{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "**Практическая работа № 3**\n",
        "\n",
        "**Джус Андрей || ДРПК-47**\n",
        "\n"
      ],
      "metadata": {
        "id": "2uKGeqlRSWt5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![image.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "C6LazrO1XMm8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import math\n",
        "import pandas as pd\n",
        "\n",
        "import keras\n",
        "from keras.datasets import fashion_mnist\n",
        "from keras.layers import Input, Flatten, Dense"
      ],
      "metadata": {
        "id": "o9SwtCqL4Qcs"
      },
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "(x_train, y_train), (x_test, y_test) = fashion_mnist.load_data()"
      ],
      "metadata": {
        "id": "k0HPZ6-sIljn"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train_norm = x_train / 255\n",
        "x_test_norm = x_test / 255"
      ],
      "metadata": {
        "id": "HHGz3GPDIlhM"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = keras.models.Sequential()\n",
        "\n",
        "model.add(\n",
        "    Flatten(input_shape = x_train_norm.shape[1:])\n",
        ")\n",
        "\n",
        "model.add(\n",
        "    Dense(\n",
        "        units = 256,\n",
        "        activation = 'relu',\n",
        "        kernel_regularizer = keras.regularizers.l2(0.002)\n",
        "    )\n",
        ")\n",
        "\n",
        "model.add(\n",
        "    Dense(\n",
        "        units = 128,\n",
        "        activation = 'relu',\n",
        "        kernel_regularizer = keras.regularizers.l2(0.002)\n",
        "    )\n",
        ")\n",
        "\n",
        "model.add(\n",
        "    Dense(\n",
        "        units = 64,\n",
        "        activation = 'relu',\n",
        "        kernel_regularizer = keras.regularizers.l2(0.002)\n",
        "    )\n",
        ")\n",
        "\n",
        "model.add(\n",
        "    Dense(\n",
        "        units = 10,\n",
        "        activation = 'softmax'\n",
        "    )\n",
        ")"
      ],
      "metadata": {
        "id": "fCfUCpXqIldw"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "26hvWcmPIlbf",
        "outputId": "e0dbfebb-ddd6-411b-f2af-9b10a4eefcbf"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " flatten (Flatten)           (None, 784)               0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 256)               200960    \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 128)               32896     \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 64)                8256      \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 10)                650       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 242,762\n",
            "Trainable params: 242,762\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "keras.utils.plot_model(\n",
        "    model,\n",
        "    show_shapes = True,\n",
        "    show_layer_activations=True,\n",
        "    show_layer_names = True\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 644
        },
        "id": "Yl23XsWeIlYL",
        "outputId": "1956bb18-df2e-4edc-f0de-aba28aa40e31"
      },
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAVQAAAJzCAYAAABQ0+umAAAABmJLR0QA/wD/AP+gvaeTAAAgAElEQVR4nOzde1xU5b4/8M8AwwwzzHARBERAZpAStdwmZwvpMXVvu/gSRUTZabusdpgVkjdClAzRJMx4YVB5yddOS/EWGkrujR22cTR3HeWIeCS8AiICcheQEb6/P/zN5DRcBlgwo37frxd/+Kw1z/Ndz3rm67o8s5aIiAiMMcZ6zcLUATDG2MOCEypjjAmEEypjjAmEEypjjAnE6vcFJ0+exMaNG00RC2OMPTAWL16MgIAAvTKDI9Ti4mLs27ev34Jifeenn37CTz/9ZOowzFpJSQmPd9Zt+/btQ3FxsUG5wRGq1t69e/s0INb3QkNDAfC+7MyePXswZ84c7iPWLSKRqN1yvobKGGMC4YTKGGMC4YTKGGMC4YTKGGMC4YTKGGMCESyh3rlzB4sWLYKrqytkMhn+9Kc/YeDAgRCJRPj888+FaqbfHTlyBHZ2dvjuu+9MHYpJPOrb354FCxZAJBLp/ubNm2ewTlZWFqKjo7F//36oVCrdui+99JLBulOmTIFCoYClpSWGDx+O06dP98dmdEtcXBz8/PygVCohkUjg4+OD5cuXo6GhQW+9b775Bv7+/lAoFPDy8sL8+fNRVlZmkvYOHTqEhIQEtLa26n0mPT1db/85OTl1O74O0e+kpaVRO8VdWrt2Lfn6+lJ1dTV98cUXtHfvXiosLCQA9Nlnn3W7PnORkZFBSqWSDh06ZOpQum3WrFk0a9asXtXxIG+/MXoy3sPDw8nR0ZEyMzOpoKCAmpub9ZbHxsbStGnTqK6uTlemVqtpwIABBIAyMjIM6szMzKTp06f3bCP6wYQJEyglJYVu3bpFdXV1lJaWRmKxmJ577jndOrt37yYAlJCQQDU1NXTmzBlSqVQ0atQo0mg0JmkvKSmJJkyYQNXV1brPtbW1UUlJCR0/fpxeeOEFGjBgQLf7AwClpaUZlv++oKcJ1d/fn1588UW9sp4k1MbGRgoICOiy7FHRm20XIqGaWl/v+54mVHd393aXffjhh+Tr60tNTU165Wq1mr7++muysLAgd3d3qqmp0Vtu7gl16tSpdPfuXb2y2bNnEwAqKioiIqKJEyfSoEGDqK2tTbfOp59+SgAoJyfHZO1FRERQQEBAu0l90aJFgiZUwU75S0pKIBaLe13Ptm3bUF5e3mXZo+JR3nbgwdr+ixcvYtWqVfjggw8glUoNlgcGBiIyMhLXr1/H0qVLTRBhz2VkZMDS0lKvTHuq3NjYCODeryzd3Nz0Jr17eHgAAK5du2ay9lavXo3c3FwkJSV1K4ae6HVC/ec//wkfHx/cuHEDf//73yESiWBra9vh+j/++CP8/PxgZ2cHqVSKkSNH4ujRowCAyMhILFmyBJcuXYJIJIKPj0+7ZQDQ2tqK2NhYeHp6wsbGBk888QTS0tIAAKmpqZDL5ZDJZDh48CCef/55KJVKDB48GLt27TJ623JycuDp6QmRSIRPP/3U6LqTk5MhlUoxcOBALFiwAG5ubpBKpQgMDMSpU6cAABEREbC2toarq6uuvbfeegtyuRwikQiVlZUdbnt/Mdft//7776FUKrF27dp+7Y+uJCcng4gQFBTU4Trx8fHw9fXF1q1bkZWV1eF6RISNGzdi2LBhkEgkcHBwwIwZM3DhwgUAxo/xzr4nvXX9+nXY2NjA29sbAKBSqQz+89Nez1SpVCZrz8HBARMmTEBSUhKor5+n//tD1p6e8ru4uNDLL7+sV9beKf/evXtp9erVVFVVRbdu3aKxY8fqHXKHhISQWq3Wq6e9sqVLl5JEIqF9+/ZRdXU1rVixgiwsLOjnn38mIqKYmBgCQMeOHaPa2loqLy+n8ePHk1wup5aWFqO3q7i4mADQpk2bdGXG1B0eHk5yuZzOnz9Pzc3NlJ+fT/7+/qRQKHSnLHPnziUXFxe99hITEwkAVVRUdLjtxhLilN8ctz8jI4MUCgXFxcX1atuIhD3lV6lU5Ofn1+5n1Go1XblyhYiITpw4QRYWFjRkyBBqaGggIsNT/tjYWLK2tqYdO