{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# MNIST synthetic gradients demo" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### A project by Sam Greydanus. MIT license" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is a two-layer neural network trained with synthetic gradients as in Decoupled Neural Interfaces using Synthetic Gradients. While the code is not an exact implementation, it captures the major ideas of the paper in a concrete way.\n", "\n", "\"Synthetic\n", "\n", "The basic idea behind synthetic gradients is to predict the gradients on all parameters in a neural network using a second network. For a really good conceptual introduction, read the DeepMind blog post]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load dependencies and data" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Extracting MNIST_data/train-images-idx3-ubyte.gz\n", "Extracting MNIST_data/train-labels-idx1-ubyte.gz\n", "Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n", "Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n" ] } ], "source": [ "import numpy as np\n", "import copy\n", "import matplotlib.pyplot as plt\n", "import matplotlib.cm as cm\n", "%matplotlib inline\n", "\n", "from tensorflow.examples.tutorials.mnist import input_data # just use tensorflow's mnist api\n", "mnist = input_data.read_data_sets('MNIST_data', one_hot=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Hyperparameters" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "global_step = 0\n", "batch_size = 10\n", "lr = 2e-2 # model learning rate\n", "slr = 1e-4 # synthetic learning rate\n", "h1_size = 100 # first hidden layer\n", "h2_size = 10 # second hidden layer\n", "D = 28*28 # dimensionality\n", "synth_step = 10 # ratio of model updates to synthetic gradient updates" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Model Initialization" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def make_model():\n", " model = {}\n", " # first layer\n", " model['W1'] = np.random.randn(D,h1_size) / np.sqrt(h1_size) # Xavier initialization\n", " model['b1'] = np.random.randn(1,h1_size) / np.sqrt(h1_size) # see http://jmlr.org/proceedings/papers/v9/glorot10a/glorot10a.pdf\n", " #second layer\n", " model['W2'] = np.random.randn(h1_size,h2_size) / np.sqrt(h2_size)\n", " model['b2'] = np.random.randn(1,h2_size) / np.sqrt(h2_size)\n", " return model\n", "\n", "synth = make_model() ; stale = make_model() ; control = make_model()\n", "\n", "# model for predicting synthetic gradients\n", "smodel = { k : np.random.randn(v.shape[1], np.prod(v.shape)) * 0.1 / np.sqrt(np.prod(v.shape)) \\\n", " for k,v in synth.iteritems() }" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# model functions\n", "def xW_plus_b(x, W, b):\n", " return np.dot(x,W) + b # in some cases you can even drop the bias b\n", "def softmax(x):\n", " maxes = np.amax(x, axis=1, keepdims=True)\n", " e = np.exp(x - maxes) # improves numerics\n", " dist = e / np.sum(e, axis=1, keepdims=True)\n", " return dist\n", "def relu(x):\n", " x[x<0] = 0\n", " return x\n", "\n", "# derivatives of model functions\n", "# see https://nbviewer.jupyter.org/github/greydanus/np_nets/blob/master/mnist_nn.ipynb for derivations\n", "def dsoftmax(h, y, batch_size):\n", " h[range(batch_size),y] -= 1\n", " return h/y.shape[0] # divide by batch size\n", "def drelu(dz, h):\n", " dz[h <= 0] = 0 # backprop relu\n", " return dz\n", "def dxW_plus_b(dh, model):\n", " return np.dot(dh, model['W2'].T)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def forward(X, model):\n", " # evaluate class scores, [N x K]\n", " hs = [] # we'll need the h's for computing gradients\n", " h1 = relu(np.dot(X, model['W1']) + model['b1']) ; hs.append(h1)\n", " h2 = relu(np.dot(h1, model['W2']) + model['b2']); hs.append(h2)\n", " probs = softmax(h2)\n", " return probs, hs\n", "\n", "def backward(y, probs, hs, model):\n", " grads = { k : np.zeros_like(v) for k,v in model.iteritems() }\n", " dh2 = dsoftmax(probs, y, batch_size)\n", " # second hidden layer\n", " grads['W2'] = np.dot(hs[0].T, dh2)\n", " grads['b2'] = np.sum(dh2, axis=0, keepdims=True)\n", " # first hidden layer\n", " dh1 = dxW_plus_b(dh2, model)\n", " dh1 = drelu(dh1, hs[0]) # backprop through relu\n", " grads['W1'] = np.dot(X.T, dh1)\n", " grads['b1'] = np.sum(dh1, axis=0, keepdims=True)\n", " return grads\n", "\n", "# forward propagate synthetic grad model\n", "def sforward(hs, smodel, model):\n", " synthetic_grads = { k : np.zeros_like(v) for k,v in smodel.iteritems() }\n", " synthetic_grads['W1'] = np.dot(hs[0], smodel['W1'])\n", " synthetic_grads['b1'] = np.dot(hs[0], smodel['b1'])\n", " synthetic_grads['W2'] = np.dot(hs[1], smodel['W2'])\n", " synthetic_grads['b2'] = np.dot(hs[1], smodel['b2'])\n", " return synthetic_grads\n", "\n", "# backward propagate synthetic grad model\n", "def sbackward(hs, ds, smodel):\n", " sgrads = { k : np.zeros_like(v) for k,v in smodel.iteritems() }\n", " sgrads['W2'] = np.dot(hs[1].T, ds['W2'])\n", " sgrads['b2'] = np.dot(hs[1].T, ds['b2'])\n", " sgrads['W1'] = np.dot(hs[0].T, ds['W1'])\n", " sgrads['b1'] = np.dot(hs[0].T, ds['b1'])\n", " return sgrads" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Evaluation metric" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# evaluate training set accuracy\n", "def eval_model(model):\n", " X = mnist.test.images\n", " y = mnist.test.labels\n", " hidden_layer = np.maximum(0, np.dot(X, model['W1']) + model['b1'])\n", " scores = np.dot(hidden_layer, model['W2']) + model['b2']\n", " predicted_class = np.argmax(scores, axis=1)\n", " return(np.mean(predicted_class == y))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Training loop" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\titeration 0: smooth_loss 3.815237, synth_loss 0.009213\n", "\titeration 250: smooth_loss 2.493766, synth_loss 0.002045\n", "\titeration 500: smooth_loss 1.379297, synth_loss 0.000673\n", "\titeration 750: smooth_loss 1.012663, synth_loss 0.001725\n", "iteration 999: test accuracy 0.738500\n", "\titeration 1000: smooth_loss 1.057044, synth_loss 0.000423\n", "\titeration 1250: smooth_loss 0.777043, synth_loss 0.001821\n", "\titeration 1500: smooth_loss 0.607534, synth_loss 0.000836\n", "\titeration 1750: smooth_loss 0.615273, synth_loss 0.001889\n", "iteration 1999: test accuracy 0.808900\n", "\titeration 2000: smooth_loss 0.584410, synth_loss 0.001935\n", "\titeration 2250: smooth_loss 0.579823, synth_loss 0.000686\n", "\titeration 2500: smooth_loss 0.517544, synth_loss 0.000147\n", "\titeration 2750: smooth_loss 0.591252, synth_loss 0.001182\n", "iteration 2999: test accuracy 0.872600\n", "\titeration 3000: smooth_loss 0.504129, synth_loss 0.000169\n", "\titeration 3250: smooth_loss 0.560493, synth_loss 0.001131\n", "\titeration 3500: smooth_loss 0.454970, synth_loss 0.000051\n", "\titeration 3750: smooth_loss 0.431393, synth_loss 0.001128\n", "iteration 3999: test accuracy 0.894100\n", "\titeration 4000: smooth_loss 0.441746, synth_loss 0.000462\n", "\titeration 4250: smooth_loss 0.417226, synth_loss 0.000246\n", "\titeration 4500: smooth_loss 0.435450, synth_loss 0.000374\n", "\titeration 4750: smooth_loss 0.369929, synth_loss 0.000181\n", "iteration 4999: test accuracy 0.907100\n", "\titeration 5000: smooth_loss 0.327477, synth_loss 0.000486\n", "\titeration 5250: smooth_loss 0.254708, synth_loss 0.000769\n", "\titeration 5500: smooth_loss 0.282602, synth_loss 0.000079\n", "\titeration 5750: smooth_loss 0.285151, synth_loss 0.000702\n", "iteration 5999: test accuracy 0.900400\n", "\titeration 6000: smooth_loss 0.301963, synth_loss 0.000240\n", "\titeration 6250: smooth_loss 0.348865, synth_loss 0.000360\n", "\titeration 6500: smooth_loss 0.331123, synth_loss 0.000797\n", "\titeration 6750: smooth_loss 0.298193, synth_loss 0.000218\n", "iteration 6999: test accuracy 0.909100\n", "\titeration 7000: smooth_loss 0.308105, synth_loss 0.000059\n", "\titeration 7250: smooth_loss 0.317047, synth_loss 0.000645\n" ] } ], "source": [ "synth_history = []\n", "smoothing_factor = 0.95\n", "for i in xrange(global_step, 7500):\n", " X, y = mnist.train.next_batch(batch_size)\n", " \n", " probs, hs = forward(X, synth)\n", " synthetic_grads = sforward(hs, smodel, synth) # compute synthetic gradients\n", " \n", " # synthetic gradient model updates\n", " if i % synth_step == 0:\n", " # compute the loss\n", " y_logprobs = -np.log(probs[range(batch_size),y])\n", " loss = np.sum(y_logprobs)/batch_size\n", " \n", " if i is 0 : smooth_loss = loss\n", " grads = backward(y, probs, hs, synth) # data gradients\n", "\n", " # compute the synthetic gradient loss\n", " ds = {k : - v.ravel() + synthetic_grads[k] for (k, v) in grads.iteritems()}\n", " squared_errors = [np.sum(slr*v*v) for v in ds.itervalues()]\n", " sloss = np.sum(squared_errors)/batch_size\n", " sgrads = sbackward(hs, ds, smodel)\n", " smodel = {k : smodel[k] - slr*sgrads[k] for (k,v) in sgrads.iteritems()} # update smodel parameters\n", " \n", " # boring book-keeping\n", " smooth_loss = smoothing_factor*smooth_loss + (1-smoothing_factor)*loss\n", " synth_history.append((i,smooth_loss))\n", " if (i+1) % 1000 == 0:\n", " print \"iteration {}: test accuracy {:3f}\".format(i, eval_model(synth))\n", " if (i) % 250 == 0:\n", " print \"\\titeration {}: smooth_loss {:3f}, synth_loss {:3f}\".format(i, smooth_loss, sloss)\n", " \n", " # estimate gradients using synthetic gradient model\n", " est_grad = {k : np.reshape(np.sum(v, axis=0), grads[k].shape)/batch_size for k,v in synthetic_grads.iteritems()}\n", " synth = {k : synth[k] - lr*v for (k,v) in est_grad.iteritems()} # update model using estimated gradient\n", " global_step += 1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Stale training loop" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\titeration 0: smooth_loss 3.030677, stale_loss 0.000600\n", "\titeration 250: smooth_loss 2.297427, stale_loss 0.000600\n", "\titeration 500: smooth_loss 1.606829, stale_loss 0.000600\n", "\titeration 750: smooth_loss 1.263941, stale_loss 0.000600\n", "iteration 999: test accuracy 0.728900\n", "\titeration 1000: smooth_loss 1.055426, stale_loss 0.000600\n", "\titeration 1250: smooth_loss 0.813792, stale_loss 0.000600\n", "\titeration 1500: smooth_loss 0.761992, stale_loss 0.000600\n", "\titeration 1750: smooth_loss 0.631247, stale_loss 0.000600\n", "iteration 1999: test accuracy 0.808100\n", "\titeration 2000: smooth_loss 0.619193, stale_loss 0.000600\n", "\titeration 2250: smooth_loss 0.689113, stale_loss 0.000600\n", "\titeration 2500: smooth_loss 0.654125, stale_loss 0.000600\n", "\titeration 2750: smooth_loss 