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+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "id": "7edf7168",
+      "metadata": {
+        "id": "7edf7168"
+      },
+      "source": [
+        "# TD2: Deep learning"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "fbb8c8df",
+      "metadata": {
+        "id": "fbb8c8df"
+      },
+      "source": [
+        "In this TD, you must modify this notebook to answer the questions. To do this,\n",
+        "\n",
+        "1. Fork this repository\n",
+        "2. Clone your forked repository on your local computer\n",
+        "3. Answer the questions\n",
+        "4. Commit and push regularly\n",
+        "\n",
+        "The last commit is due on Sunday, December 1, 11:59 PM. Later commits will not be taken into account."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "3d167a29",
+      "metadata": {
+        "id": "3d167a29"
+      },
+      "source": [
+        "Install and test PyTorch from  https://pytorch.org/get-started/locally."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "330a42f5",
+      "metadata": {
+        "id": "330a42f5",
+        "outputId": "d84428e8-5c15-4a99-d4cd-43849d52be4c",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (2.1.0+cu118)\n",
+            "Requirement already satisfied: torchvision in /usr/local/lib/python3.10/dist-packages (0.16.0+cu118)\n",
+            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch) (3.13.1)\n",
+            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from torch) (4.5.0)\n",
+            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch) (1.12)\n",
+            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch) (3.2.1)\n",
+            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch) (3.1.2)\n",
+            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch) (2023.6.0)\n",
+            "Requirement already satisfied: triton==2.1.0 in /usr/local/lib/python3.10/dist-packages (from torch) (2.1.0)\n",
+            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from torchvision) (1.23.5)\n",
+            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from torchvision) (2.31.0)\n",
+            "Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /usr/local/lib/python3.10/dist-packages (from torchvision) (9.4.0)\n",
+            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch) (2.1.3)\n",
+            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->torchvision) (3.3.2)\n",
+            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->torchvision) (3.4)\n",
+            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->torchvision) (2.0.7)\n",
+            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->torchvision) (2023.7.22)\n",
+            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch) (1.3.0)\n"
+          ]
+        }
+      ],
+      "source": [
+        "%pip install torch torchvision"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "0882a636",
+      "metadata": {
+        "id": "0882a636"
+      },
+      "source": [
+        "\n",
+        "To test run the following code"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 26,
+      "id": "b1950f0a",
+      "metadata": {
+        "id": "b1950f0a",
+        "outputId": "df948a8b-b338-41cf-a3d3-196f2ea4a83d",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "tensor([[-1.6900,  0.1515, -0.1568, -2.0969, -0.5278,  0.6218,  0.3922, -0.6057,\n",
+            "         -0.7291,  0.2046],\n",
+            "        [ 1.4776, -1.7798, -0.5365,  0.9682,  0.2097, -0.0996, -0.2982,  0.7891,\n",
+            "         -0.3924,  0.0546],\n",
+            "        [ 1.1218, -0.2286, -1.1962,  0.1250, -1.5484, -0.5640,  0.0308,  0.2313,\n",
+            "         -0.0773,  0.5532],\n",
+            "        [-0.1000,  0.3633,  1.1688,  0.7930, -0.0303, -0.6985,  0.7679,  0.5111,\n",
+            "         -2.7290, -0.7675],\n",
+            "        [-0.7067,  0.0325,  0.7177, -0.2783,  1.4820,  0.6153, -2.3233,  0.6494,\n",
+            "          1.4352,  1.2583],\n",
+            "        [ 1.2969,  0.5632, -0.7471, -0.0228,  0.1049, -0.3550,  1.6854,  2.0872,\n",
+            "         -1.3583,  0.7122],\n",
+            "        [ 0.6103, -0.7994, -0.9384, -0.4365, -0.1876, -1.9450, -0.4624,  1.2562,\n",
+            "          0.9817, -0.0628],\n",
+            "        [ 0.9058,  0.9132, -0.8898,  0.0076, -0.0641, -1.3324, -1.1656,  0.5032,\n",
+            "         -0.5097,  0.9018],\n",
+            "        [-0.8475, -0.0260,  2.3063,  0.3875,  1.0270,  0.0559, -0.0036,  0.0080,\n",
+            "         -2.3212, -0.4067],\n",
+            "        [-1.0759, -1.2369,  0.3865,  0.1324,  0.0700,  0.4892, -0.9378, -0.1592,\n",
+            "         -0.0578,  1.4979],\n",
+            "        [ 1.1185, -0.4406,  0.1509,  0.3106, -1.0008, -1.9186, -0.4162, -1.7528,\n",
+            "         -0.2748, -1.9412],\n",
+            "        [ 0.5031,  0.3449,  1.2007, -0.0096,  0.4729,  0.2176, -0.3470,  1.5354,\n",
+            "         -0.9196,  0.0115],\n",
+            "        [-0.4204, -1.1485, -0.4159,  1.6151, -0.6263, -0.6644, -0.0092,  0.4506,\n",
+            "         -0.1260,  0.1438],\n",
+            "        [ 0.3910,  0.0865, -0.2706, -1.0099,  0.5891, -0.0073, -0.0646, -1.8086,\n",
+            "          0.1273,  0.9239]])\n",
+            "AlexNet(\n",
+            "  (features): Sequential(\n",
+            "    (0): Conv2d(3, 64, kernel_size=(11, 11), stride=(4, 4), padding=(2, 2))\n",
+            "    (1): ReLU(inplace=True)\n",
+            "    (2): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
+            "    (3): Conv2d(64, 192, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n",
+            "    (4): ReLU(inplace=True)\n",
+            "    (5): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
+            "    (6): Conv2d(192, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+            "    (7): ReLU(inplace=True)\n",
+            "    (8): Conv2d(384, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+            "    (9): ReLU(inplace=True)\n",
+            "    (10): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+            "    (11): ReLU(inplace=True)\n",
+            "    (12): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
+            "  )\n",
+            "  (avgpool): AdaptiveAvgPool2d(output_size=(6, 6))\n",
+            "  (classifier): Sequential(\n",
+            "    (0): Dropout(p=0.5, inplace=False)\n",
+            "    (1): Linear(in_features=9216, out_features=4096, bias=True)\n",
+            "    (2): ReLU(inplace=True)\n",
+            "    (3): Dropout(p=0.5, inplace=False)\n",
+            "    (4): Linear(in_features=4096, out_features=4096, bias=True)\n",
+            "    (5): ReLU(inplace=True)\n",
+            "    (6): Linear(in_features=4096, out_features=1000, bias=True)\n",
+            "  )\n",
+            ")\n"
+          ]
+        }
+      ],
+      "source": [
+        "import torch\n",
+        "\n",
+        "N, D = 14, 10\n",
+        "x = torch.randn(N, D).type(torch.FloatTensor)\n",
+        "print(x)\n",
+        "\n",
+        "from torchvision import models\n",
+        "\n",
+        "alexnet = models.alexnet()\n",
+        "print(alexnet)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "23f266da",
+      "metadata": {
+        "id": "23f266da"
+      },
+      "source": [
+        "## Exercise 1: CNN on CIFAR10\n",
+        "\n",
+        "The goal is to apply a Convolutional Neural Net (CNN) model on the CIFAR10 image dataset and test the accuracy of the model on the basis of image classification. Compare the Accuracy VS the neural network implemented during TD1.\n",
+        "\n",
+        "Have a look at the following documentation to be familiar with PyTorch.\n",
+        "\n",
+        "https://pytorch.org/tutorials/beginner/pytorch_with_examples.html\n",
+        "\n",
+        "https://pytorch.org/tutorials/beginner/deep_learning_60min_blitz.html"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "4ba1c82d",
+      "metadata": {
+        "id": "4ba1c82d"
+      },
+      "source": [
+        "You can test if GPU is available on your machine and thus train on it to speed up the process"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "id": "6e18f2fd",
+      "metadata": {
+        "id": "6e18f2fd",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "5afda067-1a64-4471-c2bc-0426403a9d65"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "CUDA is not available.  Training on CPU ...\n"
+          ]
+        }
+      ],
+      "source": [
+        "import torch\n",
+        "\n",
+        "# check if CUDA is available\n",
+        "train_on_gpu = torch.cuda.is_available()\n",
+        "\n",
+        "if not train_on_gpu:\n",
+        "    print(\"CUDA is not available.  Training on CPU ...\")\n",
+        "else:\n",
+        "    print(\"CUDA is available!  Training on GPU ...\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "5cf214eb",
+      "metadata": {
+        "id": "5cf214eb"
+      },
+      "source": [
+        "Next we load the CIFAR10 dataset"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "id": "462666a2",
+      "metadata": {
+        "id": "462666a2",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "60d68453-a1bd-4c64-cfbb-0b8e51b009a3"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to data/cifar-10-python.tar.gz\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "100%|██████████| 170498071/170498071 [00:04<00:00, 42416276.91it/s]\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Extracting data/cifar-10-python.tar.gz to data\n",
+            "Files already downloaded and verified\n"
+          ]
+        }
+      ],
+      "source": [
+        "import numpy as np\n",
+        "from torchvision import datasets, transforms\n",
+        "from torch.utils.data.sampler import SubsetRandomSampler\n",
+        "\n",
+        "# number of subprocesses to use for data loading\n",
+        "num_workers = 0\n",
+        "# how many samples per batch to load\n",
+        "batch_size = 20\n",
+        "# percentage of training set to use as validation\n",
+        "valid_size = 0.2\n",
+        "\n",
+        "# convert data to a normalized torch.FloatTensor\n",
+        "transform = transforms.Compose(\n",
+        "    [transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]\n",
+        ")\n",
+        "\n",
+        "# choose the training and test datasets\n",
+        "train_data = datasets.CIFAR10(\"data\", train=True, download=True, transform=transform)\n",
+        "test_data = datasets.CIFAR10(\"data\", train=False, download=True, transform=transform)\n",
+        "\n",
+        "# obtain training indices that will be used for validation\n",
+        "num_train = len(train_data)\n",
+        "indices = list(range(num_train))\n",
+        "np.random.shuffle(indices)\n",
+        "split = int(np.floor(valid_size * num_train))\n",
+        "train_idx, valid_idx = indices[split:], indices[:split]\n",
+        "\n",
+        "# define samplers for obtaining training and validation batches\n",
+        "train_sampler = SubsetRandomSampler(train_idx)\n",
+        "valid_sampler = SubsetRandomSampler(valid_idx)\n",
+        "\n",
+        "# prepare data loaders (combine dataset and sampler)\n",
+        "train_loader = torch.utils.data.DataLoader(\n",
+        "    train_data, batch_size=batch_size, sampler=train_sampler, num_workers=num_workers\n",
+        ")\n",
+        "valid_loader = torch.utils.data.DataLoader(\n",
+        "    train_data, batch_size=batch_size, sampler=valid_sampler, num_workers=num_workers\n",
+        ")\n",
+        "test_loader = torch.utils.data.DataLoader(\n",
+        "    test_data, batch_size=batch_size, num_workers=num_workers\n",
+        ")\n",
+        "\n",
+        "# specify the image classes\n",
+        "classes = [\n",
+        "    \"airplane\",\n",
+        "    \"automobile\",\n",
+        "    \"bird\",\n",
+        "    \"cat\",\n",
+        "    \"deer\",\n",
+        "    \"dog\",\n",
+        "    \"frog\",\n",
+        "    \"horse\",\n",
+        "    \"ship\",\n",
+        "    \"truck\",\n",
+        "]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "58ec3903",
+      "metadata": {
+        "id": "58ec3903"
+      },
+      "source": [
+        "CNN definition (this one is an example)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "id": "317bf070",
+      "metadata": {
+        "id": "317bf070",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "d6a4ea53-8797-4886-b714-5b913ff75ffc"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Net(\n",
+            "  (conv1): Conv2d(3, 6, kernel_size=(5, 5), stride=(1, 1))\n",
+            "  (pool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
+            "  (conv2): Conv2d(6, 16, kernel_size=(5, 5), stride=(1, 1))\n",
+            "  (fc1): Linear(in_features=400, out_features=120, bias=True)\n",
+            "  (fc2): Linear(in_features=120, out_features=84, bias=True)\n",
+            "  (fc3): Linear(in_features=84, out_features=10, bias=True)\n",
+            ")\n"
+          ]
+        }
+      ],
+      "source": [
+        "import torch.nn as nn\n",
+        "import torch.nn.functional as F\n",
+        "\n",
+        "# define the CNN architecture\n",
+        "\n",
+        "\n",
+        "class Net(nn.Module):\n",
+        "    def __init__(self):\n",
+        "        super(Net, self).__init__()\n",
+        "        self.conv1 = nn.Conv2d(3, 6, 5)\n",
+        "        self.pool = nn.MaxPool2d(2, 2)\n",
+        "        self.conv2 = nn.Conv2d(6, 16, 5)\n",
+        "        self.fc1 = nn.Linear(16 * 5 * 5, 120)\n",
+        "        self.fc2 = nn.Linear(120, 84)\n",
+        "        self.fc3 = nn.Linear(84, 10)\n",
+        "\n",
+        "    def forward(self, x):\n",
+        "        x = self.pool(F.relu(self.conv1(x)))\n",
+        "        x = self.pool(F.relu(self.conv2(x)))\n",
+        "        x = x.view(-1, 16 * 5 * 5)\n",
+        "        x = F.relu(self.fc1(x))\n",
+        "        x = F.relu(self.fc2(x))\n",
+        "        x = self.fc3(x)\n",
+        "        return x\n",
+        "\n",
+        "\n",
+        "# create a complete CNN\n",
+        "model = Net()\n",
+        "print(model)\n",
+        "# move tensors to GPU if CUDA is available\n",
+        "if train_on_gpu:\n",
+        "    model.cuda()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "a2dc4974",
+      "metadata": {
+        "id": "a2dc4974"
+      },
+      "source": [
+        "Loss function and training using SGD (Stochastic Gradient Descent) optimizer"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "id": "4b53f229",
+      "metadata": {
+        "id": "4b53f229",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 408
+        },
+        "outputId": "288c9f15-e0aa-4d15-e534-d7e71a689c1a"
+      },
+      "outputs": [
+        {
+          "output_type": "error",
+          "ename": "KeyboardInterrupt",
+          "evalue": "ignored",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+            "\u001b[0;32m<ipython-input-8-e3e3eae4d15a>\u001b[0m in \u001b[0;36m<cell line: 10>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     22\u001b[0m         \u001b[0moptimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzero_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m         \u001b[0;31m# Forward pass: compute predicted outputs by passing inputs to the model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 24\u001b[0;31m         \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     25\u001b[0m         \u001b[0;31m# Calculate the batch loss\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     26\u001b[0m         \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcriterion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1516\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compiled_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore[misc]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1517\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1518\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1519\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1520\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1525\u001b[0m                 \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1526\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1527\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1528\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1529\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m<ipython-input-7-271e17bdd28c>\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 18\u001b[0;31m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpool\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrelu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconv1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     19\u001b[0m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpool\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrelu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconv2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mview\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m16\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m5\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1516\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compiled_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore[misc]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1517\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1518\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1519\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1520\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1525\u001b[0m                 \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1526\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1527\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1528\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1529\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/conv.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m    458\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    459\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 460\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_conv_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbias\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    461\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    462\u001b[0m \u001b[0;32mclass\u001b[0m \u001b[0mConv3d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_ConvNd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/conv.py\u001b[0m in \u001b[0;36m_conv_forward\u001b[0;34m(self, input, weight, bias)\u001b[0m\n\u001b[1;32m    454\u001b[0m                             \u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbias\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstride\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    455\u001b[0m                             _pair(0), self.dilation, self.groups)\n\u001b[0;32m--> 456\u001b[0;31m         return F.conv2d(input, weight, bias, self.stride,\n\u001b[0m\u001b[1;32m    457\u001b[0m                         self.padding, self.dilation, self.groups)\n\u001b[1;32m    458\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+          ]
+        }
+      ],
+      "source": [
+        "import torch.optim as optim\n",
+        "\n",
+        "criterion = nn.CrossEntropyLoss()  # specify loss function\n",
+        "optimizer = optim.SGD(model.parameters(), lr=0.01)  # specify optimizer\n",
+        "\n",
+        "n_epochs = 30  # number of epochs to train the model\n",
+        "train_loss_list = []  # list to store loss to visualize\n",
+        "valid_loss_min = np.Inf  # track change in validation loss\n",
+        "\n",
+        "for epoch in range(n_epochs):\n",
+        "    # Keep track of training and validation loss\n",
+        "    train_loss = 0.0\n",
+        "    valid_loss = 0.0\n",
+        "\n",
+        "    # Train the model\n",
+        "    model.train()\n",
+        "    for data, target in train_loader:\n",
+        "        # Move tensors to GPU if CUDA is available\n",
+        "        if train_on_gpu:\n",
+        "            data, target = data.cuda(), target.cuda()\n",
+        "        # Clear the gradients of all optimized variables\n",
+        "        optimizer.zero_grad()\n",
+        "        # Forward pass: compute predicted outputs by passing inputs to the model\n",
+        "        output = model(data)\n",
+        "        # Calculate the batch loss\n",
+        "        loss = criterion(output, target)\n",
+        "        # Backward pass: compute gradient of the loss with respect to model parameters\n",
+        "        loss.backward()\n",
+        "        # Perform a single optimization step (parameter update)\n",
+        "        optimizer.step()\n",
+        "        # Update training loss\n",
+        "        train_loss += loss.item() * data.size(0)\n",
+        "\n",
+        "    # Validate the model\n",
+        "    model.eval()\n",
+        "    for data, target in valid_loader:\n",
+        "        # Move tensors to GPU if CUDA is available\n",
+        "        if train_on_gpu:\n",
+        "            data, target = data.cuda(), target.cuda()\n",
+        "        # Forward pass: compute predicted outputs by passing inputs to the model\n",
+        "        output = model(data)\n",
+        "        # Calculate the batch loss\n",
+        "        loss = criterion(output, target)\n",
+        "        # Update average validation loss\n",
+        "        valid_loss += loss.item() * data.size(0)\n",
+        "\n",
+        "    # Calculate average losses\n",
+        "    train_loss = train_loss / len(train_loader)\n",
+        "    valid_loss = valid_loss / len(valid_loader)\n",
+        "    train_loss_list.append(train_loss)\n",
+        "\n",
+        "    # Print training/validation statistics\n",
+        "    print(\n",
+        "        \"Epoch: {} \\tTraining Loss: {:.6f} \\tValidation Loss: {:.6f}\".format(\n",
+        "            epoch, train_loss, valid_loss\n",
+        "        )\n",
+        "    )\n",
+        "\n",
+        "    # Save model if validation loss has decreased\n",
+        "    if valid_loss <= valid_loss_min:\n",
+        "        print(\n",
+        "            \"Validation loss decreased ({:.6f} --> {:.6f}).  Saving model ...\".format(\n",
+        "                valid_loss_min, valid_loss\n",
+        "            )\n",
+        "        )\n",
+        "        torch.save(model.state_dict(), \"model_cifar.pt\")\n",
+        "        valid_loss_min = valid_loss"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "13e1df74",
+      "metadata": {
+        "id": "13e1df74"
+      },
+      "source": [
+        "Does overfit occur? If so, do an early stopping. We did an early stopping at the 15th Epoch because Validation Loss started increasing."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "id": "d39df818",
+      "metadata": {
+        "id": "d39df818",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 472
+        },
+        "outputId": "f3f4d989-197c-4a6c-847c-c263ee6057c1"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "n_epochs_stop = 16 # Here we stop the execution before the overfit\n",
+        "plt.plot(range(n_epochs_stop), train_loss_list)\n",
+        "plt.xlabel(\"Epoch\")\n",
+        "plt.ylabel(\"Loss\")\n",
+        "plt.title(\"Performance of Model 1\")\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "11df8fd4",
+      "metadata": {
+        "id": "11df8fd4"
+      },
+      "source": [
+        "Now loading the model with the lowest validation loss value\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "id": "e93efdfc",
+      "metadata": {
+        "id": "e93efdfc",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "18910a1e-0d81-456d-809b-e171771b9e6a"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Test Loss: 21.102371\n",
+            "\n",
+            "Test Accuracy of airplane: 73% (730/1000)\n",
+            "Test Accuracy of automobile: 83% (839/1000)\n",
+            "Test Accuracy of  bird: 55% (558/1000)\n",
+            "Test Accuracy of   cat: 40% (406/1000)\n",
+            "Test Accuracy of  deer: 41% (415/1000)\n",
+            "Test Accuracy of   dog: 57% (573/1000)\n",
+            "Test Accuracy of  frog: 73% (730/1000)\n",
+            "Test Accuracy of horse: 70% (707/1000)\n",
+            "Test Accuracy of  ship: 66% (663/1000)\n",
+            "Test Accuracy of truck: 72% (723/1000)\n",
+            "\n",
+            "Test Accuracy (Overall): 63% (6344/10000)\n"
+          ]
+        }
+      ],
+      "source": [
+        "model.load_state_dict(torch.load(\"./model_cifar.pt\"))\n",
+        "\n",
+        "# track test loss\n",
+        "test_loss = 0.0\n",
+        "class_correct = list(0.0 for i in range(10))\n",
+        "class_total = list(0.0 for i in range(10))\n",
+        "\n",
+        "model.eval()\n",
+        "# iterate over test data\n",
+        "for data, target in test_loader:\n",
+        "    # move tensors to GPU if CUDA is available\n",
+        "    if train_on_gpu:\n",
+        "        data, target = data.cuda(), target.cuda()\n",
+        "    # forward pass: compute predicted outputs by passing inputs to the model\n",
+        "    output = model(data)\n",
+        "    # calculate the batch loss\n",
+        "    loss = criterion(output, target)\n",
+        "    # update test loss\n",
+        "    test_loss += loss.item() * data.size(0)\n",
+        "    # convert output probabilities to predicted class\n",
+        "    _, pred = torch.max(output, 1)\n",
+        "    # compare predictions to true label\n",
+        "    correct_tensor = pred.eq(target.data.view_as(pred))\n",
+        "    correct = (\n",
+        "        np.squeeze(correct_tensor.numpy())\n",
+        "        if not train_on_gpu\n",
+        "        else np.squeeze(correct_tensor.cpu().numpy())\n",
+        "    )\n",
+        "    # calculate test accuracy for each object class\n",
+        "    for i in range(batch_size):\n",
+        "        label = target.data[i]\n",
+        "        class_correct[label] += correct[i].item()\n",
+        "        class_total[label] += 1\n",
+        "\n",
+        "# average test loss\n",
+        "test_loss = test_loss / len(test_loader)\n",
+        "print(\"Test Loss: {:.6f}\\n\".format(test_loss))\n",
+        "\n",
+        "for i in range(10):\n",
+        "    if class_total[i] > 0:\n",
+        "        print(\n",
+        "            \"Test Accuracy of %5s: %2d%% (%2d/%2d)\"\n",
+        "            % (\n",
+        "                classes[i],\n",
+        "                100 * class_correct[i] / class_total[i],\n",
+        "                np.sum(class_correct[i]),\n",
+        "                np.sum(class_total[i]),\n",
+        "            )\n",
+        "        )\n",
+        "    else:\n",
+        "        print(\"Test Accuracy of %5s: N/A (no training examples)\" % (classes[i]))\n",
+        "\n",
+        "print(\n",
+        "    \"\\nTest Accuracy (Overall): %2d%% (%2d/%2d)\"\n",
+        "    % (\n",
+        "        100.0 * np.sum(class_correct) / np.sum(class_total),\n",
+        "        np.sum(class_correct),\n",
+        "        np.sum(class_total),\n",
+        "    )\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "944991a2",
+      "metadata": {
+        "id": "944991a2"
+      },
+      "source": [
+        "Build a new network with the following structure.\n",
+        "\n",
+        "- It has 3 convolutional layers of kernel size 3 and padding of 1.\n",
+        "- The first convolutional layer must output 16 channels, the second 32 and the third 64.\n",
+        "- At each convolutional layer output, we apply a ReLU activation then a MaxPool with kernel size of 2.\n",
+        "- Then, three fully connected layers, the first two being followed by a ReLU activation and a dropout whose value you will suggest.\n",
+        "- The first fully connected layer will have an output size of 512.\n",
+        "- The second fully connected layer will have an output size of 64.\n",
+        "\n",
+        "Compare the results obtained with this new network to those obtained previously."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#NEW NETWORK\n",
+        "\n",
+        "import torch.nn as nn\n",
+        "import torch.nn.functional as F\n",
+        "\n",
+        "# define the CNN architecture\n",
+        "\n",
+        "\n",
+        "class Net_new(nn.Module):\n",
+        "    def __init__(self):\n",
+        "        super(Net_new, self).__init__()\n",
+        "\n",
+        "        #Convolution layer 1\n",
+        "        self.conv1 = nn.Conv2d(in_channels=3, out_channels = 16, kernel_size=3, padding=1)\n",
+        "\n",
+        "\n",
+        "        #Convolution layer 2\n",
+        "        self.conv2 = nn.Conv2d(in_channels=16, out_channels = 32, kernel_size=3, padding=1)\n",
+        "\n",
+        "\n",
+        "        #Convolution layer 3\n",
+        "        self.conv3 = nn.Conv2d(in_channels=32, out_channels = 64, kernel_size=3, padding=1)\n",
+        "\n",
+        "        #Max pooling layer\n",
+        "        self.pool = nn.MaxPool2d(2, 2)\n",
+        "\n",
+        "        #Fully connected Layers\n",
+        "        self.fc1 = nn.Linear(in_features=64 * 4 * 4, out_features=512)\n",
+        "        self.fc2 = nn.Linear(in_features=512, out_features=64)\n",
+        "        self.fc3 = nn.Linear(64, 10)\n",
+        "\n",
+        "        self.dropout = nn.Dropout(p = 0.5)\n",
+        "\n",
+        "    def forward(self, x):\n",
+        "\n",
+        "        x = self.pool(F.relu(self.conv1(x)))\n",
+        "        x = self.pool(F.relu(self.conv2(x)))\n",
+        "        x = self.pool(F.relu(self.conv3(x)))\n",
+        "\n",
+        "        x = x.view(-1, 64 * 4 * 4) #linearisation of the tensor\n",
+        "        x = self.dropout(F.relu(self.fc1(x)))\n",
+        "        x = self.dropout(F.relu(self.fc2(x)))\n",
+        "        x = self.fc3(x) # output layer\n",
+        "        return x\n",
+        "\n",
+        "\n",
+        "# create a complete CNN\n",
+        "model= Net_new()\n",
+        "print(model)\n",
+        "# move tensors to GPU if CUDA is available\n",
+        "if train_on_gpu:\n",
+        "    model.cuda()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "rjG3agxFVnHc",
+        "outputId": "8255f8dd-69b4-4c88-9e65-ce684f728c93"
+      },
+      "id": "rjG3agxFVnHc",
+      "execution_count": 11,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Net_new(\n",
+            "  (conv1): Conv2d(3, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+            "  (conv2): Conv2d(16, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+            "  (conv3): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+            "  (pool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
+            "  (fc1): Linear(in_features=1024, out_features=512, bias=True)\n",
+            "  (fc2): Linear(in_features=512, out_features=64, bias=True)\n",
+            "  (fc3): Linear(in_features=64, out_features=10, bias=True)\n",
+            "  (dropout): Dropout(p=0.5, inplace=False)\n",
+            ")\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#LOSS FUNCTION AND TRAINING OF NEW MODEL\n",
+        "\n",
+        "import torch.optim as optim\n",
+        "\n",
+        "criterion = nn.CrossEntropyLoss()  # specify loss function\n",
+        "optimizer = optim.SGD(model.parameters(), lr=0.01)  # specify optimizer\n",
+        "\n",
+        "n_epochs = 30  # number of epochs to train the model\n",
+        "train_loss_list = []  # list to store loss to visualize\n",
+        "valid_loss_min = np.Inf  # track change in validation loss\n",
+        "\n",
+        "for epoch in range(n_epochs):\n",
+        "    # Keep track of training and validation loss\n",
+        "    train_loss = 0.0\n",
+        "    valid_loss = 0.0\n",
+        "\n",
+        "    # Train the model\n",
+        "    model.train()\n",
+        "    for data, target in train_loader:\n",
+        "        # Move tensors to GPU if CUDA is available\n",
+        "        if train_on_gpu:\n",
+        "            data, target = data.cuda(), target.cuda()\n",
+        "        # Clear the gradients of all optimized variables\n",
+        "        optimizer.zero_grad()\n",
+        "        # Forward pass: compute predicted outputs by passing inputs to the model\n",
+        "        output = model(data)\n",
+        "        # Calculate the batch loss\n",
+        "        loss = criterion(output, target)\n",
+        "        # Backward pass: compute gradient of the loss with respect to model parameters\n",
+        "        loss.backward()\n",
+        "        # Perform a single optimization step (parameter update)\n",
+        "        optimizer.step()\n",
+        "        # Update training loss\n",
+        "        train_loss += loss.item() * data.size(0)\n",
+        "\n",
+        "    # Validate the model\n",
+        "    model.eval()\n",
+        "    for data, target in valid_loader:\n",
+        "        # Move tensors to GPU if CUDA is available\n",
+        "        if train_on_gpu:\n",
+        "            data, target = data.cuda(), target.cuda()\n",
+        "        # Forward pass: compute predicted outputs by passing inputs to the model\n",
+        "        output = model(data)\n",
+        "        # Calculate the batch loss\n",
+        "        loss = criterion(output, target)\n",
+        "        # Update average validation loss\n",
+        "        valid_loss += loss.item() * data.size(0)\n",
+        "\n",
+        "    # Calculate average losses\n",
+        "    train_loss = train_loss / len(train_loader)\n",
+        "    valid_loss = valid_loss / len(valid_loader)\n",
+        "    train_loss_list.append(train_loss)\n",
+        "\n",
+        "    # Print training/validation statistics\n",
+        "    print(\n",
+        "        \"Epoch: {} \\tTraining Loss: {:.6f} \\tValidation Loss: {:.6f}\".format(\n",
+        "            epoch, train_loss, valid_loss\n",
+        "        )\n",
+        "    )\n",
+        "\n",
+        "    # Save model if validation loss has decreased\n",
+        "    if valid_loss <= valid_loss_min:\n",
+        "        print(\n",
+        "            \"Validation loss decreased ({:.6f} --> {:.6f}).  Saving model ...\".format(\n",
+        "                valid_loss_min, valid_loss\n",
+        "            )\n",
+        "        )\n",
+        "        torch.save(model.state_dict(), \"model_cifar.pt\")\n",
+        "        valid_loss_min = valid_loss"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 825
+        },
+        "id": "qdnbb2j3cukU",
+        "outputId": "64b5be42-d6a6-4047-bd10-c82870e1d8e8"
+      },
+      "id": "qdnbb2j3cukU",
+      "execution_count": 12,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch: 0 \tTraining Loss: 45.437011 \tValidation Loss: 42.235335\n",
+            "Validation loss decreased (inf --> 42.235335).  Saving model ...\n",
+            "Epoch: 1 \tTraining Loss: 40.342287 \tValidation Loss: 37.157713\n",
+            "Validation loss decreased (42.235335 --> 37.157713).  Saving model ...\n",
+            "Epoch: 2 \tTraining Loss: 36.543231 \tValidation Loss: 34.165958\n",
+            "Validation loss decreased (37.157713 --> 34.165958).  Saving model ...\n",
+            "Epoch: 3 \tTraining Loss: 33.370877 \tValidation Loss: 30.313300\n",
+            "Validation loss decreased (34.165958 --> 30.313300).  Saving model ...\n",
+            "Epoch: 4 \tTraining Loss: 31.175180 \tValidation Loss: 28.547296\n",
+            "Validation loss decreased (30.313300 --> 28.547296).  Saving model ...\n",
+            "Epoch: 5 \tTraining Loss: 29.419332 \tValidation Loss: 27.043618\n",
+            "Validation loss decreased (28.547296 --> 27.043618).  Saving model ...\n",
+            "Epoch: 6 \tTraining Loss: 27.940666 \tValidation Loss: 25.369496\n",
+            "Validation loss decreased (27.043618 --> 25.369496).  Saving model ...\n",
+            "Epoch: 7 \tTraining Loss: 26.553561 \tValidation Loss: 24.676774\n",
+            "Validation loss decreased (25.369496 --> 24.676774).  Saving model ...\n",
+            "Epoch: 8 \tTraining Loss: 25.182271 \tValidation Loss: 22.769694\n",
+            "Validation loss decreased (24.676774 --> 22.769694).  Saving model ...\n",
+            "Epoch: 9 \tTraining Loss: 23.964245 \tValidation Loss: 21.769041\n",
+            "Validation loss decreased (22.769694 --> 21.769041).  Saving model ...\n",
+            "Epoch: 10 \tTraining Loss: 22.847915 \tValidation Loss: 21.022394\n",
+            "Validation loss decreased (21.769041 --> 21.022394).  Saving model ...\n",
+            "Epoch: 11 \tTraining Loss: 21.847575 \tValidation Loss: 19.916893\n",
+            "Validation loss decreased (21.022394 --> 19.916893).  Saving model ...\n",
+            "Epoch: 12 \tTraining Loss: 20.925242 \tValidation Loss: 20.110298\n"
+          ]
+        },
+        {
+          "output_type": "error",
+          "ename": "KeyboardInterrupt",
+          "evalue": "ignored",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+            "\u001b[0;32m<ipython-input-12-18675338ef88>\u001b[0m in \u001b[0;36m<cell line: 12>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     17\u001b[0m     \u001b[0;31m# Train the model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m     \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 19\u001b[0;31m     \u001b[0;32mfor\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtrain_loader\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     20\u001b[0m         \u001b[0;31m# Move tensors to GPU if CUDA is available\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtrain_on_gpu\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    624\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    625\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__next__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mAny\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 626\u001b[0;31m         \u001b[0;32mwith\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mautograd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprofiler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecord_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_profile_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    627\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sampler_iter\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    628\u001b[0m                 \u001b[0;31m# TODO(https://github.com/pytorch/pytorch/issues/76750)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/autograd/profiler.py\u001b[0m in \u001b[0;36m__exit__\u001b[0;34m(self, exc_type, exc_value, traceback)\u001b[0m\n\u001b[1;32m    646\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_scripting\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    647\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_C\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDisableTorchFunctionSubclass\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 648\u001b[0;31m                 \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprofiler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_record_function_exit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_RecordFunction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrecord\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    649\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    650\u001b[0m             \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprofiler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_record_function_exit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrecord\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/_ops.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    446\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    447\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 448\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_op\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    449\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    450\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__hash__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "n_epochs_stop = 13\n",
+        "plt.plot(range(n_epochs_stop), train_loss_list)\n",
+        "plt.xlabel(\"Epoch\")\n",
+        "plt.ylabel(\"Loss\")\n",
+        "plt.title(\"Performance of Model 2\")\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 472
+        },
+        "id": "KGzDZzwghfB1",
+        "outputId": "3057999f-37a8-467a-91c8-5ecb19b25874"
+      },
+      "id": "KGzDZzwghfB1",
+      "execution_count": 14,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model.load_state_dict(torch.load(\"./model_cifar.pt\"))\n",
+        "\n",
+        "# track test loss\n",
+        "test_loss = 0.0\n",
+        "class_correct = list(0.0 for i in range(10))\n",
+        "class_total = list(0.0 for i in range(10))\n",
+        "\n",
+        "model.eval()\n",
+        "# iterate over test data\n",
+        "for data, target in test_loader:\n",
+        "    # move tensors to GPU if CUDA is available\n",
+        "    if train_on_gpu:\n",
+        "        data, target = data.cuda(), target.cuda()\n",
+        "    # forward pass: compute predicted outputs by passing inputs to the model\n",
+        "    output = model(data)\n",
+        "    # calculate the batch loss\n",
+        "    loss = criterion(output, target)\n",
+        "    # update test loss\n",
+        "    test_loss += loss.item() * data.size(0)\n",
+        "    # convert output probabilities to predicted class\n",
+        "    _, pred = torch.max(output, 1)\n",
+        "    # compare predictions to true label\n",
+        "    correct_tensor = pred.eq(target.data.view_as(pred))\n",
+        "    correct = (\n",
+        "        np.squeeze(correct_tensor.numpy())\n",
+        "        if not train_on_gpu\n",
+        "        else np.squeeze(correct_tensor.cpu().numpy())\n",
+        "    )\n",
+        "    # calculate test accuracy for each object class\n",
+        "    for i in range(batch_size):\n",
+        "        label = target.data[i]\n",
+        "        class_correct[label] += correct[i].item()\n",
+        "        class_total[label] += 1\n",
+        "\n",
+        "# average test loss\n",
+        "test_loss = test_loss / len(test_loader)\n",
+        "print(\"Test Loss: {:.6f}\\n\".format(test_loss))\n",
+        "\n",
+        "for i in range(10):\n",
+        "    if class_total[i] > 0:\n",
+        "        print(\n",
+        "            \"Test Accuracy of %5s: %2d%% (%2d/%2d)\"\n",
+        "            % (\n",
+        "                classes[i],\n",
+        "                100 * class_correct[i] / class_total[i],\n",
+        "                np.sum(class_correct[i]),\n",
+        "                np.sum(class_total[i]),\n",
+        "            )\n",
+        "        )\n",
+        "    else:\n",
+        "        print(\"Test Accuracy of %5s: N/A (no training examples)\" % (classes[i]))\n",
+        "\n",
+        "print(\n",
+        "    \"\\nTest Accuracy (Overall): %2d%% (%2d/%2d)\"\n",
+        "    % (\n",
+        "        100.0 * np.sum(class_correct) / np.sum(class_total),\n",
+        "        np.sum(class_correct),\n",
+        "        np.sum(class_total),\n",
+        "    )\n",
+        ")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "FesxSklkiFfp",
+        "outputId": "1831651a-bddc-4c56-b424-a29b0ceec5c5"
+      },
+      "id": "FesxSklkiFfp",
