{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "## Import necessary libraries"
      ],
      "metadata": {
        "id": "Tf5TguXGa44T"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "UDW1SSaBZwSS"
      },
      "outputs": [],
      "source": [
        "import zipfile\n",
        "from google.colab import files\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras import datasets, layers, models\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "from tensorflow.keras.callbacks import EarlyStopping\n",
        "from sklearn.metrics import confusion_matrix, classification_report\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Load and preprocess the CIFAR-10 dataset"
      ],
      "metadata": {
        "id": "5htarRIevfoK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Please upload your cifar10.zip file\")\n",
        "uploaded = files.upload()\n",
        "zip_file_name = list(uploaded.keys())[0]\n",
        "extract_dir = 'cifar10_data'\n",
        "with zipfile.ZipFile(zip_file_name, 'r') as zip_ref:\n",
        "    zip_ref.extractall(extract_dir)\n",
        "\n",
        "print(f\"\\nFile '{zip_file_name}' has been successfully unzipped to '{extract_dir}' directory.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 123
        },
        "id": "Gy70r9_haoR9",
        "outputId": "4da42f9a-7b06-4f5b-cccc-4921d739a444"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Please upload your cifar10.zip file\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-14294308-1067-433b-bc3b-6244d12e59cf\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-14294308-1067-433b-bc3b-6244d12e59cf\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving cifar10.zip to cifar10.zip\n",
            "\n",
            "File 'cifar10.zip' has been successfully unzipped to 'cifar10_data' directory.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Load and Preprocess the Data"
      ],
      "metadata": {
        "id": "CNXvsdp5b4G5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_dir = f'{extract_dir}/cifar10/train'\n",
        "test_dir = f'{extract_dir}/cifar10/test'"
      ],
      "metadata": {
        "id": "8Mw6UmOabJtC"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "(x_train, y_train), (x_test, y_test) = datasets.cifar10.load_data()\n",
        "\n",
        "x_train, x_test = x_train / 255.0, x_test / 255.0\n",
        "\n",
        "class_names = ['airplane','automobile','bird','cat','deer','dog','frog','horse','ship','truck']\n",
        "\n",
        "plt.figure(figsize=(10,2))\n",
        "for i in range(10):\n",
        "    plt.subplot(2,5,i+1)\n",
        "    plt.imshow(x_train[i])\n",
        "    plt.title(class_names[y_train[i][0]])\n",
        "    plt.axis('off')\n",
        "plt.show()\n",
        "\n",
        "print(f\"Training data shape: {x_train.shape}, Test data shape: {x_test.shape}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 230
        },
        "id": "0-pdcHM2bwqx",
        "outputId": "66ac1ba3-3fdd-4d00-eff4-1a8153c3bc36"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x200 with 10 Axes>"
            ],
            "image/png": 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T5/jhH/5h7rrrrj9z39AjPQ+Iss5J/Mt/+S8BHqZu+MADD5xSuDk4OODHfuzHeMYznvEhU2wAXvCCFxBj5Du/8zsf9rcQwodtGPjnMjP/RV/0RXz2Z3823/Zt38Z9993HJ37iJ/LLv/zL/Kf/9J942cteth1o99xzD1/6pV/KD/zAD3D16tWtNOW4yzrrssuEjw7cc889AHzbt30bX/ZlX4Zzji/6oi/6oMfv7u7y/Oc/n1e96lUopXjyk5/Mf/kv/+UROW//+l//a575zGfyV/7KX+HFL34xd999N/fddx+/8Au/wJvf/OaHHa+15id+4id47nOfywte8AJ+8Rd/kc/5nM/5iL3WCR+dmMbghI8WvOIVr+B1r3sdn/mZn8nXfu3XEkLgVa96FU9/+tP5vd/7PUCCt+/6ru/iW7/1W7nvvvt47nOfy3K55F3vehf/8T/+R1784hfzTd/0TWit+ff//t/znOc8h6c//el85Vd+JU984hO5//77eeMb38jOzg4///M//yi/4gkfDF/4hV/Ij//4j7O7u8vHf/zH89u//du84Q1v4MKFC6eO+7zP+zzuuOMOvvqrv5pv/uZvxhjDj/zIj3Dp0qWHJRHuuecefuiHfojv+q7v4ilPeQq33HILn/M5n8O3fMu38JM/+ZM85znP4aUvfSnnz5/nNa95De9617v4mZ/5mY8YvfoTP/ETeeELX8gP//APc+PGDe69917+1//6X7zmNa/huc99Lp/92Z996vi/8Bf+Al/91V/Nm970Jm699VZ+5Ed+hAcffJBXv/rVH9bz3nvvvbzkJS/he7/3e3nzm9/M533e5+Gc4+1vfzs//dM/zb/6V/+K5z3veR/6CT8s7ZtHER9MmnI+nz/i8YeHh/kf/sN/mJ/whCdk51x+6lOfml/5yleeksPKOefVapW/7uu+Lp8/fz4vFov83Oc+N7/tbW/LQP5n/+yf3dTXNOGjF9/5nd+Zn/jEJ2at9VZKC8hf93Vf94jHX758OX/pl35pns1m+dy5c/klL3lJ/v3f//2HyQLmnPPv//7v57/5N/9m3tvby03T5Kc97Wn527/927d/PykLOGK9Xud77703LxaL/D/+x/+4Ka95wkcXpjE44aMFv/Zrv5bvueeeXFVV/tiP/dj8b//tv31E+cGf+Zmfyc985jPzfD7P8/k8f9zHfVz+uq/7uvy2t73t1HG/+7u/m7/kS74kX7hwIdd1ne+88878ghe8IP/X//pft8c80hic8Oji+vXr+Su/8ivzxYsX82KxyM9+9rPzW9/61nznnXfmF77whaeO/Z3f+Z38qZ/6qbmqqnzHHXfkf/kv/+UjSlO+//3vz1/wBV+Ql8tlBk7JVL7zne/Mz3ve87bz1Kd8yqfk//Jf/sup53mk2DDnYxnMN73pTafuf6Rx5b3Pr3jFK/Ldd9+dnXP5SU96Uv7Wb/3W3HXdqcfeeeed+Qu+4Avy61//+vyX//JfznVd54/7uI972HN/KNKUI374h38433PPPblt27xcLvNf+kt/Kf/jf/yP8wMPPPCwY/80qJwnF4aH4s1vfjOf9EmfxE/8xE9sHWUnTJgwYcKECRMmTPhow59LzvyHg81m87D7fuAHfgCtNc961rMehSuaMGHChAkTJkyYMOFDw59LzvyHg3/+z/85v/M7v8Nnf/ZnY63ll37pl/ilX/olXvziFz9Mn3TChAkTJkyYMGHChI8mPO5pNr/yK7/CK17xCt7ylrdwdHTEHXfcwVd8xVfwbd/2bVj7uN/rTJgwYcKECRMmTPgoxuM+mJ8wYcKECRMmTJgw4bGKxz1nfsKECRMmTJgwYcKExyqmYH7ChAkTJkyYMGHChMcopmB+woQJEyZMmDBhwoTHKG5ah+cz7/0sUkqs10eE4LEqY1Sm0dAamNWGC/OKyhl2FjXOGpbzBmcNRhmM1mhjUcaitAal8SFytFqRUiIU9+DZbEblLConyIkUIyEEYox471FK4SqH0opEIueM9z0hBqq6omlqxOc1yXl9AEBjUGiUUmitqaqKyjmcc1RVhdYaYyoyMPhAyhlQoBQoDShiSsQQSRkSQFZ83Xf925v1lk84gf/vP7+BGDzX3ncf3dENjq4+yObgBjkrctbU7Q47F26jaedcuPWJVHXLfO8CrmpwTqON4url93Lt8p9wdHCNa5fvJ/pA7DpIGZ0MSina2QxXObR1KGO59fYncMfdT2axWHLrbbejFPj1EX7o+ZM/eTdHhwfkHMk5klIkpsDB/j4PPPAn+KFnvT4kxYgPnpQyMSpSVGw2nm4T8CHSD56cEzF5tNbs7CyoKsts3uCcIdOT8aQIKUGMGT/I+/JzP/v6P/2Nm/ARRc55e7vZjtLjc+ScUZx+ruA9wQf6vuNotcIYzWIxR2tNCh5ywjmHdQ6lDcoYmctQZKXIABm0euiZP/zrAz5i7o0TPjie9IQ5AFVVYYwBxNVcoeRzLDeAmNP2cRlYbzq891hlMGic0lRKE3Oi8wNZgaocymgqazFG1khrLXVd0zQtIXhWqxUxRkIXGNvzcs6kKOvtFlrGhNYKaxRaK2Z1hTOaneWMtq44XK04XK3wMTHEjDGWtp2jlCJGyCnT92tiDOzu1CwWFcbocl65KaX4qZ9/85m8/493/PQv/PZ27gNw1mKNQRuDsRaUIWtLRhGRucagUVmhMuiU0Rk0CjWOHQCjyAoikMbJKGdUlgNU1oAmJUgRMpmkkoxZYJzATraLPrR1NJPJWZ4vA+nErHdyHI+3k2P5xFm3sZ88Rv7yVV/89D/7m/qn4KYF8yEECY5DJIQIKoGCZCArkNg7Iq8xQVby7wRZQ4oJxskmJ1CaGAMpRmJKpJjJShGjJ2jQKaJyIsZIjJEUo5xPKXLSkMdgXgL+FAM5GXJK5PJx5ZTJOUHOZBQyMjQpJ1LUJK2ICqLWZC3nzCDnSxmULHyyAGq51hDJQMw3dyGfcBoppRLUgFZablqTsyZljTYGrY1M8ADlE99+fcXbUMZH+VYrsiyCWmG0RStNVTlcVWFchbaOuq5wzmKtnFsrSMaQjKFyjrquyTlIMB4DMWqsc2htUNogzwKgUEqCMll0x42l3FI6XozVyRmK079vJ5zxpU14VHCzA/mTzyFz5sPvz1nmv5wiiUzwHq0V0Uswr43GZAPIuM3y4JMn+ohc34Szw3Gsc2Kjd2q+yGUNzqc+bzXmpZRCl7lHgQRM47lLcLQ9lnGzd7xJ2EZEZPl9m+9S6DLXjdc2bjC00ZLM0+pUIL69ZVApHj8/SsZr2RDkrE4F71rp4xlx0vs4M2zfaXX8j3EtGoPbbYibTz9GHiJB+/FyLOMnl6F08m/juUll9czqVDA+nv2R1sGTwfnpTEUu/z9+AfmRHvOQ+x76+h/p2JuBmxbM/8n9f0JOmfXRIcF7GguVyezUhtwYVG3pY42qHdktIFk8a5IeJwFQyoI2xJQJMRFjousH2SSUD21lJYtvVMIoCeJiLLukLIHX4BwK8NETSzY0pUhoG/Ks3U5GOZfMfAZXgrUYZQHcIEGTtZaqqiS40hYJvo4zWCgl15pK9iHGsimxDxkoE24mNqsjyEmqKbMFDGusSmTlyKqine2we+EWrHNlgoiQBlSC7DNZZeLmCL86Im425GHAKkU7X+CsY3d5nqqq2btwjmbWMlssaGczlssdzp+/gHUVbTsDMrF1xBTZWc4IwZNSycrHQAiB97//faxXPUdHBxwdrYgpSUWKDCVTUWeLMRUhRpwfiDHS9RmtFc4ZrNMolcmksoA5YvAMw0CMEPy0iD1aeFSC2Ic8ZYoe32/oV0esrl8hxcj1GEg50Xc9KWWe8KQncf7SJVyNZOjHxATHW1318FNP+CiFKdWPnJMkm5IEM0ZrMAZyLkmoMkZPBPmVNTijqayjshZiIvtADpk8xHIeh7GaxlmctTgniQznLI01EBTZl8RZDkDGGElyOFdhjMUPA/0woI3GWnnscjnHao3VCqMVO8s5bVNha0cza1lvem4cHqG1xlpJblgr83hdZ1J0LOc187beJjwksSjXMOFsMG4OtdaSyKUkFAZPxqOMRTtAGZS2JQkhm8ZcfiYgjlnyUNgTMYACbaU6PqZjQ/CEEFDKSI5fG5ytJcnKWCFIsqcsYz2f2EVkxqTdNpcn35Ftgu14L3gyKw8nkodj4oSS3YdTx9xM3LRgflXoMOujI4L3ZCdZ+SZZgrJEZUk+k01CpRpSIgUJhnPO5AQoTcYQY2IIUX76Qj8oE5PXkg2wOmM1pCQTlAQ08mGrJNSZwfcSzJfMqNWZYErpUQM5EUNAZcjGkZQmeqkwyIeVMMYSSzBPOT/jQCzlaB8SPkRyeZwMaHez3uoJj4DoPZAxSqOcLDTRVWBq0A1V01I3LdqYEszLtKGycFMyiRw9OQZIEY0sgm3dUFc1u7t7NE3L+QvnaRczdnZ2mS8WtO2MxWKB1gZjLJCJRsZ0U1elJCdjI8RACJ7NpqNtZ3jvgePMEkgGSyWFtaCUQUcFKhMC+FAyT6UsjZLK0phRyxlilCpWSnlKSj2OkVMiRU/wHcNmRfCevlsTY2Sz6UkZzl24QAxhu8EdcZJYMwXyjx3ok9lxTgQgkrnarmsoSoVyrEZLEK2UpqostXMkHwgxFAZpQiHHGK2wxpSbPv6pFEYp4fklCaLkecAYRVXJnAyJED3GaJwzVJWlbWuhY5DRQFU7qsrJaZQmpoxZmy0tRxuFq8pajyNFJRVTa7dVgkhZ9x+VT+LxiTxGxmVDlVMmlQ1kjBllwCiL0oXWrEqgjdBsxrlGlQp5CpGUUlknwSYKfVrC5t4P+OBRGJSyGGNRxsm4Lhyc4yw5x3SbE//d/pZP0mzyiSB+/HnyXKd/V+p4k3BWWXm4icG80/JGJCvf5Z1GM680e7OKc/OKeVOxXMyoK0tV1RirS0ACycuHFnMm5UDKEDNkpTDWyeeSjndakHGVpa40OVGCeY3RVgbS2OerhTITYyCmiNaOmMZJSTiFTklgbo1DK0VWnhwCeuS9ak0SFlehlCqcE15/OBHAjzs1kIW0992UFDhLpLGHIpFjIviI95Gmqpgtd9G2ZvAeSqbcWMts3mKMQmkpRc9mMy5duoTiAurOJ1E7x95yj8pV7CzPY6uKalZjKkPbtNR1jTMGo8dNZCLlRBgG+RkjOeftIuRsReUalstzXLxwG1o5HnDvI8YstDQk8A8hYY3FOUeMBm1gGBKbTo7JxJIBMOSs8F56RobBMwyhcFQf5c9jwplinHsknsv03YqD61fZv3qFB979x8Kh7ztZXJVFGUO/OsJ3nZjlbTmq5aeaAvrHGow+QbtSiqp2GGO2mfqcdckWnt7oK6BtG6rKMWsaZm1Dt15zmAPaQFINSmuWO0usszglVewx614bQ1M5SIlZWxNTJCOV6aZpsNZQ1TXOOiqnMDrhKkc7m2GtYdZWaAV+GEqgFEkkqQLohowiRIn2rDUorXBOsr9WGVIEoxQ5ZtkkWEdEk0w6k6BqgqAbeuA42O37AT94uq5nve4lM1/VaG2p6halNFbZE8kDVcZqErprEKbD0Mt5nbUlqSrhfB8GfBxIWajVdd2yt3uOqqrY2ZkX6qs5phCO1/ZIF7/N5j/8TyeD94c/bKxgHtPVzmrM3bRg3mopzVZaMvIzp1k2lkVjWbYVbVPRNjVVZbHOYcxxQ1TOknkPKW0z8IBkuI3w33JEmk5zABLGGerGlSwkMjBMBZS4LiPBfEoobYRjrxQplazENssuH4i1UmbWKZdd4nE5Jo38QORYUwZJiEkWxyxBHEhQmGLCD36aSM4QOUYgk2Mix1h6NxJaG9p2TsqKIQZSyoTosTEQoydnoU4BNE2N03vUtWM5l4z8+d0LOOdo2x2MtUSdyBqcNSWbxHFWIWdIUu2JKeJLMO+cQyuLsQ5rHbN2znK5Rz94rK3Q2pOlvYeUZWOrnJbHGQUqknLguMAYkYlPbYP5EIIE9SGWcTeFYI8XHM8zI6E044eezeqQw/1rXPvA+wl+IA7SFW3qObaqGfqOMPSk2JzgFuctN1X4GGf9aib8WTEG85TsdF1Jz87gPT7IeqSTVA1jjNsIRwNN5WhnLYtZy3zWoklCXdRQK2moXcxbnHOopKRpEVkPnTE4Y0jW0FQVMUXQEaUVbdvgnMNVrjisR2L0VHXFctlijMFZoQDFINeVkUqpNhZjS8IiyPqqS5+2MYDK6KxJscj0pYwyCqtt+RmmNfgMMZQMeoyyhq2OVmw2HYdHaw4OVqAN2tUYY2naudBilOMkVSWmJOMny1oeQ2DoesiZyoiwyZh172PPEAdSkobotp0TM7RtSzOrQCsqY6Q8NAbkpUp1qqXxBH//5Gh52ND5U5ZVtf3z2NP2p28CPhK4acG8QbKL2imU1uzOKvbmjmXjmFeGyioMEWLGd4qoC/c8wzAEYkjb8ok2GuNsocOYEiMFyEmaBJVw/JrKEoJkYeXD9+QMIUj5JmX5aRRbCgQ5o1JCjc2SpWlGaBeq3Kckk1E2ABotVIacUTmS/CCNttJSj0b4+2OzRc6ZIURymiaSs0KOoQTT8r7rUqnJMdFvNmjjcFWLqQx1u8RVlnMXzlE3pcSrFSkMZO8xRjI/WWlubHroPGYVpeSrI0nJOX3XMW8admdz2qbmwu5u4f/Jlzh4T4iRbtPLODQWax1Hhxu0djjb0DQLYoLN5rD0foiyTs7ggyeEgX7Y4P0AOmNKZcg5Gc/S5Cgb2pFyBjKGJzw+MM5fGcgpQo70mw1HB/v4vqcyBqsqtLVkMj4DKeC7Dd3qiKoW2iNKb1elcbGbRtFjB1VVoQBjDEpr5vM5TVPTDwObvttSUHJKDL1s7OqqwmjDbNYUeovFOUPdVCyW8xIASXNpXdelIdWg8+lmVXLGGs1i3spYNNLLUzfClVdaghznDG1TY53BaFBINRXAWI0xWsZo8KQYiBG8D3g/CFfe2fKzCAZkQ9JgkkFng8qIClmKct4pmD8zPPC+BxD1QE8sCc3gI5tuYL0ZRCRE95KZ33hZo5WoCGaQvrWUCGVNE3ESIIq2TKUDWqnthi5kT8iREDIhZGFzXLlMXdfE5Knrip3FAldVOOskCau0bAjGzlpO8t3HqP541jsVkKtHiObLBuFEKuRh/PqbhZuXmSehVMI4hbGavUXFxWVDazUzpzAGTA6oqPBdAJSUR7LCD3HLqVIGtLK4MgEYayWbGgbIEa0yWkHtNG3l8CrQR/nihiAqM8GHLf8JFK5IaKUk6jhKgUpJ5Ca3kmlFbqicPyPZdoUua1yWbH2C6DskMypPomEbzCckU98PschXTjgLpFCC+SicTa2Ew55iol+vqZqWppXF7cItF6mamt0L53C1LDZaa0Ztx5QjKQpl5eqNI2KI9MOhyD6qQCJy+cH3c/3KZS7tneOOW2/lwt4eO+0cY/X2Cz34gWEY2GwGhj5gtPD6hiGgVIV1LbPZkpRhvT7Ch4RSBmOV0MOCZxg6um5NSgGlQBtFXQsFZxiGwsfPRS1KAvpRFWLC4wiyiySnQIqBzWbFwf4NfLcRKV8MtTFkMgerjhADfrNmfXhAM5uP3V8nzsUUyT/G0NQVozSzMYbFYk7btqw3GyhzgrOWGCNrvUKh2F0sJXPuhApYVw5rNW1Twe5iG8SDIgbJ5ot4pTSwGm0IRRbaGs3OYiZxkskoLQISSquiOpeonEGrWuaokmGNUTiBriTwIBNCoO8DQx+IMQv10Bm0slijqJwu3OtEDAoTDSoK1zcU2mEKE9fwLPHu97yHnDNd3xFjFC47mhAzPgh12hcWhbFrlNLoJGpIqcRPIScCkphISnrgaisU6AqDVgpnNcYokgpklfAhMQyRTd+zf7TCOUvXHdHUNbdcukTbtiwWi0L5stixnzFLDJe2m4ctaV7Suo8Yv+WH/2sb7B/zeR7TwXxlhKtu0RgytZXyWeU0daXRSgk/GcW2DlGa9rTRZBJbzoJWx9JVJYjOSegTWQvtJfhI33uhU/hITBlfuPd9GLnKMjmYsYE2Hb/BYyO/0qIRL5x3aczQ2jC2NyukkSOTj2WQyvUPRcUmxEwozxFiYvCJdT+V+M4SMQTJSjFOFg5X1czncxbLXZp2zvLcBVxV0c5mGGvpBi9jBaGIpeBJIRBjYAg9Q++5ceOIGLPw05EJJBF54P0P8uD99xO6gWXTUlc1GVXoWwatoapqtDbEqMhJNq4pJfp+YH//gNVqRUZJ86y1mGSl30Mpec4k1CGlJECva7cN0nNOhODx3hMK1UySBHI+59z/q7LghD8DzlLJ5qHziyLjQyD4gW6zYbVaE7oNw6aXBEWhM/ihJ0YRKzi4cZ1mvmDoOrR16MpszzZmnU49xzSoPmpR13WhoEhmXiGKGsYY6qbG6MInj5FUhB+apqZyDq1AaaEJDkNfHjdm3gsNQknmUTPKSpbhURIP26V9vGWhRKikCKN8dM7HSnKhqI3rUdFExrOPgRQTMYzr9TZCQiR+M/0QUGShFaZE9BHiWFEvVapp/T1TDCXuikn01rexGyI4orPCqLI+lwbsbSO2kkSo0Lek1zHkJCFXCKWfUcZeVhqDJqsMOiPaKaYktbLMbZsOHyLaXqder1ltOpq6pm7EE0Frad4WQQlzKm+httz5fCJ2P9EUyzEL48SDjjPyD2mGvVm4acH83GogU1mLVZpZ7ZjVFfNKs6wNlIz1+EZkRm1a+SBNFoOKmIVrZ8obbXKGnMgl0Mol0N9senKMwpmPojffDQMxJ/okmf/aNUKviQlTBlbJm0qApBTaWLJSpN6TElTKYbTBoIjIwAhBsuwxxpLxlwG06Tw+Rjqf6L3sEHufiAl8UExTydkhDL2MF2fRxlA1LdZZLl68hdtvu43ZYsm5i7eA1vQpMcTIB27cYDMMdH4ghMjQdQx9zzD0rDYr+sFz48aKnBVNuxQjFhXIRN76B2/lXX/0RzztYz+WxtYY7UhZ5EvHTP/CWomHOMLonr4b6LqBw6MV733v/Qy+JyVk41E3UOhdKSNl4tKHoRVoY3BVyWjpTIyeTbem73vIDoXdNts6Z2nbZsrOP24wLpqZvu/oNmtu7O9z9eo1Yt/hVyuMgsaJ/GnfbUgxcf3yg3SbNcZV7F68happaV2FUjJff7CAfsJHJxYLMY06GURE77HOUs8aCepdRfBhGwjsLpZUzhHTQE6RbtjQbTZbMyittVQbcxaZypwxRVHOSN6BlAKD78pYQXrdslC2QpSxE6Jky40pFdMUiH5AaUVdVyitC2VQsdlsGAYvUpjayUgsjd0xBKLKhK4n57TVwY99Jg2yXo/PU1fVtPk8Q6w7oW6d7NmSpKn0Psom0JwI4jUGi0bhSSJJiSaRJJs/iKJgHyIahVWSFG6w2FQYExo0Gm2NSIMHT/KBq/sHKAVXb9xAa03btFRVxXK5YHdnl7qqWCzmWGuZzcVMz5zwJ1CFkv1QjIZQoxb9SNQ5pXoDZ6Imd/PUbKxYQlTKYJXCmtEEQiExhdpyo9K44VGyd9NZ6Cvimirny1kaUbMqGcpigKKyJiuK/mgsb1omlNuWN6xkhzhuIlSMaJXQZGJOaJNQMWFUIKPwvWQDsrVUWgMOrTJZR7RWZbcv5ZhYnifEKCUeH+l9ZBgi604cYEPSE13vDLE1U1KGrLQ4zWVF1IaAoo+Jo00vm7AQGHzg+o1DNn3P4AMhRcLQE4aBwQ+su45hCPT9AGiqWmhZMXhiGgjeE0MkhoAfRN+9G4bCDR0XNem1sM5RpdIeU3iktjLErFE+oUjSBIZj8JE8+iakkXtHKf0dT5Hb30sFSTaoqihMyG1ayB4PyKd+D97T9z1917PpOvymoz9aYxSESjKwOXrImaEXhYnNes366IiUoZ4vpelam4lm8xiDLrIdacwQRklCKVOC8dJcmHOSeSpLhnyUzj2pzBZjZAheaKhjH0WZj1KKoEqOXgkt8ZhhIP9NKZX7SlY/naAynMh4jkMsl/MfB0aS5pdKI6gssYRU0kf53XR83qCEZpiPFWyUNsdynRNuOsZ+LfnsHsrTG/+WEAFSISUfC41IinTkro/9jPKZisRlzJGEwkd5lCTmy6aScS+pRKu+xICJJKImg0icG2Mx2jJU0hDunCMrhTW29I9o6Qfh5HbkhGzlMRMHxhRKPhnYq+1mJn9Qqs5HBjctmN+ZSVm/MRqrMrPW4pyYPKji1GaNKbTmMUOvSqASiWRSzNuGhxRiyYCPDq5e9MA1KDQ+e2IIhbqgRJM+pVOa9Lm4xg0RfBK+vSEJJ3CDqIRokcIc1gM5Zc7NKmZ1S9tWtO0SZTzKbdAh4OOKRKQfpMFj3UlQuOoCmyFwtBq4sd+TUEQ1ZUXPEs4VN1VTE5XFq5po4DA7zGYgrgaGB68TQmS1kgD+2vV9+n5g7IfRWQJrUaKRz3joPcZa3I7CacXRek3frdDBM3MWnTKb9YbDg0Pef/ky8/mMnZ0Z1pgie6loFzPaRVs4zWAaw7WDa6wODxgeuEyiZ75oiTRcv7EmhF6kJWOGkEWiSyuSTmA0zsp5rRZFnRBk/DvnthbrxrgpmH/cQBbGlCJHR4fcuH6dK1ev8YEr19gcHXHjylWMUiyaGmc0uzNHZQ0p32Cz2WDrBls17Fy4SD1b4uqaqmk5Nhaa8FjAaPeVCk0lqUK7yxFDRCuFH0yhj0q2WxSNFOSIIpOCrLN9CByuV1hjaOpGGu+1QaPoczymIRRYq4umuKjRpChrsCoUnW3gjSIraWh0dtSKl6DHb2k4YopntMUaMYDUlBjCWVKKrMMa7wPdpiOEgDM1VouaHVqjrKVq20kI4AwxBMk4Ga1FQpJjVRARGDlmZCgSGl2SVFoMFXMkKohaqDhN1RBjou8GUsr4InIRciq+LkakSLWiMmK2qJRUH0P2pBLsKxShG9hkz2rTc/36QWnyrqkqx+7uOeq64uL5izR1TWUsVkszttFGNrlFTEVC1NE4dMzEZ+Hfl01qZuTi39z3++Zl5p2YPlTWiKGT0SWYQXhzZWKRTZvaZhPhmFullOjFarVVit9m5LUSrvy4uMQknc8pK1ISCUofxp19LhtDeQ4fIn3I2+ZZO5YGtPCuJCsgclspamIQ3VKyQesEypJ1YrS4zmP240QpRZXKwzFp8KGtEhNuJrQ+QaTKmpAyPmW6IXK06fAx0fWBEBLrVYcfAocHR3jvi2GKNFNplYgp4WOQicQHbAwMQ0fOFj/0+GFAAZV1GK3JSRrAVqsVkKkqg3OWqnLoLA2tunBYlRKDk6qyDE6jiECULFLJAhwr0xR96HFzOmbhx8yTUhitJdBPYxZLH0t9TaWhRwEfynv+kAAjn/z1T3+8euhj4TiwKlRAX6QIB+/p+oH1phOnjJSprKaxJfAzgYSi32xYHR3imhY/9CitcXXNOAufzLEdz3cPvfR86v6HX/gUVN1sbK1rTvDM8zb7nkSBrQTypqxYudw/0rRGSAY+yexUXM1NlrUtnaAVAMfzzum0ZeHTn6BcKCVqIiPfHnNqWIwmQ3nMype5bIwHxvVXqgRs58kYxUBSsvbj3Hh6LZ5w8xFL3KVUFtuUR0CmxGY5C+d9HDAlzksU3yA9Sp+Oc96xQowka8WcMyvheySdx3bL40Rx+UkeaS8lOZYSKRkSMl86V8z0Zp1co41EbXDWgR0Dc8nAb8+5Pf/x/Dv+ne3XQD82M/PndmrRq7URqzNt7aicwZGOaQflg1SlFBaD377JKkNtHI1zwvGrHCkF+q5Hk8WgJ4OPQqdZrzds+oEQFUMsDmIpY4GlEzc6YwKoiN9EVr3Y9CaUBPQajDbUtTQAPfH8bczqGWljyN6gPDCAqRRNa/DaEAaDR2QPFWJV7TIsZ475TLNcZM5fyAwxseonNZuzRFXtkjOseoWPmeuHG442G7QKaDWU5i3hpIfBk2Jis9oQo1RslBIeekoDQwh0fU+MoVBnNFeuv09k0coi6GzN7bfezs5iiUqR1eE+73zHH9E2DRcuXRTVnIsXaJuWphV/BWkoA+s0bWsJvcbkHhXWhKAJGYa+xw+B0A2EdUdKAzGJUkSuRNlp6HpQYI3FNAYXNTGNDrBxO+lNmflHCye/93/WzyCfiJxPMjnzBwnohe4Qk9wGH+h6z9Gm59r+CnLiQGucMYSwYN5WLGaJWdNwcPUq0Ue6oxU7O7vMlkts/SQq0yKlULWleW1f3geNkz7YnDeNxbOArEtj0+pIfMnH2t0pYZQu/haiwqYVaKXRQEoajcZoMFYy4jFEoS7EQQKmSkwTdWmQVUqB0ZBKdV2JwQ+MwhKZytZoY7DWiMINQoPISVzeY0riXp0yaAtFWriqKkgJVQQqwiDc+xggB42mEopEM6Np2u0mRCktc/c07s4M3g+AKq7l5Z0vCSdjSuUcScpmVWYzVRQACyMlRE8fA9pYrJP7jJFNYI6q9C4GQgSTDRpDyoZUXIpt6YpMYzNFmayUVmUDKwgpEofI4HvW/QZrDEerIypX0bgGaxyL+Zz5bI5zrjSXK7QdfWnGhJlQg1Khw44xJvm4R/Rm4aYF87UzKJWpDVidcVYLbz6DIj78K1UmlpzS9i02SqQoxSZaEznumDdaOo6HKFJ8g0/SgJo0Q5Bg3pVsu7EGpxR2pE8koeqErPBZshAGsDaii4NnXdXMmpauz/iUQOSaUVnMONKW/z9mFaQJSLr9LUobrFPUKPqQyMZPwfwZwpiKlCHlQAiZrgusVj05deS8lsnF6GLqJLz0oetJJZiHTIg9PvT44Nn0PSEG+mFAKUVSEescddVijWNuLK2TjWBOCT8MHOzfYOgbXF0xDM22scY6g906HqviBis26EZnjMoQY5GXTJKhOukqnE/na2MxyDLGoI0Rbn7S2+avcUGb8GhijHhP5q4/WEY+P+TfD8l4n6j+yT35+NRKPfLjSwYrhEjvPTkmPOCMZtNXKC0a4zFK47figKZt2Rwdoo0uFMZ0aiMhT5tPXtI2RpdX+8GbvqZ95c3HySGhilLc+IdRLjelVHrYZFOoVGlmVePvo9u5RusTWdQsQT2AzXrLRRfjRSVRfy6V96KoBWy9Vowxhf5nS6CfgURSEYrZkOxfT5y3ZPG3QUCphqdYpHgzohymlHh4OCcU3qIvH+PNDaYmnEZMJVueTnyG5edY4zueFSUtkaUxUjZ2qhhwpohSmlzWMK3UljKmlCpBci7Or3JfSEnG5Inp6uQcrE7QnsfVNEWpPGU/yGa2ZON763HGkbOo01UxoYwR2tdxbkPOlcebkvijBPPC3ri5k97NU7NpHIqMyz06J6xGgnJtcLoqgcpxIwNKU9fN+B0FICvD2CUs7pwZV1XEmOk2QqO5cTCw6T2HG8+6T4SU6aOm1oqlUdSt4+Nuu4WdWc1y7jBW87YHD7j/xppr64HLhx1SEoklIM8oEr7f0Gfou0joE84qUqUk05ASioi1mowpPzMOIwY/yA6taWqadkYfIvWqn2gOZ4jZfEGIiWsHN9h0YphzcP06WnmMGiRbGQdSDAz9uighSeOXL+ZOQ/D0wTO24mhjcHWFNQ5b11RVw3L3Fupmzo7RLIwhdEdcv3aVtqkxKtLVNShFVdd0fU/TNFy8eJ6dnSWL5YydnSXWWPZ2FjQ6ou98Iv1mzYPXj9j0nhgcRgWiyQSnyQRSlu+JsVr4ouuOlKKo5hiNsQ11XRUX2NKcHfyj/ZFMOEVQeaR/P+RP46+FJiGaWWOFRSG1Rf2QBetEEKcVi+USgMXODlXToIylL9lMhcJohT5Y0XQ9o85vO0SU9xwZxfvf9Q4We3vM93aIYZd6voOpmi2FYmwyI8PYx5ZLpu1U9n7CmWPLP1cyhsRgSR/zkAGtKgnQI8WsSYOGtm2oK4fpLLlTmJwgCc/eFVOo2A+QM828xdRum3CQDCxYZ6jqhYyJmMtYEY6+MW4b0DvrSEl058kKaxxaZ5DkPkMsVIqM+M/khEoiduF9eVxSkCURZ4zC2AptjHxjYiDEwGbTbbnaE24+YpkgUtnUjRu5RKFIF+jCp9cqlw1bIqoovRQql81mkkoQWtzTFVRWk7NU3mMulR2EmhOQxGso85Nm5ObL/HmS156KkMkxzUeoY0fdCq0MOm/Q2XD94IDKVdRNzXy+oKoqFosdYWQ4UWAyo+noKO5SUtc5UxJqj0GaTe0s5IQOoBGnSjM2ERhDipkwNsdkUfyw1h1r2GZpLtgKeZRueGMsqEzKHh8S603gaD2w6iMbn/EJfMpkDYtKyiy37e5ycafl/F6Dc5pVUPis8WnF9dUguz8Adcx+imHAowg+EEIkRkNKMnikIpglQ2+KzFJOmKwkI1qoQrXTLOc1tY9bntWEs4GralQQnwHvPd1mw/roCGciTgd89Gz6FTF4uu6AHAOq0FFGRZvOB7oQ5EvqjFiQ1w60wliHrWrq2Q6z+S5zq1gYzaHvWB+tSNEzmzliDJiqwvU9PsTSkCoOs67QZIzWzJoaR4u+sEe3qej6iM6KWaWI0RCIRJ3JBETjXtSUQqDIpUrjtslSETLGMNq05xylEW0af48SPsj7/rC7jzPqefvPQo3IsjSUim3JbBeaTVksH34+RV3XMhc1LdY5lNb4EGVcUKqLm4HOB3aalplzmJSoU2Swhv0rHyBFT3d0gK0ctpmVwjVlHtxuHY4Debng8lIe6bVPZIczw7jBU0oCXTvy0nNpmpeIeXRLHzdgzjmaumFIERc9pEhMwlevrEPljI8SrFVNja2d9GaUrLpS0rdU1zU5ZUIXttxnKJV1JQ2F4uMijp0gWXxVquoJCCkQcyq8+CRqd3nkOo8VyxK0adHUlyqlFjoOkiXu+m6aA88QMW/bQsvGv/DUlYJ4PANonYu3ECWPXfowKKadutBXimqSSOVKcjiPm4Ws5JRFseZkIkEBVo09aPrENCsDPuW4pcaMlSSyIvpBAv4gGX+9FvnMpmlYdh11XRNS+a40rQT1tni/KC0S5yhiMaPKpWn2ZuHmZebbYuPceXJka9ObEkVlRnTYT2qQhiyNEjLXZEKCkDJWmaJOIru6ECMHq571xnO0CWz6TEiKrEyRAhRDqtpmGpuZ6YGZMlRDxgbDUlnO1ws2jeKgBR893bCRQRQSQ/ZcubGP04bYRVLIBOOIztIahw617LpE1JSqthirsFG0vUPh+jkLxB6rYKe9aW/1hEfA9ev340PkyvX3c+NgxY39Bzk8uoFOAZ0k245OGA17ywXOGnaXO1jrOFr3dH1g1QeOOo+rHHXbUjc1exfOU9U1i73zWFej7BxlHC50EPtiALXPemPx0VPXNRezpm1nLHf2mM/maKXxQyCGVJqtRTbObzquvu99DN2GVldUyzm20qyD5sb1y+xf7/Ah0fWiqRxTIoZA8AFf5DWFD+gwrtrScuS75reZkQlnjQ8jdB0pNCVaHtURZNESWoP4BWjpPeKhpeTxHPL3qmoAzYWLl7jjzrvISfEn73kfXdexWq2JMdEpCEZzcLgRQ71Zg5u35E2PviaKTx947/1sjjYoU7PQDrTBKFOyWWMa/vTrHPNcf4Z3YcJHAIfrHmBLsatiFn661XKLEGIujuUWoxXzWUPlDE1dkgKDfKzWqEIt0LSlV0g3lYRHjUNZw+EwEPoOa8X0TudA9EKFCV7osKMoRPIZgiZEkYnOOZGjjJjR9TUGT8yZGBIpJGLO6CyJkMpaMFA7RQiJMBwQUhQ5QeskYVKum+KnMGvq4tUx4SyQOE0kTGNAGwMhpS1NRoJf+RlT3lJnkmQLRIq05NdFRf7ErKIyzijxJVKKLJ3RW+rLmEDNOYo4ikpsEw+ZkkxOwtVX+cQ0Vo5BUU4qfWhAHAL+sMduHKtujTGGqqoxxZxxpLtqbeRaCsXsZves3bQIc9a0kBND6ISisHVWLV3xKTFEKTuMnfQ+ibrMcTAvtBmFprZWFockQf/Bqudw1bPaJAafiRooHesahTFQ2UxjEjPtmStNNUQ0miWO81XFulHcaBXd0JFDFNnLkIghcXWzL4mwJEYUqarJdU20DTZUpfQjRgVVZUlJU5XdZ4iieW805DhgjWE2q2/WWz3hEXD9+gP4ELl6/UFuHKzYP7jC0dEBafDkfsA6Q9PWtE3F3nKP2azhSU98Em0zY/9oYNNF9teeG6uBpm1Z7CxpZy0Xb7kFV1e0i12UMRysB7ohYFbXIA4M/cDB/gHKKI42a5q2xVQzQpTK03y+QCmNLxWfkW5GjPiu49r734fvO265/U6a2ZzZzgyvHDpv2KyvibKOH7baz6JWIucafCClRFW3NLO05RLGoq4zZaUeLTxi2pyHhbf55M+8jYYlmJeGwDHYUSo/5AHqxGkKpxiNq2qMsVy4cJE77ryLbjOwWO6Q0RwerssYlCrjgdlAjGhgXlfEriNdu47vB6689366ozXL85doZwuUq9BW+NR5uwKebrI+rYdyfLVTUH82OFz3nOyvqSI4l3GVoyo9ZylJv5hT0rMzm9W0tcNVFm3EXkAXHr3TCmsM89K831ZiRBe0UCrWh4k49Bgc1kn1MHoRtQhe5CuNdqVJUYKqGAMqBOFWI3x7ZWSEiPt2JElhQMzzsijlVSUDam2F94GjgwOSylSVpaqd9EMpUEbLcypITTX1Dp0hTs1Q+Vg3PsZQqotjj6EGLcZiRsVCU4FCkygtGEqkK8fSZD5+FqulVmhU8SbSqjTSlux7lqBeccxbH5UH06hlr/KxOmI+pnrL85REdGFXDEPHepANxnV1Da10oYZp6rre0sestcJGsbb0cdxcr5ebR7NpGnJOmLwghQptHMoYMdUJAVJCZzErOVbpLO+d6AKitcUCxloSjhAjh6s1XS8yazEGOdQI91M+dQ3aMNOZ2omxz/66R6WEw6LQ7IdMHysy0FQGsPjgym4wbhfOrDMpjB986UbOCZWjkL4U5fplstteOyNfMR+bDeTTC+6Em4vL73+AmMU0RxlNM5sDGnxEDYGmrdjZXTJra26//RxNXbO7cx7nanwcQEX61NPEnqqq0KZB6xqlK5SqyBhyNoSQiT6iYkLHdCynFjO+HwgojlZrGf9oKlcxn8+kn6JuASWW0z4wbHoOrt7Ad2vaeknoB+w5SzurWS4a9s4tMDaz6dd47+n7jhhFY9dahday4bXWljEc8L4nhFAm0CmYP3Ns3/IT3/0T3PYxWH/oA8aStPfi7BtTIoQBpWCxWOCc27r/5jwqRZw4S94K/JLRzBdLLt1yCwf7hzzxYz6G69euc7B/IMFNhhQz3RDQSpITs66ntk44o3rg8Op10pA4vHINV7fMd3YwS0tSskSDLLhkUQcDRM41xe3cefL92J3tfMTe4gmPjJHuGYpKXEyi3V7nRFZ5a7KTlKKyJQCKgRiEzqe0IoZA2R6ichauehgkQ17VaAX9ZkMXAtFH6YkzFmes0AARZ3XTiOqHRuZBlcQV0miFM6rQaSSaMtpsGx9j8OQovRzayLm1UkITSxnwIlqgReXEWY2zRtzjR+GAKDRDmRenOfCsEFMsv50MoMdKo4ynMVEx+JEKNjY5l1i+ZO/H84iM+Yn22W2yQ5RwQIL4qEo/ZjlGRtYoXQk6gR6fX4nazah4czxlj9Kn0kuSH5KekF6hUn9ICZUUmVDmZS0S14BR+VQT+M3CzcvML5YSwNYVxIAqHLmu7+n6YrpUdlmS/S5vlIJsip5smRTAkHB0fsMHrq/oup513zHEgNJ2m1WwRmGcxbmKWmfmOmKM5sEba/a1JkXRHPd1IlSQdWYxszibUTSkFAnJk1Oij56UIn2GiOzgYkrSJFR0dmXASWYjlw77jJgkZCCXzKnK0m0/KTicHd71jj+UtNLyVoybsdw7x2LnPDZmXEwslwtuu/0W5vOG22+/IIoKwZCiIqsB6yJRrwl6LQpHxqFNi9YzlHKkVEnzzZDp+4DyERtFOUGoZIF1P+D6QN0eEJOU29p2zrm9cyx3lqVxWpqq/cazOVhx+b0P0K2O0FEx39nh9p0dlotzhLTAVBe5ctWw2hyxXiX6oSfGhDEOa51kALTB1qIOEaOn6zaEEAhhmBayj1o80ucimayu33D5ymVCCAxDLzKDxjDXGqs0Jxw4TjHRxahky27n3LkLLOY7GOXYv37IAw88wP3vfYAwRJIXPvOKgX4Q2VZlDfOqwmHIHq68+wGO2hvsXLiFlIA7oFnOCSQGEV/DZMmdmaIQ1oeOPgzbCtJWVSlPwfxZIJRgdrUuRkqVx1pL6z0hBKlgY3BG09SKiCEMPR5NGhKJxDDGYzmhxX6TEAeUtZjZDI3i6OCQg9UarRW1q+RWSUOsJoPSuFmDVgayQWVNHAI5RBHG0OO1iqKOtZaYIil4/NCRUgUIxad2FYpI8KIqFkugbjQop6mdoa4Mmz4yFCGDEDxaayo3GeedJUKM29+VUif8UUbSehZzACD7XoL5YhymS/O20SKakilSgihU1kUxpjy2NJaOCfuYi2EoEJAKkDBDwJYA3RRX1yza6KIxr9P2soCtr8G4s5DNxenZOmcNCcLYAzCM0upF/jAGCH15/GOUZiPSP1ky5RhGBzCtx4bRojWaRSZotPFVCvIo2Wcd2jhxdA0QQiYMgeQDToO2mpTk3bVGBkJdO2ZtjVOZGZHaQDaOqJVMGDkTVZLMQ+HUZWeYtS2QMVZUItbdISF4VmtPP0SsGZszyoeE2ibbxx3hqB6w3b2NO7spI3/m6NZiTd/uOmw1QzkL2eJypkqZ+WLObL6grh2MvRydZKWGPhKCZNnHzMDI4xzJA6nsyPPW+nzUci9ltajIOZBV0dNV2xpN2aWr7e5OodBKpK4qW5GslexmcaDVROrKsFzM8N5z/twezlr29w/xPoihmVJYW9xerUYbWRTHDO5W1nLCmSKf/EU95M6RN3jirvGQEAODHzhaHXHl6hW89wx9jzaaqqnxwbOYL2nbmcyf+njeOW32I5kqbQyuUswXcy7dcgnvPefO7QFwdOMAPwziZpgynQ+suh6dFUMVUUnjjcfQc3RjH9u2NLsL2r0dkoFgRoKqUBKtkqzb0bCmL9KuIYSSJZWy9l233vWRf7MnnIKzhpSkap2UZMFtyRaKOdQJUyWttvKSOWdiFppejEJNMBxnTtvaYU3pYSt68CeTClqbIilZ8qFFYlorU7Ls0veRTTGP1ECSoJysyIMn5VR632T+FJEM2JI3xmst1++qipyzBINaKBE+xGOVHKXRxk7B/BkilkB9zLCPso3b5vgUSWlsmC4N2Ua456Y8zmQwWWQmdREoQY2+RKHINgfxSyjrn0qapESDPkVpwtyq2ZzmzxQamgT00jc7jqiRM1/qm+P8rcZqgMifi1R0Ges5S5NuzqTYQ/Qk35P6dXnOx2gwjzLywrU59Rq01bgsRLyqbHNMOWDUBRXV7IxxNdbW9F2gXw/0XWQ4XBPjwF6tMcoQAqSosJXoyu4s51w6v4POCR0DBlnIIlIODDGgzYDSCaMsjXK0TcOeacXC99I5lEpcuf5uNt0hH7h8wOHhBmsz1viittORsyaWIGqsOmitUVpvS+RSgpYAf7vDm3AmOLh2iK1qzt2xZLF3K9bM0aahUdAqaFvH3rkZWmW63hOD5/rlFUMX8dEQs2ZIXjZwSqFNQulEwssXO5WsehhIfiDHACmjjaNul6Qg3PxsHdgGZSpxXDwZapV1SStD7Rrm9ZwLu+forKFxFqczWg0o1bO3W3P+0oxz55bs7e1x9ep11uue9XrDwf6alDJtO6dtZ2QVyErKyqNE5WazmYL5Rwkn1w9h5h1TaRhL0epE1kcp1psVB4cH3H//A7z59/5/9H3PZiPNVjcObnDu3Hnuuutubr+tEi5mIXuOPNVRBC1lyXhaY6hcxS233oq1lku3XOL9DzzABx68zNve8lZuXBvwSYzt4qZjEz3nmoGFqmhtoPKKuBm4/x3v5MrlywwEUqVRjYN5Q0qJYbMhp4SVrm4Oun02fkU/DPSDUMJGtZNPeso9Z/gJPD6xs2hIMZF8T68zTS2be6WAFEBJM6lRRgwdS1Y0Zwg+EVLAR0VIwmNXVuSjb7v1ElopDg+PGAaP96Kq1bYN7WwGJdkhOtsWozSuqjDaEH0mxULJMgatElqJAEA/9PK4Xr4f1hiMbRkGCKUYnnJEK47Vuoqe+HJ3B6NNSR6K/8xq021VRGptcK7eZmgn3HwMg3RP6y1dRZebRPUxDvjhEKUyxpQ5qjY4Y3AJbC4J1CSeMNqMGznZRG76I6GS9h0pBnZ3L7CY7+GTpopa6NzrUe602lZyck4c+7UKTvZFy/ScSSWAN6IUj1ISA5BU8TVQRd3wODGXQy8eMf0BuT+iXx+wPrheXvPNHXs3LZgfhTOO+eKCXNxeTwUW5Y0YnbJi6WRWZQeUy+PICVO+/DOnMUbhtQTzVeNwdcOirZg1TlxlB2mo1cbJ5xYGGTiV2WYKNBprLVUtaiXL5QylE71vMSZwcLCh7/ptw9k2HFdlt8lxBuLhDW15W5qZcLYYv2xjxluPGy4j2SVdKCmQiWkgFk5pTGObTi7yanJTY7BV+iZy6dXP29CpJF+1Fo3jrFA6oYxFFROxbZMXo2Z4cSqmOC5qXRpnzNa1UUx5pBKklcZZzaxt6GYtO8sFRhuGIRN8wrkKa53o+BYeodGaXCSzpmD+0UI+9dtIN8k5kWIAjueIVObG1XrF/sEB+4f77B/s0/U9m3UJ5vf3UVqzKckJFGijTz3T6KQ9Uly0clitcJVjvpizs7PkwsULxBiZzRrWK0sXkzRLJ1Ah0/tAPwR0UngttIzNak1QcLS/z8GNG5hZgzXyPP16Q04RnRI5R476Q7qw3gbzKSZZYB+RVjThIw2rNan0QCgSdeVwbnSspKSZ2FbFxzVNqjzCgDieMo6z7FppCaxjIhSKVkpIYksZUQ4RRk5Z6+UMcs6RTlPGvBrnw7ydW0eJQKOt8PZLlDK6y2oDxgiFTCW5Hmst2piiJJe2439ctbdxxM1/2ycUpBK0j4nMbZS09T/Qogio0rHzcMnEm6wKFaasg0qMyyR2lk9RCoK5KNEIy8IZDdqSjUUpTXTS0WOtUKyC0oWeI34FYwUqq7SlAY0DVmXZCKhi/YRKshZnVaQ1R/6+BPQZIEVyCiTfk4cNsd8Q+3Wp3D9GM/ODF6a5Ch6yl9/JeD8wDD0xQQgAiqQcGRj8QEyJ3ovb5nwZWSwU3g/k2OHUwPm5OMNe3DVUVjN4SEmxOH+e2c4lKgO1zYQQ6DYBtKZq5jKgVproAzuLBbO2pQ+RPkTa2YLzl27FVY7FTgskmtmtdN0S30cIicEPDMMgLENbo5QhZUtGEU9MGmRpJstZOKLbyfJkmX3CTUfVLLBVjS0lOz90hNRDXaGbGpUVfZIMoveyIdR1TWUStuy4dQQdC1VMg1KJED1JJdxoa48X6pXKZGUwVUuz3CP2A5bCY6/n6GoO1oFVZB3JDIAVxaScUVrkrExlsMHiaslSQCD6DcFHNquIUo5F1WDP7/CMT/h41puB99x/lfVmEAc8FCFKw2QualBGadqmfXQ/kMc1xjBCZoMQPZvNRjwONhvJFBWazGq9ph96HnjwQR54//u4cuUK73zvfQz9wGa1RiuFj4G93V2WO0v2zu/RNDUzPdsGWiln+sETQ2LTbfDeszNfFKUPy55dYi18+l/9FK5eucLh4TVso3nf5Q+w2t8QvGaIUiJ9MO8zc45qqWhcYPWBD6CuG3qTuHJwjdnFPc7dcTsxZ9bdhhgjwfekFBjSmoDM5SH4somZwqmzQuNEyq85t0OG4nFht4HR0A+sDlYYNVIVwFUtlTP0vtBekIRXqS+TI/RdT06Jw8NDun6g7wMxInQ/jGzsBl+C84ixmb7vMVrTd4EYE5WpcMYiGnKRSCSrIr2qJCnS1DXaGqpKE5MqHjUaaw11bR8SGFI2wQdsup4hBCjNsrJBsXQ+TDSbM0SKxw2wGqiNoSoN0pW1xFThG2mUViqiyVQxY+KAixqboW1b2qohGUNwDlFnSyQUlavIWoNzQKZZLKhmM1rTkO1Ckgf9qOIm82vI4mcQciKS2XQdw2ZNip6YOsgZk4UbosumU+eAzomUPTGVGFA5xBjKArpQ1oBB6DX+aJ/h8Aq+W+GP9okxMQyBm5lPu4mZednWK3F6oPh+SWNLiqc4dSnLLs77SIhiNx5ioPLi3CYTjQRNtQOrFcuZoXaafoCQYLmoWezOMUQsEe8VMVlQmqp1oGSSSdbSzlpm7QztB9Qw0LaO+aLCOktdS/Nq01RApKqcdMcHPeYmhP+vZOIqrxYou/7xP6epfbKUT5nRM4Pwx51kkbJMLCFmgtXE5CQLXz6jMHL5tEZZhSocBZ0VulCpcuGapuK6KZl7VTL3JftQKFfaOLTNaC1ZeV0y86ooLmWVycSyqx97RcbUhPCfxxvFeTFlT8xB3A1dpraG3Z0lVeW5cegxdqAbPCFEUtZbszXZUIpCxISzxjG/Rvop5F8pSVOe9wObzVqy9EqOOTg6ZNN1XLt+javXrnL9YJ/1ZsMwDGw2m0JvOEQrxWq1puu7Io/rStbSkFJmGAZCiPR9L2Y+dV2Csyw63ZVj7/wumchyd8Fs0WBumFLRSaSo8EQ2w4BK0LdBAqMuglcc7e+TG0s0mfrcggRshp6QIkO/IaZAUBsS4rYcY+GS5pPvy4SbiVFNQ1uhGDg3NslrmY8y9GZd1IfGzLh8NmlctoWkvL3lnEs2PuEHjx88sTT9y2db1vMxM54zKmVx186ZEMVfw2pDyuL2mSnqOid7z5T01pmx506rY5WQEtSPm1e5Xrn2kCI+eJEiVHorf4gSWewplj9DPCTeMSis0lglP5U2JOWAcaAlcpHKJUlzv8mI348WHfltXJWliTUjDA2lFEbpEnNp6bdUSZplR0UjxkhtpCEqVEhkUxINY62qKCzqHOVnCiWOHcixB2XF7ZqxGqrJ4/pa3IlzKHx5P5CjF8lz7x+jwXxx7yLHMitI9lopkefLSlMrR4iZ1SbhfeL64Zq+H8hKnGG1WQGR2PX4wyOcCuy2inlT8ZSPvcB85rhx2LMZArO9Jc3Okso52rph8J7DowOUUrTzFq0MKYgcVuMqnLVsNmtWmyNc4zBVBwpWfSLExI2DI/qNJwQwpqKqJH63xuE9pcIy5t2VZGbzcQbumF6jGFVuptT82WH3wkWMdbiqcOWSx/vARkNSCq8UxkvOIEQrDWFaTHBCFLmzkCCUzZvSFmUsSTuUsbhGNJbnsw21jqguoAfxHZCGMGmMERqXw7lKHAkVUNxb01jiA+GkFr3dpBQ+DOS+0DBykThVihQ8XXdIVgarama14a6PuRUfE1evH7DedFy70eMPpATeb3ppyjXH5fUJZwWhlGypheX996Fj0x1xdHjIe97zHrq+53B1xBA8R+sV3TCwf3TI/tGhqMs4jcZi6wqVoRt6ODrkT953P/WsZrGYs7e3UyRKLTEm9m8c4X0owVbk6Nwe680uKUb80JNioPcbkh44d9uS29Mlrq6ucfUQchBK9RAiN2JHZyOtq2idZR4tldMcXr7CZnPEsF5J1cpZYutICrrQEXJEup8QVZ2stt4I0zg8GzRWgo3Kue3Y0Ea8B6qqpq8clSqJDj/gfeTylasiN+kjISa0dnKrHba4uR4c7BND5PDwkH7w9EHjo6bvBzbrTszNCpc4Z5kPN12HUuCH4uQaE0YPwpW2gMrM5qV6uFUQKQoo5XwxZkLMpCyywHBMS4ulwXoYBlLO6JIFlqS9Kq6wduK8niVSQKGobYU1htYZaqMZhp791QFRwaDF7TXmCCmij/bRIbBXt7SuomobsIa191w5OtpuFFVOmK5Hp0TthJZ6bX2dTb6BbZbYeSfeCEoRgmf/2lWRaHY12VjsznlMuwA3p9qb47sVfYyoOKBDh8mBigGbS6UxeuKwZvAbtLYkXZOypo8alMHUM7QxNDpidSKoiCcWeg6cCAhvGm5iAyzS3VsC3HHHr9DCQS4ZTKUSOQ+EmNl0nq7vi1GFZJesycSuI2zWkpFsYd5aLp6fsVzUYBSuG6gXFW5e0zYz5vMd/DCAleBpNqtlJ59E/N+MTRk2k7VHO402spsfwkDwcVs+TLno3duEUkmaaSNlU6a2H5LK43ZF9jDHDa9jRoNpDTtDNLM52li0saUEW3TXgyHrgHaJIQEUZ7c8OmpCIBTlozFnILv+rIyU05RBWysNO5XBZC0qDbFwScdxn4u8ltHHgbxiu/E71q3NJatfsvMKCXr02CsivR+SXUqEQSRZTeUwxlHtzkhZ4UNAq8zRkZHiUEyEEOV13eTmmwkPR85jMHtaSWjU/990a65dv8ZqvebK9at0Q8+q29B7z2bo6XyREzWq0PsspIwPAdX37B/sc+XqFfqhIxPRWuOcI4TI9WuHDCWQzymVBjMIYaDvNoD8OxFoFxXLczPqmcNYJVWdnIk5s8lSGj4aemJOOJMxaNJqRe87jLPMru2g2gptFiSt6JMn5oQukpWMGduoiGHKyp8VbOEaV9ZijCnOlJraWZq6wgDJe7z3rIqE6LofSEmqeykXlTijRWELDTnSdz0heIahFyfr5MTQKQRpcFanP+NR6x4gprGfSfosHLoo6WiqagxHyqa3qM+N1e6UUvErSCVuOO498aVaIJvFLBVZcxzeCNVmmgPPFElUhKxWOK3lZhR9CnT9hqA13tjiMRBFRGK1QYeBBo0tFGa0wufEYd8LTS8rVEpUfcCkJAIqCVbdwI1+wM0ydaqw2lA7kVu9fv0qg/ekag62onULqgoq66jqCpUS2VjhvJdquMVjcySFjuQH0rAm9Cu0tmA8MWkGL3FBVVTslBPDUq1kw7HtRwGk5+Tmvd03LZhvmkrKqjqQg5Hyf5EuIydiiPihoxsiN/ZXdL3wSAfvsVYMIDarSNx0mBRxWVEZy3K5YLlT0+6co1nU2B40BlU5sJpquWT3lo/B+wE9WwCRxni06AhJ62EUPnGtrHCZlXTrRzI5OkyVWTKn8RltW5a7Pb7f4Ic1IQodKKNJuiiaJCnhFOM6KQUqTU6BHJMYFRh903dmE06iULhSFhWNweN7yVJb54jRl6AG4rgQDOIxkLcBUDE1sYamdVjnqNsKW1l2G4exhphrUp2INhMdDP2M1dEO9aZniGCsQecI0RcZy7h1n8snGniU0WhraJoGYoPSUZSTMkV2S2Otxijprk9ZEaJkDMiBrDTnloblfImzkcW84fIHrkjDYkisu/VNLfFNeDge/MD7ym/yvR+D+vVmw+HhIYdHB2z6DZtuI/Savgej0ZWlsgqVq61VePSRdXVIGALro0M2mw33338/fd/RttK4LxQEodms1j0xJEII5BRpmoqmdpASKXqMNcznNZnMkDdUc8O5C0tuX1/i8PqGG5dXQkEj06XItW5N7Q0WRw4WlxwuOrrr+1x97/3k2uJvNESjWOVIIGOSmLzknMUYLU5OxGeJ3eUCkPVIK0VV1zhnscZJk322qFlLTBWzpsIPngfe9z42m46UpOJcuUxloVOJIxUwGhoryYW6EtfLbpMhRoauk4ypNeIgq8GVRv6qkYSG1RVKjbb3FldpKqfH7liiDxwdrYucpIRCPkpDa0Kk/7LKsinc7hSP21yNkeromHgZ+zQUedtsPuFsoHJCA621tK4iRi8c9eyJNWQjxmOjgHeOgeyP0F4zv3SOc8sdzKxlYzODU8TWlTVMoROoyqBSZkgRnxNdCvQhkMIAQ4fXSno5QsA5jVKGVQ4En0ndhs6umak5umnRzYzlxdvI/Zp4dQC/wa8PSX5Dtz7A9xu6YU0/rBEtJktIsPGS3HPdBusc1aLGVIbYr0m+J/ph2yR+s8O/m+cAWzsJ5qlIWhoKcwpAkpJrSPSDp+s9h4dHdIOn7/tiNCA7rdSL+H+rNY0zOG2Zz5csFg3tfJd6XmEPe0zIKOfAaNx8weLSbQQ/gKshDVTxAJUDCU8mMgxJ1D+spsotOUHIWRzBslhGz50hRoU1LcNmYNgcMWwaNpuOw4MVMkyN8LDS2Oxa1HOK+1cib11qlTnWGZ9wFpDFKJXsTQyB4D3GOdG3jWJIkoHgPSkmhk1HilHkTJVsEo02VBbmjQTzbeOwlWXROKzVJCpylQg64nVi6FpWO3OMMRytvSji5AgpSJd7itsJKWdIqjjXaSXNXk1NCjU5DSidSzCfi5KNwWjp1Agh4v1QMlcetGZnvsS6Cmc0s3ZG6Ace+JP34VNgs+6mIOqMcfXaZYCipKSKE2Wi73vWmw2r9RHd0LEZOo7WK9Z9R7tc4FxNpSxWQ1XXzBdLcddMmn7dsX/jOqujI/QHFKv1EZUzNI0rvGLJPvog5egYxPwupwA5opCkQ1Vbzp3fwTqLmxncTLNzbsGl7jz46xxcWQnlrKiLHfQdTsNC1dhoUTliUqInM6RAdJrVUUUwsFKZAJho0cmKPGJKxfhqePQ+kMcZFjOhrYzuu7OmFjdrJQ2tVjmObcUUXd/z3vvvZ73pS0+bFu5y1qgc0Wmgcpp6XqHIVE7496YfREhgkMbnqnYY0xSbe4sxmrYRqkVTL7DWUTcNrqqoKoOrNDlGkvd0XU+36ct6aiTGz2OVS9L6KWYYJYP1sSJPVmLYqJWWZBtaTMpKJTSl9Ehv04SbhZzQChprmDnHoe/ohg6vMskpcCJtq4r5XY6BuKnRTjE7t8vy/HkCmR4kmK/F4VyXcaGCgZTwm47svQT1MZCDh9BLY3dIqJzEIV0ZDvuIjwnf9WA6dNPQKI2rGmazhtg1rA+vk2Ikek/qN/SrQ/rNEb3fMPiNqDwlRYiK9QBoS+UHrKtY6iVVrkhDRw4DMUgfmxAz9E3tFrppwbwfeqmn+Z4cpYFAq8KjFzqd6MrnTAwDKQSskSC4royYOflA9p66crSzitlyzvz8bdSLhkM/ozvS3DiAg8PEnjM08waVDMOQAEuzPIciYlOLyoGYO3IOmBCL/m4QJ7qc0SWDXltLRrOJlpgU7TwTPQybI/zmiIMb1/HxfdKs28fjHdfYzawyIytUWKNFfjBNwlhnilIqUymic8TqTGWgtbCoFG1rOLdTFyOJRsahn5XMvGQztcoYnalqTVsl6gZ2dyuqumLv3Exk3qIlp4Bfb/Bdx3zW4qzjaNVRN3NQsNyRiWJnOaepK6wtJmpb4xNkwGgxPjPWkqMo5KjS9KMRFadSW0JpqGsrHfopk1UihZ5QXuuirbh4fo+773oSBwdHvDc9eMqRb8LNx/selMx8TrIpE4aVomlbdpe7GOO4cL7DuSMeePAyKfYYbaldTVSitlC5innbEkxkqNYkLzb1gw+sVitC6KmdYaiEQmGdNP0rI2ZoqIA2CeNyqTTJptA5RzuXseha2QhcvLhHZRxExdGNFX0fODrqZDNZpF7XHkwOsvk0Bj149FoRrMLnnmAU0Yppi6IGRG+66wZiCHT98LDGuAk3F1qLeIMuTaSM0o0pYVTp5dAKbU70gInpPdv+L6SybbRlMW+xVqOMIWdw84FVF6VyaAxV7WjbutC+hL6YVcBow4Xz52jbGbP5gqaphVJrELfXrmO1WnN44xDdK4YoUsFKZbSGyhl5XjXSWAubl+K6njM+yPcjFyptyJmYwgklpWnsnRUa57Da0LqKxjk6rTE547QiW2FD6FEGtUhY2vkCmxN7ix125gtWQ8+q79BZURlxfiVnDNA0FoucK4dKgn4FpnbYRkOKBN9JdaCWrH6jIgQIJhOTJ4aevt+QncO6BmUb2t2L5LohHD5I7NaEKBTdcXOCCH0xCp6KFGVHTIF+DTY6fL8heS9GemUNz2r8Nt0c3LRgvt+s5E0Pm2Jtq8QoQqXSPHo8yfihJ/hAVdUYY2grS2UNQfX4mGmqisXOnOW58+zefjdVXXGjj4RV4MGrcHQQmS0stZ5BdGw2gappmZ+/KBMYPeRACIekNKBzRuXMsOnojtYyuYWItpZqPgdtWCdHQGOYoZQjdEf47ojL97+Xo6MjNuuO1eZQHMasKcYIok6SlAgWpizBPBl0jBPN5ixRzHh0lmC+0olsYVHDXgs7C8NtFxqcsdSuKg5z8lkN3ZrgPSF0BN9jrcbVkdkMbr/Q0rQNFy7uCteudNL3mw2+77l+/YDzu7usVj0XLxxIY9eioqod58/tMps10gyrpX9jS7dRoLTCOGnajUXaVSsZVeP1aZVLc7hiVlckMn0vkq7RrwkDVHZOs2hx5hI7ywUPXr7K/tFqyoqeMe57z30SYGx6YohUtsJZx5OedAd33XE38/kOIVma9gZvfee7iPEIayuauiXkgM+Btq7ZWSwJfWCoN+RBaGObYZBmLCK1UcysxjhD3VYYa6lnOxhrcXVCG2gaRd0onDO0TYO1EnAZI9UgrTWVqbnlYkRnxcGNfQ4OVuyvDqRJOysCcNAHopcKgDMWFSPGB4LO9KtENBCbimQMyoGyiu5wzcG+NORuStZ1wllA3mejxcNi1GlPSbLgShVqqIJswRap6JxL039RgxE2iwTzzip29xa0Tc1iuUQbw86NnlUXtv4YVeVo2mqr9hVj4ODgOkopnnDbrZzbO89yd5fZbEYikHIgDD2b1REH+4dcuyLHhrX0aegyP1pnMdaUS5ENcsrSl2EKP3696UQWW6mtJrlP4hSap2TGmWLWiDT0rK6Zu5qNNnTIptJaLQai5rhJWmvY2d2jNoZb9s6zt7NL2r/OarPBAK0xxdU1YLRmPq9xxqBiJb1ERhGsRrka1VjCEOj6tXDnZ3OMtvQ6YEJmbSDlgTBsWG8cMc8wrcj37l76GOiOuP7+dxHyASEmgvey7qoidqKkqq5zJOVI7DJJazaqJztDKsF8TlnyvFmdMqa6Gbh5DbCU0ldKIo2wtbxN24lBwpJEyDAUPX6TM1YL5YWscE6kJHfPX2Dn/Dlmy/MorTm8cpm+25B8xmIkh5AT0Q/064002ConTT+2AhIqGrEPjl46p4PBVEquUYsGbcgGkkGbBqsclVtiTI03GmsVrm3QTqEsZC3NNyi9bbJUSNekUiekvhQnGnkmnAXO7+2gjWX3wh5VM2e1rui6jp3ljN3dOTuLBRf2llTWMWukJDwG831XE/xACAMh9BhjsM7SNA3LeUNd17SVFcfE4ppIDKgcpaKUPNZkdpct2miWezOq2tK2tShKaMl4ldZwKXSPAg6jJOWYcsrlO5TlbyTGhJkkXrMoR0kpfdwsJsgBYzJN45jPas6d28H7iTN6llitVuSc6dcd0QdmzYxcS4bQGItSAe+D0A03HZtNR7/pqKtKgvnk6ZVh0xwRh0joB6IP22DYOUftKlprWNRG9LdnorKkrENpTe0M1kLTaNqZwWi9rYAahP2pURilqJ3FYlnuLLh46QLGWq5eO8APnjQUacGc6JOiD4HOe6wR4/WUgSLRWmkD1mKrCuMqurUpvSKBFMMUzJ8RNpuNZDtPzDkpJVRMEGPJcOfCTZeeBqU12hhGMxxrDHXlsCbjbKKqLLO2oW1r2qZCac2sRRryS2beVYamruT8WhGCoiv69jpnciy6l0k47s5V6JyIzkoVvm2KzCQYH/HRE1OUqmUxR2PbiyE5UmOl6X8wRiph2oh5UIoEIz4h2Eme9yzR1hXOGi6e22V3NsfazKx1RA3RglIGbaptMG9Q7LUNtXXs1A2tNsyto3MVLmcZIykRkQ3qoqgSkkT2dlX3bHoPzqCsxmRDrBwGVXQlMs5qkio8+zCQvSF0BgV0VUvWmmBFPcy1O7Do8ZsbpNARY0cIstbGkEozrghaap3RujT9Z7ZJaxjN14pp1E2c+25aMD8qcOYwkEPPaJ+kRn/KBFlpAoF1zGwCmCiUleQTwWTmlWbe1Fy8dJG7P+7jWOye48ITnkLf9/zRW+5j/9pVFjYxNzVVBh0GhtUR6/ABdlLmwhNrTN3iFgu01uS8gRzwmyNC32FVR9YbUhQ+dYyRzeBBGZrFOaqqZb68QNXMCMMN/HCdg6NrqFZBSCTrRXdcl4JL6YQWPfmis08q0mzTAnaWeNpT7sIYy6Un3kE7W7C/v896vWa5aNjZmbFcLLj11kvUVcXucgdrjFSOcqbrNhLMJ0+MvqikyaLYtg3G2G1WU1vRLHVkOpXZ1wk/HOFMxZOecI66rrl420Vc5chaxsB2YS2qN4ksa6cR6oIaF6yyGc6xqOJqJTr4RbYNo8lRJrgQ45abndNAyhFrLMuFA7XLU556V1F6mHBW+MCVD5BTZnO4JgyeC+cusLezR4yRqqpYdwMHB0dcv77P1SvXuHrlKpWzpBCJyROiZ6hXpKMNOUF/FOj7QaqBWrHcWXBuZ85OU3F+Ls2NzbwlZTg67Igx0s4VVaVY7DgWO44QEkPvZTwjjosuK0yWAEy3lvwx0FQz3ve+B7l+/YD1asPh9UOij2xixmcweiCjaZxjgSIbqRoZpaldi21qquUOtmkZNhuuZuGyhkFoOxNuPq5ekwx3XRU6latwzlFpTWOkipyRLHYyBu8DxlmqpiH6RI6ZuqnY2ZljVMQqz3LRcunSHm0j5xLxiIrB522wba3GOVP04sF7S+pbcszgI2HTEZuWaBzNvKJta7xRqCQCBOfP7dE2LdZuGHygGzb4MJzQl9cY9FYRB4oDcs6kEOkBbR3KVBiVIQW0NtR1dVNpDhNO4+LenKaqefrTPpbbzl/g2pWrHB4e4KOnjwMpZmIQqnIKCaM1F/cu0FSVpBiUwrRzahRDimxiJKZIP4gB2YXFEudckXjOJcBOYCw4R3CZhhlkcAA5M68NTVaEox7fryRA71f4ao4fEk1V0y7PUVExu3gHarEHuUcbxfroOn2IMi/3QdbvQqOpR0Mz7dEoMUDTkGMm+tGH6DEqTTkmFk/JUzJa48pkLlzSouwBoEZjCDCFEzybWWbLGfPdHdrFAjVmgkIgBY+tNLUbzSXGJtRR/H+8SUNMxkg1IBv5XVnQjhTBxwHvE0frDWAw9VB4p7mUJ8sLk+ZltFWYShV30MIqjFJ4GCsPRHHAyzmTsi7ViQlngZ3FHGMdezsL2vkSozJtbZnPG5bLltlsRts2VM5R1w6jx2AeIBGdISZLTI5RUkqXLJIxpliaH1tUj4oKWguVwVlDXVnq2uKsZEMjcVsizqitqcqx7Tic/L6L8FMJ6E9uBtVpxpZMiknkvbI4PTqnS1OYcKUrZ4jbTcKEs0DwnpQy3ntC0cEGmRpSUXfp+56hH47/FqPMbTFIQxiKXq3JCbqVNJDGGFBk5u2Mc+fOsagsy9ax9VQphj4xRJxR6KQIFXgnVYHsRSkkpkjW4PEkk8Bpkfo1juVywepow97eHtZY1kcbMQeKYrbmU2IIAaM0wUl1KSqRkXMpbU18xrGqlJTXjVHTNHhGCEGqzcaUhkEllTlrrZjXjYk1VDGJylhrqaqKPg74GLemS0aDs5mqctubtbIW107mR8nM67JxMFtuu1aZ2jmSlnUxxSiCBCEQgymGUknGh1byeCvV0JRBB1HE2ZpJFYOgnPNWBVOV6oLVmqg1qpjvmdFkSitsoRlNOBs4DZVR7Mxrzu3M0cnTVpohePogSYkQkihdhYhWmr3lgsq5oviWZc3UMt/UMYipqDFoo5jXDc5Z4bTnxKyqWdQNWWuyMUQFrm4kzgyqNK1KRt2RaDT4HBn8gFZO3FtRomanFaaaYRS0OxfQeJQR88eh6yEdSSUrDCiF9ItoxXHNfYx/x1+46fonN5dmk5EFIwTZwZedulGIqY4PZB8hZTSKtnFU1rFbJWY2c+utS269ZY9b77yDJz7tqSgsvo/0vkPFNVXacG6xw+6ipmnF6reqHdXOkqZtySETh0heB9C6uM8m4qBkEgmOBKyHwNXrK9brFQ888B5QcPddAzs7u7TzigaLDys23T4hrjFVppppFhdq4ZOOLnRBbw0xckz4fqBfd6RgCH1VFAImnAU+9s4nYq3jCXfewXyxwzB0eD9QNTV104i7oDFl0jfkokOsUDTVogRGxxSY8ZMbf+bRQTYmlMplcRqoa8ctt16QRsaqxWhNDD0peZFKUyM/ZtytjwF9CfIQ06gxkI8hEIaBGC0pRzIZMZsTekRK0K893abnxvV9+q7niU+8lVtuPU/IgRAiOgcqV7zbJpwZuvVaaDaDcOazAls7ssqsuzWHR4dcuXaF6/s3sNYwaxt0htR5cvQQAn41sB8P8T5wcGPFEAJ9WKNV4u47nsQnfuLT0dGjQ8/B4T5//J772Kw7rv7JdXwfmLeGutLUraJuFU3dslgsAS3O2pRqj9a0swV13dDOdnjS7U9i2e6haLh69TrrozdxPd4gpkBMiaMhEoaOrs4kbcgaQsgQNN3hCus9y6qidZpExFZG2IhmNvW/nhH6QZTUEqIVH2LCmIBtapQzKHLph8j44u24s1xS1XMuP3il9DckrJEGwuVixt7ekt0daeTXRW3LWlFPsrbIT1pDVdntBs4PFQyJ4Iua03rN2hpiDIRYE1ONKkkHazV1JcFc68HowBA2+FCq3SmhjaMqST0xYZeNcSZRWyeZUaPJGnISTodSamLZnDFa7Vk4yx23LXnynRcJ/R4xBAYf6b14+MQsakVisohQwLJ4aaSY6ENgiGJgNoREjEma6KGMMcW67/E+UOE41yykUp1EfjwtdgghcnBwyOAHhsN9hn5gr2o5P6847DyHXQchQMxU9YzsWnJVUy1vobKGcxcvYNOazf41NgfXObxxnSv3P8AwrDk8/AAkT607DAEVPSpHUgKfNDEjTI2yKb6ZEeDNC+bHdHvhC6kxQDn5cpKoOxitsTrhjBa5SKeoXWY2a1jsLpjvLGiXS2LIbNZrQvBoJZOMNNs0WFehrBXXT1dhjEVsyaW5FVUGSArEwRODJ8dACoHBB4Yh0A8Dm74DMv2wYhgsIfTk5MUBLIq0oFIZpTPGyoYlqyLvpUrZRSeSzuiYUSYJv0+d5DRPuNlomxrrHPO2YT5rqZ0mxApXV7haVDbGT2SbYVej26vaihNltaWzy79TCfFTHEP9Ul1KxXlQU9e1yLJZKQnnkfeuR/Ow8t2QzrITlavjjfz49Rkfe5ydlweO/yMrcpQypd8MdOuOMPiyyVDbjnszClRMODOMFvaplIdHx1/vxVNjvVmzXq/pNhL0ayX9OzEEkg/FCjySB3Ev7lYrfIxkHVBW4aylbRqyV+TsyUDf98K/X3eEzos0ZK+IQRG9IreKyrSAJgR/IphXqGSkB8kIJUIrTdu2tG2HKbrgUQv9IibwZGwS+WCy/FspUCGQvCYEyaTJBlQGn5S7H8UP5XGEkac7Vn20EkpKymPmMG9VasZkgjGiGANl/JZ5R+Y1R1U7bFGVKcVwXJYC9WiOZ62s42MDLBkqZ1FovEd4z8VgSnWAFunAujLEmI4r9WWcHJvrwbG6znGmnpxBJRJFElpL0iQp8QmxxeNFfB6nwXdWUDmicsJZqJ2mNhXkCh8SQ4iSuCoiELFUhmLfk2Ni8CLp6HJVaKR5G8zX1SBjVZeaeM5YpcVVWGlCjPgQtj0VPgT8pkOlhCPjU6SxhrqppYk6RLIWLr0lYXJC54w2FcY5arNLrVuMslhbk1XFZh1w3YpEIMcBlw7Q2ZOGjUhjlj62XAbyyTjjZuGmBfNjAOJjIoaMcQatRQItKU3WGaUjlVVcXM4IMVE3Fms1F3ZbFm3F7XfcwR1PvpNztz+Jc7ffweGNfW684910BzdYzCxzu+Dc7beye24PsziHaXeFI9/uoKsGH7y4YBbnsKPDfTGqWh0ydOttkJ5JYkGdeuYzKT+u1g8S0w3mOwusgaE7JPUdeRjIQ4BhQPXrInkZGNt5yUIRsk5hVMJZQ/Ca3qopI3WGqGoJ5mtX0ThLckr6GaxBl7LzyeVBKdBjBE8u/8/S28FxOXdsFkvlE1elw12KLgpjHc1sXs6txiNkVxDVcQBPkZ1UyK4iSWAuWU5TzpeluSeF7cSoUbisZROcFSooaq9Ig0KtIvnQ011dceBqmnnDbG+OSnCkZcKccHYIxcF1CD0hRPYPboACHwKbruPa9Ru87Q//gNV6zWa9IYbI2nsGFN3Riu7oCJUyqlCt+sGTABYSWF/5wAd4+9veRhw6Yrfi4OiQP7n/AfrNwOr6QBwSmyPJeDatoW4NlfPs3xATk77zItfW2mK2I7KWpm5xzVxchZNmvdngqprZcsk6bwgMxJjpUxZX187LRtUCMVFvBkxKcLTCkwkpMpvPyrsyJTTOCkpKeMIlVhLoGCW0Fh98CcAlKaCjqMJIgksMoDarNf28Zuhr7LLi4vkddndmNJWhqjS1cyilaLMlZb2tMoLIScpNKIbzeU3widUq4EOm6wdWQ2C42tP5DbO25tz5HYIP3NhfMQx++7MPHSl5Ku2kSVspbJk/bfFviUY05YMPkERRLiMKOI0z4iMS/LQGnyH6oxU2JfavPcj1hWU+W9LUM9qmZm7nKG1RtpKNV5bPr1+tiN7TdR3e++JFUBFCpveRmBLDIJRFX/xhNv1MEgfFtd2HgB+G4m3hGYaBpbN0Xff/Z+/Po63Lyvp+9DOb1ezunPO21fdFUYBQIChBNAjEBjHqyCVq/F1DiPeGXJMYx8gwI4nX+LOJxmvCL4lGHRpFk3CNYhOjJooQvPxUDIUgoHTVvNW/9ban2d1aa3b3j2eufc6pKvKzEs4h6P6Osd9zzn7XXrtZc8/5zOf5Pt8vNgQWy4ZrrruBrZOnWXSeeetpXGRvGUjKoqpKGrpjQDuFMhVK19RbE+oTNzK61nHitobQtXSzq8RuSXvlUUIzY/fSExJf7u4SuilJeZTNfjPmaAffEWbmWWm7hgBY0a3ViDMbKktKacuwLPAxYK2Uwuq6ZDgaMt7cZOPEaUabJ6lGExaLhrZZ0jYLCqswuqAejagmE8xoEzPYRNkSZQtQGh8cEU/nIfjAfLpL1zQspjt0y7lQH6K4IZZ1ATFQFBKA+dDQto6uXeC6JcF3ko0NQaKi0Lt6ekhd5h9mAyBl0GiUkWyb1orgWdNsjhFmZWEudBrRaI+rHoc++w37msV9eL/KiKe036yXS2U9NzNECedVzrbH3npcabS1wveLOf2eezeUQjLp/YtMIlu1SrhngnHSq/Zx9tVsUm6oBp3EWVNHccIzUWGiQvuE6iKh9XSLlrIsKLTGabWSoFvj+NCrWcUYV41bi8VC+jOUYXtnl6tXLtM0Xd7jKVz0hJRYzuYsdqeoBCYPVp+kuqOiRaNoFkt2tncI3RLfzJkt5sznC7rG03aB2CWCimiViMkSSbgOnJMs1nLRAgnXldLTkUveyczA7KJtQTEY03UOpQ22KNHGkUyfdZI5vPW56qQy+StEjJfgyboOSBS5JK71mutwXFCr8mI8MMdkOl/PN8/VO0lpJGn6ilEMznLvRgwBrWA4qKirMvPPhdtudDbOU2ZFY40pZJUZEXrTSYnBlIo0TcT7gPeBLgVm8zl7s12a0RBbWKFRNI6uEwOprusk+6mk6mO1xuTXqkm5Vw5El0mytSEbSaXM2Vda45XHe0dcl4WODb1RY7tc0Cxn1GUJVYXR0teljMVUg5wgM8QY0SniO0PvpSLKSQPJzHcSzLddpms1LSGIU7r3XhSMlF6Z08UQcF1H13X45YKl0cyGQwoUpzc3OX3yBI2PLFxg3niK3SUhabyyJKUJfckgWZIyos5VlhSIg0b0jrg4RWgXzJXCzfdol54QCvQignaQHMpkHv0BhZujwNGp2Yi4Jp0LNG2HCgpMxFqDtZYUE1FFkk0MhxYfNMvG4UNkuHktp6+/gZM3PIet6++knmyi1AjiDrFzJOelM18XDE+cYXzmOmI5IRYjFos5e5cuS/Y0lxibtsV7z972FbqmIXZLohOHTa2gqkv01hhTaK45uYUyGmUtWlsG9SZaDSh0wphEqSzae2ha3PYeITjhnqbUJ1dxuflGW4MuDUoXjCfV4a7FNY4UVT0USbZ+YxeFBpOCF6nQKCowKSaxW87BiSgkxFVw3jfi9A19KZ/HeZedZR0pUydc8Cij5bprgy0KUVEoLFqZrC0vmwqRNO2zV7ngqBJVZcEbliqSYiCEDu8g+ApxQcy6lCnmjWSkLAzUBZsbQ6yGwmqc6wjBZfMrqKsyby7WOC7UwyyxR0J7Q1KJpm0kS7ho2N2dsnv5Cl3XicsmCqsUBkXoHEYZ4VvmkopKoLRmNBxRToZMhmOG1RCXhB9dl3DyxBna1rMXGro24LsWHzzBliRb0iXoslZ966R62nULcQJVuZKkNEFbIgrPVUJMzJcdPgQCkJQhGvHV8Gq/rz9lg59SG5QpKMqaqh4+veFkjWPBxmhDaOW5AbkqpGm1Lg1lZZDkkyQKjJZ5rrQSzG+NarSfsDEcMCgKRoOKjfGA8aiiLjSl1dSlRWtDUAVRGUw0xCTZUNfFbBplSQUoq7E+Ml10xC4xd55ZF9ndXXD16pR62jFbiJnjbLrEe0/TLIgxsLFRMqgr6tJSWS303OBRyUDKCl15t9LTalZa9DmYD1GtVE/WOB4MipLaWkK3pJnvMVOK0HVUgw0GAUxZUWmplNvCkrQhVpJYUAQKq+SaF1BaQ11aUlI56ZDwnSgQXrlyleUyUFUVRVWtKC0hBFzb4b3j7MYI1zluuuYsbecYjCaU9YAuJNqQaFzk9KbDBVi04ENi3nh86HDzBe00sNAarzRBG7wV51qtCxQj9KnbKbYcpzeu5US7YHL1ArOdy8x2L7N96XG8b2mbqaz7R4QjDOYVSSmcl2aHFDTJgA0JK4kcmduzao3xgdmiIURFNTrBxumbGJ+6idHJm7BVBaomRUt0geQD2lhsoanGm9Rbp+j0AKdqutmC7b09sdHtZOe2XM7xzjHdvoJrxMRKp7CS0UphwGCoKGzN5ngTW5YELaWWshhhVEnSHmM8Bi0GUM4R5nLeFGS/H1XPlpAmxqIuKCixlaUeWpRaZ6WOC0VZiZa3NnmXDT5LPKYUcgkuiKJI08rfwR+4Pwf0fVd9yM3T+Zi2lcd0bSMZnyTBf1lXVMMaWxTUgyHWQmGUbBCVmItJo2taUW5WZByVsNYQCpN9CgIheELIzYr9wqWyelLm4xVWQZkbKIFkJCsSgujvKgWFtUfb7b7G01DVFTFGihBAS3O88w7fOpowY7o3Y7G3J9zhnG8stcEqCVi0MvQW9n3uVKMYVDWD4YhBXVMVJXiP17LojUeblIWnXVjQThwwE0RdEnVFyOXp4BOdk81p51pUjFl2RhOUwqNwMbJo+ybZUiQMxd1MAn/T93rIz5gLX0kZyboVJbYsV0okyFHr3OgxYTAYopD5R2slzr9GfAdMIVcuZkMlUbqNFEaBVYwGFfjIsK5WgdRwUDKoipU6V2lFk94pQ8CgV1SbiFMqm+DJrKMLhfcBZSSZ0brArHHszlu295YUC8dyKcIRi0VDjIEQpH9tslFQlSVlft5EIvkg9MTUe3ECpKyIoyVZmNLKaVsjUsB9/8AaR4/KFpTaEJ3DNQsabaQHCI02FQVQVIP93grEV8hoRYqFuK8XmsIgPT1KEnMoS6Jf4zzL+R7BKYZ1wXBYY60Vycp+bQ8BPxoQfOSaUydxPuAiWZVLbp1PTEYR5yK7s47OeYz3tDGw28xwbcsyJpoEwVhcWaNtRTk6iTEVw40xVikGm6fR0VFvnmJj7zKXzz9M285pmzltt+QonaOObH0X/dCAUYrCaJIVeoMxYFVuvBEGKMZCWRQMN2/EVgNuvvM2brjtFk6cPUM1HBGjp5nu0i1mkDwQRfA/wXK+R717lUW0NNFy8YnHefT++yQjSSTGQNMupXE1dOgkcm1GK5zvmM8dIXXo0uBCYOPkKdCG4eZJbDmgLCcoXRLCEt94XJdwXrr3G58IPpGiLFQ9Z8sj9GgdIiYEqmHEDGFdYT4+3Hf/ObTWPPbkFWxZ0bSLnK3uMmVKJnYJyDtx1WyWMkFku3NjhZusjRFTEqQzPcZIu2wI3rO3u0PTLqV0HAL1aMB4YyLmKZVsKIaDCdYUTMYjkXWrLEUhspX1oESngI0eRb+YGlxZYlPAGrNqHpfFSUwqktJoI5WlelJTZjOLqilJBqJODEY1ykhpuq7tOog6ZhS1GKIMtcKHQOwiySVi0+GaDuUCYytO07LR77UlRVI0gmQ98tjTgDaGrvOk+ZKrV3aIPqKCh+DpvKdpGloXaDpHFzxdFF14nCcsJaAJIeTKqJjdKV0AIfseQOeD6CnHhItCHbOm79hWWVtc7fPUMsVQysjyXrVzdJ0IC9hCxnvfi6LWI/H4oBTaWIwRiUqtFdoojJXMZczUKqk8yqbfaMvWlmI0GDEalIzqgrquhR62uh3MfvcGVPKU1iqKIktCrpS3tJhE5cB6vphzZXvGbLagdYEYFUaJzrzzkjghabROeaNrVptNiLmKJGO1/7mffGE1h5M8KQiPOvhwpJnRNQ5jVJdUpcUqIERc0xJ9ZLFwXLq8hw+JpQtoWzAab1CUJVubmyJ7qpLQA10iaFBKGA59MA99T1uiKjVqXFPXlqoUam1hha5qKUjREIz0cdZVIbx7H/P8Bi4qlm1HCAsIHhOXFClycmSJQ8O4DLSdZdZ2TLuOxnfsLRc4LNvzBVEZdFmLIpiOFCpik8IOx4xOneYafyuL2Q4xCW3tqHCEwbwov+hc9ldW6n1GJbTK2aYUpTGgUNiy5Jpbb2W0eZLbn/ccrr35DurhJtVwTDPbZbl7lXa+CwQg0PoW7xPzvW2K0jJtI7Mucf7cOc599CNCnykks9W5BqUSmxtDqspSZvOMtl0ynU5pfUPQkok62UR0YRiMzjAYbkAsIGlC0LRNoG0DzkProHER7yIg7nNOjGVpEzggtREaxziUDE8csQ7oGofw0Y99QjKJpiYpw3I5pW2XdO2SrplLWJK57j5rHu/u7kpTTQ5qBsMRg9GIsqoYjkdopSi0kWbE2QLXdVy4cJ7pfI+27eicYzyZsHXqxMpJUQymJpRFwenTpxgOB2yd2GA0GrC5NebEiQ0qoxhZhSJItssaQlXilXRTq8wLDfn7JFrLiMmU1VRlLY3XpcZ1noBIztnaoK0spYOiWodQx4xyUAOgq4IQE2HREZaOphHHV+Uck6IkmoRSFlA0zuF8IKhE1DETf61oayP0vbZ1tJkiNt+bMywKxlVF6x2LeUPnA03rJCgP4rDtusDS94GMcIo1Mra0Tmh0rj4Fms4zX7aZcqNQxohyV2Z4qZx1lTOBbDOy1KqSDCxKXmfZelCGosyyJnBgE7DGkUK6+jE2e11ojVZgrAT0Mfe0SV+bXJPCliilGZQjFJIVtToxGAwwxuYNgUj69gmGlEQtTvxYentqk432eg6WRkWhwWgFs9mMi5cu41ygc5EQQEVpGPdeVKCsVvI4JRVWekW8XPWPKa38G5adeDpoLf0kPuRkTYriMp+zuGvzxuPDZFBTFkbinhBwoaGjYzrfZnfesL0749HHn0Rby+aJUwxHI557991sbm2yNa4Z1iU+OrrkVm6xoEDLXGlyomtQagZlTVEIRccYjTUyBlOhZLdaGek7yuqKjQs4H/FJ4SJMZwuWixnJOWxYoGLixGSCsQXtUOG9Y3s2Y2fq2Z0v6Xa3cR6uLA0tmqYYkbRlXJdUheHsuOTkeIOxVWwMSvZ2LtE0M7zrjuzzPjo1mxysk5LwPlOvyJe//H2zDUqaq6xlsrHB5okTOQug6dqWzu3QzPaY7u2xmM9z5jSIY2sKNIsZy2lF48F5RegaVAwoDaaXHLSyCFalpSxEstJ1bTZ1idm8wOM6x3KxRKmC+d6U4KTPNQVwyym+behCJKqKqEu6VOCRcrhSGgqFtiBKt4qoE1EloXokCbDWOB5cuHhZmlHrCcqUtO0S5xrm012mO1fEprwoV/JmQsdqhD6Tg3ltLUVdoZMlpChyfG0rVIdBzWhQA4HNZkLbdXTeMxgOGE3GxJTonKgndG2D61piChSFZTrbYTiouaY9gzWaWBUMxzUqiiOnUZq6rEVizWiS1hhb7GdvYaWoo1MialF2UFZjEP3ohEHbfYnN3lJtjWNE32gd1SoI1tlYREfJ3gxsubKfTwgt0amQub7S0KXQWUJN1LKSE/rUUrVEFwjGEgsZf/NmgU+JwpRoWxBzVj7lKpRWOjeh5sZqkvgfaEUMkZA5qSFm8oxW6MTqcSIHt9/I1RsDpQQ6m0T1DeTOe9GEVrKxVf0isMaxIKaEiuCDrLfKgsp685EkFL6ckddZt713kQ5OZP1IiWSQcekD3htpko6KGIUemDv7pSlVZaWvFGRNzGteDLLO6iwX6Z2naZarKnfQCZ0r3HJeEZNIiAa599LwqlIuJZBpEtkBu+kcIUSUDih0rrr2s2R2BkWxngKPD7s72xTWcPmixi9rtJGm1+29BVd2Z+zsTrlw4aJQYouSEANPPPE4u3s7+LMn2ZqMsHgs4m6ubZWloyWY72GLbPoYE8qK5KXPY1LGyoExEyXtIJs9aJ1n2TouX77C/ffdz2LZcPXKLqA4ffosdTVgOBlTj4ZsKCUux1rRLheUPtFqTZM0kmZOVDpSoEiupV12lDFQVhVVPWQ0mRCcO7LP++iCeTLHN9uiCidTAvieNalUkqyPsZRVzbXXXcepa65nMtnAGsPO7pTd3TntYspi9xLNdEfMf7qOZrGA6Ni9eongWlosXbL45QwdOwqlpblCK5Qu0UYzGdaUZcF0NmXeiFpNDLIx6JoWrRbsXNlmuXAoNaas92hz86xVEaMSi9YT7QSnHfM4wEfhZxmjKYwMqkJb0AbnO1rXYowlEtfr2DHijz/+AFobBievxVZDUhS/gEtPnOf8o+cY1DUnt7Yoy4LxaExMkd2dKU3TEHIjrC4LqvEInQJeJYJ3LHb3qGzJTbfdzmQ44qYbbwASnXe4ENBaMuvLpuHy1Ss0TcuFixdpmo7Fo3OcdwwHNVVV8Jzn3IlSitMbY05WZ1FRsq9aGQbjCSolvNZEpdBVrmP3wRKQmfcolYO0yqDK3plWkXQk6bDKyK+H3/Ei9XK0USgpOrtXWsCEhEUxqgbQN50CrXM03qGsRRVWjMxMQfIRv7eUxEPriR008xZFokyKGo0LgYVvMWXJyZtvoKhrtLY0bUvTtPimQ1uNtWLK430rlARtiRq8j3giLiq6kHs5kpS4jTEYW5BUQGclp5jVnERLXAutQd65CA8sO9kAO3F61FqSHWscD3yWm+xAFF5UidIK5xyp7SSzHTxaa8qizE36FhK0bUPXOqwBoxOLtmLZdhijCSGrevmYXVZzpUeJalZA5HRBZ58ZsrdLxCooraZrl+zu7hKSwSeLVQavvZxDWxkr2WXb+9y07QOdDuJLYwyeRONFR3xvscR54WMfmiRV7kfSisJmb441jgWPPXJOmlnnV5gMKurhiKKquXDlKk9cvMzedM75C5cZjseYsqSuay5duYQxhs957u3ccM0ZapuojajTFUW9ytAnwGWlrPFoQllWRFMSrHDlpV8sv5A+4aAgIBUlcow2m824srvHJ++7n3e9678yny+Y7s4w1nLH7c9h88RJXnDPSzl96nomm47oPTuXL1O5BU1InAjQBHhsCU1IoIUCFhcN02nLxqhmc0sa0U8vr/vspNkcLKmm1C8MuU2lN8lJSdQ/jCxay8WS6d4eqtymc4n5oqNdtnTNUlwwvQPUSm5KLN7IEmnyNTZaUVordIQkZWrJbSXJPIXc3Bgky6Vz9j6FQHCO5XxO9JFpcZWyWtK2DT54jBb9+OVijo9BDIcKg9IRSiWmnpnSpbOpFEaBMdS1ZVBXGLueSI4Lm5tbKGMZbJ7AViNiWJJix+6VS7nKr4QPr63s9pPQUYSCkK3BraUaVlSDmmpYETpDaFoGZcWJrU02xxOUEcnJznt88CIdqDVN11JUlqZpsaWlbVum0xnOuZW9eF0P0MqSkhZ3xNbRLhti12CjhyRa3p5ERUlVFGitsYVhn+SQKQ8rHnNvgNX/94GN82fkSvzZhc9uhj6ISoeOaaVIY4tCrkkQ3qfzHh8jnRPKFwfciZUxuWmaTI3I+4OYkyQJVFT4GPEhklQg+Ij2IWdEs19Cnn+NMiQd8eTsVR4/SWmZRfv5FehLOyt+9KrVUKpDClZkaZUdDoWKIRQeMU1jFeiruM6OHhec9yilspa8yQ38gZgFAFCsMvExr6MxBUhiex9izPmDlL0RWowWfnERDMYIZabUJTL1aMmmZwMgklSmV2pgMWZ6l1rphHch0HpHYURz3CiTZac10SiSVkJRC6LuFZUXTfmEfF+8x/mQXUJDHqP78kkK6TPSKZPL1mPv2NB2jmA086VI4HYoCh+YzRfMl0t8CAyGAyaTCddcc5aqrtne3cV7z2w646rVjEqNK2WtLoomS+RWpATOd5nxkfBlRSgqCltKFTLEXIjJVdFMC/PIWMdIML+7t82TT17g0qUL7Oxu0yyXLJYNxhiubl/GBc/ebMpWs6RQCps3koUxBCIVMhfWJlegsrJLjIkY+t5QMFpT1zUxhCP7vI84mI+kFEQOUFvR1I5Zx1aKXyhrqOoB2hScu/8B9EOPMtp8lGowoapHVIMR0bW4dkHomuysWVCVNSparLEYpcGIdvNoULG5OSZ6h+8aVASrLcREs1jQNkoGmY8YNIOiJCmNb1tC53CLFq0t2088iTEWHzpCFMlBtBI1iBBwqaPYKmSSqCLKRJT1KB0xBEzS1ChMKhhPhlx34+lVE+UaR4+Xv/LPo7Sh2LgWXQ5x7RTvl6QQefKxRxmNxmxtnlpp0YcQKMqB2AckRySycWKD6268lmo4YLg5ITpPuzFiXA954ec8j9NbJyhqqcp03uOy7GVS/TgRPmfjGnwI7O3NaNuOvd0Zi3nDYDhiNBxjdGJ3e45b7HLl8Yv4Zo72jhQCV2dzFm3LNbdewzU3n2U4GjAYjPKEISVuVciGRBupKKiUGxKJkBtm9bosdOxYLJakBL4Ta/LCKaxXGFOysbmFbxyLbk7nOy7v7NJ0HXuuYxkDdTY8U8agihKUkwxrEt+OpIRuE73HZHm1JPVQTFLM9uaY1rFsxOrc+wAetNWUtiTGgFOdKNUYK34JVnScKQpslSk9KYlOd4hI3l7oNyGJq61By2vUCqMko1oVFcYYyqrGFCVaa3nNSTL563jqeDCdL7K6ywittWwUQ5BGvOQoioLBcECKia51mT+fKVKdNIyKEpKc6/yFS4yGNUolUcfSCmM0Wyc3GAwqKA0U4DtwTUSpRLBASgTfEYMk3KxW+M6xmC/YmTuuzlqGRcXJ8QalsQzrAYW1aFUDiq6TXrUYO2JqZaxZQ4iRZdfhQ2SWRQxSUnmLqbP6k/ToiXXf0WVF13g69hYdRstmsm4MZm+G0prdvSnbu3tMNjZ5zt13cf0NN/L6r3w9tih5z//5O1y8cIHHH3uUx841nBzXnBzX4ixsxMOlqgb76x8wHowpy5LhYMSgGuaeMqFW97z6XkmwjVHmUCMV7z/+449x7wc+zMVLV3j00YdEtz73fezs7WKLkuGJ0yRTcHpzi1NbWyilKG1BUpFBCmhgq4I2QjBZxRGFT1CqBN5RaM3JrZOfnWo2qz6TJP9kMb3V/6v8j8qlZ5Csd6IhJkvbdIwnooYTgyN6sTbXWmTPVFmhosnygzlD36vjaM3KRzrz9FXu2F8tUFmVQStFRGVef8THDq08XQKttcgVpkDSSgoBWXYy4tEFktGqIhjhFiYdSMlAVGhlKZRo+lZ1ibXrGvNxYTyegDboegBFBalFKb+Sq9S5QXVflSHn5LVGJY1GZCKLwoo5itGo2Cs0ZO7zocBkVdcFUrY2LyXDX5ncXJiyZBYyNgpRu4nBsVwu6eYL5rMFvpmjnCg77O1OmTUtg90xo9kSlPD1RSZQytwJhdbyJhIJnStVqQ/g1wmpzwiil8A3BsnSxCjXSikj2oAmERD1q9YHGuclwxhFSabIFHOVZU9DvsVe3rRX78gOsdIciDTje0/MZiq9rGrfC5JS6omOQG90l2lcuRqgrZXxFNO+rGRCmi8yVP+YfFNabtpkjrzWq56U1YPTejAeFzrnxEQpBEKU6o6OANKw2o+BhPRIhJirOCn/nkSdLeTxtGgalFYsmxZvvFTDtaaohLYVPJRlYrlsmM+XsrYbLRVz06+9fSN0rzoTcM7TKYNzDiKUVigSIUZC0IQgDbohBnz0MsZiJCRRvumrCD5GqSzlmANAE9EEFBHDumftOKHELQmfoIsJnaRSJHNcRBnNcDxiMpmwubWFLQoGdU1RFMy3xdzThiFFknFsrMYYS1d3Uk3KkqrJR8qikP6h2ue1WdZ2a7NZnZKepDZJMO+1zLu7e7tcuXqF2WwqpmTWUFWS+Z9Olzjvmc+nTKe7jOuKGCeQ6YUqQm/qGL2X+ZUopqjJZ2ZIgBhQJNlYHJxAP8040mA+JQl4rJGueGPywqH7klyBtYVk6ruOxaLBh0g7m2JtQTx5GtOeppcg0loxHAwwqqbaHKNVxFiNUrBYNMyaOU3T0E9TotOcJyal0EEWHast1mS3w5Tl2uJ+KIaKpNgQo3RCq4TYAkdP1JFgPcl4qs2OaDyuXBJ1wBFxJIxTaK8Y2zHjcpN6YphsjNfB/DGisIqQItvbl2g8tMspXbdgd3dHyr8xiuIS0GXziRADimwRrhQqBXy3RBY/MaCYXtlmbgs+9vGPMh4MM70m0LqW1nWicVvKxq2sB2ijKCoxU9ne3qVtW0o7wJiC0lYUumC+vcPDD3ySdnaV6RMP4bslyUlW/9LeHrNlw5XFnMeuXGWyMeHMNafzpCPqEvWgxmQzNq01VaGytr1CG+Q1FPZIJ5I1ngFOVK5wQIgkDAEtfRAaWh3YUaKGsB0TTUgsvchBhqYjzhaZM29FMzlk+chMB/WZNpEnMqFUaI0i4ecLdJMz6jnQJ0HqWlhkdaRMSUi5kTEZgwHpYyqLQw7ISu1TupQGm4xsHJV4KGilREZVi+Ge0HQU+zvlnv6l17zlY8KV7W2M1mitqF21MpkbDCvGowEJRddJE+yy6Q4E8wdOkhVhwrxlsdxlNKhFXlJpZntTYohMJmPqqqSqa6qyYjqdcuXKVYQDIc6xz33OjQwHFSpUEMURdjSqWQYouwRJMVssKUxHCpL5N0YTqoJlU2CMwoUOF1oxpoo+q9oI5aZz2RAqb0yFEZaIwRFcg9WaYWnWvo3HiBNnrgESxnqSkiRFSpFkLbaqqUcjtk6eYLw5QVsZpxuTDRabCx657+M89vAj7E0G7G7UGKPFRVgbiqpcNd33WXKjDXVZU5alUHJskWNPWROtEZOnYAxJKxa+o42eBx98gMcef4zBYMTdz7ubwWDAddddh/ee9//BH7K9s8vlS4+TkseG53JyXBHCEltq2uiYL+fM2pbHL19h4Tp0WaCMobYFpTFEXRFtFmTRR0t1PcLoMouWZRmpnnsnCXMFSpO0wWiz4r0H3+GdhxgI2uIGNWFYI33sWtQ6yhprFHVVYzWEKJnziDRwhUOmEMJ97leymHIBLnM5VVYtIu5nMfuNQIqBvnE+JfBB3MCiCQTlwERMKfQaXwbQnhizk2iQjH8yNaZUmFJjC7sO5o8RSqVMrWqZtZ5muUfXLWiaJmco08pUSfTnhWfX84CVQpqjO5FLSwp819E1Lcl4dnZ36ZqWedPgnGPZNrRdS1EUlHVFYQsGw5FIR9aWROTq1W3atmMy3mJQj6iKgFYK1zl2rm7TznaY704JXUPMWst7e3NmTUusCjqtWLaBpEW3ezgoMcYwHAWMNZRlgbEGV2rKQjitpjBYa9Cml41b49gQtUweUUHUK056SElMmVB0QAvizJoSPmdCfYg450U6MuZ+n5yh72PkmESoV6hdEliTs+EpBFSKokrTVyKRbGfPpc7l0v16qVKiapI58ynJ88I+3x6tQKu+jWwlU6my+o7KPSPStHSQZ9/ngdfR1HGhbVuMMXSdwxgjFe4YKCu7CnhD3hD6GHJPmVyzvvk05FbnEDxdbFBKsVi2qATbO1O887StpyxEi74sK3b39rhy+fLKbXUyGXDTjadFJIKClKRptrCyJhprUSHiuzwfaw8JvPeYrL7T35yPhOjpgst9QjZvbNOq+KPI1acEIUj2HpOIyazTGceIsqoRvkSz6rER5a4s22wLSXwVxeox1kog3raOvemMAk+BE556dhwu2oJVv5gCm7PwVVFS2JxI6/vLjFBQS1vKnFZYktbMuoYmOPame8wXC+rBiJMnTzCZbHDjTTfinOMT993PYrmgbRbsbl9hMd/DuZYQHCiRPHW+o+taFosp87bFVCXaGkxVU1QVMShiMKgkG8+jxJFFlwqZMIwp0DbkJr2IVgarLdoU2HIg8m1ZwrIuNKUxGAVaBUxsCMupBPPKgLU4A5QWUw6x1gofP0Y2Tg4ZbCW2L11kPl8So8LTARrJz0N0ojNbliXWSobIKJXNd2ShdCESQmRv3op7bSeyWC51+OSww0S1lbAKxrXGWIPPG5K28zgfMU6jvSKVlrIaYUyFd/tZrjWOHs1sj855Hr7vHJev7rI332W5nLPY22G5mBG6htA2AFmeLYnKQxSeOUp6Onb39jDGYouCoijYnGxQ1AVBV3hT00VP4wO7s5bZbE8spH3IijKGGAPLbo5zHVeuXKBpllx79lq2tk5w5x3P5fl3fw67O1d4+KFzdIs9ut09one02bxq7jxtSEyvLFCzQFHOqR7ekQ2tBWMURVmgjcbkCawuDWWhqbNM5tbWKW6/c4viwKS5xtFDY/Y3h0SCFypA1zmapqFrOube0YaA14pgTDbvkSDcObdq9o9RKAlZYE/m1tJiCpEUFKaLxhgpbSsjzYimlwXOdBdtpFTdUxyVIgfjWfEhc+R72o7WMW8ccpJEiyqE7qk1+Xn3f6pV93XKFMeDrps6bzbWOHpEgJRYdi0R0W03WuFCZNl2oq5kjNC1kiImjfMu0xdCpkd5wIs7rNb4CLOF9Jw98eQO7bJlUM+FzpAFBDrX0TaNsLasomk9T168ymLRMLIdGkOMnroyjIYVG0njG0fnFrlZUTani+WSzrVMJhWDQUnolXFSlhZMCd+JIlOvH977aRd5kxAQapqLSRSa1kPv2BBVboLOlKrecKwoM2vDWJrOsWwaptMZxlhm8zmz+YLd6Zyd3Rkp+CwaYSmtSFIeym8rqaQbJZn7wpjs72IwWucMvaYocjCf58XdxZx523Dx8g4kxdbWCV74whdx4uRJ7rjjDpxzXLp8hRNbj/Hkxcs8cf4RTp4Ys7kxIHiPbzqWrWO2N2XRdLjZNr7rULFCFRYfWzpfEhpNOxc5y8oajnIAHl0wryTANdoQjckcPcnMGyOZ6qqqhOYQHSll90tjch4+oaMjdEtQhqQNigIfCnSQHbkuSskGxUhdFAyMZblYgrYkFVYXPeXPzwfpqLcmkXSWVVuVjxPkjvsQA/Ompek8i0Wk6yJBOYJyVBrJuKHRhUFZjUavmoe8SySfMAESBluUaFMQQlq7zx0jfNfQNS1XLz7Bk09eYntvh/lyjgoOFT3BdYS2XQUfK8WF1DeOSpPfYrFAa40xBZPxmMlgE4Ul6YKoLT5pXJCM+XQuOvXz+ZwYEt5J1n975xJtt+TSpSdo2gXbN25z5tRpNiebuNvvZLmYcfXKZbrFnLBsiMGzdA6fEo5IAJx3uHkgsQQ1Q6tIpUM2gZESpco0mzIH8+PJBlsnT3LNtZazN0AZ1g3YxwlFzlhnVS0xKAt0ztM4R+c9bYi4GAkKUs56kyQrHvKmECWBdTzAdVeKFS9U9OuzbX3+ucq6p/0Men/s/m0/oF9lzvN3IT3l56rAuXrMvuLNihcvf/QHrc6Zcr9Sf+41jgf9mOm8l41XaTHKEmM6VPWBzIYic+dDOBBzeEieZMiV8ETbBVzn2d1bspgvqUqH0VYaZkPffC/rqy1kLOxNF3LtK41VNq/DirKyDKKhjeD6cajlZ+c6QsjuybnClLIaSchVhc6F1d99tQryeLdmxfsX9Zv9/o81jh4xCzHEvjqXN/vGFtiINNb7QNd5mrbFaPGlaNuWZdOyaKSBVqW0os701Za+70flYF4rRWkNhemN8KT5tSgKCeqLSvrLlCIq2J5NmS0XzJdi4jQcDLnhxhs5ffo0t912G13XceMNN+Cd48mLF9jdvcr29mWubl8ihShiGJ2nXS7pmo7QLohdRzKRlCyRgI8Or6AhiW9ScbTMjCM7u8mNeT5GWZSMAa1JGJK2RNRK/UMy9gmNcNq0MqJaUw4wZY0xRrSRAd8uIQY679G2oKxHaFvQhUDnA+VwgzPX34TvWtrZHsF3LOZ7eO8IXhzjjPaA8IiNMatScAye+XxO23n2duYsu4APmpAUugRbFlSVYTiwFKVCBSBFVFeggqac1aiuYGBLaltwotxio95AJcvO1eWBevYaR43xaExhS05sbtE0jkSUJtYU0EnoLfZAMAK9fF6S5j2lMWWBKSxlUTEcDtnc3OSW2+9mOBpy8uRpGT+qoq2XzKZ7BB9YzBdcubqN1oayHCJ5LSUygMairGW2WJC4zKUrV7h05QrTpsNsnEIXAxpdkGKkMIZCKeo8AWEMGEPwjq5tSL4jtFNilIkwxUTUok6ijUxmoyXMO4sqtpgtoF7vJY8VXSfSaU0jNC7X+bx4OfGvcNnMRyvKssxN2WJ4o7VeBeb9+DSFZD51VmjojxH5wT6YN5k60wdp/aTTB+0Hg3i1EiKQhsT9W59N7/8OWVt+P6DXK3qN1vvlY1msLUaL/KrJr73ffPTqEmscPerBEK0UdV1TFJbCGqwxRJJk5o0ow5HEmClGqUzH3J/WjxOtDeic4Q6JedPhO4/ziZA0ypTYsqQe7a+nqa8wJY+ylulczPhsKKhMQfAORcRqRVloVGWJo1oM86oarRSh6yClnFCJVLWlGlTgpUKgVBQ54FVjN9lUMjK0hnpQYa2ooORRuF6CjxGLZon0LHaQsgeLUqID7wN6NufJi5cIES5evoLRhgsXL3Lh4iUWTUtAEZQm6IKEJkYta+yikZ63PCfZ7CdUmOx5oPbnpcKKUldhC0na5blx1ixZdp3Qt7NC4nAwoCxLnHM456jrmtF4REyR5XLOxYtPUhWG6AOhc3RONrStD1yZL3AxYhdCsym0wWqDKSxFJVSiOBgc6dx3ZMG8VmTN2UgMAaWycoiyJGUkmI9edNlzMsnoHESZEq2FhmNsRVkWDOqSzjmW012RVfMeGxODgchX+tkc75cUww1OX18S2oZmuk3bLPMOX4xLXBewxu9f7CInsjKHejGbs2g69nYXLNuAshXKWKrSYgtDVZUMBzWmjOjoSEHBvEB5QzHXWBfZGA8Ym5oT5SYb1QZNE7h0ZXHAVGWNo8Z4NKYoHFsbm7StI2SzBq2EUqVULgqrfWv6fqY3tkQbm7VobQ7ktzh16hQ33y5NMoOylGwABV214Pz5x2WiWS65ur1NUdZsnaiFH53ErAJrUdEyWy5YNEsuXZGAftZ22I0T+HKIjwUxRoajEdYWEvxoTVEWFKWlW86Z713FtXOmVzuSg7adywTU86gTxKQYLmDeWsrhgvkyEdcx1LHCZYv5thV5SNd5+ek8XZarTEjpv8j9DsaK8lGPvnKklDTC9sGyUocD+V7XvQ+s+74QOBjQH5x/clVyNSbSoerUfpUqPz5reEd56KFA/nAwj8gF91JyOUOfUsJkp+81jgf1YCBSoYMqS0nKBsv7Duc64dGnzJ33cr19EFpVykUio1VOwiVCChCkWda5QBfAR4WyJbaqGY2GDAaDrLEdcMHTLBdom5gtxHxxQEmygRi8ZFxzMK+jhWGNVppBWQHQhEAMgeVyQQgtJ+wm42JIUhobZExqg/S8rTLzEswba6jrkmANhbWy/neew929axwllu0yJwKEedGb5ok8eRIqYYjECJcuX8UYw5MXL3Hx0iUWTUNEEZURUzs0REXrEjvzFp/pNyklbJ6DZLzKc0vfm0iY9ln6vlIp1SoxeawHQwbDsSjpDAZUZYl3Du8cVV0xGo1IKbJsFly8dAHXLgnO49oW5wKzWYOPiRZROdSllWRLEnHUalAzmIypqkrajfTR8eaPbGYd1IoYwVkHvl2ZSfTucSLtk8vFJgdUViO9U5KBSjESvCNoiMGQQoQgKiW7V7dZzBeEBPVwwd50xnS+kMbV4EjBEbqO4CULr7TIWEYTV2VG6DNPUTYIzmWdZMuJzRFjD6asUKbA1hpbKapKY5NGeYgLJVbtS43yijJqlEps1BNObI7ZGE4YVgNC1+AaJ1rPaxwLurbDe8/GZIOEYTAacbptMHnzqDnA+80SkytTHVOgtFk1XhelNHeNRkN8u6SNnrg0qJRY7O3RLZfMdveY7u0xm81ZLJdUKFzM6iNak2VlQFv6HOfefMH5CxfxPkFZYZVlGEUqs65yRcpIllMZ0FrRKZHL8iFKVo1EUVYorQkuEEPCFiXKlJiixkfDsvFcuHSVqqo+k5fkzxxEWQuck+9+LxMZgsw3wiOVgLwgU1G0Qgfp4uvlc1cBfTb4UTnpAazoNCpLpRpr6Aku0vd/MKBPK1Ub1L4kcJ+d77PxITsoHszU2/zoVTD/DJn5lBIrynx+nv61a3XATGqdmT8W+BBQUdF1Tqo9VYlRWZrXGFA6C0ZItSclkXJMUaYqpUGZhNYJTUTFREKy4iEITUJbgy0ttrIoq0kqkVQkahl3ujRoA0kbkjL4kHAEyrJkYzKCDlyX6CJ46yCBCx0akeCVvWCHD156RlKvtqNISYnZXkxCs4GsqmSlYdKaXCFI8v2xdl0dP0ZEEtlCGpCxFpIkd6MPaOfpnEdpwyfvuw+tNI8/8QTbV7eZL5vc2+FJqpUTJoXP8r0hJtyBKqJKCaOQmDI/v1IK0891OhyYF8XIL4SALaRHUhq529XNdZ3c8twdQqRtOqZ6TvJiMBp8lDk9QTJmn9aNjEONlgSGsWhlCD4R1WehzvzGRBE8+L0G5ee4JBcyeNmJkb/0+5klg6oLrBXOKBiCD6QoBrzeWMkeBE3oAucfexwULBYzRuMhu7szprP5SolEa+FQRe9RaLS2WFtCghi87MycQ6nlKpjvd3lFbdiYDOUxdY2xBVFHko6iIx/E1tfNIUZQS4uOUJfC2To7Oc21Z08xGI6YDDbo5onltKHr3FF93Gs8BfPpnITizOmznDwt8nlkjXib5Uz1Af7wQX6xzoYjIfQqIgEfpDm2ne3SpoRykqWaz+a0Tculixe5fOkyV/d22ZnuMkyJkRdH2GSs7GRtCSEQo4cUuLy9y8fve4DBaJOTp6+jHBjs5BSgKLWRzL8pMEoTQieUMWYsmxZiyBKYmpg8znvcoiW4QDXcpKw3iAlcTOxMO+574NF1A+wxYzafA+Q5L+GclJd9vvW8XgBTSKAhVcSwn/Cg56ar7J0hzWMgnFRRh2Cl714UZhVo54Pzj7RSF+mhVsH84eDae0/XqzgdvCEc+p43f5AC1D+H3J9fb4bROpuzmfUYPEY0XYdSkFLAmuxcWRTSIKhLYox0XrxbqlKk+8gZermECWPkppIk0lJUOBcJQeiIFkU5KKlGlZiDqUjUovSBBmsKrFZgDVFrOhdQITEc1Jwd1Ji5w886GhSuaQk+0LZLjDacOL1FVVbs7FxiuVjivVSzYkwoDCkGutbJ5jhvfIvCUBaKqrRUZRYgIJGUFvO1tZrSsaGn+vUu0j3l2udAuQ/Ed3b32N7ZI8XEpSflWi+bBhcS3bJh2nZZ8bBXKAo5498rdUmALKt2P5v1FMLcw9H/XL24vNZbT1EKVXU+n4vZXVHQdR3LxZJm2eA6h/eR2XxBu2wl6Itxv/FaKeKK3shKMdEooYdXtkIpi+vSkQ6/Iwvm61oTvaYdKkxSLF2i9dlKOniSkm77fs0hJWIMhKAIpoMgcoDiOi3Sat5H2qbNut4NSSWa+R4aj+9aVOxd7MQ2OmhFDJGu64SjlW3Uo1akqCWITzE3qklVoCiEQ1UUhXA8rUYZRdLiGBaVZAGICrxCRS1GHElR25KqtAyqiroqMUqLZXWXS+zdOjN/XNC5ZyML0+Bch28jRWGIpXS6l4WV3XSeKMQOPKKSQiWk0WVle55QKqGNNEp7L1Wf4Dq8dygU1lrqumI8HlMPBkIpUJLBUtpIR31KIm/lpSG7aTtsmTNnWq26DGOQaSHm7G3ynpSlNJWSTH+vlrKaRXKa1tiSspZydwgJY23+fU2aP048lXe+WmQONJCKZOiqWxWTzH6TaQ7mDzaVptW1lixU0mn/2P6Wkxn0uagktBu9konssb/wHYTQD4XuFTMvte8nSUoWL1koez78Uxpbc+Afk0bFmF+P9AL0G5U1jh5SBZHf46FNWd8zoVE5obbPM5aMt9I5m62T3CKymcxUPpluskmY1RgrZlCRQFIJZZA+nhAIKHyU6kxQURSZrMFqjTUBmxMsRWHQCsnaZndZazWFNfhCKGZ+VdnKG9meGpn7nIrCZu8ZMcvyOWmX6BvD12PvuGBzw2dKatVhLe05abXmxhTRSuO8JwUZc8bYrD5jVkmDlHlUKUW0EYEAHfSB+yVGXBmdp0M/Vj+fevVjluptmpbdvV1QMBwMcN6xWC6ZLxby2hK5AhTlOeKqzVtu/WuIkn2PaKJKeBdomw6lA8qmZ3gFn8bP+6hOfOqkJcXEQBW4ZcGV3ch00bFYRrFmThofRX2jrjVJJ7quRRuxbDZGFjKNYhk1MVqCTyybQIyBLiyBiEoLFoOSwlaUpqBpO5bzpZhPOSfUntzFPhkNKAoDMUCKtF1L0zQoYygyr2o4kAyDzc0aPuZFWSvQRqTlnLjS+VZkvUxUGKU5OZ4wnlScOrHBia0xi6Vnd2eP3Z0p0z3hWK1xPKiy6Y1vItEHLl2+xM7uDoNhzWhYU1Ulk/FI6AVBbOwXi6XQsrLXgUoRHSODUc3kxISqMGwOKlKI7OxOaZsO3zja1mMLy8bmFvV4xOTkCZFerUc5YHeQFOPxJikGZtNdlosFziWmsyXaVPiulepUDr46L4F70gVRaalqpUgMHltWqGQogBA0cZal2tCgFfVowubJM8QQ8T4wHNTrAOozgL5PQ5oVVK5CmlXwrrJk2qqcSEIZCVi0VqvjeqWRmBeCns+ulVQxtdEr0xWVm59tXgj7IFvUQJ6iGZ+RlYFXAV3/GvvgWza6vcMhK3Wwg+d/Kt8+l0CJQIxWXq/W62D+GFFWpfyS6Sk+Rlw/trRGaVEJkb4Hc2i8qUyzMXo/gYHvAzBRkCFvForKUA4tXaYlGGMojKVzkaZb5qWzxhvDqMwN3EVBWRRURaQuHBqLikNR2YkRpTRVJcZ4o/GAopDmxma5XElTBi8N5EZrqnqAsb3TukYRWC7mzBcN0+mchAFVrYP5Y8R4PM6/iSuFazu8k/417/yK1x5CpFlKU2s9GFKWNYPsbB3pN6KRhCTbovd5nskZ+hCzC3Y/l2VX1ijzVh9o07fw54SYSuB8IC2XXLl6hfvvv59Tp05RVRUhRJ588gJPPHE+S53LNO1zm5F4xKvVLSTZvKYQV75FUQW8jyybFqUt2tZHOv6OLJgvSkUKirIEQqIowBYJ6yO6y5rreXGJUcogkgUVrr1IuskHJW6dieDFACLGQIgOpZJkRg0S9itNDJ4Q/IoHJRdy3wSlX0Q1CpPVFUBi9VWGjL6M3FcF2L8/bzBi/k0jBhvGaKqqYFBX4rapNN5F5vOGxaJl2fgVT3+No4fWkhFIyHhplnP29nZxbonvKpFFDZ18QfOYWuRSLs6jYsDmhhpTJIweY42iqizJB4xK6FxlcjloM9ZSGo0upckbY4FAVZSZ/16JAkPX4doOrXSehITOoyB/LxIpB/PBiLpTz7Pvq0ZEhYryOvt8a5+hLYuSuh6I6ZX3VJWMSXOEzTdrPB2rZicRzs7l10QIQkNRWnwqJNkuXYfaSL1FZHNlEeyD+bwOreYmAJ32TZtkEjuY/dkP2lVvipf215Nn6gVcUWXYD+7lsNxXsv8iPvUb7wsCq8MOK+Ssg/njgTE6F2b6GSJTrRLCJWb/GveXZP/a55sWF2mAlP0HWB17oCB44DlWSKzch70P4qRuE17lzUE2NtMKrFIUVqGSpiptfk2RlLwYTBVm9X1SuWlcaSQ5l+k1xhrRHDcakl8FefvfhvSMY36No4Ey+wNEkee2KINJ5Sqyznx27zxRaak2x55C01eBcjCfPCmJgVlKkZQlKkOI+87YcX+uiTGiXc/AyLNXkpR6n8HX2dyuc46dnR2U1mxvbxNiZG+6x3Q22++vZF8NKfYVffItJzp6Cd6oxNAvJo9OUt3SmKdRGj+dOLJgfjgyxBBxM0/yHfVIEazC1BozhK5LLGY+6796VNIYW4EyeJ8DsQhE0WcO0eGDZ9EtISVszkSF4Ok6cD6idId3QUpxPhJ9XC2EKia61hNDoiqNNO4UJQOtiVHKcTEk5vMFSimqqkSbA9zPmHmDgDElqIDNCSiTuapbp05w8uSEsiroOrh8ecbHP/E4V3eWPPzEDt6vaQ7HhcKkbE/e4LqW8+cf4YFzD+bNV6SwlmFVAWnlP9C2LTFEdC4HTiYjJuMRN+ubuOWum5mMBpw9sUl0juXVbYJrWbolu/MGFwO2tAxKcYD1PrJYeKIpGJ2dYKzh7NlT1HXJH3/kwzzy8DmsqTBYiIrQOZJSBC8d+ql3SClKgjbC+deGsq44PTiL75YsdqUpDCWKN7awaBQnTm5xww3XEbzDu466kmrRUXbSr/F0TCYTAFQSY6cY+wy2LFYASUt223snuswaTMy8d1ReyFLmZ8qCoVOfmEAWJq1yZhWhKyINYvvLRo6un4bMQT1AWUir82Wt+56fGiS7G4irgF89ZTytek76ptecCFk1qT2VjrPGkaIoZXlXSACVlKLzTmSjc+XZGiONrgRQ+oAoAKCgLEuqqhQhirYh4PHGo2JC2yQqcEkcWYWJYyFpvAfvwHcSQDk/x2rNUBlSpYjzhWRrY6RM4v1S1ppUaTaGRvp9ujnLZkZpLHVhITd++xQhBkpdsHlikA0pxVBIJDbFLTbEgEIzGg5JSRHifrJujaNHpE8gSMIJrTCFxdhCqKx5niHBaLQhSay87q2U/3qaTQzE0EkQH/1qHpUs/P4cCRJQi5txoO1aGQ/eE3PzbUpxP4mW5NjFYsGHPvxhhsMhjz72GDFGPv6JT7Czs8vedAZKGlzjoa1hfo9JPIakqpUT1Cg8rOZJrS02+SNNZBydznzmrWmTUDpirKZIEBSUCpJKmDahAvi+BHLggqQsmZWkakFMoisak1zIInflp1zeiCmSckY+xniAIwg9HzWGSFSQoixCWinR786c/ISYuqAQS14OZC5Sr7Hccw1BKTHDUlm+yxYFRVWu+P3LpWN7e87uXsts0eHXnOVjg1LCcd9XKmpp2yUpeggeawyuLCT7nceMd5m7jkYD1iqK0uJjQFtRgLCFFd5cpmCGXLoOaV/KzyhNzMlTozRlXVOWJVubWwwGollf2Kws0e/w89gUQ4wIeazIgpQDLJI06JQFnYo0WbbSZNc7netI1lqqqiBo0CpSlqLssM7MHy/EFVPlYEqteJcxJXTYXwxiiqgkvTf7mXG1Cn775lVhJbOqXArSgflI5jCVF5Vch+SZA/kDdJsDc2WKSUzwOCxReZBzHVMUmkYO0J96vpUhFT2DSB1+vjWOBf3mve9L6zdWKfOVWV2nvup80CE136ez0k3mvq+OVgivPolTtoyTA7SwmAOymMd9imBEq15UJbPohBIShizFeZzo3tiqI4aALgzWalDSe5QiGALGaqpaquApSX9RT/Hqgz2FSKT2VaL1EDxeHKzV9NVDlVfYntIHeS+WcsUm7Y9TqTpqUvQEr2W8Jb8K+A826JNPkWK/ofMYq/eD+dhn9SUW7JMU3nsSiel0Std1GCu0wOl0xnK5JCahox0qOqXUf6Hye3v6O0/7B5MJQ0fxEa9wdDrzhWgAFpUlRsugVOigsB6s81RtwhSJ4BVtI3QDMUDUWDvAmlKuq0mE6ImhRauECbKk9cYkWhcobWWRS0qyR6G/0EDfMKaA4GV3ZSD2SjooCcptgfeOZScZMh+k0aKsyrwoI3Je+fioFE4ZMYNpHcYEpkvPoJFB5GPgkce3+dgnnmTWeC7t+aepSaxxdOi6jpC/5MoYrr/hJqrhSHb1oUOnPPgPlIL766NzBqiua6pBzfjECfYaT+cXdE1H8p4r846lC3htxBRlseDylUsyOTiHtSWj4Qb1YMjJ02dE8xnwzqNRYk0dFSoFUvB0zmVahQIMuSqIT7l5MYpOb1kM2Nia0DUVrplRFCVFYfGu4/yly8zmS5azXXYuPynNX11HXZbQLdaZ+WPGYDCQX5IEE6T9gN7nPo3Ou0y/EXMwmcvy3JUXtBhFqUFctMk14n4R68kvQtPpufcrmbQVR2cfKdeH+6bWmPaPFdk2T/LpUGY+5sek/jXEKMo2/XP0UKCzHr4oOqhVQN9nqda50eOBzVG8iDnolUyjVghNUOU+CyX84xUU0uehFKqTc/iuwzVN9hsIaCXV8ZyHw4dcCQ+J4BK+y8+lxXW2GEhQbcsKjBXjHR9JOokMZqZhoBTJaGJK1LXFe02Z6TNlWVGUA1rfQddIQkUnIOBdIISE62TtXvWjoA7RvtY4TugDzfiI6pa4NqHS4WB+xSfMOy4xtOu5Xgqi9ExI56mErTI3kSk4POUCS2Kj59V7Hw5Q/dKqgTqm/WbqEAIoxXyxIMXEeDSmrgcsW58VnMIBDxCZB0PqSWx9vJlW1dI+IdPTX1fv9YhwZMG88NL1qtO9MLlZwCeSDSidFzXXb8iUbFyS6HxrXckko8TsRiUn2SsruzPp1DdoZXLmKy9MkC2ccwTfV2sSOc2vpAE2qrzTF96W0ZqQmzFCCARABdHRVbkk+V3/9n1cd3LE3/yalxCVwieFT7B0Ae2hdYHOR97/R4/z7f/qnfzfv+xuLl6esnSJB690LN06M39cCDFbgCOBxMbmJraqsgdBJ2XiECSo6Xf1+qDDpsaWBbYoKAcDOhf5lV/6JX79V/8T/+Kf/TOUj3QRYq7udM4zmy9olkseOffHDAYTnvf8z8MWJUVRUJXSjBaD5LeMzs7DufwUQiRpUJmgeu/7fosE/Lk/92X0XggpSYNjNRgAUfTklVQQfHCYK9si19YtWS6meO/wnSP5gsru64qvcTwoimJVXSTtK9H0vUEEL8TK1PPgNR/94If52B9+hNd/3V+SBsYo3GBSrgAm6QdZVaEPROr7uaADdJaD61vaP2o/o3XwsLSi9fSc0xBCv1XYR+bRq4PnyUeoBFh5Pzqro/TZ3KcaTK1xtNB5I2Wzvn/PeVcqO2gc4Mr3m8P8KylfN8lcSvbSO1mscydHvrZ6NWbe8Tv307aeL3n57XStlznUWLTRVGUhjanGsLvo+PFfuZfX3nMTd99yQsaGUmL4IzsNYlJYK3z43k24KCxVVZI02OikKt5XX4PP1IokXhtaSzaV/N3qK+vriP4YoVYVw6cIRnIw0AUOU/CSjC298tJQJJXQykBfn0wpV2Sk2fXQtX1a0mA/mO+phf0GIOWKuveethVZ4KZpiClRVhWlUhgbcF5Ub5xzq3P2CcD9n2m1pqvsp3GwJ+Woe4WOLJjvOk2KhkgBusToSKkTyiqMhbJQFMbwX353ikLzBS+Y4LpAio4UFoTk8SHhfVxJTKWUKFUtTX7GysVOEiCh5DKbJMG/VqBt3gXSd91ni/FCTHgk+JeGHmM1FkNVF/ig6FwQWcumJfpAUZWrhbFzgcZ5ru4saTrH1Ut7pBTZmkwILuzrS+cJxxjh1as1Z/7YUJaGmBJVTCirUcUGozheNUjrlKTnATKpRoG1kJsStTHCkFOycdRpf0IydkA9qChHgRNmgF207OztsDvdwxixjzbWYMqCpGE+n+JDxzDW2VNBMRwOsnmGx1gIoSEElfn7aWVTvn3lApDQxqKtISXHxuaYpmnYnc3pmiXL+Q6+65hN9/Btw2xnG53SyrDCkoS2sw6kjhn7lIREXnSimPm4lYGU36enHFjq9CqhuE918T2v/WBwrdhfJlcLhtrPEh1+OX3iaL/8rfoM/SrMz9TBnvuuV2fpj1+d6wB1qKcpqkxpJCdT1P4jSSmsXbCPEWWuJBvVizYkkpIMp9FiytgbGfaBRu8kbKxBGXHstdZiFRSI1GTsWhSGwaCUzH5h4YAL8cbGJmmYx3OmgOmyNxTT+xVQaynrGmX3+dNJZSdNYDAQ7nyBwSiNLUqhE0aDsQWy0w0oFLZQGJPQJktrZuqhbEijrPQ+PHVbusaRop+HDtCznlKXW/Xf9IF9riwqddA4L2e6owTyOtMJdTiQeEj7za0cDJxzsK2NXs2lkKSPKUn9smkaHn70Cc6eOcNkc4vxpJ8HJb5sfe7B8NKP2c/BIMXQPjkCrDYW+kBP0YqeGD9LaTbOaVLUxFSALrDWo01AW1G1CV5TaMM73z9jPNB86Ss2aJaB4KFtGnzncCHhfDbucVIWLG2FVvJYkU/zJIJwsXLTlVGSWTD9SopMFrbQq8bZnu/UDyZtNVYZysqiHHSdl4unEimEQy6HzgeWrWdnKko1T5zfJfjI9We3sTpx/emKH//O1/KJT57n4fvTKrOwVnE4PthCSsBVSCijKYcFSdtVqUwnKJDdv0WCeIoCjMGWRr78viMFaSJtmoZek88UFfVkk5QSEwpM3TK+vMFwNEYpzR3PfRllVYoRkIJFM8eFDnSksAZtFPWgInpHEINiQuiIMdG2ThbMHOTt7lwW4zRboIylKAuarmPZtuzNFzSLObtXruDahq5ZEINnsbdL8o6qqhmNxkSjpbFoTXD4jKLnm8foCSGb3QRpDjuYyQGhG676KfJiFUNcBdQHlbd4CpUF9qvWz/g66APzzMmnX4xW6f6czRTK2SpjpvYf3yPGlLXJ0+oAlZ/hYNVA5QemtJbnPS708qR5JK26J4wSyktA6Iiw70SscvXEFgXW2lU/DlqRFATv6YJHKxgNhxijccScQNNopZmMx2j26RMpJTweELOdyaDk7/7lV2AVMtcWZn9sKSXFKqWorCRZTNIYNNqIsZXOKnRCMYsklPDitfQLpb6hSalMxXCEkLLb7RrHhzzqpDx++O6M/Wb6wxl76DP6B5Cz7asKs2FFBVQHZXEPPihPhMroHLyLfmVSoPPrcn7B/Q88yGRjk2uv36CwvT5+jveCiGSsfDfSAWqh3n+yg9z9lRJY2qcrer+fuDkKHFkwP5uKGo1faKKz6DqiiyiGEgqRXytyVc0oJmNFYQPeJ6xNOKexjaJtwbuEQ1zflA5SWqPPQEnDYL+DWzUk9P/mSQaVO4yziZBSmduUROif3P0eXUfKC2xUGhciPkaxo06ibTvdmzJbOqY7MxbLjmbRSTa1k01HqUs2xwM2hmNOTEa4oCh25mi1XsiOC97tZoqDWD0rKlAW5x3t0uE7T7doUElhtSwSqq5RxlAPSmxhKTQUWhpkVQqr721ZGKrSEEICPN63GAODQYG1I+palBWUTsTkmE53peSrkjSvdt2quct3LSK/Kl33zbIlxEDITTmz6c5+MG8L6t2a7atX6NoG76RaVZUVhdFsjIeyyI7GDAZD6nrIaDSmqmomk839suUax4M86feNWmKKF1YZ+RATIWbeZ86Q9pnStnX75dtVL4cEKQelKZ8x0dhnpw7kxVXO3PdKSdYWq/P2JeieU92Hfk/LPSQIqVebCIcWNuHGZg9G3T9XJMX9k/Q8+jWOB1J8FrOwPpBHqdzHsB8wyWHpADVK79PBkCykykaM0rdTZNpU9iXQZn/9RbKoNjcMxiDGjOTvQEhRKuhGS/VJy2YhJfkugEJbmT/7Ao98f0Jum5TMvgR6GnGn6pd+RcjyhCGKKEEI8UAQtU5mHCeCz5t6JXW7PjgXclbPac/urXkzKZSa/XOkflgdKkX20r65vV8424AwMw4lHXKOoqcCxuy50CvgKFhRW0HoXb6vVOa+SxfCym32YEVTDuqrjvtBfP9+eqO8GOOR8+XhWQbzDz/8MD/wAz/Au971Lh555BGGwyGvec1r+MEf/EFuvfXW1XH/+//+v/Nd3/XT/Nr/53/DLQzRW+px4N0fmvLPfvYq/+F7z3DD2Yq//P9+lCeveMDxFX/3QQBe9JyK7/1/XUfXwcOPR/6///UqH3t4gfOJ605WfMk91/GCG08A8uW97/wOP/Jbn+Abv+h2Luw2/P59l2hd4K7rNvjLL7+V0hr+04ce5oPnLtP5yItuO81f+oI7sEYLFSFFYuh414cf4789sM3OwjGpLc+7ccLL7zqJjpI9E9lvmRg+/Ikn+N1PXGVn6amM4qSGDatxjSd0kQcfnfID3/8OvuX/9rlcc2KTziWqJ5uncZZjjPyrf/Wv+Imf+AkeeOABNjc3+Zqv+Rr+6T/9p5w4ceJ/7sr+GYdrL+YguS/xlSgKYteynLbM9uZcPH8FEhRayrdmMMQUBaPJkLouGQ8qJsMKyItcFqf13ZJ/+a/+DR/44AdQSvOCF76Iz7nnZYzHNVpp/vN/+gVOnT7LHc99Ps55Hnrg4zx67hO85OVfzJVLT3D+0YeIKfKSl34Bbdtw+dIFLl26QPAOYwtGwy1cbibZ2b5Eih5tS5QpUEpRj0akGHGdE2O2wQCjBmxtTqirkvF4wnAwpB4MGY8nGG0pirVx1HFjPyvjswPvvqJC74URvM8NsZlKkzmZs9mUB99/HxfPn4cE19x4PS/8vJdirEVrafS/7yN/zGMPPkSzWFLVNdfdchN3vuB5uVFfAv/f/rVfY7K5yS3PeQ6f/MhHmO3u8ryXvITb776bS+fP84mPfITpzg4pRqrBgNPXXcedL3jBfpY/JR78+Mc5/8gjNMslZVly5vrrueGOO56iG6/EaC/3F2mArCTVf39UrlCucTzohZXVAZfzQzxl9rOHIONVa51Vr3p6VyRG2RhEH3j/hx/ioUevMG86qsJy+sSIV77sTs6c2aSPcLb3Fvz2f/sgj1/Ypq4sn/u8W3nhc69bNRruzJb8u//8AV735+7ixXffiDGW//hfP8LHzl3gr331y3jn+x7gsfM7lKXlRXffyOd/zi0QRZLVJKFPSoIuoXQBiCpeSqzEL9rO0XbdKphSSrL2axwfJJiHVUCvepETQOm8AevldrMJnt7Pxj+V3pc7b0jkipM1LBYLPvD+e3ni8cdo25bBcMgNN9zIyz7/5Xjv+aOPfIgnHn+c2WyKUorTZ87yohe/hI2NTWIIXLx4gf/z//fbAHzwgx/ggx/8AAAvfslLueGmmzO9MRAOUCEPbRbiPk8esoKZOmgOmI/N362jxLMa3ffeey+/93u/x9d//ddz44038tBDD/GjP/qjfPEXfzEf/ehHGQ6Hh46PqiSqiUjupY5IB1wlMSJR8Xe/4Tr+j7edZ1gb/vpfOksIia2JYTAeMb3i+J6ffpC2i7z2ZWepjOH3P3qVn3rXOf4fX1bx4tuvhaSxdQPAf/3oRQqj+ZJ7bubi7oLf+dh5iuJxlFIsu8DrXnYH5y7s8v77nuTkxoi/8OLbEE505Od+5wHed/8lXnzLJn/+uTUPXV7y3k9uc2nq+arPvx6t4ypbsL3w/NZHLvPc6ze4pvM8enXJE13gxOaQydYmo8kE30l2bTAcYbcSrYvYYvtpwdSb3/xmfvqnf5o3velNfMu3fAvnzp3jh3/4h/ngBz/I7/7u74o50Br/Q0jZTImUd/EpoJOI7lkVCa5ld+eKKCoEQGlMXWNswYlTW4xGA9TWhMpMVuW7rpWx9r3f/084deoUX/2VX8Uffeyj3Pvf3suFCxe48+7no5Aejq5rmO7t4EOUwBz4+EfeT1lVXHfDrXRdw2w24/LFJ7h86UnKsmIwOYHrWvb2LuUsmaauC7QqMEWNLSrGoyF1KY2vo9KiSJQ6YbQ4HFdlST0YUFc1ZVlRVXUuSxfrvNQxow92Y+wbSvf57xxYHA41R+WfH37fHzAYjbj7hZ/D7s4Ojz74EPVwyAs+9x5Qig/93n/jsQcf4rqbb+S2u+9i5/JVHvzYJ5jvTfncL/yCfTUFYD6d8qHf/31uvvNObnnOc5hsbDDd3eV9v/3bTLa2eM7nfA5KKebTKbtXrx4qD//h7/0eO1eucONttzGaTNjb2eHxc+dYzGbc8cIXAvtZ3f4xfYZNK7VSrVCoA662axwHzGpM9YSHvuqyXyWxWYbvqdxln30FVO6hsErzu++7n3OPXeGFd17Lyc0BbRc4f3mPy9szTp6cQJIg+hd/8/0897bruOvWa/nEufO85w8+wcak5MZrt4D9IC1k2tm+5Cm8/R0f4YZrt/jil9/F/Y9c4r0feICu6Xjp827C2oQtwMVAGzsJDns2TxRpyrYV182m7WhdR/+EKxrRehY8Nuz3o2ZX4b75VEFScb/KCKs1tk/dH54n5EE6K3fFzMtYzmb851/7T3Rdx5133sXm1ibL5ZJHHnqI4D2z6ZRHH3mEm2+9jfF4zLJZct8nPs5//a3f5HVf+dXUdc3WiRO84HNexB//0Ye59fbbOXXqDAAnT53GGDFr1GFfP15egaiI9XVPlQ4H9P37OfgejmPee1bB/Otf/3re8IY3HLrvL/7Fv8grXvEKfvEXf5Fv/MZvPPR/0WwSTCKmlqA8ISngMUK8hqgGvPoVip/4j1c5tVXytV/9AuG+k2i7xE/96ifZmXr+j3/4Cp578ykWc81Xvzbw9/7Fu/nl33+EV37ei1AYhtM8OWnDt7/xy1AkvPM08V7e/4nH+Jzbr+Pb/rfXglI0LnLlbb/FvZ+8wJe87MVoAucvb/O++y/x5+++nr/6Rc9h3jV0MVK8/zHe97HzPDbV3HrNFtaInu32rOVrXnEXt1yzyZW9JS90gV973/082QZuuf0WrjmzhdqWBthTZ84yPHuaxdIx/NhFzF6z+mx+53d+h3/zb/4Nb3vb2/iGb/iG1f2vfvWr+fIv/3Le/va3H7p/jWeH5F2mF+hVSViTKPAMTMItd3nk3H0sFkv29mZismMLjLHcfMtNnDp1AnfDdRTpWpxz0nC6sw1AVRR84cs/D+8Dt99+K9vbV3nkoQdxvsNYQ+c6dne2CekBlBKlGxDKxNbZW2iXc1xQPP7YY0x3L+WMWYHrRJ6yLCrarsEWhlMnN6irinowph6MGYwmbGxtUZUlm5MR1lrGgxKrNdYYTDYQksVL5xLifmC3xvFh33DJrzKE/Q1yudnobNoqjaZ9OXbr5Ele+gUvByQQCd7z6APnuOfzX8rVy1d47MGHuPnO23nxKz5/tRBWg4oHP/ZJrl66xNnrrieviixmM17x2tdyzQ03ALLQPPCxjxFj5M+9+tUUVbXfSBb3JdiefPRRrl68yEu+8As5dfYsANfFyGhjg09+6ENcs7vLeHPz0GOB3AypsNpiV66dgnUwf3worYyl1QYSIEUUZkUDMPmYQ1QuoOtanHO5qTBSGMMjT2xz1y2necULb5QNqNa8+LnX4dA0TUuMifmy44s//y5ecPt1GGN40Z038GNv/20++sB5brxmS2gzOZz3MeB8wFiTJaUjN1+7yWu+4G4icOP1m3Sd44Mfe4xrt2o2NyZU1ZDWdyy6hTjAlj0vWxOTYjHvcC7Qdh2d9+K8nbn/5br5+jOAHMir7I+R2/dTpuX1st+rxEc2mDLWrIzrhA6T60xJvF0U8Afvv5dmueQvfPnrOXnq9Goj+sIXfy6Q2Dhxkq/6S3951YiqgDvuvItf+aW389BD53jhPS+hqGpuvvU2/viPPsw1117PbXc8Z7+iGsVgSh9osN3/v0w7SyrTDuOnblLqP4kjnvueVd5/pZsMOOe4cuUKd955J1tbW3zgAx945tMrC8qSsKunS8kAZhVooXIAYvZvv/eBKzz/zk1e/PwzoilvDMNRxWs/7w4u7yx44vJM9MCNnPOVL7qdsixR2qC04fbrT5OAL3rRHaJMoiWwufW60+zMlllVQfPRRy8B8OX33IJSJg8Izec9Txa+B5/YlXNmvvFkUPLcm07LZGgMdVXynBtPc2VvwaJ1q9cKIj/YqwH0PNIeb3/729nc3ORLvuRLuHz58ur20pe+lPF4zLvf/e5nc2nW+JTIitt556zIyc8olAbnOrqupWsb2qahbcUx1juXKRAhc539asJ5wXPvomtb2q7FdR3XXHctAPPZFNd1qy938O6QWslwclICpSz11rlWXp+2B5proijiHODeWWv2b8ZgjBhFFUVBWRSUZSkyWmVJUZSrxcsYk+lBOUu65sx/xnCwOeow9jPyByf72+66I98t950+e5aubXGd48LjTwBwx/Ofu38Wpbjtbvn74hPnDz12OB6vAvn+/iJLpT752GOfsinr0vnzDCcTBqMRXduubidOnwZgur39TG/00PP0smwHs23rgP54cOhzzg3NTzlAGq0PZORXyJz5vo8ipURZaC5tz5kt29UxPR+510q1VnPnTadXGwhtNNee3mBv1vBMWMn5ZbzwOdesni8luOv2s8SUuHB1dqDCJQaS4eAG+amb0XiwYTH9d75/axwZ0uHxdPCXp16Kw4347PPcVwce1CES6fHHH32E62+4kZOnTh8+T+blGGNWlc6YHd5tUbK5scn2lcv7Y/5gw+3B26p3qC9tqafMYQfuf6a3/wzj7UjnvvQssFgs0nd8x3ekG2+8MSml+l6ABKQ3velNq+O+8zu/Mz3Tqd/61rcmIJ07d2513wte8IL0qle96mnHVlWVvvEbv/Fp9//H//gfE5B+7dd+LaWU0rvf/e4EpP/wH/7DMz7X7//+7x+6v39tly9fTiml9OY3vzlprVPXdU97rq2trfSGN7xh9fctt9yS/vyf//NPO+4nf/InE5De+973HnpN7373u1fHvPGNb0y33HLL6u/Xve51hz6/p96+6qu+6mnPs8ZnFv3YefDBBw/d33Vd0lqnN7/5zSklGSdvfOMbV//fj8X3vOc9hx73sz/7swlI73rXu572XC95yUue8Xuxxp9u9GPsySefPHR/P4YeeuihZz1nveY1r3nacYvFIr3yla9MQDp9+nT6uq/7uvRzP/dzKYSwOuZ5z3vef3eO+pZv+ZZP4ztf4391/NzP/Vyq6zpprdPnfd7npe/8zu9MDzzwwOr/X/WqV6W77777aY974xvfmG699dbV3+fOnUtAeutb33roGK11cs4deuwDDzyQgPT93//9n/43tMZnLZ588skEpG//9m//lMeEENJb3vKWdOeddyZjzKG569WvfvXquHvvvfdp4/GzEc+KZvN3/s7f4a1vfSvf+q3fyite8Qo2NzdRSvH1X//1hzQ0P9XuY98969OPT9Ut/KnuT0/ZNR13tijGyNmzZ3nb2972jP9/5syZY309a/yP4086dg5WttZY47+HP8m89T8z7gaDAe95z3t497vfza//+q/zG7/xG/zcz/0cr3nNa3jHO94hfNEYeeELX8hb3vKWZzzvTTfd9Cd6/jX+dOBrv/Zr+aIv+iJ++Zd/mXe84x384A/+ID/wAz/AL/3SL/G6170O+JOvt2uscdT4vu/7Pr7jO76Dv/7X/zrf8z3fw8mTJ9Fa863f+q1Hrvn+mcCzCuZ/4Rd+gTe+8Y3883/+z1f3NU3Dzs7OoeN6JZadnR22trZW9z/88MNPO+enWpBuueUWPvGJTzzt/o9//OOr//904JZbbiHGyH333cfznve81f0XLlxgZ2fnac9z//33P6254ZOf/CTAIUWf/yvccccdvPOd7+SVr3zlOsj7LMN9993Hbbfdtvr7/vvvJ8b4rK4/7I/h++67j9e85jWr+51znDt3jnvuuefT8nrX+NOFZztnfSporXnta1/La1/7Wt7ylrfwfd/3fXz7t3877373u/kLf+EvcMcdd/ChD32I1772tWtqzBoAXHfddXzzN38z3/zN38zFixf53M/9XP7JP/knq2D+fwYxRh588EHuuuuu1X3/I2vrGn/6cebMGTY2NvijP/qjT3nML/zCL/DqV7+an/zJnzx0/87ODqdP71Nz/rTMbc+KRGuMedoO+4d+6IeelnG/4w7he77nPe9Z3Tefz/mZn/mZp51zNBo9bTMA8BVf8RW8733v473vfe+hc/z4j/84t956K89//vOfzUv/lPiKr/gKAP7Fv/gXh+7vs1Gvf/3rD93/xBNP8Mu//Murv/f29vi3//bf8uIXv5hrr732T/y8X/u1X0sIge/5nu952v9575/xM1njfw3863/9rw/9/UM/9EMAz3pBe9nLXsaZM2f4sR/7sZV5C8BP//RPr6//Gp8Sz3bOeiZcvXr1afe9+MUvBqBthRP9tV/7tTz++OP8xE/8xNOOXS6XzLPT9Rp/+hFCYHd399B9Z8+e5frrr1+Nl08HfviHf3j1e0qJH/7hH6YoCl772td+2p5jjc9+aK35mq/5Gn71V3+V97///U/7/5TSM8arb3/723n88ccP3TcajQA+69fcZ5WZ/8qv/Er+3b/7d2xubvL85z+f9773vbzzne/k1KlTh4770i/9Um6++Wa+6Zu+iW/7tm/DGMNP/dRPcebMGR555JFDx770pS/lR3/0R/ne7/1e7rzzTs6ePctrXvMa/sE/+Af87M/+LK973ev4lm/5Fk6ePMnP/MzPcO7cOX7xF3/x06bZec899/DGN76RH//xH2dnZ4dXvepVvO997+NnfuZn+Jqv+Rpe/epXHzr+rrvu4pu+6Zu49957ueaaa/ipn/opLly4wFvf+tZn9byvetWrePOb38z3f//384d/+Id86Zd+KUVRcN999/H2t7+df/kv/+XTlIPW+F8D586d46u+6qv48i//ct773vfy7//9v+cbvuEbnnUmvSgKvvd7v5c3v/nNvOY1r+Hrvu7rOHfuHG9961u5/fbbj+jVr/HZjmc7Zz0Tvvu7v5v3vOc9vP71r+eWW27h4sWL/MiP/Ag33ngjX/iFXwjAN37jN/LzP//z/M2/+Td597vfzStf+UpCCHz84x/n53/+5/nN3/xNXvaylx31213jfwFMp1NuvPFG3vCGN3DPPfcwHo955zvfyb333nuoUv8/g7qu+Y3f+A3e+MY38vKXv5z/8l/+C7/+67/OP/pH/2hNO13jafi+7/s+3vGOd/CqV72Kv/E3/gbPe97zOH/+PG9/+9v5nd/5Hb7yK7+S7/7u7+ZNb3oTX/AFX8BHPvIR3va2tz1tbb3jjjvY2trix37sx5hMJoxGI17+8pcfqr5/VuDZEOy3t7fTm970pnT69Ok0Ho/Tl33Zl6WPf/zjT2v4SymlP/iDP0gvf/nLU1mW6eabb05vectbnrEB9sknn0yvf/3r02QyScChpr8HHnggveENb0hbW1upruv0+Z//+avG1x59s+nb3/72Q/f3z3Xvvfceur9vMLt06dLqPudc+q7v+q502223paIo0k033ZT+4T/8h6lpmkOPveWWW9LrX//69Ju/+ZvpRS96UaqqKt19991Pe+4/SQNsjx//8R9PL33pS9NgMEiTySS98IUvTH//7//99MQTTzzt2DU+s+jHzkc/+tH0hje8IU0mk3TixIn0t//2307L5XJ13KdqgH3qWOzxIz/yI+m2225LVVWll73sZek973lPetWrXrVugP0ziGean1J6unjAs52znop3vetd6au/+qvT9ddfn8qyTNdff336K3/lr6RPfvKTh47rui79wA/8QHrBC16QqqpKJ06cSC996UvTd33Xd6Xd3d1P75tf439ZtG2bvu3bvi3dc889aTKZpNFolO655570Iz/yI6tjXvWqV6UXvOAFT3vsU9e+T9UAOxqN0gMPPJC+9Eu/NA2Hw3TNNdek7/zO7zzUlL3GGgfx8MMPp7/6V/9qOnPmTKqqKt1+++3pb/2tv5Xatk1N06S/9/f+XrruuuvSYDBIr3zlK9N73/veZ1xbf+VXfiU9//nPT9baz9pmWJXSujNljTXWWGONNdb4zOCv/bW/xi/8wi8wm80+0y9ljTU+K7EWnl5jjTXWWGONNdZYY43PUqyD+TXWWGONNdZYY4011vgsxTqYX2ONNdZYY4011lhjjc9SrDnza6yxxhprrLHGGmus8VmKdWZ+jTXWWGONNdZYY401PkuxDubXWGONNdZYY4011ljjsxTrYH6NNdZYY4011lhjjTU+S/GsHGCfDX76zf9PUoKu9cSQUFqhtYLJCDYnOGtYlCUDq7l5YDHBceWRh5hP9/jwg4/x2KWrNCHShohSoJRCKQWAUhpTFBhjGE3GlFVFZRWlha3JgDMnxpw6scXdd91OYS3Be2KMTJctzgd0NUQXJVd39rh4ZYcLF6/wsU88QAgBjAKlKIy83oExlFphdEKrREyREAIpQghAov8Hqy0ajSPgiTQ+sfRABBVAAb/6u79/VB/5Ggfwtg/+ZboucN9H99i56qjsWUp7gsKWFEWFVhqDxhhNXRmsMUyGJyhszbDcorIj6qqiqiqW7Zzd2TbLbsrF6cNE1TE82WCrwOhESzXwaOPQxhN8i+uWaK0oCotSBs0I0KQYSSmSUktKjqocM6g2McZibYlSEcOSFCOLpcP7xGxP0y41vqsIrsKHgs5X9I0uIXjmsx287+g6j/eBtmvoXEciAhHXeebzhpTg3//j3/sMXpU/W7jpzrshgcKg0KhCoaxivDXkxNlNlJb5zHvP5QvbNI0jJQ1oyqqkrEqKwlAPC4xW1FZjjWFzc5OqKll2LZ1zNG3DfLnEFgXj8QSVwM87ogu0e3N821GXJXVZMhgP2Ti5RVLQBEeIgcV0ineOtu3wzqNQKDQpQPAy3y2WC2KKlMMSXWhiinKLeT5MMjemlCBFAIzRGC2v2VpLSgnvPST40O/94WfuwvwZwff/1kOHrktKiYSsQ4aERmH7dVVbQBMShARd9HTBA6BVQqUErkWlxKAqsEZTl5aiMKSkiBF8hCZoEgqVX0NQiZifF4CU1/DVEav/yK8s/+z/BPq2PjlLggNtfv172j989SBA7d9/4HwKxXd+xa3/E5/sGn8S/ON//G3EEFju7uDaFt95gg8oU6BNIeNOKaq65pprr6Gua85ecw2Dugbk2k739phO99i5fJnzDz/Mcj7n0sWL+BCIClCgjEZpjbEFxlqqsmRQ1RRFQT0YYK1lMBxirWU0Hsn99ZCqqrFFRVEOVvdpYyjrUn6W8rMoKoy1KGNR2oJSecyp1dgjv5b8wkkJVGL1vQNQWr5rn3fP847k8z6yYD6GPIHESIoxf3kVKQZSDIQgi1hAQ9QQI/1XXiuF1hqTQMckgbzOX/88+ex/bokYIzEqYlSEEPE+EGIkJYj5ux8TxCjH6n5ySP3EkFAHLoZCNg9aKWxejLSKaBUgKpJSRJUfA30sjwK0ktdvUFirsChSkM9hf0Zb4zgg1zGhVCSmgPOdbARNIZOAUqgYiUkRkybEiAlBApQY8iIYgYTWoA0Yq2RzaSLKRFCRSETRB+qQklotWoLVVz5vSOWmlTowhvL3IxlSVKQYiSESgyKGRIysbilPErKx7BdqnnY7/MyH1sA1jgEp5bAm5UAk5d/zPKTICY5+/pAJLm8A9gOeFNPBnAFaK4yWQFkfmA8laJGoxRiNThBLiyZRlQVVWVD2AZiCoCM6QllatEqkFFEqodE5mE94Jd8hazQhJbRRGKtRMb/GiKxaJCIqP7+GBNbIZtkai7VW5l6jWE+ExwMJMvJcdCDQkJ+qH46rwAOVSE8JsvfH4YEA+qk/8/zDwcceWBxVfnh/7qcH8gcfqw7/uXoP8l3af5p0KP5XT32QUoeG2eojUM/03GscBZTSed20WBtRaIyJaFNiipK8CFPWFbYosUWJNhZljMydSR5rbEFRllR1TQiRsq4xPhBUknlVa1BgjEEpLfFeDISgCXlD6r2DlHBtS4oBrTSkRAyJFBUxSJLBGENKAW00MQa0McQYMd6ibYk2UYL5fhz167nOvyv25/w+mD+0vT06HFkwP9++JO8mRFSSYEhpaMKShW+ZJ7gSEptVyU1nT2BUgraD4Kmrks3JhNYHah9X51wtdiR8lA/JNS2+c3Q6YTR0TUPTNAQsN7WJMiSC88Tgmc0XdM4z0obKaFJoiWGJSh2FjbKgKQnIR5WltJYzW1tMBkO6ZkHbLgjB41xLiJGu7fLaqSWbrxNaRcYDi6kNXlU4NaBdtuxc3iOG+Ck+rTU+3dCFfL+KkaPsGnYun2e2d4HR8BST4RmsMZRFgTUKFzRWG1wLVpe0NVRlS+drXKxRNjDc0hTJoE5q2XvWczAtwS4JyoFTEBUqgg4lyqjVAheSAzRaFYChKjXW1BS2pLRA1CRXE6Omc5vEAMt5g+88u5enzGctUt5JJBWJKuCcYzqdE1PE6Aho2jbhXcQFTQyWRAAgBU2MirVw1TEjSBCfUoSkZHPoQVkNOwuKQjMaFqQQsRqiVihlAYPVFpM0yiV87FBWFsbCajaGNaPxiLqxtK7ATiOuWaBTAN9irWHz9IjSWmpzmkJrqqKgKiy2tFSjmkSii56YIl27RQiBtmnpOodGqlaucyymC7rOcWVbki92VKNLC0amvYhslLVWmMpIIsNotFaUtqTQBm0M1vSL7DqpcWzQRgIKrSTRkAPfGBMhRhTglNRhTDoQ/ypQaLTSKHJWPv9HH6jEBC6EXP3TqKRlM5fP11fSExAPbGRXwc6fEH28rvrs5ioBElGknKBL+4m7gw9Wz/DrOpg/Now2tiDBaDiGGBkNR9TVgLKqqeoBCUVQMj9U9QBjDEVZorXGe0+MgY3BJpMzkVPXLbjupttYLhZcfPJJXOdo2yU+BLqmwXvPfDFnuVzgu4ar012UUhRGo7WmKouc+JB8emEtxhisrbGFPLdsJgxFXaGtoawqtLUMhiOKsqSsRpTVCFtYyrLOx5RobSjrCm306hxKW5Q2+4ySnHw5ypj+yIJ51yyBJJn1JIF20gmvDY2xLENi5gKFq4gbQ5JWxBCIIWK0pigKEjpPFnAwsylZSS+TUgikILu0VfFDw3zZ0jlJOUQfCD7QOY9zLtNkAsQA0aMIGAVJy+KrlaK0hqqwTAYDNsdjFiqhksd7UCkQgifqnN3XMoFZIxuBqtKUw4KoK6IdMtea2c6MsJ5Hjg9Krr0pAqYMdKFjvoxoPaC0jmgjSitiAq00QQcIDUZFjFqSksZYsCVYA1WZQCcGGqJOdEVHVC0uNYTkSNGSvMYkg42WvPEXZNqBVJV0nkTAGoPRiZQUIRpisPiulqpVo3Cdp50vWEwj2ia0TaAjGKk+LZuWGCOD2qIUhJDwvs/ia+Q7E3J1Kq2D+eNG/3Hnyx9DJEZwLtB1DjCkKFOwRmG00FtUDqZ1n0T1kZzvxihFVVoGVQlEtE60jcVKPgFNwCjNaFBS1yWbwyF1WVIWhsIajDXYykoiJHlSijhfEmOkXbZ0rcuVRU3XdJQm0baGzpd0TmFHJbq0srmwioRsLrVRFIMCbTSlNbKA2orCWKmyahmP8WCpaI0jRSKno5Paz7jnRH3s/46sMucqCR12VQFCywFIlpRVblyykTEmfM68axRJpVWmUqn95z6IZ5uhPBiES7X88GtTeZqL/Yt+hrGVVqdQn+qQNY4AZTWAlNBFiQK2tk4wHo2pB0MGgyFRaYIygAZj6K92SomoHCl4rBUK7GA4YjKa0CwXKG3puo7FbIb3jsV8Ttd1xJTwzuO7ls51qCSVRa0UobNoBcE1OTMvm0Jra6ytUdqgbSHB/EAC87KuMdYyGI0pq5p6MKauJxRlyWAwxBSWejDAWEMIA6yxxLrG2CKfq0Bp2UxoEkkf5OJ8+nFkwfwVLxzJ6DtSDFgVMCriYkEXK5okmcyUAoudOU5Fzm/vstfOOb83Z2fR4r3QCFYFOKWEe0nC+xyk5EnDGI0xChdg6RI7845Hn7xMVZUMCskwtD4RogRwcj6wWm7GqFUp21rNyc1NxsMhp06dYnM0ZliXLAc1bbtkvtjFdR0pOGKCohqjjYVc5inrRGETZVlS1xU7IXLVlKgYjurjXuMp2JnOSSlRDi2bZkgIhuFIM6xPM65PSSbRQAyeZj4jtpG95YIUNJuTjsFgxNJVNLGiGHgGpiOZOa58nGiWpPIqyTh8iAQS3kf80lCgqJXQDKJNaCMbU60Npd3AaOHGJzJNJgZSUEK9cJp2PsJ3isUVjWs6rj4e2N5ZYIcRO/CUg5LBhiLpiC00ISL8+JRwncf7mDcRGuccbdcSgqfr/DqYP2ZYI/oCKVMdJN2QUMJxwVAwqodAws07dITgIUWpElqt0UrOU9clZ05vMRzWnD1zivHGmKZtabuOqi4xhSxO2mjKsuDs9ScZDipObk0YDmpUkv4JpUHlWT8p2WX0o8J3nugDKSbwsjC2izHeBW6YnyLEiC5L4Y4WCmU1SUeiDigDptIos5+ZL7TFaLOiApE3lWscD2KmZ6WYOPSxpz4pJqG6iuBTWNEOAQnMYZ+jChhtZDOgLUFpvJRa0EpjtUbS7gc4pyt6zkGKzjNQDp46JA4E3Su+cX4tSfcZ/5Q5yazouU89T+pff//3euwdK86cvUZ6FXckXmpdQDUtGEtRVigNxhoSkeADIPRqKbxIMB+iJyqFNYZqNKEcDKlHE7zz7O1cxXUdzWKB7zrc9TfgXcdyOWO6t4NS+3TEuqwgRbavXGS5nHP54nl2d7dR7QwV55nKbaS300ocZ6q8blcV1hZYIzdjLWWZM/FlhTGaqq4x1lAPx9iipKhH2HJIURSUZSW8/cFA5sEXPf9IPu8jC+b3goEUaV0ixoDBC+ccB8kTlAYkMG/nDY7A1fmcnWbO1eWC3WULIZLCPotPa02ZSgB8iCtO4CoLoI3MJT6xaByXtvcY1BVb42GmU0iSNOWoXSswWihXWoPJpcDCaCajERuTCRuTCePhCGs0ZWFZLgwxtBgFzVKafeq6RtuSaErQmsJ2WO0YFJbNusAvPVZZolqLBx0X5stGymyVoagsMVRUVUldbDAoN+iz1q5tmE0drnPM9zqCg5QMnfdEXRJtSaU6Yr2AYkGwl0C3KDUFFQjKEJKm8wnXJVCWQqucKe8z8QXWWKpyiNGVbABiIKaO0NMOYiIFhWsqXGPoppF2rphdiexe7Sg2oAiRkYaKClREW+E1d84TQ8D7KBxAIfDhfaJZdsQY8MGvOfPHDJOpJSBzjk4KnZBgPimMMrLIkCiNJZkAHkJMfQEGqxWlMQwKoR6OJgM2tzYYT0ZUXUnbdSgDIXl68nBZWU6cHDMc1Zw6vcVoWBOCIwYnlc5MeFd9Nt9IOTgFCdyCC8QuEH3At0NSTHgX5L2YQha93MwbTZLXbUCV+ZwGlFYYDCbPeX32d43jQ+izVn3jWEbPHT/I6RXefDyQCZdjVCbVK2T9RSmStiSlCEkqTUbrvEEg9yjlqsDBwX+Qv34woH9K5n51/FN+79f6xD7lJqXD/7+/bXgq3+YZzrXGkWNr6wTee5bzhs4FOsk8UZRe+tOUFkZESoTg5doqmzeVwpoI+WIZM6CsBxit2TpRErynKEu6tqVbLAjeYbRUbhbLOXvTXZSSfh1jDKO6JsXIY488yN7eDjt7O3RXLhM7R+qEV69U/4/81IWVxlpjMFqjkso8fiP8fC1CLFqLYIGxhuFog6KqqQabFPWYqqoZDoeUZcl4PF5Vh44CRxbM+1yta5WU4jQBnTwWKLSW3ZmtMRqij8Qo2cPGBdr+wq+aYlNeDCPO5/3+isKQny8aTDTYmCiA2XLBhcuXqasKH05QlQWDylKURW5kKKgHQzZjIGA4s/Q452mbFqOMZDw7T0SjbMnkRM0Jc4bp3lWSjiyXC5quI0SIqiQkURkJWKqQqIynVBFMB60nOAjuqD7tNZ6K7e0OpRTDUUVhDdVgSFkOCC7RdDu5QcYRfUDZiC4SPjo6F9id7jBvlqRiTDkZY5SD0oH1RCUKHs2sJeCIsSBGjepqFLlDn4hSBmtKrLXU5QhjLKSS4C2zqWPZKApTUBqLVgaLxUdNKBUBjbM1zliWbsD/n70/e5JkO9I8sZ+exczcPbZc7oalClW9zExPk9OkCCkU/v8PfKWwheR0dxVQAAq4W2bG4u5mdjblgx73iLz3AuieqYxiSYcCcSMyFg8P9+N2vqP6LfvDyG4TCNgFJEaxDmwJ1CLULKgKzhsFrRYD9jllcjYqRXvZwZ694thlec3Ae7FGpvHWfWBwDmkGa4J4mgvUZo4PrSYD0F6Q4khSmQ8PQObddwPHwwNrzuSSKbUSvVFoxs3IOEYuLndstwPjJhKngGtGvxKnOGeAy/su5j7RJko1So8Xmne04u063pTWDybG6XdIMDAvUZBpMM3rqL1zq6iYW8p58+oNlB92S1/q09eZZvKDf5+Ey08g8GND/Qfdc0VN8yXd3ca57msteBT/AwlqO9/mk079/86n3cwuDP9LP2CYecEPOvh9AvajOgtpX+o5ahwGovdcXl0RBwPgpRZySSzLsTtc1TM1y4ngxQxPgjmJUE9mAarWiBVzTlKEGAdEoeZE064pQ4njyKW/wTlPDCPeeTajTUC/Qrg+7jmmhPrA7fff8f6bb2w99WuhiLO7VMwQgNonQp0y7pyjOmeHDu9BhHU2Os3xcLDm3fABHydCiAyD4YDNtDk7mH2K+qRgvomwiJKlgVZEK5PYg+FCZNhMBG3UMqO5sK6VZS2subKW1sfA2sGIQgXXx4Ee6eNqO4WLq7jiiLFRBaoqWZVpHGni2Gwmfvb5G6Zxg4sjzkc2G7MpcnFiVc+6Ju5u79Fqopo1dbedOHD56hWvX7/i/fc7UlkIh5HDPJNLYy4jpXkObSJpZFsyVVY2quBWdMmUFUr5VI/2S/2wvv8+4b3g/IRsA9vdjnG44u7DwsPhHaUU1rTiEMYw4BVKSywpc1wSqkK8eMNFFaJUZKyoK6b7aJW7h4VcVtABCGz8yOh8Z5U2hEDwG4YQmcYrvI+scyAn4f62cntbmeLIZgiEIEwboYmnTI7qPTlGkm8c1y37/US4dGxUDMyPgm9CbZGShXVxuFZxHuPfp0JKmZQSOfWOrXvRfj13xckhCK53dFRNiByGSAyBwYVuWSsE8agEUitQGq1VclOaB7LDaeJwP1LKQqMQh0iuhVor02bD9uKCcRq5eXXNOEWury8Yp8i0HQljwGM8ae8gBHPEib5vWq13x1Kh5QoVyM0686JG04jGyXDNGZoKAh7CGIi7AfXQBqUJFCk0DPidur7mCiVnK8GXep56dGx7AtaflMjHWhp9YgF9/pkOgmstCGoTdhWcC4jY2vrxzFk72/6f6G948vHZoppT01+eHEf+wi+VJxODl/qktRkGu67c3LCmzN3tB9IhsaYV75QYImgz0B2jTbGdrcno6Z3wZrqMPt3Rs6ObMAwG1FNaja6qRhGM08hmuMC7SIxbvA9MwxYRx+7mNaWsLKUgw0Bpyh//+Ifz4dAhXX9k3WgnglI7Ha12uqJNHhV7vdhx+ERP63QzN4AEc/QR6X9j+KSb8CcD804yoo3BV7woXgWvwhQd28HTPGSpOMyNQ735uDvAY2BdnTmHoI9cvr4lcOonGcfKOpXiHGGMTNuJYRjYXVwQh0gB1tpYSiOUigsDm90lOa1I8ly4gZ8PW47HhVaFtCaWObGshTkV1qq4cWJ784pjmombHZIyc26sqTIXpSgcK2SFzW7DOEV8NNdNvGcYCmdF20t98joeTXR92FaaVmgrtc6seaEx06SikkBM0e49bC82OImkpNRqQuqqdsC8u0+oS2TXqCpo2SAaOVlKigMnDe+VIXjiEAh+wLlIqw5tkNZGTkIpoM2BOhwRcJQWyEzc645cB/ZhIA2NMl7DuMfFRvAV1JNzplRlns1XPudCLSaKbbVRSqaUbKNL1d5Ne0Hyz1276wkUQjMw34qgtXeXSqYujcO9fe86J2qu5ryVi13vWoMGHiiucrx7oCyBsiZC9JRqFrzrdkOZE+M04Wpj2gxcbjxSRjZTxMUuyAoB7yAGE+pHb1qiVppRabRR1aiNWpSaGnlOZiCwWF4IFWgmAlcHcTcwlg0EQSdnJgIR1FiU3RxAH0HUE1vhl/q01Z6i2qeA+AffJ0h3XALx8sMvnn/0dAlxYnEszoF4A0FeK9rcE3Dz8YHg/PP69LPyVOH6+Lm/AMq1A6/zXOFH4FzPYOx0oBSeuHm9XAufpR7u71CFXLU/Z0Zt1mb0Vq2101DNw92jRjGUkzORmOnzSfPx+IQb9hOHOulZB5HDfuY4HwjBMwyRYZgIfgARmiqOdj78uRiJo9Gj1XkzRSnVpj3OpgTqDNy7k/BazaAAbKilJ6oaGLVV7