ANN_Basics.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "ANN_Basics.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyPLPtMFm7tbD6obWhXeg+1x",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/gouse95/ANN_Basics/blob/main/ANN_Basics.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uuhQtIEgZzQQ"
      },
      "source": [
        "import tensorflow as tf"
      ],
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "OcY2Ja6cahTx",
        "outputId": "94ccc307-2687-410b-e8cf-8707af659580"
      },
      "source": [
        "tf.__version__"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'2.6.0'"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "0ETL26icat0r",
        "outputId": "7b8092dc-b380-4e49-e91e-713b43f665da"
      },
      "source": [
        "tf.keras.__version__"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'2.6.0'"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FDXKBpAta5aK",
        "outputId": "1238e933-da2e-4966-c501-e96cde3cd9d0"
      },
      "source": [
        "tf.config.list_physical_devices(\"GPU\")"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cpzgnF_za7ue",
        "outputId": "7c69d708-54ec-4c89-d450-1dff4225165b"
      },
      "source": [
        "tf.config.list_physical_devices(\"CPU\")"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[PhysicalDevice(name='/physical_device:CPU:0', device_type='CPU')]"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lds1ALQra9sZ"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import seaborn as sns"
      ],
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CawpKUCEbALt"
      },
      "source": [
        "mnist = tf.keras.datasets.mnist"
      ],
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d9NsJYTUbCTb",
        "outputId": "05cd3abc-a4d8-41e7-c191-16398a6016c1"
      },
      "source": [
        "mnist.load_data()"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((array([[[0, 0, 0, ..., 0, 0, 0],\n",
              "          [0, 0, 0, ..., 0, 0, 0],\n",
              "          [0, 0, 0, ..., 0, 0, 0],\n",
              "          ...,\n",
              "          [0, 0, 0, ..., 0, 0, 0],\n",
              "          [0, 0, 0, ..., 0, 0, 0],\n",
              "          [0, 0, 0, ..., 0, 0, 0]],\n",
              "  \n",
              "         [[0, 0, 0, ..., 0, 0, 0],\n",
              "          [0, 0, 0, ..., 0, 0, 0],\n",
              "          [0, 0, 0, ..., 0, 0, 0],\n",
              "          ...,\n",
              "          [0, 0, 0, ..., 0, 0, 0],\n",
              "          [0, 0, 0, ..., 0, 0, 0],\n",
              "          [0, 0, 0, ..., 0, 0, 0]],\n",
              "  \n",
              "         [[0, 0, 0, ..., 0, 0, 0],\n",
              "          [0, 0, 0, ..., 0, 0, 0],\n",
              "          [0, 0, 0, ..., 0, 0, 0],\n",
              "          ...,\n",
              "          [0, 0, 0, ..., 0, 0, 0],\n",
              "          [0, 0, 0, ..., 0, 0, 0],\n",
              "          [0, 0, 0, ..., 0, 0, 0]],\n",
              "  \n",
              "         ...,\n",
              "  \n",
              "         [[0, 0, 0, ..., 0, 0, 0],\n",
              "          [0, 0, 0, ..., 0, 0, 0],\n",
              "          [0, 0, 0, ..., 0, 0, 0],\n",
              "          ...,\n",
              "          [0, 0, 0, ..., 0, 0, 0],\n",
              "          [0, 0, 0, ..., 0, 0, 0],\n",
              "          [0, 0, 0, ..., 0, 0, 0]],\n",
              "  \n",
              "         [[0, 0, 0, ..., 0, 0, 0],\n",
              "          [0, 0, 0, ..., 0, 0, 0],\n",
              "          [0, 0, 0, ..., 0, 0, 0],\n",
              "          ...,\n",
              "          [0, 0, 0, ..., 0, 0, 0],\n",
              "          [0, 0, 0, ..., 0, 0, 0],\n",
              "          [0, 0, 0, ..., 0, 0, 0]],\n",
              "  \n",
              "         [[0, 0, 0, ..., 0, 0, 0],\n",
              "          [0, 0, 0, ..., 0, 0, 0],\n",
              "          [0, 0, 0, ..., 0, 0, 0],\n",
              "          ...,\n",
              "          [0, 0, 0, ..., 0, 0, 0],\n",
              "          [0, 0, 0, ..., 0, 0, 0],\n",
              "          [0, 0, 0, ..., 0, 0, 0]]], dtype=uint8),\n",
              "  array([5, 0, 4, ..., 5, 6, 8], dtype=uint8)),\n",
              " (array([[[0, 0, 0, ..., 0, 0, 0],\n",
              "          [0, 0, 0, ..., 0, 0, 0],\n",
              "          [0, 0, 0, ..., 0, 0, 0],\n",
              "          ...,\n",
              "          [0, 0, 0, ..., 0, 0, 0],\n",
              "          [0, 0, 0, ..., 0, 0, 0],\n",
              "          [0, 0, 0, ..., 0, 0, 0]],\n",
              "  \n",
              "         [[0, 0, 0, ..., 0, 0, 0],\n",
              "          [0, 0, 0, ..., 0, 0, 0],\n",
              "          [0, 0, 0, ..., 0, 0, 0],\n",
              "          ...,\n",
              "          [0, 0, 0, ..., 0, 0, 0],\n",
              "          [0, 0, 0, ..., 0, 0, 0],\n",
              "          [0, 0, 0, ..., 0, 0, 0]],\n",
              "  \n",
              "         [[0, 0, 0, ..., 0, 0, 0],\n",
              "          [0, 0, 0, ..., 0, 0, 0],\n",
              "          [0, 0, 0, ..., 0, 0, 0],\n",
              "          ...,\n",
              "          [0, 0, 0, ..., 0, 0, 0],\n",
              "          [0, 0, 0, ..., 0, 0, 0],\n",
              "          [0, 0, 0, ..., 0, 0, 0]],\n",
              "  \n",
              "         ...,\n",
              "  \n",
              "         [[0, 0, 0, ..., 0, 0, 0],\n",
              "          [0, 0, 0, ..., 0, 0, 0],\n",
              "          [0, 0, 0, ..., 0, 0, 0],\n",
              "          ...,\n",
              "          [0, 0, 0, ..., 0, 0, 0],\n",
              "          [0, 0, 0, ..., 0, 0, 0],\n",
              "          [0, 0, 0, ..., 0, 0, 0]],\n",
              "  \n",
              "         [[0, 0, 0, ..., 0, 0, 0],\n",
              "          [0, 0, 0, ..., 0, 0, 0],\n",
              "          [0, 0, 0, ..., 0, 0, 0],\n",
              "          ...,\n",
              "          [0, 0, 0, ..., 0, 0, 0],\n",
              "          [0, 0, 0, ..., 0, 0, 0],\n",
              "          [0, 0, 0, ..., 0, 0, 0]],\n",
              "  \n",
              "         [[0, 0, 0, ..., 0, 0, 0],\n",
              "          [0, 0, 0, ..., 0, 0, 0],\n",
              "          [0, 0, 0, ..., 0, 0, 0],\n",
              "          ...,\n",
              "          [0, 0, 0, ..., 0, 0, 0],\n",
              "          [0, 0, 0, ..., 0, 0, 0],\n",
              "          [0, 0, 0, ..., 0, 0, 0]]], dtype=uint8),\n",
              "  array([7, 2, 1, ..., 4, 5, 6], dtype=uint8)))"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GKk_BTtPch8r"
      },
      "source": [
        "(X_train_full, y_train_full),(X_test, y_test)=mnist.load_data()"
      ],
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aKltvfowdIGQ",
        "outputId": "39d0daf8-9aac-4be9-f71d-07eb78f885c2"
      },
      "source": [
        "X_train_full.shape"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(60000, 28, 28)"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "id": "FC0Bi6podLan",
        "outputId": "75a3b43a-e1e3-494d-8832-6ffd7f5ee383"
      },
      "source": [
        "img = X_train_full[0]\n",
        "plt.imshow(img, cmap=\"binary\")\n",
        "plt.axis(\"off\")\n",
        "plt.show()"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 884
        },
        "id": "XZgmWZR3dS96",
        "outputId": "22c2a349-c658-46d3-c528-a28fed85f5ce"
      },
      "source": [
        "plt.figure(figsize=(15,15))\n",
        "sns.heatmap(img/255, annot=True,cmap=\"binary\")"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff1595907d0>"
            ]
          },
          "metadata": {},
          "execution_count": 59
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAyYAAANSCAYAAACQhNrlAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzdeVxU9f4/8NdnBnBFkW0AQdTEJcOV3HciVCT1uuS3bnZN81vdculq2lVxw6zMPe+9UanXrbKse1XcUQQ1F0TBPTVRQBgUFEX8ssx8fn8g8/M4w6I5zJx6PR+PeVxmzue8eDvNnZnP+bzPQUgpQUREREREZEsaWxdARERERETEiQkREREREdkcJyZERERERGRznJgQEREREZHNcWJCREREREQ2x4kJERERERHZHCcmRERERET0WIQQK4UQWUKI02VsF0KIZUKIS0KIZCFEu4oyOTEhIiIiIqLHtRpA33K29wMQ8OA2FsA/KwrkxISIiIiIiB6LlDIOQE45QwYCWCNLHAbgIoTwLi/T4WkWWAb+aXkiIiIiqgrC1gVUhhDC3r8f/y9KVjlKRUkpox4zoz6A1Ifupz14LKOsHapiYkJERERERCrxYBLyuBOR34ytXERERERE9LSlA/B76L7vg8fKxIkJERERERE9bZsBjHxwda5OAHKllGW2cQFs5SIiIiIiqlJCqOJUmHIJIb4B0AuAuxAiDcBMAI4AIKX8F4BtAPoDuAQgH8CoCjOltPq5N/Z+cg8RERER/T6o4hu/RqOx6+/HRqPRJs8jW7mIiIiIiMjm2MpFRERERFSFfg+tXNbAFRMiIiIiIrI5TkyIiIiIiMjm2MpFRERERFSF2MplmV2tmMTFxSE0NBQhISGIinr6f2zSmvlqrp35zGe+evPVXDvzmc989earuXayY1JKa98qpbi4WAYHB8tr167JgoICGR4eLi9evFjZ3W2ar+bamc985qs3X821M5/5zFdvvp3XXhXfbX/zTavVSnu+2ep5sZsVk+TkZPj7+8PPzw9OTk4ICwtDTEyMKvLVXDvzmc989earuXbmM5/56s1Xc+1k3yqcmAghmgshpgghlj24TRFCtHjahej1enh5eZnu63Q66PV6VeSruXbmM5/56s1Xc+3MZz7z1Zuv5trthRDCrm+2Uu7ERAgxBcC3KPkrmkcf3ASAb4QQU8vZb6wQIkEIkcC+QCIiIiIiqkhFV+UaDaCllLLo4QeFEIsAnAHwsaWdpJRRAEpnJLIyheh0OmRmZpru6/V66HS6yuxaKdbMV3PtzGc+89Wbr+bamc985qs3X821k32rqJXLCMDHwuPeD7Y9NYGBgUhJSUFqaioKCwsRHR2NPn36qCJfzbUzn/nMV2++mmtnPvOZr958NdduL2zdqmWvrVzlnhkPoC+ASwC2o2QFJArAjgeP9a3kGfaVFhsbK1988UUZHBws//GPfzzOrjbPV3PtzGc+89Wbr+bamc985qs3345rt/kVtypzc3R0lPZ8s9XzIqQsv9NKCKEB0AFA/QcPpQM4JqU0VHbu89izJSIiIiKix6eKv1zo5ORk19+PCwsLbfI8VjgxeQrs+oknIiIiot8NVUxMqlWrZtffjwsKCmzyPNrN3zEhIiIiIqI/Lk5MiIiIiIjI5iq6XDARERERET1FNr3ylR3jigkREREREdkcJyZERERERGRzbOUiIiIiIqpCbOWyjCsmRERERERkc5yYEBERERGRzXFiQkRERERENsdzTIiIiIiIqhDPMbGMKyZERERERGRznJgQEREREZHNsZWLiIiIiKgKsZXLMq6YEBERERGRzXFiQkRERERENsdWLiIiIiKiKsRWLsu4YkJERERERDbHiQkREREREdkcW7mIiIiIiKoQW7kss6sVk7i4OISGhiIkJARRUVGqyldz7cxnPvPVm6/m2pnPfOarN1/NtZMdk1Ja+1YpxcXFMjg4WF67dk0WFBTI8PBwefHixcrubtN8NdfOfOYzX735aq6d+cxnvnrz7bz2qvhu+5tvtWvXlvZ8s9XzYjcrJsnJyfD394efnx+cnJwQFhaGmJgYVeSruXbmM5/56s1Xc+3MZz7z1Zuv5trthRDCrm+2YjcTE71eDy8vL9N9nU4HvV6vinw118585jNfvflqrp35zGe+evPVXDvZtyeemAghRpWzbawQIkEIkcC+QCIiIiIiqshvuSrXbACrLG2QUkYBKJ2RyMqE6XQ6ZGZmmu7r9XrodLrfUF7V5au5duYzn/nqzVdz7cxnPvPVm6/m2u0Fr8plWbkrJkKI5DJupwA81VdIYGAgUlJSkJqaisLCQkRHR6NPnz6qyFdz7cxnPvPVm6/m2pnPfOarN1/NtZN9q2jFRAcgFMCtRx4XAA491UIcHBAREYExY8bAYDBgyJAhCAgIUEW+mmtnPvOZr958NdfOfOYzX735aq6d7JuQsuxOKyHE1wBWSSkPWNi2QUr5SiV+R6VauYiIiIiIfiNV9EjVrVvXrr8f5+bm2uR5LHdi8pTY9RNPRERERL8bqpiYuLi42PX349u3b9vkebSbywUTEREREdEfFycmRERERERkc7/lcsFERERERPSYeLlgy7hiQkRERERENseJCRERERER2RxbuYiIiIiIqhBbuSzjigkREREREdkcJyZERERERGRzbOUiIiIiIqpCbOWyjCsmRERERERkc5yYEBERERGRzbGVi4iIiIioCrGVyzKumBARERERkc1xYkJERERERDbHiQkREREREdkczzEhIiIiIqpCPMfEMq6YEBERERGRzXFiQkRERERENsdWLiIiIiKiKsRWLsvsasUkLi4OoaGhCAkJQVRUlKry1Vw785nPfPXmq7l25jOf+erNV3PtZMeklNa+VUpxcbEMDg6W165dkwUFBTI8PFxevHixsrvbNF/NtTOf+cxXb76aa2c+85mv3nw7r70qvtv+5puHh4e055utnhe7WTFJTk6Gv78//Pz84OTkhLCwMMTExKgiX821M5/5zFdvvpprZz7zma/efDXXbi+EEHZ9s5UKJyZCiOZCiGAhRO1HHu/7NAvR6/Xw8vIy3dfpdNDr9arIV3PtzGc+89Wbr+bamc985qs3X821k30rd2IihBgH4L8A3gNwWggx8KHNH5Wz31ghRIIQIoF9gUREREREVJGKrsr1JoD2Uso8IURDAD8IIRpKKZcCKHOdR0oZBaB0RiIrU4hOp0NmZqbpvl6vh06nq8yulWLNfDXXznzmM1+9+WqunfnMZ75689Vcu73gVbksq6iVSyOlzAMAKWUKgF4A+gkhFqGcicmTCAwMREpKClJTU1FYWIjo6Gj06dNHFflqrp35zGe+evPVXDvzmc989earuXaybxWtmOiFEG2klCcB4MHKyQAAKwEEPtVCHBwQERGBMWPGwGAwYMiQIQgICFBFvpprZz7zma/efDXXznzmM1+9+WquneybkLLsTishhC+AYillpoVtXaWUByvxOyrVykVERERE9BupokfK29vbrr8fZ2Rk2OR5LHfFREqZVs62ykxKiIiIiIiIKmQ3f8eEiIiIiIj+uDgxISIiIiIim6vo5HciIiIiInqKeLlgy7hiQkRERERENseJCRERERER2RxbuYiIiIiIqhBbuSzjigkREREREdkcJyZERERERGRzbOUiIiIiIqpCbOWyjCsmRERERERkc5yYEBERERGRzbGVi4iIiIioCrGVyzKumBARERERkc1xYkJERERERDbHVi4iIiIioirEVi7LuGJCREREREQ2x4kJERERERHZHFu5iIiIiIiqEFu5LOOKCRERERER2ZxdTUzi4uIQGhqKkJAQREVFqSpfzbUzn/nMV2++mmtnPvOZr958NddOdkxKae1bpRQXF8vg4GB57do1WVBQIMPDw+XFixcru7tN89VcO/OZz3z15qu5duYzn/nqzbfz2qviu+1vvjVo0EDa881Wz4vdrJgkJyfD398ffn5+cHJyQlhYGGJiYlSRr+bamc985qs3X821M5/5zFdvvpprtxdCCLu+2UqFExMhRAchxPMPfn5WCPG+EKL/0y5Er9fDy8vLdF+n00Gv16siX821M5/5zFdvvpprZz7zma/efDXXTvat3ImJEGImgGUA/imEmA/gcwC1AEwVQkwrZ7+xQogEIUQC+wKJiIiIiKgiFV0ueCiANgCqAcgE4CulvCOE+AzAEQDzLO0kpYwCUDojkZUpRKfTITMz03Rfr9dDp9NVZtdKsWa+mmtnPvOZr958NdfOfOYzX735aq7dXvBywZZV1MpVLKU0SCnzAVyWUt4BACnlfQDGp1lIYGAgUlJSkJqaisLCQkRHR6NPnz6qyFdz7cxnPvPVm6/m2pnPfOarN1/NtZN9q2jFpFAIUfPBxKR96YNCiLp4yhMTBwcHREREYMyYMTAYDBgyZAgCAgJUka/m2pnPfOarN1/NtTOf+cxXb76aayf7JqQsu9NKCFFNSllg4XF3AN5SylOV+B2VauUiIiIiIvqNVNEj1bhxY7v+fvzrr7/a5Hksd8XE0qTkweM3Ady0SkVERERERPSHYzd/x4SIiIiIiP64KjrHhIiIiIiIniJelcsyrpgQEREREZHNcWJCREREREQ2x1YuIiIiIqIqxFYuy7hiQkRERERENseJCRERERER2RwnJkREREREZHM8x4SIiIiIqArxHBPLuGJCREREREQ2x4kJERERERHZHFu5iIiIiIiqEFu5LOOKCRERERER2RwnJkREREREZHNs5SIiIiIiqkJs5bKMKyZERERERGRznJgQEREREZHNsZWLiIiIiKgKsZXLMq6YEBERERGRzdnVxCQuLg6hoaEICQlBVFSUqvLVXDvzmc989earuXbmM5/56s1Xc+1kx6SU1r5VSnFxsQwODpbXrl2TBQUFMjw8XF68eLGyu9s0X821M5/5zFdvvpprZz7zma/efDuvvSq+2/7mW/PmzaU932z1vNjNiklycjL8/f3h5+cHJycnhIWFISYmRhX5aq6d+cxnvnrz1Vw785nPfPXmq7l2sm+PPTERQqyxRiF6vR5eXl6m+zqdDnq9XhX5aq6d+cxnvnrz1Vw785nPfPXmq7l2sm/lXpVLCLH50YcA9BZCuACAlPKlMvYbC2AsAHzxxRcYO3bsUyiViIiIiEj9eFUuyyq6XLAvgLMAvgIgUTIxCQKwsLydpJRRAErPVJKVKUSn0yEzM9N0X6/XQ6fTVWbXSrFmvpprZz7zma/efDXXznzmM1+9+WqunexbRa1cQQCOA5gGIFdKGQvgvpRyv5Ry/9MsJDAwECkpKUhNTUVhYSGio6PRp08fVeSruXbmM5/56s1Xc+3MZz7z1Zuv5trJvpW7YiKlNAJYLIT4/sH/6iva54kLcXBAREQExowZA4PBgCFDhiAgIEAV+WqunfnMZ75689VcO/OZz3z15qu5drJvQspKdVqVDBYiDEBXKeXfH+N3VP4XEBERERE9OVWcvNGyZUu7/n585swZmzyPj7X6IaWMBhBtpVqIiIiIiOgPym7+jgkREREREf1xWeV8ESIiIiIisoyXC7aMKyZERERERGRznJgQEREREZHNsZWLiIiIiKgKsZXLMq6YEBERERGRzXFiQkRERERENsdWLiIiIiKiKsRWLsu4YkJERERERDbHFRMi+l0yGAxWzc/NzbVqvpp9/vnnVs3Pz8+3av6FCxesmr9ixQqr5k+aNMmq+d98841V86tXr27V/KlTp1o1f+bMmVbNJ/o948SEiIiIiKgKsZXLMrZyERERERGRzXFiQkRERERENseJCRERERER2RzPMSEiIiIiqkI8x8QyrpgQEREREZHNcWJCREREREQ2x1YuIiIiIqIqxFYuy7hiQkRERERENmdXKyZxcXGYN28ejEYjhg0bhrFjx6omX821M5/5v8f8+Ph4zJ8/HwaDAUOHDsWbb76p2F5YWIipU6fizJkzcHFxwaJFi1C/fn3cvn0bEyZMwKlTpzB48GBMnz4dAHD//n1MnDgRqamp+L//+z/cu3cPzs7OeOmllzBy5Eiz7Dlz5uD8+fOoW7cuIiMj4e3tjZ07d2L9+vWmcZcuXcLq1avRtGlT/Otf/8L27dtx9+5dzJs3D0uWLIHBYFBl/p/+9CfTmGeeeQahoaHQaDQ4ceIEDh48aPG/V/PmzTF8+HB8+eWXyMjIgEajwYABA+Dt7Q0pJXbu3ImrV6+a7de0aVOEh4dDCIFjx45h//79iu3t27dHv379cOfOHQDAzz//jGPHjsHFxQWvvfYahBDQarU4dOgQjhw5Ypbftm1bjB49GhqNBnv27MGPP/5oNqZLly4YMWIEpJRISUnB4sWLAQCvvfYagoKCAAAbN24s899e6ujRo/j8889hMBgQFhaGV155RbE9KSkJK1aswOXLlxEREYGePXuWmwcArVq1wsiRI6HRaLBv3z5s2bLFbEzHjh0xZMgQAMDVq1dNf5l+3bp1uHbtGgAgOzsbCxcurPD3Pezrr7/GgAEDkJWVhcDAwMfat1RISAg+++wzaLVarF69Gp999plie4MGDfCvf/0L7u7uuHXrFt544w2kp6ejR48e+PTTT03jmjVrhpEjR1r895d65pln0LdvX2g0GiQmJpr992rdujVCQkJw9+5dACX/vU6cOPFE/65S9vjeaQ/ZVZFP9sluJiYGgwFz5szBqlWroNPpMHToUPTp0wdNmjSx+3w118585v8e8w0GAyIjI/HVV19Bp9Ph5ZdfRu/evRX7bNq0CXXq1MHOnTuxbds2LFy4EIsWLYKTkxPee+89XLx4EZcuXVLkjho1CkFBQejXrx98fHzwxhtvICoqCt27d0ejRo1M47Zs2QJnZ2f88MMP2L17N1asWIHIyEiEhoYiNDQUQMmX+qlTp6Jp06YAgG7dumHo0KEYNmwYFi5ciKVLl8LT0xNvvPGGqvKHDx9uyhFCoF+/fli3bh3u3LmDMWPG4MKFC7h586bieXVyckLHjh2RlpZmeqxdu3YAgC+++AI1a9bEK6+8gq+++kqxnxACAwcOxNdff43c3Fy8++67OHfuHLKyshTjkpOTsXnzZsVjd+/exT/+8Q8YDAY4OTlh4sSJOHv2rOlLJwBoNBqMHTsWs2bNQnZ2Nj799FMcPXpUUae3tzeGDBmCDz/8EPfu3UPdunUBlEyIGjdujIkTJ8LR0RFz585FYmIi7t+/D0sMBgOWLl2KBQsWwMPDA2+99Ra6dOmChg0bmsbodDpMmTIF3333ncWMRwkhMGrUKMyfPx/Z2dmIjIxEYmIi0tPTTWO8vLwwcOBAzJ49G/fu3UOdOnVM2woLC/H3v/+9Ur/LktWrV+Pzzz/HmjVrnmh/jUaDJUuWICwsDOnp6Thw4AC2bt2K8+fPm8bMnz8f69evx/r169GzZ0/MmTMHo0ePRlxcHDp16gQAqFevHk6fPo09e/aU+buEEOjfvz/Wrl2LO3fu4M0337T4Wj1z5gy2b9/+RP+eR9nje6c9ZFdFvj1gK5dldtPKlZycDH9/f/j5+cHJyQlhYWGIiYlRRb6aa2c+83+P+cnJyWjQoIFpn379+mHv3r2KMXv37sWgQYMAAC+++CIOHz4MKSVq1qyJ9u3bo1q1aorxNWrUQMeOHXHq1Cn4+/ujVatWyMnJwQsvvIC4uDjF2Pj4ePTv3x8A0Lt3byQkJEBKqRize/duvPDCC6b7zz33HNzd3WE0GuHr64v69evD0dFRdfkPq1+/Pm7duoXbt2/DaDTizJkzaNasGR7Vq1cvHDp0CMXFxabHPDw8cOXKFQBAfn4+CgoK4OPjo9jPz88P2dnZyMnJgcFgQFJSEp599lmzfEsMBgMMBgMAwMHBweKXhICAAGRkZECv16O4uBgHDhxAhw4dFGNCQkKwfft23Lt3DwCQm5trqu3s2bMwGo0oKCjA1atX0bZt2zLrOX/+PHx8fODj4wNHR0f06dPH7Ii9l5cXnnnmGWg0lfvobtKkCfR6PbKysmAwGPDzzz+jffv2ijG9e/fGrl27TPWXriw9DfHx8cjJyXni/Z9//nlcvnwZKSkpKCoqwvfff48BAwYoxjRv3ty0SrZ//36z7QAwePBg7Nq1q8xJIVDyWs3JyVG8Vps3b/7EtVeGPb532kN2VeST/bKbiYler4eXl5fpvk6ng16vV0W+mmtnPvN/j/mP7uPl5WV2FP3hMQ4ODnB2dsbt27crVY+rqysOHDiAoKAgeHp64saNG4oxN27cgE6nM2XXrl3b9IW1VExMDEJCQiz+Dk9PT8XPassv5ezsrNjvzp07cHZ2Vozx8vJC3bp1cfHiRcXjer0ezZo1gxACLi4u8Pb2VhzNB4A6deoo8nNzc83GACWTpvHjx+PVV181rWgAQN26dTF+/HhMnToVsbGxitUSAHB1dVUcMc/Ozoabm5tiTOlk4qOPPsLHH39smnxcuXIFbdu2hZOTE5ydnS1O3B528+ZNxX8XDw8Ps6P1j6tevXrIzs423c/JyYGrq6tijLe3N7y9vTFz5kzMnj0brVq1Mm1zdHREZGQkZs+ebWpJq0o+Pj6K1an09HTUr19fMebUqVMYOHAgAGDgwIGoU6eO2b9x2LBh2LhxY7m/y9nZWTEps/RaBYAWLVrgrbfewrBhwyy+1h6HPb532kN2VeST/XqsVi4hRDcAHQCcllLusk5JRET2y2Aw4OjRoxgxYgTq16+P5OTkx844c+YMqlWrhmeeecYKFaor/8UXX8R///tfs8dPnDgBd3d3vPnmm8jNzUVqaqrZqk1lnDt3DidPnoTBYECHDh1M57EAJROZpUuXwtnZGSNHjsTp06eRl5f3WPlarRbe3t6YMWMG3NzcMG/ePIwfPx5JSUkICAjAxx9/jNzcXFy4cAFGo/Gx67c2jUYDLy8vREZGwtXVFREREZgyZQry8/Mxbtw43Lp1C56enpg2bRquXbtmNsG3tQ8//BCLFy/Gn//8Zxw8eBDp6emmlTCgZOLbsmVL7N69+zf/rl9++QWnT5+GwWBA+/btMWjQoCduUyP6PbRyCSH6AlgKQAvgKynlx49sbwDg3wBcHoyZKqXcVl5muSsmQoijD/38JoDPATgDmCmEmFrOfmOFEAlCiISoqKjy/1UP6HQ6ZGZmmu7r9XrTEbunwZr5aq6d+cz/PeY/uk9mZqbiaPSjY4qLi3H37l24uLhUWM/mzZuh0WgwYsQIAEBWVhY8PDwUYzw8PExH94qLi5GXl6c4Ur979+5yVxse/vKnxvxSd+/eVexXp04dxapEtWrV4Onpiddffx3jxo2Dr68vRowYYTrhfdeuXYiKisJ3332H6tWrK47+AyVHtR9dAXm0FSk/P9/0RfXYsWNmR9xL69Tr9YrzOYCSFYaHVznc3NzMasjOzsaxY8dgMBiQlZWF69evm1rOfvjhB7z//vuYPXs2hBC4fv16mc+Vu7u74r/LjRs3yl1hqYxbt24pVnhcXV3NWqtycnKQmJgIg8GAGzduICMjw3Sk+tatWwBKXiNnz541e36s7fr16/D19TXdr1+/vuL8GADIyMjAiBEj0LlzZ8ycORMAFKtoQ4YMwebNmxVtgpbcvXtXsQLy6GsVKLkARulrKTExEd7e3k/2D3vAHt877SG7KvLptxNCaAGsANAPwLMA/kcI8Wgv7XQAG6WUbQGMAPCPinIrauVyfOjnsQBCpJSzAbwI4NWydpJSRkkpg6SUQZW9ikJgYCBSUlKQmpqKwsJCREdHo0+fPpXa19b5aq6d+cz/PeYHBgbi6tWrSEtLQ2FhIbZv347evXsrxvTu3Rv/+c9/AAC7du1Cx44dKzyCtXTpUjg6OkKr1eL69esoKirCnj170L17d8W4bt26Ydu2koNC+/btQ/v27U3ZRqOx3DYojUaD1NRU1eY/LD09Ha6urnBxcYFGo0HLli