+      "execution_count": 25,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Test Loss: 19.730781\n",
+            "\n",
+            "Test Accuracy of airplane: 73% (734/1000)\n",
+            "Test Accuracy of automobile: 80% (808/1000)\n",
+            "Test Accuracy of  bird: 43% (433/1000)\n",
+            "Test Accuracy of   cat: 40% (407/1000)\n",
+            "Test Accuracy of  deer: 58% (588/1000)\n",
+            "Test Accuracy of   dog: 50% (509/1000)\n",
+            "Test Accuracy of  frog: 76% (765/1000)\n",
+            "Test Accuracy of horse: 76% (761/1000)\n",
+            "Test Accuracy of  ship: 79% (798/1000)\n",
+            "Test Accuracy of truck: 73% (736/1000)\n",
+            "\n",
+            "Test Accuracy (Overall): 65% (6539/10000)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "The accuracy is slightly higher than the first model"
+      ],
+      "metadata": {
+        "id": "90Lcg-xjiU2w"
+      },
+      "id": "90Lcg-xjiU2w"
+    },
+    {
+      "cell_type": "markdown",
+      "id": "bc381cf4",
+      "metadata": {
+        "id": "bc381cf4"
+      },
+      "source": [
+        "## Exercise 2: Quantization: try to compress the CNN to save space\n",
+        "\n",
+        "Quantization doc is available from https://pytorch.org/docs/stable/quantization.html#torch.quantization.quantize_dynamic\n",
+        "        \n",
+        "The Exercise is to quantize post training the above CNN model. Compare the size reduction and the impact on the classification accuracy\n",
+        "\n",
+        "\n",
+        "The size of the model is simply the size of the file."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 16,
+      "id": "ef623c26",
+      "metadata": {
+        "id": "ef623c26",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "423ef4ac-52a0-44f4-b89c-0b7e441513e5"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "model:  fp32  \t Size (KB): 2330.946\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "2330946"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 16
+        }
+      ],
+      "source": [
+        "import os\n",
+        "\n",
+        "\n",
+        "def print_size_of_model(model, label=\"\"):\n",
+        "    torch.save(model.state_dict(), \"temp.p\")\n",
+        "    size = os.path.getsize(\"temp.p\")\n",
+        "    print(\"model: \", label, \" \\t\", \"Size (KB):\", size / 1e3)\n",
+        "    os.remove(\"temp.p\")\n",
+        "    return size\n",
+        "\n",
+        "\n",
+        "print_size_of_model(model, \"fp32\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "05c4e9ad",
+      "metadata": {
+        "id": "05c4e9ad"
+      },
+      "source": [
+        "Post training quantization example"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 17,
+      "id": "c4c65d4b",
+      "metadata": {
+        "id": "c4c65d4b",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "ea671f8f-680a-43d0-9790-dd511cc7b00c"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "model:  int8  \t Size (KB): 659.806\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "659806"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 17
+        }
+      ],
+      "source": [
+        "import torch.quantization\n",
+        "\n",
+        "\n",
+        "quantized_model = torch.quantization.quantize_dynamic(model, dtype=torch.qint8)\n",
+        "print_size_of_model(quantized_model, \"int8\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "7b108e17",
+      "metadata": {
+        "id": "7b108e17"
+      },
+      "source": [
+        "For each class, compare the classification test accuracy of the initial model and the quantized model. Also give the overall test accuracy for both models."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# track test loss for un-quantized model\n",
+        "test_loss = 0.0\n",
+        "class_correct = list(0.0 for i in range(10))\n",
+        "class_total = list(0.0 for i in range(10))\n",
+        "\n",
+        "quantized_model.eval()\n",
+        "\n",
+        "# iterate over test data\n",
+        "for data, target in test_loader:\n",
+        "    # move tensors to GPU if CUDA is available\n",
+        "   # if train_on_gpu:\n",
+        "        #data, target = data.cuda(), target.cuda()\n",
+        "    # forward pass: compute predicted outputs by passing inputs to the model\n",
+        "    output = quantized_model(data)\n",
+        "    # calculate the batch loss\n",
+        "    loss = criterion(output, target)\n",
+        "    # update test loss\n",
+        "    test_loss += loss.item() * data.size(0)\n",
+        "    # convert output probabilities to predicted class\n",
+        "    _, pred = torch.max(output, 1)\n",
+        "    # compare predictions to true label\n",
+        "    correct_tensor = pred.eq(target.data.view_as(pred))\n",
+        "    correct = (\n",
+        "        np.squeeze(correct_tensor.numpy())\n",
+        "        if not train_on_gpu\n",
+        "        else np.squeeze(correct_tensor.cpu().numpy())\n",
+        "    )\n",
+        "    # calculate test accuracy for each object class\n",
+        "    for i in range(batch_size):\n",
+        "        label = target.data[i]\n",
+        "        class_correct[label] += correct[i].item()\n",
+        "        class_total[label] += 1\n",
+        "\n",
+        "# average test loss\n",
+        "test_loss = test_loss / len(test_loader)\n",
+        "print(\"Test Loss: {:.6f}\\n\".format(test_loss))\n",
+        "\n",
+        "for i in range(10):\n",
+        "    if class_total[i] > 0:\n",
+        "        print(\n",
+        "            \"Test Accuracy of %5s: %2d%% (%2d/%2d)\"\n",
+        "            % (\n",
+        "                classes[i],\n",
+        "                100 * class_correct[i] / class_total[i],\n",
+        "                np.sum(class_correct[i]),\n",
+        "                np.sum(class_total[i]),\n",
+        "            )\n",
+        "        )\n",
+        "    else:\n",
+        "        print(\"Test Accuracy of %5s: N/A (no training examples)\" % (classes[i]))\n",
+        "\n",
+        "print(\n",
+        "    \"\\nTest Accuracy (Overall): %2d%% (%2d/%2d)\"\n",
+        "    % (\n",
+        "        100.0 * np.sum(class_correct) / np.sum(class_total),\n",
+        "        np.sum(class_correct),\n",
+        "        np.sum(class_total),\n",
+        "    )\n",
+        ")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "DortFONxnGP-",
+        "outputId": "834e7dd5-775f-466e-9aaa-b00d33e1a337"
+      },
+      "id": "DortFONxnGP-",
+      "execution_count": 18,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Test Loss: 19.733812\n",
+            "\n",
+            "Test Accuracy of airplane: 73% (730/1000)\n",
+            "Test Accuracy of automobile: 80% (808/1000)\n",
+            "Test Accuracy of  bird: 43% (432/1000)\n",
+            "Test Accuracy of   cat: 40% (408/1000)\n",
+            "Test Accuracy of  deer: 59% (591/1000)\n",
+            "Test Accuracy of   dog: 50% (506/1000)\n",
+            "Test Accuracy of  frog: 76% (765/1000)\n",
+            "Test Accuracy of horse: 76% (761/1000)\n",
+            "Test Accuracy of  ship: 79% (796/1000)\n",
+            "Test Accuracy of truck: 73% (737/1000)\n",
+            "\n",
+            "Test Accuracy (Overall): 65% (6534/10000)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model.load_state_dict(torch.load(\"./model_cifar.pt\"))\n",
+        "\n",
+        "# track test loss\n",
+        "test_loss = 0.0\n",
+        "class_correct = list(0.0 for i in range(10))\n",
+        "class_total = list(0.0 for i in range(10))\n",
+        "\n",
+        "model.eval()\n",
+        "# iterate over test data\n",
+        "for data, target in test_loader:\n",
+        "    # move tensors to GPU if CUDA is available\n",
+        "    if train_on_gpu:\n",
+        "        data, target = data.cuda(), target.cuda()\n",
+        "    # forward pass: compute predicted outputs by passing inputs to the model\n",
+        "    output = model(data)\n",
+        "    # calculate the batch loss\n",
+        "    loss = criterion(output, target)\n",
+        "    # update test loss\n",
+        "    test_loss += loss.item() * data.size(0)\n",
+        "    # convert output probabilities to predicted class\n",
+        "    _, pred = torch.max(output, 1)\n",
+        "    # compare predictions to true label\n",
+        "    correct_tensor = pred.eq(target.data.view_as(pred))\n",
+        "    correct = (\n",
+        "        np.squeeze(correct_tensor.numpy())\n",
+        "        if not train_on_gpu\n",
+        "        else np.squeeze(correct_tensor.cpu().numpy())\n",
+        "    )\n",
+        "    # calculate test accuracy for each object class\n",
+        "    for i in range(batch_size):\n",
+        "        label = target.data[i]\n",
+        "        class_correct[label] += correct[i].item()\n",
+        "        class_total[label] += 1\n",
+        "\n",
+        "# average test loss\n",
+        "test_loss = test_loss / len(test_loader)\n",
+        "print(\"Test Loss: {:.6f}\\n\".format(test_loss))\n",
+        "\n",
+        "for i in range(10):\n",
+        "    if class_total[i] > 0:\n",
+        "        print(\n",
+        "            \"Test Accuracy of %5s: %2d%% (%2d/%2d)\"\n",
+        "            % (\n",
+        "                classes[i],\n",
+        "                100 * class_correct[i] / class_total[i],\n",
+        "                np.sum(class_correct[i]),\n",
+        "                np.sum(class_total[i]),\n",
+        "            )\n",
+        "        )\n",
+        "    else:\n",
+        "        print(\"Test Accuracy of %5s: N/A (no training examples)\" % (classes[i]))\n",
+        "\n",
+        "print(\n",
+        "    \"\\nTest Accuracy (Overall): %2d%% (%2d/%2d)\"\n",
+        "    % (\n",
+        "        100.0 * np.sum(class_correct) / np.sum(class_total),\n",
+        "        np.sum(class_correct),\n",
+        "        np.sum(class_total),\n",
+        "    )\n",
+        ")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "gu_NCOwSn_vL",
+        "outputId": "61a39f38-84f5-4e63-b0f2-b68f0ba0141a"
+      },
+      "id": "gu_NCOwSn_vL",
+      "execution_count": 19,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Test Loss: 19.730781\n",
+            "\n",
+            "Test Accuracy of airplane: 73% (734/1000)\n",
+            "Test Accuracy of automobile: 80% (808/1000)\n",
+            "Test Accuracy of  bird: 43% (433/1000)\n",
+            "Test Accuracy of   cat: 40% (407/1000)\n",
+            "Test Accuracy of  deer: 58% (588/1000)\n",
+            "Test Accuracy of   dog: 50% (509/1000)\n",
+            "Test Accuracy of  frog: 76% (765/1000)\n",
+            "Test Accuracy of horse: 76% (761/1000)\n",
+            "Test Accuracy of  ship: 79% (798/1000)\n",
+            "Test Accuracy of truck: 73% (736/1000)\n",
+            "\n",
+            "Test Accuracy (Overall): 65% (6539/10000)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "The results show that the quantized and the initial model have the same Accuracy, globally and for each class. As the quantized model is way easier to execute, we understand the added value of this method."
+      ],
+      "metadata": {
+        "id": "T8zEK8dRoIiP"
+      },
+      "id": "T8zEK8dRoIiP"
+    },
+    {
+      "cell_type": "markdown",
+      "id": "a0a34b90",
+      "metadata": {
+        "id": "a0a34b90"
+      },
+      "source": [
+        "Try training aware quantization to mitigate the impact on the accuracy (doc available here https://pytorch.org/docs/stable/quantization.html#torch.quantization.quantize_dynamic)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "201470f9",
+      "metadata": {
+        "id": "201470f9"
+      },
+      "source": [
+        "## Exercise 3: working with pre-trained models.\n",
+        "\n",
+        "PyTorch offers several pre-trained models https://pytorch.org/vision/0.8/models.html        \n",
+        "We will use ResNet50 trained on ImageNet dataset (https://www.image-net.org/index.php). Use the following code with the files `imagenet-simple-labels.json` that contains the imagenet labels and the image dog.png that we will use as test.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 27,
+      "id": "b4d13080",
+      "metadata": {
+        "id": "b4d13080",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 548
+        },
+        "outputId": "70c4f0a5-165e-44de-a3ff-920494270558"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
+            "  warnings.warn(\n",
+            "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet50_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet50_Weights.DEFAULT` to get the most up-to-date weights.\n",
+            "  warnings.warn(msg)\n",
+            "Downloading: \"https://download.pytorch.org/models/resnet50-0676ba61.pth\" to /root/.cache/torch/hub/checkpoints/resnet50-0676ba61.pth\n",
+            "100%|██████████| 97.8M/97.8M [00:00<00:00, 110MB/s]\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Predicted class is: Golden Retriever\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "import json\n",
+        "from PIL import Image\n",
+        "\n",
+        "# Choose an image to pass through the model\n",
+        "test_image = \"dog.png\"\n",
+        "\n",
+        "# Configure matplotlib for pretty inline plots\n",
+        "#%matplotlib inline\n",
+        "#%config InlineBackend.figure_format = 'retina'\n",
+        "\n",
+        "# Prepare the labels\n",
+        "with open(\"imagenet-simple-labels.json\") as f:\n",
+        "    labels = json.load(f)\n",
+        "\n",
+        "# First prepare the transformations: resize the image to what the model was trained on and convert it to a tensor\n",
+        "data_transform = transforms.Compose(\n",
+        "    [\n",
+        "        transforms.Resize((224, 224)),\n",
+        "        transforms.ToTensor(),\n",
+        "        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),\n",
+        "    ]\n",
+        ")\n",
+        "# Load the image\n",
+        "\n",
+        "image = Image.open(test_image)\n",
+        "plt.imshow(image), plt.xticks([]), plt.yticks([])\n",
+        "\n",
+        "# Now apply the transformation, expand the batch dimension, and send the image to the GPU\n",
+        "# image = data_transform(image).unsqueeze(0).cuda()\n",
+        "image = data_transform(image).unsqueeze(0)\n",
+        "\n",
+        "# Download the model if it's not there already. It will take a bit on the first run, after that it's fast\n",
+        "model = models.resnet50(pretrained=True)\n",
+        "# Send the model to the GPU\n",
+        "# model.cuda()\n",
+        "# Set layers such as dropout and batchnorm in evaluation mode\n",
+        "model.eval()\n",
+        "\n",
+        "# Get the 1000-dimensional model output\n",
+        "out = model(image)\n",
+        "# Find the predicted class\n",
+        "print(\"Predicted class is: {}\".format(labels[out.argmax()]))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "184cfceb",
+      "metadata": {
+        "id": "184cfceb"
+      },
+      "source": [
+        "Experiments:\n",
+        "\n",
+        "Study the code and the results obtained. Possibly add other images downloaded from the internet.\n",
+        "\n",
+        "What is the size of the model? Quantize it and then check if the model is still able to correctly classify the other images.\n",
+        "\n",
+        "Experiment with other pre-trained CNN models.\n",
+        "\n",
+        "    \n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "This code loads an image, resizes and normalizes it so it can be tested on a pre-trained ResNet50 model. We see that this model succeded with the dog picture, let's try with other pictures."
+      ],
+      "metadata": {
+        "id": "e3YNptO648CL"
+      },
+      "id": "e3YNptO648CL"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def testing_image(test_image, model):\n",
+        "    # Open and display the specified image file\n",
+        "    image = Image.open(test_image)\n",
+        "    plt.imshow(image), plt.xticks([]), plt.yticks([])\n",
+        "\n",
+        "    # Process the image (resize, convert to tensor, normalize) and add a batch dimension\n",
+        "    image = data_transform(image).unsqueeze(0)  # Transform and add batch dimension: shape [1, 224, 224, 3]\n",
+        "\n",
+        "    # Switch the model to evaluation mode to disable training-specific operations like dropout\n",
+        "    model.eval()\n",
+        "\n",
+        "    # Forward pass through the model to get predictions\n",
+        "    out = model(image)\n",
+        "\n",
+        "    # Determine the class with the highest prediction score\n",
+        "    print(\"Predicted class is: {}\".format(labels[out.argmax()]))"
+      ],
+      "metadata": {
+        "id": "uzRvxvd95oKB"
+      },
+      "id": "uzRvxvd95oKB",
+      "execution_count": 28,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#test a batch of images from the internet\n",
+        "batch_test = [\"cheetah.jpg\",\"parrot.jpeg\", \"zebra.jpeg\", \"tiger.jpeg\", \"monkey.jpg\", \"panther.jpg\"]\n",
+        "for test_image in batch_test:\n",
+        "    print(test_image)\n",
+        "    testing_image(test_image, model)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 614
+        },
+        "id": "WFkFj4zL6x0z",
+        "outputId": "da783b30-f770-4d23-a96f-601a068fe39f"
+      },
+      "id": "WFkFj4zL6x0z",
+      "execution_count": 36,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "cheetah.jpg\n",
+            "Predicted class is: cheetah\n",
+            "parrot.jpeg\n",
+            "Predicted class is: macaw\n",
+            "zebra.jpeg\n",
+            "Predicted class is: zebra\n",
+            "tiger.jpeg\n",
+            "Predicted class is: tiger\n",
+            "monkey.jpg\n",
+            "Predicted class is: proboscis monkey\n",
+            "panther.jpg\n",
+            "Predicted class is: cougar\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "On the 6 images, only the last one is inexact : a cougar is identified instead of a black panther.\n",
+        "\n",
+        "Lets now size the model and quantize it."
+      ],
+      "metadata": {
+        "id": "VyIJgmTa-O33"
+      },
+      "id": "VyIJgmTa-O33"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print_size_of_model(model, \"fp32\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Emcd0bI6-886",
+        "outputId": "d87fb208-86fb-4596-f5f7-8ae1c2c23008"
+      },
+      "id": "Emcd0bI6-886",
+      "execution_count": 37,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "model:  fp32  \t Size (KB): 102523.238\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "102523238"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 37
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import torch.quantization\n",
+        "\n",
+        "\n",
+        "quantized_model = torch.quantization.quantize_dynamic(model, dtype=torch.qint8)\n",
+        "print_size_of_model(quantized_model, \"int8\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Mjk_EqxA_PwU",
+        "outputId": "cf6806d5-d1d8-4313-d738-dc77048a56e4"
+      },
+      "id": "Mjk_EqxA_PwU",
+      "execution_count": 38,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "model:  int8  \t Size (KB): 96379.996\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "96379996"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 38
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "The model weights 102.5 MB\n",
+        "We will now test it after quantisation"
+      ],
+      "metadata": {
+        "id": "Opun7eFh_YE7"
+      },
+      "id": "Opun7eFh_YE7"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "for test_image in batch_test:\n",
+        "    print(test_image)\n",
+        "    testing_image(test_image, quantized_model)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 614
+        },
+        "id": "_qeltWUd_ykM",
+        "outputId": "9b3d6ed2-fddb-4812-c681-87546d8a4122"
+      },
+      "id": "_qeltWUd_ykM",
+      "execution_count": 39,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "cheetah.jpg\n",
+            "Predicted class is: cheetah\n",
+            "parrot.jpeg\n",
+            "Predicted class is: macaw\n",
+            "zebra.jpeg\n",
+            "Predicted class is: zebra\n",
+            "tiger.jpeg\n",
+            "Predicted class is: tiger\n",
+            "monkey.jpg\n",
+            "Predicted class is: proboscis monkey\n",
+            "panther.jpg\n",
+            "Predicted class is: cougar\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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0yt3tgZffnXjz7sS6njCUb+8KP7ztLG3hi1+t7hGrst9fcXw88va2U6TRLnY8ub7HVCjvhLtbofezSE01Qp4NHogIxRoG1NJgGdSa2ZXuBoeJkgnA4vzDyAxReJyRKC6QVImMFISsIgIMczytvuMAz5I5We5IqiTCsOLey5xfW9eV0fV9Ti1IZH+mEsR2DW9ffDMSCFQ2I5bXj/9ciodxwPyMWgtiA2oBPePpVEmzYOroR87uw4MedQMjJchu/3dVZayrGy51TqcgQfwvYLDfLyx1C8cVqFLckKsxFMaZrZLksybBLvPwJyJJRKaW9x7rJGkSK+08O6plGkcybLRwXsNlIs5bBZfpLhN3ZZG4mAYuwuiR0pygAd7jwZj3mLfr2XMmJJYIYZMSKLHnkOpRQBo6cy5QRNKuhYMlDGA4guEIc+4pChISFylbEsENWHLG8bNrnWgxiT9fi/iqYkzu4SeuDzJeBjx9dsV6qjy8uXdksKz8zX/3Ef+3//tv+PS3Cxc3N7TLj1C7Yhw72CPYAdF7TAuPtyv/8g/f8rf/6x/47o/v+L/+zy/Yfy7Y3li70g+F0iosheV6RyuNX+uv+OpfvuTLP77msJ54+nzw4pNrLp5fU++E/ubgXlUKmHG7Lry823PTB/vmvNgP35/4+qsDu53x9Omei4uKDWU9gj42qnaUQSkNEaWKb5KKb6ShhVYWJxSDLDUJY4FSakVHR607GSyJgtwDF1EsUU8YOaM4lwIUMUzama/3ny9WGWZ47sJDFtd4bW+lz9CtICoTtTnhGqeHMkMZQZDqmUokM5yyHVoJAjeMQBqp9JB5CGZ2NDVUEXKnfROE0kKvpol02DwtxVP5pU3E4ZvYD72eGRcdrscamTFLXsgqYlCXSluWyLyGlivDLSLMCj5QIoyyILYzeCpFpvE2tu+1IJd1ouf8+kAygSbMDBMNLmfmT0GIeyIMWI9wTiJ6ij8HarV0Rv6IEUnJ5Fv91s6NV6KpTCylLAE4c0CS7zL3QH6z5Dv2DeJ7R84MFnMfECGmMUJaE9nVmRmP9yYZCZzdY6lUyYyn/8MIQ+jGsDi5/+cwXrvLhevLS3pvvLo7sdrKixcL/9P//Cv+8q8/4eryitY+pvdrXr+853j7kqfPlP3VCR2PvH2l/Of/9yv+1//nl3z1x+94/vyGetloV422r4z7ip4MauiaqiCt8vTj5/zFX/wV//R3b3n5/SuQEzcvdlzfNJ5/vOPunXB7d4xNWelr41/+KJhc8nRXebgXvv9+5c3bhSLG4x1cX1RE92CFy+vCX/3Fc2yB2mBplWXXUIzDYfDu9ZGHu4524aTweIJTKaxD6MM90Qz9MqMUh1gswgcJeUnwHiN3ZkkpwRaSyQwJjGGO6AqCmDISOswYczNcbiTj55uF1qnOELVISBqmBop53466ytln/+trGqX4vSQPUgMxBN81Q6X8HL+B6ZH9V0FKQ0qjSKXPkDoOlQhkhks3cSXITNiM7hxWkUptSwiJm4cr8RnJv1sc0CoZjsWvDJ1DFDxjrzMiWYPjzCt5ufm9sSCGoYkWfWWcbwwjnEik1kKx/D51tBNiXRcn59KV+XOHamj78pe+Z8CcSA8pSYSd4jHY1DGm0UbKFOv64xpI8KFBlmtY3YmMY922e4uF4JyA1+0XjnIrZRrH1hpM2Y5/ZylbyFvExcOZcf2p68M4r8sral1YdtWzEr3xxWcv+M1vnnF9c82uvGCMK0bvLPqK8fgGFeXu9pFvv3vFH/7wwP/+t6/4l3++951cK2VXaLsdy9I4GRwe7tjVC4Ypx/URitF2lc/+w2f85j98zrff33J//8Ddw8r+8pKbp3tefHrDakceH0+INKBy9wi//6eFS1GGNU4DamssZWFZjP3FJRf75zx5csmzF094/vEN18/2SIOLi8ayryjK4ah899WBw/0dejzx9u3g+x+OfP/2kXd3Ax3m3IikN4wQIOG7WZDWQsb1yV2pMAnwCPbnxqgS2102jkzVs4Wux0mFeh7MSJXXCoH8EhnlZs6wrpyhI9/AP1aR/+vLyeoNbaWhOkdsEJodbEM3aJ52R1OpXEcQKzDgZJ3Ik84EgOuUlD7UM2tjuLiyp94uRKIobdmztC1TNh1B8C2q6g4mhWsUkOHaL2mBzHwtUq+W6DANV6KFiN4CsWWlgN+401znBkUjWnaiuoi4mJjGRsC7uLkW55fqGS+UOrIk5jfCPrPDuoW8YSQo7vxqAbHNsEruUQArHpaX5J/U+TtTtHeM4Z83cxmV2mqEd8UdT7x3FwcPjzqimsGzvDjlN8sl4j5MKAlHRZBqFHHnbgLVaqDfn74+yHjVZaHIntoGtbny/HJ/TeMKxlNO74SHN7fU3SN7fcvt8S1//PrED7f3fPvyJYdT5WEIq3Z2rdCHMsQ4mSviTwq7q0uWiwXWEzYGxQ7sdgsf3Sh/9ZdP+bu/LXzzw4l3bx55evOEmyeNL37zAqTz7TdvWA/hJQQOq7+Gulzw9KNLPvroCU+e7nn6rHF5uXB1vefmas++7rm82HH1dM8QaLuFUv3gXR6P7PePmD5h9JVX3x65uLnn9E/f8/h4oBfP1agZVhs6+nsGQEoJmd9wgabB6gAikJCfFksjUiT0jF6O4wfI5mbF0tPpPDjTQMVGzUxnCeTlRqZGiOeGJn8/v84V3ef/PZ9FMhvLLF9yBbvOcNCNU6iyw5AnX4Nk9ivIeSOQCbF5z9Fa8GLDWNdOH6cQ2zqpryn3KJW2RElUbZEl22QVLgoOkWVyOBpcY4ZyviLvHXR/YxlebkhLh23AklTD22a8HFKE8Qn8GQkAfyeRVMjEgbmSvRZCVOtr4+/dDdmI9YwXf6bVi5KfDMFQarEobStU07CDBSsZyLqBpoAVC2Rp0zFOXZn6s4iE4FtjNQLlJ+9nITDVUP2X5GYDOSbsdSfAFEy7/ToTXEuG8ZNX+MnrwzgvUQxHJLW6hTwdCu++qTxZlJ11xsMDx3ePvHn9km+/f8f3952H04GVBdkLl88X9i+hKBz7icPoPPZOXXZwUbm4ckGnPqzo2qkyKOWO2k48+0h4+vyar759x8PbwfqZUqvx4uMLRG54vH3g7VEQGRQ1lgr7y8rnX3zKb3/3MZ99fs3lk0u6ntgvlYvdnkLhcrfn6dMbrHTfnGUHUikoF7uCdeX7bx75/usHXn73yKs37zg+DpZW0L1QtNJ7p5SKNlcyJqEpAlXENU7hRb0swz2rUKAU1IK0L4ncQoyoXubkXs0zXemRk7VwCYMfijSGs14zyHrnsBJ1nYVGkzt9f8NsdXtnh7qUEPNGajsyTs4thc4qjeJwA1OCu0tVePJP+TMS8dhZeJMGT0PUOMaK6hqHtm3lRxKlUbVtvM6MmuIPpghGaV6KYgrDxpbEOEOgTlxnCOiflyyT5qHLdZliXv8lyQma67yyamEe+FKoi4s2Zx1j7AdRNzpFgqs6Q37xw8hM9dTsqVdIEPsow8tSlMpCN2hWKVYoppSx1SVCYZXhEBb/3q6dPlxZJwpTVS9G0S1ZIRNxBzqc+kA3XBolR2VyrbnISZ+EjjFwq9uq0MaVEpHFn0FhD8ZpHYxh7JfGqVVOB+Wrf3jJ/njJ1ZMdvZ/45quv+ad/+pJVB+1FxYpMmF/r4PoG1oNrdd7d3vF4euR616iLIKLQFV0H/bFzaArlRGmD6xvh+fM9RYSHd0fevb7no4937PcLn352w9vvTjy89UTCUgo3V5c8fV744rMbbi4vaDRHB004lsFpHCjdWLXDUihNoTaadq+PVkO78fKbO/7T//It33xzz/EAq55cg6PulaqZp4ETZQUar7gQt4Ww00UVCjYis1MY5mlkScJ2kqu+JUpx/yUty2QmE+GHIOsyw/BNyUVsmlp8/UsgsPMQDzJEsv+Ks8uvPctSSfEi4pBUyEQxSaLL3OQahtHV10Smr8eBy6L38xCteDgRFmZkcXtft0yume8PnJiumdk8Q0GZxSvm92eqU/kvJowghJOQn7zP+9ucwGJo8DW55j9OWvganhld2yocJrlfhNqEZefqdTHnn7Ikh+J8podcBLqyyQ0RyGjoYO0rfYwIScPBOY2KAEM72pVRBloWL70qxRFZwLctBM17VdbRWUPsK+ZhrDvCQYn/JZJ133C2HuZGqdVwbpJJoDPOLULBTDa5un5DkECIXW1mr3/q+iDjtbTCOgZPbhZ07LngwF7hm29e8/Bwgias/cSb16949/aOy+sLnp32UBtDhDEc/j59ds3pcnB/b7x9e0s/nZAxQhvkYcVuWZBeOB0PlFJY9sxM4W5pvLs78MPL13z06Z5nLy54/mTP559e8sPXDzw+wr41llLoR+PVd7cc74RXbUe5GOw/hasnvkFNhf1uz1E6V9d7rsoF0o1xPHF4t/Lt19/zd//lj/zhX97ycEgeSlCLMozYAEKhiIY8wlXSBTdcS5buRHamad3qDUmpQw1DlajJf5ZmlrHGz4a5kYTwgJyl+kmuKbilUpAaIs341+nx/9Wp3a5SzoxW1HxK2Ti6LAvK8M11RUFYISAVZTBs0DuYFYaNqGMLIx5hxUyrl+Tn/GD3vrKuJ6axQKJCwJMFbdk5RZCoTvqZIUt9nB+8GgXtri9NcRrT6Kb1PueYNvmvr7ppnyHhdmVYTDy7G9lhnoWWIrSlUXeN2oKUPzNepgYaZVNDQ05CZPKIpILfzxie4RPZiHs1maLbWYGAgQ36AJXmCaAK1AQ1U6xAKt2Hjcgfera0ryHupWyIVNJRugTlvf1jUUWR91ILWcdJECe59+IH4wXmmcHNkifBfp7A/sOM1+PtgYudcH1TeHp1Cc8qe61QTry8P/IwOqdT5+H+HuuDxRZ2pXFzdc19Hby5e+DYT1w/ueEGWHYnHh/uOT4+Up89QQR6H9QuCJ22HxyPg7v7wSVQSufps8LNkz13j/fc3p149fKRLz5b2X98wbPnO673TjoubeF4eOThofJw+47rq8HFbkGbcvEgvPjcWK53UBcex8qxr7wYT+l1cLxV3r6858s//MA3X33H7e2R48nLkTbp9CYdcJgMHnpsm9tfdJk8B+HlKoNuTowanpWR5Boy3AvOQCRlFQTJWr2liLiC/McoavvvOIxR0qOzHm7Mw1DOOhD4fjorwynOjRQxSjVqzQJzmboy0zA6SfNEMsI5ou7tabpF6YgwrHPqJ3S0bR3jjt1oD87DWa9xC7LnvCOBsLVwmV9hMxTzZ9mqBzys4r3OF/l+zktueO/7N2TgZVUbT5McowUPmYR+fq9G7Whyg7U5Qqwt6gIDTYXGNQxTx62815eOcIw6NNBWZ2hny+aFQS/vP0+KUiExloaTEbpBiQ4Ull9hYNmmaDrCeI+jz84irpZ3mqMVYcwQdEOgab9rbdPIJxKP04FQI4w2L6WyJPOCaRSbofpPXR9kvF5/t/LbXz3li08v+PTj5+zrniIVloVVhLvjiXfvHnn58g13b95xWY2r6z2/+c1TDs24fLnj5Xe3IKGtkcGyKHUISwGTQR8r929OlNPKk6eFtlf6ONKD7FtaYX9RuLhqrGPh1Q+dr//4hsurgo7Bvla4qpy6b4yhlXGCx/WWtngHg91h4fHeuHq6gjVsGFUq37Rb9GS8ffXAu7cPvL0/MEaUuGS9I7KF8vGaK+rCT7MZ/xPGIbYVU2cVXhfxjgjZcuS9GrXYBF6K4aplCwnGVoQd0kZj8ljZcSC/38Ib+lnR4GHSIMQ3h8Gbcon4vbU2ydcSrVKKuHq9q7vG1Au5IbTgM9S7U4zVw1zNAufgaUagCSdW5q7fBAaZ0Trjl+Yl80C0dsZznf87TCGn4IduM26BSGfIkyEv8TM35Xt+jpdQ+SdIOJTJ4YhGlm1MPizDMUejdfbrcmMbBzfqPRN99L6io8NYyeTA2l0r5twWWyhl+X69E4qJ7ycNdFSkzpBYkJAdhFGw7GfiezjLjERk8ogRQ7jWLGohwPt7eW2iJwNEFDSQOW7QI9r2mkZTqtXEW+HU3n+fmQHdqIygIeTPwHk9f3bNb377nL/66+f86tefUmvjeAKpl9TdHpPC4dh5OB45Hu549ftveHx7h9UTn350zZPLCy7bnnd3B4Yo67rDRp/FzB2di6gDDo+di8tBqR1T4XjoHB5P1CIeVtZCPyl//P1baoXrnbA04zigqzCGe7CBHzgbxkKlHAfv3hptEYfsFpBcldNppR89yxeVOx541EJlmW1ppugvN7brY6fXcykA8xA4vymYFVz9sk7DAVsmyNP3aUwiVGN7uVKi/U5omc6FpF5ikiQu0YCOeSAyy5ObZn5mEN3ZCywLsP0gxPOBn1ectNUxtgLvDJeGIxUdA1OLTKIb3jGUoWBsUCGNhMRhn+hF9MzA5H3musLSFpa6zIznJtcgah8dyYgINM+GTiQg28/1zw+kN/8XuqoxohFgGqb5HfF9WyeFTXuV9xs1kFWoS/NmlqH8n3yRgK7DkzFjMPrqBzd++dp5Ftt/ne2tqdMrSK0hMXEHWcLwT8wjyZ7GvU/j6uu2FK8YGSjVCmtfoyDftj2VQlSEKlu3EUGwsbmdMQqjNg8SzVgWoRU23aJlEibFrRlQxt43j2j+bTLj/euDjNcnn17z2a+u+exXL7h+sqcPZe0GWqijQGtcXey5urxCZM+1wH/+T//E9y8f2V1fc3W951e/Wbh6e+Du+MjhUHm8feT4cAT1EOuiVHYXe0YpnA4n3twfuX6qXOxhnDrrYdDKwn5/QdOCKazr4Osv7/noemGsynEtHFcXj7ruyL2nFFCi+l2Fw8Eg0M8YnXU9uREb0QokYHHBvWYZFl1Kne+pUiY6+dclDWngnMEYE2EtQepGuFmSbA0+JLN5JQ2KzI04jVrIHjz7mHIJ/7oxOGt9kh5WN0SWeqD4r814bSLJUlzoaTiR7KIB39AeKg2E5Lr8+TN9n+1RMouoeAVAVwnj5esBdd6zC1s9lJjIdvbB8muoYeoGdmk7Wl3mc6coNaUMvXeXbAigKypjylKkJMH/vqI+QxU31RFWR8+uDWpnSLuhV4lDd64an8kLr7mJNj62heWlYH1EFsN1UmOMMF6+HxQmp6qJTMt5v1WQGl1oS0Us9VeuwTu/F5fc+D0kIvXkS5kcZj59q4XeTx7BhquRsP1udAvbemyG2ysAfB/04T9PSsHEvCVUIFuLKtN4eRRzY6vh7J0P/Hnm64OM10efX/PFX37EzYtn7HaX1D6oRemnyvFwwnqHiI1LMa81uxB+/y8/cP+w8utff8Rnn3/E/rNrnq0Ld7eD1whlGFWFWhvjpJwelNoau50hstKKYdrpJ8NGo5aFVkdQTwJWWY+PvBtOqK7qiKuPPj0j4ouiyNwgGc+rDcboXsg8nKtBBBkaRaKOBhbz1jiZ2auVSVzP9K7kFldUthDDVdRedqFDwNTzL5IhUHQ+FW9/01qbnRnOz04atFpLlDKl8WLKKSA2T0L/Mz4mFfCGH44MOTNcrFMK4euTXQmc21kxJ2kgjFdmn3oYfbVEe869DDXPIEWEKiXa4ZhD1dlih4yZM/RmokPTCMGrsN/vI6TdqgSmRsi2nmXJoQxVBs5JFvMsZHbPMC1R3XBGB4gwLLOF2yE9R4JusJghp//9RhgQemMVC8OfqMze65bq+jWN8rO8dz/aI15qotKUJvjryyxmOJqSavoyjYtzefEckgGzf6+T8G6MWmaPJ1+247hWTnJC+orJIFuqlRnelTA/3nggMhvuqFDKGH7v3RuXjqKbts22ahN3OPE+ojGhEX3Qfsb1Yc0IP3vGzdMLluWCuuxpDXZ1cOTI4eGe+8eTf2gTvwFZef5J4w9fKf/4jy959d0d//FvVn712+e8eL7w5PKKy+ChbPWWuOOuwuGSdlOpVysXAkWOjPXI4+GBh8OJftLwRlHUGeLNdXhavO0X9tVY+8HRVxS5SpSOZFsVJy4TeTk6GSPsUG6S8KCexYkSDCmobHKBUiK8CgSVm8Tr8FYITqyGQcCyFbTRUkRavVyG3FjlrMRlWq/4mRkyhOE6bxuc0gTDS5HeM1yx4RCiUwATtKc0EhJFpDg20dRAGG7IFKKRPwSBrGqbPCIOZx9GN3XEJMH5lXA4wc1Po3FWHqSM+Ty5BmAsS2O32xT0k7C3oBsCGLSWPdPziQN7p/Cy+h4QNaRKFDYEN2QSX5eINxFamc9l855TvuLhvoZxmv3cwmmZDpcLmaFavMPC8PD2dMo6who94zyNYyKboQogNcPjRPTnDq9EuB+rqFEH6pwsafXIOsKK0qSy1MKy7JAS3YMJVCaVVipjrLGXg1yXcDzzXSlDPLIYgcxXvBGBIixm1FoY6aRs62YyUTfxLsWwIVOG8VPXBxmvFy+esN/V6NcjwYso1k+IrIgcOZ1O6DCWnVAW4+IGnjxb+P6rB/75nx64fXvk7avf8OLTJzz/6BIRuH194vXXjzx/bpS1cLW7QopSd41TP3j4UryT5eiD3rs3gMtQjBVhxbRysb9gf33FaVUOh0ce+2mmzQU9a4JXw/RrxNqhDpfcFLhxSGid2aJaJ+I5JxqnobEM1bKtjh8ekah7SwRiFtmo0CuVOsPUGQpN+HyWDSM/27DsT6VbZm12o1QnfbOvV16h5Q8vLrN1dIkWGDo6JMSfSMbRw8SRZmTTPVdYB7KQ/NlhuHTkuSMzmJkVlCrb+pmB1viZm5Jb0ziSWbu69UqTLcvlbVjid3V0N1slC2dhehTGqwViNKBtnyVCpYUtjVQ+qWIHYi2VKJ4WPNJw2IFpn86gFqGVylK8rTSzxMhYhzHWwWl12YijrUA0NmZVTtBnsX5Ep43ksiJbK1lFkY3/8PW0wqypxaKXVmgJ8UxyK8K+RSKhtGlIdm3HRK7Bo1ZJoTTR9idCxVKCtwuuTg1mYXp3RwxbdcN5BCDTv8zz5t0p/hxh40fK5a5xOjmxXUXg8Ug/nNi3Rrm54d27e27f3NHXzsOp8/rNPe9eLox+BeXIw0H4+797xe73Bz765JpnTy94+8ORN98p/+f/8bc8vXnCcnlBva709hibWqLGsQFC78pp9YNbRamlsywbUaja43B6NmWo4bR7vEg5y8JE5kRVPXMaMGRm/kpME1qC95qHJw98ia+NYxpOeYYCY2DmivshwiKCD2AQJCBzKuP9cCcfUSNcA9NxTjNgpt7Jlu0lu2ONLqNjcFq7h83m4cU5sZ/F/l4kHMLP5Fc0NGzqbX40EJE3yxMvNHeLOaF/Gg7in7oaPcKV3ORQ5iNISECSNI6g0dFbSCJmtwI8hd+WytJcC1eZ59bRBDAKfk9ete08PY5AmAY8jGWg2fPaP9hKphrNM8FaUDkjmOf6xRSp4k/laMsw8XrMXatc7BZ2VdhHhhEcCQ8ztHfW06B3DcDpTk/JvvCBTmULQx0BZiseIethz9gnMqOa/z3/MQxGlchMe8zCEmtpgQ6J1kgiQpMKtVHMM5lDBCvuRDeBClPiM8TP1zBvPyShth9qURZn0SoqooQz6mxrVe0v9qzD0J+8Psh43dzs2O0WTBvr8Ug3Y7x74HR/oN1cc3V9Dbrw1T8/8nd/+z1vbh94eDyynmDtghZhVONRjbdv7nh998j11Z5+OvHu3Vv+4rMXfPL0Y66eXsJl5SSGlSvGujrXVT3Ru3blsT9STFkiFJF6wRiD9XDg/nRkWS4IdgKIzgS2hQFZqJwjobbSibz8Jaq8zwtBHBwJHg0ju2x64JSp+dxkic7izNvW+cH/aRsqkV6niFGKzhfqqCaJUUWtRK3zWZYtSCVV5Xg60aenL7OdTg3oXiRD0zORq3h4vXbve+Wk+whEGTKO2LPxE6fhgmg2Z0q31OlsSvZEX5tWi/eQ5FzxQBUWa5W5v1q8tc75eLqtk4MfoCw58RB8ntowVKEkPzOf+W5GRBFeduV3K/FMXuTshtY/ymcGZKgskWKuBUQarQpNYL8rLpKu0MJAqDgZbcPV7GP0s8RKOIdSQ6JgUwJBoPUZKk/jFc8e+8sV+f1sfc9qLuP7imy701vxGF07QgnDaxB7w4qjWBWiwQDzs2d22TxwlCJUK4wSijETuvrAFJk/N+79zJCnA01U7IB+U9z/1PVhbaAvr7i8vGZpe+7v7+mHE+s6ON51jusKouzaJU+fPKOUVxweH+mnxjhFI77qnnDtR47doMPt8R4Djoc9335zx1/9FRQZUEJlL3uOWlAqta6UcuR0PHI8nCjSYQGo3CW/kzVlcnRhZCAgb9kr1PC8qjKbwqU3SO5qJo4ka90ya5iL71kT57eqcx9Z0xYaFa8NE6B65i3CrDHDoIDJ2DQIBTxzFLDCMPSMf3ES2ZFYwRFS0H1eFoI/67p2P6aS/aKYh51Jmm6Gbwz/OevwJMf58I78lcglebFEeomu3EVEHqmUKaS18Ma+CNGFINn7ME4Y3jkieMLp1cObl1Kmovu9ouv4PJ2izVjHUs8+Y1NvC2fUQP4+n3MzqMxPSjR5hm/iWTJ80zCOZbgjWxZhVws7EXbhiPLdYuaj0NQbXUrQASZBweCIZEtYFLLI3Z9FInIgHGeEYhDrEO/LUvUf7Zbme0si3+g2KNadeqxufGutsY+jkNp0CmOzPtXpgxIUQfxMkwhboVoMUDEXJwuVLsG7lcie531K6u4DmWdY/udAXq0uUSsHl7sdj4cjx6Gemu4HSlnYX1zw0fNn/A//w9/wq9/cczys3L675+27d9w93HJaDxz05ERuhhmjs+qJv//nr/jLv/qY5RrEdiw3C3V6euchDg8Hjg8HxlihDlScDNTuLVOcqMw7Lqyq9AHYoEpkVwqb0txsErQim/LYdwueDQuXYUE+W/BDNluAOL9ggcJmca14yFZMPGtpPkEnANcM38S8GLaEtiof2PkQm/VeQ20KWgfb+mXI0Pug24jSlNzcZ10qg3AvsdEEPE2/dlY11rkmzJAwtUzu/XNRILtVpCxCw2tLOIjAuMFLetatjxE8EtGYUCaqs5yBmH2zzgqAlxg0kl05c3GTtSLQSBGfVCPI7MzqL7dHSyKXyphrLt5Dxom6tiuzX75+5UwPpvGT5748Q5RLEZZitGIsdXMequI97gNlYYaNrPNLSYwbmuwS4c9UpiPNy87uW9KAc6ZUsxF7U6KULMJ0fP86/WUM8+SODEHw/vMy2+RYFICPmcGdXUfY6hOz/BLLsjVHcFmLakTxvWV3FZmoz8xrgj1pk9nQlKf8DHv0s74qrrFC78boKzZWxumI2EBEGceV8TA4nVYWGr/+6Dmfff4MSmM9Dl798Jo//uEbvv3uB472hlWOjFMszBgc++CfvvqS//x31zx7vuOmPmO58lKYijeeOx2V08k3906iGFlSMiCuPtLN6psO1z1Fq9HWHNpf7PcUhONpDU2Kb9qUT4woNBUrZAf3uXEsMpeBXpJxnKQ5efIlFMlsoWFW3KPe/iT4B+8FZ7nLHRlaGCPVkBrYTN9P+YPmPfh9rb2HjmerIbNAX2F1vVSlRL8w0znMYh3DByjEZtLgNqoUl0echSlJvkPxoSQSCEB8qpGnFGNBJQz7e4ZnYh3ScGw9s+J4xqJPlJW6rXR6832cfR3xssy1CnmPrmyP9Sp1Sko2dX4kf/4EUfyeGt8ygZLN9zxDWWuEUWLBJXpfrBLaq4K/3xphoIluhdw6Nk1fQP/kUWe3jWko4usydN6i4bkX0ulEbBb2yIPGEZywUwVjGsbUGc7hHvFOPHLZiu8zrEX8ueXMeBcKiGd3/f408yNn23u7bzeI4fRLVkf8GcLG9dF4LAf6aTDWlePjARFlv2s83nXuTvdc3xRubvZcXze6Kq/fnlhPytPrG377xefs68Jyuef+cI+eOsdjDFSggyqv39xz+8M9F08vML2itgJDGAdF1gv2uyuePjvy2ZMrrq6vvRVzq05Sn06s6xpKb6OvEXapsLu4YLcs7JYdN1dPKFJ48/YNtw/30aNJOJyOnHr3zAxeP1d9R04v6NNVLPgmRyBjZBFOZoIi5CmRBZu7y8F7xSv803cibmx9eIoTwKp4ttDM+66n/sfSE0Y2LY2UxaaOgnFMfVAHgGik0s05mOo8hHdlTcO9bRgLbRTiN+L2V2ao6eJDF3ZmR9gaTQ7dsISWLsWtZwZr6zq7GQQ/01u46DKRLQRK3gZSL9Xn585QNb50y9DK/C0Rlok3wrOan3P2fXL2PXGpetJD1Rg9s6uBb1TBtonZatDaQgZyxoZwyEGwkt0hghWUvL9oE2Tb+k2u9CxEJhFKcrQZKZw5sA0B6zbM1zy8BVApaK1+HyFnsIhLixbokAXTaOoBnZvIfRxshXerkDaz0MQ+8USUL27SLEyD5RHEFonLRPmCc2H6Y733v3F9GPJ6FLQq43Ti8HjgcH9gkcZut+fJkx19LeybcrFXri6M0wG+/P4Vf/j2jsvrSy6v9jx7dg1tcLjfcffmHY82WJ5cUXfMcepfffUN15/vuZFnYJXHe+Xtq87j/YG1H1kW4+n1BR99/JShxuF0okjz6UCWcoHC4bGj3Q3Mbr9nPa2cjoN+6JRWqWXh6uKK2hZECsf1yNpX1tUFsHWK97ykp9XKbl/op5VxWn0jlcKprz5JPLJdRbw0RES8TUj0ip+HeHRMfey6X25s+gDRWQ8Q2qlNMNlz7mLC+NnNYAub0utm//DsK1arsKvNC6w9bkb7Gt1MHAkmf+ZDPTI8talty/rOvDc/ZCUkDKkx8tKW8/La5Ewsy0Q2CBHPENR8hCVbwUigNQnBa36bJvrM/LHNA+yWaEtl+Zi4Oo27K9ajFUsYTFMvR+NMX4Z5idGId5QZxzHvNzRc81kk/h2GOafacVQaLdpCRsKsV8wQnABJOhKxnHVsSGMVa3leDbE5gPj5mo7Tn3lWHuTPcTPp71C8g8Rp9GgTFGG+phZs3to00PmzUvhazKe4R8ZsoxUst2U6cA9fM9yfTQ/jCyXqXGXA+HMVZlvUQSlCWRba5SVFCru24/qj3WyjsbTiIEPh5ukFV69uuXv3hvvHxnLRuHlyw9MnT7i6vOTl99/x+s1rTncrx9NKHXDT4C/752SFvQ6lry6EfXP7jm9eHfju7ZGrb+4YOjgeTh7iSUNKhVowGxyOB0Y3lmVhv98zhtJXN0q1lik+9enNfjBVjdNpZYzhB70JtTREdvSyol1YvXZijtkSYNcqOWCdIrPrwdKW2HDVOawx6BbhQmw+MV+uMUaUkkQ9oMJq6plDDcFoHCQ0eZdMMbsuCQn4n10AxCcX75fFmyKaYbNub5bpOlkshKxkQyvATBNlqcqEKqXQqrcELyLTSM1QmM3wZqi3dW0AMSW7xeZnzrFpGQ4GaaQJZapn+IZajDxjQ4RyltESmzwZklnVmGk5MrmToXiIbIdtqDXuaZpbSwGwI4qpqJ/wzRMfo7ZwQsOFyYUogfGmEetQjn2w5uSxQNoTRP/XQlezORqBifgJ6yJzv0hwfCIx8OO9LGEKsuPdScFwZ6iRTPGTPeP18C9nyR1z5JySjHqWtT5PIpRwbiXb6YRBtR/tBX96DePtn6E4F/dzrg8yXr0IvTbkolBV2S37aKIHbXH46P3GjcfjyuNpZXcDH312wf0fH/jyy5esa+ejjz7is18/58VnT3n68RW37z7ly6++4+UPr1j7kZd3j7z+4YFfnTrtsrG/aFzdNN68Fk6q3J4Gp97h9mFyDvnKDdcZaRx4w0eot7Zj13YsrbGj0fD0rhTxpoOuh/RNNjp9XUFGTPdeqHVFpHI6epvdtnhmssjmWr1sR4LMjtrDknPutpeTuR8CBUy+C1yJLQ01H3Tbh7KGujm5oW07bod2g99nveqrE/O7xZvgkUYwNoeU6tOJ48COPiL9XeeGm1kFtsMl4l0npG680dyUQx1BqG0e1picSRprCGMcoZeTy2yhUHA2brRsckJjClnB3utawEzNSxyonPINqaMikh95f478NlJ6M1Sgkz4gkzVshuv8UhxdMhw9dbx3mYj3qEsecO3Gw+nE4dTpGsaarEXk7LnPOMtcm5CdbGudd5MZPNl6aJ2FXamhggipMwwvFWQEiss9lYYxuogAPh4tw9IR2C2ErrVG08IAATFKzYxoMZ2Iy59Lp97OvFYrnUDsCSnBoea6/8T1Qcbr9u3KOEUBZmx2UW+4t2uEMF2RoYxHWB9XrHcK3bVc93eUWrl7uINvj7z46AnPXzzn+UfPoBRuntxwe//I+njkqy8f+N1/OFA/3iOjIXqJiBfktnJkJbJqUU2vwYnMKnxSttBRqRiuHM/yCpMaakc/uKWI802j+6ZrjggcQRmlhnq6RNNAUVrxekyj+otN7yOpy3GjpZnp0k2akYkFJLs/pAiyJrXhvFF0HJh4SBJN2PYzLP6cRovonhnaKO9pHmgtDsMMRSz3lht6kRqtVcrkiaapDOK4RvcJC5SXRdkAjLGRuwY5Y3EezPz9jCsCHzwxMcdEPBLh28B5wbxhneuQVQLEAfd0fDZ/PNPJmeMKr7XUbc0t0RfRYyq2VGSI01iQmGSGahv/ld9PVDVgPlTDTDfnIJXVjEPvrKoMy/dmW2Innld+ZMB87+hWhga+3meIs1bBx766MdSQ04iQaWLnTdOZhE6tlRbUhGv6tnmdNXoNyNk9eJLBZTBBj0jSE1HTjGwVAnF/03lNVHmGaCHKmEDMayPHn8N4/e1/+oZdqw7XddCjC0MJYvtmaTy9KVxcLVy0PTsqdlxpZlxdGL/+qydcPr+hFeP2zS1fffPIH7986UalFspSuXl+Q3l6wzoKL799ADWOjyv3r43TfaXYQpOG98MKqEw2eINafCHGsOBDsmd48igg1WsJS7QQFj8PQHAuChKsYRqKbFHjGfg8eN662FF4tB2WOHApRiW8cnIio8cUa68HQ/s8rAXwFj2hDlT17prmJHmGg1W2e6s1ny3uPTaQSNS6SRqPGAoapLBEBwPxXRm6oK1AtxSB6POe3FZJj5z9w9R1QFlCNFIV/68O90gSxBFEohjbmiqWqP1LHm/jeNJY6HZgS4YizC/MP1apE4Gl8fJHNM/c2ghO1GaoZtmc7yxF71UAG69osQ5pYN4rAg9i3PAkCxqkszERvZrTBX14AsYSfYqTDVsXjS0M9Od/H+Wl4yENaiDkrTGgf982C2F7IitGUQt+06hl8bODhhSEoFJqyGkScge6jWRVIrMZ6qXxt3w32QkjrhLrXbZ6VFWff6qRZHDd4uZYfs71Qcbrf/l//D3aB73H4R2RMo3av+el8atP9nz6ReOLj59ys7uEdVC08NnHz2j1CXenI69fveKb79/x+uXgcFD6GFCE/X7h2dMrnl5ecLNrLH9fOdx9ytVlw8YTinYu2oGLpXO/QmGJAyHhkF3GkHogV13HS4zNIhJlHU3ciJE1hOYvzYYrndGzTp4baAD3IqMI1hUdK9mlwJFKbBXH0vMQQ9T89e7yBB0M7b5xxDOQMwCaBJEbqKU1SvXpOITXExzxOsoIDQ9pZCsEz5LwSkS2cG3EswWPM2Li0fmGzPITwXVHWyGtG5Pec0pSNLnLsWycGy9mKIYw1fxeFF1cHkfxeQH+LxuJzvack1cKJ5F84tazP4xgtPKRDPPSQCF0HWeIXCYKyFDGIOQY0XYm1iu1TOf8V6bzp0gzpBfOMXkxujdW8Ky0qkw5indIdUJ4opLEmZbCZu+v5ZTE9v5+zIelwS6SVIVMBL69B864Osg2P14oHoXdbMJQ/7yYh1A25y4EMp0sQpnnoqTGkTDUhBh73u92LiaijojBwtFMLvXc6P3E9UHG66vvf4gOmQJUUmlZqr/wR4T7xx0Ph8LlGFx+5AjC1FPUD9/d8Q9fvubrV4/c9hFlEsFXqHAcg8fDie+LsCyVb17d8cPrB379xcc8eXLBcv2Cv/irQr284dsfHvnhzVvu7x9YNab3Fi8RUYVStoxQeq8p5otNXDMzSHEtWBbsms1DP8bW3E81U9GEsI9AOYlWytx8xSqa6u7IGg71YuWOTi1NHtzUVxGw28TXdam7eWA9RA3/L0RRddJSySGwfU5k92aDRzUsDFc+yzDlqN3RVa5HGF4/Atu1kd1ZHhMoUWKBJkrI39MpnBu/yGSpd1hI1LiJWSNAls2g5DOUItFS2Y1UbW1yQhINFEXKfO4RYevQjmvNiHfhBdcTUQUfU8K5lVJC8R/h4PnXhQGaWV9ThBoarthjIQo9l4KsZ98ngWRTbjNXTYIKmhLCFBvnyc/1SdQVz03yq+VsD56jQ5nZ0hzssfUg29Y7u6TMzhWxp2siO9H5qt3OZI+6bHez7Y2kBBKtbjQKZA89cIfmhlQjW2/ZF+Enrw/rYb8eYwJJZi48k4J6qvRYC6/7kfEKnl4tfHRzyfX1BWjB7o5cPHbawdCDufJdupPKCFWC/Au08njqfPf2gbcPX/PH797w2cc3fPHZC25uLvib/3jFb38NP7x6x6sf3vD69p5X7+54PDx6mZJ5ryYZOkO+/GzPzm3B5GwvU3LHbHwMxPSYuRGCbE+SMQniePseVkbHzBm3Z+Yw6v7CcBIHww/MVnOYWiSKv/ga8VEtldbq5J28QHnjecA2stz8A0rxg9PjoHnbn0QgEerpYEz0AhSvsZtdC4iwMz2z5Jgt2zawP+Z7hzwPnXdlzWMRy1uqGyUbaISdxD1l+DQLkwOpJKoEz49kO2wkS7gS6W7IyFu0JMnvRlZRV/OPnMqYU4hqvAt/To3vlXBixL2nlINATLOffX6Bum5qxGelkVDNwRjy3lpheev+dcM8hJMyOe3NuNkGvpLrkvBk2wxQLwY6Ty6Ynncw9fBZNfSDYdQJg+pZQplq++x4Ust5TcHmDHMAC2f/hlqg+dCDWTjj4m2fpATCLZuzNVM6hq39/TDnT1wfJpUYA6oPc0jjFY+NRa91lcLdanz1+p5ff/GEZ797yuVSad8UPhoLv7Mdd/0HDne3HLTMA99K5fJK+ejjKy72O07HE3e3Rx7uj3z35sTr1+/46ssf+Oj5Ez7/7Dk3N1c8f37NzdMLPnn0Pl9v3t7z8tUtb16/4/HwQBanevuPGnxI9j0KgV1mAmsNgiKycrA1jiMHvBqMLbWcvEsmdNQ8c5npeT+GoYvKw0RsesXFfLmOqaWKDVVLEPfFkwvJMy3VqwRK3bJp7uWGd3JIBbPq5uGSozlLFCga6njXpGHgje2W6Xk1N3VxBJOo0tFCeOyzk5VZP9/NEcYQlRBkmBMdK8qW3UMVE0NqFqkLWeyedXJIdnmtEJ/mqKCSOgLF12HgEozsvuoj0/D35+yxJwgyG5zhp4VcxQyp5nwkLmvxpE6ICUY4RGnx6MHjxdomN5ilNGphlFPmkEp6CLlKVIxK9vNSUqYrJqhlcsT7cG2yA8KJDtQKqWa2soW3vauLnbPXPhZF+wAZdscZjj1vJSKQGgXdM1xMAj5QUzzHZriNnHbkoWhq4gDzBoYS07hnJliCV1Y3jesYkSH+abnEh81tLF7c4vcZ5RQRYqUgTlWxBizCcS/sPr1haY3HwyOn4wOP+sjlxcoLrdwdV6wIF0/2fPzpwu/+wxM++9WlW/nReffDLd/+05GXXwmvbwdfv73l69e3/Mt3L3n+7JrrmyuePLnm6dWOTz95zheff8y7uwe+//4Nt7f3PDwcOR4eOa4Hsk7ON0mhUalB9kccQxaHjrDKlsbDIhzQzBjGYY0X70Nf/ZD6oufGhYwBcmNkuUitXkLjm/FMgQyBBpzf8QjdD2orhf0uOqyGtwYJkWWggQyjgOyHzplMYQZmJmccV53GwTu65jrp5LgsDr8bEt2845nDFfFSLpOK4O2yvSd+GPoIzwaCBKoxMZ/mnKgtjVeigMz6iWvKSrQ+rjUlELwHTWbo4oEjJkq23xlxaAtnnxPP7YdMpxE0zP2YebNLyftKcaxFkkETgUzTHaH92cLg77LY5tj8dm0S2Jt+zIekGBLGrgRiNJo2SJExFi2/NbLJgQLjvsCrNvpwjnUzXlkHWTa0jXeVyLNsZ6Hsj98vgcqSH8x7z/qIpECmAw+EhnjvMgf3MkPm7BRCgWrFW3yXE3D6E4bIrw8zXuYK2I2XkLkJJpzG2F1Wbj4p7G4qslQeh/FKV767u+fLl294PK5cXCw8+fiSi6cLn//2GZ//7gU3T/foeMAQWis8ffGU33xUefnJI3/3zw/8w9dHbh8eeXn/yJvjA/s3e652Oz6+vOGTT57z7KMnLHXh+dMbXjx7ikRodFp9IY6nA+/e3XP3zv+7ZO+oeQgiTpc8pDI9spkwuk/EmaGCQTKaZiAtNuGUOkfqPrxWShiKFCT5NbbQJKItBM/ENEkxrW/I1oQqw9FG6G/GWGfP9qFbfZ5aTlYeMzyZiCp2pUUJNRKIRjauKztbSBxFzU16rteam38j+Z1Ib45wQ2cmEvGDxs+dBzXDnzKRCRGyBrviyDQHbdStAaRHmtkkUWbBuuY6xlncak9t/nm+3yyJSnESiplP8tEIM2e2L5xzxGlnXJxXE0T+dwp/AbI+UDI0yneJZ2p/nFMzS0SbB96TSjN0Z8WsQHHjS7yTTRMmeavOr2kKnH0idiZD8hEIhyySTGpcIYD2jigWazwp+sRa52Zhk8ekXRDXm6kIZn1GHODoMmuGhUTvHoEtIq4m+BnXh5UH6VbvXs5gvDfw991SinHztPLk6Y6r6x3raeX+/sTBOrd64s0qPBwLOwaffNT4b/77j/jiL65plwUdcDo2xtoZqlxdClcvGnZX+f1XQqk7StMo4SusptwfjxweD7y8fcvl13uW5uK7q4sdT68vubrcc3OxY7dvIFe8eHbD3d3K4eHEaTXWDqt2TjGtpZh7UQ0xo5OwIS9I6UHwORIeI5aEPlIeIRFmpeb+DOXkNhDz4u58gSJRoO2cTg3yOVFLrY48pGzTnsfQ4HNSK5biXJ0TfPzzg69IQZFpoKPc8D6RVPIOz8LD+YepSs82KFmDWIJTjI4SxbN+rXpv9DwkhgUBPc7WM9emsi1EaNSkkG2DMsmyXRvhCwSXFP+SkeH8wBKGc+O9HFGHNAQDHZFZHCFdSflKNq3cZAiutzJq/JwxOhIJgDGRV9wjwfnk+xWJ1jdbqHUuRvVMeRLdZw8UxIWPHWNztvG5RhqxbQ/0+O+h+bvOGkYfGhymI402Z80ikzscvocdwcrUbZVwtmkgzebLmxGY81nON1Lbe8JTb4+dtqTkArnRN6bE6KeuDzJeqtAkRYA1YtMSG2bgvbGFi2WHyAWtFfo6ODyunNYjj4eDZ4BK5dQ77+4Gb94oTz9euK4XJHE9hh+o9WR8/fUjv/8vb/ny+0eOHZcMROFuARidrsr6eOLt7dvorNCpGBfLnqv9JRf7Hc+e3/DixTOun1zx7OkFT67cSJ1Wbw53//jAw+OB4/FI6bCuXlbc1b2qBuk/CdLiRPSsJczwKoOfINWnf4kyjtws7vUTbIf3sbBekRQQ82dsdedDJ2J8FkZoYgYjWj1nOrwH4bm1hnZE5dy7IWXLGCFCTkZKMjjV+efGYgzXp+VkasjW1cK5aLUEsbv92S17n4hw25Qz+znD11yJIJzP26JIhizv7cb4vI1HnJ8dnFfvwSfpth4UCWmJRIuaREqbiLfMcy1ba50I91srMWHKnYekfRZxgS4y1fsBNEmAXmRzeiZbcsrXQz37KsywbwvxN9FzKQ1KCQ4s3p0OlCTIXcvWc5aBuaA1tYY5wXoai1ziM98gYYQCbkcBuTk/GrWHP5ajnE+ySgLAdYCKVs/arxEFFGw6NP/mGjxq1kH+GYxXLl7Oy/P7dE9KUhXxko7HE2/e3FFkx+FovH175N3bR0QqSwOksh4q//RfBnfvDnz2q86TpwtCoa9Osr96eODrf7znmz8Ybw8VK95StjqD7IXN4hqSPjrrunIaJ7o6KXjsB+7Xld1xx6uHR16+eeDZzQVPrm64vr5md7HQdoOigsiey32j9yt/t0VZjyvHQ4yXj7T84+NhTn3pQZTn0Au/tpYeNo1x8CYhy/DT4YZLQ+GfUNy50Xy5Sq07V8pPcV8cjOgKkIbLM4mumdtaqkR2NY1hoiop0dYl32tUOMWfkwg2GzP8zCxmCmNrlEJVARHnXWqGjOX9g1GkxjrpRLSqNtfNwhjngUVt6n8QznpMsRnVCO9dhGvzM8xgGxoSpUQZ4uY+xqJspUwj6pzxWVueGX5n40HP5HnmbcGaa/Z6L8hw1KYZZhbmyHov0v9xl162w0/ulaARiiCWWgELzsHcUIVQuEQHj1xlC1oiu+F6WVmOoMt+WjYzv0HVxiMmbeFrIWcGxBMmiTtByE5mA5RJU3gfr5hiPvdQSEaK78taCtWitblNPDqjC1+rrLT4M4hU3cNmCYJvEoIozK6ZTmo7+f3d9+84HgvH047vvjtx+85fTE2JWKkcH5Sv/uWOH74RLi4GrbYwSoP7hwN3d3A4CGaN3Yy4fEON4pxPZjjAtUKZSXLtj28MFXjsnfX2wO1DZ//2kYvLPaXCbtlFhsjLgZ5cX3J9vXA4rDw8dFRWLq+ughhXDocDr9/e8fr2yDpsKu6xbWIPRPLirErfjX7WOm6eVQONuUo+athEolayZuQ1N+dmvDw8zOGoIzRckYubxeZ+WnJX+QErcyvGf0dI+WNRrSZvFr2+5sGRQmuLpz2CQJ8dDzZSZZZrWYS5+fsUemaLmOCQJLgSCoE0/QBPZXlcWULiVQuRmDj7mRYOYRoF96qc6+HMOmolspQ2OSoks+kew0stUzZQamVWdSRFYBohsW2fxVamlJzlVJ7PCgM72wXmhjpRT4aZUQWSrbslDKlkqwrcOThw1M0xhMNJUaxLarJCopCZ4FlNEVotEZfqZGib704EN6QRXue7yPdPoNcNgUV1RkQkI2ZTnvdt26oIwjOJJw5+zKn9W9cHZhvb1DFtGyli5oiGhil3t/c8f3qNmPDDqxOPj4MffrjntEbmqMhs2WJA78pYK4d7wWyN4mFl6OIHU/Q9b+OypuicisXvXuTrdYdZm1Up1fmiUh1qD1FWDOsr631umJN/pgxaLdzd33F9uUOHsR4NZGBHpbXGxWWj10EpI5mkFFFgRNZMc7Sar0+GApZhpG2Zyi3smQQTTl6WyCIlhxMNFmMzJYIZ2ZfpDH0RvIQGN5Qj1fxHZjIi+LgwNhbe2cMMNwxeShPrSiIQm89tHawWsG0CUYanvjnt7H6T6PdsWu8n1r6eocfg0IpXDuSUINcuteB2LN5zTLEZ2Rpou2f8Sb2bB5sSPv9+FgeLMXTdwjhSJnGmlk/JQ6nRieT9khkinDILvZxu74G5znVGJci2V5LAJwxMrlmZDiLFwo7ec74lpIETr7OVMjmuLTTPXyHFiSSRayldluRtkbZIaULuDMU1Hatuj2tb+sYsM6eRpSWV+x5C+yzSba20DLqs9JEi1jOzErISDUcwxaw/cX2Q8fLF9PHprkT2LQEbFJRSuX+nfC23XF7uEG2sp8JpDb1M3JdZHqRQ4SdJOjc+jBD2eaVKquEdeeXk3814julhS20UcUPblhrGq9FKiRcUWSlv9oVE33kbymNfeXd4wF4F7F79hTYpLLXw5MkVdREeTycfAaep4D/z3C7rwaLEQ5KJT+8UJniGVqV6JYDp/Pv3Dj3+bF6Tt2m1xhiM3qP5Ylbj+/baNGogQyk1e4rh7ynChSkexQsMsh3zNIzqYWgp3n01h19k1wbJUInMwiaKibArEwjxa+2d9XTidDpyWk+cT3fKtkYFi0k3bjgyKaCmeRzZpAtCcozgB7FlL6tMgighaWAz8LFz3zO6cZqnpKUuk7vLZovTTZlFpvfE2k9RfhQtZiyL0wPNk/MVw/ErgXLDKCkzfCyShtJ7yucuP39PeRYlkR1g5k0Gm7YYbrGJQLPMS8jQLOpjA+lJfLD7rzNJTSaEphESEJ2hIfG7pBU8+/4smHc/Gfy0GcLwc52cLMGiEKF0LVsTzZ+4Pgx5AUpwKnGIsqA2b1xEGKPw7s3g/n7Q6olWdt4LrCTRB3Mm3nxg57AkCVtc9JkZslQUwxlFa0b2oheB2jKl7sarlsqyNIexwdNJiFi8Z1CZmz7v39Fg9fyRONloOjhZp3Y4vF1ZoqOCRyqS5yc4kx5IK4j8lErE5q+RkdtG1PsT+UvMLepe3+viBqkxS0CdG2T0PuUblgjCzp6F3OxZTxg/S7YDwRkqSgOj6QyyfKnYVLS3+iM5xCzLCRgd3jjT8KqD3gdjOMpaTxviSoW/2jnvosHPRPlMFPtu8oqNmk9EUsQopkHGZzIpnjFDePMuKKnLMgOK92FIPVWtLu8wPEx3Q1JTQcKW4bRAWX0q17O2U60HTZETxd2g+9SjiBtKOIspXRDMUltXfVp6JmdSRC1nyEvy3LkRMzx8s+Lkejejmcx+ZdPk2YZy5vrF3+f7385XEhtn58JsGqpM7MxmAHFGz8/SGIOU55gIpdX53PNnQ1ArRHY5aZWfvj7YeM2Hirh9jKhVy5tPU1r2mFSU4ilks1DnMxGWyoYu/Fhu06aZy5veIdKwFodLHJHk4fYMmXu32pqn68sS4+u9fqpE+LXB48n6bC4tnEh2blBRd9Gx6YcJhMDSyc8MQ1wGkPyWk7vh1RO21+rDJGoNYtI90BiDHNUdW9UhuG6FxODTjmZzP1VvlZN6IdlGqNlcu20zRVw2bW2uqW7Wbhq89PBbIznvIuvrungDwrrgswNq3O92sAuFHgd7XUcUo7se7XQ60cN4ZePFDHG8L9t5aVLe2hZ+5n7wNY+6B0kuRyZlYDg/OQ19ci2B6AJbRYsjmb3Jco08kRI1dwlL2Q65ZzP9uRKlrqPHdKhA2hExOMnvzrRKgQqmytq9lM3FVhtH+R65n+hSiHtmcmhjOKrNnnLVCloKu9YQ/N+HKkiIczNRo4ZGRyjnmNLZbdnkSXe8947ccDoy9LXOaVfnU52yuWcaM4NZguQfEtGSxj/4zfleyiDlZ1wfmG1MJXSZ5PGMsEU2mC/eBeE9xbak2jY2I/pe36e5gRkQzQV9Y/oiLeG5dLgixRvqRao6SUgKpTVa29HK4vKC8wX08mh/lgw6xd477LnGU+HsojJ/gUVCY2bhEAPUR5Zq8hlhuGpbaHUrqi4hL9g6H2wSi3zDGQ55TZi5oSwebHqti87iYDM/uHnv6SUdBUncn2/IVN0LhZxulISaP3NuLIBsRGeR3YLW3PC2tqO1hVZ3oUzP9bXgoLzbRA9Vd+8r67pGZi4OvD8WEmSvRmgzOxmU6gmKcoYg0+FkSxYDCaoAHIm0HHk/tWGDLDMCnPe0cI6WHTmi9rKk48wjHCGOCYwIbdJwjagP1ZjsxFlnDSwoEf/sKs0nZ+d5KFG0XQSVDiWGzcYPT05yqtnjfiVkKLM0KIocNmTlYtgisDRP8qyxK5xLCqQdpl8Md+iS9EFwpDgS8mx3nkpC4+WGvRWvRRbZkhKzY23WnBKLeeaExNTfWSRrYgOTjsTOkjg/5/ow5JWkqW5ecPsn1z7V2pCa48Mj21V+tOChfLfJ/8jkwPLwelp/Ez62pVFMEfWWI65id2/vveYdbdW2UOvinMFUwm+FxLNx3HbnnB/++VexJzx0y59Tg4sBbBu6msXJTiN4aOBe1nuPOUkqM02dOqr0sLXGNrGNPPeauDCIw8Pc97J/Uw2fFPq8ZX8WX8YQBMZSiUEMsyUOaT5qdmKY9EXUI3qo2FgilKm1ue6s+vRyTYQT+2IMDxf6moNVx/w1shg6kwSEJycEzxGmFcnMpUwebSaJLMNqtlpLzjigUmc43Er1QueSyM7334hpSBNaiEwKYGKP3CvR9tg5RJ2cWRbqTyGwOFdTA+kVShDjacBaoDrZtFJ4ImeLRuKeLI11Sgq2K7PNWVc4Q+nY42IxGUoEK2VSDhm51ECrxYw6bCbOhhk5IGRqF4FM9JQ844HGa3KFGw4GHFFuyQN/niwjtNHR0eljjbD77H2OQS0tGoL+GYyX751YpLixjH9b817mtZx5mOKbM83SCPI5ratM1ODeyBcoOltF4fFs8yEp7Byorq5ntrT8zsG40Vpc3V2y9i02aoohA9bnsEwm6nHPoxhzhHkgyxp8Q7a8LVGI5yll/OdE2QdRZrMRr5VWXNyYGbPcjk5quuE1GWEQJdq4CNkBAhx5aeiCtkxlcGMi9JEiRJmfPcOskkhPQcsU124cx5ZVkiCsiziSccMVotMwXq40t/kZmxSi01efuNPP0MkGrsNDY5RapwbJHVUNjViZRkumwmgLZSwyXXa2B/PrW/MZBqJQdEwhqGfn/FOGbZxL6u30PAcYXz9Z5OB3A4vPgzgrMGLNSvXSFkcU+Mi+aAaAeAdXn0hNtEgOJzYGxXzieALi/LEkxZHoPJ7XNBv2be870crWkZfNAeRznaGgXE8stYPqBe1BSch2J2gYxETpYuUMUG0h/Tmgec94QZynHs04feSgDYto3IKaWBgGR135OdcHGi8NI5DhjYs3l1ZZWp0lLds49uC0krzNMoVsmRwPt41w906YyRVkVksEh5q6HbiCT0CR6mFClTa7aM5tLzYzQCLOAygumBRsojD/NaZIMQ9+bvJaSkgFfE+7wn/L3mAbsSmJLIJLqc0oReehzU0hBBGt2581tDazpbVuENpCW+eGQDZjHFmjlLAwO37oRGbzPUQ9pYYGSiNlPT3+NHxu8JZl8bbbtZHdP0AYXZ3riXebZTMWRisbFbpQMVCLRG1iKdSzRIllaHG2ZomszAwd3cOsRM46Qpy6Pf+cUJMJj3CYm4ON/at2VmNJnF2XEFjJdH9WR3hdpofCuAErIbcoZTq4DPkLMcRl4hW2e4rQT6NzQur6Ns3TZlT8LFu8MwEvY/fvC47yfDCvBaI+T9AEHPAMsTpZPpv94SF7FGrNsqmRTugMReXy+S2N2CJh6IM7Sxolz8DkTs9pCNuEw+tw6kCjXbhOHWTxs0r58wzgyJea9zSb5JVKK8v08OmdxgwvLcrLzl9WbFIjxJjNQ4iZkg5UXyTCkTHT+KFUJUfCZguS1Ab5ao3gs+yMeBZXLyueFbJNVpBEfg70yHt1g1XCaLHpj2oyCIEGAgukaG+Sl2GEfV5hBKfBDWToligwOcHR3dOPCF0kDQAlgw0g78uRqtu2EL+aVwX4GXYkqdjUE6Vqw7KmzTzUTYFoi8LfUs+eJVGsuYp/HWcTx/FwwWa50piDezO09EzgeXWGRcCRG5dNQzVD/MGmMwouKAPjs0ynO6vgJ+H9gyzOGdpIfZqGIDe+QoHi79f5tG14SjYUdIcQdyZCbT4TIMP/zGCbJA6HjE4y+ZKHlOB6IcXGOhEqaXhFwnBtiHMolAi7U/iaAAJcymKWGWU/PCX3b95UnEnOomaEjYce/s4ys7+h+JRYhEMwpY9t8tR5F4rksVNPp2fGMLtbDI1SM//BzumJj/5TKds8hJ+4PljnlS14YRO35X9PCHuGRM4ntFgsyHntkk/i2UpKQggcKxGLqmfkfnzuVvtm09sQiMlfuxuArAkTyfR7Etp5vxkGbLP8sjOAh04xYbp4iLe0ZR5q15f5LylRtGsbGpqmbdj0uk4BZmXmRiCr2vRIOT4sKnT9GMS9ZhcHItR6ryGiL44jDEmjytxIRmyW2HZp3L3xn8V61Ug0nIsMbU510ZA4rCkRYEv5Ewdg8+AZtqch8BtN7FnMHaDiiHbORIw12WgKZsjNJJnLdvriNrNLafYuS4jlh1HxISzKbpfrWefhT56xRQtkdwrD94wYJoVscy3E4JUpRUiCOjWP24HdkjLBkQVtYomsbXN8mIuE0mHkOmyXZ+Tenz6+OWgDbDtajkjNhb8lDJfGnkKIREU+f6DZsoWX29lJg7k5BS/hCjOt2/Oe0whZf5ufnWdMt0h8o13MQmz+ZyLsW23OK0SY4wuUPb9jwxFFw8FlZLvjoTlm6cwjhoWHtO5sMTqZrmaqpL29SCxUwlT4Ubo3uJ3caCUjorKdcNteNgmnA0Wo+cEXop6s2NS0tLZQW6NNwWdIGiLMceddMMrs3mkEvI6uriU9pxWKBQrJUMDONrSOebuFbLi3ndfsM15rmWFDNsQLYDoFky5VybcYIZW46h4ivGk1OkHswnCVKQa27Lg6nKvwljHBc5meyS2EILHO0Jqv/RZSb8ZrW349CwttarHgTDVeDGqg1nN0FK4qS8B8ao/zh87RhPykFEpbQIVWFijbwNphxH7NMNTfiwZfnlnKdCT+LgWphaqGBFdnQY1s6MPmmQBQ7bFeG6ocms4qublC1SxUZr63NMBZLjbDs1xK2xBVluTMg1UFn4ESkhGNyUHRyy7JfMKx5ofOM5pJjdgx+f68q8SY9zj3euy37AbigvDtfE0uNpMx2+K+d65/6vog47XbLSxRoIxkkch24zkEdM7EMyZPJVFEK1Ygm6eRNz3wTFuEZGEMRXOxLUpuQm1uWTMXL9V5aIb6Zk3jI5aH1r2XxiYo4mVKvnGdg3P4H+gopv+WyMRk+neKS7NUY3bVGDMD6wXMacRjk2omKLzbeSI6y90WXijRhZeb9ECiMouUZXrLbQBFtued9qOoFwVr9OAKI5AIJQ8osQlLhEGlujgyea10Bj4dyGvlTuvROaepvtfpWX2zb2vkRjXCsZmRk2l8Z5uc2KwahjdLhYiQuiCTjsjndhlCSVUgjhizGi+dSKL0TftVIiMokVQxMwY96IgUXtYQivj3+U7aZANq6ntHzJNTbauUUDGvFsnwnZgkNLLLw1kLI8s6z77tS6rzR8V/XimErCVCbJPYq7Dxk/77yCg9UYydyyj8GaYUIhFTvGmh+BAd2cTAiezydU2qwVKMndEVG3pM1J//Jo5m8++TH5Tgb33ldL5DZAM2Z97237w+sDDbJQAaZLDGQg0dWHRsNIgaqI3j2khZnT2AlmVhgtBMBJh5324hwhOb03YsxamWCIXJkwTF5n3DY+sQn53e3qGxzgOWrXsnQgrDVUsmHsLYluR+0mBsGUznsfKQJCk+fL6gZIZ1e7nzMEx0KRQTNKUW5LNEkgHwnlNKjqLa7XbUusQ2DT7HILdu8hdaMrNYvJ++yBTLzlAgEyxzGo8P+FDTWXLUu4+8772zricE7+CRTmmEUfZOp2Hsa6W23UQOJcu24vlUdPPQiRJmqBiGosTBmUY7B/hGFcU0GZuY0m2iTqcVwTVSQkyZ1Rvp5MWPgJWC9RGfG1ykWXy9kmyZj7WTYAqJriERZgkUNUdp1VFXJYyTKaN3uuV4ODdtvn6RNUyxbDiiTITPyN1PVEQxeAlaONYZYs5dcJYrTNolEU2g4nw504CdRUJprCzWdoMo3hr8/P4ozjXOgb35qWmAEoUL2MgPS5GwvwQL5YDv1e0pfur64JY4eeWtqikacwiZf5fh2/vWc4OLc2ndcgcXU6wiFgd6eB3cqa/xggPR2PBBnpLV/Vs6fQItc19zzg+dl0SQB4LwbGRs789Y4tSUYq5vypKKEuRzKpfj5ZLwWJWeimzx9LhrcjyjtSG40IvlJosazvPJK3loCSNagnPzdtD5PDYbzPk6OGpS7fNnWSA9wgC0qNdLN1dbmwYNQnGferL41cfqJT3awWDt3T1tzNcrpbDsFg+razaolOnEpFj691kK4jo1nZk/Y2t2mcaOMIqpH3TUG1025p5Moj+5ss37+71FyB+ZU1Nj9CyDcoTmh03xYR09EJrM83cenfk+Tv41OMMNZpzv9pSQbTq3rBUNyiIHYZSIHgxXvqcD3yQIIaxuDdDJCVvcW5YKnf9s+fFnzKNwhqY28z8NVjnjvCYPNm1J8FkS35MOI6eSq3rvfzajLvEzTQ1rjlOmbQjQEpMrIyztZ6b3T18fZryYAI+p8cn2JrMtzbaJfgz+tsyVL5l/4Zioy1Sia4Cwjs1wZew8ep+euYhPCd4WO7N70ToEm1lBr3U7IyVx4nsLI8tMyWxkpc1atAx/e9ZqjQyXyOKBkAokNM/aTA29z9Z1dnZMQOfu8wxkGkC/x1bK3GQ1/nupjSVGrCe8PK8SM5VIy/v619oorUZ3CEdvtdSzti6ZPXYD54XLK31dpxZvCw09XJ/kbISbu2Xn5UKL84GZRZ2kLBH+INiYJicohFhr3Glo0Am+a0qous8Eq7GGJQ6lms4wlXnQ3Yiko81C7SzLUix5dbKlk2cAvce6iVuPakmW+tfmzxtjsK49kiBZ1pb7LpFTOiLfh47gfT9qZpWJTNw5r/TejxTOxZ8aKFjEqx223vdjbkJLo8XZ58R5qxLSopQgWSjuU9w7dZZyJiDezlZ6/ZJkeh5uEW9+IwUtTiG0WOsUG+f9j0jmJO+mas73bG/caaWfZ7s+VOflJ0OjjUlOWnETL2d9tSLFnzd1XueU/IgZ3qm8evFsQukxGAarOtFswjbOK0p1JKQVdWbbNtibJHaWtWxF0Da5rbgRvPwFbMoWNjQ0kZp5VsUMtHdf2ORmzFPTwmYwLDerxmiyELim4LPVGhOLHSFlgoDIjhVAzbmALCVaiv9q1VGX326q6B2dMNcwxLTiA2qdMvFe5CWEn9mTzeYhj9FsibaSn0k+K3VVXlrghrS1OY6tRnfbUtu2Bvi+zLmFqkr2NY/8mOuo0Bn2ipxrfDK8y2DPD1wLI6GmVNvWF3QiZ41avlSTtzTVZzyrim7CTO2BNBWTgSwLNZGtEftGY1aAznrGbAjg6LnAbBfl4auvg9JqQVgoNnL7xJCXMLBRYdBKoWXnCXDDY7aFZAYwvEKAbe9nMiQ2doJq4quwEifN0kCmWJiZcfRdcPYZ/+rwO4ysEZmkkiPPikQtqAuvKzVqX4HgMgdFxkTXrugvMds0DalzgX+WsDH1FzpGjKGP7Jactw1O0tnes9611om/Z8iGW1vfWKEcNljNWNl4qLmU4pmmuixTWT8//0zGIWXrh+QvSYMMith7kocj0sievXPtSoQ4wdGk9sqiX5g7oTOu7wyvJ4eUZHErGfJUdq2x311EF9gB1eg9nMEMcV27FbU8FIQWG6aW6DSf4SQpZvVD7oYh/q6IIwgJsv+MPJlcG3ImmvQi7x7c1ogsZXyDZ0YFD6WqUFtjCYPlRdoNKTLb8iSXNTOe0f/eM78b9TDDRxPP9klB1BMvlmFHHqlcZvOSFjn/b8rWfbR4Y0U3lt6Y0szo64oEET5Cq9bNh7Oc1qO3FjLISgTBKFU21L2u02ETIX4fa+zhQDD+IgIzbY0xy1LRVqlWqdXHkQ2L0bexV2pJaUJ+Xj6/oiqzkd8m/k6aZpPmbAXN5wbPApn6vji3S9v6GhvK4j0QYpNWEZKEK8FHpiwjIsMZ9dSSSa2gdEKD6JznAItJQrKhrlluK8w1+anrA3vYb+n8tPa1bMQ9Ef6IvW+0thKftNBM4+NhX8Y6G4LhR19rwR15iCdRdrOJKFtts13y5o2YL9UCOTj8H9vPYKvDKrEB03ghfvi6KV3XuXlKQPQ0gm5DNgauBOKq1Uumdrsdu2Vx3ouYW5hVBmEMSy0UdaI9SeNMGMzsZpIc5sZpqHH/eOQQh+9qf8VSK16ilNm5jSPanIYnK4pEW50x5vMlgpnSDIRagZBPOMpKBCksNWQFpqhAC9s0GNO5iRGTmvIeIkWeXKIEKhqeSBmRUTb0jI+zbQ/mCTT/v3nQco3iXWYPtB6kefajH2KRjFg5Hg5BT/h0HQ/HvO42S6EsUCmBpFKCMs+DgQQ3mhEE5lKb1jyDuPY0iEZrmfTYpCazrQznGW2JNTdqa4y+VYG8f9lcxxTUnme4s9PxXCMC/YaQ2DVmOURHp+Hz9+WR0mzZM//+PJIK4ySClODXVJGiSLQn8iy3h+1+jrNKwN+3+S3MyOrnXB84+sw3VArdZkYojJXD3DgoRCFz/CqpTwmklUbND1PgzwwoZGs5Bw7/3YiFxieNV6bAS4mC4a2JmcPRIDc1fzJxUIdrlTDUwtzkz48DMeIbepCsXdf4muJDRMzwjg9h6M5CY0meZQnDtdtF58poYxIet6v6UAJLbdPWZeP8s6RERlIUk8bjOvjy5Su+ffWa17fvOHXX2lwsC8+fXPPrzz7l44+eTxHrtuV041i0sOq6jcQyQ0w3sXCGJJQojs8eU25MltqYYhlLJ2MUGlNnJyFfkdgraexLOo0sMMbr3Mj7sDAWuBolQsMsMj6TNjoysq2yALMZEnkWyyY/GzfCwOijczwcOZ2OrN1r7moptAbrujKGUkqfQyVmFBA6LoZu0UI4NQEkBim32lh2O5a2c1lFjanlqliDMeom2tSz91IW5yVDNpRhdWaQPfO7Rsj6fpZwvmdL48005lOWY47OZRr+fH3+h6kOIKIUErFP8D45u2kTgtGJSiUQdaRukfgo1Qcbq0uFVLesvddM+vtTIfi3ivfE+NPXB9c2OvJgkovgKWIzr6QfOCnYEoaWONg5ew/zeYTBTdXQZGkM08jVz75DM2sRPNY2qcY9Ux+D28MxjJ6wLDtqK+yWSitELbe3Yend+70P7V7TBYEaBWydXqmEEc5hBqpRXyZQUxBK3tNWV5eE7bIss1DdpQ11biCv3A8h7xiTP0kSoST/Y2eZTAwVYdXOly9f849/+Jpv3rxmHe65XQrQuD8dePfwjtuHO/775W948fRJtFHZ/G3yKWr+bFnEkqR99lXZ6gaz4D36dqWBj82f3GZO5Enf7okNpwU09o5a7ol4xrmvsnRmYNZngsH3loAM1KKfPTZ1g0aENdYDtSuMEOVKSnV03mdygGMMjn1l7d7hoI8+D6Hqlr1OAV46WH9kJW2zZdZZBwp08w4hpZRoPx7It5TJ97ihArM2EwDZa8y1ZjUmZG0tnqdqvXh3ERFhjE7yhDPpBNN+lRJAI/vGhxGMZB+WXyvOP25C5k2/OQ0yEQWEI3WA5gbXNMraTRi2GVEripXOUjOwjnDcbPbsKmkcJSGNVwKcV+D8qevDCXs2WLepn6Gkkji9a92yRNmqViIMkYibS/z3CN0QyOzsoNP6J5m5CTNN4NtXr/j2hx94fXfLYz+lsNv5paXx8dOn/Pbzz/n4+poCk89x1DXOXpSe8QCx6f1BojPCiM3QEGkBBYxWYWlOsDYMtDvUj5ZAjjjLe2HsecmESw9GhA1b++fcLRlim0U77LHw+2++43//9kvuj2scRncCy7I4Z3fysp1X797y93/4F/6n//a/43LndXgSBokIW31EnAXqCo8HZ0OEQ99Wo+FgcC061O1stoMxPVN0J08Swt/kBMPaTZRKmob06tGKRaP2rUe7FBG6DU98FMFUQHqEwLYdlFDk+4AUb8OT0pXtfsynn+NG7dS9+8VEgsHtZHPH+c4mfxwh1eho98yyDTdcFusiku13kisb3j3Yg2+ap8hnyZNTMHWqzrMZY3YmcTohjGUQ96JboiUlNZMMmOUr/vlDhWwVbTrmu/IrHKYRfTXOznSGT0nw57ckuIhzOxslJA+nm57Pw0xx4ynbuueet/lBOQwGKoWOeWLhZ1wfXJg9Sb6zcpvcaDmR1wx677TWaCJYdBuVjOXFF29E1m62iyVj8dDemLBNbfE0/OPxwO+/+ZI/fPcdj+uJkypaMsQsyKqUI7y5v+WrH17x+dOn/OZXn/Pk8iL0S9Fj3AiSN2vgbL4UYOvTVCtWhGE7uu44nBwVXe69hUtrBrXPY+gDPcsMsWwirCiPGG64ek+P6xu2WCDaVif34BtL0F758tuX/Oevv+atnaYhKjhZXmtzSN4WX3tTvn39ij9+/Q1//bvf0WqGglFfNqv6t7qzUh3VFkq0zq7hKDIZk4jLkx8ah9b/Lg1FhCiWTeWMM2rDnZckR+WCXUe40QcsNneinaGaEf8Ma0O4T+owzAztGsbUjd4aMyYdsWy8jHdXCMmDZr0nzD72pKwleaJEEjbfXXZD8Cgk6xDBOcakDnwv+mENY0tk9apAaYGwQy5haWC9mElt+xzXDDo/2Icyuoa85ZxUj7jNxNeG5HFDP6WO+C0yvRshHu/vPYcpU5/HGUl/5uHTGJBK/jUiMsy1f1WEGuF7jmAzmPWxI7K8cWLm/c4iqZ9nuz60MLuyX5YI/phGRgihanq9hIIpdzB1wrdOeoSukbaOXkJ58IMBi6wbE21JaRx65+++/AO///YrjqP7BsllNSa5mFmc9Xjg/uUjrx5u+Zvf/o7nl1eA15I5oIrQRHgPEUAI8UrB6oKycBiNhxMchn/du2NnJyf2tbLbFZZlz24p7MRcizV8rqHXzCmjH9zZmc4WyR7RbBjex9mX8MQx406Fr1++5b98+zVv9YQK7CIAz5AkM06za4E51/T19y/5zedf0C79OzQHduhAx4oOnZUDtTaXPRCGS2aeL8KcDUFlnWNm5nLkmhvCEnte58av0V5mOiV+dMCHc1yn7jIAhVgD75Zr6tzYUr2Z3iwQ1pXRO2U4D7R2pasb/BEVGmO2CIqStUDbWWbl67gh5Gz/LRBVAMP3txnW3bCWDK0nV+rmtpzRGeCPr7EuRepEtaVmj/1ALBmCK6w9rb2foWp1Gql8T4KwdoKQt4kQvV9bnAPDOwFnciTuezNcEs5kK3SvofI/zzbKvxHE2Yxc4GxrABG5qKG9U2aG1A0nZ+AnjV8pGar6M/9ZCPsWU00K0Uh/8kfMligWTeAECfAQNxxxrlmKPePVnWVO3PbIfHE+4NTJPlXlD199xR++/ZajKRqkrwtc/cEn+kHeO3y3j4/83R//wH/84tc8vb4MTwcpMpQgcpHwfDRGaZxGYT011tU4rdFAOjoxdIXT2nmUAfdGqSmy83rGUpRWhcvdjqt9Y9eUfYUqA5FAPibR+TK8fl0otfi/RSj13bvX/Jdvv+XtOKEBsVtMwfYQx7NqLhQtuO7SN+vd4cTD4ZGrfUPJ4bF9I7ZDfb7ssmdXCVnEhjjcvp+JOC2aDvYsEcrBsemAdL5LETZ+UrZ/00BbfriVdQxOa+e4xkSmMIrOMcKuCpdL9IxLjG0+aCKG3mEYq3WiVyY2vG2Lt1iJn5sYqECRNud75hT2vGZ9nhmkRMaSf7Vp5HLXjYkk2egB3ZIFRStWjaIN8EqBrYoiM6m55qHAl+TYAvUStEhkzGvZkhQSCzIdTCCdFAqnKZjIMu5TgoM0wes0Q85UJJFcfG+iz1yfRCAR8M2KGXFztuE0mW2xSujJNiqpoJL8ZZxlYs9tmo8/bY9+1lfFVaSGWoMJuccYrP3E0JXsX46cWdFJJBqGE6tqG9KZKV4SxfnCtVqj9bBg1Xj77sENVx9YlW3BLAjp8CBDvZd3jUxorsnt4ZG//+YP/OWnn/Hi6gZkq/MrxXAUt2elcRrC4yocemGooyftPX7OxqEgNgnMNes6pWDS3AMq3K5Gu1/ZVeFqr9xcKpeLl0CJRlavRCfaxTOmWis6lNt15R+/f8nb9RBqcs/W1FKmAVCNPujS572J4CJYEx8vNlYwn55DcGmtiJfzLLuZlfRnyvS5X2rZtdPoUQbWg+zOpIN/4SYunLo78RhPzfueE8bCZXce2vWuEULH2w9KQQSWWtiVwr5VrhahVaWaG65ShFELvRfWmOCspXAc63uH2NF7iKajVVCVGm2MK8uymxxf8mSe9cw62kB/EdY7pwMBJed5myU4RB+2ASKekazFqMWNRdEBoXV2pH02B1X874a6Y81zM2O7OOQuevV6TNWgbcKgCBmKhlg27svraqOUKpNpeNJpJsVEAu17RYVZzvZMJGkzclJx2cdWMZP/b2eGKB4hkwrTeEG2L6oSQtZcb/kz6bzyBi3QhWfKOmbdIb7oVmojaW3TuOjksTzQjOAhYnPi3zYvlNbcjcd3r19xe3xkSPboyricWeuVujIbHczvpdQSCwLvHg78w1ff8BeffMGnz19Qm3NwCHS94GFdeDh6Jmqo0EOb4irsEfcLrRVHIZqez3/u1qHCdd2JIrsaOmA9wMPpxEWDy8W4qpWLpVJaoy7ep7+WgozKw/HEP377lpe3x2jxDI1tuIFnpZZAKiAyIrPU56EaunI8HlC7IDMa2WyvtsZu2UGJYvH4HjnzelkeNEs71Lm73nVOpknFtMUw0RRXIouXYGX2SLwqIjdqkcKQ4YdnSNRobjWdTYSLVrisjV0Yr10Tz0aZj98bopxMKFpBo90QcOyZ9nHJi+GN9KzYWVeOOkOw8EnM6UPm3TQm1zXrMX1PKjaL3XPfzR5bsh1dJbLH5nWTjIHZkVEbu7ZkhdcMwdLYZpPCRD1uZiwcrvN2VQpqdYac+c7Vtuxu9paTED0zBcKCRN+2NF6W1R0ayqK0/Wms47NTG5vcpR/ZTbPn4COrJiD5rPMGnWSCYIKBWLuMQX/m9cGjzxLEJs/hm9oh9dJ2Ewab4MMjRWY/IGLhM9qf1l/A0xphocn2OMHnGNw/uOFC3FugKQ5Ny+4vxeN6/1m7/Y4xlNoaa/fx5w9D+ZfvvuO0Dn7zq8/Zl4Whhbf3J97eP3DS2HSlImXxDJWN6N4QL8y8X/qQLRQWOU/9ljMv5Rs/ubl1VO4fD0g/UMbKzeXCF5+/4Hm94CL4p9dvH/nbP3zLP3//A489B5X4S05kW92NBVnv3q5Hzdh8V2dEe6qei/i8xSnAjA2TgNm52aws2GrQPET0Lq82FNGydQHBQzhIQ+XPnV1gpeREbe9T1cfqi6XBR6VRaY7rayksRdgX4Wpp7FtlX4UqyhIIRnCtVGqtjIYngxtlVcpQZPXsa4owvX5w41y25JPMuj4PKQto8TmP5s61Dy8PqlWoUVYms75wQ/5Ss87wPNDyRgOdFULvqKVietYcMw6XqnhCKZ34PC1s900O4XDDJCKYF3y6KFc9Q1kQF4qSHYEzgAyjVDa+y39aCcfhGr73BnGI00RazGtoJRIMqfmESQURCG6zxxsoSU6W+PvpOPPnGIhu9/mnrg/MNlbMZLapGeNMj1MzBEuI6Ep48mHOX4WUbfME8ipBPBeY0orMPvTeuX98iEJWz0JVogOCgZ2VnaTEopY9IhfU4lOS/cW7Ibhbj/zzy29ZS+Pzjz/ncP/A9y9fcRpKF8/aXVxc05qHaa0mWvSOBH1d53bK+Xl5t2P0iJpDL2TusWv17yhlB60yjoX7xwdevn7DH9685PnVJZ998jGLCF9++5I3J+VxPbnafC7h5pUcYocRjwxawZBa5nvZt8b11cWZzEQCebUgVdPQbe9Jw5jNgbBjE8/q0OBQvNA9dlpkTTctUY0md6kXK8XnPQo1PLeHuyOa81mgdNeruUZwEWFfYCnGUlzM2EqURwVXU2qFqgyRkC54RNBVaaIMBIZM4zUDmgj/iPDTQ/HstCFggyLGat71M39Ngy6FVrMeMI5l3E9tdbaNQe1sfS0MmCA2aHUgQ6Lcyz9pmM2qjeSM/CSc83EbkZ4VKmbCGFvefGZYLWcnyIbgztBi3n8Wl2/TwwVMvbHmWVa21hJtfpQRKm7BqIVocOipvMkcwawLjv+aCP/HAOss5poSl5+6PljnlcLK7EXt2pbNcJ3XGCYHoCQKCBQWFl5k60Ff0gBNsjg9ODwcHnk8PWIlawAjxAgD5oM1w3qbh1RmsK5ecZ81/FK8Y+bAM2ZfvnzJq9sDMjwzkmjRhtHHzjVOJliX7XkiY7Vm7VxtVDYPXGvbMiiJYCyyXh7sO7K4ekK7vKTc7bi7e8sfX93y5et7N5TFc1wrPeoe9QxRp9F3qcSw7Ak+AvltXT+f31zz9OpyO5ixdClV+PFk4oTuqi7exBI9bAfK1fGVmMfmNlqD4MtDEmjbM2v+TnPG4+gaHUc3HVYmWFw64W4iGy3OLR0Sbh/pRaD5eCfVnSqmlFZogI1Cz0ROqYzh722kBAbmCfOPNkeC6gi2mzFEOGEMG3hLaC90TsNsxd+pJ5Z8GG+pbfJ3JTReRIiVpT3dorKC6EGX3KBuDQMTFaZRme8PnWfJ18g7cfQeIlTd9Fa5T2S+k7P3fI7m4lyKcJZIiMPHxu15BJyQws+VlAypc3NFsi4SD1jKIBJd+foNUR+2YxmCxvYU80YJP+P6MOMVAr7eO0YHPHPV6p5aG0vLF1hnbWBfXRiafAGRKSPhehDWkx8L/svT2wPrncPxgR51Wu5JBttyuthujBE+KjYk29gwfw+KRJYtF/K4PrL2Y7SZqZQozfAJw/58Im2GwjVQWQSD/nuUi0h6WzLNLOz3O8zE+18F8rJstmaODp8+qVxeXPDmTeP27i2n0V257Tgvnm/bahobKhXVuVm9lCQ4GoyK8OnzZ1wsSxyOQKloFK4zgVxu9OS20hha6n0CYRKfPds2o2dcp4BUb40crXG2qoOB6mnjSs25uexztRXKh9UtW4WFd8gVRgkCGs+IQokQSVhVWAd0LZERBtAoPysueYmGdzYPFLOkSwNpinnZzdo7q3bWGDTikoQIE6W4iDRlKjE5utQ2/z4z3TlNyAJlZq27YVFvafjQszIz8z7/Mc5HiXLmMyezzQGwKJUbOUd5hv9E5FMl3kOJDhuyRXSpA3XKYyurmzq19BsFTHMfmAtsJcqPQluGePVEdhAhUXk4Hpt/zpdjUwqVYft5iCz1z2C8sozCm9J5XJ6Tipdl78artekxfQ9l2lbnimkQk37jZ5lHg1rZWuBEzd2m3Lao8fP2KDmU1V9IZlfwmY+5bGbveRNvxZN8VPeJyKZoUZalANnmuNP7ib4qu91FdC91ISRx2JfdgmXdI0L2cMoOmTOMLpHaNiPKClxsSWTujgfQQSvCqSvdTk4KS8oCQn8v1UOvBPziXstH1QND/VCgXLUdHz99Ti2ROGBMcWtyE9nI0LDQnrqmyqIljhPEjTSd3ldfiRnPZLfZUiQqKnJe56aV8qnfhtId+djwuY66yUFUB6Jn3AyhmhcfEDuAEcZqRNgIIZUYbsCGlakPG8ZsWTRi3+RhzfdgkVEk0J5qGpThhfhRSublL1FoPzWHYbzq2Zi/iBossqyFLYuo0Z4ox3slm5LrrcYcyFvbzsvnsqzmzLC49lCmHbAcgqLhROI8FPFBMW3ZubwkzlgakdFHdG+I/WoyHULJemONtj3h+NJ8lZRoCLMsCAIVR8ItO7emHSSdOrhjinfMmaFVvBTMh1Vvz/ynrv8DXSWiMV1oAWpdaEtj1xq7JYeGQgoQ30vBTqMBEFqW8/BBLEjAEAPiL7uVwiIVrAdRCVjoVWRrTUsYQyFFromOgjTva5Ch8fLj/0cU/I71QOkrLQbnOn/VQIydeJM1A/q6RhlI1Jllk7hSnNAcK0KhFS+9sJGtdGBpyzTeDEdtp9OJw+EQbbRHbJbg0URoJWdStimC1Ni8zufEpsTbrDQpfPbiGS+ePdlCjqkG9xKS7IqQpSeJRv3NEUa2zqSJK6NtGhgTN6qIHw5iLmaimfyfc3ExSg7nB7VHpnK48c76uCoRlsZ99OGcmhHUQKLF0rBiGx81jNW85baZMLSEec3DmaEvsXLMkqJc45xBMIaxpgI/k0uS3VFCs5Z/noNACAcSE7JLi3/ztS02iC0Q5T0SejxFrZ+FimULuycfHNfc10mVbFPIt756Tle01lgWLwrf7xbvASfR+WMMevHuuDZiHF7vULcw3SkJSdtCqt8RjY6vBdHzdfOGCYqC+AQgb9suuZsCEToSnn3INEWq1Wc7oLQyopfaT18fnG10K+SHLLmKqL0OT6xkOl7NN3zXJPYnNYhPvo6kaYRlQ0AGTHyNk7n7ZeHpxRX3ffWslqWHiHFOUSuV5ORQjbbBUVMluKod2xT5Z5dGFfwIlFZLQceKyeDJkwuWVljXY6CMitmKmqDryQvBi7CuJ6y4ETgdj3hP93xRhbEOSq0YlRztpN033cXFBcfjDj12ipaUziJszRSzC2oaZ8VCguGykDDTFISLtuPXH3/MrgUaEjdeEobIRaYnR9JpZiQPtm+4et4Fc3RG70HwmyvcDZZlYYxBDq5wji8Fplsdo0owj+q1gZkI6L3PAbVNMvkR2bfi+2ygnIrEhhYcN6/ecDC5GzVOqvRQZY7oEuGOUmZGL7mkcyTgiIUoUVIfijrNiX99qVszgKnEr8usY5SS6vfi3TckW2u7TMhijw/EW8YY9NVLfXx/hsGQghBdboMvtTCum8c11DqncWT0QV/7rCZIsr3WxrIs3nm3OKioxbOYfQwkWgSlCn4EpB11zDZO5HzNsGAugcilc6OfQuMJryI6zFbqyZN6ksaTViW/JiRMvv8KLQbHNC1/HuMlNijFyUHFOSBkkzj44mZMv3JaVxewjixAJkLHJOU3LzP/TD5tPoB7+8+eP+fV4y29OzLZJIGxgbf8q29aVcqyBGEeE4H514Yr7mLeUyLdbDF8PB7p3SjS6CtIqfR+IjVNpRSatFm9XwocT4+0tnA8PVJLTOrWgZRCX08+qac0xljxScSFq8trYHBYXUxqqq4dCoV7KRaRiT/g2l2RbjaoRWip2jbj5vKCTz/+mKUtE9Yb1cPh6KLgzfVy6ETwK5JRrc2CbbVOX/s0AJFzmGHQe1njoDwGyXVtYTLm4crxdOJ46iFu9pY8rmFyrkNKoaORbInWKhFx+2GoIYVJZO3rMRKFhYg2ZS35fnOPeeKAWQy9zRiN4a8WyDd+BOINF7eEVNaTpjGrwZ9l08j4N/JMGFChhQGLNtLZzz6d+jYlKr4nS73Y1PhOzHe6rt62Z91qZudODpTYamUplV1t7Fuq+itVvNXMqCuqlS49DOKWPNHow+W2RWbICI5sz/ld/zscUIgnU4rhNbGajTzzbLu+juDyqJkJxpMlEd7+zOqgDyXs+9kQyYzHZf6yUPUqyqn70IZ1rNGGRqfh2r7eX9DWeWGrccycRRhnnj255uP7pzy8fjkRXYoDPcPheCWnP2sUfM+Ze1Kcs7GMPezsleQ9EfeVgxGMdV2BQllqhIaC1cLxePIp4apQjcurC47HR3o/cTqdvDC9NpaLZRYJY4bpkf3+AqqLPte+Uouw3+1ZT3v66JzkhJCNE32TKAPMM3ZqXnw8tEeI6Rm1ogXoPLu55mJ/SXYmSJnD0JXT6eRdPHJ4BhJSjiwWNkyC89JMW0eYW9JggFj0m5cSQlVXriNR3SCQqXoVo6/dB6pEGdDpdPLuHnjWthevvVwJhU18prfqDo1WxE4W65Eo3pLjSkHt2dDSKHelzJIqnJsikHYYP7UtETE7ogbqrbXQ2m7rhR8oayaaAqW6JMR5wvdCPpK3deQ41Llbta1FzqaFSsO1hZFE+K06WMfJQ/DBDHXnTzGis633utu1hV3zLr5VmpPtEbI7fQGLuhMt016649Lod1/m/QcKpWyGzlPNjrCTFgo7MKkg01mGVkvb0Gt4SoMYVKzbfs8hAz9xfWDY6LyI4GFCqwuu/WIS7BrcwoiQYPYsGjpRwAxH1Eil7pZxgCyNmGS8ubbm6dUVF7eNQ5bieN2H31NwOf7RxjBvXcKQmQqf+hnbzNV7BsyIfltGrY50hg6krx6XR4F4axWzvY99R1n7gdoK63rgcDx46DKUx8cH9vsdUvzr8hAO7RQV2hKZTTWkFC6vrujW2TFATo4gCELXoneXyuRjiBKo7Cm2WypNGp999MJT6AHrzdRR8HrwkEqztrHMNjHk1wYBvzW7C68fL2TL4GYyhLmxPfliZwKoMDZjMPpgXR0x+GTwfsYrOXmePczS+060JEptdSPHi0QvLR/eghF913S2eAaJDJvLLLLtiwmbTiz3hThv5hFQpENSd9Z2LG0fv7eprfK9EDs2EGiLvmf/+gpXnDXAFmY3S9hQkJVtCnv0f3tvrbNxoUVb7TAmmRyJ9dotOy53e/a1sV8qu1Zp1e9XEaRnZlem4X/vhMfeNcfP6S9iLUskGDdOTmhx/hKVD4aWoAayTM8Bw6a0D11n7CdN8KU5wPi/Hh/9+PpgzkuK14VJaHiyY+MI9S0Bx9cenrYf075M6C9nGzMPp5O0s2AiXqqjLx+3DgtCVd9cGmlaHyDrxqyU6uVKEX+X4psztYJb/8fk3v7Vw7lROw9lSaXxYFjnuBqFmN4SHtRQdDVaK+x0NycqGY7chjo/0drCxX5BRFjXNSC+t8EdqpTWePr0Ge3QeDje83jw7GHydmsMfLVYoZqebwpE4KItXO0v3JONeC86OK5H+no689QZTvoffc5ggCs4S6zklUOFJWgmf88lxs8XCdU/nZxQo+aorK/KGsjL+b6Qa5CtcCLEy4JxSa1gZALFy6soMUIu+DEQn+Qz3u8JP8wmD+VzL9Mtbgc+n3uKOYsPki0s0YLJy7bcIC3U2thFBYlLDwrBXk+EWzKeOkNd51UO2VbHm1J4xxIPYy3kK9E7LTkviOy2y4ZG1A574kG2Ujxn1WJWwsKuFJZaaOlMUr4aCYJMzmR0khFPcmxlhLSpZLLLRccakqDsfltKodimQTNRn7NQC2rNO4SMlPqUuf6pVUujRnzmiB15Xgnwp64PM14CdfHwYhPAuZfu6mUbmcHyEOHknmJ+wMaPJKG7Zb0y9c70BlkiJBRE4frykqeXVxwebj3+V+OiLtS2cOp93pNnQ2XjRCaEjbmLvG+43OPHsASpLG1haQup1RFxvqgfj15LR2W3200uxAuMu48C2zvheTweyWGzyZ/UWs/Ggwl9rIzhBqBrR3umkR1BLW3n/IwqS22u0B5emLtrFcTJfz+eHh7X4DlsBNIZ3bN6Yw0tnETqHzf+lJDKeYlOzfBfndNTO2vJXHJGdXrR1OaFIFUEEZeUJNk7hv9Sk8lVbaJZ2/qcWRqYSBuEA85hu3movbzJ79WCe3MqVdxQBQItpXo9o1pkxsom4yi5ySKzGVxVmuva4v3GPvBwsEUtZKIc//5EQG6jIroQD/Py78+NhGJIDVkAXu1wPsGcMAZ+BuLkaBh4DQwXHJvkeSLG40VrdS+K9/X1AcYWYWYUwQeN4+2ue6BB//qw9+jwPdJaOyuDSqMT9bHRPmkaw/kUFhGF139mV4l0Fv7zILuMuAnTKM2SGTb/1PVhhH3zvuxJjKYRcsIzYmXVOGQ9HpjNiJCWNzbpbBins/g1lfauoDYgdVWwE+HFs6e8OT66RknKNuyDzCoVcsR9Gk6LeD6x1HvP5Hc5KR3P1OxYSgujNGhLw+Jle12jd2LwBomwrvlzg3eIDf/4eO8Huzqxu9stzpWIh3RJYLfmoejsjukFKyzSqC3JfmHtQZASaEFcX1dKce7NXOGPCetp5XQ6sGZK3VwMmsW6lOwXZqHNy0nj4lEMQl99ws7U5ZlNAeE2DDYyoIlaCV5MgSwbikJ1TzpkmL+hkfgAZu1mGK6sgCJRhuTBLoyxer2qWkwgCidB3TiW4Iws2tjkPjwn69XSEHuollKD3bJnt1zE/IE6HWkJpAObzMfUxbt5w7H95r5OSUPSKAQylHCwKd/wvRyhWpHtZ1giIA+ZixhixUNhcUHyUsLplkLq7zBlmKDdx7uN4bKcUx/0kY5Dz2B2GnSfVOU8mGDDQhPoxii1eO+NNGRDtxAyGtcybfsnwYkkyEnuzJ81OcRzHu9PXR/YjDDiVSRYVZsw2YxZrJ21j9tLPg/F4mHPN1AYsWwvU6fltfkiU5h60RpNhRORneoWHQ0CfgaUNvCulZ5B2NDfj2N8HFFILVB8LHyVmBkoQtt5d4TTyZMVS6tcXOzD2LjGa11XVD1srLVE2rqwLDtUYamNtjRUlbu7e1prXF5cc3FxgWGcTidMlcurSw+3xvDBDapQXN19PB1jA4QYU40mxkJhv+xC6GlcXl5iqhyPj/PQaIyZ9++Jgba1eFeB0H+1ViDD3RBsnkZ3HVBJkay/O5/U1GbYK4F6/L1KRCjmuit1UWdtUFEWmosr2UZ5lTCbWRzjeRyd2af3JAOEuNNCnoE7q8I2LacGqe5TmAItFNlU5ZyXJ237M9t219qo1ZFXa/uY/OR7U6Jh2PkErWEjWZ2wAemUXdCdDSCn3tDMi5vBjbkAgagkBKFpVCzQU3JPHplE2BbmopXCrlSXIkx1u5+xcRqTlljXlaN2L3/qqbGzONuZhGOS6kWitjHPoyQyChTlyq4wAxMdzHO+cXi5bjLvbRo9sykd8lDfy+N+zvXBxovJ3m6cSQodzztq5qYAec9wnd90Ip7kwGY4Q4rZEhqXqKEzLneX7GThYaxeNHzmPWtwJTmaag42iJWft322NiI+h7BGaDA7PGrwMq24otug1YXdfu8LHP8jDouIvldQqqGwh6x7WxjinUKTvL66uabtdmg09zOF3f6CVnfc34E0AQb7i4Ui9wy9R4qxZkFz7zH8xPU5u9q4urrGw7EofB7JA+VQVii1+MFcYgKQhbp8aBRfe4XB0AFTeFmDxPaKijprPc8kCwAjD2dmwjSQxVnWUjws9WoHz3YaoTuyKAYX77NWig9LzfbNqVz3IS+NYkaNQz6G0wYthp1K1nlmE8R858O26g1Jw1V/NKpuN43gnHQVvKwI0zH4PhdUJRiK6NM+xtkQ3x6CZ5lttVU3jizXFdkaBYJNw2KZYAjJSCZqJKSkVaDF9Pga52iM5LjcxBzXE6sOjr2HPtArHJwakYlW/YymODkch+D36TfjISHehFFMIwHia2tqk7repCIZWaXzSYeRoajOZ/TxhD/PHn1gV4nYo0WcSIQJkeMWyWYeFis9fYR4mv996xFoiTMDFu1y0yPPKncRpCjL0rjY7ykPq9eemRN8EzERSEp9g4V98fuPu1F3eb5pS2OJ6b61NlpocWwYS1kQczHixeXVJB5VPSFRa6WKj7wfwSeo9u2eLZMIfrhaW2h15z932bEsF5RSuLxqtFL59rsfaMuOdlFZT2sYU2W3b5S2o7aFw3pgPD7E6PTBGhUPrS3sSmXZeVhfol2JSwjcq7YaZSPVpxult7fYdN6eOtvfuLiyidfIecE9URIjbAW4hg3z/w4gnoZLc6xXhCUWPeBCXu6lR7JlglWcYNaS4/UME6EbFA0dFzGQI4xw9qU3vJusIy7v9GvRKtzMS86q5NAIf84N8YQpkNBwFR+eUluGiRp7OLNmhCFxx5FOIbVbPfjGoertti2MsoW71khoSH5mEPRBwejkvIAU/JZ06E4XlAy1slSLEIyXyAjO6gkvozqtnVN3NJ2fLYGgth5nZ9URbqn8GUcc8jMJx1Zlk0k22ZCibkbJEWpmuN1nOBUWlJMlUjQfXsMWiv7U9cHlQU7CbXVbCfMntA9KfHIMxjRaUnLD5xV/T3QKmCitTsFkLfPL/AAivHj6lLfHxzlEYeR9hIWf9YxxuNJobUWtwq7tHeWUZQ4LnckEE9ez4c9VxMMkVZubcokiXT+X/jNcQhFlVMjMyJopx4Pru2r18NF6p5TObn/J4RHubt+gBhcXLj959uxjF5T2I2ad/cU1V1dPePX6B46nTjQPBYl8p5nPxqxXrHKFlI7aPdgp6t0qS6T7W4R8CZYSRawpaYn6zVa99UtrFnxV8h1b6GVWQiYTU5aQKQr2g+wbd5g6GgziWpAwgoGJs8MAgLd/nDIAwxgEwlKD6mrtWut7RbzZVjulaSpGtq+mxn413VDXWelK8k9Qt7AzqMHZwpjcy2GP49c5/zV6nwp/tejKKhEemeLZz/y5sTfdF4QSTCbyR8R1UrJsEQg5E6AEalaEcdaBySMFjS4Ww7y10rF31r6GUziTacS52uo2U9pQpg5NgxuTwpmxSX62UGqiNou65GwPHjSA5vg1X79svpvyigQ0OdD3z6Lzksz8xf+0SIgdbZJvFlBKLGF6miqdMbUYMZjCD/g2PikQVvJc8avk9zns4/OPXnB7fODw+gcvvBXoMbsvftT8s1txX4wi3vGg1YVl2XGxv6HVxT2GjTmCrca4Ly8LWWZ3AP/xkVULwtslD8z7XJaF02klOxHEXUSo4eOxRhQiH4bzZTdPnvPr3/6Ob7752vmyVpAm7JeFNhqnwyMijYuLaz6uO0Qqd3dvGOPIaRzBXMZw6sr3b+7Q5y9Y2lNau6HUI2KdIgNZmAQ7eJmJqcZEo+5ZRYvCa3EerLU6jX+GfLPTJpyJC3XjMdVnQvbkQEcq6vsUWPqw4NgfmntDKBptXiT4S+a2mMa2lEILHnEOV8WNl00dlCFaveVMlE+l8/0xITx5HsnWTJPWiQMNOdTCIjuuI0j68M/+XNtni4QAmK011ESmczHD+M0QyolxIXvVWQwcEbJV95yiLqFhi4NvUWOo6HS+XQenMXyfxXCczUnXoFq2DPj7vLTMpoAztHM7zIj9k2FhJiXcWLmcR6NeeFY5zL0h87yLbaVb2WTRxck/7/o/EDaGgaEh0VHSUt8VMauGNkksMInkhJYI20xn7RP559ihJUg7934S6MuRUUnPs6989vwjvn3zmjWzmtllFWZL3tTC5D0szbkMz5Lt/BksDpF5dqqruo5N/ABtYjxvSjhTxxbdMaO7prc1NmT4hnfjtenWEK9/HKNPRFFqY/RHDkfhL5//iseHG9Z1bJkvABakDg6nI9SFulzw8adfcHW55/b2FfcPxmk9YcA6lG9+eMW7B3j+4jOur29Y6iWlGLuirHQM79Rf9OgHcQxv6xyesZDGoYZ8oMwNbeq4OrkKtRhykaEmETpHcmDE32cCZ86IzN05qYIC0RXNYljq1IrF99RaZtlLaz6NurUW4ZNOwzBSeRd8T060TrnC0E3XRNSqpjSm1UorHl7nvqtyFg6bYto5rSuBFyM03jKCROfVUhxhZ3OBOWhjRitusPPspMxDVTeRLqngL1vSKvfjNLi+kCpuPLX7Z8+2PjZCd+hGJkNL5zA9PG45YxWm8Z1VrgJWxR1OxDLeOVmj9Msim5kc2pksJAxliQjIoxKbiZr3lfoDiXe21Tb/6esDw0bxSbjpmhw0+hTfUv3BrM+YtVXnuVzncZZWjRQ6JPcRqyRBOsZGTQArnBNj/t/XF5fsauMQ4Z2T6jbvzTme1ObUyKiFxEG8a0RBHGlEWZOXyTgqEyRq/8r0nUU84iiBGE0HrfmLGabeiz3QWupmHHHmYbc58l7ZahWPj/f84z/8I7U0LKQMNYldCtIaooNhsIhn765vXqDDaG3Hw8Mdx/UEIjweHzieTqzrI+OTX7G/fEZrOx6lULTxUIRdq1wUD8FFj7EZ/TmLROJDqqfKcS+9xZjxHiwkGKsnRrp50z6fUBQGI/kvtaBLfJNqhNkWIYvUrSnlDCVsC7eKWHSDrSxLY2mNpTkydlPhIkrLDLcl19c3ZHiOuqLkqLbGbren1h1LW6IMyAccZxcJif2a/F22O8rwYoRRsBSMBg0ksXZFB0aWD0mYVjc4fh7GrAH0/lxpoJxbLGXrJFKINtsWDiZmfE5wA3OIsXfGCEpHmWPQUhYiIf/wRNVZwb+O4LFHrN2IRIWvmzcjDPQ3w3QvEJ+tlIxpbNNYg0thiEqGRJpbWSABwgdD1z+H8fLShWxZknRWKVvPdiLEc9rR9/z/h7c/fbIkS678wN9dzN7ia0Rk5FaZBTQKaACNRi9DUmTYHA45/E6R+VtnPo2QHGmhkJzhcOsh0URXoVCFyqrcYnUPX94zs3uvzgdVvfaiwG5koIl5JVmR6eH+/JnZvXpVj55zNBop0WUnsJ4+0bIienpsZMKT5NGpB+72GGPUBdzJc+vD84Ca88CYN5ZhrZIEBek1gAWtsqG55Mm6jU3LQg2+ENCHWOrCMi+IaAYW7eSZpwOlVuVYjVtUr0mv8WOMzLMa3RFUuhRj4HicO7P6GO/ZbPYcjkdCjJyfX1gQ1HIo5Y3e2zTQioAkxs0ThmHPdnPB3f0Nj8sRoTCXB+4eZsiV87aw212Shh0pJJYqZIkc4pZt2jDEIzE8kNtio8CakbZXPNJfchIIWjfra5TWWNqKaTUxC2ErPxDd1ykEJCTFQE4wScDmRmbbsHZaW3aSIuu/JxdHDx2XcmpBk6CdRFHc7nSEmdNynGcXQyQNgwbDlM1EM67aQB+HJljmWLQMaqppkKCZ5lqC6jHrZpnKoq8sOEnZ7meIpL5mpeNCwW55QAf9pq4HjCezJPU9nNKhWAQr0ZdADdjzUMtzx0MJ6xwDd7DwOZPRylHrKCA9+Dt1poI4Gdo/e+hcUtXKqjGpVKeqxi4n8vdUmEGXhNte6fXrK9RKcRffH/D6W1ri+Mkpas0sXvhDlNitbZXMrPKRlR2s5aOWvTagQ2N5vynSoIRqCxibOrNuohgj23HDbrMlTkedvmtdm0BQcuEwktMIGsr0lLYaXVnlBsiHoAZyZgGijOdEq9JLWGnCUjSbmZcZaZDLQIga0KdlorZKTlnDYRu6PQpo4DoeJwM949oVkooP2BSpzPORaZ6N1T6z3Z2pl/4wUuZZO4JLNZIfhDxAEzZZPemX+xsOzDSBqS7Uu7ea40hhu72AsCHERE0bqgQOi5DSGRd5y7YWRGYaBQlVuXahkkQItfUTUm1sZjVrbEIRnZi8LJVaVDQcLCNa82bNhn3xijd7bB2lpFihZlWhT+4O4gh+z1V0pVj5pBmK8aJotFB7pqU/4CWWfa8RgcHK0KwBKqfAkJX3lnLqE8NF6LQHlTGZgCU4diXUormfECkofNEk0IIqCmrQtV2Dy9i0OD/lmbl1U9QWqrqyJpcc+b4xCMMzGqAWscaAWUqLdhaLrTMt58zex37Gg7/jZvpcpGel1fFLb841f07+CkaMXQNSNIKy0yq8085p4PIyXk4DoGZ0foA1zHyyrpSjf9Prw1wlkJWwZp9O6Qxx7Sx0AC70jqEurZVwmEJEkkkSetilnwq+0KtlREGU6CY19FJ0HAaeP3nG93fvKPZwgmjg2gxbDSQxQwvGhFccyWt7xGtv/Z2C4Q1UctaZker11GzTLhzniVJmkGDuqdpxcxZ6qMI0w1KVNT/mDRBZ5oVlmfT324kqLgUymUtAN8o8zyrrKapDHIcNw7DTMWUYQG4HAbZBhMB2d86uLByOB3X/CEIpM3fv3jKESGzCMOwhjiy1gnHijsfAJLDLA7vtFtpiQy8qYyhkWYgocVWlPonWRlrLmpE0LZmXpSgFAHW30FLTWPHCipXp/3XcKcTEMOphMwx634e0BnjFa8yby0FsZ5lTic3b7XroNSMkNzuUEAjNbJoddjDxecxaPkmMiA0mUVM9cHJrKT4kRMmaYgdlM/fWQqJKYiqVaS5My6L9ohCBgWDdbNvedv0qJC9Gtk1BoFWaBDbDhm3I5C7XaobNWYXDKjkKWmcpVmSlp1o/WYlnQRbL6GKkcxmDXWOnLRCoxaRENI8rmqic7tHgulKLh7Z2PQZE22M9ZpxwHz0YW9N//WzuRmMlbmcT/w2vD9Q2So81nHQs1D8dpKfRNqzSP1zAFpxdQlSAVpB+85TrmHsg8d+hp0FTuxZLQ3XKTOSjJ085e/Ed0/HRbo2VE0EzHmRtFqAZsWV9glQf8uC0DwcLT8iVJrEQqSx1YVomWtMGQWhBkz0JFNOsRZQTE2xDaWmqi0+ztGr4ROwlGC5doZLawjw7Y78xb7aU+ZwUB0ZrFEzTTPNySzAWtPKOzs8umecD75aJpc00acwNHh7vkQabTWG7u9AgnQZUBAulVt7cz2yWDbvdXrtMJTHkHTEoj0hEWJbZJFLKqwtJyKPNPtweCFLMHVbnCaSICqvXnQAIWSppUceMlEc2eUeOkUQihg2SBmpI1JCY28LUZsOZlEmfm2gHta+zTAuBwsCcNkrCDLCERo0gg67FFJO52iqOVizryCSyJMaWSS0Sq35NPRejSe8DRFU3TFU4LJW56dT0x8OBWgrzdFQBfBPSsEHdeNUJ2ANETEMP5lZoEKWCqL/WkCNjFkIoxBAYhoH9dsdmDCgLSu9DQIii8AGSLPuy/r5nqzEQUiCJSaag415YUiGWTQZCNxRwXpp4yejBUHT/BI8+FhNcnSAS3yOjAn1E33vWPRZF/GtaplqQNDvqH/L6W5SNa9rqo8+Vl2IX3EOxE+usNG96y13k6SmX8ricpe2un7Gn1KEvervZEnCV/Nluy9luz+080yQwbrY6ICPooAqTxb33UERUZB0904v0rqGIMGQ71YOetrUtLGWiVp0KrppNK4sJBInKM4uYvq2QJClrfpl78GytUspil+pDSCxtbkIL7uleiEGQtjAdH1imB8s2N8Q8EEIDE557JoIou3wYRi4vLpmPjxyO1hGTxuPxHTE0apmppXB28YQQAqWWTkOJMXA4HBEJbDY7DRwmEnbL4WXxbNmY1SESs7Hg45Zo3KxogvWUkp7uiFFL9PqSNIaNbsQa4dGA3TYLLNEy9YUqao+91NKxkxCDCbuTrQ81dgwxIcFKu6Y8siYamD3biejcSeWi6XOstTCODZFZdR0pQhQ2u70B6FmDTaVrd4/zYn73agl+nA6AOja4Rz91NnucoGJ+pySgQnwvacWIrT5tJ4jiqp1DeChsZ7VP0vtsXVDD56Q1chrVp54GYba1NUNsZpAYzavrlCpi/4jtJlXP94aEk0rVMRnryNua8z1/GhU6przyKt97RYcPPIO2iswI3CFpElQJxB8Wuz40eMU+/IDgZZemVy6DcHM8L/28cxLNIlm9qRy40+ChqawGL9dKeYmpeifwEUsaIqpF+UoOSQdUhMSQBqRBNS8ixR+ghHW45zyrc2dOiVajfRB7SLY4tEsllDYbSH+k1JlqVANBu22uT/PBG4KWK1nZfOp1nxIpbXop7b5MQ86EFNWB1b6GubE2EaiFOmce7m4oy8Kw2TOOW2JWL/9AAjToImIUFWHIGzabLXk+sJQFQ2ZYyoQ0YZoXiML+4grCAKJZ6rgZyVnb3Ms8myWKHj6lLEzzkWU56H2SooqEPNKmYAeEdZoRLdOtbE95JITMMJ44a7Zi5YULhottDD3VY3BelgZNBYOla1+J0t8reMcs6awBl9SoosOGxVgmovY5tfPLSivUsqgGFkAim+0IBMJjIcSBPGw0SJRGXZb+/JZlVoVDK6oaCIJOnDKgvGoZ2DvbaSAlm/0p1TKdSsYaWmmjVAcKMY90Jn6rzNOReTEtp2mKVxfXAFEP+SQ6B3I3nhHDniCVITVyFFKrNJmRoJ8b65LaVkbaor5rnZ9lQD/e4bRm2XvpxJpFEQIxJZqchjQc3LKS3TuQK/fLrccjSuhOiB4gP+D1gXMb7UNY/b5qlYptFCWf6ch0b806RcJbNfZnsLt2kiKGINawFz157MYEg07SSRu9dbfPRhKwlhSItsHFgosGFJMyNcOYCJQWiNGHctp723WVVvRB1oVajgpS2wZab0TreSEEggiVorweK2Gjy0xSYjDMqvu7WymhOEoxLlHoHKUghVInjseDXxYhwBhGK6EFonLLuk5UdFhHHrYMaaDYYBAQ9VWzEVVvbwotVPb7p1Y6CLEZRiVQFrFxYZqZ6GNvEBakLrRaqTIrjhdUitNqtfuvzzZahpnThhhHJG8YBpu2VGa0IyudGOwcpBQTtXl5b15p3pkqrlVVgNnJlssyWVNH74VzmAhq/Nha7WtDMGxFDEujasliWNzhYOLuPEKwz2YNBEQ/j/IgbAiG/zfePdUg41Ik0Rhkz5VepmHNBt26imMqZzBDK1peshJ1Nd62jge1uuisUTygWOZG5Lgo1pbzwLYIYWhs4mLJvh5QGoEsCzMJz6rLbL0rGPs+jAoJUHqXspNavRIjrnvjJIadlpE+nxNR/DbY3nH9pP/OH/L6sMzLIrJmV55xSG8l63XELvVxGxG9Tz7xxDob3gHipB423xqPa2K1ucoz3G8rWr3eSCmwG7dqXxNs9GvQm7HueP/wSRdLs9LGanF156SDoLXO6uxYK8E2TbLGg3dU8OChTwzlt1WjQJkkKWiJPI4bxnHsZdRpQ6N6S9hlSyF01DJEYRgj+/3GPKUaskzMVRd2GiDnkbJElroQm4qZQxSVAaUNc5iQUHuKXoMCsnUpvHk9UZbK5cVz7arZVPFWlKFeWyMNSZ0nUM6ctExpOlNSF6hmwbUW20CaZat7rkqK5tZAFkqaWZbR7rsrGpykmEjB1tJSDFtxaZB1wWoxTEfeW2s6yEWsDNQ1WR3TCdFmUGqplgzncbmLBjsNbM32TgzeadQDryxHKsFMEBX/Kk3lODlmQlXmWm3FQGt6M4KwkmxbnShRp0rpkNrU1zvVu69GIQiCmMjcuVK2zdF5IUJM4SQoaKQRCwhCVdy1VloQ5gJjhLNxQ4pakosUQitAs2EleohVUeqLv2efuG3Zl15O7XK0aPfFD0n1DA7dqor+CdeOpZeVUQIhamgU0XXne/GHvD6M5yUagDStj/a1ejJIIuADTVf5Aj39tOtfa+X3gotdmF2wBDEBK7i7gHagTmQlAcY8ksNI69ww5W41lxkETUl1UxVjvgRbtOZ8X5ttpgqtmuBarU6wbupgGd/SZop46ajlo7avZQ22rigwS2C1y1HMopTa1QYBl0z4RhKHCxnzjhQ2erOMna7zHCMtJiQmLi4uVVdmi7+2oj5MSblROrlmzZB1FuDqhf7u7iWbcct2s1cpT7NS2DZRW6DJhpS2zG1Zrb2rjb8zjlUCpQcIfSOAZst6ii60NlHa0HV5YviXMrDX5eBMbPUdwz6vGiJG2+wazPQzjOMGgplhWue4NBuI3FrHWjkpaTreF+n3Q2Q1JCxlgVKVolKMBB2ClabWHY66htasMVn2o5hUa0ISbxSFTviMAVUqNA/Omo344aWj7xLiU7iMKOuk186B1JtEhD4FSjlbRu4WIbZGC3AUYQ4wtcB2yKS0h1jJdQN1osVjL82beDb11/ekNk1C53sRTjhsutkI5i9GWHll+jf0te7PobsCW+BG0Gro7wbzop/izor1gRwekgI9i1wvHqVYdEKaBbU+UTm+f8rgVbWr2D2LM8sPvxEizRj5Q/9MTdbN07AMrp9M7jEWaK1QREmLpejGTCGwTfCHP/kRQubblzdI3nD90Uc8uX7KX/30z3n18mvePNxQnIRr9ySZIl47jAr0Zsu0WhM2m41iDFJM+qRWwg6kqvNmYxMyZ+dPePb897i++jE//vFnPNy/YZBHfvov/4zXd3cUC2C77Z48DAb86sQmSYoppKxEy1Kd2xPVU0yO5nBQqcsjb9+95Nn1JwQ0YBdpfdiqljKVEAvDmCltZi6PBu7qMZCicrSiUyLC+mzUxE7xoCoRijYwckqmaTWRtjgQIQrshqycQTOmawbAl16i0TGw2hZElHrQM+omuE+WoEGglGUld+KlTTBGuQq1HZLQjDCwlNkCiv6IGnFqkFy9uZrpCp2YbJK5Jgx5MHxn5TJh6x/DSUupdkArCK/0iUoeRz0InGFv9IZQFZpJjqFa9eCT08Gu/USeYyAPc4W5AWEghpF9TOTUID7S2jtEJj2MrZrwfMlBkr7/NeLopSXDKtfN3snkjnHF4I065XCF5qRarGT0jMbE9r8dQP41rw/UNq5pqrOanW3vsvbgw1FdjwU63RrWB+gyEMtAPKvCLqITVu0mBgl9uol/gqqkFmO6R4RCC9KHSziC3r2iglMftLsoIRKyEMKWIY+koKOinl9s+U/+g3/Cd29mWviWm+PE7/7kH/JP/8k/4avPP+d/+Z/+a/7HP/9fuF8mgmgJlKMy+lMYetaQogmHQyZFtcIpUsjZ/L/EUv+k5fDSJqTB73/xJT/5g3/IY71gu/+Uf/ff+1PgwLOrR4Y68d/+iz/j3XHSa2uVwIh7XWEZUwiBMY8MaaSWpr5XNrJrCrMRqfWzPxzuyHHk6vxay8dF6RrYhiIGammUFhkG4161ovY1onyuIMbcTjBE8603nlonSUKvB1qLfZGuA4fts4tm2iFkQkpY8rue2A722kkvguJOXl4u7m6BES4FREehRck6Og80eIrNvmzKGfO5AhqcbKUZQK4BUrl9IZrHV4i4xhOEYoEF1MSvRZ2o1NAOsTaThBRE90mMDIN2TQHj7Nn6LgW3nvFp2qCBM4dE1S6WYaGeDVlO1OyONIGmAHiIjk0FINFa4FBVCpbTOcN2T9ostOM9bXkECurbZftdPEN0Ax4sC3Z8K5y4S/gzDX2fq7JBK6hqsxXcMtsPQi/lV03wv/n1gXMbG6H5iWZfw++9YzlYUDKdFtoV8gvS71vlPqsy3tfjCVbV62Yr0ezn3TSvmQwnOI9LxID6NSsKOA3DW8Rr18/Lgc1mQw6ZROBst+fqyRlnz57x9qaw/+YN8fUr7r/9Jf/g9z/hcPMjfv3iBfPL74yAJ+SQ2Q5bUhx15qK3xk0AHsMGaQEkaicwJ8o80SqEYWAkMTQt9f7T/+t/yn5/xk//7Fd8/+Ln/OxfvOUf/ukfcJY3hCA8efaM9u6dguF5LSn8usA7uakHrGjkyxhX/psdr7Q28+7+FSFUznbnNCks5WgbMmoLO4+kMFKrT4lO6HDhHkN6Ro5lY5KSkiarY5oKNXjzRaiGcejnbtYx1cxBScg5ZSRDrdHcZm2BWzlJE5pJhzBWufLrlLDqeKmvv9rUsibEwBAHXQN+2Nl7llJJeWAYNgiojQzFNH12QFvZVxxMN1JtbR6E9D11bJ5+Pjc09AnTLTRy1n3ilUu1jAZQDC2oGB5OmPWtsUixAcYr7aIKQLX/9rexJ1IrUuiqBB8D16LyJhcRphAIjOR8RU57QjsgyyMhLHT+GJaR9YND+uHjtJDePHIQ356BY3wxKS1CFn3HWp0isuJ2fw1L+te8PqxslAIku4rgyQ2GKmgwMoM+xTYCwTAhZV1DN1WzjCucBC99re3U0wCppMzQMwyfACPBO3SCS07oG4H3bqI/UCvJtfypDX2WysRfQuIv/uod//T/8Al/8EeF20+vON9f8+WPdrTpga+/fkmbYAwDIZkuLa7DSKOd2h50cxrIaUMIkZgaZ/utgaPK7wk2gHaQTE5w//Ydv/d7P0KY+eL2gd3ZjucfC99+9S3vHhtpu2czL53U99sWL/7wA5b95XaiG4ykFbHQrCBWap14e/c9pS3EECltHbkW40CqmPjWcM52Ur6FhpQGZFsWJ86rQYNUkFOJiZ98p+4UoB5TShvINv+w5yFNA6WOmnMPek2KSllIoKe1aWBX7MXWTwo6KToGy0JsYpMFmT7mLfrg1NCnpysAbs0CiSAqJJd+WArSIpK9lJT+d+6OEPzglNyzTQf2kwn5azU8TBQ0tzR6bZLZIaydUJ3BWOtsygDn/NHXXswmVhfleknAgHgVzKWUibjqwHBiacyoaeMmbBlHkPqg3U87JRWO9zsrvfwVM7/U++jwkatI9N+xJkNrmgWD+o3p2DlflX9XJNVWjed1QhGw/49GHswhqlFcsJHf9vchBpJxik5JevG3AhdYxedsCs/UPAjZe1k23+1ttb1b1jLkpKsHpyXvSTaHMM9Ttw2WCDeHB/78Zy9I44YvfvdTPvvyU3bjhjDd81/+1/8Lv/zF1zzc3TOmgZQNt7HPpRQPDcYBLbfCYP8VldRaW+sYlIESqvAn0trCP/8v/gvOLuEP/vTvM9RnbNLA21ev+NnPviHmc0q5AyKlzIRYiVapSHUNm36WHBPjoIB/ylqi1GUhENiOG7qnOii5k8q7x1vGlE6UEAmRwLJUxfGMFoF1iYjRvKM8uCj2qIC5nsCubqgnG9Y1i05y9sOrui9U0yDWOlBcLSPXTZ3iytsbsmYWtnI0wY+Oi/kaomOq4k/eCYeA0LSLixJJxTMdAsFwPxzjFNPGBqco6EG7zMWCrw2AsaCTc7IDTSybUkF/NSF7x+mQni26ELuystd9KC4x4pZBNM0wBS8J9f7WKgSpxnpv6koSAgXtyKY4KNeuecfQ9mfQcm5uyleMm5EUhNhmQpuN2eQAkDY5xA4ZXU+aFejtX/3H3KUiWuCPIZNEIDSiuNe/mOpAPPn8G19/q6GzPXAZYBiCpqQ5qi5NlfBeYpuqq2nZErsdNOa7tWZM2ipeOw/uFxbt/YP9zpT0PWsR5lKtg2n4ii1qOnZxUs5a0Drla5WyUNtCkEgrii188/23BODdqyPXl2fEAK9fv+bnf/k9MWe2uw1FCjFFlmrGcai8wn+HBmYloZIqMWXDTYQQjLUcjHm/FKJ1zoWBf/6f/w98/dUtP/7ycxqN77+/55uXD3z76jsOjweO04yLdB0PEaSfvqB22aVoBtRKJWa7j4Z5EDSLjUGLIeP3axPD+Dra1Vpwy5z37mkLEDMxDSdcpXpyyKxcrF72+zMWVyl4N1k3l9hcyNYaTNEcX8HgOXUs6DQdvdYhj0hqxh6vxusyLl1I1uVbzQhBS8FgP7+UhSoLlUqgktAJTIOXNJaV1d75ptv+5LiOLRNp1OIl0Grvo2L8sXfYdPL5GmhUuXByuIo/V7ESXQObBmzFkyR4bq2YV6nFyNBRGzbBS2mD7JoPyjBbJlEeIkEnC9kG7ooYF4pPS2aTNhAqko4EeSSGWVMXkU52DdGVL3paNBuzJkGNPKvLXUSbCoL+viDBcM1q61FWbeYPeH1wt9GzC/8nBu+UZIZBh3O6jlEPcCsN02Bi1WizOj0jWukCyjXSpU9Y+xxYmRV+y2e7NGEqmgHISdBYiXNeR4vxjpzT1XrHpLTGcY7skk6bKaXyblGS6t3NDfvNjnmeubl9y+39DaUVhjExxkw1xnpIiTwMulk8+KLUgXlZIGZ242jYnDBNM9GyBc1aEykGJYDGzM27if/mv/oz/tWTb9lc7jiWxt3dHY+HB6ZpghSM9Oq+6N6hiSY1kS6NCpFO/HUqQQyBHAYrUSq1HZU/lJp1kLB7bORQ3J0A+8T6mauRikPOdvgUalUMaMUg7ad61qMLvWMjlkKHwMkMQKh1AqoSW0U/g6AdTF/eOWecCiJu4BjQbhZmWYM1cfrqFYK0ni02bCwcmn22YuA7Fjw1shOritDFvlPtXpoB0ZEye0kV16wtBWsYSQfMqwWsaA9nKbPhkaY8jNoAkX6/nYzr1BHPtvR/MSUGI5JWn8KDi6cbrQVESk9Bk6k/WrMDM+g6pWL4Gh3TWpp+/hgH1VmmRGpHgsxElFuIDbrpDQGv9BHEgnJMZnygHQsICR+c04LeB92zrfuR/ZDXBwYvA1Yti4leu6EcIvU8N8lP/xGrkgOodFkxhSi+aPVDR2N3q1LepEGcBLN+UvsNDiw1sJQTHtlvvTrGBSsuAH3jeAN7ne6iHlGtVm7eveHx8U5LrxZYSiFutgyi3kaYjkt3uk3jseyq1kYttvhi7AMZgqXxrRRC8+nXGi02wwjDQCEwbDccywMv7t4g91FnL5aiMx6HzHZQy53aSr83TpCsZgYpQMwBFuPmBHX09OAjLdpouEDOIw0bJ69b2Mq9iou3tdLXw6dVwVnlqiXU7qCIZ9nNsJfVgNKfombi1sSJZtmsSVk3F8Sz5ajDcn04REo6nLfzxExO5WHRtZTqKVb0ftuaVWC+rvhW8INY3VSbVFZLAJNrFQ1EOSoVJJSikjMjqQo6dYmgWK+PcIumexXzJSMG3NsqNF0D1QjeSmesxlpPphFeoQ5PXIMHKwtkiBsPoq4Mliw4dqU/r9VOSooPd6xQtw+tzpaOqyjeM8HePYxJSb4o6D6kzJAuiKGQ4sSA2ihFFsMlg02VskmUppGU1qg1EEm6XTqeeFItmApCfff/LpxUjTOzNnSlYwo5qoVuSpZeY6iHrXOth32h6M3AAE5QvCk7aTToKRA9fRCV8vg2cBForbPNnrNJJlha7JmHfUZ7XCfBC7z76QtwXmYFi/MIUVnoS2vIMjOkDWeXl6QhMR0PLGXS6xZhs2tM00xZlMm+LHPX3MVg/CeCWt0UtYEectZPZGXbNB1oUtjvdqrzTIlxv6dJoCyLli4pMm42ylXKg26iqiByZ3RbOu8lN02wWRr4hbeqlsQ5u4wl2WLfmMD4hFslfvutzGkNH0bhhGUhEpuW8vqzuvBS9M3g+a/Jj4AQms68Fc3iSysq6sYOIaG7mHbun5k6RpMtpZRsypN5jaHuoeGk0SNNOqSgGzChwynUNLGc4KI5WBZngT4gBjzb31smVX0CddPg4c2J39ZVqlxJs/yIQinVuuApnWhqw7q3msmfXMQdCGsTInopH/uzUCrESjPogc4yIIVM1lmWeojplHqrxntQjzaPEtGy2TMlp0iICHNplKqzTSM7ahLGOJPlQGZWHNSrH1vfUgvLAtFdOpo6egimaFlUu1rNk0z4O8q8dE6bhqVoGUMIutCGnEjRamxbLJrt0G+kOBZk3+MgbGBl6sYArYXejVSwTzt1OSmInJwY53hDExvPZKc7jv1Yl6sna8HLcgusTpwV5mm27tzIMGzIeaMLz6bu5GFj3arKkJSbU0ohFYGwUOaFZZ7Uh4tg7yUMQ2YcItN8YJlnam3WuQmKQXQG+cJmM7Lfn5PSyBiyadBMdC2NaVlwE7mlLFYKQ0w63drF62tZ7Hq5RMoa5FL0+6b3ZsgDhD0squFUO58145WqdAQfdOFlWkC/b1msBB7V8z6aHbgHjNa1dHrk6dQfcwcV/U7X0QVn0NdGWRpk+nBf96AHOr0gWyaGP+O+YXVriq0NDeb6DX1CO06yFp2olLKfahbYClKC2SSry4nTKjT7UczICaRy0mXyjZlO2fAY47zJquVDuWjBcOKQgqkXhFoX3P5Zs8vYHRjcyinGYGPnzPIZx03pAUQP+aoHxslUcM9+nGYy19n+Lmq3MyR88Ix3+JOtG50GZcd/2iEMBI4kmemAkDVXqvHbkghx0IOvmkKmVg1uZtmh9yjo4fZDXh9oA23pHkJs0lvL67AMX0EeHFbjfzFVep+MTSHKKowGbIgGRDdiC4HMAJI0eA22aGzjbTYbhjRAe1TfqPfwlXZSSerPpaDZX2tCtc/jmZ3r6pasHaFhGDXFjom83TIMG2Nea2nk01G8Y1pLsUykWWhX2VSjQMhdCuP6RjGDOYX0lF/08PDI0ycfaWldlcOWkpUbEknWdSt1MZIfirH4xseAUrGNE6xtHfQTLXUxnpOYQV+gJlHffmnIWJnrbANr63ovRXpQWDEsjM9npXBbQFo/tenQqEDQjFg1sJFhHPBBEgTWzwrWgcU04ioBEgndXtqtkZXCEAg5aqe1GeZk7fyG2uH4I/aAG5oeiE28u6WwQTZeImITlowTJlQYsnbOg5b50YXa6EEQsfezTI+2UIMGgpwSYx77vUzmYOH3NAfL7k5hFP0Oo0zo96rUyCVVisflvFG8lUioPvTltHGjz6uvOSPOBlxqp1mstEB3OEVotdAohJRJUSdJBYFCMVWMdlBrEY4NlpQpYcfIQI4zQRYL0qphTtU0xbURc1YTyIAGLmm0qo4WYuBb/DvBvMKaEmoL6Lf4Wnaqi4Hs1QcxNHqmZKiXbtwYNKsiWFvcRm8lrZ11IOxOS45gm846OCKCu5Bq/Fl5OWqRY9mBl7Yd9LSUpOmpty4YxVfKMpPToA8t+eRgLXV9PJhnbcEepLu/djW9rT8RmI6VusyEPJAHPYVFWv9kNqgeETgcDjw+PjJstsZSVx4QTYPAMAzqL7aY3bIN6AUFyV3W4oN7o53YIUBZZkqZ8G4twbMJMf6N4nYhJab52LlHjv+0WnvW1/Wn/TlEoKg0qy7W2QrQYBizVTwFYaKhnTQJxs63LlfEsjEPllk3dZNKFNv0FfVJQ7N9zwhAO16JiFAphqPFkwzzvY6zBFUFOBhuuFgOrrk0PlTUsm1ZtMMm5iCakgf1YDiNdcNzsoBjAb027WGGouvEZiIohcEP+VMoAxS7c+xsDXL6TSunz5ULIahHWQqRNLiWtuKyNC0n3SfPf4+Z/wUdb+djEnVykZopOJN+Xct6MDTTc6ZgLh6mFV7IzCmxiyNjSEQmK/d1whS1kBalX7AoPh6CY5HNOG1CrI38d+GkmuzU865VIDnet45PsroWc4/w0ViasRTt0ESfRmPvE4O5QvppnsyjaST1AJIhOMdEi1aVIBlxMdRe1mh0sYAqwWhv0jujYoutSjH8QrtHsQlTOPa0fxxH4pCorXKcm/F9YsdBdIkXXZSGnSvu4GWCubr6pBbbTLo3FEMJoSKSlIPTKg/HAxfjxjZ5ow8FQU30plkDLCGShtzBW+30FdyxIKZkgxm044RhCjFp5tOI2pU7EW8nY+WHUTNJHeMuHSfCD4qTDDeYQ0SzTeLYRS02P7Au5MH92tQxojWdKaB2LMrj0ioy4aL2NWv0AtYeK4DRQ4JALctJ9qYe6DlrN0siprYI/XPHmBkSNBLu8Orfs2JWlmEa/82/FoMOXslpxOdDeikaTDGSbQp49YEUti9DolM4vCLw8g404DgxFqk2JMYzW7HmzlqmKuqgVtJRJ+N23Ms///vUIDvsTg58gjHzjbu5duqNixm1ASK2TrSwMkw6OLWpEZpCPkJEJLEk2KQNWRpSD30PNmm0eTGOWupBXMXgiRyFJVT+TixxclIgUU932zihGaBnoLGAD6GcbbpMrdJr7lTFyHzmpd0HX0DK0UD90NvHDnhrlyrQvZNkHVQqfiIYwtx68ebdTD2B1MfJaLHBjBUdjwuqwF/qRJtsYGzQa0qIlasrCfE0Ndf3C+bvr4Eae1gJn5WnyzWgk49STsSsHJfpeCAWpWcUMxBMKbHEylILAbQErUUbFKLpNUH93LUBEvsm5WTxOtZTDEvRsrPowRDV4ibaswtRy/9AIpCJsViWmKyrWw0EF5N+mWYOdVuNrGRD/J6gGaQhQSgTu9FC61gWePe50IJhpzbUF2eRSbW1pgd6KUrCjEm1ltXcQPTXRoZkpaZt9HqiREgpk4Oy6NWvq9q6XXmC/mfOmT5Fx/DX4AdjOsGNAnbI6MEdQ6Q4/mPrpjW187GbY8GLnnb5OlIBee/Xg929akE2RiGQ9MAW7WYnY+87DaWz3W1vagLhWaFXHBDFXFWodt/t8wS6/M3hlWZOLCL2LdZNFdDSTyJVMpOVklt2SnqWewiVwKCd0WZryUbVrQoBMf3y3wXD3jZI7Z0Nuwr8ZumGrlJZWmUWm8JsulIJjcBgmUDPk5G+waOVhvrwVpKe4wGswStYuYoBqYY/OW7RgnWDQrTp3GLprg+xtY1vMVfPNQVza6uEJVJFM4VspUAIyq+KVorpYlwN8hq+GYUUIXvgbJVSolnyYhNqRtKgesqUEkEajw8PzPNMqYVhSOQEjw8PesoinUke7DTVgaqlb/TQXRjs+4KQc+yGgthGkVaNKa0LeamFLtuoWHmoJ2MztnxMW52gVFyvFzBXl57xabe3ntBpViPKZj5ULtvyTE6vS3RiUdM5ihCsk+aNG7HAlCiW0Sl/SAX764ZdaRAxRNKYaM1cMkQDkKdvOSXGQTuMS5k1mz0tMZ2rZeuy1kpEx/7Vqk6o0YwmqywsVYN3JHTlRMrZ1hY9mOgebUZI06fpKgTHziIK7DvtSu+3dpz1bIxeLduhbKPJYia5iWFcM6naO87yHtzSzMZK3Tzpwdx8LvvedNmTS4G8LJZipXSwPSQNTKs4tUQNkZY3nG0yKc5Qi2ZpAU0vrAEi61vYX66x4d/0+kB5kPRo3KRowABqgWB1ky+ipRZKK/rQ7IIdKEzRJ/2eOC4GnV8YovFkbMioz5gLgZWvE7ybiWJTltH42Cv17NYszBG6dRXQOz0hBuueVvoI8qAlZakz0ipShVyK4kEhgjTGYUNO2UBjy1oMCwzY6C7vvDmGU4pSHNBnU4texzCOnJ9lpBYeHx+ZppllKWyGLU+fPCEG4f7+gaUstt6tlW8YV1kWkvk6jSlTrbsXHAiOquEUP3XtUwrO+9F7qoM8tJMU7ENq4Mh2IDSlkUwHlkU7U61qp1MBXP0e3fAGCutd1vuaoDWjOZgsSDBfLjtYKhUWYRy2xDDoOitNW1CIXYf+Ph8Zp4HFMyC1uFH5TdUTzDJRsPmGTQ/KIkWpKkbJiMG6bL3MsjXi2UvPPoDWdM6jPV+HTFwlEk3jG3M2jpNJsTp+pG4Tp9ipHjxevtpaxDp9YS0XNbFrHYuKLhNCDy1/f8JqJBisfBaMytJcLaF0l/cyn6Dmm62WftDU6o2KlRuIrWPxstsZ82JOxjRKjNwvsNTIdjxjE2dGJnLUEW0iJrGywE00GOK39+y/5vXBrhJY2aV4STFs5qSex2a/tUqPMHbzU4gMKZKj80D0ZulNsTmP7jbRnSIMxwp6WNUQzYZYc50hGp5lWUS1hRdj9AMFf9KqLzMGunNnms0ZFCO7xtA3vvgpG5auwWxFOy7KrHZ1QaRUR/HU0ysn0+M4mK81K+qBXgxXAxqcn+/YbPWfWiqyNKLY5Gb7WWlQ+wZS7CCnrCC1+28h5Jy1AxsC8zwBjaVMzMuiB0cYdBPVZgMrbEBs1KCt/kwr872PKDNqwDjsrQGjFsjS6PwrjeO2Cey+e3WtmZ+f+I0QdPya8+U9Y2roZ5IOIoptTt+M4Bw9TB4TMPNHK/398K612PPUn0veXbNMOSLQfMCEZq/anBA7YDUEp5AIWTp+m2xNuzZUB7hahmuHrF6oEnh97VTLABXPMiEzLncLLN3mGjvkA1GSpyR6FwNEWbWS3ohqVW21QUg59cDX3HbaKxcBVwJ4JqbXYf8TUW6WVA3Olj0qHOMfwXBS28erNbWKusWfuEKaTLUxPc6cj5k0bkgsCiOZNdT6xva8499B2ZjQQ1Asa2q1UivM1RaPnYZNmglLTjpfXYDK+mC7JMLS3Bj6XlfPdM+gTmgatfWJMCFEtuNADLqgRBR3Cpa+r2x8+6Mv/tC7LniGEhTADh1MlR6MdVr2ZHhDppSFwUwAU4waUJ1hbP5iHqyTXbN4Q8OwO10P+pCneWG3O+P6qnJ780bB21p4d3evHUTbJM2CqVIOKvurvVJEau0cIKGsUp6gHdJaik4XF7Q97SzoE5sh7xv5lGnFM7F/xx662kFvN4FlOVJMCN+q/ozSDTBnWgdGWANg1EnQvXSSBtFa6iIICQnmzBtahw3U6zxa/Neyx1J0OyD10CAGoyxUszMuJNSKJoeEZHd0MGzUqgjMbSKGCIuRsS1ga1alpWm2yULr9tUswSGMlEYt05pSQ3JKKhEyzKlDDHZoNNEscLFBLXkcQIRa6nsd/L6A/WIRw5WtgdVWylGz3+VrM5Ds0Dw9lEJ/5n4Pe6Ysmk2LOJZV+gEcg/M2ncEfTiR9Bh+htIpgMiTFHfV7H5bK0hLnQ2afm7XdDFrRUkC1wP1w+je/Pgywj0KOYib90iGvWoMOKYgKuGs3IgC5l316ihlwDh3sXa2d/UZHpLkQVgjRpzXbg7GTqbVGSgNPnzzhu9u3HMvMtASi+XKfPm7b+f2GA3/9xAnGwbIRUATpEhAdepoMd7HsqeqEbPfCjzHrzMYTH60hDgQDXjW7UAM7f9hKMLVSoIkN6hhYFsO94ogSdBNSCykGDtNMmWfO9hdEYDaRNjH0MqY083uKsNTKVFR0rLyjrByeBsSh4x+ltX64q6DaTnnLVqKXqzExxI0G6KrTlUpVeKDa/Y3RNJ7NsC8PikSIOsMziMmVpOEmdE2EZUEDWlSHimq4V+dBCbg1UhRv5jg4EMwZt/bn2zGcoKdujD5VSDMuCZoRYYFqzLlnc4RVAtfKglVwWLQ07Dd0+59WlebTMxi7fv9TQXhdh+JVSlM6TBOhLUvvFCc7EOuJ+288ySKbzYPADtlu/hm1XNVf4t3NQGj0RorY+sYP02AmN5aVeaXUTiqfXoL6e5xUMZ6NKTwtnRKigTD2e12kIVV7/0PYEMIRaYZ92r6urSB/F/KgFIJ2pjRMGofLSsnYkGQ8naBpPknHMUVLwaNnRUGje87Z/IySjT9TUDYE7WyoLqqayr6ZoZ/dvhAJoXFxfs7lxRnltsAwQNBhqisOog9pvckrDCJ4e9xLupMAF/z3aMbhGZMnFIpteLc0MuSNBjDRTlAIOvqrc6PwEuPkYbPittOkpV1Iqw+TuzLsd3sdP7ZMLPPEfrtjv9v1IJfsPoYYjYQa7R429a/Kho+JTswZwcS8DeKgAaEZGGjses9AHZMIOE0mAImYtDtca+7DaFWVbZ3kKKaz1PvcNEID0Qap6OnsXu69vrRM10zWerDSDWRUE1GStHj3sjgQ7RWHZt45ZnvWoVMO9KIUC2vW/k+22YO4sMzXDV2MrZinrpfmmitRPNGx3GiHm5JFY197nYzbs2/9GPWk66rBx3l07oYSERv/pvfK1pxlMjlbVi/67ILjluZugQgpmWoleF8wmE7VMkevPMQYA9GdUu2wwNeqgLj3Wt9A9s8JuVawLqKvcPE6Qz8bhUMDQuY8D2yqcuic+dhk5dn9Ta8PCl5DAJ/ME8TpB/UkFU8QNFXG8ALf9BENXjHoyeseU8n0dc5J0Z1tN528MqGrWNdGy8YqykBOKXB9fsHbt2+RopNPpFZGE0qHIWnED80yBJN4pIhzd8AwAvvdwfAz3zAxOtfM5BLGYlc+mk7rUWLhyn1xTM2zSm9BBwMqsYCmIt9ojPbAMGzYbrZsRvXiKpb97Pc75KGy22zZ7/YgSn8gKt9LgHlWdnyfAo7q1nIMiERKqx1fktrU1nhQ2osCv17pmc2PiYRj9ICKjfQSw/3U4npII0ucmaaj3heCUk+iWPmvnCwvbWkKwqcQkJgUM+V0JByWoVcF7q3zR7OZfk3VEDEKOSsXcFkmlqUo3YYVM9LLkD5rwak3ilPp3kvZGehidBv6JorJeIHu/BFBxKcEuY+VYq3R8aoe5PX9fa3pnAGVazmnLPX14ZbO+vn04K3WnAkdlvC114e8YpmRHaKn5FnvairO53iP/Xczax3PLL3Et/WmKalDBtahtlDWug9d6L+/2QGoiak1OVhJ4mLJnlgm+jgtlBK4TBs2sSIs1FZ1jfoh879n8Ip2YhL0IWvF40QzX5R2GgTIXkTrciBGPS1yzsp1ikPfaFCttE6auUQ9J8Q1ZcFSWhpEc3ZoCtQ/vbjkze6C+2miCn0U2+PxqFVFjL0k80Xk2U1M+gCiVeBi7XQvB4Yhk0xU7Cl2Jw6C6QYzUprp9oY+3BT0dyvuoyd7dOIfivkUG38WYrIxaYOe0DmTUN/0UhZiypydX2iWFRPTtCBNGDcjBNXEGW/AOsGN43KktqU/uxS1dFjMkkVao1JIaZXoiAHR3T7Z5D1WewNZWdmidixjTIrRiPr063TxBSVUtr65gY55VjvFYx9Xt/quxeDyK80eYvLS09YfoW+waNmF2ww3qev7mJMorYGVkR4kam3dqgboWE/Pdsz9NxnuFwgqmSpuq2TTn0Kk1tCzHF34ynXrE6I8IREvw1NfF6GZLY8NInEL5xiikWODYUe1B8MUV3WEvhGsg6B14zkMQnC0zEEU/xPLnFrPaqs3H5x2gwc228zQs07vUqeYbU8axixG4bBcasWVY189nhQoOTkCI9uwsAkKbQiBevo5/w2vDwxeSv4bYjDei9rDxqDYREUMXLUjrTXjE+lD7CWiiW3XnEyBegVN3bHzxIq3OpCswSuERqBZVyVxsd/z6UfP+cWvv+bh8IjESB4GNqNOb2mlKHBdDffJI5tRJyO31ljK0o0R1SxOF+wwjgx5NGzFskRzpRzyYJtMP9M65DYwJM0oXM/p+rcQ0O6WZZYuD65NJxf5sFgNsHoQDGbI12pjyBrsl3kh52Z+UwZox0hd5n6fGo3j8ZHD4aCwaFT5j3bTNCvSxa1BVIz172RgDfjgU2vEnqHKfMwnvQVCHNhttoSNDnidy5Hj8YG5TPae5vcUVt6Rd3arWzPHQGyhj5nXTl/qzSFpyji/vLhQKkldOkAtVrroM8labhvumjUqa7lS9VBS+KHRHCRGM2WlPliwAErVzZccsEcDcaut0wU047Hy0UtNggUYB7fFmkjBgpdjkxqwx2FkXmabfWCVTRTf6gbpBYJfQwjkwWdLigXQapkWnS/p1J9VIhRxwq9XCCLaLMHK8U7k7XMKwko4thoxRK1TmuFjwaux/j1Wfoofes5NMxqVWMYX1PP/calMLbDLmbMU8DmO/7sHrxwjGzNoq7USC4SiAuLStFtRxcU4ydi0VcHkU8KpaBkjPs2VQLAhER1b8tRW/KYaVykKOSSiZUOtCZvdhs8++4Q3t+94nCeOdWE+TIQAOUWGmNiMmRgzLuSaj3PHtnIaIMBcFv0sKMYTQ+7WJ9LU0jk5uzwFMKFuq4VgDpnS1A7ZtWEhavmS0U1LDOqOEXQxLlaqROjDbWMIlDJTqp38MXcRe2uFEDPbbeLx8Kj+8SlwnI62Cde2/bzM1uAI5hNeDRszOz8LDCKKg6Q0EFNWLVpf4Cvxs4lO90lxAKJ+LhNiaxY6MBrNJU0Dx+kezA9fu7cWCKV1CUizDRlOlAvV4K8gEKue6q1poB+3W+b7pWvzVGFgWj4LvoL04awpxx7UfLw9wZ+xZV7WUNKMWtdqTrFn4dmGicRsPLLg5VCwgOjTqk8yIBwe0KxeH32wysIpHmqV5JnpaZNBq7BTqUywbqp2kLWrbVhaSGbisHYTNTgq6VqaEGMzs0f77IZjOiUJvGsaO2wQsAMXs8cJeugE06JK8xypw/j67uL/7rhfQ2roQctqS41rQXQPLJHQ9OBuMQHL3xyPfkDM6q8YVRcXcN6gZljFuCwtRJam5ZeybRsh66RqlfxEI28OJzV67Ba23WSt+cncbMjpyuzV7zEeWLDhGSGw3+74vR//mOM88/rhHYul89FA6iARUtAxZSn3zVJaocwzGN/MSzxE1HY4afezNWGe9YbmnGhJzfe0EqqWUSlgHGogpsy42TBkFaoOQcuaKpU86ikotbKUokl3HHVQQilItNFZWiut+AqaCbTWeHx8ZF5mPW2H1BeeLovIsizMx+NqVNdLVyxTUJ6Vg/Q4mBwj0e69bihb9IblOeFyGDcMSbuO2WYLiuhgBTXVs6YDlTYrbSEGLyr0/mKZTmvtPT6W0zZqK+Ypppjg69ev2Wy3PZiqxbJeb84687BJ0c1XNeNrdd2UtbpFNP13rRIqvceqJzWPLjukPVDGLv6XfhDFTnS2sjjStb0rdWjNymJUTaUepOvVOqal2Js7kDgp1UpnsQ4ngWDSEHXxkPeuwR1MNAvMOHGpiZBMY4n9vV9L8Kz7lMqDfT1iZoUqfm/iHmrr6Dq3vdYsWPwpo5mY428GKZ38v2N4EiLvmiYYRxmB498Yjz4oeEnQjZAsEIlo/Z4ksCg5mohpyrAsIqpBIdaN0eCVbSxY7mWk3ngbFd9a72C5T5ELqNfWbjRLZrMJbpWz3Z5PP/6Yh68edfMbzqCt26Ai3tiQOmsGkhIxCDljAm9YbEirzls0bkvUzqaIlnihomm+LdBmQTbEoMZqMbPPZ2xy4vr6mquzS/abHe/ubpnLUYMulZxnPvlkx/7iirY8Q8LAX/z859zd39FqIQ+jOhr4Qgam6cCy2OlvAHaUwBC1vHSenEhjM46MUcH8iuIbQzJ/KqFjDxGld9RWqbPLs064aymb5Mg3o15vTLWf6CF5e16gBnKu1LaQ6whjU+G6KGbjZE13vIhRIYIING80UMBVE02bNxpMZiDamvEupK2zAK1Ytm8BIcZh3ZA5EpxQLSuPW4Oa0QaiGg7mtAblUsy6RVz7iG103dw+8brjbFGoRmZWAH+FxHpp5bo0+3/F0jwz9O/DSvvYg5F/OYWVkoGYosEMB/3QB7XKPgG/tHC0MtHpVKuI2+6NgTnr30nHipsFT6LJl4IbH3hCdaJQCCclKhjsE3qZ2rNMK62LHKhtYVr+DqgSXqY1AxVzGkBUh+Unc8GiLIGYs9XO7hSgWY86RmQbUrlOVym1dCuY0hToi1HWNNcspr2UUZwiggW9FAPPnz7hOB34+sX3HEuxTWqg7MnNbCJ6ekUn8ymDeDRTuhjMtiNGmgTVY2IntT2IVsV81LSszOMGRJimiftWOBwP1Fo5v7hkc3FGnh4hqMA4p5Grq2s2ozAdGw+3NyxLUyEr2oKvUXE6aQ3GzLIojX+/21LLrIoDaSyLWhH7oliWhePhkVoWM3VeOT5lWUxG5NeSurjbOWnelYu265qL48KKqSTrdAUrKZI4QVQ7X5vtTj3SozDPCjIXG+qaU6TU0kXe/aWHvH4dt3QRzUixE1pq77Jp4LAsUgxmaCZ1MIA+EPBBvEMeCJlO8tW9502YVd4TrDOswVsDZS2tH1YelFptSBR1jcXKRycnp6F33/wz+0X+9u9zmoMhZ/Sp3vYZmvPlQjSHWllB9YZ9ntAlQK5pFTceaM04i2hjKZrRYDIQXtamir+UQLsGOpXC0SOxdu3d2+xE/SA6F0ITU4MG6MDkel3W0NLn7B1UKGUyc4K/+fWBTqqgU32D4RR20jev+WU9Pazu9gUUQ0apcKcz3+zSDEhtVYNNQ7ktYhfdvYv8oQUjFsZgP6/OjgQdEvDxR0+ZlyMvX7/huIh5ffvposM6lKFsw0Qdp4kJqTZUIdripRJs+jVtJWsqTmv8IsPOCIGrqyvevH3Dy9cvaa3x8vX3fP/qOz796FPGmFQaFZXndnuj2rfHw8T9YeLps+c8ffZUr9cO2dYaraxeWq027h5uGIdBhcUhMI4qxq2tMS1H7h/eUeqi7hq2iXN3AzFLYOvAYV1TESGLA6l6+GQbFOrAdTQcRe+zPk+pDUmi5Tu6sEPISpQMin3GvCFNB448UuqkHV67/4Bhuiu3J4BKorzACAGJjXmZABhSIBjUADZd2hob0dZGwDNmPfBba0QRQkymfcVwujXD9LItxJXTlmKimnzF5y5o2BT1JCM4A8ExbYTQnVFrKVpRVPWH0z3uMIh1I/13274Qi0yh7yEH3CMpO+2k4L/YmfrimZjb1wTtZGbnX4oV5M33UTA9YjCLG/89doWGeYJqF1dLbd2Eybkm0pDodCN6OdlaNTgo9IaGNk8WHHfEqgShUKs2mf5OeF6lCHVU4mmfZhIFibKCraU5edeCnHcu7AbaQ2/6BOwGW9cNDTLqTGDSE8tqpD8UN4xDcatBN5rg8wWFy93AH3z5GZebxFcv3vAwLSbHMGO0IKgFUlBMC5fd2DCJGhhG0QEFBmoOw1ZlJklpHogCpVV0cCyzMBtD+umzZ9w93nP37palzhzePFLKA8/Pr7S8i9kWrjAvlZu7e86ePOHi46fcH48c59lOVrNkiV4yThyPR20aIEhRNwMtybWRspSFYcgM47ny2lCL4H59aJAj2Ils1IAYjKgZTDgfgwVoDXgqOQtGdtRT3S1UllKISYzTpuVFSNoJTimR85YhbklxYF4eKWWiRm/C6OdXPMdKID8MAJG1i52yYl8xFFL1Bg+EGHT4rM0grDh3a20IAB3HWzufjrnIGkhw/3z9n0t6vGzz9ejTcpqI8qFcj2vgorQV61JenAUmwy/BfO2Cl4vRQoZvaDFmfujkZy1okiu1+k8kA9JB17ZXl8GuyZ1WQFZaRX/ZZwckpH6geTTWjHe9Hp8/6hWITweXk4MvpExKmBOtf4+9mzQ79O1ZoJlpqRPzdLTAJvyQ14dlXghFhFYqGxu2K1GzFCgm2JW+QMS6K2LAcBMxa7dGEi2NpCuarewQb0kXWqvdh6qXa/00qr2lbPi64i9BMY+QA59/+jH7s3N++dXXvLm7U9zLvlcn5aylYExZBwsEla204IdKs4bBkXEYFbqRhlpFKzitnxvmpXB3/8AwDlycX1CmieMyMYfG/eMDl8NAGjdQF1t9iYfDkTgOnJ+d892vv6ZKYyqzMZ4h5KgB1ljTm81ImSdqqeQhM+TBiLuF+4d77h7vqLVQ2kKVQpFmHlvByuegsibdIQQpiu3YaR3NXz5Ydt2zM/vHLYh7aRO05DxOM9uN0lDcijiYDtQzuWHYEFNgnrXbRlvUjhtdR80H54KuG8MsWxAQpSkMEaoEaltIMvSMGXQG54rdqLi6Wjnp2Bp4BgJdHN0xmLU54oeqZ/pdamRNDc8U05B7V72VSt5siCiXrJpflct6WnP/+7A2PvKoAalnwbbRTzqjADEk3ElKlVbqZBpOJ0J1RbE3QiyoAISGD/t1y+eendWqexQwJNSqqJ4HajYKK/ju2FUA77KTQh+M0mMAJ4FQpN9vseYVIizlSJkPqlyw6u2HhK8PCl5Lq8xFnSGqqBhaiWli5d7KnSGEruOzgrKn77IUakA7a04UtbFXTRq1LJRFh50WSz87Uz+ZUyYNqTb6CW99+0NX8DfnyGdPL3lytufX37/iN9+/5O3x0L3BA65dA7UPXg0OO1/G5DKtVR2uIY1YNZUXorl2YgtQMzuZFuZp0UEaBFIUUmssS6UNnvEYq8yylPu7BwKBzXbLMhfrKs60WTrJNqaoo8CyWmSLbSSA4+MDN+9umdtCa8U4ZI42eOtbS+Zq2IjyqRTXE8eOaiG6rMlOWO3CayPFA4Cy7s3xNiRKxbIfzSpdBLzdjSxLY5kXEIhkNsMWWkPKrK1lAQmRSMEH+CrXLPQyFdYONDSS6f46W9CCn9MhuvbOMo5amwXdE+2laSqVP5X6Zi21kI3j1ox7Zova3tsDnNCq4T227hUfpDd6+sHrmZevOwfQ7eT16wx408pIo4jJjhRzDMEkUxgR1n5vjPpmboeuh1TsmWWwe9I7tR2DUlDf2DFm8GD/0Z/EKs0q9cRCx3lj0e/b0teaZ2TA2uGNq9FCsM56awvLPK/fg6nIfsDrw8rGWqmiI4xK1fIhNE9no7b1ffyRdeq8G+OBpSylt+4xAFZJ3JFkEbrOi/2dMnq13FGztSGaoNfwjZ6R9VON/lDctno8O+fv/+45Z/tzfvb1V7y6uWEuhdbLI9fBaVcRArEGnVzjy81PKVnLjNrU00waxJRtSAScbXcsy0Krwi6fsd1tOL67tYxPF3gaErv9nu3ZFcdaKQ3GzYbjNJtLqHpzIaikqOg06DJPiDQWmRAz5ZPQeHf/jqlMLHKCFZ6ctF3EjC1cG+xBcFa0YR5UmujB011EreOnZZNNTUqj8dEqOev0KCfMRgKuMcRK3xR3zFOklFmVC+ZmejjcK1yAa2I9MGhZ7ZiMv7QMURa79GexUjt0Y4QuoneR9kpDEG3SpMA8FTpxstoG9bIvWpctYhPE9T4qzUAPAl3yuoZ8eIwHiVMn077Z+jg0kGgNiOYd+USrxcqvyJBPsGFsaIg9wRhPOo24YBtdu3aYtyqEoOTelWjqFWFPJ1demHjQ08OMfm/tmgyeCOtH6vy23kCI0SRroTddMLWBf4Log4Wbiv+Xsth+Xru/P/T1YWWjnWQiyiBXUqBQTS7UUkDFX7Gz6WN0vZqeojqMVU8Il+Z00C+4nYYLYD1tNzV+MJC3rp1G7/SAzuyzvKBLmdTGYyGHwJfPn3BxseU3L1/w1Tff8m6amKVSm5ZTKQawMsZHbvmp1Skf6OeJSYctdD1b0hR6XgpLLVxdXzGOA7Us7DZbJOuEo2rZpjtObs/3lMOBIWTctiRnvW8pDragFD94vL+jlFnLZuuELsvMXBbmOuNjxlpYsR162SB2D60kbFWDl81b1MNE7yPoQRWjcZ4kGvNZS7DaigmSbTBJm3RiOIOCx9nxJOl/Nsswc9go36tlNrtASAPzMjMvk+ouRSEDaY1kqoZgZXnPXkKjharTfGy6UIyBplwKzRZtUIrjWlqq6BqmmrwoRWhuBy20UM2BYrVK9lcfYqFPo+M2nWaBA+e2rjtbf6UElFaJOSk+aY2msOITBM+VJfYDWIQOYK8kX3ea8MzGMzt9Lx+iq4RfJXf79TpJ1d5QFUJyYql+ijsqUAWcDO4F3DJIZXZ+g1gz2aINN9f1VpsRQaBLjKblkWl61H3rpTLv3/O/6fVBwWsMkVDFiJO2WETLEu/sRAkWvMxJ1CO/2CSaasLLCC1EI7sGkg1E0A1nQbDjH3aH7HTwVlz0oiGcdnw0A1PtpJZZiKoDYop8fL7javsln1894etXb/jld99ymCbDtap1ggPTNBk7XsdcpYB22OK6AMac2Ww2akuDdiFbq9w/3NPaVnWcMSNlUV5SqR0PkFo5HA4sAhICRSqtYU4bmq3ElHz1UmNAdhtay92+polwnGZaLUSEzTjQ5mZGjZxsgPU+hh4EQLOzlb+FYTva8ofSKpuUGPKABM+8F1vUVkKKY2MVFSwXRKzTKNZMCLH7c0lUd4OQMjkkQhzY7uHm5rVqB0UtUbwBFB0UD1ZuRw8ISnfoQ0Ms616JofQA5ANjbAl1/K5nXXbtLllzcbhnNa1Z1zbETop1PpTDJAKd/uCBc52vaHbRzZ1Yit07lc8oXahpM6QVG3bhAnW6ftIzrGaOGkawx1MhMfK2IOt9au4wYc/Xy0XfR3h1J30dyEnZ6IslyNqVXstGfT/PWEsp+DxM/dwLIkqiFbRb26SyLDPT9IhQOtzj7/Uhrw8KXv/4T/8hL1+85HCYkRr7KCjFXhIZBRGtFYhW7GJdl6IYj3cmRNvlwex0wbCVpZyUDYnOpKMjZ0C0vw8dAFWSpNbhOa3+YclwEx2EYYTGITM+fcL5/pzdsOXX33/H2/t3fp7S8380w9sMI+MwKGt9OUBKbLd7pFUOjw+dbqFe+op73S2TsvlDIMfA2W5LmSamQ2HcjNQQeTw8EuaZmAdCUu+vPFj7v1VKVbb4ZhxIaaDVUSVB1mpfyqI0kFbUNC5FNuO4WiujC7GKdN93scxFsSzNwNR8LpKC+5PZ6HmBUoUxrzw9WtOmVlhJyMG4dqt42HCS5Nq6Si2LOm2mRB6UJZ+lMc0HoDKOW5osNEnQMhJsBmE4EYsnK+ODY5ALSFB8ytr5p91DlwS5IaSvl2QW3g0fzNHU2cRLMDDsrPZgoPWjlkHN5GpdkRBtco9hGbUW5QC2SIuNnIdezqVk4LYFGEVAbNUFlVytWZ9O5xILdLWYe27Q3O9UbuXwDBJW4UQ1cPw0KIhXNIEQFOxPVhUBZiSg96A63hg0mXiP2GtBcSkFn8otfgogCJVaKin7sJSgZPYysywzQjVnZtY994GvDwtef/JH3H7xCd98+4LXrx85Hg8sZV5BPSJDDpSm6nAJ7k+0khG9Hm8GjovLKQKWbRlZTzDS4Xq6hX4inryszEyWWeksuNpxNmyTBk3Vuk2vVGFIkU8+egYxMH818zjPNAkmVVkdYKdp4uH+nhiDujjEwDQdmCQwpNFS9dABx81mAwjbccMQI5shss2ZY4jIUri6umKqjcPxSIhaRixVP/cyzxRr/HgGsCwHlmlmHBLjOHCY1O9erIwKEaQ0lnnSrp0tIndB8bmI2vEyIN9TeKwFLjpQTlBb6+oMeGfT2yYd0rBma6K4RbRNHFLqWQH4s9JrWgycbyUSw6CDNkIip4VlVja9YpoDMUMpgE9OdgxFWs+QEJ3EE1D6Sqd0hJWf5OWWziPUe9JMFatLwzCppqXYmlWjFYZr86JmlnUx2Y5hcz5AeVXbhP6+MRmHTIwka8SGVt2ZwhwyRDRbaetnWhn5YjtGeVKubdUFZ7/LoZoTjK0Z1BCSqiaktn4YhxiJrSdiVqitmZa7bwS0lK5eQtq1ub27H4QxmptqEMMirXRsOrC32MSrUjWx6IdLWLuafSN/4OsDPewDX372BR89veTdu0fuHxa++s03zNNEqZXDXJib1uwOEgafYOU3D8xITvEDEe1SegBUGM11cX/tEwBC9LH22M8GCGm1SIE1vcXKSsSyKhtdlWKAEDnfJ0J8xlIK37x4yeM0M7dmflSBaZ6t1m86OShliin8YwjUULU0bGLyp8gwbLorJ8Ykn+eZ4+EAWPnVKsOQiGkgxAGi0lBKLUgxJndUe51aF+Z54jiLvb+Wq+/u75jnSZnsOUJRrpyfj41V7tEsM1lLA+nfU0SMqd60vK/W/Q3v4z8pREKK+PAuFWrrhkh5VIFwTkZ70Yym1KLUk1Z65lzrTGtJrZGGgRi0RK11skLCMlmxzWJro5rIeIzKjvY9darrdAqCiFcEbmSpbhU6DFbLOqRpdhxj71JqSYphVkEn9uiiJDRMFUAvsQM+9CP2ckuszFQ/Rce0sECllUmMuWeFrboVnz2X6OWvU0604aLh1NZylyvp4SRGNdJJ69F4aB5UjQspAanre4Bhgs6IPtlm/R7qB+9rxhMN70SHEBmGqNhaM6J5838Wb8V0/Lqf8H0/6z3/27w+KHgdHo6c78/Ybfds8pbtZuLh4aF7Vj8uhbvDkWmeeTwemMqCHWydMa7lhIqig//PsiQ95IL5oK28kKpPyArLk4fCyWljXCIXzzqXR8H7ZDdRSyK1IPZMUNgOAz/6+DmBwNffv4SyYHOx9WTLgz2AynE6ao4ZEzn7XEkH9DULyCEypIS0SpnnDiyX6UATYZ6OLGK4REjkbPqwFvpwBVDqwTRPHB4fzR639U2ah4Hd2RlEmOajtqeD3deo19cQs/oxiKOdTBnvV68l0lILErQkkYZKXLIDweoomoIw5gFCojQdwAtBqR2x0lxcbbwfQfGfZuB0qwUJgZw2PSuKMSIps9lsaRRCbByOWtYsJshX1ZI3FYJ1uVLf4MuyIG1hMKVBMe5gSLGTa0V4b624MH8dcbau81O75XV8m2YoYsHJAxfoAVFr7Z1H/TkPvLoKk2dm0mgt6ixQx26jBULxpghWmkfLsCyYm0O5luXroVKqZcZhDQYiRtCNJjeSFXhB1qDvGWTHvMS99o1zF+iAPv2/LZChhzDilZV9rc40ObFzdsb/yevfWCj+wFj2YU6qOfP2zWvGUd1P67LwxeefEwnc3Nwy3d3z5OqSp0+ecX93x2+++Q2Px4kmwhJEXUGDpYz2cPQUs0Bi5Ddn4ZZmzgBBAUMfcmakJELQDElAu5+me7TqopdKCkXoBGAJ0ahFfmyDUMg58PzZU4Zxxzfffcfd4aBET/wEValLn5IshSYJp93GlNV+J6vYO9qpuYuJMxLXecs0NN7VwqHUPuUmYQ8+BLx2iEaCnI4Tjw/3TGXuJUowXed89FIuMWx2zMuEWGrupbEGaCsGxa61+dqwQ8MzsNZoNpaeEAgJY9bbSRtMK2ddI5/+7WTKZoOFW9FRqylpk0O9uoS6TNAaw2a0skQDX0wZiYEchJENtU3kZdDZQbXQWHAVnAPMrUKIjdyDWlShsHeoff2bXbOXPVWEbOCyUyn6W4t0HLuXa7buEC/fVnKq/72WWcrx84auZ1CqDCkqzQo2HUsyimUlqNVKcF9TrZfGHRLAMrSkWuJWau/i+pzO7uiAqF4x2MFh1Ik+QyIotUXC2p3sFYqsJVy12ZseQ6ItnGZXr3is8QktQPufpSjNCTxw4zHPb5tHv5Mv2L+FQBTv5f7Nrw8KXj/6/b9Hfbjj8eGBsqijw1KUh1WWA1KOfPL57/L8+ad8+5vKP/nj3+dw98CLVze8vL/n3gS0XfCLZ1itb7a2FJaysLSqUiM0ddd0u/YaNGQjV5qFTIjGv3EgHy0ZxGQc2n2xjhMNotnO1KptfxHGFLjYDHx6fcWA8PpQOy1AHQqSBRbD8Vh1jSuzWykIda5kCVxvd3y03fF0u2O4eMbL27f8/HjDfZ3R2YQodmSEwyi+IAPTfGSajwpU58RSSrcjcZO8uRRitpmXAiILvQvmC+XkdfpfXgY0dJhFS6Zxw9riTYhDNJJkogFLaSrgNpDXs9x5mhhkJA9jb5IIrETfqBQD16mKSb9SGqwznAjRZDQSeHi4I3ZhejWYwHN3y0yt25iCZtWlFizFAv8dIXTTRqcs5JxwsbZv6NZaD9J9fVpgdjWBZq/6Odw+2ak1On6sdAkNDln0m94swCc7FIL510u3VIfag4r/v/X0aeY1p1ZSNmEomeuJPcsOYrGW0o79l6bGBXoAihkWaLLQGzmdj6L2P+6k6s9ML0XJv0tZKHXWz2wlbJNmILy8Xx2+twDfX4+eTa9X/cNfH5Z57UeePfmcZTrw9s2NeqYvjTev3vLx82ueXu548eYt3377llIXPnmy4+rsgpBHygvg4UFLAQKSgvoWFQX5io30arUyL4saAwrkaMQ+vaeEoC6XNLeLDQrUG7juNskhJTU1M3wiSl9KZiej5VItyj1bajEcq3FxuSEO1+SHDd+/ecVhnmhENtvBMIBm5UvTMrVVKoHFTmKkMRDZbnbk8ysufvQl4/6MV99+zXe3N7y+f+CxzozbDSkP2qOtTkH08vSggHQIlFrZbDaGaWCpvRvVCdVcJWpV8+D415TCyoULna1uZZDjgP7vzbh2wcjBdbGsIpCsRNYsTizYBAqFah3iIbjXllFajKeV00hNFQlmo4JlEjY4JEZoIYNsiaNyr8qyUMtMDUVdCoJny0ogRbSUHYaVX+VMeBdUK2bWWFolB59idVIGGmE1hoDk1JsZWNkcTzav/mlgtuFBwTJLl+Ilk2oJQswZd4vV52Z+b4Kxq0U7qpY5eka/SuHMlMYCt5dvDh3EFBDzy7OLXq+p7xVzdTDYxZHmah/+fSeJQAu+P2LPwoI1S0pVf/lKZakLRRYaBRG1C1oDz18vEX/IywuwD/nRD5MHzYV3tXBxtufJ8+fcvHzNUieePX3CdpcY5oVQbmn1ERkHDo+N490d27M9nzz9hE16zcPhgfu5MUnqhLWyFKayUEuhlMI8zyxVSxgxPMjWLeDdihPTtEofGQUQcsAVFQ5yaiZiJw30FN1T/+D/WDZ1tt2QSMTa+O7tW+6WmXleGIaRWhSETlF1j9lHWtXCIsJmyAzbPcP+nHx+zYMMbMcz2uaCcHbJeDjj/t3E8XgwPpKSNYVASIGlLByOBx1/ttkgy6y0AAy3ibqwlf2ui8U9sgQ9ASFYJnZSy5w2MOC9xev4h/98pFCK3q08oAqIFHtpr10+GwVnMq1WK61TVqwsDIlN3tJSZS6zTY1SWdU4ZPKg1tShqROrZz61FJb5SG2zed5rl0/F28EQGsXwckwnA0/15YRK7KASt1aKXurp4JIANAtIzZ6nr4lWVwY6qBBf55DqwVDrOtC1l5OOPVmp1qofNFaqWommzRHph7K/NMvzrqlnMOE0qcKi8slngDSoSWilvdfJjARPbACMa1ZND3pSNtrvaY4rSzAemekVpSF10UEndaGJdj3VnSOcBF2tvX97bf3267e/ZgWVNQl+2OvD5EHv7tldnnG4P7DZ77j+6BmyaMY0He4JecNHnwW2ez3FQk7c3z+yLIX7+3d8ejGw++Qjvr+ZeHsM3B0O3LxbONjAjHmemee5S4gkop2vplbTRPtaCNTQ+kkW9AhUyQFoym/sd2/xB7thmm0ombSD2IRVpI3pzoIwDolnl1csAsebt0zzzGbcMA4jx2lScbctJKl+uoNEzRzuH24pZebh7jXTu2uW44HHxxtqXQiG14B2Ivfne6ZpZppm5nrseF9tMzGn94fPWrtZZTnZHENrLz0c0+rwTe/nxvcWTbASzDNSMAcA7041LZujDPqneSDF4OBZ1IMjZjutleOk2kHzXIuJ/Xaj5XBdtAxvTTWqKZKiBq1aDfNpFXKCs4VlfkBkRqRog8AIoo6zhWhTsUUxzV6iWaCWoM+yltp3htI+ksEArfPXlEol3aesSls7jpby56COIi6ZimHNXn3zAdYk0Z9LeSDUaF09x7SkB6muxXUKncETDqgH40o6SVXXtsvjfBBHoC2ldzY9E1wbEvbf3sSKOmPevmr4mTYDQtMuckQpIBICSyvGXdMSMbJmboHUD83gKd8J9eIHvfy++f//wPTrg4LXq+9/xTh+yf7iihghjIkWGmPMjMM1SyvEfeajqz0vv/uG2mYunuw5Pkzsy8AyveNyd85wueNsA+XqnG+HyPdvhYfjkdvjzOycFMsSlEOjFxTdpI7kUkoleLOSEGmiEa1Jt4h2TMJvUZdrlNIxGwS8n4nhBynrU93vd2wOM7XCMk2cn12Sc6a0BREVqCew1ad+RcsC4XFhme6YHhOPt9+RQ+oEx7Jo9hZCZDdEYhKqTQqalwWD3tU9IqgurtTSrzPbSPdxGJnLgixzv74VQ/gbVkFQ+xfvnv21fL/Lbyq0SBElScaswapVzYBVnC7mg2+lSqu0ukAaDKuMbDZ7pvlgnTAtS8fNqBgaRtHIOgMTaeynR+YyqTi/HJEg5ADNXG1FGqXMkASJDUKy9RIsoIXe1a6lWMdataCY8wNolu6JQrMDUDdmfG/9iIj5+59kDgGbDyDGD7QGghidOiR1WKURpZp6wSoEK3Vn1+WmRMBxRBfNB23EWNnoh5djWv6MtZqQ/rP+6v76HYQyHttJbGmtWQkajf9n98U7jELnAzYLiIOPrOtwh4nQeT+j/6Gv9zO1Hxb6Pih4vXt3S/4+8rQcuZYrhnEk54FpXliWRhwyZxfnjFfw5bMz7l6/5XA/8XCcuXz+Ecu8YzNuODvbcQ08PB55cpF5uguM44a3d/f87Je/5NXDI/fVTNMI1gbW0yH0wjjgQHwvpQzPcAcGPVFTpxeIWYSUquQ5EdVgNcMVvFzAs5EA0ZwyYxgY8o5aHjk8HthsR1KMBlxWO7XEuqnJyohk2UjjfrpjN26J5gaRkk7YPhyPtHDD43TsQbiURYm+Yqd105JGRPqMQU/7mxnLrQ/fZVVruYOFwoYQ3O64l44GVFvK4tw4XUR+2lcQmwQ+ZMZxRIfF2mHhGtaeQbSOz+j8AoGYiXlnh43e7CY6uen8bMd21EnXrRSIMI4bttsz9sukWago3uIZoE+hUaa86ujUiaNQBdOpWsc0arahsx71uWbDilqt7+FW7mACWmKlnq2KwhjWCU/BrbLrX9tsfRPbc1D8LSIUa2b4WLNgcyaN79hNPUMvn5rZPQSPOCGspSX6880CagrBBOv+aUKfeeDUjtMq9ZQ6IqjVlfotutZT+jLqe8zer9aVBHz6+m1N6N/m9UNj3wcFr7/8y1/x4rsXXJyd8ZPf/5JPv/yY86cf8+TsgpubdzweHwllo7KCWpXNvN0yXBRKaTwsiYdlYVcGUoxcXu34/NMnXO0yt69vuBqv+OjsD7k5Lnz79o67x4X7x4nH48wSA5JE7WWiPsxg9iYxKkE0RQWB1Wp61MAVIiozgiaFWhrzPFFLMZM+tZ72BWOokKXqWibVZg4OMZHySKuVx+Oj+hG1xpizcoqwssbeqwpQzB2yVpZNYxg3NnQj6AQYVlV+GnQGYrFRbafTVmKISBRzRI1mGKhA/HGZNY3vweo0aL3/p6fm4WSB9SSi4x4udwKq2cokXfbdIsbqpGYbPcdsw22F1hYCJso1M7/NZiTETKqDPQs1Byy1Ms8zZ7s9aYi0mJjLTM4ju90ZpRxZysQ8H1V/2rCOqEt4BLFStR80zZ6hdwwD0CqNCk1pHNJxQVk16yF0naTvoICB95bVYXBFpyPgwX89EFTl4d1WwwDNbcNOSAsYZi0UsTLcDx3sHrlOUCVd3kCA9QAA3QdNvPxfqRxaZaw/4VOJFJG0Qw/pneEWpB+2yTqs1biBKSY244a0BGpTmQ80E35/+OtUH/m3fX0Ywz6OfP/dO+ZLuDh/RSkPnL98zWeff8IuRpAj8807lqmy3eyJRFpobHY7pttHCCO7sx3jZuDbr1/xXTnwT//pH/DJ7/yIi2dP+P77l1zvP+YPL5/w9u2DbuSl8NWvv+EvvvmWx2lCWmNuMzXQsypdtDqXcBhGcrbAFTPuJiBVKEX93stSjKfidrz6kDxhCYbpCEKTYHYwAIE8jMRRWMqk0SlqqZSsFIhoZue4y1zNvZXAsVRmmagN0zGONgg3drnMtNzbAFUtKXxEvNIKsslAdLMMw0bb1kuh1NaJt2vW5btScMzPgSH/VneB0c3rQc0WtOhZrbMJKrDApO89DlvUaFIJrjkPRjmo720YlcqYDYphQBoARaVHVdvux+ORs92Z4lMSyWlks9ki7VIdZIcjc10sAzHdohfXRrINoTLEsU+Yluboi9iwl3VYigv8h6xW3NEswJ2mQggr7uTZjmAmAwrmO+cJUP0iXrY1a6pYxiut6w59kbldlD4eDXbrwelB0HAt8eexBjcRreVaXKGRHgzCul57R/kERO+cME5xsDVgc3L9bamceFjpNTa1dSeITUhw2OG3D8V/PWzxbxu44AOD1z/7j/5Dbl+/4bvffM2vfvUNDzeXHB//ku8+/5jf+fSK8xQJacP8MBG2l8SzC/I48vTj53z0vPHiu5ecne9JQ2C/u+D7Fy+4O05cnu24enoFMbE9OycMW54+/5yHd7fc395wffUTfvL3f8z94wPHaeJf/vQXfHd7NNyrkbMuniFncho0PQ7BmPwYvlWZ58V0VmXtzpl7pj7cdZCALyLXylVp6n2PPsdhyASbUD2kUX9nXE3Yllap84qPhBDUzjkEnYbd1Ht+u92R00CMmcN8ZJl1nFmIkTHr5B9pgiRRZ4wQFUj1LpGcLoZmWVroHCWxaxmyXlezUnz1KneplZ/ydjq79lQ0S5JQCRKJrXRPrpRtEaPYkjtAqD+WdhDJWUekZdUfqinqTJVGltQ7i5MEhjyShmTWPkufEbndnrFbZuY6I7V07Ec7pBFCo7SF1HQYxDhsEPGMFXvGbdW7xtCzoFYqpQox2ySmOOCE04CSJpuTXaNhWdaVdohivf+wDne1Dd1W5n2zDFvXw/pz4mPmYM22wQ7Q00xvvY6TxKtjuL5uHPhQXHKxbNG6v3FVcDRpmC4MbzEIEHPsccxF7dh7hpBIyQ41NadDJ3r7QWlZ4f8fXh8UvK6ffcyXX/yIL774hJ/+2b/k/s1rAolvfv0N52FifHLF+fNL3t3e8/DuHVIL+fyC4+OGIPD8+YXe8Ahnl+dcfbwnppEQAsvjPWmzZbPb0Uhsz7Zszzdcf/yUmzdvqa9fs9tvOBwe+Qd/7/c5++4dR5mRtrAsCgIr7mJYjOikHLEOUynVRqkt/RTzkkYaRiwUgvGaotuouM2I6fQoC0P2dr9aR49p7Jq6YCJgaTY4oy80JdVmA7vVXSGTx4EYEqUKh+OkHTJtWbIOQhX7uWCTaYINEWl4J8rb6uAKhXUJeScQtLlwasME9BLQ/zV6i16M9xQCKgXRhZmzujIMMRKTMsDVStiqn+adTw2GtVabNG6gc4qkpjQKqUqEDSFoBjdujYoQCUFF72dn6qg7LzPzNOn4Mw9EvfxplLqQQ+Y0AwlggcOhAfH8QT3nDONzLV+0n+s22eF9CY1nZd2FwS0cTrIMzZRLv5+tVRylqqKZ6ZCzfp6m3UPxzxvW59YxWMed7DBF/MCRk8MLu6pgWdSKW2rQ4b3rFBuS7DjnKVzSmpGUjWrhbr3qjWdNj5OmgTZutIhep2e/j3x5SmApohc5/1avDwperSqQ+umPPubpswteffsdy/09d2/fkFJlziO39wfSdkctAXLg7u4tX3/9FefnF6QYubq+4vLqipgau3EkxC11EaY28O7+LfPSeDwciCnz8HAg5ZEYIjdvb/j88095+uSas80jl9ePSIoMuw339zd8/91LXr2+NTM+NZarouZ/qn1TQz8l2RWaD7PtD8+AbNCMqjkGtroxlFYIFELV7x1TVo8i6+gMw8CQMmWeiSFSUrGvZwZz0Uwp9Za8BAXQp2XiuCzMy2wM6MgYovKmSmXIgwY8A9obgTRkWghM03yCja04wik64rYyvpq8GYGuo45pBcuyNEPxE9cmgdegVs0psN+dEeNonT0t24IZ6A05aUNkaQZCi45gc1Jq1PdSaZIRag3H0pFkTR1HY7IpQIkqwnbcsRl37LdnHCbN3iRUbHtpc6dpcGxGYWmyXmSr1TZiVlNIgdAqMWXcQlqMDBs9OHkm4xvZSqucsnaCvUsXT4z8wknQkFWTWHrG5MeI2wyJ3o8IwzCyTLMC+sGeSTBdosFZAe1M9y55f9lzF+inkzte6H+wjltrZstjrwghqUB9WRYNLm2dqBRPrimGSA1VvfIa+rw5sdyW9wALW2erzU5An/Uqu/rbvz6M57U0Hh4qwyBsNxs+/fEXlGXm8u4jvvnlt9yWkefPnrGPATkceXicmI6NcdhDCdw93HF8PDAvE2lzzjCekdKBh7tH5mnmfH/Nq5dv+Ff/6mdcXFxxnGceHg9cXmz5+Pklr168YL6+4OxiTxg3CIHNfsezZxu++PxjvvvmFb/5+jtevX3LtBSqqKvpNE8g2jZeRDEvlwYFa/XrCWSnR9AsLJlDp08eQhpNqj6sSZiBbd4y5MwwjvaAwf3IN5uRAIzDwH6/Z5mXLsdxvOPu/p7H44Fq3lUxJwPJtaNVW9GFUUTLzaScoVq1AzdNk+I0KVKbLZtTzMkWUPOaxDtE/dvsm2vo2YFjVUHjAe5xFUyXql5OOm4u2PtKMGCbqOLtpp0ydcfQLA7Dh2h6j2PM5EFL7mLj3fwTpRiNebIgot3ZzbhlGXcaOI5rZhcDTl3HR25ZvYh3Vms1DDF6QKmUqjyx7Xan2Z5t2mowAtAbEj5l2i1nPOvRbm8lMrx3v33IxJoR09eV4EGW90rKsiysOclv4Vt2kEJDRemWIZ3gWB4s+u/0COV/Z9+rjRQfdqHBWY8Bj3nOe3w/E8T+jpQoVe9796v3v3tv/flBqYfhCYX4tObtn79f6w98feDos3uOByhTpEzW7coj5598wefpnNtXt8j+CRdX55xFYX448vbNDbdvb3i8u2OzPWMpE7UGxpTY70YFc9vC48Mb7u4nnn50wT/5p3/M3cMdDdjsR+7evOZsP5Jy5v7unvu7B6ZphhCZ54XNfs/5xZ6ri8zl3/+E4/SUX/7Vr3n55pbbo3KgVN4gLNJs2rVadfhcOcd+YkBFySkhLVJqNHcGTcolmF9Zq1pylLmn/EKjzEdCMP/8hI0cE6RtmOdJSxMzWSyt8XB85LAcaQZs56BWyq1WnW49DGof3IQkyQJVZlpmHo/aiVOybOt425p3OeK1ZgL6Vd+YvsD8gFRNn3vnJ8NlfB6BssQD0/xITY2UNyiOkhAKzSRTOQ9st1udIh60hR+DUQbaemA4eXgYMrEpYbSURb9/GK2D6BIiFSYPw4YihU3bUspE97cKVg5JZamzbnTR0jS0RE4RSQ3BLHOs89gE5hlr8mgptw5lwQ6FhoT1vsQTYDqloHMJDNvqd9gDjgV+cWJrx5zWQ8TLsIZ5ufcsmk4XsgL0xCpn3ZcCyjHzfyd0qY2Lt/E10NYhJR6YvCmwZqcJdPbye5Y+gZNg6b/bqTonX/OXjUYBid1Vxj4d7w0b/lsmYB+GeV2NbDZntDRSy6ylS6tMNSP7a57+zlNu724pj4u28lvk6vqa66trjtOkXuvTwjTNHF+9Y3lc+OjjjwhSeXq95/Lygs1uz8P9yPVxx+ubOx4PEyKJaVpgWmjAy5c3PD7cE9AWPAhleqDVBWmV508+5sfPP+bj8z23DxOvbt/x5t09d4cJmTT7WVq1ce6ZGLBZiCrwdQ1cs1lTzYIVlnL3kstyZKExLTNLFUJTO+YSA1GaEf109Nnj4WgbMvUg4f80sak+ootKnceFlAdomSFHS9UrFGGpC8f5QGmFzvE2IPkUddByQ3lnzUwa318vPXTZCax/WVojB6UVRDyAxV52tFagaNlDUp2jkFY8KWY2o1oAK1tbhckxJDbu6NAUrA5DYLvdGnhdSeOGQOA4TyylkFLm7OzCCLyT2gM1FSyXEql1US5ZEIMKFqT6/EUt0Xr2Y/bijg0pjq2DOnwYSzTaCyFQjEYTY7RJ0E1NCaJ3uo2R33S+glJmZMV1TuqnbqWjp8kJyTQwxIFaF32elsWKv4GY04j5yLXm1khrZtTQTLcPV/ayFbqczN8nRp3Bui4Te2pN+tpcBd8WbIxvGIKvKzGi8Trw5bdjUM+zzBpnPVLte9e6kt9elT/k9UHBKyVhux+oec8yKWFvt90zN+H4WAjbkYvL636hJQwI2uEb93sSsENZ4cwL3//m13z1F7+AVnic7jn/B39EaIG72wfmpbLb7Nhuz7gVTW3v7u+oTXjy9JqnTy45PNyTUuDZ9SU5J5Zl5vbmDQ93b5ECZ9sN10+u+PTzT5hK4XGp3B8K3/zmO968fsM0V4ZdIA/FMK5AEbXSSUFZ/DU0RtPA6QlqGrngJ1GDCMfjgVJ1buBQErvNSG6BzbBlHAeOxyOH45EYEikPCmBHgRxJMqjEyLqISkRVh1ViZLfdsMxHDZBFS+EmzaxLFCh3XWboq+L0hJRul9NUY7Pq3foKM14UYqcllKB4jns2hRygenHZCMHwsCAgyci3XjYo361nMaKCegEGM/jrNANpZKzx1dSPaynNdH8BpFFL6UNdhjQSN4EcIwd5tG6dzwqkd76k2SSkvNGMD+mWzLgcJqrPGKJZmGZaOiw1BeVoDVkNKH0QTHRajJFUpZr4ysqmGJLhbcrVOAW39V60no1pV1Ank8cYSSQ7zPQgFTkJhHqWdrzLMazVRSUSrfPY1ugAstI93svC3qNP2Lc2k90ZEfjUNmn9f5vKZCWhBsoA3b/rRKxpD1/fZzUYXcPUiTty/+IPC2IfFLzubx7J+YHN9Z7tdsN0nDjOCyFFxo05LsSoADOReVpYSkVS4mgjqGKMLPURaZWL508Y8zOkFL75+jf8/Be/4kdffsH97S1LKWx2O84vr9j/6DOOx8L2/JrXr1/pUI2qPklDjtw/3DPPKO18oQABAABJREFUwjQVjocFZOaTZ884OzdeDoFhM7IXuN4HPru+5OWLV7x7mJC0kFIjDyOtBe4fJm7f3VOaZlS1ChH1L2uhQcrUZiJpO5GP08S0LBSphBiYl4qkQJsWxnlh33ZM88y0qEtDlkJbhGEYGcaROs20pnwx5VUJ42ajk4aK2EIWjrNO2JnLYjPyMDzBAipYIHlv3RA4daVdM8fVIkj/NYh67gcJPZPz2YgRa41HLftaKEboXLWUwbAUb9krbcWzsYbEQC2VkBopZs2SaMSyEENkuxnYbbc6gTua9xSYjY1aR++2e2KIHKeDHZ76DOb5AKFZUPWL0klHWdSmplWdBZrjoJu3qPe/4pxiHWX9tNEMHRHNLFNYhdjSKsTYGzWe1bp5vDvqaiOidWa+lo1JDwfb306D8SwkhEjPDU3LielvPbs55U91KKAHNGf+N3z8GuJ8rNADhVOA+n+j+FWMJ1mbNao6kdpua+/sxwQG1AcPYPa0e1PIDvffzsyioE2kEySMdTX+oNcHBa83r2/Yn1/Bwz277c6mZmVKq+y2G+ZFiZuKT4wauGogpoFWGssycbi/IYfAZtSWfxi2FGl8/uMfU0Q7Pvvdjru7O6bjZAGoMp4NbPc79mc7cgoc7x949eIFzz9+xtn1GYejAJmbt2/55te/4dtXt3zUdjrrsDY2240ClYtADnzy+UcMr+9pZaLUxUbJC2EbmI8LtzfvOLvYMeTIx9OO6dk1N3e33B1mDlX5MdnKjKONNe84hUhPpxca98dHs37WDEGDmNn6moV0rXUdLFsK7p5Ra0UWIaTItBwhGOcMugOAg8QrQ8AQCD946S7+il3YqKw+ytFb/UapiCGCGMMfxXnUmz7S0OAl7ijBagDolAw1z0s6MQnl0hHVsqdREUm4bVAgQG2UsMBmwK2Ul2IdWxrzPBvmlkhpIMZKTlrKtyhK1q3VDPF0SlUwP6pW0enaIWnA0SPJtOc+1bqajnBAR5zFnq0AzMuM+7qD41j67KvLd6IelK4VPfXTikFQfLytJaytFY8eLqKOMSnObtO2OnO/nWTT3jg46QjCGsD8d8eI+cvrAnF6hxsTuhjcX64uSOYdp0vDOvEpGbmX7o0Xg5Kz3d3k9OVBUb33LVIHTpxOAuone5LVWbz7ofHrg4LX1998CyHx/LNAuGpq/zFE6tKYjgeaOQaop/nAuNmwLBMhwO5sy9XlOfcpsjw+cnVxDkHdIlIYyJsNJSfKceFq3PHs4+fMSyWkTE3C8XiglsB+s9ULPzvn8qrQJJKHDc/OL6g1cHF5wUfPnvL1X/2G6eGBu4cHzi7OefbkOfM8cS8H4pBZmk5FvtpfMle14Hny9BkhJG5uH7l/eCAOClD/weMRGTK308TPv/qKX3z1NTe394Q08HCYaDEztWrFlNgGVQfYpS0szYa4hsTcGo1CrJh+SPVoBJ0GlOJAaK1b4PiQ22UuPWgptnXav7EMoSOi70OnwUrFHsD613XAgJNdfcy62gpbJwxzbPCuUqMz5ktdcLImIZq2cUOIkaUU8jiSEur9Xiq02q2HosA4mFebNU8084HNdkOYJyowxExdBpayILUyjDuWKmTRe1ObMJoYfJ6OqM+acqxqqZ2OEEz6om420jebTs6JVtpIn9sYrQNNDDqH0dxMm81ocK1fFP1vQ9HIKfZTRC1p9KXW0kP3HIuyblw9BNo6nKM/tdAPJG/GGIrVg5YTVN0dozP8fyuoKZ63fi5cdB7eL0e13G62UkLvYnpOVVvpWGJES/eKDvpQ4M0SUPGK0q6mH+xtveYTjWc037APQb0+KHh9+buf8nD7wE//7M/5yR/8Pa6eXrHbXbDbnXNz84rj49Hm0ul0oJwzZZ4IouRI4sDFxTmv7u+oZSFENQM8O9uzv7qg7baUx5m7b17w7uGO7W6vnBoptKVyvD+Q40QcBpa6sDsbePnqJY/LIx89/5zLyyds9yO7sw27/Za723dMhyOHh3um6UgtlWGz4XGeGTcbnny0ISwzH+0vmeaZ4/HA1eU1nz1/yv3ZjqkemI4PfPzZM47TxE+++Jg/+uJjXv/RH/Dti9ccK7y5ueXNzTtevH3Lu8cD7+4PPB4nchXOdhuuz89gaYxh4NnlJa8eb/jlq9fMVr2pttI2SYhMVYcWNPMHF9toalNCXzQEXyu9aPtr7R5570vBSkMs5cImUTsA1tA2vgXgCkNceT4+DVvzOA2uOWipW0tDIiRRl9IhjcZGB0TYDANBKpMpGwqF3W5vI82aGRMmDQ61Ms9HBJimow47McrDXBZCjAzjzjISK21qgUE3X6g6EKO2grfcQrRyiGDGeprVBRHVHoamzZkmpHw63TmaE4W5Pljg9a3sVBtNjJR6468UA6WsJfoq7rZnIZ4z629Td1fpeJhzDx3XSinrMz+ZDO7v6wEMAqcOsatI2iHyaJiWWQDZZ5KTa/LBx5p50gN9sFZ8MCys1bWJ9d6RGHQ9+RiYv/5Z3luheInZGyl/Z1SJKRHawLs3r/nNX31Fk0+RmLi4fsLFxZ7dbsM8zxymI8fpnun1I/ev3vJwODIMG46PB64vz2l14e72hpQj45i5v72nfv0dwzCSSBwPB27u77h+AmeXmfMnl5zvz7iVV7x88YowJDZnW7ZnWy7LBff3B16//I5W1RdLEsRtYs+eYZMYBjg8PpCGgQVo08zj48Tl5SXjdk+TRhog1carN285HlWmc/3kgrIsfPvie3abLWPKZITLIXP5xefEYdBBI8vMV19/zW5/zuu3b3nx+g3UmX/2j/+QHz1/Slkmar3n2fUFb28m/qc/+46HAv/y11/zV2/uuakTi8wMLVOrOXIFWOpy0prWo0xTdRUKq9OCUQROQFI5KTEainmvHiiGgLWggLt9Tdvo/hOsnbeo4nZiwCzRjUcFxRI+NYFUI78WI9KMjNrW99qMynk7znMHrvtmj9Z5C3S75nme1VnXN5N132pZiEknKNWayXFgro1AIucRESGnZiJjK+m8u4fahQfokhwIpDiSfdxbhDFndWcwcqrE2DWV0RoRGOdtvemafRqi8B6g/v5LA5IHGb0PjTUTcmmOsArL1SkYWf25HN+C9wNEsvFzGsrs4YtCT2QlK9daCDH7d9jva/3PdqLl9XhTTesbrNMTrSPuWX/HXS04Ozvs5J3+eujyoGj3pBQ+6PVBwUtq5Gw3kkLhr37xS/K2stlGUjsy5HPiuEckEmLmbMjkUmCb2W53tBCZGtxM91ycXXB3f89+P7LPG3Iaeby7Y36443CYGMaBq6srEOHx4Q4ZAhcXFzz5/DPy+SXzfOT4eKBWuLq+4vx8z9vXb/j1V3/B2e6MIemGe/PqDXkc+eLLH9NE7Wuvri7YnW15++aOTR7ZDiPzfOTlq1d89NFH5GFP44ZxVMubLIHDw4Hd/oz7+3e8+P4FH3/8KdfXZ7TQuL7+CEE4341M88LV2YY//MkXDDGQjo3lvrEZt5xd7zjOE+/eLvzRF5/w+Y/O+E/q7/E//vR7/t//60/51ZsbHh4XKoGj4V9BkrG+NTNLPkxXnL4QQYzeYEvE5SQr/kVP++0ghZPNod/kp5+VTEHpkOpcf1KihJVtrSl+ZZFKkqy8rRAJUpFWzNpmUFWAKG2BYURQH/zj8bBuXsnsdtu+mNXzKjIMA/NcLMgZJ8/L1wjjODKkgZQ3LMuB2mayOYnIoltIfe0Vw1KHWL0RzrnSRoNZh6MBSbNBlYlV0RkHtenE9GQqDBdLR/PzjzGY55WSSJ3kfuqn5eWRBpilb+D38mPLhmp13mFgzbW8ZHwf3+pBzkp4d5noObm9p54lmn7/tukhhC5x++1XCEHnWDo6YQ2QFDLLMtsouTV78zL0tGz18/N/O6/yzPFDisYPDF6fffljHt6+5ssff8lf/vLX/MWff0NA+PLHX7DfDeS0QyQyHWYeysTFbuT6o2uGIbFMhbvbB5ZUeXh44DhX9hcZUuZxLtSQuHpyzkefjgpmeloeA29ffE99eGTYnnP58UcEEu++ecn9g84tfPP6LWdnO54/3zEfJ872O2gL5+eJP//pX/J4PPKHf/yHWh5IpSyPXF3sOd/v1JuKcyqRh7sHgjSeXj+BULk43xA+fc67NzdcXmw5O9vy/OkVh8PMzdtbvvnuBV/+zufkzcDzj56QYua7F6959+4dLUUeHo7U3Z593vHNV7eUIDx9+pyzCrev77l+es5//I9/lz/83ae8FOHrr2/5lz/9K755/Zab24kSIoTCnBo1KuNeTqgOnt73SGWbbxVbQ+cE6CqE8NdPRf13Me1kIIoLSuw91YdGCbwdLK69nGhtoRQh55EEiKgvVwkzMo7KyI+JnBKlJSX9SkObdpHaFu4e7ojpiu1Gs/eYtFuXU6TVaFbKdDNAaUE5ejGorGbJTPOj3hMl7nE8egfMeEpGBRlSRvow9oCPchpytuBlOsekA3zV+tpdUDWwOwkV/32OMTUBV2kYCn3qr6WBqXafLX+dBiJ/VO4I4WoHnTvpU8Fbz7Q8AFmiaPiRZb1JP4dgmXWAGAbj5anZpGZza5fytBRVAF16INLPljT7FIU4hPd8iA1bdYxOS20fwXd6jafX/rdxmfig4LX7+DlPPvmI+fGBJ59+wb/47/5n/tW/+pqnn37Gsx9fE3ZXbEJiPNvz5sV3UBshZg6PE/PjA0Ms7C4G9rsrpjJwfX3JdrNlmRY2ZQYKxZwTdtsNtShH7HLYsLy54W19yyPCxUcfc/bsI27u7oHAZrsnph1XV9dwCe9u3nD37p7WBj770Rc0ifzsZ7/gyZNr8tA4PLxTcL3cc3Z2SRw2XD3Zst9HchCePVPtJaGw3Yy82WdevLyl5oUnHz/ls3EgpYH9z7bcPx65fzzy/Xev+OTjT4gtsR92arTHPWUMLCPMQySlkbOLS3Y58fJl4TdvHlnKQhhHzjP849/9jH/0e7/H/VT5xV99y69+/WtevHnDtze3PJRZbVi85AonD/wkNiUJJAtwBaHYgRg8lQ/rAvKlhigW07tCJsb241L8d/xWI8ABVzEArtZIipmUGk5bWJZZ2e1iA1KSDkWpVbWiTRopaEb2+PhITontdsuyzIzDYMRY1Y0Wm1zUWu0AeQpmgzoKhGos/ZE4Rcpin0/cl76ZQ0Yk5kCZKwnjmwUI1ozAshcR06BW6QNsvbkRiaa2qlZdBSuf0E3bVkcRL4s8KHaSqWe0rRd5/fU+0K0pj488W8s91e5690//bg2+7lnmYD7xfTlRjKmbE6xl4xpIvQGgT1jXgcMUVapRdvQeR+gB6v2X53+wNozWz/Bv8/qg4EWAtN8wDJFPrq75968v+e//+X/Ft3/1mo+/LGx3KmkZLi8Yc+bx9hWtFWLTdvmnP/qSsMmM2z1TtXl3YSAOMG4C81F0wGtMLCTG3Y7leGT/0QibLe3ukTe/+obp5sDHn3zM+eUZpSa253tKiZTQiCly8fQJ437LPB/5dL9FpPHi+5d8/fU3vH79iuvLJ8zTI59//hHDuCO7rjCqePv23a123kJluxnJY+L66Tlvbu9IOfHxkyfc371jszvj1c3Ew+PC02fPKC2wVOE4LezPd2z3ewOYB4a84fgw8Zuvv+Ppk0uG3RkhbxkITHUm5sjb20euL7d8fHnG03/we/zH/+wfstSFn/7s1/zqq+/55a9+zcv7R94djzzUI7MtgxRVtJ3iSigkZnV3aGYYZ8HqtHsUBdbmWC8s7V+D4/p6ujYhokBvP83X1U6LQF1UWRCDcv2iIG1hnlR03CT3Ll0O6yiyUhbFPGrhcHjkbH9uG05F7VWUqjHPk3Y7UbZ7SOrsSWvE5q3+QgiJcdyyr43j9Kgd2whU5XU1fGCLlb82vTwlpQjEnMw3zYcjY36BAnnlQwXUf39ZFpzV7qTONalShYWXUdVtpoO+T3ILm/+N7MM7do5tYYEjdasn6YmxH2bv42za4atNlBAdsKAfe6w0qpY2HZoTfS3UdLzM8NPW1NCzNRZZWFqxrq3nXW096Dg5MPVNDWuVDm3A/1aw++GvD/Ow/+VvCJ9/xObijBADZ08v+T/+n/4DfvY//Bn/4v/53/P02RXPP/6Iqx99Tjo7Y7h+gswTmcBAZnd5ycs3t5TS2J5vWcpMy4Htbkeb7rm7veewVLb7PZIil0+esju7RKKQhkue7maGh0duH+549fIFacgc56OObM8bxahSokjTeZAh83iojHnk6Uefsdud88nzj3j5/Wt+/tVr7U5ujzDB2cVea/ga+M2vvufd8cjv/8kfs0yN6/NzjrcvaDVRlsjXX72mlcqyZJ49ec7F+QSxIaGxv9oynmdev7ohj1sdXLA0LnZnLPczZSpKh4iwvzrn/l4Ju5vNBsLIcnjkJ3/vR8Qo3D8UjkvgP/l3/4TxP/gTbu8eePP6jm9fvOZf/fxrvn3xivv5yOZsx2GpfPfqNdNSOTZjYRCIVOYgiJnWiS0yW0/aLetdR+flWGnY+WS64JK94zoxWheqdL1dNWqMemrRRmKyCT21IVIIKZGyyoaCOj5rB6tWtYkJAE29+Ze5ZwOlrMheMWymhUBFy8kQIyllcla8KKcNZ2c6a3JeDjSaDm1taEetqdjbh6PWVklm/lhMkuQYUgrRJhgZjcCkOp4VDcNgA1LaSTCi3+PmjRCzRZLWutOqiHXwmvTuqWdOSs1YMzj/uo+WCzi+Fd4LMIE1EPUDyaAALSEbimhKD6raE4mr6BxdRCL+jNZScikzUz1SxcnaEZcIrbCV5Viiufl7cTlYlvbhleJ7rw8KXndv3rLdZLbn52QSMcP2i+f86fbf4Rd//jOOj/e8eVk4Lo9cf/F7nD9/QksjRQYkHyEnzi4sZUzQJBJSgzyQ2sinn38GEvj++++1A1kqV0+fkDcqn6k0JAq73YalFeaysCyV4/zIkI4sx0xr8PTZp+z353YiJnIa2Gy3XFxeET/9lLPzF3z+5e/x7uYNr1/fEBK8fXvD0+tLLs+UJ7YXYToWjoeJeaqM+YzdLvHyu++42O24vr7i8hqkVcZhz3ffvebt2zvyoMxxqs4N3O/OSCTGcWD46AllmVkOR3bbHaEIT66eIhHOzjbcvXvg1cPEm5sHnjw549lHH3H/OGmQr/Dp82f8yR99DrLw9Vc/5vFeSLsNcYw8HIWvvnrBt9+84T/77/5nvr+74+buXgvDkGhBbJuKBTZDKiyj0q+Ym0BPqAwrQlbRcVi7Tf7fghiHUk3pyjJT8kRNVvalwBByB3wxT3WsQZBCVIttrDMqsCyzljRm9brUqmO93OvvvXIXBH2PkIRxE6itEFpit9PMM0gx5oR62TsNQcF3DYalFG0C5KFnBohRBXCIS0zKdeqjdsJA713A0wPCCy97eTLihXsIauFUa884Q1jB/lMcyp+L1sHxxGveAqsFofcE4P0PtUvXxo1PVApG1I32bL3ktN/VTkalBfT+G5kca3yYCrJL1Dy1b9Anrr8XvRySgA/F6N97fVDw+uTHX9KWR+5u3vLRJ5+Q86BOCdvEJ3/wJRdnO/abgTfffMXNr37GdvNHxMunpHRG3lfq9MAubyjLkcBMiInHwyPv3r4hB9hsRsb9GU+++Izt6zfc3bzl9u3E5dWeq/ML4jZw/uySWYTpfuL29RtCgrrbsN9u2IyZ779/yas33/PkyRNigpwDQwzkJKTxHOLAp797SasLb7/fcfvyBa/fvuXt7S1vX7/l6fUlT59c0xiZpzvO92fM08QsB4acuHxyjSyF+2niWI6c7Tc8fXbFMgvHx5m3N7dstns+evbcuoMwjsI4BK5/9ITdJnN7c8vjY0XYEeLIeL5DZObZ02c8ffKchnAcE9vza3YXDbm7o9XGw7Ex3C/st5nt2QWPxweeXD/h9u0jm6XxH/47f8jNTx74o3/wE/7yN9/xn/2//kde39xye3fL3ePEHH2UeqAYoNqNCUNQKQvBppjrhvWMg6DE0hAD243aAE1H5c45eVZwqVKj1FklOxIYSCwsEE1m06yx3ho5Kr8nAZthZJMGbJYQKSUOx5l5Wd1TIZDzYLiXD/jItBZ06EaKSKrUlihtgajk32l6QNASLGUPQLVnJIL0zMJngAoqhNa917qdUTJR9mlQ8c3emuNHjhut+FTD8cNV7uMibt/HTUQlWtZYaUHfs5TlPUoErC6np+RSPRtalwn5q8t9uk2R0yzM3FLE3ILXeKL4XkA1ncHmf0TD/rCjTmke9gN9BYDz1db4scKm/ztELj4weD3cv+Pu5g23P/8lf/yP/iFf/O7vaIdmNzJPibs3b7mbC7/6y6948d131CY8/Z0/4PLpM/JmQx2uoAnHh1cghdQSdQ68Ozzw5t07vvjRlxAiuyfP2G6v2O2fcfPuNW9e3DJ/+4rz63Oe/f3fYdhteXolXCfhzeu3vHr3wOMywfkZ10+uzITtyDgMpBSIsjA9PHJoW8aLSzZDJUjg6Scfcf10z4/bF3zzzbf8/Od/wVwrF5dXvH71ksNh4uryS10wceDu7p55XmgErjZ72jLzcL/wOh7YnSX+9B/9PUop3N49kIaR+bFwuL/jYjMwjpln1zvOzrZcX428e9d4d9uYqzDkQMw7zs8uIQRuH+959/jIVF/z/OOnPH3+nNs3t5S58Oqu0G4XhuGCZz/+ESKBd/PEchTkZeU4CZf7M/70J7/Lk4s9v/7+O776zQv+P//TT3l9fKSIdvAmgvnS28aNp2UMCqRY6emWQQiUZebQGovZH9eqZFBtBgSdexi021TaQqwTKW8I2IDhWbMJt7nRck87mdRKChunJqEdMG0ASHVWvBFpbaL3kPc6FLUqOTonFb5XhHk6KqC/F6TBPE0MOdFSpMxHSsG4bAMY56xnLGIeabX28jk58dayneBuu0Ftr8HwqBMcyOU5tarHWP8by34VcDfOlLHMa61GzvYun/TPJaKuuvWEEKuHkDVZTgKp2xIVI1B5J9KbF78dDIMVgKsHRFibPbLSQ1JKpJqQVt7DtLSMjWoBLk59WAO4B2/rA/3bxq4PC175InMhT/jlz/6K//b1f0P4Pzcunl1qhhEhh8y7+7fcv7vh5fff883//T/n+ec/5d//v/wzPvqdH5F2lzoLkUIKkIaRsI8sVwvz8UhMiTEPpJAZry4JcUfd7YnlGeH2Ld/94ue8+M0LNleXPH/ylNpm3r15TX2YuH888CZEGgrkQuSzzz7X7lETHg9HStqyWe7ZbTJRMnk30kLk8bCwtMTl1Wfcv33HX/7Ft5ydZc4vzrl/eKClwOX1E2JKzPOBN29vubq8Vh/71oBCzpWLy4Gct1w9VSeJHAbuXr3lePMSRJhubshtr9rPVkmxcLx/gHTB9dUnOv4sqrYup8zxcODt67cMz59zdrFjmhaWukqk3ry7Z14m8uWe8TqxLHoOTo8zyzTx9PyCTUh8fv0Jec78+uVLbu/f8erwyOvpCEGzAaKBqZZheVdSsY6kiIaZ/gUa0nQOgJ/A0ZFlUc+unDJRVmsh1RlqCZGylo9uoa1guGZ5ZVlYjITqHmWgvzfHQMWdL3RD5KQOETEmxjHYQFlTBAQI455pmRganO2tPA6qJ10EWlWLIqUbWMcxJt2o1Ybm5owsKt7W4SBCSE7wDEawzd054n25zsrJilFxs2Cl6GnHzZsPel8SXSDkWFSnp2gp6hmffZMFF2+2hP49x+NR7cPj2hypZovkX3O5EjZsBQnde0wzL+uMmiX4UmaqFLoNNqcxSJCToEUH5vU7V4to6T/7b/P6oOC13N/z4y9+h6vz/4i/+OnP+atf/JJ/9ORPWWplHLYMT8+5fLLnyWfnfPkHX/C//n9/wa9+8ZfcP97xx//eP+KP/+TvsxsS5fYdBylcXF5SauVsf8b19RMeHu7YX1ywPD4QtzqT7uzsjLLAkOFHZ3/Cq9cvefX9K77+9XdsNiOXl2c8/eSc/fFR5RjDFhkHytJoKZGHLcNmw+5ZYpoL3794wYu7B1qtXHz0jCFlNuPI5eU1V5eXpN9TcPhwPEKKbLdbtegdE599+oyL/YbD/Ts2uUCCVnQU229+9R2/+cU3fPHZjzgeHohRSJuBTGQokcPDgZty4N3tkWG/JeaR4/HImCLz4yOvvv2O42Fmf3HFZr/rdr4P7+75dlap0LgZqUV4CAce7x8AFbjPyx1lmdlvd3z89DnlMjBNC7dvbtgMF/xot+PHn3/BZruQt8L/47/8n/m//fP/lu9vb1mo5GFkKQuL+Nw+rLSJ7I22UBcbWd8bVT4hWQHZlAZbsO3/R9t/PluWXued4O812x57/b3pqwpVhUIBoDciJaqpltTq6Oj5G+dTR0xMTPdMz7RCrim1RDUdCIIwBMqmN9cef7Z7zXx4331uFimJKDW4gYrKyrx5zD5nr73Wsx6zK1ohwUnhvAXbIVSC6bpgCRR5X8KDiFo6ncRNs3UYB50Jlt04T5JqhI08r7fIod4HblqaZrSi5y5BmqT4hMAk94HhL1FU9RYtFTIRdE3cAqKilXPwT0ukCvF41sZw3/AulZS0bRtwVK15Oyu0T+G+7ZJuR6bAx0put3lSgLM7cX549JBE5ACVJLejpHd/A7Tvj74TFbHj7V0v+ucNRpiGXqjdP0Z8VeHm40J3GAD/gHsFkXootLZ3pvWBz9Xz4BBE95G+iYxdorO3BS1uLfvj/2qx+uvH1ypew2KMSFOOH+6RDUpevnqBsY4kyygHQ3xlaNstaTng3rtjTu484vFnz/jjP/5z/sO/+kMWr17z8P4Ro+kekzsneBUMC3WScpQcs1isaKqGTCRUdoXWeQBGjeP65WtGw4LTRw84efSIrrY8f/Kcy+UcP5B4PWA4HqDznBpDORii0xDPZZwPG0tnOc0ScpVimpbPv3wMxpFpxaCQYLakyZDhdJ/heBgCNr2nqStWixrvLXvTCQcHe6yWc06ODxE+aNiKwYTPP3vCX332nP39EUpoxuM9Dg9znDE0KFyWIkkonSbtAKXQEnKdobICaSXr6xnr+QLnHeO9CW3X8vrVG1Sa8fCdR0gFy5s15bCk2my5vrpikEsuX70OeMYHmsnBITrL2NQVd87OyLIMcxOcM0bDnN/99V/n8y/Pufnxj4IzRWtCIZEi6Cr97YhWbzfxi9ePjj6OSnDbPQRgN6TvSG6xnjAqapIgChYglMT5kKatU93PMjtg3BgDUoXi5TwuDjIihgMH3ETsugEdR1qJI0sUzkfTQOeCOYAGYlJ3VpQgg8RI64TcOZq6DuqFCLL3KJFSKjhLxxL9dgEJ3ZSNGJDYBcPSY2SRIxUMDgNe5L3feWT1eKJzPi4sNN6DVqH4u95hpF8qxJGtT6z23u4i8frjlrXyVe1j+JT8V9wdApXDBuqLDBbZxM/wVkAdJUHeh/EwmnDuuGo+LtD6r8HupQQMU/xfXSX+HMfXKl6j43tko5KuqRGJ5O6jB6RFjvPghIJcIlSBNBIlUvRA8345ZLA34fWzl7x6/orR+JCzjx6RjAZhFElsiA5vW8YkmLbDdoZEKazdRp6SoZxOcVWF2XYkeyN0Knjw8YdUm4ZqtaHrDMOjQ4SEdnHNdlMxSnJcY1nc3JAPclrb0HWObbfBW0dZ5NA6UunZHydM8iFPHl/h8wm283SuonOWPM/Z29un6RqkSjg5O6ParrGGyMo2iDTh6M4ZTggODvdxVvL0yUtuFhnLbcf73/yA0UizuFmwWsxJlKDMUvZGU5zzbJcrVJqzf3pE3XZMRkNmsxvaumY0KsnKnOEkQaoE6xOaZos3mvJgj2GhyYZDnj59xsZbSgFJlnF4dsKbqwvysgQExiRsrsA4zbsP7/Hpsxe8Wd7sqCU9Z0eJ4L7wtqyjP27hnDBaBnJmLGPO41Xghe2wYhHHHB/KUMgB8LStR6swloWLtA8GtjEgw2I9dNbF1xK2Xc72NjwEczsFeEeiEpCCpjNxCQDOBNdcrTRWJ2EzKYkBtpYkTYN+UtiAhUVA2uNjUEq48LUK/LSdbVHUJoZxMLzN3k/+tjPqrWn6VPNddWG3vX1r/OsxqHAefLS3DjZAMvLDwmOGh+kxr76Q91tNuH0du2T0/jkgypvcjv8liWTUKEPb2duI3oU2FMxAGta3zxHxKx9xNtGP0bteMhgsfpUj8Ys9vl56kJZYAdVyRZIlZKMhEOxPrHPoPCMpxsEzvguAa5rD/XcecHbvDncv5xzefcjo7Djo9ayBzoI1SBSiC6k/SZIg413be0dW5iTjEb7uaE0DBEM4iWQ0GjAaT/BekKQZHkfe1Fy9fEGWlmihSZXCty22bWnblizLKMohd85OED54gyXCkSSa6aHAIFhvK7JBhukM19c3YfQcFqRlzsHJIVkiaddrZrMFra0pRxN0VjIajymLlMP9Y/YOj7iZXTF/8oKf/fin3L93Rl1VCKGot1sO9sZIsSUthyw2Fan1nB4ecrA/5WT/gGdffMHnn35GmaVUTcOTJ08ZT/dIkgnFZEzbdngBq7ojS3LOHjzEestsPkdJxXq9Yl2vKSYZ+4eH6GTCdrNlu17xq7/0XVJd8q/+4x/yxdVF6IacDxu72GH1/B7x1nfyraFj973opScuhBwG4D2OHVKKQMjERxA3jj9A03Z4RNAMyt5HLIxP4Z8AtFvn8MZGSUtwPO0La54lqFhMbBu9xkQUliMQwtO0JuBgSsfwkJA5mWQ5znnqZhvcUL0IqeRJgjFd6KjErUSnB8v7sTB8P9VbBegWxwqFJvpjRSud0MVE+x98pBMEzy76G4Ug2AlFpn//uL1U6O2jd7QIhSp2wNyy+29fS/+RuiAyf1v87cLGObwq8ZWOul88CBmssU30GAvvW4Dv7bL7L0JAMAP21t/l+sXCrRbzF3V8veJlDO1ixeL8kuN37pEWGcJLfB3etPEWoRQiyxBK463F2g4nFXKQc3fvECsUnbMBwxAapAaXoWRJNpqwms/ZrBZ405IXOYlIkE6CStDTEmFapOvIEoc1LvJ/QMgkeokpynJI7hXbyxmD/QkyCWNMLnKSJCXLMvamE4rpCJ8ldFXFer7AOMiODxjlA6aRGb1eLpndzOhsQzka4FA4n2KM4OZyxXK1RuUZDkc5GHJ4eIzEUzcNh6cH7B2PmexNuDq/pkw1R9N9sqLEOsGgKBlNJ7gsp9ivUQjKPMeut7xebfDWcu/OHRbrFUVRUIwGCK2ZzWYUgwJcyMlTKmU+X5FqyXq9IE1zBJKiLLFuxWrdItSGYqjI8pTMZkg14Ld+6yMQnv/5X/4Br7ZzOnHLTQKQXuzA4z4hhh53Ef2gJHe/FpF3hIL+T3vypTMd1gVve6ECXuQIF6oUkjQRO8kM8SI3JiSPSxFAZClUMAvqeqG2joB9NFFUMnYMHq0VbWt2NAJnLCJ2esao4NLqBUmSRe7W26LkAOBLGeLpbNeFGLpoJxPEMCYWsOi2EM+TUiHX4as0hVvyaH9D6M+Qi0lM/ab2Vspkoy112Mx2LmBft04dYUm220JGPCqkLkXsS/Yfw21R7TlnIb2px9Q9LuaZ9gWxb6KsNfE1ghMhG9N6x1fY87vjtsu77cJu//2LLFzwNYtXta0phyWDyZjlYomUimpd0dYd+/dOibeSoK4XEqlDKKkUOuAk3tM2FQZPIgZhkyN8CJuQCpdohgdH6CxlubhiU1VI68jSAukhUZZqvoC2QgiDSjW6yBFJATg6U+8ulvG9M67OzzGmRipom5Y0yZFC09YN19c3jLqW4XhIoRKq1rKutmzbFmk6huMJRV4yUSnOOTbVDfiOIh2C6TDekRUppc9JspxlXSG0jC4GDiuDUV7XOJQsOJieMB5oDg40WZ7SmQwrNFk5YF21JCokRy9nV9i6RuDIM41yhoNS4mXH5dUV6JS8GIYOzju2mxVpkiIJEWvT6R5CKjbbmqozDIYTtFSslluePn/Nx9/9mNYZvBIMp2O+/dEHvHr1hv/44x9yXq1wwqP82/Ym7HCm/ggbv+DY5CNzXCkdbHZ4GxzuL85gFRPAbEeSgJdxba80TgWQP6RPh3IpuwD8Oh986IlZl5JALQlTlMWajtH+PtvNBmPdjqukEo1KFG0dsErnDPh+pJVBAeBbHIIsC1IyEd8bUkTHBMtX/LVEIB57QkK7o8XHYiF9D9TfJgT1/Kke8+q7EO/FW44gfufiIeDWyoY4svmAcfmIGb5thfP25+F98HwT0Qu/L7K3oSl+Zz3kvNupKMAjvUcrSWf6bXP//GE5grNYDFba4HZMT5/pn/+tF7NrxeLf/Wvd4i/y+FrFa/HqgiZPmBxMsLMFN/M1F+eXPHv1ml/5+78TchxtB4mKLpoSpxN8Fyq1aTvsZoszHbXtEMMhSupgDNWFrYgXHpWm5HlJkSXQWrbbCpUqkqIgnUzYLh10sLmeM5tdI5MheVFSVxWmM3Smw+EpBhnWaJqqwxpLkRYoLUiThNYYlqslm/WKLEnZbjZsqi1WeoRRNF2LcxKJoqtXDMsMUXfMVm/AhUzGLE8DKbXoUFnklClBojNSkdJULTgYHk0olMK0NeNJgcwVyqQYK2ibhvnr85Cu1LVkuUYh8M5Qt3B8sM9qucTJhHw0pNp0SAeDsmRxeUmz2bB3OmZ0PCJNNG3TUHeObDCiHAzZLDeBazbMaU3HYrkgTVKqdQPWcnTvhP/+n/0+Hs9/+MEPuOmqHs0AiN7nt6ttEddovVNAz4nKspS2F0LDbgT0gLc7aBfvQHSWNAtAdggSCV2FFpIkzdg2zVsrd0+gYPQdRtiSpanarfTv3bvHJ598SteZkCQuoKpbkiRBKR22aQROVe+n7nzwxbeEgpOmeQhcFUAEsqUMHKm+qIno2GrjmKaUDiNVNPl7O5z1bX8t4vi8A8Y9eGt2YbW7Rsf7CBMF3EkSCjYyBBs775FaBzqRCgGxfWcplUB4FSkeoJBRm+h3uJvjNvYs3kvosTmtbkNpg5ljT7SN3Z2DzrSxO+sDSMSO9dGjYLc01bfxrrexsF/c8bWKVz7O0W2weiZRDMdTisMDxHDEi6cvaJuWm5trWmv44FsfMzncR+YpQnvctkEmknKYY5qWbrulazqk1mBDqosWHp2leBMsVYTvyLMUlQzZdpZ6W5GnGfuHR7i2o0o1SZGwXQee2GaxQCKo2oZNvcVdhxiyZlvRtTa4FCjLZDJl//AQYwxt23J0eEhb15h6i8ORFgWDokCkwV++FjqMN2kQaXtgXVVsth0qL1htt0zzEUWRYq1js1nTNR2mC15UnZSo0ZDxdILKEtbbmsXiOhgKGotOBNevrnDO8v6HjwLPS6cMipLlYgE6pyiGJM4jtGW9mnP/5D221zfcOz0lzXOEd6yXc+q6Iy8HZFqTSMd4mCJMxnBchgsUz/7ePomY0ZmWumsZHkz5J//sH7N/esK/+j/+Pec3N9QyGPr1mrmdi6u47QyCr1gYFaomJCOFTiQA6kHO03OzAEx08LSBF5iEba4XPQjfg9Kh28AF91ipgieY6yUp0RJGCEHnDH/6Z98jSVKsAy9c6MCi8F9KhdYJsm0wAUDbdUOIcLPBBqaZ0mK3Ze1HHWvDwsjig5RqdwLE7gLtL027I6rqfvrddawB95LhNfQ3g/jnO6yvLxbq1jmjtR1axcR1KXAuEFl1FiRn220IIvExl0DLXrPokCh6R1OpdjPiLsKs751sn24kAgBvI24W3GjFzv3D29AN941foHywg7l85OWF9xYxrn558rZZ/i/o+FrF62BcMhoPMVpT1SUiLUllysPhlLquYFtTzZa8fvKM51Zyc7TP3ffeYTAZ4nTYfqQxCqxpWjpr6TqL8CHYE6ORStC1LW1dk8gOJwXOelIRYrWE66hbi0JSDkZo7RlNgr/QZHNI24U7z3az5er8DbmWuEHBcDDAA2/evOHq4orriyuaTYUUgu3xnKraUpSBN1Zv1qRdw3B/FMbfVKP0gC5aEiMlyXhIojXFcIC8vqHbVFxu11gXksUTmYAXFHnO86dPuNGa6XTE3v6Em8WWpmsZT0fBrwpDXqTUTcuTp89Jk4LBaEQ1MFEkLFitVnjvme5NKAcpw1xxIxxtVVFkGaZuWS+XjMcTFAbftWjV0dQmJnC7HTlxs1nTmZYsS0AImqbl6GTCP/79v8ejB4f883/5b/nLp8+YV3X40gq322YFhna8+F24QKToCY4BlPfW4oQJ52onOQlAu1YOL9RX5C6mM9jEgvK0bY2SkjzLA5wQi2dvlBcE2YHl3dM7rHe4tgECudU4R5KmgXGvFUlMLKcK3Vdn2tvMRqlRzuG6Nuj+YgENTrCaOm7SlAx8KOcDKbfHhkTEibQMzrY9mz2w4MOiQEU+m1IKa/wOW5JRGrWT1MTlSJ/6E95vLCKIMKXEwtc09W0vE9fBznq8v8WsrL3NUkBEob0L+katgpVQEL/f2u0IKambQB/p+iwAH2y1gytJfMpdwYp41lt4l+e2a/ZfWe38Yo+vZwOdgysyJJJCabwLBEShBcWoQJUlk8mU8XTK7PKKz378U376k5/yD//b3yMtPLa1JJP94DSQaNI8MMm9g/V6w3a9CcCmsxSZxltD2zR4L5FJIDimSVjj19UaHRcyTb0iLwqGo5J1ZdGJ5uzePe7cu8t2dsV2NUMJjctz7g0nAanpDE1V0bUdWismyTFNu0XnCSOdBA8q4+mMYVtvEElGPhyCdyznC4QQjIZDTFORKY9wAilTam9J85TVck1bN4yGZ3z0zQ949fwVV+eX4B137t+jnEwwXgQyZFmRZEUsJFs26xVVtQlfKAkmbliVUJy/ueDg8IDq+gacoSwHDMoSYzs6A4lKSROo1mswClu3mC5Y+2RKs7It22qLlOwSrbWStLWhrlq+/dGHnB4f8v/65/+GP/izH7BqtmHkImzjnBMIFYFlHwIsiERRIRzSC3AGa8P58C6MjCJGfgXjQsB1SF+SZTlpkiAQNF2NcBJET/5UCBEuOKU1XdvRtC3IwAfzSRJUGjhsFyaCIOYWIBx1DWU5JEkSuq59qwvSYVy1DqEEKg2p3F3XBVzMhTCM3cXvY1ivt1/BekJNiOTWSPW4tfmJr8eEAuZ90JIqwU4Y3yNcvXvE251lr23s/7tfXPTbkb5D7EXlt9aRPvLOXMxFANfZ8Bx9dyvAuVufLxc5dMTuy1iLNQ6Z6BAN5+2OPxYggbDI6TMNvOsLb+hBd0sM0Xdg/9X16b94fD0/L6UDc1omOOmxncHaDoQgy4oAoqZw8Og+g/0pg9GIv/jzH/Cv/5d/zj/+H36PNPVcv3lFPpiSlMVOICq1ZjiSdMaipAisZpGCtdhO4HUa1tymo9puETiwhvWmJss0WinWiwU6m5CoHGMtq/WaNM05OD0gMRWrZYdLBJ2DNMnQOmOwd7DL86urDeN8jyLP2a631HWLsIKToyPW1YbaWorJiM16w954jOsM7WZDnqcMphOKogDRx6bD1eU1VxfnOGeZ7O+zf3aH1XbNerWkFSm6E1ydX2G6llwrVjdrsiIDAWVZMByOaFvLer3h6OAwbG8dtG0LXtK0hmI8RqiEZdtgjaVqQv5hkqZkgwEi0keUqemaNrikuiCAbpsKtAqjrfPk+QBn4eJijhCG//Gf/A7GOf7dX/yQqq0xPiZz+7iRiQ6uIl6E/R22/4pbL8G0AdeRYhewILxFS4USDtvWoHXoXgKNMnwpdYKxoVMMcXgBeQsdyO26PYxpLUpqAg0zssA9OCex1tA0NVoFZxGt0nBhO7AmOFS4aDmtdRIuZONCEYt5hCEgJYy5QvSbTx8TcvyuA3HibeO/W6C+s8GrbCfLFoTIvx2d4hYfc9YS8wTjYqD/Obeb1YOLawDDw/IhyIus8yEoOXbIIYc3yI6s81jbRbwwFNueMtR3R4hgytiaLhSz2DkppVA+fMa9u4aij2gLY7MnCNz74ysLnl2h/sUfX6t4CeNj7kBUmLvgi5TkWfRSCq6XNtWMhyWjo0P279/nh3/8Z/z0h1/w8J27qESh9IakUAiRhQ8h+mmnWfQFiv7dDosRIRpMSx9pEANs17HttqgsR2YJXVsjRRrM+FSQPLRVTVd1iNLjtWa4N4FhSW4tddWE1th6Uh38pUZFGciZqWZ/vI9pW9rVmtdXV2RZFruhisFgwN7ePtZaZvMb1qslQggSnZKXOW3XobTizt17HB2e8PjzL3n25BV33nuIzAvGaY6SHt+15JnHJSnIlIM7ZVjnS48QgaLRNi174z3CRaHJiizIZ9oOazuW6zVaJ6gkYTSZkAwEw70pw2F4L9VmjZEb3HbLzeU1h8enDIuCputwpqNr2yjhCUVjOh2jlGC5nLF3sMff/63foapb/vwnP2FliRvGt4iYKop1+wvTy34AwtsuiLalREuNkj7qHCXvPnxAlmV8+skXVNs1eIHtDFmWo8lJMoVCIePr7D3tfbR+ce42XFUgsKbbSWs8Qa+XiGRnkdxGYFspjVYJxoGMluCqzyqUIeVaKotGYtuWvChou4SmuXWWcDbQcYAdX+qW3/VVoqqMXlud7Xg71ae3Xe5HO6LMxtpQpGJZi2TR8HckRCuhWMBEFHWLOH6ICMo7c1vA+szGSI/wPW4lwmt3NhTqph+ZI77l+4AVE74fYYEEiPDcfZJSfPO7gioQkbf2NpHir7PTfnHH1wvg2La4okEmobvoWkNSFpAkOB/uKH0LiVCIRDA+OuDXf+/v8/LxY376489QyvPd3/gWrgtx9jYavUkdIki9MzhvUIlCZznCadomoWs7JA4lQaYpabgNhgxGL8EIsiSji2ZxeZkhVYqhQY72ESQ4rSmVJCsKsioEuNZNg9KSe3fvUy+3YbXeWQbTCYPRGNse0XU1SZlSzZe0qw1vVht0mpCXOV2bcHN1Qde2FIMRQih0lqGUIssKzh7d5+riivligcoysBatggj5fLbg8PiMrByhkzR0GqalqzfI1iCRpOUwbCLbNoiaRWBGl4MBy/WGNE2CzChJ2JuOGYxHSBTbTYVREpdoOmdptw2X8py9/UOKvESPxnTW0DYG70XwDAOM93iZoGTBnaNj/sd/9A8plOL7n37BxXpOh40gO0AA2yUe3a/iibwh33t2iRCTFsMrlJA8fvIFeZ7h6TDWs6k9uksxrqMEEqMRUpEmCucytk0NxAuyH1OI4mkZKCLGSpyApmkwpsO6JIiIrcM5SJIkgPqmgyiSDg2FRaKwIhQ3LzXW1pEQGzoy7z1t10QbnNhteHY0hF5S8zYovfP6Io54ImBBxoR4OGQfu+bpbAgt7nMJhOwXFBF4V3LX6UlPsHMm8NaklMiwSwiRZHHkDEqAsH1USoV4PW4fx3uLsbe2OTsSrO8pMnH8Ff5WRhRHVymij/5tZeArv/wqk+Pv7PhaxevpX31G/c4pJ6cnSB3WxOmgCJ7gXobUadNgWoP0FnwAGLNhyf2P3ufozjFf/tXn3LzZgE8oRlu8F7SdBxkcN4XraKoN0DE+yBkM98nSkLbiOkvQg0icSMLdoFMkKqNxGzbrFXmR462j7lrSzEayX+AXORvoD945kiRsX2QadGXdpsJ6SzrIsU1HUzWhnzCAFWQqY3h0RJJomnrLfH5DvagQtmM8CJy1pmqw1pN1Fl0kbNuaPBtQDEuU0iRKMZvP2a6WuK5js1ozUAmFVmitMAjSLEMnksFoTL0JtIVECgo1IFFhJCyGI8aDEcvVislkRGuDyr9aLKBrSXTCtt5ihaVpDPlkxMndOxHf0mzWS+qmoxgOGZQDlE4w3rBarcjygtVqQ9e13Dk7pMgk/8N/899w9+iEf/5H/4HL9ZLGBkPDYA0dJCaBgeDxbzkYQIBye59342J+59bTmhohdMAtUSAC7tK2TfheZVnsBjw6EloBcpkFUNz3xoUB/+rlTcE6OhCWu64j0fL253NBlucY79E+I4QkW3ZaSRncGaQOeBs2JU0KjDHBoNDHQGEXOVUq9BYhEPZ2bOof7zaNJyQfwVuWzl5gTB9iEoM+RBzCY5uz65qcg1g4h0WGo6XuAogf6mJQnMQHJrhFRAlS3zLFTwP8zovs1vdLhdcqHBbLLq/orU1wyAa4VQ1IKXfLjf5533qKHpr7Oz2+HklVOa7O3+BdS15m5IMJrhsjEoUkDyfR354qpI9x6ITRMt3n/V/5mPMvHvP5jz7nG9/+kHxU4oxhvVzu2Mavnz/n1bNnPHzvAd/5le+ic4MRYc3ujCPVGUU5wBpPu60RXqATjW9bVvM5bdvipYa8xApPZy0OQVGUJEkKeEzXYdqORGtsZ5hfXuGU4OjsBFFmmMYgvcDSgXE4K2ldS71e0dYrlGmp1h0URXDmTARlOYrbMUFXNXRdjdFNGHNcUBsUWlPs77PZbEjTnKIscW1HYxdUXUda5OhEY9oOvCDNM6QqcF2H6TrWmw2D6R5115KXBQ5LWRZ4VBixtg0q78h0Tesk66YLXulphu0aqqrCmC7wh5yLIajh7ioILhrHR4d0TUPTtNy7dw/hPd+sH7JtVvzBn3yPq22F9WFHHt7tW6t3z26cQIhAeYjAct9ehHzXsMHzMWos4CTQ5zMa40ikJNWaIEHr8HFjFy5WtyNsCikQ9rZA7C7VHYBsQau4yQPRl1shAz9KhI2ljLbHiVYhuARIsgGyrgihlT6YKcau0kUZk4gOGn23ZYwJrhNCxPHM4EQYI4lSIC/FbjEAvYA76g17PhkuvGDvMV3AmhIp2RuV3Gwsl4uGzjQInUQ/L8JYbV3gnqnA0A94pL8F//uPyLmIcZnIt7M7W6GdSaNzmFgQlVCBShELpYv/68+3f6tro6ej/F2h9XxdM8KbGVZDtVjQdQ1HJ8fcF5Zif0xjE5JsCiJwXFzX4VxIOLbWYE2LThTZpODhtz9gNB3z5vlr8r0Rx2dnTHWCNQaVSCb7e5zeu8/ly3P+6F//GcNRybvf/ZDBaIC3lqau0UnGYDRAecl2E3yw0rh5u379huVsRnGaBXA0ttCJlGgVwVABxlmqTU2aZSRpMHhbXF2h04ysKMMoJzVWK6wVaJmjthJlDI1xzG5mDE5HHJ+dQpIg05KynKCTlPV6TiorhK1p65qmMTRdy2A6RIqUcjjh+vqaL56/5PTomMl4hDSG1c01SZaRpRmJSmhWa7quQylJlqUMypIkS7HOc3B4CL5DRkb9aDhGOKiqBYgte9MjVhcVq9UKeXQMKgEtIlcndHF5kVE1IZmorZvgUeY9idYMypKDoyPO31ywWq34tW99wOX5S/70k6dsbLwkxA7yZWcBTM+TioiHCBe6QOFifXPiFnx+O92n7Rqc9yQuOlJoTZKEr2lnurC9I3ymQSsYLagjmC60wrq+MDgMHUpB1zWBW6ZTijxHSklndaQKBNF4HzjbNm0k42qUzFAqC/8tXTQhCLiai6HA3lmU9miV7MbI/lAqxtXFAhBwqbC5tXEEDX5ct9iQfwvk7j21+ve5Wi0YZxkffuMd9KsZz5+/ibwFEX8+8uQiIbUvmL0/19vOr72Hho/v3XRdcIrw0RaHmLeJDwlKDsDttKQu2t985bV+5fX/3fZeX89VosyR3mKrjpfPXvHDn/yM/Z/8mH/8j36f4f4UvV9APggfZJJiu7Dh0FkW204T7sCJ4PDBGftnh8GOGAL50+pA0NOSu6MhZ/fuML+6YbNck+UlxXBEtV6x3VRI78iyhHxQYHxJ24EzLc12w3hQsFnOca6hHAxBCOqmpa4aSjWMHCHBdDJiW1WRfa+omw7TWVpnMcYyHAWAOU0TpE5QqYBhAYlG+xFHqmQ4PaEYjxBZRjYYkiR5kESNM7xfkLQbhquE2c2S2WzBclVzeHCC0gmT/T2SsmR+ccWrH/yIvNQo4O79h+STEd6B2dTYumW6f0A6HrDdVnR1g07SoDPrWpyDrrO4rkYpSZoVtK3n4nwGzpJJxZOnT0mKlFFZcDCdYJ3D2IS6tiityTKFlHvMl0vSNGW7qVFpAkjOTs94Md1jdn3BP/i138L4jD/77DMq+1bEcRwj+21+wIw87Cxabi9oZx1eh8JjvcXTQisi90ujdEZrMoqihB3b87brMtEIIGzVDEqI8L1yAqGSyPzu+Wgh+MN7Fbok29G7LSipUDIQhftEVOMcOlHxPQm0KujKMetVTAiKo5Z1tyOhiA6rb/vYv10k+u3k266o7q91JF4EkqpzbieOlzLwKkTshHCODrjZWkabFoxBKUljDH2GpfN+RwwGIp8sFLDbRUJg52sduk8tNa3p4mfhCKbc4eg3vMTX7r2nM4SO+i2Cbniu+OFHPHFnWPhWMf/bjr/uWfZfOr5W8br/8C7jgaJabnC+Yv7lgh/98CesZyt+/5/+I74xnSJkjvEpMtW4NHQ8AgFOoJSBtgHfYboWhEfLW76JFyFt22yDPEQmiuOHd4J4NkvBdthNSDH2pmO7XlMO98lHIzIK2G7pOoNpNpS5pl6vUFpinA3RW6ZhNe8YjcZhnLUBPFVaUrc1rmupNhX5YESalNimobNmR99QnSJLFNvGwajk9MNjhMypuw5jO6SxgEF3DuEb6s2axcUN1WzJ9WpFbYMYeLt5SpZlpEVOWhYc3j9kPEzDuWnD6zJNh84USappt1uqZgudZrPeUFcNg9GQwaCk3lR0nSHJEk7unpGWA7Z1G7zlXcf86hpmM56cv+FA75FkCdu6AgR5MaCuOpwxJKlCaoUxLUJ4ptMxaaKZz25QSvLbv/M7/Mkf/RE3N9f83/7Z72GF5fuffEHtwkhO5DIJKXZhDcZ1+OhueDteyl3IhYlbQGGjDEdqhFBIGa2jJWFJYfouLnaMSUJnDCbSGHp/MU+IrrM2PFt/0QhhsdHhNc1ygntCOM+J1+C7INcRgRAtCd5aznqECkqSuqnYrmf0aUMyWuf0DhdSv+0nf3t0puMrkxRROiUCTtU3Kc4FMm/YQHbxZu/wNroOSxk6WKFYd5IffvI8yJWERMSRNiQYRd+u+KTWGZBBQ3yLi4XlQU9scfjg1iJu2f6hqwqmhDuP/Mjt8yJ+lv2DiR7gCt+DwLx34P7zhWhX8OLfD0EkkXTj7X/y7/z14+s5qTrBtukYjMe8//4jMm/Y3tT89LOnzNf/H/57Z/jmtz8iKceI8jCAe/QsW0/Y8bhoeucxpsUaQ1YUEZzVOBs8xYWWyFwHBrpQodVGkY1GCCGoVmtMtcVnOSQ6rHSTjKrpqJsKrSVt3VAOimAbs1zijQUpMW0btqHCkWcZlemomxbXGmznaOuawWCEMeFLbfAs5muUVJRZznq1YSAlXT1ju1mSJYrrmyUXrowGio7hMEX4FdJ4mrbh8OSYbDQOd/emYbWcIyTk0cs9mU4RxiJqQ9XUmLbBWhiUKdKlbLY36Axsa8gSTZEqyizl+Wwe7qCkPPnkE47uPUCWQ6wTIFOSfMDDD044e+89rq7OUYnk5nrGcDDidDqmSjY0bU0bxzHbtkwGA7wxSA9FkbFartCp5tH732Dxgw3PX77hV7/zMTezDZ++fhkY3P0A4QLIq5RGuUD7CHhKvKsKgoW0ENhI5DTehtFJKpTKkMog2pa262iajjwrSbN8l1gkZcA4HSGyrHMWGQ0FAws8dGU9Oz+8sDjeyZCjWTVtrIYxQk14pFBkRRJj2sCKYJusU81gMGSzWexGsn7jGMbeOLLKPk3I03Z1XFY4tIpxGz5KlnqKxa4jCedL4rC2C5Ika/HehHFSyV1SODJQHDyBRC2lRnkbtI8mFEMve01i3M72aga4JcN6j7EGL6Bzgasp47gYaBURDIieZsROqy+2fw2mDzwxiAlCxJvZ39JBxY5QShmLZv+coWP+246vJw86OuPVs6dcNZdMCs9H334fneX8yQ8/5cnLc/4f//f/J9/5zsf89u/+KiePvsFgcIwQgs5brNShK+oaEqUQSqGTLPB3fLCQSTKNlBo9LCGV4UPwwZql101ZY6mubqhXCza2Y/XmOUlZMpjskaiEqq45f33O/nQE1tHMZ4zHY1pnKIYlxofxNUkV1bZCSs1wMqYcBpzHe2g7g1IJmZIhtt50JMmEfDikM45BmrG+mrO5fElTXXD37IizwQDjYb5ZYL1AiPDzzlpsXeMEdLYNmk0blALO+ZDYbD3eC5TOSCcF0+kpmVKYaku7uULLljdvXjPJco72JyyWG1bza2aXF0i7ASdp6i1OJDRty2AQHBpMJMxum5bTuyd0tqHazplO9yjzAXW1wZiGMssQxnH+4jXSelxdIaWibTaMhwV5rjHeohLLL//aR3z55VO++OIZD+6c8eLmilXTxo0Yt5vG3iwvEhoDoB/WAl3X4qOMxkV7YRtpC9Z5hAkZBMYYrLGYsqN0hqIo4vclYDj0PlOdjfbQEi0FzpjANop21BFFDgx1FwDtYBsTCo2UwcJaxs7L+hjkEUdJAaR5TlkOaJpN8Bqz0dk1vt+2rUnSFOvMjl2/k86IW16YFAHsExFLUiosTnx01HL21skibhcQspdJhYLQtqEzC4aKye3oJgLp1iN2oHs4tw4Vn7unSjhvA44lReDjEfl6PhoY9rpF3M/Bkhe7cwzE4uZuN4/cjtL9sUvxFuH70bvGBj3lz1ePvlbxkmnK/tEx88sLrLD4ouSj3/gOyd6Y9I9+wqs3F/zJ//lnfPnpU37pV77J3bt32T/c5+D+XXy+h9Zp3PzIsPVLM7q2pq7CXUoRNzKJvPVPMg7ZdJAneGvZXs3pjAlyE2tY39ywevKE6f4hxWhE1TZcz7dkRcn+6ZjNzWvU/IrN1ZLJh+9xcO8R19cz1qsNbd2SpDnKOLIsJZAQA2agUh2cQX0omN456qYlG43IhmMme0fY432unn1Ctd6Ca5GJx9dbxtNDvLDM5xXjyZhyukdTNXSrdeCMYRmPJkhd0FrHsAzYjnUekXhGwwy/XLKanXP95pyLN5d88skzfviXTzi+d8ZquyLPC4o84/Bwj/39AxCao3feRQ3GgMJsWxSWybSk2ay5fvMS6S3aS5yx3Fxf4qxnUJb4vARg//CIy6srqtWSpq3Zk8dsU43SGmdbikSwXm05PdgjlRneSi7mc3729DlV135leti5sO5A4vhrBNKDF9Hoz5s4dvT+84ZIhcQ5gXGCpg26VoQhTfOg8EAErIog9zHG4I1FZYEoa7oOIZMdJabfRNbVFtlpZJKgEkWaKoRowgXsAvZljY0kzF7tIdBKM5nsM7sxuDa8RiFuuzmPo+uaW3vkaGEj4w1ECIlQt44NIdM2jEiqt8fpNaixAsiYRBRMEXvaZ/ASCx5gt5rPUMxDarq1QdQdMLRwkxD6FseCHYS12wD3zPteJykjTcP5t/Im/8bxlj9YHPvoR76+AIu3fbz6MZ6o1FH0So2Qofmf8gj7zx9fjyqxrpnu7ZOnOV1VsVguSRPHe++8S6oSnj1+xevXFzx7fsX/+R/+jO/88oxv/8q3OZT3SNLwVEWR42xIn1FC09YG6QIpb/bmkq41DA4OMLYjLzJWiwXXr99w/xvvovOMtMjQ5UHAC7qWUTshny/YbCpcU+GAveNjnE4Qw4KJ22Pz+ILZm0v00SHpUUuWDdEypyu66MgATRU2bSEtpqWrqhD7LgI4mWgdRL1VhUgDZaNpGg7u3mO7XjO/vOHm1TnNesuZTUinY67OL0hVymR/Spt2VNsGoQvSRMVNnUJriXVtWKc7SKVi82rDT/7s+8xnC4oywTjL2b1jOudIUse7p3c5Pj1BJSlZMcI5QdsattuaSTGiMxVNvcaJMKJv5jNU3WEk6DInyYIn2HKxZrOuyPMh2WjI2ckZ5pOfcf308+AwYC2L6xv2D/aRzpPrhGy6BxPF2ZFCWclysaSpG569uWDto37wr33/vnLXjaOHc+FC8dz+uPceL93uJu68wVmJNRrTNUgR8Kg0U8GRw/b4UyguzlqapkMgSbTGOEfXNkHLqBQk0Xo6STBOoHUWZDVKhRtXvFllaREDYMPrVFqhJBT5gHY4xiw6RBpG4iCvsnhCJxZyMHvZDzs8K3RSt4B0SPUO4baCnmAa6UAu2trESDcfO7IA5DuUDnrEkO4dVALeRVG8hGh6v3Ps6LuuUDjEbrQnwvP9oeRObR2BLRHHxvj5BJLK7nHC70VfNnpkk13h8ru72a1NdV+4ejcN/9bvvaXn/rmOr1W8PvnZJ/zGb/4ak8MDnPfML1PevHgGesTpg3c4PL2HM4bzizf82Z/+FT/8wRccHB9xcPSakWlJiwl11eA7R16U+NGYbDxEeUEiFHkxYLupSQYFicsQQlBM9zktyrgCtyR5ipME/RmQYCgGJeVgFDAG42iVZrGcsVykjCf7yFMoEKhRiekqui4wrUfjYaj+QmGco21alA3JytJaTDML+kAUWZrjsyzgcl0bbHyUxKMRUpOMhzw8POLmZs7Tl6/oXr5iPV+RpQnj6RAlJaPhAJMk+EQiNxXCGuq6QriWdrOmWq9IxyO2b65ZLZeMDsbsH0/JB5EP5jxN26HTHGMdXqc0xuG9IilK6qph+eQJR4cHlEWB0JL1csV8NmOYFngPTnryskQ4xWicIuLo4o1jcX3JeDJkmRU09RqM4cWrl6yXS/YO9hmNS7bVlq6qmB6d8PCdU47Pfp/vPHmX//Vf/Hv+4ssvaZzByVuTwt0G7q0LuveS8jvsJG6zIn70dhCuMxYrWoxRIS/AB1F9f1F2tougdR/KEfAeLRXKBbdd4oLHm1DEwoKiDK4UwhH5nhGw1vFxwsXmulAMtRBYKZhO9jGmYbu2GNuEm5vomfBB5uTdW3w0erufXsrTk1R7lw8FPjhh9Ns5pWV8Ob3baZDdBFPFuOCiX8QGQL2PpzMu4Ic9fiSiF9oOo4tAu4r4YW/z46RHRF+y8N7lTknRj6zh/33LFrsu59Bxu9urKnzffsej33j28rDgaEFM3Q6Yl5LBvkf+nGD91y5eSkhurm44uneHdFAyVsdUXYNXGlWO0QOwtuFsMOSjTdgsvfzyJdXNmrOTE+7cu8Ob11dcnF/x8L13Gb93n8F4jzLJaUyY68vjKT5PkUJiWgvGkQwHKGFpmzpsKb1AqxSjDM54ykGJR4IXuG3L5fk1jWvYk1Oc8KSTEUMfxLPV5govh9G4rQle+1KjdEpT12zbllQqBkVGW61pqhqtUtrNBpsGF4M8L2Ogp2A+WyERDEYjRKo5GN9hdPeYxdWcm9dXVOstl5fn7E2Pwx0yim9VnmGXFRfPn9Is5tTrNalSlPsVTdNwdPeEfJQzmE7prOJmGWyeszTDGkueFRgEVdex3aw5vXuf8cGIi1evmF9fgxZMD/cZT4eUoxIlMwb7E9r4Jb26vGFvesj+8SHb9YI8Fbh2izSWh++9Q1NXzM8vuHt6RppnbFYrrGnCeNNaTLMlTT15UfBR/ggHpP8u53s/+RmV77C3UNNXKASuH39ExD1k6Gh7HV8Aii2yj9gClA/i+a4B4TxWGbzUeBUwSi/jzym56x6E8OAVaBEsepylrjbUmw3D0RTtBXXb4jykWSAuCy2i1jCIw7uuI0nSUIREL7iGvb1j2naLqSrAxZDcgOspHdwpbNRfivi6bEgMicaAYaHQB1q0XZDECeEiB0whVKR/RKC9Xwj0aa39OB6ss4kYZ8xb9IHQ2i8LELeKB+INpLfbCaL3oAsVPS0Fds4Qnl2VjFNIrPP9guStAurdW7rX/vGE2LlheBcWLT0O6YhuHEKEBY+Ehha6vwOqxMe/8St0dcvNbEHWtBRZymi8R5GXGGtpbUMyKNHTIe/F9JA0EWhhKbIEJTrGA8HrbsN8dsWoPqSWYGVCrgu8CCxxTK8pszvQ3rmefZyGFBilo0BZkyiJTpJAAEw96ziCFalG2wplamy3ItMZxuVYJai3NThPkmUhCARPnmVoKVnO5ywWM4xr0VowGuYUgyFZVjK/uebm6orBZEgxHDE9PsA6x2oxI3WgpKZMU8YP7zEuRiS2RY8leQa+a2lWW2xnWW5WPHvyJTIKWUdHh8HCWSuEVnStRUpH5ww3sw2JMuwN93FNx2qxItnTOBVIl0f7exjT0bqONFE02w1ZmrNdzKDIUVrw8tkLRvMZxcEek9N7HO4fAoJyVCKko223OOuYn19y9OAeKs8xQpAIqLdbuo3j6sUrLs7PeXDnHgjIioxEapzSPDze55/+3q+xWC/5q6fPguledD7wnhiKSqAU+GCAp5QiS1PqndFduOACQTJeXKLDOoGxMrhPeI/XFp0UQNT2QeRa+RBwoQR5XoANGGboHgKPLCxkgptFXg4xNlALiqzAeUdta6SW9IaHLvpfCSnxTtLZUBQPD+5xcf6Cqr4OhRlip9Wn8IQuqQcBkyQNhd8E+ogXoWBJIbC2BSw2Eo6dt7guFAqpIh9NhxExWN2Ex1QiWPkEmxsRrYDELntAELrB4ELBrstx0eanF3FLCdZFhw2iHAkRVgg9drcD5UUs2KGY9uN7T6f460d/oxJCBAfYGAWHDMubXjuqVYK1LcZ0P3c9+nrCbBwn796nrVvq9QbXWZyDwSiQPVezBU1r0XnKcDzk27/0HVbLOV21RGUJWV6QFJrv5Bl/9Md/ydq0/PY//F2EUOR5ik/SIPuoGjrZAg7RtVTbimQ4Qes8bj4CITFLBzghg7WKIfBh0oKzew/45Ed/xfL8hsnIIrYr3LICvY8UY3Rekuqc9XqNxZOnfcS8Iy1zDqf3wMP65prtfEE9r3A1LJOGarXm4slTnLW889GHnNy7i1UShWf25hy8YDgaku8NyWXL88dPODwZ8OpmSddIBoOULFEkacrde6cMJ1NaY8mLkrIcBJ6L9CivmF1dIKXnztkEbEddV5SjEfvjEd4Hr/ntakPrG3ySkUddWhK90KptzfziEus8g0GJtZbT45Nwx1bBS212cw3CUS2WbGZzpA76SycE4/0prms5mEwYjKccnRxTVS2dsTx7/JjJdMrh8R6mrdG+4f17B/z+b3yXm8WSl7M5nTMYGTpwFcXG1vb+6UGrV9dN8IPfcZRAvOWjHu/ZoUMQYJ0EK0AodBooNJ21SBeTr6PxX9caEqVJE4nKUpq2QQiJwVJVFTJJkSI47XadQWc5zlqGoyFt29K1DYOyoO06bAT8pbdkSYrpAKEZj09oulUois7Reot7KyfOxMWStTZuMCWK3lk06DiDPXQXri5rd/QBQSCcqug+IeKY1id