day_5/5_yingzhen/classification_solution.ipynb

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   "source": [
    "# A Hands-on Tutorial for Bayesian Neural Networks\n",
    "\n",
    "(Part 2: classification)\n",
    "\n",
    "[Yingzhen Li](http://yingzhenli.net/home/en/)\n",
    "\n",
    "(As part of the BNN lecture at [ProbAI 2022](https://probabilistic.ai/))\n",
    "\n",
    "In this tutorial, you will implement various Bayesian neural network methods based on variational inference.\n",
    "\n",
    "We will go through classification tasks to see the applications of uncertainty estimation in practice, including a case study on detecting OOD examples.\n",
    "\n",
    "**How to use this tutorial notebook?**\n",
    "\n",
    "*   Read the descriptions in the text;\n",
    "*   Fill in the missing code whenever you see a block that looks like below:\n",
    "\n",
    "```\n",
    "### beginning of your code ###\n",
    "[insert your own code here]\n",
    "### end of your code ###\n",
    "```\n",
    "There will be hints provided in the code blocks as well to guide you through.\n",
    "\n",
    "**GPU usage**: For this demo, it is recommended to use GPU to accelate training. To do so, click on \"Runtime > Change runtime type\", and select \"GPU\" for the \"Hardware accelerator\" option.\n",
    "\n",
    "Let us set up the required packages, and then, enjoy 😊"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "1ZmBUecUY3E4"
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import numpy as np\n",
    "import math\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.distributions as dist\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "from torchvision import datasets, transforms\n",
    "\n",
    "EPS = 1e-5  # define a small constant for numerical stability control"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "P_gLdppUaIlv"
   },
   "source": [
    "# Training BNNs on image classfication tasks\n",
    "\n",
    "In this part you will train a BNN on an image classfication task. More specifically, we will build a Bayesian convolutional neural network (Bayesian CNN) and test a number of approximate posterior inference methods. The dataset in use is MNIST (hand-written digit classification)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fodHNzqXe0Kf"
   },
   "source": [
    "Let's set up the MNIST dataset first."
   ]
  },
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n",
      "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to ../data/MNIST/raw/train-images-idx3-ubyte.gz\n"
     ]
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     "text": [
      "Extracting ../data/MNIST/raw/train-images-idx3-ubyte.gz to ../data/MNIST/raw\n",
      "\n",
      "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n",
      "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to ../data/MNIST/raw/train-labels-idx1-ubyte.gz\n"
     ]
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     "output_type": "stream",
     "text": [
      "Extracting ../data/MNIST/raw/train-labels-idx1-ubyte.gz to ../data/MNIST/raw\n",
      "\n",
      "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n",
      "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to ../data/MNIST/raw/t10k-images-idx3-ubyte.gz\n"
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     "output_type": "stream",
     "text": [
      "Extracting ../data/MNIST/raw/t10k-images-idx3-ubyte.gz to ../data/MNIST/raw\n",
      "\n",
      "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n",
      "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to ../data/MNIST/raw/t10k-labels-idx1-ubyte.gz\n"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting ../data/MNIST/raw/t10k-labels-idx1-ubyte.gz to ../data/MNIST/raw\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# setting up the MNIST dataset\n",
    "transform=transforms.Compose([\n",
    "        transforms.ToTensor(),])\n",
    "train_data = datasets.MNIST('../data', train=True, download=True,\n",
    "                       transform=transform)\n",
    "test_data = datasets.MNIST('../data', train=False,\n",
    "                       transform=transform)\n",
    "train_loader = torch.utils.data.DataLoader(train_data, batch_size=100, shuffle=True)\n",
    "test_loader = torch.utils.data.DataLoader(test_data, batch_size=100, shuffle=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "I2ATbBeZay9s"
   },
   "source": [
    "## Implementing a Bayesian CNN with MC dropout\n",
    "\n",
    "In below we construct a Bayesian CNN with MC dropout and set-up the prediction function with dropout activated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "MgJ1eCUZbT4f"
   },
   "outputs": [],
   "source": [
    "# construct a Bayesian CNN for MNIST data\n",
    "def make_bayesian_cnn(in_channel, n_class, dropout_prob=0.1, activation='LeakyReLU'):\n",
    "    nonlinearity = getattr(nn, activation)() if isinstance(activation, str) else activation\n",
    "    net = nn.Sequential()\n",
    "    # add in conv layers\n",
    "    channel_sizes = [in_channel, 32, 64, 128]\n",
    "    for i, (c_in, c_out) in enumerate(zip(channel_sizes[:-1], channel_sizes[1:])):\n",
    "        net.add_module(f'Conv{i}', nn.Conv2d(c_in, c_out, kernel_size=3, padding='same'))\n",
    "        if dropout_prob > EPS:  # if setting dropout_prob=0.0 then constructing a deterministic CNN\n",
    "            net.add_module(f'Dropout{i}', nn.Dropout(dropout_prob))\n",
    "        net.add_module(f'Nonlinarity{i}', nonlinearity)\n",
    "        net.add_module(f'Pooling{i}', nn.MaxPool2d(kernel_size=2, ceil_mode=True))\n",
    "    # add in the last fully connected layer\n",
    "    # the image sizes goes from 28*28 -> 14*14 -> 7*7 -> 4*4\n",
    "    net.add_module(f'Flatten', nn.Flatten(start_dim=1, end_dim=-1))\n",
    "    net.add_module(f'Linear', nn.Linear(128*4*4, n_class))\n",
    "    return net\n",
    "\n",
    "# define the prediction function with Monte Carlo sampling using K samples\n",
    "def predict(bnn, x_test, K=1, use_dropout=True, reduce_mean=True):\n",
    "    y_pred = []\n",
    "    if use_dropout:\n",
    "        bnn.train() # this will turn on dropout in prediction time, if applicable\n",
    "    else:\n",
    "        bnn.eval()  # turn off dropout, if applicable\n",
    "    for _ in range(K):\n",
    "        y_pred.append(F.softmax(bnn(x_test), dim=-1))\n",
    "    # shape (K, batch_size, y_dim) or (batch_size, y_dim) if K = 1\n",
    "    y_pred = torch.stack(y_pred, dim=0).squeeze(0)\n",
    "    if reduce_mean and K > 1: # \"ensemble\"\n",
    "        y_pred = y_pred.mean(0)\n",
    "    return y_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wPwN7r3X5W_M"
   },
   "source": [
    "Adding some helper functions..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "qPHRp8RCxtCy"
   },
   "outputs": [],
   "source": [
    "def to_numpy(x):\n",
    "    return x.detach().cpu().numpy() # convert a torch tensor to a numpy array\n",
    "\n",
    "# helper functions for training\n",
    "def train_step(net, opt, dataloader, device):\n",
    "    logs = []\n",
    "    for _, (x, y) in enumerate(dataloader):\n",
    "        x = x.to(device); y = y.to(device)\n",
    "        opt.zero_grad() # opt is the optimiser\n",
    "        y_logit = net(x)\n",
    "        # training accruacy (on a mini-batch)\n",
    "        pred = y_logit.data.max(1, keepdim=True)[1] # get the index of the max logit\n",
    "        acc = pred.eq(y.data.view_as(pred)).float().cpu().mean()\n",
    "        # training loss\n",
    "        nll = F.nll_loss(F.log_softmax(y_logit, dim=-1), y)\n",
    "        loss = nll  # note that by using AdamW optimizer, weight-decay is implicitly handled there\n",
    "        loss.backward()\n",
    "        opt.step()\n",
    "        logs.append([to_numpy(nll), to_numpy(acc)])\n",
    "    return np.array(logs)\n",
    "\n",
    "# define the training function\n",
    "def train_network(net, opt, dataloader, device, N_epochs=2000, verbose=True):\n",
    "    net.train()\n",
    "    logs = []\n",
    "    for i in range(N_epochs):\n",
    "        logs_epoch = train_step(net, opt, dataloader, device)\n",
    "        if verbose:\n",
    "            print(\"Epoch {}, last mini-batch nll={}, acc={}\".format(i+1, logs_epoch[-1][0], logs_epoch[-1][1]))\n",
    "        logs.append(logs_epoch)\n",
    "    return np.concatenate(logs, axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "leKbq9FN5cCn"
   },
   "source": [
    "Now we train a Bayesian CNN using MC-dropout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 732
    },
    "id": "97QSPqBgxasT",
    "outputId": "4a1097ab-247b-49f1-a484-e0871aa34a9c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using cuda:0\n",
      "Sequential(\n",
      "  (Conv0): Conv2d(1, 32, kernel_size=(3, 3), stride=(1, 1), padding=same)\n",
      "  (Dropout0): Dropout(p=0.1, inplace=False)\n",
      "  (Nonlinarity0): ReLU()\n",
      "  (Pooling0): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=True)\n",
      "  (Conv1): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=same)\n",
      "  (Dropout1): Dropout(p=0.1, inplace=False)\n",
      "  (Nonlinarity1): ReLU()\n",
      "  (Pooling1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=True)\n",
      "  (Conv2): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=same)\n",
      "  (Dropout2): Dropout(p=0.1, inplace=False)\n",
      "  (Nonlinarity2): ReLU()\n",
      "  (Pooling2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=True)\n",
      "  (Flatten): Flatten(start_dim=1, end_dim=-1)\n",
      "  (Linear): Linear(in_features=2048, out_features=10, bias=True)\n",
      ")\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:780: UserWarning: Note that order of the arguments: ceil_mode and return_indices will changeto match the args list in nn.MaxPool2d in a future release.\n",
      "  warnings.warn(\"Note that order of the arguments: ceil_mode and return_indices will change\"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1, last mini-batch nll=0.09077185392379761, acc=0.9599999785423279\n",
      "Epoch 2, last mini-batch nll=0.04196837544441223, acc=0.9900000095367432\n",
      "Epoch 3, last mini-batch nll=0.024989120662212372, acc=0.9900000095367432\n",
      "Epoch 4, last mini-batch nll=0.024478323757648468, acc=0.9900000095367432\n",
      "Epoch 5, last mini-batch nll=0.019361017271876335, acc=0.9900000095367432\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# constructing and training a Bayesian CNN\n",
    "device = 'cuda:0' if torch.cuda.is_available() else 'cpu'\n",
    "print(\"using {}\".format(device))\n",
    "dropout_prob = 0.1\n",
    "activation = 'ReLU'\n",
    "dropout_bnn = make_bayesian_cnn(in_channel=1, n_class=10, dropout_prob=dropout_prob, \n",
    "                          activation=activation)\n",
    "dropout_bnn.to(device)\n",
    "print(dropout_bnn)\n",
    "\n",
    "# start training\n",
    "learning_rate = 1e-3\n",
    "weight_decay=0.1\n",
    "N_epochs = 5\n",
    "# note that weight decay (i.e., prior loss) is implicitly handled by AdamW optimizer\n",
    "opt = torch.optim.AdamW(dropout_bnn.parameters(), lr=learning_rate, \n",
    "                        weight_decay=weight_decay)\n",
    "# the training loop starts\n",
    "logs = train_network(dropout_bnn, opt, train_loader, device, N_epochs)\n",
    "\n",
    "# plot the training curve\n",
    "def plot_training_loss(logs, dropout_prob, weight_decay):\n",
    "    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 4))\n",
    "    ax1.plot(np.arange(logs.shape[0]), logs[:, 0], 'r-', label='nll')\n",
    "    ax2.plot(np.arange(logs.shape[0]), logs[:, 1], 'r-', label='acc')\n",
    "    ax1.legend()\n",
    "    ax2.legend()\n",
    "    ax1.set_xlabel('epoch')\n",
    "    ax2.set_xlabel('epoch')\n",
    "    ax1.set_title('p={}, weight_decay={}'.format(dropout_prob, weight_decay))\n",
    "    ax2.set_title('p={}, weight_decay={}'.format(dropout_prob, weight_decay))\n",
    "    plt.show()\n",
    "\n",
    "plot_training_loss(logs, dropout_prob, weight_decay)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "woEZgYdi5iSc"
   },
   "source": [
    "Training is finished, we now evaluate its test accuracy. Remember to turn on dropout in test time and do multiple forward passes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "x5Tac3ju1h5U",
    "outputId": "1e4c1d6f-97ca-45bd-fffc-4a390e1983c2"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:780: UserWarning: Note that order of the arguments: ceil_mode and return_indices will changeto match the args list in nn.MaxPool2d in a future release.\n",
      "  warnings.warn(\"Note that order of the arguments: ceil_mode and return_indices will change\"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy: 99.19999694824219%\n"
     ]
    }
   ],
   "source": [
    "# test the BNN\n",
    "def evaluate(model, dataloader, device, K=50):\n",
    "    accuracy = 0\n",
    "    for x, y in test_loader:\n",
    "        x = x.to(device); y = y.to(device)\n",
    "        # the \"predict\" function: by default dropout is on and we use K > 1 MC samples\n",
    "        y_pred = predict(model, x, K, reduce_mean=True)\n",
    "        pred = y_pred.data.max(1, keepdim=True)[1] # get the index of the max probability\n",
    "        accuracy += pred.eq(y.data.view_as(pred)).float().cpu().sum()\n",
    "    accuracy = accuracy / len(dataloader.dataset) * 100 # accuracy in percentage\n",
    "    return to_numpy(accuracy)\n",
    "\n",
    "accuracy = evaluate(dropout_bnn, test_loader, device, K=5)\n",
    "print('Test Accuracy: {}%'.format(accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-pq1rMfHCwkO"
   },
   "source": [
    "## MFVI for Bayesian CNN\n",
    "\n",
    "We will also train a Bayesian CNN with MFVI approximations. For this we will use the ```Bayesianize``` package. ```Bayesianize``` is a lightweight pytorch package which can convert any MLPs or CNNs to their \"Bayesian\" version using variational inference.\n",
    "\n",
    "First we clone and import the package."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "0ez5JMLVDTN4",
    "outputId": "cc4ef5b7-a89a-4da9-fd45-380a56d8d0ee"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cloning into 'bayesianize'...\n",
      "remote: Enumerating objects: 74, done.\u001b[K\n",
      "remote: Counting objects: 100% (74/74), done.\u001b[K\n",
      "remote: Compressing objects: 100% (66/66), done.\u001b[K\n",
      "remote: Total 74 (delta 14), reused 0 (delta 0), pack-reused 0\u001b[K\n",
      "Unpacking objects: 100% (74/74), done.\n"
     ]
    }
   ],
   "source": [
    "!git clone https://github.com/microsoft/bayesianize.git"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "vbEzUhvoDXvT"
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.append('bayesianize/')\n",
    "import bnn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BZJiHXvB60Fh"
   },
   "source": [
    "We prepare some helper training functions in accordance to the usage of the ```Bayesianize``` package. The idea is similar to the ones in the regression tutorial: we need to compute both the \"data losses\" (i.e., the negative log-likelihood terms) as well as the KL terms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "hsprjWfBF4Sd"
   },
   "outputs": [],
   "source": [
    "# helper functions for training\n",
    "def train_bnn_step(net, opt, dataloader, device, beta):\n",
    "    logs = []\n",
    "    N_data = len(dataloader.dataset)\n",
    "    for _, (x, y) in enumerate(dataloader):\n",
    "        x = x.to(device); y = y.to(device)\n",
    "        opt.zero_grad() # opt is the optimiser\n",
    "        y_logit = net(x)\n",
    "        # training accruacy (on a mini-batch)\n",
    "        pred = y_logit.data.max(1, keepdim=True)[1] # get the index of the max logit\n",
    "        acc = pred.eq(y.data.view_as(pred)).float().cpu().mean()\n",
    "        # training loss\n",
    "        nll = F.nll_loss(F.log_softmax(y_logit, dim=-1), y)\n",
    "        kl = sum(m.kl_divergence() for m in net.modules() if hasattr(m, \"kl_divergence\"))\n",
    "        loss = N_data * nll + beta * kl\n",
    "        loss.backward()\n",
    "        opt.step()\n",
    "        logs.append([to_numpy(nll), to_numpy(acc), to_numpy(kl)])\n",
    "    return np.array(logs)\n",
    "\n",
    "# define the training function\n",
    "def train_bnn(net, opt, dataloader, device, N_epochs=2000, beta=1.0, verbose=True):\n",
    "    net.train()\n",
    "    logs = []\n",
    "    for i in range(N_epochs):\n",
    "        logs_epoch = train_bnn_step(net, opt, dataloader, device, beta)\n",
    "        if verbose:\n",
    "            print(\"Epoch {}, last mini-batch nll={}, acc={}, kl={}\".format(\n",
    "                i+1, logs_epoch[-1][0], logs_epoch[-1][1], logs_epoch[-1][2]))\n",
    "        logs.append(logs_epoch)\n",
    "    return np.concatenate(logs, axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IQbuHwZZ7PjY"
   },
   "source": [
    "Now we train a Bayesian CNN with MFVI as for approximate posterior inference."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 662
    },
    "id": "H5WrGFI6D53-",
    "outputId": "fc5148a8-9746-414e-98ab-43f1e4f98de2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sequential(\n",
      "  (Conv0): FFGConv2d(1, 32, kernel_size=(3, 3), stride=(1, 1), padding=same, prior mean=0.00, prior sd=1.00)\n",
      "  (Nonlinarity0): ReLU()\n",
      "  (Pooling0): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=True)\n",
      "  (Conv1): FFGConv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=same, prior mean=0.00, prior sd=1.00)\n",
      "  (Nonlinarity1): ReLU()\n",
      "  (Pooling1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=True)\n",
      "  (Conv2): FFGConv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=same, prior mean=0.00, prior sd=1.00)\n",
      "  (Nonlinarity2): ReLU()\n",
      "  (Pooling2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=True)\n",
      "  (Flatten): Flatten(start_dim=1, end_dim=-1)\n",
      "  (Linear): FFGLinear(in_features=2048, out_features=10, bias=True, prior mean=0.00, prior sd=1.00)\n",
      ")\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:780: UserWarning: Note that order of the arguments: ceil_mode and return_indices will changeto match the args list in nn.MaxPool2d in a future release.\n",
      "  warnings.warn(\"Note that order of the arguments: ceil_mode and return_indices will change\"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1, last mini-batch nll=0.08229530602693558, acc=0.9800000190734863, kl=322783.625\n",
      "Epoch 2, last mini-batch nll=0.03010711260139942, acc=0.9900000095367432, kl=261299.515625\n",
      "Epoch 3, last mini-batch nll=0.11150902509689331, acc=0.9599999785423279, kl=205361.4375\n",
      "Epoch 4, last mini-batch nll=0.03091660514473915, acc=0.9800000190734863, kl=158070.546875\n",
      "Epoch 5, last mini-batch nll=0.1486053615808487, acc=0.949999988079071, kl=120133.6953125\n"
     ]
    },
    {
     "data": {
      "image/png": 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mzu3albXemoXfT7aS4sQ+UyWjnTvXfF+bbFLzkTGSKSmurKQk3CTZqlX4UPT993FHJFJvcjsx/t//4Isv4o5CROrbW2+FhA3CDWK1/Qq9pm68MbSEJbpqSG745JMw0cSZZ8YdiSRsvnn4NmHWLPj1rzMfeUQkz+R2YjxxYtwRiEhD2HPPsm4GyUlxqlbebGrSpOH6F0vmunSBo45quA9Ikpm994Zrr4Vx42D06LijEakXud3HWEQK07BhoQ/yc8+VlSVaoBLJ0IQJoc9nVWbODOPxSv34xz9yo/uH5I7LLgvjQ597bhjOUN+2SIFRYiwiDe/hh8NzcotgYnKJxFi7FccFTmXHHSuPeyvZ069f3BFIrmnUKIxQseuuoVX/7bdh443jjkoka3K7K4WIFJ6LL05d/txz8JvfhKmKRSR3tWsX+ht/9RWccor6G0tByY/EWBedSOG4+ebU5dtvDzfdpH6lIvmgb1+44YYwbfq998YdjUjWKDEWkYZzySXll6+7LnwdK5LjVq5cSZ8+fdhll13oFoYi3ArAzB42s6/M7L3o0SMqNzO728xmmdkHZtYzsS8zG2ZmX0SPYUnlvczsw2ibu83Cp0Qz29TMXozqv2hmVQxE3YAuvjiMAX7BBWG6cJECkNuJ8ciR4VmJsUh++frrymW9e8Pvfle+7Morw7BcIjmuWbNmvPLKK7z//vu89957ABub2R7R6ovdvUf0eC8qOxjoHD1GAPdBSHKBq4DdgT7AVUmJ7n3AqUnbJabwuxR42d07Ay9Hy/Fr1AjGjg1DuR15JCxZEndEInVWbWJsZh3M7FUzm2lmH5vZuSnqpP1kXCeJ4Zs0yYdIfunYsXLZGWc0eBgi2WJmbBRNKrNmzRoAA6pqtRkMjPVgKtDKzLYEDgJedPfF7r4EeBEYEK3b2N2nursDY4FDkvb1SPT6kaTy+LVpA+PHQ2kpnHSSGrIk72XSYrwWuNDduwJ7AGeaWdcKdVJ+Mq57dFF4utBE8t9JJ8UdgUidrFu3jh49erBZmJlxmbtPi1bdEDUK3WFmzaKyrYG5SZuXRmVVlZemKAfY3N3nR6//C2yeKj4zG2FmM8xsxsKFC2t3krWxxx5wyy3w7LNwxx0Nd1yRelBtYuzu8939nej198AnlF2sCek+Gdcxuig8tRiL5I9//zvuCETqRUlJCe+99x6lpaUALcxsJ+AyoAuwG7ApcEkVu6izqDU5ZWuRu492997u3rtdQ09tfe65cOih4T6Ct95q2GOLZFGN+hibWUdgV2BahVXpPgFX3L5mn2YTd6crMRbJH4sWxR2BSL1q1aoVwPfAgKjxyN19FfBHQr9hgHlAh6TN2kdlVZW3T1EO8E2isSl6XpDVE8oGMxgzJkztfvTR8O23cUckUisZJ8ZmthHwFHCeuy+rzcFq/GlWXSlE8k/TpnFHIJJ1Cxcu5LvvvgNgxYoVABsDnyYlrEbo+/tRtMlE4IToHpw9gKVRd4jJwIFm1jq66e5AYHK0bpmZ7RHt6wTg2aR9JUavGJZUnltatQrDty1YAMcfr0YtyUsZzXxnZk0ISfGf3f0vKaqk+wRcN2oxFsk/jVJ83t5qq4aPQySL5s+fz7Bhw1i3bh3rw3vSMnd/zsxeMbN2hJvx3gNOizaZBAwEZgE/ACcBuPtiM7sOmB7Vu9bdF0evzwAeBjYA/hY9AH4HjDez4cDXQO6OcdizJ9x1F5x+ehiF5vLL445IpEaqTYyjT64PAZ+4++1pqk0EzjKzxwlD0CxNulGg9tRiLFIYtqz7LQcicerevTvvvvvuj8tmNh/A3fdLVT/qC3xmmnVjgDEpymcAO6UoXwTsX6vA4/DrX8OUKfB//wc/+xnss0/cEYlkLJMW472A44EPzSwxPuPlwDYA7n4/aT4Z15luvhMpDH/9a9wRiEhDMYMHHoB33oGhQ+Hdd2GLLeKOSiQj1SbG7v4G4Suiquqk/WRcJ+pKIVIY1GIsUlxatgz9jXffHY45Bl58EUpK4o5KpFq5PfOdulKI5B+r8nO0iBSLnXeGe++FV1+Fa66JOxqRjOR2YqwWYxERkfx14olhcp/rr4fJk+OORqRauZ0Yq8VYJP+oxVhEkt1zD3TrBscdF6aOFslhuZ0Yq8VYJP/og6yIJNtwQ5gwAVauhCFDYM2auCMSSSu3E2O1GIvkv1NOiTsCEYnbDjvA6NHw5ptwxRVxRyOSVn4kxmoxFskfyV0pHn8cHnwwvlhEJHcMHRom/rjlFpg4Me5oRFLK7cRYXSlEaszMBpjZZ2Y2y8wuTbF+GzN71czeNbMPzGxgHHGKSBG6/fYwO96wYTBnTtzRiFSS24mxulKI1IiZlQCjgIOBrsBQM+taodqVwHh33xUYAtyb5SCyujsRKSDNm4fxjd3hyCNh1aq4IxIpJ7cTY7UYi9RUH2CWu89299XA48DgCnUc2Dh6vQnwn6xG8NprZa/bts3qrkWkAPz0p/DHP8KMGXDhhXFHI1JObifGajEWqamtgblJy6VRWbKrgePMrJQwnfvZ6XZmZiPMbIaZzVi4cGFmETz1VNnr/ffPbBsRKS6HHhqS4lGj4LHH4o5G5Ef5kRirxVgkm4YCD7t7e2Ag8KiZpfxf4O6j3b23u/du165dZnv/+9+zFqiIFLAbb4S+feHUU2HmzLijEQFyPTFWVwqRmpoHdEhabh+VJRsOjAdw938CzQH1eRCRhtWkCTzxBGy0ERx+OCxfHndEIjmeGKsrhUhNTQc6m1knM2tKuLmu4rhI/wb2BzCzHQmJcYb9JEREsmirrcKwjp9/HlqO9X4vMcvtxFgtxiI14u5rgbOAycAnhNEnPjaza81sUFTtQuBUM3sfGAec6K53IxGJyb77wvXXhwT53uwOkiNSU43jDqBKajEWqTF3n0S4qS65bGTS65nAXg0dl4hIWpdcAm+9BeefD717w+67xx2RFCm1GIuIiEi8GjWCsWNh663D+Mbffht3RFKkcjsxVouxSP7q0yfuCEQkn7RuDRMmwDffwHHHqVFMYpEfibEuDpH8s3p13BGISL7p1Qt+/3uYPDn0OxZpYLmdGKsrhUj+0lSvIlIbp54Kxx8PV1+tcdGlweVHYrx2bbxxiEhmXnqp7HXPnvHFISL5ywzuuw+6dYNjjoG5c6vfRiRLcjsxHjs2PN91V7xxiEhmpk8ve33hhfHFISL5rUWL0N949Wo46ih1zZIGk9uJ8fz54fmbb+KNQ0Qy0yjpX0qLFvHFISL5b4cdYMwYmDoVfvObuKORIpHbiXGib3FJSbxxiEhmkq/V7bePLw4RKQxHHAHnnRe+OR4/Pu5opAjkdmI8MpqT4JRT4o1DRDKTuC9ARCRbbroJ9twThg+HTz+NOxopcLmdGLdpE5433DDeOEQkM7pWRSTbmjYNrcXNm4cW5P/9L+6IpIDldmKs1ieR/NK0adwRiEghat8exo2DmTPhtNM08ZfUm9xOjBN0AYjkB12rUqBWrlxJnz592GWXXejWrRvAVgBm1snMppnZLDN7wsyaRuXNouVZ0fqOiX2Z2WVR+WdmdlBS+YCobJaZXZpUnvIYRad/f7jmGvjTn2D06LijkQKV24lxosVYb7Yi+UHXqhSoZs2a8corr/D+++/z3nvvAWxsZnsANwF3uPt2wBJgeLTJcGBJVH5HVA8z6woMAboBA4B7zazEzEqAUcDBQFdgaFSXKo5RfK64Ag4+GM45B2bMiDsaKUD5kRiLSH5QYiwFyszYaKONAFizZg2AAQ7sB0yIqj0CHBK9HhwtE63f38wsKn/c3Ve5+1fALKBP9Jjl7rPdfTXwODA42ibdMYpPo0bw6KOwxRahv/HixXFHJAUmtxPjBE0JLZIfdK1KAVu3bh09evRgs802A1gGfAl85+6J6VlLga2j11sDcwGi9UuBNsnlFbZJV96mimOUY2YjzGyGmc1YuHBhXU41t7VpA08+Cf/5T5g6Wv93JItyOzFesSI8X3ZZvHGISGZatw7P++8fbxwi9aCkpIT33nuP0tJSgBZAl5hDKsfdR7t7b3fv3a5du7jDqV99+sCdd8KkSXD99XFHIwUktxPj778Pzx9/HG8cIpKZ6Ktmbrgh3jhE6lGrVq0Avgf2BFqZWeNoVXtgXvR6HtABIFq/CbAoubzCNunKF1VxjOJ2+umhxfjqq+Fvf4s7GikQuZ0YN8rt8ESkgkQfY127UmAWLlzId999B8CK8G3mxsAnwKvAEVG1YcCz0euJ0TLR+lfc3aPyIdGoFZ2AzsC/gOlA52gEiqaEG/QmRtukO0ZxM4P774dddoFjjoHZs+OOSApAte9eZjbGzBaY2Udp1u9jZkvN7L3oMTJr0WkqaJH8kkiMdeOsFJj58+ez77770r17d3bbbTeAZe7+HHAJcIGZzSL0B34o2uQhoE1UfgFwKYC7fwyMB2YCLwBnuvu6qA/xWcBkQsI9PqpLFceQDTeEv/wl/M857DD44Ye4I5I817j6KjwM3AOMraLO6+7+y6xElExvriL5RYmxFKju3bvz7rvv/rhsZvMB3H02YUSJctx9JXBkqn25+w1Apf5G7j4JmJSiPOUxJNKpEzz2GAwcCL/+NYwdq/9BUmvVthi7+xQgnvFQNPSTSH5RYiwicRgwoGzyj1Gj4o5G8li2OgLuaWbvm9nfzKxbuko1HkpGibFIflFiLCJxueIK+NWv4Pzz4c03445G8lQ2EuN3gJ+4+y7A74Fn0lWs8VAySoxF8osSYxGJS6NGoRtFx45w5JHw3//GHZHkoTonxu6+zN2XR68nAU3MrG2dIws7zMpuRKSBKDEWkTi1ahVuxlu6NCTHYZZCkYzVOTE2sy2iKSsxsz7RPhfVdb+AEmORfKPEWETitvPO8Ic/wBtvwEUXxR2N5JlqR6Uws3HAPkBbMysFrgKaALj7/YSxFU83s7XACmBINO5i3a1bl5XdiEgDCTOCKTEWkXgNHQrTpsFdd8Huu4dxjkUyUG1i7O5Dq1l/D2E4t+zT/Oci+eW888KzEmMRidstt8A778App8BOO0H37nFHJHkgt6enUmIskp+UGItI3Jo0gfHjQ7/jQw+FxfGMPCv5JbcTY3WlEMlPSoxFJBdssQVMmABz54buFMorpBq5nRjvs0947tcv1jBEpIaUGItIrvjZz8KkH5Mnh7GORaqQyZTQ8WnbFjbaCHr1ijsSEamJRrn9mVtEisypp8Lbb8NNN8Guu8LRR8cdkeSo3H/3MtOwbSL5Ri3GIpJr7r4b9toLTj4Z3n8/7mgkRykxFpHsU2IsIrmmadPQ37hVKzjkEFiUnSkXpLDkR2IsIvlF162I5KIttggz4/3nPzBkCKxdG3dEkmNyPzEGtRiL1ICZDTCzz8xslpldmqbOUWY208w+NrPH6iGIrO9SRCQrdt8d7r8fXnoJLk35L1KKWG7ffAfqSiFSA2ZWAowCDgBKgelmNtHdZybV6QxcBuzl7kvMbLN6CCTruxQRyZqTTgqTf9x2G/TsqZnx5Ee532KsxFikJvoAs9x9truvBh4HBleocyowyt2XALj7gqxHoWtWRHLd7beH4WCHDw9JsghKjEUKzdbA3KTl0qgs2fbA9mb2pplNNbMB6XZmZiPMbIaZzVi4cGE9hCsiEpMmTeDJJ8PQsIceCvofJygxFilGjYHOwD7AUOBBM2uVqqK7j3b33u7eu127dg0YoohIA9hsM3jmGViwIIxtvGZN3BFJzJQYixSWeUCHpOX2UVmyUmCiu69x96+AzwmJcvbomhWRfNGrF4weDa++ChdfHHc0ErPcT4y//Rbuuy/uKETyxXSgs5l1MrOmwBBgYoU6zxBaizGztoSuFbMbMkgRkZxy/PFw3nlw110wdmzc0UiMcj8xFpGMufta4CxgMvAJMN7dPzaza81sUFRtMrDIzGYCrwIXu3t2R7pXi7GI5JtbboH99oMRI2DGjLijkZjk/nBtIlIj7j4JmFShbGTSawcuiB71FUS97VpEpF40bgxPPAG9e4eb8WbMgM03jzsqaWBqMRaR7FNiLCL5qG1bePrpMF30kUfqZrwipMRYREREJGHXXeGhh+D11+H88+OORhqYulKISPapxVhE8tnQoWHSj1tvDTPjnYSgEhMAACAASURBVHxy3BFJA1GLsYhknxJjEcl3N94IBxwAp58OU6fGHY00ECXGIpJ9SoxFJN81bgyPPw4dOsAhh8C//x13RNIAlBiLSPZ16RJ3BCJZNXfuXPbdd1+6du1Kt27dADYDMLOrzWyemb0XPQYmtjGzy8xslpl9ZmYHJZUPiMpmmdmlSeWdzGxaVP5ENBY5ZtYsWp4Vre/YUOdd9DbdFP76V1ixAgYNguXL445I6pkSYxHJvkb61yKFpXHjxtx2223MnDmTqeFr9c3MrGu0+g537xE9JgFE64YA3YABwL1mVmJmJcAo4GCgKzA0aT83RfvaDlgCDI/KhwNLovI7onrSUHbcMQzj9uGHYSKQ9evjjkjqkd69REREqrHlllvSs2dPAFq2bAmwAti6ik0GA4+7+6po6vVZQJ/oMcvdZ7v7auBxYLCZGbAfMCHa/hHgkKR9PRK9ngDsH9WXhjJgANxxBzzzDFx5ZdzRSD1SYiwiIlIDc+bMAdgQmBYVnWVmH5jZGDNrHZVtDcxN2qw0KktX3gb4Lpq9Mrm83L6i9Uuj+uWY2Qgzm2FmMxYuXFinc5QUzj4bfv3rcFPeo4/GHY3Uk9xPjLt0gdatq68nIiJSz5YvX87hhx8OMNfdlwH3AdsCPYD5wG1xxebuo929t7v3bteuXVxhFC4z+P3vw7TRp5wCb70Vd0RSD3I/Md55Z9hii7ijEBGRIrdmzRoOP/xwjj32WIDvANz9G3df5+7rgQcJXSUA5gEdkjZvH5WlK18EtDKzxhXKy+0rWr9JVF8aWpMm8OSTsM02YaSKr7+OOyLJstxPjM3U0V0kH+irWylg7s7w4cPZcccdueCCC34sN7Mtk6odCnwUvZ4IDIlGlOgEdAb+BUwHOkcjUDQl3KA30d0deBU4Itp+GPBs0r6GRa+PAF6J6kscEiNVrF4Nv/oVfP993BFJFuX+zHeNGmlMVJF8sHhx3BGI1Js333yTRx99lJ133pkePXoAdI2GZhtqZj0AB+YAvwZw94/NbDwwE1gLnOnu6wDM7CxgMlACjHH3j6PDXAI8bmbXA+8CD0XlDwGPmtksYDEhmZY4dekSWo4PPhiOOw7+8hcoKYk7KsmC/EiM1WIskvt0k7wUsL59+5LcSGtmM6Oh2Sal28bdbwBuSFGecjt3n01ZV4zk8pXAkbWLXOrNAQfAXXfBWWfB5ZfDTRpFrxAoMRYRERGpjTPPhJkz4eabw3jHJ54Yd0RSR+pjLCIiIlJbd94J/fvDiBHwxhtxRyN1lPuJsfoYi4iISK5q0gTGj4dOneDQQ+Grr+KOSOogPxJjtRiL5D71MRaRYtW6dRipYu1aGDQIli2LOyKppWoT42gmnwVm9lGa9WZmd5vZrGjmn55ZjVBdKURERCTXbb89TJgAn3wCxxwD69bFHZHUQiYtxg8DA6pYfzBhfMbOwAjCLEDZo64UIvlBLcYiUuz23x/uuQeefx5+85u4o5FaqHZUCnefYmYdq6gyGBgbDTY+1cxamdmW7j4/KxGqK4WIiIjki9NOCyNV3H47bLstnHFG3BFJDWSjj/HWwNyk5dKorBIzG2FmM8xsxsJMZ8lSVwoRERHJJ3fcEWbFO/vs0PdY8kaD3nzn7qPdvbe7927Xrl1mG6krhYiIiOSTkhIYNw569oQhQ2DGjLgjkgxlIzGeB3RIWm4flWWHulKI5Ad9gBURKdOiBTz3HGy2GfzylzBnTtwRSQaykRhPBE6IRqfYA1iatf7FoK4UIiIikp823xwmTYJVq2DgQFiyJO6IpBqZDNc2DvgnsIOZlZrZcDM7zcxOi6pMAmYDs4AHgez2MldXChEREclXO+4IzzwDX34ZJgBZtSruiKQKmYxKMbSa9Q6cmbWIKlJXChEREclne+8Nf/wjHHssDB8Ojz6qIS5zVLWJcezMNIOMiIiI5Ldjjgn9jK+4Ajp2hOuvjzsiSSH3p4S+//7w/O678cYhIlVT64eISNUuuwxOOQVuuAEefDDuaCSF3E+MV6wIz198EW8cIlK1Zs3ijkBEJLeZwb33woABcPrp8Le/xR2RVJD7iXGCWqNEREQk3zVpAuPHQ/fucMQRMG1a3BFJkvxJjFevjjsCEamKRo8REclMy5ahtXjLLeEXv4BPP407IonkT2J8zjlxRyAiIiKSHZtvDpMnh1nyDjoI5mVvbjSpvfxJjBcvjjsCEamKWoxFRGpm223hhRfCxB8DBmgCkByQP4mxiGTEzAaY2WdmNsvMLq2i3uFm5mbWuyHjExGRJLvuGiYA+fxzGDSobNABiYUSY5ECYmYlwCjgYKArMNTMuqao1xI4F8jeXR9qMRYRqZ399guTfrz5JgwZAmvXxh1R0VJiLFJY+gCz3H22u68GHgcGp6h3HXATsLIhgxMRkTSOOgruvhsmTgxDuamxIRZKjEUKy9bA3KTl0qjsR2bWE+jg7s9n9cj6Jy4iUjdnnQVXXgl/+AOMHBl3NEUp96eEFpGsMbNGwO3AiRnWHwGMANhmm20yO8jtt9cuOBERgWuvhW++CVNGb755SJalwajFWKSwzAM6JC23j8oSWgI7Aa+Z2RxgD2Biuhvw3H20u/d2997t2rWr+siJFuNNN61l6CIi8uPseIccEoaqHT8+7oiKihJjkcIyHehsZp3MrCkwBJiYWOnuS929rbt3dPeOwFRgkLvPyFoEmqVSRKRuGjeGxx6Dvn3huOPg5ZfjjqhoKDEWKSDuvhY4C5gMfAKMd/ePzexaMxtUzwev192LiBSVDTYIN+J16RJaj6dPjzuioqDEWKTAuPskd9/e3bd19xuispHuPjFF3X2y2loMajGWgjR37lz23XdfunbtSrdu3QA2AzCzTc3sRTP7InpuHZWbmd0djSf+QXTTK9G6YVH9L8xsWFJ5LzP7MNrmbrNwMaU7hhSBVq3CBCDt2oUJQD76KO6ICl7uJ8Z//3vcEYiISJFr3Lgxt912GzNnzmTq1KkAm0VjhF8KvOzunYGXo2UIY4l3jh4jgPsgJLnAVcDuhOEVr0pKdO8DTk3abkBUnu4YUgy22gpeegmaN4cDDoBZs+KOqKDlfmLcW5NyieQFdaWQArblllvSs2do9G3ZsiXACsJQiIOBR6JqjwCHRK8HA2M9mAq0MrMtgYOAF919sbsvAV4EBkTrNnb3qe7uwNgK+0p1DCkWP/0pvPgirFkD/ftDaWncERWs3E+Mk7+WXam5CERynrpSSIGbM2cOwIaEmSM3d/f50ar/AptHr9ONKV5VeWmKcqo4hhSTrl1h8mRYsiS0HC9YEHdEBSm/EuOrroovDhGpmlqMpQgsX76cww8/HGCuuy9LXhe19NbrhVDVMcxshJnNMLMZCxcurM8wJC69esFzz8HXX8NBB8F338UdUcHJ/cQ42eLFcUcgIun88EN4VouxFKg1a9Zw+OGHc+yxxwIkMpJvom4QRM+JZrx0Y4pXVd4+RXlVxyinRuOOS/76+c/h6afh44/hF7+A//0v7ogKSu4nxnqTFckPhx0WntevjzcOkXrg7gwfPpwdd9yRCy64IHnVRCAxssQw4Nmk8hOi0Sn2AJZG3SEmAweaWevoprsDgcnRumVmtkc0GsUJFfaV6hhSrA46CMaNg6lT4dBDYdWquCMqGLmfGItIfpg9O+4IROrNm2++yaOPPsorr7xCjx49ALqa2UDgd8ABZvYF0D9aBpgEzAZmAQ8CZwC4+2LgOsJkPNOBa6Myojp/iLb5EvhbVJ7uGFLMDj8cxowJN+UNHQpr18YdUUFoHHcA1VKLsUh+0TUrBahv3754Uj96M5vp7pOixf0r1o/6Ap+Zal/uPgYYk6J8BmHK9orli1IdQ4Rhw2DZsjB19EknwcMPQ0lJ3FHltfxKjHVzj0ju03UqItJwzj4bli+Hyy8PU0k/9BA0UoeA2sr9xDiZ3nBFREREyrvsMli9Gq6+OiTHDzyg5LiW8isxXrcu7ghEREREcs/IkaGf8fXXh+T43nvVta0Wcv/jRPId7mPHxheHiIiISK4yg2uvhUsugfvvD/2O9U17jeV+i3HyL9U9JMr6ekBERESkPDO48cbQcnzbbeGb9nvuUd5UA/mVGAN8+mmYFlFEREREyjODW24Jo1PcfHPoezx6tJLjDOV+YrzBBuWX1V9GREREJD0z+N3voGnT0Od4zZow5rGGcqtW7ifGzZrFHYGIiIhIfjGD664LyfHIkSE5Hjs23JgnaeVfu/rFF8Of/hR3FCIiIiK57//+L7QejxsHQ4aEBFnSyigxNrMBZvaZmc0ys0tTrD/RzBaa2XvR45SsRvmTn5S9fv55OP74rO5eREREpGBdcgncfjs89RQceSSsWhV3RDmr2sTYzEqAUcDBQFdgqJmluvvtCXfvET3+kOU4RSRfPP103BGIiEhF558fRqh49lk49FBYsSLuiHJSJi3GfYBZ7j7b3VcDjwOD6zcsEclbH34YdwQiIpLKmWfCgw/CCy/AwIHw/fdxR5RzMkmMtwbmJi2XRmUVHW5mH5jZBDPrkGpHZjbCzGaY2YyFCxdmHqVGohARERGpu1NOCfdqvf46HHggLFkSd0Q5JVs33/0V6Oju3YEXgUdSVXL30e7e2917t2vXLvO9KzEWyR+aaUlEJLcdcwxMmADvvAP77Qc1aawscJkkxvOA5Bbg9lHZj9x9kbsnenL/AeiVnfAi556b1d2JSD1SYiwikvsOOQQmToTPPoN+/WDevOq3KQKZJMbTgc5m1snMmgJDgInJFcxsy6TFQcAn2QsROPzwrO5OREREpOgddFDob1xaGpLjOXPijih21SbG7r4WOAuYTEh4x7v7x2Z2rZkNiqqdY2Yfm9n7wDnAifUVsIiIiIhkSb9+8PLLoa/xz38eWpCLWEZ9jN19krtv7+7buvsNUdlId58Yvb7M3bu5+y7uvq+7f5rVKFN9Nbt4cVYPISIiIlKU+vSB116D1atDcvz223FHFJv8mPlu/frKZW3awHffNXwsIiIiIoWme/cwUsWGG8I++8BLL8UdUSzyNzEGDTEikot0852ISH7afnt46y3o2DGMczx+fNwRNbj8ToxFJPcoMRYRyV9bbQVTpsDuu8OQITBqVNwRNSglxiKSXUqMRUTyW+vW8Pe/w69+BWedBSNHFs3/9vxIjFu0SF1+ySX1f+wPPggTjLz8cv0fS0RERCQXbLABPPUUnHwyXHcdnHYarFsXd1T1Lj8S4622Sl3+5JP1f+x//CM8P/NM/R9LpBDssUfcEYiISDY0bgx/+ANcfjmMHg1HHgkrV8YdVb3Kj8RY6s+yZfDf/8YdhRSS4cPjjkBERLLFDG64Ae66C55+GgYMgKVL446q3hROYrzzzqEPTH0p1L41XbrAlltWX69QJS54yR6zuCMQEZFsO+cceOyxMGrF3nvD/PlxR1QvCicx/uij0Acm2wr9Tb5A/7Azkviwc+WV8cYhIiKSD4YOheeeg1mzwqgVH34Yd0RZlz+J8d/+FncEUmgK9FsAMxtgZp+Z2SwzuzTF+gvMbKaZfWBmL5vZT+KIU0RE8tCBB4aJQNatg732gsmT444oq/InMR4wIHX5t982bBxSOAowMTazEmAUcDDQFRhqZl0rVHsX6O3u3YEJwM0NG6WIiOS1XXeFadPgpz+FX/wCHngg7oiyJn8S43T+9a/U5StW1H1Ykddfh9mzw+sCTKKK2pIlsHBh3FHUhz7ALHef7e6rgceBwckV3P1Vd/8hWpwKtM9qBIXe/UiK0sknn8xmm23GTjvt9GOZmV1tZvPM7L3oMTBp3WXRtzafmdlBSeUpv9Exs05mNi0qf8LMmkblzaLlWdH6jg1ywiLVad8+5EkHHRSGcrv44oIYzi3/E+MzzggDUFe04YZwzDF123e/fnDHHZnV/eQTePPNuh1Psufss8Nc7+lsumnZTYe1TeRWrKj+A9PFFzd0org1MDdpuTQqS2c4kLafkpmNMLMZZjZjYWF+kBDJyIknnsgLL7yQatUd7t4jekwCiL6lGQJ0AwYA95pZSTXf6NwU7Ws7YAnh2iR6XhKV3xHVE8kNLVvCs8/CmWfCrbfCoYfC99/HHVWd5H9i/PXXoSN4QvJEHLWZ4/uKK2rXX6ZrV+jbt+bb1dTatbk3E+DTT4dh37Jp2rTwSbS27rknjEG9zz7wzjtV161N4vrvf4cPX/ffX3W9W2+t+b4biJkdB/QGbklXx91Hu3tvd+/drl27hgtOJMf069ePTTfdNNPqg4HH3X2Vu38FzCJ8m5PyGx0zM2A/QtcmgEeAQ5L29Uj0egKwf1RfJDc0bhzec0eNgkmT4Gc/g6++ijuqWsv/xLii/v3LtxTfdFNIfI49NkxvOHp0+m3Xr4ff/jZ9f+ZMTJkCf/1r7bevyqefQpMmsMkmtd9H06Zw++3Zi+nzz+Gww2DYsOztE8IkEf361X0///hHmM6yKqneY9zDkDTpWoS/+CI8N8QkMzUzD+iQtNw+KivHzPoDVwCD3H1VA8UmUojOim5kHWNmraOydN/cpCtvA3zn7msrlJfbV7R+aVS/En3DI7E64wx44QUoLYXddgv5UB7Kr8R4hx0yqzduXNnrS6MuXI89FvrB/PrXqbdZuxZKStLvM9M+xnvvDYMGZVa3piZODM/Ll9d+H2vWwIUXZicegB+irqqZfDpcsQL69CnrF76qgfKxdevCh5277kq9vlGKy2DcuHC37Z//XFb2xhshif7885q3MjdcH/XpQOeov2JTwte5E5MrmNmuwAOEpHhBQwUmUoDuA7YFegDzgdviDEbf8Ejs+vcP7/Ft28L++8ODD8YdUY3lV2L8xhv1t+84+wdPmBD/1w5jx5bvhgKhM/1NN4WW9OnT636Md98N+/nVr8KjeXN4//267zfZ6tWhO0yyf/0rdI8577zU26RKchMtwolnKEuSX3qprOzVV+Gaa0JrflUaKDGOWpTOAiYDnwDj3f1jM7vWzBKf2G4BNgKejG4Ymphmd7Wjb3mlSLj7N+6+zt3XAw8SukpA+m9u0pUvAlqZWeMK5eX2Fa3fJKovkps6d4apU0NiPGIEnHtuaHzME/mVGLdtm93WzqpUNwzcunXZ6+t75JHQs2fl8s8+g4cfTr2NO+y5Jzz1VHZiGDYsfNJL9sADocX91ltDS29VfX4zSfwSdRYsKOsXftVV4fn770NSm87ixfDll2XL69bBSSdVTqzHjAndYWoiXVeKdPUqrrv66tCnqioNOKqJu09y9+3dfVt3vyEqG+nuE6PX/d1986QbhurpKw6RwmZmydOGHgp8FL2eCAyJRpToBHQG/kWab3Tc3YFXgSOi7YcBzybtK9FX7Qjglai+SO5q1Sq8z59/Ptx9NwwcGEaDygP5lRhD9m5mWry46tbK6voZt2oF226bnVgAvvuucln37iH5g8oz1K1fHz6RHXVUWdnKlVUnyhX/l1bXypmQ+Dn9+9+V16VLFh94ILP+Rc8+G74J2Hhj2G+/9PW6doXttitbnj07fGg4/PDy9apKrtOpqktHctKc7lyh+nnjc+2GSRGpkaFDh7Lnnnvy2Wef0b59e4C2wM1m9qGZfQDsC5wP4O4fA+OBmcALwJlRy3LKb3SiQ1wCXGBmswh9iB+Kyh8C2kTlFwCVJu0RyUmNG4d7mh56CF57Ldw79PnncUdVrfxLjLPhxRehTRvo0SN0FIfKrYYff1x+2T3UueSSsLx8OcyZU7Pjfv013Hdf5f2mk0jy/vlP2GorePTR1Nv9/vchtjPPhCOOKEtIX3ghdNNId6wFteheOnFi+e4FycniPfeE5cWLQzeMvfcO6954I7R+pzvXn/88PKfqzvLf/4bnb74pX54YK7Fiv/Dafo2f7obJr76CHXcMcSS+Crr22srHqS7xLaYGHnWlkAI0btw45s+fz5o1aygtLQX41t2Pd/ed3b27uw9y9x9bMNz9huhbmx3c/W9J5ZW+0YnKZ7t7H3ffzt2PTNwU6+4ro+XtovWzG/K8Rers5JPhlVdCi/Huu4ccLIc1rr5KATrwwLLXBx+cOmlZubL8ciLxufnm0O+2OkuXhk9KO+4IQ4aEsv79w/ziQ4eGFmdInzAltyB/8EF4/uijsrLk7RKt6Il+wIltDz64fN3kbebPL0tca2Lw4PL7Sk6MEzPfzKswCEIi8R04kBrbcsvKP6PS0jDCCJQlxh9+GD44JN94WRNffBH6IU+YEG4WuCF6vxo7tiyOhIULq28NX7q0/IgVajEWEZFi1bdvuN9n0KDwjfzNN8MFF+RkQ0pxJsapVPfLqWqYt1QGDSpLnhKJcWL4nMGDQ5L8f/+XfpaYZ58te52qC0NyspiI/cMPK69LZ6ut0q/75S/L9gVls/9VHA/4H/+ofui3iUn3dU2aVH1cADNnpl938cXlu9OUlISuEN27Z7bvdG69tay7yq9+Vf2NAldfXblsxYpwQ+Gf/gQnnFB+XTG1GIuIiFTUsWMYBvXEE+Gii8J8BQ89FCYJySHF2ZWiooceKht2LBOZjAM8Y0b55QULyvqhTpkCI0eGFuBMWhJT3UyWaJWtSUtkpsnZ88+XT8anTg3PFc97n33KEt+PPy5L8pOTyhtuoEbM4Ikn0q+v2Mf8o4+qTqQruvnm1F02kvtwDx5ceX0mEhN+pJod63e/q90+80VdJmMREZHisNFG4dvUW24J90Ttvnvm9zs1ELUYA5xySs3qVxwZI1XrYvLYuJ9/nnoM5p13Tn+ME0+sOoZEVwIIfZeTpRrn+JhjwkQc1anYhaQmPvkkPP/iF2VljWvxJzaxwuhhycOjpZJqRI90En3E68uNN8LcuZXLr702DOtWqBpqTGrJeYk+uCvr8r8khzRv3pz27dvTpEmTuEMRKQxmocW4Vy84+ugwGUiqm+ljosS4rp54oqyrRLLk5DTTiUlWrAh9XI88svq6Vd04517++J99FvreZtL/doMNqq8D8Mwz6dclt75WNWlKOu+9V375gANqvo+4pEqKi0Gq0TukKJWWltKyZUs6duxIvs9c7O4sWrSI0tJSOnXqFHc4IoVl331DF80jjgiPiy8O35DXpkEti9SVoq5SJcW1teGGoW9qJsnpyJHp11Xss9OlS91jq+jQQzOrN1s3UBeFVLMHSlFauXIlbdq0yfukGMDMaNOmTcG0fovknPbtw/1Kp58eulfss0/sDUz5+W72xRfw9ttxR5G7Xnst7gjKVByhQkQKXiEkxQmFdC4iOalZM7j3XnjssTBvQo8eZZOAxSA/E+Pttgv9Stu2jTsSEYHKI5aIiIjUxNCh4b1km23C6FAXXVS7SbvqKD8T44SFC8vfhCYi8ajJqC4iIiKpdO4cJjU74wy47Tbo16/mk6nVUX4nxhBuzKqPPrQiUjvZnCpdRESKS/PmMGpUGNbtk09g112rvuE/ywpjVIocGxxapOgkj5HdoUN8cUhuOe+8yqPM1FWPHnDnndVWO+SQQ5g7dy4rV67k3HPPZcSIEbzwwgtcfvnlrFu3jrZt2/Lyyy+zfPlyzj77bGbMmIGZcdVVV3F4jgwbJVLUjjgidJs9+uhww/+554aZh5s1q9fDFkZi3KJF3BGIFLfttos7ApFyxowZw6abbsqKFSvYbbfdGDx4MKeeeipTpkyhU6dOLF68GIDrrruOTTbZhA+j2T6XLFkSZ9gikuynP4U33ghzENx1V3j92GOw/fb1dsjCSIyfeQZatSpbHjUKdtoJ9t67rKxJEzj44MqTR4hI3Q0YEHcEkosyaNmtL3fffTdPP/00AHPnzmX06NH069fvx/GIN910UwBeeuklHn/88R+3a926dcMHKyLpNWsW/pfsuy+cfHLoWnHXXTB8eL2Mm59RH2MzG2Bmn5nZLDO7NMX6Zmb2RLR+mpl1zHagVdpkk/BV7nffwVdfhU7b/fqFslWrYPLkcGfjCSc0aFgiRUOzgkkOee2113jppZf45z//yfvvv8+uu+5Kjx494g5LROpi8GD44APYc0849dTQ1WLRoqwfptrE2MxKgFHAwUBXYKiZda1QbTiwxN23A+4Absp2oBnZZBPo2LF8WdOmcOCB4fVhh4XW5K22CtMMr11b+YfauzfcfnvlfZ98cuWyFSvKXv/xj/DCC3UKP29Mnpz9fV55Zfb3WR/OOSf9uu+/h6uuqlx+3HFhRp9Cpn7+kkOWLl1K69at2XDDDfn000+ZOnUqK1euZMqUKXz11VcAP3alOOCAAxg1atSP26orhUgO23rrMBrZrbfCX/8K3bvDyy9n9xjuXuUD2BOYnLR8GXBZhTqTgT2j142BbwGrar+9evXynLF4sftdd7k3auT+3Xfh0b+/+9Sp7uvXl9X79lv3ZcvcH3zQ/be/DWVr1rh/801ZnQ8+cAf3li3DM7gPHVr2OvFwd//hB/chQ8Lygw+Gstdfd2/SpHL9mjwOPLDsdefO4fmBB1LXHT7c/csvw3E7dKi8fuFC96OPrhz7qlVlywsWuC9aFMrff7/yPvbdt/zygw+6z5sXHsn7POKI8vXmznV/8cWy5b/8JTzvsIP7q6+G1+ef77733uF1x47hec89U5/reee5v/RS5fIePdyvvNL9hBPKlx9/vPthh4XXX34ZYly/PsS9zTah/Cc/cd9ss3DshBYtwrqDDgp/SxkCZng112Ocj2qv2eTfpRStmTNnxh2Cr1y50gcMGOBdunTxwYMH+9577+2vvvqqT5o0yXv06OHdu3f3/v37u7v7999/7yeccIJ369bNu3fv7k899VSl/aU6p7y/XkXy3TvvuHfp4m7mfs011VbP9Jq1UDc9MzsCGODup0TLxwO7u/tZSXU+iuqURstfRnW+rbCvEcAIgG222abX119/XZecPretXw9vvQV9+4Zld5g+Hbp2Df1lkr96XrIk9JFO7iuzbh18+y28+264K3PNGthsszBl4jnnhPU/ONOxVQAACOJJREFU/BCO06JF2H7xYli+PAyOPX06/PvfkHx39WefQadOYV8//ADt2lWOe/780CVl8eIwZEqvXmXrli+HpUvDJzYI0z0/91zVrajJ1q6tPAf6vHnhWDvvHOL63//C8d94I7S0QvgZbL99OM9EXBtumPoYX3wRvhF4663wc2vePIx37R7OHeDGG8NXMG3bwujR8JvfpO+ntH59eKSau33lyrD/LDGzt929d9Z2mGW9e/f2GTNmpK8wejTssgvsvnvDBSU555NPPmHHHXeMO4ysSnVOeX+9ihSCH34IE4H07QvHHFNl1Uyv2QZNjJPpohUpT2+0UgiUGOcGXa8i5WV6zWZy8908IHlg0vZRWco6ZtYY2ATIfo9oEREREZF6kkliPB3obGadzKwpMASoOObZRGBY9PoI4BWvrilaREQKUiH9+y+kcxGR6lWbGLv7WuAswg12nwDj3f1jM7vWzAZF1R4C2pjZLOACoNKQbiIiUviaN2/OokWLCiKhdHcWLVpE8yzeSyAiuS2jCT7cfRIwqULZyKTXK4EjsxuaiIjkm/bt21NaWsrChQvjDiUrmjdvTvv27eMOQ0QaSGHMfCciIjmhSZMmP84uJyKSbzKa+U5EREREpNApMRYRERERQYmxiIiIiAiQwQQf9XZgs4VAdVPftSVML10sdL6FK5Nz/Ym7p5iOMDdkcM0W0+8TdL6FrrrzzffrFfQ7LWTFdK6QxffY2BLjTJjZjFyeWSjbdL6FqxjOtRjOMZnOt7AVw/kWwzkmK6bzLaZzheyer7pSiIiIiIigxFhEREREBMj9xHh03AE0MJ1v4SqGcy2Gc0ym8y1sxXC+xXCOyYrpfIvpXCGL55vTfYxFRERERBpKrrcYi4iIiIg0CCXGIiIiIiLkcGJsZgPM7DMzm2Vml8YdT22Y2RgzW2BmHyWVbWpmL5rZF9Fz66jczOzu6Hw/MLOeSdsMi+p/YWbD4jiXTJhZBzN71cxmmtnHZnZuVF5w52xmzc3sX2b2fnSu10TlncxsWnROT5hZ06i8WbQ8K1rfMWlfl0Xln5nZQfGcUd0UwvUKxXXNFtP1Crpmk+l6zcu/X12vNND16u459wBKgC+BnwJNgfeBrnHHVYvz6Af0BD5KKrsZuDR6fSlwU/R6IPA3wIA9gGlR+abA7Oi5dfS6ddznluZ8twR6Rq9bAp8DXQvxnKOYN4peNwGmRecwHhgSld8PnB69PgO4P3o9BHgiet01+vtuBnSK/u5L4j6/Gv4sCuJ6jc6laK7ZYrpeozh1zbqu1zz++9X12kDXa662GPcBZrn7bHdfDTwODI45phpz9ynA4grFg4FHotePAIcklY/1YCrQysy2BA4CXnT3xe6+BHgRGFD/0decu89393ei198DnwBbU4DnHMW8PFpsEj0c2A+YEJVXPNfEz2ACsL+ZWVT+uLuvcvevgFmEv/98UhDXKxTXNVtM1yvomk2i6zU//351vTbQ9ZqrifHWwNyk5dKorBBs7u7zo9f/BTaPXqc757z8WURfY+xK+JRXkOdsZiVm9h6wgPDP5UvgO3dfG1VJjvvHc4rWLwXakCfnWo1COIeqFOTfb7JiuF5B12wk3+OvTsH+/Sb8f3v382JVGcdx/P0pyyxDE2yTUVlCP8CMIioLgkioVYuJojKxlm3cRdgP6A+olZCLFkYSYSlJm0ITwUVolNnvklZGJERZBoXYt8V5Jm+SGpPemXvu+wXD3PucM2ee753zgeec85w55vXM5nWmDozHQnXn+Xv3//KSzAXeBNZU1S+Dy/pUc1UdraplwCK6I9Crp7lLOsP6tP9OGpe8gpkdN33bf8G8DuP3ztSB8XfApQPvF7W2PvihXc6gfT/Y2k9U80h9FknOoQvtxqra3Jp7XXNV/QzsAG6lu1w1qy0a7PffNbXl84AfGbFaT6APNZxMb/ffccwrjH1mR73/p9Lb/de8DievM3VgvAdY0u4+PJduIvXWae7T6bIVmLwLdBXw1kD7o+1O0luAQ+3yyDvAiiQXtbtNV7S2GafN53kZ+KKqXhhY1LuakyxMMr+9ngPcTTfnawcw0VY7vtbJz2ACeK8d3W8FHmx31F4BLAF2D6eK06bPeYUe7r8wXnkFMzvAvI7m/mteh5XXmgF3H/7bF90dlV/TzSlZO939mWINrwHfA0fo5rU8TjfnZTvwDbANWFDH7sBc1+r9BLhpYDuP0U0Y3w+snu66TlLv7XSXcfYBe9vXvX2sGVgKfNRq/RR4trUvbqHbD2wCZrf289r7/W354oFtrW2fwVfAPdNd2xQ/j5HPa6tjbDI7TnltfTSzx/pvXkdv/zWvNZy8+khoSZIkiZk7lUKSJEkaKgfGkiRJEg6MJUmSJMCBsSRJkgQ4MJYkSZIAB8b6D5LcmeTt6e6HpFMzr9JoMbMziwNjSZIkCQfGvZLkkSS7k+xNsj7J2UkOJ3kxyWdJtidZ2NZdluT9JPuSbGlPwCHJVUm2Jfk4yYdJrmybn5vkjSRfJtnYnsIjaYrMqzRazOx4cGDcE0muAR4AllfVMuAo8DBwAfBBVV0H7ASeaz/yCvBkVS2leyrOZPtGYF1VXQ/cRvdUIYAbgDXAtXRPnll+xouSesq8SqPFzI6PWdPdAZ02dwE3AnvageYc4CDwJ/B6W+dVYHOSecD8qtrZ2jcAm5JcCFxSVVsAqup3gLa93VV1oL3fC1wO7DrzZUm9ZF6l0WJmx4QD4/4IsKGqnvpHY/LMcetN9Rngfwy8Por7jvR/mFdptJjZMeFUiv7YDkwkuRggyYIkl9H9jSfaOg8Bu6rqEPBTkjta+0pgZ1X9ChxIcl/bxuwk5w+1Cmk8mFdptJjZMeERSU9U1edJngbeTXIWcAR4AvgNuLktO0g3RwpgFfBSC+W3wOrWvhJYn+T5to37h1iGNBbMqzRazOz4SNVUz/prFCQ5XFVzp7sfkk7NvEqjxcz2j1MpJEmSJDxjLEmSJAGeMZYkSZIAB8aSJEkS4MBYkiRJAhwYS5IkSYADY0mSJAmAvwAmAnphhWXztQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "activation = 'ReLU'\n",
