stud-ai/1-intro/4_cifar10_tutorial.ipynb

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"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
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"source": [
"%matplotlib inline"
]
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"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"Training a Classifier\n",
"=====================\n",
"\n",
"This is it. You have seen how to define neural networks, compute loss and make\n",
"updates to the weights of the network.\n",
"\n",
"Now you might be thinking,\n",
"\n",
"What about data?\n",
"----------------\n",
"\n",
"Generally, when you have to deal with image, text, audio or video data,\n",
"you can use standard python packages that load data into a numpy array.\n",
"Then you can convert this array into a ``torch.*Tensor``.\n",
"\n",
"- For images, packages such as Pillow, OpenCV are useful\n",
"- For audio, packages such as scipy and librosa\n",
"- For text, either raw Python or Cython based loading, or NLTK and\n",
" SpaCy are useful\n",
"\n",
"Specifically for vision, we have created a package called\n",
"``torchvision``, that has data loaders for common datasets such as\n",
"Imagenet, CIFAR10, MNIST, etc. and data transformers for images, viz.,\n",
"``torchvision.datasets`` and ``torch.utils.data.DataLoader``.\n",
"\n",
"This provides a huge convenience and avoids writing boilerplate code.\n",
"\n",
"For this tutorial, we will use the CIFAR10 dataset.\n",
"It has the classes: airplane, automobile, bird, cat, deer,\n",
"dog, frog, horse, ship, truck. The images in CIFAR-10 are of\n",
"size 3x32x32, i.e. 3-channel color images of 32x32 pixels in size.\n",
"\n",
".. figure:: /_static/img/cifar10.png\n",
" :alt: cifar10\n",
"\n",
" cifar10\n",
"\n",
"\n",
"Training an image classifier\n",
"----------------------------\n",
"\n",
"We will do the following steps in order:\n",
"\n",
"1. Load and normalizing the CIFAR10 training and test datasets using\n",
" ``torchvision``\n",
"2. Define a Convolutional Neural Network\n",
"3. Define a loss function\n",
"4. Train the network on the training data\n",
"5. Test the network on the test data\n",
"\n",
"1. Loading and normalizing CIFAR10\n",
"^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
"\n",
"Using ``torchvision``, its extremely easy to load CIFAR10.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"import torchvision\n",
"import torchvision.transforms as transforms"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The output of torchvision datasets are PILImage images of range [0, 1].\n",
"We transform them to Tensors of normalized range [-1, 1].\n",
"<div class=\"alert alert-info\"><h4>Note</h4><p>If running on Windows and you get a BrokenPipeError, try setting\n",
" the num_worker of torch.utils.data.DataLoader() to 0.</p></div>\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz\n"
]
},
{
"data": {
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"HBox(children=(HTML(value=''), FloatProgress(value=1.0, bar_style='info', layout=Layout(width='20px'), max=1.0…"
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{
"name": "stdout",
"output_type": "stream",
"text": [
"Extracting ./data/cifar-10-python.tar.gz to ./data\n",
"Files already downloaded and verified\n"
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}
],
"source": [
"transform = transforms.Compose(\n",
" [transforms.ToTensor(),\n",
" transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])\n",
"\n",
"trainset = torchvision.datasets.CIFAR10(root='./data', train=True,\n",
" download=True, transform=transform)\n",
"trainloader = torch.utils.data.DataLoader(trainset, batch_size=4,\n",
" shuffle=True, num_workers=2)\n",
"\n",
"testset = torchvision.datasets.CIFAR10(root='./data', train=False,\n",
" download=True, transform=transform)\n",
"testloader = torch.utils.data.DataLoader(testset, batch_size=4,\n",
" shuffle=False, num_workers=2)\n",
"\n",
"classes = ('plane', 'car', 'bird', 'cat',\n",
" 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let us show some of the training images, for fun.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\n"
]
},
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"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" deer deer ship ship\n"
]
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"\n",
"# functions to show an image\n",
"\n",
"\n",
"def imshow(img):\n",
" img = img / 2 + 0.5 # unnormalize\n",
" npimg = img.numpy()\n",
" plt.imshow(np.transpose(npimg, (1, 2, 0)))\n",
" plt.show()\n",
"\n",
"\n",
"# get some random training images\n",
"dataiter = iter(trainloader)\n",
"images, labels = dataiter.next()\n",
"\n",
"# show images\n",
"imshow(torchvision.utils.make_grid(images))\n",
"# print labels\n",
"print(' '.join('%5s' % classes[labels[j]] for j in range(4)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"2. Define a Convolutional Neural Network\n",
"^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
"Copy the neural network from the Neural Networks section before and modify it to\n",
"take 3-channel images (instead of 1-channel images as it was defined).\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"import torch.nn as nn\n",
"import torch.nn.functional as F\n",
"\n",
"\n",
"class Net(nn.Module):\n",
" def __init__(self):\n",
" super(Net, self).__init__()\n",
" self.conv1 = nn.Conv2d(3, 6, 5)\n",
" self.pool = nn.MaxPool2d(2, 2)\n",
" self.conv2 = nn.Conv2d(6, 16, 5)\n",
" self.fc1 = nn.Linear(16 * 5 * 5, 120)\n",
" self.fc2 = nn.Linear(120, 84)\n",
" self.fc3 = nn.Linear(84, 10)\n",
"\n",
" def forward(self, x):\n",
" x = self.pool(F.relu(self.conv1(x)))\n",
" x = self.pool(F.relu(self.conv2(x)))\n",
" x = x.view(-1, 16 * 5 * 5)\n",
" x = F.relu(self.fc1(x))\n",
" x = F.relu(self.fc2(x))\n",
" x = self.fc3(x)\n",
" return x\n",
"\n",
"\n",
"net = Net()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"3. Define a Loss function and optimizer\n",
"^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
"Let's use a Classification Cross-Entropy loss and SGD with momentum.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"import torch.optim as optim\n",
"\n",
"criterion = nn.CrossEntropyLoss()\n",
"optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"4. Train the network\n",
"^^^^^^^^^^^^^^^^^^^^\n",
"\n",
"This is when things start to get interesting.\n",
"We simply have to loop over our data iterator, and feed the inputs to the\n",
"network and optimize.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1, 2000] loss: 2.215\n",
"[1, 4000] loss: 1.864\n",
"[1, 6000] loss: 1.681\n",
"[1, 8000] loss: 1.594\n",
"[1, 10000] loss: 1.520\n",
"[1, 12000] loss: 1.470\n",
"[2, 2000] loss: 1.418\n",
"[2, 4000] loss: 1.395\n",
"[2, 6000] loss: 1.381\n",
"[2, 8000] loss: 1.360\n",
"[2, 10000] loss: 1.316\n",
"[2, 12000] loss: 1.297\n",
"Finished Training\n"
]
}
],
"source": [
"for epoch in range(2): # loop over the dataset multiple times\n",
"\n",
" running_loss = 0.0\n",
" for i, data in enumerate(trainloader, 0):\n",
" # get the inputs; data is a list of [inputs, labels]\n",
" inputs, labels = data\n",
"\n",
" # zero the parameter gradients\n",
" optimizer.zero_grad()\n",
"\n",
" # forward + backward + optimize\n",
" outputs = net(inputs)\n",
" loss = criterion(outputs, labels)\n",
" loss.backward()\n",
" optimizer.step()\n",
"\n",
" # print statistics\n",
" running_loss += loss.item()\n",
" if i % 2000 == 1999: # print every 2000 mini-batches\n",
" print('[%d, %5d] loss: %.3f' %\n",
" (epoch + 1, i + 1, running_loss / 2000))\n",
" running_loss = 0.0\n",
"\n",
"print('Finished Training')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's quickly save our trained model:\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"PATH = './cifar_net.pth'\n",
"torch.save(net.state_dict(), PATH)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"See `here <https://pytorch.org/docs/stable/notes/serialization.html>`_\n",
"for more details on saving PyTorch models.\n",
"\n",
"5. Test the network on the test data\n",
"^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
"\n",
"We have trained the network for 2 passes over the training dataset.\n",
"But we need to check if the network has learnt anything at all.\n",
"\n",
"We will check this by predicting the class label that the neural network\n",
