[404218]: / Code / PennyLane / Quantum Parameters / 02 Class 9 Depth kkawchak.ipynb

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{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 226,
      "metadata": {
        "id": "UJOq3mdA8PAH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "1377d9b6-d3aa-47db-9457-5d954816a518"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time in seconds since beginning of run: 1695628060.4766407\n",
            "Mon Sep 25 07:47:40 2023\n"
          ]
        }
      ],
      "source": [
        "# This cell is added by sphinx-gallery\n",
        "# It can be customized to whatever you like\n",
        "%matplotlib inline\n",
        "\n",
        "# from google.colab import drive\n",
        "# drive.mount('/content/drive')\n",
        "# !pip install pennylane\n",
        "\n",
        "import time\n",
        "seconds = time.time()\n",
        "print(\"Time in seconds since beginning of run:\", seconds)\n",
        "local_time = time.ctime(seconds)\n",
        "print(local_time)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 227,
      "metadata": {
        "id": "5ljdosVS8PAP"
      },
      "outputs": [],
      "source": [
        "# Some parts of this code are based on the Python script:\n",
        "# https://github.com/pytorch/tutorials/blob/master/beginner_source/transfer_learning_tutorial.py\n",
        "# License: BSD\n",
        "\n",
        "import os\n",
        "import copy\n",
        "\n",
        "# PyTorch\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "from torch.optim import lr_scheduler\n",
        "import torchvision\n",
        "from torchvision import datasets, transforms\n",
        "\n",
        "# Pennylane\n",
        "import pennylane as qml\n",
        "from pennylane import numpy as np\n",
        "\n",
        "torch.manual_seed(42)\n",
        "np.random.seed(42)\n",
        "\n",
        "# Plotting\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# OpenMP: number of parallel threads.\n",
        "os.environ[\"OMP_NUM_THREADS\"] = \"1\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1AFilzYk8PAQ"
      },
      "source": [
        "Setting of the main hyper-parameters of the model\n",
        "=================================================\n",
        "\n",
        "::: {.note}\n",
        "::: {.title}\n",
        "Note\n",
        ":::\n",
        "\n",
        "To reproduce the results of Ref. \\[1\\], `num_epochs` should be set to\n",
        "`30` which may take a long time. We suggest to first try with\n",
        "`num_epochs=1` and, if everything runs smoothly, increase it to a larger\n",
        "value.\n",
        ":::\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 228,
      "metadata": {
        "id": "5LRcEYZg8PAR"
      },
      "outputs": [],
      "source": [
        "n_qubits = 4                # Number of qubits\n",
        "step = 0.0004               # Learning rate\n",
        "batch_size = 4              # Number of samples for each training step\n",
        "num_epochs = 5              # Number of training epochs\n",
        "q_depth = 9                 # Depth of the quantum circuit (number of variational layers)\n",
        "gamma_lr_scheduler = 0.1    # Learning rate reduction applied every 10 epochs.\n",
        "q_delta = 0.01              # Initial spread of random quantum weights\n",
        "start_time = time.time()    # Start of the computation timer"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NlU2Q7zd8PAR"
      },
      "source": [
        "We initialize a PennyLane device with a `default.qubit` backend.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 229,
      "metadata": {
        "id": "0prgZPLK8PAR"
      },
      "outputs": [],
      "source": [
        "dev = qml.device(\"default.qubit\", wires=n_qubits)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "54jRIpbZ8PAS"
      },
      "source": [
        "We configure PyTorch to use CUDA only if available. Otherwise the CPU is\n",
        "used.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 230,
      "metadata": {
        "id": "23nQUjLm8PAS"
      },
      "outputs": [],
      "source": [
        "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-AJzWJGi8PAT"
      },
      "source": [
        "Dataset loading\n",
        "===============\n",
        "\n",
        "::: {.note}\n",
        "::: {.title}\n",
        "Note\n",
        ":::\n",
        "\n",
        "The dataset containing images of *ants* and *bees* can be downloaded\n",
        "[here](https://download.pytorch.org/tutorial/hymenoptera_data.zip) and\n",
        "should be extracted in the subfolder `../_data/hymenoptera_data`.\n",
        ":::\n",
        "\n",
        "This is a very small dataset (roughly 250 images), too small for\n",
        "training from scratch a classical or quantum model, however it is enough\n",
        "when using *transfer learning* approach.\n",
        "\n",
        "The PyTorch packages `torchvision` and `torch.utils.data` are used for\n",
        "loading the dataset and performing standard preliminary image\n",
        "operations: resize, center, crop, normalize, *etc.*\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 231,
      "metadata": {
        "id": "XaNa12un8PAT"
      },
      "outputs": [],
      "source": [
        "data_transforms = {\n",
        "    \"train\": transforms.Compose(\n",
        "        [\n",
        "            # transforms.RandomResizedCrop(224),     # uncomment for data augmentation\n",
        "            # transforms.RandomHorizontalFlip(),     # uncomment for data augmentation\n",
        "            transforms.Resize(256),\n",
        "            transforms.CenterCrop(224),\n",
        "            transforms.ToTensor(),\n",
        "            # Normalize input channels using mean values and standard deviations of ImageNet.\n",
        "            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),\n",
        "        ]\n",
        "    ),\n",
        "    \"val\": transforms.Compose(\n",
        "        [\n",
        "            transforms.Resize(256),\n",
        "            transforms.CenterCrop(224),\n",
        "            transforms.ToTensor(),\n",
        "            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),\n",
        "        ]\n",
        "    ),\n",
        "}\n",
        "\n",
        "data_dir = \"/content/drive/MyDrive/Colab Notebooks/data/hymenoptera_data\"\n",
        "image_datasets = {\n",
        "    x if x == \"train\" else \"validation\": datasets.ImageFolder(\n",
        "        os.path.join(data_dir, x), data_transforms[x]\n",
        "    )\n",
        "    for x in [\"train\", \"val\"]\n",
        "}\n",
        "dataset_sizes = {x: len(image_datasets[x]) for x in [\"train\", \"validation\"]}\n",
        "class_names = image_datasets[\"train\"].classes\n",
        "\n",
        "# Initialize dataloader\n",
        "dataloaders = {\n",
        "    x: torch.utils.data.DataLoader(image_datasets[x], batch_size=batch_size, shuffle=True)\n",
        "    for x in [\"train\", \"validation\"]\n",
        "}\n",
        "\n",
        "# function to plot images\n",
        "def imshow(inp, title=None):\n",
        "    \"\"\"Display image from tensor.\"\"\"\n",
        "    inp = inp.numpy().transpose((1, 2, 0))\n",
        "    # Inverse of the initial normalization operation.\n",
        "    mean = np.array([0.485, 0.456, 0.406])\n",
        "    std = np.array([0.229, 0.224, 0.225])\n",
        "    inp = std * inp + mean\n",
        "    inp = np.clip(inp, 0, 1)\n",
        "    plt.imshow(inp)\n",
        "    if title is not None:\n",
        "        plt.title(title)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ANdmcnR98PAU"
      },
      "source": [
        "Let us show a batch of the test data, just to have an idea of the\n",
        "classification problem.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 232,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 207
        },
        "id": "QzIKQxS78PAU",
        "outputId": "74298fbb-8dc9-4a21-b00c-8da7e26c2f65"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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35WuCT3ziExw8eJDXve51j/Wu/KPglltu4Sd+4ie+KcgJwCWXXPKwcvYppvj7YhpBmeIbHl/4whc4e/YsEMR7N95442O8R1NMMQWEZQh2hbqHDx/myiuvfIz3aIpvJEwJyhRTTDHFFFNM8XWHx7TM+Nd//de56KKLSNOUG264gU996lOP5e5MMcUUU0wxxRRfJ3jMCMq73/1ufuqnfoo3velN3HbbbTzhCU/g5ptv3rOSnmKKKaaYYoopvnnxmKV4brjhBp7ylKfsqbqdcxw+fJgf//Ef541vfOPf+V7nHGfPnqXb7e4tLz7FFFNMMcUUU3x9w3vPcDjkwIEDX3bdqsekiqeuaz796U/zcz/3c3vbpJQ873nP45Of/OTD2ldV9SDHwjNnznD11Vf/o+zrFFNMMcUUU0zx1cWpU6c4dOjQ39nmMSEoGxsbWGsfVmq3srLCnXfe+bD2b3vb2x7kCrmL//OBD7C0sgKTINCjiaY8ctzIA7sLqO0yO4cQ4ku0f+h7xUP2wT/IHXP3b2FTaB/+3d2n8Nw5D3iElHjnqaoK5x3eOQaDIVVVkRdhpdZzZ89SVjUCQafTZm1tjc3NDZzzxHGEc47GGNI0ZWF+niiK6XTazMzMMD8/jxCSJEnodNokSYIQAqUUTdPgfVhYLI5jwGOM3dtv7x3eg/cOax3Oe7yzWOuwzuKsxe4+nMMai7UGY8I2YwzGNDTGYJoGYyxNU9M0DU1j9p7XdU2kYHlhBhv1sHWJtQatFVJ6XF0xHvcZ5WOipM3cwjLzC/PMzy+wONsjVZKiv8ba1ia5i0G1sF4R64yk3aPdbpEmMVhLWeY0tqEoSnaGY8ZFhXWeajxi0N/EO0vU7oGMGPU3GG6fJx8OKPICax1SSrTWJK0WrZl5er05siwiTWLm5xfYv2+FudlZtFSYpqJuarx1kwvCAQ7vLM41CCGQCsqqpiwMrc48s3PLpFkHIRXWOuq6xlqDJ1wbts4ZDzY4ceJe7rn7bjY3tzEOWu0eS/sPMru4wPzMLJcePsLC/ApKRzRNTlEM8a6hlbVotVokUYStc+69904+97efIR+XREmbVm+R7swCAHk+oC4KrHMIqYgjjXMGY2rwAu8M1lboSNFud8myGbyH0XjAOB/S1BbrFe0s4cCBFQ4eOMBMbwYEHP/i3dx24jbuK87u3UPee7wFZ8GaBrwna7VJOx2k0CgNIhbEkSbSMR6J8OFaVkIilCTcfhKQSARSaKRUeO8wpqQ2JY2pwvnEISZ3qBAy3LsepHVooxBO4Dw4LN5ZaCzCeJQE1VJEmcbriNoKKuOxtkEIw5XVYa6tjzEzM49a6FGZhg98/n9yrT7M/NIKNra4SvGXW39Fn4pX3vDPufrqK9FSUawXlGd30NqwsXqeoSxojCDuLLJz/33km+dYai1x1Y3fQhZrGu+pZi2fv+d2bl//LCezc0gl8dKCdzjrcAZMA86Ec+utD/96t3fsXoTXAAKJ92KvxwvdgUMIjRQSlEBIgRQinGrvADn5rN33OTw+XCc4LvUdfPwpUOsII2hKx87Y0zhIE5jJJLNph1gomtqw1S84WxsGGagMsghmI0gjSKXHABLP2Amsg0RJ2ipGosFrnBfUpsDgKFxD5T04qBXh95Pht1UN0Aga4ymlQGjBTDSDjjPazuNFQ21qsnQWWw9JpEYhKKVFm4JRUbLV9wyGMHKeVEpaMSRdyUIk2dcyzKSeSII1nnEJ58ewaYA2ZJmnrQUZAi803nuqGsYWam9wXuAcGAvUApFDWYOxHgvUCbQzz0JPcKiTESUpwoMXKd4rnDMIAFdhRB+HB+FII42WHmMbyhJ2xoLhQFBXktJHxLWm1hGxD/dS7BXOKKrK0tSO3NfEvsFEDhE5tAYXQW0g1W2uad/Af37n/6Db7X65QfUbwwfl537u5/ipn/qpvdeDwYDDhw+zvLLC/gOH9gb2cAN8eZKye7NN3rS79YGHBxA45xHigiZfigA9Agl54E9/N7NxbpcAhU7YObf3r5QS5xzj8Zjt7R2Mq8mLktNnzjAaDjlx4gRnz55FCIExhiRJWF9fpyiKvc9uTAM+lN+eOnWGNE0RQjA7O8Py8jJaR8zOznLgwAGyrEWv20FpRV3XZFmLNGvRbreRUuK9RymFcw5rLUIEQuacwzmHMWbvb7sk5MLnFz52yceFz3f/VXWNkAqEwiPR0oX9zmYxdUHT1CgFUgJxhKBCSkt3folDhy+l25sNHaQI5CDqdUB5ZGsRoVoMhiOq2pIkilaSEkWK2jYYY9Bxykpvkd6cZTDM6Y/GjFSM8Q7hGpJ2D6k00lc0psR7SVk56nIE3iCUpUGg0prGGGIXk7Vn2LfvMIcOH2S2N4PAUxQ5eZFjTbM3GMjJ9edchQCUVjRNTT7OkUrTbndodxeI0xZCSMqypK5rnGvw3uBNRbeTUVUF586dY2tngBLQmZnjimOP49DhAzhn6Ha69OZmydI2ztTkxRBjCuIoot1qESnJaGCwnkA2cQilkDrCCwneI4VEKoH1HilBKoXzdo+oSuGI4ohWq0XWahFFESqOidsxnapFXRuKyhNFEfMLS8zNL9Fqt3Hek2UthIZalpPB0YKTgKA2HmsNSoHSFqkaolgiI42MFT6K8FGEVgopFEpEaBWhVIRSEoRAIsEH0uFwWFfjG0tkQViFcXU4nyLcz1IIpNRIBMprpFOEHTN4Z3DWImoDtUFIT9JuE2UpFTWubvCNIJIRUZzRLZax99fYuOGKi64j67U501/D33eKur9DNJfRMhmpzkh6+3j8tU9hIZvDnhugS6AZk4wFpk45uP8AveuPoTopJ249wOc++H+47gnPYTaeYzwY4Ocka6tb+H5OGY8RHRnImLWB6CEw3mFd6NuEEgglwTm8E4TNgao5L5AePAJEIBzeewQCLyJAIoRESoHUKvSbEgjDXiAkk0mXQAWC5B2giX1Mlhl0apG2hSkcbVWyMa5AO9ppxFx3jm7Uw1vFTNrHbJ9lp2woI0+uoVBwUEISe2LhUcKjBOReoJH4WCOJ0TYC79CmJnc12jucFTjriZRAR55k0tfLCPoOaEBOSFmtHSL1WGdIlEdZgdIVWayhFqSpxAGJUzhlGJc1jKFyHu8kCEiVx6eCJoM4c8TCU9Ue24DUEuU8Rnls6hExCKWInaLCEmmBNDB0jraFVAic8NRN6IsT73Eeagu4QDhLB6O4ZiE1RLTwYgjENB4UAilqHBIjCpCWVEOEobE1XlmUE9S1oLAKmphGZ0iRYJUlRiJMg5L5hJBKhK2pdUGtHNoJrHTEQmMTQykkSRw/4lj5SHhMCMri4iJKKVZXVx+0fXV19RGXB0+ShCRJHrZ99wAfnQ7F7xGC8PJCArFLcCY3k/AP2/ZAuy/x6Q8hJBd+14V/213q3PuwP7sDfpgV272UlrWWzc1NTpw4webmBjvb25w8fZp8NObM2bOMx2PSJKGpwwCbF3nYP++RejcSEqIwu9+1sLCA95777rsfay1pmrK8vMzc3Cz79q3snc/FxSV6vUBkOp0OrVaLOI73CInW+oKokN8jLLuPXYK1+3z3sft6l+wopdBa7z2/8LW1FiUDS5Q6QnqPBoSwKAVRmiJFGyTML8yxsrJIpDM2N9fp97doZmZoaY3QbYTQeC/Bg2kKTF0yGGyF2f6EOMVJhuktoJM2aRphbYwwGbZpU5cjtAfpHApJGneQbUVd1FRlQdNUTEZ1pDNIV6NJaCUJnU6XOG4jRIzH4oQCIfAiUJPdcyLweOPBW/CKSKe024K6qRkN1iiqinZ3nlbWwzqPdQ7vPHiPEBKtI9I0IUkVaarRUcry/gUOHlxk/+Iso+EOSjYIX2IbSdMYrAkRr9I01FVBXeacXzvL+bUzYYKsItJ2h7TdwtgGV5c0TTheawzeGqxpqJuSpqnQShInKTrOQETkeYlUhrnFOWZnOlibUVcVTW1RKiVJWhgkpRFopfEiRDWcM/jJPaikBBETJxIfe7RWpFlCFGlULFGxJo4ipNJoHROrGCUjlIiJdDy5riakxO1GZcBjkCh0lCCVRFmFchLrTBhABSgpQ5RGSoTQYVC2DufAWxmiEkrgtSLWXb7jSf+clYUV/vzOT/I3Z24h1jlJHLM0exHXZ8/nzN1/hkpaLC3OoJdibrzsBj57fJ3twSo9s0QkBQfnL+PI4lGWZYLQNePxiM2tNUb9dcx6H+9K5vav0F4rKbeH9JTi2GVPJo4U584fZyhrhjs5g42cre2zbM7sUIwN1nqs8XjrEU7tdWkPxDbAC4+XgPNYPMLLSbRD4JGTe92jhArtvUfgwt+9BCdCKMITrm9v8V4gxCSgItibkE0CKcRSkskWkZqlxqPdCGE1jSuIyUhUh1j3EFJD1rBSpZwvDccrh/IC4yBNwUeeeQlSCmIZIgneWnzj8MphqGmcp2k8wgicEtQKIhNIWI0Pg74OO6acoBaTqIyF0tT4YhsVRygXkTGZHKqYoTCkooX2BuslyltaaodUOtpeUEuHbASmEGxlEBmIC0niPXUNeelpivBdlYW0Cr+KTTwIiTWexjvwjqQJnD2SnkgLGgWugVEDtvFIIzATklZWnu2yQmnoCFCAUglOiBBPVFkg80rhKZBKIrxEOInHYpQnFgIvPI0XSJNMZocCQYXwCYgYpTwiMeAbnAyyDYdDek+Dp+vEo67KeUwIShzHXHfddXzkIx/hpS99KRAG7I985COPavGpr4a690Jy8wCB2CU+F37TlycnDyI+D8Hudmst3nustXtRkt2ogtaaqqoYjUYMBgOGwyHOO86cPsOZM6fZ2txkONihyHMGozGj4YCqqjFNg3ch6mKN3dtnL8J5ddZSVzUej5SCNE3pdnucPXuGwWCA1jHj8ZjRaJHV1fM0TYNWmoWFebrdGZZXltg3iVa122201iilSJKEKIoedoy7g+1uxGW3M7qQlOxGiXYfu8TkkR5ycl6dC+dOKomSklY7odPWlKnETQYmZx1WNCA8jbH0hzm5llR1hcOgdIY1NU1d0jQl4+GA4WCEjlNarQ7WVHgpSL0DGRFpQTtLqKsYVwuaKqcsG/rDPmXdYOomRNqYRA4kCG/xtsGaEkmLVprQaqXEOgodOhKtM6LY4ynAGwCkDmFyh8CZ8HsJIVA6JkZSlTmj8ZA8z+nOLCGkxjqL9A4hHApDU5fUVY4U0JuZpTc7z6EDy7RTsLYgiRVJEqN1eN0fDqnqGmcK6rqiqcZsbp5nfeMcRZlTW4vUMd1ul263zWA4pqxzTF2EczUhUjgQrkZ5C8TgFc54hsWQshwTxRFZJ6PdzciylF6nhUAhZUocdxAyQuuUOI5ROqQUdyOdEoWQMVpnqCRFKoGOJCqSyFghtUYriZQRkUqJZRrIiVTEKkZHUVjZWUwGReEm6Qkfkj0iQkqBxRPSEQopDI4wafB4pFDAZJB2Ho95IEsrAalBO9Kow+OXL+XAkX0cS45wizjAh7f/AKtrLlk4xmGzxHraJYtbjFe3iPsxTT0mN30qWzLe2aRQlhsufx52ZLj/k7fSmp3BbQqMq8hHA/rb55DWMru5w3C7wIqKQVKRaMna+ml8FCGkY+38KjurJxnUWwzSirKqwUlESHAF8oVACEmIjYSI0m4yh8mgJMMvgJ30K2LyZzfpYwQ+jJbCAnKS9g0dqJAgJhFp6zxC+gfSPV6E64dJWskJpK7RMiaSGZ1M0BhNrFIQ0STd5MJ+eEXLS+LGIRxgYFMKUidZSDWpinHe4JQF02AaiW08nobGQF2H3xEBmRU0hIhRbAVeg3YC7UQIlDUgrceJkF5svENHbWKviQHjFY1sIURNRdiv0nmkV2gpiCJNXBlwksh7fOFpRoJRJfDak0oBJqRvKgeVCqfSWUEzSbtFqg73gw2/g5LghEcrQaQ9qfeUbUHdSHQxmbBUMNDQLqEaScbKEusSqRI0FUoqVKSQWqFVClLgiQJxFw7kDlIYtASlBIkIKTl8BUagiTBaIoiohSCKLS6zIAS1c8RWYb3AhasNJTzp3yPDcSEesxTPT/3UT/Ga17yG66+/nqc+9an86q/+KuPxmB/4gR/4+3/I5MJ/dAjRkEeKuuzO+B9oJwg974TmX/h9j/C1j0RO9vLnF6RwdtMaVVURRRFlWdHv75BlGXmes7a2ztbWFptbG3jnOXf2HFtbG5RFzmg0xJompHGswdtwwyip9wb73dC1M4Zer8fO1nYY3AFrHMfvP87a2jqDwQBrDUIEkrS1tUVVlawsL1PkOTs728RxzPnz8wwvvpidfp+5uQVmZ2dptVp0u13SNN1TYu8SD+BhEZQLX+9GUnbJypciKVprrLVIbwCPaapJ5+aJk5h2u0scC2zjSWPLsD/m/voEadZDRwonNDvDnMY0IRKjI1ptGUL9UqK1JFbgbENeWOI4IkslSQRpHGPQk+9JkRFkLc1oMGQw2CYfbpLnFaYxVOUYM9FFeE/Qh5QFTRlh6gqFIVEiTCqtRUhFO2sR64gcSd2M8XiUnGgi9GTm58xeilFoaDCUo01GgyFFkROnHbTW4XwKUDTUxYimqZmdW2JuKaM3O8fy4jxpnJAmMXGnh3eeurZUdcVwOCAvCmxT4KylKHbY2lplOOwzLgxV2RBpyXhUoOSQ8aBPPh6Ar5F4kkjSyTRZpBgXnsHIkTeO3JQ4l1MWOWU1QkcaFaUIKdm/fx8zvVniKEPIBEcMUqKU3s0LhGtGBsIgpSaOW8RRF601XlqkcsgJKZEqDoOsiJAiQkw0JlpqhFThund2L0oQQh+7fcDk4QVCaLQEJxzKa6y3kzREGMh2YwggcEKjlA/ijT39WQxSsXnmLD06YDxP6R1jlN/Ire5WZFUw3tkkkpKxGXL69BfRScp6tUUtDJlN2S7XccIy2tygGDVsnD9La7ZNT8yi4hbWKrabHfalPfLtEbkagDVsJavsU8vE8Ty9rM2JtTvZ3jpNf+McVcdTViVuVxsyuR89YdCVQiCJQqqQkM4IsgyB3NXFTViJYPf+DimdiSINL3eTOA3WS0LwSQT+JgNpQfhAjrwHQurQeYvXnrpxaJ2jvMK5cK+maobYi/Abeih9jrcFedOntDkNnjqoYpBeoIwnFl06ukM7aSOEIXU5xg9oTEXpHXnjqStBYTzeCxoNMYJSgBRgFYwlZF7gLOhKUFdBn1MqPxloU5omwkYpXsREApxLSUUa9DwqxJNqEhpAT66b1Ia0kbcQjwSDGHLpaUmIFdRKUKceJ0FpgZWTS7WGIvHhOAHlIPaCSoPTHq0iUi8R2tBoSKSgVh5ZSWTtcMrjRo489UTS01Y+3B8iQUUQa08sBV52ML7C2BDZiryllg1aW9AOLwVCeIQ3uKbA2Bp8RM0YR4yOPKYlSZQjcoKqMdRGo43E+Uk6TD26sMJjRlBe8YpXsL6+zi/8wi9w/vx5nvjEJ/Inf/Inj2qNiq92hfGDCYbfpf48mI18+UjKI33eLkHZ1WxYaxmNRlhr6ff7bG1toZQiz3PW19eCUHNnB+E8mxsbDAY74D2NMdjGIBEkcURVVUH7IX0It0++0zQGBGxubu5FbZjsQ1HkFEUeOpdJemk0GjEahddFUVDVFfm4JI4jhAgh09NnzrB//34WFhaYm5tnaWmZ2dnZPXHtrsD2wkjSLhnZPf4LIyq7pMRa+6B0z+5j97XYJXjOIqVHSUe306GVtimKnLK2OOcZD3c4f/4cWWee/QeOIuJWCOEWOU3VkLU0Qkp0lIB0eGswSYvOjKIxDqV1GKusJVKKdmeWLGsRacXs/DzeWorxiEjBeLjBeFxjbY3zDVIKUCFtg/c4G1IvQghMXVDmIyIdUVtQMqHVaoN32KbBVA0IA84HguJsyNkLiZJh5okzONtQjrYZFYYoH5O2ZknSNkqHc55ogTMlcdbmokuWmV1YJI4ilBAkaUqvN0MaRwyGQ4bDnKZpsM5QNw11afDOkRehY0XEVHVJWZTUwnDu/Fk2NzeoqgJna5IY5jsZB+Z6HFzskUSStQ3L/fmQzbyksgIlFXVRMC4KhIyQcoO01WV5AbTKSLMOOkoRKgupA8D5iU5BqPAeL9E6JY3bpHGGl56GBidrkAmR0kQkaCnxE2LusUEzIgMZ3f1cvA8zQBciKLu3t58Qkd2ogtyNoHqJFWbyW4DYFXwKFZIdwoZre0J0pPAYX3P33Z9FFI5ZZhBac7k6wkazxsbqGuPBAWpXQdXn7GpBEiWM5Jjzw3OkeMauohN3qAYVtXEM8nMMTcJQbNOJWog4paxH5AhUuUkzIRnV0GC6OTtlwYnVv+aurRMMbZ+5bJGTuo/1DoFCCMly+2Jq5ymabaSAZ1z6LRxoX84Hbn8vg+rcRIbnw303+fwQKgkpIIGY/M2CkHuif4EAEbQlzrmQzvFukhabRFoEOG/w2EC8AZSnaqChpoxqEt0m0hGelEgopK8wpqLBYe2YQTVguzSMvEcBhRa0vEdJRYZEuQRNmyhSJHQZT0IsjXeICkYOmgYaD5HxVLFAiTAYxjKQKgvUHgrrKWpPYQOx0ApiYykiw6yUSJWAbiHI0JHGmhojEgQJmJzaxlRUgMQCkZc0AnTjqb3HeLARZC2IlCeKBCmgREgpCaCcjBuJFFgddJFeBs2aF5B4F8iGlIjIo2KLbwS6kaja4SND5RxR4am0IFIWpUFNIpiJdESixnoFaKxrgp4MG7Rv0lJpKEVIaRoDxkkEFluB04pYxghdkMQVSRz69kh4Rt6AUwhnJ/OAR5fkeUxFsj/2Yz/2qFI6Xz08nHBcOPP/alnD7A7Cu8/38q5A0zQMBgM2NjbY2tqiv7PDzs42Wiv6/R1AUhQlUkjKakxVlygZbnTnPEhFu91mZzCiMU0ISTuPtRNNi7MhZOvcJAwrJvOfyUznguPdTTeFyZXkzNlzaDXp4KWiLCo2NzcRQlLXNTvb23R7s+R5QZ6P6fVm0FqTpilJkuyRlF3sEpRH0qPsRlIuJCwXEhStNbiJiFQIlLTEEWRZglQRTeMwjUUgibRCYPDekCYtuvP7MM2YNVeyU1ZhdigUIb3ukTIl7bZI2hLnDM6WOFswzvuopMPKzDydVkYURXTaLeIowpkK3wwZDjZIszZlVTMc9BlPyOZuZUOWpczPL7Iwv4T3go31swwGO9SNxTtBnGRIKfA2fK9UHh1n4bwz6bmFJIpihLTgaqoqZzwasDPIEcMRcdKn1elNzi90uy3SJCLN2iwtHWL/yjLONxRFRRRntFod4kjQ2IbRaETdNAgRZjfjqsZaD1Gb2eUO3e4CVpygqVbJ8zHjfIRgQp6EJUsielowE8+wr5uQpAm+yTl1tqYY9fG6xcLcDE0EVdNgnAIkTWXJc0NZW5LUEcWCLElQcRaii7Yh0npCMILeI45bZGkHpSWVHVG7QZgda0WkQ+RFSxmqR5RHK4XSGhVpIhnhxWSm7oKA0MmJpsz7UIUjLN6a3dhAuF+Fx0mP3NNfeMJc2E2iKYGQaxGFFCQh/G284wv5XbjbCw5197HU3U+aRDwtuprP9u9i4IYIbZG1ZadYQ3kQmQad8MXqfjIiolabtDVDM9wmt0MWfIbQMee3TnJo8QiyBU1t2O6fB5ngY0+S9FjbWqU/3OZ8foYTcpVa1fi5GNNt8dQDT2e17NNSFT96848wGAo2tjbZ3lzj259xI8lCizs2budzp3eYzVZQKuNc/4s4X0wIi73gPIQBXE7SOQ+kxEME0XmH94bGBv2b8BMBLR7nQ6WX8WESoFSETzybjcMaQ+R2aPmcjk5pq0UUGV5FWFeR5+uM6jH9wrFuYazASuhY2EnCoG5djLNhP5TwoBURbbyvsSanEQ1aQOnAV546EjTS01ECM0nZCQHKCLQFXftAZiqBsGCiBC1aRE2PKp6jVhlptI84XaSVdVjKFpntLVDkfU4c/zT98V8hOUVsLaWBWnnSBryGyE4iJDqkwmTkaauQMRVO4GuPcyC1oBEupBJVEDdHhDSPJAIXg+qi9RxxbImjbVqxwziNsw2xKBC+prKGpCqxqoWTMT7SKCsRsUQokM6GakDncSaMH1JAqiSZ8tTKgzSYSRpIWIFR4I3ApxVVXNCNasg8WRNibd4KrHOUSiAcCP8NkuL5+sGuAPaCLXsk5YFS4wcSzrt4IJLypUjN7qC7K4DdfV5VFePxmJ2dHe666y6qsmBra4uyDBUqYpJlsg6iOKauDHVl0NpjjUVqjW0aqrpBAEkUg3c0zuCEn5AQgbMha+wm+g/3CALeXfKweyy7x+28R/oQORFSYq1HK7CNpapq7M42SkqMqSnyHK0V3V6PbrdHmmZAyL0+VDT7UD3KQyuXHiqSDcItBUxKOJUgyVp02jN4FJ4wY5NRTKszi5cKoTOyLGG+12WcS5RKUVpNzotDSIHSEXHURusIZALOUOYD6krQmIJydJ7NVYlwlpmZWVrtlCgSGCdJkhbz83MkaRsvFIPRmKoscUIhvWDU30RhWFlaZGlpmTRNsKahX4RSZW8dYzxKiJB6MxUIB1IHUaIIugCpYqIkodNuESlPvz9gMBozHI/A5QhdUJVjhHDUpmE8nqPT6dLpdOnN1BhrAItSgixNAsGVIoids5idc+cYj2oGwxE7gyFJ0mJp/0EO7FvBmgKpYqq8pKoKqrLCNNVkQHKYqmFDa85u5GStgoW5CIPGCo3xglQndDodjHWMG431mk63h9KavKwYDIco6fBI4qRHojROWqxrAI9Qk2icVyQ6RUeKxpdUdoShRGLxIkeqDnEEiQ45dCVD5EbpGCVDKboXAuEVTgjEXjVK0DQFyhFSas559him93vaCXZTGYIweuBB2Mmgu0v9wXmF8A3nu9skpwzFcJtRe5NuZx7XSjjUzNHPt/BA09TUrsSaAi1b9LIMaTWNksxmK4zyHe47cz+tdsSo2CJSEbWGcbHFan6WyrXpmDmESrCNR+VjBttnMYmkTCSN9RSq5o76fq5oPZ2ffMb/h61UcurMCS67aD9iJHCX7qe8r0+MxOc1h5IFFo4+mxc/+9spK/hP7/8vHB/+TdBy7PZ+Xu5p2bwPM/hwPibWAzCpAvKESp2JJs5PzpGrsC6kXPERfpJOOmc9zglmJFhRISXEcghOEckaL6F0JUNjqQSYWFLbELUpvSP2YLynNjVNozDO4HwL7y1SKKKoh4k1OhkQNTVOCfpKhHSJEfgm9IdOgxYOvKABnIe48dhCUJsZ6M6RdY6xf/8xFrsJsXQcXDrG0ZWDzLZatOKUbrsFCFYvexJ/9ZnL+Ovb/hdr4zvBSKBBhgwXyoeUUhEFHYrWgrYK2pLSh+uymUw4EVAoT+wg87uXqECJDl4fJFt8Jpfsvx4lYu6562/YHPVZWLiMje2zjIfnIZaY4gSmuZumFhhV4ITA2QgnJiJv4ZGNCFEgM0YJhRQRSgqiqCKKoNAeoRpEHdQkCYZSleChijyt2JMpiw65eHTlaBrAJaHy0j06yjElKBMIsRtVuICw7HZWXyqdM/n7gxJDD4nE7JbYAhOPjzB7Xd9YZ21tjY21VbY2N4IOwFlGo3GQsCmJkhFZq0VVNxRlgRRgjKXT7RLFmiJvHij99Ux8U2woI/RM1PUBzgXvASFCd3ohWXjofu/COosxlqqscZHHRaE23ntLVVm2tzdRCobDAb1Oh3pCvHq9GYSQdLs9kiR+xOqeh1b0PDSispv2CccmAYtU0Oq06M0s0e3N4p1jZyelKEYIEcKTSRbC8MbUeGeIVIxOMtIslM+mWjLX6+KFnOhyFDrOQgjSGaSoaXtJVY7YWr2buhwguYRWth/pJNZUOOHpzszRmVVkrRkaP8nJy5imKDl/8m62N08Ta0iTiG6ng7MNw/EIYwTWGXarx+SkzNXaCtPUjPIRVW2QUiFUTJS2mJtfQGnNYHuIdRHIjMY0+LpBlzVKCxpTs7F2jv5On97sApaUxnriyJNEEaBx3qMjTRRJpLAM+uucOrlBkddYPJ2eQOuYXncGrecoypLtjVWqssB7SZ7nWGMAgdIJuVGc3KrwaclYJNhS4KIOi8sS7xWlkUiV0p1J8Eg6nQ7tdos4kTSmYDgOmockbSOFxAsfSJUPOfJIRUgnUdJjXEFp+jhXEusIhEIREcmURLeIowxUiLLt/hcGQhcIipwINgWTqhQXkvleTcqIFdIZnDWhMipQX7wA7TVyEgkInh/sRbiOZFcxl61wx/ZfU9pRmMrEcHbfEHXaogae7f5p5nv7WewuMmh2yKsR2hpmuhnbsmY82qQVzXMoOkQsPLo0bJdniG1Dz7Q5PzpLMdxGZl1WR31GZkxNzWJnPw7NTr5OZQtOydNAxIxoMxu3WTdDNvs7XF6OMP0tLlk5wqHkGH5jjKk948qwdvou4mGbtk54SvdiFi9e4tClSwxO57zwwE383l1nWW/unaSIw4EHjx6JmBDeSbHqpOcMJM8ByEAIhQ+lsM5bvPQI5ZBeItC74hZcI6g1FMLRcoKCCsUW2lpiHyFkSePNbrU5XjtwEHtovAwpE2XZrEZ045i4rhHaoX2IriqRIpRFiRglGzIvMN5jDNQOtjW0GkEqPcoLGgkOh5YCHyvyRhO5jKWVZ3LzM2/mCYcvYq4VMS6GKBnRy9okaRbOgYckjrnk8EWszH8P8zPLfOAj/5X67N2oWuNVgRWSyDsSJbDahbJ5BUQ+VAoaQelCSs14T9yAVAKlJ8J5r7FekIiDXHzpq3na42/m8PwySSzZvOZGRk3Nytw8RV0zKAqklBw/fR8f+tR72F77OHW1Q8kQbWJSFya42oPxFbhRSKMiUIATobxZJdBSEUIlNMrTuATrJFLU0IBuLB5Hox2xE8TWU0TgSyh8TUxK+vcdkCeYEpQ9+ImmZaJh3yMsE/qxK133FxKWh5QsT3Ch8HM3alLXNXVd0+/3GY1GbG5scs/dd7G9vcV4NGSnP6Q2DWVZAGEW2G53GBcFpgllhkoF8ZkxQXDmnEVMBvWyqrHOTcKbYpJn93s6k4cSkAuJyUMrmXb1E0orjLN7fhtaa7x3SBkqeOIowhvDuCqRztFUNTKKGI/GpFmGUhKleg8jI8DD0ju7ZG43cnJhBEV4CRiU8nQ6Gd1uF52kYD1x1iYucrwLqR0pNc5bqmLEOC9AJGgZE2uJkhatLN1OhoxSyrqkMR6tI1wDSkniOEYL0KKmzIdAhdKepsmDt0cUsbyyQrvdwnuJ0hHGQ6RjBIr+9jZ1vkZVrBElkk6nTXdmDtM0FLWhMUXQgfrJvF3sXmyglScWltJUGKHxziOjjHFhAim0EXF3H700lAN774iiGJVE6HrIzuYG41GOc54oaVM2FuENOpJk2Tq9VotWJ6XXa5OPtxgM1lhbPUlVWaKkBQJGO+vk4wWWl5ZZnFug3ekRJzFxrDBGYQQ4JFHWJu7MoluzNKrHdi6pSo9OexyME7aHOcO8IM0idBTEo0mW0Ot1WJztkKWa2hqqqmA03KQqxsRxTJZlIf+OIpIRwnucranKnNoO0FoTkRL5lEzMksp5YtVCqShEoXyouPGE6jXrdqvXACnQCJDqgXtaBjIICmkFFomgCmXGeBQCaEAI5uKDaBWxVZ/CY5lTK3zb0RdwyWVH+PDn9nHPxhc4XX4B60vsjGCjLljYaHBWs7p1Bmu3qYsc0JRNifdjsqQNVYuZxYtxkcEOx4yrPkUzoKO6eOPw1rJdj4lMSdu06fo2PgatBI22DPt9GtlQdBTGNyxaSRHDIK+om4r17XXuPnUHtA2daJa10yfRQnNuXHJu825U0yG2giuXrmKh1WV8aofh5g5PXl7hD+9MOLHTJ9Nt5tsXs5GfpbLbiCj44mgVo1WEkAoh5UTk7BHCBULoANdMoisOT4MT4FVI9+xujzxsCJjxkgoQDQyEQTdbWKPpxY40EiihGHrHtoFGeXQd+jbjg48IQ8s5v44zfRZtm3YW0s9CepQLYmotwMcObQRl7al8KOt11qK8JDGCWHnyEATCiyDUzhau5BnXPZPrjxxhtp2SxDHKeSpjMNahJl5UzjnqpiGRkk475QU3vYBO2uW9//vtrJ+9E+MVUrhARrRgHkHmQxQv957MCYR0OAVVDVYEawVvIMHjVdBHetemFV3KjZc/kyNz82gV+pG5bod5IdBaM9NqsW+2h/Bw6fIiRxZX+B//27Gz9kfUMlSAukYgoyiI0jUoZREukCEArEdJTysF167xjSKmxahyeGORpqFRQVfUMRaMp9ahKEBPhhbvHCNZ0pXTCMpXEf7BT/dEs7uziQfaXDjgXxg5qesagKIoyPOcjY0Nzpw5w9raKhsb62ysrwGe0XhMXuQEV0sX9B6NoaoqkiSlqmq0lljrgHpS6ivJsmzPpA3vGZpxSExJyW5c9qGRki/lG7PXJmQP8cgQinVh5m0ri5CCJMmJoyiYukmBmUSGjDHoKMIYQ7vp0G51yNIWQooH+ac8tNLnoVU9DxXMhtJIUBriJKbTaZEkMU3tiHSKjrJQTeErrK3BOopiwPbGeWTUhqZAYlHKEMUQx4o4SYi0oqwtQsTUrkFIFyp8UGGGk7bYv/8oS0tLDAY79Hf6HDh4hH2LSxSdHqNxQX8UnH1F4minKVnqmel2MPsO0ustMLe4j05vLnhvqIThaEA57tNUOc4G0yxr6lC5IBRJ0iK1Eq9TdDpH2uohhaZsLDpto7RGW0/aNsRx0H4ICcVok+HOEGNyymJMMR5Slo6qrKhNQZYoluZ7zM6mjAYdynKMrSviyFHkJXXpqJRguHmO4c4i+5aWmel16czM4oWisRbngw+GFAqpUuK0S9SaR8RdCmupjEUIhZchj40AlQTDPWcswhl2nYdt3YBrGJYV41GOlJpOu8NMb4aqLpEqIlZtrA1mfqUdYH2OoIV1jiROiekivQppgkmkZLdaREkNXqKIsJTB7dg2WCxeulDhoxRShMohKYJ5m3ME0evEI2KXSEaizQuPvphLjhzlw3d8hLPbJ/n2hZs5NnOEuB1z89U38vSta3n/XR/g7uJvsa4kX6y5t1zl8M4caTrP8fVzOFGBVgglGG6OUNGIuJNgfYGnwrqa0hlEY4micN+PqwKrIPVg5BhZWJTXrG6dQrazoBmIIxoRIqhCSTZcSW5qvBDk+Yj17VXSDcW8mOXk8c8Sq4ihk+TlFkoVFComSgTVuM/amXMM7Jh+v+T4+S8wq2d51TNez1OvfDq/9v7f5s/u+m8IrdFxhE0STJygdARSTrifmOhVbCAhrt6LnoAPZn7WIbwGp4J4OXYY5xhrSSyh9h5bQ209mfW0XItM9yCWtOMxNh+yVdc0TiCdJ3ahcmaEZ1x7lCggahAqpUUcvJQmQl4zqU6SOqStTe1xXlIaSJ3EJzGZCmX71hoSC23d4oqjT+DaQ0dIJhNFZ20oTJjoDK0N/VsURRODSoMUmqSV8uwbn413nvf+z19jrX8HVrlAOnAYPMYGUtYIgTDQWElpHGZSzV4rT2RgjEB5gTQRgoRW51KyrIOSCrAYExy+H6jq3LWzECilOXb0Ep593Yv5oz/5NE19kiqqqUxC4i2xUsjIh8pIV+F9hFQSoxTeObJY4joCUVqq2hHJCCfHgaAZSBqHc55GghWOOvZUKkTGnA0Ot4Po0ek7v0kIygMai0d+vvt6t/kjtwnK9d0LPfx/1/L9woG3aRqAvYiJc47+oM/29jYba+ucuP9+zp0/w+r5VYrxiKqusA5MXdNYi5109E0dLM+bxmBsA4Rqk909axpLmqQY62i12jRVgVICZ0IOXU10J0G09oDuAx68v6Gdm1jsB4GgUgrvPNbbwOZtMLhq6obxuEAIyblzqygl0VJS1jVaKxbm53EmEJbhoE+aJUip9uzzd0uNd4nSl0vvKKXwVoVksLNoKelkKVmsKVxNK40p4oSyDN4nUoSSxCofUBRjkqRHO2mhhUTrhLnZJebmZkFEjPMqqNUtCGeQwof+0iqcTzHeYqygqi3DwYCtrS3a3XmShYSmsRRFQb+/Q93UVLmmyVKaKkcKycL8Cr25faTdRZKshZxUBRlTMtha49zZEwy21/B1SVOVeGeQcUQUxaSJxyc9egtHQGcURYlWFhXFCBGuLyUEszMzxElKXRfQVKRpm+GwjzElW5urWJeE8LaWzPV6zM3N0m4nmKbE1zULMz0iHGeibfqDAmdLiqJPWY5o6hIdKRbm5pidmWc0HGEaMSl9DuI554J99aD0SAHeBCM91yiQGb2eZna2hfAwznOwORvra/R3dpjpZCTaUliPRSO9xHtJHKe4coCQGq1aIEJFk3ASRYaihZRdHDFFVWLxxNagdcjfCKmJNago5lDrCr5l3w3cvvE33Lr9KbwLlRxOGLz0RF6DDveww+GcwBqDMxOKLgBnQ4jbW0QxYDlu8e2LTyNPbuDIviWUspizFUnhSTspL5x7Ntd1nsg4H/FX4/+LvyiivG9MTI5rHKfOn2asGtq+TapDye04L/BOkVdj+uNt5jr7aOuUUTWkYUwkNYiGvMnpuwKVpSRaUYy26SjPcm+RLVXQ5zQrqk3lHSfHG3gRBuS1coOt4WkuMUcY24Zh5fD1gEpYNos14jjlwOKlbJXHqQvN2mgHXMSnjn+RJI74sRf/C571jGfDGK49dA0f+1wE5Rgfa6gabFSitMJJtWesJ0KZCV7UeBFSeUqrSQTVB1GyN+BDBLUbC1qNYCw8MzqU0SYGNIIWgkRktPUiOulifIETZxkO17jDmXAdOg/WU0tFVjsskqJZxrefS3vhEhQFxXgV6U4g87uRYgMvapzyJB5U5RFS08muYv/sEXoyprI7DIf3s1ltIdOjXHL0SnpxEEVDqKg0LizzYYzZ61ulFAihcBNRtneOOM141tOeTT5e5/f/8Ncom9M4CMaTjafUAtUEcpzvmj2WHmFAO0gnOhUP1MZROE8cL/DkK5/FbKcbUpqmDkUZQiFwwWhtomcMXk0WpSSPu/Ja/uJTV3NudZUqqqjrCNtkWNkgZLh3lDU0TYVzUdAdyRYqcrQzDzOWom4QtUDWKbbRON9Q1iWZ9dTWo4NPHwooAwfFGI/B82jyPN8kBOVLYZeA7D4mWVThJ8r13bp/gbGWsihQWhD5Gp128CIKYrDdTxMP2L4DjMdjtra2GA6HDAZ9tje32FxbY+38GdZXz1NMZt9VVWNsWB8jqKjDv0IIoiiiriqkkjTe0tgwCFvlJjdFqN6pm3Ivxw5+L5WihAgi0sngr7Xec5cViGBwJQTW7kprQxrLu3AenHdYHqgOAo9SEtNUlGVOpDVKK3rdLvtWltFKh52whjwfsbmp0DraOze77rO7fii7pckXGrftvt4TyxoJ1iNthXAQ6zgEs7xnptvFO8Pa6oi8HFEXBXWVU5VDyrpE6hYzvWWiOKIT9dAqJdYJUohgJy0No3FOPurjfBNKIqUGEeG8Y219I/gCmApvGjbXV9FSUzchEtBKMjIdU1U5a6un2dnaZDwe051dYGbxIpK0g9QRsVaksQbfItOKqhiys3aSphhgmxLvLVaGqJW3FlBkSRcjFDYWxGlGmmVUTU1ZFZiqpDKW2o6pqlHwVxGhlLNpaspyG2MkSZIxMzfD4vwCK/svot2dYdhfY+BPo4UljYLR07nIMyqaMPMfbXHmzHGkShj2R3gvSdMeWifkeY6zwaXYmpLRaJuqLonimDSOSaIUoTydLCZKNL1WjHQVHe0wTclaf8DG2LKmI9pZRrvTJst65I0hL0rSpCIRNUpK4iiCJkHKJIhmdUKUdoKoz3hqZ7B1qPyRKGbSFbyoMLZhJT7KCw7fxDVHD7Hczjjev4/z9Vrw4PAwkx3i+Ze+kM3ROf78zEeCvb0Nxnd75FnpsLYMGuk1x8/dzxG/SKwiuqpFU40Yrm1jGonRnnYT06Gk61uMm5ptcZAnPOHZ+Kstv3/Lf+XE5n0M2SRK54hbM3Sy2ZD+3byfjZ0NQFJUBYsdw9isUzpHo2pqW2Njy9CNUUKTzPYQXhFVYLxhtTpL0dVU3jLnZtlghJWaJJ7B+4osXeTujdPoE3/O0cWL2PTnSX2LjWILYtgutxmPP4/oGpRI2TE5SZVwzYEncdncxTxp6TKqjT5R7nnBdU/h0/e8nOPnPsV2eRzReObiedpijhODezA0QVvibTh3kQdlECpMgkQUlgkQxuO9mUhQgjfHogdjBLMyWNVHQpL4kB50UqOjLolMUcR0EpjLSrrDLTa9QHvIhER6ReJ6SLnM7OK3ceMTvpPLl1ZItaRqSnaGO9x39h7uPn4b9566FVPdTeLGWOOpkhn2da7gcNzF2hHd+BD79l1CZ+s422qRQ3MLaILdQchkGaxt8AQ9mzV2MvmaaNKE23vtvKfd7vAtz3oxZ88f57bPvAfjNjGyoRESnMcbSTaplGpccILFCUztIAqRIiPBeYlvBHF6kF57DjUp3XbOY10oJPCE6Il1D2j/PIGo9DpzHFi5hlNnP8a4GdOqNdbqsP6QiPAyIdYeb8LyI85rpAOLIZExPnOU3SFNEeNFCq5GeI82obBD+hAYU01wyh4qF0zuLPS1e1BY4MvhnyhBeaQIyENPywPbd82H3GQ1Mjd5BB+Lhryo+MLnPsPJ++/hiVcuMz+fEc1eQdo9iI5bDyon3tWblGVJv99ndXWVjfU1dna2GQ8HbK+vkQ+38c6RpjGj0ZDGBm8T42zQkNhd4a2kbpqwCJ+zYa0MD0JJmqoJRlVCkrZiqjIIGK0Lgj/8A3b6u0QAwux7L4IxOUPW2b0Kn12SEvoNP3Fb3PUtCTdmWRUopajqCqkUWqoJ0ZGMxmOkUqQ2RGSMdSgV4axFRxFxHBPH8d7SBbti3Yfa31+Y6pFSBv1AFCIC1k5y3Q7SJGV+ZgZlSmQz5uzOGqOdDcbjIf1xhfExW+0R7U6HZedY31inlaW0U01T51RFwdbWNv1+H6UV7WwmlPaqiFYXlHTBR6TVBeHI8x0GgxZJ2iPNOkiVUBRjqIJxnqtHDHdWKauc/Qcv2fsdrJ04QfhQQZS1OggcTV3iXPBSqWoDXmKsR1vDMB9QOYGWilS3J1qQkOLq97cZDrYxdY1WAjXxSTHGTK4nsC44OijRpd3u0unOMz+/Ehxt64LC1zSuYK6XokWHnWHOqMw5e/YEa6trGAej0ZjtrR2ckERRSpR64jhlfmkBrWA4GBIJRTdNaLciOkmMt5pIOxINth5h6yGRa4iUpcWYUVMzyiV10SBlgnMVVeOI45TZmXlMsQmFC7NIPFJooniOpNNFxWlIEZkG4w3Ceoxp2J9dzGue9mq2Vk/y8RN/zXccej5H1Dy2MrQqyWG/yKnmFN57It/ieZfczLOPPJ6N/DB33vtp7s9PAorISdAKpyWxSDnaupij7YtZTBa5PDtIPRqwuXk/AsV8Pc9weI669NTCkawJWkA1GHFyeD9+cYV9My1caVg5cJh7/Al0K6Y7lFx89HFcfekTuPP2j2HWI5Bga4s2is3BGn0zYCZeROmEoexjmxoRa2hJsl6LQV3QJEMSm9IXBTvS05IJbdvinK549XVvYKZ9mM/f+wmesf866mLAfdu3c+/wFrZ3NlkW+9lsNvDWYhuBLD1j+lTGkuQdnrDwFC5eWWZjoDi1cz/RZkJXaA4fOcK/+Z4f59xgm//5V+9nptfm6ZddTyZm+M0/+3+45vDFbG82/O/b/ysVO3vC/BBl9gjjCYvETPqZXbdgLYg1NN7jpCIRQQQrpMA1jtrUE3G5xfsG6QSZ7BCzTWo9idcIFIp5lg69kOuuuoHHX3oVR2fniGRYI2im3WW20+PIymGeds3TOH3uNLfd+X/5whf+hHx4Jy21n9k4RiOCHQBjTFOx1LuYQ90DdJQEbyb9U7A0cM4gZBzIgA2LpXqYpJTEZD0yM6lGlMzNzfOd3/YKNlbv4ez5T1AhmW1lzPcSqmbMsA4LxBofIVWGUA2+GuCbEi9CebHzMS45wLVXv5D9i8sg/MSPySJlxIWT7d0xQO3pg8ICsCsLh/BGUjchQmwaR5qEUnEnPEIa8BrTVBQuVE4pF0zzdKTI2jVFu2A8tDgvEd5icRjnSWQwWZQKrPLMeEGBp6+g14gvVXLyiPgnRlC+FDd7IJ2xu+6Jx4PzoZqgv86gv8NouIPEsnluFSkhiiWxNvT7Qz7+yc+iEET1KuiII5eMWDxcM794hChOJhqMMPvK85zV1VVOnz7NPXffSZ6P8E1DL9UIUxAraLW74A2rq2s4YydrWjxwHEJIejM9xqNhuCEQaK0mGpTJAK4jnLfUVYUzlro2D0QgLojs7BKUXb8TICz2JgTGhDJdD5PQ5a5XyqT8F4cSEu9CJZLwYRXd3XCm0gqiBOdhfWsTtdOn1WozN9OjpyNMY7DGsbW5hY40cZIwMzNDFEV7UZILS48fmahIcJpOd5YoSTHOIHww0PLeoaRkptOF+WVMsU3KJlu2YX0jZ1CMGY9zOmUXNVm/RYpQXVNVOVVTMc5LxkVOHLVIEo0gRmWCuShGuDpYwytN7Fs0efA7Eewq6KAsCkzT0GplqKUlnC0YFCVlvoEp96F8hlGKUvpJtCrok7xUiChCOsAJVDZLnM1QN4Yo6SC1J7IWZ2uKfEiSRKRJSpak2CojH+zQODvRCk1mc84F2/CJ266OQ2ehlABnKPIcZxyDQUExCv4IiVbM9lpksWdta8iJc8cZjy3GBj2UtZIobeN8C6USujOzXHb55SQJnD1znlQkpLHEG0caa7TSCFtiyoneps5DlUAEsbK0dQlGUNSOzU2PUilZu0uvO4uOe9h6hMkLqvEOQoFoTRbgS+KJfoSwBIIL3+mN5zn7n8W1hw5RRvNE4xYrcYvt86tUowS7MaI9gqoa471gJTnI1Xo/9akxjpx9dYehaeh0W9hNsLFioDTLyVG+feWFXLqwDysNqVKY+ZQTd22wdvpezHCd3A7Y2djCZQo902WehNF4m5PNOsuDlPLEOSrtiZxFdBLS1hEudpdwdGEfn73v/3D/+TvZ11smbmI28y1KXWG8pVSKxe4MbQ+rDeyIisgaDpgFpFY4Jdlql3SsJ51ZYChX2e+XWZw9wpOveBlPu+gaZg4uc8VcAmvrbIy3OaQ7/NV6SVF4TutVhn4L7fRetZ70CcZINFsczA6gNzrB0bSeYW3rDG0Jsl2RiDkOCcP/95kvoHf0MJGylELyM5e+nkPLc9x+2xluOfmHNHmB9VWIKBAEsw67p+/xhGUrEID2pEwiBJP0hBOO2hkUknE5ZlBsY2WNc5LaWryviJFkPkO29pN29jHfPcANVz2dGy65nLluj0gKlJI4a6gqu9e3ZGmbKy65gqMHDnH9Zcf46Mfey7l10CKl9mCJQ1TTeKSWtJKIetzHdNoopbET0xTnQU9sAbwPOhTvPEJP1tYS0DTBrVsKhdaKg4cu58YbX8b/7333UJtNhtkcLXWY2USgraUzewVi5iLanUWkc5w/+Tl2zn4cV57EYWllBzh82Qt41uOfTjuNEUDdhCUD4vgBJ+bQf/rJMg8XeGDhaaVt8LNYk+Pqktq1aVAk3iOtwFkVVnI3UNSeUkAkQKgSJQRaS6IW+KzGFW3wFiUAHfqeaFcIHUGmYBgBVVhd+tHgnxhBCXhAhDmpaGEy+HuPtRWjUZ/NjTXOnrqX9XOnOHv6OMiYKMtYWWhxzx33EceKw/tXWFhZZHbxIC9+2eMRQlPkff7X77+Hu+5f4/k3Z9S1YeXAJZRlyWg8xlnP9vY2x48f5/zqee65+268qZHO4Od7nDp7jmHR0B+NESKIwIKLpsMJiZCE6g08w8EO3gffDg102m1Go3EwPJNh5QwlPEVVEMkQZhQC5udnsdaxtbkdwqqT5eIvXFxRSYlWmroJqx0/YPs9OXf44CEgg2uknUSa6uaByqXdz3PKMh6PqIqSVtaiqYPZVpykKGuJoxhrDFVZMBoNQ9RmojPp9Xrs2v/v/nYP1aOECIpkZmaGbm8GISzehrJr7wihVTxpq8XK0jy9aJuOKtgYGGpv0FFCJ8tINRSjIWdPn6Y2luFoh1YrY2lpieWFRaK4S5zNIWQCvsZT4oWnamryqkRKQZK2SZMEJTx5MaQsa+pyiDMFaezRrZjZ2VnSDiRaYMs+xjVYJE3TkLYSrCmp65I4bdHqzYfTHmUsrFxGb2YfZVXQmIJIAU3N+bU1imJAk8ZgKvK+ob+zQzUeYpoK4yx1OaTIC7xQZO0WOo1ZWJyn046wNYzzMaurZ3AulA2fPnWW8WidNDbMtDSRMsTS0Y5ANDmj/oCq8YBGRxlKxyGNpCKSOCZSmnYSc3D/UZRIKMebDDZXEa4h63WoTcVwuB1WeXYOqUA5SSeNgrGTrejvlOz0R8Rpl6UoQUQJOmthqwRTGJqtGtURqFTjNBjfhBgyEolGCYHVljZdjpl5yuMDBtWYWacpzm9TFEPqWuDGBjVuaJoxUmY8qX2M6uwap6JVxm7EnFfM+hFxo9nIJb6WZO39PGf5WVx5YIXtc2cZ5WOiSKN7HhVZRqMtzp7fpMEyrgV5WTCTzTOUbUb1NgPvSMrznDtzP6rXISPi8u7j2ddbYH3nbj79uc8xSD3JzDKMPTrW9M9X7CQFcWuGax//Uq645Il84ZN/jCmPk6YdrDIgY6y3tFRGHXeCqVtnHjHYYEF0WVye5YaLL2Zn53683Ga8c4r7znyO81tnkE7jS0tLtagagxYpi9kCa6NtWpHmyUtP5cTJde4vb2PcGTFmh6wrGDYNpzfvpesVsqdJ3BZ2dYNuexaiEWo+pdly7LtoP6Y/QmD47qe/kqLf8P7P/g/69ot4NbFJF4SUiA9VPjoKpo6S4Gq00Hj8ZF0k6RSVDf1TUQmK4XGW5DKpyCjrgtyMULGmvfA0brzxe7nhoiO0sFTDPsVwgPbQagVBvZQCKRTOTlIfRqG0ROuYSy55Ip3uErd9+lZ2xgW27FPXQcAaVk2P0CqhaUqGowFptn9ia8Befxq8SkJ0aNeuQghQSk+Ii8VYg9LBfPHGpz6bWz/9Ye7dXufYE17A1ZdcgxaG0nouO3Qx+2bnSJMYLRWj8XP5v399Lbf8xe+hI3j8457Nk695BgszXcBR1xVVXQfDQh1PqtL2RsO9Z0GfEiIYWgmMAIwA47FNjTcJPpYgNFKkeFFjsUE7YlwovY497UmUJUoUva6jLnNwklq6sEig8EQSEico5APr8MRMCu0exVj+DU1Q/CM8Cwg+B877iWdHEDOePXuG0yfuZLSzw9Z2zp13f5ZUhDVexmVDlsbkFx1l6dBFHLvq8Vx62VV0Z+eIk2xSdiwwtubQRZfx0T/9Iz784Y9y0zNuCmuoqIz19TW2t3Y4ffoMd9x5F1s726ydP0cxGmBNw/EkJa/roBkxwUDL+2CEFkkNJpigSWRYVlt4GgMoxeLCHNsbG0ipiOMI4yxxrPDG4IzBSEGkFdZ6tjc3sT64KUoRrKS8C8ZTklCK5qwLIfK903dBxEVNSqUvIHqB6ARPg7DfQY0vAGMMo1E+iYqAVJKq6pLnOa1WRu0cVV2D92ilWW3OE6cZCwsLoXoCghvuBZqUCyt5pFRY7zE2qO2rusI3hkhnOE94bS1eWnSa4pMeLq5YXO7RW0jRStNLIzrdDAusrp7k3No6o3HOwuIKraxNu9Ujy9rEaQp46mJEUQ5J05g4ycLsSyjm5xfotLt45xjnO+TjDcaDTUw9ZiTCQmtapyzMzCNFwtbONloOMM4jdEKaxnhXk4+HCBSd3jxZe47e/EFmFg+idIvGNDTFEO9KbDViY2OVnfVzDLe3cc4x2NmiyMchqqM1KlJU1lL4FNluk7Uzut2EI0f3sTCbcu7ckFNrO9x7ZoumKCiLgvGwj7UNWZaRxDW9lqSbOpoyVB8Yaykrh5AOISKcNygpyOKMTAn6G6v4psfC/GHm5g9QVvs5LWPGg3WG4z6mGtI05YTIskeI0yQCYRkVBmsKnA2iQq0k7UQz143ZzhWddD/f++xXoVXJn63ewrpbwxmHVAlZNM+1R27E5UPuWf8cLTGDzwvWTp4hjwrK/pidLKXrBXXj2R5vQD6mZSOO7HsyT7/s6ayf+CI74x2KZkx/Zx3rYopc0m5tU6qUK7JLeOrKJdhmh+OnPs/G5pB2HLG0bx4lBUl3lp3tk1TakMwdIMrm+My5T3B04Xo6yQIdscBas0o/kWR5nwNLl9Avv8CnNz/KsCnpzio6O4oi09R5jmFMoSoKAy942vfxwm9/KfF8Smsm4dx7ziB1zkjmWDQIRU+0cCZltrXM6WaHOdFDOPirtVvRn87ppR2ynQ53nryD1dEqWwzIGeFSgbQRXb2Ib6Clu6QYljrL1NsN5do2ndYswggaBmz2NxCjNpvDbQaRo1utUBVnKd02i+xjtNZnobiIqj9GtyGO57lqcZ5rnvCdbKzm3HbvX9Lf+iJSh34kGN+FiZTSmjiLwwTEBZ9eAzQ+rNMTWYn1YTAspKDJC4Q8R6ZiGldSeYtOD3P1tS/geVdczoHZHpESnKdmuz8GFNY7jEknaeWgzbDGgLBEk+Ev0jHL8/u5+tgTMRLOn/xbvvjF+6mNx3qD0nGIfqBo6rAMSJZloXTdPzDxYxLR9RNzOinlxKRz13rCTsztPJ1Oj2c/+yU8qVE87sprWeh2QjsPWmm0iieRT0kSLfCsp307UWuWbneBg4tL9NodhNDgPVUdihKSJH7w+Oh3J5MP6CJ3S8HTtIXyMbjgVmubUCyAjxEqRriIKD1IiqR2OflwjabcADMpj0YipCNJBd2OpSwteEjEhKAQVlp2UlAGy1saGUjKo8E3NEGZhEUmSYndSEmYjQ9HfTY311k7fR+j4Tbnzm5x38mz7GyeJlaK89t9TNNw8MAyVx57HPPLh7nssis5fPgonZmZUOMu1QPWJ5MLzzvP7Mwcz7v5O/jv/88qf/GXf8VTqore3H5O3HeGe754L+sbm5w+e56t7W0Gg/7E0VKReIlpDHGSUjU1zgm8myg+JoZFUopJ+alDTpYNB89g0EdHwRhJCIFCkqYZ43yM0jJoU4TcizgooNpNywj5QIlvUP/CRDy7Z+A2EWeF0CAT/U04zbs2+EqpvRvSTWzd/WQ1Zgi+u2PnUFpRlAVplqAn1TuNNWRJilSKrY2NUAonJGVehDSRgCRJJjf+ROB7gQalaCynzqzRH1VEkUdYT7u1SBS3cL5BKYsUDY2O8e2jaLfCclIjpSbC0uQDIgntRFHGcDLfYbAzxDtPlqRUpWF+0TA/78hSgXMjhKiQMiVSGtWKUDqh150NqQbT0OtmVLlgvd5ma/UcpnEknRnm5vdjraAohzhrQv7XWRprKMoCISyRVDRVQdpqEWcdllZW0GlGXobz6qTEO4kXirm5RTY3Nzlz9hTj4YjxaMhoXKCjmLm5Oeb3HYZ2j9ZsTBIn4Cqcr1nbsYxqy3AkObvWZ9TfCbMdU2NNQ9LqMZ8tYLwnaffYGm2zuTpgMBSIKCNTUIyrkNKrSmxcIrMMU5bkg1A+2krmmZ2R9LoLXH754+n3Nzh36m+ph+tEkSLO2mEhRWeQSiCVIooTlIpIowYdJ/RmZ+h0Y6Ss8M0I6RuuWLqcb3/Kt9CWmiedPsa7vvj/cn9zBqlirl6+nlc+/UUoa7njs1eyttGnWR2TM8a0HLKGygxxdY2ficmrAbKGI+znWYdvYm45Y7zdpTq7w1a5w9nhBkePPpWrsmOc9quczL/A5STI4SY7YodTa/ewsbpFJDSbxRzZvIJum7W4pNXZh1GOo0uXcefmX5PpFiuzF3HF0SciOp5mZwNTjFkb38/fDj9LLkpEJCgXBEluSYzCSoWsPCKLuHrpel509fPIRiWWiovaPVbmDrI9ug+dZDgRykml82QiQaqUfpFzwLTpZyNW/Q5/tfF5FtMus1WXOtXkpaM3e4RZFrh/7ZM4U7CYRTg1SyQzZtuShWyR7Y0BIzuk0+6wbcdQp7TTRbxSXLNykLuGd/F/z36M2tTk+Raz9QwrzQLXpTHGOVprKQstRZEMKVYlt97+Gc6Nbw+GbWGBidAfQTDkjQQ6C32adSFdogBscP813gV9AwKpQ9nxoK6oouBLY6SklbTY1+6gnaWsK1SSMTe7TH94nKLIJ+THTaLLIU0eVm8WWBsmQI2pwdZkvd5kpffrKSvPqXPnSKUkjjKSNJpo8QRVUeC7PaQOa3s554kitTf+PLBSvQ4RFDdZamHyNyklcZLylOuew7goaaXBM8qYBi0FUsiQkt0d0/AkcczTrr2B4GocEmTAJKXrQ6WNDxWMfiIED6Xzk4KPyWTTujCudDodYplSmAzvNdZpjO/h00PMzF7FfPcKeu1DaNWmrEruuPdv+eTfvI9heQbr10hlQWQ9UnlUGpYcyGNPS0BEiMBLKRAKjHIYBBaBEY8ux/ONTVAIYTVnLHlVMx5uc+78Sc6dPo4pC47fc5KNrbMoIVnfKugXQzrthH0Hj3Dltddx6MhFHDh4mLm5OVqtNnEU7bmxNk2D1yFHKpBhATMXvtO5oCG49gnX8f73/nfarZTLLmnY3Njki1+8h+2dAdv9AaPRAKk0xloiEcSlCM+4yLHWEqsUK0KCNhISoQRaCcqqIorChY13wTjNOuJkN0KhJpoDQ5om5EWFFoJWu8v2TlgATOsIYx3We6wNCu/A5P2uCx3wQP3SAwZ0uxVME+xFYh4QtAkvJ6sD60kpcljRtcKHFWmdp8gLsiQB6ycOpsHMqaoNw8GQxlQksabVaqGiCKU083NztNLsYcZ3QoTyz/NnV9lc28A0Y5qmpNOaY3Z+gYWFGXqzM8RZilY9ZrMWva5jc/08xtRoX1PYBm/HSBytiJA+cYbxcJvzpx3VeMBouIN3BzlydD8zs13quo0gaGSSJCNKu0RJWJunwZOkbZZXDlA3FYPtbYSQtNpLeBJGozFNNUZ6NzHYMzT1iNGoT91UxHGCVhLnS7pmf0hH2iZUITVB49LUFXjP4tI+vDfU9YhxMcLtzkicI+ouM7P/SkhnKBqLEDXFcJu69tT9irp09Ptj+jt9imEgTFIrvI8QjaE/LunNzBMlM7hRxTCX7AwtaRoFciw81jvKssKYTcoyZ9hP6c20mZ0v0SqhrA0L88ssLS+zsLjMYHuGcuskWZLQ6bQZjQbUNSA8xlbkRUVVGZQQtLptlpZmaHVThHA0pgyduRmxceYMYmGZeT3Hdyy8kA9vfJwzYsATl66iHceoxnK5OkiGpb9xAttVqHgWZ8ZEjWPc30KZLnNxihGa5abDoSZj48QZ+uMtBk2fncEqM9EiPd/jwMJRHn/kKdx75nLGZ09xdvUOclGzVW/T15KnPPtFdHodNtfv4cSZO4iyedJ0loW5I+zbdzE3ie9isTXLwblL6YmGluiyUXawEk4O1hi6QfACkQqbOPJ9mtmTHuUlM7MHWE46POXAU3FlztaWw9YxxaDmkoNX8cXTfXK1ho8gKsFqg05TSl/TEoJOFlGv5KxkGVLDlhlydrzOInPMLM7yihf9BHPtQ7z7A+/jo7f+FkM75NjKDVw6cxX3FvdTjNfYaQYUSUUv7nLs4ut4yg3PpX+m4PN3fZADrR5De5QvnP5bhKpQStDUMSe319lq/pDYxFwmL+ZodDmuivn0nV/gQ3/zMQZ+E2KFlzApMwuGYBM3WhUFEf7iwrcwak4xqs7ginW0K5HSIG0Y/GI8owgwwVG1pVq01Awky8R1TVnVtJIEoyxxljHb67C60WeUj/EuDS4F3pOl6WQF8GCbIEWIejR1NRnYHYsLB7j6mifS7cxy8vQJkBGRjojTJCz74UO6JkITaT2RpIZj8y70sXuRQ6WwBPPLUHUZoVQYvFutNkqqULXp/UTHYmnqEiFa6CgOlglViWnMpLpwMpn0k3HIWaxzkxWfQyGDnKw0HeJFYW2kMGUNPb7wnjiKiGWGJUXIOdLulRy9+EauvOR6Ds4foZVkYYI+ia4//sgxrr70yZxYO8Md993Gzvg+ysE9OHMPaVpiIkEtPbWAyIAyHiVATVL2MYLU+Yf17V8O39AEZTwecOL+MadO3sXqxhanz55nY/U8ohpTWs/q2jppHHPJkUMc23+U5QOH2bf/EK2sFXLqk9Db9naf0WhMFEVorfcWvsuyjDiOw3oeF5ivBfv3EqUkvbl5/ub2e2gpwWhkObe+ydbmJtY0oRxTeEzdEKmIpgGHpyobpIS6qkBKslZKGmmSrI2OIra21pidW8AYS39nh6IocB4aE7Qn3lh6vU64MXxDOtOmGI8ZjwZ7CmlrDUkWU1c1jXtASc+DwpEX6FJgIlaFVrsd3BCroG+QQqAnGhcpJY0xE5FtqDKK4wgzCREmiaYoKxj0EQJ6vRmSNEVHobx5OBozHI2omwpjzhAnCUkcMzPTI0tS6naF9XbvfO9ez+FGNdTjnGK8yWi8xTlzkk5vhksvu4S0dYx2bwEp2jgfMy76OCZRGBsRpx1MXjAebzEcb4OrkZOS3J3hDs43oA1ziwnGrtCJZ0iSiKap8M4QxxlpmhHHCa2sjXMpdZLhuzPESQtTW4YjQ3dmH1VjGfZXcabBmSqsASMMioaZTkpVWipj0OkivaUD9GYWUTLCO0sxWmd9YxNrwfqadprgOx2UiphbXKFUHVbwbK+vMhiUtOcPkHaXiVpdKAuaeoRxAus09YREltZjrMVYjxQROmmhUklV5ozGQ5Ksw/mNAcP+mFFeUBYNdVUH4y0ESkVY68JihfmYSGtGeZuyKZEohAXlKoQ0ZK0kXINS0xjDOK+xNoS78Q1NVbPTzxnkDUJFLMx12beQIdI2vd4cs7PzlINtztan+ZvP/jlXrjwOG3tEBc/tPJ2/dfdxSbyCHlhMVWFGFjkwFKMRvfYiUSM5W6ziRzAjJPlog7pdUVjDAj12Vk9Tqpy8LBmP+/SHfZ5y5CZ6QrN9/l6Sbs18W+GF5vzgDPe5E9xXneHJT3kFz7j5erSQjDcvJf6/PU6rMywcvpRjVxwjazwHoi4UJX6rYbtYwy4dQIuImpgZ3UOOBUY3eBlWW65altGCZnZHsLx8hHJkWUrmKIZ90pmZ4JSsYp586HEMqw2i7kUY7/Cnz2LMiDTt0Jc1876FWvEsHtbEWdALKZ0xGsD63ed48qGruOjSS9BxxMue/xLuOv5pXHmG5135fI4cvorD507xnk/8DkI6fCLY3zvE8654Eb2jC7RcwW1/o7jL34HKluml+3HxDgudRXqtHjujnFPnTrA4M8N9oy3uHH6M7e2cO0/dy3YxQLfUJCLr9iY+HjFZYDEsJwCel9z0fcwuLrCxvcaptfs4eepzbK3/DcX4BLmpsc7Stg2VrBEmJo7adNNF0riLr0tGeUEnTScpz4SZ2XkGw5y6MdRNHSz5RZjsJN4TRcH6wDqLEhIdtWgG67TSCCc13d4Ml19xFUkScW59hyiKiLRGxymRhrouw4KlE02NEOylq73bXYPN7UWDd9Pqzhp2YwxBm+FYP38/RRH6mc3zJxiMa44cPcCho1cTZV2qqpnoXHigwpKwLMSu+3Y0meRJGfSM1tmwPrgME+wHNIMSh8cZj1Ap3cUn8bgnfgvXP+HpHFk6QJoECwaxm28CvAhanhuOPY7rr7iawZOeQb8Yc/t9d3DLJ36dwc4nSH1DTwbXW+cEuvQQCZQVpNaTOUcx0eU8GnxDE5QP/vH/ZHN9i6YckMQZp86tY6zh4iP72b9ykOue8iwOHzpMu9vDWUdRVQwHA9Y3Nqmqmqoq9kSZWdYiTRPiOCFNU2ZnZ1lYWKDb7RJFEc45mqahKAq2Nrc4e/okJ+69h0wK7l7d5PxGwfLyLJ0IzlYVEkGwzwiak3FRhhVXJzmjSEVYE8rtqiaISrc2N2iMZd+BfYzHeXDuFJKs3aaughlS9/9P3p81yZal55nYs9bao8/uMZ55yqEyqzJrLlSRBAECJDiBRBtpoNjdJjXVkv6B+kImM91IF+o/IDOZLiQz0azFlpqkqTmCBNlgYSxUFWrIOc98Tsw+u+95DbpYO+IcgGZq4LKsPS8yTmYcD3ePvdf61ve97/P2uhhr6Q871JUhiRImuxPm0ynT2RoK5yt4HEpIP14IvQdda3PFMvGQudfaf61PHucvfCkFpnWpqEB6MiivtWJbgJxzUNeXADaBkA2r9doLgFOvp4iJEUKw2WxYrTfUdU1V1z6tWUriKMRon0Dc6IY0jQlCb0UOgtDf5MJbrHVTIYUmDjSrTcnCNCw3O2jrFwDjKuoyYzY7wzkf+d00JaFUSBWQrzYU1RIpDJ7D5U9QSEsQSXr9DoEK0cZ3oQLpsLTWbUQbPObHPXEc46whDmNu3lxzdDJlON4FoYhDyUzXlFmN1Q1K1UQhJHGHOg4pbMDBnfcZ792k0+l78WNTEkhLvT3ls88+x4mQmzdvY3b3aXRNlA64PbhGHAXM+j2ePX1BvllxcX5K3K/o9XtEcUyddmlUgFBDBsMJjTxhsVgjgwZnLHVjCQJ/Oqqqkmy9IZ4kaNNQNxU4Q6QkYSjJK01ZNgSBQgqJsQ26aajKCqt9xyPP58SxoNgIbB2DrgnCEFvmbNcFAuuBa7YhL0ouFgV5Bd1OTBhBGDj64wGD0QRjQ5pGk8uKR5tH9KqYaDygSQy9IOFdblJ/cMTp6YY6bKgXNdJGiChhYMCUOev1ObqIEL0AAsfji8cIo+jLfc6XR+huRVkXWGOQYZe9neuM+oqXR5/y8IPvk0xGbJjxePuMjzYfU9djxr0x2bMVPZEQTTVf3/siB8GE8RvXGKYd3EXNstbkqy0ahxMJZVEShJo0iuiZLhSSWmkPUxT+dFtHFWE/AQH9KkWtHVVQIc4LFNCsS5rA8dZbX+PuW1+klpJPnnzAx7//T4jqlCZ2YCPCazP645QwimgMaAWDwwHFwrKr95AvLNwXHOz3+Xvf+gc8//h3maRD8npJzxluDu7x0faYQTTi5w//It1Oh3peYcuaIN7hpFzw8uIHrPSUSESEQUwSdxgzYhweMkgS8ixjN97j07PfZVZuUYFAKRC1u5w0AJeiUp9u7LSBEOIgZG88YX+8y7v33qH62l/h/OKIjz/5XT7+/Le4mD2lFluELeiIhK7YYxgdkEQDZFOSZ1u2cYyS3j0ThgnjwYDz2cLbf7WhkQ1KyqsN+NJJaLAESpIvzun3uuiqRMUhSgbcvnWPJD1nW1RgBUEY+ky0pmodla03pqUPOy7BmK/DKBVKKl9QWI3R/j7Ll2d8/pPf4l/+f/97Xi4KeoMRt998n3t39vi93/ynfOeXYw7vfOGV6aMFYvifaK6s2pc6PQ9QvNScmNbp6UnOrwqU9u+EKe9/69f46pe+wf0bN0hjH/7n3aSv7Mlte+gV6FNKxoMe436Xg/6AxcVTfu8HPyQQCyKgQuHcECl3idJrBGlMZU+w+SNCqb1j8c/w+JkuUB5+/oj5cslOP6U/GPKtP/cd3nn3fW7evEOadNHWsJwvOTs/4+LilPlswXR2wWbjraJ1VWG0V1Z3Oh3SToc0SUnTlMPDa5gH9wFH2ulijaEoS5aLOS+fP+fRpx/w/PFnlHnB4WSXz5++5MHNPW7f2OfxyzOM1ggk2vhN3jqwIdCm6Xprb9vW09rP6aWiN+yxf7jP40dP+dKXvsyTx4+Zzy+I44hr1/aQEuazLVEyZLU6Y39/n739AeNRwsE1w3aVc3ExZbncenKrkj5BNitaW66/0JLEQ7def1zCfAaDPru7u3z44Ude6OUcxvrXijNXbI/LWt5TEwE0wghCJVpBrKKpKuow8kVallNXNYFSVIBuNM45mqr2fBPr6HRSxpMx49GEKIhQscIp364UtsCYHIFtA94s1jXUuqJqcrbZnLJoqLKaPC/odYboqsbUW6Kk44m52lCXjReECYkKnC9Id3a4fusG167dYDQeU9eSqipbHo7GiBKNJO106VofKNYeyfzozNTUVYZzlqQ7ZCiu0VQFpsmoTY1F+o6dABGnjMfX2b91nyBMMFqTF1usrhBoBh1BIEqapiFbL1FSeSy7ksRRqz+IJeic9fSUPNvS27tDIG5gcXTTBNlNGYwmjEc7GCyrxZK6KCk2Kw8DNJbICYQuWc1e+vZzYInDEJGEvHW9zyiVfP+zKXlVYm1EFEcI0ba0Zeix/EmPxjqKqqTT5ARK41yOxKBN07qMLFY3bPKSZVZR1P70ZkRA2Qgqq7jWH9HvDZgvSupG8376HlEQc3L+kl15gHAxwTCk3uQ8PHlJd3aImficpkCHqCTidP6EsoQ03SGvV6w3W7ZiwTrKmM/X3DIRqZjSD7pM6znFZo2LEjbiDCssdq/hyZPPiYM+VWIIOofsFwHrZsXm6REXIiGrUkbBGCcd421E+CLDvdH1ozMVUAtfICfpvj+h1zmBNdyUd4iaEbosqIUBGqzz7gkZOGSSMEx2EKqL3RqKJqPJM5ooZF2ec/P9d+lvJcFOSPT+u8xOHlEeTTk4vMmz7AeQ1MRBiBSOCo+Kt8oyur1DMk+9U6P0m9p7d++Tnm2Ybs4JREJWZ3xl7z5P+Zgvdg45DA5YLRdURUl2pvnOu9+kFH+R//of/58xkSZWXdJoQDcd0Y8UC33O0+OXHHau82j+Ic82H1DrkhCHJPRhga2OTarW3SJAOHWlvQMBtk2NloIkTrl96w1uXLvNV9/7BX700+/yw5/8ezabExLhCF2PwAZEzkFTUpYVtdbUTUNY10BIbzBmk+fULYLdaoNWmkApb6uWok1l9sRt4TTr5YpJmBJ3QooyQ0Ux49EucVKQZwVBq/dzumlhli1kzblWUC5w7hXOASBQCisFGMd6fsIf/OD3+OjDH3FxsUBIxaLu8s6bHWy9IU3h84c/4sbwEENAVZVI5UdSrl1rpBQe5ujAWi/IddZ6b4jTOKc8kBA/xhJtxMDrotnd3X3+7l/7NdKo7XiKS+1Ka35onZqXQE/g6s+XEoEgCHlw4w1+/ycTEmqkGhH2vsDdW7/IWzffZ3e0i5KKT58/4bc+/A1Oz56xnD79nw4HZe9gn+/8hb/IF999n/39a3S6/dZBUrFcrphOpxwfHfH02TOeP3/KfD4nz3OKovAdDmOoaj9PjaPIj3U6HeI4ZrGYE0V+xug3Y0m23XJ2csKjTz/h+MULz9foj9mu1rx8/ow//LDPrZvX6f70EavNphWatsVIK2LVRiMFLUtDEoaBnxVagXWOuq45PTklUJIf/+gHfpxUVQRKgYjZbLfcunnAvQe3uXdnj48+ecxoMiSKEvYOO7z5dp+XT4558ug5Zxdzuv0+QoXo5pTD3V0WqzWb9ZaiKK4q8z+Zz3MJLruyEVv3Ctz2WjV/eaVpY67Eu8IZwiDGOcNsPgMh6GaeP4HwBlGgnQN7gW1dVWw2G4zRVP0uURTSTXteUNbi5JSETmop8pq6qsnyhlxbAhz5JmN2NkXnDWWxocpKkCHCtt2TwCICSVNYtqVmUxoqbdsNPyTp9NjZPeSN++9y5/Zb9Hp9NpuC5SqnLDV1XVFWNT0hKcuCLN+SxDHWWXSTk62nnJ6+YJtVTFB0u15sl62m5EkHp3371lpD2TjSUZ/D657qmm9XLC8uvNFcVxTFEm0NYRxTNZrlekGlNZ1ej0G/QxKmJHEAtUC4gqZYU9YG7RyYkiBOeOP+XXb3xsioSxQn7O/ss1ksWc3OEUVOEKVeYV/6zbSqCvJyS68/wBqDQNDvRASi9lkqeEeXRXhHg1QQJDROEsZ9ojikMjWbPPcFDh5YVxQZda3RDhpjWWQ1tVUMJkOCICFJOuwdXmP/8I4H5AUJSjVYZxmkHa4He3zgjnn54hNu3HyblA6mrHl+9DFDXRLbEcPODmVkqZOAzUVBoxu6+xMy1qRlzOn2nPNwRljFNFFFrf14x65qVtsli27Od5/l7PYHJKOAs96Si/UjbA7X0ve5O3qX4+oFx0fP6W4aBru7hLcmxETIzgRzkZGpCzrDLp2gg7p9wOnzl1Bpal0gAgfVit1kn2/v/jzfX/2QZ6sPsUZQmxrdVFRGM68u+PobvwDzBflygeh6ACQbg3YGpw21KxErgSxqblx7g2WTcD57wnp0TiIFldZIY2lcTI7AOM1g0qeoa5pyjcoGGKWpXcamnrFeSZI6ZqVL+trxBtd5O7jNWqzR24pgJbBTSOuYwTDlP33vV/m9i39NQ0M3GlDkGU/OHnK+fcYk2uHhZsOPTj8mq9eYxoEWyFBjWmSQkPIV0qCtUl5ZYu1lve+FoM4hhENJ2N+/wS/94t/j3S98m+997zd4/PkPELbBaEsja1BrRLEhy7vEUUgcev1UksSMRwNmy1U7rXBYY9ooEq/3E0hUGCKkoDPos15PGY7HBGKMtWuEhSTyxXkUxzRNRVU1OOmDU5UK0E77zoVwranBi2UvB1q2BcGszj7le7/5j/j+D77P06MNJT0OJvs8ePc91G4C8zNkccqNnTHvf/uvM94/bA+x/rULDOBFucaBthqHz/txWLT2RYuQqi2QfBqxswYrPGH2snBKk9Rzr4R4VZi8RkH/k5Ekl1wV93rxYi29Toc0vMFk9B5fevNXuH/jXSaDXS8JcD5B/Ntf+DJffeNdTpdz/slv/Ut4dvqn3uN/pguUX/97/4A7d+8hkFjnSX5VVbFYLDk6esnDhw958uQxz54+YzadUpQl2mgPyTKXGQoNYAmU8rbNOCGJQ7LthsGgSyeJ0HWBkCGb7ZbTkyNOj14wnV0QRBGLbcXDx0+QxvDpkxfs7O9z/do+y00bvS4uqbD+AlDCb7nWOsIAmqoiSSIGwwGN1mzWa7JNhm6aq43fWkfS7/Le+1/h6ZMnvPf+W3zxvTd4+fxzJnsHfPLxJ6xXFXEi2NubIETI3uGItNdlNp2zXq/odrusVivKovKv5rUL7fV/y9YJ9DqX5PWH4/Im9DkTfkFpKxXnPJvCGNabDUIplArIi4I4TggChZIhSZLS63XRTUNdNzR1Q1mVGKuR0oPP6qpoE5wVON8JGvS7rBYz1mXJOq9pGoNzPs11dvwcO+jTNCV5XmNFSBAtQDniOKG2miZbkVclQiiiMCYONTLqkHaGDMeH3LzxgJ2Jx/VLF2A0+LeqabQhVApwFKXfCAUWXW3JtwvqukQGMUEUIpQkVBFBGIMMCcIYZyQqlBBGpN1d4qhHtpwyP3nJdnaOdJa8yjzevIGqUT5NVwgCBWksGPZTRqMeEkG+EgRookhiJejtObN8SX/ngDS6z62DXTQKIyKaUYfFaMR8NMGUJaGSWJ2R50tsVWCaBmsNVSDQ2n+uP348xdqGrJHESY8gSuh2e4ShD+frD/sMRzvIIPQdHheQbQvPzCk3rLYZZVGTlxojQsKkSzLos9PtsX9wQBj3idMew8mItNejaKBcrSmKHLDkmw06T6mmS6brOVVTIm7chSYjDWPOjj5jJ3mL3WiXqBeRBzW2P8CKc47qp2yagrIRNCqgpycIpVnEayZiwnRxQS88IJ50+Cz/hE+mTxgtEnpziegkHEZv0jQ1vajLoDOgH3+Vk+Vzzs6eo4sc4Rw3br/rdWQS9NMV1c6WVPeg51C1I3t+RNCPSQ9HpOEeZVNxS0xY9e5wvPiQpg1MtI2/v6ZVTj/uMnPnxEKAhDjukb14QdKVdLcRqh/STDesmJHois36gnN3RNWpCGVAYSTIgDpIqIUkdBBFgjKsmT07Yvh8RnR9yGJxyqI6J61SwkXCtpfjipoH8jrOOnKzpi4kad1jNT9jm6WEmxVhvWE07qC1L/ZfzJ9xur3AKMOjxQuOL1bkVYbvhvhcHtFcEpQELvT6DGeBwOeZtrINn/be1IRt8rA/yrRjZwFhGHLr1pvs7hzy2f0v8uMf/CbL+THGZEi3QVQ1TmtMU1HrECV9sGqnNyDLL1PLX+WQGdNQN8JbjYXPwUq6I06On1FX1ynLmGqzQYuANPWus8lkh816iRQ1UgXeeq+8lkWqACHasEyHjw5xfnxc5wt+9Dv/hs9//FsoMee9OxMe7Ke43pj5PMPYLfkmIs8ilpuSL1+/w8HBLRB4cwGeFCukxBGjjQ8oFFfjeV+4+P/vuxuXnRLnvGDWWYfBtCiHS0yeuDp0vt7xudTLXO1c1gdxAleal1faRcF7D36Vr777FXZHh0gh2nwzh5AKJQKfpp4kjHp9/ud/+df4//zf/29/6j3+Z7pASdPUdyW0oa5LsixjsZhzfHzMxx9/xE9/+gEvXrygLr0bQltDVdc47dHuprV/4Sw+c1igioo4VDjbcHH0lOkwwZYbGmM5Oj7l6ePHTE+PMU5hBJycHV9lIGw2G56+POX6rTs8fPaSIivb+aR/7qbRhEGIaRktgfPtuqZpWK/XFGWJ09qrn5UkkAIV+Nwda2q+9/u/j3WO4+Njfv/3f0Bd54zHE7abjKOjc8CyWGzI1ht2JgO+8PYdrK1YLNcYB2VVt3NT/qOKWLxWZLzeHXm9QHk1U/VFiNexvLohpJDU2utAgiBAOq970dqfMHQjiGOLdQFVmaNU4J1TbZFTVV6XslyvkcK7mZxzdJIQpRTjUY/5/IzT85pG+5FTYwybbMP04ghbdRBYqspSWwVKEUcxdZRS51uk0/40RAdMSVkL4k6HwWDEzs4Bw9EuUoTkec422+JsQzeNSKIBjbYkaYyzmqrUaKXA1J6LUFX0RruMogFJpLA6x1lLEAr6gx5NItDWkaRdwjCh2xsiGk25nGNWC0Tpx1YuWyKTAWE6oTvyIWkKSxQ4oigiTWICKX0q9mxOWTlGk0MGu9fIsxXnp6fkizNOz87Yv3EbGSrioKHI5lhd0U8SslBiq4wqW2J1jacSa5wxFFtBGAU4J5ivCpyzxEmHOOnQHfoRjLM13RZuNx6P2RZb6qKi0+1RNobV8SlFtqBYrzF1Q60dncGQg4MHDEdj0iSl0x8TJj2iJCYKA5oyZ7m+IM9yjInQjSGrtyy1otT+WmjynBcvXyBcTiBismZGffYZoXMMygE2CRhNdvjg7ENOsxnXigPW1YrVsAAZMDQJRSTI04Iir8mrFxwFc9a2wDjDRheEWtDJYu5e/w5vjO5wfXjAbnCIShxpDP8h/wg2kuZJQDXfcv+tr5FVa2yVI4oBlT7j5PlTTFYj1pZ+PKJZbQiv7VNXjqS2HHQnjFWH3c4DfnT2Q3Bws3+fn7v1TZpyjtUlLu4QppL87JzF9IhheJNuZ4gWirP1BZ/Xn7OczSg2K4qDBitAE1CKBBml1DJEYAlUQOoirvMWF2crVFzQ1Q31MqNutugsIzeOhpCtLklNiHUd3MsMMYmZFs9Znp6SdEeE1nKWz1gEU/b799huN2zyksClPD05Yb5YoWsPhbQta0lIiWwkSIuQfrsz1kELgCS0uEsHYN1QlhU28gJWKSxOyBYj77sRQlg63T7vf/nnuXHjHt///d/g44/+gKq+wPW21HVNU/tDT9jiDeI4YjDoM53PQQQoGVyNGLxOw3fInTUknR6mKck2C5JeFxFGBDJCObBWoKRgvHPAdr3wqcGtMFa0HR8lWwuz810L5xyLsxf8xj/+b/jhTz+kqhwlDao0XNtJeXt3zP2f+woy6bFazvij2UuM2OGLX/8FVMuUUlJitD+8CZEgnLnC6lvj6bHSo6jbfslrQtoWGSGF9TEpr/1eXNtR4uqzsK91RtxVceJHQ/bKyXr5vTiH0Q2j3pi/8LVfJIoS9GWQrQBxGQzZ6nIuDRi9KLkaGf1pHj/TBYoxl8LVnO1mw+npCZ9//jmffvoJn3/+GecXU8rcCzED1VqmrG9NeW/6pbL8Sh7k80yERtchq+kZFy8C6s2MvKh49OQFi8USJUO6nR7PL6ZkWUYn9ZCd9XrO+dk5b7+7RyftUmTF1WsVl/Zga5HC+/0bbej3OuR5hsszGq3BQlVVXjwaBjRVTRJHWGM5PTlGKn8znJ+d4qzlJJ1RFAVCQBAoZtMFcai4uFjQ1CU3r18jTedst4VXjUt/LtFtK/9PXitCvF6BtyLatsASwtMfjXa8qrfdleLbSYs2eAuccwjtQXBxFHnuRhRhtaYuS5/fEyvKosQa31kyVlNWFeuV10k0xpAkCVJ06cYJ9x58AeUalkuPeC8rTVNritKw2Uik1UhhwQmCIAQR47SiMSWmqggDQRyEuAiCwBDGim6nw2jYp9PpUtcNs9mci+kppqnpdhK6aZc4DjCRwglB3ZQY3aCCEIHBmIbGBCSdHYSMKMoC7QxxpOh2I2zTR9chRW2wzude2OUWVVtk1YDWQJs15CtTervXCDtjlJBILEWxRNJQNw62FZvNim1WMdq7RdAZcP3mXapshWtqTs6mPH74GTvX3qA3HGHrnKOnL9ku1xTrOdnyjCrfACBDH5XgAKTCSembyGFEKCSmqUEEhHGHvb0bRJEiz+bs70zYP9hBBBEXiymi3tDpD5BScnY2pcq31FVOXTZIGTCYdNg/vM31Gzc8lybukXYH3q1lKoyuaaqK+fycoggJnaZyBdMyI2OLkxHddOi7nOEQFzqiqE9R5hxfPGa7jAknYw4O7rDfvcbi4QarSuZqxtZpqiak48Z0SPlg8xBpS6rGcGoyNG0SM5Za+7TmF+tjfuHWX+fGsEeg+uhihckshztv8NPmQ74T7qJUj+XsOeloggsaBv2EunIoWUIcUgnDcnZO343J0ilGhYTdhH7snYJSVLzZf5dR2OfP3fsFrjmIBCQ37lBtC5bLKevTl2SmYTzsU6WK7fSCl2dHfL75hA4pOjIszRrVGELr3SsqjHDOx8cqmZC4Pjc6N9HdBVudszj6mNW6ocpzzLoAKsLuLjhBWS5IVczKzhFhn+XynLwpGR7scvtbX6Gz2LB5ZNHNnKzcolzE4+OnLBYVjfYIe4sgEBFWOJyxOAtSBUwGu9y/+1U+ffGYTfEQnPGdxlZ3IoW3zZZljrPeCmytQrVEaYfnUAnhs6z2Du7wi7/yn7FzcJff+u6/YJtnFGVJp9sh0r7jqVSNkook6dJJc7ZZRbcboVq+lMPSNHWbzC1BxQx2brBezul0EmSY4NylWwYcXZJQIXpD6qogLyukVEjZ0mNbuqxou+bb1Sm/8f/+v/Ib//a7rHIDKkUlPeLOgGoZ8/THOd8c7PHesI+oH/H+3X0O3/7LXL9+3ZsiVNCupRIpolbrZ9r9yxd7PjpDoITFOpAoH/D6OuJe4Bdx57927ZjNd/J9OO3rzKsr7Uk7WvLuTeFDElumlrGasiiJopQg8I5LxCsC+JVr1D/FlUHjf1I246oq2KyXLOdzjo9e8ujRQ37yk5/w6NEjsqLEOYii6ErYY7Ruravuqgq8VEVLWtYHr8Ly8jxnOr1guVwyX25ZrwpQkiAM2Gy2zGdzHI5av0K7L9cbTk/PGAwHLBaLVmviuw5JnFDXFc55WI4TgqwoEUJSNxrTWvGENogWaOOcb69JKUmiEOOcx9MjUGGIlIput89g0KeuapI4xuiKOBRMLxY4p+imHYqsRAEqEGgj2kCqVxdNey2ilFeFm1ZIaQVX1bSUCiEd2rqrCvxKp+Ic0vrvNe1NraQkChTWaPKy8JqS9gIPggDnDHm+xRjdFomOpvZFR13XbDYbzs7PsGZE//oOo+GY9P4t6mzKp09OeXm2YrMtsFhqA7N1CUbTTQKGXUWAQziLMZUPG1MgEoV1AitCOt2QpNOh0+0gHLw8PiHbzFgtz4jDkF5nQJJ0COOANO3QWIsUfrEMbeTzlGRMLPqU6xUXFzOMdsRxRJqENHXF/PycLM8IwphOHKGkICtWVCogUI4yz9CNpmkK6mJNsa0Jujf8OMk0SGlJ45BARZRVzWazpsg3jMeH3H/rBkYo+qMJptphfnHBYrGkXM+5OJuxzhtsmXF2csri/ITFxQnZaobDEsWJb1NLSRDFdDpjOr0O2XZNpzsgjiOmszOsdfSGI8Y7u1S6QumK0kpqHZDEHWTUYb6ckh2fgjGstzlOW6wN0FbTIi+IlfAsiXRAEHZJ4i6NM4Sqh5ABTd1gLLx8cURdZUCXzFa42EfG17rkrDohTWNcZBBdv/FuyhWrekFwuibt9+lGPfrpgJPNEbojUS7CNZoLu+EtN2G63tKRChs5tnmBiARSOfwJFAJChnGXQFqoK4qsopIhQbzDO0HEXGf83tH3+YZboUbvkGhN1wSYvMQYxah7i2YosPsJ02fPKXGsF+eMb94h7u4jm4i/fOtvc5yd81dufZVES25GI3A5JgIRxZSdCDHsQxgS5Ft6h3tU9YqL82PyqibPM3pJl7nLmW7XjHt9XJyg4g4oj3MXwguZXRVQrjbUbk5HBGTOMT07odE5tS4womC/GeNqycn0KXmVIVPYpQdaMrhxg9tffJO9W2M63QSab/Ds+APeuPE1fvuzj9DNJ7xz/Zv8+MXv0I1jbh28zS9+4xeYz3K+++Pf4mz9kFt7N/nf/m/+D3zj69/h//Lf/Gv+23/1f8LhXYrW+VN5msaESmLcZcCeIQxDDIIgDNpEdFo0gldXdLoDvvFzv8zB/g3+8Ec/Reva6zCs9UWv9KnYUqUM+0OK4pS6Kul2On7VtwLrapraFz1hJDm4do/52Qu2m4xOrxW225qyzBmMhhhrUWFIIixlbQgDhW5qnDXg1FU32lQrjj78l/SCF/z5L3f5+IMLXq4L8tWc5Sz0I87BgI//8A94+EdbymzJX/nV/yVf+dZfajubrRbV+gOXbMcy1m9Y4FwLdLvU9rSdD8mVZgx4NS6TgsspzmXKsj9XuldfX3FKLkW17UET8UqM6wzOWBrdXNmcLxkuQRC039P+bCH+GBPmktPyZylRfqYLlGy1oMm3vDx6wacff8yHH3/CsxcvKcqaKIqRrVioaTwKuK5r/4G2xYm9qhT9KqqUIgjklcV2vs4oy4boMnE3CAnjhPm2ZLEp8bZbR1OWuChub56Aly+O2DvYb08Br4IKy7IkigOUimgqrwWpWouu71Z40ZJ1gLVUaK8XaJ1AYRihghCH971HcUg3jmm2GWFZ0Es7jA4OeHFyQm01lZG8OD6jE8W+sg19FymOAnAlTotWR9K2/vDtQG/FUwgkNRrhQEnlU5W1RarAq8h5NSqClhN5KbJq1frWOYq6RpkAZ2xL1ZXUdYlyinEv5rzxo4BLwVzTaIq8xFnHwi2IlMAeDqjLOWkEb989YG8y4ns/ecznz058VoRxNEZ726IQBKohcYLg8gq3DY1zUCkaDY2I6Q+HDHcm7OzsEwaK5eKM1eKExlTEaZ/j408os4pOb8BgvI8KlWehxJLBYJdO2sc6RVlbsryiKkuKIqOpEuoc1qs5Dz//lNV6y97hTe7fvU+/P4FUoKsabTW1K7CmxhmDtQptG4psgUViTUMQWIQwyDikzNecX1yQJH0mB9eRyYDJcMioP0CXWx+RbiuKbcEnP/k9dq4/YNTrUxc5y8U5ebZEG+MdTVGKkL7AjJKEJPX26jAMGQ0GqCii1CUg6Q9HdPtdYjFARCmbzQo3W9NrFFUDjZPk8yX9tMNovEdZlUinWc2n1K2d/PzshO5wh93OEItlk2cgJUEa+wyROGE02acqV8xO19RFSU+kDAYTFixQTcBQjDgvZ6zcmr2+ojYFVbklGY0Y9nv0hj02y5LuwRhRL9mJQzZCE8mKY7sAIRmGE6JGUW7X6NKgEgdxu5BbgQxgmZ2wKc+xK0knGqCtJIwEHSm4qcbceecXKDcr5rMVk2iM68eURUFZW6yRCFEzunfAempIxmNsbShWp+zG1xmle+wFu5TDyncN1hvMdkMy6NEUOQSg6zVK58R9g06HQINbrtHbjOn6ib8/neWsWeASRafXJ+oOkGEMOISpUTJASUWVV0zPX3IxPyFyM1ykWdUXOBt6fpGEVT6j0gUX2xl3k5D9vduc6WesNmv2d/v0ckn0uMI1FddNjxtv/wXGX7jG+PqbLBYXhMbxpHPAg/EO/9Wv/1fs39olW2eUheQ3f3LMf/7e3+bn3/o2oob/5P1vsZn/Fxzlp3xy8u8QsgYEYRjT6XTIsoxLUmpd137TM34dieMYJWS7+Vpw0luB773NZO86n332KfPVhrIOiaLAI9xbcbpSIcP+iPV6Q601cehR8oKg7dzmKNUn7fb8qEd7nce22DAaDfyYRddoYZAqxmhDbzD2I+W6akfeFiECTJ3z0e/8c55/9Lv0U83dgzFvXB/QOM12k/P4OOPhiwXTMuPk5YbJ3g5/++/+r/mrf/VvIZwhrzQ+66dF5wNCGByqdQy1oxgc1glUO7bxRcAre7A1r5kbhA9lFFyO8x3GXLKm2vXb+r3QZ6Gp9v95Zo9t35+zfn1+/aDvu0iXrp/L7sxla/4S/+8P6a+bMf40j5/pAmVx/oIiz/n4k8/4+POHHJ+coQ0MhiPiOKGpa4qiwFzO7Oxr/nTwEizhb4dOmrKzM0EqQVUV6CIn2xpcbJCdFBXGHtITptS6ZL1e+TZiGJIkCdvtFoFgu8kAqJuGJI5pmtqHYgUKow26Ma1l17dhZUvre9UO8xeOr579jRliCYQkUt7iORyNAMd6NiOOY9AeJW+zNcfzC9YGKmMIQ4UwkrLRpGlCFCiauuayQqZt84mWMaKkD5ESQrQC3zbhWHqRlRd7iysB7estO9e+diu4ct5Y42i09voU2m6MNVSV8GLlToQ2Mdb4+a23xUFTNx4UV3vNTDP22TemKrAC+r0xcTdg9+WSJy8vsEZ7gJX1qvyyNmibk9QN3cgXgPoS6S8lVgQEvS7d8YBrN65z69YtJAEvjzeUZYMMBSoIvJB0fU6Rzcm2a4aTCbo7oEcPYxXaQhAGKKOp6gZdN/7zdZZAJPR7fXbGA1bLOZv1itpYeqMdBp0+VdlgGs0iCClXJzgTkHR7pCLEhTHzxRTTlMSxQlDTFNZrW5qcoLdP3NsliPs4EaECxSpfoesNwtU0dY3KzkncLdAldVX636WShHHCaO8a/ckO29UFG+3HbSp0lHWGtZq8yIglDIdDlJB0eilRIumEKQ6JbmqyMidvGnAaJVM6wz6DbscvYEaD8aO6RucsFjOePXtMb7jDYDgh7YX+PjSgtUegR2GIShMOD/fQxZp6uyGvHSumTIMlOqtIwy63e3dZmRVmW5HLNaay1FVDmW54evJ9xp3bBEnI/eEtSpeTy5w9OeCsWrM2Gfu9PWTtmK5KbC1wxkHlvFhTCApR8ax6yO/J/4Hv3Pp5qmJNkgbkL5fovOR58wE/qR/xN9/5a+xfvwkuYdNkTE+f0p/sUmRrkA397BB6E6QNOLzzJtX8BXVT46ios5zcVgRxQr0u0MWakcypGwm2YZsvsFrTGfWpVgvC+IB6W3C6fMbzi08ZHl7DSIsdWQ4P9ukO+4SRIlBRi1LXPtAxjKhTzenmGbFLOFmuOFcP0UHDYX4d6wribp+NrcjKDBWnnNsTirpkNc2wRyVhkrKJpzQ7jlxu0LVmqPsE5yUH24a/952/w6cnz3jn+jt89d4b3LzzJtPpjHjR8Fb/LueHb/FLt38Zdwp2VnNdxvzv/hf/JcfTkv/9PyqYm99FCIHWDUopumnCOsv8IQhPQXXaoZQffcdxfCXyFO3oWaqQwWDC++9/haOXzzk5PaeoKpRK8Qh4f8Lv9HqUVU5dFYRKEbSbqpACo81Vxk6cdMnWM5JkQqUzpAz8YVdrDz+MBSqIW9dbTVFkHsDmLNZUfPi9f88//e//LUcXC5qqoBdL3r3T5703dtjbjTk43OEr79acrx0vpwV3v/qr/LVf+TWSKCDPM4RQV2trq9zBuVbbgtegWGdaeqxfqH2x0LRFSNA+h7x6Du+IlG0nxmP+TatheV0k65dzj5DQTU3TFCgVggivOiC+c+KdqUoFVwWPsw7az9RcxqrgP3u/jYS+w/VnePxMFyjnR085O7vgw48+53S6wDnBeDQi6fRwDpq6pmkan3ZrvRj2lenEJwYLKRiOJ7z91ltMRiPquuL84pSzoxcI50iDkCiMEXGCEAGLrGCbbYkjL2J6vTNzKQbFwWI2ZzDoXXnukziksJ6VEEUhgsA7QaR+JXhqNR+XF4vCzwyF9CwTqRR7ewfs7u2xyTLq1RqhFEEY0TQ1ui5Q1hAYh41aAWKLQq7rhiSOUEnCZrNFSIlfAvwM2FqL/8cXK8a2QJ+2C3UlqOWVtOp1ga2AP1asOOvQWBwC1epaPB5fXln9qromiiqSOEUFPieivcPQxngzQHuCcM6yXi8oRE2qJKWBrNC0UzHiUIGzaA21sTQa6tqQy8q3KoXnEYRhRGeQMt4Zsbs3YjAc0O30yLY5RhtU0EXXW45fHqHzHKTD6Apd5RRFQmcwZDg5pNMZUNUNyJCm9jzMpNNvTxD+ZBOHMXfu3qepC86mG7bbNdoaEA6LxmKIkhjcmCiWdAdDisZydHTmxX5lQVODkhanC5TzLplGG4xTDPsjn3VhLEmSMBn3GPYTylowGk3o9fts8y0i8NdPksQ0zjHcvcbe9RvEiUK6irLIiROf8aIbQRiFjIc94hCm65ww8E6C7XpKkRvSKCbpd4nSiEBCvu0QqYC61mRFSahiNlmGbrOEyrphtd1wdnZCfzhhYixBlBBEIZ7b4K+jMAxJhmPmnQ7n6oIFZ5xXF0R1wpYtZ+6M4WpNR/aIgohSrHGhRAc1q2xLsS7Y7m2Y6wqna8bJhIVYEIkDrqtDFi7nbjBEScG2TFHdxLsbjIO6AeMQ1rDa5nwv+wP2spTrt66z3mqa1YZg2eG97pucmBn/7tEP2f/aAx9+WFvSnQNWiyPMooYa5jzn5le/hKgMotZYa9muZtjmnKzYUEeKJ8UT3u5/gShULFZnNKQEVuAixWL2nOc/fIyMU3rfGFI2GVNpsWmfNEjJOzX9gz6dvQAV+3A554S/gyWEQYySIbpjmUdLJvMIlUvqKsQ5A1HAxhRUEYgkpRsfkE1nHLlzgpMpZupgZTmc3WY6nBHHjspsieuAdWkJqohtdsaDt25ya3QDe7rgoD8kmy6xmzXZtKRaLPjW9S+iZAd9ltPMGrbJhoPgJtfjLjdHt1gtQsC7eMqqIAoTut0uRV4ghD/UefuxQmCpqxoVBq2msD1kCYXAEccd7tx9g16/z9HxKVVZAD6MVSmFCgIG/SEX0yllVTAIezjZakYsmGpDoyRxmqLNgCQZ4FpDQ6gCfFdHEytJlKaEgUJGHTqdHk09x2rH+cuP+Vf/7J9xvi0QvV1EBy6qht/+bMuPHj3nwc0eb90eIUXBxUby1b/463z7l/5O2/HxmrbL0NTLbolPOZMY4wXFiFZcbNvCoO1YeDez11YqqUBejn2EF0HC1edmrGkP637Fvsw883uPxFrtRa8iwiGxRvt75TUrchAEbZHjD6sy8N106/z6T9uNuSyALt/f6+Lc/7HHz3SBcvTymOdHp1zMVzggSZNWc2LZZts2A6TEGY10BqQXxPk2lySIEvb29/nq17/J/fsPiMKQ+WyGdZZsMUdYgwwCSmOxeUVjCmbrtXfaBCFxLDFF6XMc2tekjbnqSKw3G5I4xFhHXVUoKen1e4zHY/K8IM8LIKDIS1+RApe/vCAIkErS7Xa8YDaOUEFAlm9g6rsZO9cOccIRqBhVFpTrBbosCKXm4OZNiiKnjhRZlnkxKb6t3OkPqOsaUVVoy9UoDCExDvrdDnVT0zQaqbyKvslz2trr6vq6bNf9SY7KpQ4FaD9vxyVgCASN1j7d13rqrQ/bigiCiCAMPTTI2tY259CNBgQyVKRRQrWZ8/xoxWy+8Or59rl9C1G2c1qHbgzbxv8sKSRJGjPsRPR3JhwcXmM0OaDbH2MtzOYLqroijFKEMJRFhlQJQexoyhorJKOdPa5du8P+/iFKBp5dYyzWCtKkT3+gfRqy00irURhkGnLr9j1qd4KQEbPlmnVeI61DYXG2Ien3GI6GTPZ2OD2dUdUvEFIRpT20rjGuwRpLpQ1WxYyHfaLQW4zTJKUoNgRBhFAxQdwl7UUMxxMO9nYRC0lZNQwah4kDVtsVMgwI0oTBaISrN6xXwmd6IAjiDqVxFKXGaEOZZWxEinCC1XqBFAE7oxGTbo/9gwHWFJTdkLyynM6WvlMTKgwSGSQI4WMaqspwfHxMrS27B+fsHxywu3dAoAyNrryVv0V0a23ZuJJZswSrWOkNyxCsrFGiotkYRmGPUpQIJYjyhk2Qk5kCMTd00glLNWMVFJzWGzQR99Q+p2pL5BTSQRDHxFHft7WNA62xtcEUNbrSzIotPz36CLtd4VSOahT94CY91+fXbv5dfio/4b/74f+Dt4f3eHd8l0Qr1s+WCBuinMK8WDB4o2Y9fcnRZ49I0oRktENdrTmbHTN58A7LDE7PjjmYHBCMd6lnG7KLY5pIc3H+kvliyv7121ycPOHJxXMugi23b98kKCzbSU3vICVKFQ6fgOtdiZpIRvTlDoXL0U6THxTI51toLPvyGt2dQ0b7B1zv9nj04vvUZsZk5xrL0xckdUA9q1mtN8RhTKz6bOucKgeMIDQhhal4uXzJWpwxOB8RLTIa5bh4fI4Ot1RNTlE7nGx4f/ddAqfYbtdk51PsgYS1o1ppROW7DgifiF7XBucawjCi2xHkZek7CVjquiKKIr+2NhVWRAQqfHVYutyAg4i9vet0Oj2Oj4/Ii4pSKsIwRimI0pTxcMB8uaBsJEky8A46DNrUyLokChOUCmh0DkhvqLCePC1VgGvHTN5erBiNhhTFhqaqePnR73D+9HNOMkfjIsIwJky7yDglp8NHM8GLbEOkMt7/2i/x3rd/lX5/4A0b7SHQE1sV2umr4szrTzy5+4qr9doabK3BmqbNz+Kqy+K7P9Lbn/F/V7QNdGdtezi9dNl4Ya2PGfH7gcCbUS51Qda0dFrEa10XB1yyUpwnpLfrvneNehaYEH6t/7M8fqYLlE1WsskL4kARSW9FLfOcsq4o8hzTNEhnfZYOop1teutuf9Dj5u07fPFLX+btL7zLeDzGaC9o7XS7dJIY6obGCYQRNFhWq7UPbtIG56rW++6FuFVd/TGrrnVgG4uWvqrFiTaPxDto9vb2uHXnHk1V8MEHHzCbzqnrGqlki2oOvfvFaW/Xk7Kd6jnqMveFRreDDCUYx3ZbIKKAyXCPpizY39slKwpmFwajNU1j6PX7qEBRlh753LSVs22tx0oFBAJPt8SSRAHaOmrtBa+++9d2Of7E43XoG7Q3jvD/9q1U69N5W4U5Qvii0YJpfEFnL5XpLewoAKqyoqoKpITxsMcgiTjbLjk9O2K9XSGFHytZ41k2QnpbY9Po1qnVCr+UIOx0OLx1i7v37jDojRh0Jwz7Q6y2GFP6Oa9zxGmHwfAmTblgs5ih9RLtNIPemG5ngNa1j9MgxOiGvCzJ8y3ONSRhQNDaPP34w7F7cBvVu0GnM2C1LdgWGXEUEUUBaTpCpjFb3ZCdXDC7mJNlW3Sj6XQ6RGGEFIZ1k0EwYnJ4m9v33yKKYpzbgAtYL+acH3/OalPQGxwiU8twOMRhQYZoERJ2+uhmQxxrwiAgjWOaKiROYnq6R1lpXLn1gLsgZl026DqjWC+ZzxbESZ8o8eBCRU0nari+J0k7AVJ22DY+vRpXUxdbRBvpXpcZgQSQzOf+vc1npxTbO0jboPZ3QDXYpsFovMA47fkQzV6fyTbmJDpDHg6IMKQXEpziws58l05aBmqHDilVTyLqinEwpuxaNqqmtI5zveFGsM+uHGKF9O6oKCBRKaY9PQttcU2DDiVNqXE4ngZz9rIBHacY7SnKskS6Fe4sJy9e8FKueLz5bZ5NH/L18B5hHRGUvfZkqZkevcS4nM3LGVliGQhHZVeoMOSHD/8HTqOMt/p/ibq21GdHxHsHrM5Dzj/7nBfZE0wN14Xk4viIKlDUVU1ZFnSVwk0sUUf5yAznRY5OOrAQqpA9ccBT/Yi6qlnKLbXKuSl3uHb9Hu+9+/PEd3apVUAcDjh9+iH5eoOtNCl9tszJlEY6hZVQuoy6qBEmQlQVIpQ+oDSqmb84xm4cdTdivTnzo0JXkyQjhCrZV2Os0WzKLdtsTlJ2KU4KZvMMUVa0FkGM9QnujgaHJAxCOh1FkWc4J68gY1ZoJALdNK1mSCHEK9qp7/oGDAYToijh/PSY+WpJXgaE4QAlA7q9Hlm+8WTYMCUOY8/9kDFNYxFKI5RnawnhCIQXtjvbMD64TRAGRFHs826cZyqNhkNmLz+iZ8/4xS936fz4gk+WFbbZUmynZGFEFI0ZdlNkLPjaN36Zv/P3/1dMRr44UXh7tlR+87/ScbQic5887zsiPp4D/1k4dSVs9VJF2Ros/KHwksvyOureOoszDm0LBAEIdZUZ5Dsd7jU5hO9eX2YLXTJQLq3fl3uBc54pZlp2l++aXBZQ7etBtpC8P/3jZ7pAqY2flYVhgNaapiqpqoaiqrCN3xyiUOEDknxHMAgjhsMh9954wPtf/ipvfeEd9vYOCMOQ1WrlRVnWIAVUzlFojbOaUjfkpa+ofXCfBt202Qf+YhLOebhQOwoxzqK1IAz9vNNaR1UVHCSHmLog35zT7Y2J4xjZuoOE9PkV2ta4uhUjta/97bfeIk1SPvrwp3zr576NVCF10/D0yWPW2y27gy7Xr9/g+PQE3VTEsbfJSQSBkiRxgFKS5XzhQXXtGMe1biYPhpNI59+LUhbT+AVBhSF17d1DlwWEda/U4vDHi5PLnxtFIePRkNls6m948IFUylNk/ehH+0vdgbKGMAzasYLXu2hjEVhCUWNqw2w2Y7VYgq0Y9WOq2rt4wqRDGMXUlWa5XFE5S2RCXCCIex1u3brNvTsP6HR6ZHnNYr4kiSTCGQQVRhfUdUm/12c03KMMG7LlGXEkiNIhQdRBiIA8q7BCo2SMQlCXW6wuwTTUWmOUFzFKoZGhIggTdnaHJEmKU1vyvEAI6ePaw5Qg7hBKx8unH/D4s0/INhuCMGiV85Y4lFgZ0unvs3twC+dCojBhu5qznK9Zzc6YTU+Z7Fzn3hu7nMxXoPrMiwaC2CfJYhBhQncUkXQ6lHUDzqGCgCAMiWUKIsA5GA730E4zn2bopmK7WrGVK4bjCUGksE3BsB/jZETa3yW3PmvHGlivtywXSwLVQQUJOO9yKCiRQpAmHcpKc3p6jLWGpJty8+Ye5/kJVVUQpfs4GRMSYCtD0O+wJ3dZhopgt8u2mbMjJJkRzOsVCOim+5g0YtyE6CBFqZDD7h1Su+XcNGhnOVcbviBvIIxqXXUBQgQ4YRBBazUWAYoEGTuQklIpHpVL3l4O2S4bkp4le7ElSCL6Er507QEfVJ/zaXHC5CTikAGxrUnTITawLI9eIpOGbLvGVBWqHPngThRJZ8gyv2A2fYYbX2P12Sek6QV73/wypycvWYUV46RPEGiMSQhDOOjdYzLapzQrwlighAcb+jGnQDpJIBVCCbTTCCNp8oJhtsdT/TG3xjvcP3yXZGMIVxWyttyIJzTpdVaLjwiDiHxbIkIQI4kUMZXLqZstaTWiDgoowRY1EDFSY+aPX2K7IUVjWS3PsXFFoFKSzpBeI6jXWxbRKVlTUVGSNAmr5QUnmyWL5sSzQ1rvpMMgpULrCiF84m6aJJRl5ZkorYvHCEkoJdo0KOEQwrsZr6yxQrQd9S43bt4miiNm8xmFEgyHYxAho8keF2cvKLIt4aANiRX+ntN1TRQlPjMnX5F2RzRlhq4N+7dSOmnk10ut/c+VjkAKXnzwe3z2ySOKjWS33+XvvNOhGxYsZ4ZHJznnuUMQ8/5bf56/+au/zmQyaQ+IGnjlALpCyl86FwCrfSK979jaK1eME+0I3PpOrhf+vvoMxKWo2BoC4R2VXsfidTc+6+xSENs+p7sc6QPOXnVOwiBAygDdjmkuD6HGONxr+9Tla5eiPTDi5/BlU/Dk6OHVPvGnefxMFyjWNARK4oymKHN0o317SRuiQNBN22qPABnEqChmd/+AB2++wbtf/BL3H7zJ3t4eURRTFAXb7ZbT02POjl62ibuOUvuQwaZuEFJ4Kmhr6RLSV/5eBN22yWh/0eKy1WYx5tIm5scp0+kFD27uUG1zPv3kERfTRXshegBa3bQiJOtQyp9ab928wc2bN/nuf/guP/etn+O/+Af/Jddv3mJvf4/lfMH/6x/9t/zb/+7/ybOnj/jlv/a3+OzhEwyWbpJgdA2VpCpLb2829iror51eAr5A6A3HjCdjVtMzTFO0iH2I4gitL9Xl/ub8k9fZf6TQdv77Vps1frIprpTm1ni9jROtP986hNTtKUEgCbww1jqqljcgrOb47DnPXx5R1d7h1Ot26QwiaicYDCfEcZ98naEb5733UhJEIXvXbnD79n263TFlVSNQVGXOZtGQpBFpmvjE3qyBZsX0tKIqt1hjSTtj9q7dIYyjVhujQARYbaia3EOZrEbXbe6MwYtVyzXWGHrj6/S6MUEYszOOGQxqiqqhNg7tfJCkkgKHoig2FMUW1YRIGRFFAqEk/cEO/fEe3TSlaRxWC5bzBRrhu2hRQtgbcXj7PrYz5Wze0E06bLcr+t0YkgHbwCLDiE6/R5VvuZgtMLVjZ3yHr77xJS6mRzx+/DGbYkNd5uTrFVVZ4nRDZUqcM6TdFNfEOG5AkLKuFSeLnEVuWK5zVos1y8UcZRc4K9hu1wgalFR0ul1kEJOmPZwxnF3M2Z2uGY4nLJcbtusFZxcXrM4v6MouA12hS8du5xpmO0NPYurbO6wXOd2ZxRYQE5I0gmAQs4hzJrMAgyFOuuyYkDQ/I9c553rBrfSAXdHxXbzG0qD9+Mz55FcB3qkXSVQYIaRgnjheujW7S4Faztnr3WI02GO+nHFPD7GDt/k8f8LKCMYziRFbVB0zHO2TrZaUT6eYuqauc7af/xSkoKgcn9445/29rxLPB3TG11hsPsUEEesX59z4+jeYPtnSLJ/xcv6MXu86Ro64desBO9dukq0vOCi31P2Ul+IxTmzRxrUdO4lDUtgNUd1Fbubc0/f5YfV9trJmoEOapqC6cNR1hMkrbLakXp1ggHM7x1rDYafHGzvv4awgtJJJZ49TPadoLmiqEiUSlkXNdjsFEWJdSKQTljZnMIE07nOdPTblikpH4CwqCJFOkJ8sebR8xNyeME77xE1IXtZeQ9NGgNRVjbWGOI6JEyir0gs1AWsMRgjv6DEGg0Wo2AvgnW3ND6o9vCYcHN4kjUPmi3OKIiCOIzppl53JHhezOUWZkCYpSkrCUKBpaLTB1g2uqZBKEUqBlQKpLsc7fh1rmgolLLOjT/iNf/7P+d5n52S5BWl4vwu/+MU+Dx50+VK2YrGsKeU+X/jFv8LutRsIJ9oRi9fZeCu11+uJtjjxYxHb8k8urb7eZCGv3Dh+LZbCIpzBucD/XZm0z/Hq0NjmG7bPEeO1LV6naC/DD680I39cb+L3s5YwjvOOJWNomrrt9IgrR1AYBlfFDvjCarGec3T+O//RvvH/7/EzXaAMUoVyacuPaHCmIRQCEUuSKKDXjbEqRMZ9uoMJu/sH3L57jwdvvMmNm7eYTHaI4/iK5Hp6csSzJ58zPT8nrwyNcdSNxiIRQeAhZy2ARrRgIedsK4x1oEBaQWMsl0NCh0Brf8Ep6agbQ6+nOL1Ysd28ZL7Z+pNrq7T2DuhWv9GKlozRCBnw8cefsZzN+YM//D6bvOTv/c/+Pr/0l3+Znb0D/txf+It88sFP+PTH3+MHP/wjCCJkIFqFvCEKJMvVtgX8WO/M0dZrD9r2nxOOuq4QQhKnXbZ1SRAonLEUhbdVv674vqrUX9OgvIL8vLr56spTCqWAsB3DCCAMFQjZwpwUKlBew9DOfZ2oAelPD05yMat48vSEdWGQSZdQQme4w87hdZwQxKFCuJSFipkt5uRlRqASBuM+t27fYn//OkEU0+12CQKFtZrGGVwNo+GQXtTjvMkwxYbVdE5eNgRJys7OHUY7t9FacTHP2mC9iF4nRtJgdUO+mbHNtkRhzM5kgnCaRy+OWW8KdmuFkR32Dq4zHPZJYoWxgqw06FpTZBvyqkTFKQc37nNxPqXI1iyXC/qDHkKF9NIuo+EQR0hVw4vjc1ZZw87ehEg5amsprWOald72W24QZoutC3b6KVE4YJMmOCSDwZCL+YJu3Cfopuzv30ZGI8J4iwh6lJspm+U55XpOXRbtbFoSKt96FyIkqxoevZxS2BWLZYUxgqaCvKqpsi1FnmO1xVkN0hEGCiEV680Wh6LXiYmiLs+PztluM8r1Oacvn7OaztnbGXK4u8ve4IDldkXgAoaiQ750uMOI5pqCbsT4eUi4lQQyIMok9lrCPCzZ2VYktSCJUgayQ2U1484uj6uXTNL3UKXXajS6aBO7TQsYkwgJIgyQYQBYtDA8Gxu0MQxnDpefEiQWJXrEG8W90QHvXf8qX568xQff+02m02MyvSLenuNiyF2OCi3ORMzWJ2hnkEHCRb3iuye/w98e/zpB3EP2h4h+wsXHn7D3rfe4cf/L/OAPHxJhCK/1efcL3+b67oj9vUMWpxHb81Okuo1zjlIUPHOf402zEcIp1mZNWCTEW59JdWfwgHvhG+jaoUWFnpUQTZhdPGVWHlG4DRuZs4639OKErkxR0neBiyqjNhsQllIvKYsSZSIauaVxW1ymiFSfRmqEaNiZ7OEM5OUGUgNiwF48IdvmxEQ8nZ/y4/kPISj5C8EXqbVjvlwwX2/ZG418vASWpvEdlSiK6EjLNq9QKmgPNwIZXWokHM5pr1F53W7bumGUDBlPrhElXRaLU/TmOcnwAb3ekKLIyPMNgQpQKkYQeLaIc2izRUiBzpYIm2FtAMZiFQQyQNsaXWbU2Utefv6vKO0aG4TUSlA3GX/w6YKzC8XbN0oS4eiNDvnKn/8bvPH+t3zKPK+KB/+1155cQjD9Xi9xrsZY7TN1rkbpXl8C3k5snUMJgzAFTnZ8sSKiVjujcGicUjgrrooJ1WahWXfp0Gy7M62+xOsSaUF5rWblan2XGNPgdIlzfg13zl4VWca8FoliLdrUHJ3+mKZ5DNz/U+/xP9MFitaaSFom3YCwElS1xKLQQnkx6mSHznCXdLDDzv41rt28yfUb19nZ2aPf7xOGXgBaFDmL2YyXTx9z9uIZVaXRTlBrgzaWqqmxRtNNvSbAGIeMY4y1NNpDeiT+IrHSeZaBbTsECCIlr8RB3W6MsVA6S1E27ZzOY/gvH1fOGCFJkpCD/X2scXz2ycdYa5gvFhwfn/CH3/8+UoXcuHmLhw8fc3R0TKHh4bPn3L17HyW8ktr7M3yvxLQx3H7EoFBW4LTv+mAdRZZxenzEeDykPxhSFRsPJwpCtKk8Q4bL0fErT4/DXVXm8McdPl6D3tqPhcf429be5zUjrZXZv3sPR2q98842WBxFWfHho2PqQjHcu01XBBgD4/093rj/FtvVlO1q5sd4WpJPElAT9vavMRwN2dk5oN/vg3D0OikIj9av6xptDFor8iLj7PwMk0+pG41M91HpHi7aYVsFbLPCU3SbnKIqca4mDQW6zii3S6qmJOmMSCJFJGrWyxnTizWNCam14WB3h53hAZ1OBEBZWbKsYK0aklJimz75cI+s9IvtyckzFusNq7xiRyb0GkVZ5SzmK1bzc6SKKTRIUVE0krBWfPzwOev1nHy9xNQFURoRBSF1XlBsclCKRW+EjDq8efcAYTUvL56yWi0YDMb0BtcpK41aT/3YQCWoQJFEnruj4oQwGWBVnxcnS8rSWyBFlCKcQjvpibQtXyEM20K5/e1WVeGLnVASE5Ovc6YvHpEtzlienyCF4vBgFxlEpCbC7YfYwhHWik4usGVI1nVUoxArLf2nNbHzC/C47nE0aVB5SXy+pXf7kHvdu6zyzzhI9vko+4B1WrEnQhqpPevCmXY67mmdKogQMqS9fcFJjLKcjCVsNEFlWKyXmHrB9VASZgmHAfRDxRtvPGC2OCKrMsTmAlYGJy2bcoswFh04GmtwtsIhWJuKIqjZ5huC6/vkiylhpHg2/Zz7X/kWn+3eQRjD9Qdv88bt22xOztB2RVqGiM5NhO0QNAHjYMxK7OGUQYqIXOdsijU7m5BkK9kPuvzVu3+BwTZlWS1AWsaj66yqjIv5I84XT3z7tJsQMyQxUDvBs4un3BrdJisE0+Uz6igiTBKydY2yDZPRLpsqZFXOqIsZddSQ9vqIoMPi7AVJL2a3N8GZIZvVDFPWvHgx5SfVc454wt9Mvs6by0P+KHjCertmuV4RhxHDQZ9AeK1eVfkDU6giOjFkRY6QAa4pr1wiHhDm4WHeJAHOeh0J7cEIFN3eiCiKWc4l2kCUhOztHnB08pKiyAgDhYq8W1I6g41DVvMVVVBiyg2qf9A6frwLL5t+yvmjf4XZPiWVJX/rb9yn/Mcf8uMXAVZE6FrzbBWytfBgL+QXvvOXePDNv06URjjXYJ1fG6+swvZyzWy1IG0Mi9UW0454TGtmwBocpsXr+yXUeYCW/94gIgpojQ3GZ4nJ+NXPcAqEQ5umLTqkd3ddOjvb9fn19dwXMX6N1kZjdIMU7tIg1Nq/X414tOZqFHU8e86Tk+9iXfZn2uN/pguUo9MZuz3BtYHkTn9AVhs2VcCmCehOrnHzzj32rt9kMN5lZ3eP8c4Og4FPmlXKO1eqqmKbbZnNznn59CGr5RInBEZb6qZB17Xf0JVCBiFOSrr9PkGgKMoC2fhQKqsblINAWJzycDLX6kllIAiVt2qmScKt23dZrTYYbdFmSZLGlI2mbowvIK7AbJJOmjAcT3j8+WekUcDBjbuMxrv81b/xN3jzrbf50R/9iNliyUc/+SnL6QVWG2SkmC0WTIZdemnCNgy8Luf1AkL48cllu020DhglBXWZU259wnM3TYmUZlM2NO17fWUnfq1X517lOQBXN5onLHvxl5KCUAX+tNKmGTvAWN+VanTjXTta+1QJqVDKP19V1ZSN5Nr1t9kdD4mSkLBtxyehgCQi0DEIySAKCUWXPW7wzvvf8dyOTe67OWXOvMo8uVQbjIUwCKiKiuX8jOViSZGXRL1d9nfeIu5OCKI+zimaxtKY9pyaDKjKNbP1imxxTpXPCRWsVxvOT88QtuL8/JSmAWRKnMQU2zW6aXA2IokjAmmwukSpPlVZkJcJFh9qpsKIvZ09lusVs/kULWPG19+g1+ug7YLpYoVzkovZnDCSDEf7OJtQb6ZMj16w3c5JIkFTSzZ1Q7neMp+tMNbRG4y59+6XuHHtHscvH7OeHrG332XQH5A1ls12RRClqMhTdMM4ZTgYIAOFdRDEXbSNWK+XWKNIukMP8jMV1jiipO+tiXXDoDcmjCV5sSJUMU4KjG3IshXOgalyLo4eYeuKdDBisLNH1O36IDRrGIcdMlcRNhGmrOhvUqqkoQ6gHoasH0jci5qx7jNcCKp0jDkUcBrRDQ94Oz3k2eYMIQO0sGxdwY6QaOmpwLYtoJUI2hOp8EdXF/iy5dJBEVuymym3tx1k6aiajNPZEzIRUlYZ4bamN+gz7A/JQs9eqjYbet0eWpUss1VLMdboyFAqTQNc6CnXyluM7t7myclTbD4jlYdsZk+5feddXn78R+hizXIxxVUNWbMl6qdEcYdKNWT1in7Y4ct8jSP3lNJmjPSEkGuMiOkkN7iWXkf1I5zqsD2fk83mLJcvOT4+5Xx7RF6scN2EyXBE0ChcvabUgnqz4s5eQ+MMF4sXJJMdhp0R2pxBUVDokEbGFEWOtTU4wfWb98iWOYEt0UGNESHbTcPi6UcMBzeYFzkfJye8LUa8sbpGqUtsz/HTx38AnZA4UEig3+8ShpEPFK0qiHzx2O8qtlnmtQ9aI4IAa30ejXS0ugoFBO2m7xfhywyxJOmys3eXbLvBYYmSLuPRLtPplKpKCMKQQIBzNVGkiPsTbLUhiPtEnR4CTZWtWBx/xqff+0essqd004ZRv8to9wa/+K3rlMWGZf8LFCxYPH7G/RtDfukv/Qrf/KVfpz8e+jHJa4F9zvmODc6jJvxGb1GKdtTv/7s/RFpPi7WXxznrOxXO4HONCoIg8WA2FEpIiir3623LtLKOluj6yoVzyQeTwmceee2IH9lc5rddFh4efuoPd1HkowOumC1WXBUll66e5XrJd3/4T6nKj+kFnT/THv8zXaDk2YZoNOTe/Rv0Y0mpFfMyZlkEJKNDrt++y+HNm4zGY3q9LknSaa1qPpWxrr0qfrvZcHL0nJOXzzFWIAPpcfMtkj4MY0aTCQ5Hnles15v2YvICJWO8mtnhCAOvN5FCIpTXL0dBiAx8myztdHjr7S/w4x9/QBDFyCimNqCCGGUKBBInFYGUDAddbt++w4uXR8QCdnf2+U///n9G0hvwzW9/mziO+eSTTzl6+ZI/+P3fodxuEBIP2KlLtuuGNPQz26qqPQ2wZcCEKkQ7Td3am30eSavbwLLdbsmlQkp/IzSNbU+cl4r5FsNzWeG81jGBVm/S+qbCQLVp0bIlOErflrSGIPTUR4FnmTRae7+/uxyLKS7j2bv9Pvv7e/Q7CSoQPnVYhTgLYTwgjmLvnmkS4qBkZGMiaamNZnpxwXQ2J99uEBicbRBWEwYCFSYIEVEWG8q6JOhdZ3jtCwwPHviFQEUEUUIUO6psi5BRmyXUZ2+wT7czYXbxjHJzgSnW6HrFcr0GpwlEyHpxStNUCBkjgoj79+4QjkdIKeh0UoK6wdiGwXDE/sE1hBDMz1/QH45wSlFWJevljIuLKVbcoKwsQobESvqU1d0dkCFPnz9kOj1Gl2tMkyGMoMpLitWWorQIYoQzVHlGtlzyyaOnrKcXSCsoixWhLZkMBqx7Y9a9PToupJPEaKsZ7h0SRBFFbdC6Zr2es9luCKMBkYNIhmR1hTCG0XiIaRKqvCIMI6qmZrh3QJB0yJdrhh1wdcnZkw+oy5pOr4vsdCHq4OI+YdRlmxTYSDB2kkQqjAjJyy3xytAbxiwC48eivZDNfUXwrGa/7PNWeYf1LY/yH0QHBImhE8VYKenGPVZkGNFFytBbT2WIMXXbEvTXmWyvXg+88jh0Zy3LsOJpXDMsHYMwYNMUXEQ1uVmSnv6UfTNAJZZyNSNNxqg4pnYNWlYUosI2AqUETTemifzGlIkt2XbOzuFNjGmYLk+4mQ/47ZM/ZDDZo9ffZfryJWdqxK4YIcYJUniRdC7mhI0jqA1G1byIXrC1G1yp+Ub1DvfkbZJunzBQOFtBEJDsHZIOBzz58Y+Yn53BYAhminQwy1a+UFQhW1sQ4ShNQZSO0blGOsl6vWRTLCjXS0ykIeqTlSvCOKSrYiSOYnpGw4xisSRM32G7Cigrg6y3zOM1UWz4881XCUNFrRNMo5kvfszHLwbc3ZsQhzEqVPTSDlEc45xFm8Z34oKQNE09cdY0SKlQeCiklBonFcaAkkFrYOCKAXL55zBMGAxCr1s0mvF4iK5LVtsNQaCQadyOVwLCOCFfPEeXNevVT4jFko9/7zf43u9+l9lihRaG3gDuPjC8LUdYDTE5ol4ySAN2b3f4+W9/k6/+/K8x2d+96mC3DH3fOXau7eRJ37Wwr3UsrMPYBne5ngrp89ykROLFqXWj2+6HRYV9LxoW/gBempLtdsNwOGlNEQ5sjdaiHe1c8k8M1vpOvgpCpPSFSN14/ZhqM+CMMRijW7CezwrSxh9MlbocW3nHatM01E3Fb37vX/PjJ/+e/UmBlCGXXfc/zeNnukDp9Ibcfvvr3Hz7PlIYtBH0TMhuBYQJuweH7O7t0+/3iOPYB8ghuEyD1FrTNA1lUbC4OGO7WiFE5BkkVe0tskGAUorteoU21sekG4M1Gom9sg47p66CmByGOFRedY1ouzV+Dt9NU7qdlLe/8CZ1mTHod2kay3R2gVGSMIjAOaJAsL+3R5YX1FnGoE1u/u5/+A98+Zs/RxrHzGdznj19yuNHDzk7OyFuasLEzwCbpia3FmGU76oICa5pW3weV6zNpZ/e3xTtWBTjBJU2KGlRwoOsmrar42+gS4X3Jc0RLhFul+4dIT25NJB+1hlGIWmcEIehr4bwN5C2FtWeYo0UPrvnsl0p/YWuhCCKI27fu8d4NEFYi7M1znlQWhR3vdbBJQgMBBF9auzylJef/S6nM8vLkynnZ+dstls6iaAXK8LQEIf+TQdBCmGPdHCdweG7DCY3CKKuh+XhWGUVKkyJU8jyNc5YsA5T5+yMR4xHXdKoxNUFQbXmx3/0Y4bDHuNhwm/97k/IVzXPPisRWJr6G9y7/4DJZOQBUkqRJgmCgM16yRzLaDShrjI0lne++EU+efiCLMspj0+JVUCnO8CZkkY3bPIcTM358RMvsHWaptzSYLC6pspLRNilk3YwxhCnXc7PZsxmK0aDPlEYo3XDs9MjisaQrxbsj/bJki5RHCBoCOOodWUFxJ2YrSmJOwOQEcVmjtY1db4iy9bUpc98Go93CZIYu1nRHe+yt3edpXjM4vghxWKG0TUuTCHpk/Z6FFVJf7zLYDxh5p5xnm6YV1v28i5DF9Pv71IVBePtiCxpqCU44bCJZHM3IH1uGLqY/VJQdro0qzW99JCD/gFnxZob3Ztsm5yws8PbgxscyAJjcn5y8j3ypkAS+Hm98Ne2d0ZojK0wtsFS8iwUjCYhB1WCqDVD1SUc7lDLPp+fPCQUCqMgKzZ04yGmLiEI6Q/6KC3Y2AoRh9zq3uciP6EpC7b1lpNHn+DimFJqTlXGU3NOvyl4c3Sbcd5neXHK9f0bJPGQgU1R+KL+K+t7nOsjNt0Lr5fQDbosWa5muPAmVvapdEYQdMmOzwm7EI52iYZjRu98Aee2XEyPkVZTN7XfEIKAGTn9nmGRr5mkB/QPd0jSMWezF1gFIgwpq4bA1eAcsQyhUWzmG/IAzvQ5+9GQ7SanyB1H7ox5/gnHacW+6LM4e0rRCTBZgt6rMVrz/PjHnD74KpHq+Vw0B/1+n0AqZCDb7qMjilN6QrFazijbsXGgFEr4DdPhR+9R6N2CV3rASxGn8x3dTrdH05Q0lWF3bx9tTqjrjXcRhl5gGsQdyvyc6vwM0Rny3X/2u3z28CnbDYhoyGRvhEoTNs2Ap48znn84pdCg6+fcOzjg/Tff4Z2v/TmGOztA27lo+SNI/x4lvkvnqXGX3WfpD8FcJsiDtgaL8WuucyC9LuTKYC0VIuwgCLxDUAg2iznO2aviARymWQMJzvp96bIr7osVdfU6tW5jYaREa9PqIX2BItsOC3h2yiWD6vKAaoznsPz4ox/y/Z/8Y7S7wCYBQVTwZ2HJ/kwXKLfeeJt77/0cezeu4XMJoKwtee7nlsPRiH6/R5KkV+KdS/GPaWmmHhBUY+scYStq7RAqQEpBHIUt1tffGGEUt2ppX3hIGfoq1FgvtrOgL1XT1rWgMSjqklAFnu+g/ezu5YunKCXZ2ztktVqRbZfQ0vrCNGbU73Hnzm3+8Ps/JI0iQFIUORfn5wwGfc7PL/id3/5tfvqjH3Jy/AJdl0hr6Xf6fn6KoG4sqfQ3Z900VzNEi4fzyJbAeEkRvLK5GYPRDqd8ASHcpSamPWj67iSSS2vfK4LgpS1O4Mc5SRwRhgFJktDpdEgT36otygolpFfLaz9Gc8a+hmAGhSIIAj8blhJnfHJ1GvU8L8Bl/qYKFBJPJZXCQRCCXhFJS2IWZBdzXjw+Z77KqLRmEwqyNCCOJIOOIgmhljXpYEDU2SdJRygZEcmAcW9ErmvyKiNNIg739jk9P2a7WZOkCf0O3Lq2w7A/wAPaLNnsBLYZm+mnhPmcYSKZbgoKZ3j28APyYsPFfMpX3/8ae/s7xHHqUe+ioBM6pNCMRxNOT7b0On1Ud0A6qCnyDFlpZG/or19tqPItzlmKqkAKS5pEYAKqcg1GIIyH2KVJiNU+Lyjp9AgDRZR0Cbs76KbGlIZSL1kvLyiK3NsTsaRphzCSxBKqKqOxcHDtDt3gGsfTBVLGlKsZ1WaJqzNcU6MbiwkcKo55570vstmsEDYim52wPfmcajmjMQaZDNEIkIob1/YRSvHuV75OYDbMzp7hpCWLCkxkaOiyv5kQBH3MUnOQDDnprdBK0w33+M4X/xK8vSX7ZEmc18SVBJexzje8ufdFpke/xxvJXc6puTZ4g2f5Qz46/33W5cq/FpS/roXPLbFGo5sG09RoU2OFRcoGG0hmkWOdahIC3qgt3dWcInQMDr7M7OQjwqiPDg1i0KOYn2GwuNqSiYJGgUDSCWKu9W6Rr1d0r+1y+uQDxreu8+xC8bk6gxh64y63gjeYfe8j7EqS25skmWFw7S4i0sQWOv0RZvYUJR1pEyBcAk2f1ExYreekShMkMWHZ4MKYdaXJHn/KajPj9OQ58Y6iP75B0RzRUQIZBjRKszGasm/YrXPKMiMLpgyp6O11MLsjLh5vkCIgN0sqmZMS0Et7bOyGHwQvyN2cTbLm2fIEZwPGd+5TuB5n5Qe4puHzquCevIG0NUJKTOCI66c8/Ozf0P/y3yGOFZvcC+r7nS4qUAhn0E3T5pAlTCZ7LJZTisKDM12cEkpJiMQJQWMaQhH67rAzYINWk6FBqLY7myJFgAo1O7ua8/NTyipHyqEf8SQ9BodvkuVH/PSP/oAfPp2BteQl9MYReQm7996iV52TpAE37o6oHp8zCDRfvLfDrVvvMOw4THFOZXpoI1BB1HZ17CvXjlQI6V+noJ0AobHai2OF01cjIK+r8R0/11qvca4VCyuCMERKSZ6tWW9WTHYO/Ht1UNUlde0PGpfRH0bXLePEs8Sc9d1tISRhqDBGXxUyr4qTdsQkJEFwidUHsL5Tb+HlyRH/5rf/IUV+RBRBYPvU5Z9wev6PPH6mC5QvvfdV7t57k8Fg6FmAWhOXFSrYIqVkMOj7BTYMucolaF0ol9k81likM+z1FHd2Ep5PGyprMVJhbenjuPHtwTj2eQRIifP0KcraJ/FK4YiiECVdK9ryP6esPFAI6SiKnE4n5fjoJU8fPcE4wcnZOVY31HV1dULdG/fZ3z/g4aMn6LLybA+tsRoOO12cdfzgB9/n3/zrf8Hx86ce3Ga99a7UhjgJ2qjwBN34cCzTsk4Ql8GElzkPXOGI1esZEM5T/4SQSCGuBK6mtZFdWdzw2GTP5Pe/F/9zfEUdJQnD0ZibN+9w9/591qslq8UMsZiRZTm10f6EpASBa730tK1CKVv4mkI4y+bsCaY35u6DrxOEEuFUe5CQqCDEugA/SFPYoEva6TNqMm7tKZ49N0ynDaaxZDXoxpLGAVgHiUJFMUkwABliTEOR5+jGIoKQQa9PGCtfqCrJzRuHrJeKybjHm3dvI2zNs4cf8/TjHyHDmGR4yCabUc8fEYeGW+MDSiPYFJZsOUfXJWVREAQxXxLvc7CnwBmP5rYesrbMBI2ckJcliB7j/Qe46Us2Wc52NmMw7DPsjVloQ55lSOFQMqA3GFFXGWXTwWmNrhy9TgerG7abFUGUkMQRSX9AHPexRrPNt9jGEgQR1XaFbkdv3d6QMExZL08oZwUqdHQ6XVYXU5JuF8oNlhyhS/LtApzm4GCX3rCPcTWChmy9QDQVy2efMD1+SFmUxMMJWMF6vfEcFukIRM0X3njA3RsjTl6u2kI4QKG9+2cfXLjhcDUiEAnRWiKDkON0wdfu/Dx/8+s/TywFF/dWfPLb3+f50Qvu33iH/ffvEqaO/geKXgUrYTl58QnaVdSmvLJNtpezn+8Li3ENpc7RdYPVNUoFyDBEELa6FEmuNJ+kS96sYG+l0Ts7JAc3yTZLwt4QEXWp8i6b1TFVUZHbCjXuEseSi/yM+5O3yAdPiNKQR7NP+fIb+9gHu6zDOd1hn4PBAaNmzHO7ZuQOCNYOGzRkm4zQCWqtKfMNvWjM7WLIoT2kEjlnTcXp9ohGdHCiRmwr9LYkGg+QKuTs6XOK5Tk7966zLiuuX9vh0fMpXdFglGRBRY3ABRGNC8iDLWZbQQUiXlNFJVlSE5RLtBXkwjCrntHvrFj0Qp7EK7oBKF3SGY34z9/5Nb743i/xR58d8V//8P9IKXPCyS7VxpEGEqkC7h0e8OWdmDz7kJ98Oib++t9lTzdUVUMSaYQS7SbrqMrSw9LChMl4h/liitWCigInFTL264W1XqgZhsqvVNb5Q6XVBEHYHjZ911oqRX8wRjcl8+WSRtcEgbce93ZusXgc89Hnz8lsh+02p9vpM1tUpDvXiaYZiAVsnyPFAe9/44s4Cg4ObzHZH1IWS4onP2KwfxfZ2fV4iiajznOqKsfphk46ojvaQZuGpvEj1KbMEVaDE1RlhiVARTFRmtBJewRxxxdm0uvjRJtaLKVENwXT6SlRmNJJe+2o0jujHBFaW3z4n2u7515sa41tE47BA99e5fZ4LlPLuJKCOE7ajsulkPayEwOL1YJ/8W//IbOLDxGupgaezmeMmiG3/wx7/M90gXLv/pvs7h8QBN5z7YP55FUWQdrptKnGr4oT4Oprn7NgESqgM9plvLvPxfYUU5Y0ZUNZlr4lqAIEgizLcW0+hLCm/aX7E6qSvtoPQp8+fDkvjKOgLXoVUZyyu3/ARx9+yDbLvbNH+BZdHMUopUjSiF5/iHaKi+NT4tDzR6qmIYgjZBjx7MkzPvnoJzx7+Bmm8S1W4cAp2OYVKMHOMMUJyfRkjtEtlE222RbA5WzmVQUvr4oT74s3SCEIpMRfg5eVr7c+izagSkmvBXGOlsXg2hYSOPyFPRxOGO3s0emN6PVHLOYXKCVpjCHPS6zR7Aw6DIZ9qnbsJmRAIAPCKCCOY19M1gXz6Zab996nE4/8feEEQvqxUahCMBXO1CgFYTqmYw0H+zVfepCRFxUns4Kq8acPKRVF7b8edHpIFWN1QVmsIHAETeXpqaF/HZESOF0yGMe8c+8tlJBURcbnH/whTz//kLC3y/7tr0PQpX76MaVLUc2GySDmrfFt/uijZxhtqKuKxfSETz/6EVGckmdb35Wzju0mZzabU4gBRWM5O5/SnyQkvQHXrl1np9Gs8oo4Thn2egy6HaanT1ktjgmVH9WpMCXtDMAaurt7RFIwm83oyw5Jd8zO9bv0+iGLsxOmpycs1yuMdggZkKRdxrs36A93sCpCyoQyX5LnSxKVoBvDxfknCKFomgptLMPBmCSw1LpBBY7JZIRQ8OzTD/n89AmiyTBVQ2e8x+TO2wTdAfOLKVleEEchEk0vkbx/b4Q1L6i250ghiKRnLyAcWjqWOxodLDlY9uioEZNthzjqc6d/jaAWuJOGXdPnC4fvIWzEZG/EwAbEVcS3bn0LnW94ePGcfHyNRFeM9QGFPsI2tT+dXgIIrcWaGmubK0GjUrGPYpABQbtsKqdpqPgsmlN2a1R1zjKquXbtFtY0DKMd+ncnfPLhvyWParSQFGOIFGhTkSQBnUEPU23YP7zOIB7SubbPr1z7OZ4GD3mj+yZ6k/Hu6C1uVwf0kz6lLTlfPSMpI8TSkjUZqegRGMtYOIpkxMLO2UkO6KXXcIGj2K6pq5p6O6PcrMlXFyzzOUEeEwI3rr3H8fEOjb4gDBOWeuHpzBHMXEmUHXMQD9joEhnG1BvLsNsjGu2xffnCa/p6MU9HFfMkwzroR33Gesy98Q1udu+zfTGF+Sl9IblnDulXCWVdMhVnGLtL1zUstpqnswXz+LuEn+3ynS/9CkGzZb0xdF2XTqdDKFWLdXcYZQnjlPFkj8167Wm0TUVlG+IkacckDt00PrNM+UR2IeIrTQd4tokUEiX7CKc9nLDMCEPfUVDA82cXbHXIdjOjibqspgsyNSDQp2yrZ3zzgeOn3y/YfbvLe9/5Oqpe09u/TxxbjO1R5Bknn/2IeHAdhGG7PkdXBU1V4KwiSvugAnp7dwjTBNdcQJ1j5AjrIoK0Q+hKEJqmLCl0g1vNkUFAlPYIkz5BmhIEEmcbphfH1E3D3v5N4jjiMsBPqQDVXuNG+wy4MEyuxkXWmfbg6pWGTVP7osbZ9nNSV2yuVzZi523Q3ovMYrnin/zzf8hHn/87oPR0ahMhXIqQh22G0J/u8TNdoAwnu3Q6XYC2JeX/u5+DSaJ2078cQVxWl5dz/zAMCUONCiNK2WfaJCwKy2K1pWzN6KFSoFqnjjXYlskehgFFVWMdqDamXkqIwhAtDBZLqBRdJakaTRQn7Ozuc3p6wdnZGU78/8j701jbsvQsF3zGGLNf/dp9d/oT50STkX3ndIsbbOuCwVBXVqEqBLpYQjIS4gcSEghhISEBP8BIBVdclYBbpq5EqewSZWyuO+wknZl2RDaR0Z44/Tm7X/2a/ZxjjPox194RaZsmS9wquWpKEYqz99on1l5rrjG+8X3v+7wX5ECFxaJch43hkCAMccMWjx88IHKaEZJe5SC4SBbzGb/7u7/D6eEzdFmAbax2CKgu1OpCkq9a+UVpyNK80csIhRZN06CpIT4kVhKr+O4LxDEXeprmVm1w+42wSn+o+9L0O8Sl3qS+GPXQ6FPaUcDacMBLL71MGAbcv/cOpsxB10hdUZUZtdZ4TodhO2q8+lKRpNWKj9JEE9R1SZxMMdawmB3S768jZBtra5RymyLKVChXYXCxZaN+d92IdqvH7taCF+MuxhiORgW61iR5E8zlBi28oIOxhrJMkHmAdQS4AVXhU5UOvhOyud6mE/ks5nMkfeajEe9988tM5gu2XvgC3Y09Kq2I05ze/qvEz+9xMntKOp8S7OzS29hgMplSGQgcj26riyskSZLiOg6e6yIE+KJgNnqf2emYeDJlOn7O3rUX2L15g61+i/NpQprXbG5voJw93lYarxVCXTNfnJHmOX7Yph32cFRNNh8Ttddod0OE47Gzt8fBbpsvnT2mrHKsrkA3vINWq4Xvh2hTkqQJYW+T9nCn2bzLhPliQZbGFHlKXZdIJNQVbuChMOTxjONHJbbKSc+e4GDpre0xvHWbwbBDbBSt9hrLZYYfBigkjvJotbs4XsBk9IxsOW0actaCUM1njhorXeJuSe0s2JgaekWPtbSDfbggyRcIZZEzB2s0O50eURDgI1DTlHm8RLR81gc7pHXFcl7zsc1X+cjwE5ynC46SZyzyMblJsKbA0JwSpeuCbU75QrpE7gDX9bFCo22BtAmVzXgcxFh7zM3WiyTVglvDlxHS0vUHTG5+hMWDr1I5mrhVc9PdZuKcYvwFG9kmoav4+Ec/T1pVvNJ9lTW3S9aesLkcMvnmewQnBfkaZMxQRlM/zVnokmh3h3h2Qqe1jXBc/MglHA7ZjS2LTLJM5/gWbLtmOlrC6TnpMuPo5Alys0/PC3A8j6SYcOXqC5welUTr11gePUQ5AuFZnBryWlNkFaaWeHGM42s+euszRMOX+Y3lv6bO58hWj0Vr0Tj2gIHX5wW5zzW1z/nxI8rK493FO+yrHpHrcmzH+GVK1jEE0rCIu9wfj6kUuGLB8dNf4Zt+i0+//L0oXZOnORhBq9vCd4NmjTK6sQv7If2+YrmYUpUZWgi0MURRB+ms4GKrwgR7kZz+AR7/QvivlCJsD9gUDvXxM6oyw3MiZicPuH/vAVltyesAV6RMsoLKKXEKh+v7LvnJjN2b+1zZqVmePsRJF0gdMC9r0vlraK1IkgW1CpCixnUbYni7v8Vg/6M4QUAej3FEieeE1PFjPKHR4RrWOuj0DNITnHAA3hZFVqIRqLpC1wVVlhDWPUTUZjqbM1vM2Ng4oN3uroCXq0DaCxzGCmuhHK9Z91fgTbvqwlhrycsSbUqg6bDIlfj10n25os9izeXruUwSfulX/w1fe/NXMW5Ib3iNGzsvcnX/Lnv7N+mFPX7jF3/hv3qP/yNdoPi+dxlu1Niy1GWglDGNXfMiY6BRKtPY0Vbe+YsApPlsyvHZOSeTJaOkIC4rpHRxlESqptK2QjTzcq2RUpDlJQhJEARN8eMqNtfWUaoRGeV5Tiv00FVOMZ6R5xnj8YT5bIo2huFan7rWZGmK53lsrw1YGw6wrs/9++9Tp+nKumlAa9xVgXB2ekyRZdiqwGh9CXMTSlFb6IcB+xtDzpcFnu8jlUNtG/hOVTehe5e+dbnSlazsZMbUTWiVsKvNoSlklKOayG1rsHWNlGI171wB21aJoEKCI8XKNty0E9M8J0mXPH/2mJ2dbaQuCRyHSRLjoAkcySTPyKsCIWDQbaOUy9StqLQFIfEDn1obslrT74fE82Mq/TLdzjrYZkZrbVOgeo5CSwFVG1tnGGUwekCvX3Blr6KqJfP4jOmyoqpsE+IWdpGO26jW8xJLjBdJonZEr+1wa7/D+to6vU4PayxVlvDgvTfIlzOs28Hb3Ee2txGOj+fWdIWDzddJnT7z4phlmnJ1X7G7fxujDlnMp7Q6Pfb3r7E+3CZqt5tF1DZdKyVgvddldPoM6gWB8unIjO1BwNV+gT1/l8eHZ4je97F94wWEeoEqv80yXrBM5hyfHZMsYuoi5fjwGaKusNZHBj6UCY+fPEKbbaL2gK19weh8htUW5bi0ukNcTzGfn1NrxcbONfIyRSlFaUzDC8FidYmpCoq6cfX0xBq7uz28OmNx/KBxXhAg25vItRv4/U2ijoeDYJrEpFmC57rEywTX9Xh2GvP1hzEKH41PlU9JifFCH7wmb0XJppGXtWqO3SXlNGdjCZOjM6Z6iN9W9Mo2VCmm1HhKIcqMLE/JJguyuCba6jGLRyTzGV+df5VxNmMj2GZv7Qav7r6K77d5PHqP155+EbvKKGk0WAppFH23x1Zvj47fxXdcpIW0nHFWnzLOT5n5BTeCfbrRGlLmmGzO9d2XmE8PCXXBYzUhdNpEnRmyP6c/foFua5PtXhuhHRQe58cJL5u7tM893P2XmNljYqVReUqQO9SLJe5ah7OTEyaH77O9U5J1PQb9O8wPn5EUU0zYYT49p+95tDQE7RbT9094dvSE8/iYK3duIDHsD/dxCstgQ7HdWeft2SElFcoD35cEuY8rLU7pEleGQBk6rYiN7gGR6LLW2sTb3uHqje/lf51+lQeL13GlwrcuUIIEGazz+Nm7nJjnDMI276gjups+G7pDWp7iW8tP/PhPgyy5/+DrPDt7m7g85ujRL/MNL+ITL3wOz1rquqTIHJxQ4bjq0kLcOEoUnV6PNF2SZStrMoIoaq06vPUqsBWUBGHdhkOy6q6vVkSUcojaXTa39jg5eU5VF5w8uc/peM5kbqmVII5zrAoIXcvObsitAwc7CtnfjfFdhZ7OsV6X5fiYoqgpsyOE2yXo9pGiJFssSJYxtalYzGZMpxVeGNLqDDh78A6ttX10fEIUOfgbMdKp0NMjbHaGox2Ieig3AAtFniC9kDgeUy5nVLaiNBB1tlhf24QLporRzfp+mbVz4bq8sBg3qHylGhNEWRbUK72JUs3rIuUHUokL3H4jjm1G7Wma8Jtf/jWenDzhlZd+gBeuvsQnP/Yprl+9RhS1kcpjNp3wm7+fOP6fuf5IFygfRqsL8YGS2K54HxeZMRfXh1MXL362KAqmoxHT0zOqPMfqVfS2Ng1y3Upcr8mwMbVGItGAlRLHcanqVTy2tcRJjLCWTqcNAoo8p9MK2N7e4fTsnNrUCKFxnQY7X9Ua3/NZG/S4dfMam1s7/Npv/UeW0wWhcjDG4Kxaz0Y56KpqApmqktroFSvC4LoutRC0ooAbV/eJXME0LgjDkLDVYrmYYmXj5mmsZqz0Ik1acLMxKvTK3bR6sZrCxG3CqxzXR1cVSrECW+lv6065jttEeANKgBESXWsm0wXvvvsOeRLz/GEbV1giBQc7G5zNEibxMb7TEBQt4AiLrhsgnB9ERK0OfuBS14aisqzROKqybEZ3sN44eHSJ1hmSpgOhrIJgiMRQ5QpTG7rdNsYMUdLheJyRVzFZoZGOh+sFH3yIdY20BZ3AcOfmJteu3mRtbYjnhCyXC0anz6izJRjJogyY5jVpFVPaU/JywN72EKkFTtBCRGtUokVV5Rwfn7Dz0g12r7xAP55TFkvORs8JOx202MT3fBzZFINuEKGCiFc+OmR4eI8sjXEoqZIp7tqQO+sl4fm7vPZaQrfbYn84pCgqynafSne4uj2kKC3vvvs2p88fUuY5QtZ4jo/WmsnZGYt4SSdStHqbtDq7pMmcLMkoy4xkMSVPFxjhsTw/bsCDeUKZpyghEGrV0bBNrERZ5xAtsZlgfHaMLQvqYEAhWxitqKqarJJMU4O2ME8qytoSZ01Kc1akjOczpmnBWr+N45eko4zRckp/2CXsRijfuywWHOWBY5l4Nd6yIIyPeKp99nYPKLMU5SoKXbA8P8OtFVVi0WVJPJ9QxQlY03B2tGW2OOXk7ClvP/0afhQx6G4QBa2m+LfOqivb/H+N1UyyEZ2gTZxPyOuUvMqAim7U5aXBHa6tvYhXalqmJptNqccx0eY2t658ksPj9wi8IV7gs7XZodcRqAl0wiEqd/CokBgGkcOgWCf1Unq9Tcrpksnjd/CMJFMWLTQb6/ucfOs14vkxnc118jzGqVOOn77La2ev89mr3086PcPfHCKKgE4Y8cwTnC1O8XttPD9gp7vPQXuPoOOjVUFtJG+O3wFXorwmQywULtuDA7xFybk+Q7kxu70dlkmC5pSZKvj0xkv8wGe+mxfsp/kff/n/xOf616hzjyS5z8df/T7au9c5iqE9foq/s8leOqQ3jln4h0zdjK6w9Ieb7F+5ygsvfJzR+SGPn7zH8clDlofv8S3t8NLtV1mLOiAyJIJIRii5wjmsNlmpFFHUAZOQFwVFmYM1tFqtFQ3VUmmNMmIVvro6ZIkPgGTCNjlN3W4fXZdMxoecnzwnKwpyXZAVAXklUE6BryJevmsZtgriecF0MscqybXbn6ITerhKYqyPdj5OuPUig51XUCZj/ODXyE/+HUrPMJ0NDOeUtYfRgjJ7RvL+Q5SIWUgXNSqwIgI9x1MC36TI9BirFEK5uI4iq0qKNGNRlKtRvUVagalurRgpK6eP5fIgKkQjbL0wjTSTeYvWZVOc1FWTxOx6q4Rk2TzeNnC4i2R6x1GURUFeJDw/fMIgbPEjn/tR+v01bty4zc72dvN3qMb2Lb+D4gT+iBcoF9cle0M2vFK7GjE0GyiXBcuHfmKlWakpy5w8zyjypKHC2lUSMQapm+6J7/sgJW7gUBUlZd0gmC+6M77v0+91aUURSiqKIsdqTdQOsRYWyznK85puS12svOsGIRRRFLG3v4fjt3j73kPOTs/xhKQ2FocLsp9AYtFVjdVN10evWtBKSIyUaAsv3brOzuY6771/n6qqSNMU1/M+iAeXCik+QNFffDC1tqx0vcBFcXdBBVzJVUyNaXzGKEfQJIFbHKVWljJ74eZDyIZLcPHzVVFQ5zGpLRr5a+Qy6PeprSDPuziiR7fdoR1FKGlZpinzRUHQsgRBgKsClLTghLTamzheq5kt1xrHsTiOhxQGaEIFrTVY1wfdxpoaUyZ4Xki7swEE3LqScTorqE1zcyRJjO+plfDLYWdnyCc+8SluXL9L4HdZxgsePnmDfDnHCVosMsvhySlFYahwqeuKsqoQyiHLOpSVwfUCgnaP0gqMcFhOJmzWJWF7DS9oU5UplS4ZjZ6yXI4ZDDbodLpI1QSnpUXCS3c/Q7fbYTE/xxVgq4Ljo0P80SMiNeHkyYKvfrHNYG0T1w8YbuzQ73fot6AKQq5fvUlWVpwcP0EKl0obirIm8CO8KKDKE8Z5RhT6xGlClqQUaUwez6mqHCFdhCnZv3qVwPfJc0GexhhdNxu21ijHIQgDWmFAMp+TxjlaeuS5QUayEa9bg3ADhCOYnJ2zSA1If6VZaizlpjbMFwuCKKK0iiovycuEzHNwPQ/Xo1kkLXTVBlv+gPeze0Q7B8zmC47Gz2ktuoRRiC3mJNMp9cLFqxwyWaKWCsdxcXPFQLQYqRIXhS5qRKmx1BRCMzIljhcipd/wUOyKiSJAWktZxbw/epsVAxRDw0M6S895PH/Ek/kTPj94GRXdptsZULgttJSsqSFi7Sqn+YR+fw2xtWCt3mJ38BKRJ8ifnuJ216hsjdUS4VYoFbBYjJicn5CPJhjPoWyFhOt9VMvHD7vU/S1aGzt01juMTk556/2v8Ub5Hi+k20TRgFNvRLc3IKok5ZYPkSS8usVmZ43t9ibjxRi/rRgt52wql5d2bnLufoHKLzlKHvFq9zp/+nv/B07zhF/+zf8rmENa/W1SESNqh+3+bZzEx0wS9m+s8X+4+afZ9gJEMOD47ABfBPAsZmu9T3/7e/jIx7+bB++M+ProF+m+skPa2kUdNyJkx3FRrS77YZvNrX1G5y/z/PApi+Wc6dkRwfYBkQiaNUY0667juijZjPS1LlFCErbaGAxlkVOWjRslDMNGq4imLJrcH9dZbX8rsra9AIzZZk8JPIljauLFlFKXWCNx6waw5zg11+943NpvcfzwhDgp2NwbUNUh4dYr7F29SZWNcdq7RJ0t3DDC9UKIv0x7+x66U1En57Q3jvA7LjVDrPDIlwVHj308x9LujpFRj6L2GT19D51nqOFdKrWFsSt8RVVgykNcfUzOGpUYMljb48oLL+H6wYrI3fBPxOqzJm1jPGAlH7AYJGol0K0bPpXTyBYEDcbecWQjVGaVF4RZ7Z8FWZZwfPyMJFnS7w1ZX9/m+vWb9PvDy0PshX2Z/38sUODbuynO6sb74GsfFCcfTms0RqNrswKCiYtaoJlNWoHB4KhG4d1kFwhS2wT4NcI5tZrXwWKRoNQqPKks0XVFgmUwGKC1RQlBt9PFcx0mozNc1yEMfNaHfYQQpFnGu/fuIbRZoZYbMmK56nhImnaarc3KVQNKSIRSVBb2t9e4sbvJdJ4wmS0JWiFVWSKF27AddL3SnTRcDyWa/km1CteSjaYbhUCzAhwZ2yQ7qxUG2TawtlYYUAjbBHsJkE6j0Wlmk82LaC+EuQKs0Sxmc2wnZNiKKLOMUVngRQGfevUOWMF4MqXbCQk8h7gWJOdL4vQcISWe7zDsdelGmzjBkH5/nTDqNJ0aXTd6HixIhRBuA0AiwwqJ9PpIr0RVEKohQmVc2at468GE+WJOXebEizn9dsj2wS4f+9Qn+dirn2e4tkO8THnzja/x6N63SJYTjPSJM814MicvMpoNSiGkoNffYq03YDFbMJnO8KMOQvlYIRDKocxzstk5rf4WyvFwfQ9H5aTZmOnkjPn0hO296w2ozmi21gfUpqDTW6cqC+oypcwLHj5+l/0IythnsZyjn94jjcdIpXj65H1uvXCLsBUxXViySnHlyh5rwxZlBZP5nLy0BF6A50jOTirSxRmT0zl1VdHyW9gqx9RFUwxbw7Q8pq5y2oNd+v1dUtU8vixKjLb4nkun1Waj50NRMRUe80IgtGGj76KlZJEVqMmU07pAa02eJWTLOabK0VXDG1rGKSfPD7EWAqHxA4/I8TF1RZUX9NvbrDk7TOsJhc1oy1sceFdRyuOFvY/zrP4mCxOjliVZOieZLUiaZExkaBG1RNkeWkJYgOe7BNLHGoMUoK1FaoOpDFoYrNOI4C1NCJu1Fo1AWxB189mwNJua4mJUClVZMk2npPKMjY3bRK6HKwIe3/8abekhuxv0t3dQvRntx3usd7bIzo4QOqF2dqGuUI6kTHIOjx8zXpxwdH5IpU/4xPf8KPvbr6IXOVQlL7/wGXIR47c6TEdPGVzZZzB6ie3znCxZIqzLtEpRfojvRdi25OiKYFuEiFnCw+h1Kh6B7PHee0t+/PYPsdHZ5IrZ51vptwiUx3prC+Uobl+5ivrkf8/r7/4yPSfAej6D3jp/9taPcP/9e0xHh7hSo85npJFHu21pRx2WZ+c49YSoLrnRuov33LAfRVz7kT+HdiL+b4e/zHPx1Q+t140eT7l99qI2G5u7nJ8fc35+xGx8jO6vI9uSQmar98ciXL8pNrQFYXFch253wHJ2TlaU5EWCtRrPc/G8ACFsk5iMvWRjNYVJ43Qp8phv/c5v8+zp+0hRUVPR8wQzpybF0taK/kDx8s2cLFWcjDOGm0O89j5td51+x8OpR3ihJYgsODmeiRHZiHr6b6mT+5TLUzzHAbvAVh7CJFjzDLfO6PmaeL5HMNQE/n1kcEiw/hjltmjvn2LCq9TBq9SlYTa6T7D494TqlLPqe1mwy607H8H3+w3nyoAQutFQ2otCTGN0tcpgaw6vpc4pi6LZ51bFSXOQtZeH8QvtSVWVK9gcxPGMk6MHlKUmanXY2z1gd+8KnXb327goF+/vHwiU/S9c/z9ToMAHBclFoN+HC5MP8m0a5PGFUPZSwa0+ZJeiOdkFvrfqQMD6oMPpeIqUDobG+21W0J+q0tRCM1/MV2FMKyhbr0dda5QQJGUJSnHtxi1MmbO+1kcqh629A87PRzx5/wHJMqbtODgWkDRQuAvrVrGaH66eoysVQkkqoBX53Lq2T5zGTOdzECuNiNG4XvNctdaXLh0hBGKlwFarDskFvfVC7GRpBLG1tgih8aVYIaSbv8t1HApdY7ErhHLjIBEfetXtReFnDEVR4jqKWCqiwENYwebGOq9+5AUm5zNMXTDotPE9DxH0MH6Xhw8fNIF5rQBnd0i3O8AJ2rT6m4RBZ8UIMDiqhZTNnFkI1USzA9aUmFqjlIvyelRFBY6k3ekz6EQcnc0JXUXoWA4Otvjv/rufZGvnJtPZlLff/hLfeuN1nj68R7yYURYZZdnkZ2itLzfWdmdIls