3ZQTU0NnT17lkaPHk1OTk5UVlZGRMbth66+Jz11+/ZtUigUFBERoSvLzs4msVhMycnJVFdXR+fOnaNhw4bRs88+26u2hGgvOjqaANCZM2f0ys32lN9Ys2bNwvvvvw8HBwc4OjoiKCgIt27dQkVFhdF1NDc3IzU1FcHBwQgJCYG9vT1WrlwJsViM7du3660bGBgIpVIJZ2dnhIWF4fbt2ygqKhJkW7qq28rKSneE4efnh9TUVNTX1xvE+KAy1fZPnToVdXV1WLVqVW83QTC3b9/GlStXoFaru1w3ICAA7777Lq5evYr33nvPYHlTUxM2btyImTNnYt68ebCzs8PIkSPx+eefo7KyEps3b9Zbv6P90J3vSXetW7cObm5uiI+P15VNmDABUVFRiIiIgFKpxIgRI1BfX4+tW7f2qi0h2hs6dCgAIC8vr9exdMbk81C1111/P7WhMwUFBWhsbMSIESN0ZTY2NnB1ddWdErXH2toaAKDRaHoYbceMqXvMmDGQyWSdxvigetS3v7y8HEQEmUxm1Prx8fF47LHHkJKSgpycHL1l+fn5aGhowJgxY/TK/f39YW1trbts0p7790NPvyddOXDgAPbs2YOjR49CoVDoymNiYrB582YcO3YMDQ0NuHz5MgIDAxEQENDuk5n6sz3tfrl582aP4zBGvyfUw4cP45lnnoGzszMkEgmWL1/e7Tpu374NAFi5cqXefLJr167pLlibK4lE0q2j8YfNw7r9zc3NAO5tnzGkUim2b98OkUiEV199FU1NTbplNTU1ANDuvQh7e3vU19cb1UZffE92796N9evXIzs7G0OGDNGV37hxAwkJCXjjjTcwadIkyOVyeHt7Y8uWLSgtLUViYqJJ27OxsQHw237qK/2aUIuKihAcHAxXV1ecOnUKtbW1SEhI6HY9zs7OAIBPPvkEdG/ql+7v5MmTQoctGI1Gg5qaGgwePNjUoZjEw7z92i9sd860AgICsHjxYhQWFmLNmjW6cnt7ewBoN3F2p/+E/p5s2rQJO3fuxA8//IBBgwbpLSssLERra6tBuVKphKOjI/Lz803aXktLC4Df9lNf6fB5qH0hLy8PGo0GCxcu1N2F6+i5gp3x8PCAVCpFbm6u0CH2qezsbBARxo4dC+DeNca+uPxgrh7m7df+KrC2trZbn1uzZg0yMjJw5swZeHp6AgBGjBgBW1tb/PLLL3rrnjp1Ci0tLXjqqaeMqluo7wkR4b333kN1dTXS09NhZWWYNrRJ/saNG3rl9fX1qKqq0k1nMlV72v3i4uJidBw90a9HqNoBk5WVhebmZhQWFhpcD3J0dERpaSmuXr2K+vp6aDQagzJLS0vMnz8fu3btQmpqKurq6tDa2oqSkhKDDjaltrY2VFdX4+7duzh79iwiIyPh6emJV155BQDg4+ODqqoqpKenQ6PRoKKiwmC+Xnv98aDoq+3PzMw0u2lTMpkMKpUKJSUl3fqc9tT//jmXUqkUS5YswYEDB7Bz507U1dUhLy8Pb775Jtzc3BAeHm503V19T8LCwuDi4tLpz13Pnz+Pjz76CFu2bIFYLNa7fCASibBhwwZ4e3tj4sSJ2LJlC44fP46mpiYUFxfrYn3ttddM0p6Wdr+MHDnSqL7rsd/f9u/uNJKrV6/SH/7wBwJAVlZWNHr0aNq3bx99/PHH5OLiQgBILpfTzJkziYgoKiqKHB0dyd7enkJDQ3W/bFCr1VRUVESnT58mLy8vsrGxoXHjxlFZWVm7ZXfu3KGoqCjy9PQkKysrcnZ2ppCQEMrPz6eUlBSSyWQEgIYOHUqXLl2izZs3k1KpJADk5eVFv/76a5fbtmnTJnJ1dSUAJJPJKCgoyOi6w8PDSSwWk7u7O1lZWZFSqaQZM2bQpUuXdPXfunWLJk6cSFKplLy9vemdd96hZcuWEQDy8fHpsD+M1dtpU+a6/UeOHCGFQkHx8fE93jYtIadNRUREkFgspsbGRl3ZgQMHSK1WEwBycnKit99+u906ly1bpjdtqq2tjRITE2no0KEkFovJwcGBgoODqaCggIjI6P3Q2feEiCg4OJgAUGxsbIfbm5eXRwA6/EtMTCQiosrKSoqMjCQfHx+SSCRka2tLTz/9NH377be6uvq7Pa2pU6eSu7u73q+qiISfNiXYPFSmT/t7b1My5U9PzWH7jSFkQi0sLCQrKyvasWOHUOH1udbWVho/fjxt27btoWyP6F7ilUqltGHDBoNlD/w81EdJd25QPIwe5u1vamrC0aNHUVhYqLvh4ePjg7i4OMTFxRk8Fckctba2Ij09HfX19QgLC3vo2tNavXo1Ro0ahYiICAD3rtGWlpYiJycHFy9eFLStRzKhXrhwweC6THt//bnT2YOlqqoKzz33HHx9ffHqq6/qyqOjoxEaGoqwsLBu36Dqb9nZ2di/fz8yMzONnj/7ILUHABs3bkRubi6OHDmim/N+8OBBuLu7Y/z48Th8+LCwDf7+kJVP+XsvOjqarK2tCQANGTKE9u7da5I4THXKby7bb4y+Gu9Hjx6lqKgowetlxktPT6d169YZPLVKCOjglF/0/xfqaF+rS339EAHW5/g10l3j8c56QiQSIS0tDbNnz9YrfyRP+RljrC9wQmWMMYFwQmWMMYFwQmWMMYFwQmWMMYF0+HCUnjy0hJkn3pdd4z5iQugwoQr13hlmOp988gkA4N133zVxJObr5MmTSEpK4vHOumXOnDntlneYUH8/v4o9eLTzT3lfdi4pKYn7iHVLRwmVr6EyxphAOKEyxphAOKEyxphAOKEyxphAOKEyxphA+iyh/vTTTxg2bBgsLCwgEong4uKC+Pj4vmrOaPv374dKpdI989TV1RXz5s0zdVjsAbJgwQK95+a2N36ysrIQHR1tMN5eeuklg3WnTJkChUIBS0tLDB8+vNP3LZlKXFwc/Pz8oFQqIZFI4OPjg+XLlxs8SPubb76Bv78/FAoFvLy8MH/+fJSVlZmkvUOHDiEhIcHgQefp6el6+8/Jyanb8XXo98/zE/r5kM8++ywBoOrqasHqFIJarSY7OztTh9GnTPkKlAdFT1+B4ujoSJmZmVRQUEDNzc16y2NjY2natGlUV1enK1Or1TRgwAACQBkZGQZ1ZmZm6r1TytxMmDCBUlJS6NatW1RXV0dpaWkkFovpueee062ze/duAkAJCQlUU1NDZ86cIZVKRaNGjSKNRmOS9pKSkmjChAl6+aetrY1KSkro+PHj9MILLzxY75QydUJtbGykgIAAg3JOqH2ro343t7qFfKcUEdGHH35Ivr6+1NTUpFeuVqvp66+/JgsLC3J3d6eamhq95eaeUKdOnWrwoObZs2cTACoqKiIiookTJ9KgQYP0XoSnfQlnTk6OydqLiIiggICAdpM6v1Oqm7Zt24by8nJTh/HI6ct+N9d9evHiRaxatQoffPABpFKpwfLAwEBERkbi+vXrWLp0qQki7LmMjAy9V10D0J0qNzY2AgCKi4vh5uam9zNeDw8PADB4PXh/trd69Wrk5uYiKSmpWzH0RL8n1NTUVMjlcshkMhw8eBDPP/88lEolBg8ejF27dgEAkpOTIZVKMXDgQCxYsABubm6QSqUIDAzEqVOnAAARERGwtraGq6urru633noLcrkcIpEIlZWViIyMxJIlS3Dp0iWIRCL4+Ph0O94ff/wRfn5+sLOzg1QqxciRI3H06FEAwOuvv667DqNWq3HmzBkAwPz58yGTyWBnZ4dDhw6htbUVsbGx8PT0hI2NDZ544gndTx0/+ugjyGQyKBQKlJeXY8mSJXB3d0dBQUGv+rmniAgbN27EsGHDIJFI4ODggBkzZuDChQsAet7vfb1Pv//+eyiVSqxdu7Yfe0tfcnIyiAhBQUEdrhMfHw9fX19s3boVWVlZHa7X1X4w5nsEoNOx11vXr1+HjY0NvL29AQAqlcrgPzrt9UyVSmWy9hwcHDBhwgQkJSX1/ZsZfn/I2h+n/DExMQSAjh07RrW1tVReXk7jx48nuVxOLS0tRHTvtEoul9P58+epubmZ8vPzyd/fnxQKhe6Qf+7cueTi4qLXXmJiIgGgiooKIiIKCQkhtVptEJexp/x79+6l1atXU1VVFd26dYvGjh2rd4oQEhJClpaWdP36db3Pvfjii3To0CEiIlq6dClJJBLat28fVVdX04oVK8jCwoJ+/vlnvf5YtGgRbdq0iWbOnEn/93//12VsXenJKX9sbCxZW1vTjh07qKamhs6ePUujR48mJycnKisrI6Ke93tf7tOMjAxSKBQUFxfXre0V8pRfpVKRn59fu59Rq9V05coVIiI6ceIEWVhY0JAhQ6ihoYGIDE/5jdkPxnyPuhp7PXX79m1SKBQUERGhK8vOziaxWEzJyclUV1dH586do2HDhtGzzz7bq7aEaC86OpoA0JkzZ/TKhT7lN2lCvf86U0pKCgGgixcvEtG9Qfv7hPfzzz8TAPrggw+IqH8S6u+tW7eOAFB5eTkREWVlZREAio+P161TW1tLQ4cOpbt371JTUxPJZDIKCwvTLW9sbCSJREILFy7ssD+E0N2E2tjYSLa2tnqxEhH9+9//JgC6ZNWbhNqX+7QnhEqoDQ0NJBKJaNq0ae1+5v6ESkS0ZMkSAkBvv/02EeknVGP3Q1ffI2PGXk/FxMSQr6+v3o03IqKVK1cSAN3f4MGDqbi4uFdtCdHel19+SQDoq6++0it/aK+hWltbAwA0Gk2H64wZMwYymUx32mMK2lfRaqdiTJo0Cb6+vvjyyy91pxO7d+9GWFgYLC0tUVBQgMbGRowYMUJXh42NDVxdXU26He3Jz89HQ0MDxowZo1fu7+8Pa2tr3am5kMxhnwqhvLwcRGT065Hj4+Px2GOPISUlBTk5OXrLerMf7v8e9dXYO3DgAPbs2YOjR49CoVDoymNiYrB582YcO3YMDQ0NuHz5MgIDAxEQEIDi4mKTtqfdLzdv3uxxHMYwm4RqLIlEgoqKin5r7/Dhw3jmmWfg7OwMiUSC5cuX6y0XiURYsGABLl++jGPHjgEAvvrqK7z22msAgNu3bwMAVq5cqTf37dq1a7qL6+aipqYGAGBra2uwzN7eHvX19X3Sbn/v077Q3NwM4N62GEMqlWL79u0QiUR49dVX0dTUpFsm1H7oi7G3e/durF+/HtnZ2RgyZIiu/MaNG0hISMAbb7yBSZMmQS6Xw9vbG1u2bEFpaSkSExNN2p6NjQ2A3/ZTX3mgEqpGo0FNTQ0GDx7cp+0cP34cn3zyCYqKihAcHAxXV1ecOnUKtbW1SEhIMFj/lVdegVQqxdatW1FQUAClUgkvLy8AgLOzM4B7zyale5dYdH8nT57s0+3oLnt7ewBo9wvbV/3eX/u0r2m/sL+fRN6ZgIAALF68GIWFhVizZo2uXKj9IPTY27RpE3bu3IkffvgBgwYN0ltWWFiI1tZWg3KlUglHR0fk5+ebtL2WlhYAv+2nvtLh81DNUXZ2NogIY8eOBQBYWVl1eomgp/7nf/4HcrkceXl50Gg0WLhwoe6uYXtPdndwcMCcOXOwe/duKBQK/O1vf9Mt8/DwgFQqRW5uruBxCm3EiBGwtbXFL7/8old+6tQptLS04KmnngIgbL/31z7tawMHDoRIJEJtbW23PrdmzRpkZGTgzJkz8PT0BGD8fuiKUGOPiPDee++huroa6enpsLIyTBvaJH/jxg298vr6elRVVemmM5mqPe1+cXFxMTqOnjDrI9S2tjZUV1fj7t27OHv2LCIjI+Hp6YlXXnkFAODj44Oqqiqkp6dDo9GgoqLCYL6bo6MjSktLcfXqVdTX13f6ZdVoNLh58yays7Mhl8t1AzwrKwvNzc0oLCzs8PrVm2++iTt37iAjIwPTpk3TlUulUsyfPx+7du1Camoq6urq0NraipKSEoPBYGpSqRRLlizBgQMHsHPnTtTV1SEvLw9vvvkm3NzcEB4eDqB3/d5X+zQzM9Ok06ZkMhlUKhVKSkq69Tntqf/9cy6N3Q/G1N3V2AsLC4OLi0unP3c9f/48PvroI2zZsgVisVjv8oFIJMKGDRvg7e2NiRMnYsuWLTh+/DiamppQXFysi1V7Cay/29PS7peRI0ca1Xc99vu7VELd5f/pp59o+PDhZGFhQQDI1dWV1q5dSykpKSSTyQgADR06lC5dukSbN28mpVJJAMjLy4t+/fVXCg8PJ7FYTO7u7mRlZUVKpZJmzJhBly5d0rVx69YtmjhxIkmlUvL29qZ33nmHli1bRgDIx8eHioqK6PTp0+Tl5UU2NjY0btw4+uyzz0itVuvdGWzv78CBA0REFBUVRY6OjmRvb0+hoaG6X2Ko1WrdVB+tP/zhDxQdHW3QF3fu3KGoqCjy9PQkKysrcnZ2ppCQEMrPz6eEhASysbEhAOTh4UE7duzodd9r9WTaVFtbGyUmJtLQoUNJLBaTg4MDBQcHU0FBgW6dnvR7WVlZn+3TsrIyOnLkCCkUCr3ZFsYQctpUREQEicViamxs1JUdOHBAN96cnJx0d/V/b9myZXrTprraD8Z+jzobe0REwcHBBIBiY2M73N68vLxOvyuJiYlERFRZWUmRkZHk4+NDEomEbG1t6emnn6Zvv/1WV1d/t6c1depUcnd31/tVFdEDOG2qp7S/l36QvPDCC3T58mVTh6Fjbr/lN8d9KmRCLSwsJCsrK0H/U+xrra2tNH78eNq2bdtD2R7RvcQrlUppw4YNBsse2mlT7enOBX5TuP/ywdmzZyGVSnW/4mDtM/d9aqympiYcPXoUhYWFuhsePj4+iIuLQ1xcnMFTkcxRa2sr0tPTUV9fj7CwsIeuPa3Vq1dj1KhRiIiIAHDvGm1paSlycnJw8eJFQdsy64Rq7qKiolBYWIhff/0V8+fP17tTyx5uVVVVeO655+Dr64tXX31VVx4dHY3Q0FCEhYV1+wZVf8vOzsb+/fuRmZlp9PzZB6k9ANi4cSNyc3Nx5MgR3RzygwcPwt3dHePHj8fhw4eFbfD3h6zmcMofHR1N1tbWBICGDBlCe/fuNWk8HYmJiSELCwvy8PDQ/czUnJjTKb+57tO+Gu9Hjx6lqKgowetlxktPT6d169YZPLVKCOjglF/0/xfq7NmzB3PmzOn7hwiwPhcaGgrgt9dJM0M83llPiEQipKWlGbx+nE/5GWNMIJxQGWNMIJxQGWNMIJxQGWNMIB3+ln/Pnj39GQfrA9qf2/G+7Jj2ISHcR0wIHSbUOXPm9GccrA/xvuwa9xETgsG0KcbMjXZqCh9FMnPH11AZY0wgnFAZY0wgnFAZY0wgnFAZY0wgnFAZY0wgnFAZY0wgnFAZY0wgnFAZY0wgnFAZY0wgnFAZY0wgnFAZY0wgnFAZY0wgnFAZY0wgnFAZY0wgnFAZY0wgnFAZY0wgnFAZY0wgnFAZY0wgnFAZY0wgnFAZY0wgnFAZY0wgnFAZY0wgnFAZY0wgnFAZY0wgnFAZY0wgnFAZY0wgnFAZY0wgnFAZY0wgnFAZY0wgnFAZY0wgnFAZY0wgnFAZY0wgnFAZY0wgVqYOgLH7HT9+HCdPntQru3DhAgAgISFBrzwgIAD/+Z//2W+xMdYVERGRqYNgTOvYsWP405/+BLFYDAuL9k+g2traoNFokJWVhcmTJ/dzhIx1jBMqMyttbW1wdXVFRUVFp+s5OTmhrKwMlpaW/RQZY13ja6jMrFhYWGDu3LmwtrbucB1ra2vMmzePkykzO5xQmdn5y1/+gpaWlg6Xt7S04C9/+Us/RsSYcfiUn5mlIUOG4Nq1a+0u8/DwwLVr1yASifo5KsY6x0eozCy99NJLEIvFBuVisRivvPIKJ1NmlvgIlZmlCxcuYNiwYe0uO3fuHIYPH97PETHWNT5CZWbp8ccfx/Dhww2ORP38/DiZMrPFCZWZrb/+9a96d/LFYjFefvllE0bEWOf4lJ+ZreLiYnh5eUE7REUiES5fvowhQ4aYNjDGOsBHqMxseXh44I9//CMsLCxgYWGBP/7xj5xMmVnjhMrM2ksvvQSRSAQLCwu89NJLpg6HsU7xKT8za5WVlXB1dQUAlJaWYuDAgSaOiLGOmTyh8nxCxphQTH18aBaP74uMjERAQICpwzBLc+bMeeT75/jx4xCJRBg/fny7yz/55BMAwLvvvtufYTEzcvLkSSQlJZk6DPM4Qk1LS8Ps2bNNGYbZ4v4B6uvrAQAKhaLd5aGhoQCAvXv39ltMzLzs2bMHc+bM4SNUxrrSUSJlzNzwXX7GGBMIJ1TGGBMIJ1TGGBMIJ1TGGBPIA51Q79y5g0WLFsHV1RUymQx/+tOfMHDgQIhEInz++eemDs9sHDlyBHZ2dvjuu+9MHYpZysrKQnR0NPbv3w+VSgWRSASRSNTuL7OmTJkChUIBS0tLDB8+HKdPnzZBxJ2Li4uDn58flEolJBIJfHx8sHz5cjQ0NOit980338Df3x8KhQJeXl6YP38+ysrKTNLeoUOHkJCQgNbW1p5vuDkgEwNAaWlpPfrs2rVrydfXl6qrq+mLL76gvXv3UmFhIQGgzz77TOBITaM3/aOVkZFBSqWSDh06JFBU5mXWrFk0a9asHn02NjaWpk2bRnV1dboytVpNAwYMIACUkZFh8JnMzEyaPn16j+PtaxMmTKCUlBS6desW1dXVUVpaGonFYnruued06+zevZsAUEJCAtXU1NCZM2dIpVLRqFGjSKPRmKS9pKQkmjBhAlVXV3d7m9PS0sgM0hmZPILeJAx/f3968cUX9cp6klAbGxspICCgyzJTECKhmlpf92VPE+qHH35Ivr6+1NTUpFeuVqvp66+/JgsLC3J3d6eamhq95eaeUKdOnUp3797VK5s9ezYBoKKiIiIimjhxIg0aNIja2tp063z66acEgHJyckzWXkREBAUEBHQ7qZtLQn2gT/lLSiYZ4u0AACAASURBVErafU1Gd23btg3l5eVdlrGeMce+vHjxIlatWoUPPvgAUqnUYHlgYCAiIyNx/fp1LF261AQR9lxGRobBG2GdnJwAAI2NjQDuPRrRzc1N76ffHh4eANDhu7z6o73Vq1cjNzfXLH711BMPZEL95z//CR8fH9y4cQN///vfIRKJYGtr2+H6P/74I/z8/GBnZwepVIqRI0fi6NGjAO797HXJkiW4dOkSRCIRfHx82i0DgNbWVsTGxsLT0xM2NjZ44oknkJaWBgBITU2FXC6HTCbDwYMH8fzzz0OpVGLw4MHYtWtX33dKB3JycuDp6QmRSIRPP/3U6FiTk5MhlUoxcOBALFiwAG5ubpBKpQgMDMSpU6cAABEREbC2ttY9vAQA3nrrLcjlcohEIlRWVnbYl99//z2USiXWrl3bzz0C3fYREYKCgjpcJz4+Hr6+vti6dSuysrI6XI+IsHHjRgwbNgwSiQQODg6YMWMGLly4AMD4sdHZ+Oqt69evw8bGBt7e3gAAlUpl8J+c9nqmSqUyWXsODg6YMGECkpKSTP6rpx4x8RFyr05pXVxc6OWXX9Yra++Uf+/evbR69WqqqqqiW7du0dixY2nAgAG65SEhIaRWq/Xqaa9s6dKlJJFIaN++fVRdXU0rVqwgCwsL+vnnn4mIKCYmhgDQsWPHqLa2lsrLy2n8+PEkl8uppaWlR9vYm/7RKi4uJgC0adMmXZkxsYaHh5NcLqfz589Tc3Mz5efnk7+/PykUCt2p3Ny5c8nFxUWvvcTERAJAFRUVRNR+X2ZkZJBCoaC4uLhebRtRz075VSoV+fn5tbtMrVbTlStXiIjoxIkTZGFhQUOGDKGGhgYiMjzlj42NJWtra9qxYwfV1NTQ2bNnafTo0eTk5ERlZWVEZFx/dzW+eur27dukUCgoIiJCV5adnU1isZiSk5Oprq6Ozp07R8OGDaNnn322V20J0V50dDQBoDNnzhjdJp/y96NZs2bh/fffh4ODAxwdHREUFIRbt26hoqLC6Dqam5uRmpqK4OBghISEwN7eHitXroRYLMb27dv11g0MDIRSqYSzszPCwsJw+/ZtFBUVCb1ZgugqVisrK92Rl5+fH1JTU1FfX2+wzd01depU1NXVYdWqVb3dhG67ffs2rly5ArVa3eW6AQEBePfdd3H16lW89957BsubmpqwceNGzJw5E/PmzYOdnR1GjhyJzz//HJWVldi8ebPe+h31d3fGV3etW7cObm5uiI+P15VNmDABUVFRiIiIgFKpxIgRI1BfX4+tW7f2qi0h2hs6dCgAIC8vr9ex9LdHIqH+nva6a3emaBQUFKCxsREjRozQldnY2MDV1VV3atcea2trAIBGo+lhtP3HmFjHjBkDmUzW6Tabu/LychARZDKZUevHx8fjscceQ0pKCnJycvSW5efno6GhAWPGjNEr9/f3h7W1te7ySHvu7++ejq+uHDhwAHv27MHRo0f1nokQExODzZs349ixY2hoaMDly5cRGBiIgIAAFBcXm7Q97X65efNmj+MwlUcioR4+fBjPPPMMnJ2dIZFIsHz58m7Xcfv2bQDAypUrdfMURSIRrl27prvw/qiQSCTdOro3N83NzQDubYcxpFIptm/fDpFIhFdffRVNTU26ZTU1NQDQ7jV8e3t73ZOyutIX42v37t1Yv349srOz9V4dc+PGDSQkJOCNN97ApEmTIJfL4e3tjS1btqC0tBSJiYkmbc/GxgbAb/vpQfLQJ9SioiIEBwfD1dUVp06dQm1tLRISErpdj7OzM4B7z