"outputs, and checking it against the ground-truth. If the prediction is\n",
"correct, we add the sample to the list of correct predictions.\n",
"\n",
"Okay, first step. Let us display an image from the test set to get familiar.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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tXkWbNdfQNOORM2ziGrWw4r1OLiaOGbwzVmfTz4cA4um7K4b+Bi2ZZT5RSmKeYV4SQqUQVY9bahRQ1HyxWSNZHfenhVeHmayOhY6CJ1VYpTghdR5VJc0TOS0wZ8gzDswTd+A0I2UmBrjeOWKALgjRh7qx1ESKVKqjVgBHIWixJKh7922+TM7989KsusEAb5AAjHeMGe5SYF5mXt3dk3Nms9nQD71BZznjRNj0RpvzTiwK43zrf1N4513D2ElKkGLJNAqiGcG8ZSfFEpNS8GS8Opwu5qljTAPnCuIsnxOkJjIFfE1OqWSUjGqmqEWdJdn89QSQAKorlqshAH7FqFejd3FRtRpuVeXlq1e8evmSnBPzOK6YdRc7Qjgj9auRrpvVGhEXR0u+26NUzzaj1ZvWUgy7rf9+lwW3BGaP0OMqGKIiqDMPNGk1es3REkfngq2X6I0h9uoO9+1XpCWTjhOIMLx4TrzaG1c/dCgOnK3zgF1zVWXJiaIFr4qr8Khr37npcF2Aokh1EtoMKsFTGjWzVOOtICoUKcwhoyhunpGUkH6H292iIVBiRwmxuix2/1ps4hFEw4oWiAgUu7dOPa4IUtpVf7/xo0MoogXXJoCjej1AxRcNYrAsN1K968sE1Oo8X1DKLsMeZ9533/fs9ntKTpychZlz8hStn2eXFwgo3pKr1YhnzICPS+bhWCgiEALqLDlTXKSIs59FWURIOHKeyJN5X30QvIDmgqaFLgo+eLriVu8DsRD8TK2rWKSrMMKK43//DX7TeP9GuPWaI/gtDONFUNOCHD2floXT2pwjZZ4Th8PRjJBw9tCThdmx8pL1HRVAP+QYf91rLz3s5l0KbYFbPGZ4t9bUdjXwusZ6aMXLoWHm5fwZYhBNbs9VQ9nwby0V5lOl5Lw6Jefjb7/p+aLqeTPMpXA4Hnn5+rXx1pcZ7xwvnj1bk29tiZwNNPU86+fJmfFx3n0bRFeNfLux9QPf5TDY6g24GsGCq1ChUsTikJabMnaWwznbCPu6Ues8wsMrypyQRzPgcQgMEUQ8SAQxh0rxBCd0Yiy0PM9oydXbVRyOWDdCKRlSQIoiOdu9rdc2x0COARR8rjapCJIhe0X6BBT8OOLm2Y57c40qzC4gPthmV5oBt3vlVu6snD3xVmBYr4E0IsR7jh8fQmlTSCKNGN/8A61Mj3UiCXX3b9nk6tHUMHOdYOuwwiBxwu76mo8//wlpnnn57TfM08hpOZGnERCyWqISjaCeaSmMUyaVzGGaWbLyOCvj4pHQ0cc94iPqO4rrKDjLrivgIm6ZKWkhdyOUjOaFpBnxHnEdxSunklgWS9IVtYS0BmcJsmokVNWSe5eZol9xg9/Ewt9lhN/XMP+5eLXSOLX1HmETtKRCKbCkTEqF07xwGieWlIh9R4jRuNM1KdTu9SXM9GaR1A89j3c+L401Iqt9Mry3GeUCJVcP1bDylsNpRki1UEr1VsXZpuNBJKzOyYqHakVD1wSmQUdaMmlZUJQuBkrwZoy0zQnDku0UHEVhrnS5aUlMi3G2V89RbY6VYhGPvauG8quBrutLLWHYPO1S7HyElog1Q99ghLfX3cXFXEfbsNrGI6sXXy4Ml2H5niyxmr1oa9J56DsQIxOkpUBa0OkRVchiEbHVyBgpcSmp0ont2LwKQQMg6BQh+lqXYZtoqTYmx0CKwepBsiIFXLagOAfIW5sLejgi84JfHCFeQ6fQb8FXuKpWt5Z103LViLeErpyhNrl8vNe0Bv65MOBgqybYTbo4eEveVZxMGmG+Jgob73RNrDQjd4G4tcSSd9y+eM7v/NV/ifF0IhU4PD7yMJ7IxxOqQi6uwiYdqp7HceL13cS8JL67OzJOCddtcf2OKAOb7obYDajvwUeKeJIPhuF2EyktaF4oy0zOC8fHe5Z5Ns6wEzILj+kBIZFUmTP0AQSHdxC9cdVVU8160/Yw6q9PxvclMb+PKvdf1mj0zxXorEYkqZWFj0timhaOx4mH44m0LIQ+4mOgC4Gu761ow7VA++wztqXw7i8+v+aHHS+Vj23/UoVS/63Z4D00VVilGaWEFMWp1S4UKo+51MKrapddY11csq402feUCgEW48JpTszziKrSd5EYvCXy1eCOVmQmAq5WTM7Lwpwzp2nhOC44lOjMgcnF2FJSIFS2jy9tU3pqxM3IZ0px5JLI2apP7dq4lQZrkUMtePteyK7eAVVawZpQN5WSERyu2AZftNWEQKLDicPR4bWzXXXTgTiywjJlyuFAefUazYUsBqWUABrMaE6uUC6SvlEdMQeLkoJDGwSbC2fMXMldYOnMgLvFdj6XDU4pnSNdOzPgD0f0NBOfAfEW2RR0Z4Z8lV2oQIpSs2siq/mmQaYilekE34OOfu/4kQ240MhYqEfV17J0aEmi9vtlCA6cwzraT56EclJ3PTP4mZQW5mlknszLW3JmKUqqE3tJVgCQUiKXwnFMnJbCvChTMjg7qCOKbTTigj28Bx9AXC3tp+JnhvNpNq/J+YiPumpeUApLsQkUpOKMOFK06xJ8O4maDFkn2Pf4Ov8lGOXvq2jU9T/1kCtOi56r1JTqAarpbixLJuXCsmTmJZNyNuMBZyOi6zRvR8BqENYfv+a85de+4u230PIoevHv6hmprpjvCrNcOA7nSoHmY7XPLE+hiSe4cZ3HzRXnDKFYcndhWQI5NzZF9e5QnG9yDFalnFJiXmbGcTRj20X8eh3PCePmGNlbz1dZSzEjo7kmCcsFzi1PrvdZG6ZcXIN3jHWC6PrvtnGskXODgYC1LkMBcag3fFv8AOIgFjQoGoIxUsQcQBVvxjs02CKfv1cV1cooQyjRo95Z0XYVY2nWpESP9BGKolJaoGRwXyfQVUezq/mCLlr47N254vUNR0MuIsc6jVhzefIe8/h7xo9qwIs4kvSWGMiRlM7ho3PKJhareJNErKXU0gyZFjS3SWAJinUiVW9PVUlpJOWFb778OfPsWJbMy9cHpnnh1cPEwySMU+b+cWZJhftDZpzKBQYOU4kUF3Bxixv2+GHAdwO+63GhQ0KdFJV7HiRSsmeeIKcFL4Hd1VU9t0TJmXl2PB4eSAscXSZKZtc7RDr6YKXMnXO1MrUZCfPoilzKf52HVYrpGzS+P58h7zKEIuvXrObggm6Vi3mKp3m2DTMX5iVTijIvmVyUeUnMcyKlRBGPBCEVmBbzTPtYw1ptbPw1vnq/4/415/Su1wdRohQLvzHKoBfzpbQk8pLQnCpFzrxobUk/kZrszOZV1Q3bubJ64JRkjwsjXur7ixYKmVQS8zyRUqbkxPFwBLk0uoaDbGVHv+lJJXM6HjmOE99+8y0//2f/jM3Q88mLj0CEjKKOmjw0eCeoqwClJVRVL8SyJOOl1E1jRouawyJ+xdpBSXmh5GRaRW9cy1XWoRSDAYuecxjNrqKVyguNb08pJleQMxI7uLpCQk/c3qDOg0wUSegwQDQTpsMeYkduWf+ckeMJV/MI9nAsPoJz6HYDvUlrOMM4SGr8bt9FfBetqnNa0KK2FVgIggyVtz3OsGT8doe/fmbHOnjwlV2llpCNzk64FtGjtc7Bjsloh0Zbbo/3Hz++By7e2BwlMJeAaqZkxUnBeaGILYCWYvTNq9GzhyOsxB5YPRkbpRh4dTw+onzLkuHxmFhSZpwzcxbGpDyO5gm+epg4jgnxAQlW6lu06p64gISI+Ih4b5Qh73DeV6NSPU/nKED2jUrkCF1npb+LI8kCyTNnYclQkrJQ8CIsqRZQqKs3t9SiJGOklAZFvHklL55qRo56nb736jdP+dfdpYsPP1d6wvcDGBXcUBPlmlNmWpZ6zZMZ8FRWT3xOmZJzLS+2TTPnQva6euxF7eEuvX5YveQ3nnzyox445oPKr9wBLqmBTrQa5nNuxgxRrgb0zMM+H0Dlj7fwmKZ90miE9prV2WgXUxql0jYN1ULOiZwz86yklAw2XGPs5qX3NA8zVfrg6XTicDigpZBu84WWTzWYpayb7KUX3bB7UEpxlOLsXKsOEerP31ta8rWsePlb4wIfbyqcTyKqioc37N0JlAqVihZcUdQ5CBHpOtxmi3rLixWd7f3LbPd2bwZcPWSnSMqGXS/prLvhPDlG8B6utjD0a+LQIhPbSlwMSN+Zyz3OaC4UZ1CYeIfEao36xb6nH5Btb8lLLxdzslWJto39kgraNpVmwS6iyx8wflQDnguckkB2nHJkLh2HwyOvXr9CpPDs2jH0wrOt46OdwwNB3Frm0IYIq6HUuhhs1BAIYZon5nRHysLDhBXeZLFyXh/qTSkWOmnCxYjvzIDnhpHFsHoysQ90fSRGo7elnJlmk4NclslU2VImVn0FHywZV9JiinaquNjjcCzzkXmxqOPhuLBEx7739MGvibHVSHxPwijnwlIV5Vr227xmOYfncn7uiY2Ty0W12jouXrFulJfXPNfim0voykvAi8EluRRSLpymhdM8M82J4zRTilajAinZaywqWdBSWFLicHR0MXA6nogh8Ox6z2YY2AwdIYZqUH/N9qO8Qdp5E355R+iq4ErGlUQpCdWElGSshZIoy8wyz5RlhpwMC03G4cYLLjhKycas0mKl6ZhXLmWx25cXNOdajKlPjqnG9BRNLGlmmZdq4AwuCTGcISpAQkBiJKXMMs2UJSEK3lm8Ms4zOMeru9c4b6X2XXB477i5uqLveyv8qdXOOS3mnTvIDttY82x5WgfiAlkUrbo/mmdIiyV23xxynlvnTcKevNz6V+OuldmTFvz9PX6cyd98hX73JbrZmfZbjGi9RrokqIV6bqmf7wpFMrIkwmHCLQlxvmoaZENVXLZEYxaL3Gv+IVQYxJdEWDKaC0wzUgqu82jnIYE7nI2wAJJPpGkyxltv9kSHgbLZIjhK1VNZ6wjaxNRKU5SLYOwHBs4/rgFX4ZQETY5j6ZjKwNf3d/zRn75EJPPZJx37rQc23Oy24M5UruY5tLAVTJvirB+SV0dLBabpxDgXUnYclkBSh4YewgZCwXW9eUrDiEjCdZG42dhnLFYJRog2eZ0Q+8iw6ei7SBcj0zSRp8VCyulImmeM12pUxhgtETdPRmHUokgYcNIxzQvTYgUXg8zM0XG77dh2xh31reb5ojz7zZFyZl6WGp3Yc+4i4bQ+55vSo1zCdKv90Es87uJ7zujnJQZaSDWxthoUNd5QUZMIXXLmOM08niZO08zhNJ2VHaEaczMU8zjXkDuDZoJ3DF0gxoBSuKrVuNvdZtWWOeOr5yN9c7zL4X6n8cYWl2hGSoKyGNSRF/t3zuR5IY0Tea6Gi1KzW4IEh2SH04JXuy4e24C9ZvscQFcDLlwKw5gzViqkkUjLxDzPzPPCsiRCDHRDb95z9d5qGoVclGWcyLNVgAZnXuI4zyRVvn31ijnNRrMLVimoWthut2vJWimZtBj1LjoT7Cq5oGleDayJbxVKEGOUpMmuQ05vXcun90BX6miL3tZpVp+nFKsQTQv+/g7/eES//pL89Z/C1TW6DWg/gFjOyXTnjFXiFnt/ZiGz4OaEexztZ2i5KnuPOodIgIWzTIdzhN3GNtuU8TkZ5XeajKmy61DXwZLhfjbope+RGNA8kdMRBFw3IN6jz16gXY96KHhWh7tG0l7PSIK7TCH8QCP+40Io4sB3oFA0kPFk9WQ8orBkz5wdqTiyOkvGyMVWpZfuYtvlhMtwLVUK1ZyFKVWdbTE9XgkdLvZ4LfiQ8BRCKMQi+BDxPti1LGphcdVOMHqVhZZOIsE7sjP2SKnHo3Uyggc1D7OFm+eQUmv4aficUliSFZIsWZmT0jkleGmWlQadXQ4L/8yrXatU173e3tT48ZeO9fo5b8Avtrbk7ecvfiomx7osM3AWXGoJy/Xcaqi9ijUFj1v3E4tuRGy7bToSWj2SptyGNkNfzlCAXN7+6tFJM4jniySXF+xdC+MtG96cBNbkmhn19rPUAjRM6gDwFXZ1RZFcPe9K/XROcKr2t1I1NOomJU0yQswjk/o3zckog/WaNT1pnFsx6iUXy8/MmcfjREmFw+PIsizc3T1wPI24JbGoNZ/Qkjkej1ak5CDGwDIv7Hc7hq5jO1RhKbWke2mYdYNIiqJZURKeCKleuLzYRqctYfj04l7mTtrcWufIOkNtXjpVfM64lGGeYZqQnHHNuC+LrROnIB4pguY2RxYrqnEF52vhkVPUK6UxPcDK9NUh82I0wQsDjhdIwfDz1Az4aPfLZcRlmAs6LxYQV49dUyJPJvGsBYNS5tnmApUCK9C07Q0fPxMvWgTbGCs/ZPxaAy4i/wbwPwC+VtX/Wn3uOfBvA78P/Az4O6r66gd+t2HJ/XNKmMmnwJwdi9uQ/B4hM2aPzMJp6ZhTQHyh+GQ4mZqXLSo4bVWUlmRBMdoUcEywKJxm4TgWUwfrB9Mm3l4Thz1uysw6EpbCxIibF8QHfNdZItR3NpFzIlfx+2WaSE7wm4FtF3ElM3uPlIzTTMkzUjzOq+l8LIt91jzXpE62cudkkYLznVELJ6OC3Z0Szjt2PcRYCXTnWf90qLLkwnFJVUkvVv67XxObpVZ4ap1L7sLOvY2+XRRKvXtSIMA0jdzf3+G9ZxhMEzv4WrBRMjnNpJRxTuiCdWbx0TbsdiopZZaUKbmweNOftqYJaVXxc96RVJlSYimlqleeueG6qklyNtb1vJ5crstT0rOtf3rmtihiDXcz4FuxR054LQQKUZVehU5hQAg4QrIqW0GRypsOqnhvsJ+bDeGWZUaWhODrfG2bjaBLIWcgJfro8C4yyIA4zzjN3B9OzEvi9cOBcZo5HE7cPTySlsTx8VThp5HDabSNvXq6Jn2Lef5pIgbP5598wtV+z08+/4y/8vu/S9dF9puBGD1xKSRfyMnmqWaDV0rKyNCjurOLtYxmONP41rW0TdvbPKxAkiXybR5qxcAF2wRDKvTLjD+M6OsH8sMDYV7wPphz8vBICRM5DKabgjMPXM1rpxT8xuN31Wnqiu2sZFQKToUwL3Yk44TpndaKWxHKY0C9R3I2T7tk9HQil4zf9/htj2YlTxi/34N4SKcjp1evUVVCZxBKDAPxakJCqLCNw6qrTWIjVRa+w6qrU7GINV9Es+8z3scD/98B/xvg37p47u8C/4Gq/n0R+bv13//6D/heG+Jxoa+7qlSZyAAuopXQXxCKWnXkSsYAGuUHODMvWja3/q4FlqLMCktWlmIqhZ2zRKQPHSH2hJLwIdviDAmf1ZTOnJXGG9tIbPK3pFo15lKTL9YdpHZdMRcU4x+Z155zYa2us4NevXkwmEWL3UhJ5mHNudCXSjBcEx7vvr2lJgxFlVBDbHuclQ4vvW+9+PcTO6ZvGvPvH4b3LzX51dUS/3Ms2BJYDmqjjvbdrMfT/LMipkKppZBFyLkmgETxXtboYfW+BS6rMp8aY1mhs3eey/cY7/O720SrhSer4Fr1zitW67QWeag9yGWl97UEpakIKuRK/VOFnM2rExOpah64eR4Gi5sHbswiH4yyOqdMziaNOo4GR71+eOS7716xVAOekiXo52QUxLm2gnNkRAwOSdOJ6D1S4LA/GFvl44/IuWeIwUSqgnWOytmMuOZEmq04LTmlpGjJ9jSvEcObU7PlWy6K9uu84LyQYZW6FVVcLkjK1vQkZ0BwPlCcQ7KJTyOZapnXa60p2SbbmVlUlOLrGlgnrF1frfdNSuXj12pvq9Rx9r3JHmWeLKc0i1nLAiQxNoxmVIx+nFOComTxuAwl1XucTVahnajWY9a60FZtlRX6/WHj1xpwVf2/icjvv/H03wb+Vv393wT+Q34DA971AzfXn1l49/WBU54I3UDXb/Fe+OSz51zvB55vE7Ff8MyUMlsYuYYlldspDqEHiRA6un6DZmV8/chhWlAJuBiJ3cD++mNC1+PjBh8HcpnxPuFzqvUVRhezVmpnqpwZBkdeEi+/fclj9AQxHncpBe89seuIXSQuHSklTqfTUyZFsc9xzjFE0/ZYUBYtaBampZBEuT/OQMFJx/XeNjnxybLmb3bsEDE4qDf9YfWeUj1wM3ylbnLVsFYsbt0Am0n9NbPn0snVamxLzsYUal2FLpJ3fRcI