diff --git a/backprop/classification.ipynb b/backprop/classification.ipynb index 90aeac9..0538f01 100644 --- a/backprop/classification.ipynb +++ b/backprop/classification.ipynb @@ -29,7 +29,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 3, @@ -61,14 +61,14 @@ "source": [ "# network architecture\n", "i_size = x.shape[1]\n", - "h_size = 3\n", + "h_size = 30000\n", "o_size = 2\n", "\n", "# hyperparameters\n", "# learning rate\n", "lr = 0.01\n", "# regularization\n", - "reg = 0.01" + "reg = 1.0" ] }, { @@ -131,10 +131,10 @@ { "data": { "text/plain": [ - "array([[0.31812171, 0.68187829],\n", - " [0.97175638, 0.02824362],\n", - " [0.17924857, 0.82075143],\n", - " [0.42695823, 0.57304177]])" + "array([[1.00000000e+00, 1.41407784e-53],\n", + " [1.00000000e+00, 5.95214899e-24],\n", + " [2.37883995e-01, 7.62116005e-01],\n", + " [3.90731965e-56, 1.00000000e+00]])" ] }, "execution_count": 8, @@ -154,7 +154,7 @@ { "data": { "text/plain": [ - "array([1, 0, 1, 1])" + "array([0, 0, 1, 1])" ] }, "execution_count": 9, @@ -194,7 +194,7 @@ { "data": { "text/plain": [ - "1.4402032156938522" + "45.332964995365955" ] }, "execution_count": 11, @@ -210,7 +210,6 @@ " loss = np.mean(log_probs)\n", " return errors, log_probs, loss\n", "\n", - "\n", "errors, log_probs, loss = log_likelihood_loss(probs, sample_y)\n", "loss" ] @@ -245,7 +244,20 @@ "cell_type": "code", "execution_count": 13, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/nc/y5cxzkzx56l5xp4c_yfnw1gm0000gn/T/ipykernel_79361/858393899.py:10: RuntimeWarning: overflow encountered in exp\n", + " probs = np.exp(z1) / np.sum(np.exp(z1), axis=1, keepdims=True)\n", + "/var/folders/nc/y5cxzkzx56l5xp4c_yfnw1gm0000gn/T/ipykernel_79361/858393899.py:10: RuntimeWarning: invalid value encountered in divide\n", + " probs = np.exp(z1) / np.sum(np.exp(z1), axis=1, keepdims=True)\n", + "/var/folders/nc/y5cxzkzx56l5xp4c_yfnw1gm0000gn/T/ipykernel_79361/1046977659.py:4: RuntimeWarning: divide by zero encountered in log\n", + " log_probs = -np.log(errors)\n" + ] + } + ], "source": [ "hist = []\n", "for _ in range(100):\n", @@ -287,30 +299,9 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# these refer to the axes of features only - not feature and target\n", "x_min, x_max = x[:, 0].min() - 0.5, x[:, 0].max() + 0.5\n", diff --git a/backprop/classification.py b/backprop/classification.py new file mode 100644 index 0000000..2f4e4e8 --- /dev/null +++ b/backprop/classification.py @@ -0,0 +1,162 @@ +# --- +# jupyter: +# jupytext: +# formats: ipynb,py:percent +# text_representation: +# extension: .py +# format_name: percent +# format_version: '1.3' +# jupytext_version: 1.16.1 +# kernelspec: +# display_name: Python 3 (ipykernel) +# language: python +# name: python3 +# --- + +# %% +import matplotlib.pyplot as plt + +# %% +import numpy as np +import sklearn.datasets + +np.random.seed(0) + +# %% +# load a classification dataset from sklearn +x, y = sklearn.datasets.make_moons(200, noise=0.20) +plt.scatter(x[:, 0], x[:, 1], s=40, c=y, cmap=plt.cm.Spectral) + +# %% +# network architecture +i_size = x.shape[1] +h_size = 30000 +o_size = 2 + +# hyperparameters +# learning rate +lr = 0.01 +# regularization +reg = 1.0 + +# %% +# sample weights from a normal distribution +w0 = np.random.randn(i_size, h_size) +b0 = np.zeros((1, h_size)) + +w1 = np.random.randn(h_size, o_size) +b1 = np.zeros((1, o_size)) + +w = [w0, w1] +b = [b0, b1] + +# %% +# work with a sample of data +n_samples = 4 +sample_x, sample_y = x[:n_samples], y[:n_samples] + + +# %% +def forward(x, w, b): + # input -> hidden + z0 = x.dot(w[0]) + b[0] + a0 = np.tanh(z0) + + # hidden -> output + z1 = a0.dot(w[1]) + b[1] + + # softmax + probs = np.exp(z1) / np.sum(np.exp(z1), axis=1, keepdims=True) + preds = np.argmax(probs, axis=1) + + return z0, a0, z1, probs, preds + + +z0, a0, z1, probs, preds = forward(sample_x, w, b) + +# %% +probs + +# %% +preds + +# %% +sample_y + + +# %% +def log_likelihood_loss(probs, y): + # select probs for the correct classes + errors = probs[range(y.shape[0]), y] + log_probs = -np.log(errors) + loss = np.mean(log_probs) + return errors, log_probs, loss + +errors, log_probs, loss = log_likelihood_loss(probs, sample_y) +loss + + +# %% +def backward(lr, reg, w, b, z0, a0, z1, probs, preds, y, x): + d2 = probs + d2[range(y.shape[0]), y] -= 1 + dw1 = (a0.T).dot(d2) + db1 = d2 + + d1 = d2.dot(w[1].T) * (1 - np.power(a0, 2)) + dw0 = np.dot(x.T, d1) + db0 = d1 + + dw1 += reg * w1 + dw0 += reg * w0 + + w[0] += -lr * dw0 + b[0] += -lr * np.sum(db0, 0, keepdims=True) + w[1] += -lr * dw1 + b[1] += -lr * np.sum(db1, 0, keepdims=True) + return w, b + + +# %% +hist = [] +for _ in range(100): + z0, a0, z1, probs, preds = forward(x, w, b) + errors, log_probs, loss = log_likelihood_loss(probs, sample_y) + hist.append(loss) + w, b = backward(lr, reg, w, b, z0, a0, z1, probs, preds, y, x) + +# %% +z0, a0, z1, probs, preds = forward(x, w, b) + + +# %% +def plot_decision_boundary(pred_func): + # Set min and max values and give it some padding + x_min, x_max = X[:, 0].min() - 0.5, X[:, 0].max() + 0.5 + y_min, y_max = X[:, 1].min() - 0.5, X[:, 1].max() + 0.5 + h = 0.01 + # Generate a grid of points with distance h between them + xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h)) + # Predict the function value for the whole gid + Z = pred_func(np.c_[xx.ravel(), yy.ravel()]) + Z = Z.reshape(xx.shape) + # Plot the contour and training examples + plt.contourf(xx, yy, Z, cmap=plt.cm.Spectral) + plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Spectral) + + +# %% +# these refer to the axes of features only - not feature and target +x_min, x_max = x[:, 0].min() - 0.5, x[:, 0].max() + 0.5 +y_min, y_max = x[:, 1].min() - 0.5, x[:, 1].max() + 0.5 + +h = 0.01 + +xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h)) + +z0, a0, z1, probs, preds = forward(np.c_[xx.ravel(), yy.ravel()], w, b) +preds = preds.reshape(xx.shape) +plt.contourf(xx, yy, preds, cmap=plt.cm.Spectral, alpha=0.2) +plt.scatter(x[:, 0], x[:, 1], c=y, cmap=plt.cm.Spectral) + +# %% diff --git a/backprop/intro-to-backprop.ipynb b/backprop/intro-to-backprop.ipynb index 5408fd0..fef6746 100644 --- a/backprop/intro-to-backprop.ipynb +++ b/backprop/intro-to-backprop.ipynb @@ -179,7 +179,7 @@ "\n", "Derivative of the error:\n", "\n", - "$$E' = \\frac{dE}{d\\theta} = 2(\\theta x^2 - y) \\cdot x^2 $$\n", + "$$E' = \\frac{dE}{d\\theta} = (\\theta x^2 - y) \\cdot x^2 $$\n", "\n", "We can now perform an iterative process to update our parameter $\\theta$, starting from an initial $\\theta = 0 $:\n", "\n", @@ -195,7 +195,9 @@ "\n", "As we are minimizing the error, we take the negative of the gradient and use it to update our parameter:\n", "\n", - "$$\\theta_{1} = \\theta_{0} + E' = 0 + 4 = 4.0 $$\n", + "learning rate\n", + "alpha = 0.1\n", + "$$\\theta_{1} = \\theta_{0} + E' = 0 + 0.1 * 4 = 0.4 $$\n", "\n", "Which is not so far away from the true value of $3$.\n", "\n", @@ -336,6 +338,13 @@ "\n", "You now have all the tools to derive update equations for all our weights and biases - do so on paper." ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/backprop/intro-to-backprop.md b/backprop/intro-to-backprop.md new file mode 100644 index 0000000..2fec7f0 --- /dev/null +++ b/backprop/intro-to-backprop.md @@ -0,0 +1,349 @@ +--- +jupyter: + jupytext: + formats: ipynb,md + text_representation: + extension: .md + format_name: markdown + format_version: '1.3' + jupytext_version: 1.16.1 + kernelspec: + display_name: Python 3 (ipykernel) + language: python + name: python3 +--- + + +# Backpropagation + +## What's So Important About Backprop + +Backprop is a cross-disciplinary computational tool, that been discovered and rediscovered many times. + +From [The Master Algorithm](https://en.wikipedia.org/wiki/The_Master_Algorithm): + +> In fact, Rumelhart is credited with inventing backprop by the Columbus test: Columbus was not the first person to discover America, but the last. +> +> It turns out that Paul Werbos, a graduate student at Harvard, had proposed a similar algorithm in his PhD thesis in 1974. And in a supreme irony, Arthur Bryson and Yu-Chi Ho, two control theorists, had done the same even earlier: in 1969, the same year that Minsky and Papert published Perceptrons! + + +**It speeds up training of neural networks**. Alternatives include computing the directional derivatives via finite difference (which would mean doing millions of forward passes). + +## What is Backprop + +Backprop is a mathematical technique for quickly calculating derivatives. + +It's the application of *reverse-mode differentiation* (the application independent name for backprop) to neural networks. + +Backpropagation is not a training algorithm: + +- it’s an algorithm for computing the gradient, +- gradient descent is the training algorithm. + +Backpropagation involves two steps: + +1. a forward pass from `data -> input layer -> output layer -> prediction error`, +2. a backwards pass from `prediction error -> gradients -> output layer -> input_layer`. + +The error from the forward pass is backpropagated from the output layer back through the network - updating the weights in reverse order. + +From [The Master Algorithm](https://en.wikipedia.org/wiki/The_Master_Algorithm): + +> With backprop, you don’t have to figure out how to tweak each neuron’s weights from scratch, which would be too slow; you can do it layer by layer, tweaking each neuron based on how you tweaked the neurons it connects to. + +From [The Singularity Is Near](https://en.wikipedia.org/wiki/The_Singularity_Is_Near): + +> However, backpropagation is not a feasible model of training synaptic weights in an actual biological neural network, because backward connections to actually adjust the strength of the synaptic connections do not appear to exist in mammalian brains. +> +> In computers, however, this type of self-organizing system can solve a wide range of pattern-recognition problems, and the power of this simple model of self-organizing interconnected neurons has been demonstrated. + +## Gradients + +The gradient is both a direction and a rate of change. Both of these are valuable: + +- the direction can be the direction that reduces error, +- the rate of change can be the largest (steepest) rate of change to reduce error. + +The change is always in the context of something else. For neural networks, we are interested in how **error changes with respect to our parameters**. + +## Calculus 101 + +Calculus is the study of change. Below we introduce just enough calculus fundamentals to understand the chain rule and backpropagation. + +If I have a function: + +$$ f(x) = x^2 $$ + +The algorithm to find the derivative is known as the **Power Law**: + +1. multiply by the power, +2. subtract the power by one. + +$$ f'(x) = 2x $$ + +$$ f(x) = 3x^4 + 2x^{2} $$ +$$ f'(x) = 12x^3 + 4x $$ + +$$ f(x) = 8x $$ +$$ f'(x) = 8 $$ + +If I take the derivaitve with respect to $x$ on something that doesn't depend on $x$, then the derivative is zero. This includes: + +- constants $10, -1$, +- terms that don't have an $x$ such as $y^2, \theta$. + +$$ f(x) = 2x + 5y + 8 $$ +$$ f'(x) = 2 $$ + +If I have something in brackets raised to a power, then the derivative is the power times the bracket to the power minus one, times