skynet1.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "skynet1",
      "provenance": [],
      "authorship_tag": "ABX9TyNr3UR3Lbg/8/v/qWBVCnRO",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ucfai/team-latex/blob/main/skynet1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tJimWGHIp1-i"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torchvision\n",
        "import torchvision.transforms as transforms\n",
        "import torch.optim as optim\n",
        "import time "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m07oF_jovtHq"
      },
      "source": [
        "batch_size = 64\n",
        "epochCount = 20\n",
        "learningRate = 0.0005"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tw3u-YSGs_WM"
      },
      "source": [
        "batchrow = int(batch_size / 8)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mULla7jfCHMM",
        "outputId": "669d0af8-3e01-455b-92a0-104caca6f822",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "dataset_0 = torchvision.datasets.MNIST(root='./data', train=True, download=True, transform=transforms.ToTensor())\n",
        "\n",
        "dataloader = torch.utils.data.DataLoader(dataset_0, batch_size=10, num_workers=2, shuffle=False)\n",
        "\n",
        "mean = 0.\n",
        "std = 0.\n",
        "\n",
        "for i, data in enumerate(dataloader, 0):\n",
        "    img, lbl = data\n",
        "    batchSize = img.size(0)\n",
        "    img = img.view(batchSize, img.size(1), -1)\n",
        "    mean += img.mean(2).sum(0)\n",
        "    std += img.std(2).sum(0)\n",
        "  \n",
        "mean /= len(dataset_0)\n",
        "std /= len(dataset_0)\n",
        "print(mean, std)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensor([0.1307]) tensor([0.3015])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9ICz3FlrrRVi"
      },
      "source": [
        "transforms = transforms.Compose(\n",
        "    [transforms.ToTensor(),\n",
        "     transforms.Normalize((0.1307),(0.3015))])\n",
        "\n",
        "dataset = torchvision.datasets.MNIST(root='./data', train=True, download=True, transform=transforms)\n",
        "\n",
        "trainset, valset = torch.utils.data.random_split(dataset, [50000, 10000])\n",
        "\n",
        "testset = torchvision.datasets.MNIST(root='./data', train=False, download=True, transform=transforms)\n",
        "\n",
        "\n",
        "trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=True, num_workers=2)\n",
        "\n",
        "valloader = torch.utils.data.DataLoader(valset, batch_size=int(batch_size / 5), shuffle=True, num_workers=2)\n",
        "\n",
        "testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size, shuffle=False, num_workers=2)\n",
        "\n",
        "\n",
        "classes = ('0', '1', '2', '3', '4', '5', '6', '7', '8', '9')\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Nk8yQli6e05I",
        "cellView": "form"
      },
      "source": [
        "#@title Under construction\n",
        "#under construction\n",
        "\"\"\"\n",
        "exp_labels_list = []\n",
        "for i in range(100):\n",
        "  _,label = trainset[i]\n",
        "  exp_labels_list.append(label)\n",
        "print(exp_labels_list)\n",
        "trainset.targets"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cVL42jyniwO8",
        "cellView": "form"
      },
      "source": [
        "#@title Under construction\n",
        "#under construction, code for dataset splitter with equal distribtuion of classes\n",
        "\"\"\"\n",
        "eqsplitloader = torch.utils.data.DataLoader(trainset, batch_size=1, shuffle=False,num_workers=2)\n",
        "\n",
        "clinp_list = [] # list of lists, containing images sorted by classes\n",
        "cltgt_list = [] # list of lists, containing labels for images\n",
        "cl_count = len(classes)\n",
        "flag = 0 #constatnt to flag if the image of this class was already sorted\n",
        "inp_list = []\n",
        "tgt_list = []\n",
        "\n",
        "for i in range(cl_count):\n",
        "  empty_list = []\n",
        "  clinp_list.append(empty_list)\n",
        "  cltgt_list.append(empty_list)\n",
        "\n",
        "for i, data in enumerate(eqsplitloader, 0):\n",
        "  inp, tgt = data\n",
        "  inp_list.append(inp)\n",
        "  tgt_list.append(tgt)\n",
        "\n",
        "print(inp_list[0])\n",
        "print(tgt_list[0])\n",
        "\n",
        "\n",
        "  emptycl_idx = 0 #index of first empty list in list of lists\n",
        "  clinp_list[emptycl_idx].append(inp[0]) #sorting the first image and its label\n",
        "  cltgt_list[emptycl_idx].append(tgt[0])\n",
        "  for k in range(i): #running check\n",
        "\n",
        "    if tgt[i] == tgt[k]:\n",
        "      flag = 1\n",
        "      break\n",
        "  if flag == 1: #if there is list for class of next item in dataloader\n",
        "    for j in range(cl_count): \n",
        "          if tgt[i] != cltgt_list[j][0]: #find it\n",
        "            continue\n",
        "          else:\n",
        "            clinp_list[j].append(inp[i]) #store it and its label\n",
        "            cltgt_list[j].append(tgt[i])\n",
        "            break\n",
        "  else: #if loaded image is of new class\n",
        "    emptycl_idx += 1 #move index of first empty list\n",
        "    clinp_list[emptycl_idx].append(inp[i]) #store the imge and its label\n",
        "    cltgt_list[emptycl_idx].append(tgt[i])\n",
        "\"\"\"\n",
        "  \n",
        "\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qi9e9kmuYr7j",
        "outputId": "cea3f03b-e551-419c-d57d-0abec0d4f869",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101
        }
      },
      "source": [
        "print(len(trainset))\n",
        "print(len(valset))\n",
        "print(len(testset))\n",
        "epoch_count_train = int(len(trainset) / batch_size)\n",
        "epoch_count_val = int(5 * len(valset) / batch_size)\n",
        "print(epoch_count_train)\n",
        "print(epoch_count_val)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "50000\n",
            "10000\n",
            "10000\n",
            "781\n",
            "781\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uu6hMYzMy2L_",
        "outputId": "24f83629-5374-4f9b-fd1b-63147f5f6704",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 538
        }
      },
      "source": [
        "def showimg(image):\n",
        "  image = image * 0.3015 + 0.1307\n",
        "  npimage = image.numpy()\n",
        "  plt.imshow(np.transpose(npimage, (1, 2, 0)))\n",
        "  plt.show()\n",
        "\n",
        "dataiter_train = iter(trainloader)\n",
        "images, labels = dataiter_train.next()\n",
        "\n",
        "showimg(torchvision.utils.make_grid(images))\n",
        "for k in range(batchrow):\n",
        "  print(' '.join('%5s' % classes[labels[j]] for j in range(k * 8, (k + 1) * 8)), end='\\n\\n')\n",
        "  k += 1"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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SpUuZOHEiISEhLFq0qNryPD09mTVrFkOHDmXnzp3MmjWLwMBA/P39sbOzw9XV9dGDVIKvv/6aRo0aIZPJEELoVyIOHz7M7NmziYqKQqvVotPpqv1E+d///oezszMAaWlpbNy4EUmS8PPzw8/Pj06dOtGtWzfc3NwwMzNDkiR9ZGT5Re/g4KCfE1eWK1eu4O/vj1wup7S0lLy8PJ544gk6d+7Mzp07ycvLM4pfpVyZPWo60blz52rLqMoyrZ+fH02aNAHuWFXVWXo1MzPjnXfeISQkhJMnT/LRRx/x66+/UlRUhEwmo3fv3nh5eWFlZWVwRmitUAKSJDFnzhwmTpzI+vXrSU1Nve9+ZWVlpKenY25ujoeHB6amptXyrqvVambPnk1ISAg//vgj77zzDikpKfz8889MmjQJOzs7WrdubehpAdCnTx9sbW2BO74PNzc35s2bR2ZmJrGxsUZZZrp9+zY6nQ6VSoWHhwc3b95EkiQsLCywsLC470Wk0+koLS0lMzOTkydPcuTIkUr7H8opKyvj9u3bFbadP3+ezMzMe6ZYNY0kSfoIP2OOWR7sVY5MJmPq1Kk0aNAAjUbDd999x4YNG6o87sSJExk4cCA7d+7khRdeqOCULSsr49atW2g0GqOkhNcKJdCgQQMmTJhASkoK48aNe+B+5evESqVS/+SqqhKwsLBg4cKF+inF9OnT9UpHCEFUVBRNmzbl2rVrBp1TObdv365wo9ja2iKEMGp8wOuvv063bt1o2rQpcrmc+vXrP3BfnU5Heno63333HYcPHyYsLIzCwkKjVUd6nHUMFAqFPrxbCMG5c+eMNrZKpcLPzw8rKyuOHDlCaWkpMpmMwMBAOnbsSFlZGStWrGDOnDlVXlVp3749o0ePJjs7m3nz5t3z+fJl1bstNUOoFUqgXNsqFAoaNGjwwNwAc3Nz+vfvT25uLmFhYdVK4pg4cSLDhw/n6NGjTJkyBY1Gg6WlJWq1miFDhvDkk0+SlpbGkSNHDDyrh5OdnW20SEGtVsuiRYsYM2ZMhWlMUVGR/juSyWQUFxdz+/ZtvvnmGw4ePFgjT2o7OztMTEweS6Rd/fr19UlekiTRvn17o4yrUqno1asX77zzDl999RWlpaVYWlrSokULFi5cSMOGDTl9+jRLliyp1nn27t2b+vXrs3XrVn7//fcK7ymVStq1a0fnzp3Jy8vj5s2bBj8waoUSSExM5Msvv2TChAnMmzePJUuWcP36dQoLCxFCoFKpcHBwoHfv3jz//PNcunSpWjepqakpo0aNwtzcnJiYGIKDg3Fzc9MnEz355JNcv36dFStWsGXLFuOf6B+UlpZy4cIFoyUQlZWV8d1337F+/Xq9kwnu+AcuXrwI3Al0ycrKMsj3UBl8fHxQq9Vcu3bNqDEQ9yM+Pp7c3FycnJwQQlQplPZheHl58emnn6JSqdBoNPj4+BAYGMjzzz9P/fr1OXDgANOnT692arZSqUQmk5GWloZcLtc/ABs2bEjr1q0ZP348jRo14pNPPuHHH380+HusFUqgrKyMJUuWYGZmxqBBg2jYsCHHjx8nISGB0tJSHBwcaNWqFcHBwWRlZfHJJ59w9uzZKstRKBQcPXoUS0tLRo4cyahRo1Cr1aSlpem99MuXL+f8+fM1dqMolUqKiopISUkxes6ARqNh06ZNRh2zqjRq1AhTU1Pi4+Nr3CeQmZlZwVn456dqdRk4cCDu7u7k5eXx/PPPY2Zmhq+vL0VFRSxfvpxNmzZV2X9yP0JCQtBqtZSWlqJUKmnevDleXl7k5+ezdOlSfvjhB4OToqCWKAGA9PR05s6dy7Vr15gwYQKTJ0+mqKgIIQTm5uZcv36do0ePsmrVKo4cOVKtCyw/P58FCxawe/fuClmCWVlZpKSkEBcXR1JSUo0+Ka2trVGr1ZVejqptODk5UVRU9FgqMF+6dIldu3bh7u6OWq3ms88+M8q4Fy9eZNu2bfTp04eePXty+vRpfvrpJ86dO8euXbsMriVw+PBhAgMDad++PTNmzADuWIdxcXFs2rSJPXv2GNVSrDVKAO7Mk1evXs2pU6ewsbGpkNOfkZFBVlYWt27dMkhGXFxctSOvjEF5Wmh5meh/miL4/PPP+eGHH4xWdedh5Obm8vnnnxMbG8u4cePYs2ePUcb95ZdfiIiI0CuVpKQkMjIyyM/PN0po9YkTJ5gwYQIODg56R6oQgtzcXGJjY8nOzjbqg6hWKQG4sxZcnfjo2kJYWBhvvPEGUVFR/8gCoMYwk6tCcnIya9euZc+ePUbzQZSVlXHz5s0aqVgEd+pnPCqc25jUOiXwTycxMbHGpxz/NoqKikhISHj0jv9SaqQhaVUxMTERDRs2fCym793mVZ2sOln/JlmxsbHhQoh2f37/b2MJCCFqfMkI7sy5/6mygDpZtUSWXC5HkqTHKutB/G2UwONuQ/bncNaaknW/0NnaLqs8jfVxyZIk6bHIKm9D9jhklbchexyy/qo2ZHXUUUctodYrAbVaTXBwMG+99Rb29vZ/9eHUUcdjIyAggIkTJz40F6Qy/G2mA9VBkiSGDh3KK6+8QqtWrdizZ0+NVfypo44HUZ7Q07JlS73p7eHhgbW1NZs3b652LcqH4e/vz6xZs/Dx8eHw4cMGdY+qtUpAkiS6d+/O+PHj8fHxISYmxuCCjndTXl1n8uTJHDlyhK+//tqoNfYqi4+PDykpKUaLe/8rMTExoXnz5jz33HP6eH64U+xj4cKFRg8gMjU15bXXXqN79+6MHj3a6N+hSqWibdu2hISE4OPjg6urK2q1GplMpi+C6+3tzcKFC43ars7c3Jz//Oc/dOrUiY8++sjg4LZaqwQcHR0ZO3Ysvr6+3L59m7FjxxrtIurWrRv9+/cnJCQEDw8PfTZaSUkJkiRx5coVzpw588C6BoZib29Pt27dGDNmDN7e3ty6dYupU6fWyBPlceHq6sro0aMJCQnB09MTlUqlVwJFRUXY2Njw7rvvGrX0effu3fVJPdWt7nM/yi3QPn36EBgYqC+Hv3//fq5cuaJPKx41ahTBwcFkZGQQFRVlcEl8uVyOv78/b775JllZWVy/fp2wsDCD+0XUSiVQ3qCxVatWKBQKLl26xMWLF42y3NKlSxe2b9+urykvSRIdOnSgTZs2+i5BOp2OhIQE5s2bx08//WSEM7qDWq1m5MiR/O9//6Nhw4b6gh+urq74+/tXWQl4eXnh7e1No0aN8PDwYMCAAZw7d46IiAjy8/ORy+WcOXOG+Ph44uPjayyev169erzyyitMnDgRpVLJqVOnOHDgAEVFRbz44ot4eXnRr18/Ll68yNKlS40is3PnzsyaNQtvb29ee+21KpVFexi2trYsXLiQZ599FktLS7RaLTt27GDJkiVER0fr6w3KZDJ99murVq1o2LChweXuHBwcGD16NMHBwcydOxdvb2/y8/MNjzUoL5P8V75UKpVwcXERQKVezs7OYtWqVUKn04mwsDDh4+MjJEmq1Gfd3d2Fq6vrQ/eJjIwUxcXFori4WBQWForTp0+Lt99+WwwYMEBMnjxZnDx5Umi1WpGamio6d+78UFlubm6VPq/OnTuLmJgYodPphE6nExkZGWLx4sVCq9WKjRs3PvK8/ixr5syZIjMzU2g0GlFUVCSKi4uFVqsVGRkZ4vbt2yI1NVUUFhaK1NRUcfToUfHcc8898rsBRKNGjUSjRo0euZ8kSaJevXpiwYIFori4WOh0OnHr1i3RsWNHIUmSkCRJ+Pr6ioKCAlFSUiIOHjwomjZteo8sd3f3Sn+HkiSJtm3bikOHDgmdTidKS0tFSEiIkMvlj/ysm5vbQ2V5enqKsLAw/bgJCQli0qRJwsLC4r7729raihMnTohTp06Jxo0bV3jP1dW1SudlZWUl3nvvPZGXlyfmz58v2rZtK3744Qfh5OT0yM+6uLiUy//tfvdfrbMEyjvbdOrUiYKCAj777DOuXr1q1MirPn368P7771NcXMzWrVvZt29fhfdXrFjB4sWLGTlyJMOGDePUqVNGkVtQUMDNmzexsLAgKSmJefPm0a7dnQCv6iRGXbhwgb1795Kbm6svbhEbG0toaChpaWnY2dnRu3dvGjduTLNmzVi1ahUajYbmzZsbPH+WyWR07NiR5cuX07RpUyRJIiMjgxUrVnDlyhX9/ys6Opru3buzevVqfHx86NWrF9evX6+WVadQKPD29uaDDz7giSeeAO7E+RsrBLtDhw54eXnp4xZeeuklQkND77uvJEmMGjUKf39/Tpw4YVCZOIVCQe/evXnttdf49ddfWb9+PREREUyePNk4jvC/2gqoqiVgb28vPvzwQ5GZmSl27NghPDw8Kq1NqaQl8KCXTCYTbm5uYu7cuaKwsFAkJSWJ4cOHV+np/LCXJEnC3t5eeHt7C0tLSwGIr776Smi1WjFmzJhHntf9ZFXWQvLw8BAJCQlCp9M91LqBR1sCcrlcdOrUSVy7dk1v1WRlZYlFixYJBweHe/ZXq9Xi448/FlqtVmzdulU0a9asgqzKPDFtbW3FgAEDxJ49e0ReXp5IT08XJSUlIjExUXTq1KlS38PDLAGlUilWrlwptFqtyM3NFb17977veSsUCqFSqYSHh4c4dOiQSE1NFe+9954wNTWtsG9VLAEPDw8RHh4uIiIiRFBQUJWv23+UJVDeRmvYsGEIITh//jyurq76SrqxsbEkJycbPfmmvNFj27ZtmThxIoGBgWg0GrZu3cq2bduMJkcIQXp6ul67W1tb67sFRUZGVnvMqu5riAPLxMSEnj178sknn9C4cWP99suXL7Nu3br7WhhlZWX6KsudO3emZcuWVco2dHNz48UXX2TChAmoVCq+//57hBC88MIL3L59m8TERIMtRWtra1q0aIFCoSApKYno6GhMTU2xtLTEyckJGxsbXFxcsLCwQC6XM2TIEDp16sTatWtZtGhRtdvJSZLE4MGDadq0KStWrODYsWNYWFigUCgoKCgwih+nVikBtVpNYGAgbm5u6HQ6xowZw3PPPad///jx46xcufKRPQoqi0KhwM3NjXbt2tGjRw/69OmDm5sb+fn5fPzxx/z8889V6ppTVfr06UODBg04efJktZVAdSjv71cd+vbtywcffEDTpk2JjIwkMjKSoKAgrl+//sBCn0VFRYSHh5OXl4e9vb2++vKjUKlUODs7M2vWLPr37092djabN2/m008/ZfTo0cjlcq5evWqU/1FOTg67d+/G19cXV1dXpk6dSlxcHB4eHjRr1gxHR0fc3d2xsLDQO5Bv3LjBypUrDVKqDg4ODBkyhJs3bxIVFcXIkSNxcXFBrVZz5coVtm3bZvAqVa1RApIk0ahRI0aOHAmgLzp6N+7u7iQmJhpFCTRu3JghQ4bQtWtXfH19adiwYYXKtYWFhUbNdVCpVDRp0kQf461Wq3nppZdQq9V89913Rm1Mej/Kl+0kSWLYsGH6TruFhYVcvXq1Uj4CpVLJoEGD9BbAhx9+SHh4+CPbf0mShLm5OTKZjFu3blVqmVCpVDJ06FCeeeYZ+vbty2+//cbSpUv1T8rWrVtjampKTEzMPd9debyCVqutdK3DkpIS1q9fT/v27XnyySeZMGECGo3mnpLtWVlZJCcn06xZM0xNTXF3dycsLOyR4z+I/v374+3tzapVq9DpdISEhGBtbY25uTlPP/00vr6+vPfeewb5BgxSApIkxQF5QCmgE0K0kySpHvAz4A7EAUOFEAZH8cjlcvr3769PXiknPz+fo0ePYmNjQ6dOnejatStqtdqg0ktyuZwRI0bwxhtvYGNjgyRJlJSUsHXrVpKSkhg6dChvvfWWvtKRIUiSRGBgIP/9739xcXFBLpcDd5SCl5cXcrmcNm3aYGFhUaGfnbOzM40aNWLz5s3Vio8wNzcnICAAW1tbEhISGDZsmL4y76BBg+jevTspKSkcPnyYpKSkSikBPz8/vcl86tQpdu/ejbm5+SNvMhMTE3x9fVGpVBw9erRSVs8zzzzDtGnT8Pb2RqfTsWrVKsLCwpAkCTc3N5o3b45GoyEhIQFra2ssLCyws7PjiSeewNfXF39/f44dO8bHH39c6diEGzduMH/+fFq3bo2LizQxq5cAACAASURBVAuWlpbk5eURHR1NeHg4ly9fJjk5meTkZN5//326dOlCYGAgGzdurNT496O8QvK+ffs4d+4c58+fRyaTIZPJePPNNxk2bBibNm0yqPq1MSyBnkKIu9XQ28BBIcQCSZLe/uPvaUaQo5/rZ2ZmcunSJRISEti5cycnTpzQd7Zp3LgxZmZmBtdfk8lk2NraUlRUxL59+1i3bh1nz54lJyeHsrIy/vvf/+Lr62uQDEtLS9555x0GDhzIL7/8wqVLl3B0dCQ4OLjCfHrChAkUFxdXCApRKpXExsayf//+aiuB3r1785///IeioiLq1auHiYkJFy9e5OOPP+batWtoNBrS0tIqHYnZpk0bGjRoQHJyMuPGjSM/Px8LCwsKCgoe2LfRzs6OZcuWERQURGRkJF9++eUDS8rfTX5+PsnJyXrradasWUybNg2tVktWVhZubm6Ympoyc+ZMJk+eDNz5zqytrVEoFJw9e5ZffvmlSk/QRo0aMW/ePBwdHYE7ma/Hjh1j0qRJZGdno9Fo9E1bpk+fztGjR/UWVXUJCAggNzeXqKgokpKSKiisCxcuMHDgQL3yri41MR0IAXr88fsa4AhGUAIymYyWLVsCd5aVJk+ezO+//64vWrlr1y7i4uIwMTExuMFFWVkZ+/bto0ePHkRERLB48WKSkpL0TpicnBwkSdI/tauDpaUlu3fvpk2bNnzxxRfMmzcPMzMzxo0bh7W1NcnJycyaNYuzZ8/Su3fve1qAxcXFcerUqWqXuMrJyWHfvn2YmZkhk8no168fFhYWLF++nA0bNtzTZLMyuLm5YWFhQVpamr6ST0ZGBhMmTLhv8c2OHTsyefJkBgwYANxJTY6Pj6+UeR4aGspvv/1GYGAgwcHB9OzZEy8vL+DO/6+8jZqLiwtCCIqLi9m0aZO+m/PVq1dJT0+v9FKktbU1H3zwAV27dmXu3LmoVCpmzJiBUqlEq9WSmZlZYX9jRJOW+x/Wr19/zxKxQqGgXbt2lJWVGT4tNWRpD4gFzgHhwMt/bMu+633p7r8NWSKUyWSib9++QqfTiYsXL4qgoCD9so9MJhNvvfWWKCgoEEeOHBFWVlYPXUqr7BKhXC4XMpmswnJbmzZtRExMjNBqtWLs2LHVWrYDxJo1a0RhYaHYtWuXUKlUws7OTixcuFBoNBqRmZkpxo4dq19WKg+s+fOrsrIe9JIkSchkMiGXy8XevXtFYWGhCAwMfOTnHrRE2KtXL3HlyhWRm5srXnnlFeHs7CwUCoX+e1QoFEKhUAh7e3sxYsQIkZSUJIqLi0V2drb47LPP7rvc+6glwvJzMDc3F05OTmLKlCli+/btQqfTibS0NNGvXz+hVquFWq0WMpmswv/zz6+HLRH+73//E6mpqeLy5cvC2dlZWFlZic2bN4uTJ08KPz+/e/Z/6aWXhFarFd9+++19x6vMEmG3bt1Efn6+mDNnzj33wogRI8StW7fEhg0bhImJyV+6RNhVCJEgSZIjECpJUoXC7kKI8ov1HiRJehl4GajUE7WsrIwbN24QFRVF8+bNmTFjBjqdjujoaEaOHMnMmTORy+WcPn3aoMCM8mMpLS295ynh6+vL6tWradSoEXFxcQ8MFKkMrVq1QqVSMXfuXDp06MCYMWMYOnQosbGxLF26lI0bN+odWsYMhLqb8ovAz88PFxcX1q1bx/Hjx6s9nlarpbi4GDMzMz799FPGjRtHWFiYvgp069atEULQokUL/P390el0pKamsn79elatWsWNGzeqfQ4ajQaNRsMnn3xCdHQ0/v7+SJJEXl6eUaaG9vb2KJVKVq9eTUpKCmVlZYwZMwZPT099A5fyfW1sbJg8eTJardag6tflvQ7Lm5GUdznu168fr7/+OjKZjEWLFhmc2GaQEhBCJPzxM1WSpK1AByBFkiRnIUSSJEnOwH3tIiHE18DXcKfGYGXkxcTE8Morr7BkyRI6duzIzp070el0mJmZoVAoiIuL49dffzXklGjZsiWurq5cuXKFmJgY4I7p1ahRI959912aNm2KEIJVq1YZ9A/Oz8+npKSEb7/9Fg8PD0pKSjhx4gSffvopoaGhj6UufzkmJibI5XJatmxZoYx7VTl27BgHDhzA1dUVS0tLWrVqRatWrYD/byVX3tcwLi6OLVu28M033xi1xHtZWRkFBQVotVoiIiKMUmC0vPuPiYkJtra2ODg4kJOTQ05ODuHh4UiShJmZGZaWlnh5eTF06FAaNWrEmjVrWLx4cbXlajQa8vPz6datGz179sTMzIzx48fTrl07Tp48yVdffcWFCxcMfkhUWwlIkmQByIQQeX/83guYC+wARgEL/vi53aAjvIvS0lLOnDnDtGnTeO211/D29sbR0ZGbN29y48YNli1bxu7duw2SERQUxMKFCzl27BiDBw/GxsYGPz8/Jk6cSLdu3UhPTycsLIxdu3YZJGfVqlVcvXoVhULBb7/9Rnx8PN9+++1j73kgSRJNmzZFrVazY8cOg5Owli9fjpWVFZ6enjg6OuLs7ExxcTEJCQmkpqZy6dIl0tPT2bhxI7GxsUZXdkqlEhcXF+zt7bl48