0.602515, stale_loss 0.000600\n", "iteration 2999: test accuracy 0.844400\n", "\titeration 3000: smooth_loss 0.567582, stale_loss 0.000600\n", "\titeration 3250: smooth_loss 0.572366, stale_loss 0.000600\n", "\titeration 3500: smooth_loss 0.524966, stale_loss 0.000600\n", "\titeration 3750: smooth_loss 0.444738, stale_loss 0.000600\n", "iteration 3999: test accuracy 0.856900\n", "\titeration 4000: smooth_loss 0.429342, stale_loss 0.000600\n", "\titeration 4250: smooth_loss 0.325885, stale_loss 0.000600\n", "\titeration 4500: smooth_loss 0.444577, stale_loss 0.000600\n", "\titeration 4750: smooth_loss 0.451115, stale_loss 0.000600\n", "iteration 4999: test accuracy 0.876700\n", "\titeration 5000: smooth_loss 0.461882, stale_loss 0.000600\n", "\titeration 5250: smooth_loss 0.443143, stale_loss 0.000600\n", "\titeration 5500: smooth_loss 0.383808, stale_loss 0.000600\n", "\titeration 5750: smooth_loss 0.401103, stale_loss 0.000600\n", "iteration 5999: test accuracy 0.885000\n", "\titeration 6000: smooth_loss 0.417234, stale_loss 0.000600\n", "\titeration 6250: smooth_loss 0.368787, stale_loss 0.000600\n", "\titeration 6500: smooth_loss 0.358992, stale_loss 0.000600\n", "\titeration 6750: smooth_loss 0.432486, stale_loss 0.000600\n", "iteration 6999: test accuracy 0.888500\n", "\titeration 7000: smooth_loss 0.458806, stale_loss 0.000600\n", "\titeration 7250: smooth_loss 0.403893, stale_loss 0.000600\n" ] } ], "source": [ "stale_history = []\n", "global_step = 0\n", "smoothing_factor = 0.95\n", "for i in xrange(global_step, 7500):\n", " X, y = mnist.train.next_batch(batch_size)\n", " \n", " probs, hs = forward(X, stale)\n", " \n", " # calculate gradients\n", " if i % synth_step == 0:\n", " # compute the loss\n", " y_logprobs = -np.log(probs[range(batch_size),y])\n", " loss = np.sum(y_logprobs)/batch_size\n", " \n", " if i is 0 : smooth_loss = loss\n", " grads = backward(y, probs, hs, stale) # data gradients\n", " \n", " # boring book-keeping\n", " smooth_loss = smoothing_factor*smooth_loss + (1-smoothing_factor)*loss\n", " stale_history.append((i,smooth_loss))\n", " if (i+1) % 1000 == 0:\n", " print \"iteration {}: test accuracy {:3f}\".format(i, eval_model(stale))\n", " if (i) % 250 == 0:\n", " print \"\\titeration {}: smooth_loss {:3f}, stale_loss {:3f}\".format(i, smooth_loss, sloss)\n", " \n", " # update model using stale gradients\n", " stale = {k : stale[k] - lr*v for (k,v) in grads.iteritems()}\n", " global_step += 1" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "45.071011% same sign out of 4788 nonzero gradients\n", "Plotting grads vs est_grads for W2 parameters\n" ] }, { "data": { "image/png": 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X1Hrd+uf8AF5L8U/VrxiakZGRkXEVICJ/b8NPnxnOfzx4/iLgr+HXxjhkt/mqm3A5/ckK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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# first, flatten all gradients and estimated gradients\n", "g, s = np.zeros((0)), np.zeros((0))\n", "for (k,v) in grads.iteritems():\n", " g = np.concatenate((g,copy.deepcopy(v.ravel())))\n", " s = np.concatenate((s,copy.deepcopy(est_grad[k].ravel())))\n", "\n", "# now keep only those which are fairly large and see if their signs agree\n", "eps = 1e-3\n", "s[np.abs(s) 0)\n", "\n", "percentage = float (num_matching_signs) / (len(g) - num_zeros) * 100\n", "print \"{:2f}% same sign out of {} nonzero gradients\".format(percentage, len(g) - num_zeros)\n", "\n", "# plot first 2500 gradients\n", "print \"Plotting grads vs est_grads for W2 parameters\"\n", "sample = np.random.randint(len(g), size=500)\n", "plt.figure(figsize=(12,16))\n", "plt.subplot(121) ; plt.title(\"Actual gradient W2\")\n", "plt.imshow(grads['W2'].T[:,:50], cmap=cm.jet)\n", "plt.subplot(122) ; plt.title(\"Synthetic gradient W2\")\n", "plt.imshow(est_grad['W2'].T[:,:50], cmap=cm.jet)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Visualize training loss" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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JSbFpaWnlrs0LL7xgjTF22LBh3rZt27ZZY4ydNWuWt23EiBG2f//+la7rGWec\nYXv06OHdnjlzpjXGVPrst9xyiw0JCbFHjhzxtvXt27fceT369+9vR48eXan9p6Y2f+88fYABthEy\naJMYEbbW/gf4D4Cp3W2Z1wFbrLW3ube/NcYMBiYDi2vaOTERKIhjb5ZGhEVEpGZ5R/PYlLnJp+fo\nmdSTiJCIEz7OmWeeybJly3j44YdZuHAhy5cv57HHHiM5OZkXXniB0aNH13iM0NBQ7/fFxcUcOXKE\nzp07ExcXx+rVq/nNb35T5X5r1qxh8+bN3HPPPRw4cMDbbq1lxIgRzJ49u9rzLly4kLZt23Luued6\n21q0aMHEiRP54x//WGWdV155ZbX15+TkUFhYyODBg3n++efZtGkT/fr1Y+XKlezbt48HH3yQ4OBj\nceiKK66o8lxlHTp0iCVLlvCXv/yFrKyscu+dddZZTJkyhd27d9O6dWvAGb32jLp7DBkyhCeeeIKM\njAz69u1b7fni4uJYv34933//PV27dq22r9RNkwjC9TAI+LBC20JgWm12TkoCCuLYn60gLCIiNduU\nuYm059N8eo5Vk1YxoPWABjlWWloab731FsXFxaxdu5Z33nmHadOmcfHFF7NmzRp69uxZ7f4FBQU8\n9NBDzJw5k507d3r+ZRVjTKXgV9bmzZsBGD9+fJXvu1wusrKyiI2NrfL9jIwM7/SMso4X/tq2bVsu\nxHps2LCBu+66iyVLlnDkyBFve9n6MzIyMMZUOnZwcDCdO3eu8nwe33//PdZa7rnnHu6+++5K7xtj\n2LdvnzcIA7Rv375cn/j4eMAJ1TV54IEHOP/88+nevTt9+/bl7LPP5vLLL6dfv3417ivV+6kG4VbA\n3gpte4EYY0yotbawup2TkoD8eA7mKQiLiEjNeib1ZNWkVT4/R0MLDg4mLS2NtLQ0unXrxoQJE5g3\nbx733HNPtfvdcMMNzJo1i8mTJzNo0CBiY2MxxnDppZdWecOZh+e9v/3tb5x88slV9omKiqr/B6qg\nqhUisrKyGDp0KHFxcTz44IN07tyZsLAwVq1axR133FFt/bXlOcYf//hHRo4cWWWfigE7KCioyn6e\nXzKqM2TIEH744QcWLFjAokWLePHFF5k2bRrPPfec9wZHqZ+fahCut8mTJxMTEwsr17J+dQHnLTmP\ncePGMW7cOH+XJiIiTVRESESDjdb6i+cmtd27d3vbjjcb8e233+bKK6/kscce87YVFhZy+HD1A0ie\n0dzo6OjjXyAeAAAgAElEQVRyT2arrQ4dOrBx48ZK7Z6R5tr4+OOPOXToEAsWLOCMM87wtv/www+V\nzmWtZfPmzaSnp3vbi4uL2bp1K/379z/uOTwjxiEhIfX6nMdT3ezQuLg4rrjiCq644gry8vIYMmQI\n999//086CL/++uvemwU9qvsXB1/4qS6ftgdIqdCWAhypaTR42rRpvPfeu4SePoaYce159913FYJF\nRKTZ+Pjjj6tsf//99wHo0aOHty0yMrLKcBsUFFRp5HT69OmUlJRUe+60tDS6dOnC448/Tm5ubqX3\na1rHeOTIkezcubPcEmQFBQW88MIL1e5XsXZrbbn6i4qKeOaZZ8r1GzhwIMnJyTz77LMUFxd7219+\n+eUaA39ycjLp6ek899xz7Nmzp9L79V2v+Xg/j4MHD5bbjoiIoGvXrhQWVht5mrxx48bx7rvvlvua\nNq1Ws1wbzE91RHgZ8KsKbWe522sl0hVHbommRoiISPNy4403kpeXxwUXXEDPnj0pKiriiy++YO7c\nuXTu3JkJEyZ4+6alpfHhhx8ybdo02rRpQ6dOnTjttNM499xzefXVV4mJiaF3794sW7aMjz76qNw6\nxB5l/2nfGMMLL7zAqFGj6NOnDxMmTKBt27bs3LmTJUuWEBsbW+2T7K655hr+8Y9/cNlll/GHP/yB\n1q1b89prr3mnQNTmfvrTTz+d+Ph4xo8fz0033QQ4S8VV3Dc4OJgHH3yQa6+9lmHDhnHppZeydetW\nXn755SrnKVf09NNPM2TIEPr168fEiRPp3Lkze/fuZdmyZezcuZOvvvqqymtUVsX2tLQ0nn32WaZO\nnUrXrl1p2bIlw4YNo3fv3qSnp5OWlkZCQgJffvklb731lvfzyQlojKUpavoCIoGTgf5AKXCze7u9\n+/2HgVll+ncEsoFHcZZPux4oAs6s5hze5dOstbbDrx+zLe6JO+7yHSIi0nw15+XTFi5caK+++mrb\nu3dvGxMTY8PCwmz37t3tzTffbPfv31+u77fffmvT09NtZGSkdblc3qXUDh8+bH/3u9/Zli1b2piY\nGDtq1Cj73Xff2U6dOtmrrrrKu39Vy6dZa+3atWvt2LFjbXJysg0PD7edOnWyl112mV2yZEmN9W/b\nts2OHj3aRkZG2pYtW9pbb73Vvv3229blctn//e9/3n7p6en2pJNOqvIYy5Yts6effrqNjIy07dq1\ns3feeaddvHhxlbU+++yztkuXLjY8PNyedtpp9vPPP7fDhg2zw4cPL1eTy+Uqt3yatdZu3brVXnnl\nlbZNmzY2NDTUtm/f3p533nl2/vz53j4zZ860Lper0p+1qq7d3r177ejRo21sbKx1uVzepdQeeugh\nO2jQIJuQkGAjIyNt79697SOPPGKLi4trvJ5NSVNcPs3YWkzS9jVjzC+AJTgfvKxZ1tqrjDEvAx2s\ntcPL7DMUZ5WI3sAO4AFr7avVnGMAsGrVqlUMGDCA/lfNYG3qNZTcV4zL/FRniIiISH2sXr2atLQ0\nPP9PkKbtiSee4NZbb2XHjh3lVmKQn5ba/L3z9AHSrLWrfV1Tk5gaYa39hGrmK1trJ1TR9inOk+jq\nJSEiDowluzCb2LCql3ERERGRxlVQUFDuyXUFBQU899xzdOvWTSFYGlyTCML+kBwTB8DhgsMKwiIi\nIk3EhRdeSGpqKv379+fw4cPMnj2b7777jjlz5vi7NGmGAjYIt46PgzwnCHegg7/LEREREeDss8/m\nhRdeYM6cOZSUlNC7d2/efPNNxo4d6+/SpBkK2CDcNtEJwnuzDjuP5xARERG/u+mmm7QagjSagL1L\nLLWlMzVi+z4toSYiIiISiAI2CHds5cwL3nFAQVhEREQkEAVsEG7bqgUURbD7kIKwiIiISCAK2CCc\nnAwUxLHviIKwiIiISCAK2CAcGgquojgO5CgIi4iIiASigF01AqBFaTwH8g/4uwwREfGTjRs3+rsE\nkYDRFP++BXQQjixJ5UBJhr/LEBGRRpaUlERERAS//e1v/V2KSECJiIggKSnJ32V4BXQQTjSd2e76\n1N9liIhII0tNTWXjxo1kZmb6uxSRgJKUlERqaqq/y/AK6CDcqkUXvmuxk4LiAsKCw2reQUREmo3U\n1NQm9T9kEWl8AXuzHED76M4AbD201c+ViIiIiEhjC+gg3DWhCwBbDm3xcyUiIiIi0tgCOwintIHi\nUDbs/cHfpYiIiIhIIwvoIJzS0gUHu7JmR9NbzkNEREREfCugg3DLlsDuAazZu8rfpYiIiIhIIwvo\nIJycDOwayOYjX3O05Ki/yxERERGRRhTQQTgpCdiVxlFbyPr96/1djoiIiIg0ooAOwi1aQGx+fwDW\n7Fnj52pEREREpDEFdBAGSIqNJNTGkpmnpwuJiIiIBJKAD8LR0RBSGsvhgsP+LkVEREREGpGCcDQE\nF8eRVZDl71JEREREpBEpCEdD0NFYDhdqRFhEREQkkAR8EI6KAlOoEWERERGRQBPwQTg6GmyB5giL\niIiIBBoF4WgozYslq1AjwiIiIiKBREE4Gopz4jQiLCIiIhJgAj4IR0XB0exYzREWERERCTDB/i7A\n36KjofBIHIWFRyi1pbhMwP9uICIiIhIQAj71RUeDzY/FYskuzPZ3OSIiIiLSSBSEo4GCWADNExYR\nEREJIAEfhKOigII4AK0cISIiIhJAAj4IR0cDhRoRFhEREQk0CsLRQG4yAHtz9vq3GBERERFpNArC\n0UB+IvEhKXyz7xt/lyMiIiIijSTgg3BUlPPavsVJfL3va/8WIyIiIiKNRkHYHYRTTD++3qsgLCIi\nIhIoAj4IBwdDWBi0LD2JLYe2aC1hERERkQAR8EEYICICWh09nSATxJ0f3envckRERESkESgI4wTh\niIJuPDTiIZ7+8mkO5R/yd0kiIiIi4mMKwjhBOC8Pftb2ZwDsz9vv54pERERExNcUhIHISCcIJ4Qn\nAHAw/6CfKxIRERERX1MQxhkRzs2FxIhEAA7kHfBzRSIiIiLiawrCHJsa4RkRnjVXI8IiIiIizZ2C\nMMeCcFhwGK6SCOa9f4B9+/xdlYiIiIj4koIwx+YIA4SVJkL4QT7/3L81iYiIiIhvKQhzbEQYIMwm\nQNIm/vXFev8WJSIiIiI+pSDMsZvlAFyFidBnHq/G9KW01L91iYiIiIjvKAhTfkSY/ARv+1tv+ace\nEREREfE9BWHKB+Hi/HBv+0uzjvqpIhERERHxNQVhyt8sl9ciw9t+IC/TTxWJiIiIiK8pCHNsRNha\nKNnTy9ueVaxHLYuIiIg0VwrCOEG4pASys6Hk/Se4vfViAHJKFYRFREREmisFYZwgDLBjB1AcRlqr\n0wDIRUFYREREpLlSEKZCEAbaJkUTRAvyXQrCIiIiIs2VgjDOzXJwLAgnJhpigpI5GrKfkhL/1SUi\nIiIivqMgTOUR4fh4iA1Ohoj9x9YXFhEREZFmRUGYY0F42zYwBhISID40GSL3e584JyIiIiLNi4Iw\nx4Lw1q2QlATBwdA6IhXit5CT49/aRERERMQ3FIRxpkIAbNgArVo53/dJ7A/J6zl0RE+XExEREWmO\nFISB2FiIjoZ9+yAlxWk7KeVkCC5iw75NdTrWnj3w2Wc+KFJEREREGpSCMM684NRU53tPEB7Q9iQA\nvtm/pk7HeuopuPjihqxORERERHxBQditQwfn1ROE2yTEwsHObMqqWxDescMZWdayayIiIiJNm4Kw\nW8UR4chI4HAn9uXvrNNxdu8Ga+HAgYatT0REREQaloKwm2dE2HOzXEgIuAqSOFxUt6fL7d7tvO7b\n14DFiYiIiEiDUxB2qzgiDNDiaDJHSuoXhPfubaDCRERERMQnFITdunVzXj2BGCC0NImc0sxaH6Ow\n8NiUCI0Ii4iIiDRtCsJup54Ka9ZAr17H2iJsMnlkYq2t1TH27Dn2vYKwiIiISNOmIFzGySeX3440\nyZSao2QVZtVqf8+0CNDUCBEREZGmTkG4GnEhSQDsz615nnBREfz1r873nTtrRFhERESkqVMQrkZC\nWDIAmXk1zxP+5BOYP99ZfaJXLwVhERERkaZOQbgayZFOEN6fV/OI8OHDzuvatdCyJeyv22ITIiIi\nItLIFISr0TI6Aajd1IicHOc1MhKioiA315eViYiIiMiJUhCuRmJcCKYgvlYjwtnZEBYGwcFOGFYQ\nFhEREWnaFISrERsLNj+OrIKaV43IyYHoaOd7BWERERGRpk9BuBpxcUBhNIfys2vsm5PjTIkABWER\nERGRnwIF4WrExgJF0RzMqV8QruVzOERERETEDxSEq+EZET6cV3MQzs4uH4Sthfx839YnIiIiIvWn\nIFwNz4jw4VpOjSg7Rxg0PUJERESkKVMQroZnRDi7sO5TI0BBWERERKQpUxCuhmdEOKeo7lMjQEFY\nREREpClTEK5GdDRQFE1uSe1HhD/N+JRZ2+8FFIRFREREmjIF4Wq4XBBmoimoZRCOjoa3N7zNG1uf\nAhSERURERJoyBeEaRLeIpsBmY2tYC80zNWJn9k6yi7LAlCgIi4iIiDRhCsI1SI6NxpoSCooLqu3n\nmRqx48gOLBbCshSERURERJowBeEapMTHAJBdlM3B/IPMXDOzUp+SEsgrzuFz+xgZWRlOY/hBBWER\nERGRJizY3wU0de2SnMWB7/7v3WzK3MRn2z/jvB7nkRCe4O2TmwukPcfbR273toXEKAiLiIiINGUK\nwjVIbRUN+2HG6hnetr05e8sF4ZwcAFNuv7B4BWERERGRpkxTI2rQsU10pbY9OXsoKC6gsLiQTZmb\n2Hc4FyL3levTIlZBWERERKQpUxCuQbfUykH4tXWvET41nInvTWTwS4N58ZvpELOjXJ+8vk+xtvBf\njVWmiIiIiNSRgnANund0B+Hs1tzQ+jUiQiJ48asXAfjg+w84kH+ATYfWQvROusT04epTriYlMoX8\npOW8F3mBHysXERERkeo0mSBsjPm9MWarMSbfGLPcGHNqDf3HG2PWGmNyjTG7jDEvGmMSqtunPlrF\nR3FK7Jkw7036B/2a1lGtve9l5mUC8MOR9RCzk1+0GcmM82YQGhza0GWIiIiISANrEkHYGHMp8Dfg\nPuAUYC2w0BiTdJz+vwBeAp4HegNjgdPc2w1dG6tvXkzkgSHk5EDraCcID2wz0Nvnx/xvITaDtjFt\nAWctYRERERFp2ppEEAYmA89Za1+x1m4CrgXygKuO038gsNVa+7S1NsNauxR4DicM+0RUlLM6RKuo\nVgCc1fks73vF9igEF9Ehvh0ApbbU+15hcaGvShIRERGRE+D3IGyMCQHSgI88bdZ5nvGHwM+Ps9uH\nQCtjzK/cx0gBLgbe91Wd3iAc6Q7CXZwgHBES4e3Tq2U3ALrEd/G27cnZ46uSREREROQE+D0IA0lA\nELC3QvteoFVVO1hr1wLjgXnGmCJgN3AIuMFXRXqCcMe4jiRHJHNqW2cKc5/kPtyc9AE8s46fdTgF\ngC8nfsnowwsB+PHIjzyx/Al2Htnpq9JEREREpB6aQhCuM2PMIGAmcC8wABgJdMKZHuETniB83anX\n8b+J/yMiJIKE8ATaxbSjK2cTcqgvQUFO3/jweDq2cOYQz10/l8kLJ/PzF39OcWmxr8oTERERkTpq\nCk+WywRKgJQK7SnA8eYV3AwstNb+3b39jTHmeuAzY8xd1tqKo8tekydPJjY2tlzbuHHjGDduXLVF\neoJwREgEHeM6AtC/VX9OSjmJ/B8hPLx8/8SIeCgO5an/PQU4I8NLf1zK0A5Dqz2PiIiISCB4/fXX\nef3118u1ZWVlNWoNfg/C1tqjxphVwAjgXQBjjHFvTz/Obi6g4vBqKWCp+KzjCqZNm8aAAQPqXKcn\nCJe18LcLcRkXD30OYWEV+xvMjrbYuC38/tTf8+zKZ9mUuUlBWERERISqByJXr15NWlpao9XQVKZG\n/B2Y6F4buCfwLBCBM/0BY8zDxphZZfr/C7jIGHOtMaaTMeYM4ElghbXWJ3enVRWEg13BuIyL/PzK\nI8KRkWDmv8b0s5/i3l/cS5eELmzK3OR9/8nlT/LGN2/4olQRERERqQW/jwgDWGvnutcMfgBnSsQa\nYKS1dr+7SyugfZn+c4wxMcDvgceBwzirTtzhqxqrCsIeBQVVB+HS7YOY1H8QoaHQM6mnNwhba7l5\n4c0AXNb3Ml+VLCIiIiLVaBJBGMBa+wzwzHHem1BF27M4I8eNorognJ9feWpEZKTzmpuLE4QTezJv\nwzzAmS8sIiIiIv7VVKZGNHn1GREGJwiDMyK87fA2cotyWbFjhe8KFREREZFaURCupRMZEQYYnDoY\ni+X/vvs/lu9YDoDBcLTkqI8qFhEREZHqKAjXUlSUE2pLSyu/V5sR4W6J3Tij/Rm8vOZlfjj0AwAW\nqyfPiYiIiPiJgnAtRUWBtc7ob0W1GREGuLTPpXy45UN2Ze9iQGtnCbed2XrinIiIiIg/KAjXUlSU\n81rV9IjajAgDdIrvRIktYcP+DZzSynkcsx69LCIiIuIfCsK1FB3tvB46VL59/XrYv792I8Kto1o7\nbUdz6ZXUixZBLdiVvctHFYuIiIhIdRSEa6lfPzAGVpRZ8KG0FM44AzZsqN2IcJvoNt7vU6JS6JbQ\njbc3vk1JaYkPKxcRERGRqigI11JCApx8Mnz88bG2jAzwPBK74ohwSIjzVXYqRcvIlriMc8lTIlN4\netTTfJLxiZ4wJyIiIuIHCsJ1kJ5ePghv2HDs+4ojwuCMCpcdEQ5yBZESmQI4ofgXHX9B66jWbD64\n2Sf1ioiIiMjxKQjXwcCBsG3bsVHe9euPvVdxRBgqB2E4Nj0iJSrFu615wiIiIiKNT0G4DuLinNcj\nR5zXsiPCLVpU7l/VQzhaRzs3zCVFJAEKwiIiIiL+oiBcBzExzmt2tvO6aRO0dnItBw5U7h8XB4cP\nl29rE9WGxPBEgl3BzraCsIiIiIhfBPu7gJ8STxD2jAgfOuTcQLd7N+yp4gFxCQlw8GD5tlHdRhEW\nfGwehYKwiIiIiH9oRLgOPGsJe4Jwfj706eN8f+GFlftXFYTH9BzDk7960rvdJroN+3L3cbTkaLl+\n/978b1bvXt1QpYuIiIhIBRoRroOKUyPy8iApyXn0clUSEmDt2uqP2Sa6DRbL3ty9tItpB4C1lnPm\nnON8f99xDi4iIiIiJ0QjwnVQcUQ4Lw8iIo7fv6oR4Yo8q0jc9MFNFJcWA7Bh/4bqdhERERGRBqAg\nXAehoc7qEP/+N7zyijM1oqr1gz1qE4Q7x3cG4J1N73Dd/12HmWL4aOtHALQIqmIpChERERFpEArC\ndRQTA2++CVdc4WzXNCJcUOAE5uMeLzSGjJszAHjhqxcAeGvDWwAUlRRxML+GJC0iIiIi9aIgXEee\n6REeNQVhqHlUODE8sdz2Z9s/I71jOgBbDm2pY4UiIiIiUhsKwnXkuWHOoyGCcERIBKFBoeXaLup1\nEQA/HPyhriWKiIiISC0oCNdRxSBc0xxhqDkIG2NIjCg/Kjys4zDiw+I1IiwiIiLiIwrCdRQUVH67\nIUaEofz0iNCgUHok9SAlKoV9ufvqUaWIiIiI1ETrCNdRbm757eqCcFyc81qrIOweEU6NTWV4p+EE\nu4JJDE/kQH4Vz24WERERkROmIFxHOTnlt6sLwsHBEB8P+/fXfFzPiPBtp9/G70/7vdMWoSAsIiIi\n4iuaGlFHFYNwdXOEAZKT6xaEkyKSyrUdyFMQFhEREfEFBeE6iowsv13diDBAy5awrxbTfD1TI8re\nNKepESIiIiK+oyBcR++/D488cmy7phFhTxBescJZg7jiiLJHlSPCERoRFhEREfEVBeE66twZJk1y\nvm/RwpkHXB1PEJ4+3QnBu3ZV3S8h3FliouLUiMMFhykpLWmI0kVERESkDAXheoiKcl5rGg2GY0E4\nK8vZLiiAo0crP3Z5aIehjOs7jlZRrbxtiRGJWCwT35vI4YLDDVS9iIiIiICCcL2EhEBoaM3zg8EJ\nwvv3w2F3js3OhvT0yo9q7pLQhTkXzSHYdWyI2TNd4uU1L/PGN280UPVNg7WWacumaeqHiIiI+I2C\ncD1FR9c+CB89Ctu3O9vZ2bB0KZSUQGFh9fuWvXEuJjSmmp4/PduztnPLolsYNmuYv0sRERGRAKUg\nXE9RUbUPwgA//ui8Zmcfe2/16ur3Lfu0uSOFR+pYYdPm+Tzr9q3jm33f+LkaERERCUQKwvUUHV37\nOcJlZWdD+/bO959/Xv2+ZUeEswqy6lhh01Y22D/8+cNYa/1YjYiIiAQiBeF6qu3UiNaty29nZx+7\nce6rr6rft0VQC7bctIXU2NRmOyJ85+A7mbNuDme8dIafKxIREZFAoyBcT1FRtRsRjouDX/3q2PbB\ng3DEnWlr86CNTvGdiAuLI6uweY4I3zH4Dm4/43ZW7FxBqS31c1UiIiISSBSE6+nWW+GWW2rX97XX\n4OaboVUryMhw2tq3r92jlwFiQ2ObZRA2GKJaRHFKq1MotaXkFB172khJaQmPL32c3KJcP1YpIiIi\nzZmCcD2ddRaceWbt+sbHw7RpThDets1p69mzDkE4LLZZzhGODo3GZVzEhsUC5edBL9m2hD8t/hNP\nf/m0v0oUERGRZk5BuBFFR1cOwrW5Ryw2NLbZzRHOLsr2LgkXFxYHUO6hIftznd8SXEZ/REVERMQ3\nlDIaUXT0sakRvXpBcfGxB21UJyY0pllMjfjToj/x67d/DbhHhFs4TxXxBOGyn3Fn9k5AQVhERER8\nRymjEXmeJmcMdO/ufF+b6RGxoVVPjfjlq7/k7Q1vN2CFvvXdwe/YlLkJcIKwZ0Q4NtSZGlF2RHjL\noS0AevKciIiI+IyCcCPyBOG4OGe+MNRu5YjYsMo3y1lr+XDLh1z+zuUNXKXvHCk84g27ZYOwZ0R4\n/DvjuWrBVUCZIJyvICwiIiK+oSDciDxBuHXrYw/aqO2I8JHCI+UeOlFQXABAfnF+Q5fpM9mF2Rwq\nOASUD8JhwWG0CGrBoYJDfLXHWVz5h0M/AArCIiIi4jsKwo0oMtJ5HTkSEhKcKRK1CsJhsRSXFpcL\nvWWnEfxUHCk8QlZBFqW2tFwQNsZ4p0fsPLKTie9O5IeD7iCsqREiIiLiI8H+LiCQbNzovF5wAQQF\nQWJi7aZGeAJjVkEWESHO4+x+ikE4uygbiyW7MLtcEAaIbBHJ/rz97M/bz8y1M7ms72W4jIt1+9b5\nsWIRERFpzjQi3IgmTYIOHeD0053t9u1h+/aa90sITwBgf96x4eOyc4ZP9Ils2dkwa1btlnI7EZ4l\n4A4VHKoUhMs+OKO4tJgr+19J14SuZOZl+rYoERERCVgKwo3orLOcdYSDgpztbt3gu+9q3q9rQlcA\nvj/4vbet7IjwvtxaDCtX49FH4corYf36EzrMcR3KP8T7371P3tE8wKm9YhCueDNgl/guJIYnciDv\nQLm50SIiIiINRUHYj7p1g82ba+6XHJFMbGgs3x04lprLBuEfs348oToinNkW3qkbDe3Fr17k3NfP\n9W5n5mVypPCId14wQFFJkff7IBNEamwqiRGJFJYUegO0iIiISENSEPaj7t1h1y7IzIQ77oDCwqr7\nGWPontidbw98620rG4R3HNlxQnXEuAdmv/nmhA5zXJsPlE/7G/dvxGJpG9O2Ut8WQS3oGNeRkKAQ\nEsMTATQ9QkRERHxCQdiPunVzXt94w5me8NVXx+/bI6kH//n+P7y14S3ACcLxYfEYTLm5w/WR716M\nYv6uafzy1V+e0LGq4lkKzeObfU7ibhfTztv2QPoDDGg9gNZRremS0AVwPjOgG+ZERETEJxSE/cgT\nhFetcl737j1+3/Yx7dmTs4eL511M3tE8JwiHxxMfHs/B/IMnVEeee+bBN+1u4cMtH57QsapSMQh7\ngm37mPbetnt+cQ+rJq1iSIchpHdIB6BDbAdaR7Xmi+1fNHhNIiIiIlo+zY8SEyEsDL51z3ioLgif\n2flMHv78YQBW7FjB4YLDxIXFEWSCTjgI51d4JkdJaQlBrqATOqZHUUkR27OOLY1hMKzbt46IkAjv\nE+XKevWCV4/1NYbT25/O0h1LG6QWERERkbI0IuxHxkBsLOxwT/GtLggP7zSckntLiAuL47Ptn5FV\nmEVcWBwJ4Qkn/NCJvAr3ojXk09wyDmeUW96tTXQbcopyaBfTDmNMjfuf3v50/rfzfxSXFjdYTSIi\nIiKgIOx3sbHODXNQfRAGcBkXg1MH8/n2z70jwokRiRwsaJipERwNc+rIqaGQOthyaEu5bc9ScGXn\nB1enY1xHCooLyCrIqrmziIiISB0oCPtZbCyUlDjf1xSEAfq17Md3B75zgnCoMyLcEHOEXS7gqPMM\n6D05e07oeGXtzN5Zbvt3p/wOOPZwjZp4pk/46kl6i39YzJg3xvjk2CIiItK0KQj7WeyxpXRrFYTb\nxbRjV/YudmXvIjkymYSwhpka0bo1cDTcqSO34UaEd2fvJjki2bt9ad9LAbw3xNXEs9ZwxQduNJQv\nfvyCd799l6MlR31yfBEREWm6FIT9rD5B+GjpUbZnbadTXCdnakQ9R4RnrpnJ7uzd5OVBq1aAyxma\nbsipEbuyd9E6ujUX9roQcNYJzv1zLo/+8tFa7R8b5g7CPpoa4fkl4kSXoBMREZGfHgVhP6tPEPbo\nFN/JOzWiro8h3p29mwkLJnDjBzceGxFukQ04UyMO5h/kYP5BBr80mAWbFtTp2OXOk7Ob1lGteXPs\nm+T92ZmMHBESgcvU7o9eQ44I/3b+b/nzR38u1+a5MbAhw7+IiIj8NCgI+1nZIJyVBQUF1fcvF4Tj\nnCBcn8cQf5rxKQDFpcXk50PLlFIIzQHg8WWPk/hYIhfNvYgvfvyC8988v97zhnfn7KZNdBuCXcGE\nh4TXeX/PiHBDzBHelLmp0sM5vEG4AaeDiIiIyE+DgrCfeYJwsnsa7b591fdPikiiRVALDIbU2FTv\nY2lIeDQAACAASURBVIjrOj3ik4xPAMg9mkteHkTE5gIQhvO85bDgMD7e9rG3/6bMTXU6vseu7F20\njmpdr33BmUoRHhzeIFMjco/mVppP7dnel1vDhRcREZFmR0HYzzxBuKuzqlitllBrG92WtjFtCQ0O\nJSE8Aah7EP7iR+dpbd/u3UJeHgRFONMiLjCzKLm3hKnDpwJwRvszAPgx68c6HR/AWsuenD20jq5/\nEAZnVLghpkbkFuVWuk4Vp0bMWz+PHw7+UGlfERERaX4UhP2srkEYnOkRneI6Ac484WBXsHeqQ239\neHgHHOrEztwMcguO4gp3gnBpbgIu42Joh6EADO0wlKSIpHJPh6utg/kHKSopok10mzrvW1ZsaGzD\njQjnVz0i7JkaMfG9iTy/6vkTPpeIiIg0fQrCfuYJwl26OE+aq00QnjxoMrf+/FbAmSpxQc8LeGbl\nM7W+Ya7UlnK48BDsPJVSSjjS96+YUGdd36KcaAD6t+rPqG6juKDnBaTGptYrCO/Kdp4UciJTI8AZ\nEW6IOcKeEWHPk+6KSorILnJ+Adibu5f8o/lkFWaxI3vHCZ9LREREmr5gfxcQ6DxBOD7+/9l77/g4\nqnv9/z2r3suqN8uW5N6wsUXvYHrHtEC+cH9wLyRAchPKzU0gN0BIgEAgoSQQnIQAoQdIggHTbIyN\nDUaucrcsyepdK2m10mp+fxyd3dnVNq1WVvF5v15+7ezMmZmzK9l+9tnnfD5gNgcmhC+ZdYnL8+vm\nX8eFf7+QirYKpqZM9Xt+m7UNHR0OlcLc1+g76X/ZpwlxaG0XQjjcFM6/rvkXgBDCHcMXwrWWWoAR\nO8LJ0ckjjkbYB+z02nsB0cwjOTrZEZOIDo+moavB4QpXdyghrFAoFArFkUBQjrCmad/VNO08w/OH\nNU1r0zTtS03TpoRuepMfKYQTEiAzMzAh7I4Umq3W1oDGO3KytYtYsut9APb3rwHA2pEwZHxB4lBH\n2GKzoP2fxlvlb3m9T22nEMJZ8VkBzcsbSVEjzwh39XU5tmUcQj7OTJtJvaXeURlDCWGFQqFQKI4M\ngo1G/AToAdA07Vjge8BdQBPweGimdmTgLoTrgqhSlhKTAgReYsxROaEnFduOs6FmEbusImPc1TJU\nCOcn5VPZXukSvShvLAfgX7v/5fU+NZ01pMakEhUeFdC8vBGKjHCXzSCEB3PC8nF2+mzqu5xC+FDH\noWHXZVYoFAqFQjHxCFYI5wN7B7cvBt7Udf2PwP8AJ4ZiYkcKeXlwwgmwcGHwjnBydDIArT2BOcI1\nrcIRjtVSqaoC6o7CplvRdBP1h4bW+s1LzMNis2CxWRz7tjVsA/BZEULWEB4pocgIe3KEZcm02Wmz\naexqdGSae+29QxbVKRQKhUKhmHwEK4QtgHlw+yzgo8FtKzD8rglHMDExsGaNqBoRrBCW3dcCFYub\n9wqRd+JiMy0tQN1CAHKjp3OwQqO83HW8rFVsFIdSCPfZ+7zeR3aVGymhyAgbHWEZDSmrKyMzLpPZ\n6bOx63Z2NO5wjFHxCIVCoVAoJj/BCuGPgOc1TXsemA78e3D/HKAiBPM6IglWCIeZwkiMSgxYCO+s\naIG+aE49cfAzy7aruKnkPtbe9DlxcfD2267jPdUqlh3afOWSazprRlxDWN6/paeFB1c/yC/X/DKo\naxjd7OaeZgb0ATYc2kBpXimZ8ZkAbK7f7MgzKyGsUCgUCsXkJ1gh/D1gHZAOXKbrurQKFwOvhGJi\nRyJmM7S1wcDA8M9Njk4OeLHcvpoWwvtTyc0d3NGdxk+O+zkF5gxOPRU+/9xtXrGDjnD3UEfY1z1r\nO2vJiR95NMIcY6Z/oJ/XdrzGv/f82/8JHpDRCJNmYvXB1WT/JpuP9n/E0pylZMRlALClfgsLMheQ\nFJXE+ur1I563QqFQKBSK8U1QQljX9TZd17+v6/pFuq6vNOy/T9f1B0M3vSOL5GTQdejsDOLc6OSA\nHeHatmbiNDNms3PflMFaH9nZiLiEAekIN/c0o+s6Hb0djtJo3u6p67qIRoTAEZZCfHfzbsd9h4uM\nRlw19yreLH/TkQ9emruUzDjhCHf0dpCXmMcVs6/gpa0vOeoNKxQKhUKhmJwEWz7tbE3TTjA8/56m\naWWapr2saVpK6KZ3ZJEy+M61BmbsuuBLCNvsNt7b9Z7jebfeQqyW6hDCM2eKZh4gxHib22XiIxII\nN4XT0tPCdW9fxyl/PgUQTTe8LdDr6O3A2m8dcek0cGaUrf1WajprgqroIB3hX572S8wxZn552i95\n+IyHObnwZOIj4zFp4q/CgswFXDv/WiraKvim5psRz12hUCgUCsX4JdhoxCNAIoCmafOA3yBywlOB\nx0IztSOPZFH8YYgQDYSU6BSvMYV/7PwHF/79QkfutTesmThTqqN028KFrnNwv/+772rYO83sbTrA\n6zte59u6bwFYmrPU6z1lx7bEqMThvxg3pCMMQgwHs3Cuy9aFhkZBUgHV/13N/5z4P9x5/J1EhkWi\naZrD/S3NK2V+5nyAoLrpKRQKhUKhmDgEK4SnAnKJ/WXAP3Vd/wkiO3xOKCZ2JCKFcKgd4d3NuwFR\nHxegL6KJxPA0iovh7rvhySdd59DWJiIakp07Qe9O5a9b/oLNbgNEa+epKVO93lNGEeIi4ob/YtyQ\njrBENuoYDl19XcRFxqFpGtHh0V7HLchcQHJ0MibNRFN307Dvo1AoFAqFYuIQrBC2AbGD22cAHw5u\ntzDoFCuGj4xGBOMISyG85uAalv1tGQ+techxbG+LKPks6+T2R9eREpFFWBj86leQnu46h/5+6O52\n7jt0COg202xtZEqSCBOXpJaQEp1Cm7XNY5a2u09cIC5y5EI4NiKWqDBnUw75OoZDl60rIFEeFR6F\nSTOREp2iagkrFAqFQjHJCVYIfwE8pmnaz4ClgGwvNh1QdaeCREYVgo5G9LTy7DfP8uG+D/nppz+l\nrK4MgH2t+wAhIAf0AfTYesxRnrO7RlfaaoWLL4b164EesWDu4pkXk5uQy3TzdFJiUhjQB+jsHbq6\nT2ZyYyNihxwbLpqmucQjglkwJx1hb+z6/i623bLN8TwtNs2lSoZCoVAoFIrJR3iQ530feBq4HLhF\n1/VDg/vPAVZ6PUvhk/Bw0Wo5mGhEbJhwhFt6Wrhg+gVsa9jGMxufYWrKVL6o/AIQQrihoxXC+kmP\nyfR4HWNOuaEB3nln8ECxeFiau5RLZ11Kdnw2B9oOAKKEWlJ0kst1QhmNABGPqOmsIdwUHlw0wo8j\nPN083fV+sWblCCsUCoVCMckJSgjrul4JnO9h/w9HPKMjHE+L1fzx/vtw1z2pcGkPhzoOUZpbSuqU\nVF7b8ZpLhrfWUsuBpjoAMr1UczAK4Rhjj8BU4SqX5pZSlFokxgxe21NOWDrCoYhGgBCmEaYIilKL\ngmp20dXXRXxkfOD3izGrjLBCoVAoFJOcYKMRaJoWpmnaZZqm/XTwzyWapoWFcnJHIsnJw3eEN28G\nukRTiD0te0iNSeWorKNcBGpBUgE1nTVUtYjWdTkJ/oWwxWI4sO8sAKalTHPsSokRoWZPJdRkRjgU\n0QgQwjQtNo3Z6bMdXe2GQ3lTOXmJeQGPT4tNU46wQqFQKBSTnGDrCBcD5cBfgUsH//wN2K5pWlHo\npnfkkZIyfEc4PR3oFiverP1WUmNSWZS9CIBj845l7217uWD6BdR01lDRIlIsOUmeoxHGBXtdXYYD\nHz7CL6I70GTBYcQCPXB2l/v5Zz/nyjeuBEQUwaSZXBa5jYS8xDzyk/JZlLWITbWbhlVLuLGrka+q\nv+Kc4sALmphjzCojrFAoFArFJCfYjPCTwD7gGF3XWwA0TTMjxPCTwHmhmd6RRzCOcGwsDkcYRCe4\nhVmiOPAJBSdQlFpEdnw2Wxu2cnfD9dAXTUay55hAdDRERYk5RBk1rB6GtSPBda6DQlg6z9/UfsOW\n+i3A4OK0iDgX4TwS7jv5Piw2C9sattHe286BtgMu7rQvPtj3ATo655QMQwirjLBCoVAoFJOeYKMR\nJwN3SREMoOt6M3DP4DFFkATjCNtsQJezBlpqTCoJUQk8f8Hz3LrkVgDOLTmXqclTxYAIK3E+orvJ\nyfDoo/DNYGO1++6DuXOHzivcFE5CZIIjGlHbWcuhjkPYB+x093WHLBYBIoaRn5TvcLo31W4K+NwN\nhzYwM23msLrcmWPMtPa0Yh+wD3uuCoVCoVAoJgbBCuFeIMHD/nhEjWFFkKSkQPMwjUibDbBHEWEX\nlRtSY0Sps/9Y9B8UJhcCcFT2Uey/Y7/jHF9CuL4eKivh17+GyEj4+c/BbPYs0JOjkx3RiDpLHXbd\nTq2lVlRpCNFCOSOZ8ZnMMM9gRdmKgM+p6qhy1D8OlLTYNHR0r53zFAqFQqFQTHyCFcL/BP6oaVqp\n5uQY4Fng3dBN78gjNxeqq107u/mjt1c8hllFPEIKYU88M70CfrfTpxA+9VTnthyXnAztHjobp8Q4\nm2rUd4mFeFXtVY5oxGjwwGkP8O89/2Zt5dqAxle2V5KfmD+se+Qm5gKwvWE7bdY2fvH5L3h3l/rV\nVigUCoViMhGsEL4dkRFeB1gH/3