3xgojJyQN2rtzBjzq4ng/G4iiHUgoMmuV0xO5tB4Qi9FvkacnofEIYdtjZ2aPTbvP02SESTZbn2PmYXneNVqvPKF2QxUvyJCdzfM6THtYsqbIUU/XBQlkkjZ5kNKcuFWWdcf3GDmHbkuWw1j2gli5G17jWx5MB6WJCWpbYqiYuZlhdrMZ8jRvNWpiNR6Rpxs6VO/QHO8SzMbGdAZIg8FnrR6SzE6rSUCoXqyxB2Ni8tRFkRc70/Bg/atPu9al1jqRuKM1hyNrWPmsbmygFyXyK9BVBO6CjehRlhbUCY3Nc4fFS+5Ostzbpun0+Un6UWM/ptoekvRM8o6mOxrS7XZbpgnyyJF2OcDsOqhZEocZUAdbkyDqnJX1WPb/mA2ck6FU2FRpzwaJovts8bKWvak6lCiVdPBXgeQG3Nu9we+0Kt4IN7GhMNjtl58XPUyWNjZSBz0YY4rYD9sJrRDe61LFGZy3qbMLJw/dxXUVnuEad1iR+Qdwp8bM+H//sF7h+5Ro6W9Dd2UTkkuJsSq0CEk9yOhqjTx7R2txnI1mnWIAtU9LZhPNQEnXbLKbn7JkBB2WXdH3M+fIZngrp2cdEsmC+WKDSmO3uJldbP0zP36YaTyiKBBtDeyNk5+Qm8/wt1pxbtIIeDjlrazvk6YzleMn55JC6bFHkUyrHJUljHAXab5H7LpomtVz668zOx5DGq/HDRWe3OWkLBEo6RK02B9EdNjf3OD8/ZDqdkYgGnqnqZuRspMIogZCrkdwKONYdbOIkC+J4Tp4btPaQysNzHYS1lGW+gry5zXplmi7se1/5NX7+n/9PVK6PG/oMeiFXb95lOBxxcjriPEu4sisJsDx9aEnNGq9ce4W2ighVij77Tc7PDEGrQ90LkVJRe+CrhCp9hijPwWYIoTDFFEMbY2J0WTfp6a4g0RGz8TY985S6OMVUJV7YwyRfwjX3qYsZZ6clRXpGwAOM7+M4iqG3wC/fQQSfBUwDrKXpbqtVxlBlLgJfBQKNrgrKqjF8eE7DZLosThwHRzVdxLrWaFOscuUUy8WE4+OnaAO9wRYHBzfY3NxqHKmrkf2H65FV0/47uv5IFyjfPrb5oED5/V//8Pcv2k0XFaGUCqTTJGeyajebC2iNxHEdWmELoyVVJS7dK03+g2q4CbbJz8mzGiktURg09lspEK5EeS6BcHjh9m1uvfAio9NDBsM15nHK4yfPGU8mHB0e03JdvGavx1iJXVm3tGlSh+0qJ8qREqSkNOB4LrcO9uhGEaPJjOPxlLKsQDYiYg+F4ynqWjQLvTEoyQp/vdKeaEGtNUoKzOr1kyusiZLNKbIyBsdC4HqXxFxHOZR1hasc5IX4BEleVpdaFikaPc88yVgWJdMkZb3fYavfJYpaRL5HtOnhO+vEcTPewWtz+9YtPC/g8aP7ZFkG0LhtWgO8cEBVliymD/GiNkPnKmEUNSeoy/ugiR7H5gjpoVyQlcaaEikKhh0Ydlw2N7e4eeMG23vXeffNd/nSF1/jvXffYDI6I00SyrLCao3VjV6CVXeuynMcJSmzEqNLUhVTp3OS8+f4UQdtNJ7rU2ZJU2gazXJyxuz8KRrD+uYeRVERBhFXDq6xt3eds3GMlSFpnpJOYtQiYzIbI2pNnuecnx1SjE5QuWG8zJp7UVd4XoDTGjIM2wy6IdYohntrnB+/z+zRt5jMa/Zu3Wmw7mlFP2qjvBZqo83x8xZF1iJLlkQe6EpQFqC8JlCvKssmD8kYsuUE4UhanQHpfEocp+i6pu3CNC+YplDi0+oNCFsdXDfAFwKjJW6ng1KCusyQgON6hK0ea/0BOwcHLJOEJMnJkwpv2MbrBkRhByetkA4ga870E5bFjM1whx2xSSgVQxPR3hqyXn+a4uwxJ+45xSxHOAobKPRZSVHEdN0BpUgwOkNLQ5WlBIGHFFAbEFZ+4KQ3AmqLFQ0/CFZdupWovKFBNJtk5Lb43NXv4e7+i1xb3+Vkdkwo2/SG13Fch9BpI52Yta1NlPJAK0yiOXr9IY4N6bvrlCJneT4nMg6uv8Z4dA9btvDWfAZ3r/Fy+SptR7E4eoxN57hFgm/XcQIXz5PUQc7tT3+Gb3z5S2wM2/iHbc7i9+maFt1wQDnLOJ8+ZzAX3CluUl+DN48fc3VbgT7nrfsLnDwkaS1x9nY5mT/gC72PMexu8sbbDzjNZnSvXadWBdv9DZ69X7DhZQgpWE4mhE7E5kdfZKkN97/1GqQniCTDdduI3LKzeRUdDJnFI8TyjM5gk/Ek5+nThxw5D9G2bk7bqEu+0qVLUChcx8F1hkStLpsbc0ajU9JkgXIdZNU85pKTIlTTAV9pLKL2ACVhOT+jKDSOGzaBghiEFFRVhlghBxCgq5S3XvsdxklKbGKW8wXSC7mzf5XttX1e2jBk9RGWh5SFoVgW2NKnWIw52FtyY29Bt500mTfaBSMwlUsRuyxLjzIv6AQVrbbTdO9YEbqFxPGCBlqpFYPhgicPXLJkg15rhOcrHFmh82Ooc6T7b9kNK7STUc1PMOYGXvcqvd1PIVUz8rfWIK1tNCdKNswTfeGyBDAUZU5RFgigFXYvDRNSOCtEB4020axiTkRj1hiNTxmfHeK4ETsbu+zt7dHt9ldjoVU8iTWX7JQP+Frf2R7/R7pAAT7QU3zoz3BRqX3QMfhwuu4HhUoDIat1jTYG86E8DiElZVEw6LYZ9rtMpjMCT6BxSLOm2rwQm0opUStwmxf4uL6H7zqUWlMmjaI8arfo9bs8efoEL4o4Pp8wmkxZLmN0vTpNOo2DQwguMcO11k0XBbCiscgZJSjrBkF8bXcbJeH4bMpysUTXmqjVQjqCLMuQyl19AC+KnoYJYFm1TlbzSWel1rYIqqq+RES7rkMralFWjd2s1k1EZb3iqSilQAgc122sznWjRzErj7IxzRnUIBFaU9YGY6Aqa4LWMwaR4pOvHLC5uckbbz7h/cdPSfWYV/pb3L1zFykl8XyMVC7t7haO2ybNCuaTOcuzNwk7PRwV4DgHqxFbjakSTBU3iaamxliB44S4ToUiBj2j0xbceekmn/70DzDobvDF3/oPvPvwMVmWURUVZVmidYXWjRnRVE0QnhYrqL9pIHVSZKt7wKFyHJZ2htEVpkiYFBVCeThugPI6VHnC03vfIOp0GbRCPAtrO1cYrm8wmidURtHpbSDdBCkleb5kMY+hziniKaOjp8gyIVaKqL9Da5phjWXQa7O+d41WK6LXC6mLkk4gMUnAfDZifn7Ot46fYaJ1HL/N2rBP0OqwuT7gxReuoq9vcHx8gi8rHj++jzROoxNREo1tYtmrnHQ5YXt3h0WlkG6AkCm+K8iXU4R0SeoC60h67TZe4GOFwQ3aRDhUuiJbzpC6pttp4/VbnBzOOZ9OmCcJuq5wMPiOpR/uEoohn+h+jPVBh9eyr5CpnC13D9/xOckf8nJwHcf1cBxFMK4I/HWSKMYNljyb3UPPDJ2NPXAFRaLJbYrSPrVqrMvSEYQp+EJSCQu2KeCxK/u9beCDui7RtUFK3YweZMOBkI7HZveA27sv88k730Xfi7g3esw8ndHZ6tPZ2KEdtDl//z5H775F/9Y1Wr0+No7Jk4Su66OCLvOjEwqZc/rkCe40YefFVynNjI07+4RxxtXgGr2wz9d+69+xv7ODdC3L5YQiaEYbQkvOHj3D6W2xd+OA4d4ef/LWXU5efkA+mhG4HtPFgkScMdwyBC9rEi9mb+nx5a8+4qVbHqfPcz63/RGu7l/nSyffoCpHiPomdWtB4WgOD59gIwktn65yOQiv4CufuFownR6xu38d61jaVrC5c5PR89+mVDEDx6XnDAnDHWS/z/HhErIRVtc8MznvT9/mqHvKmvY/cA+Ki9T1Zn0Sl7TqJjoj6g7Yb3VIFhMWcYKRHRo2U6M1kUJQ1yXCmMaxqTyCsIMUhjjOyNMpxpR0umu4SlLVBUWR4PstpFQUacJ0Midsu8yPHpOUCpPmvLlMuOd12Fzrcf3mPleuv4rPhHR+nxcO+tzeGPPCtRFBkGDKxgwhbQa2RusevhS44Sau0ASRxvFcdC0wOsXaJVa4GJPjuAGuKfHClN0y4d23epgr1xn2HyKWU1xXgbfqfNucOokx1ZIgGtPtn+JEPbRhpQG0GF2jqxzpBqscoBpBEyxYVxpd1yjhNDo+2ewLjuPgOEGzZ+gGAOc6stkTy4Kj40PSbMnG1hV2dw8YDtZQK7S+WKlxrV2hKS7qTHFx+P/OKpQ/0gWK+RB47aII+U9dl3bc3zcTaxIYNbauUUKuqo4mPdf3JLvrQ/Iy4cqVHYos5eS8yeTRxlLk5SVXBQHtViPEqsqaMs9Js5yNtR5RuMb65jZXrtzgN3/zNzk/G5PEMXGSUhQrlomy1KailgpWAUyNuroZuTRmAom5KAKEYmutixSGJC8xJievNVY5l5C6qirxHQ/f8ynzDCkbLUxTXdmVj30V6OcqPNehXjmYLlw8vu+jV7TNYlWkWLMqlEwD77GieWzTRhRYu0qARlDXtrH9imb+iTUs44yirMjyguV8hnRgfWuHR4cnPHz8nKzUDIYbfOxTn+fVVz/G0fNHq45WxOGzp+RpSr48JnBjNnVBGD0lbPcIW91VommKrqYY3aRS1/UM8KiyE/L4KVk2YffabT5963tZzkq+9JXf4fDsiHbgUCYFeV2j62bk1YraxPMpta6xWjfvgWneE4O5xP4bbUiXM4yvcKhwpaYucipT4Po1rh/gBgFVWdLzI9w45XR8TL1/0ECXnC67+y+w2Woxms7o9/qURc7jR++TLAvSJGY5G+Opmlx3GLR7XL92hfPRmHR+jLe/i+d1qa3H1laHIk/pbV/l+pUDWsFvcfbVr3N0eIbbHdIK73JwdY+XXrrD+sYW5yeP+fo3m5yo7fWIb731JvGzOVVZURuN1AprNWWRspydMTo7IVkukFLie4q8KJmlJUlucIIm40rWFVVtaHcj0mRBFs9xRI0uMnJpycucdD6nLCscRxH6Ho4SlEKj9TZdMeAz7qfZ7QxZ6wz4nelX+cHBj3DQWeP1Z7/HeHREz92kH/UoRAElOJ0Bnc0YsqfEeULPDfE2hqS6JCOnXExQkceg06PbXUcnkju96xwXNct8RlIukKwKdgE32nc4mjxiVIwvO65aNbELjtMEqL20/3Ei2eJwfMwkjjkYXmE92iRPEpb3nrCcnrB+7SpBq0+dJlgSaEu8RYskz8CUaLckvLHL5Ctv407OmE2O0KXi7g/8MIP2PkdHx2jpINa6lPMUUVsmz96iyAtqofBbAbIcUYSG40LRNUPytEDXllE6pt2XrO+1OC+e8ijzePbOERu7hltXQx68l/Jye4/99ZukZcyWdTDxkDJM6O7t0F3GvHHvK3gDh47ZZFn5BMaSpCnL+DlVsmA32ydbpJiiRIQOD7OnbEQdxskzXOFyPHlCmT5kMj9DJGM8HXJuNE/MIefFmL7euuycGGtXItiVSP+CdroqHC0CR7l0B5uE7YJkOafIYrAhWjVQtjxdgFC0O208KbAo/Ggdxy9ZzkYUWUosFN12B6nchuRdFbiOj1QuXtDixq0r2GxOaxCTTSzLwpDqOYcnCePpmMn8Np/97s/z6e/7NFHxJbwi4fxkg831GFGXIEqMNXi+h7UJjtuhMF28YNKQyI3FVM0eZmoBwsNUMZWTo0uNUpLhlsMdxrz7piEI92n3ThrSq46xaZOVU6R5435UFbL8MiJ/FevcAOshaIo+pfyVK62RCAhRY0zRrNeOxPdDpPJANGO1RtBqGv0WBdIJ0VYyn08YjU/xvRZ3XniFteEGnud92z7bFJhghfmQa6jZd5s8n+8kief/jQLlt3/7t/kH/+Af8Prrr3N8fMwv/MIv8Kf+1J+6/L61lr/9t/82//yf/3Nmsxlf+MIX+Kf/9J9y+/bty8dMJhP+yl/5K/zbf/tvkVLyZ/7Mn+Ef/+N/TLvd/o6ei1nh6i+87H+YAOfbCxfLhULiAmjmeR6+7zddBbPif6y6I1v9DlmeURrN08fP2dvZYGezR1FX5GVN2ThwsRj2trYoipyiKui1W2BcXN9DSZf1jQ0+9ZnP0On2ePL4EUdHR4iVP71ht7iNcKvMsUogjEJb84GHZ1VIGWupaoMRko1Bl83hGq7TWHC1BM+3SFWT5xkRzcSl3e4wj1osl4sV8VZitUG58lJ3ohyJpyTaWMpKX4pbjbbkRdlAeWpNpZuRkxQCqRqxrtYWB0VZN86O2tKIsy7eI+xlLpGwjXe+lmDLmrOqZpEWHI6/RKsVsFgmVHmJ5wiePX7A7bsvc/3WC4SB5PjpQ+7fe5cqTZq5qbUMrn0ExzNMp0e0B/u4foQjV4VQc+TCCfoU0/cokwcsZofkpWFj//PEeo0v/sev8+j9B2TJiP7akJdfvM7Xv7YkiVN0UeL5IVG7RbycYo3ACtFQGEUTrGh1g9k32mBXuRlGOZRZjfIl0pTYsiQrC6oqxK3bzQ2jfKLuOs74lAfvvINxHTb2btBf3yTwXQa9Pt1Wi5mpEcqhKktmk3PSNMH4ikQ7dLRABUM6fUWeTTk7e0ZUWKTTxVf7PHv0kO2tHXTYgvaA7uYmh/PnmDLl2tVt/tRP/CSeZzl+/ohH77/FYnLCHMNkOidOGv0VSLYCj9k8JSkNeW559ugBeZ5jrCAMAnxXIEyFCiNEnCGsZb6cE5QerusxHj+nyDN0kZNVTXq4HY9WFke7ugcN+A5SKGrTNPcHpoNYJtxbnDNY3+Cz7qfplC6R9rhl9piXCUl6jCwrBhtDVMdSFBUy9OkMN7mXf4st36Hd3eTKrVfpDDRlITl5/pzzwydYN8E3Hp8a3OU3pu/xkSs/zrC1xnjxnMeTeyzzOUmZ8snt7+LX7/8ymgZKpYRsSJrC4WDtNt2gTV1rlsspRT5jPBU8efpNbObwUtynSqYIJ0dacNoRUW+boqqo8xl1fkpvfZvUPWKyXXDgfx8bG+vc8ANcGdDqDElHU6p4xp3PfAK3FTBe5FQs0X6LspYU2ZzM8ZmPJujrG9x79rvIoubHb/3vuPfG1zg+uUe4UETVOc/imHD9OpXI6IZteuUmnswZeusM2xvsDF7k9PiMcjZCXg2IOi1evHuXb76+zeNHD3hRtKldn/bede6P3yebHrMRblPlKXmcEJczUhtzZOacJUdUacGnELSSBQs/YZqP6IZdupXBCQKmShOXMdZucPGRvdT3iA9pSlZr96VzasV1ct2A4VpAVWQkWXppqc3iBSUKP+zg+SvtgxR4Tkh/uMVyMSFNl8xNTRR1mpGPNVR1hnQ8Dm5cwY4ecL62wa2r6+SnD3nzLctMS3QNZVXw+N63qKYnXD1oc+flDe5uuiwXhsFgF5+n5GkMQjZmgcpinDbSt0i7RJcVxvHROms+B8LHVCkgERVI5YAUaF3R2zDcvBvz+GHEYDggDEvqMqOqEqrCoCuQnsRUOSZ9COqXkOEfQ4seUgZIBhjhYI1oTBBSYaoVNToIcD0HqSRCeFhWduJmhwTTiGLzsuDs/DnWWA72rrCxsYvr+ohVYKBYTREuMnpqXa9GPI2j9uIg3BCa/zcuUJIk4aMf/Sh/8S/+RX7yJ3/yD3z/7//9v8/P/dzP8S//5b/k+vXr/K2/9bf443/8j/P2228TBAEAf+7P/TmOj4/51V/9Vaqq4i/8hb/AT//0T/Ov//W//o6ei13NuBrxze8vUv6gg+eiBdX804x4XMfFcR0csUpwXAFolJDsrfeY5zXzOCdo9Ugrw0bH4cXr27z3+IQsLZHC4Loe3a6Lp0LOx034mCcM17fXWN/aAr/DzRfuMhpNGY/Hq4rXIQgcqqqiKiscz8NKS4XGUy5CKqxQWGHRGPTK/iykZHtjjV63x2BtHVNprFLMF3PW1zc4H42pqrJpy9cNnEhKgbAGzMrDIwVVVeFIQRCsgHRCsMyrxkWy4sVQa6g1zT3VdF0ArBBoLVaBig5GG0rdCNOyorqcM14QaS9GSRcOHwwI2djeqlozXSTMFkmDYl7FBkwnU44On3Hj9gv0+wMOHzd4fQksFjO8yCPNNLPxOZ5d4Afv4Ych3e4Ai0JXNcv5CW5wnbISpOkC5fbotPZ5cmz55rtf4/T4iMX4BGELFCXzjR693oDJNCMYDomX50xODzF1jasUynGpq3LVMVsFKUqJMBYwq1PgiljMipyuLUZXFKs4c4nl0YP3GfbXefHFj2AevMuT0xnn5xPKt75Bq9Wn0xowHKxTFBlZukBXOfFyRqUFodfCj/qcn50yGo1ot7rceukjTCZnnJx8k+PDEV9WPu1WwNVrV2kFFYvpmMHmFXrjlHanw0de/QxlnfMrv/S/MB/nPH70kEePn+IHLq3OGp1On6KoccoaIV2kEFRFM6uPc4lG4Xs+7TBA1ilJYaikT7fb8IJcT+E6EmtKZmfHFGVJnmVNp8lqEBD4AdiVm6CGrJBUlSTwBFVVYKuMyfIp02JKXM7ZXt8hGZ8yVQq8Br9f1xmO7xEZj07uIqoaaT3WwwNuDw3DwQY97dNu94h6uhFiD9YoygXnzx7ghBHdcI0X119iUc6IpeHu+h2+/84fo9AFZVHSbbdZyhGvP/tmgz53XBzHZdjd52PXPktWJLx39jq/++SLWCv47z/65/naw6/y/dd+iHI5ZuvVj1JWZyT5iO2Dj+MHEW6cIWRNtNHGLCri/IT17Q2uhHdouQ7KKIKw2yT8vvca79z/Oun5hM/+yT+D0+5RxIJk8Rxcl9I4vP3G1wjW+ggVEgRtMpHzxQf/jg5Dnk/e5Yq3yWwkeefBCCGfc3U3RiSfYOjssHazi9+NCK4MsesOu9fucq9eYh1FPhoRhm2u3/gIv/al3yN49i4/+kP/A507L7B8c4Ovf+V/wQtHLNwzynOXzE1pVZawvUlqYmblEe8n77NejVFhC3yHylfcnz5krFJGYUplc5pRRDNm+3AQHdjV4bMZUdsL3Z/9ILZEKAc/bON4AVkWkyUZxSpIM0liAr9x+KwejXRcev0NXDVnmSxIU4AI13Wa/QS486nPEH/xKeudGV1/jeGVbZ48PSVPLca6GFkDOfH4CYd5hiP7fPJKhzrfJs77WH2Ktl2sXqK1Q527iHATUT/BuClC1EijqGsXJR1M4WHLKY7nUBuJFAXCCbDaRdicjV1NVYx5+ugKt1+ucN161Z1IMXVD8EZX1JlC8AYye4qUXZQXYYM/Sy2uNtpFmj3SFR6OcHHdsFm/ZFNMXLCxmhgTTV5nTCYj0ixhOGjAb34QNEyTVXYPK63iJU/lYoRzCYCzl8WLEII8S7+jPf47LlB+7Md+jB/7sR/7Q79nreUf/aN/xN/8m3+Tn/iJnwDgX/2rf8XW1ha/+Iu/yE/91E/xzjvv8Cu/8iv83u/9Hp/61KcA+Cf/5J/w4z/+4/zDf/gP2d3d/QN/b1EUzShkdS0WCwC0bsBl0BDw/mBx8oc/x8trVVk3HQFFWVUN4tpaXN9he/8AbzxF6ykVltCT1MKj1fbY297AMmLQ7xCnJUZDUpT02xHTxZLSCFqDdYbbB7T662xt7fDg/kO0trhOSBj6WBp3S1lV1FWNkBZXAVhqC5Vp9CfGrmyQQnB1f4+1wYD1rS3KLCWpNa2oRRhdaEsgDJocHrsSQTlO41Yqi3I1ThJ4K1eOlJK8atxCaVE1PBMBasVBadTe9kOvXUPpxRj0SlQrVnoWYWuMBddxLqvoSzDPh153s/qXFKIhs9b16v0QCNt0PiRw8vwZ4/NTer3e6hlI0jTh7OwQ6Snclkc7cDh8doiuSsKoTRh+Ams8kqTg5PAeQZihSBFyHdff5rU33ueNtx8yn8ck8RJpKlxpKfOc0eicdjTA9xySPGl+X8NKwyMJWm2quiKP49UHUWFsczJpCDxNh8URq1/QNO3UCy5MXWTUSrCcGe699y3C1hfobN6B7DmJrclOx1A8oS5TlBvSh6n8vgABAABJREFUafcxusbRCcJUXL3zKrsHB0hpSCYT8tTQ8gWdXg/ptJi9/Q7lcsoizZkoyWh0zPZWA6fzui229vfxneZel0KQJjm/9Vu/zXKRUOQ5eV4hnGOEdNHG0Gm1KQvNlYMdzNMTrK2ZzFOKPMfUJToS5EVCrD2Sck5ZNSRc13HxQ59Wq4WuK+qyIM8TsE06qlIORmt0na8Q5JLIDxG2RNmmK0pdMqtmxGbJWXKGDBWRjXBmcwqToIKIvJzitx08BSJLMIuE/mCdOkvwi4o8nrO/9SKe4+IYyKqcajyjF3Q5ryTBmoMMFEPbYxQfktmM16avkT39TT598F3shx5VPuMH9j7JxsYuubA8mr+HVpJP3fwCYVjzfPKM97O3sF3Ntd4daq9gZ2ef7V4P8WIbrzNgfe06Hpbl7JxqLMCWFFWCkw6QbsQiy+licG1GMS8ZPXzG3pUbVFVOuTxme32TN54/5/jpI07eeoAyFXUH3M1tnj66j1IBm7fvMnEzjuYPOZw/xj2uuJFt4kaCk3hMHvfYlIKNW22ubdwlrIfklcD3K0qhiYspYmIIA8Pa3iZ0HCbpCJ0v6a63qNshU19jpYJ5wYt7N3kYvsB49AZTZ0AkDHYNtoKb/OXv+WssjOD/8fq/4enZb5FUMyKWxDbDsT4Tp+TUy5jbkgsXlVJNlhmX3RP5obV85fCRErXq9DaskmZMfSEGjKIuwlpm0xFVVZJmCZ2y07g0nQZz3yxVinZ3gOu5jcMnXaJ9HwFkixHJ7IRBJ+DmtQ2SakG09hJXryrS98+prYMWFY6tUVWFNhVdOUPlc7qtDsZKjC0o4oK8sKRpAbJF0JkS+ePGfCAdrA3Q5QIjM5RXYIVHXdW4qqLWoKoaqRoruxKSvWua+996ytP3N7l208XxwI8UdQR1KZBOhePMoc6xdYa2J8j6CsZNkW59GXAppEI6LYT0EDTFSVNINIgIow15kTKbjVgs5oRRi6tXbtFq93GUXEk0GxgcQqy6x6vVeZWD15g6mxFos8eoy73594+E/kvXf1MNyqNHjzg5OeGHfuiHLr/W6/X47Gc/y5e//GV+6qd+ii9/+cv0+/3L4gTgh37oh5BS8tWvfpU//af/9B/4e//e3/t7/J2/83f+wNerqqKu65WXfXU6/09cFzjfD18XYlnf85FKYVcnfSMEW8MuZV4yXcwpjSXNUiaRyxc++nG+9trrPH16yM72Fi/d3iXNauLJhNxqvHZITQeDakZDR6dUT484OzthNJpw98WXePDgEdJxWSxmVGXVQON0hWU1HjFl88aLi4RKRTsKGXbbvHD7FsZafC/g6PkRTuA3DItWCykFruvQ6feIk5i6rggdh3anh1InaJ0jBHiOQ+i7JFlBVWhKvXptbCOSFYLGJtbMc7BSXyqwV05k7GVbT1GWH9LiWENZVTRYErkqcJpipAlXXCUlW4GVFmE10kp810VKgeNIWq2QXq+LqXKOnz5CXrkKxlIX+WpeXGEQuOEabuAwntcU1XOCzju0ulfwPI/JtCLPBMKcszFYIy0jvvjVN3nn/lOqyjQUSlaV/WotTOOEYXedIPRXTo0AU9VNt6QuKYuU7tY1dPWYIIiIp+dY07ifmjwii1IWuRLSOqIpnLUFjMHUNbooqKTl7OgxXyoqxPA6ImzRjvoEjqSOJbOzEdPxCUunQ11XOKJis9flxt2P0On1Obhyg/fe+ibaeKh6xnxZoq1iND4jiwuqqsDYZnRyrg0qaKMSiAKHtd1NjJDM53Oi7lqTc1TVpGkJFpTVrA/aTOYL0CVlBceHBY5QVKJ5Hx0BrgTHFmR5hQz7iColy1KwliiMgBolGqt3VWa4UqGcZjzi+wHKERRFtZqTg62XeK6L6/pIIaEqmWXPCdb3ODt7l/S05pM3f4zJ8pjK5qhOl0GvR9uT1NmSHA2ipIrn+NKjJUPORg9xJWxEa7jS5WzyhHqek48T+u19Yk8TDfa45rZYG+xzms24tnudYbvDZDrmZjegU3cYx2e8+OInOaun/PuHKbXnsrHW4cvTf89Sz6m6Bd1ei7ubr6BNwbXOBm9lb/CC+xJH9+9j0ys8e+8rDPb26EYO3U6H1qBLOa8I/D6fWftuRFWRnj0nUB3anRY6XSKsZrC9Q2BqPrn+JxBOi6Pj36Su5hz8sc/x2luvU5w+44//if89z7tTvpW8TWyWeK6HqzXz6THb++uULpy/8wRZlnxX57Psdz+D4xXEzy0nTx9TijPm0zHr+wcEUYCDiy5LynyJVQVuXuG0AkQ3ZPzskGqZIwZtDjau82j5ED9sE/geSZzib3tEnRa+4/ORnc/wIH6DaV0zswvqKiETKcUwJMktom4CVQFqrZGrDnEzNv/gMClFsy5Bo48TQqzYTE3n8uLgZAHXD2i1W1SzinQxI251UKoFQq9AlQYhHKwAP+qsOi8LdFWyHD3hW7/+PxOsXeel7/3zPPjy/4Xnx89wowNuvBIxHf0Os3ndxKiYGk/UbPcsr9yuKeOyoSk7O5yfV6SJpKwUxli8sItvzpBuQ1JW0sdIjVA1dSWwipXzyKBNo+MztgbdCFONKcEGXL0NR08nnJzcYHNzhOPUOCpH5xJHhgjRoq5iVFhTL0uUe51GgZMi3B6OEzbwRdkcpRpPiETrJkk9Wc6ZzUbE8QLPD9je2afXXcP1vKYoWRWNQjRZR3oVtfLhDonneZdwNvhAH3rRYakvDqP/ldd/0wLl5OQEgK2trW/7+tbW1uX3Tk5O2Nzc/PYn4TgMh8PLx/z+62/8jb/BX/trf+3yz4vFgoODAxzlftASvMCYAh8e68DFjX5hcfqQg0dKHOU0xYk1q41FEIYht27fIl3OG+V3XWFrKLOIk+MTdJaAqRoOiZXMlzHDfpdellJ4bY7OTpjPY6KoTZof8eTxExxH8epHXuXluy/w6N49FvMMaw2e71KVDfwGBGblkpGr/lmn00FYw53re3SiEM/zKauaLM8IwhArmtwfJYOGDggEYYgX+Ewn5zgSHC9YBZwlCCxlqamrlUPIiJXtTCIErMxI2BU3RWBXM1zZGBsuhMk0s8ULO/GFoPfiv1l9Hz6wf19KgASr00yzEtlVkKPvunRbEeuDLpvDIWEUshyPmIQBAoMuFmhT4roBlTU8v38fgSbNDWUJD959iK5+m43NbWQ5ZWu4R9tvUZuKtx+e8uhwDDIgajlkyRLf9zBaIkSjxSnSnDjJ2NjY4cWr1zl8+IBH995BSTBCoMuCxdkJaE2RNJoeKR2MKVFC4KiVNkcprPBQvkPgltgkp7aNM0TXJaIU5Dam5hTXXSdwQ5KyIo9TAl1ijcIVHnWe4bgea/0NOp02soaPvPpZXFfzWEq29g7Y2vkM0/mcw2ePsEZiTE1V6SbSPc8ppcRBNrBAGpplpS0PH7/P0eHz1fvVaH2FbLpjs0WKNeA5LmmWUhSW0FXcvrLFM9/nfDLH9xykzilqy2J8Tr0CnnmeRxC6+IGPUoIsLVGyWbhc16HV6qMchdYVvutQFSmmrpC2wtRN4KUxBhW2UFVOXid0B1vEOuPN+a+j5xm+CgjMgAN7QF9XhG5EjaIsj1FiiCo79OOAZ/GSXzr+Bb73ymfo1UMKuyQrM8osp9Ue8N7p71L6kvXhFdrekH7vCuPFCdP5iBe3rrHmbDR5VrttBAZfRvzk/o/zpnjIu8XvEgcTBOAbh5Yz5NrgFovZCW9579JvbaDm0BsO+NVf+nkol3zh5g1GZw8xaZ/u9g1E20dIQ/V4RhLFyDSlOxyi/RTtG0SoIHeQSyhEhj+IeJIf0okCrt16ld/88hfZu7rDeDDjbfkGhSkR1hAoj6HToZBHDDo9rl7d5mtnER3H4+rGSzjCodQdOnJBYTo8HZ2js5SNzjWsUcyrObms6cWGtvCwtkINPXKvwhjFeDKjWJ7i6ZKPvfR57rzyBSbT55RPn5JXBe5ZSuYusVlGmLeYuxNEAVUUkIaSvK4wGKxquiMYKKuavC5xlLo87FyM7S9W8w9vevVFxIiQqwnyaogjJa7n4wcBValJFhNsndHpDQl9j1pnKDds9C0IHMel1e5TVQWLk5zu5jWuf/LH6Q/XsYXAQdEOBN2r1/jYp8a8/fo3SJc1odDsrpd85jMBa72MtABfbOA4AeNzTV6lWMD3XYTOUCJpNCnWYKqYBnZlqLVAF3kD3nRBCBdrBMprxvsYgxUKRIUbCK7czjk7PObJoz7716DVmeK3K9xWiAjamDghm5yDcBH6GJIv4XU/B9zGWIWpK7RuKOp5kZPnCWkSk6UpdV3j+iEb67sM1jbxwxbyItRWipWcYuXylA1HzHGcxlosPrASYyxW2Msis6GVN3tF+aFJyH/N9UfCxeP7foOb/31XsUgw6wa/Ezauiz/k+qA4+UAgC83NrpTCDwKCMCRwXJpbthFhjc8OEWXOIHCpZwmBlATK0gsisrbPfN4mi5dMljWFljw8m9NWDpEwBL5Lb2+b87NjxtMZrqtoRSHPnzzk8RvfoFzOKFCXxdVFJ8Je6jSaqxVFDHptdteH+L7HJMmp3AVZVtLvdxtvu7V4joOQkizN8JxGEzKejLHWNu14K3A8l/XNTeLFgrJsLLO1WXU0YOUUEiCacYUxBlbQJCUFQiqM1Y2WhSbB+MPPtdHXi8vX9sPXBZHT8kER2YioWJ2MBGXVFGaVNQSu17QkDeRpwnQ8Zjjscv3gCvfef4DRJXlRsNZqU5cxXqvV/E7plOXhN+jYK9zY7tFyFmRVzJvPKnLaXL9xi9HJCZPpAqvrhmNiBEp6ICqE1KTZlP76De69/TaKBr9tTIXWzfM35RJsI5h0XI8wapPFYxzRjDccqYgcia0KMpODFo0YRTS2VUGFrlj97kucbE65DKhnS0IpWMzOqKoSaTQ2j/E6fba2XiAMI+ZnD/niL/1rPGmIF3P+2E/8H7n54kc4OnrKL//C8SqxWqCkRQqz4uk0cQ5UBuU6eG7EcjZhmcZ0B9tcv3WXdvs545Nzjg9PKEpDnhRYYZjHKYHvoByHTtSmKnOUhV6nDbbA5BVF3VjNlaNw3CZMLIo6tDpdqiKhUAJf+fhhC9dxVuOdlSPKlHiOwsiG4OmGEa4fIB2FcHy2brzA7z3+DdrtfSaz1xhXGlk7KBtx1f8ofm8d2W4+G8U8xZZ71KYmrebgwmbS41vjmG+MXuOVzgu4g4CynFM7LqVbsuVscPj8DRajJ7SdNl5nh9M6JXQDYneLZW+KpxxKXfH0wX2eTU+Y1znTMCHbjnE6Ci0MEV0+vfb9tFTAN6p3GAWn3IhexhUB7/7KrzJ6/h4vf+Z7iGdTinjBzt4LdDevcPToPUbn9xCtDvPpgmivhYja6HiCi4Pf3cLpaJbpcx7de4uP7+zz0ic+TyA1a8GAP/VdP4JtWx6Jc2rRcCp0ZVCLkmKcYLci1jo32eEj/MhnPo1T1MhiTqrPqMNNsiqj9BL6+7fobKxz8+on6Xc2ef/hN3n27BuUG0tifwBWstO7wt1onat3P0KhJG/+7heJqHjp9mcJow6zWY/j6ROshSxckkeKZHJKmSVoR6OEZBkJSl013dqVBk3YZtU4PXlO0GrR6/VphVGTEi+bkMgPH3Au1pyLzDWsuTxwNjo3SeCHFG6BMRnSDfCDkOXsjLkxhK0urZaL5zlY0fyMlArfCzm48wU2r30UqLA6BVMQeD6uzpHG48qNW6wPPGwxYit8i7BKcb2YZA5JIaA4op/VZEXVsHUAEYCj8qb4oMkqM7gI20JXU2pj0EXTtfYBIyuEdUCBoUZYizQNxdxIjfIc1rcmVPmcKt3ARm2kKqjSOcq6pIs5JtGoyMHzF6jwLar4CZOzm4zzF9FGoo1A28bKbbTFmgrXDVnbuEJvsEkYdnBcd1Wc0KRG101mkeO4BEG4Cg+UlwYVe9le5wPEhG1Sk+u6wFqxEsrq398/+M9e/00LlO3tbQBOT0/Z2dm5/Prp6Skf+9jHLh9zdnb2bT9X1zWTyeTy5/9rr+DxnGCzRg4lDWbt268PZA9N4iIfyrYRQlxWf34Q0Gq3EY5s1AS2Js8SAt+j7ymu7+zSa7ksdYAcvUmVLCjzBXkd8OTZc3RZ0++08YZrdIdrZFXJ2fkIow2+kvR6XeazJaLVIs4TcttsHHVtcV3323+nIERrTeA5bK4PefGFFzg5Oydepk00tpCI1ehEComUEAVNbHqcpKRJghWQZtmlz73V9tnsR2RJRtBvg21zOplS5Q0J9lLUim2q/FU3xJgGZiRs01FhhcW/LDK4KDL4tunaH1agXCwu364TspeFi6MUnuMghSTOCxw/B0chrUOZZzhOnxt7A1TsEYcBhi69bpd4lrGIoBNF9H2PbsvF6zSW1SQreDh3yZ1NfNGo0R3HAyztTpcsTdE6bQS7SlKbkiLPEWhEWZGWeTOjpaFN6lUaaPM7gTU1ebJo2DVSooRgveNxe9twPqp4PPdWoDoobdPF8DAoUzcfWJpwROUqFA7p6AnpfEEQtnHtEl9PCLTL5mBAGEXkniaenLBIjrj64neT5jlvvPEGuzu7rPUGuBKU0PhOQ0d2HIWwAl1WKD+gP9xkuLXJcj5lHk+JFzPqqmTQW8d3QtI0Jl2k5FlNVYPAUOUafIXRkvuPT/FdBY6HrRJqrUmKZgzouC5CW6RsbgiJxPMjvDIFIZEr51OSzrC6wuoaVzVYbeW1oC5w3QDPCxtLIiWlzLl1/fOkacynuj9MYuYMWrtcGbzIwdUr+Dgkk2POj0aNtTzPGg6OtSSLM1RtuGE2Oc6nLMQCRxYEbkTHbzOr5zhVTVArZk6CUzci9NzGfGtxSJk+ZtRaQ8iAeZny3tF9zucTzidTNnZ2+Wz3VdxhwFTlXHXvUFQp30x/h0P1HF+22It2+MbT/8jho2/SjwZsXb0BRcnB3e9isH+H00cPmTx7SjZ/zODgY5z3Fnxq4w5nb9zHRVC1NJO332C4d4fW5iaf3vxRbFLzgz/8J5k8eoprfFpSYdCUor4cewgraJ9aCptQ9nwiEWFzELmmKhOKacFpMSJrzwjLLqKE7pU98mRKWU7QQZe9nWuYsmC8fEA5PWUxneC4Czo7ByTLc6LBBkN3iDEnpHmMWUhMESPdDpPxMXlrSRU7iDLh0/uvEPa3+J3Ja5w7TwHZIAcAMM0GaSFN55wdvsN83KHd3WCwtkWn3WqopisXYKPGZAXME5eWVmMuKLSglEMQtinKgtrUDRjQGTJcb5HFM5bzM+aTI8KoS9TqN6Jlz0EicZQiDLtoU6PLKdsHIa3xI07GU0xw0OwVLrRbLTZalvQcFgtLnkNmBI5rWCxOKcqqQToo0QjlhcLxmnVTIhGqBmKEI3EEpCnUFTiuRfogqKnSZn103IZNoleBfz4uRle4Xol0nlPGLl7Hx9Q5Oh5hq4ZX6YcBbitAl2N0PqdINkiTc7QIsMIF4SGVix90aHcG9AbrBGEPpfxVtEvT2dZWI2WzL7met5JTfNAxv1gPoQGKCiHQ9QepyVrXQA00sTCO6/znlBh/4PpvWqBcv36d7e1tfv3Xf/2yIFksFnz1q1/lL//lvwzA5z//eWazGa+//jqf/OQnAfiN3/gNjDF89rOf/Y7+f+tuRMdKzr78BuGL1/AHHewFDv4/w0S56J5AM14KwhDlec0HAUtVW/K8Yn1tjVudmuubETfubhP1NhgfnfBv/t3btH2PWVVxcvQMIRVxsiAvUvIsYRlndNst8qxkb3ON48NT4sWMJE0RdcXGxgDh+sRxugpcKhvLrjFYY4gCj0GnzbX9feZJQl41b7ilvhwlKCGbIioIMLrm5OgcoRR1WbKYa6bTGXWVs+Y6DNeH6DrjfDxFKBdhKza6Ic+LOY5omCvNKaQ50VhWJ5TL4qXZdBwhqFcTtaZ/sprXXIrPvr04vgjdEgiEaro1XAKYmu6JxVJrTVmVuEqha0NRVMRpusqxUXjakCQJb7zxe+jFIQElgXII60OGG230+hCrUxzpY1RIZV2KQnM8Bd0+wKshjecowI8CWp0evueT5M9wghBhm1NEA8VzMU7E5t417r3zJlY4q9fC4AcBYdRlMj5hFQaAsBWOA0q5bPQcPn5LgY55O5PEpcJVNUYIiroZ25Xa4kiBJ0yzSfsewhFUWUk+H+M4LXzXoa1rBu2SwldY5ZFmBQqJLM/o+xkdc8qv/8I/x/ibbGztsxgdUmlDpZtF35FNdGKTw2TwPZ87L32cl179NN94/T9y9uwJp8+eMh4tSfOisQ5Xmk63TdSGorRkeUaVZXQ6bYTn01WSV250eePhjEWuWOayWTiNoSgLqrJ5r4uiJIln+GGA1hVSCOJyjlh1DQU1ymo0fjN31ylgWC4WuEWG2exTWUNqKqx0OFi/w260w8nyPmLQYa93BacUPDt8g8nRIwI5xHEEejGlHufUTkWd1vhOn51wB2pBli+xLMiDFkEWU6iayjO0bMDD/ITKrdhOLWtewCyI+Or5O7z9SKJrl52DLfbubNFbupx/+ZzT0zHxvOQHP/9dvPvoCe+/+Q5+PyLfnLEnd2gHA96N3+Ad9102tgfc3Po0V3bucvrkXQbDfUxS4VjLxvouRccjm5f0WhH1SUo+SVBbm1S1ZTk+BxsQXN1hfX2Pp6P3qEzF9u07qFyxubaBCXxeXASceSekIqGdudjlHHOthdeNcFKoqhkyL8HRSBOQzDNeO/w6n3r5hzG5JZZzlvE7XIv38I4DVDhERh2GcRsra2ZlhWctrTJELyrG6SHL6RkyWrDQ5ySLOYt0gVUeo/l7dIs1cBwG7XXu3jzg4O6n2Xt6m//p3v+ZsT6kQ4u0LoirGUVaYXxDXWnySlDbglKfES+nRK0Og7VNut0BvuMh1cqFebHD2RXGQH6IVIpFKpfAD6jrmiRZkqUxUTik3dskavdJlxMWixHL2UkzIlJNYeMo1ThiTEE8b37H7bUxs7MZDx4+Jakj1tYHHPRr5tmCYmFJCsgrKCvJcH0dmz5rUAoaogg6XZcybXR2QmiktBgNQlZ4Xpu6Kuh2KorUYspmlCxdgakVfjtCuR6mLMHmNDJBhXI1ZayJej2U5+L4GRbB8jSlLsFaRZ2kVPMpQoX4UYehM2ZRrVGrIcrvE4ZDwqhDFHXxggihGg2gQK2WaInrNR1kx2lgnMbab9OQXGhLgA+NcWp0rVfFosL3PZRqcZGzVPxvPeKJ45j79+9f/vnRo0d84xvfYDgccuXKFf7qX/2r/N2/+3e5ffv2pc14d3f3kpXy4osv8qM/+qP8pb/0l/hn/+yfUVUVP/MzP8NP/dRP/aEOnv/c5X7iGureGeGzM8pWiB8F4Cns6oVqCpFGQKu1wdoGgHNRwjUEWIXruI36WDVsFGs1jtNkxjxblrxyI0BlCw5e/gg716/z7LQk+9Yxdh4jZY4RzYjibDRma2MNz/V5+PB9Xr51g6I2HJ6P8MOA4fo6h8+eIdISx23eUNd1yfN81cqEwFF0o4BWp02uDUVdU9Y1nVbUzDRDHyHA9d0GLuQ5GGuIkwwpBXVVEc/mLJYLAt/BmJosS9FGELT7CAuOrHFNzckkxVizArhZpFIo0QT/iYvxk2hOKE37VODKi3FNo7RvaLeNBRgu+iJc2s8+3DGRNLITsdLXXLRvrWhyh7KiWM2fm/cF5OpEFFFrOB4b6iLClgZHGZyshR8EbPfbHJ9mIB12hlu0PA/H8UlRbO7e4PToKYVUtNo9hPRwXJ/5bM5gMES5bZazGWk8a+IJvADlRTiOIOr0WM7Omlama3H9kCAIkUKidSPwVAKkEmwMPV65Ihm0Nb93TzHNoDSS0igqLHVtCD0XS1OI1mgc3yfqDjFhh+L8iDLN8UIPU8xpd6Dj+RinT5xoFqOHZKO38UyMlIbHR19kNNfk9glHjx/geh5lWaPrpsi1Kx2TUi5ZmQK2SRrd2EMoj7PjU4rSY/vW5zk6O+Hk/W+QLzM2UHzq89+HF3m8+fpXGD17TjofU+c5rcEabz1dUmuBsQ0pWUizEjc7gEDrmqquWcQ1JAnGaORqsfJcp2kTS1CuhxWKcpVpJFavY60bX1invc61tVdxtEIHmvZan5evfQZPhDx5+jZvj99EZgpXOhCMMMuUbu0TyYB8kVOSkZU1VZHTlh1qt2CZjkjKGGnAoGhv9BtcfS15JztGepa1vOZaf5tr167w/vvvMxrlGNFkWlW1pR1FzOcpD99/h9ki5tnxhPlkRrfd4u4rNxC7kufiMa7jcWfr4/Q/3+Zq62VODw8Jh7ucHR3hmgJXC8L2AD8IqRbHvLD2EdLZKdqBLEtoRz2sbZPMY+L5M7569BV8FO26Rdhr05FthsEuJ2bMRrvFK9Hn+N2zL+OcLMj7wFablg0JRoqKmAAXowXWLaiM4ayVcFjf5xNXfoDffvLv8XTOyfgJrurjly7zsyfMRu+zKMf4rT4b7S2uXrnN2s0XOZ9MOc1fI1ges7m2w6xdkTpLjGtY6CUmr+gFa7x8+3PkUjN+9B77VvGD/e/icfwt/sz2n+DdrOB/fOcfMZ08x2wYOu02vuNSaU1R5OhKU1clWbpkErXptAf0esMmRmTVGbwcMVuaxsyFDkII/CBsxr7+KnCyNgS+gyMDemu7tLsD0mROvBiRJxPyZEldFegqQ+sFuphi42fkzhH9nkv61phvvJfR379C+NEAz63Ic0NuoDCgiRBGkyRTkBbHCnxH4HsVkg7CcTB1DUIjnaaQtzpusp10s85K5WOpcMMhIuzi+SMwS2wgCCKPKq/ApghXIkWAcCLqagppihUOaEVnq490PaTXQgaSKpnge23a7TbXOh9Duzdx/V7jalo5e1ity43zprHRK8e5lAoa7AqW9yF7N2JFpm32WrsSOEspCUPvkjB+8QZdHHa1/najyn/p+o4LlNdee40f+IEfuPzzhXj1z//5P8+/+Bf/gr/+1/86SZLw0z/908xmM777u7+bX/mVX7lkoAD8/M//PD/zMz/DD/7gD16C2n7u537uO30qLJ+c08ejc/cK3L5KUVUYBcvlEmsNfhABmrouqWvTtNwugTJNy1lJheu6tFptWmGEUktk1ZxAKyR57dLavkKvZ6iyEXXdY+P6Ln/26gECw5dfu8c8LXjryQgrXA52N3n7vcdcPbjC6WzOs+MzrITdnT3W1nucHZ9y7cYtnj15TFEUKNUE3iFge3ODfjskL2u8IGSxjOn3+8z0BEdKyrq5EcaTKUmaUdeaSmvSLGexaLIpZrMZuqqodU0YuGitiZcJyvHY2dnm5PiYvCi5vtPiaBSQFRVlbfE8h6quqUwzBlMrloe/sgzXdWMnc2QzPiitRiEwq4RLWLVam/2lKVjEBwXKBRPlg+6evRwRSQFylaqZ1xVeXVFqjbdSite1QQhFhUsl2kgvoq4TluMc6VqenyxYLJZ87tPfS3/9APIlyyTl/uEpanjUWIGtoj9Y5+BqB7/VYT5f8vTRI+K4oKqhLFLqqqLUkOUpQtcIW6PrqunzWI3OY84XjbZHCsEFt6HX6fHivsuNzSVPzzRnM9mIk5UkLZoPZ42lMgZXWoRpbH1CQJGnOFJRLk7QRYbwfCI/QmCY54rzOmb01usszx8hqwWuA3VtiVodiixD25LcQuEGFGWO67q4SuE5zXMrqwLqGlPmnBw/5+HjR6RpQp7FOP4u11/8AiZ4i7PjJywWc6bLjKNnz9i7cY12b8hyfE6dZshyiV4KCKIG/X7RhRSgLooT07gUHKUavk+lsZiVgL2mcuqGXOkKXEchbJPzcdHuV6bGmIqqKvC8kOHaLpH2cAKJ5wtEVpPMn8PZhH5riB+1OT55yMLGvCW+yV1e4vNrL/Ps2T2k71PaBdqWJGmMdV0c4eMKj8RJyMqUveiAWnqsJzFvxs95R2s+3rrB9YMNDl7cob/u8u9+6feYzzOen8SovOTWjS3SwpKlNfPJhI/c3cdTdxmfpags4v2vP+T2x+/Q3m9R6BK5GZJkMYkpkVVBt9UiPnvE+nAfa0C2FP3rOxCFPPjGA6698mkWJ1PisyXnR6d0ByHHxznvVe8RAP32kO35AVsv3+bw6AjV7VDUihda+7S2uvzHt/8NxU6AsAa3akYK1qjm02YsRZowKRdUocu+s4ljYbfb5+bGxwlqB7yI0ZN3EVlMISzf0Pfo0OPl+i6LfEKQnRL22xxcvcvx+6fM4zOWQqNCQei0cIMWjifY7O42h7wSno+WLMp7bOPwQvAK1wY32ew4/Idvvszh8jkMLQcH19jcWmM2mTCbz6jLJVYLisxQ15oyT1gszomiDt3eGu12B9dxmoT3D5YUoFlzXC8kDEvAUtWaqqoJff8yvBRl6fS2aXUGVEVCns7JkxGL6XPi+QnF4hAbH1E4ORiH6zcdzqfw/OiE5+sO4bDG5lAaKKtG4JosnlGVOQLF2pZDvyOQaHRhkKqF4znoeonjCOqSRgtSg67A9SXtrkddaWyd4YUumATXU+iqCTF0XEtVeAjWCTa28Lz7pLOCqrC4kYvXdfBaCl2CqCzWcwFJXUmcwBCFNQRtcAIMzkpJ2AiNleOgpHupKbnsjJgPtcaFAWuotKYuqw+BRhsoo/iQHsV8SJNyuf7bD4wT/7XXd1ygfP/3f/+3s0R+3yWE4Gd/9mf52Z/92f/kY4bD4XcMZfvDLuc8Jd+OaH/0Glop6umENIWTsyNavR7ScRtAFAbHcZDSuyxOLk7xjqNotTts7uzSH/SRx40+ptaNSJBaY6INRukJ7aLkys0BN168Snx+xtlphiNDfuW3XqPtt+j0Ih7cf8RWq7HwZisx4M7ONrdu7DKfzbGiZj4+Bt1sftYafM9lc2ON0PfpD/qcTSagDZ7noOsaXVbkWUqvP2iQ/FjSxRxrNALBydkptTGUaU6RZ3iOwnN9wqiN6yg2hgN6w3WKLObw8Bl5nuOoPpvDHs9OR43+w3UbMq9dtUxtE1veioKGfFnVqw3IYgBXrCpkY5Ar58WF2ntlCmqYBr9v4CguWyxwUaR8IHxrvlmvxhTGWMqypChStNFYo6ith3IUnt+ipRZoU1HWirWBokiWTOQJSkjeff6c83FM9ntfYX1jG+W3CVp9rr9wl96gj+N4PLt6jfsPD6m0Zjk5pSpKEJaz5/eRlFirEGiM1UhlwWiUak4KWjd6nU6/T6vbQbo5swTefW6ojcJRzSnKALVpNumqrPC8ximlpMHWBaeP3yQMAuLxYcMQUZa1/hCdZmgjSeIFxSyhyFLQFiU0wmqEI6i1RcoaRwqsqfFch7WDfVq+SzdwiKdTjk5GKFGhy5jx6RNOnrzDYnaC4/pMTx9y8uRtFAJXNY42bWtmo0N2r+6ysbNFPHpOWqWECpRjoC7wox6lCNAqJstyHM/DGENe1FjTpKU2rXbZjC2bSFWqyq70PKsBmQFbW4wwuFKgMFijwVrKLOHp42+hpEOWztn2hmx1tmi1fNb9Nh3bI3UFj3WJSi26NJyxRKyFVKHFJgWVyslshnYLZukIXwVExoEgRLqwrGckeo4SkqEX8Cwbcdja4ZWwx9HjB2TxBF3G5HlENl3g6xl7N/fZ3tviydMR7753TDdwuP7CTd7Xz/nki9/N/fSbnD5+ilPmDG7s4w4ctgd7lNE6ZZyzNlynFUQks0PKckkoAoLhJkVlGKytMRuds/vqR3nnV3+NB8+/xUf3fggzzdkPDpi1x3C9TdvdYjHJOMwnnL/5TVoDH7F+DWfg8PKnfoj/9fEvI5cpunBQQmGlS5EuqauKWX5G4Zb8sSuf4HNXvwcv2uL21Ts4RlAsZ1gv4ujkAVl6SOU4JAKm8Yhh/pzx7BhxolDdkLWWz6nxOBs9wWcLuYC0mjAtJzyan9LvDZkUz5C5xdiEt0/f5dbmC/Tbt1hmY/KsYlcHRKaxlTuuotVu02r1WN/OKBeP0MWM85kiKQ1lXqArQ10WZNmSqRcShS067S5B2MZxndXmqsE2hyYvaGGswaQJWTrHd12CwG8OqQSNpk45qLCP73fo9NYZrO8Rz7Z4Xs6YjRPiNMZaRTsS7O/UzOOaJ49degGEEoyGKhM4pmA+q2l5zabaCi2dfk0RK8qyQEofsJhCUBlBgwAyKF8StUOsyTA2Johc6rJGORlV3mhYvDCiKjRS5rSGA7R+hVpsYe0TlONiChd/uE7ghdRlQpGkSOEhshSsgxN6oJ8isl9Bq22kuo5SLkJ6SOk2Vu/VuKZaJcRfcGfEqh1uTIXRaaPxwsdxXDy/RRNfshLElsnKjuxyofPECoxtuizGmMat+v8tkez/p69qEFB3fXKbMZ2e0W4N8DRk8ZSyLimLkuOjh2xtHbC2vnnpBLqwS12MgMKoxXAwpD8YEHgeaV5SapokV6t4djYmdDx28BmdHrGYv8/s8JS334v5pa+8z3KZUwoPp9vi+pVt5pkhDEveenRIr9/jypV9vDDEiVNCP2I2H1OVDSrc6hohQWqN6yjmyyW+6xJFIUmaYB2HPE/pdCI8z2E+n+EoyTLPaEcR8XLBfLGkLEoEFs9zCcMOa8Me169fR9clw81NBusbvP6l98jjJZ7j8ugsIytKjG2KEl2bRjWPQa1cN0pJXN/D833SJG06Kbbx3SgEpa6RAhwpkMqlqvVKV3Ix6hGXZEEpm4OLuVC3C7FKTDYrwaihuR2bMVVWFERRgLrgqyDQQmKokdJFuS42qUiWMybLJde2hiwnz5FIahlwMltwenpKHPjs7m2gPIkTRly9foO1fgupJO0g4PHTMbrW1LZxQZRZjtQpkhyLh1IST7m4yjb2PBNQlBopNK1Oj05/izQdc+/5Ao+apFI4LtS1QAmLkg51pZFSoesKJcB1AAymhnJ+jqyi5l5zIsJWF9fvkCcOEkNR5ORFU6g1ciCDY0rsckoQCBxhkVJToQh7G2xdvcHOsEfXqdCLGNd+mXeOapZFxejkKbPpAVmcUJYFdR3zzu/+IgR9sniGFBa1IuHOJjPqOkUpD8/zm8AxJO1en7W1Pp3NdYoi5vT8jPcenyCswVMGaxqRnjZ29flysKKxtF/MqV0/xAsjiiJfMXMMxpGUtVh1O2sir4UbCtJqwfW9G3SFT8sPUBo8p4NQDcr81Z1P8t7Z1/FLj3FySrKZU2woltNzIhHgOyGdQR/le5zNTslSgy0UnX6P88UpC3dJ5PfZ9AYcJUtu736erLA8eviMPF1y9+YdhJAsz4+wTs36MGBnq0ec5yTfTPjGa+/imi7JeM7x+CH7G2vceGmP+fEp9eOEGwe32Lu2z7g+w1/bxBQZyeiceP6UskjZat9AKo2OS7yowy/+P//v/EDgUG2EpMrQ2tjDOcu4d/IVyn7F5z55h9JWnD56zNe+8R+wcslB/4DJ0Tc5OZzzyVd/mBveXUo9p55PGKcn9HZuUEyWRL6P1+6zt9Xmc7e+j57sY4sKT0JpNL7fJtU1XqvLaPaQvMxpt7uc11MOgxn/L/L+q8myLE3PxJ4ltjzaj+vQqUVlZumu6ipUN9BoAD0zxJBjhBEk70he8HZo/Cm8I2k0kmZDI22GaoDGAOjuQjWmUFplpc7QEa7dj9x6L8GLfTyygAtaNy9IK+NOC0sP90iP9H3OXutb3/e+zzurTxFlD9ce45sAI6ChJaotz2aPeSiuOFdX1LLgNDvh9vICmzpEXPLK8C2++uafEqo9nn/yPsv6CVFQ8r/4+j/huVmgdUoQjUEIwriP709xrmV8o+MqNXVJW1U0bbV5j1Rky4piPScII8IoJU56RFFEGEabA6kijntYYzB5znJ+hRmMSHvphvO0sdNxTRjXSBWhhKCfZtQ9qCoNfo4nYDJx7E8ky3nLeuaR4w7O6LynbTqXXBxJpBXUuaFKwLWG0bgAU+GEoilkB231FlNDEnh01GIqgbcCby0SD14S6AhcDaIiSmNcLXHtAslPEUWEVQ1BkkAUgrfU6zm22oSkpgNsu0RFkqAX0NYZbX3F5fwv6O3+p/TH95CiI7Fb13YjGNelFXcKQ4czXa6WdwYlW7RsCOIeUvdxTm6EsC3dmV8AnV7vCwHtFxrG7vdfmAz+ptfvdYGS5xlbr9/kwdMPqY1hOlgx2rpLGM1ZLjMuzo8os5wgiBlPpkAHBHLWbt7AcrMJRwzGY0a9HnGo0VrSGIttDIQBP/ntM2bnp9TrO+xth7z36hgjA4YHA777R9+kvTrmfNHCzss8enZMrDRVW5EOBkynExarFZ/ff0Q/Unz1jZf49OlTyqKkbA1ICAPVWZ1DTd60bE+3cdbQti1SlHjfbXTSC1aLJWkvpW4bYhthjUPjCBNNXtbEScrO9oQ//sM/4O6rr/Ps2ROePD8HFHleIBBEUcxstcZYS6gDvDcY21mIpRKbVOBOYmlbiwy6TAwpJcp21NnG2hd8FOE3Mdyim+/IDU8DOqy+EBt6rBQvqnPvN2LZjRhXOLA4dNCJtKq6IS+rDvol5MbeNqatMhCCMluRZSvKsqbO1sx8Trg1QQ32UYMJo+1damNZL5dcLBtefaVPGgmKoiZNA/Dw6Wf3+fjDX1FWa3rJgMvFDOEavHRI3YHXZBLgnUP4zmaNs0jRjQ9HwzG+XmKbJeumQ/xHkUaL7ucLbPe6dUtgN2dWUnYFyiY9tyc9tqpRMiRJB+zs3WS8c0h29RlhKFHC40y7Oal096bxhkBqtJQEQUjZSnQcc3jrLm+99U0m4x75+afE0x2+PY1Y/5u/4uPnS64uT3n4ySdUjaXMG6SMCCgwZUFia4SCUGqkaTl98AGtaQmUwolO75TVK65WBbPZJZPLC+7e3efe/gghHA8enmKMRSJorcc6sRFES8zGIYPvqtP1KqfIqw7OJTp9WKg70JOxlqaxXK3O0TZmlAQEZUa6vYsOoJ5l6MEU6wrWl8+xz46JjedA3uCXi/c5efqEUTzlt/ITdm2PsR3gggARGmQcIPAY39LEDTbwrESLdpapHPHO4be4tbfL3u2URw9+QRyOGG2NWa/m+OE287PPePbppyxPz7j/fMbDB88YJRmv3HqL27tvsB1ukxrBIEyZjBT94RaBHVA9XxCVJeXshJOzI/LLCwbjkKyY05tlRFPBxfE5Ze0ZHhzyyx/+Je997x/w5T/+j9BBRLu8IB32SIzjpewtPrn4JRO5T70bIF4ZIuVtHv38zzlSFVcf/Dn/+Mv/lA8//Q0X1YzL9RnEMToOGb90CzmM+eT8A371+Y/4k9vfo6rXyLiH9B4VDFnmSw5evsnhl17meHbBgEuWwQrpHdnM039+RjU1lFkOoeVze0QvPyVzNVIk7PZvc+ovyBrD87OPGPS2qdcZt0f3aNaGpn7GvC3JVpd86+57vHz7Jf7Lz/4KNqJzfHcYEVKjpEYHgigB78dcb3Teu66zbExXsNQFbdNQrM7JNllOcsNT0UoSaE1rGgQts8uMqp4w6PcRwuOdwZoaa2vackaVPWP29N+xOP8VVd4gXI1vPVI39Ac93n2vockd8wXUhSDoTls0maG1cOeOpxdp2iJAUKMDh6sNtg5QYY2OFG3tcU6i4w7/4KoGV0PaU8hQUMwMQhjiYYqpPM26Jh55jFH40hL12w4A10+RYY4UKT5MaeZn1Iuisyj7s84l5HvUqytathDqkMnet1C9bYQ3mNZ0B8ZNceavCxNru04mXXZPGIYIFXeBqU5A04DoHGPCz3HWI9R4M4rvIJzCC4zrDn1iUwAiOibS3+b6vS5Q1MsjFn7Jslhycvo5syDl1df6zK+OCaM7bI33WHBFkvQIgrAjz1qLVPLfs/eGYUA6GNIb9EhDST8KcMbhbUsv6GF9wNOTOf+3HzgOdhP+/N89wdWeNNL4ICGvJeW65eLZb4iCiKfLJVnbss5zLmczmrZFywA/6XN6ccFLh3tMJ1N++fFnLJfrjRjJss4K1Gauakw3y5ei+3cUhTgsURQilWLQ6yFEZ+vspQmrdU4YJBzu7vHld77Ey6+9hQ5Ud+qvCuIkYLw1IV/PGQ5Hnf++yEnSBJuXOGsIgm584rwj0JskRKCpaoJAozeFgxKSGvMiShsh8L7ruFjr0UpixXUF7V4oZ6+tgNcTwuvkUjaeGO891thOj2IdVdNS1S3GeqxtqbMroiRFOsfF7BjvKjQGJSzLwhCGGaHy7GxNidYZ2zsHHN56hV4/pW1CmizjFz/8Pr3JhPWq5MMPPuDs+QOMsSRhgKLF2gbTegINSaTRMqRuPU27KeJEl0w8mewhXUNbXxFLQ9F4dABaWhASp0CI7gGHzmZrEVhASdBSYqxB0uCsJQ5HxHHEZOcmvdGIQDXgNe5aiOa7e+ycRekQi6awUDchKpTsbe/y0ivvMLs64un9Uy5PHrK9f4NvfO3r3Ln5Ec8u1xRlg8+X6DDFeYtpa0aTXpdaXLfEWnUqflN3MKymxlwXlELhcJSNpbI1RTOnrlteujNhqy85SlOW5YrWbsIm/Wa8szmZsemsOQu1M3jaLhpAdnEBLRAE8npqCNpxY/ce926/hFI1q8dH6CBGElEXc7LZnKya4duS7XnMZPAaa3nOybMjvvPGH7HzB7f54MkPefjkU3bPJhhRUJiCoJ9QRiUnZIROkllHJQLupbt8894fEA06UWx/MKEpDKYGFe2TXzykXq45eVBzpi+JoxLajLUNKNYZ0V7K9ugAu35EqHpYWsRlS7I7oC0z6uyK9XLBNB6jtwPKdgVyyMNf/ZZwcJPRK4dUnz/jj7/3H/Pkw59Rn57yzlf/iPnxFa+9+i0O0zc4uTjjbHHEB+uf8WbzFjdeuscyuMJWkkXfss4N+ew5l7Mn3Di4xzaa4YlFBLD7xptcXF3yl7/9Z/y0fcj/6O53KLIlQniMC7C+grZktTqlaK5YLi74tD2hDFb8/df+lLf3vsdvil9wcvYbBnpKIEOCCGw44qk5Qo1T/snkT7kx+jI/PPq3/Lj4a2Rd8NKgz+Lqkl68TXXyKVJJcrMmTgJu7d7Eq5AOWC3wtkbpfgco4wvIFy9Woo0w30tkqAkjQcpgs5Y4rGmwbUNTlxTFgiJbslxfYZoSr/roKEZLSVVcsb4K8L7B2wrTVrTNClMvqIsrVldXnJwEaOG40Y+ofY1Ukjjuo0WFCdZY19mDhe3swQLBcgWt9YSRhbbTngSRwFiw1uNbhwrBtAKt+qSTijD11IXGVwHO1UjncFYicQgywiilWkqUbEhGMfksRQU5/b6hLnLMqkUqi68kQscYVggnCMMJQS/BmxLhDEkvhGhAG2xjvaNuSvCdBsV6j3VVlxWmgq4jFado1R2urGkwxnZC2Wu3JwZv55jiOQQHeFniUb/Dy/JAi9bRxhkB1oZ4qr/VHv97XaA0pmL14DHnV6eINMSLEU4IVHQLKROUlIwmW4zHU4y1lGUJUpAm6QtSa6dDCRj0h+zs7XNz2ieRlrp19ANIQ8HFbIZDcnFyyWTrFfqhgcjz9LLg+OqEfm9EmMSsizVyFPDme2/zgx/8mKbaIOAF7E4H3Lp5wMnTJwwGEbmx7O3ssV7leG+om4ZASfZ2tjstQtAJXJXqdDJaS8o8Q0uJaw3D3oC6LiGQnJ5dgtTcPTzk5sEBXio++eRjhG159vQhQTrm7XffJQ5jzo6eUdcNWutOawIo3RFRBQKlJMJBL+1R1uUL100v7VGUFaEOunCpjSgS2GxEm26Ksxs/fLfZIEWnW9nwDpx/8X59wUfZaGXxL5DI3cyybRrKsqQsK7x3FKsZZbUmCTVlsWY03ad0a5TOicOEZGuKQzNbXDAcj/Be8Prb7/Dd73yTo2fPef74McurUx49+pTTiyWm6bQnVVF04DHfdgRW2yLR9OKukBVV18VoRadlSnojQm2psjlp0ImMAw2DniKNuwV0XRjKyuEFHdafa1G7Q2vRnVgFaC1xKJywhNoRJwFXZ48IheF8VpBlvutIeBCbGAatY9qmRoVDtJZsjUfs7R5wevSYo8cfc3n2jHVR8KpI0b0dRgevcPjshM9PS3Q64nvf/Rb/+q/+FUePSy5na5RU9IaDro1eNxswXXca8qITygkVYr0Bui5S0cDFMic6ldzYDdkbp8yymrIt0cEGoiXAGPvCiS7E78ikffe+MMYgNUgngODFe2m3t0dVXfH4/ozdyS6r81knWE6GuKClmJ8g2j5xfJPhVNFUOX86+B4XzQkyc+ze3edLwTfJ5wtOTo7pSQ3eUamcSrWswpqxGDKQCc9Wl9we3mSY9lHhOY8/fg5iyHQvpG5yTu8/oGczbr96m/Orc/oqZ3/LcjCKKG1MUxe4bImvLaOdt7DFKVJbZJhQNRlNe4KOY27svs3R/YdcnS9Z1TOSaIBNS7SM2RofMPrSELRG2IK2LBAWqibj/PlzwtAyvTFGB0NUI3n85GO++7U/5f5Fhd2zjHb3mC5ClvUSUcBLd/aJ9R3K3kPGoz5NY7g8/pBb0wP2B3d5L30LrfqoNKBtS2phcKGnCApOnz3jaPaUUzHjpdEhd8VtRv0+b7z+Ff7V459Sr56hdY/T4piTQUNiNGmlieoWGTe8eeMGPzjv8Wt7yWnxC3ZkxFZ9SR7P0CaiPK/RsWV5NaN9ek5dFggp0VGX9v0f6tb8BmPw70sXvujCwsYWG8SdOLY/ZjjZ70I664wyX7NezchWV9SNQ7bQyA6aiK2wbUFTr6mLjCrLyYuWIBri7C6lOyUIWvCWpr6EUONx2FJQF57xVpe9pRBY43FNQrloMI3ZaGtSkmGObS22ksQjxXgvBO+xjcW3kPQGhLEgDCVVVuOaABkqnDVI36C06A5NsUUyx1Qt3guEjHGyj5MBToaUWUevDXtpVxC0HW/IWE1VbaNsiVXvY9WrOLWLEAFSdTyUME7ROkRK/cJp2G46KVJKlNQdJ8xbcDWuPsc7i5NbSJlsujAgpEfSITyEB67DHkXXzapU9f87Dsr/t6+TJw+5fPxbbrz3DY5OnmKKR8Rf+hY723e5OD2m1w/oJ2PKoiLLC4ypGE+2kGnaMTa6MAKUVKT9Hoc3bvHq66+zc3bO1eUZzjRMh5aLpzPatiKIU7777S9x/5PPeP+jx3gEt/a3cTLk0ZMjbt+6wc27t/nw44c0xiCUZDRI6EVg6oLHDx8TSM/V1YJVVnLv1k0eCsdoNEZrTRSljCdDnh9tRK9tjfeWJIqQXqLDgHVeE0URqyInDAKeP3vOcDji5sEO29t7DAcj6jyjWTZUTdWhkm3DZDLlrXfe5cf/9i+ZL3IGaUK2zjq6qDEQdIKmKAxwznUJoC5CakVZ1TixIZ9sqI1Kd+py7/0mZXTj5lFdtsN1p0Rr3Y3V3OYkLfjCIrg5OQHgPHbztWAjumqblnWWUZQ53hmkbNnaukMYRiwvrrhcFITxgP7IocOA4e7LGKfIZ2uSNEVIx+nZEfOs4Nbte/zohz/l6OmnnSh1dc7i7BTb5Gjlu0h0EeIpNvHjHmOKzjXTWiwOoQLisE8camx9RaJLlOrGVmmkSSJNGIDxgiDwhNrha4MXDqRAoQm1Iww8rjWb7goEkcLgCAPFeDTi6enHKAVZbWlaR2s7vZQVQKCxzpLEPZIoYXtrynSyRbFecHT0lKuLEy6XK2or2StbRBTT332J6fbH5M0lJ+fn6PEef/i9b/Nviu9zfnqJMw3OtiRJD9duTOS6W5zMJg5SyI0mKfBY2xWlddOwzhvKAhKajci1c00opbo5v5RoLRDSdy1lu9GnbER0G1gx1kHdtGgtUVKQiIg9N+bo8QfU0TFKRuh4C6ENrfJsvfFl9m7foZqvufz4MbLwhOdLAhtwfPkZE7GLqdfcFFuc6yM+Vhf0o4S4p2gDSz8csKpa7qRD6qtLLpuGqrqiV12xOM2Q6RZ5WVJVhjyb8+ZuSDQIaE0IzZrWhURpj3wJx1dLXjqsaauCotUk6YhscUl6GFEunrE4e0hvcEjU3+fgzh2K2lCd1zz66Jfce+trDCfb/PJf/je89c1vMz7c5da77/L8k8+YXT7m8uRz6ipjcbSgeGAw90bc673JCR8ziWK+89J75MmAt9tbJKOI6NYQ3Q6xQhH2HDvv/kNsvsasM3YnO5QNXDz8GFVBdDOkPz3ABZqyrql8y27YoxURH9QPEYVg1KQslif0LkboQhLe3Oek/IT15TPOdw1ZoomXEe2q5HH+WwIdoYdD9tjl5/UxZTYjElOyYk7QJqyLC47XR7xxeI9q/ojLxRlV3AJ0oXLwgvr9IhhXiBddWEF3wLnuwl6L7F9g1r/4AkoHpHpCko6YTPfJVlfMZufUVblZf2qa0uFxhHFAoFOausHYc6oqp6lXuLJgFBmEFQhpsK3Ftx5UV5Ro7RBesn9ji+2dIaGqyK4uCCNBXUg8BqkEWgvyS0FvS6PChrg3YHGsaXKIVR+tLpHa0ZtOcfSJhyVCGJbPGpJRSBBVNLWiyTxRPwKpUNEYPVHY0hGnCdZp8pMlTVZjyoLezhSVDgmHMdnCYuq7BL1dwmhIko6ROkHpCI98gZKw9jqBfuN2lZ0mpevkNnhXdmN6hjQmR2LROu4KSCFx1m+yhBygkEJ3nX4hYTPO/9tcv9cFyi//9f+F0d4e+sHPaHWMciHr2RmD8S6LxYrt3X1MbclWp5RVQV7npHFE2+tvZmPdJYQgimO2b9zkta98m5NHDzmfzVjna4Z5yM1pn2dXGTqwaO156ZWXwLfURjFbVjx5cswbb77KYDTmpz9/n/W6Io5CtO7ERUopauMIdIcXPr9aMez3iIOAG5MeTmta253cZ7MZqyzvtAdNS9W27O7uYL2lKTvhaF23VHXNxdUMKTR70x1effUVvO9mtlnuaesGb02XOuwtZdUwPdhnPJqS5w29NGGyNeHi6opA6U0XQ5CkKdYYorgrTqxzhIF7kXysZEd9/SIwynUtP/FFmrSlG+8430GJfncO6bzrFhopXrRwu5BBXpyGjPNouk81myTrIAg5uHmLKN2lqgO2du4R1C39wZTL049w3vPk4ce8fO9VhB5gjSCOU5ZXF/yb7/+AKJry+OEjzp5+xiDWFFUJxjJIAtrWUpqObGp9J27Fe0zrqTE0raBuBUE4oBcPads5saoJlaRsPUmq0EGHe/dY2sbijEP4zS/xxQjL+G7EI4WndRbnJVIroiBm5/Am/TShWZ+irWWdO1rjMd6jpMTpkEBpAilJ4pjheMzWzjamLrk8f8bi7ATrPHVtaD1k2ZoiWxJGPXQyIdYnPDw64n//v/vf8vf/9E94/Z3XqMqS5aztZtESlJYIH6CUJhnEZMscbyEMNbenW9RNzdOnlzgh6ZZ2SVF6epHgxnRIVtZkRcfG8R0ykUhHOCewTiC1RBi7WdA6VsvGiA7eowOJVAKMYyJ3CAfvcJ49YFEf04sdwu2jc83O3gFR5XE9RaVK5sUVPjKEPUWW5ZjjJxTlnFzM0X2JGgScyYpRGnMQTJClofQGI2C73+dkPUc6S/HoKUIe0BqLsYqrRU5kCloxZHmxxiQvcTJXbDdzBGtWRYANah5cPmUvDLl58BZNa0i3brI6P+pEgekWem+XrCqgFMSTMTfHPW6+/To3Du5y9uwx1eycapFhJ4Yw6qGswbQFqQIb92iikpGM+fWn7/PW3/3HvHbzTxmkE272d3BeUk8PqdoG5QLm1ZpoOkTZCi7WpF7goglqMMWcP2X7zg3iUR/d71OWFUVe0UQhH/z2R5RRgd1Lqe4k+POS0/k5nzz6Ja5sCEQAJuPTyYpoXWLCFCccpjYU3vFh8YjgNOLrvX/M/3Trn5LMFD9pf8CxyijKD7nVTNlO77E9uMk6tlw1x1yWp8ho6wVDQ3g275vuOfSbw8q1ww94UdxeryveOZy/3gS7Nf26u/Ji7ZEB/eG0C9J0IIOEyXgLJR1Nuaatc9arS6qqZr1aIfMlcErXKRbQeoQGrTytEUSJYLqvCTDYRrB3Z4e9OyPM+gjjNhEquaXNKySesNdZvduiQUtLZdbE/TFBWIBYUC0abOnRSU2v12CbjCBWJJNtwiQH4ZHBhHCUo4cBMk5oVlcUT1eEvS28EwRRgh5MKC8XHaFZxUT9FO8hTfu08hbx5C4y2kHKGITCer/Rx23GZ1wj6v2mEOnw9N52mhRPJ0FwWLwICaIBftMxca5bwz2d+URKtel8dfEx3pkua+7/X1w8VzW4xZyr498wfOkP2L/zHrVp2J6McNxhZ/eQs5Mz9g9ucnl5hBeOxXxBnA4YDEZ4KV8A0sIwZmt7H1PWPPjwQ1azFaaquXxeEY76TIYRg3HKzW3DeOsWItB88tkxAx/w3T/6Ns/Or/jrH/4EnEM4iAI2WQeOopbkbcsw6JJjnfeUTcPpxTlSC7I8B6loqpLHT58xGPQY9HsY5wijhMY6suWcUHUgnaKuWa675NitrTHjYUoahmRlhaRH2zTkWUYYSJSWBEpSLOccHO4xGE3oLxeEUcQgbanbAW1Vd6Cta71LEJAmKXmR07QNg16PfprirMO2lkBrnPC0G0DPtc2Ma/87HXm1W1O6gkRei6Pc79qKZefg8O7fC6WS19HrgMFTNi1Cyi6OgDXz+QltU7Kz/ypt2bBeXGLqJWl/RO0kN6Z9roqW2XJFWTcIccTs6kOuTp9QrFeIWqBDTRwoOvmuQ+lOH+GsINCaIAQdaqpWULagdEKaplTFDO1X6MRjhSRKdCcWE75zOVlD23R2JS0FGo/y18wUj209tjU4JMsarLdQ50SJQ+mE2flzpCuxDqzpChktFV5rtPREEnpxxLCXsr93gzQJuJifUszOMU1B6zXOeqxzLJdLlvM5QaRIx3v0zj5F2oz7H39GUZT8oz/7B3zjOxE/+Td/zWqVk4gQqR2maggDxbCf4BpP23p2phOuzk4ROCLlscrTSwOSfgJKgGsZhxJ5XfgLSRgENE2XzG02ADm7AT5JKboecPeHkaoTV/sNIrtyDZfhJbVfIaMx05u3KauM5dkRe+o2pq5Zra/Ig4KizSjKObapOdy6wf6eoFjNmS9bnpw9Yu4yepFgdzzFKEHtFQOpOXCaynoOJlucrgRVXRGrlNK0OFtRmZYmm6GcIC8q2rpEhQW7PcNLOw6SEU3VIy4DHj9/zJ3+NrF6xv7dW1SLNYM0om0ybhx8lf7ODkK1OGMJl31cYZjemtIoy26zS/i1b2CyBcXpCcFwm97gkFX+GUE4YGcyIekNSFRA/+4uvSDmrdvvkqZDxDpHVDU94TFtS77MmWXPuD1+g3S6g7YB7XJFUV0gbIzSluXpBQ9/8QFtnjF4aY/h4RucXJ3y44//JfP9FQUSlUqCOyFLY5hu32A4OuD4+DFBmdELY5w2aCvx3hBs8PILtybzlszMScdjvjf8Kk+Lj6lFyUxV3BvEfO3O19nbe5uFtfyvvv+/4Qcnn/Lt3le62e+mSu7Gw18wlb7QVf7ubMB/Qam+Tub994oSfofD0fVZEJq0N6ZYr6mqgnUW0O8PSEa7pHgG0xv0BkO8aWjrNQEPUAaKQhBpCBWESTfSqQpHWRjisWY4dJjiGbNna6SfoQPF9u1d6qzk+a8vwFq8jZjeAmcq8itQ2jPcB4RFBQHWBJhKE/RabFtjc4sfStrqEluV6FgSxDW0HlfuIbWjXDW06xbkkkBEeCUwuSEI+7Q2wBhFJEGqHt5tEab76GiCVFGXakywSQfqlmnr7EYzVnUHKtvNbaQQyDBGIPFiU7D4lv5gGynUZs33BJvE42shs3OWtm2xGwefd56yqv5WPZTf6wJlfHiHdnZKNHgZka2YP/gZd195A6VDbty4Q2scQRThXEN/OMariGdP77Ozf/giKrq7ROcUUQG2ralnz9hLJUp37oXLokITsC5b/tf/p7+mH/fY3RnyB19+i+OLjH/+Fz9ltlgQ0M3gvATrBXVj8d7inOncKt7ROodUmqZpWawzispQNy1R2KVEWuc6p4ZxNE1LYwywOSGEMU5IiqpCSUE/SNgZjwm0JEl7nF8tePTsiK3RiCROQBiiUBIow/OHH+FdQ5GvcHh0EBFGliio6EUxtTFIKUmTBPCoMCBVfUTeBRCm6YC8rglDNoyUgLKqMU2x4QrQjbU29uFNpiBSyhckwk4823VRnL0+2agX3ReJ3ASEdW90rXW32PiumNFBjK2WhOYc3xvz2hu3+PFf/jNcs8ZjiZIxjU+YHt5lsDzl6uKU1TxjfnnajZe86R4oLFEkwFm8DIl7E5azEwyA9kSRJIw0QaBpN3lJW+MJvilx5ISBp6ghiDTaC1rTCWi9dZsqxxBp6EUQlJ1tF7nZjwUYH1J7TSNMR051vstsGk5JAkcUOla1wziDVJ4w0BglEUCoBIMkZm//Jjdv32F+/oRyvcQ0OV1b3NJ6R+s8q/WK8/mC117aZzyesOxvsT/KycuGi+fH/PP/5z/n29/+Jrfv3uL+J59QrmYMewl57dgdp2hgZixVVvNsXSBsS6AFoXKEWoGtKJdX6P4OYW9ALCpGkaBoBN4JBI44DKgbixCeMJC0bbexaCUIAvWCk+C9wBvP5ghNnhUs9JomKpi+us9v/+pfsDiao0zI8N0p52dPaauKumfJmpxFeYFGs9A5adAyN2cYAnZ7B5zrgvIe9AYDtJeoGsgtxhliG3f5IMszfvDhv+JP332dbJ2B73QJfvWU8c42oj0nDXL6UYvqe5LxmJv6JjIbcLB/jx9/9nPOZ0fsKMk463Fj9w5JMsQLgQ/BuhKRW3QvRerObpmdXZKtr0iSgDSNSW6O0S6GUDPe38IGd5inM3S/R7K3w9WjzxCLnGh7l2q+JO0nuLDFlg1WpzgE5aIiSrc5+fQzdl95hcn2PepIkB9doqUCqXB6wMXqfS7nR/SiM+6mW1ye3+feV77C0v4KrzKk0gQi5FuH7/L1u3/GcPsGvf4tjj48oVAVsVAEreC23+E/e+sfcnv0Hj/+9Becz3/Eh+ufcmgzrCr5J/t/l72Dl6kbQbs4JmOJuHrAfB3hly1Z1W42M7fZyDqQl9SqSzlXqisvvOc6uL5bsjvRuPD+hU7uiy+JL0bJm4Vo4ylEBSG90YTq6gpjLdl6xuxsRd0adJh03Y7BAcm4pDWGvPkFzhwBLXXbPceu7lABTe2p6ogoMsi2pi4uCIWjLFPK1QrXWKY3YT1XiEARRDmkgnodsLhIkMGCKDVI3eCaPr1pjE4KfNzHZJpo6xA90WSPP8bMcmotkFqxPj7Ci4h4b49CFtisQkYZ8fgm7mxGnCq2tgbIaMTyskQGLb2D99D92widAJ1w34tN2Ii/xj5IYDNu0xHe6y+Kid81NggIQrWxJH9RNrbGYIzZFDBdkdgag7UeZ7s1cr1e87dpofxeFyiT6S7GleQqxCqJHGxRmhaPQAchdZ3RSyOuruZoBO//6qdcza944813vlCHC9FRQo2hNQ1lviQKFcN+TL1uWbWQW0uLps4Ms9bT1i1NueDxoyesK0tbt6SBwuCRylNVnrLqYq2vuwXjfohxHo/CbOb0vf4Wi/wMIxRtY7rqUgiKsqaumxehTVVVsru7RxClLFZrrHEMe33uHOyRBAq05mqxQkmBcg6JJIgDiqxjo1RVw4PPPuHo6Iwiy9mdbm8KgE6IG0URkZTEcUwvSTG2fdHVUDrAS0j6KWEeobWiyHK8g16vT121nTXNuW4MYS1iozFw13Pka/3KhiJ4fUK6vl7MmNkcguhw+07TdTOkxDpLWWTYck7dFDRW8OT+h1TZFaGGyfY+N2/c5WJd8ZtPPuXObp+7Oz3aMmeRQZGvMU2DEJLW1HgnCQPVWZu17ELuaIhCQZSEKC0QWhOmEf3RkFSX5PWcKHBkTdDpNoSjkaKjP7quU4TzhNJt0p5Bie57e9FZcIVUVPRwXiBF1p0YpWA4mbC1PYTlZ8Sh46TROKEA07W/N6OQXhQyGY3Z2d0lSQOO1zPqfI6OIgLtWa8rNp1Wmrbm6OyCt998mcODCeur18hWV4wzWJae2dklf/Ev/4r33nyF6eEdro4ekxclQniyZUnTeryTxGGIMQ39AJrWbngwjhZFYWuErgjDiNiV3BwnrKuasnU4C0GgiOOAquqskVr5boy0EVqzce0Y4/DW0ZgWYx3r9SVnl5q7730LqgLTKNqwO2GvixnF847Lo0cDXF7zbPU5PrPMqysGkz51cUFqUrb627yZWj6enmMngNK0xiFyh0w09mhJYIYcbg15sDzhZH6BriV1a/BNxmg0AA1VvSLo97lYByxzxy075a2334W85fDgNW4cPeTo8px3XnqLeDyhbssO7CcVxbwkiAISPaBcrcnKkmo2J97qE4WaKFHYokGFLdge0kBgBc8ff8R6/oTLZcs3/uS/w/Yr9zj9qGA9P6UY9wjXGto1uJCmqJGRQmwP+fjDv6R3MsPna+JvDanrEtNWJHoEhebOjUMme/8R948/Y7E4QfYjPlp9iBBD3jj8Mh/NfovxFdJ46rrk8dVDpnVBLgzlOKKOBakXvOr2+Jb+Lofb7xIy4K3puxT5BR8//yWn1RGNNbz92tuEeclY3+SD4oKL+YeE4oQH7ZJfFb9A6K5qV1ohdQDebbpo3RjQmnYz+v0iHFBcFyfd4rHRu30RRPoCJPrCCLH5vYe2Krm8OGe2WGDMRZeFhUVYQ10+p6xKiuySYnVJ0SoqvwecIxvTaTSuBFp5+v2ucZgvC4QRjMcWW4JIEnZf2upEpyOPH4WkWy3zp2vqwqMkBP0eNw92cO6KZpXTFF3wqzVLZHBIOBDIsME0NTqS6NE2V79t2LqTEo495cyxOmtw0RoZe3wT4p1G4Ni+t4O1Lb7NcGLMfGbZ3rX89tf/HAaSt770d+j3k26eK9SmkHBAJ2b1XoAPvxjb+C/UyfaagSUl1hlM26EV7KYgMS9CArtiE9d1iM0mviArVzw7evD/FvT6H16/1wWKLy+RdU4YRwgR4GtDGARUVUmRrwi1pFxnPPn0Ix48/Iz3P/6Ae6++gZT6RSbANTyqqSvKfEVVrFlnObPVmqasmbeeSgoab7FOUJRQNY656lIbjROEWtPl/Aia1m86Jx6kR3qIgwAlBWXdIpXG266VtsqWlFVNkZebOZ0HHHXrOrpnEDIcjRgPh4zHW1xezclXa6JAE0cBVghu3L3JYlWwWq1YLZfEYUAgLUpqauHISkdTt1jVssjOu83BOYyzlEVO2utGQpPhAB1FXbHiI4RSnJ2fdSOiMOxU3a1By26+6pwjDEOiKMLYFmMM2guMFzhnOrTx9R4kBEJJvLGdKsFtqpHN16QS/0FR7XC+C50jCDb3RSARlGXNIqswvmE++yWmqtjanfLHf+fb1JnDuIpVtubj5RW6ueTOTsqoN+ZJk7MsGpq2QokaKWOkcCgMTXmOUoYokCgtuhwK0VnupuMbJNqxvDymNhUniy7HBWfRjUfrDcfFC/AGHKggoGygrsFYjZIGpEQ4D8JRVgVgsW6TBaUkg8GEre1tZpc/QrqW5drifVf0dVwRwzjV9JOA8WRIEDpMXbCcX+CDkNF4n6OjZzSGF6wBbwz3P/wNf/Sdb9Mfj5keHpDPbpHlD1nXNc4J6rLkNx99RtLvsTvcJRTdYl2uS3SaEJAxHg0xTU4iDfV6SZYbai9BhUjbYEyL1EMCkXAnNpCO+ejJOUVtqZoWAZuC1zMc9DbFU4tWAa0xuFYQKIHWHf5bCsFczAiCPrd6kssHD4l8Qj8e4oSjNAtcZbuIhsBQrI65rJfEQhCvZ0RxiNYD8mbBaXBOYx1i1WInhkAF+EBhQks7UMjUUnw+IxYDXj28Q5hcsiuXHBeSwmmWtaNelAzGKZENqREk4z4vv/slxuNDymGNNJqd3i5Pjj7m7PKSQfoZfjdluSyJZELZFKSj2yS3bmDmcyIdo3Z3GUxCbFiSnZ8S2ZRmtsa6miBoSLa22L/9Jcoi58vfeJnhrV3WyzXhYw/FmuzkgrDfIe3D0YT1+VO8N+T5FfJsSXtakE2XPPzZD9h99euYNqI3nqL2AypbEpQ9grvf4KJaEPb7hLd3uL/+hOP14y6/LNJ4ozm6eAaNpC9jrIrJzCUiEvTDiKkdU14seRo/wDWesm5YVBlP3JJzOaONDBO3w3K+JrInPMie8vD4N9St4SwWnLaLTYYOG6hft0my6che6xdeoNc32ojrS8gXi8jm0NONCK/HDF36eleg+M33atuK5fyK5XJN27bdwYJOG+VsSZ2vqYs1bd10cQ1Niamh7Yw8JCE0a4mxju0RBMrTG3pkAKbWxFvbpH1NU2sQDVoGjG4O8M6ipcETkQy3kRQE8RRxZ4/lWUl2vMBbRb0qaIqKphToJMCYknQ7Qn39BtQr4vEQLwuMXZFfLAlST1UYnF1hjQU9wDgIpcM3Nc+fOc6XAUXdkJ/8iEDCjTvvMpne6BLIhQBnNsWcwyO5bvf6jcvVb8bw1tkNO8VuBLUWsykmr/WErTG0TUvTdPevqmuW2Rmr9WNWi0dcXjzD+72/8R7/e12gZKvn+DZCihLVk