfd\nZ3V7I1IQijIkJGTP4+oq88ezGHHfzW7LzcuTqS09vtPNmaX6ztYF2ENcvKvl5oDbHEXjcrLXzZYp\n+/b+HTSLvDZO3siam7/68X+EFd//0T7RpoNuuoFuTlaCw4L74Wxx0kUKkKGb9z1haHHILcGOaiwe\n336v0VjVZHSmBvgnuajpkikazJ7KHbgZ4/njxxiaKyLoFaAUCqrsnXXslzcLtAtJrOoZni/cxUJ0\niLRT477iq3z6/T/D1Kuv8a6v/A1aq92AxM+N+uo/+ijzJ47v1Nu6pegBfRnBL2gSMXp7Jg+iPJJg\nuCMPB/xjFvQJNG60ImJISEjITcCOaSye2L6b4xWTkdluN2dXDWZP53G1gzVYmJ06QX6+hJDBg9Ov\nnXuA+7/7w65tiiqCMn4AfyIZYCWS5fv5O1mKDpFxatxb/CFHm4v83Yd+nHd89es9bv6bj3+MxePH\ndu6N3UL6raL6bt76CitAaSTB8Gzo5sNCOJAN2T9IychsraeZOxKG5mrbGhgrrkesZoOAZjKC3ItV\n5oTgtfvv5uiFYs8JWAr4/AOP8ueR9/Lvv/jp9nZNE8TiCs0NRSWEgMKQxkoky18c+QCuUJFCoa4n\n+EJilETZ4PIdb8FMxHnrt75DqlxmeWKcFx55J6WRkZvwZm8O/ULXPQHWgN7IQTRTEVYmUuQXG6iO\n54eCJzUSVbs1lQxIWJ5I4R2wC7mQkJCQNlIy3MfNhbkqS8e24WbHI163kXvdzQ/c09fNjUyUJx9/\ngqd+5hme/ad+vqyuC6IxgdGUG5+K/JDGciTHXx75AI5QoMPNKxMpGpkozWSSe579DqlyhaXJCV58\n5J2Uhodv0hveffrlYHticG/kIJrpCCvjSfJLzcFunkyH0VIHgHAgG7Jv0Cw3uJk7EG1sfTUtWTIo\nLNTXH9w6od1wH7FdwNMUliZTDM/VurYXRxPtQdmTjz/B//pXv43R9DCakmxOxfMklikRwi/vnyuo\npLMqXyncjSPUrlglRUJhsUEjE2Vuaoq5qamb+RZvKmZc81tDmW47r2Jtdrue236P10Ym6veGXYvn\nFoKi6xGr24BfrCIMXQoJCTnI6EZwtWMBxOo35ualI2mMPVhV1tMUlidTDG1w8+pYsu3m933uEb74\n2PN8588VjKYkV9AoLvs93dfcnC+opDMqXyrc03Lz+pFUJOQX6jTSEWZPnWT21Mmb+h5vJmZcw2m5\nee0IrLm5lr0ON2djNLKxXjfv9QWMkG0TDmRD9g8Dcj+3ejrSLJfCQn095KT19/BslZkz+Vb1QUm8\nZpGoWniKoJ6NYcX7/1QUxyNq6fSCDgAAIABJREFUODia4s8c7nAT7GYmykxSJ16zQUqaqUjXCp/i\nODxdG2Fyerr9llQFJo9qCEUhGlPQWr1jF2NDgQk3wpMorsTr02N2R5GSRNUiWTaRAurZGM2UfnOa\nhwvBwvEMhYU6yaoFEoyExup46sZmZjv23VMVf3AbEhIScsi5UTePzFS5dibvTwi23BGvtdyci2HF\n+rtZdTwiu+jmRiaKMcDNqm3zW382xsTMTHubosLkMR0hRICbe/dPvZVuzsVoJm+um/MLdZIVvyrW\nmptvaDJ4o5uvY1AcsrcJB7Ih+wanT3iJBKwthp4kWi1XAm+rWtSyUUauVYk1bBTpP3eqbFIaTlDd\n2D5FSnKLDTIlA9maXnUiKgvHMjseSuqpCvU+J+C7vvs9RmZnu4o8uS7MTDsUhlWaDQ9FgXRWJeEY\nGGpwqXnZp/fdZiiOR6pk4KmCeiaKVATxmi/DiOkiFUEtE6GWj6MbDsMzVTRXti9w4nWbeibK6sTW\nCorcKFJVWJlMs9IxUxsSEhIScn3YfQaTEjBjW3NzsjzAzTWLeibK6HSVaLPbzcWRBLVCr5vzi3XS\nJX9Attbab/Emu/nuZ7/L8Nx8t5sdmLlqUxhWadRdVFWQzmokXANL7V15Xmvfdj10urmWjYGAeNUi\nWfHd7Cm+s6v5GBHDYWSmirrBzbVslOL4zXGzt+bmidDNIVtn4EBWCJEBRqSUFzdsv0dK2adR1tYR\nQjwG/CagAr8npfzVG33OkAOMEKyOJSgsNLpCT4AtV1UUA0r6Ck8Sr9ntQSy00ikk5Jcb1LPRLgkm\nKxbpkoGQ68+rmy4jM1UW+hTLUFyPzEqTeM32e4sW4jTTEYQrSZeaxKv+9mohhpHskJqUZFZLaBY4\nkRi2DqOz0yiey7kXX0ZzXCRQHJmkns4hPBdH1bik6QzPXSFdLbK86LB6m4JEIjrmyT3A0QQTl0rg\nSTzVw47qqK6fN1rNx/r2WM3PVUmX13sKFBYauJpAdWT7+AHklpukiyaq47VTVNrHREKyYlItxLC3\nmad6Q4SSDNmnhG4O2VMIwepogsJir5tXtjhBKbxBbvYnmtcGsbDu5sJSg0Y22hVRk6yYpEpmy83+\ntojpMjxb7VtLQ3E8MqstN2uCSn7NzR7pouGvAqsK1UIcI6mvP3CDmx1dMjI7jZCSsy+/gub2cbOq\nMTx3lUzNd/Odn2jwzN+ne9xsa4LJiyVA4ikdbo613NynyGWgm1WB6na7WV9ukC4aaK3uDxvdnCqb\nVPPxvgsJu0Lo5pBt0PeqUQjxceB/BxaFEDrwC1LK51o3/z5w7428sBBCBX4b+DHgGvCcEOLzUspX\nb+R5Qw42tXwcJ6KRW6qj2h5mQqc0kthy/9lmKkJm1Qic+W2mImSXextpgy/lWMPuChlNF5uBTbcj\nhoPqeD2VGoXrMXGpjOJ47fzMyGyVSiFGqmygWZ7fKxZI1ExWR5JUhxMU5he4/6lvc/7uhwHwFAfF\n88gv1bjthWfQbRtbj/DCuz6MEU/iahqdOrpy7q3oZpO3fvuvec8X/pwrZ+9i+sw9SCHwFAWhqOi2\nRCD9fBIHoqYNQhBrOKRLBgvHsz3h1bGqRbps9YSOqY7s2aZIEK1BbCASYnX75g5kQ0L2IaGbQ/Yi\ntYI/2MktNVBtDyOhU96Om9OR9sRwz20pnfxiHzcL3x2dbs6sGoFujjUdFMfrWZVVXI+JyyUUR/pu\ntiDSrFLOx0iXDTTb9WOCpUOiZrE6mqA6lGB4bo63P/0dLtzV6WaXwmKNsy99i4jlu/kHj3wEM5bo\ndfNtb0M3GrztW18h8T9/hZNn7+bq2Xtaz+W7ObLmZgC5wc1Fg4UT2Z7w6ljVDHazex1uBuINi2p0\n/7f4CTmYDIqxeBK4T0r5NuC/Bj4rhPiHrdt2YrrkQeCClPJNKaUF/DHw0zvwvCEHHCOpMz+VY+Zs\ngeUj6S2LEvxZzHomiifW+7B5AiqFOE5ExVNE31RcKWBkZobJS5fRTAvN6l/EIqiBd7pooLhe149O\nkZBdMdBNpz2I9Z9AobBUJ1Jv8qE//jPevOMBPE3H0/RW43WN4sgkxdGjCODCHQ/QSGZw9YifAytE\n1x87Guf77/sHLE6cYOr8K7zrr/+Y+5/+PPmlWYTskFvn4/B/6IqEwnx3QQuA3HIj+L33OyZ9j5Z/\no3edoc0hIYeM0M0hexIjGWm7eWWbbjbjGo10pMfN5aE4rr65m0evXfPdbPV3swSUgJVf382yx825\nVQPdcvxBLLTdWFisE6s3+OCffo4373hwg5t1VkePUB4+ggDO3/UQzUS6v5tjCZ77wEdZGj/G1PmX\neeQr/5n7n/48ueX5bjd3vD5s4ualHXQz/qA6JGSvMmj5Q5VSzgFIKb8nhHgf8FdCiGMMLLuzZY4A\n0x3/vwY8tAPPGxLSHyFYHU9Sz0RJVtaKDUWx4n6oUD0XIxWQqyOQfPiP/oCo0QQJmm1z8Y77mT35\nFqTaLWvNsXEivSf+eN0OnFEWUiLV3p+i6jhM/egi9UweT+m9IPA0nbnj5xibucTSkame/dj4vgF+\ndO+7yX3t/yVqNknUq1Tzo1sK44mYLsKTXXm0ijt4FrcHKQe+Vmc/tzMvvsS9f/cMUcOgkUrxnQ99\nkJnTp7bzaiEhB5XQzSEHDyFYmUhRzzokKqZfST4babu5lvOd3bNiKyUf+ezvE7EskBLNtrlw14PM\nnbit1822jaP3ujlWs7bt5hOvXaCWKSADiid6ms7csTOMzl5mafLEltz82n3vIffVPyNimS03j2zN\nzYbb41YlYOV1IIPcLKCxVjVaSs6++BL3fvNbRAyDRjrFsz/2Y8yePrjVlEP2PoMGslUhxOm1HBwp\n5ZwQ4r3AXwB33oydAxBCfBL4JMCYHu/qmRkSshmxpz7a9X8pJeXPX2Xx06/hrpqkPzDJ2CfvQh9b\nD5t54WsKz3xOQVFbM5UC7vne10hVKl3PdeLCyywdOYmjR/E0DTwPxXO58+Vn+Of/+m5it3Xn4nz5\nd1XeeE7408fdewWe7F6RBRCChzMzlN3BzcDNaBwvQLb9WJ44wZHLPwJAt0ycyBaq+AXsdiMdIVM0\ne4U5QIobm5KvzbovHc20KxPe9e3vcO8z32rfL1Wt8oHP/X88/ZM/wZW33Lb5voaEHGz2nJujmYPT\nazrkFiElUz96nTuee56o0eTaqVO8/PBDgD+QteI6peHEeiSQAIngnme/SrLW3cN26o2XWJ44scHN\nHrf/4JssT36QaiHf9dKupiBxAwZ/rapSG3wmFd/NxUFuFgIzGkcGTEL3Y3niBJNX3gB8N7v6FtoO\nBai2mY6glW7MzSDRo/DT/63LkXMLAMz/h5dY+JuX2/dIVar82Of+nBOfeZTcTxzffF9vArf/+p/y\nwpfDFKWDwLu2eL9Bn/anAEUIccdaboyUstoqAvFzN7qDwAxwrOP/R1vbupBS/i7wuwC3x3M7Mdsc\ncsCRUuI4/vnaeN+fd922vGizuuy0qwiufOYNSv/PG0ydibXL4N8OnFAizMTH0KVDfmmWhasG3obX\niVgmDz71F8yeOEdx5AixRpUjl14jXStR+vgsuUL3z+vOaIE3J9+Ho6xvF9Ij3qhiRJPd4TueR8Ro\nMPqDHyErLsLb+OqgODbj0+d58Z0/vq3j43bMDh+9+AoX73pw4EBY8VzO1K7yL770J13bLaHyhyd+\nClvpKM8vJYrnguf5oVYtNMvk3A+/y7U7305DjyOkxFVUzlYu8+jy86gXZOvhkjdeM3r2QQDv/+Jf\ncfbNrwe/J0dSKbs4jkciqZJIKoiwYMSWeduHt97r8aCRvNU7sH32nJvTE2dvuZu/5P3Wrd6FG+ag\nX4B3ulnb0E5mad6iuOq23XzH3/+Au1/6ASdPx1A77ttUoszER9fdvBTkZoMHnvpLZqfOURyeJN6o\ncvTSa6RqJT71pc+Sy3cf5/noEF+cfO8GN7skG1Ua0WSXy/A8YvUaw6+/gVd2AwtI+m6+wAvvemwb\nR0fgdnj46MUf8uad9w90s+o5nK1e6XGzKTT+cOqncIQW4GbpD+5b6JbB2R9+j+k776WpxxASXEXh\nXPkSj6z8PeovSgz8z27h1V43A0z/s28Se0twDq3jSKotNydTKvHE7rr5hbAZy6FDyAFVXAGEEK8A\nnwV+HYi1/r5fSvnwDb2wEBrwBvABfEk+B3xCSvnDfo+5PZ6TnznzyI28bMgBx2h6zF6zsC3/e62o\nkM2rDA3pIODi6wYbv/JCQH5IZWQsePazWnGZn7EIGEsGoigwfiRCOtM7E3s+dZxnhu9DIvCEYNgq\n8Z7L3+QFc5QLdz7ohzIJQaxR44EXv85tEw5XL5nMJ8d46cEPAAJPVVFch+zqIo6qUi2M9c6y9mkt\no7gO93/zCyQqZYTwj8/CQw/xw/y5nhApxXUQimDMWOGx+WfQZe9gxxQa3x56O1eSR1Clyx2VCxx5\n/SUuahNUMwUU1yG3vECmvIIiJKdvj7EaL9BUooyaq8Q8q+v5XEdy4fVgWQKcuyPWI8FGw+XaFb8n\nrJR+GlI8rnD0RCQczIZsyrte+eLzUsr7b/V+bJfQzSH7iWbDY26m2825vEphWAcJF98IdnNhWGN4\nVA94RqiUHeZnbeQ23DxxNEIq3evm11NTfGv47YDAEwoj5irvvvxNfmCPc/GOB9pujjeqPPDiNzg3\n4XDlTZP59DgvP/B+EAJPabl5ZQFbj1ALCg8e4OYHnv5L4rWq72YN5h96mB/mzgbeVyiCcWOZH59/\nBl32rgybQuNbw/dyJTGJJl3uKp9n/PWXuahPUsvkUV2H3PI86fIKigKnb4uxEh/CUCKMmStEPbvr\n+RxbcvGN/m6+7c7egWyj3nIz64vBiaTCkeOhm0M2Z6tu3srUxUPArwHfBtLAH7H1Fd++SCkdIcQv\nAX+NX+L/M4NEGRKyGY4jmb5sdg04PReKyy7lVZfRCZ1Wu9cupIRGvb8J4wml5zGDEAKSqeDiCGdr\nVzlVu0YxkiHqWaSdBujwgHGFya++STkzhGZbDHsVjhzxT/bHpqKkVpbJPf055iemUDNxtHqNV07e\nh6tFgkOFhADHho7ZXE8RVIYSnEo3MHWVaEwhnVE5U3qJ+yuvcUUMMW8n0Gp1cmYZbSLPiKiRt6t9\n32tUOrxv+TlYfq69zRnSqF2cZnhhup2xJwSMjGuoimDELPZ9vm1EYQH+LPHstNV1ISM9/6KpVHTI\nF4IvgPrhupJS0aFScnFsiaYLhkd00tmb2HogJGRrhG4O2Rc4jmT6itl1nvZcWF12KRVdRseuz82J\nhArS7nv7Rga5+bbaZc7Urna7OQL3Ny9x5GtvUk4X0C2LEVlh4qjv5uMno6RXlsg//TnmJ0+ipmNo\n9XrLzfoANzvQERnlKYLYQwonf2BgRjvcXHyB+8uvclUUmLcT6LU6OauMOr41N79/6Xtd25xhjfqF\nq7gLV7vcPDrmu3nUXO37fIPSfINou1l2bvM/z3LJ7VkV3wzXlZRWHT/yas3No3rggkHI4WIr3yQb\naAJx/FnfS1Judf5rMFLKLwFf2onnCjm4NBsui3M2piXRNBgdD55RrZScvgNOz4PiSv/b9YACEGto\nmmBoRGNlqf/jYT1c6sjxCMqACrwqHsNWqWtbKq1yLqVg22VURaBq63mriiIYGtEZGoFT4hpfGXqU\nuclhfwW136ym9EBRka08IjOhUR5OYCZ0fu2/+OWuXHPbllRW6sSNGrfHBPmChp5RwJnr/2YHoGmC\nqdMxiis29aqHpgvyQxrJ1ObCEUIQjQlMo/dAx+O9IUmmIQNXyqWEStHd1kC2XHKYn+m+KLJMydyM\nheNq2x4Uh4TsMqGbQ24pjbrL0rzvZl0XjI7rgef5ctHpW4bMc6FUHOTm/i7VdEFhWOtKFwpCCP++\nm60EBrk5k9VIZ+Smbj7ZcvP85NAW3Kz4bhYCM65RGklg1XTe8wl44cvrkWG27VFZqRE3qr6bh3bA\nzWdiFJdt6jWvffwSyS24WRFEogLLDHBzovf6yTBk4Gey5ubtDGTLRX/lvRPLlMxds3DHNXKhmw81\nW6mp/Ry+LB8AHgX+sRDiz3Z1r0JCWtSqDlcvWf5J0QPbgpmrFsWV3jBX2wo+ca5hGpKgukZCQH54\n8El1aETn6Ak/XDgSWX/cGoVhlROnopw8GyUau75S9UIIIhGlKx9oDcvymF6Q/EXmncxFh/0CEn1F\nKX2RKgoCgQJEm05X24EnH38CANPwuHzBYHXFpVH3KK64XLpoYjRv7HpY0wQjYxGmzsQ4eiK6pUHs\nGkdPRHpmfxUVJo8HhH4Pik7aRuSSZXkszAbP7EsJS/MO3lZjy0NCbg6hm0NuGbWKw/TldTdbpuTa\nFYvSaoCb7cFuNpoD3Dw02M3DozpHjkdIZVT0ADcPjWi+m89EiUZ3x81XF+Avsu9kPjq0PTfLlptb\n7fo+ovxy+66+m02KHW6+fMHENHbAzePrbt7KIHaNYyciPVFTqgZHjvW6WTCghPp23Gx6LMz1d/Pi\nQujmw85WpkT+Gynl91v/ngN+WgjxT3Zxn0JC2mxcIVtjcd4mV1C7ZlfjSYVyyR0ozKPHo8zN2DQb\nHrRaso1N6MTjmwvOLyLkn8VdV7ZDnpJJBUXdvXwP0/T4jnWM1x96x2BJQis2SyI2mEKRkF1q0Eyt\nC+fJx5/gU7/9H3tWNKUHC3MWJ07FdvBdbB1NUzh9W4x6zcUyJZGoIJlSA2fSo1GBqoCz4T0IAdlt\nzPhWNvneSAnXrlgcm4qGuT0he4XQzSG3jLk+bl6Ys8nmN7g5oQw8xwoBR45HmV9zM36tg7EJndgW\n3JxMqe3JUteVNGq+35MpZWB01I1iGh7fdk9w/qEHt+jm3jGcIiG33GS+5eYnH3+Cf//FT7MwZ/e4\n2fP843v85BY6DewCmq5wZqtujgkUBdxAN2998FweEGkH/vXKtas2x8KaGIeWTa/0OkTZue2zu7M7\nISHrSCkZVN3esiTR6PqJK51WWYk4gaEvAOmMiqYrHJuK4jgS15VEIuK6Tn6qKm5KboYtVL6ReStX\nj9y2aU854bpI4YFU/My2DUTM3pnyajPwrhhNiZTylolBCEEqrfmZf5vcb/J4lGuXTeRasScBiZRC\nNrf1z8f7/9l78yDZrvrO83vO3XJfal9eLW/RkwRIAhkDwgJJYAJE2W5bNs207bYxMbajn3sYR9sx\nQz/P/DExMx2ObuzocUxMN55uYv6YdowXAQZjN5jNCC0YJHggCdDy3qtX+5b7ctdz5o+bmZXLvblU\nZVZmVZ1PhAJerqcy897P/Z3zO78fa2PKCnrZrb4YjUsolxhyWffHGYtLPc1qCwT9QLhZMCw4997S\nUcWyXLdWicYkHOzZtUJPDRD3fqXOzczhUI7j5hOoaWARCV+OvwXr863FmJqpuplw4tmXVjEaJwWu\nr1zDr3/ijz1fqxroD4te3Dy/oGF9tdHN4QhFrIfvh7XvPggA0EsM+ZyDaKzRzfGEhGBIuPmsI+pU\nC/qGaTAUCwyUApGYBNPg2N02YegcVALGxmUkx+Wu5dTpcc0TrYS6xRcOdk2k06yhmIGqEkzP1rWD\nkUlL+f9Ro0xV/PnC4zAkrauZ3kJcQ6iwC9kMN7TYqaLqRQATDbfZigLJMFpfkhLsaOOQwDFhpntr\nrn7CBIMUl68GkM85sG2OUFhCINjbRVAkKiGT6rwqm8s6KJcZsunDx+YyDhJjEmJxGeUyg6IQhCOi\n/Y9AIBgNWt3MsLttHbp5QkZyrHs3d4I2V9mlBEuXNOzvmsg0u1kjmGpyM0bczSVJw19ceByG5FNs\nsQrnAHHdHM5vgzoxOB5xlVYuoNnNpixDNVtXvblEsa2NQ+YOxs3MaLs5RHHpagCFnAPb4QiFJM/9\ntO2IxKSOmXY1N1cKSdW7OTkmIRKXoQs3n1lEICs4Epxz2BaHJBNQSmo94Kpsb1oNVQgdG9jbsbG3\na4MSN3CYnFYgtynkAAChMEGp2HoGkyRAUVtPiJJEMDWrYXKGQy9zGAaDqpKB9y7rNwwEf7Xw/o5B\nLAfAJIrt5ThsVcLFlzex+OoNrN79lobed9S2EUutA1hqeP4rD9yPe1/4LmT7cLU2NT2PHz74brdX\nAQFUx8Tj29/EeFMRjFGCSqSnVOJmgiGKSFRCId9emIyjIYgF3N94+sBBpvr7J4BEgYWLGlSP36hA\nIBAMCs44bNt1MyHA3o51eG6Cj5u3bext26BSnZvbBJOEEIRCBKVS68lSluHpdUkimJ7VMFXvZo14\nFvEbZRwQ/NWF93cMYpvdHHtxHQuvfh93rj7Q4uZoZhPAcsPzX73vPtx94wZk+/C7O5i+gB/9xLvd\n/GRCoDkmHt9+CmNmtr9/ZB+RjunmUJgiHKEoFljHFOPmgJdzIHXg1K5NCXEvaxYvap7Xj4LTifgm\nBT2T3rfw2o903HrNwGs/0rFxx6g1Mq/+B7SW0ndvdPd55LIOVm/qYE77lM75RRVKU0E6QoDFi949\nXw8f4waviaRbke80iZID+MrU21GSgh1ne3NJDZuXErBVd5p38+IyLtz8Ia7eeBbBQtaVZHoPb/jO\nV7F1ab7lJb73rp/CxsVl2JIEU1VRiMTw4lsfg6VosCQFFlVQlEP4/NyjyOTd8vfHLTYxihBCMHtB\nwdyCikiUehajIARQZJ/fNQ5/+5y53RU210zvBwoEAkGf4ZzjYN/Caz8+dPPmulnLNOnoZrhpnLmM\ng9XX9Y7bLeaXVMhNbqaVCbx2NLg5dPrc/OWph1CWAp3dPBZocvNFLLz+Eu76/nMIFHOgjo1oag9v\n+PZXsHXxQstLvPDIu7C5vAxbrrg5msDLb30UlqzCklRYVEFBDuHzs2ffzXMLKuYutHez3IWbWdXN\n68LNZwmxIivoiWzGXVWtP2EU8kc7eTqO+3rJcf/S6ZRSXLoaRLnkoFhg0DSCSOx0ya8XylTF1yd/\nEnfCc20rH4JzZMcDyExHGu6avb0KEIKZjZugzMHq1QdQDsdw+5634GCqNZBlkoSv/8I/QSSTQfwg\nBVOJI1BqmuEiBDYjeMmewsTWnepNiCcpJqZUSAMsdHWSuHt/JESi7j6b9VXDzYCr/NYTY5K7ypFr\nPzNcxTR4rd+dQCAQDJJcxsFBs5tzR3dzLmO3bWtCKcXlq0GUSg5KBQYt4J4/z66bNXx16m1YD810\ndHNmIojsVLjhrrlbtwBCMLv+OiTm4PbVB1COxHDr3geRmppteSkmy/jaEz+PaDqNWCoNq+Lmhncm\nBBaneNmaxPjWWvWms+nmmIRITEK55GB91Wxwc7LiZuQO09bboetu1sKoby8TdIcIZAU9kerQS7UX\nOHcL6HRDMHT2N+3n5RA+feF90Kl/OjGHWzFhZzEGI9y6Kv3A089CYgwbS1fx+hvfBia7h3hBmcD0\nWg47S3GYgdbDvpBIoJBIILldAC157JklBKZyWMWYcyCTYsimdSwsq2fuuwmGKpWT8wwO4wiFKVSV\nwrI4DnZbi2b5wcHRTa8Bxtwq2ARu9e1BVtoUCARnj4MOvVR7oRc3h0ISQmfs/N9MTg7j0xfeB4P6\npxN3dPOzz0FiDOvL9+DmG36ill5sKxOYvpPD9lIcloeb88kk8skkxrYKIPBxc11v23o3Ly6rCJyx\n7yYYkhrcHA5TKCqFZTLs79rdxLGukbs8VoSbRx8RyAq6Qi8z5LM2TK+qg0ekWuhB4PKtsfthyBrA\n/UQJAARbyzFYAe+Z8lChAEYIbt37E7Ug1n0aAeFAYreE3cWY7xj0sIpI1gBt/poJQfxgp3VMHLhz\n28TCknrmKvdS2lr9UlEIZuYUtzl75WviPosestJdQbFC3qmlOlV7781fUBGOnq3PUyAQ9J+qmz0r\nAh8R4eZGnht/ALqk+U5JVt28uRyH7RGMAkCw5uYHG/bI1ty8V8LeQjs3KwjnvN2c8HHz6m0Ti2d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3/0I7jrxvcRyeYwf+sWFMOo7R9wJIrs2Bh2Lsz39X31sAJblaAYjmdmDQNQSETw1MrjePuX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\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