JABmLmK2LqrmAVxU/9JH+9PBuYDBxAlBOsMTggDns0msNkOHFHeseBEGVxDRYlOiaJEEQZxNG+0g9YatO7/XYxb5bHX5BB6ME/wFgC023H16opps+Hm1WsQ4bhWUq7cL5nmAr+cdly/+ZzleGTeP3C93fGvX7/h/v6BWjx3d3f83Xe/Zp5nXv1sYSqKv7jh9S/+miwwvfuG+5T5cMwc5syxbKgtsLRGVeX11Q1Xr3cMbSW3GY2Z3UVkKC9g/rnq/buG9wJSmDYw7x4Yx0SpK0UWqms0Xwl+ZLO9xmmEz0bShfKwX1jXjIuOtWbWw8pdPqAUqjMP2s3uteWs6IJQTYBNZoiR7TYQw8g4XuDEk5JHG+wfKmmFtIAWZxcUN1GJLHXLsW34Q/6MtY3kYUfdCeniA+5SCZuZIc5A4nicSblwd7uYSDxXtDUDgq2S0sqyrpbjUKtNwuKnvZC81I/r7c+uQCEWRarSEmiC4zFxdz+zqrK/62i+Ge84HVdqKuay1QrqFclKW4X3+WDTpmB0K7OFVKbtlt3FBdNm5Pjqmu3Fhq1X6vWOy2nCDxs248BmO+I9DME2zGg8BSoG2Fdttp5SpS6N9Zho7/aUlJnvj9RcqKuJZDONQmO62rL7/AaJHtkNSHDEq9HW28YhwyNgEzlvhf88T8h/Z1U7tekptP7ovWrvPHa/7e5mI+7jPrs664i6/lQGB9ELQ7Qpj5ZKy9Umi066U47jpwktT/fAR8D943//eV7OmWV75s8/+d4Osgyz/1cIbl/qk9Qff/87xDmm7SU+RmvuOqGWzHFZcT6ypmp2lGEghAil4ZxQmlGoag8ji53iIqrnnqhzgohnmkZCEL7//o98/fXvid4zDoGLi2s24w6nUF2hOkdpSmlCGDdsLq/x05bqBnJJrOuKAwZvtLHBhXPWkO/CypM+87SEPvZaPNHKFFqxF40+oZp94nX36Trz3u55kIAX2KFMwBQiG2+0gEMrDIJ15l3rHtqtW1xBHCKb3SlcAGqtzPNsHM7u2uG9x4vHdfGDc+CdMMbA1eUFKg6JxWyLWmNeFtY1sa7ZEjNzI9TWR3X21lQoTcm1sebKnIsp911AnWXnVRVSaaTSyM1R1VkYghsZhg1x2CLVGcd/iFxeeWp9uYo8V7VqPOWcFO+U1RdQZxCkv8h8cDbRMZMGo0UILBlKs9jeWrMJBrOn0cjqcF4Zx9CdRhTnKt6UjSDQRKkoudZupWad+VKV2mkVzgniFHWVRiBXx5IdH+5gKVDWRstCqh78iLiGYKA9JygZannchBXOiZxN23k87py9NgT30pB65nr1xsC8WwpSGsv9ypqz9WpaResp9Es6YgKtptB3WDaHFwiieBFCs/dejbrTcCgQm+CLIqmiS6aKY/6wR0rlbjuhpaDlggDI4HBT6LeLdWMLtK7p0QyaGm0uMGfaIVFTou4Xai6U1XQmSQupVVqrqHfIEHDrgERPbAU/BqKMBB+MJ9pFki9XwOerk6/8n3rZn2yeu3K1a17th07pnPROpjhB1CFnL8sOoFujlUJNCXzAhcijCuORpvMItk989if36ikOR/lJUezp9/2Zz//wy4//fjqh+BMPxkv9k1dKK4hYpoHz5HWh5oTNjJplG7iMODEKV3cr7AlMZ/qUGSoZd57TFAk4BYamtJLWmcPDPQ+3H4jBk2LAqVDSYtSZWLvRkjUyhmFku91xff2KN2+/IK0L87gHVbtfGDiW873px2LhiWj8qXG6PpkEdYoQcn6tyOmA+gn34E/Xme8BIm/GLRsfeIXjUoUxbJhC5EOuaD4yeNgMgSyFEhNrTlRfUa+8en3F2y+/YBpHdrsd67ry7rvvWNeV2w935FxwzmyEaBW0EqUxuMbVbuJv/+avcGHgISnrmvnH3/yaD+/f8/b6msEFE06kxFQUHbY87I/sl8xxLSwF5iK8P6zIhz23c2ZtjrU5luaYM9zPhcNcySGC2/Dm8jW7zY6b11dsry9I68J+PjJOwv/wReQTah9e6gdVs0cdLIdGTZmyKkvMuNDwUQljYHsxEXxEIgiN3ZuIViG5QgkLGgpLXvEtEnRLrYnDIeE8bMKEnzyb68Y4KcoRZCGjHFvG6cKSHxCs+4565tVAuHjPZuvxQ6aFSqrCffJ8d+f4j//Pyv6YaFoQbVyvnk24QiTiiJTVMecjtTZyetJNA6o2cq3Uvov5EKzbYVD+n/X5+O+x/pf/y/+INiW/31OOK3/4uz/wzf0dUjIuL1CUtJgjSOtgPqjiVBm9ZV0EpwzeukMXfiA4zyaap7G1SsXizKvHrYL/sFAfVv54nPHR8/1v/pFpN/LLX/2MX/zqZ1xfbrn47Mbcubr/cTk2tDRk3wiLibvL3R7dH8l/tCj29e7BPJxzorbKfl055BXGiOw2SAz4iw0yeIbXO8I28tm/+oIrf21T08hHQsWXeqY64Ql9BCWPDXD5SJD3kZpBjLp66myLmq2pUwNipSmSM+RGPs6khwfCZsd2swXvqDhjZOnJ2dL4OnruuvMToP1jkC9PDn8/ev8j5P5f8Vi8rL1nrf3RgjIfbj+Q1hVXK9KUzeWO3dUVEiK+NkodGaeJGAeGYUDEn0WxTYWqUGplTQvBe1wfMteaqTnz7puvebh7x+/+8/+Xf/j1fybGwDAMfPb5V7y+ec1md0WIljLrncM5z9vXb9htN0x+4s3158zHI7cf3lNLZpkPZue8WEhoWhdqMTOJUjKtVnIp3YWn9l68ra1TwrtZ8j5mbHh/0gX8C+TMm/+7I/rAGCJTE0aFQSGUwqBmsxd6h6jKaTDXTKTlYIqBq2lis5m4vrhgDQE9HFidR48L+SQ2EDEbywZbgUkbG4FdcLhgHXHvG1EVXxut2xlZx6qQ/MJxf2A+HFlX656V2mjNbP+WlEi5UJp18GMcGMaJ3fYCJbNvgSaC7x7LqkKtSirKkiphDGzGgH9B889YtmO1qlQxz/fmMc6vKC7YJlNbI9eEF88YgvnCb2Bqgg9CiGJBEiFizDiPqNKqoAVoFu5TxZ1vL9WCw1E1mzqfAdFTFLn525veQ1GpVK2spbIshf1t5n7fUDWxzeQSg1RKLqS10FyjuA6MmpzYoB+N/swz17QkJxgvPyFRe6lPW1c3F2htLGshY5MgbeaP6+jgqk9ltbZ+MLNufPTCFMzdYfAQnGN05uc9OQtSEd9T6rrH5FlsW0FXsytNDwc0J+bbB5bbByZV6taCTjR068wlo6XCWiBVJDVIDVKFVNBUaUum5Uwr2axO12TR6MUmDBIDvikSPS0IoQyUNZs7jnNPQBgvoOqZqtRigNgUg2inOZ3DpMQ6iMC50wgGlN0TrvwjUAEDWPbjTW0CeeI060mkeOpcIh9h7B8Ccvnoa50H/2fcZk5M+cf7xQ/x/0fkHDn/zNNvfFl7z1UWVNhI68xyPCK14apZKg/TiDSlidnilpyM8tUqqgZLT4FLJwDcWqW5x858yYWcE/PxwP7hgfm4J81Hag7UnFnmIymtxGGltUJrfePtE4BpnLi8vCK9SSzbmRgjJWeOxwdqLSzz3owyliMlJ9KaSGkl54KklVYrmrKt3b7MnO/UnG6J6Z3ZWAbvGcfxX6YAdime6ByRgY0MuLxS18whH3nImbid+Ndvr2wf0oJvjRuEpMZZWgflrybPr7YjV9stn19d0qaJo0JeV+5CIKdEWRdaLSYyrI4YlaEuXC17Pn//PbjIcGykUhmDkC83bPd31HTkdEXbI3z929+xX1f+8ZtvOC6J43Ik18rD/QOI8ejv9veEEPmbv/pbvnj1BZ/t3nB7d+D/8R9/zYf9jNMDJRXefXdgfxs4znv2+/d8eb3hb8bPGMMLoHquitjxXUulNiVut+ymLcu6cLg/4uaF4zqDM5pMCJ6vPnvLZjPxxa8Cwd8Q/UAMEW0RLRuWZWH8NpFrpuaFY2nEwVOroD7TXKOuM+WwJ/iR7VCJfuRqEwg+EjeRoZm42yFUoADHnPjuwzu++TbyzX+ZebhT6voBdKVd7pmnleV25X63MGxge6WIV3yffp0suUQ84iAEj2BCxlYbgsOLdehf6vnql3/9ilYa71rl6K3xcDwkKI6LYSI5tS5PbRzbQtPKbnBsg+N6DFyP0cKjaDg5hTA5dptICNE8jn0w0NQPcBZQJgyjYfySEjXNzL/5I398f8v6+prN+3vGIZI3I17M/Uhro2WjL7ZU8asSsk0Kaq3k5WDgvXVa17zSlkQhsdwdcSEQtysueupxIWwH0ufX5Ksd4VJxkx0cWvuLD9tL/RPVh2/fYQ2ILj505qwhTjh1FE5x8yfQdOYlu96Ykr62usbC0ak1qkYtxSFxR7iewAeOpaE1s+CoIjjpx1Z5tCV9hNNyPiCcBKpnas7Hc4InB4EO6E/f+ZEI4NEaQ0+ffEK1UVH+6Tx2Xuov1Wa7peR8Frzmw5GyLixpYc4rYRgZtxfEcUJEGMaJGCNjLYybC6Zolo4xRFQbpRhFp7VALYV333/L8bDn13/3X3j37R95+PAOaY26ruRlYb+54/b9e3IuhOmSYcz4YUCcJ/jAECNT3PD2zRcW4FgKrRXWdaG2QlpnSunvc+K4f7C345Hb21uWeeHd+3e0psQh4r3n8vKKaRyZpi3juLEE2xCJMbLdbj+pEcUnA/NNHaoWSx7FIQ2ojZoSaTkSBsdFjCCmdo7OM+HY2iyPwcOVF268cO2FN86h3pOGgQJcTiPZCUkrNVuHU52zUWBr7EpmM88gmXyshNrwKCU4yCual+4n6qm1cUiFY0os+30P4bExSinZggnWlZQS0TkuLy6ZwogvwsXunou//0eOy4yTAs2xLoW0OubjPQ8Pd1yHjJQdTl5EiM9Vchrr9qwCE7J4UDGqizaKyyAVdSsxenLbMTph2G7ZjIEhBKKPaI20FHAuMm0CLjXmpVAp5GzdfG0V9Y1MYSUxBCV6u0i1nrTgvcf5ky8z5iDS0xeXtLLMleXBsdw16voB0ZnVzaxkZrfiWWkSGLcD/jT+Ftd9l+2vPm2QzvnOMTxtb/4Fyj9z7S5Gaqk8jIE12kG+loZvNrFUbQRnHVChAdVCooKyicLF4HGYGOtkx+ucY/BCDD1pNXhOrg/eeWIIOGddfXHKkhK5VOphZqmJVYTl8gKGyJCLgfkuoNYqUG2aJVWRZmIzQdFq18RT55XaoE8vS6m4UHE4NHhcdDa9Wot15uupU/ukm/pSn7zmee6NSAPzRjHoAtfgz7x4ESEGC+tx/ZrhndCafb05bwMgb519aacuvumSvAu4IVqQVNOuGTIrVJw8sb/sa6DblFqSq5ypP48AXs/6ivOb/qArf7Y34dx0t9t55OTr+Zr4CP5fjFGfr3wIT/jtzbrby4KLEZkHQm3gAw1lWWZUlXVZOrC3tFTX6V6tNJs0idFrSikcjwf2+wceHu65v78jryt0Hn2plZINu8UxUUrGeQ/eG4fee7wPxOBxW9+n2TYZSDl1I4nlTLMpObO/v+Vwf8v+YY+I4zha1lBrjWmaCCFwc/OKzWbLdnvBZrM902tijOx2F7h/iT7zlxIZRLgWzw2OKUYiwl1d2c8J31aOZSbGwG63YaoD/+7yNQc30vrY9/UKb779wBj2XHx7Z44daaW1yk1KBqDKKUXCupJqfln4OTH89veoOIZUaChlcDRvYxptivTW6FEdk3r2pbCuK3OtbGgUJ2wpDJpw657l/h1hs2FzdcG4GRiCY9gM/NXnr9g4SMVRqrAWo1rktpDSgeOh8O678NKZf8ZajisiMG4qRFjSEVkaRQtxtPGwpVqaYLR65fvv7tjfH3nYRcbBM8TAMERi2DAOVzRp7G6EoQj5tpDzyiHdccwFPxRcbPjo2IyOIXq2UyH4Fe/uEBdwMuDE4ZnwEtE60NqAd6BVbBzeZryr3LydGYaZL1/PXF1kNmNmGjLTtrG7MtFaU5Po1BrMl3w2mlir2pOTHU5O4S4/mEe/1CevzcZTkpLSyn5/oOaGl9HcFjRCqwTNNK0IBSg4haCerXe8ngYTvnbrLj2JqVxDWybnak0JZ8JDUXNNEnEEAkEsnVoYiFEsz6Aq5faBxXvYW4y5NS4aqInPtAFNyNnSZX0Uxm3EeXO6aVXN/i1Eai5ozdSq5HLExcA4RaIPDM0xEjpobJxiQdiaC+AAAInJSURBVF7qeep37+85wWHBtDXu1IkXdwZLAmYPKNKbBHpO02wiVLGU1904ELzneggM3rP1SnRC9J5hCN1kotj0Re2AmdJKUaU2s1HVLtKni/NF5Py7T0j9NC1oPQAPHukypytYa62nZ/dQofN0wRH6wUQeVQL2KIh+bNf5Up+0xAXEK+O0oWxWlvsHUsq4lPApUWplKQXvA4f9Ee89f/z6a0KIvH37JReXV4jzODHryFpyP3h6Ukr8/h9+zWH/wPv33zPPMzXnc8hU69MjOFEZ7SBYa6OpBf7UWvEhYvbvjiAG9qOMKMowbW31qB1wb999x20YcH5gXhLjtOPy6hXOeS4vLwgdsA/DQBwm4jD26Zc702z+RXLmJ+cZRNggbBEm7y2Z1QmFQtFMqsn4U+OAa8pX445cBK8rzlU2RdneH3HiCLIH1DzD0fPL1J+7R32UJ+Y203Ilv7+1J6UWG7Fdjmj0NipuCklhVQY8zU2EVrnOhdjMXCsLDDQCFckreT6goydOEWmW4NhUeX21pa0LD3NlyY2iFvrStFBqIiVlv38g+xcw/1xV1oI4JY5m719qYk3mr+ajRde3pAbkq9JcY/8wsy4rKQkxOsbRNqnNZsels273sA24IvhjtTVc9tS2ElECyuQHoh8ZQmOI1TZFZ1Mg57K5y0jDMVjiXY29c0UnwmecFHaXid0mc/06c32RGGJljI1hgs0OWvOkFKx7poI2R8mVvKZOr1GCj3jvnlBFXzay56xhcNDMqWFdk2UX4PESCOKpTvGY4NUM6KsdwHCMTtiFgPeOIRiQt1RtJffE7FptyTjfwZFgnHwHHiWIuXoFZ0EozsHQoB4X1Am6ui43simkcxFOEUAqtFYtMdYLYejzpGb31XvboFyulpCo1qH3tSG54kojNCFgkyOV1icIj93Vl/q09f44d4Dcg256J94BXkxL4+lzO9+BNUbr9dZUpwlklOA9ebthCIGBDTIITcwe0Isn+O4HXutjQJrCWjKlVEv+7NPu1prpOzroDuFRGKidDiTOWYc1lz65sjrRFGqtlFofb08e/wbacDbGkI/AvP09L8vveUrFGaUlRuJgroS11v5mNso1Z3COeZkBR1Mx55tcuT4eicNIjCOtVkrJgK3LtC589+3XHA57DoeDddNL6TbmJw3H47o5TXy0O+LUUmmugRgv3wuohI/0ZiEMOGfXOe+EmjJpnklrZhxs6m7C3cj19bVRhKYNIQRcHIwCqXZtdN4zxPhJH+9PBubfDpEAbHMilozXhtPGNjjeXF2ZD/FxNuFVCDiFbSs0gehMJBi1MOTC6YxtlyU7Wzd5wo87i2dtBCgoIvYCV1FqFNu8WoNsY2Rtiha7eAzi2EnFS+OvQiCpds6fw+12yOUFb1ol3N6CVvIQEOf6kSLz6vrS7v9iybX7UjiUxnI98epmy+sx8PbNjvjDQI6X+nTVR6xdh01pBUqxU3MccC6AQimV41IpKMl7WnbUDN4rYciEmJg2maWsBO8Z4mSjWmeOOH7cASMxQojWCb3Y7IghMIXJrAR7F6ypHSJUE1XreRRd6oZ13pGzECPIzvHzX2y4vhautgvTUNlOwmZjHdZh8uQk3L434WReSwfymbIWW9u1EQaHD0O/oPUJ1ks9WwXvab6ZY2lr1JxJywIScBLIpSJYcN4JaWiLtBapLVCbt2Cevoa9mPeyNrO0tMRERwiOOASc94TgcA5KzdDMTlC9INHbuLqDbrMSPwGjhLZGawltQq3mNy8ouIa2yuAdPg7Ma6PR8M4TguBcRdX4zq1as2X/cCTVyv5uz/b+SJhGom6MRqEfJ46+1CesONF5UfbvJ7zy2mHuScJw8pY/ucQHlKAmci1ARUm5A/VaoEIcAlP0TAPsRkHVoeNAbcqSK7kq+6Uyp8whFQ5rfqTRIIgrvaOejBrYDxjG7T+B+S4wPHXmO52mdFDYmtJahU5Bc0769d2ZMkkMOZx951/6ac9WpSmtWYfeh4jz/rzOWqtdj+FopbLMC7VW9oeFUhsfPtwyjht8iHhv5hPa1LKEhkirlbv335HTSl4XWikWppgSwQmxi2d98LjgzgdZE3p3Zoba9cvor77bYnI+7bVma2z/cE/Jma//8fd8/Yffcn93x7fffoP3nouLS2KMrDkRY8SHYA27YEF9J3tK6fqUT1mfDMy/GQJBYbOsDNWAsgjmMX95SamF5TijKYMPCMK2VhzK4MA7cDUjtT5G+WKCQZsCW7eqhXB+wXoA7VxPAS8VvFAiqBNozYRe5RQJfBpCNnZSmRxsQqQBhUBznrbdopc7dtoIt7eIVsrgkBhhMyFaubm+YAiRbSqk0ti1xrE2ynpJml9zFT2vL0fCJ34yX+rjkhOX0kFt5qwRB0+MBoKkBaRlWl7NuUgC1TtWV0EaLiRcyIwbWNodIQS2086AjB+IIRDCFueMdhC8sJ12XE5XeC8MZ1qVBae185i5GrZ2gHPUGkhroGZHjDBEz1c/3/DZZ0LgFk/l4mLg4sI67T4I8wEebqGoUtZimQlLpq7Zpk6qqI94sc59ewFQz17BGZh3AM0sUtOyolJwYrQE0Wp8+Ya5E7VAawOtBRP1OxAbPRrfUhVtlVarbRgOQnAMQ7dBC5bqWotRCwWHNmfNEZx10GvBtROnWM2it1VKatSipJRZ12xreDQOfvSeODiySxToHSuH80b1as1Sk2mVtp9ZSmF/f+Ti/sj2yjNia1D1hejwbBXHx4+fvP7PGuQnT0Q7f0JAGwEhdhBcmyUD51JwKK0U1AtBPGOAbRQuB/r00VGb8nBsrH2CNK+Z2znzYc5njr5h83q+EyIQxBuvH8E7Z2sy5/N9PG2fAhaI1+kUtdb+dQP0JgL3j5qizq8/Jde+bMPPU7UfBsX5H4B5E/7jTB9ZS+V4OJBS4pvv3rMsq6E5scOYnOhYXRN0ddGFpDVBd14SlDUlckqM0fRuzjucNzcZ6Ta+p2ueqjnQnAB7CPE82TmJ/HO2r93e3XHYP/D7f/w9v/uH37DfP/Du3ffEGHn1amUYTmA+nFYzLgwG5rstpT5Zp5+qPhmYT/OeqkpJxYJRnCKi5OAp4mm14kvDtUK731MB1hWtlawWHuWkIlLPqV/9Jcmpu9DgUSHTLxDShTXnEoeOG/AOlwVpQhVFGrToaYMFqAyG9ZHczGKwWChLqUqbE+7DPTVX0v2e+XBAxpFwc0WujXR3R1oSpdmYqNXuJLJmSv98Hm068FLPUyUXxEE0KjLB2XOurZmlVG3WhZKCd/U8zQGx07z3iFfwDVw9HwbmxZTw22nEu4h3gRDcOcxH6kTLA14dbgiYe6qBeaXYdEianTQkoM6Ri2P/4JhnTxgc0VduXg28fqtELvAo4+gYh9NO5HCuguYO7DKt9gnWOSSqu9oIID80iXup5yjpjYVHIWl/XpxjiNEOWF6QUvGLpRNa6qElH+aqiFPr4DsAfw5Paa0xBs8wTgzTyLgZAaX20KmmzUSozagFrTVb+2LWuY1mu213cbAE4UrJjZwyeV1p3uFE8V6ILp7Df5pq58hrp1CYPWFpgCg5FTzKYT/zcHfAvxnYtr7NuReew7PVyTroZDn55Es/OtvLD76gj5JlfwrS6XZEa6l4ER4WE7euVVlrO4+QalP2cyKVxn6ppKLkqra+TWP9KFjltB7UqGBiIMvR+rTHOIIf75xKa84acu1RhyGYLWsWbDKPo/+2s92mkyd/60t90orjRCvF9F399e/6NaRWs+hVnNkwi33NO3urfeKixtkyVyRVnJgGyQloSYi2Lvp33WHJrkW1g+dSMrXUfu2VHqxoe6PrdBoUaimsy9Kn9Wbr+7Df2wHjmz9yf3/HH//we7797hvm+cj9/R0+BErJhBB4eLg3gS22Wp0PiDdnMef849/8CS9+nwzMH26/J2BWxUVBnVlD5XEgy4ArjZgqvmXK3cE6Nq4horTBI14MyEvFixJUobszK1C1d+abHRJUHF4e/Yw50XCcx+1e4UIkHh2+QA72Em9XE+V6wmklthVtjSEXA+LHRqvKWhrlYUbvjyRV6hAp25Gw2zL9/EuywOH2nmOulGFH85GaLc23LAvpeGTNjiWaU8VLPU+ty2LUhFWoZu/SVfHV0gq1AglHJYZMa+DEqAvDMBDHaMwHVxFfyWUFhGUpRD8wxVdIGBnDJUMc+rhIIQ9UHQmjJ2wH2zx8sXRjXalawTWggYsgjjV53n3vOR4Cwyaw2ypf/Szzi19EIg3PBtVE00wtkJPgJdOahVvUvFJzF09KIMbQ1fqOUzK7nKzhXur5qju+uGZnQq/gEaIPbKYRBQYtrLniDzNS1RoI6kjFhPRKJfhkm0KMZu9YC602hiFydbljmEam7ZZcCvv93hKAazFQ74MBqFqp1Z31GcY1NuGrJvOOz2shp8q6JOZ5IQQPWoghsBmCudU0OzDkXFlTI/VpZGtK6a41OWdIwof3D/hvPxA/m7ipp+mCe3EUea4qxR7p9vh4y4lq8PgZnnyRxx69udI4bQSsUy9qE6R5LZRiB8bDmonRMQweVaGoozY4rIVSlWMyML8UJSnkpiwnYeLpd/czxxlsP8nVPB2APzaXp7vbeKP2nCf3er49o3TV8+2cvvTSlX++2l5cUnPitjsGotK95M3ZxkQZHtWGF8uBid5Rg6Ol7rCl5njUxOF9gFZZseZbyytoY7PdEmPsEyRLb8+lkHJmXVeGKfVGlyNnm+jEEC0wEodWJbdMzca5X9eZlBNff/1HDoc9//APv+Hdu+/58P57Pnz4npzz2XXHe2+mFt0dyrS2BuBxvlvB2nXT6GCfrj4ZmM81m9VQgUW7x6so1UENjqCAN/Bd1OgHTum0gI537JmxB6J00pt39iCNI+o8agoeGweefvlJ+KBqXahiamTrCDjUdyJqP51pF00ggt9Mxq+L4KoFy7pmJ7eWM1IK9aFAqZRppIhDlhnXFNGAeqWtSs5KmmfWw4Fx4ylbbMb3Us9TvQPT+km9ZLu4m1uIIK7hgwGeE+BVrbZndGckCQ3v7dBYTcUHzaHiOiWic+CdggrS+lhQA9octXAOWFEgZ7WOQ61G+RKleWFZhXkRUgYXKmFQQqyEUHsiKNTmoHlQhzS7SAwx0EaYNka/SSoUVzv9x+O94MLJy+Zl7T13lZwpp6ClftiKrh+0nDcaTOte26flhVJpFK3kVnHaztDmRHM3iottfCF4Yn9TrdaxwqzZ0Eat3XVbf2CLq5xpXydDgHZ6q41aWhfF2veVMuKddeWVHrZWCrlWSm02hW32M6nZxr2mzLIkct8kmzu9El7qOUq7ClCfAFntoPcRzsuT99aJt4/M+eXsUU/XpUl/noE1G2jOzd6aCrlZqNSaK7VxDl+0oLR+e13PdNLwPP5bH2U9crp/p/v/MZh/CupPf9Pj0tLHqf3jo/Hxz7/UJ69pmijOqC7mvmbXpaYVbQUXAsGfu02cxzVPTlynlSonrruKNd44kTGkX5+K2VHWSgCab+Sc2e/3qHjef3jPMG4o/fqVc2aI8Ww4UGsl52RhUctMyitff/0HDocD777/ltvbD+z39yzrQi2FUrpNZqtG76qnrIQTUeREE5KzU1Nrn3bxfTIwv18OeIXbNZOqiaZUG263w9GY4sA0bdFSOa5HnDa8C2bFVoVYhZZAVzuh5dIstWI3IMPE8NlnyDhQWqJqocyJuiScCkG7ELYVE7zuV2po4Lc452AaYAgwuO4A0dAlI+OA/+IVGiO4EXCMh0xbG/nhgbzfk/cHlvfvzMf72/c074mbAQ2BtmnUOLLuM/tj5jAfeNjfohcj83BFfLGmfLZy0UBSqZWalHUpaKusMbAOkXFSLrrF4xA8TYU5r7QCSgYXGDYwbh3r6jkcAzSPtBFlQIunOUf2FVoiEPEEICJuS83KYZ8R13BjQ6VxWIoBoJS6ij+A87x/73j/3tMUrq4XhstK3MzEKeNLwtVCrUItA60MaJkIsnJznSnbwnYcKbly/z6xzieRbzA6j6t2gXyJOHj2mvcH0pJZHg6s+yOuwW6YGKPpLaqaVSoC6hrqKtkVPDArHLKiTtjgznob1x1qNCibzchuOxDHgWkKiBSCNxpZWmcLDNOGxkAbjRN68jlWVYskbw2KNTNKsbeUK8uSAWWezT7YvJKDgTaBpRQeloWHNXPIhdqUXM0OLteCOri7P+I+PHBzmCmlok5oTnk5Vz5PtfqkM80JZMjHIuQz0ncdQ/X1gXbnFyVII+CIYpeRJWeaCqk4hs5HdiJUhVTs9rw8CmwVcFoZxahfdugzuhdqAXpdpgqYhan2g9/Zn76De31s2nM+AMAZRD35c3nS1Tv/8wXLP1+9fv0ZeV34epzAedac2R+PFCpZK5vthuvp1VnU7E6+8iI/mKD0o6XaQbF1StfojUZaTl34VEi5QIxE53jYH/iH3/6Ocfqe7+6OhGFinDb4EBiGkRgCHocXz/6w5/vvviXlxPH4QEor3373Nct85GF/fw6Sqq2eOfXA+VrWOqXttD5Pa/vpivsXK4DNtdIUUi2E2q0aWyOMhdB6tykGcIIOEW3VfEmR3uUGqebKoZ0jSlPUGZ9ZpwmZRmgOaRVpDpqD2sx7nmYcaaF3BhQdAho7kI/e/JprhlpsJN6fEOHEbxdz2mkNP43G8erhA6KKrsl4pX3SEMbezaoVLRntHTmtJoA4X4he6pPX+WWj3Ya0e2drxZIuQ2fauJNXEt1z/uSJjHXeObk8eBBP8LFbPgaLanae4DyegMNcSpx4kHreNLVVlErKmVQyaV3IKaGMKIV5bpQiIIoLFe8zykptGWm539GAEHASwU/E6NhsJmosOInU3NA0MPhGiNaZRyoqpV8pn3S9XupZyrQzBq5brTjM4ebEG9XWbK1pw3nBRWeHUC+oNHLL5OapKjg9uXdhdqMIwbseGS6mJXNyflM1zmirlerkI/BmjdJm9+90XW3m8mVcU6PSKEYTQoSczZu5qRq9EQNlVZWiNt7OdpmlNPMfKVUpxW5Pe4dYXxDV/1/W4+o6vf9xnYSGJ1BVWuemq5yFr+VEmene0edOJTaYDmJuda0Z5eYRZ0vvxuvjD3L62kf/Od/jJ8SaJ/WDz5xvR398Ey/1SSuGiHZxaYgRFUfFeO21NWp7nBqdJuinFYBYiJM8mSydrh3np1ScaXBaPVtRntZbU8ilcjgeSaVS/Ht8HJg2O0IIDD1d1mHBpvvDgXfv3pFz4jjvyTnx8HDPuiwsy0Iuya5hNPv9+vF6OnXd+9nzTP2yz+ljh/4T1qfjzOeMV+UuraTOU9ZcmGJgc3lhvsWvr0wQttvgayMkJVbFPxzxa6KlZpHhqqw0NER0CLjthH/1Cr/bIlQ8DTlk/JzRhyP64QHVbF6mQJEKMTD+1RvC9TUlLdSS4OEe7j70Z15BKtzt0RAoGlBxuM0OtxkZrz9jGAfWd++JY6DOM/P7W2iVKTlUG1MU2jZynGceWMlkBqdEB8GHvgm/1PNUtRdVaUhTpmFkGINxl4viktAWM99uoQtU8kppleOxkGqhqFm4ocLgB6If2E03xDBwfXnFEEcutzuGOJ4Pk94Fggt2iPRKo7CmPaUlbh/esawL+/s983EhLZ+xzJXvvvPk1RHGyjjtGaaF4/pH7vYLk9oxwfOKIVzghwuG3VtUM69fb4walM1vfr735FVQio0ypaBkmjajvf1zPyX/nZWWSiuFumbqvBLFcTFtupPXQmqFfZrJNMbLAe8Hdj2Qp80Lt8cHihvZ5EuG1kXcXnraoGe7mZiGiI8O75QYYDNFRBpNTfy1JnNsKGXsTjY2kjaOvOmEtIA25bhk0lo4zpnD0vn0VEKu+GFPiAEJAZwjAYtW5lY5dppN7q5JRY0quaqQ1VHUBL1g9+ulM/88dXZ/efzg/O/T4e70/vF7Tj/9uFe1/skg2sX1RvfLQK4QvBCttWoCViB4s5lcS6N0d7nROQYP2wipKh/mSlHInRN5ok6cjCxOVJszbNdHqtAJ9J+PH0+aoI8gq/GC3v/5anARF5Srq1eUnIkXO+oQqbVreARysYP+PPepde22qSHg/ck84GQlbnaWVZw1gZ0ZSEjwJtJuljuE2DUnr4n9t99SFdZf/xYFs7v0ZmLhzydOo93M87HnzhSjBGVLdzXSlu9iXPfxtAdA9Ym1qzyhgp0Afz+gfuLr3icD8+YLYtZ5Wa0L1Eol1EZFaSIwWCee1qAorRaKNpyRnbsDQ7VuaedMNbFxTPUe8QFOPrKDcYp1rVQ/d06yt9Gu90jwsB2Riw16LLTV3HPJKzzhQ5PMtaS2Yl3ZOEIYIAb8ZiJsJ+JuAhoSHNJap8IrwYMGYfAQvf3b946ZOGeiiJd6nurWU6ejvhdP9BGnRj2QJmg9CS5OIipBVYwvLI2QhJDAu2BcZxcZh5EYhnNEcwwjMYxQTYfhpOswRGwrUaUUC0hLaWFJR+b1yLzMLIcL5n1iORa0ifH5Q8WHTK0LOR8JMlpH3tkFw7tAiINNj2REUKQNoJ7BDZTkqTVRWzJQL5naqlEqfmRh8VKftFStUVCNl+7UkjRrsdFwabaBVafEaSBExzQFxuigJepcqVrJzVwcTtDGe7NoM4FzH0mrQS7nT5157c4N1qE/2fihDdXesT9NPY1eT6mNXBul6mM3HQVprKnQVPtkoTvkYDqlokpV8yI/fe6UAHryUdKnj8kLwHqWeuwE/unH+7+mW3jipYPiOrTXpxSX0+1Id70Rmxq5fg2UZtqLU34dYuLuQ7LVkU7Tos6XljNP+uNZwcd9+B/rL05A/+n3/uQl7+Uw+SzVnf6Jw8A4TfgQTeeoFbq24qTTKbX1dNbeie80LXEn6oo++Z97Qq2SMwY82Z4q/dpUGyllams8HFeaKkM89qm66wFk/ftrJefcJ4iN08o23ZB7EmpGf/+UBvaDlfpkfT2u0k+/6D4ZmH/9+jXSGgN3uCXhikB1OIKJWZsQ8NbBSY2SCr+/e6CsiYtlZsqJizpzgXXmc6u0Vch3d8iaadMfcZvteQNhmGAYqFOkfHFFq4VcjDv85vPPCdNE2V6jMpLme+r9gntIuDmjztGiR2ui3X6gNajHglbwl/fIZkPZ7Zh3O2iFtplgimxuJlqtrIeZpsp4FfA7xxf+gnA98s27Ww7pSBgDcXvxGErwUp+8pGYQzi/awXmmEHBqDg24zl5BIWSc9G6SE9a1URaj4+RFuNgMXFzdsHEbbrZvCTEyho2JGEskN4/TgKg32k5zVCprg5QL397esqYj7/fvWPJMXRM1V9qitKPHr7DTzNZVPt/A5QShVWoqVGc5CiIrIns0NLteuIYPR5zz7KYtXka2wxVaJlKZycW0JLUVBIdn5GUXe95ySOdk2ihXm1n1ppRZlpWslVQLfhP5+a++Yvtqy9s3Oy53I+/+/vd8/19+hy/CYc3UoFxvJpz3jMPANEacQCkZaWodrVpp1bQhtRSjxjSlesd8nNkPwXZGzd32zwLGcjLwPs+ZdSnkrDSCRSeLpwH7OeGS42IcGFxEQ7DrbTZ3EvMqkA7mu793GHDDBMEcdQR9Imx8qWcvNcDzZw/1J2eY3mC0iYviabR1pQooExAQZwE5U3Bcbrytd4HoPTfbDYMPbCfPEBzRWwdfsCVwSIVfvzuwXwu/u13Yr4XUhE65f7xSdWxU6yMwPwH2n7rrT0GVqvKC5f/5Ki+JVgu7zQ60sru8YtruWBbIJVNq43icaU1Z1tytJGvvhtPZVq1TlHtjF6FSLPkcztkC52mTGGUn9RyCtb/PNRuNR9XyEDBc8AjOT1OqJ/Suk16jJwz/0N5ZO53m45fTD1dcDyw7jZU+YX0ydLndbqBW/H5GckOcRYNbiAmgmNWZNkpR1lL5dlk4Lgs3eWVbM61lBm02+miVWjN5mc2m+26PWwoJNevBK0FCpARHGUaqelJTxmHgzatX+HGixQnFU7NS5oJfK+SGBqUNnQe4VOvM361obmhRZF6pKSMp44aA30YkBOJoMcNZGpRKGD1xEHYuUqfAw3LEB8EFj4sjPr6A+WcrbbbGOt849NAJ7xreGee3tdav7Eb09M4mNC0JJSvJ7EWozhN0Q3RbprgjROPMnwJ5WnOA8eYVCzRr6igF0qoc9ivzOnM4zqQ8I9WoP1oUskOqpR2P0rgIsIuC0863VkUdNKlUVhChtNB7ExmRQIhC8AHnJ6Rt8UlwxdnarIUgA6O/4GUbe95yKmcCgRMHzTpAtdikJGuloQQnXL++5Prza3721TXX1xv0sGf/7Xe0YyPPBV8tAkrE4sVjCOfQE7ou49GdpnWf5maxPH1zSylh6VSl70TmNJKygfmUKylX68qLB2zjUxopZ6RVpgZBBHUODZ7mxBJCVcnQXcW6BZ1znZbTJ5Lyyfezl3pSPwIZjxwVA+x/qmP/5AcbPVegNVpJ3fnLg2CZFiJE75iix4sw9ICxm+3EFANvLwd2U2AIQgyCqJ0RH5ZErpW7OXN7TJQqZrt7cl869+PhDMyfMhvg7BLyo7/1qfBQ+fEB8kU89CxVs1H1YhzYTFuGYSQMAy6bzXPrza7W1ELAqmG9Eyg/c9O72+FpqteaPfe5WIJwONlCPqGO1dqozdxzTlNJS78ufV7QLU/l6WRJOmjnfDs8+VhODYkn9ZeG3U9ebn/5m/931idDl5dXF2ip1LsDuhbCaOMKN0ScBAIeL5BVOcwz98eZ//jt97w7HHkb4MIL/wPKjoBQEbGOASnTmrB8/z2EgXmM5OCZW2FOC02UIoqLjvEisvGe4zSiw0hLK9QFuX9A7h6QdUZaQatDVxNChuZtkZVKzZn2cI87HuFwhOmeFj1lE5ExMLzaWgzw7pIoAlUpD0eieHbieD0N/OyLN1xcXfLq9fULmH/GGicLuTnF3LvowYEbPDE4832Xai4irdFQSJYxIEtFisWLe+dxOUD2FlBQtsAIbQMEVEcaEScRcRHULN1yXViXypoDbX4LeUcoEW0rXnvXVj4nyDXahPvljwyjcC3KlRNuwmumcGFJhiJQPaUlimbWdgCpyLIQQsBTGcKWwSW8bKlitpelNdJaaU4JcduTkl/qucoTCSLspmsud413sZD0wKqVmYwMwvZ6w+5mx89/9ZY3X73mqy8NzI8ULjaR++/u+eN//oZQlewKa1VSyfhV0DV32NNoWozRg3m/r6mxFsiiiFTGVJhyxdFw9GCpZvSfJRVKaXbNs/x1nDfqWcNC9NYKNCUWpVZY1FNkIEujsFLQczp30Yy0RgsOGT1hCMQhgGsvVK9nrKYnQ4dHRHGiHv4YyD+h5HRKjZ4oCw4T50sm0tiNgeCF61evuLi8YTt5rrahc94tQO9iHIne8/oysBs93qnRURW0CVNwrKXysGb2a2UTE/+4z8yz6dxEO0GrTwpcb9GfKQsnJHbm1ug5RfKR/KMv+Rr/jFWLgXnB7HSHOLIZN2g1LZEgOHFGh8kZETUXJCdGATx15asFh8qJhtWfdukBY7WaOP/ja4uRtU4UwzPQP//3xOv6mAJ4BvZPbu+pvWRnAP231el+/YvtzO/MdnKJkeo9IZqTAyECZrTvREAb87pyd5z5z+8/8I/3e764GLkeIzch8tc+EhCGE5gvGa2NvN5SxXO43JLGyPtWuMurqaWBcTtyc3GDes86DkiMlOMeXVbG/YH4sKephQ5IxTq5Ak4CNEctFiWtKeMUCEfwgTV6jlPAbUfGAHEz8urNFTFEyt09ZV3xcWATAldD5LO3N2wvL7i8ucT7F878c9UwBAPz44APHh98F2h5ogQDQE1QKtUUgJAqWiqSFFfMNcR7jyseioMSoEzABHqFENE2gQZwA/jYgy4aNUfWQyJnj66vocz4EtCWiDi8ejzXBHdB0oxP3zKkwKVcc+Ucl/6aMTSqVBqNUjO12VspC6oF5UjwgSk4yrCDEaK/oIkHCdTWSElR76hOeckse96ySY0wDRdsN4rzdxRVEpUF8zm+ejVy9XbLFz97xec/f8OXX11zdbVh9HCxnfj6t9/w/v0dOhfyseBaI+dCwFGy8d5twyq2NzkhlcaalVQBGiImRFxLsQC+DpJatY5Vytmiy0+5CL3r2lRpFWqzvJAmylCVVpWEp7jBbOaIVJRirypSMyCmXmySOXgTz8rJovgFXj1HPYL5j1m9P41u9eOPzoC+U25EcWSCNF7HwnbwfPVmw9vPXrMdHBfTKZndflMQh3fC6wvPZpQzONcmtCZMwaM09iny7f2KILxbGzqbtse0QB2QY1N841Fb7sdH9/WklZXWAZdNZZ/y7fnBxy/16atWA/NgjbEYTXPWSqaO0+N1QOoZoLsuyKnaXcDaE5fBH0z2pGsVWwfr0g93J4iu+piW/VGdz7b65OMnX/7J69OTZAY93Zf/xtWkn5Zq88nAfNIVtKKaEJLFgoulyqmz8V3SwtoqS1HWotQmloAonuQ8iwscfCRqoVJRhCSKSqNqpQlkLSSFOcG+lT4mEbwo6XBkXyq/+/3vGEK0EXFTLqXCGIiiNjIulboWI1H3TU77Qiw5o6WCc6jzlOCpKaBpIAWlDQP7QzIrwHmBXKgx0GJAwsDlODGUAsc97QXMP1u50+u39u5iXzNVhCImACwl2wbg7YUaikeaJVR6YNCBqANRt0S5JHCF1GtEJmhXKAOeCSEiBFBvL9hW8TqxjZHBZeTqgloTS91TWkYKUJQ5e44SGOSBUY6MLjDohlEiu3Fimhz7fKDU1O+R6xubdWRriyD+0R1Ab3FyoLpAdYECpCrUOtkhhZfO/HNWSY2SFcQjPtqhyoPfRHbbC3bXG37+r7/g5vUlbz674PpmYpw8PsD2aoO2V1Rt/PzDA+n+wPLrb1mXzO184Dgv1Gxgu7Vq1ywHeEepjTlVUmnEaPHoeIcLHlo1bn0znVJtjVQapfV+Vd9sjNrYWGuhaiNjriYPa+Yowu2xcHesHNZEbt1urlMhjDMPLgbiNBAH86hXLcZ9fkFUz1J6Cqn5SJD3BAj/uUOVPAIcOYXoxQvEKYQRQjSwVBLZedYSzomYqjbtUVWOq6V7allpOSF+QMKGqsoxFeZcWVejtrqmBkikh9bK4514BPBGD/uI3WzkZbRVzuDu9GefEb0idtrlY+j2Up+qcl5QbRwO96S0UEu2kLsYGIZoQXX9uTuB43EYAAjB90ZFoRZ7XtHH9GqQj+weH613T41wPfvBn7nwp8/37zsfbp/Sz/5kPXnlfHSgeCTeCI+/+3yffqjb+ISNjE8G5hedoRUcM04X68Q7B65RRSnSjBpTC8eizAWK2hA4SWBxkb2L3LnIAGzUOk9V+gtZKk2UhcyiysOauF1gEM8oEamFeYDZC+/ev8d7z5c3N+zGEUfBbwPeB1yYqMeZcpjRqrS02iKrFqiS1iM1rfYcNKV5T42BGgNl3iPeU4bv8OIYOuQqwVGDx796xasvvoSy0u5vzRP1pZ6lzF5dUdegGjiRIBQtZIw7l9YVAXywi0kkEIGgjuoEz4hvI4NeMbhXRK7x9Q2iG9BrlAHnN3g34FrvCvS0wyhKHD4DUW521rNMbaFqIR0zZS3crQ80d8vqFjbugY0ENuzYiHC9ec3mYmC5zxxzQjWanFIEyEaWaBXw5o4ilTx9CxSai6gPFDxLDtAix3yLob2Xeq7KS6PkhhKRMNFioETwm4Gri4nXn1/xb//D33D9+oIvf3nD5dXW7FMDXL7Zcfnqgs3VBgbH3Tcf+M/fvWNJM/PdCmuzaVHtdJiqZgQeHFWVfUpUVSRaOrAEh4uelpWSGrVUlmWlVuu6Vz1x+81eUlsl18qcMxUliYH0w3GhrYm7Y+XuWJlzY2mPybCqRrcRhDAOTLuJcTMyDgOlwVr1z4PIl/qnq6eE3VMyz09+28fPx6OfjP2ck4BzATeMOA8yCERnHOS0QBdLe++ZvH3+w7yQcmU+zKSUyPOefHzAb3YMl28NiDXLIjgeMzVXvDZGscl6AVQfW7GP99AEd7bezsjNQFsrT8Bb98X/6O+WH7x/qU9ZOc/UWrm7fcfxeCCn1ZKqh4jW0dZPNaDueq7POI5457t9rqW4lpyt+VBrd4ezMDQTvtoU8dyFr93xptG78o8ZF3A+1/1J0eqPyGdPgf4P1tGp0S4ffT9PDhzPe2z8ZGD+NFZzanZWRpWBKuBECTSkj1BaNVFDDMGcGjYbpu0I4pnFoyhBg3ULvEfpNpTiaGIWbN47RjFP7oggopS0gkCpSnOO1QX8WNh3rmgWRZ3S5oW8zNbFTQ2t5jphseal+47aBaRJ6xun4ouJwtZsMermRgrFCcUJwXmGzRa3GQnR0z0sX+o5ajXOJ6qoNwGXZrM1bQI0h9MIYGnBTzRTVAcqeDaM/oLoLnF6CW1LWiPe+bOIxgdBnSKnF28P48GZTWAnDBrQdyOegegLEhoxNsKwEuKAD+akU7VQayK1A6FlCAk/NKQFpHmaeqoGVB1BXBehDYCQS6LJDBpRjahMxBgtWK0Hsr/U89WcVkvmPd5zuz9SyAy7wPbVlpufXXPz9oLPvnjDxfWGaRq6kEtRrZwSBH10XF5f0tbC5vqCmhsP9/ekkpAakOp688FaTVofuaIIDMExjbGHiFlfqmqjtEauSmnK2oTansjCOjAvQHGmJ9HgQJRUO8hPhbVUmghhciakTZmTK/PZdVnM8KDmQtETf/UlPe/Z6yecXX4o8Pvoa48t8f51Ye0OIrcJjlVhn1nbTBwjY25454jBXENu9wbml4d7yjJT5gfqfM/uujFdvgYVarY9dpQGAa5GR1bPsTT2qZ0nOEJPlBUeveifqKml33/TgrfOt378e6tC1U4BehkLPVvd3r6jtsrt3Xvm45F1PaJa+ySxdNsJ23stndoxDSM+eHIyqnP3r6R1Lv2JGngWpMrj2m09hO9kM/5Dd5q/yLPqFJ2fajY87b4/+faPv+fPNCmeo4HxycD8iZfpteK0shHPDqhOqa4RqfhWkFrJuVGqsNtseR0ir16/Yne5hVy4TZUN3lTwTvDR24bTPKpClUbVzDhsuR4nnAq+QdPKsr+3BNljwinc71fWYeQYzS7rslauazMh7MMH84NOlogo5sFFriulZYo2cqv9EGFJnEO270nHBS2NQQzMZ1VyawwPBzZrIl5ecCEN90KzebbSe0AUHezgVV0FqRDELEIl4N1kOona7ZX6yV+budSMu9dcbb5kjNd4/RJNA4e7ERED9M55alSCbzbJ6fzlnCvOOWLn7YsHcXJOn/NBGD3k3cByKazLkTCOSGysZeaYEw8pUbKDUZm24NtIaKOlLOaMqEfaCLgubK3M656a3uHdiHcD4l6z210jOiBl4gXMP2992N+S1sJvvv4t3/3hA0kbl59v+Ot/+0v+j//Xf8/F9cQXv7wiDg4/VEQatWZKLdTOTfej52d/9SW77YbvfvUNbrPlj9/c8WE94OuAq8GSW+tppGv80ylADI6L7TXX1xeMg117qjaWUiilcshqndHiKc2dV4dzgnc2PV1cQb3gNxFE2N8dOCyZ+6VwSJVhO7K7viDnwvohU0vrTuQeFY9IoJbGfLSpVG75pTP/TPWjV/uT6Qn8aTB/7kZ2los0TwFuk01u/jDbdfJ6f+AizAxTZNqO1tFvSm3K4ZjsAHf7Ne14jyy3uOWOn/3V3/DVL34JCvvjPdoa12NERo+PgZuLwNcPiaVko0KoAflNMGHtFIw2ZunHcnYgab1j25qSU6K2xlLswDpXR24v177nrr//9f9KrZUP775lXWZstqfkZMAe53AhIk4YR8tOub6+YoiR+TiT18QiwtJpVU2wa2MrqPaUYXonHKPkpJTohsAm5lf9SSR+bt7J00/8dP1L0Vp8MjDvmvRgAIf28b4IeG2IVnyruJKRUtFqqSXjENkEz3a7Ybvd4uaFVFcinqbBsgG8Bf2IWPdU2mnrgEHMEs0j5FqhFCgVnwoOIVQlNLtwZTUrtjVl3JpwKVt0ea7QsERPhdLj1i2yvNskVcVJRfq4pzTbSLNacEqq1vnSlJF5RYdIzfmlI/WMNfoJRFBvyV0qggoMYWSME+I8LsYu17LRXMurnfp7AJR3G5zfIbKBNtCI1GxTH/WKcxUvzmjyxboNpVRyLj3e/OQJ34PDRKhBu2+8cZtLM49uFW8uOK2QaiNVxVehemeTKF1BHdrDoKzhL48j8T6hqr1j4TArzjiMSBtx/gXMP3e56HG1USlkzfgxEsPAxfWWq9c7truBYYz4iB00xTalE4VAaTgXCN4zTJFxt2HczWgw1UTD4lOqWif+RDvwHkKIDINnHEemcSQE62whjqZiCYkNSjNdRW6PI2NHw0uj0qgeXBTGyw04h18y5Io6oaKEMXL96oKUMmtaSalSqnXza2mUXCmlT1/7+PulnqdOjozngf9ZHGjXjFMX8oR1TrHzJ2vAH7byq5rWba0G2KNrOFWad7hcwJqoRo9IyabaeaXlFckJSYlWMtI9v0taqM2Sqp33jH7iKkSOuXG1BASzQfUi7KInOmEMEDwdzJu/uDj3COa1kVdPa5VDqqy5cbsqae6BZi/nyGerw+GBVmvvys8M0RPCD/agE69cHwWwp2wYd3auOdG+TsC8r+Jza76nvXOiWDUa7ry2H7UVfIzKnwB56e//KRoN/1zNik8G5oc00ornoWzItbGRyiSNoSS2SXFzw98FwlJp8x4pyts3V1yNA6+/+Izd1SXluw/s6wc8hSaWPHi6x14NRMVktAYHjEBwjhgD85yZ93ukNHYLRAm89SPTuGMflNk15rSg7x7weSbOe2gNqbZgDMwLWap1/8XGzq2Z2JCiZnPpHCFEXHAsJ559reQGfkmE93dsteHfXONfQqOerX7x5m8NwA8B9Y7mHOocu3HD5bTFx0CYJk7xy601DvsP5LxyTEqqyrj9Ehe/QN2GnHc0hNwKQiMMM945col45ygpW6pnzqSUz8DKOmEVQdhsJkIIhCHig+fhfs/t/Z79kkgS8TTu0oGyNqY9LEGQbYDo0fSelkyTERCCm9hGQSTggoFArUJtgeg3eNmyGV9zFb/EscFzwwuYf966+eINy7ziLz1tKrz+4hWv377ml//qc77865ueQVHMJrVlVCulFRsVgwWQBQijZ7wYefXzz9AQ0d1/YgmWGOwq5FpZVzvgeSdMLnB984qr3cRnn33Ozc0luWRyLazVs5JZWuEuV1JW7pN2MN8QUZzLOJdwA8QL2F5M/OLf/TXDODH/v39L/vaWgztAa7z92Wv+z//3/5njcea//K+/Zn8/8w9/f8syV/aHmffv7nn91UTJ2pPQHT8mfLzUp6jTq/1k1ujFrABVLHHVbEQ/7tKf6AtnOoqCZROIUVUUjsWujyIGkDUXptSpDhW0VYZ8QEui1AOVmaZ2TdS8Ug93lFK4f/81KWdUFHHCl3/9r/jFmxvebEc+305EL1wMjiE4Xm0nhuAYPHinhOAJ3mx7zUaVM/2i5kJrlfcPC4cl8//5es//6w/3LFV5qO1TahBf6kl984ffoc1ogSUnbl5dM4StJVgP0ehPnQtfasaLwwl4LzgxUH4ih9amrDlTtRl9VgQfo+VZ9LyMMyVHAa1nQf756f6BC9JH3XZ5ckDgfzsg/1M/99/sfPO/oT4dumwC1bG2/uYaWSBqw7WKqwWXE5Ib1IKoME0Dcbdht9uy3W053O1ZRKhYd7I92QZOBzSHbWq+u4gEESKQOX2u4ZsQnBLFMfhgNpkBxM/melQVKdXEi60/+81+QXatp8w6CwPqTs2qzmzcVBAfUPFUhCaNXIUkZpTickFSYU0Z/0OLpJf6ZLXbXHUwby4izXvUOy7GjSWmxkicrHsvKrRaqKXgXCRTqFnBjTSN0DzULu5r1Z7Yks/uRE3cWahTSiHlZBzO3nWotfRRcCP4QCzGYV6WxWwBS6Vi4TupFWKt5CqEKkg56T8SZU0EHIN4mlMiK95VcyuRZlzEZvoTVev+B2+UsMDAC5h/3orTQEMZtyPTxcTuesvlqx3by5FhMocQ5cQhfxqK80gWFmuV47xjmEbG7YgfIhI9VNN/VGw6eO4uIfgQCXHAhYA4T5NKaZAbpNNbsS7rWpXUFOc6mKfhpBLFMU6RsB24eHXJOE2Muw1hPOLCjAqEMXJ5fUEYPFc3OxAhxAdYKikV5nk1Ln1viYo7x7W81CeuHz7KToyicjK5edqRf1qPjhyPffyT3qIpZ+cimyBJ57SfblBBG6IFbYVGNZ50B1y1ZNJyNPetWmit0GhIE6Io28FTu4ZjCI6r0Sixry+mc5KsF7WmiDdTDec6mNcTmM/UHswXBXbREfoUq3dXPs0D/lIf1TofO+1ppWSjoZ5daLxHTvz3zmywUdJjL/1kPQm2RmvTvnZPF8Y+V/+Jp7M9vaXzz3z8PT9q1D/pzP8QfD9aX/KTX//pW/74jn1qQP/JwPy8Wprgb+bK3VyZJ2Xv4XOUUQuSF/wDhNSIeWEKAxdfvsK/ecXNm8/Z7i74h7sD35c/MrbKsWaCKM2Z2LR2mz5pytBgPSzkw4IOI7KZGGvh7WaHxgpttQCgIeCnyJuv3iBvLsm//Zqsjnp3y3q8752xfjJz5hGdESoNH0Z8GMEHCJHmhBw8eI/utkjwFDNO4XBcmJdEqZmcMpv9kcPX3+L9C5h6rvrlX/1P9oKfRggeGQYIgU2MbOKICx4fB3NrUE8pGRc3HOc9y90RSByy5y4lo9P4ZHSZKDgnhLraGFADqCOt5lBTtVJb5hQzrdrIq9F3Sg/RGGIgBk8ujZwb83xgKQVyZr8c0KFRyjVaBpa9+Xcf7h/YP7zDN8E3R/Qj2/GeGAOXNxMuwrHekttCngJldPh2YOse8K4ifuIlNOp5a3tzwVg2/If/2//Cv/53/5rNbmLaDOyuNzS/9s3K7HAtss4hMhiTsI+MvfM0LYhXrl5dgMLnX31GOmYO71aWu0xJ5uQh4nASEBloPpIl8N39gbtl4biu9jYnbu9mUq7cHjK5Kkd1FAVxBZFKiJXoCq+uLvny3/6CV29u+Jv/w98yThO/++6e+zXx/WFm7XaY49Yz7S7499O/4uFu5v23K63dcn93x29/XXn9RWT/8IZhE9heTS+uXs9U4bTfiAGlMXgGHyiq5KaU2si1/llOsGDTbungSeDcPLuMwuuN4/V24MvLkVoq87JSC6xLpkrqYm6zeD7OC99+/w7/n/4T4zRx+eotmxCtE+sdV1fX7MaR3eT44rVn9I7LyRO943IaCL1jKwIx2OdPNBvtVLPWlDklci7c7g+UulJrotWMVpD2wwzPl/pU9fD+gwnpOwVLK6AO7weGwbMsK8u8oKoMYbBJd06gllrh+8Q550yqSi4Y1S9aOGNaUxfTZrQ1nDjGYSRXqIWzs5Gt7zOn5s/e5xOgP9F+Pq5+S/IxMH/qoPTx73gE9P+iO/O52IXirjTeZeV6UCaUy1Pzu1ZI1pl3rRAkcLGbGK4vuLrcsdlcEGOgNCXXRikNaATfLz5yiqewzjylP4MIEgJelY2PNBzZ534atLdxt2G4uWa5PcBmQ1tmKsa7y51N2Jw3MK9mlRVdRDqY1zigTijBgffIOCHek3BUFdYqzM2RVmWp1pmKh6N1UF/qWery6o0955sJYsCNBuanEJlCRJyzrqU4BAPz08MHclNcqKhTchbmXHFS8b7gvDAgiLeIexExb+PmyEshp0LTSuMxZlpbI+VErZVlmSmlMMZA9KfpjnX1a6vkVkk1WzBPc7TmKbWSmnI8Jvb7A1IFV4TgV/KoxCHixkRojrWuZE0EnwgukX0i1wXwNGfCoJd6vgpDwEfPZ1++5ebVDT6a+1EYBRWj0sipGyXmtCGO7od9cmywMTIOhnFg2k5cXOy4uLqg7CG52g9pp6uhB/E08VQR5pxZauawLByWlWXJ7NdEKo1jrpSmLN0OULQiYrQfFxS3CVy8ueLy7TVXn10zjhObyw3DZsAFdx5iOi8MQ2C3uWEcRza7kRgDaU3c3z1w3M+ktRBiT1R+aWo8Szl30qqZdic4IXhbZ40+OYSf6FR/vE+dqMnQn28szXXwwjY6dtFzMQSKA802JapSgYroY9pwqQb2b+/u2NXGqy9GxmlDCMZ/H8fRQHqMDDEyBgPzwTm2YyA4UwidwHzoYJ7+Gsn5RNswA4OmrQsme/iQAt2z6aU+faVlAUCbaXVsAGnXKLOizJRimsc42OdaM80NaKfQnA5pNhFyKoh4QM+BefTuviCmhetdfpWTMsTqMU7qsf7UQfZPY+8/ffT9KYqNPMlK+NT1ycB8Xe7IuXJ3fOC74xGnwm0S8jYyupELEd62SlNlcBUX4Obygunmht32knHYEpyntEKphZIKiFkMNpRK9xrFeHNSKr42WBP59HgL9kR3MdlxPVL2wvJ+JEhGD3uI5gm/hoHilOQ9TRxlGFHnSDlTWiVuNsRpY1QIrAO/9Nv11XzED3MhZQtgKTiSwlIrF0WZloXwAuafrT772V/ZK3IYwHkIgnoh+EZ0iojHEawzHzxrMtvUVBv7/crt7YG5LMx5TwieIUab1O3tIhPjgHeeMW6JPhLEMUwTpRZydcaXrxZH3UqllkJaVvPMXSCZ+gxUWcoD6/IBXGJthdhgaStRoUlDnOIk4LlkXlfuP+zxPrHdZYbBs8YNcXQ0v6KukrUy5wPHJXE4LozxipvdbOnGL/VslcoKgB+MiidOT23N8/dYoiXd253OVZbzJtRUkVZoDcZdBNnyt//2V1xf3vB38lvq8jXaHHnuXSMXUOdYpCKuGnVxcBSXWRUSQq6OWgUXHF6V6MwueBonhkG4ebXls88vefPFDX/zP/0rLq62jNcTzgnb65Hr1zuGfxxR9cxL5vvv33N9veWvf/kZ0+T5/PMr0px42B95//2B9+/uuH1/B1zy6rNrgns5VD5HDYNZ7zpnNJlaC8d1oTRLB67dyk+Vnl9h9ZRZLKqINkSlk02Ft7tI9J5fvZ746mpgdI4pOJI2vOdsEdlqoeSVtM4IME4brm5u+Nkvfs7l1Q1//dd/y7jZMMZwBvNDjEadOR1QP7KStA6+AUE7IKwpMc8zx2Xhj9++Y06Zrx8WDqny2w8H3h1Wvn1o7ItSu2/9yy78PLXkruMRA+o5JZZ5NhsaZ8/DEG1PMp48tJ4qF7y3kDuUUjK1Kloa6hzdLBet2fDdyZoSQI1fbxa4j/flNFmy+tPo+s/aSz65hdPr5U9R1T4qeR5R7Cfb3Vs+UnJhvx65W2YEz6F6Lnzg88m8kW/UHDiCKM4pu83EdnfBNE7EMJr1n7bu995wYh7JJ/cRMLW7IFAV1xRyoWhDnEOcpb0pFku9poXqhPRwj3ONuCaiF2rwZB8pDpZhoHlPniaa86zrSq0G5sNmIrXKoRbraDW7bdGCVsftvLCuFQkDhEhWYWmVVpU5J+ILmH+2un7zua0QH1HnUNdQVxHJOMlGamjRXuSDo7lqgrCqzEvmsF+YCyzVnEHaOHb+u4lZhzgZH33n8YMwDubp7rutm3UYqgWRVVvDJRVzedBmGyTm7JTrAzkfcKWQWyUjZApJHy8+Ih4nWxsf32ecV1ZdiaNDt5mhOPxYcUGpmlnLkcUV5jmzHa9xbngB889cuSajW4VgU5rehhD3RGjYNRt6Ti0/cUAfn3vVCghxHHDi+fJnX7CbLnn/h3u+/8MH8qqEWPrPmdA7ieKkEQfBbwKlOEoy3X7LQlMT11porHHlNxcDm03k7Vev+eVff8Grt9d88VdfMm1Hwi6itTHuBraXI3GwrIOUCvf3D4yTY3cxMI6em5sND7cb7u4feLg/sH84cHg4stlOOPE4eQHzz1EhhO4QAqAsJbOWRKkG5psqJxmXTbt/Yn9SPWfGnED/9RTYDpEvria+upmsaVFMgG9r2njzqpVaMjVnEIjDwHa34/Wbt1zfvOazL75ks9kwDhHvhFp6B73X+d7o4yeMIy/QrCub1sT9/QN3Dw/8/W9/x/1x5dd3C/dr4etZzcmmRZZq1qrPwHZ4qV6p2IR69A7Rx6BGF4NNxYHQdWfe2ZSy9cNl8OGshajVEq6pFW1C69NMbfWccnwSup4oMqaheAq///IT/5P+8v1TJ/7+47//K7rzJ9/6v/ib/2nqk+3uv7qKzNnxb18NbF0hZaXkxv1c+A0rn02OrXO0Bi4an33wkUECUqG1gna7taqNXAsY7kLEosXt/9KDgIwLV7SRcx9hKxbpm3PfNANlWHEpIe/vCKkyrIV1TVQfWVHeA6k1HpaFIoJ268zgIGihohTRnl7WtbI10xCWZBdJFzDrQSc0J/jguBxHhhcw/2wVBuPDNzEr0+YqKp0X7Lr/rBPUCX4QXMtom6n5yPpwy/HDe7J4qjiGzZZNHKil8nA4GPDyGe88oUIbEmwbslFzImmVmhOHwwO1FOblSK2FdTlQcsZp6x2vgpDJekRZumLafLoPyxE9OqI3WkKMnsvLC1pzzDNAw8VqvMIm1ApRBO8VxTqvqVZUV2gz+3CHe+mIPmup9u6QnAxEjSrYmuAatI/cFU6dHutIagdDfdtCgOIqGmC62SA+MN1sCbsBjomkHcyLdbQ2n11w9WrLL/7mS25eX3G/33O/f2BdC/v9amFR2Zwfsthr4+pyYrsdePPmmi+/fM3mYmTYDrhByG2ltsqwcVxeT4zjiJNIScrd7Z7tNuJdI4zCmzeXpGPh++9veY+gFUq21Fmt+hJE/EyVUuofGbpJpZILNO28cbE8gY868yfRoRoQOdmVRi9M0TNFz7/58oZXFxuuJ89mcObiRSY069BLEe5LRkvmcrfjcjNycXnDxeU11zdXfPXVV2w2W7bjQPSOKIpHiUGgp1x7JwQHU7CDRE4rqTWO80zOmbu7O+4f7rm9veXrP37Nfkn84cOeRYX99JrkR/ZFSApZ3UlGaeLcF0T/LFVaM+KfFKCS0oxzjaFNDG7CO9hdbI025a3xmvOKtkrJhZwyOSe0GV0riFEKg9hzOG43KLCmYvqPUinN7J5b+9gz66lLzQ8//nPrQZ6eKM+61s6R/0vrqH/PcwH6Twbm/81NZCmO7+8HrkPht+8T3z1Ubo+ZeYXjzvNqGvECLgZCDIwuMkikFaW2YicyaVStpFLAme+2OKH1KLjOorONUYTUxbJaFXJFWiOsGVFoS0NCQN/dgneWSotQfKDEiRXlu5o5tMb3aWVV86X3gNeCL4Lzgg/eaK7VOF0pZ5rCqiOViFMTPzbnUA8+eK43I6N72cWeq+I0AdaBbIbbaag9ma5nz2lDvOAGh6sJrTN13TPfv2f/7lt0iDAMuAF2wzVLq5TjnloaWaxL6rJShhGHcVLPwCut7O/uKCWxrjO1VdZ1oTVbk04VNCMkqjtCnG0c5z1V4DDvKaJsNyPDEBiGS3bTJc5N5DJaQEZZENdQXWi1X+g8tJ7sWWoll4VaHIO8fwHzz1ytW0yqmCNR59F0ao2A66NhpHttCKhD+5ptrXXQZRzQ6ioShe3rHdNO2LzaES5G9O7I0spZSxTCwO6LS159dcO/+j/9G372yy/YH/bs93uWJfHwcLQJ1FrM/UgaKsrN1QXb7cTV1YabmwvUQfUVpbK2hVoy4yZwfbNlmkacDKS18eHdA5e7Ae8rQ4h88dkVUoTf/OZrBOmOFpWSe9y6f46t7aXWdbVOZTXAXvpxUs+NsA5yfoBJ+vnTghoxClZ0jtfbyOVm4H/+xWu+en3JkgopF6O6aCOoZ/IOHGhJtJy4udyxmSZ+8Ve/4me//BXjOLLdbfHOMXizFozdgjAEo9uE/uZECWJhQPv9Skorf/j6G+4fHvjNb3/H7//wB7795lv+/u/+jrU5Dm4L45bdv/kPhMsdqTlKTwLVs+m+Oaq81KevUnsGkBh1Zl0OaEtAI0ZHjAO7zRbnPTF4VJXD/oGcM8dlT1oTaU22T9OI0nACoQdwjrsN4jx3+xldM6k0Su1U7J8UpP7YseanXGs+rh9z5JVOf/yJ2/hTbjjPUZ8MzF9uHGOFX7waGSLQhJHEmoU1V9YM3x4zg3dsh8iojrwsxMOB3DxVhZoS0qk4uZ39a/pDa6FOfbs7C1UXccw+mHdzD5Uaij3woY93tPOnTM2vaLAwn4Qyt8bcGktrpLPLhCnmvXd47wkxIGo9s9YaRTowlIZQkLZCVsaaGXBcqhCLEtzLJvZcdX4xnf2TLWhezYvUOp6itmEVqGVBywoloXmhlQUfBeciTgttXWnrSl2OXYydEPGsIhaEIlBLOWs11nUhrcfuaGRgPqeZWovBNgXIiGTEZ+IkDJMwToFxMm6/c4B4VJ2NrHXBBdhtp553cAlU3HCHuMzgE8FVarM74d2IC1uCG9FO5Xip5y8b/Ro9EJ7wLNujvZ/xjE9rVmjNbNtsgcq5RaoILjpLxpwCYTOgwbHUDAjBidmWbgLjxcB4ERkuIhs/waCMeWS4GKm1kdZion+1Ceh2GhnHyLCx7IImxvEXMH9y7xmGyDgOxCF24aLRZpwL+BCJw8Dl9Y60wnY3Egdvwv/uFeeddM7zS33qaic3j3Mn8QTg6Z7/j+K8H0If6ddN74QxOHaj58ubLdfbgettZBsdrdgBITqhxYBHYTMRHbx9fUNaN7x9dcNuu+XNmzdcXezwITAE676HrtVw/Sjb6korylILrWZKTszHPSklvn/3jmVZ+OPX33C/f+Cbb7/lu+/fcXt3x+39A9WPlKsbXNhQMIex1mzdnYkWT1W8L/XJq9VsXWln1y1VbxoItSRYRBFnfPlpmgB7rnLOzIeZlDLOeaZpOpuT2+HzRKG2pseZYsPZO4bHJ/nH15ofAu1HB5sTVniiaTpNqk7/VT76vtNtnW7jT9lb/ot2s/nixlGbEIYrDqny2XjHP05Hfvch85t3if1S+E/vMtth4Jdvt1w0z/LhHk/giCMBad5bwlyrzDXRGZ79ImRXptajfmetzCiLc+x9sG6Qq7jWmHB4hXEa8TF2/lWzhNh5xY0QxsaMcpsze208NKUoOG+bkQuBYRyIITAOQ6dpZGotlLKCKl4ySsXljCS4bMrn6rmpju1q2oCXep6SzhPVZi/S1ipVK00KzWXj0PuCaw1dG3mZ0eUBWffo+kBb7hkmxzRMBM2U/T15XlgfPlBy6WIqR11nQpg4PNzhw4B4hwtitJp1prVKSkdqKyzrvttW9jXsCiKFaVQuroTdlefqemLcOcYRQlBOuKe0RNOEj1vevLlBdMS3G1Qrqf4jjSMu3CJuprpI1UCQHYNc43HwYsn2z1P6SLc5pQCD8eMVPVsDqhodrPdCjWbYjF/vxZuAsSMxNwoShHg5MN1saYPjPpn97iY4th62ryYuP9uyfTuxfTsxlMCubqEfJLWdwnUaSy5Pos+b3U+nXdNhINA7C+3bbjfopbDdTUybyDhGvB8JcWQYN2y2I1/8/A2b7Y5Xby/ZXQzEGGjVwGMMjvjDFMiX+iRVuoPLKXn8ZKknpwMi52ER9dQiE+lpFHYAHZxwNQTeXk78+1++5vWF8eR3Y6CmxNqaiVfjRKuRi8FTy5bLyaGt8eUXX3B1ecW02TFttmhrPXcDMyJAcVqB1pNCF/YP99x+eM/d/T2/+Yffst8f+HV//81337HfH1jWxSadau45/uI127dXhM01WTZoi6bEbc10A098yV/qeaqlo4FbzHyCKtCMXopWS5p2SoyOy6sLvAu0y2tKLrz//gPHeWUaIrvrDd5B9D1dOFeqWlZGrc3855t2+pjtc8499Z//y4D+VP81QlXF7of7CabFU3D/kwFUn3AT/mRgXrCRyBgEVcfVFDjuIndLYzs4qiqpNnytzLnic6WUeqbWNORsJ2Uv2EYTuuBBEa/9lAcnYYKqol34qijVKU2EMtioe7jYQoy4Zk4jBcjLSsBEPopaOpk6ArYBe2e0HhWzRpJm91nU0jidCNGHHj3saSK4okhtbFG2qmxQRgcve9jzlfZDnnbbqto7nU1MZKWuoC2jNIRKTYlWCq17wZ+6BuLFhK+5WMprWs1K0vgPeBe7l63JG6UJ0pwdHmruYVSJ2jK1rR3MW6CTaLWxgBfi6Bkmz7QdGSeHdxUTvp6EksY7tG23dWu40bocfjRh5RAgBNZqrxfnHcGNeBHCi8f8s5e2kyjr9JnTBd7emmpPKXwUInLizDfjzDsRXOf8dnIYJzaoC44wBlzwj3QdMaeIEIPZlnpnhiAdvNmkUc8gTpoQVOyw15odMqSbBnzU7+pO+D4QgoWehWhp28MwEEN3jXKBOEbGbWWcooVO9Quf6E+Ptl/qU5X+4P3J2vHRN97wiE2fldPqA5qiYpOeMcAUhIspsusWkSeVoXQOuhMHwRPdiLaAl0vQxm67ZTNNxOjxQje0sINqruYTXtJCLYX7+zvm45H7+zs+vH/H/cMDv//DHzgeZr77/h3Heebh4YHj0XjzpWTr2viIuICLAy6OcOqOnt5OI4mPBJEv9alrCA4RGAejTY1DYBwiMQZzq3HmkKTaurGEw/tgh/4YGeLAOA5M44h3indQSmHNC7U18o9oNf06JfDTz/NfFkA/7aw/3sZPIfA/x7OX89sPu/Wfcvl9Qp95E99MDsKg/PzVyOUQcM6TinK3FP5wv1BT4+uHA7smfLGuDDmRvaOI9EQ3oVZlyYXQHFoyOI/vm9Z5pNFnLU4cQ4jQGrPaiGe5tMTP3VdfEHdbXJ9ev//dH/jueGByyk0uNO+53uyYECiVpI3BQXBKc8pcE1JhWWei81zGAe+Eq90GsA6aiODmFbcmrnLhdU1cCbzZuMcQj5f65JWKJQaUbMmDpSRqKzQyinW5WzsiNCqFNP//2nvX50iyI8vv5/cREfkAClXV7CaHwxntSqYP0v//j6yZbKXRzO7OksNmV3dXFYB8RMR9uD74jcxEkVytzFhYa1McGgg0kJWZQN6M6/f48XNGpsOJ+TxaYRwF1wm+99S5cjqcOR4OPH/+aL7xapKaXEa6fkM33BHZLs1AVAs1z9SamOdnSk3M5Ymqs2UTeIeI4rzihp792zvevNvw7a/f0/We5/MzuSTTCEZACsIMxZHnA957dtsNXgSVB1R6GCbUKx/TxGkaGYYHNsM9wQW60K1ODq+MOdtnaUP7zlnyIWJCr6IwZ3NqSNn065dNoBXRoQbUdbDMBQFJMqIQNoHtw46w7akumIOXD/jQsdlu2e22OO8otZC1kJvvt9TcCjGT16gU0+RraU5LXJ7DkvHp8CCOrtugm8h2P7C/89y9GXh498D+/h7xGwg9mzcKneftt3u++W7Pdt9bIrhK662ueD1Yh0Vpm6GYWDWIdZyH5niTWjG/JHLaLH7hLsLb3vHNxvObNxve7Ezil5JJYagJ7yKdd8QQ2O42WK1vtoR93xO8NxKuzqQ0Mj4/M44jP/38E+dx5Ic/fc/xeOT77//Ex08f+fz4yMePH5nnmcPhYK4180QpFrxXG5kmCq7r8Ns7wu6B4e4dfntnB4taze1EbQhTqrv8PVa8Dt7fdTgR9ruOGD2b7Z6+3+DjltBtLKtHCynPHA7PhNjx7v49XRx4c//APCa2257tZmjdTRuA/vnpxDQnDqeRVCqpLumwep3YXgZPb6Qw/72w4vt2vnUhQvTmv663/Utfuxa0pi1j4TXwFb3q9DJI44E+OLY97IbA3eCZa8U7QGzKPuZyPWE5GyA1XZSxUqUNatVSbXtZGHptj6NNXqqKW+y0tJr+FCOm1MmNx6mg3i17jLXjnKMTh4qjd4qoEL3ZZqpUatNYU40x9YDHEZy9kMEyHG3zU6XXSkelc8rQCd6v1dRroTZWRnWR2jRipunrqGpORRSKZoubbgfDECPDpjdmMwi6DGTXjDZ2XCU3DXOiaqBqMmmEOFSCxZe3uPJSElUTzhdrcfuKeNpnxUUldI7QOWJcmEwLPoFlSM10gCJqvvMeYnR450AiSKaGgHqPd7YOHQ4vwSwpdS2hXhvNNY1lVOaFfRpLt8g2odpmg6T9b+k2Vme3MUlhu19XTQvcGHgX/KKhuFjyeh/wLmAsv7bNUFu7oPlgtkCfW8Xpwvovz3HZDKss07rNA99UZjgvxBDxIdjQrmLXb+8I0RO7ZjF381D/HZ3sFX8DGEHZ1oWqyUW9I/hAjIHoHLsuIGKuL1W1BSwprlk9b6LQO6VzEJ0jOMdc2prEOtNewIvgvbMutReceNv7nbGzOSfSPHM+n3h6fOR8PvPTTz9xOp/54YcfORwO/OmHD3z89Imn52c+ffpkLOx0ttmSWq665sawO8EY+dDjQocLEedtzX+5yPTCyq+L77UwdKZYGLpAbKx818VW15lEOmdta+lMzIV5k+hUcN7T9R0hRJz31Ao5l4trTcqFVOy/L7r5m5d28Q97WWy/fH5fSmqushhsn/4L/+aviHNuPt90Hl95uX29Yt755k9rp+ht5+h84LezedLeHybGkhgzPM+Jc56ZRJm90O/3hM2G4YdPuNBRJVCqklRJ02jpncWKb++CsfGtSpummTpOqFY2pVBEqOcZQmCsQhn6SziFP41sReir4uuM08pD6ckOggoJkOCRKHYacCC54lIhOs8b3xNEiO0w6IoV+2XOlHFk0MTWzdz3nl+9C8Sw+ny/FuysJ01Hp3gXTZNXqkleciafMrUmpnpmnmd8Fxnu9/zu3/+Od7/9jonKKJXRjTwfDmg/sf/GU2q8sKQ29DyhviPj8WFD7DrSVBlPZ0qemfMz4jIP30T6bQ8R8MKcJ6Y0snmj7N5ENruIOI/iyLOQkrDZRrw4KjNKIERjXYc4cHfnCM4jbocSmMonkk5se0fZRYawJfiemuBwTmsR9cqYy3WYUAQyFV+taK+NNVzkYM47k8TUl9HgtSopZ1SF3A6lcUnCdMKwHej6iAsO5zw+RkLsCWFH8HtqCaRZKVSKJKgFzbMRH7Xp9wsvDrvL4GSphZQSgpCdhyocDxPjITOOI3OeUNQe3zmO59H6XKkw54ILgX7YIhKZ5so0FaapoH9lS1zxt0Vc3KsEwPGw23C3HdgNPQ+7LUP0vN2a3/eYErlYCFPKBV8zrmYGKbxxE3cuodkciXKqlFrx3lsoWYjE0BGCb0SW0EVj/KfpTM4z3//xT/zp+x/4+PET//X3v+d4PPHH7z8wjiOfPz8xTROn8cw8T6ScmVNCW0bLsjCNcF0GIK3L7zd3+P17/O4BiQP4iKOCZjvEtLRQWGYH1ovga+G793eICMOmJ4RA6Lf4OHAaM4fjM3MpnOaJWpWSwfvA0+cDQ79BRHh4+8A8TxzPJ06nM5+fnjmPIz99eiSXQmkEQb2RLV54Cb6UzPy/41K4X9Q1XzDxNz9XzGDF3WTBvLyz/y9/qb8NvmJ1eaNTUyU0tnDbOR42gTFl9p0p9Z4n2+CKmsbdx0g3DIRoxQ3iKGqa9ZoL1SlINsanWVU6lIBphX1OVFVCY7pyAXKhHk/knImlUvuKy5mI4JchHHV0WvEqbNr94YztwgviLOBFMkQcPWZvGbS5Qy/UVClozgRXiF7pAmx7T+xWmc1r4eV7uGnYEFQ8Tj1VnVm2tZjxUioSPL6L7EQIm4FjHtE8kicgFhyFfu9bjL0dEkoyXT6uolLBmRVpTkKphVIbK0+h3wxs7yKEavZ8s1BGJfQQOk/oPM55RJoYQQuCt4FZ9ajaz2MMxOgJnUnAxHkgkOaAKx7vzbM5eOsVFVXSfGXTVrwOlr936/qiYl1Fm6cwDc6VATIGs+pimwoXdr4V+DmbCN/5xo8L+HYIENeuT7JIKQIQ0GJWpWY/WS3gp1nG0To/qnph1a3xaO+LusSlq7Hw2uQVc0rknC09FMVZ4gs5F+bk2oG5QtPYK0JKxsJZBPtqq/QaCLZQWqmjbPvI/abnbjPwzd2GIXq+2UUE5TQ5cimcJ3uNXRV8hVCVoSgRq5wWB6ZaLQk7BG8ub87ZkGkrqBbT6JJn5mnk6emRH378wE8//swf/vBvHI4nvv/+A9NkScEpJet+tg6SuY7VlzVS00ILcpGUSYhIHJDQI8634h2TkcnN0P9NB+o10jhXwKa3UMau6/Ah4GI0N0Hy5cA2jSafmqZszjXdlpwKu92Ovu+Z0sScEudp4nA8MU4T45wu15dlgVvH88q2L0vxtqD/66/7zSxFe7/cLJyb210r9C/lO7ooROAiTXxBzctyb19v7X1Vqlgvf+GKUxCt3AfFbx1OA0/nnsepMuUCwSG1UFOm73r2uz3DZkccNtRwJKkxW6UsrUAbvMkuI+Jb6rMV5oNWIhB0CXcaUQRfl4LtCCHQTzOBSgC6tiC6NKLOs8WGWacpUxJtuCbaRHaxtqVLZyrKXEyiUUtjtKYzNU887CP3b/Y8vLvj/bs9Xbcy86+FUrS19BcdlX2IOlCH4PEhICjR9VA83XmgdDA/Jtw48XDneb+5Y/wm8vx3bai13rWBHShZ+fD9ieNTYp4jczJmNPYDKc2kZJr5rhe6PvLtb+55882Gw/zImE/IXHEbuHsbeXh3z26/Y7u7J4SAcztyLvhuRnwitda2ao+qJ5XE4/FPeOcZ+ohzSkqRnDd4lE1UnA6UpJRkIRxrMf+6mFrRqsmGmZ2znIqcE/M82fB8tGJoMwx451CnN4V8K7LbAPfyGqo3giQvw9ooEux253nkeD7z6dOJzf7EcNfhvIcwg59aamIGFaQ4qipTzpSq5JJbDPoylFvJ2Zj56kGL8vh05PB55Olw4DyOpJKaQYAyzbMdaqujJCVlJSXl+XkkZWVz13M6NyeoFV8d/+vff4sAXagED+/3Gx62A4N37IKnc3DXtVAmEUp1TNGRizent6xQFJKw61ry6o2EwDteBDxpyczzGajMzyY7/P33P/Dp8Yn/+E//wn/8p3/h8Hzgpx9/JqXE8TRSSrVrG45qo9ksVAl6lX2JC/aYPiJiMjLnPW6zxd894Lf3EDpLbARYDgUvlprVIuvqex30GxtG9t0GcYEikaxCRi6SZycenBCDEREpJ5hOFAp+9Dw+PfL4/MR4Hnl6OpKLJavXRVvdZkEuvDHK7Yv+ZSH/5/r5pdjWy9p2jX1Z0mVphTpqQ+GLoL5qbTbQZn5h33eXe11K+cU+3X7+t/wLv8RXqy6viYZwmXpXGDyE3jFnz7tNwEnlT0GpznTwWgrBe/p+IMau2f0FUypXmtON/YeINEGqs6GXpqOLTafvMJYpz4seWm3wcJpQ54gIPfbi+aUVVxJoAdeBOk65MFPx4vEuQoG6OD+UZMxunmzjq3bt0zLbhuk7Nvue7b5nvxvWYv4VUUo7HevLXAJzQDL20hhFj4vRWKhNIBBwR7PP2g4Duzc9M8KWxrxLD0qTwSjjCKWcqRJI1SHeCnobPMyoFnwQut5x/7Dh7fst9flAPhfUVzQKw9az2Q1stgN9vyH4iPd2MUn1mVzPlDKDJtCA4ii1cp6e8N7jwx0eTymOkjscQheEmqMxacUY0ZWRel3kVszXYnpf5wRXhXlOTNNk8w5EVJs8ockiXmxALbZeq5rTV13SgxupoRb4ZGYA9cJ4nY4zx8PMeMoM24yLGQmZRTOv1YrzqjCn3DZJ+7w8tqo2lwk7BGuBcZw4Hs+M00zKyZj7ZgObcsYlEIKRLi28zAJdCsfjyDxXS2Be8dXx3dt7RGDbKdHD+13Pw7YjVKWrZpW89ZZ/kDHiI3lnznGp7bXOLJp7fzO704oo1zgSJ8b8a83k6YzWTMomr/nxhx/44edP/Jd//QP//J/+K9M4cng+NGLihSqipbq7VnAvw9ftp66ZS/iA86F1pDyu63DDBtcNLXrdAdcBjT8fgFyZ+ddC6Gx+QUIEiShCrvY6L1k/LcUH32x7S81ohqwVnPB0PPD4/MQ0zRzH0a5JF4l6K5mXIp5b9v3PC/ovv4aXmvhFY3+V2+ilboTrOr29h2sn9frTpeLgtjPE1y3k4VWKebE3qVof1wG9g7vO8+v7LUMs/HwUkvP46Uw5HijjSJkTAD4G8J6CkEUoCAVt7jUKNXOJdlKLSK9iny3MSVFtthIVs26rVurXxuYvoS5U19p7Do8xUqqmbZ27mRLHi0ZLsdY1qMkmgJStcyAtFMENge39ns1uS/DB2p4rXgVa2gBfaeuuvUlVE7iEp+L6YC1jKrkkTofPHA7PfPrpZ46PB+CeLtyT/EjuDtQ2LCvqCLIliOAoiGa6sEOGO2K/JcaIc4FFTdANPd3GgWA65Dwxzyd88NztOzaDRzQznc/84fd/QnB2SBUIw4gLCdVMrYU0K4fcWtDMOOeYcyL4iFOH6BbvAr4LVBepEkCEIcd1E3tlzLM5ei2DiK4NqDrvjVlsspiLNEbc5dBVSrPprUrO9nmR/F63DVu7CLgg5FqZ8sjzyfNf/vX3HMYjcVtRecdmB5tt82FeLp3tsVIytmuRz2jraC4w73GPFuV0tnb3NM3G4mN6f7yx/LlWqIWcqw2nVWVOMzknnp7vOZ2mL9IZV3wt/O9/92DWgBG8Xwi1yun5mQ8/faR3yruNw8uFCyc3R6PT4ZHT8yMxBrOWLOYFj2bS6Zl5nikpUXMm50KaMtM08fT4iZwTp/OJVDI/Po0cxsyYhc3dAy6OFHXNBrXJyFpXvNZC1YKWDHm2PVSaInrxxVfsQCoRDT10G/ywQ7oBK9RtDmVh9fVSRMnNz9f19xrot3tU4TwLqWSOU7LU4FyMQMg2W6NFSdlIhLlok1B5xAnjNJObl/zSs3Hu2nHXGynLzWw0L0vul0X8S3K+Fe+X7xk5DNgBEq526Bfp+PWmCy1xvXu5+XBNRnnz8Utk5gFUpBW+cvlLByA6uOsC39139KHw4akwqsfPI+V4sLTNNFtbrYsQPFmErFCbO420N2xtjjUoSPuZFfRCvtTOpT0h8wHXWkGa9lkE0XrxtIcEKvja0sqKFfNTCEwhUp2jBI86QSPgLFkWgbldCD3VOgNDYPtmx7DbEH2z01zxKriERaVizjVOW6LqbPMWQfEEilbSXKEkzsfPPD1+4tPPP/L86Zk+Fu4GR+5HsnuiaCGVjEig63qcCzfFfEf0e3w3EIJJG0o1Njb2kX7jr8V8mpjnM/tuw91uYNN7RAvzOPLzxydyqYROcQHu3lWGneLF9PIlZ6akVE3kesA5Ya6FGHp28S2d3xBDT/Qd6sQ6Xs5dnFNWvB7mZIREaIW7DeQ5vPMtOdX8uaV5D0FL+i3VCqSUTGJTrtIX4FLQ20HU1rZ4QV1lyhN6Vv7193/g8fDEu1/t6fseakfnI9rYIhtwNWbJivnMPKVmkVkvMkYfPM45PEottRXzZ6Y5XTbYRbNf1BxQarZifnEnG6eJ4/HE8/OJ03m+ED0rvi7+t1bM+wDOKZ+ejzwdz4ynA3/4w+/ZenD3HdELBGO1zd9L+fzxRz7+9IH9fs+vvv2OoRYromthPj0zno7M55E8zZyOJ56eDpxOJ/704QPTPPPz84E5F0p3j4YN50sxf27d60rNNnNRWjFvQ40FzYmaLKSs80tpZPt9mqfWDfKo7yEOuM0OCb0V8O153mYkWIEnsOzzK14F/WZHrcrzODLOmcfnE4fTaGHQItRSm3V0Jc3ZXqc50WyQEOcYp+kSCrVc/pachFty41LIw+WLv3adud5Pu9XiYtPuTOU6aG03VJuHvJwCmkvecvvrCeJGK++4