2ZEyCIlGtUQMohIRliWzLKni4dVAV6ihaW47J7WFmGfY9KiYZBGUtwx0g37aOa3VhrzfxpRT4uH+nrQkcl7W5OrpeGRZFtKyUEpmGkfuHx6I3jN11t6vVEPmnaOPEeccXecJwRtaONpRTfNi0FGtJ2C9V1SYKFv+IJmadyYjTjiNM3ePR8Zp4dX9icNp4nBMzMmTFVy3IfoCsSDZ5luslZ7BVWhuPHGqpdzjZEyOb775ju0wsBl6Ts+fsekNUtkMHSUn5vFIyZllOpGXif1+V+FDSNOJnBKPh+Nb5seJI/gAPtj5uVaRq+dduE4sUXNUZi9IH0kfv0Cu9/TpObFMZITJOaPQVoNuIlJG6WMxD9z1AbeJVdRrecKO8WqQiygktZggi7LUSdLkLEJRUjZm1DSOaCmEIRL6uMKy6hz+6go3DNBFhtDbBh+MpsjNDXOsydMq8W0bfc39PAE6TewuwwoRvu/4TTHwT1X1y/r7L4FPf5MPibHn5vktoHz9+DVyLPjYE/sNXYy8+OgLXjy75to90MlrXDmh8z1Fl0qAd6jzRvTHo/SgER93xP6WvGSmlHg8KbEf6IYtod+w3T+nq8kQFc+SBO9GnDM+OblKXjYMy9f+dSo4ceSUuXv1Ciew63s2fWfGO3aWsAwdMSykJTOO05OiFGkhpVjnF+/F2AzZsWAG3FF4ONnkG3pPFo84rSHfJXh9HhIirtu8keCq06OWsrdScKH1CDzT8p7Omrd989XYXwCaqrXZ7+p5nz1V76CPkewLizX1rGGkqbV1/YDznmVJzEuyDcpZQkqq7o2xLlrDYHvOOzu/yrNZ/fh2TGc4yI6p8A6I6PsDGXLKHB4fzVDnXKOMxDSOJku6LGjJzPPE4fGR4D1zsOYA8zyzzBMxBq52O4L39EskRm8MBNe403VDdR7nrd1Wm2NLXlbDnYRKrTTJ2dO88HAYGceZ+8cTh9PMNGfmXMPxEKqXqFacUk9TxMTUghNG/0CaJrRYBWyaF15+95LgHNtNT0mJ7dCvj5IT83SsSeYDaZmYppEuBJwTptORnBaOp5ELT8cus3N43yqszeC2Qqc1nGoeuhpFdnECfYQXzyxp7By9c0+0zYszLRW8s442dc1SFBcCLgYUZSrG9mr4t1NBixE/vViCPzlhCTY72mafVPEV5pzHiVIyXQxoCGQnJC+oE0Lf40MgDAN9tzeXwplgV94NLNFXy20TM66nLivW3SDMRki6LO96n/FbJzFVVeVX6JyKyB8AfwBwc3Pz9G9OTJAd6IYNm01mM81sd1d0MbDZ7thsdvSSiIyWFFhMGEdX2Ujr+iEuMGxuiHFP7K8Ydi+Yl8InqWfYn6ztmULXbejiQPQ9tc2hsQGqd70KuNPYwOZdo1T8e0G04LXUlmgtCXGekz54uq4j5UyMkVyKYd0169ZEtry3GxyileBrDvgQoWSWVDiNmeMYOI6J6IXBG6aob5D9VZW7u3t+8eUvK23NwldXJ2lVuzRPNlrvxSEGNjEY7/ViylzCyU++4409Q5XKromEEN6W0KRCIDUJZ07X2YAHbx1d1PtKB5bVG20wici5YKQ0PZgmCqVa2SotDjfMNQQTK+pisJ6RbYVUE/5E6vYdI6XEw/0DpzjW7zQIYZ5ncs4cx5FlNoEo562DTvTeaJVpIaeFGAJLzgTvTdg/1AbHNZRuTbKdC2Z0xeHquc9pqYJeQvbeNkdXwAVOo1Ex55SbvQJxa7K9LcOSyoWJqPuVnnuleu8RL+w3PX30bLabCn+ZlnkuxRhNWkhp4XR4MGXDaSSlhSKeODzgRJjHEyktHMbpDftt8897Z4m+2ppI6uRpYl+INjq4fb/UvpSdbUKK9XZIBWbmVRasYG9vpdvSNoOiSLIIbi6Wc2iNVHxdpA6pRTWmw5Lr5F6NarEIRkvtK4GgKtURg6V+dymKL3YPuqGHNToWCAFT52sO1xtuxIUTpm1BNRBF37UC3z1+UwP+lYh8rqpfisjnwNff90JV/UPgDwG++OKLJ0fmnKcbtjjnuL5Vstviu4Ginhg9H338Kbc3e7YlsitCmSPj/C1FE01hQNWjpcO7no8/+Ss8e/4F2/0Lbp79hJwLL37yHafjiW9fvuKbb7/Dh8huuMWHwONxJE0TeVFc8TgtBOeJ3lvllVY63GLl9fM0Mo0jXmATBQn+SchT6kzshwHvjbqUslHtjqeRJWW7wc42iM5FVE1fIsWO4D1pmclp4XB64CGNCMq2jwyd58VNZOgC+sZty7nwxz/7GT/75uWqWwzGBXZiXmxX6Xgvnt0wDD2fPH/GJy+eGXa/wuu6egh1VlVvnrMrB2u/UR+D0dC8J0SLQhq8Y9WHHg9sBkcXI2t3JOcM1xVH8kZps13GjFiqfQ2XnJim0QSaqgRCWmaW5okty8p5R4xtsd0OhBB4/uyG/X5vvSa9ewKjrKfyjkjmNI78yZ/9nMsieDN+xiKa5omcE68e7vnFd03itS1P2/JDCOw2A95bb81QmxW3zizOmdfpvDeKmwjiIoiw1KSuBI/0vRWdVPmGx+PI3WFkWTJTKmS1TS8OXZuB9Z4t5FoTIC0SyYsl6xT6vicGz0+/+ISbq739LS0VBjJmUZpGjpoZx5HvvvvWGh/nQirK5vWRb+5HRMS84pzIWM7qcvjg6LoOQqREi3Zt7VaL26pb63wTEQgRhxDiBgekJZFSZtaJA46s55jKclF1U6qefMmFMpvcQK64snfmtBh9z+5T9G7tOarFV1tl0WNJ1s3IaJMC6vBJccUKfbIldYiu4FH2oWe7262AiChIkFbBwxplXNhwaWvrIgD+Pj2jXzV+UwP+7wH/KvD3689/9zf6lMaU8OZFeB+IsWPYbOiiCRnF2BFKxOdoUmBy7k8J5o/lgl3kuKHbXtNvrxn2t5SsXI2ZGHqOp4UYHvE+EHzAOTMg2tw9qg8urXVS00g+X1hLIFmppNaCiiZCLxfqiN4bFGAZdL8mWc9QgFtvoKrW11CrOisTo0BOVrE4zkYps7zIpW/VhnIaR1KWNhOwCNMMTIyero90XWSoJcLz1VIjgvUtTxJLaAvfm4fUYJIWaehqiJ963+6J5K9SscX1nplX0jxj5861aeeWouYF5pxZ5oVUMuM01e7pM/M0UnKTCz1XQbYCnxgDS9rX+ybnE6q3ejXb73DFc84cT1OlZLo6R1oRirIs5omLS0iy+702H66CRKFK4nrniIs5BNaVyeaYbz0uvccFXz1wa2a7JNOIkeBx4k1XXBQkM06L5QxSPkvD8pQxdJnkP5929XbrPDSmUqDveoahZ5lgTkt1aC3aaZ10bPOcmZeFJRu8oDLjohnwZZ6tcMlHQnxqwFufzta+x+RyL3NKjUJ4AfdUiFAqj92EtUzawkRc60qVmpNcP8zMZ6PTNt65HYdy4fAjmDRD282bamAjSeT6GetkX6M3s+e5ev7O6okMqqx6SGvyWi7P6FfFfFx43z98vA+N8P8A/C3gIxH5OfC/xAz3vyMi/xrwJ8Df+Q2+GxGH8yYVeZoW7h8OOO/59PMv6GJgd3VNt+kIywm3dJQUURXjWlpcRFrg/jgTYiCxwW+e44ZnSHeD5ELsJjR7uvBIdB3OB4LvbLPwM9EHkst2YwU2m4HQRQ7jyOnx0SazE7x4ihamaUK7SOz3bIaem9vnfPTJpytWqqrETtdqQ/fwaHdZjG0QYkfsO0opzIsVruQW4rlAiD0injTN5AzT4nl5NzP0mS4IS1Jil+n35+toxIbComlNLEJTjnPMszCNVtk4hECaF55d7Sm5cceLOQulFUY5nPNnb1zW4M6+r02eGBm2O0QcwfsVGmowSJOqsW4l58UJUnHzs8FJaeFxfCTlzGEcGWereDweD2ZE5tmExioOXUohz/V8S6aUTOwiNzd7+r5jt92y3+2QoKi6dzJS3rVg5iXx9as75iXhfa0F0MrQ1cKSZopmQojE3opqvLdEWisK8S5xXPJFd3chOEfnQzMzNv+dQ3zd0Fyszolp0LsQ8HNAvCOrVWOO08TDw7GKVhUzMnKuti1Vz2RaFuY5VdaTeZXbLtAFb8e98RX2KczzaEVupwNd8CybgHcdt1d7rvdb7u7veX3/moIyi6kanrIw3httsKjBi9u+sAtPZYptk7ICp9DZOl/SQsqyNuZYk85S47O1WjFXbDyhZFQyIiYJ2CARQWt6R9FS2TZt0xCQWgDXmkULlSXWPP4qWdDq40qT9qVURVihsYNaw2iTWrF5HHC1CYMpJD6BGVUJzZmqRt1dTDpdf9Wz577+9/3H+7BQ/pXv+dO//IO+6V1DHOItdJyXxHEc2V9d8ez5CzM2244YHF47pJw53uY41qWQleMx42OiSIfr9rhuD2GHSCGEAxqU4Hu8i2YkXcD5gHdhLR6ppoq+64gCc07VMFQRH+fMc12S8Z1DpOsHdvs9Nze3LMvC8XgkZwM4FCyxc+GNihglrOt6cs6kbCJIbdIZXmx9C53vEJ+t8OuYmFPhamue/e6q0PHUIJVcyCVd9GOsBtwZfDALpBg4bo5QipVoF2OklDqRm3dinmST9j0zcS6CFRSjd3Ut8UgNgesCbiGh3We5gCwa2PDUgKacORwPTPPM/eHA4XRiniYOh4NtdlWmwCoDja6XFpPaNc2Qha6LQGEYequMzdkagcB6TPLG975pxJecef144DTNdLEnhI6me6JaSGWhaKbrlcFb9BFoGLgldQUISzonz0QIztNHK5YpxUJ8XHNErIu5iAMXEYlIgFAS4hy5qhJO08TxOPIu3RGl3n81R2JJdm3SYsezCVu883QhsOt9vS4WUSyzJSZVg2Hc0TMMHc+f36IosYsmLqaGGadcmMap3tra5T0ouzeXd42wnBd8NBioqKPUrlItCmvTphVQWaxWKUlNerepOUotqsPkliuvhay5RrrnaNC3fFatSHYi565IVRfdivIaha86KY2NDFbiTluf9rstgkopqFoqpYWy6/FT8XGtm8XlhbkIjHjjDz9w/MiVmFIVzuyUc7YFagLyajfJeTO23rLA3nuj8NTEXHC14axmvv36Gwg/4+bZxKe6xwHzeCRPI7kkGvOoCaf3fQC3QSmEh7PUpnG/haHvLJQvruZKDJsVETabDbv9nv1+z35/xTiOxjhRK0rJalSklDNLPgsKlQoNqKol2aqgfdFkGe3YIeIJXW9JEm+VZYgwzQkvhS6lNy6j8Oz2lnD1jJIzabbUS/BGb2swdgie22vzULebrXluVUJ35TmrQSMNQqCGwCv3GtZroWv3kNa4tS7oN0SNm8e0WtC2KdCy74aEZq1NCir3eUV2S6Hve4ssVCxszYVlWmqIPzLPI/3QsdvuGAaD3kTc6p02P2c9hO8ZisEESakdkJqRqjpyDgRP7Ac2m93ZYACzUhvemuoiQBdC7aAercwaIaeZXHIVnzM2lYs9OI9zPeI6o4O6iIojg6n9ibFWmkQyWtZ7q4ZHVZpa9SO9YbQiwvXVlpv9jk0XuN72eCeEYEbp2PmqlohpkJeFJd0gzubMZhjIRTnmhVyy6abUOUClp74F3coZQnE1CqHmI1S94dIV4msCVlZPUAl2TRvceVx0BBH6WhW6zicqZqx1U1S1xGmF7Ly4NZHuq558m6e6QlCWKLe5qOvG2ngCWiOMmoM1x7EacOftPvng7RykuSZnI34Zu7bgpMUYT64VbSN7R6j4K8aP2xNTHC5YGFoK1XPIpJTXCi4fOiI9nQy43NPFDp86vDOZz+yU3itLnvjjf/xf8E//+Gt+8nt/g8wVMQTc+BrSREoTPkhlNBWcK+x2Azu/xUd4fe/RObHMVrQQvLDfbas2+ELKlQJWVeFunt3y4vkzXnz0CR99/An39/c8PD4aflY9oHlZDApYkm1OxRr3uiXhnNB3Xb1rU4UqAkMJa/GEcwEvBScJlczhdGCeMv3z+Ykl8s7x05/8hM9//6+ZBz4vAJZAc75eawsv+xjwzrEZwlpKvEzTWXWuFJx4S8KKM2U276vmsuHv1mtQMQZQ7ZbeEnMXeLhzjX3RjHqd1HIursjtodbkIanhv510OHqu9oYVlXQ+tiCBkgunw0haEo+Pdzw83rPZDrx4/qKKXm1wcoZzjG3z1AN/F4RSVJkLjFlNx0LtPLpoXraXgIiy3V9zc/PC5m7OtezckZZCygtjpdXJLuJjh8QNYXtjG/HpSErz2tzXeU8Y9rjQ4f2A971hvsVyJLOuPQcIXcXj84xqoqkmgnVEV8AHy3toKaRgMM6nHz/j04+ec7Xd8NHtFd6JRRMl83D/mlcvA/M08vq7b0jLzGefPMd7oesD1zdX+Bh4fbpjWUayUnFm8/yB1Qg22yPUIiTvcF4I0Tjbiq/zOhC95ZGsQ72yzKWus7MKaQiW3A9aCP3GtmJ9egfl/OtqpC3ycev68C25/ua9V9bmGM2Aq0CpfXjPxWi6RqgeV6Efm+fqxGRsL7rpODWK7uX/7G/VeNd1s87JGq2sYnPvOX50OdmW1FNtugTlgjJWy6XX0NvRqH61MNeSQ84m1DyNjOMDx+OBaRopOeCXGUkzKS3knNa7bXkHh+8iMQZC9ITscWLGz7lKScuOEKxs/9IwxdjVJrEGrzTsF6he9jmSeJIbrOfZzqvd1CbsbtK05oH6HPCUleVQaiK1vOnuCMQYGYbBvP+KX3ddNGya8w7fVWpduGgZtiaKUibnZI17qwceaqhbMNZeLoXTNBqXGYf1sGI11s41r7+yP2rSrF07adewsi/OMEv1lLwjEtaw17cKTW9G0osnuGieblaS9yxpYF4mhr5n6Hr6vl8TyWiloFOz/r92eTT9GAvxTI6g5gTMNQRK9bwr/izONHpELpLiLYHr7V6GiI8REHyMFRSw/1k3KmtagKuPChWW+sjlIqnOO3ISFYcQsYIRVTuPGIo1NO4iQ2fia0Pf472QshjkMvVshr7hBRWqsuYRqK5JT8Eqie37K6wmrNHUW1dSnj4Qg5NKPc7VeyZTsGrGZZ5rPijWn101as5096tBPWMQzYjbXb3s2/QuA/7msES+W2NCBcpqiButr80Ki+MuVtT647zC5cmPs8sg68upEHC7XyvESrMH7z9+ZA9cEKnE9yqHOU8zaV5IldXhfaDMjmlW8qJoLbUXZjxK5+FqELoF7u8eOByPPL7+iLvX3xKDR+Y7yBP39/fc3b2mHwaGq57oA5tdz3a/B1e4ebiiGyPpVSFrMb3v2Bl3fLAFdDqeeNXfMWy2XF1dc3V9gzjHaRwtyZbNgziOE6fTyKlyY9cF5pxViC2JXASkGIRSzknHrnNVSGhDDB4niUACnWGsf3vHtWxiQxS9aLDLWrQQXUCcrAUYSmJZrLnEkg3WeXx45Hg8Vv6u0QKvbq7oh4HTZC3PjqeRn/3pn3H/cKgVkXafQrCf0VvzYe+dHb9z9J1tdCEEus7YMB999BHD0K//7rvA8+urcyd2tfnhW8uBqpfixK0J5Xm3IefCzWnD6XhFP3S8ePHcKmFjgGJe1TLnyhG36GNdSO8w5Ybndvjs2Gz2bAarCo7ButOM0yM51c4384TgaD1LU0os2RLW/XaPc46rZ8/Y7/fErmezvQIRuu2OXDJLjdBsA7dWfrk2uc7FKglLKSzzREpzzcmY2qCqcTJacs3YLWb0RAuejAvQDT2d91zvtuy3A9uhs7Z+3rHptzgnDN6z7ToOjw+cXr/ikAvLOPPq25fkomw7kz0mJU4P96bv0Vl3nEyTwHjH+kZrBHluZuEFxHnyUhjnE6UUxuVAKgvj8cjx8IgTx9Bt8D7w7PYZ++sbO6+6Kefm02plmjRuv14Y8GYYeTesd144nCGOamxd3TyfRhRnKHKdQBcL0dU3mHTC+fUGm9vmbw5nw1HqhlOdP4OSGlPp/ceP3xOzbs8NG26PhoW56p0vyYj1lrbw600KAn0QjL85MY/KeHpkPD2SQoD5EfLE8fTA4fRgesFkxCmxDwzbnmEZGLYDRZTw6HFTxbgIaM02FxX6vieEQIyRfhjohwFErMS6any33oTjNBndSzl7dFQcPGfrnVgpVWtxiTMmiitKjLE2NrZkCaWQpnPh0FtDqVobF5OuutjGgvD1pxnwlI3FkJuITlFO08zj4VgNsmHy3XbAxY7TNHP/cOD+4ZE/+dNf8N3L1xRxtfDibKxjMN5z8J4uRrx3bAbjZvddx2bTMwwDm+22Yt0OkUgIjo30dtzNh6qRFlATlrkWJxm1b4kGN3WdY6g0yf1+VwuHmhoituBF8b5uDJw9x7eWizRqK4TY0/WDFesEqcnUI6lGUSkli6SqI9gqbsU5Qi1w6jdbhu3eIrZhsA2idGtSMmuNPmurEqU1INZ1HbTGDq0/JSsoVJN4NUHnancXccbgCA6GaPCPyR4HYvC156dj6KJtttg6cih97Jh9ICfrAuSced+qAjmTphHRHh97EKVI0xt512aoFw+LOVqD6qyZNCVSTozTkTmNnA6PHB7urTXhMBNDR9lu8HplsF7FthP1nqpe5G5YDWybP5ferR3P28e4Yt4tENRqk9qa5Ol7n3zGZcu9s5O9RgPSIutq1C+g9fra87G1JKv7gV74j2/AV4NjIWJ0jk3fs+l7YrQJ9jgv3L+8h+WAnBKSlH2oCZGqVudLwbuCk4yWmXG0MmddjlBmxDv211cMw4YQraOOD5HQ9cR+YNjsKDiG4cSyWKXbkqkLym5JCIHNMNQQ3ZvYFayGMJfGX66PUlYOect2N03kUkxcyHAvXzWvHVDDxOJJtVDAk6F4Qu8hB0J4oxKzKN+9fMXifm7eWTZpz+At2RtcoA9d9YYjznlCV3FJKYgkM/Sho9/u8T7Q91aM1G12hH6A08SclHFK3N0fefX6kYyQWnbfG+Rh32mVln01ENf7PX3Xc3W1YzsMBDFKXR8infPE2orOO22UWFtPlYurqpTFlA0v5/baRMJD1wWcd9bzdF6Y66bq6ibhnGOzKZYXcY7gZS1pvxxOHF3syZRaL1AjgGyJw/bykgrTNAOVfVKUeV6MOoph57nAaVoQfyKEtIqpibdoxfSsahFJaeF8qzDWFaIJwTrhOLXrbIumGvAKcbkq4eCcMw53yXTesRusMUjfmeyxoCYHkBNOM957U8osBY+w3e6shViG168fiDGy2+3P0F0p5GVhPh6tW07tcJNz/N4lvhoyueD6F10dn3E8MS8j03hkng9oUU6P9wjCfHrg/tW3xK5jt73Gh0AcNpZXcEa3bPDgE2C7+c+qQKtROJfVt5c6ZIXW2nubU3XZK5S2ISA87Yr0hiGTS/O9TtQKaT2Nnm2vqJmEZjtqgvh9x49rwBuOZfg/gtKFwNV2w2YzMNRu3IfjzM9/+RLJJzY5EVXxG6GvzQmCNyPrvRnxnEeOh9eGJ6cDlMRms+Xm+hld1xP6HhcDoevp+g39JrPdXSO+4/A4kpI129VpWTFIBboY2e12bDdbQohVrVDWastmuFPOLI2zXM5YeGnMlCqxWooZmM12R9/32LbsoSieSE6WPAniIXuKRkiZLnouLVkphS9/+Q1/9s2Jpj2OnhXoogt0FRLph4EQPNe3e66fXRGjZ7/t8N6ZTkwc6LqO7e7K9F36iA8efTgyzYXDaeHbl4/88uvXVuJc10m9FTVqMNy07+3+fXT7nP12Y92Ubp8RXGATO3Zdb53nKz7ZNL5bwrexErQU0myG2aIcK2yK0Yx2CJbsyrlwOp3IuXA8nhjHyaCczs4hpUI/mChWjIGpRk2XwzlP329Qr7bRi4Na0KLr4hJSypyOo0EAYyvusYIu5ay7cThNTEshhsgwpSq9OxBCNJlpFypklld9GqniY16clW9qrLLA1tgCakAnDWk3D67rOqOOqunp9MFxu+vpomPbd3RecFpYphERKHOlE9bw34nj+uqGLvY8Pj7w9Tcv2Ww2hDhYdFC1zpd55jhOxgbbbPExkrb9U8zBjvKc26mRWssXNGrovBjXf5qPjKd7xuMdaVl4fGU/v+k39P3AdrPjo48+o+8Hbl98YjUi3UC325+9YmGFVtYJJUJLYQtnCGM9THc23ud2gRfslrX13fk1rZH0E2N8MY/eqqasdk713KF+/UM1+A1+fZdT8avGj0wjtP9Y5vgsyRoqjtp2y8YMkKy4ZKyFpWD4Ww21151PrOx5XiZ8CaZrgCLe0/WD0cu8Vb+lXBhnK3pYspKzJTS8D5QC3pvghFaDApyTlfVxTgKeH1DZGN50QHKlEdr9clQJFNOCcZfMDVdzAsVEm5wlpKT2WXTeDLx7IyGjwDRNnIotRq36IaIWbAYX6PxMCJ4i0JXINM+1BZyrWtGuRhWWsHPBen7i3Ao1WN8Jt8JI0nC/CwMevRmFLga2m4EYA9dXe3abLfvtlu0wWBKtJScrV7kldxtTJ1X4oFTa2ul4qga8kIup8zUDbnRBa3YwTQslF47HkWma8METSyYET9f3iG9t91jpnG/OSXEO5+w+aoWhmtynvaQuYH36OCvsCY16KGKRmqz39/xwzvIT4iBi+RHnTMYYzh6gOHC5NcJt87CF2gYjWORQk79qmvRdMGjrXDZuB6qVY61YpLCeY9E16VqKMs0L4nxVGhTmWkDVGlajVfe8uIuK2Hcv9HXJONPvN/E389r7vkdcQXRBdGKZHFMMtnGWxDSeEODx8Z55mQhdjwLDsNi98p7gw1mls0EZKwYu1U6fSQNPVs+lUT8716gYKqDrKpP1vly87PzzndDmhdetTz3wy0m0ev0Xn/k+48dNYkItK7Zwe9NHNkPHZrCklreGdSR1jGrSlofRqp+64Og7UwUO3kTxTaUsMy4nXt69JMSObReIwdFvdzz/+DPzmiWiIry8O/Lta0s2fvfy0XDs4hn6LSFkQliYU+IwPjBOs4kMrQwDV8v4tXrcmWlemJdEiJHNbme98cSRUubhcIAlEWp1pEiVuRCh783rbUlbK6o5mZnICdIJR2bYBIIIQx+eTsGivHx1x9f3L431IS3sN9U576V6xD1f/ORz9n4HpwNJEsEJh3urFHz+7BlXV9d4Z4VKzjuj96VEVsWHQD8MfPLZJ8S+Xw1tY5x4Z30W97stu+2Wjz96Thcjz652bKq63dXeVPqGGNElcTxZj9CUMsdptIKe04lxmmsp/VwNuMm5WphpRTIm01o1V7w3tkYt8lmWRMqZEANx6IldZM6Z/dWeLgb6oePxeFqTpuc5KdXgVqrckmgMBEushmrca9NftQSk822BO5CA96ZUt9ns6fvBStcHgzi6zqKhxnK5pGmcWVctp6HkWmkaguUVmsa4QbVl1Q4PtXkyeYG8EL2w7RzRGeuIkmpUY8U92bU5fIb9fIxEEaasvLw/wP2BL7/5jlwK3333ipQWy5mkYti3c5SUKVfbusG9A8BdN0UHtXBut4sM8YpSCh+Va7LOLNOBeXpgPB35av8LxsOBVy+/4+71K06nB+5ef4dznv3VczabPfurG55/9CldP3D77DnDMBBjpOu7Oielev4X9Dx50/S+PawZd6lwZHkCfCggVfxu9aYV3mGa374Mq4E/wzat8jSrkpW3IsJfN378Qp6GoVbaniXA7NGqshQh40k4UrJCjikbT5ZG3HcNg1JSSSY6pEofHF4dPnQMm131vK3MezzNjNPCOC2cTlakEEWMglYxs1LxazPuTxMb1F2zed4t6eRWL7XUgpKEd54kuTI0wtlrFS7U/Ew7WhWSqxtSKcb3lUIMQvTBJGgvhqKM08T9w1jxZ6N85TSuMI0PwqYk5pxIWpjTgpusJVhxEJzjen+14olNqkAbLgdV+8Sz221JKVc2RjUetWjoxbNn3FxfcX2157PPPqXvIje7DUMX6UKgjzbltORadLQwjRNzSjwcjywp8XB45DiOxvSohvx0OrFULe7cWDuhVQFaoZclEts9MW/ex0CXE3GJXE8jse9sRnln1YpveeA16VRlLYrW1KJw9q6dv/C2DXahaccj1esOOBcJoaPrhgrbmEa4rwVpUucCTU+n5gIsslOa5F6uxUExBIahq3mTmmsrmVbU47GfmgVNRq/tgtQilBqZNclf7FoJVnyUagOI5kTkooyzUW9bVetpHOuGUR9iTU5KnSe/ZqlDxeoRk6bofG9rTDyFRFo6liky9gOnh0eCczw8vCZn009fpgQIy1I49rahh9gzDBuDeir7qYEircJ51WRBVrutF/9tnvV5NZ3zctAglPVMqqfcqonfxrbfmYPUN7aB1hpL2zx7I5p7z/GjJzEF41wP/WBVdJ2F1wI2SVImhshuf8N0crx6+I6cE6M6JhGyZJCF7DK+z/QbIQ4dfW8VUj5a551hu+fm9gXOB8RHisKf/fxLHr8z3YtVl6RMiFp2fEmVXVKy6VsHYTs4hljQ+UAahSUqiy+UPNFFM5zTNDFOE6dx5Hg8WqJmGlmWRGwwSl203knFjYthlzmhmpF0QNIjnc74mPGibIK1xXqLEqXKMo6cHu/N0+56TDrW4buezbZnf701bHvbEQJM4yOPdyNBYOeN/rcJ1nw3Dj3TPNUKQvMQyzIxRIfsBn7ni0/4+PktrVeiqzxj7z2319fs97tqbHqcCNN0ZBrLOVwsem6MMM3M48ySM4+nEylnHk9HTtNUlfGs5D/ltMqhUg2qS65i4bXtXN28nAixVvlZ0qs3VkjX40JAqap/74BQnPP0Q2eScwpSKovH2/dq7u3+VFFSJyZ/cPakLaTva4u5YbOh63q8N0VI20wrDCZ2fe1tfg3v18bSFdpIyVgo3l8IYdV5o2LXk6KUlEzn+/jIPB7wAnMw6t4chc5b8rYPlWPvA05crRhuxSxV0tUHXOxZCozzgZyTbd5xQ1cUn8wgZedRcfh3UifOeLSs0UWFIC7gDBFnBVqhr8U/PZ9+XljGic1mw7PbW07HI99++x1pMWN+Gg/1ehs0WrSw2+25urqi3N4SQmS321bv3+bCE0+5wlOXrK4VLLlgt7xlrxoUU6+V5S4vePBrEvOMhT/5FAvjVliy3ukVOvnVYsdvjx8dQmn0maEf2O/2DP1gYuuKtahSJYaO/fUtOGGWnjEnThoZiQTJiJtQMq7P9Fuh2/SrAQ8h4kLHZnvFzfOPiV1HP+woCl/+8iUPD8fa0b1qnywnNM9mwJe5ZoUzzkHfCWTPJig6P5BOmSUoizN9jq4z4f6UZk7HA8fTyOHxUDPtk3VwxxaOF6P2NalRaQa8WPdylx6R5YHoC9suVwlbS9gmbzrFbSgwjycO93d0ISJb62C+2+zZbHpun1/zyWcfWYQTDXN9uHvg9TdfE0SYQ6QLnt6Bywuh7zicTFisGzb4YHrTQ+cZOs/V9vM6D23y+RDYDBtC8Oy2W4ZhYFkWTqcjaVm4e31gPJ1Y5plxNG9uPE3Vw7YcRMqZ4zSRinl6Y1UazE2Yq+ZEvLcoAKiiRcJQbDEFH4idebddZTE57wmVhx67Hh+sFH2urcfeSmJ6z7DZ4jVYE99iTaX76Gs+Zq6Ys8dJIPjI1dUNMXYVEjFYpCVADYqqvPjoVuMrTYWx4a+tWqs+Z3bCwoBltkI0CzBt82oFViuWTaHkhZJmxsMDj/fWcGT0tsbG6OiCUQfZbixpHey6tuQZiOmzA/iIiz26JE7TTM6Jm6stu01PKkpfG4SPqZDUIrg3Tc+ZzcEabZ/zVY1CpxW7VmI3IDLAUNhvr9CSefb8lsPDJ7x+9QoROByOvHp1z/F0tAToOBFjR0oL2+2ejz6yed731lEotH6k9eCaVEbziE2WoIlYXRr39u9zdaXdmoaZ154BcvGWdo7r+ev5O9+yfDXCXV9rRIwfBqD88+CBSxN+t2o158O6O7VdO4bApu9Z5h4Rq1gr0pGkRySTEFQyLkzEzkRv5ulAyImhu8L7QEqF4+GIn2emyULncTxSykLJibRUjek0o3lZNUxK1aYGY3UEyQRJRJeJkpAyk5cq/l9DVOsk7okVElL1lf9akyClmCxsUav6omqQkM1AlIQriUihc0pfhXSC1JLeNy+imkc7n45ojHhRNEfi82t2m4Hr/Y7nt6ZtMS+TNWkV1h6e5tlVyKrFcEaRwIslOKVVJppLY/euTkJjVvTVcJqsaMoLKS8saWFeZqZlZlksp1ByIWltyeE8eNOwiAi+FPMAQ6R1RAcuEqyuQifUZKAlwRpHf7PZ2nF0kRBipZpVymf1ZltydFlS7WF5MR8xL9ypXxOXJshkvSJD9b2ceARLnnkfTBrW+5WJskoR+HOCelVIkotiopVBcWnA29G04u5at1nVDgWgNANeQHOdw9ZQYllm5nm2exxNia9443KvxS/FClZo4TsXeK5iQmWdqWZeX19TSubF7RX73cCcMscpkXLBnSbmlGsz63d7jyv9rp50O+UmAYBQJYibNK9tglrzQ1r2LEtif3VtSdXHERGDc9IygRbG0wFBeXwIDEOk7weCN2ZOPwx0XW8QjjTj2U74wgtuHrqer8UZxK3HyfdU8r7rSUtS1F8vDPsbL16B2XZsP2D86JWY3gdCjPTDjs12NsqSekrxVS/BcbXbGU7rIXRbdEws/pbRX5MkUWRCSHS7gO+PPB4PvPzmT4ndlv3+E7p+y+PjgT/+45+hqCmulcJ3331LTo/M08jjw/2ZlaBKKjDldieNJx10IsqRvVNuwolttAKTKT2S8WR6tAjDEHBuS6jVe/OSavNfM4zLMqMlMPtI9krQxRD+MpHyA04TnZ4IMrP1nqve9De0iuEX1LqC1KGqnB7ueP3Vl4QQOG16drstf/2v/oTf+fxjvvjpZ/xLf/2vktLCz/70j7i/v2cIjqvNQHDCVbRqvU0X6GobvyjWFm3bmT57CIHYW4hrutuuKhlSpQVqa7DqYZ3GxOPhnnGaePVwz/F0ImclpQqDOA8u4Dtn2hIiXHkzjy05Wk8OYIUdVg+2YdVimjJd15vw0maoSU3DW60bkkmNznNiqph6SgvzyaKjJ3PSOaLrESIlmbhaDM48e+foe0/wFf8u9fXRHI8QTfcEZN3szgwUQbWxhy6MyBuPczCvq0RqLjO5LORlYTqdKt5dnR9MC6XkxHR6IKWZh/s77l6/JgaPbjqi9/TBgUSKCnOuOh2O2pXGtf5Wq0Hvh4Hb22eA8vlnHxOc47NPnvHses/hNPLdq3vGaebLr7/l8XhitxmeeKp1ga8bl7QiIwTx5rBUNh5Fmtxv83itYB11xOtnXF9ds9teQ3EcDo+Mp8yh6uCcTg8WzeSRLkYeXn/Ft1//Gf0w8PHHnzIMA89efMTN7TNi7NjtdvWe2HwyR8rX8y7rpm1e+VOt+pVmeF55lydb//0O43xxYaQVDTXT0iKwC9bS2xfy+8ePbsCl3mR36YFrI7kKgqu4bkfXdXbxxVOIJDrAE0RwknChx7mEHA8s8wmwEFTEMc8LDw8PlX96qomxA6Us5DyzLKcarttiSwVLkirV6y2IqKkfuoyXhGcxLLU4inTm5XCZkK1l5bmsAjstwZbFeOMAohlIaF6QNIEmvE9Ep0Rn/QzB2kq94TCuI6eFZTpZWzanpGgJw/1uw/V+x+3NFfM80YVYKwtrcYezBrwxWMceX7VMvDMdFKOh1erTzpgpMUbEu1VF0KiFVsZtPG1AjD2RcyIVw1hLsbDbKHShMhPaYvLrJlBSvsC7qV5+82RZNwlXvbauzo0WCTT1OxFqcg7r6ZjLqomT0rLqtz+Zk7RWdP5M42zUykoLjcFXOVJTzFyTji1J16KUBhfI5SKu+Ofbax1oOGwr/MrVYzad71wSKc1QO8MIttl6wbripLQyelJKOJRSYhVmaoU00uqjao70XPl5NqHgXSB2Hd4JfWfR5LPbW57f7omPJ9OljxN3D4+klGux0Rvru967p5WJ7QpcCD9BZWXU96wYedNTj+SU2e+vEHF0tZDOcgMLZJgnKGmmlIWUJvppoO8i87ylqxGalkzfd3ivVt3ccBW9KMWkeeJvG+Qzmn/xzDmIePt+vmHfLxCy77n93+Pd/4rxo6sRxi7SdR3DxsqrQwio92TnyOJJVtNMQIldbyXs08SyZO7uH9kNHTfPrgheyUtBcyAOmdg94nzHOI64+zseHx5wzjQ0TFfCxPlzXqygYDQYJHQDzluJ9rSYEemCwztPv91zFQaCU757/cjL1w+8fBi5P84Mu2ueffzTirFaD71pHHn96iXzvPD4eKzd12sW3gdKtsKObScE07sniOHjQxfooxJb23tM66SoWgXcG3c650xaTBP7+vqa22c3/OSnP+Wv/Ff+Ci9ePGe32dHHns8//YKr/TV5mUnzjHfCLnQE59jvtmyGwZoJ9H3Fg3fE2Fu1ay0gcrU3WF5mY7NUb7pJ4yoQuoGb5x+xXRLd5oZptm4y07zU+26fb96NeWb+ItnU3EGtP8taWJFXXPxyhcwpo0vicDpWBMiMXkl55Y+nxZhGzZ7meVw30TaKKkvOJBKOmgj3tWclME0L82T4/bIsoLJ6l/ura3b7Pc7XaEVq04ILRwWagcDqC2rRTtaMaXrneuyZkquxztZIuSwG9YESVwMXapclh5cNOcdVTTN4z37TE7xjtx0Y+m41qkUcSZ1pWZdaA3FxPZ13bAaTne2Cw3thnkbu7jLTtIAWghNur64Y+gG9YLc8WeP2detm29T3nLA2YNOKBTfjjUjreWEbVzZ++n5/jfeR29tn3N/d83B/z/HxoTYxEYpk5jmR08Q0HUhpIoSOu7uXfPXVNcOw4fbZc2Ls2O+v6PuN6fp3plzpKpPK1SRjq/FQKlJVz2Xlm9fjfZqkZI2knvzl0mKvl/q8aQlPhbjed7xPR57fAf4trPO8An+oqv9rEXkO/NvA7wM/A/6Oqr76Ad+Nc1I9P9OJ6De2uJtAUhKP4FAfCSKErqfvemsYnDIP85HgHHHY0UfHMi/kRYj9TOh2iHimaUb10fDXeakejXlezUlKaWGuAvU+DoaxK8zJaEsNz+yGnuu9J80jr19+zTSO/OKbV3zz6p5nLz4hbm7o+y0+RMR5pmni7vWdJfTGuWb6bXJ47yglEYKjcz3EaDi389a9ZeMYOsE7BVfQpl2numLQbVjnFTPgToT91Z7b21s+//wzfvd3f5ftdsN2syXnzCcff8rN9S2NR+xFGHzEi9D3XeUZt05JDu9jZViwNqJVmrGxYg8frF+gkzNC72PP1c1zSils9tZBZppmjqcJcY5hs1kLRlrnHqkQQ+sYfpYhODMxlmQbbmMKqKr1yUwLKVlTjVQ97JxMPyXPBoG1IpQQPLHzaN3I37yWKWcWnEnv+rh6/6gyzSMlmxEfp7EKWUmFlITQRWJUYt+d+ceuLUvb+FqBTi5muIsW5mRa8iklUl7Qksh5NMtRMqLFJBKScbh98FX72tN1AVTxlXYqaM2/eLa9VdluehP4KsVU/wASDtEqBZEvORC6ipCZATfGy7LM5OVkLQyL5VCur/bsinIaTxyPxycRzROVPZqB0qcGXM4G3L5aViPeEqC2Vh3b3Z4QItdX11xfX5OWeeW1Gw3PscyFufbNfDzcI+J4+epb+n7DZrPlo48+pu8HXrz4mKurK4Zhy9XVDd4HhmHAEWllroqwYBpMq9EV1o2YuhpWOP0drJNzlx59YsTfQpvOM+Qdf/3+8T4eeAL+56r6/xSRK+A/FpH/K/A/Af4DVf37IvJ3gb8L/Ovv/c0Aa4LGKFKhcoSLmnq6Zddbhtew1q7vGIaedJqrR6WkjHGjJeL8gI87hs2VFXZkZRonpnliHCecc8alDU3KtFiJedXmHoaB2G+ZdYJTWjmapSgqDhcirhR86PGdMmz37LPQDxtSKohM5NNEUTgejitFa6m4rnNWPWgJsZmgjl6ETYAohY1Xgih9KJjDW5DK7zVpf0hP93xEpKrd7bm6ecYnn37Oixcv6IYtBceSlOPJaHtLglxMo7lJeyYJJvFZHCVVL6OShJ1LtAa01UWi1O6EpzFxGhPeQ2bGuaZsaA9j9ijznGs/0cwy2xfkbPdzXhJzap6sLYwYDMrRC6PbusIvy8w4TebxVRVA4wmb0NM4jWuyr5QqdNTEtNusqx6b5repYkLDlk3neymQkjJPNleW5USp3ZpytuYe1hfUsG6DkKoscpWYdS1RqKkapHL2tqsBT3lZjZH1+NJq4IweaRocVUcFg00cTZdDa2RUe6CGSOl6g8IqZGaNU1rbu5aoa8UochHX1zRd3ThzKcxzq+xdQK2YLZe2IZ2x7Xet7ydwSetN2bjVUmgty6xzjb51P+xjtOYTqpdclS3dkyKo8y1ulbbU9W0J3plJhIf7O8Z4QlDG04Fh2HA6PtYE+L7mMyIhWLVnKXYG1p/U11qNcJGUbDzwsykXLP1s1+yc11gtvcIT+opcHv2bv//q8T4t1b4Evqy/P4jIPwJ+Avxt4G/Vl/2bwH/IDzTgqoqWBTQQO89m15OzsiQ7gUQtNqhxWAiBm+trvBNepVccDyPTkhmXgoqnC3t82LDZd9xqYJkX7l89MI8nHg8HHh8PbLcbfud3vmAYBqsqy4ko0RoAeMf2+gXdZkd+ec+397bDp1ypQBLw3RZcpN8v+GXmo+6G/XPTWhjHmeNx5O7+gdNpYpoXow+mzDiaOuEwOLquJ7rMwIFI4TpseN4PqzSuF604e13gJZ2nhwiJfN71MYOzv77lo89+yu/+3u/x3/ib/x2ePXvG1bNPmItnPiUeDg+0EuiCaWp7HxEgIYgKx7mlbfIaDjc4SFYpNUVJKNYfdJomRBZ8SCjC4/HIqcJRSzIN77JkNFvz5uCDfUI2w3X/8Mj9w4OxECqEstls6IbeNFCSQQrjaEmraTJuvTbWz3mbp0UHwCprG5xj8NG81dYBKStLKWh+BwYuRu9UdSzTXLuiz0zjaZVzVS1rxV8Ike1mY823YyCXjMuOnO2eqRaKc6SS11L0VpDU7q2oVsOpK3bvnK7dlIIPNbSHWCOBvJi8rPemZe1ELIcA9fewtnJzYkwiVys4oRaLabHCJzvx6i03DF7J2SKYebZzT/NomLM4nBjzZr+/JnbNa306bNlaktW6fVrUJGpOG2K02kvn4BJXrhPwfF+qymTXWfFO7CJrB5s3E3/a5gV23MvMdHzk8f4VIsIvq5Z/11vCP8TI1dUtfTew2e7Z7W6MDhoGnHiGYUffb4ldh+s7k4Jo1E9t1IJGU7RzKei6EdJOb92v7L6tgm11PjcNlvcdPwgDF5HfB/6bwP8D+LQad4BfYhDLu97zB8AfANzc3LzxV107wThRvJcaPV0kVOoOJYA4WXU4Wuf4ht/lQlUNNL1wY7M4RA51MmbmeTb9BbEimlz7FzpxhOhX6lR/oYRoHni5KMhya9LVqdIRIVAhgoWlcr6Px5NJttbknT2o+LfHOyU4JUqhc/bovawsEAOSDFdsRsnm+Nt8W0To+oHt/ord1RVX17fsrq5xPrJkUycsSxPiryGrU0rLs0H1flqxDeskapK3YjEwyNmAL1NinjNIofbB4HCYOJzGKrdqSThdzFs2PXCb0EvFow+HI4+PBxRZKXi5KH2uOiiV9nc6jaRlYZxGTodj3fyrZyjwVLpU0BiIxTRwCh4c+HrClwUb73L4mid4TnYuzPNU4RZ7hAqTudof1eakb7PaoAyRmmy0z2r63qm2JqNVUXKhqaQGLTpa0c65Ka8XqbRURbMxnC69zwZZOO9NBwhWBsxlElWasVShtQxrx611DuRV3jlVlcVsVbFpRsSbcJwP5FLWHrDvXP8Xa/2cIm2GWtcXnDVH3p7b7Q+t+Gc9FW1OYI3UWgRUdE2WmlHNOGudQVlq6fqy1AhwIpeZGCKq0HeD1QdUobEQ5yo4RrUFma7vKCFYFNv4/OtFPENRzcl64mydD/3J8xeX6QeN9zbgIrIH/k/A/0xV798grKuIvPOrVfUPgT8E+OKLL568puTEfHrEaYIC0duOlKQaLK3cUjVcz4uwGQYohbvYgThygcO4sGQ4+VKpSQ7n94Q+c/sikK9Nv+Hlq7uqDmgGf14sIdP3Hdv9jq7ruL15xu7qmiUJr+5HK1G/e8U4zxwGx2Fr1ZZIwAdHFwIez+k0cvc4Mc2Jx9PM43Gs2X0xu+giLsKwMZijl5lbB71beLbpedZHgofB1eLcwqrvrcWvm5VeGNc2nHN88dOfsr39CR998gnXz17QbQbuH2ceT6+tS08yA57rQhFviccVc7twglaDzrk0/QzQ1XAXw91bWbuqLZ7HwyOn8UjTsV4xKFW62DF0A43KmUvhcDhxeJypbiciwjgWfJjM68vWeX6ZZ2NZLIs1ZOa8oL2XtYTaV7aPV48UwzIzZcXvvZqanHUL4gKjtJFLYVxOzNnz+PBordGqwfHecXV1bbzzYWCz3dXQ22iWl0ZpmsY2/+18tTDnxSKpdtyh6rOLRQyrHK9vBpzV2K5mul7P5riIVKGzhjVL23DrO5QLZlBLpvr6WRZpGRxkkNM0jlZoNZ6YxpGUTUzKnhuZlxlfGUMxdrhuS5FgVa1vr/4Kf2jtY8kKlTRoheqtqrxp0JqxtoR9m0+tefjDwz0PDw88PNyTUmKarObC3qk0bfqmhY/39ZpeOECq1iwj2/x7/fBQnbOIdy2qMOhpu71i2OzYbrd88snHdF3H1dWWYWNRWN8b+ynE3uA07wku8KRyQ+umUu2CHazQ8Har7/1h470MuIhEzHj/71X1/1yf/kpEPlfVL0Xkc+DrH/jdlnibRpIoosE6rbiMI9N8cEGaABtOoO8iJferXklWZZpNv1vEms7GYJWdziv7EBHNvHz5ykTyS/PWTcLRxKc6YuzpazXo1f6Kx+PMfrtFgNd18k5jxzTOq8FwzhOddbufFmVJhWnOpq0yLZiwUd2EXEAQYr9hs90zuJkrRnpnPO9d9HgxjxyMh55rdx1XjaMl7d6GCZ04Xnz0jNtPrUvQ5sqKlw6HiXmeKiZt3rAV0DR/yOCRVeODJrp59ibWRNLl0pKzYYHWfMC8t8PhkXFqcMO82nsBuriwGer5Vc96PM1Mp8rFbkqPYzaN6ZJJ2RKWZV4qdp0tmdcw31aRWEPaVRhfpYoOWZWiNuy3hnM+nItpnkTsdbOYknA6HTkdj7UwqFsTaVdXV/TDhu12Z9WqnUWEljhdqgTAXAuG7DyTFmYSItb+LlReexdr16LaLSh4Z1WNImeWhuaaCyiVoVJzMqq2OUtB3bkLu0Ulrt6/utZo3l+7Xi0KKVVCwhKoTXPmeDpyOp1qFfG4aqHM81xzRj1dl7i6mU0d8I08A7Q9v820N73PS2/86Xu03pjVuRCP1ER9TtmUN08njqejRbppISVrJLJ2n/eWoPXe44LDi1oE35hAGLMpL5m5yhTPKVk0nwsp2bVrWP92e82w2XF1tedwes0wDHz88S3XVzuzG3vT0d/IHokdjq4ylwSo1a0tyalqET3rQlqvz+V1ep/xPiwUAf63wD9S1f/VxZ/+PeBfBf5+/fnv/oDvBYz6djweTUu52+KDIws4KTXHUUMh1xqV2s7qvYk+tTZdDVYZNj1dDCzLyOn0aFVr+QQ5MS4ZFzvwgdO8oG4yLLjb0G/2bPe3lsDsBsvsx47ddgPAZthSKhTy8PBYbYXNsEU9i3qOp5HXjyfmeWZMyqLOusForrt6h/OBRR2nOYNXlj7gPcw+MHuPo0YfWpipWuJ1Z1aURY3TvLQJUYeituByZpqtHN05zzRl0mLMFW10KGlLp1YG0gpMzt1AVkhRmlmvC0qw111Q49alWEBVcBVft2rZS/NocEauXlcDI6ghv8Fl9nLrd1mMoVFx6pKyhQMtlq3qk1I3YlBcjVocUKo6ojrBR1+Tfb5CZrbRo9AIiet8d0ZxVO/Y7YsV53jTD/feyvGlNvJIOVc8t8I2FdduFaSXeubeCUMwZkrs4tlYV12S5jGbk5FB7fONgbPUHpVNsyczzyM5J4a+Z7vdmsSAmId5OTlWo6kWfdkZG+RwnE4slcEzzRM55SdGe5wmckqrLs0q6CYm7qbAq1evOI0jQ3RsOv+G8dE6O8/bvzkMrjoP1ZS3Rh5tIwaDd9q8wSqN7+/vOR2PjMcDaZ6gZKL3oGbY85JW26Aq5GDvd1LMmDvFhcaHP8OyTm2DCF5wzs7PNs3asV6tdqTUeo3vvjPSQ1oeudv0lQZtPQJ2+2u6WPsM9FvEeWK04rLgA6E6nlZRLtVpaTDgeU2+73gfD/y/C/yPgf+viPy/63P/C8xw/zsi8q8BfwL8nff+1jqWeebVy1f0fcfNc8+m61BXSFK73mUz5C6Y7oRJx5rKX9cZG2UYBqMg9j0ff/wRV1d7vvrll3z11Vcs08Qy3pPTzDjOxM0evOfuMHKYMlf7K7b7G3Y3tzz/5CcMfc+mN+9ou9ny/PYZQz9wfDwQvCdPD3z19beVRWAe0MNUOM6FORWOUw1Dl0IuYeU9mwRmRww9Y3a8Oi7MXWG/6SAGTiHQBW/GoPKBT0VZinGkcYECJIwu5TXSnEkwr/H161d8czfbRPmTn9VQOoCe9afNWAZaxZdceAjQKhzdGt63xFErYTemhaxZePHB+hRWw42Cp2PwHqElqkwuoFQRo1QXdW6RhPf4rjODXUvbc05rkjNno35KbolV20lMxMlMhEEAFqE4rV6ut0KkLgaidEhVt7TWXJZnQKHIE3uH955N3BGJbHZ7mvCSrzonXdfhgt2PaZ4BZaKtxdolqob7hs+aQY+xY7PbrsVAjYm0lpM3PnhVf7TodKLkwng6Wq3CPHE8HSobxzRlbm9v+Kh59Q0eOEPLK4TcOr20vELKibuHO8bptIquGUvJIKt5NukDq3a03pWueu+WUyowTTw+PqKqfPbxC373i0+ecqRRLFFZKxyLwYEUiwKlwielzeWayG7Y9iWWPB5HvvryFxweH7l/9R3T8QFNM5su4KVw93BgnhfTOup7q9r2heJtY80ur4qPJo0g1ZBaj1WAKmjJsiw4qXIayRLOh8NELoXHh8Dh8TuLQnorcHLeE4NJN9zefsQwbNjtrtjvr+lix9X1LbHr2W+v2W12+BCJ3cZyJkHOhHgJZ8P+nuN9WCj/d75/S/iX3/ub3jEaj1gR9qrn3Uc4JwNUOVdK1R1ddTWgQBXm8Ww2G/b7PY+7HZvNFidCWo7WPip0dIMxIXzsrfS5H+g3W7phS+w2hNhZgwBn3O8YO2LKxK4ndlatmVQohbXhwDhljlMhFWXJWrP6phuCF+tn6gMSOiR0qAQyjoSQiCzAqJ6uuHq+9XO1MBcLH7WxBoqnFOjx9E/SQ9jiPhyfeM0iHUKbtL4muKoBd+FJiEfFT5vQkvP2M4YzD7otAF+NuvhoDwSHQUSUvOYsRMzby05RMXpdK1fONcGZU6JUfZSczIAbO8gKcVIxHF1KNtyr4TdiTWDNlVLOTJSylomjjqckXmgtrER0xcXfHMbbPncyXw14pfS1S1+01JBfa09Gdw4jLm9O+1y7yjQo+GxpLwSWKhPB2B/GZR9Ha04xz1PtOJRJaa6JeeP3+5rUbyyGdkxWbWlQS9scl8q+mpaZabH2c/NSi50qtLWkZF2lcqrFRU10quqpXNA7jVlz9daVbGtUq0Fek8Z6rkpenZB6a5/Ag9qgjqZdYwllYK3c3W43+DkwTqkmHmu+qD6kFItgW15ALMpqkZWKkN3ZHrVck0iqOQlf71hGskXepc7xiURa7Dvnai82m7Gqc/a2UTpHTslkHfJiRWjiCCXXTayx4xtU+cPGj1qJOc6JL18e6OLC9gXsQ28MNma7kUbANk6ymqc5zcYDPp2OnE6P9H1kM0Surrb8/u/9hJ988QU//ckn/O7vfsHh8ZF//E/+Ma9fv1qz0957NpstwQeur2/Y7/emJ3z7zErfSTgKcePZXnmkm7g6JqTfMw8bpn5gnEbuv/2WaZ65H+EwQ8GTxSgkYeit52P1ci0s31QD6FHnmKPjO/FEhdejEFMN7QEoTLMtsqJCUtOJzhZZ81l/zRdchMel8O03X/FP/+gXVlK9LKhSu5Sb5nQIXfXCW6cja5f8RNbSNyN19sBjbexgFEBfJ3/rRzjgQo+IJ7jeNooVitGzdxkwVYOLDTgn80yt9Lt63lWzxLjRLfS2e++qsbw04Bo8OCF6WXWvQ7BEZuehi1I11BXnlCAZj9rxlbrZvDEU04rPwrpxOXGrjjVyxpMdlnxridNVpxtBgnVQTmRyAs2ZZZxxTkhrgU+tauUicq6Qy3Q68fKbb5inifu7O46HQ+1k34pXbIML3nN9taf0he0wrOyq1gIuV9hjniZSlU0exxO5ZE7TiaVKCyxPKI62zuZpWg1ag4mcc1aYdrQah8OjFcndXm1Xh6pdyVb+70o9Hoq18mzOSoXTtKI+WhR7QQPmoKRETgvTNLPMiZwK+90VXgTVT/jd3/k9lnnhF199w8PhwP39Pa/uXqPZ9MydtE28sN1s2F9bWf2nn33G7e2NJc69J5fCw+MD0zTz8uV3jKN1sHqxv1o1/H0IBmNN05rwXdKCiLHcRJRh2HB9dcP1zQ3Pbp+feeOukjamEz5ZY2rnPE46RDwpz2vNyA9p6vCjGvCUC8cxETtYsoCLIBfiQto8cKp3YgvfmqEuNVGUCNHT9x3Pnt3w2WcfcX294/r6ivuHB17e35PX1SGrYl2r6NrV6q5us7WQtlQ1wAhxULIEhu2+lh1nKImkjrk4TosyJhhzhTq8Zaxj3BCqdGnsNmtmG3F1lxWydxykxyM8JkWSZepbv+K56h5nhbk0DNN266vScamKpqocDo989+3XpGVhGY1mF3xXhZ06w27lwttWM+ItgWnhq1gi0AkucDbgVV3RJmLVv/YeFza4uMG5QPSbtXO4o8ED9aOjwAWjgmrAW1Xg+nvjTjfUtJ6g9T+qi7pCQeIEcu1qE6sgljfDadQ7akPnWvXndN1UpNIzv0/zefWhpcnDmjqePP0rK4olrMwSqL/7qsOTq4+uptfd6l/AjPeaT/At8rRjmpeZx8dHk2N4/ZrD42P1RBt33QzdOI4sy1I7EjUPslEAa0ejnBlP1YtfZg7HuhkkoweaZ2x4/VLfNy8LU6rRz3qedqzNwKdl4XA4GLwzTW9dy/a5LScgUqotlQrt2D2uKY36/rIG4dDgsbRy0lVLVRo07feu66yBNY7+/oFUCi/v7