[404218]: / Code / PennyLane / Algorithm, Learning Rate Studies / HRyERyT1 / 0.00052 lr 25e RN50 B8 96.7% kkawchak.ipynb

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{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "id": "tvTsxVFhcEu4"
      },
      "outputs": [],
      "source": [
        "# This cell is added by sphinx-gallery\n",
        "# It can be customized to whatever you like\n",
        "%matplotlib inline\n",
        "# from google.colab import drive\n",
        "# drive.mount('/content/drive')\n",
        "# !pip install pennylane"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "id": "YzU1v9emcEu6"
      },
      "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 time\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": "NMuS6xLqcEu6"
      },
      "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": 38,
      "metadata": {
        "id": "mwV5eRZ2cEu7"
      },
      "outputs": [],
      "source": [
        "n_qubits = 4                # Number of qubits\n",
        "step = 0.00052              # Learning rate\n",
        "batch_size = 8              # Number of samples for each training step\n",
        "num_epochs = 25             # Number of training epochs\n",
        "q_depth = 1                 # 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": "_1o4Rd7xcEu7"
      },
      "source": [
        "We initialize a PennyLane device with a `default.qubit` backend.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "id": "VrhtaB9JcEu7"
      },
      "outputs": [],
      "source": [
        "dev = qml.device(\"default.qubit\", wires=n_qubits)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mV03xQP_cEu7"
      },
      "source": [
        "We configure PyTorch to use CUDA only if available. Otherwise the CPU is\n",
        "used.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "id": "8wyZgRPFcEu7"
      },
      "outputs": [],
      "source": [
        "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KanGTiTWcEu7"
      },
      "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": 41,
      "metadata": {
        "id": "MTp-0NbLcEu7"
      },
      "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": "kUCmDIHEcEu7"
      },
      "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": 42,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145
        },
        "id": "y_nK-SxNcEu7",
        "outputId": "dc0b5f4d-dc11-45e6-de52-56eb59ff5f7b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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+/Mjukk/TiRoTpZR6z3veowB15MiR5WNr1qx5Qn+gSy65RF1yySXL3z//+c8/oSblifCZz3xGAeqaa655qk34gVhaoX+vbEkp1fDw8LLW76677npCmf5e/Ch9+HsRBIG64oorlK7ryvO8H/n8J4Mln5R9+/addPzrX/+6AtS//du/KaWU+s3f/E2l6/oT+iOdiCfbh58IP4rsrvik/BTjH/7hH06yz3784x8njmNe/OIXA7Br1y6KxSL/5//8nye0487PzwMwMDDApZdeyt///d8zPT39fcsBy6uBJeTzeTZu3Pi40NafFJZWX+qE1ZZSir/+679+ytfMZrMA1Ov1k467rovv+ycd27BhA4VC4Wlr3xLuuOOOZXs0wPj4OF/+8pe5/PLL0XUdXdf5xV/8Rb7whS/w8MMPP+78E9/Rq1/9aiYnJ7nmmmseV87zPDqdDpD6c3wvllaCT3d7V2T3qeGnSXafqH2NRoNPfvKTT/mauVwOeHz7arXa4zQuz5Ss/vM//zOtVmv5++c//3mmp6eXZXXnzp1s2LCBv/iLv6Ddbj/u/CUZ/FH68PfKqmVZnHLKKSilnnafnI9+9KPL/1ZK8dGPfhTTNLnsssuAdHxJkoT/9b/+1+POjeN4+d092T78bI67TxUr5p4niTAMueyyy3j1q1/Nvn37+NjHPsaFF17IVVddBUCxWOTjH/84r3/96znrrLN4zWteQ39/P8ePH+drX/saF1xwwbJA/u3f/i0XXnghp512Gm95y1tYv349s7Oz3HHHHUxMTPDggw8CqfPbpZdeys6dO+np6eGee+7h85///A/de+Z973sf73//+7npppu49NJLn3Qbt27dyoYNG3jnO9/J5OQkxWKRL3zhCz+Wg1Umk+GUU07h2muvZfPmzfT09HDqqacSx/Hy8zzllFMwDIP/+I//YHZ2lte85jU/8JqXXnopN99885NSXT8RTj31VHbt2nVSCDJwkmr3T//0T7nppps477zzeMtb3sIpp5xCtVrlvvvu45vf/OYy6Xj961/PZz/7WX7jN36Dm266iQsuuIAkSXj00Uf57Gc/y/XXX8/ZZ5/NBz7wAW655RauuOIK1qxZw9zcHB/72McYGxvjwgsv/L51PXr0KOvWrePqq69+yinIV2T3qeGnSXYvv/xyLMviJS95CW9961tpt9tcc801DAwMPCFhfDI488wz0XWdD33oQzQaDWzb5vnPfz7/+q//ysc+9jFe/vKXs2HDBlqtFtdccw3FYpFf+IVf+IHXFEJwySWXPOUsvT09PVx44YW86U1vYnZ2lo985CNs3LiRt7zlLQBomsY//uM/8uIXv5jt27fzpje9idHRUSYnJ7npppsoFot85StfAZ58H7788ssZGhriggsuYHBwkEceeYSPfvSjXHHFFSc58X4vlsKCP/nJTz6lFP+O43Dddddx9dVXc9555/H1r3+dr33ta7znPe9ZNuNccsklvPWtb+WDH/wgDzzwAJdffjmmaXLgwAE+97nP8dd//de88pWvfNJ9eP/+/U9Zdp81PGM6m58iPBVzz80336x+/dd/XVUqFZXP59Wv/MqvqMXFxceVv+mmm9SuXbtUqVRSjuOoDRs2qDe+8Y0nhbwqlTpIveENb1BDQ0PKNE01OjqqrrzySvX5z39+ucz//t//W5177rmqXC6rTCajtm7dqv7kT/7kpHDSJ8Lv//7vKyGEeuSRR55UG0/E3r171Qte8AKVz+dVX1+festb3qIefPDBp6wyV0qp73znO2rnzp3Ksqxl9fnCwoJ6+9vfrrZu3apyuZwqlUrqvPPOU5/97Gd/aB137typhoaGfuS2KZWae97+9rerf/mXf1GbNm1Stm2rHTt2PKHKe3Z2Vr397W9Xq1atUqZpqqGhIXXZZZepf/iHfzipXBiG6kMf+pDavn27sm1bVSoVtXPnTvX+979fNRoNpZRS3/rWt9RLX/pSNTIyoizLUiMjI+q1r32t2r9//w+s7+7duxWg3vWud/3IbV2R3Z8v2f3P//xPdfrppyvHcdTatWvVhz70IfWJT3ziKZt7lFLqmmuuUevXr1e6ri+bfu677z712te+Vq1evVrZtq0GBgbUlVde+Tg5+F60Wi0FqNe85jU/ctuWzAj/9m//pt797nergYEBlclk1BVXXPE4E5dSqXnvFa94hert7VW2bas1a9aoV7/61epb3/rWSeWeTB/++7//e3XxxRcvX2vDhg3qf/7P/7ncd78f/uZv/kYB6rrrrvuR27skg4cOHVKXX365ymazanBwUL33ve99Qmf6f/iHf1A7d+5UmUxGFQoFddppp6k/+IM/UFNTUyeV+2F9+MeR3aXr8wybe/7bkpRVq1ap+fl5VavVnu3q/MRxzjnnqFe+8pXPdjWeFjSbTWUYhvroRz/6bFflGcHf/u3fqlwup2ZmZp7tqjwjWJHdn1187WtfU0II9dBDDz3bVXlG8KpXvUqdc845z3Y1nhHEcazm5+fVl770pZXonmcK4+Pj9Pf3s3379ie0Wf6sotls8uCDD/JP//RPz3ZVnhbccsstjI6OLqt/f95x00038Y53vIPBwcFnuypPO1Zk92cbN910E695zWs47bTTnu2qPO1QSvHtb3+bf/mXf3m2q/KMYPfu3c9a5I9Q6ika9n+GsXfvXqampoDUoe/8889/lmu0ghWsYAUrWMFPJ9rtNt/97neXv59++ukMDAw8I/f+b0lSVrCCFaxgBStYwU8/nrUQ5L/9279l7dq1OI7Deeedx1133fVsVWUFK1jBClawghX8FOJZISnXXnstv/d7v8d73/te7rvvPs444wx27drF3Nzcs1GdFaxgBStYwQpW8FOIZ8Xcc95553HOOecs516QUrJq1Sp++7d/m3e9613PdHVWsIIVrGAFK1jBTyGe8eieMAy59957efe73718TNM0XvCCF3DHHXc84TlBEJyUDU9KSbVapbe3FyHE017nFaxgBStYwQpW8ONDKUWr1WJkZARN++HGnGecpCwsLJAkyeNCKgcHB3n00Uef8JwPfvCDz/pGTytYwQpWsIIVrOAng/HxccbGxn5ouZ+JPCnvfve7+b3f+73l741Gg9WrV3PXXfd0d+Q8UZvyeOtVatFST/jb90JKSRzHhGFIkiT4vk+73WZiYoLZ2Vn2PfoI83MzzM7O0Wy1QQmCIECJNCV04Ac4GYd8vkC1ukhfXy+FQh7bshgYHOTUU09l9eq1lEolcrkcSqnle0ZRtKw18n0fz/OW939pt9v4vktPuQRmhqxjkMtq1Ot1+vrHiGNJFMXESYLlZIiCFvWFOcIY+vr7KfSuRkpBdWGGxfk5JiYnadYbLMxPIIRgYHCALVtP4cJLXkix1IOUCrdTQ6kETdOIAxdBQhR4NJptMsUxDNNC1xLatUl2P3w/+VIvp516CoVcHtNwWKw2cTsNmvUZjh4/QKPRYXhkLVGs8NqLGEJhGoJWJ0BpGaLQx3IsNp+yhcHePkyrgJ0tI+OYf7/xbznWmcS2KlhWHsMSmBkL03TI2lkcO4djZrq7fUoSKVFKksiQOAlJZIhUEiFAIJAyIUpiUIrTK2fyhh1Xc+8jd/Pdzu1c5J6H3oroGxvl9lu+SKexyNoNZ1Ip9pLTS8zMTyBcl57h1XT0Dj2ixEJcZe/4g9xZOIarKzR0NGGgC5NYhsiuVVWhoRQIAQgNlEATBkLo3bop0ECQkFGC7dl9FDIdIs8giBTlYglLaByaPcJ3G22ko1g/bLOplCcUPoHnEgSStieIdYiEAEuR0QS+By1XEQuNUqGHASOLpRfRhY6KfRYXJpiYDdBQOAVBMQOOULRjie9ATwFsW5EEimYsyFqQ13QW5xWz04rYgEq/Yrg/Q94uknFsMraG0mJkEtLqLDA+K+nMOUi/gDANwEK3QkLRxA8VQkGfNcAbX/VBypVeQBD6Pu1WCxm5FIplJBqWkyWTyyIUeJ0Ort9B1zQqvf0opYijACkVSkoMUyeOEjyvTW/vAL7vEYY+MlHYpkkQ+DTqDeLQo1gqoesWmWwOqQmUUmQzme7eOQrTckhkjGna6IZBEkegwLIdFAIpFUkSQ/f1+r6PrhlYlo1SIGWCYeiAII4jNC3dk0fXdY4cfITVa9Yzunrt9xmdFEpBAigFSkkEKpUZFAINIRRKxYAGSkclCVHbw52foN5oMV+r0woVfhKgpESqBGEYWBoo3ULTDQxNIpWGEII4ClEyQTMsNF0QhRIhE4IowSTB1CCXyZDLlqhUcjhZRbZQQhlFTNPCNHU0zUDpWnccTmsr1WP7ED14123c8N53ENSaVEURx4rRpEVNwKZiB5EfQCPm8ESbkbJPHOos6v1s2rKKtZX7eXh3xPFaL/NtWFMRyOYiStMZG87gaWWmJ6qYto6T68eIpjC1hF7hollZ2lqeIIhZNRbRmO2w+2ie0kgPpmGxuFhlaxF8zaTQ18+GUoP+yjFq+wQP1CwsHS55QYczLwiIjgjaD0PgCjAVyoRMH+R6wMiCnldgdaegAJCCO+fPJnv2n5LLF06yCHy/fy89r6W/UkqUTJCq+1dKlFIoJUmLqPQ/JUEpFDIdgYSGEBqI9B0jBJqWHlu6nVJp30mvp0AppExQSiKTBJXEKJkeS9KXCQI0IdB1A2EaGIaFrhtomo6mG+i6ga7r3H7bjfz27/zhD9xy4EQ84ySlr68PXdeZnZ096fjs7OwTbjENYNs2tm0/7ng+n6dQKD7BGSeTkWWSItT35SlLL0NKSRRFdDodxsfHmZyYoNGoM7+wQL1WY3ZmGt/zMI10AIgTmZKMJO4OQpJOu0273UbTNBYWFmm32pimQa1Wp1Gvc3zVcTZs3MiGDRvJ5XI4joNt2yRJgm3bBEGAZVmYpolhGGiahqZpmIaGZRmYjs7Q0BCe56OLFkcOH2Dtxh0kWohXWyRXypFxDEzDJAgiHEejUiowMLwGwTYWZif45vX/SeA1yOdySKWwTJOsYxF5DRpJjG3nUFGEVBG6aad10W10IsYXj7GwuEChPIBlmtim4OJLLqdULpHL5inkM0xNz5DImJYb4EsL0ynizdQ4cvgQSsYUMjpnbV/FQCXLLfcdYLrhkcQRjabOyGjAKVuG6ekdwrALNOqLOJkMDjnKhQEwJInuknMK2GYOw9LJOlkcK4tCImWCpiQKiZ5ITGUglZ12siUBUJARCQhBI6py6P772JIdwde2Erea+O027uFF6uEiHWpooWL8+KMUrDICRRS2WVicAl0xEU/yYOMhZMaiUhpjTbHCmt5tEIesGV7PZ+/8d0CjFSwuTynaEknpIk4CZJKgoaNkQiJDbDNDO1EEcZNSvoeK1gN4LPrzLEgP6QhiW5C38/QURzCshEbnGLOLHVwXklhg2QLbEmgCZBt8XyA0E5nPY2VGKObGyGUqqDig3roRS59BodAiaEmFKglyJY2iSOucCImWVWRicCyNsl4mSUxaHY/YV6jEJZCSsmVQzJUoZhICGeIFimLOpKcQIGvg+QlxAoZjUugLMU1FswmhD5ZhYBoGxWIJKSWBaZLJ5mjV5yj19BInCeVyL3Eck8QxkaboKVcIoxBUAkpgGhaaJkiiEF2HQq5EPpejWa+i0DBNEytrgVSYponXapAvFShWKqAEupGSB9N2UsKiFCiJk8miAMtyAEm71UAXgmKxRCIlRw8fQKmEteu3pv3ZsrDtDHGcpKQmiVFSkiQx2WwRITTCMEAphWEaFAoFisUnGtNIZVeB5LGJA9IlmmCZ+SJkjPQ6uM2Y2vwiU9MzNL1FwiQkNnPYeYeMrmNoCg0QQqI0A82w0YEkdtGMDJpKiOMoJdK6ja4LkiRByoRQBghDkSQSvxPQaDao1quUcjbDA4Le0VI6blsS3S4gNG15wpVIQEOSksBcNoejQ2koR6eVQwQtcpmEdpQljmrMHZym10pIkiyTnQwjFQdhVFi7pp/W4WHmak0sYTBS0qj06jiFLEmzQ9AKGHfb2FrIxEzI2nLA+g1Z2rHEMXQmazmmFhts7fe5d28JofciCwLlLSJ8n+0lxcCQ4NARjajVJDq1h+OHLIKFmP5YcfbWDlsGJXKPRjwHui0Y3gy51QqtrNCyoHyBiICMgBhogjIFSVPg4JDP58l3J+snIidKqccRlaV5SsoEJePld5KSFonqykdKYLvzn0oJx2OERCCEhqYZCE0DIU66J0vERqXzm0xiZAxSCpRIFzoo1S3WlUNdQ9dNDNNGN00M08IwTDTDTAlK915LG3c+WVeNZ5ykWJbFzp07+da3vsXLXvYyIJ3Yv/Wtb/3Qzccej+/XyKXjavklpw9e8P00LUopgiAgSRJc12ViYoLbbr2FmZlpOq0WoKEQtFrpzptCaGhCIFBImWowlJTwPQ8+SRI83yeOTUzDol5vYOhTeL6P22nT399H/8AQuVwBx7HRNA3DMDAMIx1ILYsoioiiiDiy0DTIF7L09PQzOTmBnclTRKNSKhJLDbfdwDINMk6WYnkIFcd0WpO0Fg+RdUxWr1mPwSDbtp/Cus2n4AURXrtN2F5kbHSMVqOK702mbZEBSeTiRyEInWyhjG1qzC/WaLVnyC3Og6YolfvIFfrQBFimhWHZxInP0WPHabZ9Nm87jTXrt9BpfYVjRw8TeD5+1mFywU07qG6SyQhsuxcv0kAYVOs1Mtk8vfkeTF1gGAaOmcWwBG5cR5gxtq2RzxQwDB3TMBGaSrURWqqtkCp9H3ESo5IENIFQOgIBWkKPPYgbt2moFrdM3chp8RoGi70seFUit4nrddIVs3JoNKsEXovY9khkQCdsU44DGnGDWlJjWlukZcH7LvoDSgMDDBRL1Mbn8UVE30VvxnYK/OV//RW1YA7oDhSagRAaqitDkQzSNXHiE0cxeaUzoSQaLiNGTEUpNALm/CZ1g+66CFpBgB+G5CwTx+7FsROU8Gn74EjQTDB0IAHlWhT7n8ulZ17FQDHH2sH19JUqWKbJA2ufy7998c+pVccRxCgDlAUlB6II3FAhhcKwBTkEGXMdp57+doZ6NnN8ZgqFRsOd5+Dhr+H7B4kzcXpjPyYIW2jo5ByNhq0ImzoyFkjlIzMhjq3wQ4UfGWi6jud6BJ6HH3hEYYTveVR6B3AyeXzfI4oC2vUqQRgjVYI7N0X/2DpkkmrQoiBICYVKtRRKJsRRAEJgGxpKQOR7WLZNFHqQhBiaCVIReG00IYjjgHyxDCh008KyLOIkQdMEQiikEszOzuDoglLPEO12g5tv/AYvecVrEEKg6zqWXexqPQRB4ON7bYrFCpqmIaXCMMCybIQQWNbjF2KPG6vSpTFCpRM9pOObWPpNCuJ2yMKxwxwcn6EWSoQu0CwT2ylScPKYlo4mdCzDQEMQ+h2k0rCzORQxSSwQwkYFAYlmkSQJQmjomkAZOpqh45QGyDoOiZT4kcdCbZHZw9O0Gh0WOxPkJhbZNFyhd2SETL+F4WTQDCOte5eoC9KJShMCGeuUNqymMtNAa3fo688zN5mg9AI5p0ErtsjmC/iRyZqtG9jem+WhW28n9g0WPI2M3uCCU4cZ7LPYfSCk3qwSWza2JsglMatzUM4YTE3XmWsl2FYOGfr0FiTNUGAZIRmtiR5qOEIxXAKVizGLPRSzkulFyfRBjw0Fg4f8MtvGdDw74DvfkTR8g6lWjpydcNXFDUqnJ+n7iEB00ndCSEpSbFAexA2BRrw8XSxN2E80cZ+sQVFdLccSQemSlC75lTLpEpITZsLu/0R6EZTokor0AEiJEgIluvNjlwAv3UcmMXEYkEQRSRKnmhuWyqR11jUdXdMxTAPDMlMtimGhm1Z3oZ0uthFiqSZPGs+Kuef3fu/3uPrqqzn77LM599xz+chHPkKn0+FNb3rTT/xeQpxo5ln6ezKJUUoRhiFhGNJoNDh+/Dj3338fB/c/yszsHB3XxTQtdM3A9VoopWEYOrphEEu5rLpUgJKyy1LFY8IlZZeseCQyQRMauXyO6vwcfqfN4mKVoeERxsZWYRjG8gD3eKJiousx/QN9lMq91Bpt4jghkZJmYxEr00PW0SkXLXp6h/FDReh30FQBrzWLSpp4nQUKhSJnnXUeHT/EMCyq89McO7yH4ZFREAZzc9MEXisVyrBD2G7gJTqRNEGzcEqr0TMhdsah05rj6OGDxDIlJ6VChtGRXhqNWY4cfJBYWoyODLNx7U4GR1YxOztJkiiMbA911cvBmkGmPIin6mBkKOYz9FQKGETMzU3gui6mpmFqDqbQaXWm0XQoWEPk9D5sI5tqyKRGnMQkBKDJVPuk65hmBhEEhMoFFVOxBtnUu525+iSvOvVVLEYtvnHgKyxqkxw5OsHgbIPA7xD6AZoDg5VV6HMJteYEdmKx0J7EViaYOr7eYiqeo2m5BEoyU68ycXQ3ZbmVgw/dz0RzlnKljzM372C+WaUSmoyVT2OyOsvRxYexsjlMK4Om611VbEwiAxQxSlPEMiRSECNoRCG+nKZHt+nLFqjKDlqYoCLFfL3DAf0I68QQjulgCAvb8CBRzIcCoQyKjo6QMVa+nysv/hUuPf1MSGIQOjkni65pXHTuZZTzvfzDJ/+IhnuAjCGxAb/bhdxIYQCmphDYjPVexgt2vBzHsTh7y1ldbafi0Pjz+NS//waeW0WVCuhGjKalBMGyoFxSiI5Gs5MQhgFJHBHmFJgQqBi/O3h3Wi3CMCROIjTdQtc1gsDFcbIoGWOYFlEUo+kW2Z4BkjgkjBJ0TaDrGiiJQBLHApkEBF4HDYVh51Jzplsn8l3i0Mc0dRQxgddE03TajRqe5yGTmCIaykrQdR3dMNF1E4FA03RKlQGioIMfeFz35S9y2hlnUypVlsefsOvwL6VC1zQKhTKBn17XtDOAQRyHqYpcaF3DjXriYfx71lcCuTyaKaWQYYRf9Th24Aiz9Uk6KkJ3CjgZB0OPcTIlLD0DSUTkhoRxTBQEtJqLeH5EIVchYxsYIqbT7tBpVUmESUyCkgmGZlIoFsiUixR0m4xm4ZQLSE1SyJaJmj7tRod2y6XRXqTZqtM7VWfLhhH6Vq9BqxQRWkqQltaMqmsmiJyEu/bMEUjFc9fEdITB1sGEwT44dlRwfCoi7tQQhR609gQjw33M5tvMRwWqQcAp/REzxyfoLEiwRwjzA9QCk3WOz1w7QRkGroBIy5LPucTKZOvqHIemJJYOl+7soe66PPhwCymhJnrxaiELsz6rBk38rElc82mFkLMllb5ejvvgNiMemQU/1Lni1U3GzorBJiVjUiAdAe2uZdcB5QriYwJNSYTFSRoMeLzm5MR5JP1dds06XQ3HkvakSyZkkn6X3SWMQEvNLUsEYVmL0tVqyWTpRl3y+BhJkSpGxQlJHBGFAUkcd03movvREVraD3TTQDMsTMtKNSm6iW4Y6Uc3u4uyk+/9ZPGskJRf+qVfYn5+nj/+4z9mZmaGM888k+uuu+4nsD/JE9lyTtaexHGEIEHXra4NuUsgPI+pqSmmJid5ePdDPPDAvczNzNJxXTqej5ICTddJla1gGgaWaZJxHIIgQAoNiSRRCl3XkVKmKwSlSKRCCJmWSxJqQmCaBl6nQ7lcYkTXqS0uYNsOpVJp+SU+TqMSmmgqoJDNkstY9JQquK06JDEz449gOWVy+Sx9Pb2USwVqTZewE6DpJkorkyiT6akJBodWYxoOvtcgjmq0m4sMDq+jb2QDuUyW4ZFV7Hv4Lmaq4ySRj2NnyOSGsYtjJIhUISWTVPODZG56kmNH9mMYObZv35iOPlHIxjV9HJ2Yozp/jE57A6VCGccpEUUahmnTiTRU28AiTy4PpWKWOHCZnjhCbcHBzmRRTGBoGobuYGpdfwArj5QOrU6LJFZYlsOa8gYuW3MBX9r/Rea8GXpz/WSNLPPuLHGYIJUADfI47Op5LkZepxw7DGs5hoZewYH5Q9R2BMzsuY+D83vI2T3ocULWb+IGHfJ6nlrUwBMBNc0lqxVI/DphXoAymA3rJELw6MRubC1hbnKCqqozmBtBTgQs1iLOGDmV17zotXzt9tv4u6/vJek0MDOSZtJJVz5mhNITDMtAJpLYNCkUFFVNYSLISJ2C2UtPeQ2aPsVU7SidWBHqOu1ggJ6RN7K2b4TpuUdpJzfT7DxK0grpye9kdbYfV1vALY+wdWQVQkmCKELTUpKuGwYowTk7LsD33sk//fsfkLBIEIH0NIglylOYSqFp4OOwafWFmIZOFAboukkSJwhNsGFsAxtWX8qeQ/9CT+DgZDUc28D3A4RuUaxA0PZoNy2iBIJQooQi0gWeULQSiWWZCF1PtRaJoJDL4rYaKUnQDMIwTDURlkXgtlFCJ18YQEmJ73aYmx4nX0i1bPlCniSIcFtNBIJWo4ZhOcSBS+x5BJ0GmWwGJTNIPYE4xG21SaREEz3IJF2hGpqOYVrLI4qUkmw2Q7VV47++eC1Dw8OcesZZINJxJkkSLMsCBEKkmg+lBEEY4tgOhmGmz143Hpt0UEj12DJKnDCMLZkq05WoBiJBKAEKkihh8egU+x/aQ0P6SMfEzhRwskUsw4TQI26EBHFM4Hq47Tn8KCYIAzrtKm0vwNBMlOfjtj0SkRDGLo6dIVEKTTeRCsqOjZUtsXHdVvr7hsgVJLmCjW4rSoUiQmrEkaQZ+NT8gLYb4HU8tjZd1p62BaOn/zFl8/KEJah3TGqtGC2OufdhhW5V0Ywsg6tG6B02ma0l5GQDP6wxO+6xON1g0TXw9AwbbZ/52ZDB1TGxl7DQ7LB+42pOXV3h+ORhxu8Hx0lYO2Jw31Gw9SJb1pQp9hbJhmX82T3sORbRSYoM9Lmols9UO6KUE+BHVGsB/ZqJZyREEmyzyMOPtlizKkezXicfe1ScGIcQTYA/q+FUFI05ndkDFmsHAsyMQgmI9wlEAEJPtSs/TKtwohZFdTUXS2YYpR7zQVnSakglQcapyRaBpikEMtX86xpC07vP/ORrLOviugsSRUp2iLtaGiVBgN4lPJpI/Uw0XU8/holupOYd3egSlK4/ilg6p0tSnkxEz4l41hxnf+u3fuspmHeeCN/jf0JXnagUUejidpoEbos4DIgTj7vvvJs1QzZjm86mMriJJEnJw/j4OPfeczeLc9NMHjtCHAa4bhvfC0iiBCU0ZBIDKnUkVanpKgj8lK/qGsiUqUp5oj2vWx8pSdSSzVAyORUwv2Cxft1aMpksAkGSKJqNBvmuXXrJ9GOaZvdjYWCiCwMloa9SRnllWjOPMjt+mMWOYHh0Fb39g6gkYrG2yPxCjUJxECufI4kTHNtmYWGGnt4xsk6BWrtOfWECYeZYv/lMNKGRzxfJZbNEQQepIjQthyE0osgnX+4jny9Qqy1Qqy4Qhz5hEBCEHsViwkD/MMOjW4ncNv29NTJmyMziBNf/1+doNF0kBsWeftauX0Pg+RQygmK2B0tkSLxFEtEm8Focn5bkS8Nkcj309fVDEGNInUppDCtfII5j4jjm/MELWZsfpt/oY43Wy2jQw4K3yCvWvwIpXP7l2KcwEgMtW+Di0Ys5tWczScNjcfY4E0mbThKQ13VarQkuPP8lfKUzhVRDrOs9nfnJR4mmW3S8Fi2jg2NlmQtblCoVPBuCepOqFbMht5FfOONXCdottHaNbx34Bq7r4RkdrEOPUM73cUb5fHaMbeHYIw9w+ebNbCn8JS3NJZspM9Oc5/998x+Y9vaBqUgAoRTCSMjZEGhgCYGOhh9HSCnJaAWKygRRYLCynZc+79c4d/NpZC2LszZewqU7Xsfufd/htls/Q85Yh6MktrOejUNbMVRMkoQkSYhSEMcJmYyWqo2V5KLnvohDh+7m9nv/leFSHwqNpDSKKpv41fvRoyZDYxewee2pxHGAEDoKRZxEGMJEaDpD/Zu46+EEPwjIZFJ1sB9ENKOQjDDJ98TMz4fgie4kqNCN7oozVjQXFwjabZTQKJV7aTaqoGkUCibHDu7FsrPkyz0gE7xWA6/VJOq0MW0LXdPJZjOoyMXK9KaTcquBUNBo1phbmKW3tw/H0GnXq3iei0KSN2yS0ENJlfaVTJbF2SlkHFHoHSaRCXEYohsmiVxyXFTcf/st9I6u5bmXvgBNS31MLMtBCG3ZkVYpSRimGhPLsskXisRxhO8HhGHQNf+kavruPARdDyYhUiIiTlhwKbFEYBJkLKhNLPDAXfdSTXzsQp6sYSJ9ReB7tLw6sd/B95uEKELfJYwbdDptGu0AIo9OlBArhUwiEimI4xCVJAjNxNRE6mSpC+ptUJqF21lkZGAESZZSuUS2nMXIW6RO6wlSKdxOG1toHPNdjLhNX1+BYqaAns2AUMsTtBAgtRiiAGWarB/LMTUrqRRiDh5sIrGY9UNGs4rBSkLT86i7glU9AqFcBnvaHKmaVCObrHSZarhMPHiI/UdMOq7C1hPiWKPezOFGbaZrNRIZYU67aHGVxY6JDCrY1jjr1vYzOWOxuVRmoKKxZ88Mj0