aJRMkuLi4s5ceIEvXr1YuLEidjZ2XHhwgV9U1oTExOaNm2Kj48P7dq1w9zcnH379vHJJ58YZIVERkayZs0aRo4cycaNG8nKyiItLY1PP/2UNWvWGFRD4G4MsQScgK1/OOEUwE9CiL2SJIUBGyRJGgvcBIYafpj/j06n4/Dhw1y5coXWrVvTrFkzrl+/zokTJ4ySKVYe2NG4cWNmzpyJm5sb/v7+ODo6UlRUxPfff8/ixYsNdvysWrWKVatWGXy8hiKTyfDx8cHa2ppff/3VYCVw7do13n33XaytrfH09MTHx4eCggIuXrxIQkICycnJBqe+PgyFQkG9evVQq9WkpKQYnL5bzsmTJ1m5ciVDhw5lzJgxFBUV6c9Dp9ORkZHB5cuX2bJlC5cuXeLgwYP3OAurSlFREYsWLSI2NpbGjRuTnJzMyZMniYiIMOp3WG0lIIS4AfjdZ3sG8KQhB1UZEhMTSUxMNPjJ/2f279/PqVOnCAgIYNKkSQBcv36dzz77jOvXr3Po0KF/RIGPcsrnsIWFhaSmphrF/1C+Vn7lyhV++eUXIxxl5SkpKeHGjRtEREQYTQHAnano0qVLOXXqFN7e3hVWoEpKSoiPjyciIoLU1FSjFp9JS0tjxYoVRhvvftSaiMHHRWxsLK+88gpubm76bZmZmURHR5OTk1NjTrq/ivK8iJycHKMpgb+S8rX7cePGVSreoCrk5+dz6NAhDh06ZNRx/2rqlMB9KI95/zexbNmyx9If8HFQ7rCro3LUKYF/OUVFRYwdO5aioiJ9zYE6/l3UtSGrk1Un618iq1a0IXscTTjlcvljlQXUyaqT9ZfKKg80ehB/GyXwT25DZkhQ0d9RVnkm5+OSJUnSY5FV3obsccgqb0P2OGTVtSGro446HsrfxhKoChYWFjRq1AhXV1cKCgoIDw+v0Qo/dRiGQqGgadOmmJiYEBkZ+VhDout4NLVOCVhaWjJkyBCee+453Nzc0Gg0fPHFF6xdu7bu4qoi5Z1y2rRpg5eXF56enmRkZLBz504uXbpkNDnW1ta88MIL+pJbOTk5Rhv7Qbi7u9OvXz9CQ0P14b113J9apQTc3d2ZMmUKwcHBeHh4AP/fDWbr1q33zVk3BAsLC/z9/WnTpg2WlpZ6T64kSVhbWxMTE8OOHTsMiuFu3Lgx7733HhYWFpw/f57c3FycnJzYvXs34eHhNbJsp1Kp8Pf3p3v37rRv355mzZphb2+Pvb09BQUFeHp6Mm7cOKPIkslk+Pn50aVLF77++usaDRm+m4CAAF5//XV+//33GlUCNjY2eHp64uXlhaOjIx4eHpiYmODk5ERUVBTp6ekUFxcTGRnJb7/9ZnCZOHt7e0aPHq2vi2CMa77WKIGGDRuyePFievXqhampaQVvZ0BAAO3atePAgQNGk9e0aVMmTZpEv379UKvVwJ1CEVqtlpMnT2Jubk6vXr3w8fHhtddeq/L4CoWCl19+maeffpqePXsil8sJDAyktLQUlUpF3759OXfuHEePHmX79u0Gp8MC9OjRgx49etCzZ099sUqNRkNkZCS7d+9m27Zt+sw1Y+Hg4MCrr77KhQsX2LlzJyUlJUYb+2FYWVnp/281gSRJODg40LdvX2bOnKm/Jq2srFAoFKhUKnr27KnPlSgpKeH3339n0aJFREVFVVtumzZtGDt2LGVlZRw9evTfpQSee+45goKCOH78OJs2baJ9+/aMGDECc3NzLCwsmDhxIqdOnTJKvLiPjw+7d+/G0tKSM2fOsGvXLi5dusT169fRaDQUFxcjk8n4z3/+w4svvlgtGU899RTvvvsu9vb2+j551tbW+vd9fX1p0aIFQ4YMYfLkyTz//PMGm+jTp08nICCAsrIydu/ezdq1a7l69SopKSnodDoKCwuNum4tl8t5+umncXNzY/78+QbVeagKZmZm2NvbG1T56VHjf/7553Tp0gWlUsnWrVs5ePAgGo0GMzOzCjUpbt68SXZ2Nk5OTvrmMoagUqkwNTVFoVAYr7/iX914pDKVhRwdHUVMTIxISEgQ7du3FzKZTAQEBIgzZ86I0tJSUVpaKoqLi+9pYXW/V2UqCw0aNEjk5uaK6dOnCwsLCyGTye6p5PPkk0+K1NRUsXTp0ofKul+1H2dnZ7F3715RVFSkP/67XzqdTpSUlIiSkhL97+Hh4fdt3PEoWXe/tm7dKoqLi8XatWuFXC6vdGOSP78q24bMzc1NxMfHiy+++OK+ssor/TysRVhV25DxR9WeH374QWRlZVWqUtLdx/soWZIkiTlz5ojCwkJx6tQpMWXKFP13+ajqT38+xqqeFyBGjx4tSkpKRHx8vGjdunWlPvOoykK1Yolw+fLl1K9fn8jISHJzcykrK+Ps2bMcPXpUvypQXoHVGMTFxaHRaAgODtbnh5c/IVUqFd26deP777/n5MmTfPDBB1Uef9y4cXTs2FGvyYUQlJSUkJmZSUZGBunp6Xz33XccO3YMnU6HTCbDy8uLkJAQg+on7tmzB51OR0xMTLVqCFYFExMTZs+eTVFREcuWLasgS6FQ0KZNG6ZMmcLnn3/O2rVr9VV1jUF5/UdjXQ93M2jQIN566y1OnDhBUFAQixYt0n+XDyjBZ1TKqw0Zk1qhBEJDQ/W53OWdaUpKSli8eHGFRJ9yZ6GhnD9/npEjRyKE4IsvvuB///sf9erVw9bWlhdeeIGVK1dy8OBBxo0bZ3BdgfIeBtu2baN79+506NCBTp068cYbb1Qo0Glqaspzzz1HkyZNqi3LxcXF6BfQgxg1ahRDhgxh06ZNREdH67crlUqeeuoptm/fzvz583nxxRfp0KEDkyZNwsTExCiyFQoFJiYmNXIT+vn5IZfL2bt3r1FTlf9KaoVP4Kuvvrrv9j8XqJg6dSonTpwweAlKCEFoaCgREREMGzaMQYMGoVQqsbOz49lnn+Wnn35izpw51fL0yuVyfZOPcq5cucKUKVPuiWJMSUnh6NGjdOnSBWdnZ7p06cK0adN48803q+UovHnz5mOJVbezs+PVV1/l8uXLzJs3T79doVDQuXNn5s6di52dHbdv32bfvn3s3r2befPm0bp1a86cOWOwfGdnZ1q0aIFWqzX6snHnzp2RJOmBJdRrErlcrq86XVhYaHABmHJqhSXwMDIyMtDpdEiSRPv27e9pTlJdhBAkJyezZs0ajhw5wqRJk3j11VfZuXMnH3zwQbWXeho0aECzZs0qPPUyMzPvG8acl5fH2LFj2bNnD3DHGujcuTNt27atluzY2FhkMhlNmzalVatWDw0lNYR+/frRpEkT5s+fr1dWcrmc1q1bM3XqVHQ6HV9//TXTp0/nrbfeIiIiAkmSDO7jUI6JiQmWlpZcv3690j0TKkteXh6SJNG1a1dat26trz34OHByctJ3qY6Ojjaao7VWWAJ3I5PJaNCgAe7u7sCdG6i4uFg/v5bJZDg4OGBpaWmwtlYoFLi7u9O4cWO9Bz0zM9Ogp0v5WnJlmlIIITAxMcHFxUW/zd7enubNm3P06NEqyz516hQ///wzffr0wdnZmYsXLxITE8OJEye4ffs2WVlZRkloCQwMJDc3V9+yXalU0rlzZyZPnoxSqWTu3LmEhobqrTg3NzeEEAaX4yqnfN6cnJxs1OVOgB9++IFWrVoxYsQI2rZtS2pqKtHR0WzatImoqKgaTQhydXUlODiYoqIio1a4qlVKwNTUlEGDBjFixAgaNmwIgK2trf6GkiQJHx8fRo0ahYmJCQsWLDAoQcPJyYkpU6bQpEkTPvnkE0pKSnj99deJiopi48aN1TKtyzMYxR9NKx+GmZkZ77zzDl26dKnuKVSgsLCQmTNncu7cOdq1a0eHDh14+umneeGFF0hISODy5cts2LDB4IIqHh4enDhxokJ35YEDB2Jra8v8+fM5fPiwPgjKxMSEwYMHk5+fz2+//WbwOZYjhODWrVtGj04MDQ0lLS0Nb29vWrVqxYABA+jTpw+BgYF8++23bNy40eiKB+4ErgUGBlK/fn3CwsL49ddfjRZ4VWuUgFwuZ/jw4cycObNCz767USqVzJw5E1tbWxITE2nSpEm1lYBCoaBr1660atWKJUuWsGnTJkpKSkhNTeXDDz8kJibmgV12H0Z0dDTHjh2jWbNm+vZR9erVw9XV9Z4pgZmZmb7NlLEcerdu3eLrr79m/fr1WFhYYGFhQefOnfH39+eJJ57gqaeeIjk5mRUrVrBz585qySg3xwF9L8WgoCC+/PLLCgoA7kRMjh49mpUrVxots7NcOd+4ccPoUaR5eXn8+uuvnDlzBrVazapVq+jatSvjxo3jgw8+IDU1lb179xptvl6OiYkJ7u7uSJJEenq6UWMuao0S6N69O/Pnz8fBweGBN4QkSXrveUlJiX7KUB3s7e2ZMGECV69eZc+ePXrtfujQITZu3MjSpUt54oknqmz+FRYWkpSUVCFyzsPDg4CAANavX19hX7VajZmZWYXzlclkmJqaIpfLq32hFRYWVki4unLlCuvWrUOlUuHt7c3KlStZuHAhqamp1XbUeXp6Ancu3rZt25KcnExoaGgFBdCwYUOWLFnCuXPnWLFihVFMaXNzc/z8/KhXrx4lJSU1Zp4XFxeTkZFBZmYmly9f5urVq3z00UdMmzaNiIgIo6eqW1tb069fP0pLS7l+/bpRi93WGsego6NjBQWQm5t7zxJN+XtlZWVERkZy8uTJasuzt7fHz8+P8+fPV2gCWVpayhdffIGTkxM9evSo1tjLly9n8+bN+hvC1NSUgIAAzM3N9efg7u7O8uXLqVevXoXP1qtXj169ehm0VAh3ErGcnZ2BOxd0Xl4eGRkZnDp1ijlz5lRoA19V8vPzkcvlSJKEEIKCggJu376tfypLkkTLli3Zvn07CoWC0aNHG6VcPNyx4KytrQ1uBFpZhBAUFRWxf/9+vvvuO7y9vRk/frxRoxVlMhktWrTAxsaGuLg4Dh8+bFQLp9YogT8HSZSUlOhXBUpKSsjPz9eHvh46dIiFCxfqYwqqikwmo2vXrqSmpt63ZHZ+fj4JCQk0a9asWuMXFhayYcMGvTZXqVSMGTOGLVu2MGLECLp27crixYsJDAzUX0zlJm5mZiYHDx40KPzU2dmZmTNn0rdvX/02mUyGiYkJ7dq14/PPP6e0tLTapbNPnz6NlZUVrq6uaLVafvnlF7Zs2UJZWRnm5uaMHTuWdevWkZKSQt++fY0+b5ckyajBY5VBCMHly5fRaDRGD1eWyWQ4OTmhUCjIzs6uUiflylBrpgPlfe7KNbydnR1wp1VTaGgoa9eu5bnnniMhIYG5c+capCnLE0FKSkru+4RydHTE0dHRoIywgwcPsmfPHrp3707Dhg2xtLQkODiY4ODge/YtdyIKIbhx48Y9ZnVVUKlUjBo1iieeeILz589jYmKCtbU1LVq0oGfPnrz88svodDqOHz/ORx99VC0ZERERyGQynnrqKTZt2kRBQQGRkZH4+fkxZMgQnnjiCUJDQ5k9e7bR1/GFEPo+kk5OTlhaWtaIo+7PqNVqnnzySWxtbY0+tiRJODo6olQqKS0tNfp3VmuUwLlz59izZ4++TXdBQQFpaWns3buXtWvXcvbsWbZs2WI0eTKZDCsrK9q1a6efwysUCmxtbZk2bRr5+fmcOHHCIBlvv/02pqamTJkyhaeffhp3d/d7niJ3K4C0tDR27dpVIQKvqiiVSnr37q1fZh01ahSBgYF06tQJIQSxsbEsW7aMvXv3VnvJbuPGjYSEhDBv3jwCAgKAO/ERDRs25MaNG8ycOZNdu3YZnFZ7P4qLi7l58yZZWVk8/fTT7NmzhzNnzhgtSMrR0ZHMzEwkScLCwgKlUomTkxN9+vRh2LBh5OXlGT0oSyaT6aduqampRumveDe1RgncunWLN998k3HjxmFjY0NqaipRUVEcPnzY6AEhZWVlHD58mBdeeIHJkyfj5OREdnY21tbW+Pj4EBAQwKuvvsrVq1cNklNurbz//vv8+uuvjBs3jg4dOlCvXj391KdcAaSnp7N3716WL19uUDpuUVERK1eu5I033uCll15Co9GQkJDAhg0bOHfuHOHh4Qbn3wshmDFjBklJSfoIt9TUVH788Uf27dtHfHx8jUUuFhUVcfToUdavX09QUBDPPPMMV65cMdo1MmHCBJKSklCpVDRq1AgLCwv8/Pzw8PAgJiaGNWvWsGnTJqM6JJVKJUFBQeTn53Pu3Ll/rxKAO4k9M2bMqHE5QgguXrzIhx9+yIgRI5g+fbq+Nfrly5eZPXs2Z8+eNZq87Oxsdu3axdWrVxk8eDBjxozRz6evXr1KQkICR44c4ciRIwYH1Oh0OjZt2sTNmzext7cnPz+fW7ducevWLaM+mWNjY3nrrbeMNl5VuHnzJh999BEHDhzA0tLSqDekQqFg1qxZqNVqMjIyyMjIIDExke3bt3Pw4EEiIyONXjOhPJBKq9WydetWo44NtUwJPE60Wi1btmwhPDwcd3d3ZDIZycnJelPT2OvAOp2Oy5cv88UXX3D8+HFsbGzQ6XSkpKSQnZ1NQkKC0ea2RUVFHD9+3Chj/R0RQhAfH18j1auXL1/OmTNnUKlUZGZmkp+fT25uLrdu3aqx5i1arZYpU6agUqm4fPmy0cevUwIPQaPREB0dbdAcvKpkZGT843rd/ZNITk5+7E1WS0tLOX36dI2NX2uWCOuoo46aoa4NWZ2sOln/Ell/+zZk8M9r/1Qnq07W30nWg/jbKIGSkpK6NmS1RFZdG7L/RyaToVar9UFCCQkJlVodqGtDVkcd/xBcXFyYPn06oaGhbNy4ETc3t7/6kKrM38YSqKOO2oapqSl9+vTh5ZdfxsTEhKNHjxqlP8Tjpk4J1PGPpkuXLgQFBaHRaFi+fLnRCnFIkoSHhwfPPvsskiTxyy+/sGzZMqMn95RjYWHBwIEDsbCwYMeOHRUyWw3lkdMBSZK+kyQpVZKkqLu21ZMkKVSSpGt//LT9Y7skSdJnkiTFSJIUIUlS9Yrh1fGX0bhxY32qdG3H1dWVN954g7fffpsZM2YYNbnH3NycqVOn4uDgwJIlS1iwYEGNlhcrVwKzZ89m+PDhWFlZGW3sylgCq4FlwPd3bXsbOCiEWCBJ0tt//D0N6At4/fHqCKz446fRUSgUdOvWjf79++Pv768vMX3z5k22bt3KgQMHjFazDu6UMevVqxft27enY8eOyGQyvvnmG3788UeDwkRVKhUDBgwgPj6ePn366G8+R0dH/dJOSUkJ8+bNIyoqqkaXlBQKBZMmTWLYsGEsXryYlJQUo41dXkh04MCBAHTt2hVzc3MWLFjAjh07jCbnbvr06UNAQAAqlYqcnByjZt+ZmZkxZMgQwsLCWLNmDfHx8UaPIv0zQggcHBwYPHgwx44dM1o5tkcqASHEMUmS3P+0OQTo8cfva4Aj3FECIcD34s6VelqSJBtJkpyFEAbbLiqVinbt2tG2bVuio6N58cUXCQwMxNraGhMTEyRJQqPR0KZNG4KDg9m5cydvvPGGwcUXJEmifv36jB8/nlGjRuHg4KBPivHw8KB+/fosWLCgWmN36NCBGTNmEBAQgE6nw9zcXF8w9e5lHSEEbdq0YeDAgUbrFqxWqwkMDCQjI0MfQvzMM8/wwgsvEBYWZlQFYGZmRv/+/Zk8eTLNmzdHkiRMTU2RyWQsX76cjIwMgzMy74erq6v+ifnDDz8YrXBJeRPc4uJijh07RlpaWo0rgLtlu7u74+DgYLQxq+sTcLrrxk4Gym3HhsDda2/xf2wzWAlMnTqV6dOnI5PJKCsrQ6FQoNVqCQ0N5eDBg2i1Wn7//XdGjBjBSy+9hK2trVGKSnTo0IFly5bRqlUrZDIZ6enpHDp0CCEE/fr146mnnuLLL7+ssrJp1qwZmzdvxtnZWZ8pCP8fPFJWVsalS5fQaDR4e3tz/fp1EhMTDT4fuHMhtW7dmnnz5rFixQpOnDiBEIIePXpgYWHB8ePHjZanoFQq6dOnD59//nmFKklxcXG4urpib2/PG2+8YXQl4ODggJubGyqViqioKBYsWGA0S0Amk9GnTx+2b9/Oxx9//Ng6Ld8t35hNZAx2DAohynuuVQlJkl4GXoZHBzPA/9fMLy+sUFZWxrFjx5g8eTI3btwA7pizbdu2JS8vjwsXLhjkqZXL5QQFBfHFF19gZ2fH/v37WbduHQcOHCA9PR03NzdatmyJqakpZmZmVVYCWq2WsrIyJEkiJSWFFStWkJaWxunTp9FqtQghiIuLo6ioiAYNGhhNAcAdr3bbtm1Rq9Wkp6cjhMDZ2Zknn3wSmUxmtCemUqnklVde4e2338bGxobw8HDWrVtHaGgocdmUewAAIABJREFUSUlJ+Pj4MG3aNGxsbIwi7246duyon7bFx8cbLblHkiQ6duyIs7Mz33777WNrslqTVFcJpJSb+ZIkOQPlvbgSANe79nP5Y9s9CCG+Br6GO2HDjxK4Z88etm3bxpAhQ7hx4waRkZG4urpWcPZ4eHjw0ksvkZOTw8WLFw2aq3t6erJo0SIsLCxYu3YtS5YsITY2Fnt7ezw9Pfn8889xd3cnIyMDMzOzKo8fHx9PZGQkDRo04Pz583zzzTfY2dnh6+ur7zpraWlJbm4uQggsLS2N1nVGrVbTsmVLfflzgL59++Lk5ER8fDwbNmwwWIZMJqNdu3ZMmDABuVzO+vXrWb58uT4FW5Ik8vPzef31141ejsva2pqAgADc3d3RarVs377daEqgfv36fPPNN9ja2rJ9+/b77iNJkr47sZOTE+np6UavemxMqqsEdgCjgAV//Nx+1/ZXJUlazx2HYI4x/AFwJ+d+zZo1PPXUU/qGpNu3b9dHGZqYmDBnzhy8vLw4cuSIvvFFdZDL5YwZMwYPDw++/fZb5s+fj4WFBe3bt+fVV1/lueeeQ6lUkpWVxZYtW/SWSFWws7OjTZs2yGQymjRpwurVq/Hx8aF+/frAnQtJp9Ppm5UuWrSIs2fPcunSJVJTU6t9UZcXrRw8eDAxMTGkpqZibW1Nr169MDMz45tvvjGKJdC2bVu2bNmCSqXip59+YuLEiRXet7e3Z/Xq1ezevZt33nnHYHl3Y2VlpY+SO378OKGhoUbL8Tc1NaVx48b8/vvv97xnYmKCs7Mztra2BAUFoVarmTRpEl9//TWLFy82eFmvvM6msftJPlIJSJK0jjtOQHtJkuKB2f/H3nmHRXGubfw322DpiEgXRBB7JyoGFCsGrNFoYtQYe8zRxGjUND0xJqaZmKIJ9hJLEo169CjBbrAhKgZEpKuIClKkSdl9vz/M7oHYgF088h3v69orcWaYZ2Z35n7f92k3d1/+nyVJGgekAS/8dfi/geeARKAIGGusC9VqtURHRxMWFkZAQABOTk5s3LiRGzduoFKpePnll3nuuef07bEMackshCAyMpKysjLatm3L3Llz8fT0xMfHBw8PDxQKBRqNhn/9619s3bq1RjaKi4sJCwtj1KhReHt74+npya1bt7h8+TJZWVmVOuG4ubkxduxYJk6cyOHDh9myZQvh4eGV2oZXFSYmJnoJsoiICM6ePYu/v79eFdgYZcy2trYsWLAAlUrFmjVrWL169T3H+Pv7G01AtiJMTU3p0aOHvhP0yZMnjRol6tSpE1qtlpUrV1ba3qhRI7p3785zzz2Hg4ODfikiSRJBQUHs2rXLqLH9x+oTEEK8+IBdPe9zrACmGnpRD8LNmzdZtWoVXbt2ZeDAgRw4cICMjAwCAgL45z//SVlZGd9//z179uwxaNqs8zfs3LmTvn374uPjg6mpKWZmZsjlcgoLC1m+fDlr164lMTGxRjby8/N5//33kcvlpKSk6Gsnbt++zY0bN8jMzNQ7Cb28vLC0tCQgIIBhw4ZhbW3NpUuX7jsaPQqmpqYMHDgQrVarjxCMGDGCBg0akJCQQGRkZI3upyI6deqEv78/e/bs4aOPPrqntZeNjQ2vvvoqJiYmNe5o/CDY29szZMgQnJyciI+P58CBA0ZVD3711VeRyWSVmnuYmZkxaNAgpk2bhrOz8z0O6ZycnBo/JxVRW7LndSpjUKPREBMTw/Hjx+nfvz/t27fH1dWVUaNGYWZmxgcffMD69euNsm7Oyspi/vz5bNq0CbirrzdmzBjkcjmbNm3iww8/NDhFND09nXfffZfr16/r7+9+P67uATp06BAZGRm88MILdO7cmZSUlGovCxwdHXnmmWdQq9X0799fP6uSy+V88cUXRmn/3atXL+7cucO33357395+48ePx8/PTz8bMiZsbW31sm179uzh7NmzRg3fmZubI5PJGDBgAJGRkfTt25f27dvTs2dPnJyciIyMJCYmhldeeUU/YBw7dszofTCNiTpFAgAZGRl6kYeJEyeiVCpRq9VMnz6dX375xWisr9VqSUlJISUlhebNm+Pt7Y2VlRUHDx7kyy+/NFqv/OpUTmZmZrJhwwa8vb0ZN24c0dHRnD17tlr28vPzOXPmDP7+/tSvX5/69esDcPDgQX7//XejvDANGjQgPz//HvUiPz8//Pz8GDNmDBYWFhw8eJCoqCiD7elgaWnJjBkzqFevHklJSRw6dKhWHHJyuZzRo0fTtWtXHBwcsLKyQq1Wo1QqSU9PZ//+/djZ2dG/f3/CwsL4+uuva7R0+zt0/gAbGxsCAwM5efKkUfw3dY4EysvLSUpKIjU1lb59+wKwfft2tm3bZtRpnw6NGjXi7bffplevXvz666989tlnj6X880GwsrLSL09qIi2ekZHBtGnTGDRoEA4ODrzwwgvUq1eP/fv3G43Y1q5dS2BgICNHjuTixYskJSUxfvx4QkJCaNKkCRYWFshkMvLz843alNPR0ZE+ffpQWFjI1q1bOXLkSK1kWAohsLKyon379pXOL4QgKCgIf39/TExMWLduHdu3b9fP9AxBTk4Ov/32GwMHDkShUPDSSy+xcePG/00SsLGxYfjw4fj7+wNw5MgR3nzzzVqJ19rb2zNs2DB69uxJWloaK1eu5MKFCwbnh+vUfnT5AFVF/fr1mTZtGr6+voSHh9fonjUaDX/++SfJycmMGTMGhULB1atXiYiIMFoYzc3NDTs7O7777ju9ArNSqUQmkxEbG0tOTg7PPPMMy5cvN9pLqlAo9BmdOTk5ev+KsTFnzhz27t17j8yZTh/CzMwMtVrNrVu3WLt2rUFRqoooKyur5Fhs0KABw4YNIyEhweDErjrTT0CSJLy8vFi2bBlvvfWWPjafmJhYKwozarWanj17smDBAu7cucO8efM4cuSIUabLgYGBnDt3ju+//57nn38eW1tbvXafDpIkIZfLMTExwdzcnMDAQObPn69PV01MTKzxOlMIgZmZGS1btsTCwoKff/7ZqMUvGRkZJCQkUF5ejlwuRy6Xk5aWxtKlSxk1ahRbt26luLjYqLMAX19fZsyYAUBSUpJBOpQPQ2xsLOHh4ZWyBIUQlJeXU1RURGFhIevXr2fSpElEREQYtaDo+vXresetroqxJrPBv6POzAS8vLz4/vvvCQgIoKioSC9JpoulGxMqlYrXXnuNt956i7Nnz/L+++9z/Phxo/2gXbt2xd7enkmTJjFx4kQ2bNhAWFgYV65cIT09HVNTUxwcHHB2dqZz5840bdqUgIAAfW7C+vXr+eKLL2qc3y+TyWjdujVDhgwhPT2dsLAwo4bRwsLCOH/+PG3atNHndXz22Wd60QxdzYIx6xM8PT1RqVQUFhZy6NChavtKqors7Gxee+012rRpw+LFi/XP3tGjR9m5c6c+Ua02+grk5eWRnJysD+caK0xYJ0jAxMSEDz/8kICAAGJjYzl16hSBgYG4ubkRFxdn1H7vpqam9O3blzfffBOFQsGePXs4d+6cUWcb//rXv/D09KRLly44ODgwatQoRo0apVcDUqvVODg43MPyN27cYN26dXz99dcGrTNtbGwYNmwYFhYW/P777yQlJRl6S/cgIyODjIwMwsLCUKlU+t/I1taWDh06kJqaSkpKilFseXh4MH36dODuS1obxUgVce3aNa5du8aePXtq1c7foZtx6JYeZWVlRhkA6wQJ+Pv789xzz+mrzXSFJ/v27ePf//63UTyvcJdsgoOD+fHHHzE3N2fDhg188803Rg/vnD17lgkTJtCjRw+6dOlC165dcXR0xM3NTa85V1JSQklJCaWlpcTExJCRkcGZM2fYsGGDQQQgSRJubm707t2by5cv8/PPP5OammqkO7sXOuluHbp3746/vz9//PGH0dKFmzVrpo/N37lzx6hJOU8SCgoKSE5OpqSkBFNTU6Kjo40yANYJEmjbti1qtZri4mK6detGo0aN2LdvHwsXLjTaaAJ3R6k333wTa2trzp8/XysEoENZWRlhYWHs27ePxo0b07BhQ5o0aXLPFK+kpIRjx45x5coV7ty5Y/A6WleKWr9+fTZv3kxUVNRjaXutg873YWVlhampqVGmzQcOHKCoqIiuXbty/fp1oqOjjXClTx7y8vLYsmUL+fn5mJqasnHjRqMMgHWCBHbs2IGnpydt2rRBqVQyY8YMTp48aRRPfUW0bNkST09PoqKiePvtt2uUkVddaDQaLl26xKVLl9i3b1+t2xNCEBUVxdixY7lw4cJjHzWdnJwwMzMjPj7eaCW4OhHSw4cPG+V8TzIuXrxo9OeyTpBAQkIC8+bN0/cISEpKMrroI8C5c+cYNGgQeXl5xMfHG/38TwJqU6evKtixYweRkZFcuXLlsdfhP8X9USdIAO5myxlSFFQVZGVl1VqjyKe4C50C8lM8OXiiZMgehxqLzoH01NZTW/9rth4kQ1ZnkoWe4imeonbwxCwHnsqQ1R1bOhmytLS0x2JLkqTHYksnQ/Y4bOlkyB6HracyZE/xFE/xUDwxM4GneLyQy+V4eXnRpk0bTExMgLsVmmfPniUxMdGoPfqf4slGnSYBtVqNu7s7zZo1o0WLFshkMq5fv87+/ftrJRX2/xMsLS0ZOXIkM2bMQK1W61NS9+/fz9q1a43anPNxw8bGhj59+ug1FWrrPszMzAgMDMTe3p4TJ07UWui6tlHnSEAul9OqVSt69uyJu7s7TZs2pVGjRri7u+t1AZo0acLMmTMNtmVmZkb79u2xsbHB2dmZNm3aYG5uDtwNdX3xxRd1tuW0EIKEhATWrFkD3FUE8vDwoE+fPnh6emJpacmaNWsem6iGMWFjY8PQoUMpLS3l0qVLteb/kSQJFxcXxo8fz7Bhw1i8eDFHjhypc0RQp0hAkiQ6dOjAt99+i6enJ2q1GpVKxbFjxzh69CgREREsXLgQHx8fg+y4uLgwfPhwgoKCcHJy0vcXtLOz0ysEFRQU0K5dO77//nv27t1rjNt7bHBwcGDYsGE4Ozvz008/kZiYiIODA/Xq1WPRokV07NiR2bNno1KpCA0NrTER6Ej0/Pnzj50sJUnC2dkZCwuLWrNRVFTE1q1biYmJITg4mLFjx5KQkFDn8iDqFAnoavx1rbozMjJYv349y5cvJzc3l5dfftkoQo1Lly7F398fCwuLB7Z41tX4X7161WASkCQJe3t7HB0d8fX1pXXr1nTq1Ak7O7tKx1y/fp23336b06dP17hBp6urK1999RWBgYEUFBRw4cIFjh8/rk/EGjNmDCtXrqRz587MmDGD0tJSNmzYUKMptaOjIz/88ANLly5l6dKllfZ169aNiIiIWvM9CCFwdHSsVRIQQnDr1i2OHz9OXl4es2bNqpEGxaOgE8Lp0qULnTt3vu91rF+/Xp8CnpGRUa3U4jpFAmVlZaSmppKXl8e2bdtYtmwZFy9e5M6dOwA0bdrUKJVpjRo10pNJxc6uFeXC4G4SRk1/dEmSCAwMxMTEhM6dO/Pyyy9jb2+PXC5HJpMhl8v15cs3btzAxsaGTp068dlnnzFx4sRK3W6rY/O7774jKCgIgDNnzhATE1PpmMTERBYvXsz333+Pu7s7ISEh7N27V98LoDqwsLDAwcEBS0vLSttbtGjBxx9/zJAhQ4zaU6AiJEnC29u7kvRZbUH3jBizDbiHhwdjx46lQ4cOqFQqunTpgkKheODzHRAQoE882rBhA1OmTKmyrTpHAps2bWLz5s33VL45OTkxceJEbt26xapVqwyyU1JSoh+hsrOziYuLIz8/X08Cfn5+2NraolAoeOaZZ+jTpw+///57tWy0aNGC9evXY2dnp68RLykpITo6muXLl3PmzBkKCgoQQiCXy1mxYgU9evTg9u3bNeorJ5PJMDc3p127dhQVFbFhwwaWL1/On3/+Wek4IQQ7d+4kKyuL8PBwPD09ad68eY1IYNKkSWg0mntEVCdOnIilpaXR243roHsZa0Oo435QqVT069cPb29vgysyJUmiY8eOfPvtt7Rv3x5JkvTfk45sKhKBrtp1+PDhODk5kZ2dXW3tiDpFAjpU/KJ1jL9q1So0Gg07duwwWOp68uTJjB8/HqVSyc8//0xERESlJqYzZszgk08+QS6X07BhQ4KDg6tNAgUFBaxbtw4bGxsOHTpEZmYm8fHx97xscrmc0NBQevXqxe3btzl37ly1R09Jkujbty9r1qzB2tqaiIgIZsyY8cCpuFarJT4+nu3btxMcHEz37t2JjIysVudemUxGYGAgZ86c4dChQ5Xux8rKivz8/IeSgFwur7EvQq1WY2trW+sEoFKpsLCwoEuXLgwdOpTMzEyDiqLkcjlt2rThk08+oU2bNuTn55ORkcHHH3+MEAK1Wk2XLl3w9fXF3Nwce3t7WrVqxeDBg1m4cCEnTpyo0fKqTpKADnK5HCcnJ5YuXYqvry+pqal8++23Bnu0o6KiHtoKu2JmY0U9v+ogNTWVuXPnPvQYpVKJr68vHTp0oLy8nD/++IOffvqp2rY8PDyYMmUKNjY2+nXsox4WrVarl0tv3rw59evXrxYJWFlZYWZmxs6dOyt1ZVKr1TRu3BhTU1Pc3NzQarX3FSS1s7Pj6NGjVb/JCnBwcDDKqPwwKJVKevfuzZAhQ+jevTvl5eV88sknBi1vnJ2deeutt+jSpQunT5/mp59+4tChQ5XW97pZbuPGjRkxYgQTJ04kODiYli1bMnHiRPbv319tu3WaBBwdHfnHP/5Bx44dyc/P56OPPrpnjWts6LzOcPdFuXnzJufOnasVOx06dOCf//wnzZs359y5c4SGht4zta4Kxo8fT2BgIHK5nKysLH755RejX+/f0bVrV5RKpf5FliQJS0tLmjRpgqWlJXZ2drz77rtotVocHBwqhdXy8vLIzs7m9OnTNbZf20sBuVxOhw4d6Nq1KwUFBZw+fZo///zToPBg48aNadmyJZGRkcyaNYuoqKgHDjBJSUl8/PHH5OTkMGPGDNzd3Zk/fz4xMTHVJqI6SwJmZmaEhITw0ksvYWpqymeffcavv/5a63b79evHiy++iEwm02fY1Ybd+vXr8/rrr/Pss89SXl7OihUr2LVrV7XP4+rqiq+vLyqVCiEEK1eurNKaUalUYmZmRklJCZcvX65WiE+lUjF16lTOnDlDWloaLVq0oHXr1rRt25bWrVvj4eHB6dOniYqKIiUlhbS0NL1zF+6WjWdnZyOE0NcpVBdCCAoKCmrN71BaWsqvv/7KmTNnUCgUDB48mMaNG5OamlrjGYinpyfNmjXjnXfeITY29pEzTCEEoaGhFBQUMHfuXBo1akSrVq3+N0jA2tqaoUOHMnXqVKytrfnmm29YsmSJ0XoNPgi+vr7MnTuXdu3a6VuCt2rVig8//JDw8HAiIiIMbpclSRJ2dna89tprBAUFodVqWbp0Kbt3767R+by9vfWFMVqtltWrVz/SsShJEk5OTvj6+lJQUEBsbGy1+ix4eHjQqVMnNm3axDvvvEPnzp2xtrYmISGB48eP4+XlxaZNm1i3bp3RQ4RKpRInJyesrKwoKCiotcQdrVbLhQsXuHDhAhYWFnTr1g0/Pz9OnTpV45wIXXv2srKyKi8xy8vL2bx5M/Xq1WPevHm0b9++2h2qnmgSaNasGW3btkWpVKLVajl27BhKpZKXXnqJF198EQsLC0JDQ/n666+NosRyP8hkMnx8fOjduzdDhw6lffv2+vpsnaz45MmT6du3L9988w0//fSTQUpIarWa0aNHM3HiRGxsbJg5cyYbNmyocbMThUKBQqHQT42rQpRyuZwuXbrg7OzMH3/8Ue126506dUKtVjNgwACUSiU7d+7kt99+Iy4ujuLiYvr06YOTk1OtTNdNTU3x9PTEwsKChISEx5KkVFBQQEFBAc7OzpiamtbYZllZGWVlZbRr14769etXuaq2tLSU+Ph41Go17dq1q7bdJ5oEZs2aRVBQUKUsPZ0WW3l5OZs2beL777+vlT55kiTRqlUr3n//fZo3b65PPLlfnNbExARra2uKi4sNmn5KkkSXLl14+eWXqVevHr/99hu//vqrQd2OgoODcXR0BGDLli0PPZdcLqdr1660adOGWbNmUVpaSnJycrW7G/v6+qJQKCgvL2fkyJHExMRw+/ZtNBoNSqWSy5cv07BhQ/2oZ0xYW1vTsWNHJEmiuLi4ThVCxcTEEBkZyZAhQ8jLy+O9996r0sxSqVTSvXt3JEmqkRjJE0sC9vb2uLu7U79+ff3IWzHxY9OmTXzxxRdcuXLF6F7gnj17Mn36dHx8fHB3d79HHQj+E6bUarUkJyfz1VdfsW3bNoMe6q5du7Jy5UqcnZ05deoU7733Xo3i8xWhewF1I9T9IiempqbY2toyfPhw3nvvPUxMTMjIyOCTTz5h8+bN1Sahzp07k5eXx5AhQ4iOjq70+5SVlZGVlcUzzzyDSqWq5AswBuRyOaampkiSxIkTJwz+/u4HSZIICAggKSmJq1evolQqMTExMXhmc+bMGfbt20eHDh0YMmQIubm5bN++/aFCKs2bN2fr1q24u7uTnZ3N2rVrq233iSWBzMxMDh06RPv27bG0tNQ74rRarf6HVqlUyOVy/UMml8uxtbUFqFGopmHDhgwbNowPPvgAMzMz4D+JJ7pEDZ3gw4kTJ8jPz+fPP/9kxYoVBueLOzg4MGLECBwcHDh69CizZs3i0qVLBhNcWlqaPnY9ceJEJEni5s2bxMXF0bZtW+RyOe7u7vTs2RNra2t9N+IRI0bUmGBtbW05fPjwPQSgw+nTp3nxxRepV69erU3XCwsLycjIMDrJAHqH7SuvvALcTVTz9PQkIyPDoPC0Vqtl3bp1tG7dmqCgIN555x3eeust8vPzWbRo0T1LsrZt2zJgwACsrKxITk7mu+++q5EgyhNLAnA3Juro6EhQUBAqlYp9+/aRkJBA37596du3L05OTuzatYvc3FwkSaJTp0506tSJ2NhYxo8fX624tqenJz/88APdunWrNOXX5QFcuXKFS5cusWLFCq5fv865c+eM0i1XkiQcHByYN28egwcPJi4uju+++44LFy4YZYZTWlpKSUkJQghkMhmTJk3S5wAolUp9FmR5eTm3b98mOjqadevWGURqly9fJjQ09IHX/+eff9bKy1kRBQUFXL16tVbsDBgwgNTUVH2Kt6OjIzY2NmzdutVgKfSUlBTeffddrly5wqBBg7C3t8fe3p7FixcD/1Eh0pHN7du32bJlC59//vk92Z9VxRNNAunp6UyfPh1zc3M8PDz0opk7d+5k/vz59O3bl86dO1fK2y4qKiIqKqraXvoPPviAZ599thIBaLVasrOzOX/+PD/88AP//ve/jf5Qubu7M2HCBCZMmMD169dZsWIF27dvN9oSJyoqis2bNzN8+HCaNGmCTCZDJpPpdfuysrK4desWSUlJ7Nmzh3379nHt2jWDbPbs2fOh+2/cuFFrJKBQKPRJUbWFn3/+menTp/P++++zZ88egoODUSgUxMfHG6X0+uLFi8yePZv169czduxYunfvrt9XVlZGcnKy/jfavHkzp0+fNqhnwhNNAnA3BKITedQhJiaGjz/+mNu3bxMYGIizszPXrl2juLiYkydPsm7dumr/GA4ODvpYui6rLi4ujrCwMDZu3FgrNel2dna8+eabTJ06laysLFavXs3WrVuN+gAXFBTw1VdfERERwZgxYyqR3JUrVzh27BhXr17l4sWLtR5i1SE3N5eTJ0/WSnTAysoKHx8fEhMTjSqyWhFr164lKyuL0aNH4+fnh5WVFRs2bCAxMdFoNkpKSoiKiuLcuXO0atVKv12j0XDt2jWjRsOeeBJ4EM6ePct7771HQEAATZs25eLFi+Tn5xMVFVUjrb7r169TWlqKUqlkz5497Nq1i1OnTpGYmGhQyO9BkMlkzJ49mxdeeIHCwkK+/fZbfvjhh1oJdRYUFHDgwIFqF5bUFoqKili0aFGt+APS09OZP38+qamptaZMXFpayvbt2zl27BgdOnTA3t6e/fv318r9aDSaWslIrQTdyPegD7AKuAnEVNg2H0gHzv31ea7CvrlAIhAP9H3U+YUQqFQq4erqKoBa/3h4eAg3N7d7tnfs2FGMHj1avPrqq8LHx8dotho2bHjPdktLS/HSSy+JnJwcUV5eLk6dOiVcXFxqxVZtfNzd3YW7u/tjs+Xh4fFYbDVs2PCx2XJzc3tstlxdXUWjRo0EcPp+719VZgJrgO+AdX/b/pUQ4ouKGyRJag6MAFoAzsA+SZKaCCGe+B5Vp0+fNihXvTpwdnbmtddew9zcXL/0qA09+6d4iqrgkSQghDgiSZJHFc83ENgshCgBUiRJSgSeAY7X+Ar/HyIlJYWXX35Zn9hRVFT0lASe4r8GQ3wCr0uSNBo4DbwlhMgBXIATFY65+te2R0KpVNa4WKQ6kCQJhULx2Gzp4vB/hxBCn12oUCho2LBhrdkyNnTJW09t1S1bD0JNSWAZsIC7a44FwJfAq9U5gSRJE4GJgN5j/Ti6wFSw/9TWU1v/U7YehBqRgBBCn44nSdJyQFfjmg64VTjU9a9t9ztHKBAKdwVJn8qQ1R1b/w0ZstTU1Fq3pau2fBy2dDJkj8NWrciQSZLkVOGfgwFdJ4+dwAhJkkwkSWoEeAOnamLjKZ7iKR4PHjkTkCRpE9AdqC9J0lVgHtBdkqS23F0OpAKTAIQQsZIk/QxcAMqBqY8zMuDj40Pr1q0pKCjg5MmTtZYsYix4eHjg4eGBvb09paWlxMXFkZCQUKvZbk/xFH9HVaIDL95n88qHHL8QWGjIRdUEbdq0YcGCBQQFBXH06FEmTJjwxJPAu+++S2BgIJ6enhQXFxMeHs6aNWs4ePAgeXl5/+3Le4r/EdTZjMGK8PPzY/78+Xp1ol27dhmlZTOeAAAgAElEQVRlbSyTyfD09KR9+/ZERUUZXd9Qo9GQn5/PmjVrSE9Pp2vXrnzyyScsXryYjRs31kqm4lMYF9bW1jRo0ICsrCwcHBzQaDQkJyfTpk0bJEnC3Nyczp07c+HCBcLDww3WRVQqlcjlckpLS2vU4PZ+qPMkMGjQIGbPno27uzsrV67k008/1fenMwS2traMHTuWPn364O3tTUJCAtu3b2fv3r2kpaUZZcq+cOFC7OzsuHHjBgUFBbRs2ZJZs2Yxffp0Ll++zL59+2pFC9Db25t3332XnTt3sm3btnv2t2rViqSkJIOqJJVKJY6OjvTo0QOAa9eukZubS9OmTTly5Ag3b97UH6vRaGqtF6CxERwcTOvWrZEkSa8BMXr0aFJSUmjSpAnl5eUcP36c/v37I0kSJiYmODo6Eh4ezvnz52s8OJmYmNCzZ09GjBhBvXr1CAsLY8OGDeTk5Bh8T3WWBCRJYsCAAfrOP19++SVffvmlcb4UhYK5c+cyduxYLCwsUCgUuLi40L59e8aNG8cPP/xAYWEhmzdvNsjOlStXKkUpTp06xYoVK/j0008ZPXo00dHRNaqDeBh0pcu9e/fm+PF7c7gcHBwIDQ3llVdeIT4+vkY2nn32Wd544w1atWqFtbU1cDffXqPRYGpqSn5+vr75ihCCy5cv67sSx8bGcuDAgUptyh+E1q1b06JFC2bOnKmXV6+IkydPsn79euzt7dm5c6fBo/DWrVvp1KkTFhYW+hLzkpIS7OzsKCsr0xegde7cGUtLS311qxACGxsbTE1Na2TX3d2dN998k5CQEIqLiyksLGT69OlEREQ8JYHg4GBatWrFpk2b+Pzzz422jp49ezavv/56pbCKQqHA1tYWa2trFi9ejFarJSgoiNDQUI4dO2YUuxqNhiNHjhAeHs7gwYNxcXHhxo0bRnUU2tjY8NZbb3H58uX7FhTZ2dnRrFmzGrWp0sHf35++fftiampKYWFhpVHf2toae3t7AP19eXt7ExAQANytGl21ahWffPKJXh/x77CwsGDq1Km8+uqrODg4PFBvsHHjxgQFBWFmZka/fv14/fXXDZrdeHh44ODgoG/aqnvBY2Nj0Wg0tG7dWq/0pNFo+O677/TVr9euXasRoderV4+PP/4Yf39/QkNDWbVqFcOGDcPDw8NobfXqJAnIZDKee+45unbtSlxcHIsWLTKYAGQyGSEhIaxZswZLS0vkcrl+zaXRaEhNTcXGxoZ69erp5clHjRrFs88+y7hx4zhy5IhRXtaCggJu3LhBvXr18PLyIjo62qh98jw9Penbty8bN24kISHhnv0DBgwwOIFF1zW3tLSUt99+m1WrVum/y7Zt2zJjxgwOHDhAXl4ecrmcevXq4evry+DBg7GysmLYsGFs2bLlgSTg6upKz5498fT01Degtba2xtvbu9JxMpmMBg0aAHd/q2bNmrF48WL27dtXoxF07NixTJ06laZNm+Ln54dMJtMX4eg6Qle85opr9po8G+7u7mzbtg1HR0fGjRtHWFgYXl5eaLVali9fbrzemlWp8qvtT3WrCJs0aSK2b98ubt68KYYMGSIUCkWV//ZBVYSBgYEiNjZWlJaWirKyMlFeXi6Ki4tFYmKiWLRokejUqZMICQkRhw4dEgUFBaK4uFiUl5eLO3fuiCNHjoguXboImUx2j62aVPZNmTJF3LhxQ4waNUoolcoq39ejbKlUKrFkyRJx5syZB1Ytbtu2TWRmZoqmTZs+8DyPqiIcOXKkyMrKEiUlJeLgwYMiKChImJubV/p+LCwshKurq5gyZYqIjo4WZWVlori4WGRnZ4vr168Lf39/va2/V9sNGzZMpKamivLycrF582Zhbm4unJ2dRXBwcKXPRx99JKKjo0VeXp4oLy8X5eXlIjs7W7z00kv3ve6qVhG6uLiIXbt2iZs3b4rbt2+LwsJCcevWLfHee+8JOzu7Kv1ej6oidHNzExERESIjI0P07dv3nv1yuVyoVCphZWUlbGxshKOjo3BwcBC2trZCpVJVOtYYVYRPFNRqNcHBwXTr1o2vvvqKffv2GTxSyuVy2rZtS4MGDfRTvKKiIk6ePMmOHTtYuXKlvhPOxYsXef/997GwsGDgwIHI5XJ8fX2ZNGkSaWlpBnflAUhOTubWrVt069aN7du3G60jb48ePRg7diy//fbbfRtwSpKEl5cXZ86cITk5ucZ2tmzZgq+vL6NHj6Zr164sW7aMrVu3sm3bNq5cuYKFhQVTpkwhICCAFi1aUFJSwoULF0hISCAsLIwzZ8480B9hYmKCp6enXsj11KlTlJWVce3atXu++927d7Nx40ZmzpzJiy++iFKpxMLCgnHjxrF79+4azx7T09MZOHAgfn5+BAUF4ejoSK9evZg/fz5arZYff/zRoL4QCoWCoUOH4u7uzocffkhYWJh+n1KpxMHBgRYtWtCoUSP8/PzQaDS0aNFC3wYvPDycffv2ceXKlSo9O3WKBHTio0OHDuX8+fPs2bPHKI0cHB0d6dChg15mXNdsc+LEifekdSYmJjJmzBiUSiXnz5/Hy8sLhUKBmZmZvjW6sdC6dWuD1uYVYW5uzsyZM8nLy+P777+/7zHOzs5YWloSHx9vkLe+vLycxYsXc/v2bYYOHYqHhwdTp06lX79+pKSk4O7ujpeXF7dv3+bixYtERETw3XffVUnpyNnZmQ4dOmBqakpOTg779+9/6LXqBEJ0U3OZTIa7uzu+vr7VFumoCI1Gw9GjRzl69CjW1tZMnTqVSZMm8eabb2Jqaso333xT41bxnp6ePP/88+zfv5/Vq1cDd4mhcePGdOzYkRdeeAFPT09ycnLQaDRs376d9evXo1arCQwM5NVXXyUkJIR33323Sn0H6xQJmJub8/zzz+Pl5cXy5ctRKpV07NiR+Ph4g0px3d3dadWqld7DnJGRQWho6EPzujUaDStWrGDRokUANGnShMaNGxslP8HMzAyVSoWDg8MD9eiri4YNG9KxY0d2797NqVP3ZnJLkoS/vz8ODg4GRz3gbrPRhQsXkpCQwKxZs2jevDk+Pj74+PgAd/s3bNmyhZiYGI4fP16laABAVlaWnqTMzMwYNWoUGRkZlZyPcHd2Z21trdc/1FXSlZSUcOjQIeLi4gy+Rx3y8vJYtmwZBQUFvPPOO7zzzjsoFAqWLFlSo67XXbp0wdTUlJ9++onS0lIcHR0ZOHAgISEhODo6cv78eTZv3kxUVBRlZWWkpaXpSS4sLIxevXqxYMECmjZt+v+PBJycnBg5ciQWFhb06tULf39/FAoFkZGRbNy48b4Pd1UQEBCAq6ur/t8xMTFVehEqOmZatGhBs2bNOHjwYJXttmrVCgcHBwCys7NJSUlBCEHz5s2xs7MzqlNQpVLpJbr8/f0xMTHB3t4erVaLUqmkpKSE/v37I5PJjPaCWFhY0LFjRywtLe/Zt3//fn788cdqe+vz8/OJjIwkPT0dT09PJkyYgKmpKWfPnuX69eskJyfj5+eHubk5zZs3x8nJiWeeeQalUokQgvz8fLZu3Wp0PYKcnBzWrl2LQqFg+vTpTJkyhf3799eIBDp27Eh6ejqnT59Gq9XSp08fJk2axIkTJ1i6dCknTpx4oGNTkiQsLCy4devWAx2rf0edIQGVSsX06dNxc3NDJpOhVquJj4/Hx8eHMWPG0LhxY4YOHVqjWPDFixe5du0aFhYWSJJEmzZt+O6779ixY8cDVYAVCgXjx4/X/7u4uLjaHXQXLVpEo0aNALhz5w63bt2iqKgIb29vLC0tOXXqlMGxbR1u3rzJ4cOH8fHxYdmyZWg0Gq5evcqJEyeIjY0lMTERb29vcnJyDFI8grsPoi55y9fXFzMzM3JzcwkPD0cIQb9+/ejTpw+ffvppjc5/9uxZNm3axIQJE2jQoAFjxoxh0KBBFBQUkJubi6urKwqFAktLy0r5A0II4uLi7psfYQzk5eURGhqKr68vQ4YMYejQoTWSCre3tyczM1M/APzxxx8kJCSQnJzMzZs3HxhpsLKyYsyYMYSEhLBz506ioqKqZK/OkIBCoWDQoEEoFApu3brFmjVr2LBhA2q1mkWLFhESEkLPnj3597//Xe1z7927l6ZNmzJz5kysra1xcHBg7NixhISE6EeqiiIkun/rXmBAr9lXHSQmJtKnT59KTR80Go1eVtvZ2RkTExOj6BvcuHGD8ePHY2VlhVqtRqvV6l+aoqIi7ty5g1ar5fDhwwaTQI8ePVi8eDHNmzenqKiIPXv2EBoaysmTJ4H/LE2GDRvGypUPLEN5IK5du8bXX3/Ntm3bGDNmDFOmTNFLrcG9vxWgD+fdvn3bYG2Ah6GgoIC8vDxkMhn9+vWr0Tl0DWJ095GcnPxIR23Tpk1ZsGABjRo1IjQ0lM2bN1d5iVxnSEClUqFWq0lJSeGjjz7i8OHDZGZm0rRpU8zMzJAkqcaj5p07d/jll18YMGAAHTp0QCaTYWJigovLf5oi6aIGFaHbJv6Swa5urv+iRYtwdXVl0KBB+m1yuZySkhIkSeL555+nfv36TJ06VV+3oCOI6uaNa7Xa+3rQdWjWrBnOzs6EhYUZlJMuSRI9e/bE29ubsrIy5s2bx4oVKyguLtanQEdERNC6dWumTZvGqlWrqh1D12g05OTkkJOTw7vvvsvy5ctRKBQ0adKEl156iezsbMLCwrh48SI2NjasWbOGRo0aVWyeWy24ubnh4OBAbGxsldqy6+zUNEPw7NmzenWoh0Emk/Hss8/y2muv4efnR0xMDBMmTCA2NrZajt06QwL9+vXD2tqapKQkwsLCKC0tpU+fPsyfP5927dqxdOnSaq3H/46kpCR+//13VCoVLVu2RCaT6V84oFJiiA46abS0tDTOnDlT7b79GRkZDB8+nF69etGvXz9UKhWXL1/mX//6F66urrz//vt069aNM2fO8M9//pPt27fTqlUr5HL5fXP+DYGDgwMmJibs2rXr0Qc/BL1792bMmDFotVoWLFjATz/9dI/T79KlS5SUlNT4JamI4uJivQ8jJiaG3377DfjPi1i/fn3u3LljUCLXm2++iYeHB9OmTatS4xvdc1PTpKuwsDBGjBjBzJkz+fXXX/nzzz8RQujrMVq3bs2AAQNo27YtXl5exMXF8Y9//IOwsLAaibrUGRLQaDQIIfD09GTJkiXY29vTsWNHtFotv/zyCwsWLDC4qmr+/Pls2rSJzp07Y2ZmxsCBA2nbti1qtRqFQkFBQQFarRZra2skSSI3N5dr164xZ84cwsPDa2SzvLycvXv3snfv3krbY2JiOHjwIDNnzmTixIl89NFHjBkzBhcXF9asWWNUEpAkiZEjR2JpaWnw0sPLywtTU1OuXr3KZ599VunlkyQJU1NT3N3dUSgURq+SvN9If/v2bfbv34+npydyubxG5eVarZaQkBCuX7/Ohx9+SG5uLkIISkpK9GpOOlhaWqJWqxFC1Div/9y5c8ycOZNPP/2UyZMnk5iYyOnTp2nZsiWNGjXCzMyMnJwcjh07xvTp0zl9+rRBik51hgQiIyNJTk7G3d2dwYMH61WJtm7dysqVK40m/BAfH098fDySJLFlyxYaNmzIoEGDqF+/vn5Ue/vtt1EqlcycOZOysrIqe2Gri5KSEhYuXMi6detYsmQJ3bp1o7i4mPPnzxvVjq5eoKioyGASCAgIwNzcnLy8PHx8fMjOzqa0tBQTExPc3Nzo3bs3Y8eO5datW3zyySe13kCltLSUK1eu6IVsaxIViI6OJjU1lQkTJmBnZ0dUVBR37txh586dODk58eyzz+qPnTZtGk5OTmi1WmbNmlWjaxZCsG/fPkaMGEFwcDA+Pj5IkkRERATLli0jLy+PmJgYrl69apRy4jpDAmlpabz11lsMGjQIMzMzoqKi2L59O6mpqbVSbiuEIDs7m+zs7HsUYEaNGmV0ew/DlStXGD16NBMnTsTJycmgZc/9YGdnh4WFBREREQZnPEZGRtK1a1ccHR3ZsWMHERERZGZm4uzsTJcuXXB0dCQ1NZV58+axfft2I93Bw6FbjshkMrp37467u3u1+iNu3boVlUrFtGnTCAoKYujQoZSWljJs2DBsbGxo1qxZpeNzcnI4fPiwwbn9CQkJfP311wadoyqoMySg1WrZtWuXwWvWuoqCggK9Mm1tITo62mBCXbZsGSUlJbRt2xY/Pz9Gjx6t33fz5k3CwsJYvXo14eHhtULe98PRo0cpKSnBxMSE1q1bM3v2bD766KMqE15RURErV67kxo0b9OrVCz8/P9q3b4+fn1+lmUxycjKJiYmcOHGChQsXPrb7MxR1hgSeovaQnZ1NfHy8UWoUioqK+PHHH7GwsKBTp06VRsnr169z/Phxrly58lhfkNjYWI4ePUq/fv0wMzOjb9++bN++vdqznl27dnHy5Em8vb1p27btPRGjlJQUUlJSuHbtWp0hAHhKAk/B3enrhx9+SElJiVEe3rKyMnJycti7d28lh6lWq/2vNVGdM2cOcDd6ERERUaNMPoDMzEwyMzON1kPiScBTEngKysvLH5gZaSielBHx4sWLTJkyBVtbW27duvXEN6F9nJCehPbWJiYmwtnZ2WiNEx8GXQLG43g4n9p6autJsKXLeUlJSYkSQnT8+/4nZiYgSdIjNdOMiae2ntr6X7H1qKSlJ4YEnsqQ1R1bT2XIDEedlyF7iqd4iv8/eEoCT1FrkMvl9OjRA1dXVywsLIxSK/AUxscTsxx40qFUKvH396dZs2Zcv36d1NRUysvLiY+PNyhv+/8zTE1NWblyJYcPHyYtLY3c3Fx2795NYmKi0ZzAHh4eBAUFoVQqK8XtMzIy7hE5eYr7o06TQIMGDRg9ejTJycns3LnTqK25/w5PT09mz55NYGCgngQKCgpYsGCBUZtUKBQKOnbsSOfOnYmKiuL48eO1el9w1znl5uZGy5YtcXV15cKFC0RERBj8ojZv3pwGDRrw8ssvo9FoKC4uplOnTsydO5eUlBSjXPukSZOYNm0aKpVKrwcAd0lg9erV/Pbbb8TGxhqtWev9YGlpSY8ePejcubN+tpOYmMjatWspKCjAxsYGHx8f0tPTH4vfq7qo0yQwbtw4Zs+ezbp169i9e3etviy67De5XI6LiwsuLi7cuXOHpKQkzp8/b5SKOCcnJ0aMGMGwYcNo0qQJhw4d4vz587UqTiqXyxk4cCDjxo3Dy8sLa2trLl++zNixYw3OHRg+fDimpqZcvnyZn376CW9vb7p168bYsWP58ssvDb6vbt260b9/f32D14rhbicnJyZPnkxAQABvvPEG58+fr5VEJRsbG8aMGcPkyZP1Ogjl5eXk5uby559/8ueffzJjxgxat27N4sWLH0oCtra2BAQE4O7uTkFBATKZDDMzMxwdHbl+/ToajYa0tDROnDhhcOOXiqizJNC+fXtCQkL0Mle1iYYNG9KzZ0/s7OwqbTcxMaFXr160b99eL6NVUzRo0IAFCxYwcOBArK2tkcvl7N692yhdhR6G7t27M3v2bNq1a6ePXZubm2Nra2vQeZVKJb6+vhQWFrJ69WqWLFlCw4YN+fLLL5k6dSrh4eH88ccfBr2YPj4+1K9fn+TkZM6cOUOnTp2IjY0lMDAQtVqNnZ0dXbp0ITg4mIsXLxqtVZsOJiYmBAUFMX36dIQQLFmyhMjISNq2bctLL71Eeno6o0aNYvz48Zw8efKRkYD27dvz/vvvo9FoiIiIoLCwkC5duuDh4YFCodA3nLl58yYHDhzgl19+ISYmxuBcgzpLAhYWFvoW4StWrDD6D6yDXC6nZ8+e9O7dW9+vLi8vj3379vH888/j6urKgAEDDCaB+vXr4+3tzebNmxk1ahRZWVmcPHmyVqexLi4uDB8+XN+oRIeUlBSDm43quv8UFhayb98+8vLyiIuL49ixY3Tq1Ak/Pz9OnDhh0P0dOHCAzMxMbty4QWxsLK1btyYtLY3Zs2czceJE4O7vN23aNG7dukVoaKhRZwOurq688sorWFtb89VXX/HDDz+Qm5tLXl4ex48fZ/HixXTt2pXMzExWrVr1yLD01atXSUhIYMeOHezevRutVqtvENuwYUOsrKxo1aoVw4cPZ/LkyQwZMoSRI0dy7tw5w+7rv60+VBMFIkDIZDKxevVqIYQQXl5eVf67BykQ3e9jZWUlJk+eLOLi4vQKNrm5uaJPnz6iXbt2IjMzUxQWFoolS5bUWBVI91Gr1eLll18WFy9eFOXl5WLNmjXCwcHhvsdKkiQUCkUl5aXqqh2ZmpqKadOmibNnz4pVq1aJ9PR0odFohEajEYcOHXro3z5KgUj3cXZ2Fj169BDW1tb6ba1atRLnzp0Tt2/fFs2aNXvkOe6nQPT370KSJP3/85c6T0BAgDh16pT+d9u1a9cDv0/dp6oKRLrnr0+fPiI3N1fs3r1beHt767/XkSNHihs3bog7d+6Ic+fOiQEDBuivTfe5nwKRJElCpVIJuVz+wHuVyWRCpVKJOXPmiKysLDF58uQHHq/7PEqBqM6GCN3d3XFycqq186vVakaOHMlrr72Gt7c3QggyMzMZPXo0+/fvJzY2tsq98h8FSZJwcXFhxIgReHt7k5aWxq+//npfFRulUsmIESNISkoiJSWFli1b1shmSUkJq1evpk+fPvz222/6CMeNGzf0xTaG4tq1a3rNQR0uXLhAVlYW5ubm+Pr6GmyjYjch3X91wq6ffvopycnJ+rbd7dq1M9ieDlqtloSEBFJTU5HJZPr1+4wZM/jhhx+wtrZm1apVTJkyhZ07d1ZppBZC6NWbH7Rfq9VSWlqq13s0RsZhnV0OuLm56cUmjQ1Jkmjbti3ffPMNMpmM0tJSLl++zLx58wgLC0Oj0aBWqw36ASqq17q6urJw4UL69etHbm4u69ev59ixY/c4OuVyOd7e3oSEhNCgQQNOnjxZY5+BTmqtV69eLFy4EHd3d/Lz85k7dy4nTpyo8X1VxW5qaiqlpaUG+x0ehVOnThEdHY2Li4v+5TEmdI7hNm3a0Lt3b5o2bcqLL74IwI4dO3jjjTeMbhPuLoWbNWuGmZmZUcLTdZYEdLh69arRv+gGDRowa9YsZDIZGo2GqKgovvjiC8LDw/W+h4EDB2JlZaV31FQXLVu2pH///hQWFhIYGEhwcDA5OTmsWbOG0NDQe6rcJEmiSZMmLFy4kB49enDkyBE++OCDGqedymQyfHx8GDFiBI0aNUKSJJYuXcrGjRtrdL6qQqvVcvLkSQYPHkxwcDDffPNNrZUX63T5/P39uXDhAomJiUY9f0FBAefPnyckJISPP/4YpVJJRkYGe/bsYfbs2bVCAJIk8dxzz+Hn50dWVhZnzpwxOJRb50lg//79RpuWw904/SuvvELv3r3RarUkJycTGhrK3r17K7HuggULsLS0JC0tjSNHjlTbzqRJkxg7dqx+SieE4MKFC6xdu/a+zS7at2/Pa6+9pq+HX7BggV6hpiawsbHh5Zdfpn///piYmJCUlMTSpUtr1RGpw7lz5yguLjZYAr2q0Gg0nDhxwugxepVKhaOjIwqFAoVCwaFDh1ixYgU7duyotaiOQqGgbdu2uLm58csvvyCE0JO4rslpTExMtWT5HkkCkiS5AesAB+46GkKFEEskSaoHbAE8gFTgBSFEjnT3l10CPAcUAa8IIc5U817/K1AoFAwcOJBJkyZhZmZGRkYGn376aaU1M9xNgqlfvz5arZaUlJQaFSPpRomKMW4PDw9effVVDh48yPnz57ly5QpCCNq2bctnn33GM888w+nTp/nqq684fvx4jUNDCoUCX19fBgwYgKmpKenp6Xz66adGjT3rYGZmhouLC05OTmg0Gr3U2uMoYbe0tMTDw4OIiAi2b99u1FJ1GxsbevXqRUhICHB3VrB58+Z7nhVj2bK1tcXLywsHBwe9NkazZs1YuHAhWq2WwsJCiouLOX36NBcvXqzW+asyEygH3hJCnJEkyRKIkiQpHHgF2C+EWCRJ0hxgDjAb6Ad4//XpBCz7679GRfPmzWnUqJFRO+82b96cDz74gIYNG1JaWqqPxVacaSgUCqZOnYqpqSn5+fl8//33Nepg+8MPPxAbG4tKpcLKyorAwEACAwOZMGECzz33HCkpKfzyyy94eXnRo0cP2rRpQ1RUFJ9//jmHDx82KDZsZ2fH+PHjadKkCUVFRWzatIkdO3YY9eFVKBT07t2bSZMmYWdnh5WVFVqtluzsbORyOfXq1cPS0hKVSlUr4V1TU1NGjx7N888/z/z58zlzxnjjkJWVFTNmzOD5558nKyuLAwcO0LNnT6MpSOvg4eHBiBEj6NChAw0aNKB+/fqYm5tjZ2dHUVERubm5xMXFce3aNW7evElqairJycnVV1iqbjgP2AH0BuIBp7+2OQHxf/3/j8CLFY7XH2fMEOG4ceNEZmamWLNmjahXr16V/+5hIcJu3bqJ3NxcUV5eLhITE4WLi8s9x4wdO1Zcv35dlJSUiLlz5wpTU9OH2npQ2E6SJGFiYiJMTEyEubm58Pb2FjNmzBAxMTFCo9GI0tJSkZGRIfLy8kR5eblIT08X/v7+wsTEpNq2Kn5MTU3FsmXLRH5+vtBqtWLbtm2iadOm94SwHvapSoiwU6dOIiUlReTl5YnLly+L2NhYkZmZKYqLi/WhyIKCAjFp0iShVCofaquqYTv+Ct15eXmJefPmifj4eHHhwgXh6+tbpfuraojw7bffFpmZmeLo0aPi2WefFUOGDBEJCQli0aJFwsbGpkrXeb8Q4d8/c+fO1X9nJ06cEP/6179EeHi40Gg0Yt26daJNmzbC1tZWqNXqh36HjwoRVpcAPIDLgBWQW2G7pPs3sAt4tsK+/UBHY5PA3Llz9fF0Y5HA2rVrxZ07d0RpaanYsmVLpX1mZmZi8ODBIi4uTpSVlYnz5+e6V88AABsjSURBVM8LJyenR9qqbuzex8dHfP755yIzM1P/spSWlor333//ofHgqtoKCAgQt2/fFhqNRpw/f17069dPyGSyan33VSGBLVu2iMLCQtGjRw9hb28v6tevL9zc3MT06dMr5SRkZ2eLRYsWVcp5+LutqpKAWq0W06ZNE4cPHxZ5eXkiJSVFjBw5UqjV6ir9/aNIQCaTiTfeeENkZGSIpKQk4ebmJmQymRgwYIBISUkRW7duFc7OzlWyVRUScHV1FXv27BGvv/66sLe3Fy4uLuKjjz4SGo1GTJ48+aEv/t/P8zASqLJjUJIkC2Ar8IYQ4nZFp44QQse0VYYkSROBicAjNdfuB12c1JiwtbXVF6Hs2bMHuOtFd3d3Z/LkyYwePRo7OzsuXbrEwIEDDe4r/3fcuXOHS5cu6VNchRBoNBrKy8uJjIw0OD3U0tKSOXPmYG5uTlFRETt27GDv3r21sj7XfY9xcXFkZWXpZbR0Ggd5eXlkZmbi5ubGq6++SlxcHGvXrq2RLUmSeOaZZxg8eDBvvvkmMpmMmzdvMmfOHH755Rej+QJatmzJnDlzqFevHidPntT7guRyOQqFwujf49WrV+nfvz9arRatVouFhYU+bFxeXm40e1UiAUmSlNwlgJ+EEDr9qxuSJDkJITIkSXICdHGydMCtwp+7/rWtEoQQoUAo3O0xWMPrrxXI5XJeeeUVIiIimDVrFj179sTV1RW5XM7q1av55JNPjFYFVxEKhYJ+/foxY8YMHBwcOH78OCNHjjRKtyBJkpg0aRJ9+/ZFCEFCQgIHDx6sNQfdxYsXGTBgAD/++CM///wz165dIyQkhFGjRqFUKpkwYQJbt24lICCAgQMHGqTiFBAQwKpVq3B3d9ffT2Fhob6W4H5RAV1VY3WKzhwdHVGpVBw9epQPP/wQuPus6HIu0tLSjO4UrO0KUqhadEACVgJxQoiK6hc7gTHAor/+u6PC9tclSdrMXYdgnhDCuEMm6L2hxoSuJbZMJqNDhw5s3LiRdu3a6evUc3JyiIyMrJXWZDKZjI4dO/Luu+/i6elJREQEU6dONZqtJk2aMGnSJABu3brFl19+yYEDB4xy7vvh119/JTg4GH9/f4KDg/Wj8e3bt/n666/1SVcHDx40SFFJrVYTEhKCq6trpe2NGjViypQpDB8+/B7HY35+PufOnWPx4sVER0dX2Va3bt1QqVRkZGRw9uxZ6tWrR8eOHRk/fjzJycls3ry5VmXP4T+anAqFwmgh1qrMBLoCo4A/JUnS6XG9w92X/2dJksYBacALf+37N3fDg4ncDRGONcqV/g3h4eEMHDjQqOdMSEigRYsW2NvbY2lpSfv27fX7iouLWbFiBT///HOtxNJ1HnsvLy/279/Phx9+SFxcnNFG6okTJ+Lu7o