54nU38Gs5AE9TQAIejgPwp2h3SDK7+BrF3oO2m5eEm0+u2c2+SkMze3yFCDe3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K4gFmi6ebfp5p6TH02jOHdBbdf6AcDO8jL+/c/97FBupkEZR63nhefmWmL8DYQI46jsBEwTIBQb\n0UXIzMH83m3szZ0H78gf9dx8C8D58S74FPifPvkbgV2Ne1FViotXOtxMgXRawlyPmzn37pUP9pz2\nSWIqI2Fh6Qy4WaWYX5Sxt3N430EIkJuXu+b1WhZDft/raaFFKLJzctdG/NI5FTevdzdL0yL93Xzu\nvIqNW152ScvNuXm5a2N72hCB7ATYuNV9ktgP3pznWDhwkA3YyTyLuA6H63IoKhl4oaqW3eA6PALU\nai5Saf9L+3/71H8LcI5kPg9HUVBPJodel3PECfj2xST4BHYK+YCnZJTgYHERH//DP4atRlFNZUE4\nB6cU2d0NlHLDDUYPO+ura/i939xG49Ev+T4XiVJcuhoB57zv66lccrC/293mvlj00tQXz01/B8VM\nVkE8LqFS9nbAE8nuFv+NRncHyYbhdTG80JE+KcsEV+7SUK8xWBaHppGBw+4lieDCZQ22xeA4HKpG\nvbQmgSCktLJ8eoOw4K/1ajuLeQeZGcky8KYkDOnmPv0o6jUXyZTfzfGEhGv3RGBZHP/qJ/8p6onh\n57oe5eati6mJdN7ihIBzHlh+ITPHc9Orf4vGB6KoJjNtN+e2b6GwEB/7ek+LoK7GQQzl5qKLgx43\nlwouCICFgI7W00YmpyCWkLz3DIBEz/idhtE9s7phuCiXXFy8rCHSbMQoKz1ujhBEowPcLBNcvKLB\nshhch0PT6MByoGlABLJjpmEwXxrAIDgHSkX3zAeyrsuxddtqpzwQAiwsK0ilZTDXk0FnSgkdsHnU\nm3ryxd//NF56tjl8nBCUc7ljrU02HSTzDTgy8TWT4ABKaQ3OJFKXWmsgzRE8nR8DwCSvbvb5x38a\nj37p/0M9noKt6dDqZTCZ4nsf+yio44DJ038Z+NxTSwPlOejGq/Mktg0HigUX8aSDWHz6fz+KSpGd\nC77h292yAztI7mxZuHwt0v4YIQSxuITeqi7OvWZQpaKDeo2BEIJUWkImJ0NRKZTpv98QzAANg/tS\n7gbR6qx71gNZn5spsLikINnPzX3iSgK/mzv5rZ85Oo24F8V0kMg34EgEshvg5mwE7oRKfkAACg5f\n5w7OEWGmV1f6znU8+od/hFqHm12FYu/89Lu538zZXga6ed/vZs69MXrxpAM9Nr2/nxbqADfvbFl+\nNzPv45euHt/NlBCkMhIy2eap7hlx8/S/CqYAzjhMk4NK/bu1Dfz+kDZY5ZzDtjkkiQSetliml65o\nNhhkhSCbkxHVg6WyectCvc662pDvbNo42LPRKuWMJyiWzqmQZIJU2l/zCniyjMUPLwrrq2vAsyf/\nGSNVC/MbFZDmxmpHp3QwApTmdFRy0ZM/wR1CGfcFsYC31laTp63Ll/CHv/bPcfW113H19deh1ypg\nhOChP/1zcErxZ7/0D1BYWBjvwkfE+uoa3v/JIj71mS8M/T3OgHb/Gzdt3PXus90dvF8jFrPBB+6W\nA17a0+3rVs/IBI6DPQe1KsOFy+rUp4AJzi5n3c2yRAJPWzrdrCgEmTkZ0Wiwm1un1O3Hdr1xXPu7\nNmwbAOlws0SQysgoFYNPZfVY8A37SUa59Lq586/HaNPN2Qm62eXgQcexhMBKeyfOm1cu4w9/7Vdx\n5bXXce21bjczieLPfukfojg/P96FnyLDns72wxmQNn77ho13vftsdwfv15G4YQzhZpPh9g2/m/d3\nPTefv3R23CwC2RFTLnrDhwFPescdkN5q5Q8AtaqLvW0bpskhSV7NWSYnT+TFWCw42OuYR5VISlg8\np7Rv+Os1F7dvWIcyMziqZQux+GHNbwvb9mpqejsScQ509iOqVhhuXTdx6ZoGLUKxsCRjd9tpNeoF\nAbBySQWl5I5mnLVhDPMblWYjJY9WMFtLqjg4N3z606hghBwuqvdzHTcwRiKOajqFRKkMK6Lj1tV7\nUU1lkSju46Nf/nN8+Vd+eah6pGngpWfTeOkY8oxE/c0PWnAP2rkuAAAgAElEQVR46f1RnaKYd1Cr\neJsymZzU3pSp11zs73qz37QIxdxC/w2bMEKl4DHER70cOOcBQWzrc15gYNQZ9Nj0/C4Es0Op6GC3\nw82DsnyC6GyoUqu62N22YbXcPC8jk52Qm/N2V91dIiVhcbm/mxsGR6VsIZY4rPltYVksMMDnHF4Q\nCwAcqJYZblkmLl31Uh5btX+dP/75pps7eeiVz3vBznEJcDP1loJqSkN+efKpuZySvg2oCu7hUVg9\nkUA9mUS8XIYZjeH21feimvTc/PBX/gxf/qfT7+b1EwazkQgd2PG6XmeIRHvdLLdLYtputhg0jWJu\nQTnsNj4FUIp23XjvxwddWzjnuHXDhBMwGpZzb/PaMBj0KbpPGYQIZEdIw2DY3uwePuwM6AJPWldi\neC82QrxW5Nk5GUadYePmoXxcF9jfdeC6CGxeM0pqFddLR+z4uVr1d+cuqOCc+37u9vdWvcL1zs7C\njo120flRWBZHw2CI6hLSWQWJpIx6s6GMHqP4kVUXj9NTCGIBxIpm39POaC0kw6MpQTWpIVY2u6TO\nCFDORrq+9O6XXkZDT+LFj34CLqUAlVDJzGH70t2Y29zF/srimBc/WlqbGUcJdH5RwY23g7tEUuKl\n1l1/q6OZguG93vU4gSwRlEuHpqnXGG5dt7ByUUUsPh2SiMUoKuWAjsQZaaAsGwaHM6gJWlOYIpAV\nhA3DYNjpcZQ7wM2UHvqJc8/VqurdNNdrrt/NOw6Yi74NV0ZFteJid7s7HbNS8naplleabt7o4+aK\nvx+H6/Dh3WxymA2OSJQgk1OQTMleqQH1rjG9J2frq2vASYJYAIlio7+bq+Hoxs8pwdXaTbwduwC3\no3MxI0Cp56T47hdfghFL48WHfhqs5eb0HLYvvgvZ7X3kl6f3VLbFsKnGncwvKbjZx82EAK7NcX2j\nAbc5prfl5licQJIpysXDHdq64x2EnL+kToWTOOfQ4xTVPm4ehGEwuP2nUXobzfWzE8hOz9bEFFLI\nB9TeDSCRlHD17gjmFmRkshKWz6u4dFUDpQT7u375cA4UDhwwNt78pn51C9WKC9flYC5gW/3XVMx3\nv8M0jQz/eyLdjy3JBImkhHhCwv/4s7+Bx+lnh/0xjiRWsfruqA5qsjRuCosxGDEFjHgNnhgBKpkI\nqunuQFZyHLx5/0fgykr7+IFTCa4kI1IL6aWAc2h1G4kDA3rZBE7wWl9fXcMDj/W/S22dIPR5elgm\nC+xgWq/yriC283u2N8JxM3UUe9tW4GB2PUaO3CBjbmDiXBtC4eseLhCEgcLBMd2c6nHzSqebg32Y\n35+Am/eC7xMqJc/Nrjs4XbPXzapGj+Vmq6ODviQTJFKemzuD2MhzT9xxxlR0gJvDdHr5sb3v4ryx\nDYm5UFyr7eZaqnvCgeS6ePP+B8E63SxJcGUZ0eokVj4EJ3DzI08ax/rbR6MU8wv93Ww02GEQ20Gt\nyruC2M7v2bo9JW7esVELcHMsTjF/xAYZcwN7jLUh5Gy5WZzIjpBBtXe9u5yUep37drYs2CZHNEYR\niR52BzTN/ukVjsOh9hkqPgqCUgkBAMTbwZWV/umuAMB6rjpU8oY8Bzbd6YX7h2k/8JhzOgEs54jU\nbSimC068+XR9loB6LDwNPjgl2D+fhOQwSLYLW5XAJX9g+tZ73wMrMud/AEJAWQgvaoxj8VYZasMB\n4d7mQZYSbF9KwVGPt5P4OP0ssNr/dDaT807269XDjqVew7HgWuyjcBxvlEeYa2tti6EQVGdOgHRW\nOXLtEX3wTW7vrFqBICwc182xuISdTQu21XLzYVdQa4CbXYeDjtHNg34u1/VqZge9Z3sDb0nyMsLy\nAZvXPgLc3Mv66hrw1BGPE/jYh25mhLT7PwQsAbV4eNyscBef2P4malIEV/7n+/Fr3/yRvm5u6Fn/\nAxAK6fil2yOHMI6FTjdTIEMIdoZ083FSjTNzTTfXWbvxESHA4rJy7A0pYDrcbFksuAcM8eYuH1UX\nHI0e4eZmvHFWEIHsCInFaWArf0KApRUFxbwLx/bEGI1SbN0+TE8yTW8ExqVrGlSVQtMo6k7wFa13\nLlsQnHHU6wzM9f5dKXstzBMpCXPzyrHab0d12k5X6sW2GUpFBlUlsMzgd1I8IOUyN69A1Sjy+zYc\nB9B1gmqFddUHtNKHO2V5KrWw8ILWxRtlyLbblbLUqr3txYqE763jyhSu3P9G4q333YuLb+a75sm2\nYCG8qCfzBtSG006ZJhzgLsfcRgXbV9Inesz11TU89wvfwLd+9eWujxNCsHJBRb3mvTcoBVIZb55b\npcTQd1dmAGaDI6qP5vdqGAyFfQeOwxGLU6Sz8rHH2/StC25mV8QTEswGg+tyaBH/+BxJIsgtyL7x\nCACgKMDKRS3UNwuC2SUWp2gYfdx8TkGx4LlZj1GoERLo5svXNCgqhar1r+OThnAzY15n0V43J9MS\ncvNHbyh1EtX9ZQKtn8u2GEpVBkXt7j3RSdDN7dyCAk2jODiw4Tqeg6tl1+/mOO2af9lJ1+SAY3J8\nN4fvBv3L//oJ7+fvs7Qf3H8fLv6gAE78XxBKNx/0uJkBBBxzm1VsX04N9Rj9XNwLIQQrF1XUqgzV\nSrebSyUX6HOfOQjL9FLgR4FR91L078jNfSabtNysxygs0xtVGYn4x+dIcv/DIUU9e24O3934GSKV\nkVHIdw9WJ8Q7/Umm5PY8Nc453nqz4XvBMebVwZ47r2JuQcat61bX1xACZLISOAeKea+gPRqVEE92\nz5DqrOHpfY5i3kW9xnDpqjZ0Y4q5eRm1il9kiuJ1eR20E0QpfEOvWySSUtcJjm3zZnqFC0KBVFpG\nbt77nT349P149JmHh1rvMGR2alAsd2A6RguOwYX2YYUpCkq5mFdf1PGTMgJU09qA75wM8VJ33S/g\n3biolgvqMLABQfsgHn3mYWD1Yd+OcLuFfc9Gi1ejbh175/e4jd2GpVR0uur7GoY3z/LStchQm1ot\nqNQ/c4IAuP7DBizrsEZubkH2jQHLzSmIRikKeQeuw6HrFImUBC2EN5MCQYt0Vkax4MB10OXm7JyM\nZNr7B2i6+Y3+bl5uurmrsWHHY3mjQhzYltfXIZ7odnOt6mLzVrCb8weemy9eGd7NuQUF1arZNbLD\nSyMcws1S/5reREpCItXh5gWGvW0Htarn5nRGRm4u+HbyTicHZLaP6eahvnI8tDfaj/j5maKgnIki\nXjJ9bq70lAiFgb5ubjigLgMLOHUOop+LeyGEIJ6QfBst2ZyMzTC5ueA1d+118+VrkaE2tVpQaXDf\nmOtvmbA73Dy/KPvGgOXmlXYjLNfliOoUyTPqZhHIjhBJIrh8TUN+30G14kKSvOYQvel2rhPcNRQA\n6lXvE1FdwspFtbszYk6GHqd4uxkEcw4UqQtlzxt4LEkErst9ku2Ec6+BUr3Ghm5Oo2oUl65q2N91\nYNRdyApBNCah2CfNIxanYIwjFpeQzshDv6EVheDcef+gq/XVNeCZoR5iaPRBNTe9EMCYdGox57jw\nwx/i3S+8CLXRwI177sHf/cgDcNTBg8EKCzFIDkO0ZoMTAsI5jLiK4rw+poUPT1Azj/bnTuHxh90R\njsWl9u4mMFzjEy2CrsHmpwVn3NdojXPAcYH8vo2FpeEHw8XiNDCOJQSo1dz2qU3rufZ3HWgR6rtO\n6DFpKppnCAQtJIng8tUI8geHbs7mZMR73OzYPLBrKOC9RwDv9b9yUcXulg3LOpwoEI31uLngQlEI\nLl3RQJtu7mwS5YMDpnk8N2ttN9sw6t5oHa/jenB5RMvN8YSEVGb4kyNFoTh3YfC15lSypTgf2Kui\nF0KARmzCwzE5x8U3f4B3f+9FKJaF6/fcgzc+8AAcdfA9Q34xDsnliHS4uZ5QUZqb3Aih/vQvLztB\n8tKJuxrHEydzs6KevpsZ49jd9rvZdYH8gY35xeFfl/GEhB34G4oS4m1+9bp5b8dzc6+HgzbmzyIi\nkB0xkuQ1TRnUOIUQfyOZ9vd3BH16jGLxnIJy0QGlBHpcwvam1SVazrxmSAd73k1ttTKgdVnH9zSM\n4WUJeDfpnSLbuGkG/gyUervfp5GPf+JW/UdA+sxibdH5KU6AYi4KV5nsxeEDX/s63vPCi1CaMxDS\n+we49tpr+PI/+cdwlQHCbNXT2i4Uy6unnfTP0o9qUkWy0Oja+eUAbEUamEJ9HIbdEc7NK0hnZRh1\nF6WCi1qVtVPbeHNhrd3RSJRg5eJoTrhNiwffJ3Cv6yiWhn8sSgnOX9K63rucA7l5CQd7wY0yCgfO\nTIhRcPaR5CHcTPvfHMtSgJtLh27eut3fzfNLKirl4dxsNo7nZk2jWLlweP25faO/mzM5eSTv59Mq\n+SEDuqIDAW6e0+Eqk21c+MHn/hp3v/Ryj5tfx5f/yS+DyQNuuSnB3vkkJMuFYofbzbWkhkSQm1Xp\nxJlSw24s9xLo5paPmwsbh5v7ldLxppvnjzEUwnOzits3ra5JJp1Be+9zFA6cmd1QFoHsBHFdjp0t\nu2+9aSsNucXuto1S4XBntXAQ/H2tLoULS/1PenufR2l2MGs0WHtIelSnw6fQDvi608jCvZNW/f2Q\nLRe5rSo0o7mbh/4nfYYuw9Fk1FLaxOtjo9Uq7v3OC5A6+qvLrotYuYyrr72OHzzw/iMfw1XCK8kW\n5Tkdes2GbLmg3Euz4oRg/9zpzwgcZlSPJBHEEzLiCRlmg6FWZZAkIJ6UQIh3wylJZCQnsYdrQN8d\n7+OkLrWI6hTX7ol4tfwMiMYozAZDfj/4BMcd0PFUIDgruC7HzqbdN9hspQ4DXvrxzqaNcmk4N5dL\nDPNLXtfvo06QCPVOP4EON6sE0ejwbh70ZaOokDmNIPYkbq6mNNgTdrNeqeCe770IudPNjoN4qYSr\n3/87/PC+9x35GK4qwT1mM8NxU8pFEa3akO3TdfOwG8u9DHQzgIbJII/YzVTqv+l1nB40LaK6hLta\nbuaeqxsG65tyPKgb+VlHBLITgnOOW9dNmAG7OC25ZHIyUs2B60addQWxR+M9iB6nwM7gr6TU+7rb\nN8zDBjDN4PbCZW2ourt0WkKtErw+/Q4GUJ/Wzq4PxrF4owSpY4TIoF/t/koisNvgJJjf3IIrSV2B\nLAAotoPzb78zVCA7DXBKsHU5hWjVhmbYcBQJtaQ60r/DsClOWoT6OnRGxzCTTVEotAhBw+h+tfZu\neg0LYxy1KvNS/2MSKCXQIsEdDwkBYmeo06FAEATnHLfeGezm7Jzcrhk1DNYVxB5F6zFicSlwdE8n\nlALRGMGt6+ZhQykCqE03D7N5lcrIqFWDU5ijd+DmXk7tFJZxLN0ogU6hmxdub4BJEnqHeCq2jZW3\n3h4qkJ0GuESxdSWFaNWCZjin7uaTphoDwW4ex7xUVe3v5myf+vFBtNzMGYfedHNkgJvPUhfi4xKO\nd/8M0jC4l4oQ0DUxmfZOSeYXlfaua7UyfJtx7zG8F7WmUaQyUt+d10iU4OJVDYVmY4lWPQ9nXqrE\nsPMw9fjh87T/ocDKRfXIVuH9GFkQC0CvWqCsew5mUHkHB2BG5dCIEgAaehRd3TyaMEJQj5/+aeVE\nIQRGQkVxIYZqJjKWv8P66tpIX3t3yspFDVqEgBDvRrclyuOOujHqLt56o4HtDQs7Wzbe/kEDB3s2\nKCWYX5K7rhmEeCe+JwmWBYJpwqgzWAFz0AkBUmkJ1+6JYG6hw83l4wWxrc1pLUKRTB/h5isaCgdO\n+1Sm5WbT5NjeHM7Nsfjh8/jcfEpHsqd5vdQrllfu0/Gxfm5u6GFzsx54XMYIQT2RmMCKRgghMBLa\nyNx81Pz3MLJyIcDN88cvravXDt28vdnhZolgftHvZlkmSGdn182z+5NPGMtigVdnzgHmEl/ThUHC\nodR7GM68F3UkQtrdfQFgYUlBPC6hWPQmRyfTMqK6N6O29TylYnAdTeu05qhW3YQQLC6rSGe9WZyU\nemkdx207Dow2gG0hW27fuliG5p+GAEwaTSrrSdErFXzga9+EYju+dCsmSXjjAw9MamlnjvXVNbz/\nk0V86jNfmPRSupBlgsvXIjAbDI7DEYn6R+McBWdeE7jeRjYHew70GEUm643c8MYIAPHEycYICATT\nhh0QxAJNNzMEuLn/Y9HmvT1ruTlKkek4nVlcVhBPSCgVHYAj2M19MrGqFe+05qiNYkIIls6pyGQZ\n6jUGKnmnN6fxXh6Fq3vH7HTS7WaKg+UQublcxgNf/yYU2w528wP3T2ppU8tR89/DhqzcuZsZ85rA\nBbtZQianQIsIN3ciAtkJoWk0eOwFQeB8q2RKChxKTghw5S4NhsFhWwyRKPXVthJCEEtIA9MCB+0o\nH6e1uab1nyV3FOM8BbMiMjj15p91wolX/8EpgaNQGHF1NIVEJ4Fz/PS/+38RL5W6JOk1WVDw/Cd+\nGsX5uUmt7kzy0rNpvHQHaU6jRItQnLRtRb3P3MtWd9WoLoluxIKZpF8dXX83yygc+IPNtpvrHLbt\nza0McnPQWJFOBroZw3dwD0q5PCkPPOZ4QcYIsDQZnPi71ntu1sEp4CgSjLgSGjcTxvCJL3wRsUrF\n52ZLVfH8Yz+Nci43qeVNPSdtBDUp7sjNNRaYSs+5N3ovqqvCzT2IQHZCRKIUkah/KHtr2HMvqkYx\nvyRjb7s71WJpRYGsUCQUoO+07SGIxyWUA5pOaZr/dHgUjDuVsxFT4ChSu5EQ4DUscBQJ5Vw0NILs\nZOnmLURqNdCeOxtGKV7/4I/ixrvvmdDKzj7DNIOaJhjrX3fG2Ow2jRAIIlECLUJgNrjPza35sp1o\nEYr5BRl7u003NzOtls833Zy6s/XEElJgQ0gtQo7MlBoFo3a1Ee9wc/NjjHgdccu5SCjdvHzjJrSG\nEejm1z78Qdy85+4JrezscNJGUNPGIP8O07x1FplIIEsI+VcAfhaABeAtAL/COS9OYi2T5PwlFfs7\nNkpFbzc3FqdYWFL6Bo6ZrIJEQkat6gLk9NKDAGB+UUG95sL1so/btTRLK6OdyTaxWkRCsH0xhfR+\nHbGyCcBrKV+cC2cQC8C329tCYgyJYmns65lF7qQJRZjQY/0zQpJJsb85qwg3e6ekFy5r2NuxUe50\n8/IAN88pSKRG62Y2Zjf38uDT93vBxKghBNuXkkjvG203V5MaSnN6eN1cLoMEBCASY4iXyhNY0dnl\nrDi4H7GYBPDgGbKtBnOCbiZ1x/IXAP4F59whhPzvAP4FgP9hQmuZGJQSLCyrWFge/ntkhQSe2N4p\nskJw5a4ISkUHhsGhaQSptAxZGY04xibFfnAOvWIiWrVAOGBGvPb9YWoc0cv+0hJIQJ6ZrcjYuXD+\n1J6Hui4k24ataaG9cZgkZ+F0VpIIFpZk7G4flisQ4nUxjSfD+x4QjBzhZnhuXlxWsRgCNysdbm60\n3JyRh5omcFqsr64Bz4zpyThHrNzt5lpKAw9xDeD+8nLgJrOtKNgVbj51pi3V+Dh4861l7O10uJl6\n0z/iCeHmICYSyHLO/7zj/34bwC9OYh2CbqhEkMkpyIz4ecYqxT6kDgwkD4x2WnGkbmPpRgnbl1Ow\ntXCeSJXmcti4ehnn3r4OxfHS2FxK0dB1vP3e99zx40u2jQ//5XO4+trrIJyjnkjg2z/1E9i8cvmO\nH3tUEMahVyxIDkNDl70Zv2MS/LTLNJ1VENW9RjOuAySSEmKJY8yOFpw5hJvDiSQRZHPKRJ573FlT\nqX0DybzfzVuX03C0cJ5IFRbmsXn5Epav3zh0syTBiMfwzimU/Ei2jR/76l/iyut/B8I5askEvv1T\nP4mty5fu+LFHBXE59KoJyeEjcfNZTjXO5BREYxLKBQcua7o5LtzcD8KP08lnFAsg5I8BfJFz/m+P\n+tp3R9P86bsmeIonuCMeeuXzeORJY9LLAGEc53+Qb4uyBQdQT6jYXwlvm3zCGN7z3Rdwz4svQXIc\n3Lj7brz80EdgRqN3/NgPP/snUCwCR40gdbCD7N4GHFnGn/7yLyG/uHgKqz9dlIaDxZtlEM5BuNcM\npKEr2DufGPtu9VmU6azw0Ve/8l3O+QcnvY6wIdw820yi7GeQm2tJFQfnQuxm1226+WVIrovr93hu\ntiKRO37sv/dHX4bkSHCUCNIH28jsbcKVZfzJP/40Cgvzp7D600U1HCzeKgOdbo4p2FsZjZuFf88m\nw7p5ZIEsIeSrAJYCPvVbnPM/an7NbwH4IIAneJ+FEEJ+HcCvA8CiEv3RL93z90eyXsFoCdNcTtl0\nsHy95JMlANgKxea1UZ9Jh4/EQRlzW3UABEySQF0HidIB7vv2X+DWXdfwtZ/72UkvsRvOsfJWEbLT\n3X2XEaCwoKOaufPA/rj81e9E8fx9vzv25xXcGbMWyAo3C45iUr5WGg6WbpZBA+pNZ9XNqb0isjsN\ngBAwKkFyHSQK+3jf33wVN959N77xM49PeondDHBzfjGGWvrOA/sg/oT9H3jxP4Yzm05wMoZ188j+\n6pzznxj0eULIPwPwMwB+vJ8om4/zBwD+APB2fU9zjYLRE3nuCXzuqaB7psnhyjSwnoXD61o8c3CO\n9L4FJh+mrjFZQSU1h62LdyOV35/g4oJRLBfU9Y+QoRyIl8yJBLKPPGkAZ7wRhWD6EW4W9GPSvStc\nhQbOG/JGzM2mm1MHTpebXVlBOTOPnYt3IXWQn+DiglHMAW4umiMLZKdt5qzg9JhI5TAh5BMA/nsA\nn+Sc1yexBsHoWV9dC10QCwBcoqgmNbCeaJYToDQ3/gBo0iiWi6BphEyWsX3xLuydO0bHkzAw4Vvq\n9dU1RJ57YrKLEAhOgHDz7LK+ujbZBowAmERRT6jCzU0U0wUPSMVlsoytC+/C3kpI3dwne5iMQc5h\nyv4TjIdJtcD6PwEkAPwFIeRFQsi/mdA6BCMg8twTob+Y5JdiqKS9YJYDcGSK/eU4TH0yDTUmCe9n\nnSav/tiHx7SS4bFVCSygiyUjQC110lHkp8fnnloK/XtAIAhAuHnGePDp+0N1rTpYjqOa6nHzSgJW\ndPbcPBiO1z78oUkvwoetScHBNwGqY3Lz+uoaHnjMGctzCSbPpLoW3zWJ5xWMnvXVNeCpSa9iCAhB\ncTGO4kIMhHFwSma2nb2jUi/d2mZdIS1hLvbO5VBNpya2tr4Qgr3lOJZuVXyfGpcsh2F9dQ3v/2QR\nn/rMFya9FIHgSISbZ4swTBDwQQgKS3EUFoWbbU0CkyhoT70pcV3sXJhHLZmc0MoGQAj2l+NYuN3t\nZk6AWnJ8bhapxrODGEokOBXWV9dCtas7NIR4s2NnVJQAvKBwJQFGSXsXnBGgloxg73x20qvrS6zq\nDQ0nHf+AA+d/WMD5H+Qxt1GBbLkTXKHHS8+mp/O9IRAIziQPvfL58F+ThJs9N5/3u7maimB/Jbxu\njlYtcNLtZsKA8295bs5tVCCNyc2hf50L7hjR4ktwx4gLxelDGEes1IBmOLA1CdVUBEwe3b6THZGx\ncVcGesUCdRjMMc9kPQnxkulLiqaAZ3vXmy8bqVrYuprxmohMmNb7ROwQCwSCSbG+ugaEYAzetDJu\nN1sRGbe73KzAiob71j1eMn1TISgAMADgiFUsRKsWNq9lRvq7a7EumjCeacL9bhCEGhHAjgbqMG88\nkMtAubcDmzpoYPtiEnZkdG9ZTkko6kuHhRwxOozA65SY3K+hsBye+YPrq2tiVIBAIBgrYZwgMG1I\nDsPS9SKoy7vdfCkJWxNubkGO6OnUcnNqr47CcnwsaxIbyWeXyR9TCKaOsDWHOGuk9+qQHNbe0aTc\n2wXObVUnu7CQYejKkT0QCYBYxRrHco7F4/Sz4j0kEAjGQlgnCEwb6d0aJIcLNx9BY0g369Xxu1k0\ngjp7iEBWcCzC0KL/rKNXLF/KLAGgmi5IwHy204IwjkTewMLNEnKbFaiGPbLnOg0Ki7F27RDQf+oO\nHd2v7I4Ro3oEAsEoERtmp4detYPd3HBB2OhGyxC3183hDsTyS8O5WXInMytPbCSfLURum2AoxJt+\n9FCHIVYxB6fMjqhmlbgcyzeKkGzvJJjDC6jzi7GRDTC/UxxVwua1NOLFBiJVG5GQy70fn3tqCRA1\nPAKB4BQRzj49qMMQK5vAADePKiQjrldq1MrSmho3X226uWYhYky+6WIQonb2bCBOZAVHIoQ4eiI1\nGytvFZDerYNwvxQ5ACOmeKMIRkCiaLSDWOCwhiW7U4PScJDaqyO9WwvdKS2TKMo5HYWlWN+vcQPm\nzYaRqe38LRAIQoW4jpwekZrluXlvgJvjCjAqNxcaXaVGbTfv1iCH2c0yRXlOR2Gxfw1sGNwsUo2n\nH3EiK+iLkOGY4BxzGxVflz8Ob/YaADgKxcEImyLoFcv3/K1FLN8ote2dKDRQS2nIL8ZC1dHYViW4\nEiD3bPxyAOVsOHet+7G+uobf+81tNB790qSXIhAIpgjR0OmUYRzzG9Uj3CzhYGkCbmbAuR43V1Ma\nCiNcy0mwNQkuBeSeEh8OoBQSN4uZs9ONOJEV+Ig894QIYseIZji+uhvA23l1FAm755PYupIeaZt6\nVwp+bAKvA2FrFhzlQKxkQgtbGi8h2DufBCPNDv/w/m1GZVQy0Umu7ER87qkl8R4UCARDIxo6nT5e\nuYo/iiQAbEXC7oUktq6kRupmdgw3x0tm6E5mQQj2O9zM0XSzLqOaDZebhXOnE3EiK+hifXUNeGrS\nqzh9JNuF3uxeW0+ocBVpwivqJbjCxlUozJgy8mevZCOI1O2und9+NT+EA3rZhKmPfl3HwYoq2Lgr\ng1jJhOQwNHQFjZgSqpPj47K+uob3f7KIT33mC5NeikAgCCFf/P1P46Vn05NexokJs5s9BxIE2dBV\n6FgcWMlGoBnDuzlWtmBFw+VmU1ewcS2DWLnp5piChh5ON4u62elDBLICANMvw0HECwYyu/X2/0/v\n1VFY0FENyUmdGZXBA2TJCFAd0+y4RkxFKRdF6sAAJz4fWecAACAASURBVASEczBKQBkPngkXQgEB\n3u51JWS7vHfKS8+m8ZKQq0Ag6GF9dQ14dtKrODnxvIHMXojdrMuBQSMjQDU9Hjcb8eO5mYdTzWDy\n9LhZzJydLkQgK5h6GQ5Cslxkduu+GpPMbh2NmApHDcHuLyE4WI5hbsObRdcKaetxFfWEOrZllOd0\nVDMRqIYDJlHYCsH5t4q+r+MEUzWc/awg5CoQCICzkQIpWy4ye8FuNuIhOZklBPmlGHKbPW5OqDDi\nE3KzTOHIBCv93JwUbj4txOnsdCBqZGeYWeiSqlet4DwcjnY606SJ1GzMbVYBgnatrBmVcXAuPvaT\nTyZRNOIqrKgMLkvYX457tS0d/5RyUVgRsQc2KUSXRYFgdjkrztYrVnC2D8LkZgu5rR4367LXeHFS\nbo7IYLKEgwA3F3NR2MLNp8pZeb+dZcQrfgZ54DHH69I2A7SaIfg+PvaV9KFPx2Kt4UAvW6hP+OTT\nSGrYiCnejQUHGjElHKfYM47osigQzBZnufyni1ENZD0ufToWa4YDvWKhPuGTz3pSQ0NX2pv1RlwJ\nxyn2GUT0qgg34kR2xlhfXZuZIBbwUoCCakY4wVjTdvvhdSz2m9vrQNiYwIr8MImimo6gmtZAXQa9\nbEK2wjngfNYQp7MCwdlnfXXtzAWx9bgSXM9JMNa03X5ofToWtzr3hwEmH7pZcoSbR8lLz6bF6WxI\nESeyM8KDT9+PR595eNLLOHWUhoPMbg2a4YBTgnImgnIu2k77cVQJxbko0vtGO42JN9Njw3+yGJpz\nY1CHYfFWuUuS9bg6kfRnQTfidFYgOLtM681zy80RwwELcrMmt5sYdbq5OKeHyM3BHYvDBHUYFm+W\nIdsdbk6oE0l/ngVEr4rwIQLZGWB9dQ14ZtKrOH0ky8XSzRIIa4Z8LkfqwIBsM+SXD4eCV3I6jLgG\nvWKCAKglVDhaOF76ZlQGJ306Fo+pK+IwzG1WoZhuV2itVy1Y+QYquenoRHjWWV9dw3O/8A1861df\nnvRSBALBHTKtASzgNXJaulFql/ZILTc7DPmlQzeX53TUEypiFavZREmDo4UjiA1Dx+JhmNusQLF6\n3FyxYEYaoZvTepYQjaDCg0gtPsM89Mrnp1qGR5HMG4dBbBPKgVjZBHVY19c6moTynI7SnB6aIBYA\nQAj2VhLtZg0c3r/rifF2LB4EcRkihu07H6YcSBTDkf4s8Hj0mYfP9HteIDjrPPCYM/Xv4WTzlLXX\nzfFSkJtllOZ0lOf00ASxAAa6OQypzwBAXYaI4QS6OSncPHKm/X16VgjRHb3gNFlfXQOeNCa9jJGi\nNfwXcAAAARTThSlPxz6NqSu4fVcGsYoF6nI0dAVWNDxvTcI9iQeWM7FwpV1JtovMTh3RmgVOCaop\nDcU5HaCzlWK1vrqGv/qdKJ6/73cnvRSBQDAkZ+XGWO3jZk4IFGuK3BxTsHFXBnrLzTElVB37CePT\n42bLRWa3hmjNBqcElXQEpbno1Kc/i1TjyTMdVxPB0Jz1U9hOLC049QcccNTpemnzZkOlci4aqiAW\nAJhE4AbceHAARkhOjQFvd3r5egl61QLlXjpbotDA/EYFksMQK5nQy2boBD8qHnnSmJlrgUAwzTz4\n9P1n6r1qa1IfN/MQ1b8OB+t0c4iCWABwZdrXzfWQnBoDXh3v8o0S9KrddnMyb3hutj03RyvT7eaz\n9P6dNsL1rhTcEbNwCttJORtFrGx2zaJjBDBiog39qUIIDpbjWLhVbqeLMeIFuMU5fdKraxMrNkAY\n96WzRWo2Vn5YaN9YEQAN3UtnM3VlAisdL2LHWCAIL2exh0U5F/XNiWXNbsRBgZfghPR1M0UpRG6O\n93Nz1cZKrQDefJ1Mu5tF3exkEFeUM8AXf//TM7kb5GgSdi8kYTV3fxkBqikN++cSk17amcPUFWxe\nTaOcjaAeV1Cc17F5JQ3WcVNCHQatZkOyJ9P+XzMc38w/wJMjgXexo83/jtQdLNwqI3EwOxs/s3iN\nEAjCzFl9T9qa7LlZ7XBzWsN+RxNGwelg6gq2rvS6OdXlZmnSbm4McDMPdnM8P51uXl9dwxd//9OT\nXsZMIU5kp5z11TXg2UmvYnK0LuKHW3rTXW8RZlxFQnEh5v8E58hu1xAvm95cQO6diu+fS4y1NtXW\nZLCaHSjMXloCTe/XUUtrYNJs7OmJ01mBYPKc1QC2E1NXsHVVuHkcOGp/N+e2atArZnuS0ETcrEpg\nsIc6OWu5ObNXRy2lgU+hm196No2XxOns2Ji+V4gAgCfCWZDh0BAydaKUbBtXXvs+7n/+21h5+51D\n4U8ZyQOjneJNmZcyFK3ZyO7WxrqOaibSbkzVyVGvCq1uj2pJoWV9dQ0PvfL5SS9DIJg5Zs7bU+rm\nq6+9jvu+9W2ce+f69Lp534BeMUF73JzZG6+bK5lIcEOqI75v2t08c+/1CSFOZKcQ8eaYfhKFAh77\nv/8dZNuBZNtwFQXlbAZ/+o8+BUcNT5OGYUgUGr5TUMqBWMlEfjE2tpsYVyLgON7uHOFAas+AEVNn\nrrPxI08agNg1FgjGwoNP349Hn3l40ssQHEHyII/HvvD/QHIO3VzM5fDnv/QP4ajTVbeZLAa7OV40\nUVgYn5uZRPt2V+6HlzFlYHvK3by+uobf+81tNB790qSXcmYRgewUIQLYbqjDkNmpIVa1AAD1mIr8\nUqyrNmSSqIaBzN4+jHgM5Wy263Mf+/J/hFY32kEXtW2k9g9w//PfxguP/L3xL/YOoH06DRKO/nN7\nRrSOfk/Vd0QBAMVykcoboWqOMU5EurFAMFrOYkOnQVCHIbtTg95yc1xFfjE8btYMA+m9fdTjcVSy\nma7PfezLX4FqdLs5s7eH9/3N3+DFj03XRsRAN48Rwng7tbmXgW42XSTzBspT7ubPPbUkNo1HiAhk\npwARwPohjotzb5e6ghe9akG77mDjWnqyqUyc44GvfxP3/u13wCQJlDEcLC7guSd+HmY0CrXRQHZn\nx3dyKLsurr7++tQFsmZUQaRu+2Rkq9JYd1IZ9cYEyQ7r+jgHmg3BODST+YfHw9uhntVAtoXouCgQ\nnC4PvfJ5L/NhhqCOi5W3S11davWKBbXhYPPq5N38gb/+Ou797gtwZQnUZdhfWsRzT/w8rEgEWr2O\nzP5BoJuvvfr61AWyjaiMSN0/09fSpLH+HZhEwCgBdbsjWQ7AjEgAH+Dmkjn1gWwL4djREI7tMUFf\nRBAbAOdYvl7yncAReLNE9YoF2XSR2alh7na53fp9XFz+uzfw3u98F7LrQrUsyI6Dua1tfOyPv3Lk\n9457p/Q0KCzq4PRws7XVpTK/FNB8YpQQgvyCDka618IJcLAcx96F1IBvnsJf/AgQtfcCwemwvro2\nc0EsGMfyOyXfqBUCr3NutGpBNh1ktj03x4oNYIxuvvr69/Ge730PkutCNT03z29u4eEv/8nY1jBO\nCgsxcBIONxcWY2AdL4qWm/PLceyf7+/mabwnGoRw7OkjTmRDinih90evWJCc4DRSwr3P57aq7blq\n0ZqNRN7A9qU0uDT6Xch7//Y7UByn62MSY1i6dRuRWh2NmI784gJyW9tdO0mOJOGte98z8vWdNrYm\nY/NKGskDA1rDgaXJKGejcLTxz/I1khp2ZYrUgQHZcmFGZJTmonA0ublWCYrpdr12GAFqCW3saw0z\n66tr+KvfieL5+3530ksRCKaKyHNPeKmEM0isYoG6A9xctqBXrS43J/MNbF9OgY8he+e9f/sdKLbf\nzedu3IRqGDB1HcW5HDI7uwFufu/I13fa2BEZW1fSSOQn7+Z6UoMrU6T2Dci2CzMqo5TT22uxVQrF\nYn43J6erZ8iwiNPZ00OcyIaMh175vAhij0AznL4vXE6AaNUC5Yd1F5QDss2QKIxnd1wzGt5aQLrO\n+RilUE3vc19ffRxWNApLUcAB2IqCUi6Llx98cCxrPG1cRUJhKY7ty2nkl+MTEWULU1eweyGJzWsZ\nHKwk2kEsAJSyEQCHO9QMgCNTlOai419oyHnkSUNciwSCY7C+ujazQSwAqEb/ESscXvmP380u4hN2\nMycEqunV837tZx6HFY3A7nBzcW4Or37kx8ayxtPGUUPm5otNN59LdK2llPMc7HNz7mykFQchZs6e\nDuJENkSsr64Bs5aKdAIchYLBvwvDm/8TtK9LubdbPIpaC8I4kvt1xJsjaF750COQXAIjnoLk2Fh5\n5/u4/MaLcCUJlXQaAFDJZvAf/qtfw6U3f4B4qYSDxUVsXrkMTsXe0qhQGg5y27Xu1wcBqlM6q25c\niNNZgWAws3wK24mjSP3dTILTRCkHYmULlREELIRxpPbriJVNgAOvfuhREFAYMc/N599+HZffeAm2\nqqKaSgIAyrkcnvnMr+PSm28iVirjYHkJG1cuT90IoWlCDXAzabv5bP/exczZO0cEsiFASPB41FIa\n0vsGeEcdTsuPgy55bBSpS5xj4VYZasNpt7k39Ux7Ha6i4vbVe2FGoti+mO0KVF1FwdtTmK40raT3\n674bKcqB9IGBSjY61S3+R40Y1SMQBLO+ugY8NelVhINaSkP6oI+bB9Q6slEEK5xj4WYJqum23WzE\ns11uvnXtXphaFJvXFroCVUdV8Nb77j39NQkCSe/53Uw4kM4bqOSiM7GJIFKNT444hpgws56KdBKY\nRLF9MQlbk7oaGZCOf3zfQ7yh3KeNZjhdQWxrHV3PLcvYvvgu3H7Xu079+QXDozb83Rtb9HY6FgSz\nvrqGyHNPTHoZAkEoEKn33TC56WY1BG6uO11BbGsd3etVsHX5bty+evXUn18wPEo/N3OvSdisIPx6\nMsSJ7IT44u9/Gi89m570MqaWVhMD6jCk92qIlyzf17T9RYBqWkM9MVzTAMl2kdo3EK3ZcCWCci7q\nfW/ArqDacIZqeMspgWQzsFlLYeUckXodlqaByZO93DiKBLmnCVcLNyTzDacBMRNPMOuIALY/dkTG\n1lXPzZndGmLlYDe30o0r6QiM+AncLBOUs1HUk8GN+rSGM1THW04JZIfBnjE3E8agGUY43KxKkI1g\nN8/aPZPw6/ERgewEWF9dA56d9CrOBkym0ALmpLUwdBn55ThcpdlUgHMoluvNHFX8TQ+ow7zRPs3O\ni7ID5LaqUMwoSvP+Gh5Hkby8hqM2DblX2ztLXH31NXzoub+GbFkAIXjz/vvwnUc/Di5NptlEcU7H\nwu1y1w49a9XhiLTiY7O+uob3f7KIT33mC5NeikAwNkQQOxxMplCNAW6OKygsxoZ2s2S7WH7ncOxe\ny82y5Qb2vnAUCk4BMoyb5ck1QJoE115+BR/8669BtmyAELzxwP347iMfn1iPjtJcFOrtis/NlXRk\nZt0sUo2HRwSyY0QIcEQMuM5V05G2FKPV5lie5tw6W5Owt5LokmYyb/jGB1DufbycjfiaAhlxBYxS\nEMa6aoJ6W8hX07PVUOjc2+/gwT//atcJ6N0vvwLKGf7mJ3/C/w2cI1q1IbkMZlSGrQ1/aYpULWR2\n61Ast92BuJbSvNNyAFZEBgiBGVOwvxxHdrcOyWHgzZS2YsAGhWA4RKMKwawgsqhOQv8j0Wqqw83t\nkXmD3NzwzY6nHEgdGKhkor6mQPWEiswu6ZppG+TmSjpy5hsKdXL+h2/hI1/9T11uvufFlwEOfOfH\nH/V/Q4ebG1G5awrAUXS5WaEo5Tw3a4YDTg7d3IipOFiOIyPc3MX66hp+7ze30Xj0S5NeSqgRgewY\neOAxB4/Tz056GWcWI65CzjcCOyU2milLsulibqN7x09tuFi8Wcbm1XQ7bThSs4PjYkKgmi5Mnfo+\nvn0pifnbFajN+aSd0uTwdhvL2dka7/L+57/lS+OVHQd3vfIavvvxj8NRlcOPmy6Wbpa8m5jm36ee\n8MQ2sMkD50jv1JAsmu3fueIwZHdqyG7XvJNy7qWO7a0kYOoKjKSGjYQKwgBOMRNNJMZBa5NOBLSC\ns4jIojoZRlyDUmj4nMoBNGKeAxTTwdym380Lt8rYutLh5np/NyuWAyuq+D6+fTGF+Y3+bi7O6ahk\nT78+N8y8/5vBbr7npZfxwsc/1pVmrJgOFm+W25v/wJBuZhyZ3RoSnW62e9wMb+zR7vkErKiCetIr\n/xJu7kakGh/N7BwRTYj11TURxI6YcjYKJpN2di+Hl+lbWNTbaSmJQsPfFQ+A5DJoHbUZjkKD95A5\n71tH6SoS7IDZbATetbiWjMzcRTleKgd+nBNAMzpGTHGO+Y0yqMtBmbfDTjmgVyzESmbX90qWC61u\ngzoM4ByLN8tdQWwLyr0LW+vxJJdj4XYZxG2+QgjxduBn7G8yDtZX1/DAY8G1TgLBtLG+uiYyqe6A\nci4KV/K7Ob8Ua3eJj/dxs2yzdlYNcEI3qxIc1f+5tptT2sx5IFYOdjNwOGcXgOfm2xXPzbzHzT11\nz3KAmxNHuZl5bl681REoCzf3RVyH+iNOZEfEg0/fj0efeXjSy5gJmEyxdSWNRL6BaNWCK1OUc1GY\nesepn+P2zUDu7IpXzkURrdldYmUAzKgMR+1fR6OYwY/PCYFsu3BnrD52f2kJ5996y39KTinq8Vj7\n/8s2g2yzQOElig3U0hEQxjG/UYFWt9uzCI2oPLALsY+mgGvp2dp9nwSP088Cq+J0VjDdiBvHO+fQ\nzc0GTQpFOdvj5oDrP+BtekrOoYjL2aiXMeVzsxJYU9tCMfs/vmwzWDPW6C+/tIhz71z3/U5cWUIj\ndpjKK1suJCfYzfFCA7WUBuI23Wx4bgYHDF2Bap7Azangpl2CQ8RM92Bm6x08JtZX10QQO2aYRFGa\n17F9JY29C8kuUQLexZUF2swLUltYUQUHy3FvF5l4smvEFOytJAY+vxWVA3eLCeeBp7VnnRc/9lG4\nSvfvxFZkfO/hj3Y1e2rVRAXRatKR3a56u73c28UlHIjWu0ceHQVpnswKxsf66hoefPr+SS9DIDgW\n4hT2dGEyRWkh5rn5vN/NjViwmyn3vNrC1BUcLMXg0g43xxXsrcQHPn9/NwN2wGntWeeFjz0MR+5x\nsyzjhY893NXsifQWFHfQ8nZuuwrNOHQz5YBes4/lZnCAurMzYudOeeRJQ1yfehAnsqeIOIUNL7V0\nBMlCA3BY+yLb6ljbu5vbqtWQbQYmkcP275xDbbheQ6KIDNaxk1vORhErmwA7vPa3Hn/W2scDQGFh\nHn/66V/Cj3ztG8htbaOeiOPlBz+CG+++p+vrbFUCowS0J8jk8MYD5DYqiFUsn09J62uGXA8nQKPn\nBkoweh595mFg9WFxOiuYCsQN4vippiJIBLi5ko74UobrqQjqSa2Pmx1ILve5uZSLQi+bAO92s9fk\nafbcnF9abLr568ht76CeiOOljz6Em3d3z7m3NQmctEx7CIcXeOY2KtBPwc0Qbj4RohHUIYQPOBEJ\nG++OpvnTd4UzUBQCDD/EZUjmG9ArFjglKGciqCeD58P2ItkuFm6WINuHHRBrCQUH5xLt71dMB5nd\nOrS6DSZRlLMRb9C7qPcYiFazsXC77O3M4lCbpOe/exlWlowA9biKg9apOueI1G1Ql8PUFTFDdoyE\nMaD96Ktf+S7n/IOTXsc0E2Y3D4tw+OSgLkPi/2fv3qOku8s60X+fvXfdq/p+e+9vrkAwCSgiCfGQ\nVx0gaQiuoMPIQsfBI8zqmYmuhOPCds3M8Rx14ZqAyppxGc6Yo47gBE0Gowl4mzeeCUQkYN5cCITk\nzXu/9b3rXrX3fs4fu6qvVf12V1fV3rv6+1nrhXTX7amqrvrt7/7d5opI5ipwDQPZoXjTvds3MqsO\nxs8swVzTNuf6ophfsyBRpFRrm4tsm3cinq9g9Fy2s21zJuodRwHr2uZSMrLuhAQ1F8R2tR222zYz\nyO7S7S88gDs/Wbz6FSkY6ivj7nBvsonXFxDdMNdGARRTEcwc6mtnhXuSWXWQWiojma0gUnauOudB\nAZTjprca5ZqvsI3vj2MKFsZTKwdFVm0VRqP2dyAAloYa7xFMnRG0RpdBdveC2DZvFwNsQLTYNu87\nuYBIZXPbXEhHMHuQbfNutdI2l+ImYldpm21LsDi22javrJBcyySiXm/6UoM9gmmzoLWr7bDdtplD\ni3dhenIKYIgNFNN2kchWIKoopqOrCzSpon+2iL75IkS9FRDnx1Mr2/NseZ8Vp+GCEQIgka/Cqjhb\nLgRFjYnjIpWtwLBdlJMRLA8nkMxVGjaUG88Eu4Zgbn8GsUIVQ5fzm+bkKAA7Irh0dGDd8LPxs1mY\nG/YJ7psvopywtvW3QLvHrXooCLgtXneZVReJXOO2eWCmsLKzwE7aZqviwKo0bpuTObbNrWrUNqey\n22+b5/dnEMtXMXSlcdtcjRi4dLR/dWi3KsbOersXrGub54ooJyIrWzVRc3t5qDGDbAvix+/19nai\nQEkulTB8Kb/y88BMAUvDCSyPJDF4JY/0YnnlSzVSdTF6PosrGxeGUkWsaMOqevNg7Zi5aRP2jRK5\nCrJ7bJ/Y3YoWqxg/6w0nFgVUiiglI3CanI1X8RbsshwXpUQE2aE4nIiJoUu5hgtLqAALI8na1g6K\nUsKCqMJwmqyQvFBikO2y6ckp3HrPIj708S/4XQrtMeyF7a7UYglDl9e3zYsjSWSHExi6nEdqaXPb\nfPlw3/q9YVtom+P5KnIMsjvSUtucisCyXZSSEWQHE3AiBoYvNm+bF0cTtRMXXttsKDaFWMB7/PRC\nkUF2m/bqnrMMsjs0PTkFPOh3FbSRYbsYvrT57F//nPclvDbE1okC/bNFXDkcWbmP8TNLsKqrK+gV\nU1HM7k9tOefD3eFQqD1PFaPnczDWLFQo6m14n+uPwS2uX5FYATiWASdqwlUDpXR0W/NaRy7mV96z\n/qtc13AbtLjUcSceH8CJPdjwkj/YC9t9hu02HDUzMFtAKWEhvVTevI9srW2eOeS1zWbVxfjZJZhr\n2+Z0FLP7tm6blW3zztT3jm3UNg/EESttbptty4ATMeFaBoqpKBzr6q/5yIXttc0Cts2tmN5jbSpn\nUm9T/Pi9PIsbYIlcpeHvReGtJtzoMgCRirPy88iFHCIVd93m34l8BZmFEubHkg2X8NfaYgW0fZGy\n03C5fUOBWMnG4kgCrmBlmwXH8IaMZxZKSC+WMXpuGcMX84AqcgPxhls3SG1xCmnwbyNXgHwf30M/\nccsT6rTpySmGWB8ks83b5vRyGdro+xsb2+YsrI1tc66CzGIZC6OJ5m0zR9nsSLTsNAyOhgLRko2l\n4Y1ts8Ba0zaPnVvGUG1UXLva5kIf95dtxfTkFG5/4QG/y+gKBtltmJ6c4lDigNvqHKA2WZlQAVTi\n3rAjcVzEi9XGw04Xy8gPJbAw4jWYCu8L1hVg5mBmTy7h30nZ4STOXT+I2QNpzBzIrBy41Bs7Q4Fk\ntozMfNFr6FJRb19BrL4vjQ6O6urvIWrXr8ZM5PrjHX9edHUMs9QJ/LsKJjVk4+4u3u8BVGr7rxuO\ni1jJbjolJDecxOLGttkArhzsg5rskW2n5ZEkztfb5v0ZGKqb2ubUchmZuSIcwxtyvKlt3uL+N7bN\nlZiJHINsy/bKnrMcWryFvfAH0CuK6SgG18zBqdPaGT01BH3zxfXDYgRYrK2It3Fo01pSO0OZHUki\nN5hAvFAFanM2d7rCInnB0TUNGPb6Xtn6vrsAoKaBUiqK9EKp4X2IAoMzRWjtHMLCaBKGwttbULBu\nrvSm2wKoWoJKwkIxHfN6Y7kNQ2BwMShqF7bh/iumIxi8svn3KkC+FlIyC6VNbXN9tVpxtenw4foK\nt8sjSWQH44gX7NU9w9k271glZnonFzbs6+4KkK+1ze422uaBmcJKN1m9bXZqJxWGruSBzQOyvNti\ntW0upGPb3h6RttbrQ43ZldQEG8BwcSwDC2PJlTN+9TOA2cE4KgkLSyMJLIwlYVsGXAFKcQszBzKI\nlWxk5oswHBd2ZPPHQbF+6LCagmImimI6yoayVSKYOZCGa2Dd2dpyIoLcwPqeUV35nw13gfr8Ge/f\n4EwBuYEYcgNxFDNbn8Gt/13MHujzGmc2lIE0PTmF2x6+xe8yKKTYhgeDEzGxMLq5bV4eSqAat7A4\nmsTC6Jq2ObG+bRZXG66J4G2xs/pdr6aBYibqLdrHtrk1Ipg5kNnUNpeSkZWTzCvq2yVtvAt4wWJj\n25wfqO0NvEWngStAdiiB2QN9KLBtbqtebk997ZEVkQfgLZ00qqqzftZSx8YvvHKDCZRSUSSzFYir\nKGSiqMZrf+IiyA0mkBv0VhdO5CoYPZ9due3ADJDPxGBWyyubf7vinX3kPmbtV0lEcO66QaSyFZi1\n1Q7LCWu14Vq7XdI27zO5XEFuMA41vMZ49Hx2pae9fh8Kr9d2Y2CmYDr26B3A5B09fTY5iILYNm/X\nbQ/f4v3dUGDkhhIopaNILnsLO21qm4cSyNVW/k8slxu0zVGYdmVd2+yYBpZGuFtAu5WTHWibsxXk\nBuJQ08Ds/jRGLuSat80bAzO1Ta+2p74FWRE5BODdAM74VcNat7/wAO7knrChZ0dNLA9v3biJoxg5\nn920imIqW8bM/jRiJQeRioNSwkJ+IM6VDztETaNpoEwvljcPBV9zeaNl+tcuUlFKRVBOFJHMVlCJ\nZwB42+8U0lEsjSQ5rzlkpien8NSnEvjazZ/2u5SeF7S2eSemJ6eAR/2ughqxoyaWr3JSWBwXIw22\nbUllK5jZ7/XSRioOSkkL+X62zZ2yddtc2lHbDF2dngV408Aq8QISuSrK8T4ITLbNXdZr7amfPbK/\nBeCXAPy5jzUAqDV+DLF7RiJfWd29ew1RIJGvYmEi7UtdtGpjQwmsvmWN5kvV97IDgFihgPf+yReR\nWl72bqeKS4cO4vi9Pw7X5J6CYXXnJ4t7co88HwSmbd4u9sL2hkSu2vD3okCiUMXCeKrLFdFG/XOl\nHbXNEKzsAxvP5/GeP/kiUlmvx11UcfHIYRz/giWPRgAAIABJREFU8XugbJu7qpfaU19OfYjIBwCc\nV9UTfjx+Hbd82MOazNPYatEn6h7Taf5GFDKRdcv6u7VFQ+pD1d755FeQWVhApFpFpFqFZduYOHsO\nNz/z9U6XTV2wl7YV6LagtM07MT05xRDbI7bsX1U2zkHQaOu8ukK6QdvcH0M15rXNdzzxFWQW17fN\n+06fwZv/8RudLpua6IUM1LEeWRH5WwCN9qz5FQDT8IYubed+PgbgYwAwHmnffIheePOoNfWzgxvV\nVzgm/5XjFuKFzdshOZaBuX1pFPI2UsslAN6cmvp7alar2H/qNEx3fWNr2TZueP55nLjj9u48Aeqo\nXjqb3G1Bb5u3i9OBek+RbXPgleMWEkV70+/tiIG5AxkUclWklkre3Oc1bXOkXMbEmTMwN+xTa9k2\n3nDiebx42zu6Uj9tFvahxh0Lsqr6Y41+LyI3A7gGwAnxJo8fBPAtEXm7ql5qcD+fA/A5AHhjYmDX\np+QYYMk1DcyPpzB0Ob/SA1vfCqCU5I5UQbAwlsTE6SVvfg1qw5YEmB9PAYa3OmUxs3mze8NtfrbY\ntJ3OFUy+mJ6cwmc+cQmlY4/5XUpoBLVt3glOB+pNrtWkbe6PeQsOke8Wx1OInV6CNGqbRZq3zU7z\n9te0Nwdj6q4wnxzu+jeDqr4AYKz+s4icAvC2Tq+M+Ja7bNxt3NfJh6AQyQ/EUU5GaqsoqrcAQaLx\n2WDqvmrcwsWj/eifLSJWslGtLeJVTm79HlVjMSwND2NwZmZdb65jCM7ecF1niyZf3P/gRGgb4CDx\nq23eKZ6M7m2b2+YYKgyxgVGJW7hUa5ujtbZ5aSRx1eOncjKJ5cFBDM7Nrfu9Yxg4c8MNnSyZdiCM\ne87uiW8HNnzUyHZWUST/2DELcwcyO77dV+9+D97zJ1+E4TqwbAfViIVKLI5v/fAPd6BKCor693zY\nGmHaHrbjewfb5mCrxizMttQ2vxfvfuSLMBwXluOgalkoJxJ47o53dqBKatX05BSOf/BpPPPR5/0u\nZVtEQzSB/o2JAX34+u0v6vDIQx/GiccHOlgREQVRPJ/HDSdeQP/8PGb278drb74JdmzzcCfqTTsJ\ns+988YlvqurbOlhOz9tp27xTDLFEvSGRy+GG519A3/wCrhw4gJNvfhPsKNvmoPLzxPB22+ae7ZGd\nnpwCHve7CiLyQymVwgu3c/GIvYq9s72BCzoR9ZZiOo3nb7/N7zJom8Iw1Ljndh7mljpERAR47UH8\n+L1+l0EtmJ6cYoglIvLZ9OQUbnv4Fr/LaKqngiwDLBERrXX/gxNsG0Ikfvxevl9ERAFy7NE7Avu9\n3BNDi4P64hIRUTBwuHHwTU9OAQ/6XQURETUSxD1nQ90je9vDtzDEEhHRtk1PTuH2Fx7wuwxa45GH\nPsy2nIgoBO78ZDFQ39ehDbLTk1M49mjnVkkkIqLeFLSGeC+bnpzi7gJERCETlDY0dEGWvbBERNQO\nbEv8xdefiCi8grAQVKjmyJ4fGGUvLBERUYgxwBIR9YZjj94BTN7h2/oToeuRJSIionBiiCUi6j1+\nfbczyBIREVFHcUEnIqLe5sdQYwZZIiIi6hgu6EREtDd0e89ZBlkiIiJqu+nJKfbCEhHtQd367meQ\nJSIiorZigCUi2tumJ6cQP35vRx8jVKsWExERUXAxwBIRUd39D04Ak1MdW9WYPbJERES0awyxRETU\nSKcWgmKQJSIiopYl+5UhloiIttSJhaAYZImIiKhl35Mxv0sgIqKQaGeYZZAlIiIiIiKirmjXUGMG\nWSIiIiIiIuqadgw1ZpAlIiIiIiKirttNmGWQJSIiIiIiIl9MT07hLXfZO74dgywRERERERH55m7j\nvh33zjLIEhERERERke92EmYZZImIiIiIiChUGGSJiIiIiIgoVBhkiYiIiIiIKFREVf2uYdtEZAbA\nab/r2IURALN+FxFSfO1aw9etdXztWhem1+6Iqo76XUSYsW3e0/jatYavW+v42rUuTK/dttrmUAXZ\nsBORZ1X1bX7XEUZ87VrD1611fO1ax9eOwoR/r63ja9cavm6t42vXul587Ti0mIiIiIiIiEKFQZaI\niIiIiIhChUG2uz7ndwEhxteuNXzdWsfXrnV87ShM+PfaOr52reHr1jq+dq3rudeOc2SJiIiIiIgo\nVNgjS0RERERERKHCIOsDEXlARFRERvyuJSxE5D+JyHdE5HkR+R8iMuB3TUEnIu8Vke+KyKsi8km/\n6wkLETkkIsdF5Nsi8pKI/ILfNYWJiJgi8k8i8pd+10K0E2ybd45t886xbW4N2+bd6dW2mUG2y0Tk\nEIB3Azjjdy0h8zcAvk9VbwHwCoBf9rmeQBMRE8B/AXAXgJsA/JSI3ORvVaFhA3hAVW8C8A4A/4av\n3Y78AoCX/S6CaCfYNreMbfMOsG3eFbbNu9OTbTODbPf9FoBfAsDJyTugqn+tqnbtx38AcNDPekLg\n7QBeVdWTqloB8N8BfMDnmkJBVS+q6rdq/52F98V/wN+qwkFEDgKYBPBf/a6FaIfYNreAbfOOsW1u\nEdvm1vVy28wg20Ui8gEA51X1hN+1hNxHAXzZ7yIC7gCAs2t+Pgd+4e+YiBwF8FYAX/e3ktD4bXhh\nwPW7EKLtYtvcNmybr45tcxuwbd6xnm2bLb8L6DUi8rcAJhpc9CsApuENXaIGtnrtVPXPa9f5FXjD\nSz7fzdpo7xGRNIBHAfyiqi77XU/Qicj7AFxR1W+KyJ1+10O0Ftvm1rFtpiBh27wzvd42M8i2mar+\nWKPfi8jNAK4BcEJEAG/4zbdE5O2qeqmLJQZWs9euTkR+FsD7APyoct+oqzkP4NCanw/WfkfbICIR\neA3l51X1Mb/rCYl3ArhHRO4GEAfQJyJ/rKof8bkuIrbNu8C2ua3YNu8C2+aW9HTbzH1kfSIipwC8\nTVVn/a4lDETkvQA+A+Bdqjrjdz1BJyIWvIU3fhReI/kNAB9W1Zd8LSwExDua/UMA86r6i37XE0a1\ns76fUNX3+V0L0U6wbd4Zts07w7a5dWybd68X22bOkaWw+M8AMgD+RkSeE5Hf87ugIKstvvFvAfwV\nvAURvsiGctveCeCnAfxI7W/tudqZTCIiWo9t8w6wbd4Vts20CXtkiYiIiIiIKFTYI0tERERERESh\nwiBLREREREREocIgS0RERERERKHCIEtEREREREShwiBLREREREREocIgS9SDROQrIrIoIn/pdy1E\nRETEtpmo3RhkiXrTf4K33xoREREFA9tmojZikCUKMRH5QRF5XkTiIpISkZdE5PtU9e8AZP2uj4iI\naK9h20zUHZbfBRBR61T1GyLyOIBfA5AA8Meq+qLPZREREe1ZbJuJuoNBlij8/i8A3wBQAnCfz7UQ\nERER22aijuPQYqLwGwaQBpABEPe5FiIiImLbTNRxDLJE4fcQgH8P4PMAftPnWoiIiIhtM1HHcWgx\nUYiJyM8AqKrqF0TEBPA1EfkRAL8K4I0A0iJyDsDPqepf+VkrERHRXsC2mag7RFX9roGIiIiIiIho\n2zi0mIiIiIiIiEKFQZaIiIiIiIhChUGWiIiIiIiIQoVBloiIiIiIiEKFQZaIiIiIiIhChUGWiIiI\niIiIQoVBloiIiIiIiEKFQZaIiIiIiIhChUGWiIiIiIiIQoVBloiIiIiIiEKFQZaIiIiIiIhChUGW\niIiIiIiIQoVBloiIiIiIiEKFQZaIiIiIiIhChUGWep6IvCQidza57E4RObfFbf9ARH6tY8WFiIgk\nROQvRGRJRP50B7c7LCI5ETE7WR8RERER7R0MshRqInJKRH5sw+9+VkServ+sqm9W1ae6XtwWNtYY\nEj8BYBzAsKr+5HZvpKpnVDWtqk67ChGRoyKitYBc//fv11wuIvKbIjJX+/ebIiLtenwiIiIi8pfl\ndwFE1H21UCeq6u7gZkcAvKKqdofKasVAk3o+BuDHAdwKQAH8DYDXAfxeF2sjIiIiog5hjyz1vLW9\ntrXhsX8gIgsi8m0AP7jhum8VkW+JSFZEHgEQ33D5+0TkORFZFJGvicgtGx7nEyLyfG347SMisu72\n26z3X4nIy7UaTorIx9dc9qKIvH/NzxERmRWRt9Z+fketrkURObF2SLWIPCUivy4iXwVQAHBtg8d+\nU+16i7Uh2ffUfv+rAP4DgA/Vej9/rsFt3y4iz4rIsohcFpHP1H5f7z21ROS2Db2oJRE5VbueISKf\nFJHXar2oXxSRoZ2+fjX/EsCnVfWcqp4H8CCAn23xvoiIiIgoYBhkaa/5jwCuq/17D7zAAwAQkSiA\nLwH4bwCGAPwpgA+uufytAB4G8HEAwwAeAvC4iMTW3P8/B/BeANcAuAWthacrAN4HoA/AvwLwWyLy\n/bXL/gjAR9Zc924AF1X1n0TkAIAnAPxarf5PAHhUREbXXP+n4fVWZgCcXvugIhIB8BcA/hrAGIB/\nB+DzIvIGVf2PAH4DwCO1YcK/36Du3wHwO6raB+/1/eLGK6jqM7XbpwEMAvg6gD+pXfzv4PWivgvA\nfgALAP7Llq8UcFpEzonI/ysiI2t+/2YAJ9b8fKL2OyIiIiLqAQyy1Au+VOtBXBSRRQC/u8V1/zmA\nX1fVeVU9C+Czay57B4AIgN9W1aqq/hmAb6y5/GMAHlLVr6uqo6p/CKBcu13dZ1X1gqrOwwuFb9np\nk1HVJ1T1NfX8Pbxg+cO1i/8YwN0i0lf7+afhBW/AC7hPquqTquqq6t8AeBZe2K37A1V9SVVtVa1u\neOh3AEgD+JSqVlT1fwL4SwA/tc3SqwCuF5ERVc2p6j9c5fqfBZAF8Cu1n/81gF+p9aKWAfyfAH5C\nRBpNgZiF15t+BMAPwAvmn19zeRrA0pqflwGkOU+WiIiIqDcwyFIv+HFVHaj/AzC1xXX3Azi75ufT\nGy47r6ra5PIjAB7YEJoP1W5Xd2nNfxfgBaodEZG7ROQfRGS+9hh3AxgBAFW9AOCrAD4oIgMA7sJq\ngDsC4Cc31HcHgH1r7n7tc99oP4CzG+bNngZwYJul/xyAGwF8R0S+ISLv2+I5fhzAnQA+vObxjgD4\nH2tqfxmAA2+BqXVqQfnZWiC/DODfAni3iGRqV8nB69Gu6weQ2/DeEhEREVFIcbEn2msuwgufL9V+\nPrzhsgMiImsCz2EAr9X++yy83txf71RxtWHKjwL4GQB/rqpVEfkSgLU9iX8I4H+H9/l9pjYHtF7f\nf1PVn9/iIbYKchcAHBIRY024PAzgle3UrqrfA/BTImIAuBfAn4nI8MbricgPA/i/AdyhqstrLjoL\n4KOq+tXtPN7Gh6/9f/3k3EvwFnr6x9rPt2L1PSciIiKikGOPLO01XwTwyyIyKCIH4c3LrHsGgA3g\nvtoiSvcCePuay/8fAP9aRH5IPCkRmVzTC7hTIiLxtf8ARAHEAMwAsEXkLgDv3nC7LwH4fgC/AG/O\nbN0fA3i/iLxHRMzafd5Ze57b8XV4vci/VHv+dwJ4P4D/vs0n8xERGa2F4MXar90N1zkE7z34GVXd\nGJB/D8Cvi8iR2nVHReQDTR7rh0TkDbUFoobhDVN+SlXrw4n/CMD9InKgNnf4AQB/sJ3nQURERETB\nxyBLe82vwhsu+zq8uaf1+aVQ1Qq8nsSfBTAP4EMAHltz+bMAfh7Af4a3ENGr2N1KuLcDKDb4dx+8\nsLcA4MMAHl97I1Utwuu1vWZDfWcBfADANLwgfBbA/4Ftfs5rz//98IYrz8Kba/wzqvqdbT6f9wJ4\nSURy8BZ++he1Wtf6UXhDhf9szcrF9Z7S36k9178WkSyAfwDwQ00e61oAX4E3x/ZFeHOV187lfQje\nHOUXav/+svY7IiIiIuoBwiljROEjIv8BwI2q+pGrXpmIiIiIqMdwjixRyNT2Vv05eCsWExERERHt\nORxaTBQiIvLz8IYMf1lV/z+/6yEiIiIi8gOHFhMREREREVGosEeWiIiIiIiIQoVBloiIiIiIiEIl\nVIs9RZL9Gu8fW/e7Q+NVuN9dbHIL6hXnB0b9LqFlBxZnOnbfYX5dWnGDXkFhSfwug3rId0tLs6q6\ntz5IREREPSBUc2Qz+27QH/iXv9Pwst944ne7XA11U/z4vbj/wQm/y9ixJ93P4rkvd/Z80fTkVEfv\nP0j4Oad2e+eLT3xTVd/mdx1ERES0Mz0ztHh6cgq3PXyL32VQh5SOPeZ3CTv2G0/8bsdDLAB85hOX\nOv4YQbBXnicRERERXV3PBFkAOPboHXuqd2qvCVNvXDdrDWPIb8VeeZ5EREREdHU9FWTrGGZ7Vxh6\n5fwI3GEK+UREREREu9WTQRbwwmz8+L1+l0FtFvReOT8DZRhCfquedD/rdwlEREREFCA9G2QB4P4H\nJ9g724OC2vvod11BD/m70Y25xkREREQUHj0dZOsYZnuP36Fxo6D0GAaljnbq5Z5mIiIiImrNngiy\nAIca96Jb7wnG/sHHP/h0YHoMg1JHO/VyTzMRERERtWbPBFmAQ417zYc+/gW/S8BTn0rgmY8+73cZ\n6wStt5qIiIiIqN32VJCtY5jtHX6Htq/d/GlfH7/X+f3+EhEREVEw7ckgC3hh9vYXHvC7DGoDv8JO\nkEPW8Q8+7XcJRIE2PTnFk5pEREQhtmeDLADc+ckiD2R6RLfnywY5xAII3HDnVgT9Nabw4vc+ERFR\n+O3pIFvHg5rw6+Z82bAErKc+lfC7BKJAYS8sERFR72CQreFQ4/DrRsAMS4gFwj1/l1vuULsxwBIR\nEfUWBtk1ONQ4/DoZNMO4R2tYe2W55Q61S/z4vfxeJyIi6kEMsg3woCfcOhE4P/OJS6HcozXMvbJE\nuzU9OYX7H5zwuwwiIiLqAAbZJqYnp3Dbw7f4XQa14LkvW21ftZc9hN0Txp5vCpZHHvowT0gSERH1\nOAbZLRx79A4eDIXUMx99vm3DasM0L7aRsNUfxp5vCo7pySmceHzA7zKIiIiowxhkt4ELQYXT127+\n9K4XDQpbCAy7sM7pJf9xRWIiIqK9hUF2m7gQVDiVjj3WcpjtpRAbloDIOb3UCn43ExER7T0MsjvE\nA6bwKR17bMehtJdCLMCASL3pLXfZ/E4mIiLaoxhkW8ChxuG03XDaayE2LPi6005MT07hbuM+v8sg\nIiIinzDItohDjcNpq7D01KcSPR2mevm50d7C714iIiLi8qC7ND05xYAQMs3er6890eVCaMVTn0rw\n9aerYoAlIiKiOvbItgH3nKWwCOqiT5zDS1vhXFgiIiLaiEG2TbjnLIVBLwVGVcXiQhUnv1fC914u\n4uypMkol1++yqM04F5aIiIga8T3IiogpIv8kIn/pdy3twDBLtDOtDs2fm7Fx5aKNakXhukAh7+LM\nyTLKDLM94baHb+H3KRERETXle5AF8AsAXva7iHaanpzCW+6y/S6DqKFemNPtuor5WRuq63+v6gVc\nCrfpySkce/QOv8sgIiKiAPM1yIrIQQCTAP6rn3V0wt3GfexNILqKVufsVqsKSOPLSkX2yIYZvzeJ\niIhoO/zukf1tAL8EoGePPHlQRtRcq3N2LUsAbXxZJNok4VKgTU9O8fuSiIiIts23ICsi7wNwRVW/\neZXrfUxEnhWRZ6uFpS5V114cahxuCuBMch+Oj74dfz/yNlyKDftd0q595hOX/C4Bxz/4dMu3NU1B\npt+EbMisIsDwKHcVCxsGWCIiItopP4/43gngHhG5G0AcQJ+I/LGqfmTtlVT1cwA+BwCZfTc06YMJ\nvruN+4DJ3pif2E3Fgotc1oZhCPr6TUSi3T33ogD+buwdOJ3aD9uIAOri1cwR3Lr4Hbxt4aWu1tJO\npWOPAT6Hh2c++vyubj++z0K55KJc8r4WRICRMQvJlNmO8qgLbnv4Fs6FJSIiopb41iOrqr+sqgdV\n9SiAfwHgf24Msb2IPQ/bo6q4dL6Cs6fKmJ91MHvFxuuvlrG02N2e7QvxsdUQCwBiwDYsPDfwJmSt\nZFdr6SXiunBdha5ZrclxFEsLNhbmbJTLV59tcOm8jUp59fb1hZ7samjPd+0pXNCJiIiIdoNj8Hww\nPTmFJ93P4rkv8+VvppB3sbzkrFuVVhW4fKGKdMaEaW6eB2nbiqV5G8Wii1hcMDAUQSTSeL6kqqJU\ndKEKxBMGDKPx9U6l9sOWzT18AsXZ5D7ctPxaa08wAJ50P9v1/TnFdfH9f/+/cPOzz+J7CkQigrF9\nERgGcO50ZfWKl4H+QRNjExHIxvHDAKoVF7mss2nVYleBhfkqRsejHX4m1Cr2whIREVE7BCJJqepT\nAJ7yuYyu4lDjrWWXNocUAIAA+ZyDvv71f7qViovTJ8tQ1wu8+RywMO/g8NEY4on1Aw9KRRfnzpTh\nut7CtwpgYn9k030CQMS1IdBN6woJFJYb7nnPz33Z8tYM76K3/+3f4boXv73y3larigtnvQC78f1e\nWnCQTptIpAwU8t5Jh1TKgGEKymWFyObbQIFSUWv3pw1DMPlnenIKeNTvKoiIiKgX+L1q8Z7HocZN\nNMkfAkAaXDhzqQrXWR9s1AUuXaisu57rKs6eKsOxvctdt3a981VUKpuHs96YOwWjQaJ2YWC0NLej\np7TXRcplXP/iS4jY608AqDYIpLXfz83aePW7JVw8V8HF8xW8+t0SlpdsRKPS+EQHvAD76neKeOXb\nJZx8xbs++ev2Fx7gdx0RERG1FYNsAHDbic36BjavSAt44SaV3vxnm883nlNZLilcdzXx5HONr1eM\nJXGhEN/U8zpQzeGO2W/CdG1YbnU1dani0UPvxTNDtzbbBSYUurl6cTKXg2vs7CunWHDXnXBQ9U46\niAEkEkbDv5FiQeE43n9Xq4qL56pYmKu24RlQK6Ynp3DnJ4t+l0FEREQ9hkE2QBhmVyWTJgaHvDC7\n9t/+Q1EYDebHNpniCtRuV+c4uq4nr5DK4Nl3vR//+KP34m/f8n78yeFJXN6wvc4bs6fw06cfR9yp\nAFBABI5pwTFMfLv/eryeOrj7J+yT0rHHuvZYub4+WMbOYn+zkxnLiw4OHI4i09f4hMdGVy7ZqFZ7\ndrvqwOJ3GhEREXUKg2zAcM/ZVaMTURy9LoaR8QjGJiK47sY40pnGW6v0D1qbA42gFnRWL0imVv/k\nXRE89867kMsMwjUtOKaFbCSNv5h4F167IFhetFdW1S2ZMRTNGCDrPzK2YeGF/hva84R7nBOJYDSz\nOZyKACPj698/ESCyxfBh11WUyy6KxSZzqRtYmOPnqls4yoSIiIg6LRCLPdF6XAhqVTRmYCh29fMt\nI6MWKmUX+Zy7sghQLC4Y3xdZuY66inJREYsLyiXF/PhBOKYFbBju6kJwcugaOCdfwtKig4NHoqhI\nBIYqnAaPXZZIg99SI8OjFkwLmJ914NiKeMLA2EQE8YSBvn4Ty4sOKhXvfaxWGidUESAWN3Dm9UrD\ny5up7zdLncUAS0RERN3AHtkA4wHh9okhOHA4hqPXxTBxIIrD18Zw5Nr4yjY9lbKD114p4cL5CkpF\nbxXiSiIJbTBnUy0LpUQaqt4czXzOxVBlEdJgNqzh2Bh4/SQuX6ys2xM1TJ50P9u1xxERDA55ves3\n3pTA4WtWV5WORAwMDlnILbtwmnSeSq2XfbmF/YRjca5g3Enx4/fyO4uIiIi6hkE24HhguDPRmIFM\nn4l4fPVPu1pxceq1ircAUD1rKpCZn2m4OLJZrWJg/rJ3NQVyWQcmFO+a+UeYru2tPATAsKuIFXI4\n8Pp3sLTgoNBkwamg69Z+xtt5nOxy86HC8YRg/6EoJg5EUCzs7KSBYQCDw+w575TpySnc/+CE32UQ\nERHRHsKhxSFQD7Mcarwz9dWKz52pNAxHmaV5DM5ewOLoftiG91EwHBvxQhYjF0+vXM+sTcu9Nn8e\nsVe+gn+KXYdSPIWhK+cwce41mI4DhbfvaSrdeA7vXveZT1xC6YmrX8+2tWmQTaaMpnOk19q4v2wk\nIrCiwJmTJdg2EI0KRici27ov2lr8+L0MsEREROQLBtkQmZ6cYphtYHnJxtyMDdtWxGIGhoZNLC05\nyC1fvYf05m8eR+ntt+DlzLUo24Kxcydx6NUXV/aOFQH6B7yPiW0r9MI8bsjNNryvsA4tBoBb71nE\niccHOnb/210duT7MuJHlJQcjYwoRQSpjIrvUaMYycOBwFPGEgULewcVzVVSriuqa3XcqFcWFsxXs\nPxRlmN2F6ckp4EG/qyAiIqK9ikOLQ4ZDjVepKs68XsLFc1VUygrX8ea0nj9b3VaIBYD+PgO3LL2C\nD537Cn7y5BO4/tXnEFEbhuGF2In9EURjBhxHcfq1UtN9aEWAvoHwnhf60Me/0LH7fupTiW1fN5ky\nEI01vsxxVvcBHh2zNi4gDQAYHjORSpsQABfPVZv27qoCM5e5t2wrHnnow/weIiIiIt+F98h7D+NQ\nY8/8jL3juZJrGSYwMrY6bzKRNHDdG+IoFlyoej8btQ1qlxZsb45tAyJAKm0gneF5oUa+dvOnt31d\nEUGmz8LczObFnNQFSkUX6YyJSNTA0etimL1io5h3YFqC4ZEIMv1eD2s+724aYrxRs1WRqbnpySng\ncb+rICIiImKQDbW9PtR4caH1fUFFANcBXv1OCcmUgfF9Xs+riCCZ2jzctJB3m4YiywJyWRevfqeE\n/gETI+ORlQC8lq4MV+bquVuJRqVhCBXD21t29XoG9h+MNrwPVYV7lZxqRfg+bBd7YImIiCho2IUU\ncnv5ANPd4SLB8YQglTZgRdaHpELexemTZdh28+QTjTYPPfX5l64LLC44OH9m/f6mtq04f7aMV75d\nwivfLuHM62VUyuFc4XinWjnRku4zN27tCwAwalvvbKVUdHH6pDfcvMFuSeuMjPE83nbs5e8YIiIi\nCi4G2R4wPTmFRx76sN9ldF1qBwv1mCZw8EgMI2ORhnuUqnrDh5sZGLKwnY7U+t6z5ZJb+1lx9lR5\n3ZzdYsHF6dfLcJxgDW09/sGn/S4BAGAywEG4AAAgAElEQVQYgsPXxhBPrL7g8YTg8DWxhj3dddWK\nizOvl1EqXv11jUQFff0WVBW5rIOL5yu4dKGCUnFvnGDYLoZYIiIiCip2SfSIE48P4MQeG2o8Om6h\nkHM2zV3NZASRmIGlBW9P0lTGxOi4BdMU5CtOw2GrqtgyxERjBg4cjuLS+crK44k06RUWoFJWxOJe\nb2+1ujlYqQssL9qB2tv0mY8+D0ze0bb7O/7Bp/HMNrbcaSQaNXDk2vhK2DfNrc8ieHsFl7ecE7tW\nOm1AVXHhXAX57Oqw8eVFB8OjFoZHg/O++IEBloiIiIKOQbbH7KV5s5GIgWuuj2NxwUax4MKKAIPD\nEcRi3kCD0fHNt4lGjYZhR2TrrV8AIJU2ce2NcdhVhRiChTkb83P25iGsCkRjAlVFdtmBNgi7qkC5\nHKwe2XZ75qPP7/o+rhZggXqvd2XbQ80NAxgctlDIu8jn1s99VgXmZmz0D1h7cg7tW+6ycbdxn99l\nEBEREV0Vg2wPmp6cwq33LHZ0S5WgMC3ZUe9ZPGEgnhCUirouwIgA/YNX/ziIyMqCQ4NDFhbn7XWL\nCtUDcSQiOP1aGZUmK+NuJziH2Wc+cQmlFntjd6pUdGFfZZi2YXi954mkt7BXJGpgbrbS8CQDBMjn\nnG39PfQS9sISERFRmPTukfQed+LxAR6YNnHwcAx9A+bKnNdkysCRa2OwrJ31wFkRb95mIul9jLy9\nZE0cPBLF7EwVlYo2HepqmkBf//bn+HbLrfcs7ur2VqWKQ9/7HmZ+4E+7NgfYtoGt3rl0xsANb0rg\n6PXevNv5WRvZZQcizetrtEdtr3rLXTa/K4iIiCh09laXwx7Ui0ON3VoX6FYL/2zFMAUT+6OY2O8N\nS93NdjixuIHD18Q2ba2TXXKahth02sD4/mjL9QfNke98F7c88w9ILS3DqlZhpaO4lK9AFRjfH0H/\nQGe/ZhLJxsPFASCZEuw/GMXSoo3LF6or18suO4jGmr/+6R0sJBZmDLBEREQUVgyye8D05JQ31PPY\nY36XsiuVsotL5yso1lalTaYMTByIIrKLuYzt2tN12/cjwMSBKMwd9v52y4c+/gWc2EG4edM3vom3\n/q+nYToOXn/j9+P8NW+Ea5qI53O44YWvAxfOI5k0EIl2rovTsgSDwxYW5uyVoCri9ZgfOByDKtaF\nWMCbC1spKzL9hreitHi9ugrg4OHeOcmwFYZYIiIiCjMG2T3i/gcngBD3zrqO4szr5XUrFBfyLs6c\nLOHaG+NtC6Tt0jdgYmFuc69sPC6BDbE7Zdg23vr0VxGxbXz3lttw+eC1cC1vvnIp3YeXfvAYbn3m\nr7C8tIDh0c6O1R0djyCRMLAwb8NxFJk+E4NDFgxDkM81X6nadYBrb4yjkHcg4u0z3OshlgGWiIiI\nesEemglGQHj3nF1edhquSuu4QC4bvL0/h0cjiMVkZa6lGN682H0Ho+uu5zqKpUUbC3M2yuXgPY+t\npJeXAQC2FcGlQ9ethNg61zRx+oZbVoaCd7yePhOHjsZw9Lo4hkcjMGorHotsXli6TgyvR7ev30Km\nz2SIJSIiIgoJ9sjuQWHcc7ZadhvOg1TX20MUCNacRsMQHL42hkLeRanoIhIVpDPrg1Ih7+DcmYr3\ngwK4DPQPmBjbFwlcD3MjxVQK4rooJzMwXBfOxrdABIVMP9IZf79mEkkDhgAbthuGCDCwR1Ymvu3h\nW3Ds0fbtEUxERETkN/bI7mFh6p2JJQw0ynZieAsuBZE3VNXE8GgEff3WuhCrqjh/1tv+RV1vmKsq\nsLToIJ8LR89sNRbD6296I6xKCWo0eA9cF4P5ecQT/oZyEcHBIzEYpvf3IoYXYgeHLaTSwToB0gnT\nk1MMsURERNRzgpkAqGumJ6cQP36v32VcVSZjwtqwqJMIEI0Kkqnw/RkXC27D8a6qwNKC3f2CdkoV\nVsXBs3cew6kbr8eBk9+GYVfXXcVUF+8svRyI3uV4wsD1N8ax/2AU4/siuOaGOEbHt7//cBjd9vAt\noTpZRURERLQT4UsA1Hb3PzgR+ANeMQRHromhf9CEYXrzTQcGTRw+GgtEUNqpZtvFXO2ybjj+wae3\nvDxStrH/5CL2vb6I8bM5zO5/M0YunsG13/4mYsU8DMfG8NJl3HPxOIary12q+urE8IZ39w9Yu1rp\nOgzYC0tERES9bm9MEKNtCfqes6a1uv9r2CWSBpqtgZTuC+75JXEV46eXYbiKehSsxhJ4/rZ34x1/\n+2c4eOo73vUEGLwhDvR4YAya2194AHd+suh3GUREREQdF9wjZvJFWIYaN1Iuu5i9UsWVSxUUCw7U\n767NLRiGIJNp/PHLB3AV5rpktgLBaoitUzFw5cC1635XzG9cXok6aXpyiiGWiIiI9gwGWdokDEON\nN1qYr+L0a2XMzdhYmHNw9lQFly9UAx1mi4XGgTWXc7u2ZU0jz3z0+aaXmbYLaVC2a1kox5MrP4tg\nZfubIMjnHJw+WcL3Xi7i9Gsl5HO9E7Ljx+8N3eeViIiIaLcYZKmpsBwc27Zi5pK9bm6pKrC85DQN\ni0HQaF9cAID6P0+2mXLCgjbIp2a1iv75yys/iwCpdDC+XnJZB+fPVFAqKlwXKJUU589UkMuGP8xO\nT07h/gcn/C6DiIiIqOuCcaRJgTU9OYXbX3jA7zK2lM862DTWFV4YzC4FN6ykMo23fonGBGaAejPX\nKicslJIRuGvKMx0bqew8RmbPQwzAsoCDAVqEa+ZSddOJAVXgyqVq4xuERFhONBERERF1Ahd7oqu6\n85NFIMALQW2VlyTAp2pGxy0Ucg5cd7UHVgxgYn/U38K2IoKZgxmkF0p4QzQP57Ul3Jg9hZsWX0H1\nUBSGIYgnJDAhFgAqlcbd29WKQlUDVet2MMASERERMcjSDgR1VeNUxgQubO5dEwH6+oP7Jx6JGLjm\n+jgWF2wUCy6iMcHgkIVINMDpGwBEkBuI48e//Acw65vhChBNN+5h9otdVSwt2l5vfYMsa1pgiCUi\nIiIKqeAe5VMgTU9O4alPJfC1mz/tdykrTFOw72AEF8+tD7PDoxbiiWCHQtMSDI9G/C5j26yKg+GL\nOSSLFfz+tT+Bw4ULeNfMs0g4Zb9LAwA4thdeC3kX+Vzz+dEiwPBIeL7+4sfv5VxYIiIiojXCcyRH\ngRHEocaZPgvJG03ksg5UvYWGAt+zGTLiuJg4tQTDVbgwAAHOJPfhz/f/CD509suNpil3Vbnk4szr\nZeg2FssaHrUwMBSOr7/pySngQb+rICIiIgoWHulTy4I2zNG0BP2DXkBhiG2/1FIZouv3kFUxUbAS\nOJ8Y962uuovnK+vmGzcjAmT6zcAPK37koQ8H7jNGREREFBQ82qddCcOqxrQ7kXIZ173wIsbPXYLR\nICS6ECxFMt0vbG0NjqJc2v6eRflscLdlArzP1YnHB/wug4iIiCiwwjG2jgItiEONqT1Gz53HP/uz\nRwEFruy/Bq+9+QfhWuvn9AoUQ5VFnyr0lEo7C6ZB7YxlDywRERHR9rBHltqGB+HtVSm7mJupYm62\nikq5uz2Iv/HE70JcF8e+9OdY7h/D3PhhDMxdRqRaAdzVvXlN18FQZQkTpdmu1rfRzOWd7Qmb7gvW\nCssAPz9EREREO8EeWWqr6ckpHP/g03jmo8/7Xco6qopSUQEo4gkj8PMj52aqmJuxV+Z7zl2xMTJm\nYWikeyscj527iG/98D1wDROAQA3B6LmTUMPE/L5DMEVxQ/YU3j7/gq8LPa2+t82tfbsnDkRgWcF5\n/29/4QFvVAMRERERbRuDLLXdsUfvACbvCMxQ42LBxfmzZWi9U1OA/QejSAVs39M6ryfWXrdokSow\ne8VGus9EtAsLWSmAaDGKahSArD7ezIFr8MZ/ehpve+WrOHQ01vE6tkNEIAZW3981DAO45oY48jkH\nAm/PYdMMToidnpwCGGKJiIiIdoxDi6ljgjBU0nUU506X4diA69b+OcD5MxXY1e0vDtRN2WVnXYh1\nRTAzcRinr/s+fM+YgNuF/s+FaD+ijrsuxAKAa0Vw4egb0T8QrJMAAwPmpnmvIsDAoAnLEvQPWOgb\nsIIXYomIiIioJeyRpY6anpzCk+5n8dyX/flTy2YdNIurS0s2hrs4VHfb1mStSiyOb90xiWo0Bse0\ncEYdvOgU8YHzf4e4W+lYCbaYkCb72Ggsgkx/sILs8JiFUslFsaArgTadMTAyFrz3lwGWiIiIaPfY\nI0sdd7dxn28H767TeF9RVcC1g9kjm8ms9i6+cvM7UEok4USigGHANiNYtNJ4evDWjtYwXF6ANDgF\nYDo2biqfCdQcY7uqOPN6BaWiekOMFUj3Gdh3MAoxglPnW+6yGWKJiIiI2oRBlrrGj4P4RMpoOhDX\ndhTapNfRT9GYgZExCxBgduIwYGzo/TQMvNZ3BE4Hg7gJxbErX4fl2jDUW6XYcqsYrC7jpuzJjj1u\nKy6cK6NSVqiuzpPNLbtYXnK2vmEXTU9O4W7jPr/LICIiIuoZHFpMXTU9OYXPfOISSsce68rjxeMG\nMn3mpnmnALC86AJaxb6D0a7UshNDIxGk+szmG56KgZmsYGKwczUcLVzAT5z9Cr7Tdx3yVhyHCpdw\nbe4cTHR3K6Ct2NXGKxarAgtzNvoH/P2Ku+3hW7zFz4iIiIiorRhkqevuf3ACmJzq2qrGEwcicBwX\n+dzmwJNddjBSdRGJBG9wQixqwHJt2ObmeZ6iLuaRxgRyHa2h387jh+aDtZXSWq7bvFfa9blDdnpy\nCnjU3xqIiIiIelXwjt5pz5ienMJtD9/S8ccREZTLzQNPuRS84cV1Y/mZJpN8gUEptOUx7KpieclG\nLutAtwiGQRSJCowm32LpPv++3jgXloiIiKiz2CNLvurGnrOqCsdudpkXhoLqHcsv4kupMbjm6kfV\nsG2MXj6NsYwD7HIrntkrVczP2oB49yQCHDwSQzwRjnNcIoKJA1FcOFtZyfsigGl6w7O7jQGWiIiI\nqDvCcbRKPa+TAUC1cadmXSwW3I/BaHkB7770NJLFLMR1YTg2Dl18Fe/JPrvrPVELeQfzs/bKIkmu\nCzgOcO50OZCLYDWTzpg4el0MA0MmUmlvoayj18dhWd09QcEQS0RERNQ9vvXIisghAH8EYByAAvic\nqv6OX/WQ/zq156wIYFpo2CsbiwW3N7buSOkyPnLhSVTFgqkOTCjQhs7GxfnNC2ABgKtAseAimQrW\nXrFbicYMjO/zZ9Gu2194AHd+sujLYxMRERHtVX52RdkAHlDVmwC8A8C/EZGbfKyHAqATe86KCEbH\nIpsWABYBRie6P/y0FQIgqrYXYtuk2UJJAq93lq5uenKKIZaIiIjIB74FWVW9qKrfqv13FsDLAA74\nVQ8FS7vDbP+ghX0Ho4hGBSJALC44cDiKVDo8vY7tluk3G+7uowokk8Edbh0Etz18C4cSExEREfko\nEIs9ichRAG8F8HV/K9m5SNlGZr6ESMVBKRlBdjAO12IIaId2DzXO9JnI9O3d4LpRX7+JpQUHpaK7\nbqGksX0WjF3Ov20X11VklxwUCy4iUUH/oNX1ua8bcVsdIiIiIv/5HmRFJA3vsPAXVXW5weUfA/Ax\nAIj1jXa5uq3F8xWMnstCtDb0s2Qjs1DCxWv64UQYmNrhbuM+YBJtX9VYVTE3Y2NpwYbrAqm0idFx\nC5Ho3jkJISI4dDSK3LKL7LID0/J6ruPxYLwGjq04fbIM21aoeiF7ftbGoaP+rKrMubBEREREweHr\nEauIROCF2M+r6mONrqOqn1PVt6nq2yLJ/u4WuBVVDF/Mw9DVDVAMBQxXMTDTnv09aVW7h3FeOFvB\n/KwN2/bmg2aXHZw+WYZjh2e13nYQEWT6Tew/FMX4vmhgQiwAzM5UUa3qSm+xqvdeXTxf6XotoZsL\nqwrDcbderrvGsF0YNidFExERUbj4uWqxAPh9AC+r6mf8qqNVhqMwnc0HfwIgka+2+bFc9M0WkcpW\noAJkB+LIDsXRcIJjD5uenGpLz2yl7CKfczcd47susLhgY3g0HAtAtapYcLG8aMNxFYYA1aoiGjUw\nOGwhGqCtiLLLTsPfV8qKatVFJNL5WuPH78X9D050/HG2y6o4iJZsOKaBctJq+B2QXihiYKYIw1Wo\nIVgciiM7nNh0XaviYORCFtGy9zpXIyZm96dRjVuAKlLLFWTmvfspZKJYHk7ANYPz90FERER7m59D\ni98J4KcBvCAiz9V+N62qT/pY07ap0TxEultctlPiKiZOLcGsuivd5wOzBcSLVcwc7Nv2/ZhVB7Gi\nDdc0UGpyABwG9Z7Z3QTaclkhsrmzSmvbzvSymctVzM9u3oeokHewtOgEagEs71xX4x7Fi+cqOHQ0\nVrtOZ0xPTgEPduzud0YVQ5fySC2XV4aAOIaBy4czcKKrX+OpxRIGrxRg1Oc8u4qBuSJgCLJDiXX3\nN356CaajKyNKIhUHE2eWce66AQzOFJBaKq/cT2a+hGS2gotHB6ABmT9NREREe5tvQVZVn8bqqNzQ\nUUNQSEWQyFXXjc92BVgejLftcZLLZZi2u+4xDAXi+SoiJdvrPYHXa2s4CjtirA+p6g11ziyUvJ9F\n4IpieTCJaMWGbRnIDcThRIMRXrZrN72zkYg0HXEZhn1lASCfczBzqYpyWWGawNCIhcFha8tgVym7\n60KsK4Jc/zDEdZFengcUuHShimtvMDoaELdrYNDE3Izd8L0qFRXLiw76BzvzFRa0FYlTS2WklmvB\nciWkujhwcgn5TBQLEykogMGZ1RBbZyjQP1dEdnB1FEciV4Hh6rovYIE3dzyzUEJ6qQxZcz8GANgu\n0kul9YGYiIiIyCe+L/YUZnP70hg7l0W0ZENFYKgi3x9Dro1BNl6objowrYuWbDgRA8MXckgUqlB4\nAXtuPIViX8zrxbmYQ3q5snrAqt7B6+BsAfX+rr75EmYOplFKxzY/iCoMV71e5gCEm7VaDbPxhIFY\nXFAqrn9hxQAGhoL/kSgWHJw/U1kJeI4DzF7xFq0aGWs+LHp5aXWo7tzoAbz8A/8bVLz31aqUcfM/\n/h0y2QXYNhAJwOjqoWELuayz6X0CvN7z5aX2B9mgBVgAKyejNn4P1D+NqWwFiaz3GW/2CTWc9TdO\nLq8PqivXUyBatKGCTZfXT6AxyBIREVEQBP+oPcDUNHD5SD+ssgPLdlCJWW3feqcaNeEKNodZETiW\ngbGzy4iWnNWDWEcxcjGHXKEKq+ogkbc3Hdxu7IURAGPnclgetFHoi6IS94YepxZLXg+Po1ADWB5K\nYKnBXDs/tTrU+OCRGC5fqCCbdQEFojHBxP5oKFYtnr2yuZdS1VvRd2jEgtFkaHul7A2bLsWTeOkH\nj8G1Vj/+jmnhudvfg9v/5oswAvISiCEYm4jgzKlKwxHG7f4z9CvEmlUHqaUyLNtFKRlBIRNd9+Ti\nhSpMp8nZLHifXwNbD29xrDUjNVSRzFUbXl8B2BEDkm9+GREREVEQMMi2gR0zYcc6MzQ3PxBH/9z6\n1VIVgGsAQxezsJzNB7CiQGax7P33Nh9HAPQtlJBZKKEaM5EdjGPocn7NXDugr1bH0kiy5efTKTvt\nnTVNwf5DMbiuAop1+6aqKvI5F4WcA9MS9A9YsCLBCe/lcvN5vLatiEYb1xpPGsguu7h06HqvJ3Yt\nEagYyB0+DNO83M5ydyWeMGAaXq/zWiJoW2+snws61bfwgnphNLVURt+8iUuH+4HaCYl07bO8la3+\nOl0BFsZWP7Nmdet54Jmlxo+nApSSEazshURERETkI55eDzjHMnD5cB+qEQOueAeT5bgJcbRhiAVW\ne1l3eqhZ79mJlB0MrgmxdYYCffPFbW3p4YdWetQMQ9aHWFdx9lQZF85WsDDvYG7GxsnvlZDPNV5B\n1w+xLVYWtqzm73p/vxf8KrEE1Nx84kXFQGI0tfsC20hEcOBIDIbhDf2ujYRG34CJdGb3X1/Tk1Nt\nDbGG7SK5XEY8X73650QVIxdy3rZd9dsrECk5GLmYw/jpJYydWYZZcVpeTMAFMHsgg0Lf6rSBrUaN\naG30x8bHqz+TkYs5HHx1AdFie1dmJyIiItop9siGQCURwYVrB2DaLlQEiVwFQ5fzLR/cKrYOuQaa\nH4OL6/3Teg5SRaxow6o4qMYtVOIWxHGRXiojUnFQjlso9MW2XOW5naYnp3DrPYv40Me/0NLtr1yu\noFhYffL11+HCuQquf0M8EIsgDY9ZKJ6qrHuPRIDB4ebDigHAtAT7DkYwO3sBlw9fD8daPxFWDOBA\nZaZTZbcskTBw3RviyGUduA6QTBm73ibokYc+jBOPD7SpQk/fbMEbPSEAIHAN4PKhPtixxl+z0bID\nafBBMwAks6vz2l25+me2GTtqoJiOrvudGoJ8X2x18ag1j9No3ixqj71ymaMYP7uMc9cNQrkdDxER\nEfmEQTYsROBEvPRo2W7TA87tavXA2DUFWjt2NRwXY2eWEams9lZWYiYiFReiCkOBlJQxMFvExaP9\nbZ8/3MyJxwdwooWFoCplF4vzjYdduo63NU8y5f/qzsmkiQOHo7hysYpKxVu1eHDYwtDI1T/Off0W\n3m7P4lJxAQvJITimdxvLreK63FkMVrOdLr8lhiHo62/P19X05BTweFvuakU8X0X/XHHNqsIKcYF9\np5dw4Wj/ui1y6rT57kLrPpuG1qYTbPj91T6/CqCQabCA2//P3p0HWZZfhZ3//u7y9jX3ysraq1st\n9a5utIAsJCGEURtsg7cxxmiwB4IOD3/YzNjuPxwxngkGOzBhCA/MeCYI443xInYJJAa1MKIlhJaW\nhHpTddeW+/by7e9uv9/8cXN7+d7LyszKrMrMOp+IptVvufe+LbnnnvM7B1gdjzPvuZoXH4JSVEbT\nlJbbu67H3b7xbN2nUTq8xnZCCCGEEPshgewJ5KVdjGofOJiNy5Mdkp2wb0dkQ9xkygminoxNZTSz\nuT5uaL4ZZ5W2PTfZiYPajdssAyrUlBebrEzmD3bAB7TfdbOV1d75qtt5neMRyAJkczaXHrIxx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T0bSaEXqXSoHt9pqF3ak6lmVpMkek4gs3WkFkKRbOFWiU0yd6ffBRGZ6r9yxDsIDichvWPy9t\nq4EzrUPp9CyEEEKIY0Ladx5Dxrbw15sVzV0oUl5skmoFGEvRzCXI1eO5mdspIHAUy2fzRI4lJZV7\n4GVdViZzDM81ei8MmN5OuBBnw8sLDZxAY5RCGcPPP/pD/Pir/wXL6l1XC2A0zM8ErCyGjE+6ZHOH\n/9k0G5rapdH1TOzOYzZ8auL9pKMOH5n/Y0b9yqHvH8D3NNO3fMIgnlNrgPEzLsXS4D8zLzz3PHz8\n4PtsF5JM5xMk23Gm0Es7EsAOog0JTw8MUp1QEyZstG3Ryrqkm0HX70KruGpECCGEEOI4kMvrx1yY\ntFk6V+D224aZfmiItbFM3yZFBvBTDn7alSB2H1r5BK1cAr0eeG1k9qrD/TN6iri7tGXA1gbLQLIT\n8m8u/WVefO6/Y/7clYGZzyAwzNzy8bzDbwDluopMvYLSfZrxKEVk2TTcLL8z+QECdfjXr4wx3L7h\nEfgGY0DrOIBfmA3oDGh4dZAsbF9K4WVcvIwrQewuEt7gsmAFRNuyrSuTedpZF7O+zEErqIxmaOdP\n1vgmIYQQQpxekpE9YYxtUS8myVW9rmyJUVAdydy/AzuplGLlbJ56OyTVCtCWolVIYJSiuG3d4AZD\n/5mlAI2Gw2tPfQehm+Dc9Vf7lhkbA5WVkInJww0ISsMO566/ytyFhzHW4AsZWineyk3xtvqNQ91/\nu6WJ+sSrxsDaavfrHTQbVhwtYymMou+FsMhRmG3r6Y2lWJ4qYIUaO9IEri3r7YUQQghxrEhG9gSq\njGepDqeJ1tfPekmbxXMFgpRclzgoP+3EzbbKKbRtYSzFykR2M1O7ka3t1+m4i7J447F3w48+0782\nGfD9w1+tmkpZnM22eeqlT5GtrsQRZJ9IOlI2bXv3jr8HEUUDXy5huHUc+2noJA7IGJLNgNxah2Qr\n2PweBAmb0LV6KgYMDGyMpR2LIOlIECuEEEKIY0cin5NIKWojGWqSgT1SrWIKP+2SrXpY2tDOuaRr\nHvmqPzBogzig+8zS4+TeP8mTn/80Cd/buk9BOm1hjMEYg2UdzrUkYwxrqxGFaJlv+8PfZnnsLK88\n+wG00z1L1jaaifbSoexzu3TG6puBVgpyeZtv/8Y/4AP/qDfDLQ5ORTruXh5ovLRDJ+tiacP4rVrX\nvNcgabNwvoixFItTBcZv17DDrfR5bShNO5+8Hy9BCCGEEOLAJJAVYhdhwqY6unXBIEg4ZBsBan19\nbD8bQW6zUOaVZ97PU5///a37FDQbIavLW+sVh0dthkdd1F2s72y3dFcgObw4Q6GyTK08inbin7mj\nQybbC4x7KwfezyCOoygPO1RWQowBP5milS+RDxr8i7/+o/xzCWIPldsJGb9VQxmDMnGlQJC0CV0b\n14u6LrS4XkRpsUllIkeUsJm9XCLRCbEjg5dy0NKJWAghhBAnkASyAsePyNR9DPF8VWkWNVjkWsxe\nKpGvdMjUPVx/cBdYlMXayCSV4RFGqJMJAtotjdfpftjKUpw9Gxk7+LrZndlQBTzxhd9n9sLDLF68\nSjKheKT2Fo/Ur++aTb4bo+MuqYzF50aeYfrMVWwT4bsJhubbLJ91utZgirszMlvHWp8jDfG6V7cT\nkehEveN1DORqPpWJjQcr/LSLEEIIIcRJJoHsA66w3KK40o6zOkBpuUVlLBPP4QRSDZ/SUgs30AQJ\ni7XRDJ3sg925VDsW1dEM1dEMmZpHebGFHfYPaLWl+MSP/DCRYzEyO8tz//5X+25zZSkik43IZA92\nEaFfaa9lNOduvsa3BW9S2GUEzn512ppWU+M4kCvYWNsC1Lem3s7s0BW0ZaOxUQZSrYCh+SYrkzLX\n+DDYQYTT5wKKBYNnBR/hDGMhhBBCiPtBasoeYK4XUlxpY63PTLWIszflxRZ2EJGueYzO1El6EZY2\nJDsRo9N1Uo3+81IfRK1CkpkrJRqFRN8gInIsIjsOOYorq4MDDWDmlo/WBws4LEsxcdbtmj6jrDjA\nzRcPJ8NujGHmtset6x5LCwHzcwFvvt6h095ab/mN4sOEVnfQbBnI1j0Jpg6DMeTWvIFZdaN6g1kD\ntLOSgRVCCCHE6SKB7AMsU/P7juJQBjJ1n/JSq2cd6EagK7ZRirWxLJGt0OsRxkaX45Uzuc3Zpmsj\nI7tuRmt4640O9VqfWbB7UCg6XLyaZHjEoVS2mZxKMHUhcVdrb7errUU0altrcY2Oj3nmtodZv7Ge\nHJB1NaAOGKSLLdmqR2G13T/7r6BeSqKtre+hVhDZisqArsRCCCGEECeVlBaLvjI1DyfoMxgUcP09\nBlrGkGrFTY066dM9wkM7FrOXS+TWOqRaIUHCplFOESa2sqErE+OsDQ9RWlkdmFGLIpib9tGTLsUD\nlAMnEhYj44d/fcpow+J80Pe+KISffddfozI2yuh0jXQj6Hl9oWthbLludrc2Kih2MoCftFkbzVId\nyZCrerhehJ+yaRZSGPv0/vaEEEII8WCSQPYB1iokKK70dpNVQLIToRXYg5JoxsAumb5U02d0psFW\noaNiaTJHJ3d619ca26I+nKE+POABSvHJH/5bfORX/xMjCwuDy0MNLC+GBwpkj8rqcojuf10D33FR\nJr6zMpYl1arCeiMiQ1zuujIh62MPgxMO+BCAhXMFsBQGRX0ofQ+PSgghhBDi3pMUyQMsSDq7rtkM\nElbf+42CZDvsc0/MijSj03FXVUuz/o9hdKaOtcuJ+IMgTLh88m//ELMXLxC4g9cthoHZLNc9DtYq\ng7PwoeNQGR2N/3fCZvZSkVo5RSdl0ygmmb9YxHvA1miqyJBd61BYbpFsBYe2PthP9l/vHDnWqa54\nEEIIIYTY6fikfMR90c65ZAaUgmrbQtEn8FRxsDpIpja4GVS25m1li4zB9SKMpbpKcE89pfiDv/ID\nXHr1Nd77e5/GiXqDRNvmrte2GmOIQrAssA5QWroRSCulBgbVBnjpz38EY21dE4tcm7UHeE1mYn3G\nK5szXtt4aYfFc4Vdqxj2ojKWZex2rau8WCtYHcvc9baFEEIIIU4SCWQfcGtjWdJ9SkFXJ7I4fkSq\nHfasyVMGvF3mUFraDGwiZa03/MlUOwwttOIA2hiCpM3S2fwDM8PWWBZvPfoODPDtn/p9nHArw60U\nDI/d3U+zUY9YmPXZiJFzeZuJSXdPAW0QaOamfdqt9c8qa5HOWjRqvRcvqkNDTD909a6O9VQxhpGZ\n+ub3HOLvfbIdkq907rrk18u4LJ4rxCOx/IjQtVkbTT/wI7GEEEII8eCR0uIH3PZSUC9l0ywkmL9Q\npJNN0CymCF17swMqxNmf6lAa7Qz+6nSyLqZPvGRUPAbE7YQMzzextYnLjw0kOhFjt2sP3IiW64++\ngz/58IdoZTMYpeik07z0XR/io9P/EACtDZWVgFvXPaZvejTqd2601WlrZm/7hGH8dhoTB7Yz03ce\nm9Ruhbz1hrcZxAK0mppWQ5MZS2yWQ4e2jZ9I8Ed/4aMHfOWniDY4foSKNI6vsfuUz1sGclXvUHbn\nZVwWLhSZfmiI+YtFCWKFEEII8UCSjKwYWApqLMX8xSL5SptM3UdbFvVyinZu9/WOfsqhlU+Qqfub\n2VytoJVL4KcchueaPRlbBTiBJuFF+KkH62t57YnHufb4Y1hRhI5rivngP+5gfc+P8Xd/8RfwPbMZ\n37eaPuVhh9HxwZ/B6nLYcz3AGGg3NYGvcRP9L0KEgebW9f6diT3L4aXH34e2LcZmZqkODXHticfo\nZB/cEmKMIb/SprStYVo76/YOchVCCCGEEIfuwYoYxL4ZS1EbzlAbzuzreStncrTyAblqBww0Skna\nuQQohR1G/Tv2KvXgNoNSCu10/xwvvvY6jcjBMSHz564we+FhjGUxcfsa7wtvkXT6R0ye1z9rqxQE\ngcEdkMCbuT04Y+uGIYW1Cn/6XR/iW089ubfXdMzZQYQy8Xrwg6wvzVY9SjvG4WQa/S8EaKBRTB7w\nSIUQQgghxE4SyIqjoRTtfIJ2vjdqamcTJPusvcWYBy4bu5tzb76FGwT82TMfYHX8LNqJs7Bv5Uqs\ndS7zlxc+S7++0pa1sdq5m9aQTPbPxurI0GkPTiVqpVgdHz/YCzlmHD9iZKa+OQ9Z2xbLZ3L77qzc\nb6Zrv3DYAEHSpl5OHeyAhRBCCCFED1kjK+65RilJ5Fg9a29rd1h7+6BpZzJUS8NdQSyAdhzW0mWm\nMxM9z9Ha4HX6B6RuAmynf+ZR71IOawA/meDG2x7e1/EfS8YwfqtGwouwTLx21Qk1Y9M17ODO64+3\ns6O91xC38gnpKiyEEEIIcYgk/SXuOWNb8drb1U689tZW1IZScenxHmSqHUpLbexI4ycdKhPZU5nJ\nfeOpJ8nUA4zqDe5D22UmNcb51lzX7UFgoH9CtmvdrAEiZWEbHa9PdhRuQhH43U80QGRb/PaP/DDR\nLnNvT4pUK8DSujdzaiC35lEd3XsJvZeySbXC/mXy2zeteGC6cQshhBBC3Cun7+xfnAjatqiOZvYV\nOADkl5qUVzqbwUOyEzJxo8rchQLBLiOBTqK10RGuP/IwltYYugMhV4Vko3bPcxxHDWw2lFhv8vRW\n9iyfH36appPB1QFPrr3G02uv8pt/9W/w3f/5v2JFEbbWaKUIXZff+u//Nq1i8dBf3/1gB7rv+2MB\nzj4zsmtjWcZvVsFslRQbusuL43FWKs7ICiGEEEKIQyOBrDg5jOkKYmEr+Tgy22DuSnnP20m2QzL1\nuLlRs5g8thndW49cYuraKkob1LZX7uHwn9/zIZ741Btdj7dtRb5oU69GXRlYpWB41OF2epzPjL2H\nyIpfr28n+Nrkk/z+w++mOprhN3/0Y7zt5ZcprKyyODXFt554jCB1etZ2Dpp/rBX7HmPjpxzmLxQp\nLrdIdiKChEUrn4zXzkZx07LIsVg6m8dYUlYshBBCCHGYjufZuxB9uF7Y93YFuMHeux2XF5rkqt7m\nCKDcWofqcJrayP6yw/eCsRTz54uMztQ355MaS7F0No92LF547nl++hO/iI4Ma2sh7abGTUCuYNGo\nxY+3bBibcMlkbT419PhmELvB9wwFv011JE2zWOAr3/n+e/4675UwafcdDRW6Fs0DZE2DlMPyVKHr\ntkYpieNrUAfviCyEEEIIIXYngaw4MfqtFd2vRDskV/W6us0qE3egbRWShAkbFWmKK/HsXGMp6qUk\njVKqf0BiDKlWQLIdEjlxMGTsw21YFaQcZi+X4i67Ju6Au/1Y/ukHPsYP/V+/RBRtWwer4MxZh0zW\nIR5NGz++5uYG7seODNGAZlCHSUWa4nKbbM0DBY1CktpI5p5lLVfO5PDSHfJrHkobmoUktaEUHNb+\nlSJMyppYIYQQQoijJIGsODHChIW2wN6RfDVAa4+jU9KNrUxsz31Nn4aT4syNKnaoN4Pd8mKLZDtk\nZTLf/QRtGL9dI9EJUSZu6lNebDF/vkBw2KXKShEk+2/zqc/9MZ6OGzdtMjA3HZJKh4DCthWlIYey\nX2UuPdZ3+5F9sEAuVfcoVDpoS1EdShMlbPIrbXI1b7OzbzvnsjqWId0IKC+2UGytJS1UOqRaAQsX\nivcme6kUjXKaRjl99PsSQgghhBBHYtezbaVUARg1xry54/YnjDFfv9udK6X+PPDzgA38P8aYn7nb\nbYpTTCmWpvKM36rH/0kcxGpbsTo5ONO4ndklUDJKka12uoJYiEe0ZOo+VT8iTGxl2gqVNonO1jxc\nZcAYw+hMndnLpb5BmdsJGVpokmyHaEvRKCVZG83gBJryYpNUM0Bbino5RW04vbkNt9Nh8sYMhhRe\nOgv4nH3rVewo4uLrb2BrjZ9IMnvhbdTKIyhjCNwEKIvhhWmmrr9K67bPVfUV5p74CGzLbmtAYzj/\n+ipgUCYisi1QNoFrUx1N918/agwTN6okvK0mSZlGsNnwaPurTzcCJptxY6Sd+WrLQMKLSLbCfc9y\nFUIIIYQQD6aBgaxS6q8B/xJYVEq5wMeMMX+6fve/Ad55NztWStnA/wF8NzAN/KlS6reMMa/czXbF\n6eZlEsxcKZGrdHD9iE7GpVlK7bkstVWIm/H0y8q2cgmGFppdQewmBcl22BXIZqt+z2MVYIcaJ9Bd\njwWw/YiJW1WUXn+cNuQrHVwvIt30UcaAsrAiQ3mxQaITsDxV5OIrr3L1G9d487F3oZVFqh1hhZp2\ndoqn/+h3cKKQVjbPV/7cXyCybYztxDXGSoEx1IbGuP7I0zz8tZeYvHmNx1u/z41HnqFRKOMEHn4q\ng6O3vQLlrGe9DXYUkpius3ImR6uQ7Ho9ubUOCS/qO35m520q3tzAUTXKxB2oJZAV2332Z9K89Pi/\nONJ9fMeRbl0IIYQQR2W3jOwLwDPGmDml1LuAf6eU+sfGmF9n8PnofrwLuGaMeQtAKfX/An8RkEBW\n7Cpybapj2QM9N0zYVMaylBebXbcvn8mhHYvAtXpGqGywQo9HvvxNXD9g/twUiY4N1t4Dr8JqZzOI\n3dymgXQzwIo0xt4W+Fo2uZqPV6nynk//AX/y4b+Ctrd+rtpx6WRyzJ9/mHPXX+Fbj72b0HHBWs93\nbmSDt/37jae+g3a2wJXXvsLw0u8A8JX3fS9BavcmV5aJS6Zb+URXljm/2tlTEHun2wGMtd4YaRvH\n80m3mjTzebQjqyB283M/Nb/v53Q++GtHcCSH66VP3O8jEEIIIcRxtdvZoW2MmQMwxnxRKfVB4HeU\nUucYOKlyX84Ct7f99zTw7kPYrhC7apRTtHIumYaPsSxaOXezQVOjlKJQ6XRlbA2gdMT3/cq/R2Gw\nwjgLefvyo9x45OnuIMsYUu0WodM7Cijhhf0DP7MjiF1nRSHnX7tNs1COs7U7aMdhafIC566/wtro\nma0gdhClmL7yKBPTb5JtVAFo5Uq7P2edHerNdcAHtpEl3nkzoLfNWlVRxPs+8XtcfP11lDEYy+Kb\nzzzNVz74gbvY+cG9+IOf2/dz2v/lK7z8u/cu+O5IwCeEEEKIB8xuZ1p1pdSVjfWx65nZDwC/ATx6\nLw4OQCn1Y8CPASQLo/dqt+KUssKQZ/7wj3jo69/ACQJWx8b4wkc+zPLkGQCihM3iVIGRucbmLNAg\nYfEdv/fruGH3+J+pG6+yMjFFvTSCtmysKMIymnd8+bNURz/E4tRU1+OfmXuFV4pXMGpH0GoAo3sC\nUWNZfNu3vkLdtwau7XUDj3pxaM8dnY2ClfGpzUA21arTSCTv8Kw4Y7oziK2XUwytN27qfnCfgNUY\nlNFgwFh21+3D3hrfs/DHFN6Is+Sztz3qta3GVUprHv/TL/Od119mdHz/I3Lu1ucPFCRKBlkIIYQQ\n4ijtdrb1E4CllHrHxrpVY0x9vUHT3ziEfc8A57b999T6bV2MMf8a+NcA+TMPHUYmWBxzey2TfOfI\npb7r56LI0G7GNbyZrIW1bf3szG2PZl1vjqkZXlzkL/yH/8jFK0kSya1g0AA1J4djQsxak7mmz85J\ntZbWPPXSp1gbnqA2NEqy02J09iaODvnYi79Gaaj751VzsrxeuES4LZC1dcjw2gLLhTH0tkBWRSHF\nlQUmE21mFnySnRbtTL4r2LXCgDPXX+Pl937Pnrv9WsZgRVvNmS69/lW++ewHu8qWd3J0yBOrr/ET\n3/pm1+0axcfPfjerye6sbqZZpZXOxce0HmCXlue4+soXWXnyKW4WzoGCdOjx3pWvcqU5vflco01X\nELtdZSVidLz/MbZbmspKSBgasjmL0pCDfcAuzEIIIYQQ4vgbePZqjPkagFLqz5RS/w7450Bq/d/P\nAv/uLvf9p8BDSqlLxAHs3wD+5l1uU9ylT+pf2PNjj6p0cq9lki/1ua26FrIwGwBbM1XTGcXYRALb\nUV1B7AZjYHU5ZOLsVrZPAcWwAUBjl2NQQHllnvLKtuDbgkSyN4gqhE2+f+ZFPjf6ThaTw7gm5JHq\nm7xz6Wv82Ss5Xnv8vTTzuZYHNAAAIABJREFUJZQxjM9c5503v0Ru0qZYtHnyT36fl9/zEYJEGojL\nbcdvv8lr73w/kdu/o3B8gL3HMTZ/M44vDZxrzDG2+AU+N/IsnpPceq6KA3VLwWPVN3i28s2e7VgY\n/srMp3kze47XCpdxdMgzlW9izy0xXUvSyJVJtupkG9W4uZUN71z+AuHqlwiVQ0p7Pdlc3T+G3Tws\nY8zmTNwN1UrIwlyw+ZI7bc1aJeLileSBg1ljDFEUXzew7tF8WyGEEEIIsXd7iUTeDfwz4rghD/wH\nDqHRozEmVEr9PeBTxON3ftkY03u2fEo8+f1re37sX//x/3iER7K7l09wSaTvaxZmg55Atd0y3Lru\nMTLmbDTy7eF5gyOoTNbq+5xBXFeRzvQv9R31K/zlmT/obijlwONDNca//Ds0OhY2EUNlm+Ez8Wcx\nPumSb/qMffk3qRRGUKUsy1GGVy8+3XdtLQBKYbdbRKk0rK8zdVzDB+a/yNRZTeAlSCQVyZQFrRmu\n3pqhE1rM+Gl0M6BktUgO58jiY/fkorftBrjavM3V5tZyd112qFdbpJabGL0VS5+ZSqCUwjURron6\nbs8a8HIG0dqwMN/9mRsDUWiorISMjO2vC3K7rVle9Gk3tzZYLNuMTbg9AbQQQgghhLh/9hK1BEAb\nSBNnZK8bY3bJm+ydMeaTwCcPY1s7ffZn0vt6/FGPeECasRzY6nLA8mK4ufSyNGwz1metZG0tGhhw\nGgNrq4PvT6YGrzG1LMXkuQSzt/07BrT5gs34mTsHPTvvTSYtzl3YWKvaHXwppcjmbLI5m5wT8htn\nv422SgwOYgGMIUql0UqBUnQyDiuTOX74fy7z+R+9TSq19dBWM2J5McTzNIlEi5Exl2zOATq7v9gB\nLEtx/lKSZl3TbEY4jqJYcnDcOweCSilyeYtGvfdPTL5o9byvXsdszhPezhho1KJ9BbILcz5rq70B\ndrUS3zZ+5t6vzxVCCCGEEP0pc4czc6XU14DfBP5XYAT4PwHfGPNXj/7wuj2SLplfvvq+e71bcR+t\nLgcsLYQ9txdKFmfOdjcpGhSIbJcvxEHS9q+9sojXyCZ2b5gUhoZ6NcL3NI16RBTFAZPjwuSUSypt\nH1nWrtGIuNYu8rXH/xyddG73DsXGYBSobeGyVlAZy9Aop/mk/oXNsvBmI2LmVneArhRMnkuQy+8z\nPXpIjDFM3/RpNbeC2UzWYup8ArWjzNf3NDfe9PpeYMhkLc5dvHMjK4jX2N6+0X87Gy4/nMR199ZU\nS5wc3/Fnn/iyMebZ+30cQgghhNifvWRk/44x5kvr/3sO+ItKqR8+wmMSYtPyYm8QC1Bb04yfMV3r\nF3N5m2plcNZVWTBx1mV1OWJtNSSKIJ2xGDvj3jGIBXAcRXk4/smMGUMQxNlAdw/PvRuNRsQflJ5l\n4dHLcRZ2t2BZx02uFN3HZJl4jm2jnOaj1k/Cc/DTn/hFFud7S7GNgcW54L4Fskopzl1MEoYG39Mk\nEtbAbG4iaZFIKryO2bENNj+rvajXwjtm22+95XPpahJLmkgJIYQQQtx3dzwD3xbEbr/tbhs9iQeE\nMXEwEoX7bzhtjNk1uAj87jszWYtsrv9XWikol20sy2JkzOXqI2ne9mia85eSpHYpKx5EKUUiYR15\nEFtzMvzGpT/P/PmHMI6zaxCropDQNaD7v2lO0H1R4IXnnicY8LkEgeFO1RpHzXEUmax9x5LkqfNJ\nkkmFUnGiWikYHnX2FYjvJZEeRYa1SojWhtXlgOvXOly/1mF1OcAMeM+FEEIIIcTROLmdfcSxYoyh\nUdPUahG2BcWyg+dFLM2H8fpFA9lcnP1USuE4e1svuRt7xzaUitey1msRy4sBgb8VoBSKNiPj+2v8\nc78tJsr8xtSHMahdIy2DwShFZbLA8OwNkl4GL5PrfpDWpFtrQPcs5kY6R7bR25fZy+X4ZvESttFc\nbE6T1v5hvKQj4biKC1eSeJ4hCg2ptLXvbsWFokNlZXA2H+JMdbMe0ahrOu2t8vTlxZB6PaJQsGm3\nDG4CSmXnyC9yCCGEEEI8yCSQFQfi+xqvY3ATimRSMX3Tp93aOrmvrvWuVW3UNY26h1Jx8HHmbGJg\nd98NpSG777rXZKp/MKyUolB0KBQdosgQ+AbXVT1B73EXKIffOvuh3YNYY9CWInQdFi4U0LZFednh\nyle+yGtPvz+eS2tZqCjC0hGZxgLwUNcmvv7ed/PsZ/8Qd1u29uZDj3PzbU+iAIXhj0ee5kOLX+By\ns2fM87GhlCKVOvhnnExZDI85rCzeocRYqa4gFuIAt9MyeO2t51ZWIqYuJMhk7095thBCCCHEaScp\nA7Evxhhmb/vcuOYxP+Nz6y2P69c6XUHsnbcRlwXfvukRBrs/afxMgnyx+2uaTCnO76GJj22rODt3\nwoLYUFn8+tnvIlJ3WA+LYflMjrlLRbQdv0dz588zPH+bd37uE4zPXCdXWWL89rd4+r/9Njfe/nDP\nFt546km+9u3vxU8kCB2HytAY1x95Gm05RJZDaLlElsNnxt7D9BLM3vZZWw0Iw0NpXH6sDI+4XHoo\nyeiEg93nEp9S8WilQT3bdwa3czPBfS/PFkIIIYQ4rSQjK/ZldTmkUY9LMDfO0YODVp0aWKvcedbn\n5FQSPRmvtXVca09lySfVUqLMp8e/nYab3TUTC7B4Nk+70B3QP/LVlzGWRaa+huu1aZ25QKtQZmXy\nIt/32pf4t+Mf7d6WUnzz3e/ilWefIdVuk2pAfs3rGQ9ktOHNxBkmFt+iXoOFuZBkCs6cTe46uuik\ncV2LoWGLfMFh5paH75nNj2H8jIs2UKvuXoK8IQoNYRBXLQghhBBCiMN1es5AxT2xtnrn7q57tZGZ\n3QvLUqTS9qkOYheTQ/zW2Q/tGsQaILIVtx8q0y6keu5/5Ksv40QRb73jWWYvvg3tOGjbIUik+OPR\nZ/ixF3+j/3Ztm3Yut/taXNX958LrwI03PSorwd5f5AnhuoqLV1JcvJrk3MUkVx9JUSg55Av2nhpD\nbdg5LmgQr6NZXgxYXgzwvNOX7RZCCCGEOGySkRV3ZIyhuhZRrYSE/afhHIhSkMqc3sB0v74w/ASh\nNfgnuRHEzl0qop3+ay9dPyCybGYvxEHsdqHl8OXyo7seQyufILfWQe28vqAUw4vTfZ+zOB8SRYah\nEbdrHNJpsHMsk23Ho4Fmb/uE6x2flaKrQmFDasA67p1WlgJWlrYuEK0uhwyPOgyPnqzmZEIIIYQQ\n95JkZMWujDHM3PJZnAvotA+Wil3vOdTDdqBYkmspG2ayEwPvM0BoK2aulAcGsQCzFy/guwl6aoPX\nNdwMP/2JXxz4fC/t0Cgm0SrepwGsKOTKn32RhNcZ+LyVpYhrr8WjaE67VNri0kNJLl6J/7nytiS5\nfJypVevfdTehOHPuzuu4fU93BbEQB8QrSyG+L5lZIYQQQohBJIoQQLyeb2nJp17VGB03VBqbcDEG\nWs29N3LaSSm4eCWJ4ypWl0OqlQhtDLm8zcjY6cvgHcQLzz0PwJm3KiT6lFobwFiKpXMFuMP79aUP\nvJ/n/u1/wNIRemfHImMY6VR2PxilqEzkaBZTpBseKMV7Pv0JxqZv3fF1GBOPokkkLHKF092tVylF\nIrn1WUyeS+B78Vgex1WkM9Ydx0cB1Gv919saA41axNCIXGsUQgghhOhHzpIEvqe59kaH6qpGR+vj\nRNqGW9d91iqD18S6CdXTUXg7pWBsIp6nqZRieNTl8sMprr4tzcRk4lSvd92gtWFpwefaa22+9Wqb\nuWm/q1PzRhALUB1Oo3e8JQbwUjYzl0sEqTtfd2oWi/zm3/kY77j+VaxwW3bUGBwT8a7VbwDwcz81\nv+t2/LRDdTRLdSTDS9/73bTzObRS3Ol6hjGw8gBkZftJJC0KJYdM1t5TEAuDlyTHt5/+34cQQggh\nxEFJRlYwP+szKEJp1PTmGsDtlIKhYYfSkMOC7VOtdGeWsnmLiTMJHPfBPhmfudU9X7dWjWg1I37l\n7/2PhIlE12NbxRRWZCgvtzff8HopxdpY5g5jeLYorfnxG79D7fXbvKPa4q2Hn8LL5Bj21/iO1ZcZ\n9eOMbOeDvwbbgujd1MslPv7j/wOTb13n6T/6Y4aWlnYNsQ5zHfVplyvYLA+YXZsvyHVGIYQQQohB\nJJB9wBljaLcG59mMidf99QS6CvLFuHx0/EyCQlFTr4UoIF9ySJ2ikSwH1WnrvvN128rl8jdf5Y2n\nn+x5TmMoTaOcwg412rYw+yy9fttXX6b2mdsYA47nESZTGKVYTpb5g/H38j3zn2MoqO37tRjLYubq\nFWauXuHSN1/hnf/tc2Tr9b4BbTYrn/1eJRIWo+MOSwvd0f/oeiXDfhlj8DrxyKBEUu05MyyEEEII\ncdJIICvuaOpcgtkZH73ee8ay4Oy5BLa9dZKczlikM4kBW3gweZ3+zXrcIGB0bq5vIAuAUkTuwdaY\nPvzy1zEG/GSKr7/nw2hnq/NtzTj89tkP8kM3fxvHaD6pf4GPWj+5731cf/QdXH/0HVz92td51x+8\niLuego0shYtheFT+rOxHedgll7dp1OPvS65g4x6gkqFRD5mbDjZ/p44DUxdO15xfIYQQQogNcsZ5\nihhj6LQNnY7GdRWZrKLTNkRhHGjaTjzaY3UlREdxxmb8jEuhaFGr9g+6MlmLTM7mysMpvE6cWkym\nJNOzF25Cxcscd2RkQ8dhbXjoaPYZxOtT56euYHZ+RkoRKZtbmUkuN/uP0tmPa08+QaNU4vEv/AnZ\nep258+f5xnveRatQ6NsZOYoMSwsB9WoECgrFuOHX9gsiDyo3YVEe3lvAaYxhdSVkbSUkiraqzvWO\nn3AYws3rHhcvJ2nU49L/XN6WwFYIIYQQp4IEsqeE1mZzPeYGY+KT3I01rokkbJ+g4nuG6Zs+Zy8k\naLV8wh09ehwHJs/FGT2lFKm0BBz7kc5YVMpD5Ctr2OtRhgG0ZXHt8ccOfX8TN2+Sq8dlw14qg9nZ\ntRjQKFp2CoCXf9eB5+5un/MXzjN/4Xz8H8aQ8CIS7ZAXPvoT/PQnf2nzccYYrl/rEG2roF1bjWg1\nNBevJuXCyD7M3PK7Oonv1lHcaLh+zdu8oLKyFFIashmbkOoJIYQQQpxsEsieEqvLYd/1mMZsnej2\nGwNqDFSWQy4/lKLZ1NQqIQYoFm1yBfl6HNRT3xvyUesnSbZafPvvfpqz16+jgNXRUV763u/By2QO\ndX9Ka97/25/YzP6WV+aZu/BwV2