    "# use dropout_prob = 0.0 effectively constructs a deterministic CNN\n",
    "mfvi_bnn = make_bayesian_cnn(in_channel=1, n_class=10, dropout_prob=0.0, \n",
    "                             activation=activation)\n",
    "# use the \"Bayesianize\" package to make a Bayesian CNN with MFVI\n",
    "# inference='ffg' means using fully-factorised Gaussian for VI, i.e., MFVI\n",
    "# see https://github.com/microsoft/bayesianize/blob/main/bnn/nn/mixins/variational/ffg.py\n",
    "bnn.bayesianize_(mfvi_bnn, inference='ffg', init_sd=0.02)\n",
    "mfvi_bnn.to(device)\n",
    "print(mfvi_bnn)\n",
    "\n",
    "# start training\n",
    "learning_rate = 1e-3\n",
    "N_epochs = 5\n",
    "beta = 1.0\n",
    "opt = torch.optim.Adam(mfvi_bnn.parameters(), lr=learning_rate)\n",
    "# the training loop starts\n",
    "logs = train_bnn(mfvi_bnn, opt, train_loader, device, N_epochs, beta)\n",
    "\n",
    "# plot the training curve\n",
    "def plot_training_loss(logs, title):\n",
    "    fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(12, 4))\n",
    "    ax1.plot(np.arange(logs.shape[0]), logs[:, 0], 'r-', label='nll')\n",
    "    ax2.plot(np.arange(logs.shape[0]), logs[:, 1], 'r-', label='acc')\n",
    "    ax3.plot(np.arange(logs.shape[0]), logs[:, 2], 'r-', label='KL')\n",
    "    for ax in [ax1, ax2, ax3]:\n",
    "        ax.legend()\n",
    "        ax.set_xlabel('epoch')\n",
    "        ax1.set_title(title)\n",
    "    plt.show()\n",
    "\n",
    "plot_training_loss(logs, title='MFVI')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "otYKHXCKKtqm"
   },
   "source": [
    "Training is finished, then we evaluate the accuracy of MFVI Bayesian CNN."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "sEjZezhBIXGt",
    "outputId": "f8edb599-01d7-4b9d-fdcb-b65ac83a5290"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:780: UserWarning: Note that order of the arguments: ceil_mode and return_indices will changeto match the args list in nn.MaxPool2d in a future release.\n",
      "  warnings.warn(\"Note that order of the arguments: ceil_mode and return_indices will change\"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy: 98.63999938964844%\n"
     ]
    }
   ],
   "source": [
    "accuracy = evaluate(mfvi_bnn, test_loader, device, K=5)\n",
    "print('Test Accuracy: {}%'.format(accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jzYt8P6t-i5R"
   },
   "source": [
    "# Case study 2: Detecting adversarial attacks\n",
    "\n",
    "Neural networks has been shown to be vulnerable to adversarial attacks. In this case study, we will see whether BNNs can be more robust or not, as well as how to use uncertainty measures from BNNs to detect adversarial examples.\n",
    "\n",
    "\n",
    "But first let's set-up the helper functions for the adversarial attack. Here we consider the $L_\\infty$-norm projected gradient descent (PGD) attack [(Madry et al. ICLR 2018)](https://arxiv.org/abs/1706.06083) which is one of the strongest attack considerred in adversarial robustness literature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "cNuL8oCJ_dC3"
   },
   "outputs": [],
   "source": [
    "# PGD attack for one step\n",
    "def pgd_one_step(x, x_grad, x_clean, epsilon, lr, x_min, x_max):\n",
    "    # use scaled element-wise sign of the data gradient for attack\n",
    "    x_adv = x + lr * x_grad.sign()\n",
    "    x_adv = torch.clamp(x_adv, min=x_min, max=x_max)  # make sure x is valid\n",
    "    # project the attack back to the epsilon-ball (L-inf norm)\n",
    "    x_adv = x_clean + torch.clamp(x_adv - x_clean, min=-epsilon, max=epsilon)\n",
    "    return x_adv\n",
    "\n",
    "def pgd_attack(x, y, model, predict_func, epsilon, T, lr, x_min, x_max):\n",
    "    x_adv = x.detach().clone()\n",
    "    x_adv.requires_grad = True  # we need to compute gradients w.r.t. the input\n",
    "    # perform PGD attack for T steps\n",
    "    for t in range(T):\n",
    "        # Forward pass the data through the model and get loss\n",
    "        y_pred = predict_func(x_adv, reduce_mean=True) # return prediction probabilities\n",
    "        loss = F.nll_loss(torch.log(torch.clamp(y_pred, min=EPS)), y)\n",
    "        # One-step PGD using gradients of loss w.r.t. input in backward pass\n",
    "        if type(model) is list:\n",
    "            for net in model:\n",
    "                net.zero_grad()\n",
    "        else:\n",
    "            model.zero_grad()\n",
    "        loss.backward()\n",
    "        x_adv.data = pgd_one_step(x_adv.data, x_adv.grad.data, x, epsilon, lr, x_min, x_max)\n",
    "    return x_adv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YfD0zwEO9ZW2"
   },
   "source": [
    "We first test the PGD attack on a deterministic network (as to validate the implementation). For this deterministic network, we just need to turn off dropout during test time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "2i8dSWHmGcDc",
    "outputId": "55f4d4f7-87e2-47b3-dc4c-1df0736389da"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:4: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  after removing the cwd from sys.path.\n",
      "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:5: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  \"\"\"\n",
      "/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:780: UserWarning: Note that order of the arguments: ceil_mode and return_indices will changeto match the args list in nn.MaxPool2d in a future release.\n",
      "  warnings.warn(\"Note that order of the arguments: ceil_mode and return_indices will change\"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "entropy (clean) 0.048386477\n",
      "clean accruacy for this batch: 100.0%\n",
      "adv accruacy for this batch (%): [95. 79. 29.  2.  0.  0.  0.  0.]\n"
     ]
    },
    {
     "data": {
      "image/png": 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CwBo50iCEEEII6TgxVlQjRWQ2gI8B+N3NZgJo3+sXIYQQQkiJxExwGjazAmB9AH81cWsAeCPPA/N4mAwtT1mTtAMPPDAJT506NXXdBx98kITvueeeVNzjjz+e+aysJdNQnvy7hbzZxhJrNj4Qy+lZxOZliSWWSMJepGbNSK1ICgCmTJkSlb7dhXyllVbKvO6OO+6ISi9EyGTRm/uWsWQdQlXb3gE4VIehOFuPeVwVZPWV5ZZbLnXdD37wgyRsRVJ58lgUW4++vu3Y1c6zY+uuDNFDyOz8u9/9burYmoL/27/9W+Z91hzYt/ONN944M+6JJ55Iwn//+98z0y/aLkNtsVGX7YioLD6dkNdu226KmrJb8+ltt922UBqWou33tttuSx0fddRRSTjkabsoMd/GVhOcr7njf7rj5QD8Lj5LhBBCCCGdJzjBUdX7WsT/qtzsEEIIIYS0T5QnYxEZAeCLABpuEqcAeExV214Hjt34zIt/zjrrrCTsl7Gz+N73vpc6tuKrZ555JiqNEN7DZ4gJEyYk4Z///OdJ+IUXXmg7H0OFe++9Nwl7z8V2mfSdd95Jxdmldb/8a+/bfffd285jSKs/tGmm1eoPeYUtwyuwR1WT5/tl77KtGEObw5YhJlp11VVTx9bq7Nprr828L1Susfny3l2LbhBaFqGNQC1lWG/azW6BePHNQgstlIS9FdWuu+7aNAwAr776ahI+4ogjUnF2nCiDTns5zrOhZujZVgQT8rS/ww47JOENN9wwFXfmmWcm4aIbH4ewaXpr1NVXXz0Jjx49Ogl7y9ei2D6QJdYNTnBEZDiAXwA4EMA8ABpfll4AH4vIxQCOVtX2BP6EEEIIISXSStvxFwB2Q22Cs6iqDlfV4QAWBXBAPe7ngfsJIYQQQrpOKxHV/wfg26o63p5U1bcB/LeIvAXgagBHdih/hBBCCCG5aTXBmRdASGD2NnL6wSnLfPOnP/1pErbyPb8TtJUDrrPOOqm4r32t30jM77T6+uuvJ+FlllkmKk/zzz9/6vjFF19MwtYkGgDGjBmThF977bUkfNJJJ0U9qxuISCIjjtm5tR1sGXjymOaffPLJSXj55ZfPvO9Pf/pTEp44cWLmdV63JlY+Hatb5inbvDmkc+O9KFvT3Txlbol1axDCpv/jH/84FWd1Naybh1b5KmOn9FBZtmuW38DqT3lCbaqMnefvv//+JLzAAguk4kJeibOwnsSBbC/jQLqv3nrrrak46/E8hNUT8vURqp+Gl+M8bg3yYPuEf+9QnWa5k1hkkUVSx+ecc04S9l7fzzjjjCScx0twEbwn4yLkyVNMn2sloroHwDki8int2fq5XwC4OzpHhBBCCCFdoNXP8kMB/AHAayLyHIA36+cXA/B5AM8CyN5kghBCCCFkAGjlB+d1EfkCgK0AbIB+M/EHAfwFwDhVzeX+saxlMitiCKX35JNPJuGrr746FTdy5MgkvO6666biHn300ST8+c9/Pgl7MdTkyZOTsPUoCaRFVM8//3wqzprUvfzyy0k4tETul+Ss+KSMJWqPiHRcNBVDqEysiSQAHHLIIUnYLqv75d5f/SrOhZOvU4s13czjvTREGZs92nrzbcaapfoyse9Q1EQ6ZLoZejcbZ/O4/vrrp66zfSqPOau91ooi8nhVtfnqlOfwon2uiGn49ttvnzr2JvmWWNNnKxJ5+umnU3G2P6622mqpuOOOOy4zTetF+eabb07CIXFoqF5DbhvKINQ28oius8rciqSA9DjkN9sM5avopqYW+z6bbrppKi7WtUBR8a4dk7K+fy17Un0Cc2f9jxBCCCFk0NPZTXEIIYQQQgaAtiY4IvJcfadxQgghhJBBQ7sKFucDWLjVRX19fYkcz8v9rPzQy1SLuLEPyV69rO/dd99NwuPHj/eXJzzyyCNRz/bmndYM3er7AGn59H33Bbf8SvDy29AOxlZfoB33/FnyaVtX3t241Q2Kld0XNUteb731UsfehLLBqaeemjq+/fbbk7DPv5Udez0VW5Zlm0QD5eh2iEhijhrS2/K6HjNmzIhKP7ZfFnX5vsUWW2ReV4ZOWGiM6JRuTSzWNUMnthRYeOH+4drrJIbqy441Vq8RAB566KEkfOyxx0bl4y9/+Uvq+OCDD07C1u0HkN6Wx+6ifuONN6aus23d62RYHaVOmYNnEdJfi21vVrfmq1/9airO7r7udT1DZD3bt4PQt+QnP/lJEvY6N3bboffeey8zH7Zt+bIKjUm2LWSNxW2NFqp6Xjv3E0IIIYR0gugJjoj0Amj8PH5bVcs32yGEEEIIKYGWExwR2RnAjwCsZ66fLSITAZylqre0kwG7ZJ7HZLMIfnmtqLfZLLx45MILL0zCXtRxySWXJGG/W3YRinpgbUWjTvKYPjY8gza7L3YH29DzrKdTvwu5xS5hH3/88ak4Kxr1y6BlLGHHigg7IRLp6+vLNG+3y7qeIiJhLwLznlpjn2XLYa211sq875hjjsmRu+5h+0BZXo19GZWxS7jNp0/fjsW+T1jRx+67756Ks7tDx7o5sGMEkDZ9Pv3001Nx1uO2FVdZVx5A2ouvF3XEuN/wecqDqmaaKhd14WHf4Vvf+lYS9uPtBRdckISLioVD522aK6+8cipuzz33TML+PU877bQkHNsnQiIpX6cx84WgkrGIHAzgWgB/A7AHgE3rf3ug5uTvGhE5sOVTCCGEEEK6SKsVnKMBHKqqv2sSd4OITABwDIBLmsQTQgghhAwIrczElwJwfyD+AQCf2qeKEEIIIWQgabWC8yyAQwAckRF/cP2aID09PYkc1cuRQ/LJMvQTQmnEpm9lwFk7vALA0UcfnTq25o7WJB3INufzsspYPaGibrhjsTorQFj+GdJhKbKr8+KLL56K23DDDZOwN5217cuarHrdE5t//24hYvNv9W687DjU5ssw77fu/v2zrDllSA/Gl6tt9/Z9ytJjs27e99tvvyT817/+NXXdnXf2O1QvY/fwEL7erMsD38bb0d+Ixdalrx9rPu/zYtu370sWW2aPPfZYKu7www9Pwlbnxt9n8W0jtNXFtddem4R32WWXVNyXvvSlzDyXQSOf7epOWR2mLPK0S9vGvvGNbyThBRdcMHXdFVdckYSL9onQfTYf1pwfAFZcccUk/Oqrr6bislyv+L5jyy00Vob0B7NoNcE5CsAdIrINgHFIb7a5BWorPNvmfiohhBBCSAdptdnmfSKyJmqrOHazzSkAbgFwkapO6mgOCSGEEEJyErPZ5iQA/1nWA2OX54H4JfqyvPZmERJLWa+SRx55ZCrugw8+SMI77rhjKs56n7T4/Bd9nzKW6K0H6k4T8vZpdxAG0kvwftnyyiuvTMJ2p3ff7uxydKy3ZSC9lGvTDJnw+meHTNTLaL+qmpmOfddQX/ReSbNEUaE0vKm6XZr2S+LbbLNNEh41alQS9l5zy9ht3RISQ4XwItqyTMNj+5wXh1gRVZGlfCA9jtodvIFiZe3LJNTnbD348TZr/D355JNTx3vssUfuPJaFqpbWBhrY+rDlc9FFF6Wus2WZp55sGw6Jp+1YYkVSHr97fKynZNuWQ+NJETcy3GyTEEIIIZWDExxCCCGEVI72d66LILRkHotfSrYeU63Iwi+vle2t2LPttv061lYkBQD33ntvEvYa5rHLdzH3VAG/NLn++usn4SWWWCIVt8ACCyRh7830pz/9adP0Q8vHecrVti/btvKIV63oxltwWTFO0c08rRWVF2WEnm2XgP372PqxfdFbRFhRiU/f1oEvc2txaO+74YYbkEXZloKtsHn241Fo0+BO4PtLaGnflpO1xpk2bVrquoUWWigqjVh82wiJGLbbbrskvO6666birLjUhg88MNvHbJ4+nceKspvYvvvmm28m4a222irzOo9tp76NhETGlqWWWioJW1EyALz//vtJ+Prrr4/Khx8bYzbN9PkFSvBkTAghhBAyFOEEhxBCCCGVI3qCIyLLisgS7twSIrJs+dkihBBCCClOHh2cSQCeB7C6OXc3gFUAZNv4ImxCZ2X9IXNsjzXLtboQMd4km90Xi5ftfuELX0jC3oOo9aTrPRl7HYcG3jzXlok3rbTXht5lKOju+F2obdmF3u2JJ55IHWe9q5fXxu4Y7u+LbTMh1wVWFt4pD9SNdhPa3dv3SVsmsR61rc6NTyNUdv6+jTfeOAlbL7o33XRT0+f6ZzV7Xmy+LLF9xeszFDFh7RR+bLHv5N1VWMp2seHLZJFFFknCa665ZiruJz/5SVSaU6dOTcIhM/Q8dENnqgh2fL///v4dk/bdd9/UdXfccUcS/uUvfxmdvq0fWx8rrLBC6roxY8Yk4dCYERpTbX/xfT+27xTpY3kmOPsBmObOHQNgwSbXEkIIIYQMGNETHFW9rMm5W0rNDSGEEEJICRQyExeReQF8BcD/qeqrra6PJSReCplB2qXvPGKuWOwSr/dWvM466yRhuxEgADzzzDOZado8h8xPrVjKe8vttJms3SS10/zHf/xH6ji0wd7YsWOTcJZZOBBePrXLnaGlVW9CmmUu7UU4RZfLyxAnikiSn9DGdiGRhI+LFc3FLiP7ZfZFF100Cf/hD3+ISiMkWvB9I8tra1GyRMxl0GhXobEs1L583dm8epcLlpA37jI4/vjjk7DdUNXjzdcnTZqUhPfZZ58kHBKjDiaRfchUO4S99sQTT0zCvo/Zsiy6MandQNWPGVa06D2v23e76667op6VRzUk5OE+RrQYNRsQkctE5NB6eC4AE1DbfPOF+kachBBCCCGDhtjljq0APFwP7whgftQ23jyx/kcIIYQQMmiIneCMBPBWPbw1gBtV9S0A1yBtVUUIIYQQMuDE6uBMAbCmiPwTtdWcg+rnRwDIpWwQkql5mWqs+3MvF7QU1VOxclprwurN8N57770kfMYZZ0SnnyWL9TsBh+TFVmbuTciHgmm45aijjoq+9phjjknCRd8zpHcTajOh9msJ6YV1WtfB4t0OhPRusrah8Nj8+3Ycu9u3NT31xO7o7evJPq8sXSaLbTNeX7CsLWF6enqS9y9rd+os3cbQ1gxl4HUSV1111ULp/O1vf0vC1i1Et7fqCGG3R/H4PliEN954Iwnvv//+qbj/9//+XxJeeeWVC6Vv0/D85je/ScJ77bVXKs72f7ttQ1mE9NBiXH3ETnB+D+BaAJMBzAEwvn7+31HzjUMIIYQQMmiImuCo6ski8iyAZQFcr6oNj3azAZzZqcwRQgghhBQhjx+cG5ucG9vs2mY0lvb9Mp73/muxS6t5dmu2hJaqQ0uc1gPs1VdfnXndtddem4S9V90QWWKKPMuZIY+4ZdDX1xeVbhliwDwsu2z/7iB2l12PXeK3u1UDadFiyNx0wQWz/VjaJf48IjZbbz/+8Y9TcVbcWkad5nGbYN/bl4ntb7FiqBAhj7q33357Zlw3Ra/eRYIVk5chdmhGX19fMjbkMSm2hEzYY834t91228y4Sy65JHW8wAILxGXMEBJn+Db7ne98p+l1IS/goTg/XjW+M+14cg5567ftKNQfQ2075NbigQceSMJ5vkGW0BhuyyUktvUuCKx5f1FsmYbEwllln2cvqnVF5HIRmVj/u0JE1m19JyGEEEJId4n1g7MHgEcBLAHgD/W/xQBMEJE9O5c9QgghhJD8xIqoTgNwvKqebk+KyDEATgVwZdkZI4QQQggpSuwEZzSA65qcvx7A8U3OZz/Q6eBYuZqXOdtrvfzNypltnN89OaTHE5J5Pv98v3HYkksumYS9zod1P+6x8krv8j/r2bFmyIOJonpONs6bBoe27Rg3blwS9vL/WFPF667rb87//Oc/U3GLLbZYEj7ooINScVnpF9FDAIApU6akjk899dRC6WQRcq/g5elWjl1UHyHUFtZee+0kHGsyPJDuDj7++OPUsZX5d0oHByime2Pr0ufNHp9+ev9v1F//+teZ6VndwrLwWzBkceutt6aOY3W+Qm02ZFLc+M6E9EHzUFQnMXRfyDWDxfd3q//j+3vIzUjWdR6bph9HLXncw2Th9Zfs97xdHZx7AGza5PymAO6LTIMQQgghpCvEruDcCeAMEVkP/Vs2bABgFwAnisgujQtV9aZys9KD658AACAASURBVEgIIYQQko/YCU7DleFB6Pdi3OA8E1YAn1qL6unpSTz0xpopAuklw9hlrNByWigNb+JmxVIWv5v4W2+91fQ64NNmplnEes4tajbaDln1ZcWHZeys/NBDD6WOY3fFLeo981vf+lah9K3ozC792914AeDRRx9NwhMnTsxM35p4dhvvNTt2ib6o2GinnXbKTOPxxx9Pwl5EUQZ2Cb6oJ2lb917kUaZ36kaf96KmkJjFxnkxvXU9cNVVVyVhK67yhMakkKjJuk7wfWfq1KmZaRxwwAFJ2IttY/Nl8WN9jKg2xjNuFj09PZnjvX22f0bs+9j2678Dtl3meW+L7Y/+PeyzvSjeutvwagVZ3y5fBrHe1b16S8ycINbRX7wzDUIIIYSQAYYTF0IIIYRUjuAKjog8BGBbVZ1WPz4DwFmq+k79eBEAj6vqsoFkGmk1PW+Xyb3VgiW0lGeXv/yybshKa9SoUUnYWuZ4TjrppCTsvayG8h8Sx8VaS9lluW6LqELLrrHEijO22GKL1PEhhxyShEMbGXpR4hprrJGE7SapIaxFFZDe2O7dd99Nxd19991J+JlnnolKP0Row8hO460SQu0r5Ek1lu222y4zjeuvv75Qmln4NmPf1S57h5bHfXmE7iuzbzbGsDzlHOojdgx55513kvBGG22Uum7nnXdOwkcccUTTPLXCip58+7IWgjfdNHhUNdsRTcXgLWjbxYuSy/4u+M2rQyoI9pvn82G/cXb8CG2O7Z8V8vwc0yZbreBsAMAKdL8PwG5B2wtgqZZPIYQQQgjpInlFVPEawoQQQgghAwR1cAghhBBSOVpZUWn9z5/Lhd2V2uscWNlcyJtiCCvr87otVjbt9Rusl9oxY8Zkpn/XXXclYS+vtfLtPB4ss+SmPn2b/5CcPZR+mSas3eLCCy+Muq6o11CrQ+Hlvtb80MuAbfuyzw7tvh2iUzo3jXbk+4NtF3k8txbRUxg5cmTq2L6rNwU/77zz0AyvxxZyT2DL3NdHlkmpTyNUJrHmrO3sSh2r9+bfx7bTWFccfudpe+x1Eu1Y6XeCt3V58cUXZ+bD67N1kiLjQkg3JIZGH8njCsXWo9cpsXEhr++dZr/99kvCvn+ccMIJmffZ94kdP0K6qUW8h7ea4AiAK0Wk0WPnAXCJiDRaQvsOUAghhBBCSqbVBGesO262qeblJeWFEEIIIaQUghMcVf1uWQ/KWnqyy04hk7Ay2GabbVLHZ5xxRua1VqwTMl8vSlZ55FnetMt+ee6LJSRajKXofZ3GLv+GPGKG8j+QG0GG6OvrS9qs9/5pRVSx3sHzEBLb7bDDDkk4tuz80rb30muJfR+bRy8qtm4fBrNoN4+4ukg9P/jgg8HjLEKiOjvmhbw0D9YxoxWNMdi37dD72DLy5uSd6J9FsF7ZzznnnFTc+PHjM++LFUtZkVtoDuDTC23InKQXlQNCCCGEkCEEJziEEEIIqRyc4BBCCCGkcsTuJt42DVm3l5vF6reUIZf1rslD2J1w7U7RXr5qZeF5dtUuYuo3kDofZT07dlfnTsvhy0g/Ng2vb2DlzCH35kXLvKenJynnonJ8/262rsowUy2j/EPl402ts2T7vg3GvpvXeWvHNLzT2HEpj+5OEezu1b4sbVmHTPw7Mc7Z9pZHRyYWq6/osedDzwrt/B0qk9h3i03Dj0lbbbVVEg61n06P2b7PWZ2lrHfjCg4hhBBCKgcnOIQQQgipHF0TUTXwIim7VOnFV3apLHbZsugy2QMPPJA6/vrXv56E7TK/NSMF0nn0Zmx2Sc0vYecRZw128phSx5rexi6nhu4bLOamIdPH2F3li5LH+2eovGx7tUvpZe+U3A5l1Hds++zUDtQhUUeI0Lt3WiyVld9QWXZbpBcq026K/r0Yyvb/0K7gtg692Dn23XwbsXGh62w9drsttSta5AoOIYQQQioHJziEEEIIqRyc4BBCCCGkckinZMmph4hoQ9boXcdbvZuQDLJqZLm0D7mf9mVn5dghs9jp06c/pqrr5c2jiBRqHGXoQth2GTKfHAp4PZXYbTWK1ltvb6822kNRXaYmeYlKw9ZVaGzxukfWpLibelQd0r8oVG9AfJ/zuhBFthvx727rII9uWNZ2O77+Qy5BBsvYr6qF9ryJrTff97vx/e0kZehDltQHm/Y5ruAQQgghpHJwgkMIIYSQytEtEdVUAK92/EEkizGqOjrvTay3AYf1NjQpVG8A626AYb0NXZrWXVcmOIQQQggh3YQiKkIIIYRUDk5wCCGEEFI5OMEhhBBCSOXgBIcQQgghlYMTHEIIIYRUDk5wCCGEEFI5OMEhhBBCSOXgBIcQQgghlYMTHEIIIYRUDk5wCCGEEFI5OMEhhBBCSOXgBIcQQgghlYMTHEIIIYRUDk5wCCGEEFI5OMEhhBBCSOXgBIcQQgghlYMTHEIIIYRUDk5wCCGEEFI5OMEhhBBCSOXgBIcQQgghlYMTHEIIIYRUDk5wCCGEEFI5OMEhhBBCSOXgBIcQQgghlYMTHEIIIYRUDk5wCCGEEFI5OMEhhBBCSOXgBIcQQgghlYMTHEIIIYRUDk5wCCGEEFI5huwER0RURD4UkdMir99fRKbX71upw3m7V0Q+FpE/R16/Sj1vc0TkgE7mbaBhvQ1NWG9DkwL1dlL9ehWRYR3O2yQR+UhEroi8vmttajDAuisBVR2SfwAUwErmeCMA092fAtg1dF+H8nYvgAPcOZ+3OQB+0+q+qv01qbdVANwKYCqAdwD8EcCqre7rRr0BmBvApQBeBfABgCcAbBNT31X7a1b+AC4G8AKAPgD7xt7X6XqrnzsMwEQAnwC4LPa+qv1l1NvaAB4DMKP+f20Xv1z9vmEdztskAJs3efY99bw97+O71aYGw1/oPQHsXY/37X7A6s7EbVLPw6kDXXdDdgXHo6r3q+qIxh+A7VGbSNxVNE0RGSUiw0vKn83b4gA+AnB9GWkPcRYCcBuAVQEsBmACahOewpRYb8MAvI5ah10QwHEArhOR5UpIuwo8CeBQAI+XkViZ/Q3AZACnAvh9SelVAhGZC7X+dSWAkQDGAri1fr5omqNFRErK4tUA/gpgYQA/AXCDiIwuKe1KICIjARwL4NkS0iqz7lDvv78C8EhZabZDZSY4TdgHwA2q+mEbaWwB4A0ROVtE1iwpXwCwK4C3ANxfYppDElWdoKqXquo7qjoLwDkAVhWRhdtItpR6U9UPVfVEVZ2kqn2qejuAVwB8sY28VQZVPV9VxwP4uKQkS+tvqnqTqt4C4F/lZK0ybIraxP1cVf1EVX8NQAB8vY009wPwSl1EsnzRRERkFQDrAjhBVT9S1RsBPI3aeEn6OQPArwG8XUJapdSd4SgA41BbfRtwKjnBEZH5AOyG2q+TwqjqtQA2Q20JfpyIPCoih9Zn0O2wD4DLtb5mR1JsDGCKqhb+MHWq3kRkMdREam3/ciKfpoP9jfSzBoCn3NjzVP18IVT1TADfBrAogIkico+I7CUinyuQt5dV9QNz7sl28lY1RORLANYDcFEZ6ZVYdxCRMahNmE4uI29lUMkJDoBdUJvd3tduQqr6jKoeDWAZACei9gvoFRG5RkQWyJtevRFsgjYnX1VERJYGcD6AH7abVgfqbTiAqwCMVdVB8eukipRdb+RTjADwnjv3HoD520lUVR9W1UMALAngQgDfQW017ncDnbeqICK9AC4AcJiq9pWVbkl1B9RWlY5X1ell5a1dqjrByb1CIiJ71LW8p4vInT5eVeegtlz6JGrKsGsCKKIvsBeAB1T1lQL3Vpa6nH0cgAtU9eoc93W83kSkB8AVAGaiprxK2qSL/Y2kmQ7ATxQXQE2JviUicqypt0+tIqjqJ6itCD2BWn/JI2psK2+fAQ5FbfXt4SI3d7LuRGQHAPPXV2EHDZWb4IjIMqj96rs8z32qepVRBN7GpDdCRPYVkbtRU6ZcCsDuqrpmQTHK3uDqTYq6CGIcgNtUNcokskGn662ugHcpagrQu9b1hEibdLG/kTTPAljLKZauhUixq6qeburte43zIrKwiBwmIhMA3A2gF8DXVHWDnHlbQUTsis0XYvP2GWAzADuLyBQRmQJgQwBni8h5MTd3uO42A7CeydvuAI4QkbYMRtqlo7byA8ReAB5S1ZfaTUhEtgZwHYAHAfwWwC31WW7R9DZEbcCm9VSdutjhjwAeVNX/KinNMuvtQgCfR80k8qMy8lcV6pY3PagpqQ4XkXkAzCy6fF5mvUnND8gw1Abr3nreZqvq7CLpVYh7UXNRcXj9V/yB9fN3F01QRPYHcC5qFqsnAbirvgKXC1V9UUSeAHCCiBwHYBvUJl9UMq6xL4B5zPFNAG5A7QdYIcqqOwDHA/iZOf4VapaMpxTNWxlUcYKzN4CzSkrrBQCrqerkktLbB8BNTonus87OANYHsIaI7GvOr66qrxVMs5R6q+tLHYyaL5Up5kfvwap6VTtpV4RxqOmTAbVfkxcD+BpqH9EilNnfjgNwgjneE7UB/MQS0h6yqOpMEdkJwO9Q+yA9B2AnVZ3ZRrJ/ATBGVd8pIYvfBnAZgHcBvAZgN1WdWkK6Qx5VnWaPRWQmgPdV1est5aGUuqt/05Lvmoh8BODDktpEcWKc5QzGP9RMU98DcErk9d8FMK1+3wodzts41Cr7nsjrV67nbQYyHKZV5Y/1NjT/WG9D869AvZ1Qv/5jAL0dztsLAN5HTXF/ULWpwfDHumv/T+oPJ4QQQgipDJVTMiaEEEII4QSHEEIIIZWDExxCCCGEVI6uWFH19PTosGGDz2DL6h/F7jc2a1a2G5Thw7P9kHldp6zn+fR7e3uT8Jw5aeu90PNcmm+rau4N68qot6LvHSL03jadnp7++XtfX7zlss2jz39smRfF5nPOnDmF6k1EohTr8tRtlq5eqH59e7XYdu3Tia0rW7/+Ph9nn2efNXt2MavxUNnNnj27UL0B8XUXwpdtqB4sRftLEUJ1FyK27+ehUV59fX3o6+srtPFk7Fjp39OWg89/kfrweSjavssm9B0rep8rn6Z9riuzjmHDhmGRRRbpxqNyYSs/drD/5z//mRkXesdQww6lP2LEiCT83ntpa8DYMv3nP//5atSFjjLqzXewrHIOlasnlCebzrzzzpuEP/wwfs/Vuebq31j5k0/Sblg63Y5nzuy31v3Xv/5VqN4soY/dwgvH72eaNVB+/HF6n01bv769WhZaaKHMdGLrytavv8/HLbBAv4Nc2xfffPPNqGd5QmX35ptvtl1v7eDL9l//au4f0beNeebpd7GSp78UIVR3IWL7fqjd+7hGeU2blrLCzkXsWGn7N5Aea/wYWGT8GjkyvW3b1KmDw8Letsms9pj3Plc+TfscRVSEEEIIqRxdWcGZNWtWMjtdYokluvHIKGJXbd55J9tX0WKLLZYZF1oyj8X+Cu522dl6CxHKV2wZh9LwebDHofvsr54FF1wwFfe5z+XeLBcAMGPGjLbTCGF/0ZVBaDm46OpFUewv5+nT0/vx+ZWyGEK/an2cXSGyq6J5xDn2Wt+f/S/zovT09CT5e//996Pvm3vuuZNwrFiiqDijrL6UlRcv9izSNvKIQRp9rugYDYTHSvuNCK1exI5lntGj+yUzvk7zjKtZ2Lbl6yIkQrLtJHZc8/kN5dGuymaVD1dwCCGEEFI5OMEhhBBCSOXgBIcQQgghlaPrttshmZrXZ7E6AiHZnI3LY41jCcmVY2XAeZ6dJRvNo2cTshrwlhSdxL+3lQl7/aUsS4OQPsh8882XOray11CZd0JnyepEhayEQvnI0weqhLXSsXUIZJeJlf8D6b7oLUZCejBWRh9bbx6b/06hqlHjjS8Xq1dU1DTYvl9I16ITumc2zbfeeisVV7aLkUUXXbTU9FphxzY/TofyktWe/XhYtHzsuBTqO15fzhLSdbL9zNav/9aG+mO733au4BBCCCGkcnCCQwghhJDKMajcC4fEFKHlqaJiKYt3WJa1bOaXB+3ynY+zy+I+LtbUOUSnzcaHDx/etmO7MpaDTznllNTxs88+m4S/8IUvpOJ22223pmk89NBDqeMJEyYk4SuuuCLz2b5tFVkyzdM+yzLdbiyF+yVkKwLLYxZrRY2xItsy2qcXC40aNSoJ5ylXK9IJiZrscr8X9YTuK8tjrKomaYVM2H0dWBFVLP59/BiY9ewy8HVnxRb+WVnvlsfb8mBxT5KnnrJMyn29FTH3BuL7sW2Hvk3a/hJKr4xvdJE65AoOIYQQQioHJziEEEIIqRyc4BBCCCGkcgwqHZyiWLmg1xkJmZpbvDw3SxciJJcPmbuFXG2XoY8T0hXpNtbFvH/vWPP4//zP/0zCu+66ayrum9/8Zuazs3QF/v3f/z11vNVWWyXh++67LxX3/PPPZ6ZvsXn25vDWTN+3Jatv4E0882xEl8WwYcNybaQZg9V9CemWlYFN0/e3+eefPwlPnDgxFXf44Ycn4RtvvDEVZ/UDOqFD1I6bf4vVe8ujt2Bd4cfqA/nrYsvFb5T5y1/+Mgl/4xvfSMJvvPFG6rrNNtssCft3s2bEfhzNMksP6Qz5dmn7p23L3SZkYp9na44iFNnyAgjrX3VaN8vWo9c9imnnXMEhhBBCSOXgBIcQQgghlSNaRCUiYwAsXj+coqqvxt7b29ubmMcV9SIawpoi292egfhlZr/EXETE45cfrUfO2KW8ot6Q83jLjaXoLvDWS61fRszaYf26665LXbfccstFPeuFF15IHd91111JeMUVV0zC22+/feq6adOmJeHPf/7zqbhHHnkk83lZ5erLx9Z9yEt2aKf6oohIUrZF20HIRLnT3patCMyXq+3rXqy8+OKLJ+GiIlqbph9LOjF2eVQ1l/lzM2I924ZEPCGWXnrp1LEVS4WuW2ONNZLwiy++mJl+bN2FxD2+rjotsg9940L9JfYbYUU1fmdu+25+J/abb745CW+66aapuLJFdVtuuWXq+Morr0zCoXez46Nvk3Ys8OoOfoxqRssVHBE5UkReB/AygL/U/14WkddF5IiWTyCEEEII6TLBqb6IHA/gaABnAvgjgIa25GIAtgRwkoiMUNVTO5pLQgghhJActFrLPAjAvqp6kzv/OoCJIvI8gN8A4ASHEEIIIYOGVhOchQE8F4h/AcDIQDyAmq5FQ17q5ZNWjlbU5KwMM+tOUIYJnZVP+rILvbeV+5bh/r8MnR4gnZcll1wyCS+77LKZ93iz7eOPPz4Jv/3226k4K6e1smprFg7UzHEb+F2pixAqH19vndw9FwjrcVh37SGz0VDbtbpTIdNWb9ZpTeBj+6kvA7sVh5fJn3/++bnT94TMvWPLrhuU4QbA6+rEjldjx46Nus6biVtCOoNF24a9z/cxu1u212Fp6MG1s92G/cb5dKxeSZ6d2Itsq+LHOdtmO20e73Wx9t9//yS88cYbZ95n68rrK9p+FtILzKJVqU0A8FMRmctH1M/9pH4NIYQQQsigodUKzmEA/hfAWyJyP9I6OBsBmAFgi85ljxBCCCEkP8EJjqo+IyKrANgTwAYAlqlHTQHwXwD+W1VzuV8MLU2OHj06FTd16tTMdDbccMMkfNBBByXhKVOmpK6zy9he1HHHHXckYW8SmpVnvyxqTaK998wyxA2xpqk+/RgTuoHGlo+IpOJsPVqvxkB8Wf7oRz9Kwssvv3zmdX/4wx+i0gOyRaqhnXq7LTadPXt2pliyDNFKqPztsroXH4bI6iteFGO9FYdEJWWJVLPwY5UV97TzbLubeAgvZrFYFwghQm3BigIBYOedd07CIRcOr7/+ehL2fXqTTTZJwl7k8uSTTybhWK/veeJCHsIbdfnuu+9mXtMKW29eNGvbhv/OWI/n06dPT8XFiqXse2+33XZxGY5MLw8XXXRR6vi0005LwiussEIS/uijj6LTtN+/IiofLR0mqOoHAC6s/xFCCCGEDHra8mQsIsNFJFs7lBBCCCFkAGh3s83VATwOoLA8JHb53osAzjrrrCQc6/XWc8oppyThZ599tlAali9+8YuZcd5j7f3335+EzzzzzCQ8efLktvMBlL8JWid47LHHkvC3vvWtVNwHH3yQhP/+97+n4kLWANZa4tvf/na7WfxU+8wSZfplabvcXNQ7dTtkiShD7cL3MUuWOMN7abXL6qFn2XoCskUuq666aurYWqFcc801memHyjG2Prx4LCQWKgsRyfRE7C1Msiij759zzjmpY+8hNwtbP14UscsuuzQNA8Crr/Y7xrfjIQDcfvvtUc8uSkgVIpa+vr5kbLBqC0DaqsqPE1acWNSKa++9907CvlyPOKLfF2/shsx5xiCbph+XrWfxhpdnIJ+IKkSMBTb3oiKEEEJI5WjlyfjlFvd3/icNIYQQQkhOWomolgBwOYCsndGWAvCDUnNECCGEENImrSY4zwB4SlXPbxYpIl9AzgmOl71buWPIJNrL9y68sN+oy+oOPProo6nrVl999SS87rrrpuLs7qobbLBBKs6aOy6zzDKIwes9WE+eXq5pzTAnTZqUhC+44ILUdQPtMbVBp02drQze48sg5PX0uOOOS8KLLLJIZpp213G/I7nF607FegO1cngvkw/Rid3FY7H5DOkllNEWQvosNh/eRYBtJ143K6RDlKV34/us3a08No0ysSb+XgcotEu4H1eLcPfddydhvxN4rAl1yMOy7ce+fVk9Sju2A2ldvRCh+um0F+re3t7Mfm719nwdFvEu7Ov66KOPTsIPPPBAKu68885LwiEXLWWQtat8HkJ58jpo3qy+Ga10cB4EsEogfjqAP7d8CiGEEEJIF2nl6O+IFvEvAfhaqTkihBBCCGmTds3Ec+OXpkMeJkOMHz++6Xm/fGeXQu+7775U3GWXXZaE11lnnVTcxIkTk7AVc3kzv7/+9a9J2G6qBqRFT08//XQqzi5NvvLKK03Pe/wGitbEsBPLrsOHDw+KecokZMIYEolsv/32qWO7TGpFhH7JPXbDwJB4zFKWCM/WfztLyA2zyTwere3GmUU9YRf1CG7jbD9ab731Ute9+OKLTe9pRRn1U8bGwHnwY2PoHYqMo96Nwpe//OXMa2M3pD322GMz8xTaVNGKlj1f+tKXkvCECf1bH+bpH90U9b/11luZcSERqCfr/X7729+mjq0H/a233jo6vaKbmmZhPVUD8X3EjhmhDTW9Gb39NmV5bqeZOCGEEEIqR7uejJ8TkeJ7zBNCCCGEdIB2RVTnA8hWmyeEEEIIGQDamuCo6nmtr6rpjjR0Y7wOTqxcvihWbheSw1oTSc/DDz+cGWf1BbwppTU9t7vGAmmdnP/7v//LTN/id5cN6Wt02iwypGthZe1F6zBkCm5Zf/31U8dZu4Zb3QAg7ebf6wNYU06vV5W103FRfRlvWmr1YIrS09ODeeedF8Cn5dZWpu3l3aF24rdkyMK66rfhVtjy22KLLTKvW3bZzm591w1T8BBW760TebHj79VXXx19nzXJfeKJJ1JxVtfwjDPOiEpv+PDhqeODDz44Cfud2u22PNaE/MYbb0xdN2vWrCTsdTGL6nrGMmfOnKTvet2T2G1DQib2G264YRL++te/noqz7hL+8Y9/xGUYaZcUId1P2/e9rstRRx2VhP172zYTu8N9SG+nyFYW0RMcEekF0NDqeVtVB/9mR4QQQgj5TNJSB0dEdhaRBwHMADC5/jdDRB4UkZ06nUFCCCGEkLy02ovqYAC/ATAWwDkAGutTiwHYEsA1IvIfqnpJKJ05c+YkS4R+uc6aPhc1TQ1hxQ1++d+a1xXFmo2vueaaqbiLLrooCft3szvkluG9tozdk0OE0gjVW+i+kCl46L5bb701Cfsyt4wbNy4Jh5bOvem/FUtliaQ8vgxsmqE0yhBJefr6+j4lWmuGXw72ogGLXZouQ3Qcqu9QnX7nO99p+9lDhVAZxYoMgfQ4FzvG+h2frWf3Qw45JBVnxT82z15MZEUWXiRidxD/xS9+kYpriFsB4Mgjj0zC3mv9Sy+9lIRDqhA+X42+0s4u16E+Z79xXs3A4sVotq6sCM+PV9b7fZ6+ab87seO09/h/2mmnJWEvvjr11FOTsC1bXwZWlB3azaATIqqjARyqqr9rEneDiEwAcAyA4ASHEEIIIaSbtBJRLQXg/kD8AwCWLC87hBBCCCHt02qC8yyAQwLxB9evIYQQQggZNLQSUR0F4A4R2QbAOKR1cLZAbYVn21YPCbn8z3KxDHTeZDOUvjXZs3o8IRnqD36Q3ljd6jR4E/KsHazLcqddhp7ErFmzouqgqNv6kIlkSD/Hm4ZnYeX1HmsaHpIJe1NnqzNjdWt8Gdg4/25WF8HLla0r9zK2avA7bIfqKrSDuCVWXp+HnXbqt1fYb7/9kvDjjz+eus666g89u4yxw5edHQd8vZXlisH2uVBZhnZS9no21hXByiuvnHmf1bPx5W512LyuSFY/9jopVu/G657dfPPNSdjrWfntOtrF6+c0jsvYkb0ZoW9cCNtXN91008zrbrnlliRcVBfT6nT58dBuPeF1GW0f8bpB1vWKrW/vMsVvQZSF34k99C1O7glFqup9IrImaqs4GwBYvB41BcAtAC5S1UlRuSOEEEII6RIt/eDUJzD/2fmsEEIIIYSUQ9d3E++E2Mkuk4U8t+Yhy/TR85WvfCUJH3DAAak4VU3CfqfVWPPjouU10B5Z8+KXHy033XRT6ji0S7j1UGxFLt4lgDVH9EvWobzY5f5YL9mhXaH9sngZLgOAbHNge96/Zxlilth25/vUlltumYStmOPtt9/OvM8/q0ib92KokEdXixe/2OX5kKlrHkLvExo/vKggFise9abgsWUb8hgcyrPtg170kLWL+8knn5y6bo899ojKY6fo5A7ztkxuu+22VJwVL+XpA7btWzFaaEfvFVdcMTO9559/PnUcm5cY5oTQ6AAAIABJREFUURMAjBgxIuq6VNq57yCEEEIIGeRwgkMIIYSQytEVEVWsNY4XI8SKcezSul9ythrbfkneam+HxBIhtttuuyQ8efLkVNzLL7+chF999dVUnN0YztJpj8R5EJFk6bgs0V8WXhRhRX9W1AekxVJ+WfT4449PwrZOQ23JWw/5zTct1qrKiqu8VUhI3BPaSNSKF9oRGcXUT0hE5fNl6z/kfdmWj7eOsKIb35Zt+ra+r7vuusxnlWEpmAebZ7+M7y1DBhLf1m073XHHHZOwFfUCnxb3WoqUta//kCjCbiC5zjrrpOJsW7Ztw469QLgOOmH5V4SQ+CcUZzer3GWXXVLXff/73y+Ul1B/t1jR5Ve/+tVUnPVQfO6552amYb/t3gIwtCmv/Z57VYIYuIJDCCGEkMoRPcERkWVFZAl3bgkRWbb8bBFCCCGEFCfPCs4kAOPdubsBvFJabgghhBBCSiCP4sl+AKa5c8cAyFZYiMDKHUN6El4vIssUM4+JZBG9Gy/LtR6avcnvXnvtlYS9zk2W6aM3E7Zmq15Oap8Xkk8W1d3p6elJyrMs09csvM6I9UIcqqcnnngidZzVhrxuVki/xbahkJdNmy/f7qxOSUgfJuSBuowy9/kK9Y/Y/mDT8ObS1mO3L/OQifdGG22UhK2Xb+ultRuE9GwsvqyK6Ad0Cq/LaN/pm9/8ZuZ9ZXljbuB1bqxn99VXXz0Vd9RRR0WlafXl/Jg6FFxqhMaCUNz48f1rC/vvv38qzrrROPvss6PzYnVfNt988yTs6816UfZuAKwOjteVtNhx2Zq1N3ueJdQmY3YXj/7Cq+plTc51d/QhhBBCCImgkJKxiMwrIpuLyJiyM0QIIYQQ0i5RKzgichmACap6gYjMBWACgDUAzBSRnVX1zlZpNJZ7/TJcaFncei4MLdfbJS9rTgekl9NDy8gh8Zg1i/Xeitddd90kfNddd6XiJk2alJmmLYeQJ+aQZ9tYr6tF6enpSZYxQ6Z8RbFLw4cddlgqLrTB3nnnnZeEf/WrX0U9yy912jIPLYP69pklFvTlY0U3eTwGl7Fc3tvbm/QdL5Ky+fLiJdv2/H32HWy4aLvYe++9U8fWFNVu7hcij+lvkXL1Y1VIZDVjxozc6TfDumbw7ST0fJtXP5bZtm7HjFDb6ATHHXdcEg6Jyuymn0B6HN13332TsBV55aHTIqnYesrDCSeckIRFJBV3+OGHJ+HYjYg91k2HFzVZNQwrkvI8/PDDUc+K9VwMhF12xGwOHPukrQA0cr8jgPlR23jzxPofIYQQQsigIXaCMxJA42fV1gBuVNW3AFwDYPXMuwghhBBCBoDYCc4UAGuKSC9qqzl/qp8fAaC5S15CCCGEkAEi1orq9wCuBTAZwBz0+8P5dwDPZ91kyZI9hsxpbVzIzNfKD/2Oo1auHNIXCJlWrr322kn4kksuyUzD725r8fm3+bR6Q74MOrlDbSv6+voSnZNOmMFaPYlYM1EAuPTSS5NwSHfKmnjbHaqB8K7HIRmxbZOhMrH1G3pWJ7C6U16Hy/Ybb65pdxT2ZWDfu+i2JrZPjRmTbZ9gTc1DhPRsQub3sToYvnxsPy26Y3crhg0blujJhPTv8ph0Z13r67HIbs0h7rwzrZq56qqrFkrHbsfy0ksvJeGiWywU3RIoRE9PD+add96mcbEuI0JYHdQf/OAHqTir+7nSSisVSv83v/lNZtzPfvazJBzasX3KlCmFnm3Haf+dtP0stEVMFlEjlaqeLCLPAlgWwPWq2tC0nA3gzNxPJYQQQgjpIHn84NzY5NzYcrNDCCGEENI+0RMcEVkXwBHoVyp+DsA5qvp4zP0N07k8JrP22pDXQrvs583JrZmZX+Kyu+yGuP/++zPjfvnLXyZhv2O4xb+nPbZLpt5UM8ucHOi8ueOcOXOixCtFl4qL5t+aLYbahRV1eJNS2xZsegAwfPjwJOzbiBVzLrXUUkn4hz/8YatsJ9g69W4H7JJs0fKxosVQ//K7+lqRjPfgbE0yQ+Ke0A7rFrurted//ud/MuO66a02JKbzZVfWbuKxdRcij9fuLLbeeuvMOCsiBj5tthyD38l8ySWXzEzvwAMPbJpGSAwZGut9n24cW1WHvPT19WWKuuw3yLv2sO4FfJuy44QVsfsx2YoCfd2HXInY8guN4SNHjkzCXs3D5j+PGoDF9jPfVq3Y3GO/m1llH6VkLCJ7AHgUwBIA/lD/WwzABBHZMyYNQgghhJBuEbuCcxqA41X1dHtSRI4BcCqAK8vOGCGEEEJIUWLNxEcDuK7J+esBLNrkPCGEEELIgBG7gnMPgE0B/N2d3xTAfTEJNOSJIVNEL3+z+ighs0x7n3ftbO/zMvWQXP7RRx9NwjbP3iW7dT/usTJCryti82zjvD6IlUGWvdtvK6zbeF/+Vtcpj8v8rLjlllsuFedl9JbbbrstCXvTzNB9lhtuuCEJT548ORW3+OKLJ2G/c+8HH3zQNL2ll1466rkeb1p56qmnFkrH0tPTE2XW7+s0pIMQcj9vCendrLjiikk4tBWHpSw9s9itOex7ejNtq2fj9QuKms57YvXeQoS2eDjooIOS8H//939npvH73/++rTw0w2/BYPnHP/6RhO3YC8Sb+Ifi7LfEj7F5tg7IwpqJx+p2AmmdFq/fYr8LdouYEL7u7fcq5CbFtnWfhi1XP97a9EPtNnb8COH1i2L6XGyvvBPAGSKyHvq3bNgAwC4AThSRXRoXqupNTe4nhBBCCOkasROchhegg+p/lvNMWAG0P1UjhBBCCGmDWEd/7a/hZRASPRXxBuvNxEO7kGc9C0ibLVqOPPLI1HFod1W7xOjN9eyOyXbp25vnhrDLfp3weKyqmcv5VvyWZ0k2i0ceeSR1bL2eevNJu1RZVISx2267RV3nRVK2nG1dvfLKK6nr7JL7xIkTM9P/85//HJWPPMyaNStzR26/zGsJtaEyynynnXbKjLv99tuT8FVXXZV5XYxpKFB8N3FbBiFxul9yb8fE2KfbGA+KiqpCZuJWNHv66SmbkdQ7LbPMMpnph0RN9j4vLrauBry4xJqCFxV52zYf8ggfMj0uSm9vb+Y4WPR9rAgmKwyE24n9/oW+hbaN+DzZ4yxvzc0IjTUWr/aRhXfF4F2qNKNjExdCCCGEkIEiOMERkYdEZCFzfIaIjDLHi4jIa53MICGEEEJIXlqJqDYAYM0xvg/gEgANleteAEv5mzzWGieYGbf0Zo/9spkV/9ilzzxYj7X/+7//m4qzojPrrdgupbcidkM+u9SmqtHpd3ojzuHDh3/K4iCGkEZ+FhtssEHqeM89+/1HfvnLX07FWZHPOuusk4pba621krC12glx7733po7tkvJzzz2XirPHTz/9dBIuumFfUVFKKxptw1sV2rbmRX8hbFsuas233XbbJWG/XG5FJyFC5RzyxJyFFzXFekX32H7SjghERJJxz28KaesuT9/PEuttscUWqeusCNFv6Bg7LlnxlfdIbC0EH374YZTNoov2eyzptJf3PBT19J5FJzY+tviyC33HQmIiO07YfuYtwkJpWDFXjEjKk1dEld8nNyGEEEJIl6EODiGEEEIqR6sJjtb//DlCCCGEkEFLKx0cAXCliDSEafMAuEREGnZdUXZg1tw4tLtpHkL6OVl4ubw1FV522WVTcVbed9NN/b4LvSzaygi9GVtIDyBr92EvX/Vy+CJ0Wx5t67jos6+88sqmYY/XiYr1KBvy8GnzbPU6gLTX05B5fKxOTifqpre3N2jiXASrrxPyCmzbq99d2Lb5q6++OhU3duzYps8N7VDs+4atG++FOIuQLovXz7E6OWXrVTQQkeQ9fPuy+fHlHhpXs7xL//3vacf0v/jFL5LwuHHjUnEHH3xwEt5hhx1ScbfeemsS/u1vf5uEvYfgPCbG7VKkfsoy9c9D7I7eITrVFhtstdVWmXGnnXZaZlyWGxOvSxPS77O6tn43gJgxttXXwI86zb40l7d8CiGEEEJIFwlOcFT1u93KCCGEEEJIWZSzQ1wL7EZkA8kmm2ySOj7vvPMyrkwTMou1y8h+STa0iZsVRVlxSadNAPMwa9asZAm1E8unsRvBeTFObF7sEmZoE8iQh89QHQ4mU1TLnDlzkncaPXp0Ks4uD/sl31hsf/DeSm1/mDVrVirOmolneVr25DFlt/g+m+VVNSRiCy2Be1PXMjZsbIXNWx7RdazY1prBP/XUU6m473//+03DRQm5R+i0yGUwYUXgseNJt8vHemU/6qijUnF333135n1Z4l/fr+z7hL4Dnhi3CbSiIoQQQkjl4ASHEEIIIZWDExxCCCGEVI6u6OBYytJbKCKH3GijjaKvtfohVpfA6w5Y9+BFyaN/MlDkyYc17fU73dp6C+10a68rKnMuqrMQwsqPi+bLti2vB2OPi26JYHek9voXWe7Tm6VhselYk+RY/Q6P7zfWrDtkhh7aLsGWq9e5yTKj9u3a1q9Pw9bNtGnTUnGd3jalLGz5+XEnZFpvdatCfcmWp7/Olm1oPAnFZZkeA/HjTlGdvhCzZ8/OLL9QPy7y7FD+88TF5mP77bePyle3dYNitubgCg4hhBBCKgcnOIQQQgipHF0RUfX19SXLk6Hde/OQtSRVdJnM7ya+5ZZbNr3OL5HbZcmyvDRbYpcf7fI8EDZ9LkLIo28IX99ZS9jedLDocmqnzU3LEHXFmkIWFU/29PREuRsI9b2Q6NWa3PudhkPPDfUVKwLxJtgWW3a+fGwbzWNumkVIvNcprGuGPBQRwXiPsvb9/D22f8aONbHevPMQarNeLGUpKhKLRVUTEab3Ih5qN2U8O/bdvMjV5is0bsa68yhK7FjvdwaI+QZxBYcQQgghlYMTHEIIIYRUDk5wCCGEEFI5xO+O3ZGHiHT+IUOMkSNHJuF333239PSdTP4xVV0vbxq9vb3a2GIjJE/vtHlgp03lvX7Rm2++mXmt1cGK1XMqWj5F62348OHaaF9Tp06Nvs/K6EP6ZCF5vZWTh8rR32f1QWx/CJVdaFfi0LVWpyC0VYNP3+p/hPQZABSqN6AzY2VWGeYxl7Zl4cvZ60bE4M3sLd3QdcpCVaXIfXas9K4fBvJ9ihAaD71OZUhXL1YfsqTxvWmf4woOIYQQQioHJziEEEIIqRzdElFNBfBqxx9EshijqqNbX5aG9TbgsN6GJoXqDWDdDTCst6FL07rrygSHEEIIIaSbUERFCCGEkMrBCQ4hhBBCKgcnOIQQQgipHJzgEEIIIaRycIJDCCGEkMrBCQ4hhBBCKgcnOIQQQgipHJzgEEIIIaRycIJDCCGEkMrBCQ4hhBBCKgcnOIQQQgipHJzgEEIIIaRycIJDCCGEkMrBCQ4hhBBCKgcnOIQQQgipHJzgEEIIIaRycIJDCCGEkMrBCQ4hhBBCKgcnOIQQQgipHJzgEEIIIaRycIJDCCGEkMrBCQ4hhBBCKgcnOIQQQgipHJzgEEIIIaRycIJDCCGEkMrBCQ4hhBBCKgcnOIQQQgipHJzgEEIIIaRycIJDCCGEkMoxZCc4IqIi8qGInBZ5/f4iMr1+30odztu9IvKxiPw58vpV6nmbIyIHdDJvAw3rbWhSsXrbvJ63PhHZvJN5G2gK1NtJ9etVRIZ1OG+TROQjEbki8vrPTL0BrLtSUNUh+QdAAaxkjjcCMN39KYBdQ/d1KG/3AjjAnfs8gLsBvAfg7wB2jrmvan9N6m0VALcCmArgHQB/BLBqq/u6WG+HAZgI4BMAl8XeV7W/ZuUP4GIALwDoA7Bv7H3dqDcTtzKAjwFc2SRuEoDNB7psB6De1gbwGIAZ9f9ru/jl6vcN63DempY/gB8AeAXAhwCeA7DKZ63esurOxO1dj/fj1YDVHYANAUwA8AGApwB8NbbOO/U3ZFdwPKp6v6qOaPwB2B61Sc5dRdMUkVEiMrzdvNVn07cCuB3AKAAHAbhSRFZpN+0KsBCA2wCsCmAx1DrIre0kWFa91ZkM4FQAvy8pvSrxJIBDATxeRmIl11uD8wE8WnKaQxYRmQu1/nUlgJEAxgK4tX6+aJqjRURKyt8BAPYHsB2Axjj+dhlpVwURGQngWADPlpBWKXUnIqMA/A+As1Ab038O4H/qeR0wKjPBacI+AG5Q1Q/bSGMLAG+IyNkismYb6awGYEkA56jqHFW9G8CDAPZqI81KoKoTVPVSVX1HVWcBOAfAqiKycBvJllVvUNWbVPUWAP9qJ50qoqrnq+p41FZIyqC0egMAEfk2gGkAxreds+qwKYBhAM5V1U9U9dcABMDX20hzPwCv1EUkyxdNRER6AJwA4EhV/ZvWeElV32kjb1XkDAC/RjkTv1LqDrXVmymqen39G3claqvyu5SQx8JUcoIjIvMB2A21XyeFUdVrAWyG2hL8OBF5VEQOLWlWKgDaHsQryMaodZTCE4oO1xvpEGXWm4gsAOBkAD8sOZtDnTUAPKV1eUGdp+rnC6GqZwL4NoBFAUwUkXtEZC8R+VzOpJau/60pIq+LSOPDW8nvVBFE5EsA1gNwURnplVh3QO2b5o8H9BtX1YazC2qz2/vaTUhVn1HVowEsA+BE1H4BvSIi19QH0RheAPAWgKNFZLiIbAlgEwBFGlFlEZGlURMptP1RKqneSJcpsd5OAXCpqr5Rfi6HNCNQ0wO0vAdg/nYSVdWHVfUQ1FaqLwTwHdRW436XI5ml6/+3BPBvAL5WT2f/dvJWFUSkF8AFAA5T1b6y0i2p7v4CYEkR+U79G7cPgBUxwN+4qk5w9gFwufuVEkRE9qhreU8XkTt9vKrOAfA0anoH76A2M43SF6iLXnZCTa48BcBRAK4DwMG3joiMBjAOwAWqenWO+zpWb6RzdLLeRGRtAJujJu4kaaYD8BPFBVBTDG2JiBxr6u1Tqwiq+glqK0JPAJiJfL/gP6r//7mqTlPVSQB+C2DbHGlUmUNRW317uMjNnay7+or7N1D7cfomgK0B/AkD/I2r3ARHRJZB7Vff5XnuU9WrjJLyNia9ESKyr4jcjZoy5VIAdlfVNfOIUVT1KVXdRFUXVtWtAKyAmkLtZ566CGIcgNtUNcokskGn6410hg7X26aoWZO8JiJTAPwIwK4iUooy9BDnWQBrOcXStRCpsKqqp5t6+17jvIgsLCKHicgE1KxFewF8TVU3yJG3F1D7sNofptE/Uj8DbAZgZxGZUm/XGwI4W0TOi7m5w3UHVb1PVddX1VGo6ZeuhgH+xnXUVn6A2AvAQ6r6UrsJicjWqK20PIjaL4lb6rPcImmtBeBF1CaVhwJYAsBl7eZxqFMXO/wRwIOq+l8lpVlmvQ1DrZ/0AugVkXkAzFbV2WXkdShTt7zpQU3WPrxeNjOLLp+XWG8XA7jGHP8ItQnPIUXyVTHuBTAHwOH1X/EH1s/fXTRBEdkfwLmoWayeBOCu+gpcLlR1hohcC+DHIvJXAAuiZnF6VtG8VYx9Acxjjm8CcAOAS4smWFbd1dNaB8AzAOZFTf/tdVX9Y9G8lUEVJzh7o7wO8QKA1VR1cglp7QXgANSW2e8HsEXRj27F2BnA+gDWEJF9zfnVVfW1gmmWWW/HoWbZ0WBP1AaCE0tIe6gzDjVdMqD2a/Ji1PQm7i2YXin1pqozUPPxAgAQkekAPlbVqe2kWwVUdaaI7ATgdwB+hpqfmZ1UdWYbyf4FwJiSrJ0OQ60dTUbNAu4S0EUDAEBVp9ljEZkJ4H1V9TpVeSiz7n6MfnHiXaiN7QNLM+c4Q+EPNdPU9wCcEnn9d1HrMB8DWKHDeRuHmkz7nsjrV67nbQYyHKZV5Y/1NjT/KlZvm9Xz9hFqS/EDXr6DqN5OqF//MYDeDuftBQDvAxjLemPddeJP6g8nhBBCCKkMlVMyJoQQQgjhBIcQQgghlYMTHEIIIYRUjq5YUfX29urw4dXwrTZnTtqCbvbsfmvhYcPSxdnT0z9/9PuZzZzZb7Qw11z9+9x98km2YZVNDwBiy/STTz55W1VHR11sKFpvoXew2DLxumD2ubNmzUrFzT333Lmf3dvbm/lsW4ftPLsILcqqUL2JSCHFOtsOQ/vv2T7g6833jyx8X7H1Y+sjNr1W2Dq172b7oceXgX1X355cPgvVW/2ZbStF+rL17btd/Dhk6esrzcFuU0JtNHbc8flvHM+ZMwd9fX2FNp6MHSt9Xdi8+LHGYt87dJ2v+9C1ZWDrwNdHVlvw5WTLxI8n/n0stg9mfeO6MsEZPnw4xowZ041HdZx33303dTx1ar/l6cILp/eHnHfeeZOwbaAA8MorryRhWzYvvvhi5rNtegCw1FJLReQYePHFF1+NutBRtN5C72Cxjdd3xMUWWywJv/FG2hlmKE9Zz15ggbTzVlsfb775ZinPLkKLsipUb0VZeumlk3BoYHnvvX6rVD9B8P0jC99XFlxwwSRs+1Rseq0YPbp/7Jtnnn5XIi+//HLmPaGPhW9PLp9drTePL1vfvtvFj0OWDz9sZ2/j1iy55JJJ2I+pseOOz/+IESMAAP/6V3H/n7FjpW3bADDffPMlYT/WWOx7/+Mf/8i8zo5drdIsA9tHfH1ktQWfR9s+/XfAt2WLHTOyvnEUURFCCCGkcnRlBeeTTz5JZterrLJKNx6ZYFdKRo0alYoLrbDYX6mxv4D8dSussELmtcsv33xn+pEj0xsn21+GsSs2ZTFr1qzk10LjV04D+65+Rm7r2P+qsmVifz3naRc2zdj77C94j/0l4PHp219goTRjCeU/9hdpWYRWMywhEV4IW/e+r2T1sdCvPd9Xpk+fnnmf/TU5eXKcH0H/bvZ5obpvZ9VprrnmSlbSYusDSJdt7CqKXT3Ic58vWyuKsGOUHUOBcD+z+HZvn+fH6SL4NBr5mjZtWrPLo7DfOI9tN75t2OPQWGBXbXy7tP3xc59L721ZxvgS6u/2uxAS99p8eDGdTdN/M20fCH1Ps+AKDiGEEEIqByc4hBBCCKkcnOAQQgghpHJ0fbPNkNzPywtDuhZZcaH0vZzfyqC9XDZWjm7lk15P5fXXX0/CWTo3Hi/bD8n6Q+9aRF7pUdVErurl9fbY6j4AaVm7tcwB0lr3Ni70Ll7XwpZJnvZUBK9HYNtFbBsJtV2vz+DLeTAS0rsJ9Qcro/d1Gqv/YdPP01eyrD3yWBjZ+i5DF6QZs2bNSo0bWfh2Yt8v1rVDHosn+7yQhZ0lVuemFe+8078PZBlplqE7l4fQOBE7TofqKvbb4rHjktUt9Lo0Id2a2DEw9C23ZeDbVujbHjO+cwWHEEIIIZWDExxCCCGEVI6ui6hChMQNReNCxJq4Wfzyr12q9qIaG+cdPJVtYuzLIHYZOcSwYcOSfPr0Yk3WvdlibJzl9NNPTx3fe++9SXifffZJxe22225N0xg/fnzq2Dr1uuKKKzKfXYaTtDxi0zLEasOGDUscZIVEGXmI7WNWfOXFUEWe7fuUbXd5+r3tt7ZfehGqFQWExoSynec16Pn/2TvvcDuqcv9/35wUAgEh9Bp6kYCES3soEgy9+COU58pVrnSuiCIKKkVBBGkiKk0pXuqlI0gPEImGYigGSIBQLqHcgBJCSz3Jyfr9sfeZvPPmzLvXzJ69TzJ+P8+TJ2vOmlmzZlaZtdfb+vRJ5iVv+98bf61w7la2A788bVeGN952uyfJIs94zHpHVqxdhjsJXS/b1p4qhx4jse2UR7VCv58ibcgdHEIIIYRUDi5wCCGEEFI5uMAhhBBCSOVYpHRwysDK8LTJ5ZprrpnK0/K9WDmmlUHqY8/U3AtKqPUArPy8N2XHIYRC8m8dHsN75xr7nIceemiSHj58eCrPHmuyItjusssuqeONN944SY8ZMyaV99RTTyVpK+/WlKGHYftrGZGfOzo6SjPR7SZL38sza589e3bq2JqNx2DHjR4rDz30UCrvhBNOSNIPP/xwKk/3Y522+gZZujo9navxdBjagXVnoMlytW/1CWPHug5WCgC/+tWvkvSIESOS9GeffZY6zxtLzZoDW6z+lw510O6QNxrvHRSljL7nXdfO/uyFJ/H0c7LgDg4hhBBCKgcXOIQQQgipHG0RURWNkBuLF6k01stjGaIge+9Yj6meWWesp15b/zJEHfPmzUvM273tbM/TsG1vLWLQZuK33XZb6rwBAwZE1fHVV19NHWvRhN7S3G+//TKvW3bZZVN5eks2dnvW81Zs8bzCzpw5M+p+Hl5k4zLQ7W1FYbrPa7EAUExEZcevNhu34shhw4YlaS0mLYodQ3oM5/H2mgftPdxix1ksuk2yRHV5sGJnLZbSLLPMMqnjnXbaKUk/8sgjmeUXnYv1uLIm9q32ED5gwAAMGTIEwMJ9b/DgwUnaU1WI9Xhs5ww9Bu2Y++Mf/5ikR44cmcorW1S3++67p44vvfTS3GVYEZ4ec0XWDu4OjojsLiJ91fF/iMh4EZkhIm+IyHdz35EQQgghpMU0ElE9CGAwAIjIgQCuB/AEgG8BuBfABSJySEtrSAghhBCSk0YiKlHpEwGcE0I4o358vYj8X/3vN7eicoQQQgghRcijg7MBACuS+hOA0xtdKCKlhA7IwotUGos1syxiZmufMdaMuKiZn35uK5+PjfLq0dHRkcjR7fvRcv08+h76+WbNmpWkbeiKjo6OJP3SSy+l8i688MIkPXXq1FSe1tEYO3Zskj722GNT502ZMiVJaxl5HnS7Wdm6F1XbC9NRRru1mqzI3BbbJ3Ufih1ftm8dfvjhSdqOlfPOOy9Jl6FTZ9tUt2OrQjWISPJO7fN5ej6ejklZ+kHdXHfddVHnWf04jW0frXtm+1TWt8P2Da2nYvU1dNtZPbduXaSurq7M+jZC605ZHSWNbdPYqPT6HVjdMJ235557pvK0LmOrzeO/+tWvpo6PPPLIJL3NNtskaU/3q+xxFbPq2FxEpgGY1cP5fQB0LHwJIYQQQkjvEbMGEjuWAAAgAElEQVTAeRgLRFU7ABin8oYBeKfsShFCCCGENEOjBY61sZ5ujvsBOD/PDb2tY2vi5olrhg4dmqS1+MFeo7e8nnnmmVSeFmFY9HWel02NfbbY68rwFNkK0UZXV1dSrt0Ct1GeY9H11O/nk08+SZ03cODAJH322Wen8t5///2oe5100klJ2pqs6uP77rsvqjwgO7KujRav86xISm8xe1HaW2nqnRf93EsvvXSSttv9+rk99wdWRJUlprVb+N/5zneS9PXXX59ZftF3p8Vqts/rOnsitmbaTY85SxEze6CYObgVdXz7299O0t2R6nvi5ZdfTtJ9+qRtWL785S8naTv3jh8/Pklr9xFAes6w40zjmRFPmzYtSVuRdBliES1atCIk3Z/tvKnNoq2JdFYfs3OG7m8/+tGPctS6Z4r235/+9KepY+0hfpNNNknS1sN5K+rSjbvACSG83SA/e4YhhBBCCOklmvJkLCJ9RWStsipDCCGEEFIGzZo2bQrgeZSkaGy36Lwtx1/+8pdJeu21144q31rSfP7550l64sSJUWV4WG+5mueffz51/PTTTyfpCy64oMc6LUpYMZrevi/qEVVvP37ve99L5en3oLeXG6G3h7/2ta8VqpfGiimyLFK8vptnm7U3g6t66OfW6ViLF4sVNWSJ5uz70OKLW265JZUX++689tDiEM/arTfwLKV0vcuwqLz44otTx1lBbC06EKcdKwcccECPaQB4++0FwoIbbrghlXfzzc17IdFzlBVJdVtfeSLVRnR1dSVWgla0pN+DnSf0ey0aIFeLD4844ohUnrY6jA1immcO0mWusMIKqbw333wzSWuxdh4RlYfu51l9nLGoCCGEEFI53J9bItIo+EOcET8hhBBCSBtptJ+8KmrhGbL2dFcHcEKpNSKEEEIIaZJGC5wJAF4MIVzWU6aIfAk5FzhFzb6sXPCee+5J0u+++26SnjBhQuo8bU6uow0DwPDhw5P0dtttl8rTZXqeKTUikjp+4YUXMsvQx/peN910U+q8MkzIy6DVuiFaBm/JY5p/yimnJGkvIvndd9+dpLVXY4t9/7Hvoag+SBlRsDVWb0M/j/b8CqT1Z6yOjNZH0XnWpDcWT79F6ylYs1fdTz744INUnj72nltTVN+gN/D0m/TzFtUluf3225P0xhtvnMqL1Q20fSoL6+VYR4235sbPPfdcktZm1tZsXo+dPHqBRaJUWzo6OjJ1aIrqdelxpq+bPHly6ryvf/3rSdq6vLj22muTtO3rZfTnNdZYI0lbT8ZF8OY/O6ZjdMsa6eA8AcCbAaYD+EvDuxBCCCGEtJFGfnC+1yD/TQC7lFojQgghhJAmaV0EzAzK2iZ77LHHevy79Z6py7f3GjVqVJK24qtnn302SevAj3arW3vgteZvOhDkk08+mcrT3jT1FqkXEM16yNSiraKm2h4DBgzAkCFDSi83L54YYZ999kkd77HHHknaM5194IEHCtVFb6FqMWPRYLLesxUdG7rdbBnapYJXZ7uVnlUXe54NyqrRoie7Fa1FFNqMd/fdd0+dp0XQi6pJfW9QRCy11157pY5HjBiRea428/X47ncXxGO2LgR037Dz6OmnZ8ds3nTTTZO0dufR2yLDLMqql56/dPo3v/lN6jwturEeqL16leFWQfe7nXfeOZUX61pARzDI8x3T80mWN2qaiRNCCCGkcjTryfgVEZnX+ExCCCGEkPbRrIjqMgDZkdcIIYQQQnqBphY4IYRLY86bP39+EnXYmpV6EbeLyNitXoHVW9Fouebo0aNz3wvwI8VqE3WrA/LSSy9lXpeFfTatt9BuebT3XrVOUVE5r5Yre3pJW2+9deo4y6T/1FNPTR1fddVVSdq2jT7Wek5AWkZc1LxUP5s1dS0jsrEeb9Y1vNaLsJHAtTzdCwvQr1+/zDJiI27b59Ttr102WFPQzTbbLLPMIixqehxaf8rTZyoD7WoDSLer1YXQbvet/tScOXOS9CWXXFKoLjqMjtXr0vo5jzzySJK2IR08/Q3dzlnhV7w5rRGdnZ2ZJs52DGbVy85DGv0t2WGHHVJ5b7zxRpL+7LPPUnm6TS1a98WbY7Xpv53zdLtZnRs9t2ldVfue9Xk2vIfXpnp+yZo3oxc4ItIBoDvYxNQQQlfstYQQQggh7aShDo6IjBSRJwDMBDCl/m+miDwhIvu3uoKEEEIIIXlpFIvqWACXALgOwMUAuveBVgawO4BbROQ7IYSrMooAUNtm6t7+9kQW3hZdLHbLXG/JW9PEotFbNXo71W7zXXHFFUnamq9rkVieaNlZtMLcuIwyYr0Q2/p7Hi311ro1RdU89dRTSfrcc8/NPM/2u1jRk77Oihm1qMyWp/thKzxVz58/PynXPpveHrbburoNvO16LbazImd9nWeG7rmL2HzzzTOv097Hy8CKD8oQEZaFnZ+Kiqy0SGaZZZZJ0p74Qru4sPc+++yzU3lafOX1If1u7Vx5/vnnJ+lf/vKXqTzdx/bbb78kPWbMmMx6WLx6dYtBrXgnDyGEKBNnTx3B88x76aULtEHsmNPfmTxqHbpNtbjKzhn6ftZtg24r62H5nHPOSdJeNHcrps/Ciq9iaCSiOhnAcSGEq3vIu0NExgE4BYC7wCGEEEIIaSeNRFSrA/irkz8WwGrlVYcQQgghpHkaLXAmAviWk39s/RxCCCGEkEWGRiKqHwC4X0T2AjAKaR2c3VDb4dm70U369++fijqq0XJAK4OMiRbaDJ68PUtm6+kVnHbaaaljrZ9jn8VGQu6mqKm8lc+XoV80Z86cpvV3rJxfy2J12VYXQpvAr7rqqqk8G+k4izPOOCMzT+sl2Db1XBdovP6p9W7ss2mdKyu3j723x/z58xNZv6cbpN+xxXO3oK8rq9/piMiHH354kv773/+eOu/ll1/OLCP23em2tzpQngm/1gGw+gD6uJmwKXPnzk30IayeSlH9IP0cW265ZeZ577//fpK2oXCuu+66JG31c3S9vGjf+nm0zgeQbq9DDjkklWddQTSL7dvd87TV3ywL/U6K6JEAadcJVp/z0UcfTdI6FAuwsF5MDLbf6TF+zDHHpPI8/RmtZ+rN9XrMeX08VldH0yjY5hgRGYraLs52AFapZ30A4G4AvwshTM59V0IIIYSQFtLQD059AfOj1leFEEIIIaQc2hJNXESSbcEPP/wwleeZ+XpkbSWXZeapt9Q8MZH2Kvm1r30t87y9905L8jyTxqx65GFRMXeN3a63ohQt6rjrrrtSedpz5/rrr5/Ku++++5K0fseeObA1G9Vb2Lbt9bmx5uT22bSIx27V2+MiaJNVa/KpxWrWY6zn2VS/E/0OrFdYPb5t+VoEYE1ddRT4wYMHJ2kr4vTEULFjRYulVlstbSNR1GR11qxZSbqZsSciyZxYlpflWLHhCiuskKSteDcrsjWQ7S7BvofY9yIiqWMrkunmrLPOSh1rMeeiRBniy48++ihJW6/7saJZ7ZEYyJ6/vDLsfKuZMGFCVDl2zoh1lWHnUTu/9ASjiRNCCCGkcnCBQwghhJDK0RYRlQ7+Z9Hbd952ut2q1tfprU97nt7u9gIIetZRHlr0NGnSpFSe3oZ/8MEHo8prtUfiPPTt2xfLL18LFt8KkZcXxE1vU1uxjQ64qANEAumtdd2mnrdoe2/P66ku0+tPetvVijM88WfZbZzHElHX2b5zLbLyxoreRs5zb+0deZtttknSt99+e+Y1RYLxNoMXsFG/n94WD3seinfZZZckbfu2vi7L0simLTFig57QYltr6aWDOOq0ta4qOne2uh9pNQwrovICnGq0WH7//dMRkg499NDM62LFV947WGmllZL0l770pVTe559/nqS1pZ1Fjxc732oLa8/q1hJj9cYdHEIIIYRUjugFjoisJSKrmr+tKiJrlV8tQgghhJDi5NnBmQzgMfO30QCyoyISQgghhPQCeRRPjgDwifnbKQAa2iDq6MaeiaEns481r7Nm5548uojejZVjajm21bXQJuSxeJ5hrTdoLetvxntqFiLivr9m8cystVdoWwf93OPHj0/lZUXrzfN+PNmu7jOeZ1bPW67G9qfY8RCLZ5LpeVW1ZuIaz7O35x1ZY597p512StJal+2Pf/xjZhl5vChn1bmozpOdZ4p6qLV0dXU13e7W1F2b7h911FFJ2j77F7/4xabu2witn2Pv9Y1vfCOqDOtmROPpSHm0WrfRG/+x89Jf/vKXJG11lLTuy0UXXZRZxuabb546XnbZZZP00UcfnaRff/311HlDhgxJ0lbnUZvwW50ujX4Htm20rqr3PrQX9liiv/AhhGt7+Nvdue9ICCGEENJiCikZi8hAEdlVRIY0PpsQQgghpL1E7eCIyLUAxoUQLheR/gDGAdgUQKeIjAwhuDbQ3rar3trNE5RQiy20KbjF2+7V2/B2m1Jvp+ptUeuteNiwYUnaepjU3ict+lk9s1It+rBbnbHmjUW3YPv06ZNsH7bClFLX67DDDkvleQH2/vu//ztJe2bERdEiDOtFNctTrxXp6HbLY/pYdoBZb3s8j+hMbyt74qtYjjzyyNSxNkWNFR17nnJtf40VS+nntGInPc9YUWiWt9286MDE2nQeiBdn2D6k5xotxssjxikDLXbea6+9Unn6fb7zzjupPB0w8pvf/GaSLjontUIkpV1qWPSzxXrttXiBg08//fQkXTQwqQ6gutZaabsh7eFam4UDwNJLL52kddBPjyJBM4G0iwAgLpBo7KjcA8DT9fRXASyNWuDNM+v/CCGEEEIWGWIXOMsB+Gc9vSeAO0MI/wRwC4DWaqYRQgghhOQkdoHzAYChItKB2m5O917UIADlm+8QQgghhDRBrBXVHwDcCmAKgC4s8IezLYBXm6mAZ17pyZxjo5BreZ9nRuq5zNcRVG+++ebMMrbffvvMPBuGIkvvxupraJmtzfNM1BcH9Dv//ve/H33dQw89lKSzzMKB9PuJjaQLpN/lEksskcqLjcys9VSsaWvZejaWPn36YODAgQAW7hfemPJM2/WxF2rCQ1+3zz77ZJ7n6a5pvDEbq2fhjTfbZ4rqM+VBRBJ9oaKuHzy3Cpo8bjKKtLkNT2Pfp8bTy3jllVeS9D//+c8krc2c82DdbVjT5yJolxr2fXvfqthQDVr/5Cc/+UkqT0fx9t6xx7nnnpuZp3WnjjvuuFSeddUQQx7dL/1+iui5RfXwEMJZIjIRwFoAbg8hdM+a8wCcn/uuhBBCCCEtJI8fnDt7+Ft2dC1CCCGEkF4ieoEjIlsC+B4WKBW/AuDiEMLzZVXGRnzW21N2my/LI6vd6tSmvF5kaA/r2VHzs5/9LEl7Eas9sURRc01PzFIG2rzfitjKoKi5pjbX9bZ1dfnWBYHGbrnrfueJpPQ7OfHEEzPPs3R1dSXpPfbYI5UX6wHZI4SQjAk7HsoWjxVtw/322y8z7/777y/9fll4/cfOD3qesW1T1viYO3fuQl6xi5TRLNaMWx//+te/brr8V19NazZsvPHGSdqaW2eJr21f8CJn6zztOVfnvf32242qncncuXMTUZcVEeoxaF0s6G+SN597oqxbb701Sdt5zpu/suZHe02sh+tY7952XHme4/WzFpm7ooRaIvJ1AM8AWBXAA/V/KwMYJyJxPrYJIYQQQtpE7A7OOQB+EkL4hf6jiJwC4GwAN5ZdMUIIIYSQosSqJa8I4LYe/n47gJV6+DshhBBCSK8Ru4PzZwDDAbxh/j4cwJg8N7QyQm3m67lGt2gZuJbT2TK8yMeePP+zzz7LzNOceeaZUedZskI1WJ2JWDNCS9HrNPPmzUtMnL0ovh6eSanO22yzzVJ5WkZvy7jrrruStJXLemEvNDrEw/vvv5/K021z6KGHpvKy5MpahyAPOhIwAJxzzjlJuqi+Sd++fZNnyKPHU5apcxY6HMMWW2yRysvSYWl1pGeL1oGyfV7ntcrUf/78+aW3g54L/uM//iNJ/8///E/mNWXo2VhefvnlqDxrtu3p1mhi81oRdkazzjrrRJ+rv0laXxRI97/YOdwLX+LpeurzbBn6W7jKKqtk5nmuSrx7e2b63piLCXcUu8B5EMC5IrIVFoRs2A7AAQDOFJEDuk8MIdzVw/WEEEIIIW0jdoFzSf3/Y+r/NJeqdADQ0WylCCGEEEKaIdbRX1OhcrVnVbuV5Hle1FtesVt0dnu3qCn1lClTkvRGG22UpE866aToMrwIzG+99VZUGbHPbbcAy9jm7tevX7IN6G0j2jbM4yG1G7t9rU0TvQi2sSIpy8EHHxx1nify1N5FX3zxxdR5WsRmPfNqkcHYsWOj6pEHLeaI9fht6+VdV7RvjRw5MklbkdQtt9ySpH/3u99llhFrRh/rrdY7z5av723HW1kiqwEDBmDIkCEAiovnbN10W2pP7GeddVbqPD2uPLcKnqhJYz3PapHLeuutl8o75pgFv5utyFijxUt2bMZElwYWfq/dz6rdN+RFt5tFz/V2PtfPY8dEltjWik71e/A8l8eOWyvCW2aZZZK0bdMQQmY5WWIp666lKDHuVZpauBBCCCGELIq4CxwReVJEllXH54rIYHW8goi808oKEkIIIYTkpZEsYTsAeq/62wCuAtDtsrcDwOr2IotnGaD/bgMnxgbKLCqm0IwaNSoz78orr0zS9913X3SZ3javfja9hewFj/RohQVMnz59Eq+fXuBBe+8i7bHpppumjvfff/8kbT2DaqzH1TXXXDNJW/FDFlZMNGvWrCRtRU96+/yFF15I0lZE4QVJ1dvGdju4qLWapqOjI9neztMvdL3sVnqWeCZP+V//+teTtN3avuOOO6LK8O4XY1UBxFtDeWIoWw89PlrtYRzw+5Ql6xl33HHH1LEWIZ5wwgmpPO3911p6xs5Z2kIwj9fgLEvYPOJXjzK+Hx55rKpisNZWZVsa2jnIBhzWzJ49OzNPjxE9NssS58bMPXlFVFKsKoQQQggh7YM6OIQQQgipHI0WOKH+z/6NEEIIIWSRRTwzLxGZD+ARAHPqf9oLNc/F3eE/BwDYNYTg+r4RkeQmq622WirPynNbiTXN1l49TznllFSeNuved999k7Q1YdTyTyuz93SItJxTy7DLltcCwGuvvfZcCGGrvNctscQSIcv0Mce9m7o+L1qnRb9X2890BFsbXfjTTz9N0p7ul4dnGpqDQu3Wr1+/YCMydxOrb2D7spahx+qYWL2q0aNHJ2nrwfmHP/xhj2Xk0TXRZrVWPyNLT6So7po14dX3+8c//lGo3QB/zOmxZHXitF6D1dHIKsNj7bXXTh3/13/9V5K2keD/9Kc/Jenf//73mWXqPmldPywKvP3225g9e3YhNYyic6WOHG9diSwqTJo0KTNv7733TtIPPvhg5nl6/pg2bVoqT0csaEIfqscx10jJ+Dpz3FNQzeuL1ogQQgghpBW4C5wQwuHtqgghhBBCSFm4Iqqy6OjoCN2ejO2WsN4KLcvsL4uddtopdXz11VdHXbf11lsnaRuEU28xWjFIUfFG2RQVUWnRYlkB6vRWuhbT2fLLCI7nbcfHmhR7926D+K1Quw0YMCB0m8jbZ/NEMlocZPuyFnt4zx3bVrHvLo+Hbu0WwJqiag+vZWyJe96CP/7445aIqIqixbGeR/JFhVYHw8yiGRFV0bnSm+faGSDUQ7tG+dWvfpXK02Jn3c+A+L6m5x1PRUOrDgALjd0exxytqAghhBBSObjAIYQQQkjl4AKHEEIIIZUjf9jnAuhQDdbsMzYCrKWITNK6Jo9Fy9ufffbZVF47dYjy6Hx4YSJafW+tC2HlsLrdbGTarPOKovVGsiLzAsV1pWLr6EU99iLiFjVh7urqSnRQbJ/0ytR6N9770sRE9O0Ja8qs5fdFn1ubkNv+74WEycI+mxe12R4vSmTpQth3FBvewjNn9kKNxLroL6o754XL0GPVll/GXNPR0ZFE3bbuSPS48saL99yxem/esxXVndNuUjysu42y9YbsPK2Ps56NOziEEEIIqRxc4BBCCCGkcrRFRKVpwqtriqwtqaLbYg8//HDqeM899+zxPLvFqCPd5hFRtdrEuOwIuUXfq43orU39dB2td0u9Fe2ZGy6qppUa2y/KNqW29O3bNxEB5YlO7nnAzSKPOEm7VPDK0WLsPOXrLWtrUqrFUrFl5rm3F/E+D3PmzCnU7rER3nXfs+9IY8VX+v01MNdtKd69PA/bRcU/sWixsPUyretsxXSxIiQtMrZlxD6bN1d655Xh2d1Df0OtqFeLvfR5QDqyfBbcwSGEEEJI5eAChxBCCCGVgwscQgghhFSOtoRq0G6sSXswst1CruP79OkTuuWceXSnytZ9abW+kiebtnimqLHlx1JGFPgywip45VgdKy0zt/o/WnfA3jsr+roXqsHqOmiZfKwOjjWH1/fzwj00GA+FQzW0eq7U7WX1WTw3+Vr/weo+6Lz58+cnaU8n0epjeWb2ZeltxhBCKBSqoX///iHLNcfiEB5D482H9hk9nZxYfciS5neGaiCEEELIvwZc4BBCCCGkcrRLRPUhgLdbfiOSxZAQQm4bYLZbr8N2Wzwp1G4A266XYbstvvTYdm1Z4BBCCCGEtBOKqAghhBBSObjAIYQQQkjl4AKHEEIIIZWDCxxCCCGEVA4ucAghhBBSObjAIYQQQkjl4AKHEEIIIZWDCxxCCCGEVA4ucAghhBBSObjAIYQQQkjl4AKHEEIIIZWDCxxCCCGEVA4ucAghhBBSObjAIYQQQkjl4AKHEEIIIZWDCxxCCCGEVA4ucAghhBBSObjAIYQQQkjl4AKHEEIIIZWDCxxCCCGEVA4ucAghhBBSObjAIYQQQkjl4AKHEEIIIZWDCxxCCCGEVA4ucAghhBBSObjAIYQQQkjl4AKHEEIIIZWDCxxCCCGEVA4ucAghhBBSObjAIYQQQkjlWGwXOCISRGSGiJwTef6RIjK9ft36La7b4yIyW0T+Enn+hvW6dYnIUa2sW2/Ddls8qVi77Vqv23wR2bWVdettCrTbz+rnBxHp2+K6TRaRWSJyQ+T5/zLtBrDtSiGEsFj+AxAArK+OdwIw3fwLAA70rmtR3R4HcJT52yYARgP4FMAbAEbGXFe1fz2024YA7gHwIYBpAB4GsFGj69rYbscDeBbAHADXxl5XtX89vX8AVwKYBGA+gMNir2tHu6m8DQDMBnBjD3mTAeza2++2F9ptCwDPAZhZ/38Lk792/bq+La5bj+8fwAkA3gIwA8ArADb8V2u3rLZTef9Zz7fzVa+1HYDtAYwD8DmAFwHsGNvmrfq32O7gWEIIfw0hDOr+B2Bf1BY5DxUtU0QGi0i/ZutWX03fA+A+AIMBHAPgRhHZsNmyK8CyAP4EYCMAK6M2QO5ppsCy2q3OFABnA/hDSeVViRcAHAfg+TIKK7ndurkMwDMll7nYIiL9URtfNwJYDsB1AO6p/71omSuKiJRUv6MAHAlgHwDd8/jUMsquCiKyHIBTAUwsoaxS2k5EBgO4F8CFqM3pFwC4t17XXqMyC5we+CaAO0IIM5ooYzcA74nIRSIytIlyNgawGoCLQwhdIYTRAJ4AcGgTZVaCEMK4EMI1IYRpIYS5AC4GsJGILN9EsWW1G0IId4UQ7gbwUTPlVJEQwmUhhMdQ2yEpg9LaDQBE5GsAPgHwWNM1qw7DAfQF8OsQwpwQwm8BCICvNFHmEQDeqotI1ilaiIj0AXAGgBNDCC+HGm+GEKY1Ubcqci6A36KchV8pbYfa7s0HIYTb69+4G1HblT+ghDoWppILHBFZCsBBqP06KUwI4VYAI1Dbgh8lIs+IyHElrUoFQNOTeAX5MmoDpfCCosXtRlpEme0mIssAOAvA90uu5uLOpgBeDHV5QZ0X638vRAjhfABfA7ASgGdF5M8icqiILJmzqDXq/4aKyLsi0v3hreR3qggisg2ArQD8rozySmw7oPZNs8e9+o2rasc5ALXV7ZhmCwohTAghnAxgTQBnovYL6C0RuaU+icYwCcA/AZwsIv1EZHcAOwMo0okqi4isgZpIoemPUkntRtpMie32cwDXhBDeK7+WizWDUNMD1HwKYOlmCg0hPB1C+BZqO9VXADgEtd24q3MUs0b9/90BbAZgl3o5RzZTt6ogIh0ALgdwfAhhflnlltR2TwFYTUQOqX/jvglgPfTyN66qC5xvArje/EpxEZGv17W8p4vIgzY/hNAF4CXU9A6mobYyjdIXqIte9kdNrvwBgB8AuA0AJ986IrIigFEALg8h3Jzjupa1G2kdrWw3EdkCwK6oiTtJmukA7EJxGdQUQxsiIqeqdltoFyGEMAe1HaHxADqR7xf8rPr/F4QQPgkhTAbwewB75yijyhyH2u7b00UubmXb1Xfc/x9qP07/AWBPAI+il79xlVvgiMiaqP3quz7PdSGEm5SS8l6qvEEicpiIjEZNmXJ1AP8eQhiaR4wSQngxhLBzCGH5EMIeANZFTaH2X566CGIUgD+FEKJMIrtpdbuR1tDidhuOmjXJOyLyAYCTABwoIqUoQy/mTASwuVEs3RyRCqshhF+odvuv7r+LyPIicryIjEPNWrQDwC4hhO1y1G0Sah9W/cM0+kfqvwAjAIwUkQ/q/Xp7ABeJyKUxF7e47RBCGBNC2DqEMBg1/dKN0cvfuJbayvcShwJ4MoTwZrMFicieqO20PIHaL4m766vcImVtDuA11BaVxwFYFcC1zdZxcacudngYwBMhhB+XVGaZ7dYXtXHSAaBDRJYAMC+EMK+Mui7O1C1v+qAma+9XfzedRbfPS2y3KwHcoo5PQm3B860i9aoYjwPoAvDd+q/4o+t/H120QBE5EsCvUbNY/RmAh+o7cLkIIcwUkVsB/FBE/g7gC6hZnF5YtG4V4zAAS6jjuwDcAeCaogWW1Xb1soYBmABgIGr6b++GEB4uWrcyqNWbSS4AACAASURBVOIC5z9R3oCYBGDjEMKUEso6FMBRqG2z/xXAbkU/uhVjJICtAWwqIoepv38xhPBOwTLLbLfTUbPs6OYbqE0EZ5ZQ9uLOKNR0yYDar8krUdObeLxgeaW0WwhhJmo+XgAAIjIdwOwQwofNlFsFQgidIrI/gKsBnIean5n9QwidTRT7FIAhJVk7HY9aP5qCmgXcVaCLBgBACOETfSwinQA+CyFYnao8lNl2P8QCceJDqM3tvUtPznEWh3+omaZ+CuDnkecfjtqAmQ1g3RbXbRRqMu0/R56/Qb1uM5HhMK0q/9hui+e/irXbiHrdZqG2Fd/r73cRarcz6ufPBtDR4rpNAvAZgOvYbmy7VvyT+s0JIYQQQipD5ZSMCSGEEEK4wCGEEEJI5WiLknHfvn3DgAED2nGrXHR1LVAWnzcv2yhG133mzJmZ51k6Ojp6LKPdzJw5c2oIYcW81+l2s6JMbWVq311nZzP6ijWWXHKBfyj7znWeJat9+vZNd/XY9rbPbctpFvuudJ8MIRRqNxHJlDt79e/fPy4c0axZs5J0URG3rYc+9sr08srodxobokff274r3W5dXV2F2q1+z0IvVL+/Pn3Sv1tb/V769Vvgnqjse9n7LbHEAiMi3Q/zYNuue57u7OzEvHnzCsVliv3G2flJt5vXp+bMad4exZZfdlvFzrHeuLJ5us76fQDp7+ucOXN6HHNtWeAMGDAAm2yySTtulYtPPlmglD51anZYj/XXXz9JP/fcc9HlDxo0qMcy2s1zzz33dpHrdLvNnTs3lacntQ8/TBunvPNOUeOnBej+Yt+515ey2mf55dOhrf7xj39kljFkyJAkPXt2OszSiisW+m5l8t57aT9Yuk/OmTOnULt52PegWWONNTLzNBMmTEjSRSdeW48VVlghSdu+prHtoSmj32nsB0E/6yqrrJLK+/TTT3W69HZrhH6f9kPb6vey8sort+xe9n4bbLBBkn7xxRcLlWfbbtlllwUAvP7664XKA+K/cXZ+0u1mx9/nny/wvfjaa68Vrls39rnLbqvYOdYbVzZPz8V6jAHAF77whST92muv9TjmKKIihBBCSOVoyw7OzJkzk5Xrv/3bv0Vfp3/Ndq+y85Jnx0WTp57Nouu4+eabp/L0r5R21gmobR12/5rWbQGkV932l4f3y0A/g37uTTeNj/Wnr4t9J/bXROx1Sy+dDtGjd1xidzw8bBn6uGjf9fB2rry8MtB92/aniROjHOmm8EQCa621VupY91e9C2R3hPR5emfC5tmdK/vrsij9+/dPfmnb9vB2y3Rd7bstG/teNHpcvfHGG6k8vYvt7Qh7eWWQtQtrxSN50N842y/1LoRFt7GdC7Q4LpY834iyd3D0jgqQPZ/Y/qPrYfN0GXaMxcy/3MEhhBBCSOXgAocQQgghlYMLHEIIIYRUjrbHovL0CqwexptvLoiXaWWLWXoYRfUWNtxww8y82DKt3D8WT27q5Xn1KkNfp6urK5HnWx0orbtgZeb6PXi6U1pmm0cHw5PzZ51XFlom7Oms6P5kLSC0Loq1BGm3nlUr8caU1YMoog9gZfDTp09P0p7+gtaR8PQGbJ30dUX0I2IQkaRsT+fG1luPxzJMij1iLQk9y1FPr6ZsnZt2Y9//229nG9V534xYK7Gic4a+rgyLLe863V/tN0GPM6vz2KxuG3dwCCGEEFI5uMAhhBBCSOVou4jKw4op9JawdSan84qKpbRJmt1yLlKmt21tyytbFFFUPObR1dWVbBHabWlvG1lvOXrb2bEOFI8++ujUsTbV3m233VJ5Bx10UI9lPPLII6njgQMHJukbbrgh895eu3l19rZrva3nMkzDtamxRW8Pe+bEdmu4iAjEmmDr7WfPYV8sWoRtseJDvUXuOXLcaKONMsv0nFvq8psxvw0hJHWyYijdJnau0e/WE53aMrPK702Kit490e96663XfMUK4o0XPT/aPpWFFf1qFYGizg89sa3+Ttq+pfuh1+903/LqaPt1sw5yuYNDCCGEkMrBBQ4hhBBCKgcXOIQQQgipHIuUDo5Fyy5j5dqx5uQWL8BfLFZ+OGnSpMxzdb08GWesCbmVW5cRFFLr4HhYXQ79PLFu1+1zjhgxIkln6dX0xPz583v8uy4PALbeeuskPWbMmFSe19f0e9ayaiu31n3B08exulO63Yrq4/Tt2zeq/b1zvLzYenlleOb9HiuttFKSvv/++1N5J5xwQpJ+8sknU3lZOgCenovVdfB0lvSzNqODo9suT1gNbTJv9Yq0fkUZuk9LLbVU6vjCCy9M0ttss02StmNx5513TtJjx45N5RVxleHN9TZcgje/d+u+ZEW/LhNPV9LrN/o6Oyd7ZuixxIYhsWOijCCgGju36HYs4v6AOziEEEIIqRxc4BBCCCGkckSLqERkCIBu29MPQgjR+2IDBw7EBhts0GNeUbM2jd6qtFuRsebYVnRShhm3Njn1ntPbiva2XYcOHZqk7VZ72ZGotadLIF3nFVZYIZWnt8FjPfXeeuutqeNp06ZF1evVV19NHT/88MNJet11103S++23X+q8119/PUnPnDkzlRf77vTWrX3/sVu31nNn2bQiIrnuh7oPWqzZqxbjWHFu1vaz7S/6uv79+6fy9tlnn4blNYPXpmVFZp43b17y3rxn0CbRFi9acxmsttpqqWMtltL06ZP+/RwrAipj7vXELLbvdffLqVOnFr5fR0cHBg0aBGBhMaB2S2DHu3Z54aHrbEVUegxOnjw5lXfllVcm6QMPPDCVV8Z71mXsvvvuqbxzzz03dxl2vor15p2lStFwB0dEThSRdwH8L4Cn6v/+V0TeFZHvNbqeEEIIIaTduDs4IvITACcDOB/AwwC6f7avDGB3AD8TkUEhhLNbWktCCCGEkBw0ElEdA+CwEMJd5u/vAnhWRF4FcAkALnAIIYQQssjQaIGzPIBXnPxJAJZrdBMRSXRcrC5HGWhZf1HzaE9foCieWanGM4Xz5IxaXulFbi7DBbuVK+t6WZf5+nmsPoDm+eefT9LWfLKjoyNJT5gwIZV37bXXJmkrN9cRpXW9zjjjjMx7Dx48OLOOseTRpdHvrowQIRatx9EKPL0brQNn+53Wc4vtk/Z9aJcBM2bMSOWddtppSboVUdl1mVZ3wtOjy4M2E/d0Z6zOoPfey0aPP49nnnkmM8+2j3YbYOuf1Za2b8ToZPRE97sLIURfY+nXr19iqm/7hhfCJ7bfeKE49LvbdtttU3laT60VY0Lz1a9+NXV8xBFHJOmtttoqSXvfMfsd0GPAy8uikQ7OOAA/FZH+NqP+t9Pq5xBCCCGELDI02sE5HsAjAP4pIn9FWgdnJwAzAeyWcS0hhBBCSK/gLnBCCBNEZEMA3wCwHYA161kfAPgxgP8JIXyW54ZlmcWuvfbaSfqYY45J0nZrXovEnn766VSeFX1o9PZXbARpuwUY693UM4WL3Wq1JqzWpLxZbB098Zs+126n6mNtbqpFRgCwySabJOnf//73qbz3338/osbAySefnKS1yAtIezK2HnE9sqIxW9GrPi9PRNzYvubR2dlZyDTY88yso5BrcYgVlWivqto8FvBdJcSKF77zne8k6WuuuSbzvFaYx+u2saJX7Um4mXt3dnZGmw5nUYa34h122CF1/POf/zxJe3PL3/72tyRtzcS//OUvJ2nryVibkFtRRBG3DXnybD8tgogkc4wnlrduTLRqgZ3Ds0RKdj7R72fvvfeOq7BD0f579NFHp45feumlJP2lL30pSVvRsv4mWNUQPY8VmdMa+sEJIXwO4Ir6P0IIIYSQRR56MiaEEEJI5WjkB0cA/AjAgQA+BnB5COFulb8ygCkhhI6MIhqit3btNr+3ragDvGlxlcexxx6bOtb3mzhxYlQZhx12WGae3VLUx6NHj07lPf7440n6ggsuSNLNaPJrWuHJtWymTJmSpLWYEUhv88Z6NQbSW8AHHHBA5nmx1gteMEa9lW4tJfR2ap4t3zIsHbTncCtKLMPKUL8Du12uj/M8d9Z1dmwvueSSSfruu+9O5cW+u9h6eZaJVjTnibvz0KdPn0wRkBeoMSuIbVGuuCK9YW8DbGZhxVIaPR7t2NSizcceeyyVp73xlkFWsMrOzs5Syvesprq6ulLHeh7yRFseP/7xj5O0DjgLpK0OYy2F88xBeiwtv/zyqbxx4xbYH+lxa0VU+rnzWFl7Qaq7abSDcxKAUwGMBvAmgJtF5BxzjkTXiBBCCCGkDTTSwTkSwFEhhNsAQESuAnCfiAwIIZxUP6ecLQdCCCGEkJJotMBZC8rPTQjheRHZBcCfRaQDwHmtrBwhhBBCSBEaLXCmorbImdz9hxDCJBH5CoA/o+YPpyliTSLXW2+91LH2kvnQQw8laR1N2l43bNiwVN7w4cOT9HbbbZfKe/fdd5P0mmuuiRhstFxdFxv9V0fg1voaN998c9S9LFZuX7YOjtaV6um4bLTeTR7T/FNPPTWqfK2/8dln2Z4OrLxeP7enz1JUl6YM82btOdzWUcu4PZcNVhau+6inmxIrQ7fvJ0uG/qMf/Sh1rHU1Pvroo1SePvZ0pzSeubelFabnFs/E3+tv+r0XHft67tlss81SefPnz48qQ7tf8LBejtdZZ50kfdRRR6XyHnjggSStvY7rvgAU9+BchudnPeYsut/Yfp/HhUQ32nMxAPziF79I0vb7ceedd2beu4z+rL9r1pNxEaypvEeMHmUjHZyxABbS1AwhvApgRP0fIYQQQsgiRaMdnPMA9PhTNITwcn0n56Ce8gkhhBBCeotGnoxfBJDpfjSEMBFAnH11k2hPqsDCpoTd2MCJOvjiX//611SeNu204qtnn302SWtxld3O1GbJ1oOoPraiM11P/WzWFNTbPtXb63m29mJZcsklUx6FW4ndLtXbqZ64Z999900da7Gjh+0LsWiRqm7Dssx0y/BkPHPmzORaKxrV/cmas3qehjWeGWxRL7pZ5sU6SB+Q7uebbrppKi9W3N3qoINlkcdsuEiwT+1dFgC23HLLzHO1CCzLmzcAHH744UnamvjrvmfFLDpQqkV7Gtb9y86NMWbD7aAsUWZWOeeff37qWJtg77XXXtHl6bnBm7+859Fzxs4775zKixVr6m+o7fNeO+pvZZZYl47+CCGEEFI5mlrgiMgrIjKv8ZmEEEIIIe2jYSyqBlwGYPmGZxFCCCGEtJGmFjghhEubrUAZOgca65pey+asPsLHH3+cpG0oBY0XZVlj669l0Mstt1wqT0da1fJoawrqmYb2ppzZRsXVaLms1XfQ7WP1qjRan8Iz3bU6Gll6C2eddVbq+KabbkrSVpdDRx739FJidVY8rM5V2W1q66hNvO1Y0ePD5ml9h0mTJiXpoUOHps7T+jl2vGk5vzfWbZmakSNHJulf/epXqbwiJreWonNQbDT0RhTVeytyz1tvvTV17Jn/63a97bbbUnnaRf+1116bux5AOlSLnfPOOWeB83ytD3nZZZelztNzkp0zrM6PprvtPvjggxw1TtPV1ZXpIsELsaH7m/dt0d8SGzFclxGr9wKk5xpvjtXj2M4nhxxySOa9s+YaGzJCU1SHL4voBU7dsV+385apIYQu73xCCCGEkN6ioQ6OiIwUkScAzAQwpf5vpog8ISL7t7qChBBCCCF5aRRN/FgAlwC4DsDFALr3tFYGsDuAW0TkOyGEq4pWwNuu8rbssrDbm1oMorfWAX8rvAwuv/zyJG3NYPV2aJ5o2RotjrGiGS3iKUPsYbdfY83SY70QW1GWNuG326faC7H37rQY8Iwzzsg8T4ukgPQ2qd2218+t37n1JK37nSfKyjJvbBW6/rbOeuzYcaTrqa+z41c/t2d66nlVtV50NVoMteqqq2ae127K8IbbCC2CsV58Y+nfv3+S3mijjTLPGz9+fOpYi0j+8Ic/pPKmTp2apHW7emJs2zfOPffcJG1Fj9oMeoMNNkjSVvTz5ptvZt5P9xtbLyuOLcKcOXMy50Q9JrzvndeHbrnlliSt2xBIR37P4wJBvwddL+sGQns8P/DAA1N5Z555ZpK2c7EWLep72efUbgCsF259rp2vPHcV3TQSUZ0M4LgQwtU95N0hIuMAnAKg8AKHEEIIIaRsGomoVgfgeUQbC2C18qpDCCGEENI8jRY4EwF8y8k/Fm3yZEwIIYQQEksjEdUPANwvInsBGIW0Ds5uqO3w7J1xbUIIIVMe6+kgaPlbKyL5eiZ6RUxO77333tSx1mPQJunAwvpAPdUJiJepWtmu1lspqoOjXf4XxXM3r/WE7PvX+lFW10I/m2fe6Ond6PtZfYBYc2bvvXruCfR1tgyt89OK8Bseeozad6Lfl9ZRsqb+RUNWHH/88UlaRyX++9//njpvypQpSdrTwWnFfGF1ADRFI3hbOjs7k3Fh+3ZRtwRaV8Xqb2h0NPannnoqlafdLGidGyD9rvU78nQc7fdA6/jZtsuKUF50XrN9tHuets9VFNtPdD2L6vto1wGzZs1K5f3tb39L0t73wr5XPaa9751uK23OD6Tnq7Fjx6bytOsV71ur2yOPmXjTOjghhDEiMhS1XZztAKxSz/oAwN0AfhdCmBxdI0IIIYSQNtDQD059AfOj1leFEEIIIaQcmg3VEMX8+fOTrSe7BRXrAdRua+njMkxt7b31lpq37bfDDjskaRvZWmMj98Zu5Rfdam+3+XEWsdvInqjpzjvvTB1rcZ/1ED1mzJgk7b073d5WvKdFi7bttUfUWNNgK47U5rleBPpWY8UqXp/UW9j6vVpzZS2S1OalgO+desSIEUlaR2m3psB63LdCDKXxPNBaWjHePO+7edD9WZtZW7RH4muuuSaV573rrCjeedpHt6t1qZEVaf53v/td6vgb3/hG9P1aiSeuLCrK1G5Fxo0bl8rT/dS+c902VlSu8b53el5Yb731MsuYODGtjpvV/t5c782pVhweM1cymjghhBBCKgcXOIQQQgipHG0RUXV2dkZ53vS8xtqtq1jxgN761B4TLUWtP3Tgs2effTaVp7cOYy0g7La43l5u9ZZ8u9FbpvbZVl999SRt3+v222+fpG0/0JZT+t15IgSb5wU41aKaWHGV3ZbWY8Fu19pt3nai28ALlKn7qH1u/WzWisazHtOWIPqd3H777anzygioWRRv/JVl/RZCSLbeW+EdWXui/d///d9U3rrrrpt5Xaw1pyd+8Nhrr72S9LBhw1J5OoijTtugpHksiDTd/XnevHlxlc1JrOWUtb7S84aeF6yoxpvbdHvEepW3rLTSSknaiqj0OL7xxhszy9BzhrWgi7GGKgp3cAghhBBSOaIXOCKyloisav62qojEa+IRQgghhLSBPDs4kwE8Zv42GsBbpdWGEEIIIaQE8ujgHAHAChNPAdAw5HcIIdM8TsuuPT0dq5sSa5ZZtszeyjF1PaxZbB4z057Ks2VaHSUtoy8jYni78fSSTj311CRt9aP0s7766qupvOnTpydpL5qxh9at8fpPlul0I3S7WT2LLJPbstByfs9k1Y5F/axe/TXWzFmPB9vPd9pppySt2/Sqq9JxfD39qDLwoipr7Nguy7y/q6urad0bq8uhn+mggw5K0lrvCfB1cMpAt53Vnxk+fHhUGV40bj0Gbft4/b7VLjViTcO9855++ukkfeSRR6byLrnkkiR98cUXp/K0Sw3bvlqfZpVVVknSK6ywQuq8E088MbOO2rO4N996nt1jsf3afm97InqBE0K4toe/3R17PSGEEEJIuyikZCwiA0VkVxHJNksihBBCCOklonZwRORaAONCCJeLSH8A4wBsCqBTREaGEB6MvaENvqhNKq23Yn1u7Dai3ZrU22betnusiGGfffZJHWuTRhugTpule6ajnihLe3+1W9f6ulaYlw8YMCDT7NOaKmbheWPVz6PNV4HsAHsA8Jvf/CZJP/nkk5nnFTX9t/1Qk+WN1/N4bMUX3lZ0GWKpvn37Jl5p7b1jxR/eeWWIfbU3ZyBtiqoDQnoiqaJmr7HjwYqE9Xzk9f9mRB4dHR0YNGhQj3mxbRcrEtl2220LXVeU0047LUnvuOOOqTz9zt5///1U3uTJk5P0YYcdlqTt3OQF9tXtVTRoqYcec9YsXH8HbBvGjvcLLrggSYtIKu+8885L0ttss01chQ060Kg2xQfSYijbNvq9WpFnFkXnZTvmYlQQYndw9gDQLQT8KoClUQu8eWb9HyGEEELIIkPsAmc5AP+sp/cEcGcI4Z8AbgHwxVZUjBBCCCGkKLELnA8ADBWRDtR2cx6t/30QgGKmKoQQQgghLSLWiuoPAG4FMAVAFxb4w9kWwKtZF/WElZNqGaQXabwoOpKzdR2vsTJ7LafXJnT33Xdf6ryXXnopSZ900kmpvM7Ozsz76ffgyb61zNa+D23Gat+dZ04ZS0dHR7SuTRax+hrf//73o8vU71ybhXt47WvR+gBWByTWFb1n7t1qXYeurq5o9/Cx6Pel+2EefRytv2Td8Wu0Do6H16a2fa2JaQxepHerx1HEJURPlGEmbonV8/DekRdtOosHHnggdaz1rCz6/Vk9j1deeSVJz5w5M0lbU3OPonofsXR0dCTjwo49/f7LaNvzzz8/dfzRRx8laS/at+3P+vtxyimnZF73gx/8IElr9x2A79olFk9Ps1miFjghhLNEZCKAtQDcHkLo/nLPA3B+9pWEEEIIIe0njx+cO3v423XlVocQQgghpHmiFzgisiWA72GBUvErAC4OITwfcW2y7ZxnS6vIdp7d/tdiKW8rzNtqt1ummuuuW7DG80RSFr1tqcUZdnvTE3W0ItqwZs6cOck7s6KZMiLAFjVfHzx4cJL2TAV1e8SKPYD0Frz16qnrvOuuuyZp7e2zEV1dXUlaR0Yvi379+iX9Jo/JsudpWKP7XdE23G+//TLzrBhYU/R+sWJBLaax4klvvMVGrm+ENhMva3wXEYnq6N72+Oc//3nTdXrmmWdSx9othHURsd122/VYhu0Letx64pgsEZsWheVlzpw5ma5AtEsEKyrT9dQRwy2eJ+arr7468zr9rPb7l9W/7PvZeeedM8uP9RbvmfDH9nP77mLmgiglYxH5OoBnAKwK4IH6v5UBjBORb0TVjhBCCCGkTcTu4JwD4CchhF/oP4rIKQDOBnBj2RUjhBBCCClKrJn4igBu6+HvtwPIVo0nhBBCCOkFYndw/gxgOACrxDIcwJhGF+to4tZFuza31C6tgbR80ppIazm3lk96OixWz8YzK3399dd7eJKFueiii6LOs/XXx/pZrLlpq02KPbTJalF9AM+kVOdZXRTvnWi9J4uV7WehI9paHSvdZ/793/89qjwvtITHXXfdlTo+55xzknRRfZP58+cn/cZzy2DRbWxNhsvoh0suuWSS3mKLLVJ5uo1vu23Bb6mi78BDP5t1g6DnHKtfUFbEcI9WmInr59V6Y48++mhPpwMoR8/G9pnx48dnnqvd/NtQAXqe8PqDl6fHe6tNxmPN6IG0bpD9Ruh+UHT8FRk/9pp58+Ylaa3/CCwcNiILPQ/lcWGi30kR1yexC5wHAZwrIlthQciG7QAcAOBMETmg+8QQwl09XE8IIYQQ0jZiFziX1P8/pv5Pc6lKBwAdzVaKEEIIIaQZYh39xerq5EZvQdntc72t5UXj9rbvikaOzfIEe/rppxcqz4rHsrbbll566dSxFtV44oZWiRS6vYXabUstUvAiPsdiTWs9z7B6C7uoCOPggw8udJ3ertXm3mPHjk2dpyPwhhAyy7PXlYH2qmrR/cS+81a7HdCevm373nDDDUn6wgsvzCxDP5dXX2+seC4OdJlWlOGNqc8//zwzLw96zE2YMCH6/rE89thjSdpGfx44cGCS1iIdS2zU6D590p8NPefttNNOqbxjjlnwu1mPHUsRs2fA/w5090U9tvOi280SG/U+1iu49ggO+K5ENLEera2IrW/f7GWCN7dlzUGeGwXb7/R3uMj81LKFCyGEEEJIb+EucETkSRFZVh2fKyKD1fEKIlLcqxUhhBBCSAtoJKLaDoB2AfttAFcBmFY/7gCwep4bWk/GehvLbsFqcY2nma63cotu4955ZzoShd4uvPnmm5P0ww8/HF1mkQB8dvtRY7cYvWCkZVtfeRr+zXhu7cZa0I0YMSJJe1YPBxxwQOpYe1y2XoizeOGFF1LHH3/8cZK2z6a3TPW2d1FR6Kabbpo6LiPwnPaqaseNt02txVdWjFNke9j2/7333jvz3BtvjHOlVYYYzbPO1Fgxst4+t/NYKyys8oxh7zmyLDb1GAOA/fffP0mfcMIJqbx11103SVtRSmyf1RaCsR5wPTyrWA/bL7v7eqxFUF7yWFXFYL3K634aK4bysP3eE+l6/V7necF/Y7+T7RBRtaYHEEIIIYSUCHVwCCGEEFI5Gi1wQv2f/RshhBBCyCJLIx0cAXCjiHQLg5cAcJWIzKwfD+j5smysrLgME2MdMdzDymhHjhyZpK0cUHs4feSRR5K0ZxaXR+cmS75u5Z1aj8R6XY2lDG+wsSaMZd1bm7N6PPTQQ6njWHm31gHwdJus2b5G62HYto/VS/IiCBelf//+WGWVVXrMi9UVsfLuDTfcMEnHmlyvttpqqWOtb/TTn/40lTdq1KioennEmsvqsee1kx1vukz7frQOWRk6acDCfdkbS7HjU9dtxowZqbybbropSdv20GbcNhL8Pffck6Svv/76JN3Z2Zk6T7ePdWtRBs3qunR0LJ4u3PQ3tIzvqWW33XbLzDvttNOiyvC8pGusrk6zY6nRAsf6xO9JE/D6Hv5GCCGEENJruAucEMLh7aoIIYQQQkhZiCdyKe0mIpk3KduEzsN6z7z44oujrjvkkEOStA3CqU3q7JZ2q4O6xfLcc889F0LYKu91ffr0Cf3717wExIoBI+rS/l5BrAAAIABJREFU49/XW2+91PGbb76ZpIv2Ea9ttKjG85KdR0zQAgq1mx5vXv09sZonbtXnadEV4Iv0supRFp6ISm+L64C/ecz7tejSivqMyLlQuwHAUkstFbI84hZFe1n2+no78dxOtPObACwQi7z33nuYPXt2IUvhvn37hkGDBgHIJ87XbkE8sWq734nm3nvvTdL2mzl69Ogkbb15axG+5/JAj1vPBL6BuKrHMUcrKkIIIYRUDi5wCCGEEFI5uMAhhBBCSOWIiibeSorK4ovIJHfcccdC99LRq62csRVmeVnkeVdlyGxDCInsNM+9tb6D1X3R9dKm2lYXooz6e22j9aOsvklsmxYxSQfiI+QWDUswYMCAxGzZul3Xz5on9EeW/Nvq3OgybURvj9go4RprburpMGgdsrLCYWhiTdR7g7L1bmw/0Ton+t3a9tf18NrYm2u0zpd9Lh1Kw5tPbPndee+//37mfRvR1dWVPJNXf9tnY8NxxEYkb0UZ1i1AFnYuKENvM9YEPuvZuINDCCGEkMrBBQ4hhBBCKkevm4mXjbfVdsopp6SODzzwwCQ9fvz4VN6wYcN6LKPV5o3W/NTzdOttyRqaNhPXprUWzxzeivS051vPq64+L4/4Sm9VlnGeRdfZ8+JbBkXN+5dccsmwwQYbAMhnBq3FV3Y7OGsL2IohrJlnTBlAeus+TxTtLOw4zRKBlRGdvAcKm4nHzpVW1KHFAd579sSQHi16T72C7bNapBhCKGQm7rlm8ETxXoT6rCjwRYl1edGbJun2e6HFXl5e1lzJHRxCCCGEVA4ucAghhBBSObjAIYQQQkjl6HUz8bLx5M8HHXRQ0+UXNW9sBZ5+hWcWGYs2E8+jy6Hv7bnuLxrKIvZ5PNl30fKLvNd2y7RFJOrdWn2MIi4Put3T94R2Q2+x7yTWZb2nJ6T1o6zumo72rcv3IhtbHasiZvStwuopxfZFbdJt28fTn/LCnmhix/SECRNSx7rMVpvZ24jVZWPbwgt74s1LZes9Ff0O6Ou0zhDgt3cRfciy4Q4OIYQQQioHFziEEEIIqRztMhP/EMDbDU8krWJICCG3/IHt1uuw3RZPCrUbwLbrZdhuiy89tl1bFjiEEEIIIe2EIipCCCGEVA4ucAghhBBSObjAIYQQQkjl4AKHEEIIIZWDCxxCCCGEVA4ucAghhBBSObjAIYQQQkjl4AKHEEIIIZWDCxxCCCGEVA4ucAghhBBSObjAIYQQQkjl4AKHEEIIIZWDCxxCCCGEVA4ucAghhBBSObjAIYQQQkjl4AKHEEIIIZWDCxxCCCGEVA4ucAghhBBSObjAIYQQQkjl4AKHEEIIIZWDCxxCCCGEVA4ucAghhBBSObjAIYQQQkjl4AKHEEIIIZWDCxxCCCGEVA4ucAghhBBSObjAIYQQQkjl4AKHEEIIIZWDCxxCCCGEVA4ucAghhBBSORbbBY6IBBGZISLnRJ5/pIhMr1+3fovr9riIzBaRv0Sev2G9bl0iclQr69bbsN0WTyrWbrvW6zZfRHZtZd16mwLt9rP6+UFE+ra4bpNFZJaI3BB5/r9MuwFsu1IIISyW/wAEAOur450ATDf/AoADvetaVLfHARxl/rYJgNEAPgXwBoCRMddV7V8P7bYhgHsAfAhgGoCHAWzU6Lo2ttvxAJ4FMAfAtbHXVe1fT+8fwJUAJgGYD+Cw2Ova0W4qbwMAswHc2EPeZAC79va77YV22wLAcwBm1v/fwuSvXb+ub4vr1uP7B3ACgLcAzADwCoAN/9XaLavtVN5/1vPtfNVrbQdgewDjAHwO4EUAO8a2eav+LbY7OJYQwl9DCIO6/wHYF7VFzkNFyxSRwSLSr9m61VfT9wC4D8BgAMcAuFFENmy27AqwLIA/AdgIwMqoDZB7mimwrHarMwXA2QD+UFJ5VeIFAMcBeL6Mwkput24uA/BMyWUutohIf9TG140AlgNwHYB76n8vWuaKIiIl1e8oAEcC2AdA9zw+tYyyq4KILAfgVAATSyirlLYTkcEA7gVwIWpz+gUA7q3XtdeozAKnB74J4I4QwowmytgNwHsicpGIDG2inI0BrAbg4hBCVwhhNIAnABzaRJmVIIQwLoRwTQhhWghhLoCLAWwkIss3UWxZ7YYQwl0hhLsBfNRMOVUkhHBZCOEx1HZIyqC0dgMAEfkagE8APNZ0zarDcAB9Afw6hDAnhPBbAALgK02UeQSAt+oiknWKFiIifQCcAeDEEMLLocabIYRpTdStipwL4LcoZ+FXStuhtnvzQQjh9vo37kbUduUPKKGOhankAkdElgJwEGq/TgoTQrgVwAjUtuBHicgzInJcSatSAdD0JF5BvozaQCm8oGhxu5EWUWa7icgyAM4C8P2Sq7m4symAF0NdXlDnxfrfCxFCOB/A1wCsBOBZEfmziBwqIkvmLGqN+r+hIvKuiHR/eCv5nSqCiGwDYCsAvyujvBLbDqh90+xxr37jqtpxDkBtdTum2YJCCBNCCCcDWBPAmaj9AnpLRG6pT6IxTALwTwAni0g/EdkdwM4AinSiyiIia6AmUmj6o1RSu5E2U2K7/RzANSGE98qv5WLNINT0ADWfAli6mUJDCE+HEL6F2k71FQAOQW037uocxaxR/393AJsB2KVezpHN1K0qiEgHgMsBHB9CmF9WuSW13VMAVhORQ+rfuG8CWA+9/I2r6gLnmwCuN79SXETk63Ut7+ki8qDNDyF0AXgJNb2DaaitTKP0Beqil/1Rkyt/AOAHAG4DwMm3joisCGAUgMtDCDfnuK5l7UZaRyvbTUS2ALArauJOkmY6ALtQXAY1xdCGiMipqt0W2kUIIcxBbUdoPIBO5PsFP6v+/wUhhE9CCJMB/B7A3jnKqDLHobb79nSRi1vZdvUd9/+H2o/TfwDYE8Cj6OVvXOUWOCKyJmq/+q7Pc10I4SalpLyXKm+QiBwmIqNRU6ZcHcC/hxCG5hGjhBBeDCHsHEJYPoSwB4B1UVOo/ZenLoIYBeBPIYQok8huWt1upDW0uN2Go2ZN8o6IfADgJAAHikgpytCLORMBbG4USzdHpMJqCOEXqt3+q/vvIrK8iBwvIuNQsxbtALBLCGG7HHWbhNqHVf8wjf6R+i/ACAAjReSDer/eHsBFInJpzMUtbjuEEMaEELYOIQxGTb90Y/TyN66ltvK9xKEAngwhvNlsQSKyJ2o7LU+g9kvi7voqt0hZmwN4DbVF5XEAVgVwbbN1XNypix0eBvBECOHHJZVZZrv1RW2cdADoEJElAMwLIcwro66LM3XLmz6oydr71d9NZ9Ht8xLb7UoAt6jjk1Bb8HyrSL0qxuMAugB8t/4r/uj630cXLVBEjgTwa9QsVn8G4KH6DlwuQggzReRWAD8Ukb8D+AJqFqcXFq1bxTgMwBLq+C4AdwC4pmiBZbVdvaxhACYAGIia/tu7IYSHi9atDKq4wPlPlDcgJgHYOIQwpYSyDgVwFGrb7H8FsFvRj27FGAlgawCbishh6u9fDCG8U7DMMtvtdNQsO7r5BmoTwZkllL24Mwo1XTKg9mvyStT0Jh4vWF4p7RZCmImajxcAgIhMBzA7hPBhM+VWgRBCp4jsD+BqAOeh5mdm/xBCZxPFPgVgSEnWTsej1o+moGYBdxXoogEAEEL4RB+LSCeAz0IIVqcqD2W23Q+xQJz4EGpze+/Sk3OcxeEfaqapnwL4eeT5h6M2YGYDWLfFdRuFmkz7z5Hnb1Cv20xkOEyryj+22+L5r2LtNqJet1mobcX3+vtdhNrtjPr5swF0tLhukwB8BuA6thvbrhX/pH5zQgghhJDKUDklY0IIIYQQLnAIIYQQUjnaomTcr1+/sMQSSzQ8b9asWanjgQMHJum5c+faMpuu1/Tp06POGzRoUJK2ddTo+gLA/PkLjEm6uroy8zQDBgzILN+W0dHRkXmuZvr06VNDCCtGnayIbbce7pekveeZMydbx1q/S++d2/rNnt1z1ICllloqdaytZG0/0HXu7EzrXtpyysbUpVC7iUim3Nmrf2xIGt0Pvbbx0GPKEjsubX379Fnwe82OlVajx2JXV1ehdgP8tou9f//+6bBSug+X8V68Ma3v5ak/2DK8uUCf27fvgk+WHeuxz5bV92bPno25c+cWissUO1faOsZ+T/T3wntXFt0v9PgAFv6mtgs7br1+ottqxox01CXdF+bOndvjmGvLAmeJJZbAVltt1fC8l156KXW82WabJel33303lbfmmms2Xa/HH3886jxdd1tHja4vAMycmRhy4JNPPsnM06y//vqZ5dsyll122cxzNY8//vjbUScaYtuth/sl6SFDhmSe99prr2XmbbTRRkl6/PjxmefZ9zVhwoQez7Ntoycj2w90nW2/K/I+8mDqUqjdPOx70MQuZnU/9NrGw3uPsePS1nfJJRc4Tf3oo/a6OtJj8aOPPiq93fLc386Nug+X8V68Ma3v5X28bRneXKDPHTx4cJJ+/fXXU+fFPltW33v22Wejru+J2LnSzuHe+NFzoP5eeO/KovuF/QH+3nu944PPjluvn+h3+re//S2Vt/zyC8IVvvfeez2OOYqoCCGEEFI52rKDM3369ORX2fDhwzPPszsSerdk1VVXTeVNnbogkOoKK6yQpO0v7jffLObvL6ue3q8E75en99yaKVOyXYCsttpqUWWURVdXV/KLw666n356gbfwDTfcMLMM+770DoL3S0T3Bb1SB9K/RHTbF8Vrm3a/c12X2J2MPOh2K4r9JZiFbTe9s2B3QvW5Ol10xyG2jna3Q+8Q2F+43i/edu8Y2efbYIMNMs8to276fvodAcC0aQtcqGy77bZJ2oqQvB3CIuOs3e/cQ3/jbL/Xuy/eboU3D8XulNoy9DjrrR0bIH5M2/p7c+Aaa6yRpLOejTs4hBBCCKkcXOAQQgghpHJwgUMIIYSQytH2WFRWx0TrYVhdDi2rs9YfWja33nrrJemiOjfbbZcdODVWt6YMnYk8sugydH5isfJzLZO3ujRaNurpyOj2tmXoZ7Nt41lAaVmvZzEUSyv0YDRlt1M7iDUNt7J2rSdidV+0jkGs/oy2mgLSug5WD0LX2dNX0VgLvVidj6Km80DNrLdb/8zez3OdoMdElquEZtD3s3NB1pxVxMUEsPCYK2OMxPapMsijG6S/XRZrNZSF935i58AyLCPtmMt6D3Y+13qBdn2gzy2iP8gdHEIIIYRUDi5wCCGEEFI52i6isttWelvLOr/bYostkvQbb7yRytNikLJNwYFsc219XyC9tWdFbNqc0m43anPKopRhTusxa9asZLvSvitdf7ulrOviOS7UW9ueyfiOO+6YOtZbld/+9rdTeQcffHCPZdx9992pY711e8MNN2Teu9XYd6f7fFGWWmqp5PmsM7TYLWs7FseNG5e7HnbLWossXnjhhczriorANLEm3XY8a2xeOxwJzp8/P9MJqH4vXj9phYiqbPKIofS53nlanKFN14Hi7j3Kxn4j9Bxo2023txaxed+OPM+iy9R921MVsX1T19F7xzrPE71acae+n237mH7OHRxCCCGEVA4ucAghhBBSObjAIYQQQkjlaFc0cay88soAFjbt1Hh6GLFupj1TtVg5b6MyNTpkhH02z6wtVlYaawLYajmy1UnSslJrglnETNY+5yabbJKk99prr1SePdZkRRTeb7/9Usdf+cpXkvSYMWNSeddff71b1zKx8u7YAKoeIpLouxQ1lbd9OdYdQqyZfjOm1N2MHj06dfzd7343SWcFXW2GLN2YMgkhZL4bPc48E2zPzNczNY9FR3EGgN/+9rdJWo9bixdB3gtiPHTo0B7/7rn1t3n6O5Cl/9NMsE0PT8dE430vtFsFG7Bz0qRJheql29/rC/rdbbPNNqm8Irp5Xj08tx9FgoVyB4cQQgghlYMLHEIIIYRUjraIqAYMGJCYC1vTLm9bLlYspbcwi0aXboVHWb0dWVSEFOvRN08U1iJY8aEnTvTqoU1vtQn5rbfemjovVsTw6quvpo71NvMqq6ySpHfdddfM66wLgqz6Atl9suj7t5GZy2DGjBmJW4IyREEe1hOr3krPE006tvx11lknSa+++uqpvGOOOSZJ33XXXbnv1Qj9bJ64qpmozVq8aNvOE+97lO1OQrcB4IulNGuvvXaSnjhxYiqvDK/jen7M4+W9jLlSe6C2Xro9sXOst2Ldbnbu1c/z2WefpfK02PYXv/hFKq9IBHeLvvfuu++eyjv11FOjysj6JgB+22hXCVliWXcHR0R2F5G+6vg/RGS8iMwQkTdE5Lve9YQQQgghvUEjEdWDAAYDgIgcCOB6AE8A+BaAewFcICKHtLSGhBBCCCE5aSSiEpU+EcA5IYQz6sfXi8j/1f9+cysqRwghhBBShDw6OBsAsCKpPwE4Pc8Ni0aY9bBmi0WwpndlmOuWrQfj6StZXZFmo7BarKm8jsicp/wsk8CVVlopdZ6Wyf/lL39J5d13331J+u23307lhRCS9P/93/8laSt/1iEMPD0YT59Cm3hnhfYAFm4bL3xFGW72PVPjMtDP7emFlDHWbRiWzTffPEnPmDEjlaf1DSxl6NhpnQWrt1XW+/baTut22Hfr9b8iejee64crr7wyqgxrxq/x3O7H9ptY/RUg3Wdt25VhJj5w4MBkzrLl6+exoVNi+42eD61JvX4PX/rSl1J5AwYMSNJl6Nx4WFccRxxxRJLec889k7TVl9FzbOx821M5PRGzMthcRKYBmNXD+X0AdESUQQghhBDSNmIWOA9jgahqBwDas88wAO+UXSlCCCGEkGZotMBZxxxPN8f9AJxfXnXi0ebg2rOtFaU888wzSdpumX744YdR9/K8uHpb37HeX8vAbu2VLaLQIilg4a3WWHT7dHZ2Jmm73ahNAO+8885U3vvvvx91r5NOOikzTz/P/fffH1WeRZtr2i19fWxFUt52fCtEuGWg200/t3Vd4BHrAdkTqZx55plJ+pxzzom+t97WjxXZeGPbEzO2aqy/++67SboMEbqHHe+//vWvo6579NFHk3SfPmkbli9/+ctJevLkyam8Vs+Pus/ab0T3eNTi7bzMnTs3ERPavuGJDz3P0ln9z5rUaxHVPvvsE1Vfj6JtcfDBB6eOX3nllSStXTp4fdeK3zzz+BjcBU4I4e0G+e3zaU8IIYQQEgk9GRNCCCGkcrg7OCIiAH4E4EAAHwO4PIRwt8pfGcCUEEJhReOiXjYvuOCCJK09ZFq23HLLJH3sscem8j7//PMkbT1rZmG9NWps+do657rrrkvl6W3FK664IureFq1VXmT7rhHaO+e0adNSeUXbTXuA1e/fboFrj5wff/xxdPma3XbbLTOvqEdX/dw6bbdddRA6a1XhWQrkEfmUjXdvLZLUVmGeSC2PRZgWiei2sZ7JtdXWbbfdlsrTXo+tN1lNq8UhrcKKjTTaQqaMueDiiy9OHS+33HJR19U+GT1zwAEHZOYdeOCBSVqLNgDgoosuirp3LHbsl+HduU+fPpkWhfo7YO+lr9loo40K3fv00xcYMlvvwXvssUeSjlWvyGNxqMu0z//YY4/1mGetHzV5Ato27ckYwEkATgUwGsCbAG4WESv4zu7RhBBCCCG9QCMl4yMBHBVCuA0AROQqAPeJyIAQQrcGZ3HNLEIIIYSQFtBogbMWlFl4COF5EdkFwJ9FpAPAea2sHCGEEEJIERotcKaitsiZ3P2HEMIkEfkKgD8DWDnvDa3JXKz805r2aS+12rTM6rN88YtfTNLDhg1L5WlZo9U/0CaZnjxf89xzz6WOtRn6DjvskMpba621eryX9tLbCM9MOUunIQ9dXV3JtV6036IeMrU5pfVIrMkTGfjkk0+OurfWiSpqHuo9d6yJsX22MrxOx+LV0Xvnngm85oUXXsjMs15JrY5XNz/84Q9Tx2PHjs0sU3s9tvNMltsEO+49naJ26+5oHQPAr5vV8yqC1oP7yle+ksqbP39+VBkjRoyIOs+67NARym208rvvTtQ+U98B6+W6DF2aomh9RYse0/Y7pvXGYqPF27Y+/vjjk7T1Mj1q1Kgk3Yoo6rrMot7Ci3pvj9HXaaSDMxbAQpphIYRXAYyo/yOEEEIIWaRotINzHoB/6ykjhPByfSfnoNJrRQghhBDSBI0c/b0I4EUnfyKAOPvqOv37989zeoIVkWgTNI1n6mq3NPW2uBVf6cBrzz//fGaZ2qtuV1dX5nl33HFH6libDu66665Jevr0tLNovdVuRSJ6i3HbbbdN5eUJRJfFoEGDsNVWWzVdTrN4W5/77rtv6thurWfx1ltvFaqL3gbXolFPhOdRVEzkISKJOMOKZqzYI/Z+elxliZMs9t56e94GOM3yjG1Fu/qd2+1+3TaeJ+8yAm+2gzzeij3XA1nsvPPOqWPPTFnPQzZ4rOaggxb83t1+++1TeZ9++mmStgF0TzvttMwy+/Xrl6R1G/emSMrDG0d55gktutFirkceeSR13rx585K0fv+N6hU7Drzn0Xnayzjgfw81ej6xY9rr1zHtT0d/hBBCCKkcTS1wROQVEZnX+ExCCCGEkPYRE03c4zIAyzc8ixBCCCGkjTS1wAkhXBpz3qxZsxLZuZVBtjPituWPf/xjkvbCAXimsDrvk08+SeVp82/r6lzrEowZMyazfM8UWZva2ndnzcbLJratisp59bN578DqCGWZ0h533HGpY+1OwOptaZmw59Zdp4v23VaYbmqsvoTW67B6Wt4716apWi7utY3tg3rse8+p9armzJmTytMRqYvqPXn0dhiHWL23Mup5yy23pI5XWWWVzHN1P7rmmmtSebpP3XnnnT2mG3HMMcck6RVXXDGVd+GFFyZprZdiTaLnzp2bWb5+X1lzY1FzZaDmaiLr+jL07HTIEmuyX9RFgP5eefpe3jd6v/32S9JW50abwOt72fek59E8oRpidHCiFzh1x37db3lqCCFOg4gQQgghpM001MERkZEi8gSA/8/emYfZUVVr/13dmUkIJCTMk4CABAQJiGgQBMKgIAG5ygWZZPjgMogCEgRBVBBUQFBRBhlEQQVkUgZlujGAIRcQwxAFCXNkiMSMdJLe3x/nnOpVK13r7KpT53RyeH/P00/v6l21a1ftXVW71zgfwOvVn/kiMllE9ml2BwkhhBBC8lIvm/jRAC4FcC2AiwD8q1q1KoDxAG4UkeNDCFd47XR0dBQSC9pop41iz1W2u6jt78SJE5NyZ2c64bpWjxVFqwesuC6PqK8IsWLX2CjEtj2twrNqEB3ZVLvzW2bMmJGUvYztVq2lVYueu7F2afRUgp6rYzNUIv369Uv6Zs+tVQ322jx1U9Y12GdK38sPf/jDme0VVc3pcAhbb91riK62Rbtqey7yFr2v3s9TSVm0+uHuu+9O1b399ttJ2Xsv6LlinzndL6ui0hF+9XE6GjywdBgQje6XnbNlRA+fN29eVDtFn/c//elPSdm+a0466aSkrNVCgB+FX9+HWHXVoYcemtq+8sork7KNTn3dddclZS/Uio66b8NFeO9f/S7Lej/VU1GdAuDYEMKVvdTdJCJTAEwE4C5wCCGEEEJaST0V1ZoAJjn1fwZQLBERIYQQQkiTqLfAeRrAMU790cgZyZgQQgghpNnUU1F9FcDvRWQPAPcibYOzKyoSnj3rnWTgwIGZrtaeDY7Olq3LrSBLZ+tl9P2f//mf1LbWW9vw9mPGjEnK06ZNS8rWdVfbMbQy0zSQdn2059Z6YC8LrrUV0Gh9tHXV1i7Aq6++eqpu+PDhSdnLXnzmmWcW6pe28/B05rGh4q1tlhduXqdSePLJJ6PatyxevDhp17Njs/YS3jzX46Pngp3X2o7He1Ys2l5Nu0k/8cQTqf3mzJmTlL3Q882wbdJz3rMNaIQlS5Yk9hDWFqLoO1DPdW3v4HHXXXeltrVLuba5AdLvLH0u7doM+LYdxx9/fFL+7ne/m6rbZpttej2mqJ2hnZe1eePZ8zWCDR9SBD1udh4888wzSTmPXWlWehQ7TtoN/dxzz03VdXV1JWWbhknb5Oj3nH336vGwY6qfOe+dnUW9XFQPicgYVKQ42wGoWaXNBHArgJ+GEGbkPishhBBCSBOpGwenuoD5WvO7QgghhBBSDo2masiNF+3XitO1KM4TTzUjq6wWcXtiP53tWEdZtRx55JGp7azok1b03Wq1lMZzfdT99MT1nkjccw/U7o6/+tWvUnVvvfVWUrYupdod0YvwGTtnPPf12DasSNmby1b8X4QQQjImXiRmOzaeSknXeaogz9Vcuznb/bSqUZf1MQBwxx13ZJ67DLWUnnfWxdb2RRMbWiAPZanZtKo8NvrzBRdcEN0Xrb7861//mpSLZDgHKmFFNDbERg3bxy9+8YuFzlcGnZ2diWrHunEXVTVrXnrppaSsTRosdpz0u8aOfez7S5uXWDd0fa363dhbX2rY945Wjdtnxwu/oVWgWXON2cQJIYQQ0nZwgUMIIYSQtqMlKqp58+YllvZWPK/FwJ543oqqskRSdj/t4WNFYzppmecJ5LHnnj1OZA888ECqTnvj6IiPHmUkZluWsCqurISOVlyqE+fdc889qbrddtstKdvEc2eddVbuPnreOBYt5tUqizweLp43SdnqVuuVoEXAngeX10cPT7XreUVuttlmSVmrKKzYW1N2JHKLPbe+l3lE6Xno6upKqbvKZuONN07KixcvTtX985//zDwu9l7rd14e9HO11VZbpeq0ik0/71aFWPTdWcY8WrJkSenPrp5TWs2VxztR36Oi92D06NG9li02CatGX4tWYwL+nNHPmfXKi4ESHEIIIYS0HdELHBFZR0RWN39bXUTWyTqGEEIIIaQvyCPBmQHgPvO3+wG8WFpvCCGEEEJKII8NzuEAbEjGiQCG97JvJlZ3PX369Mx9tb3GiBEjUnVZNjg6JTRTAAAgAElEQVS2fa1zzqO7zMLqMbfYYoukPHny5FTdCiusENWmvk4vO7OOfgykdZLLi31ObHTq008/PSn3798/VacjZFpbkblz5yZlG2U3Fs+dWePZEHn6eC/rcVm2HDXs86Bt4Kx9jtbzexGQvXuisVG5PffycePGJWX9TjjxxBMz2ygDG7YiNjquF526ETfxMmw57BzSbtZf/epXk7LOUA0sbftSNjqkg3U3PvDAA5PyzJkzM9vw3uFeZHQvQviy8u6046bn0aRJPSkhjzrqqNR+5513XlK+8MILU3WvvfZaUrYhNfSzFEJIyvad+rOf/azXPgHAww8/nJR1VGOLPs6OjZfNQGNtdLOyI2iiFzghhGt6+dutsccTQgghhLSKQkbGIjJYRHYRkXXL7hAhhBBCSKNESXBE5BoAU0IIPxGRAQCmANgMQJeITAgh3OU2oPDEr9aFXKulshKDWazoWLtcWvdL7ZoYK6a00Yofe+yxpKxVJ4CvbtDue17yRX1PrEpHX2sz3MtFJBEJWzd6LyKqPt+2226bqpsyZUqvx4wfPz61nZVgDwBuvbVHcPjii9kmYFqVUpYbt1Zp6P3s/SgS8RhoXhLHGkWjL2v0fS2qMjrttNNS21tvvXVSfvbZZ6PaL+r2Gvs8rLXWWqlt/SxalbmmERVT//79seqqqwJYWg0fm+wzdg7tsssuqW2rsiqbr3/960lZJxEG/Gi/M2bMSMpf+tKXMvfTqg+ryirDPMFj4MCBWHfdyv/7XuRyL2yDN26XXXZZUrYq4jPOOCMp2+fqvvus2WzvaPWPVlcB6e+aiGS2oZN+eljTAe9Z0tjnMYZYCc5uAGoBTfYGMAyVxJtnV38IIYQQQpYZYhc4KwN4s1reHcDNIYQ3AdwI4EPN6BghhBBCSFFiFzgzAYwRkU5UpDk1WeZQAIsyjyKEEEII6QNivah+DuDXAF4HsAQ98XA+CuC5egfr7MZexnCru/YyUWt0m57+07MbsTp7rafX7t4PPfRQaj99PpvdVqPdFIFsnbPdz3Nb9XSXXubjWAYNGpS42ce6zwK+/UOWq/BXvvKV6Pa1vli7hVu0+6E3vnmI1eXruWZdkcvILtyX6Htn3X1t+Pys42p2JjW0fVysrt0b09jxtXZ/nru37pe1CczzfHh0d3dn2mLE2tZY27/Y1A/aJuc73/lOqs5LwZHFH/7wB7dfGv3es7ZA2iZLY+de7LNZ1rtA093dnTkHvBQfRTjzzDNT2zr1wSc/+cnM4+w3QYcZ0eEDLNp26oQTTkjV5bFtrBFrc1OPGPfyqAVOCOEcEXkawDoAfhtCqDm8LwZwfuEeEkIIIYQ0gTxxcG7u5W/XltsdQgghhJDGiV7giMhHAHwZPUbFzwK4KITweL1jteujFWlpsW9WdGLAd730xMNenZepWOOpQX7wgx9EteGpJTyRvCcC1KLJZrgXL1iwIOm3FeXrfjU7q7NFizh11nGLFsl6Ymh7bcOHD++1DAA33XRTUj7yyCOT8kknnZTdYYPOjuypTYui3futyqiISNnDRmXW255K4jOf+UxqW4vLdRgA+/xmhRnIg6fS1tix0SJx79r6OpJxGe+CPfbYI3P71FNPbbh9G1LjU5/6VFK27uu77rprr23Yuafnun23x5gnTJ06NbvDdVi0aFHy/bJzQ4fYsJmzi4aT0Oh3ki4D6bnuRVv3TDnseGjee++9fJ2F/533sCrIGNVilJGxiBwI4DEAqwP4Q/VnVQBTROSgnP0khBBCCGkqsRKc7wA4M4Rwrv6jiEwE8G0A15fdMUIIIYSQosS6iY8C8Jte/v5bAKPL6w4hhBBCSOPESnAeALAjgOfN33cE8JDd2aL1k14qBY+i2Xs9t1VP56lTMHhcfvnlUft52Zl1P6zNgdaN2v42O6y/xrtXRUPm67rddtstVdfd3Z2UOzrS63Ctr7f34JFHHsk8n+a3v/1tUn7jjTdSddqF+fOf/3yq7phjjum1vU033TTqvJY//vGPqW3tnluG+2qj9hyN4M1Pm8JDPx9XXXVVUi5qc+NlZvbsbvRx1g310UcftbsnaDfnvrznvaGvSacz8K7Hs7PxUsZ4+7388suZbWrX8NVWWy1V52Wh13h1WSlWyqKzszNp19qw/OUvf8k8Tu9r7YbKmEexNnf6XPY+nn766Ul59Oi0PMNL3ZCFtTn15qGer7FZxzWxC5y7AJwnImPRk7JhOwD7AjhbRPat7RhCuCV3LwghhBBCSiR2gXNp9fdR1R/Nj1Q5AOhstFOEEEIIIY0QG+gv1lanLjaKod62WUa1W5hVZcWqZ7SILo+L7Jw5c3r9+8knnxzdhsZmgNUuzFo8aKM36+u27sxa9NwM9dXQoUMxduxYAMDzz6e1k9qttwyR77///e/U9sorrxx1XGyWecv+++9f6LjFixcnZa1Gu/vuu1P7rb766kn5l7/8ZapOi9z//Oc/F+qHR0dHR+Ka6rkzW/S+Nnt8GeJyHVLBqjW0yvCHP/xhw+eqReCuERs5XO/nicTtfS1L7aGfOasqiM0mblUAel+tLrFR2fWc9VTqVtWUpXqyquW33norKVvV71FH9fzf7EVh189OHpWOF6aj9l5t5J05ePDgTFd03a4dU309niu7vlZrxqCfVS+cRyy2jf79+2fuazOPZ6FVTXnOrd/vRd5BpS1cCCGEEEKWFdwFjog8LCIrqe3zRGSE2l5FRLItxwghhBBC+oB6KqrtAAxQ2/8D4AoANV1SJ4A185zQWkxr0ZUVp2m1jo0AqdHqk6JREq+55prUthYX3nnnnb2W6+GJebMiLHsql770zrAqC93/6dOnp+qKiHqt6lJ72XgiUuultdlmmyVlm9AxC+sxN2PGjKRsRaZajaqTAFpPCe8efO9738tsv4wx7tevX9Kuva/6+fDUV14/YlUlloMPPjjzuAsvvDC6nSz0vdQJWT28BI1WraHbt+NWxMMjL7H3OnY/++zsvffeSfnEE09M1WnPRS9ZpfZafe2111L7aQ/BWO9TD6vSKep1WBtLL2J9HrwI+UWjvnvqK+0hVkYiX/te8J6R2Lmmv/v22dHb1kRDv2/tO8nzMK6RV0WV3yeMEEIIIaTF0AaHEEIIIW1HvQVOqP7YvxFCCCGELLPUs8ERANeLSC1l6CAAV4hIzQBjYN4Tem5gti6WDTfcsNeyxepGP/KRjyTlddZZJ1Wn3aDvvffepGyzS2u8/lvXR60z1DrPZmSXLiMirnVz9+q0/cO0adMKnS82gu3kyZNT21nuiFaPrG0mPPsJ6/6b5d5vbZT0tmfP0gy7qvfeey+xZ/PmZB77mSx9t5c5WT9fQPqZstnXs6KZehGJLbH3UtvGWZs3T6+vt5vxnObB3hftFm/nrLbR8O7R7bffnpT1Ow9Iu3HvtddemcfdcccdSXnmzJmp/XQU3FGjRmX2oyhF7VtqeO7p9QghJO+RVs8NPd6N3oPe+PCHP5xZd8YZZ2TW6XAF+tm3Npu6ffs8NvrurLfAudZs95ZU87q6ZyGEEEIIaSHuAieEcFirOkIIIYQQUhaxqRoaQkQSFYHn6uW5/ZUhejv22GNT2+ecc07Ucdp98IUXXkjVea6jedQ6yyJz585NxiDP/dfiSHucVhPquTBmzJjUflq1VXTsPdWcFp96oQVsUsgikUH7kjyutN5czhIH2/20injRokWpup122imqH5qyEspqtZR2nS86no2oM2IpS91QJMqyVdtecsklvZbzoCMez5s3L1Wn51cz1CzNZuHChYl6xbo6e27W+jmwEaj1e6kv78mSJUuS8re+9a1UnY44b/uvvwOe+7q+Tpsce4MNNkjKRRLv0ouKEEIIIW0HFziEEEIIaTu4wCGEEEJI2yGx2UAbYdCgQWHdddcFUJ4NQxGd5MSJE1Pb48ePjzru8MMPT8ovvvhi7vOWRR53b31/Hnzwwf8LIYzNez4RKTQ5PFsOmzqghnUTLkPnrN3VtT64EbQNUVF3UG3f4Nn/vPrqq4XGbdiwYaGWkdripTWxmbU1Oqu9TtNhXUh1aHXPzix2LpeVyiIrbUqTbKoKjRvgj10seqyApe0Ga9h7UvRe6DHKSkEDlGNPpfts+2veeZl11n6qNk+nTp2KOXPmFIrW34xvXCyx120p28a1L8n6xlGCQwghhJC2gwscQgghhLQdLVFRFVV1FMETtXkqKiuu16JQT7TaDBfmLKwKQatg6mRRb1hFZV0A9bbnBulFCdbH6WirQNq11Wbq9jLLvx/ErvXw1Bz2Xmq0qsFGZtaqIV2288KLJK7nQlbkYtsPi6ei8o7T6sQyInvXobCKKvZdaa+1yPV598vWLW/hETw81VwIoZCKasiQIaEWTdq65XtqYf2Osu9Are5r9v3XkbHt+1X3yws5kKX6y4O+V0D6fWK/Jfr7QRUVIYQQQt43cIFDCCGEkLaDCxxCCCGEtB0tSdWgyZMdWO8b62Lo6Z+boXtvgT4/wQt3bfFcB4tgdceezY8+t2efo/F0u3bstR2JNy9ibT6srY7XfpH72pe2QFZv7d0vXTdw4MBUXZbti2dzY20KYueCl9FbY20ptM7fXrdnC6bxXOV1FmTPRqkV2PPFpuDQtjo65IGts3j2FPrexo6xtdfQ99Nmmy4rXUcNz5W9KAsWLEjez9Yuzc4VTSu/Hx7ePdb3y46NttexcyTWHtK7B3VsS+tCCQ4hhBBC2g4ucAghhBDSdrTKTfwtAC81/UQki3VDCKPyHsRx63M4bssnhcYN4Nj1MRy35Zdex64lCxxCCCGEkFZCFRUhhBBC2g4ucAghhBDSdnCBQwghhJC2gwscQgghhLQdXOAQQgghpO3gAocQQgghbQcXOIQQQghpO7jAIYQQQkjbwQUOIYQQQtoOLnAIIYQQ0nZwgUMIIYSQtoMLHEIIIYS0HVzgEEIIIaTt4AKHEEIIIW0HFziEEEIIaTu4wCGEEEJI28EFDiGEEELaDi5wCCGEENJ2cIFDCCGEkLaDCxxCCCGEtB1c4BBCCCGk7eAChxBCCCFtBxc4hBBCCGk7uMAhhBBCSNvBBQ4hhBBC2g4ucAghhBDSdnCBQwghhJC2gwscQgghhLQdXOAQQgghpO3gAocQQgghbcdyu8ARkSAi80TkO5H7f0lE5laP27DJfXtQRBaKyP9G7v/Bat+WiMgRzexbX8NxWz5ps3Hbpdq3bhHZpZl962sKjNs3q/sHEenX5L7NEJEFIvKLyP3fN+MGcOxKIYSwXP4ACAA2VNvjAMw1PwHAft5xTerbgwCOMH/bFMD9AGYDeB7AhJjj2u2nl3H7IIDbALwFYBaAewBsXO+4Fo7bcQCmAngPwDWxx7XbT2/3H8DlAKYD6AZwaOxxrRg3VbcRgIUAru+lbgaAXfr63vbBuG0J4P8AzK/+3tLUr1c9rl+T+9br/QdwIoAXAcwD8CyAD77fxi1r7FTdwdV6+77qs7EDsD2AKQDmAHgKwCdix7xZP8utBMcSQpgUQhha+wHwGVQWOXcXbVNERohI/0b7Vl1N3wbgTgAjABwF4HoR+WCjbbcBKwG4HcDGAFZF5QG5rZEGyxq3Kq8D+DaAn5fUXjvxVwDHAni8jMZKHrcaPwbwWMltLreIyABUnq/rAawM4FoAt1X/XrTNUSIiJfXvCABfAvBpALX3+NtltN0uiMjKAE4H8HQJbZUydiIyAsAdAL6Hyjv9AgB3VPvaZ7TNAqcXDgFwUwhhXgNt7ArgVRH5gYiMaaCdTQCsAeCiEMKSEML9ACYD+GIDbbYFIYQpIYSrQgizQgiLAFwEYGMRGdlAs2WNG0IIt4QQbgXwTiPttCMhhB+HEO5DRUJSBqWNGwCIyBcAvAvgvoZ71j7sCKAfgItDCO+FEC4BIAA+1UCbhwN4saoiWb9oIyLSAeAsACeFEJ4JFV4IIcxqoG/tyHkALkE5C79Sxg4V6c3MEMJvq9+461GRyu9bQh8L05YLHBFZAcDnUPnvpDAhhF8D2BkVEfy9IvKYiBxb0qpUADT8Em9DdkDlQSm8oGjyuJEmUea4iciKAM4B8JWSu7m8sxmAp0JVX1DlqerfCxFCOB/AFwCMBjBVRB4QkS+KyJCcTa1V/RkjIq+ISO3D25bfqSKIyLYAxgL4aRntlTh2QOWbZrf79BvXrhNnX1RWtw812lAIYVoI4RQAawM4G5X/gF4UkRurL9EYpgN4E8ApItJfRMYD+CSAIpOobRGRtVBRKTT8USpp3EiLKXHcvgXgqhDCq+X3crlmKCp2gJrZAIY10mgI4dEQwjGoSKovA3AAKtK4K3M0s1b193gAmwPYqdrOlxrpW7sgIp0AfgLguBBCd1ntljR2jwBYQ0QOqH7jDgGwAfr4G9euC5xDAFxn/ktxEZEDq1bec0XkLlsfQlgC4G+o2B3MQmVlGmUvUFW97IOKXnkmgK8C+A0AvnyriMgoAPcC+EkI4YYcxzVt3EjzaOa4iciWAHZBRd1J0swFYBeKK6JiGFoXETldjdtSUoQQwnuoSISeBNCFfP/BL6j+viCE8G4IYQaAnwHYM0cb7cyxqEjfHi1ycDPHripx/ywq/5z+C8DuAP6EPv7Gtd0CR0TWRuW/vuvyHBdC+KUyUt5DtTdURA4VkftRMaZcE8DnQwhj8qhRQghPhRA+GUIYGULYDcAHUDGofd9TVUHcC+D2EEKUS2SNZo8baQ5NHrcdUfEmeVlEZgI4GcB+IlKKMfRyztMAtjCGpVsg0mA1hHCuGrf/V/u7iIwUkeNEZAoq3qKdAHYKIWyXo2/TUfmw6n9Mo/9JfR+wM4AJIjKzOq+3B/ADEflRzMFNHjuEEB4KIWwTQhiBin3pJujjb1xTfeX7iC8CeDiE8EKjDYnI7qhIWiaj8p/ErdVVbpG2tgDwd1QWlccCWB3ANY32cXmnqna4B8DkEMJpJbVZ5rj1Q+U56QTQKSKDACwOISwuo6/LM1XPmw5UdO39q/emq6j4vMRxuxzAjWr7ZFQWPMcU6Veb8SCAJQBOqP4Xf2T17/cXbVBEvgTgYlQ8Vr8J4O6qBC4XIYT5IvJrAKeKyBMAhqPicfq9on1rMw4FMEht3wLgJgBXFW2wrLGrtrUVgGkABqNi//ZKCOGeon0rg3Zc4ByM8h6I6QA2CSG8XkJbXwRwBCpi9kkAdi360W0zJgDYBsBmInKo+vuHQggvF2yzzHE7AxXPjhoHofIiOLuEtpd37kXFlgyo/Dd5OSp2Ew8WbK+UcQshzEclxgsAQETmAlgYQnirkXbbgRBCl4jsA+BKAN9FJc7MPiGErgaafQTAuiV5Ox2Hyjx6HRUPuCvAEA0AgBDCu3pbRLoA/CeEYG2q8lDm2J2KHnXi3ai82/uW3oLjLA8/qLimzgbwrcj9D0PlgVkI4ANN7tu9qOi0H4jcf6Nq3+YjI2Bau/xw3JbPnzYbt52rfVuAiii+z+/vMjRuZ1X3Xwigs8l9mw7gPwCu5bhx7JrxI9WTE0IIIYS0DW1nZEwIIYQQwgUOIYQQQtqOlhgZDxgwIAwZUj/ez+zZaVupfv2yu7fCCis03C97Ps2gQT3G6tqjcsGCBb3tDgAYPnx4aru7O9uZpKOjZ225eHGPQ453zbY93YbH7Nmz3w4hjIraWeGNW1dXj02i7fO8ednZMbKu2zJsWE/csTlz0iE6dBt6nABg/vz56A07X3SfbT90nb5OABgwoHDKnijMnCw0biKSqXfW99USO5803jPktW/nsu6XN0di55b37JWBnfP63F1dXYXGDfDHzkPPy87OzlTdkiU9TjF2PhfBPkv62t97r8dvIs+5dP8HDx6cqtPteO/frD4B6flgn4HavvPnz0dXV1ehvEzeu1L3uX//dCgnbz7r+6znW55nTh9nz132vIjth/fet+hvqr1uPWeynrmWLHCGDBmCcePG1d3vzjvvTG2PHJmdjmibbbZpuF/2fJoPfOADSVkPzlNPPZV5jL3G2Mn79ts9KUVWWWWVzGP0ywMABg4cmLmv5s4773wpakeDN24vv9zj4LTqqqum6h5++OHMNocOHZqU//Wvf2Xut912PSEY/vjHP6bq9Atw0003TdVNnTq11/Y233zz1La+z/r+2zp9nQCwzjrrZPa5DMycLDRuHvq+WmLnk8Z7hix63OyzMXbs2KSc9WwAwKOPZsc489ovG/tu0n3+5z//Wfq41WOttdZKyvoZA4C5c+cm5X/+858Nn8s+S3rB8MILPdE58pxL9/9DH/pQqk4/g977V2MXSXo+2GegNu8nTZoU19le8N6VzzzzTFIePXp0qs6bz/o+63dSnmdOz1P7ni57XsT2w3vvW/Q9tdet50zWM0cVFSGEEELajpZIcGbPnp2svj7zmc9k7mdXmHqlZ+see+yxpKylOXlWtx5rrrlmUrYqEs2uu+6alO1/+xrvP39PaqOvx7t3zaC7u3spqVFvWAmL/g/snXfSwWdjV+9amqClaUD6v7HVVlstsw39n5p3j706O24PPPBAUt5pp50yjyuKHuOy5rLGjlUr0c+wlbDo+6rH2+6n27Bzyb4jNLodfZxVt8RKfvL8F5qHfv36Jf/t5jnHBhtskFkXK/WIxaqB9fa6666b2aciEkIgLWnYYostkrJ3Xd63pGg/PPQ3zsOTlOhvCZDup5VkZmG/EbpPzZqzMegx9LD3QH/n7Zjq+ZV1XynBIYQQQkjbwQUOIYQQQtoOLnAIIYQQ0na0PBeVtVPRelRPh7722muntrW3TBm2ClZ3qXWe2tJd64CBtJ7U0wkX9b7x7G686y7bXsfqrfX12OteuHBhUrbebtrGQdtdWPS1WTsJbfti70HZ1+3d42bMuzLo6OhIvEia7U2UB60zt/YAelv32Y691rXH2tzY9j07Hk1R+5xGWGGFFZJnxptf3nsoxm6uEZrtSWjRNn3ee0GPj7X/8WyUat+kZrlK6356c8izDfK8rbx3iFen50lR7zcPO0d7OxeQvifWhVyPvfe9yIISHEIIIYS0HVzgEEIIIaTtaLmKyoqEtUuoDVC1/vrrJ2VPRFcU65KmyTqfFa/p6JW2PS/gXdk0w2V5zpw5iVuxJ+q0omI9xjZol9431iX64x//eGr71VdfTcoHH3xwqm7//ffvtY3f//73qe1PfepTSfm6667LPHezaYYr+MCBAzNF8lo9Y0XinmqjiHu5nRf6fF5E6Fh31qJur/oe5FH1tMLFfsGCBanAcFl4keG9sBbLCp5qOfaZsO88fZyn7rFmErV3erNUe1oFY9Wq+v3omW9oipotWPTc9/qoXbztN9p7BouEJ7DvjJkzZyZlGy4kxt2fEhxCCCGEtB1c4BBCCCGk7eAChxBCCCFtR8ttcLRdjcXavVidWwx50j1oHV6sC5p18/vb3/6W2b7et6itRawLYLPdpb1En2W4z9r+rrTSSkk5y66mN7KySO+xxx6p7b333jsp27GfPHly9PkaxdqAaBfconNmwIABhVx5PZ22Hh99v7yx12H7Lc8++2zO3i3N7bffnto+4YQTkvKMGTMyj9O2AfYdo20M7POs74G9bm0300hI/K6urkw3Xd0fmxFbP582lUKsm3IsNpP6pZdempR1AkSLSHaibs/+RV930STLMSE1Gkm2GYvXf5vWRqPTzuRJQOtRxEbGPhNlzy07Tvr5tM9FjDs7JTiEEEIIaTu4wCGEEEJI2xGtohKRdQHUUjfPDCG8FHvssGHDUiI2jed6GRtRUYuxrFtyLM1ws9bi7qIZfbXIzorT9bXa+1sk6qNHHpdijRU5ald63eZvfvOb1H6xLrnPPfdcanvWrFlJWbs3jh8/PrXfG2+8kZRtBNMiY+Vl8bVosW4zosIuXrw4EWMXFV/buRajNrGsueaamXVFxdk77rhjUt56661TdWeddVZSvvnmmwu17+FFWNaqh0Zc/wcMGJCoeew916ovG/FVq43snCojm7i+Xmtm4KmlNLpf1iX6tddeS8pjx45N1a222mrIi/f+s+/K2rssS73dKGW4dcdGMn799