y32ViJyBoW0unFiHxMvc5w7zBrel0aBxqcvkPRmNA5doPDsQcEfRs1br69hw3ZGmuvBExIagLVShmoSq0yJ9hjlkwwj3eUPWmuU/IJ573uCYiuRl8XqeZimUR0NXVCpCbCJZ8mpVTqBKvoalrolukKmCYQSqArPSUcXcdYTTfQDAPdMFNTjmFgdI/ry+YdfVmj/VQ0KEv4mYju+f54zAlSoUjiiGarwczUMQ7se4TxQ3vwgpCecpGx0dVs2LKVM8+9lPHjh7nxmzdw7nNjNCvP1OQ0d9zxXR564D70JKDWbFFvu4SxQmk6Ski0rtnIskyUTBCaJA594jjGNDSk0gjDGEHqua5repes0LVfJ+ikjmcqlF1nJ8n09DSBF8CG9SA0Ou02lm3TNzBAT6WHKI4AlolKxsxjOQ5R2EHGOvl8ng1rBom9efypBEdTjB/ez4MP3EuhVGTNmg3kCj3IxEXXFImCQr6IIKbdmCXw6vRUSqA7tBcn8FpZ7IxNGEeUB9dQ7FtFqX89od/CbS4yOTNOu2ZTW5wnCn1ct4lmFxkdKzM0UGF6fpbZ+RqT44cxNZeePIioxcFH9xHFOvnyIH2FIQb7+yiVBojcDm5jlthv065XMXRFb16nWa8zPu6xfmOBfKmH7FSRETtH1WwhhGSoMobXbnKmXEU+KSBrbaYXaoiFJqvzY2SnAmbcSUb8kHYYsn7gHC7tP43Z8eNEBZ2pqX1M1qbo5AR2LkOkEiaOPMpAppfhDes5enQfifKwLEWzoHPlS9/GxOG9zD16A5pjY9gZ1KBDTvqsHRplXcbgWKPK7ql7afhNIglGNoOtFZBtndn4OEIk1Gs1qt4qLC9m/er1ZHM629fuoFL8Az7+9b/lYPPe1KYswLANdE2RQxIpQZwE1Lx5Aj31rM8XR7ny+f8fpw6PoIKI0PWwSPMOOabD+WdcxrqBjezd/yiTx/YTRDG2btFptSiXKtCN4JAy6Q4+GlEUkcvmuPwFr8cVec4+61IyToaxgVU4lsWDu29m/9F7ufCcX6CUy9FxO2Szha5sSzRNkgiBY2WRgU7khyiZRYgelJal02niaW0ymiBfkfhSB0PiaApMgalrEIJuWTjZAnPz03Q6bbL5IoPDo8gkRreyZLM5Yq9Ds76IJgTVxTmUJhgYGSWXzdFq1vFcD0Nv4vleOqgqSSGbI7tqLVHg0anO0Wy0MCwdJQVRmEbbWE6WvGnhtpsEvovvtij2DXVNNzpCMyCReO0md9zwNcqDw2w7/QyUUrTbbUzTpN1u0WrVKOSLmKZDnMTdFaTo/t4mSWI0TcfzXCzDIAqDdAWrJNr3W2GrE/6hNFQCjflp7rz5Zo5Nj1MaW03oxURVD7fTxnd9XNclISCWLpHQCMIAy9KJ4gBhZREm5JRAM008t41UAtf10iCCOMGLEyzLxDZ1lKFj2hkaIqZTncAUgpyXp9LuY2DVKJojQMVEcUgood1uUTA1joQhvQ8e4PRyEc0eQxjGSU3R9BwbcjUaUgfHQnN0ji8oBsodoiTBcHoZjwbx5udZVXE5Hlk82nTozcSIMKIeOSzOm2QRGE4Jgw7NapN8vkwxK/ADxZ65ABWGFLImA/056k2JZuoU8gZx4kPocPfDEY1AMehVOXioTVWUUZZg3g3od3w2rCkxPDzAvfccYmFyijWDAR0lGK8aHDxc5O6jFXLNKUYOJmw15ljbE7F4p8IZEsiawrEFZp9CaALcJfcAoLuwPpmYqGWyuuzMeoIQLJtdNA3Q0ZRCaCqVUZFqTVKCoKN1o3iWTIMK2Q0kUV1yIjjRjNNVmyB1iUj0E0hler8lzYmup74omp46xX4/7cmPk8/sZ5qkKCXxfY/q4iwTx49w7OAjHDg8TrtVxXTybN68iXM2bmPt+o2UKz2YpoVSkqGRMe5E51vXf4WR0XXcdMt32fPIPmr1OpaRql6jOEYIDUMzkLoEJEKlEQ6aaaAbOomUmKZBFKahrEKATFIysxQCmxIogUxk136Xvuo4jhFC0Gg2UUpQqdfT0LCu0+fc3BzDIyM4mQx9vb3Lpp+FxQXcBx/C1DQqPYMUijZ6ZR3DmwYoj7jg15GyzQMHH0aYOVQs2HKKjeOAYdjYToGBgRFUElKbD5ge34+T7yOXzzJ+9ABKxrTdOkJJCuVe1m8+nSCx8A0TQ1NMjB/hvgfupLpYZ2TNJnIjW6j0KTTp0o51Htp3kFZtEWTC4OAYUitwdGqWRFiEcYTvtgg7OaaOHiK3ucTI2BZaxQLjj95CoZgjiXwsK/X56SnmKJUsbN3nos2Xcv6mC/jc3muZtlx+4/LfYPrhA4T7qixk6mhNH8yEcsdkx+aLkO2QqenDbOk5n56+PorSYHryIe7bt5exdWuYy/h0VIly7xCGgNPXnQOxz2L9No5G+zF6DCotA6XrXLTjAp67cQcH7DyTc3vwjZBcbOMakrxMuCe4h6mjhzBik9U7zqdzaA9Rs8lIeQNR5LHYnMReW8awy5QdjZuPfZtANliTjLEuv5Ftw6ej1CKdeKbraa8QhoaVM7GMQcK4RhR1QClCLWGyNU/Wshnq3cG2wTFGevuZmZ5gYXGRJC6Sz+fRNA2lNIqlPs4557n0lEscPHiATCbTNUXI7spIWybMhqGTxDFxErN69Sbe/Kq3Y5pm6j9hpgPSztMv5rRt5yOEThRFJMljA6sg1S4IIJcpYMgiQh9kaPR5rB45j4zVz+Hxg9z64H/SqN+PlR3HTEKEBrZSKF0gNUhQeJ02xXyJnkofSRQiBNQXZkFKXM8l6jTJZG2cXJ5MrkAQePT3D2HbGRIpEUJHaia+1yGKAnTTplOvoesmlm0CgljpGLqOLkwW56boGxim2DeC5Tikg7GJ5WRQSuI26iAMpExTDjRmxjmw5yFGt5zGyOq1aLqB67qpgy5p0krDcKjX66gkIpMr4nouhmF2TUCg6waGoXBsB9uxCQMfjaW9Sk7wS1kK3DkB3TgMwjDi4Xv2snvPvZQGB2kuNpCdgGarRp4aD+wfZ7HVJlfK0zPQS6HUg1MoUMhnSWRMPldE65rASgMDqdktiugEHoZh0jOQmrtq1TrN+QVyxSI9A704tkPUcXE7barzNQQa2fkchf4yURQT+j5xHNH0AoJOQE/BZvdByaqhEoM9FTSjstQ0lBCYto0KC8jA5fBhQWA4TEoNEShW95lo0kGh0ak5zIQ6lp4Q+oqZ+Tp2OY+mGzS9hJ6yTqRLbCQ1XyOIYypWwpH5Kq6rEcZQzjnkhEZid2h2ImKlqBgeibCwVY3JmkbcTphvx+R7TKI4ptlaZGhMMF9vMbswxcREizU9MbNmiaYKuO8ByayeIevDQJJlfC4iN9QijBRJQ9GqCgbWCERWoRdJ+3LT5ytf+Q+ufMkrKBZLJ/sxLpMV+QQalccIRUp8U4823RSAkZKJrtZkyQ9EO1FDQ+rDp7Q0nHyJ/KRcSTuBrBgn3Psx05y2rKE5QVuypDlZOl9/jKD8uPiZJik3XPcfzM7NMTcziWFa9PX0s3nrdgqlSjeHh4YwbOYXFlFAuVzprnY6KAmP7j+C1+7w8J6HWVioIhXIOFw20Zi2RS5rk9My6IZBq9UiiSMs06SQz2CaGp22SzOJ0YSOVDFLfkjqsWBXNC31jE/iKFXrJgkIjSiKkYlCEy5zs3OYhkm+UGB+fpFmu8XUzDSrx1Zh6DqWnQbZd9oeU+MTNGqT9PQNc9Flu8gXRnCdRZAakpDq/DGE8lmcb3Ho4P2Mrq4wOHgGSioy2QL5QgXL1Mlms8QxJBSoLUzhNhbQtQBbhUSaQ2VgNbqmU5s6xNFjR+mplOkfHGKjZjE7NYGRX015cAOe16BZnSMKoNUJCBMNw7BptDroRpb5qkfsJ0il0Wy5tNoHabaq6JoOKqRQzKNrRmr7RFGttWi7klXrKoyN9pHJZDh4fDcDfoUd1qlstxT5OUlfK8dk7TAkgvn5WQwnRIYdOsePMx+3MWKLLfYaLFNnsT7FXTP3s/05V/Gc087l4fsfRCsWGbJ6cCfmUAs+Rj6DDCAKfBJLp1Mw2GxvZV1hlGCxyTBlzj39xbSsiKk99yCJKIyEOKsKSN3GP67xsl2v5uzxRe785tfZddar+MJt/45fafOS815DLQj5zn1fwo0E5dIQdd9gd3CEvY1Jbtl/B1UaYAgkKo2k0CRXXvS7lPqKHDl+P/v2fYPx6UcIojZFfYjBTD/NegO/UKRSqTA5NU2zlTrI5bphfkmS2pK3nnIGuUyeequN7dhEkY9j211TgiJJEkwzHZSiKETDoD5ziAcfuB/dMOjvq7DljIuQSqXvquvLZVk2S3lnlgZMBdh2nh07X8/zLr6SVYOrMbuRHeuHN3HhjsuYrc3zH9d9mPjAP+HrYEZFNC1HVtTSviMV9cV5NEOjWW8wMDSCrmtIJempVKjOzSIsk5ypE3Ra9A6PUSwUSMIQqSAMPSxNEPoBpq4jFLieSyaTI2l7yCQhDgIy2QJ+4HXJSIJhGsQKDE3HKZQwgtQE1KrX6XgedraIW5tn4eh+tl6wi2yxQBwGqCSdmKVKCH0fO5NFAoVihdnpCXTdS9+pTOi0msRRlE5UYUA2myFbLCNPcJp8zCflhEH+e+YqlUjmxsd58J67adSqKKkYnz+ALkPmF2bwG3M0/Q4LgaAv6KenvxfbsijkclTKZUzLQGka5UqFxWqOM844g0q5TNvt0DswkD43oRGFIR3fp9ZskM85WJZJEiVMHRsn0RV7H3qIsNrh8OHD9DWHqYVVavU6LbeN70fUfI+oA2E+Yq42R7/nomXLy43UNMHqdQX27g6RxX6G+hxyhoc82qTqaqwyTC48c4xjBw9xKOkl9nw2rLYZnwPD7MOqmAzZWY5NNigYDQIrwFVlDG2KsNngiG+iZTMM5gscq/qMDRZYt2qM2cUjHF4IKaO4/1CTjlSctW6YfKZKwYrJWxncsEZPfxbpWOT6eth81naO7XkIoYfM+QZ7Dio2VBSx0jAT6IRNzKKL0m1ascaBqsVoOaa/osivlugFgTAUKkydqW+56dvMLyzylrf8JtlsdpmofK/D7JJB6LFJX+tGmS19TiAasOzv8xiROVGboZadb5VM89cIlSBUlJIaGYAwQbPTKy5rax67XkpQlnKraMuak2WpPTGc+aR6/+j4mSYp+/Y9yumn7+DS5+3CtDIcO3achx9+iPG77sXtdIiikGKxQKXSw+lnnMFZO84CIXh07x7uuPkGAi9kenqBgUqRmdkF4liS6GlOCV1LV5h+EFIsl9ANEyuIsPJZisUchUKejKORzzs0ay4HDxwncRyaLZcwTicH2Y300fQ0L0MURUTxUlh0KoiJrjACD9/zqNVrdFwP1/XwfZ9Wq4XnenQ6bdZv2EhPpYyuxSRRiyTyqNXnqdbmqVdr+G6CjsQS0Go1kYnEtC1GV69i/YaN6IZJp+ORuB6VKCKbzWDbGSzTwC70k80VOeouIsMAp1Bk9YazKJRHaLVqqKjG/MRemtU+Sj29rB4ewEzaPPDQA/iRpHdwkI0bNmBmitRqLaaO7EfFkmZ7htB30YRi21iB/eOLuKGGaZkkSgdNp96YxdQ6KOnRrFdxfZ+Fpo+wCljZEkPDY9TrESOZYY4ce4i+/hHM2ZB7xm/CqZTxVURncZZm4DLnH6ftuRhalpZoEuqCRSvP0fGDuBaoqJ/GgWnmvGNssNYg4wTD1HAJiGMQbp01ai3fbT1IM6xR9yO2FwuYgUP94BxeErJ983bKq4a5VdnMzB+iM1ilkHWoazo9a0fQ64pNo2uIhs5hoXGc9ZVRNmk9xE0Ps5OwYeACHpo/QCHTDyqhVa1xrD7BTGMWzdBQiUAzBbomEBJyTp6NY9vYuOoULjzrSh7eewd33PElRNAhKzWa9SrNSoWeUpFiIY/nR/iej6EbZBwLIRTN6gIDo2MMD4+Sy7UIoogkjlC2083XIpdNPgLJ3ru/wbe+cT2Npke+ZFIsFRHidMbcTpqUTNO7IZAyjUxSUUrOu4OrEBqbNpzKtq07MLpO5EmSLA++lm6wqm+YndsuY2HxHtavfR7b1j+PXKbEvQfu4YEH78IPA/oGhtNkYqJOohQ5y6YThjSq89hOhsG+PuKuH1g2nyeJAoLQR6AT+T5h4COjkNg00DWdjGUgo9QPJwpCdF1g2TbFSj8JEsOwMJ0saGk5lMTO5gmAwPPpNBqEQUR7fpbxR3dTXrOZnuExfK9DrlhOF0VCIJOE+sIs+WIPnmhj2zam7ZDECZPHDiKEhu952JZFplDC67SJk5goDAFO8EFh+fvJi+mUCEZhyIF9B5iYHqfVbuM25th96DhCF3htl2azTT5jYJlZYs+j1WzR39ODrWkUbAd0nb5Vo4wOjVLMF8hYGQxDp+B0KOX7uu8SYiuhtwhr+8eIVYKuazRbLZKeEC9wKZgOhxYOs/fhA2SMAkrXiXVAU5iOTRyHZKRiqOQQxj6ogDSAPs1FoyEYHcpx5MgM8x2D9YNZEjcmH7dQTg/7j9U4MHEvlpDMhzk020Q0fTJZB5ldSygURw/vo9A7RGIVsPUEz4VcTx9hkkY/dhKNQrGHNaLKYrvFoVaLTluRJAZNZaOSBhldMtdyiSOfmh/SmzEoWpCEEsvJMl3X+dZ/3Md6w8VLEkw3oDcniTsRVt6hlHEYLdbJiZCjUwWOT49QcCS98zWef6HH2ECAZimUCyoUaInOhlWDPLJ7N3//dx/ll3/ldQwOjpxAVJasMScTl3TC17q/pwn8NC11cEmT+aVSogntJLKy5DCLjBGEEHdQMk6/ywg9aaHMMrHXQGKirCLoGcxMAQUYZpp08jFyIpZNVUtmpFRgWZ77liV26TelHosoepL4mSYpv/rrv0s2k2F2bpbbb7+N2269jfHxcTqdDnEUpY58ukY2m6NenSPvaAhhcs93bmVycoqZxQau22Ljhg0I/TgqCVNVsaankQNKYpgmjXodTUAUSyqVUSq9vaxbN8j01BxHj89TLuZZu26YmZlFgtAgjOPUH6WLKIwIwnDZA1sTdDOQCrQkfWkL1QXqrSa5bI5sNksun8dzPebn59E1QalUwrZMCnmbORVRawdYgeLQ7nuxdI0g0bEyGXKZDG6YYFkOlUKZbdvPYd2arbRa7TT5nExw3TZJEuK35/FCl4LjYDsO2UKFODDIlirkM0UWDu/Frc/TiBIwi0RxjG1IeosO8yrE1iOaU3uw8DjvrFOJlM7Q0AjN2Un81jydVpVOu4ahC/ZPBvjSIF+ukM/lGBwexLTz+EHMxPQMtdkF2h2ffM8w60YH6B1YRW9/P66vaLdbVLyYuenjNBdmKVkm9TBgw5azCRzBTH2WttdmPu6ALpmNJglVxHei3dz60M30lfvZXNnJaaNnM7cwxUN7vsvWTeegNzRMW5F4Pu54jdJIH2PmINudLdzbuJdGx8caLCBiCOIQf3GRzNo1NI7Poscxc9px0AVSmni6QU/JYWHfMSqFIrOLh2BaoRPRb1eYnTqMO9vGzBVZ378azw3YN7eb8YVJ5ut1kkShGRroGggdLBCaIAxDoiTGMAyy2QLnnr2LLRvP5u47vsahffcj6ot0OkPkMg6lUpk4qXadNH0MXcO2HTy3RuD2EIYR5XKFRCV4XtBdEWloXZNPszbPTV/9LIcfvR09CVg9MkpoD+IHCaeceRG2ZaFUiJImURSjVNyNkFuOd0EkKdmxLAutS/Qfi0w4eWU42LOaX3zhhxjsGemaPixWD6xh+8B2ZicmKfX00KzXGV2zAcMwiOIIIQTtVoOe3kE8P8J1O2gktOrzVHp6umQLiDsYQtCJfEg0ZNdHzHd9QCOOfNA08qaF42QRlo5mZciXK2iaQZxIvE6L6anjGEpHQyPwA5RUYGfIZAu4Rx8lV+whSSLiwCeSCYaR5lKx7AxuuwXtJoVKD5puMD87g0wgjDyEUhQqFfKlXhzHQdM06o368rN5bCW67DbQ/b4UfqFoN5ocPHyQTmuexeocM9UFHCNharFNX7lEHFu4vocZJag4ZLFaYbi/j465SMu0yJXKGC2fiHlUX0AzAb8T0260yZ2bRbfNru9kqgFSpLmhVKKYOzrF/Pwchw4d4Dvf/jbHjzxKEoQcaLtIzSaTy5F1smTzWSzHxIsVHR/c1gJCho8N4goSKbn1jsMcPDBD2Uo4sHuB9RvOxC6GzM5V6S0XWFPR2b2/Qb6g09ubZ2FmkunGPKsLU1RVmUIhxwXP3UK70ySf0dmyqYeBIYdSbxEjP8JCzUIXgvmDh/nODbdw4Hid1QMZTh3NYpk2uw8tYmsxcWsGYWYpZ4dAF/gGJMKkHNXw3ZBVZkTejsk6qRnMb1WxTIudp46QLQ6wMDnN3KxBo9HGSDoUKpLzTnPZuDVGGECXo0kFdtHil17/Zm779jf5znfv5ZOf+Efe/Oa3MjA4sGxaeUyHkn6WCOySP8ljcpJqMU7UlizRXEEaTiyjEBU20RMX4c1jhDNEfoAQiqjTQBFCdpjADYiUiZ4fQC8Mo+k6ktR8Yy0lVRXdPMdKdMmSQsYJSqXRQIHv43sunXaLTqdN4Hv4fkC702Zy4uiPNM//TJMUz+1w5NABbvjmN7jrrrtp1JsA+J5HHEUkShIiCcOAqaMHefhOk1q9w/TMAs0Amq009LPRdslms4Rh2DXbpMaaJJHU6zWEAts2MXSD+dlppqenePCBhzAME4BFu07GEmQzDvPzdbq+0cvaWV0TaAqSZaapkLKbKEAIfD8kimMsM0xTximJbVskcYRMJIvVKoePHEYIxRmnrCVjhFTrdWrNDsePHyVr6WScLKGXx9M0HNMkny2Q7R0kn69w+PARAq9FqVjCdmy8TpXQ10nihFxphGarhmPpOPk8vmmjlEl7ao6wViNyW5hOibUbTgcZYIiQer2B1LKced4lBG6bhx7ey77DM5CE1KeO0alOEscBSgMNHWGYxImBldFZvW4Lhhaxbv16FqpzmDpMHT9Gu17DMjOcdcG5jK7ZSLbQSxz7HD/8MJMTkyReQkvVsP0csdAIdcXBw3dTGRhAUxb1dpX5TIsSZR4JjtGMGyyIFiKKiZTNL218Dqtyw2QU3DFxD7nJ/aweWoshLDqtRTwZIeYXKI2MsrG0iiSMGBpYxbm9mxAYLMwu4BIRa7D7wAMcbx5iLlenqGURThEhDPrVMKGvGB9/mOr0caLEI+sUcLQQETfwpWDj+TsYPtLk+MIhZmcj5uY9ivkB7EyeheYUF593GXNtjwMzd6SJk2SC53pYloVlWuiGoNIzwGUvej0DIxt5YO9hOp6H5/tYZp5s1iYMU2e8MAwwTAM7W6TdXMB0CgRhSG9vL7bpEkTdZEyaRug1uP2r1/DVr95MI7axs2XO6BtkQLW44PkvI18sd51steVtHJQCTSVIqaFUAiJ13ltKp73k67L0SVfP6cAbRxHlQi+FbOWEqIM0+aFtmJTKZQqlnsf6eruJaVhEXkA2WwQhmJk4jFTQU+kBKWnXF9HMTHofYSNl0PXnEIShj0BD19M6hnFENl8mimPCyCeX7SFf7sFxcqAbxJ0OuUIJy85y4OEH2bB+E4VyhcW5WXRdY3DLGUTH7iNaXE2YKVPqyxKGAZ7n4rttRJJgZrOYThYBHHj4XpQUOLkCuqlTKJbJ5HLEkY+vUq1UGATdsJ4fpBp/7LdGq8VCbR7Pb6f5ZpSgUQ9wDINmo81Qb46p6SDNYBz6+M0ak8eO4IhBZpKIvnaN+f0PgaawLQNTs5irLrDQlryoOcPY+g30jA6SK1WI/RjdMpBKcvO3buRzn/wMQRSy0FxgcvwYlayN67Yp2CZe4tGq++iFEMuUaHYBp7ef/jVjFId6EBmLZTUB6Z+W3o8y6hSyHVqBz8MH9uEHLhnaTM8FyKRCxtFp16c57DZx7CxlOyJEsH6gyNjmUerVWU4/Y4hzzhvDsRfw/RnqiwcJ6uPo9mrG1m7BjAbZvON0tOwEo0N5qDVpRQED9SzK7bAYWAyVbWZ9gR8kFHSdWNg4usHcYpWh/gwkBc6/YCudKELN38M9u3UePeBTzD6AVw+JY9g+FjI/brBlMGbrqTGFskIEIGNQmsAYAC2C/qExXv26t2DqJrffdTfXXPN3vOpVr2bzlm3L2o/HtCpLWXS6D008thB+jJykM48QGkpJhEoIWotEzVlU2ICoTUbziVuThGENkcQIDRK3ReA3CIOHCENJohdIlCC39lxUph/dzpHZ8pyu82167TiOCAMfr9NmcW6K6cmjtOpVGtUFmvUqjXqNTquDFwQEfoTrB/hhzNi69T/SPP8zTVIeuuc73HLbbex55ACINPmZ67rEUbS8z0A2k8G2DRrNFvfetxfDsJC6xfTcPGGSZgacGJ/GXrLPizQ/iUwSvDDqaj40pIowTYXj2Ni2g0CQ0QQmgO1QazWp1lqYpoGmpy8xStK9SCwjzVYbRHGqbTtBdSulJD4hLl1KRRxF6BqYZmrjD4OIxYUqpqFz5imjnHXKGiwt4fpbH6DpxbTaAb1FRbarlXENDSPXy9r164kCj70T+7DNhEd3Vyn3jVDq6WFkdAP58iDTC4eZnj6MbQge3fsg2UIvp51yJmGkCKVBFCUEsoOM59BEiNQiavUGg2t3MjSylsXpg9x/z3fYffdtVHoGGT/yCEHgky9X8Dt18rkymoBsIVUZDo6N0u74TFdd/ADiThM7WyGOFe1GjcX5OdZs3E6iBHYmT+/AGF5rEf/wHLIg8BsefhhhZzJokSBUHTKFAnauQEUYtKRPNi4Qt1pIFWFYilC2mV48RNZX2Ikg52SYNxuMkdBs1zD0LKWNGcKWhyFCzimfxZbsNvRWQEYaBJYiV9ERxQHa1Wkm5w/hWQGBI7BKPem+JppGq9pk/8Ru6q39+J0AbKjVFqibaUr/4cpmej2Ts0rbOH/7cxGyl0MTe3jtrpcTB4rP3fTvvP8V/x/HFhp8dvd/snfyBjKOg2GkYa2JlNjKwrZtdN3izLMuZtXqrRw+eowg9Agji1y2QBhUUcpEqYQg8Kn0DtGszhKFAYaZ+pxYdgbD1mi1miShxy1f/Vcm9j/AxWcWMQzJ4ZkFJg/cw/mv/x9s3nYWURQt7z+0FHqLlu49JGUamr+UhGwpDH8pXfdy2H4cgUq1K0EYptqjbrijUilBWVIb65qB57bSfUGkIgpj2l6dJIxIkjTniedHaR9RCU7GSU1CuTQ8vaNEunrzOmi5AiqR6IaBkpIgCtF1MzWJGja6k0czbUzLTiMalMTKOOiazvDYWhw7w+LcNFnTJJcvEAU+kUgQg5uYfvi7rLroZWlUAyGh7+F3Umf4rAae10Yo1c0hYeG3G/QMjCClwnNdNE2j02ylz8f3aXkNlEoQ6MuLnO8156s0aQoySYmNJkAX6dP3E5WGDitJrdHEcQyiKCWMncVZjrcaSHeeraefxd5HHqLWXKThp1E+lp6audpSIwibCF/jwgufxylnnkXQaWP22tx+7338v2v+H51aHSEEURIhpaLWcilkDMyMhe+FICDUwCoU6ento1zpIeNk6BmoIOxSmuFUquXIzGajhZ0tYvT3YMYRTa9DJieoFEZwqjMcnVsglDCYzZEnxItAzxSw9ZCh0SIZR3HmmUOcfk4/Qh1g99372L3X58ChiKMTDzIw0MeOMzYjgCgUGFbMxOQs03MBtflZOr7B6aedQyVWLExXaXRauO0qKs7S05vD0C1Mx8Z0MpiVYW5+pMG6gQyLCxUWHYf6Qoez17ap9Ag6oYWdKTO0oY2rJCKTEo04Bi0n0HKAB7KThvs62Rwvfc3V9A0Oct31N3Dtv32G1/7KG9i0efOyWeVEB9YTlr+cqDE58XeBQoVtlDeLN/4wQfUQIgkwdYGvRcigifQWCRML3cxAZ4LW7CxxbJDECoweEncR2amS2D2U1m6lWRnCjaDTqFNbnGV28iidxiKtZoNmvYrn+liGTa5Uwc7kqOQrZJRB2/Rw9TYqjlCJwNB+NP+Un2mS8q3rv8ah8RlKpQpBGNNut5BxhOhuXNc/OMrOnWfTbFQ5/OjDJAkIy6TabCOThDAICMOU0JimgWnoaELhOA5KCfzAIwjCdMAVOobQMSybwaERAs8D3yf2PYLFBYxcftnxU9P1dBOxJducniZxMg2TIAy7IWBdG6PopodL0ux/uhAEIczOL+BkUr8RBHRcD8/zCbw2QZhBaCaa0DB1gRcpZqptNNqg6ZR7ypyydZS169fjdiKSRKfensNrzJIoRd/QCKadRUrQdBvLzmBpivXr1nB8ch5hOViOSZ5BBlaP0OiEHDjwKEnsIuIGwnDI5ntwsnkcWyefNchmTQQJdjaHmXEY27iN2aO7UUlCEilKhRyxUiSxDzKmVCow0JvB65RotT08P8YL59l3YB/lvmFG1qxDKxYxTYOhoQEePPQIc+EUQkoKSRk9hLbl01iooYoQ2jGuSMioPP1mhU7cAa9N7EZUax2+1vkSv7D+AvR2TFbPcGRgkv5iD047wp+qUx5bQ76/l6C2SKs6z7zqYCUGBTtHmARMTx8iwUQv5Qm0GDUgGNkwguNkSVRC1spRyzZJaj5z9QblUg+e2SKxHA4tHMU/6nL2mMPh/AFMNHILHa7a9FzE6IUYSnF8dpIXrbsY7ZBkLJfl1We/gg8tfJcwDDFNC1RAHMcECkBg2Saa0OnvH6ZYLDM+cRzP87GdDIVCnlarRS6fRyYRUrfIZAvoupESBN+nUM5RyhXwvQ4P3f5NvvDVb9KMDRwU64dNRvqLvOrlV/PcC59HFPqpyjdJ0oRRMkaqCCENlCaQMkLTnO4+HhqCdPO8dD+RdBWYJJI4SpAqIo5iFHRt26kDoYyTbpZ3lfqhIDCMNOFi6PlIpQiDkMDtYGik+RtkjGXmqC4uMDw8gm4YtJtpBI+UMY7l4HsuJAkySYhINwyle++YdOND0zTRLYskickYWXTzsc0As4V8ugeJZXNs/8M4Tib1JYsjrFyFQqHEwt578Lzt+G4bz+sgkyT19TIthG6kmZujENOw8H2P2uIcSegTy5hisQfDyeAuVuk0G9xz3bX09g0w2DvK99OopHlBEwQJSkjCpcRcMiFjpk6iiR/QcV1yGQ07Y2KYGQI/IGi22N/ucPzQDBCg2RquD5qZwXYMVBwS6Db5hTkmpmo8ePQYfV/9TxJNUm0sUm20aLbbREGEpQscxyT0I/r78mSFIpcv0JYdspbF6rVrGBoeJpvLUSgW6e3po3/tFjDz3f2p0gnWDVPTSNtrkC1tJXY7rCqWODw+SaSHFPQcWlwjaxpEWh7phzhOwqwXkmR1TEdn65YsWzcHdBqHefD+I0wtGBRGy5w2pJHc79Fqxtx5zz52bFvLgSMzeH5Mf3+ZRMUYGYcdp5+G67ts2jLCwEg/xl33084YaVsbU3TyGfpLUM40EUYPmlLcdfsxMlnBmkKM69Uo5CUH91msHzWZqpq0gyLDRKhgAa8jyPYrREGhmoJwHMJmSlI0TSOXL/D8F78MAXz9+m/w5S9/icsv38WZO3aw5Ih6osn0MeLymIwsa1OUhLCJaOwjrh+FxT3QnCUK2gi7QBj7yKhN7Lv4iY2WeMigTdAOUSpMFxKteUQSYSwcQ8stIKfrTLXqjDcMoiAkCkJAYCtJ02uC3yFotVlwE8LxKbw4IYrTbOyWTlfzKvBDSU8Y/0jz/M80SZlbqGIZOp1mg3a7hZbE2KYgn8uxactmLn/xS1i1ajXf/c6tVCeO4UeS6mKdeqtFnDyWzEZKie8HWGa6r0s2l2VsbIwoiti791FAYXbDjm3HIfRdSqUiVApU5+cZ6CmS6Rlg/PgRPN/HNE3iOCGKQxACTeiYBoRCoicaSSK7+VS66s5uCJljpsmQ4m74s++6JInEsjOYhoHnedi2jm3pHDq0H0NTYBpopk276dJ2PTJFh7VbTmFoeA0ZJ8PC/Dy6odE3MMJ87JLNF+nrG053aPVcQFEuVTBETLGYpdC3kVo7RIiYYqGfxJTMHttLkihMM4ufhIyu2UaxqON36szMzrFq/XaM/AiNQCfXM4yMO+RKRXr7+2lUGwhDo+ZGNOYnmZ6aoVjMM1LZwPqxVRyealNvNVDCQDcy1KoNvnP7t9m8OMWZZ51BEgUoLNp6gmEVafXGWHXJTGsBhaCgFVGBJCqYjPtVtmoVbOFgFwtkSz0QJsQtj4m4wQNHdrPaLlG0x4gm5/jHmU9wkdxAb6efMBKYC9Mk83PovTkOqBmGrTES38fzAvYeup/R3nU0vBrZ/ixqJCBfyKLQUFJSFL3MyAna2iLPWX8J63ecw/6jB3nk4VsQTZ2a6xKoiMX6NHZoUZWzFKezeELRidN9RsaKvczOHsfMm1S1AJEowijGD9IMpSIKiaXs7p6rME0bTU9NfRvWb2JubpqO61IsFvF9lyiKulsqxKkWIYnxO00y+eLyDqWFjEEwcScjYh5bKuqewfhcL+dcehU7zr98WeWsa6JLUhRxHCOlQtMSkqRrj1YRhrBQSiBVukuqlBKEsZwwbmnzzhNXg1Kq9LqkacSVUnR8F8fMLW+k12rWkXGM0NKwfxmH5LJ5LMvEbXfoNKrYpkG+VElDpiWoJEElEZpmEQZhNwdMiEySNCOm0NFUmhsl9n1clWBbNkIz020bDKO787FAoJPJ5RhZt4XpYwdoz8ygCTB7LVphREmXaEGHTrtDoVzB95rYuQLFgWE0Pd2uIZNkiKII23IARbvdoFisELgdotCj3Wpg2w6z99zCbZbG89/w+1RyvU8YFZH6qGjk83mKlRJGsYIduZQSScePUVFIztZoJwa+F6NrAZqdUMhnaMt0t/UOEtPQEdiIjIXu5LAtDaEkpjCpV/1U5gRU2zUSFCidQqGIZpjU602CwMVvRFg6+F5AqejQajXIWEXG1m+kUCySyWYol4v09/exZespOD1rUsfkJEFJRbU1z947v00bjSDy2fPIEdYNFBjI9jHp5LATn47KsHUoYd4NcIVG0TZw4wh0i/5ihjjxGRuJaU4d5N7dbWY6WfJDWZyiiRZIBkey9A8ktBZj+gaHWLd+E3smQ5rzR8jmI2KjxGKYIP2IxbbLGaesZ6jXYs/9ezl+4Dihk2FwpJ/ajKIy0MO+agElTeaSGeLZJv25HPmyw6GJEvnBAknW5ti8wvNC6tMBN16X54Lntcn3S9SiQrbTPCm6kyojU22JwjQtnv/il1PpG+ALX/gC13/9a8RxzNnnnIO2lHdrWQYenz4/VUsl6HEV2TxAOHsXylskaR4mbNTw3YhqsECzHeK6PkEkkIkiaysyDsjYwDYVidTxWxF5K6ZshuSLWRwnoa2bxIFPGCfEkUu9WqVdbxJLSCQEaEQarN18KkqzGD96DK/TRCYRYZJuDyF0gZPJ/kjz/M80SbF1QavRJAwiCpaOVcjiFHvZdtoZXPaCy9mwcTPT01PMTI7T6HRouiFBGCLRCMOQpXTCS2QlSSS6qRMGAX6ryuRsFanSDIpBGGIYBps3bcHrdPiV11/NmTvO4p+u+XsOPboHJ5fHttKVUuB7hEGEkqn9MF+qICOXRPqYhoEgIUlSNfgyutqVWCosTWB0w7mSOCbWfEw9Q+D7qCTh0KGDuK7L6OgQeraIZeZ59JF92JkOm7adxtjYJpII6ovTFLIWsTuH3wyxrQwDw2vpuDFe6CLjgKBTJ45jwsTF930G15yJnc3jhzFRLOl0aszOTuG2OhSKOcp9YxTKwzTqHfxwgVDobDvruRydbmHTYXCwHyX68b0A4fRz2s5z2Lf/AWqzUzRr88RRQuT30Fw3xkxLZ64eMzkxTXNxAb/TxDM1MrkSkxMzlPuGEEmLhelZ8jJLbGawTPA2SczpBmYTtEIWTcb0FkaYkj7zSYdVxggqUsQyQmgSo5jFsmwORB3UjMuImzCUK+MPbSDoZKi3FjDtDItziwTNKsFczPEBn8G+tTRm6pj9ZTJr1rDQmGdk1fms3bKVpppnQl9gPp7B0EySKKZQKzCvmgxZfSSTDXItRdhusECdwYFedF1gYrDYmkC4UJchiWURWAGjQ5tx24tphE8zx/2Ne+g387h+QJLE+IHCsW2II6SSxLFEiCidQrv7SI2MrKJRX6DTadBTLjG/uIhp2limQRyFSL+NijrpXhoCPK/B4d3f5trr76fRFjzv9Cw9GUF2eBuX7rqqm5AwXg6bF0IRxyFJErOUfCqOE3QRd2XZQqqEJF5KGCWQSxugdc0/2tL+NEqhVEIUpZ7+up6afYLQ48jEHWxd+/w0r1EiyWSzeK12GiVnWdi6ja5pRC2fjtsmiWPmZ6ZRYUAmXyCIJTL20ZRCI8aPIyxlEoZRGhqcpNmKDXRUNww4l81jOJnu3iRppudlnxolMSyHgZFR8sUSR4zddKqzNGaOUxpZTzWK6G/XqVUb5Ar51H/IMtEMjf7hVXTaTXy7m+cmjFiYPI5lZnA7bQxNx7QtYt8nlhE5LWb2jv/iznIfF7/818llutvZn+DDsTQ55StlVq9Zw+TMHCKOMLUqKoF6ENBTMHHaDu2OmyZ0C2LCuEnGzoAw0G2HTNf/RjNMlISMIRBJRN1zWXBrBHHcJaCgWyZCQSlfJGtniHMJviaJpUQEAX2VHoRsYdlZRkfGqFQKKN0glyswMDDAqdu3sXrzNnSrSKLS8S+MI2790qeo73+YXkMnQeFHPqZhsW9qhogcR2cWcRxJmFGEsSSRLqEBQeCiNAMZm6xa3U9/j8693/F48LBBR9Ow2/1slhHZ3DyTUwm6YTE/02bNahjbuIlXXXIqjz5ykJu/ci0z48fxjk3Rm7XIZ01a7Sobt6/hyKEpWobLc847gzPXlginZ6gJm86hI4QEXLKpzJ5DdcbGIgQWxw4pzj/7FDZsKlD92r0crVZRvT24wiRjq9S8owmEDbqmMEJIo3TEsnO0aVmc/ZxLEAg++9lruenGGwnDkIsuvmQ5K/GJ4b2PRc0kyDiA2EW0DhJW9+HVJ1HeHI16nYW5BuPTIa12qt0QQidMFJ1A0F8W9MjUL6wRSLzEZqoq2NgLwxkby1K0mx0WvUPEbcVsPcD1Q5LIR8mEIEyotdJgDj+IWbznu2iGlc5pUqJrGroOYbLUz78378sPxs80SekvCgpS0IkcKoNjjG7Yxvqtp3HKqacyMDBEGAY8uuchDj76CA03ouP5aELDcRwMs5vSPonRNEEYLQ28YNs284vNNC+KTAdiTdMol0pMTkwQhiH33ns/hVIPUpgcmZhiQOmYRuqM2vH8dH+brrNTq9mgUioglKSRBKlNH05S4aWhizGmrmMaBoaZphdX3f0+ojAksgyOT9cpZLNsOeN8+odHMIhxGy1UPEy+bxUD/SPp4CPACwJsU6c6P0cUK3rGTiVUFQ4fXyCOXJKwQas6heMUUInP+Pgsmlnkokufj+M4VGtNZmYl/aMbmD52iNmFGiLfD3WP8WPHyOazaLrOwoFjzE4dxTQUQcslihWja9dj5wp4oSJbXMX0scMgbEo9eZx8kelazPHqAYRh4wYxrVYDUzeQQKPVxM5kePCeB6hOHcS2TE7fvBVsHcfzUJqGt1GQORaRjS3m+xRlT2dDbj3TnUVa+IQqJEoCDJFuJ4Bu4uoJh3pj7PkaxXrAuvIqzlx7Dg82bmOuegyZhDQ6i7iex9G8y3rVYmigj9rMQQZOXc+kI9h6yqn0kmVA60HFGTIiS1u0ES3JqlYvOzdsJ2pD0qhz8OidtFQDs5QhcSXtoMZC/TASReJ2MGybZn2O8ugQod8mV8ji16pM+uNUk8NcILZxbOo4o6vX0VcpEwQRtm0ShQFhEgPOY4mTSPdXqfQMksk4dDptent6qNYaWFYF09Rp1jrIoINA0Vo8xvGH/424s58tGxxu2Wvx9Yd9Lj9vO69+yW+ke9mQoKTRjdKJuzsJhyRxaq4xjHQTPyV90tAFSRyHCNL9auIkJkmWkhqmA6vsyntqDopBpQmnkkQCCfuO3Um1/gBJcnGaiM0P0qgB0nN0Lc3a3Go0iOKAxHe7ZESjvjBLfXEeu1DBstLMqFG7jZSKQEYIJUjiGF1oJN3JNwwCFuZm0LumIcMwyGQLKCnTPheG3TZHGLpOszqPJGHHxbt44MavU+qp0NYNmrVJcvkidsZhZN0mUBLNNPG9Nk42S6nSR6fdpDo/h53LI/TUNOUHHl6nSafVRuqCnF3BCo9x6Pp/ASfLJVe+kayVWR7zTlTz25ksW7efzpHJaVSS0BaCcijxqhGuG2AbCWQ0hDSIkwgrl0cmknyhgG3a6SaLnksYhrhRTN5SIBP8xCBI0o0tZZIgEQRujCYEURCRzxewbZO462skDA3l1TAcndHVA5gFB02HXCHPyNgIp512GqeeeipWrgcF3W0C4IG7b+TwTV/FNIvkBIwMjtBMJIdnWqhE4SYBwsxgGjrzgU7oh4QyoC+jEwqNdqfNdFMwtrafRmOBO+9pM9XKkKlI1qyOGBzIsvdRk8NH2kwvVJFRwJbV+8ldcjnH9u/h9B1ns27jBm74/L9zeO/daELgzdfZt+coU8cOYao6SVxHN+Er//Vd+rMuh5slanOTnL59mA1jJcK4ybQ/SqcVkKloTNZC5r97H6bmc8UvnEtp9Vru/I8vMjGtk80lBJ6gdKpEL4LYv0Q6l96rQKl0c9Szn3sJhXIPn/x/13DHHd+hVK5w+ulncOK2N6kVIEZ1t2uJ3SpRexbVOE7cnKFeW8Rv1Zmcjzh0VLHQ1FBSQxcKywDHtqjkbHQtoOXHtANQYUy27KDKPRgVST3w0DybAJO4MYPbgqyRRXME852QRiuk0Un4/8n7ryDLsiw9E/uOlldfv6493EOLjEhZWVmVpVWL6kJj0OgGMQYDMMQMDCA5RhvSaHyYB9L4MDNmMNqQIAyNacB6WgPoRgEtqlqVyBKZlZU6MzK08PBw7X61OPqcvflwPLOqe2gc9GMZzlPYjQgX956z99pr/f/3p7mCqpj4ro2qqDiGgqZIVOUkuFCWMogkSVHlf0IW5PWVBTaev8xh4OB1znD5qaeYX1jEcVzyPKPf73Hz3TeJ4uTDwD/HdUiSjDxPTlgmYOgqyJNxjmVy5uxZJpOATEjCKCJPMzzXoep7FFHMz//i/4Yv//xf50//9M/4wfdeIk8TRoMeppKfWDNPWtmyVFmnScJwmKFryskCf6LSlj+ykX3QUrdtC9e2yyAoTSuFXnlGkZfIfTSL0+ev4LsmhmkjixTqVRrVgsRY5M79XXrHR+hKiqkrKKpJKnXmNz6O21xEqAqpzEH1UAwNxZgSjEognsgF77/5MvMLizz55DWadQ9Ji+PuAmk4RiiCg6MuhdVmEiREaYFumuzvPCQOjiAPiGYFCgampWObOl5FpVptUOtsUG0uUqvXKRSNyWxCIXNcVwUJ7YVFdM3AqrgQR4wPNkuNheczt3CKqZfgSBMz1Kn1FaJ1g/GGgrWncEG7jNGp01J0hvlbTPMU359jroBZPEZVNBSpoqIwczK2WyHzw4S9IEHbzJF6TKrkCFLGckqqSXLXoBsfslxbpDveYzAdUdeqdHd2aC5cxq/5OLMcX7PYUh7wVHCWq/4ldNMgb6o8uHWDcRRTCIGjuEzUCSFT4synSBUG48c4bh1RpOhpm8PkNlnRZjScsucFXBJtlALeu/st5pfnscyrNGq1UjxrO0RhSJqVrA+t0FA1o7yXVAXXrWOaXun6iGOSOMCxTQzbZnR4n/uvf51XvvlHDKYHrJ+us1g1WO1YnFpZ4Rf+3n9Ne3GltA4LgZQ5imKcHOSLk4KjbDsXeYEQGaaho6l6uckbJoZW0mhzUULZhCxDN7O87KTkeU6WJSe0SskHCLjHe5t87fv/gnmvDBAs8g/CPwMMTQchyuiLYb90PU0n2JaJroAqckBjPJqyc/s+G2fOMtduMRyMqTYa5Qabp8yCgGqtTppnkEksW6Jqgtmwj2GaGD8GrRoOuvjVOlkSI4qC2XiAaVjkccLXf/+rTEdjYtVk3O+SToZUG3WefO45llZP0e/uU613QAqyPCWYjRGFwK1U8Ss1hsM+RVHgOD55ktBeOUUQxcRhipnH+OERD/7wV7F0kxd/5u9gGdZfWPuklKiqzunTZ7lyZZfpaIguEvIsYUnTCCdDsiQhiUKkyDB1gV4k2E4FKQqm4x5pLoiKgkKqKKpEFQWgYKgCQ6onImit/BwV5USwK8mTKZ1aHScrKDSHLFORaoZvO1QdB6c9j+LUWFhe49rlK1y9ehWvUqMkogg0TWXr8V3e/N1/jiciEmMR3VfIwgDSJoarMdnfwbEiEqOC5TmMRxGp7qLGAUGYkGoqlVYVBRXTaRIkYx4eawyLjM8/C8tLA7b2xhiWxqlTDYIkZ9hLOTyKGI+G1BfWuf76Kzz5kY/yC//FP+DBrRfZvfsWN65f54fvPKSmJFxdjfmFn15CtSe8PxsQTXK2j49ZcTP6XRepzAiTGYePb2IWOruFREk2IUuZjk0qrRn7+hFxBd54x+LhG/DECzH1LwKWBOsvi6L/Yofh4pVr/OIv/W1++Zf/Offu3+e/+q/+Ic8999yHrtLyvwhykVKkIVk8JuxvE3U3KeIjBsMZj3ciwkxjGKvsT8pCs2KCKTRUv4FhmSThITf2QRGCZVvQXLA521ApkoRJZGI4BqNJgNBcqq7KKMgYjwVRUuriFuoqrqVjqAJNgVkUU0gIMsnW0YRxmJFJEFKhKDKebS//lfb5n+gi5Zkv/OfMz3dYChJ0w2J5dfUE+50TxzHhbEY6Pi7nrLpKnslywRHl7K6sRCVCquiagpACTSlhbiCYm5vj6GAf1TRYWV7i+KiLjsKg3+f6u+/y53/8Rxwf7lNrVEizDCGSkxaxRAhQ5I9wxFGSoZZSdlSl1F8rcBKoDbqmUalUWFxcxHUdgllQjqZOTpwSFcs08D0bXTfR9ZKCa7o1FFFDLUbcvfND3nrlHkeDKTVfp13T8aptli5+Ca86T7UyRy4zlvwq09Exa4tLOOoa6WzI9jvfYHD8iLe27vGnv//bjEZ9PvbxT9Ks+jQqJpN6G2l4JL2A44NjVMMmCEekwwBRJKRxQhHFqEp58p6OAopqi2nUJQgeURQ5lu0ishDDtDBrDYajGePuDkUcYPoWZy9fIB6POT56xGQ6RbE8Lpze4OrTz3Jr9AaH2ogFGjQmNTpTh0mr4IWf+ynURxHB8YTK4jxTOcYUDqO4z+P+PoqqgaqSFQlJGFDIhMeWSnfF4JQ0GMsCoSioFZs0iiCEwpIs+EtIo6Dbf8SgDXt2nyVvhcGDLdBO49pVVpI2BTss0CAb5EyLAbpqEUvBNJ3gteYxun1SAvadhLoYU8dF8xyyiqCIhqiawr38MTvmEavxHCtnn+JG9/vIbp+OOoduDtl68Ge06h0M3aDq++R5juf5BMGUMAxBKZ0gyonVHUVFN3Qqeh1tSWd/b4tCqDiVJvujx/zxN15jNE2pLqwTCxdN7HB5KeGTn3kezxak0QRUjTwNQThgWsRRQDQbk8YhuQDDtFE0vbT4qjYAs+mYdmeJJD15DmTZgdF1gzwvnW1pVgqAVVU50X2UMRTTyZg/+LNfpt/dpDK/gSwEk2EXr1InTSKkpkORMxx0mc0iXLdk+4g0JRUZqq5hWBaabrCytMzjh5sc7e1z/vITmIZClqXYrkuaF0yDGbbtEs8CEDGmY5OlCbPRAMOykaqKKARFllNkKcFoRBBMGXS7bN29SRDM+NZr18mKnMade1iail9xqUwiqq//kPbSKfxqC01T0TQLiYLrgarqDAcDJoMuRZoSz6bkWYZp29TaHbp3brLx7Kc4fs/C3H0JL+rz3r//l6AafPxLv4Rtlu/zj7MwHNvlox/5KJPpmNvX38FPUtI4xbE1hFCp1CqE8QRNUdEVnSIJST50XZXtXiEFjqrgWGUGmchL16Iqy7UR7QPdkUTTJAu+oGnGdDWo6Dl6q042GaLUOmhzp1ErTZaWV3jq2jXOnzuP55fFBJRcjW7/iJd+7f+J2jvC95oId557R+9DEFCr6SwsbnC2rnJrc4coChjOZjiaynK7QbPSZuvxNoUsOHtK5/FmSpCodFqL1JoutmFheTbv3MqZTFJcyyaczgjDlJWFKn7FYTiacvHZDeKk4O7t22ycu8S5J59n/ew5ljYuMfmt3+SN9+9yNFW4OjnmIJwRRDpVLaPjGmxOdZZrPq1c5e5uD9u1EHmG5wpkppfU5yzh3dduIjyDhpqx5dTZU1VWjS7hnsCbUyAuP8O/nHT8wSWl5NmPfoy/NRjwL//lr/Cb//O/Ynmxw/zCErbrldTkNCILR2TxmDQYMurtMTo6JpkcMJ3M6I5zVN0kFgZSV4izAiUTaEInnCh4wy57o4ytYc6ZmsrqM9fY6o2pO0u0q5KB6fJ46yHgEM8Caq5Ks2LSXDAJco3eTDCMcva6Y/Z6MwZhjm2Z1BwdgYpQTVIp8D2XZrPJUa//V6bP/kQXKWcuXcPzXJxxGeHu+/6JxCT7cHa3uNjh/v1HpHFczsQBUE+sjxLL0jH10solhcC0bA4PDtg/PEZKgW1ZdObahEFEkaQkUtLt9vjNX/2XHG9vIYA4L2g3KvSOZhRCIk4U1+qHbbzSWqzrJ3h8cYIlRkGTZetZURQazRbPv/hZZpMhd2++R28Y0qn7VCsueQG+75PM+gy6j+lcfRFVkWiGgVooSKfK2lKLy2s2/f6Q8awM67ZqNbI0YjA4wrJM2q0GVVfjyXOXGOw/5vHWJnbrLDEuWjpjvrPEztE+r7/yEn61RbvZIA5DhrOY3YMhjflVWqpEahZ5PGP38S00rYqqKdgqBOMJXq3D/EKTuzdvMJuGVOpz1DurSNVk3NtElTlBGJUWUA3arQqWobD77stYhkVr7QxFr08aTDi/Wme1GnBnrKCZGv35FGEFzAdtTrVX+eTC80gl56F6n+XTa1zqrLLZ3eLGYYZvNijEiIyMNA+JswhTtzG1Cugqj5QZIt1iuXWaeWcRGS+wPd0nauhYakJ7ycXsGqycuUpnvmB+12Mem1H/kDDqk6sZp9U5Fr1FjhiSmtDr7TA5OuZoekxldZVOssZevEuA5KiYUZscY5gV/E6b/tGArjVit9pnUa1zun2Zp+af4f3+bVSZ0S16zDkOewevIu4u4rl/HU3XqHgehZRUqnWCYEoUBYCLqignGyMn7i8F36+ytLTCweEBhmmwtTdi+2hIGAuOkkMcEdOZ2+Djz6xT9V32br2G1ByyLEQUGU51HqvaRmOMlCaiKO2ume6j6ha2X0dVXfr9fSrVNpqqkucl4K04CdXL81J/lecZeV6cJHuXWUBFURBGAV/9+q/yaOc96s2z6O65shjKshLBn0vCcEo4neJYHqZmkMYBmqETpymKopVWYk3F8SuE04DlpUWGkym3b77P6toqhmmiZgJFVbFUE03VS/2PqhCGIaqikuUpum6SRAGKohBOJ0wHLr3jA27fukPY7/LkR5/j69/8Xil6VxTa7TkOD/bJ8pwszbh54zYrnW9y9TM/i6KUXBbbdpiOh2X3SFHJihzDtBkOe2ioROEEx/MwTZP1Jy7QarZ578+mWEcvU4/7vPdv/hlxEvLZn/t7pZj1Q7dHub40Gy0++9kv4FgO77/zOqkoiKYKvtbDFDlTFWzXJg4zJrMUS9VJdQWRgy7L5FvH0an4ClkKqqrhKDpSKbsoBZzooAqqespyAxQtplGRKJpKEo2pz7VZvnAVf36B9tw8V69e5cyZ0ziu9yEDByTjYMQf//o/YXL7HRqui9W4QqV+ipb/BlJRwYBp/xE745j9QNKpakSzhChJGOykxM2IMMuJCsGb18ekac63v/8ef/8XP4pTqSIKyd17Zafu5p2AMxvQD3IWV+pYwmP98tOEccroeIf5pWVGI53u/hbrFxs0l+aYn3+a8888ye/+q19n+/orHBwP2R+WXd9rz57BU03iuz1Gky4HbovljVN8+ZPn+OOv/5DhcIbesrl2zuXVm2OW3JQza2N2eh1a84tMZxnf/8M+bVTWzkO+rSAv/Ah0+Jczej5A0H/hp36G7tEBr3/vT/jhd/6Uz33pZ9D1FdIkJBgeEo322dt5zM2bt9nfvEucJOjFjJqdk+YapBm9sCBDZRjnjBWVME1RBsdoSMYJKIrBROjEWcRC28L3CjZW57G9OYYcoBUFWiNEN2IKRSJdjV4P3nrUZ7sb0RsFCAmXNzosLS2wemoDVdV45+23EarB0089SbXe4P13XqNacfirXD/RRYrjuFiWTbV2Mi7RykwRTSsXwSiJ2epF7I4C0qzAMjQKSWl/k6DpOn6lgu+5ZQu7yAiTnP39gzLtWKqcO73OeBoyHQw+pGm+/+5bxLNp2arTddr1WtnOFieOHfnBnBEkBcUHrAhNxTRM8jwjy8qWuW6UC4EQJb8ijWYU4Rhd5mXYnl5hfWWBaaJg2DZBGDKbHICq41ZqIBI0VJS0Qq0+z9NPrDOcJLxxu0/m1lANm3A6xPNrLLUNzm60yKKAo+1Ndg76eIvXqFR8ep1r3HrvHbzlNg29SbXewjaccgyGxBETivFDaosez56bY/PhfdpPfZZzGyscdg84PDjk8YMbSNUhlyqFolBrNdEMG9erMBsfU+uso1s+wfCAUXeXNElZXOggh0P60wi1usypK0+h2SbHoylZLjgcZXj9iGgakGkFuiMYNRMy85izxz57P9ykpVeoaRbKYMKsO8QwNZLZiDSa0rHn8fwatw/fQTdsNMVizl5iobZEUUREcozltFiyVyGJGG1cZV8bYLcV5vR5rl66ipIbDIIJWZ4wFQekR7ug64wG+yxvnKFIHNI0ZKZOiNKQ27fexDu3zlJjibX6KsNdiTq5g5ma5FMVNUm5dvp57umbXDl3lj/vfo9Tosap6ilub9/FNU161Qg5m/LiU3+LsDji8f4N3r//Gs9e+gSGrlPxfFAUKpUKYTglmE1R/AqGUT4D1omeSVEUKtU2hYC9x7e4efMuwyAnzlIun1KZryR03EPSscXReEKh2eiWw/ToPXS7QZZEiMOHaHKM3ViHLCp5It4yEoVw2EUqYFTa+IsbpdakSE8OCSdaFkpNFYrEMMpodwApBUWR8fb11zFNn//TP/xlLp2/Su/omL3H2/jVOrpa6m2Ojw6wHKeEHCqSPNNB0bAdD03XUGTpHFIVHaGCIjVajQaGbfPw7r0yaK7ZxHVsXM9jNu2VJF9Vxfc8ZsUYPU1I4wjfd5GKhm447G/e42jzPs3lszz/0edB14mjmKeuXSWMQlzPZTiwEVlKEsXEcco7f/rHBAebPP2Vv091bqG0MvsVFCoIKbFdn+lkjGO5oEC93aHaaJPkGbplcPEjT5MnCTe/EWH2X6MSDbnx73+ZeDrhc3/zH1L368AHHZXysDXXbPPii58EVaFQDPrHFhEFcrhL1ddwbcEoKwj1kw0xh4wc11Cw3Uo5Xs7HKHmOWugICXkmKRQVRdGwdJ2qpdLxFBYbDrVWhUioHB70EPUWa098nNryBn6jztn106ytrWDbDrr2QUidIEoivvV7v8Lxq9+kbmgY/iL1tSeRrQqmkXFjOEIxHFzbomlXWKhnJGlCq+5zMChx9duDSQkqFAW5FKgK3H24R3+Qc2qxw/X72/SPC2xHJ8vgB28ekeUCx9D4/CfPsbxxjY1zF7l3/RUuXH0WS9OZX79I3H2Em3tYvsriqSb/u//2v+H973+U3/6Xv0Yc36awWzzcOmY4KyCPsAwTNUtQRMb7b7xHGGU0KxDlgjtxjYmRsdHMubdtMY0yIjlltanx1JUcBcGdt1Qy98Td9pdcOz/6bMvLME3+9t/9u6zNV/jz//Bb5EGPjXMXGPd2OdrfYXf3kMd7x9zfm2AZJlEUkRTQ9A1W2joVo2B3VJALhaLQSKUKJx1vXVWoGgJLk8z7CtPZlKRQmKsZ9B69i7BbKKpDGES0XQXHNFHsCqNI4WgYY1gmp1dcwiTDMTUurFZpLHb48s/+FNFsSk0d0awadFYuMpgEmPEiifafkLtHURR03Tip1P9iOJcQBaN+n1G/R5YLkkxg6DqWZRGnKSpqmefhOKWLoRCYlkUmVRxhkaYFqyuLeH6Vu3cfouUF2kmEejSbkIoCQzdYXuzQaTboTmNMy0FMZyc/GwhZnFA6ywVbU0uRoKYbJ5kKgJAnYkDB4eER11//Hq2qy0KrgmnpVF0XQ5UE4YyFRh1Fc7CdJkWeY5guKhoIHbwO5DELiyHPPw0P994hKQRFnrK02OTTn34B36nw+M57jCYhu4d9prGgEmmcOX0W3WsRS5d8OOLU1U9QFBFb2zfozC0jFFjZuMDC0jpJOCbpPUTufYv3emNaC6ucXV5ioXEeUaQMh0OSDEbjFKl5aEZO73CHYDoiDcbYlTpCZIgiQ1NVbBL6x31io4qHwTjViMZTZmFGHCfsHvZodBaYDqeERUZnpYZpeNRbcwSFzr3uHTbcVcJJn6GEPJZIQ8EoNHYO75Gm72NVKuimh46OgqQ73WMQHFKIHF1XqC04uJUNDLXC5eWnaMWHFKsTVvMzGFOBqeuQOeymI7oHD2itruM3W2j5jNTWeeX9l6jHGvXFDk6rzbguOddexDBTkkxwZfE0+84+zytnODf3JO/f/hYJKSvzF3l24zmuNC9hFQa1ikXFHXO18zHu3b/FW8V1VtfOc+bsV5hM+jx6dI/u8R6aUuLJPc9D1VQcxwMZMJuO8Pxamcyd5Rg/NrtWRMH4aIfJdEgap/gNwdULKtkwQzhtVq99Bstr4rc30OLXCR/9ObbbRXWnTIaCcTegUn3MdBigui9SOB4iHSPD95gqlzi98UT5DEqBPLHY5yfOnixLUVUNU7dL6JmmIWVBFIXs722x0Orw6Y9/jk5nAV03mAxGuL6PaXmkUYjledTnl6j4Lkf7ewSTCYamYOsqSVHyGvJCfGgHz5OMIhdoqoJfrXDm4nlEmjGdBYx6Q/a3dtk+OGKSpiiKjq6r1ByXJ84s01k7ReHXcT2f6WjE49t3aMzN0WrWSPPyPV2Yn2fv8RbTKKbRaOA7NplWPueGadFZPcXqM5/l7a/9Bh/7pX+E5VVRVRXbchhPxgwO9+ge7JMWOYvLa1TqDfK8IE4yAEzX5vInPkqextz51hRdXKeRhGz96W/wB5NDfvo//z8z11pC/SDI7UQ43Ky3eP6FjyFMna07VUaVCvF+jpkPSOOIqZKjK+UBDSHRVYGpFejZCFV3UFUFjQzHKMhyFU1XySQUokCTBRYZmgRFuDTrdXIZUF/4GPb8NWrNRXTXpT7XYnFpAd/1SsYTCoXICaKAP//qL/Pwj3+LppmieW3cudOsXz3PQEbs92cUScRwGtL2PLoC2p6NFBJLd9CNgjBO0AHV0PAUSZwLLB2icMLxYErVa6CE23R8k1warLYrFKlA0zRqvsfKQpUrF04zt77OqH/Aozvv02lXOR4dMje/QJFECNdFLVJ0LeXSk5f5P/y3/ze+9ru/yze/8W2WWpJgGjHNy+7SYBpRVyb4Sy0+caXDu3d6JNkMa3QfL1UJhxaNzgYV02C222X0eMy+Y9F7W+XMfIbagP8Yp0uRp6ThiNUFD8dQePXlVxju3mCurjE+KlOv55sew0ASCQ0NDV/VKFSFRwOBoWlYjkmFk1BLAaIoT9KeDpZMaVkCw7LoDqZMY+hNYpab0B+Pme+0WGxqjFKVUDFwTYNRLFA0C9OAwWjK+bUmplbeU9Pjx/zh//Tfs1SVvHIv5tqpNjXNQZcq5xYa7M20v9I+/xNdpMAHhYr+F1wyyo+nQwLIMjDQsmwsyyDJJYamURSS0XiCqetU/JJ54fk+q6sr5EmIYbvcvHUHoyiVI0ledkWklOiajuU6nF6ZZ++wR67oZV4JKlKR6BqIonzA1Q/smkWBrhfoqoKm6uXXAlRZnioRkn5/iE5B1bf5mc8+z9FhF0WzGAmLXu+Yi+eeojF/FtO0SOMAx7U/JOWquoflr9BZ0Fho3WUcSK5dvsgT1z7KW6+/xXtvvcqwd0ySlkAvw7Twa3V6569hGBZSVZmNeuiqpOI3GPa3aTaajCYBtu3g+XWmoy4Pd7fp9hQO41tMpuNSFCh1NjoGawunyRWXIoU333yZSe+YPJmRJwlHu4+otRepNtr09/doVD2MIiGQFpZdRdd19g/KE7PIYvxqDanZ7O/sUGn4xEywpMmGd5kr3jUcxSBzjon3jhhPZoyPdrFNF92t4yqgCRVZSPI4LxM+lQKhQJHHSAmeWeN04zwXm5cwVIPF9SsYB4+Y05fAVvCCOuPxYwwCYllwLLusP/88K50OjqgQz61y5+AOjfYawa37JMkOdVeh6TUYZJv0+28gp1XWFz/Jlxe/RDvxWF06RTK8hmnA6dPPkgSCYO8QaUiSrsT2a9TGHs+uP8P+ThdQ0HSDZmuBRnOO6WRMv9dlNtPRNQ3N9dA0E79ioKoTZtMBfqVxoqqX6LrJdHjEP/vv/u8cj/t0OisYWhe/3WfUi5hfusCpZQN99k1MtQmHJll4E10cYmCgZX1qekKhdjDTMQvVHu5cQe7YDHtHOM1HZM0XabRqpIU8cScopUaryEmSCE2zTvQnpUarKFKiKOLwcAffr3Hu3GWqlRqcjEcVRWE87NOas+j1jlAKQRoGBLLEt8+EQD0JOhR5RhKVug5FPYGGKxI0BdOymY4nKCiopkmz1aRRr5GlCStriyiGjem4iDRFCkl36wG9oyNQS0bRay+/Qtjb48yTT/PwvXeYTcaYloVbwKzXJ5QSDTh39gwr66u89vq7HB13cZ66Qn1lg97hLt/7jX/Ks7/wD1ANizAMGXWPCKYzCinxK3XCOCE4PMS0XUzT/HBds12Hq5/+JCKOuPNSglrcpZYndF/+E35r/5if+t/+X7h49gl0RfvwkKYpkvlmm49/5GNUq3X2dpbITi0zuvMNksmUyThmSopEwzR1ZEqZdKwWFGlEJBQM1T5JuNVQBRgyx9JULMvC1QTImKNhQPV4ysq509TOfY7UqKHpOobn02q2aFYauE6ltJtLSRjN+LOv/goP/ui3aSgZtmNDY47W2gbLl84zuneDJzeaHKgR1/dm6EVMVdOoVztMp7B7eExeCISqojsuKim6piClgeMqFIXk/Zv3aPsVKp5FzbOothfod7v4hkp/JllabCEVlTTqY4gpa22DzX7KZBqxvDpHFgcUhomMj1HqyyhKQppnZNGUn/7Fv43b6HDrG7+Jq6UMNZu1lTYHeztUPJXtuEUwTdnPLa7UCg4Oc1YaGY2qzhd/5gKhvsStV76OSD0e9wtcw2MQRti2w/wHheaP7Wcf7mMfhAKe/H2RxSzPexz3p6ThFH/Ow7c1PMshLlSub5bW7vOrc7h+lf54hqoZBEFIlsTIQtBxEkZBRj+hZPhIhbavkCsqg0jH13ISoTEcJqTCRUHihhNyaTILc2qe5Mx6nRwYTfoUecHGahtVUxkMJqR5wfbhCEeRzM2vUW3MmIUJ92/eIkg1TEVB6az8lfb4n/gi5YPrwy7KyRxP07QTToMoia6KpFHzKaREi8pxS5lvoKDqOopachzXT51CKiqPNwccbu5QpDF1XSNJBVmRly1GVUWqKkudNoNRwCxKMT0dXdfQ9LIroqoqilTQNA2hlaIzxzJPgpkkRVGSOA3DIM6yMtgQhePRjEmUIFB4+okJp0/N853XbnLh8rP0+n0cr0GBy4M7b1Ove6yffwFNyRDZFCHKmboiBjxxaZm5pae5e+cBL3/7+4yGQ7KsIE9ihCwR/KqqMDq2CPt76IogLRQMQ2f79mtsnL7AQnsR1ajiVKqkaczO3jbdxw+JD3YxnRqOFJw/d5lOZw6tiNi9+QNubw/RqkucOX+ez7z4NDtbVQ6277O1mxGGOdG4T71RQzctdJkxmUZIvUqlVsf1q4R5Qj6ZYNkmaRhysLtN4htc+/gzPN1YYFvZpaabzCk1OlYDrdnk3vGrTNMxSZqShQkmOY6pUNUd4jRDEaDlUGQJhSwwbAvXqfOpyz/N1aXLJGnE0uITJAddPKeOqUlGR0P60z2OH9zF12wqq8s8fe