3xyy+/mLYXFBTgs88+w7Jly7Bs2TKkpaXh22+/RUZGBhwcHODoWPIR1LhxYxiNRrPWprS0NLi5uaFevXrQarVo3bo1zp49qxjzcDvOs88+a/ryX6dOHTg4lDQN1KhRA/7+/mYtbRcvXoS3tzc8PT3h4OCAbt264dixY4oxR44cwXPPPWf6XT4+PtDr9dBoNKbf7e/vj4YNG+LkyZNlPlfNmzdHeno6MjIyUFRUhL1796JLly4VPsfluXz5Mry8vODh4QGtVovOnTvj+PHjijEJCQlo0aKFqX5vb29kZWWhVq1apufH2dkZzZo1M5sUWFtCQgKaNGkCf39/ODo6YtiwYYiOjlaMcXNzM702J0+ejH//+9+K7cOHD6+wjQsoea26ubkpXqsXLlxQjKldu7bp52bNmv3mVjt7fO+0h+yqyKenogOAS1LKX6WUhQC+BTDwkTESQOmMvy6Aso/OPFBRK5dGCFEPJRMYIaW8AQBSyntCiPIPPzwmBwcHREREYMyYMTAYDBgyZAgCAgJUka/m2pnP/N9jvoODA6ZNm4Y333wTRqMRgwcPRkBAAJYvX46WLVuiT58+GDJkCKZMmYLQ0FC4uLgoLkP6wgsvIC8vD0VFRYiJicGXX36J2rVr44svvkDjxo0hpcSIESNQq1YtvPzyy2jcuDGioqLQokULdO/eHeHh4Zg9ezaGDh2KOnXqYO7cuabskydPQqfTmR25//zzz7Fr1y4UFBQgLy8Po0aNgrOzMwYMGKCq/P/7v//Djz/+iCZNmqBVq1bYvn07Xn31VQghcPLkSdy4cQO9evXC9evXFZOUR9WqVQuvvvoqpJS4e/euaRL5MKPRiM2bN+ONN96ARqNBQkICsrKyEBISgrS0NJw7dw5dunTBs88+C6PRiPz8fHz//fcASs6tCQsLM2XFx8eb9bAbjUZ8+eWXmDlzJjQaDWJiYpCamor/+Z//waVLl3Ds2DGcOHECbdq0wbJly2A0GvHvf/8bd+/ehaOjI+bNmwegZNVm8eLF5bZyabVajBs3Dh988AGMRiP69euHRo0aYeXKlWjWrBm6du2K8+fPY8aMGcjLy8PPP/+MVatWYfXq1WVmGo1GrF69GlOnToVGo0FsbCzS09MxdOhQ/Prrr0hMTK1YZV0AACAASURBVERycjJatWqFTz/9FEajERs2bEBeXh4CAgIwevRoSCkhhMDmzZsfe2KyYcMG9OrVC+7u7khNTcXMmTOxcuXKSu9vMBgwceJEbNmyBVqtFv/+979x7tw5zJgxA4mJiYiOjkaPHj0wZ84cSClx4MABTJgwwbR/gwYN4Ovri/j4+Ap/l5QS27Ztw5///OcyX6sdO3ZE06ZNYTQacf/+fYuvycdhj++d9pBdFfn2wN5buYQQY1GyKFEqSkr5cBtUfQCpD91PA9DxkZhZAHYJId4DUAvAC6iAKK9nVwiRAsAIQKBk1tNVSpkhhKgN4ICUsk1Fv+DBfkREVerhPnNrePRkcPr/Pv/8c6vm5+fnWzX/0SPlT1vp3wmxlkmTJlk1/5tvvrFqfvXq1a2aP3VqmZ3oT0VpSxnZjH1/438gKCjIrr8fJyQklPs8CiGGAugrpRzz4P5rADpKKd99aMz7KJlrLBRCdAbwNYDnpJRlHqUpd8VEStmwjE1GAIPL25eIiIiIiH6X0gH4PXTf98FjDxsNoC8ASCl/FkJUB+AOoMyraDzR5YKllPlSyitPsi8REREREanaMQABQohGQggnlJzcvvmRMdcABAOAEKIFgOoAbqAcdvOX34mIiIiI/gjs/RyTikgpi4UQ7wLYiZJLAa+UUp4RQswBkCCl3AzgbwC+FEJMRMmpHX+RFVz3nRMTIiIiIiJ6LA/+Jsm2Rx6LeOjnswC6Pk6m3fzldyIiIiIi+uPiigkRERERURVSeyuXtXDFhIiIiIiIbI4TEyIiIiIisjm2chERERERVSG2clnGFRMiIiIiIrI5rpgQWcm1a9esml9YWGjV/EOHDlk1/8CBA1bNv337tlXzN23aZNV8sh1fX1+r5o8bN86q+T/99JNV852dna2a37p1a6vm9+zZ06r5RPTkODEhIiIiIqpCbOWyjK1cRERERERkc5yYEBERERGRzbGVi4iIiIioCrGVyzKumBARERERkc1xYkJERERERDbHVi4iIiIioirEVi7LuGJCREREREQ2x4kJERERERHZnF1NTOLi4hAaGoqQkBBERUWpKl/NtTPf+vnHjh3DqFGj8Prrr+Pbb781256cnIy3334boaGhiIuLMz1+6dIljBs3DmPGjMHYsWMRGxtrMT8hIQFjx47FmDFjsHHjRrPtp0+fxrhx4xAeHm7xL67n5+dj5MiR+Oc//2kx38fHBwMHDsSgQYPw3HPPmW1v2rQpwsPDMWDAAPTt2xd169YFADRq1AgDBgww3V577TXUq1fP4u8o1bJlS8yZMweRkZHo27evxTHt27fHrFmzMGvWLIwePbrcPKDkL0kvWbIEy5Ytw8CBAy2O6dy5MxYtWoSFCxea/jJ3y5Yt8emnn5pu69atw/PPP2+2b2hoKM6ePYsLFy7ggw8+MNveoEED7Nq1CydOnEBMTAzq169v2jZ//nwkJSUhKSkJw4cPr9Js5lec37NnT+zduxf79+/H22+/bbbdx8cH3377LbZt24YdO3agd+/eAAAHBwcsXLgQO3fuRExMDN555x2L+W3atMHSpUuxfPlyDBo0yOKYzp07Y/HixVi8eDHGjx8PoOS1uWDBAtNtw4YNVf7aBIAXXngBx48fx8mTJzFx4kSz7X5+fti8eTMOHTqE6Oho+Pj4mLbdunULBw4cwIEDByy+LwJAhw4dsH79enzzzTd49dVXzba/9957WLlyJVauXIkNGzZg27Ztpm2enp5YuHAh1q5di7Vr18LLy8ts/9OnTyMiIgLTp0/Hjh07zLbv3r0bs2bNwpw5c7Bo0SJkZ2ebtr311luYO3cu5s6dixUrVlisvyL2/tllq+yqyCf7ZDfnmBgMBsyZMwerVq2CTqfD0KFD0adPHzRp0sTu89VcO/Otn28wGLB8+XJ88skncHd3x7vvvovOnTvD39/fNMbT0xOTJ0/G999/r9i3evXq+OCDD+Dr64ubN2/ir3/9K4KCglC7dm1F/j//+U9ERkbC3d0dEydORKdOndCgQQPTGA8PD0ycOBE//vijxRrXrl1rccIBlPTBduzYEbt370Z+fj769++P1NRU5ObmmsZcuXIFv/zyCwDA19cXQUFBiImJwZUrV3DlyhUAgIuLC3r37o1bt26V+VwJIfDKK69g8eLFuHXrFv7+978jKSkJGRkZiueqX79++PTTT5Gfnw9nZ+cy80ozR48ejcjISGRnZ2P+/PlISEhAenq6aYyXlxcGDRqEGTNm4N69e6hTpw4A4MyZM6Yvc7Vq1cLy5cuRlJSkyNdoNFi+fDlCQ0ORlpaGI0eOYMuWLTh37pxpzIIFC7Bu3TqsWbMGvXv3xkcffYTXX38d/fv3R7t27dCuXTtUq1YNe/fuxfbt23H37l2rZzO/cvlz587Fq6++iszMTGzevBl79uzBxYsXTWPee+89bN26FevWrUNAQABWrVqFbt26ISwsDE5OTggNDUX16tWxZ88ebN68GWlpaYr8MWPGYM6cOcjJycHHH3+MhIQExRgvLy/86U9/wvTp081em5MnTwYA1K5du8pfm6X5CxcuxMCBA5Geno7Y2Fhs27YNFy5cMI2JjIzEt99+iw0bNqBHjx6YNWsWxo4dCwC4f/8+unXrhrJoNBq8//77mDhxIm7cuIEvv/wSBw8eREpKimnM8uXLTT8PGTIEAQEBpvvTp0/HmjVrkJCQgBo1asBoNCryjUYjvvnmG0yYMAH16tXD/Pnz0apVK8XkqUGDBujZsyecnJywf/9+bNq0yVS/k5MTZsyYUWb9FbH3zy5bZVdFvj3gOSaW2c2KSXJyMvz9/eHn5wcnJyeEhYUhJiZGFflqrp351s+/cOECfHx84O3tDUdHR/Tq1QuHDh1SjPHy8kLjxo3N3qh8fX3h6+sLAHB3d4eLiwtu376tGPPLL78o8nv06IHDhw8rxuh0OjRq1MjiG+HFixdx+/ZttG3b1mL9bm5uuHv3LvLy8mA0GpGSkgI/Pz/FmKKiItPPDg6Wj3c0atTINEkpS6NGjZCVlYWbN2/CYDDg2LFjaN26tWJM9+7dERsbi/z8fABQfFGypEmTJsjMzERWVhYMBgMOHTpkdmQ5ODgYO3fuxL179wAAd+7cMcvp1KkTTpw4gcLCQsXjHTp0wOXLl3HlyhUUFRXhu+++w0svvaQY06JFC+zduxcAsG/fPtP2Fi1aIC4uDgaDAfn5+Th16pRilcia2cyvOL9NmzZISUlBamoqioqKsGXLFoSEhCjGSClNBwqcnZ2RlZVlerxmzZrQarWoXr06ioqKzF6rD782i4uLcfDgQbPX5gsvvIAdO3ZU+No8efJklb42ASAoKAi//vorUlJSUFRUhE2bNiEsLEwxpnnz5ti/fz+AkiPg/fv3N6u/LC1atEB6ejoyMjJQXFyMmJiYcicywcHB2LNnDwCgYcOG0Gq1SEhIAFAyCSooKFCMv3LlCjw9PeHh4QEHBwcEBQWZTe6aNWsGJycnACXvT4++//4W9v7ZZavsqsgn+2U3ExO9Xq9YZtXpdNDr9arIV3PtzLd+/s2bN+Hh4WG67+7ujps3bz52HefPn0dRUZHiaB4AZGdnw93dXZH/cLtBeYxGI77++uty26Fq1qxp+lIElLR91axZ02xcs2bNMHjwYLRv3x5Hjx41296wYUPFkU5LXFxckJOTY7p/+/Zts9YvnU4HnU6HDz74AFOnTkXLli3LzXR1dVU8H9nZ2XB1dVWMKZ3YlbaQPToZAoCuXbvi4MGDZo/Xr18fqamppvvp6emKdhig5EN28ODBAIDBgwejTp06cHV1RXJyMkJDQ1GjRg24ubmhV69epomotbOZX3G+l5eXYrUuIyPDrB1oyZIlGDx4MA4fPozVq1cjIiICALBt2zbk5+fj2LFj+PnnnxEVFaVYZQRKXpsPvxeU99qMjIzERx99hDZt2uBRXbt2tdiiae3nx9vbW7G6c/36dbP3p9OnT5smO+Hh4aZ8oGRFODY2FjExMWYTGqBkpbd0ogcAN27cULzXPUyn08HHxweJiYkASlrI8vLyEBkZia+//hrvvPMONBrlV55H31/q1atX7sTj4MGDiveboqIizJs3Dx9//DFOnjxZ5n5lsffPLltlV0U+2a9yJyZCiI5CiDoPfq4hhJgthNgihPhECFG3akokouzsbHzyySeYNGmS2YfrbxEdHY2goKAyP+wfx4ULF/DTTz8hMTERrVq1Umxzd3dHcXHxUznaqNFoTL3jX375JV577TXUqFHjN2d6e3tj9uzZWLp0Kf73f/9XMflycXFBgwYNzI6mVtbkyZPRs2dPJCQkoEePHkhLS4PBYMDu3buxfft2HDhwABs2bMDhw4dhMBjsJpv5FXvppZfwww8/oFOnTvjLX/6CJUuWQAiBNm3awGg0okOHDujWrRvefPNNs5XGytBqtfD29sbMmTOxZMkSvPXWWxZfm0/yxRiw/vMzbdo0dO3aFfHx8ejWrRvS09NNOS1btkSvXr0wevRofPzxx2jUqNET/RuAktWS2NhYU7uWVqtFq1atsGLFCowdOxbe3t7o16/fE+cfPnwYV69exYsvvmh67KOPPsK0adMwevRobNy4ETdu3HjifPrjEULY9c1WKvqGsxJA/oOflwKoC+CTB4+tKmsnIcRYIUSCECKhsics6XQ6ZGZmmu7r9XrodLpK7WvrfDXXznzr57u7uys+sG7evPlYE4F79+5h+vTpGDVqFJ599lmz7W5uboqjrjdv3oSbm1ulss+fP4+tW7di1KhRWLlyJWJiYrBqlfL/2vn5+ahVq5bpfs2aNU1tVJZcuXLF7AtYw4YNK2zjAkqOYD58xNjFxcXsnJRbt24hKSkJBoMB2dnZ0Ov18PT0LDMzJydH8Xy4ubkpVmVKxyQkJMBgMODGjRvIyMiAt7e3aXvnzp1x9OhRi1/M0tPTFf/e+vXrK85fAUqOtA8dOhRBQUGYPn06AJiOns+fPx/t27dHaGgohBCK8xesmc38ivMzMzMVrwNvb2/FewEAvPzyy9i6dSsAIDExEdWqVYOrqysGDhyI2NhYFBcXIzs7G8ePHzebsOfk5CjeCyy9NrOzs02vzaysLLPXZpcuXWzy2izd9+FVFB8fH1y/ft3sOfzzn/+M7t27Y86cOYr80tWolJQUHDhwwOz5uXHjhuL/2x4eHmWuNj/cxgUAWVlZuHTpEjIyMmAwGHDgwAE0bdpUsc+j7y+3bt2Ci4uLWfa5c+ewfft2vPPOO3B0dDQ9Xrra4uHhgaZNm+LatWsWayuLvX922Sq7KvLJflU0MdFIKYsf/BwkpZwgpTwgpZwNoHFZO0kpo6SUQVLKoNKTxCoSGBho6uUtLCxEdHQ0+vTpU7l/hY3z1Vw7862f36xZM1OfdFFREWJjY9G5c+dK7VtUVIRZs2YhJCQEPXr0sDimadOmSE9PR2ZmJoqKihAXF4eOHTtWKn/y5MlYvXo1Vq1ahTfeeAPBwcEYNWqUYkx2djacnZ1Ru3ZtaDQaNGzYUNEeAkBxArqvr69ZH3xl2riAki8onp6ecHNzg1arxfPPP2+2SnHy5EnTF4zatWtDp9OV2xp3+fJleHt7w8PDA1qtFl26dDH1nZc6evSoqUXD2dkZ3t7eiraBstq4gJIrrjVp0gQNGzaEo6MjXn75ZWzZskUxxs3NzXQEaurUqabJn0ajMU3EAgMDERgYiF27dlVJNvMrzk9KSkKjRo3g5+cHR0dHhIeHY/fu3Yox169fR9euXQGUnDNSrVo1ZGdnIz09HV26dAEA1KhRA23btsXly5cV+166dAne3t7w9PSEg4MDunbtimPHjinGVPTa7Natm8U2rqp4fo4fP47GjRvD398fjo6OGDJkiOKqWEBJu1pp/vvvv49169YBKJkUlJ674erqik6dOuH8+fOKfc+fPw9fX194e3vDwcEBwcHBFv+tDRo0gLOzM06fPq3Yt3bt2qaJRrt27czegxo2bGg6p624uBgJCQlmbZzXrl3DunXr8M4775guPACUHDAqPbcuLy/P9D7zOOz9s8tW2VWRT/aroqtynRZCjJJSrgKQJIQIklImCCGaAiiqYN/HK8TBARERERgzZgwMBoPZ1TXsOV/NtTPf+vlarRbvvvsuPvzwQxiNRoSGhqJhw4ZYvXo1mjZtii5duuDChQuYNWsW8vLycPjwYaxZswZfffUV9u/fj1OnTuHOnTvYuXMngJLJxMNXJtFqtXj77bcxY8YMGI1GhISEwN/fH2vXrkVAQAA6deqEX375BZGRkcjLy8PRo0exfv36Mi8N/CgpJY4ePYoXXngBQghcunQJubm5aN26NbKzs5GWlobmzZvD29sbRqMRhYWFii/xOp0O9+7dQ15eXoW/6+Gr5Gg0Ghw8eBAZGRl46aWXcPXqVSQlJeHMmTN49tlnMWvWLEgpsWnTJsU5MJYyV65ciWnTpkGj0WDfvn1IS0vD8OHDcfnyZRw/fhxJSUlo3bo1Fi1aBKPRiHXr1pnq9fDwgLu7O86ePWsx32AwYNy4cdi+fTu0Wi1WrVqFs2fPYtasWTh+/Di2bNmCXr16Yd68eZBSIj4+Hu+++y4AwNHR0XRi8J07dzBy5EjFkW9rZjO/cvkRERFYs2YNtFotNm7ciIsXL+L9999HcnIy9uzZg8jISHz88ccYPXo0pJT429/+BgBYs2YNPvvsM+zevRtCCHz//fdmX7yNRiO++uorTJ8+HRqNBnv37kVaWhpefvllXL58GQkJCTh58iRat26NxYsXw2g0Yu3atYrXppubm01em6X5kydPxk8//QStVou1a9fi/PnzmDZtGhITE7F9+3Z0797d9P/VgwcPmp6fpk2bYunSpTAajdBoNFi0aJHial6l+YsXL8bChQuh0WgQHR2NlJQUjB49GufPnze9zwQHB5udGG00GrFixQosWbIEQMlFQh6dlGm1WowYMcJUR9euXeHj44PNmzfD398frVu3xqZNm1BQUGC6XK2rqyv++te/IjMzE+vWrYNGozG9rz96fk1F7P2zy1bZVZFvD3hVLsuElLLsjSXnkSwF0B3ATQDtAKQ+uI2TUlam4brsX0D0O/a4y/qP69Er8Dxtj1457Gkr6yjv0/I0r55jyaZNm6yaT7bz6EneT5ulvzfyNP30009WzX+4tdMaLF184mmaO3euVfN79epl1XyqkCq+8Xfv3t2uvx/Hx8fb5Hksd8VESpkL4C8PToBv9GB8mpSSl0YgIiIiIqKnplJ/YFFKeQfAk12OhoiIiIiITNjKZZnd/B0TIiIiIiL64+LEhIiIiIiIbK5SrVxERERERPR0sJXLMq6YEBERERGRzXFiQkRERERENseJCRERERER2RzPMSEiIiIiqkI8x8QyrpgQEREREZHNccWE/rBOnDhh1fzg4GCr5ufm5lo1n8heaTTWPaYWGRlp1fxatWpZNf+VV16xar6Pj49V8+vVq2fV/GbNmlk1n4ieHCcmRERERERViK1clrGVi4iIiIiIbI4TEyIiIiIisjm2chERERERVSG2clnGFRMiIiIiIrI5TkyIiIiIiMjm2MpFRERERFSF2MplGVdMiIiIiIjI5jgxISIiIiIim2MrFxERERFRFWIrl2V2tWISFxeH0NBQhISEICoqSlX5aq6d+cDJkycxceJEjB8/Hv/973/Ntp87dw5Tp07FK6+8gsOHDyu2rV+/HpMmTcKkSZNw6NAhi/nBwcE4cuQIEhISMH78eLPtvr6++OmnnxAfH4/NmzfDx8fH9Pi+ffuwf/9+HDp0CH/5y18s5oeGhuLs2bO4cOECPvjgA7PtDRo0wK5du3DixAnExMSgfv36pm3z589HUlISkpKSMHz4cObbWb6aa6+K/BdffBGnT5/GuXPnMHnyZIv5O3fuRGJiIvbs2WOWf/LkSSQnJ2Px4sUW8318fPDSSy9h4MCBaNmypdn2gIAADBgwAGFhYQgNDUXdunVN21xcXNC3b1+Eh4djwIAB0GjMP3IvXLiAhQsXYsGCBYiNjTXbfuTIESxZsgTLli3Dv/71L+j1egDAxYsXsXz5cixZsgTLly/H5cuXLdZv7fzk5GRMmTIFkydPxtatW82279ixAx9++CGmTZuGTz75BDdv3gQAXL16FXPmzDFtO3LkiMX848eP4+2338bYsWPxww8/mG0/ffo0JkyYgEGDBuHgwYOKbYMGDcL48eMxfvx4REZGWsyviL1/dtkyX821k/2ym4mJwWDAnDlz8NVXXyE6Ohpbt27FpUuXVJGv5tqZDxiNRqxcuRJTp07FwoULcfDgQaSlpSnGuLm54e2330bXrl0VjycmJiIlJQWffPIJIiMjsXXrVuTn5yvGaDQafPrppxg+fDg6d+6MIUOGoFmzZooxc+fOxXfffYfu3btjwYIFmDFjBgBAr9cjNDQUPXv2REhICCZMmAAvLy+z/OXLlyMsLAzPPfccRowYgRYtWijGLFiwAOvWrUPbtm0RGRmJjz76CADQv39/tGvXDu3atUPnzp3x/vvvw9nZmfl2kq/m2qsqf9myZQgPD0erVq0s5n/yySdYt24d2rVrh8jISMybNw8A0LlzZ3Tp0gXt2rVDmzZtEBQUhB49eij2FUKgQ4cO2Lt3L7Zs2YKGDRsqJh4AkJKSgq1btyI6OhpnzpxB+/btTft269YNR44cwZYtW7Br1y5IKRX7Go1GbN68GaNGjcLEiRORlJRkmhiUat26NSZMmIBx48ahR48eiI6OBgDUqlULr7/+OiZMmIBhw4Zh48aNeFRV5K9ZswZ/+9vfMH/+fBw+fBjp6emKMf7+/pg1axbmzZuHoKAgfPfddwCAatWqYezYsZg/fz4mTZqE9evX4969e4p9DQYDvvjiC8ycORMrVqxAXFwcrl27phjj4eGB8ePHo2fPnmb1OTk5YenSpVi6dCmmT59utr0i9v7ZZct8NddO9s1uJibJycnw9/eHn58fnJycEBYWhpiYGFXkq7l25gOXLl2Cl5cXdDodHBwc0KVLFyQkJCjGeHp6wt/f32zpNT09Hc2bN4dWq0X16tXRoEEDJCUlKca0b98eV65cwdWrV1FUVIQff/wR/fr1U4xp1qwZ4uPjAQDx8fHo378/AKCoqAiFhYUASj5kLR1x7dChAy5fvowrV66gqKgI3333HV566SXFmBYtWmDv3r0AgH379pm2t2jRAnFxcTAYDMjPz8epU6fQt29f5ttJvpprt1V+eHi4Wf6+ffsAALGxsabtUkpUr14dTk5OqFatGhwdHZGVlaXY183NDXfv3kVeXh6MRiOuXr0KPz8/xZiioiLTzw4O/7872tvbG7du3cKtW7cAAIWFhWYTk9TUVLi5ucHV1RUODg5o3bo1zp07pxhTvXp108+FhYWm9yAfHx/UqVMHAKDT6VBUVITi4uIqzf/111+h0+ng6ekJBwcHdOzYEYmJiYoxLVq0QLVq1QAATZo0QU5ODgDAy8vLdJClXr16qFOnDu7evavY9+LFi/D29oaXlxccHR3RvXt3s5UVnU6HRo0aWaUtxt4/u2yZr+ba7YUQwq5vtlLuxEQIMU4I4VfemKdFr9crjgTrdDqzIzv2mq/m2pkP5OTkwM3NzXTf1dXV9OFZkdKJSEFBAe7cuYOzZ88iOztbMcbb21txFPH69evw9vZWjDl9+jQGDBgAABgwYACcnZ1Rr149AED9+vURHx+PU6dOYenSpcjMzFTsW79+faSmpprup6enK9pVgJI3+cGDBwMABg8ejDp16sDV1RXJyckIDQ1FjRo14Obmhl69esHX15f5dpKv5tqrIt/Hx0exullR/qBBg0z5hw8fRmxsLFJTU5Gamopdu3bh/Pnzin1r1qypOIp/79491KhRA49q2rQpBg0ahHbt2uHYsWMAYPpSHxwcjP79++PZZ5812+/OnTuKFZg6deogNzfXbNzPP/+MBQsWYMeOHWYTL6Dk/cPHx0cxMaqK/Fu3bsHV1dV039XV1TQRs2T//v1o1aqV2eOXL19GcXExPD09FY9nZ2fD3d3ddN/d3d3s/bU8hYWFeP/99zFp0iSzFtzKsPfPLlvmq7l2sm8VrZjMBXBECBEvhHhHCOFRmVAhxFghRIIQIoF9gfR71rp1a7Rt2xYRERFYvnw5AgICLK5qVCQiIgJdunRBbGwsunbtiuvXr8NgMAAo+bLVvXt3BAUFYcSIEfDwqNT/DRUmT56Mnj17IiEhAT169EBaWhoMBgN2796N7du348CBA9iwYQMOHz5s+r3MV0e+mmuvivwpU6age/fuOHbsmCL/mWeeQfPmzdGwYUP4+/ujd+/eZq2alfXLL7/gP//5DxITExEYGAigpM3M09MTBw4cwM6dO9GgQQOzNszK6ty5MyZPnoy+ffuaVpdK6fV67NixwzT5ssd8ADh48CBSUlJMq8Glbt++jaioKIwZM+aJ3jvL8/XXX2PRokWYNGkSvvrqK2RkZDzVfCJ6+ip6F/gVgC9KJijtAZwVQuwQQrwuhHAuaycpZZSUMkhKGTR27NhKFaLT6RRHgvV6PXQ6XaX2tXW+mmtnfslRvoePwuXk5CiOAlZk8ODB+OSTTzBt2jRIKc1WQzIyMhRHcX18fMw+IDMzM/H666+jV69eppM079y5Yzbm/Pnz6Ny5s+Lx9PR0RXtJ/fr1zfq8MzIyMHToUAQFBZl6rUuPnM6fPx/t27dHaGgohBC4ePEi8+0kX821V0X+9evXzVaYLOUPHz4czz//vOncrdzcXAwcOBBHjhzBvXv3cO/ePezYsQOdOnVS7Jufn49atWqZ7teqVQv3799HWVJSUkz/3vz8fOj1ehQUFMBgMCA9Pd3sfeXRFYxHVzge1apVK5w9e9Z0Pzc3F2vXrsWwYcMUq75VlV+vXj3F6nJOTo5ppfdhZ86cwZYtWzBh+4E0YwAAIABJREFUwgQ4OjqaHr9//z4WLVqEoUOHokmTJmb7ubm5mU6WB4CbN29arKMspWO9vLzw3HPP4ddff630voD9f3bZMl/NtZN9q2hiIqWURinlLinlaAA+AP4BoC9KJi1PTWBgIFJSUpCamorCwkJER0ejT58+qshXc+3MB5555hlkZmYiKysLxcXFOHTokOkE1ooYjUZTX/TVq1dx7do1s1aFxMRENG7cGA0aNICjoyP+9Kc/YceOHYoxrq6upp7OCRMmYP369QBKJjGlPeB169ZFx44dzb6cHTt2DE2aNEHDhg3h6OiIl19+GVu2bFGMcXNzM+VPnToVq1atAlByVLf0y1JgYCACAwOxa9cu5ttJvpprt1X+o1eGejh/ypQpWL16NYCS8y969OgBrVYLBwcH9OjRw6yVKzs7G87OzqhduzY0Gg38/f0VrWkAFCfk+/r6mt4Prl+/DhcXF2i1WgghoNPpzNqofH19cfPmTeTk5KC4uBhJSUlmJ+8//MX8woULptam+/fvY/Xq1ejbty8aNmwIS6yd36hRI+j1ety4cQPFxcU4cuQI2rZtqxhz9epVrFq1ChMmTDC1twFAcXExli1bhq5du+L555+3mB8QEIDr168jMzMTRUVFiI+PR8eOHS2OfVReXp7p/J87d+7g3LlzZucHVcTeP7tsma/m2u2Frc8hsddzTCr6OyaKyqSURQA2A9gshKj5VAtxcEBERATGjBkDg8GAIUOGICAgQBX5aq6d+YBWq8WoUaPw0UcfwWg0onfv3vDz88PGjRvRuHFjBAUF4fLly1i4cCHu3buHxMRE/PDDD/jss89QXFyMWbNmAQBq1KiBd999F1qtVpFvMBjwwQcf4IcffoBWq8X69etx/vx5fPjhhzhx4gR27NiBbt26YcaMGZBS4ueffzZd9rRp06aYO3cupJQQQmDFihVmJ68aDAaMGzcO27dvh1arxapVq3D27FnMmjULx48fx5YtW9CrVy/MmzcPUkrEx8fj3XffBQA4Ojpi//79AEo+vEeOHGnWLsN82+Wrufb/x96dx0VZ9f8ff10MiwuboAwKuCRaaGSpWK4ILrhiJC5ZWd9S+32z27Juze5vJVlGasuddtctdbukaXsmIomKCuaGG5i4CwYICLhrCQzn9wd53Y4sWjHOTH2ej8c88ppzrveca7pmmDPnnGtuVf4zzzxDQkICBoOBRYsWkZmZyfTp09m1axerVq0iNDSU119/HaUUmzdv5m9/+xsAX3/9NWFhYezZswelFElJSfoVqa5SSrFjxw769OmDpmkcPXqUc+fO0aFDB0pKSsjNzeX222+nadOmVFRUUFpaql+ytrS0lAMHDuhTl/Ly8qqM5hgMBiIjI1mwYAFKKTp37ozRaGTt2rX4+fnRrl07tm7dytGjRzEYDNSvX58RI0YAletCSkpKSE5O1qdfPf7447i6ut7S/EceeYQ5c+ZQUVFBr1698Pf355tvvqFly5Z07NiRzz77jCtXrvCvf/0LqPwSZvLkyWzfvp1Dhw5x8eJFNm/eDMC4ceNo0aKFWf6TTz5JTEwMFRUV9O3bl+bNm/Ppp58SGBiof1HzxhtvcPHiRdLS0li2bBn/+te/yMnJ4YMPPkDTNJRSDB8+nObNm/Nb2PrfLmvm23PbhW3Trr9KiFmhprVVSh3+g49R8wMIYUV79uyxaH6fPn0sml/dIlYh/grqei3C9RYsWGDR/Gunh9mjq7+zZCnVTQerS9dfrl386djFLxf279/fpj8fJyUlWeV5rHXEpA46JUIIIYQQQohryC+/V89mfsdECCGEEEII8dclHRMhhBBCCCGE1d1o8bsQQgghhBCiDslUrurJiIkQQgghhBDC6qRjIoQQQgghhLA6mcolhBBCCCHELSRTuaonIyZCCCGEEEIIq5OOiRBCCCGEEMLqZCqX+Mtq0aKFRfO9vb0tmi+//P7ndu+991os29PT02LZABs2bLBovrOzs0XzH3nkEYvmCyGETOWqnoyYCCGEEEIIIaxOOiZCCCGEEEIIq5OOiRBCCCGEEMLqZI2JEEIIIYQQt5CsMamejJgIIYQQQgghrE46JkIIIYQQQgirk6lcQgghhBBC3EIylat6MmIihBBCCCGEsDrpmAghhBBCCCGsTqZyCSGEEEIIcQvJVK7q2dSISUpKChEREfTr14+4uDi7yrfntv9V87du3cqoUaOIjo7mk08+qVJeWlrKSy+9RHR0NE888QT5+fkArFmzhrFjx+q3bt26cfjwYQD+/e9/M2zYMMLDw82yevbsyZo1a1i3bh0TJkyo8lhNmzZlyZIlfPfdd8THxxMaGqqX3X777XzxxResXr2aVatW4ezsXGX/iIgIMjMzOXToEFOnTq1S3rx5c5KSktizZw/r16/Hz89PL4uNjSU9PZ309HRGjhxZ7XMl+dbNv/fee1m+fDlffPEFjzzySJXySZMmsWjRIhYtWsRnn33GmjVr9LKnnnqKpUuXsmzZMiZPnlxl306dOhEXF8fHH3/MiBEjqpSPHz+eefPmMW/ePD766CO++OILvWzGjBl88cUXxMTEVNtugH79+pGens6PP/7I3//+92qfm9WrV7Njxw7WrFmjPze9evVi27Zt+u3MmTMMHTq0yv59+vRh586d7Nmzp9rjCwgIYOXKlfzwww+sWrWKZs2a6WWnT58mNTWV1NRUli9fXuMx1MYW39skX/JvRb49t13YMKWUpW83pby8XPXp00f99NNP6sqVK2ro0KHqyJEjN7u7VfPtue1/1fzy8nIVFhamMjIyVEFBgRo0aJDauXOnKikp0W9xcXFq6tSpqqSkRH322Wfqf//3f83KS0pK1Pbt21VYWJi+vWnTJnXo0CHVoUMHFRgYqAIDA1Xbtm3ViRMnVFhYmAoKClKZmZlqwIABenlgYKBavny5euWVV1RgYKAaMGCAysnJUYGBger2229XBw4cUEOGDFGBgYGqc+fOqm3btiowMFA5ODgoBwcH5ejoqI4ePapat26tXFxc1N69e1X79u31cgcHB/Xll1+qxx57TDk4OKg+ffqoJUuWKAcHBzVkyBC1du1a5eTkpFxdXdWOHTuUh4eH2b6Sb538rl27qq5du6ru3bur3NxcNXz4cNWzZ091+PBh9eCDD+rl19/efvttFR8fr7p27arGjx+v0tPTVffu3VX37t3Vvn371FNPPaUGDhyoBg4cqAYPHqxOnjyp/ud//kcNHTpUHTt2TE2YMEEvv/72wQcfqDVr1ujb06ZNU9OnT1fbt283q1evXj1Vr1491aBBA3Xs2DF1xx13KDc3N5Wenq7uvvtuvbxevXrq66+/Vk888YSqV6+eioiIUJ9++qlZeb169VTTpk1VSUmJatSokapXr55yd3dX7u7uytPTUx0/flzdddddytvbW2VkZKiQkBC93N3dXX377bfqySefVO7u7mrIkCFq+fLletmFCxfM6l69WfK957eQfMm31Xwbb/ut+Gz7h29DhgxRtnyz1vNiMyMmGRkZtGjRgoCAAJydnRk8eDDr16+3i3x7bvtfNT8jIwN/f3/8/PxwcnKib9++pKSkmNVJTU1l0KBBAISFhbFz506UUmZ11q5dS9++ffXtO++8k8aNG5vVueuuuzhx4gQ5OTmUlZWRkJBAnz59qrTJ1dVV/++pU6cA6NGjB4cOHeLgwYMAnD17loqKCrP9unTpwrFjx8jKyqKsrIzPP/+cyMhIszpBQUEkJycDsGHDBr08KCiIlJQUTCYTly9fZt++fQwYMEDybSi/Xbt25ObmcvLkScrLy1m3bh09e/akJv369WPt2rX6trOzM46Ojjg5OWEwGDh9+rRe1rZtW06ePElBQQHl5eWkpKTQtWvXGrNDQ0PZtGmTvp2ens7PP/9cY/2QkBCOHTtGdnY2ZWVlfPnllwwZMsSszh133KFnbtq0qUo5QFRUFElJSVUeq1OnThw/flzP/+abbxg8eLBZndtvv11/baekpOiv6bpgi+9tki/5tyLfnttuKzRNs+mbtdTaMdE0zVnTtLGapvX9dXuMpmnva5o2UdM0p7psSGFhIb6+vvq20WiksLDQLvLtue1/1fzCwkJ8fHz0bR8fH4qKiszqFBUVYTQaAXB0dMTV1ZVz586Z1Vm/fj39+vWr9bF8fX31aWAABQUFeu5Vc+fOJTIyktTUVD7++GNmzJgBQKtWrVBKsWDBAlasWMH48eOr5Pv5+ZGTk6Nv5+XlmU0Vgso3+aioKKDyQ567uzteXl5kZGQQERFB/fr18fb2pnfv3vj7+0u+DeU3adLE7HwuKiqiSZMmVMfX15emTZuya9cuAH788Ud2795NfHw88fHx7NixgxMnTuj1vb29KS4u1reLi4vx9vauNtvHxwdfX1/S09OrLa9Os2bNyM3N1bere2727dvHsGHDABg2bJj+3FxrxIgRZlPIrs3Py8szy2/atKlZnR9//FGfAjZ06FDc3d1p1KgRAPXq1WPjxo2sW7euSofmZtjie5vkS/6tyLfntgvbdqMRk4XAYOAZTdOWACOA7UAI8HFNO2maNkHTtJ2apu2UeYHiz2r//v24uLjQunXrP5w1ZMgQvvnmG3r27Mm4ceN466230DQNg8FAp06deP755xk9ejT9+vWr9RvtmkyZMoXQ0FB27txJr169yM3NxWQysXbtWhITE9m8eTPLli1j27ZtmEwmybez/Kv69u3Lhg0b9FE1Pz8/WrZsyf3338+wYcPo1KkTHTp0+F3ZvXr1YvPmzVVG7P6oF198kZ49e7J161Z69uxJXl6e2XPg6+tL+/btzUaBfouXXnqJHj16kJqaSvfu3cnLy9OP4c4776R3796MGzeO2NhYWrVqVSfHJIQQ4ve50VW5gpVSd2ma5gjkAc2UUiZN05YCNX5tppSKA672SFRN9a5lNBopKCjQtwsLC6t8q/xHWDLfntv+V803Go36dCmAU6dOVfkW+uo31T4+PpSXl3Px4kU8PDz08rVr195wtAQqR0iu/RbX19e3yjc/I0aM4PHHHwdg7969uLi40KhRIwoKCkhLS+PMmTNA5VSX9u3bs3XrVn3fvLw8AgIC9G0/Pz+zb5EB8vPziY6OBqBhw4Y88MAD+uhPbGwssbGxACxdupQjR46Y7Sv51s2/duQOKs/L60f3rurbty9vvfWWvh0aGsqPP/6oT4HaunUrd955pz5qUlJSYjb1sHHjxpSUlFSbHRoaygcffFBtWU1OnjxpNgJU03MzevRooPK5uf/++81GJocPH87KlSspLy+vNv/aERg/Pz+z0UmofP09/PDDen5kZKSef7VudnY2mzdv5q677iIrK+umj88W39skX/JvRb49t91WyFW5qnejERMHTdOcATegAXD1U5kLUKdTuYKDg8nOziYnJ4fS0lISEhKqXNnIVvPtue1/1fzg4GBycnI4efIkZWVl1c7b79GjB6tXrwYq1wV06tRJfyOpqKi4qWlcUDlVpWXLlvj7++Pk5FTtXNmTJ0/SrVs3AFq3bo2zs7N+xaDbb7+devXqYTAYCAkJ4ejRo2b7pqWlERgYSMuWLXFycmLUqFHEx8eb1fH29tbbPm3aNBYuXAiAg4ODPm0mODiY4OBgkpKSJN+G8g8cOIC/vz9NmzbF0dGRvn37snnzZq7XokUL3Nzc+PHHH/X7CgsLueeeezAYDBgMBu655x6ys7P18sOHD9OsWTOMRiOOjo76lbCu5+/vj6urKwcOHKhSVpudO3cSGBhIixYtcHJyYsSIESQkJNT43EyZMoXFixeblY8cObLaaVwAu3fvpnXr1nr+Aw88oL9mr/Ly8tLzn3vuOZYuXQqAp6enfoU7Ly8v7rvvPn0t182yxfc2yZf8W5Fvz20Xtu1GIyb/AQ4CBuD/gC81TTsO3Ad8VqcNcXTklVdeYdy4cZhMJoYPH06bNm3sIt+e2/5XzXd0dOT555/n2WefpaKigiFDhnDbbbcRFxdHUFAQPXv2ZOjQobz66qtER0fj7u7Oa6+9pu+/d+9ejEZjlfny77//PklJSfzyyy/6hyGTycSrr77KggULMBgMfPXVVxw9epRnnnmGffv2kZyczJtvvsnrr7/OY489BlR+eAU4f/48CxYs4JtvvkEpxaZNm9i4caPZY5pMJiZNmkRiYiIGg4GFCxeSmZlJTEwMu3btIj4+nt69ezNz5kyUUqSmpvL0008D4OTkpC88Pn/+PGPHjq0ylUjyrZ//zjvv8O6772IwGFi1ahVZWVmMGzeOgwcP6p2Uvn37sm7dOrN9r3aolyxZglKK7du388MPP+Dp6QlUdrA//PBDXn/9dRwcHEhKSuKnn37i4Ycf5siRI2zfvh2ouuj9qtmzZxMQEEC9evX45JNP+Oc//8nu3bvN2j558mTi4+MxGAwsXryYAwcO8PLLL7N7924SEhLo1asXM2bMQCnF5s2befbZZ/X9mzdvjr+/P6mpqVUe+2r+3//+d7755hsMBgNLly7l4MGD/OMf/2DPnj0kJibSs2dPpk+fjlKKLVu28PzzzwOVC///+c9/UlFRgYODA++++y6HDh2q9nFqYovvbZIv+bci357bLmybdv1VhqpU0LRmAEqpk5qmeQJ9gZ+UUjtu8jFuaiqXELfatVcnsoR7773XovnHjx+3aL6wLkueP1c7JpayYcMGi+ZX91s+den6i1wIIeyKXcyRGjZsmE1/Pv7uu++s8jze8JfflVInr/n3WeAri7ZICCGEEEKIPzFZY1I9m/kdEyGEEEIIIcRfl3RMhBBCCCGEEFZ3w6lcQgghhBBCiLojU7mqJyMmQgghhBBCCKuTjokQQgghhBDC6mQqlxBCCCGEELeQTOWqnoyYCCGEEEIIIaxOOiZCCCGEEEIIq5OpXOIvy8vLy6L5c+bMsWj+qlWrLJp/zz33WDR/0qRJFs23tLvvvtui+WvXrrVYdsOGDS2WDbB//36L5r/33nsWzRdCCEuTqVzVkxETIYQQQgghhNVJx0QIIYQQQghhdTKVSwghhBBCiFtIpnJVT0ZMhBBCCCGEEFYnHRMhhBBCCCGE1clULiGEEEIIIW4hmcpVPRkxEUIIIYQQQliddEyEEEIIIYQQVicdEyGEEEIIIYTVyRoTIYQQQgghbiFZY1I9mxoxSUlJISIign79+hEXF2dX+fbcdsm3fP6hQ4eYM2cOs2fPZsOGDVXKt23bxrvvvss///lPPvzwQwoLCwG4dOkS8+fP5+WXX2bFihU15rdv357XX3+dN954g4EDB1Zbp3PnzsyYMYNXX32V8ePH6/c/++yzzJ07l7/97W815jdv3pwxY8bw8MMP07Fjxxrr3XbbbUycOJEmTZoA4O/vz4gRIxg9ejQjRozAz8+v2v0iIiLIzMzk0KFDTJ06tdrHT0pKYs+ePaxfv94sJzY2lvT0dNLT0xk5cqRV8q/VtWtXvv76a1asWMFjjz1WpdzX15f58+fz6aef8tlnn9G9e/cqdcrLy4mKiiIyMpKFCxdWKS8tLeWFF14gMjKSsWPHcvLkSbPy/Px8unfvzieffALAlStXeOSRRxg1ahTR0dHMnTv3hsdx1R8993fv3s3TTz/NU089xTfffFOlfP/+/Tz//PNER0ezZcsW/f59+/bx3HPP6bdRo0axffv2Wh+rffv2zJgxg9dff50BAwZUW6dTp07ExMQQExPDE0888ZuP53q2/t4j+ZIvn3uEPbGZEROTycSMGTNYuHAhRqOR6OhowsPDCQwMtPl8e2675Fs+v6KighUrVjBu3Dg8PDx4//33adeuHUajUa9z9913c9999wGQmZnJqlWreOKJJ3BycqJ///4UFhZSUFBQbb6maTz00EO88847nDlzhpdeeom9e/eSn5+v1/Hx8WHQoEG8+eabXL58GTc3N73s+++/x8XFhV69etWY36tXL1auXMnFixcZMWIEWVlZnDlzxqyek5MTHTp0MGvnL7/8QkJCApcvX8bLy4uhQ4eyePFis/0cHByYN28eERER5Obmsn37duLj4zlw4IBeZ86cOSxdupRPPvmEsLAw3njjDR599FEGDRpEx44d6dixIy4uLiQnJ5OYmMiFCxduWf71xzJt2jSeeuopCgsLWbJkCZs2bSIrK0uv88QTT7B27Vq++uorWrVqxdy5cxk6dKherpTiypUrzJs3D6PRyMMPP0xoaCi33XabXmfFihW4u7uzcuVK1qxZw3vvvcesWbP08nfeecesw+Ps7Mz8+fNp0KABZWVljB8/nl69enH33XdXexxX/dFz32Qy8dFHHzF9+nS8vb2ZOnUqISEhBAQE6HWaNGnC3/72N7777juzfYODg3nnnXcAuHDhAhMnTqy1vZqmMWbMGN59913OnDnDP/7xD9LT06u8DgYOHMjs2bOrvA5+D1t/75F8yZfPPcLe2MyISUZGBi1atCAgIABnZ2cGDx7M+vXr7SLfntsu+ZbPz8nJwdvbG29vbxwdHenQoQOZmZlmderVq6f/u7S0VP+3s7MzrVq1wtGx5u8QWrVqxalTpyguLsZkMrFjx44qH+B69erFhg0buHz5MoDZB+uDBw/yyy+/1Jjv4+PDuXPnOH/+PBUVFRw5coRWrVpVqXfvvfeye/duTCaTfl9xcbH+mKdPn8bR0REHB/O3nS5dunDs2DGysrIoKyvj888/JzIy0qxOUFAQycnJAGzYsEEvDwoKIiUlBZPJxOXLl9m3b1+Vb8otnX+t9u3bk5OTQ15eHuXl5SQlJdG7d2+zOkopGjZsCICrqytFRUVm5RUVFTg4OODv74+TkxMRERFs3LjRrM7GjRsZMmQIAH369CEtLQ2llN7+Zs2amXVkNE2jQYMGQOVoTHl5+U1NI/ij5/7Ro0dp2rQpvr6+ODk50aNHD3bs2GFWx8fHh5YtW1Y5L661detW7rnnHlxcXGqsc/3rIC0tjQ4dOpjV6dmzJxs3bqz2dfB72Pp7j+RLvnzusV2aptn0zVpu2DHRNO02TdP+rmnae5qmvaNp2v/TNM29rhtSWFiIr6+vvm00GvXpLLaeb89tl3zL5587dw5PT09928PDg3PnzlWpt2XLFmbNmsXq1asZNmzYTec3atTIbPTizJkzNGrUyKyO0WjEaDQybdo0XnzxRdq3b3/T+a6urly8eFHfvnjxov7B+qrGjRvj6urKiRMnasxp3bo1RUVFVFRUmN3v5+dHTk6Ovp2Xl1dlyldGRgZRUVEAREVF4e7ujpeXFxkZGURERFC/fn28vb3p3bs3/v7+tzT/Wj4+PmbnRmFhoT6t7aq4uDgGDRrE6tWrmTt3LrNnzzYrV0qZ/VHw8fHh1KlTZnWKior0c9LR0RFXV1fOnj3L5cuXWbRoEU8++WSVtplMJkaPHk3fvn3p1q1blQ/t1fmj535JSQne3t76tre3N6dPn77p/a/avHkzPXv2rLWOp6enWfbZs2drfB1MnTqVadOm/abXQXVs/b1H8iVfPvcIe1Nrx0TTtEnAv4F6QAjgAgQA2zRN613LfhM0TdupadpOmRcoxM3p1q0bL7zwAgMHDqzzb4YcHBzw8fFhzpw5fPTRRzz66KPUr1+/zvJ79OjBDz/8UGO5l5cXXbt2rfLN/82aMmUKoaGh7Ny5k169epGbm4vJZGLt2rUkJiayefNmli1bxrZt28xGbGwl/1oRERHEx8czaNAgJk2axGuvvVZn307Nnz+fhx56SB8duZbBYOCzzz7j+++/JyMjg8OHD9fJY1ra6dOn+emnn2447exmXH0dvP3223z00Uc88sgjdfo6EEII8cfcaI3JeOBupZRJ07R3gNVKqd6aps0HvgPuqW4npVQccLVHom6mIUaj0WxuemFhodkc/D/Kkvn23HbJt3y+h4cHZ8+e1bfPnTuHh4dHjfU7dOjAt99+e9P514+QXD+CcrVOVlYWJpOJ4uJi/Riys7NvmH/x4kVcXV31bVdXVy5duqRvOzs74+Xlxf333w9AgwYNGDx4MAkJCRQVFdGwYUMGDhzIunXrOH/+fJX8vLw8szUHfn5+5OXlmdXJz88nOjoagIYNG/LAAw/oo06xsbHExsYCsHTpUo4cOXJL86916tQps3PDaDRWmao1bNgw/UID+/btw9nZGU9PT/3/maZp+rSsq5k+Pj5mGU2aNKGgoACj0Uh5eTkXL17E09OTffv2sW7dOt577z0uXLiAg4MDzs7OjB49Wt/Xzc2Ne++9l9TUVNq2bVvjsVxt/x859729vSkpKdG3S0pK8PLyuun9oXIk8d577611OiNUjpBcm33tc3rVta+DkpISCgsL8fHxqXWkrza2/t4j+ZIvn3tsl1yVq3o3s8bk6l8DF8AVQCn1E+BUlw0JDg4mOzubnJwcSktLSUhIIDw83C7y7bntkm/5fH9/f0pKSjh9+jTl5eWkp6cTFBRkVqe4uFj/98GDB2ncuPFN52dnZ2M0GmncuDEGg4EuXbqQnp5uVmfPnj3cfvvtQGXHoroPzDU5deoUHh4euLm54eDgQJs2bcw6NKWlpSxYsIAlS5awZMkSCgsL9U6Js7MzQ4YMYevWrTUu3k9LSyMwMJCWLVvi5OTEqFGjiI+PN6vj7e2tv4lPmzZNv1KVg4OD/mE0ODiY4OBgkpKSbmn+tTIzMwkICKBZs2Y4OjrSv39/Nm3aZFanoKCALl26ANCyZUtcXFzMPkA7ODhQUVFBXl4eZWVlrFmzhtDQULOM0NBQVq1aBcD69esJCQlB0zQWLFhAQkICCQkJjBkzhscff5zRo0dz5swZfT3FL7/8wpYtW8zWoNTkj577gYGB5OfnU1hYSFlZGZs3byYkJOSm9wdITU2lR48eN6yXnZ2Nj48P3t7eGAwGQkJCqrwO9u7dq3fGrr4Orn3t/Va2/t4j+ZIvn3uEvbnRiMnHQJqmaduBnsBYz8EHAAAgAElEQVQsAE3TmgC/faJwbQ1xdOSVV15h3LhxmEwmhg8fTps2bewi357bLvmWzzcYDAwbNoz//Oc/VFRUEBISgq+vL0lJSfj7+9OuXTu2bNnCkSNHMBgM1K9f3+yytG+++Sa//PILJpOJ/fv3M27cOLNvjioqKli2bBnPPvssDg4O/PDDD5w8eZJhw4aRnZ1Neno6+/fv1y+lWlFRwZdffqmPekydOpWmTZvi4uLC7NmzWbx4Mfv379fzlVKkpqYSGRmJpmkcOHCA06dP06VLF06dOlXrqEtwcDAeHh6EhIToH0hXrlzJzz//rNcxmUxMmjSJxMREDAYDCxcuJDMzk5iYGHbt2kV8fDy9e/dm5syZeluefvppoPJKYFc/+J8/f56xY8dWmWpl6fzrH2v27Nm8//77GAwGvvvuO44fP87/+3//j8zMTFJSUnj33Xd56aWXGDNmDEopYmJizDI0TcPFxYWJEydSUVFBZGQkrVu35sMPP6Rdu3aEhoZy//338/LLLxMZGYmHh4c+olOToqIipk+fjslkQinFoEGDCAsLq3UfqJtzf9y4cfp516dPH5o3b87y5ctp3bo1Xbp04ciRI8yaNYtLly6RlpbG559/znvvvQdUdopLSkpuai1IRUUFy5cvN3sd5OfnExkZyYkTJ/TXQbt27YiJiUEpxddff202+vdb2fp7j+RLvnzuEfZGu3bKQLUVNK09EAT8qJQ6+Dse46amcgnxZ1Pb747UhavfmFvKPfdUO1OzzkyaNMmi+ZZWF2seapOSkmKx7OsvXlDXru3YWsLVjoulyNpIIeyaXcyRevDBB2368/Hy5cut8jze8HdMlFL7Acv+lRFCCCGEEEL8pdnM75gIIYQQQggh/rps5pffhRBCCCGE+CuQq3JVT0ZMhBBCCCGEEFYnHRMhhBBCCCGE1UnHRAghhBBCCGF1ssZECCGEEEKIW0jWmFRPRkyEEEIIIYQQVicdEyGEEEIIIYTVyVQuISzk/vvvt2h+eHi4RfPd3Nwsmp+enm7R/P/85z8WzX/++ectmm/pX2e3pPbt21s0X36ZXQhh72QqV/VkxEQIIYQQQghhddIxEUIIIYQQQlidTOUSQgghhBDiFpKpXNWTERMhhBBCCCGE1UnHRAghhBBCCGF1MpVLCCGEEEKIW0imclVPRkyEEEIIIYQQVicdEyGEEEIIIYTVyVQuIYQQQgghbiGZylU9GTERQgghhBBCWJ1NdUxSUlKIiIigX79+xMXF2VW+Pbdd8u0zf8uWLQwfPpyoqCgWLVpUpby0tJQXX3yRqKgoHnvsMU6ePAnAyZMn6dGjB2PGjGHMmDHExsbq+4wePZquXbvSrVs3evbsSUlJSZXMyZMn079/f0aOHElubq5eNn/+fPr378+AAQNITU0F4MqVK4wYMYJhw4YxZMgQ5s6dq9c/c+YMu3btIi0tjYMHD6KUAqB9+/bMmDGD119/nQEDBlR77J06dSImJoaYmBieeOKJGz5XERERZGZmcujQIaZOnVqlvHnz5iQlJbFnzx7Wr1+Pn5+fXhYbG0t6ejrp6emMHDmy2vymTZsyZMgQhg4dSrt27aqUBwYGMmjQIAYOHEjfvn1xd3cHoGHDhowcOZKBAwcycOBAQkJCbngs17PFc1PyJV/y//z59tx2YbtspmNiMpmYMWMGH3/8MQkJCaxatYqjR4/aRb49t13y7TPfZDIxe/Zs3nvvPb744guSkpI4fvy4WZ3vvvsOd3d3vv32W8aMGcO8efP0Mj8/P5YtW8ayZct48cUX9cycnBxiY2PZtGkTXl5enDlzxizzq6++wt3dnaSkJB599FHefvttAI4ePcrq1atZtWoVH3/8MTNmzMBkMuHs7MyiRYv47rvv+Pbbb9m8eTN79+6loqKCQ4cOERQUREhICC4uLhQUFKBpGmPGjGHu3LlMnz6dkJAQmjZtatYGHx8fBg4cyOzZs4mJieGLL76o9blycHBg3rx5DB48mDvvvJPRo0cTFBRkVmfOnDksXbqUe+65h9dff5033ngDgEGDBtGxY0c6duxI165dee6553BzczPbV9M0OnfuzIYNG0hISKBFixZ6x+Oq7OxsVq9eTWJiIgcOHKBjx4562cWLF0lMTCQxMZG0tLRaj+V6tnhuSr7kS/6fP9+e224rNE2z6Zu11Nox0TTNQ9O0NzVNO6hp2mlN00o0TTvw632eddmQjIwMWrRoQUBAAM7OzgwePJj169fbRb49t13y7TM/IyODgIAA/P39cXJyol+/fmzatMmsTkpKCoMHDwYgPDyctLQ0fVSiOvv378fFxQUfHx+cnJwYNGhQlXasX7+e+++/H6gchdi6dStKKdavX8+gQYNwdnbG39+f5s2bk5GRgaZpNGzYEIDy8nLKy8vRNI2zZ8+iaRoNGjQAoFGjRhQXF9OqVStOnTpFcXExJpOJtLQ0OnToYNaGnj17snHjRi5fvgzAhQsXan2uunTpwrFjx8jKyqKsrIzPP/+cyMhIszpBQUEkJycDsGHDBr08KCiIlJQUTCYTly9fZt++fVVGcby9vbl48SKXLl2ioqKCEydO4O/vb1anvLxc/7ejY90t7bPFc1PyJV/y//z59tx2YdtuNGLyBXAG6K2U8lJKeQNhv95X+9eUv1FhYSG+vr76ttFopLCw0C7y7bntkm+f+YWFhRiNRrN9ioqKzOqcOnVKr+Po6Iirqyvnzp0DKqdzPfTQQ0yYMIE9e/YAUFRUhLOzMzNmzGDMmDH8+OOPVdpx6tQpfQTD0dERNzc3zp49S2FhodnIhq+vr76vyWTi/vvvp3v37nTr1o0OHTrQqFEjlFJ6p6K4uJgrV67g6enJ6dOn9ZyzZ8/SqFEjszYYjUaMRiNTp05l2rRptG/fvtbnys/Pj5ycHH07Ly/PbKoWVP4RjIqKAiAqKgp3d3e8vLzIyMggIiKC+vXr4+3tTe/evat0OurXr8+lS5f07cuXL+sdrmu1adOGoUOHcvfdd7Nr1y79fldXVwYMGECfPn1o0qRJrcdyPVs8NyVf8iX/z59vz20Xtu1GHZOWSqlZSqmCq3copQqUUrOAFjXtpGnaBE3TdmqatlPmBQphWxo3bkx8fDyffvopkydP5qWXXuLixYtA5dqNzz77jI8++oisrCyysrL+8OMZDAZWrFjBxo0bycjI4PDhw2iaRlBQEEePHmX37t0YDIabznNwcMDHx4e3336bjz76iEceeYT69ev/oTZOmTKF0NBQdu7cSa9evcjNzcVkMrF27VoSExPZvHkzy5YtY9u2bZhMpt/1GEeOHCE+Pp69e/fqnamff/6ZFStW8P3337N79266detWpyMqQgghhD250V/AE5qmTQUWK6UKATRNMwKPATk17aSUigOu9khqnjtyDaPRSEGB3v+p8o3wH2XJfHtuu+TbZ/713x4VFhZW+bbdx8dHzyovL+fixYt4eHigaRrOzs5A5VQlf39/fvrpJ5o0acL58+eBykXZt912W5VvqHx8fMjPz8fX15fy8nIuXLiAp6cnRqOR/Px8vV5BQUGVY3B3d+fee+8lNTWVtm3b4uHhwT333APA6dOn+fnnnzl79ixeXl76Pp6enlXWuZw5c4asrCxMJhMlJSUUFhbi4+PDiRMnqn2u8vLyCAgI0Lf9/PzIy8szq5Ofn090dLR+7A888IA+uhQbG6tfIGDp0qUcOXLEbN+ff/5Zn64G0KBBA32aWXVOnDihL3KvqKigtLRUP66LFy/i7u5uNmpUG1s8NyVf8iX/z59vz223FXK54OrdaMRkFOANbPp1jclpYCPgBYyoy4YEBweTnZ1NTk4OpaWlJCQkEB4ebhf59tx2ybfP/ODgYH766Sfy8vIoKytj7dq19OrVy6xOz549SUhIACA5OZmQkBA0TePMmTP6t/65ubnk5OTg5+dH27Ztyc7OJi8vj59//pktW7bQvXt3s8zw8HBWrFgBwJo1a7jvvvvQNI3w8HBWr15NaWkpubm5nDhxgrvuuovTp0/rnZ1ffvmFLVu2cNtttwHoH8grKirIycmhadOmZGdn4+Pjg7e3NwaDgZCQENLT083asHfvXtq2bQtUToMyGo0UFxfX+FylpaURGBhIy5YtcXJyYtSoUcTHx5vV8fb21v9ITJs2jYULFwKVozNXO0rBwcEEBweTlJRktm9JSQlubm40bNgQBwcHWrRoUaXjc+2CeT8/P30Km4uLi/64DRs2xM3NTR+9uhm2eG5KvuRL/p8/357bLmxbrSMmSqkzwAu/3sxomvY/wMI6a4ijI6+88grjxo3DZDIxfPhw2rRpU1fxFs2357ZLvn3mOzo6MnXqVCZNmoTJZCIyMpLWrVvz73//m6CgIEJDQxk2bBjTp0/X10zMnDkTgD179vDvf/8bR0dHHBwcmDZtGh4eHvz88884OjoyYsQIlFLceeedTJw4kblz53LnnXcSHh5OdHQ0U6dOpX///nh4ePDOO+8AlesnBg4cyODBgzEYDLzyyisYDAaKioqYNm0aJpMJpRQDBgwgLCwMgJycHE6fPo1SimbNmtGoUSMqKipYvnw5zz77LA4ODvzwww/k5+cTGRnJiRMnSE9PZ//+/bRr146YmBiUUnz99ddmazyuZzKZmDRpEomJiRgMBhYuXEhmZiYxMTHs2rWL+Ph4evfuzcyZM1FKkZqaytNPPw2Ak5OTflGB8+fPM3bs2CpTuZRS7Ny5k7CwMDRN4/jx45w7d47g4GBOnz5NXl4ebdu2xWg0opSitLSUbdu2AZUjUMHBwSilUEqRlpamd9gsde78FpIv+ZIv+bc6+1bkC9ul1XaVnlp31LSflFLNb6Lq73sAIUStro5EWMr1l8Wta08++aRF8//zn/9YNH/JkiUWzR8zZoxF84UQ4k/KLuZIPf744zb9+XjBggVWeR5rHTHRNC2jpiLgzzXZTwghhBBCCGE1N1r8bgQiqLw88LU0YItFWiSEEEIIIYT4y7lRx2QV4KqU2nt9gaZpGy3SIiGEEEIIIf7E5Kpc1bvR4vcnaimTCdBCCCGEEEKIOnGjywULIYQQQgghhMXJTwwLIYQQQghxC8lUrurJiIkQQgghhBDC6qRjIoQQQgghhLA6mcolhBBCCCHELSRTuaonHRMh7JS7u7u1m/CHeHh4WLsJf8jHH39s0fzRo0dbLNvBQQbLhRBC2B756ySEEEIIIYSwOumYCCGEEEIIIaxOOiZCCCGEEELcQpqm2fTtJo9hgKZphzRNO6pp2rQa6ozUNC1T07T9mqYtu1GmrDERQgghhBBC3DRN0wzAv4B+QC6QpmnaSqVU5jV12gAvAt2VUmc0TfO5Ua6MmAghhBBCCCF+iy7AUaXUcaVUKfAZMOy6OuOBfymlzgAopU7dKFRGTIQQQgghhLiF/gSXC/YDcq7ZzgXuva5OWwBN034ADECMUur72kKlYyKEEEIIIYTQaZo2AZhwzV1xSqm43xjjCLQBegP+QIqmacFKqbO17SCEEEIIIYQQAPzaCamtI5IHBFyz7f/rfdfKBbYrpcqALE3TDlPZUUmrKVTWmAghhBBCCHELWfuqW3VwVa40oI2maa00TXMGRgMrr6uzgsrREjRNa0zl1K7jtYVKx0QIIYQQQghx05RS5cDTwBrgAPCFUmq/pmkzNE2L/LXaGqBE07RMYAMwRSlVUluuTXVMUlJSiIiIoF+/fsTF/dZpbNbNt+e2S77kWzv/9ttvZ8qUKbzwwguEhYVVKe/cuTPTp09n8uTJTJ48mS5duuhls2bN0u9/7LHHqs2PiIggMzOTQ4cOMXXq1CrlzZs3JykpiT179rB+/Xr8/Pz0stjYWNLT00lPT2fkyJHV5oeEhLB48WKWLFnCgw8+WKX8qaeeIi4ujri4OBYvXszKlf/9Umnt2rXExcUxYcIETCYTBQUFXLhwoUpGaWkpkydPJiIiglGjRpGX998R87i4OCIiIhg4cCCbN28GICsri6ioKP3WuXNnFi9eDMCBAwcYOXIkw4YN44EHHiAjI6Pa4wLbP3ckX/Il3zr59tx2UTeUUquVUm2VUq2VUjN/ve8VpdTKX/+tlFLPKaXaKaWClVKf3SjTZtaYmEwmZsyYwcKFCzEajURHRxMeHk5gYKDN59tz2yVf8q2dr2kaUVFRxMXFce7cOSZNmsT+/fs5dcr8qoLp6emsWLGiyv5lZWW8++67NeY7ODgwb948IiIiyM3NZfv27cTHx3PgwAG9zpw5c1i6dCmffPIJYWFhvPHGGzz66KMMGjSIjh070rFjR1xcXEhOTiYxMdGs4+Dg4MAzzzzDlClTKCoq4sMPP2TLli2cOHFCr/PBBx/o/46KijJ7bkpLSxk/fjyFhYU0btwYo9HIqVOnOHr0qFm9r776Cg8PD9asWUNCQgJvvfUW7777LkePHmX16tXEx8dz6tQpHn/8cRITE2nVqhXffvstUPn/qHfv3vTt21c/3okTJxIaGsqmTZuYM2cOS5YsqfLc2fq5I/mSL/nWybfnttuKP8FVuSzid4+YaJqWWJcNycjIoEWLFgQEBODs7MzgwYNZv369XeTbc9slX/Ktnd+8eXOKi4s5ffo0JpOJvXv30r59+zprX5cuXTh27BhZWVmUlZXx+eefExkZaVYnKCiI5ORkADZs2KCXBwUFkZKSgslk4vLly+zbt48BAwaY7XvHHXeQl5dHfn4+5eXlJCcn061btxrbEx4erj/WVaWlpTg6OuLo6IimaTRo0KBKneTkZIYNq7xEfEREBNu2bUMpRXJyMoMGDcLZ2Rl/f3+aN29eZQRk27ZtBAQE6CNBmqZx6dIlAC5cuICPT/W/eWXr547kS77kWyffntsubFutHRNN0zrWcOsE3F2XDSksLMTX11ffNhqNFBYW2kW+Pbdd8iXf2vnu7u6cPfvfKweeO3cODw+PKvWCg4N57rnneOSRR8zKHR0dmTRpEk8//XS1HRo/Pz9ycv57qfW8vDyzqVpQ+UcwKioKqBzRcHd3x8vLi4yMDCIiIqhfvz7e3t707t0bf39/s30bN25sNrpTXFxMkyZNqj1Wo9GIr68ve/bs0e9zdnbmiSeeoGfPnnTv3h0Ag8FQ5TksLCykadOm+jG7ublx9uzZap//60ebVq9ezeDBg/Xtf/zjH8yePZvQ0FBmzZrFc889V217bf3ckXzJl3zr5Ntz24Vtu9FUrjRgE1DdeJNnTTtde+3j+fPnM2HChJqqCiHEDWVmZrJnzx5MJhP33Xcfo0ePZv78+QC88cYbnD9/Hi8vL5588kkKCgooKal1bV0VU6ZMYd68eTz66KOkpqaSm5uLyWRi7dq1dO7cmc2bN1NcXMy2bdswmUy/+zjCwsJISUmhoqJCv+/BBx8kJycHBwcHvv76a7Kysjh69OjvfozrlZaWkpyczOTJk/X7li9fzosvvkhERASrV6/m//7v/1i0aFGdPaYQQojayVSu6t1oKtcB4EmlVNj1N6C4pp2UUnFKqc5Kqc432ykxGo0UFBTo24WFhRiNxpva19r59tx2yZd8a+efP38eT8//fs/h4eHBuXPnzOpcvnxZ7xBs377dbMTj/PnzAJw+fZrjx4/TrFkzs33z8vIICPjvpdb9/PzMFo4D5OfnEx0dTefOnXnppZcA9DbExsbSqVMnIiIi0DSNI0eOmO1bXFxsNhWqcePGFBUVVXusYWFhVaZoFRcX4+DgwIULF9i7dy+BgYGYTKYqz6HRaCQ/Px+A8vJyLly4gKenZ7XP/7XtSU1NpV27djRu3Fi/79tvv6V///4ADBw4sMbF77Z+7ki+5Eu+dfLtue3Ctt2oYxJTS52/1WVDgoODyc7OJicnh9LSUhISEggPD7eLfHtuu+RLvrXzc3JyaNy4MY0aNcJgMHD33XeTmZlpVsfNzU3/d/v27fWpSvXr18dgMADQoEEDWrRoUWW4Py0tjcDAQFq2bImTkxOjRo0iPj7erI63t7f+7dW0adNYuHAhULmw3cvLSz/O4OBgkpKSzPY9ePAgfn5++Pr64ujoSHh4OFu3bq1ynAEBAbi5ubF//379PldXV5ycnHB2dqaiogJvb2+ysrK4fPlylauThYWF8d133wGwZs0a7rvvPjRNIywsjNWrV1NaWkpubi4nTpzgrrvu0vdLSEgwm8YF4OPjw44dO4DK9SctW7as0t6rx2zL547kS77kWyffntsubFutU7mUUl/VUtyoThvi6Mgrr7zCuHHjMJlMDB8+nDZt2thFvj23XfIl39r5FRUVrFixgvHjx+Pg4MCOHTsoLCykf//+5ObmkpmZSY8ePWjXrh0VFRVcvnyZzz//HKj8gD18+HCUUmiaxoYNG6qsrzCZTEyaNInExEQMBgMLFy4kMzOTmJgYdu3aRXx8PL1792bmzJkopUhNTeXpp58GwMnJiU2bNgGVIzNjx46tMpWroqKCefPmMWvWLAwGA4mJiWRnZ/PYY49x+PBhtmzZAlQuet+wYYPZvi1atGDy5MkopUhLS+Opp57i0qVLNGzYkDZt2jB37lzuvPNOwsPDiY6O5oUXXiAiIgIPDw/efvttANq0acOAAQMYMmQIBoOBl19+We+sXb58mS1btvDqq6+aPe5rr73GG2+8QXl5OS4uLsyYMcMi/29vRPIlX/LtM9+e2y5sm6aU+n07atpPSqnmN1H19z2AEOJPbcqUKRbNf+eddyyaHxoaatH8devWWSzbwcGmfsJKCCHqkl0s3pg4caJNfz7+17/+ZZXnsdYRE03TavrVLQ2QyX5CCCGEEEKIOnGjq3IZgQjgzHX3a8AWi7RICCGEEEII8Zdzo47JKsBVKbX3+gJN0zZapEVCCCGEEEL8icnlgqt3o8XvT9RSNqbumyOEEEIIIYT4K5IVkEIIIYQQQgiru9FULiGEEEIIIUQdkqlc1ZMREyGEEEIIIYTVScdECCGEEEIIYXUylUsIIYQQQohbSKZyVU86JkIIq4iJibFo/q5duyyav2nTJovmW/KX3/v372+xbCGEEOL3kqlcQgghhBBCCKuTERMhhBBCCCFuIZnKVT0ZMRFCCCGEEEJYnXRMhBBCCCGEEFYnU7mEEEIIIYS4hWQqV/VkxEQIIYQQQghhddIxEUIIIYQQQliddEyEEEIIIYQQVidrTIQQQgghhLiFZI1J9WTERAghhBBCCGF1NtUxSUlJISIign79+hEXF2dX+fbcdsmXfGvk//DDD0RFRREZGcnChQurlJeWlvLCCy8QGRnJ2LFjOXnypFl5fn4+3bt355NPPgHgypUrPPLII4waNYro6GjOnz+v1w0JCWHx4sUsWbKEBx98sMpjPfXUU8TFxREXF8fixYtZuXKlXrZ27Vq97PXXX6/2WCIiIsjMzOTQoUNMnTq1Snnz5s1JSkpiz549rF+/Hj8/P70sNjaW9PR00tPTGTlyZJV9MzMzee2113j11VdJSkqqUp6cnMzMmTOJjY1l3rx5nD59GoDDhw/z5ptv6rfJkyeTnp5ebftrY4vnjuRLvuRbP9+e2y5sl81M5TKZTMyYMYOFCxdiNBqJjo4mPDycwMBAm8+357ZLvuRbI99kMjFr1iw++OADjEYjDz/8MKGhodx22216nRUrVuDu7s7KlStZs2YN7733HrNmzdLL33nnHbp3765vOzs7M3/+fBo0aEBZWRk9evSgXr161KtXj2eeeYYpU6ZQVFTEhx9+yJYtWzhx4oS+7wcffKD/OyoqyqztpaWlTJgwocZjcXBwYN68eURERJCbm8v27duJj4/nwIEDep05c+awdOlSPvnkE8LCwnjjjTd49NFHGTRoEB07dqRjx464uLiQnJxMYmIiFy5cAKCiooIvv/ySiRMn4unpyZw5cwgODqZp06Z6tr+/P1OmTMHZ2ZnU1FRWrFjB448/Ttu2bZk2bRoAly5dYsaMGQQFBdV4HDX9f7K1c0fyJV/yrZ9vz223FTKVq3o2M2KSkZFBixYtCAgIwNnZmcGDB7N+/Xq7yLfntku+5FsjPyMjA39/f/z9/XFyciIiIoKNGzea1dm4cSNDhgwBoE+fPqSlpaGUAmDDhg00a9bMrCOjaRoNGjQAoLy8XL//jjvuIC8vj/z8fMrLy0lOTqZbt241ti08PJzk5OSbPv4uXbpw7NgxsrKyKCsr4/PPPycyMtKsTlBQkJ65YcMGvTwoKIiUlBRMJhOXL19m3759DBgwQN/vxIkTNG7cmMaNG+Po6EinTp3Yt2+fWXbbtm1xdnYGoGXLlpw9e7ZKG/fu3Uu7du30ejfLFs8dyZd8ybd+vj23Xdi2Wjsmmqa5a5oWq2naEk3TxlxX9kFN+/0ehYWF+Pr66ttGo5HCwkK7yLfntku+5Fsj//p9fHx8OHXqlFmdoqIivY6joyOurq6cPXuWy5cvs2jRIp588skquSaTidGjR9O3b19cXFxwdnamcePGZtnFxcU0adKk2nYZjUZ8fX3Zs2ePfp+zszMffvgh77//vtkIzVV+fn7k5OTo23l5eWZTtaDyj2xUVBRQOSLj7u6Ol5cXGRkZREREUL9+fby9venduzf+/v76fmfPnqVRo0b6tqenZ7Udj6u2bt1Ku3btqty/a9cuOnXqVON+NbHFc0fyJV/yrZ9vz20Xtu1GIyYLAQ34GhitadrXmqa5/Fp2X007aZo2QdO0nZqm7ZR5gUKIujR//nweeughfXTkWgaDgc8++4zvv/+e0tJSysrKflN2WFgYKSkpVFRU6Pc9+OCD/O///i8zZ85k4sSJNGvW7De3ecqUKYSGhrJz50569epFbm4uJpOJtWvXkpiYyObNm1m2bBnbtm3DZDL95nyAtLQ0cnJy6NOnj9n9586dIz8//zdP4xJCCGE5mqbZ9M1abrTGpLVSaviv/16hadr/AcmapkXWtpNSKg642iNRN9MQo+hKao0AACAASURBVNFIQUGBvl1YWIjRaLyZXW+KJfPtue2SL/nWyL9+n1OnTuHj42NWp0mTJhQUFGA0GikvL+fixYt4enqyb98+1q1bx3vvvceFCxdwcHDA2dmZ0aNH6/u6ubnh4uLCL7/8QnFxsVl248aNKSoqqrZdYWFhzJ071+y+4uJioHKx/d69ewkMDDRbiJ+Xl0dAQIC+7efnR15enllGfn4+0dHRADRs2JAHHniAc+fOAZWL32NjYwFYunQpR44c0ffz9PTkzJkz+vbZs2fx9PSs0u6DBw+yZs0annnmGZycnMzK9uzZw1133YXBYKj2mGtji+eO5Eu+5Fs/357bLmzbjUZMXDRN0+sopWYCHwEpgHddNiQ4OJjs7GxycnIoLS0lISGB8PBwu8i357ZLvuRbIz84OJicnBzy8vIoKytjzZo1hIaGmtUJDQ1l1apVAKxfv56QkBA0TWPBggUkJCSQkJDAmDFjePzxxxk9ejRnzpzRF43/8ssvXLlyBUdHRw4ePIifnx++vr44OjoSHh7O1q1bq7QpICAANzc39u/fr9/n6uqqf9B3d3fnzjvvNFs0D5UjFYGBgbRs2RInJydGjRpFfHy8WR1vb2/9G6hp06bpVyFzcHDAy8tLf06Cg4PNrrzVvHlzioqKKC4upry8nF27dhEcHGyWnZOTw+eff86ECRNwc3Orcly/dxrX1TbZ2rkj+ZIv+dbPt+e2C9t2oxGTeCAcWHf1DqXUIk3TCoB5ddoQR0deeeUVxo0bh8lkYvjw4bRp08Yu8u257ZIv+dbId3R05IUXXmDixIlUVFQQGRlJ69at+fDDD2nXrh2hoaHcf//9vPzyy0RGRuLh4aGPKtSkqKiI6dOnYzKZUErh4uJC/fr1qaioYN68ecyaNQuDwUBiYiLZ2dk89thjHD58mC1btgCVi943bNhgltmiRQsmT56MUgpN01i+fHmVjonJZGLSpEkkJiZiMBhYuHAhmZmZxMTEsGvXLuLj4+nduzczZ85EKUVqaipPP/00AE5OTmzatAmA8+fPM3bsWLOpXAaDgREjRvDBBx+glOK+++6jadOmJCQk0Lx5c4KDg1mxYgVXrlxhwYIFADRq1Ehff1NSUsKZM2d+95VsbPHckXzJl3zr59tz222FXJWretrVq9z85h017X+UUlV/fKCq3/cAQog/tUuXLlk0f+jQoRbNv9qhsJTExESLZffv399i2UIIYWV28Yn/73//u01/Pn7rrbes8jz+kcsFv1pnrRBCCCGEEEL8pdU6lUvTtIyaigBZhSSEEEIIIcRvJFO5qnejNSZGIAI4c939GrDFIi0SQgghhBBC/OXcqGOyCnBVSu29vkDTtI0WaZEQQgghhBDiL6fWjolS6olaysbUVCaEEEIIIYQQv8WNRkyEEEIIIYQQdUjWmFTvj1yVSwghhBBCCCHqhHRMhBBCCCGEEFYnU7mEEEIIIYS4hWQqV/WkYyKEsIqGDRtaNP+jjz6yaH7Hjh0tmj9+/HiL5oeFhVk0v3PnzhbLnjhxosWyQT4wCCGEtchULiHE/2fv3uN7rv//j9+eOyHbsM02Zg45NZnCSIilNBlKhPp8IpHySUShiHwcS6X66FNITUjR6VPMYb5z2Bxz3NScc9wYwxzLtvdevz/k/ettY1Nm73fdr5fLLpe93s/n877H3nuV9/P9fL5eb/mbceVJiYiI/HVpxURERERE5CbSymz+tGIiIiIiIiLFThMTEREREREpdtrKJSIiIiJyE2krV/60YiIiIiIiIsVOExMRERERESl22solIiIiInITaStX/rRiIiIiIiIixU4TExERERERKXZONTFJSEggKiqK1q1bM23aNJfKd+Xala/8v3r+xo0befrpp+nVqxfz5s3L075t2zaef/552rVrx6pVq/K0X7hwgSeeeIIPPvgg3/z77ruPjRs3smXLFgYOHJinPTQ0lO+//57Vq1ezYMECKlasaG87efIkiYmJJCYm8vnnn+eb37JlS5YtW8bKlSvp27dvnvaKFSvyxRdfsHDhQhYvXmz/ZHcPDw/efvttlixZQnx8PP/617/yjA0PD2fChAm88cYbREdH5/vzGzVqxLhx4xg3bhzPPPMMAP7+/owaNYrRo0czbty4q36afOXKlfnHP/7BP//5Txo0aJBvH4Dq1avTr18/AgMD7c9Zly5deOyxx+jSpQvnzp1j1qxZzJw5M99zICsri4EDB/LAAw/QpUsXDh8+bG+bOnUqDzzwAG3atCExMRGAixcv8uijj/LQQw/Rrl07/vOf/9j7r127lo4dO9KuXTuGDh1KTk7OVet29nNf+crX6x7nZIxx6q/i4jQTE5vNxujRo5k+fTqxsbEsWLCAPXv2uES+K9eufOX/1fNtNhsffPABo0ePZsqUKaxcuZKDBw869AkMDGTQoEFERkbmmzFz5kzq1q2bb5ubmxtvv/02nTt3pnHjxnTq1InatWs79Bk7diyff/45zZo1Y+LEibz22mv2tl9++YV77rmHe+65h8ceeyzf/DFjxtCjRw/uv/9+OnToQM2aNR36PP/88yxYsIC2bdvy/PPPM2bMGACio6Px8vIiKiqK6OhoHn/8cSpVqmQfZ4zhiSeeYNKkSQwbNoy77rrLYdIEEBQURLt27Rg3bhzDhw9nzpw5AGRmZjJ27FhGjhzJ6NGjiY6OpmzZsg5jjTG0bNmS+fPnM2fOHGrVqkW5cuXy/I6enp7Uq1ePo0ePOjwvsbGxfP7558TFxREXF0eHDh34xz/+QWxsbJ5z4KuvvsLX15e4uDh69OjB22+/DcCePXtYuHAhCxYsYPr06YwePRqbzYaXlxczZszgu+++49tvv2XVqlVs3bqV3NxcXn75ZSZNmmSfRH777bd5agbnP/eVr3y97hFX4zQTk+TkZKpUqUJoaCheXl5ER0cTHx/vEvmuXLvylf9Xz9+1axcVK1akQoUKeHp60qJFC9auXevQJygoiGrVquHmlvd/ibt37yYzM/Oq7/Y3bNiQn3/+mf3795Odnc0333yTZ+Whdu3aJCQkAJfeBWzbtm2h67/zzjvZv38/hw4dIjs7m/nz59O6dWuHPpZl4e3tDYCPjw/Hjh2zP37LLbfg7u5OyZIlyc7O5uzZs/Zxt956K+np6Rw/fhybzcb69eupX7++Q3bLli2Jj4/nwoULAPbxNpvNvpLg4eGR7ztsQUFBnD59mjNnzpCbm8vu3bu59dZb8/S766672Lx5Mzabzf5YRkYG58+fB2D79u1UrlyZcuXK4e7uTtu2bfOcA/Hx8Tz88MMAREVFsXbtWizLIj4+nrZt2+Ll5UWlSpWoXLkyycnJGGMoXbo0ADk5OeTk5GCMITMzE09PT6pVqwZAs2bNiIuLy/dv4+znvvKVr9c94mquOTExxgQbYz40xvzXGONvjBlljNlmjJlnjKlwIwtJT08nODjYfhwUFER6erpL5Lty7cpX/l89/8SJEwQEBNiPAwICOHHiRKHG5ubmMn36dHr37n3VPhUrViQ1NdV+nJqaSoUKjv97/PHHH2nfvj0A7du3x9fX175yULJkSVasWMH//d//5buVKjg4mCNHjtiPjxw54vB8ALz77rt07NiRdevWMWPGDEaOHAnAwoULuXDhAhs2bGDt2rVMmzaN06dP28eVK1eOkydP2o9PnTqVZ0UjODiY4OBghg8fzogRIwgPD7e3+fn5MWbMGCZNmsTChQvJzMx0GFu6dGmHidC5c+fsk4HLypcvj4+PDwcOHMjzu1/m7e1N2bJlyc3Ntdd05Tlw7Ngx+/Pu4eGBj48PmZmZpKenO/w9fj/WZrPx8MMP06xZM5o2bcodd9xBuXLlsNlsbNu2DYDFixc7rOT8nrOf+8pXvl73iKspaMVkBpACHAKWA78AbYFEYEqRViYiUsxiY2OJiIhwmNj8Ea+++irNmzcnMTGRZs2akZqaan+RXbduXSIjI+nduzcTJkywv1N/PTp06MBXX31FkyZNePLJJ3n33XcxxnDnnXeSm5tL48aNad68OU8//TShoaHXle3m5kZQUBCvv/46H374IU8++SS33HILcOn6mBEjRjB06FCaNWuGr6/vddfevHnzfK/ruczPz4/atWuTlpZ23dkFcXd353//+x8rVqwgOTmZXbt2YYxh0qRJTJgwgc6dO1O6dOl8V9JERP6M4r6GxFmvMSnoc0yCLMuaDGCM+ZdlWW/89vhkY0yvqw0yxvQB+sCliw779OlTYCFBQUEO70qlp6cTFBRU4LjCKsp8V65d+cr/q+f7+/uTkZFhP87IyMDf379QY7dv385PP/1EbGwsv/76K9nZ2ZQqVYqePXva+6SlpRESEmI/DgkJcVjhADh69Cj//Oc/gUurCB06dLCvXFzuu3//flatWkW9evXYt2+fw9jfv+NfoUKFPO/gd+3ale7duwOwefNmSpQogZ+fHw899BArVqwgJyeHEydOsGnTJurVq2fflnXq1Cn8/PzsOeXKlePUqVMO2adOnWLv3r3YbDYyMjLsz//va8zMzOTw4cPUqlWLjRs32h8/f/48Pj4+9mNvb2/79iwALy8v/Pz86NixIwC33HIL0dHRxMbGcuzYMUqXLk3btm355JNPHOo6evRonnMgMDDQvpqUk5PD2bNnKVu2LEFBQQ5/j/zG+vr6ctddd5GYmEitWrWoX7++/VqaVatWsX//fvLj7Oe+8pWv1z3iagp6G+j37TOvaHO/2iDLsqZZlhVhWVZEYSYlcOnOMJf3UWdlZREbG0urVq0KNba48125duUr/6+eX6tWLdLS0jh69CjZ2dkkJCTQpEmTQo0dMmQIn376KTNmzKBXr17cd999DpMSuDQRqF69OlWqVMHT05NHHnmEhQsXOvTx8/OzvwM1aNAgZs+eDUDZsmXx8vKy92nSpAk7duxwGJuUlES1atUIDQ3F09OT9u3bs3TpUoc+aWlpNGvWDIAaNWpQokQJTpw4QWpqKk2bNgWgVKlS1K9fn71799rH7du3j6CgIAICAnB3d+euu+5iy5YteX6/2267Dbg0sQgKCuLYsWOUK1cOT09P4NKEolatWnkmTOnp6ZQpUwYfHx/c3NyoWbOmw4QmKyuLjz/+mJkzZzJz5kzS09PtkxIvLy/at2/PmjVr8PDwIDMzk9OnT2Oz2Vi4cGGec6BVq1b873//A2DJkiU0adIEYwytWrVi4cKFZGVlcfjwYQ4cOEC9evU4efIkZ86cAeDXX39lzZo19utfLm/1y8rK4qOPPqJbt27kx9nPfeUrX697xNUUtGLynTHG27Ksc5ZlvXr5QWNMDWDnDS3Ew4ORI0fSu3dvbDYbnTp1ynPnGWfNd+Xala/8v3q+u7s7ffv25dVXXyU3N5cHHniAKlWqMGvWLGrWrEmTJk3YtWsXY8aM4dy5c6xfv57Zs2czZUrhdqvabDZeeuklvvnmG9zd3Zk9ezY7duxg2LBhbNmyhUWLFnHPPffw2muvYVkWa9as4cUXXwQuTZreffddcnNzcXNz45133mHnzp158keOHMnMmTNxd3dn3rx57N69m0GDBpGcnMz//d//MXbsWF5//XV69eqFZVn2/JkzZ/LWW2+xdOlSjDF8+eWX7Nixw74Ck5uby+zZs3nppZdwc3MjMTGRtLQ0OnbsyL59+9i6dSvbtm3j9ttvZ9y4ceTm5jJv3jzOnz9P1apV6datG5ZlYYxh0aJFDrfohUsX3yckJPDQQw9hjCElJYWTJ0/SuHFjjh07dtWVCIB69epRpkwZGjVqRKNGjahatSrjx4/HZrPx1FNPUbNmTf7zn/9Qt25dWrVqRefOnRkyZAgPPPAAZcqUYdKkSQDUrFmTBx98kOjoaNzd3Rk5ciTu7u4cP36cl19+GZvNhmVZtGnTxn7L4+nTp7NixQpyc3N57LHHuPvuu/Ot0dnPfeUrX697nJc++T1/xrKsPzbQmJ6WZcUUousf+wEiIn/C71cGisK1PpPjRrjy1rs30tU+c+RGiYiIKNL85557rkjz9YJBxKW5xH/AI0aMcOrXx2PGjCmW5/HPXNH37xtWhYiIiIiI/K1dcyuXMSb5ak2ArkISEREREblOWpnNX4F35QKigFNXPG6ANUVSkYiIiIiI/O0UNDFZAHhblrX1ygZjzIoiqUhERERERP52rjkxsSzrqp9VYlnW4ze+HBERERGRvzZt5cqfPs5WRERERESKnSYmIiIiIiJS7Aq6xkRERERERG4gbeXKn1ZMRERERESk2GliIiIiIiIixU5buUTkL6l69epFmj9jxowize/Zs2eRZc+aNavIsm9G/vnz54s0v3v37kWaX6FChSLNFxFxVZqYiIiIiIjcRLrGJH/ayiUiIiIiIsVOExMRERERESl22solIiIiInITaStX/rRiIiIiIiIixU4TExERERERKXbayiUiIiIichNpK1f+tGIiIiIiIiLFThMTEREREREpdk41MUlISCAqKorWrVszbdo0l8p35dqVr3zl/7n8nTt38tZbb/Hmm2+yYsWKPO3r1q3jnXfe4b333uPDDz8kPT0dgN27dzN58mTeeecdJk+ezJ49e/LNv++++9iwYQObN2/mhRdeyNMeGhrKd999x+rVq1mwYAEVK1Z0aPfx8eGnn35i4sSJecZGRUWRkpLCzp07GTJkSJ72ypUrExcXx5YtW4iPjyckJMTeNmHCBJKSkkhKSqJLly751l7U+dWqVaN379706dOHu+66K98+ALVq1WLo0KEEBwcDUKdOHZ588kn715AhQwgMDARg/fr1PPHEEzz++ON89tlnebKysrL497//zeOPP07fvn05cuQIANnZ2bz++uv07NmTXr16sWXLFvuY+Ph4evbsyVNPPUWvXr04efLkVWv9PWc/95X/98135dqdgTHGqb+Ki9NMTGw2G6NHj2b69OnExsayYMGCq/4j7Wz5rly78pWv/D+Xn5uby3fffUfPnj0ZOHAgW7dutU88LrvzzjsZOHAgAwYMoGXLlsTGxgJwyy230KNHDwYOHMijjz7KvHnz8uS7ubnx1ltv0blzZ+666y46d+5M7dq1HfqMGTOGL774gmbNmjFx4kRee+01h/bhw4ezZs2afLMnT55MdHQ0devWpVu3boSFhTn0efPNN5k9ezb169dn7NixjB8/HoC2bdvSoEEDGjRowN13382gQYPw8fG5qfnGGFq3bs2XX37J9OnTqVOnDv7+/nl+Ty8vLyIiIkhLS7M/lpKSwowZM5gxYwYLFiwgMzOTY8eOkZuby3vvvccbb7zBp59+yrJly9i/f79D3sKFC/H29mbOnDl07tzZ/qJpwYIFAMTExPDWW2/x4YcfkpubS05Ojn0C+sknn1C7du18JzxXcvZzX/l/33xXrl2c23VPTIwxgUVRSHJyMlWqVCE0NBQvLy+io6OJj493iXxXrl35ylf+n8s/dOgQ/v7++Pv74+HhwR133EFKSopDn5IlS9q/z8rKsn8fEhKCr68vAEFBQWRnZ5OTk+MwtmHDhvz8888cOHCA7Oxsvv76a9q2bevQp3bt2iQkJACX3mV88MEH7W133HEH5cuXZ/ny5Xlqb9y4MXv37mXfvn1kZ2czd+5cOnTo4NAnLCyMZcuWAbB8+XJ7e1hYGAkJCdhsNi5cuMC2bdto06bNTc2vUKECmZmZnD59mtzcXLZv307NmjXz/J733HMP69aty/PcXlanTh22b98OQEZGBiEhIVSsWBFPT09atWrF6tWrHfqvXr3aXkvLli3ZtGkTlmVx4MABGjRoAEC5cuXw9vZm586dAFiWxa+//oplWZw7d86+OnMtzn7uK//vm+/KtYtzu+bExBjjd8WXP/CDMaacMcbvRhaSnp5uX2KHS/9IX/muo7Pmu3Ltyle+8v9c/pkzZyhTpoz9uEyZMpw5cyZPv7Vr1zJx4kQWLVqU58U5wI8//khISAgeHo43S6xQoQKpqan247S0NCpUqJBnbPv27QFo3749vr6+lCtXDmMM48aNY8SIEfnWHhISwqFDh+zHqampDlup4NILhI4dOwLQsWNHfH198fPzIzk5maioKEqVKoW/vz+RkZFUqlTppub7+Pg4PNdnz57F29vboU9QUBA+Pj78/PPP+T4HALfddpt9YnL+/HnKly9vbytfvjzHjx936H/8+HF7Hw8PD7y9vTl9+jTVq1dn9erV5OTkcOTIEXbu3MmxY8fw8PBg4MCBPPXUU3Tq1Im9e/fSuXPnq9ZzmbOf+8r/++a7cu3Oori3arnqVq4MYNPvvjYCIcDm374XEZFCuPvuuxkyZAgPPvigfYXgsvT0dBYtWmR/gX69RowYQbNmzUhISKBZs2akpqaSm5tL7969iYuLc9jCdL0GDx5My5Yt2bhxIy1atODw4cPYbDaWLl3KokWLWLVqFXPmzGHdunXYbDany2/VqlWe5/v3KlSoQE5ODhkZGdedfaUHH3yQ8uXL88wzz/D+++9Tt25d3NzcyMnJ4fvvv+ejjz7i66+/pnbt2kydOvVP/zwRkb+agj7HZDDQGhhsWdY2AGPMPsuyql1rkDGmD9AHYOrUqfTp06fAQoKCgjh69Kj9OD09naCgoALHFVZR5rty7cpXvvL/XL6vry+nT5+2H58+fdq+PSs/9erV49tvv3XoP2vWLLp06ZLv9RFHjhxxWGWoWLGi/WLry44ePcoTTzwBQOnSpWnfvj2nT5+mUaNG3H333fTu3ZvSpUvj6enJ+fPneemll4BLKxihoaH2nJCQEIfVmcs///K7+6VLl+aRRx6x/74TJkxgwoQJAMyePZvdu3c7jC3q/LNnzzo81z4+Ppw7d85+7OXlRUBAAI8//rhD/jfffGP/m4eFhTlsvStdurTDO7O/Xx257PIqSmBgIDk5OZw7d44yZcpgjKFfv372fs899xyhoaH2vfGX/44PPvhgoS7mdfZzX/l/33xXrl2c2zVXTCzLehvoDYw0xkwyxvgAVkGhlmVNsywrwrKsiMJMSgDCw8PZv38/hw4dIisri9jYWFq1alWoscWd78q1K1/5yv9z+ZUqVeLEiROcPHmSnJwckpKSqFOnjkOf378bv2PHDgICAgD45ZdfiImJoU2bNlStWjXf/M2bN1O9enWqVKmCp6cnnTp1YtGiRQ59/Pz87EvvAwcOtF9Y3adPH8LDw6lXrx4jRozgiy++4N///rd93IYNG6hRowZVq1bF09OTrl27Mn/+fIdsf39/e/bLL79MTEwMcOnCdj8/P/tzGB4eTlxcnMPYos4/cuQI5cqVo0yZMri5uREWFuZwgWxWVhaTJ09mypQpTJkyhbS0NIdJCThu4wIICAjg8OHDHDlyhOzsbJYtW0bTpk0dfm7Tpk1ZvHgxACtXrqRBgwYYY/j111/55ZdfANi4cSPu7u5UrVqVgIAA9u/fT2ZmJnDpGpXq1atTEGc/95X/98135drFuRX4ye+WZR0GHjXGdACWArcUSSEeHowcOZLevXtjs9no1KlTvhcxOmO+K9eufOUr/8/lu7u706FDBz755BNyc3OJiIggKCiIuLg4KlWqRJ06dVizZg179uzB3d2dUqVK2W99u2bNGk6cOEF8fLz9ws5evXo5XCdhs9kYPHgwX3/9Ne7u7syePZsdO3YwbNgwtmzZwqJFi2jevDmvvfYalmWxZs0a+4pIQWw2G/3792fRokW4u7sTExNDSkoKo0aNYtOmTcyfP5/IyEjGjRuHZVkkJibaVwQ8PT1ZuXIlcOk6m+7du+fZalXU+ZZlsXTpUrp06YIxhm3btpGRkUHz5s05evRogXfxCQ0N5ezZsw4rXm5ubgwYMIDBgweTm5vLgw8+SLVq1ex302rWrBlt27Zl/PjxPP744/j6+jJy5EgATp06xZAhQzDGEBAQwLBhw4BLk50ePXrQv39/PDw8qFKlin0l6Fqc/dxX/t8335Vrdxb65Pf8GcsqcAHk/3c2phRQ3bKsH40xPS3LiinEsML/ABERF/H77VhFoWfPnkWWffbs2SLLvhku31K4qHTv3r1I86+8eYGI3FAu8Yp//PjxTv36eNiwYcXyPF7X7YIty/rFsqwffzv89zU7i4iIiIiIFNI1t3IZY5Kv1gToKiQRERERkeukrVz5K+gakyAgCjh1xeMGyPsxwiIiIiIiIn9AQROTBYC3ZVlbr2wwxqwokopERERERORv55oTE8uyel2j7fEbX46IiIiIyF+btnLl77oufhcRERERESkKmpiIiIiIiEixK/ADFkVERERE5MbRVq78acVERERERESKnSYmIiIiIiJS7LSVS0TkD+jYsWOR5teoUaPIsl988cUiywaIj48v0vxhw4YVaf6BAweKNH/48OFFmh8SElKk+SLy52krV/60YiIiIiIiIsVOExMRERERESl22solIiIiInITaStX/rRiIiIiIiIixU4TExERERERKXaamIiIiIiISLHTNSYiIiIiIjeRrjHJn1ZMRERERESk2GliIiIiIiIixU5buUREREREbiJt5cqfU62YJCQkEBUVRevWrZk2bZpL5bty7cpXvvKdO3/Lli3079+ffv368e233+ZpT0lJYfDgwXTp0oW1a9c6tM2aNYsXXniBAQMG8PHHH2NZlkN7REQEH3/8MTExMXTt2jVP9rPPPsuHH37Ihx9+yCeffMI333wDQGBgIP/973/58MMPmTZtGtHR0fnWHhUVRUpKCjt37mTIkCF52itXrkxcXBxbtmwhPj6ekJAQe9uECRNISkoiKSmJLl26FEt+nTp1eO211xg1ahQPPPBAnvYmTZrwxhtv8Morr/DKK6/QtGlTe9vDDz/Mq6++yquvvkrDhg3zzf/hhx/o3r07//znP5kzZ06e9qSkJPr06cP999/PypUrHdqGDh1K+/btGTZsWL7ZheHs577yqgpZ1gAAIABJREFUnTfflWsX5+U0ExObzcbo0aOZPn06sbGxLFiwgD179rhEvivXrnzlK9+58202G9OnT2f48OG88847rFq1ikOHDjn0CQgI4LnnnqN58+YOj+/YsYMdO3bw9ttvM2nSJPbu3ctPP/1kb3dzc6Nfv34MHz6cp59+msjISCpXruyQMWXKFPr27Uvfvn357rvvWLVqFQAnT57khRdeoG/fvvTv35+uXbvi5+fnMNbNzY3JkycTHR1N3bp16datG2FhYQ593nzzTWbPnk39+vUZO3Ys48ePB6Bt27Y0aNCABg0acPfddzNo0CB8fHxuar4xhq5du/L+++8zZswYIiIiCA4OzvM32rRpExMmTGDChAmsWbMGgLp16xIaGsr48eOZOHEi999/PyVLlnQYZ7PZeO+993j99deJiYlh2bJl7N+/36FPUFAQQ4cO5b777svzc7t27corr7yS5/HCcvZzX/nOm+/KtYtzu+bExBjT5nfflzHGfGyMSTbGzDHGBN3IQpKTk6lSpQqhoaF4eXkRHR1NfHy8S+S7cu3KV77ynTt/z549BAcHExQUhKenJ82aNWPDhg0OfQIDA6latSpubo7/SzfGkJ2dTU5Ojv2rbNmy9vbatWuTlpbG0aNHycnJYeXKlQ7v+F8pMjKSFStWAJCTk0N2djYAnp6eeX42QOPGjdm7dy/79u0jOzubuXPn0qFDB4c+YWFhLFu2DIDly5fb28PCwkhISMBms3HhwgW2bdtGmzZtbmp+1apVOX78OCdOnMBms7Fp0ybuuOOOqz4/vxccHMyePXvIzc0lKyuL1NRU6tSp49Bnx44dhISEULFiRTw9PWnVqpV9YvP7nOrVq+f7/DZo0IBbbrmlUPXkx9nPfeU7b74r1+4sjDFO/VVcCloxGf+7798GjgDtgQ3A1BtZSHp6usM7UUFBQaSnp7tEvivXrnzlK9+580+ePElAQID92N/fn5MnTxZqbO3atbn99tt5+umnefrpp7nzzjupVKmSvT0gIIDjx4/bj48fP46/v3++WYGBgQQHB7N161b7Y+XLl2fKlCl89tlnzJ07N09dISEhDqs7qampDlup4NILkI4dOwLQsWNHfH198fPzIzk5maioKEqVKoW/vz+RkZEOtd+M/LJly3Lq1Cn78alTpyhTpkye56Z+/foMHz6c3r17U65cOXstderUwdPTk9KlS1OrVi1722UZGRkEBgbaj6/8exQ1Zz/3le+8+a5cuzi367n4PcKyrDt/+/4dY0yPq3U0xvQB+gBMnTqVPn36/IkSRUTkjzhy5AipqalMnXrpfaQxY8aQkpLyh7IiIyNJTEwkNzfX/tjx48d59tln8fPzY9SoUSQmJpKZmXlduYMHD2by5Mn06NGDxMREDh8+jM1mY+nSpURERLBq1SoyMjJYt24dNpvtuusu6vxt27axceNGcnJyaN68Od27d+e9995j+/btVKlShZdeeolz587x888/Ozx3IiKSV0ETk0BjzCDAAL7GGGP9/ysnr7raYlnWNODylUrW1fr9XlBQEEePHrUfp6enExR043aLFWW+K9eufOUr37nz/fz8yMjIsB+fOHEiz7UcV/PDDz9Qs2ZNSpUqBVx6Z3/Xrl329oyMDMqXL28/Ll++PCdOnMg3KzIykvfffz/ftpMnT7J//37Cw8NJTEy0P56amkpoaKj9OCQkhNTUVIexR44coXPnzgCULl2aRx55hNOnTwPYr9sAmD17Nrt373YYW9T5mZmZDqsc5cqVs4+97Pz58/bvV69ebV+dAVi8eDGLFy8GoGfPnhw7dsxhbEBAgMNjV/49ipqzn/vKd958V67dWeiuXPkraCvXR4AP4A18CgQAGGOCga3XGHfdwsPD2b9/P4cOHSIrK4vY2FhatWrlEvmuXLvyla98586vUaMGR44cIT09nezsbFavXk2jRo0KNTYgIICUlBRsNhs5OTn89NNPDtuVdu7cSUhICMHBwXh4eNCyZcs8d/UCCA0Nxdvb22G1JSAgAC8vLwC8vb2pW7dunovyN2zYQI0aNahatSqenp507dqV+fPnO/Tx9/e3/wP98ssvExMTA1y6sP3yBCw8PJzw8HDi4uJuav6BAwcIDAzE398fd3d3GjZsSHJyskMfX19f+/f16tWzv5gyxlC6dGng0oQpJCSE7du3O4y97bbbSE1N5ciRI2RnZ7Ns2TLuvvtubhZnP/eV77z5rly7OLdrrphYlvXvqzx+1Biz/IYW4uHByJEj6d27NzabjU6dOlGzZk2XyHfl2pWvfOU7d767uzu9e/dm7Nix5Obm0qpVK0JDQ/niiy+oXr06jRo1Ys+ePUycOJHz58+zceNG5s6dy7vvvkuTJk348ccfGTRoEMYY7rzzTiIiIvj8888ByM3N5f3332f8+PG4ubmxZMkSDhw4QPfu3dm1axfr1q0DHC96v6xy5cr06dMHy7IwxvDVV1/luaOUzWajf//+LFq0CHd3d2JiYkhJSWHUqFFs2rSJ+fPnExkZybhx47Asi8TERPr16wdcuqD+8u1xz5w5Q/fu3fNstSrq/NzcXObOnUu/fv1wc3Nj7dq1HDlyhHbt2nHgwAG2bdvGvffeS3h4OLm5uVy4cIGZM2fa/26DBg0C4Ndff2XGjBl5tnK5u7vz/PPPM3ToUGw2Gw8++CDVqlUjJiaGWrVq0axZM3bs2MHIkSM5d+4ca9euZcaMGfbJ1YABAzh48CC//PILXbp0YfDgwYWetILzn/vKd958V65dnJu58p72hR5ozEHLsioX3LNwW7lEROT/27ZtW5Flv/jii0WWDbj83XOeeeaZIs0fPnx4keZfeQMAkb8Zl9gj9e677zr16+MXXnihWJ7Ha66YGGOSr9YE/LU2+4mIiIiISLEp6OL3ICAKOHXF4wZYk7e7iIiIiIjI9StoYrIA8LYsK8+F7saYFUVSkYiIiIiI/O0UdPF7r2u0PX7jyxERERER+WvT7YLzV9DtgkVERERERIqcJiYiIiIiIlLsCrrGREREREREbiBt5cqfVkxERERERKTYaWIiIiIiIiLFTlu5REScUHh4eJFlz5s3r8iyAebPn1+k+T179izS/KlTpxZp/u7du4s0f+nSpUWaLyJ/nrZy5U8rJiIiIiIiUuw0MRERERERkWKnrVwiIiIiIjeRtnLlTysmIiIiIiJS7DQxERERERGRYqetXCIiIiIiN5G2cuVPKyYiIiIiIlLsNDEREREREZFip61cIiIiIiI3kbZy5U8rJiIiIiIiUuycamKSkJBAVFQUrVu3Ztq0aS6V78q1K1/5ynfd/D+SvXbtWh599FE6derEp59+mqc9KyuL4cOH06lTJ5566inS0tLsbbt376ZXr15069aNxx9/nIsXLwIwYMAA/vGPf9CtWzcWLlxIbm4uABUrVqRDhw489NBD3H777Xl+Vs2aNWnXrh3R0dFERUVRpkwZe1vZsmVp06YN7du3p127dri55f0nKyoqip9++okdO3YwZMiQPO2VK1cmLi6OzZs3Ex8fT0hIiL1twoQJbN26la1bt/Loo4/m+1xFRUWRkpLCzp07r5m/ZcuWfPOTkpJISkqiS5cu+eZHRETwySefMGPGDLp27Zqn/dlnn2XKlClMmTKFmJgYvv32WwACAwP54IMPmDJlCh999BHt2rXLN78grnzuK7948125dnFe1z0xMcb4F0UhNpuN0aNHM336dGJjY1mwYAF79uxxiXxXrl35yle+6+b/kWybzcabb77Ju+++yxdffEFcXBw///yzQ5/vv/8eHx8fvv76a7p168Z///tfAHJychg1ahRDhw7liy++4MMPP8TD49KO4HHjxvHZZ5/x+eefc+HCBbZv344xhsaNG7Ns2TLmz59P1apVHSYeAPv372fBggXExsby008/0bBhQ+DSNofmzZuzfv165s+fT1xcHJZlOYx1c3PjP//5D+3atSM8PJyuXbsSFhbm0GfixInMmjWLBg0aMHbsWMaNGwdA27ZtqV+/Pg0bNqRp06YMGjQIHx+fPPmTJ08mOjqaunXr0q1btzz5b775JrNnz6Z+/fqMHTuW8ePH2/MbNGhAgwYNuPvuu6+a//zzzzNs2DB69+7NvffeS+XKlR36TJkyhWeffZZnn32W//3vf6xatQqAkydPMmDAAJ599lmef/55unbtir//9f3z7MrnvvKLN9+Vaxfnds2JiTHmdWNMwG/fRxhjfgbWG2MOGGNa3shCkpOTqVKlCqGhoXh5eREdHU18fLxL5Lty7cpXvvJdN/+PZCcnJ1OpUiVCQkLw9PSkdevWJCQkOPRJSEggOjoagFatWrFhwwYsy2L9+vXUqFGDWrVqAVCmTBnc3d0B8Pb2Bi69oLDZbAD4+/tz9uxZzp07R25uLgcOHCA0NNThZ2VnZ9u/vzzJAahQoQKnTp3i1KlTwKVVnCsnJo0bN2bv3r3s27eP7Oxs5s2bR4cOHRz6hIWFsXz5cgCWL19ubw8LCyMxMRGbzcaFCxfYtm0bUVFR18yfO3duvvnLli3LNz8hIcEhv02bNg5ja9euTVpaGkePHiUnJ4cVK1bQtGlTrubee++1/y45OTn2587T0zPf1aSCuPK5r/zizXfl2p2FMcapv4pLQf8ni7YsK+O3798EulqWVQNoDbx9IwtJT08nODjYfhwUFER6erpL5Lty7cpXvvJdN/+PZKenpxMUFGQ/DgwM5Pjx4w59jh8/TmBgIHBpsuDt7c3p06c5ePAgAP3796d79+7MmjXLYVz//v1p06YNXl5ehIWFccstt3D+/Hl7+/nz5ylVqlSemmrVqsXDDz9MgwYN2LBhAwC+vr4A3HfffbRt25Y6derkGVexYkUOHTpkPz58+DAVK1Z06JOcnEzHjh0BePjhh/H19cXPz4/k5GSioqIoVaoU/v7+REZG5pk0hYSEOOSnpqY6bNW6Mr9jx47XzK9UqZLD2ICAAIfnPiMjg4CAgDy/J1z6OwUHB7N161b7Y+XLl2fq1KnMmTOHuXPncuLEiXzHXo0rn/vKL958V65dnFtBExMPY8zlt7BKWZa1AcCyrF1AiasNMsb0McZsNMZs1L5AEZG/BpvNRlJSEqNHj2batGmsWLHCPpEA+M9//kNsbCw2m439+/cXOnfXrl3873//Y/PmzYSHhwOXtjkFBgayatUqlixZQuXKlR1eqBTWkCFDaNGiBRs2bKBFixYcPnwYm83G0qVLWbRoEYmJiXz22WesW7fOvtJzPQYPHkzLli3ZuHFjvvmrVq1izpw5fzj/snvvvZfExET7tTtwaQL5zDPP8OSTT9K6dWvKli37h/NFRJxBQROTD4CFxphWwGJjzHvGmJbGmH8DW682yLKsaZZlRViWFdGnT59CFRIUFMTRo0ftx1e+q/dnFWW+K9eufOUr33Xz/0j2le88Hjt2jPLlyzv0KV++PMeOHQMubRk6d+4cZcqUITAwkPr161O2bFlKlixJ06ZN2bFjh8PYEiVKUKtWLXbt2sWFCxcoXbq0va106dL88ssvV61t//799lWLCxcukJ6ezsWLF7HZbKSmpuLn5+fQPy0tzWGVo1KlSg4X6gMcOXKERx99lEaNGjFixAgATp8+DVy6OD0iIoI2bdpgjGH37t0OY1NTUx3yQ0JCSE1NzZPfuXNnIiIiePXVV/PkN2zYkKioqHzzMzIyHJ77gIAAMjIyyE9kZKR9G9eVTpw4wf79++2TusJy5XNf+cWb78q1O4vi3qrlklu5LMuaDIwHngEeAloBQ4FUoOeNLCQ8PJz9+/dz6NAhsrKyiI2NpVWrVi6R78q1K1/5ynfd/D+SHR4ezqFDh0hLSyM7O5ulS5fSokULhz733HMPsbGxACxbtoyIiAiMMTRp0oS9e/fy66+/kpOTw5YtW6hWrRoXLlywv6DOyclhz549+Pv7c+LECXx8fPD29sbNzY0qVao4bI0CHC4Ir1SpEmfPngUuTTrKli2Lu7s7xhiCgoLsL/gv27BhAzVq1KBq1ap4enrSpUsX5s+f79DH39/f/o/syy+/zIwZM4BLKzKXJzrh4eGEh4cTFxd3zfyuXbsWmB8TE1Po/J07dxISEkJwcDAeHh5ERkaydu3aPH+z0NBQvL29SUlJsT8WEBCAl5cXcOn6nrp16+Z5bgviyue+8os335VrF+dW4AcsWpa1Alhx5ePGmJ5AzA0rxMODkSNH0rt3b2w2G506daJmzZo3Kr5I8125duUrX/mum/9Hsj08PHjppZfo378/ubm5tG/fnltvvZWpU6cSFhZGixYt6NChA6NGjaJTp074+voyduxY4NJ1H4899hhPPvkkxhiaNm1K8+bNOXHiBC+99BLZ2dnk5ubi5+dHw4YNsSyLH374gfvuuw9jDHv27OH06dPccccdnDhxgsOHD1O7dm0qVKhAbm4uWVlZrF69Grh0sfv27dtp27YtcGn14srVCpvNxoABA1i4cCHu7u7MmDGDlJQURo0axcaNG1mwYAEtW7Zk3LhxWJZFYmIizz//PHDpgvEVK1YAcPbsWXr06JFnq5XNZqN///4sWrQId3d3YmJi7PmbNm1i/vz5REZGOuT369fPnr9y5UoAzpw5Q/fu3fPk5+bm8v777zNhwgTc3NxYsmQJBw4coEePHuzatcs+SYmMjLTXelnlypV55plnsCwLYwxffvnldW2fu3wuuOq5r/zizXfl2sW5mSvvclLogcYctCyrcsE9+WM/QEREikRmZmaR5l+5qnCj9ex5Qxfs8/ij/y4WVlG/87t06dIizRdxci7xkepTp0516tfHzzzzTLE8j9dcMTHGJF+tCfhrbfYTEREREZFCMca0Ad4D3IHplmW9fpV+nYCvgEaWZW28VmZBW7mCgCjg1JU/A1hTmKJFREREROSvwxjjDvyXSx8hchjYYIz53rKslCv6+QADgPWFyS1oYrIA8LYsK88duIwxKwrzA0RERERE5P8rzjtf3SCNgT2WZf0MYIz5gks3ykq5ot8Y4A1gcGFCC7orVy/LslZdpe3xwvwAERERERH5SwkBfn8rwMO/PWZnjGkAhFqWFVvY0II+x0RERERERP5Gfv9h6b99Fe6DCf//eDdgEvDi9Ywr8HbBIiIiIiJy4zj7Vi7LsqYB067RJRUI/d1xpd8eu8wHqAus+O13DQa+N8Z0uNYF8FoxERERERGR67EBqGmMqWaM8QK6Ad9fbrQs67RlWQGWZVW1LKsqsA645qQENDEREREREZHrYFlWDtAPWAJsB+ZZlvWTMWa0MabDH83VVi4REREREbkulmUtBBZe8djIq/SNLEzmH/7k9+vg1J9sKSIirqVEiRJFmp+Tk1Ok+R4eRfue4JIlS4o0PzIyskjzRf4k57544zfTp0936tfHvXv3LpbnUVu5RERERESk2GliIiIiIiIixU7XmIiIiIiI3ETOfrvg4qIVExERERERKXaamIiIiIiISLHTVi4RERERkZtIW7nypxUTEREREREpdpqYiIiIiIhIsdNWLhERERGRm0hbufKnFRMRERERESl2TjUxSUhIICoqitatWzNt2jSXynfl2pWvfOW7br4r134j8h944AG2bdtGSkoKL730Up72ypUrs3jxYjZu3EhcXBwhISH2tvHjx7NlyxaSkpKYNGlSvvlRUVGkpKSwc+dOhgwZkm9+XFwcW7ZsIT4+3iF/woQJJCUlkZSURJcuXYql/h9//JGRI0fy6quvsnjx4jztS5cuZdSoUYwePZpJkyZx4sQJe9uzzz7LmDFjGDNmDP/973/zzS+Is58/ynfO7JuRL07Ksqyi/iqUnJwc67777rMOHjxoXbx40Wrfvr21e/fuwg4v1nxXrl35yle+6+a7cu1/Jt/Ly8vy8vKySpYsae3du9eqXbu2Vbp0aSspKcmqV6+evd3Ly8v66quvrKeeesry8vKyHnjgAWv27NmWl5eX1aJFC2v16tVWyZIlrZIlS1pr16617r//fsvLy8tyc3Oz3NzcLA8PD2vPnj1W9erVrRIlSlhbt261br/9dnu7m5ub9eWXX1pPPvmk5ebmZt13333WrFmzLDc3N6tdu3bW0qVLLU9PT8vb29v64YcfrDJlylhubm5FXv/y5cut5cuXW/Hx8VbTpk2tefPmWUuXLrVatWplffbZZ/b25cuXW1OmTLGWLFliLV++3Bo1apT12GOP2dvCw8Md+l7+Kuq/r/KdP9/Ja78Zr23/9FdMTIzlzF/F9bxcc8XEGLPZGPOqMaZ6UU+QkpOTqVKlCqGhoXh5eREdHU18fLxL5Lty7cpXvvJdN9+Va78R+Y0aNWLv3r3s27eP7Oxs5s2bR/v27R36hIWFsWLFCgBWrFhhb7csi5IlS+Ll5UWJEiXw9PTk2LFjDmMbN27skD937lw6dOiQJ3/ZsmUALF++3N4eFhZGQkICNpuNCxcusG3bNtq0aXNT69+3bx+BgYGUL18eDw8PIiIiSEpKcuhTu3ZtvLy8AKhWrRqZmZnXftKvg7OfP8p3zuybkS/Oq6CtXOWAssByY8wPxpiBxpiKRVFIeno6wcHB9uOgoCDS09NdIt+Va1e+8pXvuvmuXPuNyK9YsSKHDh2yH6empjpsdYJLL3AefvhhAB566CF8fX3x8/Nj/fr1rFy5kgMHDnDgwAGWLl3Kjh07HMaGhIQUKr9jx44AdOzY0Z6fnJxMVFQUpUqVwt/fn8jISCpVqnRT68/MzKRcuXL243Llyl1z4rF69Wpuv/12+3F2djbjxo3j9ddfZ+vWrVcddzXOfv4o3zmzb0a+OK+CJianLMt6ybKsysCLQE1gszFmuTGmz9UGGWP6GGM2GmM2al+giIgUl5dffpl77rmH9evX06JFCw4fPozNZqN69ercdttt3HrrrVSrVo3IyEiaNWt23fmDBw+mZcuWbNy40SF/6dKlLFq0iFWrVjFnzhzWrVuHzWZzuvovW7duHQcOHOCBBx6wPzZ+/HiGDx9Or169mDdvHsePH//D+SIihVHo2wVblpUIJBpjngdaA12BfGcdlmVN+12bVZj8oKAgjh49aj9OT08nKCiosOUVa74r16585SvfdfNdufYbkZ+WlkZoaKj9OCQkhNTUVIc+R44coWvXrgCULl2ahx9+mNOnT9OrVy/Wr1/P+fPnAViyZAlNmjRh9erV9rGpqamFyu/cubM9/5FHHuH06dPApYvfJ0yYAMDs2bPZvXv3Ta2/bNmynDp1yn586tQpypYtm+d53L59O4sWLeLFF1/E09PT/vjl1Zby5ctTq1YtDh48SPny5fOMvxpnP3+U75zZNyPfGeh2wfkraMVk15UPWJZlsyxrsWVZPW9kIeHh4ezfv59Dhw6RlZVFbGwsrVq1col8V65d+cpXvuvmu3LtNyJ/48aN1KhRg6pVq+Lp6UmXLl1YsGCBQx9/f3/7C4AhQ4bw6aefAnDw4EFatGiBu7s7Hh4etGjRIs9WqA0bNjjkd+3alfnz5181/+WXXyYmJgYANzc3/Pz87L9neHg4cXFxN7X+qlWrcuzYMTIyMsjJyWHjxo3ccccdDn0OHjzI7Nmz+de//oWvr6/98fPnz5OdnQ3AuXPn2Lt3LxUqVMj/D3EVzn7+KN85s29Gvjiva66YWJbV7WptxpielmXF3LBCPDwYOXIkvXv3xmaz0alTJ2rWrHmj4os035VrV77yle+6+a5c+43It9lsvPDCCyxYsAB3d3dmzJjB9u3bGTlyJJs3b2bBggW0aNGCsWPHYlkWiYmJDBgwAIBvvvmGe++9l82bN2NZFnFxccTGxubJ79+/P4sWLcLd3Z2YmBhSUlIYNWoUmzZtYv78+URGRjJu3Dh7fr9+/QDw9PRk5cqVAJw5c4bu3bvn2cpV1PW7u7vTrVs33nvvPXJzc2nWrBkVK1bk+++/p0qVKtxxxx18/fXXXLx40X47Vj8/P5577jmOHj3K7NmzcXNzIzc3l6ioKCpWvL5LTJ39/FG+c2bfjHxxXsayCrXTKu9AYw7+du1JQf7YDxAREclHiRIlijQ/JyenSPM9PAq9i/oPWbJkSZHmR0ZGFmm+yJ/kEnukZs6c6dSvj7t3714sz+M1/+9ojEm+WhPw19rsJyIiIiIixaagt22CgCjg1BWPG2BNkVQkIiIiIiJ/OwVNTBYA3pZl5bmBuTFmRZFUJCIiIiLyF6a7cuWvoIvfe12j7fEbX46IiIiIiPwdFXS7YBERERERkSJXtLcGERERERERB9rKlT+tmIiIiIiISLHTxERERERERIqdtnKJiIiIiNxE2sqVP01MREScUHLy1T7f9s/76quviiwbYMOGDUWaX9SfzF7U6tSpU6T5LVq0KNJ8EZGioq1cIiIiIiJS7LRiIiIiIiJyE2krV/60YiIiIiIiIsVOExMRERERESl2mpiIiIiIiEix0zUmIiIiIiI3ka4xyZ9WTEREREREpNhpYiIiIiIiIsVOW7lERERERG4ibeXKn1ZMRERERESk2DnVxCQhIYGoqChat27NtGnTXCrflWtXvvKV79z5W7ZsoX///vTr149vv/02T3tKSgpDhgyha9eurF271qFt1qxZDBw4kBdeeIFPPvkEy7Ic2mvUqEH//v0ZMGAA99xzT57sO++8k6FDh9K3b1/69u1LgwYN7G1PPPEEr7zyCv/4xz+uWnvDhg356KOP+Pjjj3n00UfztPfp04f333+f999/n48++ogvv/wSgFtvvZVJkyYxZcoUPvjgA1q0aJFvflRUFCkpKezcuZMhQ4bkaa9cuTJxcXFs2bKF+Ph4QkJC7G0TJkwgKSmJpKQkunTpUiz5TZs25dtvv+W7776jZ8+eedqDg4OZNm0an3/+OXPnzqV58+YAlClThmnTprF69WqGDh2KzWbjl19+4ZdffuGjjz7Kk5OVlcXAgQO6D8ijAAAgAElEQVSJioqia9eupKam2tumTZtGVFQUDz74IKtWrQJg3759dOzY0f4VERHBp59+CsD27dvp0qULDz30EI888gjJycn5/m7g/P9tKd85s29Gvjgnp9nKZbPZGD16NDExMQQFBdG5c2datWpFjRo1nD7flWtXvvKV79z5NpuNjz/+mBEjRuDn58crr7xCREQEoaGh9j4BAQE899xzfP/99w5jd+7cyc6dO3nrrbcAGDFiBCkpKfZ2Ywzt2rXj008/5cyZMzzzzDPs2LGD48ePO+T8+OOPxMbG5qlt9erVeHp6EhERkW/tbm5uPPfccwwbNoyMjAzee+891q9fz8GDB+19fv+Co0OHDlSvXh2Aixcv8tZbb5GWloafnx+TJ09m06ZNnD9/3iF/8uTJREVFcfjwYdavX8/8+fPZvn27vc+bb77J7NmzmTlzJvfeey/jx4+nR48etG3blgYNGtCgQQNKlCjBsmXLWLRoEWfPnr2p+S+//DJ9+/YlPT2dzz77jJUrV/Lzzz/b+/Tu3ZulS5fy5ZdfcuuttzJ58mSio6O5ePEiH3zwATVq1KBatWpkZWVRokQJjDHExsZy7733OpxjX331FWXKlGHJkiXExsby1ltv8c4777Bnzx4WLlzI/PnzOXbsGE899RSLFi2iWrVq9kmwzWYjMjKS+++/3/47P/fcc7Rs2ZKVK1fy5ptvMmvWrDx/f2f/b0v5zpl9M/KdgbZy5e+aKybGmAhjzHJjzGxjTKgxZqkx5rQxZoMxpv6NLCQ5OZkqVaoQGhqKl5cX0dHRxMfHu0S+K9eufOUr37nz9+zZQ3BwMEFBQXh6etKsWTM2btzo0CcwMJAqVark+w9dVlYWOTk55OTkYLPZKFOmjL2tUqVKnDx5klOnTmGz2di2bRu33XZboWv7+eefuXjx4lXba9WqRVpaGkePHiUnJ4eVK1fSpEmTq/Zv2bIlK1asACA1NZW0tDQATp48SWZmpkPtAI0bN2bv3r3s27eP7Oxs5s6dS4cOHRz6hIWFsWzZMgCWL19ubw8LCyMhIQGbzcaFCxfYtm0bbdq0uan5devW5dChQ6SmppKTk8OSJUuIjIx06GNZFqVLlwbA29vbPmn89ddf2bp1KxcvXiQ1NRVjDG5ubhhjaNu2rb2my5YtW8ZDDz0EXFoFWrduHZZlsWzZMtq2bYuXlxeVKlWicuXKeVZA1q1bR2hoqH01yBhjnyCePXuWwMBA8uPs/20p3zmzb0a+OK+CtnJ9AEwEYoE1wFTLssoAL//WdsOkp6cTHBxsPw4KCiI9Pd0l8l25duUrX/nOnX/y5En8/f3tx35+fpw4caJQY2vXrk3dunXp06cPTz/9NHfccQeVKlWyt/v4+HD69Gn78ZkzZ/D19c2TU6dOHf71r3/RtWvXfNuvJiAgwGH1JSMjw+F3+b3AwECCg4NJSkrK01arVi08PDw4cuSIw+MhISEcOnTIfpyamuqwlQouvcDp2LEjAB07dsTX1xc/Pz+Sk5OJioqiVKlS+Pv7ExkZ6fDc3Iz8wMBAh3MhPT2d8uXLO/SZOnUqbdu2ZfHixUyePJk33ngjz/Nz5swZh0lpfudYeno6FSpUAMDDwwMfHx8yMzPzPT+PHTvmMHbhwoVER0fbj4cNG8bEiRNp2bIlb7zxBoMGDcpT0+Wf6cz/bSnfObNvRr44r4ImJp6WZS2yLOtzwLIs6ysufRMPlLzaIGNMH2PMRmPMRu0LFBEpHkeOHOHw4cNMmTKFqVOn8uOPPzpsQyqMnTt3MmnSJD744AP27t3LI488UiS1tmzZklWrVpGbm+vweLly5Rg8eDDvvPNOnutjCmPw4MG0bNny/7F353FRlvv/x1/3DGAmioqICCgmVqjkbu7gigu4JGl11NNJ05PH7euWppmaSGVp5lFPlktHpTTTY4gmiAsu4S6oKGKJAgHumlvAcP3+IO6f44BoOTLY5/l4zCPuua7rPRfjzDTXfV3XDQcOHKBNmzakpqZiMpmIiopi06ZN7Nq1i7CwMGJjYzGZTDaX37lzZ8LDw+ncuTPDhw9nxowZj3UJSFZWFlu3biUgIEC/7+uvv2bixIns2LGDiRMnMmnSpMfWHyGeFJqm2fStuBQ1MLmjaVonTdNeBpSmaT0BNE3zAwr9hFVKLVJKNVZKNR48ePADdcTV1ZWMjAz9ODMzE1dX1wdqW9z5Jbnvki/5km/b+ffOkNw7g3I/+/bt49lnn6V06dKULl2aBg0acOrUKb38119/NVseVa5cOa5fv26Wcfv2bf0L9cGDB6lateoD9/3ixYtmMwCVKlUqdLbn7mVc+Z5++mmmT5/OV199xcmTJy3apKWlme21cXd3N9vUDXmDs+DgYBo3bszkyZMB9Fmi0NBQGjVqREBAAJqmkZSU9Fjzz58/b/ZacHV1tdjf07NnTyIjI4G82RkHBwfKly9vVqdcuXJmg7aCXmOurq76jFNOTg6//vor5cuXL/D1effSrJ07d1K7dm0qVaqk37du3To6deoEQJcuXQrd/G7r7y3Jt83sx5EvbFdRA5N/AmOAN4AAoK2maVfJW8Y14lF2xNfXl+TkZFJSUsjKyiIiIoJ27dqViPyS3HfJl3zJt+18b29v0tPTyczMJDs7m927dxe62fxelSpVIiEhAZPJRE5ODgkJCWZLkdLS0qhYsSLly5fHaDTi6+trMQBwdHTUf37++ectvjjfz6lTp6hatSqurq7Y2dnh5+dHbGysRT0PDw8cHR3NZnPs7Ox49913iY6O1q8Uda/9+/fj7e2Nl5cX9vb29O3bl/DwcLM6zs7O+tm/CRMmsHTpUiBv43nFihWBvH8jX19ffQDwuPKPHz9OtWrVqFq1KnZ2dgQEBFgMzjIyMmjatCkANWrUoFSpUly5csWsTtWqVVFKkZubi1KKjRs30rZtW7M6bdu2Zf369QBs3ryZZs2aoWkabdu2ZePGjWRlZZGamsrZs2d54YUX9HYRERFmy7ggbwnavn37gLz9J15eXhTE1t9bkm+b2Y8jX9iu+16VSykVR96AJN/I329omvYP8vadPJqO2NkxZcoUBg0ahMlkonfv3tSqVetRxVs1vyT3XfIlX/JtO99oNDJw4EBCQkLIzc2lbdu2eHp68s0331CzZk2aNGnC6dOnmTVrFjdv3uTgwYOsXr2aOXPm0KxZM44dO8aYMWOAvEv/Nm7cmOTkZAByc3OJiIhgwIABGAwGDh06xIULF2jXrh1paWkkJibSrFkznn/+eXJzc7l9+7bZ5YoHDhxIpUqVcHBwYMyYMaxfv57Tp0/r5bm5uSxcuJAZM2ZgNBqJjIzk3Llz9O/fn1OnTrF3714A/epOd2vdujV169albNmy+tWgZs+ebXbFKpPJxIgRI9i0aRNGo5GlS5eSkJDA1KlTOXjwIOHh4fj7+xMSEoJSip07dzJs2DAA7O3t9ce8fv06AwYMsFhq9TjyP/zwQxYsWIDBYGD9+vX8/PPPvPXWWyQkJLBjxw5mz57Nu+++S79+/VBKMWXKFL19REQEZcqUwd7enjJlyvDuu++SnZ1N586dqVWrFp999hl169alXbt2BAcH8/bbbxMQEICTkxOffPIJALVq1aJz584EBgZiNBp59913MRqNANy6dYs9e/Ywbdo0s36///77zJw5k5ycHEqVKsX06dMLfO3a+ntL8m0z+3Hk2wK5KlfBtD+yZhdA07RzSqlqD1D1jz2AEEL8hd3vb0P8WWvWrLFaNuTNNFjTvTMPJc3dMxLWcPDgQavmGww29SfQhLhXifjG/+2339r09+OXX365WJ7H+86YaJpW2P8ZNUAW+wkhhBBCCCEeiaL+wKIreUu5rtxzv8YjXMYlhBBCCCGE+GsramCyAXBUSh25t0DTtO1W6ZEQQgghhBBPMNljUrCiNr8PvE/Za4++O0IIIYQQQoi/ItnBJoQQQgghhCh2RS3lEkIIIYQQQjxCspSrYDJjIoQQQgghhCh2MjARQgghhBBCFDtZyiWEEEIIIcRjJEu5CiYDEyHEEykxMdGq+fPmzbNq/rp166yWnZGRYbXsJ4HRaLRqvpubm1Xz5S+zCyFKKvn0EkIIIYQQQhQ7mTERQgghhBDiMZKlXAWTGRMhhBBCCCFEsZOBiRBCCCGEEKLYyVIuIYQQQgghHiNZylUwmTERQgghhBBCFDsZmAghhBBCCCGKnSzlEkIIIYQQ4jGSpVwFkxkTIYQQQgghRLGTgYkQQgghhBCi2NnUwCQmJoaAgAA6duzIokWLSlR+Se675Ev+k55/8OBB3nrrLQYPHsyaNWssyo8dO8aoUaPo2bMnu3fvNivr2bMnI0eOZOTIkcyYMaPAfB8fH959913ee+89OnbsaFH+4osvEhoayoQJE5gwYQLNmzfXy3r06MGkSZOYPHkywcHBRf4u/v7+xMTEsGvXLv71r39ZlLu7u7Nq1SqioqL49ttvcXNzu29eQEAACQkJJCYmMn78eIvyatWqERkZyeHDh4mOjsbd3V0vCw0NJS4ujri4OPr06fNE5nfq1Iljx46RkJDAuHHjCsz/4YcfOHjwIFFRURb5R44cIT4+ntmzZxeY36hRIxYtWsSXX37Jyy+/bFH+5ptvMm/ePObNm8cXX3zB6tWr9bLp06ezevVqpk6dWmD2g7D1967k225+Se67sF02MzAxmUxMnz6dL7/8koiICDZs2MDp06dLRH5J7rvkS/6Tnm8ymfj888957733mD9/PjExMZw7d86sjouLCyNHjsTPz8+ivYODA3PnzmXu3LlMnjzZolzTNPr06cOCBQuYMWMGjRo1okqVKhb1Dh06xAcffMAHH3zAjz/+CECNGjV45plnmDlzJiEhIVSvXp1atWoV+rsYDAZCQkLo168fbdu2pWfPnhb1p0yZwpo1a+jYsSOffvopEydOvG/evHnz6NatG3Xr1uWVV17Bx8fHrM6sWbNYsWIFDRo0YMaMGcycOROArl270rBhQxo2bEjz5s0ZPXo0ZcuWfeLy586dS1BQEPXq1aNv374W+R9++CErV66kUaNGhISE6IPXZs2a0bx5cxo2bEj9+vVp3Lgxbdq0scgfOnQoU6ZM4Z///Cd+fn54enqa1fniiy8YPnw4w4cP5/vvv2fPnj162XfffcfHH3/MH2Xr713Jt938ktx3W6Fpmk3fist9ByaapjlqmjZd07TjmqZd0zTtgqZpsZqmvf6oOxIfH0/16tXx9PTEwcGBbt26ER0dXSLyS3LfJV/yn/T8pKQk3NzcqFKlCvb29rRu3Zq9e/ea1XF1daVGjRp/6MPYy8uLixcvcunSJUwmE4cOHeKFF1544Pb29vbY2dlhZ2eH0Wjk+vXrhdZt0KABycnJnDt3juzsbNavX09AQIBZnVq1aumzPrt376ZTp06F5jVt2pSffvqJM2fOkJ2dzapVq+jevbtZHR8fH7Zu3QrAtm3b9HIfHx9iYmIwmUzcunWLo0eP0rlz5ycqv0mTJmb5q1evJigoyCJ/27ZtAGzfvl0vV0rx1FNP4eDgQKlSpbC3t+f8+fNmbZ999ll++eUXMjIyyMnJISYmxmw27V5+fn7s2LFDP46Li+P27duF1i+Krb93Jd9280ty34VtK2rGZCXwMxAATAM+A/oDbTVNm/koO5KZmWl2ltHV1ZXMzMwSkV+S+y75kv+k51+6dIlKlSrpx5UqVeLSpUsP3D4rK4vRo0czduxYYmNjLcqdnJy4cuWKfnzlyhWcnJws6tWvX5+JEycycOBAypcvD8CZM2dISkoiJCSEmTNncuLEifv+blWqVOGXX37Rj9PT0y1mZxISEujSpQsAXbp0oWzZslSoUKHAPHd3d1JSUvTjtLQ0s6VIkPcFoVevXgD06tWLcuXKUbFiReLj4wkICKB06dI4Ozvj7++Ph4fHE5efmppqll+1alWL/J49ewJ5y/7y8/fu3cv27ds5d+4c586dIyoqipMnT5q1dXZ25uLFi/rxxYsXcXZ2piCVK1emSpUqxMXFFVj+R9j6e1fybTe/JPdd2LaiBiZeSqllSqlUpdRsoLtSKgn4B/BSYY00TRusadoBTdMOyLpAIURJtnjxYmbPns3YsWP58ssvSU9Pf+iMY8eO8d577xEaGsrJkyfp378/kDdIcnV1ZfLkyUyaNIlnn32WmjVr/qn+vv/++zRr1ozNmzfTrFkz0tPTMZlMfzhv3Lhx+Pn5ceDAAdq0aUNqaiomk4moqCg2bdrErl27CAsLIzY29g89TknPf/vtt2nTpg379u2jdevWen7NmjV5/vnnqVGjBl5eXvj7+9OyZcuHzs/Xpk0bdu3aRW5u7h/OEELYjuJeqmWrS7mK+jsmNzVNa6WU2qVpWnfgMoBSKle7T6+VUouA/BGJepCOuLq6kpGRoR9nZmbi6ur6IE0fiDXzS3LfJV/yn/T8hzkrXVh7yJutqFu3Lj///LPZhvJr166ZzUhUqFCBa9eumWXcvHlT/3nPnj36GfZ69eqRnJxMVlYWAMePH6dGjRr89NNPBfYlIyPD7Iy9m5ub2XMDec/Pm2++CcDTTz9Nt27dCl0elpaWZranwd3dnbS0NLM66enp+qb8MmXK8NJLL+m/X2hoKKGhoQCsWLGCpKSkJy7/7lkUd3d3sxmr/Pz8jfNlypShV69eXLt2jYEDB7Jv3z793z5/oHj3xRUeZjbPz8+PBQsWFFj2R9n6e1fybTe/JPdd2LaiZkz+CczWNO0KMB4YDqBpmgsw/1F2xNfXl+TkZFJSUsjKyiIiIoJ27dqViPyS3HfJl/wnPb9WrVr6Ov7s7Gx27tzJiy+++EBtb9y4QXZ2NgDXr1/nxIkTFpuTz549i4uLC87OzhiNRho2bEh8fLxZnXLlypn9Pvn/w71y5Qre3t4YDAYMBgO1atWyGGjc7ciRI9SoUQNPT0/s7e3p0aMHkZGRZnUqVKign+0aPnw433zzTaF5+/fvx9vbGy8vL+zt7enbty/h4eFmdZydnfW8CRMmsHTpUiBv43bFihX138nX19eiLyU9/8CBA2b5ffr0YcOGDYXmv/3223z11VcApKSk0Lp1a4xGI3Z2drRu3dpiKdepU6eoWrUqrq6u2NnZ0aZNmwKXC3p4eODo6MiJEycsyv4MW3/vSr7t5pfkvgvbdt8ZE6VUPNC0gPsvaJr26yPtiJ0dU6ZMYdCgQZhMJnr37n3fq9PYUn5J7rvkS/6Tnm80GhkyZAhTp04lNzeXDh06UK1aNVauXIm3tzcvvvgiSUlJzJw5kxs3brB//37CwsKYP38+KSkpLFiwAE3TUErRu3dvqlWrZpafm5vL6tWr+de//oWmacTGxpKRkUG3bt04d+4cR48exd/fH19fX32j9YoVKwA4fPgwzz77LO+88w5KKU6cOMGxY8cK/V1MJhOTJ08mLCwMg8HAqlWrOHXqFGPHjiUuLo6oqChatGjBxIkTUUoRGxvLpEmT7ps3YsQINm3ahNFoZOnSpSQkJDB16lQOHjxIeHg4/v7+hISEoJRi586dDBs2DMjbtJ+/Efv69esMGDDAYinUk5A/atQoIiIiMBgMfPXVVyQkJPDee+9x8OBBNmzYgJ+fH++//z4AO3fuZMSIEUDeFbP8/f05fPgwSik2b95MRESExWtn4cKFzJgxA4PBQGRkJOfOnaNfv34kJSXpF2m4d9N7vo8++ghPT0+eeuop/vvf//Lpp59y6NChQv+972Xr713Jt938ktx3WyF/+b1gmlIPtNLKsqGmnVNKVSu65oMt5RJCiEcpMTHRqvnz5s2zav66deusln2/WRmRN5i1pg4dOlg1f+PGjVbNF8LGlYhv/Bs2bLDp78eBgYHF8jzed8ZE07T4wooAWewnhBBCCCGEeCSK2vzuSt6lgq/cc78G7LGsLoQQQgghhLgfWcpVsKIGJhsAR6XUkXsLNE3bbpUeCSGEEEIIIf5yitr8PvA+Za89+u4IIYQQQggh/oqKmjERQgghhBBCPEKylKtgRf0dEyGEEEIIIYSwOhmYCCGEEEIIIYqdDEyEEEIIIYQQxU72mAghhBBCCPEYyR6TgsnARAhRIGv/dfCwsDCr5s+fP9+q+cnJyVbNF4Vr3LixVfMnTZpk1fzu3btbNV8IIUoqWcolhBBCCCGEKHYyYyKEEEIIIcRjJEu5CiYzJkIIIYQQQohiJwMTIYQQQgghRLGTpVxCCCGEEEI8RrKUq2AyYyKEEEIIIYQodjIwEUIIIYQQQhQ7WcolhBBCCCHEYyRLuQomMyZCCCGEEEKIYicDEyGEEEIIIUSxs6mBSUxMDAEBAXTs2JFFixaVqPyS3HfJl/yC7N27l379+vHaa6+xcuVKi/KsrCymTp3Ka6+9xj//+U/S09MByM7OJjQ0lNdff5033niDw4cPW7SdOHEiq1ev1o+9vLz0+k2aNCm0T7Vq1WL06NG4uroCUK5cOUaMGEG/fv3o168f7du3L7BdmzZtiIqKYuvWrQwZMsSi3M3NjZUrV/L9998TERGBv78/AN27dyc8PFy/JSUl4ePjY9E+ICCAhIQEEhMTGT9+vEV5tWrViIyM5PDhw0RHR+Pu7q6XhYaGEhcXR1xcHH369Hms2U9CfrNmzVi9ejVr1qxhwIABFuWjRo1i+fLlLF++nG+//ZYtW7boZf/6178ICwsjLCyMDh06FJh/8uRJPvroIz744AO2bt1qUf7jjz/yySefMHv2bObPn09mZiYAN2/e5D//+Q+TJk1i3bp1BWY/CFv8bJB8ybd29uPIL26aptn0rbjYzMDEZDIxffp0vvzySyIiItiwYQOnT58uEfklue+SL/mFtfn000/56KOP+Oqrr4iOjiY5OdmsTkREBGXLliUsLIyXX36Zzz//HIANGzYAsGzZMj755BMWLFhAbm6u3i4mJobSpUvrx5qm0a5dO9atW8eyZct4/vnnqVixokWf7O3tadCggT4Aynf16lVWrFjBihUriI6OtmhnMBiYOnUqb7zxBgEBAQQFBeHt7W1WZ9iwYURERNC9e3dGjhzJtGnTAPj+++8JCgoiKCiIMWPGkJKSwokTJyzy582bR7du3ahbty6vvPKKxeBl1qxZrFixggYNGjBjxgxmzpwJQNeuXWnYsCENGzakefPmjB49mrJlyz6W7Cclf9y4cYwaNYpXXnmFTp06UaNGDbM6n376Kf3796d///6sXr2a7du3A9CyZUuee+45+vfvzxtvvMHf/vY3ypQpY9Y2NzeXdevWMXDgQMaOHcuRI0f0gUe+Bg0aMGbMGEaPHo2/vz/ff/89kPd6DQgIIDAwkD/KFj8bJF/yrZ39OPKF7brvwETTNCdN0z7QNO2kpmmXNU27pGnaid/vK/8oOxIfH0/16tXx9PTEwcGBbt26FfglwxbzS3LfJV/yC2vj7u5O1apVsbe3p127duzatcuszu7duwkICADAz8+PQ4cOoZQiOTmZhg0bAlChQgUcHR1JTEwE4NatW6xevdrszHaVKlW4evUq165dIzc3l5MnT1KzZk2LPrVs2ZL9+/eTk5PzUL9/vXr1OHv2LCkpKWRnZ7NhwwaLs+NKKRwdHQEoW7Ys58+ft8gJCgoiIiLC4v6mTZvy008/cebMGbKzs1m1ahXdu3c3q+Pj46Ofbd+2bZte7uPjQ0xMDCaTiVu3bnH06FE6d+78WLKfhPzatWuTmprKL7/8Qk5ODlFRUbRp08bi3yhfp06diIyMBKBGjRocOXIEk8nEnTt3OH36NM2aNTOrf+7cOSpVqoSzszN2dnbUr1+f48ePm9V56qmn9J+zsrL0M40ODg7UqFEDO7s/fo0ZW/xskHzJt3b248gXtquoGZPVwBXAXylVUSnlDLT9/b7V9235kDIzM6lSpYp+7OrqanFmylbzS3LfJV/yC2tTuXJl/djFxYWLFy+a1bl48aJex87OjjJlynDt2jVq1qzJ7t27ycnJIT09nVOnTulf9JcsWUKfPn0oVaqUnuPo6Mivv/6qH9+4ccPizHjlypUpW7YsZ86cseirk5MT/fr1o0+fPmbLgO7+fe+eZcnIyNCXguWbO3cuPXv2ZNeuXSxevFifMblbt27dCA8Pt7jf3d2dlJQU/TgtLc2iH/Hx8fTq1QuAXr16Ua5cOSpWrEh8fDwBAQGULl0aZ2dn/P398fDweCzZT0J+5cqVzV7L58+fx8XFhYJUqVKFqlWrcuDAAQCSkpJo1qwZpUqVwsnJiUaNGlm8Lq5fv0758v//HJyTkxPXrl2zyN69ezehoaFERETQo0ePAh//j7DFzwbJl3xrZz+OfGG7ijqV46WU+vDuO5RSGcCHmqa9UVgjTdMGA4MBPv/8cwYPHvynOyqEKBm6du3KuXPnGDJkCK6urtSpUweDwUBSUhJpaWkMGzbMYjlWUfz8/Ni8ebPF/Tdv3uSLL77gzp07VK5cmR49evDVV1+RlZX1UPlBQUF89913LF68mAYNGvDxxx/TpUsXlFJA3qzLnTt3OHXq1EPl5hs3bhzz5s3j73//Ozt37iQ1NRWTyURUVBSNGzdm165dXLx4kdjYWEwmk81kPwn5+Tp27MjWrVv1ZYV79+7Fx8eHL7/8kitXrnD06FGzJYcPo2XLlrRs2VLfJ/PKK6/84X4KIf4a5HLBBStqYHJW07TxwFdKqUwATdNcgdeBlMIaKaUWAfk7ldSDdMTV1ZWMjAz9ODMz0+Ls1Z9hzfyS3HfJl/zC2ty9nOnChQtUqlTJrE6lSpU4f/48lStXJicnh5s3b+Lk5ISmaQwbNkyvN3ToUDw9PTly5AiJiYn07dsXk8nEtWvX+P777xkyZIjZDMm9MygODg5UqlSJl19+GYAyZcrQo0cP1q9fT2Zmpv5l9fz581y9epUKFSqYnVnLzMzEzc1NP65SpYrFmbeXX36ZN97IO9dy+PBhSmruIXwAACAASURBVJUqRcWKFbl06RIAgYGBBc6WQN4sgKenp37s7u5OWlqaWZ309HSCg4P1/r/00kv6mffQ0FBCQ0MBWLFiBUlJSY8l+0nIP3/+vNlruXLlyly4cIGCdOzYkVmzZpndt2zZMpYtWwbA9OnTOXfunFl5uXLluHr1qn587do1nJycCsyHvAHs2rVrCy1/WLb42SD5km/t7MeRL2xXUUu5+gLOwA5N065omnYZ2A5UBAq+RMof5OvrS3JyMikpKWRlZREREUG7du1KRH5J7rvkS35hbVJTU0lPTyc7O5utW7fSsmVLszotW7bUZzF27NhBgwYN0DSNO3fucPv2bQD279+P0WjEy8uLnj17snbtWlatWsW8efNwcnKie/fuZGRkUL58ecqVK4fBYOD555/n559/1h8nKyuLhQsXsnjxYhYvXkx6ero+KCldurR+1snJyYkKFSpYLLWJj4/Hy8sLDw8P7O3tCQwMtFirnJ6eTosWLQCoWbMmpUqV0gclmqbRtWtXfVP/vfbv34+3tzdeXl7Y29vTt29fi0GMs7Oz3s8JEyawdOlSIG/zdv5Gf19fX3x9ffU9ENbOfhLyT5w4gaenJ25ubtjZ2dGxY0diYmK4V/Xq1SlbtixHjx7V7zMYDJQrVw4Ab29vvL292bt3r1k7T09PLl68yOXLl8nJyeHIkSPUrl3brM7dA6GTJ09aDOD/DFv8bJB8ybd29uPIF7brvjMmSqkrmqYtBaKAWKXUjfwyTdM6Az88so7Y2TFlyhQGDRqEyWSid+/e1KpV61HFWzW/JPdd8iW/sDajRo1i7Nix5Obm0rVrV2rUqMHixYt5/vnnadmyJV27diUkJITXXnuNsmXL8t577wFw5coVxo0bh6ZpuLi4MGnSpPs+llKKbdu20bt3bzRN49ixY1y6dIkWLVqQkZFhNki5l4eHB82bNyc3NxelFFu2bOHOnTtmdUwmE9OmTWPZsmUYDAbWrFlDUlISo0aN4ujRo0RHRzNz5kxmzpzJP/7xD5RSZpe1bdq0Kenp6WZ7Je7NHzFiBJs2bcJoNLJ06VISEhKYOnUqBw8eJDw8HH9/f0JCQlBKsXPnTn1Gyd7enh07dgB5+xkGDBhgtlzJmtlPSv7HH3/MZ599hsFgIDw8nDNnzjB48GBOnDjBzp07gbzZkqioKLO2dnZ2+iVIb968yXvvvWeRbzQa6dmzJ1988QW5ubk0bdqUKlWqsHnzZjw8PKhTpw579uwhKSkJg8HA008/Td++ffX2M2fO5M6dO5hMJo4fP86bb775UGd9bfGzQfIl39rZjyPfFshSroJp+WuoCyzUtBHAv4ATQH1gpFJq/e9lh5RSDR/gMR5oKZcQwrbcPY1uDWFhYVbNnz9/vlXz7718snh8GjdubNX8ogbTf9a9VyYTQjxSJeIbf3R0tE1/P27fvn2xPI9F7TF5E2iklLqhaZoXsEbTNC+l1FxKyD+8EEIIIYQQwvYVNTAx5C/fUkola5rmT97gpDoyMBFCCCGEEOKhyVKughW1+T1T07T6+Qe/D1ICgUqArzU7JoQQQgghhPjrKGpgMgAwW2iulMpRSg0ACv/zukIIIYQQQgjxEIq6Klfqfcp2P/ruCCGEEEII8WSTpVwFK2rGRAghhBBCCCGsTgYmQgghhBBCiGJX1FW5hBBCCCGEEI+QLOUqmMyYCCGEEEIIIYqdzJgIYSWZmZlWzT9+/LhV84cPH27V/JMnT1o1XxTuxRdftGr+uHHjrJrfo0cPq+YbDHLOTgghioMMTIQQQgghhHiMZClXweS0kBBCCCGEEKLYycBECCGEEEIIUexkYCKEEEIIIYQodrLHRAghhBBCiMdI9pgUTGZMhBBCCCGEEMVOBiZCCCGEEEKIYidLuYQQQgghhHiMZClXwWTGRAghhBBCCFHsZGAihBBCCCGEKHY2NTCJiYkhICCAjh07smjRohKVX5L7LvnWz9+7dy9/+9vfePXVV1mxYoVFeVZWFu+99x6vvvoqQ4YMIT09HYDs7GxCQ0P5+9//zj/+8Q8OHz5cYP7x48d57733mDJlCps3b7Yo37JlC9OmTWPGjBl8+umnXLp0SS+7fPkyn332GdOmTWPatGlmZflatWrFhg0b2LRpE4MGDbIod3NzY+nSpaxZs4a1a9fSunVri/L9+/fz+uuvF9j/gIAAEhISSExMZPz48Rbl1apVIzIyksOHDxMdHY27u7teFhoaSlxcHHFxcfTp0+eJy7d231988UW+/vprVq9eTf/+/S3KR4wYwbJly1i2bBnffPON2etr6NChrFixgrCwMP7v//6vwPzExEQ+/vhjZs2axfbt2y3KY2NjmTNnDnPnzmXhwoVkZmYCkJSUxLx585gzZw7z5s1j27Ztes4XX3xhkZOVlcX//d//ERAQQN++fUlLS9PLFi1aREBAAF26dGHXrl0AnDlzhl69eum3xo0b89VXXwFw4sQJ+vTpQ48ePXjppZeIj48v8HcD2//skXzJl+89tknTNJu+FRullLVvDyQnJ0e1b99enTt3Tv32228qKChIJSUlPWjzYs0vyX2XfOvlZ2RkqIyMDJWWlqb8/f3VoUOHVEpKiurSpYuKjY3VyzMyMtTChQvV2LFjVUZGhlq5cqUaMmSIysjIUAsWLFAjR45UGRkZ6sSJEyowMFD98ssvKiMjQ0VHR6vo6GgVFRWlWrRoob755hu1efNm1bZtW7VixQq9PDo6Wi1YsEBt2rRJRUdHqylTpqhXX31VL+vWrZv697//raKjo1VERIRer3bt2qp27dqqbt266uzZs6pTp06qXr166uTJkyooKEgvr127tlq9erWaNm2aql27tgoKClKpqalm5Zs3b1Y//PCD+uijj/T7DAaDMhgMys7OTp0+fVrVrFlTlSpVSh05ckTVqVNHLzcYDOrbb79Vr7/+ujIYDKp9+/Zq+fLlymAwqMDAQBUVFaXs7e2Vo6Oj2rdvn3JycjJrW5LzrZXdvHlz1bx5c9WyZUuVmpqqevfurVq3bq1OnTqlXn31Vb383tsnn3yiwsPDVfPmzdWbb76p4uLiVMuWLVXLli3V0aNH1dChQ1Xz5s3V2rVr1dq1a9WaNWtUs2bN1BdffKFWr16t2rRpoxYuXKiXr127VoWFhek/z5gxQwUGBqq1a9eqefPmqWXLlqm1a9eqf//73+q5557Tc4KCglRiYqIymUz6bfny5erdd99VJpNJff/992rEiBHKZDKpxMREFRQUpG7fvq3Onj2r2rdvr7KysszaZmVlqRYtWqhz584pk8mk/vGPf6jt27crpZTavn276tevX4n67JF8ybd2vo33/XF8t/3Tt507dypbvhXX8/KHZ0w0Tdv0KAdI8fHxVK9eHU9PTxwcHOjWrRvR0dElIr8k913yrZ9/4sQJ3N3dqVq1Kvb29rRv314/a5tv165ddO7cGQA/Pz8OHTqEUork5GQaNmwIQIUKFXB0dOTkyZNmbZOTk3FxccHFxQU7OzsaN25MXFycWZ3nnnsOBwcHAJ555hmuXLkCQHp6Orm5ufj4+ADw1FNP6fXy+fr6kpKSQmpqKtnZ2WzcuJG2bdua1VFK4ejoCICjoyPnz5/Xy9q1a0dqaiqnT58u8Plp2rQpP/30E2fOnCE7O5tVq1bRvXt3szo+Pj5s3boVgG3btunlPj4+xMTEYDKZuHXrFkePHtWfxych39p9r127Nqmpqfzyyy/k5OSwZcsWi9muu3Xs2JGoqCj92MHBATs7O+zt7TEajVy+fNmsfkpKCs7Ozjg7O2NnZ0e9evVISEgwq/PUU0/pP2dlZek/u7u7U65cOf1+TdNwcnLCzs6Orl276r9zvq1bt9KjRw8gb5YpNjYWpRRbt26la9euODg44OHhQbVq1SxmQGJjY/H09NRnmzRN4+bNmwD8+uuvVK5cucDnw9Y/eyRf8uV7jyhp7jsw0TStYSG3RkD9R9mRzMxMqlSpoh+7urrqU/q2nl+S+y751s+/ePGi2RcbFxcXLly4UGgdOzs7ypQpw7Vr1/D29mb37t3k5OTwyy+/cOrUKbMv/QBXr16lQoUK+nGFChW4evVqof3ZvXs3derU0X+30qVL8/nnnxMSEsJ3331Hbm6uWX1XV1d9aVl+G1dXV7M68+fPJzAwkOjoaBYuXMjMmTMBePrppxk4cCALFy4stD/u7u6kpKTox2lpaWbLkSDvf1K9evUCoFevXpQrV46KFSsSHx9PQEAApUuXxtnZGX9/fzw8PJ6YfGv33cXFxey1fOHCBVxcXChIlSpVcHNz4+DBgwAcO3aMQ4cOER4eTnh4OPv27ePs2bNmba5fv46Tk5N+7OTkxPXr1y2yf/zxRz766CM2bdpkMfCCvKWKjo6O2NnlXUiyoPdgZmYmbm5uQN57qGzZsly9erXA9++976GNGzfSrVs3/fidd97ho48+ws/Pjw8//JDRo0cX+JzY+meP5Eu+fO+xXcW9VMtWl3IVNWOyH/gY+OSe28dAeet2TQjRtWtXXFxcGDx4MPPmzaNOnToYjcY/nLd3717Onj1Lx44dAcjNzeX06dO89NJLTJgwgYsXL/Ljjz8+dG63bt343//+R/v27Xnrrbf44IMP0DSNoUOH8t///pdbt2794T4DjBs3Dj8/Pw4cOECbNm1ITU3FZDIRFRXFpk2b2LVrF2FhYcTGxmIymf5S+dbue74OHTqwbds2feDq7u6Ol5cXPXv2pEePHjRq1Ih69er9oezmzZszfvx4unTpYjETkpmZyeHDh6levfof7vv9ZGVlsXXrVgICAvT7vv76ayZOnMiOHTuYOHEikyZNsspjCyGEMFfUwOQEMEQp1fbeG3CxsEaapg3WNO2ApmkHHnTDkqurKxkZGfpxQWdl/wxr5pfkvku+9fMrVapkdoa2oLPSd9fJycnh5s2b+rKV4cOHs2TJEkJDQ7lx4waenp5mbcuXL68vzQK4cuUK5ctbnjc4ceIEP/zwA2+99Rb29vZ6W09PT1xcXDAajdSvX59z586Ztbv7THT+83HvmauXXnpJ3xQdFxeHg4MDFSpU4IUXXmDMmDFERkbSv39/Bg8ezGuvvWbWNi0tzex3cnd3N9u4DHlLzoKDg2ncuDGTJ08G4Nq1a0DeBu9GjRoREBCApmkkJSU9MfnW7vuFCxfMXssFzebl69Chg9kyLj8/P44dO8bt27e5ffs2P/74I3Xr1jVrU65cOb0v+f3KX55VkBdeeIHjx4+b1V++fDkdOnTgzp07+v0FvQfvntnLycnh119/pXz58gW+f++ewdy5cye1a9emUqVK+n3r1q2jU6dOAHTp0qXQze+2/tkj+ZIv33tESVPUwGTqfeoML6yRUmqRUqqxUqrx4MGDH6gjvr6+JCcnk5KSQlZWFhEREbRr1+6B2hZ3fknuu+RbP//555/X1/FnZ2cTHR1Ny5Ytzeq0bNmSH374AYAdO3bQsGFDNE3jzp073L59G4D9+/djNBrx8vIya1u9enXOnz/PxYsXycnJ4cCBA7zwwgtmdVJSUggLC+Ott94y+2Lo5eXFrVu3+PXXX4G8KyjdPQiBvCU71apVw93dHXt7e7p27cq2bdvM6qSnp9OsWTMgbw9LqVKluHz5MgMGDKBTp0506tSJ5cuXs2jRIsLCwsza7t+/H29vb7y8vLC3t6dv376Eh4eb1XF2dtanlidMmMDSpUsBMBgMVKxYEcj7d/L19SUyMvKJybd230+cOIGHhwdubm7Y2dnRoUMHi/1PkPcaK1u2LMeOHdPvy8zMpEGDBhiNRoxGIw0aNCA5OdmsnYeHB5cuXeLy5cvk5OQQFxdH7dq1zepcvPj/z3GdPHlSHyDcvn2bpUuX0rlzZ5o0aWKWU9A+p7Zt27J+/XoANm/eTLNmzdA0jbZt27Jx40aysrJITU3l7NmzZu+PiIgIs2VcAJUrV2bfvn1A3v6Te99z+Wz9s0fyJV++99iu4l6qZatLue77l9+VUms0TXte07T2wF6l1I27iu8U1u4PdcTOjilTpjBo0CBMJhO9e/emVq1aJSK/JPdd8q2fb2dnx6hRoxg7diy5ubl07dqVGjVqsHjxYp577jlatWpFt27dCAkJ4dVXX6Vs2bJMnToVyJv9GDt2LJqm4eLiop8Rv5vRaOSVV15h3rx55Obm0qJFC6pWrUp4eDjVqlWjXr16fPfdd/z222/6ZVYrVKjA0KFDMRgM9O7dm7lz56KUolq1arRq1cos32QyERISwqJFizAYDKxbt46ffvqJYcOGcfz4cbZt28asWbOYNm0aAwYMQCn1UEtfTCYTI0aMYNOmTRiNRpYuXUpCQgJTp07l4MGDhIeH4+/vT0hISP6VTBg2bBgA9vb27NixA8jbzzBgwACL5UolOf9x9H327NnMmTMHo9HIhg0bOHPmDIMGDeLkyZP6IKVDhw5s2bLFrO22bdto1KgRy5cvRynF3r172b17t1kdo9FI9+7dWbJkCbm5uTRu3BhXV1ciIyPx8PCgdu3a7Nmzh9OnT2M0GildurR+WeM9e/Zw6dIloqOjiY6ORinFl19+CUD//v2pVasWn332GXXr1qVdu3YEBwfz9ttvExAQgJOTE5988gkAtWrVonPnzgQGBmI0Gnn33Xf15ZC3bt1iz549TJs2zazf77//PjNnziQnJ4dSpUoxffp0yxcutv/ZI/mSL997REmjKaUKL9S0EcC/yFvSVR8YqZRa/3vZIaVUwwd4jMIfQIgnmLU36t295MUahg8vdFL0kbj36mLi8XnxxRetmj9u3Dir5udffctaDAab+hNfQoiHU4x/hOPB7dmzx6a/H7do0aJYnsf7zpgAbwKNlFI3NE3zAtZomuallJpLCfmHF0IIIYQQQti+ogYmhvzlW0qpZE3T/MkbnFRHBiZCCCGEEEI8tGL96+o2rKj56kxN0/S/V/L7ICUQqAT4WrNjQgghhBBCiL+OogYmA4CMu+9QSuUopQYAbazWKyGEEEIIIcRfSlFX5Uq9T9nuwsqEEEIIIYQQBZOlXAWTS48IIYQQQgghip0MTIQQQgghhBDFrqircgkhhBBCCCEeIVnKVTCZMRFCCCGEEEIUOxmYCCGEEEIIIYqdLOUSNuvy5ctWzR8yZIhV848cOWLV/J9//tmq+eL+WrRoYdX8MWPGWC07ICDAatkApUuXtmq+EEKUdLKUq2AyYyKEEEIIIYQodjIwEUIIIYQQQhQ7WcolhBBCCCHEYyRLuQomMyZCCCGEEEKIYicDEyGEEEIIIUSxk6VcQgghhBBCPEaylKtgMmMihBBCCCGEKHYyMBFCCCGEEEIUOxmYCCGEEEIIIR6KpmmdNU1L1DTttKZpEwooH61pWoKmafGapkVrmla9qEybGpjExMQQEBBAx44dWbRoUYnKL8l9t9X8H3/8kb59+xIcHMx///tfi/KsrCwmT55McHAwAwcOJD09HYDNmzczYMAA/daiRQtOnToFwH/+8x969OhBu3btzLLq16/P3LlzmTdvHj179iywP82bN2fOnDnMnj2bkSNHAlCnTh1mzZql31auXEmTJk0s2rZu3ZrNmzezZcsWBg8ebFHu5ubG8uXLWb9+PeHh4fj5+ellzz33HKtXr2bjxo1s2LABBwcHi/YBAQEkJCSQmJjI+PHjLcqrVatGZGQkhw8fJjo6Gnd3d70sNDSUuLg44uLi6NOnT4G/u+TfP79p06asXLmSr7/+mr/97W8W5cOHD2fJkiUsWbKEsLAwNm7cqJdVrlyZTz75hOXLl7N8+XKqVKli0T4xMZFZs2bx0UcfsW3bNovy2NhY5syZw6effsrChQvJzMwE4ObNm3z++ee8++67/O9//9Pr7969mx49ehAUFMSSJUss8rKyshg/fjxBQUH069ePtLQ0AI4ePUqfPn3029atW/U2Xbp0ITg4mD59+vDSSy8V+DwVxBY/eyRf8iW/eLMfR35x0zTNpm8P0H8jMB/oAtQGXtU0rfY91Q4DjZVSLwBrgI+KDFZKWfv2QHJyclT79u3VuXPn1G+//aaCgoJUUlLSgzYv1vyS3Hdbzc/JyVFt27ZV8fHxKiMjQ3Xt2lUdOHBAXbp0Sb8tWrRIjR8/Xl26dEl988036q233jIrv3Tpktq7d69q27atfrxjxw6VmJio6tWrp4KDg1VwcLDq06ePSk9PV0OHDlWvvPKKOnPmjBo1apReHhwcrIYNG6Z+/vln9fe//10FBwerN954w6w8ODhYvf766+rXX39Vr732mgoODlbe3t7K29tbPfvss+rs2bOqbdu2ysfHRyUkJKjOnTvr5d7e3urrr79WU6ZMUd7e3qpz584qJSVFeXt7q+eee06dOHFCBQYGKm9vb9W4cWP17LPPKm9vb2UwGJTBYFB2dnbq9OnTqmbNmqpUqVLqyJEjqk6dOnq5wWBQ3377rXr99deVwWBQ7du3V8uXL1cGg0EFBgaqqKgoZW9vrxwdHdW+ffuUk5OTWVvJLzi/VatWqlWrVqpNmzYqNTVVvfzyy8rf318lJSWpfv366eX33ubMmaM2bNigHx86dEiNGjVKtWrVSnXs2FG1b99etWrVSq1bt06tW7dOfffdd6pZs2bqyy+/VN9++63y8/NT//nPf/TydevWqa+//lr/OSQkRAUGBqp169apVatWqdmzZ6u3335b/f3vf9fz2rVrp06dOqWuXbumAgMD1dGjR9WtW7f029KlS9U777yjbt26pdauXauGDRumbt26pS5duqSuX7+ubt26pc6ePatefPFF/djPz0+lpaWpW7duWfWz4WFIvuRL/l/ye8/j+G77p28HDhxQtnwrqv9Ac2DzXccTgYn3qd8A2F1U7n1nTDRNK6dpWqimacs1TXvtnrIFRY56HkJ8fDzVq1fH09MTBwcHunXrRnR0dInIL8l9t9X8+Ph4PDw8cHd3x97eng4dOhATE2NWZ+fOnXTt2hWAtm3bcuDAgfwXvy4qKooOHTrox3Xr1qVSpUpmdby9vcnIyOD8+fPk5OSwe/duGjdubFanQ4cO/PDDD9y8eROA69evW/S5WbNmHD58mKysLLP7X3jhBc6ePUtKSgrZ2dlERETQvn17i/aOjo76f8+fPw9Aq1atSExM5OTJkwBcvXqV3Nxcs3ZNmzblp59+4syZM2RnZ7Nq1Sq6d+9uVsfHx0c/u71t2za93MfHh5iYGEwmE7du3eLo0aN07txZ8h8i38fHh7S0NNLT08nJySE6OppWrVpRmPbt27NlyxYAvLy8MBqNHDhwAIDbt2/z22+/mdVPSUnB2dkZZ2dn7OzsqFevHgkJCWZ1nnrqKf3nu19/Dg4O1KhRAzs7O7M8T09PPDw8sLe3JyAggO3bt5vlbd++naCgICDvtb9v3z6UUpQuXVrPysrK+tNXlbHFzx7Jl3zJL97sx5EviqZp2mBN0w7cdbt3uYc7kHLXcerv9xVmILCpqMctainXUkADvgNe0TTtO03TSv1e1qyo8IeRmZlptoTB1dVVX45g6/klue+2mp+ZmUnlypX148qVK3PhwgWzOhcuXMDV1RUAOzs7HB0duXbtmlmd6OhoOnbseN/HqlixIpcuXdKPL1++jLOzs1kdNzc3qlatyvvvv09ISAj169e3yGnZsiW7du2yuL9KlSr6MjOAjIwMvd/5PvvsM7p3787OnTv58ssvmT59OgA1atRAKcWSJUv43//+x5tvvmmR7+7uTkrK//9sSEtLM1uKBHkf8r169QKgV69elCtXjooVKxIfH09AQAClS5fG2dkZf39/PDw8JP8h8l1cXPSBJOS9Lu8d/OZzdXWlatWqHDp0CABPT09u3LjBjBkzWLx4MUOHDsVgMP9YvnbtGuXLl9ePnZycLF7nAHv27OHDDz9k48aN9OjRo8DHz8+79/14d/8Bzp8/r9fJf29dvXoVyFvO9dJLLxEcHMzkyZP1gYqmabz11lu8+uqrrFq1qtDHv5stfvZIvuRLfvFmP458W1DcS7WKuimlFimlGt91+8Pr6TRN6wc0BmYVVbeogUlNpdQEpdT/lFLdgUPAVk3TnItoJ0SxO378OKVKlaJmzZp/OstoNOLm5sbUqVOZO3cuQ4YM4emnn9bLy5cvT7Vq1YiLi/tD+YGBgaxdu5bWrVszaNAgPv74YzRNw2g00qhRI8aMGcMrr7xCx44dad68+UPnjxs3Dj8/Pw4cOECbNm1ITU3FZDIRFRXFpk2b2LVrF2FhYcTGxmIymST/Eefna9++Pdu3b9dnvYxGIy+88ALz589n8ODBuLm50aVLlz+U3aJFC95++226dOli1TOLvr6+rF27lpUrV7J48WJ9hmfp0qV88803zJ8/n5UrV7J//36r9UEIIUSxSwM87zr2+P0+M5qmdQAmAd2VUr/dW36vogYmpTRN0+sopUKAL4AYoNDByd3TPw+6YcnV1ZWMjAz9ODMz0+Ks8p9hzfyS3Hdbzb/3LO758+dxcXExq+Pi4qKfQcnJyeHGjRs4OTnp5VFRUUXOloDlDMm9MygAly5dYv/+/ZhMJs6fP096ejpubm56eYsWLdi3b1+BX1ozMjLM6lapUsXizM/LL7+sb4g+cuQIpUqVokKFCmRkZLB//36uXLnCnTt32LFjB3Xq1DFrm5aWhqfn//9scHd31zcr50tPTyc4OJjGjRszefJkAP2se2hoKI0aNSIgIABN00hKSpL8h8i/cOGC2eyei4sLFy9epCB3L+OCvNf16dOnSU9Px2QysWvXLp599lmzNk5OTvpsRX6/7n6d36tevXocP3680HInJyeL9+Pd/Ye8Gcr8OvnvrbtnbQCeeeYZnn76aU6fPg2gv6crVqxIx44diY+PL7QP+Wzxs0fyJV/yizf7ceSLR2I/UEvTtBqapjkArwDf311B07QGwOfkDUrOF5BhoaiB9pBxzwAAIABJREFUSThgdvkipdQyYAyQVVCD3+vo0z8FXYGoIL6+viQnJ5OSkkJWVhYREREWV076M6yZX5L7bqv5vr6+pKSk8Msvv5Cdnc2WLVto3bq1WZ1WrVrpX+a3bdtGo0aN9DXvubm5D7SMC+D06dO4ublRuXJl7OzsaNmypb7mP9/+/fv1AUHZsmVxc3MzG1wUtowL8pa+eHl56Wv6C1or+8svv9CiRQsAatasiYODA5cvX2bnzp0899xzPPXUUxiNRpo0aaJ/Eby7b97e3nh5eWFvb0/fvn0JDw83q+Ps7Kw/NxMmTGDp0qUAGAwGKlasCOQ9576+vkRGRkr+Q+SfPHkSDw8P3NzcsLOzo3379gW+FqpVq0bZsmU5duyYWVtHR0f9S3/Dhg1JTk42a+fh4cGlS5e4fPkyOTk5xMXF4ePjY1bn7oHQyZMnC11Klp937tw50tLSyM7OZvPmzWZXgQPw8/PTn6MtW7bQpEkTNE0jLS2NnJwcIO81m5ycTNWqVbl9+7a+/+r27dvs3r2bWrVqFdqHfLb42SP5ki/5xZv9OPJtQXEv1fqzV+VSSuUAw4DNwAlgtVLquKZp0zVNy9/IOQtwBL7VNO2IpmnfFxKns7tfoVJqvKZpz2ua1h7Yq5S68fv9P2iaNqLIXj8EOzs7pkyZwqBBgzCZTPTu3fuB/sdmC/klue+2mm9nZ8eYMWMYNWoUubm5BAYG8swzz7Bo0SJ8fHxo3bo1QUFBTJs2jeDgYMqVK8f777+vtz9y5Aiurq4Wewn+/e9/ExkZyZ07dzhx4gQVKlSgSpUqLF68mEmTJmEwGNi2bRupqan07duXn376iQMHDnDkyBHq1avHnDlzyM3NZfny5dy4cQPIO0NeqVIliw3J+UwmE9OmTWPJkiUYjUbWrFnD6dOnGTlyJEePHmXr1q188MEHzJgxg9dffx3I+3IMeZvslyxZwtq1a1FKsWPHDouNyiaTiREjRrBp0yaMRiNLly4lISGBqVOncvDgQcLDw/H39yckJASlFDt37mTYsGEA2Nvbs2PHDv2xBgwYYDHrI/lF58+ZM4dPPvkEg8FAREQEycnJDBw4kJMnT7J7924gb7bk3gFpbm4u8+fP59NPPwXg1KlTFoMmo9FIjx49WLx4Mbm5uTRp0oQqVaoQGRmJh4cHtWvXZs+ePSQlJWE0GildurTZZY0/+OAD7ty5g8lk4vjx4wwaNIgJEybw1ltvkZubS48ePfD29mbBggXUrl0bf39/evXqxaRJkwgKCqJcuXJ8+OGHABw+fJglS5ZgZ2eHwWBg4sSJVKhQgdTUVEaPHg3kzbB0796dNm3aUBRb/OyRfMmX/OLNfhz54tFQSm0ENt5z35S7fu5g0agI2r1XMTIr1LTh5I2GTgD1gZFKqfW/lx1SSjV8kH4/bKeEgLwlVtY0ZMgQq+YfOXLEqvk///yzVfPF/eXPcFnLmDFjrJYdEBBgtWyA0qVLWzVfCCHu489dLvAxOXz4sE1/P27QoEGxPI/3nTEBBgONlFI3NE3zAtZomuallJpLCfmHF0IIIYQQwpb82cutP6mKGpgY7lq+laxpmj95g5PqyMBECCGEEEII8YgUtfk9U9M0/Q82/D5ICQQqAb7W7JgQQgghhBDir6OoGZMBQM7dd/y+C3+ApmmfW61XQgghhBBCPKFkKVfBiroqV+p9ynY/+u4IIYQQQggh/oqKWsolhBBCCCGEEFYnAxMhhBBCCCFEsStqj4kQQgghhBDiEZI9JgWTGRMhhBBCCCFEsZOBiRBCCCGEEKLYyVKuJ9jevXutmj9r1iyr5u/bt8+q+WlpaVbNF/dXunRpq+aPHDnSqvnvvPOOVfPLlClj1XwhhBDFR5ZyFUxmTIQQQgghhBDFTgYmQgghhBBCiGInS7mEEEIIIYR4jGQpV8FkxkQIIYQQQghR7GRgIoQQQgghhCh2spRLCCGEEEKIx0iWchVMZkyEEEIIIYQQxU4GJkIIIYQQQohiJ0u5hBBCCCGEeIxkKVfBZMZECCGEEEIIUexsamASExNDQEAAHTt2ZNGiRSUq39b7Hh8fz/jx4xk7dizh4eEW5Zs2bWLChAlMmjSJDz74gIsXLwJw9uxZpk2bxsSJE5k0aRKxsbEF5tevX5+5c+cyb948evbsWWCd5s2bM2fOHObMmcPIkSMBqFOnDrNmzdJvYWFhNGnSxKKtn58f27ZtIyYmhqFDh1qUV61alW+++YaNGzeyefNm2rZtC4C9vT0ff/wxkZGR/PDDDzRr1qzAvgUEBJCQkEBiYiLjx4+3KK9WrRqRkZEcPnyY6Oho3N3d9bLQ0FDi4uKIi4ujT58+kv8H8jt06MChQ4eIi4tj9OjRFuWenp5s2LCB2NhYNm3aRNWqVfUyDw8P1q9fz8GDBzlw4ADVqlWzaF+rVi1GjRrF6NGjadOmjUV5gwYNeOedd/5fe3ceH1V973/89U1YxLCDBITIIlIWUxURlb1ExLIpDRbUil7XWjap9KqASP2B3JafP6ve4oUqaGm95UrUgoiCLIJYkD3IooDmQiJJEBCIIJDJ5/fHDNOErMgZZg59Px+P82Bmzjnv+TAn35nznfM9Zxg+fDjDhw+nQ4cOADRq1IiHH36YkSNHMmLECJKTk8PrrFq1ioEDBzJgwABmzZpVLPPkyZM8/vjjDBgwgKFDh/L1118Xmb9v3z46d+7Mn//8ZwBOnDjB3XffzeDBgxk0aBAvvvhiia/VmWL9vUf5ylf+hZnv59olhplZqRPQEHgZ+CNQD5gIbAH+B2hU1rqFpgrJz8+3lJQU27Nnj504ccL69+9vO3furOjqUc2P1dpXr15tq1evtk8++cS6dOli8+bNs48//thSUlIsLS0tPH/16tU2c+ZM++ijj2z16tU2efJk+8UvfmGrV6+2t99+29555x1bvXq1LVy40K677jpbsmSJrV692lJTUy01NdVuv/1227dvnz3yyCM2ePBg++qrr2zUqFHh+ampqTZs2DD78ssvbejQoZaammr/9m//VmR+amqq3XPPPXbkyBG74447LDU11ZKSkiwpKcmaNm1qGRkZ1rlzZ2vRooVt3brVevbsGZ6flJRkf/3rX+3JJ5+0pKQk69mzp+3Zs8eSkpJs3LhxNmfOHEtKSrKrr77a0tPT7bLLLrOkpCSLi4uzuLg4q1Spku3atcsuv/xyq1q1qm3atMnatWsXnh8XF2dvvvmm3XvvvRYXF2cpKSk2e/Zsi4uLs379+tnixYutcuXKVr16dfv000+tVq1aRdZVfsn5CQkJlpCQYDVq1LDdu3dbu3btrHbt2paenm7XXntteH5CQoK99dZb9uCDD1pCQoL16dPH3njjjfC8FStWWL9+/SwhIcEaNGhg9evXt4SEBBs7dqyNHTvWxo0bZ998841NnTrVnnrqKfv666/t+eefD88fO3asvfnmm/aPf/yjyGNjx4615557zp577jkbO3asTZkyxY4cOWLPPPOMPfnkk9azZ0/bsWOHHTp0yPr27Wvp6emWl5cXnmbOnGlPPvmk5eXlWVpamg0bNqzI/EceecR+9atf2bRp0ywvL8+OHj1qubm5lpeXZ4cOHbJBgwbZxo0bI/LeUFHKV77ylX++sz3Ir8i+adSnrVu3WixP0Xpdyjti8hqwDdgLLAOOA32AlcB/edlBSk9Pp2nTpiQlJVGlShX69u3LkiVLfJEf67Xv3r2bBg0a0KBBAypVqsQNN9zAhg0biizTtm1bqlatCsDll1/OoUOHgOA3xg0bNgSgTp061KxZk6NHjxZZt2XLlmRnZ5Obm0t+fj6rVq0qdtTjpptu4v333+e7774D4MiRI8XqvOGGG9i0aRMnT54s8vjVV19NRkYGe/bs4dSpU8yfP5+bb765yDJmRo0aNQCoUaMGOTk5QPCb8k8++QSAAwcOcOTIEX784x8XWbdjx47s3r2br776ilOnTjFnzhwGDBhQZJk2bdqwdOlSAJYtWxae36ZNG1asWEEgEODYsWNs2bKFW265Rflnkd+hQwe+/PJLMjIyOHXqFHPnzqVv375FlmndujUfffQRAB999FF4fuvWrYmPj2fZsmUAfPfddxw/frzIuk2aNOHgwYMcOnSIQCBAeno6bdq0oSIOHDjAgQMHADh69Ch5eXkkJCRw8OBBmjRpQpMmTahcuTK9e/dm+fLlRdZdvnw5/fr1AyAlJYW1a9ee/sKHZcuWcemll9KiRYvw8s45Lr74YgDy8/PJz88vdwxyrL/3KF/5yr8w8/1ce6xwzsX0FC3ldUwSzewlM/sPoLaZ/c7M9prZS0BTLwvJyckJ7wADJCYmhncuYz0/1ms/dOgQ9erVC9+vW7duuONRkhUrVhTbeYdgByc/P58GDRoUebxu3brhoV8Q3JmrW7dukWUuvfRSGjVqxKRJk3j22We5+uqri+V37tyZjz/+uNjjDRs2LDIMZt++fSQmJhZZ5vnnn2fgwIGsWbOG119/naeffhqA7du306tXL+Lj40lKSuLKK68sMgwIoHHjxuzduzd8Pysrq8hQJAi+SQ4cOBCAgQMHUrNmTerWrUt6ejq9e/emWrVq1KtXjx49etCkSRPln0X+pZdeSmZmZpH8M7fRli1buPXWWwEYMGBAOL9ly5YcPnyYN954g1WrVjFp0iTi4oq+rdWsWZPDhw+H7x85coRatWpxpnbt2jFixAjuuOOOEuc3adKE+Ph4Dh48yPHjx4u0yQYNGpCbm1tk+f3794eXqVSpEtWrV+fbb7/l2LFjvPbaazz88MPFniMQCDBkyBBuuukmOnXqxFVXXVVsmcJi/b1H+cpX/oWZ7+faJbaV1zEpPP/PZ8yLL20l59xDzrl1zrl1GhfoL6tWreKrr76iT58+RR7/9ttvmT59Og8++GCxHb+KiI+Pp1GjRjz99NP84Q9/4Je//GX422GA2rVrc9lll7Fp06YfVPeAAQN48803uf7667nnnnv4wx/+gHOOOXPmsG/fPt59912efvpp1q9fTyAQOOv83/zmN3Tv3p1169bRrVs3MjMzCQQCLF68mIULF/Lxxx/zxhtvsHr1auVHIH/s2LF06dKFVatW0aVLF7KysggEAlSqVIlOnToxduxYunXrRvPmzfnFL35x1vk7duxg6tSpvPTSS+zatYvU1NQi82vUqMGgQYN46623wkc9fqjp06dz1113Ffn7Py0+Pp6//e1vvP/++6Snp/PFF1+c03OJiIj4SXmXC/67c666meWZ2fjTDzrnWgKfl7aSmc0ATvdIKvQpnpiYSHZ2dvh+Tk5OsW/Fz0Uk82O99jp16oSHowAcPHiQOnXqFFvus88+Y968eYwbN47KlSuHHz9+/DjPPfccgwYNomXLlsXWO3jwIPXr1w/fr1evHgcPHiyyzIEDB9i5cyeBQIDc3Fz27dtHo0aN2L17NwCdOnXi008/LXGnNTs7u8g36I0aNSr2zcmQIUO4++67AdiwYQNVq1albt26HDhwgGeeeSa83FtvvcVXX31VZN2srCySkpLC9xs3bkxWVlaRZfbt28egQYMASEhI4Gc/+1n4W/gpU6YwZcoUAP7yl7+wc+dO5Z9F/tdff13kKErjxo2LnSienZ3NnXfeGc6/9dZbOXz4MFlZWWzZsoWMjAwA5s+fT8eOHcMnlEPxIyRnHkEBigz/WrduXZHhZlWrVmXo0KEsXrw4fOSoWrVqRdpkbm5usSOJl1xyCdnZ2SQmJpKfn09eXh61a9dmy5YtfPjhh7zwwgscPXqUuLg4qlSpwpAhQ8Lr1qhRg+uvv56VK1fSqlUrShPr7z3KV77yL8x8P9ceK3S54JKV+dW3mU0AmjjnUpxz1Qs9vgt4xctCkpOTycjIYO/evZw8eZIFCxbQs2dPX+THeu0tWrQgJyeH/fv3k5+fz+rVq7nmmmuKLJORkcFrr73G6NGjqVmzZvjx/Px8XnjhBTp37kzHjh1LzN+1axeNGjUKn8PSuXNn1q5dW2SZTz/9lHbt2gHBna4zOxddunQpcRgXwObNm2nevDlJSUlUrlyZ/v37s3jx4iLLZGVl0blzZyB4zkvVqlU5cOAAF110EdWqVQOga9euBAKBYjvGa9eupWXLljRr1ozKlSszePDgYlcuq1evXvhN5IknnghfhSkuLi48bC05OZnk5GQWLVqk/LPIX79+PZdffjlNmzalcuXKDBo0iPfee6/U/DFjxjB79uzwurVq1Qp3jLt3786OHTuKrJuVlUW9evWoU6cO8fHx/PjHPy62zOnzkyB4XszpYVnx8fHcddddbNy4ka1bt4aXqVOnDnv37iUrK4tTp07xwQcf0L179yKZ3bt359133wVgyZIlXHfddTjnmDlzJgsWLGDBggXceeed3HfffQwZMoRDhw6Fz9/6/vvv+eSTT4qcg1KSWH/vUb7ylX9h5vu5doltZR4xcc6NAIYD24FXnXOjzOzvodnPAu97VkilSkyYMIEHHniAQCBAamoqV1xxhVfxEc2P9drj4+MZOnQov//97zEzunXrRpMmTUhLS6N58+a0b9+ev/3tb3z//ff853/+JxDcERw9ejRr1qzh888/Jy8vL9xxePDBB2na9J+nGBUUFPDKK68wfvx44uLiWLp0KZmZmQwePJjdu3ezbt06Nm3axFVXXcXzzz9PQUEBs2fPJi8vDwh+s1yvXj22bdtWYv2BQICnnnqK2bNnEx8fz5w5c/jiiy/49a9/zZYtW1i8eDGTJk3id7/7HQ888ABmFr7kbP369Zk9ezYFBQXk5OTw6KOPlpg/cuRIFi5cSHx8PLNmzWLbtm1MnDiR9evXM3/+fHr06MHkyZMxM1auXMnw4cOB4OWIT5+UfeTIEYYOHVrsqI/yy89/7LHHeOedd4iPj2f27Nls376d8ePHs2HDBt577z26du3KxIkTMTNWrVoV3r4FBQWMHTuWd999F+ccGzduLHbp3oKCAubPn8+9996Lc44NGzaQm5tLSkoKWVlZ7NixgxtvvJHWrVtTUFDA8ePHSUtLA+DKK6+kWbNmXHzxxbRv3x6AtLQ09u3bx+OPP86wYcMoKChgwIABXH755bz88su0bduW7t27c9ttt/HUU08xYMAAatWqFT5qVJr9+/fz9NNPEwgEMDP69OkTvux1aWL9vUf5ylf+hZnv59oltrmyxks757YAN5pZnnOuGTAXmG1mLzjnNprZNaWu/E/nNiBbfrA1a9ZENH/q1KkRzf/0008jmn/mcCQ5v04fyYqU07+VEyljx46NaH5CQkJE80VELlC+GCP1+eefx/T+8Y9+9KOovI7lnWMSZ2Z5AGaW4ZzrAcx1zjXFJxteRERERERiX3mXV8pxzoWv6xrqpPQD6gPJpa4lIiIiIiJyFso7YjIUyC/8gJnlA0Odc9MjVpWIiIiIyAVKV+UqWZkdEzPLLGPeKu/LERERERGRf0Vn/0t5IiIiIiIiHitvKJeIiIiIiHhIQ7lKpiMmIiIiIiISdeqYiIiIiIhI1Gkol4iIiIjIeaShXCVTx+QC9vbbb/s63+/atGkT0fz+/ftHND8+Pj6i+WPGjIlofu3atSOaLyIiIt7SUC4REREREYk6dUxERERERCTqNJRLREREROQ80jkmJdMRExERERERiTp1TEREREREJOo0lEtERERE5DzSUK6S6YiJiIiIiIhEnTomIiIiIiISdRrKJSIiIiJyHmkoV8l0xERERERERKLurDsmzrkGkSgEYMWKFfTu3ZtevXoxY8YMX+XHeu2tWrXiscceY8yYMXTv3r3Y/GuvvZbx48czcuRIRo4cyXXXXQdA7dq1GTFiBCNHjmT06NFcf/31Jeb37t2bbdu28fnnn/Pv//7vxeZfdtllLFq0iI0bN7JkyRIaN24cnjdlyhQ2b97M5s2b+fnPf35B5nfu3Jn58+fz3nvvcf/99xeb37BhQ2bOnMmbb77JW2+9RdeuXQG48sormTt3LnPnziUtLY2UlJQS81u1asWvf/3rUrdv+/btGTduHCNGjGDEiBF06NABCG7f4cOHM2LECB599FE6duxYYv4VV1zBqFGjGD16NN26dSs2/5prruHJJ59k2LBhDBs2jGuvvTb8/3rooYcYMWIEw4cP58orrwyv849//IPbb7+d1NRUXn/99WKZJ0+eZNy4caSmpnLffffx9ddfh+ft3LmT+++/nyFDhnDnnXdy4sQJAEaNGsVdd93FkCFDmDBhAoFAoMT/z5livf1GK1v5yle+8qORfT7yJTaVOZTLOVf3zIeAT51z1wDOzA56VUggEOCZZ55h1qxZJCYmMmjQIHr27EnLli1jPj/Wa3fOceutt/Lqq69y+PBhhg8fzvbt28nNzS2yXHp6OvPmzSvy2NGjR5k2bRqBQIAqVaowevRotm3bxtGjR8PLxMXF8dJLL9G7d28yMzNZs2YN8+fPZ/v27eFlpk6dyl/+8hf+/Oc/85Of/IRnn32We+65hz59+tC+fXvat29P1apVWbp0KQsXLrzg8sePH8+DDz5IdnY2c+bMYdmyZXz55ZfhZR5++GE++OAD5syZQ4sWLXj55Zfp3bs3u3btYvDgwQQCAerXr09aWhrLly8vssPtnGPAgAG8+uqrHDlyhGHDhpW4fbds2VLi9n355ZfD2/fRRx9l+/btRep3ztG/f39mzZrFkSNH+OUvf8n27dvZv39/sfx33323yGOnTp0iLS2NAwcOUKNGDX71q1+xa9cujh8/ztSpU3nppZdo0KAB9957L127dqVFixbhdefNm0eNGjVIS0tj0aJF/PGPf2Ty5Mnk5+czceJEnn76aVq1asXhw4epVCn4VjZ58mSqV6+OmTFhwgTef/99+vbtS1livf1GK1v5yle+8qORfT7yY4GGcpWsvCMm3wDrC03rgMbAhtBtz6Snp9O0aVOSkpKoUqUKffv2ZcmSJb7Ij/Xak5KSOHDgAAcPHiQQCLB582batm1boXUDgUB4J7hSpUolNqSOHTuye/duvvrqK06dOsWcOXMYMGBAkWXatGnD0qVLAVi2bFl4fps2bVixYgWBQIBjx46xZcsWbrnllgsqPzk5mT179pCZmUl+fj4LFy6kZ8+eRZYxMxISEgCoUaNGeKf/+++/D7/+VatWLfbawz+376FDh8Lbt02bNiUue6aKbN8mTZoUyd+yZUuF8w8cOMCBAweAYCcoLy+PhIQEDh48SJMmTWjcuDGVK1emV69erFixosi6K1asCHcqevbsydq1azEz1qxZQ8uWLWnVqhUAtWrVIj4+HoDq1auH/1+nTp2q0Bt/rLffaGUrX/nKV340ss9HvsSu8jomvwE+BwaYWXMzaw5khm63KGfds5KTk0PDhg3D9xMTE8nJyfFFfqzXXrNmTQ4fPhy+f/jwYWrWrFlsuSuvvDI8FKZWrVrhx2vVqsWoUaN44oknWL58eZFv0wEaN27M3r17w/ezsrKKDHWC4JvMwIEDARg4cCA1a9akbt26pKen07t3b6pVq0a9evXo0aMHTZo0uaDyGzRoQHZ2dvh+Tk4ODRoUHRE5bdo0+vXrx4cffsi0adN49tlnw/OSk5N55513ePvtt3nmmWeKDU86c/seOXKkyPY7rV27dowcOZI777yz2PYdOXIkjz/+OB999FGx7VtSfkl/P+3atWP48OEMGTKkxOdv3Lgx8fHxHDx4kOPHj5OYmFjkNTrzCMz+/fvDr1OlSpWoXr06hw8fZs+ePQCMHDmSoUOHMnv27CLrjRw5kltuuYWEhAR69+5drI4zxXr7jVa28pWvfOVHI/t85EvsKnMol5k955ybAzzvnNsLPA1YeaHOuYeAhwCmT5/OQw895EWtEkHbt29n06ZNBAIBOnbsyM9//nP+9Kc/AcGOzAsvvECNGjUYOnQon332GXl5eWeV/5vf/IaXXnqJe+65h5UrV5KZmUkgEGDx4sV06NCBjz/+mG+++YbVq1dX+LyACym/T58+/P3vf+f111/nqquuYsqUKdx2222YGVu2bOG2226jRYsWTJ48mZUrV3Ly5Mmzyt+xYwebN28Ob9/bb7+dV155BQhu3xdffJEaNWpw9913/6Dtu2PHDtLT0wkEAlx33XWkpqYyc+bM8Pzq1aszaNAg0tLSMCv3LaRMp48Kvfbaa1x00UUMGzaM1q1bh8+LevHFFzlx4gSTJk1i9erVdO7c+ZyeT0RExGsaylWyck9+N7NMM7sdWA4sBi6uwDozzKyDmXWoaKckMTGx2LfKhb9RPVeRzI/12s/8Br1WrVocOXKkyDLHjh0L71CvXbu22BEDCA7FycnJoVmzZkUez8rKIikpKXy/cePGZGVlFVlm3759DBo0iA4dOjB+/HiA8LfwU6ZM4dprr6V3794459i5c+cFlZ+bm1vsm58zz//42c9+xgcffADA5s2bqVKlCnXq1CmyzJdffsmxY8e44oorijx+5vY98wgHnNv2LSn/zL+f48ePh/PXrVvHpZdeGp5XtWpVhg4dyocffkhmZiYA1apVK/LtV25uLpdcckmRzEsuuST8OuXn55OXl0etWrVo0KAB11xzDbVr1+aiiy6iU6dO7Nixo8i6VatWJSUlpUKH/mO9/UYrW/nKV77yo5F9PvIldpXbMXHOtXbOpQBLgZ8AN4Uev6XMFc9ScnIyGRkZ7N27l5MnT7JgwYJi4/BjNT/Wa8/MzKRevXrUqVOH+Ph4rrrqKrZt21ZkmRo1aoRvt23bNrxDWLNmzfCJxdWqVaNp06bFhtysXbuWli1b0qxZMypXrszgwYOZP39+kWXq1asX/nbgiSeeYNasWUDwxPC6deuG/5/JycksWrTogsr/7LPPuOyyy2jcuDGVKlXipz/9KcuWLSuyzL59+8JXPGvRogVVq1bl4MGD4eFPAI0aNaJ58+bFOk2ZmZnUr1+/yPYtfOL1E7oPAAAX1ElEQVQ+FN2+bdq0KXH7XnTRRTRr1qzY9s3Kyiry95OcnFysI3D63A6A1q1bhzPi4+O588472bhxI1u3bg0vU6dOHfbu3cvXX3/NqVOnWLx4cbGrfXXt2pUFCxYAsHTpUjp06IBzjhtuuIHdu3fz/fffk5+fz8aNG2nevDnHjh3jm2++