ddTdYcddlhSPv7446P7FYv+Bu22226pOnu+LLwM8foZsGFY9L5ZauG6EhwROUlEXgHwTwCPVH/+KSKviMiX63efEEIIIaS1uBIcETkTwCkAzgdwD4DaMmlVAOMBfFNEhoYQvt3UXhJCCCGE5KCeiuooAIeGEG4xf38FwFQReQ7ApQC4wCGEEELIMkO9Bc5IAJ4/53QAK9c7SUdHR1RY5aJ4mWLbCc9NztpMbL/99km5aIh5bTtVNIy5tdHQYeR1v2rZr2voVApW/3zbbbclZesyqe1u+vfvn5S1WzgATJs2LSk///zz2RfgoK/N2hRoYjP6lsW8efMaTm0Sa/9mdd/etT722GMN9QlI6/mtHY+2NyjDjsOi7VDsvCsLz01cP+PWVVtTxpzyMnVfeOGFUW1YN36NfS8sXLgwKXvXprH2K7FZ4nVoD6DHVnLBggVR5+2NwYMHY6ONNgKQfgdZvPdELPZdr9u0dkSDBg1KymWHDrHYd+zhhx+elO180ng2Yvpa7XdM21IVtcGZAuAbIrJU4pjq375e3YcQQgghZJmh3lL5OAB/BPCmiExC2gZnHID5ALIzVhJCCCGE9AHuAieEME1EPgjgIADbAVi7WjUTwGkAfhVC+E9zu9g7WuW18cYbJ+U999wztZ+Osmkj1P7nP3Fdj810a0WA2i3SE8N50SBjxa5FojzWQ2cTz3Lz7w0vQ7yNslpDZwgH0tmmrQv5E088kZQ91cOpp56aWaezMT/00EOZ+1m0S6M3Nl6meq2qsZFNy4gG2gz0tWo34VVWWSW1n6e68eZvbETUnXfeOSn/8pe/zNxv6tSpmXVl0Cxxf79+/TBy5EgAS98vve2FwyhDDTJq1KjU9pQpcYL6W2+9NSl3dKQVBDvssENStuOjwz1Y9VWsm3tsBJsOcQcAACAASURBVGk7v7bddtte+5SHBQsWJP3M8670iJ1j+n1yyCGHNHzeomEOrOpPRxP//Oc/n5Tt/dfns+/NouYVNeoqO0MIcwBcVv0hhBBCCFnmKRTJWETWFJH8iaIIIYQQQlqAhBCyK0VWBHA5KvY2DwI4HMAPUXEfD6gE/ft0CGG2d5KVVlopjBs3rte6ouKwSy65JCmvt956hdrQHj3aqwZIe2a98MILUe3ttddeqe2PfexjSflnP/tZqk6ry37xi19EtW+JFYU++uij/xdCGFt/zzRDhgwJNc8Aew905GedaLAeWdGdjzjiiNR+Orqwjk5cD+1Bcu+992bup8/94Q9/OHM/a/2vr1ufy4rHdfteAtg6FBo3Ecl+qB2y1FAA8OKLLyZl3X8rUtaq4zzPdpYauKauqfHWW28lZasC0epDL0J0bL+8sa/TZqFxA4DOzs5Q8yi0ovyyr89Dq4GBtCrSi1zsqai8SMEvvdQTGN9GJ//d736XlBtJZBpDCCE7I6jDwIEDQ+2+WK/e2Gciz3zT6CjtNnrwhAkTkrKdT2V4Nut3oH5HAMD3v//9pKw9X/UzDKRVqrGmHMBS19PrM1dPgnMugA8D+C6ANQD8BsDHUVnw7ISKi/jX6rRBCCGEENJS6tng7A3gkBDCAyJyM4BXAewdQpgMACJyKoAfADi9ud0khBBCCImnngRnNIDnASCE8DqABQD+ruqnocezihBCCCFkmaCeBOcdAKugkpoBAG4D8K6qHwogV9jMom7Q1o5B63OffbYn2PLXvpbWmG244YZJ+SMf+UiqTus4tb0MALzyyiuZdbFcfvnlSfmzn/1sqm6NNdZIyloH6bks23ugdZ62zoumGYt2ffRcFotmcNd2BDNnzszcz4tYat2szzzzzKhza1sBDztf9Vjp/lubiNgs4WXYS1g8V2ONZz9jXY11O/r+Wz1+bITfWBfYY489NrX94x//OHNf3UfPxbpov5oxVpbu7u5MN/lmZJ7XnHTSSUl5yy23XKpfMeyzzz5R+9kox9rmy9p/afu/mAzSfcHgwYMz34PevNHPkraxArIjUtvrXnHFFZOyjTKt33N2bpcxn3Wb9hsXi74Hnp1NkRAa9SQ4fwOQfEFCCP8dQnhT1W8N4LmljiKEEEII6UPqSXAOAuAt3d8BEPcvMyGEEEJIi6gXydiVN4cQfp/3hEVFx1YV8ac//anX/axrnRZ52Si6Dz/8cFK26isdufOGG26I6qOHFeWPGDEiKR9wwAFJWUfwBYDFixcnZeuOre+dFW+WwfDhw5Hl3l82NvKyjobsqQ2sa/5WW20Vdb6nn346R+960CJyXS4a2bYZKpHu7u5MFaUXYTnWXdM+ixrrKpp1bit+f+2113o9Zr/99ktt6+e+qPu9Dq9gIzFr8oQ/aAY2qaJHkb6uvHI6T7KONGzRrr1WjaBDP+hkqJ/73OdS+82e3RNN5MYbb0zVnXHGGZnn1uYCHrHRsJuN99xaNZYeN/sM6PHXbdqx1u+yQw89NLpfnqrfOy6r7uijj07Vxao19XNrXeyLZgCoUSjQHyGEEELIskxDCxwReVZEFtffkxBCCCGkddTNRVWHHwMYWXcvQgghhJAW0tACJ4Two5j9FixYkOgNrQ4yS89YlHfeeSe1rW1prH2OTpHw73//O7PNWPsK6yL7+OOPJ2Wr79aZV6dPn56Ura2O3rZh69deuycEkbU/iM1kXgZWJ6ztaey905lvtd2QdnUE0jYaXkjxsWPT0blt9vIahx9+eGpbZyi39kv63sXOyTxzV9ui2Gsrw+5j8ODB2HzzzQEAw4YNS9Xp9CSWPC7lNeyc1/fSGzfvfu2+++5J2bqr6348+OCDmW0UpRWu4B6xdm/2vlsbthisHYyXskTba9xxxx2pOp1KRadH8VKlWLSNj81kftZZZyXl4cOHJ+Urrrgitd+iRYsy22/2uHZ3d2e6des5q20qgfTzbr9duk4/S/3790/t9/zzz+fvMJa2YctC999m995xxx2TsrW5+ehHP5qUr7nmmqhz5QlvEmNnFb3AEZFOVGLiAMDbIYQl0T0hhBBCCGkhdW1wRGSCiEwGMB/A69Wf+SIyWUTiojoRQgghhLQQV4IjIkcDuBTAtQAuAlCTYa8KYDyAG0Xk+BDCFRlNAKiI1EaPHt1QR604yroq1vAiyloRYlHX3iyseuSyyy5Lyjaz7tVXX52UN9lkk6TsuUtbt9EHHnggKW+66aaZdc3Gqh31tica1mJXq87Qx1nVonYVvu+++zLb1yJffb8tVjUTK7r10GNlx9SKecs+d79+/RL3Z6vK8NyidZ19VvT4aDWjvXexz5QXVXXBggWZx2k1StnP77KOl7m5CFYlpd+pTz75ZKpORza+5ZZbUnV6jukx0fME8NWXq622WlJesiStHBgyZEhS1vfAvut1xGOL7pdVe9aO8+ZdPebMmZP5XOtr9Z59T0X8xhtvJOW//OUvqbqf/OQnSdkzAfHQ3wv7DtJtHnfccam6Sy+9NCnrrO8AcPHFF/daZ7/d2jU8y8SgN2KyINRTUZ0C4NgQwpW91N0kIlMATATgLnAIIYQQQlpJPRXVmgAmOfV/BrCGU08IIYQQ0nLqLXCeBnCMU390dR9CCCGEkGWGeiqqrwL4vYjsAeBepG1wdkVFwrNn3ZMom4CitNKmBEjrbGNdli+44ILU9quvvpqUbZoI7QqqXST79UsPiRc+X+sgp06dmtmvMrDuyzrVhL02z9VZh8n30Pd/9dVXT9VpeyYv9YAX/l1jdc56jK0LeazLvee264UY1yHTy3BtLfrcefNcU1aKkHPPPTcpr7nmmkn5iSeeKNReq929Y+wBYli8eHFi02LHzrPfiO2bDhvg2XvZ9AiXX355Uo7NGO+F/7dt7L333kn561//eqpu22237bUNa+Oj30keWXaakyZ5ygqfjo4ODB48GMDS9zX2fnnoMCPaVR5Ij1UeuzT9jOhwG9oeyu6nw5sA6Tk5Y8aMVN20adN6Pa9Nx6DvV56QJll2uJp6uageEpExqEhxtgNQu/KZAG4F8NMQwozoHhFCCCGEtIC6cXCqC5ivNb8rhBBCCCHl0GiqhoaJFd954lQtErZiq6JZZbVYzhP7feITn0jKNpO13j7++ONTdVotpckjomtEFB6Djs5pVS6xkVOtCkOL3WOzV//1r3/NPLd1g/zHP/6RlD23UY09txZhW3F2lioiNust4M9J68K6LKJVD/b51WoDq6LQ+1r1y84775yU9f2ZMGFCZvvNUEN5onqNdaPXEaIbeS4XLVqEN998EwDw6KOPFm5HoyOg62zfHlolBZRzr7U62ao6NDakht2ucdddd6W2DzzwwAZ61xjd3d2Zz3UZLv06NIZFq/3tOOn3o3UhzzJxsN87vW3VS2PGjEnK1owkds5odbj37Nh3vQ49k/U9YjZxQgghhLQdXOAQQgghpO1oiYrK8wywHjhZ2CRcWeJAGwlx++23z2wz1lPEY889e5zIbBK6vfbaKyn/6EfZeUm1CsNTh1mvAS8xWVHVnMaLzhmLVd3oqKRa/WPVSXpeaLUTAOy3336Z5/vGN76RlIuqCrKS5gHxc8bzlNJqO3t/YtVqHrNnzy6kUigSGbimTqmhrztPH7QHzOc+97mkfNNNN2Ue0+xIxl7/YyPE5qWrq6tQ4kwP7ZlpvTQ13vNS9r220ck/+9nPJmX7jdBJHHXZehPpNm1SWc/LtNkR7WPH01Nl6/4fdthhqf08VWZsVHnvHujkpzVPsRr6/XXzzTdntqHR34A82PkZkx2BEhxCCCGEtB3RCxwRWUdEVjd/W11E1sk6hhBCCCGkL8gjwZkBwGY2vB9A4ybihBBCCCElkscG53AA75q/TQQwvJd9U8ybNy/RE1pdn6f31bYKsbYJXntl2NxYPaZ2hd1ss81SdSIS1abW2Vo3YW2nYt1uta2IvW693Wx38jzEur7qaKb9+/dP1Wm9r9UJa7ukopm59f3y2tBjY21pPBsoz6W8DNuposTazOhn2Lqe6vnr6fXtuXbYYYekPH369KR8yimnZJ47D1nRyK2bu4706+HZVZVtQ1MmOtq6jrQOAGuttVZTz61tOey7Uo+r947ICq8B+NHutSu1bb/scAN5Qn1ovGf/6ad7MiJZWx0dBfycc85J1Wn38kGDBqXqtI3oxIkTk/L555+f2u/www/P7Jd2gY/Nxh5rd2vZfPPNU9sxz2r0AieEcE0vf7s19nhCCCGEkFZRyMhYRAaLyC4ism7ZHSKEEEIIaZQoCY6IXANgSgjhJyIyAMAUAJsB6BKRCSGEu9wGFEWTZlpVgRYD6wiZ1q1Zb9s2iiQ23HLLLVPbOlrx3//+96g2gLSY0UsaFuval5VArl4bHjqBnBWfatWEva9FXIVtEs6sBHsAcNFFFyXlhQsXZu6nVRG2j7FqO6vWzIrGa1UnOuGoPVdfqqHKwIvyreedVdVo12o9hgDw6U9/Oilfe+21me1n9cMSG/k8D1oNZdsvqpawDB48GBtttBGApdXyZc+bZqukLDr5rXWz98JR6CSOJ554YuZ+3lzRKsqiCXQ9+vXrl0SMjg1pkofrr78+KVvTh+uuuy4pa1UTANx2221R7T/++ONJeZdddknV6Xfe3XffnarTrto2QWsW9lvlheXQxKqPNbESnN0A1BSXewMYhkrizbOrP4QQQgghywyxC5yVAdQieu0O4OYQwpsAbgTwocyjCCGEEEL6gNgFzkwAY0SkExVpTs00eyiARc3oGCGEEEJIUWK9qH4O4NcAXgewBD3xcD4K4Lk8J7TZSDVWr/3www8nZZtyQev3PTsbjZfawOqEs1w9n3jiidT2pEmTkvLJJ5+c2b5np6L1mF6WXc9Wx+p533nnncx9Yxk0aBA23XRTAH5mZY9YGwrv3lm023hsNnrrYq/P7d1Xqx+2YeSz0HYey7vNjYe1g9HZuO0zpfddsmRJqk6/F3Qqjj//+c+Z57ZzS7cfa2fjpVyw7wD9DFu7jbJCMSxZsiR5T9l546X/8Cgj/Ydnd5WFTV3joe0ob7017Zyr7dk09p2q74l9pssIEeIxcOBArL/++gCWfod7qVmK8Itf/CK1rV3nDzjggMzjvFRCRxxxROZxX/3qV5Py97///VRdrI2PJtbmpgyiFjghhHNE5GkA6wD4bQihq1q1GMD52UcSQgghhLSePHFwlsqkFUK4trd9CSGEEEL6kugFjoh8BMCX0WNU/CyAi0IIj2cftTSeiM66Cmu1lFZX5UGrpWyWVy129VRbIYTMussuuyyqH1aErc+n1R62j1rsautsdnHvfEXo379/pmoqKzJsK9CZpxctyjYB0/fOU2VZtanethmLX3rppaSsQwZ85StfcXqcRqtn1ltvvejjYhkwYEDiAuy5qFsxvp5fReePl7VZo7NHA0jE+0D6uZw5c2ah9j1iM4HncSf2IuXmwcsmXlTVWeS4PffcM3P7mGOOKdQPze23357a3nvvvZPyPvvsk6qbMGFCr23YObr22msnZU/tnBVSQ5sb5EVH67fofmoVLpCez3lMEDTaddteg83arjnkkEN6/bv9FmpVllUtF8F+L2JDiRQ5LsrIWEQOBPAYgNUB/KH6syqAKSJyUFTvCCGEEEJaRKwE5zsAzgwhnKv/KCITAXwbwPW9HkUIIYQQ0gfEuomPAvCbXv7+WwCje/k7IYQQQkifESvBeQDAjgCeN3/fEcBDZXXG6jC166C1P8nC6mVj3ZTtcbE2PzfccEPUfhat5/Sygmvy2EUUdSnVLFiwIHHRzJMhWZ/b0wHrsdGuwQDQ3d2dlDs60utwfVzRNBRrrLFGUn799ddTdTrrbpb+31I07L2d89/+9reTchlZjvOMvZdFXduyleH2bu1bxowZk5R/9rOfJeWiNje2fd1/PZet3YO2v7LPm953Wc4Y7tkT6ozMXkgKz87mkUceSW1/7GMf63U/G5ZDZ7a2aNdwnf4GiH/e9VyxdlY2630zyZPxvox3mcY+m0XatPNeh+Xo7OxM1a27blw6Sm2j9vLLL0f3Ra8BihC7wLkLwHkiMhY9KRu2A7AvgLNFZN/ajiGEWxrqESGEEEJIg8QucC6t/j6q+qP5kSoHAJ0ghBBCCOlDYgP9xdrqlIp2C7MiZy/7rKao2C/LrVhnxK2Hl31YiwFjryUPZagRBg4cmIjsrdhSi7pjo/t6LF68OLXdr190BINC/Nd//Veh43Q/tRrtpptuSu238847J+VPfepTqbpLLrkkKXuReovS3d2djH9R11OLnr9F1TOnnnpqUrZqDZ0t+Uc/+hEaxboCZ70H9DwGfBdvz728rEjGw4cPx7hx4wAUf3dZ1ZBWu2lX9yuuuCK135FHHhnVvr1WG3m4hlUt64i79lxf/vKXk7Kn9o9V6dg52myVoh43iw7n4ZlQxJpTWPSz6X1nis5RLwyIDpvhoTOBz58/P3M/r/9F6JOFCyGEEEJIM3EXOCLysIispLbPE5ERansVEYm3GCKEEEIIaQH19ADbARigtv8HwBUAZlW3OwGs2YR+AUhbW1uRsxbnPfDAA0m5qGrm0ksvTW3r9h96qMdR7KqrropuU4vbikb71W1YNZ1OotcMEWxHR0fSb88bylJEtK4t9QFgk002ScoDBgywuydsu+22qe3NNtssKcdGrH3zzTdT2zNmzEjKVmT6yiuvJOVnn302Kee55h122CF63yJ0dHQkqimbZFSLy71nxao5PFVXLKeddlpm3fnnN57Srkgftei8HlrVY5837e1RlsrZXk/suy3W4/Soo9LmlFo1pFVGgB9pOEtFZdEegjaScRG8ZKt5qL1X//GPfzTcp97QapY8HlYa7zhtTmE944p8D+13xvt2LVy4MKrN2G+Vfd/qbftcxXgK51VRSc79CSGEEEJaDm1wCCGEENJ21FvghOqP/RshhBBCyDJLPRscAXC9iNRC7Q4CcIWI1Py8mppGWusurQ2OJtY+xOpodXRFG5FRpEcbp21wvMziXgRRL/O3thXJE3FT7+sdV0aEzFbz3HPPRe1n77nOPn3fffdlHudlQ4/NlO5loy/LbbhR9LXkwdpxxNp46fuw/fbbp+pWXnnlpHzEEUek6qZNm5a3i+65Y697zpw5Ue0BaRuAPHYKeejq6kpsD/PYT2h7BGt3pW0UPfR+9pjDDjssKWt7HAC47bbbkvI999yTlPU7FGh+6Iei9i01Yl2e8xLr/l20/zpMRx5bSf1M62+QfocC6SjjltNPPz2zLssGUn93Ad8OToc1sMQ8H/Vm3LVmu7ekmtfVPQshhBBCSAtxFzghhMO8ekIIIYSQZZHmygyrdHR0YPDgwQCKJ/8rgwsuuCC1fcopp0Qdp92G8yTD1GJrKzZeHpg9e3YiQi2aQM6SpS4bO3ZsalsnzisqutVidu3iC6RVnlZ96Ll11pKPWpYVlRRQSYiX5Sqs3b+tCFk/m7HXY12Z9TxftGhRqm6vvfZKyrFq0zyu0qNHj07Ksf23UVU9V3Ndp91e85yvHgMGDEjmpqeWt2h1g1UxZEUyzsPVV1/daxmId8/XY2dViGW4UvclixcvTty180R219ea5z1UBlkqpNVWWy21rd8ZV155ZapOm0ZYNXbW+8Tup/uhn2F7XBHoRUUIIYSQtoMLHEIIIYS0HVzgEEIIIaTtaIkNjs5uXNSd1ursi+gkP/GJT+Q+BgBGjhzZ0HnLIo+7d9n99M5tdbna/sOOt+6XDjFusziX0f88LpOx59b64zL6aLPWe9msY9F2HBZtj2HtSLxxy3pOrW1T2eSx2dP2NF7IBo1NSeGdr2hdK9DPoGfL0Qz0vIk9l7dfbOZsLzO3972wLvBF3xOaRYsWJSlfPBscr1/WTlPv692Tou+hWBf1Aw44IKq9ZnwbY9vMuj+U4BBCCCGk7eAChxBCCCFtR8vdxC1a5JhHXJwlkioqJnvjjTdS22ussUbuNvKcu0h0YZ2xGPAztJYdvdi6gmrVRB53Vu0eqiNY5nEtLyMyqN7PiwJtxf2xqo9YrDjbE7MXwXPHjc067TFkyJDCfVkWWJbc+4F0aAbvmbP3Xc+j2Gvy1JB5TAlaeQ+LqrZ0nVWpl/GcdXV1Je/gPNHovXPriL+e6jq2/1lu4UD6PdeXIU3KVh9SgkMIIYSQtoMLHEIIIYS0HVzgEEIIIaTtaJkNTpa+f/3110/KVs/ouQRmEauHbQbNbl9nM65HkXvnYe2hdMh3G/7ds30pI+uydq2uuWb2hrb5sC7RGqubrmVzBpa2C9M6Yb2fFwK/L0ML2H55cyHW/kfbBtj7r21BrAt8nhD2Wej776UI8PqvM4Fb+zE9Z+y90tutsDvxnrk8eKHwNa20vbDjU/b7yqMM27M86PeEzULvjWkZISM01k7Ts3/VeO+5su0h89TFQAkOIYQQQtoOLnAIIYQQ0nZICKH5JxF5C8BLTT8RyWLdEMKovAdx3PocjtvySaFxAzh2fQzHbfml17FryQKHEEIIIaSVUEVFCCGEkLaDCxxCCCGEtB1c4BBCCCGk7eAChxBCCCFtBxc4hBBCCGk7uMAhhBBCSNvBBQ4hhBBC2g4ucAghhBDSdnCBQwghhJC2gwscQgghhLQdXOAQQgghpO3gAocQQgghbQcXOIQQQghpO7jAIYQQQkjbwQUOIYQQQtoOLnAIIYQQ0nZwgUMIIYSQtoMLHEIIIYS0HVzgEEIIIaTt4AKHEEIIIW0HFziEEEIIaTu4wCGEEEJI28EFDiGEEELaDi5wCCGEENJ2cIFDCCGEkLaDCxxCCCGEtB1c4BBCCCGk7eAChxBCCCFtBxc4hBBCCGk7uMAhhBBCSNux3C5wRCSIyDwR+U7k/l8SkbnV4zZsct8eFJGFIvK/kft/sNq3JSJyRDP71tdw3JZP2mzcdqn2rVtEdmlm3/qaAuP2zer+QUT6NblvM0RkgYj8InL/9824ARy7UgghLJc/AAKADdX2OABzzU8AsJ93XJP69iCAI8zfNgVwP4DZAJ4HMCHmuHb76WXcPgjgNgBvAZgF4B4AG9c7roXjdhyAqQDeA3BN7HHt9tPb/QdwOYDpALoBHBp7XCvGTdVtBGAhgOt7qZsBYJe+vrd9MG5bAvg/APOrv7c09etVj+vX5L71ev8BnAjgRQDzADwL4IPvt3HLGjtVd3C13r6v+mzsAGwPYAqAOQCeAvCJ2DFv1s9yK8GxhBAmhRCG1n4AfAaVRc7dRdsUkREi0r/RvlVX07cBuBPACABHAbheRD7YaNttwEoAbgewMYBVUXlAbmukwbLGrcrrAL4N4OcltddO/BXAsQAeL6Oxksetxo8BPFZym8stIjIAlefregArA7gWwG3Vvxdtc5SISEn9OwLAlwB8GkDtPf52GW23CyKyMoDTATxdQluljJ2IjABwB4DvofJOvwDAHdW+9hlts8DphUMA3BRCmNdAG7sCeFVEfiAiYxpoZxMAawC4KISwJIRwP4DJAL7YQJttQQhhSgjhqhDCrBDCIgAXAdhYREY20GxZ44YQwi0hhFsBvNNIO+1ICOHHIYT7UJGQlEFp4wYAIvIFAO8CuK/hnrUPOwLoB+DiEMJ7IYRLAAiATzXQ5uEAXqyqSNYv2oiIdAA4C8BJIYRnQoUXQgizGuhbO3IegEtQzsKvlLFDRXozM4Tw2+o37npUpPL7ltDHwrTlAkdEVgDwOVT+OylMCOHXAHZGRQR/r4g8JiLHlrQqFQANv8TbkB1QeVAKLyiaPG6kSZQ5biKyIoBzAHyl5G4u72wG4KlQ1RdUear690KEEM4H8AUAowFMFZEHROSLIjIkZ1NrVX/GiMgrIlL78Lbld6oIIrItgLEAflpGeyWOHVD5ptntPv3GtevE2ReV1e1DjTYUQpgWQjgFwNoAzkblP6AXReTG6ks0hukA3gRwioj0F5HxAD4JoMgkaltEZC1UVAoNf5RKGjfSYkoct28BuCqE8Gr5vVyuGYqKHaBmNoBhjTQaQng0hHAMKpLqywAcgIo07soczaxV/T0ewOYAdqq286VG+tYuiEgngJ8AOC6E0F1WuyWN3SMA1hCRA6rfuEMAbIA+/sa16wLnEADXmf9SXETkwKqV91wRucvWhxCWAPgbKnYHs1BZmUbZC1RVL/ugoleeCeCrAH4DgC/fKiIyCsC9AH4SQrghx3FNGzfSPJo5biKyJYBdUFF3kjRzAdiF4oqoGIbWRUROV+O2lBQhhPAeKhKhJwF0Id9/8Auqvy8IIbwbQpgB4GcA9szRRjtzLCrSt0eLHNzMsatK3D+Lyj+n/wKwO4A/oY+/cW23wBGRtVH5r++6PMeFEH6pjJT3UO0NFZFDReR+VIwp1wTw+RDCmDxqlBDCUyGET4YQRoYQdgPwAVQMat/3VFUQ9wK4PYQQ5RJZo9njRppDk8dtR1S8SV4WkZkATgawn4iUYgy9nPM0gC2MYekWiDRYDSGcq8bt/9X+LiIjReQ4EZmCirdoJ4CdQgjb5ejbdFQ+rPof0+h/Ut8H7AxggojMrM7r7QH8QER+FHNwk8cOIYSHQgjbhBBGoGJfugn6+BvXVF/5PuKLAB4OIbzQaEMisjsqkpbJqPwncWt1lVukrS0A/B2VReWxAFYHcE2jfVzeqaod7gEwOYRwWkltljlu/VB5TjoBdIrIIACLQwiLy+jr8kzV86YDFV17/+q96SoqPi9x3C4HcKPaPhmVBc8xRfrVZjwIYAmAE6r/xR9Z/fv9RRsUkS8BuBgVj9VvAri7KoHLRQhhvoj8GsCpIvIEgOGoeJx+r2jf2oxDAQxS27cAuAnAVUUbLGvsqm1tBWAagMGo2L+9EkK4p2jfyqAdFzgHo7wHYjqATUIIr5fQ1hcBHIGKmH0SgF2LfnTbjAkAtgGwmYgcqv7+oRDCywXbLHPczkDFs6PGTh0ENAAAIABJREFUQai8CM4uoe3lnXtRsSUDKv9NXo6K3cSDBdsrZdxCCPNRifECABCRuQAWhhDeaqTddiCE0CUi+wC4EsB3UYkzs08IoauBZh8BsG5J3k7HoTKPXkfFA+4KMEQDACCE8K7eFpEuAP8JIVibqjyUOXanokedeDcq7/a+pbfgOMvDDyquqbMBfCty/8NQeWAWAvhAk/t2Lyo67Qci99+o2rf5yAiY1i4/HLfl86fNxm3nat8WoCKK7/P7uwyN21nV/RcC6Gxy36YD+A+AazluHLtm/Ej15IQQQgghbUPbGRkTQgghhHCBQwghhJC2oyVGxgMHDgxDhw7ttW7OnJ7wCyussEKq7t1337W7J4wYMaLhfs2alW1XldW+d4xHGf0tyqxZs94OIYzKe5w3bqb9Qv3y0PfLa79//3RolEWLFvXahodtf8iQnthU8+fPT9WVPY517l2hcRORKL1zs5+hjo70/0+DBvU4gNj7qufZ4sU9DmoLF6azQOg2u7tLi3XWa/udnZ2pOj3XbP9XWmmlpPzuu+8WGjcgfuw87LiW/XwOG5aOCZh1X5YsSTvj6GfTa1N/E4Diz3EWeqyAnn4uXLgQXV1dhfIyNeNdqfup52WeNvQzZ59HO4ebie5HV1fanl1/9+3Ye/PCvAt6feZassAZOnQodtttt17rHnqoJ9jwtttum6q79dZbM9vMai8PN9yQHU8uq33vGI8y+luUG2644aUix3njZtov0ryLPq/X/qhR6Tn9+us9Djix99y2/6EPfSgpT506NbNfZVDn3hUat1ia/QzZf1g23njjpGzv60c+8pGk/Oabbybl5557LrNN+8IrA92+/ZCvscYaSdn2f8cdd0zKt956a1PHrR52XMt+Pu17evTo0Un5iSeeSMr/+c9/UvvpZ9Nr87770qnDij7HWeixAnrm0ZQpxUO2NONdqfs5ePDgQm2st956SdkuwOwcbia6H6+99lqqzht7r868C3p95qiiIoQQQkjb0RIJzqxZs5JV55Zbbpmq++QnP5mU86xMmyE1yGp/7NixhdrQ//GV0Y8DDjig4faage2Xd+/WWmutpKwldHnu1c4775yU9X+P9ty6vM8++6T20/8ReffV/pejpQv23EXwzt3KOd4MrIRlo402Ssp6HgDZ0lorRdFssskmqW39H+r06dMz++KJvfW2rfMkEJ60OQ/9+/dPpJLe+SytfDfYMfnHP/6RlDfccMPM/WJp9rXoZ19jVZJ50N84Oy+tFDIL+23M6qeH9y5uNVnv9DXXXDO1bSU6Giu10XzmM59JylnXSQkOIYQQQtoOLnAIIYQQ0nZwgUMIIYSQtqPluahGjhxZ6Dirzy3bg8LqC7X+O9ba3LM3KYrWqebRp1qbkyJovXIevbh37iy9srU38K5V277Y/Tz7nDLwdMJFWFbtqppNrH2B5zZqbRs825qsNu17xfP0agXDhg1L7BK9Z8CzC9SeTM3Ajp22rSoD77qXh+fFzkv9bbHvOV236aabpur0OHp2PN49ib1fzbDV0dcaew/y2HPGQAkOIYQQQtoOLnAIIYQQ0na0XEVl0W631r1Ou5OVrRoA0uIwHXDQw4rJhg8fnpSboRLReOK7vhTdFhUp6zo9D4D0eNs2dECuCRMmpOr233//Xs919dVXp7ZPOumkpPyLX/wis4/Nxt67oiEJNEOGDEmCFVo1qaca0qqbxx9/PFWXx2W5huf6X1SNEqt68vDusX6etboTSKuzynILt8yfPz/z3ujz635arNttlurOc+NuRhBFTdF574Vp0G3YMAHapXjBggWpumaNZQ3v2dFhUixZain7PtTXk+da9PMZ+0zYe67Hw/tGe+pefX+sutMz0YgxAaEEhxBCCCFtBxc4hBBCCGk7uMAhhBBCSNvREhucYcOGLZWgrcbs2bOTstU52pDOZaN1l7E2BnPnzk1ta/1kM3S7sTYsVj+5PLhTaqxuV7ua77nnnqk6u62xGYxrHHzwwantAQMGJOV11lknVXf++ef7nS0RL4VEUTflQYMGFXLd1TYZnm2AZ3MVG57fJmKMRSfYu/zyy1N1J5xwQlJ+5513MtvQz7B9x8Ta0TUrxcbChQszbS+0XYztp3732DFYccUVk7Ju2+4X+w7U2cMB4JJLLknKnm1Qln0csLTNjEbfa50Wwt4DPeft/I+xE7znnnsy9ymLovPGO67od0aPd+zYF01DEYu9B/p89twx100JDiGEEELaDi5wCCGEENJ2tERF1b9//0ScaNU4WkVl8bKMaspQxyyrKp2iruBlRKbU7sZFiVWd/epXv0ptX3/99VHtW9H2iBEjkvLAgQOTshbTA2kX4DJCEFhVU6zYuEjG4HrMmTMnCXtQxL0bKB45XN9X79qK9murrbZKyuPGjUvV/fSnP03Kt9xyS6H2PVoRYXfQoEFYb731APiRmi2vvvpqUrbu+VmqzjxjoM9d618NTy2l8Z4z3f4GG2yQuZ+netXqK0+9a8eq9qw04hrvZYEvQ53pRTXW7dv3nB6riRMnRvcrFv08jh8/PlV32GGHNdy+xqqTY9RjrgRHRMaLSD+1/d8i8qSIzBOR50XkBO94QgghhJC+oJ6K6i4AIwBARPYDcB2AyQCOAXAHgAtEZNkUfRBCCCHkfUs9FZWo8kkAvhNCOKu6fZ2IvFb9e/mZugghhBBCCpLHBmcjAFYldTuAMxrpgA63bPWmZbugLe944c2b4bba2dmZqV8vw8ZnpZVWyqw76KCDkvKsWbNSdd///veT8ttvv52qe+GFF5KydgW/5pprUvtpm4Wi7qGxOuxW23ctWrSosI1LjaJpDzy7mzLmzJZbbpmU7dzUbsg77LBDqq5IyIlmZFiuh+cmrq/B2jJ6YevLyIqu58M3v/nNqGM8OzprJ5SVedrDjk/scXfeeWdqu9lpKcpA27oMHTo0VafTJTz88MOpuhNPPDEpN/s99NnPfja1HUJIyjpTep7vut7XHqftHrNsHmMWOFuIyCwAC3rZvwNAZ1RPCSGEEEJaRMwC5x70qKo+DmCKqtsKwMtld4oQQgghpBHqLXDWN9tzzXZ/AHXDvi5YsADPPvtsr3VPPvlkvcProt2BL7744lSdziKrs1ADQFdXV1T7XsTgrP3yHFcULXouQwxtWbJkSaYbf6ybtVVhZLly6mioANCvX8/U/Pa3v52qmzlzptPrHk4++eTMOi3St5mzPbRY1KoJsvZrZ4pETG6E8847Lyl7WeD/93//N7Wto6CWofq2UVVbgQ6boVUWlprbcyOsssoqqe0XX3wx6jg9JiKSqtPRsd94441UnVYTWfVq7LstVi1rVVK1b8SkSZOiju+Nzs7OxEXbU5Xl+Q7odvTY22dOv1O9KO+xFP1W6e8wAFx99dVJ+ROf+ERStnPXC4Xi9SUmFIe7wAkhvFSn/rq6ZyCEEEIIaTGFIhmLyJoi8oGyO0MIIYQQUgauBEdEVgRwOYBxAB4EcDiAHwI4CkAQkUcAfDqEkB2OGBXPCm1FrSmqovrOd76TlHW0xt///vep/Y455phey0BaVPn000+n6rLUS8cff3xmn6yaRbP22muntrVY8Xe/+13mcX2J9qKyUVQ98aBVS8Vw5plnprb12FgvKg+tNhozZkzuftQjy0vIE6Xa+5Enymor0f20fdTjH+t1or076qHF8Z6qQSfbPPbYYzP38+5jrIrKa8NTTzYLrRb2aNSDDgDuvvvu1PagQYMabnPffffNrHvppR5lwWOPPZaqu/322xs+t4f1qirCkiVLkgSyVgVTdK7YqMRZ6OfARg/efffdk7KO8u5RNGK+nTPai27IkCFJed68ean9tDpfR2yuR8w7qZ4E51wAHwbwXQBrAPgNKobG4wDsBGBlAF+L7hEhhBBCSAuoZ2S8N4BDQggPiMjNAF4FsHcIYTIAiMipAH4A4PTmdpMQQgghJJ56EpzRAJ4HgBDC66jEwvm7qp8GYO1ejiOEEEII6TPqSXDeAbAKgFeq27cBeFfVDwXwXhP6VZdVV101KXd29sQatC7FOtKt1Y3utNNOSXm77bZL1Wk3U1uXxXXXpZ3K3nrrraRsbXe0fYLWP6+zzjqp/TxbF223YHWQZUTnnDNnTuIC7rnAF0W7O+p7YMnjmn/WWWdl7JnmhBOK5YnVLrja7TXP/dDXba+tldFzbZ+1rYDN0q713bFRgb3wAfbcWXYQJ510Umr7u9/9bmab2oZIu9UCS7uN17C2ZTqshKXVkY1t35qReV5z1FFHJeWtt966UBvbbLNNUrZjoG2IbJRjbUdps5V3d3cnZX1P7HgUsRMri2HDhqXeB5oYd2Zg6dAS+hpGjx6dlDfccMPUftqmxUaZ1lHa87hgx+JlMo9Fz2sbfsGzl4sZ43oSnL8BSGZsCOG/QwjacnBrAMynQAghhJBlinoSnIMAdDv17wA406knhBBCCGk59QL9vV2n/vdefW8UjbJpxWtZ4u/NN988ta0jKNvkjqeeempSvuKKK1J1OnLnaaedlq+zvWCjPGqXPa0uiRVnAuW4g3qMGDECu+22W6ltZql47LyIVf9YlYJOxujx1FNPRe1n0ffci8AZSzOSpHZ0dCTu1BtvvHGqzkvKqEW+nvg3NqmhJTayt1Y1fOELX0jV/eEPf8g8LjbirRaDexGJ+yLZZlHyuNfWsCqvj3/840nZjr9VN2n0PdRuytZlWYfGuOiii1J1Z5yRnbO5o6NH0aDHxEvY2Zd488aqobz3fZY6zkZc1vfVC2OSR9Ufe5yu+9WvfpWqW7JkSVLWqqaRI0em9tOq8TxRxmNCSxQK9EcIIYQQsizT0AJHRJ4VkcVldYYQQgghpAxisol7/BjAyLp7EUIIIYS0kIYWOCGEH8Xs193dnejZrCudtpEpI7O4JStFBJDWh95///2Z+xW1r9D6SWv/87e//S0pa3235wpqdc5629of6GvLY9dThDy63SxdqR0nrZf17ol2S/W46aabUtva5seGwNe679h7l8deQ4+NvbY86Q2yWGmllRLbKRsmXs81T99tbVOKZOP2XK49dEj/559/PlU3bty4pFxGqIJlzc5m+PDhyTVaN3GPIhnSrc2Edv+359bjb13u9bvn0Ucf7bVcDx1SY9SoUak6PY9OPvnkpHzllVem9lu0aFFm+80e54ULF6ZsYTQ6fIH3LpsyZUpqO+tdqTNzA75dmkdsSh3Pdk6HE9A2NwBw0EEHJeU//vGPSfmZZ55J7Td37tykPHTo0Kg+xRK9wBGRTlRi4gDA2yGEJd7+hBBCCCF9RV0bHBGZICKTAcwH8Hr1Z76ITBaRffyjCSGEEEJaT71s4kcDuBTAtQAuAvCvatWqAMYDuFFEjg8hXJHRRF1i1VJalQX4qqcsrAhRR5QtAyu+u/jii5OyjrYMAL/85S+TshY52uvU4kHrSu25xTZbLaXxImTGioZ1pE57nG1fi9Z//vOfp+r0mHZ1dSXl/fffP/rcZeCpCFs5NjacwgYbbJCUa9mPa2RFYvXwnilPxWLVgrqfWn07fvz41H677LJLUu7LzOvNorOzM0o1VTTchsZzkf/LX/6S2v7oRz+alK2695VXXknKRcdk++23T8p2Tmm1Tr9+PZ8sG/X9hRdeyGxf98u2HxtewGP+/PmZ7XiRyzWem7t+l1lV0GWXXZaUrRlD7DOtVeM2JIDOADBx4sRU3bnnnpuUbSR/va9WH9r7pPts56Q3NjFhAeqpqE4BcGwI4cpe6m4SkSkAJgIovMAhhBBCCCmbeiqqNQFMcur/DKBY1C9CCCGEkCZRb4HzNIBjnPqjq/sQQgghhCwz1FNRfRXA70VkDwD3Im2DsysqEp49652ko6Oj4Uy41lanDJdyT79XRJdsdbva7e/f//53qm7HHXdMyosX98RKtJl0NX0Zity6L2u7lTxu4tpt2XMF18etttpqme0ffvjhmXWnn356Zl0sRTPwFrWziU1nEIvnqp3Hji0rnHqsq6nF2j1dcsklSVlEknKRFASAf++0/Y8XesFiM6xrtN1MI8/pokWLkmfNs0srypAhQ5KyZ4NjXZZ1upq3387O4KPfE3ls2z72sY8l5RNPPDFVp+eYDhtg78e2224bdS5re1nb1pm3G6FoKhOvnf79+ydla1f37rvvJuU99tgjun19/3SKG21zYzn66KNT27ovq666aqpO2+B4z4S2E8rzvMe8K+vlonpIRMagIsXZDkDtSzMTwK0AfhpCmBHdI0IIIYSQFlA3Dk51AfO15neFEEIIIaQcGk3V0LbEZorW7o0bbrhhqk5vW3c9HTVUu4ZbFVVREX3ZZGVvr4cVHWbdS0/8bsXlHjNnzkzKntuod27PNb9sFZKljEjGWs1hww4UcQUH0ipEfQ9s1uk777wzKVv1mKeS1O7fnupMu7Dmuf9aLaXVSXnuhxdWQl9bI2EAtJt4WfNLqzqOOOKIqGN0iAvbF6uWHD58eK9t2P7HRlfX2cOBdIgN7SJtTQIOPPDAzDZbyYorrpjaLjqOWq2jr1ur8yz2XHqsvPmrTT6852/ddddNbev3lT13llrKU+Hlia6eFTlaw2zihBBCCGk7uMAhhBBCSNvREhXVrFmzEvGVVVHEivyLerNosahVdXjiaSvaz0KL4W3yuv/+7/9OyjZBXRZWPKgt2m3k2WZ7VelxK4vYJJqap59ORyLwEmx+4xvfyN2+R6wKw4vM66HnJ1BOlOM5c+Zknj92PGO9CLVKCkirf/LMnYULFyblz33uc0lZe49Ymh3J2Ou/54HUCO+++25TI10fcsghhY4rcq+9Y2zdkUcemZRfffXVVJ1Wz4QQkrJVZek2rWrfU33UnsHu7u7MfeoxbNiwxItr9uzZS9XVsCpdjVXd6Pf72WefnZS//OUvp/bTaijrGawTWeb5vmo8bzjtWThpkhcyr4ei98BGWNbf3iyPaEpwCCGEENJ2RC9wRGQdEVnd/G11EVkn6xhCCCGEkL4gjwRnBgAr974fwIul9YYQQgghpATy2OAcDuBd87eJAHr3EYzEc4MuGiVVo+0w8riEZrnKWT2mdvfebLPNUnU6ImssXsRg23/twtyXUY7zEGtfoKOZ6gzCAHD99dcnZe1eDMTrnD30cdZGJstdWtsWAb5eXNPKzOJ5iHU3tbr72Ei2tv0ddtghKWu9/jnnnJPar6jdTWzYh9g5M3To0EL96Av0u+EDH/hAUn755ZdT+9ns3GUzatSopHzFFen8zBdeeGFStjY4OvryjBkzMtvXY2czsnvPY7Pt3jSejYn3Dp82bVpSHjlyZKpOR5k+4YQTUnXWblOz//77J2UdddhmDL/mmmuSsmfLpKNMe1h7RY13D/S3FohzE49e4IQQrunlb8vm25kQQggh72sKGRmLyGAR2UVE1q2/NyGEEEJIa4mS4IjINQCmhBB+IiIDAEwBsBmALhGZEEK4K/aEnujbqqTWWmutzOM0XoRMfZwVD2qVT6xo2kYr1m7c8+fPzzzOupXqbSuS1WiRvxWB6uv2XKKbEXFX30tP9WcjAWeJIMeMGZPa9tSTt912W1L23O+LRh3WIlR7X7NUMHY/Ty21PJJ1PTYRqr4nVoSsVVu33HJLqk6Ln7VIvKg6yUsyWvR50HNev5uAfNG2PQYNGpREM7fqgFh3Y0tW5Nhmq6QsZ5xxRlL+4Q9/GH3cM888k5TPPPPMzP3KUD0WpX///okKzkYy1uNY1JRAf9f23XffVJ1+lvbbb79UnVbne+hvi1Uh/frXv07KntmFTvrpYd+VVr2fhf2G6nYadRPfDcCj1fLeAIahknjz7OoPIYQQQsgyQ+wCZ2UAtX9ddwdwcwjhTQA3AvhQMzpGCCGEEFKU2AXOzP/f3rmHa1WUf/97s4VAEHTDTs089MtTYkReppSHn2Yilme9slLymIqF5gELfD2Upj81pVdLEzM1fQ08kAdeC18VBC0vNS0NPJQKUkqypVCkLbCZ94/n2Yt73e41e9Y8a+3NXn4/17UvZj2z1qxZM7NmDXOfAOwoIk2o7eY8VP99EIBVZVSMEEIIISSWUCuqXwKYBuANAO1Y6w9nVwDZdmMBWFl20YTqioSGgvCZpvnkk9bETctlddrWw7rC1/j0bkLlmqHEmuf62ly38VlnnRVc5uabb56kdfRwH77+tTJnretg29Fn+px1v7Ll/92Nlndb/Y6ddtopSdvoxbodFi1alMobN25ckr7ggguS9BlnnJFZD1+f2vdG1zNWD0KXYfXh8ujExNwPiK93ES4kQs3sNTZ0TSw+02SNL7xO2fo5ffv2TfrL6oPo+SU0hIuP3/zmN6njjTfeOElfd911qTxtYm/fJT2+tMsAO17GjBmTpH/727S6rX2PQ7BzamxInZBvXNACxzn3QxGZB2ALAHc651bWs1YDuCyqdoQQQgghJZHHD87dnfx2S7HVIYQQQghpnOAFjojsBOC7WKtU/AKAyc65Z7q6Vps+Wg+g2vzNZ0rtw7c9nOWRGAjfmtQRbC0//vGPg8rwbU3GijP0Fp1tAxt5NYbm5mbst99+Xd67iKjdeWhubk7Sq1evzjxP19HnrdT2jY5gfcopp2Ret9FGGyXpM888M/M8u22soyNbr6RFoN+3PONgu+22S9KhZu52O1sfZ5knA8DBBx+cOtaejO+///4kHepmwGLdDGgP16FlWC/WWlRi3zc9V4WKVDqjra2toeuBD3rxjRGf7b///pnHY8eOjauYwpovH3300Znlf/Ob3wwqU8/1ecZ9x/w7c+bM4GssK1asyHxndF2sqEzPUdbVQOg41a4rrJm47x184oknOv3dXuObA//5z3+GVDGF/V74ohn4rgv5VgYpGYvIUQCeArApgAfqfxsDeFJEjvZdSwghhBDS3YTu4PwIwHnOuUv0jyIyEcDFAMK8CRFCCCGEdAOhZuItAO7o5Pc7AYSZlRBCCCGEdBOhOzizAOwFwIYL3QvAo/Zki0+urGXlWk4OpGVuVh5edhRma4qXxYQJE6LK18+j5bB5ntOXV0Qk9lWrViXy3VjzRp95ps479thjo8q3ZLkmt+bqd911V5K28vpNN900Se+yyy5B9x0yZEhoFVPMmzcvdXzxxRcn6Vjz1ZUrV2bqIGh9DKurUXR4CZ8Owdlnn52ZV4Rre9sfMeXYECra1UOjejJZ+EI1hGJ1bnQ/77777kn6scceyyzDp2dj5x07Z3Vg6//UU09llnnrrbcm6a985SupvFAdRe0awLZBrJuLGPLcS3/jijDnt8SUaa/p169f5rk+1ygarddjzbt941yPrZDo4ZbQBc5vAVwqIjtjbciGUQAOA3ChiCTBMZxz0zu5nhBCCCGk2whd4FxT//ek+p/mpyrtADQ1WilCCCGEkEYIdfQXqquTG9+2uI7UncfMNzRPY03SsyLtxooNfB6KfWIQ7QXTZ/65bNmyVF4R4oa+ffsGee7tzu1fS2i0XMsRRxwRdZ028V6zZk2Svummm1Ln7b333kl60qRJqbxzzjknSc+dOzeqHj4GDhyYjCkrqtLHPvNhO9b0sd7CtudpbPlTp07NPFebhl955ZWZ5/nQ77AdtzEiVvsO6Wf1PXcjrL/++sm8l0dEpUUANpq15vHHH0/SV111VSrP5+pAY/tVi5c0VnyxZMmSzHvpKOHWNYAm1iOxL69j3KxYsSLznK7wudTw3Vs/j+/ZdJv4xE52XMa4frB88pOfzMzzeY/W+MzVQ3nppZdyX1PawoUQQgghpKfwLnBE5PcisqE6vlREmtXxMBF5vfOrCSGEEEJ6hq5EVKMAaBXqbwO4AcDS+nETgM3sRWVQdrC0Y445JnWsgwY+88xaZ81HHXVUcJlZ1gVAWiylt8+t9VPRli2x5BFDxfSHvUYHYV1vvexhagM6jh49OklvsskmQfeeM2dO6viAAw7IPPfPf/5zktbbs9Yy4Pjjj88so+zgm+3t7YnI0noO94mlfJ7DtWhLn/fOO++kztPb57a8I488MrP88ePHZ+aFYusSgs9q0ff+WpFXUZ6MY4nxVmwD3Opxefrpp6fyfJ6GtYhKWyva90pbCJYxr8V6hC+6v3zWPmUELfZ5bI9pZxt8uH///pnntrW1BZWp5wWfiM2KsrR355gxnldEFWYTRgghhBDSg1AHhxBCCCGVo6sFjqv/2d8IIYQQQtZZutLBEQC3icj79eP+AG4QkQ57uo/kvaHPw67VpwglVK5p5bLDhw9P0llm4QAwe/bsJK293AJ+eaL2Umnlstprs5bf2zaIbRNN2TofZWC9yGaxYMGC1PGwYcOStH5uXxRfG6X26quvTtI+M3ldvpVb9yT9+vVLdJhC2xFI6yLY91TLv/V5Vmaur9thhx0y7/W1r30tdbxw4cLgemaho0n7IhRrU1qf/oXVZ9BjyJrj2jEUi/Ye7sPnMsK6ndD6U775SutrWD2b++67L0kfeOCBmXmzZs1K0n36pP//fNJJa12ovf56ubYpvm+CNW3uaG9txt4IeeZw3zykvxm+MvR1RbjssO3j+zZedNFFmXlZunrWjYE+tjqDmhivzF0tcG4xx505HflV7rsSQgghhJSId4HjnDuuuypCCCGEEFIUoaEaCsNntubLK2Lr7dprr00djxs3Lug6be4YGxCtCFFTd7N06dJEDJOn/Ys26S+i7+19fUHc9Di0986qf2ww0rLR5vZAuNmoPS/LBNe+D9qcddWqVam8b3zjG0l62rRpmffW4pcY01DAL3rS3l3zeIX1edi1bgJi0d7DY8e9r56x3HnnnZ2mgTgTeSva1P1Qtmd0LcoEECQS7AotWswjvvTl6XewO78ftn00v/jFL1LHu+22W5K2YkcdnFZjx4gePzZIbqPzKq2oCCGEEFI5uMAhhBBCSOXgAocQQgghlaPbdXBisboPMXLaPfbYI+reOmzD+uuvH1VGEeRvqKjBAAAgAElEQVTRXylajp3n3r4I6LpePhPJItB6Nr7yrR5GaNsV0cY+/Z9Y2tvbE90VG2Xeh5aFb7311kHXFBEl2OLTu9H3s+eFPqvPbbwvXIXWKbC6AdakvLvR71KsnmAsMaEOfHX0zTXaDUGonpjNs3Tojc2cOTPznK7Q4VGsmbXvWWNdnGSV4Xvu0DIsWnfOh68M7W7js5/9bFB5XZWpyXo27uAQQgghpHJwgUMIIYSQyiHOlR95QUSSm/g8GRdhIubb0po0aVLqeMcdd0zS1ovlnnvumaRDt2DLjrhty58xY0aS7sKc9o/OueyGz0D3m0WLoXzefovAinF8JpM+78Ux5/lMbn0Rfovg17/+dVS/DR061O23334dZQRf5+tTPb70uLP42tIX9TgLn8dem6dFSL53duTIkUn6lVdeySy/AaL6DUi/c1b8p+tmPWf3tIisN2HHaMf8Mn/+fLz33ntRAaUHDBjgttpqKwAfFMFoM/Q837Ss9zHWM73P5YUea3ZeK1uVQJNnrtdkzZXcwSGEEEJI5eAChxBCCCGVgwscQgghhFSOdSpUgyXGBbhPPll2VO11qXwtUy3DbNQnSy7aRN2OGa2rFSrTzuOOXZ/rMyHX8uKyw4yUQZ56PfPMM53+bvVgfMTot+QpX+s+6OjFFu2Kvq2tLZWnoxnbMBc+PZcy3jdfObE6N7o9rR5P2foVGhvtXfdX2Wbudi7omE+ampqiy2xra0u+UVYHR7e51UH1zRtFh3/xfT98ba6/vfY7XISeo69ejbrN4A4OIYQQQioHFziEEEIIqRzdZSa+BMDC0m9EstjSOdeS9yL2W4/DfuudRPUbwL7rYdhvvZdO+65bFjiEEEIIId0JRVSEEEIIqRxc4BBCCCGkcnCBQwghhJDKwQUOIYQQQioHFziEEEIIqRxc4BBCCCGkcnCBQwghhJDKwQUOIYQQQioHFziEEEIIqRxc4BBCCCGkcnCBQwghhJDKwQUOIYQQQioHFziEEEIIqRxc4BBCCCGkcnCBQwghhJDKwQUOIYQQQioHFziEEEIIqRxc4BBCCCGkcnCBQwghhJDKwQUOIYQQQioHFziEEEIIqRxc4BBCCCGkcnCBQwghhJDKwQUOIYQQQioHFziEEEIIqRxc4BBCCCGkcnCBQwghhJDKwQUOIYQQQioHFziEEEIIqRxc4BBCCCGkcvTaBY6IOBF5T0R+FHj+CSKyvH7d1iXXbbaItInInMDzt63XrV1ETiyzbj0N+613UrF++1K9bmtE5Etl1q2niei3H9TPdyKyXsl1WyAi/xGRWwPP/9D0G8C+KwTnXK/8A+AAbK2O9wCw3Pw5AIf7riupbrMBnGh++xSARwAsA/A3AIeGXFe1v076bVsA9wJYAmApgJkAtuvqum7st+8AeBrA+wBuDr2uan+dtT+AKQBeArAGwLGh13VHv6m8bQC0Abitk7wFAL7U023bA/02EsAfAayo/zvS5G9Vv269kuvWafsDOB3AawDeA/ACgG0/bP2W1Xcq75v1fDtf9VjfAfgCgCcBvAvgOQC7h/Z5WX+9dgfH4pyb65wb1PEH4ADUFjm/iy1TRJpFpG+jdauvpu8FMANAM4CTANwmIts2WnYF2BDAfQC2A7Axai/IvY0UWFS/1XkDwMUAfllQeVXizwBOBfBMEYUV3G8d/AzAUwWX2WsRkX6ovV+3AdgIwC0A7q3/Hltmi4hIQfU7EcAJAL4CoGMeby2i7KogIhsBmARgXgFlFdJ3ItIM4H4AV6A2p18O4P56XXuMyixwOuEYAHc5595roIx9AfxdRK4UkR0bKGd7AB8DMNk51+6cewTA4wDGNlBmJXDOPemcu9E5t9Q5twrAZADbicjQBootqt/gnJvunLsHwNuNlFNFnHM/c849jNoOSREU1m8AICJfA/BvAA83XLPqsBeA9QD8xDn3vnPuagAC4IsNlHk8gNfqIpJPxBYiIn0AXADgDOfcfFfjFefc0gbqVkUuBXA1iln4FdJ3qO3eLHbO3Vn/xt2G2q78YQXUMZpKLnBEZCCAI1D730k0zrlpAPZBbQv+QRF5SkROLWhVKgAansQryJ6ovSjRC4qS+42URJH9JiKDAfwQwJkFV7O3MxzAc64uL6jzXP33KJxzlwH4GoCPAnhaRGaJyFgRWT9nUR+v/+0oIotEpOPDW8nvVAwisguAnQH8vIjyCuw7oPZNs8c9+o2r6sA5DLXV7aONFuSc+4tzbgKAzQFciNr/gF4Tkan1STSElwC8BWCCiPQVkdEA/htAzCCqLCLycdRECg1/lArqN9LNFNhvFwG40Tn39+Jr2asZhJoeoGYZgA0aKdQ594RzbhxqO9XXAfg6artxv8hRzMfr/44G8GkAe9fLOaGRulUFEWkCcC2A7zjn1hRVbkF99wcAHxORr9e/cccA+CR6+BtX1QXOMQB+Zf6X4kVEjqpreS8Xkd/afOdcO4DnUdM7WIrayjRIX6AuejkENbnyYgBnAbgDACffOiLSAuBBANc6536d47rS+o2UR5n9JiIjAXwJNXEnSbMcgF0oDkZNMbRLRGSS6rcP7CI4595HbUfoTwBWIt//4P9T//dy59y/nXMLAFwP4Ms5yqgyp6K2+/ZEzMVl9l19x/1g1P5z+k8AYwA8hB7+xlVugSMim6P2v75f5bnOOfd/lJLy/qq8QSJyrIg8gpoy5WYAjnTO7ZhHjOKce84599/OuaHOuf0A/BdqCrUfeuoiiAcB3OecCzKJ7KDsfiPlUHK/7YWaNcnrIrIYwNkADheRQpSheznzAIwwiqUjEKiw6py7RPXbKR2/i8hQEfmOiDyJmrVoE4C9nXOjctTtJdQ+rPo/psH/Sf0QsA+AQ0VkcX1cfwHAlSLy05CLS+47OOcedc59zjnXjJp+6fbo4W9cqbbyPcRYAL93zr3SaEEiMga1nZbHUfufxD31VW5MWSMAvIzaovJUAJsCuLnROvZ26mKHmQAed859v6Ayi+y39VB7T5oANIlIfwCrnXOri6hrb6ZuedMHNVl733rbrIzdPi+w36YAmKqOz0ZtwTMupl4VYzaAdgCn1f8X/63674/EFigiJwD4CWoWqz8A8Lv6DlwunHMrRGQagHNE5FkAQ1CzOL0itm4V41gA/dXxdAB3AbgxtsCi+q5e1mcB/AXAANT03xY552bG1q0IqrjA+SaKeyFeArC9c+6NAsoaC+BE1LbZ5wLYN/ajWzEOBfA5AMNF5Fj1+w7Oudcjyyyy3/4XapYdHRyN2kRwYQFl93YeRE2XDKj9b3IKanoTsyPLK6TfnHMrUPPxAgAQkeUA2pxzSxoptwo451aKyCEAfgHgf1DzM3OIc25lA8X+AcCWBVk7fQe1cfQGahZwN4AuGgAAzrl/62MRWQngHeec1anKQ5F9dw7WihN/h9rc3rN05hynN/yhZpq6DMBFgecfh9oL0wbgv0qu24OoybRnBZ6/Tb1uK5DhMK0qf+y33vlXsX7bp163/6C2Fd/j7bsO9dsF9fPbADSVXLeXALwD4Bb2G/uujD+p35wQQgghpDJUTsmYEEIIIYQLHEIIIYRUjm5RMu7fv7/bYIP8fqRaW7M9UQ8bNqyRKkWX77vGRxH1jaW1tbXVOdeS97rQfrNtop+1iPbK00/63Ng2L3vc+e41ZMiQJL1s2bKofhORILmzfZY1a9YaP7W3pw0pli1bq8fYr9/asEUrV2brpurzLLZ8e5xVR82//vWv1HFTU1NQvUKx9dfH/fv3T+Xptlu6dGlUvwHhfedDjyEg3XdF4OuTtra1UTuWL1+eyhs0aFCS1n1lsXlLl67Vfy16btG8++67aGtri4rL5JsrY+upKeK511Vi53pN1jeuWxY4G2ywAQ47LH9IiilTpmTmxZRXRPm+a3wUUd9YpkyZsjDmutB+s22irymivfL0kz43ts3LHne+e+2xxx5JesaMGVH9Fop9lhUrEsMj/PvfKYMNzJgxI0lvsskmSfr117MN3fR5FvvRzfoI+9p72rRpqWP9YffVKxRb/y222CJJ77DDDqk83Xa33XZbqf3WFXoMAem+KwJfn8yfPz9JP/bYY6m8kSNHJukNN9wwswybd9ttt3V676Ln4unTp0eVB/jnyth6aop47nWV2Llek/WNo4iKEEIIIZWjW3ZwWltbk5XZSSedlHlenpVp0atYW6+eWiXr/wla1l8/HdZDn2vzuhNfn/Zku8a2j65z2fX1tV3ZxD5b6O6IPU8/6xNPpL3NP/fcc52WUVT7692dLbfcMknbnSq9k2Trr4/t7kRRDBs2LPmfap5nL3scHX300UHn6Z0tuxPzsY99LKiM3/3ud1H37kn0N64odJ/6vgtFs/vuu2fm5Rn3ut/0Lpwtv8xn4w4OIYQQQioHFziEEEIIqRxc4BBCCCGkcnR7LKo33kiHmQmVy5aty1FEeUXIwfPoimi5Zhl1CdWd8mHlq6H6Lb48n2y6bF2E0HGSJX8G/G3Qkzo5RWPNlTXaIgnI1sHxMWLEiNTxwoVrDSl8ptKDBw/OLLNok+q8rFq16gNzZF7K0BvTYzh0jIbO7Rar+6T1pELnR6vnYa3eND1plaTraesYWi/dH1Z/acyYMZnXaYs3jdWzOeCAA4LqYVmwYEGS9un1+PrUp38V0j7cwSGEEEJI5eAChxBCCCGVo1uCbWrvnHm2u7SZoc9E2ieq8aG3v3yOzTS2/vq8dVW8MGXKlD8653bOe53uN9+zxYodNb7tRutwTY9ZK6Y44ogjOi3jlltuSR1fdtllSfrWW2+Nqlc3ENVvG2ywgetwqma3+7VoaLPNNkvl9e3bN0nPmTMnlVeE4zz9vtn3uTvbWbfBnnvumcoLFYFY0ahur9dffz2q34B031m22mqrJO2rp21LPWcV4fSvjHkuRhxj+0DP4b45yF6nvx/OuShPxqEeqH1t56tXaBl53iM9LnTbleUCIYQ8z2ZE/Z2+c9zBIYQQQkjl4AKHEEIIIZWDCxxCCCGEVI5uMRMfMmRIEgDOuu/Wsj8rH9Yywlg9Gx9ajh1avpXtFiUPzSK2/HXFXDq0Hva8V155JUln6dV0ho7qrBk7dmzqWJstzp49O5V33nnnBd+vaIoIE9G/f//E5NRnHuvDmpdqE+yZM2dmXqfNsw888MDM82JNobfddtskfeaZZ6byTj/99CT9/vvvZ5ah9Yl8Jvw+rA6Mbq9G3vumpqZkjrR6gbquPvPZWLPeUAYOHJg6vuKKK5K0L0q4fgdtu4e6VdDYPvCZsveGAJVlu/2wZOlj2bHla1dfcNUYbD/5zMtpJk4IIYSQDyVc4BBCCCGkcgSbiYvIlgA67HUXO+cW+s7XtLS0uI4IuZayo4KvK/SkuAqR5sY+M/EYj75Atnnr9ddfnzr++c9/HlT+iy++mDrWkaL33nvvJG2jV59yyilJuqWlJZXX2toadO8yzJ5DTB+7ItRk1Yc1v9fiEi3isR6DjzzyyCTtM3u14+L+++9P0qHehFevXp06fuihh5L05MmTM6/TY8SH9QrrM5U3IpZoM/HYvitbVK5FBQcffHAq7+yzzw4qo1+/fkl61apVwfcOndP1c1oxXagZdBFm4nm+QTGifp+59IABA1J5el5atGhRZl4RjB49OnWcR7UgCz3urHqLZsaMGXFm4iJyhogsAvAqgD/U/14VkUUi8t2YShNCCCGElIlXyVhEzgMwAcBlAGYC+Gc9a2MAowH8QEQGOecuLrWWhBBCCCE56MqK6iQAxzrnppvfFwF4WkReBHANAC5wCCGEELLO0NUCZyiAFzz5LwHYqLjqxFFlvZvQ8sqQwQ8bNgxZulM+l+la18Jn8mnNTbP4xje+kTr+2c9+lqT79ElLWbVOzquvvpqkr7nmmtR52py8ubk5lReqg6PJ0ze9wWQ1NLq3zxTcV4aV/4fq3ehwBe+9914qzxc5eb/99gsqP7S8rEjMjaLfudhxYt/HItB6K3Pnzg26JlSPLhZf+1gTaK3LYfVzOnQ7Hnjggei66H4rInSND5+LADvfnnbaaUm6aJ0by0EHHZQ61vq9HW5igHzm5L5zQ777XengPAngfBHpZzPqv51bP4cQQgghZJ2hqx2c7wD4fwDeEpG5SOvg7AFgBYB9y6seIYQQQkh+vAsc59xfRGRbAEcDGAVg83rWYgDfB3C7c+6dcqvYNW+//XaSnjp1airvc5/7XJL+05/+FFV+qHdZu2VWhFfaUGz5vm3MUFpbW5NyfduB1uOqxm4H6+1bvY18+eWXp87TZtwXXnhhKu/NN9/MrrTCZ756++23J+mXX345qDzAbxqedZ6PdVW8avF5FM1Cez+2xIp4nn/++STtiwJv8XlfzsKayo8aNSpJx3qI7oq2tragtvGNmyK8vvfv3z91vHz58qDrrr322iRtxcc6cvv++++fytPvqn13ipg7Q0Qdtr550HOl7923rgd86Kj3Gp9LCuuxPYbY9t51111Tx8cff3yS1t9h++7E3i/kui5DNTjn3gVwXf2PEEIIIWSdJ2rJKiIXiUhL12cSQgghhHQ/XfnBae7sZwDfA/CAiLQDgHNuaQl18/L9738/SWvLihtvvDF1nhZ1WN59990kPW/evFTeTTfdlKSPO+64JH3sscdmlme98WpsELq//vWvSVp7YC2KooOTWssMvU3qsxLwBVDVWDFUe3t7kl66NG54+TzWhm65W/Rz+6xVQtvf55W0u9FeiK2H4mnTpiVpbfHkE9XYMvTxO++kJdtaBKbFCVZMpC3vTj311Mx723YMDRaqxQtz5szJPM9SlAhaB9uMxRcsMRRrodaI+KaDLItMADjnnHOS9MMPP9zwvfJQtPqAT3StxXSAP5BlKHfeeWeStt6DP/GJTyTpBQsWZJah752nHlrlQAe7BdIBb3Wb2LEVq8oRcl1XIqolGb8LgMfq/zoA2SFkCSGEEEK6ma4WOIsBPAPgKgAdjkMEwEMATgTwWnlVI4QQQgiJo6sFzggANwGYBGCsc24xkAQWe9I5V463K0IIIYSQBgiKJi4ip6MWk+o7zrl7RGQVgM+ELnB0NPGi5J1aJ0BHqT300ENT5w0aNChJjxw5MpWno01vttlmqTwdeXXzzTdHCDZCrpY5Wz0V3e7bb799ks5jsqzpwltuVHTjoUOHui9/+csAyveC6SOPaf6ECROS9NZbb515ndaruvnmm4PrUrSOTNlR4H34nsWaKutI2loHR+vtWPL0mzaf1ffSnlgB4POf/3yS/vrXv57Ks/o6Gu1VWZ83ePDg1Hk+naIcc1cp0cRjomrnQbendqOQB+3l2Hqn1vp3ebwcP/vss0m6bHcbsdHE9TfOF4U+z/yhdfy0rs64ceNS5+nvk3aZAgBXXnll5r2LaEtdptZpBdLei//xj38k6SVLsjRfPugF2upwaozeXqfvXJdm4gDgnPvfIjIHwK9F5Csh1xBCCCGE9BTBqvHOuWcB7FS/5u8AVpZVKUIIIYSQRgjawenAObcCwAlFViDWS3CWKWGeLcCVK9eu0axp5t/+9rck7TM19zF+/PgkbUWB+lm1iMSKS7K2KYG0Oag1WdZ5sSbjffr06VHRVAe+PrVm5z6xlGb27NlRdckao7GiqzKCpMbeT4ulYoPc6Tr7PAFb9Da+5vDDD08d33PPPZllhAYI9dXjw4RIWhrzhS98IUnbgJpa3OBDm+5rE2Ug7fH8oosuSuWde+65mWVa1x/rImvWrEnm4Kyx3BlZolkf5513XupYm4lPmjQp8zo7n2hPyb7Asr55SOdZNyk6oLEPPe/kcZEQErSzcecGhBBCCCHrGA0tcETkBRFZXVRlCCGEEEKKIJeIqhN+BmBoERUhhBBCCCmKhhY4zrmfhpyn5ZNlu6LP4/r+X//6V5J+5JFHMs+LrbOuizWL/Mtf/pKkfS60fTowPt2arJAIRaGfzd5Lm/aFmib6IvD62mDnncOscbWpKZBuc3tvfb9QPZg8+jK+iORF6N0MGDAA22yzDQB/pHeLT6ad1T9W96sIU+a99torSb/wwgupvC9+8YtJ2rp8j6Fss+PuIuY5Lr/88tTxZz7zmSTt07lZvTq9aX/VVVclaT2v6TQA3H///ZllatPhlpZ0mENdzw63FQDw05+mPz/WTYem7H5ub29P3jVtvgykXQ/Y9yVU7+bBBx9M0ptuumkqT+uL5iH03j492e222y5JW52b4cOHJ2k9Fnx94fsOxBC8wBGRJgDD6oetzrl23/mEEEIIIT1Flzo4InKoiDwOYAWAN+p/K0TkcRE5pOwKEkIIIYTkpato4icDuAbALQAmA/hnPWtjAKMBTBWR8c65G2IrUPTWoW+L3HpJ9EXBjsE+izaFtNF4DzrooCR99913F1oPwO8Bsgh87RwTHdYnqrH30tvUd9xxRyrPRrTtYKeddgq+dyg+U3xfXtGR3n34zC6tWbX2Sty3b99Unm4jnzm5T/ym8Yku7XuqOfDAAzPLqALDhg3LjLrti16vsSKSLNHjrrvumjrWYikrUteuMs4///xUnvaeG9sn2h2AjeKux5FWJdBmzgDwyiuvZJav62XbsYj3cdmyZcmc61MPiL3Xvvvum6Svu+66VJ491oT2h44M4PNOfskll6SOJ06cmKStyPCCCy5I0rNmzUrSPpWA0DEeSlciqgkATnXO/aKTvLtE5EkAEwFEL3AIIYQQQoqmKxHVZgDmevIfA1DsNgghhBBCSIN0tcCZB2CcJ//k+jmEEEIIIesMXYmozgLwf0VkfwAPIq2Dsy9qOzxfzrg2oQiX/91tzhkjS37zzTdTx/fee2+S1ibpAHDNNdckae0iPQ9a1lu2zk0e83uf/oyWsfpMmPV11izy4IMP9le2jjWDLRqfPF0/m8/00ZYRo79kGThwYFA4Al/0bYvuN63TYcuIfc9vuummJL148eIkbc37Nb4x6Ws7q6OiGTJkSJLecsstU3la58Oa2Oq8Rt5F7VLDtmWo/obP3P+KK65I0tYUXJt/W521iy++OEnbiNVFcNlllyVpq/+1yy67dHqN7eN99tkn6F62XTvGzfTp04Ou7wytO5VnriwC3W957hUzv9jyffo/Ha4qgLQOjo+i9RO9Cxzn3KMisiNquzijAGxSz1oM4B4AP3fOLSi0RoQQQgghDdKlH5z6AuZ75VeFEEIIIaQYGg3V0G1Y0ztt/lqG2a1PzKLZbbfdkvQmm2ySyjv55JOTtI3cW4TH17LFUj5iPfxqcY3PTF9fZyOx+1i+fHmSfv7554OusaaJegs71BOzxdc3ZUcQ12IOKwaMdY3gaxNN6Htj0eKFzTffPPM8PRZsW/naTs8feu7QXmbzYM3v83iMDsVnLh+Ljl599tlnp/LWW2/t58BG8Pa5BihCrKo58cQTU8fWxUYH1oOvnm+7m9bW1sxnL6JN2tvX+tUdNy6tFqsjuNt76XEf++7rcTh0aDoyk66LrkdndWkUuwbQz5N1L0YTJ4QQQkjl4AKHEEIIIZWjx0VUenvTbsnqbf5QcUx3ezfVXnWvv/76VJ5vyzR0+67o7d+eRm/lhwa11BZnADB+/PjMc7WX1VCLHiteCPXA6/O66RObhopSYkWQS5cubVhsW4QINc94fe2115K09mprLZk03R2412dFVVSQwCL6zkesSE5f5ysjtk9OOOGEJP3000+n8nQQR532if21Z16g5mk4i97gEVuLEydPnpzK83lNDxHjAP42GDlyZGae9nh9++23Z56n8c2vVgyl5+YYERt3cAghhBBSOYIXOCKyhYhsan7bVES2yLqGEEIIIaQnyLODswDAw+a3RwC89sFTCSGEEEJ6jjw6OMcDsLaQEwEM6eTcFNqEzsr6tN6N1TmI0Uco24ukLV/LgYcPH56ZF4qVQcbo6uS5zofP9DGW0H487rjjkrT1bKrlzIccckgq77333kvSWkfGtqtPvyt0zITqEOWhCNP/fv36JS4LbMTlrbbaKknbaOL6OFRe7/NUvWDBglSe1t2w5Wuvui+99FKS1lGsO7tfKFnm69psGvigbo3Gp4PTqKf2MtGem6+++uoeq0dLS0uStno8um2HDRuWytNj9qKLLsos3zdmfXoqRby7zc3NKX1MTRE6VT6XF9/+9rcz7+V7Nj0WtKf9hx9O72NY3VKNdtug595YfPOfHTMhkceDFzjOuZs7+e2e0OsJIYQQQrqLKCVjERkgIl8SkWwTB0IIIYSQHkJCvMSKyM0AnnTOXSsi/QD8EcBwACsBHOqc+20X12feRG8d2m1evQXl2+bLE8xQE2OCbb0VX3jhhUnaBq+zIiuNrrM2hfN5SI0VpUyZMuWPzrmdg05WtLS0uI4AcrGEtqvehgaA730vOzrIPfes3Ti025ZZ26R5tqF9YzLUU68+zyce64KofvO9b0VjA1fq/rDiH83EiRNTx7r/77jjjiRtxZOast0m2GfTdZwzZ04qT5shL1u2LKrfgPL7LtQreBlegX/yk58k6QEDBmSed+2116aOtThQB+W0aDHXmDFjUnmhY8U5l1+vAMDQoUNdmSIqzUEHHZQ61kGdLb5gmJrW1tYkbceIFhn61C6sJ+MsivLkbr7fnb5zoTs4+wF4op4+CMAGqAXevLD+RwghhBCyzhC6wNkIwFv19BgAdzvn3gIwFUCc5yhCCCGEkJIIXeAsBrCjiDShtpvzUP33QQBWlVExQgghhJBYQq2ofglgGoA3ALRjrT+cXQG82EgFfFF4Q2WXoef5ZH+hZtZvvvlmZhl5zMJ1nX310udZXQ6to7SumqmGylvPPPPM4DJHjRqVpH394atHrP5GqN5TaEiH3o6NLK11cHx6ENb0VJuDa/PVs846K/PeefrUZ+IdijZ7t7pyvlAAeRg0aFDiGt+2bSx23gihCHcbPvPiPLzwwgtB5+l+tfpfRel9ZNGnT59kDi5bNzkgKTMAAAnISURBVOy+++5LHesI31OnTk3lbb311kl60qRJmWXa8Bia3XbbLUnbMamjnIfq4FhC58eYMRi0wHHO/VBE5gHYAsCdzrmV9azVALK1vgghhBBCeoA8fnDu7uS3W4qtDiGEEEJI4wQvcERkJwDfxVql4hcATHbOPdNIBWIihOahiGjcPtPKvffeO6pMTWi9fObFDZgiZ7Jq1arE07SvvO6Oxtvc3JykV65c6TlzLZdeemlw+UOGrHXOPXjw4MxyrrjiiiR9xhlnBJd/4403JumPfOQjwdeF0tTUhEGDBgH4YDRu671Y4xPjhIp49Fi2XpQ11tRVjyFtJl7Udn+MCMm6LtDiYj1GYssviyLmgt/85jeZedZ7eAw6CjWQFlFaUYf21BtKHjFkx9ibPn167vt00NbWhvnz5wP4oHsBjXVroce3vS5URLl06dIkPXr06FSefq98YiiNfW91pPcy8KmY+NoyZG4IUjIWkaMAPAVgUwAP1P82BvCkiGQ7oSGEEEII6QFCd3B+BOA859wl+kcRmQjgYgDFejIihBBCCGmAUDPxFgB3dPL7nQA+Wlx1CCGEEEIaJ3QHZxaAvQD8zfy+F4BHG6lAqOt7G44hxv11HrPSq666KqjM2bNnZ+b5QkhkEevWu4hI7JZly5YFXRsaedqXN378+HyVy8DK9juwLsu1noc1NZ83b16SPvLII73ldNDU1BRcR33uwoULU3k/+tGPknQR+ifWDYPWHcmjnxNjWu275gc/+EFmXhHmxSNGjEgd63bQ+jJWl8aGW9H43qki3jcAWL58ecPm4bbPtR7DRz+69v+jb731FrKwejYxz2TfxTVr1mSe++qrrybpj3/846m8UD1K/ZzWjL9sdL/5vjNWB6cIHVEfMWXa99b3Ht9www25y89Do+9C6ALntwAuFZGdsTZkwygAhwG4UESSgEXOuXhNLUIIIYSQAghd4FxT//ek+p/mpyrtAIT/V5YQQgghpARCHf2F6up0SR6xTRHbd7HXZXkGLsL8GvB7cNborW+77eoTZxVRz2HDhqEjmniHuXgHZZv3h5IlkuqKr371q1HXrV69OknrLXftfRcATjvttCT9+c9/PpV36623JumivNVmYceM3m72iaSsiEeby+sy8piT33XXXZn30xHiZ82alXleKNbUNetZfWI6W3/9Llrz1YEDB0bV06LfOTt36Xv6xo3N0/VesmRJktZuDgBgwoQJmWXqMmy07yz69El/NvS9L7jgglTe5ZdfnqR93sl93wT93LZ/ipq3s9D95sPnITrW27L+ptrvShHP/a1vfSszLzQ6faxnd9+zhYiFC1u4EEIIIYSsK3gXOCLyexHZUB1fKiLN6niYiMQFdiGEEEIIKYmuRFSjAPRTx98GcAOADteJTQA2y3NDK1bRW1Cx23dafBK7JTdx4sTU8cknn5ykX3755SStrW+6whcAU+f5tuy0iCHWwqoI8oikYsSC9hr93H379s287lOf+lTqePjw4UnailmysAHq9FiwYoq2trYkrYMA2j4cO3Zskn7iiSdSedtss01QvWIZMGAAPv3pTwP4oDdebbUV633XZ1Wh29yed/jhh2ded+KJJ0bVJYvQ8WrHyJw5c5K0tbDSW+TWGsYGd4zlnXfeySwrVJwZanF6zjnnpI5/+ctfJunTTz89lefzNBwqstIWglbkHYPPWqls0W8sPu/ePnzfPz33FCGSsp6w+/fvn3mung99hIqlfIFh7bOFeNDPK6IKD5dNCCGEENJDUAeHEEIIIZWjqwWOq//Z3wghhBBC1lm60sERALeJyPv14/4AbhCRDoFa7lDIVj6sdVFio1JreXseUzttImrNRTWPPrrWWfOAAQNiqhislxLq+beMe69LhJrRa9NTANh+++2T9LPPPpt5nW4TK/eNiSrck/pRln79+n1A96YDn96NTz/g+eef7/R3a46rzax9UaCtHsfbb7+deW4ouh+t3pPVp+lA69wAfv0i3QZWB2fMmDFJupH3bfDgwUlZRbu46IoXX3wxSY8bNy6Vd++99ybpAw88MJV33333dVqeSFqj4fzzz0/SixYtiqqjj56cH9esWZPomdj2j62X1lPy6ZQV8Q310dzcnJl37rnnBpWh6+hr7zw6RCH91tUC5xZz3Nks/qvQChFCCCGEdAfeBY5z7rjuqgghhBBCSFGEhmooDLuVX8aWWhbagywQFwzTRxnipZ6ktbU12QbMU/8Yk36fyWfZW892+zfUa+i6JJbStLe3B4n4rNhGi6+sKEsHHdXtY0VX+rxVq1al8rTrhSLEArb+WrykPS8DSMzmgXgTYp94z4rEisCOe20+rkVilqJM1rPK9JWvxYR2DJ533nlJuoz3vSdZuXIlFixYAOCD4stYdPv1pOf4uXPnJmlr3q/dYfje6dCAzNOmTUvl6Xc8JuAvragIIYQQUjm4wCGEEEJI5eAChxBCCCGVQ0KjgTZ0E5HCbxIjp7XhGLJMaS2TJ09O0tqUsrtpQG/hj865nfNeFNpv1sw6Rl7sC9MRS6jc13ddGfoA2m251VMwoTmi+m399dd3HeEgfBHDLdpM3Op46HK0vok1LffphmjKcF2gTdbtux1qphqLiWwc1W8A0NLS4kKiUvvojW4hQvFFVPdFGg95j6dPn44lS5ZEeevXc6V1neDT+YqNIJ5Vhu+5Y+fD3sCUKVM6fee4g0MIIYSQysEFDiGEEEIqR7ebiRdF1nZbUVtt1gtnCHnuHbON7DOtLJuytzBDTcsBvwgsVLzUk2apWlwS63W2DELFS5o8EclDIwrHos1Iranu/PnzO73GithiTFGBYqI4A2nXDBYt+gh9Pot2c9Bh1tzBuhqBW+OrY6iZsn3fO8blmjVrGqxdDds3WlxqXUv46qzFnnrOs9fEPrfOW1fM9O04btTknjs4hBBCCKkcXOAQQgghpHJwgUMIIYSQytFrdXCyiHUXXfa9u7v8UPPAsu8dWqbvGqvfEBouwWeO7UPLvnuj2eXAgQMxatQoAMDChQszz9NhFbpCm4ZnRebuilCT9REjRiRp229aR8bW31evLN2NonRwugP9DHn0ZfT7onW+rCl9USEGQrDvTk+GQOkYY+3t7aXfy7rU8OluFaXX1UHsd6Bsdxu+8hvVC+MODiGEEEIqBxc4hBBCCKkc3eXJeAmA7L1yUjZbOuda8l7Efutx2G+9k6h+A9h3PQz7rffSad91ywKHEEIIIaQ7oYiKEEIIIZWDCxxCCCGEVA4ucAghhBBSObjAIYQQQkjl4AKHEEIIIZWDCxxCCCGEVA4ucAghhBBSObjAIYQQQkjl4AKHEEIIIZXj/wM7atKXOOXTywAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x1152 with 40 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# testing adversarial attacks\n",
    "# we will use 100 test samples as examples\n",
    "x_test, y_test = next(iter(test_loader))\n",
    "x_test = torch.tensor(x_test, device=device)\n",
    "y_test = torch.tensor(y_test, device=device)\n",
    "\n",
    "# define some helper functions for the attack\n",
    "def run_attack(model, x_test, y_test, predict_func, epsilon_list, T):\n",
    "    acc_adv = []\n",
    "    pred_adv = []\n",
    "    prob_adv = []\n",
    "    x_adv = []\n",
    "    for epsilon in epsilon_list:\n",
    "        lr = epsilon / T * 1.5\n",
    "        x_adv_ = pgd_attack(x_test, y_test, model, predict_func, \n",
    "                           epsilon, T, lr, x_min=0.0, x_max=1.0)\n",
    "        # get the accuracy on crafted adversarial examples\n",
    "        y_pred = predict_func(x_adv_, reduce_mean=True)\n",
    "        pred = y_pred.data.max(1, keepdim=True)[1] # get the index of the max probability\n",
    "        acc_adv.append(to_numpy(pred.eq(y_test.data.view_as(pred)).float().mean()))\n",
    "        pred_adv.append(to_numpy(pred))\n",
    "        prob_adv.append(to_numpy(y_pred))\n",
    "        x_adv.append(to_numpy(x_adv_))\n",
    "    return np.array(acc_adv), np.array(pred_adv), np.array(prob_adv), np.array(x_adv)\n",
    "\n",
    "# helper function for visualization\n",
    "def visualize(images, epsilons, pred_clean, pred_adv):\n",
    "    cnt = 0\n",
    "    plt.figure(figsize=(8, 2 * len(epsilons)))\n",
    "    for i in range(len(epsilons)):\n",