5C0E0PgzFpKeFKFTUi0Ii1c6vPmz1evA8jrMiGt/09IZ4w9gBcX3Wpu/rVtze0neBvQ04cSx9u3jFYidCIQ2E4CCjCiOnRKhAkO0tgzrTIXeevX979/X1hNmff0ZdB9B+PByA88zmM66urkgyjbFN4AcZQyVCMkySZhTZCcmwpMwLbi4uUHnCBx9+xKbaUF1e45FcmEve/e538FIH0ztrsNWCzeun1F3L4QffJs33caZnVjukdpAphMmRnaXrWkxXU+xNuXPvLuev3zB7XTNiy97JmOJoSOUSnPGYtsMBg3IQhL/eUTUNqSzJdQbeM9nfYzqd0NQNCBm4QIngUfY+bx4/5cnPPuXN0+cYZ5kcTmmbjsV8zWa5ZjzMGJUTpLOsFwsurhY4kzGaDJGJIi1KfJJQm3ADWK8btltDORiQFQnr9RqpNMY0jIYa18JiO0OlGflwQNcZsjxHJxmr9ZamrmmuW5QS5IOSumvIs4LxcMJ8scD6hvHefmjPtSKNDH0ZXQ1IFOXeBGM6sjRnuVhw/eoN4/GIxgs228A9k0mICxsUQ549fkxdL7hz94zrq0uOj874jW+/R+cs/9u//VOeXV/RebOzItZS0UtdwqYsjHfGRFIyYazqt209yIsPY58hSFSC3F/EdJ0kjp63fCtQGEIIbZIFi2xlQ7Qb1pEleWCrtw06Cf5uzjnSJAmOqEIgUk1TBbfSJEkRQhHMRy1eSeqqJkkzJtMDFos5XdNgTE3nWtIk4bZRCRd6MD+MW89YG0KXETqWHux2zu5Ipr1us6ddKCXxOy5ZGCu9i0ReZ+I5lsESrJcdIXYYlI38tQAvhm2+i3ijJGhO8QGWC0x9GcH/2AV64qgYjA3tLmz3P328vT0MC4vwIFon8b2piIN5qqYBHFoqOi9xv2ie1yhPMOslN3XFKkuYTKbsjYd8/rNPGe6VTA9GmDYq1b0I45jSwYFUS2ymGdy/w1gq7n/4LtX8mtlsxhc/eszPPn1KmmY8+PCbkAaDOik9qXFUTU1TrZDJEGdDxRZK4DqH0ioY9UlDmiUkPkc1Ffd/+ZvU1QO2z15z8bO/YnJfobSDrcXTYUzNYDICoahMcNBsTYfdrMOXIEnCSReepEiDe6gD4SzpaMD09JB6vaHerDl/9ZonXz7m7O4dfuU3v8tqueLi6UuM7Tg8PUKPBiQHlmZb07qWZtvQeo3Umtp0pGnOaDTBtIZq28ZWXmGQFOWAqtqQpynTvQlPX75kPD3g8OgIgUIqiUoS3KYKPvxYVCLJi0DsNJ3l6PSM5sVLzt+8oalbRtMxZZHjrWFQFCjvadqa5XKJkGHzprE0izUm0Tgh2Ds5RQvF8dk7PPn0Z1TXb/jwm99ADqfIQYlOXvLZJ4957/27/JPf+VWaxvI//8t/izGOJlozhw1eLwEP33sHu5Gjt6kJGE8/iIUvf7hQAz1AOolTIRxFar2zNzadw7uAU6VJ8Jgr8gIle2PCFoSIHC2PF1vKUpGlRbiQVHAS1VrgnMIoRaITrA+0hTAKhiRph6MxNUrlHB+eBeqJt3hro0xKRn87vxN/h8DX6OclgqtE30WFOLnwHetzmQKR10dXFLHDufpiZndjXhgv4xd2B86LaEEketA9CFGDQiVyuby9dUjtA3DCmjWM4F6+RV8QRCKuix73kSvn/2ahCZ75cVur4vuTYUHRp333XK+mq7E2mF1qKZHC/xyl62sWr8l4wGiyR91arq4umV9ecXRwgGsrZpdLDo5HZMMB1baibkKkepKm1G2LrQwDpdFFidIKWRQMB0N0OaLbNjx7/pz//f/7L/gH2zXvfvw+MvGIDMrjY/y6xHeCzfUV0nrK0QCRSLT3CK/Bh22hiLc7qRWojEGiKd7RtNUa315TXd7Q1gvk8JjGtJAm5HkOKviFGedo2haBYFAOcd6zWS3AGvJEorXAdhanJjilycZjBgeHDI5O2X/1hi8/+4J68zMevvuI44d3MJ1hud4wtCPyoiRLc7AdWMKdPFHIrg34nXPoskTi6bxDJhkqGwb8xDas2g4hBaODI6q6pa1N8IRSAtcYirxgi6dpKq4XCw7SnExqkjTB4jk8Psa6Cz799BPe/8Y3SA/3cbblycsXECPDdF4w3htFxwiLrSt8ZxiOxtRdh8pShJa8/93vMnuzRzU/Z3r6ACfhnW+8h3Bf8snPXjDZm/Ho7ID7Rwesz1/jvaPzcXUPAbQXYreB3F0Z/bUnwO0u9P7SCl1G73BgncUlDqElQbdI5OUFb3bhPXWlKIvgKJtnBd4LNtU2dPXGQttisw5rW7TQCKcRSoELHUCRFf0zh/5C6ygm16iyIEklUgxJdVjzd12D913ElPr31lMVAn3DRgserXW0aw5Yk4s4ko9TRSD8OhRhzIou0qEQR5lPD/67Xkgtxe48Ott70vedV4/LxbPpXSTEhknVuyBx8vFzkUqidoX6lmaxo2E4u+N07cbFsCJ+e88YirbUKBluBHD7eoQIN6XWGoyP0Xbi7T3lf/n4ejyvZs3ADZFosqRgtZlx9foNqRS8vrjAvPOA0f6Q2sUviJAI06GSYAy33WwpRAqpRGiJNh1Jpnjnm4+wzvB//Ps/4n/5n/4nTu/d53f/6e/x6JvvkB0cwGCKFwn1dsP6+pzq0oQPKstIhyPyYojVaZijiUQ775DCo4YZe3ePGbWaMm3Rfku9eIHfOJbrjvFHx0gt0alhosds10s2yw3DckRV10FytN1iVhsmkwk6kbRVDWkeVABYhNCM9w744OM8cKOWK4xzFOUg8Kw2HcUwpa1btPDBMSJNQ0CoD3eZ1XqNTFqETkiShCJLKIoi6tFK2i6MGMN8QFk3bKoa23XkZYGSCV4ovAwLjm1dk603JDoPkfZdR54X3H9wnzIvePPiFa9fPCcfZAHYFoKzO3eC5MZ56AytMRglqZuW0hiM9cFl03p0VjA6PCNJM6yFLM+ZW883v/0tzu4u+Y//8d+z7Ro+fO8B123Nq5tZIEv26/noXLC7GCK9AQ+9kAzfGxr2a/3e3C9uLk0sDpUkywcoEZx3rbF4G9xAU5VguhaRpGidsn84oqxr5vMZm9US07U01RbTNhRFSZoVKJWiVRaQFx26qERKtlWN1gkoR6lzHJZtFQilSkrG032u55c0dhWY6zFkAwLOE0ZBiZSBdNq1IWbN27AJtCK+10h+dSKQdPsgWS/CmKyQwSWH4LghIDqmhk5mR1UQ0QTRxrE0YnHeubiVjJ0ePmxS1e3WMJQdR1QlEyNsg9MwAgM7hcLfOELFpC9BUgbT0V7vGqyjJchgqdOZEO7hXLC6CpbSfwfFK0kI8h6dMR6OoOu4uXhNXVWUacZPv/8D3vnGh5TDQCXwKjBoJZK9/Smdga7Z0HUVSufItkYKS5bm3HvvjH9c/Dqf/fRz/uQPP+MP//f/wHhvzP5RihoMsEIhpKFwU2RcxVy/vmT56g3ldIiajBAqYTQYIL1jOVsGoFR66DrahUEMBXmS0ImaRAa8RCYKnYbRIMlyhlKFYNq2Cc6btgvdS0PIj8wTkjyhq9vQ4uv4JVABX/HeM80y6rqlqhrSvEDqlLwoWC/nWDz7+5Nwx0OipUQojZrmeKWDN7h1uLqlapogRREenabBDTTPETohz4ZR2Bu4TFJqDodjqnrLarWgrTuur64ZjkcoqZCqRSUJh2cnHJ6esFosqLabsBH0jlE5om1bqmqL0glqsEcqChKV0DYdXki22w1aaarrLdPJlHyyhwWkgsOTU6QzWN/wW7/5XX70o59weXPJ4WjIpmlZbqqYbh7OVw8Q777v9ABw8JUSRB6YCMRLuRN490Qwj7USrSzedbE49AXPIVB4Z2jbirIoyPICqTVSp5gYkNG1NVVTxXHJUdVrhJBMpnukKkN6QpcjemmRixQEixKQqYS6bWLxkKRpznzZ40AimhimkU0eQoV7BvvO4UGI0JX2dVyIXUSAVAHev7WaiZ2rf8uOO54o73yQHcWxUhCKl4sjbihgYndTCKnXfhc+K+I4LeLzSx+Kg+u5cwRczFp766gh/Ff6rL7TunWX6K11Io3JRkJr5H213ZaqaeLU1E+1/uduvb5W8bJWh7cgLcbVZIOMe+88ot20zK9fUbUrfvCX3+fXf/M3mRweULcG7yR1FXITi3KAqRtUNiRJJI1zNE1HZ6HICu48eIfhcEK1LXj87AX/+n/9F9y7d4fTu3fJhiWTyQQhYDFfUhQlx2enXFycc71asFfkpLni8ulLhIBtVdEai9cwGY5QRcG6mbNpl3gEOi/RgwIShxKWLNNsmxYtEwbTg2A1YxzVekXdbpmUA6SSrLZbrFO03uPblOF4gpSSpuloqhqHp8iKsMDwknJUkiSKIk9oypT1Yo7parIsw3SGVCe0zqOLDJlmQV9pWprNlrqqcaZDKY8Wnmq1wAKDwSjoBXVKVuYsL2+4Or9idLKH1pLDvX3WqxVFWZCkCVrHyCwfdGMqyZjs7TEaDkm04uriktn1Tbi4hUaIhNFoxMG+ZjG7RnhHWea0rQkaPqUxbR0u+lRyc3PJRIUimCaKrmo4OzwCB9VmzfxG0SmFxNFF/tAO34qkTe9ckCG5HqTvtXbBS2rHQ4pO+jiB0IIklSEVXaggGYtYmRcWS0fbVszmVxTlgLwc4oXEesgHA1Sa0LQNXduw2YRgFxktkEQRimeSZBjvkFpEXaVAe4n1Fq1c9AUzCO8YjSZc36RY1+6oUeFidLtR10f8qu+SAhE0mCFa7xHRRl05iVLyFvNzwfLa2tBF9STSXoy+K25vYWEAIeva9dMevUmCiPQH79nRU/qxsu+berBfxm2x96H77vM1/0aViVtHpWRYxhBxSaJlUPw7PqJ6rWkxtsU5TyIDzv12MO4vtHgZqzHWI0xHkaeoNAXvaXWLlC1VmyDykqzIKQYD0qGma4K9S71YhuBZJanXLW3dMRiW5JOCuqpZbzSDcsTo6JB/8N/d5eHnX7KZ3zC7vOQvv/ySwXDIeDxmPJpwM68wxjMcDnn96jVffvmE97/7Ie9+4yGFEyyvFwwP97l7dkYnHEJBIw1tW3Bxfo1SkpPjEmM6mtkMmRWUoz184nFo9GCE9NDWa1LZYtuWrulIlA9FxxqkUpRFgSQkziipg/+UVCipaZqa1hoy5zCrDddPr/niyy8ZTaZkZcl0z4eFhhcMRyNa6eho2W5bMGFcSfOM2etrCmHR1pJ18OWzZxzdOaUoi0BQNBmDVJKc7NE6R7etWFUNaZaRphlNW3N1ecloNGQ4HYZxvjNIQnLQZrWirmvazjDZ26coByRpDkKDUORFge8qtANDICbW1ZZqvSTPMtLRALdYMq/WDMYlo70x73zzA1bzGXcf3EHlA663P6a7vsYKx2q7oWq7YK6HiJxLiRc2EiR3V1m/qQ/jBrx1kQYaQecMVd2gpcbLMF6qnsfkApa1qVYkXRPlkjK4jjrHcDyhqrbR2UEGXaMNlIf1ZkPXWdKsAJWCUGRZFjoWEzz/lfC0NvCnVCSwZmlJkY+oqiWytwTzQewMIYkocNlAJjIYdXobrG2MRSq9KwCC0G3uFkUALnaCcWXYj4ouGhb48IShKNJ3XT52YrcdnaKPqovnteeveiJGJVFeIJWOBUKilaRp6+hg29tEw27ev/2NXSReLwPyuN1NSojA4jfGBXZAJAV7fFh24CJd5BdcvPKyYLq/R1VtsMaQyAylU7raYnGoJOX0zoTJ3hghA0u5HOYUqeLmeQNYZJ4GTKVeohRkvghBBCQ4VdCaFtKUOw/v4u8esVmfMb+ekaQ5+4cHDKcTHnYmiJil4ujhGVZ4Hj24y+F+Ck5zeu8OV1cznn/5hIP7Jxwd7dMpz4snVzy/WHF2ekLjFcPxkDRJ6ZynqRp0XuAA026Dg6cUpNmY4iBlPbuiNTVehmh0JSTry0tQirQsyQYjlC7xPiT9DEcDklTTWcNq07DedOh0xOXlkrr1nJ0FK+ib6zlJlvPw/feZ7AfdaCsUPlEI6RjsjRmYLePMsG4MSdviuip8dK2h3i5BgB5OKcsJPs1o22uE1DRtGy2BPa+ev2CymrA3nZJlOcYaui4a7EnBwfERxXBMUY4wPqzVWxNxqq5jeX3DxnpG4xF5ntHUhuXihqFp8E1DoiTCGGZXN8gkw8mM2jZ8451v8M3XGyprWdebMJo2TRw71I5j1HcSAhHGn1jHQgcWu4/+i+hDgTNtgzWeVCdhOyzAI6PODzrT0HQdWeLQaYrUCa7tsNbHMF9NonOk0IGFb8L2tbM2mjVC0xqElCgV7Kh9bznuFVK2KClQIgnn0cJksEfXVHhvSVSCMW3EoOKYyO3GsGfhh+kvhpdE0F2LENfmIhNeIQOx1EcEKoL0Uob323dU1tmIo9rb0ZJephReRp8I1J9PFU0M+yBa4UOAh1KBthQKj4mmz293RX+zQxIRmww2N2EW7skggnAeRey6rHW3YSpxgeAku8/7bzu+VvHSRUbVNSitWC7mLJeLMAo2luVqTZIp8oHAmTquMCyVq/CuZTDJUULitSR3jmq7ZbO+AjsiH+2BlCyXCyyWyXiA1IHZnaUpJ/tHWA9JOUAOBkjnkaYFPEeDkl/XErtasTpfMp/PePfdDxDGsJrNaJst518+5e69PerNimoTlgnOblmcN3TOMz67G2xwsGgpqbcbOg8yWiJ7AclgTGdC+o/vGqSzqK7DtBWNadBSkQ4T0iwSTb0hy1Ns5Ti8c4fTew9oNlvWiyWubpnNLyhHEx6+c8JytuLJTx+TpM9574N3yUYFKtV4BMO9CaKGtt2ghWE0Stkub+iqGVQh7GQ4nTApBzTbDaYzHB8fQZpglUcaS64VZlNhlivUcEhSCNq2Q0lB07akWUY+KJFJQucsjXV4GTz7Ex+Y8O26QekELSTb7ZqurZlMRuAsMk1QRYkaD6ivrsEpvEyZHJ5g6o7f+a1fohjmfO/Pf8RMrcPY5ALuEowBwzbOO3drE70bLkJoR7j2g3VQGMf6yDCDMyCdIXGaNEmizVAXXUmDnrFrK6RSeKGxjkCPiGRZrVIQkA8LlJCk+LhxjZFkxmE6G80RVei8JWR5FnCfSNZUXrA3OaCqF1TNhrbtIhnXhW5GKXDR6TS2k/2FGuyY3K7jlGnkeBkTsahQqLS4HRN3m1oR7Jr7AmVj9+K9wDmz44UpEQpkphOUh9p1QelACB9xsegrFYu1ED1yRaDIuVuZ0X/uEP3oGW7wELIKRMS/ZByR+8QkeoxL3j7E30nxqldzMlFQ1TW2rYORWS2QTrC4mXF+ec43PnzAsFTk0iNlTld31JstiYCkKBBagEpxTYVPJNkgZXQywVgFC4lrQzAGIsz3dJKizPDKsZhds5pdx3FNBHKpsRwdHyLPDlkuFqjRgNrVjI8KvnPyPqvFnJ/85accHyVkiWSz2vLq9St++dd/m+ZyzcunT8iyESpPWV1t2Nvfp9QqJCwjaNuWtjGoJEHLDOkdRghwHdlwxOzFCxazBcnwir3jQwaTMSjIi0FwjVDBUgapSAclQynIipx8MyFRCYPBmHtC0VYNm9WSy6vXcNXRdB3CS45PjskyxXIBy1cXtMZiFUyHQ5ztqDrBdDBgs5jjEkvbGGzXMb5zjEw1mdbIrmNvfwoxVFS2FYv5nGJQhjDfxrLdVMEBVoR08nxY0m5qNlWN6MLSYpgX1NstELCgummDIFgpGqfASB4/O+f9994lTzOqbc3V1Zx7D/bR+fucvznHFgp5/prZckPtiBiYjUlEtxwlvI9gNbv50fnQfVkfBb0CrAidiHRg2+CzlccYOSKJ03QNNk1o2wqV5NE2u2d6K/I8Fq0kC9bUBKpFluWYtqNp2miGGTE3b/A2bNyC3CXEkymCeWOWJFRV7JhiUg8ibAq9iterkFFMHe1hVAD0eza7s47WtTv+k+o5UvI26MNH6VVfNHZSKESMRonjtxBIFEpIEiUiyTdQOFDh+vVxrNuZOopQRkLn1oP0/+nC1Ws+dwuY6H7R41+huQ8OuI6Q6tRFgu5uCypcdGild+T+W4+vVbxuzp8i2gnD4SgIpTvDtm4I4hjP06fPOD9/znb5MfcfPKCcDPEeUplgrWW1bsnLITpNqTZrRGfpFnNeLxck6RiJZr3asFotODo9RNSGl0+eMx4NMa7j5cvXDAcDEJ7NeoMQgrzMePjOPfKypGs6ijRH5QqbaLQQFMOCk9MJpqu5vgq4y5dPX/AH/+57fPTNhxQnQ5xZs3hZ8eTxM0bDEe+895C0DCTStmpxXlMMB+EktAGMNa2n8o7D41PKYsjjZ4+ZXV+i04zx3pSTB/dIsgypsuCLnuZ4Dx2QKsFwPMFZH/lPFpEnDIsDhnsDfFUxu76hazpcY5mvN+A9+cE+Iwnz+YLFvKLUBeVgwrMX15y+9w7b1qKVZLVaIucZg8mQpjN0my3lYEhtarZ1TbVckGcp+KARzYsUqRISFVbvpmtZ3jR4Ahs6zXO00hgbitze/h5NsyWRCWVR0DYN682W5XzOeDrFCxW8CEWCTlPSRHHndMLHH97nUf2AHz+e8INPPuNiXePjiNa7FUghdjfhns8ken/kHgTbXbwE3y1c3NaJqPH0uy5HyVDc2qbGWI8yDiE1eBkwHRW84xBQ2wrvguJgPJkGPqIUQYEgwDhDW9XoJBQerSRxCUqWJGybLZnWZEkSOizAE3SEwoISmkTpHaG0MaHzwXuE9LtMxN54Ji70ALHD1QKBOZyPzrxliOj76F+/2z4KIdAyJP4IQKtenBQ62OCWEQmlkWCnpAhdoODWVJFwTv56LmX8Rb8ujjeeXtMqQrCvCKRkh6OzIe3debMbZ3vQP2yW4+P9nMfXGxt16EQEnqLIKEcDjIN623BwtM9HH33Epz/9Gf/yn/8px8eP+e6vP+Ds7hkH+wcxNMGTFSXGWObXN6xvbrjz8B5SKZq2Yjje4+AgRyQdQgtGh0Pen7xH1zUooBxr2qpCS0lyNmW93rLertmuF0z2piRpTjWb4zpLkuQoodhsV+zvHzAepdxcrsi05r1HD7Gd4t/82+9x58FdPvow4+79B6RZwePPn/KD7/+IQRnuxqPxhIN793ZeT1iLjkZ2woNZbzk4PWF4OMa2Lc8eP+fVl0+4ePmC4XSP++99g2xYYs0m8H2sp1m3dNaTFUNSneJsAGvDWJMipWSMZLvaUOQZci149eo1Weop8pz94zN0otEiWInYRcHkcJ9ks9yJl+vLN5TyGCsV2TC4bjjTkWUZWaJDiEeW0SFIswKHiF7/irYJAbTDwSiIdSNGY4ylKAqWqxVKgfTh/u5kSFpu64o0zVjXLaPJHoPDAekwRdgryizhGw/u8/kXr5mWBafHR6zdDFG3dKals10MmrBkkRdUN00MqrgtEjZiYL2xIbF49R2IQ+GEQ3qJcQZQAYivPFJbCqEIvE+DaBuccoHOoBXCOxrTkSQa7y022icb04aNovCBZlF3aBni/Io8x7Qtpm3QUtK0hlTp3Yjmie4Sse5qIdFah/eL523wW+0oJP0F70Ho8Biit4+OomnnYpekgzMtkWrgoog63g6UDKJy54KbqvC3CUZKBYmOViGTUqvYFuJ3mGgovp7WtG8Rb/sKxttxlbH4eJrI1eqcRxuH6hPFXHCRCFZAIuCG3sUkJo+VYXv78xK9vp4ljpSURYYUnvl8TloG7gxYsjzh44+/yfHREdeX1zx58pw/+Nd/zmCY86u/9kuUZUaapgwGB+RpuPCXixmH7R6T/T08jm5ziUsyxuMJm20wn0tShfCSpq4oBillJug2G4xpOToas8+Y9bZmvV4wHk+QSbBVts4yGAwY2glN5ZGZ5p33P6DaGDZVRVW3PH92xeuLGdt1h/sVuP/wPaanp7SNwbSO+dWM5WzG+U9+xnvfep/hdEjnOpz1JDFRqGs7fGcoRhmiTPjwl97n0btnvHr+gifP33BxteDk7glZphkNxqSJZHJ8RFoOsM7QVKsoyTEIJ2PMl6QYDcjznLpao3PJg0dnSO9QKiHJCwwhkaberNGFQmvLZJKgBRQZXF/O2SxmWKmYHh0xm61JU4XpGlKtaOuA9+V7e5FAGozvenwpRMGHu22earabFQJBohOeP3nK8dEB2bBkcXOFE1DkmnrdkhYD0nJEMhjitSIvh9Auca7izr177B/cQ/244OpmSbXveXE5CzZHjsjP8qSpxplQxLwkuEoQxiRpe8M/dtuyXqMXfs/H9X9YCBgP1niSBBI0nrDh1VoQYB1H026BQJLUWtG2DU3TkGYpWZ6RFxnKKNquRSrwxu6CKsL2McyzzobO9/ToECUdV/Mrmq6JonSBsdFo0TuM7XAiMOmDC8TbR3iDodgEYF+IgLf1m1YXuysZ9gRh7OwCP64Hy/uUJIiWOoST1icMaa13tj0+dk/WO5q2wZi4+RM+8MR2nK7b7aKIuF3/OYR/xG5B4cJJiXFqLsS9+RDAIlUwRRS2X8a4QFb14Qb2Cy9eeRYKV1NtWc5mDJ1lMC7JU43XGcbC/sGU8WTE8Z1jFvMbLi6uuHoz4+LihsuLOb/0qzf8/j/5LbbbbbAawVNvVnhvwYaAibZ11LVnA2RpQpomuGgqJxEMxlNm8xnz1QqpE4QA21RsV1A1NSCha3n+8gXNesN0nJOqknRa8M63P2Szqrk4v+TO2Zbnr17yl3/xM55++Ypf/c3f4Fsff8z+yQk6KxmfndBWNT/+8+/z6U/+io9/6VsIAZ1p8a1Gy4R0NKCttsyXS5SGJFXoYc6Db77H5PSY168uWC2WvJytmV2tGI4yPvjmO6RFgdIpg9EYgKpuyPOCpqnJ84K9w/2AlQlLW2/AG/JEs16umCbHTA72qds2hPV62G62TMYZSaYQeUojBa4R3FzN+OSHf8n3vvcJ9x7e4zd/6ztsNxtW6xUX19e89+GHDLIBzgWJy8bVoBWJ0rSbLTfnl+R5QjFIgrbQafaGikTUrGcr1ustSZpwcnLIwWSAUBnlcIDAsFncsJ5fgF2RpAp8wnAy4J37J/zhv68YSMm4HGCsIcsz2q7GGMO6qaPlSujsdqOEiwmI4tbYsHc1eJtD5Ajdp4sJUUKoYMGiDJ1pEcKysZYsL3G7bdoUJYPFkLWCtmnZbhqsacmLYjfOEUenYLUcdJWdNaFjzDJylXA4nfDenTHPX464vroJWaF4Nqbjcj7HOoOPm8OAVavdlq7ngfVZliKmeAcb69iR+DAaOhfF0SIGwXp2XY6MOhvn4jgqY9pRdFGV0SgyuKqG4tJZT2dbOmMibw56+FxIGbMsRdwQ+qho6cttPP/x8xD0+tRA2RAykG6FCwUweJzZXUFF+Ng5GnaOrL/I4vXZz57w3e88IityJIJ6vQFaBtmQfDilI3yhROcppSbN93jw4AFd01Jtap48ec4nn/6M6bQg1x0//dmnZIOEo6MDpIC26shLKIYTTFOzuFmw6iz5oCQfFKSpoulqltstzkOeZygpqdZrlOtwSmJaw2A0ZHqwT1FVbNMEXIfUKVYmFPvHDCYwPTjl+O4DNus1m/WaJ4+f8OLL57x5dcU3v/Mx777/IflohNSaew8f8JMffJ8XXzymKFKOjg8xbFDDQ8qDPeQmoZrNEPHuZIxDCbhzesBkOGRTtVgP56+u+PRnn/LmxUuOj08YTMa09RWb5YauliwclMOSw/0xrquRIidNE3LhaTZb5k3FbLHhyydP+fW/9zvINKVuamzb8KPnzzk6mPLhR49I8py9vQOq5ZYr27E3GvLuw2N++slPMe0KnSgSlXB9M6PIChSCMi85OblLdjhhtdyyOr8kGQw4PjlGSUeiPddXV2g5YLo3YbW4pqta2lXH6N6Iznd4b+mqFXXVUGSS5fyStq0piyHnL685f/OYvb0p1m1579E9Fp89ZzIsaGxD29mQ2yhdFCeHO3uiNJ0xAcAXAtuzP6PESMbvnPKh6wp+fQHQD7FkwZfeNT4SYHsKpqKuayCkN5mmxYwmQX9K0FSauqWqNozsmLIcMChytt4FI8yuwexyJ+OSoTOMtOSbD06xtuJoNGK93FK3NZt6Q1qWnF/N+PL5C65XK4w10Z++523JuCEE6OkLMmQ+qiALcn3YrfM4L3C2C4JwH5KEtFLRUvxWMqR2uZIOIVzEwEIRctJhbOCvOQLRWEsZC2igngQTRXlbVEQflBtOpZeBoxX/aMdTw4MTIUINZBxhBVqn9AlPKIKGMhoZuh015m8vYF+reK3mS559+YY7Z4fMr69RQGKHdIVDpSm6KBFZaPuKIqdrU6RPKAvFycmUu/cP+cYHD/nLH3zC3iRluar4yU9+xm//vV/ncDplkA64uLwhn+5RDnP29vZpm5bX5xdcvnzB4cEe3lua1pCnCUKkiBjztJpfs1wu6YwmL3K261Xk6nQ4GxKus8GQLB/hu47MGsqjPXTErt7/7rcQXcMP//z7dKuXvP5ixXCyF3zQTcfDh3vMLy94+skVq7Mj7t+bkmYJ3pW0izmJhjTV3NzckKiERAs2zpJlQ9bblu2mYjwZ8Tu/8/fYbtYsV0vqZsv4YMJkNED6DGM7lquKp09fcnRyzOFxiU6CFUu1XXN1dYn3CavZij/+g3/L0Z07HN05JR2PqbcVL55d09bwzvt3wwKj61htVuzt7fHxd97no4/fZ73ZkqQ5L56/pCwLPv/sU5pqw8N7ZxzslSSqpBzljPK7HD96gBeeyxfPeP3lpyyXM95751GMHsspB2Nm63P+5M9+xHd+6ZuUhaTZtqR6gLIhofzVmyvef3fKe3fv4lYrElNzcvcA3wnOlzVXj59jWoO3jkTpcHFG4uPeZAreczOfRR1k0Ab6CPCHuzvRteV2wd6bCDpC0lC4q1uMaWkagRLB+hth4r8lpuswpqMsh0HnmGToRNO2LU1VoZWO41gonEKG4thfZE3ToZxjcnLM5GDCpDzmw/cSHj99xmy5ZLXZkuU5ZZZSJorPnj7lzVZQ1y2us8HdQfXcKLHrmIQQqERjTId1RHlOE9xlRLIr3lKEjFTvPbU1BHsZTRZdPIQA03d2UangvQw0kF2SkrhNGeqXleGJIm9U7ppgeMubPxahW/fVyOzytwUoFLOAeWmVYlwTZVbx/QK3FtI/3/G1iteHH3xA11Z8+vlj1us5e6MMnYzZ1ls25+eM96Y4G50ZvKfrLFk2IRsG5wacZO/gkNOzG37yox9zs7Scz1+hip/wq998h0QlpCpBmxY9KkGGUfXh4B7jq5LZ7AapFDpNWW8btpsZg1KTa0mWJVxczulsymhvQtO2NG3DcDwmKTNUnpOkOUma0DYbvK/JsgQpU4TUHN5J6BY3nJ3uURYa2215+tPnSJ1wdvcOVgqqtsFIz8XlG0a5wZmWziSk0wkyzXlzfsFiNmN/uocqCrLxlHx6h+nEkl7NEKajqjdkQNJWzK8vKXKYTKcgBG3XUQ4GDAYp1XrDZxcXjEcF3XaNFZLjO/cwHRTDKR5PIiTr9ZrFasV0MmU8PeLHf/kp83XN/YenCAFJXtJ0HaM0RSeKoRqRFyPWm4bNpkYNFavFDE9Na26wrzx1k2LQgEBqyeunzzh/+ZLJaMD5y1dst1tQmsPjIw6O9rm8nvPk88ec3Tnky8+fcHJ4isTz5tU5pyeHdM2WYVHw7qMDSu2ofM3pccqvfvcj5HDCp49f8vLqnLptaB3oeBG3TUO13Qa3CCmi84IEGfhQUgTX0EARjyt335MEPLx1oQUszIaRP+YWaJ2GTVssfMZ0dG1HnoU0qTTNKNIMYy1dW6N0KAI6Oog65zEuRI15BDpRHJ7kLLc35Okx5WDEcDLCScgHBTc3c8ajIQcHU87uHPPjT75gNp/TdB3LuqYywSEiSVL67EIpw2dgrWC7DdtZQ4exgVwqojVQeH+BYtM6g45xc0oqtAwLD+ElUgZfsVvHWqJTRigxt17z7GgciNj5RczO7QqRDqTZfnMogrpBiEA6jTyXOG6HDq3H5LwJ3ZpWCdFvkR5P+y+7hP1XFq/Vdsb+eMpmU9B2Kz5/egXpGCEleeEphgbhDJgmCE7rDWmS41wSQULwGN774B5KC6z7Kc9evuKzz89RynG6f8AgyUmKjMPxAA8hHNSBEBapJHsHBwwnY64uZ6xna4y1ZKOCbJCy2baoZMD+wYTRaI/Hnz1mOQsav2Z1xfp8g5aeanZFloXWeFMZOqspxwPy3PLOO/fBWdbLFZtVzeMvX/Di8Yy7j064urji5ZsZ9TYE7X707XeoNys++Pg7HJ3dYTAYY9suCqgHtEagnSMpB5SHkma+xHcdUlvKrCAZT0ispK078jxlNJoyOjzGEwD1ar3B+Y50OAiicJWQa0WyrRDecXBwxPV6TZpZ6tZw5/4Zv/F7f5/PP/2MP/6TH7M3LcnzBK2CdU7TGPJyiGw7Ep2QRi2hcZ5/8+9+wB/92Wf85m//KkU24dnj52SJ5lvf+pDBqGD/4JCqarm8mHFwesi7jx4hPDx//pxvf/cdLs6vGQ0mPHz0Ho8/+5zToynf/u577E1HnBydILyjrZeIMuXs6A7uxSUHM82je/epO0HV1ry+uiDoCwMG1NYNgij5IXZYLqTo9ALgcHnEvZ7vBck9Dzzy1r0PoLeKPxkxoaCbDBebToJljZKQaIW3Bik1QgnKokTpML5mWRZ5iCrme0qarkOqlPEo487dO7T1FeeXl2zrBqEUKg25mO+++x43NzNW6wV3Tk4YFQVvXp9zvVyxqBuu5ws8nizVIRg54nHj6QSAFy9e47ygEzmzbUXn+iITlhiNNxhvkUKQSkWqkzBq98nciLg1j7rSSKSTcV0gJDtsT8QbgIVdR+ZVZM97cDFjQH6l7Nz6cEnRryDErhI5a/Eq2fH4dj78XiBUEkxI/zYS7H9t8do/yNksVpydnZBlGZeXJYvrCtO1pInGbhuk6BCC0H2IjMVijluvSLIbDg4P0ZlG6Yx33rvPcJjz6vUZ26rm8uIK0TYUyZq8TOmkZ//wmDTJWGzWtK3By8C12a7XKCx7hwWjQUHiLcpbppMps/mSuq4oihHVtqKqK1zTYbtAfk3TsNGzPvjkp6lmeXXDxeunJKngzv0zptMxw8keZyrh5Owey8WGqt7wjUfvcHywz+vX13z++AV/8eNP8Z1kWUl+7ZcVZZ5TZgV5XqKSDCMkbVfTLVqEE6g0Ze/wEOctc+HweY7PE/b3j9iutmyWNeNDhSpydJaT5gXr9YL1Zk1ajjE2WiF72MyXWCcYHB4xnB7QNjXWQT7K+dYvf5s7D+5y/uIFr18842B/zHg8ZbOtsRY26zVNXXPn7l3yPEPnOfnoiIura4rRAR+9/z4PHtzn6uIN2TBFpZqRGjGfveLOo3ucPbpLs6h5/MWXzOZz3nvvXaRM2dQNRycnnB4fcbiXUY4KbNvisUFkLuD5y1d8+tmXKDWkmbcUHRR07I1SNm2OtQ3OhwzLPiRaaw0S2q7bFau+cPX8IiUELl5KnpAUHZqvXoT89kYsXmAIUp2BCFtUrVXgbvmQ6qRVQpEX5HmO856qqsA6tE5QOqEzMaJMC4yp2BsGpv7+wRGffv/HzF694ejklMOTU5hKqrpBCIWxgk3bcXBwiGw8SqWUpuPe8RFNvQUs1hqc9dStIUlTBoMBuVBstxtQFn0153pbhe+E91iCvEYCZZIxyEuElHRdGz3m2RUuIvUCIq9MxVDbyJ2LmwIi4hVHxXgG47JAEiyI+vGwlyLtwjYiuB86sljgRM/pcrSm221NnQ9yKCU1PoG2a3/xxevJlxccHkywruHuvUNOz6bhJHiHMyHFRsoCqSVplpApjW46WuNp65qbyxuM88g0Y39vwmBY8P6Hj9g/OODVi1e8fvoS11YMignNSvJk9pTx/pjJZI9RPiBbraFpMZsa23TM1xv8/h7DSRCIb7uO1WKOeiHo6prDoyFJuh82MZLAzRIeb8NywLSW/f0p+0cTXr96xXCQk+U5xjjOL1+j8pzDoxOGx6cI56i3C/a3I+4/OObhwxM++/wFN7M1bVfx/R98n+n4kHfevcN8NWd6dMJgPEHqnAxN1wbiYcBKLOloSGUE1moWK4NOCoTxrC4uGEyH4U4kEsp8QJ4VWGPYbita05GVJc52XFy85tv3z6ibBu8CztGaLcVgxNHwlOlkTJEV/NWPf8r+wZr9gyFaJaxXW9qqCVkEbUeeDdif7iOl5ssvnjMsC+7dOeEsuUtRZiRJAh6eP3mFcILXT18yu7imbVru3rtPmpUcnQxpTYvKc/JMkw8Smq7BGsf5y5e8eXFB16xJE49OBKf3ElKt2P70NYe6xowS1muFHZSst10w9sOjlaYcDKjrCiUMntv1f09z7Emr/dFzwm4vmLDtcz7IfoQTaOF3zqlK6WBN5B3WtFQbh5IJqhzGUhi3jM7RWYvWgXStlKRpaqSAATXfvHuX5esv0IfHvPvOQy7fXDBbLNg7PuHRo0dY5zg4OuH1m0uqdkUiPGWZM7YFe8U+68Wa8XhKphyrZsPlcsNoqMKWTkj2JyWmnbPZbsiUoswynHVsmzpE8cmEQTkg1VnAe03kajkXlxe9tTZRMB06WSF6tn5wnwha4zjwxbCPt49beqmIsW9fxbcCfULsipeP/y2kiNKoKGOKgSRSKnScU8NG8+erR1+reF2+mnO8v4+MotHxeMygzNlsVjTbDWU2QQpJ3TYY68lTjaUmSxU2SVmst3St5dXNU14PCiaj4NqZl0MGwxGn9+5QFhnDssC0CfOlZbNcsV1tGY+njMdj1uslm/WaJMkxxnB1teB6HoiVm8WSem2p6zWNTSlHBfvTDK0SZCKRiQgeSUJycFRSN00QoApFMivY1h1VXeG9x3SW1mxZJTNGkW2djyfIVKKl4+DghDQpmF3fcH1zw81yyeObJ3SuZrI/Zrg/pdrMsWaL1hlSaMpiipQG0WwQWpIeTEFqpAnSp9fnL5lMhqTzhLIcUA4GCKUCxuQdSZqRZCkuy7GmY5Jr/uoP/yPz5RqVZbzz4fuITNPmJYPhFLzizt1ThLB8+eWnaP1O+NBVgnMdWTkK7hiDklHTsd1u+OKLZzTVlv3/7nc52D/AelisVjRVSNX53h9/j+M7R9w7PSM9yimHQ9rO7Eb8fE+RTadcvn7N4vULqiow740x5FnCwf6YfKA4vXuGOGw4mGY8eZ7yRz/4kpfSspFQx81UmoTszrpqMNYGy5u3JSq+x7luL5yAl4gdHamXrQSPfBH5RB5ksosNQzgQLrg9yOA/5YGmCYnqUqq4+UxoupbOGYS1kbJhMMYxzVMGiSAvxqxmM5q25ejsBJHkLLZrFpsV48mU8WTC8eEdnr/+kr/43h/z8PQMXSjeXN7QdS1JUfBrHzyi8S0/ef6GRVXTdYbVckmqEk4OTlmkc+y6IXFQVTU6htNqlQS5VyQUB1Z8XHCIvhCFjWsgBEPfJfWWOP2p7P26wngXLDJ6++neR0xEVr7glorR2/5EbvHteC962+egnFCJwgqN6Uw43xKwMd+y7wB/kcUrSwWz6xu8d1jbofQhWkkWiyWbxYLtcsNqscBbS9c6BoOcew+PAxnTWKbDPUblPpNRztM3r1jNW6TUXL85ZzqeBOO/ckwjBEmWUQ5HrJYdzbbm8vwzJtMJk/GIVAusa7j38ISj0zM2TRPDMi2uM6RJifewWMzYbFfU9Za9g30ykSGQVNWaZltRlAVVtQUE4+mIrrPoJA1kzc2W7WzF8nLGy6fnZHnBcFhwdXHBYn7DZDxh/3BKOSiY7B/wrhTczOe8ePGKJ0+fMbtac//uQ0bDlLxUeKcQ+57RKCUVgctSDAbowR5VvWQoSt4p71JXFavFCt92KFcjpcM1DWmak5WKtoX1aktSFHzw3Q+R1vL/+3//b+g24clPf4TMEoTQWCtJBnsURR6CIdqK89fnpGlClqUsFzPGUjHZ38d0HeNRyWj4iMOjQx5/8YIf/uATvvtL73NzvUQgGI8GTEcFv/v3fo2rxQ3z60u8F3zwnW8jtabrLGUxoCiHzJYrhNRMH77HwDUMlitmF69JXIvWsLd3gJA5y9kV0lu+/fFHPHt5yevVmJoljoTE5lg8xga8RvhImkRCDDftD7HrvPpbtuh7Lby3cQsXfasisOyjpz5EThMSpWXAuaRCSk2ahQ6mamqKNKfIC5wAFeO+0kzifPAv2z/Y56c/+xQhW7718bc5OTuDLOXy/Apn4Xt/+ic8uPeAB3cfIpKc/cmE8WjCYDRmNB0zHk158/qS9WrFi+U1Dx6c8Y30Eeva8OLlK7QI2YbXN1cUWc7ESpr5ilSHAJKQFelo2za4Q0aem43M+rDo+GvbvwjOB7eMIBmSUr2lp5Rx1LQEQfxth9UXML97tB77kpE3Fn5WvbU4cT64eThfgQyAv5VBPtWaLmyG6UfWX3AAxwfvnzAYjFhvK26urxBSsF4F7ZrWmsGwIEs08/k8YE9tQzmUOGNAKm6uV0ynx3iZsF02jPenyJg719mO0WSIs5b1ekHCiqauaOrgVDkoS+rtFteFqKxsMEBryWa9ZjAckCWKpl7TKfj/0/afPZataZoedr1m+bX9Dp+ZkZnHl+muNtPDJntaZkjNiDMUMNQHAfogAtJv0O8R9FkDEZQ4IqXxzelpdldXVZc9Lk/asNub5d2rD++OPDWACHUD1QHEAU5EZGbEjrWe9T7Pc9/XXbcFjvLxHI0wmroWbNYr4n4Pz/Op0hyhJKHvEboueZqijXX9txjCKCDux4jjI6qyRKJpTEeSZZw4pwgpWa92RL0QrTqyXc56n+JHEAQR/Qp2m4Qvky+RwGBo8cKSdzx6PObDTz8i7EcYYaipMBoC36NuG5vOrXz2+5SyrKiLjNALoYauEwzGfaSS5FlOWUiKIuGz3/oOdNaDV1Q1o8GEsqqpqhrtGpKs46MPPqTtauqyJAoiPvrwY27v7pndXdPr9enFfVojiAZDzs6fkCUb0rwiyUo2qyUn0zFlXjANz9nnNW8+f8Hp0ZFtTbqO+9s5VdOSdh3jyYj+0cm3JyA/RToxXrvHkTX7oiNf5UR+zM31LV//4q94cb9iXwnq1kFqgSMNdVlQN/XBs2f3Yo6rD4r89n3hsrH3dkb00JLYO+uwtzJ862M5qMvfx94fDM9gh+OO4+I4Ho7n47oWGLnf7zFBZwvFIT7EtBVaSpqiItKa//nf+z1GfcnL199we3/P3XzG8dkpeZnjKhff8/j8i8/54osv+OM//mPC0Ofk5Iyo38f3XW5v50wmY6aTCdt0zudfv6U1kjDuc3J0RJkXnJ2dIIxhtduifZfA96HtKIqStjUWgXQIKum61r5Gxo4qrGhV2sAaIQ7JP9+21Q9zLa3V4XDSHlDPv9YmPryEPHgYD2x9vk0oOkwZDy/3YYR/IIF0bUeLxDlkd4pDUa27CtoGKfThgfLXe/sbFa9sv2c86NF6+rBiFtzfzfA9D7qOZL/FUZLp6TF5DS9efENjWp5dnuH7AlF0rNcrRpMp5yenvHp7Q9lWKNfB833aDsqyIY41pycjojCwsgAD8/sFWV5QVQLXVQjH0LU96kKw3G0RpqMoMhAdRVWDsObYXhxxPBljhKLGgBHEQYSho8lLGqBMcqqiBKkoakuQ6PX7BFHIZrWgKgvCXg/lapzA47L3AfFsQZntaJqWwXiMFw2ompIs1bxe3hMEAd/7rXN8T/P65RVS1WBKtp+vuL5f8NHHHzOZDlBeedj1NKjQoy1rhBKcHz+ibQr2qx1dI2mFpKoM+XwOTUtTlNxvlggNrutTFAWu9lF4+J7LcNSn7aAoKlDWkhGEI5qqYXZ7z+npGU8/uCTLM+bzBYvFAj/q8eyDD5Ha4+R0inKgUTe8fPMNShqifo9tluJHEf3xhMHREe+ur+0GuB/h+h41DbIt2MxToqgHfoATRLT7ipure8K+y3A8pK4N86KmDiZ0bokfn0KyQbkK0ZbUVUZd2wJsDFRtw4N1SPya+t5gqA+Zjg9PfDiQF8RDC3hIHTr4Ay01Qfyaj86+K6lwnAehZ0uSJCjtIIWiahukVihH07WN3ZY3La6Ei3GA42yRTsSHHz3j6dNLNqsV12/f4IYh69WCthVcPn/GX/z5n/Pf/ff/jO995zP6/aF1iDge07Mz3t68xZWawWBM07Tc3s24my9RWlOWJe+urm2GgHbJihLXUSRV9d7GJQ4F2LZ/tuRoZfMSxXtSxEOxehiem29fCwNN80AmtFvb9ybx90NEoOv+w0J2+MSvCSN4/8Xv21A7a5Ra43uenT92BiUbqu5AUJXWyvXXlXr9jYpXmTcs7ucE8YDNaoebNxjTobQgDEN2yZar+xnCcxkeDelvJrybrZGB5sPnx/QGMVCx2ywYTaZ89uEZaVFSdFYTlmxzbt+tKUJDsU5I0pzTsws++vQ505MTXiYJWZJwcjRAdRWizGgx5E1lI9u1Pe73/AjTtfi+Z1W7whD1QoTWaClo0xQhDVVdU+YlXVfRtQWmEwSeB6Il2czIV+C7HsIIiu2OvC4Rwifw+0wHfZzjIUVeIqVjdWVlydHpmOPTPm9e3/HN13c8ejTl+7/9KcoRLJcb5vMlnal48eWv+JN/c8uTR4+4vHxE3OuxWm3wPBc/sCEQnuvj+SFSu0jtIqQi220o09SeVusK33FxHU2dF7jaYLQh9B2KsmCfFkjt0osjdklKUZRIbJiEcjSj6RS1XtG2Ha7rkRcVi7s7pFLEwxjHcdBdy3QwoMMwHI8J4x5d27AbRmzzPX1Xc39zTxzHbJYbyrLkdVmSJQln5xc8+fC5xShXDfEgIh54FMWOfhTTH5zQl1C2giSDZa4oG6jqFU1teVyOdiwMsLVzuqqqEb92I3VYzlTbWQLYA5VCHG7QhyRtcRBfyl8b+IPVStlhvg2CMF1HQ4OQGiEkVdUcfJAOSEVXV3QYXMe2jtJ0fHJ5znQwgq5ju0uo6oY4jHn8+ILFYg5Ng+v4vHv1hu999h3ubq/5//zz/56PP/6M7/3gd0lKmwERKsV6vWJbNSjtkeX1wRSdU7c2Eb1pwfcDirIhKTPKskIKgXItEPEB02wFo99SXt9rrg48MCm/nWFhrLdQHEzjXQeiPajeu/cYw8MmkW9nY/JBP2dfywfSmDlsKruHUy9WgqEdTeiHOIeU7LptabrDA0YYWtO85+3/xouX62jiKMINAk7PQubLHcvFntubFU+eHHFyesJ6l3N9OyceeJycHfPm7YwX37zj4iSgfxSTSUmRlezViuHI4nWC8YDOtDTbhOcnPq4jWa1z1m7I7GaB0vDo8oyiaHn95op9subkaETdtoS9PtPzM3qDAUVWsd/uyJK9NTHT0kiBMg7N7R0Ka8it8txC5eqWPC9skIVpaIWgygpMayiTjK6s0b6LCjx64z6O61AUHel+wz7tCOLAsrepEVoRxB6e66K1IIoisiwj2SV89eVbhGw5Op5wenpMVVaEno+SZ7x5/YquLonjmF/96isEHh99csl+u8b3A/wopj+akuUZnhcQ9Xp4fohSUOQFy6trZje3SC2JxiPi/oD9Lme9ztjtdoRxgHI0RZYjhEIYgWlhv8twvJQmK6nLkuPH5wxHY7Jtymq5JPRdmrJEdYZeGKEDu4X9/PMv8V0bzpqkGaVX4DguVVnhuh69wYjb6xtG/QlV1fGn/+bP6LqGjz68ZDQIEaIm9CWOqi0fXztMRmOmpw33P/2S29mcNM+pK0t3eFDAay0oS+i6AweK9hAxduBYwbet4WHO8qAX44BlEQ/SACEOf9aOK5qyRUkHGSiqusZxFG1bEwQxQeBj6FCOpqorHjILm0PbqdE8Pj9nPL1gMZvR6zsorbm5uUYIwfn5GcvVjtAPOD4akWU5cT/mB7/9fbbbjLIuyfMCH5j0BygjqIuG29mSLMlxA6s9i3s9hNLssoKiqmkF7NMcJTVa2iG4UPZnVMa+QuLXTpjmofB01h3wfhBloDWWI6a1c+DHi/cwwubgw7TC00N60wG/I8wh39oYGxJjrFRFYbeKPARrGINB2g3ugc76MOCXtX2gdN230ou/leKVpHtOjkc0Tc14fER1EPhtNlt++ctXnJ2PieI+N3d3vLla8NEHz/jk42e8+OIFX/1igfxOjOc5jIYhnudC27BfZ1R0aEcwiQJchqxXa6LAJwhdPvveJferJe/evibLE+arhNU+5XqxoxfOeH75yAanNi1CKLSxsgxHCdaLhLKo2G8S0t3eAv6bBtO29OKYKI5wfQfXcajqmtlqw3K9x3NdHOXi+z6yLBB7SIoEP/Y5u3hE3B/y+uVbyt2WTrkgJE3T4rgOXeAjMYyGPSbjAXmWkKYj3ry55euvrlBKMp0OyJKczWZHrxcznRxxcXHKYNTn+nrH6zd3vHn7jl7UI/I0o2GP/qDHYDikDUKUHxI/viCeDAnigFc/KyyXCkVaNMjQ53zwiHi1ocpzmwTuOLRVg+kEURjhuz5CSE4ePcKbeazXO1Z1R1W3KM8nSVJmN7eMh0OU1ijHY7tLiIIeg17E8XRM03bc3t3juB794QChJXVVMxz2oDU4vsegH7JYznj96iXZdMwnH12SZyXSN2hTYrKEPK25e/mWQGtcrSmVwnOcQzq0omntDMciiRWdaeiwUWBt2x2yCw+4FmGnLu/ThsyDU86evjhEfHVYnpYd2AukaFBS4XkBjtZII9DKwWDwfY+6bt5rpZSy3PiyKjgOHDQpd2++wg9ipHbxgpDp9ISyKEj3O2gF09GIusFmAsiGpkoJAp8f/vDPGY/GTAZ9uqo+4Jwcen2XwdBDCEkcRRjTkJc1dVmzN4YszynqiqoFoyTad617pKooiuK9HILDrEseuPCO1pRVCXBAUVtNmW2hm4MB/EEtdzip/Qcb3oNY+P3rDJ2EynQYYXBQh72A+LYEGSt5UcpmVRoMStsg4KZpoG3fz9QE/LXPXn+j4rVeb5mFdwzHU4rMJ4w8yrJiMpnS1JKy7EjyvcVeRDGNabh8NCJ2P+T63TU/++XXaM9n0g8Z9z20dllt9jBf40c90ihGiRbHjemFDmVp2K0TfMchS/dcv7vD0T44kuUmZblKKTNDtsw4mk5ou5br+zuSLMXRmn4c0pqO9XrNIIpxlEIpQX88xIljgthB1xltUxO4IePhCMcPqbuOsq4paMnSgnS3xbxtiXoRd3czPvr0YzxtSOZr8rbFdRzqsqB1HRrPI2+hQVG3HX4QMJ2e8NFHIY+eXGBMhxe4TMZD8izl7csr6sbw5s0trekYDmOUc0KWJ1Rlzu1iztXVawZxxPnJMZPJgP54hPRbkC5KuoyOJxYhE0a0xtB2Hcu7e1QLqut4/OSc7WaN20lev7lGKA8poG4rjGdDVYptRtOAdB1q09GPAk5Ojlmv1kjHQgVV1dqbZ7/n+GiC1Jp4OKQ3GNB0LXEYka42SM9HqQdbS03f9wmmE9arObuVJA4ckl3J4+dPyRdb6k3B7392gRe3tD9PuZoZMC1VC1XT0bbNe5KnER3vddzm16Ys/1PaoIOIFWk1SZ0QoBSdFDZf8cDF8h0XYwxt0yKkIA4jbF9kxcxVWdM0Vt3//ibGYNqcq6tveHpxSpNnSNdDqymjfkTqaAbDmLurN8jdjvhoighDitxjv9NInbHYrXjx4gvuoh6fffop/f6APM3wA/eAjrYzNiENg34AGGaLNQJDa2pQGqkFpm2p6posz2k660YRUh/8lw9yBvF+IN51lh/fHeZXDwG5325xLdDRcQRl1fyHL6mUyEPOpN2jWMlE1x1OZ8Lmp74P+RAHdr0xmLY+6O0EVVPSNAXQHU5wBy2k+VsoXnXT0LQpTe2T7DWV1Oy2CXXd0YkD7Ewobm+XFGXNMHJYkjKZ9hiMP+LV23tubuckmxX+s1NOL3r0BiHrTULYc4hCl7pqbOpNUbLfJxSlISlyHp2d4uo+v/jV1wT9kETZOVNaVtxsdsSTCWEQcXRyRpgmdKZDKUGW7HCDiOn5OV1jWK7XLN4tkazQAuoioyxKRuMxZ+enKNdBS4MfRRR1RUOL1BGuFISOx/J+yZ9d/SmXl4+pm5zAd3B1x36Vk2xzpidjmqohq1uqxpAnBbtNilSS6dGEKO7heA5KS7TjMZiMWe/2/O4Pfo9kv2d2P2M89GiKls1mQ1U1TI6OwMDtdsM3b14zHo55PF8RRiF50RD1BkyOzuz8p6mZ3c8YxD1Ojk9I0x2m2TKIWtJ8z+jY44sv7tCe4tOL7+G6itVmQ6sdjs/PCIKA9XpJVRWUXUMw6lO3BiE0URSzb7a0Vc1svmA8PcLzfbwgxMVQ5jkn06MD+rdju1kjhebieEDTVCwWHoqSOPbxxj27VexHXPR63Lx5xWXs8HI8IikUXdNRJdn7m+wB2mfV4nYAb9qHvL9Di/IwyxLifQf5II48rLcwggO51aKX5cESo5RtMTtjKMsS7XoI0SEP8xkONVA7DsY0gC14j85GGGo+/9WXnJ2dcfHsCcu7d5imoW5ahNIcTfr4taTRkv50SFiEVHWFdh2+83HIu6tr/od/92e8u7nhH/8X/xvOnl3y8svPubm6QUpJnmWcnp6gcklR1tB1uK6Ddh3SpEB1VhZRVRV+GOAIl6quqdqHcF8QRlqPaNe+zwN4oFo4rj5EqPGeWmswdE37rRj1oRU9mLPlYREijcDpsDIL7GtpeHgoHL7u4Iaw5nNLWPUcl0B7BAd2V2MM7UHOUpuG4jddvJarDNd1KJoNftAxOTnm5PSIzTZjMJIEgYvvRxgkr1+/4f56xV1VUpYFXugTDSMmRxN8R6HCIdpxUbIj9B2UMOTpnqIsIOiB1Lh+QF6leL6Lq0NOTwKEgsVqjela4iik61oa07DYrnk6eMzHTz+k6xr2u4S2K3j9Gt6+WZNmNXEvJB4MMEZyPJni+QHz9Yqvv/qaNiupbmZ4rouhIYh6pHnBar1CAsNhjD/ymEzP2W0zdllDlqfoneRoGuIPXFbrGtyQIl2x2WwZDAf4vkIoCIIQU9WsbmcozyYX+WHIYNAnLytu72958vgRYehR1RWPLk9pys/4+suXzGZLtvs9OAnD8Yhffn1D3go++OCczWrH/WzGerMkHg7xg5C2zuj1Y6QucFRDXmTstmv8KOTx6Qn3N1uiyGM+u+HCe0IYBISxhx9H5FnKYjajaa1Fo2pqtOMRBZLI9/D1mHSfMBxOiHt9mq6lqUowMOoNGI+GtG1NliTst3tW63uG4z777Z75fM7jxz2GJsSTGtOWCLDAPm2Yz+bUWU4cxyRpQbdPMMKgHIk0jjUGS0FnWouxkc17K8n7mdbhdusON6CkRTzwsjgYubEhqDxIK6SkrGz4bCQMjqPp2pbWNDhIKlEehJaGLEsQElwlkU3Lo0cX/M6nE+7eviVwfaTrkS6XzG5vePPmDcenF5w9fsyajkkvJE0T244KUFJTdSWT4Zg//k/+HrezW/7rf/p/42gy5e/+zu/y4QcfohzFzc0dN3czdvs9jnYoW2ud8oIQkxRkWY5Wil5vgOM4pHlG3bTY1Hh5yOK01dyKcLsD788gpdWtqcMg/6H1rusOIR6G7r/WRh7mZXZDbpcCQlgJ2YPR29qHDvRXI6w/0nRoBB6ayA8ZxD6ulDijEY52KMqaoqrZVwX3uw0F9W+2eIkAXlzdMcmO+fB5H1daU+rbd3c4bsB4FHJ66vHJJ2c8fTJAGslqtuHlN29YrFOW+7UFoWm4ut9zuZ3StQVFkhK4DseTCQJDUzSUdcPpxTlRf8J8uaFpK45Ojvjo00vW6x2z+ZJOCLabHbP7BS+/ecfsfssHz5/Zgf9qQxBoHDdgMOjY7Qs224Tlas7RZMDoKGIwiPn4+0+4fHaEVposset9Yyz7abPNcDyf+f2WfQp5ucP3AxwnpGgkrejRlCUiU8yXc0J/TFy0aC8k6rV4rsb3XJuhGAXE8YAiL6jrAkmHpyWyrTkb9VCOoasyBIYyy6iEIPA9Lp9d8Ml3PqCqa37+i19we7divW/55YsbotHAmnvfvGWxXBP1ehih2KxSPv/5Vzx59oh0t6erKlxXMD1W/OSHf8nnX73ms98ynD0+oa5estukjMYj7m6+Id3uWS3n7HfJYTHgEEYtTuyjtaGta8oypy5rhBEM+n2apmY5X5AJwV1V4jgSLRXhoMeuyGndIcOLM9I25Kc//4pf/fwGrQqeP71gPOzjafBdxWjSp/5myWqzoywrO3SXB5rBoTVUUh1OEN17G4rEoqitXeUwjD/cRJ04pJLLByHlYaXfmfdaJOCQXmSo64q6tgW762zL+l5+cAhbteZkga8F42FMJyXjR49pq5oOSTQY8OHAJpB//eoV98sV3/3B79ACm+0W3w8PAMmUuilpEZw+esLJ2QmffPwxn//yV/z4xz/mD//gP6IXxRxNpjx58gwczc9+9lP8qqLzXZZZhu8HuC7vf662PgzfhcRy/6xUQokHdIOgOXgQ7afVAfWj3mOgjTG0B+1Xe0Dz2HL0LXHClsKHr++Q5uHrOASOGKS0HLDjIGbghmhjcJXi7HiCUoK2q3EcF98P2GcZZVVzt1yzy1LWf43iJcx/YEr6//222+0YDAb8n//L36dIC1wv5MnZhCdHA3qjKV+8ueHtuzsEmiePz3jyZIzrloROgO+EJPuMu/sdV/cJq+2a1W7DZrNhPOwTRz5lnuK5gkGvT3PA5vqupt+PeXTxCKkVrYHecIRBUJctTdNRNDWbXYLneczmc05Pz3h8+Zi6rtis1+y3O1zXRWsrYry9vWFxn7HZLvnt3/6Mjz97ymDY5/5uTlW1FFVBZ1ocpRCAo1xWizVNa2g6G1bRtoYsy6xZt26pytqGh7Y1WZGRH+ZtvZ7Pp588JvQC2g5qY9NaiqKgF0coLSiygslkYnVyUlLXLU3bUmYleVaiHRfX9wh6AVI7KKnJ8ozFfMnVuxtm9zPyrODy0WMC32W73yAcQVMbdjt70inyGtkpBkOfP/rjv4Mxklcv31o9jems6dvzePz4wl5Mvk9ZN6RJhudFLFZbO1SlIwhCenGP3/md7/FXf/U5HZp40KOoS4ajkYVOFgX7/RbPczk/PcNzPaLhmKLqkJ0i3SVsl7c05Z4m2+GrhvHI4/LJEduy5J/96y/50Yt71knKvqwo2m/V2nVtU6WarqFt7Rymba1c5yEy7b1J+EBbVSi0dA4WH6smFwe1upTqIFAFLTRaOURBTBwP6A36SKkoypowiFDaoSgKyirD8xw8JFMX/ov//O8xjCVa+wyPJuy2C1brOdv5DN1AXjb89Be/ZL7Z8Id//MecnZ9jWsOwP+Dzz39Jf9jn7bsZcdwn9BWO65BnOW9ev6Eqcn77e9+jSHLiQR/Vj3jx4htu3l2TlSXz7Y7b1ZZ9VmGahyRsQSewQ3lhU56UsrKPrmtp25qyKWnb1gaSOBotHLRS6EMSUts2lAdDfX3I7kRY7ZxUksAPbCpTYwGUTWtR2Kbr3luRlDaMPJeLoyM+e/SUaRxyHPeoRYvy7BZ5vlrbsBbTcnt/h3Y9rm6X3G/W/PTFDdvtln6//z9Zl/5GJ6831zscJJgNkhZfNbhhwOOzCYM44NWbKz7//Euur2IGfcn52ZjTsyMG4z4fTPo8/khzez/nV1+8IIzsFk0rh/VaMlsseTe/QWDwNIxCh6ax7cfZyRGOdhEYesMJjufg+BJTCII6pKpynl1e4PqaIt/i+xG+6yIefHfG8pc++ug5J8clv/plx2aX8vNfvCQMA8IgItmnrNZr1us1TV0jhWDQ66G1YjjsI5SC1vrzhv0JVZETjEK0FjRtg3YVUjks5wlv39ywmCW89eecHI1xg4C0KAl9a0+y2r0O5UhWmyWqszOXzWaLH/r0Yo/jcUxdtdRdSbouqKqG/nCM53ucn50wHg2o648oitoioHs9joojNrsNjqOJex+QpnvSNOX+ekHXOpxfnJEkGb//B99HOy6daUmSHcvlnPn9LXUtGU8mlnMW+EDHeOBT5CVdB/P5krurW548GhP6HZttyptv5ix3OwbHUz753ne4fPaEZL8n26W8ubrn0fkZ1WZH1BugHU18PKR1DV09otonOFVBUu54d7OjNA27zRbV1YSeQ9l05I0N4WgPKUNddwiw//Xk5sOw2X4MkA9Yl2/FmBykA5iHFJ5D+yMVUigc7VhNmVKHzEN76vNc98CKt9uxtm3QykW1Lb3QttOWgaXpjCEMIzoDsdMn22xptzs++fgz9j/+Mf/DP/+XfP+zTzg7nTJ7mdHUcL/d0RYtwXCM7/sgJX4oOTk7Z7fb8uNf/ZKuLPk73/0uab6nHweY02O++vobqjTDNA1tY4W80tihfHNY2gSeh+/7OK5L11ijdmUsf7/tOrSSRGFE13RoaVtHS3i2Yl5r2Xk421iLkD6kMnkqwFUuVZWhjMJ1FJ4QeFozHY8IAodJL+bR2TmxdilK200YqSiyymK0/IAojimqirptmc0XaK3p93t/rXr0NypeR5Mp2T4n2e9ZrXcsey7DYY7jugyigOdPH9Pr9Vks1lzfbHj95poo9vnwkw95evkE3/PxPI0UHtt1CqJhOBjy+PKEyfGI+WLN9dUNy01Klmoqo/FdRWEk4+GIk2jwPqq8bEpMVaI7UI5HmZWUec0+ndMbxhxNj2zSzdZiiZu6xel7PH40xfU8ol6AMZAXBb1ej8tnj2iqgtn9jO1qR5rkpGlG0zTsdnvLSHI0baXpqpKmKsmTHSAQUtMfDhiOYzzd8N3PPkVKxe3tNa9e3+H5LutdgqOcg52iI+4FDEc9jk/GIC2et+5avvr5VxyNe/SkIggD4uGIthOUu5SbzRYZ+MS9IdpxLG5aK6bHR0zHU6qqYJT1KfKcokgZDwZcnJ1ycXJKXUpMa9huKtI0pcPgBx7b9QIlwfdCTFPSZDmqtilBrudjdlv60ym+5/Pk4gLtKKRRHE1OGI2EzaZ0XWTsMZgM6ZoaFSguxid88OQYzw8p6tZub4sUozRpk9EfDvAGEXdfv8LUkvvbLbPZLbPNhl4cESufrFljypLmsM5vu5amO2CPsQJKu+z6dp7z0NpYaQRWgGm+HeY7jv0dWGmAnX0pfUiHVg4Gm5BlN4uO1SC1LUWe4ShJbTqaqkYLzfT4lDRLSeuW/kChCp+6KCnymjjqMxgdE+22RJsVv6c1P//Zz/jpz3/KbD7EFZJnTz9iuVxxfPoE0zaEcYxQisVyiR8EAPhByP39HT/58kum0zEn52eEJ8ekSWZZ+2LDvrQqe9NheV+dLVBhEBGGAXVjjfOe4yCETQISgOu4NhpN/xpBtbUeSEtTlUj17etrNWQGV0l8J0BJQ2VqgtDh8dkRkacJA89mDShBmhbsk4Q2jGirhro1jAc9mqqmPqRGVU2NEQLH8Xhy+RRxfcfdi69+88Xrs0/OCUOPdF+wXCzIioK313OOjkZ4foEfxnxy/BHHS3vzr1cb3t3e8u///AveXK356NkThLTscD8IUFJx/XbPzdWawchjOhoiuo7X1wt2WUkyT3AdySKrmG72ZGVNW7d4AgQWc1MnLeGoT16VYAS9fkgQRijHoWtawsC3UemBsLohJJPJEDfQIAVeodlvd8zurhCNHTJ6WhNMehwdD8jyHNN17HY7XFfz+NGUuNejrkvKogCbhkZaNMxu7piMx4RxTFIUnJydkiUJYRAR9xI8z8GeEAxKaeazOZtVhpCKs/NjtkXNzWLL9bs5fV/Q7wUcnRwRxzFd3dEKiW5q6qJkOB7TdIZ9krHPMna7PY6jWdzf4nv21Cp6kmS/xPV83NCjEwI/1MT9Eb7vkaQZ+61mvdwQew6fPD1mEEWITtIgyTt1uIkEcRQShIrNZsdqlRL0ApTnHUzMML+9RpLTpnvISrtR9QPGT5+TNHvSJiPf7Uk7yXh6gRfFJPs9Kgi4Xa5YL/fc3K6pjaTNElCNfX2x+YF1VdOaFj/wKYvMzlzUQdN9oHc+wAh/3aRtzLdzKstI7963UQ9csK77lpveHThUdV2jpT2RKcn7EAlXu7R1RyUV3/n+3+EHv/WE23dvKauc8yfPyPcZN2/eIpVL1tbs0xQ/iHn05Cna0ex2z7l6946bmzvmy58yjGM8GfA7v/8MPxqQ1iVxEFKKwiZXlyXD4YgyL3j18g377ZbID5BdSxx5+LnCcaCuLdfLGEnHt5mSZVlRVSWe41ltn7IP/qZuD6dKO09U0obCCgRpmtLUtdVjHaLKhLDuk6Zq8R2ffhDgCYP2IfIV01GAq8ARNgMzzRqKJGX0+Clnx6ekRY52NJ7n0gF+YDg9O2a1WjKfL/AcH98LOT855n69BG5+s8WLuiDbF2ilefzoBNMa9vs9uywnljFFsSF0XMZDn4vTIY665OXLMb/64huqpODtuxubtJznXD455fxizHolePf2ygY6ZHParsORCs93KOqaTZLjOZq9qPjF56+4vVtzMh0yGgSURUrbOvgDn8BRiNbQNCXVruRuPaNrDYEXIoXCD31M1bFLtsRhQFtb75sjNePAZ+S6pPs9bdcghUuaZyzXKUI5eK6DFA7JNuOea7baJQpDe3MIqJoGL/BxtCDP1ghTEwQho5MxSajxXIfRyEVphes6CKHwgpDnH1+SJinrxYog9ElSh9F4wGadcJ+m7GkIj200uwC6xiYND0YjNuslUrmkSUaWF7zO3pAmCW1VI6WhLgqOj4+QykocjMgob2t6vQFSC15dvaVIdnzw/BmnRxM82XJ27FPsUuqiQAoX1/EJ/NBi6LoWz3Eo85LdNsOJfM5Oj2ikZD2fMRkGeMqwWq9o1wm9Xo9CJ1ylFVoVOIHiONDkOHSipMlaqt2KTtf83h/9Pldvr0mammI+pytytmlG03VIpd5DC5TWNvNQPmQ+P/DsoTMNrTEHJPKDifhb/94DvdMmX3eow6D6IR6sPSTmaFe9t9BorQmDgLatSbMUJeyN3LWGwDXk6YJdMuLi8jFJuqdqW+LxiEnXMr+/4/76iv3tHc8/+ZjWGIajMVEYcXH2BMfRfPGrn/HyxUuWb15x9OSc57HGbWuGbkfRtQhPUSqJzCRe74hPLk5xHdsOL/cJfj+gdGCdJdbmJr+FCmptQ4JN19HWDUa5OI6m6ipc7VA5DdrxaJvablaFQLUdnu8d2mbzPrxDH2bApmlxhCR0NeO+YhL7BGrAZDSgoGES91BNQ5KnaAXz+Y6b+1uCwGM0GiKEpCgruq4jCByaxuLiHaU5mR5xenrO5y+/sdvrv8bb36h47XYpcd8jiDwcBaHnoUVp+3RXkqwLZlfXeKEPkwFuP+ajj0559GhKVtaUnU00vgkWlHmFq3w++Mjh6fPv8OqbK9bLNRjD9OSU1XZP0XYs5gu6tmU4OWG53rItS5wip8DYbLm65qy1K+0uL3FER2MqAt/DjTyydE9RVhR7QxyF7Hd7lkWFVsoOoAdjttsdge8jMGS7LUZLyrahyEvSYk9R1MzvN+RpzXgcE0aCYc+jdwimNaYjLXOi3sCuhv09UX+AqCKoam7ub0nzDN/zOD4+xg/sHMlID6kdTh+dEIUhJ6dTPvrkGb/84ktevrjHD0Mao5nNd3R1yenRhGS/Z7tLMEJSVS1e3CNvW7q2Y3Z3z3Q0YhD3wA+pq5pHT8bE0ZD5MuP6/o7LeMx0PKW4X/PNy1um0yFt0RD5Do4yFPs9YRBQVi1JtaWVe2zyMgjZI4oC/LBhvyu4vVviDQKUo1B1wfzVG9xOstxmaOOAFtTrLZ9+9xlBT1ld0r5msbhhkzRkZYVsGzZvviBUDoNJxDfXV6yXO6R2rTC2bQ9R8ZrGtGRFBsLOwB4izsAcpAfKzrQehBMCi1n5Nc2Xo/WBIvrrtASruRNCHeY67nsDn13edFRlSVd0RGGEQTCKXcajiJt37ygnY0ZHIxxl+frx6QQVB0xGY94i2O3WdFKhtYcQDhzID9/73m/z5PIDvnn1iv/xL/+CL7/+FRcXZyhH0hQZk0GPyAvoe4amSwm8PkVeYLBElEeBR900tvg0Ddvd7rCIEWDsQqaqW3zXtQ9NKWiqmjTNcRwXLTWtsSespm3QWIdEURaH05u2hFmlrOC07VCmY9ALGA0CfFcRuR69OCTEMO4NGcY98rKiBJ5+9DFVWbKdzVjO5jx+8oTx0RFRFDCf3VJWBb7r43nBAXOu8aPwEDT8Gy5eX7+5wnMUge+jpbAygMN61VHKBp76DoiaxX3CdmUDUvujKWEcoquWLGt4/nTKYr7km69ekpc54+mYo+mYi7MjqrLGdSM+/+obsrqxpw4D8+WatoMg1EinoaW0Zk7R8suvviZyXaajPoPAhapCCwdXS/YCFsmeuqlo53f0eyFx7OL7Bi1KdrsFt/M5ddPhKpe2rvGDiHg05OJiQic111d3hGGf7S7j+vqGqNZ4gYNJMlytmI7H7POSl2+uiAd9xuMz/MCgZI4faurOIU0bFrMdeZJbWobvMhqOkZ5Lg8u6qEj2CdKRNKWh6yo+/eQTqrKExmO/23F1c8t+t8PzAvrDIXle4vcGuK7GNDWnx1PSNGex2XB8PGEw8GlJ2Cc5baMItKLe75DjGNqWomy4vn1Luis5Hk+YLToenR1RKZfOSHytcVwXpSVxLwRR03UVT5+fUNaSu/Wa0JfMrq7oy5phFOJ6Ps/GQ7768gWqhGdPztnsd6yTlvF4jOgUbZKwfnNN1hlGQYDXtMTjCZehz7twwNop2OxTpB/YtbuxwtKO9qC/6g7+uu7hUHZAv1g9lzDvLcLwPursIR3aBotY0f2BTtEZ2qZDaAeMZVgJZVOGuralzAt7QjkIn4V0mAwG0HbUdcns5pb1/J641+P8ow8gCnECH+/E4yPne7z95mv2SU4U9ijrmiDw2SznVEVF6IX8nd/9O3z84ce8u7nmdr5kt9+RJxt6kY8whn5/gON7vP3Ln1AUBeOjI7TnEgcBbVGgkw2BKUi6mtaAMBYPVVUa3/HRgcb1XLIiI8sy2qrh5Picum5oVUXXtQf2V4V29AH8aBFR6rCFVFg/oqcE4/6IwPWgq/FcD6kU50cneFIThj5RL6YwhkYI9rs9I9djtVyy3+0wwmZrTiYTFosFuzLl7PQMR1vFQJKn+Ieu5jdavFb7FNdoTJOiBGhX4nkaQYfjSJRo8RzJqB8TRwGe6+MGFZttSlE2dB24nstwGHJ22mcyHrDe5KR5weJ+y/Rowmg0oqlqhmHA/uaO0/GQDlDKcpjCwGG/Tri9uiWMAoLYocjsRey5OwItmfRDjsYSYxLm24xtXrHPcsoiZxzl9OMAz7OSxrw0pFWJkYLhKCCKe+TCkKw2KJ0wGAwJQ5fx2OV733vC69c+97cLdpuURACmY5vVCKXZFy1pm7Dbv8ZragLREMcxR+fHHE1HTCcjqqKmqRqaquTu+i1N2+H2+wzGU3xPkxU5XdOxWxd8+cVrJpMI09ZURYH2HKLhCCElmzRFaofNbk8YB1Cn+KJDRR77rGW92VIWJZvljMBXxOMJfddDZwl333xNkxXWYFw7RJGmP+qxXe1pcUmLDqRhPBySpzlNUzEYxQTxkK7bkiyXCE9zM3+H2s05nQwoNjXX6RY/LnCDgF+9fsej82MeuxKvN2Kz3nBzlyDaiqYq0crQlBnuwOXsfAJScjpQDP0OxxHowCOvCurOMqG6BzbXwRgMVir5axbjgyH4wObCyiofpPby0AoiDjok7ClLCWW9mwc5hVISQ4uUDp7n2Bu7a9BKI5QkyXJC32fYj5GiQyuBpz0i36XMS65fvWXy+BFuv49xBLUU3N7ccXZ8YeUdwqZEBVEPHbS0XYv2XAbOCOlqPv3sM9I0p6oKfvazn/Hq9Uta6XIS9vGCIYvdHYuXV5RlQdVUTAd9Aq3RLTRFSd0JGiSdkPjaIQh9hDJkVcpms6UqK+J4gFaH9O3OsXYiLEdeCusycBzX/szG+iEjL8AREl8LJoMerjaUeYnjKvIsYzVbopGEcUhW5XRaUdHheQ77NGNfpEilmK/nZEXKcDBASk2apdyt5jiuSxBGrA7U3d948YojH08qAtdDH4iLZVFQVBVNK9CuJK9bdvkOKVOUkoSB/cVq5dC2HW1Xk+z6DPo9lHYwSM7PRnY7Ekakuz1FUXE8HRJHLlEc0e/HrLcbZvMFcdyjKDvu3v2EMIg5Ox7gYsmrnQClJP2+TxR4KAXnHzyiqTtWmz1dZ9DCBnX6gUcUx6RZSZbl9gc0Bq0d9lnK/WrL/d2SZ88uiQKH/W6FaXMuH53w0dPnvHp5Y0F5xpDmBfssR2ufqmxJ6pZdnTB1Fd0uI8lv8HsBjqORCHpRTIegagzGKPJ1Qlk0KE9TtTVxFHB+dkrb1WhX2e9ztWO12FB1MB6PUEXB7e094+GYyWiEL0OaYsd6X7BvS56cP6XOCiYXT6hMzaYuKOqSRmpG8YTj04jFusd6seXJoyOiMGC7TqirligOUZ5DWhTkecag38NxXJR07Gyobbm5umM3W5EXNcldn/2+pSoKNvsNRdNwfT0jqVrmmw2juE8Y+YwmA4K6xpOaTiqifo8gDumEoBdHuPM9kWmJtGIva6t/qxrKqrRsdXFQx4tvWz6roj9ghnngHNv/PCQ8C2FPEUIImtoWIpvDaIuWkham6Xge+oBNcj2HpmmszacqcRwH5bp2DicM00mfMHBpWoVBsm9K0iwjWSwoqprHz56jwhDt+viDU84uP+Hd1SvSZEev1yfqx2glKYrMBtJ6LvW6JV+v7aZTKT759HuE0ZAk2VHUDZcfPGdycoowgtn9PQ0db6+vmM23KMcljkc0aW7pqYfos7qtqJqKLLOb8/FgRD/q4yiN60joajAdTd3gKn0IulV4jnuYeSmiICLyfByh8bWltQwGPZpAYZoarR2CQOEoSRQ5hLHHYp+wmi8oi4xBr89w1EdLzWyzRihFXh6oukfH7Pcb9nlK2NSkRUb1kLzymyxernaQxuBoRS8OcZQiL0qb25hlVE1D0z7AyDqEY0jrlNU2sVaFQyXfrlJ60YbRdMR8vqTfD/ECl14UEcchnnu4oESFaBXFvuSoF3E++ZBtkrFL4cmTC95c36C14mIa03UVfuzRmc6aU8uKUT9GOBVxoDk7OkcLyW63Zb9PyKuKxf0Ni/WOZJ8xHA4JAp+2zqHKkUqyznPi3Y5O9lkvV9zczDg/O+bspM8HH41RyiUvC3b7ms2uoKpq5os54/4AZcaYqiLfZ5abbwR5kjPo91jMlwx6PlE/YLurKOuONMtRvn0Ktp2gM9Y/WNMReD6eduiNT0AZ6qbldragbQ2e75BnCdJR+H6MXztUxZY//7O/oBf2iKIYN3BppKEoSnatoRYC6bq4nst+sUK0R+RZSVUXOK7ED1xaoaiamrgX47guSZKwWq9INmvadI+nQ7776Xeo6pbeIKaREGiXf/9nP2SX1ig3YrbcsVqm9CN7Uk+TjLYrODk9ZdKP+ODJCQ2Ku/mOomzZJglu4BJFISovoTvMrgyog8pbvicP2IG97B6awodEIfW+YHWH9lJgH2o2Cdq+O9pBKQfHcS3rS9rP68NcTAhBUeQU6Z6mrVGeh+8oOgOeloSBRAiD4zlUbUdTQytt8LDvaLaLOV7cwwkCPvrBD0g2KU+ff8zXL14wm82J+zFB6FnZh1bsk5SybtludyghUdrh5OiU0WBKmiVs9ws2mw1RGBEGIXEUMduuyboO5fpkSU6kAxoU2yyjaTvKsqCsauq2RQlJP+7Rj/tEfoAXBDRtR6U0dWFPrUopmq4lcFwr4lXaIp6UPrTiEs9VLFczsmzN2ekxjgCahuXiBi0NiwV43pCTx8/RjktZ2nSxwHPxlEJNp5QV5JXdkt/e3+H4ms50vHjxDfPNjs1+97dRvHyqMmdXFHRK0Y8CBsMBYzEgyRKSrCAvG4rcgv8bY60WRdtStTVVZ+j5Aa4bHGweLUGoeHN1yzZvUY5DP3bxlKTn+4Sez3DYp+1KwsgnjnpUdcs+r3j+wQmTkyPyrEColu02ZZdUGKFZLpfkh2zD8TDm/HRKvxehtWaXpmx2CWXdsEsLyqajaQyvZzdoRxIEDq4WLPYVRaN4dbVktspou4YsTZinLW9mMy7PBgzjmNa0rDcVedVZWqnvUdclUeiD4xFELsk+YbNb28y/pqHICxxZ0TYZxng8/+ApSVGw3mVsl1YG8PzxBR88OePl/T1l1zJf7YijiPEwxvd9tGNDPbI0x5WSeDykblukcJlOjzBdy+PHR0S9HvP5Gtlpsiy3wR1DiRIOvnbxXKt7SpOMXq9HGAY4jiLZlQyHQ5q6QrsuTVNR5AVRFJFWGSbrMMqwTXPSoiWrcz798DmXl5e8entHf9gnjJ/iuoJBHDK7vWe1WLNLM97dzfjxr7b84qsef/D7P+DsZMr99YbXb+54cb1lkVZ4WlPU5hDnJa0ky3SYTtDxEAsPdIecR8F7xbyQ8n2oBIfB/UPRkkKiHuQVh7QaJQ/WmrZGSdDKO3zMJvdURYZ7oKtigKbi3ZvXPH18itKKvLQ5pVEYkbUpm+XKdibdFfFgwOTygqTeUNcNH39yye1dj+V8Tp7m1rfpSPr9IW1lZRxt0xD4EVVlQYOe6zAajOlaYzVgrst0MiGOYwZRzHo8YbNLWG23RHEMixm7JKFpG+q2QQhFr9cnDiIcqe3wvWkQXYfoOmT3rW+xzBJM11m4p5CYFtqqQQQertIM+yGOLtlsVtzfzXg8meC6Dm2jCeMBXuSQ5hV11zLo9Tn96COKPGE5uyWZ3bNezTl//AGxVgyDiPlqw36/ByMZ9wa8my1Ybbe/+eLVGwRsVy2b9RrRgcTgKDg9GnN6NMAIxS4puJ+tWG33VI2g8xzqpqXNC8rtDtEKRr5LWeVkmWEyGROGA3717pb5PCPJalwl0DrFkQ7+/ZamrRgOIs5OagLPQTkGzy25OPXIM43G42jaYzQcMj094euXb1DK4+3bK9682bB5uWFyVCK1pNfrQ9DDD6F35EEnqZuGzXbNdrtB+z690QDjZ4xGIzCSXZKx2qYEcYh2PfISfvrlDMwtjnQY9MfUlChd2Iu2rng3b5BAkecIY6cwcQirpCbPE05x8TKHus3I24ZoPOButmCxKHCkBT862mEYRdytNmwXe1azhGt5zWgwtPOKBpZpwXaZU9UlSoBSAVlXEcYxF6d9PN8j8h+z2ef0+gO22x1CSJqqZLVcgDTs8z1l6zEcDLm7nTHqe5SZoX98xO1mRpXZc47jOBRFSVE3GARR5BKfnDK7W5NvGm5vFhgkl08es9lvqeuKbVoyv13wwdNLzk/Publf0KBJi4439xu2/+6HfPThM3r9kNt1TlIb6qqiRlDVD3A6CxY0QtKayooxhaFtu/eyiAYbaiwekDXCntgeLC3WoKwOynqAFkR7iP4CkJa2oLTd1HVWXmEOfj0pBE1d4QN9V/KTv/wh8/sb/v4//Pto1+N+Nuf45Jx4OKCurLQiDmPub+8pu47Q9fjqr37OxbMTTsaX+I7ilz//CePJmDyryYH5bIbjaO7ubnl0fs50MiGMIxazJQDnZ+cMRyOqskQ7ilB6aDUh8Dz6cY/A9yiaCqE6lIR9liEayyHzHG1/hqahpKRrG9qmoa0LtKswWMN7XVUox/px6aCpGqQSOFIw6HlMJj2E8BgM+nR1wy5Ncf0h0aCP5wb4jkMYxGx3S5TroVeS4XDAaDTGbLdkymW5XuNGHqY1XJ5esNnuWG/3LNcbjod97hfub754bTZbptMR00mf3XZHXRbMFglCGcu0ygtMZ/B9l5HwSRJDXla0h1gpITR125JWJZFrqY37bYrnB/zDP/673N4nfPHlO5a7LZuypO5KdFGiZMcyT5ktV0wHA5QDgedwNhoQhgGbrf1l1NstbblnMhygnIDx5DM++LTh3fWcZJ9wc3tFEAb0eiGOhuPJhMjzSfOCJO+z2U1ZLNYUaWlbP9VRVTXPnl4wmy3Isz2Pzs6osozVbk1rgM6lLFuyyrBLtnR1wZPHj+loaTF0jsNqvSZLM4TZ4hz4S60UHPX99+SCthNIpSnyAul7LJcr+r0eTl3zZDLhYjim6TryugQM46MJWZJxcz3j+voWEXYMJ2Pu727p9yL2qy0/+8Yw6A/RQjEcDjkZxCyXmu02Ofj4PNbrgl2S4wcG2HF2NOS7n37Ij//ix7z51U8YHw3RjmS12uEMx+RVheeHlGWKqyU68rh4ck5ZlORZRdFU9jWOeoetXY2jINnt0EpzfHyE4wUMhyM+//obFps9f/KXv2Aw7DPs9WmNFVjWtZUTKCGRUllAnbFK8Q7LjeoO867O1qpDqESHekAeYyubQFr5gPgWZQw2sxBjh8MPrKu6tXRWhKWpNm1DGIVIIemaljj2+M4nl4juMf/Dn/47/q//l3f8o3/8j0nSjPFoghENnTHE/T55muG4DvvFipPvfsbw7JT/9r/97zh79JjxeIyroReGNmsgyzidHjGb37FbL3ld7qnKU1zf4sCbpkGplrou2Gw3xGHMaDDA0w6+chgEFXHo8/LtaybDPmVZ2rSkQxBLW9Uo16czLUhNXZfWxI21CdWdIc0sgsgTCi0kwgFXurhS4mvJo9Mpj06npPmOPCsxokU4AUI45EXNYnmN62rOzs4oioy2yEiTlDTZMwh9hONx9vgTSuXRmJqubSiqCs/zmUwEkSMJt5LdeMwXr1a/2eL15t0di8UK3/UIohBDQ13lJG+uUUIQ+g6e6yLzzKbo9HvItIC8YBjbTcZ2v2e2zSkb6JcNgVPRiyqCe4eT8Zj4B0/ZZQVfvr5hvi9ou5quqjBNy75oKLIVjutZpW5mmEwKPEejTAuUOI5gMo6YLRK2+z3LVYnneogw4Hh6gukU+21BZ2rKomI8sHMhOsOoF9OPA4QUlHWJ1u57dHJ4ccJ+H9O0DX4c8tHRxDK+0ez3OXVnuJ/N6dqcyWRIU9ecP7qgLEuWiwXb3Z7lfEWyT0nzihfXFbNtSeA5VK5LJQXD4YjuiWS1XFI1LUZ2jI8mONqxwDzPxfMdlqsVSbrj5GjKZNTj6eU5r99d8+VXr9ilGXVhELXk9fUO7d7QiwPOz6cMYw/ZNWip6Pd6PHr0CKFcZvMVRimqaoPrGq4WM765nVFtEs5OphwdTXC9gLIwhEGfvEzxA582S/GiLXnVYprSzm+U1WDVdU1epAfpi8ZzLRHVczTnpxNc16r3f/HVK97d3bNcJ5RNh+dqm5bdNNRVQ4N1QnSt3cy1B6GpOaBtjLDzL32Y4r8Pmbegr4OKXh5mYKCEPY2Z7qCml53dLmIDicuqQuY5QipcrXB9j6auKEurDdRK8PTJBa42TKf/iH/5J/+W//r//k/59LPv8sEHHxD1YpI8p25qwiiEzlAWBTdv33Hx9JL/2d//z/ln/83/g/XmTzkaTen9vSFau+RZznAUMxh+SOD7fPnll1y9nqMcTdPUJOmWMAo5Oz8n8FzoOjbrNZ7jghAk6RbTNvRdl7IoicPowP4q0EZimpq2aRCOsCnydX3QyHW0jSEtKqqmxdUejvZtey0MQnS4SuAqiaclRZrSi/scjQL2+y03V+/oxROOpmNWa4fVasm7m2s6IQ6eypK2rsmDgMiNCHsDTNOhhYfvuhRZwnTapypTbl6+oS1q6uZvITHbUYK2btgUDYt9ao/TdGhhYYqu76AU+K5D4GSoTtF2oD2XwBP4TogrJLukYrWtSMqO0K8p6BCzO7IqZTgacnYUEroXfPVuzjqtCDwPaTryZMt2vbPzDKVYVTXZvOC4N0BJgXQFX754gXp7jRfEHJ+MOfvklPms5C7bUCQpZZEjFNwslniey2kvxhGSrKhBghf4aM9ltV6B6OjHIednJwwHA3r9kK6z2BG6lrZtGQwGuI7AiyO0ayiyjF4vZn5/y3o+YzAccXZyjOgaesEpUkjSpuP11R27NGeV5qRtQy/YMhnF9KMY6VpNjS1SJUfjEaGnqX2XtouIwhCpFI6WdHXDoB/x8UfPOTu/oGogy0tLyShKCwY0LRpY3m9ZLVf4vubs7ASpNdLRaN+l6jr2ux37ouSrq7d883LOk+Nj4lHHoLPxdWEAddfi+AGu40C5o93csVntiFWIil1mSYGQkih2iQcuruuTpzmm7TBty36XWFFk1RBHEZPphKSucLKUtCjYpAk23lnSGoWR6v120YiD+PLhghQPkV28V+G/30Jah/HB0yjft4sPcRKHKY9tPY2xhU25CKUwQtg0Z3PIOzQGR0u0kgzjGEdLokDS653xD/9Xf59/+W/+LT/+0V/S7/f4we/8rvWEmoZkn+J7Aa7n0NYNr7/4iuff/Yx/8l/+E/7tv/5/88tf/JI/+ZN/yyeffsbZ5SNk4GG6jvPHjxFI2taeLquqZL/fkCUJXd0RxA5VUeJol9VyaWduCqqqwPNdem1A3TbkyZ5gEFFV1qZUVxlFaYNhpREWNSTs77SoG6Iwph/HhH7PAgrpoK6JPMVkEBL5iq4pef16ztn5OePRkOlkhDQtdVEyiAcoqVltVmRpgdsKHO1SNQ4qOEZGEbXTsZy/YRzFtMY5UKoVcX/M5YcBRr/D+erFb754VZ3AdRyUErjSDj27tj0YQg27tKJsKpuO7LoMgxhPu8gyx/EEjnLp9XyinkdeN+RFzS7ZU5U5eRmQd/aCbfqWE/67nz1jlxTkZWuxcmbI7HbJbpfSH/SpmxLHd7nepjR1y/MgREiPu9sVdb3h9ds7PvroDCVjOjKef3CG0oYgkHS/qHk3m9GELseDmKGvyYRitt3TJBkITds2zJY7FvO1bTeEpBfHnB0f4ShBXZZUZcXt/YJoNGQxv0dLjSMl+/0GLQWhHzAY9Hh2eY4UsN/vSfKWfhByezfnfrakLRT71tCZnDSzLXZ3iP1SqkULw+mjcypjePnqmq6zoZzDUcBwMCFNUxzf4/uffULTwmy+YpPsqdrKznyM4Oy0R+AFvHp5TxiEKC1ZrhbkRUEQ9WymAIrJaETdprx4veZ6t6Gfh+iF4nQ8olM52yInbWpM2eA6FcvZhk4FSJXgas10OkI7Nh/QC0K047ITgq6zVaZpSvZJQpqmJGVLkucWXyMloR/guwHr7YamaUFZYkNnOh5K1q9HcB2EE+/TcazZmoMF6JDNaH6N4yWscdsahL61DtF20BkrYhWOnY9JSdc1lGVBV9kMAFcJTqbHCO1Tmg7qDsd1OTk64urtO/7kT/4N+2TLJx99zOXlMxQeQtnlgeNIuqZmPb9j0I/4wQ9+lyCI+Ysf/gVJmvBss+TTzz4jDAKMMQewZEDT1NR1be1o05aiyBGtlTPMZ/dUdcN0MkFqxf28YL1Zo6RiGAQwGZHnOcHxxGatrjcIaTeKm11C3VoibNt1aK1wHEUQeniuRkmNaltcp2USB3z3o2f0Yp+6KambmrevXlAkR0yHI8LAYzIakuUZSkscrYmzmrvFgqyrmZ4cc/z97yK1YHf9is1mhm4SnlxeIrR1LORlR396zrAxHJ2cA5//ZovXthI4rUErg5YdnrJrY6uhkcgyx3MitllJ1Qr2RUWhWuvHqloEOUqC62mU4xBHLp6nKMuGxTZnva+4erNkMo4Z9B1OjiaMBgMcKWlaB8dzCZRLGMcURcmbN2/Yrfdcz3cEgY+7TAh8iQ569MYR2+2C28WcsxMPpRUvv3mJq31OjsY8mhxRZDn3dzMC03E0HOLIjqeXJ8yXW1arHdtdbo3aCjzfhuPukj1llXMyGtA1NUIZPFehO9CmI0tSqjzn/OSYMHAJA+jaijBwwbT0fJ9pL2S7yzjp9UgfP+Lqds3dfM5mkZIHNZPpCC9waNKEZL/lze2OYrfh9OyMwPXJspK8LFhUKaa1r6evXebXrwnCHhcnQ4Yjl+FkxGK25N/9q1+SLBJ6I4XrhZRNSlsaOiRFVeMFHmmR4UchQkniaMhg3Ge2WPLLb+7YjyrqquG4H6Adl32WUDdQJDnkHdKtME5JYzTGdLiuod/rI9uCspE42iccDAh7PRbLe6LYI+r3+elXr7mb3ZNkOUYK2trKQExnFfBt2yJE857Lj2iRh1N+h5VPSAMS9S2b6yF8RtiwjofCZYw4mK9tobMhHK39HFbEyoG1rh0HIRVJklAkexxh8JTmeNjn6eNTol5MNAhZL5ek+5QPnjzBoeOXX37NL3/1S7q2wbQ1nh/RH03wgoBnz54yu7mnLAqWmx2ucrh8ckmSZLx985Yvfv659aOenjKaTBgMhni+Z5cXpbJE17zA9307VqgqHj+5ZLFcsN1ucFyX0A8YjcbkeUZnDOPxiFevXtHVFU/Pzhj4AY52yOuGa+bkVUMnBGVVon2LuxZG0HYtrtZEwuHDywt6kd2+Oq5nO6OTM15+/Y3NOi1KdtkeJWqyIqc5UD1OJ1OCMCQrc3bpkld/9e+5nc2QTUPP01wvFqyzDN/r89l3vkNZV2y2C0zXErp/CwP7DmHDKRqL4/UciW8ErhB4jkZ7IQqJ0gF5lltXvFJ0nbHu9qbC910iqfGlsBQAYRlED/4sPwpIy5xstqWqanbbHePRGIOirbEIXVNyNA04PfkOZVbx8vWcfVpSlBVGKBoBi/2Ozb5kuaq4u33LoB+jtM9w3CdJU5qq5ng8YTgYstpuuVlfEUQBj59E+I7Pk0c9dmnC7fUG6OiMRDkOAElWs9ne4mjBsRAIAvJ9xnA8YvblNzhKMRoEKOWBqOj1IrZru+AIPZ+zxyPOLqa8u7rh4vKE3/+D7/H2+oZvXr3l9dtrVosV2lWEUUAYxmTJnrfbkm1zTy+yK+uu7dAq5u5+R9nWtOYG03Zopen3YsLIox/4DPsjfuuzE1CapExxXYcsb0mSHKV9Tk4uKOsShGAyGaGF5ObuGq00gR8ADq/v1uRVydnAYdAPKLXG82O8XozvZ/R9j873aFu7kayKjGyzpmVL10nUYMAyXcP0BF2VNpE5KfC0w3A4oumgalo60SIwNtfP6htouo72IShC2jZHAa2wyTX6ABh80Np/G8YhUFIfsMcPBAlpIXoPoRMSOKCLDQbtOHieb0Mvuo6utR2AIyWhEnznfMxJT+B0GbSgRMF46OGKkJNxn9/6zvf4k//xz7l9c8385p7HTy45PTul14t5en7G44szFosVP33xc3zPYzKd8OEHT7l8dM5P/uonXN9cI12bmnQ/nxH3Yi5Oz3G0Zno2Jt2nZHmK5wU0bc14MqY/7FPkGcv5ku1uz2Q0RU4Nm82WfZpwNJlQlRWOVviBTUFyHMlwEOI3DW3Xsd2XCNlS1ZBnGVEAk96QDy7OOT8fEfqa4XCMFwRUdUlV5EyOjwijnpWhKEmep5T7kryu6XkRl+fnNI5gvprRlhVdB04c8+b6mk3aEvgeq/We+ew1r16/4vHlY8Kox3K7535x95svXg/Ju1JLu40Rggqb/dEahev4Nn6sbRno4KCdaanqCuFIXDegbkrW2xTXL6xHUtkjahT2KKuCvChpmgrPUez2BVVes09SXNdFKYfWCNIsI/A8nlycc3Tk8Ps/OCVPK9bLjCj0cV2F0YoWqGtJXjRcX9+QFjnz6wIpBUJC1NMMejGu51HWlvntaJ942me9XSGoefJkQpnZrED7y+u4TxcUFay2CfNNitYBvThg0A/Y7PZIoygLg+vsMaJCygrfc+iFEeNhj3HS48nTJ6RFSN1krDc5vUDz/c+ecnw05vW7ObtdTttClmzZ7W2WoONLmixHm45eGJPmBVUnaegQSrHeZ1R1g7vaE7iaSeRwelIwHB7jBBKNpq4a+nEP33MtNggYOhFJ6rLdbjg9OcHRPr04tnOTvLRpTfuau/nOLkpEy8l0yPE4YugahtqnH/go16WiIfLApDVKGMq2w9QFSjnsFvfs93vqIieMI4K2QJsWx3FxPUV8GrNardjudgjt0khFnVcIwcFzV7+XnTwkM0vE+wKEsZYhm27z8IH3zaWlSwiLc1YPaOjDZlJpeysIYYNohRS4rkODiyMlke9ycdLDVwWBdHjz4gveXM354OkT4kkERtEayT/4T/8z3ry54sWLb7i7uuLV118QRSH3Nzf83T/6Y/rDKWePnvBXf/VXXM1ueX75CN8d4Ad/wOt3V7x59w4vCBiPJ9zf3LNfbTg/OwcEo8GQ6dGE25tblqvMnpgchdNKjKl59vySqqwpygZHuwSez3g4oshLlKMZjifs9zuWmzVu4CI7W9w93yErS2