Lei1CW6+PGT8LfHVinkXjAkMEWqoKJaO2pQPOOfYhEDSzMM2cC7CVGd0OjOdj5xPR6bpTMqJVDNJrd2XZRlYtTeq/XnMRlIUighZjZ1vYcIsVm+XCS8qlNYxbBooaW/y0k7xYwFUONdK0spUK7MW2zybpkpaa0iaDs9pRahsgmPoArvOW1gUlTInxLmve3pacYG5zKgxPdUGU20VZFRHUjkzps+keeTT5w9M44ieE7E4QnWE6pmfRx7dJ+owk+/PZK2MOeNcYLibrcsUhP0uMCfrzAjm04xibkvBMWxh2CrOZ3vzV0WzQ9QTxKO5cHx+ZpoqH/50ItfK5h5iD26jaABHh5eIZqizIFKQYAfZ0/GEczMjgmfk/cNb4l2Pj46u87g5MaVySx+seAWYbSgE76wgds7aycEDESdCF8yNa2HjU8rknC0UJ9nGJsvG0LSbpbGkl2RPJ4TOnJT29/eE4KlaOJ2O/OH3f2Qaz3z77T2/+vaO2HmGrUe8mO3qUuwgIN7eN7KEnrT6rSpTSeRUOB5PPD0fTB8P14Apvdq/lmqFvDgIUfDJZgXAEhsXFm7F10Vp0sJcjNA4TIWnU+Lj4cyPnz7Te0VmT/QW/uXEWfKvKHk8E50QvCfEDnG+saTK4ThxOk48PT5yfD5wOJ75/PTEeZz59HhkzoXDZG4fd/c7hu0dd2/fgneUNJPGM4vGRlUZcyKXwjjPzClRmo1lzYl5PFFz4vz8SCmpXVsVQo9s75F+i7ah11rSdfIRrgQpzahQ9eWheMVXRVWhVOU8zRxPM6fTyPF0NsIVk5GWbEPVZg1t5hGINPW0SUQvhfzysi5dmhsv+ZdSmFuq/Sqb+Uuv+2JLb4W5Fez6otq/zoiILOFq2ipPuSp91B59yTtCavuByRqtZPyFMvM+doANjqoqhblZoylaKsF73oSO0GV+c5o4zsr3+cxYC6fnz+hm4Hg6cE5nppyYVOmBBDiE0P54tW0ii/YqKcxiOZlz26QcVuhrsRdt8ReXVpRfLiwCydW20OzUMVXTE2ZfSJdNz6J8owuXgAynEGvGa+Wh9zxsIu82gW2AqIV0OlPFNPorvj68N50yahZnoua2UBgpemSaPnN4/jdOhwPf/+ffk6aESEdfPX0OTCVw/vHA+MPPuD2Ed6bjO6SED5Htd3f0XWXfOeJDx+MBDqcMUtCmcXbeE7rK/kHZ3VV8THa4yKCzxxVPJ4EyJT5++InDceY//fNHci28/TvPsAcdHMmDr1tc3aDJUUdPiMLuzqRih+MnSoV8PqG5w//7jjd3b4i9Z7+NnM+F45xhdRF5VcRgOt8YG7sdrJgnBPrOmPnQBvaWYdPzeSTNlm6ZUiKEQN9v4KYYKbXlF1ST2Pjo6TYdQ7/hu29/jVL5+dMHPj8d+T/+w4EYPP/4D7/lH/7hN9w/bPnut2+JvWNzH2n5OqjaMJp5d7egITU2XquS0sg8JX7++Jkff/yZ03m8aOWrFmpLzK6V5gWdEa/EXgjZ4aMNus2pIH4t5l8DCYcqpJbh8vMx8ePjyB9+fuRfvv8THYXPndJ5YTdEondsXCU6JXSRIUb6GIj9gPjI83EEJn78dOR4OPBvf/yBDz/9xOPhyE+Pj6SsnJOtA3UBHyLv9w9886tvef/NA+/fP+CAIIoTC5nSqnx6fmKcZx4PR47nkTllpilxPBz44Y//xvn4zPn5QE4jtVhx5Pod7v5bZHOPutia6mf7xRc9tvfN6csqrqXjtOJ1kCukojwfzjw9n/j0dOBwOtv8o1rBZXWWUrK9Ln3f2yxEVZxzlFzNkate5x8WJn1RvFyIhC8e/zZt+5KfvRTdf62obpJrbV3TpYg38Y3g7RtXpY2034ObLBts9nPJRGjixr/+mH8jfD2i+OLD7ewX8h7xxqNXo+aNHfLC0HkqlVBtE6EWaikXL+ailazGkCcVSxAUs4k0hU3zoFVj1jM2iD+2P6xlUy0vwI05kF6jf6UR96PYy57bApmwjsCyAIyBv+xatlmL4EXYe08XAu/6wMPg2UePrwVXQLO7eJOv+PrIugzLmLZXir2lUjkxlwPj9Mxx/MR5PDKeD+S50DmzgPRViO3irynZ6XD09k5NFQ2F+XSGXPHRAsHOWfHZUhFLsaI+RugG2N3B/h6GrRA7R9dFYhgQAinVy8VBVei7gFdH10GMy1mzBV9VsydME5QsBG8n/SllSlHm80gtmXE8MaUTIcRm+ToypyOlrkXUa2LxNVgu5o7r7AZwHbbS64Yk2GblnFmMeh/w3lth7V6mCYoTpFnmguKDY7vfIAKFO+Y0M58mxnPi86cDffzMPGeGXcewjYQNuOBaYBXXocSFtGioqmZJOaXWOSg45+j7Hh+CJcy2wduqtWlcy4WZ7zpP31sYWqmldRZWfG08TRkFUsnkWjiMmfOcW2ekUrUwZfPrrrXgnZCDSVvuu4Fhu2ezu2O33xP7gb4fUITd3R3iA/fnM7OC3+5wm525MptGwdZtCPzDb7/j3dt3PLy95+HhDi9C56xQCsHWdT9EpjSz2205jRPjXDhPiefNhmk8E2Pk8e4N6jw5TXYdi50NfaPUlja/zMaJC63j7v5iN3Jl5l8HpSx1XL26ZEELAWtsusq1wF6uOdrCOVVv1ec499dlyi9eU7ncUyvo9YtlsNSmS6nf5Fg3vL59rpf/MpOUll4tN7dSu3+7di7zl0utWKF1LkW4DIx/LXy1Yl7EWYndXGYkiDlu5EpyBefAe/Cd8PZNpJ8rH6oy54zWmZxHSpmpmkk1cyoZp8oBRxIYRJoGvbYX316ZSYUTMKvymJv3Z3txrwb/7TlePgSPI6M8aSGhjEBGmaodDnbAXizvZ1OqJeTNiYCwd5HBO3735o43Q8f7N5GHu8A+wpAmfHHkmlel6CviWD5CVXKaqakwz7YGTtMHjuOfOM8/8vn4T8zHkafHAzo77n1HZMOQABxzSczpiD9FgvQmG5sr6mae60+EIfDN//LA9t2GOWbOcmQ6V/KzIO7M/k1hf1/57T8Kdw9CP3jER87nO3LqEZl5fp7w3tP1Pd51fPvdAzhl+34mDJUuWtBPrQ6KZzoXnj+ZreF4MEsus8BUzuOZXISPbwfu30DRgX6z5zgdeDz8gdy8zVe8DrxY4e2qXdgDkehuGSRtTNDSjdWWIyCEUM2f2Qe6rmubxUQho9WhTc/edR3OOyND+sA3376lHzp+I2/IOfF//Yf/zA8ff+Zfnz/w+3/+xK9/+5aUEndvN/jNO/pNoLZrdb1J9lwizVWFUirHw4nTaeR0mhinRNcPvPtmw7AdmKYJd4bHp0CIHoeFX/lY2e49Pg50Q892N1gY0FfUja644v/+dASw2YtSefx05vA0cpoTXQfkyjmNaK1Mk81nvOk7NjGwfftr3v/mH7l/eODvfvcPhBgJ/RbBcffwjlwqb3/7Wx4Pz6RSmFMhBs9uuyE4x6aztNi7zZY+RmLXEbtIcI4umsQsNBLjPM/kWjlOiSkXjlPh+Zz4+Pkzu7s9j4+PTFV5fn7i8XhkSgk2O3BQykQ+fkYbCScixK5veR1yKRAXv/G1kH89zNNsqa3JXK9UrWBfOn6GpVhuJAa8+FheLXEOQmgpxX+Bhr9Fk8sswou/VEFfjJkuKhrFXeKn7JFdS95eCJggtqadyHUGqsluajGyrehycLiMxrIQwc593Qrw64ZGcVGwcCmd22DBorVcNKUWclMIFdBCzRnNmdpahBmT2JwwayKVq/3k8pYVsdTWrHbb9MVr7m4lSwvLzqKDgozp7YuwKPzJYve3+IcsDNqisxcRgqtEFTqB3hnzEJu2X5vjQykFt5bzr4ZcJ2Oy60QtBammZ5vSiWk+MacTOY/kMqE1QfUoiaoeJ5XgleLBe3vjOnEErcSWaeDamgvREXtHiOCjIqMFAqnagdU+2gQ+1tHx3oqwUguLfDhnW1e+UQAhCCHYYy//oxVYJZu8bMzGXJSyMPd2u1oLOU/k7MhlptSZqub7vOIVoYuuskKV5qThvpBiyuWyaJvEwj7ZpufcstL0enuwruJyEaMNwmKpsN47us0GrZFh09N1HWlUxvPM+TxxOo6EwSwyJdBSqOSGvVoKeW3SBHPUKbmYPj9EQgioRoahR7w955wLNAkFaoFW3RBsXsUJoQuNpVuZ+dfAmGxGJ+VCbbMKpR3YFpvJZU8rF3mBB98R+g3Dds+w3dH1Az4EQoxYdeOJqtyVjARHaUPaMXj228GK+WiSsiFGovfNTtK3vT7Yvru0paQzFyTnCbkivqAuklLi/v4eBe4fHsB7JsTc7rxv81BC1dkGKltBWGtj5qtau75hLeZfF7Ux81qrza3dMO0XXfO1XG+XMr1Y74qadn7pbTrnoCplmb3873wp//prfvv4N4y82PNcYhWWPIXgWvq2s+dibyHXuqb23Eu9TZ1tI7jySy/mm8NMae4IUpf3lVz/hqXiVOm7DhzsN5kaYJon0tMT09MT58dHzuczJ4EZKFqJKNtqT74XS2GNQGApxGFGOWsryPWazrUU/YJJY5b2SdeaKstyc+2wYZ71NnCruZhLTtOKdlpxThh8ZRPAM0EppLFnlIiLQuwd2S8Dt2sx/1o4jp/RWpnPT5R5RnNCS2GcP3KefibnJ2qacCUxRAXN1Okjcw24WOmj4gZPyBvbiLoBFDZZwAvuTYcbPPf3G4b7yPYgbE7CdDSZS85T0wQ6To1BL3q29aU73jzsOJ6E+ZCoqQXuoFSXcailuEYlRMXH1uAq9jnlSh6V8TFDtdh27x1vv71js++JPjCOZ2JXeD4WpmnE+XJhile8Dko2N5ual5au6X29txRfEbOptOuN6Zt9rRcrtEtBXaptitlSWSkOqR7NUFKl5NKGBieOpwPOK9/99hu6PnD4PLPb3vPDHz/x4fvPTCnz48+fmHTkzc+RzTnim0616wZCMy6wA2FhmmbSnDiPE1PK3D/c03Xby8ewCdy96RCvjPOEJJpDD/T7nvfdO3IS8iz0m0jWmZpXV6/XwPFgzHxpQ4Q5WZfPKQTxVAlUieAd+w14gbfvv2W3f+Cb3/07vv3H/5nNpmezv8M51woYYWhb+H4TKfWhsZpW/AQnLwshcZdDp2IMp1ST/5SbHo0TIfjWzcfhXCCIUPV/4ng+E/otT4cD/+e//AsfPn7kcD5wPJ+oeFQnVBzFB0uBdd4SatXbIKVcyZB1B349pPFMLpU8z9R5RrMNYovWxn0v1zoroM09UNAmf/YLqw2IdyjeyAAWSWD9i1r5RUe/HAzgzwv/5TGWAdYlj8YBndOLk5IT6KMnBkcMnhBCk0G6y+fbdbW43CwNAXsfSFOiuF+mzGb542m1+PArf96GGJZTspqzjffYVH2FsWTyNJGniTLNtlmJUFEOmL1lUSUg5PZLdK2gXy4SGTOkLJhM5nr+4/KiVcBLO/XBzdK50WrdtGGumq/b+1OcKL5N2GoVasnk5MjiyMF+6yx2uxWvA2PcK6mMlDJZkFdO5HKm1JFS5wtbEDyoV1RmICPe4byg3iExIi7ggkerbU54h+96fO+JXSBGT4wQopGcqksyZ5MpJEgz5FpQwEchRmOqRIxhKrlYKBWl2WA1Vn/R2i0MvZi9odbCNNohEe8hODyRLnQI0gYohTk5UknglLWEel1o07cvJUsthVL8RcJiBX4T+snSbrYUWLgy4xcXjsXJQwEVar0OqIrQ2r3mchO7QD907O42jOfE4+cTPthmMufEPHvmKeGCEJ3HVQjBdjZ7XC62mGYxab9TP3R4F+mHHX23JfY2B6JSqTU3ty+7sfcOP3T4IPhgg8CKXoJWVnxd1DabUO0MednPvAhBPEUKVawvHbwnOGHYbNju79js9gw7Sy2/ZiLYGrWAHwjeG+MIhFbgy+Kw1J5DE7lcFcl61Scv++kyXSILueaE6KHvIvvdDucDb9++xceO7e4H+tOJ83Rm8YzXqqhzliGDWU9bknJ7zyyiAP7b6owVf1vUUqz20ysrf5HQGNdLlZYStCglRJta4rpfmUOkUJtVpGti99qqcSuc20ykXl/nSwfgL+BLXnVZx14geMELdF7wDoYo12I+Wh7I4mTovQX+LQ47l8J3eT80SY5z2PX3K+KrFfO1SVGWgaqltex881SuGbJtak6NHd9GT0X58fEzP42f+PzhmcPnJ85zpuAuUhiHMtaCp9KrOdtELPlVWvuuIuRg/2Y5mV9OS86cJITF+bMNMGChJ9ra1iyMA2Y7SWkNh/bCmesDgLk5TGnEVYdoNplQCXgxH1/RpV2+4jUwj49YpPcT4mYkJgiZrpuRAeazJ0+d2YXuCgyVurOhWfyAuMg23hHiwDxnTueRPFbmTzOewJv9Pf3dwHbriL1wd1dJWdGUOD9PnE+ZNCqaIY2O4IGQwFV8SIRuZrP1qNsxT4nj86kNkC2hJ1w2PkSJUYguwJuITjsOMfH84dEkNyVSimc6V3zM+L5SpDKnkTl7nBd8iF+9zbfiJaa5gGJyAIVchJggxkDOJoeJlullnu2Lq5ZycW0wP277KMmGR9NsbPzxaeTp05GSlbv9nvv9nu12yzAMFFVSLWzf9tDdMTNS/EjsHMMm4DtPmu0aVqXiQsGHCs7WXy1mVOBCRxDP/t4cdx7eOmOxxAEe58EHkxCVahHn8zxTayUE6xiFaIHaPnhc59cO5SvhzdbSqpe1laWnBGVz3tL1GybxPGXs8NcLsQv83d//ll//5tf85rtv2A093jk7NF4Kby71kckPhWhB2sBV1bK4bEsrvJaCbZn4Vlp6pl7ECC/rLjVr181gXaNfvX/HdrPh+w9vyWkiTSeen+29smTNuIsI2tjdy/1d2Nm1lH9NaE2gyhAF3XicV/oEpYoRW8u1DsE7K5JDq82iMwewxcUmqTI1GUtohgAlSyMc9EJAtHPDpbC/WO8uC7OpQ5zTVmgrXireC30wR6f9pid4YRMdwRkJEZzgvGvsulyuz8tsxsLUXyBNriOuhQVayu3XxNcNjeLFQeVqKeSWxtwVAnTe0VeljEdOjxPj8cQ8juRijYuKo7QhhdIOAVmVoGoym4V1QJouXy5/eF0qebEQHduMrv7IxTUrtoU1uGhUG4Omxi9AWzDuyvIvt84lk1SY2yBH9JCyhU8l/1U7LCu+QEkjdlSbQGbEJaDgtUAQSraxZxHFdw7RStGMUsAbG99veobhHjmdOdUERcm1QPXEbmAYtiaFCdB3hc2msNlmhq0VX4I9hVqEWsB5RaTiXMF520AH6QBlPJlHYFkK+EufyCb7vRdCcPR9YLvryKMDQmOnvL03EuS5ME0FGbOtfy90XWR/t12LqFdGbnZri5MDkpvjgeUE1KKXbsvCwLcLVWvfuouLkTYWvmalpEpOSpoK0zjbhtl39F1HF5ueHUuH7TYBfM/+oefu1OOcEJzgg6NUoRRBijFiuVaCXn2dFUGcxyP0w4Cqmi++s6TaUuqFpFEVKDZHpMyW8uoEF7y11pf5k+DWdfhK2MQAKLlJEkoNVDokRkro8AXOkqmi+BjwXeTuzT3ffPOWu7stXQgXbfKlK63aujSC14VJtRRXQS4y6IWRXwr5K1VvnKlc/p9WdLUvl0tf6+DHEABht90izrHfbthtBroQ2n1cOdhFSvOXd1r9K99f8dWg5dL57oJd95zYjEWurhEXjeF23izHvclYQiuCF+tx12xFfbXXWKtlCqkKOVdqFSMgLuv1Zq3KTR26dEAdFz28dxCDsOkcXQzc7Xqzae2siI/NUljclYG/ZnG4xtCHa33bCka5SGzc5euvee37em42bZDLB6XWG83acnpWwLd0zpwRNWbeOccuwM4rvRZ8ykRV+tosI9v9u1YwgyXIiUJtR4QMrStQLlqopRUjQG4TyUvbzwIDFo5+KeLl+jhik/cBG2ztnXUCOql2iNDFscJWUXWZIkqalTkAwZFdbAM/K14D+fwjUEFGhEwVC/wSXyE4wq7jTu6hztgUqqc4NalLHtDaUatYxsGcmMdEPiXSc6I45afvP9I9n9gOQigwzZYKvH9T+Yd/Fzkf4e5+RwjK+18p/aB0O8HFSrftCUOg1kitHeO58Hz/QC4zp+kTKjOb4YgPzQGpClQPGhAJ+Ojx0SMBpJi2PnpF3Iyqs5jsudqQuTPf79h1/01rrxV/e4yjCfzqIi2onloKOUPKepEUoErOmYuPMoLzlhLbxMaUWpnmbIOMLd7cBU+/6Xn7/oFShd1uz7tfvaEbIqELiBdiH5AgPLy/N2ZJHNEHYhd483BvIS1RwUEM3YVJUmcHEc/iAmIMblWlltwGKWtjnOy63YXu8juUWgnR44O/3Na89uNaUr0S3m0t1aSoWTzPTklecNuBeRhwIULXIcFz/917hu3At9/9hvv7B2LXm6UvXNLtbxOIjQhrpqs3LnGCkXWy7LsiFwvAW+htBX/zvetaM77eO5M79tGjNfJmv2d6eODx8ZHHpyemZBkaNrgbwJl00Qysbwuva0G34pWgFRFl6G3oeajOBuq1tm6K6R6ExnDTErFv1suyFuZS6XNB8Rh1u/xcydncZErBXGWUds29fl7W7mVe0pn8xbtC8JXoHUMfCN6z3Qx45+iCXQu9lwu7fi3ilw9/+WwP0D5di85rkf91/9pfkZl3NgTl/G1btRXybUCKGgC70AiwCQHvhG2AwVWiFlzJeIVeLQjq5sxvrHi763zh+o3JFKkXdyDH9Q8piLnQuOvUsS5M6PJCYJuYaycvizMXAsb+L5KeiOn0nYJXvXRyailklOwhJcHhKdld5Dkrvj7y+BFQXDRpS8ZO7S54c2YIkW7j0RooxQZTXbRiXs8dOvfUozCfEnPz2C5jJh8zSuXThyf88czunaMPDnEd4jq29443D4F5dOx24KSyv8+EWNnce0IPcdfh+4DIFpE986jcbZU5jTwezBdeuxn11j6+FvPWUfLBG8PpwQWIvRKDIs5S9EqppNmY2SImGNtucisOV7wWpvmle5BqMWLDKS6VdvivUCspJbsOLtcbb+vUORu6qs22t5Zqm5ZaMR+HyP3bO2I/MAwb3rzd20EvFnBK6AOuOu4f9gxDj3eezneEENhsNpgD2IxKbRJIadfGhcxo6bTennspiVrLJYDHiwfxiBNisE3WOWuDizdtqS8mDxLn8H7NwH4tvNlYcGNtMzwjlclBHTqOXY+PStxuCH3Pd3//O7Z3e96/f8fd3Z7ovL1mC2nW9vAXBbFpJFrBJVf28bJ3Lsx8w+32p1985iWjujyOFwHn6II5Id3ttszTPff7PfvNFnEzY1bqcjGUVsw3xvSWCW0Kn7Wofy1IRVTpu8uINEvH2bqQDqENlMrLvek2rVcVUinE2daXj0sBbbfNqVJaMV/yogi5OhfdyqsuTHrTsUdfCb4SgmPogl0fG/Hlbor3Sz6ItAH/y8dyaLyuM32R9HrzM/26Uq+vzsyL0ysTD/b1ksrmLNRhsagKVHCOd7uOgvA4Fp7HxHMqhDGTFc5N2760Ty7jNhc5z9J8u/5x3c2pzNp3V/nM9Qm37zUNV2wvYhSHx9ELZjkJDGIuN4MXK+rFCn1PC4Zp58cLU18rpWRqXYv518LGt3ac92jTs5UmG7A3asWHQtVAmSJahZKsQEmPhXIe4RzhHKhnpcsDuWaSW6YpbJBP1aPVkZNSa7a8AlGowt2mQwSiy4hW0jiTUyHXhEvZmIHgoXbs9ztKifhuptSR5AqVkTkXSlVEu3b4tbC1bvC8/dWemgqbUAlOCX3FeQVvgTBOrx0mW+vrLvaaWOiFi9+1BFS8MUe5yWqqWbfNc7qw1yJC13VETKPpfXvlnJnxOgeiQr8rVCeEKeM624TCxoa3l4lEqY1pjxFRZ5tV6PDOE7xZUpYqFG3XPm0Wmk32k0uiqjLPc2PmK3rxUF40Nvb1chi4yDHAJEIskkYhp9WW8rWw6YJp04t1VbIvzK7iNjvCu18RBbou0PUd7795x3a3YbMZWqfFI963AXx5uV/e7J9mzXz14v6Shb9KDxZJaqv+Ra7r6EL4m8y1vigXHOIqwZsE4s39HQg8HQ9MOfPp+cCkn8gVZr0V3TTirj34hem9/T1WfFVIk2YtKghbH84kKNjXjnhRbAjyogDXJvmrtYJ4k+k4IXaxFdaNfQ+LXl6oVZaJjPYslsHr9pwa87/Me3hX8U7tfkNL5Q7LQfBGHsOVmb8W88vs0PWzPcjNx+018s+bUX9TfP1i3ssyDWtv4qYFBQ/V2wsutgU4qQSU3zwM3N9tTetXCx+PE/Hjgbkqz8k8cWe1u820IVZxtyrjq15f9XKiWt7qX6oNRK6+4V7sItBJa0mLJ4g0/3hj5K2YV3beEQV6se8HFM+i3bdDhNaCFiXn64VtxdfHfXSowBwCxYEjUPAQBHWC6wp+m5BSSfRo8uRTJU+F008T82PGzx1+7qB4+rzF10xySsGY/kJBtaNWz3xWpnPGVSGUzGYT+eabO5yDVKy4Hg+2dmWakT4ROiV0wnbzhndv7wHlLkeKThyTpdUeDhPjmHAMaAmggvPQbyO//vsHtBRcPSFklBmlkLRQakLVY4atcnNxWfFaUBbbSW8sj/MgjlIKOVnBXLMV8dM0U0ttt7MCGxyqjhAbOeG9ac/FivCNd8Rtzzwn+mki+EC3sU2mLsmKzk4CDk/0SvCBPvRWhLVBspyMfRcVmilXSxvOTONkB9ycbKNdfJa9a9d4uQyxBR/az+x+59mSR22zM2Z1Tul/6Gvy/yfseuuUlGzyhjlaNkXY3xN/LXTB8+5uw6YP/ObbezZ9JIjlrvj2+oqYOcWiL17ctUSuRIETcCwSsWvhvhT3i9TgUtC3LvbSF19Kg6qLIQWNxJWWzdK86Z3w/t1bdvstVYRuu+WPH37meayMKZNPicrV9csth+jb8l74uhXVihcQAGdstmsuSN67Vkx7vAu3t2xdk6uLV2mdSN+uez54No0kq2oyRrloL9pnsdk0u9uF1n25BoTrerz8ZKnFW8H4QgMPbdjVfVHQ+3Zb325z82/a2r94I37ldfcVi3lzCaVWY+dbS64di9pAmA2pqojJCdptooNB4GET+O5+IApM08yYC0EKuSpjaUU90kKd7I9WEZaAsLro3794kW5ttsBeWIcd9JYgqq5dDDqxVl9sZFcQJbRiPi5FvJiVkenr268o13aSBaWsXjaviU6jHRKDUDyW/lWgTJUyZ2oqZqVXlXIO1AQcg83Ljg43OyQ7qIJo06s7wW8GxFXcnSAbwfseVyOkAlMx3+8xIzOcuxEX7DkgypJ2JtWkD3V2pJqZSUz9ZGdc53HS0YUdznvm4KlhplRHnpepELUWYe+b/6oz/aFYx0tzQYtDVKjZjKOMIV0tAV8TddlPuNGcOyi5kFJpFQxUFcBfq5+bTUDh4iRi1zWTT6FQsbkI8eCjSVrM3lRa10gvaXelmEMNCk6dyWfahjlPM7lkYjC71KrWoSq5ME8ztRRSzihK6CIOC04TkeZhbnr/lG04dpFLzCkzN6nR4jqyzAaseAU0m8jl9QTFO6GLkf1uSx8dd7uBPno6b84drhXz7nYpclP0KC+LoBcFUePFVV/kAV316tcU1kXXXNV88IvaXEguxdxOCi/Wf642J6Ktsx+7ns12yzAcLYBPBSe5kf764nlfnhd61eWueDVcxp1FLvIV7+0a5N2tCPpazNuauZ2fECQ4vLd8oGXd2Su71HnK5cC2dGPk+gzajVi+fSnW2/K4rmdp/36R8rTHWzoIX3z8eeEviLvev6q7PPbXXH1fT8AYYnszl7YLtXf2EospllR5acMqVmxoZRuUwYG82/B2E/n584kHXzlOmQ9PZ6ZSeZoKc4FTFZJCVkdWC37KbdMrzWNWb4p45KWNkHCjeW9fO4RefPPjbXp5MZ6tEzU9v8DOWyE/OPMj7ZzDO5vethOaUooN4c5pLaReE3d1hyLkYaB0nsPTyDgl0nRiHM9UX9DOCg2XesgR/VRxkyM8VRgFCCARSgdlgwtK982ARCV8B24A121xJTCdR3iamZ/PnD+eOW9GyulMHDx332zMY1bB4QipJzjPWApjmcnDCak/E2PH7v4OHyL7zbeoZMiPeE48H06cjyfT+3eREBx+76E6SopQBeebjOgkyNlZR+isZFHyXMxWa8WrwdJ9b6PLCyKQUrb01caO26YR7dK41PQusASM51KpQGq2uaUVz6r233gleGNRVbJ1LJv/fM2KFjVby1Rw4skuIWKSG1Xl+fhMzonNZqDvu+vQ2ThzeHw2O8ycUYT9/R3dEMzjGSglk9LUwlTMhUecQ1U4PJ84n+cbHbRS6nodfDXUNkOTE7lUhErnhX438HC3pQ+Oh11nnu6+daXdQkZ90cxrbPryfWhFlEoroljEyk11ZXvt4laysJPX7ASzzKwKU6mUaom1UymUoqRisqyKpyrMxSReWYXqPP1uxwOe53Nmv/sZZOTpOKKlNo3/pT9/GwK74jWxFLw3nuzeO0JwhGC694WZX8rc2q4PF3cvMmg1O/EYLwOpxt4btXHN6LjhQ+T6+Nw+wm01vSzbm2L+Kp3h0mlqRvftzXErwTEa+LaoX0wmli5EK0Db43/dg+TXK+bl9gt9cSrR2xtcWKiXP3FYmlwfPF1wdN6RvNmqlWqnMy92ASoqF8N/85ttA2Jip/vL4eqizWpBPLqc8NrJT5fAgqWdeP16ubAtH3/pe4u+6vpb3jpB3P5sxdeGs8YvIk3/2Q6MVNDS5i4WprwI3H5UQXTZjGB5IyqCeEW84rwVXhZdv3wIWqBmpWYoueKy2IFVl75ye1Orw0LGMC/xUtvAqz37pYW3DNrYWuI6lY+tJ5UWze6uF82LR7leW9gr/sdBr1ehF0N+7YeXq95SFL24Tnyhs1z+fb19US9s0kUhytIV5MUgWeP1qwXYqbS5jzbMegnZuXkwrYtudek0tgfUl7+Lqt2vXPQTegmduj7v61DaiteCvrxutGuDb0PWwTvMOrveyGNe7mF/CTdc6pcP92f/RC/fuxlKZLGduD4/Owvozbr64uc374UlPdkyY9zVKe7FJ710E5DlPfjXf6cVf1tcCdMbmdWieeZawIvITRfH9q3LZ67/5nI/t48B0CReNDXE9f6/fB5f/qsbJv8vPPcX74XbNtXNcfb2mXwpy7nc4mbZfc2CXnS9uq5YsWLFihUrVqxY8YvE2ndfsWLFihUrVqxYseIXirWYX7FixYoVK1asWLHiF4q1mF+xYsWKFStWrFix4heKtZhfsWLFihUrVqxYseIXirWYX7FixYoVK1asWLHiF4q1mF+xYsWKFStWrFix4heKtZhfsWLFihUrVqxYseIXirWYX7FixYoVK1asWLHiF4q1mF+xYsWKFStWrFix4heK/wdsTTdMj/JwMwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training data shape: (50000, 32, 32, 3), Test data shape: (10000, 32, 32, 3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Build and Train a Baseline CNN Model"
      ],
      "metadata": {
        "id": "mhecbWOob63h"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = models.Sequential([\n",
        "    layers.Conv2D(32, (3,3), activation='relu', input_shape=(32,32,3)),\n",
        "    layers.MaxPooling2D((2,2)),\n",
        "\n",
        "    layers.Conv2D(64, (3,3), activation='relu'),\n",
        "    layers.MaxPooling2D((2,2)),\n",
        "\n",
        "    layers.Conv2D(64, (3,3), activation='relu'),\n",
        "    layers.Flatten(),\n",
        "\n",
        "    layers.Dense(64, activation='relu'),\n",
        "    layers.Dense(10, activation='softmax')\n",
        "])\n",
        "\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 470
        },
        "id": "_cQ3ZBItb7hA",
        "outputId": "4e4ecc0f-382b-486c-859c-4b98d8561a40"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.12/dist-packages/keras/src/layers/convolutional/base_conv.py:113: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
            "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"sequential_6\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_6\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
              "│ conv2d_21 (\u001b[38;5;33mConv2D\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m30\u001b[0m, \u001b[38;5;34m30\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │           \u001b[38;5;34m896\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ max_pooling2d_13 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m15\u001b[0m, \u001b[38;5;34m15\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv2d_22 (\u001b[38;5;33mConv2D\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m13\u001b[0m, \u001b[38;5;34m13\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │        \u001b[38;5;34m18,496\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ max_pooling2d_14 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m64\u001b[0m)       │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv2d_23 (\u001b[38;5;33mConv2D\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m64\u001b[0m)       │        \u001b[38;5;34m36,928\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ flatten_6 (\u001b[38;5;33mFlatten\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m)           │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_12 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)             │        \u001b[38;5;34m65,600\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_13 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)             │           \u001b[38;5;34m650\u001b[0m │\n",
              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
              "│ conv2d_21 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">30</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">30</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │           <span style=\"color: #00af00; text-decoration-color: #00af00\">896</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ max_pooling2d_13 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">15</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">15</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv2d_22 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">13</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">13</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │        <span style=\"color: #00af00; text-decoration-color: #00af00\">18,496</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ max_pooling2d_14 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)       │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv2d_23 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)       │        <span style=\"color: #00af00; text-decoration-color: #00af00\">36,928</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ flatten_6 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1024</span>)           │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_12 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)             │        <span style=\"color: #00af00; text-decoration-color: #00af00\">65,600</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_13 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)             │           <span style=\"color: #00af00; text-decoration-color: #00af00\">650</span> │\n",
              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m122,570\u001b[0m (478.79 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">122,570</span> (478.79 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m122,570\u001b[0m (478.79 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">122,570</span> (478.79 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(optimizer='adam',\n",
        "              loss='sparse_categorical_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "# Train model\n",
        "history = model.fit(x_train, y_train, epochs=10,\n",
        "                    validation_data=(x_test, y_test),\n",
        "                    batch_size=64)\n",
        "\n",
        "# Evaluate on test data\n",
        "test_loss, test_acc = model.evaluate(x_test, y_test, verbose=2)\n",
        "print(f\"\\nTest Accuracy: {test_acc*100:.2f}%\")\n",
        "\n",
        "# Plot accuracy & loss\n",
        "plt.figure(figsize=(12,4))\n",
        "plt.subplot(1,2,1)\n",
        "plt.plot(history.history['accuracy'], label='Train Acc')\n",
        "plt.plot(history.history['val_accuracy'], label='Val Acc')\n",
        "plt.title('Accuracy')\n",
        "plt.legend()\n",
        "\n",
        "plt.subplot(1,2,2)\n",
        "plt.plot(history.history['loss'], label='Train Loss')\n",
        "plt.plot(history.history['val_loss'], label='Val Loss')\n",
        "plt.title('Loss')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 790
        },
        "id": "DpfNdd4qwARF",
        "outputId": "11958022-5d8a-4800-ded5-2574d4695f94"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 9ms/step - accuracy: 0.3460 - loss: 1.7803 - val_accuracy: 0.5148 - val_loss: 1.3645\n",
            "Epoch 2/10\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.5636 - loss: 1.2239 - val_accuracy: 0.6032 - val_loss: 1.1099\n",