    "        for j in range(len(images[i])):\n",
    "            cnt += 1\n",
    "            plt.subplot(len(epsilons),len(images[0]),cnt)\n",
    "            plt.xticks([], [])\n",
    "            plt.yticks([], [])\n",
    "            if j == 0:\n",
    "                plt.ylabel(\"Eps: {:.2f}\".format(epsilons[i]), fontsize=14)\n",
    "            plt.title(\"{} -> {}\".format(pred_clean[j], pred_adv[i, j]))\n",
    "            plt.imshow(images[i, j][0], cmap=\"gray\")\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "# we first test a deterministic NN's robustness, by turning off dropout\n",
    "predict_deterministic = lambda x, reduce_mean: predict(dropout_bnn, x, K=1, use_dropout=False, \n",
    "                                                       reduce_mean=reduce_mean)\n",
    "\n",
    "def test_robustness(model, predict_func, x_test, y_test):\n",
    "    # first test clean accuracy\n",
    "    y_pred = predict_func(x_test, reduce_mean=True)\n",
    "    entropy = -(y_pred * torch.log(y_pred)).sum(-1).mean()\n",
    "    print(\"entropy (clean)\", to_numpy(entropy))\n",
    "    pred_clean = y_pred.data.max(1, keepdim=True)[1] # get the index of the max probability\n",
    "    acc_clean = to_numpy(pred_clean.eq(y_test.data.view_as(pred_clean)).float().mean())\n",
    "    pred_clean = to_numpy(pred_clean)\n",
    "    print(\"clean accruacy for this batch: {}%\".format(acc_clean * 100))\n",
    "    \n",
    "    # now run the attack\n",
    "    T = 10\n",
    "    epsilon_list = np.arange(0.05, 0.41, 0.05)\n",
    "    acc_adv, pred_adv, prob_adv, x_adv = run_attack(model, x_test, y_test, predict_func, epsilon_list, T)\n",
    "    print(\"adv accruacy for this batch (%): {}\".format(acc_adv * 100))\n",
    "    # visualise the adversarial examples\n",
    "    visualize(x_adv[:, :5], epsilon_list, pred_clean[:5], pred_adv[:, :5])\n",
    "    return x_adv\n",
    "\n",
    "x_adv = test_robustness(dropout_bnn, predict_deterministic, x_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5XV-VU6IQqIi"
   },
   "source": [
    "Now testing the MC-dropout Bayesian CNN's robustness. \n",
    "\n",
    "In this case we will also look at the uncertainty produced by the BNN on the adversarial examples. Specifically, you will implement:\n",
    "\n",
    "*   total uncertainty: as the total entropy\n",
    "*   aleatoric uncertainty: as the conditional entropy\n",
    "\n",
    "Epistemic uncertainty is the mutual information which is essentially total entropy minus conditional entropy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "yrCJ71w3RA-_",
    "outputId": "07753092-3ac6-4877-cc2f-1c204fa66548"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:780: UserWarning: Note that order of the arguments: ceil_mode and return_indices will changeto match the args list in nn.MaxPool2d in a future release.\n",
      "  warnings.warn(\"Note that order of the arguments: ceil_mode and return_indices will change\"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "entropy (clean) 0.031681836\n",
      "clean accruacy for this batch: 100.0%\n",
      "adv accruacy for this batch (%): [95. 82. 32.  2.  0.  0.  0.  0.]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x1152 with 40 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total uncertainty (entropy): [0.11934087 0.3424316  0.52679455 0.25995743 0.0980616  0.0801347\n",
      " 0.07231481 0.11620294]\n",
      "aleatoric uncertainty (cond. entropy): [0.11407898 0.3258734  0.50757354 0.25181732 0.09615013 0.078594\n",
      " 0.07047283 0.11284649]\n",
      "epistemic uncertainty (mutual info): [0.00526189 0.0165582  0.01922101 0.00814012 0.00191147 0.0015407\n",
      " 0.00184198 0.00335645]\n"
     ]
    }
   ],
   "source": [
    "# now we test Bayesian CNN's robustness, by using MC-dropout\n",
    "predict_mcdropout = lambda x, reduce_mean: predict(dropout_bnn, x, K=5, use_dropout=True,\n",
    "                                                   reduce_mean=reduce_mean)\n",
    "x_adv = test_robustness(dropout_bnn, predict_mcdropout, x_test, y_test)\n",
    "\n",
    "# also compute uncertainty measures\n",
    "\n",
    "def total_entropy(y_prob_samples):\n",
    "    y_prob = y_prob_samples.mean(0)\n",
    "    return -(y_prob * torch.log(y_prob)).sum(-1)\n",
    "\n",
    "def conditional_entropy(y_prob_samples):\n",
    "    return -(y_prob_samples * torch.log(y_prob_samples)).sum(-1).mean(0)\n",
    "\n",
    "def compute_uncertainty(predict_func, x_adv, reduce_mean=True):\n",
    "    logs = []\n",
    "    for i in range(x_adv.shape[0]):\n",
    "        x = torch.tensor(x_adv[i], device=device)\n",
    "        y_prob_samples = predict_func(x, reduce_mean=False)\n",
    "        entropy = total_entropy(y_prob_samples)\n",
    "        cond_entropy = conditional_entropy(y_prob_samples)\n",
    "        if reduce_mean:\n",
    "            entropy = entropy.mean(); cond_entropy = cond_entropy.mean()\n",
    "        mutual_info = entropy - cond_entropy\n",
    "        logs.append([to_numpy(entropy), to_numpy(cond_entropy), to_numpy(mutual_info)])\n",
    "    return np.array(logs)\n",
    "\n",
    "logs = compute_uncertainty(predict_mcdropout, x_adv)\n",
    "print(\"total uncertainty (entropy):\", logs[:, 0])\n",
    "print(\"aleatoric uncertainty (cond. entropy):\", logs[:, 1])\n",
    "print(\"epistemic uncertainty (mutual info):\", logs[:, 2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "G6910FAQ9gOf"
   },
   "source": [
    "And similarly for the MFVI Bayesian CNN, test its adversarial robustness."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "KBVKHfvdCRkR",
    "outputId": "303340b0-5c07-43b7-f328-717065140211"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:780: UserWarning: Note that order of the arguments: ceil_mode and return_indices will changeto match the args list in nn.MaxPool2d in a future release.\n",
      "  warnings.warn(\"Note that order of the arguments: ceil_mode and return_indices will change\"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "entropy (clean) 0.09574664\n",
      "clean accruacy for this batch: 98.99999499320984%\n",
      "adv accruacy for this batch (%): [91.99999 76.      44.      14.       1.       0.       0.       0.     ]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x1152 with 40 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total uncertainty (entropy): [0.26186725 0.53992885 0.7552825  0.7608884  0.67691636 0.5337778\n",
      " 0.4432854  0.4012931 ]\n",
      "aleatoric uncertainty (cond. entropy): [0.18721826 0.3845276  0.54575104 0.5465816  0.5060795  0.4052427\n",
      " 0.32411587 0.28704536]\n",
      "epistemic uncertainty (mutual info): [0.07464899 0.15540126 0.20953149 0.21430677 0.17083687 0.12853506\n",
      " 0.11916953 0.11424774]\n"
     ]
    }
   ],
   "source": [
    "# now we test Bayesian CNN's robustness, by using MFVI\n",
    "predict_mfvi = lambda x, reduce_mean: predict(mfvi_bnn, x, K=5, reduce_mean=reduce_mean)\n",
    "x_adv = test_robustness(mfvi_bnn, predict_mfvi, x_test, y_test)\n",
    "# also compute epistemic uncertainty\n",
    "logs = compute_uncertainty(predict_mfvi, x_adv)\n",
    "print(\"total uncertainty (entropy):\", logs[:, 0])\n",
    "print(\"aleatoric uncertainty (cond. entropy):\", logs[:, 1])\n",
    "print(\"epistemic uncertainty (mutual info):\", logs[:, 2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-XNhVvJnf6NG"
   },
   "source": [
    "## Can ensembles help?\n",
    "\n",
    "From the above examples we see that the trained BNNs help a bit in adversarial robustness but not too much, especially for the MC-dropout one. From the printed uncertainty measures, we suspect the trained networks may be still not uncertain enough on the adversarial examples.\n",
    "\n",
    "Next we will train an Ensemble of MFVI-BNNs. This means we will train multiple MFVI-BNNs independently with different random initialisation of the variational parameters, and then ensemble them together to get the final model. Although diversity is not explicitly encouraged, the hope is that the randomness in initialisation and SGD steps would allow different independent runs to result in different local optima, resulting in a more diversed set of neural networks.\n",
    "\n",
    "Please run the code in below block, it might take a few minutes to train the MFVI-BNNs in the ensemble."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "FdvAe0Smf8w6",
    "outputId": "da033c9b-d4ca-4020-93be-221337ca1a37"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train a new MFVI CNN (network 2)\n",
      "Sequential(\n",
      "  (Conv0): FFGConv2d(1, 32, kernel_size=(3, 3), stride=(1, 1), padding=same, prior mean=0.00, prior sd=1.00)\n",
      "  (Nonlinarity0): ReLU()\n",
      "  (Pooling0): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=True)\n",
      "  (Conv1): FFGConv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=same, prior mean=0.00, prior sd=1.00)\n",
      "  (Nonlinarity1): ReLU()\n",
      "  (Pooling1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=True)\n",
      "  (Conv2): FFGConv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=same, prior mean=0.00, prior sd=1.00)\n",
      "  (Nonlinarity2): ReLU()\n",
      "  (Pooling2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=True)\n",
      "  (Flatten): Flatten(start_dim=1, end_dim=-1)\n",
      "  (Linear): FFGLinear(in_features=2048, out_features=10, bias=True, prior mean=0.00, prior sd=1.00)\n",
      ")\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:780: UserWarning: Note that order of the arguments: ceil_mode and return_indices will changeto match the args list in nn.MaxPool2d in a future release.\n",
      "  warnings.warn(\"Note that order of the arguments: ceil_mode and return_indices will change\"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1, last mini-batch nll=0.10069575905799866, acc=0.9700000286102295, kl=322410.0\n",
      "Epoch 2, last mini-batch nll=0.029820876196026802, acc=0.9900000095367432, kl=260510.546875\n",
      "Epoch 3, last mini-batch nll=0.09871918708086014, acc=0.9700000286102295, kl=204004.53125\n",
      "Epoch 4, last mini-batch nll=0.04804856702685356, acc=0.9800000190734863, kl=156878.75\n",
      "Epoch 5, last mini-batch nll=0.02621012181043625, acc=0.9900000095367432, kl=119781.921875\n",
      "train a new MFVI CNN (network 3)\n",
      "Sequential(\n",
      "  (Conv0): FFGConv2d(1, 32, kernel_size=(3, 3), stride=(1, 1), padding=same, prior mean=0.00, prior sd=1.00)\n",
      "  (Nonlinarity0): ReLU()\n",
      "  (Pooling0): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=True)\n",
      "  (Conv1): FFGConv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=same, prior mean=0.00, prior sd=1.00)\n",
      "  (Nonlinarity1): ReLU()\n",
      "  (Pooling1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=True)\n",
      "  (Conv2): FFGConv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=same, prior mean=0.00, prior sd=1.00)\n",
      "  (Nonlinarity2): ReLU()\n",
      "  (Pooling2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=True)\n",
      "  (Flatten): Flatten(start_dim=1, end_dim=-1)\n",
      "  (Linear): FFGLinear(in_features=2048, out_features=10, bias=True, prior mean=0.00, prior sd=1.00)\n",
      ")\n",
      "Epoch 1, last mini-batch nll=0.03743768855929375, acc=0.9800000190734863, kl=322481.71875\n",
      "Epoch 2, last mini-batch nll=0.04986201226711273, acc=0.9900000095367432, kl=260540.515625\n",
      "Epoch 3, last mini-batch nll=0.021377606317400932, acc=0.9900000095367432, kl=203769.203125\n",
      "Epoch 4, last mini-batch nll=0.03626023605465889, acc=0.9800000190734863, kl=156212.09375\n",
      "Epoch 5, last mini-batch nll=0.12863950431346893, acc=0.9700000286102295, kl=118506.0078125\n",
      "train a new MFVI CNN (network 4)\n",
      "Sequential(\n",
      "  (Conv0): FFGConv2d(1, 32, kernel_size=(3, 3), stride=(1, 1), padding=same, prior mean=0.00, prior sd=1.00)\n",
      "  (Nonlinarity0): ReLU()\n",
      "  (Pooling0): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=True)\n",
      "  (Conv1): FFGConv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=same, prior mean=0.00, prior sd=1.00)\n",
      "  (Nonlinarity1): ReLU()\n",
      "  (Pooling1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=True)\n",
      "  (Conv2): FFGConv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=same, prior mean=0.00, prior sd=1.00)\n",
      "  (Nonlinarity2): ReLU()\n",
      "  (Pooling2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=True)\n",
      "  (Flatten): Flatten(start_dim=1, end_dim=-1)\n",
      "  (Linear): FFGLinear(in_features=2048, out_features=10, bias=True, prior mean=0.00, prior sd=1.00)\n",
      ")\n",
      "Epoch 1, last mini-batch nll=0.04194052144885063, acc=0.9900000095367432, kl=322351.03125\n",
      "Epoch 2, last mini-batch nll=0.03986191004514694, acc=0.9800000190734863, kl=260487.46875\n",
      "Epoch 3, last mini-batch nll=0.05239361897110939, acc=0.9800000190734863, kl=204208.953125\n",
      "Epoch 4, last mini-batch nll=0.15761151909828186, acc=0.9599999785423279, kl=157246.28125\n",
      "Epoch 5, last mini-batch nll=0.025944700464606285, acc=0.9900000095367432, kl=120404.96875\n",
      "train a new MFVI CNN (network 5)\n",
      "Sequential(\n",
      "  (Conv0): FFGConv2d(1, 32, kernel_size=(3, 3), stride=(1, 1), padding=same, prior mean=0.00, prior sd=1.00)\n",
      "  (Nonlinarity0): ReLU()\n",
      "  (Pooling0): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=True)\n",
      "  (Conv1): FFGConv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=same, prior mean=0.00, prior sd=1.00)\n",
      "  (Nonlinarity1): ReLU()\n",
      "  (Pooling1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=True)\n",
      "  (Conv2): FFGConv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=same, prior mean=0.00, prior sd=1.00)\n",
      "  (Nonlinarity2): ReLU()\n",
      "  (Pooling2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=True)\n",
      "  (Flatten): Flatten(start_dim=1, end_dim=-1)\n",
      "  (Linear): FFGLinear(in_features=2048, out_features=10, bias=True, prior mean=0.00, prior sd=1.00)\n",
      ")\n",
      "Epoch 1, last mini-batch nll=0.11387774348258972, acc=0.9700000286102295, kl=322786.84375\n",
      "Epoch 2, last mini-batch nll=0.13871386647224426, acc=0.9599999785423279, kl=261127.609375\n",
      "Epoch 3, last mini-batch nll=0.048318635672330856, acc=0.9900000095367432, kl=205317.1875\n",
      "Epoch 4, last mini-batch nll=0.0341961495578289, acc=1.0, kl=158691.265625\n",
      "Epoch 5, last mini-batch nll=0.06055455654859543, acc=0.9700000286102295, kl=122037.7109375\n"
     ]
    }
   ],
   "source": [
    "# train an ensemble of MFVI networks (independently) and see what happens\n",
    "mfvi_bnn_list = [mfvi_bnn]\n",
    "N_ensemble = 5\n",
    "activation = 'ReLU'\n",
    "learning_rate = 1e-3\n",
    "N_epochs = 5\n",
    "beta = 1.0\n",
    "\n",
    "for i in range(1, N_ensemble):\n",
    "    print(\"train a new MFVI CNN (network {})\".format(i+1))\n",
    "    net = make_bayesian_cnn(in_channel=1, n_class=10, dropout_prob=0.0, \n",
    "                            activation=activation)\n",
    "    bnn.bayesianize_(net, inference='ffg', init_sd=0.02)\n",
    "    net.to(device)\n",
    "    print(net)\n",
    "    # start training\n",
    "    opt = torch.optim.Adam(net.parameters(), lr=learning_rate)\n",
    "    logs = train_bnn(net, opt, train_loader, device, N_epochs, beta)\n",
    "    mfvi_bnn_list.append(net)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Uct-1Us2MuzT"
   },
   "source": [
    "Training is now finished, let's see how the ensemble performs on clean data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "pg3t3s-Gjsu-",
    "outputId": "9cd5548b-3b8c-4eca-d36e-e32b640981e7"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:780: UserWarning: Note that order of the arguments: ceil_mode and return_indices will changeto match the args list in nn.MaxPool2d in a future release.\n",
      "  warnings.warn(\"Note that order of the arguments: ceil_mode and return_indices will change\"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy: 99.0199966430664%\n"
     ]
    }
   ],
   "source": [
    "# test the ensemble on clean test data\n",
    "def predict_ensemble(x_test, K=5, reduce_mean=True):\n",
    "    y_prob_samples = []\n",
    "    for net in mfvi_bnn_list:\n",
    "        y_prob_samples.append(predict(net, x_test, K=K, reduce_mean=False))\n",
    "    y_prob_samples = torch.concat(y_prob_samples, axis=0)\n",
    "    if reduce_mean:\n",
    "        return y_prob_samples.mean(0)\n",
    "    else:\n",
    "        return y_prob_samples\n",
    "\n",
    "accuracy = 0\n",
    "K = 5\n",
    "for x, y in test_loader:\n",
    "    x = x.to(device); y = y.to(device)\n",
    "    y_pred = predict_ensemble(x, K)\n",
    "    pred = y_pred.data.max(1, keepdim=True)[1] # get the index of the max probability\n",
    "    accuracy += pred.eq(y.data.view_as(pred)).float().cpu().sum()\n",
    "accuracy = to_numpy(accuracy / len(test_loader.dataset) * 100) # accuracy in percentage\n",
    "print('Test Accuracy: {}%'.format(accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4vuukXBVMzfb"
   },
   "source": [
    "Then we perform PGD attacks on the ensemble BNN."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "m4_eVAjYjpxJ",
    "outputId": "9c73ad26-e767-4f05-cd9f-5607d889622c"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:780: UserWarning: Note that order of the arguments: ceil_mode and return_indices will changeto match the args list in nn.MaxPool2d in a future release.\n",
      "  warnings.warn(\"Note that order of the arguments: ceil_mode and return_indices will change\"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "entropy (clean) 0.10990928\n",
      "clean accruacy for this batch: 100.0%\n",
      "adv accruacy for this batch (%): [94. 77. 42.  6.  0.  0.  0.  0.]\n"
     ]
    },
    {
     "data": {
      "image/png": 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kz4jZmhgmInMAfAjAe0KaDaD1KJWEEEIIISUSs8D5Z/O/APgMgL+bvLUBvN5bASFPxkXZYYd5qj9HHHFEkvbb1ta08v7770/lvfnmm0n6hRdeyLxXFVF3i7yPkAldFSLAKtrNsvTSSydp/46tGelBBx2Uyps0aVKS9vXrjn4OAGeeeWaStiIpIL19bs0ZPX57Ocsc0W8bx5pL+/K9SKxs7FZ9aGve97VYEVxRNwpZ5a+zzjqp42984xtJ+sorr4wuP8sM3bdnfzLTD/UFL6Ky2/zeI28RUfY3v/nN1PF+++2XpD/96U9nXhcSuWywwQaZdZwwYUKSfuKJJ6Lq6AnNVdaMPmsMt4KIJPcI1cN+HwHpfh8SO1pRcCii90477ZTKa6eI6vbbb08dH3/88bnL8CLvWNcFWfS2wNnaHb/pjlcCcGlLNSCEEEIIKZngAkdVH+wl/xflVocQQgghpHWizINEZAiAjQB8svnRJAB/U9X2RpgznHPOOUl6pZVWirrmq1/9aur4/fffT9LPPPNMKfXKwloQAMBzzz2XpG2wOrtV6/Hbv3Y7z29vVmm11gpWXPP4448n6c022yx1nm2bqVOnpvJCYgRrOfWlL30p8zwrSvHbxkUo6pHbi2a899oiiEhi2eYt3Oz9vAgk5CXW9q929i1rlQOkRZDXX399dDlWTNPuwJh56OrqSsZ56D17MYVt1zL60M9//vNC14XqbD3deq+31lLyuOOOS+U98MADhepiCYmluuck72U/D6qavPdQEFD/fuyxvy7LetfP9aNHj07Sm266aSqvaDsWwVujrrXWWknafkd7y1c7Hr1Iyn5fhKwys8Z0cIEjIoMB/BTAEQAWAtA9cjoBfCgivwFwgqr2H8E1IYQQQj729PZT7KcA9kJjgbOUqg5W1cEAlgJweDPv7MD1hBBCCCFtpzcR1ZcB7KuqKTMTVZ0C4Hci8jaA3wP4Zk8XE0IIIYT0Bb0tcBYGMCWQPwUV+sGxpn3e/PS73/1ukl5qqaWS9DvvvJM6b9iwYUl6/fXXT+VtvfU8IzEfafW1115L0ssvv3xUfb1Ow+TJk5P0qFGjUnmf+cxneqzzt7/97ah7Aa2b0JVFGZ6kvbfiECF5+ve///0kvfLKK2ee9/DDDydpqwvUG1ZPJfT+7TvxOkPWRDZP9NxYVDXpi3YMeUI6Nx6rK2CfJ49ZuH1fXhch6z2ccMIJqWPbT0LmxL5PZtW5P5mFA2n9KU+oLX0ZlljT8FidppAuhK2jP8/Oh4sttlgqz47V2267LZVndTvK0C+qAhFJ9KCst3MgPd692bbNC7WvfZfrrbdeKu+OO+5I0n5OsvNh7Dzt37HV+QmNd+/JOBZbL69nWtRzdTe9iajuB3CuiCzjM5qf/RTAfb3ehRBCCCGkjfS2g3MMgDsBvCoizwF4q/n5SABrAngWQHaQCUIIIYSQPqA3Pzivich6AHYAMBrzzMQfBvBXAGNVNdv9Yg/4LS67Lee3xkLeU//0pz/luS0A4He/+13q2IqvNtxww1TeY489lqQ/+9nPRpX/0ksvpY6ff/75JD1+/PhUnt12feqpp5J0SGyQRyRVtudOa54LtNdU2G9T2n6xzz77pPJOO+20qDJOOumkJJ1nS9+eGxJDWXyft8e+XmV5Hm1X++TZQrail9B2vDUptaJcID2m8ohGs7bWfT3s8xQV9bSCiCR19fNhSNRh6+bzsrwLb7nllqljK7L3WM/fiy66aOZ51rv0PfekYzDbMrw3ZDsePUceeWSS/u1vf5uky/JIXIbbAGsmHgq2GTKDDmH7op/jbPnbbrttXIWR7W3d95dQHa34aquttsrMi8WLqu0YyCO+6qZXPzjNBcxdzT9CCCGEkH5P//QGRwghhBDSAi0tcETkuWakcUIIIYSQfkNUqIYAFwIY3ttJHR0diWwtFJk7NmJxWVjz7FBE6Vh9Hy8T/PznP5+krb4PAPzjH/9I0tYVeej9FDXHLkNW7csImTeXQWw07o033jiqvPPOOy91bE2MQxFsY9+51/GINaUORSEvih1vVWCfx5s0h/qvdaMQ0sH51Kc+lZlX9RwRam/bpv65q3DZ4J/V6tb492x1FUKhR4YPnzddX3ppOk6ynaOsvgyQ1rt5++23U3k33XRTkj7mmGOStB9Xts4TJ05M5R166KFJesSIEak86xLEvvfzzz8f/QVrJh7S1crjVsGy9957J2kfjsHqd3pdT0vRcDIhTjzxxCTtdW7uv//+JP3uu++Wfu8YWlrgqOoFZVWEEEIIIaQsohc4ItIJYMnm4RRV7Z8elwghhBDysafXBY6I7A7g2wA2NufPEZHHAZyjqrdWWL/ceG/CWV5Bq2CVVVZJHd9www1J2psOXnLJJUnae1/OIuRBtAoT1o6OjsQ8vCyTzCxC25T+3nfdNc+gL2QWabewzzrrrMzzQuIFb/4du8VcdCvaikj6c9TrbkIiKU/sWFx33XUz884+u+9C34XM+9uBfdchj74hM3HrZXe11VbLLMObgo8bNy5Jf+lLX0rl2ejQsSJd77n88ssvT9JW7AGkXVRYz7wPPfRQ6rwXXnghSfsxHZoru/PKmkNDYyLWG7XHiqh8+RdddFGSDr1/P4/a76TY+cq6cACAAw44IEn7Pmnb1M6j7fRGHVQyFpGjAFwP4J8A9gewVfNvfzSc/F0nIkdUW0VCCCGEkHz09pPqBADHqOqlPeTdKCLjAJwM4JIe8gkhhBBC+oTezMSXBfDnQP5fAMwXp4oQQgghpC/pbQfnWQBHAzguI/+o5jmFsXLPPPL8LNqpc+M59thjU8f2ebyeTZY5Xx45cOhcr4vUKkVlxyFC9bcmsiuuuGIqz5pJeh0Z+9w/+tGPknSekAL2XOse32P7Wqjvhu7tr/ORiOtEqA/ZNrUmw3//+99T540dO7b8ihUg5BagHfh+EwrNYc21R44cGVW+d5thdUCKPqvVBfJuDG6//fYkvc0226TyYkPlZIVR8XgdkKxQFnnpNpP281rs3On1hpZZZt7ewejRo5O0nyOuvvrqXPXsiZCOpX2XRx11VCpvySWXTNLPPfdcKs+O1bLecV56Ww0cD+D/icgYAGORDra5PRo7PDtVVz1CCCGEkPz0FmzzQRFZB41dHBtscxKAWwFcrKoTK60hIYQQQkhOYoJtTgSQHeo1AlVNRAd5PJ/arVC/BWuPrQfFdntD3myzzZL0wQcfnHnerrvumjp+8sknk7TdAszjEdVuPXtRSru3zIswc+bMzDy7jXzLLbek8kJeeq+44ook/dZbb2WeF8L2Ie+d0/a7WJGq3y63W9je62xZ5vjd9yhD7NsONt988yS9xBJLJGnrEgCo3l1BLF7s0O7xlidafJE+sN1226WOi3pRt4TG7aRJk5J0aK63WJNxANh///2TtBcL2/by4vtucWOrYv3uenoRVeg7yc5z/rpp06Ylaetl+uKLL26pnnmxdQy14bPPprVVqhZLWTFx1vhjsE1CCCGE1A4ucAghhBBSO9picqSqiQglzxZzSFRgt/OsqCbk7bcK1l9//STtLStskE4rkvIUDdRXRYC/svFbxXarO2RFtfvuuyfppZdeOvO8xx57LHVsA/MVJeRps6+sAfLSqojK9y1ruRHyehprrebPW3bZZZO07Rc33nhj75XtA/qLqKyb2OC0Rx99dJIu6qW7CnbZZZckveGGG6by7Nxv06GxHnoHWSKwsjzs5hlzocCodgw+//zzSXqHHXZInRcjqgHmFy9lzWX+3S211FJJ+rDDDkvl2XcWCvRZBVbsSBEVIYQQQj42cIFDCCGEkNoRvcARkRVEZGn32dIiskL51SKEEEIIKU4eHZyJAMYDWMt8dh+AUQCCttk2KrWX+1l5qJejxcpErXlfyHyyDBNyL8ccM2ZMkvam2j/84Q+TdEgmXFRvweoeeblvf41EnVUvK+cFgG9961tJevjw4ak8q6Nx5513ZpZvZbRFdb/aGfkWKCeauKom76ionoUfR/b9hd6lLT/kwdVHst5iiy2S9IQJE5L0n/+cjhTTl3oiFj+XlGUmrqpJWf5ZQ/3B1id0njXHD3l9t3NLWVivt+uss04q7zvf+U5UGZMnT07SoXeep99neSAuSsi1hK9X7D2tDuc+++yTynviiSeS9AknnBBdT6vbuMYaayRpP99uvPHGmWXYvlbULUdRYubHPAucQwFMc5+dDGCxHGUQQgghhFRO9AJHVa/o4bNbS60NIYQQQkgJFDITF5GFAXwOwL9U9ZU813oRj91m8tuiVuwS2o6yW+ZleNwMccABB6SON9hggyTtva7arcOihJ7Nbm/2pefiMsRh3vxw3XXXTdJerPnQQw8l6TPOOKPle/cncV4ZdVHVTBPQqs2bbT8Miai+8pWvpI6tiNKKHfOIpKw4sQpz/naILkUk85nt/OjNi0P1sfW2ZfhrrMgqy9tvK5x22mlJ2gZU9VgPvgAwceLEJG29xf/zn/9MnRc792epKpTlUiSPl+lYjj/++CTt222//fZL0tdff30qL1YENmXKlMxrrGjx3XffTeUtttg8Ac7dd98dda92EtUSInKFiBzTTC8AYBwawTcnNANxEkIIIYT0G2KXmjsAeLSZ3hXAUDQCb57R/COEEEII6TfELnCGAXi7md4RwE2q+jaA65C2qiKEEEII6XNidXAmAVhHRN5EYzfnyObnQwD0qvwRMn20cnovA7WywJD+iXVpHQoNUBTrOty7B7cyyXPOOadQ+fa5rUt8ICzPtdd5k88ydC26urrapp9iZcxAWlfLP9uxxx7b8v2KmpCXreNVxfvt6OhI3l8V7gNiXcPHluEJRSwOYXUrfPll6Ki1w2VAaMzZvufHRKhuNs++l5CZuB8TsTo4dl72Ohmrr756kvY6UqExaHVtQiFvYsNVeLrfT1lm4r6cmTNnZubF8vrrryfpL3/5y6k8+5206qqrpvKWWWaZqPIvv/zyzLzzzz8/Sfu5twy9t1DfCo3bGJcasQuc3wK4HsAbAOYCuLf5+X+h4RuHEEIIIaTfELXAUdXvi8izAFYAcIOqdnu0mwPgrKoqRwghhBBShDx+cG7q4bMrY64VkWQ7NLSN5bcpF1pooSTtTfvs1rvdqirLPNTez27R+XrYaNbPPfdcZnneBN6K1ey2pd82ts8ZipTuRRFVm7Ta+3V7qe4pL1YkEvJW7CNb22ezfcRfFyJWLLX44ounjm07jhgxIknvv//+qfNGjhyZpP07sO/rkEMOiapHHrq6uiqNNB8r7gm1/a677pqZd8cddxQqsw6ISCKm9uLpLFGTPw69o1hTaBvdG0iLDS+55JJU3qKLLhpV5nvvvZekfR+yz+rLO+KII5J01W5AiqKqSftkRSsHqpmLX3jhhR7TZRGaK607AWtODsSPVVuG/560766Iq4I8sag2FJGrROTx5t/VIrJh71cSQgghhLSXWD84+wN4DMDSAO5s/o0EME5EDghdSwghhBDSbmJFVD8CcJqq/th+KCInA/ghgGvKrhghhBBCSFFiFzgjAPyhh89vAHBaD5+nUNX5XH93Y2W7oUirXqcgy0QsZE4ekj973ZpbbrklSa+88spJ+sUXX0ydZ0MF+PJDEXltPe1zh+qYx5W41eWpQu5r9Uiq0Pew7eb1asaNG5ekhw4dmsrzkXy78bpZ1113XZJ+8803U3lWf2bfffeNrHE8Vsfg73//eyrvF7/4Ren36y+sv/76Sdq+4xB9GYKkL1DVROchj9sMi5/L7Pi34RK8Lo3l9ttvj6sw0vOv1ZPwuhuxujpXXhml2lkYPx9mzRlF8e1mXX94/ZwqdeXKIvS9Y91t2JAaefCuUSy2P/nvgZh2i9XBuR/AVj18vhWAByPLIIQQQghpC7E7OHcBOFNENsa8kA2jAewB4AwR2aP7RFW9udwqEkIIIYTkI3aB020nfSTmeTHu5gKTVgDzhWq1Iipv1msJmYH5PCtysNu1IVNqjy3DRjMGgI033rjHa846K+32x4qs/L3ssd8WDXldLYPQVnEs1mTV17EMc137fv70pz+l8kaPHp2kfZ8Jva+sSMF+S//www9P0v5ZQlufVtRqz/Ntb7f4rRdSf9299/P6ingAACAASURBVN6LjwvHHHNMkvbm90888USSvu2229pWp6L4fmZFD2WJ1fxcZu9ZdPzdfPO835/nnntuKi9LbA7Ei1LsPOfHhDVhfuedd1J5NiL2+++/H3WvomTNEa0gIkm5fq633zNFvXT3JX7+KoKds61YKw8hVyhZxDr6Kz/+OyGEEEJIRXDhQgghhJDaEdzBEZFHAOykqtOax2cCOEdVpzaPlwTwhKquECrHBv/zW51WgzqPqCZrqy+PpZEN/nb99ddnnvftb387SVvrG4/fMrOWTFWIoWItxIoiIpn1DllxxG6f2/pvv/32qbwTTzwxSc+ePTuVZ9+z30q3osV99tknSYfEdHfddVfqOGQNcOONNybp8eOzw7CFLPvq7o03izFjxmTmhYL9tZOQCMGKGrzH8SpEVJ4yrCGnTp2apDfffPNU3u67756kv/Od76Ty7PNmWcQC4TnpBz/4QZK+6qqrUnlWbFHFXBmiez4paw713wP22XxeGZ6ZQ/0yNO9l9XVvcRpSK3nrrbdiqlhYLNUqve3gjAZgbbi+BsAKzzsBLFt2pQghhBBCWiGviKr8bQJCCCGEkJKhDg4hhBBCakdvVlTa/POf5UJVE7l0yLtvu7ERjVdaaaVUnpUl/+tf/0rS/SmabUhmXLaeRx7z+1AE9FjOPvvszHuFyrQy7oMPPjjqmjLaNPS+66BzkxXB3beNlfl7ufu0adOS9COPPJLKu+CCC9AXeH2PkH5XSD+nTNPj7ndadOzE8uSTT2Yejx07NpV35JHzvIP4SPDWrN+aoXvdOavb5sec1fuoWgcnz1xWBlU/T6hfxt7b691YTj311CTt2/T0009v+d5V0tsCRwBcIyLds9ZCAC4RkZnN477RHCKEEEIICdDbAscHBekpqOZVPXxGCCGEENJnBBc4qvqVMm5iPRn77UBv1lYlm222Wer4zDPPTNLee2bINK6vyLO1aoNhzpw5M/O8WEL38vWy9/YmpdaLr83zAdesqMOXnxVo1ZefdU1v2HuHTH6r8IjaX/DPFhJLWULmoJtssknrFQtgt8R9u2V5Ug2JgfwWu+1b/rqyTMM7OjqSd+3HbRmuGWJ5+OGHM4+9SCRrzHkT5ZA323Z6+K1CJKWqiRm/D6jZ7rq0iu9njz32WJL23q9Dntj7yjTcQiVjQgghhNQOLnAIIYQQUju4wCGEEEJI7WibAky3fLKou3Evq7R6HrFyzC222CIzb+jQoalj64L6tddeiyrfY+X0H374YSovVuZsZft59JXK0Lspim0Pr8dg30lIRmuvC5kwenmx1QfIIwu32PdchhzZ60dYVwmxUZqroD+5PMjSqwrV0fcL21a+7bNMVvPoQIT6k9VZCrnHz3Of0LPn0W+xc659Z2W1vx1zth5l6VfaMWLnD68nVkYoi6LYaOIhqjb9L0ro+2jnnXeOKqM/zSfdcAeHEEIIIbWDCxxCCCGE1I62iKhC0cRDIgDrNTFkIh27pR3ixRdfTB2vueaaSTpkAhq6X2j7274HK27zoiy77RoS93jsdmnVW7dVmV12k8eEtKhYylK2+fdAiSZuxRchU+Ay3rE3Lc7ygOzFUKG+EBqn1iWBFZ2EzMm9i4NQvwhF2K6CPKJTW+/QfGXfhfdYa8Wq/p3ZZ7fvz5dh33tI7BxLX4qkPF1dXcm4Lurx2j9Pnd1QtAvu4BBCCCGkdnCBQwghhJDawQUOIYQQQmqHtMNsrbOzU7PkkiEz2ZBLfpJ+PyG5+Ny5c/+mqhvnLb+jo0O75ea+/FhdJy9XztLf8PJ6e17I7NZHp4/VB7L9Pk8YiqxzQ+f5d2D1rHz9LdOnTy/UbiISNahDbRh6nlh9nJB+i9fBsGWGQin0ZYTiUKR0W88ZM2YUajegMVdanbyysX0xjz5IiCx9mv5kxh2LqhZSKIwdcyF8u5eh61Zn7Pwya9asHscc3yAhhBBCagcXOIQQQgipHW0RUYnIZACvVH4jksWKqjoi70Vstz6H7TYwKdRuANuuj2G7DVx6bLu2LHAIIYQQQtoJRVSEEEIIqR1c4BBCCCGkdnCBQwghhJDawQUOIYQQQmoHFziEEEIIqR1c4BBCCCGkdnCBQwghhJDawQUOIYQQQmoHFziEEEIIqR1c4BBCCCGkdnCBQwghhJDawQUOIYQQQmoHFziEEEIIqR1c4BBCCCGkdnCBQwghhJDawQUOIYQQQmoHFziEEEIIqR1c4BBCCCGkdnCBQwghhJDawQUOIYQQQmoHFziEEEIIqR1c4BBCCCGkdnCBQwghhJDawQUOIYQQQmoHFziEEEIIqR1c4BBCCCGkdnCBQwghhJDawQUOIYQQQmoHFziEEEIIqR1c4BBCCCGkdgzYBY6IqIjMEJEfRZ5/mIhMb163WsV1e0BEPhSRhyLPH9Ws21wRObzKuvU1bLeBCdttYFKg3b7XPF9FZFDFdZsoIh+IyNWR57etT/UH2HYloKoD8g+AAljNHG8OYLr7UwB7hq6rqG4PADjcfebrNhfA+b1dV7e/HtptFIDbAEwGMBXAPQBW7+26drQbgAUBXAbgFQDvA3gSwJiY9q7bX0/vH8BvAEwA0AXgkNjrqm635mfHAngcwCwAV8ReV7e/jHZbH8DfAMxs/l/f5a/UvG5QxXWbCGC7Hu59f7Nu431+u/pUf/gLPSeAg5r5vt/3WduZvC2bdfhhX7fdgN3B8ajqn1V1SPcfgJ3RWEjcXbRMEVlCRAaXVD9bt08C+ADADWWUPcBZHMDtAFYHMBLAODQWPIUpsd0GAXgNjQG7GIBTAfxBRFYqoew68BSAYwA8UUZhZY43AG8A+CGA35ZUXi0QkQXQGF/XABgG4EoAtzU/L1rmCBGRkqr4ewB/BzAcwHcA3CgiI0oquxaIyDAApwB4toSyymw7NMfvLwD8X1lltkJtFjg9cDCAG1V1RgtlbA/gdRH5mYisU1K9AGBPAG8D+HOJZQ5IVHWcql6mqlNV9SMA5wJYXUSGt1BsKe2mqjNU9QxVnaiqXap6B4CXAWzUQt1qg6peqKr3AviwpCJLG2+qerOq3grgP+VUrTZshcbC/TxVnaWqvwQgALZpocxDAbzcFJGsXLQQERkFYEMAp6vqB6p6E4B/oDFfknmcCeCXAKaUUFYpbWc4HsBYNHbf+pxaLnBEZBEAe6Hx66Qwqno9gG3R2IIfKyKPicgxzRV0KxwM4Cpt7tmRFFsAmKSqhb+Yqmo3ERmJhkit5V9OZH4qHG9kHmsDeNrNPU83Py+Eqp4FYF8ASwF4XETuF5EDReQTBer2kqq+bz57qpW61Q0R+SyAjQFcXEZ5JbYdRGRFNBZM3y+jbmVQywUOgD3QWN0+2GpBqvqMqp4AYHkAZ6DxC+hlEblORBbNW16zE2yJFhdfdURElgNwIYBvtVpWBe02GMC1AK5U1X7x66SOlN1uZD6GAHjXffYugKGtFKqqj6rq0QCWAfArAPuhsRt3aV/XrS6ISCeAiwAcq6pdZZVbUtsBjV2l01R1ell1a5W6LnBy75CIyP5NLe/pInKXz1fVuWhslz6FhjLsOgCK6AscCOAvqvpygWtrS1POPhbARar6+xzXVd5uItIB4GoAs9FQXiUt0sbxRtJMB+AXiouioUTfKyJyimm3+XYRVHUWGjtCT6IxXvKIGluq28eAY9DYfXu0yMVVtp2I7AJgaHMXtt9QuwWOiCyPxq++q/Jcp6rXGkXgMaa8ISJyiIjch4Yy5bIAvqSq6xQUoxwE7t6kaIogxgK4XVWjTCK7qbrdmgp4l6GhAL1nU0+ItEgbxxtJ8yyAdZ1i6bqIFLuq6o9Nu321+3MRGS4ix4rIOAD3AegEsLWqjs5Zt1VExO7YrBdbt48B2wLYXUQmicgkAJsC+JmIXBBzccVtty2AjU3dvgTgOBFpyWCkVSq1le8jDgTwiKq+2GpBIrIjgD8AeBjArwHc2lzlFi1vUzQmbFpPNWmKHe4B8LCq/k9JZZbZbr8CsCYaJpEflFG/utC0vOlAQ0l1sIgsBGB20e3zMttNGn5ABqExWXc26zZHVecUKa9GPICGi4qvN3/FH9H8/L6iBYrIYQDOQ8Ni9XsA7m7uwOVCVZ8XkScBnC4ipwIYg8bii0rGDQ4BsJA5vhnAjWj8ACtEWW0H4DQAPzHHv0DDkvEHRetWBnVc4BwE4JySypoAYA1VfaOk8g4GcLNTovu4szuAzwBYW0QOMZ+vpaqvFiyzlHZr6ksdhYYvlUnmR+9RqnptK2XXhLFo6JMBjV+TvwGwNRpfokUoc7ydCuB0c3wAGhP4GSWUPWBR1dkishuAS9H4QnoOwG6qOruFYv8KYEVVnVpCFfcFcAWAdwC8CmAvVZ1cQrkDHlWdZo9FZDaA91TV6y3loZS2a36nJd9rIvIBgBkl9YnixDjL6Y9/aJimvgvgB5HnfwXAtOZ1q1Rct7FoNPb9ked/qlm3mchwmFaXP7bbwPxjuw3MvwLtdnrz/A8BdFZctwkA3kNDcb9f9an+8Me2a/1PmjcnhBBCCKkNtVMyJoQQQgjhAocQQgghtYMLHEIIIYTUjrZYUXV0dGhnZycAoKy4XlZ3KFSmPW/OnGIWoh0d89aBXV3ZFrD2PF8vX8cS45sBmP/Zut93M2+KquYOWNfR0aGDBvW9oZ3XEyvSjv592+fy5dlzQ+3dBvplu9l3MnduEYvSePxzlD1uQoT6na+HO7dQuzXLTQqyYxgIv2t7rp+HPvqoZ9dNofJ9nm3zkN7m4MGDM88ru+18+baORcetqhaqZH+ZKz9OuO/2HsdcW1qks7MTw4c3Yif6wVcU24FDZdrz3nrrrUL3WnjhhZP0jBnZsTvteUB6cl5ooYVSeWW9h26mTk1b4y266DyHoJMnT36lSJmDBg3Ckksu2VrFSsBPVkXacYEF0sGSl1hiiSTt351tt1B7t4F+2W6zZ8+zKP7Pf6r1vdc9b3RT9rgJEep3vj/NmpVy11Oo3TyLL7546jj0ru25vm5vvvlmj9cMGTIkdfzuu+9m5n344bx4qu5ZU9h+599f2W3nf5jMnDkzSbd73PaXufLjhG3/rO84iqgIIYQQUjvasoMjIsnqvaxVvb3OrtynT0/H+SpjCz3210DoPPvrKA9LL710ks76JdYTZWyXzpkzJ9nd8L/oLP4Xo20PX4+qf/Hb9xWL3c3pjaz3+olP5A6+2yt52tvy0UcfFb62TEJt4es3YsS83eXJk+f5dcuzW7fIIosk6aK/4BdccMEk7fuF3U3yfb6MnWKgMVd2l+3nMis28rsFdj70O5JZ102ZMiV13siRI3ssz+N3cOx1WXWqAj8W7S65n6/su/Q7P6EdqVi6urpS857F1jPUb/x3o90pLWN+qWJOsG3v+33W+LfPBYRVDkKqBDFwB4cQQgghtYMLHEIIIYTUDi5wCCGEEFI72m7XVoVc1lsoWWJ1X7xZ5FJLLZWkrWzVy0LLkr1nUVRuWoZ586BBg6L0U7xMNSQvLqJTFNLl8PcuA9vevm+VbQpataVJfyXUpnl0dyxlWM5YfQx/r8UWWyxJV2USrKqJroGdg3x9rFWTJ7bPhvR4fL8MzaNF+qx/t0V05zxev8Vi34F/H2Xo4HR0dLSsJ+Pfo21HO8/557S6Kf7ZirpGiSX0nWfb2Lavr3+o3Ww/DOkvZfXPj8dsSgghhJCPFVzgEEIIIaR2tEVEFTJbtaaXfnst1oGfPS+POXbstmho69Heu+g2a1ExlDWt9aah06ZNK1SmxZo+ht5BrGMxIP2OYt/XCSeckDq2HlG9aHGvvfbqsYxbb701dTxx4sQkffXVV6fyqjD5zqJuIqkyRA2x5ecZN/a6kMj5vffeS9Kh7X0/vsoQcwCNOdA7N+zGfu5NyK2jPy++suMzJLpup9fuqvuJfxYr7skSBbXiwkJVk3v6vmH7WMjha+j996V7jbLNy4uOWzs2gbh5ul6zKyGEEEIIuMAhhBBCSA3hAocQQgghtaPPw59WLVu0VC33zYPVA7Du4fPI8q1Le491Vd6KfkARc9iQnkSW6aDnpJNOStJ77rlnKi8kt84KzbHLLrukjl966aUk/eCDD6byXn311cx6FcHX0ep2hEwkBwJeByq2fYsyatSoJP3GG2+k8g4++OAkfe2116byrF5KKBq2DVIbwsv/ywpAGgprY5/Bj0ubF3KbYcdOyFQ7j57EYYcdlqR/8IMfJOnXX389dd7ee++dpF95pZR4pJnk0W0rw72HqiZtEHIlUnU9QtjvGSA+RE3sOA71maJ6PG+//XaS9m4TYuAODiGEEEJqBxc4hBBCCKkd0fIHEVkRwCebh5NUtdo9xoL4LfPYaOLt9ihbtimyNRMFwp5OY5k7d26lIkS7bXneeeel8rxYymLbasKECam8u+++O0mvuuqqSXrnnXdOnbfKKqsk6TXXXDOV9+STTybp0DZuyLuoraNvC9v2PgJxVR5yqyK0bVx0Wzq0Jb7BBhtk5g0dOjSqXlVQRbv5Och6avbiBnv/0NyVFfEamN8MNwvr0RlIi6Vee+21JG3dOQDpdn300Uej7jUQ8e/Yqgj4uaAM9wL2vXrx6y233JKkN9lkk5bvFcKKiAHgmmuuSdKx38Oeotd10+u3uIh8U0ReA/ASgL82/14SkddE5LiW7k4IIYQQUgHBnx0ichqAEwCcBeAeAN1aUCMBfB7A90RkiKr+sNJaEkIIIYTkoLd91SMBHKKqN7vPXwPwuIiMB3A+AC5wCCGEENJv6G2BMxzAc4H8CQCGlVedYpRhjurl1lmmfUV1c7xc1uphZKU9IXPDkJ5HnvAV7WSZZZZJ0rvvvnvmef/85z9Tx6effnqSnjJlSirP6ilYHZAVVlghdd66666bpIcNS3fhUETpMmhne/ixUfR5bJiA2Oi/ecZlVr1CbgC8/tXNN/vfYa0RMu8vQ8etN6yOl8ebgtv6hOaQUN+LjcZuddQ8yy+/fJL2ZuJ2vJfRNzztDpVjEZGkTULRyr3OjdUZ9fomVtfJ9r2QK4MddtghdWx1tap2k/LFL34xdWzdB2yxxRYtl18kAn1v39bjAHxXROab0Zqffad5DiGEEEJIv6G3HZxjAfwvgLdF5M9I6+BsDmAmgO2rqx4hhBBCSH6CCxxVfUZERgE4AMBoAN37j5MA/A+A36lqnG1hyWy66aZJ+sgjj0zSkyZNSp1nt13Hjx+fyrNbXi+++GIqr2wzcR+ZuMi2qDcN9abhAw27xehNSq1Y6uSTT07lhd6dNXU9//zzk/ROO+2UOs9un995552RNS6G30q14soqzEYt1hNob1Qh6m313uutt17q+Otf/3qSvvLKKzPL8+IlG33bine8OMHW3z9LSCwVK97Jg6/byJEjo871oq0i3rJPPPHE1PE+++yTpFdaaaXc5QHApz71qSRt528g7ZX9X//6V6HyQ/PCiBEjerwXME9M1IpJsvVAnce1RuieVtQYElfa5/7CF76Qylt//fWj69IqF198cer4+OOPT9KrrbZakvbtZMeOd0HQqji/V+cNqvo+gF81/wghhBBC+j0tbVOIyGARWaH3MwkhhBBC2ker7jfXAvAEgM7eTiybc845J0kX3TJ9//33k/Szzz7bapWC1gsrrrhi6vh///d/k/RZZ52VpH0AQYsXX9jgbKHt6/6EFUXYZ7Vb4EC6baZOnZrKixXv7bvvvlHnee/XljKskPz2cqzVXFHrjkGDBiVWTz6AX38KOBuDFS0B6fd13XXXZV7nxUtZlie+bez9vIgoZL1i88qyvAuJ+0IWJf6ZQt6Ls7BzUh5Cnoz32GOPHtNAOvjmccel/ceW8T5DgYm72y7Wk3NPzJ07t9D1seMxJGZce+21k7T3VmxFulXjvb6vtdZaSfqFF15I0l4MZckjkgqJ7bphLCpCCCGE1I7ePBm/1Mv1+bXXCCGEEEIqpjcR1dIArgLwfEb+sgC+UWqNCCGEEEJapLcFzjMAnlbVC3vKFJH10OICZ5FFFknSXsZtTT29PPpXv5pn1GVNAL2+hpXZr7zyyqm8rbbaKkmPHj06lWdlydY7ZwhvCm5NAP29bQRrW/8vf/nLUffq7d7+uJ0U0fOwMnhPHlNnG9l4ySWXTNLeq+q4cfP8U3rdplD9ra6T9aIcMvcMmY2W5WnYMmfOnKRuvvzQmIqlas+ylpNOOil1bPuJletXge8XVZiC5yFWl6bo2H/kkUcKXWcJzZW23/jxYvUob7311lTeZz/72V7La4V33nmn5TKsJ+NQP/H6J0XG46c//enU8T333JOk//GPf6TyLrjggiRddC6IPc97MrZYz/FeH8p+R+dp05h+3tsbfRjAqED+dAAPRdeIEEIIIaQN9Obo77he8l8EsHWpNSKEEEIIaZFWzcSj6OjowMILLwwAGDJkyHx5oeuyuPfee1uu1xVXXJGkN9hgg1Te448/nqQ33HDDJB0yKf7LX/6SOp44cWKS9mbo1qTObiuGtvVD23deDBIyxSubqk2PQ+Ifv3196qmn9nie73c33HBDks5jRmv75FJLLZWki26XVxHME5j3zvw2rjd9LoJt71D9Q3mhPmNdKmy88capvOefn6cOGCojZLLb16KmolgPzCFzWu96IKsdvGsGPwdarIh3ueWWyzzPBjy1ohMgXWc/P2WNWwA4+OCDk3TIe3VfYj0ZV80xxxyTOn755ZeT9JgxY1J5oTESW9/QefZ7bMstt0zlFfEMHRKjeZcXMdBMnBBCCCG1o1VPxs+JSN9psxJCCCGE9ECr+9UXAhheRkUIIYQQQsqipQWOql7Q+1kNGV63DkS75JQxWPPA++67L/O8Bx54IKo8L+u2pufeBP6pp55K0rfffntU+XmiUls5fKsRWXvDR262bVw0snGs/sbhhx8eVd6ZZ56ZOrZu/svQIQqV4d9BnmjDRbChGqoeb/65rUl/SAbv3TlYWf7QoUMzr/O6VFnEhlXwVKUTVYTQuAqFZgm5sLfj7+yzz07lWfNdb+5t9W4++OCDVN5NN92UpA888MDMOtr6+9A19t7WbQYAHHrooUna6ptcf/31KIMFF1wQQJzr/yxEJEq/zetH+TbOYq+99krS1sUIkHaX8O9//zuqvBDd76MbH4LBsuOOOyZpP97t9+a0adMyyyg65nwYl56IXuCISCeAbsciU1S1eGx5QgghhJAK6fXnnYjsLiIPA5gJ4I3m30wReVhEdqu6goQQQggheektFtVRAM4HcCWAcwF022mNBPB5ANeJyH+r6iWV1nKAsN5666WOL7/88iTtzcvvuOOOJO2362MJRaKumlixTqxZst+mtNuk3tT5tttuS9Kf+cxnMsu0Yim/HV8GsaIAL5az786bqNtt1yJmlgCgqsk7ixUJloU1nS/KOuusk5l3ySXVTjVF3TRUQR4XGrYfhdp88ODBSdqLiUI899xzSdqbl1uRa+y84D2X33nnnUnamoUDSFyMAMA555yTpL045sUXX0zS/UnUGCJWhLzddtslaT/vX3TRRUnaiwVDptXWVD/2u8T3mQMOOCBJe3Hb3XffnaQ/+uijzDJj3U54YubH3r59TgBwjKpe2kPejSIyDsDJALjAIYQQQki/obfl47IA/hzI/wuAZcqrDiGEEEJI6/S2wHkWwNGB/KOa5xBCCCGE9Bt6E1EdD+D/icgYAGOR1sHZHo0dnp16u4mqJmbM1t04kJZBehmePS7DxXzVHHvssalja47qI9ZOmDCh1Hv791oGnZ2dWHzxxQGE5fqhcABexlwkeq7XbQrp3VhsZPE82OcJ9bvQO4l9Ti/7LhpZ1xJrstqfsC4VrFnwE088kTpv7NixbauTx+oK+D5vQ0P4KORlEepTIR0Kq2sRG2l83LhxqePPfe5zSXrYsGGpvKz+7T+3/dnr6vzxj39M0l4Hy4friMGbmvsI1paq2qsn/HdcSEdmjTXWSNLrr79+krY6ScD80ddjse5DbDqkR/XVr341dbzkkksmaaunBaT1TKt2VZJFb8E2HxSRddDYxRkN4JPNrEkAbgVwsapOrLSGhBBCCCE56fVnXnMBc1L1VSGEEEIIKYe27GOLSCJC8Vt03gOvpS/NoGOxW7fbb7995nl77LFH6jgU7dgSK+oIiYKK0tHREWVmnEccYusZEsHYrXQbobg3rMmk97gaSxninb702N2XZuJFsWOn2wszMH9E6naKE0KERB5VUbRP2Xl01VVXTdJebGBFWf/1X/+Vyqva27eNEu+f07rYsKbBxx13XOq8//7v/84sI+SaoZsYz7itksdbsp2HVlhhhSR9zTXXlFonj5+X7XgMeSR+5plnUsdVi6UWWWSRJD1jxowez+k/cRMIIYQQQkqCCxxCCCGE1I62iKjmzp2bbG15Lf6QN8LQFlcZW6ZlYD1HWi+hAHDvvfcmaS8usduA1mtvni34kLffgWZF47FBNEMeV8ePH586/trXvpb7XnmslYr0Oy+OtNZ1VQTitFZUvny7DR8Kohcb7DSELyPksdT2X1VN0jfccEPUvdrBQPGOG2KHHXbo6yoAmF+kYEWUG2ywQSrPfkfYvnHeeedllh8S5/m5sfs4S8wRg6pmqgXYedpb04awIh/b92yASyDeE3DR70zrnfwb3/hGKu+NN95I0taTdDuIaS/u4BBCCCGkdkQvcERkBRFZ2n22tIiskHUNIYQQQkhfkGcHZyKAe91n9wF4ubTaEEIIIYSUQB5FjUMBeBuxkwEs1sO5KTo6OhIzcW8WHtLByTIPBNKyRqsHU7V5rvciaeWhXt/hu9/9bpL20VStPPTtt98uVBerr+OjlVs5c1HTWitXzvNei5ioe++l3/zmN6Ouu/TSnuLANigapTZEbDnWhNHq3ADp9+N1p4rqY1nmzJmTeEj10YVDejcWL6+3zx16B9bUOCTzt55ZAWDzzTdP0tbL9//93/+lzrNewQS45gAAIABJREFUdKvw3m3HcMiFRVVYE3+vKxLyQhyrc2dNq4cMGZJ5nu83ZWC9C2+55ZapvBNPPDGqDGueH4pQ7U3Bral8lusEEYmqQ9a13XOk7/d2fvfzdExEbCDtFfiII45I5d13331J2n7nAGkdGY/1lGzfyUYbbZQ6b+utt84sw85lPrp71cTMldELHFW9oofPivmIJoQQQgipkELbHSKysIhsJyLZ5i2EEEIIIX1E1A6OiFwBYJyqXiQiCwAYB2BtALNFZHdVvSt0fVdXV6HtXruFGjIZDwUss3hRgcWKFEJss802qeMNN9wwSd99992pvIkTJyZpu50GhMVLWfjzbKCzkAipqPmjFXXkMTEMeeu09bTPYwMsAuH2ePTRR5P0z372s8zz+tKsN1Zk4L11l+1NNXZseKyoCYgX99lxGvJE7tvbmqLedde86aTd7iCsmX7suCwTK6LyhPrU1KlTk3RIDGlFIq+99loqb/nll0/SVYj6Tz311CS99957Z57n62Xn0UMOOSRJZ3kkBub/vrB90YvQy3jWrq6upD6+35ehLnDCCSckae9yZP/990/SP//5zwuVP2XKlCRtTfGBtPuT119/PTPv6aefLnTvonQHggay57nYlt0BQPe3yq4AhqIRePOM5h8hhBBCSL8hdoEzDEC3ptSOAG5S1bcBXAdgrSoqRgghhBBSlNgFziQA64hIJxq7OX9qfj4EQLYqOyGEEEJIHxBrRfVbANcDeAPAXMzzh/NfAMZnXdRNR0dHok/jdXGsTNLrqVjZpTdbtOVYEzfvFj/WLDbEKquskqS9GZ69349//ONUXkh/wMqPrbzY656E9IaqprOzM7m/1+Ox9fQ6N9Z81+tr2Ha0+gD77bdf6rxPfvKTmfWykdn9+4rVN6ravXmIkMzf6qL0pQ5RSIch9E5i6xyKxm3HW1HKeHexJrxA2KVFHqxLjTzYeTSkm2LraXVuPN51he2XIexcYEPVAGndF69nE8KGYwmFA7Bzge+jIdcMPZ1TJkX1bix2PPpwCfZ7x7pbANLfkyuvvHIqz46RG2+8MfPeP/nJT5L0SSedlMqzul8vvfRSZhmxhNxTeL3AGN2pqAWOqn5fRJ4FsAKAG1S1uxfPAXBWTBmEEEIIIe0ijx+cm3r47Mpyq0MIIYQQ0jrRCxwR2RDAcZinVPwcgHNV9YmIaxNxU54o16HIx3brzW7flSVSsB5T7RadNx298847k7Q38Q15Hs0yew95F81DaJs6ltjt8izPoD2RZQbtI7GHsG1f1AQ+JMJYYYV54dVCIkLbJ8eMGZPKs9upXrxqt3Vt3wLKabfBgwcnLgTa7cE59ppdd901M++Pf/xjqfWoAj8PxLq06I2PPvoo8xltn/KmyGWIDS0HHXRQZt5ll12WOo71AGxNjP14t16Jl1tuuVSeNS8vOr9bcYafr8qaK0NuEarEmkiHRE1Fsd+FHttuw4cPT+VZlwshrGjRt4UtM8/aoZsoJWMR2R/AYwCWBnBn828kgHEickDuuxJCCCGEVEjskuhHAE5T1ZQWrYicDOCHAK4pu2KEEEIIIUWJNRMfAeAPPXx+A4A49XpCCCGEkDYRu4NzP4CtALzgPt8KwIO9Xayqid5ESD7tZWwhV+lW/8HqNHh5szU99/oUIVPVX//610namq16M8VbbrklSXuztVgZZBWmyFYeXFQn4KOPPkrMRa1bbCBe7yZk9mfx8tsQtr1DUeZt5Hfv3tzKqn3EXWuivs8++0TXqwiPPPJI6vjyyy+v9H59yaqrrpqkY6NVl6EfUQVeV87qqrWigxPCluvvEev24PDDD0/S1113XeZ5v/3tbzPzQibeyyyzTJL2puZetyaLm2++OXVcRdgIS/dcWVZf8+bmVneqqr5RJS+//HJmnp1/Y7/vPCE9R+uGpYjLlNgFzl0AzhSRjTEvZMNoAHsAOENEEsckqnpzD9cTQgghhLSN2AXO+c3/Rzb/LBeYtAJof4Q6QgghhBBDrKO/lvYIrQmd92Rst3pDYo9QBNiQt2K77RgyM/NbzhtttFGP5337299OHY8bNy6zzBAjRowodF0sZXjltOb93rw71jtn7HNefPHFqeOdd945SYe2tr0YM3Yb/Oijj07Sfms19O7slqw9z2+j33rrrUnaizWtyPO+++6Lqm8eQqbGfcnJJ5+cpL0XXesF9dprr21bnfJg+5p3n1C1GKU3Ytvbin+sh2Agba7rTb99hOksrLjXl/HEE/M8iniT6s022yxJe3H4QMP3hSzXGEBahaIMj8dVEBI9+egDMXiPxCGKmIZb+nZUEkIIIYRUQHCBIyKPiMji5vhMEVnCHC8pIq9WWUFCCCGEkLz0tv8zGoCVG30NwCUAus1YOgEs29tN5s6dm2hDh7Yfvedfuz1VdAs45F1y9OjRSfrcc8/NPM+KpfKIpGKto0KijliqCBTX2dmZtNe0adNSebGWASFLNYsVGQHp7dpnnnkmlbfBBhskaR/gbaeddkrS22yzTZL228TWA/Ljjz+eyrMeVz3W+spv8cdiAxIWtTwYiOy9995J2lu/ff/73293dXrEimn8fGSPqxJJiUgiqvciizKscazn2V122SWVt9tuuyVpH9AxhBVFWVHW0KFDU+d9/etfT9LW+tSTxzN6f0FVkznY9w3b16uwmA1hRZfecjGrD3txZ8ib/TvvvBNVDyvKyuPxuVXv0HlHaZxPbkIIIYSQPoQ6OIQQQgipHb0tcLT55z8jhBBCCOm39KaDIwCuEZFuYfBCAC4RkW7b6ygbsc7OzsQLoZfvWblgqyZheTnqqKOStNXHAdLmjlZ/owoZalF5vtXlqEJuLSJJ3UKm+F5Oaj0Nx5o++vd63HHHxVYzhTc378b3LatPUcRDJhCv9zRlypTUcUyE9v6GlaGH+oId3zYqO5CW1//ud79L5V155ZW56+R1CmxU5RD2Wbx7CNtPfLt1R2ivEqv35p+n7PnxhRfSjul/+tOfJumxY8em8uxc6XV3rEsEG2nc9/OQubTFf0dUrbfSPY+2osdo50pPrFm0d6FSxjwR++5CbgZ++ctfZuadcsopmXlWn63oHNsqvY0YP+v0FFTzqpLqQgghhBBSCsEFjqp+pV0VIYQQQggpi7bIhGzQxnabyVk+97nPpY632267JO0DLsZiPSVX4d3UilJCYhaf582620lIhGEJbYvafuKD9i211LwA9lYcBqRFDnZL3LeNfXc+yJ49N2ReHrsF602ibRn9FftsQLx4xLabNUkGgE022aT1ihXEignss4Sey7eb7TNVmTLbwMSedo7pp59+OnX8ta99LUl/97vfTeXZ92Lr7sXTtm/0pbl0u4kVNYXmoaqxwY5937YeqH/+85+n8v7yl78k6b78bs+CVlSEEEIIqR1c4BBCCCGkdnCBQwghhJDa0Ta77G55dlmy0CLyvi233DJ1HIo8beWhseaNXufG64dYrNw6pLPSHeICmD/MhXXX7uX2/TUyrSW2Da3OjSf07kJ6MVbO7GXO1lzUl1HE3NHrs1jZertDNQwbNixJex0TGy3buiAAWneZ3hu2L8TqZuUhq/7eNNiOYW/eG9LBKStUyty5c5P5xj9rUbcQVoctFDbAznl52tvWJaQ3kqW/1ht9qT9TBrG6mLG6i1UQ6k8777xzG2tSLtzBIYQQQkjt4AKHEEIIIbWjbSKq7u1vvy1elKxty6Jb2D4y9Jprrhl1XWhr3W75e3NUK3axW8M+SnDIC6bd5q3C9DgUIbfOlPGsITFXCGuuWbX4avDgwanj0P2yRBa+X7dTDBESAftxk+XOoQrvsa0wePDgTI/JRU3Ts0S8ITGkz7N9w3uQzsKPI9sfBrrYyRM7V/rntl61PX0pshoIxIiFPz7fWoQQQgj52MAFDiGEEEJqBxc4hBBCCKkdoqqV32TQoEHabeLs5ch1k8WWzYgRI5L05MmTixbzN1XdOO9FCyywgLYaQTlkhhs6z0ZS9josVm7d7gj0VWPfw1tvvVWo3UQkGdReDyakD2RdEvj3WrWZeJYemtVjA9J6CV5PJKQ/kyWv9/3RhkPw+hH2/XhdPzePFWo3oJwxVwahcRsaqyEdyIEw16uqFLnOjrkQXjes6nH1ceHNN9/sccxxB4cQQgghtYMLHEIIIYTUjraIqERkMoBXKr8RyWJFVR3R+2lp2G59DtttYFKo3QC2XR/Ddhu49Nh2bVngEEIIIYS0E4qoCCGEEFI7uMAhhBBCSO3gAocQQgghtYMLHEIIIYTUDi5wCCGEEFI7uMAhhBBCSO3gAocQQgghtYMLHEIIIYTUDi5wCCGEEFI7uMAhhBBCSO3gAocQQgghtYMLHEIIIYTUDi5wCCGEEFI7uMAhhBBCSO3gAocQQgghtYMLHEIIIYTUDi5wCCGEEFI7uMAhhBBCSO3gAocQQgghtYMLHEIIIYTUDi5wCCGEEFI7uMAhhBBCSO3gAocQQgghtYMLHEIIIYTUDi5wCCGEEFI7uMAhhBBCSO3gAocQQgghtYMLHEIIIYTUDi5wCCGEEFI7BuwCR0RURGaIyI8izz9MRKY3r1ut4ro9ICIfishDkeePatZtrogcXmXd+hq228CE7TYwKdBu32ueryIyqOK6TRSRD0Tk6sjz29an+gM1a7vtmm3XJSLbVVm3FKo6IP8AKIDVzPHmAKa7PwWwZ+i6iur2AIDD3We+bnMBnN/bdXX766HdRgG4DcBkAFMB3ANg9d6ua0e7AVgQwGUAXgHwPoAnAYyJae+6/fX0/gH8BsAEAF0ADom9rup2a352LIDHAcwCcEXsdXX7y2i39QH8DcDM5v/1Xf5KzesGVVy3iQC26+He9zfrNt7nt6tP9Ye/0HMCOKiZ7/t9X7bdpgDGNefKpwFsFnNdlX8DdgfHo6p/VtUh3X8AdkZjIXF30TJFZAkRGVxS/WzdPgngAwA3lFH2AGdxALcDWB3ASDQGyG2tFFhiuw0C8BqALQEsBuBUAH8QkZVKKLsOPAXgGABPlFFYmeMNwBsAfgjgtyWVVwtEZAE0xtc1AIYBuBLAbc3Pi5Y5QkSkpCr+HsDfAQwH8B0AN4rIiJLKrgUiMgzAKQCeLaGsUtpORJYA8EcA56Axp58N4I/NuvYZtVng9MDBAG5U1RktlLE9gNdF5Gcisk5J9QKAPQG8DeDPJZY5IFHVcap6mapOVdWPAJwLYHURGd5CsaW0m6rOUNUzVHWiqnap6h0AXgawUQt1qw2qeqGq3gvgw5KKLG28qerNqnorgP+UU7XasBUaC/fzVHWWqv4SgADYpoUyDwXwclNEsnLRQkRkFIANAZyuqh+o6k0A/oHGfEnmcSaAXwKYUkJZpbQdGrs3k1T1BlWdq6rXoLErv0cJdSxMLRc4IrIIgL3Q+HVSGFW9HsC2aGzBjxWRx0TkmBJWpQcDuEqbe3YkxRZoDJTCX0xVtZuIjERDpNbyLycyPxWONzKPtQE87eaep5ufF0JVzwKwL4ClADwuIveLyIEi8okCdXtJVd83nz3VSt3qhoh8FsDGAC4uo7wS2w5oLJT9cZkbA7mp5QIHjVXjFAAPtlqQqj6jqicAWB7AGWj8AnpZRK4TkUXzliciK6Ih8mhp8VVHRGQ5ABcC+FarZVXQboMBXAvgSlUd32r9SM+U3W5kPoYAeNd99i6Aoa0UqqqPqurRAJYB8CsA+6GxG3dpX9etLohIJ4CLAByrql1llVtS2/0VwDIisp+IDBaRgwGsCqDIQqk06rrAyb1DIiL7N7W8p4vIXT5fVeeisV36FBrKsOsAKKIvcCCAv6jqywWurS1NOftYABep6u9zXFd5u4lIB4CrAcxGQ3mVtEgbxxtJMx2AXyguioZiaK+IyCmm3ebbRVDVWWjsCD2JxnjJ8wu+pbp9DDgGjd23R4tcXGXbNXfcv4jGj9O3AOwI4E8AXi9S17Ko3QJHRJZH41ffVXmuU9VrjSLwGFPeEBE5RETuQ0OZclkAX1LVdQqKUQ4Cd29SNEUQYwHcrqpRJpHdVN1uTQW8y9BQgN6zqSdEWqSN442keRbAuk6xdF1Eil1V9cem3b7a/bmIDBeRY0VkHID7AHQC2FpVR+es2yoiYnds1out28eAbQHsLiKTRGQSGnovPxORC2IurrjtoKoPqupnVHUJNH7Ir4GG0UifUamtfB9xIIBHVPXFVgsSkR0B/AHAwwB+DeDW5iq3aHmbojFh03qqSVPscA+Ah1X1f0oqs8x2+xWANdEwbfygjPrVhablTQcasvbBIrIQgNlFt8/LbLemH5BBaEzWnc26zVHVOUXKqxEPoOGi4uvNX/FHND+/r2iBInIYgPPQsFj9HoC7mztwuVDV50XkSQCni8ipAMagsfiiknGDQwAsZI5vBnAjGj/AClFW2zXL2gDAMwAWBvB9AK+p6j1F61YGdVzgHISGqVoZTACwhqq+UVJ5BwO42SnRfdzZHcBnAKwtIoeYz9dS1VcLlllKuzX1pY5Cw5fKJPOj9yhVvbaVsmvCWDT0yYDGr8nfANgajS/RIpQ53k4FcLo5PgCNCfyMEsoesKjqbBHZDcClAH4C4DkAu6nq7BaK/SuAFVV1aglV3BfAFQDeAfAqgL1UdXIJ5Q54VHWaPRaR2QDeU1Wvt5SHMtvuRAA7NdN3ozG39y09OccZCH9omKa+C+AHked/BcC05nWrVFy3sWjIje+PPP9TzbrNRIbDtLr8sd0G5h/bbWD+FWi305vnfwigs+K6TQDwHhqK+/2qT/WHv5q13bbNtvsADfFXW96hNG9OCCGEEFIbaqdkTAghhBDCBQ4hhBBCagcXOIQQQgipHW2xours7NTBg3v20WV1gLq60tal9thfP3fuPEu2zs7OHsvLg483ZsufMyfbstTWy5dh6+/zOjri1pb23vY5e8Peb9asWVNUNXfAulC79SWzZs2zHF5wwQULnWfzPPbc0HkW374l6bYNuHaz78u/E3vsx3osofauGvtsfiza49mzZxdqNwAQkaiOs8AC6diYs2fHGUHZeto5zjNoUPqrITQHWmLbx48P2zfy5Fli30Ev9SoUeLKzs1O731kJsSvnI3Ye8tjvGT8nfPTRR5l5/YXYOmZ9x7VlgTN48GCsuOKKPebZTjljRjou5syZM5P0sssum8p79915lnGLLbZYko4diB4/oN95550kPXlytpXiyJEjk7SfdOzz+Mb5xCfiPFjbew8bFh+Sxz7P888//0r0hYZQu/Ulzz//fJIO1S90ns3z2HND51l8/7EDswUGXLvZ9+XfiR0ffqzH0pf90T7booumHe7asfnSSy8Varc8LLfccqnjl156Keo6W287x3mGD0/Hun3rrbeiyo9tHz9P276SJ88S+w6qYNCgQUmb+H5fBrHzkGfhhRdO0v479N///ndmXn8hto5Z33EUURFCCCGkdrRlB0dVk5V36Fed3wEJ7VjYXRtLWatne2+b9ivp11+PC7WxyCKLZF5nd4H8L6VVVlklMy/0K3jUqFFR9Qoxa9aswr8csrA7WWXsctidPCDdL0LvIPb9hLZ1bRllv6dWqKLdiuDbd+WVV07SL7/8cvDcLKp+rtA2uG1v+8sSKEc80htFx3TWdaEdHD9fFSk/RGieLppn58rQdX4XqIydn7lz5ybvc8SIbOmk7ydWhOSfzfexbuz3BZCe8/z4sN8RViLi84qOqyJt7yUiISlOqB/6ub8nuINDCCGEkNrBBQ4hhBBCagcXOIQQQgipHW0PthnSkPcy4TxWQ914uWXZ2uFe5mjliV4vaOLEiUk6pC8TslAoKh8OWX71JUX0bsrQJyqK1RsJkaeOWRaAQP/S5WkVb+ljiX2vnqrfT6h/2ntX1Sc7OjoSyxevaxFLaN6MtTKtwhKonbS7/oMGDQrq3nTj9UyLkGfOqFpPMPbelpj3FIN9D1nfodzBIYQQQkjt4AKHEEIIIbWjLft4IhK1ZZhHJJW1zR8SSXmzsunTpydpv21mRTyhMkPbbaFtbLtV7JzyZV6Th5AJaCwLLrhg4rjLmxjGOir0xD6ffXf77bdfKs96wV1ooYVSeXvttVeP5d17772p4//85z9J+uqrr46qU1lkuTgA+q/peSxVixOLvp/Yetlx401UrXihDHF6T3R1dSXi7KJillBdQvOCvc6P94FA7PuqQnw1e/bsxPWBF7+GzJlDZtBZag151A/6cg7Jurcfi1WKfrmDQwghhJDawQUOIYQQQmoHFziEEEIIqR39yhbQyxatfouX51lZndVnyWNWbeWfPrp3EfNyq9MDAEOGDMk8N0sOHJJPeorIb/NgQ2yUpXNjn8+2t9dl+spXvpKkd9ttt+j7ZUWp3nrrrVPHL7zwQpJ+8MEHU3mvvvpqZvlWN6HoOwkx0PRufB+09Q+5lC+K7T8TJkxI5e24445J+p577smsVwj7PCE9FD++ytLBsXpvHlsfbxZr7x/SMYkNzxAbggYAzj///CT9+c9/Pkm/9957qfOsftwrr1QejzSa7j7VSp0WWGCBzHaz3ws+VEMozEIWRfUrq9aPi61/6LxQng/FEaNLxR0cQgghhNQOLnAIIYQQUjvaIqIqGt04tBVXZMs5FK08tN0V8jxrCYmkQlGvY8tfaaWVUsfWU3Ke6L9FCNU/D1nt9q1vfSt1HCuWGj9+fOrYiibsluYuu+ySOm+11VZL0l4cacv0YqgqxFKWgWYmnsdDd8hjtyW0lb7BBhtE5flo5RYrMgiN2b5g7ty5yVjzY8z2Pe9xuQzvsLFexjfbbLPUsRVL/fOf/0zSXuxvqcLEvyjdor8s8XZevLfo0BjJihieh1CU+1tuuSVJf+ELX0jlFfUmnsWxxx6bOr7gggtylxFS0fDqJzH9IriDIyKfF5FB5vjLIvKkiMwQkRdE/j97Zx5v13T3/8/33iQ3iVSJmCWhSkw1RoVKTTGWkvB60NZUHkqpoY8SWsPTGvuUltZQHUy/B0UeU4uUkJrTKEJEkKJIgjSmDHLlZv3+OOes+91fd6+z9j77nHvv8Xm/Xnll7bv2XnudvfZaZ53vKD+I6zYhhBBCSOOopqK6F8BgABCR/QFcD+AxAMcCuBvAxSJycPrlhBBCCCGNp5qKSlT5ZADnOefOLh9fLyJvl/9+Uz06RwghhBCShyw2OOsBsCqpuwD8uOpN+vTx9i71yHKt3VGLcEW1FNFmqA2t+7U6VP3ZrA2RrrMu6n379vXlPBm8gZJLY0XvGcoMbV0fYzPmjhw50pdDNjfWHfiKK67w5Xnz5iXq9HN4+OGHffkrX/lK4jxtg7Paaqsl6mJdZOthG1BEiPx+/fphjTXWAJC008pCbJqRECGduW0jLbyDtdXQYzN16tRE3YQJE6L61dPsbjQtLS2p9nR5187YdyrWRuqOO+5IrdNjrEMxAPDvpD2vGnnSu2Qhi0t8DFncuPOE8wh9Th0qASiFHagQsrkpwi7tm9/8ZuL4yCOP9OWQ7ZzGjrX+jrPvp/3e6YqYDc6mIjIfwOIuzm8B0BrRBiGEEEJIw4jZ4NyPTlXV1wBMUXVbAEiPikYIIYQQ0g1U2+BYmdYCc9wXwEUxNwq5DOZhk0028eXvf//7vmzdmXXWaKvOmDt3ri/PmjWr0P5ZrEheizFDIk2t7rHqq1C0ZS3mzhLdOY2ixbhAMtKpfT+mT5/uyzpSKgDMmTPHl61IU7v+X3rppb5sRbdaJGszjccSEp1rlZ4NQaDH1LahVYt5aW9vT1VNFaFWi83GHLqXbSPtXKvuPOGEE3z5+uuvj+pHiN7gil9Bv9tZ1CB55u6BBx6YOD7rrLN8eaWVVkq9Tq+3Fq0WtvO9iLW4O8dSR33PMjZ6nQ6pq/T8sN9xWnVz2mmnRd9bE6uWsp9Nv5P6HQGSEeI333xzX7YRrkPfTyG1aYwpRHClcs4FY1c752pfYQghhBBCCqYmsYqI9BGRYUV1hhBCCCGkCGqNZLwxgH8gg6GxTZiVV31y002dnul5xfoff/yxL9uIuB0dHbna1LS2dj6WzTbbLFGnPZt23HHHLvsEJEWCNnmhVntZz4u8CdnqTVpSwpNOOilxrJ/D/PnzE3UhUbT+3AcddFDqeVokO3v27NTzLLr/WkSa19MupMapt8jdqk1jVU+NZKONNkoc62i+N998c6Ju+eWXj2pTf+4syW0bgYikjkOsl1Osl1sI+2xjCalZxo0bl1qnE11eeOGFibruHpOs2HVar19WrRJ6Xva7soJda7Sn1F577ZWou/jii8OdzUgoqeyQIUMSx1rVGEpim3cNjPEqZC4qQgghhDQdwZ9sIlJNvBIX8IQQQgghpIFUk0mvjlJ6hjQ50ZoATiy0R4QQQgghNVJtg/MCgGnOud90VSkim6HGDU4oUmHaeQBw2WWX+bK2p5gxY0bivGHDOm2gbRva9mXrrbdO1L355pu+PHTo0NR+aWzE4A8++MCXrY3MKqus4svf+c53fPnKK69Mbd8+H51dfPHixYm6vC6leciiQ03Leqx18NXaCHHNNdf4stZNW3SU1Sw2OGnP0o5NKCt4IzOGZ3HV7on88Ic/TBzr90S7Fnd13BsJZRPXYSFC703ecb311ltzXafd0NPsRgDghRde8GXbRx1l9+qrr07U7brrrl3eq6cSsgmNdQUPYaOy33bbbb5sXch1qIx6Z2W3kYyLILRW6u/UtHW5mg3OYwBCT2UBgL9VaYMQQgghpKFUi4NzUpX6WQB2KrRHhBBCCCE10hAZtYhEuXJbFVLI9VZHn9VJt6wbXsgNfeLEib5sk4HpRH5WfaXRaqi//vWviTodOfnxxx9P1GkV1bvvvuvL1g0vpF6yailNniRuIez4xUa+zCsWjVXdnHzyyYnjo446Kuq60aNHZ+5TFkL9r7daKuRq3BvQqlc79/SzK0Lk3tNckFtbW1PXPRvJvFa22mqrxPHRk3O0AAAgAElEQVQBBxyQeu6LL77oy9Z1X0ft1muqLgNJ9YldT44//vjUe++0U+dv6KeeesqXe9LYLVu2zK+5dvxCyY7zvMN23KZNm+bLoXXNfpdoN+si1ukddtghUaeTSMe2EepHnj7STZwQQgghTUetkYxniEjtUaUIIYQQQgqkVjn2bwCkZ14jhBBCCOkGatrgOOd+HXme10NaGxnt6mXtBrTO2WbOTrMxyGJ7oHWSkyZNSj0vNtv04MGDE8daV2pta+677z5f1p/TulGnuVUDyZDs1uZG64FjQ7xb2traMHz48C7rdLhtG3o7hA61r8kSXl7rYkP2URobel67FIfC9Vu7sLzPMo16pApoaWlJtZEK2avVG/3ZbNgE/f7aeaQZNWpUoX3KotfvbpsPPaeLsLG75JJLUuts6hprd6O54447fHns2LG+HHq2dl3R9iB2zdNpXHTG6u4eD01LS8tn3uki2X///X15t912S9TpkBc2U7cmlNog9CxD43jMMcf4srW5efjhh31Z26qGyDKmdm3uiujdgIi0Aqgkm5jnnKs9WRMhhBBCSB2oaoMjImNF5DEAiwDMLv9bJCKPich+9e4gIYQQQkhWquWiOgbA5QCuA3ApgIp8flUAuwG4WUROcM5dk9IEgHjxnRWhaRGUVYNo9ZIWx9v7NNJdVrt+A0k1lP1sWiVms2UXQREZhUNoVZONnqldQENqEN3HLFnl77zzTl/eYIMNUs8744wzfPmCCy5IPS8UadiOW1pkTauCDGUa19fVQ8wuIqnPvdFqKU2sOmjTTTdNrbvooouK6k5mGhGB2jnn54Vdu/TY2XVOz6XYNe/rX/96ap2dVzqC8IEHHpioS1P32nVBzwMbufz555/35Z133jlRp9canR3bqkR09mr7fRGKgFzpcyiaejV0aIYsa2+synj11Vf3Zavm19Hv6x2tWIdwAIBvf/vbvmw/93PPPefLIVd5rZKMyRBeIWZPUW0mnArgOOfc77qou01EpgAYDyC4wSGEEEIIaSTVVFRrAngkUP8ogDWK6w4hhBBCSO1U2+BMB3BsoP6Y8jmEEEIIIT2GaiqqHwL4s4jsCWAikjY4u6Ik4dmr2k3a29u9jYV17VqwYIEvWzuGkH4yzcah3rYnIbTLHADMnDnTl62bXJ7MxzZUu3adD7nd1htrYxI7Bnp8Q9gQ4No+wOrhW1o69+whu5tY7DupyavvDtlyaPulkN46hA73b8ciZJ+h9d9FZKDP8ny23XZbXz7iiCN8+ZlnnkmcZ9OhNJJQqgQ9/2qZe6E0G3q+2LAZsej5EcLao2jbCzuuaf0Npdqx3HXXXanX6VQNITdojf2+CM25ou2psth96nQ7r7/+eqJu5MiRvrzvvvv6srUveuCBBzL2MIzNCK8/z9FHH52oGzJkiC/PmDEjUadTdej1pIi1BYibZ9WSbU4WkU1QkuKMArBauWougDsAXOWce722bhJCCCGEFEvVrWZ5A3Na/btCCCGEEFIMDU85rFVSQFLMZEVOITcw7Rqu3RHtNfWO3Pq1r33NlzfffPPU87SIEUiP4GxVCtp92ooO9ee2zzUmymO90J/Huv3lEeVffvnlqXVW5H7PPfdEtRmLFTenReO1KoNYt92QGiev6Fy7GltC6sOi50fI/d6y4YYb+rKOZKxDLQDAJ598EnW/erjL6jG2n60RKmG95mV5trouNsOzdQeut/uxVj2JSKJOq2T0fN9uu+0S52k3cRtxXI+dVTsXpTKpUJSZhP48bW1tvvy3v/2tkPbTsCE79Ni/8soriTod4fqFF14ItlMroe/2NJhNnBBCCCFNBzc4hBBCCGk6GqKiamlpwYABAwB8VgweEu2G6nQ7WuRoxVZpyR2Lon///r6sPWAAYPLkyb5sxWt5xHeha6z6qmjxoLXcDz1XLaK14l8dtTIUXXTEiBFR/bJtnH322VHXhYhVdej3M4s6Sb8nNilmrGdZCOecV+XY9vU42jHU3ivWkyUtoWroPbDPRKtRZ8+enajr6OhMbeec8+Vbb701tX3LGmvUNyRXEWNTjWXLlvnnpNcWIKzqjH1n9fx48cUXE3WhhJpFY9+N3Xff3Ze33HLLRJ1Wq+ny448/ntp+yMssLaFxLZGMNVm8qOx3hkavnUuWLPHlPffcM3FeyNMzVv0Wemd0hP799kvPzlRvNW2e9inBIYQQQkjTEb3BEZFhIrK6+dvqIjKs+G4RQgghhOQniwTndQAPmr9NAvBaYb0hhBBCCCmALDY43wXwgfnbeABVw1W2tLR4W4Ci9Nhat6jtW6yNT8g2IjbzraZiS1RBZ+S1n01ns15ppZVS711EJE2rn9Ru4tZlMg9ZbJlCY5z2WYcNSwoCzzzzTF+2emptd2NdyHVW4hBabx3SU9cja7R+R+2zCrkiF0Fem7Q814X0+qNGjUocjx492pd1BPAsLrfW3igPobASWTId58U55+2R7Luhj/Pa3OlM4Nquw1IPt3Bt+6KzYwPAWWedFdWGHoO8kb5j1/pGELJD1Fx//fW+/P3vfz9Rp+3UfvGLXyTqdPRtaxukbXm0zaMdGx05fq211krUaff+PNH5a0HPzzT7nOgNjnPu2i7+dkeOfhFCCCGE1JVcRsYiMkBExojI8KI7RAghhBBSK1ESHBG5FsAU59wVItIPwBQAGwNoF5Gxzrl7Q9c757x41Ypdi0guqMV8VrSqj0PJ8kJiSi0m1xFXAWCLLbbwZRt19d///rcvW/GgVhtpcXNI1Gw/m3bdzfvsQrS3t+O1116rW/uanXfeOXG89dZbp56rE5defPHFue6X130yVm1kRblpWBF1EWqpTz/99DNu2BXqHZE2Fpu0T7uihtx/640WdVuVVCOiFetEqRY93+16olXSVgWj0eutjpQLJFUR9UCrna2rs8a6r+sklIcddpgv22cQq6Ksh0pq2bJlfnzqETE/FP7i8MMP92Wroopl3rx5vqzDNABJNZRNqPmFL3zBl5977rlc99aEkkZbUwV9btp6HivB2R3Ak+XyNwF8AaXEm+eU/xFCCCGE9BhiNzgrAni3XN4DwO3OuXcB3AygcdGhCCGEEEIiiN3gzAWwiYi0oiTNeaD890EA6qu7IIQQQgjJSKwX1R8A3AJgNoAOdMbD2QbAS1luGNJjh/RvoTqNtWHQumkbvlvrqkN62ZEjR/ryT37yk0Sd1k/efPPNibpQCO3YrNr6vJBu3fY/dG4szrm6295UOOWUU6LPPf744+vYkzCxNiwh92ZtO2Czvut0BvWw+YgNm1BE+6H2ikjTUQTWLk/bCFqbjth5Wsu4LVmyJJcdVuhZp7m3h2xusmQrT+Pee5OmmdrOytrZhNB2H++++27gzE7s94Vei23YjHXWWSe6L2mIiJ/XWVI1xKJTVNjvIJ3F24YPiOWCCy5IrdO2U9oGCkhmd8/rJq7frZDdW55xihoJ59x/i8h0AMMA3Oqcq6wCSwFclPmuhBBCCCF1JEscnNu7+Nt1xXaHEEIIIaR2ojc4IrIlgJPQaVQ8A8Clzrl/VLtWu4lb0aF2qYt13QWSorg333zTl61KRYsjrWgyJHbVLplHHHGELy+//PKJ87RYbv78+Ym6UMTatD5atJg8lOHZZnsOZaktAu0GnSWjdBHYMagn9jlq1d9mm23my9/4xjdS27CqDp05+/TTT0/UFaGW6tu3r1d9xUZKBYofq1B722+/fWrd/fffn6vNIsg7b4qKQN3a2urf7yzrYdHP5cQTT0yt++Uvf5mrzZde6rRm0KoNi1WdjR071pdDa7ZWN9p5pNfierhxA+HPVE9uueWWqPPsux1rfqBVxuutt16iToejKGLtsu98bLiNNKJGRES+DeDvAFYH8Jfyv1UBTBGR79TUA0IIIYSQgomV4JwH4CfOufP1H0VkPICfAbix6I4RQgghhOQlVqa2MoA/dfH3WwGs0sXfCSGEEEK6jVgJzkMAdgTwqvn7jgAmV7t42bJlXj9nbXC0bYrVt2n7Aavf0ykNtKuttVPRbmdWv1dJQwAAQ4cOTdRddFGnc5h2T9PpFwDgf/7nf3zZ2sGEiNVXhtzXQ7Yv+rPlpa2tDcOHl9KNWR1/yLYjj940dI21UdLpGa644opEnX6fQqG8dQbeOXPmJOr0+6SzL+clpPPX2ehtv/Ly6aefZrK9qRfWXkKn2LDu8WlkySZeBNouwb4z+n2y/S+qnx0dHf6+9vnFhrUI2ePsvffevnzPPfeknheys7HzUa9ln3zyiS9bm5TYVBATJ06MOs9iw4BoQvY5RaDdxItCj39RKVzyEEofpL/zdIiLECE7KjuvQt9xMcSOyL0ALhCRkehM2TAKwDgA54jIuMqJzrkJNfWIEEIIIaRGYjc4l5f/P7r8T/NrVXYAWmvtFCGEEEJILcQG+qvJ/62lpQUDBgzosi7kXqlFWVYErMVmWmRqxWS6/ZVXXjlRp+9nxXBbbbVVl/39j//4j9Q28qKjbNo+htDiQasG0cdFRCO2YsWQ6i+kHtGuilr1Z9VEsS6XdrzTxOBW9Kmjgdr+hkSt+j3U0UWtePrBBx/0Zfvua1WDPq/ZsHPjmGOO8eWNNkqmsNNjoN1es7hKh9DvnXbbz9J+KDJ5PaLXhtaWUGiJEH/+85+j2rDPRb/reZk8udOaYd11103UaROBzTffvOZ7WULqq6Kx31X63bNrsV6XQiEKilZXhbD9mDlzZuq5NvN4rRSd6b17HPcJIYQQQupIcIMjIo+LyArq+AIRGayOh4jIv+rZQUIIIYSQrFSTq44CoHUf3wdwDYBKyN5WAFVlf3379vUiwlASyJB1tRUB60jGui6L+G7XXXf15VNPPTX1vEsvvdSXRSS1HzqiMhBWDYUSjKVhRZ8hC/OiIqumoVVpVq2mVWdW/aOfiT5v4403Tpy33377+fKQIUMSddqbxUYJ3nnnnX35q1/9qi/bRHD6WT766KOJOj3G1htNJ7bTSQAtIa8aLf4vSgVTBFoFE9svO2dD79rll1/uy7b9k046KfO9s/RLEzsfQokL7XoUO4erkTcKdR50JG4gGTHYRjLWKuNYdZWdmz/4wQ98+a9//WvqdVm8UXsi9t3Q6jfrwZVH1Riac1nqNCGTA+v5rCnCc6qeZFVRSfVTCCGEEEK6F9rgEEIIIaTpqLbBceV/9m+EEEIIIT2WajY4AuBGEVlSPu4P4BoRqShJ27LesCg3sDS3zCy6vqOP7gzpo+1xgKRr3EcffeTL1i1O6ysHDx6cqIv9rLEupllcNYuwu1m6dKnXzVo9bMj+Jzb6ZMjGQLuUWvfcUCRSG9m4QiiacGicrC2NHittK2D7pHXTRbjp1wP7XPPa3aRhXfY//vhjX77tttsSdbfffntUmyFCNjOxkb31e27npX4X6hVheenSpd4uw36eUETZPFj7j6uuusqX77vvvkTd9773PV/eZ599EnV33XWXL2sXfx25GgDWXnttX7ZhG/R8CYUL6Y3o52ztkvRzsOEktN1T6Dsi9Hxin11o7l9zzTWpdWeeeWbN964n1b5ZrzPHXSXVvL6gvhBCCCGEFEJwg+OcO6JRHSGEEEIIKYriw29WoTvFj6NHj04c60i6NlrjiBEjotrUYn6rpgglx4tNCqnFlK2tySwYun2rgtFt5k0u19LS4tuxIlLd/5D6J+/46vZ1GUg+S6sq0HVahWTVRPr5WLdUrWKznzutzZ7k7h1Llj7HjqM+z6pUt9122+j75SFNfQikqwmt6tWqENLat27h9h3NS79+/XyCW0sRru+xvP7664nj008/vctyFvQzCz3nnqDaKBKthgqpPWNNFUJq87yEnvkrr7ziy5dcckmibtasWb4cUhHnJTbBbBr0oiKEEEJI08ENDiGEEEKaDm5wCCGEENJ0NMQGZ8mSJak64qJ1xyFd4vbbb584TssYbnnmmWd82eretf1JKOy21R9qHbdOdRAKU27denWb1g4mb7bhNEJ2NvVA38/eO5RFXaPtLkL62yw67DQX+FD7b7/9duI4r01U0YRCsDd6vGNtxoqwz9DZkkNZpm1aGd1Ha3PT3WOaxz6n0bYueh5Yl2i9XtU7W3ajCa0veexn7Npe70zpe++9d9R5RdgCWWoNK0MJDiGEEEKaDm5wCCGEENJ0NERF1draiuWXXx7AZzNPa3FkKHKnjXypRVdFZPKdMGFC4nj//fevuU39Wa2aQou7Q/0PuZDXm5aWluioxI0ktk95xZt6PGwbadFFrcpCH9t3Xqt/utO93M4p3RerotKuqToisX4eQP5nHqvi0WqjkIotFCVbRxy3Lre6H/az6Ht3t0oqC/VWRcVmrNaqFPvc9dpfdMTmRqDNMIrK9p32rltTBZIOJTiEEEIIaTq4wSGEEEJI08ENDiGEEEKaDrHZsetB//79XSX8eChVQ6jO6my1njbURnei3VFjM0pbuwjtErjWWmsl6rTNhA19rrPzLly48Gnn3MioDij0uFlbEa0TXrx4caJO2y5Y25Qi3I9DWbzTUkhYOxjdRk+0MwKAl19+Ode4iUjqpNbvpH0ms2fP9uWQzZt+tzo6OlLPC6XAsOh5G7KtsX3WaHsg60qr34uhQ4f6cih0vkWfW2WdyTVuANDW1uYq87webrf1RtuvhcaqJ63TGuec5LkuNOdise+9noM67UloDbWhDUJpbXrD+6VtV+2aZOZjl3OOEhxCCCGENB3c4BBCCCGk6WiIikpE3gPwRt1vRNIY7pxLlxenwHHrdjhuvZNc4wZw7LoZjlvvpcuxa8gGhxBCCCGkkVBFRQghhJCmgxscQgghhDQd3OAQQgghpOngBocQQgghTQc3OIQQQghpOrjBIYQQQkjTwQ0OIYQQQpoObnAIIYQQ0nRwg0MIIYSQpoMbHEIIIYQ0HdzgEEIIIaTp4AaHEEIIIU0HNziEEEIIaTq4wSGEEEJI08ENDiGEEEKaDm5wCCGEENJ0cINDCCGEkKaDGxxCCCGENB3c4BBCCCGk6eAGhxBCCCFNBzc4hBBCCGk6uMEhhBBCSNPBDQ4hhBBCmg5ucAghhBDSdHCDQwghhJCmgxscQgghhDQd3OAQQgghpOngBocQQgghTQc3OIQQQghpOrjBIYQQQkjT0Ws3OCLiRGShiJwXef6RIrKgfN2X69y3h0XkExH5W+T565f71iEiR9Wzb90Nx613wnHrneQYt3PL5zsR6VPnvr0uIotF5IbI8xv2TvUEmmzsxpTHbpmIjKln3xI453rlPwAOwJfV8WgAC8w/B2D/0HV16tvDAI4yf7sRwBwAHwF42danXdds/7oYt/UB3AngPQDzAdwPYES16xo4bscDmApgCYBrY69rtn9dPX8AvwUwE8AyAIfHXtegcbNrQQeAyzluDgA2B/A0gEXl/zc39WuXr+tT5769DmBMF/d+qNy3l2x9o96pnvAv9DkBHFqut+99d47ddgCmAPgYwDQA28dcV89/vVaCY3HOPeKcG1T5B2BvlBa2+/K2KSKDRaRvQV28AMDazrnlAXwTwM9EZKuC2u7NrADgLgAjAKyK0gS5s5YGCx632QB+BuAPBbXXTDwH4DgA/yiisSLHzawFqwFYDODWItruzYhIP5Tm140AVgRwHYA7y3/P2+bKIiIFdfEmAM8AWAnAmQBuE5GVC2q7KRCRFQGcAWB6AW0VMnYiMhjA3QB+jtKafjGAu8t97TaaZoPTBYcBuM05t7CGNnYF8JaI/EJENqmlM8656c65JZXD8r91a2mzGXDOTXHO/d45N9859ymASwGMEJGVami2yHGb4Jy7A8C/a2mnGXHO/cY59yCATwpqsrBxM+wP4F0AjxTYZm9lRwB9APzSObfEOXcZAAGwcw1tfhfAa2UVyTp5GxGR9QFsCeBs59xi59ztAJ5HafxIJxcAuAzAvALaKmTsUJLezHXO3eqc63DO3YiSVH5cAX3MTVNucERkOQAHoPTrJDfOuVsA7IKSCH6iiPxdRI7LuysVkStEpCJ6nQPgL7X0r0n5OkoTJfeGouhxI42hjuN2GIDrXVlG/jlnYwDTzLOYVv57LpxzFwE4CMAqAKaKyEMicoiIDMzRt3865z5Wf3uulr41GyLyVQAjAVxVRHsFjh1Q2ijb4yJ/qGSmKTc4KO0a5wGYXGtDzrkXnHOnAhgK4ByUfgG9JiI3i8jyGds6DsAXULIXmoCSXQcpIyJrAfgNgFNqbavIcSONo+hxE5HhAHZAjT92mohBAD40f/sQpXUpN865J51zxwJYA8CVAA5GSRr3u+7uW7MgIq0ArgBwvHNuWVHtFjR2TwBYQ0QOFpG+InIYShqKPBulwmjWDU7mX2wi8u2ylfcCEbnX1jvnOlASlz6HkjHsJgAy2wuUxXePAlgLwLFZr29Wynr2iQCucM7dlOG6howbKZYGjtshAB51zr1WU4ebhwUA7EZxeZQMQ6siImeocfuMFKGshp8G4FkA7cj2C76mvn0OOA4l6duTeS6u59iVJe77ovTj9B0AewB4AMBbefpaFE23wRGRoSj96rs+y3XOuf+nDBP3VO0NEpHDRWQSSsaUawI40Dm3SS1qFJT04J97GxzAG81NBHCXcy7KJbJCN4wbKYAGjtuhoPRGMx3ApsawdFNEGqw6585X4/a9yt9FZCUROV5EpgCYBKAVwE7OuVEZ+/YlEdESm81i+/Y5YBcAY0VkrojMRcnu5Rci8uuYi+s8dnDOTXbObe2cG4zSD4sNUHIa6Tbq6ivfTRwC4HHn3KxaGxKRPQD8CcBjAK4GcIcyFM7SziooGfHdg5I3xxiUxIAH19rH3k5Z7XA/gMecc6cX1GYh41Zuqw9K86QVQKuI9Aew1Dm3tIi+9mbKnjctKOna+5afTXte8XmR41ZubzuUNkife+8pxcMoucz/oPwr/j/Lf5+Ut0ERORLAL1HyWD0XwH1lCVwmnHMvi8izAM4WkR8D2BOlzReNjEscDqC/Op4A4DYAv8/bYFFjV25rCwAvABgA4L8BvOmcuz9v34qgGTc4h6LkqlYEMwFs4JybXWM7DiV11FUofSG8AeAk59xdNbbbDIwFsDWAjUXkcPX3jZxz/8rZZlHjBgA/BnC2Ov4OSgvBOQW03duZiJJ9C1D6NflbADuh9CWahyLHDSipqicYo9XPNc65dhHZD8DvAFwIYAaA/Zxz7TU0+wSA4c65+QV08SAA1wJ4H8C/ABzgnHuvgHZ7Pc65D/SxiLQD+Mg5Z+2WslDk2P0IwF7l8n0ore3dSyhITk/+h5Jr6ocAfhp5/hEAPihf96U6920iSnrjhyLPX6/ct0VICZjWLP84br3zH8etd/7LMW5nl8//BEBrnfs2E6XAp9dFnt+wd6on/GuysdulPHaLUVJ/NeQZSvnmhBBCCCFNQ9MZGRNCCCGEcINDCCGEkKajIUbGffr0cW1tbZmvW7Kk04HCXv/pp5/6ct++ceEx2tuTdnT6uo6OpOH4smWdjiAtLZ37wD59us8uO9RHm05EHy9ZsmSecy5zPhcpZaUFAAwYMCDr5XVj0aJFvmzfCz3GWv06cGB6vKnFixcnjvVntSpce26dyTVueedbHvRYNILQOMYS6rNuP3Rev37J1E16Li5dujTXuAGlORdznn0OS5emO/XpdcOuIbHt67U4bxv6utbW1qg2QtTj3XPO5crLpMct7ztqv5/sO5aGfg79+/dP1OnvrtjnVcQcqwd6jgGf+WxdzrmGfFu3tbVhww03zHzdq6++6stf/vKXE3XvvPOOL6+66qpR7b31VjLmkL7ugw8SBuqpm6uVV+6+vG8ff5x0BtEvrB5sIPmiv/zyy2/kuZ+I+EmWZ/zqxdNPP+3La621VqJOj7Eew1D/X3jhhcSxPldvpAFg2rRp2TpbG7nGLe98y4Mei0ZQxOcK9Vm3HzpvtdVWSxzrd+2dd97JNW5ZsM/hvffSHY0+/PDDLstZ2tdrcd429Pr1hS/UHpy40e9eLHnfUfv9ZNe2NPRzWHfdZGg1/T0Q+7x60lqv+eSTZMo789m6nHNUURFCCCGk6ejRcXCs1EaTJrWxv7g1X/ziFxPHb7yR/kNL7w7/9a9/dVmuxlZbbeXL9f61MWzYsMSxFVXmwTnnf5mG+m+fq/7lYX+VxP76i2XBggWJ4002yZ7bLXSN/dWgpXn6V3tPYtGiRT3y120R8yF0Xei90+9o6LzYfmVZB/Ji1Yyh91T3Rz9nIH3O2TU0JDEIzVv9bENrdhFSG439nCFC41ppZ8aMGTX3CfjsmqGP581LJgAfMmSILw8aNCiq/dBnseu+1UzU2r4lNAb6c2vp3QorrJA4T6subf+15NAyfPjwqv2jBIcQQgghTQc3OIQQQghpOrjBIYQQQkjT0aNtcEKk6eZCejmr3wudq/WHWsf88ssvx3axoXYQ1ibA6jnridXPF21nk0XXXjTWbiDN7sbaIcU+A3udfke1p2BvJDRuecc0NKesPY1Gj4d+5kW9W0XN9ba2Nr8uZbFZse+RJu1dDNncZPk8IbubnkJonIsYu4EDB6Z6H+nnb7+D9LF9f9PW8E033TRxrD077fdirIdxXkIeXLr/IZvQUJgX/W5ZL+KY8DCU4BBCCCGk6eAGhxBCCCFNR0NUVCG31bwi4lixaChYoHYpX7hwYaIuTTyYt7/WfV2L14oQkVo38aIJfe5QkDEbGFE/h1DQPH2/nXbaKVGng6xZt8sDDjigy/amTJmSONZRQ2+44YbUfsSSRUWlP5sVu2ZRgfZE6q1OLMLVPBT2Qc8jO4ahNaeokBBLly71qkkr1tdrhl1PYt24Q+eFQmyEyBNVvidRhJu4/o7Loi7Uamh7XdpzDa2bRZgL2DkcG1h11qxZme+VpS/23bJrZ1dQgkMIIYSQpoMbHEIIIYQ0HdzgEEIIIaTp6HY38ZgQ2lmus3pMfWx1dlrHXW+36pBuWty0CLoAACAASURBVH9O6+anXdlDutd6uImHXB811s4mFB5cPwetz7XujN/61rd8efTo0VX7UMFmnK0wcuTIxLHWF1v7Jf0sY+0p7PMOJWXVuvUiUmp0J9ZlVT+vetjjrL/++r48derURN11113ny5dffnlqG6Ex1WNv15JGhH1oa2vztj7WJiZk6xaywUtzkbfkCesPAAcffLAvn3766b5s56K2j2tEqotYKsl2bTbvLPTv39+7SdtM6drOxtrE6PfZrgU6lVARoTfyzsfQdXrdrsf8CNnQxmSkpwSHEEIIIU0HNziEEEIIaTqiVVQiMhxAxT93rnMuPRV3QeQReYUiRYbcLuuNFTdrdZMW84cynodcka2IeubMmfk7W6a9vT01OmzeKLv6s+q2tUoKiFdLvfTSS4njSZMm+fLQoUN9eZ999kmcp6NurrHGGom6kPhcZ3jOk7kcSEbJtm7hjcxAXwQhtWle9XOILbbYIrXu7bffjmojdG/9ToYi/VqKGqtly5Z9Jht1V2RxYddzLrTmxaqN7L20Wuqpp57y5ZaW5O/nOXPm+HIRz6soFWhlHtfiJi4iXmViVX0hV3D9nWSvi1VL6ffUrte33367L5977rmJuqK//8aPH584vuCCC2puUz8D+87EjH9VCY6InCwibwL4J4Anyv/+KSJvishJGftLCCGEEFJ3ghIcEfkJgFMBXATgfgCVreiqAHYDcK6IDHLO/ayuvSSEEEIIyUA1FdXRAA53zk0wf38TwFQReQnA5QC4wSGEEEJIj6HaBmclACHF5EwAKxbXnXxoV7ssGXgbSUjfqe0YrF5RuxtbN3etn7T6Wq3bTcuAnYWiMlvrz9qvXz9fHjt2bPS9r732Wl+2qRoWLFjgy9o+x2a63WijjXx58ODBibqQfUAoA71G99m6wOt3tDszpWdB2x5pRowYkTiOTUESa59j7RJ0yo2HHnooUTdhgv0dlp0sdjcabecSskuqRktLi5+71i08lJU6RNrak9cOpuJW3RWrr766L9t5q213sqSFSOtnlv43cp6FbJns+qHf71gbKPtZtM2WXteA5Bpbb5vTb37zm4nj7373u7683nrr+bL+vrbYd6bW1CLVbHCmADhLRPrZivLfziyfQwghhBDSY6gmwTkewF8BvCsijyBpgzMawCIAu9ave4QQQggh2QlucJxzL4jI+gC+A2AUgIrf7VwApwP4X+fcR9Vu0traikGDBgEoJiIjAKy99tq+fPjhh/uyFetq0frixYsTdXPnzvVlG0G43mgxnXYVtuJfLdK0LsVa9WHdD7U4Mu8z15mNLfreVn0RK2odMmSIL4tIom769Om+rF0dgXh3U33duHHjEnX6mTz66KNR/QXiVaBWLdXbKEKkH8pKbLEqxAo2Q/Ef/vAHX77++utr6F2x1EP8b9VQei7mjYAdqxrad999E8e///3vfTlNXVkNHY7BupDrqMe1uGunUe+QC+3t7YmQHrHErs2h+ajXyv/8z//M3Ics2O9JrXa0937sscd8OaSW0t9rNjJ6SOU9f/78qv2tGgfHOfcxgCvL/wghhBBCejyMZEwIIYSQpqNaHBwBcBqA/QG8D+AK59wdqn5VALOdc8GsV62trV6FUpSK6uc//7kva3VVFrRXkhbz1QOtRgOAe++915cPPPBAX7YeT1YtpdFqECsib7TKTaPVZXa800StF110UeJYe0NZUWSsuNmqpTShCJkh9DvTUz328lJvT5M87du5PXDgQF++5ZZbEnXWo6u3E4ponFdNFOvhdccdd1Q/qQtC3pah+ajVOzYidZo6vyfR0dER9d1m1+nQNXq+aNMLawKgvUD33HPPRN0ZZ5xRtU9ZCEXMXmmllRLH2ov1S1/6ki+Hksba91O/51btb9WcXVHtjP8CcAaASQBmAbhJRM4z58hnriKEEEII6Uaq2eAcCeAo59yfAEBErgFwj4i0Oef+q3yOq2cHCSGEEEKyUm2DMwwqzo1z7h8ishOAh0SkFcCF9ewcIYQQQkgeqm1w5qG0yXm98gfn3EwR2RnAQyjFw8mEdWfWx7HuxQBwzz33+PKWW27py1Z/u9xyy/ny+++/n6jbcccdfXnUqFGJujfffNOXdVbqEEuXLk0ca13jP//5z0TdNtts48uHHHKILxfl+qrdy2uJrFoh5OZn9cra5TPW5io09jaCc4ibb7456ry77rrLlz/6qGqkA09PtQGIpd52NtpuJK8rs+a0005LHGtbDW2nBTQ2+7qdD/VwE19hhRUSx3ou2boQMdnJAeDCC+N+r77yyiuJ47Qotfa+ixYt8mUbgXydddbxZWuPd+yxx3bZfm+Zi9r12Ubm1oTmpo5ov/POOyfq/vjHP6Zep0Nl5J37el6F2rCRjGPRbVrbUf3OW/uu0HdShWo2OI8C+IxlmHPuJQC7lP8RQgghhPQoqklwLgTQ5ZbNOfdiWZJzQOG9IoQQQgipgWqRjKcBSNVvOOemA6jqX92nTx8vYrOutVZUqdl444192Yq7dcTfUPK3EPq6LbbYIlE3depUX956662j2rvssssSx1pEe//99yfqtIg5JLYMoVVPoQiQeenbt693zcviEq3HKhTRNxRVNVatppP7AUmX+xDjx4+POq+30+hknnrsrYpCh2II9Uu7ho8cOTJRp9USeT/bW2+95ct5k8jWSz3inPPzwj4/PZc6OjoSdaH5nhYCw6oUrDpQ89RTT/lyyD33xRdf9OWJEycm6rS6wZoq7LHHHqlt9ja1lH0vtdqliDApZ555ZuJYR2LfYYcdgn3JQ6wK6eqrr07UaVOFEHo+2vci7V5AnAqcgf4IIYQQ0nTUtMERkRkisrT6mYQQQgghjaNqLqoq/AbASlXPIoQQQghpIDVtcJxzv445b/Hixd7exdpkaD1zPexIQmi38UmTJqWe9+CDD0a1Z3Xm2pZgxRVXTG1TZznPojPVNiw2k21IlxmLiESFw7a2NLHjpm2Psuim9TM655xzoq6xGcOti3F3YUPu21QdvQ09B1pb0zO4hFy6Q89gr7328uVLLrkkY+9KrLXWWl2Wq9EIN3TnnLevCc2JIUOG1Hyvn/70p4ljbY9kwzbosBYWvZaNGTPGl0Nr2bBhwxLH+lztEg0AxxxzjC+fd54NpN8zGDBggHeXt+uhthWxY6rtTEMccECnL4+2WQGSa7+1eynindVjY1M1aPsoe++HH37Yl0N2pnnt4GLCH0RvcMqB/Sqzap5zriN0PiGEEEJId1H157mIjBWRxwAsAjC7/G+RiDwmIvvVu4OEEEIIIVmplk38GACXA7gOwKUAKrKkVQHsBuBmETnBOXdNqB3tbmzFj1oUatUsIUJZTbsLm8n1L3/5iy9bUeHcuXN92WbLjkWrgqxKSruhZ4kQrdHu/bH9sIRcwTVZVFQ603FIxXDCCSf48q9/HaVNBZBUo+YVn2qsqF5HZrZiVv2884qXBw4ciA033DDXtbUSct0MqSy0++lXvvKV1PN+9atf5etYAej+10td1dLS4p+hFevr9zL0nK0KIw1rEpB2L9uXgw46KFGnQ33EqtjtmvTvf//bl+2aozPIa7WaVfvPmjXLlxsZ1dpixy20hsRG+9aZuvXzAIArr7zSl+sdFkKbXQDAUUcd5cs2dIGO5K+/B2LfzyKopqI6FcBxzrnfdVF3m4hMATAeQHCDQwghhBDSSKqpqNYE8Eig/lEAaxTXHUIIIYSQ2qm2wZkO4NhA/TGIiGRMCCGEENJIqqmofgjgzyKyJ4CJSNrg7IqShGevlGs9HR0d3sbC2lpYN1lNKLR30VmLi+C4445LHOt0D1YvO3PmzC7bsLpjrVO1WbVDn7veLvax2M+t9euxLtF777134jjWtTeL3Y1G9zmUaiLWPseOqXattPYGRein29vbfTtZ3KC1zjzG9qpIDjnkEF/WKQSeeeaZxHn33HNPahuxWY97CyNGjEgcx2YFt2ulXmNj7fHsvbS9S95nq+0w7Pp05513+vLChQsTdbGpcjS2j/W2yRER/5lCdqYW3S87bltuuaUv77777r6s7TeBZBqNIgiN79FHH504HjRokC/PmDEjUadDrxSRHiUUYiaNarmoJovIJihJcUYBWK1cNRfAHQCucs69nqOvhBBCCCF1o2ocnPIGJj0LGyGEEEJID6PWVA1RtLa2evGbVUtoMVMWV2Et7uxOFdXXvvY1X9aufJb99kuGDLJi2DS0CNOK6HSG70arFPKi1SCx3HTTTdHnTp48OXP71l1Wq6jsc9XvnVb/WPWhfq+tiFpnRLZjqtvMK8rt169fJtVUBf1Z7TjV+/3aZZddfHnw4MG+fO+99ybOc86ltlFvtVQoGms9sGocfRxSZet1wR7rqOmvv/564jztAjxgwIDU9vMS6r8+3m233RJ1adHUbeZs7SZuCbn4V6IJh67Pgl0LYrHff7odHfk5b9iPWOzz0WuJjeq///77+3IlW0GFItRSGmvOwmzihBBCCPlcwg0OIYQQQpqOhqioNNbyWYuZQl41IfWVFcnGErLqj0X337ahxXk2sqz2sIrFivm0yM6K6/I+k0YSEluuueaavjxt2rRE3XbbbefLOooqAJx99tmZ+2HbD3mdpInq7fPW73nIo8M+g0arQdIoQiVlRd3aS8TOZ+0JokXiVo1SBPoZ64jfQFI1Z5MHNjICazXsOxXrAXf++ef7sn33bJTaRqLn9BZbbJGo0+Ogy3nU0UD6HI5JLNxI9HeEXue23377xHm77rqrL0+ZMiVRF2v2EVJBrrLKKr58+umnJ+r0nNDnAcWopULtDR8+vOo1PWtECSGEEEIKIHqDIyLDRGR187fVRWRY2jWEEEIIId1BFgnO6wAeNH+bBOC1wnpDCCGEEFIAWWxwvgvAGgiMB/DFLs5N0Nra+hldd4WQy1soq7PWLWpdf8Xlr0LR0X6t+6TOat7e3p6o0/Ygm2yySaKuEZmJa2XRokW+bzbKpv7cNmN4KMKktrPS42tdAE87rTP0kh2nV155xZet/cxLL72Uem9N6N3SfbSfO22sbP/1+26fj3b5tHZneaLOWpYsWeKzc+txAhob7TfUvn2Oo0eP9mU9hvVwiU1bi4CkLZW1uQnZEHU3sTZToVAWmnq8G7qPJ598cqLukksu8eVQZN60DNVZsHZuofchD9YeT0cu1yEisvDLX/7Sl0899dRE3Q9/+ENfPuussxJ11kZRo8dY272NGzcu9Tz7/acjXOus5vXAjlOMnWn0Bsc5d20Xf7sj9npCCCGEkEaRy8hYRAaIyBgRqW7GTAghhBDSYKIkOCJyLYApzrkrRKQfgCkANgbQLiJjnXP3hq7v6OjwYsGiRLtpkR2nT09Pbm7FrnncxK3KQrs03nfffYk6rQYJRboNiTB1XXe6fttxy6tW0yoYrZ7RETGBcIK9559/3pcvv/zyRF2sa2LsebHvq1U16fckFBqhHioYEfH3CI1TvVWjITXHrbfemjjWLqY2erEmts8h1/yQetjO7zTsedpl1apNsxBSC+t7WDWLfo9C69xee3XmRn722Wdz9zMPZ555pi9bV+cQOlTAMccc48vWjd+qY9PIq9qKxa71edVSmvPOO8+Xly5dmqj71re+5ct2PYxFq7Lsu6XfZxFJ1Gm1kV6Xi0J/z+fJWBArwdkdwJPl8jcBfAGlxJvnlP8RQgghhPQYYjc4KwJ4t1zeA8Dtzrl3AdwMYKN6dIwQQgghJC+xG5y5ADYRkVaUpDkPlP8+CEB95X2EEEIIIRmJ9aL6A4BbAMwG0IHOeDjbAKjql6ttArLYHITC1ut2tK465BYb0t+H7AW0HcyJJ56YqPvoo498+bHHHkvUabdIq5cNpaXQaPuNLDY4ebJ2N4K0z33KKadEt/HHP/7Rl2Pdwi2x2Xn1eUC6S2neVB+2/SJscpYuXVp4mPQ0QrYuIex5Ok3A+++/H3U/m71Yv1t5bY9Cen79TO24FZViY+DAgT6ti+2nfsesW7h+b2KzWW+zzTapdZUwAxVi7Vv0ddb25O233/Zla0cSGpMZM2b4cujdCLWh7SG1S3Q9sOt0mt1hXi666KLE8f333+/Ldj7q9cradOn3afz48an3+8EPfuDLv/rVrxJ12kbx3XffRa1suummieO862qFqA2Oc+6/RWQ6gGEAbnXOVQK+LAVwUfqVhBBCCCGNJ0scnNu7+Nt1xXaHEEIIIaR2ojc4IrIlgJPQaVQ8A8Clzrl/VLvWOedVLSE3TOuSq8V5VnSlXdfSohoDYZfQkFpKu8ONHTvWl1tbWxPnaTGsVQtpd0T72bTqSZfXXXfdxHmhKJshN3ctws6r9hAR9OvXD0BYtJolYm0RaBHwoEGDEnX6eNasWalthJ6JFinb56rHaujQob48ZsyYQI+TaPH8NddcE31dvSk6unZIhWQjrmrWWGMNXw6pIG108CL6HKvas/NSvye1qBm1m3gW9Z8+N+SmbCNup3HCCSek1v30pz+NasPyxhtv+PIzzzyTqNOZvG2IiFGjRvlybIRl+6z0uNqwDRX3e+dcVNtd0dHR4Z+7VVEVoZYKoVV4Idd/O/ax/dJRxu01oecai/5uz6KSipmrUUbGIvJtAH8HsDqAv5T/rQpgioh8J7pHhBBCCCENIFaCcx6Anzjnztd/FJHxAH4G4MaiO0YIIYQQkpdYN/GVAfypi7/fCmCVLv5OCCGEENJtxEpwHgKwI4BXzd93BDC5lg5o2xTreqldNrX+1hKyHQhlT9Z1gwcPTtSdf36nsGqdddbxZWvX8dBDD6X2S+sTrf4zTX+o3RlroQh7hAEDBniX1UYTyvZtMxGnoZ+/Dc9+9913+7LNGq3fhQMPPDDqXnmZMmVK4rgIN/GQq3GIWBuP2NAL1h13zpw5vpz3Pdf3i02rUBR2fdLUI/x/UWOn0ZmiJ0yYkHpeFjsbnf1bu57//e9/T5wXSr+ieeKJJxLHeTKbh9Z6aytSWSdsGoIstLa2pobxiLUDzbtm6+9Jnc4DSNpf5bUF0umPDjjggESd/v6OXbuKylQfY08Wu8G5F8AFIjISnSkbRgEYB+AcEfGzxjmXPmsIIYQQQhpA7AanksHr6PI/za9V2QFoBSGEEEJINxIb6C/WVqdLdCTjkGuXFXFpUVZsFN+8kVXnz58fbKfCPffckzjWbnk2CqpWkYTEg7FuctYNXYsHbaTRol1+LaE2Q6oDLVbUbr5WnZE3Eq++dygq7VFHHeXLduxD6gYtxg65RWoVmI1yq93LdRTSotCuxkWRpz2rzj344IN9ORRFV2eWD4m9YzO950VHMLfUGmG1u/i///s/X7bvng4t0adP8qth8eLFUe1rdZV2/QaSa6d2PQaAIUOG+LJ2Cy+KotQiRROaVzpkSCjkhZ4Hdk7EtqGx6uNQlO5Y13r9HWfX17xzKRRCpUJNGxdCCCGEkJ5IcIMjIo+LyArq+AIRGayOh4hI7VaRhBBCCCEFUk1FNQpAP3X8fQDXAKjI9FsBrFntJtrCPG/00ZD1uU7wFlLVWHSE4u98Jz1e4amnnurLDz/8cKJOq0SsaE+rMKzFd6xaSovzbIK9kIiuCBVFXlVHSHWQpqqzkar32GMPX7Zjqj3qrNeAjmS8yy67VO8sgL/97W+J4+WWW86XrYeVvreOIBrCqhb1M2hvb7enNy3/+7//68tWBXnooYf6chGeZHnRaimr4tR9tirzeiS3zatuj0WrAgFgv/328+Uf/ehHiTq9RnV0dKS2uWzZMl+2XlPbbrutLz/55JNII1Yd1lMJmRJkUZUVMd6xaqmQSYNdAzWx0Yu7S6WbVUWV34+OEEIIIaRB0AaHEEIIIU1HtQ2OK/+zfyOEEEII6bFIyM1LRJYB+CuAitHAnihFLl5UPm4DMMY5F4x9s9xyy7nuiogb4rzzzvPl3XffPVE3depUX/7d737X5d+BcEZvndU31s3dovW5MW5xFXS2dQBPO+dGZr23iKS+HEVnEC/KjTM2omy9dcJab53X5R11GLeiseOmn//AgQMTdZMndwY9v+qqqxJ1J554Ys190XZueaO26s9jbSmsDVwaTz/9dK5xA4C2tja32mqrAQhnLA9lDC8C+1mPProz/Nk+++yTqLvzzjt9edKkSb78z3/+M3He9ttv78s6Oi4Q7n8Ra0PsmuScy2WGEfqOC0XTrzex9w49H71HsPNqzJgxvvzoo48m6vJ8VuuSHvudlzbnqhkZX2eOu0qqeX1UDwghhBBCGkRwg+OcO6JRHSGEEEIIKYqgiqootNg1FK3Yqhe0mqUI0Z6Nnrnvvvv6snYvtoRUVHl54YUXfHnEiBG52tDPx0YPNq7auUTm9VYtavd+61oeUhVoYtUGebGqAO0WqcWnNhls3ii7Jsloj1RR1UPMnketaSMN51HThD6LHXs9pjbytnmXc6uo9Jyz7rk1qDoLxYa8iFUH6qi6WdTtjWLGjBlYuHBh4SqqWEKhULoTHZX90ksvTdRplaR9P3Uk63qv02kqKnpREUIIIaTp4AaHEEIIIU0HNziEEEIIaTqisonXSnt7e2r49bypGtII6S21myIA7LjjjlFt/vznP/dlq5cPuX/H9tnab6RhUxbUm6VLl3r7l5DLal5iP4/V32p7B1sXctvPgx3ftPG2nyXkrq77Ze0stC1VETYXWewlQvZwPcX+IzbFSWwbIWwYepuKRaNThpgQDbmxdj76OIu9Rne6Kaf1w6LHxD6/tD5nsdsKpSLoTkJ90e+fXvNsuqN6Y8MCpBGaH90FJTiEEEIIaTq4wSGEEEJI09EQFdXAgQNRcaGzIrlQ9t7uykAKACKNyysacimOFa3mdUsOsWzZMq/S6M6xsMSqtqw6URNSLerrQqoZLZINPX8rutVu79a9PzY7b4jW1lYf9iDUL3tv3S+rUtbnavWVVa/q80KuobERpy36+cycOTP1PPvM7WdN64cebzsWWr1n22+0Ci+Lqin2XK0uteoxTWgdCqk5Q2tZSK1XhEqpp6iltKoJSD4TW6ejPddDtajfb/vdG3MNkFwr7XzP037RUIJDCCGEkKaDGxxCCCGENB3c4BBCCCGk6WiIDY7G6hIbqRs94IADGnYvi9XZ66yp2u3PPg+d0iFE3vDpITo6Onw/bZbXIlwVtY7e6nZDNjKxqRvy2g3pvoTsfXT/bX9D7ura5d7W6XbSQitUo6OjI9X2Rr8n2rXZYu+d9hxsG3mf+bBhw3xZ27NY3X3IRinkpqr7pcv2XdLtW7sdPafs57Tzr6eibSbsOxuyu9H2ObEu6faZ6DWjiDQbPYlFixZFfZfZZ6efq323tet8rKu/teMJrV957G7sNaE2usvuRkMJDiGEEEKaDm5wCCGEENJ0NCSbuIi8ByAuXC+pB8Odc5nTuXLcuh2OW+8k17gBHLtuhuPWe+ly7BqywSGEEEIIaSRUURFCCCGk6eAGhxBCCCFNBzc4hBBCCGk6uMEhhBBCSNPBDQ4hhBBCmg5ucAghhBDSdHCDQwghhJCmgxscQgghhDQd3OAQQgghpOngBocQQgghTQc3OIQQQghpOrjBIYQQQkjTwQ0OIYQQQpoObnAIIYQQ0nRwg0MIIYSQpoMbHEIIIYQ0HdzgEEIIIaTp4AaHEEIIIU0HNziEEEIIaTq4wSGEEEJI08ENDiGEEEKaDm5wCCGEENJ0cINDCCGEkKaDGxxCCCGENB3c4BBCCCGk6eAGhxBCCCFNBzc4hBBCCGk6uMEhhBBCSNPBDQ4hhBBCmg5ucAghhBDSdPTaDY6IOBFZKCLnRZ5/pIgsKF/35Tr37WER+URE/hZ5/vrlvnWIyFH17Ft3w3HrnXDceic5xu3c8vlORPrUuW+vi8hiEbkh8vwx5XFbJiJj6tm3ngDHrnZ67QanzGbOuTMBQERGlx+g/udEZH8AcM793jk3qIF9O9459/XKgYjcKCJzROQjEXlZL6zOuZfLfXukgf3rTvS4rS8id4rIeyIyX0TuF5ERlRO7e9zKfTxIRGaUF49ZIjK63LfP7bgBgIj8VkRmlhetw/WJ3T1uIrKhiEwSkQ9F5FURGav69nkft81F5GkRWVT+f/NKnXPubAAbN7Bv+zjnDtF/EJETReS18nybISLrl/v2QHnc/tXA/nU3ibGrICKHlr/f9PdIt46diGwnIlNE5GMRmSYi26u+dcvY9fYNjsc594hzblDlH4C9ASwAcF/eNkVksIj0LaiLFwBY2zm3PIBvAviZiGxVUNu9mRUA3AVgBIBVAUwBcGctDRY5biKyK4CLABwB4AsAvg7gn0W03QQ8B+A4AP8oorGixq386/VOAPcAGAzgaAA3Vr4oP8+ISD+Uns2NAFYEcB2AO8t/z9vmyiIiBfXvKABHAvgGgMo6Pq+ItpsFEVkRwBkAphfQViFjJyKDAdwN4OcorekXA7i73Nduo2k2OF1wGIDbnHMLa2hjVwBvicgvRGSTWjrjnJvunFtSOSz/W7eWNpsB59yU8q/9+c65TwFcCmCEiKxUQ7OFjRuAcwH8t3PuSefcMufc2865t2tssylwzv3GOfcggE8KarKocdsAwBoALnXOdTjnJgF4DMAh4cs+F+wIoA+AXzrnljjnLgMgAHauoc3vAnitrCJZJ28jItIC4GwAJzvnXnQlZjnn5tfQt2bkAgCXoZiNXyFjB2A7AHOdc7eW59yNAN4DMK6APuamKTc4IrIcgANQ+nWSG+fcLQB2AbAMwEQR+buIHJd3VyoiV4jIIgAvAZgD4C+19K9J+TpKE+XfeRsoatxEpBXASAArl9Ucb4nIr0VkQN6+kXSKnm8GAVDrZrcZ2BjANOecU3+bhhpUG865iwAcBGAVAFNF5CEROUREBmZsaq3yv01E5M2ymurc8saHABCRr6K0Jl1VRHsFjh1QmmP2uFvnXLO+OONQ2t1OrrUh59wLzrlTAQwFcA5KTNMUOwAAIABJREFUv4BeE5GbRWT5jG0dh5KaYzSACQCWhK/4fCEiawH4DYBTam2roHFbFUBflDbLowFsDmALAD+utX+kawoat5kA3gVwqoj0FZHdAOwAIM+i3WwMAvCh+duHKK1LuSlLOI9FSXJ2JYCDUZLG/S5DM2uV/98NwFcA7FRu58ha+tYslH9wXYGSvdmyototaOyeALCGiBxcnnOHoaSh6NY516wbnMMAXG9+pQQRkW8r4+R7bb1zrgPA8yjZHcxHaWea2V6gLL57FKXJfGzW65sVEVkZwEQAVzjnbspwXT3HbXH5/8udc3Occ/MAXAJgr9j+ka6p57iVVZ37oWTHMRfADwH8CcBbBXW/N7MAgN0oLg/g45iLReQMNW6fkSKU1fDTADwLoB3ZfsFX5tvFzrkPnHOvA7ganG8VjkNJ+vZknovrOXZlifu+KP04fQfAHgAeQDfPuabb4IjIUJR+9V2f5Trn3P9TRsp7qvYGicjhIjIJJWPKNQEc6JzbpBY1Ckp68M+9DQ7gjeYmArjLORflElmhnuPmnHsfpQmqN8rRm2aSTr3nm3NumnNuB+fcSs653QF8CSUD9s870wFsagxLN0Wkwapz7nw1bt+r/F1EVhKR40VkCoBJAFoB7OScG5WhbzNR+mLlfOuaXQCMFZG5IjIXJbuXX4jIr2MurvPYwTk32Tm3tXNuMEr2bhugm+dcXX3lu4lDADzunJtVa0MisgdKv/weQ+mXxB3KUDhLO6ugZMR3D0q/UsagJAY8uNY+9nbKaof7ATzmnDu9oDYLGbcyfwRwgojcB+BTACejNI6fe8qeNy0o6dr7ikh/AO15xedFjpuIbArg5XL/jgOwOoBr87TVZDwMoAPAD8q/4v+z/PdJeRsUkSMB/BIlj9VzAdxXlsBlwjm3SERuAfAjEXkGwBdR8oD7ed6+NRmHA+ivjicAuA3A7/M2WNTYldvaAsALAAYA+G8Abzrn7s/btyJoxg3OoShuQswEsIFzbnaN7TiU1FFXobTgvgHgJOfcXTW22wyMBbA1gI0lGUtlI+dc3pgJRY0bAPwUwBCUviw/QekLOJOUqYmZiJJtC1D6NflblOwmHs7ZXpHjdgiAo1BSaz0CYNcaNrlNg3OuXUT2A/A7ABcCmAFgP+dcew3NPgFgeEHeTsej9B7NBvABgGsA/KGAdns9zrkP9LGItAP4yDlnbaqyUOTY/Qid6sT7UFrbuxfnXK/8h9KXzYcAfhp5/hEoTZhPAHypzn2biJJO+6HI89cr920RgMO7+9ly3DhuHLfm+Jdj3M4un/8JgNY6920mgI8AXBd5/i7lcVuMkgql258vx65nj52Ub04IIYQQ0jQ0nZExIYQQQgg3OIQQQghpOhpiZNy3b1/Xv3//6idm4NNPP9Xtp56nVXALF9aStaE6gwY1MrcgsGRJp81kv37pqWQWLlw4zzm3ctb26zFusejPZtFjH+qfHvvQO2JZvHixL3d05HIoKIpeN24aq/5eunSpL2cZj+7Crhf6mep3BAAGDuyMZ7Zo0aJc4waUMkhXygMGJANm63va59fW1ubLCxYsSK3r06dzyQ+th3Yt03MuNDdDa6Dulz1P19nP3dra2uV5WdD3s/3Xn805lysvU0tLi6s8W/28sxD72ezY6+NFixZF309fl7fPRaCfv02Lpce+vT1pB2/e+S7nXEM2OP3798fIkSMLbfPNN9/05aFDh6ae98knnWlynnwyV3ykaIr+jNV49dVXfXmttdZKPe/JJ598I0/79Ri3WPRns7z1VmfsqC9/+cup5+kJscYaa0Tf+/nnn/flf/+7llBHNdPrxk2j5x4AzJ/f6aiRZTy6i6eeeipxPGKET3KPZ599NlG3ySadMdGmTJmSa9ws+n72nquuumqiTs+Dhx9+OFE3fPhwXx48eLAvh9ZD+/7Mnt3p2Pbyyy9HX6fR/bLn6Tr7uVdYYYUuz8uCvp9dW/R6kpc+ffr4MQmtSSFiP5sdez2XpkyJDzuj28nb5yLQ3+V2c6s3pnaczDvf5ZyjiooQQgghTUdDvKi02NWy4447+rLeyQHJ3dyQIUOK71gO8v6CaDT62S1evPhp51zmn/ShcUu7lyX0K1Sj3wMg/Jy1tEqrBoBiJAOxY2z7XATm3nUdN/JZRo3qDN5q1XwZ5n6ucQOSY7f++usn6rT6wf7i1tImqz5Le09DnyfLfAw9s56IltACnRqAmTNnYtGiRblUVKE5t/nmm/uyNSUISVzs+FewavNZs+Ji2oZUnnnRn82u7fp+22yzTWobsfPKPg+99j/77LNdzjlKcAghhBDSdHCDQwghhJCmgxscQgghhDQd3Z6LSuvfrN5Xe2FYvem663Ym4ta6OG3tD4T11nnQ+mYgqXPuTvuckB1MvQnpctNsbixZ7AEaSd57h2witMeNtS3T9+st9l7NRMizaKWVVvLl9dZbL1H3yiuv+HItnndtbW3e60l7PAGN9TyzHnAhirC7mTdvni8XYW9p544eu6985SvBc4tG25bae+t+6e80ywcffNBluRoh79oivMdC63usjY9e8/R7YLHvhfVy7ApKcAghhBDSdHCDQwghhJCmoyEqqkGDBvlAS1bVZEV2Gh0YzJ6n29F1VoxrVVYaLYaNFbOGzgupM6zIV0et1AHpQkG0QmhRJ1B8gDrtDggkg2+FsOJf476eep1+luuss06ibrPNNvNl+1ytCrHCI488kjjW78kNN9yQeu9YrNhYfzarPrQROZsV+07qgJx2HsXOv0aq7ey7pN81+95plVUtc69Pnz5eNaXXPyCsoirC5VerM4pQX2ShCLWUVm9kmcOV+ZlFLZcFrXoKvTd2vLVqK+/4Nnoc08g7b0PjqF3P09qnBIcQQgghTQc3OIQQQghpOrjBIYQQQkjT0XA3cWuPoG0XQnp4q2NL081ZWwjraqnR+tAs+u6Y9oDk57Hup2lui9YGR7sUv/DCC6n3trrWIvTp2mU1lK3cZrANZbRN0yVbF8nddtvNl0Nhvi1p2b+32267xLEObz5s2LBE3b/+9a/U9tPCGlibpJDuWLdhn1VP0ZnXA20jkTfFxtFHH+3LV199daJOzyM7V/K439s5G3K5LYply5b5dSSvW3haiH8gbGei7WBiwzsAwI9//GNf3mWXXVLPO/zww335jTfS85HGrvWWvHY8lfVl6tSpua4HkmtlyA7Ufh/p7whbl9ceM428dpSxxM4r24/QuxYKIxMDJTiEEEIIaTq4wSGEEEJI09EQFVVHR4dXHVnRtBbR2yiGIdc4LbrS6h7rmqoJqZBC4jqt9gqdF1Kx2X7pYy3CtOdpl+KQK3g9xOdLly7197DjplU8WVzUtfhci2C1SgqIV0u99NJLieP777/fl7/0pS/58j777JM4T6vEPv7440RdSEVVRFTlUBs62nZvj2ScxV1ai+dD82ijjTZKrTvssMN8+c9//nPqebHqQ4tWH1oX8lAE5CwsXbrUr4NZVAha7D9z5sxEnVaZhJ6tdksOceihhyaOtVrqgQce8OWWluTvZ60C+vDDDxN1IdVs2pjkHccsmdJjaW9v988vpH61YUv0e59XPR0yR7j22mt9WasSgeJVVOeff37i+IwzzujyvCzqT40dp5AqtkJQgiMiu4lIH3X8LRF5VkQWisirIvKDXD0lhBBCCKkj1VRU9wIYDAAisj+A6wE8BuBYAHcDuFhEDq5rDwkhhBBCMlJNRSWqfDKA85xzZ5ePrxeRt8t/v6kenSOEEEIIyUMWG5z1AFiV1F0AftzFuQm0Xjlkg2MJ6fC13rGILOEhitBV5s1gG2vHYM/T6QyKcD0OjZOtC2U2T3N9HDt2bOo1r776auL4iSee8GVrt6WPtS3NN77xjcR5q6yyii9b98xY/X2eVB89iZAtQqytUcilN4ttg7ZhCd373nvv9WUbWiBkdxObrTrWrqMomxtLv379Uu3pYt+3kP1aaExi15rrrrsutW6DDTbwZTvX9Ro4d+7cRF1oDc8zz4qwlcvCcsst59MRhbB2TvqZxz5/+9l0m9Y2rK2tzZfr/T1p7Ry/+93v+vJqq60W1Yb97tB2uNa9PMZmLGaDs6mIzAewuIvzWwC0RrRBCCGEENIwYjY496NTVfU1AFNU3RYA0l1OCCGEEEK6gWobnHXM8QJz3BfARdVu0tbW5sVjVt2gRVBZ3EqXW245X952221T29DtWzHZ+++/78u2X/VGi9u025wV0WkX7JCqacSIEYnjV155pdYuJghF1cwSmVKjXX5FJFE3ffp0X9YqKQCYM2eOL4eyld95552+PGbMmMR5+j2x/Q+Rx2U1T3v14qtf/WpqnY6aHUvoc2d5Jvo5hJ7J3Xff7cs2C3wI/c7oKMdZ+hg6V4vLdQiFIgmpZ7Qa3boi54mIfO655yaOTzvttKjrQqrsFVdc0Zft+qTVUHYtCEVwT6MeruAhlixZ4r9DrIpRj5sNqRFrPqA/z/PPP5+o02vZL37xi0Tdvvvu68s28ncR6L5YFdUf//hHX9YmAgsXLkycp8dm6NChiTr9vWO/V0KmEBWCGxznXHo87VL99VXvQAghhBDSYBjJmBBCCCFNR1CCIyVZ4WkA9gfwPoArnHN3qPpVAcx2zgUNjZ1zXgRpoxNnUUtptMh07bXXztWGjmCrVSL1YPz48YnjW2+91ZdPOeWUmtu30Uu1J0Ve8WxHR0fq+GjxoL13iDQx/4033pg4/uijj3xZqxKB8OfR79euu+6aet5zzz0X6GV2sjxj/QzyRmPNcy+LjeytVSshTyMtlrbegXlJ66dVT2jV9J/+9KdEXVoCW3usVThW3J92DRBWQ2nvlVpUVCKSyyNPj6X1CrQqqxjOOuuszNcASc9FG8l43LhxXZaBZPJNO655VFSNVv1qM4yi+qLnhH73rJpLr5U2Ivzuu++e6955sO+dTnysv0dC73eWBKNanZV2XTUJzn8BOAPAJACzANwkIueZc+QzVxFCCCGEdCPVjIyPBHCUc+5PACAi1wC4R0TanHP/VT7H1bODhBBCCCFZqbbBGQblFu6c+4eI7ATgIRFpBXBhPTtHCCGEEJKHahuceShtcl6v/ME5N1NEdgbwEIBVY26i9crWDWy99dbz5SzRQe+77z5f3nnnnX3Z6pu1nU3fvn0TdVrHbiNAap2n7XMaS5cuTRy/9957vmxdnfXn3muvvXz5t7/9bdS9gLhsqvUilOldE+uCrXXwliw2BKFotpqnnnrKl51rrBAypJMPuXHH0traGhV92+rCYzO4h+xudMRg7Zpd7bo0LrooGYUi9J7oORWKsKzdpm00VBtpXaPXAWtnU0S0cAD49NNP/fuexb1bj6W1rYq1awi5eMcybNiw1PZefPFFX7YhO9ZZpzMiiXVJb29v92UdziPvM7f2XpXnFbLHagQhezn97tl5dNlll6VeN3HixJr7pbHRw3VfYvuvvxerXVerLVU1G5xHAYyzf3TOvQRgl/I/QgghhJAeRTUJzoUAtuqqwjn3YlmSc0DhvSKEEEIIqYFqgf6mAZgWqJ8OoKp/9bJly7y40ootYxJmAZ8Vjb377ru+fPPNN0e1YdHRNLfYYotE3dSpU3156623jmrvJz/5SWrdbbfdljheffXVffnDDz+Mat+ixek2ymOe6KUhbHROLR62dUUkddMqpJA6zKoWtbovxBlnnBF1XqMjok6ZMqX6SVUIufcXjR4nIKnmCrmah9BhHzbeeONEnY6GmzdSsr4uVi1nSVNzALWpq/r27Rs1d4uIVrzDDjskjmMiwwKfXWu0Glqr7mbMmJE4T69z8+fPT9TpJJ0WnRhXY59zbKJXW1dR9VsTgyzoUChZ3Pzt+hXDyiuvnDjW5hp77rlnoi40R2LVfdot3b4j+jp7L+0mHiIUdkKbYdi9Qr9+/aq2zUB/hBBCCGk6atrgiMgMEcm/7SWEEEIIqQMx2cRD/AbASlXPIoQQQghpIDVtcJxzv445b9GiRd62wIaZ1jY5tk4T66qdBZ0CYNKkSannPfjgg7na159H2w4ASb2j1k3bjM4h+xOt67V2MEXYimj3fus+q+9n760J2QpYd1ZN6HNrXe8555yTep7m73//e1R7QPjZxer587ZfBIMGDcLIkSO7rNPPPE9KAEvIhiWU9Tj0TIYPH+7Lhx56aPT9NLH2OVlSNWjqNYaLFy/2fQrd39rc5OnP9773vczXAJ8N/aDtxvbff39fDo2VHmMguVZaG5NDDjnEl3WKG/t9kXdMsqQHSEOvlR988EH0ddoWKWRHpZ+lff7alsYSsg3TtpKxdpPWDmajjTbyZWtzo8cj9Ey0XU9ozbD2PzE2ftEbnHJgv0qL85xzcRZEhBBCCCENpqoNjoiMFZHHACwCMLv8b5GIPCYi+9W7g4QQQgghWamWTfwYAJcDuA7ApQDeKVetCmA3ADeLyAnOuWtC7ejIqtZNXKsiQmoJK7rS4qqQeK1okXwWdFRXi47qqftoxW66DVsXEj9q97q8Ith+/fp51aAdN92X0HMNiV21eDZLH++4wye0xxe/+MXU8w4//HBfvu6661LPs6JtrXKzdVo8XKtbKlCMeNyyZMkSL7a2c6ORc8CqWEKi9NB1muWXX96Xt9qqyxBdmbD3sm7vjWbAgAGpn1+L+e046nc21k39oIMOiu6XVj/Y6/QaFatCtBGp29raUs/V6nGtrrKR6bWLeqOziWuyzDGbgTuNJUuW+LI1F7jyyit9OaSaLSJ8x+jRoxPHe+yxhy9bFZUeH22GYaOAx4a0CJmwpFFNRXUqgOOcc7/rou42EZkCYDyA4AaHEEIIIaSRVFNRrQngkUD9owCKjShHCCGEEFIj1TY40wEcG6g/BhGRjAkhhBBCGkk1FdUPAfxZRPYEMBFJG5xdUZLwVI2N36dPH68/C7mJW92xPtfagGgbHF1n9ZOx+lDrspzHVmHy5MmJ48cff9yX33nnnUTdzJkzu2zDuuGF3OO1DYt11da2L3ntPEJh43U/7bPS/bJZrfX4xOpex44dmzgO2d1oQnY3Gut+qJ+l1VtrWzD92bK4gutnap+vdqvPO25tbW2+39Y9M/Re67rY99DOy1A27pANwPjx431Zu7g/88wzifO+9rWv+XIozEAstv95Mp43Cv2+hey6bPj/J598ssv27PwL2Tj06dP5VWHdlGMy1wPp6TIA4K677vLlDTfcMFEXmypH0+jQDJq055313H333deXv/GNb6Sep+05dTkvITueo48+OnGs549NzaFDr+iM8EWlkYlxx6+Wi2qyiGyCkhRnFIBKjvu5AO4AcJVz7vXaukkIIYQQUixV4+CUNzCn1b8rhBBCCCHFUGuqhihaWlpSRdchcVWoTquztIi0pSVpVqTVJdYlT4vk87rPhkTm2k1u3LhxqW3o6MXWhU4fW9GhVm9kiZ5ZBFqtYzMD6/Gw/QpFpkxjwoQJ0f36xz/+EX1uBRueIBRdNFaFocfKhjjQ4nIr7o91G43FqmBi1Qkh1ah+z0MqqSxo91Pd5ttvv5167yKw/Q+1352uxxY7d/Q423dWv4vPPfdcVPsiktpGXmLDKlx88cWJ49bW1i7PW3PNNRPHdu3sLrS6EEiqmkN1Fr1W6u+Z22+/vdYuBgmNzQ033JA41pGrLUWo2zV2PxAKK1OB2cQJIYQQ0nRwg0MIIYSQpqMhKqr29nbvlWFFqyE1VGzE1zlz5qTWrbvuur5cRBRXq27QaqhddtklUaeTdP5/9t47zq6q3P//PDPplSQkEDoIhJJLExC4hCJVlAjIRSyIFPELgoBcERAu5UtRrihfUFQsFOGHVGkKoQQEAhpAILSAlIQghJKYkJAyM5n1++Ocs+fZT2Y/Z5199plJDp/365VX1p6199rrrHbWWU/bZZddUnmPPPJIkn7hhRei3u153LUWKkV4ZG1vb0+OGa3oZMiQIUm6s7MzlafFUp5Y0DsK1UeyHvbIfdddd416TuMdG8cerXpH+FaspcUgRVjvWXS/9evXL5Wnj409CzfPisqz/PDaQc8dO+8333zzJK2PvX/0ox+l7rN9pdF1jg3Oa72N63HteU+POR7Pw8KFC5N54bWlFa1pkb1nrfbDH/4wSVsLtT333LOGmhaLFtNvvfXWqTztIVevNVZ86REjYnvqqaeiy8tLLaKaxx57LElr6yj7WbTVnLVE1td5xYxjxoxJ0ldccUUqb/r06Un6xRfTHmOK9tJurfxiLHJ5gkMIIYSQpiN6gyMi64jIWPO3sSKyTvHVIoQQQgjJTy0nODMAPGj+NhnAm4XVhhBCCCGkAGrRwTkSgLVFPh1AVbeyAwYMwEYbbQQA+Oc//xn9Qk+Gp+XhWv5mPfrGmrHGemTdfvvtU9dabq29NQJpT7pWLh5rMumhP7ctf9y4cUn62WefzVW+xuqGaF0OK//UOidWx0R/Vs9T9RlnnBFVr7POOit1vXDhwiQdqzORV1as62/1NbQOiG07zxS5CHN/7YFa9xPgR3fXeDos3tj1vNXqcWGf02bi2su3rb+91ui5aXXQsqJc23mu16daPK7mieZdDfsZdJ9YvS7dnt740nqCVv9IU4RZuGX06NFJ+uyzz07lnXnmmUnaWw8/+OCDJN3e3l5c5eokhFC4CwPN7373uyR90kknpfIOOeSQJH3JJZek8nSb67RF6+rZ77i99torSds5UfleB4DLLrsss/wisPPK08erEL3BCSFc3c3fbo99nhBCCCGkp8ilZCwiA0VkTxFZt+gKEUIIIYTUS9QJjohcDWBqCOEKEekHYCqAzQG0iciBIYR7vOfb2tqS4yXPe60VL2mxgj0a08e1WsRgj5y9I3NNrCjrq1/9aupae9O89957U3meuaY+ztRmfvaYswjxUl6WLl2atK1nUmzbLlbMovt07733TuXpo0+L9jSsA5paGmXK2x3WhNHzGKxFVra/rVfoPCxduhSvvfYagOXbwBvn+pja9mGWaCuvKMPOFe3O4fzzz48q34pw9GfLEkkBSNoGWL599Jj0gk96Irx6RFRDhgxJgo1aUY1e5zxvxRbdl6uuumruutWLNlHfcssto5+bMWNGkr7qqquStDajBnzv4VYErvHGSiwiksxr2zdFmEv/8pe/zMw77LDDknRsgGGLFleGEFJ52hzfioi1KoTup0ZgRVIxXt9jT3D2AVBxfjERwFCUAm+eU/5HCCGEELLCELvBGQHg/XJ6XwC3hhDeB/BHAJs1omKEEEIIIXmJ3eDMBjBeRFpROs15oPz3IQBWHFV2QgghhBDEW1H9HsCNAN4BsAxd/nA+A2B61kMxaL0bz/24Rcs5tXzVk7V65ofaVTiQllVr1+Grr7566r6PPvooSWdFva0Fq7uh5cpW/hkbGTovra2tyTvsuz0dHE2sDtH3vve91