1TTDZ3CAdHpEqPTAG/ZnN29RJjp4GYxaRaj6XTOo+jXYLHQy56p+hOd7nin6U3mbD98C3qhkmYRvSOH3D6yefZO3rI1tFbLNnnMSvz7Gzf5FCLuBNe50mulPeblGiqSaPZoVZrMpuOSbMUIzOAEoHv+TU0FcJwiqo1kJQApcHRPodHj7m1tY2Ox8LcPGeXn6RiBlyY73J57TaGEiBlTh42yHIDt6IhRQLCRNeh3uyyvVmh0dYxjIfo6b+lkk7RnGXqCysUqo5MM6BAyhxZJGRphmlY6IZdjnmUMhIiCiOmswlnz15hbm6+hKgpahkNISWFEMwmM3w/RIiCWqNJpdZgMughhYmQMJ3MqNdrZEGA1O2SIJ3neK6D5zoEYUKSpEhR0mZtISgyjTTLMBSBaZkYtkOl2SKPQx4+eMR7O4esFlCp1bh/5z5bd2+ysb7GD77+ByRZzvLyMno8oaYWPDtvcmssifMCx3P45Oe/hNDryHjM+edfREVy/vlP8/3HW7z02/+KT/zS30MoEEwmTId9NNNmbnEeyzZYWD6FZVns7zw6kdOXl1PxePKLX0RJAu6//m9IZo9opTHDzdf5D/+v/yvP/2f/kE988qdxLedDjYquGnSqDa5duoLn+wyPW4jjO2wfvY2qCaoVjcKsU6gWs34XQ3dwWgvoVqXE1KNRBlxqxJMhYW8XVclpNhtomkY8G+DYKpFqMNOWqRp1HMdDGgr1ZoPlziKtWqtkekjBNJjy+7/x/+HRN36buiLQPJXE8anV25x55lkUTTA93uPBcYJUKqy2VIo8JEgSTCb0J1OyQpBKaDk6G6s1BkGMq8wwQ4GuKeimystv3OL5K6eRikl/GFBvSy5eeYKNSzl2/Qzbm4/QDBge7TBfN1DSGc2KhmVrOLaK11wiH26TWy6ivwvWCkEwxmkusre9zdVnn8EUEV/76tewshE13yBpLdELI+hF2GZKx8nY2zM5v97E0mcYouD733uJH97R8W2LNNEwbZdVP+XnPj4gWIjRNO0EGlhef5kqC5DnGUWegszoNDXqrmShruHokmZFReQZt7cDZJGxWHdZ7fhEwkIoOkGcMBgMygLY9JlIE9MY4ZkFFBkIFVSbmpWRhjmZ5mG5PlqREMdT6q7Atw3CQifKJdlUUpukTEKB5TpYMiMtJLaqoMicd++NCBLBmZUGUyyqDZ3jcYzpOiiuziSKEVH2V9rbf6KLlL8c+fxBdLSigKZpmIb+QXgkrqmjUoKhbEtn+2BIIQrmGjVEUWCosLy8zHMfeZ7f+I3fot/vAhJNhRzIhOSEbkKuwMpCB8t2iYVEM010VUVzbDRVhaIAUbabTUMjiEsMcJYXZCe0zlLgqJWArKLUpKRCoKqSXAhuP9qn/3sv0ahXGA5G2H6TT37+pzje3+Pu+9cxrYI09am1Dmg0quR5QJYVjHvvkucFdvU83//+a/iuhiFjRJZhaDqpyEGIEq2dS9I8JVRjXEtlFgg0p4I3nqGnOXfef5fmesLZ808z1+5wZzTgaH8bESUstxZw1ZDpcIcsy/FMlbm1C9y49+dI3eKTL36Eo91Neju3aNRrHB2P0FEZTSc8vn+rbI27BhOpkxU5wXRElqXlWCgOkUAUhviejSkrrGYLrOVnOOufY8Na4eBon1ldpVa18RYWiZIxy62nqNY0ekddZoN9PtF5jiPDxNZ0JvEQJVF4a/81FEejXVng9NwF7u3foWr6vL77PRZTldapNcx6GzsV5GKIcm2eerZIvdFBmRTkOuRum+OdW4zGfcZmwWGmseqvsP/4Lro65CgNWK2r1ORpzq1cYzgZYRg6ndPneeM7v8MzFz/OOJ2QDLZpHK7QF0PemL7Pmd4Ia3gXoad07YxdcQgnyPOyzQBSlOLPar1VgpSSmGA6IohiFhaXcf0mhhUznYywHQ9QcCoVLl0+RXMu4u6tHnuHuwSzPh+9toS34RBMGpiyW44oVQ1dD8kzkFIrdViyQNcVVtb7HOy1qTUNsskxSZDhGRrK8HdQnE+gqWfJZaUUdGsK1VoNTbdQFB2QJPGMaRDhuT5ra6cxTQv15HAgRY44QdGLoqA1v0R7YRHbqxBHIeFoQJ6n6KpG1XWYzAR5lpKqFqamlUDCNCvF6lLBr/ocHnWpeR5ZIkniCFUz0IzS2q+iEs2mCMCyPe5u7xHFBSoa3W6fB5tbpW4tDcgVlXbVpW0rDGdgOiobZ0+xtniBt96/RzoLeOfVV5irV7lw5gJJ0Eedm8OwHZ792V/gW//613j7a/+alWsvEE4mCJnjuxZ5PmOufoY0iRFFSpolP76aARK75vPkl/8ahlLw/tv/jny4SUNGOIMHvPmr/z1bd2/ylV/6L1nqLHx4Ctc0jblKA3PDYN/zKKZfYudwB6YDzFzBrJjYjSXGbo3hMMfyV2k25ijSHFlI4iClKBLSIqJS6yCJqFU9bL/CrFrBrzRpbVykunwaqRrkqqA91+Hs6jqdxhyaqiGlYDQb8dXf/Gd0v/V7LOgZqqlRuB6622Ljygtohsbtb/4RR5ubJOGYYBYwS1Wqng2yYBArGLZDkZZuMM1Q2d7fB9VAsQVJrmLZZdctKzR6gz5Lcwvs78dImaLoFSp+jWR6yCc+8yke3HyfJMqJgzHthQ6mAXnYw7EFUXcTp9YklTazwxlOu6BQXTSZsXb6DMPDXZ544VPsH4557Xvf4v17B6hIdK3A1GOqnsOt3QLXs3n3UNKu+6ybe9x5R8GMBa2FBqeuLLK116eaDVnYyDm0+P9bpHxwqZpOUeREsx6261DxNJJCoeLrOJ7O460ZW3sTqvUWvlUwm8WMkpRcSLZ29hhNpjQbTUQUM8sS5lwDxxCMU4khUsgVclOhahSMVA1NRqRCoOgGjgO1Vh0nFWS5IJcqR+MC1/WIRYoiExxbR6QJD/amhImg3ajiej65VEmFRDFMxonKE+eXaNQ9do6jv9I+/xNdpGRZ9r/AA39wKYqCaf2Iere8sFA6D6TC8mKHOCsx2JZRxs7X63UuXHkC16sQxSmW5RIEU3KZo5hlkZKeAGnOnTnN3FybQig4KiiaRpGnJdpblmmvpl6eSKI0Jz3prERJ9mH+SZYJdEOQpCXY6IMsHyHKmzSVku5gynAa4loGh3u7pHFAnuUMukdgQZZNyZKXeOaFL9M/PCALA/LM542be1y/cYN42qMzV6FWbTAchURhgGVZoGpkcYjIczRFoCsF6olwOJ4WHO89Zq/dgcoiD3cOefz491FRoMjQDZOVc58gGPZwXJNglvHqt38XRVNZWp7HrC+wcfo008mA3/qN3+Dx1h6qpiOkguvaZFZGbzjGtkyiMGMW6RQiJNIVgsmAyWRSFpimgSgSkAayyAgnx9ybjWjPn+IoM4iVBKHOIEio63U6TpPFuVNUHB0lExwfbLK6fJqsmGDaFk9Xr9JAsra8zJ3wIVfXP8LD2btsRvd42nmeuUYTz1lCMRWKIMBVNTLLo16pUR2aGEnE8cEDNvf2UYVOpAYc9Q+wn1jlUbrF/c23aEQFwb2cxQWNjnoZpV0nthMqlQa5JrENyR4Rn6x3WG+vc/21P6Dm3+Jc+xT+s/+IP3/z17GifeKq4FE6JVVPOoMnIWN80AJW1TL4UldwNJ8iTwhmY6KwSaXiY5oOjYZBEIxIi4TZcJfLF09Rcfso0uPG+4doWZfjzW3sp6pk2RrRRCUIJHYloVLro2kmosjRNFC1HE1VMS2YX5jSP6rTWfTQ9BkqKWL8feT0PZTGP0a3n8W0PDytiaKWIt4oChmPh6iqxuJiiQnQNeNEOFsGeKqaWmZnqSamVYZ4SlGGFZqWjdlZoNlus/vwPrkske7DwQjfd8hzgZAFIiuIT7KFXMVmrlkjnkyIggDdLrVdaRRj6EYpaJcFFAWHvR794QhV16g3agRBxHQ8puOqeEpKc2mD9fVlaow4t/A8w83bTEYjLl25wtlLV9Bsj2azhWFbVKt1qrUaumkz6ncZH+9z8alr2I6H02hzPBjQ39umEAKvWqd7sEelXqfebJednw81CuWBSEFi1atc+fLfQLXhvdd+D9HbpKLGGGmP4fd/l1/bfcSX/+5/zRMXrp5YustsorpXxV91qDgeoSx4+7t/zN692+hWlUp7DfSYWXiMo2Xcu/0Ay/OJE4HrGti6pDq3RNOtkWVTTE1B001Mz6W+cgV/bhmhGSQKrC0tc/HsWRpO9cNCaRyM+epv/Y+Mvvvv6agRui0JTRvcOisXn8ZwG7zzp19HK1SsSoO2WaA5DrkiGEzKFPuaEhKdHPAUoFB1Gr5GkJn4VYlu5YSZhuk66DkcT2ZM40Mc2+TB1oCLz8wzHo2pL1+hf3jAwlyL8eE26akKiuHR3Fgi7m1jWDbBLCFJNSazKU5tjjhLSbMMz7FQZcry6bPMJlM+8+Wf5d57L5MkI0ytoK4K+scKqebQqdv0hlNcKyTVfI6NOoGYsORIKr6GUaRcPQ3FccYr36yQtH0Wn1H+F/vXXwwULFk1KBa7m7eQSYBX9RnGgnGi4bsGQSJJpYpTBJAIRplCYVToTyLG04hnn3+Ru3fuUHFt0iRmGAvadmny6CUKxjTHNDSk5lJzNbIkoO1pWKaBSs5oPEWzXBqtKuMgJ5UWtm5TcRWmgwGhEtAbxcyijHrFwXVt5loV2u0qqpkzGAVomsZwEqIoYJ5Y6f9jr5/oIuUDGNoHM7uyCi2V0pqmYVs2qqZgmAa+a9Pt9ZF6StqsQSFYXpxDhCGzzGAwCfjGN76F57ostBo8ePwYKSVJkhHHKYqq0KzXWF/q0FpYIklzsjzDsk18v0IYTlA0E920ybKcOBNl6jGgqieUgA9R9wUCSZoVf+EGlVKeYMNLzkohBK5uMdds0K5V2HlwF8/zkSIjnOTkUcLRzi4aLh0/xVTgnftTtnZH5XugWyRRSqNhsn7uHFt3bxPFEarqYmgaJQJBYOilxsa2JCLKSYMxt99/C/f08zj1BsVkzKh3gGN7nF7f4OqTH+Vw9zH1ZovXX/1eOWPPE8hz7FqbC1eeZO/wkLyAPMkReYKmq0yTmEbVx1BUsjRiMo0IMwVDN1EQFFmM59onXIWCzBBopKhqFTRJSMT7ozfZCm4x31hnSXokhYpMBOY446Wd3+dy5SyT6QBNrxFkKWeq62SGxlptgXWjwVpzmY3pIjedBwzkMZfaT+IKk0keIO7cIRwfcvr0EvNLF/Bzm+goAz2h0HIqix06FY9XXvoTFs+eRUiNzcYmw3GPSlSwsrRCMTa4vLyObc9zvPWA3WCTlfNXOMi61LMMe75KMA3Ji4BrVz6BaVsoqcKCOwdmi13lISMnZyYy3MIkjGKyPP9wXPIBjr68SqaP7XhYpsFo1ENRCmzLxLQcqrUWcTAimQ44d+1zTPoHPP38HHr6PbRwwDOXMmQ2Iww0dh+nmKbFvDcqtVJqSVtVFAXTVJBGeV9aboaiDhkN2rTmJbphkhVT8miM7r+N1Ntk+QJpPiGKwtJ1pFm0OytUq80PCywhBYZhomsayslzUWb7pKRpQhIHpJkPqkJrrkOaxHQP95G6haoZKEpaAs4kJGlOnmel7kVXsVQbTdXJ0phKrc7De/fZD3KeeuopKoZLEIxxPQep6EjNpNs7JA4Dzp7eKIMQ84yGY9ByAMPjyrlV5uab+KpDWl9g58ZbeCunWdy4wN72Q3QFug/vIouUt457tFcWsFGwq+UIxa21wTDZ39pkfHTAxac/ilepkAuJ67gIJMN+j8HhHsP5KguLC+iG/eHgRwGsaoUnfupvYlZ83vrWb5IPN7G1KSv5hGj7Zf74Xwzp/c1/xIsvfAbTME4IKOWztTK/wkc/8mlmuQ7+KbLBMY5VR68kqIsqVz7+Mf7esx8tn8FCQ0eiZWMa7Q47B7vcfve1UlQqM8a5TqvdAE3H8VxOnznHpTPncAzrZFQliZIZf/6Hv4L8wVc5bY3JNJ1Q8xB2g/mVi6h6nbe+8U18Z45zzz2Pns2INR+0CNuEIFYZ5RCPZqDp1D0dTSsoyAkTSVGkHA4M6p4gTQULjSpJnDCcJDSWW0yDglE4pNsdsXpqBakYVKoN/M4phgfz3L/9mMrcWRo1E6dzGooCbZoxnERMRkO81ir94yNc1yMYjqlUXUShYxuC06fXePFzn+U//JvfIREOiV6QK6W4N8r7LNQkSRhxcCwZeQ4JJiMdFo0Bg0HIdMfFa6wweVDQsSo/2sf+0vVBoZJlKVkSMOlt02g2kLmFaY8ZDwa8f/sIw/YYDEvchKMLZkFGIBNmacwkhTPnLvKP//H/nn/6T//f9I8PUHSdQgqqjkF/poCSYZoas0TiuoIiGFLzFXTHoOKCigqqxDMFUpMIqTMYh+wf5thWmWVXSMn9vTECFcO0qPkWa/NumQ0lE6SUjKZl90RXBOKvppv9yS5SzKCA5o/ARuWlfCim9StVXNsBKQiDMVeWPObnG4xHIx6GM+5u7tKo1alWqojpIVkcczgaEk0n5IX40dcySpvx+qk1oiRhGkSYun6SiZITxRGj0YRGs8niXJOxpdMfjilO3EAKyknYWrmpCKW0Vf74vfmhyFfhw7l0iRAvX0xzwXgw4Mz6AktugEhzKGI0qTMvNnFlgwdjC8WpU6uOKbKMWVEglJS8yIkjgaZr6JqOyGOQOYYKpq5yZl5nNE2ZRSqppLRiTse48YzgaEzcP8STYxba55hv1bn7yldZWr9Mc+4aVddFVyS6ppCnCaqiYdkeu4+3aTVbxAsBh7v7zKZx6XAwDFxDZZpkTOMcRTfRdQ3LcoGitHKncRkrbpiomoZhO6i+T9Wp4IsE26lyZeM58mBGfzQincwQkwnZ1pAt/TZ+rYbUcro7m4zsbaaawvG+y12zys5kj7s7j3nqc0/TXLpIXWuwkz9k3T3D3Xs/4Oqlj+E110njgu7+bWYtAycp+RaaW8evV/j0F34eR9HZDW0eyzfQppKcmLZ3hlV/hWB4zD35ANe1qNXnWZu7wNbmTQ67D+nkNq25efp7B8y1WvSnM3YfXUezPZ5qn+MGFsfp+6CUBff+3ia2rTM3v0Kj0cQ0LBR+JKxDAcOwqVRrhFGCosB42ANFwXE8TNNg48lPM+3dplPpcRTkPPuRdZaNfaL+gKOehm7tEScFti0xDIGilDZ6gUY0LfArEpHnZZ4NBYaloCsHULjkTIgmBYZnY4ofMNybsjd7GsWoYbsNmu0lKrV5dN0sLY+6jmFaqCeFiaTMpyqyHCGLE/Fs6Qzy/BpZlpRIesPEdjz8WpU8DknimDCK0JMyz2Qym6Eiqfo+0tLZOeqydvoMaRRQW17n9vXbfOO7L/P0pfOc3ljBNgykqmBX6zRbAZ5h8Oxzz7N3/w5hv4ejq2RSoVF1MOKA6c4QbX6JO9/6GqquoqQh977zpxwEAclgiG0ZrF24zFMvfgbb9zBMG9Uwmc0mZHGCqukc7e1gug5b929hWxa1uXlmmo5bbYDI0QyD9373nzC9/3HOffIXaCycwdDMD5Y1dNfj/Ke+gu76vPPSrxPv38ARI+YZMjd+k3v/9r8jGu/x2S/+Ap5d4YOEXFVRWFtc5Quf/CyNSo3DR9sYsx61tk66vMLyQofs4CGVap16exkQ9HuS6VGPjt/gqLWBayoMR5ssuTrdQqfRqHL+wnnOnTqPZZgn30qQZSHvff838F79FTreEKFpHGsuqd6k1jlNlinc+u4PqTfOcOryC6xdfZJg8xY7kxxGY+YrCokJw0jFMVXQCjIpqHkauaJgGB6+UzAIDAzPxVYyBsNRGQLpGFTtgmF/RhAkbN27zqUnn0ZMdyArGO6+x9KZF2h25ug+foCY7VNfPk8qTPb2D9HdeZK07KAoikqWxFikNNpLTEcjCqFgqDFPPXeVx+9eZfNgzDSJiUXE3u4h65UU1auzFwc4Sk7LFMQUrM3bFLpGv2+RzFI0RcXVIImSctT5/0OH8uFrUlJkEcH4iNE4xjYEosgYjRP2+ym6CUoh8E0IohJtPxWC3jQgjjM+/8Uvlbb9Imc6m6IqCrahIzTBMAhYant05mxEGmOrORkaugq+kZHlBnM1lSyXaEqBW/O5ubXPZDDj0qkKdqWGrbls7Q8pBDTrVSoVn5WFGotLTQ56IWmaAAJTzfEdnSiOSJX/hNw9xq09pO8jmx5FkX8Ih9I0FcMwcF0X3Sxx4YZhcHqpzpc/c5bHvYJhKnl4MODwMMAyPkBul10Y1bSoaBpBGKGrCg3fY36+Q5iW+T6WaZIkESrQ6w8Jw5DJdEKr3cJwfew8py1yDvqTD4FuH+hlRCExdRVBKaYVJxmHKHxoKfxghCWBOE2ZzEJ6wzGW67J30EXPJhhKgl+pQFEl1U4xmUjUygJW3GN5fR3T9RkOxkz7B0jNw6u7dI93MS0DVTEIZkM0FS6suSw2Mh4c6YRCKR0aikAvChzfZ7j/CCWNma/naKrO4we3kZN7HGzfZ/itlxCyfM+NsvqiUW+xtHqGt159mVA02D2+Tas1z7nFRe5ff5siK1BtizDTyaVAEzALQoLoMchSx2MYOq5llM4oRSEMbTy/zcbcE7RaTUQe8+DwLuF4SBqPaR/ZzCYjGorPLJowTQZgGliezs3wMRe8FSIz5KWH9zl7dh6xk7B3axd53wO2Wbjc4XjhkNVPvEBVrlBkCjJPaDXXMPOIYDxGUW2i/iP2qzOado2l2gUEEVfUhOuTH5CvVTAnEpFNiNOUt7N3eGrpo/Snt1g+WicNZhz1H7DYPo3vNXgwvYFyPGZiZBxNd1jTN/j42RdYz1ZZG9fJpMcf3fkqtmkTzKYEwW26fo1Wa55Go4VjOx8WtJqm4VdqZFkXRTVpzS0TR2Omo2OOp0fEQZewdx1fu8PB3SlHswqfODcinkGmOFhyjKkrVPySiqqIFEXNMFQVYaoo6JhODd0I0VQdRI7EBUUgT6zwpl1DpjPm2grW4lVMbxndcFA1A10vSbAfhgsKQVaU2hPghHFUFtBQOt9My0I3jFK3pSrIk5GpKIpyfCigyHKSNCWXEpGmZFIS6zGbu31SodHwDzFUhXarwZm1FV597Q3evnuXF7/0BabdI0SWougmlmXQshUO3/sBxbjLSq1B6jYI4ohErfDGnR0qdRtzt4/h+fT6Y5btCqvPvsCaYeP7HlIWVJodoijA9WqMBn1EOiWJAoLREMPy8CsVxoMhosioNdusnbnAdDIkTWLG3S6T/iHGuMfOd36Hw/e+y8rHfo5zL/4Nao0lNFUrCxXL5OwLn6c6N8eb3/pNRvdfwcsPmDMDzlrv0v/BNg/M21z69H+Dbs2BkqNioKkaS+027kc/xuFii+s//D7RwSF3b2zy/pvvcu25Z5hfXOJwt4vt2CSJIMozrv/gNSZBSGWhie8EPPPkOZqtU5w5e5lGfQ5D15AIkAVJMub41r/Bf+1/4NT8kFGmsVe0SdVlas2zxOOcvbu3qdQvsHb1WTaefYLaYgdt9z66oSLqHQbplCSb4VoKjmtTdVOiXEEaHvM1wWE/YlhoKLJAKk0c2+J41KfdbBMmkr2DMaqQOKYknhwSDvc5e/oUe+//EYrRYXbwNm77IsrcWYIiQxkXxHnIr/2br3H//kPOnlqiM/cyn/v8F+jMtag1faxalSTPCQ67GJ5HLlVqtuDSfM7rj8Y0HY1RbDLRbLREsDRfoTfNsJUZum3QHSpUA0FeqGwHOZ5nYiYRehT8WLjtj/SVH+wBRVGQhGNmg0P2d7bZ2j7EMgRZGtM7IckWhaBIM9K8YDjLCQudWRKUqeNSEgQBP/jBKxwdHhJHIXONGjW7oD+ZEGSQZjlBUuDqUK/bOK4JUlDzVTKhkBbguha6YRKnggc7Af3+mPOrPo2aS5AnBFFOteLRbNRoVB1Or7UYBXA8SAjCjGAWEUUJcZLhuC5u5T+hcY+y3ES3VA4P9pB6KUByXQ9Ns1FVDc/3WVpeRtffIc4KArNBrph8/MVl1lYa/N4f3+Cdh12qpkKiKQzyjEsXznD7zn2CMEaKnKX5DpWqX4Kp8jKnJE1ipqMxQoH+YEgwm1Kr1bAti6XlNd5/503mmzUG0+gkObZAUzVc1yKOkxL4BuSF+DA/SJ50XOSHfyqLFiSESUKUpMRRTJwrSNpoImH/YETD9ag2fW5sPkSxhywsn+FTX/g5kPDNb3yXN777dQaDPqbeR5E5GhlZmqDpOvVmk04jZfMwQ1V10kKSFQJLEyAzBtu3iEY92o0mQtF5uPmA6WSGKiJcx0YzZmDVWV4/TcvR2Hq0xai3x4N715lNJzjNVTTLpT8ccOrMGeo1DxlM0XOb+U4LZTAiTnOiJCfPCvK8QAqw7QLbMsjTFF2VpHGIJlVEnkJ/SlVVWNXmuKEOeSu9x6dqT9Pb7ZEUAdNkgq5p6KaFqmtMkzGz6jobc1W2t/bo7fX46HNn6I5SWvUGVWsZpRDIYU7nzCreuI2nqIS9x7g1F6fwmE0D+nt7JBRsTh6QX71AK0+5//AmzYUmy8uXeTC5jZoqpFHMKOnx1Nln+MjyR0qi6Cxl7/F13uYeV2aSi9NNKvUKRwe3EL7BkegyZ7RIgh6j3j0+v/xR+uOQN/I2KytrrJ8+TTjeZTRJGfQOmI67eH6VaqWB6/nohoFpulQqNeJohmNZuF4Nx61QZMsE4x0e93/AdHzMyqLK1g9HPPQKPA0QAapX0JnTsM2ixLgXKnkiQAHbAcuW6EaIrmZohoamVRHqaWR0E9NroqtQJCVl1tQP8a0uunseVfdQVa0c7xQFggJZpAiZgfKjwgXK71UUkqLISNL0xEIt0I0yxyeOIia9LrIodSWz8RQJ5FKQZYI8KxBSsH3Yw6zUaCkFSZiQKALUiPVTqzx4+Ajd0vgPv/t71Go1Tp9aY86pMbe4zELNpZkd8sRnP4dTb7H98D4vv3ef3WHAdDLEnJqIaIrrV+isbvDsL/wDltbPkMQRtuOcjIaTsiMkCrxKpUxDTzO8aoOjrdt4jXnm19awLIdGq4lhWbQ6S4TBjLn5FfZ3HrH38A3M5DFJd5t7f/grPHjje1z5mb/DuWe+gGf7gIKq6XTOPcVn5pa4+frX2Ln+Z6iz6zTMIy46e8j7v0wvuYFz8Weobfw8GGV3RFMkvqXQsWM6ZsYP944YHvQQecEb0ynrp9a4dOUpZnHAeJpx/94jHr57G8VUibKM+oUWTd+muryIbdgoshTgA0wH32f7lf+R+s47nFvtszcy2QlWiRvPMb/wBPu3brN3+zaudYaly89x9iNP0VxdQLfKkd/VJYcb9w8IVRfPE4gkYByFuJ7NYhuOhzO6A5Nm3SArVGaRxmR8TJKW6b1RWqb7xtKkU9UosohhGLLz+A5zLZONpz7D4HCfaLxNYK2gOjWCUDCcjfB9G0Xq7DzeY3R8zEL7EY/uvs8zl1f4pb/z11D0J5DqhP39bTQRcff2azzo7uGbBkGikUUFs3DGEQ6Nlk+hmUi1YBxIwmDKSlXDd33SYsKyF1LTYyyrhmpbZZFxkrb9l/WVRZEzGQ94781X2bl/C9PSGY1nTEJJmJocT2YUWRkwmCSCSSIRMjsRoJfvx927d9nZ2SEMppgaKDLD11I2h2V3T9MUbEtDkyfAUTIcz8Z0LJaaBkmuY9oOO3sjtg4OMJSMKJe8uZVz7oklrPFjhtMIUShYOpxZrZBkkp2dA4aThEKAkCa1uTlWVlY4tXGayWQK/NF/9D7/E12kHGsTOOiiqTp7x32EULly7Tk0rZxPe77P6toanm0RxQkP9kb825cCPjXsM+pOKbKYxUaVCI0wy/EqPvsHR7TrHgoFYSTwXQvDNNFVhSAIce06URCQFxlRHJNnMYuLC/z0F7/ANIwYTwMQguNxgq6pFEWBaegnMCcbFUiyEnJl6ipCVSnESXtdlrK5PD+JIJSSQkiKQjALoxNKromiGkTjMcPuMdIGv7FIKHWyScTp8w61ep00ThgN+yiqSjQaoDqCimdQ5BAqOb5dQdck7z8K0HUN0yjbwkiJqUtMTZIPdlFVl8XVcxTjWyThlCguEALQCnzTptZa4unnPo6Z9piba/Cd19/j4Y13CWZTJt1vYUYzVFWye+8mIstJ0oJsNmWlucCp+QXeub1DkghKAK9KIQrStGA4KYW9uqYglZAwmmCQUPU7qInEI2NhauNPLHrqiKGVYglQXRgkQ1TVIEolG/465848xfHme5xeWOL4cIeou8+gF7B5u8dXPnmOM6157EzSzFbALOgePGYy3CEumkirzr1Hd2ktrVBTqyi9B5i5w53udfYaA/Y238NuLnFGucRgeIw7tw6dDh2rSjU30HWTVMupnlpnrVCwvAaPt26SiZStdJe4n4Bb4eHoEY6qsmyvoGYe0fg+/+Wzfx3TdPArLfxqizkpEUVBUWTlrDpNmIy6Jy43CUVKkuYk4YxqvQ4yJUsmzHr3GY/uk8YJmi559kmNyTAnSRWKKGNxScXSFTQlg3xWkikzDaeilC41RUKeoZgqqqog0hBVv4/mG6CbBEeHaJaNafsUIqUoYpQiJy8CSg1NOdpRFAVVA0XVURQVkQ0olJJPJES5sCqUQYamqWOaFr3j/TJTSdXQDIPHN98r9SeWznA4I03K3xkJx6MxcVrQFgJplJ3QIM04Go7o9YeIQlAVFsfZgKPjHnfv3cNAo+27rDsJilrguhb2wmnGd7bAcTkezfBVgzlTZ1BYNJdP8flf/NssrJ4iikJM00IIQZrEGIaB3eoQRSHD7j62W6HemuNofxevMU9tbpFKnlNpNqhVG8wmI3KRYxo6umkQJRHuqc+QHN5FC25gFl2yx+/zxq/+P3j0+jd48mf/Pqunn0TXLVRFxW8s8Nzn/y6nn/wsu/df5fjxd8miN1itP0SbfRft/vcR5g8QK/8UTWsisoDpgx+w+3iL/iTleCZJVB1V1fG8Cigmo+6A1tlT6EVMpV5n9expJmnEeDYjKzqEwsDobaNJgaWvgOMhs2Psyf/A+dbLCB+mgUOXF1l6/hfJnSXe/ZPfY//6dQy1TXvjSS4+9xxLG6volv2hWeDOzpBwNiMQEXgWjqVTqUiQOf2pQaOqkxQao5lKxU7Jc0iT0q7u2CZxEmMYOqPYIE4TbMfj1uYUrXqfhY6JEiQUyjLm/EeYRoI4GNFZXipF3Krg2aeu8vbrryIkVByVihGxvqLjd9aQqsXefpevffX30JJDVF2ldzhhpCqILGOUSSxbRSVn2B2RF5LTHZ9qZ57bWweEmk82U1HTiDOrGulUMg4lHmrpZhNlUviPj/+FKJiM+nz7z/6A0cF9bKOgWvNp1l2QGY+2egwGMI4LaqZEtzRyNEahQJWyfIYMg/FoTJ5lKLLAs3WavkKrphEkBaZt4zkGAh3LErTnPNo1g0bdZpyYuBWHtmdTYNDtjemNEny7DBC9//iYl199k088s8jHri5zY3PMuY0Frl67zHBasGIvc8bxqNVb1JotarUGjWYLx/V447VX/kr7/E90kfJ47zaFkrO0eAZTt7G9GmEUkhc59Vodw7RYWVvj/FKTyXRCHkd867UduuOIcBIwiiVxKvErVbb3jxEiZ6HdwHINzm+sc+fBJlmW0bQtorSMideUcjN3TIsoimjUm1y9dIUiywhGfS49+Rw7jx4wGo+xbYssKwsmQy/hbbbjnICC8pMWX6k9UFQFKQRF8SN9v5AKioQ8z4mimPE0IA7HJbZ8NkbVDFJFI1ZV2guLnL/4BJ5t8s2v/T6Pdw94eOs6STBEFDG2ruH7DoNRiuPaeLaOkvYQuqTqa4xnBYqqlVY/DWxDIpUCbJNao0avHxEEEVkuQVNJ0VGtGnPted5+7SWm0zFf/vKXqd+5h++5dDoN9h9PadZs0jgiDUZkaUaOxngaEDzY5cJ6k2bV4Xgco6qcjNvKzlM2LUdrpqmRC4Wq5eOoKr2dLYRukcxm1GjzOV5gMOpx9tJn+N5bf0Bv0iP3C4b6GC2xudy+jGPOMK0GRT6lokome/v4csbBbAlbWjT0OdR8AEGAIMFQHYYTSRRMOP38RT75+Z9lfNxlNsu56F5lNArIJkPWl84wy3L03ORjpz+J1w5Ripxg532WrQaqbhGpgkHc5ebjt2k2mvzUlZ/nB+/+AbEc8MgesZrX+fmNn+M3en/A96fv8WwQYU730KVCOi6dLx9YFCXlOEY3LExL/Nj8WpBnCeF0QJL2GA/2GA+3EdmMJOwyOn7I4VFBVTWx1YhaXSUPJUKCZqqoGIg8AVOnSAssV8OrmWhajqI6SJGjajrIHFWBAhXNjCmEg8hU8kJDUy0kOrmolYj6ZIqm+6ha2TFRNA0pPii+i7JAyaYohgFqScbVNAVF0dBUA8etlonPlk08G9Nqr9Hb3yMXksXVVVyviiYEe7v7TMMpg2lAlhc4hsYsCplGCpMgIk5TRCHQNY0kzQjiBNeWZYcmgzRNyMIAb6FO3ZMEoyOOi/sM45zFtVNsD27RaLex6h4vXLjAs5/7Ap2lVSzbRjsZR8VRhGlZKKpGnMQoqoqq6Vi2wyyYkMQhiqpiGDq5otI7PMDQDerNNnmeIxGIQjI3t0Dr7BVmR0/x4M1lksNXsbUdtGRK/61v8t3711n7xFd44ot/m3Zzubw3dJO5hTO0Ouskz36FcfcBW7d+G3P071jTj2D/j5HWR5m6v8De5jsk2/cJY5X+IMbRVRavnGNlpcPyqQ0Mdw7draIWIatrCzxxbo6joyX2ujPu37lPkhd0xzEqJe31/vvf5tIzn6dqfAM7fZ1ClwzDOY7Uv8bZn/k/cnh0zFu/+y/o3nkfXfeozp3j9LNPs3phHd35AEJZ9mJMXcOwDMJpTpEUGK6NYyroMmSSCsLCAxmhKwWjwMJ3VSJNA5GVhbNQyAqVPM+YpAnqNCFOc968/pCqDV/59AaalXP91T/k7PNfRrN8Ht27iaLA3PwCp9bPUPFdSMY0HMHP/swnePozX8GqLZEEQ3rdI3IJ3e0Dzl9cpbG0SLc/o1AnGJZkpemytDTHVk8i4jG6oXDlybPcHWXEloUmQ3wlI1IqVDomo1EZOSJEgZQfjHqUD7UoaZryxg9e4tGDO9QdsBwX37cIpwMMw2BjtXRsjqcJApUgU0iEAjqIPMW0HRzboVatoCIxaxXmazpPnzE5OBqiajqnV5epan3aVQ3HMFhbdGnPN/F9l2auUggVy9IYjBIs2+bU2hz3t4b4js5wlvHW7WOeudTmmWvrfPLT65w6fQ6vvoBueeXepbug6OVnLMpUdSEK5F9Ch/yvXT/RRcrmjR/SWDzFudPPYdoKiqKz83iTxcUlatU6mqpx6sx5Pv35L/Lyn/4hBlMMU/LktfPcvr1F3CvTdw+Ohmga1HydOJySRgqWZbFQ9xCqxmQyJooTDNMgL8pZ32AyxdAtnnnqKqqqs/1oE02H0+fOU2+0UBWFNM0wdAPbsTH1MgxNVRWkkCiaRhSXD7yg7JaULqUPspFPsPkoFBKyImc0GuK4Fn59hSzVSP2UNDxGJBlCt9jefowiDd7/4XfJkhlaEaPKokw6JCdNY4Ri4TkOtjpA2iqmaWBoAkMtToS7Krou0BRBJqFScbGUhDCICOIUoeiYlkGt1mRxcYmdB9fZ3tsjVR2k6bO0tMTt+/d55iPPEkxGBOMpUpiYpkIUKVQMCzfJGAcZk1lGywZNhTTJUVQFXdVKxLksXVGFEGiaQk34iK2AiTpA9dusn36COJsxeGuAgcJo8yGWKNhtB1g1Cyd3QDXoh2MW9iRhXmMwGNJQCyJvDhmmxFnC/d37rNYW8R2LNDjEac7jt2osSEG9Vad77z5nn32BeDbjeO8uwWGPZJLx9BNPcnH+DJ+uvECqQ+4YtJZcZFrQ9Dsk0QRhGQziGTvqkKPlAv14wMGDdxiZATfDh4SmhnWcs2u8z+ean+VfH/06qrjORf8SwjO5sXOdv6k+9SFRWfwFhoL24TxbUTVMy8U0HWqNRfrdHYIwxHVMsnhMnknE3i3GQUauFFhmgWmreC4sLM1Tq+TkYUA6iVEMFZnL8h4wTESWYFgumpkjchdFz5G5ieraJOMpumOgGhXiaYw/3yFR51Dtszh+CzQXUE9+dnHCQykX5UKaKHob9SQluBTSKifJ5AXT6QS/3sT2KlRrTabTMUJR2Lh4CdO0KOIA9cTFkuUSqWhoalEKHrOcw8EYkWWlbkfVUDUNXTdI0hjHNNBUHctUSFEBjcPhlKVKA21ug4P9CSJLOJ5kWI7LRz71KVxSLjz9DM3OPJ7noWk6aTAlS2M0zcCyHLIsAxTSNEZRFEbDPqIQ2LZNISz63WOSKKDeaGEaJmmaYJgmtuWTn2QcVdtNzl95gvlTq9x5aZHju99BVe5SEyOycJ/Nr/06m29/jyd++m9x8dkvUa/Pl0RdVcNxa9hrz+DWV7jz3gXu9f+IFfEm/t6/4nH/IbNuRJzoCHWNNJUsznm023O4+QwlCVEcpbS7Zjl5GhHu3Wd21KXIazTmOsRFCHnI4NFtKnWLqpEwuf87eM3vEaQao9lzZPVfYGHtSzy4+TZv/7tfY3a0i6E7GPYaqxef4+LT13Crlb9IWQUKq8K4mJDJnIZtUnMkwyCmWq3SdHKCcEqheLTbVY56I0bTAt8WzBKwLQvdstFEShAJ2vPLVCoVth5tIqTGn718h6ZfZ6kd01p7kntvv4nhVjh77Sq1WoM4nGFoCs8+scCp5Yusnr3GyulLWNUVVMUkTlJuvP0mN24+YHwc4ZjbXDy7xFE3w84UZjInjUL2hgmdhs3f+Jkv8Sdf/w7//hvvsD2OqVWrzLkK60tNlk/pNGsGypt9IhF9yPGSH5glREEYTNm88w5/8odfxatUWVg+h65NQTcxDJ1202MyzKlXbRabSZkVl0hEDJmqohUC33CwHRvthBnWrFR48rTF+jz0ehMc06Rd81ivJrQaDi1fcGp9nnqrDVIhziSTMCcIZ6RJxLu3jrn1eIpUTU4tNQk3uwRJTm+Y8cTTy7RXzmP6bVTTxrQcdNNGNytIyg6pehILY5gWjuv/lfb5n+gi5WDvCFu3sR2bxYVTjIdDwnjG8fEx8wvLqCfQK9e2cfOEWRBjWQ5379+j0awRS8l7NzdJ4gQFwXhWYOk6CtAbjtBUhSyNmEUprueS5jmgkhUSWQhWljvYtkWvN0SIAlNV2XlwG8syqdVqxGnJcXEdpxypxDGmYRKGIVmekWU5OQWKECBPMOaKgqKVI6APEP+aVrZkcyGxHBct22eu47D76IAknGA5VSwNbt25STAZMesfU/VVTM9GigShadiORVaYNKouadilQJYpsklGlGfo5FiaRqRCXqgEaEyjnLrloBVjkrxA1yTS1Kh7NufPXUCkM2aDfZI0ZZrljCZj5pfXuHPnNt/+1ve5euUsSXAHNc/wHY1+apCGMWqeUrfKdFxTtalbKmmqggJ5LlCkRDfKfBopJaIQ9II+lVaN7laffLOLW/hMmbA32qRVbyGnRUnxvFDHrrrogxw51BlHBwynMBoGFEGX3JjgugpLnQ6ucY7Dgx0OO5tcPf8Udr2D6XugauiqiaonuOvLKGlCtd7Gm+viL64Q9fdZ75zCtgw0Q2E86JENA5rnn2TafQAi4+7t98m9G4x1yfX6XaaNgOec51lYuoZ8eBdUg8v2aT5y5ePsHbzHRjzjr7U/g2VWaDkuezOFN4+/zd8QAlGID10vHyDQAZAnILSyPVG+pqq4fo3RZEKUqIjCoNI+Q611lf7uY2bJNlIBXZUomolIu8QzjdpcHV0VFInAdDMmhzqVVkyRWVgVENJFdTxmBzvoVswsyUjzCEuZoVkWtu8RRTpq7TS2v4ZmOEhFRQoFQY6myDJYUBpIJNpJSrkQgjzLydKULC9I05TBaHhCT9XL8Wiaoukmrudh6jq65UDuMmo36e3s4JkGSVbe55MgIopiihNwoqbrWJqBaZsn48yCJMtx7LLrYhgGFUtjba7JZDzkO6++x15/wLNPP8nWW2+x0Fnm8pNX6e88YnB8yKkLT6CoZXCioZvYlkOSJsymE+I4ApmVTKBKg2A2xrIteg/vkmc5puuh6xZ+rYFh2WRpUup1RJkvI2WpRzMsi1OXztJsNbn50jz33/xTgvBdbKtPUwsJD+7z9v/8T3j/m/+Bq1/8m5x96rM06vNoJzydam2eZz/xDwijv8Hk8Td5fPufUySbyLSG5p7CrK5wztLYvXOduc4yuqmS2m2ceh3PtVH9Bmkmie0FmhvLROOErdvvs7rgU6k6DHYOyIMpjq1g5S6DpEPl1H9B49JXmMQJ73z9N3n4na8j4gjHNpBGm+Vzz/HkZ16ksdhC1dS/sI5LIAxmqIrEM1XiNMfSDJbqGuMkZJJarJxaIxgP6XYHpEmGbTgkWU5WqFiy3Ey1k89lOp2iqRpBGBKFCkLkvHVjk/O/8PMkWUowO6BVsRgcH9Dd3kRTJbqa8cJHLzENwfHaWKaJpiukOTy4+R6j0Zij7ogsznjnQcZi55hPvbDA/mOHpZbBO4920dIZySTiW9+7TqXus4jDiuUx6M2YBCE7RzOSRKXfaBJEBQbxSSepfA6EEITBhJe//ce88cpLzM11eOFTX8J1TQ4fvcssFhRByETJ0CyPxcU5CnRG45C1jTpv3u4TS3nS5SvBolJKalWfS6sGl08phOGMdt3CV3Nq2S7XLqzgehYLc6VMQDctdE0lDzNUQ0PPC0xbMjfXxDmISNKUjz+1RhSEHHVnzIYjalUfvzFPpbWCbrhYTgXDKse4H3aHhCgDd09gg3+V6ye6SHHsCoU0yYqCKBxzvPuQV15+iY2LT3woRAqDCcG4i6IIBrlglqa8eWMbQ4OsEChCIIUkSXN0tczBKIQgSguyvCBOEkQhKKRkrt3G9Xx6vQGL8x06nTaD4QgdgappjKcxb732OhSiDFQzTSqeh+06JGmGVBQM3UBIiZqWEe5JkiAKWSrkFaXMHZGcnI6Uk40asqI4saNlTOMew3GJQX7xo89w1B8TZDFLFcnWMKUQ2UkSdEpq5GimgWFbzFXnCYY7HAd5yanQZak5QGIqKqBi6goChbhQ0AyD5dVVbGWfMAFUBV1VWGrX6cw3uXdjE82tkI0SCpHzeHuXjz1zkf3N2/zwxhZvvP4+zYZHxdXJckG1IigMDRHkTBKF6SymWa9wbaXBq4/GBEmOokh8zwIoRcVFjhSC/XCbhbkNxv0+ZAoPt98m03Tu9q/TnDRwTQPhC+yGhbJqki+r8Ciisq8hiy3y3pSZVJGOoBAu7aVnuGCf5e6dGxyOuqyMNjGFglNfRVM8pFYAM7Q0IZ71sL0mhYyZP3OGg+CA/v5DxNIiaZLTGzyif/M+FFC1q7RrTZ558dPshWM2+6+QKhFKJjka7PJm/G16ckoDnyfjM+iWwUGY8v7gq1zunKcSL/KdOy9xL+kTqSfYbF1HSvGjTsoHBclJp01VQJ404Mp7fsrO48ckaYpCQTTrMpkkTLMmcrpLOBPUfLCNjCQFr+5jOBUcTyMPp6iqi9upgcgIjmcIoZPnAtMGozWPYeUMd8fl6V+J0SttcplwuKMw3LzJ3Ma7rJ97Htupo+ggRRk/IWTZMczSjKLISbOMLMtI04QwDJkFU3qDh2xtXufy+S+RphGGYZCn5b/xPRdN06k22/QP91BNk9bSIo8PD0t3noA4TpmFEbqi4Hgelu3QaLbI05gA0HWNNE4QucBzHRQVnnzhBS49+TTf+He/x6x3zPriIksLLeTl8yxffZb5zhyOZbP5/tsgSxF8LiSFKJhNxyiqVgpoLRvDqpKmaam5EQWGaWNYDuPeNiJL8ZpNNF0nS0uLvaZpxHFMEM7Ifow4qygK1fkWz/21L9JaW+HGd77OcP9VLG2fqj2jGs+Y7F3nnV+9w/X53+bMZ36WJz72s8y11tGUMjy14rUx1z5N98EbTGdjNMvDW7yA2zyL4QYc3rWpNXzmNy7g1puEYUqWZPiVOpbt8FhXON49Ig5mxOGU4TjDUhdQZIKiQq5YHA8FVvUMzvLn2D94yDu/808ZP7yFo0py10ZaHeY2XuQjX/kyi+fX0TTjg1/wL6zlUZJTZCmtikmmKpjNNZKoR80IkJqGYS8wOpgw36kRhgGTmSTNS2vkLJS4lkqalxrALAnpHgdUHZ3uaIaqqHQHA44PNplfPM3GhWs4tkYw2qW9sIzn2HgOxKLOcBpTr9pYlsl0NGbr8R2+8+1v8+0/+SYilzRsh8K0eeu+wk99IuWBpvPObkiR6Rwdz2jZKb3xFL/W4OLzH8NzW1SSA8gm3HjjBls7U4zdCE/R8Bc5ISXHiEIQzMa88p0/4/d/97f4/Be/wsc/81MousHR3iYFFun4Ea6SITJBe36BSVBw6pRJfThFNwzmmh7RcQwncERVUTAMjfWOydPnTBSZAirtVoVnTtc5s+bhOBaXn7iIlDmm7ZJmBf3xFNvUTyjoBobT5nOfP8cnPh4yOd5i6dQaK4uLfPcPvsuZqoPfWGNh/WkcvwEnBdcHGXdZmqCq2omQ90R7I/7XQxV//PqJLlIUXUOmMZPhETfffpm9gy7d42OeeaFFURREUcDuozvs332PKEvpZZJQ5mgC4qzUlxRSUkiBYxsYmk5WCISQzILgBLYGlmnguQ6LC4scH/eoOhYLzRphEKBoGooCYZKimw6qqpFmKZZpY9s2luNgmBaKbiKKUqyU5TkiTTFMEyEKkiw74UsCqop+UgFLqXzojMiSgmmgEoVTikzF8BdYbHlUawVhf5+DicT3daoepKFOnqVIkaEpAhSdpbqPkh+yNc7pjosyRdYsyZQKGr0kJ1MFC50qpgjpT1M0w2Kx6aKPjtA0SSo05rwqi2tn6PUOOBoM6Q8n5ELgOxZ379zg0y8+w5VrT7O1d8z2ccjBUUJU82lUqzx32mYyGrC3UyBGIbGE5vI5PnK2zvS7b3D9wT55UWpzVE1FIafhG1Q9A/tKm8HyhIN8yOrtDpmYcjA7Jl+V9OIecpYTOgnqlo5dMdA6FeQTFVJnxGRLghcxGUIhFkgjjddefcCnVzeYa3Q47Hc53qtgaBpOrGNqHtVmjXvXXyULZkjbor52gYtXP8G430dTNA7v7rJ/d4fK/BpTITk4PMDZ3kJvXCAzZ/TUlNvhLZ4+9ymsuM2N6evcSfbZOt7GKEpL3/cG3yUsCjajAbWGwcP99zicfpfEyPEWLOYqLQpRFieqqqJ9cI/IH7EVxAecBVEWL4oicRwXlYIgDMq8oixHMVqYznmCYJ8w6VLTBFEC/YGK60UMix0M06RzykdBYdqfIIVGdcVFCBjuDqiJHMN1yCJJLiSzsYLuagx2UwxHEOcdXP8MyWSf7QdvYvkdXK+JZXsoJyTUNElI4ohCCJIkJokjsiwjz0ukfaexilwCz/Px/QpJEuNVqthFuYnphomqK7ieS7PZYtrvoWo6hlqQl6Kd0jWXZ9ieS2dujqrnEcQaRZ5BnuNZFuFsRr1aZZpENOc6KFLgWCrYJqeuPM2Fj32KJz5jEUYhXrWBkAp5lpXUZtsrtQ+9Q5xqE00rw0Ud12MyHjGbjIHyMwmDgHA6wfVd1i9dwzjJ2dH1cnQcRWF5oAKyLD1Z2JQPC1HDdTj3wlPMr69w/ZVLPL7+Z8wmN/ArAxaVmFMyYjS5yd5X77H9nX/Hxme+zNUX/zPqtTV0zcRwWsxf+BniOz9kdHxIS5ywTK6/iVoxMSptdvd2SB7cR6QCmUp022b5zCpxXiAUiefBqVNNdg62eevNl2mJhCBKmFuao7O0Suv8kxzc+CPu/cHvYI36zDVUAlkh0VdZvPD/Je/PYyzJ8vte7HPOiT3umvtSWftevU3P9Mz0DGcfakhREinaD9az8ShLgBeChKHFgCD98yTLECHAf/jZePrDtiwZkvVI0FpIkRI5XGZ61p7eu7qruvYtK/e8+4094hz/ETeru2chh5TxgIEPkKi8eSNv3rpx4sTvfH/f5fN85Kf/KgunT6OsH367EdTIXl6B53nMN2yOL1lsbhdEaYCDZrL/FvNtm8fbU7otxXy7YjJVeIstBoMcaQfovMQqMsoyI8nq+A9bKaSEcZZysH+V7twy86snCL2MM6cvIcqab9Fa3CDXCfPxhF4/5979x7z51u/zeH/AW6+9yt7BIVZVMpAWsoK+rtjZ63B8rcHLbz3gXNcjH1ukrst8K2D7YEDjwWOs7CaBLCgSwbGzz/DciwF3bj/g+q1tzjU6lGVJlmXEkyHf/trv8/X//O949uIpLjz9HHFeko4nTOKcSoNlMkSZEjYDJv0D5hoBZQmriysMJhXpO3s4nodtwLJtKAs2lj0+fsFFKUESl6S5Ji1tzl35OK65x+2HQ1aO5zSbLkVcUhQVaWY47E/RwiJNC7TOGU5KPL/F4qln8QLJ8x87SfRwxP7V73Fw9x3Wn/qZurXzATl1NaNG6KpESoUwos6Wkz/oEP8njZ/oIkVOhoQLc/R2t+jvH9BaWMBptmm1WwyHfb72B7/Dg1d+n0cPHvA4LhhmBqF03aPGUJoj+KmGpTR6ZrZW7/qUrCtSYeD42hqVNjRCn8VuG8t1MUVBuxEQxSluENKdXySOIvI8mzndutiuS5bnuK6Hsmx8P2Q0GteVJbMUZ6WwrLpylULi2nLmoWIoymqWZzJ7bHzmNj6CJUrS5ICdQYwJLQYPt4lizdZ+iq6ooV8MaVJwetWj40seHiqC5fN0yx3ydAKWwHbtOt4+HzOdxuhKc+7sCVJrDyNd5v2U9+5pZDiHUD1604iX33od15WURUaU5cx3fYQVMukfcOvWHT568RSf3bvAf/z2LYxQLC7MkcVT3nlYsDbfxZl3GBU9dCbYGfQolz7Jz/xPzsNv/yY37z6mLCssy6IUdbEipcSuYOjuYz/dJr07IRnEJN2S+S9cQCvQdxPWej5JMCXtjTF7Bhm6KDXPvVHMse457l19g/7VQxrNJk+fPIuybBqWYicZkKSGwrUYbD9CFjlnGh+ne/wK9++9zoOdxyRXb/Df/C/P0zISb36Nx4nLeHrIzvZD3PkFli4/hesusrN9C7vbZGJBeGKZ3DU0qwbzapXHzn323JjVkaEoM+5EU1pWiO84PDzsMSin+EsOS2cC5lbadJjjO2+9Rel3OLW2QujVGVFyRswEalNAXdUUFV2z+n3PZ211jSRJmVY5RrlYDoTtDSz7S+Sjmxzuv0noG5qhZn8vZXFJ0bIcHr49xvYMwiqwrBZZkpGNxnWvfGrQw5ygM0dWGJQbkJUtvNDDW36BhaVfwGtdpCwqkBZCWkjpIJWNVAopJL7nI7pz71v9wwcMDOv25r3bNxmNJriu94TnkUxHHGw9ZPXUeWzlYJIpi8dPsbe3S7PdQtgZZZziugXzCwsMDg6IhkOGjSaWBFsKHCnBtgg8l9APCNtddGyxceIUnjKMh2OWOl0ufvQFWgtLBGGTeWNq8vzCIksL3dp5VVdkWYZ0Aoo8p1IVSlmMhn2mkyEYw/zSOuPxkPHeFlIKmotrOL7PZDjEVBXTUQ9puTSaTaajEWWV15lfR+PoszEGISXttSVe/Cs/z6mnn+H2t/6A3Xtfx4iHzKkBK07EfJYymdxm9Lv/V65e+w1Ofu4XWXv2l7DsJgsnPwbCJcq+R1paJIMD5loe23cf8zu//utkowlnL57mytOnCdptppOE+y+/xs5OD6vj4vkV8/MdGgvz2PkOKkrxhMfqsTMcO9Yjev2/Q9x/j1Otgp7bZFzOEwbPcumjf5lzn/gM4cLyE1fhD/3fZsMADUdQuoLdXoIzzimKexxbVQRuyWicUKgmaA9bpWB1iIoULTXtueMU5TZxnJCmOa7j4FgwiVKMrrl90gjiNONwcMDWt17mcz8FauM0w3HB/Nwclu8zOdxBK8XjwyGvvfo2b1y9ybtXr5NmmjjN8SVklUaYgjI2NKRgGic8fWURXJ/r72yTyAZN2yHK6/b24f17XDzZ5NAsMNecsLl/wMt3C0SesGRXqDKmKHLSZMob3/ljvv37/55L505w8eNfYZKUbG6/x8HBPqPhAKWntEXFRqM24IyzmLnlNpYTIJSH37RYXu4Rb49rYzWv4uSizaUzLYxQZAUc9HOStCAImyipcL0FJqMpu4cJhrqLoJGUxscJGqSFwvbqlpolXLAs4lQiTIXnwPN/8Rf46nvvYnBrU8ZKH53gmVO1heN4T9q632/3/+OOn+giRSsHd34Bkpz1E6cgbJGXoKTFb/7r/wdf++pvI6nNag6SWjrrWBIhzOxDnZmpYWrlzozUaoxAGImSNQeg4XlsHDvO/c1HdBo+i3NtCq1xZc3psF2P9mIXgMGgz2g8Js09bNtBUMtGdVVhuw7aGBzbonJckiStHVVtC200ZVn367TRlJWeFTCSbLabRihcv43rN6GMUdJnHEGWTJmfazLoHxI4Bd2lE8TjAWmec+LUKc41HvLq3QH9yEbYAzzfRkgfZbs1coOm2TQzy3JJbzBkmkparRa6Knh3y1AC6+sruEHIZNjn8HBCaWyk8nAthWXlVAquXX2dn/nMX+fSRofXWh77iaGoDGFQZzrsRRqkx1gGWKHDaDLl3//H3+Hy5ct84Su/yJlHD3nv2lVGwz7RNKojAowiudcnWyko/IrB3JTDew/xnlsnbIASNuVpg+m6LI0XGAQ9xp0RlS5plPMsrQne+Oa7fOzkc2y7W2RCcMZdZP/eO1R5H6vM2U/2eW7t84TzTXYf38QXFo3OAqOV02xuHXDq3NPsPdqm4Uv8ZkBp99gZDQlCn/MbJ1k6/zy5MNy8c4M7997g9JWP8LXxd/HjOW7svU0+quj1+5goZ611kmqcEHgOg2rC3nREbjIaaxaLlxXtOR/l2JRpyauv/Vu+9c7vc+b08zx/6aOcO32etYUFQs/FVrW0vc72EBhZp9h6SnHpmY+xvLrG/Xu3OOz1KIoCzz/JfPcTPLj+NfZVTJHeYpxq9AAmk4pmI6LTsEkPM4KWwrYnSBGSTirS3CB6Jb5fMk1G9A/ACVzaQUAiL3Hl7K/i+OsYLCy3Vg4dLVa1aqzm0khZ+1oIUcPRRzesI0v1+oGkqjR5nqOUhWXZoMF3XRzHYnq4TbiwSqnhxIVLBK0u248e886715C2S1UZyiwjG4+YHu7T9F06C/M4ooWuKhqBjxs0qXTJ5774RVaPb2AMvPjlr+A5DsdOnapJ8nmKPcsZScY9Ns6eoxrtk3cXa0sB38eybGzbJstShLAIgga241JWJdPxENv1aHS6BH7AoN9jcHBA7/F91k6eQVkO0aBHEkcEzRDXD//E9c5ybdYunGPpxHEO732BB69/jZ3N7+Jzm1ZjyMpCRFOOcFQP/c3/I6ODfwv+HM7G5+ke/1/wkUaH22+/RJwaOt0O07mc1twK8XSEI0tu37wFVYHj+TQ6Szz14keQvmL7/lVKSoIwYHH1DNVBysbpp1lZ20bt/Guy/Yj2BvTjRcr8eVaPfZnOU3+B1sbJJ5/fk/HDctaAZugxmXiYNAYjGUcWdx5mrKy0mJ/XJKViMh7TbivSrJaaLy60ONi7zXCkQbmsrC4wODxgOMui8WybOM3RBuIc9saSzb2HGJ3y6U+UnHnqWcbTgjgpsf0Og16f/9e/+vc82Nxme2cPVRQ0jKay7JpfJWtzydDz6YYe3746JpY3+OLHOqCP8dubW9iqwGsG7A0crizkSNFh3ivZGija6TYqMgQqpe1X2FoTjXrsPbrB3df+kAvnTrBw+nke7fbZ3nmXw4MD+r0ek/EEKTRn10KWLwXEhaTZCvE9Gy08lNdgqz8lL+HUaov+cMKlEw0unWzR7HQYjFP2+mMGiUKqkHnfpzCalY0zZI/3ySqXQjRotBfxGouAhZA2ygkwCLI0r2X/jkvY6uC7Ho4lMNKlvbjI4b17FOkUN2gDtU1BnU9XPglQfKJQnKHCf5bxE12krF66wPr6KZrNNmXg0xtlpNMRV998mde+/YfEpmSclKTZUfKwwLIFCF3nypQGo2tJsbQkrl23ZISoasa1qbML2o2Qyhgun15nbb5JkmumSUpaWnhSYYdtEJLe4X6NpBQlWTbGbzTQQuBYNnme4QchZVX36sojT5RK1/16LVCSJ74q2jCzZxY4tlOTHR0bKQWBp5iMUqJkXEuipQeOR+g3kJVFMjnEC5sYk7Hu7vB4ZNGPauSmyDKE0GhT4tg+QtiYIsN2fVa6SyjbwxQRUTZmfeM4wvYojcJ1fWzXo9VpI0WFsENs28VxXCgjAk9QFlP6B3sc3PsuSenyySurfOPmgHanTW//IXGaUJlaJlgZEMbgeg5FFvHyt1+i013kM1/4Gc5dfJpvv/RVrr/zFpWROL5HWHjoW5rRhSHynEaJOeSzHqWK6mLTNsTdIRN7QHOryWKyQrgxh9twEZdv8/h6wJLf5sr5DbSWTIY7zK9dwJu/QJKM8BsueRXRUau4skn+aJfYThkebvLeg3d5/c7bpNIwt9xksLuDlYV4jkN3cQXbSPLeAaLVZJz2eebYJc5f+Szfeu0af/zwDxn3xuRJgUSw0phDZxV7csywiOv5YBmcpqRxqsJvekjlIFB1gSanDOLHvHXtAe/d/g80Gy0W55Y5ffxjnN14lrXlkyx05mmFwRM/HiFrtcfS2hnmFleJxzukqaHX28FiwvOf+CLXHJvN+x6D3XvEecbSnE88GDONEqoC5EADAXOLJWVhiBOD6xni1CYbZzQ6q4Trz9Na/wQqbeG1zs2Qu4gkiWvYOBqikyHt9adwHAelJEZZMwKwRj8pUOSMGDyz+p9JMMuyBGO4+94bFHGMKPOZWk3Rnl/GCIHr+3QWV6m05pvf/CYawXA0pdvp0CtyqHKqLOHU+Qu0GwEHe7uMh2OiJObsudN05tpINNJyCGyLtTNnKcsSx3EptYaiQBuN25ijwmLvzZdoHLtAlmcgJFmWEQQ+juOQZRlho1WbNypFd34Rx3EZNwbE0ZS432cy6NGcW6AoSrQxJGmMsmrviTj/IRH2H7ypm1oDYnsuq5cvsXL+HNODv8rh7av0Hr7MtH8NJ3hA2BnTaG+jx9fR2hCMv0t27wap/AWsfBclFkjiEc31E1x/+w1CCmyvy/feukc8HvLU5bMsZC77w2tceP4ywcJx9g/3YTBENWwaocKafA8xfQkrLAguBew8PMPS0/87ls99Hqu1hLBmROXv/z/8sCEEymvTbVWQxiAMi54g6Cii6ZTJ2GDcNlKnzC2sYEcRaX/Kzn7J008/xePH95lMYlqNkHQyIMs1nquJ49o/R1o2jjK8/l5t9Pj2A0Fr4R6d5eO0GjbLqxvEhSY3Pndv3WSv1yNKKspKUlYGaQpC16LTbJHlBWWRszUua05MOeSZjz3Lyctr7D94QDQZkyQTDILU2Ax7Oxw/fYyCEWk2piFDvNIwiArOOQl3336JzXs36C4tkzvzPNrtk+U50/GILInRRY6uSuIsY+dQ8PDQp9iOCcOKS+ea5EaTZwe8d++Q0NGEnkUnaCCUxbTwcUyDXHgE3Tmaix6eH8B0G60aLK2fQzgLBJ5hbvUEnaXTNOeWcb0GasadzNKMaDygd7DLeHDIdJrQ7C7QmVvCd31OfupLbL/9Ncb7DwnaK0hV25BKKWYbEvEhBEU/sVj/8cdPdJGyfuoEfkvhLHSgsnl8/T6vfPclRv19rFaTZDQmycsnH5BlCywbtIYy1RR5DU+5roXtKGxHUWQapWeerwZcO+DsyVUutBIuPnWeuaUVXnvrFuM4J81SXL9Brz9ke+sRnucyHI3JspwkzVj2A4aDAUtLy+ztH7C0VCt4pGWTZWOgZjyWM9dBqM9fWZY0A59RVKcuvx80JoinPR4/eGfGlXFAaDyrwc6d+8SJoLXyNKrIKNIJF467OLLH7YcgVIUsMyxhoSzB0sZpjFYIIxFSooUhzzNG4xHpNELZPmtrK0TZECPq4k5KgxPYtE0X181B1GnFRkvGccXBICZNMr751i1Cb55Pf+Q8Pf2Azd0evm0wUiEsjzgusdOCoiyIppqqKjHaEE0n/MHv/Taf/txX+MwX/gqeF7D16A5SlQzH+4RFk6pToCoHb61FZaDUGiMN2tIICbmIGJ80zO13ae0FdOZaXHS+hHcC4vGEjfWnCJtLlGnOZDBExxXCeCwF61gqJxocEu8esrd1n6kTsVkmxNKQySl/fOu3eLr7abZW7rJ4Z56//tz/llhPUU5Olhke3XubRtDEd5foD/dxhoKfWnye0+cu8a9u/WuyKmONBaI8Ie4U2JbCd1y80MdpFrhtjWM1saWLEeBZDUSgWFCCcRpRlhOm6V3irYzrj34bpwhwy3ma7kWunP80n/3Mlzl7+iSe76NUjV5YyiVoLuIGGilLqrKD4zV46mMtzlx6kQe336TSOSdOPcV7L/1f2Hr0DsoyKNuh3e3wcPMReQJ2u0nLXyKODGG4jL/6adaf/Su0F86hLBcpFVJaBA0Hy/aZjgcc9Hcoi5xUbeK4Hp4f4nkBlmWjZm28owRkMfMuFaJ22qyqss7kGQ6wLQvbhubKCfbu32X5zAXyPEFZFrbj0J2f5/jps4zHUzxbIKoSW1SsrS4znYw5deYsH3/x49y9fZfxOCbJC46dOMH5p5/CdW2CRoeyyOntPGbt3FmkMDMU08KybXSWUlYV08kYLSTpdERWGqbDHp3FZXIlsG2XsiywHYeyyoimE4ypSKKI0aBPNB3VZHrXwWs0oSqZX94gjadgTB1tT/Snrnkfku7aNs3VVRorq2y8+AWqeEo+PaAcPyaL3kCH/x886x5GJ5BeY/wYimyFuw/v8u79fa5dfYBjK37+F36ek8vHGMcF5XhKo9VibnUB44bcvHMTrxGwfdAjGR/SkYL11hDXn5L3FHl8Bd38LCuf/wW8Y88hbff9wFf+9PrkaES5YX80ZS5UDDPD5mHOivQ4dtzDthL60ylGlxwe9BDkLC84RFHO9WvXanPMsuC96zexLEFZHmVI1zEbl57/GNFgn9H+faBkZ7/P733tW+wfDPjLf+nncO0dZGOe7333W2TxCCEUhdb4liYyNUfQkZo0TbCVW/N0bAtXJEx7MXsH87gNydmnN/juS++RlZIz8wpbp9jFkPu3DQ1p2Okpnj8L6wurvPPWHuOJYL21xPmPbpBXGiEd0jRnNBlz4Lq1k2wcYVuKslQcDmO+c7WgE9qMkhG3NlM2VueosghpCjwlwFgUWmIbxf7EsHR8kYVWm0arQ3thDccyvPfNf0tndYPzH/kil2wPYzSuF+J4PgiB4Cj002A5AX7YotFdYjTocbC7xeDwgGg8Znn9BMc+8iKPXv8j4p3rmDPPIyz7SVFyhJhUs425ZVk1ovLjTorZ+IkuUsbZG7Tl87S9Bndu3OPG9bfo9XZJi5wkz4nTWgKMqAOIXbc2KquqmutRzixElJJYtkIo0NQ3PVsqRKUIvIBhUnH8qfNYZsC9O0Neff1NCruNE3pYqsX+4R57+3vMt9uMx+OZsRNMRkMQcHCwz2Q6pdVqYikLYQxZmtaSOV1LJWsrcFmHANoOeaWxbftJAJvRs7ZREeFhsXLsKRyvyeDwIXub1znoT5COz6ml4wyHh2zMuaw5d/itbx2wO9QErs3S0gpBaw4lC5q+RzB3kclwG9evU4Yng12m05hKerhKcPbkCreu7xE0F6mKCbbvs7d9wMraCkpotJZgWaTxlPFwjOt6RJMxf/yd+7Rbh4S+x8pCi61BTjFJ0FmGZ1lorXFcD1NooIYCS5PX0k3H4ZXv/BHnr7zAlec+izCafm+LuIpIJiPuvfWY+XKBcr5EYRHOu0hXYauAM/bTWA2LyBnRDbosxR1W0lV0BoutZWSwREtp8ukB2vJZPrGGwmX33g38XBJ4kvRwk2Cxy+P9G4yiCVvxIWvrJ9jiFtVGyWN9DXvT5rOrL+JbLgdbW8wvL7D67Dm2Xh4yvHuTat6mp6e82L3AhVPPM7A1bztvsFA0uRCfoXNsmdfda9wrbtfmS6Zkmu3huy08p0ElcjzVRGeAkiw0GsyHGf3plKKoEEbgC0Hb7fLclZ/hU5/8Rc6df4pGo4WY7V6e9H6lwnLbWBhcv0NV1mTVxfXaq6A5v0HQnMP1Guw9usmtW3fx7BybADFNyFJBJWx+6ov/iGOnniUvcuaXz+CHHaSyZ/4+ckbyrj1bXD/EcX2CZmeW5FrUIYKm5nxlaVTn9sx4KTUcbD8JwCzLelFLoymDrQeUUZ+F1Q3Sw13Wz1ygMb9UE4qr6gmU3Gg1WV1Z4uG9+7TbLc6cPcvTz1xkaXWdzuIaX/v9/wRFRl7krK6vcfHiBfI4otM9TlnmjHuHOJaLYzsoy0FKRanrQlrriiJPGew+Jk8iHt94B6vZJWyEuH6A43qkaYIxRwuyxnEdyqpEKAu/0UBIQZYXIBS7Dx+ysLxM/2APx6mDF91Gg2I4+LHXvvr0HoXSgWU7WO053PYcrF/A6M+R7P8s+aOvEvffIS5SppOEO9uHvHttizeu3mS8NyZsNxlMJnz2Cxd54QtfmsV/lDy6+TbffPt1/virv0e33aWzvMbh5g3s2GfxhQ5ptkC49pdwTv4i0m/XZn3UXD5m9u4/NqhvDLJKCYoEy5bMh5L+JGPvQDNMc7otweJKlzyb0G7apLpNozXH7v5tbDumHdoMxwYlBFlpqKrafMS2IS3g7juv4tqCJAPPdWiFdb7b2zffY3t3k6988ixpNuUPv3uHKK/wlKbtQ5waRFWRV5CiiIqMeStHSItJoWko0FbA9772PVZObPB4e8rusCS0JHnuYlkNJmlBhmTBNpgK7m+m3B+WyM4aq6shz3zii3V8RVnQPzxgMhkzGo9phA1sy0IJgZJ7SDRJWjGJMqIooTICqQv6vSGdts9c02VpsUslXdp+QNjssLx6nNbcHLbr44YdwkaIZ9cZa+25RVrzq7UtgJh5dwFlVSGlmale9Wx+CTwvwFv16czN09vfY/v+LfYe3iL0FQunLrK3ucexNMZywicOukdIylGrR2v9o+fAnzB+oouUsH0St7FMbzTA80OOnzhBnI3Y3N5kZ3OPoqyeqGYcV+G4itrRVHNkeictie0oLAuE1DiOhSkrZAZKOegq53B/n//7b7yE6xqWV0OitoPnVqysrPPuW3e5deM9lGWxP0trrap6B6WEoDvXZRLFdUaJgcl0+kQB5Dk2krrCPALAqkqT5UXNM8BQFgVaGwSmjpCfX+HipY9juU2QVh1JH0VMJhHjyZTe4WO6DZ+TwQFvPpA82M8xQjG3skFj+SyW0Fg6JRrtU1abGCkotSaPY4xWdDotJuNDPN+jGVr0evtE0xG+o5lOh5RFyf7eLmWuEUqSFxVJkpAXJZVOkbZNYQSjuOT2fsILZ9sc9oYkucJoh3F/ghTWDO5X9c2ryqnKkjhOyfMcz3W5efV7WAguPfMpbl97maKqyEWK3jcIBXcOdinuappzHt1jAe3lgFud18AxeLTwrTYPrC3oSeLNfVRl0V5YZjLaJYtHTEZTTl7+KW48vEo+HNFpt3DDZSa9A3YO7xGriirs4MuUzEkJr7S4Mn+ZT3Q/xzvvfZtgHw7ze5hoTD5wGTzcomPN01Owt3cPv1ggy1N6wSbu6iqf2DzPcY6xunyc5vo6XrnEqBiSiYTBeBvLcrBtH23qG3SeJZg8JalKdDnBpkSakkoI5oIFXrjw1/jy5/8m62snse06KffoxvWhDBDxPg6HAMsJsJyAMk8pq4K5pQ1AoauCgoDMbqGCAYYcKTS56NDsnufCR3+RdndhJiGsVWEIkErW8mhdc77k7G8KKfGDBsaAq6sP5ZPoqqKcISVHLQxtDFVZUOR5LUmeTNgaDVC6ZGHpGMmj2yw//1m89tzsteqed61yUgRhk5PrS8RRzGQ0RlJx9+4jth7vcXDwBxz2B1w+f5zTZ1ZpNduUeURrbgGBZjrocbizTWOuy9rJs9i2TZrGlGWFlLWjr227NLoLJCZntHmPk6cvYjvurD1cf8a247C/t0On08UPmxhjGI8G7Dx+SKs9R5amZFGELgtMnhEbSHWGtCyCwIM/zYnzA6qf91PfZ+f2+79VFv7KZfzli1RFhrf1OgdXv8W71/+Yd965QzRNsJwKUUbcvnGVr6+v0G7McWHjLMvHj3H90SP+02/8Dxzs77MtJBsXrtBZWGWkQ4ZijdNLx3GOfQ7CDho5e1/mh7+XH2O4VcqgNEg8QttiPpQkVsDqssL3p8TTA4TWjBIfbVLSaY+GD9rY9MYaXcl6PRQpzUBiO7C21OLuowl5UVFWimZgEKZgnFS0GzZho8le/5C3bt5ksVnQn+Z1JISGaSywlE3Dt9BlyZxvEdiasqpYbNpEkzot+d5uRBnfZ/DKAy4+dZmPP3+K7a1NppHh42fncH345rUJwzzi3ErB6kIJepurWw2idI0gCAgCD4NPEIbkeU6WJiRJwqUrT7G/u83Du7e4d/cuk/GIPEupdIUlJZZt0wgD2s2Ak2fOcOLkaZqtDu25BWy7JqsrJcmiMaU2NUcEsKuYQGazolp/aF5JOUPujZ7J6PWTc2kMBGELZyOg1elysHmPqPeI7tlLbN25zfHNB6xf7KCUXQeVfiAst56+/39InHX8Y+yNE0SRMxkPKImoEOwd9kizEqj5JpYlCRs2jivqnJiixGieMJA936bdCGhZIWVWMOxPKC1JVpbYCNIsw224PP3J55nqHm4l6bpL3L15yM2b71EUOaaoo63dypBkI5phA9t1CAOfJMlotJokScpgOMRzbSwBtpT04uSJgsiI+gQeoSpHDp0YU6cpo2vUw3LJ8gLLcbD9OVbPfJzm/DLvfOd3OXxwlcvPdNiZttibSs6efYZBfwdLaoQuKIspuhwSNn2iqIe0XabTAVVZYrs+8XSEJaHbaWFVPdIsAakZpQohFZVWjA5ibNvFmIJ8BoX7rsCxXPKinvQVhq2dfX7uMxdZab/DvX2DsFyKQpPnRZ0IrXOEKTFVVccH2AGWZWFZCl1lbN5/m2YrYGX1JEn0CG/s8xHnLLGVQa6p4or+ZMJwe4rlC+Y3miydadFsF9z2X0XhYluGg+wujt9goXmOzUfXkL6gsbJCKwz49Me/wvXrL7M5fMhQpGS9LZbnlrl49goH0Rh/v8vro29i+4JPis9xxr5ME5e7e98mzoZkUUZaRETJkL3Dfcq0IIoyRukYX/isfvo0cmOBxvVlHu3fpZ+UVK/c5sHGNmZJQqlJiwhXhWiTo3WOpQTCErheh/1+xuPBgIanUeR0vFV+4cv/Bz7/4l/Cdd36wtemDqacORbX+TcfRlUFR0ZwtQGc5bgorbCVJE0isiJnmmTMH7tASW1klST3SZIpfvM8fqNdv7awQFZUerZTmpHLlawXtLIsn6A5UsiZGaGqGf5V7ZWglIVl7CcGVlrrmby6PtZ1PfJoxMLCAg3PZfTgOsc/9RVsv0VVlTPirSDPs5oThcB2PX7+l/4GF999m4O9Q6IowpY1r2dxscNzLzyPNBWtTpuFlWOk0QgvbNYxFFXMo5vXOffM87UlQZZSVXr2mdlE4wG222Bh9Rh6eY10MMSkCeHcwkx9ppDKkMQRVVWQpilpkmA7LtF0ijGG3t4WQbONcuxaabK/S3dpFcuxWDt+mu7iKoXe+rHWvR9c6M2HvjVP6lJZo1yqQi09xSh/j+HhlPEwJytqbySh4eGNG1x/6zqNxSU+/fnPkRQZ3/m9/0w16uMLQVEVbN17hHCDOl09W8Bd/zzaX60jE2Rt0CfMjPw84xdhxI9VqBhgZ1oQuBZVBf1UUFYBQmsOBpIloVhYaOBaoHopRWWTppDkmkqnzDUD4iRjPK2Y71gMxoKyrLj9aILRgtAVTOKKVtgizVPWFkOMKTA6w/Wa3N9OGAUKYQpavk8c52hj8MIOEkGZJ0wqjbIcqnyKlBbLLhjL4SDWbBoIPZfHD+4xv3GaSeXjMeGjn36Ou3d3aOS3mBMSics0M+wdCiaVwbPqDCvLcWbFukFbEqvRpN2dY2FxidW1Vc6ev8CL0yn5TLKfZ2ndKg4CWu0O3fk5/LBZ+6I4LkrNCgQMeZpStrr0DvYZHu6RO5LcuATzJ2o1qfygQaSZXeMSreugTCEER807ObMRUMqi2Z7HD5sk0xO4YZdx75Ab3/x9nLDN3MoGnh+8zzE7mo4fKFb+LOMnukjJc5t2p4Pv+kzjHaZRj0kcE8UxCDFbQARh06bZdDAa4iijKusVXEpBGDistec51VikUXncGNwnywqmWUUz9Ciqkqc++gw/9eUX0J7hxr0Jc+4ce7cnvPvWG1RFiTqKmFeKKI5oNkIcx8ZSitF4WnsoeC6b29ukWc5kUqGkqKvmooBC4Lr2LChOoCsDRtdfUC/0swyXqiiJpjFSKoqqdmOtqhJkyPH1Y7TVIxy7wePqOCvHKqQoeXy/wvJgZW2e+zd28GyL4WTC4d4BpTYY6ZFEI0BSVjnNVpOGpxlvv45yAtpti3EiyfLa3K4saqWSklClKZbt0OnOcbC/RVVpXDcEWcsAbz044KNnl9nrbxJXCtuWlIUmK+rdtKxqialQLmWVo5TNyrET3Lr+Fmk0wlGCy89+hlvjPsM84oqzhlcIljuSvWlJNKoo0ooqlWyPD9i/P6Qx77B4qsniRptHgUNwtoEfLjKp+lSna/THTzP6W3eoskdUsaYIAvbtQ04uLLHRXUNZNn3bRts2XWcVdatil4d0Fh2m+ZAqT+kdbjFKYsLkgPlojbScsHz8JMnuPlcfvMXzZz5O73Cf/LDHveEdkqDHUN3HLCrEYgOMYTw+IC8ThAEtLdrNeWwnpDQgKoui0EzSiiKCphtw/rm/yWde/Ms4jlPzOMxsYfkguC6OwiLFB+S9dSHzpEWAAGmjHBtPuty6+W12e2Ms/zSYuviRpoEnx5TGpyjKWmVjjjKD6oJEzvKemC1IlmXNTJtMLek3Nd9EzK7HLMlmCIV6ou452r0dyRi11rQ6LbLBLoUxnPzUz+KFbbIsodIVtu0iBNi2Q1WVFHmK43o0u4t89DNfwrYcsjxjOByii5R4MsF2XeLJFMdzqMocDJRVSZmmdOYX8VyPuZVVxoMeVZETtrpYjjODq220LkiSjPFggL+wwvVv/RFXvvRzuEFI2Kh9T3zfp9U6RZalKGWRJAnDgx2WVo4xGQ3wwwaHewcU0ZS5Y+fxQh8lBX6jSZbG5Nmfzkn54PiRe1Lz/nMGQ4Wg0IIbt+9TZBqJAlNSak0lcvL+LmG4iImnfOM//x5SgFNWZKa+RovCYBgy3tkibHZ4uNNjHBd024A2aGFmVbDhyS3NmHrT9WMVKgbHVAgpscIW0SSizBLm59s4KmVrM+fxzhDf1xjlUJUJUhj8wCVsBOz1QBcVpYZpZOFYJUJYeI6hNyopBLSbNnkxRqIoSsFkmtH0Ciwp2djYYKEVsjt4jzwvWPJcWn7IMM2YTCIaNijh0LEhCRqMc4GxfWJdv+7hYIhLRmgZhveucXKpSVqNeOnf/2eSymG9kRLrgGHR4cbNHs+cUyzEJZNM1FwtL6wdjB0Pp6qzbbSuLTAsJQiCgKrTwbJtKl3hen7tTOw4NZKpazPRsshrbuPsmsJoHNfF8byZT0nF/oNrjAdjbL+FkFZtxz8zD62B2Np7SRtDWWYoaSOVVf++kE/OrRACy3ZpdZexLnkII5kcbFNmaX3uj86sMT+ApvxZx090kbK6tEGrFZLEE3RpSFLD7sEOldHYtsQY8BxFt+PheRZxVJLnBq3rJT3wHdZb8zzbOsmCbDNIeoymI9K8rNGXVsgLn3uRL/3CxziYbnL95i1Oz/pvd289JJ5GeK5be6rMCEHatnFdr+YCKBslayvueDrmsNdneX6O3YMejmPP4O7anfapMyco4gF3dvpUQO3NVZu6WUohlcSybPr7Dwk8t55SwsZUMUa6ML7LunuDeweGd3enhJ0xju+wsr7I/uYNstzmzo1bkEcIr4vRBmvmqpiXGXle0Ap9FpZWufDUM6x6hzQbKZef7TCNCt59d5ftnQMsaaEch7IoCJtNPN/DUob+oE+clji2QzNwSKs6j+Lb37vBV54J+cuXJe/uaq5uFXieS5SMKCuDLiuqIkcqNcs4KTnYvc/KvI8whirrs7t1g5XGAn1/yuvlJpftdU47l7CX7nLoDxj1au8bY1noqmLwcMpkN2G0m5CsV1xxnqcqKzI35eD4PnkxJehd5L333iV+MCaZThikIxofOcGptc8wylLKYsJOekB/esjhwT4rnQX6xSM2xyVFULEdHxJNx2RtSSeqL9LzJ5+ne/Isk7kxb+xepeU32Xp8j8fFHlsXD9CrFsIK6t5vGRPFfQod0W11EJaNkgHKamK0RIpZ8VEaykqTeza2G9KZvzhDJnhSoBzl3iDqIqTexcJR6+dJWXK0PhwxGg0URcF777zJd7/1EkUpQIQzfoFB2Qto7aArwd7eDidOnKx3WVX9eQtRS+sNctZ/Zka8kzOkTFOVKVK5TwoVz/MYDvqMRiPM0aKnDcwMDAUwHA3J4wlLx86wdOI8ZVnUPgtSoEzdWtC65mEYo8kzQ1mWWI5HWWbkM7t5y7IpqhLHb3Cwu4k0IAkpJTQ7HcL2HFIIqnjKUqfx5DPN8ww7jai94QSW4+J5PhqB6/pk7RZFGvHg7Vc5+dwLNJrtmcN0vYs11H87TTM6C8vsPt5kZ/Mhpy9eQWI4dvI4T33802RZTpEnNJodhFIcHu7//3iFrPkh2kAUT9jZPqAsEvwA4qKkAnItURbkVUZ8GIEGYTSWbaFsh6rM0Ch0BVGakkRTBqMROzs7hC0bx1+cTSdVIzgzNG82+zBCIGa8wPfHh29UAsFKyyUWBjsEVcBINZDK4uR8h518zLTKmet0CBs2UZQwiSuEdNnemyAp6bRDXCfD9iyE8BiNUlANlhcyyjwhSuv503BtXCumsRRQZAWF8dhYP0a34aDFQ2wZcbufoGSBkjYIw7OnlnmwO2WSFwhhkScJqaPqjYWSdJtN2taU0mjmPc3hzj6WEIwYsej63Bk2CDzNWNl4bkB/N2djveT0mYiiKN5Hxkx9E7cclzyJ6zVgdl05QTCbn3VwpZxJ+6uqVoCWRU6RZUgpMTNDUiFrUYSyHJqdAC9oko56WNpgTO3Oq1S9Ztbt26NNT11cKmUjZL0BP0Jm5ROjUfNEvRWELU4+80mKonaXtV33SWv3+9GUP8/4iS5SGkEH3wvY3XrA1tYmtx/cZTgaYNmqXvQ0NYrScmu3y6KgLDWua7G0NMfJxSUudtaZU03i0ZidyV2EydAYNo6f4Of+65/l7LNrbEdvsbl1yMn1pxkOD9m51+dwfx9jDFlRt2Zs20ZKSeB5lJWm2/LJ8zq+XeuS7d1DbMsiLwsc28G2JKHnkmWSSVJw69FjXNuiqAyOlJQCLKVQsoZsHdfFcRzi8QE3Xn+AljYaRdfVrLZKjE54Y+SSii7DQY84B9dziMfbCFXRaq+QlxZlKlFeSNhx0CRsnH+G733zJTZOnsERGX6zgxSGBeuAWC6hbIligDBjAk9ipjE5Hl7QxJiSpZUlxqMhjtsgbLbQFdgmIy8zpLbo98b88dsZP/P8Gl84tca9//htHu32yPMKIRRSWTOEKKcqC7JkyrGVJRZaNtEoRhuLMhvid9b4yOJlbkQPuBZv81x6kkuNj3InvIcTPMYPBc1WQGuuW/OKRI7REtezGHOXpeln8VsurWaGtD3E+pSFlWO899r3yN5MyCtDu1xhezzhwXSTxY01JqXGCRqc23iOj65d4unl53l87S02d95ltxxy+fIVLj39Ildf+WOyOOfR4wfkqWE0HbKomzw6eMAwn7B/NsWsKowpydOErJyALNFWheP4BO4Cur7Vg9BoKpSw6oJQgMol7eUmy+05dreusTP+IiuhVatP1BFRlllhUpMXRf0tH3YgEbNdtiHPMh4/3uTdN1/l3u13ycsMOMrTKZHCwggHqZoIYfPO1ddYXV2vERwpaihYSuB9nokQ4sliJ2bFkzHOh9oTdbZMh+3Na/zhH/wnPFdRWU0mlcGyCgyKKtd8/mNfIJxfIooiHMee7dIl0lJ1AaIsirJ2qnVdryZtxzEYgefXvX3f98iTiHH/kGg4wLYVZTKhu7JBkqS0FxzCIKSyLFZPnSKJxjS78yyvnyAvckxV4Ti1ckmbCiEsqqqks7BM2Gxz9Q9+m8nmLebm5pFubWiVpDFaV+R5QZalTCdTLCHxfZ94MmJhZZVGs0FlJL3+IYcP3sP96Iu055b/rMrMH2vUWKwhHo0Zj0YgFEJZIBVlYcgBVUgskSK1QdkuVV5R5TlSuORGUAqFMDZRVtWtrDhh0D/k4N41hrFmVM0R5RqpbAK/yeriOqurGwR+p/bbmOU2vD8nPzyEgPnFNYrygLmGx72DGFfn5Am8vS3o7U9RCsbFhBPr80SpJE0ndO2ChbaD5zc5HClsu6AR+OzsJzQbkrmlFfYOp6hqh2MLFr1xTJxqokTQaip0CRYx9x5v89HLZ8jLisFEkxcV3UCQl4LQ9kiNIDeGSZqhTYxlDF7g0pZwd9hnvRWQqRb9EvxpxG6/4HTH0JrzORhrUhniOQIri1kKp8w1DEvHPE5dKLEsVcukZ/5BFnUWlxc2ybMI27apZtdWrdirM56kqk04q6pOF1ZK4YdN9AwVOfIqAZ7wwbIkxeQllz/7s3jNDrqY+RDZdVTBUVFhjJ4tJbOCBIPQH5YSf/B4qRSuH2C73pPnP1icfPBnRwXOn2X8RBcplvKQqu7x7g+m9AdDhATbsdClRgLNplvfrJNa196aCzhzeoVTaxv4KDQVW/17iOqAQgzptlzOfOwSn/urn+fk5eO8def32NoecGz+GSwrJJhm9G/0iKdjpKx3KYHn4tiKRqNBo9lkEqVESV5b2hvNZDJFSEXgedi2zcZ6B4RhNBpTGAvXcyjLkiItKTX4tsJ3aymfNmDZ7gzaU5RVhYXmWDjFSIFjORxGLgdjl9FkQvdYQKNbEk8GhI1F0niCzjOS8Q5xaeN6PrYfkJc5RQHXr75FWebs7z5meWmZRc/BHj4mtaGy1nGpOJzcJ52Ocd0WvuMwjSviLMYolyTVVJVAWYpKG6JowuJcl47jUiZjUmXYHpf8xnd2WF7IiVKQyiEIFWiNdAI812bc26vPnTRkcUzm+TiNOZR0ai4OgqZq8GLjErftPbazMe084Cn7DAdqkZuTBxxUBYXUOL7BdV08W1CVhkLkxI0JThXiR0sk9ohIThAq5NiVp9mM73IiW+Xx4CY3Hn4X6du05Dyy9JlUQxbyVeL9KbvNQ1LbAinxHIcTa1dYDDe4vPJp/uPV/zed5IDNB+9xcvE4X/zkL1C2HH7rjf+BvfUJOjVoWVJRoRwHqXxyUioSKlMgUNThOwpLuEhcTAVOw1CNNI0CRJmzvfN7/N/+Vcpq9zSf+OQXODbfZKnTxJrtuKSsmfSCo/BBM1NdaIqyYDIecvPW27zyvd9hOorxVBNjNEIotC5RzPI1rLq0EbJ2P75z4w3WT57l8vkrOLb1/q5LCJgtalrXC6xU77vJCsDoDCMkQjgIJErB+vHLZP3/M72Dr3MYaR5WgmZnFgXBBp//6BcIwwZHSFFtmy+RSqFnuTly5iGklKIoCpSUlLO1Qci6t+76AVprOvOLtNttgkaLUW+PRmeJTqeL1poiT6i8EEcp2vNLGGPwXK/+v1d1awohsZSD63qkcYRlWTz/lZ9n8/Y1rn7z97j04k/XyEM1a9PKOprCdR285SWswKPMSxzXIS8KRsMe0mgWV9dotrp1hlGW/heviUe42fsU1hpNyZII25YE3S5doSi0YjCcYKqaQ2QLENKAKVCWQQiFsCQ6tzBYVFJiqpLx4BBx5gSHh7usjP6I6c13ebTt8XhsM0orciFw2i1OX7zIRz/xRS4883nCzhKWsurd/xPeygerFcE0ThnnEb19gy5StChBukhgLlRMGEFp0AAAUKxJREFUc8NwlHFmNWJjSdAfO2gsRpOC/jgncA3S8YmiBEcluO48/YPHWGVOrjWVPUcjMHiOIk1yEDmJccjxScZDlk+c5eSxm+xsTYgnPdK8Ii0znECyezAmyTIavoOqBLKqQyqHAhoueFaG1qCNxwNCPK+OX4lGMTtTiS5iNicuC77DuNUiwWXFL0liQZEmZM6kJmEfxYFIhan0k8JFHRFQpYWYyfa1LqiqgiODRGU59RydoZN1hVK7lQvkTHFmc/qjnwYEjhfMWjjiCeoB1IoeYWa8rw88rzVGCMyPQEWOfv8IaTmSH/95ipLvHz/RRYrWJWUp0UIxGg9nyaR1rH1JieMIgkZ9s09zTXch4NLlNdZW2hRZwWQywNcll0422K4k/bjJT33uy5z99Cl6esRX3/h1smnGhWMv4tpzDA53OCbPE5ZvcrQMKCnRpmK+O8f8/CJCOZRmTLPlkGZZbak9P4cApKzzPCZRTJIWFEWFRj6RaOpK19evlIRBQFbkaFNzUer+v6EZKtrtFdbm57l5b4sgPIZsNmg6A0bxHQ529lHCUFUp1mRKOTmk6TnMr57m4cP7TJMh+7t3UdKiMoq0EKBChuOMwgxYXlwiWJjjdn8I6UN6e312N7cRsoHrtXEdC9cvKA8OGUcZWRzhhw2UErU1dZLQH1noIse2BcIJkEjGWYYzyXD8kFAG5HmGJTRFWYFxaDYDQt+l1ekQBC7GbWO7dSKnkobtYkjCAWtBh9NWF2kvUCUlushZlU1WzNNYVpcir3jc26SyKgoMhTDIUjBqlgyLO4wHI+J8QFXmVNUWxvVITMxj7x2mJ/Zwjkv8qEvupJDHiJFhqg+4lb7F4eA+gfIxEirf4trN11BDw/5wh6zK2UqHHPeX6a6cxF0+QSJzTpy5wucax3ndepervF4jfFJQaYMlXGzjUmaaqrY+RhuNZorB4KU2TihwLE1vOMZyBaoY0+/9c96eKP74j85y7MQn+Wv/8/8N54/N4wjQlSYrCtI0JkqnDHvb7B1sc7i3w+7ufYbT10mSh6BL1ub+IkanGKNB2hhT1siHrkCXGAOmKsGuVVi/9Zv/PfzP/g6Xz57Bsqwnu6WaG1OrASpd1cWO5bxv5qT89xcrUce2tzuL/NTP/E3+4298j7KcYioQocAxUEU1ohNHQxrNOhunqjRFmZPHEY7jYFkOcTTG9UKKPCdJEjwvwPF8bMuhKAts26ZQis78Astr60TTCX4QIq1aDXSEAFVZwvKJc0gl0YZZ+nlBlcaoGfeHGfFX2Tau52FZNlVVcvLK8xw/fwXl+Eil6PcP8Nyg5pcZQ9BsE/ghWZ5jWwWd+SUG/ZqofuzkGWzbxg8aDAeH/4Wr4YxxNOMClWVJUsQk2RST5yTxiI2VJap2g9PnAqbDKf29fQajiCCwkSYnyTOgxFIC2wmIsxx/OGUYF7Q7LcpK4EqLZrPBg4e3+Mzzu6xd6TDXtmm8F7Nf5cR5xXB/zO39XQ5vPWLnYzd44Qt/kYUzT2NZ/hNvHHF04xKzosWkhKZiazTgWKCIc3g0GNN0FMoJOLbW4d7mFne3ShZShyIHLRICXxIGFsrx2NzNcETFXLfB4WAIWHiOYn1BcfLCBV575VUmU02lJWmWkuSSllextVfwu7/7daJJStgIqeI+eVkXuoHrcOXkPN+7tQ2motFa4FTX4tV72zWaWZRo6aJViKtLWu0mZphxGJe1qjK0ibVFNYgp04KevUgwTrh7tcBdk5zUFcZUlEWOmBXgStb3AGEklS5nCcLvq/S0rmbeUnom3befhPiJozmgdd3qmRU979cW7xcZR9fkB5GOJxyxI1QUauWe0QgjYfZevh8l+eBrfv94sk584OvPMn6ii5Q0niCEi9Qlvu3OCvQaglaWpNm0sR1JXhpsT/DMcyuErkcQ5hhL0zUWTeOztHQcS7ic+NRlTr/4LLcPXufN26+SRRaLjTk6rXluP7zBmeIU+a3JrLKFuv9vaPgeSinG4zFBo02n2aSoDK5jgxBkaYwxFaPxhPE0rXvxQlBVM2lmVWG0wXUddFUQBAF5WcyY1AJLCfKipCw0z1w+xc69a7xzY8QozcgPb2PbCqTN+pkrLC4t8+jeTXr7j0mmA3SpaYXLaK1o+hb3twfklSJstXD9AJwIx/exphESwTs373D3gaLSBdNom/l2B89bxnEDSmGBgKKKcCyBqFJK6ZKmMVJolKkzd6rK4DXqfn9RJBRFTpVn7A0qFlfWCJDsPr5PkU5RQpImJadPn0BXKY4bgqqjC6RlY7s2UgiWmhpCibIsJmVGVqSc8o8jpxVRPKKq4HRwlqfPXOHrr/4Hru9fIydHS0NhVdyJv4OwwTQqUr9AO5AZSIoBRVliqKgE0BY0OjlRuksVK9TjgK/w09i55JW9byCUwaoEW9aAB3IbfTvh2fOf4qnqed6Zf41j+hz7+YD9975OVubMO20WirNcLua4698nMRGUgqo4IomC1gKja4VTnuToEoQCQ0DoGvwm9PcztDWg2RVYwhBTchi9Rf+9Gzz+777JyuJxlpo2Suf0k30maQ+lJlTphDzJKKeGKAe/U9FqGWxpYVQG2seYqu4LGI3WOcbUOzUzY/cbUyIkDPce8t/903/A3/jl/5aPPnOpjpiQR+6S6skCpY3BlAVSCJRUCFWjGkeLJ7JeqC49/SL/6Q8vMY5fx1igbYktBE4ha3fjZpdKawqdPymCpFRYljMzTvNRSlIVOc1Wa+aQmVDIjOl4WPNgpEXQaDMZjXF8FzB4vovnOXVsRZljuS7WzIxNCp4YWQmp8T6w40ySqA45VLV9fZrEuJ6L5fqomb+LperiJYmjWetLsr+3TRZFOI7Lg9s3CBotbOk/uf7jKKLIkyfExB93CN4nKAJUumQa9Xm4eZdHm3fYfXyPtHfIytw8p859lC/9pf8aZWxKS9EIQ/LpBE+5OJaNLnJGwz5pnlKWBY7ngRDYwiLOcjzPRiibSZ5yMOnRdjIax1YI8qucXrVonGpw4ztvsr8zIcgU/bRivP2Q6687gOKZeMr65edRbgtm51HMdK3awM4kJ0pn+WlewOmuT9w3JEUKRUr/YJ+VtkNhKh5ujSgKzVNnfPxWi739AXk1ZWVeURSCg37B+nKLzuIJrr79LhiL733ne3T9kthIhCxp+R47vZzBRCCF5ua1m2SlwhYlpqxwbQtZlkyjKW/eEVjSRZc5o2jKntfFtn1yAyvNBqYqmJYVVa7R04Qr54+zMxqz93AHVWmeOr/Ma7FCFjGD/R3CxZDjfk6z0NhegLJdyqJAfYBjUnObjtxZ61YnQlIUOboskJZCWk49Z4yZKXFqbhgGlPWDEmD4QSLrEepxdOyR2u4IETl6L0e8tx9WaHx/YfLD2jz/JdyUn+gixS0NnrFYXzjOc+c/znBQcPfxXZJkiuOA66oa71CGpz7S4cz5kGIgaB8m9PYKpBPQXJSMC8XyJ34asxDz+sPf4ubdA6rcZ6EZcmL9ItfvvMuKu8pF/wrfib/B42gLbSqUJbGVosgzdnf3UJbCH0+wPZ8gbBI2GmR5TlGU9IZjplFcE/CMpiwKqrKgnKEnxhjyIqcV+hgESgjyMsW1al5KVdUE26R0OJwYdg5H9McRUFvlt7pzXD51gZWVE1RG41oJW5sPCZtdukvrVJWhF5fYfosrz/0UthsyHfdIo0N6Ow+xml7tDpqWjGNdh7Iph6A9j6UEjtukjCIODw6wLYNtK1qBRFsBlucxHo9naJHGUNIInZp/UcS4lkWSlYR+u76gpyOMjqmqgsAX5HHGaNjH96y66pcuArCUQBcxQsC+nDB2YlzLQypJR3VQ+LiBotlY5KF5wFX3Vbo7Icvr6zw8sYtZSpFCklY5WZFjKo1le1jkFFWOXVSYRKHjiiITGO0RjTW+Y2FJQ5nGrLnHOS6OMZRDLNvjzbm7uGNBUwrMhYTkQUZ34zQXFpd49+E7bN9/gIxsJr2EaDTl3OIlEqtgJxgzXcspjYVGYEzt4VMZg6kMVVGSJwV5XGF0TT51hGajAe0Fw9bQoEyFNAKpIVcCoWAiEsrROyTRNaLFgAXfI64GTFVCy4akEkgjsRCICSAFliewFFRijBEdhKnbQQBCaxAVuqx3T7XfQYFSivmFFd78o5f4P/3a/57/6pd+mS999vO0A7fezc1CMpVSaF0vRkeusZawn+RgmZlnilIWYdiku/gsg803yBU4pcC2BMoSpElMqSHPM9Iknil6ajJfEkcoy3ri8yCtuoUiZ+qissjxPR/H9ciKkng6Ik+m+N4yvh9SlRaj/gF+2CHp74JT7/CVUoh0gBXOo4IQpRoURUVZFhRFQRg2sSyLLE8pyxLX8/H9kDRN6PUO8GbcGK01zVYbXRmiOCLLMprtLpPxCD8IKPKUzsYJhKhbuTsP7rB+4hT93o+PpjzhBxgDaNJszJ0b3+LV73yd7bsPEOMec5bNxctPs/HUxynai0RlSa/XQ6UVWT8lS1O6C8v4YUhnvk24cYZ51yXLcvqTXt2a8zyWfZ/BcIDleqwqiwvqGVzLxjBG53cw239I0Jpy5dMv0Ll2k+3NHm5ssKclk94Ot6+9ixY2hoqVM0/jhF2U5aKk9WRfP41jxkmMHwQcTgXTLCcvSkyWoEROiU1pL9CUGWVlsb7qs7UfEURT5jqgEZTMUemIppcTZ4Lp5g6rCxamUkyimMq3wJ2Dos848mh5EsvkjCcRKEW34TAZZbiOR9NR6Dxhqg29yQhPStquQy4Fm7s7mApOnFzg3LEF/uiVOyTplAob27G4szPl2EKDTW2xN04wb94jNfVadcGDY52ctXZKcggzWdQMDZEzcmxdKCDEDFERaG3I4+kTA0NLOE82BkfzQKi6PXT0oX4/f+SH8UmOvqSUT1zPj57XH0BwjgroH1Zs/EkFy4/62Z9l/JmKlF/7tV/j3/27f8eNGzfwfZ9PfepT/NN/+k+5cOHCk2PSNOXv/t2/y6//+q+TZRlf+cpX+Gf/7J+xvLz85JhHjx7xy7/8y3zta1+j0Wjw1//6X+fXfu3XsH5ElPePGraQ+MrHajk8c/4jZFFtp31n8xaWU1CZEqTkytPzHD/h4TjgzBmG90fsb8esnD+