the derivative of what is in the bracket: + +$$ f(x) = g(x)^3 $$ +$$ f'(x) = 3 g(x)^2 \cdot g'(x) $$ + +$$ f(x) = (x^2 + x)^3 $$ +$$ f' = 3(x^2 + x)^2 \cdot (2x + 1) $$ + +We use this rule to take the derivative of the mean square error. + +## Notation + +Calculus was discovered by multiple people around the same time - this has led to a number of competing notations: + +$$ \nabla_{x} f = f' = \frac{df}{dx} $$ + +## Minimizing a Function + +Our use case of calculus is to find the minimum of a function - to find the minimum of our loss function. + +To find the minimum of a simple function: + +$$ f(x) = x^2 $$ + +We can **sample data** from this function. From the data below it is clear where the minimum is (but lets pretend it isn't): + +| x | f(x) | +|----|------| +| -5 | 25 | +| -2 | 4 | +| 0 | 0 | +| 2 | 4 | +| 5 | 25 | + +We can take the derivative of this function, and it shows us the direction we need to take to find the minimum: + +$$ f'(x) = \frac{df}{dx} = 2x $$ + +| x | f(x) | f'(x) | +|----|------|-------| +| -5 | 25 | -10 | +| -2 | 4 | -4 | +| 0 | 0 | 0 | +| 2 | 4 | 4 | +| 5 | 25 | 10 | + +This shows us the value of a gradient - it shows us the direction towards maximizing a function (we take the negative to minimize it). + +Gradient descent is an iterative process that repeatedly takes steps in the direction of the negative gradient. + +## Fitting a Function + +The example above shows how we can use gradients to find the minimum of a function. + +In machine learning, the function we want to minimise is an **error or loss function**. + +This error is the difference between two functions: + +- a function that we parametrize $f(x; \theta)$ with weights $\theta$, +- a function that we want to learn $F(x)$. + +We don't have access to $F(x)$ (if we did, we wouldn't need to learn it) - what we do have access to is the ability to sample: + +- $x$ - features (inputs), +- $y$ - target / label (outputs). + +We have three things: + +1. a function parametrized by weights $\theta$ +2. samples of $x$ (features) +3. samples $y$ (target) + +Lets learn from data sampled from the function: + +$$F(x) = 3 x^2 $$ + +This is a parameterized function with a single feature: + +$$ f(x; \theta) = \theta x^2 $$ + +Samples: + +| x | y | +|----|----| +| -1 | 3 | +| 0 | 0 | +| 1 | 3 | + +Mean square error: + +$$E = \frac{1}{2} (f(x; \theta) - y)^2 = \frac{1}{2} (\theta x^2 - y)^2 $$ + +Derivative of the error: + +$$E' = \frac{dE}{d\theta} = (\theta x^2 - y) \cdot x^2 $$ + +We can now perform an iterative process to update our parameter $\theta$, starting from an initial $\theta = 0 $: + +| x | y | E' | +|----|----|----| +| -1 | 3 | -6 | +| 0 | 0 | 0 | +| 1 | 3 | -6 | + +How do we update our parameter? One way is to average over the three samples: + +$$E' = (-6 + 0 -6) / 3 = -4.0$$ + +As we are minimizing the error, we take the negative of the gradient and use it to update our parameter: + +learning rate +alpha = 0.1 +$$\theta_{1} = \theta_{0} + E' = 0 + 0.1 * 4 = 0.4 $$ + +Which is not so far away from the true value of $3$. + +Here we are seeing the need for two of the most important hyperparameters in training neural networks - the **learning rate** & **batch size**. Can you see how they would fit into our example? + +## Practical + +Do this (on paper!) for $f(x) = 5 x^3$ + +## Partial Derivatives + +In the example above we used our prior knowledge of the true function $F(x)$ to engineer a single feature $x^2$. + +What happens if we don't encode this knowledge, and instead have multiple parameters? + +$$ f(x;\theta) = \theta_{0} x^2 + \theta_{1} x + \theta_{2} $$ + +We now need partial derivatives: + +$$ \frac{\partial f}{\partial \theta_{0}} = x^2 $$ + +$$ \frac{\partial f}{\partial \theta_{1}} = x $$ + +$$ \frac{\partial f}{\partial \theta_{2}} = 1 $$ + +## Practical + +Lets pick some initial parameters (this is **weight initialization**): + +$$\theta = [1,1,1]$$ + +Calculate the partial derivatives and update the parameters using the following data: + +| x | y | +|----|----| +| -2 | -5 | +| 0 | 1 | +| 1 | 4 | + +## The Linear Perceptron + +If we have a feature of length 3: + +$$x = [x_{0}, x_{1}, x_{2}]$$ + +Let give each feature its own parameter: + +$$ \theta = [\theta_{0}, \theta_{1}, \theta_{2}] $$ + +And combine them together using a linear combination: + +$$ f(x; \theta) = x_{0} \cdot \theta_{0} + x_{1} \cdot \theta_{1} + x_{2} \cdot \theta_{2} $$ + +$$ f(x; \theta) = \sum x \cdot \theta $$ + +## The Perceptron + +From [The Singularity Is Near](https://en.wikipedia.org/wiki/The_Singularity_Is_Near): + +> This basic neural-net model has a neural “weight” (representing the “strength” of the connection) for each synapse and a nonlinearity (firing threshold) in the neuron soma (cell body). + +This linear combination won't be much use for learning a non-linear function. Let's adjust notation in anticipation of complexity: + +$$ z(x) = \sum x \theta $$ + +Lets add an activation function after the linear combination - lets use a sigmoid (which is a special case of the logistic function): + +$$ a(z) = \frac{1}{1 + e^{-z}} $$ + + +## A Single Hidden Layer Neural Network + +Three layers in total - input, hidden & output. + +Parameters from input -> hidden layer: + +$$w_{0}, b_{0}$$ + +Linear combination of parameters: + +$$z_{0} = \sum X \cdot w_{0} + b_{0}$$ + +Hidden layer activation function (sigmoid): + +$$a_{0} = \frac{1}{1 + \exp^{-z}} $$ + +Output layer linear combination: + +$$z_{1} = \sum a_{0} \cdot w_{1} + b_{1}$$ + +Error function: + +$$E = \frac{1}{2} (z_{1} - y)^2$$ + +## Partial Derivatives of These Components + +Partial derivative of the linear combination of the input layer with respect to a weight or bias in that layer: + +$$ \frac{\partial z_{0}}{\partial w_{0}} = X $$ + +$$ \frac{\partial z_{0}}{\partial b_{0}} = 1 $$ + +Partial derivative of the sigmoid activation on the hidden layer with respect to the linear combination: + +$$ \frac{\partial a_{0}}{\partial z_{0}} = a_{0}(z_{0}) * (1-a_{0}(z_{0})) $$ + +Partial derivative of the output layer linear combination with respect to the activation: + +$$ \frac{\partial z_{1}}{\partial a_{0}} = w_{1} $$ + +Partial derivative of the error with respect to the output layer: + +$$ \frac{\partial E}{\partial z_{1}} = z_{1} - y $$ + +Our full model can now be written as a composition of functions: + +$$ f(x; \theta) = z_{1}(a_{0}(z_{0}(x))) $$ + +We want change each of our parameters with respect to minimize the error: + +$$ \frac{\partial E}{\partial w_{0}}, \frac{\partial E}{\partial b_{0}}, \frac{\partial E}{\partial w_{1}}, \frac{\partial E}{\partial b_{1}} $$ + +## The Chain Rule + +When we have compositions of functions: + +$$ f(x) = a(z(x)) $$ + +The **chain rule** shows us how gradients flow through these compositions of functions: + +$$ \frac{df}{dz} = \frac{df}{da} \cdot \frac{da}{dz} $$ + +## Practical + +You now have all the tools to derive update equations for all our weights and biases - do so on paper. + +```python + +``` diff --git a/backprop/regression.ipynb b/backprop/regression.ipynb index 4d619ef..8e6e688 100644 --- a/backprop/regression.ipynb +++ b/backprop/regression.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 14, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -14,7 +14,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -34,7 +34,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -55,7 +55,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -72,7 +72,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -83,7 +83,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -106,7 +106,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -130,19 +130,19 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[1.25110487],\n", - " [1.50673367],\n", - " [0.22065717],\n", - " [0.89321702]])" + "array([[-0.2518802 ],\n", + " [-0.25564427],\n", + " [-0.25334685],\n", + " [-0.27336485]])" ] }, - "execution_count": 21, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -154,7 +154,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -170,7 +170,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -197,14 +197,14 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "# training\n", - "epochs = 10000\n", + "epochs = 100\n", "hist = []\n", - "\n", + "reg = 1.0\n", "for _ in range(epochs):\n", " z0, a0, pred = forward(x, w, b)\n", " hist.append(np.mean(abs_error(pred, y)))\n", @@ -213,7 +213,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -222,22 +222,22 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 26, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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73/v4yEc+ws0338xTTz2F7/vceOONu+yzYcMGfvSjH/GLX/yCe+65h2eeeYY///M/3+tYV61axXe/+13uuOMO1qxZw1/8xV/w/ve/nwcffBCAbdu2cdVVV/GWt7yFZ599luuvv55Pf/rT+7z+66+/nnvuuYfBwcEd6375y19SrVZ5z3vec0D3UhAEQdgJx4SJzSpV2nP51i9UUbpTli2kRBjTrIE5Rq5YJRHwWNoCnU0Z/GiGP1s5k+9ce/phFy4gbqMDp5ZTDzuWnVwXCEMsrARMLQehrkN6ysHBQRzH4aqrrmLmzJkAnHzyyTvef+Mb37jL9v/8z/9MNpvlwQcf3MVi8cEPfpB3v1u1M//Upz7FypUrueWWW7jssssA+PjHP84HP/jBXY5lGAbf/e53mTZtGgBf+9rXePOb38yXvvQlOjs7d9nWNE2+8IUvcN9997Fy5UoAZs+ezcMPP8y3vvUtzj//fL75zW8yZ84cvvSlLwGwYMECXnjhBf7+7/9+r9d/9tlns2DBAv7t3/6Nv/7rvwbgzjvv5F3vehfJZHI/76IgCILwKqp5GHkFqhNsGavy30+pdjyXLu8iiM0EGaJYJLxx+otxnHQr+VCSSEznrNktR6Q1AIjl5cBwTNVtM7yXhozhuHp/93bhr5Nly5Zx0UUXcfLJJ/Oud72L//f//h+5XG7H+8PDw9xwww3MmzePTCZDOp2mXC7T19e3y3GWLl264/eODqWUdxZBHR0dGIZBsVjcsW7GjBk7hAvAypUr8TyPtWvXvmqcGzZsoFqtcskll5BMJncs3/3ud3e4o15++WXOPPPMXfZrCJ19cf3113PnnXfuuN7f/OY3XHfdda+5nyAIgrAPKsNQHgNcvvLL1fg+zJ89jUS2HcdwmPASjNJKRW+m32thQy3JUIXDHqC7O2J5ORB8Ty16aM/v6yHwa2qbQ0ggEODee+/lkUce4be//S1f+9rX+Ju/+Rsef/xxent7ufbaaxkfH+erX/0qM2fOJBKJsHLlyl1aMQCEQpPj1jRtr+s87+DGXy6XAfjVr361i+ABXnfzzGuuuYZPf/rTPProozzyyCP09vZy3nnnva5jCoIgnNA4JlTGIZZg++gYd/7yUQBWnjwHXJcIFj1WH+v1mUyE45hamGLFpjMTPewBursjlpcDQdPV4tl7ft+zJ7c51KfWNM455xxuu+02nnnmGcLhMD/72c8A+OMf/8jHPvYx3vSmN7FkyRIikcgubRdeD319fQwMDOx4/dhjj6HrOgsWLHjVtosXLyYSidDX18fcuXN3WXp6egBYtGgRTzzxxC77PfbYY685jpaWFt7+9rdz5513ctddd73KvSUIgiAcIHYNXBMvO5sv/ecTFMtVMskYC+bNwkbHQyNNhZBnMmQGqbo6yUiQDx6BRoy7I5aXAyEYgUhKxbbE9lCMx6qqWJhDnDb9+OOPc//993PppZfS3t7O448/zujoKIsWLQJg3rx5/Nu//RunnXYaxWKRv/qrvyIWix2Sc0ejUa699lq++MUvUiwW+djHPsa73/3uV8W7AKRSKT75yU/yF3/xF3iex7nnnkuhUOCPf/wj6XSaa6+9lv/xP/4HX/rSl/irv/orrr/+ep5++mnuuuuu/RrL9ddfz5VXXonrulx77bWH5PoEQRBOaHx4ZiLAjx9cA8B5y2cTDzj4aGi4VIlSJYqu+Zw6o4nmRJg3LjyywgXE8nLgxJoms4tcC3xf/WxkH8UOvQ8wnU7z0EMP8aY3vYn58+fzmc98hi996UtcccUVAHz7298ml8tx6qmn8md/9md87GMfo729/ZCce+7cuVx11VW86U1v4tJLL2Xp0qX8f//f/7fX7W+//XZuueUWVq1axaJFi7j88sv51a9+RW9vL6BiaP7zP/+Tu+++m2XLlnHHHXfwhS98Yb/GcvHFF9PV1cVll11Gd3f3Ibk+QRCEE5ZQDC+c4p9+dC/9/UPous7iZcvw0YhiEcUjT4IiCVq9CWrlHPM700zLHpovx68Hzd+fDorHEMVikUwmQ6FQIJ1O7/KeYRhs3ryZ3t5eotHowZ/ENuqVCEuTReoiKSVcQq/juEcZt956K3fffTfPPvvskR4KoGJqpk2bxp133slVV1211+0O2XMWBEE4zvnOL+7n7/7mk6x/4Vl6F53MRW99G53kiGFgEWQrXYyToYUCsY5e/vI9VzCnu21KxrKv+Xt3xG10MISiKh3aaZYKu4cBz/MYGxvjS1/6Etlslre+9a1HekiCIAjHPJbl8s2Ht7Px5RcBOOXUZTRRJohLnhQFkkyQAaBEgkt7m5iTdI7kkHcg4uX1IILlsNDX10dvby/Tp0/nrrvuIhiUj60gCMLrwjb48YPPMPHEL/Ech5b2DmZPa0bDY4QsBVKUiGMTJEkVL5zk3JPn1suBNB/x+U9iXoS9cuuttx4VLqNZs2bh+z7btm3joosuOtLDEQRBOLaxDbxCP0++vIn+Z1X185ZTLyOvpQjiMk6GCVL4QJIqFiHmz5zOtJb0ZMmQI4x8hRUEQRCEE4nSMMNjI7z4wouY+VEC0QTJxW9gIy5xLDKU8QjgoVEmjhdN8aELFqH7zpSVAzlQRLwIgiAIwomAbUBpGIZfpL+vyIYH7wag8+RzCIQilIHNdNNOjlHSWISxCfGpC+Yxpy1ZLxOSPeIuIxC3kSAIgiAc/9iGasA4tpb+wX5++cdn2Lr+FQCWnrKcCKoie44UBmFi2PhonNQZ5x1LO6a0HMjBIOJFEARBEI53SkOQ305f3uTHz+f4xb1/AGDW3DnMa9ZopgCAS4ARmihoCWakNG65fAa6aymLS7r7qCkHIm4jQRAEQTiecUwobGd7yeSup8YYHRrlpWdXA3DGqUtIaTV69DFKWhNR3yRPhnTbdD588Wx657QeleVARLwIgiAIwvGMXcMzy3z3yQHG+zez6ZknMAyLlmyKU3qb0SiT8ksUgs3YsU6cQCufuHg+bzyp60iPfK+IeBEEQRCE45yH1vZT2P4KM91RfvDUcwCcfdpiNE0j7FtkAjUimku2q5euSIwl3ZkjPOJ9IzEvxwgXXHABn/jEJ470MARBEIRjDMf1eeLlPma4Q7y0eYjRiSKRSJgVSxfhaCHKxBn0mnE8GKlYzOtIHRX9i/aFiJfjCN/3cZyjo3SzIAiCcHTwwsYtDBsuARwefOIlAJYtXYwXShDDJIzNFq+DgGPSHHa4dEkHuq4d4VHvGxEvxwAf+MAHePDBB/nqV7+KpmlomsaWLVt44IEH0DSN3/zmN6xYsYJIJMLDDz/MBz7wAd7+9rfvcoxPfOITXHDBBTtee57HqlWr6O3tJRaLsWzZMn7yk5/sdQyf+9znOOmkk161fvny5dxyyy2H6lIFQRCEQ4VtwMRW1q1ZTcX02T5aZMOWATRN49wVCwljYnhByn6UInHC4QDvP3MWc9tTR3rkr8kJH/Pi+z7VavWInDsej6Npr61uv/rVr7Ju3TpOOukkPve5zwHQ1tbGli1bAPj0pz/NF7/4RWbPnk1T0/7l4K9atYp///d/54477mDevHk89NBDvP/976etrY3zzz//Vdtfd9113HbbbTz55JOcfvrpADzzzDM8//zz/PSnP93PKxYEQRAOKY655wbBtgHFAbYMDPDE1iJDfguPPbUNgJPmzSCZaaJEiAoxfHRi2MzunsbKBdOP0IUcGCe8eKlWqySTySNy7nK5TCKReM3tMpkM4XCYeDxOZ2fnq97/3Oc+xyWXXLLf5zVNky984Qvcd999rFy5EoDZs2fz8MMP861vfWuP4mX69Olcdtll3HnnnTvEy5133sn555/P7Nmz9/vcgiAIwiHANqCWU40SG+IlklJF5EJRKA3jGXl+80qZsq2j2zleXLMWgDNPX0aFGGOkCeCRxCAdDfCON5yBHj466ri8Fie8eDkeOO200w5o+w0bNlCtVl8leCzL4pRTTtnrfjfccAPXXXcd//iP/4iu63z/+9/ny1/+8kGNWRAEQThI6lYVHBPCcdBD4NmqCm4tD54D+a2MVDxG+/tIuiO89NQDuK5Ld1cbc6Y1YVGmRpgIDlU9yhlLT2HunHlH+sr2mxNevMTjccrl8hE796Fgd+uNruv4vr/LOtu2d/zeuN5f/epXTJs2bZftIpG9FyJ6y1veQiQS4Wc/+xnhcBjbtnnnO9/5eocvCIIgHAi1nBIusezkukAYNAMGngHHAA1e3lzBzG0hWi3z7NPPALDytKVoGmQpUtJjTGgtaK0LuOTcs46a6rn7w0EH7D700EO85S1vobu7G03TuPvuu3d53/d9PvvZz9LV1UUsFuPiiy9m/fr1r3ncb3zjG8yaNYtoNMqZZ57JE088cbBD3C80TSORSByRZX/iXRqEw2Fc192vbdva2hgcHNxl3bPPPrvj98WLFxOJROjr62Pu3Lm7LD09PXs9bjAY5Nprr+XOO+/kzjvv5L3vfS+x2NGdTicIgnBc4ZjKVRQIKZHiWpPrx9aDXYNQnP5qkMfW9hO3Sqx96RXKlRqpZIKZC5dQDaTQQkEIJxmILmT2gpOY1np09CzaXw5avFQqFZYtW8Y3vvGNPb7/f//v/+Wf/umfuOOOO3j88cdJJBJcdtllGIax12P+8Ic/5KabbuJv//ZvWb16NcuWLeOyyy5jZGTkYId53DBr1iwef/xxtmzZwtjYGJ7n7XXbN77xjTz11FN897vfZf369fzt3/4tL7744o73U6kUn/zkJ/mLv/gL/vVf/5WNGzeyevVqvva1r/Gv//qv+xzH9ddfz+9+9zvuuecerrvuukN2fYIgCMJ+YFWhPKx6FRW2q6U8rH6aRUh24KFx75oB3FoO09d59KkXAFi+4hRKeoacl2SNP5sJO0J73Oedp/age5YSPo55hC9w/zho8XLFFVfw+c9/nne84x2ves/3fb7yla/wmc98hre97W0sXbqU7373uwwMDLzKQrMz//iP/8gNN9zABz/4QRYvXswdd9xBPB7nO9/5zsEO87jhk5/8JIFAgMWLF9PW1kZfX99et73sssu45ZZb+Ou//mtOP/10SqUS11xzzS7b3H777dxyyy2sWrWKRYsWcfnll/OrX/2K3t7efY5j3rx5nH322SxcuJAzzzzzkFybIAiCsB/YhhIqZkUF6IZTyl1UGoLx9eBUoTbBhq1b2TA0StItUex7hYGRCULBACuXzSWKTdmPMuRk0AJh3rkoyvzwBOS2Ti7FQXWuo5gpiXnZvHkzQ0NDXHzxxTvWZTIZzjzzTB599FHe+973vmofy7J4+umnufnmm3es03Wdiy++mEcffXSv5zJNE9OcVIrFYvEQXcXRxfz58191H2bNmvWq2JYGt912G7fddttej6dpGh//+Mf5+Mc/fkDj8H2fgYEB/vzP//yA9hMEQRBeJ7Uc+D6kOpWVxbXUT7MM+e1glhm1w9y3zmXUiBInzAP1onTnnjSd6TGLjbhUtBiaFqAzHeCMNheMnMpS2jnw164dVV2kd2dKitQNDQ0B0NHRscv6jo6OHe/tztjYGK7rHtA+oOqVZDKZHcu+YjaE18fo6Chf//rXGRoa4oMf/OCRHo4gCMKJQyPWJRyfDNSd2KwETSAEgTCeWWT91q1YlRHaGacyOsBzG0fQgAvPXMh22rC0CE3BGhmtwoxskLZUVAmXQBg0Tf2MZdX5arkjeMH75pjPNrr55pu56aabdrwuFosiYKaI9vZ2Wltb+ed//uf9LoYnCIIgHAJ8Ty16SImMYEQJjUBACQ3Pob/k8EouQMAxyGLy/cfXAbByQRut2RTb9BS2F6bZKRIOJzirN4OeaFXH2Z1wXIklp3nX4ndHCVMiXhqF1IaHh+nqmmypPTw8zPLly/e4T2trK4FAgOHh4V3WDw8P77EwW4NIJLLP9F7h0LE3F5UgCIIwxWi6Wrx62QvPgex0wAfbwMsP8PiAy5jhk8AhUhji2Ze2AHDZmfPQ0Yh5Jj4aBeKc2hLlpGnNEG/d8/n0EPg1JZiOQqbEbdTb20tnZyf333//jnXFYpHHH398R0XX3QmHw6xYsWKXfTzP4/7779/rPoIgCIJwQhCMqAq6VnVXK0wgDFqAP67vZ1PRxyJCkRg/f7IPz/eZPbMLt2spIzRRIoGhxdCjTZy7dD56oh30vcgAz54UTEchB215KZfLbNiwYcfrzZs38+yzz9Lc3MyMGTP4xCc+wec//3nmzZtHb28vt9xyC93d3bs0DLzooot4xzvewY033gjATTfdxLXXXstpp53GGWecwVe+8hUqlYrEVwiCIAhCrEkF0pol8FwVsKtpWEaFZ7blsPwQQzSj1XI8+Hy9j9GZbwCgSJwtdBGLRLlsYSsnLZ4DwagSQ7E9uI2sqop9OQpdRvA6xMtTTz3FhRdeuON1I+7k2muv5a677uKv//qvqVQqfPjDHyafz3Puuedyzz33EI1ORi5v3LiRsbGxHa/f8573MDo6ymc/+1mGhoZYvnw599xzz6uCeF8v4v44vpHnKwjCcUkoqjKAajmVYVQdg2iWR/urDJhJwCWlGTz89LPYtkNHRxvzZ3Vi4OMQJBDQWDCtlbcta0ePppUYKg6o7KKd2wxYVSVaYkdvbKPmH2f/6YvFIplMhkKhQDqd3uU913VZt24d7e3ttLS0HKERClPN+Pg4IyMjzJ8/n0AgcKSHIwiCcOipFaDYD67Nvzw+wJOPP0JCN/HNKt/65rep1Qze9tbLmb9oIQZBTKLkEnP4+z85id6O5sk06Ndq8HgY2df8vTvHfLbRgRAIBMhmszsq9sbj8QMq0S8c3fi+T7VaZWRkhGw2K8JFEITjl1hGWUdqOdKhQVxNB03jqRc2UKsZJLItRBecR5USEWxMfC5a0ETv9Gm7CpNQFEJdKquoIV6OUlfRzpxQ4gUmM6Gk5cDxSzab3WeGmiAIwjGNY+4QGl6yk565LpsfGyRuTvD0E08CsOTMc8gEDCzCeD6YoRSXnzJ37xaVY0Cw7MwJJ140TaOrq4v29vZdOi0LxwehUEgsLoIgHJ/s5uLZkjP43aYKTw1rVEJt9D/3CLXCBOF4ijnLz8DWPUw3iKGlWdDTS09aUzEuR3Hl3P3lhBMvDQKBgExygiAIwrGBbSjh4ZgQjrM5Z/KdR/vZNjSG7Xok9SzPPfFbAOatOIcoDhU/gR2K09newQ2XnIweS6rg3FpOuYqOYU5Y8SIIgiAIxwy1nBIusSye73PXI+t4css4KTdHp15gYsMfMEb6CISj9Jx2ARPRNrpbMqyY3ckbF7fT25JUxznKK+fuLyJeBEEQBOFoZue+RsATmyZ4at0WptsDZPQqnqfxwsP3ATD/1LOYk9HIRWJcf+FClvc0oe+cmHKUV87dX0S8CIIgCMLRzE4VdT3f5/uPrafdGiBNmZyXYmign/6+beh6gPmnnU3IN4jaeQzb21W4wFFfOXd/ObZHLwiCIAjHOzv1NXpi8zjDg9tIUyVHGo8gax59CIDpS1YQT2UoOTopr4DmWq8+llVVdVyOYZcRiHgRBEEQhKObel8jz6pw99NbCNoVbALY6IwOD9K3/hVAY/ZZl1AjQtUJEApozIhbqoWA76uftfxRXzl3fxHxIgiCIAhHO7Emnu6vsnXbdsK+CfjEMXjlEdXMeNqi5SRaOrE9HQ3ItHXT1d5Vj5cp7gj2PR7SpEFiXgRBEAThqMcLRPg/vxvGM0ymUSRFhbHRcTatfRmA3pWX4/kQxCMR8rj4tMXo2S7VyBEgFDvmXUU7I+JFEARBEI5y/s/Pn2ZibIgwMTbTTZoKDz/6MACz5i+kpa0VD4d2rcC87i7OmNkMua1HvF/RVCHiRRAEQRCOYgzD4ber1xHBoUgCgzDViWE2vqKsLivPPoMsY1SJEY5leNe5S9BdY9dO0bW8ssIcJ24jiXkRBEEQhKOYHz65gaBdoUqECBZpKrzy6O/wfZ/Zc2fT2dGGg04fnZx2ylJmtTer+JZAGDRN/YxlVdxLLXekL+eQIOJFEARBEI5i7nmhHx2PAC6t5InlN7B1zTMArDj7XHRcYhgEwmEuW9Syo5jdq9hRXdc8jKOfGkS8CIIgCMJRyiuDRV4aquCh084E3Yyy4dF78X2fWb0zae/qokScBDZnpMbpiqFcRXtCD00WvDvGEfEiCIIgCEcDjllvnJgHx8TzfP7p/vWU7QDgM5tB3NwAL7ygYl1OP/tc4pi0UmSYLO9c2opem1AxLnviOKmuCxKwKwiCIAhHFtuA0pDqGm0UQQPCSZ6aiPHcliF8QMMniM0jjzyO7/vM7J1Fz/QOQFlRWhIxls2ero5Xy0Oy/dXnsaoq9uU4SJkW8SIIgiAIRwrbgInNUNgGWgDizYCGZxZ45cWXaXU0ylqaqO6xYcTmxTXrAHjbeUtoI0+JBDk9xTXLW9A1IJKEQEgJmJ2zjazqcVNdF0S8CIIgCMKRo5aDyigEoxBNq3WuxXMjDs/3l0g7DhlPx8flmT/+Ad/3mTt3NnrXIkbxCWkuc9M6p7UHQA9DNAPJDrCrKjjXryk3USwrdV4EQRAEQXidOCbUJlQAbTihXlcnGBgb549Pb8OvFWj2wABKo4NsfFnFulxx/ilEAw6GG0SLRHjT3DBB34VguF6MLqMWp3mySN1x4CraGREvgiAIgnAk8D1w60G0lgGFrXhGgdVrhqnlRmiyHcI4JCnxw4fvA3x6Fyyis7WJtDeKEwqxfMY0FnZmlasomt7VLXScCZadEfEiCIIgCEcCTVeiwzGg0A9WmTE7wprBCTzDJYxHAJ+J4SE2rlOxLuefcxp9tNMecTizO8AbZibUcZLtkOo4btxCr8Wxny8lCIIgCMciwQjEmqGah+o4xFt4ZF0/tmHjAUnKtDDBgw8/CcCyRbPJtHXiEaS5qYXlixejdy2FppnK6hLaS3G64xCxvAiCIAjCkUIPKDOC62KVxtnSt5l2KnSSJ4LJ6gGHlzdsQ9M0Lj5nGVGq5EjR3NbNtBkLIBiCyvhxUbvlQBDxIgiCIAiHG9tQmUbVMXAdCIS45/HnmOkMEsHAI8A4Wb7/8BMAnL+kg66WKEmG0YDp0U61rx5Swb7h+HFROXd/EfEiCIIgCIcT25gsSGdXAZ3tVSjmRklhUyJBlRjPbc3z4uZRArrGted0EyHPIK2EAh7TUyjxE81ApFNlGp1A1pcT50oFQRAE4WigllPCxTHAc/AiGe55eQzL0wnj0UKRmh/g7gefB+DS5d20ZNO0kyOERSIeZ3bPTGidp1KjaxPq53GcXbQ7Il4EQRAE4XDhmKp4nGeDa0Ewymi5xngxRxwDgwBhTPrXvcjWwQmiIZ0Pnd1GBAsdlwRVrpiXJmiXwbHqx3H23kn6OEXcRoIgCIJwuPA9ZXFxLNUOoDLK4+uH2G5kSJOiCQ3DK/G9hzYD8M7Tu4gnElQJEibA7JTH4q6scjfVxiHdA7oOgfCRva7DzJRaXmbNmoWmaa9aPvrRj+5x+7vuuutV20ajJ0bOuiAIgnAC0IhLcS2wqvSPj3H/+hwl4ozQRAiHXz1fYOuERToW4o1nLMIhSAybaijB2Ut6lZWlaRZE0hBNQSh2QsW7wBRbXp588klc193x+sUXX+SSSy7hXe961173SafTrF27dsdrTdOmcoiCIAiCcPgIRiCSgbH1eLU89z+9kYyRJ4tPGAPHtvjeH7cC8N6ze4hFAoQwCQDdTVnC6WngmuCj0qytsupldALFu8AUi5e2trZdXv/d3/0dc+bM4fzzz9/rPpqm0dnZOZXDEgRBEITDj2PW+xhFwaqw9vnHqY0WmI1BHIMMZX709DbGyzadmTB/sryJKjajpImFPU7riKr4lvIoFPpUB+lE23HTKfpAOGx2Jsuy+Pd//3euu+66fVpTyuUyM2fOpKenh7e97W2sWbNmn8c1TZNisbjLIgiCIAhHDbYBxUHIbVXL4BqcapH1/WO0OBOEsfDwMWtVfvRYPwB/dl4PejBEGIskJslognSmHTSUAKqOq5iZVOcJ0xJgZw6beLn77rvJ5/N84AMf2Os2CxYs4Dvf+Q4///nP+fd//3c8z+Pss89m+/bte91n1apVZDKZHUtPT88UjF4QBEEQDoJGTZdaXrl2AlEwcqwZGGfY1DEJ4RKgiSo/eGyAsukxpy3KmxenyZGkQBI0mNWZQQ9HQdcgNQ26l0NmOti1I32FRwTN933/cJzosssuIxwO84tf/GK/97Ftm0WLFnH11Vdz++2373Eb0zQxTXPH62KxSE9PD4VCgXQ6/brHLQiCIAgHTXFQCZdYVr02inh9j/Ot/15NvLQJHQjgUspPcOO/PI7j+nzpnb0sndNOgTQukIxGuHRhC8FQBLIzofcNkJ0OmqZcUU0zj4uYl2KxSCaT2a/5+7CkSm/dupX77ruPn/70pwe0XygU4pRTTmHDhg173SYSiRCJHPsPTRAEQTjOaNR02a0GyysD47ilIaKYBHFIYvLPD6zDcX1WzExxxuwMw8Tw8GiJ2ixpjxD0HcguhI750NSjUqN9H/zaCdUWoMFhcRvdeeedtLe38+Y3v/mA9nNdlxdeeIGurq4pGpkgCIIgTBG+pxY9tGPVllyFX6xeRxsFAnhoaDy53eTBtRPoGtx40TTQoJUKEc1jzrQeOqfNga6l0DobrKqq0NsoUKfpJ1yaNBwG8eJ5HnfeeSfXXnstweCuhp5rrrmGm2++ecfrz33uc/z2t79l06ZNrF69mve///1s3bqV66+/fqqHKQiCIAiHloaw8GwAPN/nP/64lkEjiU2QEA6uD3f+bj0Ab1raxty2GDZhDELorXOYNW0GaB5Em+qZRiMwtgGK/TCxVVlgjgOX0YEy5W6j++67j76+Pq677rpXvdfX14euT+qnXC7HDTfcwNDQEE1NTaxYsYJHHnmExYsXT/UwBUEQBOHQEozUew/lIRamfyzPi1sG8MmQI0UEi4df2s6mwQKxcIBrz5sGgIZPjRhzujsgFAbPhMqosrrEm9WxjaKqrGvXVFDwCZZxdNgCdg8XBxLwIwiCIAhTSiPbyDH52gMb+cMzL+GhcTIbiNhF/v7//Rf5UpVr3zCLq1bOwgdC2JjhOG8++yzCyTYVmFseVWnR8RbwHWieA6kOJV5iWUgf++EVR13AriAIgiCckISikO7mD8+v4741/WQxAY8aUR584mnypSrZdJIlp5/JWsLEsQli05NtIZydpgrRGSVomg5Ns9XxPBvSncplpGkqKNhpPqHcRyJeBEEQBGEKcbQwq/6QZ4PVwXRgAVuolIo8/PhzAFx5/nKagxY1NDbRBYEA175hBXTMqddx6YdwQvUycmoQzU42YtRDJ2TG0YkXoiwIgiAIU4FjKrHhmLusfnLrBOtHSmj4NFOkh1Ee+8PD2LZDT3c7MxctoUScIA4uQU47eRnR5m51LD2gUqLRlHAJhJV4aXCCZhyJ5UUQBEEQXg+2odKXzZKygGi6CtSNNUEoyk8e20jUK9PNKNMY5ZXhGk+9oOqXvfWNp9GkVZkgQ5way9t0rr5gOYQSUB1TbQD0AOBDNK2Ey87uIauqYl5OIJcRiHgRBEEQhINnp4BcwnHlxvFslWFUy/Pw1jIb17/EQkbpYJwuf4Sf3vs7ABYtmk9o2mIG8JkgRSwa40Nndahj6UGIZiDRrjKOagVlgdE09dOzlXAJRk7IxowiXgRBEAThYKnllNholP8H5doJ+Tgj6/j945txCBLFI4zDujUvs71/iFAoxMoL3qhqvaARw+akmTNpaWlXPYuiaWXBaVhUok07WXdq6r1Ydod150RDxIsgCIIgHAxGUbl2grE9vJfn0S05NoxXwU+SoUrVdPjxAy8AcO7ZpxNLZ7FwiVMlE4UrFybRo5k9u4FCUQh1qayihmvqBHMV7YyIF0EQBEE4EBoxLpVRKGxXWUCRpBIxwTBYFbZv28K9Lw3jmgY6Om1McO8f/0i5UqOjOck7T2unwDAWIdJBk4t6kswIllURun2JkhNYsOyMiBdBEARB2F92jnEJxiCSAauiREytAJqGV82z6aUNNOctwn6QHBlKowM8+ZRKjX7PRcuYExylRpFhvYlTejpZOqtTuYp8/4SsmHugiHgRBEEQhP1l5xgXx1SBucMvQGUCCgPgmpQdD3dsiHlWAJsAlh/ib+9/Bt/3WT6vm+Wzm6lSJRDVWRyucWpXBLIzoPMk8Fx1jtCxXzF3KhHxIgiCIAj7g2OqgNlwXP1eGlKuo+Iw2BXwXTCL9E1Y+IZDmho5Ujy2dphXto4RCuq876LFdFBgjAweaZbMaiUYTQB6vcniiVkx90A5saraCIIgCMLB4ntq0UPK4mJVwChAKALhGLg1irZDXwHKJHAJgWlw1+/WAfDus6bTlYlQJM44aeLZTuZ01rOLfBcqI/WKud4JVzH3QBHxIgiCIAj7Q6OSrVUBuwpWTcW6+IBVxbMNXh5xKXsRPDQmSPK9h7eQKxl0ZyN88IwWbDQqJIhh0Z7QIJRUtVzCCTDL6rgnYMXcA0XujiAIgiDsD8GIqpxrlesZRxOqZH8wDFqAwUKZvGETwCWKw+ahEj95egSAGy6djxOKkSNLkRg2QZqyWdUZOhiZ7FVkldU5xGW0T0S8CIIgCML+EmuCUAzy21TvoWAUajkcq8pgqUoIlyRVwl6Zb92zBs+HNyxq59zeGAE8LIJ46GSjHova0yqryK6pY7uWOt4JWDH3QJGAXUEQBEHYX1xzMnDXLIBRgeoID/Y5OEaEZioEcfnx6mHWD1dJRgJc+8YFOHho+MxmiDHSzJ89l3C2S5X413XVDiDZCU2zJE16PxDxIgiCIAivhW2o7KLxTUq0hGMq9iUSY+2gg1ceQQdcfPLFKv/0hwkA/scF08gmQ2ykmTwZZjHEopYAZ86dPlmQTg+o1GsRLvuNiBdBEARB2BeNwnT57aD5kJqmgmutIsOGzh8Gg7QRI4RNhQRfuH8rFctn6bQYb17WShmXMC6gMZJayNWnNAM6hKMQb92lA7Wwf4h4EQRBEIR90WiIGAhCIAaBEACepvOrdRUML8IQzTRT4rfrx3lgXYmADn9x2UwGtHbypKgSJY7JlQvS6LoPwQAEImoR4XLASMCuIAiCIOyNRnxLKKpqsehBcGzQQ2wq+hRKJboYo50crlnle/eqxot/enobvW1JmiiTxKBKlK64x5zgGBCA9DRlcbGryqpjG0f2Oo8xRLwIgiAIwt7YUZguAloAPAd8Dy8U4aevOMQwaKJECJu7fr+BsZJFdzbCh85pp0ASD51WcnSQ48KFrejhOEQSSgwFIzu1Gcgd6Ss9phDxIgiCIAi745gqhdm1VcE4XYNQXK3TdNb055ioVHEIUiPC6i0F/vu5IQA+fsU8AqEwESwqhHHR6W2L09HSAbHmehuA+GRtl3C83hLAPIIXfGwhMS+CIAiC0MA2JmNcHGNyXSCsrCRmES+/jYdWr2U6I4SxqVku37hHtQB406ndnDEjhgVkqBLFRI+GOWdGsJ4SHYBIWh2rgR4CvyYtAQ4AES+CIAiCAJNZRUYRPBscSxWOs8rq90Q7+DYPv7KVXGGMORTw0Pn2g1sZKRi0pmNce/5swMIkRJEkOh7nTGtF9+vdoptnQ8sciKYnz+vZ0hLgABHxIgiCIAigxIVRVBYX11KVdMNxiCRhYjMMPsdjW0d5+MUBorhMkOW5bQXuWb0NgP91xTxK4VZcTEJYhPFob0rTnMmqFGurojKVopldz2tVlSVGWgLsNyJeBEEQBKGRVeTZSrg0LCOOBdUxKA0zvulJNqzNkfST5EhRtl3u/s0fALhw6XROnZVlAoccaZookIqarJjZorpOR1KQ6la9kIr9yg2l60q4BCPSEuAAEfEiCIIgCL6nLC5O3eLiWmCbUBqE6gSeUeLhTUUMN0gUm2nk+Lc/vMRorkw2