mpxVMFJ3exKIXYd4q0X7s3tMz7+xCAfF4N1bwqR4+nE4XC0qMt7dtvdqmjY9z3TNPFwf2/RwLygZVkjjVJM+noYBrabLdvttl6vOl+KiaipKi55nM/4IpagLWntn/tD3PAf14AX4X7xBDyHHBg1Gs7rOlRzTaSZwBRqNYmIiQ1Zc4INN9d7Pv34Bbe3N+w2Hd4pfee52m9xTvjip79Dv7/m7u6BV6/vKEVIY8K5QuLEsYZA3h8NTNAFp5nTnHg4Wd/IX746cjieSONMGgvT4sjxCrTHR6HLNZnnDJ7wXQ8hgo9o7EEcRcJqwItaO6usESnWI9PVxGioaNEswQSvsO48phdcvRJ5QzhIIEbPZogsrlCWxpE2DQ3XeWJf8e1a0FxSoaSlMkgqRrmu1Manpnqg5x6fbTOy7jxbXNwgEvBhA+JxNPW5s0fq19Zn5yRbzuZ1l1yZJbRSiAp/SPP4zKP17swusI5D1tTaB892iGyHDu9g6FyVJAAvim+VrBdJ2uA9XfQUVzsfXc7JlHg43rNoYH+1ZxgGe2/L7NZ7sMYt1fAoyrzUxsclW3MFPdMfV3iKFolA13dstqYz0/UG3+mazWX1/mMIdF3t+i5dPVK7R7GLq2f+8PDAKQSW2RKTJZeVDTNNs5Xpp2SSvqVUJyGvBt+So6aBv8wz87LY/avOwmnJzMA0joynk3Vlqtj699mcSwbTRdqmbpAXGyDUJkdn6Va7Blbputlu+fjTz1bJ3JIN044hsKSEH3Y8Ho5cP3vO7vrGdNzb9a4Mru1mw8cff8QwDHzy6U94/uIF4g0SzCUT+y3TOBG6DaHb4Jxjv9uZWmoVxEop2bnnbBtso4sidLHj089/h+urK3b7K3b7a1Y2ABAk4iUYRBtCdZieluW/G9T7/vGjGvC5CK8mj5TIfeo4am8n4awZr8mUJRM7UreyJcQ5hqHn+mrHi+fP+L3f+Zzb2xuur0xoyg+RYejZ7nZMruPj08g/+ac/45v70cq5J+vy8jAqMUy0RrqqSqgFNFMqHBab5F9/d2/tpPIIKVOKZ+lvIUKUgCPY5AM7PrGmyfiAhq4m3QxvzmoUsCKOhGXLA946tjshOkCVSRYyiSIm6qWGISEUkpw9sPqN9F1kvx+Yjpl0UjLGiEAV7wL9xnBwXz3w+bQwpwlVW7Qll7Ws27ymyuF+00MVw+URM+A+7kAC+Kohg7Oy/Ooli0hVQLQsfBcjcMZk19UvWELH0erEa4Nrt1YWeufoYiQ46zKzj54uBq62G673G7yDLtoh5jRRcsJJwZFw2iiGRjPdDJG0CNMbCaO0LLx+9R1jNj5235s4WUNQ2iU5A0914VWYYppOFmaP46pPk1PjW9s9K8WS4NfXe57prXGq6yZH9aJFqTr5vupyZ1o3n3VjExiG3jzsmngUWLu9G5Ztcz2XZmzP3uJcjXqunY9MqbPy75N54uZUWHJ0nCZKxaEt6Vkx8wu64pNxkQ84F0+djbggNAaGPdGgmvNHuOBqBHjNbruvn1v/VnM7uWQ++vyOcR759rtv+eKrr9BSVk51yzt0sWO/39N1kY8+Mi2U5oGrFp6dRpaUePbRZ7z4+AsE0xN33hlZorMWcFp7BBwPB6Z5qslhg2dvrm/p+96qRfuNrYEmhYBUvZ9SGS1K9pbL0cqM0XeX03zv+FENeFZhyuZVH6bMw3HGlUQoBoV5zh3HG5Mi+EAImb7vTaRpO7DZ9Ay1+0YLcUuBXDLjPHM8zRxH696RkiWHtCjTUvA+rQYcVbwaaW8uBo/MKTHOmSkVY2DVysRMoDhQiRSJXO6bKuazqJj/J1jzW8SwbG03TGX9WVSQ0syyYDQqV8EDOy8LnR1vk98azNC85haNVhW/i8RdCAZzSIlIyfhkmh/ZVTpeqRO0NROoScd6ZnZsYiiwSMS5BfHnBs2JlpzSVfrWpAhM/2PlH1evv6Eq4gzWQSuHm3OvVCvKsA1g6CKboacLJtTUR88QPNGJwSNarDlG3exs7lTkpSZnfcX0qdWSl0PRGrK7ymU3I6ZVG6Yl0M9XvW1CanS8Sr2bqgEv2RK05wRjvaZQcepz4rHJgaBVaKzJEVeqrFSWC7BGSKVYA+dG/2zHYewJkyxofU1LKTUXZMnkZakStPlcyFNy7YHaEnraxMbOCedzU4zybsN9cS0bpNPoh1SYWyozaRULkbMHep5r63J+wy1tkcz5YRop0SqSt7t1E6yrCUFqJDOY5pJ4SmtOnFpxjQM83vf0/Q6AECsLy3c4V6MgB06V2Cu4YLe/thZ0vgMCRR1LVjtXV2EorXmTuq6QSur01FqNWk/xA3zwH9cDz/DtaIpf//kvXnG3KFe956NdR3Swk0BsuKp3+M5ZWJsG9puO9OKWzz/7lE8+ecF+t6XvA2Dl3Ydx4e7xxP/nP/kn/OKbV/yzX3zFn/78y4oLVhxRW1b9vHDW6jQJJGe9M6dUyNr4otEwbCqWTaRIqDhr8y6aARbrrYgZakOAzs1rhTqZq3ddVhgBpCi+dl1p0pa5mIfWClTOw45b84wnM3SekpU5ZVJRxGWKTgTXc3O7ZdMPlHkhT9bz8/FgfTtP44lxNgO0jLZA05JXLuzKcNAFEAKRzvUMQ8+zT54RYs9hSYwpseTEqaoG5pLImkjFMS3zGVNVqt5zjT68aZX0MRCjs16otdPP/mpP33fsNwPPrnZ0znHdBzrnULWS9Fwy82h0uFb27yr84IKnH0zzfKgNL5Z55s67p1eyWFemnCDNJ5axR4NHisEgbXGZuFSdOzWheHf3mtevXlnS7XRajX/j+Lqq6tha0aWK/Wu2RL20RgsKQTzbYUMXbG51MRoeXDvLp0pvyznXMN64DELt49MaF2ObzpINY7WqReN1T8cTeUkXc9Z6jzY8vHWZmisclOcZrRW0jYXyPRbW5mu2qKDIjBtHnFfbnFVwFFJ1RIpU8KxGBvZ7JS60ja9Nc6g1DbaC7B4oc1pIpSAhcv3sRX2xrBujVam6qg3jOBwzp/FgcGZd/y1xXkqkG56v9xmBJWEN1YWVKul8pPct+rDvWpIyL6DHRNG0HjeIqWxWh8L7Gop0DoKQpoXluFiiNj+9lr9q/MgeOJySMKvy6nHCx0fSrmfbeXrv6Hxr3mq4qnOeGLpauSagHVdXO7abwVp61RA0l8Q0zxxPJ759+YpffPUt37x8zev7x6qbnVdtknOUJ/X3asBdRMNgAvi1S0Yll9mx4+rfAgVvUb/AWjYLtErHS6aHqxg+UEWM6vc3UZt1ltZJp2ufkvqSN413/a7KIRdK3UgcrmT7jjVks76dw6aD4NDoSYs1UU7JYR1/MkkUXUypsGRq6G+fsx4fgpDxYqqJ+60VL8g0I4vDLcKiphapqS1M26igSYuyYuXGvDw3EO586+EYiCGw31j5+s1uw7OrHdE5roIQnTAvE/NssFuphk1qgtChlZ9dNWhqMjbGgL7DA7cQOVcV12R9JlHKKlZir8G5Vf62JdeM5nc0D/xkEIOvBUVSlQEvKYmleQ4tMXLxU5TaJEGJMVacWkzTprVGq4a0NRVaMfnV9ZS1UUIuhVQySy0GyqlqmyxpzXHYudQCpPr5WimFtrEl09W5gGLeFAO7HA1bl5RJS8IVK2d3KlUc2aLe0uI9ZfXqv+877BK1pKjQ8JakTWpZiF3titTWUTPk9X+qwrzYeeZi4nkK6+dZ5BOffHe71pcdodbm1Kv9UKN+ZpNPTqnJTJw3ZS/eGMYVZRX1kIU8F9KcWZYKLb7n+FENuCIs4hiL8M39gcM883oTODw+sImBn97u2Q+RbR/YeUdKifvHe/Iyc7MfuNr27AZrGeWdo6gjF+Hl3cgf/fxrXt4d+Gdf3fH1d4/cHxaW7ClqBlmdrjd/xTUssLUF5DxNwKH5XQadVJnbNrmwMuZSrGJyNcjrqJ9RV1djdAhWcXrxqvrTfls/X8+6CS50ZvD8zCWMosAyTZweHylpIS0mJrUU88B1HI0aN88chg06zUSFoGI4cZpx2ZrCBYwfbZPLjHYz3pe4u8FUCymd8H7Hpx/dsL+6IXQDPnaM08Td4z0pm95MqypsGflWXu+cWyd1Vxs8b7tIHzxdCOwqNa4bTD3OO8VPpqz4kGcomayFrAbPiDNRNB8DLnhiH4l9xAdPP/R0fTQ8s+9R1dVwXd4HE45SKHVDQCm5laDL+fjr764mmUMI9H1H9g7BFmKMVuItzuOaNkft7zkM/Urdu7+7x4lp06fFIqPj6VgZIXOtfDToo+mXtCKjVhXcWD6tj2sphanxumufV4uqaotAHwjiVy9GK+TSvO8GtWg15Ge8pM7WmutoBuzN1X1395p5egS/ge4BJK5GjJKRyuBonZ1WWAYq17pxo/WJMW+1BK1JhgKpRQzrHJPV2XEVgvXNAVw7Qvla09FOod7bRhtdVxcXm8h5s3Per85lqwFYCQHlMpqwj3Ei9Vh0zWHQCRIcmkbKeKCU6a02f79q/KgGvIiwiGdU4ZevD6CZfSd8O3j2Q4fnM15c7cgixD4wL4n7uzvSfOJ29wnX+xt2m54YPN55piykInx7N/Kf/dEv+e7ukT/98hXfvHow3eniWfls1aA2g6mlZb8NHDmDipxDOTgb8vpvk8c0DDvhL4kKF0yMc0IvBEeM3nJ1Nexdc3myim6uWtdrU2URfFVhFF+A6XwhVVnGE8eHe2NA1OrFBdMxTDkxThNL7HgI///2zi3mtiyr678x57rsvb9z664q6BuRJhAQiAIhBKIxptUISsAHHlCiGEl4MREviYHwoCa+EI2oCUIIKK0htNqCdEgwInZCfAAFMS0CDc1F6Ft1VZ3bd9l7r7XmHD6MMeda+zvfqT7VdJ9zvrhH1T7fvq41r2OO63+05O2WdWxZe+FgGUdCTjSaaTzBJRwkuhTmXVUDAFIeGCeIMfGWl+7w0oufxWe99BbedOdNXGy33Lt3l3EcOd9uzamWstW6rOYqqkYSxOtwCpw0jdm1Y8O67yx5wnXh7e6C04szpmng4dlDKzHWmBE+tg2rmyfEJtL2DbFrrcBC19G0kdV6Rb/q6deWvWt22UPGI2Jx3U3ADodpMGYcLS2fUvdTbe4qE8PS7fu+IzeRxst1da0VUijOMgkOgBajxzIr4ziwPTWn6363Y+9RDtthV5lo1lwzPREcqXDOEF0EO5DVi1KMI6cXlrk5eHKUlPFGWDctrUTHGJmYSGYDT6lCR5QwwEeQG+tCl0d5N7Zf7t+/xyf25yRt2erHUG3oQmsgTzlZAXGdk26yFnA0rQdUuZaZ4tIhA3fzmyqMU2kvlYHXAiGhce29ZbM2zJK+X9E0Hblim9cl6Xy8MPP5rywYvP1tKrM3JEqp5rGicS+26IyaC+YPAGfgQNoj4xnChMiIXDGmV9Ezr4kZm4aoUsOupgz7cSIG4Wy7p4uBNsK6C0zj5HjWllHYtp3XYrQB3Q8T2zHx8Oycuw9OefDwgv04UXH7fePVWng+4DWErlqmSyRysY/PNr4yyYirZv4LqnS2jAcO1QNdTuuSll6BupDiY7T7ialkGoyrhwDBsymbrvOsU0fvW4j6IZqjL6VMTkU78L9+GJXoiKggcSI0U3VmWcmxwRx4Xsmm4HnMGXSXVLuCZDeN7Lfn7C7OkGHHKiVLvAmBqYms24ZR1B014cBkVDMT0Qqv3JFpsxI0WTFfwSvXKLthx7A79ySniUwmBgcsaoJFLTQBaaz4QYhelX5ROb5kpc5zdrAqDxWzOuGlz35w51zNE9TvaZXSSwUfGyYzPeXJ1f5klaeiwOifp8HS5sdhzzgNXkN0TmiqDNpEf59bb0dthd1nmgzWeCrzmCyL1X7uTNkZcHGoFXNJPnjUiHzrh4A6iIgWADQ31zxqipqXp43XLEnn7GhyuYydd04d897/WhPdWUt2f5Bn0VYJ3DTiGPxAVYNHACoDP0jGcjNeCfdUN7XMWvW8n8X7bAEJs3ZccjBEJkQCuTJwZvMoi7VVlpAnJi5sO9bYKEjeEdI5Iom2TdeDgcfYmMd4Cmx3wjjuGXXk4XZgPyZ+/+XXuHe/5fzNN0jTLcgjUwKhoVudcPPWm1if3CQ0HVkDr9y7x6v3T/nN3/kIv/LB3+Z8O/Jw58h+GKZIYdqVBI+8SJU/mWStjiBc9qHOdmhMiilxnBWEJpiEX8CJzF5m4WF93y8cdsbAgy6iSVSgwt5LtYW2XcO6t1hhkxoim5wQfTh3QYT1quPWbYtjfTgOwBxRYKeN2eQepLuch2ASeHSvuks4gyaGnJhyZp8sOie5/bOMzCxXCIa0l7g4u89Hf+9D7O6/yttQbkigUWVFhiDsu5bURkcTdBtvLrjTySvTwKDJ0SWt1uYwjTzYXjDlxMW0Z0yJkcxAhiiEVYe0kWbd0G/WhKah3XTGpN2EEvuWbr0yu3ff0ziWiRWPODSfzONpai6KHYaxSOqWrm5MM5NHT+IoDr1ppA3BMjWdIWpKDGlC80TKXiDEccFdXLSb+vqTuREUbVHcF4Rg4JYoKQGaGbP6gW21Xs2fYSaYZcq7xTK57TwlECHrRAo2x6P/Zhzn31azQTHNYLU0y14QKRoUVQJdUgiRNnaItiTpUCyMNagLNk3BT3d/krr9V+bbzjHkJQxTL93HmKEVfi4beBbQzCxeotnmXIgglh8QUCtZqCb1kwvGuhzcgTw7hHMpRl7gFUQW17f7LiV2yu/GkTyNtt8cgG3yMMIgexrOiVG4efMmXdfxJPTMJfCm8UmNAUmBPImF32jifLuHNHG2brnYDwRN5tzxggomkbZY+QNhtxs4Pd/y8PycB6fnbPcTg3YeHmTqr9+4St6FZhl7fg0z87bn8+JZovHNzpSSJRjmSV0AQZkUPksrSpEaPJwQvwYCLoEbAp7hNnR9Z+n+UzTRbUGxMVhTSxpZNL5sCFWyiEUFACEmQvRiAv71SYs9WWfp78CJtDQ5qP9vjGN7dsqFCOn8jLi7oEe4GQJBlYG5JIVKcRj5PTGs7InMhVrES5oGxnFP3u/ZX5xZRMu4Z8gTKUBqBMkN7dpU49BYweXgwELBJW+JRQqfJe9S+uzARPYYMlvmXJAjhAWz1jnUrkSh5JRcRdMqfZskmyjV0kvcfcFKX9qWBeYDPlg19iKJVj8KS0nW4WSn4swcPYbb0uPLNEmRHn0dlIdpV5ZZmXI6kL6vck4WmFd1IabMo1xieHhvyn8F4jUXbdM/Df479VqqQjCEyUPh1Xqs6j4jrfcrGamKEjy+usjJpY3WyJKJjfvQzY+gDhw3g21N7qikSva1HcWM6nZuFDeLUvc6XG1yKZTGPWkcZgauyhgMiyXKnhwuaDSiunn9hbmgZ8zAObAhWXXziEpkUuVsNzEOE8JD0jCyagMv3uxo+obY36Bd3UTansHBpT7+2l1+98Mf45W79xjy5CWjkmMgmXe50DwZiZlJue3M467Lmi9KanD7p4WQFUbrkpxEiG01Cy3VNmPwwW2THuKlc6hXyhaRE2JjzrcQWa1aYmzo2siqt+tqSo7Sly6No7A6WXP7hdvELrLdbxmGkfFibx6audOkwkCVCr0axBI1RncGTgV0CpjBoKAw7cvyzzQOPHjtZfT8Ifdjx4OH52QJ3PB4+BJ5olrjWJgc0XGbJ3aaGDRxfxoZNHM6DVxME4MmLpKZULRvoIvEVU9/sjYH5cmG0FpqdOhcqvYc/tjYAd91PW3f2Zx4FR2Dg72MUu39yZlhv2W/T2ga2bctTdOwPbOErGpays4AXKI1CbgUoNAFEyxrKwGjr7VUzQQ2gSzCDWc5wxyWQhozBDd1qMWOl3Czks0KFsVTpGVZOmOy25SLczMVqd8KURfJuzDyQ8HmUbGm+HSs6VeNYlkrnpWqMI47kkZzYrqfZzbNGAMvceq4PXyWrLO3ZAlMVg4x86kUM9/i7KwQylKEN9UqzwTH/bEVuYTtzQd9rhqAmxCLzR5dmLUojH5m3vOrRT/HEXx9aMqowNg2TE2g65T1RunpuEmif2Q8r6ZnysBZnM6FgWuwElhJE+e7iR0GM7q72HJrs+L2jc+CdkPoTmj6E6RpGLOwGxKv3H3Ahz/2Mq89eGARGChJM1nF7KTuPV6GBqV0iRn6v8UZsawUMnucXZetu02swk1jTo2CdFakKcCr16iHGBXQJmuLVSeHphXWrUMF9Gv6vqONgb41iNJxvzdMCEmHO0Zgtem5cecGKsrD04cQYLcb8A7XrxbGPOJSJUKDOcaSx6InNx8dJq0U0vmm/m8aB07vvUpuGh5Owum9U6JEkrSmtnsbLNvMVMZBrLb9BRNnOrHNmVemgb1m7ueJ85xIURi6AE1k1d02M8imY33nFrGNNGtj5JR5CaFGDkXHiG/diWkM3Jh8wVO5ytCYNTEOe4b9yDTu6yHcuLo/jRNz0eTJhYDyd862LGtsltCUGE26C6GYRBamtuiCwULDMQkZMsngdpPV5zQgpdGyh92/HILQrQw50mzJs+ioikEn1AgTD29LyfwUzsCryeXynKtL/XXmXctUKh76VWSaikH9juPesHykqXjqZQFlPwLmZKNMSouK89nAnxCDJ1bvdMXPUQymVsWd/lQNBdeai0R9OC9gJe38AK61Sl3TWny/MHAbjmJSnPteRmcpdZdnBQIjpOSmNccZB4Z1T+pa1puGvOpNAz6I9np9esYMnIVKBzCrVAAqmawwqTBMym7MnG1HQhzYDokhgQYljIndNLGfJnbjZMkrMRJUDa4xz6F/5aAsm6U6MRcTe8Ady3ehMoqFR2m2fxNs4aDV7jyrmlp9F1nnEMEpeWSLHwohNjRt59WGind9cpxsS6QgJ1I7QnvQREM9ay3qous6clJC2FaVtQ63/81i2WQZSMXeq8ZUM/N4vM7E1Ztb/7JHM4ykYSBLKaIgjnOuxrgRJlFOJTOinJI4JbEj81AToypDhNxGaCLN2mzW3WZNu17R9r1pKV6Np/KpMmsyR/zUv0tTV9WIpEzuYa9SZtxv2W+HA19GyYC0jEVX3Uu/NFeGUSBmy4Xtd/LIWXEY21yYkpQBBYSMO7R9VixxZ3TGZIdBbOKMr+EqveZFGF6aJdUS2bEUYFjYyZex1/UAqH+KGGNDF1yN0wUY1uXlMU179rtz9hOcbyNJA2PwWGhqnRocfHgG49KCA1/CJSebKinZsJbTsBTAsjvHi9WwSOV1ZTxq4SlGFXAJPOVyOM/hjAcM/LHCzGVN5ZKW6r/N2RK2skD2xJGwXhHWPc26oVm1FenzSelJKvKsgJ8Dev/+e1X174nIO4H3AC8AvwT8ZVUdnvjO3rPsqdtl0SoRDY2p+z6pu5xI+8yoie7VUx6cD3zewz2ng9LkxFb37PZ77l3suH+xYztlKxIQlCzREiA8C01EvACtWAVxDbYQpiJ5WEib2Q49gsSZti4k7lIOKYbGK804xokqKY8IkyeNtG6Ts6unlJmSSd2W4QZtb+iF7WrN6sYtxyBWpqwM2wu2Z/cN2yEngiov3NyhzRIzwpIX1psTUlJu3LxJbFpOH5xfPewYsx7FEv1L4ld2s8YyI/Sq39odl2+aQy+lzLjdMnDKFFporAiATBMhJyaBMzI7lI8ycY5yP2TuiTlztq1YJZ1NT1i1tKsV61s3iW3LyY2bdP2K2LW0q97MGQ5ENQd4QhMK8JZcsic/+tpwvw939jSNnN2/y8Ozi1ni0ksHmuBp+aG+PjjzHcPEwLdaj3qZpfEDBgqVwapSi2Ab8yhHgfeyYNTgh30IrFZrNusTFJNyc0oM08TkcBG5mAg9bb80V7HyeoVxF0ZYGRZ6ODaySL7CoI9VM2MaarWgywxtuz3lwb2Psh2UV04TU4IuNI5YqV60e4aPKD4g1dkpqW6rlmC1T62NzsAX4+bRhywT6UIx8yzPooUvgaodhXk+1B3rqRzOengGSDGxlt/Zxer3liaXA9IKJ6FNJPcN0rbceOlN9Ldv07ewXgmdNFaP8wnpSSTwPfAuVT0Tq43530Tkp4G/DXyvqr5HRH4A+Dbg+5/4zouOzd7j8l5wFhJAjMknNca3GxIxWM3Ji/1ImwNZgsV5j4nBEdLcsMjSWTWHHgHhkoQmXNrMj6rXsrD4VcdNkdJ0sfizxVBLDjV7q25aLezGwxTBGEq0g6BI9OoRHuM4st/tIGcaLE66bPxlU41pxTnbMBZn3aI3i0W1lBgKs15aGC99/ZFRMc2BCuKVF4xgSomkgRSSFa7wogB7lC2ZLcoZE+einAHnwaSwMTRoFEIbkVVLWLU0q940i76j6VpPhgkeaHA4R7O5YmGOWGw8Fp8v9KoDUs2mRYwDJTSwSIVuR7MVEANBY2Xm5RYgVhJQQMNcrs6yJ8sapD7XBRNdmmG0lhRz1iaFgdvaDo49E7w4QWm3Snabdz60xRdpVOc5Vrc5L+Otr5zsxWiJ4AleZWzmpKLLVByq45gYh8SYFGJDI5boFBbp/n4sVM2zALGpazo2ZmaQq8lMxVezYOCLCZ8PwBrquDBxLNdK0firHX32b8x2dKyuqs7XP7Cv+m8fz8Dt61mEgromXUNcdbSrnhizZSJL4Ipl+Vh6koo8Cpz5y9YfCrwL+Ev+/ruBv8+nwsDrarInQomhdWapSg6BTGREebgd2Y2J3/i/HyWLcuvGhre+9QVSnnj1wRl3H56znwxNsISbqkHbGfB+sLhgkUCD4WVnzcTU2EaaLImhbFxbEzaFEqlS13LSU7KAw4OwIXwBj7OUNc+vEkNg1bcQIuvNDbr1CSLCzkH0x92FxVefPeTi/qsEEW7dOCG27ZWrI4SGtrG4+KZpTLX2Cue2EDlYGIUBU4+RmalfqS1emrKZTD4cVdmjPMiJT6SBCdhoSwPcTxds08ADTbyaJ/YB7raBvQh5tUE3a0LXsrm1IXQNq5sbuk1P7Fr6zaZCKFh27FzyWvyADu5IrtEmlw6vUpllbnHZvI/2TVBER0THwmUX8bt1KRijSdE38GFUSxYBTWQRNE9MYzG7Ma+Fx5ioZsFg1gDdmOJNsQOgcQ2gbSJN21Q42GLPNnzu+eAuR1aN+VZPSdcSC66PtOOyM84gfS3dPhW0w2HngFiXwqIACIiW9HmIbtKscU/FCidFcjZuKA5KpuVdN4cF99WYsCRmHnXJt/palpOq83qefbqFmUuZbQrI3KyBBsdt14oOOOsuzsiZcXGu3Cxh/q6d4UJcrWj7hna9Yv3iHV/fN2j7Fbq/YHz4ECSimxNon8yN+aRV6SNmJvl84PuA3wLuqxa0Fj4MvP0xv/124NsBbt++ffjhgnFXpiSFOWoddfNSG0TqxX5kP0x85OW75Dzx4gt36E8Mhvb0fMfZxY4pw6hF6onzoRyLCm2JHFGMIeecrbBuVsa8d0809fQFiw0NEnyvymIB+IldGErtkke5UKSsOh6+IC0xR0JDv16zWq89fdqK1W7PzhmHLfuzh2wfPKBtIid9Z1mHVzLwMDNvl8StLBouJi8krbrG1fswj/38tlx5n/rLS4fVhDHxs5y4nyaCBO5qJqB8PO85nfbc05GX88AYhPOmY5LIuhNWJyu6VU//5tu0fcu6MPDGIkxEQk12Wra3SKCl4MKSeZe5sPnwvivV/j0bAy53zAo+iBY4XZtfObDxiifyeMxwQZ+cRWaSawA5TzVbsCz3RzQo8GpNnvBV4EfdkR6Yw+9CMTSIQ+3GYHU43elcsyodpCoWpzvz2izoiI8zlV3FvF1ptexR1Co/ldJuk2mLl69kRUNiBa8yfDE/zDTM665KtYsVpS5YeNtDSVfHNABdhAzaL8qBvbzQwslY2UkxPc4adBFhMlLxVAge8y8c7IMSSjm/MeusdcRcOFBfa+r1N8O6oz1Zsbp5wp23vGgaJQ1CYBwu2F5cEKQh9+nAx/V69EQMXM3o9GUicgf4CeCLnuzyoKo/CPwgwNve9raDOQ7B7NFdiIzTSEyTR0F4nGU1VZQFZxs2kznb7nj1rjLlRL/uQJQHZxeMkyU37Cclq3i4V7F/+uY4wDrABtyztEKMxDxPun1c4rvNzMGCgWcwCUAWEuxC9S7OHfGJbaLV8guxoe3XnlFqGNfqjih0ttmWyArFAYlSql7tg3F21dK+M1kpqwitIxMWz/asVUhddtVuuLD3VentSioROmUDWJz3KHCqiVfzyJiFJu0IwGuSOW/gPDTsYyTHQHuypmkjm5snnNxc0/UdN9ZmLum6jrZpwZkauJoti/vLbNMOseCM2MEl1ZzU+EEWZsa4YE6PJXdOzoeePWbnnppppJomylhUGZIaR+wZmXbgzZmBc09wLcId2QuTXllRmg0atyQGIRZSmkNw+7NVZS8FG0o5taoVXGGquRxhdDmKoiyD0kYTSG0c8jQZbriX7yvVci4f+CbZuznCTTrqphB1MwgspG/BTEXM7asMepFxWpzG86Nkpi5NIpcWcA1An/tZTVlFA8l5MafLMM8CWEXhz7WHS93BN4WNUTCtX2KkXa+QJrK6fcLqxoa2byFl8n5k2F+QxkS6OGPcbpHYLhLnPjm9oSgUVb0vIu8Hvga4IyKNS+HvAD7yRq4FBgazXq0gNUzjnjzBlGHy2M4YWq9pVyJIhOwxvK/cP+W1u6+xeaXn5dfuIkG49+AB2yGxH62iTlYlTyYZtU3rjNMKBJc6j9GTOkII5lrPjW2iIgG5VCQeq1qwSoot24SFhV1btYL/lLhawQoSBDF0vfV6RdN2rE9uExsrIWEp9IGuiUwobRPRbNEWGqLhxmTDtLgU+VjmxpJg0sQwWTp2bIR+3drmHjjYuIqhDUIxM1EFlirtXimAL7SMUNRPZVTzrn9CJ8a0Y8PEK2KS+FlQhkbQVQ8nFkVy484N2q7l9smG25sT2rblxubEqu2sekLbkgQGb0YSa6dl/ZmEFD1xp2lbQ/oLXnBZoocQdjRt63U9DVc7FgCix6/ygxjvMraqxcnn/c+LAVowrsKQqwQbvMBFCB7AJLU49GyvpyZ/sRz2coiUCJKUyOOIiJCihf1NrSU9TZPVahwcj9yQFkO1VqQ0kZfoeKWx8phprh0qLjsoBavTsGd7fmrOvmmwCKkrTCjqYbOGrTJaFq64Rr0MZ1QzmdaArsL4mdPlUa1akBZnoNuo7V62L4MYMuhC6bDDSc0vYe/LfCqpWkw2eFLVPJ91XnwcyptysEEsxT/4gGY/UIhCbhvCqqN/6c3EVc/NO7c4uXXTIHkvLpjGPQ9efZWL0zNCGojjDvo1+c2fRjArEXkJGJ15r4E/A3wP8H7gm7BIlG8FfvKJ71quXR6yeKB+0i/GePGbct6lrKQxsYsjZxdbQhDGaXK7t1ZQ+uQHfQiR4FLJlCavTDNTjRFfmBMqA/e0efWMz6VyKX6/jCLV5DKrgOIqW1E/YwgWz9w0dK0XSlWLBInFQVYFYj3Y3GVDFMfYkg6kKrWNYhVcXAKMnnXn5p6D9AvVBdNgwcSvllSrHCoBLbguLrJNURhM+GDnG3JsIikIoWuJfU/TNrR9R9e1dF1L20VaL5pcmBtSxnmOMSlO08ogZT5k6zz5XB2kONfnLKTbq+ngIPaxYZ7VK+3mhcq6mCXXYqjxTxaaW2Xc5f3F/ZdzSpEMC3aHY7DknKqTvEK/LqTSOnJFuMjz+9aREumyeKscIL7utLSrmDJrVqk/0uSH3RKp8NHRvOqhxb58xc8eKzscfIM6pqrUiKIYggtlUBZlwX6xveRmqWCQF6bNeuy8hFqbs1hSpDgVdV4FB0ugMKg66T6/jYGpha4zB/zKTIIloCENFm477QfSfgCdiCnVDM0npSeRwN8KvNvt4AH4d6r6UyLyq8B7ROQfAr8M/PAT39WpViXPpp41boYImkCFKNmcJqJme14MkBUQ7plUeXC6BTGmndWqtxdYA8OJMAlERExSGYbZfup/iw2ydDLG6NApApLd7EKN7iix2iZlqxVE2O+diVuigYjWcLZ1Z/bpk/WKG5s1bddx89aGGFuGKTEkJaLsvAjrNOzZb7eoKuv1BtQKyY77zHC7h0u5WqqZpBOZhEQlNNBvGsOJmTLT6KGZJS4YqZJ3qrZenUGPDrS4spH9XuVJiM7AjWEHwQ6kJpKalqlf08TIjX5N07S0q45+syI2gfVJR9MG1l3Duo9uWjKTSBJPsgKSI9JoWMAUxII/s1DynaHXBKr6N1Rtq4YRvg4Xtvj8xOjlxMo9itmpHhrFvIXU1HzT2pxxF8jR8pnbVetvi/4OB4eFRbzYQVwSUWSakDShaSLt94iYWzO2mdB2NXU+F9OJM2BUqy28Spe1o/OhuOx7zYdwBlemXQSGYc+03zEMW9K4p5Tfu6Q3LJdNCSwxk6AaXJBEU3arn6KezDNDLuM+26uXqqGF2RYhK0iga3vT3kSqQ7NmcHq0iu11m5+C5a2+J5ZdmH1Ws0A2jpPje5s0D1KFCdMU1eZ3tUJiZHXrJpvbt4ltx+b2LWLTMu52nL38GtN2x/buXdIwMu12hgYqmUbEqpE9dnU+Sk8ShfIB4MuveP+3ga96A/d69NrgqqEgaiFyCbwkVpETLd07L/bqvGkaUpoY93tAa1SC5TksbWQWo12D/ktEgS+SGL3iugh9NBxwiyEtESf+8JJcIQa6zlAQS9yuoOwHP4DqYjMKQWgbhxvtGnqXPNe9YVzLIMiUmUaLj0UzOU1M40BAaboWTYlxv3M7Z0QvM/Cidmr24gjQtJb5lmMmhgJkpJ5MNCcVhWrv5UACX0qSZRwq83aGhEu8wc0DoY0W59q05HWPxoZ+fcKqXdH1LZvNihCF1SoSG6FrhbYpZc7sWiWRyBTUOYG6qL5Lqbuqwz6ftQZhmL9bKv4cOPM4tAEvF2XBA8GZWL13vUfJyC0MOS6k65mpHLbR1LD6ug5ocbAWiXhm4qkw4zQh04ROhhQJIO0IQSrjLoUYdLG21Svq1BjlpWS30PKuIintqiYMSNPEsN8xTkPNQl1e69GLzA8JVSCm1MEsjt+lg3pebRGW8yNCCXiVws99fmKIrLre9tJCA5oTl+bs0rJOm8rAIV+6VRmXEj5qcfSKSvYhLI70gmGkTGB8o2tM07yxYX3nFk3bsdmcEEJkOt8ynF0wnF9wce+UPI4EtRq8QZQY8Sr1jxnPK+jZFnTQAjFqZRJKVfamrG+PBa0zLGXT+fAt7Ll2PfENOD9/RK1cPAebTCvmajlISezAaEoprhDoOivrFVuvdC2mlpmjquCGmKMpVG1B6kYsUKmlao8jP6BptJT1yeyTedozDXumYSBPk3UkeIiUmwNqbPtl8k0Sm0DXNYSQCWLwrZrU8DSqBO5mn6Kho3VhumDzCFRuUfVBaoiUBHFpmDouVkEnsmo7bq/WNKHhpF/TNS1d27LqGz8ALdkmNgKNoCEwRWO6EzCplhKkKKVogeHLxAOQsGI2WUi6i/EpjjsQj8nP/huu1tMFi2gJczTLomzjLD2Xe8KCZVT+VO6+0K4VLgvBi4Oz1MmsgFKOraKqyDhAskr3edib5hcCMZkEPg5WpT1PbmJJudq7Kcz7KtL5lNZFm0s/YhBEc80EHoc9o0MOl8P+4DB95PqCZHvMEoNLrxVjHtBIQeO8PJZaVIVS13VpYMnu0FV1G7zbzMsvC/NlDiYotR4yhq+OSvGlzutGF7K3x5kbuMQlLJayHWKkbSPSRvrbN4irjn7TIwFSGji7Z36Ci/sP2D04tYzqNCJef7f0+LFCxevQs2XgbkJJSasJBbEyceagmChhOyYEXGbi5ZknSczInDWQYDkelxMWyqJbVuUmj5CT1U7sLK19vdlYdARaGZbSulBj9RjRZJllKOJV1ZOVoDe8Ec8ki2KoaqIJHffkHMjjRB4T037LsL2wskzjgOYEGM51dulScwHUOqSiITRtYLXuLPu0FVerdS7cWkwoOlcu0XqNEqo1yzH1WbUnM8dfR8crFyttFoPQ9R1d29I3Dbf6tUlHTWvVX0Kkd/z2prVU+BxMAsoSGKIx4VEtC1XFcTJkRumLIc4lymrESZGEZWbiizk3v4FlOYoUXMRwKJHWMZAZN+XAfm7XrGYVKYjui98eLG7nFAcCyCI9W7VKyKYhzsWHS4HhcRwsK3Ma7JBfODEnVQuxbFq69d73khdkmCwqpfTn8Sq5upBTWdLBempcuBkHK9C832/Z7bbYSTTvoYWb83AsFUIWM8clhUlRrF6pgzj4ClOfkYC4b6qsOV2aaUoESZEySsaqBKZxML/AwQHuvdIFv/DnIc3AdKXtB/DCi0vYfknW9sLYBS/wIUjX0t/oCV3HjRfv0G3WvmaUaT9y+om7TLs949kF6WILOSPJQmzngAldzMOT07PFAxdlHUyNELGahkkzYylIUJzC4gd0qWItQDVHCeoAPmUAUhMZY8F3cMD3UolcsfdUPUxPyEn8+4ImBS0VrI1Z9J3QNNA20DUGStTLhEgkhcmyRGMitr4pfWPkCFMQYoBNk2ljYiUTHQNREzEJQQONJ2GsGNjEibaZoDMjSQx2X83CPgc0BVbNo5uliQ192yF5TVrfMNW6HSn1HTVdZuDMtlcASvjVwvUm9ZOFOYCKcRKCEiJ+QJnZqWtbmrahj41pLiHQxNadSxGJxhgpaHBl74cAElFHgiz3LpCjQkSKhGYpbYsD24wtqJAkoUGtlmiyIgpkM3toyjSxqcBpu+3Wwt+WazJGbp4YnOeBw3FpQilmlUvSYn1eGcNSGZgZebWxamHgCzySlCqo0+SV5XUaPRU+kUeHg9icENuO9Y0Tbqx7NJtIkR07p5paFvO4aKGN24