8xXZyAEZbmiXs8psjUnz57ywa9+zNHpFYQJ/cH4xXzMue7G101NWRXUZcb64pjq8hztLUp6nJSsDTS+q3KFcFgLjQF8h+12G/y7sZ092YkOF6+8II0iBmnMxXJFGITgOwEhHk7OZ1SNoTVdW+26i6A3sdT9YQ+tFbcOb3AxXzCfz9BSkWwcI7vjIaeXC+qi6rIbsPR6PbTs0jTn6zVh1Gd374AgjrvE0GKNc5YgigmjCOW7DpGUkjRNMRsHT9ofcXx6RhJqeknacQCSGOcscZqwWiywm+6IUhrnHFEUsF5n3an42gbIdftQYJVCbBaEF6p76MY9G8uz9x3qXm6suUXbUjV98HaTz2HwFppW4HTE7s1bDEYSFfXoW8etdMbResYsd8zrhlFPcLg1JtA3OXkuuBAFbVtjTQ1BQBAnaN8xARrVaWDaznTDdDBhkFpmsyPqquZ85pivLVIYAi060bOXyI2o1qNQ3lIb2YlVW0thKuI0QtoY2RT0I0/etpRNp8sJtCSOIran28TKYss5vVjjfYOSisZDZSxxqNkd9RgMEm7cucXOjTv86mc/Iq9qCFPOr07J8hLrfZdRshEfX12e8vjoiHdfO+Rwb4Up3qLMrzheXLKuOopvkRXkZUGVxwwGKdPxNpMbB5g645vvTLg8v+LotKRpQnqjAcLN0dZT2IY4UsgwIIxStgZTduKc4XrO6YXGeIcxHbQv1rrjKOBxxoLzCC06DYP2RGE33nPQuX7uRKSv3OR0/Yzn9cdc5k9RmWUcHKClxqmGpVnSVJCXlxRiRTTdpmha1rMHhHGKadacunOINOF6hGstLvREQUKoA2xgcLcNNsooP80Y2ph+f0g0fcLHz0uOi5io12M0Stna3SLtJ5SNpzfY4nxmkGWOsDFllhF4D77i2fEjpkGAny2IU0FmFwTDET4ekC3mrM5PSQdDJrcOyatzlo8+x1+tieQe1SqjzC9IBze5sfcNDl+6jdaawIE8bxDKoIdjfFZTXVVkO12YXj9TJDu3WF08Ytkark5OmDSaxWxB1lYM9xZMd19GSk8zm5PZhgbL+TrnMp/z0uvv8fdf+++xvfgxv85+ik+6cMfaWZ7HlzQXFU1kCazEuQCR1bR9ydPVCWf5BQlPoTY0RvLx0SN+K57xxr0xk3CLz7JL8sWcrGx47jPWIifzNcYIwpHA2YCNyv5FB+Raf3a96XXjnQ6pjtywla6/vhkfXyd9SUQ3urgeWQhw7lrf5AnCmDBQYE03UrdmI7J14Fq8aUGGiHAb5UIC22DaFXX1FK0g7HUpHmkqCCJPU8HVTDDsCbCeYnEMTYiQKVFUU2eOXp3S3xZI59BJRF23eDegrXK8XTDamRAHDt+AjGOyec3iac7kVkuYCKJ0m+GNESYPoSlxxuFw5FmLLCU6DggHEToecvysR2sWTCY1Or5kML5JLe7RG+8w1AOEmTM//ZBsdULSP6DXnxInPXSwwdMDCN+hIa5Fsrb7uK6rDfG6K2Ja03Y5SU3z4mttayiqnLoqMG2DaRuqZo1tIBBTBimsNmOkv8n1e12gYCVWw3hnD1s1qGpBURcEqzkf//zfkFcwWy0JezuoIGcy3ebmzbtoHbzYINumocwzsuWcy+ePObn/W1xT4IVnCSycp2htV9ULkLKbwW2ajl8k/W46YUJ1zTElBUmcopVgUXbW1SjQVE2N844o0N2L2natzOtTRBiEpGHQOWgay0uv3KF2lmy16rKahGVntMXu3pQsWxEFAc4aisIz6A1Y5xkyHVCbLkvGOk/U6zGfzQjDgLIuUTpFOUuS9FiuF8RxhJUSv0nWDNN4I1qUG4iSpLWOwXBAnuUY0+lkwiBEioIgimnbppshC7EZMXxRgMA1lM0TadUVaB0kZbMA8TunJdepwR14selutV3GS7ZesVyskckO4+kEqRWTyYg0WlM1T+njOLl8iI6HDIXGomjqglWQcn5+BFj6aUhRdrlDVeko8hopO42QNd2DGWvN3taIe7uao+OH2DJjnsGy1phN/pHfqN6xnSTY+s7N5Dw01lEZR2scRWt4+e5NYjzZ6VPS0GPotEhCqM6OG2i2d3fQ64f01IJLW5EGDXOtaBqwrUNEmun+64wnI4LeFh999D4nR09YFjWrvMTblrptqVpLGISESqPDCO8Nv/7Fz3jr1f8BgVZs70zJ9u6yc56xqnOMv87MgKa2zNsFed4yGAxJVAvikDdfHjEah3z6aEWeNagwJHWWqnCdg6St0f0xL3/tm0zVFXL9mAfr+xRPLymKFu9sp+dqutOVRxInCUWeY60liRRRqAiUwmziAKKox9b2Hn7qSHYPEe8WNEcrxvenxCbA9BLK+hi7zql9Tu/mEKNL8kWOMQblMmzsCHZG3RjhSUmvGSPfUogpyDAm9BKwtLci2jBj9eGcX7wvubtzg3RvxmRhaIVnVQh0FtIw4uxiztXFZxSzjO+98Q6v734b4S133vgKn2ZP8EGCHG2jhGVx+ZzR7l2qzFPlnxMNd1B1i5ce/7imcRnv/+RHuCZj0N/CRjAe3+DsyWc8OX/Gt/7jf8LW7j6L03Pqq4ylmKOqmvWqwZmW6pc1WbGgrRoO3/k29EMG995i+S//ksAnXD27z8Frd7E25PzTZ4QqJxykVKZC3dzhyYOP+UX239J7c8LcGl4/fJdD9Sb/9vRfUdgZaMHqXoR5sALbPb9pOmRo+pRlTRm0rNY5rriitpYLWp60F4z7Q8Sqxyo0PFw/5bScU7U1xoHUgmCgGO/GDLZi8AFhG2BtFwoIbDopX4xs5DVkg05z8gUtfeMEpHP+dFbEbsTgXbfm+M06rWTXjYySPuPxhKv5AmUMdiPa974DkXWcpgApHUHQwwdTTHQbbxYIs+Ly1KECzySA1kJTg5KezAvGE00QR4CjWud401LmIeNbY6IxNIsVXvWoijlteYFUIdlVw8HrE1Q6oTFzon5AIkcsTjKKZUkQb1HMWqrlOaas0NLT2h7GAgraViADsI3FRnB1/JzhTkxZJdgqJKtiouEAIRVKCkzjqESBdZq6algtztBhShj1iMKUIIx/x6TQYeuNtZimpq5yjL0OcDWYtqZp6q4Lalq8853o1nviQKHjPkpppNzvClA64f7xw7/4G2/xv9cFyvTmPUTjaM9P8NWag/e+w3Cyx+OPfsLzx58hRre5+/qXeXj/I/ZvHhJECWLjIFBK0TQN69WS4+NnnDz6iMfv/5AHn96nqRoqa7lqHMvGYx3dnE6+qDE7wZW4lh1ca00ESshN6xGcaymtxzpLpAOMNTjX0UWFFNhm04KUnZj02krXuhZRW9588y1Gkymf3b+PtYatyRDhLFuTEWmvh9Ax8+WC8XhIv5d0GwGdxXSVrUl7KWVj6PcSbNPDCAG5QiiFEJ5+L+FiPiMJIwb9PghBVTckzmCdIQ5D2qbGOEsc9RHKkvn8RahgbVosm3ReNgXai6RoCf661doJPB1dAFU/DLvWv+w+b233xu4KlY6l4Taj4+s2Y9O2XC1KovQmB4f3cKZCSoO3JcvMEUWeo9kpsieRlJyeWvK6xltHUdScXq7JspxIsaEbekxbY0yD8g7rBa3tFseDwwN2B57l2RNoM1oxQPUnvDQd8/lnH4NQuLbq0M++m58HYUQUdXClojJUpqYqWxrrePzgIYOkx1Z/yHB3n6kwPHz8lKL2VF6SxiF7W2PWZz8nlg3LKmY4HnFSZPim+x5ZbVgbyeroiE8f3cf7ljgQDFPHbFFjbSdQEwKiSBHokMZ0hdDR4/scP3vGa7e28U3F7ZdfpVies6rOWOSdeA0PcZKwv7eLpKXIC6wT/OrxirduSrxruXP3Bo+fXlLLkHq1wkmHo3vfrssSqwXR1k22Xv8Dvip+RvbDf8vzZ8+pqoZqkwotPbiN46Qr+CUBIJ0DpZE6REpBX27RmDVKpdS6JdkbY4cx9kHDbH6M0wlVVlM1GWYUsffdt5EDiclqzHEBxyUia2hb253khopw1dD7YIQdKfy+xI0UItFESiF3AszrcHZ8yp5+hde/fJNnz0958GjJx58eUX9wRH/QJ00HnJwcsz+dQBIhhKI3nGLMioFWrMwMKSIGkwFXp2eEbUB5dZ+6Ubz89gHn54+4/erbSBFR+4q3v/VnfPzrv2S2mlMvao4u5vz26oyp67F78Apv/sF3Mb2Kop0xWpfo8Q7L+JjlVY5XEaus4OryOT5IaAYh7/y9v8vu3ZeZPXxIIAV33vhD8mdXlKePURHE85hmq8/ys/usV2f42wF13HCQ3GNl1miz4F76Bh/mP+68eKEkvxtia0Ny5rDrNelkBE7z1M4QGGTTkLeOS2W5O9mjJ2LyPONkOeeqXHYjWiVQqWS4mzDeiUlGmrAXEAUjgieKy+WK6XpFpEOCTfAfdDgCuVkncN3YWGx4KXBtJ+7WiRdrjewWZmc3AlnXjUi9F3jbbCInNoX5pruLEHilECLCus6YIHEEcR/rbuJNRe3uY4OGUJdkC0ct6Nx+yjOfwXzVUpeOnW2BUC2IkMlej3KWY0sI4xBaQ1VJqnmNUiVlPkEGChXHFDNHdn5BtvBI7QgHChmHtMWSal3S5jHTO3t43+JFSxALhIjpbW/TmJbFsxn33jikt91ntc44OU44uP0NRoffxnpNsS4wbUe61joF1d1H4VpMVeLblrrKEVIhpULK69ehK/y6oEWN1l23RUn5Yr1n8yxvTODdxH7TFdPqGgoqWMyv+P6//su/8R7/e12guMKxfvoEaTKEcMiwu2lHn39KFAwZT/fxpqI3iNnbP2BdlMS9hCRJsNZyfn7KJx9/yK9++gOq409QxZyqqSitYdV4LitDaTq1s3TXYiz5xcOx8ev7jTjymp4pZPeBB5zz9JMYZy1103UOlPzCMy4lv/NCd5k0rXEcbk+4dXCDo9NThAAdp4wHKb04xApBYxzZakGSxDR1hQsiPI5AKfKyIYp79IdDdBjT74/IswLb1IRBiBIdhjyQkIQhOgxRQYipayIVsJwvmW4H9Ho9Vs4SxjFpr8dqvqAxBiU7EE8XvSNpN+6dQCpaKfFad6chdR0WtfkZN3ell0Q4ITY6II8WEm8tdhNcqDaZLFJch3iBVDHD6S0GW3vE6RBvS1aLI7yVoPqIIKQRJXdenfDJrz+ibRuauqANugBCgaXdJOsqa9DSgQqI4n5HmKwq8LA1CtkewPb2mJNnT8nskHb4KsNxj6LIGU93sK3p4tKtIQgjZKjRSoGwCBWSKAliTdO2KMA0hqXNMH5AEzV8471DLpZXXB5nKCX40ts32d+Bs5Oc83Wfzy48k51thK4Q1AQhWGf49fs/YdCPsMYymowoq5pepDBoKmOIIsV0ENMfTDEEXF7NUU7gmpIf/vD7vP4/+Z8R9ufs+JzgjbucLSuq46xLRRaCwXjCcNRjfnWJp1vUj05P8e2Y7WEfpzQyDOhNE0rjMK7Ee4UKI+aLC375m58R/cFXGA32eeMr38aLit/8zPH5o1Nk2dJKg7eeOIoBh3ICrxUqkFjVvdBdHLsgdRGz5glJsEXpllhhEdpjhWG5OMHKmP4ghbEkG0qCrRi0QEYBxBF+MKJ/GtOzfUQCy/6SzJ1jiob2okB9pDuKZqIRiUZGMd6MkeGC9z86Jem/wfH5Gb/54JTFqiTQGpEJpNIkUcT2aMxwsNU5tLSgydfsJxM+Xz7nwaOPEPM99m7covQhGZKr1RU78wtOjz+mNDlvffUPSQY9Lp7OeeeP/hM++vn3maQ9jmZL6vuf0CrBb77/V9y98y4BIf0wpCcHkMBbb/4hs9maOE7p1TuIBzHZ5YqX3v0ujz98TJ6tee3bf8woCKiLkGcf/hXj3Qn9yQ4LU9DvT4kqeOWNr3Dv9t+hH405yc+p8wsu3Sm3eje5Ku9wVDzCtoa6rCmykp4V7NGjmZe0ETxZX1EUNTvDMeNkxF0X4m3LhZ9zUi4omqLToSlItkKG+zHJliIZCuIkItAhSRQipOSvfvwzjgvPK3dvsjvq0w8DtJIo3wk5pRAdWVUFqM0Bi02XpSNTX3M7us4km87vZogMviPASuGYbu/T1C1Hx89ZrCy1ty90Lh6BRONoEEqhwh6xkOjgTeLRPZQtKE6+z/IixxuIUtjeE+jYdwWdaSkKCHsSGfa6sfp8yXrWFVtxXxCmDi8sUW+bfqQ4u39KU0vaVd0F8+lOkNqUHn95jhCKIBpTl3UXhe48VQlN0x22ozTB0CM7LwjigkS8iu4lvPSVbzPY/QpBcthxTzaOOQAvNl0S3xV8mz7TBlL5hbj4xceCLwqPzZ/+Xdf373bLrztf3Quz6WFdyxeC8G+1x/9eFyhtUaCcoSIgHKbce+8bXDx/jBAhKpJEgWCVnxPECUXd0B+MOTy8BcDxyRE//Xc/4N9+/19w+vQh01jSjyQVsLaeq9aSmU1UPNdGtY34efP3X1vXrLuGkfFF5bj5mlJiM9NzHQZ48/2c8yglXzxIQl5H0Fv6SvPWvXu03tFYw85gQGMNRVnRS1OiOKGqKpBy48oZU1uDNxZDJ6S0CIaTLaIgREsoqoqiyFjM57jJmDAIabMcLzxxknTZLLZFq5DVagUegihEVQFJkhJojXGd8GbcH1LmGb00xRpLVVXoIEBJiWwlUgq0krTOgZdIulOQFJ21d1VWL5D3ks5+HWpF3TqM66A+znmc7NJChegEw8lkG+PtRrQFONexK+KU49MarcZ8er/AiBHD4RJcRt02LHJLni0IlWdreoswCFnPLzsnipUMtm4yjVNsccWbuw33pke8/3TB+XzCMmtZN/dRQYIKAiJlcVjC3oSyznEoZBhunAcCoQRSeOIkZdAa2s17qG5q6qri6vQJJ3sNy6zBIhEi4MnzNe/cmVF7+OA0YLHOMHJBHARMxgNUEKN0QJGvu+h2IVnOl1jnOUWT5Q3dItDBALNsidQpvVhjmooyz3h8/z6nZ2e888Y7LB78lKE44N7DZxzPHHnT4oWgqiqKrEA4SKOUm/tTqqIgjDSLyhLrEHBc5RmFCKAXoYxFCIX18OzZEfPFipefF7z37pfY3hrw3T/6Bwx3PuP+5/e5ms1pm4advT3Sfsrp06e0xnbPk3TdKdd2z1n27BSzZZntHne5ToAMQN8KaR4b1qsjwn6A0UP03gSpJV5IpNaEPUEtcgpdEV+l9NyAXTcg2upztvuY+o4jtj18bTF5jWgqRAOi8fSihA+fPcSUjmES0Qt6iJ7G4FBK0lYVw7jPG3uvkVQpJSua5SXl4gG2XDMyhrP1Y9TyCuG/xJ23X6etL3j9za+SzTO8aknjlCqv2do5ZO+GI0xS9O4By8sV08E+X37tyxQXC0Bz8eyE/mTQZdUoQW8YIqKIVdHw6OhTGlqSw5uk+xCNEtw85+3XXubVd75GzJhZvsB+4z20CvFKkRee9ekxTd3w2F5xebTk5uCAaf81Pso/QCI5W12QuiHloiEvMuq8ol41eDSHwyluUXeCUBOzP5jSI8E0DUu/5rLNWVQLjGu792QgSKYRo1sBvaklTBQ61GgVAN0IGu+5/+l/w+ePfsLOwVvcvfcmb778Gvdu7DKKNVEQoWTniOsORqZbW6X6nU1SfGF7lerF2L2z0XZrsEAiQsVwmtAb7bGzf8DTR59xdnFFXla0dqNvcd38RAiJUoIwjFDCoITn/OFPWUuP7kfEQYuSDichicE0QNjBOOeXEtcuO3p0kKDiPtnsjLb1DJQkTBVeKOr1GmxJvtBI57vYB9XRmueXglB1FnyhCmQgqYpOfHp5LvAW+gNPW1XM5wHGjRHJLkbeorf1HXrTL6HCEUIECKHwfqPRud6rfKfX8U6Ct5u/dxNTstnnrhEZUiqQG27vZpr2ohBBvHBlXo/m/Eaf2H2uWyP+P7l+rwsUiyYcTmhtS397zMnnn1HNVrz21tcwriGYTDjovcF8sSQvSibjCVppTk9P+MVP/pq/+Gf/JZenJzg8Z84zazRV61nXjsJ0LBOxeTGF6tJtwb248d2z1c1Er3UU1y0Ddb0hA947tOoEts523RMpBWKjwQDxoi2JlIzHE155821++dFnLBYLmiRm0k/YHm1x99YNFosluZds7W5T52uc7UYtdduQxj0q07K/v48TGtM2LOuSYFO5tq2hNZ3Qc7FaMej3ccbQi1MMZUf6VIK6rinLslNsA0VZgOjQ9U52wliEpGma7s3uwHrXwbnapiu+pKRtzWYOuXG7SEnbtAjRZbFo2Z2QlIBAd2m91nZtyI6TIvGoFzY2FfcQKiBQAqQiiRP29+9xfvaEoig3D4+iNxwT6oDV8pKmrRn1I4JAs7WzQ5wOCGVLXVW0psEHgr0bLzEWQw7lh6xbyePjBfMyRImgC2gMAiCGUIIpuVqtSJMEraKOdolHaToKrdp0fbToxL4e4jgiCCOSOOD4tCSrIAkHqKCDR023hpw+zjm+WHfamcoSRTEqFARxSm8wwJkJ+XrJfDZjlZW06K4YCiOc79D6tpXEYYRQNY4an4S4JARX8/4vfsLX3n6F8dYOl5fPuNGXTAKDtYLKd3b7ompIkhTpWubrJUqH1I1hlZX0+33U6Cb54mmnyxISGUQEUYTE451gvcr59U//mvn5EW+8epvtvVf4w+/d4+DGPcqq5JOPfsPJ0TFNsQYBYRhiXIuUAdqHGOEwTmArS/ipoh4abGRfFLTH4TmVr8iWBaVo6R/GjO4qrKiRKDwaJRSx7mFGLUVaQQH9Rcz4dIga3OVydIYZGMRI473eJIVrhvWA/b23mJ9U3P/0IX/wjS/zn37nH7A+v+Dz08fMq5waz8v7N9kN++RPPmfpJGkoyMsrIj1ENiGJdqi9PkHaw8xL3vnSnyJjzXnwjN36TV56+eswX9ATGhmmtMslr958g5+f/pBf/uiveeu97/HWV/+Y9mzJarXqDmKyId1KaWxKVlyBsoThkGJ1wfu//WuKbM0//Z//57zxzlu48y5jKJpsY9SaZPAqs3nDX//l/52z1Yd87/Y3ePu975LrD/mo+pAgbwnELvcvP6SqSuqyhVLSVC1NZWnLGls1bI+maB9SiwabZUwCjTAtiyBjQc2yLCjLGsRmjQw8vV3F6JYnmrSEaUIU9JAq6IpOX0EruxF5u2BWn7AsnvPw6N/xi9/c4O7Bu7x8+x637txhf2eHaT8mVgqtNoGisusgSKG6DVVsOgIvoEOblvZmR/V4cK4DvwWCye4t0sGIvfMnnJycMV+tqetmM/L0SKmI45jJ9gFbw5jL4/us+ltMb/wJTXWOdh9QlwWr3FGWgvXck5eS6RhWM8PVlaDfa5iMIEktOnU0RrCYK9K2ZX11RphA0g9J5ZAirzH1kirz5KWgbjW9xBBIQxQ3VHWIO8kpixV5LjG1p1URA7tDOqq4uKq5fzzhD9/775MMtlGqD9fFCd367Ded/i8Ydl3RITYd7a44cQgrNreu09Z57/FmA9K8tid7v1mn/YsOiWDzGmx0bdevgX/xud9pu/wNrt/zAkVQ2ZresMedt75Me3LBMB3Q64dUSNbtkokcs7W1gw4ykjQmz1c8f3KfX/zw+1ydHXdYdt25JUxpaVpLYx2dC7kTfOpNDslGbP5CmHWNcnfui1FG91+JF/AgpWTXanSbN8E1YXXzAnp33UrbfA5FlKSsqort6RamzZlfzTGBRMQBRV12uUBKESuHHg46jD6K9jInGY4YhjE7uzvcf/AIbxus92T5GqW6B26dZTQ6oDWeqm6I0x5BFJGfl6jG0EsTlO6KpyiMCLSmbuoOuuY7gJxF4KVEqi4N022gP4Mk7kSbdHh2rxTOdT+zdS1JFOO9oGk6LkZtDVJ2eHsp1It8Gu9BKEmgNEpvMmuSHltbOygRgKspoh5lMePps/vgLb0koiosy2wJSUg66BGGEOcW1zZkyznl6ghrB8RJgDEZWZkRqRi/eMR4kKEjyyeXO5R+3o2EAoHUAZV1NHVOGAfYpqVsGnQQEmqHDCJCGeBsi/AO5R3Gdpb3NE1QOuw2cx2SxAl5donHdjCsMOLm7ZdIeinrNkHokkRLAhl23QCpEN5j6hIdaIajIWW5pnWaYdQn2ACtlFaYpiJSFWncveeUDoiihLr2XC4M9z/5gItP/zW6mXN+foVxmnEqWDUO4yVx2mM03gIalrMr1mtLfzjGeUVRFDw+OqGX9rBOd2FmznczaaUQzhL1EozpdB9P7n/Ik4cfc/PWQ7769W/zpa98m8Xlc8IwoD/4nOePHlA3defKcoa67ezb16ezNOgRrgNWz0uqWx19UktJebNhsbOgkob+tw5IvjbCx003bnVdmrZwGqSk1gWFWJEHMaWIiI5DonXKQX2DxfaaPF3jpWcgt7kTvkqs+nzW/zXDd2N6JxFnR0veePUOL+/f4fb2lMuTI2zYY2uyx/GjD3GN4+XbX8UHLWqQ0HrBdDwl1pKoHxPtjFldXqFnIYPxLpOdQ6Kkh3l2yWgYUzx7wtrXNPM1eZOTHT9DUvLbD/5b7j/+mFfe/gPSsKYuVghb8eDxKbv3XuK3v/groukOn3/2GdIKktEOd974Clu7N6gen7LV28MuS6riCcO9bebVks8+/xHP55/y7Vtf4nvf+TPSV94lWL3Mx+9/ypP1M07PMuaLBdW6ps5bXOW6Z9AJnLVIL9hKh7jGYqyhCVrq1LGmpjEt1jus2Oj0pOiyaoYQ7TnkqEZGPULVxwsNQhDLsCP1uhqPoxYe4ypaBUndsm5XfLR4yOcPAsLeFtvje9y5+Ra3b97m9uFt9iZDBlHQJb5Lj9z8041zrrVrG9rs7wDFvBQI75BYhApQ/W3idMjW/svkqytWiwuKoqKpG6qmRuiIrekWwpbEacCr736by2efcXL8KdliRu2fYMuGZg227TKlwhhqA8uVxwBK1zhRkxVQVp4krFktoDUdRn/v5TeJVEVlTrtICOtx0mNsS9HAZKRpvccrzWK5REvNdH+KDiyrQlG2ITv7CeKopKkD4uErRGkP7wzWdAgIY7v1FiE2B+ov9DdNsSSMUmSQoJT498Y3Qkg2EWovjA9daOgmpkJe4zGuab3yRXEirndDofhi3vC3u36vC5RhEqCnN7j7zte5efgqz8ufM5zuMD48pPGevjOs85anTx+zXq24ceMG0nuePviM+fkxUoGQGqsDqqKhaQ3Gdl77rgXGCwS3Ul/g2r3vXhRrRZfS+8Kb3z0UUskNsl0S6gDYwGxsd6IWYvMQ+40L6PrxkiCcpx/HXFwuEcLx8p1bBAdTXrmzQ20Ey7wm0ltIHPPlmswIkjAhSTSDrW2k0jigtY6irGjqkn4voS66eXCQJORXM2TYzVtPz8/ZPThgvlxR1A3DMGYwGFNXBW3dMByNCINgo+Q22MaQDkJm3tHUHfjM+g55XzYG1+ZIoWhNi5MWLcButDrWOBrR0uv3AKjqenM/Nx0pupwfrTcZEdeE2U1GhJYe3AqhhyxWJ2TZFc411HWNVhF1I0AFDCY3kK6grWuKdU2ZFWAdtoWiWhFb6G/tE6WOoG4IlWQ3bRkmK56uRxzNO6tzIEDRZSJZ4xFOIAwI29CPJHGkkUHX3oyCgLzMMN7QOIdDE6dT4jQlTPp4IWnrnF4c4ZqQIHQ4GRHFMXfv3aOtT5ita9yGKiuVQunOiSOsJQgDBqMR52dHoELG0z5ShgjhUUoThTFaa4rsijDQeFMS9yJsvULIFmtr8rxChz1mJ7/kLNvmS9/6h+zu/Yj/x4/uc5oHDAd9xpMxxfIUb1uU1BRZjqOzvXtbMZ9lG+swtM7hVIAtG9IkZXt3h7OTZ5TFGrzDGsPn61+xnM/49vf+lNs3b/DVb9zl4PAm7w8G3P/4Yy6vzjEWlA5Jwk4gm8Qdkr1nevQe98hHGU2v6cakw4i9d26zerIgeW8bHzmMd0jpujGRNyAqHBKvulm/8QWrYYNEkZ5WDM77TPI+4V7IZLrDLf0qs/aUT5qfUcY5N+4cUN0umB+taJuGylWEYsykf4s4GvD8+SekSqHjlDQYsfvyK9igxXtD05a06xmqllTunMl4lybL8UlNOJrw5P6PCWYLWO8TT0KqNmc9uyJThtrBwkkeXz3EHn/Kw8cf8c2v/z2+8Z0/o2wueTj7jKtZRnpwyHT3Dru3X6I+Oub+g/vk+SWL1RpTlchyjY4lpjAMQk9RLNk+3GX65JCv3PsmjRbItqDXBhzUN/n1xW8o8xPavKYtWkzdvjggSCHBewZpj6Hq0a4L8rbmLGhYl9kXOAHXNSpEKDstUOiItzzBwBAECbHs4xAo4RAixNquoxoFAVJIIl3jfYORnsY14EKky9CipcqPqLIPOXv+X/FjpYn6dzncfps7B+9yeHCH7eku21tTxv2EWCuioKNZ469H7ZvVuZsl0nWsu2gP6QRCx6S9gDgUjIYBQg3wTrFeXXF5foSt5thqziQ+I975Hr0opK3yLtZETajEM2x7hfFd4rlKAgZhBBclFkveQHYuaEuPDgSN9TQNNJXH2ZjFxXMSdYXQ3SizqaGoBEhPFAts4CmKkMWqy/VK+gmShN1RzHD8CqPb/wiRpOy8csHU1rR1RZyOkEojhAbRdgeHpsTZjjHlrjU33vP0J/8luy99if6Nr6BViN6IlJXSXXdEyk2R97tjnG5/9BsYaXe7u0+K6+pk08C63jel+ELc/De9fq8LlPPL+7z5zb/DwdYB5uwRWwdj0q0RepCSr5Y0rSWOBpyfnnJ1dcZifkXblDz+5Dc4YVFpQlkbqrqhalrMhtEBX7TApACl2RQz3R2/7pxY63DWbUY1wEZzIkRn7ZdaIPXm+1nZQYFkR+u77piIjfgI132frfGYV3f7XJ4/pZQB7919gzdeusfWziEPHjxhNMg5vqyojWWxLoiTBImlbi0gwQvWqzVNWfDg/qckScT29ptczq4YDsdYD/PFgulkzDLLidMU5zwXFxd47xkMBjgPZxeXOAdJmnJxcYnDY02LlwIdhXghWWXrDSlVYk2DVJKsbtAqwJimw9tHIa1vN12mziU0SGJG/RTnPU3bboqQjbBNSpRSyA1b5pqD4GzL5cnnrOYxUmrqOgPb6QLCMOi8+SpAC08gQ8pZxvJqTlM2CB2jR9sMphEyX1LVBcXFFYEQjLde5jBtuDE8ZWm2+Oh5yyq7QgqNCrrXv0s+7bpAZVWhnCNOEtL+gLbJKfMVtqm6LAoVouMeUnWaDqk1g8k+TnjWFwUXV6fkeYaj07T0+n12xyNYfk7ZWKxrUFIThZ3bSssuEXW0M+Vrf/Ad/uKf/V8Z+gChgk7wK0AH0SYcDAI1pMiWTA9fJh2N+O0vfkJTuQ6v37ScLzLSgWCeLQlDxUv3XuJPjOVf/OqMxhWs5ieYakmouywinMcS0BvsUdcLivUSKT1VWXa8GinAtLS149njR5T5Arze7AMC4wynx4/48//6/8yrb32Zf/Rn/xm377yGs5LR+IBf/+KHnJ+fMx5PCUJF22ZI6cjrErdeo2TI+HLMWXSCDyRKOUbJLtwOaaPNeMB3pzmP74CAyuFFi3cgfUcwblyDHwpMaKkvWoaXMdOTW9wMXuV+/QE+dRy4A7b9Fj6q2bsz4l8efZ95O2d/cg/dpAxHe2zt32X31kuEKsQ20Otv4RuHEzHPnn3CKjtm0BvSi/pEpo9NPYfTW0ihuHjylIEPWTw7Y7UdM8+hCRrydklmPAsV8MnlI7xwhIGmiCt+c/5zqt9UbL16i4evXRKoFdGjiOKv3+fdV7/B33nz7/Pua/8Qr2uSwDCLG6QMcescEJw9vY++tYuqRrx572Vs3KeUCVIYynxFmAUUZyVt25IKxUgnzPyK1puNENUikOwPt5CVoG4bqtiTuRKnNx0TvxkTSNXphBJNmAricYOOJJEeIQjozDwavKdyGVprQtVDCEkUGkZS0ChP2eYYXxN4j7IVtQ6xqqaRK7RxrBYPuLj6AR991kOrCYm4QRrvMujdZXv7HgeHN9mf7rA9mTAeDdiaDEgjveFYfbF/CP+F+BPAmRprSpQc0JiKqsrA5WgVMNrZhrokiWLG05vs3FgQJgOq4oD5/CWq4RlVdYEQFqN3GG3dxrYfsZz/irrwVCtPoEAFnbYu6Ql6A006eJeq+AArHc26O7QWJaAhTWFdKDKjubo01G3DMFUUxhP3U1L3Je6+8p+we+vrqCBl+47qgIhK451FKI2UnStKqQitY4piRZ2vqcoCYxxlseLq+DG9/Vdxixk6TAjDECkVgQ6QqitUtN5EkLBJORa+e8Y3m6Xb3M/rDDZvN5ofxSbdvjObdA6pv/n1e12gqO0t9u+8TF2vcbrFK826LhFVRZIMcb7l6fNT5osZx0dPyIuS9WqJtBX7h7do5nMW5+fUjXlRTXZwwo1pWHRx8Ep3/JNuFtf93d75rhVnPEK4L/6fNkm/UskuAXijurbWbxw+vKjsEZ0I1LsOrHZ445C6Lnn9nZf5yvqMpycXhO2au3fepmgaDvdG3H/aUJcXVEYzGG9TlgUY22UAtQ1GlDx4/JAk6MYyYaCYzWas85z+YMR6tSZNImbLFV4o0jQlz3OybM1oMCCJI54dnbBYLuing0774RxSeqJQEzYBTWvBCUxjUGGAlgqDQCuFUa6DH/kusK6o6hetRSVVZ31tWwZRRKjVi0wkublv3jvsNTb+WgQOG0phi6ivZ5l+8+Bogk3WjfSC/iBBmCUnVyfUpcWJBK9S4uEhUkIvGtDzlqrIELZka6C4MyoJyXmwfB3dB7/+lCDto1yFMRXrZY6XIUkSoqQnSRO2tg4YT29TlhXZ1Sn98R5JnFCVOQZPGIdkyyXWNaxnR8zmF5gqBycJdB9nFYHSRElEmIRUy5RlHaB11LXBOwg/bdvN9Jezc3718x+hlKaXdGRfT3dP0RKF7fghicTZiOfPHpM/tBSFAatpTEPdFPwf/6s/50++ucWj84LPz9Z8aTfkzsGI1xaSs0ySrS6o6zWh3CTFqhgloCqvUEKgI42rLUoptPPgzOY05TDGgFR4u3mWpCJNhyA0ZZnx4OP3+UulePNLX2Fn/17Hm6Dms08+oK5yrKuobIb3LXm2Jlus6Sc77K72ONbP8b0GEfXInixob3isb8ApxCZqwdONGiV606W8Psh5vLNY32Jigdt3uLRBXh4jj1oGk5Ab4hZDNyDwAqc92XZOL/whfTlgOr1JHE8I4wFxMsaVJSqMQEmenjygOr9k+/AWRkA6GHPjzutMh9v4Zk61OONSDhAmAK9QIubq6hIbSpan51Rxiybms/UJ0c277PQ7YbtQhipsuKpP+eBpxd/bfZmtvZsciQf4/Tn+suFGv89U9lkt5szlmtx2qd/IFGMtq8WS48VTzPoJ8fiQnfiQXIKuS9rZKcv1EmMM/Sjhy/e+xD/95j9l4m7xf/jlf8FfzP9FhxDwkPiYvWCH5rKg1ZYsqdFKoFXX8RISVKgIo5gwDDs8e9higwUqSJAyRuiAUGsCldD6FqkUcTBAyRg8qKhHklj6oaZpApaNoW5bpDV4XxO5BsV1bgyUqqU1C6J6RemPWF7B02aAND0UE6ajW7z+6jt8+ctf594rLzEaDAmiiCDokrpfrNNSboT6AhVNEcEQPERSs717k36vR1nmGKcIBweIeEgqIg7uvMHh3dcIgoDF1Rknjz/i/OqMXn/C3o177O6/hG/m/OC/+M9ZLp/hZKdhFCEUa0HZCvqv7JEZw/yqRDhoSr9JOZdEU0USH+J8RFnUqMgw7vWIkm16W3eZ7r3C3u13GW6/jBchQkTdz3SdbSY3dN1OzYqSikQHRElKf7hFUxXk2ZJydUp/7x61U5jFFeiQQGvCMCYIY4IwItABQRh290p2Ixshur2O631y40jtrq6r7L3bWL1BiGvx7N9uyPN7XaCkk5TF8gPquE+/N0H6kOH4NlvTmxyfPufZ8+dcnl/SNBVlkZGv11RVxWQ8RPeHlJvixG4C267D3K4FyJ0FuKPFKvWFYhnRbb7XgteNXPzFC9R59zv9iRACJzaclI2S1m9GPIFUOOPwXtJLQ/ppHyU1Hz+74n/4j77KG9kll2eXZMsLTDDh+GzO/Y8/4vQqxwUpw8kOZ/M5O5MhaRxAlNCb7NB+9gnrxYx8vaYXRVRFThSEZNmaPF+zvTVGqIrGWIxpWa/XLBcLxsM+VVmymM8pihopA8qyZGtrwnqVkYa6WwCdxbsOoKaUItQa2xqk7kTFdV1jrOlErl5irEVIQRiHCCUoWosrS8JA05qN2HLDgelyWdoXp5rrN7X3lqZZIYwi0JIgGjKcTAmCiLrKWK8uCQLF7OyC5fySrPI0Tcu6WjHZvsP2dJ+qLsiLDDyMJgMSltzrXzKJc37+UPL8+IhF5nBNQ10fszVM0EqQxhH96ZS9G3e4OH/O9nTEdHqHxkZsH7zBsXwf4R1eesJeROAVxjv6kz5NmVPP5tiqRukBUTrpODJtg5aON1++zXTc48lFgJMxQdyZ18u6Jom7VrQTAW3VcnV6SdobcePWXU6ffUZVZHipkUHYJY5Kj/dtN35pGmzjGfVHWNuyXDass5wPPyuYDASXs4w//6vvo/7oS9wZDri9p6ijlN4g5rQturydRlDXFWEg0UEEKsKWOcbWxHEKfpO6LDUOSSAVUoVoLRHe0bSGra1dhAqYzxW9Xsrl2TH/bnbFH/3Jf5et/Rvk+Zo33pQ8+Ow3rNdrQp0ghSZMQ2wbY1zFxfwpqypjIZeM9DbDbEKjKkzehVY6rWiuQ8424vPrVr4SEMqEON5mIAakDEh8ymA0ILytMbWgVysmZkLQCML+gEbX9OIRW6MBO6MtRpMBxdUVMgwpl2eMJlOS6ZRiNkeIMW40pNfbYix3mewfsHx0hM4a+r0xRkUsVhcU5wum2y8TD1Pq1HA6f4STDXnTkIgBq7wgWM55+aU3OTt6QK4MTWg6916w5rcPvs9B9SrJIKSOPF977Vt8Zf87xMEIdZjSXp7w4ecf8vYbb3Hw0h2klBydPuHZz46Y5wt23Rwl+wStJbzIWdcZWbFkJ9H8L9/+H/P1t/6Erb27VAjeXXyH989/gZMWvGKnHJGeh9SyQe9MqUYL+mmK0N0mJSXIICDUAUJJEI7GLmhEQBqOiYMhgY7w0r7Q74U6JZDJC3eHQxArRRgEjAPPIA6ZteBLiFyLk10qeItHe0HkOy0u1hFai3IBg3TM4c57vPvWd3j3va9x88490rTXZVlt2B4vcr/+w33SeYToBNPdQTXC4wmTMQNvaOqCKl/RlCU6SBhv3+xGHSpAJxNaC8nWLW7cfZutnUOUUljTsH/3u5SP/muavKVaWwJpqUtAhZyf1GSL35L0HFqBkeBagUgk/enf4/Zbf0TbLFgtL+gNDpju3CJMhoy2bzLevoUKErzzGNftR0Jp1DXTxW/EweJ3NiZAqWDDMokI4h4IyZ3XvoKUqtNf1iVNU3caS9t0yewYrG0QQm3gnd3pUUqF1OpFZ0rrrtNynUwvX0hPuggCuzE6/G2u3+sCJRm2WPtLmuoupauJk12QUJQls6sFpycn3P/sU85OnuK8RWhJmIQYHM9PjpjN55sCxb+wB193Trpxg0AHsis0NiM45zZgsc2vF9WM9xtb2vWvTll+rSb3dG2wTnvbdWNCqcmakn6a0rSW+dWM8daYTx8c8dHpK3zp3iFueQXZMVfZjF/+9Fc8O52xrh25DbkdJqjAoWPQSco6a7h4/JDFfI53XYu2bWtM22xAZ3N6aYpQCo+nrkoC1WXEWGOI4pjnx8e0pkVrRds2ZHnGcNBntV6T7mzjPdRVid1gjFUvQWlF6y2RCrFtB3Bj4366vjrSrmfQSyjrtmNvbJD+dqPh+SLR9IvVwzmHs11CsGqWDCcTtA65fe9tgnSEDiKKfE2iE6SsuMguqbOCLKvw3hHHfbRSBHFCnAwoypy6qkgiuLeruTVy/Oqjip9+uCavC6qmIYw6IFFWSwbDLbanIxyaooI02aIsG5bnD2itJuztcnD7bc6efYiXitHWTZpiRZD2SfuCxfkJZVEz3NoDFXJxeYkzDVGgCHspe7tjaAsePzqiNp7t3UOaYkWddwGEcW8L6yFbz1E2J+mFzGfPMW2DVrqz/m06ftZZTJ11mRx09Mwo0VSlQ0uP8hZsyMf3yy5Dql7wq0+O2PnmbaTLWS1z8rIibwPatsK2Dm+6U7mQ/y/y/uzZsiu/88M+a9jjme58b86JGQWgUBNZJKuLc09mUy2LrY6QrLDDkp86/OJX/yF+tyOskO0Iqd2SFeoWm2zOxWKxWAWgABSQQI43M+985j2tyQ9r35tJhsJNvrmiD+JGIvPO5+y91m99R4/UlzZEEEKh0zyiJR5iQJZGSYd1Hutizfrhs8eAYH9nhyyRnJ0/xzv4g9//73nrqz/Ptd3XyPKSxWJFa7/EOo9SCfP1FNfUZCimZzOmZcVxNUXJUbQ0H55i5p7RuGC0WSALEBrQcVUUImNHXOdr+a9SJkPqsOLCHXPaPmNtHrDnbnNL3SF1BnG0QG9fQ6jA4qzGyjggj4sR1w5eRVWwIXL8+pyuMZyHjOkXP+YrX/91BqJjeH1IqbfogufPfv+/pVh2DL/9HbyXBCXJnaccDEG3ZKNtXv/5f8Bs9phnTz8i1IrxjbdxX5wwnR7y3W/+GveffUyyl1C8PcBkNWEgmfpT1qcz0vsZO2rMN++8h18YFvoInwmCbzj/yQcMX32LtCxQKPZv3eWt6hf517/3XzOZOez+DcJiyTxdsiYjuCVvprfYHt0irARzf846GNb2nI3dDQZBc83tcFPd4KDY5ebmLTYPrnEymPHnqx/wSfUTrDQgL8UG8RpszALTNGSqRKuid5H4WBznG1KVkYpBjFAPFSGUUWCeShI1INESbTwySOosZWksEtsjxZLMRZpESYHWJbujt/nK67/Kz33rH/LWO19jNB71p3penDj/WjhEXItid0+4chdGRwrRnRKvovjpXpJngjTJabsGZy0hxHwlKSVBZ4y39rl2+yukRRlrVILAB0G+/Q6Hf/g/E4JnUOR425IUhmQyYTiaUAy3kO6Ei4sprQtkI8Fk9w5/7z/6P3PnjfcxzZzT51+gsyHb+6+RJFn/e7zoH/LBYYzB9Zu/0vpqBgtxEb0KvoufGjvHhBBs79zoD4Ce4ionJdKmPjicdT2lI2K2VNeC83hnMVYQul6rKWL4adS99DS9fOH6SZPkRUfS3+HxMz2gPJ9/ymTrNhujO2wObiCzcawxr+JCu1rO6ZolWa7R+QgvAzkZTddydnZGVbdY12dqQPTOQ7zXlEDr+CZ722jo7cTe9sjJpXWt34yljENN/DP0A8plmZKPLdceEilIvKBqGgSKLCvY2hmynJ4zuzhjY7LBf/uv/pgP33qDw8dfcnsc+NY33ufpquY0eGrpEKHGJyt2Dkqu7d2m8wN++IPf4+zkhKZt2NzZZbGYU+ZZdO10HUWWMxwMUUKxWizx3mN6m/CgLAkBzs7PKcsBdVXHxFsRLcfL1Yrtra3+1KDxRL9/13WkSWwh1VKQZxnL1ZIrLc8LiRrWdLjkRUtm27dhvtyEGdMg+2e2v5h9CGidsbN3l73da5TlJlk5iUmIQpIXI3b27zA9vY/pAqMiYbXytKEgTwtMs+Di9Akb27fJsxxlO16dNNxMj2mM4y/vLXg+rbBeEwgYFEmas7HxCju3bjEo0mi1tJbx5nWmpw9xUmGbFedPv2Dv2qsIPErGXAdHoJqeMr0wBGcZDDfQWUe1WpDJjpVZk4832docsrm1g28XnJ0f0tQVVduQqUBZZKAUKElbV7SdJXGG7e0t2rahqhsSJZDKoZB0Lqr+F7Mp1gla02Js4OxkiUDig6MYjglA7foSQg/PZg12dIOsOKOZPuTpmenr0eNzr2VO1VlEu0Qog5IpprN0oSKoJIqzAB8cXVPFIcna/r6wrJdTQpCoHgIWCLROuHj+iE+NY+83DhhP9jm48SpplnP8/Al5PqRtGurVFHFzhwY4v5hRLToG44xG1Rw+nGGUJR8kjLcHjDYTBhsaPUzQedQPreSUv6r/XQzsCxUuOFzwgKOWKzyGu/odJndeYXFWUxYj5if3eH54n+LOHe7efJ3NzdtUJ4fkCtAO4w1nT3+CyHJOju9x7e5XuTh/zP/0ye/x9TtfJbErutWK6RdfMHj1Dm4cMz/0QLBmwemzIy6Oaw4P78POPmkeOLx4QjHIeT59hM4Kiu0N0tck9sCR+ZzX9F02ky3uDe6x2qy5/WCLzapgxTlCCbI2J/iaN/ZfIQs51ZMLdK4IiWTHbrJ14zXq509oqhUL5RFeEWSJFw3L4PDVc5quYVJuU7UdVbjgzeUOX2/e4kbxBlrmbOzsMhiPKO5sMRrdYri4CY9SPuMDgiQev3zAdQ1tNyfgI7UjJEF4QKGExkmFEgneeazv8MHiQ4FRAqlbhF8jfIqVc+CFK84Tm8PRgYFKKH3B9uhtvvHeP+OXfuG32L92jSRJYyDkJUoCL22I4W/8vRd7StHTyC+LUy79J5d/FRGpVAlFkuOsoW2biMgQF/6tvTsRYRCiL9YzeGvRRUHnJcYovFgxST0WT31xRtNYpGy5ca1g9ShhXTnSWrL3xi9wcPttpErJBzvcfG0T711P3yheEn/0w0FCliZ472IMvYnlf1orJJeIyktC1jh6IZWOPWr9xhZ8wPWR/zH+X6OUv3rOYjVHgrN9CnjvArrstnPWxj4eF3/WAH0fUqQDtdYY87fv4YGf8QFFhB22J99mMrpJUAkmKGbTC/JSc+PGLSbjDe7efYPz8yOePL1POHyI9Zbpcs5qXccOAQ9XprReaCdFDBrTSXy7HD49MXDN+T6shh5toUfStERpiU5UTwn1/BuQpjr2Q3hPRuxEKFVGUhbUdU1dV3E/CoHzi3PyQcGff++HBNtyPEh5VH+AU5LNWwfs6ISuWyBLy2S8z9beq/zJH/8x5yfHEDyt6airCoVkua5YtQatFIPhEICjsxPariPPsh4BFIwnY1Klr34f4yyj0ZAiL9BS4X2szG6aFqdEdOAEj+pbnXGeVKn+dB3D1y6Dj+NdHLAusFzXsdeoh10hqsmttb3g+CX8NVzeKA6dpGzt3WT/xlsENKqnh6RKQBk6U1GMb3L9TsLs6DM6a/HZFqfPHpPmGcvZOeic8XCEb54yVucYH/jBowmt38ZTk2SDKFBUsL29w87ODRaLFV1bkKcJxjbM5hcondAs5zRVzf4k4fj5T0EoOtNhZ2cMRhO6usLWHcYZgm8RPp74lPJoSa+5GFBORrTr+9SdI9Ep63qNFx0qaKQKLJZzpEzIZBQUf/jjD9BJAU6AcEidYumwbUNn4oknzTKCVwhCtIs7T1AxRA4vsdYRpCcEx8VswWxZs3dwjes7Fzy9uMB6QGUgYuFhcBJn1khakrTAhZTxOGexXOH6k6IPEHzXJ8FKFAJrLFJqlIqx+1pBqhOU1vjgWE2P+P6f/Pe88ea3uPPK23jXcnH2PCJf5QBpPMvVmizJuK33+aR5ymiSs5JtPDEaz6pxVDPDeaYoJgnDrZTxdk46VHRlS11cINPeuo5EkfFm+m3u6K8wc+d8Uv2AZNZx5/ktDq6/z8pMWbTnMBuxu/caSVFwLlasbMvu9it06y9ZdjMSsc3q+CHPCdx48y3kR2f80R/8v9m+8Srf/EffYWMwodaaYmuIWjacfHqPE3vO+WpOJhTlbspHn/8l7779K5zMnnHnzvs8Wj7hp09+yPj2FvbGCqsdr/nX+Od7/wdG+RbrMOP3Zv+SIljWy4q2q8gHJbpzrJslreg4mZ+TmZq0yPCpojufMZns4dZz/Noy745JNkcMdYlfGExYcbE8Rw0M+9evMx7v8+b9tzm8d0YoAvNyxkjnVHUgbFd0k4b1XDCdn6ObgBoofJ914byh6ypa1zJMRxTpBJToxZEmBkeJ6JLs3Aob6n4W2GBtAtZZBizoQoJwHd45rI9DpUGSp5JxUAyz27z7+j/nN379P+XawS2SRCPwUdT70nDi/aXl+HKNf9k++xKF/L+0t7yslxACKfULXVwSVynvXKRWfLgaVoKItLZCEoSiaQxWlowPBuS5x/sGFVVSwBQtU0IyJtuSJOOEnd1NXn/vn5IXo6vhR6L6mAaL8/7KiPFi1goEEQcOoRT0/TnOWoJUUd/Xr6qXxYuX1SwRSerFWsKjgsDTJ+sSrnQmvs9D0VrhtMK7OIRdxnEIoUmzlMFwA4j1LqFHxC/Xd2d7BObvAKL8TA8oX3/zH7C7eQNjO7rW0ZqK9XJJXlkG5SZNWxFUjGoXUhOEYLaYM53PaTtDn0H216gIKUSEDlNJmqnYrEqfeRICzoYXuScvuYuljBOrThRaRwhNqyhOSrIk6jEcmNZgqo4ky2lag2sajHdsb48xbYvxAWc6zLwBqXjnm++C7Njai705IlEoPNvbtxiX27Rryb/9t7/L55/9FAh0bQcB6qrCOkvVRsfJeDiibVvWVUXdNr1eQJIVBSDYnIwxXUuSJBCgyDM2JxPyvGCxXGFNTLINwVGva4J3ZFlKlqZoJK0xtHWNAbQQmEs12+XT1D/J1vu+8CtaQy/hRed8dDldrQmhH3DibSSEoBhskhQDmqrBth1BpoBGyIQ0V0jdkqQl+XCbYmMH052zPGpZLdZ4NNYZUjPgja01O1sl3/8c7j+rkdkG+3sy6lOkxNsVQliGww2y9DpVtSDQoKRAYsg0KB3oBDx//Al6NCZNh7TtKoZQeUtbLwjWUQ6G7G4d8ODhg4j2pCkubaLgVCsmow3C2uK9pigFUg+YzgyztYnleV4ilUCplMYonHco40mURsgc07QIBdY4TGeIamGHD44kKxgMxkwvTuOQQohN1zaAUngCVV3x+PCCu1+/xpt39/n08QXdOlrfsyKnEx1t2yFUEReZzuF9YLZoMcYDBt8H9IUA1tuoQTIW+kVLq1hUFisdIoSulST4hvn0KXX9CjJ9lWK4wcZ4izz1qESRZSWZL6DxTMQQtVewkQ449EsCAhnkFQXYtI5ubVhddEyPanQB5SRj52BEsZ2h8zjwaaD1DT/o/i0Lf44THVvFNvnNLZ6ffEIjaqbtDLd4Rl7u0DQN5fgaT778S+brFTdvvk5xcUrwgo3hNkMNA13yz377/8gnf/y7/LsPfpfnTz7ntWuvk1vPnW/+IrQtn3325wQt0bnm7rvvMdp8j9sHbzK8dpPRozEiybi9c5318hj2UkJmyBx8J/91DjbfpAlLrlev8g+L3+Gj9f/E1JzgJgLTlVi9ZrFaUncrZu05RbsmMTlV02BP1pS7OdaXJMKzqpeEPMFmLbPpCZnx7Fy7y+tvvMvWu6/iU81r6de5uP+Qs6PHLBbn5MMBO6++wdbuJvrUUoslx/aUZ4Nn0WkmBAhPa1ZUZokSAiWj2w4Z8CHmnXjXxtO88CACOpEkOkVIQTvtWK/XPBgriqyjNA58iw0diQMloLQle9u/yG/84v+eb33zVxkMy5jF0aMEl4cb73pEh17ACRBUT/m8tDv2tPNl+unLereYJHuJpMRViEv0QQhIMpT2KO8ISULXXuCsIxATcn0IGNNwen7K3t03CfIMbzRS5VECoCXBV3TNCU9OjlmvoaNAuk32b3+l7wd6sXAKCQp9hZJI9YKS6iNNXgxUUkb3X+iL/Yx5cSDsP84519uIL79F6D+170fzHnmJYHt/9bE+eCA6BqWLUoFYzhjNDb5rrsS5UmnyJEcpxWUDtffur2+4/57Hz/SAkicFjpTaBFZVS2s9mc7xruH42T2Ozh4zXy3wNmU2n9M5H/M+quZK9wAvLuLApe5EkGeKNFUoHV/QSOsI3BW9c3n9xAtJaUmSqIi8aElZpAzSnFLmSC/x1tGZhrN1hRYSlSQY62gdCKXwxl81Rfrg0NmAra0R737tbVqzJBmnHK2fk2nFq3e+TjNbUWZjbKt5+vQxTbXqL5Q4GRtj4uSvFKlOMF3Huqr6i1uQ5xmDsiTRCU3bMpmMOTo5ZVAUrFZrJsNhpAGM4dnREdV6zWgQa7nbukLiKQYDYiWGxzjHYr0mz1NSJbHmcjB5MajAC9uwoOd/g+/fLieTHn4ULwYTKXtLhkxZzqc4H9N7hTTo1KJV1hc6ClSSoLIx4703CeaMg50jHjy8x/rimJKG/c2E/aHiy/ObPLpo0DqjKATD0SYBx/ziOYuFwXYdpydfRI2OM2SpBl/TtnN8nrK5c0AybDh9/oxX3vg5lhdPOKtjh816fortViidsFzBcjVjuTin7cspJxu7DAZDrl3bIMw+YXn+kNVqRd2scF4gk5wQFE5HQZvzHhsEXgiEkjRdR8h0tNBKhekM3gs8Amvj6SlLA0pamnoRhwOh4gIiQWhJkH2dgIOj02NE/gbX90sOhg1Hpy0hyaEJgEInCc5Cazts6NA6wXYe0CA8pmsJzhAbYSGRKU5GJFKKKCDP8hxrungN4Ak2pivnScaTB5/Q1FNu3vka1++8zenzT6lUi5UVRZegScmE4t3hBGcMLlmjRMCHiG46H5M/g4mWxqqW+GBYpjXrs5bJfsbmQUk+zDBJzRfp95A6QZAigqQQW8ySFc/qz1gen8BuoMmf8fb2q/Fwo3awDXSp4fn0KSd+yp39r7N3931kJnAE8mLCN/7B73Dj3fd4fvgl1tVIkTFbn6AC7Ny+iWw6bt54nUKP0a7kxkHBQq3p6gVpdkBebjCvVqjEgLBstTvs+2u0qyWBCociETm+VZxWT8iLIVIS3289Njjq1ZSJ2KYJHcv1gvXpCW02Zn5xzK4c0HnIO8vpyUMePP+c21u3eWVjn3S0iXOO2hps6Mg2J5yeLAmTCeW1Haah49mHf4k1a1bJitO7M9a7DqUSEB7nIorpgiWRGSEEOruO4lMADErbSHeJmDKtdUmSRFG0tYGqMZgq0OYNq1QisXglKDyUasjW+Nf4J//4/8Q33nkvdiNdrtqRS+/dWi8cleKldYe/gThcjh0vtyZf0iA+hH79ufz8lyiVvjU5Jq92USCaZEi1jW1rmnodc6GEYr28YL1uyIohuBavd/G6jLUmAqDDO01XPyff2GV9csrZsiAfjqNmsh9AognyhTMnXCI3IsYgCHF5lLvURF7+/hKtBF5EZ2VEX9RLz0scLi7pmquBTEp0XysQQzb7wUwqZK/NjAmzydXT6fs1PCaP94FuzuK9Q/vkinYLf5fphJ/xAaUzgeXaMl0ssc6yv3uHYZZT10s6X6OXFjtfYbsCISWtcVRNG0/rvbj10rKK6NETLUlzQZppsixCY84FbHAx5KYPJOISLuxF0kmiUFqRpgmjYckky9lSA8qQ4Z1g1cxZ1h21sSRCRiFkCORJxqjMaLuGIMB6Q1qUfOPb7/Lmu6+ze30b4zf54MvvMxpvsD3epO6WiFBh202+/2f/jvnZ2RX6ENNro8I6hIBOIo2yriqsidxhlqZMJhO8i2FreZZxdj5jua4YjybMFkuSJMUET1s1rNYrlqs1G5sb2Kri4vyC0XAEBOq6jiLiIHAemrajc54gxZU2J2rVZA+5vriJQg9DiXAJtV5ynSLe+P3fL7UwSib9jRZP+N4afAArLc5B8J5EK5CaJNtkVa3Z2X+NajnF2DXfen2XO+MTZt1t7p2V5JMh43xAtV4yGuR0zYLQrSmynKZbEPyK19/5RexqxbPDLwlBYq2ibju2shxvLJ31/OSD30cGgw8C5zvKYcbpyXGvfE9o2hpjGyDCxPP5OTqFrcktzg9/TL1OmC5b2jYuRmlSYH0gCBV527bFmL6DyFmssRRFRp5nhOBYtBdYZ2NBo5DoNGcwzJF01E2D94bolIo8elwsAi4YBIqnR6csW8G4LHnn9ZvcP3rEsg2Y0JEXg5hNI8FZjW87jOmuEBmlJc5C3TW9/ipha2sbvV7TtTXWRkeATjN2tg84uH7AkydfMj0/JzjPuqkYlgX4mv3du2zuXGM9H3HkH/FYnHBN73JgBMIFwjp+vfG2YmvLcT4N4CIP7r0jOAgiRNGmCJhVx7TuWJ5rFscV+ViRTzSbe0PSQYpKOpRWPEo+wLuG4eaQs40pucqQWxn5ehOVSZ76B+y89ip/+uG/5r2v/yqTbx2wOn/O9P7HiM2MzKUok5IMtmG4wWTvFZ6cfA4ZeFNzbe8GWV6gTMf+5DppnmJEw3kSsHXOxu4t1GDEZLHD1E0jsrBMGB1tc5HP0O4JhYemXFN1K4KI15lt1yxbj6s7ZF5ysTpDnsHmRk4zd6zbKdP1KV1TszIVSbVgNNglSMnJfMFpfc6ro3fwec6yrmifevzas27PWTQVeTJBiwEnTx7w3J6ysmtkEXBvabgxRvYJyXjo2jV1M+8PGxJrLLVdI5Ul1Zoiy0nTMYkqcUJhvANSXOiNCcGz8jBpPJUBrw27JMgUTOJIinf59d/8F7z/lfdiMSf9Gkw8IIlLPSAvUI4XNM0l3f6C1rl6BPHX/l0g+qbjuG72meH9x17WnFxqP3T/d4GQKUmRotMBTbVgsZzz5b3PqExHVmaM032WbUplS4RwvfNFoDKHMA06P2C4vU2HoO1sPyxFsWro2+Iv0Q0hJN7ZOBQoGSNJXtbAvkSTB4gUj4w5XHHo4MqN47yPzp+XnpcXmhPROyujxkTKuF5fimav1vF+iJE9dX859Gmt45oPVzEA/0GJZLN0QpGPaLtIN2yMRiRKE4Qjr3OKoiDPB6w6gXENdbXGmFh4J2U8ccapU/Q2KUmWKYpCkRca1bc9+j6QLaIn/krYeYm8SCXQiaJIE7bKEZvZkK2kZJOSJCTY4FiZGU3bUSiJcbBqOxCSLLFIElpnUFJx+9XX+c1//A+5dqNE5JbR5IAfffpjlqs5W+MtsjxnMrjORbXmBz/4Ec8Pn/RC39D3/8TTCT2yoWTUHBhnCUCZJWxvbmKsoW068rJkVBasVqtYxuUtw8GANM3ItGR2saJtWkLwlIOSp4fPcT6KWmeLOW3Tsjkaxibi7oUiPk00zpleHBnbmp0X/SDVLxjhMiY5LgWXF/dlQeDLIjVnLXW1Ik2HGNOSZhlKxSbUpm3AO6RQVCY6SBIl6Lo1XgZuXttlI6u5eSCp6m2e1HcZbY/wZkEQCc6mhODY3r3J9OyUpl4xHhe89u6b3H3zVb7/B7/HYrVgvLHF9uQOs7NDHn72KXXTsp4vQKUYZ/uTXMJsuaar1ljTkWYlSqR0vkUrzWBUkpcJWnnqk3so/Yyz5SvsX9tn3Cnarmaxdliy3mFm8C7yucaYKJYTmta0DEcjqmrZf19JkmQ4b5hMJty4dpNnTx/hrKGpF1inUGmJt55RWdA5R1AOlaYslhV/+r0PuT0J7E02+ZX3DX/00RHz1mFN11tDPYmWeK8wNoDU1E0T0QsEic7xrsMaw9HpaVTyS4n3DuUDzhjycsTmzgHPnj2g61rausV3gjQY1gI++/QvePO97zIYbdCd3udiseKiqzjNd3ir2GXDaJyHstthqM7Qe46LGbR1XMiddYTgUDrSl5FC9Ni14aJyIALpIGG511FsJWzu5WzuDCFRHCePmAy/yma9RT4ekWdjZs+fUuwWPB/eZ+/Wda4neyzqp3xl8E0Ojz6lwpJPayqd0q4UzdEx1WzO0fKEzVffoHxrxP3D78N9yaieMRwOCHYb33ZkCMpEsTIrBpsD/uKnP+Rg5xUW1QI9TTBPK5pZw9Pxl8h1x366QZuuaQctaZ7iW81YbjC1C1b1DOEN5+tjlHJM5AivNG09J88Ue1s7vPLWd3GrlmcPP+fB2U85q84YFUO2JvuIxDCbPyWfDXFCcdg846cP/xJrTtBZRrMnabcCaiDRmyVycOnMcUgX6Lo1y9WUdbuIBzk8mYqahURl5GlBnm8gVRYbH4NDC9+fvB0ETxcgdZKVEqQiIF2GFYEuCEqxw827/wnvvfs10peGk6tTeQgvDSf9W7hSFsaDknyhtxAvUyc9UhL63SB+XcklWvLyZv0yEiwEoJIrd2L8PAhSo7MBp/d+ymeffEzrErBj6laAVP0gkV8NOlJvI/QNvBgw2dzh5OyQ05MTbhzs91USMbU1Op58H8cAUqpe3xEQXJbBcTU8vBjMXqyhyF4b4/1VkuyVu+fq0HhJF9F/vUhFr9ctzjWAREl95ZK6HEoI4ILlqm/nihWLtn8nfI+2/wc0oEgURVoy3I/JnVrquFFfnr6DRoQEFzyL9ZLFeh7zOaRE9jBe6F8VJSVpKskLTVlqklQhAlFzEgTOgXOR5rkSXHkQCtJEkyUJk7xkPxuzn24yliW5S1AIztopp/Mpy7omBEnjPEprilSTJJJ1z9vdeO0N/ov/6j8jGRxz9/ZXOV+e85cf/RFfPvuQb77zXQ4OvsKjx1/w+MuP+fB7T5ienOGMITgXu3nSjEDUeQQR0CoW5NVNAz6QpSnX9vfpOsN6XVEWJWmSoJKEZj5nMhkRrGOYZ+TKU2jF+fk5ddOyMR6hg2C1WiFEnIaNMTjvybLsBX8LbE8mvHbrJsEZHj5/yrJqY1JuDwNGcCXe8JIX8KCgL1nk8vTT770IbNdycfyAa3ffwzcV9XqNUilJMUQGT5FnXMxO8EHS1BXerhH1BSP7kK3xIzwVP3064/mFIN3p2Nsf8/CzexTFBsNxzs7OhCePHuJ8R6YMphY8vn/I8dGc08NHONNQrTxZcp10sIGtAqOtfZrqHl3XRVhYqZh+a1o66zAOQmcoc81kOGI4HnJw8yYHN26ynh9zZ6NiMlgyyG7ySiFAJIDg3r3HnMwsUoQYtBYEUgkSNN5FUaozFmMC29vXOHFPgFgI6b3EmIqj00MW6xVVXdG2BiUDmRakMvYddk6SpjkIaDvHX/3oY/Kvv8Zrd2Cpz/j23ZIPDjsqZ2lthIi9DSRakySeVdX2a1rUlQQpaZoodA5YtJYRMAoCmaRkec6zZw84PzvEu4YiVRRJQZZo0ixBpwn1+pSToy+5du2AbTVilBYsaDg2J9Sh5d3xbXZVjmgDG4NbnPCA69ck05VjdhywtYvWf6I1UmmFlwJwUfhrPPV5Q3XeoIuE+nZBe8uRlSmDPPBUP+GN5l20liyKBnVjgDeetVxiRpAVexzPH3BijhkPxpyvz8mDwDxtabqOZjXDtZaT1VMOjz+iu18y/IUR0+oRm+NbFOUI6w1+VWPLAarTzKeHZDdepzNL/vyj38ULz0Y7IdMpRjvOpo9IW4/essisZFVdkA0SZuctz84P8T6nrjucXLHUHQPbcLx4Sp6PGaYFu+NrXNv7Gn6U4XcS7myMOfnTc550H/O18StcK7dx3rK8WNNpy7Sd88Xyc863j8m/OqHdzEALEnnJpKh+A4TgPHW7YrWesmpmaCkpdMwS0ToFpVEiJ9EjtBjB5Zrp6V0eIZZrCoGyAWktQQkU4KRhIRUTJykYMjl4izyN29Xl5iheCs58oV37G2hAj57EjffFcCJ4CXLoPz+IQAj2r33+X/ta4YXQ9uWN9ur9PtB1LY++/IK/+LM/pelilYcUKQGHkEn/M0faJdabjJF6TEAh9IDhaMKTR/d47733IrLRDxFSiT6GwaJ0glQvKC3ft8ZfDgQ9PfDXh6v+z+hcktFQ6vsBp5+9pI+IkH/RhxvvcKkYDAas10vWqzU+vIyW+P7JjyjNpYsqXA2FXNUlBO//wxLJ5vmI0XDCVRdA8ATXQnDYztDUHV0Hy9Wai+kFq9UKiG4GKUD4nkQIoDUkqSLPErJUo3TsybF9d4GxUR+Cv5yW4wubJIosTRhlBfvZJjeKbTbTMXlIEU7QuYZ5d4GhiUOTeDEVK+FACA6u3eC1d97ll//+LzC+UbMzeY/Pn/4hH937UxazlO3hmEE55OnJlzw9+gk39t/h/FrK9GSGcR6kjAmOiaYzJjpopIwUAOD6zTPLM6qqpjKGJCsoBiMSpZgv1midUmYZJ6dnZHlKsIp7D8+4mC9RSrA5HrJeL3vBVKQLuq7COzg5v0ARQMXo8eVqwcPDR/xv/+N/xM2PGz56dMzzWYuSAi9iLYC9YiOjf/4ye0CKFxe5py9pVAprW06efhEdMsvnKJUjdcZqprH1HK0cTWcoNm4iTIufPyavH7A/fIZMND96NOHRyRShc4byCNN2VPUKlWYc7F/n9a+8xfnRY/JUsVhLRDrB+zGnzy5IshHDLMF2U7r6guHkAGM6rK3Z3t9jenHKbLEA6wnCY62j7RxJlpMnguEwIytyBpNNJtu3GIxGKPOcvVGJklvIvGCoOprmHKVG7B9sUjcnLOoFoV0RnEbqoh+GU2zf/7RazZA6kCQZ3hu0DiidEDxcnJ/RtgZrPVIXJGlKlmcMUkGSDmiqOtrhRaQBu67jJz89ZH94g2GueH97yu3xiKcXlp9OC55crOisZ3Nrwmo1o65bArIXFAbqrsNZGzlv5zHBE0IsEiSAUBIlIISG8UCRjIZ0rUGK2GmkZIO3gYuTB2wMEwZpybe23+bj1SMuuiUru+bD6gGvD25woy64m77OMNnlUfMJG6OKUWmZXsBqFjAmJkALFZGUIAQ6xGxe4cF1FruG6VPBem0ZbpTs7kjc+D4U8Kb9Kqdn97i5+S0Ims7XfKk+ok6n+D3H582PeD/9Lm33hKdHD7HzmHDsgOAarOgoESyOnuMfwvb1nyNJJwTtmdeBup5BWFNLx1nuOf3yJ+zvvM7D4y+obIXMAgOds5mMcLZlvXrOSdtSbmyycBdkm2MuhOH+o4/Z0ltkSjOfCFaiQ7WBpZvRuo4suY3IN2hFhl23lMmIQu+wef0r6NM/ZzMb0WBZ1jVNU7G0FcfNMSfjJwy+vo8cFTgM4GN32CUyYS3edjTdirpbY31DkWuSpEQnCUFKPILQ+1W89BgaZJCIIHHBIUNPMfS6CenAu9ghZqRA6zwmV2tJSBNs42ktBH2ZJit6zcMLyliKGAkRmZiIisRG+b5992rTjVTHC1qIF/EG/ws0x+XjMh/rZd0KxO/ng6dpaj798Mf81ff/iMXqnBAkUkQ3DGiEUNHmftmIKWREVdSY4B0+BIaj6zy+/xHL5d9nYzyKSaH0v6KU0ZljbY+kxM63vx7J4Psy6b+p9bj8mP7w58MLpCg4gq0ROkf0+qHL3/dysJNSMhiMcNZwcXGB96KnjgT0WhXnesEsoFUS9x4fETKpFBLJajm/es7/No+f6QElURlKplfBNNY1dF1NtZyznF+wWi05n884PjvlYnoa4Wb1QjgivLjiyHQiyHJJnkvSLG6QxkYRp3W9oPAl/EtrSV5kDMuczWHJtXKL18YHbGUjEjRYMMEwq89ZN+cMU8G6hYvGYIQgl/G0fe3mq3zn177DzvWUndsDVFHy4PR7PD3/HBG2uLF1k/3dG5xePGC2nBM6x9nzluVyTtc1V6Cm9R7fh/VcDhECEUPXpCIvEjKtaTuDAAZ5TproyGU6w+bmBGM6tApsbwz54uEzTs7mSCHZnAxQSlA3dT+lK+aLVRTcIsBbsjTBeU9nI6y+bhr+u3/7R2ykMUMghJqA6l0UcTATPTSohCBTiq4zKClJkqRHIwIKgRJxI6xmRzy991eAIctKamfpjMI1CxIVCLZit1mSuSk7+gnlMHBUpZwvb+MHI4x7SLAdar0kBIWxjun0DPtZzfT4AWmWkWeCZjBi5+BNjo6ekucl27t7dOsz1kuHCQIvFVk5JHVVpLGygne+8T4nTx/y9MlTECllOWJQ5uSpYzge4mUKSYYxNa6VTBKLWX3AcvP1eKQk8vhKNpRZxuakwDQDbBfwqwXBebwsYiZMkvZ9G4G2qeK1mKeYriZYEVE+oRiPh33ehGN354CmXlBowbqpMW1FkiQkaRZFi0Lz/Pic3/1ezW/9wk3u7i7Y6SR3bpaUR4LpD+csVxUnxy1tZ3DGYvtI+7woyMuceh2ROx8cGkWaFgRv6ZoVy5ljOBqzsT1ge6IY5BrTVMwu5gRv8V4RkEjfUq2XVJnFK8nXJ6/xWX3IcbfAOMNnzRMqvc9bs4Rru3sMkgH32i9Yc8rWTsPOXl+GhqCzEo/sh7sIPSdSgrAoGTAOOgfjYUGWx76ex+5TtC+5trxLSoIdGYpUUSVTCgGJLtFDxWn4jNcn30Fe73iUfMLypy1JSJiogixNaYWhXRkGpwWv/9rP4xrD4uSENM9Jt29gwyrmwpxMGUgYlgW/8e3/hKcnn9LkNa9s7fHd3d+k8AX2YsbTLz/j9OSQJmmxheS0nfIkLEjTnHdf+znCjQ0+PP8Yd+zY37jOqqo5PzkiS8Ykx+do7VhVhm7W0axXHPgNMI6T+oJVA3a9oq4rluUKcVchxwrr2ysNavAB57t4/boWFww+1KBEtI4nBVoPUDLFCRcpTSHRIkWgQMSgtrg5BVy/USqiHirIgLeKgZcEFciTAaNJyjjJyGzC7Ow+z5a/yHYGaVB9Ts8VbHIlaL0cXiKD8tJwQZwLLr8/V8fa+LmX+/nL9uL4071oqhcC/CUVFOK/+xATq0+Oj/nox3/Jg3sf0TSrKwVIQBCC623Il2hHLJQFgQ8KKQcEYYkR8pquWXPvi8/55je+Af6yJTjq8oIQvZX/MlxOXL0+QI+I9BqXvqSPl+IahNAvoUmhNy0AlCBU79KJv/3l8/Dy8DaebGKahovTz3l0bvEqRytFmqYkKiEIMLa7csgi6FFVBwHq9eo/HAQlBNAqjdZYAtFRYDg9fsLZ6XOm8yVHpyccnR7Tdm3fzhl5GREEyIBw0X6Wpoo812R5SpLFCnvrImTcdRZnXRQjKUGep2xvT9jaHLM1GLGVD9jPJkzSIdIJrAPjW2bdESfNfdbdItqgbUASSIVkY3eH23dv8x/9Z7/DYK/h+sEdLtZP+OyLf0OW7DCdQTCaydaQzckNpvMzcumxruAHf/49jg4fY7o2Ig0hROEi8SJKlUYrjbEdAsFoMIiNxN5hug6towC4SAWrlWG9XlEWBSdnpwgpGXae5bpGCoVUkRqaryqEEJRFGdEfJSnSkjxLGKSK2WpNazzLuiX4KDI7Oltwmugo4AwaZw3jLCVBYUO0EadZirGWLEtRSmJ96PsyYtBPCHEiD95j2yXnz1as6g6ZKKwcxHI427EznLCTO0bNT1EsWNuMB+ebHB5fIAaeG3c2aNqaEBxJ19C1zxFCMBptUC2mtIsLgutYrc7ZunaL4UaGPg3k422MgHVrkOkGg9GYIDXDrX2EW7E4X6MTzbvf+nvs7mzy+OFDUJpbt+/y/ntv8fEP/4QkTRE6J5P9aV4nbIaWUngqvcdyafEoxpPruG7B04ef8vRZR9WVJEVO7hLaqgMErRUI5RmONsnylLpaoZOMa9dvMzs/pmlqrAepLO+8/RUOnz5mOp1ibUwTzoVE+IYijf08zklcY/BdVNyfzRp+94fHfPvtbW5f2+Pg5psMu88psgQhPG3bYV1cmBOtYtBTH04ndYLWiizzdE1D0y6QSLIswzuLEJJEK6T0OFMh6MgzjXMpqDTeh3n8PltqyEJ6ECnvJ6/xSf2Eo+YC23kO3RljkXJroZkMN/iaeJdH4pgv5vcwomJYKsYjweZAMihShuMNBqNh//NJPBYfDMbWBK8o0jHW1X1cf4qs5wxWbzJRm6Q658bGhFrGann6XAmlAsvRF9wZfBt54Dh84yHzD2tWD2pWvmEtDZONHd5/9dv8+Z//Ed4axkpz+OQT9u++wY3da7C5Q93mVMZycvqATA0ZlXep5p/x9vVfYOD3KXc2YfM6rcw5W15QSoFuFF5Ytjc2+M2f/6e89eYvsbl7k7cPP+cP/+j/xbS9YJLvMV9ecDZ7jrYZaZ5j7DOqxZKVveBA7LCoZoSz51ga1lXNghX2Vk67EXMuotYuctudrehcjfMNiBBDLPvAxs53WDySEiU1CSlSJn2BY6TiA4EgLLFd0KNkghIZSsQwsTwBGSwmCFAJSeHYTHOGWpMqqOd/ySf3vsvB6BX204AIFhBXh1OBvBK5Xgo7od+4BUTNW0QjxCU/xOUg0pP2L7Sl/cddTiYBguy9FHHTd95RNy3HR0d88dknPPjiY6rVDNcL93s1KwQLvZbkMrBTADJoguhHJCnAJwiikSDRGZ98+H3uvvoGO5NRvLekigkCSr34OXokJ/QOn8ufLvTaPuF6pEZG9CVu95e27JcpKkm4yka53Fy5orIudSpRGKzY2NljvnjA937vv2ExOyNLRQzJS4fUcsLaCoxwJLpFS4cQKR4JsiBNA+FqCPr3P36mBxTdb8RSRL996CRd29EaQ2Ucq9ayquqYliq4EsYqGbnQ4HqqRUuSVJJlmjTVSCVwIcJlxsWuAw8kaUI5TNnb3+Ta3i5bo01GKqOUCalQ1KGlaTu8tbRmyqJ+RpBrEFEBnWuPTiWjnQN+87f+Y3Zvlezd9YzHe6ztki+Pfszh83Nsdw7NmN2NAVKl3Hv4Y4p0wE6a8+TJA44eP8R606MZ0W7m+yZmrRTj4TAOLc6Q6JgymKUpQQryvMA6R2c9btWwrjq8UKyblkAMFvr8iydYE2FdoSRV3SB1QpFlCASDwYAiTxgUSUyjNR0uNLFPIy9pWxupnEvRVR/kJUQss9Jc0mMJIQTSPMNYi5aqFxwnMcadaG0NUhAQ1M4zyHIybRnkGqUMZS4ZFIpxUqHslNZ7ar/D8ws4na4xTjLIAg+/vM/tN95hPn1K259wlJS0XV96JeN1lOdj5mdHnJ+e4UOJLiZMz8/xwTEajti9djOKdcsNvE1wPEOJlj/+3f+BxeysF4fC2fETPsskQuQ4HxgkEoWFZk3z/DlqNIetu1RiB5VCpgSCmqZbkGcDtOwItgIVO2wyqakaF2sJgqNr1jjXEuLZhMHmNnVd0bQW07V455nO571+yjFfzNBKgs4pEx27N+oVzoEVFpEKLClGSGat5o9+csLNI8srqy0++PQp06XDmbgRaK1QMvbgOGPojEWolOvX75AVOc3ynPOT5zjg0jYugsB2Dau5IxOKfKRpjEAnBUmeo5OStlnSdS3ONOT5gG05AZ3jZeAbJHyphtwLz7DO8aU/ZdBqDtIUJRWvtpvsjb/FJ9UXPJ2ecDF35CWMx47NpmPHOXQWHW5ZlqBUgkaTpoosSbHCRlG5AjHucLKCrCYJI8p2jzCOmUYmtFhvCSJhJc+R6YRd8R7tW45sb8rh/+cxq8c1+1s3efP17/Lg7D7PD3+CXRhu7r3BRXvG04cnfLYaMZ7exiw7qjCnocZceN7Y/QbbjDEzy1IssSFB1w4ZBNu7r9A0TzmpTtBB88b2XV7bfp+QRA3ArY1X+ZVXf5uPvvxDHiy+YF4vud5WiEWHUilVu0JYQzJI2dvcow5jtjZ3uZif86R7zmxzTXfTEUyCchKpPM5bOtfQ+QYhPCrRKJVEREoonG+owjxuuv3rLEkB0af2BrwwUUclQYkMSRKRFZHgXcxuFoOAmwe0FShyRl5RNg6fWpwCa+/xyY9/n0T9fV7fK8mDYzjZ4GCzIJMCVNx4QwgEL/oNtldJhXhgCvJyA7601F4uUMQ/Q19+R+84IVxpNAgO72Jx6rqquPf5p3zwwfc4OXmIJkEFF7UlwULEhYhuG99vyL6PVYhBhkG6qyEgZjn5/v8Fzgsujh/yVz/6Pt/9zi+T6SSiKAlXa/JVMmy48j6+ZAmOX8qH6Jfw3tMnpnDVoAl/A8mIhZqCrn/NMiS615lcDoDx55dKc3D9Pfb39zl/8PvopEImhtZazleaoy6wSgKDgadM44BjO8nSKpJixFfKf/C33+P/1h/5/4cPIRUIidRJTL+TAi9A6JLWKtbrmq7teNkhcsUjioAPoEQgTQVZpsj7U3wgCmOtj2hICJ7BMGNjs2T/YIO9vW02iwmZzLBNQxMaKgmNa6mbGWXVMpINTbLGi54yInBnexc1nPDOd36RV75WsH9tk7u3vsmjs0/44N5/w8VZzWoWGKbX+MobP8fm5DpVY5gME6bPH7L+YI57NOVg+4CnZ0+uRKSRZY39B3mWXekCBmmKTlMmk2EcTALUdctytaaum/j7K02uEwSKohggwoomOIJWlIOSNE3xLloFhYDN3R0Gw5KqWrOoO46Po4g2SfLYjDoaIqTFGIOU0QuvehuoIGCdo0ij3sX2hYMbgyHL1QqUJtEZUqlIwQmB8uFK0CZ8vBn3dve5Xq6Q/oja54h0l5ZA1Y25qAucGtEJi1JzcC1NvaTcOmD/9dfIjhLuf/wxSqcorRhPtgjBYdoGQheHlrUlyRJIPMvZIc4LhuMJRZmzripWywv8/AwRLKvFAii4OHpGVS3RStG0FW27RjjLeLKLdgKlNEOtKesZ3fQBy2SbqinptKYYbKFFy2o2ZXkxYzWr6GqLCBoIZPmAPM0phoK6MdStYd21IBNcgGo250ff/wF5qum6Bu+imHW5qOk6R/ASaztkmqDTHO8sMskQXUMmLNJ6uqCwQhKCYrpeEVzLvKr58uiUIhuBioWAPgjK4RDTdgipydMElRikLlBJwnI5ZT07wwdPVgywpiJNFFmqSLWPba1WsqglShdolcRCRKlQWY50gUQplqGjc0smwaKtJhEFXy1us5dMeNLNOG/P+dJdUFQpk+EmIgtMjOKXJ9/g+eacT1afMrdLFqtAbRvOlq4vN5PoNCY9K+FJlCRPNVpZJDGSP1EpUq4J4RSf5CyOJH6RcTY860/ADq06EpFizAO2mjfYDG8wffoxu9df4439XcbFa3xx/684WX6OzOOwfm/+EemoZGNvgt8UrLsZEkk9bvDOkjSK56dfcHN4i5PZI7yoGRUNhMBydULVThHAernESovPU2Z2TblyzDkGKyApGBd3eHr+YxrdcCPs8Gz6iM4ZCJ6391/n/fd/mb3X3kbuj3EDwWd/8mM++ovHtO8muCLgTI1QHhkEHosVPuaV9HZVpdQVMpEoyVDtxOeSIQRFwF7tvTJIpEgiReElqg8ycyIWWzpnKIMkzwVJDvUyUDiDMilOtgRVAIFGnDJ7+v/k9Mmf8KfJCM0WN977bb77S29zbQgKxcawYKBfpFTHLdnzoquEqyHmcr+GfpO/zGcSPf3Tb/wuxFTVpml4enTE5/c+4tMP/4QnT/4Caxfsbr3PsNyJvUvEiofYbi/BC5x3CBw+SHACQQsi5wrY6RvaLvu0Iq0SO4c++MG/Zv/6Xd559VUEDmPDVY5JuJQbvLwfvkRPXb4vhoqGqIeRXKmK/2ZpohQCL0VEv/s6iCAkAs0lJRbP9XE1LssxP//3/ikPPv4zpPkEIVqkDqjEk/iYgRQkKE1/LTmCtbR1CuXffo//mR5QejIwbshXvigNMsLXy9USY7q+6fUSpgoQYteIFx6pYjR9mml0Ek/wzsdSNGtjrP1wnLG7t8Hu3oD9nQ2GgwEqKNp6SS0qbLAoqSF05KoBt2ZzUjBfKVarBuM9o40drr/9Dd74xbdhfEi6ZXB6yE8efY+Pn/wr1uuGtrO8ffO7bE+uUZQldV2xWC5Yrg4ZTwWv73yDw/KC2dOf9Nb8F/yolIIkieiPdYYiSxmVOcVgyN7eHgHJbFXhQwMIBsWANMuuThrO+ciRpilSx4WozPMouBUiJoUSrcaz2YyL2ZJ11SKFJMvyCHtrSdu2dF2HlLLnSqE1lj6vC+sdKMloOGS+WCIImM5EqFYl0SLaY63ev+BAhRAkqSbNE956/yvsyDkPnw1ZLVNM6xlP9kEnjDeHBJXTrs7Y373Jl5/9iNV6Sd1aPrItwgWUzDGmIck0QWoECc3yiK6acrB7AGnG69/6ZXSa8se//z/TNBX4NcFNmE6f0dRLMJayzKiahvPpGd6GPoXWQ4ipxavG4PUaXQ64s3md2xuwkcBsKlmqBl8fk+hdnEg5Ojvi+Okh0+dPmF6sMS6lGO2BkigVG4szJUkLKE3AnjynaxegCySBrmmQIUXpFHBMxhN2d/d4+PgBzhls16KUwnpHU61xZorpKnItSPKMpo6akqjlChASlnU8/b33+k0WVYXUCSpNGE+2mC/mBB+Q0lOWCZ0NnJ8+RQRHlsWET4UnTdNIAw5yskxRFjlpMYYkRSV5388TUEmKzEucbfB4zrolj9tzxlnJfj5hTxaMbMYOOVvpLfzwVaa+AuNwyxpdDpCjBLdccc2lbOtvcqLnTNWKKmlxKhCkBSn7vCGPC4rOCmprY7GZF6igkMGRJxVb6i2eTA/59Pin3Elex4qWRXuG8gLlLLJ1hKbi2HzE7s4bzO8vwHkG+zkfPPs91uEcs2eQNqBWoAaKJGQgUpKuABNwpiWbS/AJeIWTLcfzB6jVnKaZUs6PCVmCXVY4N0V4KMZbBLPgQtbcf/QRN6c38aUhDBTT+RnLxSl6kKNWHcuuYuaXLLsL3hzf4sb11ymLXbAJXhSsli1s76Jub1OODNfNHtNkxXN5Qke0zguRRkHl5YYVLuMAQEtFIuImJoOO1lcX5R8iSIIXOOEjmkpsxvXR6xbpWw/jMIZMMBwKfBWwa0eVNSSpJCUiXc4FZHtI3T7mwjpUM+Dp88/4/MOvsT1MGI3v8mv/5B/z9ZtjCgkExyWWEPqIfQhXieDR8tpvvSG2s5sgeoF7R113LJZrzs9OODp6yNHzv+Lw6C+pV89ouwXC1STZDjot+6/V4XFx4/dR4+i9gdAhdUHwgkCkf5QMvaMx1gCIIAguELRHStGHTiZ0y+f87v/4fyf75/+C23sTUqGJDjXdC1z/OgUD/ZByJQB5ocdxzoDQCBe1K5dJtS9nxQiho7btioK5DF4jal1EjLEIHrwQvPn2e7z9rd/msx8+wYlomAhJQHmBDhKlwGehR7jilpWs/qZ49//342d6QAnO4ZwFEy1TpmsBgXWWtlphTX3FBUavdpw6L+OMpZIoJUgzSZolMX5YgPVRPNfZQFYqrl/f4PatbTYmOYMyBeFpqhVWN5A5gm2pV1Pe3NjgF959n+nFgkcPn+FYIXXK9miLd7/7D7j13hvorQuu7f0Wnx9/wg9++ieczD5j3hyykbxBFgpaO2N/7z0IYz48/AHr9SGvHrzH3f1vMv1wSmo0Qz2gshXBBfo7n0C8uYwxZGnCeDRiMhqTZDmmz0kRQZClGVqnjMshaM2qqpgvpgQf0FJRDgdRM2Bj945ONEWa0HQtPgi+ePCItrUURclwmNDUday89/6vDSaXqbaXUGQUeQkSnYKPmpnRaIhqVE//xM4Yj0Mo0ZfOOZx3eF+SpYL97YAVEPIxRg3Rg4zV2VPwlrTYpjWOIsmorKCYTHh270esXGD35uucHz9lNZ2RpClNvcYHhw3Q2odoArZaIE3NBUeoNOfw3k/I8gml8qTlGCUkzx/ej6cMHGlSYpzCU5KWZczJcQZPg7M1zlqataEzc+qmoVSKcLBBe7BLbXJOTk5JhgVqtaJrf8xiNuf4+Qld0yFlwbAsIUuvkIs0TfFdHWkIqSgyTTOraZsOlSRRKyVgmOWEIFiuFhhT92FpMUWyqRtOTs6Qgp4rl3RBkgiF1DlbgxE+eOq6BsCYCnTC0WyBE5o0z1HpABckzoW+TTaNWSneUxQD0iRnc3OLs+efkehAnuUMx7ukRYEzFWmRgs5iTX1eIvtAqsitB9IiRyWKNDRkicQlhlM1py4aRipjbHNGlUKsYNNrhJW0dUdXdzg3RDuFtIHMeW76CW/qu7xx/X1O10/5/OQz6tAiEnB9A6/3PgaFSvCdQQaF8IK8EBSDAScPT8iOak7c52ilsOsFbbAYa6PDRQasDjyt7tHlFqfhGWfYmxarV1TJDKU8hSrIXYk8tpimwtEgS4nvDH7uoZZIldFJzyAdMvNr3LJlsJqSZJqBKCiLXbpqjZNrEqXJu4Qnyy9RFahNTWgSZtUxZ+unpDIw95aH66eMVMa3bn2V3fI2K51w2M6ZqAF6LZifn/F8/pC7e9d4R/4ar268y2rg+H79A/6k+zcs1AyEx9ISSZxoLgihLzF2fWlcAOsc1ol4+IskQ68hc7zIKullpsK/oCbCEKFAF5BmsFp1nC8MRhds+BVpanDEezYoj/GOJqwJswWz6cccqoJi9Comn2B+9du8dn3COJVoGZABgre40B86jaXzjq5tqJuWxnSsV1Pmp884uThnOj1itbhgOT9nvZjTtBd04ilpPiNVLdJH1FppSZJJtEp6tKJDBEvwKuqpgOBjLk8kk/wV3U2PkkSVrSL4aG6QSkGIOskQLFk24sGn/4b/x//N8Z/+5/+CV65vkSfRHYN8EdEffG/p7Z1K8XvKl4aYXt7gLAGLDGmktKUg9NodLhEdEcXBVwPkJVDzsnupH1byPOeXfvUf8fCLf8t6eUYnHUYJbCpogiBkgkEKivjzbRgwf8eJ42d6QPHB07UN0koCDmcaXNeA7VAyLuSh91/HVsVwxdNdJq5mmSTPNUkSxUQ+BFwAYzxSBfaulbzy6iaTwZBUe9LcYozDqw4SgxYBLQKjQjIZFjRqTL