LWng3P88ccnaasn4bkM0Og8G3pAlxkbPTwv1oS8CHl9Z2dnolvimfB7FBUlPAs9b4B0mIXPfvazSVrrXFg83QlvrmtZvrfGeGbi3vish8WLFyf6I14IEfv5tG6jbRc9xvS49+ZHXvcCul52LdZ1fOCBBxDLyy+/nKSt3k3Wuz28z70yYPVxnnjiiSR91FFHpfK0Tp+ns3LCCSdk5h1xxBFJ+lvf+lYqb+zYLt+/eaO563HhhWOIHYOaqA1OCOE8EXkRwDoAbg4hVBy+dAD4cc1vJYQQQghpILX4wbm1m7/lU9kmhBBCCGkg0RscEdkGwEnoUip+GcDPQgj/qPZsR0dH5nFvrKjDmkFq81rvmDmvGasuX4uvrBhKRwy3pokeWabCnigoxiyuQhHm5X369EmODG10ad3m2swdyBcRu3///pl51vu1bgdPpKNNgK1Zrz66ta4LtPhh6NChmfUcMWJEkh48eHDqPs/EWJtEX3zxxZn35WXZsmWZ8807xvfmStZz9plYMYE9StfMnDkzqoy8HsDzigG32mqrJF1E1Pfu0H1nxYn6iN5+dj3WbV7WWLRjRN/n9c+pp56amecxefLkJN3Skq3+qUWUAHDooYcm6dj13BsbjRC/ak/G3tiw4jHvuyvLpYrtT73Wn3POOe69mth5oNt/xx13TOW9//77STq2XT21AuvGoF41jCglYxH5GoAnAYwF8Jfyv9UATBWRr9dVA0IIIYSQgok9wbkAwFkhhAv1H0XkdADnA7iu6IoRQgghhOQl1kx8NICbuvn7zQDGFFcdQgghhJD6iT3BeQjAbgBeM3/fDcBfqz3c0tISJZ+z92iZt0XrUOSNbqzdU9tI4Nocbv3110/S1hTOM0uPRcts85jCdYfWi7Fm0LHoqNR56xWro2F1cLRc2YYvOOigg7pN18Kdd96ZpK2L8dVWWy1Jf/nLX85VfixWd6cItLt/HZYA8GX+eXRa8urBWHcLjX5fEXj6ALad86Jd/tvPqudSLfpSWsdBu7y48cYbU/dp3QirZ6PHjdXdeeaZZ5L0JptskqStjofVrcnihRdeSF3ncdXgrTtWn7Ci02dDFNSC7jevLrWY92eN9VpcG3iuDrKwOjJXX311krYhdXRfvfLKK1HlW90i7Q6haNcnsRucewBcJCLboitkww4ADgJwjogk3zIhhNsKrSEhhBBCSI3EbnAuL/9/TPmf5ucqHQDU7+2OEEIIIaQOYh39xerqdP8SZW7sYY/49LV3XOv93TvStkehmk9/+tPd/n369LTjZs97oxaBDRkyJPM+/Tn1M0C86V0jTB8XLlzYUJGANplsb09H/CgiErgXuX7ixIm5yuzo6EjSnZ2dmfdpEZg1/ddMmTIlVz08dL95bdDTnH766Ul6zz33TOW99dZbSTpWlNForPsDj3oiiGtCCJljv4i5qEW/dv3T4gxbB2+si0iS1uujNQW/4YYbkvSBBx6YytPjdI899sh8V148j/B5RfixaDcgFs+8f0XB++7WYr1Nsf+sAAAgAElEQVTYNXvDDTesu06x1LVxIYQQQghZEXE3OCLyuIisoq4vEpGR6npVEXmr+6cJIYQQQnqHaiKqHQD0U9ffAfAbABWzllYAa1Z7ifZkbI+7soJmWuxz+pjRs/DxrA0OP/zwJL377rtn3nfGGWckaXucqY9Wt9xyy1RerFhKf+4+fdJd4ometDirlkClKwq6jtYaSltx9O3bN5WnrTNsQEIdxHHChAlR9bj33ntT1wsWLMgsX3tV1kEAPebNmxd1XyMoQtRn0WO+lvIvvLDLjdZDDz2UytNzrDfRYtNGeSteUfjSl76UutZi2xNPPDG6nCzrIytq1B7hv/rVr2aWt2zZsuh358F+X1Sun3rqqYa+F1j++0hbdFlv60VjPe3r7x1twWVFZdaTvCZ2/vdWgNNaRVRS/RZCCCGEkN6FOjiEEEIIaTqqbXBC+Z/9GyGEEELICks1HRwBcJ2ILC1fDwDwGxGpCO+yQ0ArvAi5sbo0ebxZVuOYY7pc+uy0006pPK0jsPPOOydpTy5vzQ9jvf9qHQ2rS+O1j428qilCB6d///5Yd911ASzvlTRP5GkP27/33HNP1HPWbL/eetRSvsYzBbUePj25dV79lp7Eq5ceF1Z3SqPN6IFiTHW1np7V58tjwmr7tCd0cgYPHpzMhaLMl3W7eCa/uk/uu+++VJ5eK/fff//M56688sokfe6556bu0643Ro8eXa3aCbofVlS9KC+auFfn1taecxvnfYd6rix++9vfJmntZgAAzjzzzMznYiO/e1iv05oYnaVqG5xrzHV3QTWvrfoWQgghhJAexN3ghBCO6KmKEEIIIYQURWyohroYNGhQYiIY65G4EVizYe2505qt5qGWgJS6HTxTc40N6KdFVPbo0AvwFkveYJs92ac9jT4u98RS+mi1FlHTuHHjkrTt0xWF2P613qm1K4YixIc2GG+e9vI+iw3yqudAo7zOdnZ2JuOqlnnk1Uevc7HBF+3Yvuyyy7pNe5x22mmp67zrQtFiKSv2qIhnvPlcjRCC6+05Cz2mrBl3Vl9pc3vAV1XQWDGULn+jjTZK0raftNuMu+66K5U3YsSIzPdpMXFeT/taDGW9/MdAKypCCCGENB3c4BBCCCGk6eAGhxBCCCFNR4/o4CxatAhTp07tNs+THcfKbHUZ3jPa3BsA9t5776jyb7rppqj7LLFyeq13Y91ia/0ca/qtr2uJop4HKx8u2mzflj927NgkHStjrgUtz21ra0vlabm4ldfHmnVq2bFtK+0WYJVVVkEjsdHE85qeryh6VUXUI7aMkSNHZuY1ar4tWbIkcSug3edXI4/bhrxtaXUBs9Yh+15vnc5jCl5Lm3uhAipzdebMmdHlWVpaWjL1TP7+978naatjqT+rXSf0GuW5EoklK0RFNY477rio+xq9RuRZu3iCQwghhJCmgxscQgghhDQdPSKiykujTDG749JLL01dn3zyyT32bs9jqY5mbSNba+/CVsRTNLHmpXlphKdqjedl2kb71vdaj6tahKhNKy36Pmu+7B2XWw+8PYkeX1Y8k2VCa02pdf21V+Ba8Dzv6no899xz0WXq43M9V2z5+nPbz9YTDB48GNtuu223eXk9+hYhOtBzwvarFlHpNdua8Wtxrx1PRXltrmDXSo31yl6Zj3nMvGPwRI1W3JdF3rnkoftUR3D3PATb9ckTl+l5VsT6nifaOk9wCCGEENJ0cINDCCGEkKaDGxxCCCGENB0SQmj4S4YOHRqy5MqantS5WVnQsmQrO66Bp0MI1TvAENtvHp5ukNZhsToFWkbvyeetfkGs3FfrA9Qi29XPaf0NK4vWJp7WzD22jg8//HCufhORqEltTchrMUuuF6sTpce2rpftG0/mr3WdrA6G1rnS77Zm+i0tXb/57LjTZVpdBOPCIVe/AX7fxerSxOpJWP0PT8/DC2OgP7t1ZaHJY8peFJ7em65zCEHylJ+33zx9MN2PnksQjXVrEbu26XdZfUtdRt7yG03WWskTHEIIIYQ0HdzgEEIIIaTp6BERlYh8ACC/m0hSL+uGEEZXvy0N+63XYb+tnOTqN4B918uw31Zeuu27HtngEEIIIYT0JBRREUIIIaTp4AaHEEIIIU0HNziEEEIIaTq4wSGEEEJI08ENDiGEEEKaDm5wCCGEENJ0cINDCCGEkKaDGxxCCCGENB3c4BBCCCGk6eAGhxBCCCFNBzc4hBBCCGk6uMEhhBBCSNPBDQ4hhBBCmg5ucAghhBDSdHCDQwghhJCmgxscQgghhDQd3OAQQgghpOngBocQQgghTQc3OIQQQghpOrjBIYQQQkjTwQ0OIYQQQpoObnAIIYQQ0nRwg0MIIYSQpoMbHEIIIYQ0HdzgEEIIIaTp4AaHEEIIIU0HNziEEEIIaTq4wSGEEEJI08ENDiGEEEKaDm5wCCGEENJ0rLQbHBEJIvKxiFwQef9RIrKw/NyGDa7bwyKyREQeibx/43LdlonI0Y2sW2/Dfls5Yb+tnOTot3PL9wcR6dPgus0QkcUi8ofI+/cs91uniOzZyLqtCLDv6mel3eCU2TKE8EMAEJEJ5QbU/4KIfAkAQgi/CyEM6cG6HR9C2KVyISLXici7IvKRiLyqF9YQwqvluj3ag/XrTXS/bSwid4jIByIyV0Qmici4yo0rQL8dLyJPichSEbla3/hJ7jcAEJErReSV8qL1TX3jCtBvm4rIZBGZLyKviciBqm6f9H7bSkSeFpFF5f+3quSFEM4GsHkP1m3/EMJh+g8icqKIvFn+sn5ZRDYu1+2Bcr+91YP1621SfVdBRL5R/n7T3yO92ncispOITBWRBSIyTUR2VnXrlb5b2Tc4CSGER0MIQyr/AHwBwEIA9+YtU0RGikjfgqp4EYD1QgjDAEwEcL6IfLqgsldmVgFwJ4BxAFYDMBXAHfUUWHC/vQPgfAC/L6i8ZuI5AMcB+EcRhRXVb+Vfr3cAuBvASADHALiu8kX5SUZE+qHUNtcBGAHgGgB3lP+et8zRIiIF1e9oAEcB+DyAyjr+YRFlNwsiMgLAGQBeLKCsQvpOREYCuAvA/6K0pl8M4K5yXXuNptngdMPhAG4JIXxcRxl7AXhbRC4RkfH1VCaE8GIIYWnlsvzvU/WU2QyEEKaWf+3PDSG0A/gZgHEiMqqOYovst9tCCLcDmFNPOc1ICOEXIYQHASwpqMii+m0TAGsA+FkIYVkIYTKAKQAO8x/7RLAbgD4ALg0hLA0hXAZAAHy2jjKPBPBmWUSyft5CRKQFwNkATg4hvBRKvB5CmFtH3ZqRiwBchmI2foX0HYCdAMwOIdxcnnPXAfgAwEEF1DE3TbnBEZHBAA5G6ddJbkIINwLYA0AngPtE5EkROS7vrlRErhCRRQCmA3gXwF/qqV+TsgtKEyX3hqLofiM9Q4P7TQDUtdltEjYHMC2EENTfpqEO0UYI4ccADgUwBsBTIvKQiBwmIoNqLGqt8r/xIjKrLKY6t7zxIQBEZHsA2wL4VRHlFdh3QGmO2etenXPNOnAOQml3+9d6CwohvBBC+D6AtQGcg9IvoDdF5I8iMqzGso4DMBTABAC3AVjqP/HJQkTWAvALAN+rt6wi+430HAX12ysA3gfwfRHpKyJ7A9gVQJ5Fu9kYAmC++dt8lNal3IQQ/hZCOBalk7NfAvgKSqdxv62hmLXK/+8N4D8A7F4u56h66tYsiEgrgCtQ0jfrLKrcgvruCQBriMhXynPucJQkFL0655p1g3M4gGvNrxQXEfmaUk6+x+aHEJYBeB4lvYO5KO1Ma9YXKB/fPYbSZD621uebFREZDeA+AFeEEG6o4bke6TdSLI3st7Ko8wCU9DhmAzgFwE0A3i6o+iszCwHYjeIwAAtiHhaRM1S/LXeKUBbDTwPwLIA21PYLfnH5/4tDCPNCCDMA/BrAfjWU0cwch9Lp29/yPNzIviufuH8RpR+n7wHYF8AD6OU513QbHBFZG6VffdfW8lwI4XqlpPw5Vd4QEfmmiExGSZlyTQBfDiGMr0eMgpIc/BOvgwMkSnP3AbgzhBBlElmhF/qNFECj+y2EMC2EsGsIYVQIYR8AG6CkwP5J50UAWxjF0i0QqbAaQrhQ9dv/qfxdREZJyeJwKoDJAFoB7B5C2KGGur2C0her/mEa/SP1E8AeAA4UkdkiMhslvZdLROTnMQ83uO8QQvhrCGG7EMJIlPTdNkEvz7mG2sr3EocBeDyE8Hq9BYnIvij98puC0i+J25WicC3ljEFJie9ulH6l7InSMeBX6q3jyk5Z7DAJwJQQwmkFlVlIv5XL6oPSPGkF0CoiAwB0hBA6iqjrykzZ8qYFJVl733LbtOU9Pi+437YA8Gq5fscBGAvg6jxlNRkPA1gG4LvlX/HfKv99ct4CReQoAJeiZLF6LoB7yydwNRFCWCQiNwI4VUSeATAcJQu4/81btybjmwAGqOvbANwC4Hd5Cyyq78plbQ3gBQADAZwHYFYIYVLeuhVBM25wvoHiJsQrADYJIbxTZzkBJXHUr1BacGcCOCmEcGed5TYDBwLYDsDmkvalslkIIa/PhKL6DQDORMmyo8LXUVoIzimg7JWd+1DSbQFKvyavRElv4uGc5RXZb4cBOBolsdajAPbKu1lqJkIIbSJyAIDfAvgRgJcBHBBCaKuj2CcArFuQtdPxKI2jdwDMA/Ab0EUDACCEME9fi0gbgI9CCFanqhaK7LtT0SVOvBeltb13CSGslP9QMk2dD+D/Rt5/BEoTZgmADRpct/tQkmk/FHn/RuW6LQLwzd5uW/Yb+4391hz/cvTb2eX7lwBobXDdXgHwEYBrIu/fo9xvi1ESofR6+7LvVuy+k/LLCSGEEEKahqZTMiaEEEII4QaHEEIIIU1HjygZ9+vXLwwaVLu/n87OLmOMxYsXp/IGDx5cd700bW1pHbulS7v0EVtaWrpNd3et6d+/f0G1K6HbAwAWLMh2XdGnT1fXdnR0fBhCGF3r+1paWkJra2u379afrV+/dBgb3XaWvn27d2XitaMtb8mSrsgAAwYMSOVltXlHR9roSZfpjaX58+P094YPH17Ic6aMXP3Wt2/fUGkHPQ6qofvYG1u9iddeeWhvb09d6/EZ24dAevx2dnbm6jegFEG6kh46NO17z+sT/X77XBZ2Tn/8cVdUGzsn8owH21deGV6ds/oh75wbOHBg6rqyFnR2diKEkCsuk/cdp9vZrkN6ftr20fXUVv2LFi3KU8XlsGtnhaK/tyx6nAHL94dGf+9XWae7nXM9ssEZNGgQJkyYUPNzuiFeeumlVN52221Xd700b72VNth5/fUuK/MhQ7qCIttGzhokALDBBhsUVLsS9ov+/vvvz7x31KiuUE7vvffezDzva21tTcpZuHBhKu9Tn+py4bPOOuuk8t54443MMldbbbVu/+4NXlueHgu2jbPa/MMP02Fb3nzzzSTtjaW77747M09jx3fe50wZufqtf//++I//+A8AwKqrrhr9nB5f3tjqTfKsIx6zZ89OXa+++upJOrYPgfQi/fHHH+fqN8sOO6TdkHh9ot8f20Z2PXn88cdzvTsLW4+HHnoo+l5NVj/knXMbbbRR6rqy1tsf0bXgfcfpdn7vvfdSeXo9tG2s66l/RD711FO566nJWiuL/t6yPPnkk6nrLbbYIknbDaBe66us093OOYqoCCGEENJ09MgJzvz586N213bnuNlmmyVpvcuz2NMXzbRp0zLzdJn2hCLrpMGijxj1qQ+QFqV4pxorA/oUC0j/EvF+ldh2zfoV94UvfCF1rXf5Xt/E9pM9BvV+DXh9ZetZwRvfWc90h/crN5aPP/4Yf/tbLm/uhWL7xo6TPNRyqhKDraM+wanlufXX7wrEXFTb19Jeu+++e2aeHlP6VNx+hsqpXz14Y92rY54yvblSy5zzviNi6ezszBTN6/Jr6VMt8ooVS9mTcH3Sbr+f9OmIfs5KS2Kxba7Hmu4r+z2vRXO1zJ2YtZInOIQQQghpOrjBIYQQQkjTwQ0OIYQQQpqOHvFkrE0frYxQ63bUYhmlrWK0RUwtMk5rKaDRZotaRvj+++9nPlOLnk2WprotQ9/nle/pL919991PhxC2ja5cmdbW1lDRXfF0Xbw21zJgIF7erd9XtMVcNTyrB60PpPtjr732St1XhKll3n7T8603qUUPIpa8Ojh6PHnjVd+39tprp/JmzZoV9a733nsvV78B8X1XS9tmWcd5ZdTSznn62ZbfiLES++5Kn8+ZMwft7e25zMSHDRsWKt8ndnx5a56nT6jL0d+btXzPeNap1ly7kXj9q+thTeX1nLPfA2ZcdzvneIJDCCGEkKaDGxxCCCGENB09YiY+fPjwxAmS5+XWmqdp8ZU1Fc5jdu0dk9nyPvrooyStxT+e47QxY8akrrU4yx5b6vfpY0RPhOfRCDP0gQMHJqaj1kwxyxGiJa9psD6OHDFiRCpv++23T9JWZKjzNNY51uabb56kr7322lSeFi/Z42U9hrQY0KKPwT3zSdvfRZtBNxrrvsE6fSwa3ea1mLPGjkPP/YHux6IcBHp4Jr9WvFCEZ3fP3YaHbotYM/ueFEkB6fay766Mo3//+9+5y1+wYEEuZ4haLKWdswLp8Zd3fe9JMZRH3jnhjZMYNQCe4BBCCCGk6eAGhxBCCCFNBzc4hBBCCGk6ekQHR2PdRXv6CJq8MshYWW8RAcasfo52O23roWWSug2sToPWPfLKsBShk9Pe3p7ouNigop7ejTavjQ0MZ82st9xyyySdpVcDLK/zYSMkV9hmm21S13oceubAsePH9oXuR0/vzAaeW9mwpq36s3oycm/sapf+dk3YZ599kvSLL76YytPhEmbMmJHKy9uPsXmNwIY20GPF0w3z8Nw92LU5lhtuuCFJezo4p512WpKeOTMdG1F/tp52C1FZy1pbWxtSvl4LanG3UURoE02j9Z5i54ed056ekKfLGANPcAghhBDSdHCDQwghhJCmI1pEJSLrAqicP84OIcz07td0dHQknoetaENf6+ipQNpDsYc+8sobsbYReEdqOk8fw9VyTKzFOtZEUXtpzhvduKOjIzkmtUer3lFrW1tbkt5227RzSS3+0UewtjxPLKWZPn166vrhhx9O0mussUaSnjhxYuo+bXJrj6Y9j7W6r3Sdazk+1aIb+7m1GXQRkcUbQRHiHg/9ue27rKhRc/bZZyfpW2+9Nde7izjGL0qU5Zlt5x1vVgSuiTUp/tKXvpS63nDDDZP07bffnqRbWtK/n/UaNXLkyFSeXsOt2LYIkZWn/lD53rH1rYWWlhZUvL7bdrTfa5oixFCel+7LL788SU+aNKnud3lotQIAeO6557q9L6/puqcGkEXVHhWRk0VkFoA3ADxR/veGiMwSkZNy1ZQQQgghpIG4JzgichaA7wP4MYBJACrbw9UA7A3gXBEZEkI4v6G1JIQQQgipgWoiqmMAfDOEcJv5+ywAT4nIdACXA+AGhxBCCCErDNU2OKMAvOzkvwJghJNfEzYcgIeWvzXaPXwjyJLTF+VaO6/ejaZPnz6urk0WnlxZ52nz7IMOOijzGR02AwAeeeSRJG3lvEuWLEnS2rS9X79+qfu23nrrJF2RnVc48cQTM+tStKmlHbsrqtl47Of26p+n7WxIhF/+8pdJ2upM5NW7KYKKjmG9DB48OAmPMnTo0Mz7rOuB2Oj1+r68ukK33HJLZp5el1944YVUntYZtGbi2sWG/Wyx9cw7N4teK23IHt2PNlq2xos6rrGfU8+58ePHp/Kse49Gcuedd6audZ9qNyxWpyrvOIz5bqqmgzMVwP+ISD+bUf7bD8v3EEIIIYSsMFQ7wTkewP0A3heRR5HWwZkAYBGAvTKeJYQQQgjpFdwNTgjhBRHZGMDXAewAoCJTmA3gNAD/Xwjho6znK3R2dmaKn2KP5SzDhg1L0uutt16SnjNnTuq+cePGJWkrinj33XeT9GuvvZarHnnRx7XesaU+PrXeibVJsT221CamedtYm4lXjs0r6Cje1vRfe5T1jn+32mqrJC0iqTztpda6C9D9NnVq+gBRH4WeeuqpSVqbzVv23nvvzDxLESbAXlRq3adFezKthdjjfitO0MfGRZj3TpgwIXX9mc98Jklfc801qbyiPdH2RmR3vVZaEZU2N44VSQH5xGd6TQXiXXZ4Y3b+/PlJ2ppO63mg1/Za6I3+qtDW1pasz3ad9taeWLcgWW5FLOedd17q+uijj07Sd9xxR9S7bP11X1nP9LpeVkR88sknJ+kdd9wxSXd0dGS+26472mWE/Z7xvjcrVPWDE0JYAOCX5X+EEEIIISs8uTwbiciaIlJ/8CZCCCGEkAZQzQ/OMABXoqRv8zCAIwH8P5TMx4OIPAHg8yGE+ZmFAFi2bFkSNLKoY/dDDjkkSdvj1FjWWmutJG3FV7Ho4HJW3KDRR4UA8PbbbyfpH/zgB0naE+lYb5wvvfRSktaijaLo379/4vHXihg9ay9PJKaPIEMISfqBBx5I3aePH+fOnZvK05/boseXdzRsNf57En3EbPstNjhpERRhEfb444+nrrUnceuJOdbLuG4fK4rRZdxzzz3R9cyDDcCrrWNsYF1NPaKS9vb2ZAzrQLv2/bWg6xpbt1iRFJBeyzysB2SNtqqylpFFeGVvNMOHD19OnFrBExHGWs1qSylrPaTXvGOOOSaVp0VD3pjVeIGnvYDP1qu8LkeLl+bNm5dZvjc+bVsVYUV1IYAtAfwIwBoAbgLwnyhteHZHyUT8B5lPE0IIIYT0AtV0cCYCODyE8JCI3ArgbQATQwhTAEBETgVwCYAzGltNQgghhJB4qp3gjAHwGgCEEN4BsBjAqyr/BXRZVhFCCCGErBBUO8GZA2BVlEIzAMAdALQAbQiApfYhizahqwXtFdPK2zo7O5P0yy93OVu2ZuKjRo1K0taMVOu0aFklkI4orT3uetj7PvjggyStzZ6BtIfJ4cOHd1snIC13tDJILXu1uk1aVyGvmbjG6gPo6yL0qqxnU42NquyNpT/96U9R74uV5Xvy7iLw9IkaQdGemD29mlidG4uuo/Y4DfjjJNaUNhY7zvKsYbXS0tKSrAE64j2Qf+zFtoU26/Ww+mtaf9HWWfP8888naa27CKRdS+g0kF5Hi+5joEvHR9evSGJ1XyxZc9W67Ljwwgszy4hd5/KuC/o57ZLF0rdv31zv9vrYi9JeodoJzvMAEmcWIYSvhhDeV/mfBjB9uacIIYQQQnqRaic4XwfQ6eTPAXBWcdUhhBBCCKmfap6MXReYIYQ/F1udNFq0Yo+xrFlxHkaM6IoTas119bu33HLLJO0FLzvppJMy866++urU9RprrJGkPTGUNrWzR+T6qNKacVsPs3lYvHhx0g5aXNhdXTSe+Z42d/Q83er77NG8Lt+aEWsP0R4XXXRRZp4u334WXWd9fOqZT/YmRYmk9Li0YlSN97lj66LdPhx33HGpvOuvvz6qPC9Pm+2uqGbHwPJjWwdmtevE4sWLk3SsSESvfwDw05/+NOo5rR4ApOfc5MmTk/QNN9yQum/NNdfMLMOKrDRa9N8Ij/ONHgNaDO2JGWPnh/YiD6SDn/7Xf/1XrjK9eeut5/rzfPvb307l2T6Oebd9l762becFo62Qy9EfIYQQQsiKTF0bHBF5WUSyA0sQQgghhPQCVWNRVeEXAEZVvYsQQgghpAepa4MTQvh5URUBlpe/aVm/lTl7egCx/Pvf/07SU6ZMybzvsccey1V+v379krSVd2sdE60TYNvA033Q7tStjkxMpNVqDB48ONHzqcV1u/4M2kwfAPr06Rpy1vxb48mqtR7MueeeG1UnT2fIc01uo/3qz5ZXB0RjwxloYt24W/r06bNcu9eLHk86bXUnPDNeT2dJo82E9TwB8odl0Xg6Kp7Om6cP0NPYcZPHfPriiy/OzLOm4BMnTkzSBxxwQCpPu+nYY489krSdV1rPcd11103laRceo0ePTuXtsssuSVq7+bfl6z7JO3caQWy97HjTn0/r1liTa08vqQhdQF3/bbfdNpW36aabJmmrc6PHqBeeIetdRdwbvcERkVaUfOIAwIchhGXRNSGEEEII6UGq6uCIyIEiMgXAIgDvlP8tEpEpInKA/zQhhBBCSM8jOqLzcpki3wZwOYBrAEwCUDkTWg3A3gC+AeCEEMJvvJf07ds3VI7MrZdKjTXX00fCVjyjPWZqc0prHm1NLRuJPVr9/e9/n6TtkfKrr3ZFvNDHiLb+HR3ZOty6TexRvvY0/PHHHz8dQkifLUbQv3//oCOuZ2GjHGsRgI2kq72F6v6txUvssmVdh4ctLdl79IMOOihJWw/HnohTR7616GNRT4RahHn23Xffnavfhg0bFireWe34947IY8W+sab+Fs8cVLfrrrvumqRPOeWU1H2zZ89O0o2ICK/raI/j29raknQV7+C5+g0AhgwZEipiYU+UZj1g6zlozWfvv//+bsvw1n6PI444InWdFS3bike8OfH5z38+SVuxp57jWgzy8MMPp+7T4uS8opkQguR5bpVVVgmVaOJ2Dc9qfyA9D7y5pF2V2PtOOOGEbu+z1NIfWVgRsV5j29vbU3l6jD744INJ2n5X5RX3GnFyt3Oumojq+wCOCyH8tpu8W0RkKoDTAbgbHEIIIYSQnqSaiGpNAI86+Y8BWMPJJ4QQQgjpcaptcF4EcKyT/+3yPYQQQgghKwzVRFSnAPiziHwOwH1I6+DshdIJz37VXjJw4MDEjNnKrj35m86zMntdjjY/tNHEtXvzWoh1Ta/RslAAuOuuu5K0NZObMWNGt2V4sn3bVlqGaqN977TTTknakwF7DBw4MGlbK1fW8m7rOlzrDuSNpKv5xje+kbr29G40XmRxHebCmsB7oRp0W3r6LJ4OgGeWbkOGFI2eH3nnhh6Htcj1vTwtrz/yyCOT9H9mHcMAACAASURBVDPPPJO6r+iIz3Zc6/afNWtWoe+KoU+fPplzRte1iHFi1wUdcmHJkiWpvM0337zmd9v+1m4h7Nh74YUXkrRdKxs9J3oTby597WtfS9KnnXZaktb6LEB6nNYyZrPWKG+e2nAMWo/Lfg/oeuq+z6tzY12hWBce3VEtFtVfRWQ8Sqc4OwCoaH/NBnA7gF+FEGbkqCshhBBCSMOo6genvIH5QeOrQgghhBBSDPWGaoiio6MjOZbyjqesKEib61oRTJYIaeTIkXXVNasuWey8885J2oo6tMfJCy64IJX3yiuvJGl99FbL8Z3nGdbzkJsHWy/dN56oowhPmtdcc030vf/85z+j7tPHqVYMNWjQoMzndF/p+2JNc4F84s9a0FHgPdPTvGajsd6KvecsOvK07pt77rkndZ8VnWjytKs1o9djwZpi9wTz589P2tC2l56DVpQdKxqcPn16ktauNiwDBw6MLj8WvU544+Sss85KXWeJpD0XILXUt4g1SuPVy45LT8x9/fXXJ+nzzjsvSXsi7iKw7aHnxI9//ONU3sEHH5ykrdpFEWIpjS0jRg2D0cQJIYQQ0nRwg0MIIYSQpqNHRFR9+vRJjrm8o2N95AT4x/y6HO/4SweC23333avWtRr2SPHee+9N0lbb/4EHHsjM014fq3hFjcJ6h9THz0WUrwOHWvJ6j9bei+24WLRoUVQZVnS58cYbd3ufLV+/2x75au18O2ayxFL2M+sjcts+Olil/ZxeANJY+vbtu5zYrYIOwlmE2MEroxbxlRYt62PvcePGpe57+umnM8vQfZx3TMaKpWoRNeTFEyHaOR0byPTWW2/NLCNW9KG9SQPLex7Og/aQu80226TytPdinbZ9nJdKez36qOfyzaetrS1q7uYdJ1q0uMkmm6TyvvzlLydp/X0HZFsbA/H9rT30X3bZZak8HejTfg/rtTlWNcVrH7vWx8ATHEIIIYQ0HdEbHBFZR0TGmr+NFZF8zjQIIYQQQhpELSc4MwA8aP42GcCby99KCCGEENJ71KKDcySAeeZvpwMYXu3Bzs7ORO/ARmrW0cUff/zx6MpofR39nBeV2jN/i42KbL2Mjh8/PknraMNAWl5pIxNreahO5zVZtDJOL2p7HoqQs1u0DNjWX5tFeuy///6p6yzdE08GbMdMJRJ3d+iI9/o+W76uh9X/0NdZkZjrob29PamPNeHX7gOK0MHx8Mp/6qmnUte77LJLktYuFM4999xc74vVubEyf2+c6D71dLrqYfjw4ahEpfZ0Omzb6nXD6uPptW3AgAFR9fD6Lu9aoHU5fvKTn6TydNR4qxukzY8/+OCDJG3XW42dV9qFR5b+VD16VP369ct0l1GEDqT2yn7IIYek8rSX6UmTJqXyVllllST92c9+NpWnx7N+7uyzz07dt+666yZp+/29ePHiJP3ss8+m8mLnRGy7a+/zQNwcj97ghBCu7uZvt8c+TwghhBDSU+RSMhaRgSKyp4isW/1uQgghhJCeJeoER0SuBjA1hHCFiPQDMBXA5gDaROTAEMI9bgEKezxoA3TFok1t9RGXF5jNmoDmCRqoTfKAtPm09bqqRRjWHDjLrNOKR7RIxIpf1l577SRtj/z1c3nxvKp6XmM9L8r6Xn2EaT+3Daym0Sb29ji+CHPd2ACh3tjNEpUBafNWOy6KOM4ePHhwIpbwzJ6L9uAKpAM2ekfI2lwZSAcB/fOf/5yk84rR7PG4Nu/v06dr2fPmiTcGbT/pz503uC1QWtcq86cWtxa6nax4Ro83veZZsbkmr5drjzPPPDNJeybKtu+0qE6bct9yyy2p+3Qd7bzSJsZZ6gj1mIlr7JpUBDoYqTWP15/76quvzlX+2LFd9kPWI7FuO7ue6HW7EeJ2PU7yBG6OPcHZB0BlJZgIYChKgTfPKf8jhBBCCFlhiN3gjABQ+bm6L4BbQwjvA/gjgOaNZU8IIYSQlZLYDc5sAONFpBWl05yKi94hANoznyKEEEII6QVirah+D+BGAO8AWIYufzifATA966EKnZ2diUzUmplpmbCnP2Hl4Vmu/K2MUMsn80Zh3XPPPZN0a2trKm/+/PlJWrsRB3yZoZbhe7JLT/bdCFmvRpusWryQG1p3wMrys/r4v//7v1PXn/nMZzLL/853vpOkbURk3a66v61c38vTY8jqdBWhi6B1U6yujhepO5bFixf3SiRsIK1/4rWVNcfWejE6VIOOMl4LWqcHAGbOnJmkY/W0PH0orXNTJJ2dnUn9PLcWtv10W9t1R+uwbL311knarsWaWj5f1jjVunJAuo9vvz3eAFePZat3o9G6KdZkW/elNb/PMu/Oi9XxKULXTeuKWb2xBx/sck930UUX5Sr/6KOPzszT8/Hmm29O5Wn9HO3eIS9Fu66I2uCEEM4TkRcBrAPg5hBCxQFBB4AfZz9JCCGEENLz1OIH59Zu/nZNsdUhhBBCCKmf6A2OiGwD4CR0KRW/DOBnIYR/VHu2paUlMdO0prX62NVGC9VHyXnNZ/XxoD129cxY9fGtiCRpK6J65JFHkvS7776bWZ417fNMyDVaDGWPPvW1Nc0uwlzaMxNvNHpc3Hnnnak8HXnakhWZ1jP59USXXsTiYcOGJWnt0RpIixC+9rWvpfK0x88TTjghs/y8DBw4MBGt2foX5XE3Bu9o3o6nNdZYI0l75vdaXOF5Ibaivyy3BrWIxb01KNZzci3YcVmLl/YsdNvadtbvO/DAAzPLOPbYY6PeZdHzuKUlW/1z4sSJqWtdF23aXotHZW/9qrRrUdHJPRoRhf6dd95J0ocffnjh79aeki1aXGZVNGLxPMfXS5SSsYh8DcCTAMYC+Ev532oAporI1xtWO0IIIYSQHMSe4FwA4KwQwoX6jyJyOoDzAVxXdMUIIYQQQvISayY+GsBN3fz9ZgBjiqsOIYQQQkj9xJ7gPARgNwCvmb/vBuCv1R72zMS1rNxzf55Xxq1l07W4UP+f//mfJK0jc7/++uup+7QLeIuW6erQEkC23o0tP9Zdu72vaDf8jXDrr7G6TR46krxO14KnD6B1wQ499NBc5Wvmzp2butZmqbXoeeShnrABjcQLZeERq0PkRQXXa5Cnh2D7wtPjaIT+hvdZvWjiHl/84heT9PXXX5/K0+Pe07Ox9Rozpus37gMPPJCk7byyujVZaJN+oBj9P90+dq2s6B7961//qvs93aHrb8ebF9amaPLq+3hrkv4e05HePWrpT11n7WYAWP67sjtiNzj3ALhIRLZFV8iGHQAcBOAcETmocmMI4bbIMgkhhBBCGkLsBufy8v/HlP9pfq7SAUD8T3FCCCGEkAYQ6+gvVlenW9rb25MjY2vqrI92vWNr631S3+uJr7zjMO9YNyvSrhWl6CNGT1xl0fdqz6N5vWpar8aep95YBg0alJgb22jlRaC9BG+66aapPGsanoeBAwcmaSsaPeqoo5J07NEqEG8KOWvWrCTd1taWytNio5hj1lrR5v0rErvttluStibQeizst99+SboRIjbPC7fGrh16rbJrTlFm4v369cNaa60FoBizcIuOSm3dWmix3uLFi1N5eUyAbTv/5je/SdLW0/all16apA855JCa31ULVixUMVPu6OjIXWZbW1vyHeWt4d7Ys3nbb799ku5JUVbs/ABK47WCJxb23HR46M9t56NeM7LmSl0bF0IIIYSQFRF3gyMij4vIKur6IhEZqa5XFZG3un+aEEIIIaR3qCai2gFAP3X9HQC/AVAxC2kFsGa1lwwcODA5orLWBvpo1wtSFms1UIuGtg7auMkmm2TepwNBvvrqq6k8LyidtpzyAm82Ah2EMq+IasmSJXj55ZeLqhKA7P6xwTZPPPHEJH3jjTem8jxR5rJly5L0jjvumKTtkbtGW34AaQ/F9thVixa9dvUs6DRFeDJdWdDHzXfddVcqT3t71mKUWoj1ip6XRngrtrS1tSXjKq+lVCz77rtv6lpbF/3gBz9I5WmLqNi2tPP0yCOPTNJW5UBz001pjyRFWFHFlGGtdGqhX79+UeoFtu10G9nvP01sG1jRX+zar0VI1mrKEz3pYNMeeVUv6u37WkVUUv0WQgghhJDehTo4hBBCCGk6qm1wQvmf/RshhBBCyAqLhJC9XxGRTgD3A6goFHwOJc/FFfeF/QHsGUJwfd+sssoqYcKECQB8HZxGY2XYU6dOTdJW/ql1BJ555pkkbc2xi6iLljNac8BYT8Yffvhh6nro0KFJ+v777386hNC93buD7jfiY9tfY6M2a5m2lckb8/5c/SYiPfYjxJOR9+3bN3V9221dPkCPP/74VN4vfvGLuuui9eFq0SPQ5JX5m7UlV78B8XPOrhNal8OOKW32q70OW2/qnsfaI444Iklbj8R33HFHkn722WeTtB332ovylClTot8dq6cSi/0eqMy5t99+G0uXLs2lhhHbb/b7Q+uK2X4rwpN/LFonyvbF7Nmzk7Q1Id9yyy2TtNX30Xo9eXVwdL1GjRqVypszZ06SnjZtWrdzrppW1TXmurugmtdWqyQhhBBCSE/ibnBCCEd4+YQQQgghKyL57eJqYMGCBcmRqmdiWIQ5oMfFF1+cutbHndZsdf/990/SWkRVFFr0pI8ttXk3kD6is15vtadLy4oaZFF/Vn30XMsRph5DtXjd1OQNjphl8v3mm2+mrq2ZcizW43K9WFPdWFGNR+w8bW9vT13rOVVEcErb93nGvHXz4Hkr1mIhKzrWbVKUObcVQ8WOe6+PPbNhXaZdp6+66qpu07Uwffr0JG3FDbr9rKmz9libF73uWC/aed1o5MHz5O/1m66/bTv7eWLR41TPAzu/9Vi44YYbUnmeN+E8a6CdO1rMpUVSwPLt0B20oiKEEEJI08ENDiGEEEKaDm5wCCGEENJ09IgOTmdnZ5R771pk13n0dXbeeefMPK0fYPFMgGPxPpuni6Kfs/oUnmtxz/V2LF5U6tj2ty7Zs8w8a4kWrz+3J6/X5o2rr7566j5dpv2M3mfLMte0oTj0eLd1XH/99ZO01d3xwlDkoRadG+/dWf2m2xhYvp2zKMLsdaeddkpd688aa3Zs66HHoZ2LsS4bisK+z1sL9Li3Oiv6uSJMdz2dFa0PYueVFxla49XfQ7/bjnut21a0nhtQmu8VPZlaTNn1OLU6JVlhjGx72PVFo9vZ9ptuZ28+6rA5Ho3QodX9aMd8jP4lT3AIIYQQ0nRwg0MIIYSQpsP1ZFwU/fv3D2uttRaA5Y/JjOfWut/lHZOdccYZqWsdbfr8889P5Z111ll118VDHz/rozZ7/KiPEe1RZKw447333ivcI25W/WvBM8mN9Rgd2wa1HMdrscusWbNSeVlHpvZYXfeVPbLOMs+05Wd556xGrCfjvOIwLeawogDvSFkfwWsv4kC+aN+27WLNxHX9PeyY8by9GnJ7Mh44cGCorIl5zX89tLjdilWz7rP32jztEbmjoyNJL1myJHVfo82xdXvZ8aRNlu24rNw7Z84ctLe3N9STcREuBGz9PfGVxjOP1+tXLaIgT6Tbk9x9993dzjme4BBCCCGk6eAGhxBCCCFNBzc4hBBCCGk6ekQHx9MJ0PI9a/bpmfJ+UtDtY80btb6D1SswOgJ1RxP32t/qPXk6JrHmwZ5p/vPPP5+Zp8eQbh9rgqnb1brE1+EyPLmy1iGyOhmeXFy3lw1ZYPRICtfB0eNE60sAwJprrpmkbVgQjW4f73N7WB0J2wcx2LGl62J1g/Tn1joGnhm91UWoYT3KrYPj9V2sGa6nv5Y3Grcu07aZ1t+YOXNmkrbrVayuSCxWN0SPqTwmxfVEE9f95tXLovViRo4cmcrTLhc8FwGxfWp1oLSOlI4yP3To0NR9ur/tmqHDJxQR6T0v1MEhhBBCyCcGbnAIIYQQ0nT0lIjqAwAzq95IGsW6IYTRtT7Efut12G8rJ7n6DWDf9TLst5WXbvuuRzY4hBBCCCE9CUVUhBBCCGk6uMEhhBBCSNPBDQ4hhBBCmg5ucAghhBDSdHCDQwghhJCmgxscQgghhDQd3OAQQgghpOngBocQQgghTQc3OIQQQghpOrjBIYQQQkjTwQ0OIYQQQpoObnAIIYQQ0nRwg0MIIYSQpoMbHEIIIYQ0HdzgEEIIIaTp4AaHEEIIIU0HNziEEEIIaTq4wSGEEEJI08ENDiGEEEKaDm5wCCGEENJ0cINDCCGEkKaDGxxCCCGENB3c4BBCCCGk6eAGhxBCCCFNBzc4hBBCCGk6uMEhhBBCSNPBDQ4hhBBCmg5ucAghhBDSdHCDQwghhJCmgxscQgghhDQdK+0GR0SCiHwsIhdE3n+UiCwsP7dhg+v2sIgsEZFHIu/fuFy3ZSJydCPr1tuw31ZO2G8rJzn67dzy/UFE+jS4bjNEZLGI/CHy/j3L/dYpIns2sm4rAuy7+llpNzhltgwh/BAARGRCuQH1vyAiXwKAEMLvQghDerBux4cQdqlciMh1IvKuiHwkIq/qhTWE8Gq5bo/2YP16E91vG4vIHSLygYjMFZFJIjKucuMK0G/Hi8hTIrJURK7WN36S+w0ARORKEXmlvGh9U9+4AvTbpiIyWUTmi8hrInKgqtsnvd+2EpGnRWRR+f+tKnkhhLMBbN6Ddds/hHCY/oOInCgib5a/rF8WkY3LdXug3G9v9WD9eptU31UQkW+Uv9/090iv9p2I7CQiU0VkgYhME5GdVd16pe9W9g1OQgjh0RDCkMo/AF8AsBDAvXnLFJGRItK3oCpeBGC9EMIwABMBnC8iny6o7JWZVQDcCWAcgNUATAVwRz0FFtxv7wA4H8DvCyqvmXgOwHEA/lFEYUX1W/nX6x0A7gYwEsAxAK6rfFF+khGRfii1zXUARgC4BsAd5b/nLXO0iEhB9TsawFEAPg+gso5/WETZzYKIjABwBoAXCyirkL4TkZEA7gLwvyit6RcDuKtc116jaTY43XA4gFtCCB/XUcZeAN4WkUtEZHw9lQkhvBhCWFq5LP/7VD1lNgMhhKnlX/tzQwjtAH4GYJyIjKqj2CL77bYQwu0A5tRTTjMSQvhFCOFBAEsKKrKoftsEwBoAfhZCWBZCmAxgCoDD/Mc+EewGoA+AS0MIS0MIlwEQAJ+to8wjAbxZFpGsn7cQEWkBcDaAk0MIL4USr4cQ5tZRt2bkIgCXoZiNXyF9B2AnALNDCDeX59x1AD4AcFABdcxNU25wRGQwgINR+nWSmxDCjQD2ANAJ4D4ReVJEjsu7KxWRK0RkEYDpAN4F8Jd66tek7ILSRMm9oSi630jP0OB+EwB1bXabhM0BTAshBPW3aahDtBFC+DGAQwGMAfCUiDwkIoeJyKAai1qr/G+8iMwqi6nOLW98CAAR2R7AtgB+VUR5BfYdUJpj9rpX51yzDpyDUNrd/rXegkIIL4QQvg9gbQDnoPQL6E0R+aOIDKuxrOMADAUwAcBtAJb6T3yyEJG1APwCwPfqLavIfiM9R0H99gqA9wF8X0T6isjeAHYFkGfRbjaGAJhv/jYfpXUpNyGEv4UQjkXp5OyXAL6C0mncb2soZq3y/3sD+A8Au5fLOaqeujULItIK4AqU9M06iyq3oL57AsAaIvKV8pw7HCUJRa/OuWbd4BwO4FrzK8VFRL6mlJPvsfkhhGUAnkdJ72AuSjvTmvUFysd3j6E0mY+t9flmRURGA7gPwBUhhBtqeK5H+o0USyP7rSzqPAAlPY7ZAE4BcBOAtwuq/srMQgB2ozgMwIKYh0XkDNVvy50ilMXw0wA8C6ANtf2CX1z+/+IQwrwQwgwAvwawXw1lNDPHoXT69rc8Dzey78on7l9E6cfpewD2BfAAennONd0GR0TWRulX37W1PBdCuF4pKX9OlTdERL4pIpNRUqZcE8CXQwjj6xGjoCQH/8Tr4ACJ0tx9AO4MIUSZRFbohX4jBdDofgshTAsh7BpCGBVC2AfABigpsH/SeRHAFkaxdAtEKqyGEC5U/fZ/Kn8XkVFSsjicCmAygFYAu4cQdqihbq+g9MWqf5hG/0j9BLAHgANFZLaIzEZJ7+USEfl5zMMN7juEEP4aQtguhDASJX23TdDLc66htvK9xGEAHg8hvF5vQSKyL0q//Kag9EvidqUoXEs5Y1BS4rsbpV8pe6J0DPiVeuu4slMWO0wCMCWEcFpBZRbSb+Wy+qA0T1oBtIrIAAAdIYSOIuq6MlO2vGlBSdbet9w2bXmPzwvuty0AvFqu33EAxgK4Ok9ZTcbDAJYB+G75V/y3yn+fnLdAETkKwKUoWayeC+De8glcTYQQFonIjQBOFZFnAAxHyQLuf/PWrcn4JoAB6vo2ALcA+F3eAovqu3JZWwN4AcBAAOcBmBVCmJS3bkXQjBucb6C4CfEKgE1CCO/UWU5ASRz1K5QW3JkATgoh3Flnuc3AgQC2A7C5pH2pbBZCyOszoah+A4AzUbLsqPB1lBaCcwooe2XnPpR0W4DSr8krUdKbeDhneUX222EAjkZJrPUogL3ybpaaiRBCm4gcAOC3AH4E4GUAB4QQ2uoo9gkA6xZk7XQ8SuPoHQDzAPwGdNEAAAghzNPXItIG4KMQgtWpqoUi++5UdIkT70Vpbe9dQggr5T+UTFPnA/i/kfcfgdKEWQJggwbX7T6UZNoPRd6/UbluiwB8s7fblv3GfmO/Nce/HP12dvn+JQBaG1y3VwB8BOCayPv3KPfbYpREKL3evuy7FbvvpPxyQgghhJCmoemUjAkhhBBCuMEhhBBCSNPRI0rGIhJaWkp7qc7OtIHFoEFdfoDa29szyxg6NO2HSpdTKbs75s3r0ssaMGBAKk+/z5afVYatv8bWQ987cuTIzOfysmRJl4d8+9nmzk3pjH0YQhhda/n9+/cPQ4Z0Hy9xwYJstxnLlnUp4dt2Xbq0S89T11n/3dLRkTZY0s/pdwFAv37dh9SxothFixYl6b590+5VdPl2THqfuwEU3m9FoNtOj8FGMHz48NR1a2tr3WUuXrw4SQ8cODCVp+eNHRd6LOh1C0i3CXL2G1BaK7Py9Fyy41CPWVu32PUr613dvS+L2HWurS2t06znv51zeizr+W3a3B2Lun3sffq7KYSQKy5Tnz59Qv/+/ZerI5Beo7w62rVs2LAud0X6u0X3JxDfp7Hk/a6y9dfovrJjS89Hu9brseDNR2TMuR7Z4LS0tGDw4MEAlp8om222WZJ+551s44ldd901de0tUpq77747SY8bNy6Vp99ny88qw5volc/Y3b377LNP5nN5+ec//5mkN9poo1TeDTekfOXNzFP+kCFDMuv9179mO4nWn3vChAmpvNdeey1Jb7LJJkl6xowZmeW9//77qetNN900Sc+fnzYgWGuttdAdduL84x9d8SHXWGONVJ5uS/vuBx98MLOeDaDwfiuCZ555JklPnz69Ye8Blh8/3g+RWF5++eUkrccSkJ43o0en10u9Xuh1CwCeeuopfZmr36qx/fbbJ2k7Dtdbb70kvfXWW6fyYtevrHd1974sYsfdv/71r9S1Xhfs98A222yTpNdcc80krcch4I9F3T72vsq6/fHH+cMW9u/fPxkTdg3Sba7HnsX2zW677Zak9Xec7s/unquXvGuHVw+93trvWt0mdr3VY8Gbj8iYcxRREUIIIaTp6BErqlGjRoWsXaE+ibFMndrlBNH+otA7Wn2Ssfrqq2eW5/36s7tPvUv+whe+0O3fV2TMcfbTIYRtay3DOy7X7LHHHpl5RZx4eOXbExzd/7H9/dFHH6Xy9K9EcxIWjX63Ls9e21+y5tdlQ/utmfFELPrk0IryXnnllcwy9Ti0pwC6Tx988MFc/QYA/fr1C5VfqlpEYetqToyw7bZdr6tympuJbhc7J7zTdf2cPT3Kwq639n0aO38q2LnzyCOPJOmvfCXtQ1Wf9mTVcdKkSZgzZ04uEVVe0WLR2NNor9+KJvZ09VOfSjvw16eo3lit8tm6nXM8wSGEEEJI08ENDiGEEEKaDm5wCCGEENJ09IgV1bx586J0V6wMT8vcPEspjdbWtlgZnoeWt2v5sNbHsdjPqOXAeXU58qLl1o2wdNHtY/tm9uzZmc9peb1XL33fmDFjMu/z8jz0WKvFMkePIc/yTlsDWF0BrVtm6c0xUzRWd0r3VaM/m6fr4JnQe8/pfrPWV1YXLC/t7e2JboFnOWp1cKzeTQwHHHBAZl4tunOxejcabTVVrQw9VvT8sLo5Vu8mtvzKul2PFZWHHlO2zW+//fYk7emYxK6bVudG62bZMVM0du5k9Yed+88++2xU+VYPV8/HLF0jnuAQQgghpOngBocQQgghTUePiKiGDRuWOC2y4gxt4u1hzcljjqeA2sRSGl3PWA/L3hGpl9eI4/qiRVS2/rrfvOPsWPGPFWfo56yIR/fNFltskcrLOoq+7777Utf6uSeffDKzXvqIF8h2JGgdH8aaZ9rxubKLpbxxnuc+j7xtpY/qt9pqq1SevrZiKD0OG+XwUTtFtXiiTY3nekOTdy0uglrEWrFjxXPgqPPsHK6IPmLbtxp2zdOiFetwVH822296jMWu4VqUBeQTS9n2zjvP9HN2HY1FP2fbQItws+rIExxCCCGENB3c4BBCCCGk6eAGhxBCCCFNR4+ZiWtzuDzEyhKtrM8zn9R6E1ZnIsudu3WfrvFklV69PHNNbXpXi9y/CLPVvn37LhfgrMLChQuTtP1suq+sKWeWia4NQqflq9/61rfiKozsiLZWx+eNN95I0uuss04q74knnkjSsea41qQ3y7S1GnkCI/YmRejS1MLGG2+cpG2Ymb/97W9J+rLLLkvlZc1Nz0TV6nHosbzLLrtk5tWjR9XZ2ZnZ73rt8XS8PJcaeq5anQ89p2sZez/96U+TtBcq5/TTT0/SM2fGxyPVuk9ZYRuA5ftLo6N4Wx2ZyrWNVp0X23Z6Lfb6xuoAxfaBonpGZwAAIABJREFU7lNvXOSdq7HPeeM+9vvb9o33nOfuoQJPcAghhBDSdHCDQwghhJCmo0dEVEOHDl3OC2G9aHGNFidZEYJnMqnr5B0deuaTuvxaTMGHDx+epPURpv57LTQiiuyAAQOSY1/bBvp40KuzNe3LMhe042O//faLqqON/qw9pGoRhvVAvcEGGyRpeyz61ltvJelGH+taGu2BWuNF3PbQ4j47v7w6a1GD7Y9YkZ5nXvz2229n5uU5Zrem/1oM6YlK6mH48OGYMGECgLQ37HreqdcGvT7a8mPH2/e///3UdZZY6rrrrsss3+srLSoD0p7k87ZBHm/LebHzymtzfa9Vf4hdw/V99pmLLrooSU+bNi2qvLxcddVVqesjjjii5jJqWZN0u2aJstwTHBHZW0T6qOuvisizIvKxiLwmIt+NqzYhhBBCSM9RTUR1D4CRACAiXwJwLYApAI4FcBeAi0WkZ7UMCSGEEEKqUE1EJSp9MoALQghnl6+vFZF/lf++crtfJYQQQkhTUYsOzkYArEjqTgBnFledNFo3xcrYtC6HZwoeG4XcI0+kXosXRVbjhZbwyvAirRaBlYtrebo1wfbI0tc58MADM5958803U9fPP/98kn7uuedSeVqOrfVsVlllldR9G264YZL+4IMPUnlaTm5N87UuxzPPPJOki5Lx63IaoYPjuUz3TDL12NNzyurg6Ha1eVoHxzMp9dw36L63UeutzkwePF0d3d+2b/KGhLHMnz8/cRXghUexLvk1Vs9D6w7pMvJGl7744osz8yZPnhxVhqcraddsPVZiTfB7OjSOxq7Ftj80esx6Lkg89Jyzpu79+/dP0tYVR5ZZvdW31OPEa9cvfvGLqWutAynSdVbi6dl488jq48T0Y8wGZwsRmQtgcTf3twBojSiDEEIIIaTHiNngTEKXqOo/Aeijga0BvLXcE4QQQgghvUi1Dc765nqhue4L4MdFVcaKA7xI1H36dFVdH8PZ481Ro0Yl6Y8//jiVp02dtXlxI7D10kf+npdnfSxn79PtY0VSMZFWq7FgwYJM78laLDVmzJjoMnV522yzTZLWR5gA8NJLLyXpDz/8MJWn+/7VV1/NfJc2Wxw/fnzmfTbSuOcxWrelbv+eNEOtBy1q1Oa31cgS9dq/22NwjTUN12SJ+yZNmpS678QTT0zS11xzTSpv7bXXziw/zxzwxHmNcMsApL2Hey4ubFRqLVbIingPLC9qzmLVVVdNXU+ZMiXqOW9MjR07Nkn/4x//SOXpOV6ER+FGi6E8vO8tu7bkcQvhRfs+9thjM5/zPD1rrPjQE2Xqd996662pvHfffTdJH3300Unafg/rMuy7tCjWzjntIiTLnNzd4IQQXH/aIYRrvXxCCCGEkN4glydjEVlTRDaofichhBBCSM/jnuCIyDAAVwKYAOBhAEcC+H8AjgEQROQJAJ8PIbiRHVtbW5NjO3sM5wXM0sdO1mpg//33T9Lrrbee9/qo8ufNm5erDI0nhtpxxx1TeTrYnBdQUx/LWe18T5SigzbmpaWlBYMHDwaQHaAOqO04OMv6TYukgHSb2GN1Lc6w6HHiiaW0uE+LPaqhj4djP7e9T5dhLRaKsNjT2Hmj55t3JJ7XE7O2GNEiSCDdp3Y8abGUFnPNnTs3dd+gQYOS9GOPPZbK22qrrZK0tUjJ02+e5WCjaG9vT8Rddj3RoidbFy3q8CxHYz+DtSz00P2q01bsrNdAbz188sknM9/VE32QBy1atOoCeg23ohTv8+jntHjJW/+uuOKK1HVsu3rErgUjR45MXf/lL39J0lqNxIqo9Oe0ol7dPlZEpUXeWXO62gnOhQC2BPAjAGsAuAklReMJAHYHMALAD6qUQQghhBDSo1RTMp4I4PAQwkMiciuAtwFMDCFMAQARORXAJQDOaGw1CSGEEELiqXaCMwbAawAQQngHJV842mzlBQDZpguEEEIIIb1AtROcOQBWBTCrfH0HAK2sMgTA0movWbZsWaYZl2eCpvUHtBdUIC1j1x5N+/Xrl7rvjTfeSNJWJ0CbNO6www6pvFmzZiVpz/xUY003n3766SRtvfFqXYIlS5ZklqllktbMT+v4WFmu1mnIazLZ2dmZ2W9eNGCPLB0TLSu2WA+1nklu7Gd94oknou6LLb+WNvbuzetdNgur4+aVn1fvRqPnmNUDiy1fj/Pdd989leeNk2effTYzT787Vh/HzqlY1w6NQs9Fux56UbY9c3PNMccck6teWn/OM5HWa2BFX6WC1qO0OpVal0N/JxTVBxW9QKsHWAtDhw5NXHN4noCrlaHRrj401nP86aefnlmmbstY7861rAP6XqsjqmltzfYHrD+n/b7Ra4hd9z1dpArVTnCeB7Bd5SKE8NUQgvY7/WkAK6bWFyGEEEI+sVQ7wfk6gE4nfw6As4qrDiGEEEJI/VRz9Pdhlfw/1/rCWsz89DGsPa57/PHHa3116qgTSAdgHDFiRCpPH6fqoJ+f+tSnMsu3XiTb29uT9OWXX57K00f5++23X5L2PIbqethraw4ceyztoU0f7fGjPh625nue2Z9Gi55s/+pjXq+M66+/PnWtTYU9vICBzUxs38RSS2C+2GNwfax+8MEHp/LuvPPOqDKs6Cnr3V4dLZ5bht5GrwXWs3isKOfXv/511H1/+MMfUtd6TGnRtTU11+vtI488ksqbMGFC5vu0WXHFbQWw/Jrnfbfoe+19FY+4nqpALdQiZt5ll11qLn/atGmpa+2x/cgjj8x8Lq9bCw8tLltttdVSecuWLYsqwwti6+G5mKmQy9EfIYQQQsiKTF0bHBF5WUQ6qt9JCCGEENJzxEQT9/gFgFFV7yKEEEII6UHq2uCEEH4ec5+OSu3JTW2e1sGx0Wc33HDDJK3NvW0kXc9sUYdnmDx5cuZ9uvwZM2Zk3qd1bixa/gykIyHHmLt1x/Dhw3M9F0vfvn0T+bo1C7d6N1l492m9HqszFGvOvN1222Xep6nF9bxnRpzH5b/V8/B0j7RZZJaJfi1Ys3w9P4ows7Xlx4aa8NpOR5O24Ta0CbkXST6vybtnCq71DRplJj506FDX3Fbfp1l99dVrftcDDzyQmXfdddelrr/+9a8naWum3NnZZYdy0UUXJWm7HmpT9nXXXTeVp+enNSHXLvn1umlN4z39DZ1n266IebZo0aJc6/iiRYuStJ07+vtPh5PRkdeB5fXgNN56pce6Du9Riw6O1omyOjd6nWtra0vSOhQLsLzLg1hivv+iNzgi0oqSTxwA+DCEEKdBRAghhBDSw1TVwRGRA0VkCoBFAN4p/1skIlNE5AD/aUIIIYSQnkdCCNmZIt8GcDmAawBMAvBeOWs1AHsD+AaAE0IIv3FfIpK8xIqhNN4Roydq0nlWJKKPbr0yisAeyd58882Z9956663dpu0xpxaPWXFGDUeJT4cQtq1+W5qBAweGismuPQ7WEXM9D5xW9KSPQnXkaWuCq8u077ZHnFnss88+Sfq+++6LeqYasaIPr2+yIqp3U0aufhs2bFioiDmsybAnHutNdHtpFwqnnHJK6j4d4fvhhx8uvB5e+3jj1ZCr34D0nNPeyIH0XLIiMi1usHXLEsF4a7/Hd7/73dS1Fpl4c1OLR+2aocu0IhcdlVzX2YooX3/99STtzT/77ooIbNKkSZgzZ45090w19HecJUsUZOtpv7v0+NNlaHEPAJxwwglJ+nOf+1xslXNhvUzr9cVGE9f1vOSSS5K0FUnFigjt3kF/Lzz44IPdzrlqIqrvAzguhPDbbvJuEZGpAE4H4G5wCCGEEEJ6kmoiqjUBPOrkPwYgTtuUEEIIIaSHqLbBeRHAsU7+t8v3EEIIIYSsMFQTUZ0C4M8i8jkA9yGtg7MXSic8+2U8mzBo0CBsttlm3ebFurW2ehjaTbOW7XrRSK0Mz8q460WbSALAn/70pyT97rvvpvK0iXqsq+pawi9oPY+8Ear79u2btLsNEzFu3Lgk7emRaF0dIC1X9nSidD/mlSt7ejdeyALPrXuWbN+azmpdnVqiC1s3B3lYsmRJogthdXB0+1t9CRutPou8kYc9LrvssiStI4hrs2AAmDNnTq7y9dyxehAa7X7C4j1XFCKCAQMGVL3P6mvoummzaiBeV6+jo8tna58+6a8GrQdjy9fzOHYMWe64444kbdc5vZYVgf2OqMUsOouWlpbEZNoz8/fWcLsOaTPoHXbYIUnbMBf6u6SIz+LN6W9/+9up65aWrjMSqxOnXa94a2os9jm7J+iOarGo/ioi41E6xdkBQEVjdzaA2wH8KoQwI0ddCSGEEEIaRlU/OOUNzA8aXxVCCCGEkGKoN1RDFIsWLUqO5e1RfizaXBpIm5rpoz17zOeZ6BXBTjvtlKRtJHDtkfU73/lOZhme6bzGmoZ6x35abFSPiKoi4rAiqkoEXvsuIG1q6x1Zxx5V/vSnP426DwAOPfTQqPu8SNq6XrHRpq3po24vK3ZqtAfqoUOHJqIoz8up7ZtY0VNRYinNf/7nfybp559/PknbMeKJNWNNbjW23/Txv/2cVmzTCEQk8z16/bLjV392K0LKwopqdNtqkRQQ3+d67ts6xnoBP/nkk1PXra2tSVp7y7VjQ6+3tr41mPjXjRUL6zlYy1qsPUSPGDEiSdt29cTtebB9o8s/66yzUnl77713ktZtDPhuDbLwvEznEVUymjghhBBCmg5ucAghhBDSdPSIiEoHkLOijljscaq+1ml7xOUdjRVx1K4D1h1yyCGZed7RW16tci22s2IuTzQRy4IFC5IjZ3vE74lZ9L1egLdGcOONN9Zdhj6Ste2YNWbsfZ41lD7C9jw956WzszMp16uHPVLWY0hbHwLp8arbwHrezjuWlyxZkqQPPvjgJK1FVwAwbdq0JG3HVqyHaI21SPGwAWc1en7XE7xRi/OtuEHPKzsOdT/YtUbfq0XLdm087LDDcta6+zrWwqhRo5K0tW7VYintydiuedqTsUXPOdt2FWtC3Tb1YOe0LreWQJ9XXnllktZtMnHixNR92lrJip1jxXHed6Fuu0svvTTzPvuuP/7xj1Hv1tQyd2IiE/AEhxBCCCFNR/QGR0TWEZGx5m9jRWSd4qtFCCGEEJKfWk5wZgCw512TAbxZWG0IIYQQQgqgFh2cIwHMM387HUBVm1ftWXXYsGHRL9Qm5XlN+7baaqsk/eyzz6byPFPFLJmk9TKqvSnaKK9XXXVV1fKq1cNDt6X27FwU7e3tmWaH1hQyCysn1zoaXp1PPPHEqPKtCbmWy+p0XvNJq2uUpdtk7/P0wnoSqzfiRXTWcn6rB6HHqNW70eSNtr7LLrskaa2z4OmSeR51i8DqKHloz9553TJYrD6LNmm3XlxjvLoCwE9+8pMk7ekUef1oTetj3z169Ogk/atf/SqVt9122yVprXMDpMeD9oj/4ovpKEHaM7eNeu3pYlbG0Ztv5v+t3traGjX+8upnTZo0KUnvtttuqTw99rRH6GqcdNJJSVp7If7Zz36Wus/ztK/X97///e+pvNh5oHXnrB6Ubi+7xsboK0ZvcEIIV3fztzjjdkIIIYSQHiSXkrGIDBSRPUVk3aIrRAghhBBSL1EnOCJyNYCpIYQrRKQfgKkANgfQJiIHhhDu8Z5vbW2NEk1Z76MeWV587RGgFkvZY1d91GrNRbO8uuqgZ0BazHLvvfdm1tc71tWm0/bY2Dvm8zzuWjPfPGjz/rzmzN6Rpj5yHD9+fOq+WK+Vp5xySmZe7HGwd7Rs3RosXLgwSXsitlgRnm3HWgKqZjFv3rzMI/m84pOivRdrc28g7b34oYceStKVAIbd4fWb146euEKPO897d9EBILvDivG8AK4a2y56jOl1J6/LjliRlOXMM89M0lpcZfnDH/6Qup45c2aSvuCCCzKf02JVK170+qvSXtpjcq0sW7YsWW8a4TFfj1nrZVp/f2hRXy1odRArgl511VWTtF0/9Hr+2muv5Xp37JoUu6ZqYk9w9gHwt3J6IoChKAXePKf8jxBCCCFkhSF2gzMCQGVLvC+AW0MI7wP4I4DNMp8ihBBCCOkFYjc4swGMF5FWlE5zKi56hwBob0TFCCGEEELyEmtF9XsANwJ4B8AydPnD+QyAqr7Z29raEh0UG01c62hYPR1PRuxF0tZ4ej1aluzpGOh3WfmqDpdg5Z8dHR1J2uoEaPm6F74gNgJvI1iwYEFinu/pO3jRi73216b/2qQeAMaOHWtvT/je976XpL1o355uk3blb+X1+rM2Qp7uUY+b/wojR47EPvvsA6C4MZNVjhe12ZOZH3fccanrY489NknPnTs3SXs6OB553UpoHSurN+ehze+LMhO36D6w81GbCntjVs+rjTfeOPM+HRUcSJusx+o3Pffcc6k8PbatvmJsqAAPrZdkQ3XoMmox/49l2LBhy5lvVyhiTmv+9Kc/pa61KxSryxjrrmL//ffPzNt3332T9D33ZKvbfvDBB5l5Ht53eb26f1EbnBDCeSLyIoB1ANwcQqg4fOkA8OO6akAIIYQQUjC1+MG5tZu/XVNsdQghhBBC6id6gyMi2wA4CV1KxS8D+FkI4R/VnvWO7zTW7NaLWO2Zemq0B1vPe6o1fdTRbVtaulSVrCnhX/7ylyRt6+8d5WpRnRaf2OjPscep9l1FH4t65XnmrBUz8wr/f3tnHi5XVab7d2U4GZGYQUAUBAI0IhJlEFpBUBARHAi03LZBmUWgGbS1b/QqQyug9xH6yiDQagOXsaFpBQwmzUxovBGQUUhLGIKEQEIgZOSEnHX/qKp93v2l9ndW7bPrnKR4f89znrN2rb1XrVpTrVrf5HlPZbh9enp6cnnjx4/P0vZIk81BPS+YXt+wqNQ71h09enSWtsezntdZ9tRqTWKrYMWKFZmppzWP9SImcz2teKao/8t4AweAL37xi4V5X/va17I0R0oG8mIIz+tpK1GbGRY522f4KD01Wnl/aOUz8Dpn68ZtxmXY8vj9zj333ML3Kht1nOtoTZ058rQtn+9lcYyNnM1j0YoJeSxakVfDpcby5cv9D+AwZMiQQtEgi8pnz56dy2MRflnR5umnn17quVSmTp2adN+wYa0ERujFek1PJUWEnKRkHEL4OwB/ALAJgOn1v40AzA4hHFaqdkIIIYQQbSJ1y/UjAN+PMZ7NL4YQpgH4IYCrqq6YEEIIIURZUs3EJwH4tyav3wCgdfeCQgghhBBtJPUE5y4AewGwvpj3AnCPvdny5ptvZmabnkmxhaPDpkbj9syGWX4LrB2egTnttNOy9JZbbpml586dm7tvxx13zNJPPPFEYXlWPsvXLJe18nO+z8rkWU/Fls9y31R9JQubG1s83YsqTJO5zmy6C+QjBduowan89re/zdKTJ0/O5bFugg3NUTX33Xdf7vr555/vd5lDhgzJ9MFsFHVPj8MLPcG6AlymF5HcGwf7779/YZ6Hp6fAdSmrg8afzbo4YB2csqFLWsHTAfJcY9gwLVzORz/60Sx99dVX5+5jfTnruoLL8PTGuA/svD3ssMOapi3sJgAodpVhdXD4Pmvm7lGFvuLq1aszfUmr48NrmXWTUtadwUBy0UUXZeljjz02l8c6lan6ld53uV2LuR957AJpIUNSNzi3ATgnhLAzekM27AZgKoAzQgiZFlKM8abEMoUQQggh2kLqBueC+v/j6n/MhZSOAMpHLBNCCCGEqIBUR3+pujpNGTNmTHb86Zk9W7PPVHGWd+Tl5XGkcUtR9Flr3sgiK2sCyN4/LXz0ttVWWxXe55l18pGgFW2VFUulkuph0hNTFEUWB9Y+3q6aAw44oNRzbOJtzdeZm2++OUtb0SV7vK5CJGVZsWJFKZNT75miY3z7uif23WmnnbK0dbfAY+H888/P0q2IOz0zcb62YjuGxVLe/G0XLBa2UZ09s1hPzMKf/ZVXXsnS1tPw66+/nqVXrVqVy4sxOrVuXg+7Vp533nlZmr2R23uPPvrowvJT151W+q5R5owZM5KfsQwfPjzJ47IVSXnjjcWgLAJtt0d7+93nrZX8HVSFB28rXuX+tuPfc+HRoF8bFyGEEEKIdRF3gxNC+K8Qwji6PieEMJ6uJ4YQ5rWzgkIIIYQQrRK8o8cQQg+AjWOMr9av3wQwJcb4bP16IwDzY4yu3s2YMWPiBz9Yc4BsPfV6sMjHHt8VWU7Z4zs+xrIir8033zxLWxHJZpttlqX/4R/+IUu//PLLuftSj0ytVj9bLHgWGOyFdOONN87l8Wfj+4C1jgsfijE2l7k5TJgwIRZZUbWbgw8+OEtfeumluTzrHZkZNy7bj6/VXkVYscrq1asL32vRokVZmq38PDyxrHdkPX/+/FL9FkJIkycYiiylqoK9BN9yyy25vO9+97tZ+oUXXihVPtffBu71rI6Y/gb3A4Brr722VL8Bft95gWtTxW6eyHvvvffO0kcddVQuL1VExViPxOyBnMVhlrJ9wGugbQ++Zg/2APDMMzXj4IULF6K7uzsvV0vEWys90akVyTBsJcTWRbaMKkRW3ncoB/B8/PHHc3m/+MUvsrS1sCoq38OOSfaObK20eH2/9dZbm865VkVUpTpfCCGEEGIgkQ6OEEIIITqOvjY4sf5nXxNCCCGEWGdJ0cH5TwBv1V/aHzXPxSvq1yMA7NOXDo4nV65C5l2Ws8/uDa1lPeJec801WXrBggVZuhVTOJZlWtM7Nuv2TDxZb2HFihW5PC7DmpQanYMB1eUYzD5dF7H6UYwdT8Yb74D2Wyqp/Tt8+PDc9bRp07I0R48GgDPPPLPf9Sqjo2I99rI+XKrptaU/Ojisy2Hf/+GHH87SVseIsR5eWc/L01PyzNBPPfXULG1dUrBLBK6z1becOHFilp43L2+bUjYqPcPzzNbRK7/Rl8uXL8eaNWsq18Fhc2/PfYcdv6x3mqqLWRauo3V3wuPC1v+443rd4i1cuLCwXqkuX+wYZP1Iqztl7ms65/ryg3OFuW4WVPPKPsoQQgghhBhQ3A1OjPHIgaqIEEIIIURVpIZq6BfsndMeFVZxNJnKHnvskbueOXNmlraBBr/yla9kafbA2Qpe/YuCwXnmnvY4ncuwz7FIrAoPkx7shdZizSC33XbbLM1H2O0IVjiQ2OCLfFRvXRB4ogBun3b3m4d3XJ4Km9sDwFlnnZWlqzBttXVkkUuqWbg37jxzXDsXq/Ic3tPTk40l+/7cB95RPou1LTwuvT617XfDDTdkaW++e96AWUxhTX6rWOutWKqo/KL5xyLAVunu7s7KtaoEo0ePbloPwBc9MbwW2PbnALcenisRrpcdFzwW2E0GAHzsYx/L0l6Q11SseJXngHXnURRtgJEVlRBCCCE6Dm1whBBCCNFxaIMjhBBCiI5jQHRwFi9enMmvrdzc0zlJ1c8pCttgsTo47ILao4rI1qlRtW37eKEtPNf6VehvsO5UWawctkjvxsrFrSy2v9j296L4siyZdWKAYjm/1eV4++23s3TDFXwD1vey/W31dQaLVJ0b266sZ1GF+arFk7unjnleI6zulKeTw+/N/Vslq1atynQe2D0/kNdbsevClClTcmUwrJvk6WtwXqpeB5APRcKmvFZngueV7UdvDec8Xitt9O5Uk2I7H7fbbjsAa0c/b4Wurq7CNYvXNhvehfuG3ZEA+TAFvE7YtZKvU82xgTQdFiCvO2fhOW77LTWUjQd/Hqu3ynOgaO7rBEcIIYQQHYc2OEIIIYToOAZERMVYUQofvTWOChvYSMtMFWamHnxc2e4oy9ZEneFj6VbETlXX2bZ3qlln6n32eLeK4007nhhuE8/E22s77g8rauJra3LriW6q+Nxl4SNr2yY8DrlN7GfhY3WL59E5lbJjmT9bkYsGwPeG7PV3VYwePbpQPMTj2RN1eOOesf3B4lfbRvx57TrEYiMWl3iiJivq5TK9tZ3nkucKwIrHvP5qeO5dvnx54T194alhcH9Y0SKL7excmjNnTtJ783rlRSf3zLZZDcOK/nhttutCqmfm1DFZtdsYneAIIYQQouPQBkcIIYQQHYc2OEIIIYToONxo4lXBkVatjO3Tn/50lrY6N545Z1Wu0QeLInfn1s06yy6XLFmSy/P0EUxeqejGXoRcj1SzfS/aOsuEbZvsuuuuWdpGvmU5M5sYeuPH0xWwZpcsJ2d9AxuZ2XP7zibYNgo8y7vvuOOOtkYTb0WmbduvQSshNri9rI5EEXaeePpqrMdhx5PnbiEVXp943QLWWo9KRxPnvrM6GTwW2TQbyLetNS+vwlyfdXKsLgePZzbB9lw/pIbSKIvVg0mJDL9w4UJ0d3eXshXnfvPe26uHNW23a0MD25/c3557DU8HjnWiPN0sj9Tnyupzelx77bVN55xOcIQQQgjRcWiDI4QQQoiOY0BEVCGEhQBeaPsbiSI2jzFOavUh9dugo35bPynVb4D6bpBRv62/NO27AdngCCGEEEIMJBJRCSGEEKLj0AZHCCGEEB2HNjhCCCGE6Di0wRFCCCFEx6ENjhBCCCE6Dm1whBBCCNFxaIMjhBBCiI5DGxwhhBBCdBza4AghhBCi49AGRwghhBAdhzY4QgghhOg4tMERQgghRMehDY4QQgghOg5tcIQQQgjRcWiDI4QQQoiOQxscIYQQQnQc2uAIIYQQouPQBkcIIYQQHYc2OEIIIYToOLTBEUIIIUTHoQ2OEEIIIToObXCEEEII0XFogyOEEEKIjkMbHCGEEEJ0HNrgCCGEEKLj0AZHCCGEEB2HNjhCCCGE6Di0wRFCCCFEx6ENjhBCCCE6Dm1whBBCCNFxrLcbnBBCDCEsDyH8KPH+o0MIy+rPTW5z3e4OIawKIdybeP829bqtCSEc0866DTbqt/WTDuu3fep16wkh7NPOug02JfrtzPr9MYQwrM11ez6EsDKE8H8T73/H9BugvquEGON6+QcgAph8hFTxAAAgAElEQVRM13sAWGb+IoCDvefaVLe7ARxjXrsKwMsA3gTw3za/6LlO+2vSb9sA+A2AhQAWA5gBYNu+nhuofqu//j8APAVgOYC5APZ4p/db/bXLAMwB0APgiNTnBqLf6q+torVgTpPnngewz2C37SD02xQADwFYUf8/xeR/oP7csDbXrWn7AzgFwHP1+fYUgG3eaf1W1HeU99V6vh33g9Z3AP4awGwASwE8BuATqX3err/19gTHEmO8L8Y4tvEH4EDUFrbflS0zhDA+hDC8oiqeA+ADMcZ3AfgCgB+GEHaqqOz1mXEAbgawLYCNUJsgv+lPgVX2WwhhXwA/BnAkgA0A7Ang2SrK7gAeBXACgIerKKzi+QYAJ9GasG2F5a63hBC6UJtfVwF4N4ArAPym/nrZMieFEEJF9TsGwNEADgDQWMcXVVF2pxBCeDeA7wJ4soKyKum7EMJ4ALcA+N+orek/AXBLva6DRsdscJrwNQA3xhiX96OMfQH8JYTw0xDCh/pTmRjjkzHGtxqX9b+t+lNmJxBjnB1j/GWMcXGMcTWA8wFsG0KY0I9iK+s3AGcCOCvG+PsYY0+M8aUY40v9LLMjiDFeFGO8A7WTkiqost9Ec/YCMAzAP8cY34ox/gxAAPCpfpR5FIDn6iKSLcoWEkIYAuB0AKfFGP8Ua8yNMS7uR906kXMA/AzVbPwq6TvUTm8WxBhviDGuiTFehdqp/NQK6liajtzghBDGADgEtV8npYkxXg/g06gdwc8MIfwhhHBC2V1pCOHiEMIKAE+jJq6a3p/6dSh7ojZRXitbQFX9FkIYCmBnAJNCCM+EEP4SQrgwhDCqbN1EMVXPNwDnhBAWhRDuDyHsVVlF12+2B/BYrMsL6jxWf70UMcYfoybGfQ+AB0MId4UQDg8hjG6xqPfV/z4UQngxhND44u3I76kyhBB2RW1NuqSK8irsO6C2UbbXg/pDpVMHzlTUdrf39LegGOMTMcZvA3g/gDNQ+wX0XAjhuhDCu1os6wTUxBx7ALgJwFv+E+8sQgjvA3ARgG/2t6yK+m0jAMNR2yzvgZruwkcA/K/+1k80p8L59o8AtgSwKWq6QreEEN7xJ6aoiX2WmNeWoLYulaZ+wvkNAO8F8HMAf4vaadwvWijmffX/nwGwA4C96+Uc3Z+6dQr1H1wXoyZ67amq3Ir67gEA7w0h/G0IYXgI4WuoSSjKbJQqo1M3OF8DcKX5leISQvi7upb3shDCbTY/xrgGwOOo6R0sRm1n2rK+QP34bhZqk/kbrT7fqYQQJgGYCeDiGOO1LTzXzn5bWf9/QYzx5RjjIgDnAfhcav1Ec9o932KM/y/GuLQuhrkCwP1QvwE1vUS7UXwXaoqhfRJC+C7121qnCHUx/GMAHgHQjdZ+wTfm209ijG/EGJ8HcCnUbw1OQO307fdlHm5n39VP3L+I2o/TVwB8FsDtAP5Spq5V0XEbnBDC+1H71XdlK8/FGK8mhcT9qbyxIYQjQgh3oqZMuSmAQ2OMH+qPGAU1Obh+USJTmpsJ4OYYY5JJZIN29luM8XXUJihvlJM3zaKYQZhvEWsfob8TeRLAh41i6YeRqLAaYzyb+u34xushhAkhhJNCCLMB3AlgKIC9Y4y7tVC3Oah9sWq+NefTAA4KISwIISxATe/lpyGEC1MebnPfIcZ4T4xxlxjjeACHA/gr1IxGBo222soPEocD+K8Y49z+FhRC+CyAf0Pt19+lAH5NisKtlPMe1JT4bkXtV8o+qB0D/m1/67i+Uxc7zABwf4zxf1ZUZiX9VudfAfx9COF3AFYDOA21fnzHU7e8GYLaxmF4CGEkgO6yx+cVzrdxAD6Gmoj6bQCHoqbbdUqZenUYdwNYA+Dk+q/4Y+uv31m2wBDC0QD+GTWL1TMB/K5+AtcSMcYVIYTrAXwnhPBHABsCOA41yxwBHAFgJF3fBOBGAL8sW2BVfVcv6yMAngAwCsBZAF6MMc4oW7cq6MQNzldR3YSYA+CvYozz+1lORE0cdQlqXwgvADg1xnhzP8vtBA4CsAuA7UMIR9DrH4wxzitZZlX9BgD/BGAiar6LVqH2BdzSKVMHMxPAJ+vpv0ZN12Vv1L5Ey1BVvw0H8EPUfkGuQU2p/0sxxv/uZ7nrPTHG7hDClwD8AsC5qPmZ+VKMsbsfxT4AYPOKrJ1OQm0czQfwBoB/AfCrCspd74kxvsHXIYRuAG/GGK1OVStU2XffQa848Xeore2DS1wHHBqV+UPty2YJgH9KvP9I1CbMKgBbtrluM1GTad+VeP/W9bqtQIHDtE75U7+tn38d1m+frtdtJWpH8YPevutQv51ev38VgKFtrtsc1ByfXqF+U9+14y/U31wIIYQQomPoOCVjIYQQQghtcIQQQgjRcQyIknFXV1ccPbrm72flypU2L0uPHDkSRSxalPdKPXHixKZ548ePz9331lu9Rhj8XgCwevXqLN3dndexs/c2WLNmTeG1fcb7PPzc0KFDC+/zWL68NwrFmDFjcnmrVvV6z1+2bNmiGOOkVssfOnRoHDasNkRs+zDcFwDw5ptvZmn7HPfP4sW9em1F7d0MLnPs2LG5PC6H23j48GIXKj09eaOf1OfsmExl1KheR8i275ctW5Z7izL9NnLkyLjBBv3y27ZWv3Gb8LhrB9wm3tx417vy7lx4bbHPcf25v+16xM/Zuc5wHzYpp1S/AbUI0o20nRPeHGTsfDRrQZa2ayV/3iVLyumt2vcuwps7tl95vPF8t3OT77P9b/uZ4c8dYyzlSiB1zr3++uu56w033DBL2/4dMqT3/IG/q7zPUgX2u8SO9VT4s3rzmPuUP3MrZXR3dzedcwOywRk9ejT22GMPAMBjjz2Wy9tss82y9Ac/+MHCMi677LLc9dSpU5vmfe5zeZ9QzzzzTJbefPPNc3kvvdQbUmjevLzBDteL4S9vAHjjjV7FdvuM93l4AeFB3gq//32vv6fddsu7LPjTn/6UpWfNmvVCmfKHDRuGjTfeGMDa7cNwXwDA737XG9/UPsf9c9VVV2XpxvukwGVOmTIll/eBD3wgS3PfvPe97y0sb8WKFbnr1OfsmExl6623ztJ2MZ81axZfluq3DTbYYK0+aZUXXsi/NY9XHnftgBc8b2589rOfzV3z2mKf4/pzf9v1iN/b+5LnPmxSTql+s9g54c1Bxva9WQuytF0redzfems5Twip486bO7vvvnvumsdb43sEWHtu8n12Xj3++OOF71d2M8ekzrnrr78+d8198Pzzz+fyeKPx8ssvZ2k7Zqtmhx12yF1/+MMfLlUOf1ZvHu+5555ZunEQ0moZ8+bNazrnJKISQgghRMcxIFZUEyZMiI2dqt2h8a8LS+qu+7DDDsvSt9xyS+Ezdge43XbbZWn7q5RPe/jXrN3dml/cg4Y9PeLrWbNmPRRj3LnVMseNGxcbv5jsr0f+tWd/cdlfKcyBBx6Ypcv+SvzEJz6RdN/bb79dmNcQvTWDfznZU7/UU5vjjjsu6T5bHj932WWXleo3FnOUxY6n1NODwYTnt/drj9cO2788rlv5zPx+S5YsKdVvADBixIhYdJrp1ccbb0VjltdNIL92tnKqUfTe9hRwxoxen2+p86MVeA33TjnsiUTjtOeRRx7B0qVLS4moys45rosn3eD7yq6b7cauGePGjcvSXP977703dx+Pazsm+V47/k3bNZ1zOsERQgghRMehDY4QQgghOg5tcIQQQgjRcQyIFdWyZcsyWVqRdRKwtoyN5cD77bdfLo9NBNkax5bPlhbz5+dD3HgWMiw/ZFmyrSPrg/AzwMDKStuhI9Hd3V1YLss/rdmwJ1dOtQBgnQar9+RZp1VNqsWQp1PAVmVAvq/sc3aMrs+0omdR1iKN4fXC6pDweGK9G2ttkzo+7WdL1f/oi66urmwN8+Z0WcsWxupD8jyrQrfQ6jeV1bspGhuHHnpo7prb3baPtTJtxtNPP12idq1h9QetVS7D3yfed4m33hrdsOR6lsGOV2vl2MDT7+Pv8r6wc7cZOsERQgghRMehDY4QQgghOo4BEVGNGjUqO0azx1h8zOSZOltRQdFxmy2fzdCtaIPLmDQp7wSRj28951IsvrJ5ZUw3W4Hbx37uqkVW9jiQzWlbIbVefFzOXjyBvGdWKxbcfvvtm5Z3991356632mqrLP3qq68W1oNdCdjrVFNkOy746Naa1Lf7GDmVsuOnrBiirFl9KtyufIxvRd987bkI8Mz7+yOiYrGwd5Tviag8MSeXaR1cDqQrANtGkydPztJWdFb0WR944IHcNYuhqhDhVQW3OTsitZQV53P59pnBXE+K5mpVfZMiRtUJjhBCCCE6Dm1whBBCCNFxaIMjhBBCiI5jQEI1sMt/qzPBWPmhp+fB5fBzHAoA8E3BU2FZtZUPewEvWQZpTRpZd4dlkjZ0Raq5pjU/ZHn6vHnzSrmO99zGM1bOW0bua/UNdtxxxyxtgwJWwdy5c7O01aV58cUXK3+/MgxmqAYPdqdu5wOP66eeeiqXx/pLVr8llW222SZLf+tb38rlPfHEE1nauhYwITBKvbdHVaEauO+8UAp2zBaZ5ALFn9euGRwap5U5zCFRvIjPZ599dpa+9NJLc3lewMWi4L1VmZ032nn69Ol47bXXSoVq4HBENmgm6914eZ6JtKdvmUo7wmMw7ZhXjK2/CSKrUA1CCCGEeGegDY4QQgghOo5kM/EQwuYAGvKKBTHGF7z7meHDh2eiImuayGIoz3zMHt/xMR2LpaoQSVnskSnjeVNkk1Nr0shHjnzU5nlK9sRVnkfMsowdOzZ3dFyEFSWW8eC800475a4/85nPJD1nvY+yyHD8+PFZ+gtf+ELuPjYTt6abqSIqPjq3IgI+rm23u4CBwBNLMexugb2NA/lxYt0+cJ4nbvnIRz5SmPfSSy9laa/N1+Wj+lGjRmHrrbcGsLZ7BBZLeR7hU7HjnkVUHieeeGLuukgsddttt+Wur7322ixt13rufzu+vPGQCqsxFPX/kCHlf+93d3evJX5qxqabblqq/LJiqWnTpmXpc845J5dX9Ty48cYbc9eHHHJIpeV7rhmKvhv77NEQwmkhhBcBPAvggfrfsyGEF0MIp/ajvkIIIYQQbcE9wQkhfB/AtwH8GMAMAK/UszYC8BkAZ4YQxsYYf9jWWgohhBBCtEBfIqrjABwRY7zJvP4igAdDCE8DuACANjhCCCGEWGfoa4MzAcBTTv4cAO9u5Q090+9W3P+zPLodejepeJGt2RTWc/vOeO7ZvbyyoRM8Vq5cmcmuPV0jq/+TGsGW7/vUpz5VeN+TTz6Zu+Yy/+M//iOXt2zZsizN7WVl/ueee26Wnj17di6vjA6F98z6omfDpMrnrU7dvffem6WtSwgeQ55LBca6jpg5c2aWtrogf/7znxNqvG4zZsyYwsjX3J5V6NxZnZFU0/ALL7ywMI/7i10GAHmdn0WLFuXyeH56keAZTyfD4ul3VjE/u7q6ss9n29Uz76+aCRMm5K5HjhyZpaswq/fKsHqO7IImhFLW9y6sA1lEXzo4swH8IITQZTPqr32vfo8QQgghxDpDXyc4JwH4TwCvhhDuQ14HZw8AKwDs277qCSGEEEK0jrvBiTE+EULYBsBhAHYD8P561gIA/xPANTHGPs9Ke3p61jrKbsCiFXuk7UVTZZHPmDFjmpYHALvuumuWtqZ27353r3TtmWeeKXyvdlDkWdWaZaceb3pmo2VNDLu7u7NjZo6obsu0R9FlPBlbE833ve99Wfryyy/P5bGXYxZJWVhcYkWEbMpuzRsHEuvhmk2dU71YV0WZI2zP+6odd175Nqp3A9u/J598cpa+8sorc3k819uNFWtYr8BlWbVqVeY2wppx8zyrQtThja8pU6bkrh988MGWy7RzeuXKlVl6l112yeXdfPPNWdqu4anryWCKglmcb7+3uK/s+u7NH4bFlkXfpQDw6KOPFuZ9/etfT3ovS+q60NWVF/SwiOq0007L0suXL8/dx/1mvXentk8RffrBiTEuBfDz+p8QQgghxDpPKc9GIYR/CiFM6vtOIYQQQoiBpy8/OOObvQzgHwFMDyGsAYAY4+LUN7THd2XFJxyA0R7lFmHFFEuXLs3S3d3dperhwcEFv/Od7+TyXnnllSw9f/78LG018LnO9uiWLQOsh0wWdVSBfW8+Lm9FJFV03GnLZ2/CLJICfO15Pg72AjouXtw7ZK+44orC+7zy+XNbkYFnScbX119/fS7PiqzaSSsiKT4WZ9Ff2fKtOIE/tydqYC+3jzzySC6Pr207Vm294n22/ogWR44cWWiZyeOmjBi4Ff74xz8m35tal6lTpxbmjRo1KkvbtZgtrvorsmgXQ4cOLbQ05cCvKd6OG/D6bj1/F91n+yLVO3WqpZRde/n7e+zYsbm8n/+8V+iT+v3q9a9nCVe0j+hLRLWw4PUAYFb9fwRQHEJWCCGEEGKA6WuDswDAwwDOA9BTfy0AuB3AMQCea1/VhBBCCCHK0dcG58MA/hXAdwEcHmNcAAAhhAhgdozxT97DQgghhBCDQV9m4q8B+EII4RTUQjOcFGP8datvwiZ0rcByc2umzDosm2yySZa2clA2Pbcmp6ynYr2Hsg7I+9//fqTA5upA3vT80ksvLXxur732ytLW9JWxZvSMjdzMeklVmBtbz6lV6wBYU3OmFfPPVP2Qu+66qzDP09cooyPD0eKBvLzYtqPVyVlXYN0Xju5clWkutznrAPT09OTu88YJs662Y1+sXr0608mz60mqfllZUtvWwvXyPJ4/91zvgT9HnQeALbbYovC5l19+OUvz/FuX+njZsmVJ66ztU4+i78wLLrggd33SSScVlsHfm6k6d63MaS5zyy23LLyPv7sWLsxrvhS5TLHY9khxzdCnmTgAxBj/TwjhXgDXhhAOSHlGCCGEEGKwSDYTjzH+EcBH68/8BUD1ZkdCCCGEEBWQdILTIMa4AsDRrb7JmjVrCoNBsgdea+rFx/c2ONvq1auztBXPpMKejK34h8VLfJ+HJ744++yzc9dsOjh9+vQs7R3x2vbxRFYsUijLmjVrsj6wJnqeyDFVjOOJuVLN3NkDKuCbEjLWO3JRveyxbqo5JWPNftmkf6CP2b06s4giVQTimceXrQeLV6dNm5bLmzNnTpZ+7bXXCstYHwOcArW1rCh4MI9LO/9YxJ762a17Dc8bOpvvWm+z7BWcPelyXwH5scHiKgDYeeedC997/PhebyWpZs/rEtzOngjdilyK3AVYkdTPfvazLH3KKacUlu8FJ+W0J6qcMWNGYZlWDcOKl4vgsewFlLakBJwt5ehPCCGEEGJdpl8bnBDCUyGEt6uqjBBCCCFEFbQkomrCRQAmVFERIYQQQoiq6NcGJ8Z4Ycp93d3dmSzNyhnZtM7msbx12LB8VZ966qkszboorbhkf/3117P0nXfeWXjfHXfckVxmEZ/85Cdz19dcc02WZj0M6+6aZZBWv4T1mlpxAZ7K2LFjs6jCqebXQL4PvMi3rGdj9QE8k0vW8fFk98z222+fdJ/F02dI1XWwcmUvxEMV5vdDhw7NxhHregF5k3Ur47dRfovgMmz92YTcc+vu6TYxrH8B5EO0eKTqR1ldFnaJ34o+QFX09PRkc8bq0fH893TNrA5ckZ7XkUcembt+++3ew3i73rLejW0H1hvkiOHsvsNidbzYdNiakE+ePDlL33jjjYVlptKOOcfYccNrsxdN3K55PD85tMxPf/rT3H1lzfuL9AlbcUGw7bbbZmmrc3P33Xdn6YkTJ2ZpW18vDIWHp6/aIHmDE0IYCqBRy0UxxjWlaiWEEEII0Wb61MEJIRwUQrgfwAoA8+t/K0II94cQvtTuCgohhBBCtEpf0cS/DuACAFcAOB9Aw33wRgA+A+C6EMLfxxj/xSuHj8wtfPRpxSB8dGjNA4tEUfbImSN18/H5QBBjzNL2GK6rqytLH3LIIVnaHuvzcafnydGKeIrM8luBvXNa8aFnol6GVqIEX3fddUn37b777lnaehPmcdfuCMX2SJ/HrhUncD3LeqBm834LjxM71lLnBx+de3W05XkiPR7nI0eOLLyPzZCrwIp6WKRaxvt6fxkyZEihiweuj53f3Naprge+9KX871NeozwvsZdccknumr2+s/uOBx54IHffdtttV1jm0Uf3eh958MEHc3ncHmeccUaW/sMf/pC7j9dUb6xZEUzDxP6mm24qfKYvurq6sPHGGwNYW0TFc67sWtMoG1hbdMlRu1NFs0A5FxXW3clRRx2VpW+44YZc3kEHHZSlWQXEmpq3k75EVN8GcEKM8RdN8m4MIcwGMA2Au8ERQgghhBhI+hJRbQrgPid/FoDmXqmEEEIIIQaJvjY4TwL4hpP/9fo9QgghhBDrDH2JqL4F4LchhP0BzEReB2df1E54+rTbHDp0aKHODMsTPRM6q1PA1yzztO9T1gStjEt+jnoL5F1XW5n5008/naXZXL0VPP0Hq5PTX4rchgNry3JtZHbGMxtnuB9PPfXUpGcs3PcHHnhg4X2eybLVRWD34J6ORqqOjx2fnrv8VFgfwPYb6914OjeeW/eyeGWceeaZWfqII47I0scff3zuPqtLVZTH4TCAvD6I97l57NpxnKpD1C5z8ir6gPHWj4cffjh3/ctf/jJL23Zh3UnPFYDH7bffnqU5TA4A7Lrrrk2fsTo9c+fOzdKeib+dt1XoWrErFPs9VgVf/vKXs/QTTzyRy2Pz/rIhSrznPBPyP//5z1l6r732yuVx1HNv3pYlJVSDu8GJMd4TQvgQaqc4uwFoaDotAPBrAJfEGJ/vXzWFEEIIIaqlTz849Q3MP7a/KkIIIYQQ1RDYlLldTJgwITY8kFpRDR8PeiIqD8+kkY+qW/FyzPXyvIZ+/OMfz9L2yIyjnLMpJQDccsstTd/Lwx67shm0jahu2u6hGGOay19igw02iA1PxtZMn4/5bbt6x5FFx/eeKXUrJu+vvvpqlt5oo42ytBVRcZtX4T7Ait68aO58HGzHFnv5XLJkSal+GzduXNxjjz2a5hVFqu6LMiLbVvjmN7+Zpe+5554szR6/gbznVIvXrkViU2sqz+3DLiaAlkRPpfoNAEII2YKc6u252b1FcBlsXmz5xjfyqpdlyrdwGd59xxxzTO56l112ydLsLffaa6/N3dfd3Z2lreiXx4O33sYYQ2Gmw4gRI2JDLGzHHkdb9zz5e/D3tP1ePPzww1uqa6uwuN16mWZP8vY77tlnn83SrMZQledoM56azjlFExdCCCFEx6ENjhBCCCE6jgERUbGow1r3cCAyewTsHQmzCIM1u+3xo+c1tgq4jtYzKAfpfM973pPLY4+fLN7wxHJWRMXXntXUVVdd1W8RlfVcnCrq8Kym+LPaz9Z4XwD4zW9+U1jG5Zdfnrs+//zzszQHY7P152NjTxSQamFlj5r5WLcsZftt2LBhseE53I55rv9+++2Xy0sNspfaPq2w7777ZumDDz44S9vxw9aHVeAFBLV9yGJlG8SUn5s3b14lIio7J1j0wePX4omTOADiKaeckss74YQTuB65PG4LT/yaih0nI0aMyNLnnXdeLm/IkN7f4SyiWrZsWe4+tqKyAR29sd0YAwsWLMBbb71VSkQ1adKkOHXq1KZ53pzw1hCG17Wddtopl8fin4suuqjPujaDx5oV2fN319/8zd/k8jzRH4vOUtUwPBGenQ9cT4mohBBCCPGOIXmDE0LYLISwiXltkxBC/x13CCGEEEJUSCsnOM8DuMO8dieA5yqrjRBCCCFEBfTpB4c4CoC1150GoE/b66FDh2Y6EDZiOOtGtOIB9POf/3yWZj0eS1UmaQ3YDBnIy3bZTBHIR1e1skX2MJsq07afxZPZVuFNlb1zWlJ1cFhvweLJb60MvYhzzz03d82yfB5bns6CJyO3eUW6NWWjq9u+T/X07LHhhhui4ZbBwubytg9Zlu/pq3k6HmXNidmsfc6cOVma9diAvBzejhmWyVtXBayjZj3lFmH14Tx3FFV5Mp44cSKKdDmYsqb63GY2Gndq+WW9XLOJ8dVXX53L+8pXvpKl7TrH5uALFy7M0kOHDi18L+tVns3GrQuFxrjpTzTxsqSaifP6NXHixFwefwexawwAeOWVVwrzuB+/9a1vNS0PyK/Ttm/uu683VOVLL72UyyvjIbrsOlpE8gYnxnh5k9d+XWlthBBCCCEqoJSScQhhVAhhnxBCmtmFEEIIIcQAknSCE0K4HMDsGOPFIYQuALMBbA+gO4RwUIzxNu/54cOHF4o0PNGBB5tZe6ZkfFxsg0J64quiY1frIfOMM87I0tbktKgeQL7OLDawHnf5yM4erXNdvGCYVYirrBk6i1I8EZttYzbb5yPM/fffv1S9WJxhYTNxiyce4zFkx1ORV2V7tFrWlNYT6aWycuXK7DN5gU8tLJZqR7BNhr2cAsDMmTOz9PTp07O0V39PTDpmzJjcddFxuTc3rDiSxeteQMVUsUMzFi1alGRqb0WInpd2Lo/FFA3Puw28+cKkjgXb5nfeeWeWHjVqVOFzVnzFfXT//fdnaes5nue0FXFz+9g+b9QzJXhjET09PdmaaNUwqoBFtdaE/4ADDsjSp59+enKZLFL66le/mqU5eCeQby/73uzxumxQZBb9evOxTBDT1BOc/QA0vk2/AGAD1AJvnlH/E0IIIYRYZ0jd4LwbQEO79rMA/j3G+CqA6wAUHx0IIYQQQgwCqRucBQA+FEIYitppzu3118cCWF34lBBCCCHEIJBqRfUrANcDmA9gDXr94XwMQJ++01k+6ZmBefozVu5bZEZsI7my6ajVn0nVT9h7772z9I477pjLY7ntr39dbFTGkb+B/GdjPSRbf9b5sHnDhvV2n9V14SjqZfHMxMtSpPfUig4O6z15kd55rHl6A1iKWbwAAAv3SURBVFZXgJ/zdJuqwIu8XhbWwbHzhNu/Fb0a1uPw3Pan6mbZCMh8/eUvfzlLlw3NMGPGjMI8z9zbi2LvzQXPVUU7sPqEqXhuCFJ1h6yOELcZj69WwgBdfPHFhXlPPfVUluY6ejpSnu6fHZeN69deey25vpYhQ4Zk5dhQB2XDlxRx++2356557Zw8eXKpMjlMh+XjH/94lrZjhOfEXXfdVeq9eb21elBev6WQtMGJMZ4VQngSwGYAbogxNhy+vA3gxy2/qxBCCCFEG2nFD86/N3ntimqrI4QQQgjRf5I3OCGEjwI4Fb1KxU8BOD/G+HBfz3Z3d2dHuK0c+fMRpD1aZW+LLGqy4gY2JbV5nohq+PDhWfqRRx7J0ltssUXuvm9/+9tZOtVDKpAXD/ARpjXV9NrLy/NM1stgjwdZtOLVw4oDHn/88Sxd1sv07Nmzs7RtLz7i5KNPW/9FixZlaXvU7ZncFr2XNVlmD5/f+973cnl8lMvt0Q5aaeP58+dnaS9KuBcFnq89kccPfvCDwjxuS09EZdcENrH3+jDVHNj2KZdpRVI8B/pjJt4OuC9PPvnkLM3m+EB+jngi40svvbRUPS655JLCPF5X7XufeOKJTZ+xc9oTIfJawOMc6BVzs8i/VVgNw3rAbjfshbgdHH300YV5LAIvcqHRFzzPbBlW3Ff03kUkKRmHEP4OwB8AbAJgev1vIwCzQwjNfdcLIYQQQgwSqVvWHwH4fozxbH4xhDANwA8BDOyWVQghhBDCIdVMfBKAf2vy+g0A3lNddYQQQggh+k/qCc5dAPYCYJVM9gJwT18Pjxw5slBPw4vC65mFsfzdmn8zrHdjdW48871jjz02S7N8eO7cubn7fvKTnxSWkfpejG2DsmbK7Na6rEx47NixmDJlCoC1zVI5Iq+VabMc1boFYN0p7hvrAtyDTYrL6vHwWLDjp6enJ0tvt912pcrn0Ba2T7faaqss3dXVlcvjaMntxo5Jq0/DsC4Vh0GwkYcZO3aLQgbYclL71BvX3mfxohzvsMMOWdqOC9bBsZ+tiijw/cEz+S/Sl7M6MamRnG27F+k0DRmS//18/PHHJ5Xf3d2du051Z8D32THEpuaWxnhYuXJl0vs0g83EU/sCqEZfi+dmO/S/OHTGkUceWXhfqksRq/Po6dJ4unTe2tMgdYNzG4BzQgg7ozdkw24ApgI4I4QwtXFjjHHgY84LIYQQQhCpG5wL6v+Pq/8xF1I6Ahja30oJIYQQQvSHVEd/qbo6feId5bbiWZXFV0Um10D+mNmW74m2dtppp6av24imbALcSuRmrgubdFuzOG4vG6WW61LknbM/LFu2rPDIs2wU+CJsdGnvvcqKpZjXX389S2+77bbJz3GkXRZlWW6++eYsvXz58sL72iGS6urqyiJFt+KJmu8t68Ham8NLly7N0mPHjs3lsRfZ73//+1naE+16ns+tuKVILHXggQcWlm/bwDNZHWx4DUntOytunzRpUpa24iUe66lRx61I6swzz8zSVmTEIvCyUakZK9rw1uaGiKSsmTMArFq1qtArOY9Fr2+88ezBa7R1y1FGZGXnxNSpUwvuTDPVBnyP8yxqsu5NuA2878YiKtu4CCGEEEKsK7gbnBDCf4UQxtH1OSGE8XQ9MYRQbbAiIYQQQoh+0peIajcAbOZxIoB/AbC4fj0UQL+iOvKxkz1yYm39Mpr0gH/E/aMf/ShLL1u2rPA+9lbMx+xA/kjQvhfXJTVYqIVFTdazKntuPfTQQ3N5VXgynjhxYnY86bWjPX70PluReOn888/PXbO2/ssvv5zLu//++7O01aRfvHhxlvaORZnrrrsud81BAtlaDKi1SQPPMoMtpTysmJQtDMpav40aNSr77GVFTWWPuj1RCXvAtd5wWdTrBT3kPvVECl6eZ5nhWQB69Ee8wYwfPx6f+9znAJS3EvPg5371q1/l8nitOeWUUwrLSPUEbeH1dvXq1YX3tcMijdcku2Y0rh966KHS5bOlsK2/t4bzuuoF6eTvD88Sq6wVlWfNfNZZZ2Vpa0WVKmJPVd+wbeB9j6WoYbQqokq35RVCCCGEGCSkgyOEEEKIjqOvDU6s/9nXhBBCCCHWWQLrG6yVGUIPgP8E8Fb9pf1R81zcEDKOALBPjNH1fRNCyN6kFVNwT++jlXKKymNZ7LnnnpvLY10RNpl88MEHW37fZu/NsL6D9ZDq6TRwG3iRlZcsWfJQjHHn1moMTJo0KXomgg2sHoknb+V25c9mPQan6s+s7/Th4bpUv22wwQax4YHaw5PX2/ZnHRPuQ6tTxbL8z3/+87k8NjW20bhvu+22Putr62XNlVP1D7iO9nOy3o3Vq+H5Z8c4t8P1119fqt8Af87xWLH15vqkeha3Okas02A9i3Pe5MmTc3n33NPrzJ71m2xU8CeffDJLv/jii7m8qtd6C5dfpL+0YMECvPXWW6XUMLjf7HrILgo8U3Dr4TfV1Qf3t3Wpkdp2bKZvdZQeffTRwue4jtYTNI8F1hG1ejWsl2THdWoEABSslX0pGV9hrptpvV2ZWgMhhBBCiIHA3eDEGIsDTwghhBBCrKO4IqrK3oREVJYqjh9TsSa/r7zySpa2nlWZdoioWCzFgRktfJRvRVR83OkFNkRJUUeqiKqFY8RC7NGt5zXW8/xcBVy+NevkI1TPnJX7zROdWJNlHqO33nprqX4bPXp03HrrrQH4gSUt3pg0Is9Wq1Qa2z4s9uI6Aen1Yk+tqR6PgbypseeWYd68eW0RUTGeqbD1RMvrBn8+27b2MzE81lODIFsz94Fc61uhUefp06fjtdde67eIyooIeQ2xY/Sll17K0nbOsWiRRU9VtSOL0nhds/3Lc856Zb/zzjuztP0e4HHIbWDHruetmPG+Q2fNmtV0zsmKSgghhBAdhzY4QgghhOg4tMERQgghRMeRFE28naSaB3phEFKx7uc93RfGhhFgPNm099nY3bnn5t2rI+sPeJHRy/Lmm29mugWeibqnP2NNAjmP22f33XfP3cftatunChPysmapRf1tPye3idV1YDm8NaWuIgr8ypUrW9K9acB6HLa/uc782ayOErfPJptsksvzoqozVp+M4XYuqwvk6USw6Xnq+gCUD4lhWb58eTaX7bjnOWLbiNcTWxc2IU91mW/xxiW3IY91O+7LzrmikAUeVs+D+7JovV2zZk1S2X1h5wRj24Svq9BlbAVP74axum5F2L7hNYjbxM6rTTdNi/bk9X2RnqNOcIQQQgjRcWiDI4QQQoiOY0DMxEeMGBE33nhjANUd5TKpx5bTpk3LXXtH0OzJs4oIzxYu0zMp5iNMa+ZuvVYyfPz42GOPlTJbnTBhQmxENq5CdNIKfLxpzS75s3neWPk5T5Ri4TI8c1zGi77tHUvb+cB5ZT1Qe24ZqsCLPLzffvtlads3DzzwQJZONe9vx7F9amTmVrzOmnWhtJk4e6H2RHUDDYsGrTiZveAy1lyd52A7+pXnzg477JD8XGOc3nfffXjjjTdKmYmnzrk+xk0hxiS68D5r6s9zzq6BvD7uueeeWfree+/N3Zdqum0/G8Ofs6yZu6emctlll8lMXAghhBDvDLTBEUIIIUTHoQ2OEEIIITqOAdHBGUiX/+sSLBO2EVqLsCaMrAfApqBA3nW8LZ/zyupysMt/L7Ky1T9h3QGr41B0n4Xlw57elo1Czm3OejzWtNzq9TD8Wb3wGCwLb0UHh81qbb2MeXepfhs2bFgsCj3i6f94FLldt7pMrL80mPPZG5OMNz5t+7CegsdVV11VWgeHdTm8iOGtuM1INbNmXRqrd8EhBVLnhMXT3SpDUVRwYG39L8ZGoW/wyCOPYOnSpW3VwWmFor6yfc/jwo4Zb3xzX3n6MwOtf1kG6eAIIYQQ4h2DNjhCCCGE6DgGKpr4QgAv9HmjaBebxxgntfqQ+m3QUb+tn5TqN0B9N8io39ZfmvbdgGxwhBBCCCEGEomohBBCCNFxaIMjhBBCiI5DGxwhhBBCdBza4AghhBCi49AGRwghhBAdhzY4QgghhOg4tMERQgghRMehDY4QQgghOg5tcIQQQgjRcfx/LgLULRMG2csAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x1152 with 40 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total uncertainty (entropy): [0.34317023 0.70485646 0.9586889  0.98697364 0.86825347 0.80917144\n",
      " 0.7803755  0.73641235]\n",
      "aleatoric uncertainty (cond. entropy): [0.20037204 0.44248432 0.62013936 0.69085914 0.63022095 0.58786035\n",
      " 0.5421429  0.485346  ]\n",
      "epistemic uncertainty (mutual info): [0.14279819 0.26237214 0.33854955 0.2961145  0.23803252 0.22131109\n",
      " 0.23823255 0.25106636]\n"
     ]
    }
   ],
   "source": [
    "# test for adversarial attacks\n",
    "x_adv = test_robustness(mfvi_bnn_list, predict_ensemble, x_test, y_test)\n",
    "# also compute epistemic uncertainty\n",
    "logs = compute_uncertainty(predict_ensemble, x_adv)\n",
    "print(\"total uncertainty (entropy):\", logs[:, 0])\n",
    "print(\"aleatoric uncertainty (cond. entropy):\", logs[:, 1])\n",
    "print(\"epistemic uncertainty (mutual info):\", logs[:, 2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cAMnjnwM9dkH"
   },
   "source": [
    "# Quantitative metric for detection results\n",
    "\n",
    "The ensemble BNN above makes a bit of improvement on robustness but not too much, however, in terms of uncertainty measures, the ensemble becomes more uncertain on the adversarial inputs. But at the same time, the total entropy on clean inputs remains relatively close to the single MFVI-BNN case. This is helpful as it will be easier to separate clean/adversarial examples by looking at the uncertainty measures and picking appropriate thresholds for dectection.\n",
    "\n",
    "In below blocks, we will quantify the quality of uncertainty by considering its effectiveness as a tool for adversarial example detection. The idea is, for a given uncertainty measure (e.g., total entropy):\n",
    "\n",
    "*   for a given threshold, predict the input as \"adversarial\" if uncertainty > threshold;\n",
    "*   search for the best threshold so that it returns the best true positive rate (TPR) of detecting adversarial example, while maintaining the false positive rate (FPR) to be below 5%.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "qXcybj_-Qpu1"
   },
   "outputs": [],
   "source": [
    "# below are helper functions for the adversarial example detection test\n",
    "\n",
    "def get_uncertainty_adv_examples(model, predict_func, x_test, y_test, epsilon_list, T=10):\n",
    "    # first get uncertainties on clean examples, return tensor of shape (3, batch_size)\n",
    "    uncertainty_clean = compute_uncertainty(predict_func, x_test.unsqueeze(0), reduce_mean=False)[0]\n",
    "    # now run the attack\n",
    "    acc_adv, pred_adv, prob_adv, x_adv = run_attack(model, x_test, y_test, predict_func, epsilon_list, T)\n",
    "    print(\"adv accruacy for this batch (%): {}\".format(acc_adv * 100))\n",
    "    # get uncertainties on adversarial examples, return tensor of shape (len(epsilon_list, 3, batch_size))\n",
    "    uncertainty_adv = compute_uncertainty(predict_func, x_adv, reduce_mean=False)\n",
    "    return uncertainty_clean, uncertainty_adv\n",
    "\n",
    "def compute_detection_error(thresholds, uncertainty, label):\n",
    "    # compute the detection error\n",
    "    # here if uncertainty > threshold then we say it is an OOD example\n",
    "    # where for label, 0 means in-distribution, 1 means OOD\n",
    "    # we assume the first half of the labels are 0 and the second half of the labels are 1\n",
    "    tpr = []; fpr = []\n",
    "    N = int(label.shape[0] / 2)\n",
    "    for t in thresholds:\n",
    "        pred = np.maximum(np.sign(uncertainty - t), 0)\n",
    "        fpr.append(1 - np.mean(np.where(pred[:N] == label[:N], 1, 0)))\n",
    "        tpr.append(np.mean(np.where(pred[N:] == label[N:], 1, 0)))\n",
    "    return fpr, tpr\n",
    "\n",
    "def run_detect(model, x_test, y_test, predict_func, epsilon_list, min_fpr = 0.05):\n",
    "    thresholds = np.arange(0.0, np.log(10), 0.05)\n",
    "    fpr_total = 0.0; tpr_total = 0.0\n",
    "    N_data = x_test.shape[0]; batch_size = 100; N_batches = int(N_data / batch_size)\n",
    "    for n in range(N_batches):\n",
    "        x = x_test[n*batch_size:(n+1)*batch_size].to(device)\n",
    "        y = y_test[n*batch_size:(n+1)*batch_size].to(device)\n",
    "        uncertainty_clean, uncertainty_adv = get_uncertainty_adv_examples(model, predict_func, x, y, epsilon_list)\n",
    "        # we assume the first half of the labels are 0 and the second half of the labels are 1\n",
    "        label = np.concatenate([np.zeros(x.shape[0]), np.ones(x.shape[0])])\n",
    "        # len(uncertainty_adv) == len(epsilon_list)\n",
    "        fpr = np.zeros((len(uncertainty_adv), 3, len(thresholds)))\n",
    "        tpr = np.zeros((len(uncertainty_adv), 3, len(thresholds)))\n",
    "        for i in range(len(uncertainty_adv)):\n",
    "            for j in range(3):\n",
    "                uncertainty = np.concatenate([uncertainty_clean[j], uncertainty_adv[i][j]])\n",
    "                fpr[i, j], tpr[i, j] = compute_detection_error(thresholds, uncertainty, label)\n",
    "        fpr_total = fpr_total + fpr * batch_size / N_data\n",
    "        tpr_total = tpr_total + tpr * batch_size / N_data\n",
    "    # now we compute the best tpr given that the corresponding fpr <= min_fpr\n",
    "    mask = np.where(fpr_total <= min_fpr, 1, 0)\n",
    "    tpr_best = np.max(tpr_total * mask, axis=-1)\n",
    "    # plot the result\n",
    "    plt.subplots(1, 1, figsize=(5, 4))\n",
    "    plt.plot(epsilon_list, tpr_best[:, 0], 'k-', label='total entropy')\n",
    "    plt.plot(epsilon_list, tpr_best[:, 1], 'b-', label='cond entropy')\n",
    "    plt.plot(epsilon_list, tpr_best[:, 2], 'r-', label='mutual info')\n",
    "    plt.legend()\n",
    "    plt.xlabel('epsilon')\n",
    "    plt.title('Best TPR (with FPR <= %.2f)' % min_fpr)\n",
    "    plt.show()\n",
    "    return tpr_best"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dC0_tU4fmdJ9"
   },
   "source": [
    "Now we can test for adversarial detection. To do so, we randomly sample 1000 (10*100) datapoints from the test data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "lB_VDIV6OuiK"
   },
   "outputs": [],
   "source": [
    "# get N_batch*100 test images to test detection\n",
    "count_batch = 0\n",
    "N_batch = 10\n",
    "x_clean = []; y_clean = []\n",
    "for x, y in test_loader:\n",
    "    x_clean.append(x); y_clean.append(y)\n",
    "    count_batch += 1\n",
    "    if count_batch >= N_batch:\n",
    "        break\n",
    "x_clean = torch.concat(x_clean, dim=0); y_clean = torch.concat(y_clean, dim=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iPx7CvK-OvdV"
   },
   "source": [
    "First we evaluate the MC-dropout method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 575
    },
    "id": "u_7dewHElx12",
    "outputId": "04b5dda8-cd6f-4136-c24e-6a6ea21beeee"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:18: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:780: UserWarning: Note that order of the arguments: ceil_mode and return_indices will changeto match the args list in nn.MaxPool2d in a future release.\n",
      "  warnings.warn(\"Note that order of the arguments: ceil_mode and return_indices will change\"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "adv accruacy for this batch (%): [96. 82. 33.  2.  0.  0.  0.  0.]\n",
      "adv accruacy for this batch (%): [96. 76. 38.  2.  0.  0.  0.  0.]\n",
      "adv accruacy for this batch (%): [95. 71. 24.  1.  0.  0.  0.  0.]\n",
      "adv accruacy for this batch (%): [94.       75.       26.999998  1.        0.        0.        0.\n",
      "  0.      ]\n",
      "adv accruacy for this batch (%): [90. 62. 18.  3.  0.  0.  0.  0.]\n",
      "adv accruacy for this batch (%): [95. 72. 22.  1.  0.  0.  0.  0.]\n",
      "adv accruacy for this batch (%): [91.99999 72.      34.       2.       0.       0.       0.       0.     ]\n",
      "adv accruacy for this batch (%): [96.      79.99999 26.       2.       0.       0.       0.       0.     ]\n",
      "adv accruacy for this batch (%): [89. 72. 26.  0.  0.  0.  0.  0.]\n",
      "adv accruacy for this batch (%): [91.99999  70.       22.999998  1.        0.        0.        0.\n",
      "  0.      ]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# test detection for the MC-dropout net\n",
    "epsilon_list = np.arange(0.05, 0.41, 0.05)\n",
    "tpr_dropout = run_detect(dropout_bnn, x_clean, y_clean, predict_mcdropout, epsilon_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JWjFNv08mjp3"
   },
   "source": [
    "Then we evaluate the MFVI Bayesian CNN."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 558
    },
    "id": "bSNKODDHl09g",
    "outputId": "8b2e3058-98a6-4ec9-d3e9-72decaea39a7"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:18: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:780: UserWarning: Note that order of the arguments: ceil_mode and return_indices will changeto match the args list in nn.MaxPool2d in a future release.\n",
      "  warnings.warn(\"Note that order of the arguments: ceil_mode and return_indices will change\"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "adv accruacy for this batch (%): [91. 77. 39. 12.  0.  0.  0.  0.]\n",
      "adv accruacy for this batch (%): [95. 76. 48. 12.  3.  0.  0.  0.]\n",
      "adv accruacy for this batch (%): [93. 71. 37. 13.  2.  0.  0.  0.]\n",
      "adv accruacy for this batch (%): [94.      66.99999 38.      17.       0.       0.       0.       0.     ]\n",
      "adv accruacy for this batch (%): [87. 66. 31.  4.  2.  0.  0.  0.]\n",
      "adv accruacy for this batch (%): [91.99999 72.99999 33.       9.       0.       0.       0.       0.     ]\n",
      "adv accruacy for this batch (%): [93. 66. 37. 13.  1.  0.  0.  0.]\n",
      "adv accruacy for this batch (%): [95.       74.       42.999996 14.        0.        0.        0.\n",
      "  0.      ]\n",
      "adv accruacy for this batch (%): [91.99999 76.      37.      11.       1.       0.       0.       0.     ]\n",
      "adv accruacy for this batch (%): [91.99999 72.99999 37.      13.       0.       0.       0.       0.     ]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# test detection for the mfvi net\n",
    "tpr_mfvi = run_detect(mfvi_bnn, x_clean, y_clean, predict_mfvi, epsilon_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZXgUsw6PmmkP"
   },
   "source": [
    "Finally we evaluate the ensemble MFVI Bayesian CNN."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 610
    },
    "id": "OnQzSgaamvML",
    "outputId": "b0b4aa0a-06aa-47cf-d9a1-9511b6ce57b3"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:18: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:780: UserWarning: Note that order of the arguments: ceil_mode and return_indices will changeto match the args list in nn.MaxPool2d in a future release.\n",
      "  warnings.warn(\"Note that order of the arguments: ceil_mode and return_indices will change\"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "adv accruacy for this batch (%): [91.99999   76.        44.         4.9999995  0.         0.\n",
      "  0.         0.       ]\n",
      "adv accruacy for this batch (%): [97.       79.99999  45.999996  7.        0.        0.        0.\n",
      "  0.      ]\n",
      "adv accruacy for this batch (%): [96.       78.       39.999996 11.        0.        0.        0.\n",
      "  0.      ]\n",
      "adv accruacy for this batch (%): [95. 78. 39.  6.  0.  0.  0.  0.]\n",
      "adv accruacy for this batch (%): [91. 70. 29.  6.  0.  0.  0.  0.]\n",
      "adv accruacy for this batch (%): [96. 81. 28.  4.  0.  0.  0.  0.]\n",
      "adv accruacy for this batch (%): [93. 79. 42.  8.  0.  0.  0.  0.]\n",
      "adv accruacy for this batch (%): [93.        79.99999   48.999996   4.9999995  1.         0.\n",
      "  0.         0.       ]\n",
      "adv accruacy for this batch (%): [94. 76. 38.  8.  0.  0.  0.  0.]\n",
      "adv accruacy for this batch (%): [95.      72.99999 38.       8.       0.       0.       0.       0.     ]\n"
     ]
    },
    {
     "data": {
      "image/png": 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sk9prsINFi2D9et2VLVUqY3UopWdu27SBwYNh9GhYsgRCQ3Xrz2BwJ506deLo0aOsX7/e4XWblRYeRFSUdkOpWxf69898faVL61ne5cvh4kWoXx9eew1u3Mh83QZDWiSOEybn22+/Zc+ePfj4OH4awAieBzFqFFy4oCcccuVyXL0dOsD+/VpEp04Ff39Ys8Zx9RsMWQUjeB7C9u3w2WcwaBDUquX4+osW1fVv3gx580Lr1tC3r275GQzZBSN4HkBcHLz8MpQpA+PHO/dejRpph+VRo2DBAnj4YVi82DgsG7IHRvA8gMTgAB9/DEWKpF0+s+TPD++9p6OuVKwI3bpph+V//037WoMhK2MEL4tz+rSeRW3dWoeAciU1a8K2bTBlip4Zrl5di69xWM4+9O3bN8n5NzO4I0pLRjCCl8UZNgxu34ZPP9XuJK4mVy5tw969ehb31Vd1t/fvv11viyHrkprgxcXFudga2xjBy8KkFhzA1fj5aXvmztViFxgI//2vFmODa/jyyy+TVlf06tUL0EITHBxMQEAAzZs35+TJk4BuuQ0ePJgGDRpQqVKlpFaciDBw4EAeeughWrRoYXPZ2NGjR3n88cepXbs2jRo14sCBA6nWO3LkSH755RcCAwOZOnUqc+fOpX379gQHB9O8eXMuXbpEx44dCQgIoH79+uzZsweAcePG0atXLx599FEqV67MrFmzAOjduzfLly9PsqdHjx589913mX8TbXkkO3szKy1SJzpa5IEHRKpUEbl5093W3M3ZsyJdu+qYe/7+Itu2udsi52Pt0T9kiEiTJo7dhgxJ/f579+6VypUrS2RkpIiIXLx4UURE2rVrJ3PnzhURkdDQUOnQoYOIiPTp00c6d+4s8fHxsm/fPnnggQdERCQsLExatGghcXFxEhERIUWLFpWlS5fec7/g4GA5dOiQiIhs27ZNmjVrlmq9GzZskCeeeCLp+jlz5kjZsmWT7Bw4cKCMGzdORETWrVsnNWvWFBGRsWPHSkBAgERHR0tkZKSUK1dOIiIiZOPGjUmvJSoqSnx9fSU2NjbVzyURUllpYVp4WZSJE3VwgM8+Szs4gKspVUrP3K5YoZ2hH30Uhg4FG36kBgewfv16nnnmmSRn3PsswQ1/++23pPBJvXr1SgqxBNCxY0e8vLyoVq0a586dA2Dz5s1JYaXuv/9+glNYjH39+nW2bt3KM888Q2BgIC+99BJnzpxJtd6UaNmyZZKdW7ZsSWqVBgcHc/HiRa5evQpAhw4dKFCgAD4+PjRr1ozt27fTpEkTDh8+TGRkJIsWLeLpp58md+7MB3cyeWmzIIcOacFLT3AAd/Dkkzru3ttvw7RpesXG55/D44+72zLn4inRoazDOUk6/IoSEhIoVqyYzSCn9tZrb4io5NnHEvd79+7NV199xeLFi5kzZ45ddaWFaeFlMURgwAAdy+6jj9xtTdoUKaKjLW/Zom1u0wZ69dIrQgyOIzg4mKVLl3LR4gl+6dIlABo0aMDixYsBWLBgAY0aNUq1nsaNGyeFlTpz5gwbNmy4p0yRIkXw8/Nj6dKlgBa13bt3p1pvaiGiQIezWrBgAQAbN27Ex8eHIhYfq++++46bN29y8eJFNm7cSB3Lgu6+ffsmxR6sVq1aqve3FyN4WYxFi2DdOh3vLj3BAVauXMkzzzyTNGjtah57TPsKvvOO7u4+/DAsXGgclh1F9erVGTVqFE2aNKFmzZq89tprAHzyySfMmTOHgIAA5s+fz7Rp01Ktp1OnTlSuXJlq1arRu3dvHn300RTLLViwgNDQUGrWrEn16tXTnDBIHpYqOePGjeOPP/4gICCAkSNHMm/evLuubdasGfXr1+edd97h/vvvB6BUqVI8/PDDjo2JZ2twz9mbmbS4l8uXRUqVEqlTRyQuzv7roqOj5f777xdAihcvLt99953zjLSDPXtE6tXTkxrdu4tcvepWcxyCCQ/lHMaOHSsffvhhiudu3LghlSpVkqioKJvXm0kLD2bUKIiMTH9wgE8++YTTp08ze/Zs/Pz86NChA6+99hq33eQzUqMG/PqrdltZvBhq1wYH57wxZHPWrl3Lww8/zKBBgyhatKjjKralhM7eTAvvbrZvF1FKZPDg9F136dIlKVasmLRt21ZERG7evCmDBg0SQOrWrSvHjh1zgrX2s3GjyP33i+TLJzJzpg4+6omYFl7WxLTwPJD4eB0coHRp3SpKD5MmTeLKlSu8//77gJ5Bmz59OmFhYRw8eJBatWqxbNkyJ1htH02a6NZds2Z6MqZrV7hyxW3mGHI4RvCyADNnws6d6Q8OEBERwbRp0+jevTsBAQF3nXvqqaf4888/qVKlCk8//TSDBg3i1q1bDrbcPkqWhFWrtKvNsmXwyCM6MIHB4HJsNf2cvZkurSYiQqRwYZFWrdLf3evfv7/kyZMn1W7rrVu3ZNiwYQLII488IocPH86kxZljyxaRcuVE8uQRmTbNc7q4pkubNTFdWg/jtdcyFhzg4MGDhIaG8vLLL+Pn52ezXN68eZkyZQrfffcdx48f55FHHuHrr792gOUZ47HHdBe3dWsYMgSefhouX3abOYYchl2Cp5R6XCl1UCl1RCk1MoXzU5VSuyzbIaVUlONNzX6sWaOT57z9Njz4YPquHT16NAUKFGD06NF2lW/fvj27du3C39+frl278vLLL6eaSNmZlCihl6V99BGsXKm7uL//7hZTcgxRUVHMnDkzU3XYCiU1ZswY1q5dm+q1t27dokWLFgQGBrJkyZJM2ZEpbDX9EjcgFzo9YyUgLzoVY7VUyg8CZqdVb07v0mYmOMD27dsFkLFjx6b7vrdv35Y33nhDAAkICJADBw6kuw5Hsm2bSMWKIrlzi3z0Udbt4np6l/b48eNSvXr1TNXRp0+fFAMN2MNvv/0mzZs3z9T9U8IZXdq6wBEROSYit4HFQIdUyncDFmVQf3MMicEBZs5MX3AAEWHkyJH4+Pgkedunhzx58jBp0iRWrVpFREQEtWvXTlry4w7q1dMrNNq1g9df15GVTR6Nezlx4gRVq1alb9++VKlShR49erB27Voee+wxKleuzPbt2wG9omHy5MlJ1/n7+3PixAlGjhzJ0aNHCQwMZMSIEWzcuJF27dollRs4cCBz584FYPz48dSpUwd/f3/69++f2JCxiXXLz9fXl7Fjx/LII49Qo0YNDhw4wPnz5+nZsyc7duwgMDCQo0ePsm7dOmrVqkWNGjV4/vnnXTahZk/wgLKAdXDvU0C9lAoqpSoCfkCKCSWVUv2B/gAVKlRIl6HZicTgAN27Q/Pm6bt27dq1rF+/no8//jhpLWJGaNu2Lbt27aJbt2707NmTDRs2MH36dAoWLJjhOjNK8eJ69vaTT2D4cJ2kaPFiaNDA5abYx9ChjvekDgxMMyrBkSNHWLp0KbNnz6ZOnTosXLiQLVu2sGLFCiZMmHBX/LjkTJw4kb179yYFBNi4caPNsgMHDmTMGJ12ulevXnz//fc8+eSTdr8UHx8fdu7cycyZM5k8eTIhISGEhIQwefJkvv/+e27evEnTpk1Zt24dVapUoXfv3nz22WcMHTrU7ntkFEdPWjwLfCMi8SmdFJH/E5EgEQkqWbKkg2/tGUgmggMkJCQwcuRIfH19efnllzNtS7ly5diwYQOjRo1i9uzZ1K1bl/3792e63oyglE4KvnUr5MkDjRvDpEkmnLw1fn5+1KhRAy8vL6pXr07z5s1RSlGjRg1OnDjhsPts2LCBevXqUaNGDdavX8++ffvSdf1TTz0FQO3atVO06+DBg/j5+VGlShUA+vTpw+bNmzNttz3Y08KLAMpb7ZezHEuJZ4FXM2tUdmbxYh0c4NNPtaNxeli6dCk7d+7kyy+/vCtET2bInTs37733Hk2aNKFHjx7UqVOHTz/9lL59+zqk/vQSFKR9El98EUaOhE2bYN487cuXZXBTfCjrz9zLyytp38vLKymMeu7cuUmw+pe4efNminXZKnfz5k0GDBhAeHg45cuXZ9y4cTbrSMvOXLlyZanw7mBfC28HUFkp5aeUyosWtRXJCymlqgLFgd8ca2L2ISpK54cICoKXXkrftbGxsYwePZoaNWokBXx0JC1btmT37t3Uq1eP5557jj59+tjMDO9sihbVs9czZ+rkQYGBOl+uIW18fX3ZuXMnADt37uT48ePAveGbKlasyP79+7l16xZRUVGsW7cOuCN8Pj4+XL9+3SEJfpLz0EMPceLECY4cOQLA/PnzadKkicPvkxJpCp6IxAEDgZ+Av4GvRWSfUmq8Uqq9VdFngcWS1ghnDmb06IwFBwAICQnhyJEjTJgwgVzpvdhOypQpw88//8zYsWOZP38+derU4a+//nLKvdJCKXjlFZ01zdtbL0177z29DM9gm6effppLly5RvXp1ZsyYkdRtLFGiBI899hj+/v6MGDGC8uXL06VLF/z9/enSpQu1LNndixUrxosvvoi/vz+tW7dOik3nSPLnz8+cOXN45plnkrrojhiisQtb07fO3nKaW0picIBBg9J/7fXr16V06dLSsGFDSXCR38a6deukdOnSkj9/fpk1a5bL7psSV6+KdOsmAiItWuicGq7G091SsitmpUUWJDPBAQCmTZvG2bNnmTRp0j3hsJ1FcHAwu3btomHDhrz44ov06NEj1Yi2zqRwYViwAGbN0pGVAwN1V9dgSC9G8FxAYnCAqVP1+FR6uHjxIpMmTaJ9+/Y0cLGfRqlSpfjpp5947733WLJkCbVr17aZ58DZKAX9+sH27VCsGLRoAWPHmi6uIX0YwXMyp0/rwJ6tWkGXLum//v333+fatWtMmDDB8cbZgZeXF6NGjWLDhg3cuHGD+vXr89lnn6XpjOosatSAHTt03ozx47XwWSXUcirues2GlMnI52EEz8lkNDgAwMmTJ5kxYwa9e/emevXqzjHQTho3bsyuXbto1qwZAwYMoGvXrlxxU2C7QoW0q8qcObrFV7OmXpfsTPLnz8/FixeN6GURRISLFy+SP3/+dF2n3PUBBgUFSXg2D4q2Zo2OCvLuu2BxXE8Xzz//PAsWLODQoUNUrFjR8QZmgISEBD788ENGjRpFxYoVWbJkCUFBQW6zZ/9+3XLevx/eeku/1w5IX3oPsbGxnDp1Kt0+aQbnkT9/fsqVK0eePHnuOq6U+kNEUv5S2prNcPaW3WdpY2JEHnxQpHLl9AcHEBHZt2+feHl5ybBhwxxvnAP49ddfpXz58pInTx6ZNm2aW2dxb9wQeeEFPYvbqJHIv/+6zRRDFoBUZmmN4DmJsWP1u/vzzxm7vmPHjlKkSBGJjIx0qF2O5MKFC/Lkk08KIB07dpRLly651Z6vvhLx9hYpUUJk1Sq3mmJwI0bwXMyhQyJ582rfsYywdetWAeS///2vYw1zAgkJCfLRRx9J7ty5pWLFirJt2za32nPggEhAgP5mjxghcvu2W80xuAEjeC4kIUE7xxYtKnLmTEauT5DGjRtLqVKl5Nq1a4430Els27ZNKlasKLlz55bJkye7tYsbHS3y8sv62/3ooyL//OM2UwxuwAieC1m0SL+rM2Zk7PpVq1YJIDMyWoEbuXTpknTq1EkAadeunVy4cMGt9ixerPOFFC8u4ubc5AYXYgTPRURFiZQuLRIUJBIXl/7r4+PjJSAgQCpVqiS3bt1yvIEuICEhQaZPny558+aVcuXKyebNm91qz+HDIo88or/pw4aJuFmDDS4gNcEzfngOZPRoOH8+Y8EBABYtWsSePXt47733yJs3r+MNdAFKKQYNGsTWrVvJly8fTZs2ZezYsW4LE/TggzrG3qBBeqWLj48+1qMHTJ+ugxO4KXulwQ0YPzwHER4OdevCwIH6h5Rebt++TdWqVSlWrBjh4eF4eXn+f9G1a9cYOHAgX375JQ0aNGDBggX4+vq6zZ7t22HDBp0w6Pff9SoY0AFHAwN1uPl69fTnWLly+h3FDVkD44fnZOLiRGrX1t3ZqKiM1TF9+nQBZPXq1Y41LguwYMECKVKkiBQtWlQWL17sbnOSOHVKJCxM5I03RJo00S4tOia1Hvdr3VpkzBjt4pKFvYMMycCM4TmXTz7R72RGf8tXr16VkiVLSvjIXHYAACAASURBVNOmTd06u+lMjh07JvXr1xdA+vbtmyVnoOPiRPbsEZk1S6RfP5EaNUS8vO6IYKVK2tXo449FfvtNO5cbsh5G8JzI6dMiRYqItGyZ8RSD7777rgBu92FzNrdv35bRo0eLUkoqV64s4eHh7jYpTa5dE9m4UWTSJJGnnhIpW/aOAObJoyeoXn1V5MsvRQ4eFImPd7fFhtQEz4zhZZJu3eDbb+Gvv/S4T3qJjIykUqVKtGrVirCwMMcbmAXZtGkTPXv25Ny5c0yYMIHXXnvNo8YsIyL0eGDiWOCOHXDjhj5XrJgeA7QeD8xS+ThyAGYMz0msWaP/6ceNy3gdQ4YMES8vL/n7778dZ5gHcPHixSSfvZYtW8rp06fdbVKGSewKh4SIvPiiXulh3RX28xN59lmRqVNFtm41XWFng2nhOZ6bN3VsNtCtu3RGqQF0cuWHHnqI3r17M2vWLMca6AGICLNmzWLo0KF4e3szd+5cnnjiCXeb5RCuX4c//ri7JXjqlD6XO7cOaVWvHrRsCW3apC8ZuyF1Mt3CAx4HDgJHgJE2ynQB9gP7gIVp1enpLbx339X/3mvWZLyOXr16Sf78+eXfHB7eY9++fRIQECCADB48WGKyaRMoIkJk2TKRkSNFmjUTKVRIkmaE+/cX2bTJjAE6AjIzaQHkAo4ClYC8wG6gWrIylYE/geKW/f+kVa8nC15cnEipUiJPPpnxOvbs2SNKKRkxYoTjDPNgYmJiZMiQIQJIQECA7Nu3z90mOZ3YWJEffxTp0UOkYEH9a6xQQQviX3+52zrPJbOC9yjwk9X+W8Bbycp8APRLqy7JJoK3ebNkyg1FRKRdu3ZSrFgxuXjxouMMywasWrVKSpYsKQUKFJDPP/8827rpJOf6dZEFC0TatBHJlUt/vwICRD74wMT3Sy+pCZ49U2NlgX+t9k9ZjllTBaiilPpVKbVNKfV4ShUppforpcKVUuGRkZF23DprEhamx1zats3Y9Vu2bOH777/nzTff5L777nOscR5O27Zt2b17Nw0bNuTll19OyrOa3fH2hu7d4Ycf9AqQ6dOhQAF44w2oUAGCgyE0VCdzN2QCW0ood1pvnYEQq/1ewIxkZb4HvgXyAH5ogSyWWr2e2sJLSBApX16kffuMXp8gDRo0kDJlysiNGzcca1w2Ij4+XiZPnix58uSRsmXLyoYNG9xtkls4fFh7AVSurFt9+fKJPP20HgvMSCTtnACZbOFFAOWt9stZjllzClghIrEichw4hB7Xy3bs2AH//gtPPZWx61euXMnWrVsZO3YsBQsWdKxx2QgvLy9ef/11fvvtNwoWLEhwcDCjR48mNjbW3aa5lAcf1OkoDx7UM74vvQS//KK/f6VLQ//+sGkTJCS421IPwZYSyp3WW27gGLrlljhpUT1ZmceBeZbnPugWXonU6vXUFt6bb4rkzi2SkWjmcXFxUr16dalcubLcNqF47ebatWvy3HPPCSD169eXo0ePutskt5I42dGz5531v+XL6+/mnj3uts79kNmlZUBbdKvtKDDKcmw80N7yXAFT0G4pfwHPplWnJwpeQoJOzNOqVcaunzt3rgDy9ddfO9awHMLixYulaNGiUrhwYVmwYIG7zckSXL8usnChSNu2d092TJokcvKku61zD5kWPGdsnih4u3frd+yLL9J/bUxMjFSoUEGCgoJyzMyjMzh+/Lg0aNBAAOndu7dcvXrV3SZlGc6d04Es6tfX31OlRJo21cEQLl92t3WuwwiegxgzRn+Jzp1L/7VTpkwRQNauXet4w3IYsbGxMnbsWPHy8pIHHnhAtm/f7m6TshxHjoiMHy9SpYr+lefNq4MfhIVl/8kOI3gOwt9fpHHj9F8XFRUlJUqUkBYtWjjeqBzM5s2bpXz58pI7d26ZOHGixJtlCveQkCCyY4fIkCHaWR50gql+/UQ2bMieKzuM4DmAgwf1uzVtWvqvfeeddwTwiHBInsalS5ekc+fOAkhwcLBERES426QsS2ysyE8/ifTqdWdZW7lyOgDq7t3uts5xGMFzABMm6HcrvV7vZ8+eFW9vb+nSpYtzDDNIQkKChISESMGCBaVEiRKyYsUKd5uU5blxQ2fYe+IJ7XUAugczerROaL5tm4inLgJKTfBMtBQ7CQrSUS62bUvfdQMHDuSLL75g//79VM5IwDyD3Rw4cIBu3bqxa9cuXn31VT788EMKFCjgbrOyPJGRsHQpfPWV/n5bS0Lx4jrO44MP3vtYooT7bE6N1KKlGMGzgxMnwM8PPvgARoyw/7qjR49StWpV+vXrx2effeY0+wx3uHXrFm+//TZTpkzB39+fRYsW4e/v726zPIZbt+DYMThyBA4fvvvxn3/uFcNEAcxKYmgEL5NMnQqvvaY/9AcesP+67t27s3z5co4cOcL999/vPAMN97B69Wr69OnD1atX+eijj3jllVdQJg1Zprh1C44fv1sI7RHD5I/33efcjHBG8DJJw4Y6oOOuXfZfs2vXLmrVqsVbb73FhAkTnGecwSbnzp2jb9++rF69mvbt2xMaGoqPj4+7zcqWpCSGiY8nT9699K1YsZSF0FFiaAQvE5w5A2XLwrvvwjvv2H9dmzZt+P333zl27BjFihVznoGGVElISGD69Om8+eab+Pj40KpVKwoVKkThwoUpVKjQPVtKx729vT0q50ZWI1EMk7cK7RXDJ5/Ux+0lNcHLndkXk9359lvdVH/6afuv2bhxI6tXr+bDDz80YudmvLy8GDp0KE2aNGHw4MGsW7eO69evc+3aNeLi4uyux9vbO01hNCKaMvnyQdWqektOcjFMfNy2DZYs0WJ49Gj6BC81TAsvDVq00LkI/v7bvqa2iFC/fn1Onz7NoUOHzCxhFub27dtcu3aN69ev37Nl5HhGRLRBgwaMGTOGhg0bOvGVeia3bukJwwcfhFy57L/OtPAyyMWLsHEjvPmm/eMK3377Ldu3byckJMSIXRYnb968lChRghIOnFK8ffu23QJ5+fJllixZQqNGjWjevDnjxo0zwmdFvnzw0EMOrtSWg56zN09wPA4N1Q6Zf/xhX/nY2FipWrWqVK1aVWJjY51rnCFbcOPGDZkyZYqUKlVKAGnevLls3rzZ3WZ5NGQyAGiOJSwMfH2hVi37ys+bN48DBw4wYcIEcuc2jWdD2hQsWJBhw4Zx7NgxpkyZwt69e2ncuDEtWrTgl19+cbd52Q4jeDa4cgV+/llPVtjTnY2JiWHs2LHUr1+fjh07Ot9AQ7bCCJ9rMIJng1WrIDbW/lDuM2bMICIigokTJxoHV0OGsSV8zZs3N8LnAIzg2SAsDO6/H+rXT7tsVFQU77//Pm3atKFJkybON86Q7bEWvqlTp7Jv3z4jfA7ACF4K3LgBP/4InTqBPa5SkyZN4vLly2ZFhcHhFCxYkKFDh6YofJs3b3a3eR6HEbwUWL0aYmLsczY+ffo006ZNo3v37gQGBjrfOEOOJLnw7d+/nyZNmhjhSydG8FIgLExHe2jUKO2y48ePJy4ujv/+97/ON8yQ4zHClznsEjyl1ONKqYNKqSNKqZEpnO+rlIpUSu2ybP0cb6pruHULvv8eOnbU8e9S49ChQ4SEhPDSSy9RqVIl1xhoMAAFChRIEr6PP/44SfiCg4ON8KWGLQe9xA3IhU7PWIk7eWmrJSvTF5iRVl3iAY7H33+vnY1/+CHtss8884x4e3vL2bNnnW+YwZAK0dHR8vHHH0vp0qUFkGbNmsmmTZvcbZZbIJOOx3WBIyJyTERuA4uBDg5X3ixCWBgULQrNm6deLjw8nKVLl/Laa69RqlQp1xhnMNigQIECDBkyJKnF9/fff5sWXwrYI3hlgX+t9k9ZjiXnaaXUHqXUN0qp8ilVpJTqr5QKV0qFR0ZGZsBc5xIbC999p8PR5M2betmxY8fi4+PD8OHDXWOcwWAHqQnfpk2b3G2e23HUpMVKwFdEAoCfgXkpFRKR/xORIBEJKlmypINu7Tg2b4ZLl9J2Nv7nn3/48ccfGTBgAEWKFHGNcQZDOkhJ+Jo2bZrjhc8ewYsArFts5SzHkhCRiyJyy7IbAtR2jHmuJSwMChaE1q1TLzd37lwAnn/+eecbZTBkAmvhmzZtGgcOHKBp06Y0a9YsRwqfPYK3A6islPJTSuUFngVWWBdQSpWx2m0P/O04E11DQoIO9tm2rRY9W8THxzN79mxatmxJxYoVXWegwZAJChQowODBgzl69CjTpk3j4MGDWUb4RISYmBguXLjAyZMnOXDgADt37mTLli2sWbOGmJgYh90rzZAeIhKnlBoI/ISesZ0tIvuUUuPRsyErgMFKqfZAHHAJPWvrUWzdCmfPpu1svHbtWk6ePMnkyZNdY5jB4EAShe/FF19k1qxZTJw4kaZNm9K0aVPGjRt3z9LIhIQEYmJiiI6O5saNG0RHR9+zOeJ4ahw+fJgHH3zQIa/fRDy2MGwYzJwJFy5A4cK2y3Xp0oX169cTERFBvnz5XGegweAEYmJikoTvzJkz+Pr6EhcXlyREN2/eTHeduXLlwtvbm4IFC1KwYMG7niffUjuXeL5WrVrpCqZrkvikgYiOexcQACtX2i4XGRlJ2bJlGThwIFOmTHGZfQaDs4mJiSEkJIStW7dmSJysz+XJk8etr8WEeE+DP/7Q2ZPefTf1cvPnzyc2NpYXXnjBNYYZDC6iQIECDBo0iEGDBrnbFKdi1tKiZ2dz54b27W2XERFCQ0OpX78+1atXd51x9rJli06+ce2auy0xGLIsOV7wRLTgNWumkwDbYtu2bezfv59+/bLgMuFTp/Ti3w8+gDp1YO9ed1tkMGRJcrzg7dun82Cm5WwcEhJCoUKF6Nq1q2sMs5e4OOjWDW7ehNmzISoK6taFeSn6fhsMOZocL3hhYTpnRWppKK5du8aSJUvo2rUrhQoVcp1x9jB2rO7OfvEFPPcc7NoF9epB377Qr58O7GcwGAAjeISFQcOGULq07TJLlizhxo0bWa87u2YNvP++FrYePfSx0qV19qG334bQUHj0Ud2ENRgMOVvwDh+Gv/5K29k4NDSU6tWrU69ePdcYZg+nT0PPnlC9Okybdve53Lnhf//TmYj+/Rdq19bKbjDkcHK04CVqQGrjd3v37mXbtm288MILWScbWXy8btHduAFff217LVzbtvDnn1CtGnTuDEOHwu3brrXVYMhC5GjBW7ZMT2qWTzGYlSY0NJQ8efLQq1cv1xmWFuPHw8aN8Nln8PDDqZetUEGHgRk8WLcEGzfWTocGQw4kxwreyZOwY0fq3dlbt24xf/58OnXqhI+Pj+uMS4116+C//9WTEr1723dN3rxa7JYuhf37oVYtnZbNYMhh5FjBW7ZMP6YmeN999x0XL17MOisrzp7VXdmHH4YZM9J/fefOellJuXK6uztqlHZrMRhyCDlW8MLC9NrZ1IIwhISEULFiRVq0aOE6w2wRH68nKa5e1eN23t4Zq6dyZdi2DV54ASZMgJYttZAaDDmAHCl4Z8/Cr7+m3ro7ceIEa9eu5bnnnsPLnmzczmbCBN2dnTFDz8xmhgIFICQE5s6F33/XXdyNGx1hpcGQpckCv2TXs3y5XlKW2uzsnDlzAHjuuedcZFUqbNoE48bpFp4j7enTRwteYtaiCRN0JFSDIZuSI8NDtWypJy0OHNCrLJITHx+Pr68v/v7+/Ojuwf3z5yEwEIoUgfBwcMZKj2vX4MUXYckSPbb35Zc6E7nB4IGkFh4qx7XwLl6EDRt0d9aWW93PP//MqVOn3D9ZkZAAvXrB5ct63M5Zy9oKF4ZFi+DTT2HtWnjkEd3yMxiyGTlO8Fas0OP/qY3fhYSE4OPjQ/vU4kW5gkmT9PKxadP0DIszUQoGDNCDm15e0KgRTJ+u+/4GQzYhxwnesmVQsaJuxKTE+fPn+e677+jTpw9500pO60y2bIF33oFnn9XdTVcRFAQ7d8Ljj8OQIdC1q54ZNhiyATlK8K5e1Q2mp56y3Z2dP38+cXFx7u3OXrighc7PT0dBcfWStuLF9czOBx/of4jatWH3btfaYDA4AbsETyn1uFLqoFLqiFJqZCrlnlZKiVIqxQFDd7NqlV5Kaqs7KyKEhITQoEEDHk5ryZazSEjQs6eRkXrczl2Jvr28YMQIPeB54wbUr6/j7RkMHkyagqeUygV8CrQBqgHdlFLVUihXGBgCZNnR7rAwKFNGR0xKia1bt3LgwAH3hoGaPBl++AGmTtX+ce6mUSMdY++xx7Sz8nPPQRpp9QyGrIo9Lby6wBEROSYit4HFQIcUyv0XmASkP6+bC4iO1stHO3XSjZeUCA0NpVChQjzzzDOuNS6RrVt1HLvOneGVV9xjQ0r85z/w008wZoyOpFyvHhw86G6rDIZ0Y4/glQX+tdo/ZTmWhFLqEaC8iKxKrSKlVH+lVLhSKjwyMjLdxmaGn37SomfL2fjq1assWbKEbt26uSeq8cWLetyuYkW9CiKrhKJKJFcundZt9Wq9VCUoSPvtGQweRKYnLZRSXsAU4PW0yorI/4lIkIgElSxZMrO3ThdhYdqXNlli9SSWLFlCdHS0e7qzIrqrePasFpGiRV1vg720aqVj7AUEaIEeOBBu3XK3VQaDXdgjeBGAdcS4cpZjiRQG/IGNSqkTQH1gRVaauLh1SyfY7tBBBwNOiZCQEPz9/alTp45rjQM9XrdypR6/C8oyb5ttypXTa29ff107KzdsCCdOuNsqgyFN7BG8HUBlpZSfUiov8CywIvGkiFwRER8R8RURX2Ab0F5E3LNuLAXWrdMuKbZmZ//66y+2b99Ov379XB/V+PffdT7ZTp3Ak5Ig58mjBfrbb3Ws/Fq1tGgbDFmYNAVPROKAgcBPwN/A1yKyTyk1Xinl5qUI9rFsmfbuaN485fOhoaHkzZuXnj17utawy5e1Y2+5cjrhTlYbt7OHjh11jD0/P53J/M03TYw9Q9ZFRNyy1a5dW1xBbKxIiRIi3bunfD4mJkbuu+8+6dq1q0vsSSIhQaRjR5E8eUR+/92193YGMTEiL70kAiKNGolERLjbIkMOBQgXG7qT7VdabN6sJ0BtdWeXL1/OpUuXXD9Z8cknejXDpEk6cbankz8/fP45fPWVbvHVqqXHEgyGLES2F7ywMJ3U6/HHUz4fGhqKr68vwcHBrjMqPByGD9ddwKFDXXdfV9Cjh04WUqKEntE16SENWYhsLXgJCXpMvU2blDMZHj9+nLVr1/L888+7LqrxlSt63K5MGZgzxzPH7dKiWjXYvl07KPfsqR2qDYYsQLYWvG3b4MwZ287Gs2fPxsvLi759+7rGIBHo109HH128GO67zzX3dQeFCulYXOXK6Zbs4cPutshgyN6CFxamMxS2a3fvufj4eObMmUPr1q0pn1piWkfy2WfwzTc6lLqtBb3ZCR8fvZ5PKd3MPn/e3RYZcjjZVvBEtOC1bJlywJGffvqJiIgI101W/PknDBumQ6i/nuailOzDgw9q/7yICN3SM4EHDG4k2wrezp3wzz+2Z2dDQkL4z3/+Q7uUmn+O5upV6NIFSpbUi++zQhY0V1K/PixcqMf1evTQIacNBjeQbX95YWF6vXtKUdrPnTvHypUr6d27t/OjGovASy/B8eN63M7Hx7n3y6p06gQff6xdcYYNM6HjDW7BxspSzyaxO9u0acrJt7788kvXRTWeNUsL3YQJes1pTmbwYN3snjJFr8wYNszdFhlyGNmyhbd/Pxw6lHJ3VixRjR977DGqVq3qXEP27NF5IVq31kuuDPDhh/qDef11PYFjMLiQbCl4YWF6YrBTp3vP/frrrxw6dMj5kxXXr+txu+LFdZ7XnDZuZwsvL5g/X89S9+yps6QZDC4iW/4Kw8J0RPLSpe89FxISQuHChZ0b1VhERyw+fFjne/3Pf5x3L0+kQAH47juoUEEPsh465G6LDDmEbCd4R47onmRKzsZXrlzh66+/plu3bnh7ezvPiDlz9JrSceNsRxzN6ST66OXKZXz0DC4j2wnesmX6MSXBW7x4MTExMc7tzu7dq6MAN2+u81MYbPPAA9pH78wZePJJ46NncDrZTvDCwnTQ4IoV7z0XEhJCQEAAQc6KKnzjhh63K1IEFizQrRdD6tSrp7v9O3ZA9+7GR8/gVLKV4P37r/ZtTWl2dvfu3YSHh/PCCy84L6rxq6/CgQNa7EqVcs49siMdOsD06Xpcb8gQ46NncBrZyg8vte5saGgo+fLlc15U43nz9DZmjO3QygbbDByo82J89JH20ctJy+8MLiPbCZ6/P1Spcvfxmzdv8tVXX/HUU09xnzMilOzfDwMGaE/nMWMcX39O4YMPtGPy8OF6Btdd+YEN2ZZs06U9dw5++SXl7uy3337L5cuXnbOyIjpaj9t5e5txu8yS6KP32GPQqxds2eJuiwzZDLsETyn1uFLqoFLqiFJqZArnX1ZK/aWU2qWU2qKUquZ4U1Nn+XI99JOS4IWEhODn50ezZs0cf+PBg3UL76uv4P77HV9/TiN/fj2WV7GiHts7eNDdFhmyEWkKnlIqF/Ap0AaoBnRLQdAWikgNEQkEPkAn5nYpYWFQubLu0lpz9OhR1q9fzwsvvOD4qMYLFuhsY2+9pcOZGxxDiRJ3++idO+duiwzZBHsUoC5wRESOichtYDHQwbqAiFy12vUGXDrNdukSbNigJyuST8DOmTPHOVGNDx7UUVAaNYJ333Vs3QaoVAm+/x7OntU+ejduuNsiQzbAHsErC/xrtX/KcuwulFKvKqWOolt4gx1jnn2sXKlToSbvzsbFxTFnzhzatGlD2bL3mJxxbt7UeSkKFNA+ZLmz1dxP1qFuXR1p5o8/oFs346NnyDQO6+OJyKci8gDwJjA6pTJKqf5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      "text/plain": [
       "<Figure size 360x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# test detection for the ensemble method\n",
    "tpr_ensemble = run_detect(mfvi_bnn_list, x_clean, y_clean, predict_ensemble, epsilon_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lVrAAQcGPLo1"
   },
   "source": [
    "Hopefully you can see that the ensemble BNN performs significantly better than the other two BNNs (MC-dropout and single MFVI-BNN) in terms of detecting adversarial examples.\n",
    "\n",
    "There are ways to improve the ensemble BNN further in terms of both robustness and detection to adversarial examples, but they are beyond the scope of this tutorial."
   ]
  }
 ],
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