GeNP3ngyRSCGSZEEE6SZNW0awWkzVb+iaArS01UzR6sMxe2Q7FAWF6nzcsCIeBEEQRCERryJVQZbU0KmMg6lYQgEGamUGTMCxDEokGTDtmH+8ORLALzn8jPIRjQsgpiEyJOgPR7k9CXzIV4XJpoGgQAk25RY8RxIde4a+yLsNxIdJAiCIAjBCASiUByEWgF8rV7TxYfKBGtffokwNZopEjYnuPNXT+IDZ53cS9ecBdiEqBFmghSBcITzl/QQDNTtA44BwRh4LoQTEM0qt1G9Uq9w4Ih4EQRBEASAYLjuMjJVgK1rgFVi03iegaKDQRSbAD++/znGClXaMzE+dNF8WimQJ4mOTxSbFXN7WdCZVa6o8gjU8iqWJhBWwkWyi143U3rnbr31VjRN22VZuHDhPvf58Y9/zMKFC4lGo5x88sn8+te/nsohCoIgCIIK2HUt6Fii3DpGHqoTjOVy/H5jmbwXJ4TDH9bl+e8XRtGAv7xyHpFIiAg2YSxypClGW3njioXKwmKXVaZSMKpquaQ6lYXHqqoAXskuOmimPOZlyZIl3HfffZMnDO79lI888ghXX301q1at4sorr+T73/8+b3/721m9ejUnnXTSVA9VEARBOFFptAGIJFUdllwQL7eNhzeM4BtgEcEuV7njHhXn8pYze1k4vYURwkyQIYBDhQhvPHkpwfb54M2D0kC9tUBYFadzbVWhV7KLXjdTLl6CwSCdnZ37te1Xv/pVLr/8cv7qr/4KgNtvv517772Xr3/969xxxx1TOUxBEAThRMaxoDqhrC+eC9VRfrsuz/PVVuKYBHyb797zPKWazcz2FO87t5cCMbbQToE0bRTIRMOcv2SWip2JhFWgrpEHswxmQWUcJdoku+gQMOUOt/Xr19Pd3c3s2bP50z/9U/r6+va67aOPPsrFF1+8y7rLLruMRx99dKqHKQiCIJyo2AZUx8F1VOdoz2NbzuDxbTVsQlSJ8Oxza3h24yjBgMYnrlxELthEiTghIIGBH4nyvnPnosezKnYGlIUl2aHcRdkeaJoF6S4RLoeAKRUvZ555JnfddRf33HMP3/zmN9m8eTPnnXcepVJpj9sPDQ3R0dGxy7qOjg6Ghob2eg7TNCkWi7ssgiAIgrBXGk0XHVMtxUFVoK5pBuhhvOooP36hQN6L0kaO2vggP/jdGgDe9YaFzGpLEsdkhCYGyeKFY7xrWSfdqeie3UGuDfFWSYs+hEyp2+iKK67Y8fvSpUs588wzmTlzJj/60Y/40Ic+dEjOsWrVKm677bZDcixBEAThOGbnpot2Dao5lVnkVCDarNKio2n++EKV9cMFmrBxHZt/+/lD2LbD3JldXHD6fCrYxDGYzhgasHLeHBZ3ZyDRqlxOrqUCdD1bKuhOEYe1SF02m2X+/Pls2LBhj+93dnYyPDy8y7rh4eF9xszcfPPN3HTTTTteF4tFenp6Ds2ABUEQhOODRtNFx1QCI79NVbt1DDCr0KyD57C14PGTF0tMeHE0XO75/Vr6RwvE4zHef+VK4pqBBmyhixA2sWiQN85vgWgSEh0Qiqtz+DWVCi0VdKeEw5pkXi6X2bhxI11dXXt8f+XKldx///27rLv33ntZuXLlXo8ZiURIp9O7LIIgCIKwC7WcEhWhGOS3qkDaRJsq2e/7UNyOV53gZ4+tIV8qEcJl87pXeGb18wBc+Oa3EkpmKZBijDQRbNIYXDYzSDAchXBKxcuk2qFp5uQiMS5TwpSKl09+8pM8+OCDbNmyhUceeYR3vOMdBAIBrr76agCuueYabr755h3bf/zjH+eee+7hS1/6Eq+88gq33norTz31FDfeeONUDlMQBEE4nmk0XQzHodAPlVGIpAFfZQJ5Nkxs5g9/+B2V7S/RQQ6tOMADv/kVAKeccQbTZ88hTQ0fqBCnRJxAuoOTZ88CPaiyiSpj6njBiBJJUsdlyphSt9H27du5+uqrGR8fp62tjXPPPZfHHnuMtrY2APr6+tD1Sf109tln8/3vf5/PfOYz/O///b+ZN28ed999t9R4EQRBEA4OxwSrAkZBWVgmNoFRUkG0rgk+oGlsmjBY3V8hCliez33/9d8YhklPVwsr33AeHmARIAhoaPTETC5f3I7u1JRoqZUgloLykGorIK6iKUXzfd8/0oM4lBSLRTKZDIVCQVxIgiAIJyo7B+caBRh5WRWK81QqNGZB9TDSfBxf56sP91EwQzRR4pcPPsvvHnuRUDjCuz/4QRLZFspEWcJmWsiTjnictXAuvW1ZZbXxXQhElItoxlkq1iUYgXS3CJgD4EDmb+kqLQiCIBxf7BycG46DFlSNEe1K3eLiQGkEdA0qYzywbgLHjBFA5+X1W/ndYy8CcOblb8PMzsTDpZsJOsiRCJr0NLczM62rDtThBCS6lXVH09X5glHVz6iWg9CeYzyF14d0hRIEQRCOLxrBubGscgs5NWUFSbQpkVEcVOnMZpm+4QIj+TLNlMnk1vLvv3oMgAtXzONtiyIsYyNpytQIUCCOHW5n3qwZ6NEkhGPqmMGgyjYKBJU4AiVizJIah3DIEcuLIAiCcPywc3Au1HsWuRBOQiCk4lNyW8EoMFEpsWFogG5sirbGX9z9IjXTZsG0DH924VyKuMygj26G2UwPFiHOWjyb9mkzwLNUurVTUyIJTVXWDYTUefWQSpf2vSN1J45rRLwIgiAIxw++p2q36EHwLeXK0QIqiFbTlLgIxRmrWTzcX6FkZpnJCP9870Y2jVRpige59W1zSQfKdFIkiEccgxA+qVSKOUkTSsOq5H+ySwkjTYdgCCIZ9TuoWBhNn3wtHFJEvAiCIAjHB7ahYllKQ8pdFAipxfdVRd1IEooDeLrOf/alGC8bzKTIfz03xq9emEDX4La39JBMJUhQJY5BmRgGYXxN4+y5rSpD1q5BZQQiKVXgLpyAQBjiTeonqMq6saykS08RIl4EQRCEYx/bgNwWlVlklFT1XNdRbp1ASNVdcRwY38iDfRYT4z4ZqmwbnOCf7t0KwP88r53zZkUoUkXHwyJEiiplYsxvT9He0gYEIBSu/0ypirqupYRMJKN+l5YAU46IF0EQBOHYxjZg5BUYfQWqo1CdAKOiAmhjTVDOgWuAmad/8ws44xrTaWFbGb740+ewXZ9z56a55qxWfHx8wENDwyeAS1vU5IyeekBuok0VotN8MPPQMkd1jg4EVd0YT1oCHA5EvAiCIAjHLrYBo2th8HkobAOjqOJQXAv0FBS3g1kB32XU1Fmb1zAIg2PyLz99mlzZpKclzuev7CSpORSJYxMijE0cC6IhFrUn0e0yhBLK2uI6Ko4m1QbdyyHVUe9n5E3WeBGmFBEvgiAIwrFLrg8mNkKhD4pDSlhYBSUuyKmaK3oYz8jz8sZham6ImG/xj/esZfNgjmQ0yO1/Mhc9EsDEIIBLEgNwiUYdulJpMtEwRLLK/WQVwalC8zxo6a1nGiGC5TAj4kUQBEE49rANlfUz8DTkB2BsHdSKEM2CrykrSHkY8CDdw6bBEaqGRYkYv35yEw+sGSagwWffNof5TT4hTKpEKJAkiEdSr9GVSNIc1iGYgOZe5TIyitDUC00zIN4iouUIIeJFEARBOLZoVNCtjILrKjeRVam7i0xlIdE01Q6gmqNgwHC+RIAwWzZu5Y4H+gH46EU9nD0rRowaIRxqhCmSwEejJZ0m25VUriJNV+0EzLISLOkuFaArAblHDBEvgiAIwrFFo4JuNKtK9Oe2KKGiB6E6DsGkqrvi+3iOy0hpCNtJMjhW4v/8YgOeD29e2sp7T81QJkqFKGnKGISpESUdDTBjwTz0pjYIRVTcTCgBqS7ITlMCRgJyjygiXgRBEIRjh50r6NomlMeVuAhEVSq0Y4E5qBolWmX6Siam4TJRtvn0T9ZRNj1Ompbkf1/aTkRzyBMghoVDEJcgmgbnLJ5JW1MLJLKqW3TncuUmal+oarqIq+iII+JFEARBOHZoVNB1XRhfq1KjjTJoNVWeP5wER6UtDxar5Awd23L59H9uYLBg0d0U5QtXzSIS8NDQiWNSIkaJZlrJc+a0MJ3JsHJD6QFlcYlllZtIhMtRg4gXQRAE4digUUG3sB2K/aqui++pBolGXrUa9j3AZ8Tw2F4JUvCC/M1/DbF+qEI6FuLv3zWPSDyFSQ0LnXEyFEkBLm3NTSyaPUtZbfQgNM2GTLeKrfFcES5HESJeBEEQhKODfdVKaQTpOqbq3FxqCIo4RFuUJcYsgusxSJLHimnSvs2X79vGExtzBIMBPvInZ5No0hkmBvj4aPWaLhbzmjSWz+2FQAySbaq9QDyrrDyRtCpC55giYI4SRLwIgiAIRxbbUEG4ZmlSvDSyeRpBsY0g3XAcHFvVcfF9CAfqgkID16Jk2YxVyoSMZr77+Bi/fmYIDbjqygtJT5uBx3YSGATwqRDBIExb1OXUOdMg2Qoe6jyhqBpLLFsv+29Kh+ijCBEvgiAIwpFjZ4tKOA56SHVkruVVA8R0t0p7bgTp1grKjRNOqP3Ht0BhAKwSo06IrRULDJ8nXtrGdx/cAsBbLjqdMxZ00UczGjqz6CeIqzKLQhYL5/QSzNbPEwCiacj2QKZHNXN0LVX2XzpEHzWIeBEEQRCOHA2LSqNSLajOzLGwEjC1nHrP98DzlHCxK5BohWwv5LeDU2W45vBiQSfkR3h58xB/92tVy+XS03o587STcIAIDjlSRGkhRxINnbN62pnT1QrGBLgetMwGNJUOHUmq8UiH6KMOES+CIAjCkWHntOc9EY7X368XiquOqfXJNpUS7buAzzYjRCk/QTMGz/XbfOJn/diuz3kLmvnzC6fxOHFMIgzQTAgfgCaKnN2lsWJeJ4QzkJqmRJFTA89XFiDpEH3UIuJFEARBODL4nlr00K7rXaseX1Iv8x8IqQygyjgkWsDKwvgmKA2yebzEwHiZLBrbRsp8/MeDGLbPslktfOzKxWh6kAg24zRRIEONCK4W4OxFM7lwYVJZWHxXpUW7GSWKrCJUc0o8SYfooxIRL4IgCMKRQavHkXi2chUZJTByKtZFD6hsIh9VSTcYBTzIbQPPAsdmw8Z1lIe3EyLEs7kwH//RZoqmx8LuFJ98x1IiQRcXjwA+eZLEMXAiaf7n+cs4N5tTFpV0l4p18X310zbUuSMpSHWq+BfhqEPEiyAIgnBkCEaURaU0pDKISoMqqycUUxVzjYLaznMgklAxMPX9Xsg5rBm0mUGU8aLJTT/cxHjFYWZbktvftZjWcIk4DltpxSSEKknn8f4zejh3SRf0r1ZuK89Rheg8W4mmQBiSHZM9koSjEhEvgiAIwuGnkR5dGYXR9aoDdCgO2RlKOIyuU7VVOk9S1plaXgXs+g79NY0fPDNKkmn0l3y+/B8PMFIw6WqK8TfvPoVoVAdq5IgySBsAreSJplt53xmzIKhDNDnprrJK6hzRtLLyaJpkFx3liHgRBEEQDi87p0fjKxeNWQLXUNVzNR1iGZXt4zkq9mR8CHwPL7eJBx7byAzHYbxs8Pf/8SijOZPOTIQvvGcxM5I2NkFqROinlQJJDMIkMXjn6d0Eg7oKwk20K1dUvGmytkwgrMZXy0t20VGOiBdBEATh8NJIj9YCylVkFsEzVXVb3wE/ANnpaluzqPoMja/Di2T54Yt5ioaHW85zxw8eYXSiRnM6xi1Xn0JXBhw8HDRKJLAIYRDCIsBZs9s5c3pCdaGONatYl+qEEjKN+jKSXXTMIOJFEARBOHw00qN9V2UMjW9WYsGqgW6pWitWBWIpZRGp5aE0Sm50Oz/b1M+4oWNWHL7+gycYnqjRngrxmatXUMvMop8SYRwCuNSIkCOFSZS507u5+sqLAQ/S0yZrygSjO1X2rdUtPlnJLjoGEPEiCIIgHB4cU1k78v1QHYfcFjALKs4kFFMBs1YV8lvBKqhgXtdiopjjoQ0jpC2fagW+/IPHGJ4o0ZoK89Wr57EoO84AHiO0MEqGID5DZFnHLBZNb+F/vWmeiqOJZtR5GoSiEOoCp3nvPZWEoxIRL4IgCMLU0gjOLQ1Cvg8K/VAtAPVU6HwfBGMq5kXXVePFia0Qb8JxbF7ZuAWsELVihb//jxcYzJk0JyPcevUpNDVp2JRopUQUhwAOL9PLBE2cOrudm69cro5XHa/HuexBnIhgOeYQ8SIIgiBMDY5Zt6T0qbTn8ig4VfX7wNPK4hJrUjVWiv31YnQhcCtgVRnXkjzWVyNoeOQm8nzqP15mtGTRmQ7zD+9dQKYpgolGPzEsNIy6fElSZcX8mVz1xoVKuFg10IIQk5otxwsiXgRBEIRDg1PvvNwIfDVLqvdQYSsEE8p147lKxNRyKrbFqqosn+qoei8UA9djrd3CI4MOGB7lUYtbf7iGiYrD9OYoX3lPL+1pHQsLkxAVQtSIUiFOhTBLWsO8dUFCuaGIQDgGwSaVii0cF0xpEvuqVas4/fTTSaVStLe38/a3v521a9fuc5+77roLTdN2WaJRCZwSBEE4arENKA5Cbquqz7LtaRhbXy+1X1YF5zxbWWCKqmEi8XZwHSVarBIEUxBKghair1Dj2cEipuHx0kCFT3//RSYqDvPbI/zwfa0sTRskMWiigI6Li46Gj47DrPYsbz19HsSbIdOtAnTDSdXIUdxDxw1TKl4efPBBPvrRj/LYY49x7733Yts2l156KZVKZZ/7pdNpBgcHdyxbt26dymEKgiAIB0ujZkstr8SB54Gmmh9SHgIzr2q2ANhVtegB5cKJpVUhumAUgkGIpBiv2awbN9Bcj/Wb+vmnHz5A1bBY2J3i/109nekJDx2HKA5hLJooE8UhhM2CtgjvXJJSlpxoE2j1c0rq83HHlLqN7rnnnl1e33XXXbS3t/P000/zhje8Ya/7aZpGZ2fnVA5NEARBOBQ0araE40rImAWIpFXsSnEAjApEsqp+SySjMow8T2USoavUaNfEs2q8Mlpje8GgRoT7nh/k2/e8iOf7LJ3ZxOeumk8tHGSMCcK46LjUiOHjk6VIJJlh5fQQ6FFItKmv5q6tGi9K6vNxx2GNeSkUVJ+K5ubmfW5XLpeZOXMmnudx6qmn8oUvfIElS5bscVvTNDFNc8frYrF46AYsCIIg7B3HVFk8dg1qE+pneUSlJEfTKsZEAyrDYFZUzEstr9xEegBqJdA0JgyPFwfKlAyI+i4/emQL339YWdzfsLiDT79pJrVAAg+H7XQQwaKTCUJ4TJAkGo2zcskCcAxVpyXbA5lpkO4WV9FxymETL57n8YlPfIJzzjmHk046aa/bLViwgO985zssXbqUQqHAF7/4Rc4++2zWrFnD9OnTX7X9qlWruO2226Zy6IIgCMKesKqqy7MeUI0TAxHlpqmMqd8TrSoItzqhLDSeq9xEvgbFYbCKjPlx1pXzFI0EY16C7/y2jz88p4TLm8+aw3vfMBdXMzCJYBHBJkAMox7rEqAzGWD5rGYIJ1R8C77KXkp1iXA5jtF83/cPx4n+5//8n/zmN7/h4Ycf3qMI2Ru2bbNo0SKuvvpqbr/99le9vyfLS09PD4VCgXRa0uIEQRB2oZER9HoLstkGDL4AQ88rS4uGSkm2a+p3LQBoKkC3PAa1MWWd0UJgV/Bsg00TDoMVixAe42aIm/9rOy9sGkbTNN5x8emcc+p8XHSaKGESJoBLDyOEcKmG4pzUO4O5cUtZeJpnKcESikNLL3SeLOLlGKNYLJLJZPZr/j4slpcbb7yRX/7ylzz00EMHJFwAQqEQp5xyChs2bNjj+5FIhEhEPqCCIAj7pFEozixNipdI6uDiQWxDxa7U6oXfPFutM/KqmaJtgzWhrCu1Qr1fkQaxFjDL5J0Aa0dNckaEJDZrczqf+c+X2T5eJRgMcMlb3krP/Nm8QoQ2ikyQppUCbeRJUyEa9TltegfNcUc1boymoakXku31WjFhdY3CccuUZhv5vs+NN97Iz372M373u9/R29t7wMdwXZcXXniBrq6uKRihIAjCCcDuGUGRtPpZy6v1tnFgx6vlVHxJOKEEg6Yr4RJNK+FiFlWatFlQmUepDggpUbGuFOCBbR6moRPE4Y9bLT763TVsH6+STUa54X1vZsb8hYyTZT0zGCWLh043Y7RRYCzayrS26TRHAlDcroJyE21KNOkBdV3BqBqTcNwypZaXj370o3z/+9/n5z//OalUiqGhIQAymQyxmOovcc011zBt2jRWrVoFwOc+9znOOuss5s6dSz6f5x/+4R/YunUr119//VQOVRAE4filkRHUaEgIyjoRCysBU8upHj/7Q6OxYjipiszpusroqeVUqX+7qERLvF1lGUVSEAzjVPOs2drPujEDiwgpNO5b3ccX7xvC9WFWVxPXXHUh4WQTBWo4hEhi0E8rESwKJPAS7Zw9K013OA9mGZpnq2sKRlTV3kg9TTqSEpfRcc6UipdvfvObAFxwwQW7rL/zzjv5wAc+AEBfXx+6PqmQc7kcN9xwA0NDQzQ1NbFixQoeeeQRFi9ePJVDFQRBOD7ZITb2Ul02HFfvO837N+H7nlrCSWV5MYpKwERSyhrjOCpg1pxQVpdghI3b+lizeTMBo0YXFXBM/vH+AX72bA6AS5Y0c8PlS6gEHYZxKBOlRIw28tQI46LRPH0+V50+l6BrgFNTlh49rCws1ZxyfwWjk64w4bjmsAXsHi4OJOBHEAThuMeuqcq3kTRo2qvf933l5mmauWvH5d3ZUfrfhvGNKiDXs6A4pPYvj6k4GKOkYmjiWTArPLqtxOi2jdSIEKFGLN/HX/58kDVDBhrwoQt6uOGMDGg6I2RYy0y20o0OFIlRI8QV0x2uuWC5Gr8eqLuE/HoMTxUqo9C+AKadVndRSU2XY5GjLmBXEARBOEJoulo8W7lUdsezJ7dpsHNGku9PBvoaBdUGIN+n0qQ1IBhXFXTtsrKImMoS4wWjPLduPcX+bYTRGCZL/4at/OMvt1IyPTLRAP/7yln0zullghoJauiAWU+HrhLD03Tetrid9873VWCurqvzNUh2QLAEmgetC6B55lTfTeEoQcSLIAjC8UwwolwptbyKcdkdqzoZN7J7RpJrq7iWUExZWsY2QHlYuYoKAyooN5aqp11HoWkmnhZicGyMJ19cTd6wmYWN7znc89BT/PTxbQAs6ErymbcvIJVOAS4uGlvoxCNAgQRjNBGIJvn4JfNY2Q6MbwLXgHBWjS0YBT2oBE1lBDLToXXu4bunwhFHxIsgCMLxTqypXgU3r2Jc9JCyuFg79f1pZCQ1Sv3rIVWjpTquAnKLQ5DfVq/nUgW3CsGwaq5YHoVQjOGywR/XjqJVxohTo0qal4ph7vjlK7ywrQTAW1ZM460XrmAikGCUAC0U0XEZJ0OaGg5BetuSfOFtiwk6VagWVZdqU1dji2SVaLLzquljvBWmrxBX0QmGiBdBEIRjkdcqNrfz+6GoKpW/w6pSU+tj2ck6L8VB9V4krbKGXFstyXZVMXdkjTqe59UzjFoBHQI6Ti3H81sGWVsYxUMHItgEefrlbfzzf79M2XSJhwP81RUzWLJwDkMkMIkQwaZMDA+NBAYaHidNS/DBC9phdK2qD6MFVS2Zltmqmq9dVpagSBJa5kDb/F2zqIQTAhEvgiAIxxKvVWxuX++nu1RW0e6ixyioIFzPUTErWkAttqGEgl2D/BaId0A4CloYXAPHtXlqWGNw2zgZM0eUNDqwzYzzw3tf4OE1/QDM70px3ZUrOKXZIkQRDagSp0iUCZIkMGmjTEdnBxecs3CnKr0h1Q/Jc5SomnUWFLarTKfMNFVXRjghEfEiCIJwrLC7a8fzwTNVM0S7BvFm1UdoZ9ePZyt3kV1T1peGe8Ux1TqjpIq9lYYg2akq1Go6VMaVUJhAuYUKg2DXW7FEUmwZz/Pi9lEGjSQd9a5DCWq8tL3Aql9sZKRoqjL/K2fz/rO7KQRSbEcnSg2PIBoeMUxMokRwmdYzk3NPPxtCESgNK5dUOFXPMAqpnknRlLIEOabUcTnBEfEiCMLBc6j65Aj7R6PYXCimapvYVfUalGunPKqsEfsqRkeT+lkZU+tyW1Vci2OoYNxIWu3jWDD0ohI/qWlKDFXGqDkWzw0aDBkRPDS6GSNDhXErxD8/tI1fP70dH2jPxPjIlacyd3ozeQLUCBHCY5A2XILYBElSIxIN8ZZzV9KT0CASV1aVaE19nnzUuki2fq2WEld+Tcr/n+CIeBEE4cA5lH1yhP2jUWxOD6jAWaukgmXx69aVosoMmnm2Ei+ONflsQMWpFAeUi8g21P5WSQkes6TSnEtDytIRSqn041AU9ATUxvBsmzX9eUarLhFsQsqvQ5Yyz27J8/l7BhgqKCF18cmdvOeik3AiWWpAFAOdEC46E2QpEadKlJO6Uvz5JYvRW+bUa8QUlfvKLEM4poRUJFHPLKpfz55Su4UTDhEvgiAcGHvKStmba+Jo4li3Evmeur+lYSgNqufgu6rGSrQJ4kHl/hlbr9Z7Tl2klNX+4bgSKolW1QXatZTLyJhQ7hnXrGcR1UAvg1OF1HQsdNZsyzHRP4LmqnBcHZ8OcgwbIT7/+wHueX4EgOZ0nGsvW8YZszME8LCpEMVmgiQWISZIM0aWmdECV6zo4ZSlZyhRomnKXeSElVUpFFdLtF61tzSkLEqavmtqt3DCIuJFEIQD41D2yTkcHKtWot3FlluvZlsZVsGsgSCEkmBW1AQfSkA4DfmtysqSnqZcLa6p3C+OqbYziir4NhBT2xoFZaXRUC6ZaBKMsupFtH2C1fkwVTdMiiwJTAJ4pH2b3780wZd+P8pExQHgklNncMEbziYSCTGCRQSHDibQ8LEIUyTBCE2cOyvO1ctmEIymoDahREkoCq6rhFaqUwkUPaDGG4yqCrrBsLqeRmq3cEIj4kUQDgXH+rf6/WX3PjnuTq6JQPjA++QcivHs674fi1aivYktx1SixPcBX7lSjLIK2K2Wgbobxa+7VcyiutbGRJ/rU2nOaFAeV8fEh2BM9QkKxcFzcIJZnh8aZnikSsrKkyVDmAQJqsSwWDtq8p171/PcNmXR6WhO8MHLlzOnp4NhEpQIkUJDR6NKDBsNHZdENMzHF+osaNcm3V+1HHQtU+PTAyr92a6pccWalRusllfX6rtqjFL+X0DEiyC8Po7Wb/VTJaYaTfk8D2rDanJpnCecgEhmcpupZH/v+7FoJdqT2KqMqt5B8RZVUbY0qOqf+J7aRg+qhoiaBh4q+yjeXHcP2cqCEUurOilOXRyU6z2JPAfPh9L4ANtHc6wvb8S2goTxiODgESBGDc0s8a0/9PPz1UN4PoSDOu88u5c/Ob2bcDDIVsKEcDEIsY0OArjUCJOIRjl/ZoQFnVmCkbo7yK6qeBs89btdU5akZJfKljKLqsZLLKPcXJFkPXC4XYSLAIh4EYRJDnTCPxq/1U+1mNJ0FUtRHgV8FZ+gh9T1VsbUxJNsn9pgyv2970ax7m7YS7PBw20l2h/2JrYiKZjYrMac6lIZQr6hJnUNCGcg5Kr4kYaY8euiRQ/UrSwaWC8rN0w0VQ98DZEb386aUZehUpWEWyUKGCTx0SkRw3Y1Hnx+Kz98eAu5qnIRXTg/w3svWkRrOkGGMg4OOeK4BGmmTBSXXKiJlScv5exFc9EL20HXVPCtXxcsngNNveqzYpTUT11X2yRa1aLp9Sq+lrovEqQr1BHxIggHO+Efbd/qD1RMHYx1JhhRGS5mURUJcyyojqnJyPeUqPFcaNmPPjMHax16rfteGlLHq46pOiWRjLIQ7R7kqR9lKbe7u+R2Xl8eU+9VJtR1eHXXUSipnqnvqtiVYFhZwMJJJSyDUbWE4zDRp+5TogWjWuH57VWGhoYIVsZwbB2bKFWiGEQwCRH0HX653uY/HnyUwYkKAD3NUT5y8WxO7m2liTI2HoO0EMEhjk2FIAF8uprTXLPyJHraWtXzCgQh2apcXr4PzTEVeOx7apyuCeh1i1ELJNt2fVYSpCvshogX4cTmYK0ne5toGhyJb/X7K6Zej3XGMdU3+WhaWVrMipo49ZCaUBs9dHJboGnWno/3es+/r/uuB2Fik5oAwwmVaqtpSmw5hgoGbTyPQ5VyeyBl+vf1WWi42/TQrvuWhlQgq69BaUSJlOq4inWxKxBrVYG2ibZ6kKupgl+dIjjD9YDYOFRH8OIt/GBNlZc3bsa1LVrQ6SZRL9bvYBFihGb6+of51QPPsn77OACpWIgrzlnCpcunEQgEKOCQpoZFCIsQGjoRDJoSMc5evJB5i09DN3JKRBUGVJCxXVFF7nxPWWGCcfXMfV89n0S7em7BqHpmvv/q/kuCUEfEi3Bis/OE7+xkmo5l92092dNEszOH+1v9/oopI64qpx6sq8v31ESZng7jG9SEFI6D5qtU11BSxSrYtT3fu9franut+26VlUiJ1DNYIknlPoqm1c9aXgV8wuv/Nv96yvTv6RobQsqzlegEFUhrlesZQ45yC3kBZZmwaoCnYlgCAUi0KJEyvgkKfcrSYRk41Qm25SpsGa1yf3+UilGhQIYaIYqk2EIHneSZzhDP9xv8/I8PsWlzHwDBYIDzT1/IW8+cQyQSpkAMDZ8W8hRJkiNBDAsnmuTdKxbSEvbQM+3K9VMdVZYi6q7GxudT01UcjleP0YlllRuraZa67n31XxKEOiJehBOXnYt+lYbrrg9X9XQJxfdtPdnTRLMzh7uQ1v6KqUbp+IN1dTWuyXfV/QnPVG4iszzZtdjzIBBRlpn4bvfuQF1tu1stdr/vOxdi01BWiVB88r5Hs0pEGEVllbEqaqxOTb0Oxw/OffVaIqxRpt8qT7puNG3fIi0YUeKmllf3w7XUeF1bXafnKuEVy6pKuL4DrgfZGUrMxJvVvXdq1IJZ7n4uz5pt4/h2mYhrkPQrlGinSAqLECZhTEL4hHm+f5DvPPwcm7dsU49Z01hx8lyuOmcemXSKCBYtTJAnwRgt+GiYhKnpCZb3JFi27NT6M/XVOMvD6p64JkQToM9S1qBwDNAm07eNohLDrfMm+xSF9tJ/SRB2QsSLcOLSKPpl1dQ32lAM9NhkczrbUP9s92Q92X2i2Z3D7aNvTOpWRU3KjUDHBl79m67tKlP+nthZrMGeJ4/GdZfr8Qp6GGojqlR9434GY5Pujez0yf0PxNXm+3u3WkRSSmzCroJTDyhLRWamEjKOofZLdar1Zlm5W9x6iflAGAZfmMyUCsX23321uwhrpIyH4+rZj66bDJY1CpPniGZ3tUrtLpwabrdaXu1rG/XCcQ3x0ug9VO/3YxbU74SgMoI1McA3/jjIg+NZPDQipPFJ46MxnRECuGyhmxgmMb/G4NZNvPD4H9m2ZUv9Y6Sz6KSTuGDlyfRkI4QwcQAXgCw2OnFqVAIxprW08bbeJOFEE8Ra6sXv0mqc1VFonl3PkhpScUd2VS1QL0SXAKuoft/9MyGCRXgNRLwIJy6ariZH11S+9gaBkFqq4+rb7d6sJztPNDt/+z4SPnrfV+ctDSmTfcN61BBQRkFdk2vv2zpj51WMgmfv3dURa1LHKw2BOayCdAMB9V40q9JzHRMqBSiNTt6HvVmHzJKa8DQd8NUEV62Lg0Ajc8abtFqE43V3SqU+ruRkaftqXk2UTnXXFO5oVqUWmyXlSopmlEujYQFAU/erllfXFssqa0ko9uqJdGcR5piTY2mcz7FhYiNkpitrSONzYdQFcbyl3kvIqp9/987P3Urc1CZUTJFdVTVPfNTvhqnWV/PUSmO88PIm+o0IVcNkuGzykjuDEDYJDOKYaHhYhKkQpZUCcafA2pdfYfDJ/6Y6uh1QoqXzpLNZvPINLMraTGeEJBVypPEx0AhTJkUgGmLlzAxn9qQIZ6er55RoUdYfPVh3vRoqniXdVbdGFerXGFQWOXz1Uw+oonrxpj1bLwVhH4h4EY4uDnexN02vT157wGffbp9QdHKiOZI++oYLQ9PURO15SkwYRTW54AK6iitoWAwSba++v7WcOk7CUunOe4pH0TTAV+m6RhGGX6qn4jZBIKqqswbCyhpCQtUkcWbu2eVjFFWPnuqYEok+KlbFsdQ58NUEvrMIcS21X6xJ3WerAna5bmGpj6nQD+0LXi0arLL6tp+Zriw3njUpWo1ivWkhML4e0JRgiKbVdac6J5/nzrVuysNqTI2Ucc+GsU0qyyk7S4kvmBTERrFex6QECXtXcbPzfU53qfe0IIytU9deGsCxTTYOF1i/ZQvV4jC+aVIjzHqmE0AnSpBm8vQyiIuORQiHABYWpZrJo8+u5vHV/4pZLgCghyJ0LD2X7tMuoSMbYxFb0IBxsmyupz4nMMmEDFbO7eTSJdPQAxHIbVZiOdmmRF+xLnhBPdt4k7L0NayaWlBZAkNZ9TkMhNT7rq2uTVKghQNExItwdHAkir35nvrHiaYmlZ0nILs2Gfeyr6DbUPTI++gbgiTZriwKDUuAXYGJLerb7fRlajIGlY7acEEEI3UrySgMr1EWnGhGiZ6G1SYWVpO9UZy8H5o+aZkIp1QxsWBM3TuzpCawaEaJj0aQ7M6uNs2A4RfVfY5mlPCpjqlg4uoEJDuUBWnnZ2IUVZq2a0Dr/Ekx0xhPLQfxViVSGkG6elAdoxGf0r1ciSO7qtY30AMqABmv7oIKT1Z7HVunfjb3qufdEGHVMXX+RqyGYyqLijEOZrUugnz1fsOFF4pBfrtKHY41T1oc9hT3E4zgZWcwPDTAxOY1bNo+yLq+QSJuhQxVQljEsYhjE2Iba+khgsUc8rgEKRPH933GBoZ4+rmXee7lzdiOcgAFks20rLicluUX4USbGSNEkn6KJMiRopkyFSJk4yEuOWkel8wKEfbseh+kpLo3sawSWOVhJUKaZqlnZpbVMxnfqARKw8oSitXbE3hKPHt1UR1vETeRcMCIeBGOPIer2Nuegj8bTeDsiprwG3ES0bT6lo6/f98K9+ef7/5Ylfa2zb7W7xxHEowoF4lVVpNkLa+uaWStsjjEsvWYhPG62T4FY6+oiq1mVd3rypiaXM0SZHvUca2Suj8tc9Xk5dnKvaIHJ8vMNywgjfFVJ8DIKSuE79WtJXVX28Bz6vjpafVYnLKqAJuZBn2PqeDOGSsmr7NhuSiNqMm98Uwak38juDVRnwjDicnKslpATZC1CSVKfK+e3r2TeDGr6riJViUq7Io6XyNbqTKq7p1WF6m+pu5hw3LjmFAaUG4ro6KOY9fU9bsNS1dYicPqGDTNUALKsZS1CtTvuo5VHOXXr1T5yeo+Ng6VCdoTnOz2080oLRTJUsEHothYBKkSIYJDAhMdl2mM0mfEeGbNWn73XB8joxM7LrOjo42u0y8juvANRAOgY+Mxig9kKVEhRgCP1hhcvbSTNyyZia6h7mWxX93X5tmTgcTjG9RnQQ8ptx3t6vOh6eqeJVrVdRcH6+X9E2pfa6v6nGanSwq0cFCIeBGOPFNd7G1fVp2GJSDZoSZA21D7hKKHLuh2f6xKu2/jOfVGe3VLx+7r483K2rF7HIljQr5PpcpaJbWNW7eG5DaD062sIHpAWWBq69QkmuqGSP0che31mJnoZNqx56uJSw8o11EgrOpyNKwfmVlqX7cualxLTWrxdrX/zkI0klTWk2BMiQRNVxNeNK2uLxgBp6QERWT3QM56Fo5d29VysqMlgabGneqcXN+IpTHrfYACUXV9nqMEimMrYYNfF7I1ZeHxbCCizlPLweha5SbRAkoc1grKHRKKqfTk8U1KrJgFZU1qWGhiGTWuZJvqL9Qo7V8Zw3I8nhmssqXok3dDDE4YDAyPMEKWKhHiBIhhYhFijPQO0RKouwLzpKkRIYBP2CnxyqZ+frpmLY9tzOG4yh8aDAY4eeEcFi87mWnTujC1KOswyZMkgEsTJTqYYFooT1cqwem9SZZOm05QM9V9iSSVF6+WV1lBvjv5NxKKghdSbr/SMERikOiof/bqrqNUp7LSlYeUVdBzlThumqlEnKRACweBiBfhyDLVxd72J6XVrqmJvFGC3LXq7oCsij3Y19hfqziZXa1XEvXrQsRXx66MTk7mMDlGPaDiBMwCGBvVtSc71ATgOpPrA2H1DTjduVscSV59A67kleXDc9Rk4doq1gCtHscxTQWsBiP1Eu2+ugc+arK1DbVvfquyVmRngBba1QoVDEN2pnKPNNw0dk2JvkAQ0NX9i9QtCw0hGoyoyT3WUhcIvprcAiFwUEKmPDxp/QjuHOCrq/iaRnxFgx3ZVkV13J0zrUAJF01TwcWZHiVSzHqabqNcvR6qNwKcUIKkGlbPIhBV9zIYUc8rnKwHGAdUTI9RhPF1qrdQOKmObZXU+HNboJJQ56124hUGGK35PNI3ypq+EeLGEJ3kcAnQzzQmSJHFJIRNkRgGYeKYdDJGHAODMP10EKNGEYeap7Opf5znXtrAc69spWpM3pdprWnOXj6HRUsWUohOo0KUKKNEcagSIYjHzNAE3ckAb1wwh9OiwzQlI+h6QFmLAkH1WTLqrjvfVSLWKtddR6n6dnWLkm2o4OlGBlgoqSxqgZCy4CU7lGht/A009YhwEQ4aES/CkWWqi729llWnIWDKI+p1MKJ89JHUpCm8ITwaAuVAipOV6pNwrLmeVbNTFk8tPyl8HFNNmKUh9Y+94fao5SYb1Wn1e+TZ6ht+LQ+1XnVeqwph1HUUhyYb2+m6yr7RmMxwiTfVAyYtNV69Hi+iBxoPpR6bUFaBv249sLLrpFdnhaS7laDyPSWcyiNq0nPqwajRne57OK7GEKlbg8ojSjQ17kejPotdU2IjtxXi9YkyHK+7erR6+nV01ywvqIs0b9dn7ZjqPGMb6kLOVLEYKRVTQmlYbeO59WaB9QaGiXp/JqsMla3qnrfMVfemMqJEDih3nFmup6iHAK9ev6SA5XhsKoVZW3DIO1VC7laKRpUtRpJuJuioV1qpEkHHYyYjzGYQgxCDtDCfEnmSDNJKkRitFIhgU3ICrN3az7PrtvPyhq2Uq+aOy21KRjhncRfnL+kk1tZDXssQxsGnTAKDTiYYD2W5oNXh/FlhTmpupaWpCd0oQKUuTDPT68XlPPBDSkg69dgep1a3nETUM9R0QFPCMBhWVkG7Vk9Vr+1a6ygYnrScHc4aSMJxiYgX4cgylcXe9tuqYylLQKprVzdDLa8CXiuj9QDSVP0b+cTeLTnhRL2WiD05GQdjqmS9r0HTdBVn4tlgF2H0FRUPkWhVQbKNAFDXVv/84y31YNmc2j8c3bXoWWNiSbap3wv9k0GVvqfOE0tPWhWsegZSoC6yGgXmagUlXlxDiZ9ASE1AWgA0W12TWVbXvUt/IF1ZMiJpFctRy4GvK5Hh2cqiZGfVfbOqqkx8vG6NMvLQtkB9Q/dsZZ0w8uoYqWnKpWCW1D5GRLkZAuF6obamV2d5ZWeoZ2DX1L2xDXXfC31qso3W423Kw5Dfop5LtElZhqwqVHJKGKU61Hlcqx54u0E910SbEkGNQGXXxDErDA9vpX+kzKAdxSFIQjMxfChYAXSvQJEkVcK46PhoNFEiiI0POIQI4JKgRgwDhxBxAsQx8FCf+RGylCoWj23pY9PGLTy/cQjTmrSwxCIhTpnXxWlLZnLajCRJ3cQgyjARAnikKdNEiTxxOtpaecepp9CeCKEbeWURaWSHBaPK5WeWlQCsjEMwoMR7IFR3LaaUsLXrXyj0oHoN6nMdjoHlKmtddUzFMO3+dy19ioRDgIgX4cgylcXe9seq4xTUP99oZvKfbKOfTENI7GwpyW1VE1eyffI4gTCEfBXPYOSV5aFR2dUx6xNDffxWXeAEQsoykd9az+xpmcyAcevZTk49xbQ8CJ4OOhDtrE/MJjh5aJ6jCoJpuqptMrFZCYNEqxI0oK4D6n1vbBU8GazHK/i+cntYZbVdI7U6UHeZpNohEKu/N6aeVSOIt/F8Eq3KPRSOqXgZq6KEgWOqcZaHAX+ydooHJDvVfR/fqKwcvgfF7Up8xVtUEKtr1zNRYkq82UY9oLbp1Vlerl13O1lK1BT6lfAsDQCaskrp5frzbFFxJ+UxaA5Byxx1jkBIuXsqY8o6VKtnPvk+XijBSLHMeGGYcTtMrZpnYniIfG6ILmucJFU8UhRJ4WExTpo8aZooAi4mOlkMXHQSmLhoWARIYxLCpYkCERwMItQI4TgOT/S7bNj8Es9suYeB4cmgW4BsMsIp8zpZPG8mC2Z0kAjYeOgYeATQiOCQpUQUk3DIozcbZ9HJ84hmutVnLBAEK6/qxWj1OKZYVn1WXRvscTDz4CWVqA/HlTvRKNar5jbX3ZzB+mdcg2SsHivkQkCbzORzrSNbA0k4LhHxIhx5DqTY24HUgWmUsbfKdXfQbuLIs9nRAG5ngVPL7yRcUBOaHlDb5LbWLRQ7pej6vhI7jlHvtjy9HmRbt3I0apQ0UpJDcRWI6tVjJJx6YKSRV8cyS8rqUsuDbUG1ALE29S0YAF8JhPKIEiO+rybZzAxVHAxfuXLCCSVgfF9ZVMxSfSKp98EJJ9XkbpfV/nq9NoweUMXnQsm6VSUDpX4wSkpsRDOTAc3BiHpmjaqxtqGuPRRVE5vnKOFRLQAaNLcod1WyDYJBGHgW+lcDdQtQMD6Z2VIeVuOM1Xv2+F7dmqWp22AU1eRrlifdT56jrsV1lMsrlFDXXeyv7xtQz8Vz67VTBsG1cdDYVItRGB+nWOnHtFwCmocfiGIYVYZKeQp+mAk7TMItE8VijAytBGkjSI0ocWo4BAjikaaKg04QmzA+E6QBDYsgbRQI4GISJEGNDGVcs8YzAyWe3G6xpr/EKwNlDHtXV2lXRwtLettZOHcGS7sjeFoQDY0KIcYJkcChShQ3qrEgG2BuTGNGvEYmEkUPRutunZgSuV5YWZXMMthtyp1GUKWae7aynGSmq2cWSqj7m2hTf0OVMfU3FQgrwZ2drrb3bCWkI2klcEOxugtQ+hQJhx4RL8KRZ3+KvR1oHZjG9uXRev2Ppnr11+yk6DEK6h+w5+wU8FquB5XWhU7DbeU6k0W1Cv31Hj716ruep6wiBJXrIRBRQaaarlJ7K8PKgtBwBVllZbnxqTeiK0Cx7vIxi2pS18PKMlDaDsGkmqTjLWp7s6Qm6HBMVbAN6ODUx+3bkzE0E5vV5NEyR73Gq9dN0dT6QBgsE9DVPSyPoYRPqX7vm5UICkZUYC6oANSRddAyU+3nWqrQnO/B0EvqHOFEvcpqtF5rzgPNU2IDTQk415l0eekh5Zar5NhR1j/YpmJ2bAti1K05pspYKWxX12qV1XPwvXohuW4lxhrZUuVRHNtgS8nhuUEbZ/wVkkzgEqJGE61JNX7dfBHLq7HZameb16oyd3Bw0cgTQyNKhgoWPilKxDGUpkUJXg0NFx0NlzAGIXwylEhSo0yMElG20UmAQboYB8+ib7xG//AY/UOjvLS9zCsjJp6/60e4ORHi5N5WlvS2s3hmG/GEKhq3nVb6AZMQLRRppUgIm2AkwOmze1kxbzp6KKmeS2FbvZZOuh4zFKw3dKxncpllJVarJfXsk63qMxhN17PMbLVE08oyFc2oe+ujPmu+r+57ow1CrEm5iqIZdRGxrPQpEqYEES/C0cG+ir0daB0Y21ATd2V0MoMotxVCo8pF0aj54Trqm71VUXEe0bT6vTSgrBJWtR5gq9ULm5VVwTKrANFW9c3eKkBhRGXyhJNKiITj6ri1eo2TsXqKbapbXVNxu6pz4vlKeHh1YVQZVbEo8Za65aYei+KMqkk90Qpj9dojTTOVuBjfCIZVb3FQr3Yaqmc12SZMbFW/R5JKVMWa626ZrJqk3HoAph5UrhqjXsMkO1ONtTyihFQ4OWm5yW1ScTWeowRKsk25lpyKGr9TDwT26nVWXFvdd9dU38yNorqu6oS6RiMPer04XLWo7nPDlecMKMtNNAvxZjxPY3TL84yPDLI5Z7KxEGLcCtKsracr5pJKpaiFW/ELm6A0xHDJJOyaBHCYwSBJbHx8TMYoGTFMgjiEsImTokycFKpTkI2J+vx5aHhopKkSxMFBJ0WVFFXCOMTr1pM0FUwClIlhEMVDxzGrrB1zeX7sBVYPb6IwvJ2+kSKW8+oA9O5MiKXTk6yYHuOUaRE6WzNUtBg2QRLksSjhEySGSYEELhpuOE66ZQ4rmg2mZZMEQ0FVZ8axlDiNJCcFux5Qn8dIov48y0C9SWVmGtRKykVpmirdOZxQf3ehhBIyoP72AkFlhTFLkyIyGFXr9vRlQgSLMAUcFvHyjW98g3/4h39gaGiIZcuW8bWvfY0zzjhjr9v/+Mc/5pZbbmHLli3MmzePv//7v+dNb3rT4RiqcKRpWA18bzI49EDrwJSG1DfOYFT9kw5E61VnyypANpxSJvHs9Mm015GXwPGgdVY9gNSDkfVgjk8G1BZHoLBV/ZNv1tU/9UoezAklfmJpaF1Qd5OMqknbq1tCXBuKfSobJpxQ31o9H6plZaUp1a0e0YBycThWfdJIgTdRdx/l6i6XTjUeq6TEhVmqu77qhfd8TQkFDbWUBsHvUNfRsLo0smXGNyhrUzAC6PXqspbaDx+C9ewRI6/EhlZPoy0PqfsaTinXQa2geiIlWtU3/EhKCZZIBMuyea6/iJkfYEKvkNXKJIobGKtp6GaBiGeQioyR0cv4rs2oESLnRrEYpoUCwaBOLdGFnehmsPgUxXKeASNBBxOMk2Y77YyjYedHMAcnGCfPdMZIUiOFSS/b60XyfRw0gjikqZCiQoU422knR4osFbL1yrIJaoRxaKaIjwfopKmQpso4KQJ4NJGnkxwVW2O06DBRcBjMlegbH2TThM36cYdc2drjxzwW1lnUHmFRR4Ql0+KsnBZkelqjRhiPADlUgcQ0NaqEiGERQaMSbWJOVCOaTTAzGyHT3IGeaFWCW6v/O69OqM9XNK2eo1VS8Ttuh7Jmad1KZMSbId2hRK1nq5/5bXVxE1eWlkTbZCYRTFoiQzH1mUu0q7gosaoIh5kpFy8//OEPuemmm7jjjjs488wz+cpXvsJll13G2rVraW9vf9X2jzzyCFdffTWrVq3iyiuv5Pvf/z5vf/vbWb16NSeddNJUD1c4kuzJNRSIqH++kdSu2zrWZLbDznVgHFN9W3RdwFIBuY2uw9EmtZ9nK8sF9domDTeDV1OWikBYuUvKwyrOw/NVfEBxOxSGlQDIbZ/8Jx9tBmtICanquBpXZUjV/dAC6n28etxJUU32nqsEludNmu9jrfXmia6aZJyqsu7Em+vXEFRp13ZVWVz0qNouEIRkl7p/9jZIduDF2ymMbmF8aDueazKk56gka9QizWQ0C8N4DN/xiJhj+I5FScsQDGi0aDmimk1lLEE51ELCqxDUJ8hXt+BZFRxXI4pBzQujh6NUoxXC4Qghr4qe20DB6cNNdBELDmDYr1CqGthWmaw5hu0HGKSZFFUyVDEJkcYghEu5VERjjBwpRogQp0yGClGq2IQolk3y5HHRqdCCg45BmDRVOhkHNKpEmcd2mqgQwCWIRSejtJMHPHw0dDwCuPgEsQgQxiaMTQQXgwgWQWLYxDEI+0VqpkNfJcRAVadWKROqjFIormOoaDFSMJgo1shVdqs5sxtNyTAzWhL0diSZ3ZGku7ONzqYoHVqBFFVMIvVMozImISrEcQgAHuVIkibNZVrUI5ltJtU2G92pKktfsn3SdRbJKguKWa2n5Jvqc4unhG+gngFmO3VLY78Smplpk4X5gjEVlJuervaLNU3GczXaZ1jVyc7ZwYjKzJL4FeEIoPm+77/2ZgfPmWeeyemnn87Xv/51ADzPo6enh//1v/4Xn/70p1+1/Xve8x4qlQq//OUvd6w766yzWL58OXfcccdrnq9YLJLJZCgUCqTT6UN3IcLUsjfXUHVCWQ6aeieLvNXyk4Ww0JVY6Dqpnj6bh80P1fumoKwvjcBRs6xiYMIRmH66+gdsVVQAYqPcfXlUncMqQa2orCJGXUzVJtQ/eD0A1F0t2boIKo7UhURITQaupawTWhASTfWMHRvMIk6uj3KljGOWKBCjQJomSoQ1HT+aoupHqbkaiYCFEWhirGLiGjlGzSQhzSPslMhXLQZrIcZqGppTw9BiuJaJaTtU/TB5w8MxDXAtHNfHJoDn6xhEMAiq5F3fxkUniEPVj+CjgaYT1200XcfQ40R1h7DmEdMddF0nqrk4ehhPD+LrQXw9RE2LEgt46DpkdRNDjxLVoaIniOsOAR0iuk9I99ECAXQtQFD30QNB4rpNi14mpjs06SZlPY6jR+jUi8R1h6AOZiCCrwVxfZ0Bv4VtfpuqjeK7BH0Xww8QwSbgm8xkhH6vGdexaXVHaXLGcRyboGuAY2A5UHGgZPrkTJ0JU2fAjFEyXaqmy4SpYZkmlapJsWrsqFL7WsTDOp3pMJ3ZKG0tWWa0RFjS4tHekoJICpcQYSya67VbdCCOQYkoNhEcfGYwRjAaIBaK0Rz1SESjJBNpdM+pZ/XYKqBWR4nwdAcQAs1RMUO2oT7HlTE1qEBMiRg9CKk2IKAsMNFWaJ5Rr8OT2emLQj2gN9NTrwlU/1v0vMm+U3pQFUaMt0jgrXDIOZD5e0otL