455JozJMrw5f0Jr//qb3Hw3h7Xbh2wtjk/PfoxHz78E2bTdcxtaPdZuo7Rzhb723soWfDF05/w+PhHbOiU7fUB6y/OWD44JksyijQnNOIKPYFIQznrSRPNxmhEORggtcZZD8KQJBmT8ZjSFFR1jQkO0xhM1zLICzY3t2KnSVPRdi1BePb3tqiqdWwzXqyZrdZkeclwWBBbjht8CKxWK5IkJpRaY/ruhejmSHTMpomvQUBrxXA4pDGGbl2RpAmJ7gccE29oKZKosO/tayF4xsOCV28e8MFPHvHwp5+TJRpvVgjd0VnFl4/v01QVRZ6i8xIfBJ2HIh/iQ8db732Vo8PH0QKrCopiwHL6DBksoe+nkTKhIWEy3Kcc7GPaJZNxiS62WbaW5aPHIDVpnhGkpPWScjxhNM5xLvZd+Lbj/PyU9bpCBYfWGufg84cPeH6aUX6Rkam4aK/bBW17giKQpwlSFBSDcURMVILtFfZZlvX2PYWxDbYzJIlkUBZU0xXGWpRO8c6zWq9JshyFoFqt8NaS6wQjdV8REAgCZDIgS3OKvCDPEqpqTmdD/7rWZFlcZFsTuP/8HOsC5WCCSrNodfQRAq7qBk3USIiQ0XY1R0cPSbUmLxOKchDL+oQkycpIw/pAImV074g45CitYtCcbQkhcHuww+vp9WgTpg+g8jFEw+sOIztc8IjG4JMOVxu6aU0iNC7omDrsBdf33+Erb3ybt+oZY5Xzw5/+Gat61cPZFmtNHI6JeielIzpQ6wXdomI6+5LazfAiEJKALwOuAJFKXC5wStApEdOMPX3MuUdIRxACHzQ+tOjCUQ4F6qDFNi14iZYptIJmZmhPAvrY8FrY5+/f+k2Ojp7xsf2Sdn3M2AzQKdTdMwiGlVqzsCusD5zbgDvvSDtJWoxpq3MOxlu0s5pjzmn3Au+EW2xvvYJTkrlbUdfHLJ41iCNo6KinZ9zRd9nZvEPwI4piwFvLlNNuykfZn+H6/y4RCemJGr0o1OuREBnrFi5rK65cLg7nDMFeim6jQDNqKaAPQiK7pNrLQLcOtMvAuagxpUENO0oVS1Y76UmCoRWBWjcI2yJ8glhe8JM/veDw4Xe4fftttiYTikxRaofv1tTViqarWbcN63qGaZZ4f4YJFd4ssc0JdbPE2TbWjjQWYyROAIlFyIAegFPgRaCUDqENQRgIOh4UvetRpkvNSUtMpI2ltJe0kQ+yp1EEwXeEEBPMCTY+PzJapHVaIpzjox/8S9o68Dv/m/+Sd1+/QxI8xG+FFDJWgVyBJqKvH4j7w1VDfHwXzjlCMIQQUKi+iqKvdb0SBUctEUhC7zy6osx6E1Q89wtu3LzL7bd+nR/+8EMMC2okPkAGWBkwWlIgSJ0gyECt/0b+zL/n8TM9oAhvCb2lScgIbSkJeZoyLifkyQW4eY8whBe8XXjR8ZJliiyLeQ2hpzqMjW2MWzsJr7w64fqNEfOLhuXFmoMsI0kStmSCSBRDlyBrR9CBMpTUtceblmUrSca3+MZvfYu9926wtX8NEpgeHfOT+3/IdNEigyAVWyi1QQjn7G4XXD/4Cq3xPD9+yGs3v8a40uwsxjw5esQ6LKmpaFzHCyF0hC+lFORJyqgsyVJNXcUwMyFiJkFWDBgMRgiiHkIpSRYCozyLsfbeM1/MCN5SlAUqeM4vzrHWsVy1tMYyKEex0blHSJTSaOVxIi7q1pjeXy/wziEEDEcDqqrCu4CWEp1odJbiCLRdR9e2XHZJaCVIszT2z1zezH0QUVaMeP/rv8z50TmfPnrImYvim9pB3QWkrPHG0DaSIl3QOQ/JBnfefi0mq966zY2bb/D86UNWy3MWswuUkuTDAakSLKZzVFqQDncZ79xBagEmqufPnt/j2fmCIEDpjCzfQOqEwWgTIRymm1NVy56uEIxGOVmucMbGwj8XFyjvPW3T0TqHkwnzZU3bVAzzAq2HpNkAoXoxn0op04zGhh6KjuV/xrTUrqHrLForBrmi9Sq2mALOtARvUUXJaDyhq5asewfWpVB8PN5Epild02K6FmtqjGkwZoFzDmM7qvWSNElAGhCKV157hf3rdzg5OqWtlnizYnoetSaDocK5QFXNaG2gzDO++Uu/ibEznh0+jkm4yvYWSYGWnmBb0AqZ5n3hm0cIh5AaKRwXYsEqa8mSKIKVKsF7g/WWrmzpvCEj4xt8jX27Q2UXXKxPWFVzaANBpiyrOWdmznR+wXhccGP3NVbNik8ffcBTc4RPPDaN5WVBxUa6oANee5yac5GdorYVWTYkyxVN2mB0wCmHpKcvuoDvLM5arLUEF1vP40qukAwJIQcrEWSkQ01X1LR1Rdt1kCvcFmhdcs3t8fcP/gm3b32NMr3Ps0cnPBuvuHDPUf4UKzvyThKajPmo5qhcoBLFYraiebzi7dGbfPVXfpONjZtc+/JDTj76r1l9tWV0tEFoA0lW0q5rZiwJq2fIUtLV0M6n6M0aOR8w1h3SDThfLGhTEF4RpQt9ng0yVgrYeNi7jE4PV66POMYEHw8l1nqs8djO4ZyIjnoR3ZBCEnvIPOgMEucJmUBlgbaJJgZvHYmtaVIRG4BdwCLxOFodN0rnY/ZT1X7M/PALjp/mqLRkI08Y5opMKmKRd820m1NVc1wCmXYkqqd/PBgZQApSDV4JnOnjG1RECwsJSRowQfQ0h8GKljQkICQBdcUb+X44U1cR872DKUgIphcHX4pmia3sxKA40Q8wQgo2xvvcv/8jPvyLf8Vi2vBP/4v/km+99wrjIosxGbir6HrRO3YuO+EvdZfwAv3QKr/S2zjncFgkMYsqCK76fi5hEylEH1Aaxb2XybARaRGkScqbb/8CP/hwj7VZ0rk4nrkkUAaJ9BKT9EJeLeK6+nd4/EwPKNIHkgAQXTmJ0IzKMTtb11jvt6xW0NRwOjujbuvICdLzdiKQKEGiJVpFz3ishQbnLFt7KXdfK7lxK2c0EgSnWB4bxCLhYCcja2E2banrAMZQDlImO5okaTk8W7Hy27z/a79JM7xAjyS1n/PTz/+Qe08eMMzfhFxSrU5YrJYE85Cf/9q32N6+xb3H9zl8/lNW63O28wm/8I1/RvOTGet6xjqsOe8umLXz/oJ/MUVrKWOhHYFqXfWbe4xHVkpRdrFtWKcpSie0xpJlGRsbGxhjabuO8XiIRtCaFiFSjk7nrFarKJ6V0c4spaBzsVEz9E2yhIC39mo4edGDAdOLGUpL8iQhTRMuPfhaJ9H6bAxd26KThLIoUCqe9F+o0+PvF5BUnWBda+r1OQ0DplVHVcdNK8tyEpnEziWbIZOEQVny6tvf4Mbt11DCk2YbzFcLZCLJlEfYBevFOWsfGA03yIZjbt59j/F4G+8FM+e4mB/TBEUQmqIcsH39Fbb2r5MmKd461usFbaPIsox6PaPuM1ZC8GSpRuBItESqgsXigrqLYWsbww0WywuyrKAcjhhtbKMTjfCGNMkYjnZQOuP8/JT5Yk6WQlOtABcdTkkU2o1HBevWYnzUCCgtMF1HU3sSJdBpDmoNXYeSCZ6AdYbNYkiZSBbzGYvFHGMcQgrKwYCd3R2Onz6G4GjrliwfIITii88+xFvJeKMgT03Ua3UeYxUiePIkwbqOtq344oufUBQK27bITGJs6B1ASZ9ynJIkGVkxjNqnbhWbkoMALVky55AjUpGhpUbL+HkeixcBtGBAydq2NEgsCZKMQbGBSRznTDnbWdLln/Hus7do7CaNsOSbBdf8K5xMVpiRoUxfZO6E4DHeYno7aAgB3wWkKjCFA3Kkt1H82OvedL+o00W7pxU+UiDxEA1oPIG2FqyTWJCokgKJpWtqTBcpj4kY8frkLnvpAaGpkZOUUcjBJ5xsWZ4nh2QiY281JHvQwNiT79eoJEXcyVlfrLh++xWGOzdQowkHX/kqb8++zV+IP2YhZ6i1JNRTyBLquqJzhjBTzFdL0tYyomTBCV1oYZExDzVno6c0rUF4hfeC0AsmfTya4+kLLEPcFMNli0W4RFUczjpsF3AdfVRDtNoLEWJ+B7FgMs9gQwraRvBsHdfhoYc2SDQBLTxGgHCKLhCziIQnCQEjoFWxymDg1hhfYcKMldLIQqKSMRqFoKXQK1Zph9MQspg6jocqgBWCTAisjtJQSSC0oH38GbwSkAgy4cErpHTI0PVxKya6FUUgIHu6VyJ7uiROEb5HVET/T10cBHzMdREy0p2Evp5QeMbb++RScb5e8vHHf8Ds/3LB8e/85/zyr/4ie8OMRKpeX+N7TZi6QgHlpWbkKu+kF7teBmH2ybShF9RKrV5y9oQrfcolbe+ci71ol20DIa45B9dvUBa3OV4/oguBRkMbCQuED+gA0sfbwf3d5pOf7QFFOYcORAtZENERoAWbwx26HUe7BtvFpM3T6SnL9YKui70yUscETaWiWCie1CNyMtrQ3H1lxLWbGcNxglaC7e2CyWvbbM4t5pNn/OR5x8JnTLY22R7CZOhZruY8eTzn9vv/kNfuXEcWM8oCjuef8OT4Q748/JLT45xJ6VmslkyKIXd2N5lMbrA5ust8seL+ox9hDdzeeIdv7v4642eC+ZeHJLlgtVhyuj6nts2Vg+eydArAdAZr7FU2CgG0itkTpm0x1qG6mJeSZTn5aITpunjyC566MtTrmtV6hQ2CqqrI05zhoMQ4R57nOOtQyvZ0TteX98Xp+mWb2wtV/FWEEkVR0HUdi/mCRCussX1EvOjDuiJu2XWm/9oxe0ZJSdM0fPrwnONuzNSuyUcTfHWBFx0IhTEKK8HYmqyzFGVGWl1w/94PMcFy/dptpss5aZYjQk7OkNVMslx4dLYFKscYQ1PNUQLaruHk5JDjaYVON0nLhP1rN3nrG78EqqRpG8x6gUhTdosbWLNmPT9ieXHKcrFGqQTTGZzrWK5qPDXeWVSSYqxnOj1F+dCXTCagFTpNCdZTmw67nOKCiHkPoeXkaA44BmWC1ppEeZq2I9gO6RxKSN5+7+cxtuLLe59hnWe5XNKaaDUXKhBEVC82TYU1AwaDks6VjCYTFvMzbNcicVSLKYmKcP3tW6+SFwOqxYJgWqrlitPTCkWH7bpYvIkkT0HQ0XQWR85yNoUu6Wvae8hfEEvLELggsLZFthE9wVuEc1GboEqklNF2HmRcFImuO1AMRMFW2GG326JoFYv2kIBHZZKd/A5SKuz8Y45GJ1yMjnl4/Ck369tUwzVFmlEmKTtih5PRDJXF4YcA1lmk82jvMNZhfEdQnlZalNBRFKoDwUsIHThPSKJNXeoU2Qm6rt+QQy8F7R0UpvOslwYRFOUgR4sxudJo0xGcYtwOmdgR7XxO6wwre4FrGgbOc9BsMFdr1qrlIjNsCEFRJ1wbj6lYEdScyZ036fIRi/UFhbDYeUM53AIBz8wZvjIRNTYZTV1hgsDUhnl1wUSNMc2SqhuwWqxwGtbWsMhnmO5Fo+6lOxDRn/5F36eF71GjSDX4nkr0xuOMw5pejwZxMEEQgowHmrg0MFZQ6IAdBp7PBEsZaBSgDI0UJE6w5wUXwcchQIF0gmW/gzUyUBBovMSJwMAHnPVYdxkt6zGiw0qJKiRBe/JEkIiAd4JwGYWQRLG0ABIfY987FfDxDIyU0WUmCFhqamakYQtICJg+IC3GASBcPzxIBDG/xOORxEE8UmG9qFVEJ1IQMYMXEV2TRTlhe2uH40fP8e2Uw2ff51/+d895dvw7/Nqv/Qav3Tqg1AqBiSLXEPA+IhzeXSZxv5gKLo07l4NH8OIKHcFFk8jlmn0Z6w/RIu6ciW3rUvV0XdS8DIcThoM7tCffpxYNcwUDKdEOrIOmH9ithCb5u+3xP9MDSmTJYj5BhNgEhVbIgYQtGau76w7vA8aa/q3rp1UQir52Pp6WnIDNnZxrNwsOrqUMh/JqgAkykGzEkq6zoyUnZx3pBAZpTQBOlga18TZv/r2fY/P6iMaecW37bR6efMq9p3/B8/MT2tUBhZjw9OlTtiZDRoXktVduMR6/xaPD54TQ8catbyNDyqYecUNeIzydk6caUWrOj2Zc1PM4fEj+GloRs0YirCr6CR0hcCLQdS7WdyNI04TBaMxkPCLPsghhekfXtnTGcnZxSl01pMWA4WhEolOc94xGI4wxFEWOELBaLvF9aq3vESm4zGO5HMMDWsVGXq11P7XHEq6BjlBj1XVYG7t04usU26b72wPr4mnWOcfh4WMePj9nbSWhXrO5MWQ/3SIguTifxkHABerQkWSSshwgpaBerfnpT36INSuKPCctUzoXF969W++zs/8q7fqU+fSQ+dkT1hePqa3gYl6R5Rts7Ozz9OkDgoK2qZnsHDCa7JGlmrPTZyACFydPWK5arBdsbu1Q1RUbW9dYLudUqyckChrjcdahk4SuWTEZTWiqNbY2tKsafEw+9c7ifdOL5kS/0UmUFCivESpD4NkotqmaBbJaYdqW8+MHVF10rSmlccaS6oz9vT0WixPqeo4QAWMdh4cPSWSfAJlKpIdMRVSsbmpEcCiZcP3aPtY5nj87I88yVm7K/GwaUQ2tuX17j7o5Iwkdneni6U+qq+tQyCjIVVzGkFuEikJK0zpwFUr62N1DpA3QOYlKGaYjcl1SyAEDN6C0AwZtybBLkBYUHpNYlrJCnVvStkDvlezu7HHr7gGT9i/4SPyUn+YfUTw2ZJsjZJkgq4rbbo/lZkunLPt+m+1myAN1yDLveprJoq2gExYbHCFYNDomj6sovuyCiQVpKg4pqeqVoyF2nHh3aSGNe6RtLAORssmALm2plUDqnLTO2FhMcE3NIj2jsxl6JHj3W9/ma13KyeqEsv6AL5KHTAYlYbvFTRtesW8wGx5xYRfokLCyU7JlQb1ssOuK1WyK14KZbygmCclSE9ZLTGaY+RZRG7yXLJlybJ6R1C1egHWe6VbHMlsRbcMxrjxcrisEvHAID0G8aKkNXhA6geki3eWcj8L3fp8WxLRgQb9vv5SMplRApIHNUrI/CDRL6EIsgVRWIBG0BFogN1CYSD1cFg2D7zG0+E+1C4jWIOohAzJ8so70thAMEolMAjKNCFiwgcQLsAKZCDJP7APKwJmYr5IRSEOshshUzGWKMt8ZQRhi9lt08oXQ0yAeuKwIEP7Kviv6xNYQouA+nuliIa1UfdGrkH1Xj2bn4BrhwXNaF3f89foeH/zl/5XZxV/xzZ/7X/HmW19nZzxgoEHLQJLoSL1Jj/caHS7LM3tHUy95AGKvj5c9VWeJ2d3iSsNyNaAISRCRrr68xy9fuyAlVqa4EKgFpF6SILAqOnpaDy7E50DLF8PS3+bxMz2gEER8EYXq4QSJCAGR5ogR+D0HPlIc9HyftQ2t8egkXBUwdTZOvuOtnNuvjDi4XlKWcdONaYgt3hm8gtOw4pwualdkSycFXTbm1htf487bXwdl2dq8xdm85bPDv+STJ3/M2ayhWd/A1ppldczB9jZ3r094+/X3eP3ur7BcQ7tVcHR+n/uHH7KjB3zz4H/NoIaQJ6QbA9q5YR6W6FIxZnJFrYjQhwn5QHCezlpsn6IokRFitdHfr6VCiRThHcvVAkHMhZjPFszmczpjsNbQWYNZLWND80bB5uYO1lg2JmOWyyX1eo2W0FjDJRR46be/1Dr43p6spKAsS4qioKmrqCa3jqZtKMsS4yyG0Gs0ehW6iDcRrj95Ewef6bzBmcBkVHL3zg22tq7TNDVt13L9+jW6uuH46Ijj03MGgyEHN14hSQsuTj4neEM5GEXaYn3O9PwYVe5y4/Vvsbl9wNP7SwZuxPbGJlLAs6ML6vqEYDqK8YSiKBgPt5ifz3DiGJ1m7O7fYLK5x+nJExazKfPzGYv5jLLIsMYwn69x1uNCINEZ3bqlWSzIcodt1lTrhizNSILDr9YMheiHOcF4tEUQsFyvyPOSwY0cKRxt1eCCQKUZMktIggafkCjB4uwxJmR4lZDoFCk0xgXado31PvZseI8KAe8txlmUtHjrGZYbOAdZHq2/xsZukIuzwyhUDZamajBNRZkohFBkWUqqFbWROC+RTlFqRTLcIEklznWoRMWFm8jzCyIq5ozFuzamXSYSQRpFo73ob99f5279DrLyqHUgVxKda4LyVKomGQ3Zmdyi8Qtmzz9HzmdUFxas4vU3vsXtN29xp36P1x79KX86+CPmwwv2Gs3F7IKj1X26XMM4kKWa7zbf4UZ+g48uvsefjD+gLh1SaIKMHT6R0XBYWhS6P2UqFB6HwXmHVgmJ1ojeIeiCjzUFlr6DJvCLybf43736X1F2GRer53xa/4hP3KdUDoomxfuGmT3BySHb5U0O3v4KAzdi6/iU0fEBd7ufUG7CsTnlw9OfkJ4W3MlfR4bnZOuSVs05kQbZZviuZTU9xjtDndacicC1dIvrG3fJN3eoRMX8Ys6j2ZesVcexv0Asl1glaA4U3c0Ur/rSVVRE3y43N3+Z2+EjXeM1WB/R2BqsieJjHwIyhPgx/Sk8Rsdf2qb6cDIBxvcsiAhsZYJVHlgYUH3BpQrQCIHBY4Og8yFSbULgZEBJReb6ji8RO8GaLiAWS5beMigkWluCMGgVyJREy6gvQQlwgtZKpIYE0BJyD1YFnBU0UpD1xaWtlKSyYCA3GOoNtLH4VuBEPDAHH+3Usg9sA9u7KANCaAK+1730f4aACBJCzCwK/Rp62ZI83tokTaB1gDQoFRiUp+T+D3j28efMDt9n48YvcOv2++xv7bCR50jVdxNJhbKaNEligWYf8nbVZNyn6l4aSrx32HBpRZZXgbtxmIqHYGvicygkNAG+eH7G6fw4apOQpAG8DKRIWgeNgUxE/VLyHxLFQ+/wuLRRCSljHD0CUtiYbGCNiToH02BDR5CW5focIdt4k4mIACSZYPcgY/8gY2OS9vAX0QIWOoSq8QJWgxWrLYM0mvGd2+y9+xY3XnmFja0ddrduovSAprU4l+AqjbS38U2CrxfgO9648Ro39ktu3XiPrc3X+OnjH3Hv4T2Ojg+xdsFOMebnD36Lu/o6uROsvMFqT5d4httDbhe3cS6exE1nMG1HXdU0dU3dNH2bbp9SeGlpk6G/2GQU5oXAtdGQjc0NZrN5TEr1Bu88KknpVhVZIkmUJkk0BBiUJfPZOcvFAoHHdG0vtIqvgejtzkqKfq6IepUsz0mShM3NTZ7XFUpJjAkY52i6jiIvcHXVn6ZEjE+XkrVtXmo5FlR1y7oJvPrGV9jevYWShvnsBNM1JIli9/pNRsNN3n5nycP7X9IGzdbuJmdHz2lmzxGyw1QpTTGkXc1Ytwm7N79COd6kHJRMtrfRqefg9ntMRmM6/0c8fvYEExTL+YLtnW2+8t63+PGP/4Lp/IyiHLM8e0YxHHJ+fkS9rikGY2znWa2XHBzcYLm8QAhDnpexo8gT7b5tgzWWJEA5TKg7i1lX6ERQ5jleJDHeGki1xgswpqPtLFJn0ZorYpVBmuQUaRl7eMKcLmicyDCurwzQnrppEWjKIqXrKpRSJFZh2woZPFmiCHZJqhWpliiRUhRRC2S6huAd9XpOtV4hvWCQpcisQEnJ4vwM6W3fDq7JCoVzM5qlpRiNSLIRWVFiuzrWtGOj28MblISgdK/ZSQkiZriAQNYWmiXGtjSpoy4zZK6YpmuWuuMrap+75TsEt2a8MWZ57ZAvmo94Nv2Cp0cPmGwMkV3gQL3JL4eEgzfukAnFg4c/pFJrvuwOWXctm3YXubR08znbbpub3T5f7B8RdDzxa5WBiEF31ntCcGiSiAiEOKiJfsCWvehTJRrtL5GXQDAxzfSOf5Nxepdiw5B0K4aL73BtcZvPV18wlRfkKmHuzvB1xaDbiYV/aSCdDBkvtnlf/RIuLFHJZ3yef8nzowte27xD3mwQZo4ZJ7SNIHQeZRytXeK6lreHt3g1fYON4Q7b2zfxewMW6Rp9/DlPv3jOumqps5aw42heLbFjHVPhve+ptZ7i6JNQQ4gUhnUh5v548DZgjMV2vs8CisNHLC3v49U9vf04IPpuFogZKXTQEaiDZJkGupFA1zEzxEtwEhIrEA6MDWAFTQjkMVYJk0qM9HTAMEYYo5zAOUnlWpzKGSmHJGagaBXQ/W7pECQqDibB9wOLEKQCUhVb7J0RVD6QBUESFEUyYpJco9QjvFRUbRzCX4TA9SgEl0Oe64exru8vIxYl0jczhz5qvq8D6FuKCEJQlCOGo5S5d6jUUgwtu1uwNyoYpE8R9RHnn3yPw8+/yvD6L/H661/n+u41JnlGIixSmtizpjVpmkX0Q4C1phc2g5IKpRVaR6eP6dpYddIHdEoEVdfx6NkpQUjSMmNZG+49OeSv/vx/ZHrxF0hnotEBEEFQC1BOIHvXVn4Jrv8djDw/0wOKdRFW1lKhZIYMKj7RKKQWiMSzMRhjNhqatqJzLZaOstR4X+NcFw8FEsZbnp09TVk6pLBIil4R3cUa7eCp244OzfV3vsbuz7/HzVfuMtlNsKzY2rjLdHXKp0/+B47PDpkvT1ksKwITpvMTbm6/SSJhc9wx2igwXvGTL37MJw/+HSF4RoNr5HbML9z6Nd7wd0nXnpWvOK3OeN6dYzLHzt4u2+zjbVwY2qalrltmiznT6QVCzIlwnMU7h/OR41Q9p+h6uqTIU5JEMTs/5fHTIxLpkGmGNw7vxRU1srt/wM2btxiUAx48+JLlfE6apTRtA1JFzY+I2SuXim8pBJ2NGpU0SRmOxrjOsF6t0EqRpEkMwHMOoRTW+xj01jdmegAlcSHGOsWbPJBmI175+e8ghEMJwcmzewTT0tYVxXAb8LS2Zjwa8nO/9Cs8fXKfZ08/Yn4+pa4rlE4JNCzmS1QyYrh9h63922TpkOn8jKbtyIoMlQbqek5rGiZb11ks1/jgWa0qfvDnf8Ds4ojgDXU+oCpysizn6HTGaLLLP/7t3+bDH36fDz/4iPliihSQ9Em81XIKUqGSnICgHEzAGzrjWK3j4KYlFGm0CXZtgzXRhu19i+lcTHFUURCtpERIxXhYMhxkPHv0MWXaoURJVuZIEWjqFmMtCIV1IIRnmGWxx6iFVmdxKex57yTRJGkCXUBIRTEYUK0vWK8uWK/WNHVLoXKkTuhMi0ShgidLJFpGF1aSRFeG85ZgW7ypCVmGEJ5EK7y3OG+RxAFWKOLQpQRaQdesqdeBbgjzYoXPJC4RvCXe4OfS9zixp9zzjzn1p3x58iWDRpO6Ibubb7AONUcnj/ji0Q8ZjAuKLuolsmZMWmyRbkiuHbxOkBpfK9rxczrdcuyfkK3XLNaHbCUJg0wy3zQImSCFivbLS12F99HeLOI2K0OE6523eEevnZE4LdH6Uo+hcE5wuHrE/eMv2L9xDTHIWZzFCoWJS1i7QGsqWmPxdcV8MGN8NMVLg2w8TgjqYLFrTXvkGakB54tzyosBTC0jXzLeGvCkOuF8dYTykhbB2KS8xXuUYYsizUjLAWFYkoSOdDykfSulIaMdp1BmoPqGamf6VTYOKK6ncp01eGsiMttGd07wIj5P4dJxJnpxKH1GSjx9g+o37qhhESJEwTOwDtBa8NaTeZgEwYz44YkLNAQaJxi0ULlY5CoNXAjPhpQo5fFCYkSgC4HMw0oFhsrjEo+gRiqP0IEkiYNHJqAlvl3m3woX8FKgQ8xcSlQgaEErPdJBTaAQglwVpNmQXBUEndFWBtEFwv+3vT8Pti2/6/rh13dY0x7OPvM5d+y+3emkuzOQiaQ7UUHIj4h5ygHKKqmIQSl9iB0kQCGD4kSFoP5hqYX409JQzyOYkqdAMCIaAwQCGTvpTN3p6c7Dmc8e1/Sdnj+++970TRqS9qek2+x31a17797r7LPW/u691md9Pu9hnpyMcPFzI2yU9UuiS6uvUAxxYYAMGYF0PkbRtzhiEPlf0iu08Ig0YTAo2G+PSTJBnnvyFPJUUaRq3pk8ppn8Pk8/8ijnn7qXjVOv5J67X8HJzdOsdAoKnZCnCcZ60iQhSWI+kp+rrbx3+KBiUOs8KbmqSuq5airohIvXrvHf3/cfmVYTZH9AMxpxNPws0/rzODvFwS3fWRvi6Gymoyv7ypyDVDyX6oTnWKC8+93v5pd/+Zf5whe+QFEUvOENb+Af/aN/xEte8pJb29R1zQ//8A/z3ve+l6ZpePOb38y//Jf/kq2trVvbXL58mbe//e381m/9Fr1ej7e97W28+93vRuvnVi9VbU3ZlqQqniAzKdFESZlAkqiETtZl0F2iXmsRnZTVExvUpowM8hComhLjjlDFEf1BSZL46AwbSpAOIWuCa2haRyK3eN19f4qtjXPY2UXW1+4hH2xzfvczPHrxM1zafYTd4ZNcurZDXSYkIiH4EcGkHImrfNM3voGTJ+9AiBUu71zj8s55+t1TBFehguXuwUt4RbiH3Cgm5pgjNWNPztj3Y0gFA1EACu8EvnG0KkU7MCqh1Rk+7yGCoFVNzNsxBufmGQs+jkPTIFhfXiVYz3DvgFxYWiU5Go6pWkMIkGYder0BtjVMp2PGw2MOdq9jTTvPeXEkaRZjBG6aBRG7A95BotW8ISIwTUuaaJyLEeFJolldW+Xg4Ahr4/w6z/Jb7K2bBm1KSFAymmYHUFqjEolpaobjI1xwDNa34PiQ4Im8Ge3p99dZ3bybNCsYH55n6CcoKUiyLsurp5EqJ+gu66deTJZrRkfH7N94EtuOGPQ1k6PzTA8PqaqWTm+JurakeYp3Lcf7h/HOSzmUL/FVyY2dmsomLK9tknSWEFlGp9Obd4QcVTVjdHTEbFbHlqpUCCR5ltBUJc4E+r0eQkoyDalq6eaa1npaY9BakCYaKXOklCyvrVLORpTjGUpAXQ6ZHg2xzZhuJ6WTaJZXl9FKMRnvU80sxppoVAUkSYcgFTZJKYo+zra4ZoJW0d/F+galAiiQaUKYOmzbzpO/412qFszTaIl8DRUioV8rVJqhgqcTFBaFtDWhnpKmCi1avLBRQimi74wktratqQhW4Y0D4bCpZ9Jp6YkOvVDQdQN6YptBs8GKWOPh9mE+ye/QP84YlCsk3RzTeIRM2C2f5slrq9y/eS++HjE83MVXjhW9Sa0CIknotT1OHq1wqX/Mtfo6HEy4On6MUe4pVyRtG6ItvtYQYoChkirKXH3kc0XuV1RqhWCw3qO8jHeMIppaq+DBG7xTPO4f49SljzKt76UvMkzRMulUJJlj7WrO1RsXSbICQcrlJx5BEuhtbmLrGtlAYw2zesi0HNE1KUJ3MFem3L18lge/+f+iv3GOC5ce57c/9l5uzHY5NhXrLmN/uMtK3xD0OtKOSJqWYANilOOXMlw3A0AEi/BzmfTNHt68S+3mnKG2bmmbFlc5rHE4EyME0ixFpbGgU3PeQqSYRIVkQCP8fNSOn4f9EUfzQE8lGCFoHRwrqFQgE9DOiaraRuPM0gAmNhq89xQIEhmQWpATSESgCdHpVEhP0ALfAVl4XOFI5rwTm8TiAwTCBqSIJHvdShoRyPzcWVhAoQVZDj6HLAQa4RFBk/g5OTpRZLmjNLHYEigIFoefX4uSSAQnuiTb0AfSWLT56CYbjQNldJUNsWCQUiGFQktFfyUjt55UxhGjFCBUHUfCogNCkjQWyqvsj/bZLz/HtZ2TbK2/lDOnXsnqymkGS8t005wizSnynDxLyXUyv+7GDljsfEfeIELw+cc+x2effJJRM2Vv7wkO9n6HuhrR4kmNwQqD0dGYzytoFHScQAVwUiIdFCYgc0GpA8mcZ/3V4jlVBB/84Ad56KGH+MZv/EastfzET/wE3/Zt38ajjz5Kt9sF4Ad/8Af5L//lv/BLv/RLDAYD3vGOd/Ad3/Ed/N7v/R4Q7+Lf8pa3sL29ze///u9z48YN/vJf/sskScJP//RPP5fdoXGWxrb4tiIlRPmlzhAhauYlAdIM3x9gFWhbsC43kInEE6iamml9SG1SglKg9hCiIeDwfgbEf6fJGV595ls4e+JBjD1GaoHp3s2V4TWunv9dnNMcTG5wOD7mxr5lOk5xbYBE00k7rK0NuPNMn7vvfiVH04rHz3+ES9fPUzcz0iShm0pO98/xqrU3oEVG04yZ+DEHYcSBPWLmJ1jREGSLM2EeGw7W1bRmAiGmwiK7BGAmAGtwATIMXRGDFcdBokLg+vXreJ2wlKV0lwYc3tjHWosS0On3I8PcW46PD3D1DC+gqqrYgQHsnOdig0NpTXB2rloISO8oOj2EIEqJ05Q0TeM4zXusdXQ6KVmeRZ+QucGbUuoWWVngUSp6Bvh5q9g5w3R2jFYZeXcQLx5Y+p0KhyFNAta2JHmfPO9SZwVSSrKsQKZdVrbv4+Wv/r/odrsMh/uMpiPqOnZc9q95ghWUM2iqCc2sBdlhealHlmacOLVNWw156tHPRHm3c7QeysYzqyzOG3YuP8Wv/f/+PyQ6ZWV9nRCgnI6ZjkcE76MLqbHUbXTe1UsDrNTINOY/IQQ6AesUSbYO2sfiuTEILUmShCxLEBhMPaWphrTBE8wULQV5sYxIFWkmUdLRtA2Q0ekvR6v+0MQ7SBnvzowUiKBA5phWk+hAojTWBTSCWV0TmmN6uaBMBGbqcCis8+AcMlE4fEyqlTqmpSqFQ5LlHTraYnwcT7VeIrxGOo/0DbmMxle4BmwO3kaFjoa8GKDSnDZITomz/MnidXSEYv9on6t7T9DTq6SF5J7yNHtLUyadfXaHT9EcNNSiobY1XsNmOePcnd9A0jEcXXua848+SvXJQ/xAM2WGFS2rwy6Fzjmz/CLWOjnNU4dc8I9Sd1fwCJxtYxLtvA1OSOYEzyZ2J4WYz+gj+TCOQAxSzGW5PkSJp3IEadkvjvn48PeYjY+5S95JvhxHcx3XQQrLcTniWOzQUUtMmhnNJcXp+iRBtZijNqYBdyS1H7KWd1iRfVSe8cr7/zSyv8lBeUR3Y5uXvfibOPjEr9HORmxunINQM5wckPqcxEmOh45aSy77C9RbE4KMBVeYdzMCgeADDgsuEGzAGkdbmshDqgzeRAWPIBZiQsaRZOCL3NCbgXyRKTgvGLyYK1xuKkUiVpINMva5oiv6FbQJsQMFJB6yBo59QHlILIzn44IkRHlw6kS8W1egG5gqTzdE8opQEq8dVkOaCcScNxekmPPcxLyAF5QesibmzWR4lBZoDWk6j/SBaGjpa1rXkvkYqpjkHso5GzqE2H0L8/Y88fMThEaIVUQIcTtv8aKNHMowlzUTbp0DxZy7JKQgyxNEHu/iag/TBtrW4TKNFB6LA0q0bPEYat8iy2PE3kUm44+hszPkyRZ5tkaSLVPkA7qdVZaXllld3aDb7VHoeAN58+Zh92jIJx75IE88/UuU9ohGGJJQxfGXiR2YNkAT5qGEUqBCVOjlIT7nCDghsHq+2BZIv/pr/HMqUH7jN37jtv///M//PJubmzz88MP8iT/xJxiNRvzbf/tv+cVf/EW+5Vu+BYD3vOc93HfffXzkIx/hgQce4L//9//Oo48+yv/4H/+Dra0tXvnKV/JTP/VT/OiP/ih//+//fdL0y/e+aRqaprn1//F4HBfdh1tueTe/CPgwz9SI7dcgNKlKKHSK0w5yjUx19PpQiiArpE0JUmNczDpwweGDQYScpeKlbK68ku2V+zDBUoYKYVL2h0fsH9/g+tEes9mU/ePrHA6HuBZylmhDQ1sZ+tpx951bbK6f5PLu4zx+/hKXd66SJJq6HdO0FXevvoHXbLyRnixQTU1pxxyHPXbNLjNfY1WLkB4nZpjQIoPGS43zDS5r8NaiM0VmFZk0tKHBhUArLKnSTGwkArfzTIos0WivOH1qi6PhGK2iAioA3U6Xuq5IZOD0qS1OrPX45Oeeiqx2H7kjznu0DHMuTMx4MS46TaZpEuemRU6WZl+UGxP9ZYSUzKZTiixn0k6+uMgiRolDNEeKEeJzotZ8UCqlpNPtkyY5oxFgSmyakWc5aZayvnUOLQuGh5e58ORnmVaSJN8gL1bZPH0P26dOkxU5UlrGox2O924wmYzo91eQyxtonaO1pm1KtNYondAaQ54JNtdXqcYTWuvZvfbUrRY33lFkgm7Wkospeb4aT9aqz2R0RN20WC/moipHlqT0+qs470nzbizqlKJbdOj1OkhRMx4fRSWTbRDSY5yjqmqGzoK3mLYGZwjWUGQJxdIyadEFGQu98WSENRKlM/q9PkKBtTp214n7LOfcHil9TAT3NcE3BJ9QtS1V1czbvR6d5sjEI02g9QFTewolUVqiEokXIoonhUaqlMZ4rLdY50CmBG9oXSBNYpezn+eAJUl69LpdkqxP66ENsLyxjQiOfQ5JfEbR9ljKU5rRERcvfoHR0gqdQc5ycpYTrse+vcAu57kyu8jxyoypLJFGMrVT9sdHrOsu2dpp+mdnXPn0J5jsT5G9nK6GxkzpT9e59yWvIj1dkCz3qM7nPNkec5RWtMFjnbuV6EqImT3JTVfq4CFEiYpEgY/kWEd8TsxvCJyw0V6fwMXBZexBxWhyg5PlNkmuKH3AmjEzV1KfdlTZHkePGw4nn+dA3mDD9OmmKav5gNBLM80AAE6KSURBVLSzgqsTMlquz66w5s9STg2TsBOVWEWHrbW7Obv5UsbjA3LnyUiYNi1TP2ZSTRnnU45W96juP6A/yMhcTmUcjXG3zqXBzyNBrCe0AVc7XGMIJuo8pFZRmaI0WqdIHcezUTo8V/nIOLYVN+3b54m48Z0QsbsSYicqS3qEsMGSvEKpPKGj6Gcdej4naIVsPJWtqU3ANIFVI3CNoW3A4CnxlEDaSJz3zJsjBCRGe1wiMDp2QLQWuGROQg2AFXgv0E6Q20gELeeET61j4B2JiJ1FHw3cKvYZiWUIKUnbwXqJ0JLgcgSa4KtbBNloljkv/oLDyRjrEYQjuJogEtAJXkZuTiCJqpmbWoF5grnUYE3gsAr0R5IidQg5JNcprfFYAjIVZM5hQoX3Da0H2RgaN2bmn4YgCd4gW4H3GdIPQJ8kydbpJCvk3T46T8E79sYXONr9H5TiMk46tBTRt1EEXOKwQaJsLBBViEor66HwggQoAhgfqFJB4z29VjBTnueC/0cclNFoBMDq6ioADz/8MMYY3vSmN93a5t577+Xs2bN8+MMf5oEHHuDDH/4wL3/5y28b+bz5zW/m7W9/O5///Od51ate9WW/593vfjf/4B/8gy97XHjAC7SMCgAZIolKOpBWxHOH9UjjUC6Q5xmiE23KvQDtTMzjCY7Wz4AWITSKnLXVlyNCj+3Nkyx1zhA8nL/xCa4fXscazcHwGrP2kFG5y3A0YzKq6Xc36HU7DIrNSNyyLXec7LO9dQYhltgbjqjqhjzr43yLlg1LWY+0ScBCWkBtZ+y6A65ynTqrEUGQoLAuzuy1AhFCrFRVQuZTbBpwQSGNp5AOJOhphQ9QNy1tEjAtCOlIpCKRCXpu2dzNQTqDcYYsSQmmRnvLcpbzkhOrsXpXcabe2DiCMdbF2C8T27TWWJSU0YUx0aRak6UpWsX2rHOWvNOlbRvqpsE0hvWtTeqqijwEwbwAia3Z+X1ZvLkh6sGVTuh2lkmSOOrodvuYJpK6lPKsrp2gv7yFacdMhruMJw2rJ1/FaDREpil33nUfRZ5gzYyjwx32d69hjWNpaY3B6kmkhDRLIQSachwDEoslEp0SfMXw8BKdbkEnWGaHntneCBUKOkVGkng6nSySQb2LRNnJiMPDQ4yJFvWtqUiT+BktywnV1LC2tkaioNtb5uzZu5hMjpiMxtSzHYLUBCFJdFTKNHUZ79zKEd47ilSS5gVZv4/UBc5DOR0hhCbRAqVy8k6HtOjM32dJ0xqk1HSXVgkqxbZTTDWJd25eYNsK5yxl1WCNoG48Do/zKWkmqNoG01iSRGI9CB/JoRaQiUInOVmq8bbBBEXrLN7aOI9vLa1WlCLQNLGoG2QZMuuTdgdk6YDpbERZ1XTyBBcsN9w1PlR/mJeOzqHahiTvstTtM5sccMVdoFv1GNXXOaqPmYqaRhrypQ4b0z5tPWZv9ypZcgIrKvRyxuqdp9n5zO8wvjYj1Tljt09necT65U+zzhls6lmfriImms+cvgYrkpWmixGeUT4mSE+iUzwB4aJ/kHENgoBSFnwVPT8ISJGTJilKJTgs3ppYAKQO7wRHs0OOjq5TGEWv6KPbQDfv0foWdcpTPTnhznSFVORcnF7HV3C6u8mgqSjbBpnAkZuQVEOePnqclbJL0uvRhAo7a9FJRtCai9U1VoqGmSnZnd5A5ynqhEc+UJJ2GoSdUuQZPdtj1iQMS4O1c68XJ3HW45s4zvHzUWvUWkcDsDiGmJuBEbll2NjHl1JFOYyUOCW4mfFyq8Uvbt4YRaWQlBqpMnppy4rMWNHrLCXbdNIOSgSMa7A2JqI701DWDUeTKaPpEUe14dAEZgJqAVkA6wOugcYKGieRzjFNAomA1EJL5IU0QRBMdLltlCfI+H+aQCKjaVsnAPMxhg2RyuCay5iuoau7KKcJoYsQWUzuJUqI4/krBrHGEL45T+fmGCwIwnwkpLwE9HzqdbOgmWdeJeA0iNbTTOGKVQQTqK1lq+eRMscg8ImjcoG2hcRbjG/xoka1JUGmJCFDuBk2NGA8VW1p2x7e5OBTUAk6CQhlsHpGmU5RMnITERKVQk9KnA1MRFRNISD1AhsCXS9RPo7ZALIgKdsoyyeFNJdfbJt9FfifLlC897zzne/kjW98Iy972csA2NnZIU1TlpeXb9t2a2uLnZ2dW9s8szi5+fzN554NP/7jP84P/dAP3fr/eDzmzJkzkY0tFAkJqUjQXqJcQNpIdgrWEeoWV9VIFfMEYofa0NQ1R8d7jKvLOHUeIcdIkdPv3UEnK1hZvgOVpCTJKl6t8PjVT3Ft7zrj8YggYOfoGqU5IlFLJCTcdXKFo9EQ5wQHo+uc2Fjh1EbOPXe9ip3jPer6iMu7T1DbMcPJCCFaVrodTvdewks2X852Z0BVT9mvrnIlXGGcTNBJPqebObxxCGcj10bliJAhVexoiCzDe0XVWNK0CyuSclKR7x0zGU+ZzWbM7DR6noQwD8tSPPbkJR64/xyDuwTndxKOKsuLTm3HTKO6ZL3wtCFw96kNPvX01djeJxIBrXMEAkv9PnVVIoCiKFgZ9LFz3osnoHWC8x4zD5TDhziaq2uKIsO4+Ylv7lro3Hx+NT8hKKWiEZaU5HkXRJTNCilJ8w4iS1AiJngNj3bY37nArJqxevKlrK2eZjw84vDgAsGMsW2Hum442D9C6x5CNQiVU9UtQgvKekKn06foDeJdXdGnyAsmo2islOQFRaYIJ08yGU2pgsKiSfurJP0lnJKY1tHUNaPjIdPpFGuiH0ZUmiUMluLIxTUCV9ekvZwX3fNy3vSn/xyf/uTv8tlPVZSTQ5wFEwQehU5zeks96tkQU5ekWtDp9ki6SyidYk1NNSlxpgaVka/0sKahqRoae4CSOUIqUuXxwaHzHiLROF9GI7VgMU6C0PPuT4JvDK3xCJ0jtKazJEkLx3A0jJwUwDuLAUgTpIpJx0JLnJEY63EoHDG7RUmB0il5mqFocLZlVjU4KtImkHUtbT3B2Qrb6RFWHBM35Do535i9jjMvOs3axhESw/7Y0vOn6K5kPLZzg3YiuPfeB/iGjS4ze0y5M2Kv2ufxvU+xpHP0ksdRstrf4pX3/XGuH1/i/LUnmQGj7ICHx7/D5uNrdFWf8d4VpoUjGTheJu/gjzcPcKSH/E77YVRXMKgGnNeXmOLwJvIXEB5F9NLwBJwPpImkk3ZJdeTZONNS+xlZ6LDWbiAzRy3BNAanHVLGdOH2uGL5C8v8pTf+v3jtqT9ObT1Pnf80X3jqYa7sX+LqpWusrPQQKsUksM8B7sajiPQs3RPgsxHtUUXJMe1qQLTg2kOMMOilHHkmIO7TLHX7dNKCXtrSeEPNmIFO0bLD/shStg3eQHA+Gjn6gBLzgiPE4wz4WwqdIGQ02wsxKd55G8n6Qc67LTGjZp76MtevuFtKQ2MqWiZ4axAq0FEDinSJTOckqiBNJDl9QrBRAWMbbM/QLxT7eUky9Mjag/F4I7ABlA00EtISJolHC8EUQTeJhmtOCBJLlBETfbBsIinm1xnrBeMQEFaQyWjqFnTsItYGrB0xrSs66YBBsoYWY7TOSMR6LMpjRkAsUoKNHSMECB27U8KDykCoWMAgEDLOQLzXCOHm75cmS7oUQtAGgZkFjiYO20aZtXGebtESZKAMDg3oEOXRlZjhtcAnGcvW4HyLFAapAkJLVB5i/8nEXLVgY0fK4kFH87pWCIJUZGlGX/TJkbS2oW1LWgxLDkoRSK1AWsCBcuCR5IDDY+Yuwx0nnlPV8T9doDz00EN87nOf40Mf+tD/7Et81ciyjCzLvuzxNE3JdUamEhIk0hJ9FWxk3Qdr8a2NxmypnueVSKxrmEyH7O1foA7n6S2DEl021s5B2nDu5DdyZfcxQt2jqg2j2ae4cvwok3GDtTNqW3I8HtFUklSOSZMC42F4PEXLCS86vc29d56hNjP2jo5ojaa1E6xtwSVsr5xke+0k2kpedvJ13NXfohwN2Zlc4tL0PFM1JZEJCDOnf1u8KBGqRuks2n47jQgtIgSKtECIDIXFythRSlWFr+cfuNZQyuiWqKRmaWWZ5U7KF568yPVJxZ989Ys5czDk8Yu7vOTcSdp6yrkz64yPjpkMZ5y/cRjTco3B+RBVQSGQzIP/hAg460izlCRJyYuM6Ww6TzSOltfWRDXA9tmzXLlwAde2dDpd3LREqZvBh3HGenMkpOdzYp1qvPdUdUmaxgC6pqli8SI1iYY862CqCYc3dhk3LS/+hk3W1zfp9gsGfcGNy5/k6qWMwdpdpMUyTetJbUDqnMl4hMSQZZqJNbhOjzzLceUI5w0uOHpL62RFlyzVaCzdyxdppjBY2aS7soUFnG1p2wbTlKwv96mmE2YmhhFKqUnSjBe9+MXs7V6nnk1pq5okzRmODnjk0x+jrEqWVtbpdvvU9YxZWeFcwJop3kQzqLzTRQlDWvRI0zxeKKQgTSVeZTgfmI6OSPI+SLCmBaUIKmorpBBMR9ejD0Jw8cToDM5YWtuAjEoOJz0ojdB5vGsWkjRxKLWCMW2U7tc1TVvjAmidUR2PSBMVxx1S0Ol28E7ijUEL0MHQTfusrJ9gsnuZ2fGIelqjuzlh/xpaRc+MTGeRTEhNLUt2GFGYFYZmiB0dU4WW1c4yy91t7nuJYmu74ZWv+TZ6axnVZMilxx7jkWu/x+XmCZYuC06s3InPLD1WeNFL38xdqeXkUw/z+1/4Lzw6fpJ8u8NodMBGs8ogLej0MjrGc3Z0ls5gA1cK7i3vJhEa3ddUBxUXp9dQNkf7mOEVRCClSzfEPZdakeg0kohDRuE7LNlVkpEiTDxjPWUSJlSioZtOKExCuNOyvNrhFcev4A0vfiu97VUmk2NOSjAdzerBWR698DD72YRSTEgQJPPwyFQLllJHSAPhYErQNfZOjbzq8NIR1jTjk45mpQajSK9LekXKRr/PWlfTTz2Nm6LEhESmHAjN4XFD01qEd5FPIlS07RCxEyAgFgtzgrtARb6OnNv9z40jQ5DzC7W/5fFx0+cjhDjWHc32mIYJQ+cRKiWRMSNMz5U3/qYjdfhiYSO8QQZPIQRKQiKhj6ARIgb+BcHIBPqT6KPiHNQaukFgEoH2MPKBmYcp0QhOzXk4QghUEvks+y0IG0hywAtkCLQWJsHTaRqa6QEmmVFkkKeSQW85doaDjq7lIhCEjvycm/wSBBJPEGquepo/GqKqLjqEg/AqfieSIvqshMgRK8dwWEMwgRLYWHakORglkFITEstMBPJW4G20y2hw1MpRSMiERiuJ95JEBtABQ+TWNSFER2AXOUBeSbIEemlCXyzTTQdolZA3I3ZGl5mVkQOaEVVr1oFso7zYy2h8NxWe3ChkeG5GKP9TBco73vEO3ve+9/E7v/M7nD59+tbj29vbtG3LcDi8rYuyu7vL9vb2rW0+9rGP3fZ6u7u7t557TjsvE7TQSB+gjQZQOBnNboiGQkGA6uQkXY3oKBphaX0TXWVdyeb2Nhsbd6GSKWsrpwgyZ1h6Zu0qw/ERo+mTHE2uczC+SD/fxoQZthWYUiFDh1ldMXEjppMaLRRnt7e578Xn6C91yewdXLp2mXG5SxCCqnaU9Q5tVnFu407u2Hoxd65u08ymXJ9c4srsAiM1ptPtk6WagEVJjQoKqGNKpYdgDU3b0NQluVBoXYAoQBqskngLykamvncu5i0Ez8rSgKVunyRR3Lh+BYPiC5f2UGmP196zxVu+9RwH+zuMgmCwNgBvefr6mG7R42ASpbBCBPIsxVhHlqZMx2PyPEckRAt2rcFHKbD1PpJ1nYttXmu58OTTKC2xIWCcRadplNTN+37PNHyTKjogZmn0RxaEeb5HzKtRMqCUIEk0gUBSdHj1676ZRz/3ME09i+Nlb5k1FUGknDl1D0EOYiicKHDhAOM8eUfhbAlakaUa01RgW6QM5IkiVZKkyFDKo4Sfc5SitLSux4RpSrc7wLZjpDsiFQ0awemtLpPSMCtLRhOPlnDl8lMY0xC8x2ARWlHXJY9//lPkeUJRFIgiRymHFJ5yOqUc7UfyGmDahlTP5+FUZPkyutsDCpRICc7StiVuzp2QIsF5S6L1PLnXEnw5J3CKKLW3NW1dYtoWY6e0RgA5KstRKtrV3wwd63S6aL0SR04+cHSwy9FRlCFnaUrRyZASlgbr6CCxTU0hFUmWY4VkPJthRCBNM0TbRtIwNpopJtGkyvuA1pKkSLCh4jH/aUaHV0mu16zIHpWccGP6KKKc0llfIakMB+d3SdjGBkjzAXd3voHOLOd4tIs4niHyFH16QGUrUJ7uYIsXn3wArOXq7JjOmTX+RPPtrG+sc715nI1qB3H1iBv+AqU5oGHI7mSCvrPPvj+gridzp815GNzcYTOm93qCjLlBSEGKoQjrZHVCU0051kN200MmTLHac+Zcl/6aJit65DpDqoQrly4wEIbKTakaiw7LNPaAO1ZfxMR+hgZJaDytqQlWc9BepT1s6KsOSVDoieU1Ky9m/aXLdE8MyLoDLs2u8on28xzZMVVtORoHbuxplvMOJ9eW2N5aZSkBocec0IJeR3NjVzKZxkA6IaL5IggQ0YxRyLkS5eZJWUik0oh5gKjU8WLr5ym9QkTKbByDibkXCFyZVExcixASlWtmWtDxglx5NNE00IcAzgKeEFqsa7F+Ru1a7Dzh1wY4ko5cCJSFgY8kzryEKoG8kjQ+FiJOgG8FmEApohmbDIG+EdQIsjaOgioXOJpb03eCZFl5EgmhFUxlIDMeW80oU0E3vUSSvohBuowQzU12dQzh84Fo2gZCWG6agoh5y1jNh10yViZzgm24xUMJgNMCmYHQgWBgPFSoDlDAUhajTTIdO86VdHPzzijTJggKPBQijiRFZLcmSuCTaMIvTMAFSaMDTgQmGlItWFOKRGZ0Oyus5qvRkTct6CrLjXCVg1mMX7FzWpZxnmQumXYCukSPsrblf18HJYTA93//9/Mrv/Ir/PZv/zbnzp277fnXvOY1JEnCBz7wAb7zO78TgMcff5zLly/z4IMPAvDggw/yrne9i729PTY3NwF4//vfz9LSEvfff/9z2R10kKgA0noCdq7l1jihcT5emL0KqG6C6GUxFbs1BAI6E9x1bpu8B5ZDHILrx0P2hwfMqgnCBQ6HY4zxHA5nHE9mNPkQ5RVNqyiSLQ4OrpGkKYqEbtLlxKkOd915B1ub97OxfB+ffOzDeA/TcsrhcJfpbEi/r9FumcJJtjortO2Y/dl1blRXqGRJP1+m110lFZLgHFLmOGeQweFJQM5bcMHRz3tk3TWkWsI0kKQNIUuoqwbrSnxlMXWLt5Y81QzyDCXh2rUrTCrLUr9Pb7DMExevMBkf8KbX3curXnSCoxs3YHzEZFZzYzTDEbDW0u8U1G1LlqUUnZgAbXyg1+3SNg1BRP+M1tQgBG0bjXtaO5cYK00IFkEac3uknBsnxa+msXFUI4RHqEgSUzqde1EIOt3iFtm2KPpzObMjSzPGx7sgAydP3MFr+2sclo79wxHl9IiytGyfeRln7n4J167tEdqW/vIKdWtIg6cqRzgXfRp6S2uYekaeaaQEJT2pVrRNC75kND7gaPcKAUhSwfrKAIvD1AcYU5IkgjzPyUVC04wZ9BSTWUKRKILK2Ts4YH1zg9WVPtevXsGFGK5nbEO0k+mQ50X8LNs2FnjWopMEKTVZKun0exR55IwURU6SL5MVOTrJCd4RbE01O6RpwAaDTgA/JU+6NMYjhUYlCu8NrnU05ZS6rpGyQCc9GjPFWoN1Cp3IeMGdZ4soqSnyHKegbhuyLIY+Nq3BuQrvDINeB9FUBAKqMQSpyVc3yAYrzKZTlI8jOqckQWeMplMynZB1MqTShACJ0izlS9zBXdyv76MI4BvN9tYWU33Ahc88xvh4mTte/GrSbIfLj32CanYXeS9j7/AKk+kYrTIS6xhPj/BHLTL06ISCfLXD1AxJUs2rT/xxOkcPc1RZQnfANM1I3Cai3OPS7mN0/BizNuXQHuDIGM72OOwc0KQGkCihMa6JGV8+jipCcCRzEmlmeyxNt0jGmqqdMiwOuZ7uM/JTjLQorcm6kPY0BIHxlsNkh4sHj1Pc2EcmgnZnzOT4iOHwBjpp6RZrzMa7ZEWCSD1+BnVZIeojEtkydRliXPHKjQd4yYu/kd6pFZoc7jlq4eqv8evNb1C3Nc4GKh8Yu5Lre2NOHg948V1dlrqKMhkj0oosTRmNC6alx87Tm9t5to6cd0kAAtGcTSBBRQdTITxIFTU8kkgKFTeLmXhDIufZXTttoKljwCXU1MkIg6V0PXI9QSMI1iGcid0GC860zKqa46pl0kBlPW0QKB9N3/yc35aYqKYxBXRa0AqklbgsdjeEj5wV5WGmoztqrQJIwYRAayKBuyuhSjwdDbkQ2Czgy2jl3tgAxtPWU5r0SdKN15KLJHZPUPOJtbvVIfEuzLvGsTgJIhDN2SJXRSJvuWoHAQZLIuN9qFGCFSeYtJ7WeUqnGPgAMhpr9vQSPrEUbUXV1ITWzqk/klIEpI1kdWQkL4vkpseNoJaRU2YFgER7T00cR2npSFWCFnPlIbBanEUMElS4wn5boXzsnjkX3YFDCKQiXqttgKPSc67zHK7xz6UgeOihh/jFX/xFfvVXf5V+v3+LMzIYRCvwwWDA937v9/JDP/RDrK6usrS0xPd///fz4IMP8sADDwDwbd/2bdx///1893d/N//4H/9jdnZ2+Dt/5+/w0EMPPesY5w+D8ID1eOHmYwFJ8Da+uSGGWLlEIjJF0NGuOUhP2hWs55rWzpDJGcrKcGnvcY5GFxhVu5TVFNMGdo+OETZlY3WD5aWT4BOaWcA2LU0zpZMtkacFeSa5+85NtjaW6eRbaLHNk5ce5erBBRKRcOPoGpnSnNo6QU/2OLNyjpMbZ2j9kPFsxtQNEbljkPVJRBdlBDJIEp+iZIYxCt1uUogCEzwzN0WJkpBKut0tVCio2hmpUhjr8VZiZy3tdEY1mZARuOfOu9mbTCmrEePK0Ot02ThxEoRgNDzk6cszzl/6bf7YS0/xLa85R17kfPDTj3N5b0hTG7pZitCarZUVjHFsnzjB4dEhuQ9IrchFgXMGpRTOulhNW0fbRklypjWdLI/0V8GtNGStYouztXbuanjTLl/iXFTuQMw66RYF4Eh0h8HyGkXRQcpIuqvGB5RNQ9U2TA20LsFMJswmM3LdIwTB8f4Nrl+5QOtzVlY3KIqEsqlRWiKVQkjB8HifXr8DtBzuHdBf6tDJPMd7TzE83uf4aEzTtBRFj7QIpHKG9AIlNEWnwLt5uzlNSNMcayqMkfS7cDQuEUIyHE2YzWbI4GlmQ1xbk2cp7aTBjARpnmNMwLY3JYuSsp6S6pwkSUmXVuh3Uoa718mzgk5vCaEVQqfRF0F1yQvB7rUreOsi/0MG0jzDzU/04Rkdq1nlEGKA0r25QZ7GugrjG4z1aJUglcSaOFJ01txKt1Y6o7/UhcmMpm0IBDppRodoZw8xjynrdQm+5ezJ01EF5QJKatJuj/HwGNc2IKHT0fEGXSq6cpX725dyZ3sHMsCeucbsoCU7M8BRcfn4EmuHl+mdTEjSQ5586gb9pROsrp3gJQ+8CkLNwdMXeOJTH+fq1QvY6+dxpWVtY51GNwzrPZIkY+D7iKtHPKE/RqccEIZTdiZX2EuHMCzZYBOvE+RGRlpoXCEQdj52tBWtqzCqJEiwMhblSgv6zRrLo22SYc7MT5mujTnsjynbGj+zBOnJMiiKLBJNvUdJGHWv8uiopXu0he0G6mpGs3/IxB2hdM4kq0g7CcrBCbvObjambUu0MZyvrjFxM+7tv4gmTZk2PhLbnWdiD1nPNxH7iqppIIj4/QrR+PHKJctkUrGxlrO61md5qUuWTeh0ZwSbQMiwxmPaHOMU09IwnRic8ahsHlPhY6aLIICXBCtoTaBF4oSIHjgwn+bOrdaBrFQ0jcQrgXCSA1li1JgbQpMnIfJ4HIgmcn9CK9A2UDUwNg5bRw+VRkAbBCWBwgVUCDQimsMWJg6mkpuMj7m5IIlHWUHbBhInKQUUrQDvSb1giEe3IJUgs9CkApkGihBJs2MbKBtBZgQzBUFdoOi/iLuKQeygeBf5O7FvNP8jojxdRHnxPNoZcHgBBHVLZhyIY7JECUgC3SQeR9pIMhtIdKBE0AuCVPfQOkM5SESKSmsyL5mawBiBAaQPdJ1A6ajoFMISHExEwIdYTGopcQTSIMhbgfGCkWxZEy1BNgSRkOgOUsAgSJTMSdUlDuWIchYZRuqmP5YVGBeN9drqpp7pq8NzKlB+7ud+DoBv/uZvvu3x97znPXzP93wPAP/0n/5TpJR853d+521GbTehlOJ973sfb3/723nwwQfpdru87W1v4x/+w3/4XHYFiG6V1ho8HiUk3gWYBy4FSTTpSdK5cZIHGUiLaOcdTMa1GxOuDn8b21bsHR9x5cY1ZrMWkPiQgANNIMsSxtMp03EJLqGpZ3SSHjoRdDuKO+9Y5vTWWdLiNKNJycWnPsJwdIO90QUGvQ2kNhSd6Ja6tXKS01un8GLEYbOD8w1CSjqdDGsEpi6Z1kMIilQVCCHJsz4dMWAg1jB1i20kZWOoKRFM6XqQsxZRxbu4ajhmeLjHbDpicymlt7KFF4LdnRtMq4atrZPoNGFrc52nHn+SRGkqX4KzXLh+zG80DZPSMyxrTm+uUdUth1VLf2mZ5cESZVmRFR063QbbtvgQMGYuN7aWxsa7S+ccxhoEAoelaZrY4hWRGGetnRcGczlx8PPRjrzlLJumGUop8jzjRS86x2xasjwYkBUFPkSvlbYp6fZyWtfSGs9oXCNUToJgdWWb9ZU1ytmYy1euUo6H9JY3kX7Gwf41ZtNhDDoMjmk5oionnDhzjrY84PDaVba272I6u8DR/gXwGp0NEEmf9e2N2KI2lrIagtQomVHXMRm0t3KSRMHx3vmY/ZEZTmyv0a8leweHNGXFoCspMomUgU4KOIG3Bh0SUFGlE6XZHep5tIBWKcdXL1MrjXUGVRwh04RcZpjKYlVBksB0HHOBgrcoB1ZphMrpLZ1mdLRHWbZ42yJCisrWWeqtURQ5ly4+TlNXWBvzp4I0iCRDkCClItF6Pnqbn1wTjZAFppmQKU2Wd0myHK0lhVYIHx0+y/E+eZFwfDBEJZIszVnqd5ECbNWnbh1LSyv0u33yJMcBTniuuWuEiUGPJEfDiwhX8NKlB3jR6dfy+ORTnL/8CBtqhaZQXA9PUgxr+utnCG2Btik6XafYPoE/vsiV4ye5OHqCO4fnSPOUUk3wiaJAcVgf82RzhdVmFWUNZV4TTqZM6jH71YigYSlfx6Ax1hNcizGGypTxOyyi9DYjJ3MdVscn6BwvEUaWsTxmvDyh3SgRqSCvUxAGIS1FR5OmzLNuFEI6QgGPZ0+iL11nddxBGBv9VRJF022Rm5DvauRlw9APMSueelUSSsd4WtENRYxicMcciV36jcZMA5PxAZSGMAVjYmijSuT8+yjwznN81DAeJVzbSdne6nP21DK9jkHgUDcTeE30I2ktlFNiREY3KvyquqWcGqaloWkTKp/SkOLFPCMHjxex0yJC5FQgILEBa2PHoDWeagYmcwTtKYCuEMy8QFpHOxE0E0hqQRui/YiwsUtSSo8y0f3V+vi8FwHVBqSFKgiGIr6mMGEeLyTo+UDjYeY8/SAZ4enYaFfSm6tFaQPGQCNEzM0SkBdQzQLuGA6am8dScaN7kVOnX0EqY3BfnHZEEzbmo0Eh4011HPMIRJB49NyDNzr6GhdIlMe5Mubr5ILUihheq6G0CpcGjA/YEJBWoYUgio49XR2VTQ2R8GptoA1zzl8S5eA6xAKwTWO3pBKQKoEXkVc0Mp68Be1adrmB7AuW0gFFokEJUi3ppV1E9wRYh1JTQo/5+A9MKTicBeoykFbP7Rr/nEc8Xwl5nvOzP/uz/OzP/uwfuM0dd9zBr//6rz+XX/2ssKbFmpgZ4BDRqt1ZUAKVJwiZEonngRAsXgR0pjmcTtgZXeGp3ctc2nmCshri2wG26qLcAGMqfHAUWUGSpFy6fpFcLSNcAl6w3tvixMYKQh9x+sQ9WCYcz44o3Bqfe/JjtDSYpqVpa47HO/SLAQGLNJ4TG5sknZLG1/hQR98EC84LQkioXcm0HaFUh+AmGNGy3juNLxTOQYecNTZJQ85efQ3fNlSVxU1aRFLgsZjgUZ2c1bUum4XnycMRF3dvUFvJqTN3kGQ5o/GI8089xWw2xVgTDamUYrC2ws6kpFNkbBQxeK4MjrzTpSg6TGclWiccHx6xfeo0O9cvkytF7SxOKMbTKa1xcRB5k/4lbhJsfTRtU9EO2hhDluXROTHE4CwVrWwQEhSCTpaRze2XlwZ9BoMVsiyPPhvG3ko6nk7GWAfdpW2K+pjDg+tsbmyyPFhHILj01BM89eQjdDuaM51VJjeuMzq6QTk5jOF8gxXa2QGz410e3/0Czpb0lteZTK9x8eJFqrKm6HZYyxOyfEBIlrDB0V1aohu2GB9ew5o2WkirHJUtoWSgs3QaGzrIekZZB4wtaU0dGfpaUnQyup0uKonhc8HGYrqjElyQ2FCQupysrVDCUhTLDEdj9g8PCGR01RJl3VJO9hBYpjOHkAnBJ3inSZQglYqqcdRmSpAjguixcWKZydENpuMpQilGsyEHwxY/v9AGA1U5QZDgswbygqI7mKeYxtOoSlKk0lgZkMtLNPWUNBWgfTSKSmOIYaoEadZEZcvSCbq9Ps42OFsxHI5ZW90m0Zqi0yNJcpJMMRMVWnlWl3qckGuE0DLea9idHONqxWDtLC8/k6JWBY2ZcTCekRQJnyk/jJgUhC8YVOuoj48xVU1WFMwmJZNmBiPH8jijLBwrnWV0d42lpU3GwTNJDE1WUeQFsgchFahE47XjwB4xMtPI6wqG1luCcGgt0TIjCz361Sq94TIcQ9OUjNIZR/mIUs8orCaREk2Clglee4pMI6WPTqbB4XGokDFrasZhisjXec3SvRTkXDVX2c+ndNuCU5111s70GNYHPFFcY+ZLevUy/aRgXa9SyBVqX3NwtMe0BVlNENYzmo5p6jqOG9RN2e9NYzY3TyX3lKXh8rWG43GH5X5GN9fkWcpStyATEmMtzqeEzGLahuG4ZjoLjKaOWQlNnQLx5iMoUIqY1yUkMZEnqp/cnDQZs40EtY+JuRWG1giyzjz0Lok+OoaEtPVMas9R65FtJLumVmIIpDKOY3Aww1G4GCZYKUHaQCgDbSbpEkicoFWBWkRvl1SCbolElnSuNtKBnpfINlrxlxZm1jNIYhCqVoJeLhin4Cce66AkcPX4KTaXt7mrtxEDrufC42hQF489CIkMfq7ygSAMCEUQ8/GYCAgZxy9TVyKzQJHHEZaTgmMZaFpHESQGaFw8vzQqIIPFuYpjATMlkCEGbadeYlqohSVNVQxLjPdndGxUCSVRqBU73RaSSlI1gUp4dqZ7mLpkY+UMy+kSiVAoEoQUKNUjS1YpCodNWhI8wkXXXuslu2V0JH4ueEFn8TSmpizHBBfZ4s57rHegBZnokqcDtHAEb6I5jRA0reXGwXU+c/Ej7I6exNSCo31NM6vJRC9WfaSkSlCWM1TSIIIm66zQ+oZeN+H09oD773oJ4/KAmbMcTwLD8Q6Hwy+QJAWNK8nzAmEK6nqG8yNW8wH3v+hlFH1DvtTHO8l0doTxnlE1pW1LOsVJZr5k2BySMEShGPTvYGvpblKtMa0jFII05KyOe2TTPjaxDA+PqEWDzjqYukW1U3qN5FSnz8XDGQfDQxyau++5h83NE1y5ehFXzZg2NS4ErLO44Ehkwv7hlONpidIVG6sr1KYkLzrR26TIkUpRzqZMp1PGo2Om0wmryytA9AwwTTuPWndIIaKD7Hxso9T8DpzIZpdS4JzFBubeBjYWIyK2YKWQ5EVKp5sD8Q5NqoBOCnAaKSRNU6ETxYnT59g/PMTYFucNwdVUkwnCQpp3qMohmfRImXG4d52qHJNrQdbr0jZjBv0e2xuvpjx8mp0LD3M0cpTjEc4GJDlr6xsUaYIW0YdmeLhLmnWxrWVpZY3Vzbs4uvEYdVXileVg9ypFp4PwgiTrgcyomhHldIwikOUdep2UXjcDpZAqJVEenQm42akQGY01NLUn1RnBear6mEnVYJDovMepe1+D9jOe+PTv0VRTvNUgLEVXEpzEOk1I+qRFSiY9phzjRX/OCYDpbEaQKVU1xjQVSkfSnpQKqRSmMWitsG1NGTw6yUiSJMqfVU6/v0StAkUiaCoJ3uDsFJl18WmG7kiKXJGnmiztoguN0HHQ7Tzk+RKDpVjoKKlIsg5BOqQo2fTr3GvvRUw7rJ5dZ7W3wYWnH+Hp8x9muT3B3Xe+npUTy1S7u/iQMtkdEpzhU8mHqK/uc1bdwaDf5+Rgm+V+h5ff+VKSPOfa0SWuXTtPMxtTjsbcc+qVfMsDfxLX1FzYfZTH6s+y5/c47k2o18FWNcl1hRzn2NzgUhPN2WRAKk0RlliuN+mNVxCHgaaqmImSYVFymJQ0oobW4Uc+5nsZG7meLsUmEqxCJGrexbDYmaOZgMXiOo6N3h10wgA7c1T+MoXtct/qa+hsLXPj6EkOZy3eS/o2o04EVWjYt3uEEo4uH7LW3WKJHqbQfHb6BUoxi+Zj0seLpwzxeOaj1SA9UgrwgfG4YjSuAE+SKvKiQ57kMe05BHxwWNvSNJ7WBJyNnzsXGcNz7tzc52T+nZY357xhrsyZe2koPJkP1CqgaigkiCCZaoHLPUtAkUhcV6Ibi2jmkmICZp40HGQgMTGFOrVQSxA2GobVQlDNYlBgVcCkGz1RxiHQ9YI2BFJiP6MgkHYCXQk2E3gRCHYuK2+gyWLAoA6QJpKiCIxSQSgFtobptOTx0SP0igfYFtmc8KrnEy17c7IVPVCEm5vKxYJOzPkniJhxVDaH7DTH+CyasAoNSSboeUFmA/VcHlwax8hUCBHDV2sHQwd2Hr0QAiRO4E1gLARJG5CpB6eoBLQ63hxaHdBCoonBjKIWTMqAqQR55jhqxkzLC6ytLLPd26CQAySaoCt0qunoPklRon1FW0WVlMoh6BA/98+hRnlBFyh1NWUs46jAz83D3Hy009EWm0pSHdDSo7TCVJb98Q4XLj3FeGKop32OD6eURznCS3Svi7EzPIagLVmakagOuITZbILAsboE66sjKneESAvq4ZQnLj2BCwEtE6TStFVN2xqm5RH9Tp/t1XOc2z7H1tYaVjxNbdbxLiOIDjrLcBxStkc0BqqyQpiSPMsZFHeSZ6uMjo/I8HT0Mp10iSXdx4SA1hqrLC4XGLNH7aYMZ0Nm9RF2esRRW3J1b5fjyrO6scHm5hY7Vy9xcP0aWkZSqnVmnv0QX4+kICugbWsmdUOqM5YHAybTKZ1Ol93xLqPxhMFSn3I2mQeEzeO79c156c00UxktpYMnSTRax0Ax76NFvkBgjMH4gLMWKUDKFCkkrbUkSpEmCUrErAilYhqnDxaBvGWTH0PbJFXdUpUjcu05c+Y0TVmxub3CiRNn0H7E7x5d4fBgl05bsbJ6ks3tM6Q6YX/3POtbJ+l1OjTpjPa4x7QyiFDQXzrN0qrGNLPYcm0rnKlJs3502VRgmoakUCjRIoWLQVt1iRCaTqfHUnfA5HgX4Rty5SFTrK4t08tbUhVAa7JEsrrcZVY2+KAIQiJ0QIeG3tYA2xj2b5RUdUVwMaBxUu3x4d/6rwx6BfV4Gr0qZLw787OKVCl0IkgyzdbJs2hp2LvxNNOjS1TlEBEcWiuWt86xv3Oeti5xxhKCw85dfz2W1lpy7alnDUrWyE6OS1Okb5iZWex6eYNA4nwcFSgp6fVyBAZrHCJfJ++fQmqFUJq0k5NkfUTwKCVQKgPX4ENsTgcCIzflE8NPs7a3gsxfQegHku1VptNPcv1TF6Hucq54CaEcI2tHhx73uHN84ehpslM9HrzvLXRUh/H+HpMbI06/4j6kM9w9PWLv3AWOdi9RDiecHryOVJ6kKsYkgzs4EQzppEtpnuIo3afWJdlSxuCwoF+vMEz3o4w6dFkpt+lP1pEjhSkrZn7KKJlymM0oRYUNc08g42mmDtc6grGAQKeSJo1SzxBaECnCa+qRJ5QB4QSTMOEz1adZq9cYuyNsz0AA02YYlVJ6TeUdme8RUo+2ksa33LDXUT5nxfQQjWTIiKPBhC/wKCGJBpVCxhR4pEQo5nfB0c89umzP40Ki4wkBibWWmhohksihiA0YlJZkMiGkKc45nPXx+xFiRyYm9oq57UnkVkhx04hRILJAQ0AHgdfQaQWtCwTpqWxABoFLBHlQ0JUkztKv4bCZO4frAE7E8D8ZFX9irsqRIRYWo/nFViuBCHFUM0gCy14y8Z7g4s+6JFBo0LlgIAWl9zRK0K88rfcYJE2Io6BECKTypH3QY4GpQIsQi4BylwvlJ+l0X0lfLCGDih0RF8mpyJhwzjyQ0hMLxPgneqCE4Lkx3uHAt2gCXQSZBK+gIHZSWh/jAFoLO41lRhzF6VmUVidCYGWMMmjjPQTeR0O7FkUaAtbGkZiTIZqXAniBcYKR96RTwWziqBPBkpWM6pIJDdIH1jIXE5Odw2pPojrkHUPmQKiaKrg4ZgsB757bNf4FXaBMpkPw0QfA2HhXE5KEoDVNsBgBHS3IU4k0KZNmxM7BdaZ1ycWLV0nIKNQqxWrC/uE1pvUR/W6OljlWWGyVEGRCkWdkOWxvJpw9uYrOJN3eCXb2Rzx1+TzTyQxEXARrHZnuMiuPWS5WWMp7nFm+g62NJRqeADejnLUEFHm2SaI3EfoqghRpWgZJQZatkao+mVollJrda1fZ7Gww6CcUOqeX59RNg58Z2sNjEmpCW3J9coGqHbNW5Jit03zkUw9zZb/ixJk7uP/+V3Dl0nnGk2GUHRNbp0oInI9dgUxJpHDc8+IX88gjn2RlZY3jg0P293dZ6vcIc6t1KQJFnnP56hWWOh1s29K2JrrA3hwDiihF1ELglYp26nPpsVQSrfTc7MtjTSySdJLEBGAl8V7cGvMIH3kdWndQKsUHg9bJPE05ul3qNKPo9Ch6Cd4ZZk3FmTN3RAMoV7J+YpvVrVMcj2YkUjNY2+blr3o9mYbf++Ae5bRkZWmJJgSsWkJ3NGm2wsaJOylnQ2rlWV7Z5PDGZcpqQr7cw9noI+LsjNHxjPGwovEamRZInRFkjs6WyDsF0hvaakI9m9ELLSuDnCLrUVZHpElGwDOeGZq6IQSJ9wFnK4o8xbSO2WxIU02py4ayNMymLdYFxjtXcN2MLMlJ0hwhFEJGmaFQKQhL284wZkLe7cexjLCIMGMwOElbX2Xv+mPMprM5wdEjJORpgbcBpeIotfYKJTQq9SRi3t7GkwSHmKfUWhcdR1WSojToRKLpIIJB6YQgFTrrkiRzuXxwuLZGK9CJxlRlNPsSCuc9x2KPo94+g7pLu29YO+zixlPWe3dyNH2Ez974LaajHdJc492U0uyxnC5xd3sn4iinnEpcVnM8OWZ4vI/eW6ObBdpyiG1LmjBjpgNXmmuMr9Z4HGOmdO0SwW+yfVyyJ/awskF0PXZqyaZ9lruSbtmnNxkgJwJjWqahZKxnHKczJtS0GEKIMs/gPc4aTNPiSotvo2lkyBRN4SKR2SfR+dQbykrgmzgOaPOGpzY+x+UmJ7fRVqGxho9OPkher7CvrnA0uM6J4QYuCYzTCh1AOkWwnqoeczTco2pmrC2v8k1rdzNcnvKYus5YRrKzUAKlVeSsysgRE3OVh7p55y30vMsV/ZRi9GQsMG76miQqFtaJj/4fwbm5MWZMiolNlcjBkEhCzF4AAst9Qa0kpQn0g8DKgHMQfCA1kDhNlkbb+a6TlL0EWktSOryJr2GkjwWGZy6SiI7b0kEiQDQxb+dYRB5G14LtgdegjKBxoF2I/kGpoEgkbe5IfMBlkf+xbCQOWFKCngykIlB5wUAGJhkMk0BwAWEEZhoYVle5lGvuUq+iI7uR9E6II//AF63rhEKJGD9xE15IGmu4dHidJkDXChInSQi0DhTQyJjenFtBa6CDpPEO7QQTE9/DNJEs+5iVE0Q0XmvbQDUDXKDOBKWPbq9BR8daH6IjuzaR62Nrj5sFUFC7wMhDoR1Hbh9THKNSTZIIrNZkfh4RoWNIaVCRmBtcwH49jXgms0NcqzDGRAMcGXNBSDJMCPg0xec5NRanApPqmMnsmKPhAXXZkHaXmEyOwStWlrfxssS2LcEXJNqR54pu1mVrvc/qWsbp7dPMmmOCyHn84he4tHuVujUUnWjIVLctWiasDdZIZYptDWc2TrKyNsOL6xRpgpzP/Kpqxmh2CWuPaJsRyjc4UwErJCFHskaRb5LrZba2T3Nm606WswFJK8lHjrSxVMcV4WCGnx1j3REzM6Y0R2SdlItXd7iwe8xdL76fIAWf/+wjHO7vYV2LVlE+2pg23il5T6I0eZrS7w4YHw8p0pzh0SE61VhnWV5Z5ej4iLIuKbKMajoGHxOTVaJp52sQ5oF/hBieFouN2FWxPp5wOkmOnvskWOcgBBKtUUqilUIpiTIgiRfHQCduaw2EQJp3YkhhcAgpcN7SNjPq2ZTKBfKsi7cSYxp0klJWNSIdsHnmpQTdo8hT8k7O5z//MLPJiPHomMHAc7B7iYtPPslwZPAiJUlzpA4UhaLTW+HUubsJvkaPU4JwSCVoqpK2LikyjUpXUT4h6JQszRE6pTWWWdWgVEF35RRbqktb7tPr9hmsbHPh6RGmiTJsrTRJ0qOpa9pyBsFgpUBoTTMrmY1HHE88Kltm+9RppuMxk/ERSkKWdOKsXqoo7VQanWcomaK0ZDaraOsZ1eQYjQEUwVfkSU09K/E23JoPa6HRiSbRipAJGu9i/oqANM1Jsg5KzHkESiKUJNE5WicoWSEUFFlOorKYQk1K01RkxkQ9p4vrKAWgJcE7yvERpilRSYLQMT6hnM4geKbZGDP7bb6Zb+Eb7nsDXlnWTm1yeP0aK2mHJJMcHg4RSUEjSxo34/z+I3zuY5usLq8zLfcYHewze7yic3IFaJmVxwzrfWbNmLayVHZE051ybEZkPsGODhC+ZLtYw3YcKZ0YezAJbHZOEvY8pq2oRMs0KzlOxkzELHZ/QiA4h3cQrIuScWNxrcU3Ht/MfVitpy5aXOUJOiBFivWOsmxxzqFSgdQCP/AYVUfvGt/gq4pL5W7kNaSS1GhG+1OyVtFqhw0CVEPTztg7rCgs9HTKpupyb//VrG2eZqezy0emH+fTzVM0Kn6WpVTzoL+AmluuxwDB6CIrlYgFzc3xRIhXMYG69djNz4QkQeoEgscGHX1MfBzTegchiLlZWRz1DIqCRiT4iaExAa1geR4/qNuA8BYpo1WEx5J5wSCXzHqOmQl0nCATkDtBIyIZFjwKgQnR86TRgkp4+lbQaUEoGPqoxpFAGzxGRCf/RINIA6oIZB50G6h0IHXQayQDEegnkIRANlfzLCeCYRZwtcJqgQmOWeWZphfY6bVs6lfRoYuQEuUkXtq5NFsAGoGOdNnohofDcungafbLEToN1IC20XEW75kJibDQcZHjkrQCK/y88wJNzCOk1YGekGgZOTUiC4hW4po4aYNAm4FV0fG5FwQzAsEIdBPwLbgWRANOCWoNYhxl2NMQb0q6WYvoSiBBqEAbVOwE+QDe3yp8k+fm0/bCLlDqukJ4GQPZnIuJokKi8w4kOdpWqGYKXtDQMG0mVKZFi5w7Tr6Y4GFzdZ3GBGblEcNRCVZFZn2m6GQFZ06s8KI7X4yjpXUSEza4cPkCVw+uzf05GuqmRCWCVKeMp0dMZ1NymXF6vcPpM56sMyTPU7KkoHXgXYr1mrI6ItgZqSjReYJpc/LmFGvpnSzRoWgzBp0Bd5y5j17o4jCQOuTkALM7Rk0rRGtwdYM2juUksNKRHNRTnt4fsbp9mpW1VfZ3bnB8vB87EkKSpglKCpANKig6XY33geWVFZCS48M90iRyEaxp2djcZjqbcXx8hGnbSJ6aO5sWnYK6iQoTNS865gJ/vPe0LnofmPkYScnI3nfzCPfgw7woUaQ6oZvn0UPBeLSQ+Lqhns2AgG9rvJsS1DoyXaVtDYeH16hqQ7czoL+yTnNwiAuB2XTC+YuPcfeLv4Gi6NPWLcPhiM1Tp+n3Cq498TmuX79CcBX9Tko5nHK84yCkLA/WSLMelkA9uoqiAVmwc/lpsrRAM4kOrc4SXINOc0TSwScG4fQ8JE0DirqxtMbFE54uKFY7DNa2GO08wZNPfDq2w6nIU02YS4KLTJKoPqY5wrkW20om45rjkWVSB9Z6Bb3lZVrncaMRde3ICoOXEp3Ek5fwcX3SVEdPiIkh2AZTzWJ7XTomk12kkvR6BdbWVMJHKacI0XbeOby1KBUNoFItKYoMlaZIom2+BwgSrTPE3K6zrad45+dmcZFfY2zN8PgGwbfkWSf6Q7iG4Cxpd4W008MZB0LhAzSVYdxUvLi4i1eufQP9ImN5chd6aR0pWwbTDYoTm9zz8tehpOXg6kXOX3+Ma5PzeC8pR1cZl5dZyR22OWA43mFndI3+eJ18OadyM6btkK5R3Dj+Av6oR7Lc4cDt0sqWpPVILZBVnzvbLRKvme0fM6yPmHUyQmoZJRNGyYSJmGCDwft4CypCJCXG0WksTFxj8E3At4HQxhGWsIHQWCSGEARS5VjnmcwaggCtBFLF0WaSJvOgXIeXHhcspalIpSakCXWqOM1JdHBUqqXteVzZsnKsOTHYoJt0ecnWKzhz4iUsnzjN3Wdez/3jP85/ePr/y+83H4vjHMGti1uMIwaFig9KMe/MxSJlbilLtF2TBOEJuNhdCfE7Pm+BoonOrH4uQbbORcVliLEZogWdK6bKM3UBVcULrq4DPog4fvKSQIvRghqF8oFu4glLgmwGoY3jAzmPelEIKiDxAZuATGGQCqo0kCtBJwGpAzlQ+MhVyRQYFzN6JCB8QPtALmPnppWBvhDkiSdHkKeCQhf4LIEwoy4NK0ajij79PKVIGypbUpU1e+lVrJ6yKV9Bz5+YG7elCGwM7gzE3yqjskbiOKiu88TkCRoCNoBuJU4LJtIjBfEmQUJqo6t3qkLMPBISPU9+FgZSKRBpQCpINSSJZCJDzNOysWNd6EjarVWMADBzL5ncQFIKmopbFgraR0v7ZhY4VoLlMHfXTTxaN6AUneCxLmCDw7pYVGVFQkiyW8XsV4MXdIFStgZjiQoRF703hE7JEVS2wk+PaGRDaWpqZ2iDYWZKDg720FqAtKws3cFofBHhW9Z726RKgDQs9S2bazlnt++hk57k2v55RpMxe8e7NC2sZlsMxwdU1pKKHsIovPVkoSATmju2NrjjrhwYYxqQPqE0gdoIrJ1hGsB15qTLLrgBXb1CN92kqwf00g4JAhcc06NjyvqYoD0eB9Njgi2pQ0XpZ1ShZOqnNDjSfo/PP72HXtrkjhfdw96Na0wnxwwGg1jQiZwkz/DesiwkLgTuv+s001lNMdigNS02WKbjIVIIer0+Wgm0CKRKIueurWmqEUKwsbbKwcEhet796IVOPFEz90SYx49HT6JAmqRkWYZK1NzjQ82dSj29vKDf6SJDNELCu3lWTOS4VLMZqZtQlkekgztp25a965e5fPUi9730DVRlhWtbbNPgTMvBzi5KPU5vaRnbeqyZ4ZvA4fgK4+PrZNojFLSmod8/RXe1h21K6tkR3lmKvEczNcwmY7yomFYVUmjqusEFGQsUIbFO0kwt3seE39iyvXmBcvH/JHGURUAnAqlTEiVI0gylMnrdgrYc4lyDkCHmMCWK0XjGtJzig0Tnfboy0NQlOztXqeuGJEvI8w6oGChpXB05PjIgsUgK8I5qVsX3X0iStEPS6SAwBO8wtiUrJGkW5a7OOZwRZInCZDZKaKWkSBRZ1kEken6BihwgLYlKF9cQQosjjqi8izJRkWo6WZfpZMp0ckRrLGneQQsdAwvFiDTvEHQSx1JYfBUYmBW+denb2U5P4AYte1zjC9c9SZYwLvcwtUNeO09Pp0ybiokwyE7G1mybOzbv5hWv/5Mkyxmj4TXKT/0OH//k71Odf5Izq6dYW9kgTZaoVMmO2GMkLtEfL6GEZCVfwnQl01ATnCfxaTRdKiRpJ6VMZoRuYOKnGFeTeIV2ki/6bsWLe5ABK6EVEi8VXjF37IxtfpnAUq+LTrNommUTVCtQNtDJJaSQI+k4hbIJnuhgjAwxJ8wobNsgpcT3obSGkCe0PYNNAvogZaXq00t6ZNkyarCMXOoiezluSdNf3uS14z/BE9eeZsYsBroRAzuFYN41k1+0XRcK9YyrS+w7EOWz8z6AI96QRI5n9PuQ889PkD6Oe4KLIXbeI3yU2CoKevQwymEygVcBLwOZkyjlbxHn+z7+ZhskIvGc6QrGawE1FrRNQHnBLASChq6BuhMYJIK8K1hLBGiwOhYnXgE6RN8sF4mwBYGEyFtpAOk85Zzz0Q1ExZZS5Ci0COhQEIKjSCSDgWWmM3pyg5UkJc8SrCvZddewpcFLRyuvUtGjEP15/yjM/Z9A4Jk3HJiFikvVJbIiYVPGEFOtJDoIeiGa0JkUagIqE/S0JIunHpo54XlAoFKQAx0p6CAQOlDl0OkEXC3wKhab0gdaIciEILegPQRDHM0pB0kg6YMqoMjmyisFWauQWRwHpTVkMsTCOgnQBoIJJEiyVLC+nJHqNW6a9H01EOGr0Q4/zzAajVheXub7/99vJUuSL3lW3Hb8N9nLz0SY21RFO5mbaZq3fvq2n4V5SR6e6fwXtwxzid4zKNnP+PU3W6HP/OXiS/7/Jfv9jL/Fsz536wC++DpzCdvN44KbeQ9iTlCd36094zi/7Ji/tKR9xms+cyYanmXf4++4uT9/2PF92U8+25F98YEvedtu+qI88/2/uU9RJSRv7vgX93fusXD7i8Z9/NKPvXjmGs6PVNzc9raffZZD/dJ/fzW47bPEs39Qn7nFM/brS3+p+JJfevPz/YfdqjzzmWddsa/0Mf3DcNv34Jk/9Iw36Su8TvxuCZSQz3jb42PPfK0/6PN58wIZT/7zsUvw86+ruPX+hLkV+TNW9/aD/JI3Koiv8vMdvvSfz/5z4uZ54hmHddvHQHzJ38/y+rc2nR/EMz8zt85FEMmoiPnf3HpvXHiOzMX/xbj5PfuyM8yzvQ/Phi87v/9Bv+cPf62bX0nxB2zzbD/2pWe8Z/tGfvFc+syf+kpfpDD/vP8Bv/SZv/APeqkvef9u++Z8pdf9km2etUp4lq/xH3TK+eLPC1pj+Bf/9y8wHA4ZDAbP/gM3t34hFijnz5/n7rvv/lrvxgILLLDAAgss8D+BK1eu3Jbl92x4QY54VldXAbh8+fJXrMAWeP5gPB5z5swZrly5wtLS0td6dxb4KrBYsxcmFuv2wsPXy5qFEJhMJpw8efIrbvuCLFBu5rMMBoP/oxfy/1QsLS0t1u0FhsWavTCxWLcXHr4e1uyrbSzI/837scACCyywwAILLPCcsShQFlhggQUWWGCB5x1ekAVKlmX8vb/398iy7Gu9Kws8ByzW7YWHxZq9MLFYtxceFmv25XhBqngWWGCBBRZYYIH/s/GC7KAssMACCyywwAL/Z2NRoCywwAILLLDAAs87LAqUBRZYYIEFFljgeYdFgbLAAgsssMACCzzvsChQFlhggQUWWGCB5x1ekAXKz/7sz3LnnXeS5zmvf/3r+djHPva13qWvW7z73e/mG7/xG+n3+2xubvLn/tyf4/HHH79tm7queeihh1hbW6PX6/Gd3/md7O7u3rbN5cuXectb3kKn02Fzc5Mf+ZEfwVr7R3koX7f4mZ/5GYQQvPOd77z12GLNnp+4du0af+kv/SXW1tYoioKXv/zlfOITn7j1fAiBv/t3/y4nTpygKAre9KY38eSTT972GkdHR7z1rW9laWmJ5eVlvvd7v5fpdPpHfShfF3DO8ZM/+ZOcO3eOoii4++67+amf+qnbwkoXa/aHILzA8N73vjekaRr+3b/7d+Hzn/98+Gt/7a+F5eXlsLu7+7Xeta9LvPnNbw7vec97wuc+97nwyCOPhD/9p/90OHv2bJhOp7e2+b7v+75w5syZ8IEPfCB84hOfCA888EB4wxvecOt5a2142cteFt70pjeFT33qU+HXf/3Xw/r6evjxH//xr8UhfV3hYx/7WLjzzjvDK17xivADP/ADtx5frNnzD0dHR+GOO+4I3/M93xM++tGPhvPnz4f/9t/+W3jqqadubfMzP/MzYTAYhP/0n/5T+PSnPx3+zJ/5M+HcuXOhqqpb2/ypP/Wnwjd8wzeEj3zkI+F3f/d3w4te9KLwXd/1XV+LQ/o/Hu9617vC2tpaeN/73hcuXLgQfumXfin0er3wz/7ZP7u1zWLN/mC84AqU173udeGhhx669X/nXDh58mR497vf/TXcqwVuYm9vLwDhgx/8YAghhOFwGJIkCb/0S790a5vHHnssAOHDH/5wCCGEX//1Xw9SyrCzs3Nrm5/7uZ8LS0tLoWmaP9oD+DrCZDIJ99xzT3j/+98fvumbvulWgbJYs+cnfvRHfzT8sT/2x/7A5733YXt7O/yTf/JPbj02HA5DlmXhP/yH/xBCCOHRRx8NQPj4xz9+a5v/+l//axBChGvXrv3v2/mvU7zlLW8Jf/Wv/tXbHvuO7/iO8Na3vjWEsFizr4QX1IinbVsefvhh3vSmN916TErJm970Jj784Q9/DfdsgZsYjUbAFxOnH374YYwxt63Zvffey9mzZ2+t2Yc//GFe/vKXs7W1dWubN7/5zYzHYz7/+c//Ee791xceeugh3vKWt9y2NrBYs+crfu3Xfo3Xvva1/IW/8BfY3NzkVa96Ff/m3/ybW89fuHCBnZ2d29ZtMBjw+te//rZ1W15e5rWvfe2tbd70pjchpeSjH/3oH93BfJ3gDW94Ax/4wAd44oknAPj0pz/Nhz70Ib79278dWKzZV8ILKs344OAA59xtJ0WAra0tvvCFL3yN9mqBm/De8853vpM3vvGNvOxlLwNgZ2eHNE1ZXl6+bdutrS12dnZubfNsa3rzuQX+1+O9730vn/zkJ/n4xz/+Zc8t1uz5ifPnz/NzP/dz/NAP/RA/8RM/wcc//nH+5t/8m6Rpytve9rZb7/uzrcsz121zc/O257XWrK6uLtbtfwN+7Md+jPF4zL333otSCucc73rXu3jrW98KsFizr4AXVIGywPMbDz30EJ/73Of40Ic+9LXelQX+EFy5coUf+IEf4P3vfz95nn+td2eBrxLee1772tfy0z/90wC86lWv4nOf+xz/6l/9K972trd9jfdugWfDf/yP/5Ff+IVf4Bd/8Rd56UtfyiOPPMI73/lOTp48uVizrwIvqBHP+vo6SqkvUxPs7u6yvb39NdqrBQDe8Y538L73vY/f+q3f4vTp07ce397epm1bhsPhbds/c822t7efdU1vPrfA/1o8/PDD7O3t8epXvxqtNVprPvjBD/LP//k/R2vN1tbWYs2ehzhx4gT333//bY/dd999XL58Gfji+/6HnR+3t7fZ29u77XlrLUdHR4t1+9+AH/mRH+HHfuzH+It/8S/y8pe/nO/+7u/mB3/wB3n3u98NLNbsK+EFVaCkacprXvMaPvCBD9x6zHvPBz7wAR588MGv4Z59/SKEwDve8Q5+5Vd+hd/8zd/k3Llztz3/mte8hiRJbluzxx9/nMuXL99aswcffJDPfvazt30J3//+97O0tPRlJ+QF/p/jW7/1W/nsZz/LI488cuvPa1/7Wt761rfe+vdizZ5/eOMb3/hlEv4nnniCO+64A4Bz586xvb1927qNx2M++tGP3rZuw+GQhx9++NY2v/mbv4n3nte//vV/BEfx9YWyLJHy9susUgrvPbBYs6+IrzVL97nive99b8iyLPz8z/98ePTRR8Nf/+t/PSwvL9+mJljgjw5vf/vbw2AwCL/9278dbty4cetPWZa3tvm+7/u+cPbs2fCbv/mb4ROf+ER48MEHw4MPPnjr+ZuS1W/7tm8LjzzySPiN3/iNsLGxsZCs/hHimSqeEBZr9nzExz72saC1Du9617vCk08+GX7hF34hdDqd8O///b+/tc3P/MzPhOXl5fCrv/qr4TOf+Uz4s3/2zz6rZPVVr3pV+OhHPxo+9KEPhXvuuefrQrL6tcDb3va2cOrUqVsy41/+5V8O6+vr4W/9rb91a5vFmv3BeMEVKCGE8C/+xb8IZ8+eDWmahte97nXhIx/5yNd6l75uATzrn/e85z23tqmqKvyNv/E3wsrKSuh0OuHP//k/H27cuHHb61y8eDF8+7d/eyiKIqyvr4cf/uEfDsaYP+Kj+frFlxYoizV7fuI//+f/HF72speFLMvCvffeG/71v/7Xtz3vvQ8/+ZM/Gba2tkKWZeFbv/Vbw+OPP37bNoeHh+G7vuu7Qq/XC0tLS+Gv/JW/EiaTyR/lYXzdYDwehx/4gR8IZ8+eDXmeh7vuuiv87b/9t2+T4i/W7A+GCOEZlnYLLLDAAgsssMACzwO8oDgoCyywwAILLLDA1wcWBcoCCyywwAILLPC8w6JAWWCBBRZYYIEFnndYFCgLLLDAAgsssMDzDosCZYEFFlhggQUWeN5hUaAssMACCyywwALPOywKlAUWWGCBBRZY4HmHRYGywAILLLDAAgs877AoUBZYYIEFFlhggecdFgXKAgsssMACCyzwvMOiQFlggQUWWGCBBZ53+P8DHXQCASPCoP8AAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Get a batch of training data\n",