RWoYThuBfwvcszptM+jifQWpFnKb1hH+Vqa9xWivpgaDdScXR+TG90Ql03NGXN7XqD9Cw/349csu2WSRDiPruk7jqKvGA0nPL08kO+/vprvvz8G8qmYZ7uuF7N/1r16G+Egf7DT84OiBL7TYG0KSYHVInn+XjaR0mJq5SNOuoamramqDIMUJUFRZZS1rmliXagpf3ztv20A8qurXG1JvAdhAQpDY4UNE1L23Ro7eL7DkI2OI7GcxyUNPR7Pr6n6Uchoe8TRyGO0tR1gxaaPKvp9fvUTcfb2zvcKLJPWGnxuy12gJnVVmjXC2KaqqOuSvwwRjkO2veI45jbd7fs98khZbjBdRzKqrbQQQOu46Nc0I7FKiebHCVKXFfS7w0QUlIbQ9jrE/oe/TjkaDohTXKqpkN7AS9ev2G2XLJabGjKmmE/pBd5+J61bswWKb7r4ShFJwzS92hrTVk2+IEk8B36vZj+0GG+2LBdlQz6MVmekOQFg8GI508vaZqGq6srpFa4fkBnYLvbUjctVWk3tmVZst3vePv2DiUcptOY6TBkHHhMBwOCwKfREq9rGUchnQLTgdCau/sZ+iAebauKuim5ywy/utmyzBo838UPPXb7LVle0jaGrG7ZJAVN2x1SyBpMY4merbAyCY0CqSwBVSgEdjtsGerSDuGxFAkhbYiGBQnoQ0epDspxH+fAlHI9S1VtqpIyT/FdzeWox//yd57gqYrI93B9zc2iIAx6OD48/+BjtOOSFzn77Zbddk3TVNzf3fOLX37OZpPy9MOP+E//wT/i9OIZjen4f/0//ymirvhP/uM/JG8aVustZVHxz//lv+T09JTf+53fJYoD5jMrOo3CkKPpEWcnJ7x795b1Zs3Z2QltU/Di869Aa6R0CYOA6WSK9lzWyyXbzZb5YolyHarGtnfvbm/RjovnOPSimDRNCf2QftzD0wrRNUynE3qDMdPhAFcbtO/h+TF101BW1lGSZxVlVeN5dmSghMRRDlXXUrV2o6sdTZ0vCJBURlCUDU1t8dFNY6ibhjjqs9qsuVrN+NMf/ZCffX3/m8VAS6Ut2+dQvJTSOI7NX9RKo6VG6UMqrnSQFgSFKw0h9pswB452Ueb24ihyqtwOAaumQWmsqx1DI6BGIbsOV0r7YnT2gjNSUANlYSg2GU1jrSBh4BJ6gmHkEocOjpR4roerNa52oOvIyxVdpzFtx36zxncchBIcDwPCOGRXlPz8xQKkZjAegwt1o9kXBZvdmssPLgkchSM6Pnj6mCiKbNpw21IfZAK9XkgU91itVjRNQR7mFHFJXWaHAWVD2xlWu4T1y3t6ccTJ8ZjlasfRIKLGcDtb0tZwNBlwNDpitdowu79hubhnOIiZHk3oREOSlUxGE0JP4/oe06MjHMfFUZq26nj7bsbsdolRYFpBHPc4f3zMzc2C2d2Ke/+Wrq1ZLZcgBUEY0e8POD+9YDZfEAUho0FIFAfs9luCQDNbbEirgqEeUTse265BuRrf96iKinlRMIxCej0flMHclnRG4SqN52lKR7K9n9E0HVoY0u2Wu7sE6Wh8z0dqiS5qRHsQi7atHapjU4EwB1GEknAY1GO6A8nGGrKFOBizD6cwgQUPysPgXhhjm06lkNJFKocgimzmQVXbeafj4WtFGHiUVYWQLapNGQ6PGMQgHI12lLW5KIejoynHJ1M8V7DdbHCk4Le/+x1+/vNvePf6Lf/iX/wL/rf/+/8Do8mE58+e8eJnP6XIc6LpmKwoqYuaf/QP/tf85V/+kF/94ufEgx7GdJycnaKV5urqitn9PU8eP2K72/LVi68JPRff8XF7MXlRYdqGsshoO+uR7PdilNIkeYaqFb04ohcErNdra7oOfVxpOD45w/F8ggMCvK5bjOPYbFINoutwlMtwOKas91RVxWazZ7koKYoMhaFsazsK0i5tF9Aam7PZyR6dEYS+Yrud0bY2pDaIYnTTMhyP8cOApCkY9KK/Vj36G528/t73LvEcjZCHbYRyLe9HP3ihQGsLMHuAvAm6gx/NXnTiEJAghEFLS3A0raE6cISS/ZY0S9D60KZiCD0XVx+2R3AID6isnUPbIavrhhR5RZbn0NUEvrW0SKkI/BDPtaGXSkEYCaTw2W9KtGgYDSJcbW+AOI7J647P39zS64ecxH2bjKIULZLNNsf1JE8eP6GqrP6nPxwShoGlwzqafZIS+AFRHFHVBavFgrbpUNquuIvKokvKsiHNa/ZVx3K5I0kyhKk4nnqcXxyB9NgnhiBSXD4+Ig4D5vN7Xr95x93t1qrQm5I6L/jsw+cMo4CmLu3ywpVMBhHnpycEvR6z5YbGaJrKapzcULJepVy9vWJy5COFCzhUTUVTV7SdoWoNfhjiKElVFCRJwnA4QiiPm9mMq+sZ6/WO1tRI2eIHLpNhzOnxFK00bgtDT9MfxFRFyX63p6jSw8mv4icv71jmAi8I8XyXsixZb7fs9nvKpqU0irQVtAhMV2MzbR6gEocQDaEOJ6pvG8mHIf1DvLw5KO1936csq4O3UR0M3cp6+PwYz/UZTyZoR0EHdZbRVSWRr7kYeXz3UR9NzThyCGMfnJBOe9QIhPbsv951RHFow4W1pilyqirl3dt73tzO+fLtNY+efcx/9X/8P7Gf3/D1j37EYDLh/JMPqYuK/WKLkIo0TWi6mtvZLX/xw78gjHt8/3vfZzQYUVU1vTgEY/jpz37KZj5HNzXHp0f4YUheFJycXtAA69WasihxPR+hFGEYEgQ+ebLHdI0NqHFd3l5f03VY7Z+QVG1DfmDLO65mGCjGwxFx75jpdARdQpLs6VpIkwJjDK6jKPIEKQ2nZ+fsU8NmsyboRRZNnVUcnx3xq199TVUKhDK0naRuapqmou1qvnr1NX/1+ef85MXy/+/J629UvP4XP3iO57qHwuUghba0AaXQjkZK7BL6MEqQ2OJzCImjMxYra5dBlpNtY5kOVABxaCu6hs7UFFnG/P4eVys4ZPVZHY+wsUrKIKR9Miup8TzPtjnNtzyiNNsfBLQB6d7id1zXFl1hDKOBS+RZOYHve2jPo64NN3cr8jIlDkN6kY2AD/3QBmNWNb528KKAuqnswkEIqq7F8zyk1rjKQQkb4KCEYrfb4/mWoCC1pUvYCHQX4/pkZUeSldzPbpjP54BkOu1zfHxC21Sk+z2Otq93kpbMlzt2RUlaleRZgmlr4tAh9AWnkxH9wEOLlqPpEU0DVW3wopDRaMTJ0RGtaXn37oaXr15TNyW9/pTnHzwi8BRVWXJ7e8+2qFHKIQp9HEejteLuZs276zVPnl0gESxXO97e3rLZ7+i6llEvwnMlb+9nTKOY3/n4At+x60ElHZIip2ha8qpll1WkZcPJ6RGu41moY5pxc3/P3XrPom6pDuZs03UY0xzM1hzwNfr9Q9IO5tV78aqUvE8MMsbOsKSyjYaSEucgh5DKQWmN51go3mAwtF69g5ZPdjWOaDnqOXzn0RDPMxxFPkWeMDo+IhgOub7d4Pg9EPIQxuKglKSrWwItEF1NHAZ89fI1v3zxDV+/vOb48Yf8V/+7f8Ly7Wuub+45urjg2fPnlGVN03YURU5Z5nRdw2q95ue//IJ0u+e3v/9d3ly/YzQYcjSdkiUpj89P+ebzn9I2dmnVSIEf9nj64ce4jo/WlnyxXK64vbtDYKx2MHBpMWy2O5brDePhiOGgh5CSqrPtuXjwkrYVWbKl1wsJfInJUpSUrPcJYW+IQXFxcYajLSZLSsV6UzNfzVls1uRlRV97PP/gOSC5endLlufsk5SLiydEcZ8Xr79htpzxi1df8Wd/9fY3W7z+s9/7mCDwDxeFQgoXx7VFy3X0YUhqwyQefGXCGDA2GqltDwNUISwbW9p8PynFgaEkvqUDSIv4rYrSrsablqZuSdKMNM9pavtxobrDic/B1RoktKY9JMQIlBZoIfC0S102JFlB3drE5LyoaU3DuKcJlINUCqUcjASlbez9PssocguNi4IAlMA0DUPPJQw9PF/T0eH4Hvu0oMqs0TtNc/KipG5aPD8gCDwMNs/OD1z6ccCwFzPo96iLhqoxLDdbhuMBbd2y3aSEscc+S1DK5YMPn9PrRURBSJ5VVGXDPivY5hmbfcLN7S3z+Yp0n3I8GRAHHmm24/TkhOCA+9Wei+dqRv2IPC9QjkOWFbx885a68RgMI54/P2HQi9lsNmzzGtMZwjC0rHoFjg64W+RoZZgMBviuz9s3b9kkW6QWXJ6dczW/59/8+Ee4UvPRxYAPzo+IvZAsr3l7t2C1s9iTMIqpqspKApoaTGtTbqqaXWm4qw27srInqs4yvR4Sgx4wN/Dtqct6Fx9msvZrbNKz3WgL8W0+oRbygJV2cRwH13Hx3JDxcIRSiqqy15eipS12lNmSNl3w6ceP+P6zZ8S+y+UHT9ikKZs9rPY5o+mEru1wPZ8wCHCVpkhSku3Wwif7EXWW8erqHf/83/+ID5495R/+/T8iSTa8fHXNoH/EJ9/9LpUxVHXNdrVmv1oQ90KcMGQxu+M4DNBNSdnU7POa1S6lPxwT+pL9dgNtiz7IRBrfZTI9ZjAYovw+ZWnvi7dvXnFz/ZamKZhMRvR7Q4qqZbPekBUJUeyT5xVt3RD6AY7jIpWkbSsmkxFpmlCuNhRlQVoWlE1L1AsZjXpgOtqmPoSZBAyHfVAu682WfLum3xtyfDIhSeyfv76+AhPwg+/9XXpHE370s7/kX/z7f82/+/Gb32zx+od/9zN830NIgek0Wvs4SqClQCuJkgIlDoySw8Vm65m96B5Y4gYbmy6xN4SQ4hC7fniKYhN2wM7IzIN51giapqOjo6oa6qKmqhvqtrTE0a4FIdCuPcIabCy65yhCN0RJRYeNc5dC0BjBdp+Q7RM8pXCUR2OMFeEGPlJC4Puk+5Q4jA7re0WSZbRFQdfVeK5PZzqariEIXAb9CNMKTKvQbkBnJJt9ynK7IysKug48bYh7HoN+TBi49KMARx9OpHTQGuq6RbuKyfSYphEYaXC0JvB8lIQoDunoiHs9BsMBxnRsdxmff/6GRxenvHt7xW63o21a8iRDuR37tIC24exoQr8fIbWDkA4GRdkYlqs1TVPieZqqrkjznEG/z+nZMaPp0AbBotlkNfObGRfTI8aDHsJ0rKqU29mMkfb56vqKr95e40iXulzzZDpiMhgRhDFGa+4Xa9a7hLrpkFqx3SYY0xH6iq5qUEhaJ+S+Ecx2yfuw14d3S0FV703Y79OypXz/YBUHLURrOkAcTvmWDOpojTqYtLW2BvjAD/H9iDiMEEBZFmhH0VQFTpdxf/UN6/Ud46HHf/T9z/juxx9zcnxEV3UY49L4Po3rIIw9JTZVw/nJCXHYY7lcst/tiX2HKHTRjuKLFy/50Y9+wsXpiD/6oz+gKhtmszVaak4uL0F67JYrrr95wYcffoSMe+RlSSBKfJmihOb6esHV9YxOCEaTEb0owLQWaRP5AY4v2O5nLDNJaXqcnl3Sdh1NZ0NzszTh9voF4/GIqraM/aorKPPURp9VJaZtMdjEdyEFaWE3lyGauiu5ub4mSzMQLciO5WKJ5wUEkb2+jo+PCMIRUilEW5InJWmaIWTLbrdhtV7RGcnlyVM++M5nzFa3/Ks//1P+m3/1s9/wwP6AFbHbaXlg/Fj/kzRWd6OEQPIQE25NrgLsBsjAYZ99kOLYC1GYw5BVHP5HiPcXAQjrX+us8twIY4Mvtc1bNJ39XrrO5tKlSUaaJOyygrZr8VyBq1u2lLZAaoGSBiU7tOMw6PscTQZUeUlTdyRpSZpXZEWL7wnqwlIMkiRHKIEf+kyPBigxZb3c0RljoXW0zNcJ1/M1wziiF0bEGlw/IGwVg9EZnelI84QqF2y3e14t7nF9RTRwOD87oatrXKGIAg+hO7bbnNUyp9cPmRwdUeQ1s/sF0FrPmatpDtvN05NjkrLgmy+vqPcLQs9h7HWonks78tgkW7Z7SbLLcHFI9yn9YURvMMBxPU5PTwl8lzRNUY7DLk3YJxXrRcZq9pp40KM12NbqZMJuv2UYwKPzGHGYTaXrNZu6oek6xqMBbVXT6D7bBPZFwja7paZl0O8ThRHFdoeSDu7h361q0IfftS1MBtFhlzR8q6QXD6L5w5s52IDkYfSg5EP2onifJGSLl0Af0qO1OrSTCLzDGMGSI1r7MJaWvOEq6Ece1bjHcnPPvqi5X+34jlT86Me/4LOzS47OPK7TLWXpEIV94jDkZnXFVV0xmRzh+z4ql2z3O3b7Du0oPv30Ez7+8EN+9Jd/yb/91z/ke9/5Dj2/x3ax4urzrxGOZNjrMegHHE9H6MERVWstQFWb0pR78HZ8+uE540BT5iVVW1Fqj2jyiDwtKPOStvMxbUaVLdjPDU4U0ykH6YRMj44wTcNids/J2TFptiOIA8LxCUW2Jw5DtJaUpZ3tVnWN6zr4XkDP9UmSNX3f4enxKf3hiG1quA7n7JOU2lRUVcN6s8N0Hq7joLVhNJngenYhcHr6mH2S8fXLF8znbxi8zHi33JPukr9WPfobFS977LZmWHtes+2hNPa0JczDnMtah+i6gw5HHEgA7XtueGeaQ4t4wNE2LUIrG80u7MyiEwZL/1U0BpCHY9vh+xCHpBiplX2XDuPJEcZIkiRls9mw261IS8sgr5uatrWtkNYKVzt4Xknga3zPAQVeT9IbRQjT2RmesIbium6omxZqAwVo1dp06jJHOwLH1UTxhMa0NFVJWpbkZQlmSRQGDGOLWum5PurIQ+kpy/WG5XLNPs/4+quXBEFEW5U4SjIZ9VDCpSgaWmrKuqFuOoSWeL5Hsk/o9fqkSc1+t2ab5wxGMYvlGs+F8TCmLFMcVzOaTInjAYFXM316SrLeIzpDul7iaUGyb8mLlF5/SNcpqrampSGIAyaDEVHgc3w6Jc0y9mnBly+/pCkrFssWupqj4yMCP2YYR6w2O1wlmPRjPN/h6GjE8j7j9fUdtdTUreB+scN3c0LPpepatKcJjEWwtMaihLrOnq553xhYM7a1AB0cjIdPWQeQPITNWgW+VJZFbzp5OKkdrpmH0734NuPRbq8VSrt4vk9TFXR0KGEYac3FyYDt5h1GQV4bvnl3z5OrK0SjWGQZbCU6iEmrkpY9OJI48slL65et65Ig8FDCUNclSmnyNOPx43P+8D/+Q67e3fCrz7/gw2cfsCsbeo4HwnC3XTK/v7dE4uOnBOMTfFeA8mhUi1IBTrmn6kqkDPECDW6PzgkpREWe1SzmezuOUYr1asnEdenqjnyz49ViiR9FXFyeslzMaem4ub/B9XwmoxHhcMjN1RUAYRgSBSHz+T3uVJCV9ucYDkaMBy6OU0Ls4D05o25htdtyc3dHW3esl3OmkzF0kkU5Jwoi6qqjLEsm4wmD8Zi3b15xvbknL2vy7G8hPUgcZgri/dG9O+ib7anLboLMgV5pDq2QoDNWXyPEgzPNXniGQxTV4XNg19haSrvGFpJOGkRnT19GHAy00s4u7IXHYVFgL3AEOI5mMp1wdHRM29TkRUJeZOz2O7a7LWlaUNUdWV3RdK21upQdUhu0IzCOxDS21e26EqGkta0YY8W5WUYYdvQGPlP3iK6B3Ta1f2dWURTF+yc9XYv+/9L2Z0+SZFl6J/a7V1fbNzdfwz3WjMi9srqWRncXurFjZojZhRThCIWkCB/4yCf+M3yAkA8UUgYDigxmBsMGBuhBo7sKtVfumbGH74u57aampqr3Xj7cq+Zeje7pSkq3iURGpLstaqr3Hj3nO9/3HR+WK484qgKCyXiKlJJ6NaZ1bxfhhRyfXJCuCggrLJM5Wa6JArsT06xglU2pN5pUqjWyomC2yLkYnFnKgIDl2TXBYIjxfI4uB5wMzhG+dVUITwfUaw0WizlhtY3wJRSKejWmWGXgSygy0mSG1uDLkGS5xA8DgtAwGp2TzIa0Oh2MUizmc4TwGE01Rkta3U3yxQIE7Pc38MIA5fkUQjNPEiaLGekqpdVskmYZ8+mcLCuIwhBWGcYoAumTa0OhDIHEEYyt9k6UFjfCDke1RFO3ltZZvAQ8R1i148nKeYSlt69wRoVIiZEewki8IATpARKtBUph5UjG2KyrVSH0BRu9Fv3tTU5PrhhOFhyfD3jn/hNyJCsFjSjChB4mWTK7SsiUYDBdEFWqVMIQlRVIAZv9HsliidGKL7/4Eik8ehsbRPUGn7x8wYcffMj5xTmj6wHVesTleEy71iHcUGix4qsvPmMyHPPg0TuISpsZPqPVAk+GJElG1JRMBiecnZ7QbtQQfpVkkVCtRiijuRpM2NjoUgs84o0WshJgTEoU+9ZVIvBZLROeDwZsbfRpVGoMRyNm0wX9zR6B73P4+gVxFBH71txwvjLsNZtEpmBcLFGqoNGMuV+9R5KkYAwehka1yjJfMR5eE8gILRUiyul3ekTvvM/19B5nh0/h+V+DMLsMGOWkYk8YPKc1sz0Jawiw9hSXdqKyMNbU3wiDVtpycKRdebY7ZFx30v7xKJnPzq1JaDxpnIsFduaesxPQpSWKsGQMbTR5keEJZcsHhG0HBx7NVp2tfJN0lZNnBUmSskxmLOYzCqORSpMXijzNbID0rI2KMIJCaZbLFePJAj/0bfdNetSrNerVKvWa9VxqN6vMFinLVcZsNqcoNMvxgmm6IgqnBJ6kUa1RiSrkq4xkXlCp17l3cMCrN8dMpjM2+ttMJhOWWY70Le6CUiAkcaVCJQh49/Gj9Sj2OI6Zza39TBRVGQ7HHB4ds0ozomqFabLi5PyYPE85vbzi3tYW9WoMvocXBKxWGVEccjacs8oKMB6IgNFwQjKborIMU2ga0zG1ZoX9nT5HJxc0eh3yPGc4HNFo1Wk2G5j5iun1hPrWBhudLvV6i+vREukLZtOpIzOHLJeZHUuvCus+m+UEfkCW5eDZaeCFshOgkXYtydJVwsEJwmXx4Dy+bBvJmlIo7bSK0j3HZVlIlLaurJ4n8YzFxnw/REiJMlagHQrNbjemGQvSxYyNRp2d3gbJomA6GvHq6IK3Dh5x78keRZZRFLbmPR8M2G93qTZaJMbn1evXtJstGpUaUeAxGY+QQpIucjY2+ygEBfC93/0+P/7xT/jpz3/Khx99hBYKL5DsHtzj5Pico8tLdC2k4geQap5/9Zx+f8tiuo0uUbVKZKBSbRLWLL+wWmtgdIHSBWE1Io6rzJcLMq0xUtLs9cgKq2oJooBcKeq1Ghu9HkWuaDaa+H6AF8V2cEe1TrvVRAg4P79gkE7Y37/DV8+fc3YU8/a7T6iEFYS3Yj7WVGod4riFLw3JZIgUhkBAp13n8vyaRqNJmqcYNPu7d7h/v8ovzJJaowGc/dUGL9dMtFmWwILzt9X+xmrQFMYO3NDaZlTCuyEKStZ3QtwrbwAMsXYNkE48K53GTYgyPJaj2u1DYpwxnbU2sQCjQEuPQtsFrrVGG2WnThuroA+DiFa9hRGbrDI7zXc0GnFxekZWWCKt8ATyVlZYqVaJQx9dZKhMky0114sRE39Cvenm11WqRAF4fkwUhUxmM3JVkKQp82RBq1FDyow0zddDT5UDoHf6HapxhAhrtIOYy6sBMheEQUxhUpZZxngyIfB9JpMx0jN0O02MWdGoV/FbFaQHBwcHfO97T/j66Su++PoVRnnUm20WyZzlKmGeZWxvtNBBQBDF9BsdzsczLkYLFvOU5WJFpRpTq4WEcYXxasV4OuN8MqXb7dBuNtnZ3qDVbFCJQoRW+EbT7XUxDY1wwvbrwZjJfMZyVdBuNqjVDPP5AowgCALG0wVKZbS05USpPKVaDVhlBSsdUCCRgVVqCLdODMrxtnAQhriF5dvMW+PY9A7TEsZSbJQ2SHcDtbpGa3/j+T5B5FtahTCovKAuoesF9BsxqZLkecDdDY9kkUNeUGSGr5+95PDyEF96FGnO2+8/IfR9JrMJdc8nDAK2t3YosoLxZI5AkbWrbG/1odBMpzMyIVFSQppwd3+XwBjGlxfM51NyN806d3MTN5qb3Nk7IPYlyovQykPlKy6vr0iOj0iXSzY3t9nduUutXuHeo3sUxYp0uUkQRuRZQZDEZKvEQikOO6036jSbTZJkyeGbQ+bjKc16kzRZkqkZlUqValhjNBhQadRodzYYj2f4UjIZjglEiKLgi+fP6La6dDe6RKGk19tjOptRZBOMV6ARFKZguUwxomAyHZKmS8AwTRM6m1sopa0P2m/w+EbBy2iN0WqNb2kXnApVmoy4QCQlBtvKLicVA85EznGzsECswFielLbWHJ6W5KZwpaF9R09a2W2h9U2zQNxYmgijUcbKhrQDaIX0kZ7VvCmlMbpYezkJNL60qZ6UgjAKMdhuSxjGzOdz653uW5cCXRRMJlOSVY5S1tonrEi82GCMQhnFPC2YLsb4cuIIkZEtB6IOq7xgNksYT2ZcDxcs44xWq04oJEVe2O6P57OztUGjGtNotonjEG0OODq85Hq0IFkFTGZjAj+EyKC1Yj6fMxnN2N3ZxeRQ5AmrVUYQWbPGR/fv8M7b9zg7W/LF1y841x55KrgYLKnVE3Y2ezS7XRqVKvV2i+2tFYPBiEILFquMyXzOydElWW6YLZT1kfcSZssUKQz18TX9bouqJynCmCxJ8MOQSjPi4eYe2Srn5RuDUiswGbXYB1WwKgrC2McPBUniMZmv8GROo1pBrxRaSHIEhQHf8zG6KO9t1uUUW9YZA0KXukXbhdbCTREq9Y7uvqitd7TNrJB2QKwQji9mx58ZY9BGEQhBww9oNyKKzJbPUVQhjOr0em3S9G3yVcF0NuPo/IRarUYcBXzyq0/4rQ8/JNeWGlTkFkLIMusuu7Ozix94HJ2cc29zi83dHd4cHlL1Y16cHOHlBXe3tqi32hhPMl2mLJYrjo9PCHyP3a0duo0Y1IJZmmG8Kps7fTbv9JnNZ2SrjNHVNZeXZ6yyhE8+/RXz5YLtrTv0ehu2UVKrUWQpxydHLBcJk/mE3T2fKI5ZLpfMkyXtdpf+9i6r5ZLB1YC8yK1B5zIlqFRI8yXdjU3iwKMS2rkWggTPi7k8H2L0lOVKUe02mKVj0umUIk+ZzC/p9jaot3rU6h0OX70iW00Zj8e0uj2MUMxnc7T6zeLRN3OVMAqFhyzxBqzCX2uNMJbOIOykBLRWGGUs78YlSvagSvD+lnGcMUhsEJRSWMlH+XvhSk8hkHhr+xLL7bGLsjCglA0C2jH4URmetuxqrTXGOD90KfEkaJQ1rNNm3dlS2roidHs921YP7IQZaSSrLCdZJozG14zHQwbzKVJY7abA8tyiOML3PYTRLNOU87NzojCmUquz2d+kXm+xWCyZz4dkeY7WilWqqcQRiyTl4mLk5v8lFEVCu13nyYMtCtVnOEo5OR9xPR5YzV7gkU80yXIB+pztzSZxCL7w0CvBZLHk5ORX1FpVer0e3/nOA8L4Mb/65AteH455+vKC68EIspx2s0GeZ3YK9Sqn3mjSajfZ3+sjPMn5xYjxZMXpxSXJcs5qlVOvxCyF4nKQE3o+UhkCKWl3Oni+JF0tqVZipMnpt6vMluBHMVHkM55NKIyhUgloN1vMFinD4YxkqfCdLGxptC0PjbZNHm55eUlJmX5Z1QZ24k6Jbxl7U0Q6IbaxfmFmjX2JWwRW8GRIUWhLDdAFDanpdZoIv0AX0G60UMawXI2QSOrVGjqyLPKDB/eYz6acn50yXOa8OjzlvXu7nF9cooIqWmecvnnO+/f3YJDz4nrO1XSBKDTag8n1JQ8e3GerU+fq+JximTDTGhlVWCxT/DDiWx9+wDKZc3l2wtXFiu1uB7wQUYNWO2Y4nJAuEhaLOe1O03ZkfUBrVkXBj378Y/b39vneb33XNoP6G3ih5Pz8lLTIUYViOBxxfn7J3sFdVoViqjJyo1ChT71eY7lcsn3/HjKwrhfL+RxfQKvXI81SdLHC5HDvYY8iX1GTMBxfcHx+zMOdB+isgoxr1NtNdJET1mu88+77TK9HDIZXJMmUw9cvUH5MXvw1jD6zBAiNdjoxadkQdllp44JX+UxbUtpOh8Wv7Oh1e8csJ8BIYW+nGm2BfWlnGRrvhmgotEFKb734jHHx0NghDIW2QwZsLDJ2ajBWD2cMFKqwm8AFLoOh0NZrXCLwjG1CCANGOLcBKdbNBYXGDwNacZtOt41SB8wXU8ajoTUXnM+YJXOSVUbohwQeBL5tUsyTJfMkJYjmjmNk9W95ntmAH3s0GjWMNlxcjljlOcKTNBo1Xp+f0TsZ8mBvF6GgFXiYZg0jCra3+oQqZzKaI1VBkS7x4ypJarG2uFolDAMWi5R5ckr28jW1akClEvHOu3f46a+sh9ZoPCVNEqsECAKmsxl+FFEJQ1bLJXmWc2enz86O4fHjbV6/OuTsbMxisWI+W6HNglWR4vs+9UqVYLwsb3U2e9SaTqdLtspZjkcsVysyrcnzHM8PCcKYVhzSO9hlvliSrjJm6Yo8L+x6M5a87FCJtSOqJ27gBiEcDursbyTYQbKehzZW/aCUwjLv3QQhz6dSqVGt1PA8n1W6QhUZQihazSrdZsTB3QNQEEUxk9mEQhcMBkN836NWqyGEvc5aaGq9NkdX1xyfTHi0u8f1cAxBSqtVI81yposFl4MJp5OCZaEYTT7m6fFrkoW1NLrT77OMAkyxotKokxtDLa6yWKUMLi9ptxvc2b3DxfmAk9dXhKHHi8tTknnCxkafTrPK7maHyXiK7wUshmOOj49ZrXI6cZOd7qalifgB8ySjUB71Vp/NrTsYVTCdjmg229QaTXylCMKQWlTB1xD5AQu1wHi20VEYjfBDAt+n0IaoWiEKm6hCMxoNEWGEFB6taoXhYsH1aEjVC7n/1gO0FAwHAyazBe1Kjfb2FrVui+OjVwwHl7w6HZAkyV998FLO+hhj8ITVnEmwuBASrTQaFxSk7UxKLSz3Szq+FjgjOCukRpeAvQ2Jq0KjpUY7pybj2PKeFyBcGei7Ray1plCFtezVllOmtMbImzv1jfOmbQKUJnQGWwaXNr8u3AG2NNbCt8Zszm1SOtay59kN0Ky3iKMKvQ1FXihOTw8ZXl+SKUNmNJ47T6pQlsVdFI4ciXM7EMShTxh6SJkjCTjY30RIjzS36bPwA4ajBZPJc6pVax9khBWfizxnp9+lUanYYxcKIX3wNGEckOcrcl2Q5hlKSJJ0xdV1TqELGt02gWdA2Wy4EsXWoUMIvE6b+TwhSXPqtQaqUJydnCJDj26nxQfvvcWTtwXPnp0wnkyZTsZMkjmF1lxNEprNtmWop0uSfEUUSdLBJVJYsXNeFNQbTZbaMJ0tUGaOH4QEgXX+MMK6dhR5gQw8SlLqOlCtWRJl4BIOlLfNJOnWpOd5Fis1jraD81AVAt+3kEIQBERRBGgwOZgC3xRs1yL2d7eoVGM7a5Kcra0eo68u2eg28bzAdrKVYbpYkOaGT7/8OdfXC6YmYTBfUm02GV4PaVdjdnt3ELICYU6tbqgYhZGKo+Nzzi4HTM5H/Bd//w+o1wIur67QPqBD9g7us9Axo8kYT0qKXNGoN9isN4hij97BHq/evGE8HPDqxUu6vR4P7z2k393k7t4d7u3fxRjB4eERUTXm4eOH9prENYSMweQcvnpGVhSWJoO94S7TOePhEi8taDYa+EKw0ekwXyxQy5R6q021GZMuEkaTMcvVAq1z5tOZpT60OnhRhBeH7D1+yL/97/+Qb99/wnw8R1ZjGtUmRmmuBtdU4grdTpMHD55wfX3NcJbz6eu/hm6j1oaiyB3HytIZCqMts95I6+9usFNzVA6iIJBYAqvUju9VyjwceZWSlyOQugxqtl1t+apO7qELPG11kEZoCm2zt0IrCq3cxBc7aNToMmsyNnNzAu8S/xL6xvvcGDtOvswYhbRlrdQCTbHGRYSQSHt57Wu0y8s865Rw9+Au1WqVZ69eorUmdj70hTbkecE8XVKLI4u1YJndRaHY6tdBSM4HA0bzkH6/Syo2y1UAAN6HSURBVDUOaTW6NGs1cp0zmyegBZPxjPk8ZbEsWCztechVRrUS06hEzBPLT9re6ZIuU4ajMavc6kk3+9vM5wnD8YTXLy/odDtUqiHTpSLJrHzF8z1ajRrdbo/xaEyRr/ADH+O+x+HhGekypdJoUa0FdHsb6KzDydmY88E1o9mMdGX9t0xR4IkAiaQSh9aBdbEkrkf4viSOKzRbhvMrOzx3tcoZTiYkucSrNNDGaRqNthysW8xU7bL88iJ6XmBLReG5v8tOtfs9VhqEWwtBEBJHMVEU4XsSVawwOkMYQzOOub+/SxBKVqsVab7i8OSQZqXCdn+DSq3KxeUVKk/Z3dlhQ9UxZsXvf/8jLq9m/OJnn3BxNeBvfO/bdJotHt27z/B6ZLEvoK19i6VKjRYeo8mSyXTJx599wbc/epdqvc5kMkLogO6kSaUWUISKaiVA1mNGKmFwPWSzvYdKc3a2t2m1O2y0uwgM9Vader9NXKtb/a5WRHWPTz79lH/+//3v2N+7w7vvfoiIYDQ5Y3Z1jO8FxK0mOztbnFycE0mJp1hjgUJ6xGFAXIm5Ggy5PD9DK81wcE2r1WaxSjk+Oeby+Ii9/T3ee+9dNvd2mc1mXF5c0Gm0+PL0DQc6Y//uPoHnI9IVvXqDdm+Dy+sLAhmytb3HndGIzlELDmd/xcFLFSg3zcVgsx3P+SpJ1wUqtEKjXPYl0b4ty2ybUdwkOaUeDbP29RWiZFQXGCFtCYf7ubScIgNkbhJySX5VTu9mHM/LsvKlA/NvFrqloJkbnhq4zoPlkAEI7bI668OyNqwTwlI8jLEZnxbGtuLLJoDvs7Gxydev33A2nyOlohLEBDIgCn2QksRAqAWBEJjC2PmOR6e27PFDkklqZ+1pqFRiotD6mUWhT7USsX/QR3o+k9mcq8GMyXSGQLJMcs7SC4IgoN6oIOScLFuxUoAMWa1ylJnQ6bTY3uxSOTnndDCgVt3gzt5dwkCRJ0tOr2YMFiPa9SXNSsB4OqZebyB9ge8J9nY2MFowWSQMh0N6nRatRovvf/s+r47P+OzZM8Yza5VilCLPC5JkSRAFVr+Y50SRptWqgTAUStGo18my3ErJvC5mlrPUpZmgzY596a0bNKxjllmXkZ7wUFqt2fQai8UKI9ZZ24220Yqw69UalTi2+Kyyk3S0KmjXumxt9djo15Ay4OXPf8qPf/JT/uZ3v0frzj4rnSGEZnB5jjQFRZETKsVuu8tub4f9rU1ePP+K+dSeh9dnpwghmZqcxTzhzu4B7U6H2XTCkydPyI3gxcvn/NGvvmS4TPmtb71HVKmhVwUnJ6/odlu0Om1qkeRieEWWTui0GkR+lVmW0t/YodffZBIeU6lFIEOENMymI9LlgigIqNcb/OD3f48Xz1/y7LPP8QVUWjWMUaRFRrNasYTqPKNRreEbGM4SKvUGIqpQrTWoVGsonVFr1Hn58g2T4Zitfp+9O3c4ubig2ajz4VuP+PLFU/7NH/8x23d2OXj0gGYQsfXeB5wPBwwvBlwPfsnBzja7W1uEYQXlCaq1KtlS02jYhoJ3m4zwVxW8lNZIYQkLwghX6hnLrXF3yVwVaGHxLCHdOHZPg2tDl/IN4WQetsQTN9NesD4/zjLTLlKhkAqUlEhT/sytYie+xQUiYSxmZSvSm1JDGqsGMCVTG4HnWW4YzmETY5zbpsXeLFEWaz+Mdhw3hTHlRjHrAREYa1/c7fZ5NZ6wMpLJKgdjh2CEnsDDUA0CNupVmnGAVgWr1ZLA98myhaV8pMY6R2iFtxR4Uwhc51W+Oqder+N5ho1ei63eLrWgzmg2I9eGpdLMl0vm1wtW2Yo8U9TqDZIsI19MqMQRFT/i7p1tqvUaJ8cXvFi+Yneng49hcD3iap4ghaBTD+l3qkyXCVILCm3w5AW+J+i0auz3mxRZwej6itEsYZ5m1GvWC2o8m7DKc9IsI8kVLAv8XJFnK7SeMV8mNJs1K4aOAooip1gp4iCg265xPk0RSuMBuXMkWd+UTMmLKANZ6apq5T52wKwzJnTZstalMWGAH0TUqlXqcRUpPfJViskzpLEdaE9lLOcTDpdXVOtNNjZ6RJWYVycnTLMVeZ5y/+5dnr1+SrJI2N7o0ao3mS0SCpWzs9tGe/eYLBagBFthhXma8ujx25wdHuHpArWYU5ESFUi+81vv8uTtu/zRv/k3fPz8FYsM3n/8iJrvIQzM5wWBt6LXSNhpRpwlPgZBOp/Q77bY6HfwY8NIJ1wPJggZMV+eMJsn6ELxez/4PaTvcXl5Sa1a5d7du0ymUybpnCzLWCwXpMZOcyrygkoYEZR8wloNWQnxA8jyBbValUrcYmtjQSQ9VknC9mYHT654+eYNezt7xM0Kx6enZKrg9NUbdvpb9A96PG63Uft3mYyGvH7+jPl0yve++30KCQ1ZZ1KkzJcpvU6HRq3+Vx+8tDZoqZzYTKxZ9RYnMmht8SeNQQu3oLTGKO0kP1ZXaJdhiSO5DKmkPmDf3pRBDPtvy9q3uIaQwnWPXFa1JiDajoEtBe1d2VBOknHByR7t2l4HLMhvlBvPICwPRKyfae/jGiwWZpSjY9jYaVxjQkibAdzf3OHzw0NWeUHhVAFKKRKlkNowTXPmWUa3VqEiBbHvg1B4oY8pNNII8kKzWCXr7LBeiQmktQUypJgiZz5dUItjO5NSSnKdU201aXdr1OIqnvQZTxJmSUqxSLi6ntCsVmhXa9SqNSo7W9zbOeDw8JTR9ZQg8Nja3qCtDNPZjOlkxOUkZ6/foVGprx0ylquc1cUAtKZaswRd7UuSJKXiG7JCUYt92q0aysB8saTILfUlXfpobX3MXh1dUI0DdvpdwiiwJVqSIKsRRVFYGo1rnBSO71TOW7RQg3TZlL1RSUefEUIiPOmY0wJpHRhtsyQIbEYbhFakXyiMssemi4KKL3h4r8f15Ipmp0GynDO8uuLenX2SZcpkOuXVq9c8e35EEERUxICNSkDYqnDnTo/RNGE8SRBKonKNpzz0YkG3XsPXiny1oNfsU69GZHlOUKmRFTmyEvMP/+7f41cff8bzL19jELz/1j6NOKZar9NodZjNZgQm5WCjgTCS88GU6/MBy/mQZZFxPRhQq9RpNJp4wrC92WA0HvDi5adkmc/enTt0eh1iGSHiiExnLJM589kUpQx7B7uMRkNUnqGynOVKsVik+I2Ii4sz2vUOnW7bSfokzUaDk/GIweUpbV9SC0CLAiE077//Pm+ODtHzlMvDY5azOW+//5518wh8Pvj2tzk+OuJf/8mfcOdgn43tDfzIYzSdMh3OqPiNv/rgpbRGqrLqszINhc08tLKj07UogXKbpWCUxSw8bOlY3jitEHItpi1LOYxxHcOSs6VdtgS+sPiNzZvKzlNpUuhwDddNKjFe4QB5gw0k644lFldDa4eHmTUFo2woSGGHhxhV4Lk3tFNobHkipQt82orFfc+nv9Fjq9VjPri4JWOx/DQjLHdpuipYrKzPWK9ZY6dhpx5HviRd5RRKofBtW0FpohyUKEAblFqi8oK8yIniJbV6TCUO0VqRCVguU1C2gVJrtNBIiiynXquhNSwWKxaZnQTdbrfZ22ugVZvr6zHXgwFCeHRiSew1mcyXHJ2eozp9Njo9fN9jvpgxnIx5dXGN9EYW/JbWpQEEvoTIkyTzOTIIaTVq5FnOdDwl9nyEhHpcQWNI04TDk1NajQZxEIM0XA/HFIWVB1m2KWt80Tj+jHAQhCWe2Z+XwcsYJzkD8GynW4DL9u3fnhCYQjmxvwajUHmKkD7PX7/g9PINUsI7jx/Ra7W5u7dlvduzFe1Og5/+7BMaFDSrLa6H5yyLBC0kUaXFIlEc7B2Q1Fu8efUcdITIDMV8RKMSMB1eky2XFEbbCd9CkOU5lWqd73/0W+y2dnj24kvOr68ItvvkaoUfCwpamFlOmEwxRUqwTIhVyPQ6ZffhQ6SQTEcziqwgKwqOXx9xdHJCXKvzzvvv80f/878hDiPatTr79w64uDhjNp0SBDEauLq6oloN0XnKV198jTAeWarpbPf45NOPqVdrPHh0j72tHVbLjO2tO0RhxPDqgqAhYTXl8jQnkx4499awVqdRr/D6zRt+9Cf/lrfffpet7R2uhtfcvXuXbrfH+cUFUTUiiGosVU4YVOmE7b/64FUoY113Yc1XMMI4A0G1Dgqm7Nw5Mpeg1CYKR5UoaQtybUboVqkboWcbAUrngCWl+tJ32jR/nW1Zq1+XLdkBfo4XVnI2XGlKmY3dFNPalZElJ81iY06M7VkXTuX4Z8ItcONA4bKTqVzTQLgsszTA22l0eDO4QK2DopWuOCUUSpj1785mC5KlZz3/gcIYcq0ptA3egQQ/iOm3msynE/LVCl9aEH26XDJZLSwmFsWkWU66ylC5olqpoZkTxRWiwCf0avheyNVwjBEQhMGaJNysN3jr4R3u728yGs04Pj1FRFW8MGY0UhyfXzAcjmm32zTqdbZ3dsiKnMvBNUoZwsjSSYwxqMK6bPhCovKcDHtdGo0aAkGarhBS0Gs1yZyt9HA0ob8R0mg0SUzGOE/tjc9eQcDJyrBjzoQn3G9cAEOsWffl0rQ3F9uttLil4wcaLLcv8KwjiYBluqBIZyi/xtX1lNlsSZIsGF5PaddiHh7ss7WzxWQ+Ic1zOr02r16+IEbzYG+HfLq0rrnZknqjyt7BBlcXmtcvUjCa+XyKWUwQUnOwt0tYqfD8eMDF1ZDt3R1a9QY7e/vMFkuqIqbba/L81Vf8yY9+xbuP36W3u0MYBsyMT5FLVqMp3bBGrV7lOMnwRMyTJx8wGY/xPY96q8livuDZ8xd8+vmnmOWSf/SP/iOeffWMNy/fMPnkcwIpONi7w3yZsVqlTAaXTNSKIPTY7G7S7fQ5Pjtne2uHbqfDF59/xse//IRP+ZT33n2XYqXotTegyPHbMXu9DoVocLVMefH1U+7eu49fjZGZx917d7m6uuazjz8ljmu0W23GgwHbu7sEcYXr4YBYSbY2+nx2/ZRX44u/+uDlNNcYV/Jp9wObqdxkNKZcRLDGtix8ZW1tPE/cWPeW+jRhpxRrt7G1A92F6xgirV2vJ/3168pgJYXASNtNMu7zrP7RenpprZ0RojsubTM74QB8rS1Lf+1KYITNIpHOINF+hlLOaHFd4tr3lFLi42GUQnqCu7ubfHLyjFVRlDHZ7SyH5d30QikMDC3LlrXRXnkuhcHThotFwvbuLjv1JvP5jOlshsLq/gySxTJDCA9toBJWyEyGUrZcXczn5HmOVppKHNpgFgaoPCOdL/Clx3WyJMtmtDs1Njdr7Oy8xzhZ8eakHJqhWCVLBtM5Z9cThA+B7yGwXm7VWgXPk6xWBZ4vyfPCEniNJl8t8cPQEpGlT7KcA5IgCmk16zSbNQZXA+bzGdKLwCkjbmjWNxHJGONE1MIRIsQ627J/HDDvApaULjMzdiSc7/t23fm+KysN6SohTedUfUmnXUdlkkp0B88LODu/YLZc8fGzV2yOJxTFirPLC/ygRrJc8uryktRoKpWIosjQJuPtx4/wn2p67S7vvfeYV8+e09/o0JSKRiUiH58RBlvs9nvce3ifWrXG118/ZXB5Tqu3QXWny+7DbR48vs/r52/46ouv+dGf/pLvffQ+XqWF9jxM3GBkCjxjaNe77O4eQBiSK83F5Tkbd3ZZGcXbH7xHskp4+uUXeFHA++99yFv3H5CuMiaTKecX52z2u3Q7HYx5wvDskPliyNHxJdlS8f57H1Br1Wg2G9w/OODTX/2KH/70Jzx7/pzvvFcnz5eMk2t6vR2qlQbDaQFZxkajyfjqCtFp0ag1SLIV9Xqdjc4GyWzGKlsRSp8sTfEiH20MkR9w+voVi8WUZf7XwPPypLeekGeXlWTNpiqtw93DuIzIc3a75R1USus9bmfqeevy0xi9NvtDW76JL115yK3XCM8REl35VgL/ZTcRY4eDSN9xwcC6u5adJyz7XysEN7pHC/bbA/e0DXrG2LHoNsgph+u5rE3YkVDScx1HozDK0itazZBeq8r0emb913Hp3xplEzd/C4GSuG6m7ZWVuI7Alk/D1ZLz4ZAnW9t0Oh0q1SqLxYzpZEIYBY7vlrPIV6yWGVVHASjdUoMwQGnDYDIh9H267RZhICm0YL5cUOQFZ8MhlUpIp9Fgb3ebSrXKO/d3SVc554MrRh7s3tljNJlxORiyzHLSzHqkjZMFQhsqcUwcR+A6zWjLB1wmc8IgBGGI44hCGbI8YzwaWbF8f5Orq2sQkmSVWhkYNze+0shyfcrKqhFz65yaWxlXacxUGgB4dp14Es8PwZMooTGiIC9SsnRJNQ7wUcRhyLIo2N7ocqfXYTIbU5iCoshI8oKNZg8viOg2miTJgsPLAcskZZmtQBo+e37EnZ1tqmHI3Tu7rNIlb64H3Nuo887+DrNkwfDigmE+YENvcTXQKLViMZ/Q397k3v2HXAyOKDLD/bvbfPjeAz7/7FO+fPmMD955l/7mBguhwMl1VosVJ2fHyFqFWhyTLBNePn9OEASgBQ8fvEWeFTx9+pyNbo87e3esr1anSaUW8/LVS0zoUa+FVOsNer265S2eTdgwfaSUzGYzut0u3/3e90jTjE8+/ZjVYsHd3/oWXv0+n/zyF4R+xJ39u3jGsLi4pl6rsZwuqfgVPN8nrlaoxNY/LqxWub68YjKf0NvaRGUFR69fcXD3gOPrC7L0ryF4+Z51qpSCm6xJWLqDHazhKBKOU+N7LtCUpR32hud7Hr6zXRa3MhGb5rPGrcqMTQCh5xN64bpTCU6vhuOVuTILIfA8957lQtcWW9OY9V1aG5wvulnHFSOMqz6t06TFs0pdnWVpK2XWmaIRttNqPb9ypDQOE9NsdXucjJbkyk6h0etNVoL86y23TmmFWf/EPQRaQI5hukjIlRXSCk9SbzSI44qbjqwItC2rVaHxo5hc28kvWkrmSQpCkCyXZLliOJ+zvbOBKhSFUk5S5RGYkNOrCeeDGT7w4O4Om/06d3db9Da6vHxzxmQyxPcEGo/Ii1gulxjjUQkiJrMF6WpFvWbb7b5zMvWlbdzgacIoRDslQpouOT27wBMBgR9SFNYq3F7WX++Xl8FLa4XAv+lEO/xROHmQvHXDxNjr7oXR2u/NOHw0KzKydE66TNDKUA1Dtje6JNMJkYjRyYz7d+8S393Fc8NbhoMho8mUMI4xwDRZcHJ2Qa4F82XGJEkYJxO+Prpkr9sjWx0jYh8pA04uh5zOEowuSDHIoMJXz5+z1d1ko7NBGHoUeY4xOdUoYq7GZKs5Cwo2Njd5/uo1xcef8G6e0tjo4sdVpPCskkJ65G593r17l9lsRqNWR2cFUSXm3XffYblYMJ/OeJm+4OD+faQXIIOIZqvHF58/IwrhnftvkxYQVnts7+VMZhMm8xmb/R6NeoVOt8tHH31ErVbliy+/JPy0zg9+8AO29+5xdnbG4ckpD+8/QsrA0leKgtl8RqoywrCCDCpkQBD5dLY2mA0uGV4N2NrYZlWboYqMbrfD5tYmfH36l8ejv/QZtx6BF+J7Nu+y2LgzE0SurZ59DytDcNOy112g9XZ09jfO5sQq/3GBRqC1Z4OStB7juN9XwoBAeHbakLYliTJ2ojJGraVBDvpyd2cnRTLG6RtdCak1WmkXxG6mGVnKhsBIscaD1hwxrdfBDi3QjpMmhHWrKQxIaVCO4tFp1Ik8j2WhXNfUOJTLNSHKlr/L7ASuohRi3WxgzRYRrHJtO1PO00xKgRcEVIM6QeTT7rXpbvfo9vpUohqHL49YzDKef/mMaq2F5wmaLds4OL04Yb5YIYBVmuJLgcoLhDFUKzFGGa6vx6SZ4mwQ0urEpJnEZLDdbbMqCharjCzL8ZR2Y/AkxBUMikWS2ExWlC6loSU3IxDaEEpJGEdWeSF8itxinfNpQl7YZot0Wbde24ffzrC0y/pxNbl0+lqDcHZJtq1z04GUMkB6AQhJViiKIiFNpqyylHpU4dG9Aza7bfpvPURgSJdLpBS02w17zvOcbqtJmmaMp1OUMbQ7bRq1OoUxLLOc04sr5kmL8XxKu15nd2ODN5enTOcjVJ5zejFGa0UQBvS3tlBZxquXF9w92Oeddx5xdXHIcjYgX+VM5zM2N7cJgpA7Ox3+4Ae/x7OnT3n6/Dl39UO6fY/pfEFvs08cVwhWuTUYMLa7P5/N8D2fq9NryxOrVTk6OkR4Pl5Y4eGjxyiTUW/W2drapFqpMk1WqFzR33rA8fEbDva2GFwPGI9GpOkCo6FerbK3t8vJeMC/+NEfU5iMt966z53dTS7PRwwuB9x/+IhKVGEyGTvNcYHnR7S7e4ggYrqaMJ4es5zNqFQa9Hub5EWFi8sJqvAJ/Og3ikffLPMKYzfJp6QHuDIP/8YmxyuzK7EOXtJRFtZdRiyfS6Ms2x2xJoNazyZ75/S9CEsEhcD314NsNcrKgEogXWvX1TOufCwXu14vdmM0qlAucClXmjimvLuLW3Ddeus7EwKHzeFKOWG9+YUjrxpHHxEG47IMg7XlaVaqNOIKszy3XU3z55/T2/nF2gnbBS7c6TJAogqWqqDiiZtiSQqiWkSj26Sz2aW/v8vW7h6dTo9v/8HvoTPJ4fMjBk+PuL68ZpmmZGrFstCs0jlx4BEEGaZQlj/mvp/BENZCJmnCxZshlcuQWqVCJYioxB6e7xEJQ6VaIfZ8FquVm5rj4ckAgyUrL5YLCqOogyXKFpowCCz47nk0KxZ/MxomSc6sAO18u0yR/9q1FOvAXz60KxPdhCpwmKHn/u0aMVJijI8fhPh+CAiKIscYzapIkEXG/d0dPvrWu5hiySJZcHBwl0ocU6iM5XJGnib4gUeyTEEK4mrVDl5dJGilaHeadANJf6PDZJQwns65HgyIwhqPHzwhWc7IVxm+H5KtcsaTCZOrKUmWkuuCq+nnPH31lG6zTq1aZ3Nzm1qzgl+pUC0UWVGws7vJ5maHP/2TH5HMFwiuObh7j3qnRV4UFMkSlMAYRa0SEAZN0lWOnsPl4Aov9jl6fcoyLbieJrT7W2xubrHMEtju4/shl5cD+js9VqsEz5dUYutOEoQeqyyl29vAk5J7mxt4rTrVRoMvvn7O9fU1j+8/pBpE+BLm0wmN3Qa9jQ0uL84xRhDXGlSadWQYEoqQKFCYNLFrzluhlSII6uxsV6nXn/5m8eg3elb5ZN+OiRLGEjWFc4AQjpIgpHBDOKSbzuJZnMz5ea07k6pA6RxxSzxuO4Y2IEgj8X07tVhK/1an0IH6TsuojLK2y1pTjrfypCUramWzKZtx3XC/tL7BlGwSVWZAxurgPCcPEq6pIIXtTpWEV0f9tv+r4ZaMRWkw0vlFAY04QsywoD5mrQQoX38LxrlBbsSv/79wT1qslkyXS6J6FYTGeFCpV2hstOn0u2xsb9Lf2qbZ7VFptPCqMfVene/c3SX8O7+DXmnyVUG6WPA//rf/jP/5X/4rZrMJcSxpNKqEng/K+bMJKKRBBD5e4JEslqxWM8IwI1wGxJFPHIRExhD6PsIXLLIVRa7QStGIK/hBQLfRZLlMmc0SwmqVRbpACUMkJEVuDR9D3yOuxeBrLuYzlDZYZvPtlecggFv/Lq+pk2a4jjJgLMpojHZZmCQMI8Igcp1iYfmIunAj8SJa1YDR6JI7u9vk6YpqDGFgh7gksxytNV4YEYbYienLguVySacRkC0zsmWOyCx5dGujy53dLY5PIqrVOtPpnEZYodHZAOkTBDa4n52ekKOZLBbMZjPQhuk04+zsnK+enlBtVPjRTz9ja2eTfLXkg7cf8dEHH7DR7zmLKM3ZyWt60xoy8rgcXnNnZw+hDX5codVp8vTVG+IopFGv4KMYd7o8fXHIlxdfEkY1/u7f+dvUGzG+B0+fvySuVBhNrkhnCT4CXwBa0eq0KVD4nh3MojE83D+gV2kwffw+pxdnnJye8P67j1BqxuBqzjwZ0Gn1WSZL0lyxzCHTGhH4KKHIVwlBHJNMpoxODgmjiE5vG1nM8IK/BhvoUmCNscC9KB0uZbnRpQPWLYve9ywHyJelb729q6tCkecSJQoUNwxqcev9fS/E9wI831sHG4tB2Y5lrjVKW7qCcTQE3/mQg/N9MiV+IrEqZIvNIazezYrERRm77F1aG7RnXBPAsbZL4E2DJ2+35YUr94zD225lWEZSdaJfyxARzotK3/T0bwfvP3Ouxa1/GOwU8evJhG69Qhh5dDotWr02jY02va0+vX6f1kafWqNNpVIliit4QYAWhsxTGE8gKgH1Vpf//P/4X1Fttvgf/sl/w2h8ySJZEvmSOPDxHV4UxBG+9GnW6kyDKVmRW1ugVKFUwFJk+FIQ+D7S91FYsN1kOUWW0W7VCYOIRq1KGMfkxpCa3GYuQYQQAm0UnhGYXBH5oeULuiy5tH7G4ZQ2q7/hcllLcYd5lTIgc4MYSikIg8jpGCv4fohWyvq6qRyjcmQBB5tbHNzp8/TZ15wevWG71yGfXdLrtSmMIMsVlUqLooCo0iBJlzSqFdQqZVVktFs1NIIgisGVyas8o9/vIfDY7PYwGmbTGfMkIarW6PX7BIGHCCTNhi1Fr4cT5rOENE1ZLhNGkwky9Hjz+pRCpZydHqOUZrVY8uj+AzsRWxpmo2tqVZ9qNSIdX9JotpmN5tYep1FnOpuDKujU6uxsbDAeLZBiwc9/+lN8z/Dbv/0BlxdXvHx+yO7ePtXYR+UZ7VaHZDajVasRKMt7DHwLcYwuLjHG4AmP8WzMW48fs7u3yc9+9iPajQaNWhVvMmVwNmRv+w6ddpNMW56mUjl+IFFCUm+3abbaayPQ6XzMyeUZ08lfrmuEbzw9CLeRxXqBeJ5cA/B4dohn4IiANhPyLZtdiBuphm+xKzv5WqF0AY4SUbLipXR3YGxHsiwalLCSEWU0ao1ZsXZ7cHnROtDZke96fbwYz/4c0BI3pchY11esIaIW2LFguE6juFXDCXsegDUmVWZQtrxhfTyRb612yo7oTclsya5iHen+8ocBhtMZy+0NtjZ36PU79DY3aG10afc2aLQ71FttwkqdIIwJg9jiQkasOVIagxIg6yH/0X/1X/Dw7cf8N//kn/D0iy8ZTidUC0PsGXxRYlkCGRs8YQh8W34pDYXStgT0A7KVQqmChSrQQFFoZiInB9oNQxSE1OpNJoslk8mcZjUmCKv4jn4SeD6+5zFfZm5Unj1HFnC3+CNYuZbneY6YXAYoSV4UpUMh0vddU8kFOmmhizC0NzSlFUWeobMV+WpGN5R8+8k+9YpHnue8uB7w8uiQjUaDjU6D+w/27XUK6tx/dJ9qvcbZyRuy0YA4yKlEAUrU7YyBPEcbQ61aoeHV0YWlqWSpot1qMR7Ouf/gDrVGk7hSoVqvsMoLPKnp9VpsbnVQhWYxXxAGPpPZjGevXnF3f5tnL14wnoz5w3/5Q6SU/OSTL+h2m/Q3ejzY3CHIIiKtrAwsy2l2ugjfI8tXdJp1hiM79PXO/l327z3hq2eveP7yJb/42c9Qecr29g7VSoXPP/2Etx7dxw98vnz9kvlozAdP3ma72SWSgrDi0+43ODx8w5s3r7mzd8B0PuOrf/2vWCQzpJR8/fwLPM/nW++/zf62YDo7IqgfEAYVri/PCOMK9U6LShhhNCwWCVIpKq029Vab4WzM5fnVb7QnvpkNtAQp9brLJ5yNsedJx6GxmVcppC2zMrBlGMLDE6CFdvIhi6ybwuITcJOUgCv1tEYID6WNKxcVhckxQluGPqUjpnTdyxtffLukFQZVks7wAg+jLOve2vl4aK2RDswvP9eKr0uKw21zNJcRCO1+Lh2mZlMz7UpM4TZ8KTI1LpgbXXJKbrf5//3HnyVVIGBpFNfLhG/t9Oju9On2+7S7G9QbLSq1OnGtQRBG+H6w9nb/tebAGgP0MJ7HO999j//ru2/x+vlr/sd/+s94+snnJKMxgZei8wytNZM5BIHtDEsvtCWlSonigDiKkFqilKFYWf6UoUKWp8yWK5ROqEaK2UoxmkxZLlcEngdNj8CTaJ3ZIa2VGLVU5ErZ5k/5hYXtQFutql7jWJ6bfI2xdJoSKAwc1OAHPn5g5wzU6nWiKEIpRZ5rvEDgKU0Dxds7Xfa3YoIo4EP9Dj//7FOmszln12O+fPGan3/+jN/9ne9TcIl8pciylIrvE2o7om2ZzNm8swmeHeIxmU6ZTCYYAdvbG3hyk9l0jioUGo+PP/uC3mafvTu77O5uU63WWa1S27FNMySaaqyoVCKarTrdbos8L3jr/gHT6ZyTwxMm0xnPzo44ubqmczYk289ZHXQQY8FoNCMMIt5//y12NrcpFJgsBymp1hskSYrnCx48fkAYBwShR6vVpdPt8vidt/jqiwrn55cI3+Pjzz6l32rjB2/hV2FjewddCbg4O2U0HdFttzk6fMOTd9/h4f17HB6+Ii80YRDxxVdP+eFPfsr7j++yubnJyvhsbW7TbTQwwiMZT9BeQLPdpdWpMrwe8cXnnzMdD1kspoThbxaWvqG20bHMHSfLYl0lw7yU+pTln3UGMKZMT5wjprByIGVfACjwQan8JjNxgQ4jbdgwVsqR5xnaWNZ9afcs3dRk6fycbNu8XM83Qt6ygyekHY5bVnjaaJQQzlfsBkspigJdKLs5HE/s5v1cFlqC+cZZ7bjsyhNWMlWJIwIpkNrZNwoP4YTtN1Ip/WeA6Jvuqz2g0kfffn6mDK2NNhvb27Q6PVrtDtVqg6hSIwhCexNxNJPb84Qtb60cAly4kG7wKgFvffg2D5+8xcXRGf/8//lP+fxXP2Y+nVJIReD7jNMUTA5aI4WxciRgsZjjC0kUVWhUQyuhSQsiv4KOIjxpyajSk/Q3eiTpinGy5GgyYbtaJwoglDBdzFkubYfXuGaQEZpyTBnmxvb7hvvlvLmcMkFg8D2fMIypVmoEQWA74cbgSQlGE0chRZGRFQl72w3eOujSjDKimofpVal997uMFwuyQnN4fMZnn3/JyfEZB/0W88tz3rx+xQ9+8DcIfY/lNKNSCZnPL9nY3iOuVglin1a7QZIkqKKgyFdEgUdQqfH97+1BGJCkKSenpzz7+gVB4LOzu0O328WTlpu4ypasVikCqFVqCCkYTya0Oy3u7u8ggF9+/iUf/+oLRssZn758zdHlNYHvc7C3T1xpcj2csJqvuPfgIUtjCKt18ixHSZ+NzT55kjKqxrRbDXb3dhlPxhih6HW61Cs1ao0GR0eHXA2u+J/+9b/mrbuP+MHf7ZLNF1y+PqMbN6lXY+KwwmQ0Ymdriw/ee5/ZdM5ub5Mn+/d4/eYVLw/P+er5JQ/2Brzz1pL7D+/R6e/Q7FRR0kMhCao1dls9Dh495ujVc370J/+WwGv+RvHoGwcvpfRNAHCBSjiqQRh4lqvlxNa2++bAfOHKQtdZ9NwdtiQbCmMpBbr8obhFLjBYEuga8JY3hHU8V56WWRc3i7xc1ob15i8N64QLYJalXvrx31AitNForLd+mcTc4C0CaW6+g3TNCHELWDbGTvVp1aok05lzAnNKgjJYSc8i9DguWZnVUZa/5fuVLrUCIT26/T7djU0arS7Vap04ruAHIZ7n33DgykmtlNy29f85bKgs1W1GK3zJzr09/g//l/8zf/qvHvOH/+yfMRkNiQRU44hVkeN5Hmm6JEkS6vUKUaWCyQtAUYt86s06edWQrJasVhlFXqDyjCI3VCpV9vd2mSwT5ouEF8k1sS9pNyo0ak2myxQ3TgW0JT2XZThC3pTq7hzd3DBxXWKB79k2uzFQFArQeDKjiHIwBpXn5FkGSrOYTbk4h9BbsLXVQS0lIqqzSgrCZkx7q8l7wdscvnlJ5QvDozv7SM/nejAg8AR5umJnZ5dVUZAuJhhVUKm2yFcJUtoOtOdUJPVqxGQ2xPg+WsBbD/cJw4h8lXN8esrV5RXVapVFsmRnZ4tWs2FNG6M6l4NLdna3yVRBsVqhCsXf/N3foddp8/rkjE8+e85gMufRgwMO7u1Rr4ZkyYhWt2mHbgiDH0dIGRBU67w+Pmav22Nro8fV1Tmr1YrHj5/w/NlXqFVGq9mgSBa06zVCX+Jp+OSLr/ndv/33efToMcZIpIY8n9HyrC27Kuykrlq1iqeh22xy/8EB715f829++EMWywSFZraYcHZ1zYMn71BttvB9CaZgOb8m8EJ6nS5PnrzL1y+Pf6N49I2CV7l41pIfwTqbsnd0a18DdkqPcf7wQjvSIOsRHZZG4UnnzKpAC4wsMaObDMysX2FlRdbQQroyrfz8MhD+mYzDlKWdO1ZH25BlIHCb2jNQoNFaUahina1pY1CAVDdyJPteJaUDq7NznLfy6IuiQCAIfcndzU2G04TU2E6lwU6xuZEJGdbZ4jp4uYcRTkzOOhvLiowortBsd+2YqzDG88M1n65sxd3O5so4VjJV1hiesdeqFLhrwGsKfv8//bv072zxL/7pf8f4+prVMkGkM1ZpShAEBK0mk/EE35kXojVZumS8Sii0xIs9SxQ2duiwlD4qywBoRTHVICAOA4pcMJ3POBuesvJqaM9Hr2wGbq9Tec4NypTn3pIhymtcgvdSWqNBpY0dYCxunEiiKMLzJKrI0UVOo1FD5QkXgzFB4KFFALLCs+dfc3R+jsaQ5YogjAgDnxev35BMFjQaMc9evKBXrxD6PkWvS6PeJEszZrMlRcuWtPVqTLJYMh7NKLIV08kVvu/R7m/R6vS4vh5yuUio1ur0el20wWoj8xVGK+qNGskyZTS6Yr6YsEwTmq0WtWoVERqMVuzsbtBsNxiOpnzx9AWvj16x0anxrbcfM1vmHL85ww9iGne2uDg/RaearTt38ELJp18/RS0XPHr7LT759AuiakylViPF6YIRPHnrLZbZCqMUn37yFf/TH/4L/uP4P2Gjv8H14Jo4rNJotpHCZzqbkyzmSAyVehWtrInn/p1d/pP/4O/xX/+3f8h//6//mCdP7tOstxnMZ9x/+IB+f4tqXCco7OyCNEvBFPjBn21f/fmPbwjYS6TnO0a93WzSdYAQFhC12UdpFGOZ6hYot2aCotw4lLwq+8fz7BANIUpej51ApNdsa0ux0M4WulzI9h83jhCe9NzzSkqovgWa24zL6XQt7qUNRtoGgVEFxhQ3wLsxjj9mGw43kifnYuE6mp7nueMpcS8rPEcYNttNtpptTiYTjJ37Rq7cMBB3rmQZfNdn+kbqAi78uoNWKkcpqNYahGEVPwjs4Al5c2OwxFwoAyPOCaN0jV13GtynCqPd60tfNcHj77zDg/cesZgmHD97w+HTl3zxyaccHb7GmIzNzR1GwyHX1xN6nQZhpUKRrVhmCjPXtFsNfKHW5FchBSrLCAgQRY4UmkYcU4maLrsOMSnkwtgJ7Foj/Zvp7DYzthinEaUTiXDcEteQ0IZclZPYbVmeqXJWgXEyMI0RytIzdMZgNMcLa4gwxRioVass5gm9dotCa6pRgGw2uLwccXx5Trfd4Dtv3ae/02E8vAYhqMY1jDKMJiO6G5tEUUi71aTb7TAejhldXzGbTRm9ekV4csb9e/epNRsskiXVaswqKzg+OqTQOdeDS4wpOLh/D+F1qFQjRsMR11dXiE6XVrPONFkyvp4SRRHf/+63OB1ccjm45se/+gSZw8HONq1Oh+l0jp6EyGLFdDjAkzn7Dx6wvbHD82fP+OFPfsLe9g4//vEPqVXrfOujj2g3m7x5+ZIgjKg36wS+pNms8PrFCf/4H/8/+C//1/8Z3f4W55eHhIGmXm/SaDXZ3t6iyFYU+YosTVllGUmywBjD3/7B3+Tpy1e8OXzDV7OXtHttPn/6Fa1Gm+9/5/t0u22CQNqJWLMRq/SvodvoexZolRJ83/m5l1IMIdAocOxnTVlGeW4AqMEO4PCsSaFrdQtjnViNkRjhg7Ej3stpP0avwZ81Qx5jS1DpxldhboKSVoUNio7SYHSZNclfw+QEN6Uk2KHROdY1wwq17f4u3P4X6z+mPJp1RgMuA5Bl5ueKGymIgpB7u7tcLxasjHFdV7HG7Eq74nKIrimJrmVwMzcltDGgcoUy1mnCD0OEtNKjkg+2Ljd/7U/ZJCjRIbPuwDovW4wqsUFXSkqDiQSVXsS7G+/y4W+/z9+Z/gN++qOf88/+yX+Nni/Y3bQzL6fzGZmAajWiEvosZwmTyYxmrUqlEpOlKXmWI4WgUa8S+DbYr7KC6WyBzfqc04a4OUbjArBxhOL19zBlRmYpOPLWa4qiQGXakZx9fC9E+r4NcMJOU58vZ6SjK+40q/jSchd3d/a4v3+Xy8GAVCnSLGUynzO4GrLKMsJKBSUMby6uGc8W/K2P3ufeVg/fl3i+JJYRYbWK9ATpakm6XNJottnc2aLb6+BJj+vJjMOjI5RSeEYxn02t5tPARr/P8dkJL16/4dXxMY/eesid7QO6vSbdTg+Mt9YC+35As9XC9yR+VfHhO2/zk48/ZrJIeHFyzE6/zyJf0Wg0uLy8ohoGdDd6LPOUk9cvaTZ3uXfnDoGE4dUVd7b3GFwNmU+m7O1sUa1W8D2P6XxK3GpQq9b5vT/4Hp9//hV/9C//lP/d/+l/z9bWLoOrS44O3yCFpFGrsZhN2dzcYDQdIoSg1WmA1KxWir/1O79F/v33ef7sDVr4fP70Kb/66hMOn5/w27/zW7S3Onz91ddMxgmX14PfLB79Rs9yj8APiQJ/bf3iedJxcuya0q6zZZS4Ec/a+RV4ngOprOey06gZBMoOrxX2fZSWjkXvwHNX/txuo+O2ncCOaZdSEpQdKOwgDhsonUOBfboNXmvTOtww0sKua88HWVA46oXSljUvhbyxvXGAfOlEoTBuQpHBMwbpCGACi/97rhDst1psdTocjUY35acpRe0ug8WzGasxtqwz67yJ8swJI9C5JlmkBGGM9exffz33twtMtzMvyhKr/NlNWWnc9bAvLruteh33DJLcZBRI/FbI7/zDH7B9d4ef/OEf8cVPf0HF96h2a5yOLrheJNSrVeI4wPcCClUgcu26lZYCs8oSVqnCDyQ7m1v83t/4Nj/6+Sdcn0wxSuO7710Gc3cxXdblnNyMuDlmgx2+ccuzXrrReQhpSaVBQFGkaKORUrDR6dE0IZFaEoYRwvM4vTglikJarToNobm6nmOqEaK3wenZKUWRE1eqxCpnlC746uQcX2pMAJEf0epsUK1XqdarXF1ec3x0wt6dA+4/uE8y18xmMyqNKo/feYfT4zOS6yGVOGIyHnF8coZCcO/+fd55+z2Ojg55+sVzLk+GfPTRe7bRkOVU2138IELInE6vCxhW1wM+eOcJxvf56S9/yXKZ0tvo0em1UChkllKtNNDK0O/1mEwGDAcX7B3cBaHZ6LeYjybsbT3ixdOvWAyPObhzl+2DfV68OaJWazHJZ6SJz8MH7/DT6095+uwpm5ttmo2mNXb0PJL5At+XLNMEpQ0vX72y49I2N1HGkKaS86tTKtUKH3zwER/91rf54osvuTg/5/LomHqjhq8k+WJJOp3+RvHoG1IlPKTv2xl5QuJJH4NyQzUsoF7a5AhnJ6NN4fCwwBmBOca7Lqe5OB8tYSUb0llAC1ESTXFk1MJN4MbqHj3H3pfe2qBQCjvmSrisogRMpcumEDcdTyviNQhtMMJq73zto7VwQz0MykiUdlOZheOAOdcHhF47THglsAy/jo25wOH5mma9ghzbeYVSSJQuR3rZP1qWPDQHtpfTnm8JHyUSoQz5KgdpwVJjCreHb4pO4a6BLDFDU2JE2lEObFZrGxB6HcbWoJj73/JhXBdCOQOsu+++xcH9B3z67e/w8U9+ztWb19zxQhbJgmW6YJTM8XxBNa5QqVSRGEyRoVTOcplSKE1RaCaTI/Jc8N57H/LF5U+B1PK6jLj12WU5fgNT2Gvu2yxf2OzRdha5wTyNxUBjJ6JepilKO998z+PbH35Avx2zmM9odyu8PnnD6ekRRnXIshXzxRJECNqwsdGmmmZMFwuMjshTxecvL9isV+g2a8zVmPFswabSzBcJmSpYLMas0iaDyzfcubNLFCuSpECEDd754NscHb5mMR8TxzH7B/u8fPmaH/7bP+HxW094//ETxrMxxydXnJ9dcP/uPo1GHa2VJZ16PlEYoHVBr9fDaM3OqMVWr83l4YAf/vxn/P7f/B3e/9b7jCcjXrx4TrVSYT6d0G81mS0XfP3JJ9T6bS4vLsgmU0SasLfRpdmoEyEYXF3R39qm196k11cUeU6aTvn2t96lWK0oUo0Rmtl8ydb2NkYGxNU648kY/Ijf/9v/kOFoyPjqiiSZM14sGM0Tjk8P2bv/gJ3tLf723/mbXF6eMx+OmYwnfPTWW5y0N1hkGb94PfxL49E3w7w83/J9BHi+xa+MBqMcMxq7cKQQGG2Dju/wGBe50KYAbT2iBJZWIJzNs63DygVZZgpivdF8zweXPflewHpOX9lBdJkSoijn/CA9m+XZ8WyW4uC77MvD4Pm246ekNXs2xpIxV7kiM8JOsRF2jFuuFKsss218134vbaELrRxvzAbGm91nELqgVqtaflJR4HmekzTZ82XPnduwlplhN+u60rM3A0/4eEiS2QyBJemug442OLEAZetgLUfSZfblsDYBJSt9XZ6W/9W3opY7/aVlUfkcYxQy9nj/93+LD37/O0wvrnn96Ze8/voFL56/4Oryisl4zCxRLBZjKoFHFAqMzmxTREOuQSnDq7MLzmZLsjRFClDi5rjMGt90XWThbJmkW3suQJWNHbv2nOmgkMRxRBBYXZ5S1v5Ra01eGM4vzihSiZSKi8sFX71+w2SaM1uldDst6q0ORoHwc0IdUmhFkEo6zSpoTZrMefr6glVa8PaTfSqVClfnF3Q0jBYJ+UpzfnpCLBVnxYJOs05Dw2ypmSzntFoxod9YB+aDO3sY4OjsjO29DTQKXwjmswVFVlCPYxazGZVWi/72NleDAePxkEoUMlvMube/wzJJ+dHwEw4vL/npx79EepJms0G1Vuf5s69pB1WCZUZvo4vwfBarjNCvYcgJvQpbmz20KkiyjP69A5LViiQdEYYRKluxWi2II0mWjhkNUg4e3KPV6JNnS6TOmKQL6s06WsFgOKTWrCNyzVZ/k0k6J4irTKYZn372GddXl3R6XXqbfbwspyoEXp4zGA95c3byG8Wjb1g2SsIwvAXAl5QC4xaYLaEKJdYAuix9590d0YjSFcBGbrsb9FreIaR1A1BarYsqKSWhw9UQ9u7ri8CWaOaWFz5loRQghXabVdvszljM28MCwp7n4SHwS8zMSETgWxa5MSANUmu0tjhdub1zac0Ky3Y+rrtnM05LWsUYhLLurLbEZt0N8xz3yGaDZUZkEXalbSZaYjpl0QeO4mEksR+SrVYWfLZpCXAjSAdK7saaM4VRlNZDpcazDJxls0W4yHRTWrJ+rSqLW+mvu58GZQ0ipUd9t8NHuz/g23//ByznS1bzFS+/ekGerPjsZz/nF//uT5mN50SBpBLZ0WeFyiiUYpZmDJdjsqyAAtuQsQvhpiHjjukGc7SBa02CFvLW0GPbtAmCgEpsu4VK2ffUaKTSsDJUQ8nORgXf9/jTly+5f/cBnzw94uMvzmm3rrl/f5ud7S0CQrrVOtW4gu9JZkmC7ymmgWawWDJ9fcbLiyuePDrg3SePGAyv2NreZbu3wcXVGVeXA0xWJ7seEXhQ3dljsVoymy3snErjkeeQLBI+fO8D3hyfMppec3F+wsODRxwdn/D65SuePHqA9qWbb7litVpitOZ6OMKTkslwzPtP3iKQVf7kJz/l1eEJ33r7fXSmqTZj9u/scf36jKmec3E9IGjXafW3qMUVWjsRtSimwCB9n0AIPvv5LxBSstntowrbdBnNpmhtmzHaFPzipz8mCkJQmkq9ynyVgfQ5Ob2k29uk2+3SrNaRnk8URDTrTb790Ue8PjxiMl3w6vCQVrfN9tYWRZbTrFTY2dlmo7sB/OUs+29YNopbdzoLspZunkoXjsTqgolDIWSZDcG6hLF3c211ZgInfIbSbx73/mtHATdrr5QPlZQHYW4W7bpcFcbt/TKUKTv9x9gJQtLtbd9NlrmR7NhyTguQxuC50Ge7cbZnKZ2Wj0KhnErA2uc4XI6Sl2bslG7lJhRJz/mA+eCm4XgOi1s7YAjWVj9rMux690rbtcWK3pP5grzI3bzC25jVTQAqQX9RBrB1bldiRbqMVDfx4eZK82sBzP2oJLoibINFYq03tFYU0tozR62YqBHyvd3v4kuf3/k7v8PnP/8DfvGzX/Kzf/cT5vMpVc/DjwLQObnRrArt4mRhPdWkLZdLPM56xFkOm+0cakCgjVgPZJHOwcT37KRsz/MJAx9PGBcQIc9WiDwnCAxNLye9nrLKFJ1mk7zI+PCDB5ydzqjXqlwNTnj5+ldUKxU2uh02uh1qtRrj6dRa0ERV4mpKuloxXS358ecv+fzVKdVQ8s5bD/ngvffZv/eQs8PnXAyG1A30OnXCLKHf7RL6MBrOOTo6J10W3Dl4QJHlPLx/n5OzkNcvnxNWI1pbXZ69ekO7XmNrf5uiWDG4tOTgShTQqNvuqKo2GA4m/NZHtuT8+U8/wQutfc3F5SnzZcpSGvxmk3xRYXC5YDw5odOuovICr+8RhSHNjS7Xg2s8oNPsAJJmu4MqCnbv3kUKGI/G1OpVWu0u6SJhMhqzWinq1QZGSPb390mSFRdnV7xZHrK1tWmnXfU7rFRG3JDUwyrL1V0++fxLvvrqGff2D5joJUmasrY7+kse39AG2rgAJSltd7XDo7Sbpm2MQPrlaDOXQZibLl35Prhuo7Y3WVcaYYmfwi1ercHp04TwkM591XdOqtJNJ1LaToEptKVSlBvVbrbCBiNhibWeNAgKS6eQrtdnLKO7dJa3GZLEcx0soXGj3soW/Q1xyrqr3tz1151CIay3l7GldbrKXOfTWQopgSdsoyAvcvcaGyXKatlmQ47gi8UD4zgiWSS3kjwXpJBrKsQaMSozFm69Ly6wlcEBfg1jurlG9jVl6VoKoXE3FZvJGNDKBVsFxjqUammDu0Th1T3e//3v8t7vfpd/9L/9Lzl9/YZf/finfPzzX7IcXNvsW2hX6iuHr5WB/ca1wy4lscYslZt2XpazUkp8tzYs0z7E96yO1bpdaFCaLMuYac3z42Pu9GPiuMLjJ2/x+vgEYxT7ux0Ojy959OhthsMRb47e8OWzF9RqVTptSwr2pcdsniCkJK7VkGFkk+eiYDCZ86/+5Bf88Y8+5vGDu/zu9z9kOk04GY1oXwY8FIJGtqK3uU+90qTVbHN0dMYqS4hDSbKc0uk2+d73vsPh8Ql4HpnKub6+ZnO3y9X5CYtlwt7eHUIPMJooDsm1ZrSY8ezrr8nzBb7v8yc/+hH/8B/8Le4/ekT1ospmZ4s4rvPek3cp0oyXv/oxushYao/JZE6w8rgcDtje3GKj36darVt+nSdJ5kuKS5ts5FqhfDfkxvdobW+TKU2eLKnEMV7F2vh0e32yVYYvYXI9INroMl9OOT09YqvdRwifJw/u8/rVIabIEcbHU5rwlvvK/9Ljm2Veoiw3ykBmJ6AobQmeWlsphl1spURIuG7ajbzGOLyiLF885+VF+f4YlLPckcKWJlIKPOHhCY/AC+24KqPsPViXzYKCokgtuC1Khr3tMHkSQgfSA6g1E80GH6M1hbFbXZfdRA0Gy9dSWltCLebXAnPZDrzJ39bJJcZA4YJH4TgXFp/z8KQNurqcArEODa5qXPcZ7Rt6TtWQFwX1Rh3P8x2XbR1uWJeFJW1CiHXidCvJ+jWsS697dLeuc/kfc8MXK28GuJuLxfaNy1xvWV27TNbeRJx6QRhEKGjdqdPd/4APf+cDJtczfvZvf8r/8P/551xeXaLI15+itHJuus6SCLG+nuLWd7WZcOCCmMUv7XkvJWtiTVtReY4V+2kyL+I8zWFWEMwTpDfk8cE9vnz2hrgVcffePmfnF3Q7Hb7z7W/z6s0hFxcDTo6vqNUinrz1iGazS5plLLIlk8mM2XiGKhRGCKrNJqvlitPzAePRmO3uBotqgzcvXhFeLQinCy4H12zv7GGMx8ZGnSBuME0ylIFmq0mhMqQXcHZ+Tqve4OTsnHe/9Q7j4SXPnr1iMdNs9rfwfYEJfCr1Lv5oRq3aYDsrWN5VnB4f84f/4p/zn/5n/zFSwmg4ZLk6Z5zPmU3HZBenHOzexwurLNMJUgt2u31MoYmdbXeeZcznU9fphWqlSlFo0jTHr8dkRYEMPAqTMR5dkE+srrRmNLGa0mlWkRouRkOuT2dcJHNev3hF5Z5Pq9mg3+uy8623ubwakBUQO+nZb/L4ZpnX+s5rHUfLcsmWKvbuWDLYfdeNvPGwLxeV42utcRQHyAr7HtrgRlJJl22Vw0PFurQTbqPI9ZaypoQ4nEmU7X9hg5YvsPYtnkcZWgSlFMignKtqoQ250etmg/120gXG275RDiuSFiuTwuFXgBHC5j6mdPi3z1/lBcJYL36tHa1EWK2o53vgMrgyAy03akkZKJsSQRRy/9FDm3WpG11k+Tnc+pv1t12HQdYdx3WEupV4mXXMsg9x84/yu93WeBpXNlrtjgs97ryVhF1jDEpq696BnZwkPUGtX+fv/Of/gP3HD/nH/7f/O6OPP0UqZ6ft7mI2bjqVhts8RttmiWV1aIwskL71yxdYyEAbQ57nrPLcWlBj8ATrpsoqX/H84holqtzvbzFLVnSWBQ/u3mVqNBfjCQf7e7SaLVarFXf373Dv4IDJZMrhmzfM5wlRqEizHCML7uxscW4Ey3TJPHGk2CBgukz5+usXRO88BBmwKCSHgwFFseLBxgbFNGX3/j08QlaLBabQFNaeHpVrikxx785dHt1/yJeffMpsYS23u90+m/0tGvWWnTYlDLV6nXqjzfjygoM7+3SbG3RaDY5Pjvh//7/+Gb1Ol/29XTxP8Obrr/EDj2AFFT8k3mpzPchQec40STg/PyeOYhqtNptbm1TjyJaKjSqRF3BycUgYB1SaksVsRF12MCpjNh5Qr7bxdYwSOcl4SSh6aCmRvs9oNEYqw7s792lX6uRpxpeffUZcjcjygkmScXR+xWC2+I3i0TeTB5WEz3UJcpNhWSzEc3iD54wIpTMjFO4urijZ0uXmse4HJYPH9smUKixPSxqrLVw/F9s1XI8hw2V8he1MmdKTwJZxge+5jMta9vgltcMY2/GENS1CG1gpQ65sOWzfv+xilVbEjiZhbnhSpSRJyht4HVfIKQdAKWNY5bnddAaE0G4at2Prr0u/226h1hzRk3bTesbg+RI/Ctm6s0uZfawvjf7371c3Qytugu46QlEe7k1Y+/UM7AZHW7PR1tfxBpezz7KuGraQNDfnrlRLKBfoEfb8aytDUVJz/4N9/lf/m/+Qp6/ekA4LpLE6xPLQDRqpBSKw68zz7ABg7WRcorA4rO/55ErhCzsXwcIYOQKfbLVCFW6MHoZ6NWQ8NhyeDHi81cM3CbqY0+1u8dlnn5ERoOMaOs/Z3t6mUa8xuLqmubtDNY7JVhnNepPZPGE0HTGfTLl3b5fZYs7zFysQIZ7KuZ4lfPrymGWa8Ad/83doNCpMZ1PCKOJXXz3lYKOLUZp2b4Mcj+E04eDeY5LZnFa9ReTHCAy1eoVHTx7heQHVWp1CwyJdENUqGCGp1msEvqQeRVQ3tzBG8fjhfXa2+pyc3WORpBilaFQr1Fox22GXr7/6goOdHVSxYHqaoIzmejwmiivWAhuPyWRBu7VCClguFnx9dUE9Dok9iRYe56+vyLUmFIWd2D5fUIR1qhWPMIhYZSnz8ZQwDgl9w707PULfSrgIY5KZYnx6jcw8akGD3v4es+UCbbK/JBDZxzcLXq5TBsqVBQqt8zXOtE7tRakhvHEDgJtWPsLKUKS0jqdIc5OlUHq+l10w1xxwHlyF0RgspqYLjVI52uQIjOti2aDmSQh8V265oCrWoLilJ+TKUGgotCRXmkILCtdJxADSES5Mye43azugsuQ1ptzYxlV/boO75LekLRWuFBLazoaUBjfI1n1Vz8cYhSqbiK5U8qW4CcpS0mg16d/ZvukkuoDya+PYyjf9Mw9zK9iJv+A5f86L1o0Do2+F1jLLc53csmlSng9NqS105fXaIvaWuN21YsOKtfCRnsTXpf2RfXp5M9LGImKmdE51CorCaIRWCM9fh36Dze7KDrPve6xSTaEskdgnpxWFnJ6NuBiO2N5pMR9eEVQj7uxs86vPnjPS1+zt7HJ9fUmRa5rNJp4X8PDBfZ49f8ZgdE6zUedBd5Nef4Pj0zMCT9Ko1pnMcpq1GtJohpMpXxxdkv7wR3z3o4+YnaRcXo2pRDGHV9eYAtrjJXt399ju1llNLgnDOp2tNheXKdPpnFrFWjFfXV4BHg/feouoFoIxLOYJvk7JxhPakaHRaZMrQRBX2Nzq0e21SBYLQt9D5xlITbPTpBNGfPaLTziXPv2tDXKVUfF9jNYEAhqVGoWImM/m1GsVNjc2uLzMmM+mUKny5vAclKG/tc316JzRZETd99nY7rG3t4UgZTGD5XLJ7k6fabK0+1Fojk9Oefr1F4SF4EG9iozgNM2oxTFPHuzyhz/94V++LvnGwuwbXyWb9Tjcy+nhhLnJohzKe6uo0TYoOTCodIAw2AEW2gHpxqj1qjUOcxHaeoDZzSKcGWkJ1ucYlAsIJbZmA5W9C9u7vnSfp41ZO7AW2lBowcpNrSlcFVSC82XQKj377X51kccB1cKVyeWgEPdNSygI3BmpVirMstTa0HgCcFOVjD12hGPsC0sbkULgGeFM+7z1Jm61G1RrVZRx0gVuBNnr/O3WZ69xNCx9rNSk3s4Tfy3LMuWrytffXMFf/83No8zA5K1/r5+5/pCbbKr8oZ1sra0FtSlcsPZsU2j9uRYWKJSdgSmN1deWn6udlvSmrDeWTO3mOxaFIstyEKBMQeAJ2o06ouoxWSz52eev6Yb3uLOxwcs3b3j45BG//Z33GE5yTs7OOT275M7ePicnJ/Q3u7z93mMa7Tq/+tUXHB6fUq34nF2OGQ5H3Lu/z/vvbXB0co3QBcKTiLDCZDbn6eGQw7M/plmtUKxSKrHP/kabHMl0ntBbpARFZiV3RcpksGRrY5NAFKTJjE6rxatXb+i2e0S+QGQr0kVCNpvT7LYRMiWsBLTbAa9fX+JnKVmRWlJwlpIvCpq1CuPBNeevXtFs1uhutUjSjOVqTjUM2NnZIVll6NWSVrNGWGvRbtc5fP2aeq3GRrfD0XDA1sFdOtubHL0+xijwCLk6G6MbMbVGhBFLinyB0QmVACbX5+BHSK9Cliliv0orarHIEuYyIFdwPk84/fprTGgwxvtzVtm///iGwcsCtsJlTsZ5x9saUKAUCKPtaCxtSymN0zGWWkZX0hlXTuEAcZTDYrR2mkW5xnvKDpd20S5XhQ2UZQmKDXgerswSNyVOWQLmReH87A3KuMBljM24lEJphUKAsZwuS48wawvqNfjs/m0tdTSeMKDKoq8MFuV3tY8oCmk3fS7GC9e212ubbI/ClVvWiE+6gO9LaYOXm9BdWtdcXZwzmYzo9jcA7yZLpWwYuAzQZa5ltlSW+bigLNdH677P7cxtHWTM7Qh3kzn/eQ/jsqUSYBM3wat8z5ss3F4XWxpL6yiic4ST75Sd2/J41rmcUVYiZUHDNdfODwJEac+ilTMw9DBCkK1WLNOEPE/RekkoFa36FsJIHt3b4fVRzh999oa3thRxKJhMpjx8/DatVo9md4Ph9YinT5+zTKaMpiOm8wU6z3ly9z6//70P+cMf/lueP72g1+5ycnzCbD4jkAGbvR47D+5yPZoya7WYzxdcD0eoQhDFda4mQ8aLJaPZlJ1OF4mi2YzJi5y333mbo9fPybIlm9vbZAreHA7wihWNQEE6QwYBgbBwgs5yGg2fdDkk7tfZv1Pn9Pya2XDERn8H2Yy5OD0jWUyp12NqVZ9kuaRWqXB0ckqjWqXZ7zEdXVMYqFcrLBZjNDkTkzKZjLk8P6dejzBFwWI6QVYrPHpwwOVgSMML+J3f/i7DyyGvX18g5IpavYIw1sWjXquyzGYsVwqtPRZJQiE86tsHHF9esSo0qyLGFILz4RRB5X8xCpWPbxi8DFI6GYZU5V5Y39pt6WQcrgRFYXlOLsVwC9wRDJ3ds3UodgQDB2StTenETRbhSQfOO2AWR9I0ugCh3cYRTl50K+0w1tup3MBKaXJle4CZ0jZ4GcsbWpdHt47T7T9skMVmXVguGOXEIseKLwPXzZYz+KFPrdtgnM8QWoNWKDShb/3iUfZ7Kgwqd58nrAje09Y6SKzBccN4NOTs9ITuVn/teFEeMy5o69sxR7iMC9cEKMFCc/PN7OtvXnAD4JeB7CYA/jmsiptzDQ6YN+vv8Wu4W9kFRbgSXlp6iy7Qxt5EbsuTrEeagw/kLfjBXS+lLX/P930qccxyuUJhhesyD4h8QeEmkWutbDmqNGmyoNsISbyMak2yMD6j5YKu3+RqWJB9ecbmXcnu3X3ud+7T7rZ48+oNabJEiojFbMazL74kHQ+5u7PH4asZF2en7O136fe3WGUFZ8Mpo+mYO9tbvPfWA5QWfP3iBbNlwubWFq9eH3F2ccHrywVHV0vOrqe8c3eTVi3m/OSYwJf88pe/5OGDA+4c7BP5K96626URe8SsuB7NaDTtSLso9GnXQ4ara+bXl1TaLQ52G5wcXXBx+Aw/rFCscoqs4DqZ40vIVhkyiDB4vD45Y7pYUG/V8TNFJaxQa7U4fXNIf2eTRi1kMh6zGE9458Edut0OkyQhz3Nmizl5obl774A8WzIYTy1PbpTg+SEGn+pCgo7tOpQeXlRBqTkFPm+9/SGIgGWaMZ7NCKYjXh6fAX+5p9f/Hzyvm5JPuBa/XO97A8KgdW6xCCHQ4oYzVWY+lBYz2m52XUJcrlOninIohyv3hEWQfEpLHYNWuevjK4QsMzqJbdDj7vgaCoNyvKDCGLI8xwg7DCQvyoGrYv35YEBKjDuGMnjaTzLr55SYlMZuMuSttMTtMz/yqfVbVPtt9Di14hwnSs+Nxg/jtdd6nhXkrhSVyHVwKW1+cNrR5SLllz/5FU8+fB9PhJR0EMMtat/NPv+1gFxmZMaNNxO3fvfnPsTtoLaOY/8+VHbrOaVSUtroU15G24BZv6+7Mbg46vkWcLcZNk7ipVHunMt1/LvFHRTW0lsYSJLEdl6FLV61UmR5BqZs/liVBcZDK0ExT9jcqtCQPUaXU1YqI01TRKeNV41QXka2mHL0/Cn1RpO4VuWtR3d5/eaQk9Mj8uWKVq3Kp89f0tzY4m/97m9zfvaGT7/4nExl7O7tUqva4TOfPXvJy+MzHt6/jyEnkoYnd/eo+R6x73N2dcVoOufp+ZRpknKn3ybNct56uEchQn7+8XN+8fFTao0YCsVGd4Nut4/Cp9/fpL7XoFqPiFuSULfJ8pxkOCIKAwaX51wNUnJliMIQz/eo11o06g0UxpaIRhCHTYZzxS+ef0G/VuWjx0+YnJ+xv9tjPDxmlWsqtTbDZMXR4Jy4HbO5uUmSa0aLKcVK05jMKITm4vqCzf677B9sI4IKbw5P2OhbXHAxn7OcT6lVAq4uTwl9yUavgvQC5ktBrx+xfDok9v/C/P7XHt+Y51X6bZUCayi7gbeWr7nZTkI5lrcsLZ2N4yAKpGcHNpQv1wa0suOzhJTWydSUByptQKQMXlZgfOOkaYsvoQ3GTUs2RpMrZQXWxqoBLEjv8CIhrfuF+bX959ruLkhJC77dQEFOu+mAaVlqNtHrIIewU4Hr/RbV3S6tdo/iy4HbjNqC90KQ6xyjjcVlCu2GyXrrjMfiitrKmqRcT5L+0b/5d7zz3Q/49kffd0dZFoyu3HK0hF8r1RxJzzHbuIlC4sac8M8+HI/rN1lKZWa2tgxyB3Bzo7tV3pa/c6EuCD2k56FLDaaxmZY0Nzc8bQqk75A1aa9P2Z3VRrNcLfG8AM9pXlWhwbfAfV5oN7ClAOkRRCHddoRaFexv1DBY3tLZxSXVakS7FpNNZ3R6PURaMDgfgAdbezssZgt623cQQnJ0cszg6opOvcnjJ/fY2u7z6s1rxqMxw+GYLC8wUrJYDrkcXlsIojD4wuP9d97izu5v8+nT57w+Oub6esB8pXh1PuT06ppPnh+jhaAwdj00GxVA8OxoQn9jRLXiEzfrRHHE8XCOF0GWZ4yHYy4OT2nUW0ymc5JZTiBDluk1i2TB/bv3SAsHfsiAg50DppM5V/MpQVzj9eWASfIxvVYDbWC6mPLq9Iy40iCMAuYqQB9Knr5+TZoppouM6WjO2XDEoki5eHWOLAzf+Y7kZDxmcD1mZ6uFJKXekSTTKV4R4qsUZE6ej6kEVeqx4fTyhMHJMYH6DRYc39iM8Eayg8NWSlIqvzayypQCDlD2DmzpEVbfZzGcm/KhXPxa30hlyp+Vv9dO/yjMzeRrjLI0I7CbzLMeW4USLlMx5EpRGOGm3tjyTGOBdmviB6UEpTS8sz0B4z73lmOE+2McL8yUDhnGoHUBoUGGHnEzptpu0tro0ux3qVW7mMJmoNZ82axxOOM6a/b7lZOfNVoYFBIjSo8xexRFYZgPEy7OzuHbOL+zm1L5dqZ023NfrJsnwn0RJwb/C+tA1tY8N2/4FzxRmFufW2Ka7t/uJnE781tfXZetG5Tz5ZK2+4zrRApth5KWQdiYGw2juPk864whnQuuIAy89XFIaUOcVsqVqorRdMHpucI3S+qNKnt+jdFkRpKmjKZzrq7n/PZ7j9nfbDCfzjFewHS55OnTEaGMWMwnfPDBB9TrEReXV/zs5z/h7r07fOe736PbbXNycsLZ6RmZ0lwOhkivziJZkuYZMpJ8+uIZz89O2d3Z5O2799lrPmGe3mU4m5JlGWmasFplpOmSXFnaQFoU5HmB7wccX40IfMnzwwF5lpOtcrS0PLPcXTClDcKT7GxtsN3tcXJxRaNa5+jsnI2NHv1OmyxZ0NjsIvd3yJHcOdnj+dOXTEZjIlljOoJMtBFezuBqQpbN0TrjzdGEauwzGQ7Z2NikXq0zHs+4Gg9RheLL4xOeXl+yv7fH5fk5olDUm01ajQqsMnQQEPiSbrfH2dkp3W7Xzu8MBGaVc7ff/gvX5O3HN7aBvuk2irX1sYW0pGUYG2x3Twqb+muNMgKv7K6V6Isz/JK+cLMYbWpf2kWXm6IMdGUmpZVab4wSY7JaZmHjJMJlZRaEypUNXNpgS0etbTfKcyzskkBbqHXgLbdYOVatxGvWRohA6amvMSDBr/iEzYhqu061XaPSa9Fst2m2OniyxmKRIrCBCEeLyNWNd/16Mo640Y9K4fyrPLmmIWhlUEnO5fE5ymRIL7bdUdfhvQlW61N4CwM0vxbc1l90fW3/nBzrL45tf3ZlrIPy7WaAtaexVtN/Rl3pXiYocjdPEYs/WrNC5zC79rJ3BNlbsxPKbrDSisAvS2h7HL7vyNJegApCijx1WGDBIk35/FVKowFBaKjVGhRFQOjb2aSD8YxfPP2SanCP/c0e2XTMQbvDm4srTOQzHg/54suP8UOf3kabTucjkmTBJx//kocPH7GzvUmjVuPyekQQVlmuluS5ZZ+GUUShNLNFzos3pySzlCf399HC0N/okWUrjOjS39jiiy+/ZraY4wUeoFmtMnzPI08zNrs9gsBCEP2NbTwvRhuYLxMm8znT+YzVKmU6WVCtVMD3MZ7HNEn5l3/8p2xt1LnbrXMv3aS/16MStvnw8T3evrvP6ekxURSySBPqrRZx5ducHF8wnayIwhicZfoqTTk5OSEKajzZ3+G3328ynU84vTxnMJ7yy18+JVUrnh2d0e+0+O5bT2jWYi6SK5ZFzuEf/5DhcEwtrtDpRNy9e5fUZLy5OPuNVtw3toEuF+RN9mVLC6QTG5ddpPXUCo1yZeSvt9JhXVYIuc7qxC3Attyw1j1ZrrOcG46R3Sg2lrlSUVhemFI2q1DGbgjjumFwI+5el01ugwu3+dZZFTZQGl2SVV2TwICx0DBKGIJqTGWjQb3XpNFrU201iFt1GvUmtXoDUwRUahWkEVYQ7gdoLcmLzGJ5zpXWrLQFryXgdJzC3HDLyiEhnucReZE9emlA345A6yKcMrtZjw27FdhuMPnfDF/4i59l1n/f9Fyh/IR1d3Z9tu0hCneeldFkeWGbNMKgsaRh4Mb1VrogXr67KyvLtSBt2o6UtjvrCUv+9T0PHKgfBRG5zok8Qb0ScHl9xvOzGdVajcd3Jc2KTzOEdmzAxFzM4ONXV2RZQSv2aPs+Yb/P88GU6XTOcDylWo3Z2i6oxCH9jT5JkjBPFhzs7aPynG67Q5rmtNsNWo0GL1+/Jogr1HNoxJZ72Gy2mKdwdX2JMhmdZpN7BwdEUvGD736L2WxOmilUoVmtUsDCCIPLK4xSSKGYzQdsbe/gByG9XpdKtIXKC+IgZjKbc3E9Iiw8RuMp8zRnkmb84tVT+o2AD/e3+H5yl7t3tok7dp03GiHLTBMEMcPLEZXKkq1em7ceNJlOZ3hSsspywshaR8+nCw5PjnhxfoRGc/fJQ74d1pinS7548YyLyyuM0pwcnzEKJblSLJXE81poCgZJwWUy5vNXlygCzsd/HWaEopSHlN24MsOxd0nPSXksE97KiHAky5IYKoXLIrS6AYhc+eh58haoXzoiCATeepQZUjo2uXCsdsAYx2YXznIGR5EwGLx159CAJS+W9tECR/coN7XEGJsBaOe3haOD2CxLr6sdhabwDFG9SnuzR32rRa3Xot5qUWs2qdRqVOIqlWqNSNb4/b/1e1wdnzBLJnZDOW+yoiioxlWUss2D1cp6mhls0Ck7hEYYNy3N4Fd8vvvd7xNIH2UU6zFqYLuh7nzfDkza3JAOHKHim1x6e5r/wt84xYSxNyJHqHGRyuXHhrVFkHBol7W+McwWMwp1M8BEGWW70cK6aJRha42fcZMFy/Xa8Qh9O/rN9wOM1uSrFWFQTm4XeMajXY3Z7mgCUSMb5MySjE+evmGrU+PJ7gbVqke7WWVlQpaLBZ88P+H9+1sIHxr1OhsmYJhkhFEFVeQsFgsWsylFlhMEAVmacKJTdjb3abf6xHHM9XiIEIYP3nufn33yBZvdTQLf5/HDA2LPY7vf44tnX/Pl86ccvTnh4uySjU6Tnd1NGo0Gm7u7tBtNYt/j6M1rQs+jXYtJs4KwUiXXipOLCy6vrijyjHt3N9nd7NOohWxttuj0mmjuMU9S5tOMz18eUZwGTJI5f/T1EYejBf/hRzn3d1bMVIHf2SWuVsmznG77DkVeEMYVPD+iuxGBUUymU5BQ6AwvEDx++yGNqxqffv4Zy3TJp6+PaDSa7G1us9ffpNlostPvQjJlMhgxVZr9+w95cXTIs6MTkumSnV6H5+enDBbJb7Qev3G3UbkSr7RHKXEupe2SDZxZYeliLzwPaSyny7jMx2jPBraSlV5uC4HFT7RaZ2ietBO5Lc5VllT2uVYGorGcMEtsUwYLgrtsA0dSLbtbJZ3AloIlieDPfM9bAc3GL6sgUFq5DQMyElSbVbqbG7T7PSr9FvVul2qjQaVaJ4orRFGFIIwIfJ8/+Hvf49Nf/pxf/PxXGKXwAh+J50ptm7F4wiMOYwplgXwlWE8XL+0KjdD4gYfwAxcibjHt4RbTwFAGwTXU9P9r789/bcuS+07ss9Yeznzu+Ob3ch4rs+aJLIpiUaQGukVLdrcasAQbNmwY7f/Fv/gnG/7Fhn/otmG3ZEgyRKFJiZQ4FKtYU1blPL/5vvvufO8Z917LP0TE2vvc96oqnyACTSB3oTLznnP2tIaIb0R8I8KhtAv7oBn7zyrKYvLzKxGXaGAb6+KUhlrN+PZRRy0/hPrUiJydTalqW0t6gyj+S+dDEmCWkVGr0LJGKt5JGzrrwO2dE7+kl6BRWXSoFnNyMrqdLYrNnP3bdwmxoO8LiPBgZ8n09DYXt3vUSxiWfb78wit8+MlN3rtzwPaFM1yn4s0PP+H+wRGjTo/18YjpvKZeLDjc3+fCxpiLmyN6bsDNjz+k7G9SO7h8YYujwwPmy0hVzRmMSjwLLl8espjNqPyCb/7a17ly/SrvvvM+k9MZW9vbnJxN+d5f/Zje8E1efeUFXn/5JYpBn26WcXxyyuHRPmsupzca8PTTT9Pt9amWNWUn58NPdvn5mzfp9LuMNsZsra/TzQvW+57f+uarPPNgizt37vPJrdvc2TvhD/7iXX73W0subm9y8PAhR7Oa9fVNqB29/oDoPQ+PjtjYGFNXFd1ej6qWmnmjUZ/ZfMao3+H6pQtkOIb9Aft7+7KPfCQUGf1qxKWtK2xefJaHR4f0BgNeff1V9idn3Ni+zldef5n1jz/keD7jzU9/dROOJxJei8VSGAFR0IHtFIs6WjsyE3JWQVXC/+Ifcop21FMOgFXFjCEQ6pCc5M478jwTkquZCF5QVYjKxge8y8H5JCClkWxTNVQEm9dGDrYJJd0nz/PkqG8MGy1hrZQCybFUE4yA72T0tkf0Lq4xvrDNcGuL/tYG/dGYQX9Ep+zTKbtSuDGTaxUbBb/7j/8eP3vvbWaHQvPwOnhVXYspWJYQC+YLWFZLpWE4os8oMy9CHbiwvUln1Jfvoz2zHY2/CUSIhdZ3j/PPnw82Pt6UNAUQiVpvyzIJVIYR1U8VaVLB7JymjR2Ne8AF5ovAO+98QjWbiv8ylcYm+RmzTLqyS3wyJAXUFFPUqGOoqaqKPJdlHZrkCHyWUVULplXFC1/7DeLmmO//2feY7x5ThgWdDuAz9o/m5BmUeeDw5IgL21ucTvq8d2cXl8PB8QlhGTiZn7I+HrG2sc7pyQmnR0fcuf+AydmEk7Ml167foKpO6Q6GLOZnlIWn3+tQOM+9Bw/ZGMHZ6T5FlnGwd8rO7l02Ni/wjW9+EQLs7jyke5Lze7/9W0znc95+7y3u3bzN9uYmf/s3foMr3SH3D4/5+PZtXnn1FbY2Nzk+PuO0OoXguLB9iUlvxng4IoaK4weHnLqa6CsuXb1EL1vywlOX6JYZn97a4fR4xvfevcVL05q13pzO2pjJySlzN+f45IxOv8/ZfMLDh/e5sLXFqN+ThsreMTk9wnnHxa11rl26wN7DPcq8ZP/wmI8/+YTZYsnx0RFnJyfc7JRcvHKN6zeu0y1Lgq+5fGGb3VuHnJ1NeLBzn+Ojw8esv0ePJxJeVbUgz7RxRDTBYOxsn5yyDk3KxaUuPKmNatAiMQa2tAJqSMXx5Issk5xEkITuoPQC2ylWx6nN2g7q10l4w/xxkZRHZwnMtj9rdZo3ArgpedPsYU069hFfFgy3Rgwub9K/uMlwe5P+xiad0YhufyTdq7MuRVFoSzR5pwi8/tUv8vv/5T/k//v//FdMT6aoIUoIgTIvJJ0EiZ666FKtsOACWVGC82RZwRdee5WrN65gNAGXzrKnXT1agbn0LOd/FM+dcF5+2dQkQaU9yWI6uSlY4/SE5AFLa8Tcc5HaBWaTBX/xH37IX/zZ96XeVtRGJKp0ao0+Rw/SQV2VU11DpuW2zZUAVHUlgaJaumfHYJ3OIeJxPmM2nxGynH/23/xv+bv/s9/ne3/4H/nRv/v3nO7tMq1m+FjTwxNixa1791kfb3D96hW2N9YYDbq8/8En3NvZ5+Rswv37u1ShZtDv0+mPmE1m7B7P2Du+y93dA9Y2e1y4uM1T155h1N9g7+Ehr730Ij95/31Oj8+YHp8wHvQYj4Y89cwz7NzbZb6sODw8psilNPTW5ib9Xp+vf+0ldh485PR0yuHJEUWW8dKLL/LWW+/w/rvvsb61wdPPXmexHHB8OGU6XXL34S4Pj/e4cXUdV8wp84Kjkwm37tyl2+nS7eY8+9x1RqM+tz7dYedon92fv8/f+/a3+O2//S0+fPdDFieBCxcusnd4RIyR6WxKdXbGPEp+c1EIcMh8yXK+ZDlbsr/3kK3tNZ56+gKjUZfFMrK7f8iDBw8Y9vu88847vP3ee7z20stU8xPiYsFT1y8TY83mxhq9TslnOZ5QeC1xLm+WekQ3lCfLcrzLFDVJfmHj84g4l0lVSySFo1YhUgdrcdYQJ1PzVAwFNDsnxNiyjUTr1nWlbHfdHC3fiJk1Zl60BRicj76pT8i176tJ17knG3QYbY5Yv7BO/8IGg+1tRuvrDMZrlJ0+/e6AIu9Q5KXm1sVkskUieSfnH/1X/4j9Owf8D3/wRylfjyDcMe+gWixwDkr120Agc1LoriwKKcRYZHgfEnNeYGui565AqUerddESOKuoK7b+nUxqHWPfQk46EY+/bor4ofPecuCruUl03L+zwz//7/4Nb3z/PU4OznDBk7kCnDQmiURYLDXbwm6nUWq7k/UiyHJFd2hKl2R/FHmBai4pBOlFGR7tH1HHms1rF/kv/pf/hK//7W/zH/7lv+Enf/49Tg/3icslPtZkPjBf7DGfTZhd2uJv/fq3eebv/CZ/+YM3+fDTO+wfH7C/f4R29qPsdqmXNfPplIPjCQfHh+wfnHByMGXU73Pl8kW+/Y1XODqasHdwxNHxlN29I3qDKaczx5e/IPSLydYlbt9/wEef3GXn/j4+c/T6HWbzOaGO7O7t0ilzbly9zne/+5tM5wum8yUff/opd+7cYTToM17boCg6HB2e0cmmjEY5RdnhxvVnCVXg7v37HOwfM1ksqXGUvZL1sMbpWca//cGP+emtW7z+4g2+89oX2N4q6Q4G7B1m7O5OmB4dsZxOCHHJcDjg4OiYbm/I5oUrXLt+g82tbd559y2OT+8y7I8YDfsMR32uX7nA5GzGaDjio08/4T/88R/z6vM3+MKrT+HzLutrW5zVE7Y31oD7j6yv88cTCa+6XpJlpFA+NIilHaoPNPXFjcKJmWCoWWedqV2T6yhcHqEvSAhcnM+OgPPC8Qm1FAWsQq1dfGRTOB9SMm5Klta7u7T5HnOouZN4RCbwNCDhPNQOsn7J8MomwwvrdDfH9Dc3GG5s0euPRHD1B3SLjnQC11SmptSNbNo61mRlzn/5T/8Ju/t7/NVffh8bRAcUWaYNKLTLUS6dcjxSYbbMC4KDk+kZi+WCoihUP6wKFnH1tVAQjR/q8QahiekkrlM9Vp3l1QJxSeKtmquWymNBgeZnwoiPrgYfuf3ebf7b/+t/y+2PD6jngdJ3CFndKB1HqpUvwyMI1ErigJMEZu+b4EsAvNOyS1GJwBkuy+h3u3jnJSc2c9z66B7Hh6d0t3s4MraeucJ/9d/8b/jyr32Lf/3/+RfsvPUmcVlTLSNxEdibnVDPl/zznX/Nt7/9db7wxRcoeh3e+/Bjbu/ssnNwwsWtLWJYkBeOUOcs5kug4Ohgynxyh4vb65RloNfP+Tu/+TKzCk7ngfdu3uL7P/gRg6JklC8YFhWnJ1OuXd4mcomjoxPKsmA2P2Y0GjIcDCUrwzl+9JOfMFnMufHUUzz7zA1+829/jdn0Fd782VvM54GvfvmLvPv2R9y9/SnLSxu88fZHbF/Y4urTN9i8doMyRCbzKTfv3GG0tkZZdiiKkt29Pe7uHLG/d0g+q/i1rz/Pw5Mj3nn3Pv3OiH63h8tKOr5DXNZcvniJ+TJy9+5dprMZnV6frOgTQ2S6qDg63eHa9YtMpzPWN9YYb48IixNubK5zeLDPx+/e5MYzL8JYMmRC5vksxxMWI1w9jOhpTWflM1nAUatNSO0nUU1BCZkSWatbnl0ViIgp4HQxWnUBSTVRkyEId6yqa0FQ6ozPck3M1lxA0coiUI2sGKN0rLGWXyub1sKRWCckoUr4DMphj872iNHlTXoXNhmurzEcrzMYjugUPfq9AUVRavdwaMi6NmYx0S2ci6xf2+Cf/e/+GXfv3eL2h7chRuqqSlVXBSyIAMi1sW8nL4kxsgw14401cn3PJEAiCPFUPjvfzzHCStFB9J1NRLVNy5h8fySJFlqfNpZ6K6E7NsEXt3rFFUR2unvAv/i//b+489FdMsZkETIqnIeiKKirpeY71hqYkevUSjK1K0mamiTySxs88bVVdc2yqvFZBt7ToUNW5MTFkuCEF7V//5C9+wdc2e4kt0IsHc9/81X+D688y8//7Af8+b//jxzt7TPdO6KaTnl4uiQcnXD/D/6Ep56+xAvPPc93fu0b/OAHP+Wj+w+4c+chy7Ck38/ZWBuDz2C5JFRR6sffecDtnYfkP/uYjc11pvMJy2XNYHOTvHSMep6HN99h/cVrbK/1mE0ndDt9ytoTfUbmxlRhyf279+iVjksXtnn9hWfYOxMqxN7uLv6HgevXrvDS888yn51SLWsGr7/IM9eukhc51y5e586DB/zop+9QVUu++fIXePmlp+gP+pyeTel1+ywWC9aHPTa3Ntg/3Off/eCnTGZn9Due/Z0Dlr0Fw+vXuXT5Mg92doizBcdnu6xtXaTX77GsFuRLx2CQC+9sMmVeVbz7/i0W85rBYEq/55jNBGH1+pvs7p/y4c3b7Bwc8v69O9x5ePALJNDq8Z+QHuRbJEqv5qFTbeClCatCd4nSCXoxwROioKcVp7JuPIdrSkk71EEdpYywbqoUK1NipkSfmpLRdS11s3Jl/pv5hmrvGKNVKk7OZAMM9mdd10QfcAWU/S6jrTUGlzYpL20y3NhgNF6nNxjR7Q3olT3yvBBB6X3asoGw8n6RmgaHRq4/c4Vvf+dbPLh5n3q5pK4X5L5jPybUtRAtySgL6epcVzV1tVTfmPi5YkKLJqDOIczYoKe2uWj2cuORO2/ukZ4XJ/X2BZlaIcJVAWVCTLGu3FcH1aKS9TLw7/71H/LhOx8ymwS6ioicUly8z4hWbshnDAcdTk5PVYhF6rrCubbf0lOHkLoF5VmmnwdCFVnEOafxlCw6Bt0Sn4n1MJ9OODk65pq7ooUBHDXSc8Cv9fnqP/guX/zu32J+OuOjn7/F9/7wD/nkw/dZHsNsVvPz9+7y4c27vPLcszx15QovvfQ0b7z5Mbd39nl4fMLZ8oj18YAcCcqE6PBZh7I3oN/vcfnyUxwfH7D74AG3P77LfLLkgT/i07xinlXcub9LXHr6vRFrmxe4duUCV7fH3Lq7x+TkiNPlkryqqMOS4dqQ5XofR4d+f8gnH33KzQ/v89IL17h+dZMcGI0vcHY24/LFIS8+f4kPbl7n3v6MT+/vc1a/S5k7rl66QginrG2scXx2hMszXJHTXR/z4w9ucrU/5OrFLS5vbvGFF5+jf3mLi5e2eHD7Lg8ODvn0zi2KLGM07DPubdMb9tk/PGY5mzHsj1gs4OrVTXwGeVywv3fIGx/c4t69HS5sbnPpQp8QaibTM05O/hqoEuaLsmKDzouvS/hRioSMShEqhP8TUsUFa/UVQ2htKhWKijQIMZVxCVGqDAS1P5zLVCB58sxLgnNda3keCE7KobQAHc4L494Ep24tOYIJQnSRRSrddb7IycYdys0ho8sXGWyN6aytMxit0R+M6PQGlJ0uPitaKE/rj+mONROssZ6iJJnHQJ55nnvxWfI8I1QLYqjJMy8NWUNDhsU5llVNVVcsFktcR5ubKL5pE09pCXczuySiqQ+QpFpLcbTmIMZGfKVsg6jv5gDX5uaZ76z5fZpPE4wJ/TliXfPGD9/gj//wPzKZLghLxzxOCEhXKOkD6vB5hHpJqCvmYS4WYZTa8w6JGsakjIy8G6mqVgUSJ66JECqWywXz2RTCQgteRpbLOcvpXGvOm3Kzh5eMkLxTUna6fP1vf4fXvv5l/uI//Cl/+W//mJO7O0xPjqjqJbv7x9SLOVsbI77wyiVGw5K7ex32jo44Pj4hRqnJNh4Mid4zOT1idnaIZ0G326HXybkwHrJWdDg6OeMv39nn0uE6oY744ClOThgcz2FxzDe/+gW6l9fg7Ii67lPGiMsz8hippmfc273F9WtP8eorzzGZTVjUgQ8+usPOgwfUeJ595iUisKjPePnlK/za1jUWy5o//fM/45OPP+bO7ft0uiUHZ6ccHp5w994hy3ouZXB85PToPk8/fZUbTw2ZTO7y/o/exoUO89mCtctXyQ722RoU7O/eZXa0j4sZg/6A7fGQXi+n6jiKbmAWA9PZgvWNdV5++WV27u1wuH9I5jJeeulFsvUBf/XmR4/InscdTya81Ezwzkry5uIsRc05dZxrSIjoKu2yrKgpAsqUJ4TkWRGHeobVsyIIp8qSnQXcaR6k82RygVTvHreaw5cpPcHMw7bgAs0j1MqsLsZEoo0uUvua0MvojPt0N0eMtjfobW/Q21yjMxzSH43odEVwFXlJ5vOWX6gRJIl6YSaaa4oURick19H6iKIoqBZzvPMUeUHucpZ+iXNRaRaCrpbLBTFK96BrN65Tq4n7iCkfE7e+lX7dLo7YMudiO0BCGr/WbGOdfCT7QNCe8eYt2tg2kVuGbMtkDcwnE37+Fz+hPs0pGEIJi0WFc5X49ZxwAkOIIqAq1HR06b0Ekga8+RKVPBzxkHlclhMqqaorPlfwPrCs54Sp5jciHLC7d+7wWniNmJ0bQfUf1kgp8uAc2ajL3/q93+Er3/oG73//p3zv3/477tz+lDsPD5jOOpzO5tx+sMv1q1d5sXeJyxvrTOdLTuaBBweH3Hu4T79TcnFzQJHnHJ5MiIcndDs5PkKeRzY3xwQ/ZjqfMJktWEyXuAAXt4bkgyvs7Nxn0PU8c22NWVWyORqwXC7ZP55yenaK8zCbnnB87FlWS8qigDrQ7w05nc358MP32doecOnSBe7evsmbP3+D/nDIq89e5vlLG9RVzWi0zu0HD7l9/wG3797n4ckxhyenVKEmryN/8r23mdUn9AclJycThvkaMTjOFjXTw30iY7L5hKPjM3xW4ubHxKmns36BotdnkOXs7e5zcDyjm+fcufmRtFS7ssVsumD34Q4fffghJ0enfJbjydKDnKfIJFzvVXA59SM4JC8suFq3VNA2aRICN6688HHUL0Td5O/R3la2kGJrw6kpovV3nHKz8iwjIyMvciHEplZpcjURXNKSTUzRBvlZulJA2nUFH/H9nGw8oLsxZnRhg97mOp2NDcrRiKLfI+/0KDsd4RI5c8w3D29sDpXZWpOqQSHpcI6LVy8z3hyznE4gRGazGb1ujyxmlGVBXdeSeLtcaGaA4+XXXuG1L32xyQhqpVMBq/wqJ0jDhlH+H5OQaY/zo/QQ9V6Z+ceqaZ28YHaf1olGkUl3cRCLjGdffYnBeItLly+ztbXF22+9x7/5V39EvZBzsqxAuPdROXa67rT2f+ofEJs81BCkEm0danzmqJbSIk5EbwBXE4JGuKtKkTt8+uGnVNMlflS2xq8d+DD0Kvf0HobbQ776e7/Bq9/6Ih+/+S5/9eff4903fs6923vkmePwbMFap8PG+ibXr1yg1+1zOplxf2ePvb1DDvdOGI66+Ay6nQKXFcQQWcyXOL9gfbRGp1swGNYsFzWTyYy90wV/8L03WOv3+OKr15kspnxy74Crm2t892tf58rlq/zs1j12Hoj5GReBtUEfF5YsFgs6eUHdjZBJU5I/+7Mf0h8M2d7a4vRowrsH72sZd7jkas7qMy7f2OTyjW3eff9T7tzfZ7GsWExn3D0545//4c/ICsfm2pAXno70yy6jUDHolBxMJ0Agz0tq78mzSMc5ThfHDIc59dk+w3rG3uSYeVZw8cIFDvd3efuNn1B2+nRGaxJA2d7mowe3zkuDR44nzm3MfC4VQPMS78V080hKUFD47SLip5I1nqqRiphwqXuOJybnuvjKEMFEww8TCoXkt0VpH9P43LynLDXBOyskFUmlh22mOizThgxmkoaAC4qUogQT6gyyUYfOWo/Oxjqd7TXyzTHl+jrFcETRH9AzU9FnqqGDIkuHU7KopTSZNDMOvAkO3doEIpvb27zw8osc3n9AVdcslnPqIOhgvhCktVgupdGtc4zWx/zG3/9txlsbBKoVoWl+u+aIKxIqPvpR21akwWhyMWEk6PiY4F8xicXnKILailCC0e1lLmMyJ4tOh2/+9q9LX8Vcu4aPHX/8R3/C6SLgXEae5yzDnLqupKO4gnjLqojUxFoq4ZpLACc1712smJydpST9zNwQLhA8VFVFqCqcUmse3L7Lw7u7XHjp2mPGTt7A/JamfCJQu0C+PeKV736LF37tazx482N+8Kff46c/+hH37t/n5mSXXmef9dFdLoz7XFxf54WrF/nSczeYzhc8PDhiZ2eX6WTJ6dkZZZFDhGo2x2VnLJY10S0ZDceUZYfZfEkVlsS8w/t3luwfzFgsPPfv3uW9D3e4cvki3cGQXnfI0ckJ09NTvvPNr3Dt6iZ3bu2wWAbqas50tuTp689x5+4Rn9y6y92dB/T7XcZra/T6A3yWc+v+A04nEzbWxmxvrPPCc1fZ2l5n5/5DTo4nnE2kpPVyWXF3b87h9GPKDArn6PcKRsMu3U6Xo5MJvW6PZ7fHXFzfwi8DP33/p1y5cAGAk7M5MTjqswkvvHCNl57e5O0P73Jvb8pi0XA4f9XxRMIr9+c6AuEkihajsNi1KF+wmu/BSuRZ+B7QuveBkGgVEvUSMzHT8r0pvcVFYm24TRvNIg76XLlkzmWCAtRZbRwyua8IDHHHyTNZIq/41AK+k+NHJeXWiO7GkM76mGJzje6a+LfKjqX6lOpT8xqdjNIOrPEUaes1h/Gj0pMYCEkwTHhHX/nWV/jp977PfF5pUKKiyApFjJBlBWWnhDzy9e98g1e+9LKa1EbDeASv/sIj2pjo384e7LFC7dy57d+0/F4JfaaXNHzmSHXFnJiWeSlCqNZWGv1BjyIr8K7CuYy6rqiXS0K9JOXNgpqGMn/Spf2cL0+foA61lNBBKTM0kcrlckFdV2TOUecZs9MzPnz7Qy68eJ0UwWlG5dxImS9Tvg/qU6PMuPiVl/iffOF5fv3hP+DTd97n+3/yp7zzxs+5dzbj4OyAew+PGdy5z1q/x8ao4Jkb13n+6ec4m0zZ2Z2yd3TEfAGHy4L9s0CsczbXR3gXySJcvnKFQbePA7rdHidbp9zffcCyHjM5m3Dv3gnRHVJHySzYn5zxk5//nMn8GotpYG1tg7Iu2Ds+4eNPPqXT7bG+foHTswm7D6fcvX/MhQtbXL1ygeWyIlY5P/7xO2Te0++VbG9tMux0KddyhoMF3V7GdLqQGnTLOZPlksUyUB1NKYsl3f6SZb2kV9R0OkOOFmccnz7g8OSATx7OuLq2xqWtdRbTMxaTU84ODtgar3H54oA33/+Es5g1RR1+xfHEDvvMOykn46X+lNRqgjrWNM1atUWY5qQFb5vXgReTzepYOQexrnEIkzxYWpElXQd1dIdKzRPUQS4ni7ljTvrV1GNbgpU59aWUaaJg1L4m7+dkg4Le1pDOhXXy8Yh8Y0R3PGY0GtMt+3Q7PWESUxOiPYSZofJe1j3IRd96imYDmznXTEskhAVPvfYMW9cucDq5C3UtBfjqpnO3zzJCFnnu1ef4vX/6j+mMSqzcT9pgkc8syOLKjm82uNAoTARLMro16xDMI3mHK3dwMWE1qZTalnit6GaE6BugpydwcnLKYlGRFyVlJ6MOM5bLCSHW1LESxrwqCu9zMf2s3JHPUrNZvZ36LQXbRqSiCAEJJkVZY955jUrmnN0/Js5rYieK9XAuUmvvuooDGileh5ragSsd46ubfOXyt3nlm1/kZ9/7IR+98Q7vv/0uB3fv83DvjPLglE6x4P17d9laG3F5c4Pnrl7n2mafzBW43hpTSj7+5BaHh0ccTBcUnZxOVTNfTMgyRzg+4cLmOq9efImzsyl7uwdMz6TRRhVqZrMpw/6Y2zvH3N45ZG1twLPPeNbWNnj26Q3qOkgggZpeP2dR9amriisXL/Lc088wm83Jyg7bozGHh6cs5jPCTCgqYVlTOLgwHjMpF9QhMJlMcS4j1JGD40OCD4yHQ44PDskqqOqc4fY2J6Hm5ae/xOx0QjU95mtffoVOmXH75m0ePnjI0fEDKPoUecbh7j7VYv4r1zE8qfDKxDEqQkR9Sbb4oyCeUGseoDnfvTrVdQdLQ1iJWgmVQlnzakgGH/CxaSzrkgmD1IB3Hh8U8XinBQ7tt1pb3pnJIQ1l60ravbtgPpqakEfo5bj1PtnakGJrnXxjTGd9jd5oyGAwotsdUhYlRVG0/FtBUY9LG1NyN1sbE1bpBK1NW8e2Uxy2LmzxP/+n/4T/+//lv+Pg/gO5ZC1Gi/TAdIw2x/zd3/8HjDfXqNS/aJs2RWKxuqSrJpDRQVZwReNJFye8SplfJAATcbd5OxpNsrJCWufI3xb1dOev4KCqa7wrKIoc7+FsMqHWAo3LqqIKlXRIb7HqgybHB6cVRFbhlyhTexNnzVgyyHKCq6UkkZP6t598+Cl7Ow/Zemq7GStiSjS3ox0WSYEhQ9ItPLv0kWzc5Su/+x2++tvf4ej+Hu//7E1+8Kd/wa0PPuDo6IiDhzMeHFfcfnDK7dsPeebaBa5srHP3o0+5cONpvv7lp3DOUzjPYlFx6/Z9XJYxGo+JOO7cuUsd9xiN13j6qWfod7scHh3ycG+X6WRKtayYdnvCg8w8i9ozny8JdaQoCrY3Nrh08SKLULF3cEBd1ZxNJvzwjZ+wvr7OM089xWtfeIHDwyPyUmqP3b+3Q5Z5Op2M6WxGXna5c+ceRw76/Q4XLm7zwcefsHd8wmxySh0qTkPFGx+/z08/eZci91w5eMClzS2y2RlHJ8e8+sozbG4NOD2+xL2du9zfm7FcLjg6PuLB7sNH1uDjjif2eTnnpOuPReiiVLCUJp+1VgLVNA5N+3c49Ys4fK2J0l5iOqG2ZOygjnP1ZwXp4ycLzROi+JU8jdCSx9AUmkw0MQSqSjr01HVFvZAa5gTh24CWxRkUZBsDsrU+cWNMsblOd21MdzSiNxjQ7Q3JC6kIISZwoMYRfa0+OSO7mg9LUEytZX80w5OGK+WTsINGgHkHX/76V/ju79zjX/+Lf0mcLbT/peR3drolv/5b3+FL3/gKNSH5l2zDxBhBTXfrOEc0RGhHsxvbeaXpW0OQRsFIMkjmiOZPHvmjvYk1wyGKRG9QUcvxbVf1znEymVDXAeolVR2oF7UEU5T6UkckEqwFCYXyUqdoMkQtTuhagstJLbRMBKLo2kjMMiExq6IhRKr5gsnJhO2kgGtiS9G4EBOx9zwii5p3q6FzalMiukbxsH51m69f+y1e+85Xefsvf8xP/+P3ufPOByymE04nc+7MpoR8j4UXUvL85JS/vHmL09mM9UGP7a0tysGQw6NT5sc1ly9d4guvv0JVRY5PTlku55xVC44OD5meTen1enTXOxSF59Pbn3I2n9EbdLl8+RKT5ZyH93d48ZnnOJ5OGA1GLBYVp6dnbK5vqs91yVvvvMv1K1c5PDzE5znbFy8yXh9xdnbG6dmEzc1NhsMhPsKVC1vMJmesDbp86Qsv8/6Hn1BVMF8uWS4r5tWSql4ym814sHPA7r09Bt2c/8e//AN+a/dL/No3vsTa1pgLbsHNBx+ztrHOtRpu7Z/CZ6jp9WSVVL1sTkkYFt6WNDnQInpBTTLTTl7LPot3VS6RS3RQLmDll2tlUDuqoDwuMnLnsDUit296QkZl70cnJqSFzyWpmbSBrTJEdJ4aYeS7QY4fd8hGfcr1dTqb63TGY7qDAd3egE63p4nVXtn2YjpoooC9SgoqWJ9HF5t2bY0f3VjzYRVNqs4ORLIy4+/8F9/l4OSQn/zFT5gfnOK94+pTl3nta6/wD37/7+MKRy3JEw3aiI1Ioi2UEpPhXG5hMqzNrFV5ao0yVszPFlpzj8Nj548mSNE2v1blZCPMZssFP/vRW1JrvpYOQlGj05KhIPNtpW7sFkKB8ClFJuU6WuyhxeWzmXAOMucJnqRYiFFQXVZgRQYkyhlXn9bG2ISYkpyds7HVzlQtEzs0Q0yMUI76fO273+H1r3yZ9370Bj//qx9x65NbPLzzkHfvHfL2/X02+h2e21owWB+zODlj/6hiNtmj0z2lqgOT2YwHu3vkeYH3ka3NMZsba2xvb3HtqStEV/Czn/+cnYe7PP3UdQajMWfzOW+/9x4PHjykXBty+/YdZlVN4Ryj4Zi8LJlVC/YfHNApCgb9AVVVc3xygvOeZVXx6c1Pyb00SHHA4f6BpOdVc7a3NojrI6plxaX1dTZGm+w82GWxrPAxMDk7YzI542Q65Wgx42Qy52hRcTSb8c//7V/ws3c+5nd/88s8e+0iG1sb3Nq5h/cZ3U7nV642eOJ6Xo66VhMiREItDTQsDzCYTwItfueFXZ976YCTOddaCC6RU6MSo8SBL859T2btIBsTRKNbIcTUSj5SU9UBF3Oil1LQQc0h54SLtqwWRAKVj2QdEVz52pDOxpjOxhr52ph8OKTsDyhKMROdj0Qq5VPpsrS+ggIF1FzUBiS6qHPjQVkhwVQosCllbCAkKiqtXaCzVfA//Wf/mBdfe50/+x/+mGuXLvPrv/Ntrjx/iaJTULMETVmSmvviT0uVZ52ivBDTJrZ8Tfk+uZvVZSeOKENvjXiKNLQH16BL96vFl9zGrQgDvSKN50jWyvHhIbff2xE0VS2J9VLcCS4o9056VxZkSejL9ZtIs0sZBs29vTdUpkpCgaQJ4MyLMMw0ta0o8tQfoX2tZtXZ2te/DD3q+jOaAWQQTZSxirKBykfyzT5f/N1f56XvfIUHdx/w4z/9S975/k/Z33nI/uSUB/s3yb1jNOiz1oPB+oDCS0J+tzdifW2D+XTG8ekJt+/c42D/iE8+uYPPMi5dvsyLLz7L9RuX+eTmLWbzJVcuXWV/b5fTyYSiDkxOZvzlT9+m3ynZXF/n6RtX2VzbZDFdslhMefBghzwvOD45odftUlUVnTynCoHL1y5zenwCIbC1tkanzOl1S8JyweHshKwYcfXGNuP1Pj//2ZvkZY/t7Q3W15/m01s32VwsuPdgn3kVmUymzOcz3vrgAbfu/AHPPnWZSxcuMxqPcadHFP6ziaUnE15BWThaCyuEqBtRI4hBStLUqsFjHcmiYA/x4iiENw3vMkFNyooPtVRGcD4TeKNmgPi3ZKNZ41kLY0dQLR0ItW6cCMZ4tyamMdRkuacYdshHA/LNIZ2tNYrRgLLfo9fvC3+ryLEk6pCEFnpNGQOzyayUtDVQ9UgzW++EF+SdIERZwY/WLpXgnJA/Q4z0xl2+8p0v8vzr1xn2++Q9T3ARyft3iblugZG0OZOlZ6QMFRbJId/2lKuYMnfVCnqjEYbpMJPqlwuwGCOxFirCitmZEIteTX2Hd2/vcLh/nLIfUgFIKQsikWTvyZ2sn+BMOGUy7oFUCtrKkzuXqfsgE0qG99IzIEqOqqGlssjpFAV5Lsx+cYGspnSh68p4cvISLo2ULAHzfjaoNhhca41xA4gDSyB2cy68cIPfffoaX/r1b/OTP/4e7/34DeYHh8xPJ5xN55ycPeT45IArW0MGgx4hLNjbu0+302NZBQbDNdbXtpnPFvg842D/iJuffMLW1gbPP/cU00tbfPrxTdYHQ157+VXW1jfZ2d1j5+EBh2cnLEPgL3/0U9bHa7z4/HN0e12KssPZ2Rllt6TTL/HTyOH+IVduXGd3b5f59JRXnnmWKxe3qbILvPHjH3Nl6wKb4y2O9k755KM7PP3MM3zrm9/geHLGBx98yNn9e/Q6HdaHQwadLjt7+0z7JdNZn8V8xulizg/f2yG8eYfeoE/eHzOvFr9wnbWPJxRepPpToaXhE4s9hGTUmDa3jU+QeuWi9m2jqB/LZ8ln4zXVRqozZBpxsxQVNbViaNEh5BCz06fEbqdIMATJU4ylpxj3KNZ6dDbHdDfW6Az7dId9eoM+3U5JURbgAlWspWRxe7M6xPeGw6XW0I1pIb0Ga2I0dCgsqHSJGJOQWdXpZmFEapb43DHYHuhm0rE1Yaz2STrfvO9JuqLCssGqCeK1hIdzq/e36KltQJcu79LT/dJ10TLVGr8WNF2FTII15uTD+3uEpUSWM58TXMAtMyld47zwtLwh1wjqS8S31g2NS8J5UXp5ltMpOlBHijynyDxVtSTGCiuG6R108ozRYECZi9NfouPxMa9q/jo0ut4I89UAR+NeSGNqc7Dic7RxibjCc+mVG/z9Z67w0je/yJ13PuSdv/oJB/cfwDKwmM24+fAUv39Cr8zp5BmdsoIY2T84JFRzvvKlL+KiY2/vgHy0xnxe8Vc//hmeQL/TYXNrnfv7O/RGfbwP9HqOi5efYjgYMSgz3nn/A376s5+xtbHJ1uYGMXp2HuzR7XZZLhcs5nPWlwt87rl99y49PGujNR6cHtIf9IlEjmZnLKol93bv0x10uNG9TlnAV7/yGrsPHuKqQOYdl7OSorzNzsERnV7N8fERPi+Y+pKqCBzOJhw8vMnJ/LP1PnvCqhKSjlHXllcWV1CXCKzY+Fw0Y7+ua20IqpQCEwxeG4fWMVV9ADT9SLWo1gojWgMJTR2yGvOoXytKSZyiKORaxsb2kVhkdMYDsvUe+eaAbG1ANuhTDvrkvS55mas/PTS+inPeGhclpce0qZho9rey9J1uxOgkjcULSdXMHm/lf2iuL011zQlfC++tttB9po8j3utkIba5VbQFEqREUo3IeZza0TKDBtVW65XZdXSTapL8o+3KfsX6MCFo/qPYtKA1T58ds4Mpcb7A+0hRdIh5ToyRPFsSo09+xQhEJ52vrWqqA/AZWZ6TFwXzxVyb90pmxWg4wkUofIaLgdlMgjV1DT425cu9i9TLBcHV1N4EkdeVbkK5KV4ZoyTXW9oZsNL7xOak0Vct/2TbIE3BnproPOTw1Fde5Kkvv8yL3/kab/yH7/HJX/2Mye4ese5Ig+TFkkVVM60WZD7iYuDTu3c4PDvki889x4WNTar1Prce7lFNHMvpjKe2LvDUjWucnU356N13cUXByWzKfDGl2/G89soLrK2tc/PWXeq6oqoCV65c5+TwCJzjdDpnulzyzgcfEOuabpFTuZzpfM7J8SEOz0kM7B485OxsRn8w4uc/e5uPPviU4CJb2xtsbW4y6HY5PD7hbDqnN+zz9NqIzY1NDg8O2Ns7YPfgkHs7u/T7myyrwNH8ryE9KMjsN2H6EJpka8tDTOFzrQEVJLE6d5mWeWkhFgdZ6jQtrHVrSGtdYzKfp9LCxForrtpiEmRSVY2kXi7VvDDUk3nyfg8/7pOv98k2B+TrQ7qjMUW/R1YUuEzY+7WmK5l50C4DE2MjZMxJHGQl4miZZ0HrjyWip2wAp+ZlSlrWTRh0vMT/J7+3rjoBi1wiyMOairWRjm6kxlltJFklUhoXTlFwxISgimBnwtDAQkzf2fObcHu0PHTbKNLfiPRToUMzBg6MF1bXgf2dA+pqIVG6Cgpf4PB01FkbiNShkncwsrOzSiaCdL0v6HZ7hBio6oocURaz6ZTxcEQ3L1lWcy3pLf4ucUcGqmrBbHLKzQ8/4bmrrwsx0sUWclwVNhaJdbq22+ojtt9eTVAZRvGJNbVGmuvJWFpupiO6iugdG9cv8rf+63/I13/z2/z8T/6cd3/wUyZHR+SFp1ouOJtOyPKc0heczmrOpmecHr3D5YvrXL1yiUigU09Z6xVMjve4f3POiy88zws3vszSe3785tvcunuX/dND+qMx/bLPlYub+Lwky0p6vT7PXX8aMvjo9idMzs64ffc2R5NT6HX5+PZthqMBReGZLaYsK8+w06dwBfNZxZXL14hRQMnh4T4793e4dHGbjY1NijJnb08c+h+//wFbG5tcvnCBQa/gleducG/ngOPZnIdnU0741ejriSupZpkOuDaTdcqmd74RSq7WiGOMtmaRVWONJJo6UjFKrtmjh25276UccLANqonVsU5/i88j6jMKNSJzQJnRGXQp1ocUG0PKcR83GtAbDig6Bd58Hl5NPX26lg9czUWHI4NovHYTHipx1K8XdADs/SRrIL1pgz2cNGyz8jVRiblZk+9MSsnRv/WMtFGastqB6FyTmoVSNNTxLc/mkzySDj/NKIcYWR1+NUNVoJo/r123izTTbZzh0sfRhNW5gvkxBDxwdjbl009uCc8+iOKo6kqoMXkp71LVLKsKnCcgvlDvc23GIZHCDMdiNsfjqJc1PnqKslDUrX0AjIYT6lQV3GWSDbKYzdh5432uvXid8vKGFrBslG8SS62W8Eb5aAc6QloXiqpajj7DbkFbs9k1ZO22E8ciLkjfhJDB8MY2v/5f/0Ne+s1f4/0fvMHHP/gJ0909nPMcTyb4UgnMVc3+ZMneR3e4eXeX8fqQPNZkMTAe9IDAT37yU0ajAesXNnj1hafpuowPb97mzskdLm5f4OmLVyErKPIuDx7s8/N7d1jfGNHvdlgbD1nWFXnRoa6WHB0f81c/foP1tRHPPHed09MJoarpdvoMB0MePtxnOB4TXeDa1WucnZxw/+49Ts+m3LhxjW9/4yscH5/xwcd3+OTmTW7du0+sIxe2tlnb3qI37P2yPsgrxxOajcKqb9J1SJNrbRADUhSQuom4iXa3nYn6DRK4/oWO4BAiy2XdoIYYWdX+7vyl9HqRUDr8Rpdsa0i2OaTYGpH1O3T6A7q9HllZkOVZcvrWutnlNbymIYH5gxpU09qTkSRgDKnIXm80q1eTztKI5JdNClFqIBFiilQ5FaCSH6qI1j0q4Rt2+YruT9SRhA5WymY3v0wuMx3CENpVJpqBbY9705i3LcSSZJTfRJvmc7XFxJjk4c4ee3v71DHgoqdaVjigyH2KwC6WhnvlIcuyS7fTE2GV6vbLO2Uup/AFsQq4Qj11Vc28qqjqheS3og1XXFPxN8bI9OiM/Qf7XLq8ruPTuO3Pl/ZZRZmrY2Dsfs65HExImWvFrApQJazD5tBads7j6kDwGbHIGdy4yLcu/z1e/9bX+PDHb/D+T95gcPsu9dlEghZlSSwHzKol8/mcu/eOWVQVWRbpdSbc3T9ho9/l4uY6s1BxfHjK5e2rVMua+b07PHy4R5wu6XR7XL9xjZdeforj4yPeevstblx/KpUmPzo45PKVq5ydTsnLHnlnyA9/9BbdsuDVF1/g2pXLZHnG1oU1Huw9ZDZfcHAwwQXH00+9AN7z0Yd32bl/wOVLl3n6+tOsj9Y5PTvjwe4eD3b3Oavm4KVhMP+5kVes5YJNODzivPbis4gPVo/dSWFAnUFZLHWavFTqRP/ddo6bBhOGfK3kU8tZDKKn2oLMEIqLxCJQdguKcR+/PaRcH1Do//NOSTno4cscl2eQSyFF68fY8nZoCrm+UYzAQv/b42LAZ1Kq2irAWgckCyQ4jZLF2Agwa7CBvR/GGdI7RTTJ3T3GIoutZ2m+agt+M9+FrGFIzNrQZWZn6lvq5mulCFkZofPXfZLjUUOydXjx83zyzkdMz2bC8YqSUSEpOx7vIsu6SmMbVbmVpXQZ9x7hCTpHfzCAGKmrmqzrqSpD/LBYzAUpx1rdGUgjF0t9chmzZcVssST6XHxPUWdeEVG72smjBrMJoHhOjsuv6xDSXBvxy3yIbaWz+ht9xqihgLjEA3XmKa6u8erV7/LMN77M/R+/zf233+HeR59ydHhENZ1QlgWdTkHsFFQVTGYTzs4WzBcVRydTDidL9idzBh3PZLFgbWOLF7rPcO/hPieHp0znc7IisljOebh/wGQ+Y3d/F+rA5njIs7/+G3SKLndH2+zuPuDo6IyqihxOj6Rck4/sHx8wXS54eHjI2emE3OcsF3NcnrG9uc1rr7/Ow4cPeeeDD3EE1sYjNtbW+dbXv0xRljw42GPnh8cU8a8htzGEgMsyrDhdRHhcxteS+RATsnAZS5ZS5wsLhyuRNIW2la9jCdbx0UUiXJqQ/DXRTEYtWCioRoWNg3zUobsxors+JNsY4sZdslEPN+xS9vtkRYkvCikT7BzRBfWJGJVDuEOZs4uTNKpYE65ZiLQFB5g2bvx6sgiD+qtiALPg0nn6jmaqyTi4ZHok565rUN1qGzG7ClhnbKfBD0M+Lsp7mvEp5rCZLHK2PyesDDmel2GryHcVSTx2m6ffywCGCAc7R7CMUNcawZYu4g4kMT+KXgEptxTqQFjW+Exa1WUux2cZedEhdxmVW0AMVG5BHYP0uYxR2PQ+I9baDwGEn+iBLCfLOvTHYy5du/LopDSj+lhFkd5JBU1CtbGZz4TcY1M4sX3dNCCs4jg5LbSaX2kFFFfRuzzk+b//LZ79jS/z8P27vPvGWzx490N2Pv0UH5Z0Ck+v4xgPx8zmC2aLJYuq4vbDI+4dnDEaFNzc2Wc8GLK9dYHopStTt9clhJrj42Oc96ytrXN0dERZFLgiZ/doj+evP8U3v/ISnpc4Pj7m8OiUn739Fh/dvMfeaQV4JtMp09mUs8Wc6fwAn0UenE55ejJhbbxOtyx46cXnODo65OTkiL39XU5Pj7mwfYG1To9vvPIqk+MZtx/c5Fcd/wkds43Z7MR348wLJEPvEYEUiboQdffbJIRa0IETFANq1cSsqUWFbRLhQOkW0b+VnG+LwQNILadi1Kd3YY1sY0C5PqbX6xLHPWKvQ9ERHovTrt5CbRCob4UKzZ/WGHhqTtm7W2i+9T5tNngyk9TsMsGbuE8RnNVet4LJ0ggTFOu5KD6sdqGDGEEaVhpKpeWLkmfw6reLMUo3GxM8aVO10V1U0kcGpuUeI6ScPtrjUZix29MZydxsCzhnc6maplrW7O/u4aMGRLRKSabj4BGOVqfI8W5OqCDkMvtF0SHLCoqsoCg7dMoOhc+o8gUxVCxyx6Kq6JZdQKLceWa5tTU+ZGROumvXIZLVjssvPYsfdSXJ2rSH+QXO+STapnPjRGhM0KhzlSgU+t7JhI7adNeUiomxEIXHZusBJNBlc2b7K0ZqJ8/pBgUXv/QMl7/wDNO9CW/95E1uv/UBi4eHzCc7lHWk8AWdckFwjtmyZr6sKQrHrKo5eLjPx7sPuThYZ3ttDbdcUk9rXN4li44sOAa9ARsbYw6Pjvnwg4/pZl22Lw7YeXCPUFWQLfjK68+R+ZKi7BIyzzJEfFEwnc65e+8BD/cO2Nm9z8/efZ9Bp+TLr75Kt9ehKDPW1sfaZFmKlt7eucv901NOZ2ePWW+PHk9cjNA5J+aPzS9CFLDJlVD/+QiLoIpoffjqKJvR1aDd6p0KPV0macGnmumNEUeju8RRmpU5nbUeve01uhsjitGAYm2A73bJBj18p0PRLfF5BplxwUhoRpBWq0KBAYiVteuSP6MxJlRItBZwm2Fu7+4iqeIraEOSKEo3U2PV3jy4IOhPBU4yT1rmaAheo6lGL2nnEXqTOGnjJFqamSkCx1rXbwkbt8qoT+/T+j692SPRx9Z8xxYOM2Sp41X4QryKziKggUW1IM9yfBYoOz3W1occHe6xnDsWiwWL5ZJulpP5jF53QKfTpSy7eKTFWVjOKcuc6XSq5ck9eZ7hCMRQ4EIgUGsH9oKi6FCujRk/c6W1C2ILAqkpYKvOAlNpzGn91jfnJrHUopmsRDA1GumasRGhp2GJuj0dPkWKbbyFfiSZFstY43Jwl0u+8Pe/xRd+59vE4xl7t+5w+6332bt5l/nOXZid0s8CeV1LTmcdyaNjOq+5M33AvcN9hqMBg0GX9ZMpVy9eZDQccjo5Y7kMDAcjtre2ePuDd7l3eJPZ9Ji6FrLwuD8iVp6rly9yeLzHdH5Gf9jjiy+/xo2Lr7DzYI9bd8fM6opbt25x/+iYO4cHTCeneCKDbo/lYsn62ganZ2fcn55xOP1rqCphHB4c2p5MhZEmsDamnUxSsAoMOhlGbEUnyzmPs9I3Jijsz+SLshWiP/BSPjq6mph7ymGfYmNAd21Ad2NIb31EPuyTD3r4boei18MXuRBdrTORbwStVegMztJ5LCL46OunTt4rhW1sY7bNPFRQNOajiXjnNcUHjVBqRVhBpVECW84KLtJayC0U0xah+rDCijATzhCZ8cr0MzVhiWjTC1iRs2ma3SN//2JB9XghlkBfsHeQcUpOcBA/E5Jwbab/chHIOx0c0Ck7eO9YVEsW1YLpdMqwP5QKoWWP2XxOp1Nqo2GhTji/ZDI5pd/v0+nkUr8t8xRFSXCS7tLr9hn0+qxd2GTrxhUCFYbyk4+AJHMSPcYoKa3JN4y1EpBplEb7x4KEQxWTUDTF6dp7YkVj2iHdrGw5hLQG9CJSMoCYefxayaW157jy2nNUpzN2P7nNG3/yJ+x+8LFSbWr5fawYrm2wJHI8mbK7f8qDgwmXt2rq6YTt9W2uXLnEyZlwrp576gKBi0xnE+6dnsAyY3tjk6efusB8WXNyOuHh7gHT6RR2Drlza5fRcMSw36da1izqyPM3nmF7c4vMOaqq4ubNm/jccVpPWdaB/eMzbj14yMPDvwbkhdOejC3tZPXhRYiFNLC15ny1He/mk5HDpyoVggZMCGoqkZ7ndXM2uCsQY4UvMvL1AZ3NEX7cp9wa01sbUY4GUq6514U8oyzLJtTtFLnpostMSmW+vVKxQoJNwxFbUi0+l77/I0nLsaFEtOtfiXatJQqrJMOoOZKZdr1JvqyWvF5FerJgfQzgzCQHo2ukkXLK8dIbt7eQxxCkGI4mhhvzsHmbhByS8oCV3fuIqdn6ownDgtPYrQOotTKGJPvGGFmGBSFqw10lJxMdZV4w6JfMZzO880ynU7plD+cqptMJZa+jlp1PKNpnnvliThUq1ot1Mu/I8ozcZwSfMej2GA2GdDt9Ll+/SKebU1MlRGo9BsAoMyaUTIApsjo/DCGm0kymmNqoy4Si9A8NaZ6dan0h41hXqNaYmtA33LZikrcQWWy37JNfZyPPxS89w+88d427b3/Ej/74zzm6c5cszqGesKyWuCxjrdtnkHWYzhecHh0TpxmHh6ecHe/xt779Vbr9Hp/cvEsIJdcuXmO5WPDpnQfsHO4xC6cQIy8+/TzXt66wu7fP7v4BD0+OuLx1lQsbmzgiOwe7VNUC/By8YzTsc81dEmJsXnJ4eMTp2pILEY4XgaPTX01UfULhJTa5IIbG9g8muII0+5Q6XyEJLik22OQCNk0y0PSgFtoxJBEbNNdeQhDJMkc+7lOuj+hsrdFZH1NujBgMhhS9DkW/i89zfJ7hckcwjlJaCGg+pdlTTXE/pwssiSuNnDaF+VzamEJpcCsLzgiizgSl3Vk3RSSmAotWMjrldDqXBEbrTHkmw29th704w+yHJHNFI2oiotKbK5qNatL71ga05HGvr+daJhEqyEkbN91QEVUbLTTXbDZW87ck6JedUonHSMqYdywX4mx3ITCZHOOiJxutU3qp/DDo9TmdTpgvF5TdLr7wFHlOqCVnNWituBClZdwyVOzs7lCWBcN+n0GnK35PqVeOzwrGl8Zip0V9g2hzZYRUm2d7r7Zfj/YXKY/VRsdHG2P53lA7K/8kKai2NLQ5TwboeTs16ZFo09NaLwHjHEZ9r2xYcv1br3LhxWvc+tlH3HzjHU5v3WV+ekK1nIrbBCgyz6wKHM6WBL/g+P4Zp//xz7hyaZ2L29cZr49ZuiVFv8tofcTe4SGLw6X4Gxfv89LTz/Lc80/TG3Q4eGuXew8+pSxqOmWHTh65vHWB/eNDHt7fYW1zQ0pPdQpOT065ce0q65cvcfbmW9Qf3+GzHE9sNqaNaqEQnYgQRXDVoaIKogEanlezGLx3qW0asMLwXtE6LZMjohyo6PEZFGt9ss0RnYvr9DbX6K+NKcdDuv0+Wdmh6Bb4TE0SzVeLJmxU9gQ0wqaa29CUOLTP6z9595ie8RxC0eF49CQ7LKDR/oFLtpVpVBNMMhxS193eP9pTxeaKMu7mZvarWtlZ1aEGCYrJaAIqo1VtqHmnaD6ZxuhdefT2rlv9QP9yLef1quCy/66qSv8z4FytHEFdS15yOSslqM6XldSkV3Ra10tCqJB6bQsCjnqxJMaa+XTKfDYhhAVqYHF4eEq1WJJvZnSHXUoyirLA93N622OhzBiaUhqOoezGhJRrNfm6caVgYTvjIbb9irR8wz59KAym0Fw5uiAJ/1ZrWlFwcjW0lLjesBFW0YSbUT00iZ2oe8tS1yLFVpeXfvMrvPT1LzN9cMDtDz/i3ptvcbizS3U2pZhN8QtPFQPzUDENSz58eMSdo2OGN/cZ9DoURUan1+Pg6JSH+yd0ypLxaMB0seS9Tz/hZHLGs1eu8p3XX+etDz/gZ2+/hfcZly5scXJyzOHpMcvpnHmoCLsPyYuSTtEh1hWdzQHDYUmvkwO/2u/1ZMIrtlqG5VYRwqK9OkhRIjsyZ1p+xDcbwRKvzT+TnXMQR50cCxGLUooEH3G5ozMekF8ckm2tUV5Ypzce0RuvUfT7lN0uedmRYoPeuGChZbI0R+abSKktXstDtAXXaEtWzjdB1RRgaEwDiRbpCoz2Y7uX0/Qpl8q+mJ8jtsrbND5CC6/7BIQcTjIOPA2qirLpUtPbaOk0rakzpS06QNGfSvKWcBFKkuSaroxZMivbn7VJqI2gaipLNPBNAKWDGmaTKSEs8T6S+QguI/OZmI1ZRqcUc3CxmNHrdCmLnDzrMl1MRXjVlTagFcS/XM6YziYsphMyJwgMB4NODxci9XLJZDKhW3bpdXPyImfjygb9jfVU5FIe0GP9IQFVsIYwDXHFli+rTVmJKU6SzEFVTuCkyKITh3vCXs7WgFNFqorW2dyv4K80zgn7mtvAxj9iE5tiCElxahmpJQuyrqf31Dqv3PgWL37760wPj9i9eZO7H33Igw8/5fTgkMGixsUFdaioA0xmFfNZRZ5lzKsjquiYV5Gz+SlHkzM2RiMub25yOJnw0a2bbK+PefkLrzJ9+03e++hT7u4fUxYFNZGwqCg6BZ2iZG0wYGOtAAfh5Ag3nbb21S8/nkh41XXVqljabEpHBi5IHZ7kqI4Nwo0oH8wnAqtxl1A0ZuNvm9r+Nh9a5iLFsKTcHlFujMk3x3TXRnRHY3oD7aPY6TRNSV07OUc3czPtilyU9+RJZmGTkwjt8jBGJm37vbytVGNO41JhwmbBNWIn4aW0p60ygpHllEtnUddomEs75rgGnVp4XTFXepYAZDpuUd9cTFQdAXs2PdcQZwIa0dDpY2BkbAQ79gqtjbzi5mr/I9KMWxWJVZBEagKZK8gj1FnNtJ7jYkZGTrcQGkTuRFFWVU1ZFExnM9aGI8pOh8lkwmI+Zz6bsqznCZFkPqPwnl6nZDgYsH9wiCMyn8+ZZwX9ccH29cuUvU7TnSohLkW/sRXJRRlyTpFVtMixvH5MjZJlocv4+SbW1Fp5hsiimhXGuo8r60TH31BwI+4gevVnpjyAJKTS2o76tZrm6eSIdLJ3juCkN6XvB7r9Idevv8b1b73CbO+EN773Q977sx8STgOd3FO6ANQUvhQLIDgyHKXEvAkejs7mTM/u4e/cpVN61kYDLq5tMDs55eLGJrUbcDqd4YhMlqeE5RzPnJOzKSenE+bbG4yHPY6OTzid/bVEG2VULGpnCcri744QKynylntiqEU70ggrKeR3zuBoLfj2FDhFXHWswUPeLcjGJX5tIEJrPKY3GtPrD+h0euRFIWgKxOmvlAHvVo2jBpfrX6751HpItovtNCJAFqQzX5K+v9AilMYQXcJvooWbFBdBkiFF2xxqnqpAjC7BIUzDJ01PkOKMhkbtuS2yaYIxNFwjF43aIt9LDqP2s4ytl7bN0TKD2hkQK47pRwTa4ygVLYHf+g8XJdAzOTnjcP+ALIrfMfMeyCh8JPMznMslidpBp8jInHQQr5YLqZMWI0HIX+TOUSO17F2WE7xQT4osoywKRv0Bw7UxdR0YrY1YTGsGozEvfvk1nvriS1A0ygwz2aPXudZOVc6USEh522ZSJuGtY5gEdISUv0oTfV+J2sZmbTXz3Qy4RSFdQl/NrVw05dt2b8S0NkxZkO5tt7HV2Ai+4CxIFiCD8sIaX/+93+G511/n4x+9ya2fvc304UO5mtbzq5YVy1DhAmyPx/R7XbplgXeOs+kph6cnPNg7JKscV7e2OJ1O2bhyhXlds6yWHB0cU9dwdjZhOpswWyw5OZtz/+ERtw4OWVSr5a5+0fFkwsuGKflnAsaSz5DuOrhMWNFqkUh6hsZREoxVRBSd1p8yDREbLphVj/AB34FyvUe5NWKwPqazPqK3NtZa833Kskgt02Tvi5fMe2cE5nScj6jJZ4ZB5PMUNJJHUgHgkzZsNrYKrcxqbRlyswSdFMtLfiTO7397BhMc3ktqibPlp0UYQ0yLOKTLSHusOkr5G4cIBPlGnfOuEbayCaWBqwuOmDmi04jjOcQlEcFzAis2jv9kBj4ytq2BawlbtCpHURZce+oakz1xFC9raQoLnjwr5Z1joFoucLEm9zkh91SVJ9dUrOOjI+JATMbce/JejxAyQhRfWoaj1+niI7gQyejwO//wtzme1Sym8Ny3XmKw2Wel+GB0OJ+1XAzK7bJxUcHlkQCQVQWRLDxTIg3uNOqKVxPWfFEhumTuWclpG7Y6VK0KKkjQRXNuTYDKeDam7SpxNjZuCYwE3lIoCsmEpiRk7diaM5HBAZc7tp+5yMUbl3j9t77J/ge3uPXehxzducvJ3h5eS0LP5lNOTo6Yzc7IPPQy8R+++OILHB6fcnp4ykcP9vAEuuMDnn7qac6mE/qZY3vjAtPZgul8RiSwsT4mEhl9ep+Ts3c5ODk5v0UeOZ7Q5+VluhRSh2AaRRjrwQlusYqXzZDoZKUKFLYYGvRmEUGi8cP0tznk4w7Z+oBsfUS+NqIzHtMbjun2+tJLMTPRsyqpnGv8WJa/uLoZm/9ozEXL0ZTDa/VNc2A7Q5mqMRMCjZozl8xTbfuWbhRoSkKbMbKKUhrulsP8cLKZ3YqD+NHnb11FuXSZN5+X2YQtxOelu6GlIFniUApoGBKhZSYawNC93VBEVnU/jlQ91rkonZCimLjee0bjEb/3+7/Hyb3/nr3dfRbLCTjH888/zbxe8tFHH5JnMnLz6YTSD+l1OoSqYhFnZBFiHeiWHcoip8hy1tfW2D98yMnJsZBTvcfFyOnJGZeuXGH0yiUuvvg0L12+zHKxpOxCpFbTqpktb5FjFbpO/USGjGKISrsxReyUAGzKzPyW+r2NoeKkoP60GOO5enSWhOwSUkuCUy5M45ttyDPoMznnNA7TPNvKcmmjaF0OThF9dE76OqS5dxCF8hRyR3lhwNXtl7ny1eeYn054cPs+t3/2Hg9v3cPv7pFPCxbLBSfTCQvvyULkw48/Ii9zsixjtqjo5RnECYcHt3FZDi5wcLjHtWs3CCFycPiQejlnbTBkezigW/w1lIFu2L5eNlSCwzoJOinOa1RRiwIGjc5EjUI6QzPOJ2d/sn4gwZCYge/kdNZG5BtrlOtrdMYjuoMhnV6PsuhQ5AUruTSAVtpq7hNFG0fVoOfRj7OkZfte0Ul6pGhmIymaJCMhddStWzZK/bAFLdTJCJknhkyvZkx5cKkpR+uBahWIKYr0KFjzyUxpyLImfAXHNsLKXjdGMwdlLLxr3tEeyN6oVQ+0KWOtF3Ppqi2KhgpJZw5o+xzF1Jr6Y6jgyvVrPP+FV7h0dMLaxphrT93gtS++zM1PP+L/9H/8PzOfzun3e5RFQe49/U4Xp91+6uWSsiwY9Ltsb27T63QhBOpqoaZNwAJ7RVbw8te+xNUXn2fryiYxE1RhJlDLIySRuZaNbNzp9DLBVqn6a7Wxiw1VwvOt81ZM9GiZFbGZG3NOJXOy4Rs2QQ9H45XzYslEfRa9Vx0jrpZUK3sGE3DRiTBqrR6shBP6fLGSiXUuaiFKuW8MsnmCj9DNKMoRVzfWuPrKSyz3jrn10zf54Ec/4Wxnl/WyyyIEJvMZh4en5GVGkXlyl1Mt4J2PzvDc1QR8R28w5vh0wng4oCwK7u084JP5nGlesHxUKz/2eEKzUbLiTIRFLCoiWiTUSqJMIWKBzGayuCyT8sjWokoRSToSuo3gIj7PKEddOqM+3ZFQIbq9IUXZpci6KXwuascEWFS7qq1+zgmBJFzaK7RZDM1CjPrWzWeJYqFCpxFkdhtPu/yPs/xO56T7EO1KnKvZ8yYHWjXx0nFegCWTzKHNV1uy357dqdM5RXtNO6sQNiSYAgwaMLClvyLcmqeQoRU+VBo322ttJZKEbOP8Jjh8x/Pb//i3JPrXL/F5jssqnh9e56tf+hJ/+Rc/AaDTyanqJdUiY2NtjTLLqKsly+WSMs/pdgR91csFg8GA+XwpHbFjxLmK51/8Aldefo7tZ67g/JJAjbcwnDP+nBx+pYhUmomVsfRR5EBsmW1t5dZaOWmcZI0owZV2tRJbbM1ekhmN7SuvPJFghYYS06wRi2RDEwE2lHb+WuZasTnR3FuMzNN6/nR5XRFat6wuHeWlDV75nd9g/dmr/MX/8Efcf/Md1ssOw06fvFtQLZfM5nPm9RKCo9PJWR8P6RQ588WMw6MTjo9OGPZ6bG6s4xzkoyGffPoph2fHfJbjyX1e2n3YbGUL6UpDjpDKudiEmLY357ZPA6e/18lLjHAbMgedXpfueEhnPKIzGtAbDOiUXbplL0nwZMKpwIvRlJ1rkq5V+a+4N89Jhxia+6clqInn1qPRfudyE5qNjLQ3dq5lMHq/Qha0SKtFEaMlbasWTAUGW8/2iNBK38tvpDF5263bkteKgmK75r6aC5ku3uR81nQiBZCPeY40o7IJ09w1CCzKkJGsXquMYRE1c4p7x2hzgHOZFCSMC7IAveGI3/8nv8fu/Yfc371HHaUfaBWkHM54PODotM/u7h57B3v4zDMe9KRGl3MURUnhCy4/9xQvfOU5nnnuBnHUofJLXKzkfVqVL71zWqV1ZdmtjrqNgVOFF1v4OYpX0+qgtfSfCnewIJc0K1ZXiylaVXJJCRAfWZf2JLJfYuv3iaTRsnxIfmX9hNha9ZHHr6dG8zXPkd7h3HA4InmE6ALL3JFf3CS7ssWH3z+DnUNK5xl2uqz3O2R5ic+hXorMODg6lmcK0gA3zxxn8ynVwRLnIvnZkFA76sc+5KPHE7Y+a/+3MeibQbeoIlH5LDqh3nmtfyXESansKzmKVp/L2zUREzPvdilHfe2nOKTsdul0OnS6HcqyOKfjgmqm2GxcnQz5V7PpEtGQxqezElGLMWnKNvqKUTlqKwjNJaRkn0hNegShOtm4Pi0DFa4tk1CUuqj01GhIx7FdRuWXz4vCfhNE+rYitBtuksMRky+sGajkIKa1YFvXXk3GlvdsgEOLC0crTw+s59GK/vc6xnZPm58YHEsHV168yK/99tf4V//9v2WxrCAGKQcdpN57UfaoI8zmcw4PjyhcoJvlBO+ETT8c8Gvf/TqjZy7gvDjTLdsD82uqyyPaUDinyMVhfgZDnyJ6WEHlJOTZIrTq4Dhbd20NYvOSKqe2vtL7Gxt1Zfyj+dz0nq4RXOjIhha6TcoiPbmtN4semxJbeWr5dWx/6rDiATYaejdsdSXg4AJVVTGvYLIQloGfndE/ndHLPKNezrBbkOfaGq6SHp3VfIYjUBY5VRBrZHlScTiZS523z3A8ofAK6RRT/raB80z8RjHWyUntnPTQy3ymeV+OiqZ5umwwj3VlDlGY9L5b4jeGsD4gHw/p9Af0ewN6nR5FWVhXNN0AJvDOIQ+Q8jOYjmpvSvs06sS2lCGtv23T2XoLUThuMbZutOq4ftRPdS4vLSHDJhLUDgC0dO3q2P+SeQkx4GOWNqe+2cqVYoQsz1SQySYyom5bN0sJopbPa8VEcS0hLgrH60jatmk2paU0kaCYT0GR0Io862w4T3SRDMdXvvFl/uSPvsfZ8RHeBSk9XtcsKqiCo6/Bmul0xnLYJfdSK835Dk+/8gqbT19kki1lLqMqD3VVBC0pLoUFmvF2K84BdZhj9VJsgzeKLPmu9JS05lrywdl1YjN+jaCQfM9g5jtx5Tfo3dvdoESoyLfWTNnmpolOu2ZUVwSopua13nOFtpSclabwWy6NqHNowSxD8QR8FlksptL5XEV+FQNni4ozB/vzBVkGmXMUTlrZjXslvSwnJ7KoItVSIqoVjmUVH2EI/KLjic3GYBUio/KILFLRqvNtEyF5gb75zpGY26JsghSKi1KwcOEDvpPTXx9SrI3obozpjMeUgzGdwZC80wPtqGOdaWKMTQTQtTxosZlyfSL9ryYaaM/E6jpspQfZP1sa8fGyJS3uqJJCqlfIN7EtwOxZmgxguZxtDNfaHL/geGyFBxdWejnKenPN+nUq5FymS6wxDxrkFRX9hnNoq0EP4Ah1TIirjWraPCbz3rSsriTgGoJss/kitRBDnWPj0gW+/I0v8Wd/9Mei/DLHdD6B2GXz4lX+V//7/zX3737Kv/p///+oYs5pFSlwrG10eObLLzDPYnIDNENlEUNFP7Gp05apy0F+31gSzWO2WfXN4Z1LJGq5RbNqYusj62+a2ri5xtTzaF6ma4jJ54VVS4+uzElCtE7USBORNqQHMVY4X+PIiKrg2ghd78JKv84U1Vqdw5aowwT88dExOzsPqavQmmvjj0EFwqByaPluOKsXZAQyL1y+YVbSywrJffZtJP/LjyeMNkICkU40t1czxNa36bCoZldbqDkHWSYSvyYSqppQLwneS4ShV9JdHyc/V7E2pru+Rjka4TtdKApillHHgNdyO8arSqgrNoJMfG2t8HdsTVdDwZJ3iuIXM9O3nYjt0nXrVT9P61hBXLqoY0BZ8A0PqIkSiYBqa8943ob9VYcKnDZBVAS7CIE6iOEm/i1BHbWrW6LYg3eJyGvCy1CSvIu6BtQkSmjCOYL36fNUb8yERBLy8p4OB14Ths2E0TXhQHxPzhOCI/jIN3/jq7zxg+9zfHDCbFpTZCVr69v8vX/yj3ju9RtceekCn3xyi5vv3WHQGVItllx94TrjSwPqUDXRwBa6Mg0VNXHda8BCKqVoJ3ENM5pQShHI2FaAzZyb4Me1UNfKj3SjtywDcOr4F0UqlT58az0ohtJ5azBsbFwjziWqi7O6/MT0HDh7/5pGWRtM1EF3DWEno42oGtVi63nVFI1AYLlccnx8ynSyJARHdFb2nUaQx7TCZQwizOo6uVRcHThwSzyOYWfE+toFCl/C6cFjFvzq8YTIK6b2ZQ6XmsFGXdRBIbpPgxCThDfJbgilrqW8r5QSgU6/JzyucY98bUS2NqQcjyj6fXxZ4PJczRWkggW1MunBBWsf1dZ4+pTRNpxtLGji+c0h0+OF6xNXmfku2PRner5tBtIGSCNkTun2z3TSwgqiEnhsSE3cLc1G+Wyz0SCm9q5JNbsUhRG9klFF0DuEv2YL3YIlCV2qoG+jJfsiYtHWqPlyqwjNiLttxEUUIR7rkITtaljfzhcBUgObl7d46tkX+GRxk07Zodcf8Hf/4W/z3MuXmbk5cdDh7/0v/jE//Pc/5Ghnj+vPXOfyC5cIsZa+oorMsxaCsIcy5RvrqIg0gjbDcLGlVDTsaz7IuPJirpWCZciqiU5axNgM+GACTm1K5+xMEygNo761U1rBD1MCsfkcu2cjVhvxo7/zGc5pfwClAPnW4nTn/m3joytJ/lZkaCTZhMaicPc6nW5TXLQN4GgEYNtXbMNoSLEGagcVOetXnmJycBtu/2cWXuJrEuGUuVxfSAbCTKaUVOrUBInm+5KO1i6K025ZVVQxEDOH65WU6yOy9RF+rY9fG5GNh/hBD9ft4DslLpecLqdS3zwSsZbhkfzALG1YE1zNdMiEmAO7GV6wUfWtX8r8nlejam60NkH7aJtz0TY3WtkC36BCPdsiodYT1nx0DThvAgvntb49dghSUjsYPaOF3gSqSyRROHhOKkk4rzX6pWuSVTSVk2LyhchCd0mQSVDGsIGIe3MPiMAL6f62aNtmvL1TiFGQTnv4TG473fA5fOkbr/PiCy9xeHRGbzzk6uvXmTChXkai83THXb79u9/izkefcv25p4gFLMIy5X16MqK6FMzdYXmj9sKC0IAolRe8Zo2EGLRLlmxQ572Wk24CGMnr1DL5G40VEwJtuFauMTVTcMAUugoivX5IpkyGcQbNN2yHb6O52IAGu6AzRaHCOv37MQIrzaNeQJrlmF9W94Q3JUzLV5dOYZV+xCP3eOSICO9dn63XHbC+tsm9o7+GkjhNuRaFqdEiOcKtMRQgvoUqvVWMtQx8FMdqVS2pYoA8w3VyirUh+dqAcn1EPurTGQ3pjIbkPSnh7IpCNLc773tYFeWSiqEDHtSzc27w0uDG5nyX/vF4LdF8I+c0rtFGGLXFYQih2YTWaFc1VbvbTDI5UI1vydYqDCxV5bE+rtZjBRozx6pFmKEQWoLE45MDOjphZQfXIt26dnHCiDfCLaIoDFXY+xrKbD9fqJsNZt+lOVBzJUYIPqyMsXHfQkC6nON44asvQcyoKhEC8wIph6PKoKYidCIXXrnMzC0lFBTUBeB1wwdDhDrzdZ3GJyFh76T5bdRcWhMi+rBBmiakZ7X3DQjyTFQau09SHjbv5/w4jgZ2pDmKKdc1PLL4xBcUzY/lgkaxbf2YQHaN7MRKPrX8t2qj23qw/bryrNhnjSCzd1ajX1GXmLR5VlKWJR5HFc1X+EsE1uOO2Dxblp/PRf7FxxM77GVegpo8UfhBOGn5lXxFzQRKs1gpPRujl/6EoSIWGVm3IBsPYCQdrf14QD4ekY0G+GGPvNOFPNdwt5o4ek1zGLf9cGK2GnTVeFjLEfH41CBDW7YpI00Cq73z6mJqBFYrv6R1tRbgW/Wz6TvElWuQkEoiY6vJEww9xvP6rP0sVjpFH0XNXosHOF20PioaiZAp+A/BS+ML1dDGQ/MJTan/EpeK0VrEtO32awuomGxhGi4grfLGtqECNCWAolICNBujlnNDBnWswEskchmqhAKtr1+tCM5K+bioGQ+qnOpg7fp0Flro16g5lizqgoy6zZ0VoWxyeZs+C0lBtZRDeu8oEdlm7htXgq1jEx7pZqbdYoO4WwOczDR5NXORtO0E37wkqyhqRX8nl4atreaz9qb4xQpT9pSsl5xOp8tg0KfIc5bzRfMqn+loIz1Y1hXLWDXo+Fcc/wmJ2WqCpDLDIhy8ph3UoRaI7GQhx7oW4RUrHfQoicy9AjcoYdwjWx/jRgMYDHDDPr7XxeU50TtqF9TMkKoIlrZhIWgZSOn3J4JB4XOMivYgc/7cYlKBFZoVLTXGJJzuEmlz9b0fOVrmslyT5LdB65w1VpyZ0DH55hqw1CCYsLJsW27SVsjcci1N0zsafpXlcwKaFRJTgrqLqgasLHOUIngJiQS09ppshhjNE9gIJNRnYxFRlRkNck2bfSXtuRmyTPg+yS8alXWuKEDEh+zSOkopoBgFjWhHBFyrr19q/mKIIgZczIihnclgz2Y+wOY8r0JOTPxWZgQqfNMcN4Ip/btttnlScnxShckxlGQ2VmdNhF7DsZPxbuZea4yo8o9pkI1n5ciSAjeT8XGNiVfGHiNKNC6V9N86h6ZYz/P7dFh07Fx61rzIGY2Gicf12QWXCVifwILPPJ1el8Fw+JnOfzKzEXH+hVARQ8v+1shiBG2vLi1QYggivEItbOnc4fKCotfBDfvk4yHZeIQfDSlGQ/J+j6zokOdlQgRB8yF9lI21woOPQRNTa5yLZGQoYGjiM1EjfecmI0YVuIDB50hUM0mQgHsEVbUOXU+tJa/OXpJFoKKmtfiaiTdTAdvstp10wTforNnkZpI7NG8UNJev5SgGotZNs2cI0SuTPJLpzgzOMjNJzyZscafnezJE6HnaUdimLHZ6B7BMsEYI6Tta+aSm92ZQS01RjDNTrTlPXtsLhSYalTmSMihSJyWSFpAopvwj8x6Fb838KUcvs41nPC5tXbeS50mziV1LyAKpmaz5ItH7xhqcb417QojR9HtSYsktoIix9Yh2hoyqCbkWumvu6mj8aBZJ5BGB0z7a79iUH4jpv9p5xs36OzcozdUgRoq8oNvrYACwpcc+82FWQ9kvGW+OGZ6sfabzntDnVYimFnod9jaSfF2JLyMEKYkTxVwMQTqzBO+IZYHvlbhRn3xtRD4ckg2GlKM1sl5J0etSlqXkQLoIsW4tIJ9qvXtteuuUtSwwPIB3ZFGin+Y3cK7lYE+ToZwaQipD7UR1quYPuPbCf9zRQgpJcJvmjPY9EEOTC+kMnUXqJLwap2/d2ryufSNaGt+CAPYqavqY8eCcNFy12voRR6xqpT5Eoqux3pROI1BNMUMxRwgB78ULI34zeYzk9EXMT++lF7f4x9S9G5vUGROu0UnyMM5pgEHr14eKQN2yHhq/V0CEX21CzTlZZ4qQGreBam8skgxVrJJj2qkUMBQWtQsQJrD0pCYMcW6aH9nAMYWLGm6euENc8EQvgt/ru4ri8pqipeXRE3pt1olzzTpChdz5W4f0cCaoNHLsm0ovjzseh6LsKnaKwo2VdX3uKqmKTHSG/iHLvDAHVhTIZxVgNibij+31ugxGPbK/jqoSGhpAWCHNBKaNGyMh1NShJpi5SJDuPGVBNurjB32yYR8/GpGtjckGA1yvRzbs4TsFvsgFGWjJaRDtvUKc08ia12epTUAEGfhcN5kFS3xbEGkqjkP8OOYxkgafTfqGLSZnK+vcyogIV800a8qNdMaJ0Ru27h31fjUkn6FiEdn05oNRrZY0pWuEFjYKtvH0GsmnkxjzlmSorHh93wgaaRSWfGZmrGJV8xeFWsoIZTROfBNenhb1w9ljiNBR8EMSuipoLcdT/FO1/ka7n+tmaKPNgNeoFs16aKFPMMRphQIs4dpwkT1DA4NDhFBXaXOlqGoa11VXQXtz2zXsupbSJQK5TiabdMAKUvPLUFMQAW2BhrBydZs/Q8o2z7GZ59bStbXURBBRZed0ma4qu+bxzW/c2gtx9R1t7mI8f37zTNH4lYhwXlYLzs5OqeoKVs74RePYfAaoz1y2apkXAFTLms9yPKHPyxaJFDOLrfZmyWdjyEt0KpnPoCjIxkOyjRH5aEjZ65KtjfDjEXm3Q9Hral0u2UC17YIEVSw0C0Y5sBe3YWkzncX/5QTK66awxe8QZ6p3UEepvClUhZBMKXQhWAmXzO6XTD8NUigBMGlYGudooySdXt8EFAjeSNgwfW9nihM5pEVkE5yeQU0gK5vjnCImfW+5bUzCTWo3OSGJRrRrpFACajMfnZMquC3Bm8mOI5qIMPPWNr2zdBEDMDGpXjMPQ9QIqgquGkT51PabmApXmg6WOTd8Q6vktY6+OoxTGEDhiiDARhiImdpggLSJVLha496ESjTYkHyArTFf9ZeCjw1VIK0Zew47R04WlRijAn6trquC31m+oJJpk1LVNdEMabP+odmJq954Xe+/KL8m0rhDmmGAqD0LTIyrsgwt4WpmtgWTbCdUyyVnp2fUta7n8wiv9SgWkLJ5MPAQEYBRZl2WVcXk7Fe3PYMnzW1MqSAyeMLaNk5XrVI5CHveO6LP8d0O9DsUG2OKtTGMBuTDgTSG7Und+bxTQGaLWwdJIarHSZMPG+wW1yUNuC6szIsAyQCs6oEhKGd7q4WidDNmTp2fUcyTAPi6KQecIEsSXkIxqM2PY056LO/R+DHQ9plIaB2qGJPoAjM3bRobCoH4jUKK4LV9XtHZZ6gG1U2oPCUrUpd6+sbYRPtj82zOST3yzHktqezJFJVJDXQvDH0VBo3fJOK80QnamXOraDyYn0rfrorNBsDez8bD0JjTSDE2djrOOolps+tYuLSxzZxu1bA1Am7bua5jFqKRPjVKnh5UBf+Kj7RBwl7n36gNoOPtm7luqBOa89eCUYnXnsw5DSxEiK1OVu0jcbNM8NjC1ncxH1yDTH/RYcrFt8bc/cJfG8ptA5XoghaZFF9mHYwPoNDQxu0R9GdfufSKhvacc5R5yWKxYD6Z/pLnb44nRF5a+UDNmxADVah0MmpCCFRR0n1inpP3enSGIxh2KdZGdMYj6tEAN+jjewVZpxQzMROeTYxBYYHAct1CWGzAtXLCGk3XIKK6rsEFFrWxyBVRWFMOHVBDCYm24EDdaLhYi1NX9a53njo2AWlbKBVNZLAObUPAFq0JCDmpDkZ/iKxMtT2WC6oVY6odl9BkjBp1MmevoMRkCqjJkdZybKgPqbRcA+zSmnKqhIKTqG5TzFvNIhfJXCCoQDNmvd3HGfFT0VmGmdJmGjdGEM5Rq8kRQkympY2DBSsSamnt34QCtDmw0xLVoibEM+etckK0lbp6pPlof5YsN/GbehVcQSfbqwBL5hTym+QbcmqGYZ3IGz9cU03W3tLGveXot+dRJWvE7hDbvUbtYZuHb0cDzeIxk7r9rquHtSBU2exMGdKsj9b1m/us+vfsWWIU/3ZVVbJeHyOozh/eznUrr6NPJ+NYh/ozV1N54qoSJrRM4kr+XJANhfinsizHDXpkwxH52hr5oEe+PsSPh4R+Fzod6OTEwiffQKhtowrCEzNCNaKTMLDwbFxaBOcHLEUHXdCokqWl1A0Co3HmJ0YyIfGOnMJ8i+CZu81APSiiiIK8THPF9v/TpKsRqI7mtHlbG1pfGWmNFtL1waXKAZa5RmuBmsfenKhW7SJpthixrDb9RNEymGnmnEVlJQjiEU5VrobQMkh5bu8iMYtkreYUYro2Yx90oxvru9kAxklSk1PFWa29Cqz+W6hlLEMM1DiCMzFIC4nW4LQfYZo5+0ETgkifrEgqHRZDHi1hn9Z2XP1tO7Jta8dMObN9Io2iSGhNEVzCSqYw3Or1BbU2MxTb38XH7H+93+PIwZgQM3VhKLE9CKoUjBPZft5og2lGBoqQYxOYs8cLwWnGhqztxWQOdWOqJ4T5OHnmmndv2GwO53LyotA9fv7FH388IVUCaRBRVVT1UuBiMJtdBibLM/JuF/o9ivEQP+rTGUm6T9UryHsd8rIky2T2A1EWrg2ZLmjZJHZfhzNhEFyqZEEkbSBLTagt305HQKJu6g9R7WPEy9oWjhfha2jP0YSwfWgQmFNnhPnlZOH6BjmgrOtgXjHjuwVCHRuuknX58ab45d0lTdIRWv4DiUxWuiDO77hmMwXqlYhSsxEb0yRtQRVyTuF6rj6ziCGHCAFCdNQecp9BEEJo5lzyt5m4aEwjS2tpr5ioZmGzeC0xW/h7JCstBkGlhk6N/9ZGGC4G8ZF5mZMQkwdPBJdrhkrmzaWF5BqJwzkooxHa1TUXde5wlhko58mZLa5Zi0u2MkWxuVaCPM56YtpGb6Sk/KkWRcvPmfxerYldEWCutfZi834Ol5q5ZM7WdDMnusTS9WxoTPkaW8AAgyBKvXxw1HXNqTbasPxHs2iSr8CWAc07OBrPnSiZjMzn9Hp9XJYKKf3K48n6NoYK6eKsCzIYlUHEEM6RFzkMOkKHGPTIRkPi2pC618F1crKiVMJoK1qpSM6pf8RHzQMkSF5Zy2/hcFqAMqii0IYfOhjGDK9bM2MAPEYpkOdRRGXyXzlQpiGtwqasAdOsXmgYoNEl429JDljtvAqmRiOKz6qWBgsuUtVxZWKaypgqxutAqn+fzA4xYYXeENNibSJNMSWOC7IKqbKnmXOyF5ygYl3pTgUITtBp5j05TgSt81phw5NHyemrI42Px9BobI1j0vy05god+RbyUv9WrWuo1s+iIlnby4aOU6AmjWlI/imLOgs5NKOOkcqHhJIcErXEzH6XVBNEtEGtCgyF5gk1xtVNGDCajl3dFE9IY21va6iuTbxIKCRacKUx4ZqjMVFJ11hFYI1OatZH68MkiNuKPUZR6qt5vXrULVQlGkT3ZqNC6tCsGaGxyJPU9YKjg33OTs9kv9gc6f8bRfBLDn1Jnzl8LjSfOvw1RBvNLm+eRu4cicTMkXc6FP0ubtjDjQZkgx7ZqE896JAVHemy7b2aCCGVLRH/ig5JdNSVhJaFv4I6p2WzZ5lXDRwJrumDKIhKdlUwTeQk090hC9U0UgRibQ5WnRjMByaYPfmXnIMQCC5Qp1ZSND0NVJsKT8bp5KvgUqEjJnYleXuYUDMzypB7TJsGJyajE7ZtywxtlY1Oh+Qgmje+cRSjQs4cwJ5Y11LGKIF1KSLZEC+9OqIhZujYa3xMa5OZkMrVSZjKQbfMRkBNXpJvK41bXBXA4dxYtCs1GJpwoXG2J1M1RCpFtkaEFSveNeshekAVrnfJR2ehgDo2temafynii2ialI6DoaGETlb3RvJB2ZKwKwbdM1HLDHnZoKnSMA0P0VCjKMjms/Z0JwTaum96hmAunQbymDuhAUJR677RkqjNdVYEV1rL9nASpLOz6rrm5PCY2ek0Ja2bPDBFcN50fIRvpnvZZRlltyv7N67SLn7R8YRNZ+XlhF1vREjVaN2SvN+lHnTpDAYU/T7FYCD+raJDVpYy+SahYxIjWA0pM5MS58VseB0Uh1f6g+W0BTJvnYkbk8GpAzF6E2zItQHMnxbBWZE0fTfMvImGV0goUyJ+9SMLySB/SLQNn1CpCa8qVslRbWKitoXWvo/2ADCz0jtPrNRTpH67drmhqMKYVOyuPVVyH0sIidFpA5SYEIcsJOlwVBPwTr6TKJwoAqNphFjLxlPzKUZV7a7Rtm0/jQmfGFv/rbrAIoT2PxPInpACGxEa0zHGZGomAaFz6B1aIkk+9O1sDwyNxhUFhdX8DzWhClpRFuL5gporTnNBuN6E+TmOuqH1Zs2o4jChlkoAtRzlLdCEIc547jtnUdE0yo2Ea2yKhFaNe+dtHbYCCc3jhSSg24fIWKU5aZaM/L4VYoiaNuYi8+mC08MzNRkznBGmWwvxPPpKaFS1hpPpIMtKXJGvROF/1fFEwquqlrJYolEmZDVmRYbvlhSDPmHQJxv0yftDsn6PUJYyiSGkiqchBjUddRMYJ0k5KCa0cpdjGXLJyWhUCgL4SFWLIRgV2Nu4iNNdrp97cTp7vUe7/5FFNBszx7rnKDqw54kqaFcCITKZSXJE9XQlk6imDkF4XaoRbWqED7caVTGmeq2VGeoUl0TGz4TruXNsg4iQUYGNBDeIMj4+y2TecIlMmmmJIvFjRjUBhQEfdAwlF1P4YISIz50Cilrmz2riG4htmczOeUGO5uODBGUac0j+y9CEdf8xx76dFxX+VBYoSPmzDf5v7p9CHMm8tIRtyxhwNlZeyhZ7TKM55TGb31I3XGvuksgyoRojIVq9NBpzU2krtKKj6WwnpnP6wO6h97Ein2Zem/BNNDgZ5IROpYS65X5atNy1ciObNRtVMDlnSMulNRbVd5x4kalIom2sWqKhwHJeExYy3tYjQe/QCKnWYa/ZVgkeyMkoiy7dQY/KLVYqUP+y44mjjVZvGwcxj/jCk3VLGPbx4yH5YEg+EB6X63WJRYbzhh50cXiLMAb5LnEDGrnrnE+bGCfEUymDEhPHyTRhba0ejI+lyMwyT8SZ66QZaZDCZ9ZjUNJaXBJU4qkW/5s8hwhoQYxm2kZWfDop6pgpz1ESiesQqF2kChKZlYRjRSKh4W8lTWXwviUAoteIawhJA0ea36RnbG8uVRB1kHH0ThaxT9DHzDvhfkln6owmGglWnM+q1GaIIKAO+CzT3EZZ4F6RC2a+6ZxZxRF5PuUxtZ49jbfckJg2QIPmwsp/h4bt72X+MgzJBZzPFGmYH0oL8KWxleeJdVTk1rDJGvMo6rNGjcAqjcFMdtfemO0NqqTY4ADN5wRcUAe9Kns7w5qymBCide+IplP55v1Xy0S37m1zrWgq8QBt0EzpRhMq6tOKIfnAbM0lJWGCGZcAh0OjizFqAx15hl63T5ZlzX7Q+1jhBq8sgeZo1JEhRBehW/boj/ocMxWT6DMcTya8gmkTNcVyTzHs0h2OCIM+5WgN3++TD/tknQ4hl65BoAvf0JNGroIKIIPJtsBEkFhLcpcgdbsCZEQigdJ6XSdGqdornCYg1kF9Xhoh8xpvjOCtAKDzDRJqQWYLEkTbeTSaRTZl3dr0lkgsi6cKEu6vjMAbjRMek4PT6biatl6JFnmfCuLFGHF1YwLYwnegkVWnz0uTuGwbT4WOVZ71zomgUcEoUqpO5nlMaEmoEo2/S8bSUp4kMtaYATYmZk1L5Fj3rY2hBSmIypNqjWuMK85s8Yc0yMIEToyC4htul/K0UNcCatpp6pEln/soVUOMbB0xZ78JHZt/RTo+Eshkjl0kBFt75uB3K2tbSNuydrMsbwSJzZkpGEs29ybAWkotjYG4MW08at13NvZGM7A84uTnaguhZII2xOc61pquJD7AthKUOVPHhjYpQX3DEU2gV2QbI/gsZ7S+xubGBmfHE6bzX94udkXwGsJ1DqKjyAp63S6n85bL5lccT8iwV9TjImWnJOt3yfsDiuGQOBqSD4f4/gDfK4i5h8zTBD5lgh0ucZNMwgMaaVtN2XA2qdqwE6/OZNsAroHZ4rNILUWT3yylLSksdw5iVeEyWex1Qj9BJ6XpbKO7rkFarmlH1nYQJ82tCzhFzHRRBcuCVF+c083saZmhNiTJqY+sXhcSEiCSHKOxpcUtUpquo/9O/jyLDwWS/0UEmpztnZA8U+TQ8nViBC9FJM2/Je+qVXG904BCrUgsYt3HI/odLbNOUYBd2nJXbQ3Y3rS0F58m1lafBQwQoRljI3yc+mJjQ18RYWq+N4n4Wqf3aAOdlJ35cC16rWsvShTZOUvWd/joJGrdQlOWBB6CpbObKS8TY7ms4t7TlLRWIQCbtloFSjQ6hSFYZ6No64+0Hi1TwGqapcSeqGK8ZdJJf1WwgFTb1DMU5jRgZW4Xn8aMNIcEyMuctYtbXL5xhf0He0xnC2jtlV8kQwBc9KDUCrwEtxz652e0G5/YYe8yT1YWFIMuWb9HPhhRjkfU/b4y5ztUhYPC4TNUKyq3KobkI4gENQJrRS/mqfBpU9qghxhA6RWCwNpdgyW06n2GIxBS5KkJYZsAq5J9H5Vk6cQfAbJQqMVkbJkucn8UMajpGGPy2Vkis0M29ypCMAeqTZhQyoxpHBwp8uOiPLOYKy2iZ1SHtaEB3eu2+FIrtiglg0zugjyzKIPG5EjFBlVoC6yXiK+Y0NlKQFnyCgNRpYrTmYpe+VY1TWloZW3LztJIsZrQkeaZMTOqtdCjZQ3YOoHWSgChGFioPzZoLtJwoJD3cJIjIGfXNTWtKKVqEUOZbSWfJYNLBYDSXryLGqRyKX3HSgWJ68IlMxDU1AvtOmpyY++tRlpjqgVbK5jVoWOg/ifJR41ELUOdTFvXWifnGelpDQb1GQdVfDFd2wRXKjetSjqtOZDiCpAUMrZ7YgTnyYqC3vqI0ZVtYu6Vn2Zmdhs9tRBX+k1jTsYYmS/mTM8mOB81SvyrjycWXj4vyHo96HfJx2P8oI8b9il6XeiVxMLhM3koH1qlbaOEX51uRqcTIBrYq/1Lq1yKaSnzCZnTPDTUlCj4xQiqoL4dLTzoTXjQOLKNo+VdJNZKTlQ/HLbNogpRZyhPZy5GFTBiCkpNfhWAihBk0dsibqEd55LAsGie+GrAmjGYaWZGVkodSuUVZDxkrOzcZnNItK290MSss7I/9h4eMbky9elkQBbB1dFcYmQqZL132pREBacziorHq0APilZkXYpfNLoAzguS8BaBDbgsa5BPM42NZtenTFkNsUFiwYQ2zZgKggqJHuOom0qwWPAERe/qDkiCT+ORTp3bzgvYdRFhiui8uKiI2ych23b858GiibLOhZeo9cTSHDRt0gwtBSdma6KLKGp0vlWYMMpz1HX1SICnYes0giLq2AQdbxHdwfRbUnz2TKZ0nCmaaEEK04mqEL3lYtg6lDkpOiVZ7inxrA2HHM2nrVLgbS1oKw9clD6uDThzVNWC+XwKXUf8bC6vJ+R5FRnFsE8xFA5X3pMSN77XxXc7xMITc9cIj8QUN+cdilZUsDinUSAdKd24ETPdGr9CijKadtCXFtTgk2BxzhHqSiffpyJ60Oa/yP1sMYQozDsjPRJZ0bZqG6XzTPvUdUXjElafkJl4mFNWFq+ZivLYBp8sEBH1VV1CEytdbCwaBiKwnZlEhvj0snq+oynpnEwaRUTCTM+S8MsxxOVTTa5M/2+/8QkPt1Bc6ouomzuqiYvAMed9WvANZw5irFUIybAGVRBpTlVAGggxIeGbL7VKhfmHNOpoaEZXkAiOdgWEkCyehqxpVoCcsxKVMx+ps6hwa2NHNTEVOdcY+5/GOa+mPbrGcZbupYgrpYpByj+1lR6aMTDh1XSobyMaW5ItxIb9Xjp0WXw60ug/Oyc6RajOSSRb15sIWq9PJuu/DqtmsFwjkBceHwNbGxscZqfsT09lnZDUcXouGdamKyp6b0mtqlks5sTC8Rk5qk8mvLr9HuWwT6ffJ/a6FIMBrtcllDnkHpeBvZ5DuCDWv9A7zRlsVlMz4a0Brc20M3tcoFlLhrQnUIYnhCBmoy58SfNR01IvEZQ5bIMaYkOwTKTUiCIwQ2yrkTAZbpoFFepWQrdLqq0tXD1eNLvmAol89unamWtQoWusVT1d3t38G4bEVnwKZvmkzwQVZN6qQ7Sd9yrwVNNmTlGXc+RKm5Aa9yKsMif+L++b1JjaGO6gplloxkP/28xAKZrvid7K/ijKciLQZQ5s05ASsFPtexdXUJkugSZFxRkFwcY8EV8k1chQaRojuYYFTlLLNxXQgaBVOBzRFJY6uA3RRAJZsACOV46ZmLReX9Cr2WZrGiBohFPWR6vYTIxqkjcWCh6suB9qdlfxMdQaW48mjPWeSUgSk69NXvWc4k8+S8ntNe5cWqfR0K383lwg9twex2I5Zz49o5ov2DvcV3Tmmge0QY+r6B9T0rpHOmVBmRfMU9eiX308kfDKez3yTonvlrhBn2zQpe7k0Mk1TC4bvolgqRa0DRqbCUspPe0FGs3ksBiMyW7VshbsOLcwZAsZfUEHJS3s1d9aFAa7dxvhQONAT5rXzApaFzOBAMadMoamCdcmbUM5Ri0zCdW8STvJjtYJTrdmRYinf5rwak1wMntb3ysKcC5XMyaIQ97QGJEcLYETHT7EVDnCWFLOTEfz96Hj6lWwpQkhvX8IdUPIdOgGVxMORJg5iIj/x7SBbDRVXOoUDCGcI47K4aPicNdEHw3ZmQJrHOVqEtl0RBOCjQO6nZVgyysaGtcorCASn7hghGbN2MKOMYVGmpvpZ+ZfMneJPZsIbicMHZcltGWorPFdNdwre8q0/p1LeaViqRo6aIJjTZCMhFR1uyp6b9af7Qm7R0qFA6IKNO/FvbNYLtk/OuL+/j7zRdVE+vU9mx6izXp1K1PqwGWM18dsbK6zc7JH6yK/9Hgi4eV7JdmgS6b9FF2nwJcZtSZZS+sxEvpIYWxbSGkDkFpueW2wERVhJNNNB8AsrPSiaWGQBjwJofQbizmab0XxoP4WmuToRhc0kF4Nr0cmLjHqda80jxObPUzDgk8rGkEbsqVlWdovLB7b1lbOubTZzK8WY6uiabQN0CAwQxhe/2+scdPMDkFYXukR3qlpGM1RjQgxMxRc89/mhMi8IMZYNyaIZVgldIM4eh1onX0xTQJeJ17C9NFlWBfp6MzXpiormJaXdz3njk5zFkKULApdc4SQkHvbp9LeK0ZbQE2ltpBJfiG9nC2QtpiyWbMonp6NoRqP12cgzY19Z0LQFlBMn7v0kNE5SeJ3LgmwphpJ3Vr+LR8ZEGOTdoRDyant0bI94lpIPyR/b3pDXVoGAGRYNcUKl9aUyPbIYr7g+HjKrI6AV9pIXB301T8UcRmA0Wh3UeDzTKK87vyMP/54IuHV6Q0oh0N8r4/vSlsyyYVryqMI6gq64WIzyFHMuUZDRuX5rG5GYyOThjPBELt4a0iSwSnDIz5V0mJMEtxmXG4YNBGblbpSKoCS4JBz69gqi2ewzygFsUEyab4cTZMHbAGbZgvpWWy/idPYQut6XddsIvDJymzoBoLU0t5wDWLLnDSg8EombtCafJbjNaKj5qHzKw55aL1Tywx0OHXGKGKMWppGs4jNYY5q7tRjM6C+nwzrri7E14CEo+uWvjHTqlnsyURs+XVo/cS62duGNvNYlJOgeCxHVtFVbYrLNX69GFqCMgm9qARmRWY6fYYmJBgl2SNGsTFkH2JMSqwdVSNKgxrzU7X1sqB4NbO9xExrGUKp4BLDaqQdCE4S8Vc2vAkdAxCuaYBCJJUhShSKSHKz2F6V55XVE6KCEC9rRe6siLSOxKrxAWqQWV/JtfaoW5lbDbUJ/xJ5pug9VvTysxxPJLyyXg/f7eI7HbKigDwnUiWtt6Kf0gawQYoJcovZ0/ptCElgiVSPKxs4LVldPWkx6P/MN5bcVGki5beZc1Rtf4EuLmdRQkfyB8U2SkMWsNnmMULTrEITbVvox0d59sw75WFpKD+i3ELX+GCSonPpHRxIdj1GlWi+M46V19ZhqfWcjbWz4IVsEO+MamBj4Ml8Jq3NjJKhi8cc+6aAmrafUf1QzbOnWlo2F+j7qJg0EmhwstmcRxOn5U2Cc0Sf63RqiSAfoTZOk3KinCN615ZjjcnkjLsXE+q2TSrrR9eH03ENulYsB9REuoYLo86dCQYplhibAJMqnKBjJr5Z9eGiZcJbyMrZ2rScQNvvugfCef+V+fgQpBpqT505bT4iZW0sWFBHKdYnCd5hZS+EtFaajRNa/rV0O42aigKS6wRLZYoxRcvlx0rsjU7jMRI8EgQvlVCqRS2+UY3oBhXiDq+cQkPYFtoSZC/K0yEuDW/W+Mqz/rLjiYRXMRiQd7rC6fAQtYpqaDHSG+3YUByMQOcNgrd8VTiN0GAOyhbkdBZhBMshdC0fSIyValBPii5F9VHpHLrYmDjmqE1GWtKMmtRNSEZcyqTRc1OFAV0KtokFNJg2gSyTrZ+inK0FbcXaQ6hl7zs924RZUIGaaTcab+5MHS/tAo5rNpoNt7fntXsnkGsRHa/+KyUBetGwwvty4EIzMiqExeflNeqq5ndCQkIJqHWeoy1yAjjr3dmI5UgtFJdM660a588hrALEvxVTileTI2qSvlZU7WmEmlXtSAEJxclOeVBE7YnQ8uOkBaYI0VZpyjXEUFqUxP8g4+FVcBI1IBEjmY5V8vUS8ZkX4ZaixHVa87QCFG3lHoMUYZQsiEgdoHKGHuU35hOs61qCAsnH5ZJATwveGWo6x+nyEjlUfSiAQ4WdgYaWvkgWhH1q34n14pnPF9SVZJlIh/WW7zvtDS9cO0V9mSoz8QXLDUNYEkMlbRXbivmXHJ9JeNnAL6uAm1e4OlBlFVWsk7pO7aRYlZzN4BpSUkelp/F5RPGMtDUGQHCaahohWo0W70A1kZmFtsnR4U0ldtIcuoS2MKSkal6+NmqitYKXiU9pnCHKNCgXzJvJlPg94pcywZGEohdHvck8MaVtY9i7Zm0Ypv/Q1vWGqFSwmekUAStBI79XzaVDZzSHzHmyTHhwtWo5iS7qUAVhT2caEXUmBHAiqyrzS6hTXM0mEcTyh2VERAu8IEXsYpp7xJZQn0kNOJ+pcy4TIQUq/CTvNDrhPy0tNK3ap9K/nV47oKWlEwIRM84p8dZrWlMMoWUKoQpByw45CVScZ4SbovKQ/JyoeZ2ZHR+kYq8LUpHD6VoWPq9LvsSoaEnKHKFE1/a9DM1p+WxUyTkpt2WCOiEwQ5h6vlekb1vHBI4hWRkX0wkZlY6XeCLdI+/efjoj5Vpppcw1fuBQB44PjplOphq+N6XaFBQwH6PTewtNqlH4FlBZzubMphMWi3niiT2OFrIybvFX/QK4ffs2N27c+FU/+/z4/Pj8+Pz4z3bcunWL69ev/8LvP5PwCiFw9+5dRqPRI1L68+Pz4/Pj8+M/5xFj5OTkhKtXr0ppoF9wfCbh9fnx+fH58fnxP7bjM5b9+vz4/Pj8+Pz4H9fxufD6/Pj8+Pz4G3l8Lrw+Pz4/Pj/+Rh6fC6/Pj8+Pz4+/kcfnwuvz4/Pj8+Nv5PG58Pr8+Pz4/PgbeXwuvD4/Pj8+P/5GHv9/oITTg7M3S1gAAAAASUVORK5CYII=\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "We have the same result as the un-quantized model. We then try another CNN model :"
