963 lines (963 with data), 234.2 kB

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 464,
      "metadata": {
        "id": "UJOq3mdA8PAH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "a74ea596-aa20-457d-b010-5ce3c5e73de4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time in seconds since beginning of run: 1695698202.6060126\n",
            "Tue Sep 26 03:16:42 2023\n"
          ]
        }
      ],
      "source": [
        "# This cell is added by sphinx-gallery\n",
        "# It can be customized to whatever you like\n",
        "%matplotlib inline\n",
        "\n",
        "# from google.colab import drive\n",
        "# drive.mount('/content/drive')\n",
        "# !pip install pennylane\n",
        "\n",
        "import time\n",
        "seconds = time.time()\n",
        "print(\"Time in seconds since beginning of run:\", seconds)\n",
        "local_time = time.ctime(seconds)\n",
        "print(local_time)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 465,
      "metadata": {
        "id": "5ljdosVS8PAP"
      },
      "outputs": [],
      "source": [
        "# Some parts of this code are based on the Python script:\n",
        "# https://github.com/pytorch/tutorials/blob/master/beginner_source/transfer_learning_tutorial.py\n",
        "# License: BSD\n",
        "\n",
        "import os\n",
        "import copy\n",
        "\n",
        "# PyTorch\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "from torch.optim import lr_scheduler\n",
        "import torchvision\n",
        "from torchvision import datasets, transforms\n",
        "\n",
        "# Pennylane\n",
        "import pennylane as qml\n",
        "from pennylane import numpy as np\n",
        "\n",
        "torch.manual_seed(42)\n",
        "np.random.seed(42)\n",
        "\n",
        "# Plotting\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# OpenMP: number of parallel threads.\n",
        "os.environ[\"OMP_NUM_THREADS\"] = \"1\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1AFilzYk8PAQ"
      },
      "source": [
        "Setting of the main hyper-parameters of the model\n",
        "=================================================\n",
        "\n",
        "::: {.note}\n",
        "::: {.title}\n",
        "Note\n",
        ":::\n",
        "\n",
        "To reproduce the results of Ref. \\[1\\], `num_epochs` should be set to\n",
        "`30` which may take a long time. We suggest to first try with\n",
        "`num_epochs=1` and, if everything runs smoothly, increase it to a larger\n",
        "value.\n",
        ":::\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 466,
      "metadata": {
        "id": "5LRcEYZg8PAR"
      },
      "outputs": [],
      "source": [
        "n_qubits = 4                # Number of qubits\n",
        "step = 0.0004               # Learning rate\n",
        "batch_size = 4              # Number of samples for each training step\n",
        "num_epochs = 5              # Number of training epochs\n",
        "q_depth = 9                 # Depth of the quantum circuit (number of variational layers)\n",
        "gamma_lr_scheduler = 0.1    # Learning rate reduction applied every 10 epochs.\n",
        "q_delta = 0.01              # Initial spread of random quantum weights\n",
        "start_time = time.time()    # Start of the computation timer"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NlU2Q7zd8PAR"
      },
      "source": [
        "We initialize a PennyLane device with a `default.qubit` backend.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 467,
      "metadata": {
        "id": "0prgZPLK8PAR"
      },
      "outputs": [],
      "source": [
        "dev = qml.device(\"default.qubit\", wires=n_qubits)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "54jRIpbZ8PAS"
      },
      "source": [
        "We configure PyTorch to use CUDA only if available. Otherwise the CPU is\n",
        "used.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 468,
      "metadata": {
        "id": "23nQUjLm8PAS"
      },
      "outputs": [],
      "source": [
        "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-AJzWJGi8PAT"
      },
      "source": [
        "Dataset loading\n",
        "===============\n",
        "\n",
        "::: {.note}\n",
        "::: {.title}\n",
        "Note\n",
        ":::\n",
        "\n",
        "The dataset containing images of *ants* and *bees* can be downloaded\n",
        "[here](https://download.pytorch.org/tutorial/hymenoptera_data.zip) and\n",
        "should be extracted in the subfolder `../_data/hymenoptera_data`.\n",
        ":::\n",
        "\n",
        "This is a very small dataset (roughly 250 images), too small for\n",
        "training from scratch a classical or quantum model, however it is enough\n",
        "when using *transfer learning* approach.\n",
        "\n",
        "The PyTorch packages `torchvision` and `torch.utils.data` are used for\n",
        "loading the dataset and performing standard preliminary image\n",
        "operations: resize, center, crop, normalize, *etc.*\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 469,
      "metadata": {
        "id": "XaNa12un8PAT"
      },
      "outputs": [],
      "source": [
        "data_transforms = {\n",
        "    \"train\": transforms.Compose(\n",
        "        [\n",
        "            # transforms.RandomResizedCrop(224),     # uncomment for data augmentation\n",
        "            # transforms.RandomHorizontalFlip(),     # uncomment for data augmentation\n",
        "            transforms.Resize(256),\n",
        "            transforms.CenterCrop(224),\n",
        "            transforms.ToTensor(),\n",
        "            # Normalize input channels using mean values and standard deviations of ImageNet.\n",
        "            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),\n",
        "        ]\n",
        "    ),\n",
        "    \"val\": transforms.Compose(\n",
        "        [\n",
        "            transforms.Resize(256),\n",
        "            transforms.CenterCrop(224),\n",
        "            transforms.ToTensor(),\n",
        "            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),\n",
        "        ]\n",
        "    ),\n",
        "}\n",
        "\n",
        "data_dir = \"/content/drive/MyDrive/Colab Notebooks/data/Shuffle Split 10 of 17 Classes Big Brain Tumor MRI Images\"\n",
        "image_datasets = {\n",
        "    x if x == \"train\" else \"validation\": datasets.ImageFolder(\n",
        "        os.path.join(data_dir, x), data_transforms[x]\n",
        "    )\n",
        "    for x in [\"train\", \"val\"]\n",
        "}\n",
        "dataset_sizes = {x: len(image_datasets[x]) for x in [\"train\", \"validation\"]}\n",
        "class_names = image_datasets[\"train\"].classes\n",
        "\n",
        "# Initialize dataloader\n",
        "dataloaders = {\n",
        "    x: torch.utils.data.DataLoader(image_datasets[x], batch_size=batch_size, shuffle=True)\n",
        "    for x in [\"train\", \"validation\"]\n",
        "}\n",
        "\n",
        "# function to plot images\n",
        "def imshow(inp, title=None):\n",
        "    \"\"\"Display image from tensor.\"\"\"\n",
        "    inp = inp.numpy().transpose((1, 2, 0))\n",
        "    # Inverse of the initial normalization operation.\n",
        "    mean = np.array([0.485, 0.456, 0.406])\n",
        "    std = np.array([0.229, 0.224, 0.225])\n",
        "    inp = std * inp + mean\n",
        "    inp = np.clip(inp, 0, 1)\n",
        "    plt.imshow(inp)\n",
        "    if title is not None:\n",
        "        plt.title(title)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ANdmcnR98PAU"
      },
      "source": [
        "Let us show a batch of the test data, just to have an idea of the\n",
        "classification problem.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 470,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 207
        },
        "id": "QzIKQxS78PAU",
        "outputId": "73854d61-51dc-43fd-8e00-ee453eb7846c"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Get a batch of training data\n",
        "inputs, classes = next(iter(dataloaders[\"validation\"]))\n",
        "\n",
        "# Make a grid from batch\n",
        "out = torchvision.utils.make_grid(inputs)\n",
        "\n",
        "imshow(out, title=[class_names[x] for x in classes])\n",
        "\n",
        "dataloaders = {\n",
        "    x: torch.utils.data.DataLoader(image_datasets[x], batch_size=batch_size, shuffle=True)\n",
        "    for x in [\"train\", \"validation\"]\n",
        "}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_ULbO8f28PAU"
      },
      "source": [
        "Variational quantum circuit\n",
        "===========================\n",
        "\n",
        "We first define some quantum layers that will compose the quantum\n",
        "circuit.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 471,
      "metadata": {
        "id": "6gMomjvL8PAV"
      },
      "outputs": [],
      "source": [
        "def H_layer(nqubits):\n",
        "    \"\"\"Layer of single-qubit Hadamard gates.\n",
        "    \"\"\"\n",
        "    for idx in range(nqubits):\n",
        "        qml.Hadamard(wires=idx)\n",
        "\n",
        "\n",
        "def RY_layer(w):\n",
        "    \"\"\"Layer of parametrized qubit rotations around the y axis.\n",
        "    \"\"\"\n",
        "    for idx, element in enumerate(w):\n",
        "        qml.RY(element, wires=idx)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0iroynmF8PAV"
      },
      "source": [
        "Now we define the quantum circuit through the PennyLane\n",
        "[qnode]{.title-ref} decorator .\n",
        "\n",
        "The structure is that of a typical variational quantum circuit:\n",
        "\n",
        "-   **Embedding layer:** All qubits are first initialized in a balanced\n",
        "    superposition of *up* and *down* states, then they are rotated\n",
        "    according to the input parameters (local embedding).\n",
        "-   **Variational layers:** A sequence of trainable rotation layers and\n",
        "    constant entangling layers is applied.\n",
        "-   **Measurement layer:** For each qubit, the local expectation value\n",
        "    of the $Z$ operator is measured. This produces a classical output\n",
        "    vector, suitable for additional post-processing.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 472,
      "metadata": {
        "id": "ONyq04RY8PAV"
      },
      "outputs": [],
      "source": [
        "@qml.qnode(dev, interface=\"torch\")\n",
        "def quantum_net(q_input_features, q_weights_flat):\n",
        "    \"\"\"\n",
        "    The variational quantum circuit.\n",
        "    \"\"\"\n",
        "\n",
        "    # Reshape weights\n",
        "    q_weights = q_weights_flat.reshape(q_depth, n_qubits)\n",
        "\n",
        "    # Start from state |+> , unbiased w.r.t. |0> and |1>\n",
        "    H_layer(n_qubits)\n",
        "\n",
        "    # Embed features in the quantum node\n",
        "    RY_layer(q_input_features)\n",
        "\n",
        "    # Sequence of trainable variational layers\n",
        "    for k in range(q_depth):\n",
        "        RY_layer(q_weights[k])\n",
        "\n",
        "    # Expectation values in the Z basis\n",
        "    exp_vals = [qml.expval(qml.PauliZ(position)) for position in range(n_qubits)]\n",
        "    return tuple(exp_vals)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4eG97j4f8PAV"
      },
      "source": [
        "Dressed quantum circuit\n",
        "=======================\n",
        "\n",
        "We can now define a custom `torch.nn.Module` representing a *dressed*\n",
        "quantum circuit.\n",
        "\n",
        "This is a concatenation of:\n",
        "\n",
        "-   A classical pre-processing layer (`nn.Linear`).\n",
        "-   A classical activation function (`torch.tanh`).\n",
        "-   A constant `np.pi/2.0` scaling.\n",
        "-   The previously defined quantum circuit (`quantum_net`).\n",
        "-   A classical post-processing layer (`nn.Linear`).\n",
        "\n",
        "The input of the module is a batch of vectors with 512 real parameters\n",
        "(features) and the output is a batch of vectors with two real outputs\n",
        "(associated with the two classes of images: *ants* and *bees*).\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 473,
      "metadata": {
        "id": "hIljGdv_8PAW"
      },
      "outputs": [],
      "source": [
        "class DressedQuantumNet(nn.Module):\n",
        "    \"\"\"\n",
        "    Torch module implementing the *dressed* quantum net.\n",
        "    \"\"\"\n",
        "\n",
        "    def __init__(self):\n",
        "        \"\"\"\n",
        "        Definition of the *dressed* layout.\n",
        "        \"\"\"\n",
        "\n",
        "        super().__init__()\n",
        "        self.pre_net = nn.Linear(2048, n_qubits)\n",
        "        self.q_params = nn.Parameter(q_delta * torch.randn(q_depth * n_qubits))\n",
        "        self.post_net = nn.Linear(n_qubits, 10)\n",
        "\n",
        "    def forward(self, input_features):\n",
        "        \"\"\"\n",
        "        Defining how tensors are supposed to move through the *dressed* quantum\n",
        "        net.\n",
        "        \"\"\"\n",
        "\n",
        "        # obtain the input features for the quantum circuit\n",
        "        # by reducing the feature dimension from 512 to 4\n",
        "        pre_out = self.pre_net(input_features)\n",
        "        q_in = torch.tanh(pre_out) * np.pi / 2.0\n",
        "\n",
        "        # Apply the quantum circuit to each element of the batch and append to q_out\n",
        "        q_out = torch.Tensor(0, n_qubits)\n",
        "        q_out = q_out.to(device)\n",
        "        for elem in q_in:\n",
        "            q_out_elem = torch.hstack(quantum_net(elem, self.q_params)).float().unsqueeze(0)\n",
        "            q_out = torch.cat((q_out, q_out_elem))\n",
        "\n",
        "        # return the two-dimensional prediction from the postprocessing layer\n",
        "        return self.post_net(q_out)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E8-EDnhn8PAW"
      },
      "source": [
        "Hybrid classical-quantum model\n",
        "==============================\n",
        "\n",
        "We are finally ready to build our full hybrid classical-quantum network.\n",
        "We follow the *transfer learning* approach:\n",
        "\n",
        "1.  First load the classical pre-trained network *ResNet18* from the\n",
        "    `torchvision.models` zoo.\n",
        "2.  Freeze all the weights since they should not be trained.\n",
        "3.  Replace the last fully connected layer with our trainable dressed\n",
        "    quantum circuit (`DressedQuantumNet`).\n",
        "\n",
        "::: {.note}\n",
        "::: {.title}\n",
        "Note\n",
        ":::\n",
        "\n",
        "The *ResNet18* model is automatically downloaded by PyTorch and it may\n",
        "take several minutes (only the first time).\n",