AYIdmeXLlxc5mb40sd5+o5WtfOUrX/nRyD4f+RK7yrsq10hgGLAdeBUYZWZ/D81+Fnjfs0IqVWLChAk88MADBAIBUlNTi30zHKv5sV57QUEB8+bN47777iMuLo5169aRm5tLr169yMzMZPv27XTq1Im2bdtSUFDAsWPHePPNN4Hg2P/CVzVauXJlsXGegUCAkSNHsnDhQuLj45k1axbbtm1j4sSJrF+/nvnz59OjRw8mT56MmbFy5UqGDx8OQOXKlfnoo4+A4DfzQ4cOLTYU6kLIf/bZZ5k+fTrx8fG8/fbb7N69m2HDhrF161aWL1/O1KlT+e1vf8vQoUMxs/BRmfbt23P//feTn59PQUEBkyZN4ttvvy11+zrnwtv3pptuIisrK7x927RpE96+c+fODW/fPn36hLNWrFhRbPsWFBTw7rvvcs899xAXF8f69evJzc0lJSWFrKwsduzYwY033kjr1q0pKCjg+PHjpKWlAcHzlpo1a8bFF19M+/btAUhLSyM7O5sxY8YwcuRICgoK6N+/Py1atGD69Om0adOGbt26MWDAACZOnEhqaio1a9Zk0qRJQLAzdccdd3DvvffinKNTp0506dKFAwcOMGbMGE6dOkVBQQGdOnViyJAhlCfW22+0spWvfOUrPxrZ5yNfYpcra7y3c24LcKOZ5TnnmgFzgdlm9oJzbqOZXVOB5zi3AeXygz3xxBMRzZ86dWpE8/2uoleu+qH69+8f0fzTR2oiZcyYMRHNr127dkTzRUQkJvni5I09e/bE9P7xZZddFpXXscwjJkCcmeUBmFmGc64HMNc51xSfbHgREREREYl95Z1jkuOcu/r0nVAnpR9QH0iOZGEiIiIiIvKvo7wjJkOB/MIPmFk+MNQ5Nz1iVYmIiIiIXKB0ueCSlfc7JpllzFvlfTkiIiIiIvKvqNzLBYuIiIiIiERaeUO5RERERETEQxrKVTIdMRERERERkahTx0RERERERKJOQ7lERERERM4jDeUqWZm//O6RmP5lSxERERG5YPhijz8rKyum948bN24clddRQ7lERERERCTqNJRLREREROQ80lCukumIiYiIiIiIRJ06JiIiIiIiEnUayiUiIiIich5pKFfJdMRERERERESiTh0TERERERGJOnVMREREREQk6nSOiYiIiIjIeaRzTEqmIyYiIiIiIhJ1MdUxWbFiBb1796ZXr17MmDHDV/l+rl35yle+f/P9XLvyla98/+b7uXaJYWZW6gTcUuh2LeBVIB14A0gsa91CU4Xk5+dbSkqK7dmzx06cOGH9+/e3nTt3VnT1qOb7uXblK1/5/s33c+3KV77y/Zsf47VXZN806lN2drbF8hSt16W8IybPFrr9HLAP6A+sBaZ72UFKT0+nadOmJCUlUaVKFfr27cuSJUt8ke/n2pWvfOX7N9/PtStf+cr3b76fa5fYdjZDuTqY2Xgz+18zex5o5mUhOTk5NGzYMHw/MTGRnJwcX+T7uXblK1/5/s33c+3KV77y/Zvv59oltpXXMWngnPu1c+4xoKYregmBUtd1zj3knFvnnFuncYEiIiIiIv/knIvpKVrKu1zwn4AaoduvA/WB/c65hsCm0lYysxnA6R6JVaSQxMREsrOzw/dzcnJITEysyKoVEsl8P9eufOUr37/5fq5d+cpXvn/z/Vy7xLYyj5iY2W+BOcDHwHNmtj/0eDbBE+A9k5ycTEZGBnv37uXkyZMsWLCAnj17+iLfz7UrX/nK92++n2tXvvKV7998P9cusa3MIybOuRHAcGA78KpzbpSZ/T00+1ngfc8KqVSJCRMm8MADDxAIBEhNTeWKK67wKj6i+X6uXfnKV75/8/1cu/KVr3z/5vu59lihH1gsmTMrfaSVc24LcKOZ5TnnmgFzgdlm9oJzbqOZXVOB56jQUC4RERERkXPkiz3+/fv3x/T+8SWXXBKV17G8c0zizCwPwMwynHM9gLnOuab4ZMOLiIiIiEjsK++qXDnOuatP3wl1UvoRPAk+OZKFiYiIiIhciKJ91a1YvSpXeR2ToUB24QfMLN/MhgLdIlaViIiIiIj8SylzKJeZZZYxb5X35YiIiIiIyL+is/nldxERERERkYgo7+R3ERERERHxkC4XXDIdMRERERERkahTx0RERERERKJOQ7lERERERM4jDeUqmY6YiIiIiIhI1KljIiIiIiIiUaehXCIiIiIi55GGcpVMR0xERERERCTq1DEREREREZGo01AuEREREZHzSEO5SqYjJiIiIiIiEnXqmIiIiIiISNRpKJeIiIiIyHmkoVwl0xETERERERGJOnVMREREREQk6s66Y+KcqxeJQgBWrFhB79696dWrFzNmzPBVvp9rV77yle/ffD/XrnzlK9+/+X6uXWKYmZU6Af8B1A/d7gB8CewC/hfoXta6haYKyc/Pt5SUFNuzZ4+dOHHC+vfvbzt37qzo6lHN93Ptyle+8v2b7+fala985fs3P8Zrr8i+adSnw4cPWyxP0Xpdyjti0tfMvgndngoMNrOWQC/gOS87SOnp6TRt2pSkpCSqVKlC3759WbJkiS/y/Vy78pWvfP/m+7l25Stf+f7N93PtEtvK65hUcs6dvnJXNTNbC2BmXwBVvSwkJyeHhg0bhu8nJiaSk5Pji3w/16585Svfv/l+rl35yle+f/P9XLvEtvI6JtOA95xzPYH3nXMvOOe6O+d+C2wqbSXn3EPOuXXOuXUaFygiIiIi8k/OuZieoqXM3zExs5ecc1uAR4BWoeWvAN4BJpWx3gzgdI/EKlJIYmIi2dnZ4fs5OTkkJiZWZNUKiWS+n2tXvvKV7998P9eufOUr37/5fq5dYltFrsqVTbCT0dXMks2sT6jjkeJlIcnJyWRkZLB3715OnjzJggUL6Nmzpy/y/Vy78pWvfP/m+7l25Stf+f7N93PtEuPKOjMeGAl8TvAISQZwa6F5Gyp4hn2FLV++3G6++WZLSUmxadOmnc2qUc/3c+3KV77y/Zvv59qVr3zl+zc/hmuP+hW3KjIdPXrUYnmK1uvizEofaRUaxnWjmeU555oBc4HZZvaCc26jmV1Tkb7PuXScREREREQqKHonSJyFvLy8mN4/rl69elRexzLPMQHizCwPwMwynHM9gLnOuab4ZMOLiIiIiEjsK+8ckxzn3NWn74Q6Kf2A+kByJAsTEREREbkQRfuqW7F6Va7yhnI1AfLNLLuEeZ3NbFUFniOmD1WJiIiIyAXDFyN6vvvuu5jeP05ISIjK61hmx8QjMf3Ci4iIiMgFQx0TD0SrY1LeOSYiIiIiIuKhaA6XimUV+R0TERERERGRiFLHREREREREok5DuUREREREziMN5SqZjpiIiIiIiEjUqWMiIiIiIiJRp46JiIiIiIhEnc4xERERERE5j3SOScl0xERERERERKJOHRMREREREYk6DeUSERERETmPNJSrZDpiIiIiIiIiUaeOiYiIiIiIRJ2GcomIiIiInEcaylUyHTEREREREZGoU8dERERERESiLqY6JitWrKB379706tWLGTNm+Crfz7UrX/nK92++n2tXvvKV7998P9ceC5xzMT1FjZmVOgEbgPHA5WUtV85UIfn5+ZaSkmJ79uyxEydOWP/+/W3nzp0VXT2q+X6uXfnKV75/8/1cu/KVr3z/5sd47T90f/W8TidPnrRYnqL1upR3xKQOUBtY5pz71Dk32jl3aSQ6SOnp6TRt2pSkpCSqVKlC3759WbJkiS/y/Vy78pWvfP/m+7l25Stf+f7N93PtEtvK65gcMrMxZnYZ8BhwBbDBObfMOfeQl4Xk5OTQsGHD8P3ExERycnJ8ke/n2pWvfOX7N9/PtStf+cr3b76fa48V0R6qFatDuSp8jomZrTSzXwGNgd8BN5a2rHPuIefcOufcugtxXKCIiIiIiHirvN8x+eLMB8wsALwfmkpkZjOA0z0Sq0ghiYmJZGdnh+/n5OSQmJhYkVUrJJL5fq5d+cpXvn/z/Vy78pWvfP/m+7l2iW1lHjExsyHOudbOuRTnXPXC85xzt3hZSHJyMhkZGezdu5eTJ0+yYMECevbs6Yt8P9eufOUr37/5fq5d+cpXvn/z/Vy7xLiyzowHRgCfA+8AGcCtheZtqOAZ9hW2fPlyu/nmmy0lJcWmTZt2NqtGPd/PtStf+cr3b76fa1e+8pXv3/wYrj3qV9yqyJSfn2+xPEXrdXFmpY+0cs5tAW40szznXDNgLjDbzF5wzm00s2sq0vc5l46TiIiIiEgFRfFHOCouEAjE9P5xfHx8VF7H8s4xiTOzPAAzy3DO9QDmOuea4pMNLyIiIiIisa+8q3LlOOeuPn0n1EnpB9QHkiNZmIiIiIjIhSjalwOO1csFlzeUqwmQb2bZJczrbGarKvAcMX2oSkREREQuGL4Y0VNQUBDT+8dxcXFReR3L7Jh4JKZfeBERERG5YKhj4oFodUzKO8dEREREREQ8FM3hUrGswr/8LiIiIiIiEinqmIiIiIiISNSpYyIiIiIich5F+6pbXlyVyzl3i3Puc+fcLufcEyXMr+qcmxOavyb0m4hlUsdEREREREQqzDkXD/wR+CnQFrjDOdf2jMXuBw6ZWUvgeeB35eWqYyIiIiIiImejI7DLzL40s5PA34Bbz1jmVuD10O25QIor53DM+eiYuLOZnHMPn+06ylf+hZDv59qVr3zl+zffz7UrX/klTH4RsdfMi8k595Bzbl2h6aEz6m8M7C10PzP0WInLmFk+cBioV9aLEotHTM78jytf+f8q+X6uXfnKV75/8/1cu/KVLxFgZjPMrEOhacb5eN5Y7JiIiIiIiEjsygKSCt1vEnqsxGWcc5WAWsCBskLVMRERERERkbOxFrjCOdfcOVcFGALMO2OZecA9oduDgKVmVuYv3sfiL79H+lCR8pUfq/l+rl35yle+f/P9XLvylS9RYGb5zrnhwAdAPDDTzLY6554B1pnZPOBVYLZzbhdwkGDnpUyunI6LiIiIiIhIxGkol4iIiIiIRJ06JiIiIiIiEnUx1TEp76ftzzF7pnMu1zn3mZe5hfKTnHPLnHPbnHNbnXOjPM6/yDn3qXNucyj/t17mh54j3jm30Tn3bgSyM5xzW5xzm5xz6yKQX9s5N9c5t8M5t905d6OH2T8K1X16OuKce9Sr/NBzjA5t18+cc//tnLvI4/xRoeytXtReUntyztV1zi12zu0M/VvH4/zbQ/UXOOc6RKD+qaG/n3Tn3NvOudoe5/+fUPYm59wi59ylXuYXmveYc86cc/U9rn+icy6rUDvo43X9zrkRoW2w1Tn3ew9rn1Oo7gzn3CYva3fOXe2cW336/c0519Hj/Kucc/8IvYfOd87VPIf8Ej+rvGq/ZeR70n7LyPek/ZaR70n7LS2/0Pwf3H7LqN2TtltW7R613dLq96z9ig+YWUxMBE+c2Q20AKoAm4G2HuZ3A9oDn0Wo/kZA+9DtGsAXHtfvgOqh25WBNcANHv8ffg28AbwbgdcnA6gfwb+f14EHQrerALUj9DzxQDbQ1MPMxsBXQLXQ/f8B7vUw/0rgM+Bighe8+BBoeY6ZxdoT8HvgidDtJ4DfeZzfBvgRsBzoEIH6bwYqhW7/LgL11yx0eyTwX17mhx5PIngi4v+eS3srpf6JwBiP/iZLyv9J6G+zauh+Ay9fm0LznwMmeFz7IuCnodt9gOUe568Fuodu3wf8n3PIL/Gzyqv2W0a+J+23jHxP2m8Z+Z6039LyQ/fPqf2WUbsnbbeMfK/abrn7UefafjXF/hRLR0wq8tP2P5iZrSB4RYCIMLN9ZrYhdPsosJ3iv4B5LvlmZnmhu5VDk2dXLnDONQH6Aq94lXm+OOdqEfwwfxXAzE6a2bcReroUYLeZ/a/HuZWAai54ne+Lga89zG4DrDGzYxb85dWPgJ+dS2Ap7elWgh1EQv/e5mW+mW03s89/aGYF8heFXh+A1QSvye5l/pFCdxM4h/ZbxvvZ88C/n0t2OfmeKCX/EeA/zOxEaJlcD7OB4E8ZAz8H/vuHZJeRb8Dpoxi1OIf2W0p+K2BF6PZiIPUc8kv7rPKk/ZaW71X7LSPfk/ZbRr4n7becfYVzar/nYT+ktHyv2m6Z9XvRfiX2xVLHpCI/be8LzrlmwDUEj2p4mRsfOoSZCyw2My/z/0DwDbHAw8zCDFjknFvvnPP6V1ybA/uBWS44FO0V51yCx89x2hA8flM0syzg/wJ7gH3AYTNb5OFTfAZ0dc7Vc85dTPAb3aRy1vkhEs1sX+h2NpAYgec4X+4DFnod6pyb7JzbC9wFTPA4+1Ygy8w2e5l7huGh4Swzf+hQnzK0Ivh3usY595Fz7jqP8wG6AjlmttPj3EeBqaFt+3+BJz3O38o/v6i7HY/a7xmfVZ6330h9FlYg35P2e2a+1+23cL7X7beE18bTtntGvudtt5RtG6n2KzEkljomFwTnXHUgDXj0jG9YzpmZBczsaoLfBHV0zl3pRa5zrh+Qa2brvcgrRRczaw/8FBjmnOvmYXYlgkMfXjaza4DvCA5F8JQL/oDQAOBNj3PrENzpaA5cCiQ4537hVb6ZbSc4tGER8D6wCQh4lV/KcxoeHtE7n5xz44B84K9eZ5vZODNLCmUP9yo31OEci8ednTO8DFwOXE2wA/2cx/mVgLrADcBvgP8JfUPqpTuIzLetjwCjQ9t2NKGjtx66D/iVc249wSEuJ881sKzPKi/abyQ/C8vK96r9lpTvZfstnB+q17P2W0LtnrbdEvI9bbtl/O1Eqv1KDImljklFfto+pjnnKhNsTH81s7ci9TyhYUrLgFs8iuwMDHDOZRAcQtfTOfcXj7KB8FGB04d43yY4dM8rmUBmoSNIcwl2VLz2U2CDmeV4nHsT8JWZ7TezU8BbQCcvn8DMXjWza82sG3CI4Nhdr+U45xoBhP79QYfzo8k5dy/QD7grtHMWKX/lHIbjlOBygh3bzaF23ATY4Jxr6NUTmFlO6MuRAuBPeNuGIdiO3woNW/2U4NHbH3wC/5lCwyR/BszxKrOQewi2Wwh+ceHpa2NmO8zsZjO7luCO2e5zySvls8qz9hvpz8LS8r1qvxWo/5zabwn5nrXfkmr3su2W8tp41nbL2LaRbL8SQ2KpY1KRn7aPWaFvB14FtpvZ/4tA/iUudJUR51w1oBeww4tsM3vSzJqYWTOCr/tSM/PsG3vnXIJzrsbp2wRPUvTs6mhmlg3sdc79KPRQCrDNq/xCIvVtzR7gBufcxaG/oxSCY2s945xrEPr3MoJv7m94mR8yj+AOGqF//x6B54gY59wtBIczDjCzYxHIv6LQ3VvxqP0CmNkWM2tgZs1C7TiT4Emk2V49x+md1pCBeNiGQ94heBItzrlWBC9i8Y2H+TcBO8ws08PM074Guodu9wQ8HWpSqP3GAeOB/zqHrNI+qzxpv+fhs7DEfK/abxn5nrTfkvK9ar9l1O5J2y1j23rSdsv524lk+5VYYjFwBv7pieDY9y8Ifhs0zuPs/yZ4CPMUwUZ/v8f5XQge+k4nOFRmE9DHw/wfAxtD+Z8RoatSAD3w+KpcBK+0tjk0bfV624ae42pgXej1eQeo43F+AnAAqBWh1/23BD/oPgNmE7q6iYf5Kwl21jYDKR7kFWtPQD1gCcGdsg+Buh7nDwzdPgHkAB94nL+L4Hlup9vvuVw1q6T8tND2TQfmEzyh1rP8M+ZncG5X5Sqp/tnAllD984BGHudXAf4Seo02AD29fG2A14BfRuhvvwuwPtS+1gDXepw/iuBn4xfAfwDuHPJL/Kzyqv2Wke9J+y0j35P2W0a+J+23tPwzlvlB7beM2j1pu2Xke9V2S31tvGq/mmJ/cqENLiIiIiIiEjWxNJRLRERERET+RaljIiIiIiIiUaeOiYiIiIiIRJ06JiIiIiIiEnXqmIiIiIiISNSpYyIiIiIiIlGnjomIiIiIiETd/weBRIv6y2NEPwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1080x1080 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VIqKoCx2eZaQ",
        "outputId": "8751ef32-63bc-4e85-d58a-2c3f73c05261"
      },
      "source": [
        " X_test.shape"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10000, 28, 28)"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZwZLGksQfvMz"
      },
      "source": [
        "X_valid, X_train = X_train_full[:5000] / 255.,X_train_full[5000:]/255\n",
        "y_valid, y_train = y_train_full[:5000], y_train_full[5000:]\n",
        "\n",
        "X_test =X_test/255"
      ],
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KSIYrvLni59V"
      },
      "source": [
        "LAYERS = [\n",
        "          tf.keras.layers.Flatten(input_shape=[28,28],name=\"InputLayer\"),\n",
        "          tf.keras.layers.Dense(300,activation=\"relu\",name=\"HiddenLayer1\"),\n",
        "          tf.keras.layers.Dense(100,activation=\"relu\",name=\"HiddenLayer2\"),\n",
        "          tf.keras.layers.Dense(10,activation=\"softmax\",name=\"OutputLayer\")\n",
        "]"
      ],
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-yVTRRjonXTM"
      },
      "source": [
        "model_clf =tf.keras.models.Sequential(LAYERS)"
      ],
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tuoaaw5TnsVC",
        "outputId": "61f99083-eedc-479b-a48c-784e4731f3a7"
      },
      "source": [
        "model_clf.layers"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<keras.layers.core.Flatten at 0x7ff156872d90>,\n",
              " <keras.layers.core.Dense at 0x7ff156872890>,\n",
              " <keras.layers.core.Dense at 0x7ff1567097d0>,\n",
              " <keras.layers.core.Dense at 0x7ff1567091d0>]"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JonGU2hRoA8O",
        "outputId": "37ca528d-da7e-4533-f137-af652b90d928"
      },
      "source": [
        "model_clf.summary()"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "InputLayer (Flatten)         (None, 784)               0         \n",
            "_________________________________________________________________\n",
            "HiddenLayer1 (Dense)         (None, 300)               235500    \n",
            "_________________________________________________________________\n",
            "HiddenLayer2 (Dense)         (None, 100)               30100     \n",
            "_________________________________________________________________\n",
            "OutputLayer (Dense)          (None, 10)                1010      \n",
            "=================================================================\n",
            "Total params: 266,610\n",
            "Trainable params: 266,610\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xA6CMcTxoIC3"
      },
      "source": [
        ""
      ],
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qF9OfC2Mgyq7",
        "outputId": "8af7f772-24b9-4bf5-b0e2-a40063afa58b"
      },
      "source": [
        "# firstlayer * secondlayer + bias\n",
        "\n",
        "784*300+300, 300*100+100, 100*10+10"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(235500, 30100, 1010)"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "NmJ1lygGiLL7",
        "outputId": "405cf583-3751-4c39-e0c4-327554b66db9"
      },
      "source": [
        "model_clf.layers[1].name"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'HiddenLayer1'"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ENpwevCWty1i"
      },
      "source": [
        "weights,biases=model_clf.layers[1].get_weights()"
      ],
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NAPHUynHuAIm",
        "outputId": "d1e647ea-6a4c-48be-a3ee-7e17de6b1df3"
      },
      "source": [
        "weights.shape"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(784, 300)"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sSVthxh-uHeP",
        "outputId": "95a2b312-313e-492e-fca1-7a3054f867cf"
      },
      "source": [
        "biases.shape"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(300,)"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LAIvc7IxuLqI"
      },
      "source": [
        "LOSS_FUNCTION=\"sparse_categorical_crossentropy\"\n",
        "OPTIMIZER=\"SGD\"\n",
        "METRICS=[\"accuracy\"]\n",
        "model_clf.compile(loss=LOSS_FUNCTION, optimizer=OPTIMIZER,metrics=METRICS)"
      ],
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "51vIFmWsu20P",
        "outputId": "a384354f-fdab-46dd-b9a5-c4094ff353bc"
      },
      "source": [
        "EPOCHS=30\n",
        "VALIDATION=(X_valid,y_valid)\n",
        "\n",
        "history=model_clf.fit(X_train,y_train,epochs=EPOCHS,validation_data=VALIDATION)"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/30\n",
            "1719/1719 [==============================] - 6s 3ms/step - loss: 0.6170 - accuracy: 0.8429 - val_loss: 0.3120 - val_accuracy: 0.9172\n",
            "Epoch 2/30\n",
            "1719/1719 [==============================] - 5s 3ms/step - loss: 0.2910 - accuracy: 0.9168 - val_loss: 0.2445 - val_accuracy: 0.9322\n",
            "Epoch 3/30\n",
            "1719/1719 [==============================] - 5s 3ms/step - loss: 0.2385 - accuracy: 0.9322 - val_loss: 0.2095 - val_accuracy: 0.9422\n",
            "Epoch 4/30\n",
            "1719/1719 [==============================] - 5s 3ms/step - loss: 0.2035 - accuracy: 0.9421 - val_loss: 0.1795 - val_accuracy: 0.9516\n",
            "Epoch 5/30\n",
            "1719/1719 [==============================] - 5s 3ms/step - loss: 0.1780 - accuracy: 0.9495 - val_loss: 0.1623 - val_accuracy: 0.9558\n",
            "Epoch 6/30\n",
            "1719/1719 [==============================] - 6s 3ms/step - loss: 0.1578 - accuracy: 0.9554 - val_loss: 0.1459 - val_accuracy: 0.9616\n",
            "Epoch 7/30\n",
            "1719/1719 [==============================] - 5s 3ms/step - loss: 0.1414 - accuracy: 0.9596 - val_loss: 0.1345 - val_accuracy: 0.9658\n",
            "Epoch 8/30\n",
            "1719/1719 [==============================] - 5s 3ms/step - loss: 0.1276 - accuracy: 0.9637 - val_loss: 0.1255 - val_accuracy: 0.9670\n",
            "Epoch 9/30\n",
            "1719/1719 [==============================] - 5s 3ms/step - loss: 0.1156 - accuracy: 0.9671 - val_loss: 0.1156 - val_accuracy: 0.9694\n",
            "Epoch 10/30\n",
            "1719/1719 [==============================] - 6s 3ms/step - loss: 0.1057 - accuracy: 0.9695 - val_loss: 0.1088 - val_accuracy: 0.9698\n",
            "Epoch 11/30\n",
            "1719/1719 [==============================] - 5s 3ms/step - loss: 0.0973 - accuracy: 0.9725 - val_loss: 0.1051 - val_accuracy: 0.9716\n",
            "Epoch 12/30\n",
            "1719/1719 [==============================] - 5s 3ms/step - loss: 0.0896 - accuracy: 0.9749 - val_loss: 0.1026 - val_accuracy: 0.9724\n",
            "Epoch 13/30\n",
            "1719/1719 [==============================] - 5s 3ms/step - loss: 0.0829 - accuracy: 0.9766 - val_loss: 0.0953 - val_accuracy: 0.9740\n",
            "Epoch 14/30\n",
            "1719/1719 [==============================] - 6s 3ms/step - loss: 0.0769 - accuracy: 0.9786 - val_loss: 0.0904 - val_accuracy: 0.9746\n",
            "Epoch 15/30\n",
            "1719/1719 [==============================] - 6s 3ms/step - loss: 0.0716 - accuracy: 0.9799 - val_loss: 0.0884 - val_accuracy: 0.9760\n",
            "Epoch 16/30\n",
            "1719/1719 [==============================] - 6s 3ms/step - loss: 0.0669 - accuracy: 0.9813 - val_loss: 0.0857 - val_accuracy: 0.9766\n",
            "Epoch 17/30\n",
            "1719/1719 [==============================] - 6s 3ms/step - loss: 0.0622 - accuracy: 0.9827 - val_loss: 0.0836 - val_accuracy: 0.9764\n",
            "Epoch 18/30\n",
            "1719/1719 [==============================] - 6s 3ms/step - loss: 0.0584 - accuracy: 0.9842 - val_loss: 0.0791 - val_accuracy: 0.9772\n",
            "Epoch 19/30\n",
            "1719/1719 [==============================] - 5s 3ms/step - loss: 0.0547 - accuracy: 0.9852 - val_loss: 0.0793 - val_accuracy: 0.9772\n",
            "Epoch 20/30\n",
            "1719/1719 [==============================] - 5s 3ms/step - loss: 0.0514 - accuracy: 0.9861 - val_loss: 0.0799 - val_accuracy: 0.9762\n",
            "Epoch 21/30\n",
            "1719/1719 [==============================] - 5s 3ms/step - loss: 0.0479 - accuracy: 0.9877 - val_loss: 0.0757 - val_accuracy: 0.9778\n",
            "Epoch 22/30\n",
            "1719/1719 [==============================] - 5s 3ms/step - loss: 0.0449 - accuracy: 0.9885 - val_loss: 0.0758 - val_accuracy: 0.9766\n",
            "Epoch 23/30\n",
            "1719/1719 [==============================] - 5s 3ms/step - loss: 0.0426 - accuracy: 0.9893 - val_loss: 0.0741 - val_accuracy: 0.9784\n",
            "Epoch 24/30\n",
            "1719/1719 [==============================] - 5s 3ms/step - loss: 0.0400 - accuracy: 0.9900 - val_loss: 0.0729 - val_accuracy: 0.9788\n",
            "Epoch 25/30\n",
            "1719/1719 [==============================] - 5s 3ms/step - loss: 0.0375 - accuracy: 0.9902 - val_loss: 0.0729 - val_accuracy: 0.9788\n",
            "Epoch 26/30\n",
            "1719/1719 [==============================] - 5s 3ms/step - loss: 0.0355 - accuracy: 0.9908 - val_loss: 0.0742 - val_accuracy: 0.9790\n",
            "Epoch 27/30\n",
            "1719/1719 [==============================] - 5s 3ms/step - loss: 0.0332 - accuracy: 0.9921 - val_loss: 0.0713 - val_accuracy: 0.9784\n",
            "Epoch 28/30\n",
            "1719/1719 [==============================] - 5s 3ms/step - loss: 0.0314 - accuracy: 0.9927 - val_loss: 0.0765 - val_accuracy: 0.9772\n",
            "Epoch 29/30\n",