5WqyUmJoYdO3Y8+o8MQHR0NIMGDeKll14iICAAuJuqfPToUdatW2e08ugGDRrwyiuv3Dd7U6lU0qBBAxISEvRaleXl5Rw6dIi9e/dWiwAAfUqvlZUVffr0ISgoiIEDB2JiYsJHH31ETExMrXbMLikpIT09nby8PDw8PDA3NzcK6TySBIQQf3DX8Xc/9LzP8QKYauB1PRIxMTFERUVhYWFhtHOGhoZy4sQJfHx8cHZ2rrQvJyeH1atX1xrTOzg48Pzzz7Nnzx4+//xzoqOjjfpARUZGkpKSgqur633lwmsDly9fZtGiRSxatKjWbOTn57N69WrUavU9ikA67Nq1S59zodVqycjIqJGA7a5du2jTpg29evWie/fuyGQyjh8/zu+//87OnTuNPjP9O8rKyoiLiyMpKQkXFxfMzMweDwk8ydi8eTMWFhZGy85KSEggISHBKOeqLq5fv86cOXM4fvw4sbGxRh9R9uzZQ1FRER4eHvz+++9GPfd/E9nZ2bz99tuPxVZUVBQffPABhw4dQqlUUlRUxB9//EFSUlKt933Q4dKlS5w8eRIzMzOjnbNOk0BkZOR/+xKMhqysLH788cdaO39eXh47/6+9M4+K6j77+OfHMoOyDaAjCAiyad0QUKKpyxGXiDYqNXGpZjGpscc3bd7To60e02psalEbbXJMooQm1iSaWInFfQFeRaUJUY+4ACoC4sKiskqEQea+fwxzCyoVmQsM8X7OmTPDvZf7PPObe7/3tz7Prl1tdv6nhfPnz3P+/PkOs19aWsqGDRvo2rWrYlmUOrUIqKg8bUiSxMWLFxU9p1WlIWuPlEzm8dz2sgXtNMyj2upUtmxtbRFCtKutTpGGTOnJP9ZgC1o3GUq19eO2ZR7ea09bzWE1IqCmIes8ttQ0ZJajpiFTUVGxGlQRUFF5yrGa5kBnIDQ0lLCwMDQajTxeq8QEkS5dujBs2DCCg4PlbeXl5ZSUlJCVlUVxcbHFNtobc9ALb29vuZpdVVVFeno6eXl5bTqzDkzJR8yRn5SOVxAeHo69vT2hoaEP7cvKyrI4lHp7o4pAC7CzsyMsLIylS5cyceJEtFotZ8+eZdGiRSQnJ1t0biEE48aNY9WqVfTr10/efufOHQoLCzl9+jQHDx5kx44d7dKTrATu7u688sorTJw4ET8/PwICArC1taWiooJ9+/YRGxtLZmZmmwpBSEgIM2fOZPPmzRaFYXsQPz8/3nvvPbp06cIzzzwcK+fMmTO88847JCYmtkv0JCXoVCJgZ2dHSEgIrq6ueHt74+Pjw40bN8jMzMRgMHDnzh3FJlCY8fDwkOfAR0ZGyglI+vfvz/jx4xURgRdffJGgoCCysrI4e/YsBoOB6upqRo8ezdSpUxk5ciS5ubmkp6dbZGvo0KHMnDlTXmxTXFzM8ePHSU1NVaS2YWdnR0REBPPnzycmJgZXV1cqKipITEzE3d2d8PBwYmJiyMnJ4a9//WubT10eOnQo9+7d49KlS4pMrw0ICGD9+vWMGjUKo9FIWloa33zzDQUFBQwePFiOABUbG0tubq6iUa/akk4hAnq9nt/+9rdERUXh5OSEvb09Dg4OdOnShZqaGiorKzEajeTk5DBt2jRFbAohCAoKYvHixUyePJlu3boBJqV3cHAgJCSEZ599Vr7QLcFgMLB69Wq++uor+bvcv3+fbt26ERERQXx8PG+99RZz5sxp1fk1Gg2vvfYav/71r/Hz85MzN9XU1BATE8PXX3/NmjVrLFrOC6aVe8uXL5dX2x06dIgPP/yQM2fOoNPpWLRoES+99BKTJk0iPj6+zUXA1taWQYMGKbbQ5vXXX2f8+PFIksSvfvUrjhw5ws2bN7l37x6HDh3i+vXrLF++nD59+jBhwgRFRMDR0ZEvvvhCzrFhrl0UFBSwb98+tm/fbnGT1OpFQK/Xc/z4cXnBxKPw9vZGkiR8fX0fuf9JEUIwduxYVq5cSWhoKFqtlsuXLxMXF0dCQgLr1q0jJCSEwMBAnJ2dLRIBo9HI22+/TWVl5UPzz2/fvk1+fj6vv/66vGjlyJEjT2xj+PDh/P73v8fX1xej0SjfEHZ2dnh6ejJy5Eji4+MtFgFvb28CAgLklORLly6lvLyc+vp6ORHJ+PHj6d27N3q9nhs3brR5lVnJJbd6vR5bW1s++OADPv/88yYhxSsqKigtLZWbHc7OzorYNKdeN+db7NmzJ3q9nmHDhtGnTx+OHDli8VCt1YvAtm3bCAwMpLa2lrfeeosTJ06QmZnZ5JgJEyYwcuRIxZbHCiHw9/dn6NCh/PDDDxw4cIDY2FhOnz4tR/qB/8zEspSioqJm99XU1JCYmMjw4cOZNWvWE4uAs7Mzb7zxBp6enly9epW3336bnTt3AqZyW79+vWI3SmpqKsuWLWPWrFl8/PHHTTIoCSHo2rUrDg4OlJSUcOvWrTYVACGE/FKKhQsXkpOTw9/+9rc2ySnwKJycnCgtLSUmJoa8vDz5Oz3zzDNs2rSJSZMmERcXZ9EMWKsWgV/84heEhoZSX19PcHCwHNXXzs4OrVYrT4A4evQoR48epa6uDq1Wa3FvsCRJXLt2jZKSErKzs1m5cqW89tzV1VVeZlxQUKBISvLHkZubC7Rudll0dDTDhg0jPz+fF154QU43bmNjg729vaIRco1GIwkJCSQkJDy0z8bGBicnJzm6cFt2Ctra2uLl5YVOp1NUbOrq6li9erUi52opL7zwApWVlVy6dKnJ9tLSUu7fvy9nk/7RioC5DyA9PZ2bN29iY2NDQEAA48aNY/bs2XKwCjDduAcOHODSpUu8//77FBYWtjrUkyRJHD58mMjISAwGQ5Nqsr+/PwMGDECSJNLS0hTviHwU1dXVrRa2tLQ03n//fZKTk+UalBCCXr16MXnyZLy9vSkrK8PNzQ17e3tFY9c1xsnJCT8/PxwdHdm/f3+r1vO3FK1WS79+/fD19WX9+vXt8hu1FT//+c+pq6t7KFZCeXk5d+/eZejQoRbnI7RqEQgKCkKr1XLv3j0GDhxIQEAAv/nNbxg+fDjXrl0jNTVVrpa5uLjIMfqnT5/O8uXLSUxMfGRSz5ZgNBofmU8gIiICOzs7qqurLU5HbsbR0RF3d3c5QIrBYJA7nAB++ctfotVqWzX2fP36dT744IMm23r27MmCBQuYPn06YBr2WrJkCVu2bJGDVig9th4cHEx4eDiVlZWKZ416EBcXF7kjra1ErT3p2rUrer2+yQhOcXExZWVlDB482OImj1WLgJmIiAg+++wzQkNDyc/PZ/PmzezcuZN///vfcrolf39/+vbtS3R0NGPHjuXdd9/Fy8uLuLg4izu8zFln+/Xrx7Rp09Bqtezbt8/i4BxeXl6EhYUxYMAABg8ejLe3N2CKlrN7924OHz5Mfn4+Tk5O1NbWKpJAQ6PRMHfuXBYuXIi9vT137tzBwcGBSZMmERkZSUZGBjt27GD79u2KPa2dnZ0ZO3YsgwYNIjs7m6Kioja9MXv16kV4eDhCCH7605+yY8cOxQOAginBiTkxqBCCsLAweeRFSVxdXQkMDGwiAl5eXri7uysSzMSqRSA5OZmIiAh0Oh29evViw4YNpKSkcOzYMcrKypocm5+fT35+PikpKURHR/O73/2OhQsXcuXKlVZNtDGHxx4+fDg9e/bEw8OD0aNHM3z4cOzs7Pj666+fKN/bg9jY2DB//nzmzZtHjx495PkHZiIiInjuuefYuXMnQUFBZGRk8K9//avV9swIIXB0dKSwsJCMjAySkpIIDg7m+eefJzAwkHHjxhEWFoZer+fjjz9+qJxbg06nY8iQITg6OpKRkdHq2tmTYH46du3aVZH03Q+ee/78+fTr14/w8HB5JMDHxwedTgeg2MKgrKysJs1eM66urjg6OioS19CqRWDTpk0UFBQwaNAgUlJSSEtLe+xFaTAY2L9/P126dGH16tXMmzePkydPPlFG2v79+zN79mzCw8MJDg5Gp9Ph6Ogo36iSJLUq9VhjQkJCmDNnDj4+Ply4cIFTp05RUFCAm5sbQ4YMYeDAgUyZMoWIiAg8PT3ZuXOnIrkCDQYDn3zyCUlJSdy8eZPc3Fzc3d1JSUkhLCyMWbNm0b9/f9544w0yMzMVER4zhYWFHDp0yOKaWUuQJInq6mqSkpIUC2oKpt9t+fLljB8/nu7duzd73Ny5c+WHRFFREcePH2+VvRUrVjBjxgyuXLnyyP1Xrlz5cYtASUkJW7duZffu3XLnjqurq3zjNIfBYOD48eMcO3aM6dOn4+3t3SIR0Gg0hIeHs3btWgYOHIhGo5E7zZTOoLts2TI8PT1ZsmQJBw8e5NatW/zwww9oNBq6detGeHg4f/nLX+SxfaWGpMwjH42XUt++fZvDhw9z/PhxCgoKiIuLQ6fTMXDgQEWmv+p0OoKDgyktLaWkpKRdAroAci4AJYfzxowZw4wZM7CzsyMnJ4eXXnqJUaNGybkiGh9nXgty9OjRVovAqVOnyM3Nbfbh9+2331pcnla/irCurk4WAL1eT0JCAn/4wx/kjp/mKCsr4+bNmzg4OMgRYx7HkCFD+OKLL4iMjKSqqop169bx4osv8tlnnz309Hqc/ccREBDAe++9x8aNGzl//jzFxcVUVVVx584dioqKePbZZ/Hw8ABM1c9XX32VqKgoi2z+N+rr67l79y7ffPMNp06dwtHRkREjRlg8hKjRaAgLC6Nv375yJp22xN7eHn9/f/R6PW5ubjg7Oysq4Fu3bqW+vp7KykrefPNNsrOzcXd3l38rMN245slrHh4eFtXgJEmitLT0ISEOCQnBxsaGgoICi0Xa6kXAjBCCgIAAxowZg1arpaamptkfVwhBaGioPMWzJYXk4eFBbGwsfn5+pKenM3fuXN555x1qamro378/3bp1a3KuGTNmPOaMzePn54ezszOZmZkPdey4urry8ssvM2XKFGxsbNi4cSM5OTn4+PiwZcsWli5d2mq7LeHevXusW7dOnnNh6Q3k6OhIdHQ0dnZ2VFZWPjSvQgiBRqMhIiKCV199lejoaBwcHFptT6PR4O/vT48ePbC3t1c8cs+mTZuwsbFhzZo1JCUl0adPH6ZMmSKHrXvzzTeJiorC1dWV559/nnfffZctW7Yo6gOYHojl5eWKiIBVNwcaI4RAr9dTV1fH1KlTSUtLIysrixMnTjRRWltbW0JDQ5kzZw46nY69e/e2aFqlVqulR48eCCH405/+REZGBhMmTGDt2rWEhIRQXV1NfHw8I0aMkLMStRYXFxfs7e2xsbGRZ4BpNBp69+7NokWLiImJ4fTp0yxYsIDk5GSmTZvG8uXL8ff3JywsrNV2W0pqaqpiQ3jmdORGo5Gf/OQnREdHU1tbi5OTEzqdjmnTpuHv78/o0aOxsbHhxIkTFBcXt3ok5P79+xQXF1NeXo6Li4viTY8xY8Zga2tLVFQUvXv35rXXXgNMiUG2bdvGjh075FGVPXv2sGfPHkXtP4gStZxOIwJGo5GUlBRWrVrFggUL8Pb2JjAwkJiYmIeOra+vp6qqimPHjrFq1apmO1Ue/J/y8nIkSWLz5s0YDAZcXFxwcnLizp07rFy5kvj4eBYvXoyvry/fffddq7/LuXPnuH37NiNGjODy5cv4+PgwZcoUJk+ejLOzM9nZ2fzxj38kLS0NMKX0KikpITIyskXfxRI0Gg3z5s3DxcWF6upqi58yFRUVxMfHM3r0aPr27cvKlStZtGgRWq0WjUaD0WiksrKSmzdv8u233/Lpp58+cWagxtTW1pKVlcW1a9eoqanh4sWLii7BLisro0ePHnLTrL6+nurqaj766CPWrFmjyGhKS+nevTve3t4UFhZa9Dt1GhEAuHv3LqtWrSI5OZmxY8fSp08febzU3O6vr68nLy+PI0eOkJSU1OIY8bdu3WLt2rXExsbK8foKCwu5ePEi27ZtIyEhgdraWuLj4ykuLmbv3r0WfZfvv/+emTNnsnDhQuA/MRYPHjzIhg0bmtwIkiTJU6PbEgcHB372s5+xePFi7t69S2JioiKdaleuXGHjxo3Mnj0bT09PvLy8uH//PqWlpWRkZJCYmEhGRgYnT55U9MldVFSkeMaoFStW8Oc//5nu3bvTpUsXzp07x4EDB9i6dWu7CgCYOs7NmZUsoVOJAJiqeydOnODEiRPytoEDB8rtSIPB0KonidFo5ODBg3Tt2pVRo0bJIwx5eXmkp6fLF2dRURGffPKJxd9j1apV5OTkyJNaKioqSElJIT09nZKSksefoJUEBQXh5ubGhQsXmvRH+Pj4MHHiRFauXImbmxt79uwhNTVVEZuVlZXExcWxe/duBg8ezMiRI6mpqSE3N5d//vOfFs23eBS3bt1i165dbRKVafv27dTU1DBgwAD0ej179uzh3LlzHRL9ydyk+m8L0FpCpxOBR3Hu3DlFzlNVVcWWLVvapCPnQW7fvs1HH33U5nYa03iU4dixY1y+fJmqqip8fX0JCwsjKioKo9HIl19+yZo1a8jOzlbMtsFg4OrVq1y9erXNk6Hm5eWxYsWKNjv/rl27rCKbk16vx8fHh4sXLz49zQEVy5Akif3795OZmcn9+/cpKyujpqZGzna7d+9eysvLycjIsHgylErbcv36dfLy8hRZxaqKwFPGg00plc7J0aNHycnJUWSIUE1D9iOyBT++dF0/9tRgahqyRgghWjyzT7XVPKot1daTYhU1ASHELaAasHyFTPvSDdXntqaz+QvW67OfJEkPrXqyChEAEEKcfFRVxZpRfW57Opu/0Pl87jRrB1RUVNoGVQRUVJ5yrEkE4jragVag+tz2dDZ/oZP5bDV9AioqKh2DNdUEVFRUOoAOFwEhxEQhxEUhRI4QYklH+9McQoh8IcQ5IcQZIcTJhm3uQojDQojLDe9uHezjp0KIEiHE+UbbHumjMPFBQ7mfFUKEW5HPK4QQNxrK+owQYlKjfUsbfL4ohHiug3z2FUL8nxAiUwhxQQjxVsN2qy7rZjFHy+mIF2ALXAECAA2QAfTrSJ/+i6/5QLcHtq0BljR8XgKs7mAfRwHhwPnH+QhMAvYDAhgGfGdFPq8AFj3i2H4N14gW6N1w7dh2gM9eQHjDZ2fgUoNvVl3Wzb06uiYQCeRIkpQrSZIB+AqY2sE+PQlTgX80fP4HoExK5FYiSVIq8GC6neZ8nApskUx8C+iEEJYFTmwFzfjcHFOBryRJqpUkKQ/IwXQNtSuSJBVKknS64XMVkAV4Y+Vl3RwdLQLewLVGf19v2GaNSMAhIcQpIcQbDdt6SJJU2PC5COjx6H/tUJrz0drL/s2GqvOnjZpZVuezEMIfCAO+o5OWdUeLQGdihCRJ4UA08D9CiCYZISRTvc+qh1o6g48NfAwEAoOBQuC9jnXn0QghnIAE4H8lSWqSrqkTlXWHi8ANwLfR3z4N26wOSZJuNLyXADsxVUOLzdW6hve2CwnUeprz0WrLXpKkYkmS6iVJMgKf8J8qv9X4LISwxyQAX0qS9E3D5k5X1tDxIvA9ECyE6C2E0ACzgI4P2fIAQghHIYSz+TMwATiPyddXGg57BWjbkDmtozkfdwEvN/RcDwMqGlVlO5QH2ssxmMoaTD7PEkJohRC9gWAgvQP8E8DfgSxJktY12tXpyhro2NGBRj2nlzD19C7raH+a8TEAU690BnDB7CfgASQDl4EkwL2D/dyGqfpch6nd+XpzPmLqqf6wodzPAUOsyOfPG3w6i+kG8mp0/LIGny8C0R3k8whMVf2zwJmG1yRrL+vmXuqMQRWVp5yObg6oqKh0MKoIqKg85agioKLylKOKgIrKU44qAioqTzmqCKioPOWoIqCi8pSjioCKylPO/wPxmWRogV+wmAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "    5     8     7     2     4     9     5     8\n",
            "\n",
            "    6     3     2     7     0     4     8     6\n",
            "\n",
            "    2     8     0     5     6     1     5     1\n",
            "\n",
            "    8     9     5     4     9     1     6     1\n",
            "\n",
            "    2     2     7     0     4     1     3     0\n",
            "\n",
            "    6     0     2     4     8     9     5     6\n",
            "\n",
            "    1     0     4     1     2     2     8     7\n",
            "\n",
            "    5     2     2     3     2     1     2     7\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bKIUf3Lt9q96"
      },
      "source": [
        "class skynet1(nn.Module):\n",
        "  def __init__(self):\n",
        "    super(skynet1, self).__init__()\n",
        "    self.conv1 = nn.Conv2d(1, 8, 3) #1 input channel, 8 output channels(number of filters), 3x3 kernel, stride 1, no padding\n",
        "    torch.nn.init.xavier_uniform_(self.conv1.weight)\n",
        "    self.pool1 = nn.MaxPool2d(2, 2, 1) #2x2 filter, stride 2, padding 1\n",
        "    self.conv2 = nn.Conv2d(8, 16, 3) \n",
        "    torch.nn.init.xavier_uniform_(self.conv2.weight)\n",
        "    self.pool2 = nn.MaxPool2d(2, 2)\n",
        "    self.conv3 = nn.Conv2d(16, 128, 6)\n",
        "    torch.nn.init.xavier_uniform_(self.conv3.weight)\n",
        "    self.fc1 = nn.Linear(128, 64)\n",
        "    torch.nn.init.xavier_uniform_(self.fc1.weight)\n",
        "    self.fc2 = nn.Linear(64, 10)\n",
        "    torch.nn.init.xavier_uniform_(self.fc2.weight)\n",
        "    #self.dropout1 = nn.Dropout(0.1)\n",
        "    #self.dropout2 = nn.Dropout(0.5)\n",
        "    \n",
        "  def forward(self, x):\n",
        "    #x = self.dropout1(x)\n",
        "    x = F.relu(self.conv1(x))\n",
        "    #x = self.dropout1(x)\n",
        "    x = self.pool1(x)\n",
        "    x = F.relu(self.conv2(x))\n",
        "    #x = self.dropout1(x)\n",
        "    x = self.pool2(x)\n",
        "    x = F.relu(self.conv3(x))\n",
        "    #x = self.dropout1(x)\n",
        "    x = x.view(-1, 128)\n",
        "    x = self.fc1(x)\n",
        "    #x = self.dropout2(x)\n",
        "    x = F.relu(x)\n",
        "    x = self.fc2(x)\n",
        "    return x\n",
        "\n",
        "net = skynet1()\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n8IYVXC0UY89",
        "outputId": "4f555510-2a53-43b0-f3eb-612f9f465176",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        }
      },
      "source": [
        "params = list(net.parameters())\n",
        "print(len(params))\n",
        "for i in range(10):\n",
        "  print(params[i].size())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "10\n",
            "torch.Size([8, 1, 3, 3])\n",
            "torch.Size([8])\n",
            "torch.Size([16, 8, 3, 3])\n",
            "torch.Size([16])\n",
            "torch.Size([128, 16, 6, 6])\n",
            "torch.Size([128])\n",
            "torch.Size([64, 128])\n",
            "torch.Size([64])\n",
            "torch.Size([10, 64])\n",
            "torch.Size([10])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d6dtp2iQLmjR"
      },
      "source": [
        "criterion = nn.CrossEntropyLoss()\n",
        "optimizer = optim.Adam(params, lr = learningRate, betas = (0.9,0.999), eps = 1e-6)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VG1gE4SgRytq",
        "outputId": "451c8660-f8f8-4c62-d2de-fe86cf3f84f7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421
        }
      },
      "source": [
        "tloss_plt = []\n",
        "epoch_tloss_plt = []\n",
        "train_acc_plot = []\n",
        "correct_train = 0.0\n",
        "total_train = 0.0\n",
        "\n",
        "vloss_plt = []\n",
        "epoch_vloss_plt = []\n",
        "val_acc_plot = []\n",
        "correct_val = 0.0\n",
        "total_val = 0.0\n",
        "\n",
        "\n",
        "for epoch in range(epochCount):\n",
        "\n",
        "    running_tloss = 0.0\n",
        "    running_vloss = 0.0\n",
        "    avg_perbatch_acc_train = 0.0\n",
        "    avg_perbatch_acc_val = 0.0\n",
        "    start = time.time()\n",
        "\n",
        "    print('epoch', epoch + 1)\n",
        "\n",
        "    net.train(mode=True)\n",
        "    print('training cycle')\n",
        "    for i, data in enumerate(trainloader, 0):\n",
        "      inputs, labels = data\n",
        "\n",
        "      optimizer.zero_grad()\n",
        "\n",
        "      outputs = net(inputs)\n",
        "      loss = criterion(outputs, labels)\n",
        "      loss.backward()\n",
        "      optimizer.step()\n",
        "\n",
        "      perbatch_acc_train = 0.0\n",
        "      _, predicted_train = torch.max(outputs.data, 1)\n",
        "      total_train += labels.size(0)\n",
        "      correct_train += (predicted_train == labels).sum().item()\n",
        "      perbatch_acc_train = correct_train / total_train\n",
        "      avg_perbatch_acc_train += perbatch_acc_train / 2\n",
        "\n",
        "      running_tloss += loss.item()\n",
        "\n",
        "      if i % 50 == 49:\n",
        "         tloss_plt.append(loss.item())\n",
        "         \n",
        "      if i % 200 == 199:\n",
        "        print('[%d, %5d] train loss: %.6f, avg_train_acc: %.2f %%' % (epoch + 1, i + 1, running_tloss / 200, avg_perbatch_acc_train))\n",
        "        train_acc_plot.append(avg_perbatch_acc_train)\n",
        "        running_tloss = 0.0\n",
        "        avg_perbatch_acc_train = 0.0\n",
        "\n",
        "    net.train(mode=False)\n",
        "    print('evaluation cycle')\n",
        "    with torch.no_grad():\n",
        "      for i, data in enumerate(valloader, 0):\n",
        "        inputs, labels = data\n",
        "\n",
        "        outputs = net(inputs)\n",
        "        loss = criterion(outputs, labels)\n",
        "        \n",
        "        perbatch_acc_val = 0.0\n",
        "        _, predicted_val = torch.max(outputs.data, 1)\n",
        "        total_val += labels.size(0)\n",
        "        correct_val += (predicted_val == labels).sum().item()\n",
        "        perbatch_acc_val = correct_val / total_val\n",
        "        avg_perbatch_acc_val += perbatch_acc_val / 2\n",
        "\n",
        "        running_vloss += loss.item()\n",
        "\n",
        "        if i % 50 == 49:\n",
        "         vloss_plt.append(loss.item())\n",
        "         \n",
        "        if i % 200 == 199:\n",
        "          print('[%d, %5d] val loss: %.6f, avg_val_acc: %.2f %%' % (epoch + 1, i + 1, running_vloss / 200, avg_perbatch_acc_val))\n",
        "          val_acc_plot.append(avg_perbatch_acc_val)\n",
        "          running_vloss = 0.0\n",
        "          avg_perbatch_acc_val = 0.0\n",
        "\n",
        "    epoch_tloss_plt.append(running_tloss / epoch_count_train)\n",
        "    epoch_vloss_plt.append(running_vloss / epoch_count_val)\n",
        "    end = time.time()\n",
        "    print('time spent on epoch %d: %.2f s' % (epoch + 1,end - start))\n",
        "print('sector clear!')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "epoch 1\n",
            "training cycle\n",
            "[1,   200] train loss: 0.002859, avg_train_acc: 99.91 %\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-122-da72cd93b060>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     32\u001b[0m       \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcriterion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     33\u001b[0m       \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 34\u001b[0;31m       \u001b[0moptimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     35\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     36\u001b[0m       \u001b[0mperbatch_acc_train\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/autograd/grad_mode.py\u001b[0m in \u001b[0;36mdecorate_context\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     13\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0mdecorate_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     16\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mdecorate_context\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/optim/adam.py\u001b[0m in \u001b[0;36mstep\u001b[0;34m(self, closure)\u001b[0m\n\u001b[1;32m     98\u001b[0m                 \u001b[0;31m# Decay the first and second moment running average coefficient\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     99\u001b[0m                 \u001b[0mexp_avg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmul_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbeta1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgrad\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mbeta1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 100\u001b[0;31m                 \u001b[0mexp_avg_sq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmul_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbeta2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maddcmul_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgrad\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrad\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mbeta2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    101\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mamsgrad\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    102\u001b[0m                     \u001b[0;31m# Maintains the maximum of all 2nd moment running avg. till now\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JjqDroVrz3K2",
        "outputId": "ac7fab2c-6d96-433b-c3ca-ae8d58b4a07b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 609
        }
      },
      "source": [
        "print(len(tloss_plt))\n",
        "print(len(vloss_plt))\n",
        "plt.figure(figsize=[10, 10])\n",
        "plt.plot(tloss_plt)\n",
        "plt.plot(vloss_plt)\n",
        "plt.ylabel('loss')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'loss')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 115
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hDBnbQfNZphr",
        "outputId": "8947441c-672d-46a4-c5e4-e9c1662bea98",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 623
        }
      },
      "source": [
        "plt.figure(figsize=[10, 10])\n",
        "plt.plot(epoch_tloss_plt)\n",
        "plt.plot(epoch_vloss_plt)\n",
        "plt.xlabel('epoch')\n",
        "plt.ylabel('loss')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'loss')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 116
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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YU1IQbEZYUkUHKQAAiYTQFmMy05I1MDeTeW0AACQYQlsMCvh93B4FACDBENpiUKnfp827G1VX3+R0KQAAIEoIbTEoQDMCAAAJh9AWg0oKgttZMa8NAIDEQWiLQdnpKTQjAACQYAhtMYpmBAAAEguhLUaV+n3atKtRWxtoRgAAIBEQ2mJURzMCt0gBAEgMhLYYVeIPNiMsrSK0AQCQCAhtMcqbnqIBuZlaWkNoAwAgERDaYliwGYE9SAEASASEthhW6veqeuc+bd/T7HQpAAAgwghtMYxmBAAAEgehLYaxnRUAAImD0BbDvOkpKurZTUvoIAUAIO4R2mJcwO/j9igAAAmA0BbjAn6fqnfu0w6aEQAAiGuEthhXSjMCAAAJgdAW4wIFhDYAABIBoS3G+bqlqF9ONzpIAQCIc4S2OFBKMwIAAHGP0BYHAn6fqnbQjAAAQDwjtMWBjmYENo8HACB+EdriQMDvlSQ2jwcAII4R2uJA926p6puTQTMCAABxjNAWJ2hGAAAgvhHa4kTA71Pl9r3atbfF6VIAAEAEENriBM0IAADEN0JbnGBnBAAA4huhLU70yExVYY8MQhsAAHGK0BZHSv0+OkgBAIhThLY4EvD7tGHbXu3aRzMCAADxhtAWRwKhZoRljLYBABB3CG1xpKODlHltAADEH0JbHMnJTJW/O80IAADEI0JbnAn4vTQjAAAQhwhtcabU79P6bXu1u5FmBAAA4gmhLc50NCMw2gYAQHwhtMWZ0v0dpLsdrgQAAIQToS3O9MxKU4EvnWYEAADiDKEtDgXYGQEAgLhDaItDpX6f1m7do3qaEQAAiBuEtjgUKAzNa6thXhsAAPGC0BaHSukgBQAg7hDa4lBuVpryaUYAACCuENriVMDvI7QBABBHCG1xqtTv07qte9TQ1Op0KQAAIAwIbXEq4PfKWmkZo20AAMQFQluc6tjOilukAADEB0JbnOqVna7e3jQ6SAEAiBOEtjhWSjMCAABxI6KhzRgzyRiz0hhTYYy57xCvpxljXgm9PtcYUxQ63tMYM9MY02CMmXLQez4IXXNR6KtXJD9DLAuEdkagGQEAgNgXsdBmjEmS9Jik8yWNkHSVMWbEQafdIGmHtXawpIckPRA63ijpp5LuPczlr7HWnhT6qg1/9fGh1O+TtdLnm9gZAQCAWBfJkbZRkiqstWuttc2SXpY0+aBzJkv6Q+j5a5LOMcYYa+0ea+0sBcMbuqhjZ4QlVdwiBQAg1kUytPklbTzg+6rQsUOeY61tlbRLUs9OXPv3oVujPzXGmHAUG496edPVK5tmBAAA4kEsNiJcY60tlTQu9HXtoU4yxtxsjCk3xpTX1dVFtUA3oRkBAID4EMnQVi2p7wHfF4aOHfIcY0yyJJ+kbUe6qLW2OvRYL+lPCt6GPdR5T1lry6y1ZXl5eV36APEg4PdpTV2D9jbTjAAAQCyLZGibL6nYGDPAGJMq6UpJbx10zluSvh16fqmkGdZae7gLGmOSjTG5oecpki6UtDTslceRUr9P7VZaXkMzAgAAsSw5Uhe21rYaY+6QNF1SkqRnrbXLjDE/l1RurX1L0jOSXjDGVEjarmCwkyQZY9ZL8kpKNcZ8U9JESRskTQ8FtiRJ70t6OlKfIR6UFn6xM0JZUY7D1QAAgK6KWGiTJGvtNEnTDjr2swOeN0q67DDvLTrMZUeGq75E0NubrrzsNOa1AQAQ42KxEQHHqNTvo4MUAIAYR2hLAAG/TxW1NCMAABDLCG0JoKMZgZ0RAACIXYS2BBDweyWxMwIAALGM0JYA+njTlZuVqiXVjLQBABCrCG0JwBijAM0IAADENEJbgij1+7S6tl77mtucLgUAAHQBoS1BBDqaETZzixQAgFhEaEsQpf7gzgjcIgUAIDYR2hJEvi9dPTNT6SAFACBGEdoSREczAttZAQAQmwhtCSTYjNCgxhaaEQAAiDWEtgQS8PvU1m7ZGQEAgBhEaEsgpYU0IwAAEKsIbQmkwJeunMxU5rUBABCDCG0J5ItmBG6PAgAQawhtCabU79XqLfU0IwAAEGMIbQmm1O9Ta7vVis31TpcCAACOAaEtwZQUBJsRmNcGAEBsIbQlmMIeGereLUVL2RkBAICYQmhLMMYYlbIzAgAAMYfQloACfp9WbalXUyvNCAAAxApCWwLqaEZYSTMCAAAxg9CWgEr9NCMAABBrCG0JqLBHhnwZKWxnBQBADCG0JSCaEQAAiD2EtgQV8Pu0cjPNCAAAxApCW4Iq9fvU0ma1anOD06UAAIBOILQlKJoRAACILYS2BNU3J9iMQGgDACA2ENoSlDFGAb+XDlIAAGIEoS2BdTQjNLe2O10KAAA4CkJbAiv1+9Tc1q5VW9gZAQAAtyO0JTCaEQAAiB2EtgTWL6ebstOTCW0AAMQAQlsCM8YoUOCjGQEAgBjQqdBmjPm+McZrgp4xxiw0xkyMdHGIvNJCn1ZsqldLG80IAAC4WWdH2r5rrd0taaKkHpKulfSriFWFqAnQjAAAQEzobGgzoccLJL1grV12wDHEsI5mBG6RAgDgbp0NbQuMMe8qGNqmG2OyJXE/LQ70z+mm7DSaEQAAcLvkTp53g6STJK211u41xuRIuj5yZSFaPB6jEr9XS6p3O10KAAA4gs6OtI2VtNJau9MY8y+S/q8khmbiRKnfp8837aYZAQAAF+tsaHtc0l5jzImS7pG0RtLzEasKURXw+9Tc2q7VWxqcLgUAABxGZ0Nbq7XWSposaYq19jFJ2ZErC9FEMwIAAO7X2dBWb4y5X8GlPt42xngkpUSuLERTUc9MZdGMAACAq3U2tF0hqUnB9do2SyqU9GDEqkJUeTxGJQVeQhsAAC7WqdAWCmovSvIZYy6U1GitZU5bHOloRmilGQEAAFfq7DZWl0uaJ+kySZdLmmuMuTSShSG6Sgt9ampt1+pamhEAAHCjzq7T9n8knWqtrZUkY0yepPclvRapwhBdgVAzwpLqXRqe73W4GgAAcLDOzmnzdAS2kG3H8F7EgAE9M5WZmkQHKQAALtXZkbZ3jDHTJb0U+v4KSdMiUxKcEGxG8BHaAABwqc42IvxI0lOSTgh9PWWt/UkkC0P0Bfw+LacZAQAAV+rsSJustVMlTY1gLXBYaaFXjR+3a03dHg3tw9rJAAC4yRFDmzGmXpI91EuSrLWWGetxpPSAZgRCGwAA7nLE26PW2mxrrfcQX9kEtvgzIDdL3WhGAADAlegAxX5J7IwAAIBrEdrwJQG/T8trdqut/VB3xQEAgFMIbfiSUr9P+1ratKaOnREAAHATQhu+ZH8zQhW3SAEAcBNCG75kYF6wGYF5bQAAuAuhDV+S5DEake+lgxQAAJchtOErAn6fltGMAACAqxDa8BUdzQhraUYAAMA1CG34itLCL3ZGAAAA7kBow1cMystSeoqH0AYAgIsQ2vAVHc0Iy6p3O10KAAAIIbThkEr9Pi2r2aV2mhEAAHAFQhsOKeD3aU9zm/7PG0u0aONOWUt4AwDASclOFwB3+voJ+fpkzTa9vrBaL83bqEF5mbr4lEJdfIpf+b4Mp8sDACDhmEQYQSkrK7Pl5eVOlxGTdje2aNpnmzR1YZXmr98hY6TTB+XqkpF+nVfSR91Syf0AAISTMWaBtbbsK8cJbeisDdv2aOrCar2+sEpVO/YpMzVJF5Tm6+JTCjV6QI48HuN0iQAAxDxCG6EtbNrbreav366pC6s0bclmNTS1qrBHhi4+2a+LTylUUW6m0yUCABCzCG2EtojY19ym6cs2a+rCKs2q2CprpZH9e+iSUwr19RPy5ctIcbpEAABiCqGN0BZxm3c16i+fVmvqwipV1DYoNdmjiSN665KRhRo3OFfJSTQrAwBwNIQ2QlvUWGv1WdUuTV1YpbcW12jn3hblZafpmycV6JKRhRrWx+t0iQAAuBahjdDmiKbWNs1cUaepC6s0c0WtWtutSgq8uuSUQk0+qUA9s9KcLhEAAFchtBHaHLetoUlvLa7R1IVVWlq9W8keo/FD83TJKYWaMLyX0pKTnC4RAADHEdoIba6ycnO9Xl9Ypb98Wq3a+iYN65Otv955hlKY9wYASHCHC238hoQjhvbJ1v0XDNcn903QL74V0IrN9XpzUY3TZQEA4FqENjgqOcmjq0f10/B8r347s0JtbFAPAMAhEdrgOGOM7pwwWGu37tHbSzY5XQ4AAK5EaIMrTCrpo+JeWZoyY7XaGW0DAOArCG1wBY/H6I4Jg7VqS4PeXb7Z6XIAAHAdQhtc48ITCjQgN1OPzqhQInQ1AwBwLAhtcI0kj9Ft4wdpWc1uzVxZ63Q5AAC4CqENrvKtk/0q7JGhR/7BaBsAAAcitMFVUpI8um38IC3auFMfV2xzuhwAAFyD0AbXuXRkofp40/XIjNVOlwIAgGsQ2uA6aclJuuWsgZq3brvmrmW0DQAAidAGl7pqVD/lZqXp0RkVTpcCAIArENrgSukpSbr5zAGaVbFVCyt3OF0OAACOI7TBta4Z3V89uqVoCqNtAAAQ2uBemWnJuuGMAZqxolZLq3c5XQ4AAI4itMHVrjutSNnpyYy2AQASHqENruZNT9H1pxXpnWWbtXJzvdPlAADgGEIbXO+7ZwxQZmqSpsxktA0AkLgiGtqMMZOMMSuNMRXGmPsO8XqaMeaV0OtzjTFFoeM9jTEzjTENxpgpB71npDFmSeg9jxhjTCQ/A5zXvVuqrh1bpL99VqM1dQ1OlwMAgCMiFtqMMUmSHpN0vqQRkq4yxow46LQbJO2w1g6W9JCkB0LHGyX9VNK9h7j045JuklQc+poU/urhNjeOG6C0ZI9+O3ON06UAAOCISI60jZJUYa1da61tlvSypMkHnTNZ0h9Cz1+TdI4xxlhr91hrZykY3vYzxuRL8lpr59jgbuLPS/pmBD8DXCI3K01Xj+qvNxZVq3LbXqfLAQAg6iIZ2vySNh7wfVXo2CHPsda2StolqedRrll1lGsiTt1y1kAleYwe/5C5bQCAxBO3jQjGmJuNMeXGmPK6ujqny0EY9Pam64qyvnptQZVqdu5zuhwAAKIqkqGtWlLfA74vDB075DnGmGRJPklH2iG8OnSdI11TkmStfcpaW2atLcvLyzvG0uFWt5w1UNZKT37I3DYAQGKJZGibL6nYGDPAGJMq6UpJbx10zluSvh16fqmkGaG5aodkrd0kabcxZkyoa/Q6SW+Gv3S4VWGPbrrklEK9NH+janc3Hv0NAADEiYiFttActTskTZf0uaRXrbXLjDE/N8ZcFDrtGUk9jTEVku6WtH9ZEGPMekm/kfQdY0zVAZ2nt0v6naQKSWsk/T1SnwHudPvZg9Ta1q6nP1rrdCkAAESNOcLAVtwoKyuz5eXlTpeBMPrhK4v0ztLNmvWTs9UzK83pcgAACBtjzAJrbdnBx+O2EQHx7XtnD1Zja5uembXO6VIAAIgKQhti0uBeWbqgNF/Pz96gnXubnS4HAICII7QhZt1x9mA1NLXquU/WO10KAAARR2hDzBqe79W5I3rr2VnrVN/Y4nQ5AABEFKENMe2uCcXa3diq52dvcLoUAAAiitCGmFZa6NP4oXl6ZtY67W1udbocAAAihtCGmHfnhMHavqdZf5pb6XQpAABEDKENMW9k/xydNqinnvznWjW2tDldDgAAEUFoQ1y4c0Kx6uqb9Mr8jU6XAgBARBDaEBfGDMzRqUU99MSHa9Tc2u50OQAAhB2hDXHBGKM7JhRr065GTV1Y5XQ5AACEHaENcePM4lydWOjTbz+oUEsbo20AgPhCaEPcMMbozgnF2rh9n95aVON0OQAAhBWhDXHlnOG9NDzfq8dmVqit3TpdDgAAYUNoQ1wJjrYN1tqte/T2kk1OlwMAQNgQ2hB3JpX0UXGvLD02o0LtjLYBAOIEoQ1xx+MxumPCYK3cUq93l29xuhwAAMKC0Ia49PXSfBX17KZHZ6yWtYy2AQBiH6ENcSk5yaPbzx6sZTW7NXNlrdPlAABw3AhtiFvfOtkvf/cMPfKPCkbbAAAxj9CGuJWS5NFt4wdp0cad+rhim9PlAABwXAhtiGuXlRWqjzddj8xY7XQpAAAcF0Ib4lpacpJuOWug5q3brrlrGW0DAMQuQhBPImsAACAASURBVBvi3pWn9lNuVqqmzKxwuhQAALqM0Ia4l5GapJvGDdRHq7fq08odTpcDAECXENqQEP5lTH/16JaiR2cw2gYAiE2ENiSEzLRk3XDGAM1YUaul1bucLgcAgGNGaEPCuO60InnTk3XPq4tVV9/kdDkAABwTQhsShjc9RU/8y0hVbt+rK56arc27Gp0uCQCATiO0IaGcNjhXz98wSrW7m3T5k7O1cftep0sCAKBTCG1IOKcW5ejFG0dr174WXfHkbK3busfpkgAAOCpCGxLSiX2766WbxqixtV2XPzlbq7bUO10SAABHRGhDwhpR4NWrt4yRkXTlU3PoKgUAuBqhDQltcK9svXrLWGWkJOnqp+ew+C4AwLUIbUh4RbmZeuWWMeqRmap/+d1c9igFALgSoQ2QVNijm169Zaz6+NL17d/P00er65wuCQCALyG0ASG9vel65ZaxKuqZqRueK9f7y7c4XRIAAPsR2oAD5Gal6eWbx2hYfrZu/eMCvf3ZJqdLAgBAEqEN+Iru3VL1xxtH66S+3XXnSwv1l0+rnC4JAABCG3Ao3vQUPX/DKI0Z2FN3v7pYL82rdLokAECCI7QBh9EtNVnPfudUjR+Sp/tfX6JnZ61zuiQAQAIjtAFHkJ6SpCevLdOkkj76+d+W67cfVDhdEgAgQRHagKNITfZoytUna/JJBfr1Oyv1m3dXylrrdFkAgAST7HQBQCxITvLoN5efpLRkjx6ZUaHG1nbdf/4wGWOcLg0AkCAIbUAnJXmMfnXxCcpISdJT/1yrfc1t+veLSuTxENwAAJFHaAOOgcdj9G8XlQTnuv1zrRpb2vSrS05QEsENABBhhDbgGBljdN/5w5SekqSH/7Faja3t+s3lJyoliSmiAIDIIbQBXWCM0Q/PHaL0lCQ98M4KNbe26ZGrTlZacpLTpQEA4hRDA8BxuG38IP3bN0Zo+rItuuWFBWpsaXO6JABAnCK0AcfpO6cP0C8vLtWHq+p0/e/na09Tq9MlAQDiEKENCIOrRvXTby4/UfPWb9d1z87T7sYWp0sCAMQZQhsQJt86uVBTrjpZizfu1DVPz9WOPc1OlwQAiCM0IgBhdH5pvp5K8ejWPy7UFU/N1rjiPFkrWdngo7VqP+D7ditp//PQOQo+V8cx6YvXg6cfcG7wegW+dN1z3lB501Oc/PgAgAgyibAdT1lZmS0vL3e6DCSQWau36p4/L1J9Y6s8xshIMibYdWqMvnos9PyL4yb02iGOhZ57jGQUPFZR26B+Od305LUjVdw728mPDgA4TsaYBdbasq8cJ7QBsW/euu26/cUF2tfcpv+5/ERNCuQ7XRIAoIsOF9qY0wbEgVEDcvS3O8epuHe2bv3jQv36nRVqa4//P8gAIJEQ2oA40ceXrlduGaOrRvXVbz9Yo+ufm6+de2mGAIB4QWgD4khacpJ+efEJ+q9vlWr2mq26aMrH+nzTbqfLAgCEAaENiENXj+6nl28eq6bWNl3820/01uIap0sCABwnQhsQp0b276G/3nmGAn6v7nrpU/3i7eVqbWt3uiwAQBcR2oA41is7XS/eOEbXje2vpz9ap+uenadtDU1OlwUA6AJCGxDnUpM9+vnkgB689ASVb9ihi6Z8rCVVu5wuCwBwjAhtQIK4rKyvXrt1rKy1uuSJT/TagiqnSwIAHANCG5BATijsrr/eeYZO6ddd9/55sf7fm0vVwjw3AIgJhDYgwfTMStMfbxitG88YoD/M3qBrnp6r2vpGp8sCABwFoQ1IQMlJHv3fC0fo4StP0mfVO/WNR2dpYeUOp8sCABwBoQ1IYJNP8uv1205XarJHVz45Ry/Nq3S6JACRYq3U1uJ0FTgOhDYgwY0o8Oqvd5yh0QNzdP/rS3T/60vU1NrmdFkAwm3uE9L/lkqtLPsTqwhtANS9W6qeu36Ubh8/SC/Nq9SVT83R5l3McwPiyqp3pPpN0oZPnK4EXURoAyBJSvIY/XjSMD1+zSlaubleFz46S/PXb3e6LADh0NYqbZwffL76XWdrQZcR2gB8yfml+Xrje6crOz1ZVz01R8/PXi9rrdNlATgeW5ZILXuk5AxCWwwjtAH4iiG9s/XG907XWUPy9LM3l+neP3+mxhbmuQExq3JO8HH0zdK2CmnbGmfrQZcQ2gAcki8jRU9fV6bvn1OsqQurdNkTs1W9c5/TZQHoisrZUvd+0sjvBL9f/Z6j5aBrCG0ADsvjMfrhuUP09HVlWr91j77x6CzNW8c8NyCmWBscaes3VsoZKPUsllZPd7oqdAGhDcBRnTuit96443T5MlJ010ufal8zt0qBmLFjndSwJRjaJKl4orR+ltS8x9m6cMwIbQA6ZVBeln55cak2727U7z9Z53Q5ADprw+zgY0doGzJRamuW1v3TuZrQJYQ2AJ02ZmBPnTOslx7/YI127Gl2uhwAnVE5W8roIeUOCX7fb6yUmiWt4hZprCG0ATgmPzl/mPY0tWrKzAqnSwHQGZVzpL5jJE/oV35ymjRwfLAZgeV8YgqhDcAxGdI7W5eOLNQLszdo4/a9TpcD4Ej2bJW2rZb6jfny8eKJ0u4qqXa5M3WhSwhtAI7ZD88dImOk/3l3pdOlADiSjvXZOuazdSg+N/jIQrsxhdAG4Jjl+zL03TMG6I1FNVpavcvpcgAcTuVsKSlNKjjpy8e9BVKfUmkVoS2WENoAdMmtZw1S924peuCdFU6XAuBwKudI/pHBeWwHK54obZwr7dsR/brQJYQ2AF3iy0jRHWcP1kert+qj1XVOlwPgYM17pU2LvjqfrUPxeZJtk9bMjG5d6DJCG4Auu3ZsfxX2yNCv/r5C7e10oQGuUr1Aam/96ny2DoVlwaVAmNcWMwhtALosLTlJ904cqmU1u/XW4hqnywFwoMrZkozUd9ShX/ckSYPOCS790d4e1dLQNYQ2AMflohMLVFLg1X+/u1JNrWxvBbhG5Wypd4mU0f3w5ww5T9q7Var5NHp1ocsIbQCOi8djdN/5w1S1Y59emL3B6XIASFJbq7Rx3uHns3UYdI4kwy3SGEFoA3DcxhXnaVxxrqbMrNCufS1OlwOgdpnU3HD4+WwdMntKhacS2mIEoQ1AWPxk0jDt3Nuixz9Y43QpAPYvqnuUkTYpuPRHzUKpoTayNeG4EdoAhEXA79M3TyrQ7z9ep5qd+5wuB0hslbMlX1/JV3j0c4dMDD5WvB/ZmnDcCG0AwuaeiUNlrfTQe6ucLgVIXNYGR9o6M8omSX1OkLL6SKumR7YuHDdCG4Cw6ZvTTdeO7a+pC6u0cnO90+UAiWnnBql+U+dDmzFS8deCi+y2MSfVzQhtAMLqjrMHKzMtme2tAKccbpP4Iyk+T2raFdzWCq5FaAMQVj0yU3X7+MGasaJWc9Zuc7ocIPFUzpbSfFLe8M6/Z+B4yZNCF6nLEdoAhN31pxcp35euX/59haxleysgqjbMlvqNljzH8Cs+3Sv1HxvcHQGuRWgDEHbpKUn64blDtHjjTk1bstnpcoDEsWebtHXlsd0a7VA8UapdLu3cGP66EBYRDW3GmEnGmJXGmApjzH2HeD3NGPNK6PW5xpiiA167P3R8pTHmvAOOrzfGLDHGLDLGlEeyfgBdd8kphRraO1sPTl+hljb2NQSiomNOWpdCW+hXLbdIXStioc0YkyTpMUnnSxoh6SpjzIiDTrtB0g5r7WBJD0l6IPTeEZKulFQiaZKk34au1+Fsa+1J1tqySNUP4PgkeYx+cv5Qrd+2Vy/Nq3S6HCAxVM6WklKlgpOP/b25xVL3/oQ2F4vkSNsoSRXW2rXW2mZJL0uafNA5kyX9IfT8NUnnGGNM6PjL1toma+06SRWh6wGIIWcP7aXRA3L08Pur1dDU6nQ5QPyrnCMVnCKlpB/7e40J3iJd+6HU0hj+2nDcIhna/JIOvDFeFTp2yHOsta2SdknqeZT3WknvGmMWGGNujkDdAMLEGKP7LxiubXua9dQ/1zpdDhDfWvZJNZ92fn22QxlyntS6T1o/K3x1IWxisRHhDGvtKQredv2eMebMQ51kjLnZGFNujCmvq6uLboUA9jupb3d9vTRfv/torWrr+esdiJjqhVJ7S9fms3UoOkNKzuAWqUtFMrRVS+p7wPeFoWOHPMcYkyzJJ2nbkd5rre14rJX0Fx3mtqm19ilrbZm1tiwvL++4PwyArvvReUPV3Nquh99f7XQpQPyqnB187Hscs4lSMqQBZ0qrpwe3w4KrRDK0zZdUbIwZYIxJVbCx4K2DznlL0rdDzy+VNMMGF3V6S9KVoe7SAZKKJc0zxmQaY7IlyRiTKWmipKUR/AwAwqAoN1NXj+6nl+dv1Jq6BqfL+ZLGljYtrd7ldBnA8aucE1xQt1vO8V2n+Fxpx3ppW0VYykL4RCy0heao3SFpuqTPJb1qrV1mjPm5Meai0GnPSOppjKmQdLek+0LvXSbpVUnLJb0j6XvW2jZJvSXNMsYsljRP0tvW2nci9RkAhM9d5xQrPdmjB99Z6XQp+62ta9C3fvuJLnx0lu566VPt2su+i4hR7W3SxnnHN5+tQ/HE4CO3SF0nOZIXt9ZOkzTtoGM/O+B5o6TLDvPeX0j6xUHH1ko6MfyVAoi03Kw03XzmID30/iot2LBDI/v3cLSeNxdV619fX6LUZI+uG9tff5pbqfL12/Xfl5+o0wblOlobcMxqlwf3Dj2e+WwdevSX8oZJq6ZLY793/NdD2MRiIwKAGHXjuAHKzUrTL6d97tj2Vo0tbbr/9SX6/suLNDzfq2nfH6efTw5o6m2nKT0lSdf8bq7+a9rnamptc6Q+oEs6NonvH4bQJgVH2zZ8IjXVh+d6CAtCG4CoyUxL1g++VqzyDTv03vItUf/5a+oa9M3HPtZL8yp1+/hBevnmMcr3ZUiSTuzbXX+76wxdPaqfnvrnWk2e8rFWbuYXFmJE5WzJ65d8fY9+bmcUTwx2oq79MDzXQ1gQ2sJh4zxp6VSnqwBiwhWn9tXA3Ew98M4KtUZxe6s3Pq3WNx6dpdr6Jj13/an68aRhSk768j+B3VKT9YtvlerZ75Rpa0OTvjFllp6ZtU7t7XTRwcWsDW0SPya4QG449BsjpXmDXaRwDUJbOHz4a+kvt0lVC5yuBHC9lCSPfjxpqNbU7dGfF1RF/Oc1trTpvqmf6QevLFKgwKdpd43T+KG9jvieCcN6650fnKkzi3P1H39bruuenafNu1hjDi61a6NUXxOe+WwdklKkQWdLq99j6Q8XIbSFw7eelLJ7S69cI9VvdroawPXOK+mjU/p110PvrdLe5shtb1VR26DJUz7Wy/M36ntnD9KfbhqtPr7Obe+Tm5Wmp68r0y8vLtWCDTt03v/+U9OWbIpYrUCXdcxnC0fn6IGKJ0r1m6TNS8J7XXQZoS0cMntKV74kNe6SXvkXqbXJ6YoAV+vY3qq2vknPzloXkZ/x+sIqXTRlluoamvSH747Sj8776u3QztR51ah+mvb9cSrKzdTtLy7U3a8uUn0jS4PARSpnB29l9hoR3usOPjf4yNIfrkFoC5c+Aembj0tV86W372E4GTiKU4tydO6I3nriw7Xa1hC+P3T2Nbfpx68t1t2vLlbAH7wdetaQ49sVZUBupl67dazuOqdYb3xarfMf/kjz1m0PU8XAcaqcE9wFwZMU3utm95byTyK0uQihLZxKvimd+SPp0xekeU87XQ3gej+ZNFR7m1v16IzwrLy+eku9Jj82S39eUKU7JwzWn27s/O3Qo0lJ8ujuc4foz7eepiSP0ZVPzdaD01eouTV6zRTAV+zbEVyjLdy3RjsMOS84GLGXP1LcgNAWbuP/VRpyvvTOfdK6j5yuBnC1wb2ydcWpffXi3A2q3Lb3uK41dUGVLprysbY1NOv5747SPROHHvPt0M4Y2b+H3r5rnC4b2VePzVyjix//WBW17tqaCwmkcm7wMZxNCAcqnijZdmnNjMhcH8eE0BZuHo908VNSz8HSq9dJOzY4XRHgaj/42hAleYwefLdr21vtbW7VvX9erHv+vFgn9vVp2vfHaVzx8d0OPZqstGQ9cOkJevLakaresU8XPvqRnp+93rEFg5HAKmdLnhTJPzIy1y84WerWM7g7AhxHaIuEdK901UuSbZNevkZq3uN0RYBr9fam68YzBuqvi2v0WdXOY3rv6i31mjzlY01dWKW7zinWizeOUW9veG6HdsZ5JX00/QdnavSAnvrZm8t0/XPzVVvP0iCIoso5wWCVkhGZ63uSgg0JFe8H9zeFowhtkdJzkHTJs1LtMumN22lMAI7glrMGKiczVb/6+4pOj1b9uXyjvjFllnbsbdYL3x2tu88NjthFWy9vup67/lT9x+QSzV6zTZP+9yNNX8bSP4iClkapZmHk5rN1KD5X2rddqmYtUqcR2iKp+GvS1/5NWv6G9NH/OF0N4FrZ6Sm6c8JgfbJmmz5cVXfEc/c2t+ruVxfpR699ppP79tC0u8bpjGJnN3g3xujasUV6+64zVNA9Xbe8sED3Tf1Me5oitwYdoJpPpbbmyM1n6zD4HMl46CJ1AUJbpJ12lxS4VJrxn9LKd5yuBnCta0b3V7+cbvrV31eo7TDbRq3cXK+Lpnysv3xare+fU6w/3jhavaJ4O/RoBvfK1uu3na7bxw/SK+UbdcEjH2lh5Q6ny0K8qpwdfOw7OrI/J6NH8GcQ2hxHaIs0Y6SLHpXyT5Cm3ijVrXK6IsCVUpM9uve8oVqxuV5vfFr9pdestXp1/kZNfmyWdu5t0Ys3jNYPHbodejSpyR79eNIwvXLzWLW2WV32xGw99N4qtURxn1UkiMo5Uu7Q4ALvkVY8Udq0mF1/HEZoi4bUbtIVL0rJadLLV0n7jm2yNZAoLizNV6nfp9+8t0qNLcFJz3uaWnXPq4v146mfaWT/Hpr2/TN02mBnb4d2xqgBOfr7D8Zp8kkFevgfq3XZE7O1bitNSQiT9nZp45zIz2frUDwx+Lj6vej8PBwSoS1auveVrnhB2rE+OOJGFw7wFR6P0f3nD1P1zn16fvZ6rdi8WxdNmaU3FlXr7nOH6PnvjlavbPfcDj0ab3qKfnP5SXrs6lO0buseXfDwR3r4/dUqX79dTa38G4DjULciuHVipOezdehdInn90mqW/nBSstMFJJT+p0nn/1p6+25pxn8EmxQAfMlpg3N11pA8PfKPCv3Pu6vkzUjRH28crdMGuX907XC+fkK+RvbvoZ9M/UwPvb9KD70fvI16gt+nsqIclfXvoZH9e6hHZqrTpSJWdMxni9ZImzHBLtIlU6XWZimZ/1adQGiLtlNvkDYvkWY9JPUOSKWXOl0R4Dr3nT9MF02ZpdEDeuqhK05SXnaa0yUdtz6+dP3hu6O0raFJCzbsUPmGHSpfv13PzFqrJz4MNl4M7pWlU4t6aGT/HJ1a1EP9crrJGPfN24MLVM6WsvpIPYqi9zOLJ0oLngv+7IFnRe/nYj9CmxPO/3VwaPvNO6TcYin/RKcrAlxleL5Xc//1a+qekSKPC5sNjkfPrDRNLOmjiSV9JEmNLW36rGqX5q/frgUbdujtzzbppXkbJUm5WWmhENdDpxblaESBVykR2JoLMagyNJ8tmqF+wFlSUmqwi5TQ5ghCmxOSU6XLn5eeGh/cMeGmmVJWZLfdAWJNToLcKkxPSdKoATkaNSBHktTeblVR1xAMcet3aP6G7fr70mDHXkZKkk7s69OpRTka2b+HTunfQ970FCfLhxN2bpR2bZROuzO6PzctS+p/erAZ4bxfRPdnQxKhzTlZvaQrX5SenST9+dvSdW9KSfzjGzeslarmS/6y4H60QCd5PEZDemdrSO9sXTO6vyRpy+5Gla/fsX807rcfrFFbu5Ux0tDe2Tq1KEdlRT1UVpQjf/cIbWcE99jYsUl8lOazHWjIedI79wWb6qJ5axaSCG3OKjg5uIbb6zcF/yf4OrsmxI0Fz0l/+4E08T+j/9cw4k5vb7q+fkK+vn5CvqTgMiiLNu7cH+JeX1ilF+ZskCTl+9L3NzecPjhXg/IymRcXbypnS6nZUq+S6P/s4onB31er35NG3RT9n5/gCG1OO+FyafNn0iePSn1KpZHfcboiHK8926R//Hvw+cePSKfeGLnNnJGQMtOSdfrgXJ0eWq+uta1dKzbXq3z9dpVv2KH567brr4trJEn9crppwrBemjCsl0YPzFFacpKTpSMcKudIfU+Vkhz4Fd5zkJQzUFo1ndDmAEKbG3zt36Uty6S375Xyhjkz5I3wef//SU310gX/LU27V1rwB2nMrU5XhTiWnORRwO9TwO/Td04fIGutqnbs0wcrazVjRa1emlep5z5Zr26pSTp9cK4mDOuls4f2Uh9f7Kx5h5B9O4O/L0ZMdq6G4vOkBb+XmvcGF49H1BDa3MCTJF36rPT0BOmVa6WbP5B8fqerQldsnCd9+kLwluiom6Slr0sfPyyVXR/cEQOIAmOM+uZ007Vji3Tt2CLta27T7LVbNWNFrWauqNN7y7dIkkbke4MBblgvndS3uyu3BcNBquZLss7+cV98rjT3cWn9R8E5bogaQptbZPSQrnxJ+t050ivXSNf/nVtqsaatNbhwcnaBdNZ9wWNn/Uh64VvSp38MrtEHOCAjNUkThvXWhGG9Za3Vqi0NoQBXq8c/XKMpMyuUk5mqs4bk6exhvXRWcZ583WiMcqXK2ZInWfKPdK6GojOklG7BpT8IbVFFaHOTXsOki5+SXr5a+uv3pW89Gd01eHB8yp8JLpx82XPB1nhJGnh2sIN01v9Kp1xHhzAcZ4zR0D7ZGtonW7eNH6Rde1v04eo6zVxRqw9W1uovn1YryWM0sl8PnR2aCzekdxbNDG6xYXZwbc/UTOdqSE6TBo4PhjZr+T0VRYQ2txn2dens/yPN/IXU5wTptDucrgidUb9FmvGfwZA24ptfHDdGOuvH0p8ulxa/LJ1yrXM1Aofg65aii04s0EUnFqit3WrRxp2auSI4F+6Bd1bogXdWyN89Q2cPy9OEYb102qBcpafQzOCI1iapeoE7GgCKJ0orp0l1K4MDDogKQpsbjbs32FH63k+lXsOlwec4XRGO5r2fSq2NweaDg//qLJ4Y/Mv4o/+WTrzKmY4voBOSPEYjQ/ug3nveUG3e1aiZoWaGqQuq9cc5lUpL9ui0QT33z4Ur7MFE9KipWSS1NbmjWa343ODj6ncJbVHEbw838nikbz4hPXOu9Np3pZtmBNus4U7rZ0mfvRIM27mDv/q6MdKZPw7OVVz6mnTildGvEeiCPr50XTWqn64a1U+NLW2au277/lG4mSuXSW8u05DeWfra8N76zulF6pVNN2pE7d8kfqyzdUiSrzC4Ttzqd6XT73K6moRhrLVO1xBxZWVltry83Okyjt32ddLTZ0tZvaUb35fSsp2uCAdra5GeGCc175G+N/fw7e/t7dITZ0htzcHzPNxeQuyy1mpN3Z79AW7e+u1KS/boljMH6aYzB6hbKuMBEfGnK6VtFdKdLvl99v6/BdcY/fFaKd3ndDVxxRizwFpbdvBx9tdxs5wBwUntW1dLf7k1+Isf7jLncanuc+n8B468XpHHI515r7RttbT8jejVB0SAMUaDe2XppjMH6qWbx+i9H56pM4vz9ND7qzT+wQ/0yvxKtbXH/4BAVLW3SxvnuOPWaIfiiVJ7q7T2A6crSRiENrcbOD64FdKKv0kfPuB0NTjQrmrpg19JQyZJwy44+vkjJku5Q6V//jcBHHFlYF6Wnrh2pF67daz8PTL0k6lLdMHDH+mDlbVKhLs5UbF1lbRvhztujXYoHBUcYVv1rtOVJAxCWywYc5t04tXSh7+SPv+r09Wgw/T7JdsWHGXrDE9ScLStdnkwhANxpqwoR6/fdpoeu/oU7Wtp03d+P1/XPjNPy2p2OV1a7Ns/n81FI21JydKgc6SK9/hDNEoIbbHAGOnCh4KLKb5+S7DlG86q+Ie0/M1g80GPos6/r+Ti4L59/3wwuL4REGeMMfr6Cfl6/+6z9LMLR2hpzS5d+Ogs3fPqYm3atc/p8mJX5Rwps1fw3w83GXKe1LBF2rzY6UoSAqEtVqSkS1e8KGV0l56ZKM38pdTa7HRViam1SZr2Iyln0LF3TSUlS+PuCS7psmp6ZOoDXCA12aPvnjFAH/7obN08bqD+urhG4x/8QL9+Z4XqG1ucLi/2VM4OjrK5bSHbQedIMtLq95yuJCEQ2mKJN1+6dVZwtObDXwX3Kt30mdNVJZ6PH5G2r5EueLBr+4mecIXUvZ/0z18z2oa458tI0f0XDNc/7jlLkwJ99NsP1mj8gx/o+dnr1dLGLbVO2V0j7dzgrvlsHbLyJP8p/BEaJYS2WNMtR7rkaenKP0l7aoNLgsz8L0bdomXH+uAiuSMmd33R46QU6Yy7g7e518wIa3mAW/XN6aaHrzxZb91xuop7Z+lnb/7/9u47PMoqe+D495IEpEZ6L0F6lxJAukgRUUAQQUVWXcSCiv7UVVHXvquurroCFmwUBRtFBKnSpPfeQ0logUASUkiZ+/vjTEyEBBIyPefzPPMwmXnnnTsv78ycueWcHfT67zLm7TiR+8UK+xbAhm/c21Bf5Ivz2bKq20s+zxJOe+f5E2MKzJw6Ddr8VYNb4JHV0GSgrCr9vBsc1zkFbjf3OTBB0Otf+dtPi7ugVFVYqr1tqmBpVu1avhvRjgn3tsYYGDlpA4M/XcWmI2dzflB6Gsx/CaYMgl8eh92/eq7BvuDIaggpLqUNfVHdHoCVub6eFBMB3w+Hd8Lgsy4Qsdyzz+8FGrT5s2JlpMD8kO8gIRo+6waL39ReN3fZMxf2zoWu/4DQqvnbV3AR6DBa8i4dCvwPGqWyMsZwU6OKzBvdmTcHNCHidCIDxq3k0W83cuRM4l83jj8BE2+DlR9Bq/skcJn1mNT7LSiOrILqbXy3BF7l9mTwJAAAIABJREFUFrJIYp+HhkiTzsG8MTA2XCoyhD8o6VC+6QtT74YzBzzTDi/QoC0QNOgjvW7NBss8qc+6wrFN3m5VYElJhLnPQvkG0O4R1+yz5TCpdrH0HdfsTyk/ExxUiLvb1mTJM115/MY6LN51iu7vL+H12Ts5l5giPSefdJLPswGfwa0fwMAJUoFk5iN+30ttreVoTCIrD5wmJS2H4b3kWDi5wzfns2UoVEh62/Yvkl5Rd0lPhTWfwkfXw6qx0HQwPLZR5hePWgc3viSJfse2hd9ekEAuwPho2K7yrFgZGPAJNOoPvzwBn3eHjk9Cl2evbrK8+qsV78O5IzB8tsxJc4WQonDD4zB/jAx/+Op8FaXcrESRYJ7qWZ+729Xk/fl7+eqPA5RY/z+eYCqUvY5C986Eio1k4/L1JeH4nKdh3QQIH+HdxueStZbDZxLZfiyWbVGx7IiKY/uxWM4lykrauhVK8OaApoSHlfnrAyPXgXX4/udD3Z6weYq0t6aLA0xrYc8cWPCylPEK6yLnQOUsw8UhRSUP5vXD4Pc3YPU42PIddH0eWt/nus9tL9Pao4Eo6az8ytjyLVRoBP3GyuoeXxV/Ukqh5HfI0V3OHIBx7SQgHvi5a/edkgAfNIPKzWHYz67dt1L+KOks8dNGUPLQAmant+XD4k8wqncLbm1WhUKFnOkurJX5bYdWwMhlEsj5EIfDEnEmge1Rsc6LBGjxydILFRJkaFCpFE2qlqJJ1VCKFw7mP/P3EHk2iTtaVeP5Pg0pU7yw7GzxG7D8fXjuCBQp4cVXdQXJsfB2GHR4Am76p+v2e2wTzHsRDq+QijI9X5cA8UqpT05sg3kvQMQyKFdPgrzcPM5H5FR7VIO2QLZ3nvS6nT8FHUdDl3/4Tq9bcpxUBdg6Td5UJghueQ9aDfd2y/7KWph8O0Suh1HroWRF1z/Hiv9K4eW/L4ZqrVy/f6X8xfEtMG0YxEVBzzdYXmYgb83dw67jcTSvFsoLfRrStnZZ2Tb+JIxvLwt6/r4Iggt7pcnpDsvB6PPSgxYpwdnOY3GcvyABWuHgQjSsVJImVUNpWjWUJlVDqVexJIWD/zo7KSklnQ8X7WPC8oOUvCaYF/o0ZFCraphvboWU8/DgEs+/uLz66hZIPgcP/5H/fcVGwqLXYetUKFYOuj0PLf+Wt3l91spc5PkvSpqm2t2g11uZvbY+TIO2ghi0gXPC5gvSbV2+IfQf571et7QU2L8Qtn0vb6S0ZKkm0PSOzPQX4SPlTeUrE253zIAfhkPvt6HdQ+55jgvx8EFTqN4W7prmnudQypdZCxsnStLq4uXgjq+hejggQdH0TVG8N38Px2OTaV4tlPIli1CqaAjhF1Yx5MBzbK15H/uaPU2poiGEOi+ligYTWjSEoiFBGBf1rqSlO9gffV56zpy9aDuPx5GYkg7ANSGFaFi51J/BWZMqodStWIKQoNxPH999Io4x07ez4fBZ2tcqyZTTd1Co9f3QO58r1j3hjw9lCPPJnVc/cnIhHlZ8AKs+lvOi/SMy1eea0KtvV1oKrP9CakVfiIOWw6HbGMkx56M0aCuoQVuGvfOdvW4npfu663Oe6XVzOODoGgnUdkyXodtiZSVBcLPBUK2NdFenp8mbffVYqN0VBn0l8/S86cJ5+LgNFC8LI5a4N5Bc+g78/qYM9VRu7r7nUcrXpCTK/LTNU6QnZOAECdwukpyazpd/RLBsbzRxSWnEJqUSl5TK8+njGRK0hKEpL7LGNrzkcSFBhlLXZARyIVkCOwnqMu4L/ct9IZS8Jpioc0nsiIpjW1Qs24/Fsut4HMmpsmCgaEgQjavI8GZGL9p15YsTnIcALScOh+X79UeZPWcWkxnDzHr/ptcdD3JNSFC+9+1Wp3bJVJJbP4RWf8vbY9PTYNMkyTuacEp+zHd/WRKRu0pijKTIWjcBgotC5/+Dtg9LxSEfo0FbQQ/aQHrd5o+BTZNlFWS/ce4bjju1WwK1bT/IBP7gopJbrtlguO7GnCeFbpwEs5+Ea6vD0Knenasy/yVJM/DAgj9/9btN0jmZ21a7M9w52b3PpZSvOHMAvr9XVkd2eVamcBTKW2CSnhwPn3TCpqewu/9czjmKE5uUKkFdcuqf1zOCvLg/75PAL91x5e/AEkWCaVQlowdN/g0rV4KgQu6dH5Xw+/sUX/oqbZLHUaxsFV7v14TO9Xy3dwhr5XOsUlMY+m3uH7dvoQxhRu+C6u1ktMWdU0VO75PP971zJSi86VVoPMCn5rtp0KZBW6Z9CyVBZfxxWb3Y9XnX/NKIOw7bf4St30ttTVNIfjk3GywBW5GSudvPkdUw7R6p8TnwC6jXM/9ty6tTu+CTjtB8iCzk8ITFb0rKlodX+cWcC6XyZecsmPGI9GDfPgHq3nT1+4pcLzWZmwzM02Ihay0JKel/BnJZg7vYpFTKlyxC06qh1CpbPHMRhCd9dxdE72JlnwW8OGM7B08n0LdZZV7u24gKpXyvdwiA2U/Blqnwj4grj+ac3CHB2oHFUDoMerwKDW/zXPB0cInkezu53TPBYh5o0KZB218lx8qbZeNEWZHTfxxUu+T8yN1+dv0igVrEMsBClZYSqDW+/eon7p87ClOHwont0OM1uOExz72RrYWvb5EPlMc2ZDtU4xaJMTK3rV4vGPSlZ55TKU9LT5WFN6s+ls+Kwd+4Zghsyduw5C35odd0UP73523Wwju1of7N0H8cF9LS+WTJQcYu2U+RoEI827s+d7Wt6fbevjzbOw++HQzDpsuoSnbiT0pajk2ToUgp6WVtM8I7i0kc6dKOxW84h2UHy+rX0Gqeb0sWGrRp0Ja9/Qth1hMQf0wCo64vXLnXLS0F9i+QlZ97foP0C/IrqdlgOeHL1XFN21ISYMbDsHMmNB8KfT/wzNyDLdNg+oPQ97/Q+n73P19WC/4pk3kfXQvl63n2uZVyt7jj8ON9kuG/zQjo9abr5tamp8FXN0P0Hlm9eG111+zXW6L3wtg2cNvHkojbKeJ0Ai/O2MYf+8/QvPq1vDWgCY2r5GOSvqulJMLbtaDNA5cunkhJlKS4K/4L6SmSY6/zM96fvwyyAGL5+9I+U0i+Dzs84bU0Kxq0adCWs+RYGd/f+I3ks+k3TkqmZOVwSMmlrdNkRWXyOVmG3WSgBGtVW7mnJ8zhkCHDJf+SRQt3TnFP2o0MSedk8UFoNfj7wjzPr8m389HS29aoH9z+qWefWyl3OrgUfnpAvrhv+8g9vWExB6WCQuUWMHyW59+/rrThG5nGMmrDJT+ErbXM3HyMN37dSUxCCvd1COOpHvUoXsRHVt1PHghnD8lIBcjn+NZpsOg16SBoeKvMIyt7nVebma1zR6QnePtPUKISdH8Jmt8lVR88SIM2DdqubP8imPW4vKnaj4JuL0hB3m3fw7YfIfYohBSDBn0lUKvd1XNZpnfOhOkPQdHSMORbqNLCPc8z51lY+xmMWOy91CjzxsDq8fDYeihT2zttUMpVHA6pKPL7m1C2LgyeCBUauO/5Nk2GmY/KtIoOT7jvedxt+sNSV/OZ/Tn+II5NTOXtebv5ds0RKodewyu3NaZX40oebmg21nwGc5+RElNxx2QB3PEtMhze602oeYO3W3hlR9fCb89D1Hqpd9vrLQjr5LGn16BNg7bcSY6DBS/Bhq9lrsGFOEl8e92NEqjV7+O9rNzHt8LUuyDhNPQfK718Lt3/Fqnb2uo+6Pu+a/edF/EnZAVWs8HQ72PvtUOp/EqMkR9b++bJ+/XWj9z/+WEtfD9Mpm6MWOS/KXQ+bAEVG8OQKVfcdMPhs4yZvo3dJ+K5qWFFXrmtEdVKF/NAI3MQEwEftZBpM2cjoFQ1mSfWZJDHe6zyxeGQHreFr0BcpHRY9HjNIz2EGrRp0JY3B36X4dIa7WVBga8kITwfLStLj66WuRBdX3DNh4DDAV/2lA+bx9ZLj543zXkG1n8Jj29ybZ4iX5eSIPOdIpZJGZo2f5eVx8r/RG2UxNRxx2VuU5u/e24xUWIMjGsvCVlHLpW6lP4k/gS8Vx96vgk3jMrVQ1LTHXy5IoIPFu4D4MkedbmvQ1ieEvu61PiOMkTa6Ulo94j//R9klZokC2eWO+fiPbIKytV161Nq0KZBW+BIuwC/PiXDIA36woBP8//rfeNEmPUY9B8PLe5yTTvzIzZSfmm3HCYLIgJVWooUmI5YJpfIdeBIhUIhkoQ54ZSUN/P0ghB19ayFDV/B3H9A8QqyOvRqVqbn1/5FUoIufCT0ecfzz58fGZVYrqK0XeTZRP45cweLdp+iQaWSvHV7U1rW8MKP0MQYCdK9/QPYleJP4Nj6A4VuGOX2HyA5BW0+MmtRqTwILiIrqio2kRJdX/SEod9B6ZpXt7/EGFm1WaO9rFL1BaHV4Pp7JDDt/AyUquLtFrmGIx2Ob84M0g6vgrQkwMg8xfaPQFgXqNFOtv/hb5Js+fwpSbzqQ8kvVTZSEiRP19apUOcmuP1z760MrNNdst2vGS+FwvOTB87TjqyShOSVm+X5odVKF2PC8NbM23GSV2btYOD4ldwVXoNnezUgtJiH5iCDb6wIdaFDpxOYtDqG+TsbM69NOsUKeyd80p425d/2L5IUAoWCYfAkqNUh7/v45QmpxPDQcplD4ivOHob/tZRhpZvf9nZrro61kqg4I0g7tAIuxMp95RtC7S4Q1lkmJmf3izw9VRbHbPlW5hre8p5/rwgMZKf3SbH36N2SsLvzM96fv5SaBJ91g6QYSVpdvKx325Nbn3aWOcV/m52v3Zy/kMb78/fy9coIyhQvzEt9G3Fb8youq8Ua6NIdlt93n2Li6sMs2xtNcCFDryaVeLlvIyq6ObmxDo9q0Ba4Tu+H74bIhNdb3stbzbvIDTChu8y56P2W25p41WY8KlUmntjq3lQnrmKt/D9ELJMUD4eWQ0K03Fc6TAK0jEuJCrnf58JX4I8PJFXA7RN8slZggbZjOswcBUGFpXZone7eblGmE9vg8xult+3Oyb7fW3shHv5dAzo9DTeOcckut0fF8sL0bWyNjKVzvfK8P7g55Up4oPa0n4pJSGHauqNMWXOYyLNJVChZhLva1mBoeA23B2sZNGjToC2wJZ2THFD7F0L4g9DrX1cu8O5Ih8+7SXbuUevgmlKeaWtenDkAH7eG9o9Czze83ZrsxR2DiOUQsVSCtdijcnuJSpk9aWGd87+gYtU4mPc81OwgaV+KXpv/tqv8Ob1PAurdsyWP4h1fez2TfLZW/k8qwNz2P2h5r7dbc3kHFsOkAXDPzy4NftMdlsmrD/PWnF2UKV6Y8fe0okV1fQ9ltfnoOSauOsTsrcdJSXPQNqwM97avRc/GFT2+oEODNg3aAp8jHRa8LKt8wrrIF8jl5lWs/RzmPO37ZW9+GiFfiqO3ea6k1uUkxmQOd0YsgzOyWo2ipaFWJwnQaneFsnVc36ux7UdJIVGuHtzzE5Sq7Nr9q9yJPykJrzdOlFWBHUZLTjRvlCHKDYcDJt4mK1ofWu6bSV0z/P4WLHsXnjuS+3rNebA9KpaRkzYQHX+B1/s35s42BWh1ejaSU9P5ZcsxJq0+zNbIWIoXDmJAy6oMa1eL+pVcf/xzS4M2DdoKjk1TYPZo+cU/dCqUr3/pNuej4eNWksPp3lm+PWQSvQfGtoWOT0quI2+J2ihB8aHl8nfhEjIXLczZm1axiWfmMB1YLHOnipaBYT+7fem9yuJCvPRarfxYyte1vh86P+s7KYEuJzYSxt8gCX7vn3flnnhv+eZWqVIzcpnbnuJsQgqPT93E8n2nGRpeg1dua0SR4II1V/RoTCKTVx9m2vqjnEtMpU6FEgxrV5PbW1al5DUeXLCRAw3aNGgrWI6skXxuqUlSfL1ez7/eP/1h2PYDPLzSP2p8/vA32LcQRm/1/KqsuGNSfmbLd1K6LPxBuK4bVLnecxUxLnZsE0weBNYBd//gnZQSBUl6qiTcXvq2zFFs1B+6v+zbPVbZ2f4T/Hg/dHkOuj3v7dZcKj1V5rO1vNfti4/SHZb/zN/D+CUHaF79Wj65pyWVQ/04l1ouOByWpXujmbjqEEv2RlPIGHo2qsiwdjVpf11Zn1qgoUGbBm0FT2wkfDdUJiL3eBVueFx61A6vlMLSHZ+Em17xditz5+QO6SXw5JdNSoL0qvzxITjSoN3D0On/JGGpLzhzQPJwnT8lpZHq9vB2iwKPtbBrFix8FWIOyHzCHq/5d5D880j5wXb/b1A93Nut+auoDbJo4o6vofEAjzzl3G3HefqHLRQtHMTHd7WkXW0/WWGbB+cSU/h+/VEmrz7CkZhEypUowl3h1RnatobPBqoatGnQVjClJMCMR2DnDGg2RFaXftFDhnkeXQOFi3u7hbk39W4Zmhy9zb2Bk8Mh9WYXvip1aBv1k+LOZcLc95xXK/4kTBkoaUVu+xha+EievexYCwcWQcIZ6fn19aSjh1fKcHjkOijfQM6Ber18eypBbiTHSrb+QkEyv80N88au2qqxknvyqd0ena+5/1Q8D07awOEzibzQpyH3d6jlU71OV2tbZCwTVx1i1pZjXEhz0KZWaYa1r0XvxpUoHOzb5bQ0aNOgreCyFpa+A0veghIV4fxJWfrf8FZvtyxvjm2S2qg3vig5sNzh8CpZoXlsE1RuIeWHfL24c3IcTLtbFkX0eC2zR9VXWCsraxe/IQEQSMWH67pJb0r9Pr61EjZ6j6wI3TMHSlaGbi9A87t8dw7Y1Ti8Er7qIwmsfam+79S74eR2eGKLx586PjmVp77fwoKdJ+nXogr/ur2p1xLI5kdyajpzth1n4qrDbD56jqIhQfS/virD2tWkURUfzBCQAw3aNGhTO2fKysOwzrJAwZe+2HNrymD54h+9zbWFt88ekqoQO2fIF3X3f0KzO72fHDW30i7A9JGSL6ydMz2KL7T9yGoJ1g4th1JVocuzsmBj50wpVRR7xBnA3egM4G72XgAXd1xWhG6aJItMOo6WigKFvVh43J0Wvgor3vedH3DWwrt1ZJh/wCdeaYLDYRm/9AD/mb+H+hVL8umwVtQs6x+jEZFnE5my5gjT1h0lJiGF2uWKc0+7mgxsVY3Qot5fWJBXGrRp0KZAhqaKlJBSWP4ocr0kA+7xmqRYyK/kOFj+HqweByZI9tnhcf8aNs7gcMBvz8HaT6HpHdBvnPdSUBzbBIvfhP0LpP5m56eh5fC/JgW2Vlbk7vhZgrjYoxLA1emeGcB5Yv5gchys/EiG5tJTpQJH52f8p3rA1UpLkakS547IgiRvp485vV9WtN/6Yd4ShLvB0r3RPP7dJqy1fDj0errVz2UibA+z1rImIoYvVkSwaNdJALo3rMi97WvS4bpyFCrkhz/MnTRo06BNBYqJ/Z1DKFuvvhfEkS45tn5/U1YDNhsiqwFDq7q2rZ5mrfSeLHpNeq8GT/TsnKWTO2UYftcvMmetw2gIH3HlINhamYS+Y7r0wMVFSnWB67IGcC4e2klLyVwRmngamgyEG1/yzbmL7hK9V0pG1WwPd//k3d7ZjZNg1ih4dG32aYo87MiZREZO3sDuE3E8eVM9RnWr4zNBUGq6gznbjjNheQTbomIpXSyEoeE1uLtdTape65sLC/JKgzYN2lSgyFj92vvfsqIzrw78DvPGwKkdUL2dlO+q2sr17fSmjZOkpmylpnD3j+7PI3bmgAwtbvtRhhZvGCX/N1fTU+ZwZAZwO2dAXBQEFcnsgavXO38BnLWy70WvScmxWp2k57Zqy6vfpz9bNwF+/T+4+R1oO9J77ZjxqMwjfPagz0zdSEpJ5/mftzJj8zFualiR9+9sTikv5jCLT05l2rqjfPXHIaLOJVG7XHEe6BTGwJbVuCYksPLMadCmQZsKJF/3lRJCT2zJfR3O0/uklM/e36SkVI/XJN+Wj3xBuNyeuZLfrlQVKQnkjh6kc0elp2rzt9Iz1nakDDG7KpeewwFR6zN74OKPOQO4m5w9cL3z1pN4aIWsCI3aABUayTlQ56bAPQdyw1r49k5ZLPLgEqjQ0Dvt+KilrNId+q13nj8H1lq+WXmIN37dRfUyxfh0WCvqVfTsittj55L46o8Ipq49SvyFNMLDyjCiU226N6jgM71/rqZBmwZtKpAcXCplefr8R4bfLicxRgKLdRMguCh0/j+ZYF4Qiq4fWQPfDpaA6p6foHIz1+w3/oTMBdzwtfzd+n7o+BSUrOia/WfH4ZBFKBk9cPHHJYCr28PZA9cr5wDu1C5ZEbr3N1kQ0W0MNB8iaS+U5Pob114W4YxY5Pk5r+dPwX/qQo/XZU6pD1pz8AyPfruJxJQ03h3UnFuauX8O4PaoWD5ffpBftx7HAn2aVmZEpzCaVfOh1dZuokGbBm0qkFgLX/aC2Ch4fFP2E+7TUyVQW/JvuBAnWda7jYESvjmp2G1O7ZYkvMlxMGSKFLG/Wgln4I8PpG5teoqkjOjyrOeLpDscELk2swfu/AkIviazB65eb1lwE3dM5i1u/hYKl4ROT0Lbh6ReqPqrPXPhuyGSMqbn65597p2z4Pth8MBCqN7Gs8+dBydik3l4ygY2HTnHyC61eaZnfYJdXEjd4bAs2XuKz5YdZPXBGIoXDmJIeA3u61CLaqUDdCVzNjRo06BNBZp9CyWx7K0fQavhmbdbKz0q81+EM/uleHuvt6BiY2+11PtioyRwizkIAz6FJrfn7fHJsbK6ctU4SDkPzQZDl3/4RhknhwOOrnH2wM3MDOBq3iB592w6tBkhK1g9XQLN3/wyWnpPh8+S1ECe8tsLsP4LeO6o91Y859KFtHRe+2UnU9YcoUOdsvxvaEvKFM9/m5NT05m+KYoJyw9yIDqByqHXcF+HWgwJr+HVeXTeokGbBm0q0FgLn3eT4c/HNkgd0BPbJaN6xFIpjN3zjcDIYu8KiTFS1uzoGuek8wev/JiUBFjzCfzxESSfk+oQXV+ACg3c396r4UiX3HA7Z8C+BVJu6sYXoXQtb7fMP6QkyGrS1CR4+A/PVa34rBuEFIP7fvXM87nA9+uO8uLM7ZQvUYRP7mlF02pXl54mJiGFSasOM2n1IU6fT6FxlVKM6FSbW5pVJsTFvXj+RIM2DdpUIMoY0unxuvSqbZoERUpB1+ehzQPeK+juq1KTpGD4njnQ6WkJaLILaFOTYf2Xkj4kIRrq9pLKAFVaeL7NyrOiNkr+tkb9YdAXeXusI10SPadfkJQq6RdkmsIlt6VkXk+7IKX2Oj4J3V9yz2tyky1Hz/Hw5A2cTkjhzf5NuKN19Vw/9kD0eb5YEcFPGyK5kOagW/3yjOhU2+cKt3tLTkGb/9WoUEplqtdb0loseAkKBUP4SJljpcNg2QspCoMnwa9PwvL/SEmzvh9klmhKS4HNk2Hpu7JSM6wzdJsCNdp6t93Kc6q2hK7PSSWLxNNgCuUQeGVzm3Vc/fNed6PrXoOHNK9+Lb881pHHvtvEMz9uZUvkOV7u2zjHup7WWtZGxPD58ggW7T5JSFAhbr++Kg90DKOuh1ek+isN2pTyZ8ZAn/ck0LjhcShX19st8n1BwTIPsERFWPYuJJyGgZ/DrtmSa+3cYagWLqWE8rNoQfmvjk9BzCE4vkXmmAUVkUTWQaWdfztvC876b25vKyK3/3lbEVn166cLhMqWKMLE+8N5Z94ePlt2kJ3H4hh/TysqlspcnZ6W7mDu9hNMWH6QLZGSDPexbnUY1r4W5Uv6aXUaL9HhUaVUwbX2c5jzjPTApSZCpWZSFaBuD50HqFQezd56jGd/3EqxwsGMu7sljaqUYuraI38mww0rV5wHOkoy3KKFNd3M5eicNg3alFLZ2TFDUqOEj4AGt/pGoXml/NSeE/GMnLSeyLNJFA0JkmS4tcrw905h3NSwYsAmw3U1Ddo0aFNKKaXcLjYplZdnbsdaeKBjGM2rB34yXFfThQhKKaWUcrvQoiF8OOR6bzcjIOk4gFJKKaWUH9CgTSmllFLKD2jQppRSSinlBzRoU0oppZTyAxq0KaWUUkr5AQ3alFJKKaX8gAZtSimllFJ+QIM2pZRSSik/oEGbUkoppZQf0KBNKaWUUsoPaNCmlFJKKeUHNGhTSimllPIDGrQppZRSSvkBDdqUUkoppfyAW4M2Y0xvY8weY8x+Y8xz2dxfxBgzzXn/GmNMrSz3Pe+8fY8xpldu96mUUkopFYjcFrQZY4KAscDNQCNgqDGm0UWbPQCctdbWAf4LvO18bCNgCNAY6A2MM8YE5XKfSimllFIBx509beHAfmvtQWttCjAV6HfRNv2Ab5zXfwS6G2OM8/ap1toL1toIYL9zf7nZp1JKKaVUwHFn0FYVOJrl70jnbdluY61NA2KBspd5bG72qZRSSikVcAJ2IYIx5kFjzHpjzPro6GhvN0cppZRSKl/cGbRFAdWz/F3NeVu22xhjgoFQ4MxlHpubfQJgrf3MWtvaWtu6fPny+XgZSimllFLe586gbR1Q1xgTZowpjCwsmHXRNrOA4c7rg4DF1lrrvH2Ic3VpGFAXWJvLfSqllFJKBZxgd+3YWptmjBkFzAOCgC+ttTuMMa8B6621s4AvgEnGmP1ADBKE4dzue2AnkAY8aq1NB8hun+56DUoppZRSvsJIx1Zga926tV2/fr23m6GUUkopdUXGmA3W2taX3F4QgjZjTDRw2M1PUw447ebn8Bd6LDLpsRB6HDLpscikxyKTHguhx0HUtNZeMiG/QARtnmCMWZ9dVFwQ6bHIpMdC6HHIpMcikx6LTHoshB6HywvYlB9KKaWUUoFEgzallFJKKT+gQZvrfObtBvgQPRaZ9FgIPQ6Z9Fhk0mORSY+F0ONwGTqnTSmllFLKD2hPm1JKKaWUH9CgLY+MMb2NMXuMMfuNMc9lc38RY8w05/1rjDG1PN9K9zPGVDfG/G6M2WmM2WGMeSKbbboaY2KNMZudl5e0RI7LAAAG1UlEQVS90VZ3M8YcMsZsc77GSxICGvGR85zYaoxp6Y12upsxpn6W/+vNxpg4Y8zoi7YJ2HPCGPOlMeaUMWZ7ltvKGGMWGGP2Of8tncNjhzu32WeMGZ7dNv4kh2PxrjFmt/M9MN0Yc20Oj73s+8nf5HAsXjHGRGV5H/TJ4bGX/b7xJzkch2lZjsEhY8zmHB4bUOdEvlhr9ZLLC1KF4QBQGygMbAEaXbTNI8AnzutDgGnebrebjkVloKXzeklgbzbHoisw29tt9cCxOASUu8z9fYC5gAHaAWu83WYPHJMg4ASSa6hAnBNAZ6AlsD3Lbe8AzzmvPwe8nc3jygAHnf+Wdl4v7e3X44Zj0RMIdl5/O7tj4bzvsu8nf7vkcCxeAZ6+wuOu+H3jT5fsjsNF978HvFwQzon8XLSnLW/Cgf3W2oPW2hRgKtDvom36Ad84r/8IdDfGGA+20SOstcettRud1+OBXUBV77bKZ/UDJlqxGrjWGFPZ241ys+7AAWutu5Na+wxr7TKkHF9WWT8PvgH6Z/PQXsACa22MtfYssADo7baGekB2x8JaO99am+b8czVQzeMN84IczovcyM33jd+43HFwfkcOBr7zaKP8kAZteVMVOJrl70guDVT+3Mb5ARULlPVI67zEOQR8PbAmm7vbG2O2GGPmGmMae7RhnmOB+caYDcaYB7O5PzfnTaAZQs4fwAXhnMhQ0Vp73Hn9BFAxm20K4vlxP9L7nJ0rvZ8CxSjnUPGXOQybF6TzohNw0lq7L4f7C8o5cUUatKl8McaUAH4CRltr4y66eyMyPNYc+B8ww9Pt85CO1tqWwM3Ao8aYzt5ukDcZYwoDtwE/ZHN3QTknLmFlnKfAL9c3xowB0oApOWxSEN5P44HrgBbAcWRosCAbyuV72QrCOZErGrTlTRRQPcvf1Zy3ZbuNMSYYCAXOeKR1HmaMCUECtinW2p8vvt9aG2etPe+8PgcIMcaU83Az3c5aG+X89xQwHRnWyCo3500guRnYaK09efEdBeWcyOJkxlC4899T2WxTYM4PY8zfgL7A3c4g9hK5eD/5PWvtSWtturXWAXxO9q+xQJwXzu/J24FpOW1TEM6J3NKgLW/WAXWNMWHO3oQhwKyLtpkFZKz+GgQszunDyZ855yB8Aeyy1r6fwzaVMubzGWPCkfMtoAJYY0xxY0zJjOvIZOvtF202C7jXuYq0HRCbZcgsEOX4q7kgnBMXyfp5MByYmc0284CexpjSzmGyns7bAooxpjfwLHCbtTYxh21y837yexfNaR1A9q8xN983geAmYLe1NjK7OwvKOZFr3l4J4W8XZCXgXmRVzxjnba8hH0QA1yDDQvuBtUBtb7fZTcehIzLUsxXY7Lz0AR4CHnJuMwrYgax6Wg3c4O12u+E41Ha+vi3O15pxTmQ9DgYY6zxntgGtvd1uNx6P4kgQFprltgJxTiCB6nEgFZl/9AAyn3URsA9YCJRxbtsamJDlsfc7PzP2A/d5+7W46VjsR+ZoZXxeZKyyrwLMcV7P9v3kz5ccjsUk52fBViQQq3zxsXD+fcn3jb9esjsOztu/zvh8yLJtQJ8T+bloRQSllFJKKT+gw6NKKaWUUn5AgzallFJKKT+gQZtSSimllB/QoE0ppZRSyg9o0KaUUkop5Qc0aFNKKTcxxnQ1xsz2djuUUoFBgzallFJKKT+gQZtSqsAzxtxjjFlrjNlsjPnUGBNkjDlvjPmvMWaHMWaRMaa8c9sWxpjVzmLf0zOKfRtj6hhjFhpjthhjNhpjrnPuvoQx5kdjzG5jzJSMihBKKZVXGrQppQo0Y0xD4E6gg7W2BZAO3I1Ud1hvrW0MLAX+6XzIROAf1tpmSFb7jNunAGOttc2BG5Ds7wDXA6OBRkh29w5uf1FKqYAU7O0GKKWUl3UHWgHrnJ1gRZHC7g4yi1hPBn42xoQC11prlzpv/wb4wVkbsaq1djqAtTYZwLm/tdZZV9EYsxmoBaxw/8tSSgUaDdqUUgWdAb6x1j7/lxuNeemi7a625t+FLNfT0c9dpdRV0uFRpVRBtwgYZIypAGCMKWOMqYl8Pg5ybnMXsMJaGwucNcZ0ct4+DFhqrY0HIo0x/Z37KGKMKebRV6GUCnj6i08pVaBZa3caY14E5htjCgGpwKNAAhDuvO8UMu8NYDjwiTMoOwjc57x9GPCpMeY15z7u8ODLUEoVAMbaq+3xV0qpwGWMOW+tLeHtdiilVAYdHlVKKaWU8gPa06aUUkop5Qe0p00ppZRSyg9o0KaUUkop5Qc0aFNKKaWU8gMatCmllFJK+QEN2pRSSiml/IAGbUoppZRSfuD/AaFnAUAbNv3EAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6-nc5JohfQV_",