nxhonO8qHuGyDRDhmdqWFFBhQYFP/b+/4OrUKCn/7kL7G6HHYF\nsRt839BsaHJ5G2MMvmcwRrL+g3Ta+mDjsLYFvWurEfmCJp2RzKwQQgghTi6JVE6JnV2D98PzDEop\ncjmbXO50z/+8F7aP1PEyGV78wb+EFYZYWvd0Kj4speVlnGArUhyenyZbq9AsDKGd+GduhQFDi9MM\ndSqb82h/7qfm+fs/O3FX+1baMH67hqU3oqX4/4zMNYgW4J9+59/lb/3f/2rg8+u1iFo1pFHTm1UE\nlgVnphJk5fvYpdPuvwRgP4yBWjWUNe1CCCGEONEkkD0lzB0nfA6WTErm6zC895ef4IMff1/f+7Tj\ncPchyGCRbZNwNGa9PFxheOqlTzF78RHmz13G0prJm28wOX2N5lmXfOHwAsRM3e9b36oAR8PoTJ2F\nwihDK4t9n19bi7r+2xiIoriE9tJDqQM1Pjqt3ITqOw5rv+QdFUIIIcRJJ4HsKZEv2KytRnd+4A5K\nwfBYb/mp2J8XnnsePr7tBmPI1H2SrYDItWgUU2jn6Eo5a0NDOJFm+zJnW0ece+ubnHvrm1s3KgjD\nrShoP/NkB7FCjdolsFIGrr/9Kcqf+/S+AigD1NZChkfl+7khk7WwbIXWe4xk+zQbUyoekSWEEEII\ncZLJ2cwpMTLq0mxowsD0zdYoFWdz8gWLtdWIKIozsWNnXNJpWSt3N17YEQgqbZi4WcXxIywDWkFx\nuc3iuQJe5oiCMqU4ez7B7eseemNd9IBYJ3PIayO9jItRDAxmFbA6PknoODhhuPdg1kAY3GXq8ZRR\nSnH+UoLZ2z6ddvd74zgwNOKglEJZcYfiZj1ifja+vLFRtl0eduQ3L4QQQogTTwLZU8J2FBevJGnU\nIlotTSKhSKUtatWIKDRk8zaFoo1lKUbG7vfRng47A9gN+dX2ZhALbP57ZLbBzJXS1ryUQ5ZMWlx+\nOEW9YdBhxNpqSLCt6lepeEbpYY9f8dMO7VyCdMPffK3baQX1cobf/Vt/k2f+8L8xMjNLJ5Nh/vw5\nHvnGNwaWySoFGVkj28N1LS5cTsWZdQPKMhijsG16GmQVSg6ZrE29FmGMIZe3SSQliBVCCCHEySeB\n7CliWYpCyaFQ2rotk5VA4CgMCmIBcjWvb0BnRRon0ISJI/hMjOGrxUd4ufx2fMslHzZ5z9JXGbl9\nk1o1QilFaSi+mHEUlidzZGs+pcUmdmQ2s64a0HZcWm3sNP/fX/3Bzee4nsflV1/FDfq0MybuXJzL\nS9A1iONsvMu7XxhxXEV5WP7UCyGEEOJ0kbPEY84YQxgadCQllsdB6sUf2DWIBTC7ZFzNEXXZ+Yml\nz/Llocfw7QQoRd3N8eLEe2mdm+LilRQXLicplpyejF3qxR84nANQimYxyczVMisTWbykTeBa1IdS\nzF0qYuzeFx4kk3z2L34/gevgJ9zNeMx2YHTC4dxFmS97Lxlj8Doa3z/KtmRCCCGEEIdDLtMfY81G\nxMJsQBjG615tO172mExaDI86MprkHnvhuefhZ+/8uHoxSXmp1ZWVNUCYsIncI/jMtOb3Vh9DW93X\npULL4U/LjzHVXjj8fQ6iFM1SimYptaeHz16+xH9+/ieYeustLK352LUXt2Ua+wt8TbUSEYSGbNYi\nX7BRlgS8d6PZiJib9tHra6vdhOLsuYSUIQshhBDi2JJA9pjyPM3MLb9r/WC03pS43YrvG590KUr3\n0SP37d/4B3zgH7X3/PhGOUWqFZBubvUQ1pZi6Wz+KA6PbM1HG9W3wrTi5HZ97t3OkD0MYTLBjbc/\nAsA/efQdfFL/Ai//bv/vdbMRdf0u6tWI1eWQ85eTWAOCWWMMWm/Mp5WAd6fA7/1b43uG2zc8Lj+c\nkqy4EEIIIY4liYKOqcpKuOusSGNgaT6gULTlRHOPgsCwtBDQrEdYFhTLDsOjveW2273w3PMwIIhV\nOm6201M2qxTLUwXcTkiyHRI5Fu2ce2RNntINf+C23XabINC4bm9m7anv7b829X77qPWT8Bz89Cd+\nset2Ywxz090BlzHg+4bKSv8xPa1m3LU38E3c7CpvMzHpYvUpdT7NOm1NdS0EA/miTTpjbX7v1ypR\n3781kYZWQ5PNx1UEQaCprIT4niGVtigNObtmz+u1iJWluKIkk7EYGXMlwyuEEEKIQyOB7DHle3de\nE6t1nKV15FO8oygy3Hyzs5nV1hpWl0O8jubs+WTP43fLwtpBxPBcg1QrDgT9lMPymRxhsrtsOEg5\nBKmj/3BKUwHBa33uMIYz09fwA8NatsxXy2+n6uYZ66zw1NqrfNT6kSM/trvxwnPPdwWzvmfoNz7V\nGKhVo55A1vc00ze3Al9joFGPmLllOHep9zM/rZYXA1aXty6MVdciCiWbickEsMuII7M1c7jT1ty6\n4WHWl8+2mprKasiFy0kSid7gtLIcsLS4tc96TdNsePHjJZgVQgghxCGQM4pjKs6Y3PlxtnyCe1Kt\nhOgdPWyMgWZD43vdd7zw3PODS4mNYeJGlVQrnoeqgEQnZOJmFRXdnyY5icctrCjoud2KQs5ef43F\n8hl+6+yHuJ49y2qyxOuFS3z83PfgesczI7vd9sZaSjFwNm6/iuHVPlUNxkC73fuZn1a+r7uCWFgP\n/Nci2u34PcjkLNSAvyPp9ZnD87P+ZhC7sQ0dxVUhO2ltWFrqfe+1huWl4/+dE0IIIcTJIGHQMVUe\ndrB2+XSUgmJZmtzsVbul+5ZPKgWdTnyGfqeOxCrSnL1W6RovA3Ewq4whW/MP96D36Id+5leZas7F\nkYIxoDUqDHj8Sy+SSxteGn+W0HLYiFaMsgiUQ2mheV+Od79eeO55XnjueRJJCzfR+31XCkpDvZnv\nQVUNSsVl5g+CZr1/wG4MNGpxUJkv2Liu6rpwplRcgpxIWmht8Dr9369mo3f7AzO8xL9DIYQQQojD\nIEWpx0AYGlaXApoNje0ohkYccnmbC5eTLC2GtBoRhjgDAvFJZqFkMzbRuybwQaK1oVoJqVU1lhUH\nM7m81XfNayKpoN67DWPATVh76kg8fqvWE8RusAw4fnSwF3KXbmXOMJObjL8Y62lLBZSTASMjGZpu\npvdJSpFp3p/A+6BeeO55/tPKz/OpXwkx6zE7xIFYodTbDTqdUbTb9GRxjeGBKW/drapj43diWYoL\nl5JUVkNq1Wjzt7Qxc3jja9XvQlC/i222owZmzl1XLrwJIYQQ4nBIIHufhaHhxrWttZv4hplbPqm0\nYnjE/f/bu/MY2e7rPvDf87v31r713v1ev42kLFrm5piWLJIek5LgmGxFDqRB5GgmhqEg8qBnhjZE\nwZBbCDCZCQIPTCuB4ABhEBAYjO1YnqEM0aEo2c7QgRSasmOJiySK2yPf2nt37dtdfvPH7a1eVfVa\nVbeq+vsBHqTu2n5dG++55/zOwcxZCyL+XjbtadiOhmnIqWtWcyut/a6qtareOcCulOtIjxiYmgk1\nXT8zamFzw20ojwSA0Z+exv/x0c8c+HjK9RCquS2DWADwBKhHA/g4aY3vjP89uGrPY4uCZyq8+f77\nMXv5BYjWLeMKq17t2TI75dNjvwH5TQ+//Sf/Fq6jEY2ptkHpyKiF7IbbsK92O9PYKqByHY1a3W+M\nNSwBVyJlYKVF+e/2ybBtyhCMTVgtG2aJCJIpA4V8Y1Movyqk+T1vGIJE0kCx0Hz9sQn+J4eIiIg6\n43SkJfqM52os3azjzdcreOeNPUHsHtWKxs3rdVy5XIO3dSQuShAKqVMfxAJ+R9RaTTft/cttuqjX\nm8sXLUtw7mLYz8xuufK+O/D7D37yUN2Eldu+XFID8AyFcrI5gO428YCS2SLjCmAlMgoDGtPX34Zy\nGvcmKsfGyPKVXiyx47RS+Fe/8r8iPWLum1k1LcGF28NIphSU8n8enzQxfaYxWNNaY2WpjnferOLG\nlTrefauK61d2P3eDzDRl62SYX1m+nV2dnDEbmjRVKx6uX6nhnTf9v/3WEuCpGQuRqNoaYeTfRzyh\nMN4mMJ0+a/nzfQU7t5masTj7moiIiDqGp8d7bCeTeEsQ1vq6/j6/jTUH45Ono4y4VvPgOhrhiIKx\nT8BeKnpN2dVtlZLXspNqNKpw6Y4IXFfjf3v0c3Ctwz+njqWgBZA2r9nixVTXxuvsRyv4b5QWDx3y\nbIQjgve//rewzTDWp89BPA9aKUxffQtLF8Z6vt5OurWrcSuhkMKZc/t3KM5uOMhu+NnD7c9kqehh\n6Ub9wNsOgmTaRCxhoFTwtyjEE0bD2Jxy2cX193a7Ozu2RrlUw9nzoZ3AUxmC85fCqFU91Osa4bDs\nexJBKcHMbAhTrobrapiWcEwYERERdRQD2R6rVDxUa7rtHrJbbY8WGfZA1rE1rl+toV7TO/vxxiZM\njI6bsOsaSgnMPeWebUcOydYevTb2a+bUTrhsI71egbcnkN1+BA1gcyIKzwwm0/SfP/Vf8bl/+T40\n9YLVGpZnQ0RwftaEfP+/IB9NwglHECkVUEnEsTkVR7hcRi3WOqM7CLZfz4MC2v1srreeo1rIe8hn\nbaQyg//ZMwxBKtP6Q7O6aLfs7ryyaOPS+xrf1+GIQjhyy3U9jULBRXbDQbXqf36TKQMTUxYMboMg\nIiKiLmFpcQ+4rkax4KJcdlGreocOYrftTWRoreE4ui/KHl1Xo1J2Ybco5QX8DqXXr9TwzhsVXLlc\nRT7nQLdJQ9+4trvfdbv57vqqg7d/XMV779Rw+a0qrlyu7jxWOmO2TICqrZLHVo4TxMZyVUxdyyNS\nsmHu+TM9ALYpWD2TQGEsuECwXDHgtXoiRFAxogCASFTh/378cXz/Fx4ExIXhOsisr+Nn/su38amn\n/gOmrl7r8ao77ziv7TZ3n7LxxRsOvH0uHwbVNh2J63Xd9vO6c9uKh7ffrGLxuo1KWUN7flO6XNbF\n1XdrB96eiIiI6LiYke2yjTUbayuOn2VEywrQfYkA6a2mLPmsg5Ule2ceanrE71zc65I9rTXWVx1s\nrDk72dNIVOHs+dBOOXCx4OLmtT3lio7G4nUbGxEb5y9FoPaMDarXvZbjPfaWegL+vuFr79Vx6X1h\nhMIKM7MhLN3wO+9q+Fmn2fOhpufjq099Bq88mzn6H+p6GF8sNY3a0QCqCQurs6mj32eHRcIulNbw\nWrwFYq4/C3c7yFNaI57PoxJP48pP3ItSKoNEbhM/+/99G8/96q9A7zfvaQAcptS4lVhcodhmTI0I\nUCp5iEQV1ldtlIseDBMYHff3gAJAPudgbcWBbWuELMH41O5lg8Aw0HKf/kFvB639KgqvVbNu7Y84\nKhU9JJKD81wQERHR4GAg20Xlkou1FachINsvP6G2jve0BrTnH0RHYwojYyZKRRdLNxtLAHObfknk\n9JneNhkq5j1srDX+XZWyh8XrdcxeCENrjeWb9ZblmrUqsL5qY2Jqd82u2368x60cV6NS9hCLG0im\nDCQSEVSrHkQE4UjzPryFuXng2eP9ncnN1l19BUC43FTMGwjTAD6Qexs/St/hz4rd/r3n4Gc2f9iQ\nqbz9tR+gmBrHax/6GDxDAaJQjSawMXUWEzdWsHJuOog/oaOOU2o8MWWhWKi1vEzEz9heeWe3KZtt\nAzev1REKC0wTKJd237j1ut+kbeashVS6/79etdaIxRUK+cZAXsSfZb3fSbJKuf0+dcD/DqtVGcgS\nERFRd/T/kdYA2260oJINAAAgAElEQVQgc1iptLF1UO3Csf0gNhL1g7P1VaflPrZ81sXklO7pPrT1\n9eY9dQBQLnlwHP8CZ5+Rqrmsi4mp3Z/DYTlStbVj715blCAaaz5QPkmp6bZoqd5+3I7qn31/H9p4\nFZ4IXk/dDgGgtIf7N36Ap3/hHzRcT4vg7bs/BG/vBmOl4EEhVN0nIgmS1ogV6ghXHDiWQikd9oPw\nAxwlOxsKK0yftbB0o3lMjdZ+MNYqY1mvadRbxb8aWF60ByKQXbpht8xGpzLqwFE5B323iQKsUP98\nToiIiGi49P+R1gDbb+/d3gykiJ+NjUQVrr1Xg21rRCIK8YTayYi024cK+FnKUA8DWXefZKS33aEU\n+2Sfb7lAKcHktImVxeZgvdVtI9H9A5njBrHi+UGTVfX/QMNp/ZxrIJBRO+0oaDy4/jI+uPEaaiqE\nqFvFz7/2efzHL1YarvfWPXfDU61LrJXbf2XF4nqYvpKHabtQ2p/Vm1mrYPl8CvXIwV9dRwlmU2kD\nlbKHfNY/+bSdiDwzG8LaanOAexDP9T//+3XeDlqt5jXNhgX8vz0WNw7cshCNqX0/r4YCs7FERETU\nNQxkuyiRUn75XYsDxbMXQshtunDqGrGEgjKA5T2lw6Wih3KphvO3hRGJKESirffxiQDWPl16t7mO\nRrHownU0yiUP5ZJ/X4mkgckZq2Ecx0HiCYXcZnOKShRQqbgormiYFmDXW98+0WL/YGbEQiissLnu\nwLH9csd8zoXrNAb8yZTRduzHSbKwhu1i+koOytVQe5pKt9vXbFv9EaA88sxD+Fd4FQBgaReWW8FX\nn/oM/vktQSwAvPeTd+Li66vwjOaPvdeHAVd6vbITxALw/1drjN0sYPG2kUPdx8LcPO79RBaf/vU/\n2vd6IoLpMyFkRj2UCi6UEiTT/piaXNZpuYf7IPWa17JaoBOKBRfraw7crc/K2IQJq8XIqf3cOit2\nm9ZAueghmfK/K1xHIxpXsKzG+1dKMHXGavje2haNCWbOhhr2whMRERF1EgPZLkpnTOQ2XdT3zIwV\nASamTMTjBuJx/yBXa42336i2LB1eW7YxeyGM8UkLpWKt4Toi/ogax9HIbtqo1/wDznTGbMgE5XMO\nlm60Lgcu5F1Uqx4u3RE+dNOosQkLxbzbUG4p4mdglm/un1U1LWCizSihWMxAbM+B/+i4xsaajULO\ng1JAetRAZqT1W/akpcQjyyUYjt4JWvd7JjRwcCecgOy3J1grhY3JJNLrZciev9ATIDcaaX2jAMXz\n9Z0gdi/T9mDYHlzrcK/BK89m8Mohs7ORiEIk0ni/I+MmSsXWe773c5STQ0exuWZjdWX3c5bLuigU\nXFy8PdwUbB64vjalE6KAy29WdzqIA0Bm1N/6sPd7Ip0xEYko5LIOXAeIJRQSSQXjEOXfRERERCfB\nQLaLlBKcvxRGPueimHehDMHIqNGUpXEctG2aUq34F4QjCucvhbG6bKNa8WBa4mdhLMG779R2bl8q\nethcc3Dh9ghMU+A4um0Qu/v4GsWCd+hOq5YluHh7BBvrNsolD5YlCIWl7TzOzIgBDY1ozG/QdNgs\njWEIJqZCDftpb/XAa0/g4RbZx6OKFe3Dd5QWoJIIuLRYa7z/+6/gzu99D9+rG/iZf/J+/Av1wQNv\nlhuPQnkeEtnaThBTyERQGI12f81HpNu8ILLPZfs57szZWMzA1BkLK4u7HcMPvE1cHTlDehiepxuC\n2J3fu/64qqM0fosnFJQAt9ZWiADFvAvnli0E2Q135zO8VziiMDndP6X2REREdDowkO0ypQSZEbNt\nJhEAoHXbQNPcU8Iajvj3lVcOIP7M1MUbdkMQrLUfGK+t2Jg+E0Ixv0/Xpe3beH4ZJHD4MkjTkoaD\n1+tXai3/BqWAeNLoyl65hbl54KRBrNYwba9tZgp7fr0dPG1MxeGZwWacHnj+W7j4xhuwbD/a+OFT\n38fHY2/g6//012CHw+1vKILNqQSy4zGYjgfHNKD7sKwYAIrpMNLrlYasrAZQDxsnev6PM6YnnTGR\nShuo1zVymw6yGy6wJ6Ep8Ocfy9Yc4+mz3Qns6nXdtsN3pXS0hl0i/om261frfgO1re+UsUkLq0ut\nG19lN5yBGi1EREREw4uBbIAc2x/VUam0n2E5NuGX4WqtcfNaHaXi7p7bYr79gWux4G7d7uB1iPI7\nt2rt74mrVjxYIUEiefjsabuuyRqdr8K971EHj6nHT3w/VtXBxI2C39RJ7z/nt5AKwYmYKCdDcK1g\nD+TjuRwuvf5jmHtquw3PQ6haxR2vvobXf/b+A+9DGwp2n5d/5seiiJRthCtbqUHxu0WvnUme+L6P\nE8yKCMJh/wTO6JhGuezCUIJYwn8ebdtv7tTNBk+mIYc66XVYobDCpTvCsOsanvY7iFcr7YNlzzv6\nXmEiIiKibmAgGxCtNa6+V4Ndb31gqBQwPmXuZD8qZa8hiD2I2trHlkgqrC7vf13DEMRigiuXa6jX\ntT/DVgFK2bhwKXyoEsnMiIFiiw6oamsWbqd0YqwOAIirMXU1D+XpQ5UUZ6fi0H0S+I0tLcMzDNw6\nE8ZyHExfvXaoQHYgiGDlXAqhqoNw1YFjGqgkrN2Wwid02EZQrZiWNI3XCfVg1IxpCWJxhXLJa9ov\nPzp+9K9zx9Eo5F14nkY8YWzNY2593e1ma0RERET9oD+OzE+hStnvBtpKZsTAHXdGMDK62xSpWDj8\nTFoRvzELAFghv6Npu2P/RErhwm1hrK86flOqrSSv9vwxO4stZmu2EosbO48jyv9nGMDshcM3kdrP\nh5++p2NBLADECjWIbg5ib32KNYBK3OqbIBYAyskEpMVmTVcpFEYO1813YIigHrVQGImikgx1LIjd\n9sqzmY6+r3phZjaEWFztfNaUAiZnTMQTRwsyiwUXl9+sYnXJxtqyg6uXa1i6WYcIMHXGaniqRYBQ\nWJAZ5blPIiIi6g88KgmIbeu2c1ZdF03B337liqYJuNvbPLWfhR0Z231pxyYsxJMG8lkH0EAybezM\nYt1+nHyudaBcKXvwXN22dHivsQkL6RETlZIHZWDrYPvkgcfC3DzwzInvpoHheJA2L4AH+Kd4NOCE\nDKzPJDr74CeQWl/HQ889D9NxmkqhPaXwxk/fF9TSBtpxSo2DYhiC2QthOI6G62iEQgI54pgbz/O3\nNez9zGsN5LMukikDqbSJcEQht+nAsYF4Uh2pURsRERFRtzGQDUgkqlqPvRAgGm8+WEylDayvNncr\nFQVcuN3f42bbGpGIajlnNRJRiPSgs6hp+vM3O6FTHYlbqUUtaKk0BbNa/GZOWgkcS6EeMTueBTwu\n8Tz8/T/+fxAplRoCWA2gnEjgO3OPojCSCWp5A+8kpcZBME059oifcslr2d9Ma3+cTzxhIBxmN2Ii\nIiLqX4HUS4rI74rIj0XkVRH5UxE5dUff4bBCPKmaYiTDFKQzzecXrJDC9Fm/3E+p3ZLC2fMhmKZC\nNOZnUVoFsYeRTBktOx1FonKobGynLczNdy2IBYBazEQtasLb86d5AtQjJkrpMMqpMOrRzu3H7ISZ\n967AtOtNH1pPKbzzUz+JpQvnA1nXMBnEUuOOYz8nIiIiGgBBbfz7CwB3aa3vAfAmgN8OaB2BOjMb\nwvikCSskME1/X+vF28Jty/dSaRN33BnBmdkQzp4L4Y73RxCLdyb7OT5lbZUo+j+L+HtcZ7o0RmQ/\nPQkkthoJZcejqIcM1EMGsuMxLJ9L9VXwulekXG5ZDm14HuKFUu8XNMSGPZiNxVXLrQQiQCrDhk5E\nRETU/wIpLdZa//meH18C8N8HsY6giQhGxy2MjlsHX3mLUoJ4F2ayGobg4u1hFAseqhUXoXDv98T1\nNHjQGum1ClKbVYinYYcU7Ijpt1nuUyuzZ1s2ebItCzduu9ixx4mUyghXK8hnMtDG6Q1qFubm8eUv\nLKH6yNeCXkrHKSWYmbWweN1v5qb1VhCbNhBP9E9jMyIiIqJ2+mGP7GcBfDXoRZAfWCdTRiAjNnqd\nARtZKSGRrUFtZaVCdQ8T1/NYPp/yS4r7UDGTwdt334Xbf/gjWLYfgDimifzICK68/ydOfP9WtYr/\n7s+ew8zVa/CUgqcU/uajj+DyXT914vvuFsP2EM9VYTgeqnELlURnOxt//slpYIAaQR1FMmUi+j4D\n+bwLz9VIJHebwBERERH1O9GHnely1DsW+UsA0y0u+pLW+utb1/kSgPsBfFK3WYiIfA7A5wBgyor+\nzNfe/5GurJeC0c2GTu2IqzH79sZOELtte9TO6rlUT9dzJFrjwhtv4v3ffxlW3ca7H7gTb9x3L1zr\n5MH3w1/7M9RDKdQjMYyu3sTkjXfhKcFf/qNPYWV2tgOL76xwycbk9TwAQGl/j7MdNrB8Pg3dhcz6\nMAazBDz4g+f+Tms9JMOXiYiITo+uBbIHPrDIrwH4dQAf1VqXD3ObO6MZ/fQdD3V1XdQ7Qe1DNGsO\nZt7LNQWyAGBbCjdvH7JZrIeQWc5iZLUGrQRaGVCOjUi5iJ/+9nNYvHQBL3zyHwa9xEZaY/btTRhu\n44voCZAdj6IwFuvKww5rqfFpxkCWiIhoMAXVtfiXAPwWgE8cNoil4RF54ZOBNtNxrdal0xp+Ru/U\n0RqprAPPNKGV//d7poVqPIkbt/0kErl8wAtsZtVciNd8JkJpIJGvd+1xP//k9NA3giIiIiIaBEFt\niPp9AEkAfyEiL4vIvwtoHdRjC3Pz/r7DAGklKIxEGkbvAP4M2ex4dzJ5/cyqudAtZi95homVs7dh\nsQ/H+uh99sHud1mnMJglIiIiClZQXYvvCOJxKTiRFz4ZeAC7V3YiBtdQSG9UoFyNesTA5mTc71x8\nyuy3n1R5Ln7wwZ/t4WoOxwkpuKaC2F5DCO4BKGTCPVnDwtw8/up3onjx7t/ryeMRERER0a7Td9RO\nPbcwNw88GfQqbiGCwlgUhbFo0CsJnBMy4IQMv1x37wWei5uXplBNxINaWnsiWJtJYPpqHg0FxuI3\n7OqVh79YGdquxkRERET9jLMWqGu++tRnWII5IFbPJuGaCp4AnvKbJhVGY9icTAe9tLYSuRo0ANn7\nTwOz72Rx/sfrmHovh1DV6cla+D4nIiIi6i1mZKkrFubmgWeDXsXgUo6H5EYF4YoDO2ygMBqFE+pe\nIyonZODG7RlEyg6U46EeNbv6eJ0Qz9eazsTtzShHqg6m3sth8bZMT/4WlhoTERER9Q4zstRxzE6d\njFl3ceZyFqmNKqIVB8lsDTPvZhEu2919YBFU4xbK6XDfB7GHJQDSq6WePd7DX6zw/U9ERETUA8zI\nUsfwAL4zMqtlKG+3j/B2yezYUhE3bzt9M27bqSRCiBXqLfot7xIA0WKXTwC0wOwsERERUXcxI0sd\nwSC2cyIlu2VwZtY9KNfr2uMqx0NmuYSZy5uYupJDtNC9eaydsDEV39nXCwDNU2V9qt0FXcbsLBER\nEVH3MCNLJ8ID9c4xay4SuSpEt4m8BPC6NCNVOR5m3s3CcLczwR5CNwvIjUWR79PZup6pcOO2DOKF\nOsKlGhL53mdeD2OBXY2JiIiIOo6BLB3LfY86eEw9HvQyhkYsX8PYYhGi/XLY7W682zzxS2mxz8zX\nk0huVhvKmQE/k5ler6AeMREv1CGeRikV8tfRpYD6yJSglA6jFjGRyGdbXsUxg18rS42JiIiIOouB\nLB0Zs7CdJZ7G2GKxoQR2O5jVWzFYPWpifbp781yjpXrrElwNTNwo7ATY0WId1biF1bPJ/glmATgh\nBccUmE5jMO4ByPfJrGDOnCUiIiLqHO6RpSNhENt5oYqDVptiBYAdUli8mMHy+TS00b2Pq2OqlntM\nBX5mdnt5Svt7eCOlPivjFcHquRQ8Q/xZuPD/VZIhFDORoFfXgJ8hIiIiopNjRpYOZdAPvkNVB7F8\nDdBAORVGPdo/b329T3zqmgaccPdH4RRGo4iWbMieaHa/5kmxYh3VRKjr6zoKO2zi+u0jiBXrUK5G\nLWrCjvTP67wXS42JiIiIToYZWTrQoAex6dUypq7kkNqoIrVZxdTVHNKr5aCXtaMeMeEpaQocPQEK\nI73JJtZiFjam4vCUwFP+Y9shtVPavJcG4HVpr+6JKUE5FUZxJNK3Qew2djUmIiIiOj4GstTWA689\nMfAH2mbNRWqjslMeu10qm9qowKo5QS/PJ4L1qbi/Jxa7ZbGFTATVuNWzZZQyEVx73wiWzqdx87YM\nli5mWpY8awGK6XDP1jXsBv0zRkRERBQEBrLU0sLcvN+cZsDFivWGctltooFosT/2ecbyNUzcLO4E\n2hDADhvITsR631BJBHbEhGsZ0EqwMpvaydK6W5najak4nHB/ZzsHzcLcPD789D1BL4OIiIhoYPBo\nlBp8+Ol78MgzDwW9jI7R4v+7NZjd/n3QWnUsVhqw6v5M2eJIsB13azEL1+4YQaRsQ7RGNWZ1tenU\nafbIMw8Bcw+xqzERERHRIfCIlHYszM0PbBArngZ0c+q1nGzfkGi/y3rF71jcHFErDcTy9QBW1IIS\nVBP+/NhI2UZyo4Jw2W75fNPJsdSYiIiI6GDMyNJAZ2HDZRujS0VYdQ9agFIqjM2pOPRWMyLXMrAx\nFcfocqnhdhtTcbhW97sBH0QrtA0IdR81VDLqLqav5qBc7W/kFb9J1fK5FNBH6xwWC3PzeOFT38Ff\nf/bVoJdCRERE1JcYyJ5yC3PzwDNBr+J4rJqDyWv5nbJc0UA8X4PhelidTe1cr5SJoJIIIVr0M5yV\nRAie2R/FCPWICc8QiKMb+ip5AhR71LH4MMYXizD2rlH7I43S6xXkJmJBLm1osdSYiIiIqL3+OJqn\nnhuGjsSp9WrT3lelgUjJhmG7Db/3TIVSJoJSJtI3QSwAQLYaKhm7Y2+2x+5UetixeD/K9RCuOE0N\njJUGErlaIGs6kG5daj6IBv1zSkRERNQNzMieQgtz88AQdCS26s3BFQBAALPu9UXp8GHYERPX7xjx\nA3BXoxoz+2vtW6XETYNudy7sH1bVwehSCeGqAwhQSoawMRUf+AZVC3Pz+Ib3Fbz8PL+yiYiIiABm\nZE+dYcru1CJWyzBKtD++ZqCI31CplA73VxALP5tth4ym59oToJTsn3myyvEwfTWPcNU/wSFbDbOm\nrhVg1Ryk1itIblagHC/opR7LY+rxofr8EhEREZ0ET++fEsN4AJwfiyCRrwHe7t5NT4BSOtxf5cND\nYG0mgamreYjWUNp/nh3LQG482PFAeyWyVUA37jVW8PfyTr+Xg2z1qRpZLsMOKeTGY37n6l7P6j2h\nhbl57pslIiKiU49H+6fAMAaxgN+RePFCCtW45QdWpkJ2PIqNqXjQSxs6dsTEjdsz2JyMIzcSwfpM\nAouX0g0lu1bNQSxf80cKBbA/NVR1Gubx7qW0Xx2t4P9vqO5hbLGIzEq5hyvsnIW5edz3qBP0MoiI\niIgCw4zsEHvgtSfw8BDshd2PEzaxci518BXpxLShWndS9jQmrxcQrth+dlNr2GEDy+dSPd2bWo+a\niJbstsHsrZQGUtkqCmNRuAOYwX9MPQ7MgdlZIiIiOpUG7+iNDmVhbn7og9hBFymVcddLf4MHnv8W\n7nj1NRi2HfSSjiWzVka44geQyvNLj62q2zS7t9uKGT/IPkouWAMIlQfzed/G7CwRERGdRszIDpnI\nC5/E55+cDnoZPSWu37xnkDrTji4t4+//8Z9AeS5Mx8XFH7+Be158Cc/96v+AWmyw5rImcrWmLKgC\nEM/XsT6je7YH1ROBbJUQ77Xfo4sGRlbKqMatgXr/3IrZWSIiIjptBvfIjZoszM2fqiDWrLuYupLD\nubc2ce6tTUy9l4NZdw++YY8kN7O49KPXMXX1WtOe0Ye+8TxC9TpMx1+vZduIFYu47zsvBrHUExFv\nnxxoD7fKqn325eo2SxEAhuMhvTYc1QvDuh+eiIiI6FbMyA6Brz71GbzybCboZfSUUXMx8162IQMX\nrjqYvpLDjdtHoFWAnWi1xoPf+CYuvvEmPBFAgGoshm/9yj9COZVCuFJBamOz6WaG5+HCm2/hu7/4\nsQAWfXyVuIVY0W7IfGoA9YgJ9PB18JTAsRQsu3G8jgZQjZrwFBArNc8eVgDihRqyQ9IkjDNniYiI\n6DRgRnbALczNn7ogVjyNmSvZpjJS2bosVqghXLYxfj2P6feySK+WodzezQ593yuv4sIbb8J0HIRs\nG6G6jUQuj4e//mcAAE+1/9i5Rn/NkD2Mzck4PEPgbb0YngBaCdanexwYimB9OuE//tavPPgB7sZM\nAhtnkvvduAcL7B3OnCUiIqJhx0B2QD3w2hOn9kA1lq9BvNahh2ggWqhj8loesaKNcNVFaqOCmXez\nUE5vgtk7v/8yLKex+Y7SGiMrq4gWirDDYazMnvWztXs4pom37r27J2vsJDdk4OZtGWTHoyglLeTG\norhxWwZ2pPcZwVrcwuLFNIrpMKpRE4XRCBYvZeCEDHiGQi1qNpUYewIUM+Ger7UXTut3BBEREQ0/\nBrID6LR3JA7V3LZvXA0gWrR35oYC/pgV5WikNnrznJm2Aw2gFonBNXaDOS0C0/E75H577jEU02nU\nQxZs04RjmliencUPPvTBnqyx0zxDoTAWw9rZFPLjMXgBjrNxwiY2ZhJYvpBGdjIO19pdSyHtB6zb\ne2Y9APWwgdxoNJC19gK7GhMREdEw4iaqAcMMC2CHDXhoPgujAUD8rOytFPwANzvZ+fUo18PIcgmx\nQh2igZd/7hehtIJnmtAQTN58Fz/x6l+jHomgkPHLwCvJBP70n30WM1euIpHLYX16ChtTU51fHO0I\nl22MLZca9/IKUItaPd3LGwR2NSYiIqJhw0B2QJzGhk7tlFJhZFbL0K7eCUq2Y9dW41e2ud3IEmqN\nqSt5WHV353HdUAx7i5hXz1yEY1q4edt44ygaESxevND5NVFL6bVK85ggDSSzVeQmYsE2COuRhbl5\nBrNEREQ0FFhaPABOY0On/WglWLyQ9md/Yk8Qi/ZBrCdAfjTS8bVEyjZM221qOtXw2IaJtZkLWDk7\n2/HHp8Oz6u3La40e7Z/uB6zqICIiomHAjGwf4wFne27IwMq5FKA1xhaLiOfrTdfZDnC1ANnxGKqJ\n0KHu26o5GFkpI1y24RkK+ZEICqORxmzqznXdQ81K1coPloLcOxoEs24jubmJSiKOajzY8Tb1sAnD\nsVue7OhKtr6PbX+3MDtLREREg4qBbB+671HH39NGBxNBuNI8G3RbKWFhYyYJbfjXEE8jXLHhKfHn\nnN4SnJp1F9NXcjtdkZXjIbNWhmW72JhONN2/HTL8Kx4UzGrACQ3eaJ2TuOul7+LeF1+CpxQM18X1\n2y7h2x9/DK5ldf7BtIZ42i8PbnHCAQBy4zFEyrmGPdR+pj56KsqKW2GpMREREQ2q05WGGAALc/MM\nYo9I7xODlNKRnSA2nq1i9q0NTNwoYOpaHmffycKqNZabJjcqTaN9lAYSuVrL8T3VuAXHMhri2L3l\nzoAfLOXGTlewdPH1H+OeF1/yZ+nW6zBcF2cvv4sHvvnnLa8vrofEZhXptTIipTqgD5Hm3hLPVjH7\n9ibOvbWJc29tIL1WhjguYvkaooUaxPPvqx41sXIuhVrEf70cU2FzIobc+PB2LD4MVn4QERHRIGJG\nto/wgPJ4SqkwzLVK01kZD0A14Wf/QlUHo8slv9nPVowknofJq3ncuGNkJ4vXLrvricCqu6jdWoIq\nguULKYzdKCBadhr26Wr4Qfb6dALl1OHKmo/i3k9k+3bv9F3f/ZumWbqm6+LCm2/hpVoNdnh3bmuo\n4mDqWg7QfrMuLUA9YmL5XGrfbsLi+mXlsWJ95zkXD0itV5Baq0CrrddCA2tnk6gkQqjFLCxd7M/n\nLEgLc/O49xNZfPrX/yjopRAREREdCgPZPsAA9mQKI1HE83WYdX++7Hasun4msROgJjarTWN5BIDy\nNMJlB7W4H/DaYQOhmtsUzIrWcKzWBQyeoVpmW7crjmux5hLm42ooA30O+PTW/+2391C0VG75ey2C\nUHVPIKs1Jm4UoPYku0X7Jx6S2SoK2/NdtUao6sC0PdTDJpyQwvSVXEO36G07nYn33Of4jQJu3D5y\n6vYoH8Urz2bwCkuNiYiIaEAwkA1YvwUgg0gbgqWLacRzVURLNhxToTgSgR3efXsbrtd6H60AytuN\nePJj0Z15sNs8ASpxC67Vfo+rabe7f4HhePve9jAOCi62L++X99PSuXO4+MYbULeUCDuWhXJyd6+x\nWXeh3OaS7e1y7sJoFMrxMHUtD7Pu+icEtIYdMpq6RR8kVqijONL5ztXDhvtmiYiIaBAwPRGQ+x51\n+iboGAZaCYojUazOprA5nWgIYgGgnAzBaxH1iAZq0d3mQ3bYxMpsCvWQgoYfxJbSYaydSe77+NWY\n1brf01bQdRJHCSr6JQB5+ecfgBOy4G1lov09qSa++7GPQKu9XzsHh6JjS0VYNRdK+xl0pYHQ1s+H\nprGzV5YOtjA3j68+9Zmgl0FERETUlugjNFUJ2p3RjH76joeCXsaJMYANgKcxfTW3ExBt71/NjUWR\nH4+1vIl42m8ktV0WrDWiRRuG66EWNRszvo6HmctZKE/vhGae+BneXJv7P8g3vK/g5eePVzTRD++x\nRDaHu1/6Liav30AxncIPfu5DWD53yyxdrXHmcrYpo60BeAqwQybC1db7ljUOEwb7PAGWLqabTnDQ\nwfrl5Ei3PPiD5/5Oa31/0OsgIiKio2Eg20MffvoePPLM4K5/4HkaiVwVsUIdniEoZKI7e2MPYtZc\nTF/JNnQ0rkWNrYZEfobRsF2k1yqIlmy4piA/GkU5FW5/p/v4q9+J4sW7f+9Yt93WD8HsYVhVB9NX\n8/4InT1fR3ubZh03kN0+YVHIRJCd8ufYiqsRK9ahXA/VmAU7wuD2ICc5qdLvGMgSERENJgayPTIo\nQcWwE9eD4Z8MIbAAAA9hSURBVG41bjpsAyatcebyJkxbN2UNaxEDyx3ugvvlLyyh+sjXOnJfg/K+\nE08jlq8hkashVHEO3POgAVRiJkI1F4brf4fd+mpqAHZIYWMqsdNwK1SxMXUtv9sRTIByMoz1mXjH\nGnINs2HMzjKQJSIiGkzcI9tlD7z2xMAEE8PAqjnIrJQwslxCqGLvXuBpjN0s4Nzbm5h5N4vZtzYR\nz1YPdZ+m7cG4JYgF/MApXHVhVZ1WNzuWTgaxQH8HHobtIr1axuhiEdFiHaV0GOLpll9KGrtNiD0B\nXEOwMZP05/NK6yC2FjGwdDHjZ923mkRNXvc7JCvtf/kpDcQKNcQK9W7+qUOD32VERETUL4azVqxP\nLMzNA1+sBL2MUyO5XkFmrbxTnprIVlFKh7ExncDYUnGnG7E/W1RjdLkE11SoJvbMePX8slPT9lCP\nmKjGTIhuDmL3ipTtjpWndjKI3fblLyzh809Od/x+TyJSqmPiemHn9Yjna0itG3BMQajWIjAVoJgO\nb+1PtlBMh6ENhXih3rLpkxZ/vvDISgmydRst/hilW213SD5uGfhpw5mzRERE1A+Yke0SZi56y7Bd\nZNbKUFuBkcAPUOK5GsLFesuAR2kgvb57osGsuzj7zibGFovIrJYxcT2PqSt5OKbymz61oAG4Rmc+\nRt3KnnYjOD4RrTF+s7jzWgH+a2HVXbiW0fRcawCOpWDaHpTbOLfXazG/F/C7UY+slJHI1pAo2Bhd\nKWNsuQxpnvSzsyY6vFeezfA7joiIiALFQLbDFubmeYAXgGjJbvl70UC8UG89Ggd+2fC28ZsFGK7e\nCbD8MS8OUhtVrM0kWt+HElSSoVaXHEm3S4D7qcTYqrktR+EoDYQrDjam43CVwBO/jNgxBabtIVay\nES3bGF0qYuJ6AdAaxZFI01il7XtW2A2UZc+/W/kjljhf9jj4XUdERERBYSDbQTyoC85++TTXaLGJ\ncus21ZhfEqxcD6Ga23S17bLTSiqMtZk4PMHOfFnHFCyfT+1kB4+rV0Hml7+w1JPHOUi77DbgzwMu\npSO4/r4RLF1MY+Vcaufkwjal/XLuzEoJynZRTId3gl5PAVrt/37Qe/554s8ALqVOfjLitFqYm0fk\nhU8GvQwiIiI6ZbhHtgMeeO0JPMy9sIGqJEPAcqnp91qAcjoM11IYWSnvBEQafsCzM+N1O7Jpyb+g\nnI6gnAojVHWgRWCHjRN3ur33E1nguRPdxaFVH/ka0AcnW5yQAddUkFtmx3oCFDNbmVER2GETic3W\nDblEA6nN2k5QnJ2IwjMMuIZAPI3xpRLQIusL+Oc06pZCJRlCNW6hGrPYsfiEPv/kNDA331eZfyIi\nIhpuzMie0MLcPIPYPuAZCmszid3M3Na/zYkY7LCJ4kgUa2eSqEVMOKagnAxh9UwS8VwNmeUSrLqL\nethoimW9raZBO0RQj27NHu1A8NPrhjl/9TvRnj5eSyJYmU3BMwSe2n2tSqlwi8xo+2B0u/xbaSCz\nWkE1ZqGaCKGSCO2bkfUEKIxEkJ2MoxoPMYjtIFalEBERUa8EOkdWRJ4A8CSACa312kHX76c5spEX\nPtl3nWDJLxGOFv3uxJV4CK7V+lxNPFvF6HJpp8OxFqASsxCpOBDtl7J64mcPl86noY3OBztBZa/6\nJtjQGtGSDcPxUI1ZcEJGw2WjSyUk8jVAt97bupcnQHYihsKoH6iHKjYmrxUgnm7YG+sJ4FoKixcz\nJy4Jp/Y6PUaqmzhHloiIaDAFlpEVkXMAfhHA1aDWcFwLc/MMYvuUZyiU0hEUM5G2QaxyPYwul5o6\nHEfLNtZn4ticiiM3GsHamSQWL3YniP2G95WO3+dh9U35pwgqiRCKmUhjEAsguVFBPF/bHZeExr2t\nTXelsXNSAgAcS2C4K4gXb8JTNVSjJmoRA7nxKBYvMIjtts8/Od0/J0yIiIhoKAW5R/ZfA/gtAF8P\ncA1HwizscIjs0+E4WrSxMZPo+hpefp7b0/eT2qw1jUsS7AayrebMlre6R8dzOTz2B/8RZr0O03Hg\nmibyIyP45j/+NJwwmzr10gL3zRIREVGXBJKRFZFfBnBDa/1KEI9/HMzCng77ddTtlH44sO+HNexH\ntWnUBACFkfBO9+jtzsP50ehOVveh576JSLmMkG1DaQ3LtpFeX8d9//XF3iyeGizMzeOB154IehlE\nREQ0ZLqWFhKRvwTQKvL7EoAF+GXFh7mfzwH4HABMWb1vVPPVpz6DV57N9PxxqXsqidZZOS1AKR1u\neVmnfMP7Cl5ms/ADVWImYkW7KfPqhBSyUwmUUxHE8jVgq0mUHfGfU7Nex+TNm1C37P03XRe3/eh1\n/LePPNyT9VOjh79YYVdjIiIi6qiuZWS11h/TWt916z8AlwFcAvCKiLwHYBbA90SkZbpTa/3vtdb3\na63vzxi9LQtcmJtnEDuEtBKsnk02dTjOj0ZRj1pdfex+KikOcp/uQbKTcXhK4G39vJ15XZ/2y77r\nURPZqTiyk/GdIHbnim0F19iOfNw3S0RERJ3S89JirfVrWutJrfVFrfVFANcB/D2t9VKv17IfHnAN\nt2oihOt3jGBjKo7NyThuXsogNxHr6mP2Wzaqn4LqWzkhA4uXMiiMRlCNmihmwli8mEEttv+JBicc\nwtr0NLxbRuo4hoF377yzm0umQ2KpMREREXUC58jeYmFunkHsKaENhVImguJIBO4tXXM7rV+zn1/+\nQl+dP2rgWgrZyTiWL6SxMZ2AEz7ca/SduV9CPRKBbVnQAGzLQjGTwcs//2B3F0yH9vAXK/yeJSIi\nohMJPCWzlZXtCzywom7p1+xn9ZGvAUP2vi+MjOD//Z/+GS6+8SYS2Rw2piZx/fbboBXP2/Wbhbn5\ngZo5S0RERP2jP4+ue4wBLHVTv5UUnwauZeGdu34q6GXQIXz+yWk2giIiIqIjO9UpivsedRjEUlf1\na0nxXoOwRhp+/C4mIiKiozi1gezC3DweU48HvQwacv1aUrzXIKyRTgc2giIiIqLDOpWBLM/8Uy8M\nUqnkIK2VhhsbQREREdFhnKpAlh2JiYgGA7+riYiIaD+nJpDlQRH1EjOcRCfHUmMiIiJqZ+gDWTZ0\nol679xPZoJdwLAy+qR+x1JiIiIhaGepAlg2dKAif/vU/CnoJREOHwSwRERHtNZSB7IefvocHPRSI\nFz71naCXcCKDvn4abgtz8/jw0/cEvQwiIiLqA0MXyC7MzeORZx4Kehl0Sv31Z18NegknMujrp+H3\nyDMP8UQlERERDU8gyywsBY17TIl6h9/3REREp9tQBLLMwlLQBrXBUyssL6ZBwVJjIiKi02vgA1me\nlad+MEwNnlheTIOEpcZERESn08AGsgtz8zx4ISIiADypSUREdNoMZCDLAxbqJ8O4N3YY/yYafgtz\n87jvUSfoZRAREVEPDFQgm/ipKQax1Ff+6neiQS+BiPZ4TD3O/04QERGdAgMVyL5xwwt6CUQNXrz7\n94JeQtcwSKdBxkZQREREw22gAlmifvIN7ytBL6GrhjlIp9OBjaCIiIiGFwNZomN6+Xkz6CUQ0SEw\nmCUiIho+DGSJjuG0NENieTENC5YaExERDRcGskTUFsuLaZiw1JiIiGh4MJAlOqLTko0lGlYMZomI\niAYfA1ki2hfLi2kYceYsERHRYGMgS3QEpzEby/JiGlaPqceDXgIREREdEwNZIiIiIiIiGigMZIkO\n6TRmY7exvJiIiIiI+olorYNew6GJyCqAK0Gv4wTGAawFvYgBxefuePi8HR+fu+MbpOfugtZ6IuhF\nEBER0dEMVCA76ETkv2mt7w96HYOIz93x8Hk7Pj53x8fnjoiIiLqNpcVEREREREQ0UBjIEhERERER\n0UBhINtb/z7oBQwwPnfHw+ft+PjcHR+fOyIiIuoq7pElIiIiIiKigcKMLBEREREREQ0UBrIBEJEn\nRESLyHjQaxkUIvK7IvJjEXlVRP5URDJBr6nficgvicgbIvK2iHwx6PUMChE5JyIviMiPROSHIvIb\nQa9pkIiIISLfF5H/FPRaiIiIaHgxkO0xETkH4BcBXA16LQPmLwDcpbW+B8CbAH474PX0NRExAPxb\nAI8C+ACAfywiHwh2VQPDAfCE1voDAH4OwP/M5+5IfgPA60EvgoiIiIYbA9ne+9cAfgsANycfgdb6\nz7XWztaPLwGYDXI9A+CDAN7WWl/WWtcB/DGAXw54TQNBa72otf7e1v8vwA/Kzga7qsEgIrMA5gD8\nh6DXQkRERMONgWwPicgvA7ihtX4l6LUMuM8CeD7oRfS5swCu7fn5OhiMHZmIXATw0wC+G+xKBsa/\ngX+izgt6IURERDTczKAXMGxE5C8BTLe46EsAFuCXFVML+z13Wuuvb13nS/BLP/+wl2uj00dEEgCe\nAfCbWut80OvpdyLycQArWuu/E5GHg14PERERDTcGsh2mtf5Yq9+LyN0ALgF4RUQAvzT2eyLyQa31\nUg+X2LfaPXfbROTXAHwcwEc150Yd5AaAc3t+nt36HR2CiFjwg9g/1Fp/Lej1DIgHAXxCRB4DEAGQ\nEpE/0Fr/jwGvi4iIiIYQ58gGRETeA3C/1not6LUMAhH5JQBfBvALWuvVoNfT70TEhN8U66PwA9i/\nBfAZrfUPA13YABD/TNP/BWBDa/2bQa9nEG1lZL+gtf540GshIiKi4cQ9sjQofh9AEsBfiMjLIvLv\ngl5QP9tqjPW/APgW/GZFf8Ig9tAeBPBPAHxk67328laWkYiIiIj6BDOyRERERERENFCYkSUiIiIi\nIqKBwkCWiIiIiIiIBgoDWSIiIiIiIhooDGSJiIiIiIhooDCQJSIiIiIiooHCQJZoCInIN0UkKyL/\nKei1EBERERF1GgNZouH0u/BnoRIRERERDR0GskQDTER+VkReFZGIiMRF5IcicpfW+j8DKAS9PiIi\nIiKibjCDXgARHZ/W+m9F5FkA/xJAFMAfaK1/EPCyiIiIiIi6ioEs0eD73wH8LYAqgMcDXgsRERER\nUdextJho8I0BSABIAogEvBYiIiIioq5jIEs0+J4C8M8B/CGA/zPgtRARERERdR1Li4kGmIj8KgBb\na/1HImIAeFFEPgLgXwC4E0BCRK4D+Kda628FuVYiIiIiok4RrXXQayAiIiIiIiI6NJYWExERERER\n0UBhIEtEREREREQDhYEsERERERERDRQGskRERERERDRQGMgSERERERHRQGEgS0RERERERAOFgSwR\nERERERENFAayRERERERENFD+f/GIE63efLDwAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f16e90b79b0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"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 noticeable 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 by 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": {},
"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": 67,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"image/png": 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Q8vL/AbDumc84NHstmv0vN5G7uJxZo8Zz9YHJPhtvVRbf8iqOnMLKJ3dPmQPK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"text/plain": [
"<matplotlib.figure.Figure at 0x7f16e8f387b8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"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",
"### 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/"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"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": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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