            "1719/1719 [==============================] - 5s 3ms/step - loss: 0.0296 - accuracy: 0.9932 - val_loss: 0.0722 - val_accuracy: 0.9786\n",
            "Epoch 30/30\n",
            "1719/1719 [==============================] - 5s 3ms/step - loss: 0.0280 - accuracy: 0.9935 - val_loss: 0.0716 - val_accuracy: 0.9786\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D3r6pjhvvN2d",
        "outputId": "6fc4636c-acb8-47f8-c738-da0accb08dba"
      },
      "source": [
        "history.params"
      ],
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'epochs': 30, 'steps': 1719, 'verbose': 1}"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j-TMjHXu4Epo",
        "outputId": "9e096fec-788b-4681-b040-5bb875411515"
      },
      "source": [
        "history.history"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'accuracy': [0.8429272770881653,\n",
              "  0.9167818427085876,\n",
              "  0.9321818351745605,\n",
              "  0.9420545697212219,\n",
              "  0.9494909048080444,\n",
              "  0.9554181694984436,\n",
              "  0.9595999717712402,\n",
              "  0.9636545181274414,\n",
              "  0.9671090841293335,\n",
              "  0.9695272445678711,\n",
              "  0.9725090861320496,\n",
              "  0.9748727083206177,\n",
              "  0.9765636324882507,\n",
              "  0.9785636067390442,\n",
              "  0.9798545241355896,\n",
              "  0.9813272953033447,\n",
              "  0.9826727509498596,\n",
              "  0.9842363595962524,\n",
              "  0.9851636290550232,\n",
              "  0.9861090779304504,\n",
              "  0.987672746181488,\n",
              "  0.988490879535675,\n",
              "  0.9892545342445374,\n",
              "  0.9900363683700562,\n",
              "  0.9901999831199646,\n",
              "  0.990818202495575,\n",
              "  0.9920908808708191,\n",
              "  0.9927091002464294,\n",
              "  0.9931636452674866,\n",
              "  0.9934727549552917],\n",
              " 'loss': [0.6170014142990112,\n",
              "  0.2910059690475464,\n",
              "  0.2384977638721466,\n",
              "  0.20348592102527618,\n",
              "  0.17800123989582062,\n",
              "  0.157823383808136,\n",
              "  0.14138644933700562,\n",
              "  0.1275523602962494,\n",
              "  0.11558882147073746,\n",
              "  0.10568660497665405,\n",
              "  0.09726125001907349,\n",
              "  0.08963682502508163,\n",
              "  0.08293931186199188,\n",
              "  0.07694131880998611,\n",
              "  0.07156176120042801,\n",
              "  0.06691651046276093,\n",
              "  0.062224071472883224,\n",
              "  0.05835587531328201,\n",
              "  0.05474018678069115,\n",
              "  0.051423631608486176,\n",
              "  0.047857701778411865,\n",
              "  0.04486462473869324,\n",
              "  0.04259417578577995,\n",
              "  0.039987921714782715,\n",
              "  0.037500396370887756,\n",
              "  0.03554786741733551,\n",
              "  0.03316495567560196,\n",
              "  0.03136468678712845,\n",
              "  0.029620461165905,\n",
              "  0.028005579486489296],\n",
              " 'val_accuracy': [0.9172000288963318,\n",
              "  0.932200014591217,\n",
              "  0.9422000050544739,\n",
              "  0.9516000151634216,\n",
              "  0.9557999968528748,\n",
              "  0.9616000056266785,\n",
              "  0.9657999873161316,\n",
              "  0.9670000076293945,\n",
              "  0.9693999886512756,\n",
              "  0.9697999954223633,\n",
              "  0.9715999960899353,\n",
              "  0.9724000096321106,\n",
              "  0.9739999771118164,\n",
              "  0.9746000170707703,\n",
              "  0.9760000109672546,\n",
              "  0.9765999913215637,\n",
              "  0.9764000177383423,\n",
              "  0.9771999716758728,\n",
              "  0.9771999716758728,\n",
              "  0.9761999845504761,\n",
              "  0.9778000116348267,\n",
              "  0.9765999913215637,\n",
              "  0.9783999919891357,\n",
              "  0.9787999987602234,\n",
              "  0.9787999987602234,\n",
              "  0.9789999723434448,\n",
              "  0.9783999919891357,\n",
              "  0.9771999716758728,\n",
              "  0.978600025177002,\n",
              "  0.978600025177002],\n",
              " 'val_loss': [0.31203529238700867,\n",
              "  0.24448168277740479,\n",
              "  0.20951774716377258,\n",
              "  0.17947541177272797,\n",
              "  0.16227677464485168,\n",
              "  0.14590425789356232,\n",
              "  0.13453535735607147,\n",
              "  0.12546080350875854,\n",
              "  0.1155780553817749,\n",
              "  0.10879182070493698,\n",
              "  0.10505549609661102,\n",
              "  0.10256797820329666,\n",
              "  0.09529312700033188,\n",
              "  0.09035097807645798,\n",
              "  0.0883786529302597,\n",
              "  0.08569000661373138,\n",
              "  0.0835791602730751,\n",
              "  0.0791437104344368,\n",
              "  0.07934141159057617,\n",
              "  0.07993976771831512,\n",
              "  0.07572237402200699,\n",
              "  0.07577509433031082,\n",
              "  0.0741056278347969,\n",
              "  0.07286899536848068,\n",
              "  0.0728706419467926,\n",
              "  0.07417520135641098,\n",
              "  0.0713454857468605,\n",
              "  0.07645107060670853,\n",
              "  0.07222311943769455,\n",
              "  0.07155215740203857]}"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 990
        },
        "id": "cIHVjBj-4JHz",
        "outputId": "cbcfc98e-3581-4ddd-d2a1-f0eea30957ca"
      },
      "source": [
        "pd.DataFrame(history.history)"
      ],
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>loss</th>\n",
              "      <th>accuracy</th>\n",
              "      <th>val_loss</th>\n",
              "      <th>val_accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.617001</td>\n",
              "      <td>0.842927</td>\n",
              "      <td>0.312035</td>\n",
              "      <td>0.9172</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.291006</td>\n",
              "      <td>0.916782</td>\n",
              "      <td>0.244482</td>\n",
              "      <td>0.9322</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.238498</td>\n",
              "      <td>0.932182</td>\n",
              "      <td>0.209518</td>\n",
              "      <td>0.9422</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.203486</td>\n",
              "      <td>0.942055</td>\n",
              "      <td>0.179475</td>\n",
              "      <td>0.9516</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.178001</td>\n",
              "      <td>0.949491</td>\n",
              "      <td>0.162277</td>\n",
              "      <td>0.9558</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.157823</td>\n",
              "      <td>0.955418</td>\n",
              "      <td>0.145904</td>\n",
              "      <td>0.9616</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.141386</td>\n",
              "      <td>0.959600</td>\n",
              "      <td>0.134535</td>\n",
              "      <td>0.9658</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.127552</td>\n",
              "      <td>0.963655</td>\n",
              "      <td>0.125461</td>\n",
              "      <td>0.9670</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.115589</td>\n",
              "      <td>0.967109</td>\n",
              "      <td>0.115578</td>\n",
              "      <td>0.9694</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.105687</td>\n",
              "      <td>0.969527</td>\n",
              "      <td>0.108792</td>\n",
              "      <td>0.9698</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>0.097261</td>\n",
              "      <td>0.972509</td>\n",
              "      <td>0.105055</td>\n",
              "      <td>0.9716</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>0.089637</td>\n",
              "      <td>0.974873</td>\n",
              "      <td>0.102568</td>\n",
              "      <td>0.9724</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>0.082939</td>\n",
              "      <td>0.976564</td>\n",
              "      <td>0.095293</td>\n",
              "      <td>0.9740</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>0.076941</td>\n",
              "      <td>0.978564</td>\n",
              "      <td>0.090351</td>\n",
              "      <td>0.9746</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>0.071562</td>\n",
              "      <td>0.979855</td>\n",
              "      <td>0.088379</td>\n",
              "      <td>0.9760</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>0.066917</td>\n",
              "      <td>0.981327</td>\n",
              "      <td>0.085690</td>\n",
              "      <td>0.9766</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>0.062224</td>\n",
              "      <td>0.982673</td>\n",
              "      <td>0.083579</td>\n",
              "      <td>0.9764</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>0.058356</td>\n",
              "      <td>0.984236</td>\n",
              "      <td>0.079144</td>\n",
              "      <td>0.9772</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>0.054740</td>\n",
              "      <td>0.985164</td>\n",
              "      <td>0.079341</td>\n",
              "      <td>0.9772</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>0.051424</td>\n",
              "      <td>0.986109</td>\n",
              "      <td>0.079940</td>\n",
              "      <td>0.9762</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>0.047858</td>\n",
              "      <td>0.987673</td>\n",
              "      <td>0.075722</td>\n",
              "      <td>0.9778</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>0.044865</td>\n",
              "      <td>0.988491</td>\n",
              "      <td>0.075775</td>\n",
              "      <td>0.9766</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>0.042594</td>\n",
              "      <td>0.989255</td>\n",
              "      <td>0.074106</td>\n",
              "      <td>0.9784</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>0.039988</td>\n",
              "      <td>0.990036</td>\n",
              "      <td>0.072869</td>\n",
              "      <td>0.9788</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>0.037500</td>\n",
              "      <td>0.990200</td>\n",
              "      <td>0.072871</td>\n",
              "      <td>0.9788</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>0.035548</td>\n",
              "      <td>0.990818</td>\n",
              "      <td>0.074175</td>\n",
              "      <td>0.9790</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>0.033165</td>\n",
              "      <td>0.992091</td>\n",
              "      <td>0.071345</td>\n",
              "      <td>0.9784</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>0.031365</td>\n",
              "      <td>0.992709</td>\n",
              "      <td>0.076451</td>\n",
              "      <td>0.9772</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>0.029620</td>\n",
              "      <td>0.993164</td>\n",
              "      <td>0.072223</td>\n",
              "      <td>0.9786</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>0.028006</td>\n",
              "      <td>0.993473</td>\n",
              "      <td>0.071552</td>\n",
              "      <td>0.9786</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        loss  accuracy  val_loss  val_accuracy\n",
              "0   0.617001  0.842927  0.312035        0.9172\n",
              "1   0.291006  0.916782  0.244482        0.9322\n",
              "2   0.238498  0.932182  0.209518        0.9422\n",
              "3   0.203486  0.942055  0.179475        0.9516\n",
              "4   0.178001  0.949491  0.162277        0.9558\n",
              "5   0.157823  0.955418  0.145904        0.9616\n",
              "6   0.141386  0.959600  0.134535        0.9658\n",
              "7   0.127552  0.963655  0.125461        0.9670\n",
              "8   0.115589  0.967109  0.115578        0.9694\n",
              "9   0.105687  0.969527  0.108792        0.9698\n",
              "10  0.097261  0.972509  0.105055        0.9716\n",
              "11  0.089637  0.974873  0.102568        0.9724\n",
              "12  0.082939  0.976564  0.095293        0.9740\n",
              "13  0.076941  0.978564  0.090351        0.9746\n",
              "14  0.071562  0.979855  0.088379        0.9760\n",
              "15  0.066917  0.981327  0.085690        0.9766\n",
              "16  0.062224  0.982673  0.083579        0.9764\n",
              "17  0.058356  0.984236  0.079144        0.9772\n",
              "18  0.054740  0.985164  0.079341        0.9772\n",
              "19  0.051424  0.986109  0.079940        0.9762\n",
              "20  0.047858  0.987673  0.075722        0.9778\n",
              "21  0.044865  0.988491  0.075775        0.9766\n",
              "22  0.042594  0.989255  0.074106        0.9784\n",
              "23  0.039988  0.990036  0.072869        0.9788\n",
              "24  0.037500  0.990200  0.072871        0.9788\n",
              "25  0.035548  0.990818  0.074175        0.9790\n",
              "26  0.033165  0.992091  0.071345        0.9784\n",
              "27  0.031365  0.992709  0.076451        0.9772\n",
              "28  0.029620  0.993164  0.072223        0.9786\n",
              "29  0.028006  0.993473  0.071552        0.9786"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "id": "O6yVIUJ64g9Y",
        "outputId": "d8c5acfd-6fa2-43ac-d75e-5ab29b86b083"
      },
      "source": [
        "pd.DataFrame(history.history).plot(figsize=(10,7))\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vwTzn-BH40Hp",
        "outputId": "f74cc296-dff0-4f55-d28d-eeb737e537cd"
      },
      "source": [
        "model_clf.evaluate(X_test,y_test)"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0715 - accuracy: 0.9775\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.07153919339179993, 0.9775000214576721]"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1NA8JspzMElq",
        "outputId": "84dc55da-b335-493f-d486-95b53678f017"
      },
      "source": [
        "X_new = X_test[:3]\n",
        "\n",
        "y_prob = model_clf.predict(X_new)\n",
        "\n",
        "y_prob.round(3)"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.   , 0.   , 0.   , 0.   , 0.   , 0.   , 0.   , 1.   , 0.   ,\n",
              "        0.   ],\n",
              "       [0.   , 0.   , 1.   , 0.   , 0.   , 0.   , 0.   , 0.   , 0.   ,\n",
              "        0.   ],\n",
              "       [0.   , 0.999, 0.   , 0.   , 0.   , 0.   , 0.   , 0.001, 0.   ,\n",
              "        0.   ]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1x_ytSeuMJQo",
        "outputId": "5d16b274-11a3-415f-a3d8-896470547901"
      },
      "source": [
        "y_prob.shape"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3, 10)"
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "awj8yyHBMMq9",
        "outputId": "b0894922-4d1a-442c-b2cd-78dd05bf971e"
      },
      "source": [
        "y_prob"
      ],
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1.1165778e-06, 1.9583736e-08, 3.5137095e-05, 1.0805331e-04,\n",
              "        1.9589375e-08, 9.3466205e-08, 6.0649437e-12, 9.9984467e-01,\n",
              "        3.4383279e-06, 7.4111026e-06],\n",
              "       [2.3019045e-07, 3.3643646e-05, 9.9989522e-01, 6.8646128e-05,\n",
              "        1.1621061e-12, 1.9639123e-07, 5.0933085e-08, 1.1131366e-11,\n",
              "        1.9808158e-06, 6.8420530e-12],\n",
              "       [5.5413329e-07, 9.9864703e-01, 6.6136461e-05, 2.6474207e-05,\n",
              "        8.5032567e-05, 6.7073612e-05, 3.0925486e-05, 7.1063876e-04,\n",
              "        3.5145265e-04, 1.4603110e-05]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oVvp0ecOMO5o",
        "outputId": "1556651d-21b0-45e0-9068-037cdd6066b1"
      },
      "source": [
        "Y_pred= np.argmax(y_prob, axis=-1)\n",
        "Y_pred"
      ],
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([7, 2, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 810
        },
        "id": "1X7Qt94YMRcR",
        "outputId": "8107a692-3eb7-428a-8303-e216e076beb2"
      },
      "source": [
        "for img_array, pred, actual in zip(X_new, Y_pred, y_test[:3]):\n",
        "  plt.imshow(img_array, cmap=\"binary\")\n",
        "  plt.title(f\"predicted: {pred}, Actual: {actual}\")\n",
        "  plt.axis(\"off\")\n",
        "  plt.show()\n",
        "  print(\"---\"*10)"
      ],
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "------------------------------\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "------------------------------\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "------------------------------\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CvEHRachMTfo"
      },
      "source": [
        " "
      ],
      "execution_count": 82,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a6J9CoMLel_1"
      },
      "source": [
        ""
      ],
      "execution_count": 82,
      "outputs": []
    }
  ]
}