5Zl8fTTT3PzzTfvWKfrOhdffDGPPvroHvd59NFHuemmm3ZZd9lll3H33XfvcXvTNDHNySJNxWLx9Q9cOPzsyTXk++qfcWlQCY14S90aUa/QqscmXSD5bWofx1LBrXqwXtbcmywDH06Avx1sVHCq76qlNKgmBl1TosjI19OcHXAC9XLxlhJGIQsiSRw9ykSpQn9uhAk9Q7yUJ+nkmSCD6eepuiHaqBGOhEhFx3C0MBMmRGrDNNvD1EyN4bLG1mKN7aUaxbJBruqQqw0wXvUYq3qUaxYTVY+q5R7+5yHsIBoOkUpEySZCZOJhmlNxmjMxlmRM4pk0s9M6TVEfQ4tQIU4EmwgmCUyGaCaIS4UIJRIksIhjkIjaOERpQiMcDdAZ08hGukgGfXQ9rNxA0WYVO2VWldumPALlfmXtwFPuScdTrqFIU71dQqxeoFCDUAQCdcGv12OT4s1KmGemT7qFAvXg8kZZglhm0l3bsIJGM8pFFE3vWrBREI4QUypexsbGcF2Xjo6OXdZ3dHTwyiuv7HGfoaGhPW4/NDS0x+1XrVrFbbfddmgGLBwZ9lRMbudaK/E2ZR43S8qdE2tW/5A1Tf3D1YLKalMrKMFSHVW1ROKZeiaQruIzQlGwbXBLMLFR/dMv1LNTohllirdreHqEYnmIfL5E2Qa/3u9I9xz+//bePEyuq77z/tyl7q29qvdNrV2WbEvyLlnGeAeLGDCx84YlwTZhnDDDkgBhsDMswSSvzeAA78tAQogxmXcgHmCM7QQvYBsDNsIG2fIqy5KstaXel9qXW/e8f/xudau1dsvd2jif5+mnu26dqjrn3qo+3/qtFr0UcCl4JhXPJk+OAZroYoQKFQYpE6aKoQxezpTpHRkhOzLE68M1do9VGcvmGcyUGSpMv2KwbRpEHZO4axJ1DMJOiJDj4IZMkpaHYxlUrQiWZaIsB9c2cC2ommE8w0ZhUjZCWChS5DCAqmFjAjVMTAMqyqTsm4T8CoZfIeIXUEph+jU8X1FVBmXfwvA9Sr5J2bcw/Qo536XoW5h+FcuvUvN9lO9TqpmUlEWpZmL7FUJ+Cc+Hmq9Qfo2qD8r38X2fmu9TqymmaOw4LJZp4Nomri1hRK5tELJNHNskYinirknctUi4BlHXwnAT+G4Sw42SdKE1ouiK+eSjneRCjRRwaWWEJHlMII/NWWyjDxcbgxoeYSqUcIlTIEGOKjYVbCohl5CTpikapsso0mrmSNomdrxZrCNm0GepOCxixBsTIZ1ok95Q3u4gNTkUFBXMBwHRplgWK2PyuWg8W+KRiiMSjK58ES/1mj7RxqB3lzmR6m/aE8Jl37IEJ0K3dI3mMJz02Ua33nrrJEtNJpOhu7v7MI/QnHAoX7JLTBuoTPToqddasRwYykhZ9nBDUFjLFNdSeUyyKZykCJr6cxUyEi8STgWZPEHV22oefB8vP8r2TI2BPT2MDg2ijEFMTGIU2eOFaCgNAT4tjFEmRBGXOEXSlEhQIInJEFEqyqA41sfv+veycyDP5oEyO4bK7B4pUznCLuzaBu0Jm9ZEiJa4TWsU0lGLxohJMhqiPeqRjEawwnGqTpKwbdHEEEkKVAgxQJosUeJINo2NoohNOSgvH6fMMEmqmESpEMZjhCgxijQyRpocPbQxRIrdtBCnSBNjhKkCNRoZC4SNhUKqDoepEqOMj8KmxjApTHxGiTNIGoUiRRHwaSBHGYuddNJDCwqDZbxOByNkiODi4WERpoKFR5wiLhWqysT1K1R9k1HfwfB9SspkyGggZHgsMPqpYtNnSOxL0ijRZQxRMhw2G/PZbnSTMPKcwQ7m0Ecro1QxqREii0sInxgFwCBDBDDYxIIgqwgilOmjEYMiFjZJ8qTI0RLE3HhY+IQYI0qUKiYmNcCmRoIC4bBPA1VqdozujiZam1qwmxeISBlMSKp5NQOVCiRiEhTtleX9bJryXnaDejbloO5OzQtKA6TlfR5Oy9hKBtJdIs4rWbGktJ4uqcyFQXn/22GJh6mWxBoTmR9k0+UmMoV0/RXNScasipfm5mYsy6Kvr2/S8b6+Ptrb2w/6mPb29mmNd10X19XfCE5aqiUJfh3dKf+83bik5lZyEw39/Frw7bAowYfUgqJsHfJPPNYUuIuy8s0z3gaGKyLHK+FVK+wazLFzzy6GSiaFiqLq5clWw3TRRxWTBEVKhDAoYeNSxiZBlhhFbGrEKWGpCttGKjy/p8yLe8s831tjy2CJQuXgIsU2oSvt0NEQYU5DmO4Gm/lJWJzwaEy4xCIOlqGCEFILE58qEAryW8JU8LDJE6KIj0ENGx8wqGLiYaJQsk/hYqCIBDVWLRSDJKngYONTxKGCRRNjpMkToUCIGlVsXCo0kKOGRZY4LiOAIopHjrBYkvApEsbAx8bDDOZWwSCORzNjjJAICuBDhjgVHJrJ0M4QJopNzGWUBHPoJ4JDmRA5wmSJEKZKCJ8yIUKGj2kpLAta8CnhUCROAxYJCriEqBAjASgUMXwyNFAiRDMFSgwRpYRNjQFSRCgSxqOKCAyRYj5VLHKEMTAAxQhxRknRzAhDpACDDoaoYuOj8DDJESYadjgrWiVsNuEYNSLeGF4oTshN0B4DpzAInim1hWImJBoDARGU0Q/HJEXdssFwRLTbQNNpIq5NW1KTnbDUCfJrIuRLo0EGXZDqbBkyzk1IEcHiKHSsDMR/RoRIcRhqSiyQ4YR8tuLN4npNdh06U2i6dZU0mmPMrIoXx3E477zzeOyxx3jXu94FSMDuY489xkc+8pGDPmbNmjU89thj/NVf/dX4sZ/97GesWbNmNqeqOR4UR6H/VfldKYGfCRowKknfDUVBVYJsIBtSnUGjxnpzvJz8887slWDbah5Mh4pv8UpfkdHhfnYNl9mahapvkMKgjREKOGSJ0cIYYSqAyyhxVFC/o50hyoRxKgV+3VPg2T2jPL+nwot7S4wUD3T3hCyD+c1hFrZEOaPFZl5zBLexjXlJg1Yzh0+NKi79pIlQIU4+KBGvcKlQIESGMGAQosYgLi4ePiY17KCxniJEjVqQRlvEwcTAxGQXLcQoEUFcAnlCDIsNAIcaISqYge3EBnppJIlLgjzd9DFCggbGUFgUcbGpBVk+Jv00kCZLM1lMijhUqWEQwggyehR9NOARwglqo/STRLr81KhiUSBMCI92hglRYw9tksGLTY4wDh4+NlnKQR+eHC4VFFDEoUScAjEilIng0UsjYyQpY9FADkUUH4LZmLQwSgmXQeI0kKdGiBwhfAwSlLDwyBEhQywQLgY5wuylhaoRoiUKZ0UMkqEoDZUMvjLIEqXJ8WiK2rQ0NmKbSDaPaYnLJbsHbA9CCYnNKueRYHKClhBFKbdfLYgod5JiEQw58pNcKGnnI9vkeZUnWWe2K5+H5BypreN54Bpyv9MiQkjVgirEQffn8SBcJorHxZuCSrkmYBw+U2i6dZU0muPArLuNPvGJT3DjjTdy/vnns2rVKr72ta+Rz+f5wAc+AMANN9xAV1cXt99+OwB/+Zd/yaWXXso//MM/cM0113DPPffwu9/9jn/+53+e7alqjiXVkgiXwpCkfEbSkp1TGJwoYZ7rl3/wKGicP9GDp96vKNsLmT5802IkX6RsRdm4exfrdwwRL/XjUmUnbRQIo/BJkw9SZaWCaoUQo8QDC0IIs1bkd3uKvLqjn/U7cmzck2P/WmKOZXB6e5jlnRHObA9zWmuI5sYUPWYnrYwRpUSOMCMkGMUiQg2XStDBxiRHhL00soi9hIJNdJQ4LlWieAyTpIcm5jDEIEkMTCxq+EHNDx9FDQMfkwJhMiSx8alhUsRliCR9NJIjRjOjFAjTT4IuBmklwzBxMsSIUaCCTQ2fNkYwUUHmi0GWGLLsIjFKDJMijEcLo8GmHyZNnhIhdtNClBIDJJjHADnADuI/QDFMlDA1slh004uHSR+N4+4XhUmUMjl84uQAix200swYZSwUFgpFC8PUsBkkST4Qd2lyRClTwMFGEaWAwqBICBOTOBXiQUaPS5kaDiO2Rdj2KJGkyTII+WXGzCRzI0lWdLWysDFCc2UvJpa4WIZKYqUwDNmwy9mgSWOTtDyojEhDSCcplZEruaDqsoKQHXSrLsLgZnkf+zUJxLUsETqRtKQtWyERGtEmwBDLYrRBrB12SERQrk+C05sWSBE5vzbRbTs1X8R7dq/UAoq2yPNVC2KlsV1xM0UajpwpNN26ShrNcWDWxcu73/1uBgYG+NznPkdvby9nn302Dz/88HhQ7s6dOzHNiY7BF110Ed///vf5zGc+w9/8zd+wZMkS7rvvPl3j5WRgOn2H6l2F463yj9sKSQO4UCyojBt0JI42S+qm7cq3zaFtkN2Lj0Vm4HV6h3O8WGyk3wszphxS5WFspKBWEYcwFVoZIU6eOAUGaSBPhBglskQZHM2zactrvPD6IJt2Dx/QT6Y9GeL8OQ7ndoY4vTPO/NYUtiVWkDIhwKcGZAkTowSBhSNKhRwRCkQw8YNNXrZjA+ilmQbGcKjSEAiJXTSxhS7AoJEcPjbVoAS9D3g4WEFchYXBAGmGSQd2GMliKRNmN22UA0sIGETwqGExRDwoxpYjQZEyDiNEKZInSpUSDjZQwiVPnAZGiFOmFlhbyliE8EhTQGGym1b20MoSdo3H4fSRJEaZWFBC38GngTFsyjiBC6iCSR8tVLEJ4RGhQgd91LDYTQt7aaKLAZrI4mERo0gFgyFS9NJIc7hMC6O0uZBXDVQ9GwNod0pEQmDhMeSH8cwkykvju45E3VhlHMcVK16tGLhQFkDDwiCGyoPqXnE9OkkRHYlWSM2TDTuSkNgS05IYErcqrR8qWan5Y5hiLQm7QSdvJ2hPMSpiPZKW97sZWFssVwSSExF3aLhRRMnwponPiO2AigZFEntFcDiJoIJy4Fp1A3GSbJfx2V4RO4k2yRCKJKXeUD2r6HCfzSl3Ym/UAbya48qs13k51ug6L8eBqfrHS5mgFH5NrCy5AckcMvZ7vkpeaquUsvJtNtoC+QH8vhcZG+pn82Ce7QNDpEpD2MAgMfpowQ02cC8QFRliDJImhEcjWVLkyCuHLXszvLZlG09vHqJ/cGTSSycjIVbMS7NqXpyl85qZnzaZZwxSwaJKiDwRHEq0kmUPjVQIkaLAa3QRwQviUmpUcMcrtUYp8zrthPEYDFwqcQqB5cXBJxS4gmLUgAayJMgRwQNkHwJw8AhTpJksZUL00ESNEAZ1a0eZbXTQSzOtjFAmxHY6cKkwjz4SFABIM0aUClVsirjUMAN3mkuFEFki9NJEnCJtDGPg00gWA0UIjwoOI8R4mUXkidLCCM2M0kAmqCPjUsIhTJkUY9goqoZNxW0glW6iqzFFxoyzNe9SLpXx/RrnuztpMIuMGUleKDSRKxTppJ+0USRkQNT2cJoWYKeaaan20mkMY6OC948RWDgKIgzyA/Le6zwXCI4P7RBrXjkj5fPxRJjEmiHeKVYLryqWP1+JCHAS0gwz2iBuTScGLUtlbK0m79vhHeIyMoNyxr4vlo5wSuKxaiURJuXRoKJtl4gZA7GEKFPcOtm94hoygy7ibkqCcasVaYroxsU1ZAbdoLFFjDTMF0HhxCHRLoKikhdxlZ4nAe/T+iwXpZWGm5xogLkvSsk5bJgXpGZrNDPHCVPnRfN7wFT8414JBl4Ts7fyg67FJYjEg7LkQUfnWiWoSWHLP+GGFF4+w7YtGxnevYntu/eysZDExaALMIkSpUwjOaTCagQDFWz8JWpAL02EVJnNPf1sfGUzT7/WRyY/URfINAwWdzexfFEnF8936WhJYRqQpEQvaYbwSVPAwqdIKCgUbzBKjH7ShKkRpiS1OyhTxKWMg4kfxHJYQaaNicJikAZMoIMBVGBNABEfZWyq2ESokCFGijxJ8kGnHI8KFkWi9BBmhChhPBrIkiOMTY09NLOVOUFmUYVeGigSJkuSprDFvBaDud5OGpVNphbBDNoXjNXChFWUhBnCCIUwnTBz0wswa2WaqxEM5VEqJClXwfELFOxWCmYr3eE0DWaBmBGm0fPxxkp4fo1CJEXY9nFKRQwjSqR1KXPtAVobmrFbFkpwda0mGTWJRfJ+2JaFsg1Ni7g4Gbgk/Iqkv+cHJW6keZEEYw9asKc/qIocxHaUguwy35NKyRURsERbYXS7BL8mGuV9GSmKRc92JaZK+RIIa5jiUillpABc2wpJQ86PBlWOCYJmXYk1yfdJpo8Tlvd9cUgCxS0zCLqNgt0g4sSvylyVH7iD4rKmcj0rLimWlEgDtJ0h93kFcF1xUZmWtLNwUxLcXq946yaDTKH0hCUkFJ2obTRdDDNwe1UP7MIOE+0BDPPA+zSaY4gWL5o3xpH84wObYHCLZD048aAUeQUK/XJMKdkYShn5R+7XguJ0NZ4qz+cn6wdQg6/RWBpA4QSxDnkMfNLkqGIxQAMxiqjA7qGAkPLI9O/ltxtfY8PG7QxliuPTizgWqxamuGhxA8sWduJH0oyQJE6B3cRJk6eIi8IihxukIxeJU6KIQxmbDFEiVKkQYogG4pSxqVDFYpg4CUo0kmWEGCMkcPF4hTlsoZsw5fHg0aawgRt2sS2LaKqFRFM7pVyGUn6ISCROtJajb3CYwVIR300xP1kl2dhJTIVRezfhVYo0WR6e24BrdbGsZrDI2E04sZjT0/MIR+IsbUvQHq5h5vpgzxAMD0DDXNkoc71B5eCmoNx8UTbjeUsAH0YTslGVcyIuTUuudT1QdXhAvqGH28BdKEIjPwhGCMLdQcPHMBhzpZCaH7jlzHr/JVteJ5IOapcEhQmNEGDIBh1vEyHTda7cLgzLhp4fgeJAIJg9wJUMHCct78FqFlRKnjecDjLROsXNY7tiHXTjYqmId0E5qI9SHgM7GtQ6cWUN+WGx6igTMGU+yhA3Zyoqx3J9shYrJuLIjYv10AtcRuXMRJsHpcTNY86R9ViuWIz8qqzHAZqWSDfuSJOcM9+T52paLNct0hC4VPcTGW9EYNTPS3FUPsP7Uy9kp11GmuOMFi+ao+dI/nHThu1PyqaXmhvEBFSCLIuINBrM9IqPvlrAx2QgV2L7rl1sy4V4eGSYcq3MnGqVFAX20oiJT5Qi7QyTJE+JMMUgLdbEZ6xY4umXdvLoCz3sGiyMTyXqWFx0WporljVz7vw4EatGGYcYWaoUyRPGxqOLAUZJUCJBhCoGBrtoYwG7sLFwqFEgRCVIp1bAsJEm6sIcI8dcNUw7VQZJ4RnNJCNJ3pSKU3SaUMVmjHKMhnCSKzrSnNsewmnolm7TXl4EQqxJrFmjO8RtgSFF98yQdH12UuLuKOegJzDb205wDQxxaVSSUuvDNMTl5pWCjK5sYF1oEZeFiVwHq0PSZ92kiFEMcXMYBrSdLq6O0Z1yrS13Iii05omVwbAl7deOQPMysLeLIKpkZCM0QoG4VYF1zpbmkl5eWic4MVlfrl/GlMaCANZQUIStAI2LoO1Mec81zpf52cF7qDgsj7MN8A2ZZ7ILIi1BZ/CgfH55VMSTZYkFw7RlrXY46Kg9FFhjYjInrxTc1ygdurP9kp4fa5NzG28X4TO2U8RSOCliJtkhj88PBbExCbESqZrEckUaZR2ljIyLNIi4suyg03ljEFdSlgDgaimwVo7J9bIdqedihw8ULvDGBUakQaymxdHJ1tT9C9lpNMcRLV40R4/yJzJ/DkauT+IBEl1BpkROXAa2IxYWvwr9L+Mp2Lg3y3M9A5TLJhmi9NNAA9uoIbezREkE6cAONSpY+JhYeERUkfU7Cjz4Qh+/fa1vvB9NyDI5b1EzV52R5sqFLoQcyrgkg1TlQpB6bOPRyQAmiiohTBQZwuylgXZjjAXOMA2GQRiHXTQwSAuu4zAnViFBntaWFtJtizArBTH9Q7DxBpu66UL7mVwea5LNyyuLdSIUkZiFcAK8uHwbL2VkU421Bg0isxKXEWuVDaue3lrKiLio1w7xynLb6pBzbAUf7VDQbC8UCzJhjKBgX0XmmJ4XFPcLeju5cWg5XSwOvi+bvhUSMTS6U+IhYk0yt8weMFXQBNOSlPZoo2zW4YSInEiLWBgMG+manRIRhS/vi6aFQT+dObBng4gnOybvjVpFxEGiA+acN9HduGGBbK5uUqwVIyEZS+DuiCQDMRaVLt6mCQ2LRLSM9cg5rVe19WtBkcMg4DY/IM0KG+bLua0WJtxKzYuh/SyJY1FqYmMPhSeCy0MRGV8pyONrNTBqUu05nJbr5JVkTGlMHhMPOjfngrmGAzFTGhXRUxoTq41hi/CJt05YoGZDYITC4u4dj2MryrnRhew0JxBavGiOnv394/VOz4YpcQi5gaCwXBmoyn2hiIz3SnhjvWzb9iq/GYpQ8S1sfCo4DJNkiDRd9DGXPl5hPgXCzKGPIi55wjQxSrZc48EXBvjBs0PsGZ2IY1ncFuPyszp58+kdeOE08+glShGTMnFKxChh4KOAPaTIECNqVUmHPLKxLiJWiDPDFmZDN63J05hf24VdLQM1lrpxwAM7KKBXq8jGE2uS4+ll0LxQrAjVkvyjN0OyYeWHZGO3HPlxg5YEENTdaJcNK9srYiPeIpv6wfrJhJPBBjMapMvu01wv1yebdLJr4pu5ZYuI8Tplc7RsiSepb+KVnGzU6W6xbESb5HktZ8L9UBia6HpdC0RatE02/HqFWK8sj6kLA5OJaq6h2MS6yznZuOvuRtuFzrNheLvU8KkWJQsnfpq4uJQS4VTvVRWKSQyKYcrz14LeVJYddEVGhI8TBw+oFcCMSaCpE5WNuZKXeixWaEKkRFIiwGxXHlstijXLTYpoMAyZy75u0lizzGnodbGcRBrkXOX7Za3JdrFYxZpkzsUhmX+1ICKrfl2NoC5M/ZrF24LOzr48X6RJah3Vx9vh2RMYuj2A5gRHixfN0VP3j2eDisj1Ts+GJZtitle+wRu1IIPDlc3eK7Nt22a27tiMXa1SJcIQqfFqsGlylAlRwSFGkSQF9tBIB4O0MUrPSJFvrt/Gz17so1CRGIqYa3HlGU2sWrmQdHs33QxQwUYx4TqSEvpghm3C1HAxiIUVzY0OSTeOqaoQt2WTclPQcpoIr75BiKUDd0tQu8OvBAHGBmR3yzlww7JRQVAdOHCTZQfEFWBYYoXxg8wVfBE5kbRs/IYhm1zD/MDCcoQGeHXzfqUw8e27VgGM4PmZsLD4nmzUkRRYzXLcSQaZM4H4jLfDgosnslb2pTgi3/Tr/XCUCsrZGxMl6/P9cl7wxUpgxwBLxFS4Hl+Sk/vizUHKcXnitSJpcVMVO+R1nNiEhWf/gPBqIXhvtUiTTTcioizRLgGxNU8aF1byYg2rFkQwRJvkee2IWIassMwtFBarBkGmUUWqM2OYIk4wxI1XK0+4Sb2yiM1KXl7PjoiQsx2xMBnWhPvGcsSaAoFFJy/3YwQWpoJY1mCyNQWCNOtGcUfte12OhcDQgkVzgqLFi+aNEYpM/AOPNEAoLn7+kZ3BhpGS5oduHPwx/OIYP9+4m+zIEGmqYvWgTAPZIFsIIhRJkaGPRoZIYwEuNZ7vKfLL37zIi1v2jqcPz2sKs/b8+bzpzE7aQ0Vep4MqFkOkKIZbmE8fcatK2oCGkEkqkcJx42KGL+fBLAFBlhO2bLRu8O3bCzZiJy4uh0S7FAwr5yUTZHSvCI5aVTaseIdsyPVYh0pezoERxIPUyrK5R5qgMS2blFJBfZyj+Oa8v3nfG5Pj4bRYGKrFyd/KE+1BcG0qyAYrBFk2iCBrXiyP259qScSZV5T1OXF5nlJWunmnu4MaIojgCkVh5HXpyG07IirMkMzTciVDJ9Yi50P5E6+xf7o9gDd88IDwWIu43pygiJvvi8ioZ9jU+1z5noi0eKe8V2tlKAftJpqXTLYuKTUhlOLx4JgfXN+gdku2b8LCVG8cGoqIMHITcn7cwG3lxEWkhJNyTepCINEunw/LnhBI9esO07emaIGh+T1EixfNG6NalH+skfTEt9z6RjnWA5UsfnaAzGAPLw+UeH2sSq2iWESGPGGGiNNCFoWFHRR+C1GllTFCKPpIsXHXEL946qf07Ng+/rLnL2rkD86by5L5nZQNB6hQQQJ7i+EYZyxazML2RppGfUx7LgxtnUhLrVVFgPjS7QZqULMgHAFUYFUIS6wFNXF/qerE5mg6ULYgnJX1t5wJjfPE8pDdA1gyyVpNxEG0WW5HmsSlEQ5cTtEG2QQT7VMrIHYwQmGgIUg/Lsvca+WJ6xLd51t5fXOuFuW1/TT4ZREObkLmcTCKI/LY9FwpElgrB+NbpSZJfljWZNpyjpyYiIL8sDx/sj1If48EwamJIHA7cDseKt0+1y/uktRBGq0apoikWlmETD3GBGSuY3skwDU5R+YZb5HrbhjijtnfDVdnXzHoe5PFg2FMuEn3bRw6PidD4mWcmIiVxgUiduod0FWQSVUtStpztEnE3f7XXbtrNJojosWL5ugZzzaKyTfOUFQ2Ya8iG3kozMBgL89szdGXKdPKKEkMPBzGiDGMxHuEqBKiEtQ6SeFjgvLYumMX3/v1OrbvEreUaZqcfeZi3rF6AcuaDPJE6CWJhcf8sE9nspF012l0NzdgJzulIu9YEKBqhSFUkQ21brexI1LpNJKWb+52GBrniCuomhfBUu8B4+8TzxNrkc3P9yA/BnPOls0qNyivlWiTWJ9Yo1gaIg2yyZupCeECQZ+mopyzoy34te/G7yYP0Ydmn+eeFIgZrCfeeuhv9vtmlKmIxG2M7QU/sKKEG4OU91rg1gjLPNJzxYWS7Q3SeffJjPEq8phIEFuS2Xtw64qblNL5+zbp3Pf+cEpcP9GmwAWjxJpUGBLhkuoWMRVtnHCjFUdF5EQPEdB6JFeMm5B1VfIHXrN6EHEkLesJRcUFeDRxKVqwaDSHRYsXzdFTKQS1LQxABcGTMQnGzA2yO1PhVy9spliqEaEc9NKxyBAljI2FT4wqFaygXH2YtMry2va93PnkNjbuyQIiWrpXXsgVF57B8lSZVkYYsyLYTopVnQkumNtExA2JGyOcDIp3xUVAgZjnfQvMRqmiWgkqrNp2UIjLkm/Gpi1/pzpEwLhJ2XA7Vso3/FJGHlt3J0SbxDVQykhGCD5YESlqlmqXb/35Adm83ETgbqhMbOJvtOCXV5aNv5wVAVLncH1ophsnsW9GmWGIKLHDIhrqfXycOHSeEwSTRvYJEg7cO/UmgdWSVLDND8m5tkJSobaSlfOzP3XrSmlMBMj+RdOc+ERxOcsJsoJiQGCtqce31GNNYOrl7Q91X6RBzmlpTGKglBIRWy0G5z09IUrrAerakqLRzDhavGiOjmpJhEs58OmHorIZZ/bA8DZ+ubvKf7ywm6WeooiLj4HCoIBLkhI+oFBBJ2IblypDe3fz9V/08tyODAC2ZbLm7MUsW3UJC1avZUGsQld+I+1+jHktKZxwVEz3lgIMcQ1UgwBd2xXxEWuTTdb3gk3+TPlWPrIjMOc70ufGjQUVXzuDQNYgFTnWMjkWoTgcBLiGJM6hOCruqGpR0lyTbUHgsgrGBbVE4u1yfuoxHnD09Tjq8SHFYRjZJYKkngGz73MdbqOe6mvun1FmuxLjkmiTeVSyEnsS26ercT0A1U1MxN7kBwMXiidjo81yvgrDImaaYuPetok5BplJub2Tz1sd34PGhTKnwlBg4QhaBMRaxPK1b6wJTBYWR0MoLK6hwnAQ7GyJ1aducakHpe8vSrVg0WhmFC1eNEdHPQ4i0R7UoJDDfrXEYxs28x+7w0T9CqPEGSVBhAqd9NNABh8bD4s4RdLk6M+U+Mov9/DIy9JnyLYM3nz2ItZceDZuvInlCzu57tIuEQJbd0LVkGJtsWYRCJWsbCDRBiiakl7sxGSO6bmycdpOUDk1IZtOXXA0dMu3+liL9JCJB/1j4s0S0LlvTEOoY3IMSWE4cCstEZFgO+KeciKyITsxmUtxWCw25n7ZJUdTj2NfN5EZCmI3IrIerzQ5U+iNbtRw6Iqr9XRv5UNLkO57KNdIJB2k0dfk2L4WlEijiJrCoJyr/XFjUAy6ORvmgfVMEu3yGvXrUs5J9Vk7MuEq2peZKG8fTkHTIonJcZOBNWWfNekqtBrNrKPFi2b67BsHEZKN2u/bxKt7htnw2hYG+gaZH/T/2UMzFoocMapYnM42XKqYKFSlwj8+08e9T/dQ9iQO5arlLVx18bmkUwk8N8Kbzj2Ds7sbgzLwYcnoGa/TkRG3QbxDamfkB8RV03oGEnhbk423MCSVWLNI6nOtDMm5cn967kThN68cVHeNQPvKgze1q29I+1cXrjeiLGWAiDy2nqWSaJcCb4YpBc78N1CPY992DPVy/QbyeqWMiIxEm4ydqT40R6q4WhcQh3KNeME531+4QFAttkmuUbz1wPv9mhSyO5w4gonXC0XEIlMcPfhaZkpY1M+JVwrigZSuQqvRHEO0eNFMn3ochO9DfoBdPTt4/NlX6BnJEvKkq3OaPDZRMkRJUCZCERuPEB4FZfHky738719sYihXAWDFnDg3XnEaSzpSlMJxLlzYTGcihN2Zkh4vpik/0bRsGol22ay9YpDmHNQWSXQEFWsr4sqqB9iGU1K0rV77w7BF7FiubP7F0SAeJD4hRKZyDvatLhxOi2WkFIgq3wvEii9WhXpl2qONe9hfMFmOWBdKmYmg32phIq5mpjbqqVZcPdTrHKkSc7RZCuYVR+T5piuO9udYlLfXVWg1muOKFi+a6WOYsjHnBnjsuVf4jw07Gag2EMelgyF8oA+XKBUSFBkhSRcFmhlj12Ce//HIFjbvHgKgORXhhssWc/nSNE1Rm3O6IoSjYYiHoBK4SDqWT9TvCCck1qZWkRgXpcRtVM7JhhEP0pJtR8RLOSOm/bpLxQwyiUxT4iVMU1Khw2FJqY21HOgGONQ52L/77r5VcutZLbWqBI7OxIY2HcFUnWELwBspiHakTsWmKdYiN3n4mjdTfb1jJSx0FVqN5rihxYtm+tgu1DzuW/cCD73YT5kwYJAjxgA+3fSzlwZGMGlhjBoWlarP99Zt5ddPP0/N93FCNteuWcgfXdBBLJHiwjkuTWY5SLmOiAXESQA1KYTWeoZYSMyghky9yJoK+urU++nsm74aSQdNCYckJiYUllorY3vEHeGEJ+qwuAmJVYGgT9ARXC2HigWxXakjYphyf7Jz5ja04yGY9udo1jKVTsWxZkm19sozIwSOpbDQgkWjOeZo8aKZPl6ZJ18f476NGRooUCIBKCx8LGrkcXGpsp12TBTbtu/iV488xOioVIA9e3Ebf/KWlaRamjivtcqCtmbM/ABgiVso1iTCxYlIdkq9ZLvtBhtgWr6pT+qlZIq4sfYTEvtXM7UdaTLoB0Xn6mm2derPP5UN6XDuCScuLqyZ3NiOh2CaKabqytHl7TUazRTQ4uX3jTf4zdb3FTsGxvjnp3aw22skRJEIFVwqRCijMBglSooSyWIvP3nscTa//BIAsXicd111AWvPTLC0q41z5rdgj+0Wq4ETlhRX05nojVQv+d68RNxE0X02QNMOxtTEVRI/SF+YQ1UzHa80+waDLY9H3MOxFkwzhY4R0Wg0M4gWL78vHKx3TD0wdYobx5b+LN9/eie/fGU3/miZEi79NDCfvSTJSzAuEQwMNmzt4/GHH6aQywEGS867kCsuW8OblzTw7hXN2MVhESSxNkltrVXADvrmpOeIC0ghwiXSGJT2dyQldng75HeJaDFtiLVKJ+TpdNmdqY30WMc9nMwiQMeIaDSaGUKLl98HDtU7ZlIJ+UOUhg82mS3DFb726Gae3zVKrewRNRQdqh8XjzhFQnjSGbpc5pnH/p1nX9wEQKqxicvf/g4uOf9Mrjurg3mRoDGhG5PqsKVRKfYWS0nsiRUPUmpdsZbE2yZSfmuVidoqjQsnN84rDMs6klPcHGd6Iz2Wm/DJLgJOprlqNJoTEi1efh8ojgT9YYIS7IZx+BLydStNYQi/5rE3V+XeF3Js25VnDlk63WFy5X46a72EqZAnTB6XsZ7Xuf+BRxjN5AA46/wLWHbFO/kvF6S4+OyVmLUijOwWK4rviYBxFoIyRVTVKhMFx+KtE9VR63EolcKBPXDG17jPOqazOZ7MG+nJPHeNRqN5A2jxcqpTHIPhbSIMSmMT/YfqwmD/EvLVkozPDbBtKMd9G3rYPVIgUyzRXSsRs0OEIxEG7CRDfolWNcx81cOPn97Jg798AV8pUqkkV13zB7QuOI13rD6NS+ZUIeRCNCUpvdl+SLQEcShhKVBmh6XuSrxFrCr1dgPFUZlXKCop0vUaJ/sz1Z41mpnL6NFoNJrjhBYvpzLVknTlLY0GLhlHBEEpI/cl2oMS70VxHylfeuWM7uZ7z/Zy70tDFGsWNop2csyhj2Ga2VFuJuHWsHyb3jH43r8/w6vbpfPzgtPP5Pqr15BKNfCOVd2ctbQ16GkTlKi3w4AHmICSTszlYUmN9mtQrYrI8r3JsRyowxc6m4lS+Kc6MxD3pNFoNCcCWrycyhRHJB4knBZXkYEUe7NCQSfkUbFo5AelXopfg95X+Povt/PEXgsTiFLFpkaYCiY17GqRCkWqEQdv91bu+eG/k8sXCNkWb3nLZZx54Zu5er7NpQtSUudN+eIG8j2ZC0qCdO2Q9IYpjUAoIaLGCkFhAGwbmhZLOnQ4JWup1145VKGzmSqFf6pytHFPGo1GcwKixcupwMHcAPVS8pG0pAKXMxNVakGKuRVGwNsjGT5OHCpFHnxuK/17eziPKgYKhwomCgMfFdRy2VXO8puf/4JXfv0oSkFrawt//keXsfqMObz1nE6c8gjk+6Bki2uoaZEUlCuNykYZioIypPZKar5UvTVtETBeVP7O9gfN/1zZVKdS6Ew3wzs0+/ZEqnO4uCeNRqM5gdHi5WTmcG6Afd0s9UqzpUzQuE5Jo7zRHVJHJT0XLIdScZgXtmyngXKQ+lwBFHZQfK6Iy0Ahzy/vf5w9O3cCsOqCc3jn2jeztNnhgu4YjpeX+BorSGEOuSJcIg1Sl2V0p4gpw4RYu8zDK4NT74RsSZl4kHXtu6kei541pyL790TaHx0vpNFoTjK0eDlZOZIbINo04WapV5rN9Ut6ciUvjy+PQno+AL5S3P2rjYT8Mg2MEadIEznKWNK0mAqbezN86d4dDGeL2I7D2mvfxQXnncOKJo817R4tjc1SJM2wRLCAuKy8ilh+nLg0WVQEpf19iYcxg27LtRok20VgVYJeRftuqidzjZPjyZEaI+p4IY1Gc5KhxcvJyqHcACGgOCwCou5mcQh63pQl0yjaKFlI4QQYBrt2buOJXVXWb+1hKQYNZGkkAxiMEKWGyWMv9fL/PryVSk3R0JDihg/cwAeuPo8Ot0RzIoZZ75wcToh7Kj8UdHtWUuK/OCKWmI6VMLIDhl8P3EVm0A25KBV1vbJYUgwLTFdEzb6b6sle4+R4cKTGiDpeSKPRnGRo8XIycjA3gFeeaFbolYO046CBoVeAUhb8ilSrBREZfpSdBXjo+e1sGapi1cqYKHxMDKCAC77ifz2xiX//rbiJLljUxNq3v4X3XLqAM9pj4EfBjYJhi/VDeeKecmJi7alVgzL9BenaHIrInMIpqGTkfsOQ225c5p7rh8YFIm78Q2yqWrBMHR0vpNFoTjG0eDkZ2d8N4JUhG5TYD0XAjkhNlLGdUPMkELZalgyf/IC4b5oX49dq3P/zZ3lqd4FILUPEyxGnQBmHMeJkKwb/8B8vs36zpEG/86LTeNfFy6ikW1l25rnQukwsIYVRGNwkmUO2C+GkzGFfMeVXJQg3mpZ5tp8pc8zslbVEGoKMJE/ETCiqN9WZRMcLaTSaU4hZsRNv376dD37wgyxYsIBIJMKiRYv4/Oc/T6VSOezjLrvsMgzDmPTzoQ99aDameHKzrxsAZEOqVUQ0WCGxflTzUC4EWT4Z8MvgBW4DrwAjO/j2E6/yzOu9pMp9NHgjRCmRJkeSHNtzNn/zb8+xfnMftmVy8ztX8443L6fiRLj2rC7MeOtEKnPjPGheKu6o1ByJcymOTGQ4GaZsjuUxGHpd6rg4cUh1Q8N8cRHl+mSu4ZRYCap5vanOJPV4oUg6sNxlJtyOOk1ao9GcZMyK5eXVV1/F932+9a1vsXjxYl566SVuvvlm8vk8d95552Efe/PNN3PbbbeN345GD5Eh8fvMvm6AELLphyIT9xeGJOYk0iA1VMo5EQyqBuE0fqyF/3huO0+/PEJKFbCooTAZIcUQSUb69/K3P3qNoWyJeMThY9etZvGcJoyQ4pKlnbS3NElA8L4k2uT5KwUJtvUq4ASxLIYRpGtnpaZMPZg31gLpbhE7pZGJQnm+J+6tRJveVGcSHS+k0WhOEWZFvKxdu5a1a9eO3164cCGbNm3iH//xH48oXqLRKO3t7bMxrVOLcTfAsHyDtiMSP1ItioCwQjKmVoF8r2QYGYr+3gxP79nBE7sq2MohQoUMMfbSRI4I219/nV/f/wqlikdHY4yP/9GFdLdGsR2Hsxd1s3jOHGhbIfEr+1L/Zp/tg7FdUqulVglqt5TkJxSBRKe4s8Z6pCheol3ib8IJGV8cgXCDWHM0s4MWLBqN5iTnmMW8jI2N0djYeMRx3/ve9/hf/+t/0d7ezjve8Q4++9nPHtb6Ui6XKZfL47czmcyMzPeEpy4WDEviWOrxJqGwNDwEcQ9l+yA/DKEoQ6NDvLS1h0pujDN8lxFSlHBxKZEhyp7nn+LZR+5FKZ+lc5v59B8ux0o30dncwLKONK3pJDQvgZbTDj2nRKu4h5y41GwpjohwCQe1W5QScWJaYiEyLUh2TcRfuEmxuGg0Go1GcwiOiXjZsmULX//6149odXnf+97HvHnz6Ozs5IUXXuDTn/40mzZt4t577z3kY26//Xa+8IUvzPSUTw5C4QkLRWkkCHoN0o0xYXQX5AagmsNXNdbvGGQkM0rSywEVLHy2006jyvDk0//O+l+sA2D+8nP547VnYzsDnDGnlXMWzsF049I0Md0V9Cc6BIYp95uW3K7kJ7u0fC/oHN0sIqcYiC47rOu1aDQajWZKGEopNdXBt9xyC1/60pcOO2bjxo0sW7Zs/HZPTw+XXnopl112Gf/yL/8yrck9/vjjXHnllWzZsoVFixYddMzBLC/d3d2MjY2RTCan9XonLfsWrENJ5lGlCAMbIbsHvAqP7fbZvruXFgaJUKOITYQyRRXiX36+czwVesWFF3PuJVcRM0rMTxl8/F1vxk51igAZT7dNQ/IwpeQze4N4nDCM7QYnIX2VQIKH69YVpaAwKEG+Tky7MzQajeb3mEwmQyqVmtL+PS3Lyyc/+Uluuummw45ZuHDh+N979uzh8ssv56KLLuKf//mfp/NSAKxevRrgsOLFdV1c9/d80wuFJdNneDvk+8etLdRqYIb55bZBsn09LCFDmAo5wrjUcP0SX3voNR58aQSAd15+LueuOoMqfRTtBJeffxa2ExarSF1YTKWUfD0ep5yVuJZaRYJ2q8Wgn05axvlVeW4tXDQajUYzDaYlXlpaWmhpaZnS2J6eHi6//HLOO+887r77bkxz+lnZGzZsAKCjQzeMOyzVEhSGRQA0LIB4Gwxthf4tvLKzh9f7snRSJEGeLGGqODi1Al/49208vimDaRjc9Afnsnz5GWSBDnOUhYu6OL8rJVYUvyaWFycGbmqizsyhmFTGPyfWlXBaLC771m3RdVw0Go1GcxTMSp2Xnp4eLrvsMubOncudd97JwMAAvb299Pb2ThqzbNkynnnmGQC2bt3KF7/4RdavX8/27dt54IEHuOGGG7jkkktYuXLlbEzz1CHbJ3U7TEtiTIojUPPY3reH3p7XiVGmQogsMcDE9bJ8/r7NPL4pg20afOwPz+XS5Z2kyBMOG1y4pJ23LYxKTIrtiuiwHHH5jO2WuJUjlZIPhcW11Ham1HJxYhBtkOepVcStpOu4aDQajeYomJWA3Z/97Gds2bKFLVu2MGfOnEn31UNsqtUqmzZtolCQmh+O4/Doo4/yta99jXw+T3d3N9dffz2f+cxnZmOKJw9e+dA1OaolES6Dm4Imh/3imnFj7O3bw/rdI9i+SyMZ8rj4mFAt8pkfv85T2/I4tsFfvut8zljUSQWLWriB91x+MXONAXke05KicXZQUt4KSYqzm5i6tSSSkrG6maJGo9FoZohpBeyeDEwn4OeEplraZ8MPxIubmNjw60G6pTHpBZTth+GtUKvg+z73PbeDcm6MCCW66KeGQaZiceu9r/PsjizhkMF/v24h6flLcfApmS4Xnn8hSxcvgZFtUPMh1Sk9hpyoWFvqBefCaUmXnq6753BCTKPRaDS/18xawK7mGLFv9tC+fWiKoyIg6vEkXhlCMYl3GXkdDAW2y7Obd1DMZalg42BgYFCqGnz63u1s2JEl4pj8P380j9O6GxmgTJ4IsaY5LOluF1cRQPsZEG8V0VLJBV2qk2KJqZUPH/NyKLRg0Wg0Gs0MoMXLiUhdmNSzciDI0nFEwGT7REA4Ufm7OCSWF8ti23Ce53ZniWAQo0yaPGOezed+vJkNO/JEHJO//+MzWdAVYxQXF49a2GbNWcsxw0mxrMRboPk0sfR4lX2sJUG8yqE6PWs0Go1GcwzQ4uVEwyuLq8g5RFVhKwSFAflbJWBwi1hqanl6BnK8sLdE3K+RJo+HQa3mc9sD2/n1tjxuyOSWPzqbRV1JKtRQGChTsWpeM13hqgiVpoViXfFr8hr1eJc6OkNIo9FoNMcZLV5ONOppyGZo8nGvDKVRST3O9orlo1qCTA+4SbxIG6/s7SNXskhSJkKJIT/KP/z7Fp7aPErIMvjcdcs5ozvNMAn20kSUIm9dMY8Fy1dB0yJJh/a9CZdQcXSy26pS0BlCGo1GoznuaPFyomEELhm/Kq4iEOGS7ZXYk0pBis/VitD7AuBDpIHvv1LGLNn4GNQwyCqX//Hgq/xi0wi2aXDLH57Jwvmd7CFBH03kiPIHi9s4fV4XpOeIq6hOcVSEjO3qDCGNRqPRnHBo8XKiYbv7lOEPxEtpNBAuOWlmGGmBWDOMbIXCEI88+Ss2DqfoJkw7o4RUif/nke088XIfpmHwwWtXs2hRM4PEyBAlSpFLFiW48vROSWV2E5Pn4ERFMCU7pHKvzhDSaDQazQmEFi8nIvXy+sXRIMZleKL0fzgFblUsIlaUjWNZCgM9nEUfZRw8FD/61TZ+9vxeTAPe+46LaD9tOTtQmPjEjDIXznFYc1o7JOeIeNm3cSKIm0gVRbTsf59Go9FoNMcZLV5ORPYtrz+8DXavh8KICBcsiXupltjas4NfvdqPRQIfgwxx1v2uh/+zbi8A77/6bLpOP5s8IQZpwDLgT8+Ks2ZxIyQ6pQhdvertvvjVCfeVRqPRaDQnGFq8nKiEwkADVDeC6UCyBYwIVDKQ2cH2vgFe2LqTFt+nhEsPzfz8lX4efOxZAK598xksPescdpCkgkOUEm9f0cZlZ84FNyZ1W1RNCs7tj84o0mg0Gs0JjBYvJzLZPvArElDb/wqYZfAreMpmw7ZBcpUQTWQoY7Jr2+s8/JNfA3DRecu4eM25bKYDhxpFTCKRKG9ZPl+6WTlxsexUixNVc3VGkUaj0WhOErR4OVHxymJlsRwI+UFjxBGwHF7qLzNSMrExKBPm+T74px//Bt/3OXPZYt585RWUjDIhahQIs5sW/uzKldjNcYmhSXZJrMukFgQ6o0ij0Wg0JwdavJyoKB+UEquIV4T0PBgs8PSWXtbvGiOCDxjszCi++aOnqFQ9FszrZM0117HXiJGkgIkiTY63LFrEpUtbJvdGAvkd6gBPZxRpNBqN5uRBi5cTFcMEOyzuHK8I4QZ+PRjmZz0hQn4CH5NQeYwv/Z9nyeSKNDc1cs27riVnRwlTZYQkFWxWtIe55fqLxU10KGGiBYtGo9FoTiK0eDlRsV2w3KCXUI3K2CCPbxpixItg4eL7Hv/fAy+wt3+EeDTM+/6vtcTCFj4lKtgoIBWNcOt73gK16vFejUaj0Wg0M4YWLyci1ZJU1B3eDiM7IdfLT9bvgFKVFqCgHH702AZee303lm3znuvXkk6l8FD4GFRwKOHwoSvOwg6FoVY6ui7QGo1Go9GcgGjxcqJRLUltl7Fdks7ctJj/+XyGnYM1khQoEubJ9a/y/LMbAHjT26/H7ZzPdqKUcHDxiFFg7ZIIb04Pw1AJQlGplqsLzmk0Go3mFECLlxON4gjkByTeJZzkqa2D/GCLicsS2hmm//WN/OrxJwB402WXsmTpUqBEK0OMkiJkKq4/Zy5XntElqc/ZAUi2QX5I3FA6i0ij0Wg0JzlavBwvvPKBGT5eGYrDctyJ4dUU33lyGz5QJMLGYZMnH3gEpRQLVp5PetW1jODRQZk2RpljDfOHa85i7qK5kqWkPLAscJPBc49IdpFGo9FoNCcxWrwca/atreKVJCDXSUCiHWwHKnkJsFWKX2zqIZ8ZJQoUyjWeufcuquUSXZ3tXPKWtxI3+nCpYlFjJ+28s7PI3HAZRndDvEnaCcTbgg7VoeA1G3V2kUaj0WhOarR4OZZUS5DZA6WMiJT8YCAoihCKidCoZiE/zN6xl9n09OucripUlcX//o+HyA/1kYqH+bM/vJianaGKRZYorYzSGne44pzTwbTBiUicS6wZFFDJioXH93Tgrkaj0WhOerR4OZYUR0S4lDKQ3SMBudFGsbQMvibtABJt9A308fPnNlHK+ZiEefzJZ9myZRshy+Sv//AscvF2LMChSpo8FUK8Y3kTpleBsC3F6PzqRIq0YU64qHSzRY1Go9Gc5GjxMtvUY1tqVbGy+FUoDImIcGMyppoFJwrKxy/08/D618gVitSIsnnTFtb9+rcAfOjqpZzZGWMvfShMMsSxKNDVGGNJa1xcUOUiJEPyt/Ll9cNJeX3dbFGj0Wg0pwBavMwWk/oG+ROZP9UCFIfEhZPvh1IeqjnJBCqN8uQrO3AKOaI4ZIb6+eGDvwLgrecvYPmK08lSJk6BMhHCVAhFE7x1WSNmOSeCJdoK5Ryg5LflAIZutqjRaDSaUwYtXmaDemyLVxaLihkKRMswDLwmwsVyoTwqXZ3LeTBtXukdoTS8mwZMtldSfPO+31Gq1FjZneQPLz8bA6hhUyJMDZM5cY9LzmqnJe5ArQymA+kuKI1J7IvtiGCJNulmixqNRqM5ZdDiZTYojohwiaQnjjkxsGzI9UnmD5ZUvsWHXD+Z/BiDfSUi1FDK4wePbGb3YI7GWIhb37mYoukTo0gNi0GSRGNpLltq01LaCSoK4TQ4rsTRtJ0hnaPDCd1sUaPRaDSnHFq8zDReWVxFTnS/4xXwPBEZY7sAQ6wh5Sy+qrF7cIxUtUQel59s6OPnrwxgGga3XLuMlrjDGFk8bMDAQnHGom5amhRkSpJqHQ9SrS17vMCdtrRoNBqN5lREi5eZRvnyY4YmH/dKEveSmgOlYSjlgtgXn5cHq3ilImkKbNhb5suP9QHwny/t4JzuKFnCRKhgUmYT3RAO86ZWHyqjkOiClqXiGjINaFgAlYIuSKfRaDSaUxadNzvT1NOR/YN0cq5VRdg0nQYN3WA77MiUGRkZo4bNnpLDLff3UK0pLluS4IZVjUQp00CeDBH6SZMjwppFHTiOA8k50LYcmheDaYlVx3LE6lPOihVIo9FoNJpTDC1eZpJ6WrTlivVjX+ywiIpCv1hl3CTDZYtn9pTIK5sxFeEzDw/RO1ahI+XwF39wJgNGI4OkGCOGh8N22mlqncOa+U3yHOl5UoiulBHREk7La5mhCQuQRqPRaDSnGNptNBMckBbtSZqyV4Zog4gJA8kAKufBjuCHEqzfW4JSAQN48PlBntg0imUafPqdpxELO/hYFAnhYZPHZl7C4E9XNYMyQFWhnJFA4GiDCJd6YK5f1QXpNBqNRnPKMmu72/z58zEMY9LPHXfccdjHlEolPvzhD9PU1EQ8Huf666+nr69vtqY4M9TTooujIh7cpFS4DUUkzqWUEZHhlSHZCS2nge3y2KbdvN6fJUWO3QM5/sdjOwD4wCXdtHd24VBDUcMCskQxwk2sPWsOZvMSWPoWmLNaXsOyJwsXEKuPm9BZRhqNRqM5JZlVy8ttt93GzTffPH47kUgcdvzHP/5xfvKTn/DDH/6QVCrFRz7yEa677jqeeuqp2ZzmG2PftGivIn8bJsRbRdCEopBonSjV334W//rj+ynt2UiSAoWq4u8e2EzFU5y3IM2lq5ZRwWE3rZSxiFClZrtce8ESWucsgsYF0nDRDoNpSrVee0BSo/2qCBddkE6j0Wg0pzCzKl4SiQTt7e1TGjs2NsZdd93F97//fa644goA7r77bk4//XR+85vfcOGFF87mVI+Oelq0aUlfomoBVE1qrYSiEuNSC8SMFYJqkS8/+CK79xSZS5QiLvc9vpEdgwXSMYe/umYpyrDpJ0WGGHHK9BPmgqWL6Fi8Etw42BF5bduVTtSmJQLKdkXQRNK6IJ1Go9FoTmlmNSjijjvuoKmpiXPOOYcvf/nLeJ53yLHr16+nWq