OPPYc6xc/Si4HvH7/W9y+v0U6Ulw8vcpzVz7Ho61dQtng481PEOxrxioiljlBI6wlOKUmyfOaDV1qinxM0xjarRZpkrB/eMhgMEQj6Hbn8HyfJIlJo8mM9FRPSjB4jovrelRlgS3tOvBP1+Rcreub1/bOITtjzcqxY2xYNtE0Znd3nzAMGA+2GQ+28B2wbItLH/s5kvE2uhixs3tIJVzaC00cL6SzdJzO4gq33nmZvBRIWcvbyjIlSQoUGs+xGY8jgjBAWiVGGBzXRZoKx3exbIskjojjehdQy1Er8jxlZ+sBnmPhOxaYHG0L5rshk+mUqL9PnhYszrep0gG+ZaGLEuEp0BXClOTphMRMcR0babusinU2IoeWCbGFTdNuYhzN8O5DNuaf5b/6xN/gW73/zO6D93ByQ9O3COaWeb64zDec7zHxMrI8oTQlStoIC1Kd4DZshNKMTUmZGvzC5+fEz2PKmGvFNdqDFnvcp338BP/TU/8rTm2+zG7jHvv3t1iyjmHhcO/+O6TThOkwZsfJWWcFy3WZjve4Fb8H/U0GHSg0H5Dn1kZkVWHIopIsyWsXViOQQmFqqQS2Mqy2YasBpTDkhSDTBrsQlNRouTCCuDCMBhWeyZEOzAcSyzVkSIwS+EqQ5/WCoqTB83wcWSKyCiMlVLruXQuN0HomQ6wQusJgY6Sg0ezgeQ4H/Tv80Vf/W+LB5zl/+ec4efxZus3aIdN13CchmbWvgqYqK4ysuSoIWaeEV5ppVjDKNakR2AiyUlAJQUtCWdReOkrZNBodhBRUZUEcT7FsB2bmhkr5lGUxazWVKGXjuH6N3ul6ofXDBkura1iWhZRWzWdBkGUJeZLQXT1FWRbkWYLvt6kMOI5Lnqd4nk8+IximaVLzN6yayFsUBePxkLIssW0b13VRlsNoOCCJIyzHmUnFIUtjgjDAGHBdr5abOg55njO3uEjY6mDb7o+99umZpNyYkmS0xdWXX+J73/oGhw+v4eYJJ9fXuPJTXyKdP8vrdx/RG3yXopSYwuITT71AU4RESBikvPnGOyRFjBcErK7MoysY9kf4bsDi/CJhMySeTGg0muyWKd3FJZZXF2k4EFvzeCf/G5z9d9HyKmsfuYwJH+L1JniTnN1exmj/AdffyplMx5w72OfS8y/S6G4gLDGTosPp5YB9Kiy/y9b+AcNxRsu1ETrl1FqXzRGMhmOmAoztMEhKqEp6PU3oNkhLzSRNOLEqyKo2khJkiuN20Vqw1Mxqj6Zih+MrDpuPR5SJi1UULISSpNSYeEqSZ1Rocu3SdXwaUrLQgOF0yiBPCVXtQht4AduDioe9bToO9CealqeZZilZT/N4extXWtjKZZilxCrBM20GueEpJ2GlU3DP1MwxIVXdXkU/cYCtvUqqmuMlFUKUWLaFcNwn+VxwVDwcFSofLka+HzGpPkBu//5MnSNE5ei5HzY++Fp/EjLyw4qYP62Q+ZPGn6kqeOmll/iVX/kVXnjhBcqy5B/8g3/AX/gLf4Hr168ThjUE+rf/9t/md3/3d/nN3/xN2u02v/qrv8ov/uIv8u1vfxuomcY/93M/x8rKCt/5znfY2dnhl37pl7Btm3/yT/7Jn+nNV1UNHXvKY3V+mWfPXSHOpyi3JM76aEpWNhSnTgX4vk9VJiSMyY4FnN/4GKc/eonu4mkeH25y5+E7bO7EhHKRK1fmWVq8wKvXX2I6GPOlpS9xLOvwiIfIRYcXP/ECWZwx6I/Y3tlmMBxS5LUzrERTZCm2gN2DA5LphErXRM7l5RVGgx5ZNKkJSsa83+OTgkYY4rkuKZp8piwQou4rJmktqzR2l4998iLJdI9o2uPE2af5uO9ycHjI1uObtQzMCgjnzjC/epKpl7O0tMI0e4X+Vo+OG/Dw1hvsPHwP5bg4tkfQ6DIeDTEIssKQZgUSqIwgO+xzzBEY7WNbktL1EMKgpKLdbDCV29iFAuWQZTmVtgm9Wq1DWeBIg+OFNBoN4skhvYN9TKHxPZciSnCVRRBapOUEy+7UkQLUPVpjO0AtkTN5QeSlzAUdsCW5dDmTbRAu2jw+vMGJg8s8V36S5ukOvcM7PJjsohJB90CxZre4td7DsQJMEVMVtZRQoKiq+uJ3gzpDKYwbXHI/RUdXLE0XuVe8x0G6iZjOMb90mkutj7A8WuTr6e9QPfIIBjb3elcZjnu0cPjo8ousrZ9n2M3YfvhbbLtDqnVJ5dbGbaXWFEVOmeUUSYmpBFWpa/TkaHGa6XG0NKQ5pNrgNwRpbqiMhhTGmaEqIXQVhTC4WhAnOZOgou2VhDaIWVZVUtSoVGAZYg25ESx7yyz6y0QDSZF9QJ4MCGKEMQjjIrBnsLPGshVziyHpdIfVxRg//Sp3v/ctXnnleU5e/ItcOv00C+0ujSDEdZxZi0Zg25K8yIgnBXFR8e1XvskwmfDo8TXeu/E7WKZWfyQF+IAtBa4XUBQlVVVh2/WuMUrHhI127Uxb5LNCIqMscqJRn6DZodFyZiGeuobPyzrYM8syJsMBeZbQmVskjqa4gwMcx6UoippvYtmYGeVYCIHj1gUQM85NmsZYll27QFdlzWvRGokhiWNUs0OSRDWCk2UMDw+BqkY6OwsIAeP+IaYEXSZYYS0rVWFY89H+NDj8QzvhCqMLkt4Dbl19nde+8Qq9e9cJTMrq0gLP/Mwv8EBryq2v0bt9k36/IMlsvNVzNNbnOLl8AoWFruDS+cs8enCHKitAmbrIayyzun6cuYUFLNsmjScoW3D/0W22Hr3Hu9df5eKZUyw0DO25RYKNn8JdeIbxo1dpTkPi4iaumRBmhriKOdy9TX8yJEoz4umIFz77s4Rz6yBqt+Ruq8OcLri/vYstNM1WAyEE46hiNwvpBCVplhA25xhPxuwnGZ1mG1VOub89oe07pCVQKg6HBRsLFUZIbt04oKzA8TzabQ/XDDncVwgUukxqRZ20KcqUqDRIu3ZprQyMS40lNFkpkRrKsiKNEqRUrPuSTCmivCI2DpZImQ9hmGkyoymkR0vk9ErJUuDyyY6m247JxjCZzrH9YEi2MUMbi4xqJrFHzRDHWUEvqDleIFDKmTmRzzYnT9qDtRz4yOQQPmxBf1SgHBHk69//Qd+SH4WSfD8Cc/Tv96cZf7+K70cWMX/iJP/B8WcqUn7v937vQ4//5b/8lywtLfH666/z2c9+ltFoxD//5/+cf/Nv/g1f/OIXAfgX/+JfcOnSJV5++WU++clP8tWvfpXr16/zh3/4hywvL/Pcc8/xj//xP+bv/b2/xz/8h/8Qx3F+7PczSkb4RYuW2yK0PNYWVvnIlY9w4vxpcpMzjB6DewvbHVPpIcqyOTX/FVaeeRrQ7E9GvHT1q1y9/SrjYU7LafHJZz/G/MIJ3r71Flk24fnl53nGvcx29Jhb+SOcluCY2yGPSuxKU6XzCCMYR1NMliINKAO9/QOqsmAyjVC2i2PbTAb1TTrLcjT1idRolFD4nlfbmwsIwwZxXBtC2bZNUdbKBaUEfqOFEzTRJgOTocuY9ZNP49iK7YfvsXbiBZbXL5BWhsHhHqP+gDwaoeyAY8cCqmJCOd0hmwiG44LVk2cImiFVVbG3MyKaTikrQ7sRAhrXtnCtGcO7yggDD893GB7uE496OFZJZ76NUh5RlFIUKcJSlGVIVZYoUSJQlBqqPCdLc7QWWMpgWQrpOjURURtMniFthaNypKyQWJR5jHQCpn7O2e5plqeLLCSL3OMOb4++Td5L6af73Nu7xbPHL3Lw6AG5NKyZJcbTnH66x8H0EdmiVUPM2FSmoqgqBBZlWRNHhQTpVBxm+/zW9X/Dx70XqKwENy65OXjAw1GP0eQAkZbEecS8CWlOunzyxZ/ncNTnt77+/6RpHKbjAaPGPqOyIDkGsa8QMq/5J5WmSEqmw4hskqFLjev7WI5bkwmFRAgbowWIemFqWiH9UqAsg1tIZAZxZrBSgdA1GRsFsQErNJiwQLYMwhUIWXMW0kKQVAbHhsADJcDoetHzAkVZAKZW5gijMDizxwYtQGpTuwNj0150ORQ10mI7GiESBru/z3vf+C5Xb1xgffUTzLVP0W4us9BZptuex3dr5cHXXvr3fO/d36bXu0YUTSl0SaYMlRB4Re0Kr4HSqRdvqWpHzpq3Ma1dXG2PPEuRSs4k6zGu5xG2OnhBg6Kok4T9mQy52Ww/2ZVWRcZi9xhVWdTGWON9wuPnkZZFqVMarS5wJNmsDbXKsv7esmx8r/a4ycsC23aetIFs2yVozZNmKWmacrC7zcHWJstrx0izGF0UmFYbL2iwfvIsjuPSbLZIswytax6N9I9cP//kUSP7Gl0VpMNHPLx9jdvXb7J/5yotX7DcbHH88iVee7TNnc33KHfv07s7ImwonJUm7119l6WLrxAsdFltL5NFMYunj7FyfB1hK3RZxxPEwzGt5fknChO35bM72OP67n1uv/sKu/f2uHVznaWleZ65fIW5pYJj6+ssPfWzNFefR4ZfJb/xCo38kBIFIqU3OeS9t75DmkzJioJPfvZnaS2fotQVg9jiYCDZTqDtN/B0wTCNoBJMBgd4oc3zay77acZ+PKEZeGAMrmVYbkiQAWUquPVwimM7HPQERtgIYWEpKAtNMq1wCousShkVmoXQw7YC0mRCUtSOsr7UlBUEtqCfZkhT0vYbNIOAJSfHdmr/m0onjPoJc8snSEpNQ5e0Gx5nVgLOXjrJ1799HSuO8EzFYssmcA3PbiSMB5I3hjb7/jrdhpmRUs2R1+uMWwKII8XNkUGeqRH3mZDiw8XFDyIjs9nCUUlQc0w+jKL84Nz6cJHx/c99//Pfj9T8sPFDC5X/MYmzo9EIgLm5OQBef/11iqLgy1/+8pNjLl68yPHjx/nud7/LJz/5Sb773e/y9NNPf6j985WvfIVf/uVf5tq1a3zkIx/5gb+TZRlZlj15PB6PAdBVrd+WpcCuLELpstSep9taYppFuKOYpHIwhHjuMVbnP4bvbbA1eMSjvXvc37nB3n4PmxZdX3Hp9AZ+o8ur115lONlhPZzj6e6zRJND7lbvMXEmmEZEJWOMUoikoGWHFI6m2jNE2hClCSrPCJoBoarzd5q+y1rHw3cFu0VBkmVUxqB1fWJ9z6XVbNambVKQJAmueyRJE0itZlyN2vWv3VnA82wKTzK/tEyWZfSHMXOrz/DsC1+m0Wjw6stfx1QVq8efQQrBvADbtpkMHrAtS+7dfzDL/dlGNReodEWc1HbQtmVhNDSaDdbWN8jiA3Q6wFIQT6fsb42wqGi1Wii/S5qVKFXS7C6TxNN6B2A5VNkEU05IopR4mlNogXIbxNMMTwu0stDKxdgeRZ6TpRVxlRI60Altms0lpNfGdmxGZkwrbeHsJ+xnd5lz55BZyrvTWwyClH50iB12kXOH3HvluyTlhO5gnfWnnuXiXkwUPUI6LlOrwLNsUl1SlDmSirhMwCh81yYXOW/rV8keHDBX+Zgoob8yJOsPKTZznFgz117GKKhKyfZwF9dtsNo+xfDwHpu9B+zqXZIXe4RrERR1mrUuDEVakU0zdKZrSaYj64KuKkDXRZuyqpmsFzCGhj3PXOWi5iRL1nFcYZOkOXGS0RuM2Y4n9EuDMIZSCxILKktQKFAlVKUADaMCQiVYcEFi6E92Ufoevugi1AKimvFShMRgoUWFqZIZyVsBM2mhA6kx3NgShM6EduCAU1LEBxwOp4zjG1gmQOQZVeqh9Rqu00W4JYfJWyTZLhKFa0NWQoXEqiR2BbmBGKgsAFMrHp4shtBqtWv/EgGuH6CkBbMCRNn19eJ69VxK0wRt6lZAUdaGWa3O/BN3W1FVTOOUjhugdY321UPUlv8zwmBVFWDMDFEBoRRq9jkVeU5VlUwnY8o8wwsaxOMRve1H5GmEF4Q05xYokgi0xuiSslQIWbJ/sEdVVVjKwrZrd9re/i4nT5754Yvt0Q6WikrnROPHPLz1NtffvMqDd95kuWuzvjbP0soce6Xh3e++zKTfZ61l03I94tJm//qQwCt56d/9NvG4z6mzF+htDXnquWe5dPwsHa+DZQlkadFcmuPR/hauZ2F5ims3rvOtr3+NydZDbr/xGmfX5xnf3CW673KwucOpC88wPD/mhWeeI1g6xbmf+mt0TlziwTvf5OGt62hrhOUohuMJD298jyIbY7KEF3/6F9BlyWQyoDfo4zsWXQ/SRBNrRYUhzVLu5zmWM8+kslCq9pMq4yEtX9GPHWTggyqI0pzu/BpJPGE07ePbNu32AsP+AbYdEHgNrGltHkmSMVUWaaFZa7eRtodVjNgcJeRGENqKJCsQukDaAYMkpzE7F/0cKkthprt4lotQFWUyoQp9JBVrXoEbKNoBnF9KCIQhkLB+RrNabdFdVhy2FlBWrc6xZkhIfY6PZmJN/BbifZL0h+7vxjwhof9Qya8xGFM9QWg/WKB8f57OBxGSo/HB535UEfIntW5+WIHyP6rjrNaav/W3/haf/vSneeqppwDY3d3FcRw6nc6Hjl1eXmZ3d/fJMR8sUI6eP3ruh41f+7Vf4x/9o3/0Az/3pItrbFRcIXKDmabYnkWBoNffYRDdImy5LC0+jVId4tLjtet/wEF/h52Dx+jSwiJAoblyboN2e52HO4+J0j4r7SU+uf45tI55M36bqRvVZjoqI7AtTO6TVwa/bdFsL9NpjXhw9yHpwS62bTOOUp45s8pKO6DVarPYtHh8OKHUs6A1IAxrV9WFuS5lpRHUwYfGgCUl1cx8qw4YlFi2UxMIpcD1QhrhCRzH4tb1tzl5+VOslCXpdIt42kQbB2G5JFlOGPr4YRPX9SjyESdOn+Nw7zGFu4rbmieOJ0yHfYq8REib1bVV4tGATrvLUx/5BDfe+R7j4YC0NLgKlJA0Wi2U55NGYwwG15ckSYawmjUqoQyUiiyXVCiyUlKJgLnFLo43Ic8S0izHdjxUaaiobzhCg+3WFui7ewPsIMZShvKU5Eb5gJ9e/RKiv0XHXkC14K88+ylKEfPurde4efMN2s0GL3zi53jz7jd4c/gW/avbtHOXDbp8uvFZ3rXeJQsz3pneIMlylHCQs1wOz2mgjMDObSInoZfv05xzaDa7/K+f/1Va/hwvv/q7XH3wKmM/wZscIm8rwvYckTtivzlGhlOS8zbBnM2CE9IONIFdsrVvmMY5ujLYtou2j246AnRJmWXoSiCFjRS1ZLAyDtNkQlpVLPqLrIUrBE4TYSrKLKXffoy7lcC4JE4hmYLdg6EtEBWYSjCpILPAE4J+CiIB14e0iBmN32VOdeiE5wjsEwj0TEygQBiQNffDzBwoMQbP9hGV4NE9Q5FpTp/K0W5tgT5JYtAZLTHClwItSuLpYwaFIXc0RQCWbbPUWMWTPg9H2zBNkLlBZwIPwbTUOEGtiKuVCoZk0iNoLaCNwXHqYLWq0uRlhhC1eVutJMvJ07QmZhtBu90hTZL6/QuOMHLSNKJKhtj6KNJC43o+eZYQx1OUVHheCMJgWQ5KaeJ4UgcoVgqjNUk6wbJcyiJnMuzj2A7RuE8SJzQ7CxidEUdjOn5I2FlAiJn9wGhAEk1oNFsz8y1du++WOc3WbN00sx3w0Xr+5EZQI1rJeIdHt17jvbfe5eY7rxMQc+bCBp25EC19dt/dpP94B60E28OMhicJjMFpSWxhqHr73Pv6S1z/zuv0h1Me375B/hd+mrmldU6uneTR5iMePbzLN77zLbStCXybyeZj7r9zi3NrAVZWcLjdY/l4SBQX3H/7LWRZ4SjDsdVlTp3s4jQXWD3/GbprT7N6/g3eevkP2Lx9C8+GeDxm+ugWj5TBNmDNrVOYgkmhOdmQHF902e8rdlJD23OpipyszEm1Io3j2gVYKpYXAhwMrjfHQQppOWWlbeGXEZQTsqzARWPKiLQsyaIRk7So7RAULPi1bFxXkvXFFqOoYGtaz9FS19k6ofTp2oJLaw7fzgr6UU7b93BczUIrAOWwHMJWv8ARmq1HB9y6f8DZtg1KIANBa1HQ9Q22BkvDMb+i7BnSCqRQSGXzQYVNfco/TGz9YRk4xui6+K2VwT/QwjlSoBqYuf7+aMLrn/bzH9UeOhrfryL6cV/3xxl/7iLlV37lV3j33Xf51re+9ed9iR97/P2///f5O3/n7zx5PB6P2djYwMVCxnl9Y680uAq/0yHVU5ARa2sncUKPSWbY3XrI1v4jeuPHlDlMh4YiT5lrWnz02WdZmD/N1dtvsb1/D1tmPHPmcyx35nnv0WvEXkbDayKwkCajkhVxkrEYLlDkPrHRZOQIbWiFIb7rstcfcH17wqevHOPSaoBQNlfvHyKlIvDr1o6ybSxVu9XmRUJcZAghSdMMz3WxLBttan6uUhLfc+sCQIIftHAdRVXEXLj4MUrbp6wigqBFJbp0lyTDYZ8sHSOFosojpG1oNn0GSYlSppbklhEOQzYWbVqez0G/IEkmuA2HrEi4+uZ3aLcChAnp7++QVCm2kihZEfg+3sIywhjSZEyuSyQZuijRRUGRjInHQ7LSojISYdkIIZibn6cqCw72dtjZ2acZeix053DLgsbCMkYqjLDpD/uoiaDZbtN021gN2D18QFt4HB5ucubUR8gThdda4mwzYe/+DQbG58pzX+LU8Y+RVyk4io/Mf4K726/xcHiN2N1nbyUiLiZoIM8qyqJCyxhrpGhPVhlUB9zzNlFtzcXLXebkKr29MVMDltNlbmGZfnGfLEt4cHCNxfEic7h84VN/A3u9y2/t/Cdee/gmd7Vkea7N6VM2pzcidh1DfyipCkFeQVWY2oZcSWzXrc20kHXcSb194nZvzNRo5rsFQTkgI4WiwOQF42RMUlQUupZTi9SQ9yAPJJky6MoQF5Abg9SCojBsxoYWgrYN47xkOjlk5FecObaBKxRaSCqT1wTmGdBc399npnAChAeWK9jeF9gLsNrwmLctJqOISVQxkQYvVFi2xPI0cWWYCkGEYc6RzDeXWfBXaPsdbm/dpDeJyTKDrAyuJUjiOsTMsW3yIqfZXWEw7BOEDXw/II1jlKXqQjeOaLTnahVQHOF5HkYbRofbhL5HkiQsLK/VfiimwugSncc4tiCYW65bGW6dZoyQ+H5jlglUPQlETJMYozWO7RNFY8pSk6UpVTmlKAqm4yFhs0MSTVGWxeBwj/nlE0z6PaRUVFrTas/jBiHKtlheOzYLYFPs7WyRJVPmOi2CRoP399HvQ/VHw5iKLOrx6M4rPHzvHrdvvAP5mIXFFq7fxvGXGMYFyTDFR9Ef5awshQwmEYXQnFpfZDIu6VgZJpkwebBDURqa55Z49Xf/A7Hw6aytsX3rLp2WS//6HbZ392i1XJZCWGuUhPmEzpyLKAwVLlKVpHHMw7v3mZtb5uDxNqdOXK4RK8sjaC9x/MoX6ayc48abL3PnrT+mfXCDahphZ7vkowfs9XqcXekyZ2tKbbGb2PTyGu0IhMR1JH3pUBYp0tQk5lYQcDhI6U0TumGFIyGKYyJLsj8+ZCkQuAIc5TOOCxQQhA1yPWXehoYHymrgWSHVpKJQmhxN17M5mJYIQBcxnXYbKeCN+wm+3yIuBoS24URHMEkzDsZTjCPQXkDheshil4bnc/rSCWT/Np//YkboGyzAEqAnIAvIR4Yyer/+/FFk0yMya33+v8+TBAGmNlqrD5EfKFDMBw8E+aNN2D7kk/KBv//DpMU/qoD5/uN/eFH151P5/LmKlF/91V/ld37nd/jGN77BsWPHnvx8ZWWFPM8ZDocfQlP29vZYWVl5cswrr7zyodfb29t78twPG67r4ro/yHzXaU4mY0odgQLd8sGzcAR012Czd8CtO3fY2t8mjks8q4FSHslI4xiPteUGT1+6hLIWeffuNfaHjxEy4+LKMzR8eBy/iwgrvNSGRGJLj465TFVq9tjFC+cp0hJRRCSHA0KhcRtNdvp9FpZWiJOE//C1N+hdXkVaDr1JQrvVJGy2cVyXKI4oi1qmmRcFURwTuLXDalWVeL5PVpQIIWoUxXJZmOty6vgxOt15lJLcvXUV47SIY4MUPpWWHPYeoVRIUUwpi4hBf4gtMiaHht7+deKoYGHlGKAodIm2amJr4G0hRczu0BBFCcqkJMOIaqIR0kUJQ65LHLeJKRWWcggbIZbtIPopSW8fJ/Cw3HniuCDNBJa7SpSPSNMROquwbLe2c7ckzVartn5WihCDkgbXFXieQ1HWmR+j8RDX8vCskLP6PN28RVUatIrYG24SsU++0+L5Zz9Px2ry8rd/h97XfoPWXAuEZry7x7cO/5hxtc9YZsi2TeQK0iAnzqYUboI0km6yRDiY48A5pLdwSDaMmQ99bFeQmwm/v/PrBOM2xXRE1EkJbBcvV4y9lEHvLhfnL+PaS9imyWq6wnQnAksQDRMODj1WVkPW1x2OrxtMKSlTi+EwRYuCLM+YRgVJ4pBoCeL9HnUxFUwK6OlDHieHhJakjDR5H7IYotQQaUhN3d4hg3YhaiKu1HjaEKWCVP9/2zvTILmq8+7/zt177+nZRxuSAGGZJSCDLDuAKygs8RuTQFUIoWxwCI6J7Dhlx0WRxCGhKoEyVeZDKqH8weBU2TF26jVQZWPnZVO8IINREFjCyJIQGi2zz3RPb3d/3g93plEjIZvYCC33V5JqdM65d859+unbT597nv8DUSS0XdA8KOaSfSCHGsJYdQ5yo6ytrESLQZRGjAGECzeYGBEdiSOEiGxJYZuKZktoozCNPEWl4dgusSdUI6HgK4qOQjOhaQixkXyLbLWEqlOjJ9tHT36Eswc1Xo9eZc5sQayo1wWvITiZXJKFs1DJWFdCHIbMTU8mkve5ZP+JncnRmK/itdvU5+cYHFqCrun09C8hWyhi2A7tVgM/CCiWymhA4HnoMeTKAwuPdMKOQJauJ1pAppkIyIVB8oFlO3nq8zU812V2chJN13DbLRrVWaxMjtr0roX6PQ4ZJ5PcXC2bTL7I/OwUjdoMc1Nj9A4OYzsZPM/H91wyGYdiqUS+WGLy1R1JgLgYoIi8kU5KTODOsXfHM+zbNcqunS+DN8eS3gI9lTKF/hUMrr6YqZeeTxR2M2ViZTCwJIPVmKFWc7FUTGAoWq0Iw9bJmzqFbBmlDIZ7fNxIiKqHMN06bVqUSkV6DMX47DSFnEUjKiIa9Dk5olqbvQdcMk4WG8GrN5idqlKr1ukUvVx4zGDZGj0DZ3DRpQMsW30ee376Tfa9+ARubYLaXJ5pqSSreGgcmm7i2Fl0NDKGQRT7mJmYdjPA1CyUZpAzoNV2sfWAs5b1c96SMnv2z3EghjByCRFaIfQ4JmJoyR4iw8BrhxAJYuVptF0KZovh4TwZw+Fne6YpZGyypkHOzqBE4UUtevqyrFwxzNPPvoodJWnuk7U2YaCoZLP0ZBVxFOAQMlIymK2DoUUMm3uxe3x6y0JPRXDrirCh0JxkUTLOgW4m4ovAEY9bjpYNc9RifSqpH764V+mN45JHpMmq6JH7S452vl/G0STv3zzXxf8f7Tr+t7ytIEVE+PSnP80jjzzC5s2bWblyZVf/unXrME2Tp556iuuvvx6AnTt3Mjo6yoYNGwDYsGED//RP/8Tk5CQDAwMAPPHEExSLRdauXfu2Jl9vzBH7bSKJsMsFLN1E4dIIamzd8wLbd/+U+qxB6BuIignNNo6y6cnlOGvFMJhwcHqKiepOXL9BEDYYKvawbHkPnj5OvVHDDYVGu0XkR1Qqq7DKPRTdPLrkmG9XCeKYiJhiUaPVVrw222BgeDnTUxM0GnXiOGbnWAPLMtCdHGUnk8gaSyL2poswW5vH932QmLbrks1kOiJvcRxh6En2T7GQo1wuserM1Sil47ptbDuDr+WJq5P09Y3w0gubafttNDNHvTpBsVigOraDmZkxNLtMu+3SU+5Fz/SQyxeJvRq12jSYRbJli3I8y1TtAMrQ6K0UyGYzmAupoH4EftvG9SMOTdUoLD2HQ/tfo93ykw1q6AS6gZIIJ9dHterRdpv4vksYeHjtJtlCASOTx9Qssj09OLqHRB5iC9m8QzHrU6wMgYJmo8mS4REyuQKe7tGrl+gpFBndt5vBobOo9FbYNTbDC95m4u1Cf1hkeGQZs+40E1O7sa0MZ77nYka9vcwZUfJBWFfUWg08q4VC0RctoTBVwauF7DOmqBs1DE/QsHAsE0WyM35v6wBL9Zjf6l/LLuMXrMm9l3p2hh3NV8nVswRK2PH6Vipjveyv708ElzRFrAIaTZ/XR9tMzmbJZ03KxRyWZuBrFvVWQLUW0m4u3GSs+I0CgsRoseCHUK9G+JFCyjGGo1MnotoEbSEVWWLBS4Q7adQV+QzEGUUooAXJI5uso7ADaHqCFysKtoZpxkxNx+w4tIOhci+9WgHQUNqCwiSS3Ag1RUxANaxi5UGzITDAj4W620Q3oUpMG4XyFI04xtIUoShEh9gAfJidEbZPjdJc4rGidwm26dBTGsQujYPvIQr8tnQqi4tE1GamMWwHzdAJGl4nzbnRbmIWE0XaXKGAbeqUKn20WvMYC9opi5k4tpmIArYWApawUWO41IfSNNzGPLphYFl2532n9GTvQ+y5hGFEJD7NRp1mfZ7a7DQ9/UMErkvgeQR+sgnabc6QzRfRdJ12s0Hv8FJa9QZTh0bpHRgkk81hOVka8zVQOplMjvnaHKaVPDYSidi593+oDPaTd3p4o6JwTBQ2OLjreXZu+ylT49P4rTGG+5NHyX0rz2XFRVeQ7VlO7fkXCAyb4rKl6GRoGi7VxlxSv8wwiGvzZERjdrZGb6WCHynmatMcbGj4vo7kHWjPElZ9Ai+gVCzgWCbtQGjWXN5zdg8Hqi6FwTLWfMyAs4Jcxabmj4FIkuEXhJimA53K3Bq6obDzBsOrz6XU24tjR/zP5v/H7ld+gZZbzY7980SNJjYGc/NNco6BqUcUDI391RaB6OiGhhYnG3lzyiXEwHV9Zto+++bqZIxMkuYexbTdNr6KUbGezCsSdHEp6CFuHFNzQzwvgmqdSKCSTQpIhkEbL9IYzOWZaLjsPVinXvPodyLm/TY9GYuq79NwI2bDgCU5IRNGIC0OHRAa8xFnLgtYfVbA2rURugmqBY4Gs3NQ6oMwAtMmCUDfvMJx2ALa4Volb71S0b1KIXH8RmyrtGTPysKjw18lDfhYqynHWkU51nl+Xd5WkLJp0yb+4z/+g8cee4xCodDZQ1IqlchkMpRKJW699VY++9nPUqlUKBaLfPrTn2bDhg28//3vB+DKK69k7dq1fPSjH+WLX/wi4+Pj/N3f/R2bNm066mrJsTg4thtD14hNg4IM0JPVQBn8bN9LvPrqQRqzJdyWi2UBuoEZ5inkDc5Z7VAq9/P8jpeZrk1hW1l8v8lQuZdzz1yBGPsJKBAZBrPzu1GeSckaQK/bSDugkiuDruMEJrv83TTaB4mjFgdqLZYsO4PxQwdoN+v4voeuKTQnR63VIq8LhUyGubkqEBNHIRIlYlESR+hKYZjJS6JrirbbJopi8tksAmRtG003MEwrKQxlmCxZtpKJqTlGRnrprZRYvfoMfvLsDzj7vJWsPmM5QeRTsedpt5rk+1YTxR5uc544FLx2Gx2fMIiYn2/R2ztIKYwpZQ5RKvdjW0ImkyiSRpGQsQWJYGp6lmrdZcdL/0PG1NA1C9PWyGUtlgwvZ3ZqH825eXQtqSjruS6aMghjn1ZjHgl8nGIeHY+MqeFGgp2xcLIZMoVBssV+NKWTy3kYmiIMPRqxzwuNrbwvvphM0eZnu3/EmuBiVEPoC3JIH6xaciH9E8upDFfYv+9FZiZnGRm4CNMbQJux2O9sIQpjMvUi2WyBUrWPqA4T8QwT5hyueIRzbfz9HkRQ1jIwDEErwqvGjA2NYdlJDZ4aIbM5H8fNEmfbjIb7mdk3gx7q1Pur9OSyNI0AzdTQjWSDrB+EzNWFpgea7i/s/1Bodp6MFi18GC4U90pyEbHzYCrBaoPbFOyyxkjWwBlQNOdDolgIRRHEoCIhjmB6ZqHiaznRZgkF7KyiXAAvJ4ib3MSyBpRLMDYF9fl5Xqk9x/sqH8TRcogKFzblLW7Yhrrb5PXmDJkCOEYiCNcOYKzhMqdD6CZ7X3SEVi253/qaEBkgmiLwwJ2OOdAM8Ftj+F6TgUKOyBYKtpUEdjMhUV0IozCpVRUm88jlCyilUe7pxzBNpicOEcUxjdocSjeSSsSGSRBGxLHCdV2MwMNttymWe8kXSiBgWQ75Ui8Hx/bS9nz8+QamZYPS8QMfJKlg7HkuoOF7Lr7XZnTXK4CO20oeMY25e3EyWZxsHq9Zozk/h5PJ4vs+juPQqNfQNIVp2pQKRQr5AgGK+bkZCuUelEre37lCEtSYloPnery2+f+i5ud43+XXUS4OLsic+1QnX2Xn1s00xg7gzhxiacXBKlZYcs6lrLnodyn1L8NzA1qeIrCKTAg08TACDU3vxVFV4kgnnzUJwhi7rlFvuNiqzexEi2Y9oCk6vueT04XejEUY+sw1PXqyDkoiRnpLVE2boYLGgchkaLhC/7lnY2UdirVBqvOzhGFMGLQRyRyWIqtQykBTEUpXOLkKS8/9P8xMzlL98fPoKsJUip6iyWQjQteEiVoVS1esf99KyrNFxqfa1FoNQt8nQPFby/Mcmg0wdIOD4w3C0KfSU2C85mMqk0B5hEDGMNDimFLOIg4CmhH0lDTKWYtdEy30+hwr+8uMLCnys4NN9k6GrKgUqbsN9Dik5OTJWha+CujVIuY9jx5LI2vozHgB7UZMpdemnNWYnPHJZRwaqshUy8Ppn8EQwfcUlqmwdWhOQKasYTuC7idVtuGwoOGwAOXNGTS6rh/xGdi1EiLJI9lEz607MHnzfpfFtkWOpQp7xOrNYce8ce43Fmx+XfG2N/O2gpQHHngAgA996ENd7Q899BC33HILAPfffz+apnH99dd3ibktous63/nOd7j99tvZsGEDuVyOm2++mbvvvvttT/7g2J7kRTZtylqEm9FozbU5NHmA0LPIORa6ahCHIYWMw+ply1gy0kfdbbP11e1U6