        "outputId": "f64459be-a597-4cc8-afc1-2f82f0af8741",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 840
        }
      },
      "source": [
        "dataiter = iter(testloader)\n",
        "images, labels = dataiter.next()\n",
        "\n",
        "showimg(torchvision.utils.make_grid(images))\n",
        "print('ground truth labels:')\n",
        "for k in range(batchrow):\n",
        "  print(' '.join('%5s' % classes[labels[j]] for j in range(k * 8, (k + 1) * 8)),end='\\n\\n')\n",
        "  k += 1\n",
        "\n",
        "outputs = net(images)\n",
        "\n",
        "_,predicted = torch.max(outputs, 1)\n",
        "print('predicted:')\n",
        "for k in range(batchrow):\n",
        "  print(' '.join('%5s' % classes[predicted[j]] for j in range(k * 8, (k + 1) * 8)),end='\\n\\n')\n",
        "  k += 1"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "ground truth labels:\n",
            "    7     2     1     0     4     1     4     9\n",
            "\n",
            "    5     9     0     6     9     0     1     5\n",
            "\n",
            "    9     7     3     4     9     6     6     5\n",
            "\n",
            "    4     0     7     4     0     1     3     1\n",
            "\n",
            "    3     4     7     2     7     1     2     1\n",
            "\n",
            "    1     7     4     2     3     5     1     2\n",
            "\n",
            "    4     4     6     3     5     5     6     0\n",
            "\n",
            "    4     1     9     5     7     8     9     3\n",
            "\n",
            "predicted:\n",
            "    7     2     1     0     4     1     4     9\n",
            "\n",
            "    5     9     0     6     9     0     1     5\n",
            "\n",
            "    9     7     8     4     9     6     6     5\n",
            "\n",
            "    4     0     7     4     0     1     3     1\n",
            "\n",
            "    3     4     7     2     7     1     2     1\n",
            "\n",
            "    1     7     4     2     3     5     1     2\n",
            "\n",
            "    4     4     6     3     5     5     6     0\n",
            "\n",
            "    4     1     9     5     7     8     9     3\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LulKEYBohY5-",
        "outputId": "beb99237-1a18-457b-aae6-8d0f6fa16eef",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "loss_test_plt = []\n",
        "correct_test = 0\n",
        "total_test = 0\n",
        "test_acc_plot = []\n",
        "avg_test_acc = 0.0\n",
        "avg_loss_test = 0.0\n",
        "\n",
        "with torch.no_grad():\n",
        "    for data in testloader:\n",
        "        images, labels = data\n",
        "        outputs = net(images)\n",
        "\n",
        "        _, predicted_test = torch.max(outputs.data, 1)\n",
        "        total_test += labels.size(0)\n",
        "        correct_test += (predicted_test == labels).sum().item()\n",
        "        avg_test_acc = 100 * correct_test / total_test\n",
        "        test_acc_plot.append(avg_test_acc)\n",
        "\n",
        "print('accuracy %.2f %%, error rate %.2f %%' % (avg_test_acc, 100 - avg_test_acc))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "accuracy 98.80 %, error rate 1.20 %\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HEO4P0zjSo8M",
        "outputId": "3a59b214-5652-4b09-c38f-1a1ee07d5650",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 609
        }
      },
      "source": [
        "print(len(train_acc_plot),len(val_acc_plot),len(test_acc_plot))\n",
        "plt.figure(figsize=[10, 10])\n",
        "plt.plot(test_acc_plot,label='test set accuracy')\n",
        "plt.plot(val_acc_plot,label='validation set accuracy')\n",
        "plt.plot(train_acc_plot,label='train set accuracy')\n",
        "plt.legend(loc='upper right')\n",
        "plt.ylabel('accuracy,%')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'accuracy,%')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 117
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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qvvZzAsycObPlcXl5OTNnzmTHjh0Eg0H69+8PxFYD+Pvf/96yX05ODgDHH388L7/8MkOGDCEUCjF8+PD9vteB0L+yneCzcjcXP7qY3DTXPsPY/pw2vCcvX3M0i9bX8Gm5myWbanlt5U5mTxnAjScehc1mdWLl0tUEIgHq/fXUB+qpbKqk3FvOVs9WKhorcAfdNAQaaAg2xMJWyLvX8amOVLKTsslKyiIzKZPuqd1Jc6aR4cqgILWAnmk96ZHWg5zkHDKcGaS50nDanAn4pCLfTueffz7PPfccO3fubAkITz31FFVVVSxduhSn00lRURF+v7/N5964cSNz587l448/Jicnh0svvRSfz0e0eQ3rYDjaEoaMAY8vhC8YxheMUO0NEDUGrz+Myxdih9uHzeFke70Pu83CFzY0NvqpaPC3HB+NGm751e9YumQxb73+H+4cMZJ//fcdGnwhbrrzt0w8bmpsv+b3/3jR+3gDYdbu44K2F//xNFu3V/DkS2/hdDo5dUIJlXUemoJh/KEoNd5gy3kAIlHDqu1uUlICOO02HDaLSk+QQCjSUnON20skEqbaGyAUiRLAQUWDH2Pgyqtmc+111zPzvOksWLCgpWvz61x++eX8+te/ZvDgwXFb2FyBLM42VHm56M8fkZXi5G+zxrcpjO0yrFcWw3rF1uUKhCPMefFz7n97PV/s9HDPzDIykvUL81AXjASpD9TjDrip8lWxpWELmxs2U+4pp8ZfQ62/ljp/HU3hpr2OTbYn0yOtB9lJ2XRL7caA7AGxwOXKJNOVSUFaAX0y+lCYXkiGK2Mf7y4ie9lPS1ZnmjlzJldccQXV1dUsWLCAaNRQW1dPXn43wsbGG/+dz+bNm2nwBaltDGKAygY/VQ1+whHD1tomho8az0OPPkG/4rGsXvU5K1asYEtNIzs8QRxJKez0Wazc+iUvvfIqA0vGsnKbG2dyKss37KB32AVAOBplS10T/YtH8+gNV3Pe92djjOGlf7/Ab+57CK8/TNRAXVOQSNTg8YVoCoSpaPBjWRY2C2yWRfnmjQwrG8XwEaN55603qN65g+OnnshzT/6FE084gSSXk43rv6RXr14U9cgj4vfRNzcVW/M5YueySCXAwH69KO2XxzsLFrC9fCuDemRSOP10pk+fzm/m/Iyc3Fwqq6rJyMph8vHH89qzT/C9K3+IPxiiwd1ARk4+1VWVbCzfSVJKKq+88gqTJk9le72PYDgW6ioa/FhY1NXXk9c91gL5+OOPt/x8TjzxRO6///6WsXR1dXXk5OQwbtw4tm7dyrJly1ixYkVc/iwokMVRNGq46bnYD+ZvV4ynMA5XSyY57PzmnOEM65XJnS+t4qz7P+DemWWU9M7u8Lmlcxhj8Ia8VDRWsN69ni/rv2R9/Xoqmyqp8cXC1r6CVoojhT4ZfchPyadvZl9yknLISc5paeHqltKt5XXLUkupSFdgmluHIsYQjZo97vnK831vd+T1pbrOTXZ+ATXRVKq3uxk19Qwe/+sFlJQMZ2jJCPoPHMS2ej9kNGEM7GzwU9MYJGIM3kCY8y76HrdefxUnHz2KgYOOorh0BA67jeLhpRQPL+X0Y8bQq3ch48ZPICPZQY/MZL53+RX86NLz6dmzF6/85w0cNhv989MYN+RoNn/vMr4//SQAZv9gFueffCybNm0iyWFjWK8sjDEUZCbjtYcZXpjV6t+jW2ffybp16zDGMHXqVE6dPJ5TjhtHQ/V2zjh+EsYYunXrxgsvvMCx40eT5HJw3IQxXHrppa0Gxl9y8f9wxhlnMKK0lNGjRzN48GAAhg0bxq233spxxx2H3W5nxIgRPPbYY8z74x+YNWsW/3jqCex2O/PmzaNswgTuvGMOF591AoWFhZQNH0p+ehJDe2aSmuRgQLf0lvp/+6tfMOvSC8nJyeH4449n48aNANx2223Mnj2b4uJi7HY7c+bMaemKnTFjBsuXL2/pxuwoy+zR5HeoGT16tPmmuVYOpsc+2MgdL63i7vNLOXdU77if/8MNNVz/zHIqPQGuOX4gs6cMxGnXhbKJ4A16+aLuC9bUrmGrZyuVTZVUNVVR5aui2ldNIBJo2ddm2eiT0YeeaT3JTc4lNzm3VdDKTc6lb0Zfuqd2V9AS6USRqMHjD+ENhLEsi7ptGxl01ODm8Uu7A1MkGnve6j5Ky+PWIQsM3/x71LIs7JaFzRZrSYo9jrUK7X68+3WbZWHf47HNZmG3dj+3mluTJHGmTZvG9ddfz9SpU/f5+urVqxkyZEirbZZlLTXGjN7X/mohi5OttU38v/9+wXGDunHOyMJOeY/xR+Txn2uP5Y6XPufe+et4a00lv54+nOLCrE55v28zX9jHRvdGdjTuYGfjTnY27qSiqYKKxgoqmirY5t3Wsm+qI5Xuqd0pSC2grHsZ3VK6kZ+ST35KPkdkHUH/rP4kO5IT+GkOYcZAJAghH4QDEAlANAzRSOzeGMDE7qPh3bdICKKh3ftFI2Aiu1+LhGLnioSazxvc4/g9jomGYo9P/jW40hL9bXzrRaMGTyBMgy+E2xeiwR+iwRduvg/R4A833zdvb3kce80bCLc63yNn9iS6s2G/77lXOLJZuGw27M49QpJtd8Cy77m/zWoVtuTwUF9fz9ixYyktLf3aMNYeCmRxYIzhZ//6DAv49TnDO/V/LVmpTu6ZWcaJQwu4/YWVnPHH97lgbF9uOukoctJcnfa+hyNjDA3BBrZ5t7HDu4Ptjdv5sv5LVlavZH39eiIm0rKvy+aiIK2A7qndKelWwvSB0xmSN4TBuYPpltLt8P2faiQEAU/sFvRC2L9HiGkOPZFg8zZ/8y0AwSYINcWCVKgptj3k2x2MTLR18InsOqYxdr8rfIXbPpC5Qyw72Bxgs4PNCXZH7PnUOQpkcRSKRHH7QtQ3hXD7gs33sef1vhDupiD1X3nubg5h0f00RlkWZCQ5yExxkpnsJDPFQd/c1FbPM5OdpCXZsbDIcdZTmJOyR4jaO3wpSMlXZWdns3bt2rifV4FsPxZvrGXu61+0PLdbFj855ShG9G3dX/z8J9t4/8tqfnF2cVzGjR2I04b3ZNLAfO6dv5YnFm3mlRU7uGdmKccP1vQY+xI1UdbWrWXJziV8WvVpywB6T6j11T3ZSdkMyx/GlD5TGJw7mJ7pPemR2oPc5NyDE7qMaW69CcZuQW8sDPkbINgIYV8s2AQb97h5YiEm2BgLNOHA7sAUCTaHpnAsCAHQ/DnCvuYQ5ANMLIRY9tjjcDAWhvYIpW1mc4IzFZzJ4EgGZ0psm2Xtfi+7MxZ4XOmQXhDb35Ua29+RBPak3cc7kpv3d8buLVvsXFjN53TEXrPZ995v1/vZHLFtdtfue0dS7N7maD6fQOwKvPqmILVNscHktY1B6hqD1DaGqGsKUtP8PBiJthzjstvITnWSk+oi1WWnwR/eK3C5faG9Wqr2ZFmQmewkO9VJdoqTrFQX/XJTyU51kpXS+tYqaKU4SXc52nQl+urVXvLSkjr0PYnEiwLZflgW7Pl3+7Ntbu5/ez1/umR3968xhkfe28iQnplcOLbvQa0vK8XJnDOGccHYvtzwj+X84MllPHzxKCYf1b1N54lGDe9/WU1umiuh3Z/RqOnwtB7eoJfNns1scm9ii2dLbD4uTznr6tfhCcbCV6+0XvTP7k9pt1J6Z/SmML2Qnuk9KUwrJCsp68CClzHNQakemmqab7Xgq4uFqF1hqaUVKRwLSwFvc0tToLkLLbpH8Gp+7QDGo7TiSI613rjSwJkWCzB2V+zmzGwdTGLFNx+X0hx+UmKvRXeFNmt3SHGmQFJG7OZKaw5Fe4SYluDj2iM4JcWClV1XA++PMYb6phCVngBVngDeQIisFBd56S6yU5w0+ENUeYLUNAao9gSo9gap9gao9gao8gap8QZwN4VaLv232SzGFuVySnEPThxaQHaqi6ZgmJrm42q8sXPVN8VamQyxwehuX4gab5C65uBV1xSk1hvEs5/QlJnsIDfNRXaqi2Tn7nGsjcEw2+p91DUFaQpGWoJVVoqTHpnJHNUjg+wUVyxsNW/PTo193thjJxnJTuya3ke+hRTI9mNMUS5/nzWh5fn//WcND7+7gYoGPwWZsTFBn21zs3pHA788uzhhc4QNKsjgqe+P57t/+pBZTy7l0UvGcPSR+d94nC8Y4Z/Lynn0/Y1sqG7EZsF1Jwxi9pSBcfkHsbyuiVdW7ODtLyoZ1S+HK48bQOYeU3YYY/iy0ssbqyuYv6qCT8vdHNk9nTFFuYwuyiErxUkoYghFooQiUYLhKKGIIclhY3jvLAZ0S8cTdPPhjg/5YPsHfLjjQ3Y27mxVQ0FqAb0zenNSv5MYVTCKMT3G0CPtK5MrGrO7Jap+K3grY7fGqj3C1q7AVRu799fHAszXseyxVp9dLUB2ZyzcuNIhKR2Ss5pbbJpbbVqFnqTdgcqVBkmZza+lxwKUM3X3uVxpsXNIQkWjhrqmIBUNASoa/M23ABUeP1WeAOFIlGjz/EtuX4gqT6B5LqQDD982C3LTkshPd9EtI4kj8tPISomFFwtoDEZ4d20Vb66pxG6zcNlt+ELf3MKZ5LCRl+YiJ81FbpqLfnmp5KTGHu+67fk8O9Wpi4lEOoGusmyDjdWNTJm7gJtOPorZUwYCcMvzn/GvZeUsvvWEVmEjEeoag1zwyIdsqmnkymMHEI5G8frDePxhPIEwHn8IT/PAVk/z4Ndw1FDaO4vvHd2ft9dU8sLy7UwamMe9M0fQLaPtTfmVHj+vrtjBSyt2sHRzbJbkAd3SWF/VSE6qkx9NPZKjemTw5upK5q+uYHNNbPqH4YVZjC7K4ctKL0s319EU3PMXicGye7ElVWJLqsDmqsSWVIU9qRrL4QYg05nOuJwhDEsrpMiZTT9HGn2sJJJCvubxT57mVqum2POmGmiqhsZq8Lv52lYpmxPS8iE1D1JzISUHUnJjj5Ozm5/nNO+TH3uclB4LU+r+OiwEwhF2uv1sq/exo95PhcdPZXPw2tkQe1zp8e8zXOWlxcKTy2HDsmLBKSvFSbeMpNgtPYnumbH79GQH7qYQNY2x8VOZyQ7y05Oab7FQ9E3/6TPG8Nk2N2+sqsAXjJDXfGx+ehJ56S7y0pNiIa7lKr3Y1DrfVvu6Ck4kXtp6laUCWRt95+FF7HD7efvGyfjDEcb+6k1OHtaDu2eUHtQ6vk6NN8BFf17M6h0N2G0WGckO0pMcZCQ7yUhyxJ4nx+4zkp1MOao7Y4pysCwLYwz/WLKVn7/4ORnJDn49fTgnDdv3Uh27ujm/2OnBEwjj9YdZs7OBDzfUEDUwuEcGZ5T24oySXvTNS2XlNje/eW01H3xZA8TGmkwcmMcJQwqYOqT77gl0I2HCjTWs2bSOFZWL+NT9CUua1lMZ3T1vV5qx6BeO0j/gZ2AwwBi/n+JAkP3+WtmzW8+VHgtYLSEqe3crVEpObCxTevfY60mZClaHsUjUUOUJxMKWOxa4Wh67/Wyv91HtDe51XEayg4LMZAoykyjISKYgK5mCjCQKMpPp3ry9e0YyLodakrqyRAey+vp6nn76aa6++uo2H3vaaafx9NNPk50d/zkpN23axMKFC/nud78b93N/myiQdbIXPtnGdc8s5+krxrGtzsdNz63g2R9MYExR7kGtY3+iUYM/HCHFaW/XQPQvdnq4/pnlrNrRwDkjCplzxjCyUp1Eo4ZKT4B/fVLO0x9tobwuthaiZUG6y0GPrGROLe7BGaW9OLJg7xnijTF8uL4GX0MlE/L8pPh2Qt0mqiuWs6pmDWt8O1lrhVnrcrHZ6SBqWaRGo0zw+RkVggH2VAY6s+iWnIeVUQBp3XDbsnhsaQ1bvDbOnziE8YP77RG80pq7+jJiV8sRm55k2ZY6GvxhfM1LcAwvzOLoI/O7VDeMNxDmi50eGnwhAuEIgXCUQCi6+3E4SiAUIRDZtT26z/3y0lyM7Z/LuP55DO4R+5kEI3ufq1d2MqmuQ28Egz8UobzOR3ldE1vrfJTXNlFe78NuWeSmuchLc5HishOMRAlHDI3BMDvq/exw+9heH+taDH/lsr00l52e2Sn0yk6hV1YyPbNS6JWdTK/sFHpmJVOQmUxa0qH3XWpogOEAACAASURBVMneEh3INm3axLRp01i5cuVer4XDYRyOxPw5W7BgAXPnzuXll19OyPvvksjvIB4UyDqZPxRh7K/mM2Vwd7bV+ahtCvLmDccddtMeBMNR/vj2l9z/9pekuey4HPaWJTMAxh+Ry0Xj+3HsoG6kWyFsDVtj468ayqFhO3h2NHcVNg9k99XFugl9tRANs9rl5LmMdBakplC5x1+43o50BiV358j03ozuVsaoPsfgzOobGzv1NdxNIa786xI+3FDLyL7ZDCrI4MiCDLJSnHibJ4HcUtvEog01bK3d94LauWkuTh/ek+LCTBoDEZqCYRqDEZoCYbyBCMFIlJ5ZyfTNTaV3Tgpb63ws21zHJ1vq8IUi9M1NpU9uKt3Sk/CHIjQFI4QiUbpnJtM7J4XeOSnYLAtfMIKv+XV/KIIvGKGp+d4XjNDgD7Fmp4dNNY0cyF/NJIctdnPaSXLYcDlsJDl2P95e72sJznab1fLz+yq7zWJoz0xG9cthZL8cRvXL2ecVw9GoYVu9jw3Vjc1hMRbsHDarpXvN5bCxuaaJDdVettY20RSMEAzHxgAO6pHBycN6UFKY1dL95g2E2en24w9FWsJi7D4WGCsa/GypbWJzTRO1jcGW8YSeQJgqT6BVfS67jcKcFKLG7HNgutNu0SMrmV5ZKS0Bq1d2LHD1bN6Wmew47P4+y74lOpB95zvf4cUXX+Soo47ixBNP5PTTT+f2228nJyeHNWvWsHbtWs4++2y2bt2K3+/n2muvZdasWQAUFRWxZMkSvF4vp556KkcffTQLFy6ksLCQF198kZSU1n9/n332We68886WhbffffddIpEIN998MwsWLCAQCDB79myuvPJKxo8fz+rVq+nfvz+XXHJJqxn0vV4vZ511FnV1dYRCIX75y19y1llnAfDEE08wd+5cLMuipKSEJ598koqKCn7wgx+wYcMGAObNm0evXr1aBdG5c+fi9Xq54447mDx5MmVlZbz//vtccMEFDBo0iF/+8pcEg0Hy8vJ46qmnKCgowOv1cs0117BkyRIsy2LOnDm43W5WrFjRsszRI488wqpVq7jnnns6/We5LwpkB8GcF1fy1EdbCEcNt5w2mFnHDjjoNRwsn5W7efz9tfSK7qS/tZNCs4NByfVkh6piwcu9FbwVXznKinX5JWXGxlO50vElZ7LG5WSFFeY/wZ2s9FeSZHMyudckSnqMYWjeUAbnDibdld6uOgPhCH9480s+3lTLukovtY2tu5myUpyMPyKXiQPyGds/l7x0F6kuBw6bxXvrqnlx+Tbmr67AH2p9CX9qkp00lwOn3WK7208wvPv1/PQkRvXLJiPZyZbaJrbWNlHTGCTFaSfVZcdht6hoCLQ6Zl+cdotkp50Up530ZAdHdk9nWK8shvbMJD8jaa/QtStsuey2AwoO5XVNfLShlvVV3pbA5tp1TocNh91ifWUjSzfXsXxrfctA8F1XxYWjUfyhKI2BMJtqGlt9R98kP91FWpKDJIcNm2WxrtJLJGrokZlMn9wUNtU07RWq9iUj2UG/vFjgdTlsuBx2Up12CnNS6JObQp+c3YF4z3FWwXAUfziCy27Dabfp6j1pJdGB7KstZAsWLOD0009n5cqV9O/fH4Da2lpyc3Px+XyMGTOGd955h7y8vFaBbODAgSxZsoSysjJmzJjBmWeeyUUXXdTqvYYPH85//vMfCgsLqa+vJzs7m4cffpjKykpuu+02AoEAkyZN4tlnn2Xz5s1f20IWDodpamoiMzOT6upqxo8fz7p161i1ahXTp09n4cKF5Ofnt9Q9c+ZMJkyYwHXXXUckEsHr9VJXV7ffQDZ06FAeeOABILZ2ZHZ2NpZl8ac//YnVq1dz991389Of/pRAINBqjUmn00lpaSlr1qzB6XQyceJEHnroIYYPH95pP8P90Uz9B8HMMX15fNFmHDaLc0bGf4mkhIiEoW4jVK+N3Wq+hLrNDK/bzNyG8thEnru40iGzEDJ7wZEnQnYR5PSD7L6x7Rk92OGr5pPKT/ik8hM+rfqUtXWriARiv+gHZg/k5pLvMe2IaWQlxWeajSSHnR+ffFTL82pvgMZAmIzmSSD3N3D5xKEFnDi0gKZgmNrGIOlJDlJdjr3G/0SjhgqPn621PnpmxVq+vikQRaOGam+A8vpYK1VKc/BKddlJdsUed3ZXae+cVHqP+voWxj2FI1HW7PSwdHMdSzfXsaHaS5LDTrLTRk5qKpMG5jOwezpH5KeRl+5qaY0LhKPUNAap9gTwhyP0y02jf7c00r/StVffFOTN1ZW8vmontY1BJg/qRlF+Gr1zUkh2NgdFu40kpw2XPfa8IDOJ7NT2TXrsag6vIt/k/xb/H2tq18T1nINzB/PTsT9t0zFjx45tCWMA9913H88//zwAW7duZd26deTl5bU6pn///pSVlQEwatQoNm3atNd5J02axKWXXsqMGTNa1mJ8/fXXWbFiBc899xwAbrebdevW4XJ9/d83Ywy33HIL7777LjabjW3btlFRUcFbb73F+eefT35+7Ar/3NzYMJ633nqLJ554AqClda6urm6/38HMmTNbHpeXlzNz5kx27NhBMBhs+W7mz5/P3//+95b9dq0nefzxx/Pyyy8zZMgQQqFQwsJYeyiQtcPQXplMOCKPXtkp5KcfgpMKhgNQ8TnsWA47PoUdK2LP91h/kbTukNsf+o6Pha28gbFb7hGxKwz3oSnUxH83/Zd/LfwXy6uWA7EFs0u6lfC94u8xPH84xfnFdEvt1ukfcVf3WVukuhz7HUdls1n0zErZfQHCAbDZLLo3D/Q+FDjsNooLsyguzOKSiUVtOrZP7jeHvuxUF+eO6t0pa72KHA7S0navCLFgwQLmz5/PokWLSE1NZfLkyfj9e69ekZS0+986u92Oz7f30IwHH3yQjz76iFdeeYVRo0axdOlSjDH84Q9/4OSTT26174IFC762vqeeeoqqqiqWLl2K0+mkqKhonzXtj8PhIBrd/Z/8rx6/53dwzTXXcMMNN3DmmWeyYMEC7rjjjv2e+/LLL+fXv/41gwcP5rLLLmtTXYmmQNZOf5s1PtElHDh/A2xeCBvfgc0fQMWq2GSlEJsPq2cpjJsF3YdB/iDIHxjb/g2MMWxwb2DxzsV8vPNjPtj2AU3hJooyi7h+1PVM6DmBI3OOxGHTHzMR6dra2pIVDxkZGXg8nq993e12k5OTQ2pqKmvWrOHDDz9s93utX7+ecePGMW7cOF577TW2bt3KySefzLx58zj++ONxOp2sXbuWwsLC/dbldrvp3r07TqeTt99+m82bNwOxlqnp06dzww03kJeX19JlOXXqVObNm9eqy7KgoIDKykpqampIT0/n5Zdf5pRTTvna9yssjK0P/fjjj7dsP/HEE7n//vtbdVnm5OQwbtw4tm7dyrJly1ixYkW7v69E0G/Kw42vHrZ+FGv5qlwdu1Wvjc3A7kiGPmNh4g+hZ1ksiOUUtWlaB3fAzcLtC/lg2wcs3L6QKl8VAD3SenBy0cmcPfBsRnQfoUHRIiLfIC8vj0mTJlFcXMypp57K6aef3ur1U045hQcffJAhQ4Zw1FFHMX58+xsCbrrpJtatW4cxhqlTp1JaWkpJSQmbNm1i5MiRGGPo1q0bL7zwAiUlJdjtdkpLS7n00ktbDeq/8MILOeOMMxg+fDijR49m8ODBAAwbNoxbb72V4447DrvdzogRI3jsscf4/e9/z6xZs/jzn/+M3W5n3rx5TJgwgZ///OeMHTuWwsLClnPsyx133MH5559PTk4Oxx9/PBs3bgTgtttuY/bs2RQXF2O325kzZ05LV+yMGTNYvnx5SzfmoUKD+g91vnrY9