wJ7AV+EJqpHZkUFIhqDbLN8f33w8GDvs+xDXrwukUIYWPzCXdi\n+6ZA8wyfQnjVKjjxRLEdPxglTkry7AinRKfQam2lubuZ/oF+QDiwXbaukEYjjFw661IiTBGOhiH+\nqGqvoiCpYFj3ODrnaEpSS3jm62e479P7uO+z+3joC9Gt7x87/0FHb8ew561QKBQKhWJ8EZQQ1nW9\nTdf1ixCd5C4f/DNd1/VLdF0Poi+aQjJl8Bv8ykpoaYF774W//933OVII29pETvinP0rlySc9j+3q\ngrAwEXnwhskEqYOmstER9iiEY0RHO2O3t2e+fobN9ZtHJRoBYNJM5CTkcKjzkN+x1n4rjd2N5CcN\nzxE2aSZuOfoW3ix/k7J6Ibjbre109nZyyauXcOKKE4Oauzs//vDHzHl6TkiupVAoFAqFYngEXDVC\n07TH/Aw5VVYI0HX9v0cyqSOZgkHjsrLSGY/Yts37eHBGI/TODEwDUfzl+Ri+LIHbbx86tqtLiFt/\nxRxkPWEphL05wsnRyexs2kmdpc6x77OKzwC4YPoFvm8yAnIScqjprPE7TjbfGG40AkQXvf6BftZX\nrwdEZ772XpEP2VK/hf2t+/m65mte2/4abyx/Y9jXB/jNut8EdZ5CoVAoFIqRM5zyaUcFOG4Y6VaF\nO5mZEBHhGofwJ4SlI0z7FAbacwANs5d0hBTC/nAXwqmpnsu6pUSLjLBse3zB9At4b/d74txRcoQh\ncCFc1V4FMGxHGJzNQ2x2G/Mz57Olfgv1lnrH8VX7V7G+ej3v7HoH+4CdMFPw/WRGer5CoVAoFIrh\nE7AQ1nX9VP+jFCPFZIL8fOEIS9e2vBz6+yHcy0+rtxemTYODX9+Bfcu1gIhVeCJQISwba8iMsNkM\n3d3wySei1vBxxw2OixbRiJrOGpKjk3nnqne486M7+c263xAbHlhGuK8PVq+GjAyYNy+gU8hNyKW8\nqdzvuKoOIYSH01VOkhWfRWxELN193ZTmlrKlfgu7m3cDEGGKYF31OvY076F/oJ+azpphi23ZfQ+g\nuaeZjLgMH6MVCoVCoVCEmqBbLCtGjylThCNcJTQcvb2wb5/38TabaKoxLS8O2qdQWupbCMd77qXh\ngrsjnJYmHk8/HY4/3jBusHzah/s/ZEnOEjRNozi1GICo8MC6yj33HJxxhri2LcDie8NxhM0x5qAW\n7mma5nCFS3NLARxCeFnxMr6s+pI9LXsAqGirGPb1dzbtdGwbnWaFQqFQKBSHByWExyHFxbB7txDC\n0wabp+3Z4328zSa6wJWUiOcnnSRiDJ5KsAUbjZBCWNLTIx5TYlKw2W18VvEZ18y7BoCiFNFlO9DO\nbLWD6+waG4Uj/Pbb/s/JScihzdrm4qp6oqqjKqhYhMQhhPOEEN7VvAuAs6adxe7m3TR0NQBwsN1P\naQ8P7Gjc4diWpecUCoVCoVAcPpQQHofMnw87dggX+Oijxb56Hzqpt1dUgSgpEc7wokVgt0Nnpziu\n67B9O3R0iAVvSUn+5+AuhN0zx19/PXjc5DxwycxLAKd4lAvo1q2D88/3fq+2NiGAc3LEB4AHH/Q/\nv5yEHAC/rnBVR1VQC+UkRSlFRJgimJU2i6iwKIcjfNXcq1zGVbRVYLPbhtWJrqq9iujwaACHoFYo\nFAqFQnH4UEJ4HDJ/vnB5v/pKOMJpab6FsHSEv/99+MtfnO6tjEf861+iRXJRkehal+q934YD94yw\nuyO8di3U1MCVS07n/qNf4OAPDpIULRT2xaeK0hdJUeL5j34k5uCpIQcIIZycDJ99BmedFViLadkx\nb1uD75WEVe0jE8JXzrmSu4+/mzBTGFnxWexu3k1UWBTpcekszl4MwHTzdCraKoh6IIorXr8i4Gu3\nWdvITcglLiJORSMUCoVCoRgDxo0Q1jTte5qmHdA0rUfTtPWapi3xMz5S07QHNU2r0DTNqmnafk3T\n/t9hmu6oMn++c3vZMsjKgro67+OlI1xcDJdf7hS6UgivXi0em5pE9thbRQkj7o5wQoKoZiHZs0cI\n4T5rJHP7bnBpWLFtSwS8+AEvXPQCANnZYn9lped7SSFcUgLLl8OBA2C1+p5fUWoRC7MW8tfNf/U5\nbqTRiNK8Uu4/7X4AshOy6bR1khiVCMAH3/mAVy9/lfmZ8x0Z4bd3BpDrGKTN2kZydDIZcRk8uu5R\nVh9cHfQ8FQqFQqFQDJ9xIYQ1TbsS+A1wH6JM22bgA03T0nyc9jpwKnADorHH1cCuUZ7qYUGKUICT\nT/YvhG021wYZ7kL4q6+c+6qqAnOE3YWwpjld4dxcIaotFvG8sdHDBfadRVqsOCFQIQwwcyYMDPjO\nREtuXHgj7+1+j+Zuz1lki81Cm7VtRI6wkex48UISohIA0cFv+Zzl5MQH1tzDnbbeNsdiw5rOGq59\n69qQzFOhUCgUCkVgjAshDPwQ+IOu63/VdX0n8F9AN3Cjp8Gapp0NnAicq+v6p7quV+q6/pWu6+sO\n35RHl5dego8/FgI0ECEcZSjQYBTC/f0iz3uBobdFII6wezTCeN68eQEIYQNyPoEKYYCdOz2PNXLZ\n7MvoH+hn1f5VHo/LGsLDba/sDSmEpSMsyYzPZF+Ls6xHoDlh6QhnxWcBwZV4UygUCoVCETxjLoQ1\nTYsAFgMfy326ruvAKuBYL6ddAHwN3K1pWrWmabs0TXtE07ToUZ/wYeKaa+C008R2oNEISUKCaKPc\n0iIWn3V3wznnOI8H4gjHx4vOdKec4tyXlibqHM+aFZgQlqXQ7IO68KCXwgpGIWw2i/vsCsDbz0nI\nYW7GXD7Y9wEAN75zI+aHzWxv2M6N79zIW+VvAcE10/BEdsKgIxyZ4LI/Iy6DvoE+x/Mt9VsCup4U\nwp9c/wnL5yynpcdLzTuFQqFQKBSjwpgLYSANCAPcVwvVA1lezpmGcITnABcDdwCXA0+N0hzHlOFG\nIzRNiN2WFrE4DmD2bOfxQISwpsETT8D06c59aWmi6UVmpnchbCzZVi26G9M3qBEDcYQBCguhosLz\nWKvVeT0QZcw+3Pchuq6zav8qWnpauPOjO1lRtoKffvpTSnNLmZI0xf8LDgBvjrB7I4xFf1zEfZ/e\n5/d6bdY2kqKSyE7IZknOkoDqIisUCoVCoQgd40EIB4MJGACu0XX9a13XVwL/DXxX07TAujhMIDIz\nRSm0bi8lc92jESCc1cZGZwm1lBSnAA4kGuGJKVOcVSxaW53VHZqanGP6+53bUsxKZ9iTI9zfLwS1\nUQhPmeJdNMfEiAWEktOmnsahzkPsb91PTEQMAF/XfO04/sTZT6DJFn0jRJZskxlhiaeOcL9Y/QuX\nhhmeaLe2kxyd7Li2xWbBYrOEZK4KhUKhUCj8E3CL5VGkCbADmW77MwFvPmgtcEjXdaNqKAc0IA/w\n2ofthz/8IUluhXSvvvpqrr766mFO+/BhXGwmM7RG3KMRAAUFQnhKIZyQAOnpwiUOxBH2xP/9n2ik\nsXatcH5l5zujI2ys9iCFr3RwD3lYTyZLqhmFcEGBKLfmjU8/dW4fly96Pa+tWkubVSjzxm4xoea7\nmkmNCfLFekBGIxIj3TLCcc5f3Qurt/GbX0dR8rsSDrQeYGaahx/YIDIaAU63ubazlhJzScjmrFAo\nFArFeOWVV17hlVdecdnX7q3W6igx5kJY1/U+TdO+AU4H3gXQhIV3OvCkl9PWApdrmhar67r0SWcg\nXOJqX/d7/PHHWbRoUUjmfrhYskQI3ZUrPQthT47w1KmiWoQUwvHxQgjv2hW8IxwXJ/7I6hHS8fUm\nhPfvd84PhIvsjnSVPTnCui4iGr5IiUlhTvocvqj8gjZrGynRKbRaW4kKiyIlOiXg1xYI7lUjJA5H\n2JrIu8/P4fvX9AK+m2RY+6302nudQnhQZFe0VfDcpue454R7QiriFQqFQqEYb3gyIjdt2sTixYsP\n2xzGSzTiMeAmTdOu1zRtJvAsEAv8GUDTtIc0TfuLYfzLQDOwQtO0WZqmnQQ8DPxJ1/Xewzv10Scx\nEc44w3vrYU+O8NSpoh5vZ6foNhcWJvK9YWHieiPBkxCW2WCjEN67VzxKIdzR4Vw4J/EkhAsKxHXc\nF+F5ahkNcHz+8Xx84GNsdhuz0mcBkBWfFbJIhCQ9Lp0wLWzIYrm4yDhiI2KhW7wxz/wuiqSoJJ9C\nWLrXxmgEwOs7XueRLx/hg70fhHTuCoVCoVAohjIuhLCu668BPwZ+AXwLzAeW6boupVAWkG8Y3wWc\nCSQDG4EXgXcQi+YmJRdcAF984bnRhPtiORBCuL1dxBcSBnVberrICo9UH7oLYasVurqc2yC62Ekh\nbFzc5t41zpsjDENzwhZDEMZ4nZlpM9nfKuznWWlCCEuHNZSYNBO3HH0Lp087fcix1KgM6E7jvPPg\nn/8Ec3TGsIRwQmQCsRGx/H3b3wHY0bgj5PNXKBQKhULhyrgQwgC6rj+t63qhrusxuq4fq+v614Zj\nN+i6fprb+N26ri/TdT1e1/Upuq7fNRndYElBgWg00eyhd4S3aATA5s1OIXzCCXDmmSOfS0qKKKPW\n0eHsNidbQPcO/gTmzRNNMXTdVai7xyPkc3dHGISjbcQYGzIuvDOK3tnpojyGdFhDze/O/R0nFJww\nZH9iWCZ0p3HHHUL4R/Zl0NA9VAjf++m97GraNUQIa5rGMXnH0GkTWZbypvJRmX8o0HWdjt6OsZ6G\nQqFQKBQjZtwIYYVv3LvFGfEWjQDYssUphL/zHXj55ZHPxWQS7jKIts4AtbXiUTrCc+cK4drSIoRh\nxmCM1l0INzWJuIZRCJvNolbx3/7mOrbDoL1chHD8UCFs3BcKrFbfbZ+PjbkOti/nqKOE4x4zMNQR\n7ujt4P7V9/P2zreHCGGAe0+617E9nh3hm967iaRfJdFmbXO8DoVCoVAoJiJKCE8QfAlhT9EIs1ks\nkGtqcgrhUCJdW1lnuGawBK5RCINwhW02UQIOPAths1mIa4mmwV13wbvvioYgEqMjbKwzbHR/Z5hn\noKGFXAjfcAPcfLP34wts3yNq53cxm0UGO6o/c4gQrmgTk67uqHYcMy6IO7nwZF69/FUePO1B9rTs\nwWa3hfQ1hILyxnL+9O2fALjk1Uu49NVLx3hGCoVCoVAEjxLCEwRvQlhGD9yjEZoGubliezSEcGGh\neMzNFYvxnnsO7rjDKYRldYuDB8X8vDnCjY3OzLGR5cuFOF6zxrlPCuH4eCGwJcZoRHpcOo8te4zl\nc5YH/+IGaW4WuWwQFTjKfaQVampEmTtNE+52eO9QR1gK4aqOKnY17SI3IZe4yDiXMcvnLOe4/OPo\nH+h3ads8Xtjbstex/VnFZ2xr2DaGs1EoFAqFYmSMefk0RWDI6IC7EO7vF2LY3REGyMkR5dJGQwjL\nBW3x8eI+q1aJPzKDnJ4uhGxrq4hGZGc7nxtpbHTGLIzExkJJCZSVOfdJIVxa6tqCOT4ynvjIeHr6\neoiLiOMHx/xgxK+vocHpYtfUCAfaVzRCCmEQPytTdwYNkQ3ous4jXz5Cd1+3w/2taq8iMiySGWkz\nPF6rOFXkTfa37ndUwZDous5LW1/iqrlXEW46/H99u/tcu7o0djfS2ds5pKScQqFQKBQTAeUITxDC\nwyEpaagQlqXJ3B1hEAIVRtcRjo113gec1SNiY8WiutZWp2OdnDy0akRTk2chDLBggXBk33tPPG9v\nF47r4sWw061pW3Z8NsnRySMumfbyy8Jt3rzZue/DD8WHjfp61855kupq0eRDvifJyTBgycBmt9He\n287dq+7m/z7/Pw60itV/0hGeYfYshHMScogKi3JUwjCytWEr1719HWsOrvFw5ujT098DwPzM+Y59\nB9oOeBuuUCgUCsW4RgnhCURq6lAhLKs0eHOEYXSFcGenM4IBzuoR0dFOIdzXJ+YnnxvxFo0AIYTL\nyuDCC0UFia1bRf521ixRFk6KbhDxCOPCs2C59lpYutT12u+/Lx4HBpyvz8hDDwm3+MEHxfPkZBho\nzQNgw6ENjnEV7RUANHU3sbVhq1chbNJMTE2Zyr5WEY2oaKvgYJtYHVhnEc0W67s8TOQw0N3XTYQp\nwrEoEfAo2BUKhUKhmAgoITyBcBfCK1bAjsHiAodbCMvFck1Nro6wrB4RFSXmKx1hX0LYmyM8b55z\ne+ZM+P3vhSsu88fGhXQ5CTkhEcIgXGt3ISyNZk9totvaYPZsZ6WO5GToaxRPVpStcIzb17KPklRn\n++Tp5ule5zAtZZpDYF795tVc9eZVgLNbXb1l7IRwbEQsS3KWMDNtJrERsUoIKxQKhWLCooTwBMIo\nhG02UcXg0UfFc1/RCPdubqFACuElS1yFcE2NmIumCeHb0iLmGhExVAjruu9oxLnnwrPPim0ZzxHj\nvQAAIABJREFUAWlpgRmDRqpRCN913F386oxfhebFIYSwpgnnu6ND5JLBsxDu7XV9/5OToashk5jw\nGN4ud7YD3N64nZOnnOx4vjR3qdf7T0sWQrjOUsf66vWsr15PRVuFQwj7atYxmkghfEfpHXxz8zcu\ngl2hUCgUiomGEsITCKMQ3rtX5FVXrRLPPTnCcvFWZ2fo55KUJKoq3HornHMOXHyx2F9bK2IRMDQa\n4e5oWyxCRHqLRoSFibJlRiwWcd34eBGPkByVfRRnTDtjRK/JmP/t6oK4OGfs46KLhJiXZeKMeBLC\n7W0ahcmF9Np7HRGIAX2Ac0vO5Q/n/4HKH1RijjV7nUtRahHbG7cz7xlhi0eFRfH69tdHRQi/t+s9\nlwiHL3r6eoiJiCHMFEZsRKwSwgqFQqGY0CghPIEwCkkZiZBf4cvMrhFZacL4NX+o56NpIrP74oti\nn3SE5XEZjYiIgKwsZ3QCRCwCvDvCIAS0LL1mJC/Pszs7EnoNfQmlEJaVKk44QTjfgTrCra1QmFwI\nwHkl5zmOlZhLuHnxzeQn5eOLZUXLOGPaGWTHZ3Pd/Os4b/p5vLr9VacQ9tC1Llgu/PuFlD5fSk9f\nD7e/fzu1nbVex0pHWCKda4VCoVAoJiKqfNoEIjXV2WK5vFyIy74+IdI8CeHZs+GWW+AnPxn9ucXF\nOR1TmUmW0QjpCLuLVymKPQldI7m5opzZb38rFs6BuFZ1dWhfQ0+Pc1sK4cRE8XzxYlEyztjIQ+Je\nxzk5Gbq7oSBR5ITPLTmXx9Y/BkBRSlFAc5mVPouPrvvI8fy17a9x5RtXYrFZgNGJRry9821+t+F3\n9Pb38ocL/uBxzBAhnDKNA20HGNAHMGnqc7VCoVAoJhbqf64JREGBEJrd3cIRLi2FP/8Z3nnH8/iw\nMHj6aSEaRxtNczrA7tEIuVguL0/kbWWr5C1bRFm4GZ6LJziQ8z/1VOeCtNEQwsY6wZ2dQgi/+qp4\nf2NiRBc9Yy5Z4t7iWjrxOdFFmDQTS3OXkhqTSl5iHjERMUHN7byS84iNiGVXsyigHCohbKwL/OiX\njxIZFskLZS94vX5Pf88QIWyz26jp9JAZUSgUCoVinKOE8ARiyRKx8K2sTAiymTPhu991dp0ba+Q8\npBBOTRWCsrtbuMUyb/vCC0JgbtwIc+Z4XuhnRJ4nm3jIfaGORhgd4UOHhBDOy3O60FII67rreZ6i\nEQDn5dzIB9/5gISoBLLis1wqRgyXuMg4zi4+W2xHxIVMCBtjEN/WfcvNi26mf6DfUfPYne6+bmLC\nnWJ+Wso0QJVQUygUCsXERAnhCcTcuUJkbtggIge+srVjQUqKeDQ6wuDqCAP88Idw1VWi/NtRR/m/\nbkmJWPiXlOTcl5cn3PFQVsQwOsIVFUIIG5k+XQh791rC7kJY/lw6G5MdC/jOKzmPC2dcOKL5XTTj\nIgBSY1Kx2Cx02UYW/v7kwCfc+dGdjufhvenccfQ9gOgY5wn3aITMQSshrFAoFIqJiBLCE4iICCEc\nN24Ui7iMwnA84O4ISyEMYu7GMmvHHy8eSwIwSW+9Fb76ynVfXp4QwZ4aXASLUQgfPOhZCMPQeIR7\nRnjmTPFefPaZc9/DZz484tbP5xSfA8DFM0WJjhNXnMiAPhD09VaUreDtnaK8W9RfNmB/bD+fvCcC\n294cZ3chHBMRQ05CDntb9gY9D4VCoVAoxgolhCcYCxbAtm0iZzvehLB5sBqYMRohiYx0FYtvvgnL\nlsHy5f6vGx0N+W5FFqS77N5qeSQYoxGtrUOF8LRpYDLB66+LTPSePWK/uyMcFibyzLK0XahIj0un\n+yfdPHH2E7xw4Qt8W/eto+NcMBjF6/ITj2Zqbjx7dkaQGpPqMyNsjEYAzMuYR1ldWdDzUCgUCoVi\nrFBCeIKRkQGVlcINHW9C+IILxOOWLeLR6Ai71znOzISVK6G4OLh7zZsnnNdHHgnufE8YHWEYKoSj\nouCkk0SHO4BNm8Sj+2I5gDPOgPXrQ1+6LiYiBk3TOG3qaQDsbAr+k4BRCGdnaZSUCHGfEZcRsCMM\nsCRnCRtrNqK7h6cVCoVCoRjnKCE8wUhNFS19YfwJYdlUQ5Ycc49GALz3HqxePfJ7hYWJsnArV4Zu\n0ZwUwmFh4tFdCAM89JBzW/4c3B1hEJUw7HbPDThCQX5SPrERsZQ3lQc03mKz0D/g7BjSbm2nqbvJ\n8Twx0bkYcLhCeGnuUhq6GtjRuIMb3rmBdmt7EK9IoVAoFIrDjxLCEwyzoRnZeBPCYWHCrZbZ2NhY\np1MqH88/H048MTT3mzVLPDaEqKSujEZIl9qTED7mGOF4p6c788mehLD8ORk76YUSk2ZihnlGQI6w\nrusc/cejeXjtw459+1r3AbDiohVEvlBGYqLIa+/bB2kx6QEvlgNYkrsEgGe/fpY/l/2ZjTUbg31Z\nCoVCoVAcVpQQnmAYc7fjTQiDyPLK1s6a5nSFpSMcSuR70doamutJR1gu4PMkhEHEMnJzoa5OPHdf\nLGecm2yAMhrMTJsZkCN8oO0Au5p3sbZqrWOfjEWcPfVCbJULHI6wzQYxA94dYdli2UhWfBbpsel8\nfOBjAKo7QlzgWaFQKBSKUUIJ4QnGeBfC7kgh7J6hDeW1Q+W6Wq1iMZxs2uFNCINoF11XB/39MDDg\n2xHu7xfd9ULNgswFfFv7rd8yap9XfA7gsqDtQOsBEqMSiegXv1CJic7GJgOdnoWwruseHWGAgqQC\nhzt9qCPEBZ4VCoVCoRgllBCeYIznaIQnpHAfDSGclCRc51A5wj09okKFbODhSwhnZopoRG+veO7+\n+mJixLWam+GOO0Td5FBzxZwr6OrrcpRA88ZnBz8DoKazhsYuEXmotdSSk5Dj6PKXmCgalqSmQket\nEMLui9/6Bvqw63avQlhHjFeOsEKhUCgmCkoITzCksNQ0iI8f27kEwmhGI0wmcf1QOsIxMc4PGwM+\nSvRKR1gKYU/d8cxmMbd160TJu1AzLWUaJ085mZe2vuRz3Jb6LZxSeAogusd9tO8jai21ZMdn0z64\nrk1+qDj6aGjcPZX+gX72tOxxuU5PnwhRu5dPAyGEJdWdSggrFAqFYmKghPAEIzlZCJaEBCEExzuj\nGY2Q1w/WEdZ1aHIWTsBqFS6unLOv6wYihFNTobFR1DoereoRJxacyNb6rexs2ulSBcJIVXsVpxWe\nRnpsOhe8cgFn/e0s3t/zPtkJ2S6OMIg23gdWn0C4KZyP93/scp3uvm4Aj45wfqKz0LOKRigUCoVi\nojABpJTCSFiYEMMTIRYBTgd7NBxhef1gHeGPPoKCArBYxHMZjZBtn48+2vu5mZnQ3e28tzdH+Ntv\nxXUtFtGeOdQUpxZzqPMQs56axZkvnjnkeHdfN809zUxJnsLPT/k5NrsNgE5bJ9nxnoVwY3UCC8xL\n+eOmP1Jvcbbu6+oTWWRv0QgQ1SxUNEKhUCgUEwUlhCcgqakTRwgfDkd4uEJY1+GFF6C8XIhUWQZN\nRiOmTRNO7+mne7+GrCzx8aBp6s0RXr/e+Xw0XOHiVGdHEuNiuD57H5XtlQ53Nj8xn5sX38zT5z5N\nhEl8KsmKzxoihE8+WXxoyew8m7K6Ms59+VzHNVftX4VJMzHdPH3IPKQQnpsxl8buRkeMQqFQKBSK\n8YwSwhOQiSSER3OxnLz+cKMRVVXwH/8Br7winst4hIxGgP/5Ll4MixY5O9t5Gm9c2AijL4Sz47Md\n2z/+8MdM+e0UKtsrAchLzCPcFM4tS25x1P2VjnBYmPgAAOLbhjPOgI6Vd3LTopvYVLuJnr4edF3n\n6Y1Pc+GMC8lNzB0yj8LkQgAun3U5EaYIbvnXLfTZR6FUhkKhUCgUIUQJ4QlIZiakpY31LAJjNBfL\nyesP1xGWHeF27RKPstavjEYEgqYJMV09mALw5ggDnHKKeBwNIZwRl0F8pFg1mRCV4Ni/oWYD4HSJ\n8xLzHMdKUoWdLTPCiYni9UguvBDWfh7N9XNuBmBrw1aae5rZ2rCVK+dc6XEemfGZrLlhDfeccA8r\nLlrBS1tf4so3xNgXN7/ItoZRWC2oUCgUCsUICR/rCSiGz+OPuwqX8czMmcK9Tk4enesH4wjLSglS\nEBsd4ZihBRG8MmWKc9uTEJYd7+65R2SFR0MIa5pGcWoxZXVljtJogEMcb6zZSFpsmksTDOkiy2iE\njEVIpkwR8ZEs01zCtDA21W5y5IKN1SHcOaHgBACunX8thzoPce+n97K/dT/X/+N6luQsYcNNG0Ly\nmhUKhUKhCBVKCE9Aiov9jxkvLFkihOpoCfdgHGEphCXSETZGIwLBGH3wJITvuEMI6zPPhJyc0asc\n8YPSH/Dq9ld5f+/79Pb3EhUeRUKkcIc31mx0qegAsDBrIdHh0eQl5tHRMTRmI53s7o5o5mTMYcOh\nDS7iORDmZ86n197Ljz/8MeAU5gqFQqFQjCdUNEIx6oyme52eLqox9AxjbZa7EJaO8HCiEeAaT/Ek\nhBcsgKeeEmXucnNFNnk0+O7C73Lb0tsAHB3hpPDc37qfqSlTXcafV3IeB+4QneXa24c6wsaueJfO\nvJS/bv4r/9j5DwAy4zIDmtMMs2hTJ5t9eGvZrFAoFArFWKKEsGJCI9shHzjg+fjKlSLzakRWSpD8\n8pdw+eXDj0b4E8JGCgvh4MHArz1cMuOFQH2r/C1sdhsDurMbiBSlEk3THM6uxTK0g550hJub4Scn\n/oQScwlPbXyKhMgE4iJ9tNszUJBUQFSYeFNOKTyFiraKIZ3qFAqFQqEYa5QQVkxoiorE4759no+f\ncw68954QuRJ3RxjEmEOHnIv7AsEYKfBXZaKwECoqAr/2cJHC9gcf/ICXt76MxWZxHPNU7kzS3z90\n7rJpS0sLRIRFcGzesS73CIQwUxjTzdMxaSaun389XX1dXht+KBQKhUIxVighrJjQZGcLF3fv3qHH\njOJX5oDBsxC22YSrvHhx4Pc2Rj4CcYSbmpzNO4bLqafCs896P54em+7YttgsLkLY3RE20tcH4W4r\nBWTravmezU6fDQxPCAPMy5zH0tylLMhaAEBFW8WwzlcoFAqFYrRRQlgxodE04Qp7coQ//9y5LXPA\nO3b4dmaXLAluHoE4whBcPGLdOvjsM7j7bu9jIsIi+NGxPwKgsavR0QUO/DvCnkrbmc3ORYhz0ucA\nwxfCT5z9BG8uf5OpySK/cqDNS35FoVAoFIoxQlWNUEx4vAnhzZud21IIz5nj+RrJyWC3w3TvmtEn\nJj8fKWWptYoK73PwhnSCly517mtrgzVr4IILnPsePetRPjnwCQ1dDQ5HODUmFXOsW2cPA54cYRA5\n4ZE6wmmxIkSt6zpxEXFUtY/SakGFQqFQKIJEOcKKCU9REezZM3T/9u0wa5bYbnKLp0oX9Fe/gu9+\nV5Q4O/ZY/4I2WLKzxT2DyQnX1opH42v41a/EIsA33nAdmxGXQUN3A122Li6ddSl/uvBPPq/tzRFO\nTXU6wvlJ+aREpzAlacrQgQEgF+fVd9UHdb5CoVAoFKOFcoQVE545c0STka4u1woI27bBMccIt7ip\nSbifkqOOgrIyuPpqKCgQDqvdPnpzDAuD/PzgohGyNFyjs18G69aJx3vuERUvJBlxGRxoO4DFZmFx\n9mIunnmxz2t7c4TNZudcTZqJr/6/rzy2Vg6UrPgs6ix1QZ+vUCgUCsVooBxhxYRnwQLRCW2boYvv\nwACUl8PcuaLMWWOj6yK5xYvF84LBRmnJya4NMgJFZn8DIT9fVKYYLnLRX2OjeJ2dnUIIz5olahMb\nq5JlxGXQ0NVAV18XcRH+S5319fl3hAFKzCWO7nLBoBxhhUKhUIxHlBBWTHjmzBGOa1mZc19FhXBS\n58wRQripybUVc3v78JpneOPLL10X5fkiLw+qq4d/D6sVMjNFZYv2drjzThFpuP56sc9YFzkjLoM6\nSx3dfd0BdXPr7/fuCBsrbYwU5Qgrhouu6zR3h/CXUKFQKDyghLBiwhMdDTNnui6Oqx80H3NzhRB+\n7TV48UXn8YYQNTrLzoaTTgpsbLBCuKfH6Vy//jr84Q/wzDNQWir2GSMT6bHpdPQKZRxI8wt/jnCo\nemBMBiFstwfn6CuC48uqL0l7JI2/lP1lrKeiUCgmMUoIKyYFCxe6OsKdneIxIUGIusZGuP9+5/Hj\njju88wOnEB6uuLRanUL4ySdFHOPmmyEjQ+wzivqMuAzH9kgdYZsNuruHN1dvZMVn0djVSP9Af2gu\nOAb8/e8wY4ZrfWrF6LGvVZSCufHdG+mz9/kZrVAoFMGhhLBiUrBgAWzZIrLB4CqEjdlhEM037r33\n8M4PhBC22YZWsPBHT4/IF4N4LVdeKeonpw/20DA6wkYhPNKMMIQuHpEZl4mOPqG7y+3YIRZkeuti\nqAgt9Rbxtc6APkB1RxBfpSgmBWsOrqHN2jbW01BMYpQQVkwKFi50FSkyN5uQAA8/7HQ9TSaYOlVk\nig83uYNFF4Ybj7BahYiW3HSTeDSbhSA2OsJzMpxFikfqCIPrgrmRIGsQT+R4hCx9t3v3mE7jiMH4\nu3KwPYhyK4oJj33AzpkvnskL374w1lNRTGKUEFZMChaILr6OnHBnp8gOR0SIphP//rfYn5Q0erWC\n/SHF7HByprouhHB8vHCAb71V1E0GIebNZldHODYilgiTsHhHmhGG0DnCOQk5wMRusyzLye3aNbbz\nOFKo66pjaa7oInOwTQnhI5H6rnp67b0c6lDhfMXooYSwYlKQkSEWrsmccEeHcIMlMlqQknL45ybJ\nyBCiczi1hPv6RNwjOhrq6uCpp4Ze033hX2meWEUXEx7j9/qHyxHOScihKKWIlXtXhuaCY4ByhA8v\ndZY6CpMLyYzLnNAfoBTBU9leCYgPRQrFaKGEsGLSsHAhbNoktjs7ITHReUy6scnJh39ekrAwmDZN\nZJQDRS7Mio727GSnp7s6wgDPXfAcF824KKAGGN4c4cREMd9QOcKapnHhjAt5d9e7DOgDobnoYcRm\ng5oa8V4pR/jwUG+pJysui8LkQhWNOEKRbdkncqRKMf5RQlgxaTj6aNi40dl0wugIx8cLETyWjjBA\nSYloB22zBTZeCuEYL+auJ0d4ZtpM/nHVP4gMi/R7fW8tljVtaFONkXL+9POptdSyo3FH6C56mJCN\nS445BvbvH+vZHBnUWerIis9iSvIUJYSPUKo6hBCu7awd45koJjNKCCsmDUuXiooMBw8OjUaAiEeM\npSMMQgj/618QFRWYsyjbK3tr/uHJER4O3losgxDCoWyqMcM8A5iYOeEq8f8xS5aI93tg4pnaEwqb\n3UZzTzOZ8ZlMTZ7KF5VfcO+nY1DqRTGmOKIRyhFWjCJKCCsmDUuWiMcNG4Y6wgC33QbXXnv452Wk\npMS5XV7uf7wxGuEJT47wcPDmCIPICRsd4bVrA3Oy6+pEzV13suKzCDeFO77unEjI9tzTp4vGGm2q\nmtOo0tAlfqmz4rP43pLvcWrhqTy/6fkxnpXicCMd4VZrK739vWM8G8VkRQlhxaQhM1O4vt98MzQj\nDKLs2CWXjM3cJNOnO7crK/2P9xeNkI5wMB3g7HZxXiCOsMUCJ5wAP/yh/+ueeCJcffVQ0RxmCiM3\nIdfxn9toYGyj7Y+qqsCjHxaLeCwsFI+h6kyocKLrOpXtlfT09VDTWQNAdnw2+Un5/MdR/0GtpZbG\nrhF8/aGYcFS2V5KXKBZ41HfVj/FsFJMVJYQVk4rZs0XkwFM0YjwwY4Zz+8AB/+P9RSMyMoSrG4xD\n2T/Y5M2bI2zMCMu6zOvX+75md7dzMWCth1hfflL+qAnhL76AnJzAxfAVV8D//m9gY7u6RG5advgb\nSRxF4Zkff/hjpvx2Cvd+eq+jgUZ+kij3siBL1EfcXL/Z6/mKyUdrTytz0kVtdE/xiHtW3cO5L53L\n9obth3tqikmEEsKKSUVJiShv5SkaMR7IyxOL5ZYtC0wIB+IIQ3DCrG+wa603R9gYjZCOqD8ndPVq\n57anesn5ifmjFo2orBTvV01NYON37gzMlQfx+uPjnW2tlRAODc3dzTy/6Xl0XeflbS8DsLd1L1Xt\nVUSHR2OOEXX8ilKKiAmPYUv9lrGc7qTBYrOwq2n8lz+x9luZmjwVGLpgTtd1Hl//OO/vfZ8P9n0w\nFtNTTBKUEFZMKqZPF93l2tqGRiPGC8XForvdcISwL0cYgvuqXgphX46wjEbIltX+BKAxauBVCI+S\nIyznGMgCv9ZWkfutD/Db1q4uiIsTVUfCwpzv965dTtdeMXxWlK3gpvdu4pVtr1BnqSMjLoPazlqq\nO6rJS8xD0zRAxGrmZc5TQjhEPLPxGU7+88ljPQ2/WPut5CXmEaaFDXGEm7qbsNlF/qq5O4SrehVH\nHEoIKyYV06eLbGpDw/h0hCVSCPvL9gZSNQKCcyhlNMKfI6zrTke41896FYtFRAhiYjy3ks5Pyqe6\noxprv3X4E/aDFMJNTZ6P67pzTvJDSKBCWDrCJpOzm19fHyxeDM89N7J5H8msrVoLwLVviVWsl826\njJrOGqo6qhzZUElhcuGo5suPJKo6qqjvqqenb3x/irP2W4mLjCMjLmOIEN7fKuoYhpvCaer28pde\noQgAJYQVkwpjVYbxLIQLC4Vw85ft9ReNSE0V4my0HOH+fjFPKTLBKYo90dkpBGNurmdH+Ni8Y7EP\n2DnhhRPQg1nh5wN/Qvjpp8Viyt//Hs46S+xraAhsoaF0hMFZqWPPHrF/586Rz30yce5L55LwUAKf\nV3zuc5yu66ytXMu0lGkAzMuYx9yMudRaaqlsryQ/Md9lfHZ8tiqjFSJkVY5ay/iuz2vttxIdHk1W\nfJZXIXx0ztE09SghrAgeJYQVk4opU5zb4zUaAZCVJR79OZJSCEdFeT5uMkFaWnBCOBBHGIQrbBS/\nvhpKWCziA0henmchvDhnMS9e8iLf1H7jqAwQKuSCPm/RiB2DfTxuu805xmYLbKGhxeIUwrJSx9at\n4vm+fcHPeTLyReUXWGwW1lWv8zlub8teGrsb+f05v2fPbXtY+Z2V5CTk0D/QT1ld2RBHODs+WzVW\nCBEOITyO38/+gX7sut0hhN1F+4G2A6TGpFKUUqQcYcWIUEJYMakIC4N16+AXv3C6fuORzEzx6E8I\n9/QIoepNrIJwKEeyWM6XIwxCNBodYV8LzPw5wgDHFxwPwKbaTcOcsW/8OcJpaZ73BxKP6OoSrwuE\nEG5ogG3bxHMlhF3pGxC/WFJseePLqi8BODb/WIpTi8lJyCEnIQeAnv6eIY5wVnwWrdbWUYnVHGnI\nn02oP4yGElk3ODo82uO3Aftb9zM1eSppsWk0dTdxqOMQP/rgRxOyhbtibFFCWDHpOOYY+NnPxr6d\nsi8CFcJWq/dYhCQjI/Csq5FAHeHmZuGIxsSIsb6EsHSEfQnh/MR8zDHmURPC3hxhKewl554rHgN5\n74yOcF6eyBpLR/jgQed7eaTTP9DvEKr+6r6urVrLnPQ5JEc72z1KIQzi2wMj2QnZ4roWVU92pEwE\nISx/j6LCojxGIyraKihMLiQtNo3m7mY+3Pchj61/bFy73IrxiRLCCsUYkJQEkZH+Iw1Wq/eFcpKs\nLNHNbbgE6ggvWwb//d9izrm5znbDnpCOcHq6d2dW0zQWZS9iU93hdYSNPP44vPSS2B6uIzxtGlRU\nQFkZzJsnRLCv9+RIorNX/BBMmsmrI6zrOnd/dDfPbXqO4/OPdzmWGZfp2F6Ss8TlWFa8yBON91zr\neMc+YHdECcbzeymFsDEjbFxX0NLTQnpsOuYYM03dTY4PXo3dwdU23Neyj5998rOQr11QjH+UEFYo\nxgBNC8zJ7enx7wjn5AReO9eIP0dYCj9JQoJYbOZL9ElHOC1NZHa9tWSelzGPHY07fM7v/ffhww99\nDnHBnyNsrHiRl+f8MDJcR7ioSHyIOHgQLrpI7FPxCEGnTfwQilKKvDq3hzoP8fCXDwNwXP5xLsci\nwiJYlL2IJ89+0lE6TZIdLxxhtWBuZDT3NKMjxN5EcISlEO6199Le2+443tHbQWJUImmxadh1O7ub\ndwME3X3wjBfP4IE1D9Dd1z3yySsmFEoIKxRjRGZmYNEIf45wTo7o4jZcI8OfI+ymQ4iP9y+EpSMs\n87jeRGlWfJbPDKmui+jCsmXe7yVp6WnBPmD36wgbRXlurnh92dmBfYgwVo0oKnLuP/988fOReeHx\nzIoVcOONo3sP6QgXpxZ7/fnKD0C3Lb2NK+ZcMeT4Nzd/w22ltw3Zb441E24KV199jxD5c8lLzJsw\njrCMxRh/9kYhDM7fq2Ad4Yq2ihGdr5i4KCGsUIwRgQjhnh7/Qjg7Wwg144K2QPDXYtkd6QgHkhGW\n9Y29idLM+Ew6eju8Lnzavdu5/dZbQnz32ft4ffvrLl9d9tn7MD9s5r7P7qOzU7jbgTrC8tFTvWOJ\nros2zLt3Ox3yKVNEtQ5NE9GIBQtgU2hTHqPCp58Oz2EPho5eUbqjOLWYxu5G7AP2IWPKG8uJDo/m\n8WWPExsRG/C1TZqJzLjMceMI2wfstPS0+B84zpCO6VFZRzlaWY9HjEJYVhAx1pGWQtgcKxYzOIRw\nAI5wQ1cDjV2N2Ow2dF13lGIDVAWKIxAlhBWKMULWo/VFba1zYZ03cgbXFw03HuGvxbI78fFQUCAW\nwQ14WZjt7gh7q2aRESda4nlzDf/5T+f2ZZfBd78Lq/avYvkby9nX6swhfFP7DSAWXnV2CqHe2up5\n8ZoUwiaTs3ydP4e7uRl++UuxLR3hyEhxXnExxMbCokUTQwjX1ooPJqMZgZTRiOLUYgb0AY9Csbyp\nnBnmGYSZwoZ9fU9ltMaK+1ffj/lhM/0DE2ulpPw7tzh7MZXtleM2E+suhMO0MA60ik44vf299Np7\nSYxKdOTK5e9eII5u4W8LyXg0g5LflfD0xqdZtX+V45gSwkceSggrFGNEII5wRYVovuG+Itd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VWsLL958c0sK17mOC87IZupKVPR0PjkwCcAtMV87Tje1N1EWmwauYm5kHCI2Fj4QOgXGhrEArlQ\nRSOCcYRlObf8fGc95TlzgJhBxa3pPLj+fxxOcKtVCOF/7/k3O5t2Ol5jRJ+Z2Z3CMc8qrmHzZt9t\nlmUjAxBNK9yR0YjSvFKHIyxd4v86+r9cfgZ5iXmOPOaq61bx8fUfB/ryA2ZZ0TI0NFaUrXDJ644m\nvf292Ow2UqJTyE3MJcIU4RLbOG3qaS4tgSXGSiVjga7rLovlEqMSmZI0ZVIJ4R8d+yNuWHgDm+s2\nDzlW11VHZrzvf1hnmGcA8OkBUWqlsbsRa7+V/5+98w5vqz7b/300LFveluW9t7OdAVkEEghJICEv\ngbBHgZdRyo9AgbYUXjoo5W0LbYGXFsreUELZEAhhhBBC9iLLcezY8Z6SbEvyOr8/Hn/POdqSJa/k\n+7kuriMdHR0dDeL73Od+nqfD1iEVaz567qOYnTEbf97854COzd3/T0PBH0d4e912LH5lMfIfz3f5\nHXIc4UKYwxllcnIoQ8qEFIscDEUIp6fLnRycRVfNYIT22WdpGYwjnJJCx8mGaEwdvNKoFMJ6PW3n\nyREWBAETkyZib90BYPEvgJRd6LXEuAhhALhs0STgd/3I1E7DVZOvwp7GPYAg4r4rz8TSpfJ2rFgG\nIDc5XBOOlKgUbK3dCgBoDZMvZzZ3N8OoNyI9Oh0N3bVYtAj49NPBcc6N9F9rq/+OcEuLZ4E5lIxw\n9Yk+4PZ8lIvrJEf4vPMA6GXr+f3mR/H09qcByNGIx394HPHh8fjvsv8GAOi6CpAaTSItPqsOHR3u\nO5UwrL1yVwilo8lgovf09NNR2VGJrp4uSYCyKn4lD5z5AO6acxfOzjvboetDqDBGGjEleQru3XAv\n7v787pDvH6A8uXIUMRO0MboYZMZkIiMmwyHyoVFpkBWbJX1+vf0+Ls+MEF29XegX+yVHGKAuHics\nXuaMjxL2PrtHIXz4tsPYdbP7aIIgCCg2FLsVh42djUiJ9N6GJzM2ExGaCGlKYkt3i3S1gZ3sqFVq\n3FB2A76p+oZiWm4QRRE3fXgTfmyiqya/+/p3UP9eHZJWf0ohnKhPRI2pxkVkf3GMxn9OS5mG+Ij4\noF/zZIYLYQ5nlGHOKeseMRQhHBkJLFoEnHGGLISdL8OzgjzW5iwYR5hFI6xWKoZzJ4TZfW+iKy8+\nDzsbfwDm/QUoXAf0uDrCALBgAQBRhd27gUsnXap4vuMLqgQVIgQSYz12+uctMzYTrVYSj80aOYMi\nOcLR6ag112LpUmCj9UlE/jESPT0i+vuBo0f9zwj39pI77g6rlU46urv9H4RRccIMJBzDPZuvQfZg\nx7FlyyA7woMwB5dFI+z9dlxYciFKEksAAEJ7AZIMYTDqjQhLJJt5l5d4Y3evPDnEnasmOcKDhUUH\nmg9I61iLKSWnpZ+GR859xNfbDYonz6MrBgdbhifrev0H1+P2T+VpeEz4x4bHYqJxIiYYJ7g8Jzc+\nF5UddBbY1Ts2Lk+z3sfKE5bkyGSPo85HE1u/Z0e4yFCEaSnTPD43KzYLJrsJFrsFtj4brn3vWtSa\na9HQ2eBScOmMSlChyFAkZYCbu5qlTjXK564qXQW1So2Pjnzkdj9muxnP7HwGV797NQDgtX2vAYD0\nmwgGpRA+M/tM2PvtLr2rv6z8EssKlmHdVesQF+7H2fwpDBfCHM4o4zxYYyhCGAA2bACWLqVBFoCr\n+8gEMFv29Q1dCKemUvSC7ausjArlpkxx3E4ZoXBHdmw2aroVbcDsMcjIcP96AH02GpUGby/7Fnjj\nPURHu2b9npt+EHh2s/Q5ZsbIAdtm1T7JnVNGI+osdSgqAgbm/BnWvm7AQG3BbDYSub5Ip8FWkuvu\njNUK6X356wrXNpDAbe5uxhVXiFi7lj5n6Fsh9EZBI+oBUFsyW5/NIdJwy8xbpL66fc0FSEigdnUW\n1CElxbsQVvYJZk66EovdgkhtJKamTIVGpcH2uu1eHeGRYF7WPNx+2u3SyUCoOWE+gYr2Cum+8v3+\nfenf8ebFb7o8JzcuV3KElcJlNIZ/MJiTzaIRAHVhGOl8tS8GxAGYbKYhRSMAOvkFgBpzDfY07MHL\ne17GmnVr0NDpOxoBADNSZ0i3m7ubJXeZTbAD6CSoMKFQcnydYVEldpKRE5cDIDSt/tjvSafW4cbp\nN0Kn1mHdUTmbb7FbsKl6ExblLgr6tU4FuBDmcEaZ+HjqvasUwmFh1JlhKISF0eX8deuAfyk6YDHR\nylzL3t6hRyPS06m9GBN1mZkkAhcvdtwuOdlxOIgz7I+DdD8tGqed5rpdZCRNcGMud2nkfODwSrf9\nfSdmpQEn5kgCnAlhoTsJ/eiRXMPmLjkaYbKbEGvsAjoHFXeOPIptxgz4pHCwBqy83P3jVquc821u\nBq64Ali1yvs+TzTKgrSu70dcdBF9r7q4Vmh7DXihoAvYfwmOtlS5CMDYrlnIGywUtJ7Ih8EASfCX\nlTkWZzrDHOHMmEz8UPuDy+OWHguiddEI14RjctJkbKvbJsUlonWujvBIkRKVIrW4YnxZ+SVWvLEi\n6H23dreiziKH7pXOaoQ2wu1gBdY6DnDstDFSOWZ3sONWRiPGoiO85tM1eP/w+y69e/2F/T9fbaqW\n3Nz1x9bD3m/36QgDwOJ8+R+yNmsbttdtR3p0Ogx6g8N2pcZSj1ch2P+TrNiud4BOwFkRXjB09nQi\nLToNdXfVYUnBEizIXuAghF/c/SJ6+ntw6cRLveyFw+BCmMMZZQSBXOEjg1e2WMcIfwZpeCIpCXjz\nTWoD9u23tI5dkg+FI8zczcOD3aCiosg5dT7mQIXwOfNjpJHOSgTBcV9MzEe70V3OAz+YO6SupCIu\n1mPUoVgOgBhVC0QOvkDul9L+psuxY48kJdGJyxEPk3W7ux2F8BtvAO++C3zyied91jXLvV1Z8RsA\nRCW1Ilw0YOZMAB05KG+pkvLBkWu/QurzXXjkEUDTXgp8+yvg6DJyhKPSsL9pP0qnt3l3hAczwgtz\nF2Jb3TY0dTXh7R/laXUWu0WKQJyWfhq21m4ddUcYICHcam11EE+3fnwrPjryUdBOcZu1DfWWeimH\nyQS3IcLg8TkZMRlot7XD2mt1cISVgnqkcecIJ0clo7m7WRocMRbYVLMJgJx7D5S06DSoBBVqTDWo\nMcntGQHXXtTuODtXntI2IA5gQ+UGzEhzPSMuTfQshFmmvKe/B79c/0tpoIy7qyyBYumxICosCgkR\nVKV8Tt45+Lb6W+lqwzM7n8FFEy6S/u3jeIcLYQ5nDLB4MQmjnp7Aewi7g8UjAODWW8n9dY5GBOsI\nA5CmsbkTpAC9j6Ymz0M1nIWwN0eR7QtwHeesxGCg3sjOjrDYUgSjqhC7Gnahp78HJrsJxkijNKnq\nf76/DYivgqY7DSj4DFDbPb6GM4JArrC/jjDL/H7uOghOorFFFsLKfrRZJa3ISjRQltqUjSZbtdQT\nuTA1CZdfrMennwI11Wpgw8NAdyIMBuAn036CVmsr1sVeiPozLsYrW91nG1k0YmHOQnT3dmP126tx\nydpLZHdz0BEGgFlps3Cw5SDqO+shQECkNtLHJzV8MIGjvMzPhi8Ekx2299nR1duF3oFeaXz0jvod\nyInL8VqExEb81lnqHIRwraV2yMcSLO4c4aTIJAyIA1KOfiyQHJmMgoQC/Hv1v4f0fK1ai9SoVNSY\na1BtqkZqlFykmRzp+x9XY6QRM1JnYFUpXbbZWb8T01Ncz4hLE0tRZ6mTPlclLBoBAH/e/GdUdlQi\nLjwOR9uOumwbKM7jnQsSCtDd2y19h8faj2F2+mxPT+c4wYUwhzMGuO466jrw0UehFcIXXUS9cf/5\nT1k8tg0Wv9tsjpPWAsFZCHsSi8nJVKTX1ub+8cyYTECUbWRvjmJSEn02330H3H2359dVq+VtAdkR\n7u9IRbauDDvrd0qCJlGfiOy4bLx50ZtYf2w9AKDvy/uBcBNQ+Any8z0ejguFhZ4dYauVYg0xMSTm\n2XfhqaMGADS1kyBlk8oYcamtmJRvQFgYENmbg370SW3MUuPjcM45wIkTcis4gNz6eVnz8OLKF3Gg\neyMw4R08/O0f8diWxxyK4wA5GjElmQLfrCr+/UPvAxgUwoOO8Kz0WRgQB/B11deI1kV77c863LCO\nFFl/z8LLe14GAMkx+8vmv0jHHyjKbhHMzd1Wtw0z02Z6fZ4nIeypy8BIYLKbIEBwEFFMGI6leITZ\nbsYZWWegyFA05H0UGYrwXc13qDHXoCSxBAtzFgLwzxEGgO03bcdDix6S7rt1hI00ZU55xYah/N0w\n5mTMQbutfchON8NZCCujID39Pejq7ZJ++xzfcCHM4YwBJk0iIbVxY2iEMKvHufJK6ibx9deOQthu\nJ+HtTyGYO6KjSdSxaIQ3RxjwPEhCp9Eh3joTkV3U2N+bEGbRiPPOA/bs8f66yiK9goQC6NQ6oLUY\nOeHTsLdxr5TbS9TTB3DppEvl0a1HViCiYxouvG+t1yytM0VFro6wxQIUF1M/4ogIOkFpbpajHd4m\nAFq6yREuSSxxEMKNXY2SeDFo6Q/g3sa9AIAsYzwWLKDIy8svkwu9d69cxLiieIU0Vetg5/e447M7\n8MyOZxxel0UjsmOzoRbUUqHQ2oNrIYoiRSMGHeEJxgnQa/X44tgXbjtGjCRKgfPOwXcAyJfD3zv0\nHv7rrf8a0n6dhXD/QD921u/ErLRZXp/HCquUQjg6LNrhuxxpTDYTYnQxDq3eWPHYWCqYM9vNQcds\nbpx+I76s/BIfHP4AmbGZeP+y9/HBZR84uOG+YCczxYZih7gEg/UcdnfFod3ajrjwOLx50Zv07w+A\nuZlzASDovr7OQjgrli43VZuqpRgQb5nmP1wIczhjhJISElKhEMJsyllpKbm3jY2yEAaAjz8OTggD\nlBNmjnCkhyvi7H14ywlP27YVk02/BuC+/RYjKYkEdYLC6PDkaCuFcKI+EeU3NwDV85ESng2T3SSJ\nEaNezpBsum4T8pvvBMzpKAifg6OWfQEVLBYW0hAOi4Um+zU10ffJXGK9noRwbS1FYCZPJkfYXROB\ngQHA3k9CuDSx1FEIdzZKop0J4kOth4A+HdKTwxEZCcydS99vTg69DkMlqLD5hs0wHv6lwzolzBGO\nCotCclSyNKp2c81mPLfrOQdHWKPSYHrqdFj7rA49nEcD5XeZGEE/bHbCw/DVseFQyyEXh1wZGaiz\n1OFo21F09nT6dIRjdDHQa/WSEBZAfbND0T5rqJjsJhchyH5LjV0nlxBePXE18uLzYO2zIiM6A9G6\naKwoDqxwMkYXg5Z7WnDgZwcQoXUtXogMi0R2bLbbEdXttnYkRCTg0kmXSkJ1TsYcAMG3UOvs6XT4\nt9IYaUSYOgw1phopkhEfzoWwv3AhzOGMEYqKyGENhRA+fXCSa36+LAo7O8nFzcqiyMShQ45Z4kBJ\nTwfMZnI6PWWN/RHCLS2yoPPHEVZ+Np6uxGdnO8YU9Crqo5kUQQ7PvsZ9AGRHGAAKDYVI3vVXAALm\nlxahvK08oElQRYNXcY8epdefO5ciCoyICBLzLA4xeTIVMLqbSGe1AtCSM8scYVEU0dvfi1Zrq/R5\npcUZAFEgR7gzBamp9IGw7h2sNZ8SvVaPJFWpdN/5Mq21zwoBAsLUYVK28oayG3Dt1Gtx44c34uuq\nrx0iEMyZ+tmsn/nzMQ0bapVaul3XSRGG5q5m5MbRdEHAt9grfbIU5712nsM65giHqcNQZ6mTCuWU\nbfncIQgCVfYPCuHIsEiHThKjgclmciiUA+j702v1J50jrFFp8PRyGjaj/P88UAx6g8vJohJPnSPa\nrG1SPIEJ4cnJk6HX6gNyhA+3HHZZ5+wIqwQVMmMyHRxhHo3wHy6EOZwxQmEhUFFBIihYIfzww+RK\narXy8IvOThJrbw82ADhxIjghzKadeSsmY49dfjldondHayuQFkuulK9iObvd+3hgxpw5wI8/Au2D\n9Sr2wRa7yXoSdnsa90Cr0rr8sWUdIpbOKoatzxZQnpO1UHt/MIpaUeHYV5hFI4yq+3oAACAASURB\nVFgcgsUV3OWEu7oAaORoRFdvF1q6WySHk13OTk5SQ9OTSMVyllSpY8Y559DSnRAGgKzIQum2c8ux\n7t5u6LV6CIIg5W6TI5PxwsoX8PZq+vEUJsjPf3Dhg7h+2vUOLadGi3VXrsN5heeh1lwLURTR0t2C\ne+begx9vpV6v7kQFg7nF3xz/xmE9E8ITjRNR1VEl3fdHaKRFp6Gus05y8HLjckd13K07RxiAJKLG\nAmwMdCg6kJyTdw42X78ZN8+8OQRH5h5PnSPabe2SK/vCyhdw/xn3w6g3BnQytOXEFpQ8WYLPKxyr\nap2FMEBiu8ZcI/0+eTTCf7gQ5nDGCEWKuhDWVWCoaLWyUE1JofZdjY0kTDMVRlYwQrh00FT0FIsA\nyLGdMDh0a90618dFkRzR4oQS3D3nbilD5w42Mc9TZwYl8+fT8uOPqTtGz2BHrdQocoT3Nu5Foj7R\npbjr0UfppGFiMn0ZztOavBEfTx0rHn6Y7hcWuhfCzB1nkQVfQpiJzhPmE1JBE3OEjUbqjwwA6EyR\nhPDMmXQysGCB+2MtTJB/bKzPKsPaa5UuAzNHOCUqBYIg4OIJF8P8KzPunX+vtP3MtJl4buVzXl2z\nkWJJwRLMz5yPWkstOmwd6Bf7kahPRF58HlSCyuv3qZz+1tUj327tbkVceByKDEWoaK+QhIY/07rS\notPw+r7X8c3xbxAVFoW8+DzUWepg77P7fO5wYLK7OsKA4xS80aazpxMixJC14puTOQd6rT4k+3JH\naWIpjrUfg63P5rC+zdomidHM2Ew8uOhBCIKAnLgcVJmq/Nr3h4c/BADc+dmd0gQ7a68VNeYaF5c7\nM3bQEebRiIAZ/X+5OBwOAEchPDuEnW+Yu3z0KAnhpCQ5yhBMRnjSJFp66gjB2LoVyMtzXzDX2Uki\nNdmoxV/O/YtXccHeR7sfLWHz8kiEX301CVOWZY7TRyMqLAqHWw+7vVwaFkavkx2XDa1KG5AQZq/L\nRLfJ5BqNUJ54ZGbS+GbPQtiKMFW41PXihPmEdPmaOcJJSUC/mQnhVGkCn1oNbN4MnO1a30PHmWKA\nev1jWJK/xMURtvZZEaFxFMLKaVzRumjoNENsNzICpMeko83aJjmcxkgjdBodcuNy3Vb3M5SDLlgH\nEUC+xF2QUCAJ4bjwOIcohicmGel/kg2VGxAVFoXcuFyIEHHcdHyoby8oTDb3jnBu3NgRwmOhJ3Ug\nlBpLMSAOuLRFa7e2IyHc9apBenQ66i31Luvd8enRTxEdFo0DzQew4o0V2N2wG2sPrIXZbnYZlpEd\nm42qDhquE64Jd5tp5riHC2EOZ4yQlgbccANN/QplFyrmElZUkBBWq+X2Z8E4wkwIm30MyoqMpPfm\nTgizoj5/BLkyLnL11cAPrkPPJASBWsYBwHPPAcuW0W2dThZ3xkjPb16j0iA/IT9gIcwK9G66iZzu\n4wq9090tu9oAdbzIy3MvhLu7AWhs0KnDkRSZBI1KgxpzjZRxZQVORiMwYBncqSXVYf/eSEsT0P/d\n7SiNK0N9Zz3q66mn8St7XsHzu56XHDRWNe9vy6mxQEYMTXvZ3bAbgFxEd1r6aS6xByVs+ptKUGHN\nujXS/TZrG+LD45Efn48T5hOotdT6nb/89Rm/xsNn0yUCnUYnjb32FtEIhgFxAO8efNfjcAxPjnBO\nXI6UQx9txpsQZn3InbuBtNva3cYTWG7cF2a7GbsaduFvS/6GT674BHnxeXjo24fwyt5XsDBnIQoN\nhQ7b58fno76zHifMJ7gbHCBcCHM4YwRBAJ59FigrC+1+mYC02+UYA5sMF2yxnL8o+/oqYYViBs8D\nuiTi40nEA8Bpp8HtKGYlN98M/OlPlJVmaLWQxqQuyPKQGxikyFAUsBB+8kngtttIeA8MALt3A8uX\n02OTJjl+3tHRQG6u92hEhCYCKkGF9Oh0yRGO0cUgXBMOYFBYd5H6jUKq35MCmXMc3k9jia+7XsSS\n82245r1rUGupldwkJoSVAwnGOqxt2a4GGp/HTnjOKzwPO+p34Oef/dxtYRgTYK9e+CqqTdXYVE3T\nzcw9ZsSGx0oidkf9Dr+FsCAImJc5DwBQY6pBRkwGjHojttVtC+Ideubb499i1b9X4bJ3LsPdn9/t\n8ri7YjmAxJzZbnYYAjFasO/B3XGORVKjU6FVaaXJcQxPn3VadBqauprQ298rrWvsbMTzu5532I4N\nycmNz8WywmW4cfqN+LT8U+ys34kzs8902W9+AjU939mwk+eDA4QLYQ7nJCc+Xh6lzIrXWE44mGgE\nc61VfvwropwKxzCZKK7h73GoVLKjGuvn30jWPaOsDPjtb4GJE4Efm6hw6uIJF3t9blFCEQ63Bubc\nrVgBPPGEfJydncCSJZSFzshwFMJRUT6EsNYqCd6MmAwSwooewoCjEE7Q+e/aMiGssabC1mdDr8oM\nlLwnPc4c4XPzz8UrF74iicDxAKvQ/7rqa4RrwqUIzJJ8GrH9ty1/w4u7X3R5HhNgs9JnIUwdJk/S\nGxwpzYTG1tqtAVXkT0uZBoAmygmCII2lHg7YVMC1B9bi0e8fdZl45qlYLjeeXM3RLORjjDdHWCWo\nkBWb5eAIi6LoMIFRSXp0OkSIDpGkS9deihs+uMFhFDj77lhcbEbqDHT1dqHV2opJSZNc9psXnwcA\n2F63nTvCAcKFMIdzkqNSyfGIUAphgDogHPcj7ujOES4spG4SgH+OMNsPQFPa/GHGDMr9Xn458Jvf\n0O0rJ18JgIZBeKM4sRhVHVXo7OnEU9ufcjtG1RPKGMcMxUAqJoS1Wopp5ObS59fvdCVbcoS1jkK4\nqqNKuvQPDDr7g0I4Re+/a8uE8EB7Dt1I2wbkyblYVvim0+hw1ZSrRnViXKBEaCOQEZOBPY17pCI5\ngJzhRxY/AgCoMde4PI8JsPjweOTE5aCivQKAPFI6NSoVeq0ePf09AQlhJoaYODk9/XRsrd0a0hjC\n4ZbD6LB1SHEOxv6m/dJtURSlgRrOsMv77D2PJuNNCANUU6DMfff096BvoM9tX3R2lSXr71lYe2At\nAEhXH8rb5Epg1taQucpTU6ZKj7kTwqlRqQjXhKPD1sFbpwUIF8IczikAuzwfTroKs2cDs2bB70vp\nnsjNlWMW3khOpolqA4q2vM2Dsw4iImjYhD8wgemvEI6KomK922+X1/3j/H+g5/4en+KuyFCEAXEA\n//Pl/+CnH/8UV797td/iRZnVna6YM8GEMDshyc2lrhZ1TpFBJoQjwyiiwITwjvodDoMr4uIAnS0L\nGFAhK857X1slej0wbx6w7b1ZmJYyDXui/xeIroe6hz7YsTRudyiwThvOTvZdc+/CiqIVbttXMQEW\nrYt2aHHFHGFBEDA5iVp9uCuC8saeW/Zg9y2UWT4t/TS029pDJjp7+ntQ8mQJrn73ammCXdevu6BR\nabC5ZrN0Amfts6Jf7Hd7ud6gNyAzJnPYnOpAUH4P44Wc2BwHR9jSQyckzi3OAFkIA8Dvv/k9Wrpb\n0C/SmXB5q6sQZo5wUmQSUqJSEKYOk65OKBEEQcrDcyEcGGNGCAuC8DNBECoFQbAKgrBFEATv8yvl\n580TBKFXEIQAhqFyOKcWN91ES1bMtWoVCcSRIikJ6Otz3/HB03Q4dwQqhAFg6lTH1xAEAVq17zOA\nIgO18fj7D39HZkwmPjzyod9RCWVvZeVrh4dTNpiNhs4lI84lHtHVBah0VoQrHOHytnJUdVQ5jPYV\nBCCn/1zgn/uQ62+l3CDXXw98sV7AVQW3ozV2A5B4EP3HafLVWBquMBSYAM6PdxUM+fH5bkWo2W5G\nuCachEZ8viSElT1bWcwhUKExJXmKFNlgzh4bi63kxd0vorW71WW9N1iP2RpTDSw9FkRoIqDX6lFk\nKMIvvvgFznjhDADAvV9QyztPI4bnZc3DdzXfBfTaw4HZboZeq4dG5WFKzxjE2RFmzrw7Mc9qFAC6\n6vTRkY+kgS/KmgTJEVZ8X9NSpqE0sdTjZ8N+97fOunWob+WUZEwIYUEQLgXwKIDfACgDsAfAZ4Ig\neL1wKwhCLICXAHwx7AfJ4Yxjpk0DnnoKuPde39sOB0zAuusc0dHhus4TgUYjgiE5Mhlh6jAA1BAf\ncHRsvMHM5iVLXB8zGmWhzNz02lrHbbq6AI3OJmWEF+Uukh5zHu2blakCmidI8Rd/WbWKssudlRNp\nRXwVUEf7Ntn9j4GMRTw5wgAVFVW2V7p0VlAOcWCOsJT1HLzEzYRwZJiX5tk+SI5MRqI+UZpuCFC3\nh4bOBlz3/nVY9PIiL8925NW9r+LWj0n0xIXHkXs9KL5YJGRf0z60W9vx+NbHYdQbMSN1htt9zcuc\nhx11O1z64Y40ZrvZ66j1sUhuXC6aupqws578OObMu3sfyn7bXT1deOvHt3BG9hlYkL0AR9pICD+2\n5TG8d/g9RIVFOYjePy76I55Y9oTH43jr4rdQ9/M6nJbuo5KY48CYEMIA7gTwtCiKL4uieAjALQC6\nAVzv43lPAXgNwJZhPj4OZ9xz882BdXoIJUzAehu17A9DcYSHiiAIqLi9Ap33dmJR7iLotXqHDJ8v\nWluBDz5wXW80yo4wc4fdCWG1zib1852SPAXZsTRlhRXFMJiYDlQIx8WRI91wWJ7eEt5DO9OqgszM\njDK+HOHegV4cajmE9w7JBYLOQrirtwtNXU0O4pLlyp17LweCIAiYkjwF+5r24fEfHsd171+Hs18+\nG9e8ew0Acor9LVp7fd/rqDHXoDSxFCfMJxxE+zMrnsHZudRImgm0Dy//UJoW6MzczLnoHejFjrod\nQ35vocBT27GxzMqSlZiZNhPLX18unTwB7qMRAPCHhX+AIcKAQy2HsL5iPS6beBmKEoqwv2k/RFHE\nH779Az44/IFLX/Wy1DKckX2Gx+MwRho9fr8cz4y6EBYEQQtgBoANbJ1IQbwvAMzx8rzrAOQC+N1w\nHyOHwwmOtMFYHBN8zsVh/jJnDmVbvY11DiUZMRmIDIuEIAgoSCjw2xEGgIQEKs5zJiXFsetFWppr\nRri7ezAaMegIA8DuW3Zj9827XbLNygmCgTJtGnBgWxLQS69TlJqK+ce+wN6fepiHPU6YmzkXi/MW\nY1a6a8KO5Ssf+vYhXPjWhdKkOKUQZgWJJ8wn0NXbJQmaspQyxOhicMXkK4I6vslJk7G3cS9e2/ca\nXtv7GjYe3+gwxIO1fvNEY2cj3tz/Jk6YT+Bns36Gm2fcjFpLrYNon50xG48vexwA8En5JwDoEr63\nYwpTh0miebRgA0zGEzG6GPx6/q9R31kvnTwBnnPO9y24D7fOuhWVHZXoF/uxKHcRlhUuw/6m/fjv\nD/5bap02XlrIjXdGXQgDSASgBuDsFTUCcPtPuyAIhQD+COBKURQH3G3D4XDGDlFRJAxZhwkWhygs\nBF56yf/9zJ8PbNoU2oEj/lKYUBiQI+yJhx8GHnlEvp+e7ugIf/UV8PqPL6MrZb2DEI4Lj3OoHGcE\nI4TLyoCN3whARw4AICshBf3lZ6MksSTwnY0hkqOS8fnVn7sVVDlxOVAJKkl4siInc48shNkAEZYl\nZi5rtC4apl+Zgr70PDV5KsrbyrG1dit6B3oxMPhnzKg3IkIT4dKT1plX9r6Cy9+5HMfajyEjJgMZ\nMRmw9dlw3HTc4XJ8YUIhwtRh+Lj8Y4Spw6RBLO7QqrWYkjwFOxtGVwi329rHZfsvVlNwpPWI5Ah7\ni3iwNohalRZ58XlYVboKd825Cy/sfkHaxp8x3pzgGT9p9EEEQVCB4hC/EUWRVTz4/WfxzjvvRKxT\nE9LLL78cl7M+ThwOZ1jIzpaFMCuae/ppYOHC0TumQChMKMQb+98Iej+lpY7309KoWK6piSIkixYB\nWPNbQBiQohHemD0bmDIFyMkJ/FimTRu8YcoGjIeQGZ+KA+O7Ts4nYeowZMZkSsVNxzuOY3rqdAdH\nmIkUVrwU6g4GF5ZeiOs/oOSfRqVB30AfAHKrO2wdPkcws8lkXb1dyIjJQHoMZZ4OtRySBBlA4rY0\nsRR7GvegIKHAIZ/qjukp07GldnSThu3Wdq/O9VilIKEAAgQcbj0s5Xo9RSMAeWx5fkK+VLw7J2MO\nRMidaTwVNp5MvPHGG3jjDcd/V02mka1RGAtCuAVAP4Bkp/XJANwFsaIBzAQwTRCEJwfXqQAIgiD0\nADhXFMWvPb3Y3/72N0xX9jPicDgjQna2POWNCeH4cWT8lBpLUW2qRmNno/RHLBSkpwOvvkpClpxh\nEYgmi1jpCHti0iRgz56hvfYCNlxv0BHOMSa5LWg82chPyJeF8ODSbDdLnR20ai0S9YnSFQBvgmYo\nxIXH4eopV+OVva/ghrIbUN5Wji8rv0RefB5au1ulY+ob6MNPP/opJiZNxB2z75CerxzRyxxhgBzs\nGWmOxXBn556NPY17kBnju73e9NTpeG7Xc7D12fz67Q0HbdY2lKWEeLzmCKDT6JATl4MjrUeQGZMJ\nnVrntTsNO9lSXn1hxZgMwX+Pb9zizojcuXMnZsxwX9Q5HIx6NEIUxV4AOwCczdYJFII7G8BmN08x\nA5gEYBqAqYP/PQXg0ODtH4b5kDkczhDIynJ1hMeTEF5etBxatRav73s9pPtl+WmrFdi4EYC+BdD0\nAIB0yXy4iI0dnAzYMA15sYVISw5DZydllE9mlEV0LBrRYetATJg8xCE1KlXKhA9HF4MXVr6Atl+0\n4anlT2Hdleuktm3ZsdlSNOJPm/6EZ3c9i3s3OLZ7cRbCKVEpktvrfKwrilcAAFqtvtuyTUqahH6x\nH0fbjgb13oJhPBbLMdhYdk9T5ZSwk+kSgyyEc+NzERUWJX2XrPsEZ3gZdSE8yF8B3CgIwjWCIJSA\nhK0ewIsAIAjCw4IgvARQIZ0oigeU/wFoAmATRfGgKIrWUXoPHA7HCywaIYrjUwgnRCRgRdEKvLQn\ngFCzHyiHiXz0EYAYOTB8rMN18EOoeewxADtuwq6b9nptc3cyoRTCGyo3YHPNZhxtO+owqCA1OlVy\nhIdjuINapZYEn1atxdrVa3HLzFscetKyorkBcQA9/T3Sc5VCOD06HRqVRuoq4iyE52XOA+DqNroj\nJy4HAHxmlIeT8Vgsx2BCuLOn0+fJU2pUKjQqjcOUOJWgwozUGTg9nWbDs6wxZ3gZE0JYFMV/A7gb\nwO8B7AIwBcASURQHZ08hBYD/Y5M4HM6YIzubnMbWVhLCarXcRmy8cO3Ua7GncQ/2NAwxi+CGRYuo\n0O2cc4DXXoMUiwBGZrDFbbcBvT0qxOjD3ba5++47OsaBk6gsmQnejJgM7G/aj3nPz0N3b7fDZeqU\nqBSpen8k+tquKF6BtOg0ZMdmo83aBovdglZrK7Jis9DT34ODzQcB0KjkOksdVIIKhggDIrSUIy80\nUO9kZ9GuVWtRc2cNnjr/KZ/HkBqdCq1K6zAlbSSx9dlg67ONy2I5gITw0baj6LB1+IzTROuisf3G\n7bh00qUO619d9SreXv02gNBHcjjuGRNCGABEUfyHKIo5oihGiKI4RxTF7YrHrhNF0WOXcVEUfyeK\nIg/+cjhjmOzB+pfqahLCcXGj0/0hGJYWLIVRbwypK5yXB9TXA1deSfEIxNRCgAr3zvwTXrnwlZC9\njjc0g9UiTAgrHeGPP6ZOFs69jsczZ2afiSsnX4lbZtzisF4phFOj5H6sIylIZqXPggABr+17Da3d\nrViUuwgCBGw8vhGGPxvwSfknsPZZ8f9O+3+4f8H90vPYEBF3oj0jJkMSzN5QCSpkxWaNmhBut9Kl\novEajSg2FKN3oBf7m/b7dRVhaspUlylxrPjxjYvewOurQhvD4rhnLBTLcTicUwA2zKOujlzh8RSL\nYGjVWlxQfAG+rPwy5Puew7qmx5xASmQK/nj+L0L+Gr4wGmmpFML7BgeglZfLrdrGO8ZII15d9SpE\nUcTqiatR/H/F0Kl1UrwAkFuoAYBeq3e3m2GhyFCEK6dciQc3PogBcQDZsdkoSCjAa/teQ5u1DR+X\nfwwAWD1hNeZlzZOex4RwMFPvAIpH+OpaMVywns7jORoB0ACT+Vnzg9rXZZMuC8UhcfxgzDjCHA7n\n5CYpiQqz6uqAHTuAiRNH+4iGRn58PqpN1SHfbxHrehVdi8zYjJDv3x80GsBgcIxGKIXwyYYgCCgy\nFCEzJhPFicVQq9TSY2dkneGw3UhyUelFqLPUoaGzAYYIA6alTMMPtVQHzpbOE8RY5wiz3RzUa2fH\nZgfkCH/+ObX/CwXttkFHeJxGIzJjqVuEcggLZ+zDhTCHwxkR1GrKwlZWAlu2AGeeOdpHNDSyYrPQ\nbmsPeUW3pLWiGpESPYTpGCEiORloGGxcaTbLnT5ORiHMuHLylVhRtMJh3Yy0GdJI5ZGGtXEDgER9\nokOh2+6G3dCoNC7t0Njo7TC1m3GGAZATlxOQEL7++sGCyxDAohHj1RFWCSqpFzWfCjd+4NEIDocz\nYqSlAe+9B9hs41cIZ8aSAKkx1aDUWOpj68D45hvgpu8tiNWN3kCBrCy53/OPP9IyLQ04cmTUDmnY\nefich92u33XzLrR2+247FmqUItegNzhMGBsQB1BkKHLpUVuWWob3L3sfSwuWBvXaefF5aO5udhgw\n4o3WVvlkKVhYNCI+Ih7ffw/odMB4a/u/rHAZXt7zMm4//fbRPhSOn3BHmMPhjBhpacChQ9S/dqrr\ntOBxAXPrhiMesWABoI+zjOpl1ZwcoKqKbjNn+KyzTm5H2BNh6jCXCMJIkKhPlAZasGiEEk8jsC8o\nviBoR5i54AeaD0jrHtvymNRTWYnNRv+FSgh393ZDo9JAJYbhkkuASy4B+vtDs++R4unlT8N2n83t\nOHTO2IQLYQ6HM2Kw4RHz51NUYjySHp0OAQJqzDXDsv/Ons5RF8KVldTvuaOD1s2aBRw7Nv5EyXhF\nEAQp82vQG5AanYqLJ1yM1RNWA3AcwhBqihOLIUCQhLAoirjjsztw9stnu2zL+oFXh+ic0N5vh06t\nw6efAidOABUVwDvvhGbfI0W4Jhw6jW60D4MTAFwIczicEYMJ4bPOGtXDCAqtWovU6NRhcYQBaqI/\nEn1rPZGbS9ngjg76LzISKC0FenpCJ3g4vmHxCEOEAQDw9uq3cdWUqwAg5JEcJXqtHnnxefixiXIx\ntj4bALg98WujJANaW4GuruBfm412fvddGh1eVgasWxf8fjkcb3AhzOFwRgzWQm285oMZWbFZw9Zi\naiw4wgDFI0wm6vdcSJ25Tsl4xGiRFZuFMHWYw2+hJLEEKkGFspSyYX3tiUkT8WPzj+jpAVo7PXeh\nYI4wEJp4hL3PDp1GhxMngJISEsOHDgW/Xw7HG1wIczicEWPJEuDnPx9/BTDOTE6ajO1123HuK+fi\nq8qvQrbfAXGAxrMOw0hff8nNpWVlJTnCcXFUQKfV+hbC+/eHrpXWqQ5r66Zs3VZkKELDXQ2YnDx5\nWF97ctJkbK3dipWXt+C2u2Uh3N3b7bAdc4QB30J4X+M+xDwcg1qz58kszBGuq6OrR8XFJIRFcUhv\ng8PxCy6EORzOiJGeDjz66PjNBzPmZc7DgeYDWH9sPe778r6Q7ZcJjdF0hBMTAb2eHOGODips1Gho\nAp4vIXzzzcD993vfhuMfd86+E1//5GuX9cZI47C/9q2zbgUArMc9+Owri7T+UIujPcscYbVaLrD0\nxDfHv4Glx4ItJ7Z43IZlhJkQLimh12hpGdLb4HD8ggthDofDCRDlRC/WTi0UsN7EoymEBYFcYaUj\nDFA8wpcQrq+nIidO8ERoI6SCuZEmLToNy4w3oz9nPWyi7AgfaZV76HV20m8kMhLIz/fdXm93w24A\nwK6GXR63sfbZoBbD0d4uO8IAj0dwhhcuhDkcDidA8uPzpRG83i71BorFTu7baBbLAXILNaUQzs/3\nHXtoapJbrnHGN93HJwExtTDkyRWS9ZZ66facOcDvfkej0ouLgcOHve+PCWG2dMe+gzbs300dF9LS\ngIICmkbpa98cTjBwIczhcDgBIggCPrr8I9xQdkNAU7h8MRYcYUAWwqxYDqCccE2N57xmdzd1Dqiv\nd/84Z3xhLp8EANAXUZQhMyYTdZY66fH9+2kZEUERBm+ubW9/L/Y37UdCRIJXR7ii0g70Uf/k1FQg\nPJyWoepTzOG4gwthDofDGQIz0mZgTsYc1Fnq0NPfE5J9WnoGHeFRLJYD5GhEe7sshDMz6XI46y3s\nTHMzLS2W0LTS4owupmPFEEQVrIbvgQENcuNzUddZ57JdeTk5wlVVNFzDHVUdVbD327F6wmrUWerc\nTutrawMaWm2SEGatFtPTgdrQXXThcFzgQpjD4XCGSHZcNkSIeH7X8xBDUNo+lhzhri5y4pSOMOC5\nl3BTk3ybxyPGP7XHw5EgFKBFuxuwxyAtKs0hGhGtOFcrLqYrBUePut9XczedJZ2VcxYA4GDLQemx\nfY370NnTie++A6C2A/0UjYiNpceHUwh3dABFRcDWrcOz/9GmupriK+wkleMeLoQ5HA5niOTE5QAA\nfvrxT/Fl5ZdB72+sZIRZCzVgaEKYxyPGNz09QGMjkBk+OLjDFoN4bZoUjbBayfnX64G//52iEYDn\neERzFymxuZlzoRJUONh8EJ8d/QzrK9Zj/gvz8ejmR3HsGCBobZg9MxxXXEFFm8DwCuF//IMc7ffe\nG579jzbl5cBvf0vfFcczXAhzOBzOEMmNy8Wa09cAAL6r+S7o/TFHWK/VB72vYHAnhJOTqZcwF8In\nP7W15PDmxxfQCnsMtLZUSQiz7/f994E1a6jlXkKC56K2lm7qf5YWnYb8+HxsObEFS19binNfPRdm\nuxnrDn6HigogPNKOonwdXntNfm5GxvAJ4X/+k5ZWK3U7efRR4N13qQVgfz+dEIxnmACOiRnd4xjr\ncCHM4XA4Q0StUuPvS/+OZQXLsLlmc9D7s/RYoNfqoVaNbqPl+HjqCgBQ7rlOaQAAIABJREFUeyyA\nqvczMqhgzpmaGuDNN0k063RcCI93WAu8ian5dKMvArCkwdJjQWdPJ0VfNDbEJMphcG+dI5q7mxEf\nHg+NSoNSYyme3/28w+Nbqn/AE/83AK3ehnB1uMNj6ekUYeh2nOURND098vusrqbf7913A9dfDzz0\nELndOh3w6quhfd2RxDzY+S56dC8wjXm4EOZwOJwgmZs5F1tObMGAOBDUfjp7Okc9FsF46y1g6VLH\nKYBZWe4HJ9x2G/D55yRYUlOBujq6TC4IQEXFiB0yJ0Swk50ZueQIqyPbUX+EqtfqLfV0orN0De7f\nfan0HHedIzYe34j9TfvR0t0iDQJJj6Y56yuLV8obhpuBxIPQ6GzQaXQO+2Bj2UPtCjc2yvuvrpb7\nIHd0AOecA/zpT9Qy8KvQDY4cccxmICyMBD3HM1wIczgcTpDMzpgNk93kMHAgUPY37cf/fPU/6B3o\nDeGRDZ3MTODTT4GUFHkdG3nrjN1Oy9RUEsvV1cCmTbTuiy+G/1g5oaWmhorVJqWTI6yJbcYX71BI\nvLKjEvX1gJC6Gwfa5J7AzBFW1oye+eKZmPzPyWjqakKiPhEA8Kv5v8KbF72JF1a+AADIjRrMIafu\nhCrMjnCNqyMMhF4Is4LO00+nolDlsJj/9/+AO+4AzjoL2LkztK87klgsPBbhD1wIczgcTpCUpZQB\nAHbVe+6R6osXd78IADDqh3+E7lCZOJGEcH+/4/q6OuCyy4Bt24DsbHKNWaTCbHbZDWeM09REJ0BZ\nsSR+7ap2tFdmQyNosb2yHM88AwiGCtRaamHttQIgIWw2u+8Y8ureV6XfdVZsFi6ddCniI+KRF5+H\nIv1soNsAxFajX7BBp3bvCId6YqFSCDc3A3v2yI+V0f/OmD4d+PFH+URvvGE281iEP3AhzOFwOEFi\n0BuQFZuFnfVDt4/qLHWYYJyADddsCOGRhZYJE0gUHDsmrxNF6jk8cyaJlpwccthYL2GTaVQOlRME\nra1U/KZRaQAAS/KXIFynhkEowLpth3HgWAcGwqkXcMETBTj/9fORlNMGwLGFWoQmAgAgQpQcYSVv\nr34bK6J/B5gyoYqvcesIR0aSOx1qR7i+nnLvM2fS/fZ2upqRlERZeIAEcW8vieHxCHeE/UMz2gfA\n4XA4JwPTU6d7nZrli2pTNWakzkB6THoIjyq0TJxIywMHgMJCut3aSoM2cnLofnY2OcQsg8mHIYw/\n2toAg4Fut9zTgsiwSEx9EOi3F6Gu/wji8irQMrhtnaUOdZY6LMh4EcDPcewYCcwde62w9lmREpWC\nhs4Gt0J4eup0/GABBHMWll5Sg001NhchDAxPC7WGBhK9U6fK6559lsQwa902iYbr4eBBx6z8eIE7\nwv7BHWEOh8MJAWUpZdjVsMthsEZXT5ffgzaqTdXSpeixSkoKdYZQOmSVlbRkLdeYIN63j5ae2q1x\nxi6trbIQNugNCNeEIzsbULcXo0L4DJ1n3eSwfXZsNr6uWQ/1nQX4tmIbli0D1vyKHOPLJl4GQG6h\n5u61wnsyUWOuga3PtVgOGB4hXF9Pv2eDgfLsq1cD8+ZRxIMRHU2Oan196LtWjARmM3eE/YELYQ6H\nwwkB01Ono83ahmoTKb/+gX7kPJaDN/a/4fO5fQN9qLPUjXkhLAh0uXjbNnkd6yLBhHB2Ni13D9ZR\ncSE8vNhsoe93qxTCjKwswN5AxXO2BMcI0Kz0WVh3dB36YyvwurgSiUl9gJ6E78oS6g7Bpso509YG\nRPVnotpUjZ7+HreO8HD0Em5ooOJOgATwv/9NLdOcSUujDipGo2Ov7PEAj0b4BxfCHA6HEwKkgrnB\neESNuQYt3S3YVrvN29MAUEuqfrEfmTGZw3qMoWD+fHLQPviABhFUVwNRUfLgjcxMEsysCr+mBhgI\nrqscxwurVwN33hnafbKMsJLsbKBz+0rkNN+K6UffxosrX8R/LvkPvrzmSxQmFErbWTX1iE6vASLI\nEc6KzUL/A/24aspVbl+rrQ2IU2XCZKcwuXOxHDB8jnBysu/t0tKA7dvJEWadUMYLPBrhH1wIczgc\nTghIi05DUmSSVDB3tI2qhg61epg7q4C5yGPdEQZICLe0ACtXAq+9JjtrLFep09H9gQG6bbdTpvhk\n49tvgeXLHduFjQY7dwJbt4ZufwMDjhlhRnY20FqdDOPWJ1EqXoxrp12LC0svxMLchZIQTgYFbhts\n1YCehLAhwgCV4FlqtLYCRp18AugpI1xf79qtZKgMDNBvUhmD8ERamnx7c/Azc0YU7gj7BxfCHA6H\nEwIEQZBywuuOrsMre18BABxu8TBuS0GNmSYYZMaOfUd49mz5dlsbCWFlr2FAzgmvXEmO1H/+M2KH\nN2Js3Qp8/DF9BqOF1SoPLwmVIDebSSi6E8IAsH+/7P4zCg0khOfEXwAAaLJVQxvTCgxoEKPzrsTa\n2oDUiGzpvqeMcH9/6KIJR46QSJw1y/e241kIc0fYP7gQ5nA4nBBRllKG3Q27sey1ZXh5z8sAgKqO\nKqnXqifarG3QqrRjZqqcN2JigPffBxITqbdrfb2ctWQw0ZSSAqxYAaxdO/LHOdxYLLR0N2lvpGCF\nip2doeuz20pGrkchbLW6CuGJxokwRBhw4cTlQFciEFuN1IIWwJoAQPD6em1tQGac3CnFU0YYCN17\nZBl3fzpBMCEcHQ3s2AH09YXmGEYCXiznH1wIczgcToiYYJyAE2bHv9YiRCkm4YkOWwfiwuMgCN5F\nw1jhgguAGTNImHhzhOPiyBXet496D/eOjaF5IaGzk5ZMjAbDDz8MTVAr+zm7m/g3FJgQds4Ip6dT\nWzTAVQjHR8Sj5RctWD33NMCcCcRWIyG9Feg2SPvzRFsbYIhXS/fdZYSZCD9+PJB34pnt24GCAiA+\n3ve2TAgvWUJFicrPXInZ7FhEOtr091Mvb+4I+4YLYQ6HwwkRpcZSh/uGCAM0Kg02VHofksGE8Hgi\nI8O3IxwXB5x9NuWHZ80i8cyc1PEOex+hEMKzZ8tdNwKhooJy2Dod9boNBSzq4ewIa7WyKHQWwoyI\nCACmLCC2GnoDCWFfLi7LsaZHkyvszhGOj6eCzFC574cPyz2CfcHe8/LltPR0wnHddcBpp4Uuxxws\n7ESNO8K+4UKYw+FwQkRJYol0+4EFD2DDNRuwqnQVnt7xtNd+wuNRCGdmkhBra/PuCBsMdAm6rY3E\n2u9+N+KHOiwwoeFNnJlMw9s+7tgxIC+P3M0jR0Kzz5bBdr/OQhhwPMHxiCkLiK1Bs2YP0FbotdvD\nwABFLfR6SINk3GWEBYF+U6ESwu3t7t+fO2bOBH7zG+CSS8hd9SSEmVM8VgbIsNHm3BH2DRfCHA6H\nEyKiwqKQFZuFCE0EfnPWbzA1ZSpunH4jDrUcwu6G3R6f125rH3dCOCNDdg+9OcIAcNVVwDnnUKuv\n778fuWMcTvxxhBculD+LYLnySuCLLxzX1dRQf9+CAjopCQUbNtDUwIgI18f8EcIFhjwgoRzlpn0Q\nqhZ6dYStg9H5yEggI4aCwO6iEUDohbA/sQiA3Pbf/pY+j9JSz847E9ah+h6CpYbqb5E+dgdVjhm4\nEOZwOJwQUppYikJDodQyam7mXKgEFbbVeQ4QjkdHmBUwAa6OcFER8MgjJH4B4I47gPXraZztvn1j\nu6+wzQbcdBOJJW/4kxHeNThx21tHB+VnYTJ53ub114HzznNc39xMY4Lz84Gj3mPofmGzUYePyy+X\n2+Ep8UcIf/SnS6HR0huObl2Iu+/2nKvt6qKlXg/MSqMWDp5areXkhC4jHIgQVlJS4iqEq6vp+2Wf\nyY4do9tJhMF+D/n5o3sc4wEuhDkcDieE3L/gfjx89sPSfb1WjwnGCdhRt8Pjc8ajEFZmLLOc2h+r\nVMBdd7lelp0yhZzUUAma4eCbb4BnngH++lfv2ym7RngSulotLTs6PO+HiUFAHkLiDBPdzsWGTU2y\nEK6qCr6jwfff0yX1iy92/7g/Qrg4LRXXl12HKclTMHtCBjo7gaeecr8tG1scGQncM/cefH7V5yhO\ndN/clznCwbaJE8WhC+HCQkdR39xMn8kjj8jv5Z57gAkTgjvGUFBeTvnmyMjRPpKxDxfCHA6HE0Lm\nZ83HeYWO1t2M1Bn4185/4eJ/u1cY41EIp6dTFX1Li/+iYsoUWu7bN3zHFSysuMhXt4POThKgNhvQ\n2Oh+G9Z5wVs8gGU5ARIv7l5X6RTb7fLt5mYa/ZufTyKYXQ4fKg0NtPRUuHf66ZRJdo7COPPEeU/g\n2+u+xYcfAmec4dk1VzrCapUai/MXe9xnTg595s3N3l/bF52dVNA2FCGclUWvzyId7Hv/3/91PNnx\n9Htwh80G/OtfoR/McvQoCXeOb7gQ5nA4nGFmSjIpwHcOvgOz3ezyeIetA/HhQ/jLPMpotf4XHQHk\nUCUmAu++O3zHFCw9PbT0JYQtFmDyZLrtSegxseWvEH73XRr7+803jtsohfCePbTs7SVnMymJMsJA\n8PnU1lYgLMyzizhtGr2GL5cxTB2GGF0MwsJoepunz0fpCPuijCaYB92ijEVehiqEAfmEg4nftjbX\naIq/wvbpp4GbbyZX+fHHAz8mT5SXy78Ljne4EOZwOJxh5srJV+KCYpq6Vd7qeP1bFEW0W8dfsdxQ\nEATgoYeAF1+kwq/vviMhd/fdo31kMjYbLf1xhH0JYeYu+yOEc3Np8Eh/P/D8847bKIXwSy/RknV3\nMBpJoGk01DmisRH4/e+HlsNubaUTm1C2s87Npc+nvByIjXWMnDBH2B8hnJtLv5Vgiy1DIYRZJxBl\njry5mVqorVnj+FhXF/Dgg/IJljOsKPHXvwbuuy80zrAockc4ELgQ5nA4nGEmOSoZL/0XKZgjrY59\nrrp6u9Av9p8SQhgAbryRiunefhvYuZMExGOPjZ3+q+yytzchLIrkCKenU/zBUzcDlun1JoSZyF28\nWBZB77wju6XKbX76U+C55+gzY+OGk5LImZ8wAdi9G/jgA2r3tX+/17fpwEUX0bhoJoRDSW4uOaYb\nN5Lov+su2UlVRiN8IQjAnDmyEH7lFeCTTwI/nmCEcHo6HYc7IQyQY3755XSbbfPWW8ADD9Dv3R3s\nvff10cmVtzy5v9jt9JtRjofmeIYLYQ6HwxkB4sLjkBSZhPI2R0e4w9YhPX4qIAg0peuzz+QsZV9f\n6MbnBos/jrDNRo5rVBRlV31lYP1xhM89l5bnnEPP27hR3kYphO126kbBsrJJSbQsK6P17Fi++87z\nazof43/+A6xbNzjlLcRCmPWUVjq57MQhkGgEQEJ461b67P/3f4Ennwz8eIIRwjoddUhhIrejAwgP\nl53/+HjX+IRusBvc/v30Gbz+uuM+lSc8QGj6Tgf6uZ7qcCHM4XA4I0RhQqGLI3yqCWGAhPDx48Cm\nTbKI8NRia6TxxxFmHSOio6l4zNOxM0HiTdwwIbxoEX0Wa9aQ86jsGWwyAWo1te9Sq0lIMkfYaKRl\nWRkVIbLBGps3e35NJezYWaHecDjCAAlzFgNgXUMCcYQBGszS2UmX/Y8dG9pvhglhr0NBvJCV5egI\nx8fL+4qLo4y3VisLYdZK7YcfyC3+yU8ch26w3xsjlELY38/1VIcLYQ6HwxkhigxFDkJYFEU8vf1p\nAIAx0jhahzXizJlDy02baPSyIIRmVHEoYI5wd7fncdCsnVlUFBWDeZo2xoSetyI2s5kES3w8OeTL\nl5MrvEExldtkIpGs1VL/5qoqcoT1etn1Kysjt5jFBfx1hNmxMSHMOl2EiuRk+pwOHaLoQHi4oyOs\n1cpt5nwxdSot162j76myMvAsdHs7ncBoNIE9j5GVJQv5jg4Sv7GxdD8ujloHpqfLQphlub/6itbp\ndMATT8j7G05H2N1QFI4rXAhzOBzOCFGaWIqDLQcxINJf7yOtR/B/2/4PDyx4AAUJp06Jd3w8Cbv+\nfhrVnJ4+9hxhwHO/Y6UjPHEiUFfnPtvZ3U0FdcePey6WMptlVzw8nJZnnUV531/8Arj6ahLCTGyx\nfrpNTbIbDJBbqtPR8RcWkkhkgt0brNtBVRW1Twu1IywI5GQDlFnNznZ0hANxLZOSqHXbf/5D9+12\noL7edbu+Ps89lYfaQ5iRmyv/Vtm+2PfHnOGCAuDAAbrd2krf3U03UTHkBRc4nqSw39udd9K+f/97\n4P335fd3//2urrEv2PbcEfYPLoQ5HA5nhJiUNAmdPZ2oNpHtU2Mm2+jaadeO5mGNOIIgZ0eTk+XO\nAmMB5ggDnp1cpSPMhicw4cPo7ychM3UquZaeCuqUQpjBnM9//IOK2NwJYTZVjhEVBcybR7eXLaOl\npwEdSioq6PsYGCDHMtRCGKDRxAAJYeWo5O7uwHOsU6c6tpdz9x1dfz1wySXunx+sEM7PJ9e2p0fe\nF/tu2HL2bMoDiyIJ4enTqU3ahRe6Tsjr7qbi0b/+lcR7UxPwX/9Fj330EXVZefbZwI6RRyMCgwth\nDofDGSEmJdE4tv1NVNJfa6awYFr0qVfezbKjSUmUsw22B26osFopfqDXex5bzHK9LBqhUrkKYSZG\nmKj1ti8moBglJbTPri4SWwcOyGLZkyMMyCOtzzqLlocPe3mjg1RUADNnyveHUwinppIjzIRwoI4w\nQEM9lDhfSbDbyTH+9FP3TmoohPDAAIlhFo246y46mWDfx9y5dKJSUeGau87JoYww6yhitcqfAev7\nyyb4sUI7lgcH6PfiHKdwhgvhwOBCmMPhcEaIjJgMxOpisa+RRqvVWmphiDAgXBM+ykc28iiF8MSJ\nVFVvMjlGCEI9bcsfbDYSEAUFnsX53r3kZKamUpwhPx/48UfHbZgYKSykbbwJYWdHOCLCsQfst986\nOsL19eTeKh1hgATZ00/T5XejUS6c80ZtLWW22bS44RDCLBqRmkoO+v79lGUeiiN81120zMggh/nA\nAeDaa+XOHF9/TQLbZnPsvMEIhRAG6LfB9rVoEYljlsmdPZuW331HGeHERPn5OTm0LTve7m75ee+8\nA/z85/QcUZR/Q6zgDqCCu5//3PsxciEcGFwIczgczgghCAImJU3CvqZBIWyuRXpM+igf1ejAhHBy\nMhV6