zMIibpmj1Ni7ZklSj2TKE1RqzVoNn2MOCQXrWAwOIvh0pmsKK0kWxeqB0aZHp3AbRzAYx+7D01T6h/h0IFR2m4TXdewTJN8xsHSDdokstWe5+K5TcLAx7IcPNdNdEAWwt8oijolsOMwwjINsrZFFEREXjI29l2sfJlGfYZ4QQzO9zzavkemZynvuXADbrPK/ok9WKbJ3FSDQmUp2ayGlang5nM0Gk28Vg1d1xC9D9PO0/bBzPezas0FRO055udnMfQQQ9MI2i18IibHJ5mtBvT0LmFq4iB6Txk7YyeFp3SD6elx/EYVTTew8CBoJo6mKyxdQwG5TBZDT2SbbTuflAQwTSwnn+he+B6WmazIhEEi0Fer1/HyEWPxfi5dtZFieYByth/d8ChUD3Jw7895bT7HobF9OLO9eNEMjflxDrw0zrxdJ2zX6Y8GCFDEtQjDshiPJ5myZmlLQBQKoefjtVyCWR8JhHbFQnkObTcgDCKCQsD4ioMQauypPU5MzPBcL4YFbcPFG5tnhT3EVQM3oFaX+Pr4t9gTvo7SkhpPSlPoeiLtn2gwqSTlGFCmDSqRlA/DkDAUdFFUyjaTQqKJEicqy9gxmTxYPeD6kI+EhgbRwjvaVULogucDWiICp5lJsUI7I1g+DCjos3R0VzFV1FCag9+Y47X8DznDXoejFZNsHpWkOEbi8sr0q7Q8H93RCPSYWMCJFboHgVK0PcFxFFlb0aqDNw9BAcRJNHekKcQ1CFtQ02MOqjphX4N8r46yNJJbLFhGIpmeKNQKlmUnukKaxsTYAaxMFhb6UYpmbY5GbTZ5vJLNgwK3WSeXL1AsV7AsO3nfeS1sy8G0MzgLQZFuGh2hOMMwiCXuyNXreqL82241yJd6ma/OMT1+EM/zyGQdtNBHlIag09NboVgZYPzAAQo9FfLlHiwruY5CTx9GJs/swX1kiyXMVrJKZFkORjaHSPJFJY5CinMG09//Ps8c2s+6a25gcGg1kTvJ7p9+F1u1yWstKv1g9w3T/97rWHX+5RTKFTTdIPBjTCfDvK8xMTeLp4GdyeNoNjUUvUroKWWZnZ3HyRnEgUuhYDM+28YPQmKlYaNwgDOWmuz4xTy2o5EtZAk9oWb7uFMBQ2tHmH25hipneG1+Gr0FA1aOXDHZ1E3UQCijSLKgFrfZKKWQWGhUp6jX5ll6weU0Io19e2b4rQvOZ9+hafp7YwIR+t02BC776hY9S1bQG71O/4plRLEQhREUC0hwgFXLRxjKKl7ae4BmvYWmNJq+hxvH5CydlhdQtE0qtjAVxqBMDGVh6CE5yySbtdk365LNF8iaQr9joQnkrEQawlcKI5PH0zWmZ+fBD5hre6zuK3B2FsygjdPXy+REgGfqxLFLtWnxva0xH7hKoxxHmD2Cu0+RzcD0lGJqFPoHFVYlsUccxQsFMY8UPVvcUHu40Nrihtg4lk6Qs2Dgzi6mowUVi+d8O6scb/Vop1uJdlEpN3rjdT5KoJKUxviVf/Ubx8lvOuw5DtRqNcrlMp/5+HXoho5umFiFHLpj4sUBdbdOFAVouo7vtjE0RS6rMdC7jKbnUa1X8f0Q12+CJLLDA5U8xXKErukEoRAGAiIoP0OWXrJ6hoyexxYb8X2CuTq1Zo2ZYJZZqdMIDfwgJPA8FqvPWoaOk8kQxkK71cRykvpCnptkJVi2he8Hyb4UWciDB4wFifHkwy2piCxhIsP/ngvOZ2SgF2XlqM5NYdo5vLZHozmP0k0S8VqfRnWGKPKxnRKxRMRRQBS0CcIIQScME5XbWBmAhqabneJqSgl+q0oU+piGjkResize9mi6AX4A6DpRGGFZSZyr6SrRw5AQXYFuWRBHuO1woeBZTBQlq5u2ZSdyz0qhawrPb2MZFk42m3wLUAaWmSGpyJqsrlXDGquLZ5NxMsSeRhC0KWQrhEGbanOCrJGn3N/Hvtd/ztx8lWKhTBC0mPcbWLqFsnQi00ATHVeaBHZEM042RC4I/hIt1t/wktev2GvS22/jtzXmxwKsXoVVTPZIBEFSayfbzmDEBp4RUJi1KFu9LB04k8xAmT0ze9jZ/DmQfMvQNLVQxVQt3FBUJzB54xl+opUQhiGWGJR0l1mvShiCFoKWEcwsECrcNgR1iEPwYoiVYDvJfhF0ASPZ1hBFgmlCNivoCjRRZJRORun4XsRsSyejcijNp603KJgFHDWYiJYsTN6LW4y3x5AwRjcUsZEETjbJaxiJJPoShsJQ4HtCFCliSxBDITFEDWjXYkSBYSQS+Nlckm1kOYmkgNvU0KXE+ed+kGwuT6NWw84kCq2B79FuNnByOaIwwvPaaJqJZRr4bgtRSTXYRBo/RGnJYyPTttE1ncUS9HGQ1D5Rmo7XaqDpOoaVrL9rSu98GMRxTLNZR9cSWfL56gyNag2UQtcTSXuldCzHJpsrojRF6HtohpGoRuuJNkWskvex225CHJLNlwgCHyeTQzeTGkJ+4FOdmybeP4nym7h4WANlBs9YA2GL2bGdWI5Fe26GrGNiFlaQ6z8Ly0k0dlCKMAzZu3sXBw8eZL7lEhBj2BYmYPo1spYi1hUVZTI510A3wTR0Gq2III6JtSSb0NahWHaozbYpZC3QNUJNw3VDbEMnX84xUxNiy0E0hRKSTJiMRU8+z/Iz+jHsCkolwVhCosMTRwG1mQk8t55Uf4/aTE/O4IZZRl8fZWR4mImJCbIZm2y+wMzMDH0DgxhxjNIVczNTlIs9aLk89fo8OcfCiAMOHJqk1WoThxFuFKKIKTgGXqCSx9WENMKYnkyOQGJcz0uqZ8ca7cAj7yRfJn3fhzhA1xToOkEYYWoWkUSYhiJjKmpND0tTFLM6hoTMu2DFisDILKyG6+Qdj4vXuegL6fNRSxHWoV2HVh0cR9GuDJI/60NYCyUflFJdSjnJ3nnVCWCSNnXYB/1bPLLp/LMw5E1BQ2clZfGXdH6fWjzgiPPJYn9nySRpVYf9vPDnLee1eN5dv/g5X7j7PqrVKqVS6S3GHnbUyRikvPbaa6xe/RZ6AikpKSkpKSknNPv37+9KvHkrTsoCg4vicaOjo79SJHY6sJiWvX//forF4rs9nXed1B5Hktqkm9QeR5LapJvUHkfy69pERKjX64yMjPxK40/KIGUxb7xUKqWO8yaKxWJqk8NI7XEkqU26Se1xJKlNukntcSS/jk3ezuKC9suHpKSkpKSkpKQcf9IgJSUlJSUlJeWE5KQMUmzb5q677nrbuiqnMqlNukntcSSpTbpJ7XEkqU26Se1xJMfbJidldk9KSkpKSkrKqc9JuZKSkpKSkpKScuqTBikpKSkpKSkpJyRpkJKSkpKSkpJyQpIGKSkpKSkpKSknJGmQkpKSkpKSknJCclIGKf/6r//KGWecgeM4rF+/nueff/7dntJvnHvuuYeLL76YQqHAwMAAf/AHf8DOnTu7xnzoQx/qKrGtlOKTn/xk15jR0VE+/OEPk81mGRgY4POf/zxhGB7PS/mN8Q//8A9HXO8555zT6Xddl02bNtHb20s+n+f6669nYmKi6xynkj0AzjjjjCNsopRi06ZNwKnvIz/4wQ/4/d//fUZGRlBK8eijj3b1iwh///d/z/DwMJlMho0bN7Jr166uMbOzs9x0000Ui0XK5TK33norjUaja8zLL7/MpZdeiuM4LFu2jC9+8Yvv9KX9rzmWTYIg4I477uC8884jl8sxMjLCxz72MQ4dOtR1jqP51b333ts15mSxyS/zkVtuueWIa7366qu7xpxOPgIc9Z6ilOK+++7rjDluPiInGQ8//LBYliUPPvig7NixQ2677TYpl8syMTHxbk/tN8pVV10lDz30kGzfvl22bdsmv/d7vyfLly+XRqPRGXP55ZfLbbfdJmNjY52/tVqt0x+GoZx77rmyceNGefHFF+Xxxx+Xvr4+ufPOO9+NS/q1ueuuu+S9731v1/VOTU11+j/5yU/KsmXL5KmnnpIXXnhB3v/+98sHPvCBTv+pZg8RkcnJyS57PPHEEwLIM888IyKnvo88/vjj8rd/+7fy7W9/WwB55JFHuvrvvfdeKZVK8uijj8pLL70kH/nIR2TlypXSbrc7Y66++mq54IIL5Cc/+Yn88Ic/lDPPPFNuvPHGTn+tVpPBwUG56aabZPv27fKNb3xDMpmMfPnLXz5el/m2OJZNqtWqbNy4Ub75zW/Kq6++Klu2bJFLLrlE1q1b13WOFStWyN13393lN4ffe04mm/wyH7n55pvl6quv7rrW2dnZrjGnk4+ISJctxsbG5MEHHxSllOzZs6cz5nj5yEkXpFxyySWyadOmzv+jKJKRkRG555573sVZvfNMTk4KIP/93//dabv88svlM5/5zFse8/jjj4umaTI+Pt5pe+CBB6RYLIrnee/kdN8R7rrrLrnggguO2letVsU0TfnP//zPTtvPf/5zAWTLli0icurZ42h85jOfkdWrV0scxyJyevnIm2+2cRzL0NCQ3HfffZ22arUqtm3LN77xDREReeWVVwSQn/70p50x3/ve90QpJQcPHhQRkX/7t3+Tnp6eLnvccccdsmbNmnf4in59jvYB9Gaef/55AWTfvn2dthUrVsj999//lsecrDZ5qyDl2muvfctjUh8Rufbaa+V3fud3utqOl4+cVI97fN9n69atbNy4sdOmaRobN25ky5Yt7+LM3nlqtRrwRgXoRb7+9a/T19fHueeey5133kmr1er0bdmyhfPOO4/BwcFO21VXXcX8/Dw7duw4PhP/DbNr1y5GRkZYtWoVN910E6OjowBs3bqVIAi6fOOcc85h+fLlHd84Fe1xOL7v87WvfY0//dM/RSnVaT/dfGSRvXv3Mj4+3uUTpVKJ9evXd/lEuVzmfe97X2fMxo0b0TSN5557rjPmsssuw7KszpirrrqKnTt3Mjc3d5yu5p2jVquhlKJcLne133vvvfT29nLhhRdy3333dT0CPNVssnnzZgYGBlizZg233347MzMznb7T3UcmJib47ne/y6233npE3/HwkZOqCvL09DRRFHXdUAEGBwd59dVX36VZvfPEccxf/dVf8cEPfpBzzz230/4nf/InrFixgpGREV5++WXuuOMOdu7cybe//W0AxsfHj2qrxb6TjfXr1/PVr36VNWvWMDY2xj/+4z9y6aWXsn37dsbHx7Es64gb7eDgYOdaTzV7vJlHH32UarXKLbfc0mk73XzkcBbnf7TrO9wnBgYGuvoNw6BSqXSNWbly5RHnWOzr6el5R+Z/PHBdlzvuuIMbb7yxq6LtX/7lX3LRRRdRqVR49tlnufPOOxkbG+NLX/oScGrZ5Oqrr+a6665j5cqV7Nmzh7/5m7/hmmuuYcuWLei6ftr7yL//+79TKBS47rrrutqPl4+cVEHK6cqmTZvYvn07P/rRj7raP/GJT3R+Pu+88xgeHuaKK65gz549rF69+nhP8x3nmmuu6fx8/vnns379elasWMG3vvUtMpnMuzizE4OvfOUrXHPNNYyMjHTaTjcfSfnVCYKAP/qjP0JEeOCBB7r6PvvZz3Z+Pv/887Esiz//8z/nnnvuOeXq2PzxH/9x5+fzzjuP888/n9WrV7N582auuOKKd3FmJwYPPvggN910E47jdLUfLx85qR739PX1oev6ERkbExMTDA0NvUuzemf51Kc+xXe+8x2eeeYZli5desyx69evB2D37t0ADA0NHdVWi30nO+VymbPPPpvdu3czNDSE7/tUq9WuMYf7xqlsj3379vHkk0/yZ3/2Z8ccdzr5yOL8j3W/GBoaYnJysqs/DENmZ2dPab9ZDFD27dvHE0880bWKcjTWr19PGIa8/vrrwKlpk0VWrVpFX19f13vkdPQRgB/+8Ifs3Lnzl95X4J3zkZMqSLEsi3Xr1vHUU0912uI45qmnnmLDhg3v4sx+84gIn/rUp3jkkUd4+umnj1g2Oxrbtm0DYHh4GIANGzbws5/9rOsNtnhDWrt27Tsy7+NJo9Fgz549DA8Ps27dOkzT7PKNnTt3Mjo62vGNU9keDz30EAMDA3z4wx8+5rjTyUdWrlzJ0NBQl0/Mz8/z3HPPdflEtVpl69atnTFPP/00cRx3AroNGzbwgx/8gCAIOmOeeOIJ1qxZc1Iu4y8GKLt27eLJJ5+kt7f3lx6zbds2NE3rPPY41WxyOAcOHGBmZqbrPXK6+cgiX/nKV1i3bh0XXHDBLx37jvnI29pmewLw8MMPi23b8tWvflVeeeUV+cQnPiHlcrkrO+FU4Pbbb5dSqSSbN2/uSvFqtVoiIrJ79265++675YUXXpC9e/fKY489JqtWrZLLLrusc47F9NIrr7xStm3bJt///velv7//pEkvfTOf+9znZPPmzbJ371758Y9/LBs3bpS+vj6ZnJwUkSQFefny5fL000/LCy+8IBs2bJANGzZ0jj/V7LFIFEWyfPlyueOOO7raTwcfqdfr8uKLL8qLL74ogHzpS1+SF198sZOpcu+990q5XJbHHntMXn75Zbn22muPmoJ84YUXynPPPSc/+tGP5KyzzupKL61WqzI4OCgf/ehHZfv27fLwww9LNps9YdNLj2UT3/flIx/5iCxdulS2bdvWdW9ZzMJ49tln5f7775dt27bJnj175Gtf+5r09/fLxz72sc7vOJlscix71Ot1+eu//mvZsmWL7N27V5588km56KKL5KyzzhLXdTvnOJ18ZJFarSbZbFYeeOCBI44/nj5y0gUpIiL/8i//IsuXLxfLsuSSSy6Rn/zkJ+/2lH7jAEf9+9BDD4mIyOjoqFx22WVSqVTEtm0588wz5fOf/3yXBoaIyOuvvy7XXHONZDIZ6evrk8997nMSBMG7cEW/PjfccIMMDw+LZVmyZMkSueGGG2T37t2d/na7LX/xF38hPT09ks1m5Q//8A9lbGys6xynkj0W+a//+i8BZOfOnV3tp4OPPPPMM0d9n9x8880ikqQhf+ELX5DBwUGxbVuuuOKKI+w0MzMjN954o+TzeSkWi/Lxj39c6vV615iXXnpJfvu3f1ts25YlS5bIvffee7wu8W1zLJvs3bv3Le8ti9o6W7dulfXr10upVBLHceQ973mP/PM//3PXh7bIyWOTY9mj1WrJlVdeKf39/WKapqxYsUJuu+22I770nk4+ssiXv/xlyWQyUq1Wjzj+ePqIEhH51dddUlJSUlJSUlKODyfVnpSUlJSUlJSU04c0SElJSUlJSUk5IUmDlJSUlJSUlJQTkjRISUlJSUlJSTkhSYOUlJSUlJSUlBOSNEhJSUlJSUlJOSFJg5SUlJSUlJSUE5I0SElJSUlJSUk5IUmDlJSUlJSUlJQTkjRISUlJSUlJSTkhSYOUlJSUlJSUlBOS/w8hj8SNbKSypwAAAABJRU5ErkJggg==\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": "xAyxAXAicEu8"
      },
      "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": 43,
      "metadata": {
        "id": "CRFFQeBucEu8"
      },
      "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": "gFSYAOD4cEu8"
      },
      "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": 44,
      "metadata": {
        "id": "tnveQZD0cEu8"
      },
      "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": "octf05h1cEu8"
      },
      "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": 45,
      "metadata": {
        "id": "R_1TsI-zcEu8"
      },
      "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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z4jMByjkcEu8"
      },
      "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": 46,
      "metadata": {
        "id": "-rTgki5ycEu8"
      },
      "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": "1gJgwg6GcEu8"
      },
      "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": 47,
      "metadata": {
        "id": "qPT5EdxxcEu8"
      },
      "outputs": [],
      "source": [
        "criterion = nn.CrossEntropyLoss()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GepjtiALcEu8"
      },
      "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": 48,
      "metadata": {
        "id": "Iox9dWlOcEu8"
      },
      "outputs": [],
      "source": [
        "optimizer_hybrid = optim.Adam(model_hybrid.fc.parameters(), lr=step)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sbLNf2wccEu8"
      },
      "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": 49,
      "metadata": {
        "id": "xJcZ5B3JcEu8"
      },
      "outputs": [],
      "source": [
        "exp_lr_scheduler = lr_scheduler.StepLR(\n",
        "    optimizer_hybrid, step_size=10, gamma=gamma_lr_scheduler\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gogMl00McEu8"
      },
      "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": 50,
      "metadata": {
        "id": "BCfFACd6cEu9"
      },
      "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": "XpEBNYtocEu9"
      },
      "source": [
        "We are ready to perform the actual training process.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "voQ2Yb7QcEu9",
        "outputId": "5b8a437d-68c2-43b3-d162-da1935e75e60"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training started:\n",
            "Phase: train Epoch: 1/25 Loss: 0.5122 Acc: 0.7705        \n",
            "Phase: validation   Epoch: 1/25 Loss: 0.3870 Acc: 0.8627        \n",
            "Phase: train Epoch: 2/25 Loss: 0.3569 Acc: 0.8852        \n",
            "Phase: validation   Epoch: 2/25 Loss: 0.2870 Acc: 0.9346        \n",
            "Phase: train Epoch: 3/25 Loss: 0.2854 Acc: 0.9221        \n",
            "Phase: validation   Epoch: 3/25 Loss: 0.2541 Acc: 0.9412        \n",
            "Phase: train Epoch: 4/25 Loss: 0.2830 Acc: 0.9180        \n",
            "Phase: validation   Epoch: 4/25 Loss: 0.2239 Acc: 0.9542        \n",
            "Phase: train Epoch: 5/25 Loss: 0.2897 Acc: 0.9016        \n",
            "Phase: validation   Epoch: 5/25 Loss: 0.2190 Acc: 0.9412        \n",
            "Phase: train Epoch: 6/25 Loss: 0.3371 Acc: 0.8525        \n",
            "Phase: validation   Epoch: 6/25 Loss: 0.2193 Acc: 0.9477        \n",
            "Phase: train Epoch: 7/25 Loss: 0.2279 Acc: 0.9344        \n",
            "Phase: validation   Epoch: 7/25 Loss: 0.1934 Acc: 0.9542        \n",
            "Phase: train Epoch: 8/25 Loss: 0.1857 Acc: 0.9590        \n",
            "Phase: validation   Epoch: 8/25 Loss: 0.1849 Acc: 0.9477        \n",
            "Phase: train Epoch: 9/25 Loss: 0.1699 Acc: 0.9672        \n",
            "Phase: validation   Epoch: 9/25 Loss: 0.1949 Acc: 0.9412        \n",
            "Phase: train Epoch: 10/25 Loss: 0.2586 Acc: 0.8934        \n",
            "Phase: validation   Epoch: 10/25 Loss: 0.2709 Acc: 0.8954        \n",
            "Phase: train Epoch: 11/25 Loss: 0.1931 Acc: 0.9385        \n",
            "Phase: validation   Epoch: 11/25 Loss: 0.1957 Acc: 0.9346        \n",
            "Phase: train Epoch: 12/25 Loss: 0.1384 Acc: 0.9754        \n",
            "Phase: validation   Epoch: 12/25 Loss: 0.1845 Acc: 0.9542        \n",
            "Phase: train Epoch: 13/25 Loss: 0.1770 Acc: 0.9467        \n",
            "Phase: validation   Epoch: 13/25 Loss: 0.1783 Acc: 0.9608        \n",
            "Phase: train Epoch: 14/25 Loss: 0.2017 Acc: 0.9262        \n",
            "Phase: validation   Epoch: 14/25 Loss: 0.1853 Acc: 0.9477        \n",
            "Phase: train Epoch: 15/25 Loss: 0.1826 Acc: 0.9508        \n",
            "Phase: validation   Epoch: 15/25 Loss: 0.1756 Acc: 0.9608        \n",
            "Phase: train Epoch: 16/25 Loss: 0.1523 Acc: 0.9713        \n",
            "Phase: validation   Epoch: 16/25 Loss: 0.1842 Acc: 0.9412        \n",
            "Phase: train Epoch: 17/25 Loss: 0.1495 Acc: 0.9672        \n",
            "Phase: validation   Epoch: 17/25 Loss: 0.1780 Acc: 0.9477        \n",
            "Phase: train Epoch: 18/25 Loss: 0.1809 Acc: 0.9426        \n",
            "Phase: validation   Epoch: 18/25 Loss: 0.1732 Acc: 0.9608        \n",
            "Phase: train Epoch: 19/25 Loss: 0.1478 Acc: 0.9549        \n",
            "Phase: validation   Epoch: 19/25 Loss: 0.1691 Acc: 0.9608        \n",
            "Phase: train Epoch: 20/25 Loss: 0.1654 Acc: 0.9590        \n",
            "Phase: validation   Epoch: 20/25 Loss: 0.1771 Acc: 0.9477        \n",
            "Phase: train Epoch: 21/25 Loss: 0.1824 Acc: 0.9508        \n",
            "Phase: validation   Epoch: 21/25 Loss: 0.1694 Acc: 0.9673        \n",
            "Phase: train Epoch: 22/25 Loss: 0.1476 Acc: 0.9713        \n",
            "Phase: validation   Epoch: 22/25 Loss: 0.1810 Acc: 0.9542        \n",
            "Phase: train Epoch: 23/25 Loss: 0.1782 Acc: 0.9508        \n",
            "Phase: validation   Epoch: 23/25 Loss: 0.1812 Acc: 0.9477        \n",
            "Phase: train Epoch: 24/25 Loss: 0.1671 Acc: 0.9508        \n",
            "Phase: validation   Epoch: 24/25 Loss: 0.1718 Acc: 0.9673        \n",
            "Phase: train Epoch: 25/25 Loss: 0.1456 Acc: 0.9631        \n",
            "Phase: validation   Epoch: 25/25 Loss: 0.1818 Acc: 0.9542        \n",
            "Training completed in 4m 49s\n",
            "Best test loss: 0.1691 | Best test accuracy: 0.9673\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": "OitpTjhacEu9"
      },
      "source": [
        "Visualizing the model predictions\n",
        "=================================\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OGIiHKWHcEu9"
      },
      "source": [
        "We first define a visualization function for a batch of test data.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {
        "id": "lW9ytOLBcEu9"
      },
      "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": "837ToUo2cEu9"
      },
      "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": 53,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "id": "dMVtD1GScEu9",
        "outputId": "edb790fd-f9d4-429e-ae5d-a734d07192c5"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 8 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "visualize_model(model_hybrid, num_images=batch_size)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HywUAsp3cEu9"
      },
      "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": [],
      "gpuType": "V100"
    },
    "accelerator": "GPU"
  },
  "nbformat": 4,
  "nbformat_minor": 0
}