        "inputs, classes = next(iter(dataloaders[\"validation\"]))\n",
        "\n",
        "# Make a grid from batch\n",
        "out = torchvision.utils.make_grid(inputs)\n",
        "\n",
        "imshow(out, title=[class_names[x] for x in classes])\n",
        "\n",
        "dataloaders = {\n",
        "    x: torch.utils.data.DataLoader(image_datasets[x], batch_size=batch_size, shuffle=True)\n",
        "    for x in [\"train\", \"validation\"]\n",
        "}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_ULbO8f28PAU"
      },
      "source": [
        "Variational quantum circuit\n",
        "===========================\n",
        "\n",
        "We first define some quantum layers that will compose the quantum\n",
        "circuit.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 233,
      "metadata": {
        "id": "6gMomjvL8PAV"
      },
      "outputs": [],
      "source": [
        "def H_layer(nqubits):\n",
        "    \"\"\"Layer of single-qubit Hadamard gates.\n",
        "    \"\"\"\n",
        "    for idx in range(nqubits):\n",
        "        qml.Hadamard(wires=idx)\n",
        "\n",
        "\n",
        "def RY_layer(w):\n",
        "    \"\"\"Layer of parametrized qubit rotations around the y axis.\n",
        "    \"\"\"\n",
        "    for idx, element in enumerate(w):\n",
        "        qml.RY(element, wires=idx)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0iroynmF8PAV"
      },
      "source": [
        "Now we define the quantum circuit through the PennyLane\n",
        "[qnode]{.title-ref} decorator .\n",
        "\n",
        "The structure is that of a typical variational quantum circuit:\n",
        "\n",
        "-   **Embedding layer:** All qubits are first initialized in a balanced\n",
        "    superposition of *up* and *down* states, then they are rotated\n",
        "    according to the input parameters (local embedding).\n",
        "-   **Variational layers:** A sequence of trainable rotation layers and\n",
        "    constant entangling layers is applied.\n",
        "-   **Measurement layer:** For each qubit, the local expectation value\n",
        "    of the $Z$ operator is measured. This produces a classical output\n",
        "    vector, suitable for additional post-processing.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 234,
      "metadata": {
        "id": "ONyq04RY8PAV"
      },
      "outputs": [],
      "source": [
        "@qml.qnode(dev, interface=\"torch\")\n",
        "def quantum_net(q_input_features, q_weights_flat):\n",
        "    \"\"\"\n",
        "    The variational quantum circuit.\n",
        "    \"\"\"\n",
        "\n",
        "    # Reshape weights\n",
        "    q_weights = q_weights_flat.reshape(q_depth, n_qubits)\n",
        "\n",
        "    # Start from state |+> , unbiased w.r.t. |0> and |1>\n",
        "    H_layer(n_qubits)\n",
        "\n",
        "    # Embed features in the quantum node\n",
        "    RY_layer(q_input_features)\n",
        "\n",
        "    # Sequence of trainable variational layers\n",
        "    for k in range(q_depth):\n",
        "        RY_layer(q_weights[k])\n",
        "\n",
        "    # Expectation values in the Z basis\n",
        "    exp_vals = [qml.expval(qml.PauliZ(position)) for position in range(n_qubits)]\n",
        "    return tuple(exp_vals)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4eG97j4f8PAV"
      },
      "source": [
        "Dressed quantum circuit\n",
        "=======================\n",
        "\n",
        "We can now define a custom `torch.nn.Module` representing a *dressed*\n",
        "quantum circuit.\n",
        "\n",
        "This is a concatenation of:\n",
        "\n",
        "-   A classical pre-processing layer (`nn.Linear`).\n",
        "-   A classical activation function (`torch.tanh`).\n",
        "-   A constant `np.pi/2.0` scaling.\n",
        "-   The previously defined quantum circuit (`quantum_net`).\n",
        "-   A classical post-processing layer (`nn.Linear`).\n",
        "\n",
        "The input of the module is a batch of vectors with 512 real parameters\n",
        "(features) and the output is a batch of vectors with two real outputs\n",
        "(associated with the two classes of images: *ants* and *bees*).\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 235,
      "metadata": {
        "id": "hIljGdv_8PAW"
      },
      "outputs": [],
      "source": [
        "class DressedQuantumNet(nn.Module):\n",
        "    \"\"\"\n",
        "    Torch module implementing the *dressed* quantum net.\n",
        "    \"\"\"\n",
        "\n",
        "    def __init__(self):\n",
        "        \"\"\"\n",
        "        Definition of the *dressed* layout.\n",
        "        \"\"\"\n",
        "\n",
        "        super().__init__()\n",
        "        self.pre_net = nn.Linear(2048, n_qubits)\n",
        "        self.q_params = nn.Parameter(q_delta * torch.randn(q_depth * n_qubits))\n",
        "        self.post_net = nn.Linear(n_qubits, 2)\n",
        "\n",
        "    def forward(self, input_features):\n",
        "        \"\"\"\n",
        "        Defining how tensors are supposed to move through the *dressed* quantum\n",
        "        net.\n",
        "        \"\"\"\n",
        "\n",
        "        # obtain the input features for the quantum circuit\n",
        "        # by reducing the feature dimension from 512 to 4\n",
        "        pre_out = self.pre_net(input_features)\n",
        "        q_in = torch.tanh(pre_out) * np.pi / 2.0\n",
        "\n",
        "        # Apply the quantum circuit to each element of the batch and append to q_out\n",
        "        q_out = torch.Tensor(0, n_qubits)\n",
        "        q_out = q_out.to(device)\n",
        "        for elem in q_in:\n",
        "            q_out_elem = torch.hstack(quantum_net(elem, self.q_params)).float().unsqueeze(0)\n",
        "            q_out = torch.cat((q_out, q_out_elem))\n",
        "\n",
        "        # return the two-dimensional prediction from the postprocessing layer\n",
        "        return self.post_net(q_out)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E8-EDnhn8PAW"
      },
      "source": [
        "Hybrid classical-quantum model\n",
        "==============================\n",
        "\n",
        "We are finally ready to build our full hybrid classical-quantum network.\n",
        "We follow the *transfer learning* approach:\n",
        "\n",
        "1.  First load the classical pre-trained network *ResNet18* from the\n",
        "    `torchvision.models` zoo.\n",
        "2.  Freeze all the weights since they should not be trained.\n",
        "3.  Replace the last fully connected layer with our trainable dressed\n",
        "    quantum circuit (`DressedQuantumNet`).\n",
        "\n",
        "::: {.note}\n",
        "::: {.title}\n",
        "Note\n",
        ":::\n",
        "\n",
        "The *ResNet18* model is automatically downloaded by PyTorch and it may\n",
        "take several minutes (only the first time).\n",
        ":::\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 236,
      "metadata": {
        "id": "lnJnW_ra8PAX"
      },
      "outputs": [],
      "source": [
        "model_hybrid = torchvision.models.resnet50(pretrained=True)\n",
        "\n",
        "for param in model_hybrid.parameters():\n",
        "    param.requires_grad = False\n",
        "\n",
        "\n",
        "# Notice that model_hybrid.fc is the last layer of ResNet18\n",
        "model_hybrid.fc = DressedQuantumNet()\n",
        "\n",
        "# Use CUDA or CPU according to the \"device\" object.\n",
        "model_hybrid = model_hybrid.to(device)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5k96EBuZ8PAX"
      },
      "source": [
        "Training and results\n",
        "====================\n",
        "\n",
        "Before training the network we need to specify the *loss* function.\n",
        "\n",
        "We use, as usual in classification problem, the *cross-entropy* which is\n",
        "directly available within `torch.nn`.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 237,
      "metadata": {
        "id": "BKvfgR5N8PAX"
      },
      "outputs": [],
      "source": [
        "criterion = nn.CrossEntropyLoss()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UUvuVdii8PAX"
      },
      "source": [
        "We also initialize the *Adam optimizer* which is called at each training\n",
        "step in order to update the weights of the model.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 238,
      "metadata": {
        "id": "bPI2SbMQ8PAX"
      },
      "outputs": [],
      "source": [
        "optimizer_hybrid = optim.Adam(model_hybrid.fc.parameters(), lr=step)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a8wMKvP48PAY"
      },
      "source": [
        "We schedule to reduce the learning rate by a factor of\n",
        "`gamma_lr_scheduler` every 10 epochs.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 239,
      "metadata": {
        "id": "dLQsPIzy8PAY"
      },
      "outputs": [],
      "source": [
        "exp_lr_scheduler = lr_scheduler.StepLR(\n",
        "    optimizer_hybrid, step_size=10, gamma=gamma_lr_scheduler\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q-xTUZhq8PAY"
      },
      "source": [
        "What follows is a training function that will be called later. This\n",
        "function should return a trained model that can be used to make\n",
        "predictions (classifications).\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 240,
      "metadata": {
        "id": "rppVRya_8PAY"
      },
      "outputs": [],
      "source": [
        "def train_model(model, criterion, optimizer, scheduler, num_epochs):\n",
        "    since = time.time()\n",
        "    best_model_wts = copy.deepcopy(model.state_dict())\n",
        "    best_acc = 0.0\n",
        "    best_loss = 10000.0  # Large arbitrary number\n",
        "    best_acc_train = 0.0\n",
        "    best_loss_train = 10000.0  # Large arbitrary number\n",
        "    print(\"Training started:\")\n",
        "\n",
        "    for epoch in range(num_epochs):\n",
        "\n",
        "        # Each epoch has a training and validation phase\n",
        "        for phase in [\"train\", \"validation\"]:\n",
        "            if phase == \"train\":\n",
        "                # Set model to training mode\n",
        "                model.train()\n",
        "            else:\n",
        "                # Set model to evaluate mode\n",
        "                model.eval()\n",
        "            running_loss = 0.0\n",
        "            running_corrects = 0\n",
        "\n",
        "            # Iterate over data.\n",
        "            n_batches = dataset_sizes[phase] // batch_size\n",
        "            it = 0\n",
        "            for inputs, labels in dataloaders[phase]:\n",
        "                since_batch = time.time()\n",
        "                batch_size_ = len(inputs)\n",
        "                inputs = inputs.to(device)\n",
        "                labels = labels.to(device)\n",
        "                optimizer.zero_grad()\n",
        "\n",
        "                # Track/compute gradient and make an optimization step only when training\n",
        "                with torch.set_grad_enabled(phase == \"train\"):\n",
        "                    outputs = model(inputs)\n",
        "                    _, preds = torch.max(outputs, 1)\n",
        "                    loss = criterion(outputs, labels)\n",
        "                    if phase == \"train\":\n",
        "                        loss.backward()\n",
        "                        optimizer.step()\n",
        "\n",
        "                # Print iteration results\n",
        "                running_loss += loss.item() * batch_size_\n",
        "                batch_corrects = torch.sum(preds == labels.data).item()\n",
        "                running_corrects += batch_corrects\n",
        "                print(\n",
        "                    \"Phase: {} Epoch: {}/{} Iter: {}/{} Batch time: {:.4f}\".format(\n",
        "                        phase,\n",
        "                        epoch + 1,\n",
        "                        num_epochs,\n",
        "                        it + 1,\n",
        "                        n_batches + 1,\n",
        "                        time.time() - since_batch,\n",
        "                    ),\n",
        "                    end=\"\\r\",\n",
        "                    flush=True,\n",
        "                )\n",
        "                it += 1\n",
        "\n",
        "            # Print epoch results\n",
        "            epoch_loss = running_loss / dataset_sizes[phase]\n",
        "            epoch_acc = running_corrects / dataset_sizes[phase]\n",
        "            print(\n",
        "                \"Phase: {} Epoch: {}/{} Loss: {:.4f} Acc: {:.4f}        \".format(\n",
        "                    \"train\" if phase == \"train\" else \"validation  \",\n",
        "                    epoch + 1,\n",
        "                    num_epochs,\n",
        "                    epoch_loss,\n",
        "                    epoch_acc,\n",
        "                )\n",
        "            )\n",
        "\n",
        "            # Check if this is the best model wrt previous epochs\n",
        "            if phase == \"validation\" and epoch_acc > best_acc:\n",
        "                best_acc = epoch_acc\n",
        "                best_model_wts = copy.deepcopy(model.state_dict())\n",
        "            if phase == \"validation\" and epoch_loss < best_loss:\n",
        "                best_loss = epoch_loss\n",
        "            if phase == \"train\" and epoch_acc > best_acc_train:\n",
        "                best_acc_train = epoch_acc\n",
        "            if phase == \"train\" and epoch_loss < best_loss_train:\n",
        "                best_loss_train = epoch_loss\n",
        "\n",
        "            # Update learning rate\n",
        "            if phase == \"train\":\n",
        "                scheduler.step()\n",
        "\n",
        "    # Print final results\n",
        "    model.load_state_dict(best_model_wts)\n",
        "    time_elapsed = time.time() - since\n",
        "    print(\n",
        "        \"Training completed in {:.0f}m {:.0f}s\".format(time_elapsed // 60, time_elapsed % 60)\n",
        "    )\n",
        "    print(\"Best test loss: {:.4f} | Best test accuracy: {:.4f}\".format(best_loss, best_acc))\n",
        "    return model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a_XtRwDI8PAZ"
      },
      "source": [
        "We are ready to perform the actual training process.\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from IPython.display import display, Javascript\n",
        "\n",
        "# Run this cell to keep Colab awake\n",
        "display(Javascript('''\n",
        "  function keep_colab_awake(){\n",
        "    console.log(\"Colab is being kept awake.\");\n",
        "    document.querySelector(\"#top-toolbar > colab-connect-button\").shadowRoot.querySelector(\"#connect\").click();\n",
        "    document.querySelector(\"body > colab-sandbox-output > div > div.output.container.output-wrapper > div.output > pre\").innerText;\n",
        "    setTimeout(keep_colab_awake, 61000);\n",
        "  }\n",
        "  keep_colab_awake();\n",
        "'''))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "p2W621Tsy2hY",
        "outputId": "eb439460-a07b-47ad-c499-609305a8be9a"
      },
      "execution_count": 241,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "  function keep_colab_awake(){\n",
              "    console.log(\"Colab is being kept awake.\");\n",
              "    document.querySelector(\"#top-toolbar > colab-connect-button\").shadowRoot.querySelector(\"#connect\").click();\n",
              "    document.querySelector(\"body > colab-sandbox-output > div > div.output.container.output-wrapper > div.output > pre\").innerText;\n",
              "    setTimeout(keep_colab_awake, 61000);\n",
              "  }\n",
              "  keep_colab_awake();\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 242,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "5VgfdD3-8PAZ",
        "outputId": "7bccdceb-6974-49c4-8318-a1f04d4ee33b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training started:\n",
            "Phase: train Epoch: 1/5 Loss: 0.6814 Acc: 0.6230        \n",
            "Phase: validation   Epoch: 1/5 Loss: 0.5236 Acc: 0.8497        \n",
            "Phase: train Epoch: 2/5 Loss: 0.5052 Acc: 0.8197        \n",
            "Phase: validation   Epoch: 2/5 Loss: 0.4442 Acc: 0.8824        \n",
            "Phase: train Epoch: 3/5 Loss: 0.4618 Acc: 0.8484        \n",
            "Phase: validation   Epoch: 3/5 Loss: 0.3646 Acc: 0.8954        \n",
            "Phase: train Epoch: 4/5 Loss: 0.4334 Acc: 0.8320        \n",
            "Phase: validation   Epoch: 4/5 Loss: 0.3371 Acc: 0.9216        \n",
            "Phase: train Epoch: 5/5 Loss: 0.4373 Acc: 0.8197        \n",
            "Phase: validation   Epoch: 5/5 Loss: 0.3053 Acc: 0.9542        \n",
            "Training completed in 2m 35s\n",
            "Best test loss: 0.3053 | Best test accuracy: 0.9542\n"
          ]
        }
      ],
      "source": [
        "model_hybrid = train_model(\n",
        "    model_hybrid, criterion, optimizer_hybrid, exp_lr_scheduler, num_epochs=num_epochs\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AG82Ot6Y8PAZ"
      },
      "source": [
        "Visualizing the model predictions\n",
        "=================================\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cwycKwbd8PAZ"
      },
      "source": [
        "We first define a visualization function for a batch of test data.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 243,
      "metadata": {
        "id": "_8R2rHzF8PAZ"
      },
      "outputs": [],
      "source": [
        "def visualize_model(model, num_images=6, fig_name=\"Predictions\"):\n",
        "    images_so_far = 0\n",
        "    _fig = plt.figure(fig_name)\n",
        "    model.eval()\n",
        "    with torch.no_grad():\n",
        "        for _i, (inputs, labels) in enumerate(dataloaders[\"validation\"]):\n",
        "            inputs = inputs.to(device)\n",
        "            labels = labels.to(device)\n",
        "            outputs = model(inputs)\n",
        "            _, preds = torch.max(outputs, 1)\n",
        "            for j in range(inputs.size()[0]):\n",
        "                images_so_far += 1\n",
        "                ax = plt.subplot(num_images // 2, 2, images_so_far)\n",
        "                ax.axis(\"off\")\n",
        "                ax.set_title(\"[{}]\".format(class_names[preds[j]]))\n",
        "                imshow(inputs.cpu().data[j])\n",
        "                if images_so_far == num_images:\n",
        "                    return"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LQvJfmme8PAa"
      },
      "source": [
        "Finally, we can run the previous function to see a batch of images with\n",
        "the corresponding predictions.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 244,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "id": "mKBJn2x68PAa",
        "outputId": "c5e4518b-d0d0-4242-9f11-651b06ee0f25"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 16 Axes>"
            ],
            "image/png": 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hLQ9DBroNWmBriyxaQ8YpUrkYy8N4PqpQIrNi0qyHERnKK+L5RWxhYRKD1QlJkxi7UiIoaIQlUJaLJXzUYKcoJk5AGwIy8F0SoRECRJzR7DRpJBleZHCxCOPOLY9vOCe0EoORsAuXVxUOs3f+XkpT84hiDZCYMCWJJEa6NDsBWdin7irWNpZIL3+FPfMzmNpeCgsOun0J5Y2wciFD2QGM+aTtJkoLTBag/Ay7H5PGGTI2mJ4gDCKyjcv0L24i1ju43//D+HOLCOENY3i5XO5FYJXnwKsQtG6gkj6SDCMkSIEq2ESujVMeR6oCxvFBdlAZGNsBoRB2gcDKSMM2TqFMQfqYJEUHESpKIOkipCRxQBdtRLWA7ZcYUQ79LEaGGfH6JsSDWaoIY6TlMeqWcGzn1sd3y68ApImhJgUvscbZWztCdWoByy9AZjBxRNDpkwQROtU0MklpzyyP99YI05haZrH05U9x94FXIKRGOnvBbVId6WJ8j+YSuLUiCTZ2skkqM1xXYmUBygIlBa1QEyeG6FKH3n/9OP1Gn/3/5J8yfuAoys/XXHO521FmrRG3r5BqiVeZw0iNNoPZJEKj0zaZ6ZNFXZyewCUjqvq4XoFEZLjGELdbSKOxvQL9uIVCoQoW0veIs3hwuEinqDBDdLqEaR2tNaFOSaXAqY0hi0XQGXvHRlkxAVq6OOltsuYqMs1hStzpLVIdmcXxC4hUQxBDlmFpiRKK2GiqtRoz4yV++7/+F5Tjc3R2N43wOc5fXOJVxw8zP3WM9a0NLLuKTitQ2cLqR9S8McIxjzDuo90U348QvQ5J0kfHyeBUlgHRjFj9759EbzYxP/WTTB67cxhDzOVyQ2a6DZQ22MUx8MAYjSUEWhqSNMPEmqzXJhUa4ZRQwoJ2j7QXYMjYIsXXNpFOECUJdjq441+oEqcRviijbId+2EXHCSZNSaOMDAsRK5zMoLfqaMdCO5KkXGVfeYI0TTDure8yGkq41hLJXabKuDOBPzKGDiNM0B8sHGuDbSCJI1SSMF1VTMxOcGS8zP2H7uPIwiGeOfl5Hl1e5qONi7z5X9zDmS144vNfZF/BZ0+xxnS5SNJN8aWDVyiStes4vk+KRHYEpZKm2e0gM40yIPoZ6x9/lKgfcPhn/+/cvSevipXL3W6U7aEsiUl76L7BOB5SKkzcxwQdLGURSYNJEiwkuD5SgIlCSBOkzkAZCkKi6020CTFFj6imSIuKl48d4ouNcyQ6xZbOoEqe8AijPm0/Q1guRhXxbZ8oCbiSbDG51icI+xhukxta+xObfe4ovl+ANEJlGaQxJCFIAVmEMhojIGpGrFy7QhwEpM0NkplpXv6yVzC+fp0vXXyCT33p08wu7OGBH/oRfve3fosfPbhAc2sTKRXJjWv4vo8KAqYqVfrdDkXpMuIKSuUirXqdThwRp4Y0Nmx8+lli9bt8/w/97DCGmcvlhkikhqQfoE2GUDEqdkgthVYGIyWW1mgdk1gumdCYbhuZGJACIQzaJGS2RcWuEGmNFaaYZkSnfoWCX6DZcSGp4zlFYiukZwm0AOO4eNqmZBw20jbdoIMUElBsmAgxUmTcmbzl8Q0lXI9mDiNeBSU1JgzQYY+02yDzJN2NLbxiiUhq+mFAPWlx7uIqz26GiO6zlHZNcTVdQ0zOc2zq9XziLz/EH76vRXWkSqve4lrQp2aViJOYuOCDVKRxQuDYtH2PMWUxrg1j2jA6NooVtVnZ7GFlkCaajU/eft0lc7kcaEsjEoGNg5YGIzKMHBRmypIEkUKWxeAaMstCKBsQ6DCGLEWQkaQ9Do2PsSV9rpgYYoEKU2j3EaKDam2C0x1sFRUZiYKeMgjfp1f08SyLljKD47TNJtKA6ns0ZOOWxzeUcF3QHko6CBeEr4hswXrY48LHvkAQRhz/wR8gLToEyRbnzz3BtZWIiWKBpSjizOWzpH6JQleRJRHzY2M8c63J6vomFtCVLjNTUyTNJoWRGhvLK5jRGuf6AZmGZmuLC1nGtJHctXuGEcsibMWExGQZkOQbsnK521EaxSAshKeQArTOMJGBNEPGGiMltrEwERCnGFeQ+IMgFplAGhetM75cX+Et976Z9rlP4ZRH+Nff+1M8d+U5li6fJGpewMgejusx4bmUheZ0r4NWXTIBERmWGpykt6QkIyONElqyf8vjG0q4FhOJ5ReIwpilZ56mdWMFdeAopZld7Dp2BAoe9QvPceYLn+XLT1whQbGwa5JGu85Gt0Nvq41stGl2e9hZyJExh0sNST/JsB1Fqewh7SqWLODYHs1eh9Zmg1KtRrvVJNCadpKSraxxrFKk6ihkIhFyULA7l8vdfqq6RGBS4jQdVLMzGiEhtTLwXKTjDW5ERTFkGplmOL2Efj8gkwZtK4TrEMYJpzbPsG98DyUNB4OYml/mj+NNrGoBrQ0vmdhLN+2ynCQoC3SaINIM17IxIiPRMW4KWtmE/R4m++bX/80MJVx9r0yWdViPbFarVeKREU4cPYgtJKvXrzMyNk774hkYmWP20FFmDxykuXWNhZ7F9ZaN1emCbaEtixuNkIqTUXMku8fHGC1WiEQBy4rp9kIsx6OsDcUxSS8KqY6O4Ta32EpCrnd72L0+855Lqezh6Rhh51uxcrnb0eHiHCtRizXZIjOaVGtECsoITJZgUk2mJMaToCVEmiSIMIOmUYjYIKIIaeDq5dM8OLOftXaTv3ru3Zw9dZq1agtnfooD46NsmRWuhRk61RiZoiQEKhscVtCaLA2IUGAkrrIJndtkK5ZT9glMzLJto6enmD58hG6vR9Tq0A0DumvrGL9GueRz4rvu5/Kzp0kUXD3VZGRslLGpWYKwT6mWooOQKAw4Me/TixRxPyUNethWgYKd0mjW8aVEux7FLKFb8CimZcIgpmFSzhpDvx+wUHAoFxyckXzmmsvdjhzlMVdwOehOsdrt0FcRW0mHWKckOiVJE5TIkEqRGo2QCu1aiJuVmgrKJQoCiplEximPL51hNwkPX20xbcFLD9/DWvsi61trnNOScmmEom2zlYbIJMOEIRqBwSAzQ7laoWF6ZLZE3/pmgeGEKw5c2VrnfOKiI0292aHR6eBIxd4DB0mkIskybE/j+UWKxSKWZxPO7qJQG6E8OsHm2gphP8CJU8Jum8TA9GwZabtESYptJApDBXAFSMuioTW2koQICraFYyniKKEpYOboMZ67eA5/cQjz+1wuN3TyZnsmoQUTXg2NZswpcqW/SdcECClJRUaGBjnojSeUQKeD1jBdE2A5ElJJmkFH2pzXCrFgc8Ca4viuu4mKinavy/fNHeN6Y5WljYuIgiKuFjBODZUYkkYbuoZOvYFEYyQIeev3aoYSrm2Z8szmBjdCgbO2zpmvPMb8sTtZOHCIsNNh9sBBttbWsKTk2jPPYvkOa48/TqU2Rq1cZbQ2SrSxQXVigolKhX6zRdDrYnke0nXITIbluUTdLq6l8IUiBTzHoajKNNOMJI3QvQC/4FOdn+PYj/4Y1971y0RD2K+Wy+WGz7UclBLEWYoRAAopLMrSQwuDpSSbUZvMZKA1AoFjWYAilRlGgZGC0LGQcYYtDQVjMZbarPS22LfnIGq9x9Ljn+DxC1vUpSTR4Cobwowk7BKhyQqgK0VMqpEpiG6M1YtueXxDCdfLN5a4FiTE/RWi8jjluTmULVH9Dna1RHtlFdf3kEIxtWeBsNMiy1Is38f1C2w2G1R3zeF5Pp3lZZQdIB0Hr+hjOT5RGuPaFrLgIV0X3euQ9TpMjE/Q29qgUCthOYqs00CNTnPH678PnabEqSZo5rsFcrnbUZilVJWLUg6GQalQJ4VW2MOTGbFO8bFRGXiWQ5ql6MRgI0iEgMQgFRhHk9iSuGhzyCpgjToUQo+/+OzvU1MjLDc05arFhFdgU6Y0UgG2RZjFKAk6A9GPBg28LQUVCyr2LY9vKOHaCPr0M41G40ZtrLBNdyPGTE7y+Y9/jLWlJebmd+PXarjCZc/Ro9ij4/TCiHKrTavdJs4yHDFoLOgArhIYneGT4itFGIUUhCBThsyWOJ6LIzJEuYDV06RkGFmktmcaR2R85Tf+I3Ya0b71mre5XO5FsLsySpSm2JZFKwrJjEFgM+6NUEg6rPSbGG0Q3GwZJQWOEbjSpmgNijIFaUgYJKRKUxMZgWrRVC7l1jIb1Hh1v8qdvYD6qOBisMVMcYy97iRrWcpXNtfRlsTYYnDTbFARmizRN7tZ35qhhGvdFggjKFdqKGySpAMm4amnvsTqtRWmxsapr67id7qMVEc59Td/xVqnxdTcXkyhgCcg6rRIkhDhF4gKRUYmJ+nUN3CNQ5rE6CTF832SOMUvFgikJMoibMfG1w7aBeOmRGvneebUo/SyDu58AdJgGEPM5XJDNlNwEMpHKUUrdFnpdMEwmMFKixHHJ8oSpBQEWUKqU2wAozBG4yoX23IR2sIC/H7CZhoTixj6XRppyKPdZZTd47lUEnkeq2mLtNEkizKkMeg0QyUgQkhtiXYNuBY31yluyfO6lf7QQw/dbPonOH78+M7jv3Y64am6Jk7aGJlgsj4kHTrL1zA6o9fcotPv0NpcY/PiGXqtOm7QJ2ptoU2GK6FcKeGOjhEHPaJ+hzAMcC1FNwlIwh6FooewJJYyWJbB8xVJ0kM6CmkLPN9CeuDVfHQhY/zOBYp75+hsd4W9+fHBD37wln9puVzuhfnaDPnl//k3ibMUx1JMlwvsrhaZKRfYV60yW6xQc3wK0mbE8plxqxSVh0SS6oxuFtFL+wRZRGJSYp3RNAIhHNLYYIRLqDVrRYuny1V6oSYKQlKdohyFKTvoiotxHVASaQQqzJDtGLnUIft/P71znb/2a7/2gsb6vPcpjY+P8573vIdf+ZVf+brHhQJha1IZI0SKbRKCOMJ3NI5IGRkpIUVGp9/n2tXr0G5itRqYNMZ3HOwkxsQhUkLWrpO1tvA8myQKMJZBGY0ShjSLSNMQSYyUKanpYBfBuOBXLJwRC2vUxjmwm67oU55weM973sMv/MIvvKBfUC6XG67tDHngHz9IK9ZkmcaSkprnUXJsKp7DntFxDo1OMVOogBDUHJ+D5Ulm/FE86SD14K17ojMinRLqmFTHaDRKKja3Aja/ska3E2E7HqlQpAkk/YSg2Sdp9cl6ITpNwJIYSyGUHOxgEAL1wBTvete7bmmcz3tZoFgs8ta3vvUbHhcShAtapJRcl167g1GaWMekWiC7hupYlfX2KpYtiMlYW7qGrJVQaKIwwC1VqIxPMjY9hQ4COo06Ua+LqlbwXYvOxhJ9nVJQRSxLYGxNHDfB90gJcaw+7ugYoyOHWapvEcuI2q5R3vrWt/LpT3+aX/7lX76lX1Yul7t12xlSL15jqx+x2esxJQWeY2MrQZImCCFwlaJoO6z32yx32iRZymyxwlypxsX2Op2kjzAZSloYNL04RgkJApKtmPZXNvCnPERx0C9LIpBSYjSYNEXFGSkGpEQjQIjBNixbYB2r8aY3vYl3vOMdL3icwzu+pAyWZ0iyhNSz8KwapreJUJrSyBRWlLF5YxXL1lRrRXqpxlgxm5srLB4+ymS5RhzFbK0t0VxfwhIG6RcIO3UcX9G7toZTKmG5iijpYHll/JEKYdii3b+BZQtEwSHNepRmDxP360RxTN5CK5e7PQVRwKTvoqQAYxCAQJJkhkwngz54SuEph6rjEaaDHnujnsMdY7u41GmwEbRJswxH2cQMTlVJIbHUINoyzM1jAobMgDAGSyqEEYM+WhmDtt5i0BEWzKB/obr14BheuEq4vJ5w8pKhFy5R8QXHF8rsGq0iooh6q0WsUnzP4QubHVaDjDgzlNoJ99mXOVZSuCPTeJ6HFIYkCvnc+Uuc22jTjS5R9GyO7x7j9S89iAZavQ2KrsPZlTU+e2qdjXaCNlAu2ezfv8nR4wpZtgnTfLtALnc7+usLzzLqF5gsVVl78hpPP/Isq9fXCXohY9OjvOwf3c3RVx5FAJ5t000jPvbLH6U4WeIlr7+LZz7yFJvLW3gVn30PHsY64JPpjNbJNVb/4gIAmx9d2vl5lTfNY80VCFa79L60RrYRQWIQvkLN+vivngKjMQZMcpvsFgDYaML1NcP+3YPWtZdWDF840+buxT5eZvCFQDuKTy51AcMdUyVkHHE1MPzN2atE86PcJTVusYxyfD508jrXG23unh9louyyFWm+dG6VtVabN79yASMMV7YavP+zS4xXLb773jJWwWGrJVm60ebQnSWwBvW6c7nc7edae43rXUmh6XHpLx5ndHaUu19/DxXf5/xTF/nIuz+B1oZ9rziMFIo9lRFsKelv9Xj49z/Dwn2LTN61i+XHrnLqg4/z/f/L97Pk9EnmKoy8ZIbGYyuUXzaNqFlonaFGXXQ3ofORawhP4dw1Ao4ka8dkV/pkaIQQGAHC3CadCABaXXjtd0O1PGh5uzAp+MtHNZc3U07MK0ykObuWog28ZpegagcozzBXFjzVdvnycoO9pQA32eJMPePyZp8ff9lupisK6RhK1QnKXsZfP73Ola0WsxOSi2ttMg0/8oYR3BEb4Y4RJTGdSGBECkJg2/m6QC53Oyo4Lq7lYEvF0bffh7AUynKoFWv84KuO8uHf+Chf+svHeN0PvIIoTUiyDEdZ9DZavOynX8343nGCLCXbW6T5G1/k8c8/S/nVu7BrLv7uCo3HVqjuHcPbXSKI+oRpQnS5j4k0hTfOIcZttDFIYzD3jZEZg2SwBcsMoXf00JJnfAzGJgTSNmAZnAqMj0KzC6PjHqktWO/CVFlgbNhMNW0M2tLMjhjizNCxXGTB40onoeZLRkZ8ItumG0WsbF5hbtoF4PpmFyyN6w1+EZduRMQo2kGLIK0DfZAR0o6w3Dxcc7nbkUJgS0mUpWTSINA0oi6PLl3k8xeeo7owyuZag/V6EwGUXR8BTM2Nc+cdByjbHgeqE7x88SCVyQpeTzNXHGHcK2PffMuqTcaY4/GSiXnmK2MUC/7g8Wt9hGYQpsYMjmkZ0Aw6HRh56/tchzZzLVVBu6BjMEZgAL8EeguE7eBUNKkOuNY0XGv+7e+OAWhFPcbKNu0wpRkYfvMvz/2dP6sdBERGsX+PYPqi4q8ebuN+scP0nM3uRcnCAYEmQyobLfNDBLnc7aiXxgRZTKQz0pUe9c9dp7fc+ob1zv/++OeojlcZ8UqEaYI3VqRiu5QsCwlUyxVqlSJRP6EVBxyojDE1AVc5TcX26Kcp59qb1ByX2X2TRAfrNB/dRD7dwJr1Ebtd5GIRLI0RAqM12twmhVvg5oEGC9Kbn6fGIG6+uioIdk/V4EsBM2Nw78EizWYfRxlAYIRBKBgvG6yyi5ARoyXD/cddLNsiTVOE0EjbBiGo1BTGztC25gfeZLOy7nPpUsLStZirFwznnxO87h/7CAcMt16XMZfLDV/Z8WjFfbJGxPU/PoU9VmDygUVU2SEyCd2LW0RPNWj0GzRbPa62BGGasBV1+Ztrp5nxRyhYFsnWBu1ocON6TLmYNKMTDgqv7CqOsHtyisutTdIsQQrBoR85irWZsnlug+Vza3Q/t4V6pkPlTbtRSpIYTaxvk3quAJ02JAqyDDJpSG0IgsENJbea0UwSLEuQScP8fIFCNSYN00HBBgssPWjR3c+6VMqSzWbK7NSgm0CqM5AG6aYgwHYzMmnAAW1LphZcRufheJzy3BOaU182rCzFzOyT2Fa+LJDL3Y6mvRKWhuUrG5jMMPOjh0l9EAgqAsKrLSJAi8EWqsE79UH9kaXmOo1emzG/RNUtYgBtNP00JjGaRtABYCPoorolprwqRQUb/Q5XO02qkz57d+3j0GsPcfHJazz7Z8+gL/UoHBnBERJH3nq4Di156huwtWUGZcAURJFhYxUmpyXtJGZ5q8HsgsfGJtxo9vGLLqGECIO2oJ8aMs8QWxkzUym90HDyckBgQmJXk9iaUMUEIqaTRlhFi15miLOMMG4RJwFCwejM4HriNCMxCaGJhzXEXC43RM1uRMn2KNiDeym2lHiWjaskOsqIz7S+5tnbt5gEYEizhGbUZaXXYKPfItUpxhgcy+Hw2DQHJ6cA2GWX8CxFP0tBKjzj0E8TLrY3OdNcJ8gSRmZrAERJQqgTPGUx6hVueXxDm7kWa/Clv4b5Q4O/Ya6fHzx+9B5JvR+RApMLEWs34KN/02NxQeB4hiyBThc2NuH7Xw8Sw+ysYWYVvnJSs9oQzMzYGDLqLcO1K4YHv0/ij2U88eWYlWXNrj3glyEI4dKpweej8wYjB610c7nc7Se5atEthTBdRCjB0p+cpnrXFHEY0z65jvAkdGGuNMKmCon1YC1WColSisxowixiMzDEWYoUkmbQ4bktjVUEIQWP/fUp7n7lEaQlCPeM8tyjV7jxpUuMHhwjrlicjLdondxEOorKYo0gjekKCylupxtaU1CbEFx72hD2oFiFIy8Dq6Tp9jTaElgKTrwctq7B0pIhisB2oFyBI8chEWASkFLwspcZLl2BK1cMS8sxlg3FMhw8LiiOGHpxwORuQ6sFl56DKATHh4ndcOJVArsAKEDl9VxzudtROfUpthxiyyV54A6Wn7zA+sNXsIs2k3fNIgo2Nz5+nrVeD1mxBnf1b7KVwmgDhp22L9poGnFAL41xlcX89x5k9QvX+Os//jxGG17/L76HvQen2bq2RePMJmE3wnYtSjMV9r/xMGO7aix3tuhmMXoIsfG8w1VrzebmJpZlUavVADBDuLP2YsmyjM3NTVqt1jd/ci6Xe9FtZ8gH/vN/3smQv7ff+v/z+P9+ixf1NYwxbG1tcf369Vt6necdrtevX2diYoJjx47xzDPP3NIP/1Y4deoUd99997f7MnK53E23e4a0Wi0mJiZu+XWEeR7TztOnT3Pjxg0ASqUS999//y1fwIut2+3ypS99aefrO+64g8nJyW/jFeVy37n+r5AhaZry6U9/eufrgwcPMj8//7xf53mFay6Xy+X+fvJNoLlcLvciyMM1l8vlXgR5uOZyudyLIA/XXC6XexEM5RDBhz/8YZIkQWuN1hopJcViEdu2iaKIKIpIkkFfnGKxSKVSwfd9LMtCa82VK1e4dOkSe/fuZXZ2liRJSJIEgH6/TxzHeJ7H3NwcrVaLra0t4jjG932SJKHRaNDtdhFCMDIygpSS5eVlsixDKcXP//zPD2OYuVxuiFr/9h7g5iEAMzh9pTXEcUoUaTZaKSaMmN9dwfFtpDAgBsdfBwWfbIzJMFoPagsIm/VQUZEJrjKsdg1FmbHaTvnoimZfxWZcJaylFiQpu2eqdDtdAgF1A5NxSuwW+NJqnyRJ+K+Pnr+l8Q1t5irl4EialBLLsiiXy5TLZWzbxhhDqVRifHyc0dFRfN/feb7ruszNzTE6Osr6+jr9fp9er8fq6urO6wkhUEqRJAmlUol2u02326XX6yGEQGv9dc+pVCoIIXauJZfL3X4y4SEsB6TCIBFSIS2J5ztUKi5754rsOzCGW3SRSiIsC6HUoBqUlIPur5kA5SAsl1Zpio8vxWx1E4SUREGIBJQFdhwQRRknG4as3uB4KWNad1hwM3bLDNmLaaQC6RfoxzFPLm/d8viGkjy2be+EnFJqJxSVUpRKpUFfrJudArdnskqpnedPTEywd+9ems0mxhhs26ZSqRDHMVJKwjDE8zzq9TrPPPMM3W6XYrFIHMcUCoMCC0opLMui1WqxsbFBlmVYlnVbnx7L5b6TpWGA8m42Ekw1ypU3j7IaUIKduZ/YLtdiMMaQJRqpJGkGK+2U6VEPS4DYWOGI1Wei6KOUYNeogxSC3bbNmxddTm3FaAklJWglhpJSlDyLsjH03AKPrfZJm33Ggj57bpYsvBVDD1fLshBCEMcxUTS4QCEExpidmWQYhjSbzZt1WgVRFGFZFqVSiX6/z8jICJZlsbi4SKPRYHV1lRs3bmCMod/vE4YhQgiyLCMIApRS2La987Ns296ZMcu8/Wsud1uyLYHRKUoJpAMw6ABrzKBmAGQIMShBiBh0DDBm8OdeWhKjXCb9jLjfR0sYKVjcv1hEKglKURwdxxiDjmJGPQ/TanKjHvDSxRKO0jg6RmeKZiIwYUjVtTFRyPEDs8zXyrc8vqGEq+M4+L6PbdvU63UajQaWZWFZFrZt47outm3vPBbHgzKAxgxqM25tbVEul5mZmSHLMjqdDkopTp48Sa1WQylFs9lEKUWxWCQIAoIg2Aln27Z3jqsppXZmrdtf53K524+QGikHs9LB7PSrRQWF/GpBFhjMWoUUiExjKYEU4IgY4ykQAiEBYRBSwsgI1vHjyEoRlE13aYXe9Q32BZLd1ipjRfA8l8xAN4WyzFjNDFYYkqaGjZbGmhy55fENJVy3375PTU3hOA7dbpc0TXc+kiTZCdlyucyBAwdYWlrixo0bO7Pc5eVllpaWmJub21lXXV1dZXV1lVKpRKFQIE0HBWw9zyMIApIk2Vl+iON4Zwa9vTQhhPi6/4NyudztQ4ivDdavCdPtr3eaBG4/cVAxT1sWxnYRSQwiQyo1CF4pEHO7MPv2I0emEV6BrL3G8tUNNlsphckRCsQUSEBIHl2Leawluaeq2O2nOBm0tMV6KugEt94eaijvmYvFIq1Wi9XVVSqVCnv37sV1XdI0pdfr0ev1CMOQJElotVqcPHmSixcvYts2juPs/FLDMOTMmTO02222trZ2blo1m82doFRKfd33bL/111rvrK9uz1rzJYFc7jZ288/w1/7vV//boDi2AbBtsGzAIMRgXVYkEUKAtCyElAjHRh4+jDhyHFmpoY0kXF8janSYnC2js4CNfsbVQBMgkCalEvfwl1c4dXaFdqiZqthsZoIkSBDi1uedQ5m5+r6P67pcu3YN27YZHx+nUCjsrLsKIXAch0ajQafTQWv9dVuzvnYJIcsyrl27hlIKYwxRFO2E8MrKCtPT0zsz1O1/W9agz5aUEmMMSZLsBOz2um8ul7vdSAQao7dbuNwkBMYMZq4GgTYanSQoMfjzrYTZXjfAANKxYP8BzO49CNdHKIWQBneshlRjFMwe7vEV9RtNlvrrNMKQoqM4OOGxyxUkRhDJFFvZVFxYsRz6UXbLoxtKuBpjKBQK9Pt9oiiiXq/vzCQty6JSqSClpFwu4zgO6+vrNBqNnX2x2+Fs2zZSSnq9HsVikenpaY4dO8b8/Dzr6+v85m/+JoVCAc/zdm5Ybc9g0zTFcZydXQjGGNI0pd1uD2OIuVxuyCT6Znxu14QWZMaQGUU31tRcgxQGaQ/avpib6whCqZ0AzoyA2TnM6DjKUjTXtvAKLt5IDen66LCHcDzcSoVaYuiPFJCrIWmcIqWgNOJhEERacEZXaJouptPF/lsdaF+IoYTr9g0kpRStVgspJZ7n7azFbq+rbm/qn5qaIo5j2u02WZYxOjpKoVDA931qtRqe53H06FGq1Sq+72OM4dy5cxhjuHr1Kvv27du5Mfa14Zqm6U5Aa60Jw5BerzeMIeZyuWG7uesqTQ3CUrQzB1sK1jsxj9YFD4wkjJUlCEmWaoQrkUIOjhAIAVJg791PVqsiChWE5VKdcgcvrDN0GGIyja7fINja5MKj5+msrTAZpMS2RFkW0pIIKTCJRqyvYBKHqSQhCsJbHt5QwnX7rr3v+/R6PdI0pVwebGXY3NzceYu/fUNKa8309DSVSoWlpSW01uzatYv9+/fjui6u6+4Esud5JEnC2bNnKRQKtNttGo0G5XIZfbOnzvZaq+d5O49prQmCYGdnQi6Xu70YbTBak8UJUro02n26VoGmP8UGLR670eFVe4t4iUZHCYlTIAsTHM/BUgK1sIg4dAyiLkmWIQ0IZZEmGcR9pK3RcUTSbhB3YuKwT9yJ6HQ0xpe4TobjSJRjk6Gw5+aYW28zIgNGd90mW7G2Q9P3feI4ptPpUCgU6HQ6O8dYJyYmSJKE9fV1sizbOVVVq9UYGRlhenqaYrFIqVTCtm2SJNnZqnX+/HmuXr2KZVk7uxGOHz9OmqaMjY1x5cqVne8Bdm5+RVGUHyLI5W5TBkgzBiezhGGhKtFpn6UgIdy1i7HCLlZXzzGp29hphoj69FsJYWYxduQw8sgx0iTGIHGrI4P+sMaANqjCCDrukwYtkm5E0OnR29yivxqhU0GaSAq+xNcWLppYC8zyRWZsweS0h+fZtzy+oYTr9okrpRTlcplms0mSJBQKBVZXV3Fdl7W1NcIw3KkBYFkWvV6Pq1ev7vSqWVpaotFocOTIkZ1ZbaFQ4Nlnn8VxHIIgoFKp8KpXvYpisUixWCRJEvbu3cuTTz5Jvz84E5xlGUmSEIZhHq653G0qjqEXQqUw2ItuGEymnH4H//TjTB+ewzt2B6w8RxYn9DZ6FIsupWOL2MfvBNdH9RtkcXxz54HE6AhlGdL2OmmvRdTuE2w0Wbm4RLce0GhmeAgwkBkwIsNIgWULdk84GCnwfRtL3fpOo6GE63agbb+ld12XVquF4zi0221c12VjYwOt9U7wZVnGuXPn6Ha77Nq1i0cffZQwDCmVSoThYL0jiiLSNMXzPFzX5ciRIxSLRa5cucKuXbt21ltd1+XEiRN0u12iKOLZZ5+l2WwSRVG+HSuXu01dD2ysqE/Zsxis5hkQMFq2qBwoImWbZP0y2fgM/XYfixhrrIZ19ATYNgZJHCYoZQEahCLttdBxSNLpEbU7tJbXaXcj+u0W9UDwZDtlREmODX4aUgmkyjBICq7CdiQm08TZbXJDa/sUVJZlCCHwPI9Go7Gz3rl9kKDVahHHMdVqlYmJCV7zmtcwMjKC53lEUcQjjzyyczprOyi3dyLMz89jWRbLy8u0Wi3m5ubY2Njg6tWrPPvss0gpd3YV3H333Vy+fJnLly/j+/4whpjL5YbsybbhoIQZY5BIto+/SgVOwQZhuLG5hhP06ZVGqY1OUj28CI7CILj+V58jSQMWvuelZHFIFkWk/R6da8vE3YAkzQiCiM7WJt1OyFeu9bkUZhx3Fe14MFt1EokVG4RM0TYYI9A6w7Zuk5nr9pHT7T2m/X5/50MIQaPR2DkSWyqVKBaLvPa1rwWg3W6TJAkjIyP8xE/8BN1ul4cffpgLFy7QarVI05SlpSXOnz/Pgw8+iJSStbU1PvzhD3PixAlOnTpFrVZjenqaiYkJjh49CgzWf1//+tfz6KOPDmOIuVxuyNa3eszaAbpWAmuwdUCYQXEWozWpkITKRWpQm+v4B/agi1WC9Q5WTZA2u4zdvYek3SBLEtIgpLO8Tn1lg36nTdjvEvQTuq2Qx68EXO9kBMZwJjWMJYZ+CK5n8FyDublcazQoBXIIJzuHEq7b5f22N/1v7y3dvXs3vV6PpaUl2u32zkmrEydOoLXmE5/4xE5pwYWFBUZGRvje7/1e7rvvPi5dukQYhrRaLarVKo1Gg5MnT7Jr1y62trYIgoAPf/jDKKVYWlpidXWVMAyRUnLo0CFmZ2d54okn6HQ6wxhiLpcbspdtbjC3p4hSEsz2flfYPpolKxV2SRs1NUv71Gmcqo8UfXSWcfazZ7m6tM6sr5icdim6Fu2VJhsr63RbTZAWTmmMTtDmySstzm0l9A1EDIpIdTIz2EsbSarlwc9LM0gjTVnJodyrGUq4djqdnU392xWuLMui3+8jpWRhYWGnFGCz2WRubo5PfepTvO9976NcLrOyssLi4iKtVotCocDhw4dZWVnhypUrzM7OorXmu77ruzh37hxbW1s7uwGMMcRxjNaaRqPBysoKr3nNa9jY2OCZZ54hDMOdalm5XO72srCvRGnUH9QQ0INDV0iBRKLR9OtdlrdCPFkmiA01I+ltdXjq3BW+/OQV/GKVM90lHJnwst1FOt2Iq5fb7F8sMz4zSX2tztmzG1xtxlhCUL1ZbHvKUlTk4PxXlGmkGdQBUBiUHMxah1GRZCjheunSJaanpwcvaFlIKUmShJWVlZ2aAtvHUDc3N1laWuLP//zPWVpaYmRkhK2tLVzXZXR0lL/8y7/kQx/6EJ/5zGd45StfSbFYxHVdJicnd/a+br/u9j7WUqmE7/usrKzwsY99jB/5kR9hbm6OkZER9uzZM4wh5nK5IRuZLiEtiVRicENLDN6SG20gBZUlfPlSk11cwSn5LNoOXzrf5jPn+hRqszz4ln/C5/7Hn3L96hLZepuSFtgiIY4crp69xNmz65zbTPAthUgNCk2YGYQBSwkmHMlMQSGURCiJrQSOLUEoMn2bLAs8++yzWJbFrl27droDwGAXQRzHO7PL6elpoihiaWmJ/fv3E4YhaZoyMjLCsWPHOHnyJKdPn+bs2bMcP3585whsFEU0m02mpqao1Wo7xVwWFxfpdDpEUbRz+uvYsWO0223K5TKWZfHII4/wpje9aRjDzOVyQ2R7FgiBVAp5s84AbB8KkhRLFiMlm8urXcZ3O1y/vsX7//JZrnYS3vj938els+dZWmlQb4Yc2D/JBG1sS7C5vMGlGwFP1hOkEOhU4wlBSQjGLMGoJShaktGyolRS+K7CsxWOI7FsBQq68W1yQ2t7GWC7kwAMdg5sH2/d3oJVLBZ53etet7O7YN++fUxNTXHu3Dk2NzdpNBpMTEzwkpe8BK01ly5d4tKlSziOw2te8xre+973Mjk5SbFYZHNzk2KxyOzs7M7P2LdvH6VSCSEEly5dQkrJ7t27hzHEXC43ZPJmjQChFMZIkBaCDPSgWLbMNCd2lbh6LaHbTTh9cYWlZpfNMONDH/04Zc9mrdVltuwQ9ArosQJrl67x1ErAmVY6CG5jqCmxE65VSzLlKsaLioIv8VxF0RX4rkTZCmkpNBbXQsnBWxzfUML1pS996aD9QpbheR5hGLKyssLy8jK1Wo3x8XEOHz7M4cOHcV2X06dPc/LkSer1Om95y1u48847ef/737+znLDd0SCKIuI4ptvt8p73vIfJyUnK5TJCCI4cObKzVHD//ffz3HPPYds2jUaDq1evcuPGDTY3N6lUKsMYYi6XexGIm6eqpNruNiC/WhjbaOYmSuyut9k16nCtnzI/XqOx2mKj02WtY5BC0I0Vm1dWqFb28ehawtONBFtK4kRTsBV9oIKgZElGHEm5qCgVFQVf4XkK21FYtkIqtVPmUCW3fmx+aDvst2sKpGm603RwYmKC+fl5du/eje/7LC0t8YlPfILf+Z3f4eDBg+zatYvHHnuMU6dO7cx+t6tkbWxsUCwWKZfLO91gkyShXq+jlGJ2dpYHHniAhYUFkiTh2rVrnDlzhkOHDrGyssJ3fdd30Wq1eOyxx4Y1xFwuN0xCYhjsWR1krCHTgiwDbQSJkRgjODGqKCQBcWmMf/pT/zdeemAPrhyE16hjMSM09X7ChRsNEnfQN6sRpTQzzVKS0U41BSUYdyVjJYtaUVEuKUoFC8dW2LaF5QyaHypLIZVkvHSbHH8Nw3DnJJZt28RxTK/X4+DBgzuNBB9++GEmJyd55JFHMMZw7733ct999/FHf/RHO11bt/trlctl7r33Xr7yla8wOTlJHMcopVhdXd3ZfWCM4ezZs2itWVtbY2FhgUajwW/+5m9y4cIFnnvuOZrNJt1udxhDzOVyQ6YNhGGCrQTSyhBSEuOQSRdLGFYabS52BWXlEY/UaNcjrp17joqrmfZd0mhwL+d6P2PNshi70WbCl3QzQwbUlGTSVkzYit0Fi/GiRaWkqJQsPFch1GA5wHEkUm53OxiUPmz3b70O9NBmrkopOp3OzvarYrGI7/tMTU3x2te+lvHxcb7whS9Qr9eZnJzk3LlzRFFErVaj2+3iuu7OksKlS5fodDocO3aMer2O67o7vbWuX7/Oo48+yvz8PMePH99ZCoiiiLGxMdbX15mamgKgWq0yMzMzrCHmcrkh6gcpN9YDwjAhjlOSRJMGEVkUEcQZjpDMmIR2N2VlK+LKlTVuXL7ISLHAomNTSDVJnBLFGc1+yLnVBv1OxEzBpiwFc47ikG9xpGgxU7KoVRS1skWloCh5krJv4boKS4GSGiU0clCem3Hv1nvvDWXmKqVkZmaG1dVV6vU6UkoOHDhAtVrdad0yNTXF4uIiJ06coNPp0Ov16Ha7LCwscP369Z0DAI7jsLq6Sq1W45577uHpp58mSRJ836fRaOzUDHjyySf5+Mc/zktf+lLW1tb42Mc+Bgz23Far1Z1GhtsntnK53O2llfkYO6MVaK6sJZSmylTHqmglMO0OGkNvdAQjPa5dXiVJMtYv3mCuHzEexNSloJcZsptFWASwEiTcsXuMtZUGs1Iw40nGCgrPVxQKioJv4TmSgitxbIVSg1mrsB0ATBqTpSk3OgmHbnF8z2vm+tBDD+1s4D9+/PjO4z/zMz/De9/7Xvbs2UO5XN4pcN1qteh0OiwtLdHr9Zient65WeV5HteuXaNQKFCtVgmCgHa7vVOmsN1uY4zZOTrb7/fxPG+njfbHPvYxHn/8cS5dusTKygq+7++021ZKMT09TalUYnV1deeahRB88IMfvMVfWS6Xe77+ruw4t9Hj7ved4n99osHW6BiPr3b4H49e5NSjZ+itraGSAN1rcn6zTVe5bG60iJodgjSlZiumbcmIknhK4EiBLaDdj2l0Qk7sm2Kq5FAr2RRqPn7JJnQ8tO1gWQohbzYxldvtYhRa3zyVlWXs8jKeeuqpW8qO5z1zHR8f513vehe1Wu3rHk/TlH6/T61Ww3XdnT5ZWZbRarV2ag1orYnjmKtXr7K8vMwdd9zB4uIiKysrO6e7xsbGsCyL9fV1Wq3WTjuXcrmM67pIKen3+/i+z4c+9CEqlQrGGDY3N7Ftm/X1dYIgoFwu02g0eM973sNzzz3HL//yLz/f4eZyuSH529kR9AdH0y0ds6+7yrxOWZGS2aP7EZ7LZ544w/nlLU5u9pkqOFQx/NDuAp9ZjbhiFCPKMGIgTjVgSPUgYIPNNtaIT2WsiHEtovERNppt1nqSKelzKAsYN4JJSyCFBiVI+oP9+ZYtsW1JuWwzsbBwS9nxvNdci8Uib33rW3njG9/4dY9blrVzeEApRRiGOx8bGxtsbGzsdIJdWVlhbW2NIAg4ffo0UkpGRkawLIssy6jX67Tbbb74xS+itcZ1XeI4ptFo7MxMx8fH2draIoqine4DU1NTjIyM4LouYRjutJ5561vfyj/6R//oef9ycrnc8Pzt7NhTGsztqkqzZ8xlbtxBOYKnWjFf2Yj41HKfRxoxPSFZdGHCEjiW4O6qw537Zqke349yFEUJvpAUpGBMSeZcRbHdRfgOjVDz5HKL01shidacXe/z2eWIjbUeq82IKEzBgHVzl4DOBu27bWeQSbeSHUO7oWXbNqVSCcdxdk5Qra6uArB//36OHj3KoUOHdva7zs/PU61WUUrRbrd3lgpgsPtgeXmZzc1NpJQ7H0IICoUC8NWtX5OTkzsNCY0xjI6OkqYpcRyzvr6+E7y5XO72ouVgnVNIENJwrhnzFxca/OXnT9G3i/zo2/8natUygdbI8QkmSi6bmUWWGeKlVQ7s283uw4uUbMWogpoUzLqKhYpD1Tb4SYKlUxqdiE6sUQYKjqQQJhTjmDFP4DgKjCHOoKs9Umy0Bm1ukxNaMJi5fuITn+AjH/kIzWaTsbEx7rnnHu6//34OHTqElBLLsjh58iR/+qd/yqVLl4iiaKduwNjY2NdV1wJ2bnrV63U8z2Pfvn3cd9999Ho9sizj7NmzbG5usra2ttNqplgsUqlUqFarVKtVWq3WsIaYy+WGKOgEAGRJxp88V+f/8+V1NsMMWwnOfugT3HvgFMf3jHPPwWmKykZ22vynpZCzqw0Cbahd+xivPDjD4bkaN643yDLDpCVxtObjmzGfP99kK8rwLcXuapHJkoNsdRhv95CjJb5UT/j/PrnE2UZEqg0TBZcHF8f4f91Tw5G3SWttgJMnT/Lwww9zxx134DgOTz31FJ/4xCeYnZ2lVCqhlOLMmTP8wR/8AUopjhw5guM4LC0tce3aNYrFIvPz81y9epUoimg0GgRBwF133cWBAweo1+t88pOfJE1T3vCGNzAzM4Nt27znPe9BSolt24yOjpJlGc1mEyklcRzjuu6whpjL5YbImMEy4iM3+nzkSoeXjXlc6mVc6cRc2OzSDq8yVvIo2BZeknJqs4v0XI4VbIQxXI8zPvzsEhtVl+O2ouIoJh3J76/2ONdPeN2uIkXXZb0d8On1NkkQ8dBMlVrVZhXN2z9xlSOjLj93zwRKCJ5tah5b67AqF6jEbW51E+fQwvX69ev81E/9FFNTU9i2zT333MNv/dZv8ZnPfIaxsTHK5TJPPvkkSine8Y534HkeaZoShiHvf//7uXDhAgcPHuTw4cNcuXKFpaUl/vW//tfcf//9O90EFhcX+c//+T9zxx13MDs7y7PPPgsMCmNvH2DYvvG1XYB7cnJyWEPM5XJDFPmDZcALnYQ/fe0MJja893KAaymerfcJE02apBgDV1p9Em34lbc8yGf/4nM0g4h7bYXqJzzajrh3qsScb/NclPBsN+E/3jPG0TGP803NHYse7ztn8Z+udnBEzOyEzyfaCbE2vPd1uxj1bTZ6htERi9csSh672uT+SnLL4xvamuuePXs4ePDgTndWKSX79u3j8uXLCCEoFos8/fTTHDt2jFKptNPbSgjB0aNHd2oK7NmzB601s7Oz3HnnnTsdY1utFseOHQMGbbNv3LjB1tYWMAjX7Zqu222+YVCV69ChW92tlsvlXgzt9mBZ4M5Rl92VIqVykf0lm12exahr0YlT5ksuJTSrYcq4o3DKRdoG+tqghWBv0SMxgBSM+JInujG7fcXBigXCMFcySFfwhgMlAK7ZMDXhMXrzkMBfXeuSpBoXzVy/zeGkxVHZpaBukx5aAJOTkwRBwMbGBkIIbNumVqvt1BvY2NggCAK+/OUv8+Uvf/nvfI1yucyJEyf4sz/7M5aXl/nJn/zJv/N5/X6fYrHIXXfdRbvd3rnxtd1CZt++fTu9trY7y+ZyudvLhjV4RzpWLvLFqMzlZsS1ROIBo0qwaUB2I5IkIwWuhSn/4tff/3e+llCDm1Wrccb1MOP7P732dz6vkxpiDT+wUOJ9Fzv83OfX+OVHN3j5VIEHdhV540wZSwmsWz+gNbxw3e6fJYTAcZydt+jAzr8BXv7yl/PKV74SGMwslVI7BVsmJyep1WoYY5ifn+cnfuIndjoOGGN2dgxMTU3R6/VYWFjgR37kR7h8+TInT54kSRIuX75MFEX8k3/yT3j22Wd5+umnhzXEXC43RI3uoMtzXSuudEIur2yRxgmZEYQ3N/SvBwnJzRvcY7bkzffv58r5FbwoZcRR2AJGXMmRqk3BHdQH2Fey+H8cq2HbEkuJQcUtBELAtK8QmaEbGP7wDcd4fHmDT19v8pkbfT52rct7L3R53+t2YYvbaLfAdhUr+GoX2Hq9jmXd3MtWre7UCDh8+PDOrgDHcXj88cc5fPgwvu8jpWR6eporV65w7NgxhBBfF8Ke5xHHMZcvX6ZYLFIoFNi/fz9zc3Ps3r2bRx55hE9/+tM0Gg2+7/u+j/379w9riLlcbohmbxaeWml0CSfLLJQ8XG1RcxRnmwES2AJsbq5fGqhdX+NeBZWyy6gnKXuSkidwbYlrC+aLFue7Ka+a9nCcm8dblUSIwSRPG0jijDgzRPVNDowVuW/M5v95l+F/e6zOH52v8/DVDq9frN7y+Ia25nrlyhVWV1d3wrRer3Pp0iUWFhZ2ql0dO3aMxx9/nIsXL5KmKUEQYFkWr3rVq2g0Gjsz05e//OXU63U+/vGP77SAMcbguu7OIYTt7gbb9WIfeOABZmdnWVxcBNiZDZ84cWJYQ8zlckP0xc3BzHWtG1DvhmA7hFFGu97lWhCz6FksuBYoyaQlaaSa1U6MJyU1W1B1JdWCJACKjqDsCN6wq8B6mPGhpT5KCZTcfucLQZoRpBnKUnSRFFxBIzZcbkMzEByrDvbddvspW83wlsc3tJnr9PQ0v/u7v8udd96J67o8+eSTALzhDW/AdV2SJOEVr3gFly5d4p3vfCf3338/1WoVy7JYXV3l6aef5rd/+7cRQnDffffx2GOP8d73vpdr166xuLiI1ppms8mnPvUpfuqnfoq9e/fymc98hvPnz3PPPfcwOjq6s12rWq2yf//+nRtpuVzu9mNuLgtM+jYfvbjK/bNj1KI+n20GCODHpwroBDaUJLQUf9YO+eN6wIkw446ihYkEV1cznmhEfPYfzeI6kh/bX+Jv1kN+8fE6X9mKeemES5rBhU7KX1zt8J7XznPHiOT/OLnKVzZC7p4sUvJ9kl6fj1zvMOFI7htxGSkMIRrN8/C2t73NLCwsfMPjgPnZn/1Z8973vtccOHDAuK5r7r77bvPwww9/w3PX1tbMz/7sz5rdu3cb27bN9PS0efDBB83v/u7vft3z4jg2v/qrv2qOHTtmXNc1IyMj5t577zW/9Eu/ZFqtljHGmE9+8pPmh37oh8zs7KxxHMfMzs6at7zlLebcuXPf8HMffvhhA5gPfOADz2fIuVxuCL4Ts0PcHODfy0MPPcSnPvUpnnjiCSzL+obiLbejLMtoNBo7jQo/8IEP8OY3v/nbfVm53HeU78TseN5z3+vXrzMxMcGxY8d45plnnu+3f8udOnWKu++++9t9Gbncd7zvtOx4XjPX06dPc+PGDQBKpRL333//C/7B3yrdbpcvfelLO1/fcccd+amtXO5b7DsxO55XuOZyuVzu72doW7FyuVwu91V5uOZyudyLIA/XXC6XexEM5RDBL/7ijxP0U65f2aDZCMiyiErNw1I+wlSo+gvsnj5KpTqKtns0got0wiv0wlX6vQa9BpigStmZplaZplzehdGSscoiigS/OMqp504xO7WIrYocv+sAxvaJk4TP/PWnMGKLWmmEYrGG5UkKVUVtIkX5TSK9wk/8s/9lGMPM5XJDdObMmW+oP7L9b3OznogxhixL0dmgH5/WGcZoMAaM2flcSImUg8aDUqmvvo7O0FqjdYZOU3SakKUJOtNkOkMIsCwHy3VxHB/LdlC2g2XZ7D9w8JbGN5RwVVaG7WoKFUm3H5MEEdpI0szDpDbadei0+5ikiFEOmdmF42hwFZgYHXdIZYQRPbQdoK0GaaJ54ukLLM4dxomvcX35MaL2Oofv2c9qsMrq1Q43LnXpdusU3QpJ5BOqFlnSoRW10HaJotFk3HpdxlwuN3zbXVW3Pwf46v31QbDqbBCG6c2W1zpLBw1LjQEhEINvRopB8W1hJPpmbZNB8Gp0lpJEIUkUkmWDcDbGIIVE2TbKsbEdD9t1sW0PZdmDrrC3aCjhmuqIKI7QJgKRYTng+gKFh8zGsUSVOM6oB00s6VEZHaVo1YjFFBhJ4p1DGo2kg1Vq4VYVfjTB/PRhWq0VTM+n3+kjJvv4k1tYRZtSLSPVG2RZCmZQBMZkGUaAq3xMbBP3oRf1hjHEXC43ZF9bLe9rH9uZtep0MOPUGTrLvjrr1BkCkMpCKWunJslglhsDgu1wNjpDJwlJFJBmKSBQN2e3yrKxXR/H9bFdF8t2UZa985q3aijhGkUxYRiRZRrbURRKinLFIeoLdCIIgx5R2AelqVbLRCZFpw44EaVCBUvspqva2I6hUe9y44qFClIWFgzlhSbXLqwwO7EfW/j42V4KlsAUUibHSmyEW5TLPmNjYxit0cLFdUCZjLAT0G37wxhiLpcbsu1A3Lb9ubn5dv9rlwbg5ofRSAFSWijbQiobKdXg+TpDZ+lgyUAKBGLwOQapFLaUKGUN3vbbDsp2sWwH23ZQljP4b9ZXw/pWDSVc+/2EKATHKbB7zwiFokQYi7UVwdZmC5llCDT+aA9Tdkm9VVJTwrMUVb9ExfaoOjHKC9BBj9WzkqrrUi3OUrDv50b6NxTLRSbGdqGNQGcKHXk4SjA6KhgZ8fAdmzSRCCnQOiWO+mRoep1br26Ty+WG78qVS+zZs/cbQnRnTRUQgkF5UjWIKqUUCIlUCiktpByEoNYaLUCy/X0CIdVgDmvbg2wWAiEllu1iWfbOLFVJC2lZqK9Zqx2GoYRrErlIbEYmSszsKuM4gqCf0uv1WL7aI+6GFIoOVV+j5TrGXSZLbMJoCt9MYWUVfGySOGb10nUWxme4cf06zfUIXdIcu+MN3PGSvYTdhM3gC4RZQtAfJ+kW6LdiRioeWkvS1AEjMEai44RUGoKoP4wh5nK5IfuNd/0a//ynf4YjR459zaNfnckOukErBoWuxU7AwiBwBzPMQa1WrTU6kxglETpBosEkGOmB9JDK2rnpNZidqq/eAPuatd9BwIuvu44XaijhGgbgeS4zM7NMTJQQIsaxe1RHIorVlCyLKY37FCsGZcXYtkChiNuaIFbYiUeWOhg5zsHFw0zvruA51yh604xOjHDuzCUuPHaGoy+ZYvRQTGOrzvpFm/5GCSMVYZjR7yZkSYwUNpoE4aTEmSTu5d1fc7nb0YOvfiV//Ie/zw/+6I/xkpe87GYBfQDNIFDN4CaVkCiluHkP66ZBZwFMhkn6qLiJHdwgi3qYuI+UkjCRaLuGVZsHr4JbKKMs5+aNsEF3gizLSHVGEseEYZ+g3yMKI/pBn9nZH76l8Q0lXONIUS6VcewiAhtJiuMalG1QyuB6NqWyxC/GOEWN57lExsZ1LIJOTKvZJgo1RgnSOKa+6XH96inqW7tY6de4sbrOhDfFhfObLIp5Oq0RLG2D3aAXbcCmj5IOAkmSJgglcbUFMsZzOsMYYi6XG7I3/shbGBuf4OMf+XN63R6vfs0DN0NU7iwTCDFYZwUQN/9BGEwWETVuENcvI8JNnKyJ6VxFZxodNAnabaLYIjU+3vzd2GOLjB39HrSO6fc6NOvrNDZWuX75PJ1Wg36nQ6vRoN3u0Gh1aHf7vOEHboNw9dUkFiWCHlh2jO1khImh33GwmWaiVmBiXFGsNpHOGp5vUy27qAmLXiOmbqW0lrucfvoyruPi4lH1Z1jcdzeje0ZYPCjpbGyyvnSZVNsUJhPawbNkVo/25ZA4qVIrjeF4LknWRAAqKyCNxFa32n08l8u9GCzb4RXf83qqI6N88AN/gjGG73ngtTtdRL4asINZ5jaR9sg2TxFf+SL9jYtIkxFkIXGvQaJdTHeNoNXFGAcdGpLGNQq7FjhTX2F1q0+32SDsdVlfXaFeb5EYm0za9HsBWRYThiH9/q1v4RxOuFpjxP2I+lpIv2+wvJBuN6axZlEQVaYnZ5ie9FCFLSJhsJ0m5ZKFY1kUCxZCRWRIaqtVNpbr1EYnObTnLhzXRUmN55VJvYA77riT0T01GvF5wmZA0urSC3vYfgHbBstKcYEw7qAR6MxCx9PDGGIulxuywbqp4I57XkahWOYD7//vRFHE937vG7AdZydgd5gMkg7UnyK5/hmi1eeoL6+xVU9pd2OCyCAEjJTAVhZhBDKIODzdoyTr3Fi9SL0e02o0aDUaJImmFUTEBoqVGp2gSxonBLHBsgq3PL6hhOvk+AHCuE+/1abZaOKU+2RZiK18jKNwfJtyaQRsQ5rUwDRwXImtLHTs4No255+7gE41WRoQhR3WNi4yXzpCY6uP7aSMjc+yeuUs9XgdVbuBEoJapUw0oxCRhe87KAVhmpFmAWFssC0XrfLW2rnc7Wh7RqqU4OCRY/z4//QQ7//v/x3Lsnn9937fzSWCwe4BnSWkQYt46xzp+tPUly6wfL3Os2d6bLU1rgLHtfEcSdROSWINno9jF1lAk7YTevUz9Hs23X5Ko92n3k6IYoOSGSK+TklB5kmKrkuSZrc8vqGE6+L+fcSBzcZ6k5XNUygCPD8jtQShSAjikDg1SGzaDU2mQjzPwqLIjQseSSKoVircuLKExGFmdoGiP0M3kEhhMzE2jcRhazPCJ2J2coQgiXAsie8XAQchMuIspBvUSZIYkNiWN4ybfrlc7kWzvQ0LFhb386M/9o/59f/4a/R7Pd70wz+M6/mkSUwatgjbN9i6eoruylmWrzW4tqq52rJodFOqrqDi1kjjHl+8HrJ/zGW8XMYvuyx1UkQnpRNm+Br6GVS8AkXb0A0iWr0IQ0YzSFlrhtiuRxjdJg0KK5USrSxCSYmtihhdIIk0UggsJ0GohDTWkFn0ujbtIEPqFN/q0W0Juu1dTI5PcDZbR5ou/W6HgwsH6asmre4yFy+36XdiRN+jKHahYojjDq1mB5GWKXhllKUI4j5J1r/5t44EQkyap2sudzv62oMC2/YfPMzb3vYQH/+z/8bRA3McOHon3cYaG9fPcvLpJ7ly5mnCzhYFFRGnkkasaMSGjb7GNJqkWUqYCA4sjOCWyoxMT1HwfLrL5xm363gFi4nxMn99qsfTF+s0uiEnDs1z5Ohx1tZWsNfrHDl2nAtnnr3l8Q0lXFeubuJUU4Tdw3UFYeISZzFKSuLIYFsxUagpFqaYqnwXSeCydmMZz+oyPVFharKLiu/h4ux+2u0LNDa2WFm+xvwde0lTxerqebIYFmcP4JcmsEwJ0TWEzVPY2scp1VAKNFtkIiTRPXTqEmcRcTyMEeZyuWHTWu98vn3jSgjBfS97KaZ9hQ+9+zc4fHiRoLXG1evrPHu9RyfU9IKYsm9R9TWdvsBgkUiBEpKCY7GrmqGTLkvLCVbWJNIOlYKHVbBJSyWur2a0goyxms9IxWXPpMehKY+qcXnJwhyTs1XmS/O3PL6hhOuFk6dYvM9FOAnVmo0VeGzWO0RJhM4yWs0tCs4EStfIEoXDKFudy/R1l0qhgGP16QcfJ8guEgYhkyN7mN4/ThLGRHEDr6KJe4LKuI+yDZ3WGmSakjtGxStTq82SZBFRluK4NwjjDRKdkEUpvW5euCWXux1th+lXdwQMZGlMrQhELVpLz1IrWcyNSK6uK4ywObB3nk6Q0usFOG6PMTug0QmJtaRqC4q2ZrMHUZaw2oqpFvrMTBa4up6xb14SZJJiwWFstEzQ77OyXmdr9dPsnp+isRbTWOtAdpscIpg6EJOqi5Qqe7Clg9MVGGvQPTHLNO1Og63+GTKTgrZo9zfpB1sonRIGFTKrRJLa7Dm4wKXoGlutM1QKD7J46A5a4SJRv8+5M89w/rkvkMYp09NzFKpl9i8cJksGywCWcij449T0QYK4QRj0SFNNHOZT11zudmTbNsA3LA2kSYxJO+yedCh7MD1m0et0SNOU3aMlLEsRyIz1jXVKxTLrgU3FidH9GKEVZVfSSC28YpEoDigWDLbnUu/BUj0jwcGybZSSbDT6rDcjDu+dJrEKrKeS0miNkufc8viGEq4imUDHq1DskAnQso/l9XBkgqVc3KIk1utEZoVOJ6XR2aQftCnb42SJoloZpWKNsn71KtLyGa2MUStMcO7pkyzescjSlSWisI1TEnipTT/qsXRplUPmMLgJU7um8NwS3W4R5VogYKN+gVCniHJlGEPM5XJDtl156m8XbEEottauUakV6fZCImOz0UqxSQk7LaJAslzvcuT4PVy5fIk41niuJDaC9W5KybMpFCTIHuOjFp4LQhhqIyU6scIvFsmSLq0wZHmjy8RYjdmpGiO1MlEqCBLD1GTplsc3lHA9/eQZjn33NBu9y2jTotcXpDrFtVNc16VYLFC1i6SiCWoDTQfLUhSVgzY2vjdOrTbN/oOKbm+DaLPJ0tIlwjTjc391lq3mCvXGBsePfBelWplMVSl2E4qlKhkWihF8t0SWdUi0oZZVydIZjOMT3vp2tVwu9yL42zNWYwxpEtOtL2OQjI2VaTWbfP7JDmFocETGVsewEWyAXeAt/+yf8a5f/4/00wjH1Wy1I3aNFZFWhtABtaLCL1qUCxLLTnH8IjcubOKKmErZ4+SFdSzXZ2Ksyr6FUeqtmCxL6fcC1tfrtzy+oYRr0O3Qb0yxGbcIoxBLzxEEAi3XqVQCpncV8csCx8owQuP5McmoxFEZIlZoYyOER6ejuXZ1mQm/yNL1axy687t59rPvRzhQqy3g+1Ms3Vin31lmbnaMKAHPL9HcWgdCwmwTLSMcv0C1ugdhu7RNfvw1l7sdaa1vFq8erLlqrem161w88xTnL1wjDLpsbGm6gSbsx6y3YlqRoB0k+CWHRx55hEZ9i4qnEBiagWa3ktguTI+6FHyLStnCCIVlOyytBjx2ZoM3PziKW3CJU8PM1Bj7FsbohLC61aPT7WNZDk6hfMvjG0q4vuzlP0hWuEq4FBN0StScCp522WjXkTKgH8UUlcFx+hRkSrEEaQpRoEn7Ll6hSBBozp9fRWmP5ZUtTLzJ2ITF5OR+9izOcOnKVXrpEiqNmCh71DfXiHuGiSmXIIkRKiOhT+YtYTtVKsVR2t2EdrcxjCHmcrkhS9P063YMxFHAlz//N1w5/QVKRQ/XEWy1Iq5vdvBkhhGS7Oae2DAMeeTzn8MiYbQoCPoRynHwXBvfN0xNeIyMFHELRcrVEteXm6ysrYCQfPbkOm94+RzTo0Xmdo9z4OAB1usxC/vnuHN0krndC8zs2n3L4xtKuDa3Nik5DhP+FBttMHGEpSSj1Rp2ZYnyaML46BxeejfNVonZ+QqN+ONc6V8iCDShpamUMhr15+gEW1iiwuWtL9F4+lEWJqZ56hlY34o54X0PFoZuv0UQxBSUx+pKm/KIwlIeWTxGHK/T7V8g6Es2tgLW1jaGMcRcLjdkOktBCIyBNE145qlH+eLnPsXMeJHqSAETdziiRlld69APwCiBsAaFoMqVKo4lmd41zv2HHD75lZADC1PsmozYNaY4cWIX5WqFXiTQOiNKNFgWY1Wfq8t1mp0J3v7PXsfu/fdQGp1F2j5CFTA3W8co69ajcThVsUSbXk/iyT0os0ars0GxWGNsrkB5d5W5XS6+a/Olj6+hoxYFf5y0LPGdfURMEkcKZ7TMP3vrv+UjH/oQ1689A/S55+6EH/mePq3+Lv7my6NYqU9x1sEvVjj33AWu1k9SGyuyOH2EQiUi2AwQqSRNOsRRSpqlWHY6jCHmcrkhMwh0pkniiC9//m949JFPcejEyyhYEf32MgWTUaqWObh3gnorwOsYgtUA27dxHZtqqcB3H/eYroRMlG3KhT7337WL2akik9OTSKnIOhmddocoVXTafe4+PEEWJ3RbffbtP8D4nkO4xVG8QhWhBvUMhAGhbpM2L8fuPs6NayHCGMrF69TrT1MsJkzNWozOORSLPkl3hpnpA9RXVikXK2wmZbK+TdkZx3c80iQji+AVr3iQL6kmjV6L2amUrzzdQimLV903Tq+5l5G5o4yOV1B2ic8/8iFGSjWkNmxtrKPdLZxCA7dvaHc0UZBg9PAqi+dyueGJoogo7PHlz/0Nn/mrP+eH3vLTlGvjXD7zGHGvTsHqUByZYt/iNLuTlKfO1VnvCLRQ2BLuOVBgcUbR7UQc27cLW4aMT04yuWsCpKLR6hAlEGmXYydOcOKOO6gUFS85fIP1kycpj8wzNX8cbUBnGUmSYAxkWiON/uYD+CaGEq5Xzl4n6GpmFhaYmpij3lyhNplQrqW4lqC95bK5usm1tcs42W6ePfk4o5MvIWt1cRwbx3I5+cRzxF3BgcNzdPsdMi04eUGwf3aU776zTD+J2Vr+r7S2fpjl2i4W5ma4sXiYfqvLtStLzC1OU54qoEqblGKPVidG65QgiIYxxFwuN2S9bpunvvhJvvI3H+G+736AjXqLp585TWNzhXEZMDsBMusxPzsOdoGlOvirCZWC5I7FAnt3F2j3Q+rtlPLoLhwr5PJKSLFiEBLirESoJcI2RJmN6xbAtnjpa47zqbOXCTsdMq0xRoCQ2I731cMM5jY5RHD13BlSI4hMb7BeoTSOVcAkEhKPS8+ucv6JIgcOL1BydvPkl59kftbh7le8Ftt16LYCrGyJZmOL1dWroCWdRsqpTo84rqKcEhvrS1y+FEL0EQ4d+i6sQycYK8zQTJZwC+BWNW4hwisLRgplLEfiKpcrzwXDGGIulxuyM4/9NZdPfp6ZvYe5stJg6fop1lZWaLVa7N9VpOhUGBUKH49mK6MfZhzaXaRaVBzaP02sbXp9F7c2iq9sZnYfoNPrI8sLTMwdxi+OIi2POE5obK6TJBGV2ji1sUnmTjzBxuWz7HnJ96IsFxicFPtqJa7bJFzn9uzFcSqcu/oMjfYSnmVTqMywte6QGcnhO1z2750jbE7xlc88ydwhh3uP30O9uczGZg9bpYyM7+OZM1/i2pfbOI5NQdVZqm/Rqrlcv2yhbI9+sIFrrdJqrHD6VMLU7BjlkSrK9pCySJx0KShJsSiZna0QtFx662PDGGIulxsyx6tw6GXfRxhrtupNgm6Xdn2TrpJcXG7T7oYYFHt3B7gyRaGxPRekTScbYWrXIntO7KLgW1x6+ovc+5o3Y3lFPK+A45eQyh5s8coyqhNzbK6tsLGyRLcbsOve72b5yx8mCbuokgcMmh9qrXeO5d6qFxSuDz30EH/4h38IwLFjx3j90bfjuSVmpw/RDTaQVkaxqnDLXbA3QPaozK7QT8bYd+iljI5N47o1Hn32I1iWZrwyRacZ0wu7NDtXQafYxsamhDQl6vUEr+zi+j5p1KLb2yKM+ghbI4XL2OgonpjBk3tJwqfpdp6j0474N//2nQD8H+/933jnO9/Jz/3cz93yLyyXy71wX5sdBw8e4GN/8VE67RatVovFvfuYn9/N6VNPs7a6ShxHKClY3+ozNTnK0SMHGJ2YoTY6wcjYBLYF0vZxRErBgtrELpRTuPmWXoABbQZ9uDyvyNzCfsYmZ1i5cp5eNyN1amwtX2P24BhCfLXWQaPRYPfur27FeqHZ8YJnruPj47zrXe+iVqvxud+9ysr1qxx5xX3Mx/dgTI+i7VBwm5TKdXyvwKnHUh79y5AHX7NIZw16WxcpF8ZQTg+vHPCZRz5J2O9gtCbq9/ArM4xOJFxbazJdq7A4VWHPyDhXz18j0QGWWyLRIWFvi5LrE7Q0rjNONxqlHXZIkowfftMbOH96jWfOPf5Ch5nL5YZsOzu0zigWiziOQ21khIXFvRw/cYJXveYBwn6PKOijlKRaqzEyNkaxVL7ZFttC64w4Cli7cZ36xhqOVwFp7zQ0PPPcc/zZn3+Yt/zTf8z8/AJSCoS0KJaqLB65i/7cAlmqufjY5ymP76I6Pr1TRKZcLvNf/st/odPp8I53vOMFj/MFh2uxWOStb30rAB//33+Vif276LTb9DsJcdrCdYvUMnCcEkkc0awHOE6Jdvsqm/VNJqYWObL4Uh598tOsr64wUhnn/Po1srSDZ7toHREHGWevnWW8eA+L8y8hjDT92hg6zkiSmNWNq5QLDr2gwdrKOv2+TVKoI4IQg4cvaowVHCAP11zudrGdHRsb64BAWTYYQxKHqEqZYtFHSYmybGzHvRl6gy1bRuvBW3cpKZZHmN9X4uzWCkL5aCMQBjCG586c5Vd+5Vd4+cvvZ35+DwiFlIPOB0LYVEYmOfJd30dnaw3bK2CMIcsyhBBYlsVb3vIW1tfXvz3h+rUu3niCBQtsXyF0mzRrIx0PZXmEsSAIM2YXPYLNKo5fw3a3uHDucaJeSLO+TjvcZN/CHtKoQ7N1gyjssW/vAZbW1gGo+PuoWK8kbF2HrM7u6Sms0gibW2s4lsZyHWoziqLn0Y8P8slPnialR9q1CfP7Wbncbcno7P/H3n9HW37d9f3/89M/p9fbe5neNBr1bsldwjLYBoSNLRMgxE745htSvskvCSWUeJGEJPzAYEI1plim2MJyU7fqjDSa3uf2du7p/Xzq/v5x74wkDF+Q7zHMWv481pLu3DOnfN53Lb207/7s/d7IsrrRwEWSkCQT4W/s2lIUFVlRAAnXsfF9H00PgSQjb4at74MkyaSyQ/QOT4HvI6nSm+ZMJUkGNrbWyrJ89WhuSZIIR+OY4ejm9tvNa+rCjawruhKuQyP99GV6efrEn3N59STF2jquZ5NO9nLD/huQHI3b7u9DTl7g7NxF/uyrf0VPpp87wy7PnvhjSpU1nj8WZvvAQSZH9nB55ihHTr3KTO48AF85+ghfOfoIAHfv+34SsSSN+izPHf8iy/kZPN8hk0lz/XU38Ykf+0/cE49z8dICbmEXAz1VXj7fjSoDgUA32Z0WiqohyTKff+QLPPLIFzh79hz1ep3x8TF+7Ed/lI9+9MMggW6YSJLCdQcPsnPnTn7yJ/8F//k//zRnzpyhv7+ff/fv/i0/OLEb3/f5oz/6I/75P//nALzvfe+7+nmPPvoo99xzD0ePHuXnfu7neO2112i1WvT29nLXXXfx6U9/+k1Nu7eqK+EqWyHaTZvTc0dIRDPsHrsR4Ros5i/ylacfZSi2l6n9CXYfSnLqcAEhoFQr8shXPs1w/zQxvZdCfYHjc99E8n3602NI4QiVRplSc50DU7czMbyfaHQYu2HRtFr86ZP/i5Ae4ebr7mD7nh5sv8nTT7+Epf45aniBWCxLWJ6k3VrrRomBQKDLNCOE5zogBL/7u7/Pzp07eec734GiKHzj64/zb/7tv8NxHX7sR38UwUbbQIC5uTn+yT/5UX74h3+Yhx56iM997nN88pP/nAMHrmP37t3ccccd/PiP/zif+cxn+Kmf+il27NgBwK5du8jn87z//e8nm83yr/7VvyIej7OwsMCjjz76LU27t6or4TqbO8GBnj4+/J5/TjSa4uLMAkJJMzVxgFcuPUKhME959p2Ue100RUVRZWr1Mh9/6EcY63sbreUhLsx+lS+++t9YLM1w56F3svP6G5hbOk+puc6uvWPs33MDpTWZc+vPMbdymY7d5Ife+S8ZHuplcHKQqQNpPv7wj7Gy9ir55RHi0p3E0oNcujDfjRIDgUC3SRKyoiJJEn/5F39BOBJG+Bunrn784Y/x4Q9/lM/85m/x4z/24wjE1XX9Fy9e5Mtf/jK33XYbvu/zvve9jwMHDvDHf/zH/Jf/8l+YmJjg9ttv5zOf+Qz33nsvd9555+bHSTz66KOUy2W+9KUvcfDgwavTAD/90z/9Le0Pt2rrG2gBT3FQZBW/LTh58gzrpQpry+dJpqIc3H8DzXadbLaXy0d6GYzfiyypJCJZbpz8CEZrEtEWJEJjmGoU13N47dIRLp0+wXDPxMZF+gmqjROsV16i4y/jOi0ATl0+THnd5vJRi3PPmxQW+mmWM8i+ifAFKyt5FpaDcA0ErkWeY+M6Np7nYBgaQmysoqpWK1SqNW6//Xbm5udpNFsoioa62Uxlx44d3HLLLVeba2ezWaanp5mbm3vDXOvrX994nEw8vtE8/7HHHtvc7iquPm9jTvbNr9+KroxcDTPC6YsvkOkf5KWLX6JcX8PzXZ5/w1xn78gEiWyBwZEBZEUmFo6yUjhKLBOiVFjkxOKrgI/jWfiOw4XzJ7DtjeNtq7U8mVY/lu0QCscZIMpgPcfhs9/g6PmnGe3dx7v0D9Mz8iGK6zauNEuhtMbcKZtiOWg5GAhci2RFRVYlZEnm5cOH+eVf/m+88sortNtvvgtdq1VJJpNXTy4YHh4G4QMSsqwgyzKpVIpqtYosy3/jDqsrj9155508+OCD/NIv/RK/9mu/xh133MEDDzzAD/zAD2Ca5tXnXzPh2qjZtNtFvnrsEWLhJLcdfAcIQSY5TLme55kjXyS3tozNeZae/Sau1wFVg55zFNoqHbWBK+pIksDQTHZOXMfy+iXqnY1gVEwH11XIpiZoihKYDj+0++NcWDzNucvnaHQqfPqzP8Wjj/8m/+yf/hCybNFu1BHIqGZXBueBQKDLZHljRcDc3Bwf+tD3Mz09zc///M8zPDyMrut84xvf4Nd//dfxPBd/c7oANnZSAW+6y3/l+yuuhOMbg/bKyPRzn/schw8f5rHHHuOJJ57gE5/4BL/6q7/K008/TTQavTrK3aquhGur3qJqFfCFxw+99yepdUqUSznGh6ZpXN7op5ovnSMuTyKcJSRUEpFxdo3/CM16i9ULjxEKzW780CRBOt3LzPpRKq2NpVhmrEnYjDOQvJEcS1RKZ7E8i3Sqn3tuHefQ3lt48dhZfu/P/yMnzz1LMrQD17eoNNYww0HLwUDgWuT7PrKi8NWvfhXLsvjc5z7HyMjI1dHns88+C7x5OdUVG+H5erJeuRn1xl/z/zaSJHHzzTdzyy238HM/93P86Z/+KR//+Mf5whe+wMc//vGuLcfqyrBOMQzSiT4A8oUSlVIR33FZWLjEi689CYDjNjGlKBMDdyF8Gdf2eOpLj1GYy3PfvR9huO86JDSEEJy5+CrFSgfX3bi8Ut5EJU2zVabZrFCuN1hcKVIu2CSjk7Qdld7ejcOyluddzp8wmZ8xkaQUuhkcohUIXIukzRC9MhL1fR8hBL7vU6vV+OM//uON50nyt4SlJMnIinJ1qgB4U7BGIhEAqtXqm5ZWlcvlNy23kiSJ/fv3AxstELupKyPX97/nYRZmL3B0/jkeP/I5Joa207HqLORmCZkhmu06zYrGZe9lMPbi+x7VeglfeFTKZVbWnmb/9lt48fSXabdrxPUkGkVCegKQOPzKS6RCu/D9GSTLZal4kdOzRxkf2E21lcd+2eLc4isYusmu/ncwsW0/laJDo7XI4WOPdqPEQCDQZcL38QXcc/fd6LrOhz/8YR5++GFarRZ/8Ad/QE9PD2tra1dvNr0xPK+cFPC3dbDav38/iqLwP//n/6Rer6PrOnfffTePPPIIv/Vbv8X73vcgk5NT1Os1fvd3f4d4PM673/3uzfeXu9FxEMS34WMf+5gYGxv7lse/9KUvif379wvTNMX4+Lj41Kc+JX7nd35HAGJ2dvbq88bGxsT999//La+/++67xd133/2mx37rt35LTE5OCkVRBCCeeuopcfToUfHQQw+J0dFRYRiG6O3tFQ888IB45ZVXvuU9Z2dnBSB++Zd/+dspNRAIdNF3U3ZIQrz1jH744Yd58sknOXr0KKqqkkwmuxDz3SWEoFgssri4yPXXXx90xQoErgHfTdnxbU8LLC4u0tPTw549ezh16tS3+zbfMdVqlZ6enn/sywgEAn/Nd0t2fFsj1zNnzrCysgJANBrllltu2fKFdJvrujz99NNXv9++fTujo6P/eBcUCAS+q7Lj2wrXQCAQCPx/C1bYBwKBwHdAEK6BQCDwHRCEayAQCHwHdGUTga7rW36Pb92BIYEEsiRjGDLjIzrhWILRfe/g3e++n5GhwY2OOo7NUG8v/dkszdoS7eZFoI3j2FQaKdptjXe/+3u3fH2BQKC7xtKbx1hvHmstCRBI+GJjY6sPm4cMbiyP8sVGvxYBm6/ZPItQgGDzUMLNPwkZwgkY3CUzuGOA3ZP30eq0qLYqRDQVIRlYThtDNfE8H8e1wfdxgJgRQ5bhN3/hz7ZUX1fC9S2RpM0fARs/GQmkzT3CEhJC2twxLIGuKfQOJDh0cISbr5+kZ/B6eiduYWx8ikjIIJ9bxWo20GSfVmMJq/EqVusCrq/SccJUGyqSFP0HLzEQCPzdFAWEkDbDUdrcbbX53/+VsJUkJH/zqwAhiathK5A2olS6kicbX2VJYmpvnB13DZNMDiEpUYRwkSSfkBbG8lxiukkqnqXaKhELxVEVA9uuIBAUa0uUa6Ut1/cdD9e/3mFGeuPXN4xWr4xcZUlCVmRSqSgHDoxw3cGDTE1tZ2rbTpLZEaLJfqLRGK7dRhMeugmSv0ylcA7cNRr2ELV2Ar/j4rugx7Y+qg4EAt2nyhujUjZHq2JzlCpdORlbbI5O5Y3QlMSVMN5IUd9nYzAmveG1SIRCGrunx+gLDyMbcdpOh0a7hue7hENZTDVG267geGVCmkyxNkfdqiKEgqZGkGWNdHpq6/Vt+R3+DuIN+4H/tjVf0magJlMpMj1perIJpiaG2b59kpHRaQbHtjMwPEYskUTXdFyridfMo/qruPYKllfEth1anSiVdhTHDeGtXCK3tIwZisC7gmmBQOBaIykCZXMUKgnparCKNwSudHWUuhnEAhRfwgckWSABsrw5gPM2RrF63GGmcZlKYZ2EN0zUHKBlt1EUBVnY2E6Rjl1DkXQkSSEVGyWVVLHcDqYWA1+QjqS3XN93NFy/ZR716r+u/EEgywqRSJjxsWFuuPEQfX1pohGfVFwnHOtneHwn/SMTJJMJZOHQqcxSXD5DSFnGc4o0rShNK41lG7i2h67GcKwm7bZLZsc+7HrzO1liIBD4NqmyhL85ChUIZAH+5mgVAfLmr/lXRrBXjs32pc3n+q8HMEjoBgzskhg5ECOa6KM/uQddjxNSo7StBtV2iVpnjbAxQCoyjCqBI3w0FOKhNIqkARI+glb7WpwWeMMc6t/2d5IsI8kyqqYxNjbKrTcdYl9vGLMnTc94BkNt0Vgtk8yECOkqirCoF87jti7j189x/uUFth2cptLWWC9HMY0kiUSKSqdArVxhZXEevV1jdGofg3vGul5iIBDYOllm4/f6zZEpAiQffImNxzZHsyAhSW8Y1foCsTExi7Q5Z2tGDO54xxTqyAqCLEgxCo11IlqVsjBQtDiZxBhgU29UiZtRNNVARsL2fcrtdUKyTrXTREgSttXYcn1dCVfpr82dhkMaHcvF897c3FZWFJI9PQyOjZLuHyBqaEwPDrB9agJ5/jRWaYHk/hROU6NdLNOszXDx8By9Q0mGJl0i4Ra+p+KHEzScKSzLYebSaUyjQibby/LiHJFIlP7hcUQ5h6aFWNvcahcIBK4tsizeMI96Zf5UIHNlDvXKCFXgiyvnYEmb87Fw5Q6WQJBJRMgvlFk9WkPWW+y8cYjM6G7aboOh1CSeUNEEIBvEQj3k62vIiowuaehKCEWWQZIwtRi6qkNo670FujpylSSJPdODfOCe2zl2cY5nXz1BpWltzJOoCqPbd/Ku9z7A6OgIuuoj+zaGomGoCnXXwlVDlIsV2iWQzTSNoochKyydz3HihVUGtmcY3b2XcrsAK2V0WWIoE8dXIkiSxIF9+4nEEkjCZ71VQlPcjf8VBgKBa44sv+Gm1ZUbMuLKqgCQNqcMNs4cEJurCTaDWGzkje9vrDRYXS+yVpCIZKF3l4cbBUn2ycamEJJE3IzTsVoIXGKmQdgYp2lVkQREjQT1ToVSo0gqNoQhSyBv/UZ418JVkiR0TWXn6AhHz11kanIXWVPhG8fO4Cg6mb4+br7zPvbv3klvNotwO7hOG9uyEU4V4dvYfoT8YgvdVanmi6AkkWhhlUt4TY+zL86yvlDj4L3vQlMkFE1BaDq1/BL4LUglaNs2iuRjO3UKa3N4uvl3X3wgEPgHJ0vS5qzA68dmC/GGwJU2vsqSeNN0AW94jiSBAugRhaG9ERLjCooZIhRJ4kkqYS1Bx62Try2jCQ9FhXrHIxUbJhTtAUmh3qyQCveSjg3jORa1TgXZ3fqpBF2bFpAkCcM0MGSJwxfnOLla5X2338DD7+mlonqEskMMjkzS15NBV2SabQff83DcDrLvo0WTrFyYxY6mEJpKSAujlRZQo3FUz8WtFPCbZRbrRWK9A0ztP0i1UMYBfCWMY7VYWziPmR1FU2Us4SO127i+0o0SA4FAl125yy+uLte8stZVvB6ybG4mUNhcWbAx9+oLgaZpRGJh9FSbgf0GqYEx0ondJM0BBhLTLJYug/AZjE9RbZVpdcrIsgGST91q4AnwXQtDM7C9OiElRsg0KbVsipVLW66vK+EqyzKKppHt6yOaSLLz1rsYGpsgkdLJmjID9TpaJkXvyCiGadBq1vE9G6vToNmoonk+TVunL57BdiQ6RhxD0yCUpNNo4HXauJ6LhEBqFJl9+UmWzp9GyfYSHhhidGgMM5KhWc5RunAKzwgRQyCpNq7odKPEQCDQZW+aFpCu3NjauFsvvWFEK119zpWR7cYSrIGeBIfu2IY6YOCZOqnINFGzH8n3aVhleuIjSJKGhCDktUkYYzi+h+u2yET70bUITatOo1Wi0a5TqF5kvXyBerOE7Tpbrq8r4Tqyew/bdu5i1+Q4vZLF9Mg24tEw6XgUQ4VKfp3y7Ay5s6fRDRM5HsH1XOqFMorVRvhg6HGa/gqO5aHaZfK1KrqsYoRCNIRGWZbBl1GMKAnTJb59klLDpjedRdMdqtUakWiE4socnXoVIxajWi6QGdrWjRIDgUCXSW/YACDExhTB1SVYYuPm1kb4bn7d3Ip1ZcPBSq7A+pdKDO+Kc8/7PsBAag+6omG7LpqioUgavvBxHJuWZCDLPj2xASzXodGu0CzP0WiXKNXXqTXy2G4dT4BppjH/1lX5f39dCdef+L//LZOjY+jCoTJ3jqHd1xExTRRJcOLwy8zMLBLTY2gzl2nlLuEPbkOYKoaskTATNF1BZeECjcoKotMhkkmy4/7v4cRXv0yz2cKRFBTPoeFYmFqCQq6AfeookenrKOVW6evZgZEKocoqgxPbmL98Ac23aXUE0U67GyUGAoEuU+SNZPUAJHF1Z5b/hkCVpI2dWJK02TNACGR/M4glmcndfdx0/91ooRTRUAJVlnFbJQzVpOM4uJ4FEsRCSdp2jbZVpd6qcG7hMK1OkY5VwXE6SJJOOBQnGeonZKQxla3fq+lKuL7t7ruIhsLU1leJKIKJ6e1IvsfhZ5/m4swSIT1Cef4c+aUzpMZHCesaVAvEBkYoF1exXBmrUUBXQEtE6DtwC2M3v51Xz1xkYmiYxtoq/vwFtFQKIxKltqpQzTXYdc8ISjRNOtGP1W7SrJeRfIfpnfsQ7SZDcYuWHfQCDwSuRbLkI66sir+65fXKrvjXl3duTB9sdBLwkUAGQ5MY2ZHm7vc9iBwawHc6lGuLxMxe7E4DPEE41IPtgu1azK68ynppiaZVwHUaePioSpRoeBBFUdFkE084pCJ9pGJ9uF04Zrsr4dqXyaAoMsXZOtFkGt0wqJYKLMzOkkxkqJw6grd6kkv5Crff83b6RkeonD1Do1SiVlxB0zXiYwNY6+soXovy8gKPPfIHmNE4B+55B4uvvYjdLhCTZFQjRDwxRfXSZdZmF7jnA3fg+zZOq4KqmvQNTeA5FnW7idssU6139yzyQCDQHRtb4iWUzU5W/uauzStLsTY3aiGxsd31SvBqqsSOfSFuvP+9CD2D79QI+yq6I+MqbUwzhSyrLBSOs7x+idX8JTpOA1MPI8saPelJUvEhMtEx6p06nteh026ALBPVIgjfp2HVt1xfV8JVk0ESHs1SjqGde2nUStQqJfxOk/Z6joS/zsWWhQhFMUIaUUXQtGrU202E3yEaC+MOjhLpG8XQVNrVMtbCOQZvfjerC/OUOw492w/it0p4noMv6Wj4NPMLzJ1+lYnrbkePC8JJCUWWCYUMOvUq8Ww/K8XL3SgxEAh03ZUOedKV3fBXe5BIm2PajdC9Or4lZKgM9EcYGBshZo6itdrUVpb50uMvoOsq9zz4AbRkhnx9kbncYTzHIxpNMxzbTzrSRzraj+12WK8uoKs6uqrScQTxcIqW1aTQWALJJGamtlxdV8LV9Wzsdht0DdvqUFpfpl4tkzAcImqVeiyO2j/FzlQItVai7HXwsr2YvSGUpQtImTTbDt1KpVZFlQxEu07EVGnmLpKOKiRNhVq9Ta3jsLI4Q7ZvGFfV6dl9EEOCucNPMH3Tnfiug9VpUKiWOHPmDKWOYG5+qRslBgKBLpOutAqEzWB9w2YC4ErmXongkKkzNJigEZOQs/0Uzpxi8eIK0UiYiaF+2nabhbVjtMoSYT3G7sG3gSIzlN2BJmt0rBYSEolIL6oaRpJkFKeG5beoW+voko6umChymFJtfsv1dSVcq5UKVrWMa7UpLM1RLq6gIpFOp2i266ihNpGkhxFPEh4Yxpc1QvZGICdHp2FgjNWlebLZYUr5FXzbpm96L/W1GZTGOv09o1w4d4rlxXlkRaNZrxGNxhib3o1hqITbTdbOvoISi7O4nKNWrXJ+ZpHFQp1d4wPdKDEQCHTZ5n6Ab3FllcAVsiQxMJRkZEcvLz5b4F0/9F4WTp2jZedJ9G1jfm6GbWNZBkf6KBkm/cO7MPQ4ltXG9h3qjRxDPbtwXZe20yZXvIhjV1G1JI1miXgoQygxwXLxPAJotMs4wttyfV0J18rSHLXFGfKLl3FadXwhMGJxfFUhMrWbdCxMx7GQVAPPbVIuFzGUOJmBLGpxibaQsG0oLC9imCGiyTBL8zN0XEFat2lcOkoyJONmszhCImzqpHr6KRZyDAwOEh+aJLc0SzVf4rWTZ+jr6WF8dICJqSnGBvu7UWIgEOi6KxMA4q8NY6XNgN2YMpBlibBhUKyt05CbPPbZL2M3O6QTKT55z4NETKjklyhUTPr3Xkc2NUq73cRXIaQmaLTWaHVqtNoV6p0qnmvTsJuktBjxcA+qZiCjoKsGpU4ez/PxvWvkhlbu1MvY7QpEokipXoZHJxkYGubSzFmiho6myFw4+RyZvl6mBhy8ThOnHaNvaIq1lVl808M0TNZX54jGkpTWG5Tza3i+ixTRSUQjqKZET38Uoej0xcM4QtBuVXDtDCuLl7h0+jiKonNo/35U0UERUdRQDN0Itr8GAtcqeXNGdbMv1utzsLwevUg+VqhB3+gk2tI8A1KUdkHi1KUVfvGXf5U90xkiyRBLK1XGborQaJZoNQp06lUiuobVLoMvo6kmkpAJmwmEJJBRqbaKqJpGSA7juk1S0T5cz6XazG25tq6EayeSRorE0HzwPJlsbz992QEMI0q7UWR1dQ5NSVLNVWjFkthNhXA0RqfdxpcVGrkFWp6gXFyjXMhTq9ZpN0qMT00hKRLh9ABSXGZ9aRYdD0WJUpw5htY7wszMDEtLCwz19bBt5z4kRaZ4+RSmLuN0mlitYjdKDAQC3wFi89bVxsqAzSNb3jBfoKgS49eHGLtxF8JL8K539bP+8hzlUpV0VKNUrPD1YpXMeJKPffQ+ls4dQ9ZtXE0mlRpBCqcwwhk6TplYJEnMi9G2amRjg9RaFXqTQwg8XKeFqsWx7ArIGpbb2nJtXQlXT3gkwiqdtoPVrFFez1FamSeVHSLT14eq6yRiGWzLxXYaRBJlevrGKedXaSs6+bWLlGwfWdbIpnsIR+M4nTjhSJSBgSHi4TBevYVjW0RCMcAlM7mHpQvHMfqnuOfed6LqBuvzlzEkFy0Ux1Z1muU8cnO1GyUGAoEu88XrBw36Vxu3XDlhT8IIw/ghBS0jsfZqgU69SUhVaeUbtC2HnTsHOLdUxmvY3Hfbzbi1PG5YwxWCnlgP4VB8cyOCwNCj6JpBJtFHqSbh+T6qIiEpErVmnVQ4S0Yo1Ns6lcYiw6npLdfXndUCVgOh6fiySSyW4NL8ZUYHhukdHCaZTJJOpjHGd1Ct1bk8dxnkKJVynvXcMolElumd+yl3LPRoCtPQaeSXEIaEHorSLJVx80UqzTK6ppEenCKd6mFlaRa71aBPV9AUiWq5hG5GSWfSdGSTlbmz+KEEjlXtRomBQKDLPH/z13/x+llaGyQyGZXxGz2UlMzqeR9FadHJe0jIFCttiq5gOmRzx30HqXcs7nn797CwfokTl19mamInxeYiHbdMLDRAKJQipCQw9DCtVp2wEaPjtCk2SyTVYexOAy02SKG2RibagyLLVBvrW66vK+GaiSq4Soh4PIPnOxSqC2zbez1qJEKuUCC3ukQsmqDZqOHbFj2ZHuaKa3hWG79TxRRNoqaBZhqIRhHDEVhthcryRSTdQHI6GMkEvSPbGBiZprE2S2V1CT+UxqrVKOWWMI0QesjAkwQrMydZL5TpyaSJJ7a+Xi0QCHTflTOxBK+3HESAJEuMDGXp1FeR6mH0dob52TK1XJ2QIgglDA5eP8zEwWnOHJ6l4fk89sQfsbR+gUxfkmOnLxGJ+qTSgwymryMaTuDh4XsuhhnBapaoNFcJq0lqrTJClqk014kYYVpOmVJlDbcLbaC7Eq4ilEGT1c0WYjCYTSLJEo1ahUq1ghaJ4SsyA8NjuO06tVKBeq1MWPdBEjQlE8l3kRp5rHyR4uwc7Y6LGomgI+P4GqrtofgOc6eO4q8vYwqNWDiLnh1jfXYBWfGZuuVOGvU6qGE8L09f0iQTCY7WDgSuRZ5/ZW5VvGl9q/AEx06u4QuZ3TuGcZqCPtOgpblUbIvedJpmscHzjz9Ffs0lohiUFhZpNjuUzpS46dA0QztjVHwoNNZYW1/Bsyyu3/U2LEmiVC3ieVBv5rCcFopiUG/l0GSFkJ5AQaJmXSNnaEmSjaqpGKaM58roWpZKpUgy20M8mUSRFVRFJiTLuIRoKwIhq7ihLKGwgSZ5GO0KjZqLW6vQKa/Stl1Gpt9Jc/4sUjhFyEijry1hl4voqqDpm7iuzPrcLF5+BlWymDUV6q5DPl9jsLefyR17iRhaN0oMBAJd5nuvJ+ob17sKwHFA1QzaHZ90X5aKYpB2l3ELdWYXcihS82oXrY4h4bgeviuwJZczZxaYWRF4GjTqDrpksPemMU4uPslifoFK1cG2PCRho+kKmh4iZiaJhGMYioYiy7TsrW9/ld/Kkx9++OGrjbH37t179fH73/sxPv3rv4/jtEn1jjO253qSmSy+6xEKhQiFI0iux9FXj3DmzElm5udoWQ7vetf93Hznu0hnhqlXGqzMLVJeOA+tIqYusOaPU186Tj13Hj0zSKR3DM0qU1hfwvfb6FYOZ+Ew9fwKopknLEnsvu5WBoeHUKJpapbNKyfOXb1mSZL4whe+sOUfWiAQeGv+puwQAl6b8VnI+xsnufpc/cfzBL4nePn4PIuLqyzn8rQ6Lvgg/I07+b4QCAQdy8b1PEDg+4JCscnC5RbLZ5s016A3mqJayvH8My9w9sg8C8dXWH5ljcZihWqjSi6fY35xhnMnTjB/8SS+DIIOx44d21J2vOWRazab5Vd+5VdIJpNvelzXQ+zZcxu2L1hdXSGaTGIaOrKs4to2X3vsz/HsDuPb97P9wC0MTjcot200XSOaiKCksrTOzhKSJVwzTmLnIQy3hdLKoltLlC68QEc3USM9xGNxnFCS3pvvZeXyBUKFNUzZoW9oHNXtYBg6tt/hwvHn8YTOZz/7Wc6ePcsv/uIvvtVyA4FAl/z17HA3N0EJNgL1yirXK50ELKdDTzzMzNwyHatDxPSR5Y1jXa6cT+hfedXV1QZXVsluHGZoOzYnzyzCmSvHdG+sSJBlifiQRHnFoVUFv2WjKTID94Rx/AZrpTnGxsa2lB1vOVwjkQgf+chH/obHY9SbLY6/doRkKkU0HMbTTGq1GrX1FfK5VdJ9WTQNqtU8ku9hhA1Chk5HC7E+P4fu1QlFdHqnpjj4oR/g4ktfptNYx2mr+O0c8f4poqMHCccTmEPbiWZ6mbz5HoTjIDwX4TqcevrzxMwI9Y6H6hTJDPfz/u//CE8//XQQroHAP6K/nh1vvIl15ZiXjW/F1cdtq40sBFFzY1usdGXDwebNMJnXpxSEeOM0w+vvcfXL1XaGAt+TWDzrX/1cXZXZtT+KJVVYWHSornZIpVJ85CPffnZ07YBCRdVo2TYj49MYuo6u65x97VlKpRIrazkq9Sr7b7qFVn0dXfbpH5kiksxQzuc4fe4cEU0hGoNI7ziD192F7F6mUp4jPTpJfPstmEYYXdfRwmEcu0Ypf4l4PI6iGRRKq8zNnsOyPbRoDLPTJhxaJ5I2CIWCo7UDgWuRv7m49fXTXzfSTwjxhnauAuHLCNnbfO6buxH4rz/tSmOtzSC98t3rI1Zg8yjv18P2SluYsWwKuQmrR+s0q2Xs1jVyEgGAJwTffOZZ/vsv/RJLS8sMDPRyz9tuoSebQpIEPX29lNfnUJQQn/3s7/H4175Gs9lkaGiIhz7yYR54z/20iqvITpuWHKe0bPFHj57llZN/RaX+aySTCe697x4+/OEP0izPEU+PYRoh/uwvvsD/+c3fZG52Htd1SSYi3HbTLj7xsdtpOk1ypWD7ayBwLXpjTpabPsU6OC4YGvQlJSLmxihV11Rsx8fHJ18T1Nsb0wiaCpmYRDKycTKBtHnAoS8EpZqg1t6YelBkQTQE6eiV+dONkG1agnLTx3FhZn0dVYawAamo/KbGMd+uroXr049/gz/70z/hnntv5brrdvL0My/yR5/7Ij/w/e9m985xejMZNMXk//oX/x5ZUXj/e95BKh7lxVeO8Us//wv4vscH3v8A1eVLSLLgP/yH/8zp0+e58eBOJoYHWCnV+MIjf8GlSzP8p//4r6m3ZH7vtz/Dz/2XX2BkOMv733cLulwjl28yMzdPz+D3EWYPA9GgK1YgcC3yN0eXTQtqbUhFNvq5VpqChYJgonfjxAHXd7Fdn8XSRhqnohKqDI2OYLW8cRMrFb3SUFuwXIS2DYkw6CpYDlSaYLuCgdRGaFquYK3io6uQjGwErutBx7nSnnvruhauMzOzfOJffoyRoUGy6SiHbhjlZ3/md3jt+AXe/8EPoKhJfvVX/ie+6/HrP/1viEoK7VKZg5qDVyvxa//rV3nPgW04dounnnqB4yfO8MNvv55tO7ajmQrbB6KY5r185WuPc+70DNGwz9HDX8V1PT76oW2osszQyBSyrLF33w5CyQmikemNPrOBQOCac2XLq+3CSFbC1Da2qsZDMJOD9apgOLMxT5qvb4x0J3pBUTZel4hIrJQE+RrEIhvzr7UWtCwYzmy8H4AwQVMFhZqgY4GhS7StjQmF/s0G+69PJ2xc1zUVrmPjQ2yfHKO3N0MqmcUMxdl/YA8nT5ylv38at93g8MtHuG33duqnj1M1o7jtFr7dZGcmwpGL8xw/cZwezePxr32ddCxMtV6nViwRi4YQvmAks7Eh4LWjT/A9b4szmNlYknHp8iIPvPsg2YEhZC1NvelTarUwQ3P4VoNdO/d1q8xAINAlYnP7q6mBqW7GmZBQFIiagoZ1ZV7WpWVt3NQSAjxXbE6pSoQNiXpbYFkQ2vyzrm6Eqedv9tUSENI33r5tCwxdQt78uJa18VmSJG0ckLi5WqEb6dq1cB0a6qc324OuqqiqTG92kP5shldtm2ce/QJj49M0Wy2+8coxvvHKsb/xPSpNi56swdp6nmKtyRdfugAvXfiW57XbeTLD23h3TOapF1f43J/P81dP5Lhu3wh33nUd2yb78KQ4yYTMytwl7n77+7tVZiAQ6JqNoaumbJ6PdfWRjflU0QHH2/gV3RcbUwe19re+HsD1N97D8TZGwrO5b30ObOwKk4CICbUOFOqCUkNg6hAxJMLGxtREN3QtXGPhOL09vTRqTTxLo7g8g92oAbD7hjvQlY0LvnVqmOtSEnK8DwcFTZWJpDMM33A7AzETXdMQSPRmUrz7ve9CCAnTCBExZexqAd1dZWpfLzg+Qmh86mc+wMlzBY6fWOCFIzM889wjTI4P8c9+9GMI28eMxrtVYiAQ6KLX16aCJ0C+shrLl66uCnjjaQUxE2KhN4wqxdVDY9HV11cd6CpkY9IbbphJV1fPqsrGa2RJYiAp0XEELUvQtiFvCUwN+pKiKwHbtXBdXl6hkSvwzFNPgmpw/W1346lhTNNkz003IUsQCYeRzAg7JgdJDU7jeoJOeZVQKsxA0qNdnsM3wvSloswXW7zz7XcTTvSR7eml1ahz5uRLhDsmw9Nh6vUWTtMiHCmzazrLwX3T/LMfewe/8X+e40//8jlabY8btu9heSVYihUIXIu8zXVUjrcxReBzZR3qxuhT4vUtpFcOKggbr4felTv6V7YewMaI13I2pgGuHHD45t/xpavzq5IEIV0ipG+c1FVu+lSaYvP1W1+K9Za2v/5/OXXmPCdnl7jrgQ/yoR//F4ztPcgLLx3m7nvuwc7niOgG3/uBD3DkwgzK9uuJ9QwSi6dQJRWrWmH22Cs0c0v4rs1d1++kUq1xebHI5I6d5MtlLlw6T7u5hhGWWFou0nEMEoNDlNodqpaNke6lboeY2r4xvxpOpjEjCRYXFrtVYiAQ6KbN/LJcsJzN47UFOK6gZV2ZJ5VAkokYEo0OdGyB8DfCeGN0K/D811tYRU0Jz9+4sSU2jzfcSFMJz78yDys2gl1647oAgbE51Pzra2m/XV0buU5OT/Hf//un+YEf/ADhaIzP/8kXEELwkz/5SVqldarzZ/jYQ9/H49/4Ov/6l3+Vd910A5ODg6yv5ji3MM/51QK/97P/N0aqn/c8MMrRVZuf+U//ia889leMTU5RKeRYXlzg0qVZHv6h23jPu0f47J+8xstHZrjxxl1kMw6zlxc58uopstksIdPgxRe+ydzspW6VGAgEuuhKhmkK5Go+MXNjSVS9s7nkKvz6etN0WKJjC5ZLglgIdEXgi41gbtuCiZ6NEWlUl2jognxd0LY9TH1jesD2oNkRDCQlDF2i0vLo2BDWN6YKPB9qbYEib9xg68piLPEWfOxjHxNjY2Pf8jggPvnJT4o//MM/FNu2bROGYYiDBw+Kp5566luem8vlxCc/+UkxMjIiNE0T/f394r777hOf+cxn3vQ827bFpz71KbFnzx5hGIZIpVLi0KFD4md/9mdFtVoVQgjxxBNPiAcffFAMDg4KXdfF4OCgeOihh8SFCxe+5XOfeuopAYhHHnnkrZQcCAS64LsxO6TNAv9eHn74YZ588kmOHj2Kqqrf0rzlWuR5HuVymeeff573v//9PPLII3zwgx/8x76sQOC7yndjdrzlaYHFxUV6enrYs2cPp06deqsv/wd38uRJDh48+I99GYHAd73vtux4SyPXM2fOsLJ59z0ajXLLLbd82x/8D6XRaPDSSy9d/X7//v309vb+I15RIPDd57sxO95SuAYCgUDg76drS7ECgUAg8LogXAOBQOA7IAjXQCAQ+A7oyiaCj37oASRJQlFVdNNECyXQjHF8qYEWS2P5Kqu5Y3ScVTzh0Wp2sFo2mqITjSRo1urohoQW9bE7Dl7D5B1T9+JZLpfqNQ5e9xC4KrZTYnXpKJIqiPbvJ65GcHPP0fYL1L06TbcFbHa4YaMxLpLg13/js90oMxAIdNH//w9uYXTkALumH6TWWOHVE59ndDiKLxwSietZL76EZZkgqTz+2GOEzEliccHgcIRoxKMnK+FYUb70xSNktHsZqz1IbDBO31Q/5cgf8MLxz2HoMpFwlj17foD5hdeYufwcPdl9TE6OImsSjusQCiVZWp6jkC8wPjaOY1+gZc/z73/C3lJ9XQlXVVWRZYVkJgO6ihKeJtrzIE47T2Ygi6YIkpFBKtUKldplZvLPkk0MMjQ8ysr6BfSIhO/bdCyXsBGl1mrzzcVnaDU6KL5M5dUSkhFHVXRiiYMkZA+lcYJlr02rPYPAe8MeZOnKpjc2Ojt0p8NNIBDorlZrhYXLNnrhPuLx7ewavRcp/AyhUD8el1DUVZx6hnq9QKPhUa2tEI0PoygZkokhetLjRCK9fO+HbiDcvpn816K8+Phfkb4wwb77b+W9754iHu0nnRqkWp/n+ec/iyRrSFKSixfX0Y0ZGu0KrmugqiqDw7sYHdmB6/dx9nx4y/V1JVwjRpRUJI3r+Ei+iWymcTshVCuGWmihKR4DSpxIzGTH5DZw1inUl2h3WkhoKIqFQEY4IZSQgRGz0QydiCnTaDQo1s8R80cZmHg/vh3GUj1s0yNkW0hmio5dw/PtzVDdDNPN4xy60/Y2EAh028TIHcx802euVcdyq4xN7GfH9/Uj6xa54nlkkUWWc6SSg9z/wG3EEkOk0wbhiI+qtnHFLHXvONm+CNXDUeJ9fdx71wOsl1qEisOkphfx5NNcnP0SyysvYJgyihulVFrC6jjsPZhnJDrCwryD1ckz3D/AQP8IzdYI26a2foJJV8K13qlhey5jqW2M9eyGyA58pUVLVwgJG9n18TUT0a5SyttUagU6Xpm1fANkGU3XUEUIp6NgGikiiSEa1TyNRoPevkHq5Rr7pm/DDMeIyuC7cfxaFt0YIjQcJb96hLpXo2XXNxreSps9GaUu7REOBAJdl1spkPLfj912QfZorLo0z+/BTc2y3plF1SRSPS667uC5qyjqSQq1WaS6T09iH67n4tuDVI69ndwJm4RSZ2RkknhWIOsqi0ePwvTXmV88Rz7XYe+e92IaUY6dOIokCyCMrocYHRlEkdM4zgrV+gK6up9EbOvR2JVwlSQJSVNR9RApKYLfcWnKHTohQSsaJRyNYzRKyNlx5paOUazl0PSNBreS0DBjGvF4H1J6L4a6A80IYzeeIBw9Ram8huobFAqLDI2METEihEwFN7oLR7SwRR0tniDlCnr8LFW7QdOq4Xk2EjJd6nsbCAS67Lp9H6Lp7CC9Z5gTh58HT6ZywUQydhDbGUZEv4xjKVRKPaSj28mGbqRTGSGe2EVPfAf1xmVOnXmZ6nEPta4QHYuhmjp+x8L3HUR1gPxyi/mZGrHoMIloL0url0hmQkxNT2Bbadp1i/X8WZLpOCYGwh8gZPSxtHxuy/V1J1xlCd+zaFgFzhRewUtfh5rdS6uyjDc7h2+5tBJR+nbfyNL8cxghjWjcwHWgN7GdiYk76NQ6OGIUU5awhEEkNozlHsMVNo16ndPzT9O2y5jXfQRF0XDaglpTwowPMtqXJd1cJh5JsNYp8vL8YTq+gxyMXAOBa9bQ2DCWHsFZNJnauYeVS4vUyhVc30cfnMaX7ke4DWLmGD3JHkKhCfbvuoNGO8/i2mtEjRTReIILif/FTRO/SFREkXWZ9koTOa5SL3isWTF6spPs3HkT5Uqe3Oo8jiiRTCRIZfoJm1UWly9z5nSeG246iKb24rkea0trW66vayNX4fsU2+vU5DJ2fQ49902S4Snk5K3UWmssrnyd07OP4vgFIrEoiixjmhl2bnuQ4lwZPbkHrfQaQjTIDN7A5M73cfG8xszlx+goS3jCZ75whsaR36U3OcF47wFieoYRTSHqmmQjAyyUZzi2fgzLaSJLErIkbfZ0DAQC15pC8RtIfpx6/j0YoQyRSAhkCdU0KFebDA8Og8hTLKxy4fwL7N57H/090+hanMmR25CQCZkZFlaPs7r+ClYjRSjWR2WtTEzvQ4vLHLzlNvoHoyyvvsb84nFCUYVMdIBYwgblIqXaHKVSm1plCFkawvdkqp08ly++uOX6urNaQFExtRCyqpDJTBJOjuJKFr4yil2PkQhBfOIdzCw9w3K5SL3YJhyNMrX7e3ALILXqZHcNoLKIU7bpVX2ot8mmJlgM6YTdEFbHR1Y0fLlKqXge2grjE7eyUPaJGLDYPMNKa46m10JW5M2lWBvHOQQCgWvPZ3//M+zaZTCRiBGVv5dEby+KpiGHw4QTt+M7T5FIGfhOjJXFFun4OBcvvky13WLH9pvJJkcxw0mGeu6kMjPF+so68ZBGs9Ugrg4wOX4QLdvAleZQzTTXHboDnwK2M4cnTjA/02DmgkSjkUaWDBTFxLIdiqU53EZmy/V1KVwNspEsYSOMHt+PzDBRxwY5gZs0KK0sYneW2TF5F2uvHSduRNGVNG6rjpZVKS+s47z6Vfomd6ANpLlo+xTP/gXzxcPopk08nsQNC2xLYHUsWnaLlfXHiadNYuE4rXITlyoNv4GsysjSlTWuUjAtEAhco2bn2sTT0+zaU8f3n8erTOHXXdLhFFH3FqjuRXJLDI8lmIhHcPzz5HPP0rTr1PrzRA0D8DAjKmE9gpmMgZAp1msMhxVAot1sEkk5JChTq5+nWlmh2bCRxCQnjiyRWzXZd2gftfpFbG8F359mvTCDQXTL9XUlXBXVIBruJRNKEU9P4ioqdKCYr2BQZyzsY4kUDaOFqttYsk3TatFZepRGo5e19jzVhSbSpSzRWAolXCMck0G4OJYKso1j+ciyhtWpYdsW4WQCUxfEUShLDQp2EUkW6Cgbo1VZQpJlJEnpRomBQKDLRsehL2syMngzlp3H175JY1nFbyaQ3D6K5yUcOU7f0Bi580u02jqDIxP0ZwcIyRO4VYmi/ySqZpCM9+I4JsXcCrZqYztNQuEVpPQXWS0eJ7dapF716esdIx6VWF2C3LKLqsVJp/vw/DV0TdBsl8ivz2NVr5F1rulQhla7QgwNipeQ4xOsWbDaOIXX7LD/wP3UKgscfvV3aTTrqKZKNBSnbRdYKObo2D5OSCCJdXK1JXpjKdq2xHD/HaSiWYqNJrXWKoX1k4RDGQYGDhAydVB8OlQRsoeh6zie9fqoVZaRZQVZDsI1ELgW5dfD9KTLFMqnSMR3cPTkb9LfN0ByKI2WP0RmtJe2JZh9pcyxx88wMB1h+61TyKLF4uE4hhZhWZtDbx9i7pUFonqE5eVLDB1SGLlNgt6zrJQNVhfHqZSTmGYJIVJYnTbrK6s0qx3GphNkMwMYhk80PEK5XMDulMn2DW25vq6Eq6HoDIT7yIYGkCNjLMkyjlihas3TkMIYxcvMnXuacmkVV5FQPY12s4OQNDzh0ah10E0Nw1RRJBlVNnHaFuFwimj8AINjPayu1RmNv0gonkZWx6nUCkj+RdYlFc9vgnDQFOXqEbqSLKMoCpIctE8IBK5FI8MDHLjuAMtrr+GKKD4JItFRpOwqHflZ7EtDyPYull+1aNRrWO04UXEztjiDoaZoFhwGtr+H+to2qtUL1JQcxraLHPzBCH7kFUKh7UyG9pKOrFKpX6RQfIlSUSIVn0CRnkORWmR7ElSqeSyrSbW6im3LhMPQP2hsub6uhOt6bZFKI4cqn0VJrIGZpl0+Sq3dZCl/lNX8EayOgx5JovkW0dAkshKl0biI41jIsoJrC6LZOB25g9OxiUauJxa7gdyleYrORcxQlsTAHly3SqYnRCi9h9lzyxhhlZBhouGj+jKe520cySvLyLKEFIxcA4Fr0u59d9Kwj1OvOxSrNSxbkMsvoxspYimPGf3XKZ+bZHrgn5CwIwxuz5KOb6e8PoRXXUGRDaL6MB0lRjF/lr7bThHZc5b18o2480vIkctks3uJRuIMR28hGdvOgnqZQn6BcgW27b6eicntzCyeQ5YdJqdiaKrFyGg/6fTWc6Mr4er5Dm3hIjwfqfgSihYmOfEgfj2Ho/poSoqEthfF01ESTdpiCM9VqNWeo2K9iFqX8C0P32+QSIXo2Bap/gyGJtDVFoPju8jnyyi+z3BmnIjlgemSSu9BcZu4QqLl51BUhabfxHFbSMpGwCIHN7QCgWvR7bc9yLEzJeJKDdf1GR1PMzH0NkYGD7G8eoKnv3mUdu08O697P3fdeBvCc3jlK2eorrfpVCV6JlLkL3kU5ldJjKyDU8Vvy1yefxXdGWL76HYW5s7Sti8xMLCbRGyK0ZFpenr66OufQPg6ttXhmy8+xvDIAKqiYll5fFGjXFvccn1datyiMZodx5IcLMXH0fpoOnGEXaEnPMH46J041SaNXIlY9lYadodG4RLRgV6kSgrhChypheu5tN0aimpQLj7HCwsv06hbDBdnmM+dYSg9TvTmh3AabTQqqG2fSG8PmpGF2kXKoorwZBo4IPlI8sbcayAQuPZcvnyK/sydNFoLrOcv05udoiczznLuVc7NHCGWTvOu+z9E/2QL/HmymWE6lk15cYlIeBRci/LyOnMnTpPI+OQLCtGRHhYLZ5BtjYS/ylJxkZXiJS4nC5jhlxgYHmfXzlsYGhqm1amyspLDF20Gh/qIRCUWFtfZueMGouY1MucaDyfwPcFgdISeUJYzXi+5qorU6GFssJdQrUnEdunty9JaWMUMt4moJTqOhqNfT1s9jNBsHKuDg0Q8FcP1XDypjFBd1p1X6Phllqtljs6Osi07RY8awkxH8RSFkOUjG0kUqYLS8VGEjI/YXC0QhGsgcC36r7/0X7nrnoPcedfbkeRV6rU85ehJypXz1OpH+cEPfBxUlVzlVSLhFVRvnZqxxqtnn2Q8Pc3OvpvYe+teomnBn//ZU4R72/SNT9EvD9JaUXnyG68gKzJvf+fbePb4kyytzhOJmzTrNfbsvRUzZKDqEj29g/T3D9Bqz1AqlUknJtBUd8v1dSVcW+0G2Ug/KSVKVMRJ+yHMvgh21MF2fXIr58gmBunINrZmkpR1wiJMDZVlu4btVVFUGdeRUWWFRsNBwiOVuo5EIkKtVCWdzVPIX+LI0d/jUmSEm27/J0RbDlE5xlDCIKvrpLw4hqGBItNwKkhSEK6BwLVq1+4m1doRzlxooGohMqnTrKwdo1pxGemdZn52hqeffYbxyRHe8fYfRhISX/3Co3zz+eNsG7+AJS0QOttDfSlNYaWfsFJnIPp28utLzJ5/ifMXcxQrDe5/553sGpqgXlinWevw9De+wtFXX+Se++5jYGA327btJ50cYKBvlPHBB3G8HPV2bcv1dWnO1WW1toIhQjR8G9l2CMlhlHQYO1QnJ1Y5evlF6p01QuoQu6fexeDAFObIOPLpLyCKgo7bQlVjhPQk0VgWy27S3/M9CDdKf69MMXeCQn4WSe7QcNY4dvRz9KVuY6p/N71mFMs1kYWg5BRo+S0URd0M1mDONRC4FsVTCvGECwIqRZvDywVkpYmpS0xP7uTwK8/Tk0nx0Af+Fc12k8/98ac5tfASQ/tSrM0X+asvnWLPgT52jL+LTqmFb1r81n9+gtve28+FMys0mx3sdoe5hfPM5ObIJhIMaWMUahZrq0UOv/gU737vBKPDU6QSvSTiErV6iXKjSE9m35br605vAcDq1JnxzuF7Hlp8H0ZyH7a9wrHDj2DV8+TtPAIfVSlSPrnESONWYoUs5eITmHoEiJHUDhCWImTifTSpIcrrxOJ1GnaaVnOduBmloykoukEkrhMLmeiyRb5Yo6Mu4mlr2LKHImubbQeDlQKBwLVKllK4TpJD+3+UV449g/AzVMrnsHST9VKV8W3jWC2HZ1/8S1568TleePEipgHbBsaRZJfVNZeOmGdu5gvYxhrCq5GrN2h3+tm/N8P6+hoYPosrJZ47uUyr7dCsrzHcn+Chh25Cj/Uhyyaq6uCLFrZtkC/lqTctdoz//Y/Q/tt0JVx1M4QZiWDbDr5t0amfpN28gGxkUcJx6DTJhqaxZQXPXcEwBau5Zyk3FUxDwbVUhjJ3IjsmlcsvEZbvoYPCaLRFTNPoT4bJhPdxWSyz3lnA9n069QpN9RIttYeEtIqqVtFUAwmZjuggJCkI10DgGhZrvoP9t26jXi8wODBJZDrJpYttGrUOjmNjS+ewvSlmZ5epVV1sW5CIGTitKsuFOk5IZaFYo2Cv0Ol0GPCjDI5HGZ/Yz+EXzyMJD1+WOHl5lo7VwQyb7Nu/nVtu28/o9jgTI7eRL1Vw7Catdh5V0fF9n1a7TLtT3XJ9XQlXfJAcg5gex0oYtFFw7RaKMcFo5D5S13m0a1U6nqBSepq19Quoho6KBB7UGxUWxSskjR5EKoRSnGOleIHRG95Pj6ETjhvEUnuoN3KsnJzBVT2qlRKKZnBv+iCpsEGhIza2xzotYLPH7GZ/gUAgcO3piR3Acdd4/oVH2L33QUaGdjI8tJMz5RdYms8hh1eJhYfI5aqsrZbodCSqdRevWabUsJFdAcKh49moUbCEw4W5FeZmlrhwAi4vCZSwTsyVOXBwBw++/70MDk/h+RVq9Qv4no/rejSbVXKFi8hillRqBNvqsLh6kn3TW6uvK+HaG86gSSZRfQJSu0GW8FWLipSlWKmhCKiXLzG2bS97Jr+HxZWznL38HPFUBgULz/WRzBZV9yLNdp2ms0TRadCKRlGzu1mYOcPM+hnOFV5ECpvo0iDJ7CJmNESNGCEvxICpsGbnEAiQlc1wDW5mBQLXqoSxHVXUcVyX8+dfIBxOsrI6TyFXp1yyUaMuoi9HpmeS+MoC6XQIiTahuMu2PRlOHc+hSaBKAkWRkTWHEC0Ov/QMq+UCKCpv/55b+ZFPfAxJsTBCDVbXFyhXZoiGFTpOB0WScdw2iqSxtHSOfH6OVHY368VTW66vO2doaXFs28EL9eB0ZIRoojRWScoNoqEEFbdKX1hDq5VYXS4xefBO1oorOM4KQ8kswrFoKhahcJSFVpO62sL3PV49+Rf4smBp+Sna+WVuPPBetPg2qq04tnMOzVvCVzIsuhJh9wKev9Fu8MpoNeiIFQhcu1wH4vV3cvctZ7m4+Dgzs09Tq3Y4f3mdQt4mGhF4Xo0PfPAg99x3C/Wawvnzx7h44iJnZisb2919D1nIyMJgfd0mFvFQekLIikEsFeeHf+SD7Nqxj5X8KVbXXyNsphjYcQOVxlkQLSZGdrO8OotpKIyO3UinZXNw7/dQqr665fq6Eq6lTpkepQ+tuYYTT+BYdUq5S8Tje7CaHc6tP4ue2k6jUwBNsLrWxNPirBdfoFRbwPU9Gg0HTVXQdAPhC3RNAbXE0ROfxupYhJQhMsMHEH6Uyvoimhcnkb2DptPG1AtIko+MzMYhWrB5R6sb5QUCge+Ak8df4sKJGHu+b5V2GxQljRmS8PwLeHg4rszqUoNzZy6SzM4wPjHI9TdNMDkygVc+zMXZV/GEQEFgu22QBbWW4NiJE3iKztjOQcZGD4BwiUVShEfvp1Q9hibXCelRfK+DoYep18qkkxKSFMLzdUwzSlIa2XJ9XQnXjJEmo8eoSiY1qUMu/wJCNFlY/SJFOUy1tUY99wqKIqPIGrsnb6fSPIskyYRDMcq1Cs1GB0VRSaWjJBJRCrkSuqZSt0u4wqHWrrGwXKcvJjGYSOJUF+gJgadksPVhROsomlpCkWVs30FILuLvvvRAIPCPpFFt4OoKxfIeVLWHTkvQscr09SXYviNLpVRhbaVIueDylUcvkuo5xcH9k1hLwwhfY6AnwvJ6HcsV4PsoMrRcB1mUEchUXivzP/5/v80nfuY+CrVLRMNpwuEMjYZHMrqTesPC0MN0OjXabYep6etptNbxxDqDvbduub6uhGvNrtAOeZStBTqLr6BJMmvtGp3IdcQYAHmGcPwwpdIallPm8trX0cMGmhqi7di02x6aGUISkEm/DV1NEx+X8KQKlcqjeK6DLHdYXjnMetumd+gGOlYJuZGkz5XI0kKRTEQ0iRyRyMs1bAQID4J510DgmqQJA9+Hqd73Mdf+Cq/NPUOtModtO5w5VadZ72AYCvV6m7V1l2Qmjmrs4oWTp1m90MH3wEfQcXx8AZoscARokko6pSCrKnO5cxSrO9HVKLPzzzI6Mk04PE6lMUerVUFIdxGLRbg08zRDI8PEExrNdgldLW25vreUPA8//PDVu/B79+69+vhP/Y//ymf/7PMktB4yfZPEUwNM9d7MUOR64rEUQ+lhGmUfXQ2RyqRRTQOEjOuC60okklESmRihSJRs9gAj2e1sHztAKnoz8cg2DCOCEDUuzv4Z53Nf5/Ar/5vc8gvknSozIY0LqkZBH0AiDo7HkJzBlDaa3S4uLl29ZkmS+MIXvrDlH1ogEHhr/qbsmOjdy//68i/w3//brzFz5hLl4jxCtFA1m7XVGp4fYde+G+kdGGRqMsaB63cxPjnNjpujqDGLQrVFSA6Tlfrok4ZISL2k5TQREcW1JaJxibe95yCO7xKJJdmz8wMIXISYxfdBSHWa7RWy2QEmpvYzM/88Fy59k2otx6W5lzl27NiWsuMtj1yz2Sy/8iu/QjKZfPNfCEFcH8BzEtihEL6mohLHbixSa9v0JkYptS5QL9aJJMLUanUSiQyTI7toOiXafgfFHSck+VB8DSO8k8GhUUZ3/hTLM89w/txf0HQbKCJEPJpC9pO0ai2a1UWyZgopEyeuJnDUFnWaWKKJAFKpFJ/97Gc5e/Ysv/iLv/hWyw0EAl3y17PDatgA5JbXoJ1msL8fz68SiUkM9ieR1DSHrn8v9WaZwcEs+3ffyNmLx1lYOI9qhjGVKCkxgCNZeELg+RotUQc8ZCuG47aJJDwazRmQG4TMOLHQHgyzCjjYdoFiZYaB/jHSaZWl1SaNRplWy8Nx1hkbG9tSdrzl35kjkQgf+chHeOCBB970eFiLoER78bN92B2FcKuF0ayjRzLI0QyeW6VcWiMSDeFZPrKikIinSaZv4PrrfoBEpJeJ/jsQ7RZ94X6ynRojcptUw0bUSxhmmES0FxmVZqNFq12hWskTcTqEPQtRKVCszFPvlHBwN/u5yoQjIT7ykY/wjne84y3/cAKBQPf89ezIlZYB0AyVfdse4vzZOvn1JoaRZWBwhFgsihkKoSoq1WqTP//C59m1bR833PAO9LhHTMqCq260FpV8bNFCQqDLIXQpxOqKw9cee4qZ2edoty6hqxrNTgXL6sEwBglp2/A9nVgkRq2ZIxSKMjhwHfHoEPFEnFQqtaXs6M4mAsAXPkqzhetImJ0GqqIR7pOptCpUFw5TbyyghF0st0wklEV2NOYuzWJEGlhOGNlSWJo/QaV0iezN72Owv59yUzC7eoR87Rye69FotujtvRtJ7md99Zv4jbP0je6jWKlQbKzTrj7DdXumiEeGiKkaqBotx+5WiYFAoItaehkAz3Moz/v4rRHWOqeIJZrcdPMgflGgaTrJRJZY1CTkj3Lkcz6SMkFPzcfza9hqB+FL+JJDJGQioSNJKsgSwwMe3/O97+DWO2/A1DNoWphWu0THcig3c+h6kkaziO3U6UllmF+5QDK2F12LEYls33J9XQvXRCjJa5de44/+6jMUSquM9E3wnnu/jybrFNbnEYaNosn4nse5o2dYmlvFsV3OHD3DgVvuZmRcY3nxRWqdJuI1m1L1Dp49/JecOvEMrWYLI6QzPLGTnVMHEMSwwqfJl49x4uXf5sRrhymX10D4xL4c4vrto3z4ox+iqYTwgzUDgcA1SYv7ALiOxxPPf4XjF+YoFC2OvLDI+bOP8r7veweeb+G4HcZH9qHOvIs/fuY3uZQ7g+05mJJJv9ZHVNVIRGOoagjXF3i+x0prhfJskZ94+H+TTOs88L038mM/eQe+66MpY1idGl95/Kt88U/OcenCIq7nkclEedd74/zr//AJas3lLdfXtXB97dIJnj3+Avfe+SF8u8UzR77Gpz/3Ka67fR96WCMWMtGExItfO40kSYzvHEE1ZPLLRZ79yp8zvXeCnrEsalhntnCUb774JWrFKtv37qK3Z5D8eo5zp06giP/GBx74BcL9ezFdl8//+W/Qk+3nttvuJ2q4LMyf48LCCvW1NcTgKL4IwjUQuBZpugnAWnGF2dxlHnj3+zCzNf7kD7/Gc0+U2XdglVh8iUSklz75fv7dH/1bfN9hKrMNHJnV6gqz9izTkTF6Un04jotwHC6VZqnbNa6/aYJ41uHimUX+4Lee48jLh/k3/+kg0EepqPGL//ErTExl+PhP3E8hP4dtRTly5DWOnf99Ll98gVv3/siW6utauC6vr/Jv/ulPMbj7RlyvCEmXv/rLP2fm3Bx3vOt2BC5nXrwASLzr++/C0CaImvso15/m2a89zey5BXrH+ohFw5RXy5TzZa6/Zw8jfWPUinX6xwZIZ/p57qmvI995hv2D+zh79Gv4vsdHH/4xCmtlBOsMDR1CqhRYLLYY6PPwpSBcA4FrUbvTAaDYLPIj932Cif4pJt45j8dp/vh35/nmE3P8wA9+kvxaiS9+7VF83+MDN36UWilPo9QgFgoxJyTmqsuM9U5g6AqFUolqp8J//pnb+d7v/xH+7IuPcPHcMsmM4PQxm3/3k4c5dEuEdGoPniv437/xr7D9NYrrSdK9A5QbOiE9yf7dH91yfV1bBDo8MEA0YlCbvYizVqe//wCDQ+PUSg08F1xHYX2xzPD4KKbSR8gbIar2E9FH6B3qw3M9yut1dNWkuFokGo8QSZjk15epNsvUqmv0pmIAvLp4ggUTpIQGwMmzdfq2f5DJ7bcwvmMnFaHRRsbHw/e33lE8EAh031zhAgD9qSG2pfZCJ0xt1eH2t42ya3+CC2fXMDSDuQszPPPCNxjvm6ZV8KktObTrDmYyQk98GNd3qbUcVClMsVmmrzfDrffcxcrKM9x24/vpH+hjx+4EALFYlNGxcQb7N3ZgPfXEy4QNC0U/xuXZz7O2NEexkKe/f2rL9XVt5NqTSZPI7KCoRWk0l1HkJrFUBH/Ro5gvYRomju1w6cxFLp25+De+hyzbSEobq9OhWWvxzb988/7eVzgOwNlLj+OGFomlUwyPbuerj/53nnn8t5gcn+a6G3fTNzFOOhPHFz6e73SrxEAg0EVu4iwAO3bswYwnMIwIUlFCzVxi34GdnD72MivLBc49U6Fttzi18CqneEMmNF//Y8fu4LsJOn6T2nqTd9/zS5t/8wdv+szp7WN87MP/ksmx2/j6V47wyz//RT79P1VGJ8PceNsEU9sdVhaOU8jPc8uej22pvq6Fq4eL17hMv7qNhUYaQQgjPAKcxHUFpVYRgGx/L++89+PE/TCOk8fSokiGzYXlpzGjMh42EhBNhBna3ocsg+dIeI5PJBrCFxCNxTBNmcGeCT7y8IPMnznP8uyrnFt4hc/9wR8ytW2Cn/gXH0MSPo4bjFwDgWvR9t1xHv8yJDMxbnzgRk597TKiMURE3IAkPQVA61IPIWdjlDnRM02f3se9P3w9A5MxLLtKpbbE8uIy6WQvE1NjnPxpjURvkp/4qZvR5Ah2o0ZPeg/p9F5c0WJsdIQbDtzL0trj/JNPpDn8Ely+4DN3ucnv/8Zx9hxI8tDHBZYV23J9XQvXRrmN4sWRjAFivg2dKq1KCUVR8JUmWnijLRiSgxnOsX34fTgIGk2LXPMV+vvTWJKFLMlEExHKhRo9A1kURSG/ViHZF6Knt49IKEar3iJfXKXVqLJ/2uCGu9/NzYfeju/nePqF/8OXvvxFTp8+zdT06EYLwkAgcM154P4f4Nd/+ae5MPcyLXmRQilHQkqTW+ywOJ/HNA1KlzrESaPJOr7j84P//mbuuv8efL9NpZbD0HtBupOWM4vtLzIxneXCmRwTkzHOnjpJyBgn0XeORMJlevyD9GS2sbQyw8/+/Kc4cuQkn/y/Ps67HjDpdKq8+GSN3/7Nv+SZJxUO3ZzYcn1dC9eZ5QXUiknWUCimIhQKM1y6cJhENo5mgqbKpAcSFFYqLJXmkMLPcenyCZyOi+2WyOhh2nEPM2QwNNHL2mKe8moTM2aiaQahUASr6WK3yrhuB6vdRrRdLi8dodK0mJi+B1XxSQ+EAHBdD0mSEL7frRIDgUBXbXStO3t6ia98/U9oFZOo7MGLJTj8/Bw33ng9dttl9qULDJlDLNXnGdieRNKWaDbP4il5qk3BhXNHmJi8FV05yNBwnReeucQ/+9ifYYSgJ7vIvv1h4vHXuO2mJqOj9/Nbn/ltHv/6Wbbv6WVy8hChkM7/+F//D6W1jdMHzp7wWV89yy/9h61V17Vw7c1m+Lef/X+4687vx3LXefnlpxAIJnYNEIrIyEJj18EpjlRO8cSXnqVn6DSKrqL4Eq1mi2PFOrfcfwCr7dI7upNMf47TR86Q7k2T6k3TLLdwLIeVuWV2HBon1hNi/swip147R+/ACS6cewXNMzn62uMkEnG2b9uOrkLbtrpVYiAQ6KJzJzduaE1vH+O3//CP2Dt+HecXTnHiGy8gEHz4oe9l9akCi50j3Pe9vXzp8Rwf/eDPcu97hghHGzhWjLMn8sxebvHBH7apVZ7izMk1VB2qeYFmQF8mxqXzCjOXVvmdX/99hsf+EjME+68b58K5JT71C7/Ke9/zQVKhm3n8pccIhSChwNpseesFirfgYx/7mBgbG/uWxwHxyU9+UvzhH/6h2LZtmzAMQxw8eFA89dRT3/LcXC4nPvnJT4qRkRGhaZro7+8X9913n/jMZz7zpufZti0+9alPiT179gjDMEQqlRKHDh0SP/uzPyuq1aoQQognnnhCPPjgg2JwcFDoui4GBwfFQw89JC5cuPAtn/vUU08JQDzyyCNvpeRAINAF343ZIW0W+Pfy8MMP8+STT3L06FFUVf3W5i3XIM/zKJfLPP/887z//e/nkUce4YMf/OA/9mUFAt9Vvhuz4y1PCywuLtLT08OePXs4dWrr58x8p508eZKDBw/+Y19GIPBd77stO97SyPXMmTOsrKwAEI1GueWWW77tD/6H0mg0eOmll65+v3//fnp7t34meSAQ+Pv7bsyOtxSugUAgEPj7Cc5ACQQCge+AIFwDgUDgOyAI10AgEPgO6Momgo88dBejA4M01sscOzFLJmawe+cIkVQaRdXAd3Ech1hIAUmwtFahVc2zkKszPTrB8FCWlfUyiViM1WIRIWys8BIfft8q99zawe341MoShTWJXFnjy6eSnF6O4Tkdbh66g5ieRdMMWrUaHm1