1y1VVXjR9btmwZc+fOZd26dbM5zaNH+VIhNz8sriErBE5cfheHxJ2UH5QO0rUqT23czovbdhGhQhmXZ17r5xcbtgPwZ9echxdroUgYhUmJMFvpotp5Pm9702oRJqGo1HKpY7ticUm0Swp2wzxIdmjhotFoNJpTmlmzvHzsYx/j3HPPpbGxkV//+tfceuut7N27l6985SsHHd/b24vjOKTT6UnH29ra6O3tPeTrlMtlyuWJlOBMJjMj858ShimWl1pZ6qyAuI7KGSiOwehuMBVUy3hOgu89sZ5GCnQyxECuyv0PPwnARatWctqCLirUGCZBGI8eWkh2LOE/XX0aZrVXAnP3rdhbx68GmUwxHeOi0Wg0mt8LpiVebrnlFr70pS8ddszGjRtZtmwZn/jEJ8aPrVy5Esdx+Iu/+Atuv/12XHfmNtnbb7+dL3zhCzP2fNPGMEEFf3sVyA+IpaWaB8MHKwqqxv2/fpZUdZR2hmhXA3z7kVcoFEu0tzbxlksuYC8hHKr00oCNTzjZzH+9qpv5KQsqTWLNOZg4qRR0t2iNRqPR/F4xLfHyyU9+kptuuumwYxYuXHjQ46tXr8bzPLZv387SpUsPuL+9vZ1KpcLo6Ogk60tfX99h42ZuvfXWSUIpk8nQ3d19+IXMFMoXiweGZBVV8hIH41ehkoNoM9hhftuTZ91Lm4lTpYTDf7wwyEtberAtkxvevhrfcigQxsIljEcvTXzo8hXMb4yBUuBEJPU62zeReq2DczUajUbze8q0xEtLSwstLS1H9UIbNmzANE1aW1sPev95551HKBTiscce4/rrrwdg06ZN7Ny5kzVr1hzyeV3XnVFLzrQwTElXDkUls2hkh4iNclb6DYUi7Bgt8oOntlKohQjhkB0d5n8//iIAb3nzeTS2tJHBIkQNA4VFldPbI1y4MC3CqC5U/JrE15SC2BrD1MG5Go1Go/m9ZFZiXtatW8fTTz/N5ZdfTiKRYN26dXz84x/nT//0T2loECtBT08PV155Jf/zf/5PVq1aRSqV4oMf/CCf+MQnaGxsJJlM8tGPfpQ1a9acmJlGIFYPy4VcL3hBKrSqSbyLX2XT9u088PIIVsmgSpKc7/DAT35FpeLRPaeDiy84EwuPOewlR5wyDral+NPz2jFrFekWbRhSPXf/1GvdLVqj0Wg0v6fMinhxXZd77rmHv/3bv6VcLrNgwQI+/vGPT3LvVKtVNm3aRKEwUUb/q1/9KqZpcv3111Mul7n66qv55je/ORtTfOPUq+rmB2BgM1Qy4AfBL16JbXt289vXM1TLYbooEaXIT3/7Gjt378VxQrz9mqtwTJ8EBRKUGCPBMAnet3opc7oXiIWlWpJMorpIcaITdWS0cNFoNBrN7ymGUkodedjJQyaTIZVKMTY2RjKZnJ0XqVfV9coS21IclZYAe56HwhCeafOj325nTzFEAZcwVXIDu/i7f32CWs3nmrWXctFZC/EIEaFAgjKDJJkzZy43XXWBCBYjiKMJJyHeJq+rlGQyNcwTd5VGo9FoNKcI09m/dW+jo6FeVTcUlSBaOyLiJd4KhWEee+F1Roo+bWSIUcKreXzqP9ZTq/msWNzO5Svn4OMTpkCCPCMkmTenk/evmiO9j2KexLWEIkGNmIq4jnTPIo1Go9FotHiZNpOq6vbC6E6olqVbdLaXp1/dRmk0TyMuMfLEqPLtp3rY3p8nHbH4+7VNZI0RNtOFhU/M8XnrimWcvWKlCJdqXiwusaBztFea6A6t06I1Go1Go9HiZdooH0pj4ioqjYnlxSsABs9u76N3NI+PQTNjGPhs7Kvwg6d7APjwWxfSEHNQ5GljBC/azpuXn8YZnQ1QykozxWpR6saUMtKFGkMyjeqNH3VatEaj0Wh+z9HiZTpUSzCyC3b/Dgoj0stobBcYIZ7NuGzsLdBBgShlQniUfPjqQ9up+YqLT2vkTcvaGcWggIsdtnnHqvl0JSNgALUS+GFxF8VbRCRle8GNyd86LVqj0Wg0GkCLl6lTLcHwNhjZBpWiiA2vBEaI9TsH2T6YpYscHfQFvYtC/H/PFNjSlycetvnwWxahAA+bEB7nNfh0hQpQKkvMTMUAFLgNEuvie9A4X4J1nah2FWk0Go1GE6DFy1QpjkBuQOJQnAR4RaDIi6Pw2mCVuYyQII+HTRaTncNV/vHJIQBuvmIhjXGHMgYAcbfKmR0x6UkEgA/lggTlJuYASltaNBqNRqM5BFq8TAWvDIUhwAfTkd/RZvaOZtmwbZAWMlgoRklg4OMruPWhTVRqiovmR3nH8hSjhLGo4mHR3tSK4yTEuqIMsG1w09C8DLrPCbpHa0uLRqPRaDQHQ4uXqaB8ERqGKdYR2+GlvWP8+IUibSqEhc9IIFzCVLjvuX6e310gEjL4m7UdpIw8oyRw8QiHo6xYvABSHVDzACV1XdLdEE5p4aLRaDQazRHQ4mUqGKZk/igflM/zgzXue7aHcllRxCFEFYWPg0d/psS3f7ELgA9f2kZzKoJNlTgFDNfkrDNOw0x2gBsHKwzJLhEvblwK0NXTojUajUaj0RwULV6mgu1CtAnyw3i1Kt/57TBeycbAJkKZMjZRKlSVyf/7050UKj6ndyVZfe4Z9FMlRolSOMrKOZ20zFkGTYvEyhJpgHBCXqNW0QXoNBqNRqOZAlq8TJVIA8Rb+OoPHmW0UEZ6RFcZJkkvzSTJ8+jmLE9tzWCZBu9bex5FI0qaITLhRlavvoqO5jaIpKBhAYTjk59fF6DTaDQajWZKaPEyVUJh3v3DvfT0xGmnTAqPCBVMfLbTTqo8wL/97NcAXLl6GV3NKRJkGTZTXHz+xXQsOR9QknJdzUHIkcwlvyrCRReg02g0Go1mSmjxMkXe/+1f8/TuItDKKDFaGCVGCYcKjWT4P09uYCRXoiUd5bo1i6lhsZkuLjjrHDoXzJeU52oREq0QaZQWA74nsTTRJp0WrdFoNBrNFNHiZQr8y6+28KutIwC4VGhjBJcKOSKYuJh9G3lp/e8AeNtb3kx/qJ0hs5E3rVzGZWe1iXVF1STl2kmA7UBVar5gGMdrWRqNRqPRnJRo8XIEXt2b4as/2wyIcFlED3PZS4mQDPAVDz3yEEopli5bwtyFC/Esiw+e28iK0xvFTRQKS28iJyEipjgKTnzCbVQcFatMslNbXzQajUajOQJavBwG31f866+3Uaz4uFQ4je2cxWZC+PiYxMmz7vlN9O/di+O4XHbFZVgorl19GitabBjZCdEGsCyItUpArgqq59axHIg4ImCKIxDqOE6r1Wg0Go3m5ECLl8PQM1rk9YE8pgFdaoAz2EGcCv2kUBh4hTEe+eVzAFxxyfkkEjEuWWhzTosTBOTa0LBQLCqRNOT6IHSIbCInKnEwXqPOONJoNBqN5jBo8XIY8hUPTIO47bGouhsDGCSFj4WB4tFfPUuhVKG7NclbzummYcE8rljVGRSgcyUdumWpCJdqUQrQmaGDv5gZAlXUReo0Go1GozkCuiLaYYg5Nh0Jl063QpORYYQYNUyilOjv7ee3G14F4O1XXcjCtMUHzklBtBFSnXJma1XpEA0TBej86sFfzK/qInUajUaj0UwBbXk5DF3pCEvakhQGTVq9ImbZx1I+MZXnqUcfRgHnnT6Py1fM413LXLBDEE7KgxWThYjtgpuQ2JaIc+CL6SJ1Go1Go9FMCS1eDoNpGqxdlsLfA6m8TZQco7UQGzZsZUdPP27I4v++8U28dakrrqJEp6Q+lzLSYNGJTnYDRRrEfVQclft0kTqNRqPRaKaNFi9HYFHc49qzO9jkz8UcfJVteYOHf/EsAJ+67jzeurRZgnBTc8XyUquI9SUUA9Rk60soLMG7xREJzlVFuT+S1kXqNBqNRqOZIlq8HA6vDOUsc1Mh5qxazlBPlHu/8W+MZfMs7mzkM394DuR6oWUZNHYHac9pcJOBeyh9oBsoFJZ0aK9RrDKGqV1FGo1Go9FMAy1eDofywSuBV8aMtTLsDfDtf38GgK994EJcS4HbIrVcQkkpSOf1y283cXg3kBYsGo1Go9EcFVq8HA7DlBiWWgVl2Pzl332dqlfjmotWcM3aq8XS4pXBssErgBODcl6Ei66Wq9FoNBrNrKDFy+GwXXCSUOvhgUee4JEnn8UJ2Xz1r/9EAnK9oggWJyqCJdEpzRZRumeRRqPRaDSzhBYvRyLRBqM76I77rFpxGldccDpLYnnoHwAUWCGxskSbIWkG1peMLjan0Wg0Gs0socXLkQiFofk0zj1rL+v+qZvq4DbI9IIdEcuMD2R7JXsokhaxo4vNaTQajUYza+gddipEG6B5MWatjEsJMMHLSaxLqg0SHVLbpfclyTJyEzogV6PRaDSaWUJbXqaCUlDIQCULyS6p5eJXglTqDFgRSLZBdi/kB6WfkUaj0Wg0mllBi5epUBwBywLbAa8qRegMA0pZyA+B40G4Gwwb7LAO1tVoNBqNZhaZFbfRE088gWEYB/357W9/e8jHXXbZZQeM/9CHPjQbU5w6QaE6Yk3SdNG0xBLje9ISoKFbsowiaYi1SbVdHayr0Wg0Gs2sMSuWl4suuoi9e/dOOvbZz36Wxx57jPPPP/+wj7355pu57bbbxm9Ho9HZmOLUUb78RBpEpBRGRKjUa8AYNlQLUM3K/W5SB+tqNBqNRjOLzIp4cRyH9vb28dvVapX777+fj370oxhHcKlEo9FJjz3u1DOH/Co0LoRMj2QXxdulSF0lD8UhSHVDpFEH62o0Go1GM8scExPBAw88wNDQEB/4wAeOOPZ73/sezc3NLF++nFtvvZVCoXDY8eVymUwmM+lnRrFdESSVgsS6dF8g2UWFQcgPQHEMEl3QsADiLboztEaj0Wg0s8wxCdi96667uPrqq5kzZ85hx73vfe9j3rx5dHZ28sILL/DpT3+aTZs2ce+99x7yMbfffjtf+MIXZnrKk4k0QLUoadDhNMy/GEZ2Qr4fQg40LIRYs+4MrdFoNBrNMcBQSqmpDr7lllv40pe+dNgxGzduZNmyZeO3d+/ezbx58/jBD37A9ddfP63JPf7441x55ZVs2bKFRYsWHXRMuVymXC6P385kMnR3dzM2NkYymZzW6x2WakmyjsrZiW7QlguRpLQK0K4ijUaj0WiOmkwmQyqVmtL+PS3Lyyc/+Uluuummw45ZuHDhpNt33303TU1NvPOd75zOSwGwevVqgMOKF9d1cd1jIBxCYQh1gNc4IV60YNFoNBqN5pgzLfHS0tJCS0vLlMcrpbj77ru54YYbCIVC057chg0bAOjo6Jj2Y2cNLVg0Go1GozmuzGrA7uOPP862bdv4T//pPx1wX09PD8uWLeOZZ54BYOvWrXzxi19k/fr1bN++nQceeIAbbriBSy65hJUrV87mNDUajUaj0ZxEzGrA7l133cVFF100KQamTrVaZdOmTePZRI7j8Oijj/K1r32NfD5Pd3c3119/PZ/5zGdmc4oajUaj0WhOMqYVsHsyMJ2AH41Go9FoNCcG09m/dSlYjUaj0Wg0JxVavGg0Go1Gozmp0OJFo9FoNBrNSYUWLxqNRqPRaE4qtHjRaDQajUZzUqHFi0aj0Wg0mpOKY9KY8VhSz/ye8e7SGo1Go9FoZo36vj2VCi6nnHjJZrMAdHd3H+eZaDQajUajmS7ZbJZUKnXYMadckTrf99mzZw+JRALDMGbseevdqnft2nXKFr871dd4qq8PTv016vWd/JzqazzV1wezt0alFNlsls7OTkzz8FEtp5zlxTRN5syZM2vPn0wmT9k3ZJ1TfY2n+vrg1F+jXt/Jz6m+xlN9fTA7azySxaWODtjVaDQajUZzUqHFi0aj0Wg0mpMKLV6miOu6fP7zn8d13eM9lVnjVF/jqb4+OPXXqNd38nOqr/FUXx+cGGs85QJ2NRqNRqPRnNpoy4tGo9FoNJqTCi1eNBqNRqPRnFRo8aLRaDQajeakQosXjUaj0Wg0JxVavOzD3//933PRRRcRjUZJp9NTeoxSis997nN0dHQQiUS46qqr2Lx586Qxw8PD/Mmf/AnJZJJ0Os0HP/hBcrncLKzg8Ex3Htu3b8cwjIP+/PCHPxwfd7D777nnnmOxpEkczXm+7LLLDpj7hz70oUljdu7cyTXXXEM0GqW1tZVPfepTeJ43m0s5JNNd4/DwMB/96EdZunQpkUiEuXPn8rGPfYyxsbFJ447nNfzGN77B/PnzCYfDrF69mmeeeeaw43/4wx+ybNkywuEwK1as4MEHH5x0/1Q+k8eS6azv29/+Nm9+85tpaGigoaGBq6666oDxN9100wHXau3atbO9jEMynfV997vfPWDu4XB40pgT7frB9NZ4sP8phmFwzTXXjI85ka7hL3/5S97xjnfQ2dmJYRjcd999R3zME088wbnnnovruixevJjvfve7B4yZ7ud62ijNOJ/73OfUV77yFfWJT3xCpVKpKT3mjjvuUKlUSt13333q+eefV+985zvVggULVLFYHB+zdu1addZZZ6nf/OY36le/+pVavHixeu973ztLqzg0052H53lq7969k36+8IUvqHg8rrLZ7Pg4QN19992Txu27/mPF0ZznSy+9VN18882T5j42NjZ+v+d5avny5eqqq65Szz33nHrwwQdVc3OzuvXWW2d7OQdlumt88cUX1XXXXaceeOABtWXLFvXYY4+pJUuWqOuvv37SuON1De+55x7lOI76zne+o15++WV18803q3Q6rfr6+g46/qmnnlKWZan//t//u3rllVfUZz7zGRUKhdSLL744PmYqn8ljxXTX9773vU994xvfUM8995zauHGjuummm1QqlVK7d+8eH3PjjTeqtWvXTrpWw8PDx2pJk5ju+u6++26VTCYnzb23t3fSmBPp+ik1/TUODQ1NWt9LL72kLMtSd9999/iYE+kaPvjgg+q//bf/pu69914FqB//+MeHHf/666+raDSqPvGJT6hXXnlFff3rX1eWZamHH354fMx0z9nRoMXLQbj77runJF5831ft7e3qy1/+8vix0dFR5bqu+rd/+zellFKvvPKKAtRvf/vb8TEPPfSQMgxD9fT0zPjcD8VMzePss89Wf/Znfzbp2FTe8LPN0a7v0ksvVX/5l395yPsffPBBZZrmpH+w//iP/6iSyaQql8szMvepMlPX8Ac/+IFyHEdVq9XxY8frGq5atUp9+MMfHr9dq9VUZ2enuv322w86/o//+I/VNddcM+nY6tWr1V/8xV8opab2mTyWTHd9++N5nkokEupf//Vfx4/deOON6tprr53pqR4V013fkf63nmjXT6k3fg2/+tWvqkQioXK53PixE+ka7stU/g/81//6X9WZZ5456di73/1udfXVV4/ffqPnbCpot9EbYNu2bfT29nLVVVeNH0ulUqxevZp169YBsG7dOtLpNOeff/74mKuuugrTNHn66aeP2VxnYh7r169nw4YNfPCDHzzgvg9/+MM0NzezatUqvvOd70yppflM8kbW973vfY/m5maWL1/OrbfeSqFQmPS8K1asoK2tbfzY1VdfTSaT4eWXX575hRyGmXovjY2NkUwmse3Jrc2O9TWsVCqsX79+0ufHNE2uuuqq8c/P/qxbt27SeJDrUR8/lc/kseJo1rc/hUKBarVKY2PjpONPPPEEra2tLF26lP/8n/8zQ0NDMzr3qXC068vlcsybN4/u7m6uvfbaSZ+jE+n6wcxcw7vuuov3vOc9xGKxScdPhGt4NBzpMzgT52wqnHKNGY8lvb29AJM2tvrt+n29vb20trZOut+2bRobG8fHHAtmYh533XUXp59+OhdddNGk47fddhtXXHEF0WiUn/70p/yX//JfyOVyfOxjH5ux+R+Jo13f+973PubNm0dnZycvvPACn/70p9m0aRP33nvv+PMe7PrW7zuWzMQ1HBwc5Itf/CJ//ud/Pun48biGg4OD1Gq1g57fV1999aCPOdT12PfzVj92qDHHiqNZ3/58+tOfprOzc9JGsHbtWq677joWLFjA1q1b+Zu/+Rve9ra3sW7dOizLmtE1HI6jWd/SpUv5zne+w8qVKxkbG+POO+/koosu4uWXX2bOnDkn1PWDN34Nn3nmGV566SXuuuuuScdPlGt4NBzqM5jJZCgWi4yMjLzh9/1UOOXFyy233MKXvvSlw47ZuHEjy5YtO0Yzmlmmur43SrFY5Pvf/z6f/exnD7hv32PnnHMO+XyeL3/5yzOy8c32+vbdxFesWEFHRwdXXnklW7duZdGiRUf9vNPhWF3DTCbDNddcwxlnnMHf/u3fTrpvNq+h5ui44447uOeee3jiiScmBbW+5z3vGf97xYoVrFy5kkWLFvHEE09w5ZVXHo+pTpk1a9awZs2a8dsXXXQRp59+Ot/61rf44he/eBxnNjvcddddrFixglWrVk06fjJfwxOFU168fPKTn+Smm2467JiFCxce1XO3t7cD0NfXR0dHx/jxvr4+zj777PEx/f39kx7neR7Dw8Pjj38jTHV9b3QeP/rRjygUCtxwww1HHLt69Wq++MUvUi6X33Dvi2O1vjqrV68GYMuWLSxatIj29vYDouT7+voAZuT6wbFZYzabZe3atSQSCX784x8TCoUOO34mr+GhaG5uxrKs8fNZp6+v75DraW9vP+z4qXwmjxVHs746d955J3fccQePPvooK1euPOzYhQsX0tzczJYtW47pxvdG1lcnFApxzjnnsGXLFuDEun7wxtaYz+e55557uO222474OsfrGh4Nh/oMJpNJIpEIlmW94ffFlJix6JlTiOkG7N55553jx8bGxg4asPu73/1ufMwjjzxy3AJ2j3Yel1566QEZKofi7/7u71RDQ8NRz/VomKnz/OSTTypAPf/880qpiYDdfaPkv/Wtb6lkMqlKpdLMLWAKHO0ax8bG1IUXXqguvfRSlc/np/Rax+oarlq1Sn3kIx8Zv12r1VRXV9dhA3bf/va3Tzq2Zs2aAwJ2D/eZPJZMd31KKfWlL31JJZNJtW7duim9xq5du5RhGOr+++9/w/OdLkezvn3xPE8tXbpUffzjH1dKnXjXT6mjX+Pdd9+tXNdVg4ODR3yN43kN94UpBuwuX7580rH3vve9BwTsvpH3xZTmOmPPdAqwY8cO9dxzz42nAz/33HPqueeem5QWvHTpUnXvvfeO377jjjtUOp1W999/v3rhhRfUtddee9BU6XPOOUc9/fTT6sknn1RLliw5bqnSh5vH7t271dKlS9XTTz896XGbN29WhmGohx566IDnfOCBB9S3v/1t9eKLL6rNmzerb37zmyoajarPfe5zs76e/Znu+rZs2aJuu+029bvf/U5t27ZN3X///WrhwoXqkksuGX9MPVX6rW99q9qwYYN6+OGHVUtLy3FNlZ7OGsfGxtTq1avVihUr1JYtWyalZnqep5Q6vtfwnnvuUa7rqu9+97vqlVdeUX/+53+u0un0eHbX+9//fnXLLbeMj3/qqaeUbdvqzjvvVBs3blSf//znD5oqfaTP5LFiuuu74447lOM46kc/+tGka1X/H5TNZtVf//Vfq3Xr1qlt27apRx99VJ177rlqyZIlx1xMH836vvCFL6hHHnlEbd26Va1fv1695z3vUeFwWL388svjY06k66fU9NdY5+KLL1bvfve7Dzh+ol3DbDY7vtcB6itf+Yp67rnn1I4dO5RSSt1yyy3q/e9///j4eqr0pz71KbVx40b1jW9846Cp0oc7ZzOBFi/7cOONNyrggJ+f//zn42MI6mHU8X1fffazn1VtbW3KdV115ZVXqk2bNk163qGhIfXe975XxeNxlUwm1Qc+8IFJguhYcaR5bNu27YD1KqXUrbfeqrq7u1WtVjvgOR966CF19tlnq3g8rmKxmDrrrLPUP/3TPx107Gwz3fXt3LlTXXLJJaqxsVG5rqsWL16sPvWpT02q86KUUtu3b1dve9vbVCQSUc3NzeqTn/zkpDTjY8l01/jzn//8oO9pQG3btk0pdfyv4de//nU1d+5c5TiOWrVqlfrNb34zft+ll16qbrzxxknjf/CDH6jTTjtNOY6jzjzzTPWTn/xk0v1T+UweS6azvnnz5h30Wn3+859XSilVKBTUW9/6VtXS0qJCoZCaN2+euvnmm2d0U5gu01nfX/3VX42PbWtrU3/wB3+gnn322UnPd6JdP6Wm/x599dVXFaB++tOfHvBcJ9o1PNT/iPqabrzxRnXppZce8Jizzz5bOY6jFi5cOGlPrHO4czYTGEod45xWjUaj0Wg0mjeArvOi0Wg0Go3mpEKLF41Go9FoNCcVWrxoNBqNRqM5qdDiRaPRaDQazUmFFi8ajUaj0WhOKrR40Wg0Go1Gc1KhxYtGo9FoNJqTCi1eNBqNRqPRnFRo8aLRaDQajeakQosXjUaj0Wg0JxVavGg0Go1Gozmp0OJFo9FoNBrNScX/D/pLUlyCL48WAAAAAElFTkSuQmCC", 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", 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" ] diff --git a/test-driven-development/README.md b/test-driven-development/README.md index 12fd235..c611a86 100644 --- a/test-driven-development/README.md +++ b/test-driven-development/README.md @@ -4,4 +4,3 @@ 2. [introduction & practical work on TDD with Python](https://github.com/ADGEfficiency/teaching-monolith/blob/master/test-driven-development/tdd.ipynb), 3. [a quick look at Hypothesis](https://github.com/ADGEfficiency/teaching-monolith/blob/master/test-driven-development/hypothesis.ipynb) - a parameterized testing framework, 4. [notes on Python testing tools](https://github.com/ADGEfficiency/programming-resources/blob/master/test-driven-development/python-testing-tools.md). -