            "Epoch 3/10\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 4ms/step - accuracy: 0.6374 - loss: 1.0311 - val_accuracy: 0.6456 - val_loss: 1.0205\n",
            "Epoch 4/10\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 6ms/step - accuracy: 0.6701 - loss: 0.9425 - val_accuracy: 0.6750 - val_loss: 0.9294\n",
            "Epoch 5/10\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 4ms/step - accuracy: 0.6933 - loss: 0.8706 - val_accuracy: 0.6943 - val_loss: 0.8812\n",
            "Epoch 6/10\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.7228 - loss: 0.8020 - val_accuracy: 0.6998 - val_loss: 0.8695\n",
            "Epoch 7/10\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.7381 - loss: 0.7477 - val_accuracy: 0.6953 - val_loss: 0.8706\n",
            "Epoch 8/10\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 5ms/step - accuracy: 0.7505 - loss: 0.7096 - val_accuracy: 0.7147 - val_loss: 0.8295\n",
            "Epoch 9/10\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.7725 - loss: 0.6438 - val_accuracy: 0.7117 - val_loss: 0.8425\n",
            "Epoch 10/10\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.7821 - loss: 0.6212 - val_accuracy: 0.7230 - val_loss: 0.8173\n",
            "313/313 - 2s - 5ms/step - accuracy: 0.7230 - loss: 0.8173\n",
            "\n",
            "Test Accuracy: 72.30%\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Enhanced CNN with Regularisation and Tuning"
      ],
      "metadata": {
        "id": "Qvd0p2kywLoO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_opt = models.Sequential([\n",
        "    layers.Conv2D(32, (3,3), activation='relu', padding='same', input_shape=(32,32,3)),\n",
        "    layers.BatchNormalization(),\n",
        "    layers.Conv2D(32, (3,3), activation='relu', padding='same'),\n",
        "    layers.BatchNormalization(),\n",
        "    layers.MaxPooling2D((2,2)),\n",
        "    layers.Dropout(0.25),\n",
        "\n",
        "    layers.Conv2D(64, (3,3), activation='relu', padding='same'),\n",
        "    layers.BatchNormalization(),\n",
        "    layers.Conv2D(64, (3,3), activation='relu', padding='same'),\n",
        "    layers.BatchNormalization(),\n",
        "    layers.MaxPooling2D((2,2)),\n",
        "    layers.Dropout(0.25),\n",
        "\n",
        "    layers.Conv2D(128, (3,3), activation='relu', padding='same'),\n",
        "    layers.BatchNormalization(),\n",
        "    layers.Conv2D(128, (3,3), activation='relu', padding='same'),\n",
        "    layers.BatchNormalization(),\n",
        "    layers.MaxPooling2D((2,2)),\n",
        "    layers.Dropout(0.4),\n",
        "\n",
        "    layers.Flatten(),\n",
        "    layers.Dense(256, activation='relu'),\n",
        "    layers.BatchNormalization(),\n",
        "    layers.Dropout(0.5),\n",
        "    layers.Dense(10, activation='softmax')\n",
        "])\n",
        "model_opt.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "fcsTpqbLwMAV",
        "outputId": "597d75da-e63c-459f-ea69-e45a4746b8d1"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.12/dist-packages/keras/src/layers/convolutional/base_conv.py:113: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
            "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"sequential_7\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_7\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
              "│ conv2d_24 (\u001b[38;5;33mConv2D\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │           \u001b[38;5;34m896\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ batch_normalization_7           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │           \u001b[38;5;34m128\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv2d_25 (\u001b[38;5;33mConv2D\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │         \u001b[38;5;34m9,248\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ batch_normalization_8           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │           \u001b[38;5;34m128\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ max_pooling2d_15 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dropout_13 (\u001b[38;5;33mDropout\u001b[0m)            │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv2d_26 (\u001b[38;5;33mConv2D\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │        \u001b[38;5;34m18,496\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ batch_normalization_9           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │           \u001b[38;5;34m256\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv2d_27 (\u001b[38;5;33mConv2D\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │        \u001b[38;5;34m36,928\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ batch_normalization_10          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │           \u001b[38;5;34m256\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ max_pooling2d_16 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m64\u001b[0m)       │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dropout_14 (\u001b[38;5;33mDropout\u001b[0m)            │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m64\u001b[0m)       │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv2d_28 (\u001b[38;5;33mConv2D\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m128\u001b[0m)      │        \u001b[38;5;34m73,856\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ batch_normalization_11          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m128\u001b[0m)      │           \u001b[38;5;34m512\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv2d_29 (\u001b[38;5;33mConv2D\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m128\u001b[0m)      │       \u001b[38;5;34m147,584\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ batch_normalization_12          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m128\u001b[0m)      │           \u001b[38;5;34m512\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ max_pooling2d_17 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m128\u001b[0m)      │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dropout_15 (\u001b[38;5;33mDropout\u001b[0m)            │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m128\u001b[0m)      │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ flatten_7 (\u001b[38;5;33mFlatten\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2048\u001b[0m)           │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_14 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m)            │       \u001b[38;5;34m524,544\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ batch_normalization_13          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m)            │         \u001b[38;5;34m1,024\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dropout_16 (\u001b[38;5;33mDropout\u001b[0m)            │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_15 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)             │         \u001b[38;5;34m2,570\u001b[0m │\n",
              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
              "│ conv2d_24 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │           <span style=\"color: #00af00; text-decoration-color: #00af00\">896</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ batch_normalization_7           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │           <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv2d_25 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │         <span style=\"color: #00af00; text-decoration-color: #00af00\">9,248</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ batch_normalization_8           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │           <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ max_pooling2d_15 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dropout_13 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)            │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv2d_26 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │        <span style=\"color: #00af00; text-decoration-color: #00af00\">18,496</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ batch_normalization_9           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │           <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv2d_27 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │        <span style=\"color: #00af00; text-decoration-color: #00af00\">36,928</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ batch_normalization_10          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │           <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ max_pooling2d_16 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)       │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dropout_14 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)            │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)       │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv2d_28 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)      │        <span style=\"color: #00af00; text-decoration-color: #00af00\">73,856</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ batch_normalization_11          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)      │           <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv2d_29 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)      │       <span style=\"color: #00af00; text-decoration-color: #00af00\">147,584</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ batch_normalization_12          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)      │           <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ max_pooling2d_17 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)      │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dropout_15 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)            │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)      │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ flatten_7 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2048</span>)           │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_14 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)            │       <span style=\"color: #00af00; text-decoration-color: #00af00\">524,544</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ batch_normalization_13          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)            │         <span style=\"color: #00af00; text-decoration-color: #00af00\">1,024</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dropout_16 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)            │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_15 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">2,570</span> │\n",
              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m816,938\u001b[0m (3.12 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">816,938</span> (3.12 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m815,530\u001b[0m (3.11 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">815,530</span> (3.11 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m1,408\u001b[0m (5.50 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">1,408</span> (5.50 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Compile model with tuned learning rate\n",
        "opt = tf.keras.optimizers.Adam(learning_rate=0.0008)\n",
        "model_opt.compile(optimizer=opt,\n",
        "                  loss='sparse_categorical_crossentropy',\n",
        "                  metrics=['accuracy'])\n",
        "\n",
        "# Early stopping to prevent overfitting\n",
        "es = EarlyStopping(monitor='val_accuracy', patience=5, restore_best_weights=True)\n",
        "\n",
        "# Train model\n",
        "history_opt = model_opt.fit(x_train, y_train, epochs=20, batch_size=64,\n",
        "                            validation_data=(x_test, y_test),\n",
        "                            callbacks=[es])\n",
        "\n",
        "# Evaluate\n",
        "test_loss_opt, test_acc_opt = model_opt.evaluate(x_test, y_test, verbose=2)\n",
        "print(f\"\\nOptimised Test Accuracy: {test_acc_opt*100:.2f}%\")\n",
        "\n",
        "# Plot results\n",
        "plt.figure(figsize=(12,4))\n",
        "plt.subplot(1,2,1)\n",
        "plt.plot(history_opt.history['accuracy'], label='Train Acc')\n",
        "plt.plot(history_opt.history['val_accuracy'], label='Val Acc')\n",
        "plt.title('Optimised Model Accuracy')\n",
        "plt.legend()\n",
        "\n",
        "plt.subplot(1,2,2)\n",
        "plt.plot(history_opt.history['loss'], label='Train Loss')\n",
        "plt.plot(history_opt.history['val_loss'], label='Val Loss')\n",
        "plt.title('Optimised Model Loss')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "exlyyJfxwacG",
        "outputId": "70ffb2da-493f-49da-cb57-861152ab767e"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m37s\u001b[0m 20ms/step - accuracy: 0.3493 - loss: 2.1099 - val_accuracy: 0.5986 - val_loss: 1.1354\n",
            "Epoch 2/20\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 10ms/step - accuracy: 0.6021 - loss: 1.1240 - val_accuracy: 0.6852 - val_loss: 0.8977\n",
            "Epoch 3/20\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 9ms/step - accuracy: 0.6762 - loss: 0.9229 - val_accuracy: 0.6679 - val_loss: 0.9417\n",
            "Epoch 4/20\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 10ms/step - accuracy: 0.7271 - loss: 0.7792 - val_accuracy: 0.7108 - val_loss: 0.8594\n",
            "Epoch 5/20\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 10ms/step - accuracy: 0.7536 - loss: 0.7068 - val_accuracy: 0.7689 - val_loss: 0.6645\n",
            "Epoch 6/20\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 9ms/step - accuracy: 0.7765 - loss: 0.6441 - val_accuracy: 0.7916 - val_loss: 0.6027\n",
            "Epoch 7/20\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 10ms/step - accuracy: 0.7956 - loss: 0.5927 - val_accuracy: 0.8009 - val_loss: 0.5837\n",
            "Epoch 8/20\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 10ms/step - accuracy: 0.8030 - loss: 0.5621 - val_accuracy: 0.8124 - val_loss: 0.5526\n",
            "Epoch 9/20\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 10ms/step - accuracy: 0.8208 - loss: 0.5167 - val_accuracy: 0.8182 - val_loss: 0.5250\n",
            "Epoch 10/20\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 10ms/step - accuracy: 0.8287 - loss: 0.4841 - val_accuracy: 0.8084 - val_loss: 0.5630\n",
            "Epoch 11/20\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 9ms/step - accuracy: 0.8426 - loss: 0.4556 - val_accuracy: 0.8039 - val_loss: 0.5812\n",
            "Epoch 12/20\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 10ms/step - accuracy: 0.8504 - loss: 0.4272 - val_accuracy: 0.8358 - val_loss: 0.4967\n",
            "Epoch 13/20\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 10ms/step - accuracy: 0.8601 - loss: 0.4011 - val_accuracy: 0.8419 - val_loss: 0.4812\n",
            "Epoch 14/20\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 9ms/step - accuracy: 0.8607 - loss: 0.4026 - val_accuracy: 0.8308 - val_loss: 0.5056\n",
            "Epoch 15/20\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 10ms/step - accuracy: 0.8689 - loss: 0.3758 - val_accuracy: 0.8335 - val_loss: 0.4943\n",
            "Epoch 16/20\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 10ms/step - accuracy: 0.8692 - loss: 0.3671 - val_accuracy: 0.8418 - val_loss: 0.5033\n",
            "Epoch 17/20\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 9ms/step - accuracy: 0.8744 - loss: 0.3552 - val_accuracy: 0.8301 - val_loss: 0.5220\n",
            "Epoch 18/20\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 10ms/step - accuracy: 0.8826 - loss: 0.3353 - val_accuracy: 0.8124 - val_loss: 0.5897\n",
            "313/313 - 2s - 6ms/step - accuracy: 0.8419 - loss: 0.4812\n",
            "\n",
            "Optimised Test Accuracy: 84.19%\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model Evaluation and Metrics"
      ],
      "metadata": {
        "id": "Uh8-G0mmwuKM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Predict on test data\n",
        "y_pred = model_opt.predict(x_test)\n",
        "y_pred_classes = np.argmax(y_pred, axis=1)\n",
        "y_true = y_test.flatten()\n",
        "\n",
        "# Confusion Matrix\n",
        "cm = confusion_matrix(y_true, y_pred_classes)\n",
        "\n",
        "plt.figure(figsize=(10,8))\n",
        "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues',\n",
        "            xticklabels=class_names, yticklabels=class_names)\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('True')\n",
        "plt.title('Confusion Matrix for CIFAR-10')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 800
        },
        "id": "kiANm8_Wty-P",
        "outputId": "487fe4cf-280d-4b92-fb8e-5e530bb9ce0d"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 5ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Classification Report\n",
        "print(\"Classification Report:\\n\")\n",
        "print(classification_report(y_true, y_pred_classes, target_names=class_names))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tbKypCRwxEI9",
        "outputId": "c63e7b9e-1ee1-4ebc-a47f-6b7f10e95601"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classification Report:\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    airplane       0.81      0.88      0.84      1000\n",
            "  automobile       0.95      0.89      0.92      1000\n",
            "        bird       0.82      0.75      0.79      1000\n",
            "         cat       0.73      0.70      0.72      1000\n",
            "        deer       0.79      0.86      0.83      1000\n",
            "         dog       0.80      0.74      0.77      1000\n",
            "        frog       0.90      0.86      0.88      1000\n",
            "       horse       0.86      0.90      0.88      1000\n",
            "        ship       0.86      0.92      0.89      1000\n",
            "       truck       0.91      0.91      0.91      1000\n",
            "\n",
            "    accuracy                           0.84     10000\n",
            "   macro avg       0.84      0.84      0.84     10000\n",
            "weighted avg       0.84      0.84      0.84     10000\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model Performance and Save Model"
      ],
      "metadata": {
        "id": "oZQs9kepyJ9r"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_opt.save(\"cifar10_cnn_model.h5\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A290n0xTxymL",
        "outputId": "61d2b862-98b3-4297-8218-9ca6d297f099"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualise 10 predictions\n",
        "plt.figure(figsize=(12,4))\n",
        "for i in range(10):\n",
        "    idx = np.random.randint(0, len(x_test))\n",
        "    img = x_test[idx]\n",
        "    plt.subplot(2,5,i+1)\n",
        "    plt.imshow(img)\n",
        "    pred = class_names[y_pred_classes[idx]]\n",
        "    true = class_names[y_true[idx]]\n",
        "    color = 'green' if pred == true else 'red'\n",
        "    plt.title(f\"P:{pred}\\nT:{true}\", color=color)\n",
        "    plt.axis('off')\n",
        "plt.suptitle(\"Sample Predictions (Green = Correct, Red = Incorrect)\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        },
        "id": "bRs1RXG9yOsz",
        "outputId": "07350c5f-f5d6-44a1-a051-bd38a26ebfb1"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x400 with 10 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "4G0Sok9zyROS"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}