cX0FGwbcs1gslfuNPH+tGmYFAoIv+2Sf/BYnEAIOpQ/T0DXLm+GGa9Q6eD5FYnXr5NPtu2EsiM84X/vzzpKIjKLLO7PJJ8ssKmqaS6nVIpmQ0rcW9t/8giyvrPPfSUSxboEoejt9BMiT6+pMoEkwO7KRYb7Bt5BCzC+eJxtJcXHiNTmONGC59sTArHQ+73eYvH3tiS/V1JVydtIYqy/geRAyZydF+JMBQJCzPxeu0UYRD21fp7e9hZDTNmWMrDPV6jA80mRrPkcyo1GrL9KfWEFadVLLD7jGBpOuoroWhQywqkH2b+7dXWMopOG4fsi8T0jU6VotOu0ksEgFFo91uUW82MXWtGyUGAoEusz2PaKgPYWs89+Q3UWWLTEplOb+K25CIpYdYL7Q5dfYlquUm7fI8fdk+YjGNNZp4lszqkk+5FGVoUOKZF75Cq5PADIdouotYroMke0T1GLVSDZBx+wW37vs+zFCcocwOVvPrFKNL5KwilUYdW2pSrraRu/A7fVfCNRuO4TfbhHW4bs8E/b1pFNWjTZtCpUUhVyVsRJjatgNXTzI5Oss775slkeyweGKOvqzHrWkJIcNLj8PioqDTlohEAEkAAlWTiCbBakJSdbg1W2FmrR+r3mSlvUq1UkLTIkiyjuzV8awm1dU1Gq3m33H1gUDgH8NC4TQRY4i6E6JQzOO5LeZXVmj6K/T19pErFYnrEgkjRCzqgtdhbuUyjpsBYaFq4PsSvqegJYcZ29vPxcur1JdySJqGr1Tx/DaRWAo6MksrRWzLZS03QzqRpt2UuTRzCU2W+b4H7id34mVOFqq4cg3baW+5vq6E6/bJu2nmzlDKzdKfSeMIidlambpo4nuCar1E2FQxTB1HSGQyFtPTdSRZELlBol0EzxKEYtBcg6gOyYRAj6iAiySBqgmQJHQdTEOwJ9PBbpepe9uwXRer0wLTRI3qxIwEtEo0a1XyhUI3SgwEAl2mGyrF6ipSRcK2bXypRdurEU8laFsd0BTK7RpruRU8U8ZDwjdCOK6DpsfotBx8IdDDDs2axenjRUrFFq6TwuuYyLqHUDukU0naRYtarczLrxxmIGpjGFFsx6bjFDHDq3zl6+dJGqBqCcKmzO7te7ZcX1fCtXd8mhPNElZ5nbzaplcNM6wmcb0YA70z3PjDS/jeIs89fpFmZZxi1sOXZRQ8QmmB15JolaGTA7cDIROGJiR01YHNplaKAq4l0BUJ14BYXBCPKeTWmtTrDYQMtXwBv23R09dPyDCI9o8xom29dVggEOg+GYP51bNI5TrpxBC9gz2sNxYJJyU0E4SsYzV7qDQkfNEA2UUoTZRQA6vtY+o92G4Hy1mmXAiRsNJILsieRESO0Gr1oYRUrIaNHo+haAaRmEpHOYce7kP2dejU8SNt3I7FWsdneEhmcnyC48fOb7m+roTrbG4eydFQQwNYtQWsmI7rCXqkDmP9FtsnmiAEdrHNmcNFbjzkoqg+eCBrYKbAqsPKeQjpEArD2G4BChvhKm1+kAuGDp4OqT6497o1zOejfPOlDCEzTiSkIHwbp93GbVdoYlLyW90oMRAIdJllWbTtGq5yGcOXiUYOkRm6iRtu2sn83CyNdola1WV9qU0inmGucBxV1omaGoleQb1QRpZkJFnlwI376O3v5ZVnLxAO9zG3mEMxoTcTwfYE2VQKz/NRdZXUYBgt1KbVKWG6HiG9h/7YMPNzJ7FFmwszJ8iXq1uuryvhGnfDRIeirNYvo0kKDdclKTu8470vMzTQQDKgU5OwWtBuS1QKMtmRzSGpD1pcIMkS9SKYBvRNQLxPbCwU89n4KkBVARMUCWJDEM42SETPY4Z28fWnHHr6x1AlFYGgmm+ysHyelhScRBAIXIsst4OiSyC36YQuMtdscM++t6OiYNUdrHIEyalxz213kE738sWnijieQ0h3iMZ8tu9Og4jgtHrRFBXbb7J9xzRHX5zB8ywy8Tj7906QGUhjGCGeevybNBsd7t59Kz4OHbuNImukY2OE3DH8Toel3GU6vo2W0LdcX1fCdfniazRDTbSoxcSEy47UHDdvb9DTW0ExXZCgU5GYPS9QJYOF0/2M7byIZgrwQNJAViUGpyF3QZAdAdnk9XCVNgavqg6yJLCFhJYASYJ0us397z6O4w1y+KiOamZo1FoUSmWWc3l8gmbZgcC1SEguminQfBlPblO2llAUn6efeZJWrUVMz+D4K2h+gohd49Z9h+jpHeLs5ReQJJWYkkR1JLbfdhsLC5cIJ2P0D0+zvtDA1ONIuuD6m3ejmTqNZp1DN+7BtyNMZu7AEmVsaRnP9hjq3U1+oUo6GkPV72SxcoRYeutdAbqzFGswj2u36Y17hItnkfKLGFMCRQaEhKQK4lnYdkBi4UycejHO4nGJ8RtAckE0IWEKQkMgl8GMANpGeAoJcDf/0UGSFFTVRwkJECD5YJoeH3hwmd5+h3w5yucfCVErueALek2lGyUGAoEua3faCASGZoAk0261+cazj6F5KulkllJziUpjEdsyaRo+JcWg3loiHPZpV6qoWZ1yrsb6xZMMZjVK6+vkyj7XHZpibb1JtbXAE9/8OnEjTtjoZ9v0IbYN38zlc+ssrZ5hYMykuXaeC6EZbth3AFVWsNptelID7Ji6Rk5/VWJxjEKb944fJ7t9hcuHBTMvS2y7GWKDAiSQVcGOQxLhhM3yhTbLJ6FTloinoG9aoGYEoioRDYNhAIqEEAIhJIQNsiY2wlXxUA02RrUOCFmiVoC507C+XuHImQbra1Gatgyex8X1TjdKDAQCXdasdfAlF2ISiqpgtS0WmpfRNJPkQIKRkUGURY38ik1eX0EP1zkwnWZlxUbSNNbKNfr6B3GX5njhYpmRbXu58cBezlxe4OLCy6yX5jdGXxjs3/k2btx2D0eeuUCpUqRKlYXXVjGpM5issDKnUMnn0DGYGLqehYvFLdfXlXCtlwvY5Uu0OyqTBwSpNJz5hmDmeRjaK6H1Qb4ik4jKZIar+AywckShMOsyvhsUE6ymjBKB0PTGTIBbUVEiDq4F2DJC9lGEDipIioUQ4NsSrbJM5bJAciQefVxjfl7DbQt04bJWs5BEsMM3ELgWuZbAQ9BSLHRDBt9HQsGxbcqVMjEzStNqstY+j1936FHCFPIa6WSG1fVFFDlBNCRY0TrERqZwXJW/eOxLnL+4RLlVQFcFuyYOsl7Jkwz3Ua20uTx7ESF3sKUVXGFRa1eh3KCxPsPUtm2YusepY0fINbwt19eVcC21F0n1pLElC1+C1IBgcKfE8ik4/qzE515MkGsMs3vvAD/w3mVqlQlW62127ZihbfksHgGrIuH6gqGdAh2JctEjjIRdBEogOxLRUR8p7iEbIGwJvwVyy6evF9L9gl/8qRaPPxvm+HGYnfOoNMFxulFhIBDoNt/18IWE05YxZIOBnh6ioSTrxTVyq6tYVRur1uTAxDjL5SqToxNsm7iBldU85VWVarOI4gtGRrIsXKpzaWGW1Xabcq2C7dhMjGzHjPfwods/TLNice7scRJpg1cvPY2vFInHwggk5psdMhqIfI5qqUnNVbHF1nd2diVcPR98R+PJCz1Ajlt3tukf92lUJL76FYN8aydeKM2zLy0hpH3Um4JM/Ca8iy1KzVUO3uRTKkJ5RVCISKgedJqC/h0QlsC1BZIrQLjIIbExekWgRtmY13UkNBkMz+d7393gvffB0y/H+L3fDbFSCNI1ELgWKZpCT6yfeCiJZujcvO+d7N52iAuXX+Kbr3wJ2+8QTZqEIoIeNUTErPLYVx5HWINcP32IxbU1Tp+YZ3W5QKle56473sb1qsmLR17m3MVzzC9eZiCzi8JSDadj03QWmVk7jC+XsByLekugqSbxZAg9IZPvOEhJHdV3aNW3vrOzK+HqOk2aCzk6xSqPvSKY+KcKA/2CU69Bq+AylFbJWWCZGkdO5NDNOC1bxc710yrlGez18R0Y2u6zeB7iPYJMPxhsrBIQPqhxkBOAKSHJAiFtLiHwAGNjakUSoMrgq3DrwSa1gsdvfm7rSyoCgUD3aYbEYN8guyeuZ3JiN6rbx8xMkXZZ5+DYHbxy9lnKboFO3zgD2QyOaKNKfXRswezcHLlajo5lcf7yMtGwzuzlOVxfYbh3kt6efl47+RKWVUONtpldPUUq6XMgtA0jrJGrlVENk6OnT6OqBunMCPFUglqrtDHloGz9RnhXwjXj5NBmq8gtn3SPRCoF1SZ4Msi+S7x5ikbdwEiOcHGlRFzTKFUc5gsuZ6QBhqYr7NyWpNSOUqXN2cfWufE9TTQNDF1CliT0lIcUE0gyCBe8IhsrEUyxcbPLlZA1CJugWuDj8973dphbCVYLBALXIjOksZC7jGUrLOcqTPfdzIkzR/FFnbGRXg7suplSfYnc+hzNUhk0g0J1hZA0yYVFG19pI7Qa45Pb2T4xyOETx/mxj/4Ely8WOHb6GaJRjeXSGR5/uUC5kOPA/n2ghTh97DiqrjMyeT2pSI3x4Qg7J7fTPzTCcm6dQqFCMnaNLMX60B6LJ44LJAVUSfD4FySUFHgmTNwMp56uMqjpVP0Yu8bHyK8t06n7xGMpkhGT2cpuWsXrOXHsOMXlBXZO91Coz5K28iiyIJISyEkAiWZFQuoI5LKEPr4RtnggqQLZ3xjlKjqE4xJyx+em6+1ulBgIBLpsZHCEaq1FsbmE7Vj0hLcRjSQ5ePA2bMdhfDJG3Vkivz7DM898nZbVxJM8hN5GSB74dcKmQzTUwpQL7JwewWpU8NwcutnCL8vkSxVW8iWSqRhtYVFZL+ErAt2UWVqcQfF9wnqIRAgy4RBlSSamZ7C9rW8+6kq4Pv0Vm6Zvoplgyi6zr7qoikBPS/ROwfabo9jyO3C0PtbXckyNTOK5LYyEgqxrFDo2ufPL5CsOZmaCS7kcg+P7KJSfQxMORkzQqUnoClw8IlGqQk8/TA+AX4NWWUI2wYiC64OiyyiGht2U+cKXVD70iW5UGQgEumnPzgMsr+Yol+pUynWOn36RocweOm2X3v4Y52de4chrLyH8Jom4iiklsDxQFIdKsYxhxFDNBs1Ok+WiwfiOXSDaNMrL3LL7ELffkOTzX36U9XKFcDgMisy2vWMszgnK+SUEdfr64oxMpDEiKZ577hki0Tiar7G8uvWGT10J18XYNoZ27seL2tSsAsb6Cvpyk1rBprBcom9vkuvuu4e5i+eQjO0ULIN6aYlybhlf8imVbDxPMDzaTywapmQ18dSd1PxT9GhVFOGgVXxaBXDbAs0QWA4UViDpg1MBT0DHlEARGDEfQvD5P4py/KTVjRIDgUCXZaOThIbGGD20g1dPPMWJ069SauusrkRIZSfQ5BUGewW+EqfV1Bgf2su5xQtIiky4p0IsESIc1ijlWuTyRfqHKnTMMD29fRw7foo9+/Zx0/47uDgnqLcWqZdCVNZXWFhbJG6qCM9mpVjkiW8+x2Aqw8pKGTQZdAMleo0sxTp+chGtJ0rS6cNQMvjDBmo6gTebo/TKCxReWGf++L9HqDKr6k7U1HbqzTblYpld06NsH09Qq7dxWy1W8jkGh4ZJpEZQZjqULZ+I79Nuwvl5cCIgRSUyAxubCuolGc/28WRwLZAU8DyJ3IzLy89ZmHIw5xoIXIs0OcJodoq1mRKTPTew7e3jWO48kr+GZDTIV/JE4yEsz2EtXyMV7WcgKTObO00kYqArMqlEP1ND01y6OIfsydiNdYor69x5y024Ioahpzl36ml6ouMId5nFhUWEpqCGomhOE1lTqNZaVOsW0XiMSCSM7Qm8ztanE7sSrp26y+knZumfXKM/NUn/VC+xPpdt6TI5SWH9ooVbt6hbEufa8xBukkim2DM5xe49Oynlc8ytn0TSNNLZEHsObOf0a68RK+oc2unTqHvQkNA0F8cBXdtsO2BLtFbAroMIgR4XaBGQFVhYkclVfMrBSqxA4Jr02okX0bwqwvFx5VUO7Oshkwxz+vQRjl+ok8kmmR7qpdXK0+nXOD3/KIlImgPbr2cx9zKZ8BSF8jzrq/OMD2TxvBrVhsuufcNcvnSO2ZkGsdQo2VSUatXi9kN3k0qFOX72KJ7t0puKMrNaoeMIJMlHd3x0R8KyHFqtre/s7Eq4moqEZquEXB270qB2Kc977ltDa5cpOgLFB1kGIUkUGxZJo0PY1Gi2W3zt608wvW2AgdFRXn3qGayhDE8u/S77emsslkPEalkSQ7NEshJZxaXsQSgFpinQJAlPF3SAVhnaloTpCEIxwXUHfYaGJRZL0t95/YFA4B/exMQ2LrzWQJYkmmKdy4s2u3dvo02aHVOjTI6FME24cGKeidE4tqWyut5ife4YalhiOX+ZUsmlJxvj5IXLpKIxxiMqL8/lcOQY+UYRIz7ADfvfzpmTl7DqbdKJJJlEHBkLx27jSw49PUm2j96IIpmM9mdYLxR45cKLW66vK+E6P+MTCbewHJkd4zHuv28FuVrhua9BNQeKD0IDxfMxcVFlBd8VVC2fUrnM/Ok1IvEot0+1iKsVpgc9YrrPgR6J1IBgYAREXaA64AswQqD5EmZboCU2Rqp6GxxfIEug6RLLayozixJ+0BQrELgmnT57AreVRtcSuJ7ByqJMrXqKTruK4jVJx4Y4u+hQqbhMRMPEzP249jxLyy9hxMHQ0rRqKnMNj3bLRZM8LrQUPEJIkkUonKRUalOptkin08hyg2ahwXjPAKEwLK4tMTYYYWp8N7fu+hhLi0UwCzhynmQsveX6uhKuIyMmt90+SF/PMLo7h2LXePkZqOdBeIAOHWdj9LotaXO52aARa+F2ygzGW4z1qKTby7TWOgxNCFKyT0iHxHZBZhQ0CWwkjJigL7qx+NjICJQQ+I4gZkn8v+39abRk2V3feX/3PnOcmOPGnW/OmZVDVdY8SVWSUGlEAmQmm7aWwW2M/YCXH+Oh3aYfbGj3I0NDN25sPDAaJMwgGZBAIKGhSlPNY2ZWzuO9eech5jOfvZ8XtxBm4aeXpQyZ9OrzWatW1Y26GXH+8eKX++yz938nMUQhRAnkaE6dlmz1JEFW9HMtFG5H+2dr6CqcurCNUZpgz/whTt6xQJ716Pa3OH/lGc5eO0O9Okc+PM7VxS4XL12i1WwwiLuYpma6dYjVjQHkml53yNEjx5hsH+JbvuVxzp69yuc++yJ/+JnfwbYU955sU3VNhrHElCZbmzF3H9/LY3c9ws3LVxiONA2vwZ7po9TLt36CyVjC9ZFDgv3hJe6oX+HAiZzn/0DR6+w+wVdy91PiAAwT3nwy4/GZPp2dC8RdOHTnPubNKxw4HmDXoLcoSZZg7kGYOqEZdmHnoqDe0DjzEMeQDiEbQeUOMCwwpMZ2oVQRJDkMRmDVNNKTWFnxQKtQuB0Jo4RZ87j7vgbdbsyhGZvRdsjmVpdz116jH93A92qUzaP01hx8b4pK6RqD7ojWtEsWS5KkRzbawUCwb/KbWLveIRtt8hu/8XEuX7lBnEaEaQezpLiyss3eaoXLG0OiTHJ9cZmDe2vsP9jAL/mkicPVK12GXYv29MIt1zeWcH3nE5p77sypVDNWrkN3a/f2XcndRthptnvr3p6Dt31Ac/KxEeFQ8Ou/7LLPPU+jCUkq8NCMrimq01Ce1+DsnqflHtboEegA3Cr0A41hSq6f1jTnoFKCZAiL64JSXfDllwx+/Qs+tC3Kxq0vqSgUCuP3+rnXOXrwTpS5Tb1d5cbyFrYMWFvfJs40d955P5tbi9y4tkqWBEy0JlAyQ8sdhn0LU7Vp+A1WGJCmEVdXL1C1Juh3FRvby/SiLfphhyDroYY2+w6c4PzidQbDkJsrG6RZxnMvnOejH/t17jr2IHHocPl6n0RL1vq3yTrXUV9RqmqyXNDd0WgBwgRTQ6kB4WD31v7hd8E9b9odwS5egz3lhHhVIWcEMtbsvKopN3Z3WOWB4NJTUK1p5u7RCEDFEK7sNmmxK5qmCSsXBUrA5RsG//KXbLo5dGOJNaVwKoKsPo4KC4XCuA2ClHPXzzGIuqQpTDsnqfmT7GzvEMcJDb/FoUP380ebp1m9KejZAcrd5sBBg9WNlF6wzaH9jyNvLCNFgtbrhFFKfyjI8ow+XUwvRwnF9HyLU2fOYUlFu1GmO3AIQ02WxXzqj5/h7Pl1ZmbvpBeV8ErQ7W/ecn1fU7PT7/u+70MIgRCCO++886uvf/+P9vmnP7Xbn3F2H9Rau2cLGga86dtM3vJdguOPgl8WdFbh/JNw/guCiglTxwQH79E0JzSTB6E2B+UmIDT+BKzdBGUBBsgylA5oTA/SQIPS1Boa29I89nbFe9+vcR2bJIRkkKNQKDP96jULIfjYxz52y19aoVD4+vznGfL0F7/AjeWrDAZ9hsMOveE6eRqwMDdHo1Vm/9wTNJy3cPLEo/hlk63OdUCxspZgmjmd/hp//MyvEulFhNNDJwGjaAOlM0p1wZFDsxzdv589szPcd989HDy8wGgYUa+XqZQdfM9iFGb0hhGjJCH3hijjJlcvn+G3f/WXv3qdP/3TP/111fo1j1wnJib4mZ/5Ger1+p95PQwNWpP57rlWbeit7J6LNbVgMDWbkT+62zpw8bSgtwEH7lT0lwRH36SpzQvqbRApbFyD/jYMBxCE0NsRpInG8IF4d5rBndeMbgjiAaAhGgh6q5of/GsRb38851/8K4vnzkBs5yhD8uEPf5hz587xoQ996Ov6kgqFwvj8SYb84x/7n0lVQjyI6fUGbKo+URpz57H7OXrsDq5d2SAMBmDmHNjbIEobTMyus7Z1k0EoyPM+JcfGdkYEw5xhqEGkVByHfm/E2kqA7focPTiFjiP8isu+fVO88tpltM7JMoXKNZZpohKXcODjNiLqWvDoOx7mu9/3V/jhH/7hr7vOr7lNv+/7fPCDH+T973//n3n98qJFvyfod2Bzefed730nBJ2UXBgIAWUXjj+umVjQbC9CZU7T3KeRzm4DFuFAbkCiIM4gy2E00sSBAA3YgNrdheUvaGwf0AKvCkkMG8uwMJnw7e9McAUMNyKyJOODH/wg73znO7/uL6lQKIzPn2TI9HyLSsXHdATSUGQ6ZGnjFE+/+jvcXFyl09thZnaSNO6zuX0a294BIbEwCfs5hmWBFVHyyiRpRkpKkg9Z377O2uoKFy6cZ9DrYFkOnlvFDBP8kkeWKbq9EENCtWTTrE0w6luI1CLJArSR0Zqv84EPfOCW6hzLnCvA8prg1XNtrOE2azdzDt4leOQdmo0bcONVmDskWL0BtVnN+efAyMFvCRQCiULo3VFpEAiUBZ3O7hrZYQ+GO1Ct7M7jYu4u7xI2VPdrgvOQj8CfhDiC3g7s3ZPTPpwSrQmEV6wWKBRuRzMTLYTpAAqVp3imz9sefztREnHu9Rc5OvswUdpkrbvE5mAHw0spTZssbmyxvZ2CoUkSxdyMT7oSIQybKIywHQdp7Y4bXdvj+vU+S8sv0rITTl1bZhTE5ArSTOPZLmnSpt52EaUNhr2I9c0N0uTWt3aOLVztms+//cxenvtUn34v446rmn2PwZsf1GwtSi6/uHu7X8s0/+qT8OoNCBLN4X8JP/y34Xu/C5TYnQ7wG4Kr5zW/8SnFl85C9xeh3YK//K3wv/2T3U0EOgfDEby8Av/rT2gu39w9EWFqAt50n8YsG5QPulhuaVwlFgqFMar7JZxSFc+yUfU5Xj91lp//uZ9nFIRMTrbZPz/La2c3uXTjBQZBH2uo+NS/vYBfcfGrZTZWtomjhIunbtCertJsVzEdRRAErCzuPu0/f/F14HUApmemiNOULM0IRwGDbs4KWzQaA46YJ3igfQ87lwf0O0OS+NYbPo0tXEeDlC984hT3v/1OKlbCmWdP8b6/As98Co7doblyBq5c0fyzD0GewrvuhMk23EjhB/4B7HThb3y3IEkFnfOaH/tFzaV1eOwQzNRhM4Kf/RW4dB1+91d3r/z1s5q//LfgxGH4f30nuGW4sCT57LOC0l6JqAqyrGguUCjcjo5MTxJhcffRk/zrn/sVJpptThw5Rg5cvHKNX/yln+fEsfso1wVawHCUoYE4zBj0NpmamcA0JBtrO6wudij5FbI0RylJueoz7I+Ynpqj2ZgnTTM6/SXSJGM4GCCFoF6v8a4n3k2/p3jxlad5r/irdPoDpDAxxK1v7RxbuO6sdvkr//g7OXTHHFWh+JG/fp13fkefH/vf4WP/UfPg+wQ/8R92b91/4q9A2YFSQ/DE34Dv//uaD/0MfOAdmnAo+K3f05xdgb/3Tjg4sTvfKiW0HM1vfBa++BV45EHBZ76sSRL4w9+CtCNY3xCIMz7PbxhEcYYMYxzr1g8aKxQK41cpl8kjsOUUP/g3f4RL514nTzXVto9XmWZ7e4vFmxd49/33cP2qi1YGhjQZDUMeevgu4jwhy3KONmq88vJ5dja7lKolpGlgWLvRJk0Dw61guD5lJYnjy6A17ekm9WoVnQY8ePejvP1N38nFxTMkSYhfKhHLW4/GsZ07PTFfp1wSlJREKQvHK/Gt74Y/fhLyZPdAwS+/BvccAC1hawDbA9jqwdsfg/4Q/vhjsHZW8+J1mKlB04HOUNMZaXYGmn313c/6xV8W/PrvVxkOd+dTP/7HMEgFv/UZi9/4uESFEgMDNdSMloNxlVgoFMYoMSY4cecTzLTvIug77D1whH3HW9huxMk793H0+B0MBiO6nRjDcdi3/whaC0q+zygcEkYp8wv7+Bf/359gcnISlQump45Src4gxBsNmzSQpyThkJLrU69PAKC0wrRhcW2Zi9fOs7h2k1NXnmYw6BIEIXF0G825lpsO2U6KaAsM16HbbXFg/xpBCGursLm4G6BfeH33n10a/vWfvsfKElQnYGMAaz340d//L3/WhfOwlNi89RGXRx8a8bf/PjiOxixpSnWQWYZd1xg2mE7RuaVQuB2dv3iGcOTRrjm8+MpTvH7pS1y/fpkk+bO9VIUAy07xvQpaaRzXw3Y8Rr0e8cjk6tXN3ROolaTkV7GdEsNeh+5WF8MwsG0HlEGaxVguNCcbbK512NroUq30se1ZyuUReWQQBxBlfVR+G00LGEKxsbHNofZ+chVyavFhFm/2gJvEseDcqwCaD7wbvv1dMAot+ls5xx9SWAKiCAbnYLC6+5fNbAPecxdEQ9BvnBWmNbgehI7Ba72Uly/YfOhHEz7+u/DRp0y2NjI2r/WwXJNy4FCqSHKvCNdC4XZ08cpN+t0Sttjm43/0i9RqDd71jm/nwN672NlZ5+VTX+Dsudc4fvAJljcvc/nKxd2BqDKIY4tyuU1vR/FLv/gbJHGM0CCzhDxMsU0XANuxGA62kKpGmgXYpRoHjlWx/ZsEvYgkzPjCF/+A55//AtMHFrAsE51rxBg6lY4tXLvrQ9Zb19mo7sNVCZnvc/p1GykF//v/4TJTA78UkmaCO477XD4/wZ37FnnwMUGeahCC7aPw5O8I6iXNxhCqtqTkafJktzfBwWNw+FGTn/pPddwdE7vn83f+kWRjK8BsOtzx7hm2rwQsv7qGMVMhkwY6LbpiFQq3o+bkAitbF9lefRGlct706OMcO3ovcZRieMuMom0Aqhxh8sBdtOwlXnr2NFoJUFVMMyWJ1wh3bJI4R0rBsBuQZYo83o22yeYBOtshSucgUlwxTdPyqB+ZYH1rlXAEg17A+upltraXqU3U8Hwb27n1ZzVjm3PdXgvJs4DYuoFlpiwtL3H24nVKnsuTz1T4w69UmZ2u8Kmn4MNffAC3GnH4ThgNBUEoOHceShV45D2wf0rQGcCvPC05u2nQd21e2LS5FLt8+Pkqg+oE83NN2m6De/YdoTpRZbSTsPr6BvKNkaoz6VDe56Pl2EosFApjVK3Y3HFsP1Zp99Z0anqGE/fsx3AS1lZS1lZ2l1Ntbi5y49pF9swewDJNBGAaZco1jWmnlFwfKSXSFMSJQTDSuFYFgO52D0sKDCvHMh2SJKU37DNRncNxPEI2UW4XACEElmvh+g7SuPX18WMbuVaqDmef65InZ2nKKV585QoazWP3nkBlKUsbm8xNLLCycZV/99Nf4vxjbT7x2VnW17psdBLOXcj5iX9s8/yrgsWhxpAxvSDjC1ckzpIgyzSvreZkWZdDj1RZ7iWc31yhN0qZmaqxraC/GLFzLcD0DHSSMLwSMdwsHmgVCrejhx96mFZrirmp/Vw4c4FP/uHHGQV9zr5+lsuXL2DbNnEcsbOzQy8OWO110FqhtUYb27i+ZjTIkOUempw819SqDbppH9OwAMGN5bM0apNYtoHvT7HTucGlG9c5e9EFQ4POCXoRQgpsz4FckUYx8e1yhhbA3NQkud/n6tkOrw+38T2P/XNzrG92MCS063XqfpnH7ruTm1tdnnl+hTCMEIBfd9h34gA/9jNLpNnul2dZJjLPyXJFEO4+uTOFweThJp1rPXZGKSVHYBlwZXGTME5Bgl02mX24jYhzthaH6GLKtVC4LV29tsb1xSGzkzP8pW//Tp75ynP8zn/6LRzbY2HuMNPTU3zxy5/GtG1UuIkpE7I8x9QZaSRZXupgiJxef4tcJ6BgafM8nmVz+MABtnuH2e6ssLx+GdA88b4J/F6Z9FKZzk6PPNudSrBci0rLx7bBt218z6OX9m+5vq85XJVSbG1tYZrmV5u36D954nQbyvOcra0ter3eX/SlFAoF/jRDfux/+Yf/WQOoH/qv/NO/9g26qj+ltWZ7e5ulpaVbep+vOVyXlpZot9ucOHGCM2fO3NKH/7dw+vRp7r333r/oyygUCm+43TOk1+vRbrdv+X2E/hqGnWfPnmVlZQWAcrnMI488cssX8I02HA559tlnv/rzyZMnmZyc/Au8okLh/7n+e8iQLMt46qmnvvrzkSNH2LNnz9f8Pl9TuBYKhULhv06xTqlQKBS+AYpwLRQKhW+AIlwLhULhG2As61x//Hd+jTxTmLbFzYuv8vu//cvc9fgDNGsOn/v9L5OHIG2QKkaqjIN3V+ltj9hctnj8W9/DhXNn8CoTfO/3/wOkSOmPdshVG88ucaw1RZz77Cl32ele4sSRw7RqRzGFD0DKgEBtgxYYEmw8vnD50/z7j3ycaklSbfX5v/7mp8dRZqFQGKN/83O/QJ4rDENimiZS7DZlGQ5HlEolLNuh211n0O/w2unX6fU6TExMsn/vftIsYXtznUatSqYMms0WE60Wm9tbTLUnUQpcz0OnPYZBRG+oKPslhsMh7VYT2zIxLAvLsvFKZTo765TLVUzLYTAYsLm1wd/74b97S/WNJVyHw01K/gRaZVTbLQ49dIQ3v+NNXD77ZWwzJzIMtBIoYaEQXHh2gEiAuubKmVOEQUAwyhkMhkxMNGH7PAcnFthKG0jb5c2z8zRrBxHcB2TA7s6AlC6S3QYNliyhiOhxE53mmEZCkgy5eXM4jhILhcKYCaFpNmusb+5QkgLHc8lyTaVSA3LSNCbNMgzTZd/egwxHI4IwQkqTpaVrXL9+ha3tHebn57nrzhOkeYrnupR9j+4gIE4SVKoYDEdk2W7U+eUy3cEQx7ZpNZt4jkMUDukPU1S+g+uWCIOQLL1NumINOx0ct4xh1zEtB0dHbCy/zNKFc+hBiuFKpg5OsL64RR4bSAcMW5EbgrXNDTzPIu4OuHL2deqPPYGO4YEjU5TMI7xw5RKxChDYZOzQS84jUo+Kt58eV2nKk+RaE+ktTGAtXGY07FMtKdY2uwyjYvtroXA70lojpaRRrZArTdkvMRqNcOwSGpMkSWk1psjzlM31VZ588nMMhyNsxyJOYmYmpzhy+A4qZZ+KX6fiV5mdnsbzbDa3OwyTIa1Gk9aEi1KCkueTq4zt7W0G/SHVahVpGJhSUq3VMclJ85xXXjvNxMTELdc3lnDtrnbALLPv8F62N64wWu3wlbVFtm4MiAKNFBotR0gSckMhGzlCSESUE0cZ2Sgk7youvfACR+68G9dt8vrSOR49Wue9954gy1M0CkkFSy6Qy5xhfoXV/ktUG0fIkoxXFp9hp7PNhaWzbN1cZbuzQ5zEOGNowFAoFMavXm8ghYFhmBhSsbO1ysbGBl6pgTBNGo0mnldi9eYmK2vrlMoVZucWqDeabKyv4rsWk+02k5PT+CWfSqWGMEyCIGB7Z4tmc5Jy2SdNYrQWSEOwsrSO7/vkWY7rOmiVI4SgXvHodDqAYO+eBUqlWz97byzhahsGr3zpK+w9eJLtlSWuX9ggTjKUllhCIpWkezNEqxS7mpONJLqnEbFCKI02DaqTNrVmRLB+lnLFJ1YVXrr+Au88No8rPQAEBlVzD5igaHIzf4lTS8/y2oUX+ezzn2MUhDhmjsgVWuV4jsnC7PQ4SiwUCmPmOC4CkFKitEZImF+osLS8CMJkbnaOOFH0+kMmJ6cwLQfLLlEu15ibnafuu7ieR9n3EOQs37zBluNiGIp6tUrVLxGFQyrVGnEYEYUdhNAIBEpn9Dpb5LbGcsuI3CYIQyrlKgvzc4RRdMv1jSVca+02N//w41y98DLd/gq0XDzLQBoZ1gja+6dJ7IjWxDyLV6/TH2rSJMVSAs+2mD8+AeWITrbFINzB9CSJdlna7LGzb5sJb+o/+zRNwiaJXiEIBvzKx3+Oa4sb5JhUXRNDKxCSNInBSAmN4gytQuG2pDRaSrJcIaSmVKowHHQxDEGt1qRU8ljbWiFLUyy7RKPlcnj/fsIkx3ctPMcky3PqZQvLdnAcj05nh0Z9krJfYqfbIc8S0jSnNxgQjga4XhkhcgSKc6eeoVIuccexewiGQ4IgwrVtojylVK7ccnljCVerZFCuCC6dfooo28YoSUAhZMLkXTb33L+P1y+d4+Sbj+FOSkaHU84+eY14O6PU8qjuKREGKb3OgAtXXqS8PsNGL8S0S/yR9Vnu3nuIkmkSxx2246sM9DI6gs5al1F3BwMPyxziORYNr0GUDonyAWs7XaZdexwlFgqFMcuyFC0MQGAaBrnSIE32LOwnCEK2tjZxbQutNYMg5ciBg0xONLBtF601o/42eTQAXScIQrqdHrkSmLZLkib0e30c1yGOA2Zm5hgMBly/foXpqQmmJ9vk+VEmJ+oIaRHHESWvjFfyiEKNadx6NI6n5aAtwVN0t1bY3l4lCiJ0CoZrsF1SfOZ3v0jQT8n7TyEdydZOTGnexK5JBIrllW1AY4gKN2/uUPJgGIBTclldv8qTz38GlY9I0y3q5TIHZ+9jGGguXb2ClVVoV4f0k4RO1CFKuywurZFqjTBtPOPW/wYqFArjp5FkWYoQYFtlkCYqT5CGpGq5DEchWRywMDNNjmByokXJL2OaECc5Ku1jqj6aJmmmaDRb+JUqruOwtbGGX/apVqsIIAojBoMBM1MzSDIsqZhut3BLZTo7qwShpuTaJIlDuVLFMG+TcI1GI9IkYRisEKcpwpIIQ2H5GaNORtpVqAAsVSJLAoKdBMsxqNZ8ht0QlUhaU/O02odxvTIIiyQfkuYJaTCiO+xR98tMNo7QqEyR5SYKheXUiMUiUkRsbffB1mwPR2gTolFCxXY4e+b5cZRYKBTGzDANtFa4jkWeZ0itsaQmCEfUalWiUJMZknq9gmWZ2JYkjocoZdPZ6eKWJjEqk4RRwk6ni+e6WKaFUoqp6VlUnjEY9FFaEwwDLNOgUSuTpQnrGxtsbG6wd/9RpFnGc1OiMCCI1tm3x8Oybv2OdzzhGicEaUyS5ghDok2BlJIsT9A2GA2JWVPc3FknTTPyTOAaHq5VZaRS0lHC9s3rdJdXcBoNGjP7cLwSQoBrudRrDSpeld5oyFpnE9MwMSWEWU4v2aRmmpRdi2EcolLIlUIKQRhGxOGtT0wXCoXxy7OE0SjC9994oq80hmGSpTFpkmBIge04SClxXBfPkvT6A0zTxS9XCAYDavU6cZzTqJRJsgSdx7vTAkmM6ziUKxXCIKBWLTMajVDaAGlhe1WOn9hDmKSsrK3j2SbtyVl0nr3Rb3bzlusbS7hqJFko0QiEoRFCkQcZaQxmwyJPFVpL1HZOuWRiKId0lBGpCMeySFMwDI9qcx+VySlM16bsWbTae6hU2uR5Tr+/TZSOEFITZyOCLGAYXqHsaNI0Za49y5XrN8jTiDTLMaRDmkRIVTzQKhRuR4ZhUPJd0jQhz1Is0yTJcyyrRJzmeJ5H0h/BG8u1kGJ3VYHQ5EnMcNBHSoFjGVimSz7MiJMArTWbm1v4JQ/DckjTlJJXolKtEIQRnm1Sr9aoNyd45fXzdHsj7GYFKSW5kgRBgF/ybrm+sYRrHocQZ2ipUKZGSgU6x/QFaI0QBpYpyCONcG0MYTDoh4g8oNZs4M9OMzlzGMvxCZOAMNlhGGpERzIcbKB1Sqp2+ywm2ZBUpSTpBg3Xom7tIZaCIBySZunutIQ0yPMcaZgQFu0TCoXbkWFYSAF5lqOUIopHRFHKRKvKaDik1wuRho3KUrTKQDjYtkeWKobDIbbj0O11sUyBZZo4lkUwCAllTH/QJ0lCHLdEtz/i8MEDkCkkiq2tDRr1JmEwoupX2d5YRaP5g09+nNFoyHvf9d43zuC6NWMJ17i/TpZm5JbG1AoyDbkgGwjyKEMIgfQNPF8iLYHjONimSZaCYUjypM/W5mWsUo1caJr1NtVqiyga0h92EQKkNEjyhDRPyPSQlu+yp7mHJJNsr54nSXbwDZuhFRPFilLZIc0S5MTYjgkrFApjFIwGOLZJEvUZRuC7NuVKGduSbPbXCRKTybn9oHKyPMOTLqZt0e/3uHr9Mq1mg9/9vd8j15rv+EvfSbs9g19zGfa7ZJkmIuX60gWmZ/bw2ukzLC0vcfTwUVAxbdsmTjL27Zlla/06L7/8As++8AKtVovl1ZtocZs80NpeXcSsSFAphJD0QAUCNGhDI23AFWQyJxnm5CqiVPYwHE2aJJQq01Qr+1AyoVav45Zc0mwIQoOGNM+BjDgPyPWIdtVjqlxnkPr0hiEpM2yNtmjXTeZqPuvrEf1BiCbH8YpwLRRuR0oppGGRK4tK2UcKhef5ZEmfJA5JcxPHBCEdBsMRZd9H5wmbG2uoNOL82dNcW7rJZHuSpeV1+oHAsRR5uM36dp9qtQoa8mjE8vISn/7c59jY7vDQPXcTBCGDXo/Ll85zY+kG165f45GHHsCyTIJRQBjcek+S8Wx/7e8wNdMkCoZ0FodkHdCZRloCYQhkRSBrGTmSPNUM1kakjYxqo0Kz3QZLEITr1JsTDEcbBJGBxCUXFhILkxSthtj0aTRmqPqzhKpMkGjCTBPmHWIZM8j6DLdzRkONgcCzJZlOx1FioVAYsyiKcUslTMcDrXG9MrkCKW0qzXkq0kIIjW3BaDRiNCpjmwaDXpdg1KPdnqBRq1KvVRmN+kxPz6GUwK+1yTa32djcoF4pYxqCNI0puS6ra6tcvOSRxBEvvPQCG5tbDIZ97j5+hFqliuV4zEzP4Xm3yZzrzuYOCE21UUbuhZ0sJOrmYIPwwJ6SCDJ0JHFLEtl0ydOcJErob62SqpzG7FE21q9Qq0/juy0UBmW5w6HJy8w0Fql4IZapEE6DMJ3h+nqT86tzZIlmMOpCFqPTEr3hNmmgKNVdagJSq5hzLRRuR7Ztk8cjDLNElmsQcPrUyzQbDeYW9qAx8HwXlWdkeUa/16Xk2rTaU0xNzXLm7CmGo5AD+3yaVZ962WE47BFFuw/QF+Zn2Ts/TzDsU61WaDQqBFGEaZncWLzB0soq7377EywtLzLbrhJlBvVqFWmaeN5t0lvAeKM5yrCXoJCUZy3cliAaKbSrcesQLWrygaLvxFTbPtJ3kEjCMEELGA5WKVfa+P4EmUoo21u87cTTzE32MQyQBmgNQq8jjA2mKzBXbfPxl46T533IMtLIwTRLSH9EOkq4qUGIYuRaKNyOoihEawffEmiV0etu02pP06jXsUzrjQfTijBMaU9MYVkCy4CJiRbd7W06nR0UMDszxXAwIAwHdDrrLK5sEEUx9568BzQ8+8U/5OXz15me38dwsMrG2k2mpmZ47zvfydz0NJZpYFomC/Umg16PerWCKcUt1zeeloODANO0MC0TMBGAV7XxGimJl2I4EMUSMk2uobM6wi1bOK6FaRooNFF/gG0Jzp+5wWS7xLvevsrCzAjTATSoHHa7PAiE0BgW7Jna5JvvfJ5ff6aFtmuYZpMwTUnTDEWKygV5dut9GQuFwvgFwx6XL62wML+XcrWB73tYpoltSlSeIdGEUYZpGiRhjOGW2dzpINjtZDU3NcnxOw4jpSBXOaMwZDAK2NreZn5mGtM06PZ2kGWDiJzVzSUqvs/MZAvHsTBNEwW0Wy2kab3Rd8DcnaJw3VuubzwjV8cBrVE6R5gmWhkkUQauxqyamJnGqwp0SZMlCtM0yBQkyW5iSgnRMCQKQyYXpnns7gF7F0YI608PplUapNztAZkpQO3+997pkCeOdXll5U4SVaOUdEliSMWQPNFokY+jxEKhMGZJntMfDnnl9GsYps0jDz2MUppypYLtekihGAwGCBykIej1dnj9zCkMSzM7NU+OYs/CAo1WGwGsbayyvHKT2ZlZThw7jhQ5vfAm/TCkNeXjS59qZYJRGBMmmr37DmKZFrZf5ebyMr3FRRbm5jBNgyiKb7m+8TRucQ20UgitdwMQgZIWoqwxbUW2/Ub/VktjojAtsA2JzgXonLK3+zeR39zP/P46x/dfw7Q0WkGWgxQC0GQpCLE7gAVAgJSaew5GbCQLXLp+jamqzWjUp6cVUZ5ipbc+vC8UCuP3qc98lp3tTYIoIE5S2hNN9u89iFY5QRAgpUnJr2Aagq3OEi+eOovjVjl65Dimodnc3KDRmsY0LRzbodPrcvjAIfbv3YdSAy5eO8elxRU6QYTITNpz8xw6cJw43G1hODXZZHlllSAM2d7Z4NnnX2BqcppHHnqYudk9t1zfWMI1HA4xhEQgQBoYUoAJZlkjlSTra1SaY5kSyzJQQiMMzURN8fZHUo7ekSNNGEXLdIYblEspWu0u5coz0ABaoDSY5m6gakBrgWFoKl7GnPk0d9y1QauVgYJBAC+cFbx8uQjXQuF2NBj22LswTxCOEKaDbVlkecYwCKn4ZbRWSCFZXb7GmTOnGIwy5mYP4jo2g+GQZquNIXcHXIaZI3UfnYa8cuYqO6OUne6I5Wsr5Bkc2LePg/sP4Jd8GrUSjUYTpQSu6zEMIiZaE3zze76Z3/vEJ5icnCRNbv1ZzVjC1ZTmbtiJ3Vt3pTWmBZYvyfsarSRaKfJYIxEIJ+XgXM53f0vO5JRGo1FKUK10mW5J+jtlpBUSh4qzrzucOJl8NVBbE3r3wZYAKTRSCEyhOb5wE1kChUBKTbkqeP+EYrJSnERQKNyOjhw8zP59B3Acm62dDqPRkHCwTR6HDFwPaRj4foUw0tRb8wh7yHDQZWtD0B/2KPkGYRwQxUM2t9fZ6Y8YJru9W3vdEWkYk6cmlmlQ8+ukUYZyYiyvgsDEK/k06y26/R6XL5yh0+1RqZa4dv0aSmW3XN9YwlUaBqDIU4VSOXmuMCdMDMsk7iWgBIYhUVlOpjMqJck3P5Ezv6BJE8hTyBXEseTpL1Y5f6EGeoDQmuV1n8XFEQ8+MCDLFWkqaU8q5BujX8Tu9AEAGgzjT8J3N+AfuKt4oFUo3I7e9MhjVMplhJAcPGjg2jYqz4nCAecunOWV068jDZvDh44wGg05e/4sqJxvestD+OWA66tb7IQKpQ263RFhHJIMhqRRjpVIKn6L6QMzdDp9rl67zqFDR7BcjxyD5dVlptqTUG9gGgbTU1MI0+bE0WN89smnGN4umwiC7XB3N5XSCFdiOQZ21YYc0kChEWidk+c5QgnedG/O3oU/Db04BrTBq882efmFGmGUkeVNcp0QJBVePaVZmAuptxIW5gEpCYY5hhQYUpMGoIVAo5G7/xt2ZxWwnXFUWCgUxm16egqdJwitSVXOKAyoVatE4Q4vv/oypy9cJRiNOPX6a7QaLXzfJ1MpFy+/zKFjbYKwz85mwGiQonNBGOT0OwP2zu9h/vAsE80JHNvhxtIave4Ozz3/NI88/GYatQZ5stvFL4oSRoMeSljMzS7geWUuXLzMysb6Ldc3nq5YSqJ1huWaWJ7EcE0s3ybth5BClmSgFVoLJhoWb3lTgBCQpqCy3bnTa+ctLrzm0OlkaCFptSUTtYhuN2V5eYdhT9FqSHSa43ua1AQJoASj0W4lQgi0BkOyO6IFMPT/3aUXCoW/II7pgiUxJIRxzrA3IopT4ijm8MGDWG4VhMQyDaoVn263S7Xio9jgzCvX2NrJmJqao+XbKKWpzjWRwmBmss301DS1ahUhJGkqOLJ/liTscfHaWc6cvcQdR44wPxixd+8+NjfXGQz67JmdZDDocM/dd7L11G3SclBKQZpCGisQb3S+AgYbu1MFWuW7o0jbZmraoDWpyJUmGEAaCdaXTV57xqOT1MnFENdOmG52aJQFSafD3UcUe6c0rhAsXZPs359hWwLL1IQDQaJ2MzTP3pjcfmOSGwGimHItFG5LYdzDc8tI00KkMa6pSaMR0q5w9z1v5uiJ5I3+zxGOY2MZFjudDb7y9AbamOD4iXkqfoVWawIhBK1mizTN8ByLZqPJYDig6pfYMztJqjS9PKPRbGM5N7hy7ToLcwtcuHiaiWYbyzI5d/kSwShgdmaGb3r8zbdc35i6mggQcncLFRZ21SMZKVRsIsixHMgyBcLErRjEESSZIBzB9rrkK5/1yfOcO04MOHS8y7lXBTOTIw4eVjz6NsXUrIBEkSQSAbjuG60MDQEVGIS7o2AhwbJ2L0OK3RGxkMXItVC4HSWJJkt7uK6L69rIapk4CNjc6bOwsB+tNUkckucxjm0TRSOa9QoPPPAYKkuoN5sIaRFGCXvmF5DkOK5DniUYhqRSKZOlKW7Jp+n56Czm+ZdeIcs0a2trbHV2qFZrNBot6k1BjiYKA0qeh2PfesvBr2nj/fd93/chhEAIwZ133vnV1zcv9Ai3dhehCkNilwVJ18QwHQzHwnAspCHIkxQMB6UFeQKDLrz4tMOrrxtcXp8kCj2mplucuEuQawtH5/gCSMApgd/SaK3INFiWIAggGO2eR54rQb7bRnY34xGYhubaFf3VaxZC8LGPfeyWv7RCofD1+c8z5B//yD9iNOwTp5o4HNLdXmVjc4VR0CWOQ/J8dwOQ75WwbAev1KAXCibaM9x51900GpNYpsnehXmq1Qpeycf3fVzXxzAsHNvGMCT1ehOkwPXrHDh0FEGONCU3l5dJkoz19TVGgwHtiRmOHr2bMEz5Fz/1f371On/6p3/666r1ax65TkxM8DM/8zPU6/U/8/ru4n6J6TqYpkeaZFiWIBW7B5BZaJQpGWVVwuEaSaS5fEZy+axAeg0EGZvrmkq1xYHDFVx9FbeyO0K1LM3qhsCrazoh2CNwGmDmUKkq2tOSSxf56hItw9hdC4uAqWn48Ic/zLlz5/jQhz70dX1JhUJhfP4kQ156/gUM28WybSzHws8zRlHCdKuKUCkKgWFa+L6HlgadTo+p9gyWqen1BrtLM1tv9AOoN9Fa0R/08VyXpaXrPP3MMzz84P3Mz+9Ha0jjFN9zmZ5s49gO9548ie/XcGwT0zTx/Cqe62GbJj/w/T/AsRPH+OEf/uGvu86vuWWU7/t88IMf5P3vf/+feV1IgWGZlKtVVCARwkRaFtJ0EMLAdF3sWplBVme7a7GzDudf0zilFKVzkkTT7UR0t9fZWUswvFkmZg0qExKtwK1Cpy+wfEEQ7vbjdkuQxjDqa4Lh7hIs09oNVyF3/12pCT74wQ/yzne+8+v+kgqFwvj8SYY88uADlDyPPM/Z2ArIhIcQknrVRagRlvnGdkxhkqQK3/exLIMgDNjpbFNybVQaofKM4bBLFA6oVXykgEuXLvDrv/mb9IcjpGUhULRaVer1Gs3WBPfec5LRaES9Xse0PVyvguM4CDTtdpt3vfOb+cAHPnBLdY6tk7TWIA1JFATIzMKyyxgG6NxESwfDNrDdCmGa8/rFCmI9IcuhWs/ZGoTY1YN0d66zxBYyWGVhxuDGZZvpdka5ltJsaLJc4PtQr74xpyohjASREqBAqDeWYr3RRQsgK5piFQq3pVQbCA2GzHBtk87WDfKgz1D0sbwK22s7tCdnEKJEFIaAxHPN3SVX/S6T7WnK5Rot1ydJEmq1KqDJswT5xjEtWkEcRYSJQinNyuo60zN7KDvW7ojZsrBtB6UgiUMc26RU8hn0bpPVAgBaKYJOyObFDirVOBWX6TvncGsmSoFhGZiGIhj2+J3fTOmtZGQZ1KowPdNH6pSZfXfS3d7g8vWr1CcGXL6U89opzdY2tNrwjncKfvDvCfJYIIE4hZdfVnzk1zWXLu52zppow1vfIfj+HxIkseDTXzB4z60/+CsUCmM26O9gmQLHtHjx1VN87vOf48biDcIoZnJignc98Va+/f2TaJWQpyMq1Rp//fv/Bq1mk/d983v50E/8cxaXFmk0Gnz3d3wnb3r0UVzH4nNPfp6f+3c/D8CP/viPffXz/sEP/080WpNsba7xm5/4FKura0RxTK1W5fjR4/y1v/bXsS0bpXPG0HFwfOGahRnJMKMyXcYuV+gubnLj2SsceOwQpg9SGKRxypXPX0FomN5b4eBcwPZ2zqnTQ06ceA3K+ymVpzD1Av/pY6/T6Wm+47sk+/Zqrt7Q/OZ/1Fy/qvlX/9YgC2HxuuZH/olm/wH4ru8W+BXY2daceVWzs2XwzLk2nz0fjqvEQqEwRrMz82TpkDhSfPIPP8n+ffu4/55jBP1tzly4wa/+x49iSM37v/Uv02pPEUchSim63Q6//B/+A48//lYef+ytfOkrX+Tn/t2/Zc/CPuZmpnjkgfu4/t5388k/+jTf8v5vYd/e/SgNk5OT5FnGL/3KL+OXfN7zrvdSrlTY3NzgxZdfIMk0tZpPFg/R3Ho3vbGFa55oJo7UaCzMI0yD8rTDjS9dZ/PiOnP3TyGEwfLpTdBw13vvpuFv8r3fHuH78C9+NOf0ayGPvfUaeb7D+XMB65s5/+AfGzz+NoFtau7sCzxf8Sv/XnHqVU2tKfjyM5o0hb//jyQTDUGlqjFtwcaO5CO/b9GXNeJoMK4SC4XCGLmeS+ZYDOMuH/rn/yuO55EnIwaDEW8ddvlXP/fv+f1PPck73vVetnfWyXIDpRWbWx3+Pz/yz9izdw+NWo0nvult/I8/8Dd46ouf5/u/74N43iQzU5MAPPrQI9x73wMEYcjFSxdZW7tJEAT8rb/5t5mfm6fkeVi2w8OPPEpnZ3N3jWy9SqiiW65vbGegmJ7EqUuUTgj7W9i2QXWmymBjgFAKoaC7tENjzwQZBgvTXUrljFRpHnyzIIrAcWMOH95ifX1AcwIsR/P8i4qnX9Ksbmne/d7dsfozz2kqdZjc/f54/nkwfY0sCQwPZhcU3/WelIreIOkV/VwLhduRYHfp5FSjTKPho9IILQ0GQUqnn7J3/yE2t7a5cvE0G6vXCbqr2KbF/PwC8/OzTE3UsSwLv1JjZmaGza1twmBInsY0m7U3PkSh8hTHNgnDCM/zAbh85QqO65ErRZ4mZEmEgcISmjAcYVi3vm9+bCNX0wWhU+LBNlpZGLaF4Ql0rsmSFEjIk5yNC6tsXFjlNeBXfgreaCgIgDCh2tT0eprtTfgXP/5fbrpy9RqsrsPMnOTQHTm//POK//RbcP9Dgm96J3zTu2DPguIDb+tz/krRcrBQuB0Fox5JLnFtuHjuCv/h136VS5evkiTJn/m94TDm0L4WXqmGYUgmWk1azTqW7RFEuz1Jy+UyQRBiujWCKCXLd59oZ3HAaNhBmiXqjQa1PXt45OFH+f0/+Dif/dwfc/zYCY4dO86RI3eg8pQwHFF3GsjbKVwNz0C6Gh2mWE4FhL+7aQBQQpK9sSC4ub9Fc67OyUNrvPnhEGnsHp+dK5idBdMGw4SFffDd37u7m0sIjW2AbUGSQb0hGPUF0oD/7ScNrl7WvPyi5sXnNZ//DNz/MfiX/16zZwEWqsX+10LhdtTd2aBUabK10eFHfvRHmZqc5Lu+6y9zaO88SZLw2ukz/OGnP400PVIqZLFAqRx0js4zwmBAEqcIudtLWghoNCdZX116Y/87WE6JamOKIIqp+D5aK/7JP/mnnD59iueff5YzZ07xkV//NaanZ/if/tH/TD+IMOyAcrl+y/WNb7VAKqjM1chGoFOTeNAl7sUIKZCmidYZ0jTQGvzpElSmOXnPDfyKQrDbFyBNdhti790Hly/AE++EJBWkmcAWYEnYGeyuCjCEoOrvHrv76Jskj71FY9nw4V9W/Juf1bz8guD4XRBlxfbXQuF2VK23MC2TLzz1Cmma8f/+O3+H9tQstbLD1laX06+fAyCXDik2k/UyACrPWFu5ytzMHCoK0OSoLEYaVlBCyAAAJ9VJREFUBipPsF0f40/WYgoDU+72LMg1VEoecRQwN7/A9xw8iOCv8pnPfJpf+pVf4NVXX+atb3kbWabI9a1PJ45tzjXYTAi2I6yGid0CZQbE/YxSq4TOIYsSKjM+3cUd+uvb3FjUvHaqRBruzrsMupqSC40qvOu9gq1N+NwfaSYamqkWtJuaehmqJU3F10y1NVmmcT2B42hMEwypOXzH7vUkMVy9Lli99baMhULhGyDNTKJEEL2xFt12PLI0YWunT67hy888DcBEs84oGNHrDchzRaY0eZqQphGbG8tsLl8jigKiMCAKeggEpZIHQLfXJck1QhhYpkUYhqRZysbmFlql9DobNJt1APIsJwgiwigkiW/9gdb45lw9wY0nd6juGWI4Fv0bEUjB1D0NNDE6S2nsrxDuxFx/epXtlsXPnjM5eRyCgebFFzQf+ajAKQm+7QPwuU/n/J8fUpx6SXDyPtAZXL8Cn/2s5qf/L0n7hODf/aritVc1b34MZucFnQ587Dc1k1Nw4KDg1z5ukxcrsQqF25LteiAE83v2Y5om//Jn/yVPPPEEcZTwuc9/nlq1RrfbxXYrNHyPzY1V8jwjGPbpbi6xb7pGzQjYGIzIc0UQRFy7sUSz2aZcriCl5Ld++7fY2ulS9sscOnSYl158gU9/5lPcf/8DNBs1Njc3eeXV1/A8j2PHju82bFGCMAhuvUD9Nfje7/1evXfv3j/3OqB/6Id+SH/kIx/Rhw8f1o7j6HvvvVc/+eSTf+5319fX9Q/90A/phYUFbVmWnp6e1k888YT++Z//+T/ze0mS6J/8yZ/UJ06c0I7j6Eajoe+//3794z/+47rX62mttf7c5z6nv+3bvk3Pzs5q27b17Oys/p7v+R598eLFP/e5Tz75pAb0Rz/60a+l5EKhMEb//zLkE5/4hD558qR2XVfv27dP/+RP/qT+5V/+ZQ3oa9euffX39u7dq9/3vvf9uT//1re+Vb/1rW/9M6/9wi/8gj5w4IA2DEMD+sknn9Qvv/yy/p7v+R69Z88e7TiOnpyc1O9///v1iy+++Ofe89q1axrQP/VTP/V11Sq01v/Vk5Lf933fx+c//3lefvllTNP8c81bbkd5ntPpdPjKV77CBz7wAT760Y/ynd/5nX/Rl1Uo/D/Sfw8ZorVme3ubpaUl7rvvPn7qp36Kf/gP/+HX/D5f87TA0tIS7XabEydOcObMma/5A/9bO336NPfee+9f9GUUCoU33O4Z0uv1aLfbt/w+X9PI9ezZs6ysrAC768oeeeSRW76Ab7ThcMizzz771Z9PnjzJ5J/sPigUCv9N/feQIVmW8dRTT3315yNHjrBnz56v+X2+pnAtFAqFwn+dsS3FKhQKhcKfKsK1UCgUvgGKcC0UCoVvgLFsIjj2zScJBjs09s7iy4CF2hIbImSYSFRu0yo9yHvf8TYmZ1uYpsGnP/wHfPIjXyFPcspmlYP3vo2H33WI5TMX6LyyyEP7HmT+u+qsjV5i4+VzZPE0R9/yAxw6cQclJJZ06OcR/WxIGAWEaUaYpAyCIXEW45gOvswoha9RG13hu3/wD8dRZqFQGCOtFBqIB1f4vX/3T3n1y6cht7FNsNIYX2Q4jotfrSJti94gpD8MiJMcIXZPHYkzk16egWHjC0XTk8hMYRgGqRI4tk3FhnbJwJECk5RcZSjTIcsMkALTNSi1axx+5zuYfvR7EaIEAqS4tbHnWMI1SxN0pkh3Ntl/uMOaiBiGBiifWmmeEw/PkVauszJaxZIe937r3bT37eXSs0s09x/j5NuncPyMvW++i+2LbV79+ZewPnE3vcoKlUNv4/DD76Bc9sjTG2jto+0Wru2QGR4CiZAJghitFC1ZoWrmlPQSgi6L4dY4SiwUCt8ASoec+tJv8/or5+gnJnmaMWFraoamYnkY0sRI9O6heGh8t4wkIU3j3ZBMIxxpM7cwj9xZR2iJtjXolLIJloqpOi4eGY4wyNMc33GRUtBLI6LcgiSH7pDlrzxFZeEY/sJbGMdN/VjCNYwiZman2d++xs04JsJEZB4nDz/E/IkumXWZQVhBCBOlDaS2qB2p8cjRvUgiEnWVcGNIsrLE8PJlansMTi2XeXDPt1F6u8mIywSpQKQBRpZjZg0qzgFMGoCJY9vY0qRp2nhZj/7wSZ7a/AJLoyVikfF3xlFkoVAYKw0MN8/y8rMvYrSPMYpXGa5tU0o1U1ULbVo4poXUmuGoh6EUIpeYhkQrC7SBbWXUdYZz8ya67GNVfKouDLc28C2DqmFQMcABHMNECYlAkpOjc0F3pEjylJkJE3crZu25T3Fg5m6k1bjl+sYSrjLLOdBcZsuMCUcuwiphjSK2L7+GU7ZxJyyk1UOKElq7KKUZhCtknQS1mZBuBOSjBOmCdXCC9sOTjDYjLnz0HCfummN0YJU8U2SpIo4DkjTGN04xaR+lZh2iYk1Q1gKDhCDb4OWNF3h+/QK50Ihbb8tYKBS+ATQpV888yzCrsrTdYXm9QxmTJEtQ0kAYFpnOIUwwNdiGySiPMLTGM02GaYwpwVU2aQ7kOV4ckQYJddvFERYeKa4Gy7YwTYlSmiRLyHLFKMw43cnJSjVCaw5haMSVm8Rr5ynNP7rbzfsWjCVc7z2Y0jFCRoGHZTW4476DlIybXH1hg9c+0cPSAtc3cFyJyiGLNHlk4Hl7md/3OAdOHqM53yD3VtlIn2agLyEXcsLjQ3Y+X0e3lwmtLbIkQ6UGeWqSRD0Id6iaK9jMM9IWgdUmdiYY2jWUuXvyI9k4KiwUCuOm0j5Xr19jeWvEzvo6ZWlTMSSGShGGRmcxqQaJwpYmDjaOr+iECZaUaClJc0mcK0xT42UZZphSLVmULI0nU0qGItcClQtSnaK1xHUdBqMhO3HKlnQZJSmj/oCJfcep600Gyxfx5h9G3OLUwFjCtVsOCWIbmTTYd/wu9u0ZkGUe8u0NopFg1A2JuylGYpArg4a/n4X5b+KOfU8wUZ0nVxolQIojGP0aYT8j5DTOg11u/uYs+87U2DpyhkwF6BS8rMqUmmEi2yIa3eBU5hHax3HLJ6iZLSzhId5owC1uvXNYoVD4BoiDLVZXBizf3CAMU6ZqDRZaNeL16+SZwMDEzhNcbZJlgkTEKJHhSVBaY0iBbRikKMoVG5CQ57iGiWVk5FKRGwpT2ng46CQhMQVJBtr0CG1J2SnT70Z0tjtcWuuy5/AC60tXaT8Yg7y1eBxLuI4CjzwyObnvh/je97wPy+wTZyFR2md7cIVuuEovWAYNde8oB9qPM1XZi84U3X6fKMnA0GhhUjGOcLD6P7A4+kNWjE8S3n2a/tOPYdV8dDmlSZV6ZmGkWywlfVbzhNj08fQWXrhEnB8lzjaQ7I7qzWL/WaFwW1q7eoWba1v0gpCSKTl25BCuyNnZuIYjDaSUWLkkSVNClYFp4GDjSolhSkwpCWKQ2sExPTxHYGaKatkkimPKpg1KQW6QC43jehhWDp5LGiiaCxVOtvfhX1lBDwck21uszkySmQNUMsIw/Vuqbyzhmgx3511F6SZLi5/g9IWnqNcf4N2Pfj+HWo8QpwmxSkmzFNcsUXZdhFYkcQq+oFaGRGVESQwoiCepqAcw4hXsA6+yeuUiJ196AnnfS8Rqg5Vshw0dEZoKYQhkHpOnHbbjVZa6rzIIAhQaw4BGMedaKNyWXvi932DQ2SGKYjzHolb2yLZWqVng6hQVRuTCIFIpkQTyHBuJbUukIdmJUyLToBcGMBKUhU2jZOCakCQSrQW2YSOFgRSKjATLtNBSI6VAZRm+X+Jdb3szzz//ZW6sdLg5HFKrOsTDLlbp1nqQjCVc7zv8nbzw3Ee4fP0/cnV1wNYgxHO+giHavO3e96KEwDRMTG2htSbWMYYhQAocxyJOY5I0Jk4ihsMuy1uLdIYZc943Y1l70A9e5Pqnr/Oo826y4+epVE3uqh3ixbWfZ6h7JBmQa5RW9LMeMbuHHTpA0x5HhYVCYdysrVUO+RnrLsRRxqnTp6iGffa5JjIPkFKQaU0mIYoyDOmQKoM4FSRRhM4UWZoTWibaKqNESqXikakQYdikwkJmUCpJLAssw8Q2NblKqXguc2RY000mWg0m223WtkN2+l3kzCznf/PDPPB3//kt1TeWcJ2Zv4x6LmE0MKj4knq1Qsl1MYRiNIqwTQtpCxACqTPCTKN0RhTHxFlKpjLSLCUIR3T7O+jMYE/rCGW/RJrsoZ/PYz9+ildfeZ1H0od56K33UK80aQ4dvtT9NYYsY6NQUlMzYCggNAEB5XEUWCgUxi63fKZExEP7HE4vhfTWb1LWOaZZxfNMTJkRxAmmFghyEpWQS8FI5RiGhQv0UVh+GatexSbAKJUgNxBJSJpoDMvG8D1KJQNLKTKpSEcBcZihooyKMUGGweZOH9uyOFw1MVcuY+W3yTEvnz/1eShJolHGpOWwHY7IlIFnVdjqbJDmORqBbTm4tgUEJKkmSmIyFWFZIUJrwsTFL0n2zFcJAkUUp6TKpq+aDLwm5bfkbLQl+456jDovkbwQ8pbK36VTvcq6d5GdfBEdd1DxCEfGeBVwZXG0dqFwO1KtEuzETPsBclKytpLhJSlpEpNZ4Lmaes0kGGkGAfRyIM5olUykBEMocqOENzVP1XexdpbJRym4BlJaROR4fgl/eh7/YAu5fpU8T2BzwGgUIjPJ6T/+OEva58bidY5NNpkYrlKvubQnbr0t6VjCNUgl5ZZksJYTR5DLHIyUzeFzZLFJkiryXOF5FUwTKuYr1OQFSlWfPz61yOHDdzDTGlDBolZtUqtN0wuus7op6a7tI4gUSrfZc+AEDz1yJxVPohYeonf9Gr/3f/wCvvTZMztLxXuAplvGbVUJvRDT1+TVjXGUWCgUxuz+934LT374V6nYJRaaCb5K2VoJiJMRkZCU620qk7OUtMkwucjm1oBhkjNZcZB5RqQFvl0h0xJXGFTrTQwjx7ANRJix0+1QbZbw90xhl1zYtw9zMCTrbCPNGJEros0uWksemmqwr67YM+Eg3RL91h3M3mJ9YwnXKMvxbQfHTslTRaXsMIgSXln8JCfbOb2deRzbp97QWEbAIKsziOHukxscvaNOfxgzO5/gGSuofInNtSUCYeH6HlMLl6nW2+wN2txxdII0W6c7AmnYHH3LW6m/8DRf+v3zvNK/ysIDAsPS6Gs+rfAAs6/NMtOag78+jioLhcI4+V6Jja0tQneCmYZLazLHkoqk0yFTBrntQ9ljMIqxfA+90ycVgtw0yBWkhoPjuextN3BGAVa9jO2ZCKUItnfo9hMG/ZitSzeo1WuUT+xBOzWSlUtkizZBf0DVNmhXXabrHrVmBdmqQr3M/vd9zy3XN5ZwLZklhFTkFqRRRqliQg6LGxu0nC8zZ52kWq6ytL1ElFxjonqcbXUv9spFTh48i6g8j1rscW05JU4Ffq1EZaZBe7ZMlIVEE7OU3QMMs4StbgW0wLJ8DGEze98sk4PXWfuSYPtyjQf+0h4m3jHNXPUo28tX+aNP/RZ/n58ZR5mFQmGMvNoUvifp93oYmclUs8LUnjIj1ybeGbK9tQElnyDOGXYGGMJC5QbC8kgAv9KgNtFkz0yDrSs9DLdKP07ob++w1k3RQkHUp9/dYpRFNK9rGgsz9DsON9d76FKZim3QnKrjl0tkfplKuQ6uhVVq3XJ9YwnXCd9kbRggXYHKcixpkOYJWarY2lokZZHlGwmr3RxhwjffN8/xhYQbWxf42G+8yijSuAp0DhUHkjxmKEe8423v4ZuOl3j9tY+Smq/RWvguLKtClgukYWJJl2Pz0/ylf/RBBj9YJnV6eNUhFW+CuneIKJxn4eCtDu4LhcI3guM3KJU8gq0+QUexnYRMz85yxwMPcO3sWeLFDbZuriAdn0xLUmGB56BKPo5hU6k1cR0Pv1RnBchtj1gaGLUm+WaPqmNiZxGjrRVK7h7SJGf1tddYOnWezPZo1MtUKmUc3yPPFMOtPu29e7Am2yCNW65vLOG61AuxLU0mBSiJVgKZ5RiR5uxOjONo2lWHI3NtMHPIr/KVSy9ysT8gkZpcQF9qtAlbGpIYJDG//+Sn8bzvpj11F6trrxKsfJZK5Ri58rAsE0vGaHUZnYfsmz9KbN3PIHbZ6a1wbfkrrK2tMDlx619SoVAYP8N08fwSthxg5wIxyhisb7FZ8zjy4AOo/AV21nfYWF1BYRNh4roO2nAQjoP0bBzfZpiN0LbJVm+ENAwGwwhZKmFKg2g0IssVib1BNBzRWdpCeBVmpxtYJQdHmmTDAaYp8dOQLImozhzmlhsLMK5NBFFGHgNag+2Sp5qm51JpzXB0317K5lkq2sOfeoztOOYLL3+K7byHMw0VIRB9zSCGREEegm2DaYHpRHz+uU/wbW97gjg+g+YGSZaRqUka9QqRPkeSdnh18RClU89RN1+kVj7GgQNHsKbaDGpNYuGOo8RCoTBmQhjoOEYqDUohLYG0JFqUuHD6PLV2k0Sb9KJNtoYJWQ6UPLRtkGWKznCA1or+cIR0K4RCE21vE+UpiZQYuaKfa7JMIdIOnjek1iqzd/9elGWRD2Oc0Q6YgjRNdqc3FXizdyJul3BVgUKbGscQmCh0ZnD30WM8cPA+mlW4fvU5wnQP/UixvLrKymqCaApEpFG5xorBE5KGNIksB+GluKWciiupmglXbpwH5ZBmQ1wvwDZiovAaSdLB5DCf/VzKuQsZ5dihtXGTh++HB+6BoXGT6nQPjvzYOMosFApjJE0babwRYkJglF2wLeIkYBRBb9il6jlMTfhshzlhpplQkvsffIju6jKbnR7djU2Cbg+/0aIx1SZNdptob62sUQoSKqbEsyWlkqTS8HFLLv1+D7M+gVdvY8kh+ShBqwylLeSwg8ozxnG/O5ZwbVgCS2osBRVSkgyyXFN1yyTJGQbBArn3GCsbNzh9+SYq9ZBDQRClVCyLpqmZdRPmGiUC9wGUY5ERIbMAT/UYDVPclo9lmQidoLJFBr0hWeby6le6XL6aUm343OEcx40ldGe5+kILa+I4X1z/Td4zjiILhcJYCcunNNGGyx3QCb7XYmr/QbY315A6JxcmK9t9rDzjjuMzNLcH6GCTUqOGHm4S9TNMC3KVEPS3UEkPV2qq5QqhqTGbBr5tUDUyGi2HMM8QSYbv2xhZhjlpkfcNyDIc00A5BoZvgeGghb7lsetYwrWCJupBqgSZaSNL0I8NtJGxsX4WbX8LmwHEucETb3ofc1PTZHmP85deoKICJhiSJSso26ReP8BITBHEIXGSstW9SCde4+49s1QrGXF8g163h1Y+66ua68sawy9xdMYkvnyOLopRdJm4V+WQmOfwvuPjKLFQKIyZkA6t+YNcMy5DDoaU+H4Tz3ZYXdmh6vls26uIPGHY3WD/ZI0b12N+72d/hYmZGvVWg5l9M8SjbQwl2F7cQKcZQb3DQgtmZlqMYo+NG2v0YxvXMnHKVQzXQFs5BDvkfgmxs0KuLYRbwWzOIKwyQovbo58rysJ1FZZwaTSnGbDBKNhhrXuaYbAPSke4c49EB0NWb1zn9KVrZJmHbU0x3W5wfM7hcv8PeG7xJhPWVWzPZhSFRMkUm+EkG9ur3ItH3TcIxIDYzIiJaE7CQ2+rsr7pgBqQmx2GOayvDinZXdztHY6me8dSYqFQGC+JxezBO7GcT2OMQOR9PCcmUhK7ZBLGPTzbZWmlx+pKiOeU6YUD4iTG8MD3fTbOLWInNtJWVGo2mysDRqMc7QsalNja3Gb/3fdTyvvE0QCnWift93AcG7ftEJ67hE4ztOFhOhbG3BGEsNFjmHUdS7jqUYPWHkGZnHuPZzx/ISeM+1xbWaTEu9lfDpjVOV94+gy//rEz9IfgezZzjYTXHcnNuyvIvZucXompDE9zeD5GxxKlSmRyQD/aoN8vkbc8LFHGNWJCG6qtTWpTMdODJlJN0LzD5LXPrzG4pgjI2UhCzsbr4yixUCiMnWBy4U78SoVBNyBMDPo7Q6r1KRyrTxImTDYaqDjnzOVNXr7UQecmntRkoxEkQ7xyEyUtwlzhGjmNkkOUKLY2DV4eduhpjzv2zZFvZ4TXOwzjZWzHwrUbBOdXSHcUWrlI38OdbeLvvRu49VErjClcLSsjCmrcecjBS2wWlIuKWtQ6FepGRtjvstmawdFTjAYvMRxGvP0Ok+96RNFyYKPTxYkE4YzBCzsRy+vXsI0GZbfPVv8iO71NlH6Iml9lNFxBaJdArTOKMhwnw65GzLfr7Jk5yeHH9rO91ieIh0T5DoHcGUeJhUJh3ITCr8xQa0zSX90iCCPCTpdaa4KS5yKHMY4wqE1U8csSoQ3QkooNexolpgzFoX010kEXXapy8/Uh3cRlK4xZDxTdFKoTPqlTZms7hFxjmgK/WUKVHIzthCQKyKWH4xj4h+/GKC8g2F34dFtMC7jWgEPzR7jvkTezZ+YIN4c/wWsb15ioTTE4e5VPPG9RrdiIPOLE4aOo8ALfdjKiLuH6TcmLNzTdXHLkTSXiIGQ1CZlu1SHeIEiW8KuSyYkD1CsuebyBIGFzGJAlYEXgWgJjxiXTNqLUoDpfxgz6mGGZpJuPo8RCoTBmSm9jeD6NmUMsXbmKISWjwQAShZNlRLmkP+iwsbNDzXYYjAQzdZ+2u7vqNU1MojTAN0xWr11CmwHVqoV0JJWaxTBOSVXCxRdfoapiPEMgbEk8ypDdFXq9AaPUwnJtJqcX8I68BSFsdo9OvPV0Hc/R2gjKzh3MTN1PP3oB4XYQhskBNvnoqz1OX47RaJJEUfJsnjiaMxjAF18XfPmy4so2mJbBg7SYnEkJGx2CYQffDnhkf4VO2mbPzCy2nWBKASgGYU4cQJ4Iyr5FZxJMa4gpPXQmyZMSUlkYajCOEguFwpjlK1/EnH6Qub3HuPDSC4yiEU6i2OjHlExJlOQE0YhhP0aFKb7pUHEllmOjtGQ4GJHsrFOfnAaVYHglSrUS4cYGnmGQKE0SxfSuLdKaq+KXHTa7A0zTRo766NQkEzZWtUH55MPI+j5Ag9BvHMB3G5yhFfY1Kxtf5MqFIcJd4+gd93Lt+hk+8jshp5cEoAnCGKUUeZ7y2k2T164LVnogrQqPPf42LLnJtRtLWB3JsSPzbOk1zFKXab9KubbARK2CIXfQKiPXiiBRRLEgjSwMOcHGzoAsl5ScOjb27vG5mUmaeOMosVAojNn1z3yCfY8HtCd9LLfEVq+Plhn1UUhqpMRCMhQSUZkjETGeZZGmGcoUKKkIwpiN9S5ly0VEGXmU0hcJUgqiJMFOc9IwJNDQ3+nhGBNYxgSh9sCFKOgRiZSFPQu4B+4DaQGgdUaul5AcuqX6xhKuJ++YYmlrlVOvf5n2XEwamZQ2Ev741QC35FMulwjjBKV2Dxa7sppiGhbT022i2MSwHK5dW2PU7yAMn2jYwM18Ll4fsLQac/eb69R8D51pdJ6SK41hOLiOTcVtMTExjWMYxFEIaUJumtiGTZrmxOF4FkQUCoXxuv7cafKgz/Thh6j5Fqteia1I4e5ESDvFshtMzS3QTGFncw2pFInOCROBFhmmUnS2RmzLdQYZYHtkSYwhDIw0wvIM7MY0zWN3s/ry00SDDISg0ZREI4t+oCi1JqjM7Uc05tEiQ6gY0guw/DwcvA3C9aE3fQeDj3cYbb5OvXadY2aXd99rcPV1k6DUwswHbG6DEGAYu0Pteq1GMBoyCmOe/vIn0cC+2TJRrtGVbcKOzdH5eS6trCJVC1NGJDojzwW5hnptgXp5Gt9p0KxP4tqSwfAGWoEpXEzpkikLSTCOEguFwpiFvYylV6+RbQV4Osf2y1Cxie2E+dl5bMshiGKkgka1SjAcEEUpie3iGDmkEpVmpKkiV4JgFKOyGGVIlBRQMinPTuM0PXq2S266yFSxdnmFOIholqvMTO/Bmp5DyhDSFRhcRV36EsOXnqPxQz94S/WNJVzXNhNk5Ut01t/Doem3c7D+s5h5xgFf8tROSBgFWJYBaAxDUvFdJmo2cayBnFEYo7VEGx6HDjZ47zsfJIk8Ds4F/N5vvogZGfzaz7zE439J0x+GbC+X0MKn1KpR90uUS+CYLo6YJY1HSExsWcU2TfzyxDhKLBQKYxamEjnI2bq2imE5mFkITovOZgfXrLAejnDSEK1yyBVWyQclMMnxXBshFHamQBpochzDIRaSNMlIDIHMbERiceW5M5RLZSbqbWbuvofTX/gD3EYZvz6BLJdxRYi68nlkd4V05QzdC2cZrI1o3GJ9YwnXc5+8jz3357ze+SgrHYsRCdEG6Mxgutnk+nKfPNfYlslUe4K/8j+8i9Hwi7z6lYQ5GuROl8UlH8+t89e+569z530zfO4zL/Krv32Z555eY/+1F/EaPvU9ipXFdZ5/yiIt5/jfKSmRMhADYmVRzh1KicIxfHzvAFK0oFTMuRYKt6NYaqTUqEzgiIg9KmI4iFjD4fTSZSZbC/R6I5IkgVgzWSth2Q6GysgSMKVFLDNGKkVrQT9KEEKQGQaZY2J4VS7eWGWgBLMlzWh1hW6WQS7wW00uLV3HVAO2Tq9jJwNUb5m82yFJDVLr1hs+jSVc731wjtrMPbzpze/i2o1f4sL6sxBYrKO4a+8E25tXWEsztDJ54i3fzoP3/wAXeyNOvf5FyDPs1CHPYmpOicZUky99cYX/9JtfZGdjg0PtPViOi20OGPa2ee25kJs3Tepln51rQ/YYNZp6Fl9WsUQLaZbJtctwJOgOttjaus63fOAvj6PMQqEwRsp3EOUShoQ87tM0U2aJmSXmtZ7D3nqF1SRg+eYiKlF0VMzUZJvhMCBKAlxDYAEiyKiWXMI8IcjAcEt0hzFxZ51+ArXmBKZQBJmi0+sxW68zWLnOtEqx1q+zuhlhWhK3YlFtL1BrtbEb7Vuubyzh+p7vuYvuekSvbzM3/b/Qlxc4u/4ssftl2m7OwYUqSzsxtl2h07X4+G8/Rc8oY8gJdN1FbHT4e391imZtmgvPvcpv/O5XqFgOjxy4E2k5ZG7AKOuyds0mz3JaLUHSzwlfajN37J1UKz6xcOgOe3SWN1hdXWRz6wrdaIsoHwL/ehxlFgqFcXJ9ypP7mZhrUXUUSdBBRQNKacxdA82LZ56h3thHpTLBxsoaq0FIJ+3QrloYOiFMYholg4FZZrUboJVBnGc4pqQ3yBDxkEZjmumKT9Ld4PD+PUw1E4zRCnlToZUmTTKUAqfdZPrwnVQPn0TOHERXbn06cSzhunl9xNJri3zlc09yaulZNvJVcj1EixQzjnh9Q+M5FeI04nNf+CVUlmGZBqZhcmDSx3DqPP7oFIMNzUa3xN/61vu5eXGd9W6GWQO7YbNQr7G+7HDvozfprFRYPDtJNgj59O+/zOGHp9jcXGVl4wpBtoL0ephuSO5o2pU94yixUCiMmSENKuUGsxPHmNjTRlQMlJGiRjEHtrfI7K9w89IaJx59hPBzX6Kz1WEnDhh2FEoaLJQd7trbwqr4XL66Tnc7IE1zMjFCipxWu4VXLRP0N5idrfPQyQb9lQskBuhSCa1Tcs/ALlnMHrmP8onH0HNHwZ5GcJtMC2xfWmPp1CJbGyskqsN0RdGPXMLEYmVgMllvobUAJbh3/g72TvZJ3assrpmM4gqPvq1FY77GxMSAB/a8CSk8ess32Vi6xo2VRTqZZnGpx5G7ltBBgwOl/Wxfjji6MIPlKNTFHSznPMOoS807SFD9IqNQc2TPm3j8we8YR4mFQmHMkjBAyQTHd7CaM4ipNspwkUmC21rjcb/JRy79G66dP01tfoa5cpkwGOGKnMzOWRmkvHizzyNHq7QbE8h8izyHWrVBZzjEtwzY2WRmdpJH791PyR0QGAnYJaSwAAshXepTU1T2H4bpPQhnHoEF+jZplt3f3mF76zqdYJncGIIYMQpNlIYsTdEROLaFY7ikyuWhxycwWj3sUsSZL6Z0+g2WN48wO1VjqOawpKayd4LqvjvZEwXEQUTU/SQr/Zv0NsqU1IMc/sEZ9sxMUakCOqHTF/zh0/dxenGRxcvP8PC9j/D+R/4Z09Mz4yixUCiM2WjQZXNrkWFygKZ5CKwqyDrIFEyXumnzwDu+id/98MdwS3VafpORtsnSEF+YyJaHMEP6mzfJ0iqOYxJkMEpTHG2g4ojmVIs7jkwTD66z2Q2Q2HglEykshDAxDAO/2kLX6wi3tNu/ADGGza9jCteZeyxSq4kxO8OF6wEb26vkWY8klziOg9YQRBEDNeKFyy8w9enH+J6/8SDtvZsc/2GTnYuLXD23zfMvu7x06gUeePODPPb2BQwJo2GEY1v4lQX2eU1Eew1DpDiNO0njFMkQFa9hhdehv8Ty62vcffz9vHX/3ybZOEhUssZRYqFQGDONZH1lkXPnvkyp7TFR98Ez0aKEtmyEBcePHeeV/dMsL66T5TmO42KUTDTQnmqzsrLC9lCRRz28soNMcmQ6QAqB36hyz0NHEf2rxMEA23QwDBPTNDGkgRACKSWIBKEihErBUCAk42iLJbTW+lbf5KUXnyFPFKOdgPUrO5x/5lmeffE5rvfW6CdDsjyh7rrMTFQZjRIado3777qbh775EEfv20H1L5GkB9DhZfJkhqmFMpfPx9y8MaJUcUlUhTiX+OUBhpMCc1hWjckmDPojzr6+Qm+wTBonZNsDRqGkVj7AcCQJ3BY//zvF0dqFwu3mZ//qoygVYZck8/unuP8tjzJ515swynsAA4br5BsbfOXzT/LkZ54hCjJMx0UbCmwbr94mJ0Rs3sRIMqp+DUuBDTj1Cgv756mVRzDsU/ZsTMvAkgLTMNDyjfYBQmCWoHrkHqxDD0D1CMgWYL8xdfD1G89JBNYk24s9rj25xqXLl+mGO1RLFQ7kGltOUypb9PKQm8MuQaLpxj2ufPkLPHPuOnvnprnzeJV79gfUpsGrK4a+x+E39dl3Vw/XqoDsEXS2GHQC4kiTZ32GvQExbXzd5WA7QdcECycqtPa26axvEPd91k9N8+RzV8ZRYqFQGDO37tLvg05g+cYm2ZOf5p7eJvOH7sGoTCHSAGPY4cBsnVdbFTaiLYwsJtGSNB8izIx+bGMnJUTYxZAxvoDqdJvKVAstI3qdPlXHQ0oTUCidkWQ5OYI0TUnimGg7pIXJnNXEnLPQ5R0wHbCO3VJ9YwnXUTzEauUcf98Uh0Ztti8d4+pzZ7i2dIbLnSUurN4k1hG1kk+W50gJzUqLteEqw0vLTJhH+cTrA3JHYxibrA+XeOtjM9hll2p5ldnDTRo1SBs12InxZyvYkcTUJlbrENNmh3BTMtqs8vpTgsvXy6ysCq5feZWVXtEsu1C4Hd318D1cv7FGMIxIgx22Nwacf/kMYWfE5NQMtmEQ93YYbu9w5+EZTiWCjfVNpKmwpcKRLolh4rTniZdz3FIVv+bQmG1QLdtkoyGe18S0Bdo2QWjiPCGOI8IwJIki0jghEYowW8MwzjExUFiNGtK14NCthetYpgUKhUKh8GfdWsPCQqFQKPwXFeFaKBQK3wBFuBYKhcI3QBGuhUKh8A1QhGuhUCh8AxThWigUCt8ARbgWCoXCN0ARroVCofANUIRroVAofAP8/wDaisccR35WqQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "visualize_model(model_hybrid, num_images=16)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "seconds = time.time()\n",
        "print(\"Time in seconds since end of run:\", seconds)\n",
        "local_time = time.ctime(seconds)\n",
        "print(local_time)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "D3AaQc2xMk-G",
        "outputId": "37681043-d30a-40fc-d7d1-9e6091e901ba"
      },
      "execution_count": 245,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time in seconds since end of run: 1695628218.1321762\n",
            "Mon Sep 25 07:50:18 2023\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# from google.colab import runtime\n",
        "# runtime.unassign()"
      ],
      "metadata": {
        "id": "fALJ8tZXA0to"
      },
      "execution_count": 246,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0yhgWSns8PAa"
      },
      "source": [
        "References\n",
        "==========\n",
        "\n",
        "\\[1\\] Andrea Mari, Thomas R. Bromley, Josh Izaac, Maria Schuld, and\n",
        "Nathan Killoran. *Transfer learning in hybrid classical-quantum neural\n",
        "networks*. arXiv:1912.08278 (2019).\n",
        "\n",
        "\\[2\\] Rajat Raina, Alexis Battle, Honglak Lee, Benjamin Packer, and\n",
        "Andrew Y Ng. *Self-taught learning: transfer learning from unlabeled\n",
        "data*. Proceedings of the 24th International Conference on Machine\n",
        "Learning\\*, 759--766 (2007).\n",
        "\n",
        "\\[3\\] Kaiming He, Xiangyu Zhang, Shaoqing ren and Jian Sun. *Deep\n",
        "residual learning for image recognition*. Proceedings of the IEEE\n",
        "Conference on Computer Vision and Pattern Recognition, 770-778 (2016).\n",
        "\n",
        "\\[4\\] Ville Bergholm, Josh Izaac, Maria Schuld, Christian Gogolin,\n",
        "Carsten Blank, Keri McKiernan, and Nathan Killoran. *PennyLane:\n",
        "Automatic differentiation of hybrid quantum-classical computations*.\n",
        "arXiv:1811.04968 (2018).\n",
        "\n",
        "About the author\n",
        "================\n"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.17"
    },
    "colab": {
      "provenance": [],
      "machine_shape": "hm",
      "gpuType": "V100"
    },
    "accelerator": "GPU"
  },
  "nbformat": 4,
  "nbformat_minor": 0
}