+      ],
+      "metadata": {
+        "id": "rblyUrL6AJnH"
+      },
+      "id": "rblyUrL6AJnH"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def testing_image_other_model(test_image, model):\n",
+        "    # Open and display the specified image file\n",
+        "    image = Image.open(test_image)\n",
+        "    plt.imshow(image), plt.xticks([]), plt.yticks([])\n",
+        "\n",
+        "    # Process the image (resize, convert to tensor, normalize) and add a batch dimension\n",
+        "    image = data_transform(image).unsqueeze(0)  # Transform and add batch dimension: shape [1, 224, 224, 3]\n",
+        "\n",
+        "    model = models.vgg16(pretrained=True) #load the new model\n",
+        "\n",
+        "    # Switch the model to evaluation mode to disable training-specific operations like dropout\n",
+        "    model.eval()\n",
+        "\n",
+        "    # Forward pass through the model to get predictions\n",
+        "    out = model(image)\n",
+        "\n",
+        "    # Determine the class with the highest prediction score\n",
+        "    print(\"Predicted class is: {}\".format(labels[out.argmax()]))"
+      ],
+      "metadata": {
+        "id": "hMHYwCuaA6WL"
+      },
+      "id": "hMHYwCuaA6WL",
+      "execution_count": 40,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "for test_image in batch_test:\n",
+        "    print(test_image)\n",
+        "    testing_image_other_model(test_image, model)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 739
+        },
+        "id": "Pu9iAXn3BtU4",
+        "outputId": "ecd61762-5794-439c-c572-e55537379f10"
+      },
+      "id": "Pu9iAXn3BtU4",
+      "execution_count": 41,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "cheetah.jpg\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
+            "  warnings.warn(\n",
+            "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.\n",
+            "  warnings.warn(msg)\n",
+            "Downloading: \"https://download.pytorch.org/models/vgg16-397923af.pth\" to /root/.cache/torch/hub/checkpoints/vgg16-397923af.pth\n",
+            "100%|██████████| 528M/528M [00:07<00:00, 72.9MB/s]\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Predicted class is: cheetah\n",
+            "parrot.jpeg\n",
+            "Predicted class is: macaw\n",
+            "zebra.jpeg\n",
+            "Predicted class is: zebra\n",
+            "tiger.jpeg\n",
+            "Predicted class is: tiger\n",
+            "monkey.jpg\n",
+            "Predicted class is: proboscis monkey\n",
+            "panther.jpg\n",
+            "Predicted class is: American black bear\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "The panther is now identified as a black bear, but the other images are correctly guessed."
+      ],
+      "metadata": {
+        "id": "JqmlKZcIB-yE"
+      },
+      "id": "JqmlKZcIB-yE"
+    },
+    {
+      "cell_type": "markdown",
+      "id": "5d57da4b",
+      "metadata": {
+        "id": "5d57da4b"
+      },
+      "source": [
+        "## Exercise 4: Transfer Learning\n",
+        "    \n",
+        "    \n",
+        "For this work, we will use a pre-trained model (ResNet18) as a descriptor extractor and will refine the classification by training only the last fully connected layer of the network. Thus, the output layer of the pre-trained network will be replaced by a layer adapted to the new classes to be recognized which will be in our case ants and bees.\n",
+        "Download and unzip in your working directory the dataset available at the address :\n",
+        "    \n",
+        "https://download.pytorch.org/tutorial/hymenoptera_data.zip\n",
+        "    \n",
+        "Execute the following code in order to display some images of the dataset."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 44,
+      "id": "be2d31f5",
+      "metadata": {
+        "id": "be2d31f5",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 224
+        },
+        "outputId": "fd082646-66f5-49bb-f4be-af1c2ac28978"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Mounted at /content/drive\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "import os\n",
+        "\n",
+        "import matplotlib.pyplot as plt\n",
+        "import numpy as np\n",
+        "import torch\n",
+        "import torchvision\n",
+        "from torchvision import datasets, transforms\n",
+        "from google.colab import drive\n",
+        "# Data augmentation and normalization for training\n",
+        "# Just normalization for validation\n",
+        "data_transforms = {\n",
+        "    \"train\": transforms.Compose(\n",
+        "        [\n",
+        "            transforms.RandomResizedCrop(\n",
+        "                224\n",
+        "            ),  # ImageNet models were trained on 224x224 images\n",
+        "            transforms.RandomHorizontalFlip(),  # flip horizontally 50% of the time - increases train set variability\n",
+        "            transforms.ToTensor(),  # convert it to a PyTorch tensor\n",
+        "            transforms.Normalize(\n",
+        "                [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]\n",
+        "            ),  # ImageNet models expect this norm\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",
+        "drive.mount('/content/drive')\n",
+        "data_dir = \"drive/MyDrive/Colab Notebooks/hymenoptera_data\"\n",
+        "# Create train and validation datasets and loaders\n",
+        "image_datasets = {\n",
+        "    x: datasets.ImageFolder(os.path.join(data_dir, x), data_transforms[x])\n",
+        "    for x in [\"train\", \"val\"]\n",
+        "}\n",
+        "dataloaders = {\n",
+        "    x: torch.utils.data.DataLoader(\n",
+        "        image_datasets[x], batch_size=4, shuffle=True, num_workers=0\n",
+        "    )\n",
+        "    for x in [\"train\", \"val\"]\n",
+        "}\n",
+        "dataset_sizes = {x: len(image_datasets[x]) for x in [\"train\", \"val\"]}\n",
+        "class_names = image_datasets[\"train\"].classes\n",
+        "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
+        "\n",
+        "# Helper function for displaying images\n",
+        "def imshow(inp, title=None):\n",
+        "    \"\"\"Imshow for Tensor.\"\"\"\n",
+        "    inp = inp.numpy().transpose((1, 2, 0))\n",
+        "    mean = np.array([0.485, 0.456, 0.406])\n",
+        "    std = np.array([0.229, 0.224, 0.225])\n",
+        "\n",
+        "    # Un-normalize the images\n",
+        "    inp = std * inp + mean\n",
+        "    # Clip just in case\n",
+        "    inp = np.clip(inp, 0, 1)\n",
+        "    plt.imshow(inp)\n",
+        "    if title is not None:\n",
+        "        plt.title(title)\n",
+        "    plt.pause(0.001)  # pause a bit so that plots are updated\n",
+        "    plt.show()\n",
+        "\n",
+        "\n",
+        "# Get a batch of training data\n",
+        "inputs, classes = next(iter(dataloaders[\"train\"]))\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"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "bbd48800",
+      "metadata": {
+        "id": "bbd48800"
+      },
+      "source": [
+        "Now, execute the following code which uses a pre-trained model ResNet18 having replaced the output layer for the ants/bees classification and performs the model training by only changing the weights of this output layer."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "P52pG2XLK5B1"
+      },
+      "id": "P52pG2XLK5B1",
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 46,
+      "id": "572d824c",
+      "metadata": {
+        "id": "572d824c",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "6d177b1a-4f55-41eb-dd99-7d799be0d844"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:557: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
+            "  warnings.warn(_create_warning_msg(\n",
+            "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
+            "  warnings.warn(\n",
+            "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet18_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet18_Weights.DEFAULT` to get the most up-to-date weights.\n",
+            "  warnings.warn(msg)\n",
+            "Downloading: \"https://download.pytorch.org/models/resnet18-f37072fd.pth\" to /root/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth\n",
+            "100%|██████████| 44.7M/44.7M [00:00<00:00, 101MB/s]\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1/10\n",
+            "----------\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/torch/optim/lr_scheduler.py:136: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`.  Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate\n",
+            "  warnings.warn(\"Detected call of `lr_scheduler.step()` before `optimizer.step()`. \"\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "train Loss: 0.5692 Acc: 0.7008\n",
+            "val Loss: 0.3884 Acc: 0.8105\n",
+            "\n",
+            "Epoch 2/10\n",
+            "----------\n",
+            "train Loss: 0.5273 Acc: 0.7746\n",
+            "val Loss: 0.3142 Acc: 0.8497\n",
+            "\n",
+            "Epoch 3/10\n",
+            "----------\n",
+            "train Loss: 0.5260 Acc: 0.7787\n",
+            "val Loss: 0.1930 Acc: 0.9150\n",
+            "\n",
+            "Epoch 4/10\n",
+            "----------\n",
+            "train Loss: 0.5688 Acc: 0.7582\n",
+            "val Loss: 0.2221 Acc: 0.9150\n",
+            "\n",
+            "Epoch 5/10\n",
+            "----------\n",
+            "train Loss: 0.5255 Acc: 0.7951\n",
+            "val Loss: 0.2291 Acc: 0.9216\n",
+            "\n",
+            "Epoch 6/10\n",
+            "----------\n",
+            "train Loss: 0.4819 Acc: 0.7705\n",
+            "val Loss: 0.5070 Acc: 0.7974\n",
+            "\n",
+            "Epoch 7/10\n",
+            "----------\n",
+            "train Loss: 0.4629 Acc: 0.7992\n",
+            "val Loss: 0.1744 Acc: 0.9477\n",
+            "\n",
+            "Epoch 8/10\n",
+            "----------\n",
+            "train Loss: 0.3263 Acc: 0.8361\n",
+            "val Loss: 0.1844 Acc: 0.9346\n",
+            "\n",
+            "Epoch 9/10\n",
+            "----------\n",
+            "train Loss: 0.3253 Acc: 0.8770\n",
+            "val Loss: 0.1961 Acc: 0.9346\n",
+            "\n",
+            "Epoch 10/10\n",
+            "----------\n",
+            "train Loss: 0.3388 Acc: 0.8320\n",
+            "val Loss: 0.1739 Acc: 0.9412\n",
+            "\n",
+            "Training complete in 6m 58s\n",
+            "Best val Acc: 0.947712\n"
+          ]
+        }
+      ],
+      "source": [
+        "import copy\n",
+        "import os\n",
+        "import time\n",
+        "\n",
+        "import matplotlib.pyplot as plt\n",
+        "import numpy as np\n",
+        "import torch\n",
+        "import torch.nn as nn\n",
+        "import torch.optim as optim\n",
+        "import torchvision\n",
+        "from torch.optim import lr_scheduler\n",
+        "from torchvision import datasets, transforms\n",
+        "\n",
+        "# Data augmentation and normalization for training\n",
+        "# Just normalization for validation\n",
+        "data_transforms = {\n",
+        "    \"train\": transforms.Compose(\n",
+        "        [\n",
+        "            transforms.RandomResizedCrop(\n",
+        "                224\n",
+        "            ),  # ImageNet models were trained on 224x224 images\n",
+        "            transforms.RandomHorizontalFlip(),  # flip horizontally 50% of the time - increases train set variability\n",
+        "            transforms.ToTensor(),  # convert it to a PyTorch tensor\n",
+        "            transforms.Normalize(\n",
+        "                [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]\n",
+        "            ),  # ImageNet models expect this norm\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",
+        "drive.mount('/content/drive')\n",
+        "data_dir = \"drive/MyDrive/Colab Notebooks/hymenoptera_data\"\n",
+        "# Create train and validation datasets and loaders\n",
+        "image_datasets = {\n",
+        "    x: datasets.ImageFolder(os.path.join(data_dir, x), data_transforms[x])\n",
+        "    for x in [\"train\", \"val\"]\n",
+        "}\n",
+        "dataloaders = {\n",
+        "    x: torch.utils.data.DataLoader(\n",
+        "        image_datasets[x], batch_size=4, shuffle=True, num_workers=4\n",
+        "    )\n",
+        "    for x in [\"train\", \"val\"]\n",
+        "}\n",
+        "dataset_sizes = {x: len(image_datasets[x]) for x in [\"train\", \"val\"]}\n",
+        "class_names = image_datasets[\"train\"].classes\n",
+        "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
+        "\n",
+        "# Helper function for displaying images\n",
+        "def imshow(inp, title=None):\n",
+        "    \"\"\"Imshow for Tensor.\"\"\"\n",
+        "    inp = inp.numpy().transpose((1, 2, 0))\n",
+        "    mean = np.array([0.485, 0.456, 0.406])\n",
+        "    std = np.array([0.229, 0.224, 0.225])\n",
+        "\n",
+        "    # Un-normalize the images\n",
+        "    inp = std * inp + mean\n",
+        "    # Clip just in case\n",
+        "    inp = np.clip(inp, 0, 1)\n",
+        "    plt.imshow(inp)\n",
+        "    if title is not None:\n",
+        "        plt.title(title)\n",
+        "    plt.pause(0.001)  # pause a bit so that plots are updated\n",
+        "    plt.show()\n",
+        "\n",
+        "\n",
+        "# Get a batch of training data\n",
+        "# inputs, classes = next(iter(dataloaders['train']))\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",
+        "# training\n",
+        "\n",
+        "\n",
+        "def train_model(model, criterion, optimizer, scheduler, num_epochs=25):\n",
+        "    since = time.time()\n",
+        "\n",
+        "    best_model_wts = copy.deepcopy(model.state_dict())\n",
+        "    best_acc = 0.0\n",
+        "\n",
+        "    epoch_time = []  # we'll keep track of the time needed for each epoch\n",
+        "\n",
+        "    for epoch in range(num_epochs):\n",
+        "        epoch_start = time.time()\n",
+        "        print(\"Epoch {}/{}\".format(epoch + 1, num_epochs))\n",
+        "        print(\"-\" * 10)\n",
+        "\n",
+        "        # Each epoch has a training and validation phase\n",
+        "        for phase in [\"train\", \"val\"]:\n",
+        "            if phase == \"train\":\n",
+        "                scheduler.step()\n",
+        "                model.train()  # Set model to training mode\n",
+        "            else:\n",
+        "                model.eval()  # Set model to evaluate mode\n",
+        "\n",
+        "            running_loss = 0.0\n",
+        "            running_corrects = 0\n",
+        "\n",
+        "            # Iterate over data.\n",
+        "            for inputs, labels in dataloaders[phase]:\n",
+        "                inputs = inputs.to(device)\n",
+        "                labels = labels.to(device)\n",
+        "\n",
+        "                # zero the parameter gradients\n",
+        "                optimizer.zero_grad()\n",
+        "\n",
+        "                # Forward\n",
+        "                # Track history if only in training phase\n",
+        "                with torch.set_grad_enabled(phase == \"train\"):\n",
+        "                    outputs = model(inputs)\n",
+        "                    _, preds = torch.max(outputs, 1)\n",
+        "                    loss = criterion(outputs, labels)\n",
+        "\n",
+        "                    # backward + optimize only if in training phase\n",
+        "                    if phase == \"train\":\n",
+        "                        loss.backward()\n",
+        "                        optimizer.step()\n",
+        "\n",
+        "                # Statistics\n",
+        "                running_loss += loss.item() * inputs.size(0)\n",
+        "                running_corrects += torch.sum(preds == labels.data)\n",
+        "\n",
+        "            epoch_loss = running_loss / dataset_sizes[phase]\n",
+        "            epoch_acc = running_corrects.double() / dataset_sizes[phase]\n",
+        "\n",
+        "            print(\"{} Loss: {:.4f} Acc: {:.4f}\".format(phase, epoch_loss, epoch_acc))\n",
+        "\n",
+        "            # Deep copy the model\n",
+        "            if phase == \"val\" and epoch_acc > best_acc:\n",
+        "                best_acc = epoch_acc\n",
+        "                best_model_wts = copy.deepcopy(model.state_dict())\n",
+        "\n",
+        "        # Add the epoch time\n",
+        "        t_epoch = time.time() - epoch_start\n",
+        "        epoch_time.append(t_epoch)\n",
+        "        print()\n",
+        "\n",
+        "    time_elapsed = time.time() - since\n",
+        "    print(\n",
+        "        \"Training complete in {:.0f}m {:.0f}s\".format(\n",
+        "            time_elapsed // 60, time_elapsed % 60\n",
+        "        )\n",
+        "    )\n",
+        "    print(\"Best val Acc: {:4f}\".format(best_acc))\n",
+        "\n",
+        "    # Load best model weights\n",
+        "    model.load_state_dict(best_model_wts)\n",
+        "    return model, epoch_time\n",
+        "\n",
+        "\n",
+        "# Download a pre-trained ResNet18 model and freeze its weights\n",
+        "model = torchvision.models.resnet18(pretrained=True)\n",
+        "for param in model.parameters():\n",
+        "    param.requires_grad = False\n",
+        "\n",
+        "# Replace the final fully connected layer\n",
+        "# Parameters of newly constructed modules have requires_grad=True by default\n",
+        "num_ftrs = model.fc.in_features\n",
+        "model.fc = nn.Linear(num_ftrs, 2)\n",
+        "# Send the model to the GPU\n",
+        "model = model.to(device)\n",
+        "# Set the loss function\n",
+        "criterion = nn.CrossEntropyLoss()\n",
+        "\n",
+        "# Observe that only the parameters of the final layer are being optimized\n",
+        "optimizer_conv = optim.SGD(model.fc.parameters(), lr=0.001, momentum=0.9)\n",
+        "exp_lr_scheduler = lr_scheduler.StepLR(optimizer_conv, step_size=7, gamma=0.1)\n",
+        "model, epoch_time = train_model(\n",
+        "    model, criterion, optimizer_conv, exp_lr_scheduler, num_epochs=10\n",
+        ")\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "csZtjCLgYZ_s"
+      },
+      "source": [
+        "Experiments:\n",
+        "Study the code and the results obtained.\n",
+        "\n",
+        "Modify the code and add an \"eval_model\" function to allow\n",
+        "the evaluation of the model on a test set (different from the learning and validation sets used during the learning phase). Study the results obtained.\n",
+        "\n",
+        "Now modify the code to replace the current classification layer with a set of two layers using a \"relu\" activation function for the middle layer, and the \"dropout\" mechanism for both layers. Renew the experiments and study the results obtained.\n",
+        "\n",
+        "Apply ther quantization (post and quantization aware) and evaluate impact on model size and accuracy."
+      ],
+      "id": "csZtjCLgYZ_s"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import copy\n",
+        "import os\n",
+        "import time\n",
+        "\n",
+        "import matplotlib.pyplot as plt\n",
+        "import numpy as np\n",
+        "import torch\n",
+        "import torch.nn as nn\n",
+        "import torch.optim as optim\n",
+        "import torchvision\n",
+        "from torch.optim import lr_scheduler\n",
+        "from torchvision import datasets, transforms\n",
+        "\n",
+        "# Data augmentation and normalization for training\n",
+        "# Just normalization for validation\n",
+        "data_transforms = {\n",
+        "    \"train\": transforms.Compose(\n",
+        "        [\n",
+        "            transforms.RandomResizedCrop(\n",
+        "                224\n",
+        "            ),  # ImageNet models were trained on 224x224 images\n",
+        "            transforms.RandomHorizontalFlip(),  # flip horizontally 50% of the time - increases train set variability\n",
+        "            transforms.ToTensor(),  # convert it to a PyTorch tensor\n",
+        "            transforms.Normalize(\n",
+        "                [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]\n",
+        "            ),  # ImageNet models expect this norm\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",
+        "###### modified part #####\n",
+        "\n",
+        "data_dir = \"drive/MyDrive/Colab Notebooks/hymenoptera_data\"\n",
+        "# Initialize datasets for training and validation using the specified transformations\n",
+        "a = datasets.ImageFolder(os.path.join(data_dir, \"train\"), data_transforms[\"train\"])\n",
+        "b = datasets.ImageFolder(os.path.join(data_dir, \"val\"), data_transforms[\"val\"])\n",
+        "\n",
+        "# Combine training and validation datasets into a single dataset\n",
+        "image_dataset = torch.utils.data.ConcatDataset([a, b])\n",
+        "\n",
+        "# Retrieve class names from the training dataset\n",
+        "class_names = a.classes\n",
+        "# Set the device to GPU if available, else CPU\n",
+        "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
+        "\n",
+        "# Configuration for data loading process\n",
+        "num_workers = 0  # Number of sub-processes used for data loading\n",
+        "batch_size = 4   # Number of images processed in each batch\n",
+        "\n",
+        "# Define proportions of dataset for validation and test sets\n",
+        "valid_size = 0.2  # 20% of dataset for validation\n",
+        "test_size  = 0.1  # 10% of dataset for testing\n",
+        "\n",
+        "# Randomly shuffle indices and split into train, validation, and test sets\n",
+        "num_train = len(image_dataset)\n",
+        "indices = list(range(num_train))\n",
+        "np.random.shuffle(indices)\n",
+        "split1 = int(np.floor(valid_size * num_train))\n",
+        "split2 = int(np.floor(test_size  * num_train)) + split1\n",
+        "valid_idx, test_idx, train_idx = indices[:split1], indices[split1:split2], indices[split2:]\n",
+        "\n",
+        "# Record the size of each dataset split\n",
+        "dataset_sizes = {\"train\": len(train_idx), \"val\": len(valid_idx)}\n",
+        "\n",
+        "# Samplers for selecting data during training, validation, and testing\n",
+        "train_sampler = SubsetRandomSampler(train_idx)\n",
+        "valid_sampler = SubsetRandomSampler(valid_idx)\n",
+        "test_sampler  = SubsetRandomSampler(test_idx)\n",
+        "\n",
+        "# Data loaders to batch and load images during model training and evaluation\n",
+        "dataloaders = {\n",
+        "    \"train\": torch.utils.data.DataLoader(image_dataset, batch_size=batch_size, sampler=train_sampler, num_workers=num_workers),\n",
+        "    \"val\": torch.utils.data.DataLoader(image_dataset, batch_size=batch_size, sampler=valid_sampler, num_workers=num_workers)\n",
+        "}\n",
+        "test_loader = torch.utils.data.DataLoader(image_dataset, batch_size=batch_size, sampler=test_sampler, num_workers=num_workers)\n",
+        "\n",
+        "\n",
+        "# Helper function for displaying images\n",
+        "def imshow(inp, title=None):\n",
+        "    \"\"\"Imshow for Tensor.\"\"\"\n",
+        "    inp = inp.numpy().transpose((1, 2, 0))\n",
+        "    mean = np.array([0.485, 0.456, 0.406])\n",
+        "    std = np.array([0.229, 0.224, 0.225])\n",
+        "\n",
+        "    # Un-normalize the images\n",
+        "    inp = std * inp + mean\n",
+        "    # Clip just in case\n",
+        "    inp = np.clip(inp, 0, 1)\n",
+        "    plt.imshow(inp)\n",
+        "    if title is not None:\n",
+        "        plt.title(title)\n",
+        "    plt.pause(0.001)  # pause a bit so that plots are updated\n",
+        "    plt.show()\n",
+        "\n",
+        "# Get a batch of training data\n",
+        "# inputs, classes = next(iter(dataloaders['train']))\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",
+        "# training\n",
+        "\n",
+        "\n",
+        "def train_model(model, criterion, optimizer, scheduler, num_epochs=25):\n",
+        "    since = time.time()\n",
+        "\n",
+        "    best_model_wts = copy.deepcopy(model.state_dict())\n",
+        "    best_acc = 0.0\n",
+        "\n",
+        "    epoch_time = []  # we'll keep track of the time needed for each epoch\n",
+        "\n",
+        "    for epoch in range(num_epochs):\n",
+        "        epoch_start = time.time()\n",
+        "        print(\"Epoch {}/{}\".format(epoch + 1, num_epochs))\n",
+        "        print(\"-\" * 10)\n",
+        "\n",
+        "        # Each epoch has a training and validation phase\n",
+        "        for phase in [\"train\", \"val\"]:\n",
+        "            if phase == \"train\":\n",
+        "                scheduler.step()\n",
+        "                model.train()  # Set model to training mode\n",
+        "            else:\n",
+        "                model.eval()  # Set model to evaluate mode\n",
+        "\n",
+        "            running_loss = 0.0\n",
+        "            running_corrects = 0\n",
+        "\n",
+        "            # Iterate over data.\n",
+        "            for inputs, labels in dataloaders[phase]:\n",
+        "                inputs = inputs.to(device)\n",
+        "                labels = labels.to(device)\n",
+        "\n",
+        "                # zero the parameter gradients\n",
+        "                optimizer.zero_grad()\n",
+        "\n",
+        "                # Forward\n",
+        "                # Track history if only in training phase\n",
+        "                with torch.set_grad_enabled(phase == \"train\"):\n",
+        "                    outputs = model(inputs)\n",
+        "                    _, preds = torch.max(outputs, 1)\n",
+        "                    loss = criterion(outputs, labels)\n",
+        "\n",
+        "                    # backward + optimize only if in training phase\n",
+        "                    if phase == \"train\":\n",
+        "                        loss.backward()\n",
+        "                        optimizer.step()\n",
+        "\n",
+        "                # Statistics\n",
+        "                running_loss += loss.item() * inputs.size(0)\n",
+        "                running_corrects += torch.sum(preds == labels.data)\n",
+        "\n",
+        "            epoch_loss = running_loss / dataset_sizes[phase]\n",
+        "            epoch_acc = running_corrects.double() / dataset_sizes[phase]\n",
+        "\n",
+        "            print(\"{} Loss: {:.4f} Acc: {:.4f}\".format(phase, epoch_loss, epoch_acc))\n",
+        "\n",
+        "            # Deep copy the model\n",
+        "            if phase == \"val\" and epoch_acc > best_acc:\n",
+        "                best_acc = epoch_acc\n",
+        "                best_model_wts = copy.deepcopy(model.state_dict())\n",
+        "\n",
+        "        # Add the epoch time\n",
+        "        t_epoch = time.time() - epoch_start\n",
+        "        epoch_time.append(t_epoch)\n",
+        "        print()\n",
+        "\n",
+        "    time_elapsed = time.time() - since\n",
+        "    print(\n",
+        "        \"Training complete in {:.0f}m {:.0f}s\".format(\n",
+        "            time_elapsed // 60, time_elapsed % 60\n",
+        "        )\n",
+        "    )\n",
+        "    print(\"Best val Acc: {:4f}\".format(best_acc))\n",
+        "\n",
+        "    # Load best model weights\n",
+        "    model.load_state_dict(best_model_wts)\n",
+        "    return model, epoch_time\n",
+        "\n",
+        "\n",
+        "\n",
+        "\n",
+        "# Download a pre-trained ResNet18 model and freeze its weights\n",
+        "model = torchvision.models.resnet18(pretrained=True)\n",
+        "for param in model.parameters():\n",
+        "    param.requires_grad = False\n",
+        "\n",
+        "# Replace the final fully connected layer\n",
+        "# Parameters of newly constructed modules have requires_grad=True by default\n",
+        "num_ftrs = model.fc.in_features\n",
+        "model.fc = nn.Linear(num_ftrs, 2)\n",
+        "# Send the model to the GPU\n",
+        "model = model.to(device)\n",
+        "# Set the loss function\n",
+        "criterion = nn.CrossEntropyLoss()\n",
+        "\n",
+        "# Observe that only the parameters of the final layer are being optimized\n",
+        "optimizer_conv = optim.SGD(model.fc.parameters(), lr=0.001, momentum=0.9)\n",
+        "exp_lr_scheduler = lr_scheduler.StepLR(optimizer_conv, step_size=7, gamma=0.1)\n",
+        "model, epoch_time = train_model(\n",
+        "    model, criterion, optimizer_conv, exp_lr_scheduler, num_epochs=10\n",
+        ")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "pB_A4hywW-Zw",
+        "outputId": "93c62617-1955-44f9-d12b-5865d516dcbf"
+      },
+      "id": "pB_A4hywW-Zw",
+      "execution_count": 52,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
+            "  warnings.warn(\n",
+            "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet18_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet18_Weights.DEFAULT` to get the most up-to-date weights.\n",
+            "  warnings.warn(msg)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1/10\n",
+            "----------\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/torch/optim/lr_scheduler.py:136: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`.  Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate\n",
+            "  warnings.warn(\"Detected call of `lr_scheduler.step()` before `optimizer.step()`. \"\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "train Loss: 0.6051 Acc: 0.6882\n",
+            "val Loss: 0.1484 Acc: 0.9620\n",
+            "\n",
+            "Epoch 2/10\n",
+            "----------\n",
+            "train Loss: 0.4705 Acc: 0.7778\n",
+            "val Loss: 0.1482 Acc: 0.9620\n",
+            "\n",
+            "Epoch 3/10\n",
+            "----------\n",
+            "train Loss: 0.4658 Acc: 0.7885\n",
+            "val Loss: 0.2591 Acc: 0.8861\n",
+            "\n",
+            "Epoch 4/10\n",
+            "----------\n",
+            "train Loss: 0.4696 Acc: 0.8208\n",
+            "val Loss: 0.1403 Acc: 0.9620\n",
+            "\n",
+            "Epoch 5/10\n",
+            "----------\n",
+            "train Loss: 0.4905 Acc: 0.7885\n",
+            "val Loss: 0.2154 Acc: 0.8861\n",
+            "\n",
+            "Epoch 6/10\n",
+            "----------\n",
+            "train Loss: 0.3723 Acc: 0.8710\n",
+            "val Loss: 0.1817 Acc: 0.9241\n",
+            "\n",
+            "Epoch 7/10\n",
+            "----------\n",
+            "train Loss: 0.2563 Acc: 0.9140\n",
+            "val Loss: 0.1702 Acc: 0.8861\n",
+            "\n",
+            "Epoch 8/10\n",
+            "----------\n",
+            "train Loss: 0.3036 Acc: 0.8746\n",
+            "val Loss: 0.1820 Acc: 0.9241\n",
+            "\n",
+            "Epoch 9/10\n",
+            "----------\n",
+            "train Loss: 0.2920 Acc: 0.8817\n",
+            "val Loss: 0.1941 Acc: 0.9241\n",
+            "\n",
+            "Epoch 10/10\n",
+            "----------\n",
+            "train Loss: 0.3867 Acc: 0.8530\n",
+            "val Loss: 0.2133 Acc: 0.8987\n",
+            "\n",
+            "Training complete in 6m 16s\n",
+            "Best val Acc: 0.962025\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def eval_model(model, criterion):\n",
+        "    # Initialize variables to track test loss and accuracy per class\n",
+        "    test_loss = 0.0\n",
+        "    class_correct = [0.0 for _ in range(2)]\n",
+        "    class_total = [0.0 for _ in range(2)]\n",
+        "\n",
+        "    # Set the model to evaluation mode\n",
+        "    model.eval()\n",
+        "\n",
+        "    # Iterate over batches in the test data loader\n",
+        "    for data, target in test_loader:\n",
+        "        # If GPU is available, transfer data and target tensors to GPU\n",
+        "        if train_on_gpu:\n",
+        "            data, target = data.cuda(), target.cuda()\n",
+        "\n",
+        "        # Perform a forward pass through the model with the test data\n",
+        "        output = model(data)\n",
+        "\n",
+        "        # Compute the loss of the model's predictions against the true targets\n",
+        "        loss = criterion(output, target)\n",
+        "\n",
+        "        # Accumulate the test loss over all batches\n",
+        "        test_loss += loss.item() * data.size(0)\n",
+        "\n",
+        "        # Determine the model's predicted classes for this batch\n",
+        "        _, pred = torch.max(output, 1)\n",
+        "\n",
+        "        # Compare the predicted classes to the actual classes\n",
+        "        correct_tensor = pred.eq(target.data.view_as(pred))\n",
+        "        correct = np.squeeze(correct_tensor.numpy()) if not train_on_gpu else np.squeeze(correct_tensor.cpu().numpy())\n",
+        "\n",
+        "        # Update the accuracy metrics for each class\n",
+        "        for i in range(batch_size):\n",
+        "            try:\n",
+        "                label = target.data[i]\n",
+        "                class_correct[label] += correct[i].item()\n",
+        "                class_total[label] += 1\n",
+        "            except:\n",
+        "                pass\n",
+        "\n",
+        "    # Calculate and print the average loss over the test set\n",
+        "    test_loss = test_loss / len(test_loader)\n",
+        "    print(\"Test Loss: {:.6f}\\n\".format(test_loss))\n",
+        "\n",
+        "for i in range(2):\n",
+        "    if class_total[i] > 0:\n",
+        "        accuracy = 100 * class_correct[i] / class_total[i]\n",
+        "        print(f\"Test Accuracy for {class_names[i]:>5}: {accuracy:.2f}% ({int(np.sum(class_correct[i]))}/{int(np.sum(class_total[i]))})\")\n",
+        "    else:\n",
+        "        print(f\"Test Accuracy for {class_names[i]:>5}: Not Applicable (no training examples)\")\n",
+        "\n",
+        "overall_accuracy = 100.0 * np.sum(class_correct) / np.sum(class_total)\n",
+        "print(f\"\\nOverall Test Accuracy: {overall_accuracy:.2f}% ({int(np.sum(class_correct))}/{int(np.sum(class_total))})\")\n",
+        "\n",
+        "eval_model(model,criterion)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Dt3ucPlhXlzd",
+        "outputId": "8f391999-c1da-41f9-8236-c3bfd4e67d55"
+      },
+      "id": "Dt3ucPlhXlzd",
+      "execution_count": 58,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Test Accuracy for  ants: 73.40% (734/1000)\n",
+            "Test Accuracy for  bees: 80.80% (808/1000)\n",
+            "\n",
+            "Overall Test Accuracy: 65.39% (6539/10000)\n",
+            "Test Loss: 1.071686\n",
+            "\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "\n",
+        "# Download a pre-trained ResNet18 model and freeze its weights\n",
+        "model = torchvision.models.resnet18(pretrained=True)\n",
+        "for param in model.parameters():\n",
+        "    param.requires_grad = False\n",
+        "\n",
+        "# Replace the final fully connected layer\n",
+        "# Parameters of newly constructed modules have requires_grad=True by default\n",
+        "num_ftrs = model.fc.in_features\n",
+        "model.fc = nn.Sequential(\n",
+        "    nn.Linear(num_ftrs,512),\n",
+        "    nn.ReLU(),\n",
+        "    nn.Dropout(),\n",
+        "    nn.Linear(512,2),\n",
+        "    nn.Dropout(),\n",
+        ")\n",
+        "\n",
+        "# Send the model to the GPU\n",
+        "model = model.to(device)\n",
+        "# Set the loss function\n",
+        "criterion = nn.CrossEntropyLoss()\n",
+        "\n",
+        "# Observe that only the parameters of the final layer are being optimized\n",
+        "optimizer_conv = optim.SGD(model.fc.parameters(), lr=0.001, momentum=0.9)\n",
+        "exp_lr_scheduler = lr_scheduler.StepLR(optimizer_conv, step_size=7, gamma=0.1)\n",
+        "model, epoch_time = train_model(\n",
+        "    model, criterion, optimizer_conv, exp_lr_scheduler, num_epochs=10\n",
+        ")\n",
+        "\n",
+        "eval_model(model,criterion)\n",
+        "\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "zWdpBQpkcki7",
+        "outputId": "5ee822f9-57c8-43c9-ff81-9e29dbfd6cae"
+      },
+      "id": "zWdpBQpkcki7",
+      "execution_count": 54,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1/10\n",
+            "----------\n",
+            "train Loss: 0.6396 Acc: 0.5842\n",
+            "val Loss: 0.3509 Acc: 0.8354\n",
+            "\n",
+            "Epoch 2/10\n",
+            "----------\n",
+            "train Loss: 0.5438 Acc: 0.6989\n",
+            "val Loss: 0.2769 Acc: 0.9367\n",
+            "\n",
+            "Epoch 3/10\n",
+            "----------\n",
+            "train Loss: 0.7084 Acc: 0.6595\n",
+            "val Loss: 0.2347 Acc: 0.9494\n",
+            "\n",
+            "Epoch 4/10\n",
+            "----------\n",
+            "train Loss: 0.5043 Acc: 0.6882\n",
+            "val Loss: 0.2325 Acc: 0.8987\n",
+            "\n",
+            "Epoch 5/10\n",
+            "----------\n",
+            "train Loss: 0.5727 Acc: 0.6738\n",
+            "val Loss: 0.1864 Acc: 0.9494\n",
+            "\n",
+            "Epoch 6/10\n",
+            "----------\n",
+            "train Loss: 0.4690 Acc: 0.7778\n",
+            "val Loss: 0.1940 Acc: 0.9494\n",
+            "\n",
+            "Epoch 7/10\n",
+            "----------\n",
+            "train Loss: 0.5026 Acc: 0.7348\n",
+            "val Loss: 0.2486 Acc: 0.9114\n",
+            "\n",
+            "Epoch 8/10\n",
+            "----------\n",
+            "train Loss: 0.4637 Acc: 0.7419\n",
+            "val Loss: 0.2120 Acc: 0.9494\n",
+            "\n",
+            "Epoch 9/10\n",
+            "----------\n",
+            "train Loss: 0.4250 Acc: 0.7778\n",
+            "val Loss: 0.1957 Acc: 0.9620\n",
+            "\n",
+            "Epoch 10/10\n",
+            "----------\n",
+            "train Loss: 0.5271 Acc: 0.7240\n",
+            "val Loss: 0.1930 Acc: 0.9367\n",
+            "\n",
+            "Training complete in 6m 12s\n",
+            "Best val Acc: 0.962025\n",
+            "Test Loss: 0.970537\n",
+            "\n",
+            "Test Accuracy of  ants: 85% (18/21)\n",
+            "Test Accuracy of  bees: 94% (17/18)\n",
+            "\n",
+            "Test Accuracy (Overall): 89% (35/39)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "We observe that the new model is a bit less accurate than the precedent one"
+      ],
+      "metadata": {
+        "id": "JKX8supteaMv"
+      },
+      "id": "JKX8supteaMv"
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Lets now look at quantization\n"
+      ],
+      "metadata": {
+        "id": "BfXSGYFKeq-X"
+      },
+      "id": "BfXSGYFKeq-X"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import torch.quantization\n",
+        "\n",
+        "\n",
+        "quantized_model = torch.quantization.quantize_dynamic(model, dtype=torch.qint8)\n",
+        "print_size_of_model(model, \"fp32\")\n",
+        "print_size_of_model(quantized_model, \"int8\")\n",
+        "print(\"-----\")\n",
+        "print(\"non-quantized model:\")\n",
+        "eval_model(model,criterion)\n",
+        "\n",
+        "print(\"-----\")\n",
+        "print(\"quantized model:\")\n",
+        "eval_model(quantized_model,criterion)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ESI9kKjvetqe",
+        "outputId": "7671eb81-7b50-44ba-eb7b-c6e7abce9cbb"
+      },
+      "id": "ESI9kKjvetqe",
+      "execution_count": 57,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "model:  fp32  \t Size (KB): 45831.61\n",
+            "model:  int8  \t Size (KB): 45043.622\n",
+            "-----\n",
+            "non-quantized model:\n",
+            "Test Loss: 1.296086\n",
+            "\n",
+            "Test Accuracy of  ants: 76% (16/21)\n",
+            "Test Accuracy of  bees: 100% (18/18)\n",
+            "\n",
+            "Test Accuracy (Overall): 87% (34/39)\n",
+            "-----\n",
+            "quantized model:\n",
+            "Test Loss: 1.061615\n",
+            "\n",
+            "Test Accuracy of  ants: 80% (17/21)\n",
+            "Test Accuracy of  bees: 100% (18/18)\n",
+            "\n",
+            "Test Accuracy (Overall): 89% (35/39)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "We observe that one image is not recognized in the non-quantized model and is recognized in the quantized model. This is quite unexpected."
+      ],
+      "metadata": {
+        "id": "79tI2vbrfTf4"
+      },
+      "id": "79tI2vbrfTf4"
+    },
+    {
+      "cell_type": "markdown",
+      "id": "04a263f0",
+      "metadata": {
+        "id": "04a263f0"
+      },
+      "source": [
+        "## Optional\n",
+        "    \n",
+        "Try this at home!!\n",
+        "\n",
+        "\n",
+        "Pytorch offers a framework to export a given CNN to your selfphone (either android or iOS). Have a look at the tutorial https://pytorch.org/mobile/home/\n",
+        "\n",
+        "The Exercise consists in deploying the CNN of Exercise 4 in your phone and then test it on live.\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "fe954ce4",
+      "metadata": {
+        "id": "fe954ce4"
+      },
+      "source": [
+        "## Author\n",
+        "\n",
+        "Alberto BOSIO - Ph. D."
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3.8.5 ('base')",
+      "language": "python",
+      "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": "3.8.5"
+    },
+    "vscode": {
+      "interpreter": {
+        "hash": "9e3efbebb05da2d4a1968abe9a0645745f54b63feb7a85a514e4da0495be97eb"
+      }
+    },
+    "colab": {
+      "provenance": []
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 5
+}
\ No newline at end of file