EQVFkTAJ072jUlxr4hp0QblGHVU3FgoPoUTjYnj0jhsWlp+w1ZJjZjw5QyOJaY3bUcptFXnRX9LQMnFAm4HBbFZONOVmwNiwixs6S8wwN8ZtxFqi7V4IuJq4xRMd/UiVxqaAqxGWlqqTPjQeoZwdK3hN4ip9ZNRxvaes0gmT40xJhp2p7UM5uIVWvYaBSlEaV3oedJ6Zky8JvNwBdu7lnSQ3U4zBCRRaU/CIYok6gAETSgetjhWW2eXy834WUJ/JEIDrUIjoKBEmNR0yGE4gAdXcCaTTR5ky+1A1RtocS4J8hIiDtifoioELNdv2KohMxbbtumTbczuUjbYoOQ8wpV5WYfDjZlEOH2+jab7sQAj+68w6W8PKugRZOuw6eL5z60ywV8JXOaP5fy8oDR4rbmYpssWDJSmVo5BLLIYWy3S06C0GDmJl3eVoNJcrlg0sA0FWdxqhtXZHcgNVenIbPzsLQlpcT24uJgzm7fvMFXfMkfttJvi44esCap/zxCj5d2Fx8uJP85MmS2gc/MZraPL23lQE1YKgBoihqTL5L90kz4mHZU/nSFyFd9BmUvuZkip0fRB0WsCMESrlkkcHL7RfrNTXJWXppyVUrmuy9X3pJzLqwZy0Vbr71of7XTlz0hl+5x+LSILFeZfF5PV8mLg2RJWjS/6Fph2/iB5J83yvTCzRrDr6UYRBUByiFiB1QIkdX6xmPbcZmeKQNvJXOn3b/ONz6ZMjGrP0+PCpO+Ihj7yuyp0r40/6as3cvx+gJ0l99Y0uNOZqFrerrmSVGEnz293syKPGZWFxLRocr/xuyGr0dd2/LSC2/6tF3v/2dq+zVtv37WzXi2FIDVZ25fvl5Gw5GOdKQjHek5piMDP9KRjnSka0pHBn6kIx3pSNeU5HGB/J+Rm4m8ApwDrz61m35m6EWudx+ue/vh+vfhurcfrn8frlP7/5CqvnT5zafKwAFE5BdV9Suf6k0/zXTd+3Dd2w/Xvw/Xvf1w/ftw3dsPRxPKkY50pCNdWzoy8CMd6UhHuqb0LBj4Dz6De3666br34bq3H65/H657++H69+G6t//p28CPdKQjHelInx46mlCOdKQjHema0lNl4CLytSLyQRH5kIh859O896dCIvI5IvJ+EflVEfk/IvId/v6bReRnROQ3/e9znXstIlFEfllEfspfv1NEfsHn4d+KSPfJrvEsSUTuiMh7ReTXReTXRORrruEc/C1fQ78iIj8mIqvneR5E5F+KyCdE5FcW71055mL0z70fHxCRr3h2LZ/pMX34R76OPiAiPyFW57d89l3ehw+KyJ99Jo1+g/TUGLhYRZ/vA74O+GLgL4rIFz+t+3+KNAF/R1W/GPhq4K97m78T+FlV/QLgZ/3180zfAfza4vX3AN+rqp8P3AO+7Zm06snpnwH/SVW/CPijWF+uzRyIyNuBvwF8pap+KQZq88083/PwI8DXXnrvcWP+dcAX+OPbge9/Sm38ZPQjPNqHnwG+VFX/CPAbwHcB+L7+ZuBL/Df/wnnWc01PUwL/KuBDqvrbqjpgtTS/8Sne/w2Tqn5MVf+nPz/FGMfbsXa/27/2buAvPJMGPgGJyDuAPw/8kL8W4F3Ae/0rz3v7bwN/Ai/Zp6qDqt7nGs2BUwOsRaQBNsDHeI7nQVV/Drh76e3Hjfk3Av9ajX4eK3j+1qfS0Nehq/qgqv9ZrRA7wM9jBdnB+vAeVd2r6u8AH+IPWHHsadDTZOBvB35/8frD/t61IBH5XKy03C8An62qH/OPPg589rNq1xPQPwX+LjP24QvA/cUift7n4Z3AK8C/cjPQD4nICddoDlT1I8A/Bn4PY9wPgF/ies0DPH7Mr+ve/mvAT/vza9mHoxPzCUhEbgD/Afibqvpw+ZmWEh/PIYnI1wOfUNVfetZt+QNQA3wF8P2q+uUYFMOBueR5ngMAtxV/I3YYvQ044VHV/lrR8z7mn4xE5LsxE+mPPuu2/EHoaTLwjwCfs3j9Dn/vuSYRaTHm/aOq+uP+9stFRfS/n3hW7fsk9MeAbxCR38VMVu/C7Ml3XJWH538ePgx8WFV/wV+/F2Po12UOAP408Duq+oqqjsCPY3NzneYBHj/m12pvi8hfBb4e+Bad46ivVR8KPU0G/j+AL3DPe4c5DN73FO//hsntxT8M/Jqq/pPFR+8DvtWffyvwk0+7bU9CqvpdqvoOVf1cbLz/q6p+C/B+4Jv8a89t+wFU9ePA74vIF/pbfwr4Va7JHDj9HvDVIrLxNVX6cG3mwelxY/4+4K94NMpXAw8WppbnikTkazGT4jeo6rIc0/uAbxaRXkTeiTlk//uzaOMbomVJsM/0A/hzmOf3t4Dvfpr3/hTb+8cxNfEDwP/yx5/D7Mg/C/wm8F+ANz/rtj5BX/4k8FP+/POwxfkh4N8D/bNu3ydp+5cBv+jz8B+BN123OQD+AfDrwK8A/wbon+d5AH4Ms9ePmBb0bY8bc6yA0vf5vv7fWLTN89qHD2G27rKff2Dx/e/2PnwQ+Lpn3f4neRwzMY90pCMd6ZrS0Yl5pCMd6UjXlI4M/EhHOtKRrikdGfiRjnSkI11TOjLwIx3pSEe6pnRk4Ec60pGOdE3pyMCPdKQjHema0pGBH+lIRzrSNaUjAz/SkY50pGtK/w9slreZJVzbvQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"GroundTruth: cat ship ship plane\n"
]
}
],
"source": [
"dataiter = iter(testloader)\n",
"images, labels = dataiter.next()\n",
"\n",
"# print images\n",
"imshow(torchvision.utils.make_grid(images))\n",
"print('GroundTruth: ', ' '.join('%5s' % classes[labels[j]] for j in range(4)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next, let's load back in our saved model (note: saving and re-loading the model\n",
"wasn't necessary here, we only did it to illustrate how to do so):\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<All keys matched successfully>"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"net = Net()\n",
"net.load_state_dict(torch.load(PATH))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Okay, now let us see what the neural network thinks these examples above are:\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"outputs = net(images)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The outputs are energies for the 10 classes.\n",
"The higher the energy for a class, the more the network\n",
"thinks that the image is of the particular class.\n",
"So, let's get the index of the highest energy:\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Predicted: cat ship ship ship\n"
]
}
],
"source": [
"_, predicted = torch.max(outputs, 1)\n",
"\n",
"print('Predicted: ', ' '.join('%5s' % classes[predicted[j]]\n",
" for j in range(4)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The results seem pretty good.\n",
"\n",
"Let us look at how the network performs on the whole dataset.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy of the network on the 10000 test images: 54 %\n"
]
}
],
"source": [
"correct = 0\n",
"total = 0\n",
"with torch.no_grad():\n",
" for data in testloader:\n",
" images, labels = data\n",
" outputs = net(images)\n",
" _, predicted = torch.max(outputs.data, 1)\n",
" total += labels.size(0)\n",
" correct += (predicted == labels).sum().item()\n",
"\n",
"print('Accuracy of the network on the 10000 test images: %d %%' % (\n",
" 100 * correct / total))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"That looks way better than chance, which is 10% accuracy (randomly picking\n",
"a class out of 10 classes).\n",
"Seems like the network learnt something.\n",
"\n",
"Hmmm, what are the classes that performed well, and the classes that did\n",
"not perform well:\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy of plane : 70 %\n",
"Accuracy of car : 66 %\n",
"Accuracy of bird : 39 %\n",
"Accuracy of cat : 34 %\n",
"Accuracy of deer : 56 %\n",
"Accuracy of dog : 37 %\n",
"Accuracy of frog : 61 %\n",
"Accuracy of horse : 59 %\n",
"Accuracy of ship : 63 %\n",
"Accuracy of truck : 56 %\n"
]
}
],
"source": [
"class_correct = list(0. for i in range(10))\n",
"class_total = list(0. for i in range(10))\n",
"with torch.no_grad():\n",
" for data in testloader:\n",
" images, labels = data\n",
" outputs = net(images)\n",
" _, predicted = torch.max(outputs, 1)\n",
" c = (predicted == labels).squeeze()\n",
" for i in range(4):\n",
" label = labels[i]\n",
" class_correct[label] += c[i].item()\n",
" class_total[label] += 1\n",
"\n",
"\n",
"for i in range(10):\n",
" print('Accuracy of %5s : %2d %%' % (\n",
" classes[i], 100 * class_correct[i] / class_total[i]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Okay, so what next?\n",
"\n",
"How do we run these neural networks on the GPU?\n",
"\n",
"Training on GPU\n",
"----------------\n",
"Just like how you transfer a Tensor onto the GPU, you transfer the neural\n",
"net onto the GPU.\n",
"\n",
"Let's first define our device as the first visible cuda device if we have\n",
"CUDA available:\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"cuda:0\n"
]
}
],
"source": [
"device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
"\n",
"# Assuming that we are on a CUDA machine, this should print a CUDA device:\n",
"\n",
"print(device)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The rest of this section assumes that ``device`` is a CUDA device.\n",
"\n",
"Then these methods will recursively go over all modules and convert their\n",
"parameters and buffers to CUDA tensors:\n",
"\n",
".. code:: python\n",
"\n",
" net.to(device)\n",
"\n",
"\n",
"Remember that you will have to send the inputs and targets at every step\n",
"to the GPU too:\n",
"\n",
".. code:: python\n",
"\n",
" inputs, labels = data[0].to(device), data[1].to(device)\n",
"\n",
"Why dont I notice MASSIVE speedup compared to CPU? Because your network\n",
"is really small.\n",
"\n",
"**Exercise:** Try increasing the width of your network (argument 2 of\n",
"the first ``nn.Conv2d``, and argument 1 of the second ``nn.Conv2d`` \n",
"they need to be the same number), see what kind of speedup you get.\n",
"\n",
"**Goals achieved**:\n",
"\n",
"- Understanding PyTorch's Tensor library and neural networks at a high level.\n",
"- Train a small neural network to classify images\n",
"\n",
"Training on multiple GPUs\n",
"-------------------------\n",
"If you want to see even more MASSIVE speedup using all of your GPUs,\n",
"please check out :doc:`data_parallel_tutorial`.\n",
"\n",
"Where do I go next?\n",
"-------------------\n",
"\n",
"- :doc:`Train neural nets to play video games </intermediate/reinforcement_q_learning>`\n",
"- `Train a state-of-the-art ResNet network on imagenet`_\n",
"- `Train a face generator using Generative Adversarial Networks`_\n",
"- `Train a word-level language model using Recurrent LSTM networks`_\n",
"- `More examples`_\n",
"- `More tutorials`_\n",
"- `Discuss PyTorch on the Forums`_\n",
"- `Chat with other users on Slack`_\n",
"\n",
"\n"
]
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