        ":::\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 474,
      "metadata": {
        "id": "lnJnW_ra8PAX"
      },
      "outputs": [],
      "source": [
        "model_hybrid = torchvision.models.resnet50(pretrained=True)\n",
        "\n",
        "for param in model_hybrid.parameters():\n",
        "    param.requires_grad = False\n",
        "\n",
        "\n",
        "# Notice that model_hybrid.fc is the last layer of ResNet18\n",
        "model_hybrid.fc = DressedQuantumNet()\n",
        "\n",
        "# Use CUDA or CPU according to the \"device\" object.\n",
        "model_hybrid = model_hybrid.to(device)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5k96EBuZ8PAX"
      },
      "source": [
        "Training and results\n",
        "====================\n",
        "\n",
        "Before training the network we need to specify the *loss* function.\n",
        "\n",
        "We use, as usual in classification problem, the *cross-entropy* which is\n",
        "directly available within `torch.nn`.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 475,
      "metadata": {
        "id": "BKvfgR5N8PAX"
      },
      "outputs": [],
      "source": [
        "criterion = nn.CrossEntropyLoss()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UUvuVdii8PAX"
      },
      "source": [
        "We also initialize the *Adam optimizer* which is called at each training\n",
        "step in order to update the weights of the model.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 476,
      "metadata": {
        "id": "bPI2SbMQ8PAX"
      },
      "outputs": [],
      "source": [
        "optimizer_hybrid = optim.Adam(model_hybrid.fc.parameters(), lr=step)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a8wMKvP48PAY"
      },
      "source": [
        "We schedule to reduce the learning rate by a factor of\n",
        "`gamma_lr_scheduler` every 10 epochs.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 477,
      "metadata": {
        "id": "dLQsPIzy8PAY"
      },
      "outputs": [],
      "source": [
        "exp_lr_scheduler = lr_scheduler.StepLR(\n",
        "    optimizer_hybrid, step_size=10, gamma=gamma_lr_scheduler\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q-xTUZhq8PAY"
      },
      "source": [
        "What follows is a training function that will be called later. This\n",
        "function should return a trained model that can be used to make\n",
        "predictions (classifications).\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 478,
      "metadata": {
        "id": "rppVRya_8PAY"
      },
      "outputs": [],
      "source": [
        "def train_model(model, criterion, optimizer, scheduler, num_epochs):\n",
        "    since = time.time()\n",
        "    best_model_wts = copy.deepcopy(model.state_dict())\n",
        "    best_acc = 0.0\n",
        "    best_loss = 10000.0  # Large arbitrary number\n",
        "    best_acc_train = 0.0\n",
        "    best_loss_train = 10000.0  # Large arbitrary number\n",
        "    print(\"Training started:\")\n",
        "\n",
        "    for epoch in range(num_epochs):\n",
        "\n",
        "        # Each epoch has a training and validation phase\n",
        "        for phase in [\"train\", \"validation\"]:\n",
        "            if phase == \"train\":\n",
        "                # Set model to training mode\n",
        "                model.train()\n",
        "            else:\n",
        "                # Set model to evaluate mode\n",
        "                model.eval()\n",
        "            running_loss = 0.0\n",
        "            running_corrects = 0\n",
        "\n",
        "            # Iterate over data.\n",
        "            n_batches = dataset_sizes[phase] // batch_size\n",
        "            it = 0\n",
        "            for inputs, labels in dataloaders[phase]:\n",
        "                since_batch = time.time()\n",
        "                batch_size_ = len(inputs)\n",
        "                inputs = inputs.to(device)\n",
        "                labels = labels.to(device)\n",
        "                optimizer.zero_grad()\n",
        "\n",
        "                # Track/compute gradient and make an optimization step only when training\n",
        "                with torch.set_grad_enabled(phase == \"train\"):\n",
        "                    outputs = model(inputs)\n",
        "                    _, preds = torch.max(outputs, 1)\n",
        "                    loss = criterion(outputs, labels)\n",
        "                    if phase == \"train\":\n",
        "                        loss.backward()\n",
        "                        optimizer.step()\n",
        "\n",
        "                # Print iteration results\n",
        "                running_loss += loss.item() * batch_size_\n",
        "                batch_corrects = torch.sum(preds == labels.data).item()\n",
        "                running_corrects += batch_corrects\n",
        "                print(\n",
        "                    \"Phase: {} Epoch: {}/{} Iter: {}/{} Batch time: {:.4f}\".format(\n",
        "                        phase,\n",
        "                        epoch + 1,\n",
        "                        num_epochs,\n",
        "                        it + 1,\n",
        "                        n_batches + 1,\n",
        "                        time.time() - since_batch,\n",
        "                    ),\n",
        "                    end=\"\\r\",\n",
        "                    flush=True,\n",
        "                )\n",
        "                it += 1\n",
        "\n",
        "            # Print epoch results\n",
        "            epoch_loss = running_loss / dataset_sizes[phase]\n",
        "            epoch_acc = running_corrects / dataset_sizes[phase]\n",
        "            print(\n",
        "                \"Phase: {} Epoch: {}/{} Loss: {:.4f} Acc: {:.4f}        \".format(\n",
        "                    \"train\" if phase == \"train\" else \"validation  \",\n",
        "                    epoch + 1,\n",
        "                    num_epochs,\n",
        "                    epoch_loss,\n",
        "                    epoch_acc,\n",
        "                )\n",
        "            )\n",
        "\n",
        "            # Check if this is the best model wrt previous epochs\n",
        "            if phase == \"validation\" and epoch_acc > best_acc:\n",
        "                best_acc = epoch_acc\n",
        "                best_model_wts = copy.deepcopy(model.state_dict())\n",
        "            if phase == \"validation\" and epoch_loss < best_loss:\n",
        "                best_loss = epoch_loss\n",
        "            if phase == \"train\" and epoch_acc > best_acc_train:\n",
        "                best_acc_train = epoch_acc\n",
        "            if phase == \"train\" and epoch_loss < best_loss_train:\n",
        "                best_loss_train = epoch_loss\n",
        "\n",
        "            # Update learning rate\n",
        "            if phase == \"train\":\n",
        "                scheduler.step()\n",
        "\n",
        "    # Print final results\n",
        "    model.load_state_dict(best_model_wts)\n",
        "    time_elapsed = time.time() - since\n",
        "    print(\n",
        "        \"Training completed in {:.0f}m {:.0f}s\".format(time_elapsed // 60, time_elapsed % 60)\n",
        "    )\n",
        "    print(\"Best test loss: {:.4f} | Best test accuracy: {:.4f}\".format(best_loss, best_acc))\n",
        "    return model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a_XtRwDI8PAZ"
      },
      "source": [
        "We are ready to perform the actual training process.\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from IPython.display import display, Javascript\n",
        "\n",
        "# Run this cell to keep Colab awake\n",
        "display(Javascript('''\n",
        "  function keep_colab_awake(){\n",
        "    console.log(\"Colab is being kept awake.\");\n",
        "    document.querySelector(\"#top-toolbar > colab-connect-button\").shadowRoot.querySelector(\"#connect\").click();\n",
        "    document.querySelector(\"body > colab-sandbox-output > div > div.output.container.output-wrapper > div.output > pre\").innerText;\n",
        "    setTimeout(keep_colab_awake, 61000);\n",
        "  }\n",
        "  keep_colab_awake();\n",
        "'''))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "p2W621Tsy2hY",
        "outputId": "694f1a6a-6ca1-42c4-8a1a-f0a33a44d9a8"
      },
      "execution_count": 479,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "  function keep_colab_awake(){\n",
              "    console.log(\"Colab is being kept awake.\");\n",
              "    document.querySelector(\"#top-toolbar > colab-connect-button\").shadowRoot.querySelector(\"#connect\").click();\n",
              "    document.querySelector(\"body > colab-sandbox-output > div > div.output.container.output-wrapper > div.output > pre\").innerText;\n",
              "    setTimeout(keep_colab_awake, 61000);\n",
              "  }\n",
              "  keep_colab_awake();\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 480,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "5VgfdD3-8PAZ",
        "outputId": "4068aabb-fc6b-49da-a317-5367401b08b3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training started:\n",
            "Phase: train Epoch: 1/5 Loss: 2.2162 Acc: 0.1752        \n",
            "Phase: validation   Epoch: 1/5 Loss: 2.0872 Acc: 0.2139        \n",
            "Phase: train Epoch: 2/5 Loss: 2.1094 Acc: 0.2015        \n",
            "Phase: validation   Epoch: 2/5 Loss: 2.0520 Acc: 0.2024        \n",
            "Phase: train Epoch: 3/5 Loss: 2.0247 Acc: 0.2571        \n",
            "Phase: validation   Epoch: 3/5 Loss: 1.9175 Acc: 0.2987        \n",
            "Phase: train Epoch: 4/5 Loss: 1.9743 Acc: 0.2780        \n",
            "Phase: validation   Epoch: 4/5 Loss: 1.8619 Acc: 0.3209        \n",
            "Phase: train Epoch: 5/5 Loss: 1.9467 Acc: 0.2775        \n",
            "Phase: validation   Epoch: 5/5 Loss: 1.8630 Acc: 0.3086        \n",
            "Training completed in 21m 11s\n",
            "Best test loss: 1.8619 | Best test accuracy: 0.3209\n"
          ]
        }
      ],
      "source": [
        "model_hybrid = train_model(\n",
        "    model_hybrid, criterion, optimizer_hybrid, exp_lr_scheduler, num_epochs=num_epochs\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AG82Ot6Y8PAZ"
      },
      "source": [
        "Visualizing the model predictions\n",
        "=================================\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cwycKwbd8PAZ"
      },
      "source": [
        "We first define a visualization function for a batch of test data.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 481,
      "metadata": {
        "id": "_8R2rHzF8PAZ"
      },
      "outputs": [],
      "source": [
        "def visualize_model(model, num_images=6, fig_name=\"Predictions\"):\n",
        "    images_so_far = 0\n",
        "    _fig = plt.figure(fig_name)\n",
        "    model.eval()\n",
        "    with torch.no_grad():\n",
        "        for _i, (inputs, labels) in enumerate(dataloaders[\"validation\"]):\n",
        "            inputs = inputs.to(device)\n",
        "            labels = labels.to(device)\n",
        "            outputs = model(inputs)\n",
        "            _, preds = torch.max(outputs, 1)\n",
        "            for j in range(inputs.size()[0]):\n",
        "                images_so_far += 1\n",
        "                ax = plt.subplot(num_images // 2, 2, images_so_far)\n",
        "                ax.axis(\"off\")\n",
        "                ax.set_title(\"[{}]\".format(class_names[preds[j]]))\n",
        "                imshow(inputs.cpu().data[j])\n",
        "                if images_so_far == num_images:\n",
        "                    return"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LQvJfmme8PAa"
      },
      "source": [
        "Finally, we can run the previous function to see a batch of images with\n",
        "the corresponding predictions.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 482,
      "metadata": {
        "id": "mKBJn2x68PAa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "outputId": "291d1154-16e6-42ad-c678-c4f1d8943d53"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 16 Axes>"
            ],
            "image/png": 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kB/zMM88wduxYhgwZws0330xxcTFNTU189dVX1NbWsn79+t5qYjf0ej0PPPAAt99+O5MmTeKKK66gsrKSefPmUVJSsl8XqMwTTzzBlClTOPPMM7nxxhsJBoP85S9/wW63H1Vt5KlTp/Lee+8xffp0LrroIioqKvjb3/7GwIEDu+1XHSkPPfQQsDf1B2DBggV88cUXAPz2t78V582dO5fdu3dzxx138N577zF16lTS0tKorq7mnXfeYdu2bUkBJUeTmTNnigj7O+64g0AgwLPPPku/fv2SAk4efPBBPvvsMy666CIKCwtpbm7mr3/9K3l5eYwdO7ZX2qbw/ebJJ5+koqKC22+/nTfffJOLL76YjIwMWltbWbFiBR988EGPcSMHIj09nV//+tf87ne/44ILLuCSSy5h+/bt/PWvf2XkyJFHNV1q6tSpLFiwALvdzsCBA/nqq6/45JNPjkq6YFeqqqqEqp8sdyuPLYWFhad8dHavGeJDZeDAgaxZs4bf/e53zJs3j7a2NjIyMjjttNO4//77j3l7brvtNiRJYu7cucyePZuhQ4fy73//mzvuuAOj0XjAa88991w++ugj5syZw/33349Op2P8+PE89thj9OnT56i1cdasWTQ2NvLcc8/x8ccfM3DgQF599VXeeeedA2pBfxfuu+++pOcvvfSS+H9XQ2w2m1m0aBHz5s1j/vz5/P73vycQCJCTk8OkSZN47bXXyM3NPaptk3E6nbz//vv8/Oc/55e//CV9+vThD3/4Azt37kwyxJdccgmVlZW89NJLtLa24nK5GD9+PL/73e8OmvqmoNATZrOZjz76iAULFrBgwQIef/xxvF4vqampDB06lL/+9a9cd911h33fBx54gPT0dJ5++mnuuusuHA4Ht9xyC4888shRldX985//LNzroVCIMWPGCD2Eo0lFRUW3sUR+Pn78+FPeEKukw/CdzJo1i6VLl7J27Vq0Wq1IB/m+k0gkSE9PZ8aMGTz//PPHuzkKvUg8Hqe9vZ0VK1Ywbdo03nnnnW55lgqnFqfquHc8OFX732GviGtqakhPT2fQoEFs2rSpN9p0XAmFQhgMhiQ39CuvvILb7e710ooKx5+NGzdy2mmnHe9mKJxgfN/HvROFU7X/HdaKeMuWLdTX1wN7k8jPOOOMXmvY8WL58uXcddddXH755TidTtauXcuLL77IgAED+Oabb5IEQRS+f3R2drJy5UrxvLy8nIyMjOPYIoXjzakw7p0onKr977AM8alAZWUld9xxB6tWrcLtduNwOIRW66nwg1BQUFBQOLYohlhBQUFBQeE40mt5xAoKCgoKCgoHRzHECgoKCgoKx5Hjnkf8fUXOS5UkCUmSUKlUqNVqVCoViUQCtVpNbm4uM2bMwOfz8dVXX9HZ2Yndbsdms4mqKFqtlkQigd1uR6vVEo1G6ejoQK/X4/F46OjoIBaLkZ2dzejRo9m5cydLliwhGAySSCTEe++LIuiuoHBicqCc9q59WaVSodPpSElJEWNDOBwmHA4jSRI6nQ6LxYLBYCAYDOL3+4nH4xgMBtLS0jAajYRCITweD4FAQKhpHWi3UpKkpBKPCkcHZY+4l7Db7T3+oGWDXFZWxpQpU6iurmb79u3EYjFcLhdWq1UYbI1GQzQaRZIkYrEYarUarVaLVqtFp9Ph8XhIJBJEIhEaGhrQ6XSMHDkSlUrFhx9+iNvtJh6PdzPESmdSUDhx2Z8hlvuxPIa4XC6ysrKw2+3069ePjo4OWltbiUQiRKNRsrOzSU1NFZPy+vp6QqEQLpeLPn36kJmZSVNTE1u2bKGlpYXq6mpCoZBYPPSEMnb0Dooh7iVSUlJQqVSi8yQSCfF85MiRXHLJJaxYsYKGhgbS09PFdTabjWAwiMViQa1WC91q2RDrdDrsdjuJRILOzk70ej1+v59YLEYoFKKzs5PRo0djs9lYsGABra2tAGJlLP+rrIgVFE5MDmaINRoNRUVFFBUVEQqFuPjii4lGo0yYMIHq6mr++9//UldXR0pKCpWVlbS3t5OWloZGoyEvL49hw4Zx6aWXEo1GcblczJ8/n1WrVtHW1sa6desIBoP7NcaKIe4dlD3iXkLuNJIkJZUTKykp4cILL2T58uU0NDRQUFCASqXCZDKhVqsJBoMYDAYMBgMajYaUlBSMRiNqtRq1Wo1GoyGRSKDVaklJSUGn04mVss1mw+FwsGHDBrxeL9OmTcNgMPTYLgUFhZMPlUqFzWajvLyccDhMeXk5U6dO5Sc/+QmDBg1i0qRJpKam4nQ62bx5M7W1tfj9fnbv3o3FYsFoNHLeeeeRl5dHnz59sNlsjB8/nkAgQElJCf369RMeuZ7GCmX86B0UQ9xLdJ1Nyq4km83GtGnTWLduHR0dHWRmZiJJElqtVryu0+nQ6/VIkiRWuvI5kiSJPeJwOIxGoxGuaqPRiEajEa7tdevWYTKZGDFihGhL1/1qBQWFE599DaJKpaK4uBi1Wk0sFsNqteJ0OtFoNKhUKkKhEH6/H5PJhMfjwWazkUgkiMVi6HQ6zGYzzc3NSYuD6upqotGoKLvYtVyrMlYcGxRD3MvIxk+tVnP++efj9/vZsWMHVqsVq9UqzpGDKEwmE4lEQgR0BQIBQqGQWBUbjUasVitarVYYeLvdLvaTNRoNRqORSCTCxo0bGTVqVDc3uYKCwsmF3H8tFgv5+flUVFTgdrvZuHEjK1aswO12s2HDBj744APhMTMYDNhsNpxOJxaLBYfDgU6no6GhgSVLlrBy5Uo+//xzPvjgA9RqNY2NjaSlpVFSUoJWq016b4XeRYma7iXk/Vj5/2VlZZx22mm89957uFwuUlJSSCQSYsaq0Wjo6Ohgz549+P1+XC4XJSUlpKWlEQqFiMViIoDLYrEQiUTw+/2YzWYADAYDkiSh1+vFPnN7ezuRSIThw4eLqkxd26WgoHDisu9KWKvVMnLkSIxGI8FgkEgkgiRJLF26lB07dggvmV6vJxQKEY1GxdgSj8fxeDyYTCY0Gg0tLS3E43HcbjdZWVmsXr0anU6HSqVixIgRNDc309DQkORFU8aN3kNZEfcSXV0/RqORSy+9lE2bNiWtWOWZq1qtpr6+nk2bNtHe3k44HKa+vp6NGzeSSCSIx+NEIhHhgq6urmbVqlWsWrVK7Aer1WpMJhPxeJxoNEpqaioqlYrGxkbOPvtsbDab2GdWUFA4eZBXwwMGDKC4uJjs7GwMBgPxeJy6ujra2toAyMrKoqmpiUQiQUtLC5FIRNxDo9GwZ88evF4vDQ0NYsyQS4VqtVrKy8spLy/H6XQyfvx4xZN2DFFG5V5CDngAGDx4MCkpKTQ1NZGVlYXZbBYrW0mSaGtrY+fOnUSj0aQo67a2Nqqrq9FoNCKAq7a2lp07d4q8v+bmZrZs2UJ7ezsAwWAQ2FtOLC0tjR07dhCNRhk8eLA43nWSoKCgcOKTmprK2LFjSU9Pp2/fvpx22mkUFBQQCoXYsGEDy5cvJxgM4nA4MJlMhMNhzGYzWq1W7B9HIhE8Hg/t7e3k5OSwfv16vv32W7GXnJaWRkZGBjk5OZxxxhn069cPjUYj2qAY5N5DMcS9hOzKMRgMTJ48ma1bt2I0GtHpdJhMJsxmM2q1mkQiQW1tLbFYjHg8jkajEatWjUZDbW0t0WiUrKwsmpub2blzJ7FYLOl9/H4/e/bsIR6Pi0AvOdnfbDazZ88ehg4dmrSvrKCgcOKyb87w6aefTl5eHunp6SINKRwOk5OTw4gRI1Cr1XzyySeYTCaCwSCBQIBoNEogEMBisRCNRvF6vYRCIdLS0qisrGTFihWUl5dTWloK7C0B29LSQlZWFlarlbPOOktZFR8jlBG5l5CNYWlpKXq9nurqaiRJIhAIEAgEklbMcjCXbJglSUKj0Qghj82bN7Nx40ZhbGVj3fX6zs5O2traMJlMWK1W8ZrVaqW9vR2Xy4XT6QSUma2CwolMVyMMYLFYOO2008QkPhAIsG3bNtRqtVDIysnJIRQKsWnTJvx+P83NzUJDwGg0Eg6HMRgM6HQ6tm3bxscff0w8HhclHlUqFdu2bWPlypUiQ2PQoEHk5eUp48UxQDHEvYRsXMeMGUNNTY2QtozFYgQCATo7O4nFYkIZq6u83L7u41AoRF1dHfF4POl1OXhCTmXwer1J6VBy6lMsFiMcDjNs2LDj8VUoKCgcBvumPjqdTpxOp3Azb926le3bt6PT6aioqGDNmjVs376dzs5OKioqaG1tJRwOo9Vq8fv9VFZW4nK5sFgshEIhdu/eTVFREXl5eaSlpWGxWES95V27dlFfX4/dbsdkMlFWVoZOp0tqj8LRRzHEvYjVaqW4uJhAIIDJZBJRz/LKFxDasF2R93S6alTLRrhrsv2+OtZyyoFGoxE5yXIaQ0tLC4MHD07qVAoKCiceXT1dsFdtz2AwYDQaCQQCVFVV4XA4aGhoIDs7m7KyMgYNGoTT6RSKe/F4nGAwSH19PfX19dTW1lJdXY3X6yUrKwtJkggGg+h0OgYMGEDfvn0JBoOEw2GqqqrE3nBRUZEyZhwDFEPcS6hUKrKzs9HpdAQCAbGSldMLzGYzer2eYDCYpAfddTYsdwZJkkQRCBnZCHd9GAwGYaB1Oh1GoxGTyYRWq6WzsxOHw4HD4Th2X4KCgsJ3Rh4T5PiRcDgsUpbkwKxBgwah0Wior69HrVaLqOpQKCREglwuF3q9nkQigdPpJD09nUQiQTAYpKOjg4qKCnJycrDZbEQiETo6OlCr1SIdSqH3UfKIewmVSkVhYaEIrJLlKWUDqdFohD60XGHJaDTi9/tJJBKkpaVRVlaGWq1m165duN1ucd+u77GvAZdXzLJhDofDxONx1Go1kiRht9tpamo6xt+GgoLC4SJ7u2SFvYaGBkwmE5FIBK/XS15eHt9++y1qtZpIJEJaWhrhcBi1Wk1eXh4Gg4Hs7GyKi4tpamqiqqoKp9NJS0uLSJ2MxWIiytrlcuF2u+no6KCxsVEEeylZFr2PYoh7CZVKhcvlwufzEY/HhWKWPKPVarVEIhFUKpWIpu7Tpw+VlZVYrVYyMjJE+oC8t+x2u0WCflepStkIy0EW4XBYlESTg8BCoRCRSASbzabs8ygonATIE2p5q0oO1GpvbycUCmGz2URAFoBer8diseD1ehkwYACRSIS6ujoikQi5ubkUFhaSlpbG5MmTWbt2Lbt376ahoUGsnFNSUjAYDNTV1dHU1IRKpSIcDiuG+BigGOJeQq1Wk5GRQTAYRKvVEgqFMJvNmEwmdDqdkLCUj9lsNuFGOv3007FarYTDYaF2IxtwOehLXg3L/wfEHnEoFBLubr1eL86V95kUhRwFhZMDlUqF1WolEomQmppKVVUVTU1NZGdnEwgEMBgMxGIxCgoKUKvVNDc3CylLSZJE1kQkEsFut+Pz+fjiiy+w2+2YzWYyMzOpq6sTRWNsNhttbW1YrVZ8Pp/Yb1boXRRD3EvodDoyMjJobW0VQVaymhb8LyBLNrRqtZoJEyawevVqcnJySE9PZ/Xq1dTV1WEymYSLu6fVsByoJRd8SElJIRKJiJzCWCwm3q+rhqyCgsKJj9FoJB6PY7FY2LNnD0ajkczMTCKRCJFIhMzMTOLxOO3t7WJ82b17NwMHDqSgoIDm5mZaWloIBAJ4vV7a2tqQJAmHwyEm6X6/n4aGBgwGA4FAgNbWVjQaTZI6l0LvoQRr9RImkwmj0UhzczOwVwtaTiuS92vlKOpAIEBlZSXLli0jLS0NlUqF1+slPz9fzEhTU1OB/0lndtWxhr2dVS6dKMtohsNhsQe9b26igoLCyYHFYqGpqQmPx0N9fb0omWo2m0lNTcVsNhMOh/H7/dTW1rJt2zYKCwspLS0Vq2m32000GqW1tZXm5mbcbrcI6nQ6nUlZFwaDgaamJrxeLy6XSwnYOgYoy6NeQpaMk/d5gsEgaWlpwiUdjUaRJImOjg78fj/xeJzly5eTnZ3NjBkzaG1tFUEaXq8Xj8cj7t01tUn+vxxVHY/HRRCGPIuWtaq7djYFBYUTH5VKRWZmJhaLhcrKSjo7O1Gr1TQ1NaHVatHr9Xi9Xjo6OoQL+vTTTyczM1NM+uW65XIAp1qtpqamhvb2djIyMrDb7cJgm81mVCoVzc3NpKamiviVUCh0vL+K7zXKqNxLZGZmEgwGiUajYo8lGo2KjiDv71ZVVSUFQzQ2NvLWW2/Rt29fEWFdX19PKBRKMrxAkotaVtPqGj1ttVpFylQ8HhducGWPWEHhxKVrv7bZbEiSRGtrK3V1dfj9flQqFQ0NDaICk5zna7PZKCoqoqioCEDEjxgMhqR7ywp71dXVVFZWEovFMJvNpKeni+hsp9NJJBIhGAxisVjw+XzH9ks4xVAMcS+Rnp4uZpPyClXuYHIglbxSlY2jLPTR2tpKa2uryP2Tr5XpGqQlG/GWlhaRsiBHT8PePWU5HzAWi4lyigoKCic+Op2Ozz//nEGDBtHR0SE8afF4XIj8qFQqioqKGDp0KKmpqcIwy+OKvM8rK3P5/X6ys7PJy8sTEpctLS0i1TEUCuHz+XA4HGzbto3Ozs7j+RWcEih7xL2IXBc4EomQkpKC0WgEEEpZcmqAbDT3TROQa4ra7XbhWpLZV3lLTmva97h8n0QiIUqnKYZYQeHkoK2tjUQigcvlEkFWsnytSqXCZrMxYsQIzjvvPPLy8jCZTOj1euH9kifi8laZXq8HwO/3o1arGT58OBMnTiQrK4vOzk78fj+wt4pbeno6hYWFSQFbijetd1AMcS8hlynsKuAhF+qWBTY6OztJJBJidSsHRXQNrLJYLJSVlQmXdteVbtfiD3IEpVarFcFZsmvJYDAINS+/3690JgWFkwSNRkNGRgbxeJyOjo6k46WlpcyaNYuxY8eKgg46nQ6NRoNWq0Wn04nSh5dccglnn3220LePRqNCWyAlJYUzzjgDl8slxiJ55S1Xe1LoXRRD3EvIijTp6elC4crr9Ypgra7pBnJQxb6qWRqNhqysLDGT7WpAu+4Tyw+5GERXXepwOEwsFiMtLU2kJigrYgWFEx+5D8sZGPJqVpbPHThwIG63WyhlycZYHkt8Ph/bt2/HbDZTUlIiDKzsFZNTIYPBoAgK66rA5/F4qKmpEeOJQu+h7BH3Eh0dHYTDYVQqlXAxy7rPkiTh9XppamoSRhT+V3u0q160HEAhJ+Z3zR+WZ69y59k3VUmOmg4GgyKdKRqNHrfvREFB4fCIRqM0NTXhdDopLCykra1NxJwkEgkxuff5fBQXF2MymWhtbaW9vZ2WlhZisRj9+vVjwYIFYiWsVquTYknkh8ViAfautocNGyYqOinKWr3P92pFPGvWLBFZPHjw4OPaFq/XSyQSISMjg9TUVOFKlg1oR0dHknyd/JD3dVUqlQi+8nq9pKenC4PbNQdZPl9Wxun6/m63WxSVSEtLQ5IksQd0vJg2bdoJ8zdSUDgUjva4olKpeOCBB8TzefPmoVKpqKysFMe6Zj84HA7cbjfDhg3DarV2S0FMJBKEw2G+/fZbPvnkE9asWcOePXtobW0lGAxSU1OD1+uloqJCaNqbzWZg72RdNuqBQACdTkdeXh5DhgwRGRf7tqk3WbduXdJ4+O677/b6e54IfK8MMYDL5WLBggU8+uijSceLiopQqVTcfvvt3a5Zvnz5fv/omzdv5pprriE3NxeDwUBOTg5XX301mzdv7nau3KFUKhXbt2/n6quv5r777mPhwoV0dnaKlbEkSQQCAaqrq9m6dSsVFRXdVrpykv0LL7zAa6+9RlNTk0hRklGpVLS3t7Nlyxb27NkjSi3KkZJyxPUrr7zCwoULCYVCh2SIKysrkzrDgR7y4PHwww9zySWXkJmZ2W2g6cpdd93FggUL6N+//0HboaBworC/cQX2GsJXXnmFyZMn43K5hKreeeedx9///vduZU4Ph64xJaWlpWRlZRGJROjs7CQQCACIDAy9Xi/0BCKRCKFQSMSIpKSkCC3q1NTUpLgS2LuVtmfPHiRJ4owzzkCtVtPS0kJVVVU38SBInpwc6DFr1iwAVq1axa233srw4cNFIFlPFBYWsmDBAu69997v/J2djHzvXNMWi4Vrrrlmv68///zz/PrXvyYnJ+eg93rvvfe48sorcTgc3HjjjaIow4svvsi7777Lm2++yfTp07td9+CDD/LEE08wYcIE/H4/y5Yto7m5mcsvv7zHgKxoNCrcx11LGu7evVvkAMsVVbZt25Y0M+3o6ECn0+H3+8XKWavVkpqaKjoj7J1Zy6v0g7ma0tPTWbBgQdKxuXPnUltby5/+9Kdu5wL89re/JSsri9NOO42PP/54v/ceP348AC+88EKSYL2CwonM/saVYDDI9OnT+fjjjznrrLOYPXs2mZmZuN1uPv30U2699Va+/vprXnzxxf3e+9prr2XmzJlJ+b4yiUSCrVu3MnDgQGw2G/3796e5uZlgMEhVVRUDBw4kFouJLSdZP0COUbFarWJirtVqMRqNYp9YDiaNRCLs3r0bv99PXl4egwcPZuvWrbS3t3dLnZT50Y9+xLnnniueV1RUcP/993PLLbcwbtw4cbykpASAhQsX8sILL1BeXk5xcTE7duzo8btIS0vjmmuuYfny5TzyyCP7/c6+b3zvDPGBGDRoENu3b+fRRx/lqaeeOuC5u3fv5tprr6W4uJjPPvtMGByAO++8k3HjxnHttdeyYcMGiouLk66dMmUKc+fOJSsrixkzZtDR0cHatWuprq6mqKhIRDADIufP4/GI2ay8qt2zZw95eXlUVVWhVqvJyckhEolQW1sroh6DwSDZ2dm0tbWxfft2UWs0kUiIkoqwV3KzoaFBFI84ED0NOm+++Sbt7e37neRUVFRQVFREa2tr0neloPB95q677uLjjz/mySef5M4770x67Re/+AU7d+5kyZIlB7yHvG21L/IWVCwWo7a2lrq6OgYPHsyePXuorq4mEAhQVVVFbm6u2P+V40YsFouQvDWbzbjdbhGjIhtXeausurqajo4OHA4HY8eOJRKJsHLlSrHiltvSlTPPPJMzzzxTPF+zZg33338/Z555Zo9jxE9+8hN+9atfYTKZuO222/ZriE9VTilDXFRUxOjRo3n++ee55557DrgqfuKJJwgEAvz973/vZlhcLhfPPfcc48eP5/HHH+dvf/tbt+slSWL37t0EAgFGjhzJ2rVr8Xq9QhlHFlwHsNvtQm5ONsKBQIBoNEpOTg5VVVXCgPbp0wen08mWLVtobW0VJcwAtm/fztixY4Wajl6vF51J7nByJObRRlbzUVA4VaipqeGFF17gggsu6GaEZUpLSyktLT3gfebNm8f1118vJrMysus4kUjg8XiYP38+v/jFLxg+fDihUIjOzk5WrFgBwMSJE1mzZg1tbW2YzWZGjBhBTk4Ozc3NLFu2DLfbjdls5qyzzhKGu6Ojg127dlFVVSXEPKqrq3G5XASDwaRA0iMlMzPzqNzn+8r3bo/4YPzmN78hFov1uNfTlQ8++ICioqIkN0tXzj77bIqKivjwww97fF2SJKqqqmhtbU1SupELe9vtdhF0kZKSQiwWIxgMCsPa0NBAVlaWSFmQIyRjsRgdHR0EAgE8Ho8wwkVFRXR0dNDQ0EA4HCYSiWA0GsnOzgb2utB2794t9m4UFBSOjEWLFhGPxw+4FfZdkQu2qNVqodC3YcMG5syZw+jRoznnnHOw2+3C7bx48WKMRiPDhg1Do9GwfPlyqqur+fzzz9FqtQwaNIhYLMbSpUtpampiw4YNfPPNN9TX1xOPxxkxYgTXXnst5eXl1NTU0NraKsSGlNSl3ueUM8TFxcVce+21PP/88zQ0NPR4jlzlZOjQoQe8V3l5ObW1td10WD0eD5Ik0dbWxn/+8x9eeeUVtFotGRkZwg1lMBiwWq3odDosFgtGo1HIypWWllJTU0NZWZkw4rKSjixrKSfj2+12HA4HgwYNwmq1smXLFsLhsAjmkA15Z2cnlZWVSqdSUDhKbNu2DaBbJHUkEhEyta2trbS1tR32vcPhMFqtFrPZjNFoxGKxYLfbqaysZOHChZx22mmce+65mEwm4vE4NpuNcDhMU1MTDocDSZL4/PPPycjIICsri2AwiMPhIBqNsmLFCpqamjCbzYwbN45HHnmEn/zkJ+Tl5REKhbBarcLVrXBsOOUMMewNLDrQqlg2rF3TgXpCft3r9SYdP/fcc+ns7KSjo4MPPvgAg8HAww8/TFZWlkgJ6CrIPmTIEPLz8/H5fJSWloqo5T59+gg3stFoFKvo7OxskQt45plnirSGfv36sWPHDuHWjkQijBw5EkCIxCuGWEHh6CD3e6vVmnR84cKFpKeni0dhYeFh3Vc2gPIkvKtmgEqlYuHChdTU1DB06FCcTic6nY7+/fuTkpIiJC271jpvbW0VSn/yfUeMGMFZZ53FBRdcgMlkYsuWLXzyySdUV1eLdsjnK/Q+p6QhllfFf//733tcFcsG9mAVR/ZnsJ955hnMZrPQfZVLGF5yySXEYjEikQg+n0+sctPS0pg4cSLxeJza2lr27NlDTk6OyDEGROlEWYC9sbGRwsJCDAYDwWCQ9vZ2UlJSCAQCNDQ0EAgEyMrKEvrWLS0tScn7CgoKR4bc7/ctijBmzBiWLFnCkiVLOO+88w77vrIBlAO4uvZbtVpNR0cH7777Lt9++60Q/bnwwgtxuVziIctcygIeNptN7NO6XC5Gjx7NmDFjaGpq4k9/+hNPPPEEK1eupK2tTUwwlHHi2HFKGmL4317xY4891u01u91OdnY2GzZsOOA9NmzYQG5uLikpKUnHR40aJTqCHDT16KOPEgwGGTJkSFLkshyIodfryc3NZfPmzTQ3N1NSUiJqCsvnyYUiqqqq8Pv97Nixg1deeYX58+czf/58li9fDkBtbS1Op5PJkyeL2bX8nsoesYLC0UHOhd+0aVPS8fT0dM4991zOPfdcEaPxXejJEMoGeefOnbz77rsiBbCoqIgLLriAkSNHCk+aLBCkVquxWCwUFBQAew1xeno6O3fu5Nlnn2Xbtm3CDW42m7FYLMoYcYw5ZQ1xSUkJ11xzDc8991yPq+KpU6dSUVHBF1980eP1n3/+OZWVlUydOvWA7yN3iPb2dpYtW8Z5551HWloa4XBYuJ0lScLj8VBUVER9fb1Qt7HZbOIcuTyZXq8X4h3jxo1j7NixXHDBBUyZMoVJkybRp08fdu/ezejRowHEZ+u6ElYk6xQUjpwpU6ag0Wh47bXXjup95T7ftZ/K7umuRRmam5vxeDx4PB6+/vprIpEIBQUFnHXWWZhMJhwOB2effTYTJkxg4sSJnHHGGcBel/qSJUv4+OOPaW5uRq/XJxWN6FpcRuHYcMoaYti7VxyNRnn88ce7vXb33XdjMpn40Y9+1C3Ywu128+Mf/xiz2czdd9/d4727upXk/VxZIeuGG24gGo0KlS29Xk80GiU9PZ1hw4Zx5plnEovFcLvdSftPKpWKYDBIdXU1+fn5ZGdnk5+fT1FREU6nk+zsbAoLC0UnTSQS/POf/0y6vquMpoKCwnenoKCAG264gUWLFvH000/3eM53MWjy2LFvzv++e8eylywUCvH++++zYMECXnzxRd577z1xbjgcpq6ujjVr1vDBBx8AezUSPv30U2pqakQbuz66lj38rp9B4fA4pfKI90VeFc+fP7/ba6WlpcyfP5+rr76aIUOGdFPWam1t5Y033hDKMfuyryycRqPB6/Xy7rvvcvXVV3PPPfewYsUKgsEg4XAYjUaD0Whk5MiRRCIRYrGYqJ4k30+SJCHmkZOTI+4dCAREWtOAAQNYv349//znP9Hr9XzzzTcAItJazlN+6KGHAJgwYQJjx4494u9ywYIFVFVVibzlzz77TLzHtddee9gBKwoKJwNPPvkkFRUV3H777bz55ptcfPHFZGRk0NrayooVK/jggw8oKys7rHuq1Wr0ej2RSEToPycSCSKRCGq1upvWtFxBSaPREAqF8Hq9hEIhmpub+eyzz7oZ0mg0SjgcRpIksY8sT9Bl7el9pXSP1BhXVVUJtb41a9YAiPGhsLCQa6+99ojuf7JzShti2LsqfvXVV3uMELz88svp378/f/jDH4TxdTqdTJw4kXvvvfeAAvCyW6lr/WC1Ws2CBQs499xz8Xg8QgVry5YtmEwmXC4XarVaBF1Fo1Fyc3OBvZ1To9FQXV2NRqMhMzMTu91OIBDA7Xbj8Xg466yzmDhxIjt27GD58uV0dHSImbEsaSdz3333AfD73//+qBjiF198kU8//VQ8X7ZsGcuWLQNg7NixiiFW+F5iNpv56KOPWLBgAQsWLODxxx/H6/WSmprK0KFD+etf/8p111132PeV64/LMrVyFLTRaOzRMMr9uyeVLvn8rqVT5fFJDuaUvXOyEteBVLW+CxUVFWLMkZGfjx8//pQ3xCrpe+R3mDVrFkuXLmXt2rVCb1nhxMLn8xEOh7n00kvxeDzdAl0UFE40lHHl2CHXaV+xYgXTpk3jnXfe4bLLLjvezep1vnebhTU1NaSnpx+VVZ7C0efaa68lPT2dL7/88ng3RUHhkFHGlWPDxo0bSU9PZ9q0ace7KceU79WKeMuWLdTX1wN7k+zlKEGFE4cNGzbQ3NwMKH8jhZMDZVw5dnR2drJy5UrxvLy8nIyMjOPYomPD98oQKygoKCgonGx871zTCgoKCgoKJxOKIVZQUFBQUDiOKIZYQUFBQUHhOKIYYgUFBQUFhePIKS/o0VsYDIajch+tVpsk4B4KhY5Kgr2s2KWgoHBicaQFF+RCD3Lt8q73lPWqjwQlvvfooxji48T+Otu+0nL5+floNBoMBgNarZbq6mq8Xm+3zrC/zqF0GgWFUwObzUZOTg55eXk4nU4xiZcVusLhMB0dHbS0tFBTUyPKsyocf5T0pV5i3xXxgWa5+3vNZDKRl5dHR0cHFosFjUZDNBqltrZ2v3WFD/Tn7PpKOBQ68AdQUFA4LhzuithkMjFw4EAGDRpEW1sbfr8fj8dDNBrFYrEQiUTw+/0YDAYcDgfFxcWoVCq+/fZbNm/e3K3Iw8FQTMbRRzHEvYSs4Sqz70oX9upPyy5nedXbtfKKXOC7ra0Nk8lEIpEgKyuLuro62tvbRTUlWTu2q5Z01z9rT3/ikGKIFRROSA7HEOfn53PBBRfQ0tLChg0biMVi6PV64YKORqO0tbUJLemUlBQsFgsGg4GBAwei0Wj48ssvaWlpOeT3VEzG0UcxxL2ELM7eFfn5vsbXbreTlpaGyWTC6XQSCoWor68nGo0Sj8cJh8MYDAaCwSB2ux2dTkcsFhOat/F4nJaWFtxuN9FolFgsllR7eF/Rd5VKlSTqrqCgcOJwqIY4JyeHO+64g3Xr1vHFF1+Qm5tLeno6Go2GjIwMDAYDbreb6upqUlNT8fv9VFdXE4lEiMfjRCIRTjvtNAYPHsybb77ZY132nlBMxtFHMcS9xL6GuCejbLFYyMnJoX///sTjcdxuN3a7HYPBQEVFBZ2dnahUKjo7OzEajRiNRmKxGE6nkyFDhtDZ2UldXR0ul4v09HTWrFnDjh07CIfDJBKJbqtiSZKEUQ4Gg8fsu1BQUDh0DsUQWywWbrjhBjZs2MDu3bs5/fTTGTRoEEajkVAoJGqgB4NBYrEYVquVUChEW1sbO3fuxOfz0dzcTCAQYMiQIQwcOJD58+fT2dl50PdWTMbRRwnW6kVkwyf/HxDuZIfDQV5eHqWlpQwbNoz6+npMJpPY09FoNPj9foxGIxqNhlgsRjweJ5FIYLFYSE1NJRaLodFo0Ov1lJSUYLfb0Wq1rF+/nng8fth7yAoKCic+KpWK8ePH09nZSU1NDRdeeCE5OTlotVpSUlJQqVRoNBrcbjeJREKMIVqtlszMTFQqFbFYjFAoxPr169m4cSM6nY4JEyawcOHCI46qVjh8lDziY4xGo6G0tJSzzjoLs9lMW1sbgUAAu90uXEuBQIC2tjYAgsGgiHyMRqNEIhHcbjcmkwmj0Uh+fj4FBQWo1WrS0tLIy8ujpKQErVabNLOWV8T7C/JSUFA4OXC5XAwbNozVq1czbtw4+vXrh81mw+FwoNFokgK0EokEBoOBSCSCwWBAr9djs9lITU0lLy+PkSNH4nQ62bhxI3379qWgoOB4f7xTEsUQ9zJdjZ5araZv376MHz+eUChEWloaQ4YMoampSRjaVatWsWrVKnw+H7FYTKyE4/E4wWAQv99PTU0NX375JW1tbWRnZ+N0OlGpVKxdu5ampibKy8vp27dvt9W4goLCyc+oUaNobm7GZDJRUFBAOBxGq9Wi0WgwmUyoVCqi0ajwloVCIbElJW+JWa1WzGYzBQUFjBgxglAoxI4dO5g4caIIGFU4diiu6WOESqXC6XRy1llnAaDX62lvb+frr78mkUiwbds24vE4VVVV+P1+cY1arRar21gsRjgcRqPRsGPHDsrKyigvL6elpYVvv/0WvV5PJBLBbDYzadIkmpqacLvdSZ1QMcoKCicvJpOJ8vJyPv30U/r06UNWVhaxWAy/349KpcJkMhEMBuns7CQej4ttLY1GQyKREEIfgHBhFxYW4nK52L59O0OHDiUnJ4eamprj/ElPLZQVcS8j/+h1Oh1nnXUWffv2JSUlhaysLLxer8jhk93OkUiEUChELBYD9ibpyznJsVgMi8WCWq0WK+PVq1fz9ddf4/V68Xg8mEwmBg8ezJgxYxg9ejQ6nU7sS3dtj4KCwslHaWkpkiRhNBqxWCyEw2Gi0ShpaWniHHns0Ov16HS6bhkT8n6xPC7IqUx6vZ5NmzZx+umno1YrpuFYonzbx4j8/HzGjx+PwWDA6XTS2tpKPB5PWqVmZGQwYcIE+vTpg06nQ6fTievlIK1oNCpmvunp6TgcDhFcIRv1zMxMBgwYwCWXXEJ+fn6SRGZXo6ygoHDyoNVqGTFiBPX19SQSCZqamgiFQgQCAWKxGDabTYwNBoNB6BLIOgPAfj1jGRkZ6PV6rFYr2dnZuFyu4/ERT1kUQ9yLyAZPr9dz5plnotPp8Hg8hEIhPB4PNpsNSZJETnDfvn0pKSlh+PDhjBw5EofDQSAQoLW1VbiknU4np59+OhdeeCElJSU4HA4ikQiSJKHX64lGo2zatAm9Xs+oUaO4+OKLMRqNYoarGGEFhZOTgoICcnNzMRqNuN1uoTGQSCTw+XxJGgIGg0EY3UQiIQK3ukZEy6/F43GsVquIQ/H5fJx++unH8ZOeeiiGuJeR9aLPPPNM3G43W7dupba2VnSCcDgsXEmSJOHxeKitrWX37t3U1dXhdrvx+/10dnbS3t5OOBzG6/WyatUqqquraW5uJiUlRXRIs9lMR0cHbrebtLQ0LrjgAgoLC0V7uqZUKSgonByo1WpGjx5NW1sb7e3tBINBXC4XjY2N6PV6sQfc2dkp9AfC4TCSJImtLL/fjyRJIrVRNsySJGG1WrHZbHi9XrxeL3379sVsNh/vj33KoBjiXkaj0VBeXk5+fj5+v5+qqipisRhqtZpYLEZnZyexWIzm5mZWr17Nhg0bRKfpKrohz369Xi/t7e1CyH379u0iKjIcDhMIBHA4HDQ3NxMKhcjIyOD000/vlsqkoKBw8mCxWCgrK6OlpYWdO3eSlZVFQUEBDocDSZIwmUxEo1FxviRJ6HQ6oaxnMpkIBAL4fD5hrNva2ujs7ESv1yNJEpmZmVRXV5NIJLBarWRmZh7HT3xqoRjiXqKrW3ro0KHYbDYKCwtFlKO8urXb7aSkpGC327HZbOj1erRabbfZqLzHazabhZs5EolgNBrR6XRYrVaMRiORSIRgMEh7e7sw8l1LoClGWEHh5MNmswlZXI/Hw/Dhw9Hr9ahUKrRarYg3ATCbzWLVG4vFRC6xXDhGHgNMJhN6vZ5AIIDf7ycjIwO/3084HMbv9yuG+BiiGOJjgJxsbzKZKCoqIhAIIEkSTqeTjIwMnE4nKSkpGAwGotEo4XCYjIwM0tPTRXCVVqsVBtdqtQpBD61WKxL15XN27dpFfX09Ho8HSZJob28XM2MFBYWTD7lAjBxPkpOTQzwex+fzCSOs1WqxWCxCDlfu8/J4IacyxWIxDAaDyMiQPXRpaWlkZmaKfWSr1XqcP/Wpg5JH3EvIe7GhUIjPP/8cq9VKe3s7tbW1NDU1iQAruYOFQiHC4TDBYJBoNEqfPn3QarW43W5gb1nF0tJS8vLy2Lp1KykpKcDevSOr1SqS+LVaLQ0NDWzfvp2+fftSW1ubNAtWUFA4+UgkEqKWcEFBgSja4PF4cDgcWCwW4TUDRAS13+/HbDaL6kuhUIiOjg4RlyIb6HA4TEpKCuXl5WzduhWHw5FUzU2hd1FWxL2ELEupVquprq4mKyuLtLQ09Hq9iFIMBoOieHcwGKS5uRlJksjPzxcuZ5fLRUpKCrm5uWRmZhKPx0X+oCzwkUgkiEQiBAIBkcwvB2io1WpSUlKUqGkFhZMYjUZDPB6nra1NSFlmZGSQkZGBWq1GkiS02r3rKlmTXhbwkDXsTSYTdrudjIwMkeoou68NBgNqtZqsrCxhqPct5arQeygr4l6iq66zHJSlUqmora1lx44dZGVlAVBZWYlOpyMajeL1enE4HGRlZdHY2EhjYyM6nU7sC1VUVJCZmUlRURF1dXWiPGI0GiUlJQWNRkMoFMJoNLJr1y6Ki4sJBoOEw2FF7lJB4SQmOzsbvV5PZ2cnKSkptLe3k5KSgtPpFNoCgHAry8ZY9rjpdDpRp1ij0dDZ2ZlkrGVBEKfTiVqtJhAIkJ+fj8FgIBwOH7fPfaqgGOJeoquSjVarpaqqisbGRoLBIEajkaamJrFiTU1NJZFIEIvF0Ol0+Hw+9uzZg91up6ioCJVKRTwep6GhgerqarHXU1dXJ9zSoVBIdLR4PE57ezvbtm2juLhYBGnIUZWKMVZQOHmQUyCNRiMpKSlYrVbhRTObzdjt9iStgK769PKWlRxBrdFo0Ol0Qq1PjriW847D4TBms5nGxkZSU1OF902hd1EMcS8jSRJut5tPPvmEIUOGCBEPrVYrSpTJxjkjI4OOjg4aGxvFfo3sHkokEthsNiKRCDt27BD7vm1tbUSjUZHqJEc6yso7sVhMdNZQKKQYYQWFkwzZvRyPx3G5XGIiLnvDUlNTMRgMIphLzhWWUxplmcuuMrdms1nEqVgsFqHQFQgEMJvNhMNh+vTpoxSAOEYohvgYUFtbK+qEykpZXWUptVotfr8fu91OZmYmkiRRVFSE2WwW7m05F3DQoEGsWbOGUChEamqq6EByDrHX6xU6tHIaU2trq5gBKygonFzI21tyfIm8upXdz4BQ1ZJdyXKlNtgb6JlIJMT1Go0Gu90uPGSyboEcAKbVaoXuPaAUizkGKIb4GOByucjLy2PZsmVs3bpV7Muo1WocDgd5eXlC4MPhcJCZmYlOpxMdTTbGarUai8XCwIEDaW5uprW1Fa/XSzAYFLPgWCxGNBolFAphtVqx2+3A/3SoFRQUTi4SiYQQ6pHFfOTtLIvFgk6no6amhng8LgJC/X4/9fX1aDQa2tvbSUtLw263i5WzbJj9fj+hUEiskNVqNZmZmWJMkVfYCr2LYoh7GYvFQl5eHsFgkN27dwsRDpfLRTQaxeVyiUAMWeBDrVaLGa/X6xXuJbnyksPhEEUhZLUtOVJalroLh8PU1dWRk5ODwWCgX79+tLa2JqnvKCgonBzI8SCJRILGxkZKSkpEEJZGo6G1tZVQKITNZhNR0oFAgJqaGpGJMWzYMFQqFT6fTwRoyfvF8kRdHlPy8/Pp7OxUJvDHCCV9qRdRq9X069dPuJvhfwpZJpMJtVot0pBsNhsFBQXCHQ0QDAapqqrC7XZTUVFBc3OzuIfFYsFsNotkfIvFQmpqKgUFBWRnZ2M2mwkGgyQSCfLz8yktLcVmsx2370Lh1GbWrFlCnGbw4MHHuzn7ZcKECUyYMKFX36OyshKVSsW8efMO+Zqamho8Hg9Dhgxh3bp1QqpSHivy8vLEZD4QCKDX6zEajSJwCxAxKXJQlqxjoNfrRcBWNBqltraWkpIStm/f/r0L1FKpVDzwwAO9cu9169aJ37hKpeLdd9895GtPGUN8tAeCff+g8+bNQ6VSUVlZKY5JkoTP5xMrYDnaUc7RKygooLi4WIhzdN0Pll3RckmyzMxM9Ho9gJCvczqd9O3bF5PJJKKu5chrOYrSZDKJvelj1amO5Aep8P3F5XKxYMECHn300aTjcmbAueee2+N1zz//vPgtrVmz5lg09YTD7XazevVqzjjjDCoqKkTao1wCMTMzk9zcXMxms1DSysrKErnGGRkZxGIxQqGQqEcs5x3LBlhOkYS90rxbt24Vwh9d6dq3930UFRUB0NDQwD333MPEiROFuMjy5cv3+/ni8Tgvv/wyEyZMwOFwYDAYKCoq4vrrrz/o31ye2MgPjUZDQUEB06dPZ926dd/p+/4uFBYWsmDBAu69997DvvaUMcSw/4EA9s4WX3nlFSZPnozL5UKn05GRkcF5553H3//+9+9kxCRJoqKigi1btmCz2Rg4cCBms1lEL5pMJgBRpFve45UDMLr+uOSOI8+A5X+zs7PJyckRqQter1dUbJJlL4PBIN988w2BQCCpfV0nJwd6zJo1C4BVq1Zx6623Mnz4cHQ63X7FQY7kB6nw/cVisXDNNdcwderUbq8ZjUaWLVtGY2Njt9dee+21YyYusXjxYhYvXtyr71FYWEgwGOTaa6895Gui0SirV68mEAiQk5PDzp07xUpXLvqSkZEhxhSVSoXdbic9PZ2UlBQcDkdSHrHNZiORSBCNRmlpaRHBWmvXrqWgoID169dTW1vbY1sWLFjAggULMBgMjBs3TjxfsGABTz75JADbt2/nscceo66ujiFDhhzwswWDQaZOncoNN9yAJEnce++9PPvss/y///f/+Oqrrxg1atR+29KVK6+8kgULFvDSSy9x1VVXsXTpUs4444xjZozT0tK45pprmDx58mFfe0rtEcsDwb4Eg0GmT5/Oxx9/zFlnncXs2bPJzMzE7Xbz6aefcuutt/L111/z4osv7vfe1157LTNnzuwWnRyLxaipqWH06NFMnDiReDzOli1b6OzsJC0tTcjUya5mnU4H/K96ipz/K6ccyMFb8h6ynNLgdrtFulIoFBLBGXl5eTQ3N+Pz+bq1+Uc/+lHSKqSiooL777+fW265hXHjxonjJSUlACxcuJAXXniB8vJyiouL2bFjR4/fhfyDXL58OY888sgB/iIKCnsZM2YMq1ev5q233uLOO+8Ux2tra/n888+ZPn06//jHP3q9HbLXqTdRqVTfaWJRX1/Pjh07mDJlCm+++SaBQIBoNIrBYMBoNApDKxeBMBqNZGdn4/V6RdaFvJUlR1DL7up4PE5lZSUtLS2cd955fPjhhyLqel/kMfTHP/4xxcXFPY6pw4cPFypg7777Lpdffvl+P9fdd9/NRx99xJ/+9Cd+9rOfJb02Z84c/vSnPx3S93P66acntWXMmDFccsklPPvsszz33HOHdI/jxSm1It4fd911Fx9//DFPPvkkK1as4J577uH666/nF7/4Bf/+97/ZunUrw4cPP+A9NBoNRqOxx1ViIBBg586dWK1WpkyZQt++fYlEImJ/OBAI4PF4aG9vx+v14vP5CAaDBAIBmpqa6OzsFLVEAaGIE4/HhaupoKCArKwsDAYDJpMJm83G+PHj0el0fPHFFzQ2NnaLfjzzzDO55pprxGPKlCk9Hj/zzDMB+MlPfoLH42HNmjXfadanoLA/jEYjM2bM4PXXX086/sYbb5CWlsb555/f43Xbtm3jsssuw+FwYDQaGTFiBP/+97+TzpG3jVasWMHPf/5z0tPTsVgsTJ8+nZaWlqRz990jXr58OSqVirfffpuHH36YvLw8jEYj55xzDrt27erWnmeeeYbi4mJMJhOjRo3i888/73bP/e0RL126NGkCvC9+v5+PP/6Y3/3ud6SlpfHMM8/w8MMP8+CDD/LPf/6TeDyOx+PhySef5Pbbb+c3v/kNq1evxul0otfrCYVCRCIRPv74Yx566CHuvPNO7rrrLp5++mmqq6vZuXMngwcPRqVS0dTUtN92HApyoZuDUVtby3PPPcfkyZO7GWHYO67Onj2bvLy8w27DpEmTAIS7vSdmzZol3OldeeCBB7qN5UuWLGHs2LGkpqZitVopKys7al6/U2pF3BM1NTW88MILXHDBBUkz8a6UlpZSWlp6wPvMmzeP66+/noqKiqQ/rJybt3TpUr799ltGjx5NSUmJKM5gMBjYvXs3sViM008/nc2bN9PR0YHJZGL48OE4nU4aGxvZunUrXq8Xq9XKGWecQVZWlqiu0t7ezsaNG2lubhZylhaLhcLCQj7//POkvOUjQSmLptCbXHXVVZx33nns3r1beGFef/11LrvsMuEp6srmzZsZM2YMubm53HPPPVgsFt5++22mTZvGP/7xD6ZPn550/u23305aWhpz5syhsrKSJ598kttuu4233nrroG179NFHUavVzJ49G4/Hw+OPP87VV1/N119/Lc559tlnue222xg3bhx33XUXlZWVTJs2jbS0tIMakk8++YQpU6ZQXFy833MkScLr9QJ7x61IJEKfPn0A+M9//oPFYuHTTz+lrKyMadOm8fXXX7No0SJycnIYMGCA8IqtWLGCkSNHcuaZZxIMBvnyyy956aWXKCgo4LrrrmPFihXHLLti0aJFxGKxw3LTHyq7d+8G9sp2HimbN29m6tSplJeX8+CDD2IwGNi1axcrVqw44nuDYohZtGgR8Xi8R/fKkSAB8f8/YlGtVpOTk8OoUaP417/+Rd++fbnxxhtZtmwZu3fvJhqNEolE+Prrr8nOziYrK4vq6mo+++wzSkpKqK6upqSkhP79+7Nu3Tr++9//MnnyZOG6bmhooL29ndTUVABRoenhhx8WhSIUFE50Jk2aRFZWFm+88Qa//e1v2bp1K+vWrePPf/4ze/bs6Xb+nXfeSUFBAatXrxZbQrfeeitjx47lV7/6VTdD7HQ6Wbx4cZIu81NPPYXH4xH59vsjFAqxbt064bpOS0vjzjvvZNOmTQwePJhIJMJ9993HyJEjWbp0qQiEKi8vZ9asWQc1xHfffTcOh4OvvvrqgIZDVtDr168fY8aM4Z133mHcuHE0Njby9ttvM336dKZMmUIgEKBv37489thjfPPNN/Tp00doEzzyyCNCdUutVlNaWsqTTz4p4lf27Nkj3qe32bp1K8BB95EPBVksKR6Ps23bNu666y6AA7rFD5UlS5YQiURYtGgRLpfriO+3L6e8a3rbtm0A3SKpI5EIra2t4tHW1nZ4N+4iM6fVakVu3m233cb27dvx+/3ceOONnH766ajVaqLRKPn5+fTp04fs7GwGDx6MJEns2rWLPn36UFBQQJ8+fRg5ciSxWIw9e/bg9/uFCPygQYPIzc1l1qxZ/P73v+eee+4RaU/7uqSV9HyFExGNRsMVV1zBG2+8AewN0srPz+/RXet2u1m6dClXXHEFPp8vqZ+ef/757Ny5k7q6uqRrbrnlliR347hx44jH41RVVR20bddff33S/rHcJnmCsGbNGtra2rj55puFEQa4+uqrRUGF/dHQ0MC6deuYNWvWQd258qTabrdTVlbGhRdeyMaNG3E6nUiSxIgRI0TAp9lsxuVy4fV6RTqTbORDoRAej4eamhq++OILXC4XPp+Pr776itra2mO2IpZX+EcjtXLOnDmkp6eTlZXFhAkT2L17N4899hgzZsw44nvLi5x//etfR8W7uC+n/IpY/iHsWwR74cKFSTNqi8VCZ2fnId9X/mPJ1U98Ph9vvvkmp512GmazmW+++YYf/OAHXHjhhSxfvpxAIIDNZiMajaLRaESdUL1eL9zics1QgI6ODqERq9VqSU9PZ+rUqfTv35/q6mq2bNmSVKNYRjHCCicyV111FU899RTr16/n9ddfZ+bMmT3GXezatQtJkrjvvvu47777erxXc3Mzubm54nlBQUHS67KBbG9vP2i7DnatbMz79u2bdJ5Wq+1xD7Ir8rVlZWUHbYfM2rVrSU9P5+yzz8bv9/P222+j1WrR6XRCQ1qSJPR6vdCPlusOf/bZZ3z55Ze0tLQkecscDgebNm0SegXHAnk86ymY9HC55ZZbuPzyy4Xy2KBBg46atO8Pf/hDXnjhBW666SbuuecezjnnHGbMmMFll10mMlaOhFPeEMszsX2N7JgxY1iyZAkATzzxxGHvBcirUPmPJEkSHo+HL774AoD169eTmprK4MGDsdlsYsXs8/no6OggFAqhVqvRarVUVFSg1WpJTU0V95NnrC6Xi/T0dBoaGnjooYdwu91JK2BFnk7hZEKOofjZz35GRUUFV111VY/nyRPd2bNn7zeQa1+juL8CBofSR47k2t6gtraWpUuXotfrGTlyJB9//DFerxePx0N6errIrJA1CeTYkTVr1vDee+9RUlIiCtCcddZZrFy5kurqampra4+piEf//v0B2LhxI8OGDTuie5WWlu43F31/7C8Fc9/tPJPJxGeffcayZcv48MMP+eijj3jrrbeYNGkSixcvPuLiGKe8a1r+IWzatCnpeHp6Oueeey7nnnsu2dnZR+W9ZO3WYDBIc3Mz8+bNw+l0YrPZsFqtXHrppYwZM4bBgwfTv39/tFotZrNZqGXp9Xrxw7HZbPTv35/+/ftTVVXFV199Rf/+/SktLcVkMvUY3PL/N2LvQ0HhBOXKK69k+fLlDBgwYL+DsxzUpNPpRD/d93EsleQKCwsBukVSx2KxJJGfA127ffv2Q36/RCJBZWUl77//Pl9//TVOp1NEd8vKW3V1dUQiEaFVHQ6HWb9+vZDKtNls/PCHP6S0tJSmpiaRU3wsmTJlChqNhldfffWYvq9MWloaHR0d3Y73tF2hVqs555xz+OMf/8iWLVt4+OGHWbp0KcuWLTvidpzyhlj+Ibz22mu9cv999xNkl1E0GuXFF1/kb3/7myhFNnHiRKZNm8aVV17JlVdeidlsxuFwMGrUKEaMGMHo0aPFjC8/P58zzjiD8ePHs2XLFsrKymhra6O2tlbMhBUUTkZuuukm5syZw9y5c/d7TkZGBhMmTOC5556joaGh2+vH2qCMGDECp9PJ888/n6RG9dprrx3U9Z2dnc2wYcOYP39+j0Zhf8g1yt977z1aWlrQaDSMGjWKL7/8kgULFrBo0SJaW1vx+XwsXbqU//znP9TV1REOhyktLeUHP/gBnZ2dvPTSS3g8HqHYdyzJz8/n5ptvZvHixfzlL3/p9noikWDu3LmHJOjxXSgpKcHj8bBhwwZxrKGhgffffz/pPLfb3e1aeZJ4NDwIp7xruqCggBtuuIHnn3+ep59+mttuu63bOd/lx6lWq4XghlqtFipVcidVq9U0Nzfz4osvEolE0Ol0VFZWUlRURFFRkahRnJmZyYUXXohKpRJSdPfeey+ZmZmUlpayadMmotFot1n3fiOlFQOtcIJTWFh4SHrAzzzzDGPHjmXIkCHcfPPNFBcX09TUJAKO1q9f3/uN/f/R6/U88MAD3H777UyaNIkrrriCyspK5s2bJwo0HIgnnniCKVOmiJz9Q0Wud97R0SHqnF988cU0NTWJbAo58NRqtVJSUsI333xDVVUVr7zyClVVVYRCoSP56PvloYceAvam/sBeRS55a+63v/2tOG/u3Lns3r2bO+64g/fee4+pU6eSlpZGdXU177zzDtu2bWPmzJm90saZM2eKCPs77riDQCDAs88+S79+/Vi7dq0478EHH+Szzz7joosuorCwkObmZv7617+Sl5fH2LFjj7gdp7whBnjyySepqKjg9ttv58033+Tiiy8mIyOD1tZWVqxYwQcffHBYgRTwv+IOsoycRqNJKtgtP/d6vUQiEVQqFffccw/Dhw9nxIgRZGVlEYvFCIfDeDwezGYz0WhUyFTW1NTw7LPPsmnTJiRJEsIecomzrivxozXHraqqYsGCBQBC/1XubIWFhb2SC6igsD8GDhzImjVr+N3vfse8efNoa2sjIyOD0047jfvvv/+Yt+e2225DkiTmzp3L7NmzGTp0KP/+97+54447Dqqkde655/LRRx8xZ86c7/Tecn9//fXXSU1NJSMjg/z8fGw2G/F4nBtvvJHW1lYqKyupra1lx44dvb763TeI7qWXXhL/72qIzWYzixYtYt68ecyfP5/f//73Qspz0qRJvPbaa0lBd0cTp9PJ+++/z89//nN++ctf0qdPH/7whz+wc+fOJEN8ySWXUFlZyUsvvURraysul4vx48fzu9/97qCpb4eEdIpw3XXXSYWFhft9PRaLSS+//LI0adIkyeFwSFqtVnK5XNI555wj/e1vf5OCwWDS+YA0Z84c8fzll1+WAKmioiLpvKefflrq37+/pNPppMzMTOknP/mJ1N7ennTO+PHjpUGDBnVrU2FhoXTRRRd1Ow5IP/3pT8Xz9vZ26frrr5dcLpdktVql888/X9q2bZtUWFgoXXfddfv9zPuyevVqCZBefvnlHl9ftmyZxF673u0xfvz4/Z7/zjvvHHIbFL6fXHfddVJ+fr7U0tLS7ff/fSYej0sOh0O66aabjndTFHqZWCwmtbS0SP/85z8Pe9w7pQzxqTgQHA+O5Aep8P3kuuuuE5O2niad3weCwaCUSCSSjskT9FdfffU4tUrhWPHtt98mLU4OZ9w7pVzTNTU1pKenM2jQoG5R0gpHj40bN3Laaacd72YonED88pe/FOp1++bsf19YuXIld911F5dffjlOp5O1a9fy4osvMnjw4KOi7qRwYtO3b1+R8gp7VdUOFZUknRq5LFu2bKG+vh5A6DUr9A6dnZ2sXLlSPC8vLycjI+M4tkhBofeprKzkjjvuYNWqVbjdbhwOBxdeeCGPPvqo8vtXOCCnjCFWUFBQUFA4ETnl84gVFBQUFBSOJ4ohVlBQUFBQOI4ohlhBQUFBQeE4ckpFTR9LZOH1roo6sroWICqEyHrRRqORqqoqGhsbkSQJq9WK1WoVkpgdHR1J0nk6nY7MzEzS09MBqKioYMeOHTQ0NBCNRkWyfk8hAJIk0dra2psfX0FB4Tui0+lEv+06fsjHNBoNNpsNk8mE0WhEr9cLvfrq6mq0Wi0Oh4NIJEJnZycej4dRo0aRlZVFW1sbjY2NWCwW+vTpQ05ODq2trVRVVbFy5Ura2trEmLPv+8scqxKJpxKKIT5G7GuEU1JS6NevH+Xl5ahUKrZu3UowGMRqtWIymbBYLAQCAeLxODabTVRKkasv+f1+vF4vgUCAoqIiysvLsdlsfPvtt9TV1QljLFdf2bctCgoKJw9y39doNGRmZtK3b1+houdwOEhLS6OtrY1YLCYU/GKxmCiDajQaRZU3k8lEJBJBo9HQ0dFBQ0MD/fr1Y8SIEbzxxhts375djBH7GmQltrd3UAzxMUA2wnJnMplM9O3bl/79+wsjrNFoyM/PJxQK4XA4RG1Rn8+H0WhEq9Wi1WpFQQetViuk1aqrqzGZTOTl5YmOWFtbK/Smu3aefVfpCgoKJx5yH+06blgsFkaNGsWwYcOor6+nsbERnU6H3W4nJyeHYDCIXq9Hq9USCARQqVSYzWYAGhsbOe2001Cr1bS1taHRaCgsLGTjxo3s2rWLzs5Oxo0bx49+9CNefPFFNm/e3OOqWBk7egfFEPcisgGWO5NKpUKn05Gfny/KuK1fvx6VSkV6ejoWi0W4n2XDG4lEgL2i8mq1mmg0ik6nE+cZDAZUKhWVlZVEo1FycnKIRqMEg0FaWlqSapIqKCicXMj91mazMWXKFAoLC1Gr1WRmZtLW1kYgEBCu6erqajGR9/v96HQ6NBoN0WiUjRs3otPpyMrKwmq1UlZWhslkoq6ujuzsbHw+H2azGbVazQ033MD//d//0dDQINzUXccwhaOPEqx1jJALPchVk+x2OzU1NVitVgoLC7HZbGg0GmKxGKFQCJ1Oh8FgACASiQi3VDweR6PRoNVqCYfDqNVqDAYDJpOJ1tZW6urqyM3NpX///qSkpHTrOEpHUlA4uTAYDMycOZOCggJWrVrF2rVryczMZMiQIaSkpNDR0cHatWvZs2cPgUCAQCAgvGfyeOL1elm5ciULFy5k8+bNuN1u6urqqK6uJhaL4fP5qKiowG63o9frmT59OjqdLmlLDRTXdG+hGOJeoqd92bS0NPr160dmZiZVVVUEAgEyMzMxGo2o1WoikQg2mw2DwYBarRb7weFwWLinupY31Ov1YsZqt9vJy8ujpaWF9vZ2CgsLKSkpEStmxb2koHDy0NUtPH78eMrLy3G73WRnZ6PX66mqqqK0tJQhQ4awc+dOPv74YwKBAEajkXg8TiAQQJIkIpEIoVBI7At7PB527drFV199RUdHB3l5eTQ1NYkSkvLKt7y8nEGDBh3nb+HUQTHEvcS+xs5oNNKnTx/y8/OFsTQajeh0OvR6vTjPZrORmpoqjKzsno7FYmJvWC6vqNfr0Wg0hMNh7HY7qampuFwu6urqUKvVFBcXk52dLeohyyizWgWFE5eu/TM7O5upU6eKCXhlZSVer5fPP/+c1tZWNBoN1dXVeDwe8ZAn8LIXzul0YrFYSCQSSJIkop4dDgc6nY54PE7fvn2x2+1UVlZiNBoxGAxMmjQpaWxS6D0UQ9yLyDWJtVotubm59O3bl2AwSHNzMzabTaxw9Xq9qCdqMBjQarViP9lut5OVlSXc1HIdYzkaUt63kfd3zGYziUSC3bt3o9Vq6du3LykpKQCigyooKJy4yB4stVrNxIkTRf8OBoMEAgH8fj/9+vVj/fr1fPPNN4TDYXGtvHWVSCSIx+NIkoRGo0Gn0yXdOycnhz179tDY2IjH46GhoYHMzEy2bNmCz+dDkiThveu6T6x403oHZWTuJeQfbCKRwGaz0bdvX/R6PbW1tVitVlJTUzGbzej1epGGIK+A5UAstVqdNIM1mUyYTCZhuOXzU1NTRSCX1+tFpVIRj8cJBoPk5+eTn58vDLiCgsLJgd1u58wzzxR5w6WlpYwdOxZJktizZw86nY6MjAwcDkdSQJUcMR2Px0kkEsKLZjAYsFqtFBQUiJW0bOSXL19OTU0Nra2tbN26lUQigdFoJDc3F1C2s3obxRD3IiqVCr1eT2FhIdnZ2dTX14sfeDgcFoY4Go2KaGitVisMbSgUor29nZaWFiKRCHq9HpfLJVKb5GAtnU5HR0cHoVCIlJQUrFYrsViMtrY2QqEQRUVFpKWlKTNaBYWTAHnCnJ+fj8vlEhoCJpOJkpISYrEYwWCQXbt2kZmZyfjx48nKysJoNIrtLo1GQygUwu/3o1arKSwsZNy4cUyYMIG0tDSam5vF+GS1WjEajWzZsgWv18vmzZsJBoNJW2L7tk3h6KKkL/US8o83JSVFpAe0t7djsVgIh8NkZmZis9kwGo20tLSg0+mEWo5araazs1O4lKxWKwaDgXg8jsFgwGw2Y7fb6ezsxO/3i70g2QUtR2R7PB7q6uooKiqisLAQj8ejqOIoKJwk5OXliS2pzs5OQqEQq1atoqmpCYfDAeytgaxSqejTpw9OpxMAi8VCW1sb33zzDcFgkJEjRzJ48GA2b96MTqcTKY7yRN1gMJCamorX6xWT+paWFlwuF263+4AqWwpHB2VF3ItotVqysrKw2Wy0trYSj8cJh8N0dnaK1AJJkoR7OhqNkkgk8Hq9uN1uoY7V2dlJZ2cnbrdb3NdqtaLT6YhEIkSjUcxms7if0WgU+YLBYJCmpiby8vJE51VmtQoKJy6y50qj0QB7t6ja29sJBoOYzWbhSZNzhPV6PQ6HQ8SNlJaWijSmSCTCF198wbfffks0GsVisZCdnY3RaKSzs5O6ujp8Pp8IBI1EIvj9fmGAu8rqKuNG76EY4l7EbDaTmZmJz+fD4/GQSCTQaDTk5uai0+nw+/10dnYKQ+z1emlvbxc5xHK+sOyKtlqtSXs/BoMBo9GIJEkYDAbRicLhMJFIREhlNjY2olarycnJEcIgCgoKJyZycJSc7wvQ0tJCXV0dTU1NZGZm4nQ68Xq9BINBotEozc3NYh/422+/paamRsSRBINBWltbyczMRKfTsWnTJrZs2SLGJJPJRHp6utjmslgshEIhQqEQNptNSX08Biiu6V5CrVbjcrnQ6XS0t7eLTtKnTx/sdjs6nY7Ozk6R+ydHSQaDQZFb7PF4CIfDRKNRIeoui35YLBYR2CWvgo1GI42NjcJAp6Wl4fP58Hq91NXVkZ6eTkpKilLwQUHhBEY2drW1tWzevJnBgwfT0NBAY2OjEPfp6Oigvb2dUCiEz+fDYDBgs9nw+/00NTURCATEvWSDnEgk2LRpE263W3jVYrEY4XCYjo4OzGYzJpMpKY6lT58+InhLWRH3HsrSqJfQ6XQ4nU6x6rVardhsNuFK1mg0OByOpNQl+F+KUSAQwOfz0draKl6XO6jBYBCaspFIBJ1Oh9FoBPbuD+Xk5JCZmUlqaqqI0PZ6vSQSCTHzVVBQOLGJRqNUV1fT1NRELBajvb2djo4OIpEIdrud9PR04vE40WgUv99PRUUFNTU16HQ6CgoKyMvLE9WZLBYLfr8fj8eDyWQSaU16vZ54PE51dTVVVVVUVlZSW1srJvBDhw4VmvaguKd7C2VF3EtYLBasVis+n09IWzocDuGC9ng8uFwusrOzRZUUr9dLSkoK8Xgcv99PIBCgo6NDdJx9K6DI6Qt6vZ5wOCwUdORgDXmfKT09HY/HQ2trK6mpqSIAREFB4cSja86uRqMhEokQDAYJhUJCK8DtdtPW1oZer8dgMGAwGJAkCbfbTSKRIC8vD5PJhNfrxWg0Eo1G2bRpk8hFlpW25Hvb7XYRp6LX64UXr+tWluKW7j0UQ9xLpKSkiOhnObBKpVKRmpqKxWKhpqZGBELYbDZisZjI8/N6vXR0dIgoaVkZSy5v1rWYhMViSTLcTqdT7CN3dnaiUqkIh8OoVCoCgYB4fwUFhRMTOedfp9NRWFgoShj6fD6ys7Npamqio6MDm80G/K8gjJxB4fV66ezsRJIkSktLhTyuw+GgublZrKLlf2OxmEij9Hq9mEwmoeAlSZII5lLoPRRD3EvYbDZhNGOxGPX19XR0dACINCPZLeT1ekU+n6wxLRvpjIwM0WHcbjd6vZ60tDTi8ThtbW1Eo1FxP4PBgN1up729XehTy7Pr3Nxc4vE4kUhEMcQKCicwsscrNTVV9HU5+yIWi4k65XKubyAQQK1WEw6H0ev1pKamAuDz+UTd4VgshslkwuFwiHt4PB5isRiRSISKigox9rjdbnJzczEYDElBX0oVt95DMcS9hFarxePxYLPZKCgowGg0iv0djUaDxWLB7XYTDofx+XxYLBZSU1MxmUwYDIYkA7xnzx6xLywLc/h8PqLRKOFwmObmZhKJBKmpqUiSRGtrK+FwWKyOZbUdedYrz6QVFBROTCRJwuFwiFgRSZIwm810dnZiNpux2WwialrWG0hNTRW5xDU1NULUQ44L8Xq9QgIT9u5BRyKRJC+bLJfZ1dUtLwoUI9x7KIa4l9DpdEkR0HI0YjAYJBaLkZaWJqqdpKWl4ff7xZ5PWloaDoeDUChEW1ubcFOlpqZit9tFoEVaWhqSJAn1HKvVKtxT8r6RVqslGAySSCREJKViiBUUTlzk4Eyfz8e2bdtoaGggJycHq9UqZClhb1W2WCxGZ2cnJpMJu91OR0cHbrcbm80mPGXyeKBSqWhra8Pv95NIJAiHwxiNRrRardAwkNW0GhoakrI5ZM+e4qLuHRRD3EvIK1GdTieMpLz6DQaDpKSkUFhYSCwWE0Y1HA6LvECDwUBbWxuNjY1otVosFgvRaJSWlhYyMjIwGAx4PB5gr4vb5/MRCoXE3o9c1UnOXZYFAHw+H1ar9Th/OwoKCgejtbWVpqYm/H4/dXV1Io6kqalJiP3I21iJRILGxkax2pW3n7oaTp1OR3Z2NvF4nJaWFuLxuKhzLkmSWA1Ho1Hq6+vR6XTk5eWRlpZGNBpl586dyqq4l1AMcS8hB0zJbh+NRoPdbicej4tEekmS8Hg8Ql/aZDKRlZUlqqaYzWYcDgd1dXV4PB4h0A57Z8NNTU2iXKI8Y5VLIsoFvuvr6/F4PGICIO8bKygonNiEQiHWrVuHy+USOvM1NTVEIhGhhNXVpRyPx7HZbDgcDjQajVgMyMVjZOEgeWtMrtAkCwF1dHSI4jHRaJTa2loKCgooKipiy5Ytx/vr+F6jGOJeRKvVCvdONBpFrVaTmppKW1sbHo9HuJjNZjOhUIhIJEIikRCvqdVqoZyl0WgwmUxiNRsMBkUakrzilt3Pcu1SuZSirJQj5xorKCicuHRdxQaDQRoaGrDZbGKMkFexJpNJuIslSSIvLw+73S4Mr1y3XPaKaTQaMTbIY4pcptVsNuN0OonH4zQ3N4sCNX6/n6ysLLxeb7e2KRw9FEPcS8iRiR0dHVgsFtrb28WKVv7hx2IxtFotBoOBQCCA1WolGo3S1NQEIHIIuz5kQysXgpALe8vpUSqVCr/fL9xOcseMRqOoVCpRyUlBQeHERq1WM3LkSGKxGDqdjqamJqEBLbuS5bxih8NBWlqaWCXLgViy/K2sGy1P9uX7y8ZYXkGrVCpCoZBYQWdkZLBnzx6am5sVT1ovohjiXkL+0coSlXJdYkDUF9Xr9cBeFxQg9nTlAAxZ0SYlJQWDwSCCKVpbW4WkXTgcFvs5KSkpQjLTZDIJndpYLCZmySaTSZG4VFA4wZGNrc/no6ysDK1Wy9atW8VKV9agz8vLE9K3XQV/5P/LEpeyop+sUy9L48pjkRzMBXvHJ1kJUKVSsXHjRjFGKfQOitZhL5FIJNDpdCLFQNZxDYVCYhUbi8VEMj0g3D8GgwGLxYLZbEan04nOl5KSQkpKikhb6ipT5/V6RVS00WgU0ppyLqEs/q7X6+ns7Dxu34uCgsKBkfN5YW8akqyMJQvzyDK2RUVF2Gw2McmWBX/k/8v3iUQihEIh4ZaWV8Cw12iHQiFhhOV0KIPBwPDhw4lGo+Tk5IjIaUUet3c44b7VWbNmCRfJ4MGDj3dz9suECROYMGHCfl+X5SZlt5JcT1gu2CBXRJFdPlarlc7OTuLxuFjR+nw+/vjHP4pke1mwIzc3V0hhqlQqHA4HFotFGFw56tpisYhcwtbWVnw+nxCMPxlJTU0Vv43bbrvteDdH4TD4vvTro0FlZSUqlYp58+b1+HpX7Xmfz8fXX39NOBzGbDajUqkwGo04HA5MJpM4T175yvvCgEhFkveRZWMrr4q7Bm7KRlauezxgwAD69OmTFE39fUClUvHAAw/0yr3XrVuXpHr47rvvHvK1J5whBnC5XCxYsIBHH3006XhRUREqlYpzzz23x+uef/558SWsWbPmWDR1v/h8PsxmswiQkGuFypWXYrEYra2taLV7dwfsdrtYMTscDhwOhwiukvd9wuGwcHHLrihZIcdut5NIJISqFiBmwJFIhOrqasxmMz6fD7/f32ufe968eUk/xv09ioqKAPjvf//LDTfcQL9+/TCbzRQXF3PTTTfR0NDQ7d5///vfWbBgQa+1XaF3+T7062OFbEzlVKNgMEj//v3FOLA/r1ZXw6rVaoXR7al6khzkJf8rV3JKS0ujX79+RCIRvvnmGxoaGsQiQo41OVi/bmho4J577mHixInCxb18+fL9ft54PM7LL7/MhAkTcDgcGAwGioqKuP766w/6N5cnNl31uQsKCpg+fTrr1q07tC/8KFBYWMiCBQu49957D/vaE3KP2GKxcM011/T4mtFoZNmyZTQ2NpKVlZX02muvvYbRaDwm+xmLFy8+4Oter1ck0odCIRG8JUc2RyIRXC6XiFLMyMggPT09aW9Zq9Xy05/+VFwnz3DlWsZycJfX6xVlzTo7O4U7Sg7sksskZmZm8s033yQV+z7anH322d2M5U033cSoUaO45ZZbxDE5+vtXv/oVbrebyy+/nNLSUvbs2cPTTz/Nf/7zH9atW5f0N77iiisAuPbaa3ut/Qq9x/ehXx8NCgsLCQaD6HS6Hl+XxwD5X3myPWTIEKqrq2lsbKS1tVUEfsL/DLdsWBOJhFjlysGZ8v26SujKY1QikaCzsxOtVktWVhZlZWWsX78er9ebdK5KpWL+/PnAgfv19u3beeyxxygtLWXIkCF89dVX+/0+gsEgM2bM4KOPPuLss8/m3nvvxeFwUFlZydtvv838+fOprq4mLy/vgN/rlVdeyYUXXkg8Hmfr1q08++yzLFq0iJUrVzJs2LADXns0SEtL45prrmH58uU88sgjh3XtCWmID8SYMWNYvXo1b731Fnfeeac4Xltby+eff8706dP5xz/+0evtkPd194ff76e9vZ3c3FwaGhpEwQc571ev12M2m0X1k3g8TjgcRqfTEY1GaW9vx+v1imhp2X2t1WoJhUJ4PB6i0ajozMFgEJvNJpS0AMxmM9FolFAoRJ8+ffD5fL0e/VhcXExxcXHSsR//+McUFxf3OAj/8Y9/ZOzYsUl7TxdccAHjx4/n6aef5qGHHuq1tiqcOJws/fpoILuXD5VIJMKGDRtwOp1MmjSJZcuW0dTUhM/nE7K2+65uu64Q5RRIWddAXh3LaY9GoxGfz0d7ezsWi4Vhw4ah1WrZtGmT2P6S2y1JkujHB+rXw4cPp62tDYfDwbvvvsvll1++3893991389FHH/GnP/2Jn/3sZ0mvzZkzhz/96U+H9D2dfvrpSW0ZM2YMl1xyCc8++yzPPffcId3jeHFCuqYPhNFoZMaMGbz++utJx9944w3S0tI4//zze7xu27ZtXHbZZcLlO2LECP79738nnSO7VVesWMHPf/5z0tPTsVgsTJ8+nZaWlqRz991LWr58OSqVirfffpuHH36YpqYmli9fzieffJIk0CHrxG7evJknn3ySRx99lAULFrBr1y7+/Oc/88c//lGIgUSjUV5++WWqqqoARA3iLVu28Nprr/Hoo4/y8MMP869//YuOjg6hthONRlmyZAkPPvggtbW1LF68mHnz5vH666