D5sfDfWClaxktgkp1asq7H7UOgxHIqOgd5jYsvbtENVUxV/Xvlnnv3iWYLRIJmuTCb2msiEXhMY02MMvdN7K4SJyCEl0YP6pXNMmzaN66+/nqlTpya0Dg3q/zbwVsGKZ2DlP2PjwEw0th5hn7Ew+WfQbyL0GhG7wrEDoibK6trVvLz+ZZ5d+yzhaJgzB5zJuYPOpTivGLuW7BERkS6ivr6esWPHUlpamvAw1h4KZF1ZOBibv8uzIza9RP3W2Biwtf+JraPYayQc+xPofyz0Hh1bA7GDQpEQi3Ys4s0tb/Je+XtU+aqwWTbOOOIMriy5kj6ZfeLwwUREROIrOzubtWvXJrqMdlMgSwRvJWxZBDs/i4Ws+i2x0BWNxFq7TKR5UlXv3semdYfxV0HZRdD96/vd2+rz6s95du2zzN8yH3fATboznQm9JnBc7+M4uvBo8lLyvvkkIiIi0i4KZAeLvwHe/x2sfik2xxeAZYvN25XVBwpHgj0pts2yYpOqpubsXlsxq0/slpob17UVA5EA9y+/n8dWPkaKI4UpfadwStEpTOw1EZe9fXM/iYgcKowxGv8qcdee8fkKZJ3NGPjsWXj9tljL2MATYMT/xMZ59SwDR+JCz+c1n3Pb+7fxZf2XnDfoPG4cdWO7Z8oXETnUJCcnU1NTQ15enkKZxI0xhpqaGpKT23YRnQJZe+38LNai1W3Q1+9Tsx7+/SPY/H5skP13/ga9Rx28GvchGAny5pY3eW7tcyzeuZjuKd2Zd8I8ji48OqF1iYgcbL1796a8vJyqqqpElyKHmeTkZHr3btsE2Apk7bFhATw1Izbea8otMOla2POKQ2NgyZ/h9dvB5oRp98LIi1vvc5BV+6p5evXTPLv2WeoD9RSmF3LNiGuYedTMuC1fJCJyKHE6na2WKRJJJAWyttq8CP52AeQNgPwj4c07Y1c9Tv05+N2xQfpfvBqbFX/A8XDmHyGrMGHl7vDuYN6n83h5w8uEo2GO73s8M46awfie47FZWuNPRESkK1Aga4ttS+Gp82OLal/8IqR1g8+eg1dvhMf2mGE5OQum3QOjLovrAPy2Wl65nGvfvpamUBPnHHkOFw+9mL6ZfRNWj4iIiOybAtmBCnjgr+fFrnK8+N+Q3j22veR86H8MlC+JBbXsvpCal9AgBvDaxte47f3bKEgr4LFTHqN/lprlRUREuioFsgO19r/gq4XvPLV3F2RGDxgyLTF1fYU36OXhzx7mLyv/wsjuI7l3yr3kJB9a63mJiIh82yiQHajPn4eMXtCn/Yu7dqZAJMAza57hkc8eoT5QzzlHnsOt427VXGIiIiKHAAWyAxHwwLo3YPRlYOt6A+EXbF3Abz76DdsbtzOh5wSuHXktw/KHJbosEREROUAKZAdi7X8hEoChZye6klYqmyr57eLf8sbmNxiYPZA/nfQnxvUcl+iyREREpI0UyA7E589DRk/o03XCzvLK5Vw1/ypC0RDXjryWS4ZegtPuTHRZIiIi0g4KZN+kC3ZX7mzcyXVvX0duci7zTpinqSxEREQOcQpk36SLdVf6w36uffta/BE/j578qMKYiIjIYUCB7Jt0oe5KYwx3LLqD1TWrue/4+zgi+4hElyQiIiJx0DX64LqqXd2VQ85MeHdlOBpm7pK5vLLhFX444odM7jM5ofWIiIhI/KiFbH92dVcOS2x3ZUVjBT959ycsq1zGzKNmcsXwKxJaj4iIiMSXAtn+DDoFZjyR0MlgF21fxM3v3Ywv7OM3x/yGaUd0jRUBREREJH4UyPYnKR2GnpWwt39mzTP8ZvFv6J/Vn7sn380RWRozJiIicjhSIOuCItEIdy+9mydXPcmxvY/lrmPvItWZmuiyREREpJMokHUxURPlx+/8mPlb5nPhkAu5afRN2G32RJclIiIinUiBrIt5ffPrzN8yn2tHXsvlwy9PdDkiIiJyEGjaiy4kEo0wb/k8BmQN4LJhlyW6HBERETlIFMi6kFc3vsoG9wauLrta3ZQiIiLfIgpkXUQ4GubBTx9kUM4gTuh3QqLLERERkYNIgayLeGn9S2zxbGF22Wxsln4sIiIi3yb6zd8FhCIhHvz0QYblDWNKnymJLkdEREQOMgWyLuCPy//I9sbtzC6bjWVZiS5HREREDjIFsgR7beNrPLryUWYMmsExvY9JdDkiIiKSAApkCbSmdg0//+DnjOw+kpvH3pzockRERCRBFMgSpM5fx7VvXUtmUiZ3T74bp92Z6JJEREQkQTRTfwIYY7j1/Vup9lXz+KmPk5+Sn+iSREREJIHUQpYA//jiH7y37T1uGH0DxfnFiS5HREREEkyB7CDb6N7I3CVzmdhrIhcMviDR5YiIiEgXoEB2EIWiIX723s9IciTxi0m/0ASwIiIiAmgM2UH1yIpH+Lzmc+4+7m66p3ZPdDkiIiLSRSSkicayrGsty1ppWdbnlmVd17ztDsuytlmWtbz5dloiaussURPlmS+eYUqfKZxUdFKiyxEREZEu5KC3kFmWVQxcAYwFgsB/LMt6ufnle4wxcw92TQfD2rq11PprtXC4iIiI7CURXZZDgI+MMU0AlmW9A5yTgDoOqkXbFwEwvuf4BFciIiIiXU0iuixXAsdYlpVnWVYqcBrQp/m1H1qWtcKyrEcty8pJQG2d5sMdHzIga4DGjomIiMheDnogM8asBv4PeB34D7AciADzgAFAGbADuHtfx1uWNcuyrCWWZS2pqqo6OEV3UCASYGnFUib0mpDoUkRERKQLSsigfmPMn40xo4wxxwJ1wFpjTIUxJmKMiQKPEBtjtq9jHzbGjDbGjO7WrdvBLLvdllcuJxAJqLtSRERE9ilRV1l2b77vS2z82NOWZfXcY5fpxLo2DwuLti/CYTkY3WN0oksRERGRLihR85D907KsPCAEzDbG1FuW9QfLssoAA2wCrkxQbXG3aMciSrqVkOZMS3QpIiIi0gUlJJAZY47Zx7b/SUQtna3eX8/qmtVcVXZVoksRERGRLkpr93Syj3Z+hMEwoacG9IuIiMi+KZB1sg93fEiaM43i/OJElyIiIiJdlAJZJ1u0fRFjeozBYdOyoSIiIrJvCmSdaG3dWrZ5tzGp16RElyIiIiJdmAJZJ3pp/Us4LIcWExcREZH9UiDrJOFomJc3vMzRvY8mNzk30eWIiIhIF6ZA1kk+3PEh1b5qzhpwVqJLERERkS5OgayT/Hv9v8l0ZXJs72MTXYqIiIh0cQpkncAb9PLWlrc4tf+puOyuRJcjIiIiXZwCWSd4Y/MbBCIBzhxwZqJLERERkUOAAlkneHH9ixRlFjE8f3iiSxEREZFDgAJZnJV7yllasZQzB5yJZVmJLkdEREQOAQpkcbZg6wIATul/SmILERERkUOGAlmcLdy+kKLMIvpk9El0KSIiInKIUCCLo2AkyJKKJUzoNSHRpYiIiMghRIEsjj6p/ARf2MfEXhMTXYqIiIgcQhTI4uiD7R/gsDkY22NsoksRERGRQ4gCWRwt2r6Ism5lpDpTE12KiIiIHEIUyOKk2lfNmto16q4UERGRNlMgi5NF2xcBMLFQgUxERETaRoEsThZtX0R2UjZDcockuhQRERE5xCiQxYExhoXbFzKh5wRslr5SERERaRulhzhYW7eWGn+N5h8TERGRdlEgi4OW8WMa0C8iIiLtoEAWB5s9m8lNzqUgrSDRpYiIiMghSIEsDtwBN9lJ2YkuQ0RERA5RCmRx4A64yUrKSnQZIiIicohSIIsDBTIRERHpCAWyOKgP1JPlUiATERGR9lEgi4OGYINayERERKTdFMg6KBAJ4Av7NKhfRERE2k2BrIPcATeAWshERESk3RTIOmhXIMtMykxwJSIiInKoUiDroF2BTF2WIiIi0l4KZB3U0mWpqyxFRESknRTIOsgdVAuZiIiIdIwCWQfVB+oBDeoXERGR9lMg6yB3wI3D5iDFkZLoUkREROQQpUDWQbsWFrcsK9GliIiIyCFKgayD3AG3BvSLiIhIhyiQdZA7qIXFRUREpGMUyDrIHVAgExERkY5RIOug+kC9ApmIiIh0iAJZBzUEGjQHmYiIiHSIAlkH+MN+/BG/WshERESkQxTIOqBlYXGXFhYXERGR9lMg6wAtmyQiIiLxoEDWAS0Li6vLUkRERDpAgawDFMhEREQkHhTIOmBXIFOXpYiIiHSEAlkH1AfqAQ3qFxERkY5RIOsAd9CNy+YixZGS6FJERETkEKZA1gENgQaykrKwLCvRpYiIiMghTIGsA7RskoiIiMSDAlkHaGFxERERiQcFsg5wB91kuRTIREREpGMUyDrA7VcLmYiIiHScAlkHuINuzUEmIiIiHaZA1k6+sI9AJEBmkuYgExERkY5RIGsnzdIvIiIi8aJA1k5ax1JERETiRYGsnVoCma6yFBERkQ5SIGsnd1AtZCIiIhIfCmTtpC5LERERiRcFsnaqD9QDCmQiIiLScQpk7dQQaCDJnkSKIyXRpYiIiMghToGsnbRskoiIiMSLAlk71fvryUpWIBMREZGOUyBrJ7WQiYiISLwokLWTO+Am06Vlk0RERKTjFMjaqTHUSLorPdFliIiIyGEgIYHMsqxrLctaaVnW55ZlXde8LdeyrDcsy1rXfJ+TiNoOlDfoJcOVkegyRERE5DBw0AOZZVnFwBXAWKAUmGZZ1kDgZuBNY8yRwJvNz7ukqIniDXlJd6qFTERERDouES1kQ4CPjDFNxpgw8A5wDnAW8HjzPo8DZyegtgPiC/swGAUyERERiYtEBLKVwDGWZeVZlpUKnAb0AQqMMTua99kJFCSgtgPiCXoANIZMRERE4sJxsN/QGLPasqz/A14HGoHlQOQr+xjLssy+jrcsaxYwC6Bv376dXO2+eYNeQIFMRERE4iMhg/qNMX82xowyxhwL1AFrgQrLsnoCNN9Xfs2xDxtjRhtjRnfr1u3gFb0Hb6g5kKnLUkREROIgUVdZdm++70ts/NjTwL+BS5p3uQR4MRG1HQgFMhEREYmng95l2eyflmXlASFgtjGm3rKs3wL/sCzr+8BmYEaCavtGu7osNe2FiIiIxENCApkx5ph9bKsBpiagnDbzhGKD+tOcaQmuRERERA4Hmqm/HRqDjYBayERERCQ+FMjawRPyYLNspDpSE12KiIiIHAYUyNrBG/SS5kzDsqxElyIiIiKHAQWydtCySSIiIhJPCmTt4A16NSmsiIiIxI0CWTt4Q14ynBrQLyIiIvGhQNYOnqBHU16IiIhI3CiQtUNjqFFdliIiIhI3CmTtoC5LERERiScFsnbwBD2kudRlKSIiIvGhQNZGwUiQUDSkFjIRERGJGwWyNvIEY+tYagyZiIiIxIsCWRt5Q14ATQwrIiIicaNA1kYKZCIiIhJvCmRt5A02BzJ1WYqIiEicKJC10a5AluHSoH4RERGJDwWyNvKEYoP6NVO/iIiIxIsCWRs1hhoBNO2FiIiIxI0CWRvtmvZCE8OKiIhIvCiQtZE36CXZnozT5kx0KSIiInKYUCBrI2/IqyssRUREJK4UyNrIG/JqDjIRERGJKwWyNvIGFchEREQkvhTI2khdliIiIhJvCmRt5A16NSmsiIiIxJUCWRt5Qh5NCisiIiJxpUDWRo2hRo0hExERkbhSIGuDSDRCY6hRXZYiIiISVwpkbdAYji2bpBYyERERiScFsjbwBr0AuspSRERE4kqBrA28oeZAphYyERERiSMFsjZQC5mIiIh0BgWyNlALmYiIiHQGBbI2UAuZiIiIdAYFsjbY1UKW4dS0FyIiIhI/CmRt4Al6ADRTv4iIiMSVAlkbNIYasVt2UhwpiS5FREREDiMKZG3gCXpId6VjWVaiSxEREZHDiAJZG3hDXl1hKSIiInGnQNYG3qACmYiIiMSfAlkbeENeTXkhIiIicadA1gbekFdTXoiIiEjcKZC1gSfoIc2lKS9EREQkvhTI2qAx1KgxZCIiIhJ3CmQHyBiDN+glw6UuSxEREYkvBbID5I/4CZuwZukXERGRuFMgO0DBSBCAZHtygisRERGRw40C2QGKmiiAZukXERGRuFMgO0AREwHAbtkTXImIiIgcbhTIDtCuFjKbpa9MRERE4kvp4gDtCmRqIRMREZF4UyA7QLu6LNVCJiIiIvGmdHGA1GUpIiIinUXp4gApkImIiEhnUbo4QLrKUkRERDqLAtkBikabW8hs+spEREQkvpQuDpBayERERKSzKJAdIIMBwKavTEREROJM6eIAadoLERER6SxKFwdo1xgyu01dliIiIhJfCmQHSC1kIiIi0lmULg6Q5iETERGRzqJ0cYAUyERERKSzKF0cIC0uLiIiIp1FgewAaQyZiIiIdBaliwOkFjIRERHpLApkB0gtZCIiItJZlC4OkDHNM/UrkImIiEicKV0cILWQiYiISGdpc7qwLCvZsqzMziimK9MYMhEREeksjrbsbFnW5cB5gN2yrI+NMbd0Tlldj1rIREREpLPsN11YlnXmVzadYIw5xRhzInB6e9/UsqzrLcv63LKslZZl/a251e0xy7I2Wpa1vPlW1t7zdwa1kImIiEhn+abmnuGWZb24RzhaYVnWnyzLegT4vD1vaFlWIfAjYLQxphiwA99pfvkmY0xZ8215e87fWXYFMsuyElyJiIiIHG7222VpjPmVZVk9gP+1YknkdiADSDHGrOjg+6ZYlhUCUoHtHTjXQaEWMhEREeksBzIgqhG4Dvgj8DBwAbC2vW9ojNkGzIX/397dB1mWl/UB/z4967IsWuwCIwGWhAURC0kEHCiImgrLS0AIC1VWBQJhRWSNUUBjEkCqxFQlFTQIwapIsgEBXwoIsMAWUeJCKFJWImZYEJa3sMiLrAtMDCBBeev75I97G5thpu9h6N89TffnU9U1fc7t3n7m12d6vvuc5/wmH0tyU5LPdvfvrV7+11X1rqp6flXd7Fy/xghmyACAUdbNkP2rJK9J8oYkD+juRyZ5Z5LfqaonnMsXrKqLk1ye5NIkt09yi6p6fJJnJvmeJPdJcqskTz/L519ZVSer6uSpU6fOpYRzokMGAIyyrt3ziO5+SJIHJnlCyi7+/wAAFspJREFUknT3NUkekuTic/yaD0ry4e4+1d1fTnJ1kr/d3Tf10heTvCTJfc/0yd19VXef6O4Tx48fP8cSvnE6ZADAKOu2vbi+qq5KcvMkb9052d1fSfKCc/yaH0tyv6q6MMlfZhn2TlbV7br7ptWs2qOSXH+O//0h7NQPAIyybqj/8VX1N5N8ubvfvx9fsLvfVlWvTnJdkq8keUeWs2m/W1XHk1SWt0X/8X58vf2iQwYAjLJ2Y9jufvfp56rqRJI/7e5zejqyu5+d5Nmnnb7sXP5bm2KGDAAY5VzbPU9J8l+q6pX7WcxBtr1Ydci2dMgAgP31Df3TSTu6+4okqarv2N9yDi4dMgBglEntnqq6uqoeXvW1A1Td/bkxZR08iywDmRkyAGC/TU0Xv5bkHyb5YFU9p6ruNrCmA2mnQyaQAQD7bVK66O43dffjktw7yUeSvKmq/kdVPbGqvm1kgQfFV2fIBDIAYJ9NThdVdeskP5rkx7PcquIFWQa0a4dUdsCYIQMARpk01F9Vr01ytyS/meTvd/dNq5deWVUnRxV3kNiHDAAYZepTlr/a3W850wvdfWIf6zmwFr3QHQMAhpja7rl7VV20c1BVF1fVPxlU04G06EWW/6oTAMD+mhrIntzdn9k56O5PJ3nymJIOJh0yAGCUqYHsWO1qD1XVsSTnjynpYNrubfNjAMAQU2fI3pjlAP9/XB3/xOrckaFDBgCMMjWQPT3LEPaTq+Nrk7xoSEUH1KIXOmQAwBCTAll3L5K8cPV2JLllCQCMMnUfsrsm+TdJ7p7kgp3z3X3nQXUdODpkAMAoUxPGS7Lsjn0lyQOS/EaS3xpV1EFkhgwAGGVqILt5d785SXX3R7v7F5M8fFxZB49blgDAKFOH+r9YVVtJPlhVP53kxiTfPq6sg0eHDAAYZWrL52lJLkzy1CTfn+TxSa4YVdRBZKd+AGCUtR2y1Saw/6C7/1mS/5fkicOrOoC2e1uHDAAYYm2HrLu3k/zgBmo50DxlCQCMMnWG7B1VdU2SVyX5/M7J7r56SFUHkBkyAGCUqYHsgiR/luSyXec6yZEKZFtbOmQAwP6bulP/kZwb2227t7M1+RkIAIDppu7U/5IsO2Jfo7t/bN8rOqDMkAEAo0y9ZfmGXe9fkOTRSf50/8s5uDxlCQCMMvWW5Wt2H1fVy5P8/pCKDqjFwgwZADDGuSaMuyb5zv0s5KDzlCUAMMrUGbLP5WtnyD6R5OlDKjqgFlmkYqd+AGD/Tb1l+R2jCznothfbObalQwYA7L9Jtyyr6tFVdctdxxdV1aPGlXXweMoSABhlasJ4dnd/dueguz+T5NljSjqYzJABAKNMDWRn+ripW2YcCtu9rUMGAAwxNWGcrKrnVdVdVm/PS/L2kYUdNG5ZAgCjTE0YT0nypSSvTPKKJF9I8lOjijqIBDIAYJSpT1l+PskzBtdyoNmpHwAYZepTltdW1UW7ji+uqv86rqyDR4cMABhlasK4zerJyiRJd386duoHANgXUwPZoqr++s5BVd0pX7tz/6G36EWq7NQPAOy/qVtXPCvJ71fVW5NUkh9KcuWwqg4gM2QAwChTh/rfWFUnsgxh70jyuiR/ObKwg8YMGQAwytR/XPzHkzwtySVJ3pnkfkn+Z5LLxpV2sOiQAQCjTG35PC3JfZJ8tLsfkOReST6z96ccLjpkAMAoUxPGF7r7C0lSVTfr7vcnudu4sg4egQwAGGXqUP/HV/uQvS7JtVX16SQfHVfWwSOQAQCjTB3qf/Tq3V+sqrckuWWSNw6r6gAyQwYAjDK1Q/ZV3f3WEYUcdDpkAMAoEsZEduoHAEYRyCbSIQMARpEwJtrubYEMABhCwphIhwwAGEXCmKC7lzNkW2bIAID9J5BNsOhFkuiQAQBDSBgTLLIKZJYLABhAwphgp0PmliUAMIJANsH2YjuJW5YAwBgSxgRf7ZDZGBYAGEAgm2C7dcgAgHEkjAm6O4lABgCMIWFMoEMGAIwkYUxghgwAGEkgm0CHDAAYScKYQIcMABhJIJtgJ5BV1cyVAACHkUA2gQ4ZADCSQDaBGTIAYCQJYwIdMgBgJIFsAh0yAGAkCWOCnZ36dcgAgBEEsgl2OmSesgQARpglkFXVz1bVe6rq+qp6eVVdUFWXVtXbquqGqnplVZ0/R21nYoYMABhp44Gsqu6Q5KlJTnT3PZIcS/KYJL+U5Pnd/V1JPp3kSZuu7WzMkAEAI82VMM5LcvOqOi/JhUluSnJZklevXn9ZkkfNVNvX0SEDAEbaeCDr7huTPDfJx7IMYp9N8vYkn+nur6w+7ONJ7rDp2s5mJ5BtbemQAQD7b45blhcnuTzJpUlun+QWSR76DXz+lVV1sqpOnjp1alCVX+urgcwzEADAAHMkjAcl+XB3n+ruLye5OskPJLlodQszSS5JcuOZPrm7r+ruE9194vjx4xsp2AwZADDSHAnjY0nuV1UX1nIfiQcmeW+StyT5kdXHXJHk9TPUdkaLxWqGbMsMGQCw/+aYIXtblsP71yV596qGq5I8Pck/raobktw6yYs3XdvZ6JABACOdt/5D9l93PzvJs087/cdJ7jtDOWt17NQPAIyj5TPB9sJO/QDAOALZBPYhAwBGEsgmMEMGAIwkYUygQwYAjCSQTfDVjWF1yACAASSMCdyyBABGkjAm0CEDAEaSMCYwQwYAjCSQTeCWJQAwkoQxgQ4ZADCSQDbBTiCzUz8AMIJANsHOLUsdMgBgBIFsAk9ZAgAjSRgTmCEDAEYSyCbQIQMARpIwJrDtBQAwkoQxgVuWAMBIAtkEOmQAwEgSxgRmyACAkSSMCbYX29mqLRvDAgBDCGQTdDpblgoAGETKmGC7t92uBACGkTImWCwWObblCUsAYAyBbAIdMgBgJCljgkUvBDIAYBgpYwKBDAAYScqYYNELu/QDAMMIZBOYIQMARpIyJnDLEgAYScqYYLu33bIEAIYRyCbobh0yAGAYKWMCM2QAwEhSxgSesgQARhLIJtAhAwBGkjIm8JQlADCSlDGBQAYAjCRlTGCGDAAYSSCbwAwZADCSlDGBDhkAMJJANoEOGQAwkpQxgZ36AYCRpIwJdMgAgJGkjAnMkAEAIwlkE+iQAQAjSRkTLBaLbG1ZKgBgDCljgkUW2bJUAMAgUsYEZsgAgJEEsgnMkAEAI0kZEywWixzb0iEDAMYQyCbQIQMARpIyJujYqR8AGEfKmGB7oUMGAIwjZUzgKUsAYCSBbAIzZADASFLGBDpkAMBIAtkEi17okAEAw0gZEwhkAMBIUsYEZsgAgJGkjAnMkAEAIwlkE+iQAQAjSRkTdLcOGQAwjEA2wXZvp6rmLgMAOKQEsgnMkAEAIwlkE5ghAwBGkjLWWPQiSXTIAIBhBLI1dgKZDhkAMIqUsYZABgCMJmWssd3bSQQyAGAcKWMNM2QAwGgC2Ro6ZADAaFLGGt2dJDm2pUMGAIxx3qa/YFXdLckrd526c5JfSHJRkicnObU6//Pd/TsbLu/r7HTIKnbqBwDG2Hgg6+4PJLlnklTVsSQ3JnltkicmeX53P3fTNe3FDBkAMNrctywfmORD3f3Rmes4q+3FaoZsa+6lAgAOq7lTxmOSvHzX8U9X1buq6ter6uK5itpNhwwAGG22QFZV5yd5ZJJXrU69MMldsrydeVOSXznL511ZVSer6uSpU6fO9CH7ahEbwwIAY82ZMh6W5Lru/mSSdPcnu3u7uxdJ/lOS+57pk7r7qu4+0d0njh8/PrzIxUIgAwDGmjNlPDa7bldW1e12vfboJNdvvKIzsA8ZADDaxp+yTJKqukWSByf5iV2nf7mq7pmkk3zktNdmY4YMABhtlkDW3Z9PcuvTzv2jOWpZR4cMABhNylhDhwwAGE0gW2MnkFXZqR8AGEMgW0OHDAAYTSBbwwwZADCalLGGDhkAMJpAtsZOINMhAwBGkTLWcMsSABhNylhDhwwAGE3KWGOnQ2aGDAAYRSBbQ4cMABhNyljDU5YAwGgC2Ro6ZADAaFLGGp6yBABGkzLW0CEDAEaTMtbwlCUAMJpAtsZiseqQbVkqAGAMKWONRVaBzFIBAINIGWvY9gIAGE0gW2N7sXrK0i1LAGAQKWMNHTIAYDSBbA37kAEAo0kZa3R3EoEMABhHylhDhwwAGE3KWMMMGQAwmkC2hg4ZADCalLGGDhkAMJpAtsZOIKuqmSsBAA4rgWwNHTIAYDSBbA0zZADAaFLGGjpkAMBoAtka272dSpkhAwCGEcjW6G63KwGAoSSNNbZ7WyADAIaSNNZY9ML8GAAwlEC2hg4ZADCapLGGDhkAMJpAtsaiF9naskwAwDiSxhqLXmTLMgEAA0kaa5ghAwBGkzTWMEMGAIwmkK2xvdg2QwYADCVprNFpHTIAYCiBbI2df8sSAGAUgWyNxWKRY1s6ZADAOALZGp6yBABGkzTW8JQlADCaQLbGohc6ZADAUJLGGgIZADCapLGGGTIAYDRJYw0zZADAaALZGjpkAMBoksYa3XbqBwDGEsjW2O7tVNmpHwAYRyBbwwwZADCaQLaGGTIAYDRJYw0dMgBgNIFsDRvDAgCjSRprCGQAwGiSxhpmyACA0SSNNcyQAQCjCWRr6JABAKNJGmvYqR8AGE0gW8NO/QDAaALZGmbIAIDRBLI1zJABAKNJGmssFosc29IhAwDGEcjWWMTGsADAWJLGGoteZMsyAQADSRprmCEDAEaTNNYwQwYAjCaQraFDBgCMtvGkUVV3q6p37nr786r6maq6VVVdW1UfXP168aZrO5OOnfoBgLE2Hsi6+wPdfc/uvmeS70/yF0lem+QZSd7c3XdN8ubV8ey2F3bqBwDGmvte3AOTfKi7P5rk8iQvW51/WZJHzVbVLnbqBwBGmzuQPSbJy1fv37a7b1q9/4kkt52npK9lhgwAGG22pFFV5yd5ZJJXnf5ad3eSPsvnXVlVJ6vq5KlTp4bW2N1myACA4eZs/TwsyXXd/cnV8Ser6nZJsvr1U2f6pO6+qrtPdPeJ48ePDy1w0Ysk0SEDAIaaM2k8Nn91uzJJrklyxer9K5K8fuMVnUYgAwA2YZakUVW3SPLgJFfvOv2cJA+uqg8medDqeFbbvZ1EIAMAxjpvji/a3Z9PcuvTzv1Zlk9dHhg7HTIzZADASFo/e9AhAwA2QdLYgw4ZALAJAtkedgKZnfoBgJEEsj3s3LLUIQMARhLI9mDbCwBgEySNPZghAwA2QSDbg6csAYBNkDT24JYlALAJksYeBDIAYBMkjT14yhIA2ASBbA+LxapDtmWZAIBxJI096JABAJsgkO2h00mSLcsEAAwkaezBthcAwCZIGnvYmSE7tuWWJQAwjkC2Bx0yAGATJI092IcMANgESWMP/i1LAGATBLI96JABAJsgaezBDBkAsAmSxh7csgQANkEg24MOGQCwCZLGHrqXO/XrkAEAIwlke9jpkFXVzJUAAIeZQLaHm59381x6y0tzwXkXzF0KAHCInTd3AQfZ/W9//1zzqGvmLgMAOOR0yAAAZiaQAQDMTCADAJiZQAYAMDOBDABgZgIZAMDMBDIAgJkJZAAAMxPIAABmJpABAMxMIAMAmJlABgAwM4EMAGBmAhkAwMwEMgCAmQlkAAAzE8gAAGYmkAEAzEwgAwCYmUAGADAzgQwAYGYCGQDAzAQyAICZVXfPXcM5q6pTST46+MvcJsn/Gfw1DjprsGQdrEFiDRJrkFiDxBok3/ga/I3uPn6mF76lA9kmVNXJ7j4xdx1zsgZL1sEaJNYgsQaJNUisQbK/a+CWJQDAzAQyAICZCWTrXTV3AQeANViyDtYgsQaJNUisQWINkn1cAzNkAAAz0yEDAJiZQLaHqnpoVX2gqm6oqmfMXc8mVNUdq+otVfXeqnpPVT1tdf5WVXVtVX1w9evFc9c6WlUdq6p3VNUbVseXVtXbVtfDK6vq/LlrHKmqLqqqV1fV+6vqfVV1/6N2HVTVz67+HFxfVS+vqgsO+3VQVb9eVZ+qqut3nTvj972WfnW1Fu+qqnvPV/n+Ocsa/NvVn4V3VdVrq+qiXa89c7UGH6iqvzdP1fvrTGuw67Wfq6quqtusjo/MdbA6/5TVtfCeqvrlXee/qetAIDuLqjqW5N8neViSuyd5bFXdfd6qNuIrSX6uu++e5H5Jfmr1+35Gkjd3912TvHl1fNg9Lcn7dh3/UpLnd/d3Jfl0kifNUtXmvCDJG7v7e5J8X5ZrcWSug6q6Q5KnJjnR3fdIcizJY3L4r4OXJnnoaefO9n1/WJK7rt6uTPLCDdU42kvz9WtwbZJ7dPffSvK/kzwzSVY/Hx+T5HtXn/Nrq78/vtW9NF+/BqmqOyZ5SJKP7Tp9ZK6DqnpAksuTfF93f2+S567Of9PXgUB2dvdNckN3/3F3fynJK7L8Jhxq3X1Td1+3ev9zWf4lfIcsf+8vW33Yy5I8ap4KN6OqLkny8CQvWh1XksuSvHr1IYd6Darqlkn+TpIXJ0l3f6m7P5Mjdh0kOS/JzavqvCQXJrkph/w66O7/nuT/nnb6bN/3y5P8Ri/9QZKLqup2m6l0nDOtQXf/Xnd/ZXX4B0kuWb1/eZJXdPcXu/vDSW7I8u+Pb2lnuQ6S5PlJ/kWS3QPoR+Y6SPKTSZ7T3V9cfcynVue/6etAIDu7OyT5k13HH1+dOzKq6k5J7pXkbUlu2903rV76RJLbzlTWpvy7LH/oLFbHt07ymV0/kA/79XBpklNJXrK6bfuiqrpFjtB10N03Zvl/vx/LMoh9Nsnbc7Sugx1n+74f1Z+TP5bkd1fvH5k1qKrLk9zY3X902ktHZg2SfHeSH1qNLby1qu6zOv9Nr4FAxhlV1bcneU2Sn+nuP9/9Wi8fzT20j+dW1SOSfKq73z53LTM6L8m9k7ywu++V5PM57fbkEbgOLs7y/3ovTXL7JLfIGW7hHDWH/fu+TlU9K8vRjt+eu5ZNqqoLk/x8kl+Yu5aZnZfkVlmO9PzzJP95dQflmyaQnd2NSe646/iS1blDr6q+Lcsw9tvdffXq9Cd3WtCrXz91ts8/BH4gySOr6iNZ3qq+LMt5qotWt66Sw389fDzJx7v7bavjV2cZ0I7SdfCgJB/u7lPd/eUkV2d5bRyl62DH2b7vR+rnZFX9aJJHJHlc/9WeUUdlDe6S5f+c/NHqZ+MlSa6rqr+Wo7MGyfJn49Wr27N/mOVdlNtkH9ZAIDu7/5Xkrqsnqs7PcljvmplrGm6V9F+c5H3d/bxdL12T5IrV+1ckef2ma9uU7n5md1/S3XfK8vv+37r7cUnekuRHVh922NfgE0n+pKrutjr1wCTvzRG6DrK8VXm/qrpw9ediZw2OzHWwy9m+79ckecLqKbv7Jfnsrlubh0pVPTTLMYZHdvdf7HrpmiSPqaqbVdWlWQ62/+EcNY7U3e/u7u/s7jutfjZ+PMm9Vz8rjsx1kOR1SR6QJFX13UnOz/IfF//mr4Pu9naWtyQ/nOXTNB9K8qy569nQ7/kHs7wd8a4k71y9/XCWM1RvTvLBJG9Kcqu5a93QevzdJG9YvX/n1R+wG5K8KsnN5q5v8O/9nklOrq6F1yW5+KhdB0n+ZZL3J7k+yW8mudlhvw6SvDzLmbkvZ/mX7pPO9n1PUlk+jf6hJO/O8onU2X8Pg9bghixnhHZ+Lv6HXR//rNUafCDJw+auf9QanPb6R5Lc5gheB+cn+a3Vz4Trkly2X9eBnfoBAGbmliUAwMwEMgCAmQlkAAAzE8gAAGYmkAEAzEwgAwCYmUAGADAzgQwAYGb/H9wj8T17baXVAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dmCAswyypaNS"
      },
      "source": [
        "version-batchsize-epochnum-lr-dropout-error\n",
        "\n",
        "1-64-60-0.0005-no-1.19%\n",
        "\n",
        "2-64-30-0.0005-no-1.06%\n",
        "\n",
        "3-32-30-0.0005-no-1.02%\n",
        "\n",
        "4-32-30-0.0005-fc1,fc2-40.78%\n",
        "\n",
        "5-64-15-0.005-fc1,fc2-40.30%\n",
        "\n",
        "6-64-15-0.005-fc1-1.66%\n",
        "\n",
        "7-64-15-0.05-fc1-43.58%\n",
        "\n",
        "8-128-15-0.0005-fc1-1.39%\n",
        "\n",
        "9-64-15-0.0005-init-1.07%\n",
        "\n",
        "10-32-15-0.0005-init-1.10%\n",
        "\n",
        "11-64-15-0.0005-init,fc1-1.49%\n",
        "\n",
        "12-64-15-0.0005-init,fc1,fc2(0.1)-5.26%\n",
        "\n",
        "12-64-15-0.0005-init,conv1,conv2,conv3-1.33%\n",
        "\n",
        "12-64-50-0.0001-init-1.27%"
      ]
    }
  ]
}