dXWRYGJ/5HfsIPFXVzeyx2W1knAtKHAVr3Y7iZ6HH6YYAiu4KipyFc1MjERFkXjyFFOor3cU\nSoxJdPEAOh3FLJgQZm7hvn2ujnBYGGVR1Wpyqp0d4f5+utyuPMHo7CQhzqaa+RqfPBTYaO3cXGoB\nd8YZwC9/Sd95oEI4Oprc1i1b6D2+/Tbw8svA55+Ty/rnP5PYzMigFn3OBCuEMzLoe6+oIEfY3b7i\n4+lEZvduV0eYfX/MFVc6wvHxJKK7umjfLHfOCu4Ael1fPaK5EA4MLoQ5HA5nBDk9/XR8XfU1znjh\nDPxj+z+QHn1qC2GjkboJAFSs9dZblJX8/nsqSnMnZoYTq5UcOndCuKKCqv9NJuDMM+X1ytwrQ9ka\nLCfHtRtAayu1ONuzRxaKSubNI/f0tNPofnS0/FoAiT5nR1iJOyH86KPAihU0yIRhsZBYv/hicpnZ\n64WS/HwS7gsWkFg//3zKhA8lGgHQiUN6Op04sc/90CGaWPjNNzSZb9Ei11HVQPBCWKOh74A5wp7a\nsJWW0jE5C2HWZ5jlhJWOMEDFo4AcvQCod/LatXS7udlz3pzBu0YEBhfCHA6HM4IsL1qO+s56bKre\nBODUzAcDwNlnA3/4AzmfiYnktAkCuV8bNwIHD9J2yn66I4HNJgth524PyjyqOyHMnFa7HfjwQ7od\nGenYe5Zx/fUkdk0m+URAyZo1JFj/9S+axrd6Na3PyKD8MODqCCspKqJohNL9ZZ+lso9tZ6cssjMy\nPO8vWCZNkkc3Z2eTCK6uDm7oA8seAyQ6Kyros164kET37t2O46kB7+LVX/Lzad+dnXRFwx0lJbRN\nV5ejEA4Pp6Ec7hxhwHEgBzv2qir6/k0miknU1dFvzBOsLd1QW8SdanAhzOFwOCPI/Kz5iNXFQgCp\nAr321Lx+qdcD990n/7FetAi44grKfa5b5yiERzIrrIxGOHd7qKykqEJFhdyhASAhbLFQ66vjxynr\n+etf02N6Pbl8zkJYOW3OnRBWqei1SkpIDC9aROtZL2HAtyNssZDLDlAsgjnBzI3s6SFnOSrK2ycS\nelg84ODB4C7fs+wxQO53Y6MsTBcsoO9POVhEFIN3hAESwps2ybc9HRv77J1z18oWbM6OcHIy/T+h\ndIQZrO2aKHrvN9zdzWMRgcCFMIfD4YwgWrUWf1vyNzx3wXMAgKiwEVYhY5QXX6Ss57x5FIs4eJCG\nbTQ1+dcGLFjq6qhwTekIA47xiMpKEnF5eY7PZXGF3/6WRv8qj1evJ5evrU2OSwDy5WujMfBcLns9\nb45wcTEtWcEcu0wPyEKY9UMeaSHMjt9sDs4RZkI4IYFOTmpqZCFcUEDf1R//KLfE6+qi2E0ohDC7\nUuD8W2Ao3WpWPMdQFmKyKA5Drabfy0MPUeeLSZOAu++mx77+Wt7OWzyCC+HA4EKYw+FwRpjryq7D\ndWXXYdN1m3D/gvtH+3DGBIJALuicOeS2NTQA111Hj33//fC//vnnAw88IDvC6enkyCqF8LFj7oUP\nczgByjgzNxsgocdyn2zaWE8PieXzz6eBCSwy4C9MSHpzhPPySFSxnDBr7zZtmiyiWD9kFo0YKQwG\nOjYguOK8jAxqFXfbbeR4b90qC2FBAN54g347r7xC69h45VAIYYBOIDydjDCRPn++qyNcWCifLLkT\nrS+9JE/Oy88H/vIX+o0pM8/e+m47xy043uFCmMPhcEaJeVnzTtlohCfY+GWABkNMnOh4eXs46O2l\nSvxDh2RHWKUiMensCLMiPyXKrg/t7SSGGcwRBuTL2UePknD7xS+A228P/Hj9cYTDwuhYDx8mR/Gf\n/6TjnDFDdoSVg0FGEkGg9w9QHCaY/Xz1FXWiAMjhVmZ258yhyX5bttD9UAvh/HzPJzFxccAHH1CX\nDmcKCqjozWRydYQBEs+sawjrFjJxIgl9gIT13r0U/fj4Y9foEHeEA4MLYQ6Hw+GMGaZPB5YuBd59\nlwTfnDlDE8Jsqpc/HDtGl8yrqmRHGHC8hC2KnoWwIJCgXbvW0R0GKNObPtgYZOVKei3mziovnwfC\n4sU0fcxXV4CSEjqmRx8lwVha6jjieLSiEYD8mShzvkMlOVnuxexcvDZrFrBtG90OlRBmVwU8xSIY\nK1a4DksB5B7RFRWeRSvbNyvsmzyZlhoNcM01dLK1fj2wfDmwfbv8vHvuAZ54ggvhQOBCmMPhcDhj\nhrAwykayMbNz59KwCufqf188/zw5oM3NvrdlUYaqKsfiJdZC7b336DK7xeJeCAPAY48BF11ExwtQ\nr1zmBGu1NGHOZgNeeIFeLzHRu6Prjfnz6UTBF2vWOBZVZWdTN4nmZhrSMVrRCIDaxin74waDIMiZ\naHdC+Mcf5d68QPBCmLn87DUDhTnK5eXuHWFAFsJMSLOx2X19wE9+Qp/dX/5C69hVC1GUxzFzIew/\nXAhzOBwOZ8wyZw79gf/hh8CexzokeMtSMpgQtttJDCuFcGUl8JvfAD/7Ga3zJIQZ7JL2vffKzisA\n7NoFrFpFOc8DB4buBgfCOeeQG/zXv9L9qCgS62eeCVx2mdzVYDQcYYMhtFPsmLOckuK4ftYsihDs\n2hU6Rxig/ta//OXQnhsfT8e5c6fnPC8TwixLPX++/NiUKSSmN2yg++yqRWWlLPZ5D2H/4V3mOBwO\nhzNmKSqirgDffw+ce67/z2Nuqz+T6Q4dIufNZKK8sDIa0dtLeUyGr8vha9ZQMRxrd8YQBCrsuvtu\ncmadHx8u2KS+5GRgyRK6tP7SS+RmPvAAPTYaQjjUeHKES0rosz98mL7fyEhy6IMl2EjH8uV0lUE5\nmlkJi9gw8a5WU/Eo61Zx5pmyAGZL5aAU7gj7D3eEORwOhzNmUamo/dTmzTSggLmYvmDiTunKeqKh\nATj9dPm+0hFWEhvr202MjKQ2ajqd62NnnSV3jJgwwfdxhZIrrpAd2OxsElWVlSSw3B3reGPOHPp+\n0pzm04SHU9eO8nLqST116ugcnzOrV8tdRNyJVnblQdmn+PnngVdfpdvKgS5MCO/YIa9TDoLheIcL\nYQ6Hw+GMacrKqKvDBRcADz/s33NY71h/hLDVSq3ImIhijnBWluN0Ll+xCF9MmiQ7faEoEgsGNtY5\nIiLw9m1jkUWLKPvsri9xYSENo/j8c+Dqq0f+2NyxcKF8250jnJ5OVypYJMfd8zUaOgFgwzn27pWv\nhPiTjecQXAhzOBwOZ0xTWkoRh5oa2f3yBRtY4a8QjogArr2W7vf20lKjIfErCOScBiuEBQF4/30S\nL7NmBbevYGFOIyuYOxnwFHkoLKTe1Go1cMklI3tMntBq5T7ZniYnFhfL47SdYdMKb7qJRma3tZFw\nZvEhLoT9hwthDofD4YxplIVl3iZqKRmKEL7lFrrPWnsBFI/IzCRX2t0o5ECZOpViHqEo2AoG59jH\nyQxrV3b++ZQ3Hyv8+c8UkXCePOcvqanUTUKvBx58kP7fWLCAHvO3dSCHF8txOBwOZ4zDCp5Ekf7Y\ni6Lvy/lMCPsjnJkQzsoih1SZ2bzkEsrS/upXoSmyGiuw1m6nAkwIX3PN6B6HM4mJwL//Hdw+kpOB\nO+6gUdIAnTTef7/cbo3jGy6EORwOhzOm0espW1tVRf1gW1sdp7m5gwnh1lagvt77KF9lL1fnjOlP\nfjLUox7baE6hv/6LFwNPPkmdGk5GbrhBFsLFxdTDmuM/PBrB4XA4nDHP0qUkaAD/egN3d8udGZRt\npdzhaajBqcBoDNMYacLDgVtvPXnFv7Kl31CHtJzKnKQ/Cw6Hw+GcTPzzn1QQZDCQM+yr2Ky7my4T\n19fTMIXzz/e87akqhNvbT46OERzgnXeovSD/PgOHO8IcDofDGRf+Uh8tAAARMklEQVTEx5MQ3r/f\n97bd3RRzKCvz7ggPDNBEuVNRCMfFySN8OeObVauA3/9+tI9ifMKFMIfD4XDGBYJA/VPXr/e8TUcH\nidvubsoWT5sG7NnjeXvWb/hUFMIcDocLYQ6Hw+GMI5YsAX74QR49q6SxkVzjJ56QhXBREUUpPE3a\nslppyYUwh3NqwoUwh8PhcMYNixeT47txo7zu0CGKPzz6KN3fsEEWwoWFtL2nAjsuhDmcUxteLMfh\ncDiccUNWFmVbDxwAVq6kdQ88QBPnamvp/pEj1GaNCWEAKC+n1lLOcCHM4ZzacEeYw+FwOOMGQaAB\nG4cOyetqa4G9eykaccEFwOHDQEsLCeH0dBK55eXu98eFMIdzasOFMIfD4XDGFc5CuK4O6Ouj2zfc\nIK/X6wGVisYJcyHM4XDcwYUwh8PhcMYVTAiLIv1XX0/rw8Jo8AaDjUouKfHcOYILYQ7n1IYLYQ6H\nw+GMK0pKALMZaGigdml2O60vLSUxnJVF95kQPvdcYMsWiksAcpYY4EKYwznV4UKYw+FwOOOK0lJa\nHjwou8FGIzBnDt3OzqYlE8IrVpBz/PHHVFSXkQG8/jo9xoUwh3Nqw4Uwh8PhcMYVubnA/2/v3qOs\nKu8zjn8fEMFLERABk4ioqKhkIQoaryQhUYNFxVBNtC2pbRqztKjpauOlijFV01bFeCGr0SRGvBVt\ntZhCvEFsghdE1BhBtICxEQUUCuKgIPz6x7uPbo7DDDD7zJmZ/XzWOmvYe7/7ch7OOvPbe9797i5d\n0iNlH3sszZs5E66/Pv17wID0s1Lc9u0LhxwCM2bAsmVpXuWhHC6EzcrNw6eZmVm70qVLugHussvS\nMGkAe+8NXbumf1euCK9Z8/E6gwenfsUrVqTpSjeJSiHcrVvtj9vM2h5fETYzs3Zn0KCPi2DY9Iru\n+PEwdiwce+zH8/bbLw2rtnx5mq5cGV67NhXQnfzb0KyUfEXYzMzanUGD0s8BA1K3h7zddoN77910\n3v77pxvrKsOuLVyYfq5d624RZmXmQtjMzNqdSiE8ZQoMH958+8pT5WbNSj/feSddFXYhbFZu/mOQ\nmZm1O6ecAhMnwqGHbln7ffZJT6WbNQv69UvzHn/chbBZ2bkQNjOzdqd7dzj//C3v27vDDmm0iYjU\nleKgg9Jwai6EzcrNhbCZmZVC5epx795w4okwbVq6ea5nz/oel5nVjwthMzMrhUoh3KsXjBiRiuBf\n/Sp1mzCzcnIhbGZmpTBsWPq5cuXHT6d78800JrGZlZMLYTMzK4XKMGt9+6aHblT6BvuKsFl5efg0\nMzMrhZ494Zln0lPmOnVKQ6o9/7wLYbMy8xVhMzMrjWHDPn6ccqV7hAths/JyIWxmZqU0dGgaU7hX\nr3ofiZnViwthMzMrpfHjYc6c9KANMysnF8JmZlZKXbvCpz9d76Mws3pyIWxmZmZmpdRmCmFJ50ha\nLGmtpKckDW+i7RhJD0taJmmVpCckHdeax2ufdPfdd9f7EDo8Z1xbzrf2nHFtOd/ac8YdS5sohCWd\nDlwLTACGAi8AD0nqvZlVjgUeBr4CHALMBB6UNKQVDtc2w18OteeMa8v51p4zri3nW3vOuGNpE4Uw\ncAHwrxFxe0S8DJwNNABnNdY4Ii6IiGsi4tmIWBgRlwCvAqNb75DNzMzMrD2reyEsqQtwKPBYZV5E\nBPAocMQWbkPAHwEranGMZmZmZtbx1L0QBnoDnYGlVfOXAv22cBt/B+wETCnwuMzMzMysA2v3j1iW\ndAZwKXBSRLzdRNNuAPPnz2+V4yqjVatWMXfu3HofRofmjGvL+daeM64t51t7zri2cnVat9bYn1Iv\nhPrJukY0AF+NiKm5+bcBu0TEmCbW/RpwKzA2In7ZzH7OAO4s5KDNzMzMrJbOjIi7ar2Tul8Rjoj1\nkp4FRgJT4aM+vyOBGza3nqSvk4rg05srgjMPAWcCrwHvt/CwzczMzKx43YABpLqt5up+RRhA0mnA\nbaTRImaTRpEYCwyKiOWSrgY+FRHjsvZnZO3HA/fnNrU2Ila34qGbmZmZWTtV9yvCABExJRsz+Aqg\nL/A8cHxELM+a9AP2yK3yTdINdjdnr4qfs5kh18zMzMzM8trEFWEzMzMzs9bWFoZPMzMzMzNrde2m\nEJZ0jKSpkt6QtFHSSY20uULSEkkNkh6RNLBqeVdJN0t6W9K7ku6T1KeqTU9Jd0paJWmlpFsl7VTr\n91dvki6SNFvSaklLJd0vab9G2jnjbSTpbEkvZO97laQnJJ1Q1cb5FkTShdl3xXVV853xNpI0Ics0\n/5pX1cb5tpCkT0manGXUkH1vHFLVxjlvA0mLG/kMb5R0Y66Ns20BSZ0lXZ1l3SDpfyT9QyPt2kbO\nEdEuXsAJpD7EJwMbSOMG55d/l/RkuT8GBgMPAAuB7XNtfkQaNWIEMBR4Avh11XamA3OBYcCRwCvA\nHfV+/62Q7zTgz4ADgM8Cv8iy2sEZF5bxidnneB9gIPCPwDrgQOdbeNbDgUXAc8B1/gwXlusE4LfA\nbkCf7NXL+RaacQ9gMWlUpEOBPYEvAXs550Ly3TX32e1DGqFqA3CMsy0s48uAZaTfd/2BU4HVwLlt\n8TNc98C2MeSNfLIQXgJckJvuDqwFTstNfwCMybXZP9vWYdn0Adn00Fyb44EPgX71ft+tnHHvLIuj\nnXFNc34H+AvnW2imOwMLgC8CM9m0EHbGLct2AjC3ieXOt+UZ/wB4vJk2zrm4vK8HXnG2hWb6IHBL\n1bz7gNvbYs7tpmtEUyTtRRpZ4rHKvEjDqD0NHJHNGkYaJSPfZgHweq7N54CVEfFcbvOPAgEcXqvj\nb6N6kN73CnDGRZPUSemBMF2B/3a+hboZeDAiZuRnOuPC7KvURW2hpDsk7QHOt0CjgTmSpih1U5sr\n6a8qC51zcZQe6HUm8JNs2tkWYzowUtK+AJKGAEeR/vLc5nJuE8OnFaAf6Y0vrZq/NFsGaVi2dfHJ\ncYbzbfqRLud/JCI2SFqRa9PhSRLpLPk3EVHp/+eMCyBpMPAkacDwBtLZ70JJR+B8Wyw7uTiY9CVa\nzZ/hlnsK+AbpivvuwOWkE7nBON+i7A18G7gWuBI4DLhB0gcRMRnnXKQxwC6koVfB2RYiIiZlJ8gL\nJH1Iuh/tkoi4J2vSpnLuKIWwFWsScCDpDM6K9TIwhPTlOxa4R9KI+h5SxyDpM6QTuC9FxPp6H09H\nFBH5Jz39TtJs4PfAaaTPtrVcJ2B2RFyaTb+QnWicDUyu32F1SGcB0yPirXofSEciaTwwDjgdmEe6\nOPFDSUuyk7k2pUN0jQDeAkQ6g8jrmy2rtNleUvdm2lTfkdgZ6JVr06FJugkYBXw+It7MLXLGBYiI\nDyNiUUQ8FxGXkP4U9G2cbxEOJd3ENVfSeknrSTdZnCdpHelKgjMuUESsIt2cMhB/hovyJjC/at58\n0k1H4JwLIak/6SbEW3KznW0xLga+HxH3RsRLEXEnMBG4KFvepnLuEIVwRCwmvemRlXlZeIeT7jIE\neJbUgTrfZn/Sl8uT2awngR6ShuY2P5L0H/Z0rY6/rciK4JOBL0TE6/llzrhmOgGdnW8hHiWNeHIw\n6ar7EGAOcAcwJCIW4YwLJWlnUhG8xJ/hwswi3RSUtz/pyru/i4tzFunkeFplhrMtTCfSSBx5G7P5\nbS/net9duKUvYCfSL7aDs0DPz6b3yJb/PekO/NGkX4YPAK+y6VAck0jD0nyedPVoFp8cimMa6Zfn\ncFLXgAXA5Hq//1bIdxKwEjiGdMZVeXXLtXHGLcv4qizfPUnDxVwNrCedeDjf2mRePWqEM25Znv8C\nHJt9ho8EHiEVE7s638IyHka6W/4i0lCLZwDvAl/z57iwjEUaluvKRpY525bn+2PSTW2jsu+KMaS+\nvFe1xZzrHthWBDuCVABvqHr9NNfmctKQHA3AQ8DAqm10BW4E3s6+WO4F+lS16UG6grSKVBjeAuxY\n7/ffCvk2lu0G4M+r2jnjbc/4VtLYtmtJZ8MPA190vjXNfAa5QtgZtzjPu4E/ZJ/h14G7yI1v63wL\ny3kUabzmBuAl4KxG2jjnbc/3y6TfbwM3s9zZtizfHUknzYuA90gF7veA7dpizso2ZGZmZmZWKh2i\nj7CZmZmZ2dZyIWxmZmZmpeRC2MzMzMxKyYWwmZmZmZWSC2EzMzMzKyUXwmZmZmZWSi6EzczMzKyU\nXAibmZmZWSm5EDYzMzOzUnIhbGZWMEmLJY3fivYjJG2Q1L2Wx2VmZpvyI5bNrPQkzQSei4jvFLS9\nXYH3IuL9LWy/HdArIpYVsf9tIWkEMBPoERGr63UcZmatabt6H4CZWXshqXNEbGiuXUS8szXbjYgP\ngboVwRkBkf00MysFd40ws1KT9DNgBHCepI1ZF4X+WXeFjZJOkDRH0vvAUZL2lvSApLckvStptqSR\nVdvcpGtEtp2/lPQfkt6T9Iqk0bnllX11z6bHSVop6ThJ87L9TJfUN7dOZ0k3ZO2WSbpS0m2S7m/i\nvfaXNFXSCklrJL2Yvb89gRlZs5VZBj/N1pGkiyQtktQg6TlJX23k2EdJekHSWklPSjqouf1u43+Z\nmVlhXAibWdmdBzwJ3AL0BXYH/je3/Grgu8ABwG+BnYH/Ar4AHAxMB6ZK+kwz+7kMuAf4LDANuFNS\nj9zy6n5qOwJ/C5wJHAP0B67JLb8Q+DowDjga6Amc0sh28iYB22ftB2fvaw3wOlApbvclZXBeNn0x\n8KfAXwMHAhOByZKOqdr2PwMXAMOA5cCDkjo3s18zs7py1wgzK7WIWC1pHdAQEcsr86WPeghcGhGP\n5Vb5P1JBXDFB0qnASaSCb3N+FhFTsm1fDIwHDgMe3kz77YBvRcRr2To3AZfmlp8LXBURU7Pl5wKj\nmtg/wB7AfRExL5t+rbJA0orsn8srfYQlbQ9cBIyMiKcr62RF8LeAX+e2fXlEzMjWGwf8ARgD3NfU\nfs3M6smFsJnZ5gXwbH6GpJ2A75GKzt1J36PdSFdsm/LiRxuNaJC0GujTRPuGShGcebPSPutC0Rd4\nJrfNjZKepek+vjcAP5J0PPAo8O8R8WIT7QeSrkw/otyZAdAFmJubDuCp3LGslLSAdBV9W/ZrZtYq\n3DXCzKxp71VNXwucTOqacDQwBPgd6U//TVlfNR00/R3cWPsW3cgWET8B9gJuJ3VRmCPpnCZW2Tn7\nOYr0PiuvA4E/acF+n2lmv2ZmrcKFsJkZrAM6N9sqORK4LSKmRsRLpNEeBtTqwBqTdV1YCgyvzJPU\nCThkC9Z9IyJ+HBFjSUX9N7NF67Kf+RzmAR8Ae0bEoqrXG7l2Aj6XO5aewH7A/M3s97rcfs3M6sZd\nI8zMUp/Vw7PRE9YAlf6yjV2BfRU4VdIvsukrNtNua23tNm4ELpa0EHgZ+BugB03cLCdpIunmvleA\nXqQb/ir9dn+frTta0jRgbUSskXQNMDG78e03wC7AUcCqiJic2/xlWT/jZcCVpBvmHtiC/ZqZ1Y2v\nCJuZpdEYNpCKs2Wkm7ug8aLyO8BKYBbwn8Av2bS/bGPrNbadLWnTlH8C7gJ+DjxBKuAfBpp6iEdn\n4CbS+5xGKqDPAYiIJcAE4AfAW6RCm4i4FPg+qSvIPFJBOwpYXHXsFwI/JPVb3g0YnY2P3OR+zczq\nyU+WMzPrALKb2eYD/xYRE1pxvyNIYxD39BPpzKy9cdcIM7N2SFJ/4DjgcdKoFeeS+irfVY/DqcM+\nzcxazF0jzMzap43AN4DZpPF8DyKN97ugDsfiPy2aWbvkrhFmZmZmVkq+ImxmZmZmpeRC2MzMzMxK\nyYWwmZmZmZWSC2EzMzMzKyUXwmZmZmZWSi6EzczMzKyUXAibmZmZWSm5EDYzMzOzUnIhbGZmZmal\n9P/V6xyNfOGe5AAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(8,6))\n", "plt.title(\"Smoothed loss\")\n", "plt.xlabel(\"training steps\")\n", "plt.ylabel(\"loss\")\n", "\n", "train_steps, synth_losses = zip(*synth_history[1000/synth_step:])\n", "train_steps, stale_losses = zip(*stale_history[1000/synth_step:])\n", "\n", "synth_line, = plt.plot(train_steps, synth_losses, label='Synthetic gradients')\n", "stale_line, = plt.plot(train_steps, stale_losses, label='Stale gradients')\n", "plt.legend(handles=[synth_line, stale_line])\n", "plt.show()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.10" } }, "nbformat": 4, "nbformat_minor": 0 }