/T1NSEJEnU1NRw6aWXkpKSQlZWFnPnzk36fJFIhPvvv5/hw4djt9uxWCyMGzeOZcuWfdc/XY+cffbZ3QJAzj77bBwOB1u3bj2q76Vw4nKy9Gu5xu8555zDrl27urXnmWeeobi4GJPJxKhRo/j888+73XN/e8RLly5l3LhxwmDKilmwtz9u3LiRnTt3smvXLlwuF7t372bVqlWsWbNGjBGhUIht27axatUq1q5dS3NzM+FwGK/XK3KPOzo6aGhoYM+ePaxfv55vvvmG3bt3o9Fo6N+/P/369WPHjh3s2bOnWy30Q80jloVFDkZtbS3PPfcckydP7maEYW8E9+zZsw+6Gu6JSZMmAVBRUbHfc2bNmiXc6V154IEHun3WJUuWMHbsWFHnvays7Du5oXvipDPEAFdddRWrVq1i9+7d4tjrr7/OZZdd1qO7Z/PmzZxxxhls3bqVe+65h7lz52KxWJg2bRrvv/9+t/Nvv/121q9fz5w5c/jJT37CBx98cMjBQY8++ijvv/++qAvc3NzMhg0bhHspGo3y+eef88EHH2C32znnnHPIzc3l1VdfFaLssqRl17rB8j7Phg0beOONNwiFQowcOZKhQ4dSV1fHSy+9JPRhzWazMG7/+te/0Ol0DBkyRLi0g8EgkydPJjc3l8cee4y+ffsye/ZsPvvsM/F+Xq+XF154gQkTJvDYY4/xwAMP0NLSwvnnn9/r+y6dnZ10dnbicrl69X0UTixOhn49e/Zsfv3rX7Ny5UquvvrqpHOeffZZbrvtNvLy8nj88ccZN24c06ZNo7a29qD3/+STTzj//PNpbm5OipruWvlIFtoA8Hg86HQ6TCYTJpOJ+vp6Ghoa2Lp1K3q9nvz8fIxGIzU1NYRCIdrb22loaKC9vZ2WlhZMJpOYkMh7zi6Xi7KyMqLRKKtWreoWS9IbYh6LFi0iFov1ynaT/DuSC2EcCZs3b2bq1KmEw2EefPBB5s6dyyWXXMKKFSuO+N5wErqmYe9MJysrizfeeIPf/va3bN26lXXr1vHnP/+ZPXv2dDv/zjvvpKCggNWrVwvjduuttzJ27Fh+9atfMX369KTznU4nixcvFj+8RCLBU089JYKiDoQsS5ebmyv2X1paWtBqtdTV1eH1evn000/JzMzkhhtuAPa6sXNzc/nPf/5DSkoKiUQCi8Ui0pnknL9QKMSiRYswGAzMnDmTlJQUce0///lPvvjiC2bMmCFm0wB9+vRh/PjxLFu2TOhd+/1+rr/+en71q18Be/dWcnJyeOmllzj77LOBvfsdlZWVSa66m2++mf79+/OXv/yFF1988bD/bofKk08+SSQS4Yc//GGvvYfCicfJ0K/l/pCWlsadd97Jpk2bGDx4MJFIhPvuu4+RI0eydOlSsXdbXl7OrFmzDrqiu/vuu3E4HHz11VdkZGQIt7LsSpbbKxv1fv36kZKSwldffYVOp0Or1VJVVUV+fj55eXlIkoTL5WLt2rU0NjZis9nElldqaqoo9pCRkYFWq2Xjxo10dHSQkZHB6tWr2bVrV7e94a75yUcL2es1ZMiQI75XIBCgtbWVeDzOtm3buOuuuwAO6BY/VJYsWUIkEmHRokW9skA4KVfEGo2GK664gjfeeAPYG8yRn5/PuHHjup3rdrtZunQpV1xxBT6fj9bWVlpbW2lra+P8889n586d1NXVJV1zyy23JP3gZHeR7P45ENdff73orLKWK0BTUxP9+vWjsrKScDjMwIEDRW6gPBuVB5NAIEB7e7vY65VTCJqammhsbKR///6iupLD4aB///4UFhayc+dOoTkrG/HRo0cLF5UkSWKAuPHGG0WbU1NTKSsrSxrs5Pxk+XPI5dBGjBjB2rVrD/o9fFc+++wzfve733HFFVcI15LCqcHJ0q/lawHRZ9asWUNbWxs333yz6GMAV199NWlpaQe8d0NDA+vWrWPWrFnCnds1+rmrAZQD1vLz85kwYQLDhw9PEuNIJBK0tLTQ1tZGa2srarVaBItarVb69u1LWVkZeXl5OBwOzGYzOTk5ZGVlEQqF2Lp1K59++qkYP2T2DSA7WsjvczQqws2ZM4f09HSysrKYMGECu3fv5rHHHmPGjBlHfG+5xvO//vWvpPSyo8VJuSKGvW6sp556ivXr1/P6668zc+bMHmdru3btQpIk7rvvPu67774e79Xc3Exubq54XlBQkPS63JHa29sP2i752q4BEwA7d+6kX79+YmYci8UIhUIkEgk6OjpE8JYkSSKXT05R0Ol0RCIR2traxPvU1tZSWFgoXNjZ2dlUVlbS1NREJBIRwSAVFRU97vXsO6uz2+1J9weYP38+c+fOZdu2bUSjUXG8T58+B/0evgvbtm1j+vTpDB48mBdeeKFX3kPhxOZE79f7u1Y25n379k06T6vV9rgH2RX52rKyMqBnF/C+hnDFihW4XC4uvPBCbDYbX3zxBR0dHaK8oSx72dLSQiwWo6SkRMSQNDc309jYKMohypjNZhYtWoTb7RZR1/u+/9FGLtXo8/mO+F633HILl19+udD0HzRoUNL23pHwwx/+kBdeeIGbbrqJe+65h3POOYcZM2Zw2WWXHRWRk5PWEI8ePZqSkhJ+9rOfUVFRwVVXXdXjefIPbfbs2fsN+Ni388iqM/tyKD/Grtd2Pb+jo4OvvvpK7Fekp6eza9cuUfQhNTVVpCh5vV4Rxdj1nrJylqxy07VwhPw5/X4/qampwtBu2bIlyYgeiK7tffXVV5k1axbTpk3j7rvvJiMjA41Gwx/+8IekPbyjRU1NDeeddx52u52FCxcqNZNPUU6Gfn241x4tuhrGlpYW3n//fcaNG8eIESPYtm0bXq+XQYMGCa15+RqNRkN2djapqal0dHSwevVqysvLGT58OMFgkIqKCjZs2EAwGBQZGvt736P9efv37w/Axo0bjzjFqLS0dL+56Ptjf652+fuTMZlMfPbZZyxbtowPP/yQjz76iLfeeotJkyaxePHi/f4+DpWT1hDD3r3Nhx56iAEDBuz3jyin0uh0usP+Ix1t6uvrhaqVXq9n5MiRfPvtt7S2thKNRgkGg2i1WmpqakQ9Ydi7epZlMmXy8/NFJ1Or1TQ1NWE0Gunbty8Oh0MEEXxXVZx3332X4uJi3nvvvaQf65w5c77jp98/bW1tnHfeeYTDYf773/+SnZ191N9D4eThZOvXsDc3GPau1CdOnCiOx2IxKisrKS8vP+i127dvB5KN3f7+n0gkaG1tZfHixcI9rtFouOCCC5IEPJqbmwmFQlxyySXEYjH+/Oc/k5qaysiRI6msrGTHjh20tbUlbYPtS2+5pQGmTJmCRqPh1VdfPS76AGlpaT0qDfa0XaFWqznnnHM455xz+OMf/8gjjzzCb37zG5YtW3bEv8GTco9Y5qabbmLOnDndUm+6kpGRwYQJE3juued6VGvaN33haNLTTNLv96NWq/noo49ob2/nvPPO46yzzqKiokJURgkGg0m5hlqtVtQzdrlcbN68mcrKSrZu3crmzZtZu3YtVVVVlJeXo9frWbZsGdXV1UCyXN7hdKSuWrQyX3/99QET878Lfr+fCy+8kLq6OhYuXEhpaelRvb/CyceJ3q97YsSIETidTp5//vkkwZzXXnvtoK7v7Oxshg0bxvz584VR6Dp29JQ6JMtT+v1+Nm7cSGVlJdFolPfff58vvviCrVu30tzcLM5ds2YNb731FtXV1fh8Pt577z2+/PJLGhoakoRSum6ndTXAvbXyz8/P5+abb2bx4sX85S9/6fZ6IpFg7ty5hxR5/l0oKSnB4/GwYcMGcayhoaFb1L1cQKcr8iTxaEiAntQr4sLCwkPSDX3mmWcYO3YsQ4YM4eabb6a4uJimpia++uoramtrWb9+fe83lv/tG5vNZjo6Ovjzn//M4MGDRdRieno6BoOB9PR0QqGQCGSQizeoVCr69+/PihUrWLp0KeXl5ZhMJlauXInRaKS4uJiFCxdSU1PTo3rW4UQ8Tp06lffee4/p06dz0UUXUVFRwd/+9jcGDhx4VItGXH311axatYobbriBrVu3JuUOW61Wpk2bdtTeS+Hk4GTr17DXw/XAAw9w++23M2nSJK644goqKyuZN28eJSUlB+17TzzxBFOmTOHMM88Uhq9rucL9XS8Ldcgr4NWrV6PRaNDpdBiNRrxeL4lEgn/+8594PB5R/KGjo0MEgu1rZHtahcvnHg6yGM/mzZsBWLBgAV988QUAv/3tb8V5c+fOZffu3dxxxx289957TJ06lbS0NKqrq3nnnXfYtm0bM2fOPKz3PlRmzpwpIuzvuOMOAoEAzz77LP369UsKSn3wwQf57LPPuOiiiygsLKS5uZm//vWv5OXlMXbs2CNviHSCcd1110mFhYU9vlZYWChddNFFB7z+5ZdflgBp9erVScd3794t/b//9/+krKwsSafTSbm5udLUqVOld99996DXLlu2TAKkZcuWiWPjx4+Xxo8f3+2cd955J+naiooKCZBefvnlpONPPfWUVFhYKBkMBmnUqFHSihUrpOHDh0sXXHDBQa/95JNPpDFjxkgmk0lKSUmRLr74YmnLli1J58yZM0cCpJaWlqTj1113nWSxWLp9b+PHj5cGDRoknicSCemRRx4RbTzttNOk//znPwf8++wPi8UiXXfddT2+VlhYKAE9Pvb3PoD005/+9LDaoHB8Ufr1qdWvJUnab7/uyezEYjHphRdekMaNGyfZ7XZJp9NJhYWF0vXXXy99++23B2yH/H0+8cQTB20zIM2ZMyfp2OLFi6XBgwdLer1eKisrk1599VXxPcv897//lS699FIpJydH0uv1Uk5OjnTllVdKO3bs6PYe+/vNHAjV/9+4E4ZZs2axdOlS1q5di1arFWHj33cSiQTp6enMmDGD559//ng354REjuZMT0/npz/9KU8//fTxbpLCIaL0a6Vff9+Jx+O0t7ezYsUKpk2bxjvvvMNll112SNeekHvENTU1pKenH50l/wlIKBTq5uZ55ZVXcLvdSVJ4CskUFxeTnp5+vJuh8B1R+rXC9xl5e/G7bKedcCviLVu2CBk3q9XKGWeccZxbdPRZvnw5d911F5dffjlOp5O1a9fy4osvMmDAAL755ptjIjx/MvLpp5+KVKz8/HyRd6lw4qP0a6Vff9/p7Oxk5cqV4nl5eTkZGRmHdO0JZ4hPBSorK7njjjtYtWoVbrcbh8PBhRdeyKOPPnrIfzgFBYUTC6VfK3xXFEOsoKCgoKBwHDkh94gVFBQUFBROFRRDrKCgoKCgcBw5qQU9TmS6ylF2pTdqeh6I/e08yLrVCgoKJxYHCuoqKipi2rRpOJ1OfD4fVVVVBINBMjIycDgcBINB2tvbqa6upqqqikAggF6vZ8iQIZx22mm4XC7i8TgrVqxg+/btJBIJHA6HENF4+umn2bZtm3i/nsaPSCTSK5/7VEYxxMeIIzXA30XZ5kiuU1BQOL507bsqlYri4mJ++MMf0tjYyPr160W1tnA4TCgUQqPRYDQaWbNmDRUVFaIYTDweZ/ny5VRXVzNmzBii0SjV1dUEg0EkSaKlpYWNGzcya9Ysrr76av7v//4Pj8cj3ldGGUd6D8U1fQw4HCMsa73u++jpte9yTwUFhZMHud/279+fG2+8Eb/fz+eff06/fv2YMGECo0ePZtCgQQwdOpQRI0YgSRIejweNRoPL5SIlJQWtVkskEqGzs5PCwkLKy8vJz89Hp9Oh0+kwmUz4fD5WrFhBnz59mDJlSlJpP6kXtaYV9qKsiE8gZEMpa8vKFZi0Wi2JRIJYLEY0GiUWi3UrTq10FAWF7x8qlYqhQ4dyyy230NLSwo4dO4jH4zidToYPH05bWxttbW0kEgm8Xi81NTWEw2Fyc3PJzs4mGo3S0dFBTU0NKpWKjIwMcnNzsdvtpKSk4Ha7MZlMDBo0iPXr11NXVye05VetWtVNa1qZzPcOiiHuZQ72w+36ulqtRq/X43A4yM3NxeVyodPpCAaDGAwGUZWppaWF5uZmIeIudxbFGCsonPx09YANHDiQH/3oRyQSCTZs2IDVasXlcrFq1Sr69euH0WgkHo8TDoepqqqivr6e7OxscnJyUKvVJBIJcnNzMZvNQlo0Ly+PcDjMlVdeKVbLbrebTz75hHA4jM1m4/bbb+ePf/wja9euVdzTxwDFEPcihzp7lFe/ubm5lJWVYbPZgL3lD0OhENXV1WRnZ2O1WtFoNDidTsrKynC73ezatYumpqakUmYH6izKjFZB4cRHpVLhdDq55pprMJlMrFmzhlAoRHFxMZWVlWg0GhYvXkxubq5YqdbX1xONRsnNzcVqtQrvmcFgIDMzE5fLRTQapampiZaWFvLz88nPz8ftdrN69WrS0tIwGo00NTVRVFTEvffeyy9+8QuqqqrEeyiGuHdQDHEvcSgGT97/MRqNlJeXk5eXhyRJOBwO7HY7RqMRv99PZ2cnp59+OqFQiFgshlarJRwOk5KSQnZ2Ntu2bWPXrl14PB4SiYTSYRQUTnI0Gg2TJ08mLy+PhoYGampq0Gg07Ny5EwCbzUZVVZXQ747H4wSDQex2OyaTCdg7vmg0GrRaLRaLhVAoxPbt2/F6vZhMJoxGI8FgkA8//JCFCxcydOhQwuEwmzZtorCwkKKiIm6//Xbuu+8+Edil0Dsohvg4IRthnU7HsGHDyM7OJj09nbS0NLRaLTabDbVaTUtLC+PHj0ej0VBdXU04HMblcjFmzBhaWlpoaWlBrVaTkpLCunXrcLvdSodRUDjJyc/PZ+LEiaJWuexmrqurw+Vy0dHRQTweJxKJUF9fj16vx2q1YjAYxNgiSRJarRa1Wo1arSYej7Nz5050Oh02m41oNMqKFSuEO9vn86FWq4lGo1RWVpKdnc3kyZP58MMPWbZsmeJN60UUQ3wcUalUlJSU4HQ6KSkpAaChoQGn04lOp2P79u3o9XpSUlJoaGjA7XajUqlIT09n27Zt4rVhw4Zhs9lEYfDOzk5A2c9RUDgZ0Wg0nH322bhcLtauXUtrayt5eXmsW7eOlJQUBg4ciMfjoaamhkQigU6nSzK6ssHUarVotVp0Oh2JRAK9Xo/P52P79u1YLBYqKiowGAzYbDbi8TjRaBSn00lTUxMejwetVovBYODiiy9m5cqVPVaXUjg6KIb4GNI18lClUpGamkpRUREOhwOdTofP5xMdYceOHahUKgoKCnA6ndjtdnw+HzU1NXg8HhobG8nIyCA1NZUBAwYQj8cJhUK0t7ezefNmUaUIFIOsoHAykZaWxvDhw2lubmb79u3E43EqKirw+XwUFxezdetWYG8VK6127xAei8XEuKJWq0WmBZBkoM1mM6FQCJ/Ph9lsFka9s7OTiooK+vTpg8FgoLKyko6ODhwOB0OHDiUjI4Pq6mplVdxLKHnEx5iuKUqyAXU4HFRWVtLc3EwoFCI/Px+VSkUgEMDv9xMOh1Gr1VitVuLxOJ2dnSQSCaqrq2lvb+fbb78lGo2Snp7OwIEDcblcSt6wgsJJSt++fcnNzaW6uhqz2UxJSQk7d+4UwVYGg0HEiUiSJIQ8dDqdMMLyatZsNosUSLVaLVIi09LSMJvNVFRUUFdXh8fjweVysXnzZkKhEBUVFXi9XiRJwmw2Y7Vaj/fX8r1GWREfB+SISKvVSmlpKYWFhWL/p7W1laamJgKBgEi2b21tJRgMUlNTI/7Nz8+nvr6etrY2zGYzAwYMYNCgQUSjUQYOHEhHRwfBYPB4f1QFBYXDZNCgQRgMBlpbWwkEAqxfv16MBXq9XmgKyJNt2e2sUqmIx+PA3jFGdkvH43EkSRIiHZIkkUgkkCSJeDwuArpisRhutxudTofD4aCxsZH8/Hyh3KXkEfceyor4GLFvvnBRUZEIjNi5cyeSJJGZmUkoFKKlpYVYLEZhYSGFhYVkZmaKma5arSYWi+H1elGpVOLf1tZWPB4PJSUluFwuMjMzlVWxgsJJSFZWFuFwWHjD6urqyM7OFkpYBoOBQCCA0WgE9o4nsvqV3N/l3OJwOEwikRAP2TDL4kDyqlp2XTscDpqbm8nIyGDjxo20tLTQ1NREe3u7Mpb0IoohPgZ0/QGrVCoR3ZiTk4MkSTQ2NlJXV0cikSArKwutVotKpRKdRO4YWVlZ2Gw2kdJkMBgIhULCOHs8HlJTU7FYLBQUFAjxeKUDKSicPKSkpIhJdFVVFXa7XbiG5ehpQKyQ1Wo1/x977x0eZ3Xm/X+m9yZp1Jtlyx0bx3RsDKYHSDAvISSBxbCEBAJhScjCLxtKdrMJJC+7LEt2Q4AAMWASCCRA6GCaA7jbuMhFlqxeZzS9z/P7w+850diSTbFkG5/PdemyZ+aZ53k0mnPuc+7yvQ0GAyaTScaGdTod0WiUdDotXdIinizySXQ6HZFIBE3TsFgsMvM6FovR0tJCIpGQj+PxuFrYjyHKNX0QqKioIJfLYbPZZHZiNBqViRYiucpms9HX14fBYECn0zE0NEQqlSKXyzE4OCjrBFtbW3E6nVitViZNmoTJZMLtduN2u+nv7z/Iv61Cofg0OJ1OBgYGSCaT9PT0MH36dIxGozS6mUwGn88HgMlkkmWQwk2dz+dJJpPkcjmpqGUwGMhkMsTjcTm/mM1mPB6PfC2bzaLX66moqKCtrY36+noikQjRaFS6txVjg9oRjzN6vR6/3y+TIyZPnkxZWZk0qCJBQtM0BgcH6e/vp66ujqKiImpra6XqljiXy+XC6XTicDhoa2vj7bfflm7pqqoquQpWK1mF4vDAarXS09NDMBjEYrHg9/vlYnx4ZrTY6Voslr306YWxNplMGAwGstksBoMBt9uNzWbD5XJhMBjke7PZLLlcDp1Oh8/nw2Kx0NnZydatW7HZbBgMBmWIxxBliMcR4ZY2m83U1NRgtVrJZDKEw2Hy+TypVIqenh5yuRz5fJ5EIiETM+LxOOl0uiDr2mAwUFpaSk1NDfX19VRWVuJ2u2lsbMTpdDJp0iSsVqsywgrFYYTFYiEcDhMIBGSlhBjv4l9hQA0GA7lcTu5YxY44l8vJBKxMJiPnF+GSFoZdnAMKNa69Xi+hUIihoSHKy8tlf3VljMcG5ZoeY/aMD/v9fsxmM5MmTWLz5s2YzWbMZjMWi4VMJiM1o0WfUeGKSiQS0hCLgZTP5wkEAuTzeZkRmUqlCIVCstOKiCmLcyoUikMbm81GNpuVugLDd6zCuNpsNunt2jMDWqfTYTabpWctnU7LRC0RQxaJXcPLoIbL45rNZkKhELFYDIvFQklJCcFg8CB/Ml9c1I54nBAG1O/3k0gkpGEVouwlJSX4/X6sVqtcoSYSCbLZLNlsViZl2Ww2WaYgDKvH4yGVStHV1UVfXx9erxe/3086nZaZlQqF4vBAKGElEgk53s1msywzGq4lLTKes9lsQRe24a1Th++mhbEV7x3eTlW4u8X8Iwy4zWajpKRE9SUeQ5QhHmOGf3H1ej2VlZWyvlcMDpFYkU6nC2TkxMARrqe+vj5ZGygGRTgcJhgMyvIlu93O9u3bZXuzoqKiApeTQqE4tBELcE3T5Fwx3JgKgQ5AGlIRIxalSYCcI4TXTa/XU1NTg9/vl141kW0tjLCYI8ROWrRetdvtKmt6DFGu6XFENHNwu90AxONxBgYGpM6rSJYQA8hut2MwGAiHw9jtdvx+Pz09PQWGOp1O09bWJtVySktL6e7uZvLkyYRCIZkINnzlq1AoDl0ikYhUx4pEIqRSKcxmMyaTCUDudsUiHZD1wcPHuUjeEs9ZLBZZNtnQ0MDOnTvp6+uT6lyA9LSl02ng74Y+lUqN2+9/JKIM8RgzfAVpMplk4+1QKITb7cbv96NpGvF4nJ6enoKBpGkaDocDq9WKzWYjkUhgsVgKBN4NBgNOp5P6+npSqRQDAwN4vV4pAFJUVITRaJTa02pFq1Ac2gwODspdrGhrKMbt8HyQ4UpZewp6GAwGHA6HzKwuLy/H6/WSTqdJpVIyTJZMJhkYGCjYCWuaRiwWk9KZsVhMGmzF2KBc02PM8C+v2Jl6vV4aGhqwWq2Ew2H6+/tJp9Oy3GC4VmwymaS7u5toNIrRaJQascII22w2ioqKGBoaksa9srJSljEML+QfvttWKBSHJl1dXZSXl8t+wcLACleyIJlMyrIiEdoKhUJkMhnsdjv5fJ66ujomTZqE1+uVC3hAZmJ7PJ6Ca4v5QUjslpeXs2bNGrq6utQifgxRhngc0el0pNNpNE2jvb2dWCyGTqfD5XKh0+kIhUJSQ1bTNDwej4wHO51OGcex2Wwy81FIYor6v1AoRCQSobe3F/h7AgaorGmF4nBg69atTJ48maKiIgKBAOl0WrqdhydaBYNBmcwpdsqaptHf308ymaS8vFzOG8M7MQljbjQaC3bT4hhxbqfTSUVFBcuWLSMajar5YwxRhngcEcX1Xq8Xp9NJPp/H4/FIyUrhPhYDY2BgQGZUC33pRCKB3W4vcEUNT7To6elhw4YNDA0NFayeFQrF4UFraytVVVVMnz5dJm7t6YYWmdROp5NMJiN3y6WlpdKTJozzcAMrFvB6vZ5cLsfAwEBBxrWQvUwkEtTX1xOPx1m7dq0ywmOMMsTjiMlkwuVyYTabqaqqQq/XEw6H8fv91NXVUV1dLQdOPp/H6XTi8/lIJpMkEgnZ2DsUCslMahEzzmQyDAwMkMlk5E56uOydQqE4PBgcHCQej3P22WfjcrkIBAJ7CXYAFBcXE4lEsFgs0m2cTqfxeDyYzWaGhoakS1ss8ocLfDgcDmmwReKXmEf8fj/HHnssq1evZnBw8KB9FkcKh5whXrx4sXS/zJw582DfzqiceuqpnHrqqfs9bnhcJZ1OS/3o5uZmEokEoVCIVCpFPB7H6XTi8XjkQInFYtx1112sXr1aNoKoqqrC6XTKQZlMJonH4wQCATKZDHV1dbLOUAi6ixXv4b6q9Xq98rtx/fXXH+zbURwhHOg5SafTceedd8rHjz76KDqdjtbWVgBCoRArVqzg2GOP5aSTTqK1tVVqzA9X0RL1w8JlLRI0g8Eg6XSagYEBenp6gN1JXiIslsvl8Hg89Pf3E4/HAeR5o9EofX19TJ8+nUmTJrF8+XKZmQ1jH95at25dgZTnM888M6bXO1Q45AwxQElJCUuWLOGuu+4qeL6+vh6dTscZZ5wx4vsefPBB+QdctWrVeNzqfhleZB+LxaSSjc1mkxnRbW1taJpGeXk5breb4uJi4O9ZkZlMhqGhISKRCH19fUSjUXl+t9vN5MmTqaqqYtKkSTgcDrkbzmazDA0NFWRMj3XChZhU9vdTX18PwJtvvslVV13F5MmTsdvtNDQ0cPXVV9Pd3b3XuX/729+yZMmSMb1/hWIkRpuTYLeR+/3vf8+ZZ55JSUkJJpOJ0tJSzjrrLH77299+6tKfdDrNH//4Rzo7O/nud7+Lw+Fg27ZtANKQChfzcLey8KZZrVZ27NhBZ2cnra2tdHV1FYh0JBIJVq1axapVqwrOI1zVBoOB8847j127dtHa2jrinDF8cbKvn8WLFwOwYsUKrrvuOubOnSs9dSNRV1fHkiVL+PGPf/ypPrPDnUOyfMnhcHDZZZeN+JrVamXZsmX09PRQXl5e8NoTTzyB1WqVMpFjyWuvvbbP10dqop1Op4nH4ySTSSZMmIDBYCAYDBKNRqXGtMlkkoYzk8nwD//wD9hsNhnTEapcmqbh9XqpqKggkUhgMpno7OwkmUzi9XqxWCzY7XbWrVsnV7Tj0dj7lFNO2ctYXn311Rx33HFcc8018jnR1u2WW24hEAjwta99jcbGRnbu3Mn999/Piy++yLp16wr+xpdccgkAl19++Zj+DgrFnow2JyUSCRYtWsSrr77KSSedxM0330xZWRmBQIB33nmH6667jo8++oiHH3541HNffvnlXHrppVgsFmD3OF2zZg1//OMf+cEPfsAVV1zBr3/9a/r6+vbqMy7mO2FQNU2T6nw6nY5MJsPOnTvp7u7G5/PR19fHzp07SafTVFdXF2gYpFIpent7Oemkk5g0aRK/+93vZBMakSAm+M53vlOwIWppaeH222/nmmuuYf78+fL5iRMnAvDSSy/x0EMPMWvWLBoaGuTCYk98Ph+XXXYZb7/9Nj//+c8/xV/o8OaQNMT74uSTT2blypX84Q9/4MYbb5TPd3R08N5777Fo0SL+9Kc/jfl9iF6/ozGSwdM0jZaWFqqrq+nq6iKXy8kG32VlZUQiEdkpBXbHc0pLS2VmdTabJRgMSu3oYDBIKpUim83i8XhkzEiv1xOJRLDZbHR0dBQMoLF2LTU0NNDQ0FDw3He/+10aGhpGnMj+4z/+g3nz5sndP8A555zDggULuP/++/nZz342pverUHwebrrpJl599VXuvffegvkI4Ic//CHbt2/n9ddf3+c59ixLgt2L9hdffJFzzz2Xf/iHf+CVV16hra1N7riHd2ESAh/w9/FdXV0tBUAAwuEwXV1dhEIhEomEDPMIJa5cLkdvby9Go5FLLrmEpqYm3n///YJF/HBOPPFETjzxRPl41apV3H777Zx44okjjvNrr72WW265BZvNxvXXXz+qIT5SOSRd0/vCarVy0UUX8eSTTxY8v3TpUnw+H2efffaI72tqauLiiy+mqKgIq9XKMcccw/PPP19wjHCrLl++nB/84Af4/X4cDgeLFi3aq6/vnjHit99+G51Oxx//+Ef+/d//nXg8Lne/4sucz+fZunUr06dPZ9WqVSxdupTHH3+c119/nU2bNvHyyy/z9NNPyzKEoaEh/u///b+sWLFCurXtdjsDAwP87W9/44UXXuDpp5/mnXfeYdeuXfT19Um5ug8//JC77rpL9jUVzb2Hl0999atfxe12U15ezj333FPw+6XTaW6//Xbmzp2Lx+PB4XAwf/58li1b9ln/dCNyyimnFBhh8VxRURFbtmw5oNdSKA4k7e3tPPTQQ5xzzjl7GWFBY2Mj11133T7Ps2eMWNDU1MRFF11ETU0Nq1atoq2tjaGhIWD3XJLJZHjxxRd5/vnnGRwc5KWXXuLxxx/nL3/5C4FAAIfDQUdHBy+88AJLly7l9ddfZ2BgALPZjM1mQ9M0otEo69at480332T16tV0dnZy//3388Ybb9DS0nIgPiYAysrKZO6KYm8OO0MM8M1vfpMVK1bQ3Nwsn3vyySe5+OKLC1aBgk2bNnHCCSewZcsWbr31Vu655x4cDgcXXnghzz333F7H33DDDaxfv5477riDa6+9lhdeeOETJwfdddddPPfcczJOK5IkYPfgGRgY4JlnnuHDDz/EZrNx7LHH4vf7WbZsGUNDQ7LDisFgKNh1CznMpqYmVqxYQTqd5thjj2Xu3LkMDQ3x7rvvEo1GMZlMBAIB+TmEQiGpNyuSO7LZLGeeeSZVVVXcfffdTJo0iZtvvpl3331XXi8cDvPQQw9x6qmncvfdd3PnnXfS39/P2Wefzbp16z7RZ/FZiUajRKNRSkpKxvQ6CsXn4eWXXyaXy40aRvs8CJdxMpnk6quvZtGiRcTjcZYtW0YqlSKTychFdTqd5q233qKkpIQvfelL6PV63nrrLZqamnjvvfeoqKhgxowZZLNZtmzZgt1ul92ZBgYGGBwcpKysDK/XyznnnMPKlSt56qmnCmLbh3ui56HOYeeaBli4cCHl5eUsXbqUn/zkJ2zZsoV169bxX//1X+zcuXOv42+88UZqa2tZuXKljMNcd911zJs3j1tuuYVFixYVHF9cXMxrr71WICt33333EQqF9lKi2ZNkMsm6devw+XzyOZEFLbSh33vvPWpqapg7dy6NjY2yWcOqVatkjbDFYpEDQYh6AKxduxaz2cy8efMoLi5mypQpVFRU8Oc//5lt27ZRXl4uhUNgdwKHKHEyGAwkk0kymQxXXnklt9xyCwDf+MY3qKys5He/+x2nnHIKsDtW09raWrAY+Pa3v83UqVP57//+733GvD4v9957L+l0mq9//etjdg2F4vPS1NQEsFcmdTqdJhwOy8c6nU4mYH4ShrckzGazxGIxbr31VqxWK7/5zW9Ys2YNs2fPlnHbeDzO/PnzqaurI5/PU1payl//+lfef/99zj77bDweD/F4HLPZzKpVqxgcHJShLr/fT2lpKbt27WLSpEl8//vfx+/38/DDD8s4s/gdFGPHYbkjNhgMXHLJJSxduhTYnaRVU1NTkCQgCAQCvPXWW1xyySVEIhEGBgbkKvDss89m+/btdHZ2FrznmmuuKfjizZ8/n1wux65du/Z7b1deeWVBL9DhYuqATIwoLy/nqKOOYufOnXg8HmbOnInFYsFkMlFSUkJVVZWM+wgXdzQaJRgMMm3aNCllKRK2Ghoa2L59O8FgkKqqKlm2INRzABlTAvjHf/xHec9er5cpU6YULGKG78hF3+NsNssxxxzDmjVr9vs5fFbeffddfvrTn3LJJZewcOHCMbuOQvF5EcZWJB4KXnrpJfx+v/ypq6v7VOfdMwv65Zdf5i9/+Qs//OEPMZvNtLW1ydpeIVtZWVkpa4FtNpvULPB6vTL5s6KiAthd1ihasIqxPTAwwLnnnktHR4ecD9UuePw4LHfEsNs9fd9997F+/XqefPJJLr300hFXbTt27EDTNG677TZuu+22Ec/V19dHVVWVfFxbW1vwutjdfpLG2Hu+d89EKfFYSFzW1dXR2tqK1+vF4/Hgcrmoqamhvb0du90O7DaEqVSKUCgE7B5QooGE+P/69etpbm5mwoQJ/O1vf5MDdc/4q2BPt6/H49mrcP+xxx7jnnvuoampSQ5mgAkTJuz3c/gsNDU1sWjRImbOnMlDDz00JtdQKA4ULpcLoKCcEHYnlIoErV/96lcsX778U513uCEWcpMPPfQQxcXFNDQ00N/fT0tLi5xPbDabTPAU2dOib7FIxhreZziZTMrn0uk0H3/8Mblcjh/84Afjmtip+DuHrSE+/vjjmThxIv/0T/9ES0sL3/zmN0c8Tuwqb7755lETuSZNmlTweDRpyE/yxdyfrKQ4RyQS4bXXXuPCCy+koaGBpqYmGU/esmWLLGWC3bXCiURClmVZLBbcbjcej4dNmzaRTCalkV69ejXr16+XrumRSg/2d28Ajz/+OIsXL+bCCy/kRz/6EaWlpRgMBn7xi18UxOYPFO3t7Zx11ll4PB5eeuklOckpFIcqU6dOBWDjxo3Mnj1bPu/3+2Vpz+OPP/6Zzz+83LCtrY177rmHZDJJWVkZ1dXV7Nixg3Q6LbOfxTgfXt4kDO6euSq5XA6fz8eHH35IOBzmq1/9Kvl8nhUrVjA4OCgNu2J8OGwNMeyObf7sZz9j2rRpHH300SMeI0ppTCbTqEIg48Fw9zDsdlF3dHTw/PPPc/rppzN37lxefvllKVdZWVmJ1WoFdg+mNWvWyEWFcGdv3boVnU7HxIkTZbeUdevWSaP8eXjmmWdoaGjg2WefLfA03HHHHZ/73HsyODjIWWedRSqV4s0335QuNIXiUObcc8/FYDDwxBNP8K1vfeuAnXf4eBMeLZ1Ox86dO8nlchx33HH87Gc/4//+3//LCy+8QD6fp6+vD5/Ph9lslp2XoFBQSJDL5ejo6GDTpk0EAgEWLFjAtGnTePzxx/fr9VO75LHhsIwRC66++mruuOOOvUpvhlNaWsqpp57KAw88MKJa055lSWONGFjZbJZ0Os2uXbv4y1/+wrPPPks6ncbhcGA0Gtm2bZssZ+ju7sZqteJyuXC5XGzdupVsNktNTQ2VlZWsWbOGnTt3otfrZZb053UxiZ398Pd+9NFHfPDBB5/jt9+bWCzGl7/8ZTo7O3nppZdobGw8oOdXKMaK2tparrrqKl5++WXuv//+EY/5LGNPGOLhcpZiF5vL5ejs7GTVqlXccsstlJeXo9fr6enpYevWrezYsYMtW7bIzmxdXV3EYjFisRgDAwPA7th2KBTimGOOwWazEY1GeeGFF+jt7S3owrTX74IGKmdrTDisd8R1dXUFmq2j8etf/5p58+Zx1FFH8e1vf5uGhgZ6e3v54IMP6OjoYP369WN/s/x9UAr1LBGraW9vJ5vNYjabiUajmM1mGhsbiUQiwO4Bf8wxx5DP56muruYvf/kLL7zwAmVlZbKfsUjE2nMQfVY1rfPPP59nn32WRYsWcd5559HS0sJvfvMbpk+fvldM7PPwrW99ixUrVnDVVVexZcuWgtphp9PJhRdeeMCupVAcaO69915aWlq44YYbeOqpp7jgggsoLS1lYGCA5cuX88ILLzBlypRPdc7hY1nIWQIyk7q7u5t/+7d/4ytf+Qo2m43S0lK+//3vs379elpaWqRKXzqdpr29nfb2duDv84/L5eLKK6+kpqaGzZs3s3r1atm9TcSYBRoHZge8a9cuqbgn5IeFWE9dXZ1Sy9MOMa644gqtrq5uxNfq6uq08847b5/vf+SRRzRAW7lyZcHzzc3N2j/8wz9o5eXlmslk0qqqqrTzzz9fe+aZZ/b73mXLlmmAtmzZMvncggULtAULFux1zNNPP13w3paWFg3QHnnkkYLn77vvPq2urk6zWCzacccdpy1fvlybO3euds455+z3vW+88YZ28sknazabTXO73doFF1ygbd68ueCYO+64QwO0/v7+guevuOIKzeFw7PW5LViwQJsxY4Z8nM/ntZ///OfyHufMmaO9+OKL+/z7jIbD4dCuuOKKEV+rq6vTgBF/RrsOoH3ve9/7VPegUHxW9vedz2az2iOPPKItXLhQKyoq0oxGo1ZSUqKdfvrp2m9+8xstkUgUHA9od9xxh3ws5p2WlpaC4+6//35t6tSpmslk0srKyrRrr71WCwaDBcfsOW4Fo82Ve46dYDCoXXnllVpJSYnmdDq1s88+W2tqatLq6upGHbMjsXLlyhHnKoGYH0f6GT6P7nn8nvPpFxWdph1aTv/Fixfz1ltvsWbNGoxGI16v92Df0riQz+fx+/1cdNFFPPjggwf7dg5JAoGA/Jy+973vjeoOVCgOJEfqnHQwyOVyBINBli9fzoUXXsjTTz/NxRdffLBva8w5JGPE7e3t+P1+5s2bd7BvZUxIJpN7xY5+//vfEwgEPlFrxSOVhoYG/H7/wb4NxRHIF31OOlT4+OOP8fv9R1xI6pDbEW/evJmuri5gd4zwhBNOOMh3dOB5++23uemmm/ja175GcXExa9as4eGHH2batGmsXr16vw0ljlTeeecdWc9cU1PzqWNvCsVn4UiYkw4VotEoH374oXw8a9YsSktLD+IdjQ+HnCE+EmhtbeX73/8+K1asIBAIUFRUxJe//GXuuuuuI+JLp1AoFIq/owyxQqFQKBQHkUMyRqxQKBQKxZGCMsQKhUKhUBxEDmtBj0MZIU85EiO1FvN4PMyYMQOdTkdbWxvpdBqr1YrBYGBoaIjy8nIMBgPxeJyTTjqJwcFBmpubqa6uxmw28/777xONRkeUtBOPh18vkUgcyF9XoVAcIMxm894iPGLO+ITn2J+Ij5gfPqkO/fD3De9TrDgwqBjxGCEM8b4GhHitsrKSuro6Wlpa8Pv9VFVV4ff7SaVSlJaWkkqlOOqoo4hEIiQSCRobG3E6nQwMDPDiiy+yadMmpk+fzurVqwtkPPf1p1WGWKE4NBE90wv4BIZ4eNOHT8snNQPKEI8NyhCPEcN3xCMNDNEhpaqqiuLiYkKhEIsXLyYej7N69WosFguxWAyXy0VFRQXTp0+nqqqKTCaDyWTC6XTKQffSSy/x2muvMWXKFNauXUtHRwegDLFCcTgymiHenxGG3RrxNpsNh8OB3W7HbrdjMBiIxWJEo1FCoRCpVGpEj9knQRnisUG5pseBPVep4v8+n4+ysjICgQCzZs1i+/btGAwGstkskUgEnU5HSUkJFouF0tJS9Ho9DocD2K1AY7VasVgsnHXWWVRXV/Pggw8yZcoUAoEA8Xh8VLeTWnspFF8sjEYjFRUVTJ06lerqajweD9FolEQiQUlJCeXl5WQyGTo7O1m/fj2bNm2SoSwxPw3/d080VL+HsUTtiMeIPWPEexpig8HACSecwK5duzj22GNJJBKEQiHcbjfhcJhoNEpZWRkTJ05kzpw5VFRUyPcZjbvXT5qmYbFYZAeWxx57jCVLllBVVcWqVavIZrOjGmLR21ihUBxajLQj3pe72el0cuyxxzJ58mSGhobo7e0lnU6TTqcxGAzo9XoMBgMmkwm/38+sWbMIBAK88cYbNDU1kclkRnRpD587hCFWO+KxQe2Ix5g9v9zicW1tLbFYjKqqKnK5HNlslkQigaZpsrewz+fDYDCQSCTQ6/XY7XYSiQT5fJ5sNks0GsVkMlFeXs7g4CCZTIaamhoMBgMNDQ1s27ZtxBXuZ4khKRSKQwudTofP55NKX8uXL6eqqoqamhosFgsmk4lEIkE8Hpc5J319fbzwwguUlJRw7rnn4vP5+OCDD6Ri3b5QO7axQ5UvjTM6nQ6j0Uh1dTXBYJDy8nJCoRBDQ0PkcjmSySR6vR69Xi9bHJpMJkwmE0ajEYPBIFevdrsdi8VCJBJh1apV7Ny5k6qqKgYHB2loaJDZl8rwKhSHMaPkmHi9XubNm0c0GmXXrl1UVlbidruJRqNEIhEikQjxeJyioiIsFgtOp5NJkyYxdepUwuEwr776KqeeeirHHHOM7JO+5zXk/1Gu6bFEGeIxZE8DKL7sVVVVpFIpjj76aKqqqmTf0Ww2Sy6XA3a7gIqKimhsbKSqqoqSkhIMBgNut5tJkyYxadIkzGYzFRUVZDIZotEoFosFj8fD0UcfTS6Xo7KycsT7UCgUhzc2m43jjjuOWCzG4OAgfr+fTCZDd3c3/f39Mj4s5gYxt+RyOVwuF9XV1VRWVvLiiy9y2mmnMWHChIJ5QsSOFeODMsTjhPiSWywWpk6dyuDgIDNnzsTtdlNaWkpxcbGM/ebzeSwWCy6Xi3w+TzweZ+fOnWzcuJHW1lY+/vhjNm/eTFlZGT6fj0QigdvtpqGhgWQyST6fp6+vj5kzZ2IwGAqur1AoDi/2HLkGg4Fp06Zhs9lob2/H4/GQSqUYGBggFouh0+nIZDIkEgmy2SzBYJCenh5pmIeGhsjn89LTtmrVKhYuXEhxcfHfr6nmi3FFGeIxYqQsaZ1OR21tLTqdjtLSUhnDcbvd2O12zGYzBoOBXC5HNBpl27ZtDA4OsmHDBvr6+kgmk2zfvp2BgQF0Oh3d3d1s2LCBlpYWYrEYQEF5k8lkorS0tKB4X6FQHL7odDoqKyuZNm0a27Ztw+l0AjA0NEQmk5E5IalUing8TjweJ5lMEggECIVCpNNpmWNiMBgoKiqir6+Prq4ujj/+eGw2W8G1FOODMsRjzPCyALPZzMyZM9m+fTsVFRV8/PHHtLa20tvbSzKZlF98vV5PNptlaGiIeDxOV1cXy5YtIxgMkkwmGRwcJJfLkUqlaG1tpaOjQzbUtlgsGI1G7HY77e3tzJ49W7rElatJoTiM0On2ig/b7XbmzJlDS0sLZrMZq9VKKpWSVRDxeJxEIkE6nSaZTMpkrWg0SjQaJRaLYTQaSaVSGI1GzGYzJSUl9PX1ydaiygCPP8oQjwMiYUq4jgFKS0sxGAyEQiGCwSADAwMyPmw2m+Vg6erqQqfTYbVa2bJlC/l8Hr1ez9atW1m9erUU70gkEnR3d0t3lMFgIBAI0NDQIMsh1ABTKA4f9kyQ0ul0TJ48GZPJRDgcxuVyodfrZTxXjPt8Pk86nZaL9VQqRSaTIZfLkU6nZaKnpmkYjUYcDgdWq5UdO3Ywf/58nE6nmivGGVW+NMaI3bBer2f27Nk0NTXh9Xrp6uoiFosRi8WkS0mIeZjNZux2O4FAgP7+fjkocrkcBoMBr9dLPp+XK+J0Ok1nZyepVAqdTkcsFpM7Y6vVitfrlUpan1ZbVqFQHBq4XC5mzZrF+vXrpREWxlfMHUajUS7o8/k8+XxeVloAmEwmNE3DZrNJQQ+z2Qzs3k17vV4aGhrYsGHDXtdXs8bYoXbE44BOp8Nms+Fyuejt7cXj8ZDP54lEImSzWfR6PVarFZPJJEuXXC4XbrcbvV7PwMAAgUCAoaEhmpubaWtrQ6/X4/f7iUajbNmyhW3bthEOh4lEItKwZzIZ8vk8brdbrXAVisOc+vp6nE4nuVxO7mh1Oh3ZbBZA7oDF4+HiP8BeylnCpW0wGPD7/WSzWXp6epg3bx4ej2ev66sZZOxQhngMGV7D6/f7CQQC5HI5aZRnz55NUVERRqMRr9eLzWaT8dxcLkdZWRklJSWyhthsNlNZWUlFRQVOp5Oenh5Wr15NW1ubjA2JgSiSvsLhMOl0+mB+DAqF4nNiMpmYM2cOPT09OBwO9Hq9nF/Eghsgm82SyWTIZDIyMWu4qzoSiQC7d8viuP7+frmA37BhA/PmzWP27Nlq8T6OKNf0OGG322VGol6vl9mMmUxGupaMRmNBCZPdbuecc87hvffeo7a2FqvVysaNG9m0aRNWq5X+/n5ZriBwOBwUFxfj9Xpxu91omkYsFitwR6sBplAc2uw5Rj0eDxMnTpSqWGLRnkgkMBgMmM1mNE0jn8+TSqVkLNhsNmOz2TCbzTJOLM7tcrmkFy6VSmEymejr62PdunWceOKJfPDBB0rOcpxQhngMGa7fqmka0WgUg8GAw+FAp9PR29tLKpXC5XLhdDoJBALSEItY8fHHH08qleKKK67gueee48MPPySZTBIOh9Hr9VgsFpmMpdfrsdls6HS6gqxIsXIerVexQqE4tNiz5LCqqopYLEYymcRisWA2m8nlciQSCUwmE/l8vmB3LPJS7HY7brcbg8GAxWIp8LqJOLNIABVu7ra2Nume7uvr2+ueFAce5ZoeQ4bXD/t8PmKxGHa7nQkTJpBOp/H5fNhsNtlhSSRZAHJA/e1vf2PXrl0EAgFsNpscfGIXbTabcbvdsjg/n88zMDBAKBRCp9MRiUQoKSmR51WDSaE4fNA0Db1ez4QJE4jH4/K54bFhk8kkjbPVasVqteJwOHC73bjdbrlYF0ZZ5KEAsqY4EokwNDSE1WolFAphMBiorKxU3rNxQu2Ix5DhyRE+n4/+/n58Ph+tra0MDQ3JASJWpcOF1zVNo7OzkxdffJHS0lJ+/etfS2MtXE5iJet2u4HdRf3BYBCr1YrZbOaoo46it7cXv9+vDLBCcRgitOlramoYGhpCr9eTTqexWCw4HA4MBgORSASDwSB3xHq9XpZA2u12aUxFrokgmUwSjUalxG5RUREmk0nq3ldVVbF+/fqD9asfUShDPA7odDpcLhetra34/X4ikQhOpxOz2Uw2m8VkMhGJRKR7yGAwyIL8XC7HpEmTpEta7IYtFos03DqdTq6KTSaTfL/RaCQYDMoWivvqN6pQKA5NTCYTxcXFtLa2yrEfjUax2Wwy7yQej0sDrNfrMZlMcndsNBqlFv2exGIxeX69Xk8wGJQNI/x+P3q9nlwup+aMMUYZ4nFAGMpYLMaXvvQlYPfAEYlaouOS2WyWPUTF+0TBvdFolAkYwtiKuuJMJiMbPoi6wWw2KzMoXS7Xwfz1FQrFp2T4glkkccbjcSnCIR4LcQ4xV6TTaVkO6XQ6pdiH0WjcyxBbrVb8fr98bzgcxmQyyRarPp9PuabHCWWIx5Dh5Us6nU52WIrFYtKtLHbBsDvjWWRSi5aH+Xye119/Xa54jUajjO+I3bNwa0ejUaxWqxT9EG7v4e4ohUJx+KBpGgaDAZvNRjKZlOIbIhNaeNRsNhuZTEZqzotYcCaTIZvNSp2CfD4vjbNOp8PhcEht6mQyicvlwmAwEA6HZZKXiEUrxg41Q48RI60k9Xo9TqeTaDRKMBgEdg8ou92O0+kskLmcMmUKfX19DA4OFijg+Hw+AKlNbbVaicVi6PV6HA6HbPggypby+TxGo1GusJWLSaE4tBFGcvgckk6nyWazuFwuMpkMZrNZJmiKY00mE3a7XXZWEvNGOp2Wu+bhVRyijlgYYrvdLt3QiUQCr9c7Yp9ixYFHfcpjhDB6w3/sdjuDg4Nks1lZaK/T6UgmkwwMDJBIJNA0jZNOOomzzz5bNn0QxlMIc5x22mlyZ5zL5bBarQW740gkQm9vL7Db/TR8RatcTQrFoc2eY1RkNgNyQS4SrVKplNw1Cy0Ci8UiY8m5XE6KfYhzi3rjWCwmBX/sdrvcUVutVgBp5BVjjzLE44CmaTIObDAYSCQSUu1GJEeEw2FSqRRWq5WvfvWrvPPOO4RCIYxGI36/X+6Ee3p6yGQyTJw4kVQqJbWqxeABCvSrhcFXKBSHD8M9V8PzRERmdDablf+PRCIFpY+wew4QGdEmk4l0Oi3d1rBb/tLlclFcXCw3Cvl8nlAoJDXrRUxZedHGHuWaHgc0TSMSieDxeHA6nQUDQmQ+iy+/yHT++OOP0ev1eDweWYifyWQIh8O89dZblJWVAchuTsLgiiQwp9Mpk7WGi74rFIpDG43dus7CRS08XrA7ybOoqAi9Xk8ymZTVEsPd2TqdTrqyheKWyE2x2WxyPhClSuFwmFAoJM+Vy+VwOp04nU4sFkvBfKVmkbHhkNsRL168WH6ZZs6cebBvZ1ROPfVUTj311P0eJwxgZ2cnlZWV9PX1YTKZ8Hq9+P1+GhoaqKqqkglVqVSKRx55RLqdnn/+edrb22WShshuFO0RhWKXiCcnk0mpXZ3P5/H5fHIlLThc3U1er1d+N66//vqDfTuKI4QDPSfpdDruvPNO+fjRRx9Fp9PR2to64vEmkwmz2UxxcTGxWEwmbno8Hux2O2azWSroDU/+BKQXTsSKh++khRE2m80UFRXh8XhwOBwAVFdXS0M8nvPFunXrChYUzzzzzLhd+2ByyBligJKSEpYsWcJdd91V8Hx9fT06nY4zzjhjxPc9+OCD8g+4atWq8bjVT0xXVxeNjY20t7dTW1vLjBkz5AARg0xkKLa3t8sSJkDGbhKJhBwUopRJlDAJEQ+n04nRaCSbzZJKpairq6O7u3vcdsRiUtnfT319PQBvvvkmV111FZMnT8Zut9PQ0MDVV19Nd3f3Xuf+7W9/y5IlS8bl91AohjPanAS7vVG///3vOfPMM2WTltLSUs466yx++9vffi69ZpE4ZTQamTdvnowJi5pf4UoWJU0iUzqfz+PxeGR4yuPxyNIkgHA4TDQaxWg04vF4MBqNJBIJKQg0YcIEuru7SSaTI84dwxcn+/pZvHgxACtWrOC6665j7ty5+4w919XVsWTJEn784x9/5s/scOSQdE07HA4uu+yyEV+zWq0sW7aMnp4eysvLC1574oknsFqt0l07lrz22mv7fH24zjQgVXFsNhvhcJihoSEikYgcVGKA5PP5Apm6s846C7/fX5D9LHqOikQMsZrNZrMyoUun07Fjxw4uv/xy3n777XHTmT7llFP2MpZXX301xx13HNdcc418zul0AnDLLbcQCAT42te+RmNjIzt37uT+++/nxRdfZN26dQV/40suuQSAyy+/fEx/B4ViT0abkxKJBIsWLeLVV1/lpJNO4uabb6asrIxAIMA777zDddddx0cffcTDDz886rkvv/xyLr300oI6X+GeBohEIrS1tbFw4UL+9Kc/yTCWx+MhFotJt7PImha7YPh7trXIExEJoj6fD5PJRH9/v4wXi+OrqqowGAzs3LmzwC09nO985zsFG6KWlhZuv/12rrnmGubPny+fnzhxIgAvvfQSDz30ELNmzaKhoYFt27aNeF6fz8dll13G22+/zc9//vNRP7MvGoekId4XJ598MitXruQPf/gDN954o3y+o6OD9957j0WLFvGnP/1pzO9DuIr3h3AfZ7NZli1bxjnnnMOf//xnjj/+eKk1ncvl2Llzp8x+FBqxkUhEdlLxeDwyg9rn88mMar1eL5tJiD7GAG1tbVLFq6urayw/igIaGhpoaGgoeO673/0uDQ0NI05k//Ef/8G8efMKyiTOOeccFixYwP3338/PfvazMb9nheKzctNNN/Hqq69y7733FsxHAD/84Q/Zvn07r7/++j7PITKeRyOZTNLU1MScOXM444wz+Otf/ypLFa1WK6lUilAohNVqlXOFqCMWc4Zo8CBixul0mt7eXsxmMy6Xi2QySSQSIZlMMnPmTDRNY+fOnaPWEJ944omceOKJ8vGqVau4/fbbOfHEE0cc59deey233HILNpuN66+/flRDfKRySLqm94XVauWiiy7iySefLHh+6dKl+Hw+zj777BHf19TUxMUXX0xRURFWq5VjjjmG559/vuAY4VZdvnw5P/jBD/D7/TgcDhYtWkR/f3/BsXvGiN9++210Oh1//OMf+fd//3dZIJ9KpWQR/fr16zEajZSXl7Ns2TKeeOIJ7rvvPn7/+9/T0dHBmjVrWLVqFblcjnQ6TTQa5bnnnqOrq0vKXWqaRktLC8uXL+fNN9/k3XffZc2aNdIoC0GQ1atX89e//pU//OEPhMNhEokEiURC1g22t7fz1a9+FbfbTXl5Offcc0/B75dOp7n99tuZO3eujB3Nnz+fZcuWfY6/3t6ccsope9UqnnLKKRQVFbFly5YDei2F4kDS3t7OQw89xDnnnLOXERY0NjZy3XXX7fM8o8WIc7kcmUyGeDzOH/7wB376058ydepUXC6XrKh4+eWXefnll4nH47zyyiv87ne/46GHHqK9vR2j0Uhvby9vvPEGv/3tb3nyySfZtm0bg4OD0vDncjnee+89nn32WV555RXWrl3Lm2++ybJly9iyZcteHrTPGi0uKyuTcpyKvTnsDDHAN7/5TVasWEFzc7N87sknn+Tiiy/GZDLtdfymTZs44YQT2LJlC7feeiv33HMPDoeDCy+8kOeee26v42+44QbWr1/PHXfcwbXXXssLL7zwiZOD7rrrLp577jkZsxnezCEajbJ06VI8Hg/r1q3DbrdzyimnUFFRwaZNm2TcV7iThq+SHQ4HLpeLRCLB+++/TzKZpKGhgZqaGsLhMK+99hr5fJ6SkhKam5upq6sDYOPGjQAFdce5XI4zzzyTqqoq7r77biZNmsTNN9/Mu+++K68XDod56KGHOPXUU7n77ru588476e/v5+yzz2bdunWf6LP4rESjUaLRaEHXKIXiUOPll18ml8uNGkb7POSyWVmSJBo6rF69mh//+Md85StfIRQKMTg4KMU43nrrLSorK1mwYAEGg4E///nPbNq0iffee4/KykqOO+44MpmMnCfy+TwOh4OOjg56enqorq6mtraWefPmsXbtWu677z46Ozv3ui+VNT02HHauaYCFCxdSXl7O0qVL+clPfsKWLVtYt24d//Vf/8XOnTv3Ov7GG2+ktraWlStXyjjMddddx7x587jllltYtGhRwfHFxcW89tprMsabz+e57777CIVCeDyefd5bMplk3bp1uN1u+f7s/xtUer2eLVu2sH79eurq6iguLqa6uprS0lIsFgvr16/H6XTKGJCIz4i648HBQVauXInZbObYY4/FaDSSSqWYNGkSb775JqtXr2b27NnkcjlZ3iQUd0StYCqVIpvNcuWVV3LLLbcA8I1vfIPKykp+97vfccoppwDILlHDXfDf/va3mTp1Kv/93/+9z5jX5+Xee+8lnU7z9a9/fcyuoVB8XpqamgD2yqROp9OEw2H5WKfTUVxc/InPK2p6hUiPyIR2Op3s2LGDQCDAokWL+POf/yx3zPPmzaO2tlZqDvzpT3/ilVde4fzzz8fpdFJeXo7D4eCNN96gs7MTr9dLLpejvr4en89HPB7H7/dz00038fTTT/M///M/BXK6ygCPLYfljthgMHDJJZewdOlSYHeSVk1NTUGSgCAQCPDWW29xySWXEIlEGBgYYGBggMHBQc4++2y2b9++18rvmmuuKUi0mj9/Prlcjl27du333q688soC4yW/yP8veSudTpNOp5k7dy5nnHEGW7duRa/XM2XKlIL3pVIp2X9U9BhOJBKEQiEmTZqEwWDA7/fLxK7S0lLa29vZuXMnX/7yl+no6JCflUCUNwD84z/+o3ze6/UyZcqUgkWM6HUsrh8IBMhmsxxzzDGsWbNmv5/DZ+Xdd9/lpz/9KZdccgkLFy4cs+soFJ8XYWxF4qHgpZdewu/3yx/hnfqkaP8vsUpvMKAbNn8kEgn0ej1PP/00J5xwAt/73veA3WNVZD6LsiZRkiS8StlsVrZLDYVClJWVUVJSQjabJRwO4/P5uOaaa2htbZULDKU9MH4cljti2O2evu+++1i/fj1PPvkkl1566Ygp8Tt27EDTNG677TZuu+22Ec/V19dHVVWVfFxbW1vwulC1EvrQ+2LP9+6J+HKvXr2aiy66iB07dtDS0kJDQwNOp1PGgcUOGnYLuO/Zr1gIvdtsNkpLS/H5fPT29nLyySej0+mkUR2t7eGebl+Px8Pg4GDBc4899hj33HMPTU1NBdeeMGHCfj+Hz0JTUxOLFi1i5syZPPTQQ2NyDYXiQCESI6PRaMHzJ598skzQ+tWvfsXy5cs/1XnFaB2uCz1cK37btm08/fTTfOMb36C+vp6Wlhbi8Tj5fF5604aLc6TTabq7u0kkEsDu+U68J5PJYLfbWbVqFRdccEHBXKEM8fhx2Bri448/nokTJ/JP//RPtLS08M1vfnPE40Ta/s033zxqItekSZMKHo+WwfhJvpjD37vnl3r4497eXh588EEWL17MO++8w+rVqwvaHxYXF0vDKIyzEP0QtcdGo5GioiJ0Oh2BQACAiooKfve738kSrj3LqPbF8Pt7/PHHWbx4MRdeeCE/+tGPKC0txWAw8Itf/KIgNn+gaG9v56yzzsLj8fDSSy+p1o2KQ56pU6cCu/MwZs+eLZ/3+/2ytOfxxx8/4NfNZrMsXbqU/v5+0uk0JSUl3Hjjjbz44ot0dHRIfQKj0UhJSQlDQ0MYDIaCxbfb7eaMM87gnXfe4a9//SvnnXceAGvWrKGvr69g4Q2ApsFhKgR0OHDYGmLYHdv82c9+xrRp0zj66KNHPEaU0phMplGFQMYTYRRzuRxr1qzBYDDwve99j+rqan7xi1/g8Xjw+Xx4PB6GhoYAGBgYwOFw4Pf7AWSihXB1NzU14XQ6iUQiPPDAA3tleAs+zQr3mWeeoaGhgWeffbbAkN9xxx2f8TcfncHBQc466yxSqRRvvvkmFRUVB/waCsWB5txzz8VgMPDEE0/wrW9964CdV4y2PRfRw9sXRiIRXnzxRalN4HQ6+fGPf0x/fz/bt29n+fLl6HQ6QqEQDocDi8Ui49THH388d955J7lcjjvuuIPZs2eTTCbZsGGDnHMU48thbYivvvpqDAYDxx9//KjHlJaWcuqpp/LAAw9www037DXJ9/f3SwN3IBnNJTw8gSuTyfDRRx8RCAQ46qijyOVymM1ment76e3tla7wSCQiV78ul4vm5mYqKytpaWlh8uTJnHLKKdx9991YLBb6+vpGNbifRqpO7M6HTwYfffQRH3zwwX7d75+GWCzGl7/8ZTo7O1m2bBmNjY0H7NwKxVhSW1vLVVddxYMPPsj9998/YmXFZ3Hv6vR6yOXI53K78zqGJY3C3/NOhIBPLBbjpptuYu7cucycOZP6+nqsViv19fXcdNNNGI1GIpEIgUCAe+65h127dnH33Xeza9cukskkmzZtQq/XFzR/2Pum1G54LDmsDXFdXV2BZuto/PrXv2bevHkcddRRfPvb36ahoYHe3l4++OADOjo6WL9+/QG/t30ZQyFBmU6nMRgMbNy4kQ0bNuB0OikuLuaf//mfaW1tZc2aNaxbt476+nqqqqqIxWJMnjyZNWvWsGHDBs477zw6Ozt54oknPvH1Pynnn38+zz77LIsWLeK8886jpaWF3/zmN0yfPn2vmNjn4Vvf+hYrVqzgqquuYsuWLQW1w06nkwsvvPCAXUuhONDce++9tLS0cMMNN/DUU09xwQUXUFpaysDAAMuXL+eFF15gypQpn+qcIqkyn8+Ty2bJ6/UwLJN6z0xmETfesWMHzz33HE6nk8HBQTZu3MiPfvQj2fJQeMrWrVvHxo0bpb6BmIdG69amHQC39K5du6TinpAfFmI9dXV1Si1PO8S44oortLq6uhFfq6ur084777x9vv+RRx7RAG3lypUFzzc3N2v/8A//oJWXl2smk0mrqqrSzj//fO2ZZ57Z73uXLVumAdqyZcvkcwsWLNAWLFiw1zFPP/10wXtbWlo0QHvkkUcKnr/vvvu0uro6zWKxaMcdd5y2fPlybe7cudo555yz3/e+8cYb2sknn6zZbDbN7XZrF1xwgbZ58+aCY+644w4N0Pr7+wuev+KKKzSHw7HX57ZgwQJtxowZ8nE+n9d+/vOfy3ucM2eO9uKLL+7z7zMaDodDu+KKK0Z8ra6uTmP3nLLXz2jXAbTvfe97n+oeFIrPyv6+89lsVnvkkUe0hQsXakVFRZrRaNRKSkq0008/XfvNb36jJRKJguMB7Y477pCPxbzT0tJScNz999+vTZ06VTOZTFpZWZl27bXXasFgsOCYPcetYLS5cs+xEwwGtSuvvFIrKSnRnE6ndvbZZ2tNTU1aXV3dqGN2JFauXDniXCUQ8+NIP8Pn0T2P33M+/aKi07RDKzVu8eLFvPXWW6xZs0Z2EToSyOfz+P1+LrroIh588MGDfTuHJIFAQH5O3/ve97j//vsP9i0pjgCO1DnpYJDL5QgGgyxfvpwLL7yQp59+mosvvvhg39aYc0jWEbe3t+P3+5k3b97BvpUxYaSOJr///e8JBAKfqLXikUpDQ8OYxPMViv3xRZ+TDhU+/vhj/H7/EReSOuR2xJs3b5ZNCpxOJyeccMJBvqMDz9tvv81NN93E1772NYqLi1mzZg0PP/ww06ZNY/Xq1Z+4ocSRxjvvvCPLKmpqaj517E2h+CwcCXPSoUI0GuXDDz+Uj2fNmkVpaelBvKPx4ZAzxEcCra2tfP/732fFihUEAgGKior48pe/zF133XVEfOkUCoVC8XeUIVYoFAqF4iBySMaIFQqFQqE4UlCGWKFQKBSKg8hhLehxKCPUqHQ63f/7v5Ry3+u44YpXI6lf7fmciCYMjyqM9Nywd/y/1/Z3nEKhONgInXUxRofPJZqmUVZWxoIFC8jn82zcuJFAIEAsFpMa9BaLBYvFQiQSIZlM4vP58Hq9hEIhEokEuVyOVCqFxWLB6XRSV1fH9OnTeffdd9m+fbu8D20PfXzBgRT0UexGGeJxYPiXWdjUfclNaiNozI70nuEymiMdr1AoDj9GGr9CgrKqqop58+bR3NxMW1sbiUQCm81GRUUFLpeLeDyOz+fDYDAQi8XYvn07LpdLtj1MJpPkcjlisRi9vb0MDg4SiUQIh8PMnTsXvV5PU1NTgfEf7Z4UBw5liMeJ0Xa6ww3p8BWopmmyIfjwwbDngBhtoAhjvvvfA/7rKBSKMWbPnbDX6+WUU05h3bp1dHd3U1paSk1NDQaDAZPJhF6vl12WAoGA7Fmey+UYGBigoqICu92OpmmyQ5PNZqO7u5vm5mYikQgnnXQS0Wh0rx7tirFFGeJxYM/V5d5u678fZzQacbvdlJeX43A45EpYp9NhNptl55Vdu3YRjUbJZDJ77aCHG/XC59XKVqE41BnJ06XX6zn22GPZtWsX7e3t1NbWUl1dTTAYxGw2U1xczNDQEB6PB6vVKucNl8tFJpPBaDQSjUapqqrCZDIRi8UoKipCr9dTX19PJBKhpaWF9evXc9xxx/Hqq68Sj8fVfDFOKEM8zuxpjAUWi4Xq6mocDoccAF1dXeRyOTKZDDabDY/HQzqdxmw209jYiNPpJBgM0tzcTCgUGvE6aiApFIcfe84PVVVVFBcXs3btWlwuF36/H7PZjMFgIJ1Ok8vl8Pv96PV6UqkUsVgMnU5HLpcjl8uRzWaJx+MEg0HKysooLS2VO1+TyYTZbKa8vJzW1laqqqqkuNDfvWojd5NTHBiUIR4nRtqxih1xaWkp9fX1pNNp2b7M5XIRiUQASKfT0jUlBl04HKarqwudTsecOXMYGBigqamJXC63125YJWgpFIcfYn4wmUzMnj2b7du3E4vFqK6uJhQKyUQsq9Uqj9PpdMRiMXw+H7C7haro2JTJZGQfc5vNRiqVoqSkhGw2SzQaxeVyEQ6H2b59O8cffzxNTU1qVzxOKEM8xowUGxZGUq/XU1tbi9PpxGKx0NjYiE6nI5PJSFe0yITM5/PSMIt2ibW1tUSjUZqbmzEYDMyZM4cNGzaQSqX2uge1qlUoDh+GL6JramrQ6XS0trYyadIkHA4H2WyWbDbL0NAQtbW1GI1G2R7RarWSTCZJJBJkMhny+Tzl5eVomobJZCKXy2EwGPD5fNhsNtrb22XcuLq6mpaWFnK5HLW1tbItqZo3xhZliMeY4TvT4TFhnU5HbW0tfr+f+vp6WV6QTCbJZrNkMhmy2Sxutxuj0SgNdyAQkLtiAJPJxJw5cxgaGqKzs5PJkyezefNmuTNWRlihOPwQyZoGg4H6+nq2bt2KzWajpKREGuJMJkM6nSadTmMymeQ4z+fzssRI9BfOZrMUFxfLPBPYPQdZLBbKy8tJJBIYDAYSiQRdXV1s376dSZMmsX37drLZ7MH8KI4IlKDHGCOM755u4vLycjweDxMnTsRoNDIwMMDAwACRSIRAIEAoFMJqtcq6QJvNBuweWIODg/T19ZFIJOTK12AwUFNTQyKRoKysbNT7UCgUhz5i4Szivj09PTidToaGhggEAhiNRoqLi6mqqsJmsxUs+F0uFxMmTKCiooKSkhJ0Oh19fX2Ew2Fp3EXWdD6fx2azkc1m6e7uJhwO43A4aG9vx2w2U1JScpA/iSMDtSMeY/bcBQPY7XZ8Ph9+vx+Hw0E0GqW/v59sNoumaWQyGfR6Pblcjng8js1mk0kXDodDxoij0ahMtHC73ZSUlDB16lS2bduGz+cjGAzulTmtUCgOD3Q6HQ0NDXR1dWEymfD5fKTTabLZLH19fej1eoxGo6y0EBnSkUiEkpISbDYbRqNR7miLioowGo0MDQ0Bu0Ncer1e9vh2uVwEAgH0ej39/f309/czYcIEent7AeWeHkvUjngcEUaxrKxM7mCF4c3n82iaJt3OuVyOaDRKMpkklUpJYyoMr1gxi3iwzWaTsSCr1UpZWZl0aSsUisMHMWadTic+n4/+/n7y+Tw6nY6Kigp8Ph9utxur1UoikSAWi8mkKp1OR0dHB5s2bSIWi+F2u5kyZQqNjY3Y7XYGBwd5//33Zew3HA4zMDBAb28vqVQKv9+Pz+fD4XDQ0dFBeXm59MapuWTsUIZ4nBA7Y6F6U1JSIlerwpjmcjlsNhvFxcUF9cNilRuLxWSh/syZM5k1axYul4tUKkVbWxv9/f0kEgkmTZpELpejvLx8v/KZCoXi0KS6ulomXIm5YmhoCKPRKGUwhY6AcDML9S2n00lbWxsdHR0yn6Snp4e//e1vJBIJGUNOp9MEg0Gi0SiDg4M0NTXJ3JS+vj6y2Sx1dXUqtDXGKNf0GCO+wJqmYTQaqaioIJ1OU1ZWRiwWo6+vT9b8mUwmpk+fTl1dHYFAgOXLlxMOh3E6nWiaRigUor6+noULF+J0OmlpaaGvrw+Xy0UymSSTyTA4OIjb7cbr9WI2mxkcHCSRSADKtaRQHA6IxMzq6mrC4TC5XA6n00k+n8dsNmM0Gunr6yOTyZDL5TCbzZhMJhKJBJqmyfCVyWSiv7+fzs5OfD4fW7duJRaLYTAYcDgcUlnLbDaTTqfxer1SJlOn05HNZunt7aWxsZEdO3aQzWaVMR4j1I54HBADy+VyydWpwWAgFArJFazYIdvtdrxeL6effjrf+MY30Ol0JJNJIpEIlZWVfOUrX6GiogKHw0E4HCYej2O1WiktLZVuK5PJhMlkwmq1Ul5eLu9BoVAcHjgcDjweD6lUCoPBgNPplPrRqVQKo9Eoc0asVismk4lUKkUul0Ov10uFLYfDQVdXFx0dHWQyGSkcVFZWRjabxWQyyVIom80mE7mMRiMmk0kmb5WUlMgdt+LAo3bEY8zwHbHf7weQxtLhcOD3+8lkMiSTSfL5PJs3b6arq4t8Pk9lZSWnnXYar7zyChaLhYULF2K1WonH4/ztb39j27ZtMoZstVqlqLtQ18lkMni93gI3typjUigOffx+P5qmyZpfp9OJ1+uViVeibMlms8mSJTG2M5kMbrcbu90utQcSiYTcDFRWVsqa4nw+DyDniWg0itVqxeVy4XA4SKfTRCIRamtr6enpkccrDixqRzzGiNiNwWCQguu1tbVMnDiRyspK9Ho96XSaZDIp1W6CwSAbN24km83S0NDAMcccw9lnny3Lkrq7u9m2bZuUuxStzYaGhggGg2SzWYqKiojFYgCyVGG0tmYKheLQQafTUVVVRTwel7vcfD5PKpUiEokwODhIOp3G6XTicDgwGo1SkGN4hYbBYKC8vByv10s+n8dut2OxWADkDjubzUp3dDAYlBr3mqZht9uJxWJEo1FKS0sxGtW+baxQhngcEJJysDu5wuPxEI/HcTgcAFIRRwwksTsOBAJs376dYDBIMBiUJU4Gg4F8Pi8NsejSlE6nZfF+aWmpXFE7nU55HwqF4tDGYDBQVFQkNQJsNhvpdJrOzk5CoRChUEi6iTOZTEFVhcg1EfOA1Wpl1qxZ+Hw+ysrKpOaA3W6XnjiPx4PJZCISiZDL5bBYLHg8noISSlEOpRgb1BJnjBEDRAwsMYB6e3vlF324cRUlTJFIhOXLl8v+oX19ffT19VFSUsKMGTOw2+1Eo1HZsSmbzWKxWDAYDKRSKTloEokELpdL1g4q17RCcWhjMplkqZHY+brdbpxOp4wFCyEO2F2CJJKuRG2xcFVnMhnMZjMTJ04km83S09MjqzOSyaRcrOfzeSl/abFYsFgs5PN5qfAnjPeezWUUBwa1Ix4n7HY78XiciooKzGYz8XhcCneIHa3Y5YpErnQ6LVW18vk8VqsVTdNkbHi4uzmZTMrr2O122d4sk8lgMpkAlTWtUBwOiPyRXC5HKpXC4XBQWlpKdXU1LpcLi8VCKpWSeSDDjalAaBEMFwISu1/hiRMymiLDOpVK0draSiwWQ6/XS5e22F0XFRUpr9oYoXbEY8zwEgFN06ioqCAUCsmVpmjoIHbGYkVqt9sxmUwYDAaZbCXiRIODg3I3nM1mZexGDCxhpKurq+ns7JQrZZGxrVAoDl0sFgtGo1G6kb1eLxaLBZ1OJ+uKrVYrsFvIx2q1YrFYpEs6nU7LrGqx+E6n01LRz263k8vlpOCPSAYTi3ZN02T9sdFoJBaLyblLCIEoDizKEI8xOp0Ot9tNMpnE7/fjdrsZGBiQCVzw96J88a9wN8Pula1YheZyORKJhJSuFK4js9ksrycSt8QK1u12yxWvMsQKxaGP3W4HIB6Py/htX1+flLoVBlO4kGF3AqcIPxUVFVFaWio9aGLBLoxuOp2Wc4wwvKLZTElJiZTTjEaj6PV6EokE6XSa6urqgrlGceBQhniM0TRNGuJZs2bJVWgqlZJylcOVcXK5nIwliziNeN5sNhONRuUAGe6OGm6Y8/m8XOGWlJTQ09OD3W6X7muFQnHoYrFYSCQSxONx2YNcNGZIpVIyQzqfz7N9+3b6+vrk7tXr9RKNRsnn8/h8Pjwej9xF53I5uZMWC3yRICo0p4WnTSz+hdhHKBRi2rRpKmFrjFCGeIzR6/U4nU4CgQDl5eVS/1V0ThLxGlFUL9xJ4XAYnU6H1WqVg07oTwt3kyg/ELWG6XSaRCKB0+mU6jhClUsMQIVCcWhjMpno7e2VOSKiA1t/f79MxNTr9TQ3NxONRnE4HDgcDil7KQzp4OCgrNJIp9NS/EdsBkSGtTDSog2iMMrpdFqGukSITJUwjQ3qUx1jRJay0IQWNcNiVxwMBqUBFWIc+XyecDiM0WiUajeAbAIxXFdWDBidTielMkUZk8fjkYZYlEgpFIpDm3w+j9vtlnOFx+ORu1cRD+7s7CSVSlFbWyv7EwsvmRjrRqNR9jg3mUzSsA4X+BHH2Ww24vE4XV1dsnxJeOxE4qjI1lYceNTsPA4IyTmhN22xWHA6nZjNZoaGhhgaGiIejwN/L8RPpVKyR7HQkRZasoBcocLuuLBYLRuNRmnoLRaLHJgi/qxQKA5txO5UuJudTqecN4TBHBwclJ3W4O+7YKPRKN8rkjtDoRBtbW2k02k5B4gNgli49/X1kUqlqK6uRtM0qcjlcDjIZDIYDAYZMlMceNSOeIwRpQViYMTjcfmFFjHbWbNmUVRUxI4dO+jv7weQ5UyhUAiPx4PP5yObzcoMRtHg22KxEA6HZSalaKMo4sliR60E2xWKwwNhMHU6nWzoEI/HZaw4Eomg1+ulERYLfGGI8/m8jAuLxbk4X09Pj6xLFjko4XAYTdPw+XzSeycW8GLBb7FYZEKX4sBzxOyIFy9eLFeNM2fO/Nzn0+l03HnnnfLxo48+ik6no7W1teA4kZEoYsQ+nw+z2UwymSSdTlNSUsI555zDl770JRYtWsRpp51GRUUFFotF1g9mMhlCoRDhcJjS0lLmzJnDV77yFb7+9a/zzW9+k2uvvVaq5kSjUTmQhQ7t8JKm8WDdunXys9bpdDzzzDPjcl2F4rNyoOeHz4MIUQmXsfCaifIk0XtYLMjFuBYeMmGkhydjORwO8vk8O3bsYPv27axZs0bKZwLU1tZisVjo6+uT0pmapskKDSEadDArLy688MJD5m90oDliDDFASUkJS5Ys4a677trrtXw+z+9//3vOPPNMmcJfWlrKWWedxW9/+1vZM/jTItw8JpOJ1tbWgprg4d1ThOtn2rRpnHHGGdTX12O1WolEIkSjUbZs2SKl7WbOnElDQ4McYJMmTWLatGmytMFut8t2aYFAQMpmivsRDJ989vWzePFiAFasWMF1113H3LlzMZlMo+6w6+rqWLJkCT/+8Y8/02emUBwMRpsf6uvr0el03HDDDXu95+233x51sblp0yYuu+wyqqqqsFgsVFZW8q1vfYtNmzbtdaxYyOt0OrZt28YDDzxAe3s7q1evpqurS5YriRBVZ2cny5Yt4/333y+ouhDzSjAY5IMPPmD16tVYLBbq6+vlzlhkZL/33ns88sgjvP/++0QiEUKhkMxb0ev1uN1uNm3aRCQSkUZ7f1611tbWTzSnDN+0/Pu//ztf+cpXKCsr22uDM5ybbrqJJUuWMHXq1H3ew+HIEeWadjgcXHbZZXs9n0gkWLRoEa+++ionnXQSN998M2VlZQQCAd555x2uu+46PvroIx5++OFRz3355Zdz6aWXyrq+4QwNDVFXVycHVDKZlNnPQ0NDMtkqEongdrvx+Xw0NDTQ1taG0WiksrJSdlvp7++Xu9xUKoXFYpFuJ9GvVKyCRRakqC3es/vSd77zHc444wx5ny0tLdx+++1cc801zJ8/Xz4/ceJEAF566SUeeughZs2aRUNDA9u2bRvxs/D5fFx22WW8/fbb/PznP/9kfxyF4iAz2vwgePDBB/n//r//j8rKyv2e69lnn+Ub3/gGRUVF/OM//iMTJkygtbWVhx9+mGeeeYannnqKRYsW7fW+f/3Xf+W+++7j2GOPZeXKlbI++MwzzyzIZhZjOJFIEA6HpQqWcCn39PTI410uF+Xl5YTDYfmcTqejs7MTp9PJwMAAra2tlJWV4XQ6cblceDweotEosHuHXVZWRl9f335jxH6/nyVLlhQ8d88999DR0cF//ud/7nUswE9+8hPKy8uZM2cOr7766qjnXrBgAQAPPfQQAwMD+/kLHF4cUYZ4NG666SZeffVV7r33Xm688caC1374wx+yfft2Xn/99X2eQ/TxHIlEIkEsFivo5ykUcYaGhmhqamLatGnU1dWh1+ul+IbQeI3H49jtdlKpFNlslr/97W+cfvrpTJ06Vdb5tbS0AMgdt3h/Pp+nt7d3RLf0iSeeyIknnigfr1q1ittvv50TTzxxxAnp2muv5ZZbbsFms3H99dePaogVii8aM2bMYOvWrdx1113cd999+zy2ubmZyy+/nIaGBt59911pcABuvPFG5s+fz+WXX86GDRtoaGgoeO+5557Lk08+ybHHHoter+eDDz4gEAiwa9cuGhsbCxbSIi7c29sr5SdFPkhPTw9lZWV0d3fT29tLRUUF5eXlbN26VS7q0+k006dPp6mpiWg0ysSJEykpKZESvCKB1GQy4Xa7aW1t3W+MeKTFzFNPPUUwGBx1kdPS0kJ9fT0DAwMFn9WRxBFviNvb23nooYc455xz9jLCgsbGRhobG/d5nkcffZQrr7xSfqkEwiCuXr0aq9VKR0cHxx9/vNzR5nI57r33Xux2O9deey1Llixh586dmEwmGhoamDBhAmvWrKGzs5NIJCKTNfr7+znllFOYMWMG77//Pm+99ZYsMzAajdTU1DBjxgzy+TyDg4MH5LMSbRgViiON+vp6jj/+eB588EFuvfXWfe6Kf/WrXxGPx/ntb3+7l2EpKSnhgQceYMGCBfzyl7/kN7/5zV7vF5nMDQ0NrFixAthduiikakX8V9M0SkpK6O3tpbGxUWrWi8YOfr+f7u5u0um0zFGpqalhx44dUmfA5XLR0NBAR0cHXq+3IJdE1Ax7vV7ZEnEssqaHz5dHKkdUjHgkXn75ZXK53D5dUp8H4TY2GAy43W527drFG2+8IYvwfT6fjOn8x3/8B6WlpUybNg29Xs+mTZvo7OykubmZ8vJyZs6cSS6XY9OmTSQSCd5//33ee+89/vrXvxKNRqmurmbWrFlMnDiRjo4OXn/9dXbt2kUmkxmT302hOJL4l3/5F7LZ7Ig5JsN54YUXqK+vLwjvDOeUU06hvr6ev/71ryO+LsoRS0pKpDEUNcLJZJIJEyZIY+n1ekmlUlIAJJfL0dXVVdCgwWAwYDQayWQyNDY2YrPZSCQSUtN+0qRJhMNhmpubaW1tlUIh4XBYXiMajRIIBFT50hhxxBvipqYmgL2y8NLptKzjHRgY+Fy7ShGbTSaTzJw5k8HBQVpbW9E0Tbp/4vE4c+bMYebMmVRVVTFjxgw0TWPNmjUcd9xxTJ8+nbq6OmbOnEk2m6Wjo4NoNMoHH3yAyWRi3rx5zJ07l6OOOor58+czd+5cEomEdFmLhA6FQvHZaGho4PLLL+fBBx+ku7t7xGNCoRBdXV3Mnj17n+eaNWsWHR0dsl53+PsjkQjt7e00NzfLecftdsu64OLiYtlRLRaL4XA4pCHOZDIMDAxQUVEhjXg+n5chL6PRSG1tLblcDrfbTWlpKWVlZTgcDrq7uykqKpKlj11dXcDuMFpfX5/MoFYceI54QyxWfU6ns+D5l156Cb/fL3/q6uo+0/mFu0gY3WAwiMlkYuvWrdhsNpl9LMTVe3p68Pl8VFZWytiMGHhGo5HS0lIA2UZR0zTMZjNWq1XGhLu7u7Hb7bIfqUKhODD85Cc/2eeuWBhWITc5GuJ1Mf8IzjjjDAYHB3nttdf4/e9/j81mk6WJ8Xhchrrsdrus8XW73QSDQSwWCz09Peh0OoqKimTOSjablc1iOjo6GBgYwGq1smDBAimxW19fz65du3C5XJSWlsomNLDbkA8MDBAKhZQWwRhxxMeIxYAQGYKCk08+WSZo/epXv2L58uWf+1qaptHX1ycTKgYGBnC73bJeUDQDF3Ego9GI1WqV3VhEdrWguLgYs9lMMBikpaWF5uZmYrFYwTWHJ3eIe1AoFJ8NsSv+7W9/y6233rrX62I+2XOnuyejGexf//rX/PCHP8Tr9WIwGBgYGOCoo45ix44dVFRUYDKZSCQSUie6vLycbDZLd3c3fX199Pb2UlpaKhtHwO5kK6fTSWdnJ+l0mm3btlFZWYlerycQCKBpGmVlZWzatIlNmzZRVlZGb2+v7LQkFvfjqUVwpHHEG2JRk7Zx48YCd5Lf75elPY8//vhnPv9wTVdAdkgyGAwUFRXR3Nwsi/dLSkqA3YNUlCWJ+kBRdiDKo4Ynbmzbto3W1lYaGxspLi5m165d9Pb2qraHCsUY8C//8i8sWbKEu+++mwsvvLDgNY/HQ0VFBRs2bNjnOTZs2EBVVRVut7vg+eOOO07K1Iq5acOGDfj9fkKhEHV1dQVd1CwWC3q9Hp/PR1dXF0NDQ8yZMwdAGlK9Xs/Q0BAej4fW1lZSqRQtLS0ybDWcrq4u6d4+4YQTaGtrIxQKFfQ2Vhx4jnjX9LnnnovBYOCJJ54Yl+sJ45pMJtm4cSO1tbVSS1o09/b7/QVtyjweD0ajEbvdLtVzRC1gKpWivb2dmpoaamtraWtro7+/f69dsIoRKxQHhokTJ3LZZZfxwAMPjBgrPv/882lpaeH9998f8f3vvfcera2tnH/++aNeI5vNytpeUSfc2dlJNBrF4/HI8S+6M5WWljI0NITRaKShoUHOIeJcQrM6EAhgsVg4/fTTOf3005k5cybHHXccX/rSlygtLWVwcFAa7blz5wK73ecHqvJCMTJHvCGura3lqquu4uWXX+b+++8f8ZjPY8CGd0oa7ibWNI0tW7ZI5RudTsfWrVtl16Xi4mKZ7VhSUkJ7ezvw9yL4XC7H9u3baWpqQqfTkUgkWLlyJX19fXsZXRXXUSgOLD/5yU/IZDL88pe/3Ou1H/3oR9hsNr7zne/sZcACgQDf/e53sdvt/OhHP9rnNWKxGP39/Xi9XrZu3UpjYyM7duwgGAzKHarVasVms1FRUUFDQwNHH300qVSKVCol479ms1lmPvf29lJbW4vT6aSmpoaGhgasVitlZWXU1NSQz+eJRCKcdNJJUss6EolIo64W82PDEe+aBrj33ntpaWnhhhtu4KmnnuKCCy6gtLSUgYEBli9fzgsvvMCUKVM+8/nFl3e4e0en05HL5WhrayOfz2M0GmUT79bWVinqkcvlaG9vx2w2s2XLFioqKuQ5a2pqCIVCmEwm+vv75W55uCE+kANn165dUjVn1apVAPzsZz8DdstaXn755QfsWgrFoYzYFT/22GN7vdbY2Mhjjz3Gt771LY466qi9lLUGBgZYunSpVKzbk+GL9mAwKIV/ioqKiEQi7Nq1S3rLHA6HVNAbLpc7NDQkF+CZTEbGkbPZLFVVVSQSCfL5PD6fTzaJEAIhZrOZadOmsXbtWmC39rXIwNY0TY75U089lXnz5n3uz3LJkiXs2rVLVpC8++678hqXX375Z06UPZxQhhiw2+288sorLFmyhCVLlvDLX/5SuoNmz57N//zP/3DFFVd8rmsMN4jDDaYwttlslg8++ICioiKKiopkEkc+n5clCrW1tfI8oVCI5cuXE41GpRt7eI3fnrvvA0FLSwu33XZbwXPi8YIFC5QhVhxR/OQnP+Hxxx8fMRfja1/7GlOnTuUXv/iFNL7FxcWcdtpp/PjHP/5UTQsSiQQmk4k333yTH/7whyxdupRUKiW7ryWTSeLxuMwrEe5qoY8vdratra0YDAZqa2vp7+/HZDIRjUbJZrMkk0laW1vx+XwMDQ2xbt06Nm7cCCBlNQVizP/bv/3bATHEDz/8MO+88458vGzZMpYtWwbAvHnzjghDrNOOEF/D4sWLeeutt1izZg1GoxGv13uwb+kLixAoWb58ORdeeCFPP/00F1988cG+LYViVNT8cOgjmk989atfJRQKyYXCF4EjKkbc3t6O3+8/IKs4xeh8/PHH+P3+vTJKFYpDGTU/HNpcfvnl+P1+/va3vx3sWzngHDE74s2bN0ulGKfTyQknnHCQ7+iLSzQa5cMPP5SPZ82aJYVIFIpDETU/HPps2LCBvr4+4Iv3NzpiDLFCoVAoFIciR5RrWqFQKBSKQw1liBUKhUKhOIgoQ6xQKBQKxUFE1RGPEZ9VzUqv1+P1eqmurqayspKysjK8Xi8mk4lMJkMqlaKvr09qxYbD4c/UI1SlBigUhyZFRUWjvvZp5xWhJ7Bn85fRGC44NNrxgUDgU92DYv+oZK0x4rMYYqfTyZw5c5g8eXJBP+RUKkUmk8Fut2M2mykuLsbr9WK329m8eTNr1qyRqjSfFPVnVygOTUYzxOMpVbuv+UEZ4gOPMsRjxKcZNHq9npqaGqZOncrg4CAdHR2y56jJZCIcDpPNZvH5fBQXF6PX64nFYmSzWSZNmoTL5eLtt9+mp6fnExtY9WdXKA5Nhhviz2J8dTqd7HE+vOHLpx3zakc8fihDPEZ80gFkMBiYOXMmZWVlbNmyBYPBIHe+NpsNnU4nu6rAbgF3cW6TyUQoFMJsNjNr1iw+/PBDtm3b9qlcUAqF4tCiqKiowKX8SdHr9ZjNZkwmEwaDAfi7pn02myWdTn/mdobD36MM8YFHxYgPIjqdjmnTplFdXU1TUxNer1d2UxF9h7u7u8lkMpjNZjRNI5PJyN7EuVwOh8OBpmmsXbuWk08+mXw+z/bt2w/yb6ZQKD4vn9QI63Q6jEYjDodD9ik3Go2YTCZgd9OHfD5PKpUiGo2SyWQ+kw79J40zKz49yhAfRMrKypg4cSLr1q2jqKhIGmCXy4VeryedTsv2ZfD3gTB8pSz6khoMBlauXMnpp59OMBhkYGDgYP5qCoXiM/Jp3dEGgwGfz4fL5cJqtcouSgaDAYPBQDabRdM0UqkUOp2OaDRKKpX61EZYMXao8qWDhMVi4ZhjjmH79u2YzWbsdjs2mw273Y7BYJA9hkWLRLHqNZlM6PV62atYdGBxOp3YbDa2bt3KySefLF3ZCoXii4ter8fpdFJUVITP55MeskgkQjAYJJFIYLPZ8Hg8lJSU4PV6cbvdsgf6p0UZ5LFBzdYHAZ1Ox6RJk0gmk0QiEXw+n2wz5nA4pOtZ7Hx1Oh1Wq1UmX4g4j+gdarVayefzOBwO2traqK6upqGhgW3bth3k31ShUBxohhtDi8WCx+PBZrORyWSIRCIkEgm5mB8cHCQSiWC323E6nVRUVMiFfSgUkq0Slcv54KIM8UHA6/Vy1FFH8fHHH1NRUYHH4yGTychdrtFoRK/X43K5iEQiaJqGwWCQcWJRNywMsV6vR6fTYbFY8Pl87Nixg+OPP55du3bJgaZQKA5vhoemYLdL2uFw4PF4iMVihEIhdDodDocDn89HJpMhmUyi0+kIh8OEQiFqa2uprq6WHrNgMEgmkymI/6pY8PijXNPjjE6nY8aMGfKLbjabyeVy2O12PB4PJpNJuqBzuRz5fB6z2SybfVutVjweDx6PRyZniPdEo1EsFguZTAa/3091dfVB/m0VCsWBYE/jKLxkfr8fq9VKJBLBaDTidrspLy/ntNNOY/bs2TidTo499lgqKyvR6XR0dnYyNDREaWkpXq8Xh8NRkGGtjPDBQe2Ixxmn08nUqVNpa2vDYDDgcrnI5/OYTCa5uxVZj36/H9idkKXX6/F4PORyOTlYRKJWOp2Wu2aXy0UulyMUCtHQ0MDOnTvVwFIoDnP2LGUyGo14PB6Kiopka0CxQP/Sl77E2WefTVtbG83NzZxyyilMmDCBd955h76+PpnIWVZWRjKZJJ1Of2pBIMWBRRnicaahoQGz2Uw0GkWn08kdr4gLGwwG+X+Hw0EikSCRSGC32zEajdhsNgBcLhfxeJxQKAT8PXNSuK6EITaZTDL+rFAoDi9GS46yWCwUFRWRTCaJRqN4vV5phL/yla9QVVWF1WplwoQJJJNJZsyYgdlsZvny5ezatYve3l5qa2vx+XzSGGez2XH+7RQC5ZoeR0wmEzNmzCAYDJJKpbBYLLJMKRgMEolEZImByJguKirC6XSi1+uxWCxomkY6nSYUChGLxQrKlywWi4z9pNNpKioqcLlcB/m3VigUBxKDwYDT6cTtdjMwMIDVasXn8zF79mzOOeccJk6cSEtLC93d3dTV1bF582Y2bNiAz+fj6KOPZtq0aVitVnp6enA4HLjdbux2e4GIiMqOHl/UjngccbvdVFVVsXnzZilhGY/HpXs6nU5Lw6lpGtFoFL/fj9PplAMkkUgQDoeJx+NomobVagWQCV4iOSOVSknjrVAoDi/2Fas1mUx4PB6ZhGW325kwYQIXXXQRdXV1LF++nI8++ojq6mqy2Szbt28nm83icDg4+uijaWhooK+vj48//hiHw0FpaSmJRIJ0Ok0ymQQ+WfMHxYFD7YjHEa/XKwvq8/k8NpsNt9uN0WgscEsD2Gw2crkciURC1hAD5HI5YPeqeLgCVyKRkBmQmqYRi8WIRCLEYrGD88sqFIrPzGjGT1RHuFwuQqEQer2esrIyjj/+eGpra1m5ciXvvvsunZ2d1NTUkMlkyOVyVFVVcfzxxzMwMMD27dtxOBxMnTpVZlr7fD7sdrucf4bvitXueOxRO+JxxGg0Eg6HCQQC5HI5uau12WwyJiwyGJ1OJ4lEgh07duD1euXuV5Q3OZ1OdDodwWCQcDgsjbjItBZydqp8SaH44qDX67Hb7bK0saqqijPPPJOqqioAOjo6mDJlCqeddpoUCcpkMnR0dJDL5YhGo+RyOfR6PVVVVWiaJhvKRKNR4vG43BUrxg9liMcRkTgVCAQoLy/Hbrdjt9uJRqPo9XqpmgXI5g/CZd3R0cGkSZNwOp1yBy1Wu2J3DZDNZjGbzUQiEVwuFzabTQ0sheILgtFoxOVyYbFYSKfTUjErlUqRTCYJh8OsWbMGq9WKy+WitrZWigP19/fLigzhZTMYDPT19VFfX4/b7SYcDpNOp6VWwfCY8adtQqH45CjX9DhSVlZGIBDAYrFgMpmIxWJyVyxqgYWohxgojY2NeDweqqursdlschAKAyxcWLlcDrPZjNFoxGw2k81mSSQSeL3eg/tLKxSKz8RIRk9oCUSjUdlNKRAIyHpi0cfcaDRy0kknUVFRgdlsprS0lGw2K0shbTYb8XiceDxOKpWSlRnDvXKj3YPiwKMM8Tih0+koKSmhv79fupbMZjOZTIZMJiNFPAwGg+yaks1mMZlMmEwmysvL8Xg8clUqBN1Fq8RMJiMHp3BfB4NBWYusUCgOL4bHicW4F7ryIp6r1+sJBoOYTCasViuDg4OYzWb6+/vZtm0b2WyW2tpaWSucTqeJxWKEw2GZTyKkMR0Oh8w72dMAf5ZuTYpPjnJNjxPCeEajUVwuF5lMBqvVKo2xiNUIwyr0pIdnL+bzebLZrByA+XxeJnmJ9ogimctsNjM4OChd1gqF4vBFuIeFwRVhKSHw09zcTDgc5txzz6WyspJgMMjKlSuJxWL09/cTj8dlhYWQ0wWkap8omxSNZ4Snbvj1lREeO5QhHidEbCaXy1FUVCQbNaTTaRkLTiQSsnxJxGjS6bQU5BBNH8SgFBrTBoOBZDJJMpmUClv5fJ5oNIrT6Txov7NCofjs7LkrFXkkVquVcDgsF+c9PT10dnby3HPP4Xa7aW9v54ILLqCpqYmenh6CwaCcO8xmMzabTRplTdOkLK4ocbLZbHIO2df9KA4cyhCPEyIJS9TqiSxpj8cja4qFiIfYEYufSCQik7Tg77trUS8sDLXT6cRkMpHNZmXNsciuVqtZheLwYqTkKKPRiMVikaEp0fjBYDAQiUTo6uoCYN26dRx33HH09PTQ1dUl5wQhpSvmEiEClEqlGBwcxOfzSf36TCYz4j0pDjzKEI8juVwOm80m1bRSqRRms5mioiIsFgt2ux2TyYROp5Nyc2azmZqaGnK5nDTgw4/R6XQydmw2mzEYDKRSKbLZLKFQqCDxQqFQHL6IkJQw0OKx2WxmwoQJXHbZZSxfvlz2N6+srKS+vp5NmzZhNBrRNE3ODdlsVobGxE5bHCO8dyOhdsVjg0rWGieEOIdYfZpMJpn9nEwm5Zdf0zSCwaCsL/b5fLjdbnw+Hz6fTw6EfD4vpe1EFyaAaDTK0NAQ8Xi8wKArFIrDh5EMnjC8IolTLLKDwSBbtmyhpqaGr3/961RVVZFMJunp6ZGJVyaTSar0JRIJGScWc4doqSrOKyo3FOOD2hGPE0JkQ+hLx2IxHA6HrPWF3W6feDxOZ2cn06ZNk4ZaGFWr1Sr1ZbPZLMXFxZhMJhnnicfj5PN5qRubzWb3SrpQKBSHPiONWWEYc7kc2WxWljAmk0lCoRDvvvsuO3bswOfzceKJJ9LT08O2bdtkOaPNZsNkMpFMJqVuvSilFKEuk8kkw2OK8UMZ4nFCFNS73W6i0SgejweAWCwm3URiR1xSUiKzqcUO12AwUFRUhN1ux+1209nZKTOohxfbp1IpOThtNhvhcPgg/+YKheKzMDy3Y/j/hfcrm82STqfJZDLU1NRw7LHHcu6551JVVcX69etZunQpU6dOlYvyWCwm5xaDwYDZbJaLdzFvALJiQzF+KNf0ONLR0cGECROkYfX5fPj9fkpKSgrKCUpLS+XgEQlcw1W0jEYjRUVFMpkrm80SjUalpGU+nycWi+H3+wkGgwf5t1YoYPHixTLbf+bMmQf7dkbl1FNP5dRTTx3Ta7S2tqLT6Xj00Uf3edyexlCUM2YyGbxer5TGzWaz7Ny5k0AgQCgUYu3atZSWluJyuZg0aZLUFhDzSH19PRMnTqS6ulrq0efzeSmlK+aVka59uKPT6bjzzjvH5Nzr1q2T33GdTsczzzzzid97xBjiAz0R7PkHffTRR9HpdLS2to76HqF4Y7fbpVtIZC0KV5BIxIpGo0QiEZkRLXbHgUCASCQimz2I+uNYLCZrk4VhrqmpOSg74s/zhVR8cSkpKWHJkiXcddddBc/X19ej0+k444wzRnzfgw8+KL9Lq1atGo9bPeTQNE2WO4oQl8fjIZ1Ok0gk2LlzJ2+//Tbd3d0YDAbKy8s599xzGRgYIB6PYzKZmDZtGhMnTqSxsRG/3097e7t8vaioCJfLRSwWk4v74SIeqVSKQCDA4OBgwdje86e+vh6A7u5ubr31Vk477TRcLhc6nY6333571N8vl8vxyCOPcOqpp8rk1fr6eq688sr9/s3Fwkb8GAwGamtrWbRoEevWrTtAf4H9U1dXx5IlS/jxj3/8qd97RLmmS0pK+M///M8RZR/z+TyPP/44S5YsYe3atYRCIdm/8+KLL+aKK674zC0FhVspnU6zY8cOjjrqKDZt2iRrAl0ul1TTgt1Gd2BgAECKdRiNRuLxuGz4INxTQtBDaEoLRS2RhT00NDTqfS1evJjHHntsv/d/xRVX8Oijj7JixQoeffRRPvroIzZs2FAwWIcjvpBbtmzh5z//+af8tBRfVBwOB5dddtmIr1mtVpYtW0ZPTw/l5eUFrz3xxBNYrdZx0Ux/7bXXxvwadXV1JBKJgjE/Enu6pnO5HJlMhmQyKY3Nxo0bCYfDmM1mmpubcTgcTJ48GZPJxMKFC3nmmWfIZrOUlpYSi8UIhUJkMhnC4TBtbW3E43EA/H4/RqORaDRKJpORzWLE9Y1GIw6HA4Df/OY3AFx99dUcd9xxXHPNNfKehW7B1q1bufvuu2lsbOSoo47igw8+GPX3TCQSXHTRRbzyyiuccsop/PjHP6aoqIjW1lb++Mc/8thjj9HW1kZ1dfU+P69vfOMbfPnLXyaXy7Flyxb+93//l5dffpkPP/yQo48+ep/vPRD4fD4uu+wy3n777U897x1Rhni0iSCRSLBo0SJeffVVTjrpJG6++WapC/3OO+9w3XXX8dFHH/Hwww+Peu7LL7+cSy+9dERjPdxYbdmyhTPPPJMNGzYQCAQoKSkhm82SyWSw2+0yacLr9cr47+DgoFwRi9dFPEfoTYt4TzAYpK+vj9NPP53Vq1fvsw3id77znYJdSEtLC7fffjvXXHMN8+fPl89PnDgRgJdeeomHHnqIWbNm0dDQwLZt20Y87+f5QiqOTE4++WRWrlzJH/7wB2688Ub5fEdHB++99x6LFi3iT3/605jfh1jgjiUi8XJ/7Clxmc/niUQiDA4O0tvbS319PUNDQ2zbtk26nsUO8Etf+hKTJk1i69atGAwGufnQNI3169czMDAgJXErKiooLy+nu7ubSCRCIpGQrmmxGBCyuYCcQ7/73e/S0NAw4pw6d+5cBgcHKSoq4plnnuFrX/vaqL/nj370I1555RX+8z//k3/6p38qeO2OO+7gP//zP/f7WYnfefi9nHzyyXzlK1/hf//3f3nggQc+0TkOFkeMa3pf3HTTTbz66qvce++9LF++nFtvvZUrr7ySH/7whzz//PNs2bKFuXPn7vMcBoMBq9W632zD7u5u1q9fzwUXXEAkEpG73+GrfVFWYLFYSCaT5HI5qUUtdqHDmz5ks1lSqRQGg4HBwUEqKiqoqKhg+fLle6njDOfEE0/ksssukz/nnnvuiM+feOKJAFx77bWEQiFWrVrFmWee+Uk/XoViv1itVi666CKefPLJgueXLl2Kz+fj7LPPHvF9TU1NXHzxxVKt7phjjuH5558vOEaEjZYvX84PfvAD/H4/DoeDRYsW0d/fX3DsnjHit99+G51Oxx//+Ef+/d//nerqaqxWK6effjo7duzY635+/etf09DQgM1m47jjjuO9997b65yjxYjfeust5s+fTyAQIBgMEolE9orV9vX1sXz5cnbs2MGzzz7L888/z+bNm9m4cSP9/f20trZyzz33cO655zJ9+nSee+45uZjfsmULa9eu5f333+dvf/sbq1atYsOGDWzYsIGWlhZ6e3tJJpPEYrERtaU/TZzY5XJRVFS03+M6Ojp44IEHOPPMM/cywrB7Lrz55pv3uxseiYULFwK7NxijsXjxYulOH86dd96511z++uuvM2/ePLxeL06nkylTpnwmN/RIHFE74pFob2/noYce4pxzzilYiQ+nsbGRxsbGfZ7n0Ucf5corr6SlpWXEP6wgl8vx17/+lRtuuIHZs2ezatUqent7yeVynH766SxfvpyBgQHsdjvHHnssPp+PYDDI5s2bGRoawul0Mm/ePKqqquROuru7m23bttHf308ymcRqtbJ582YCgcDn+Wj2oqys7ICeT6EYzje/+U3OOussmpubpRfmySef5OKLLx7Rjbtp0yZOPvlkqqqquPXWW3E4HPzxj3/kwgsv5E9/+hOLFi0qOP6GG27A5/Nxxx130Nrayr333sv111/PH/7wh/3e21133YVer+fmm28mFArxy1/+km9961t89NFH8pj//d//5frrr2f+/PncdNNNtLa2cuGFF+Lz+fZrSN544w3OPfdcacQB2dbQ7XbLZE5hCNeuXUtlZSVf/vKXWb9+PS0tLeh0OiKRCF6vl9raWgYGBti+fTt6vZ5MJkM6nSYajdLb24vb7aa+vh6/38/GjRt5/vnnaWxsJB6PFyhqCWM0VolaL7/8Mtlslssvv/yAn7u5uRmA4uLiz32uTZs2cf755zNr1iz+9V//FYvFwo4dO1i+fPnnPjcoQ8zLL79MLpcbNXY1FgwNDbF06VJ+8IMf0N7eTkdHB5lMhpdffpmamhomTZrEpk2b5Ap55cqVTJ48mYkTJ7Jp0yZef/11Lr30UvL5PKFQSCZROJ1Ojj76aJLJJGvXrh2330ehOBAsXLiQ8vJyli5dyk9+8hO2bNnCunXr+K//+i927ty51/E33ngjtbW1rFy5UoaErrvuOubNm8ctt9yylyEuLi7mtddeKxDFue+++wiFQrKccDSSySTr1q2Trmufz8eNN97Ixo0bmTlzJul0mttuu41jjz2Wt956S7pxZ82axeLFi/driH/0ox9RVFTEBx98IBf9JpOJcDgspWrh7wbRYDBQVlaGzWbj61//Ov/zP//DwMAAfr9ftkqtrq4mFArR2dkpG8Rks1mmTZvGlClTaGxslPK6b775Jp2dndhstnHttLRlyxYAjjrqqM99rng8zsDAALlcjqamJm666SaAfbrFPymvv/466XSal19+mZKSks99vj054l3TTU1NAHtlUou+nuJncHDwgF63ubmZV155hYsvvhjYHaeePn26jL8uXLgQTdN47733mDdvHtOnT2fmzJnMnz+fdDpNc3Mz0WgUvV6Pz+ejrq6OY489lgsuuEAmeikUhxMGg4FLLrmEpUuXAruTtGpqagryFQSBQIC33nqLSy65hEgkUjBOzz77bLZv305nZ2fBe6655poCd+P8+fPJ5XLs2rVrv/d25ZVXFsSPxT2JBcKqVasYHBzk29/+doE85Le+9S18Pt8+z93d3c26detYvHhxgTtXhKPEDnW4YTQYDHR2drJ582ZWrFghY8AnnHCC/H8ymZThLJEYOnXqVE466SSmTp3K0NAQ69evp6urC5PJRCqV2qeQyFggqjpEs5vPwx133IHf76e8vJxTTz2V5uZm7r77bi666KLPfW7xmf7lL3/ZZ7jvs3LE74jFF2HPLkUvvfRSwYra4XAQjUYP2HU1TeP111+noqKCsrIyotEoPp9PKmQZjUapGSsUtHQ6nfzCRqNRKioqSCQSDA4OUlZWxvz583nqqadob28/YPepUIwn3/zmN7nvvvtYv349Tz75JJdeeumIhmDHjh1omsZtt93GbbfdNuK5+vr6qKqqko9ra2sLXhcG8pPU2u/vvcKYT5o0qeA4o9G4z1DV8PdOmTJlr9eEtvSeDSBEiaNoIpNIJDAYDEydOpWGhgYGBgYIBAL09/eTyWQ4/vjjsVgsOBwO1q9fz1NPPUUkEikwvMN1rIf/O5Y7Y7fbDUAkEvnc57rmmmv42te+hl6vx+v1MmPGjM9c6bInX//613nooYe4+uqrufXWWzn99NO56KKLuPjii2VDn8/DEW+Ihxu24Zx88sm8/vrrAPzqV786YLGA4SQSCZYsWYLb7aasrAydTsfOnTvx+XyUlpZiNpuxWq0y8zmbzcrVdjQaJR6PEwgEaGhooLu7m9tuu01pSysOa44//ngmTpzIP/3TP9HS0sI3v/nNEY8Tu5Kbb7551ESuPY3iaA1QPomh+Tzv/bSM1HVptONSqZSUwdU0jXfeeYfq6mq8Xi8NDQ2sW7dOGumhoSGWL1/Oxx9/jMvlori4WIp3CIWt4SVT4+Ganjp1KgAff/zx5y4xamxsHLUWfTRG+5z3TJKz2Wy8++67LFu2jL/+9a+88sor/OEPf2DhwoW89tprn7u5zhFviMUXYePGjcyePVs+7/f75R/18ccfH7Prx+NxkskkTqeTK6+8kvXr1/P666/T09Mji/Xb29sxm80UFxfLCWhwcJBwOMxpp53Giy++yMcffzxm96hQjCff+MY3+NnPfsa0adNGnZwbGhqA3XHUTzv5jgV1dXXA7p36aaedJp/PZrO0trYya9as/b5369at8jlhjIVx3NNgDDeWoptSPp+no6ODgYEBrFYrNptN1gV/+OGHpFIpOjo6ZIWH6LT0aXZ0B9o4n3vuuRgMBh5//PExSdjaHz6fb0SthZHCFXq9ntNPP53TTz+d//iP/+DnP/85//Iv/8KyZcs+93fwiI8Riy/CE088cdDuIZ/PEw6H+eUvf0l1dTW33nor/+f//B8sFgtut5s5c+Yw4NbIWAAAGtlJREFUYcKEglq+xsZGrrrqKlpaWpQRVnyhuPrqq7njjju45557Rj2mtLSUU089lQceeIDu7u69Xt+zLGmsOeaYYyguLubBBx8s8Eo98cQT+3V9V1RUcPTRR/PYY48VGAVRFTGa8MdIO9dUKkU4HKa/v5+Ojg6SySSZTIbe3l6CwaDUkRaGG3YLCIl7Hm8Zy5qaGr797W/z2muv8d///d97vZ7P57nnnnvo6OgYk+tPnDiRUCjEhg0b5HPd3d0899xzBceNVIEiFolC/OTzcMTviGtra7nqqqt48MEHuf/++7n++uv3Oma8vpxr167ln//5n5k4cSJ1dXWy1eFxxx1HKBRiaGiIvr4+YPeK7V/+5V/kY4Xii0JdXd0n0gP+9a9/zbx58zjqqKP49re/TUNDA729vXzwwQd0dHSwfv36sb/Z/4fZbObOO+/khhtuYOHChVxyySW0trby6KOPMnHixP26mn/1q19x7rnncuKJJ5JMJqXbWafTyXKmfTF8jtpT7Gf4cyaTSZYxmc1mcrmc1CD4JPPcp0nc+tnPfgbsLv0BWLJkCe+//z4AP/nJT+Rx99xzD83NzXz/+9/n2Wef5fzzz8fn89HW1sbTTz9NU1MTl1566Se+7qfh0ksvlRn23//+94nH4/zv//4vkydPZs2aNfK4f/3Xf+Xdd9/lvPPOo66ujr6+Pv7nf/6H6upq5s2b97nv44g3xAD33nsvLS0t3HDDDTz11FNccMEFlJaWMjAwwPLly3nhhRdGTKQYC+LxOB9//LHc5e7YsYMf/vCHciUrBsvBMMC7du1iyZIlAFL/VQy2urq6g+JaUhy5TJ8+nVWrVvHTn/6URx99lMHBQUpLS5kzZw633377uN/P9ddfj6Zp3HPPPdx8883Mnj2b559/nu9///v7VdI644wzeOWVV7jjjjtkJYfJZMJms32q+ONoxlS4ui0Wi+y2FIvFpHhQOp0eNb9kuMzmp2HPJLrf/e538v/DDbHdbufll1/m0Ucf5bHHHuPf/u3fiMfjVFZWsnDhQp544omCpLsDSXFxMc899xw/+MEP+Od//mcmTJjAL37xC7Zv315giL/yla/Q2trK7373OwYGBigpKWHBggX89Kc/3W/p2ydCO0K44oortLq6ulFfz2az2iOPPKItXLhQKyoq0oxGo1ZSUqKdfvrp2m9+8xstkUgUHA9od9xxh3z8yCOPaIDW0tJScNz999+vTZ06VTOZTFpZWZl27bXXasFgsOCYBQsWaDNmzNjrnurq6rTzzjtvr+cB7Xvf+558HAwGtSuvvFIrKSnRnE6ndvbZZ2tNTU1aXV2ddsUVV4z6O+/JypUrNUB75JFHRnx92bJlGjDiz4IFC0Y9/umnn/7E96D4YnLFFVdoNTU1Wn9//17f/y8yuVxOKyoq0q6++uqDfSuKMSabzWr9/f3an//850897x1RhvhInAgOBp/nC6n4YnLFFVfIRdtIi84vAolEQsvn8wXPiQX6448/fpDuSjFerF27tmBz8mnmvSPKNd3e3o7f72fGjBls3LjxYN/OF5aPP/6YOXPmHOzbUBxC/PM//7NUr9uzZv+LwocffshNN93E1772NYqLi1mzZg0PP/wwM2fOPCDqTopDm0mTJsmSV2CfmfJ7otO0L0C350/A5s2b6erqAnZPBCeccMJBvqMvLtFolA8//FA+njVrFqWlpQfxjhSKsae1tZXvf//7rFixgkAgQFFREV/+8pe566671PdfsU+OGEOsUCgUCsWhyBFfR6xQKBQKxcFEGWKFQqFQKA4iyhArFAqFQnEQOaKypseTT9rWazQN2ZFeOxCI8x+IbicKheLAM3zcGwwG+Vgb1hFpz+NHe/6TPDcS4lri/8ObIKi0ogOPMsSHCNpnVK/5tIz1+RUKxYFnuGEUDH88fP4YaTH/aYzncIOv5ovxQRnig8ie/UUFojuKw+HAZrNhNpsxGo3E43GCwSCJRIJsNvuZVqZqcCkUhx+jzRUjGWej0YjVasXv9zN58mQMBgNtbW00NzeTSqWk7vS+dtgjnVsxdqjypTHik7im9/yim0wmSkpKqKysxOv1kk6nGRgYQNM03G43VVVV2Gw2kskk27dvp6WlhVgsNuIKdn9/1nA4/Nl/OYVCMWaM5pqGwoW0cEnb7XZcLhdmsxkAh8NBWVkZTqdT9iJ2uVyEw2F27tzJ4OCg7GMsfvblwlau6bFHGeIxYl+GeKRVrNvtpr6+HovFwtDQEJqmUVtbi9Pp5KOPPqKurg6LxUIsFiOXy1FeXo7D4WDz5s3s2LHjU7cxU4ZYoTg02dMQ6/X6Ed3NFouFqVOnUltbi9VqlS1SAWKxGK2trYRCITKZDBaLherqalwuFwaDQbbuS6fTdHR00NraOmLTB3Hd4a8pk3HgUYZ4jPikhlgY4ZkzZ9Lf308qlaKyspITTzyRmTNnEo/HWbt2LdXV1SQSCZLJJL29vXLgTJw4kba2NtauXUs6nQY+2UBRhlihODQZyRDv+brFYuHYY4+luLiYoaEhIpEIXq8Xv9+P3W6nu7ubbdu2FbREzOfzTJs2DZ/Ph9FoJBAIyH7Hg4ODrF+/nnQ6XTB/KEM8PqgY8TgyUszF6XRy1FFHEYlEMBgMzJ07l1NOOYWamhp0Oh0mk4kpU6awadMmnE4nJSUl1NTU4PV6icVibNiwgcmTJ6NpGqtWrZIDT6FQHP6MNGcYDAamT5+OXq8nGo3S0dFBKBSiu7sbvV7PzJkzCQaDeL1eMpkM2WxWGtL6+nqGhobYunUrmUwGh8OBw+HAYDAwY8YMNmzYII9VMeLxQxnicWLPBAhN07BarRxzzDGEQiE0TaOyspKTTjqJyspKkskksVgMgEwmQ1FREatXr8ZiseD1ejEajVxwwQVYrVbWrFnD3Llz6e3tZdeuXZ+4REGhUBz66HQ68vm8nDvq6urweDyk02lsNpuM8ZpMJtxuNzqdjmQyST6fR9M0DAaDTOCyWCz4fD70ej2xWAybzYbf70ev1xOPx6mrq2Pnzp0F11dzydijDPE4I77UBoNBdudIp9NUVFTQ2NhIUVERyWSSZDJJIpFA0zR8Ph8mk4lp06axY8cO4vE4JpOJ5uZmLBYL5eXlbN68mZNPPpmBgQFisZgyxgrFFwBN0wq8XG63m2OOOYZwOEw2m6W4uJgZM2bQ09NDUVERkyZNwm63k8vlpAGH3Ymger2eXC5HaWkp9fX1OBwOKisrKSoqYmhoiHQ6zcyZM4lGo/T19cnrK8Yepax1ENDpdPj9fioqKujq6sLr9VJcXMzs2bNl5iPsjutEo1GSySR2u50ZM2bgdruJxWIYDAa2bdtGMplk0qRJ6PV6AoEAc+fOxWAwyOsoFIrDF5EZLcbyzJkzKS4uJpvNEovF0Ov1zJo1i1NOOYWqqir0ej3d3d2k02n0ej1GoxGz2YzZbMZgMDA4OEgoFEKn01FRUYHJZKKnp4fu7m5MJhM2m405c+bIuPSe8WnF2KA+5XFgT4NoNpuZMWMGzc3N+Hw+/H4/JpOJoqIiiouLMRqNaJqGXq8nm81is9lwOp0kk0lsNhvZbBaTycTQ0BDhcJhwOMzkyZMJBAI0NjZSVlYGqNWsQvFFwmw209DQwNDQEHq9HpPJxMcff8zOnTvJZDLEYjHa29tlIqcw4KIEKp/P09/fz9atWwmHw8Tjcdra2ti6dSupVAqdTifnEq/XC6g5ZLxQrulxRqfTUV1dTSaTwWAwUFpaSj6fx+VyodfrMZvNWCwWmRkpYkPCDVVdXU1HRwdDQ0OyFMHtdjM0NERlZSXNzc0cffTR9Pb2jliOoFAoDk/8fj9ms5nOzk4A6fnaunUrAwMDsnwpk8mQz+cxGAxy/hA/qVSKdDqNyWQiHA6TSqUwmUzodDoymQzJZJJoNEpdXR3BYBBQxng8UDviccZkMjFnzhxCoRCTJ0/GbrdTXl7OlClTMBgMMhkrk8lgtVqprKyUylp6vZ62tjZyuRyhUEjGkrPZLEajEa/XSyAQYNasWRQXFyvXtEJxmCOMoF6vZ+7cudJ4CoU9g8GApmkMDg5KD1k0GpUGWK/Xyx9xvuGJoBaLBbPZjF6vJ5VKkc1m6enpwe/3y+PVPDL2KEM8xuxZM1xUVITP5yMYDEojWlZWhs1mk4a4pKSEiRMnUlRUhNvtpqKigoaGBkwmE/F4HL1eTz6fZ3BwEIPBgMlkwmQy4fV65e75jDPOUPEdheIwR8wfbrebkpISgsEg0WgU2L0jFuPfYDCQy+UKpG+FAd5T0tJsNsv5Rq/XYzAYpDGOx+My2VO8XxnisUfN1ONMfX09XV1dlJSUyAEiXEhCRs5sNlNWVkZjYyPTpk2jvr6e3t5e1q5dK3fHosbYaDTS1NRER0cH3d3d1NfX89prrzF79mw8Hs/B/FUVCsXnRLiUKyoqSKfTUllPJGFlMhnC4TDJZFKWKYmKCU3TSCQSZDKZguQrq9UqM6iz2Sy5XE7GnIVokJhfQLmmxwNliMcRvV7PhAkTaGtro6KiAq/Xi16vJ5PJAJDL5YhGo3KgmUwmNE0jEomwY8cOIpEIDocDq9Uqjxe6sU6nE4/HQ3l5OQMDA+zatYuampoR70MNLIXi8EAY4srKShKJBOFwWMZ0xaLcZDJhsViIx+MySSuXy0n3dDqdJh6Py8V+Pp8nk8nI5Kx4PC7jzQaDgUQigc1mkwt5tSMee5QhHkfMZjNWq5XBwUF0Oh0DAwNEo1Gy2SzJZFJKVGazWdLpNIlEgsHBQVpbW9mxY4dc/QoZumw2SyKRwOVy4XK58Hq9nHPOObhcLtra2pgwYcKIg0gNLIXi8ECn02Gz2aitrZXZ0haLRYajbDYbXq8Xh8OBTqeTndn8fj/V1dVMnjyZ+vp6dDodsViswCgLl7bT6ZSG2Gq1kkqlSKVSOBwO6ZpWc8bYorKmxxG3243VasVgMFBSUiKTrTZv3ixVbux2u1zpZrNZWltbeeONN4jH45jNZjlIgL0GSFVVFYODg5SWlhIMBpkyZYp0QQ1H7YgVisMHj8eD0WgkmUzidrtlXFe4jzVNI5PJ4HK5cDqdGI1GPB4PuVxONnwoKSkhEomQSCRkPFiIBeXzeYxGY4FCVzgcprS0lNbWVjVfjAPKEI8hw+UsdTqdbEsmYjM2m41EIkEkEpGuZKEfKwbIe++9RyQSwWQyYTabCzIijUYj+XyecDiM2+2mu7ubjo4Ocrkc8Xic4uJimcQhUINKoTi8qK6uJhAIkM/nsVgsUqhDLLIDgQAmk0nuiu12e0H+SS6Xk8lZIst6uHs7m83Kc4r3x+NxJv7/7Z3bbxNnE8Yf2+vTZjfExzi4CYXgBHFsVYEQF1XveqIXCKlcVKrE39X/ok170YsIUQoClBZIU6CghJTENnE23vXazvq024t05lvn4/Ad4oSo85Miy0dihXefd+adeWZyEgsLC1wcJgwOEeJdZGRkhKceVatVLqpQFAWNRgO1Wg2e56Fer/Pr6DYcDrMjDvUIRqNRLrTwPA9//PEHer0ehoaGUC6XObKmlDfQP2dUEIS3m0AggEQiwZXM/s2953moVqsolUqIx+PsLR2JRFhsyamP5g6TQFMRl67rUFWVq69pGhMVcdFwGWGwiBDvArR4NE2DqqqcWmq329xg77oubt26xUO9R0dHYZomLMvC0NAQf04ikcDa2hqf6Wiaxo45wWAQx44dQzgcxsLCAi/EZrPJv4uIsCDsH1RVRTgchm3b7LQHoO/6QW1GNAqRWpgUReEjLmp7jMfjME0ThmFw5iyTySCRSHDrI31etVrF0NAQi74wOESId5lWq4VoNApVVeE4DiKRCI8ho77ixcVF/Pnnn1wNTUJMk1Qoqm21WnBdl71kM5kMisUiIpEIXNflwg5CFpMg7B8CgQDGx8cRj8dZiP1p6Xa7DUVRoKoqMpkMZ8soxazrep9dLqWyVVWFoihoNpuwLAuVSgWapnGFNb3WsixomoZoNMoRsjAYRIgHyHZXmlgsxkURtVqNh3U3Gg24rot4PM7jy8g1h3qMqZK61+txI34wGMSZM2fQbrexsbEBy7LQaDQwNTUFALwDpt9D0tKCsH8IBoMYHR3l4S+qqvJmnFLMsVisLx1tWRafJVMFNB1fkbgGg0H+rGazCc/z+o64XNdFLBaDbdtIp9M4fvw4Hjx40JdZE3YWaV8aIH4RpkWVTqfheR7i8TiArQlLQ0ND3Dzvfy+lhVRVhaZpGB4eZhGm/sGVlRWsra2h0Wggm80ikUhgYmIC4XAYiUQCBw8elNYDQdiHuK6LtbU12LbN/cMklJQujkQiHM2SR73jOPA8D7VaDYZhsNjSZh4AzyemH/8Gna4xnuexBaZEw4NFIuJdIhQK8VQk6s3TdZ0NO8j5JhqNotPpoNvt8pQlqlr0W9hRlOs4DprNJtLpND7//HOUSiU8fvwYAJBIJJDNZiUSFoR9CGWxyHCDakv81rW0Ke92u9wbTNcPAFzYCQCO40BRFMTjcRZWaqf0fxa9z3Vd2LaNXC4HRVG4bVLYeSQi3kUajQamp6cBbP2ndxyHW5P8vrD+yki6pdQTtR9QD2AwGEQ+n8fZs2exuLiIubk5rKysIJfLIZVKsYiLEAvC/oLsKKnegzbhNF3Jn0XzPA/tdpsjYyoApQ17r9djxyw6SwaAeDzOLVEAuMCTggUSeRJrYTBIRLwL0BnM4uIiPv74Y6RSKViWhUKhgLW1NfR6PS682NjY4Pclk0lOO4XDYWiahnK53Nf/VygUkE6nsbCwANM0MTY2hmKxiJMnT8IwDKyursoEFUHYh3S7XZ4PbFkWF2FRtoyqoqluhAq1aNO9ubnJkS0VdfltLun9tLkn4aZbYCuTd+PGDWlhGjASEQ8QfxTqui6Wl5exurqKDz/8EM+fP0e320UsFuOUs23b/D7yj/U8D7FYjA3eR0ZGoCgKkskkOp0OfvvtN0QiEei6DmBr8ZVKJbz//vsoFotYX1/fk+8uCML/T6VSwfj4eF/ESz/khLW9v5gmJwHgGecU4VLlNB1X0esoGOh0Ovx8NBpFrVZDtVqVM+IBI0K8C9AiKRaL+Pnnn3HhwgWoqoqnT59yb3A0GoWiKMjlclxdHYvF0G63YRgGLMviySie58E0TTQaDYyOjuLw4cOoVqsoFAp48eIFpqamcPToUczNzcG2bU5lS3paEPYXtm2jXq8jnU6zqx4AjlxJPF+2vini9bcu0Wv8wk2fSQJMrVGZTAZPnz7dxW/7z0WEeMD4F0etVsOdO3ewtLSEr776Cs+ePUO73UYoFEIymcS7777LfXvA1m6Y+oWpUMI0TYTDYYyNjbHph+M4yOfzWF9fx+LiIq5cuYKffvoJN2/e5JFmgiDsP7rdLq5fv44zZ86wPwClqOms11+8RYJM4kqP0a1fyP3PUxcGnUWTE5dhGLv0Tf/ZiBAPkO0C6HkeyuUyfv31VxQKBXz22Wd4/PgxKpUKstksDMPAxsYGNE3jlgIS6oMHD7J39AcffIADBw6g0+nwXNJcLod79+7h9OnTyGaz+OWXX1AqlSQKFoR9jOd5eP78OZaWlpDL5WCaZp+PQCAQ6KukJiEl7wE676Wxh/4CLmpR8g+G6fV6ME0T+XweDx486BN0YXCIEA8Q/06UqNfruH37NmZnZ3Hx4kVcuXIFlUoFpmmy+Uaz2USn08H4+DgvhOXlZZTLZYRCIUxOTsKyLIyNjeGLL74AAHz77bdQVRVff/015ufn8eTJEz5TAmT0oSDsR+gs99q1a3jnnXfYojIajSIUCnGxlt9xizbnFEH7rwN+0SaxJsEmq9xUKoX19XWUy2XZyO8SUjW9y3ieh2fPnmFmZga2bePSpUuYmprCN998g263i2w2yw5bnudB13U8fPgQzWYTR44cwalTp3Do0CEkEgm0Wi3cvHkTd+7cwdTUFK5evYqNjQ38+OOPWFlZ6es3lgUlCPsPWreNRgMzMzO4fPkyrl+/DsMwuKPCdV0+gvJHyOFwmMcfOo4DoH9DTj4F9B7DMBCNRpFMJvHdd99JNLyLBDy5Qg8EqmL2s91pK5VK4fz58/jyyy8xOTmJ77//Hnfv3kUgEEAymUQ8HkckEkGpVMKjR49w6tQpLszY3NwEABw/fhwfffQRJiYmcP/+ffzwww+YnZ3ta4N62Z+YKrQFQXi78F8n6DyYHi8UCvj0009x48YNhEIhHvoCoO/8lwQ2mUyiVquxmQc9T59HKel6vY5AIIBjx45hZmYG1Wq1r+2x0+nw7ySSsfOIEA8ITdcQwMvTwdv9p6enp3Hx4kWcP38e6XQaT548wb1797C6ugrHcRAMBrGysoJkMolsNov33nsPhUKBJ6YsLy/j1q1bmJubw8OHD2Ga5hsXiwixILydbBdi/2OBQACHDx/GJ598gmvXrmF4eJj7gykDRufHJKSUvvYLq79wq1QqIZvNYmJiArOzsygWi/9mKkROXf7HhJ1DhHhAaLoGAK8UY+BfiysYDELXdUxMTODEiRM4efIkjh49ilQqhVarhc3NTQwPD8PzPFQqFSwvL8MwDCwtLaFWq+HFixdYXV3lQRL/yZ9UhFgQ3k78QkzT1vyPBwIBTE9P49y5c5ifn4frutB1vU906XwY2EprU3aNnPtIWB3HQSaTQavVeqVxB/lU++8LO4sI8YAgIfbzpgiZFpKqqhgeHsbIyAi3M/V6PdTrdVSrVViWhXa7jXa73ec97YfvB7b+XQ/9z9ft+g58S0EQdprXRcT+12SzWVy4cAG9Xg+lUgmO43ARVyQSAbC1ya/X61hfX0c+n0csFuNrhq7r0HUdv//+Ox49evTa9LMI8WARIR4Q/40Q8/N/F1X5b/0LcHvv35uKsLaLrx8RYkF4O3lVROxPLdOtoigYHx/HiRMn2G3Ptm2eK0ymHo1GA7FYDAcOHGAxnp+f50LQ115H/rbo9d8XdhYR4gHxMiF+GW8S5/+V14kwIEIsCG8rrxLiV72WhsFomobR0VHk83lomoZEItE3/7xer6PZbPLo1M3NzZf60G9/TIR48IgQC4IgCMIeIoYegiAIgrCHiBALgiAIwh4iQiwIgiAIe4gIsSAIgiDsISLEgiAIgrCHiBALgiAIwh4iQiwIgiAIe4gIsSAIgiDsISLEgiAIgrCH/AWnZidxb0RrqgAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "visualize_model(model_hybrid, num_images=16)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "seconds = time.time()\n",
        "print(\"Time in seconds since end of run:\", seconds)\n",
        "local_time = time.ctime(seconds)\n",
        "print(local_time)"
      ],
      "metadata": {
        "id": "D3AaQc2xMk-G",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "738d7eca-7b1f-43f7-da69-a573d2e8b34e"
      },
      "execution_count": 483,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time in seconds since end of run: 1695699476.9082072\n",
            "Tue Sep 26 03:37:56 2023\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# from google.colab import runtime\n",
        "# runtime.unassign()"
      ],
      "metadata": {
        "id": "fALJ8tZXA0to"
      },
      "execution_count": 484,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0yhgWSns8PAa"
      },
      "source": [
        "References\n",
        "==========\n",
        "\n",
        "\\[1\\] Andrea Mari, Thomas R. Bromley, Josh Izaac, Maria Schuld, and\n",
        "Nathan Killoran. *Transfer learning in hybrid classical-quantum neural\n",
        "networks*. arXiv:1912.08278 (2019).\n",
        "\n",
        "\\[2\\] Rajat Raina, Alexis Battle, Honglak Lee, Benjamin Packer, and\n",
        "Andrew Y Ng. *Self-taught learning: transfer learning from unlabeled\n",
        "data*. Proceedings of the 24th International Conference on Machine\n",
        "Learning\\*, 759--766 (2007).\n",
        "\n",
        "\\[3\\] Kaiming He, Xiangyu Zhang, Shaoqing ren and Jian Sun. *Deep\n",
        "residual learning for image recognition*. Proceedings of the IEEE\n",
        "Conference on Computer Vision and Pattern Recognition, 770-778 (2016).\n",
        "\n",
        "\\[4\\] Ville Bergholm, Josh Izaac, Maria Schuld, Christian Gogolin,\n",
        "Carsten Blank, Keri McKiernan, and Nathan Killoran. *PennyLane:\n",
        "Automatic differentiation of hybrid quantum-classical computations*.\n",
        "arXiv:1811.04968 (2018).\n",
        "\n",
        "About the author\n",
        "================\n"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.17"
    },
    "colab": {
      "provenance": [],
      "machine_shape": "hm",
      "gpuType": "V100"
    },
    "accelerator": "GPU"
  },
  "nbformat": 4,
  "nbformat_minor": 0
}