96ke9PNdH8nT54UOmyzpdFoUFNTg8GDB5s6lB7TfmG7c4YSEBCAxYsXo7CwEGvWrNGV29vbA0C7ibM7/ST0+Nq0aRN27tyJH374AYMGDdJbVlhYiNbWVoNypVIJR0dH5Ofnm7S9lpYWAL/tpwfJQ//4vry8PGg0GixcuFB3N7Enbwnw8PCAVCpFbm6u0CE+ULKzs0FEGDt2LIB711gfhMsZ99P+mq62trZbn1uzZg0yMjJw5swZeHp6AgBGjBgBW1tb/PLLL3rrnjp1Ci0tLXjqqaeMqluo8UVEeO+991BdXY309HRYWRl+xbVJ/saNG3rl9fX1qKqq0k1nMlV72v3i4uJidBzm4qE/QtUO/KysLDQ3N6OwsNDgupajoyNKS0tx9epV1NfXQ6PRGJRZWlpi/vz52LVrF1JTU1FXV4fW1laUlJQYDJSHSVtbG6qrq3H37l2cPXsWkZGR8PT0xCuvvAIA8PHxQVVVFdLT06HRaFBRUWEwj7G9/s3MzDTZtCmZTAaVSoWSkpJufU576n//nEupVIolS5bgwIED2LlzJ+rq6pCXl4c333wTbm5uCA8PN7rursZXWFgYXFxcOv256/nz5/HRRx9hy5YtEIvFepcPRCIRNmzYAG9vb0ycOBFbtmzB8ePH0dTUhOLiYl2sr732mkna09Lul5EjRxrVd2bFNJMLfoMeTAu6evUq/eEPfyAAZGVlRaNHj6Z9+/bRxx9/TC4uLgSA5HI5zZw5k4iIoqKiyNHRkezt7Sk0NFT3Cw21Wk1FRUV0+vRp8vLyIhsbGxo3bhyVlZW1W3bnzh2KiooiT09PsrKyImdnZwoJCaH8/HxKSUkhmUxGAGjo0KF06dIl2rx5MymVSgJAXl5e9Ouvv/ZL/9xv06ZN5OrqSgBIJpNRUFCQ0bGGh4eTWCwmd3d3srKyIqVSSTNmzKBLly7p6r916xZNnDiRpFIpeXt70zvvvEPLli0jAOTj49Nh/x45coQUCgXFx8f3eNu0ejJtKiIigsRiMTU2NurKDhw4QGq1mgCQk5MTvf322+1+dtmyZXrTptra2igxMZGGDh1KYrGYHBwcKDg4mAoKCoiIjO7vzsYXEVFwcDABoNjY2A63Ky8vjwB0+JeYmEhERJWVlRQZGUk+Pj4kkUjI1taWnn76afr22291dfV3e1pTp04ld3d3vV9VdcVcpk2ZPILeJoyHnSn7Jzw8nBwdHU3Sdnf0JKEWFhaSlZUV7dixo4+iEl5rayuNHz+etm3b9lC2R3Qv8UqlUtqwYUO3PmcuCfWhP+VnvfPAP/2nAz4+PoiLi0NcXJzBU5HMUWtrK9LT01FfX4+wsLCHrj2t1atXY9SoUYiIiOi3NoXECZU9sqKjoxEaGoqwsLBu36Dqb9nZ2di/fz8yMzONnj/7ILUHABs3bkRubi6OHDkiyDM6TIETKmvXihUrsH37dtTW1sLb2xv79u0zdUh9Yu3atYiIiMCHH35o6lA6NXnyZHz99dd6z014mNo7ePAg7ty5g+zsbDg4OPRLm33hoZ82xXpm3bp1WLdunanD6BdTpkzBlClTTB3GI2369OmYPn26qcPoNT5CZYwxgXBCZYwxgXBCZYwxgXBCZYwxgZjFTalH6eEiPcH90zntTxX37Nlj4kiYqZjLd0REZNr3DPTkQSWMMdYeE6cz8zhCTUtLw+zZs00dhlkSiUTcP10IDQ0FAOzdu9fEkTBT2bNnD+bMmWPqMPgaKmOMCYUTKmOMCYQTKmOMCYQTKmOMCYQTKmOMCYQTKmOMCeSBSqj79++HSqUyeG/N/X9DhgzBhg0bdC9i+/zzz00dNnsIZGVlITo62mAMvvTSSwbrTpkyBQqFApaWlhg+fHin72QylWeeeabD79D9r8T+5ptv4O/vD4VCAS8vL8yfPx9lZWUd1tvc3IzHH38cK1euBAAcOnQICQkJD+2Dyn/vgUqoISEhuHz5MtRqNezs7HSv2b179y4aGxtx8+ZNyGQyLF26FCdOnDB1uOwh8f777yM5ORkrVqzQG4MDBgzAzp07cfjwYb31//GPf2Dv3r2YNm0a8vPzMXr0aBNF3jPjxo0DcG9++Ny5cxEaGoqSkhIcPHgQx48fx/PPP4+7d++2+9mYmBgUFBTo/h0UFASpVIrJkyejpqamX+I3pQcqoXbE0tISNjY2GDhwIHx9fXtcT1NTEwIDA7ssexT05XY/SH26fv167N69G3v27IFCodBblpycDAsLC4SHh5v9E/9/TyqVoq6uTndQov0LDw/H8uXLAQBffPEFBg0ahGXLlsHOzg6jRo3C4sWLkZuba/DmYAA4ceIEzp07Z1C+aNEiPPnkk3jhhRc6TMQPi4ciod4vPT29x5/dtm0bysvLuyx7FPTldj8ofXrx4kWsWrUKH3zwAaRSqcHywMBAREZG4vr161i6dKkJIuy577//3uA/iOLiYpw7dw6TJk3S/dvNzU3v5+EeHh4AYPCq8KamJixbtgxJSUnttrd69Wrk5uZ2uPxh8dAl1M78+OOP8PPzg52dHaRSKUaOHImjR48CACIjI7FkyRJcunQJIpEIPj4+7ZYB915gFhsbC09PT9jY2OCJJ55AWloaACA1NRVyuRwymQwHDx7E888/D6VSicGDB2PXrl19vo1EhI0bN2LYsGGQSCRwcHDAjBkzcOHCBQBAREQErK2t9V5t8dZbb0Eul0MkEqGysrLd7U5OToZUKsXAgQOxYMECuLm5QSqVIjAwUHe00tO6gXtfcKVSibVr1/Z5HxkrOTkZRISgoKAO14mPj4evry+2bt2KrKysDtfrar8YO246G3u9tX79eixatEj3b5VKZfAfn/b6qUql0iuPiYnBW2+9BWdn53brdnBwwIQJE5CUlGTy39v3KZO8a/U+6MFrktVqNdnZ2emVHTt2TPcOcKJ7rwkGQJ999pmubO/evbR69WqqqqqiW7du0dixY2nAgAG65SEhIaRWq/Xqba9s6dKlJJFIaN++fVRdXU0rVqwgCwsL+vnnn4mIKCYmhgDQsWPHqLa2lsrLy2n8+PEkl8uppaWlW9va3f6JjY0la2tr2rFjB9XU1NDZs2dp9OjR5OTkRGVlZURENHfuXHJxcdH7XGJiIgGgioqKDrc7PDyc5HI5nT9/npqbmyk/P5/8/f1JoVBQUVFRr+rOyMgghUJBcXFxRm+rVk9eI20MlUpFfn5+7S5Tq9V05coVIiI6ceIEWVhY0JAhQ6ihoYGIiDIzM2n69Om69Y3ZL8aMm67GXk+VlJSQn58ftba26sqys7NJLBZTcnIy1dXV0blz52jYsGH07LPP6n02JyeHgoKCiIiooqKCAFBMTIxBG9HR0QSAzpw506tY28Ovke6l2tpavTuTkydP7vIzs2bNwvvvvw8HBwc4OjoiKCgIt27dQkVFhdHtNjc3IzU1FcHBwQgJCYG9vT1WrlwJsViM7du3660bGBgIpVIJZ2dnhIWF4fbt2ygqKur2thqrqakJGzduxMyZMzFv3jzY2dlh5MiR+Pzzz1FZWYnNmzf3ug0rKyvdUZafnx9SU1NRX19vsO3dNXXqVNTV1WHVqlW9jlEIt2/fxpUrV6BWq7tcNyAgAO+++y6uXr2K9957z2B5d/dLR+OmO2Ovu9avX4933nkHFha/pYQJEyYgKioKERERUCqVGDFiBOrr67F161a9bYuMjERqamqXbQwdOhQAkJeX16tYzdkDm1Dvv8tPRPiv//qvbtehfVVtd6Z0FBQUoLGxESNGjNCV2djYwNXVVXf61h5ra2sAgEaj6XacxsrPz0dDQwPGjBmjV+7v7w9ra+t2byT01pgxYyCTyTrd9gdReXk5iMjoVyjHx8fjscceQ0pKCnJycvSW9Wa/3D9uejr2ulJaWopDhw7hlVde0SuPiYnB5s2bcezYMTQ0NODy5csIDAxEQEAAiouLAdx7O+4bb7wBd3f3LtvR9uXNmzd7HKu5e2AT6u8988wzXd4YOHz4MJ555hk4OztDIpHo7mZ2x+3btwEAK1eu1DtCvnbtGhobG3sUu1C001Lun0eoZW9vj/r6+j5pVyKRdOso/0HQ3NwM4N62GUMqlWL79u0QiUR49dVX0dTUpFsm1H7pq7GXkJCAv/3tb3o33m7cuIGEhAS88cYbmDRpEuRyOby9vbFlyxaUlpYiMTEROTk5yMvLw+uvv25UOzY2NgB+69uH0UOTULtSVFSE4OBguLq64tSpU6itrUVCQkK369FedP/kk08MppyY+qnh9vb2ANDuF7SmpgaDBw8WvE2NRtNndZuS9svfnbOXgIAALF68GIWFhVizZo2uXKj90hdjr6ysDN988w0WLlyoV15YWIjW1lYMGjRIr1ypVMLR0RH5+fnYtm0bjh07BgsLC11y18a4du1aiEQi/PLLL7rPtrS0APitbx9Gj0xCzcvLg0ajwcKFC6FSqSCVSnv0tgAPDw9IpVLk5ub2QZS9M2LECNja2uoNYgA4deoUWlpa8NRTTwG4dx1UqEsP2dnZICKMHTtW8LpNSftLu+7OL12zZg0ef/xxnDlzRldm7H7pSl+MvYSEBMybNw+Ojo565dokf+PGDb3y+vp6VFVVwcPDA9u3bzdI7NozlZiYGBCR3mUObV+6uLgIFr+5eWQSqqenJ4B7PyFsbm5GYWGhwbUrR0dHlJaW4urVq6ivr4dGozEos7S0xPz587Fr1y6kpqairq4Ora2tKCkpMRh8/U0qlWLJkiU4cOAAdu7cibq6OuTl5eHNN9+Em5sbwsPDAQA+Pj6oqqpCeno6NBoNKioqDOYVttcXANDW1obq6mrcvXsXZ8+eRWRkJDw9PXXX33pad2ZmpllNm5LJZFCpVLr3VRlLe+pvaWmpV2bMfjGm7q7GXlhYGFxcXIz6uevNmzfx5Zdf4t133zVY5u3tjYkTJ2LLli04fvw4mpqaUFxcrIv1tddeMyrm+2n7cuTIkd3+7AOjv6cV/B66MS3ov//7v8nX15cAEABydXWlyZMnG6z38ccfk4uLCwEguVxOM2fOJCKiqKgocnR0JHt7ewoNDaVPP/2UAJBaraaioiI6ffo0eXl5kY2NDY0bN47KysraLbtz5w5FRUWRp6cnWVlZkbOzM4WEhFB+fj6lpKSQTCYjADR06FC6dOkSbd68mZRKJQEgLy8v+vXXX/ukf4iI2traKDExkYYOHUpisZgcHBwoODiYCgoKdOvcunWLJk6cSFKplLy9vemdd96hZcuWEQDy8fHpsC/Cw8NJLBaTu7s7WVlZkVKppBkzZtClS5d6XfeRI0dIoVBQfHy80duq1VfTpiIiIkgsFlNjY6Ou7MCBA6RWqwkAOTk50dtvv93uZ5ctW6Y3baqr/WLsuOls7BERBQcHEwCKjY3tcvsWL15M8+bN63B5ZWUlRUZGko+PD0kkErK1taWnn36avv322w4/09m0qalTp5K7uzu1tbV1GVt3mcu0KZNH0N2E8agxp/4JDw8nR0dHU4dhoK8SamFhIVlZWdGOHTsEr7uvtLa20vjx42nbtm2mDkVPZWUlSaVS2rBhQ5/Uby4J9ZE55WfCeFSeGgTcu3wRFxeHuLg4NDQ0mDqcLrW2tiI9PR319fUICwszdTh6Vq9ejVGjRiEiIsLUofQpTqiMdSI6OhqhoaEICwsz+wegZGdnY//+/cjMzDR6/mx/2LhxI3Jzc3HkyBHd3O+HFSdUZpQVK1Zg+/btqK2thbe3N/bt22fqkPrN2rVrERERgQ8//NDUoXRq8uTJ+Prrr/WepWBqBw8exJ07d5CdnQ0HBwdTh9PnrEwdAHswrFu3DuvWrTN1GCYzZcoUTJkyxdRhPHCmT5+O6dOnmzqMfsNHqIwxJhBOqIwxJhBOqIwxJhBOqIwxJhARkWkfny0SiTB27NiH7uEaQtm3bx/3Txd++uknANA9T4A9ekpKSvDTTz+Z/G0AJk+ooaGhpmyePQC0DyR+qH8DzgSxd+9ek7Zv8oTKWFdmz54NANizZ4+JI2Gsc3wNlTHGBMIJlTHGBMIJlTHGBMIJlTHGBMIJlTHGBMIJlTHGBMIJlTHGBMIJlTHGBMIJlTHGBMIJlTHGBMIJlTHGBMIJlTHGBMIJlTHGBMIJlTHGBMIJlTHGBMIJlTHGBMIJlTHGBMIJlTHGBMIJlTHGBMIJlTHGBMIJlTHGBMIJlTHGBMIJlTHGBMIJlTHGBMIJlTHGBMIJlTHGBMIJlTHGBMIJlTHGBMIJlTHGBMIJlTHGBMIJlTHGBMIJlTHGBMIJlTHGBCIiIjJ1EIxpffXVV9i4cSNaW1t1ZZWVlQAAJycnXZmlpSUWL16Mv/71r/0eI2Md4YTKzMqvv/6Kxx57zKh1CwoK4Ovr28cRMWY8PuVnZsXX1xdPPvkkRCJRh+uIRCI8+eSTnEyZ2eGEyszOX//6V1haWna43MrKCi+//HI/RsSYcfiUn5md0tJSeHh4oK2trd3lIpEIxcXFcHd37+fIGOscH6EyszNo0CAEBgbCwsJweFpYWODpp5/mZMrMEidUZpZeeumldstFIhHf2Wdmi0/5mVmqrq6Gi4sLNBqNXrmVlRXKysowYMAAE0XGWMf4CJWZJQcHB/z5z3/WuzllaWmJZ599lpMpM1ucUJnZmjdvnt6NKSLCvHnzTBgRY53jU35mthobGzFgwAA0NzcDAKRSKSorKyGXy00cGWPt4yNUZrZkMhmCg4MhFoshFosRHBzMyZSZNU6ozKy9+OKL0Gg00Gg0ePHFF00dDmOdshKqopKSEpw4cUKo6hgDALS2tkImk4GIUFdXhz179pg6JPaQCQwMxODBg4WpjASSlpZGAPiP//iP/x6ov7S0NKHSIAl2hKpFfI/roSISiZCWlobZs2ebLIZ//etfEIlE+M///E+TxdCZ0NBQAMDevXtNHAnrrs4ewtMTgidUxoQ2fvx4U4fAmFE4oTKz195v+hkzRzxSGWNMIJxQGWNMIJxQGWNMIJxQGWNMII9sQn399dehUCggEomQm5tr6nAeakeOHIGdnR2+++47U4dilrKyshAdHY39+/dDpVJBJBJBJBK1+0zYKVOmQKFQwNLSEsOHD8fp06dNEHHnnnnmGd02/P7P1tZWt94333wDf39/KBQKeHl5Yf78+SgrK+uw3ubmZjz++ONYuXIlAODQoUNISEjQe0OuqT2yCXXr1q3YsmWLqcN4JPDc5I69//77SE5OxooVKxASEoLLly9DrVZjwIAB2LlzJw4fPqy3/j/+8Q/s3bsX06ZNQ35+PkaPHm2iyHtm3LhxAIC0tDTMnTsXoaGhKCkpwcGDB3H8+HE8//zzuHv3brufjYmJQUFBge7fQUFBkEqlmDx5Mmpqavol/q48sgmV9Z+pU6eitrYW06ZNM0n7TU1NCAwMNEnbnVm/fj12796NPXv2QKFQ6C1LTk6GhYUFwsPDUVtba6IIe0YqlaKurg5EpPcXHh6O5cuXAwC++OILDBo0CMuWLYOdnR1GjRqFxYsXIzc3F6dOnTKo88SJEzh37pxB+aJFi/Dkk0/ihRde6DAR96dHOqEK/SsJZp62bduG8vJyU4eh5+LFi1i1ahU++OADSKVSg+WBgYGIjIzE9evXsXTpUhNE2HPff/+9wX8QxcXFOHfuHCZNmqT7t5ubm9530MPDAwBw7do1vc82NTVh2bJlSEpKare91atXIzc3t8Pl/emRSahEhMTERDz22GOQSCSws7PDsmXL9NZpbW1FbGwsPD09YWNjgyeeeAJpaWkAgNTUVMjlcshkMhw8eBDPP/88lEolBg8ejF27dunq+Ne//oX/+I//gEwmg1KpxMiRI1FXV9dl/Q+rnJwceHp6QiQS4dNPPwVgXF8mJydDKpVi4MCBWLBgAdzc3CCVShEYGKg7gomIiIC1tTVcXV117b311luQy+UQiUSorKxEZGQklixZgkuXLkEkEsHHxwfAvS+9UqnE2rVr+7lHoNs+IkJQUFCH68THx8PX1xdbt25FVlZWh+sRETZu3Ihhw4ZBIpHAwcEBM2bMwIULFwAYP3b7cnyuX78eixYt0v1bpVIZ/CenvX6qUqn0ymNiYvDWW2/B2dm53bodHBwwYcIEJCUlmf7yklAPBdA+HMVcxcTEkEgkoo8//piqq6upsbGRUlJSCACdOXOGiIiWLl1KEomE9u3bR9XV1bRixQqysLCgn3/+WVcHADp27BjV1tZSeXk5jR8/nuRyObW0tFBDQwMplUpKSEigpqYmKisro5kzZ1JFRYVR9ZsjCPDwiOLiYgJAmzZt0pV11ZdEROHh4SSXy+n8+fPU3NxM+fn55O/vTwqFgoqKioiIaO7cueTi4qLXXmJiIgHQ9XtISAip1Wq9dTIyMkihUFBcXFyvto2IaNasWTRr1qxufUalUpGfn1+7y9RqNV25coWIiE6cOEEWFhY0ZMgQamhoICKizMxMmj59um792NhYsra2ph07dlBNTQ2dPXuWRo8eTU5OTlRWVkZExvV3X43PkpIS8vPzo9bWVl1ZdnY2icViSk5Oprq6Ojp37hwNGzaMnn32Wb3P5uTkUFBQEBERVVRUEACKiYkxaCM6Olrvu2wsIca3Xn1CVWTOCbWxsZFkMhn9+c9/1ivftWuXbic0NTWRTCajsLAwvc9JJBJauHAhEf02KJuamnTraJPyxYsX6dy5cwSAMjIyDGIwpn5z1NcJtaO+JLqXUO3s7PTq+vnnnwkAffDBB0TU84QqpO4m1IaGBhKJRDRt2rR2l9+fUImIlixZQgDo7bffJiL9hNrY2Ei2trZ644qI6N///jcB0P2H0VV/9+X4fPvtt+mzzz4zKF+5cqXeU58GDx5MxcXFeu2PGTOGSkpKiKjzhPrll18SAPrqq6+6FZvQCfWROOW/ePEiGhsbMXny5A7XKSgoQGNjI0aMGKErs7Gxgaurq+7UqT3W1tYAAI1GA5VKhYEDB2LevHlYvXo1rl692uv6HyX392VHxowZA5lM9kD3WXl5OYgIMpnMqPXj4+Px2GOPISUlBTk5OXrL8vPz0dDQgDFjxuiV+/v7w9raut0bPFr393dfjc/S0lIcOnQIr7zyil55TEwMNm/ejGPHjqGhoQGXL19GYGAgAgICUFxcDABYsWIF3njjDbi7I/IqiAAAIABJREFUu3fZjrYvb9682eNYhfBIJNSSkhIA6PAaDADcvn0bALBy5Uq9eXPXrl1DY2OjUe3Y2Njghx9+wLhx47B27VqoVCqEhYWhqalJkPrZPRKJBBUVFaYOo8e078iSSCRGrS+VSrF9+3aIRCK8+uqraGpq0i3TThe6f36nlr29Perr641qo6/GZ0JCAv72t7/p3Xi7ceMGEhIS8MYbb2DSpEmQy+Xw9vbGli1bUFpaisTEROTk5CAvLw+vv/66Ue3Y2NgA+K1vTeWRSKjanXnnzp0O19Em208++cRgusfJkyeNbmv48OH47rvvUFpaiqioKKSlpWHDhg2C1f+o02g0qKmpEe4J6yag/fJ3Z0J6QEAAFi9ejMLCQqxZs0ZXbm9vDwDtJs7u9FNfjM+ysjJ88803WLhwoV55YWEhWltbMWjQIL1ypVIJR0dH5OfnY9u2bTh27BgsLCx0yV0b49q1ayESifDLL7/oPtvS0gLgt741lUcioY4YMQIWFhb417/+1eE6Hh4ekEqlvfrVVGlpKc6fPw/g3gD98MMPMXr0aJw/f16Q+hmQnZ0NIsLYsWMBAFZWVp1eIjBHAwcOhEgk6vb80jVr1uDxxx/HmTNndGUjRoyAra2tXnIBgFOnTqGlpQVPPfWUUXX3xfhMSEjAvHnz4OjoqFeuTfI3btzQK6+vr0dVVRU8PDywfft2g8SuPSuJiYkBEeld5tD2pYuLi2Dx98QjkVCdnZ0REhKCffv2Ydu2bairq8PZs2exefNm3TpSqRTz58/Hrl27kJqairq6OrS2tqKkpMRgx3ektLQUCxYswIULF9DS0oIzZ87g2rVrGDt2rCD1P4ra2tpQXV2Nu3fv4uzZs4iMjISnp6fumpyPjw+qqqqQnp4OjUaDiooKg3mMjo6OKC0txdWrV1FfXw+NRoPMzEyTTZuSyWRQqVS6S1HG0p76W1pa6pUtWbIEBw4cwM6dO1FXV4e8vDy8+eabcHNzQ3h4uNF1dzU+w8LC4OLiYtTPXW/evIkvv/wS7777rsEyb29vTJw4EVu2bMHx48fR1NSE4uJiXayvvfaaUTHfT9uXI0eO7PZnBSXU3S1zvstPRFRfX0+vv/46DRgwgGxtbWncuHEUGxuru7v4v//7v3Tnzh2KiooiT09PsrKyImdnZwoJCaH8/HxKSUkhmUxGAGjo0KF06dIl2rx5MymVSgJAXl5e9M9//pMCAwPJwcGBLC0tadCgQRQTE0N3794lIuq0fnOFXt4F3bRpE7m6uhIAkslkFBQUZFRf/vrrrxQeHk5isZjc3d3JysqKlEolzZgxgy5duqSr/9atWzRx4kSSSqXk7e1N77zzDi1btowAkI+PDxUVFdHp06fJy8uLbGxsaNy4cVRWVkZHjhwhhUJB8fHxve6jnkybioiIILFYTI2NjbqyAwcOkFqtJgDk5OSku6v/e8uWLdObNtXW1kaJiYk0dOhQEovF5ODgQMHBwVRQUEBEZHR/dzU+g4ODCQDFxsZ2uX2LFy+mefPmdbi8srKSIiMjycfHhyQSCdna2tLTTz9N3377bYef6ewu/9SpU8nd3Z3a2tq6jO1+vR3fBvUJVZG5J1TWM0IPuO4IDw8nR0dHk7TdHT1JqIWFhWRlZUU7duzoo6iE19raSuPHj6dt27aZOhQ9lZWVJJVKacP/Y+/eg6I4s//xvweZK8xwCVflIgMaFY2EeAN1NesnZI3lFS9kNd81bnbRxBAiugQxLCKgBKIWBpOPxlAVdQUUF40rZqN+MOVqjFuBqBAVURAlCBiQy4xyO78//DFxBGSAxhnMeVVNlXb3nDnzzNOH6e5n+klK6vZzhe7fv4lDftZ/mdKdhITk5eWFmJgYxMTEoL6+3tjpdKmlpQVZWVmoq6tDUFCQsdPREx0dDR8fH4SEhBg7ld/GOVTGTFFERAQWLFiAoKAgk78BSk5ODjIzM5GdnW3w+NmnYfPmzcjLy8PRo0chFouNnQ4XVGaa1q5di9TUVNy7dw8eHh44cOCAsVPqE3FxcQgJCcHGjRuNncoTTZs2DXv37tW7b4KxHTp0CA8ePEBOTg5sbGyMnQ4AnvWUmaj4+HjEx8cbO42nIiAgAAEBAcZOo9+ZPXs2Zs+ebew09PA3VMYYEwgXVMYYEwgXVMYYEwgXVMYYE4jgF6UWLFggdEhmZFu2bMH+/fuNnYbJ+u677wBw32f8DbVPfPfdd7qdjDH22yH4N1T+JvPrN5VnoS1EIhHef/99LFy40NipmKxn6fP+rRF6ok7+hsoYYwLhgsoYYwLhgsoYYwLhgsoYYwLhgsoYYwIxWkHNzMyEWq3Wm2FRJBJBIpHAwcEBU6dORWJiIqqrq42VoslLSkrSzU/02WefGTsd1kPHjx9HREREu33ijTfeaLdtQEAAlEolBgwYAG9vb4OmI3naYmJiMGLECKhUKkilUnh5eeFvf/ub3n1fY2Nj2+37IpFIbxpr4OGkjPHx8fDy8oJEIoG1tTVGjhyJ4uJiHD58GAkJCSZ1z1yjFdTAwEBcv34dnp6esLKyAhGhtbUVFRUVyMjIgIeHB8LDw+Ht7d1uAjL20OrVq3HmzBljp8F64e9//zuSk5Oxdu1avX3iueeew549e/Cvf/1Lb/t///vf2L9/P2bOnIn8/Hz4+voaKfPOnTx5EitXrkRxcTGqqqoQHx+PrVu39uiHD4sWLcKXX36JvXv3QqPR4KeffoKnpyfq6+sxa9YsyGQyTJs2TTedtrGZ1CG/SCSCtbU1pk6ditTUVGRkZODOnTuYMWOGyd+AlwlLq9XC39+/38Xujk2bNiEtLQ0ZGRlQKpV665KTk2FmZobg4OB+1/ctLS0RHBwMW1tbKJVKLFy4EHPnzsWxY8dQWlqq22737t3tZja9dOmSbn1aWhqysrKwf/9+jB8/Hubm5nB2dsahQ4d032Tfe+89jB49Gq+99hqam5uf+nt9nEkV1MfNnz8fS5cuRUVFBR/S/sbs2rULFRUV/S62oa5du4YPP/wQ69evh0wma7fe398foaGhuH37NlavXm2EDHvuyJEjejOzAoCdnR0AQKPRGBzn008/ha+vb5czmUZHRyMvLw9bt27tfrICM+mCCkA3XXB2djaAh3PbREVFwc3NDXK5HC+88ALS09MBANu3b4eFhQUUCgUOHTqE6dOnQ6VSwcXFBfv27dPFPHXqFMaNGweFQgGVSoVRo0ahtra2y/h95aOPPoJCoYBSqURFRQXCwsIwaNAgXLlypdv5hISEQCKR6N1Z/Z133oGFhQVEIhGqqqr69L0QETZv3ozhw4dDKpXCxsYGc+bMweXLlw3OLzQ0FGFhYSgqKoJIJIKXlxeSk5Mhk8ng4OCA5cuXw9nZGTKZDP7+/jh37lyvYgPAsWPHnuq00snJySAizJo1q9NtYmNjMXToUHz++ec4fvx4p9t11eaG7hd92fdv374NuVwODw8Pg7ZvbGzEd999Bx8fny63tbGxwZQpU7B161Y8nHfPiISa7a+ns556enqSlZVVp+tra2sJALm6uhIR0erVq0kqldKBAweourqa1q5dS2ZmZnT+/HkiIoqMjCQAdOLECbp37x5VVFTQ5MmTycLCghobG6m+vp5UKhUlJCSQVqul8vJymjdvHlVWVhoU3xA9mQWzLe/33nuPtm3bRvPmzaOffvqpy3wKCwsJAH366ae6WIsXLyZHR0e9+ImJiQRA9z4NhW7OChkVFUUSiYR2795NNTU1dOHCBfL19SU7OzsqLy83OL/AwEDy9PTU2yY4OJgsLCyooKCA7t+/T/n5+TR27FhSKpV08+bNXsU+cuQIKZVKiomJMfi9tunJ561Wq2nEiBEdrvP09KQbN24QEdGZM2fIzMyMBg8eTPX19URElJ2drTeNtCFt3tV+QSRM3+9IQ0MDKZVKCgkJ0S3bsGEDubi4kLW1NYnFYho8eDDNnj2bvv/+eyIiunHjBgEgHx8fmjp1Kjk5OZFUKqVhw4bRJ5980m666IiICAJAubm53cqtu/27y3hCBeqrgkpEJBKJyNramrRaLSkUCgoKCtKt02g0JJVK6e233yaiXzuOVqvVbZOSkkIA6Nq1a3Tp0iUCQEeOHGn3OobEN0RvCuqjeRuSjykVVI1GQ5aWlnr5EhF9//33BEBXrHpTUB/vK+fPnycAtH79+l7F7o3uft719fUkEolo5syZHa5/tKASEYWFhREAWrlyJRHpF1RD27yr/UKovt+RyMhIGjp0KNXW1uqW3bx5k3744Qeqq6ujBw8e0NmzZ+nFF18kuVxOly5doosXLxIAeuWVV+g///kP3b17l2pqauiDDz4gALRnzx691/jiiy8IAH355Zfdyk3ogmryh/wNDQ0gIqhUKly5cgUajUZvaIVcLoeTk5Pu8KYjEokEwMMhGGq1Gg4ODliyZAmio6NRXFys266n8fuKqeXTlfz8fNTX12PMmDF6y8eOHQuJRKI7NBfSmDFjoFAoTLI9OlNRUQEiMnj20NjYWDz//PNISUnB6dOn9db1ps0f3S/6qq8dPHgQGRkZ+Prrr/UuvLm6uuLFF1+EpaUlJBIJJkyYgNTUVGi1WqSkpEAqlQIAvL294e/vD1tbW1hZWWH9+vWwsrLCjh079F6nrS3v3LnT41yFYPIF9erVqwCAYcOGoaGhAQCwbt06vbFrJSUlBp/slsvlOHnyJCZNmoS4uDio1WoEBQVBq9UKEl9IppZPV9qGrlhaWrZbZ21tjbq6uj55XalUisrKyj6J3Rfu378PALqi0RWZTIbU1FSIRCIsW7YMWq1Wt06oNu+LvpaWloZNmzYhJycHgwcP7nL7UaNGYcCAAbh69SqcnZ0BoN05f4lEAnd3dxQVFektl8vlAH5tW2Mx+YJ67NgxAMD06dNhb28P4OENj+mx4RZnz541OKa3tze++uorlJWVITw8HOnp6UhKShIsvlBMLZ+uWFtbA0CHO3FNTQ1cXFwEf82mpqY+i91X2nb+7gxI9/Pzw6pVq1BYWIgNGzbolgvV5kL3tW3btmHPnj04efIkBg4caNBzWltb0draCqlUCktLSwwZMgQFBQXttmtuboaVlZXessbGRgC/tq2xmHRBLS8vx5YtW+Di4oJly5bB1dUVMpkMeXl5PY5ZVlam+5Ds7e2xceNG+Pr6oqCgQJD4QuppPubm5mhqauqjrDo3cuRIWFpatvshxrlz59DY2IiXXnpJ8PxycnJARJgwYYLgsftK26/buju+dMOGDRg2bBhyc3N1ywxt864I1feJCOHh4bh48SKysrI6/OYMAK+++mq7ZefPnwcRwc/PD8DDQf25ubm4fv26bhuNRoOSkpJ2Q6na2tLR0bFX+feWSRRUIkJ9fT1aW1tBRKisrER6ejomTpyIAQMGICsrCyqVCjKZDG+++Sb27duH7du3o7a2Fi0tLbh16xZ+/vlng16rrKwMy5cvx+XLl9HY2Ijc3FyUlJRgwoQJgsQXUk/z8fLywi+//IKsrCw0NTWhsrISJSUlTyXfsLAwHDx4EHv27EFtbS0uXryIFStWwNnZGcHBwQbnZ2tri7KyMhQXF6Ourk5XJFtbW1FdXY3m5mZcuHABoaGhcHNz0w2v62ns7OzspzZsSqFQQK1W49atW916Xtuh/6NjPA1tc0Nid9XXgoKC4Ojo+MSfuxYUFOCjjz7Czp07IRaL2/20NCkpCcDDYVRpaWmoqalBU1MTzp49i7feegtubm5YsWIFAGDVqlVwd3fH0qVLcfPmTdy9exfh4eHQarX44IMP9F63rS27GrPa54S6utXdq/yHDx+mF154gRQKBUkkEjIzMyMAuiv648aNo5iYGLp7967e8x48eEDh4eHk5uZG5ubmZG9vT4GBgZSfn08pKSmkUCgIAA0ZMoSKiopox44dpFKpCAC5u7vTN998Q/7+/mRjY0MDBgyggQMHUmRkJDU3N3cZ31DdveqbkJBAcrlcNzxs9+7dBr3fjz/+mBwdHQkAWVhY0Lx584iI6O7du/Tyyy+TTCYjDw8Pevfdd2nNmjUEgLy8vHRDjAyBbl4FbW1tpcTERBoyZAiJxWKysbGhuXPn0pUrV3TbGJLfDz/8QO7u7iSXy2nSpElUXl5OwcHBJBaLadCgQWRubk4qlYrmzJlDRUVFvY599OhRUiqVFBsba/B7bdOTUR0hISEkFotJo9Holh08eJA8PT0JANnZ2emu6j9uzZo1esOmumpzQ/aLq1evdtn3586dSwAoKiqq0/fVdnW+s0diYiIRPRy54OnpSRYWFmRubk4uLi70l7/8hcrKyvTilZaW0uuvv042NjYklUpp3LhxlJ2d3e51Z8yYQYMGDWo3nKor3e3fXcYTKlBPh009i3qyg5kqoTtcbwQHB5Otra2x02inJ593YWEhmZub6/3xNHUtLS00efJk2rVrl7FT0VNVVUUymYySkpK6/Vyh+7dJHPIzZihTurNQb3h5eSEmJgYxMTF6d2EyVS0tLcjKykJdXR2CgoKMnY6e6Oho+Pj4ICQkxNipmMY5VMZ+iyIiIrBgwQIEBQWZ/A1QcnJykJmZiezsbIPHzz4NmzdvRl5eHo4ePQqxWGzsdLigsv5h7dq1SE1Nxb179+Dh4YEDBw4YOyVBxMXFISQkBBs3bjR2Kk80bdo07N27V+8+CcZ26NAhPHjwADk5ObCxsTF2OgD6YBppxvpCfHw84uPjjZ1GnwgICEBAQICx0+h3Zs+ejdmzZxs7DT38DZUxxgTCBZUxxgTCBZUxxgTCBZUxxgTCBZUxxgQi+FV+kUgkdMh+61lpi0WLFmHRokXGTsPkPSufN+s50f//86teu3XrFk9pzPrEli1bAADvv/++kTNhzyJ/f3/Bbv8oWEFlrK8sXLgQAJCRkWHkTBh7Mj6HyhhjAuGCyhhjAuGCyhhjAuGCyhhjAuGCyhhjAuGCyhhjAuGCyhhjAuGCyhhjAuGCyhhjAuGCyhhjAuGCyhhjAuGCyhhjAuGCyhhjAuGCyhhjAuGCyhhjAuGCyhhjAuGCyhhjAuGCyhhjAuGCyhhjAuGCyhhjAuGCyhhjAuGCyhhjAuGCyhhjAuGCyhhjAuGCyhhjAuGCyhhjAuGCyhhjAuGCyhhjAuGCyhhjAuGCyhhjAuGCyhhjAuGCyhhjAjE3dgKMPaqqqgq1tbV6yxoaGgAA169f11uuUqlgZ2f31HJjrCsiIiJjJ8FYm9TUVCxbtsygbb/44gu8+eabfZwRY4bjgspMyr1792Bvb4+mpqYnbicWi1FZWQkrK6unlBljXeNzqMykWFlZ4bXXXoO5eedno8zNzTFjxgwupszkcEFlJmfJkiVoaWnpdH1rayuWLFnyFDNizDB8yM9Mzv3792FnZ6e7GPU4hUKBqqoqyOXyp5wZY0/G31CZyZHJZJg3bx7EYnG7dWKxGPPnz+diykwSF1Rmkv74xz92eGGqqakJf/zjH42QEWNd40N+ZpKam5vh6OiIX375RW+5tbU1Kisrn3jRijFj4W+ozCSZm5vj9ddf1zvsF4vFWLJkCRdTZrK4oDKT9frrr+sd9jc1NeH11183YkaMPRkf8jOTRURwdXXF7du3AQDOzs64ffs2RCKRkTNjrGP8DZWZLJFIhDfeeAMSiQQSiQR/+tOfuJgyk8bfUJlJu3DhAkaPHq3796hRo4ycEWOdE+zs/tmzZ7F582ahwjGmY2lpCQCIiYkxcibsWbRq1Sr4+fkJEkuwQ/7S0lIcOHBAqHDMRBw4cAC3bt0yag7u7u4YPHiwUXN4ku+++w7fffedsdNgPXDgwAGUlpYKFk/w8Sf79+8XOiQzIpFIhPfffx8LFy40Wg5t90FVq9VGy+FJFixYAID7fn8k9Dl5HtDHTJ6pFlLGHsdX+RljTCBcUBljTCBcUBljTCBcUBljTCDPZEF96623oFQqIRKJkJeXZ+x0uq21tRVbtmyBv7+/sVMRxNGjR2FlZYWvvvrK2KmYpOPHjyMiIgKZmZlQq9UQiUS6X4k9LiAgAEqlEgMGDIC3tzd++OEHI2T8ZDExMRgxYgRUKhWkUim8vLzwt7/9DfX19bptYmNjde/z0cfIkSP1YjU1NSE+Ph5eXl6QSCSwtrbGyJEjUVxcjMOHDyMhIeGJszs8bc9kQf3888+xc+dOY6fRI4WFhfjd736HVatWQaPRGDsdQfCP8Tr397//HcnJyVi7di0CAwNx/fp1eHp64rnnnsOePXvwr3/9S2/7f//739i/fz9mzpyJ/Px8+Pr6Ginzzp08eRIrV65EcXExqqqqEB8fj61bt+qGl3XHokWL8OWXX2Lv3r3QaDT46aef4Onpifr6esyaNQsymQzTpk1DTU1NH7yT7nsmC2p/9eOPP+KDDz7AihUr4OPjY+x0BDNjxgzcu3cPM2fONMrra7Vak/y2v2nTJqSlpSEjIwNKpVJvXXJyMszMzBAcHIx79+4ZKcOesbS0RHBwMGxtbaFUKrFw4ULMnTsXx44d0xtEv3v3bhCR3uPSpUu69WlpacjKysL+/fsxfvx4mJubw9nZGYcOHdJ9k33vvfcwevRovPbaa2hubn7q7/Vxz2xB7Y830Rg9ejQyMzOxePFiSKVSY6fzzNi1axcqKiqMnYaea9eu4cMPP8T69eshk8narff390doaChu376N1atXGyHDnjty5AgGDBigt8zOzg4AunXU9emnn8LX17fL+zdER0cjLy8PW7du7X6yAnsmCioRITExEc8//zykUimsrKywZs0avW1aWloQFRUFNzc3yOVyvPDCC0hPTwcAbN++HRYWFlAoFDh06BCmT58OlUoFFxcX7Nu3Txfj1KlTGDduHBQKBVQqFUaNGoXa2tou4/+WnT59Gm5ubhCJRPjkk08AGNbeycnJkMlkcHBwwPLly+Hs7AyZTAZ/f3+cO3cOABASEgKJRAInJyfd673zzjuwsLCASCRCVVUVQkNDERYWhqKiIohEInh5eQEAjh07BpVKhbi4uKfcItC9PyLCrFmzOt0mNjYWQ4cOxeeff47jx493uh0RYfPmzRg+fDikUilsbGwwZ84cXL58GYDh/bsv+/Dt27chl8vh4eFh0PaNjY347rvvDDpSs7GxwZQpU7B161bjn14igaSnp5OA4bolMjKSRCIRffzxx1RdXU0ajYZSUlIIAOXm5hIR0erVq0kqldKBAweourqa1q5dS2ZmZnT+/HldDAB04sQJunfvHlVUVNDkyZPJwsKCGhsbqb6+nlQqFSUkJJBWq6Xy8nKaN28eVVZWGhS/u8aPH0+jR48WpoF6AQClp6f3KkZpaSkBoG3btumWddXeRETBwcFkYWFBBQUFdP/+fcrPz6exY8eSUqmkmzdvEhHR4sWLydHRUe/1EhMTCYDuswkMDCRPT0+9bY4cOUJKpZJiYmJ69d6IiObPn0/z58/v1nPUajWNGDGiw3Wenp5048YNIiI6c+YMmZmZ0eDBg6m+vp6IiLKzs2n27Nm67aOiokgikdDu3buppqaGLly4QL6+vmRnZ0fl5eVEZFh7C92H2zQ0NJBSqaSQkBDdsg0bNpCLiwtZW1uTWCymwYMH0+zZs+n7778nIqIbN24QAPLx8aGpU6eSk5MTSaVSGjZsGH3yySfU2tqq9xoRERF6+7uhhOjfevGECmSsgqrRaEihUNArr7yit3zfvn26BtZqtaRQKCgoKEjveVKplN5++20i+rXDabVa3TZtRfnatWt06dIlAkBHjhxpl4Mh8bvrt1JQO2tvoocF1crKSi/W+fPnCQCtX7+eiHpeUIXU3YJaX19PIpGIZs6c2eH6RwsqEVFYWBgBoJUrVxKRfkHVaDRkaWmp1/eIiL7//nsCoPuD0VV790UfbhMZGUlDhw6l2tpa3bKbN2/SDz/8QHV1dfTgwQM6e/YsvfjiiySXy+nSpUt08eJFAkCvvPIK/ec//6G7d+9STU0NffDBBwSA9uzZo/caX3zxBQGgL7/8slu5CV1Q+/0h/7Vr16DRaDBt2rROt7ly5Qo0Go3ekAy5XA4nJyfdYVFHJBIJgIdDN9RqNRwcHLBkyRJER0ejuLi41/GZvkfbuzNjxoyBQqHo1+1aUVEBIoJCoTBo+9jYWDz//PNISUnB6dOn9dbl5+ejvr4eY8aM0Vs+duxYSCQS3emRjjza3n3Vhw8ePIiMjAx8/fXXehfeXF1d8eKLL8LS0hISiQQTJkxAamoqtFotUlJSdNcQvL294e/vD1tbW1hZWWH9+vWwsrLCjh079F6nrS3v3LnT41yF0O8Latut5ezt7TvdpqGhAQCwbt06vTFvJSUlBp8kl8vlOHnyJCZNmoS4uDio1WoEBQVBq9UKEp8ZTiqVorKy0thp9Nj9+/cBwOALjzKZDKmpqRCJRFi2bBm0Wq1uXdtwobZ7xj7K2toadXV1Br1GX/ThtLQ0bNq0CTk5OQbdfnHUqFEYMGAArl69CmdnZwBAVVWV3jYSiQTu7u4oKirSWy6XywH82rbG0u8LatsV0gcPHnS6TVux3bJlS7thGmfPnjX4tby9vfHVV1+hrKwM4eHhSE9PR1JSkmDxWdeamppQU1MDFxcXY6fSY207f3cGpPv5+WHVqlUoLCzEhg0bdMutra0BoMPC2Z12EroPb9u2DXv27MHJkycxcOBAg57T2tqK1tZWSKVSWFpaYsiQISgoKGi3XXNzM6ysrPSWNTY2Avi1bY2l3xfUkSNHwszMDKdOnep0G1dXV8hksl79aqqsrEz34drb22Pjxo3w9fVFQUGBIPGZYXJyckBEmDBhAoCH000/6RSBKXJwcIBIJOr2+NINGzZg2LBhyM3N1S0bOXIkLC0t8d///ldv23PnzqGxsREvvfSSQbGF6sNEhPDwcFy8eBFZWVkdfnMGgFdffbXdsvPnz4OIdHfPX7RoEXJzc3X3wwUeDruBAZVgAAAgAElEQVQqKSlpN5SqrS0dHR17lX9v9fuCam9vj8DAQBw4cAC7du1CbW0tLly4oHeORSaT4c0338S+ffuwfft21NbWoqWlBbdu3cLPP/9s0OuUlZVh+fLluHz5MhobG5Gbm4uSkhJMmDBBkPisY62traiurkZzczMuXLiA0NBQuLm5YenSpQAALy8v/PLLL8jKykJTUxMqKytRUlKiF8PW1hZlZWUoLi5GXV0dmpqakJ2dbbRhUwqFAmq1utszIbQd+j86xlMmkyEsLAwHDx7Enj17UFtbi4sXL2LFihVwdnZGcHCwwbG76sNBQUFwdHR84s9dCwoK8NFHH2Hnzp0Qi8XtflqalJQE4OEwqrS0NNTU1KCpqQlnz57FW2+9BTc3N6xYsQLAw6lJ3N3dsXTpUty8eRN3795FeHg4tFotPvjgA73XbWtLo885JtTVLWMOm6qrq6O33nqLnnvuObK0tKRJkyZRVFQUASAXFxf68ccf6cGDBxQeHk5ubm5kbm5O9vb2FBgYSPn5+ZSSkkIKhYIA0JAhQ6ioqIh27NhBKpWKAJC7uzt988035O/vTzY2NjRgwAAaOHAgRUZGUnNzMxHRE+Mb6uzZszRx4kRydnYmAASAnJycyN/fn06dOtVXzfdE6OVV0G3btpGTkxMBIIVCQbNmzTKova9evUrBwcEkFotp0KBBZG5uTiqViubMmUNFRUW6+Hfv3qWXX36ZZDIZeXh40Lvvvktr1qwhAOTl5aW7muzu7k5yuZwmTZpE5eXldPToUVIqlRQbG9vrNurJsKmQkBASi8Wk0Wh0yw4ePEienp4EgOzs7HRX9R+3Zs0avWFTra2tlJiYSEOGDCGxWEw2NjY0d+5cunLlChGRwe3dVR+eO3cuAaCoqKhO31fb1fnOHomJiUT0cOSCp6cnWVhYkLm5Obm4uNBf/vIXKisr04tXWlpKr7/+OtnY2JBUKqVx48ZRdnZ2u9edMWMGDRo0qN1wqq70tn+3iydUIGMWVNZ3hO5w3REcHEy2trZGee3u6ElBLSwsJHNzc9q9e3cfZSW8lpYWmjx5Mu3atcvYqeipqqoimUxGSUlJ3X6u0P273x/ys2ebKd1JSEheXl6IiYlBTEyM3l2YTFVLSwuysrJQV1eHoKAgY6ejJzo6Gj4+PggJCTF2Kv3/HKqpu3z5coe3KXv8YWqdlPW9iIgILFiwAEFBQSZ/A5ScnBxkZmYiOzvb4PGzT8PmzZuRl5eHo0ePQiwWGzsdLqh9bdiwYe2GoXT0SEtLM3aqJmXt2rVITU3FvXv34OHh8cxOUR4XF4eQkBBs3LjR2Kk80bRp07B37169+yYY26FDh/DgwQPk5OTAxsbG2OkA4FlPmYmKj49HfHy8sdN4KgICAhAQEGDsNPqd2bNnY/bs2cZOQw9/Q2WMMYFwQWWMMYFwQWWMMYFwQWWMMYFwQWWMMYEIfpW/P87lxJ5s0aJFWLRokbHTMHnc95ngBZXnUXp4CzQAeP/9942cSe8tWrQIoaGhujsAsfaepc/7t0boLwqCF9SFCxcKHbLf2b9/P4Bnoy0WLVoEPz+/Z+K99JVn6fP+rRG6oPI5VMYYEwgXVMYYEwgXVMYYEwgXVMYYEwgXVMYYE4jRCmpmZibUanW7+4JKJBI4ODhg6tSpSExMRHV1tbFSNHlJSUm6Cd8+++wzY6fDeuj48eOIiIhot0+88cYb7bYNCAiAUqnEgAED4O3t/cT5nYyttbUVW7Zsgb+/f7t1MTExGDFiBFQqFaRSKby8vPC3v/2t3c22//GPf2Ds2LFQKpVwd3fHm2++ifLycgDA4cOHkZCQYFo3IRfq1v89nQLF09OTrKysiOjh3DjV1dX0f//3f7R06VISiUTk7OxM58+fFyrNp6InU2L0VGFhIQGgTz/9tE/iw4hToPQXvfm8o6KiaObMmVRbW6tb5unpSc899xwBoCNHjrR7TnZ2tt6cUqbo6tWrNHHiRAJAo0ePbrd+ypQplJKSQnfv3qXa2lpKT08nsVhMf/jDH3TbpKWlEQBKSEigmpoays3NJbVaTT4+PtTU1ERERFu3bqUpU6ZQdXV1j/IUun+b1CG/SCSCtbU1pk6ditTUVGRkZODOnTuYMWOGyd/RnAlLq9V2+M3G1GN3x6ZNm5CWloaMjAwolUq9dcnJyTAzM0NwcHC/6/s//vgjPvjgA6xYsQI+Pj4dbmNpaYng4GDY2tpCqVRi4cKFmDt3Lo4dO4bS0lIAwP/+7/9i4MCBWLNmDaysrODj44NVq1YhLy8P586dAwC89957GD16NF577TU0Nzc/tffYGZMqqI+bP38+li5dioqKCj6k/Y3ZtWsXKioq+l1sQ127dg0ffvgh1q9fD5lM1m69v78/QkNDcfv2baxevdoIGfbc6NGjkZmZicWLF0MqlXa4zZEjR/SmwwYAOzs7AIBGowEAlJaWwtnZWe8nva6urgCgN1V4dHQ08vLysHXrVkHfR0+YdEEFoJt/PTs7G8DDycKioqLg5uYGuVyOF154Qfdz1+3bt8PCwgIKhQKHDh3C9OnToVKp4OLign379ulinjp1CuPGjYNCoYBKpcKoUaNQW1vbZfy+8tFHH0GhUECpVKKiogJhYWEYNGgQrly50u18QkJCIJFI9KaqeOedd2BhYQGRSISqqqo+fS9EhM2bN2P48OGQSqWwsbHBnDlzcPnyZYPzCw0NRVhYGIqKiiASieDl5YXk5GTIZDI4ODhg+fLlcHZ2hkwmg7+/v+7bSk9jA8CxY8egUqkQFxfXp+3TJjk5GUSEWbNmdbpNbGwshg4dis8//xzHjx/vdLuu2tzQ/cIYff9Rt2/fhlwuh4eHBwBArVa3+8PXdv5UrVbrltnY2GDKlCnYunUrHh7FG5FQ5w6EOIfakdraWgJArq6uRES0evVqkkqldODAAaqurqa1a9eSmZmZ7jxrZGQkAaATJ07QvXv3qKKigiZPnkwWFhbU2NhI9fX1pFKpKCEhgbRaLZWXl9O8efOosrLSoPiG6Mk5tba833vvPdq2bRvNmzePfvrppy7z6egc6uLFi8nR0VEvfmJiIgHQvU9DoZvnmKKiokgikdDu3buppqaGLly4QL6+vmRnZ0fl5eUG5xcYGEienp562wQHB5OFhQUVFBTQ/fv3KT8/n8aOHUtKpZJu3rzZq9hHjhwhpVJJMTExBr/XNj35vNVqNY0YMaLDdZ6ennTjxg0iIjpz5gyZmZnR4MGDqb6+nojan0M1pM272i+IhOn7jxs/fnyH51Af19DQQEqlkkJCQnTLcnJySCwWU3JyMtXW1tKlS5do+PDh9Oqrr7Z7fkREBAGg3NzcbuXX3f7dZTyhAvVVQSUiEolEZG1tTVqtlhQKBQUFBenWaTQakkql9PbbbxPRrx1Hq9XqtklJSSEAdO3aNbp06VKnJ/sNiW+I3hTUR/M2JB9TKqgajYYsLS318iUi+v777wmArlj1pqA+3lfOnz9PAGj9+vW9it0b3f286+vrSSQS0cyZMztc/2hBJSIKCwsjALRy5Uoi0i+ohrZ5V/uFUH3/cYYW1MjISBo6dKjexTkionXr1hEA3cPFxYVKS0vbPf+LL74gAPTll192Kz+hC6rJH/I3NDSAiKBSqXDlyhVoNBqMHDlSt14ul8PJyUl3eNMRiUQCAGhqaoJarYaDgwOWLFmC6OhoFBcX67brafy+Ymr5dCU/Px/19fUYM2aM3vKxY8dCIpHoDs2FNGbMGCgUCpNsj85UVFSAiAyejjk2NhbPP/88UlJScPr0ab11vWnzR/cLY/a1gwcPIiMjA19//bXexbnIyEjs2LEDJ06cQH19Pa5fvw5/f3/4+fnpLly1aWvLO3fu9GmuXTH5gnr16lUAD6djbmhoAACsW7dOb+xqSUmJ7kR2V+RyOU6ePIlJkyYhLi4OarUaQUFB0Gq1gsQXkqnl05WamhoAD6/gPs7a2hp1dXV98rpSqRSVlZV9Ersv3L9/HwA6vWDzOJlMhtTUVIhEIixbtgxarVa3Tqg2N1ZfS0tLw6ZNm5CTk4PBgwfrlv/8889ISEjAX//6V/z+97+HhYUFPDw8sHPnTpSVlSExMVEvjlwuB/Br2xqLyRfUY8eOAQCmT58Oe3t7AA/vP0mPzWt/9uxZg2N6e3vjq6++QllZGcLDw5Geno6kpCTB4gvF1PLpirW1NQB0uBPX1NTAxcVF8Ndsamrqs9h9pW3n786AdD8/P6xatQqFhYXYsGGDbrlQbW6MvrZt2zbs2bMHJ0+exMCBA/XWFRYWoqWlpd1ylUoFW1tb5Ofn6y1vbGwE8GvbGotJF9Ty8nJs2bIFLi4uWLZsGVxdXSGTyZCXl9fjmGVlZSgoKADwsBNt3LgRvr6+KCgoECS+kHqaj7m5OZqamvooq86NHDkSlpaW+O9//6u3/Ny5c2hsbMRLL70keH45OTkgIkyYMEHw2H2l7ddt3R1fumHDBgwbNgy5ubm6ZYa2eVeeZt8nIoSHh+PixYvIysrq8Nt12x+Cn3/+WW95XV0dfvnlF93wqTZtbeno6NhHWRvGJAoqEaG+vh6tra0gIlRWViI9PR0TJ07EgAEDkJWVBZVKBZlMhjfffBP79u3D9u3bUVtbi5aWFty6datdw3emrKwMy5cvx+XLl9HY2Ijc3FyUlJRgwoQJgsQXUk/z8fLywi+//IKsrCw0NTWhsrJSb9xeX+YbFhaGgwcPYs+ePaitrcXFixexYsUKODs7Izg42OD8bG1tUVZWhuLiYtTV1emKZGtrK6qrq9Hc3IwLFy4gNDQUbm5uuuF1PY2dnZ391IZNKRQKqNVq3Lp1q1vPazv0f3T8pqFtbkjsrvpaUFAQHB0de/1z14KCAnz00UfYuXMnxGJxu5+fJyUlwcPDAy+//DJ27tyJb7/9FlqtFqWlpbr38+c//1kvZltbjho1qle59ZpQV7e6e5X/8OHD9MILL5BCoSCJREJmZmYEQHdFf9y4cRQTE0N3797Ve96DBw8oPDyc3NzcyNzcnOzt7SkwMJDy8/MpJSWFFAoFAaAhQ4ZQUVER7dixg1QqFQEgd3d3+uabb8jf359sbGxowIABNHDgQIqMjKTm5uYu4xuqu1d9ExISSC6X64aH7d6926D3+/HHH5OjoyMBIAsLC5o3bx4REd29e5defvllkslk5OHhQe+++y6tWbOGAJCXl5duiJEh0M2roK2trZSYmEhDhgwhsVhMNjY2NHfuXLpy5YpuG0Py++GHH8jd3Z3kcjlNmjSJysvLKTg4mMRiMQ0aNIjMzc1JpVLRnDlzqKioqNexjx49SkqlkmJjYw1+r216MqojJCSExGIxaTQa3bKDBw+Sp6cnASA7OzvdVf3HrVmzRm/YVFdtbsh+cfXq1S77/ty5cwkARUVFPfG9nT17liZOnEjOzs66q/NOTk7k7+9Pp06doosXL+pduX/8kZiYSEREVVVVFBoaSl5eXiSVSsnS0pImTpxI//znP9u95owZM2jQoEHU2trarc+hu/27y3hCBerpsKln0dP8LX9fE7rD9UZwcDDZ2toaO412evJ5FxYWkrm5ud4fT1PX0tJCkydPpl27dhk7FT1VVVUkk8koKSmp288Vun+bxCE/Y4YyqTsL9YKXlxdiYmIQExPT7g5LpqilpQVZWVmoq6tDUFCQsdPREx0dDR8fH4SEhBg7FdM4h8rYb1FERAQWLFiAoKAgk78BSk5ODjIzM5GdnW3w+NmnYfPmzcjLy8PRo0chFouNnQ4XVNY/rF27Fqmpqbh37x48PDxw4MABY6ckiLi4OISEhGDjxo3GTuWJpk2bhr179+rdJ8HYDh06hAcPHiAnJwc2NjbGTgdAH0wjzVhfiI+PR3x8vLHT6BMBAQEICAgwdhr9zuzZszF79mxjp6GHv6EyxphAuKAyxphAuKAyxphAuKAyxphABL8olZGRIXTIfqftZ3DPSluY4o1YTMmz9nmzXhDqFwJtv5TiBz/4wY/+9BDyl1IiImNPwsLYky1cuBAAfwNkpo/PoTLGmEC4oDLGmEC4oDLGmEC4oDLGmEC4oDLGmEC4oDLGmEC4oDLGmEC4oDLGmEC4oDLGmEC4oDLGmEC4oDLGmEC4oDLGmEC4oDLGmEC4oDLGmEC4oDLGmEC4oDLGmEC4oDLGmEC4oDLGmEC4oDLGmEC4oDLGmEC4oDLGmEC4oDLGmEC4oDLGmEC4oDLGmEC4oDLGmEC4oDLGmEC4oDLGmEC4oDLGmEC4oDLGmEC4oDLGmEC4oDLGmEC4oDLGmEC4oDLGmEDMjZ0AY4/69ttvcfbsWb1lly9fBgAkJCToLffz88Pvfve7p5YbY10REREZOwnG2pw4cQL/8z//A7FYDDOzjg+gWltb0dTUhOPHj2PatGlPOUPGOscFlZmU1tZWODk5obKy8onb2dnZoby8HAMGDHhKmTHWNT6HykyKmZkZFi9eDIlE0uk2EokES5Ys4WLKTA4XVGZyXn/9dTQ2Nna6vrGxEa+//vpTzIgxw/AhPzNJgwcPRklJSYfrXF1dUVJSApFI9JSzYuzJ+BsqM0lvvPEGxGJxu+VisRhLly7lYspMEn9DZSbp8uXLGD58eIfrLl26BG9v76ecEWNd42+ozCQNGzYM3t7e7b6JjhgxgospM1lcUJnJ+n//7//pXckXi8X405/+ZMSMGHsyPuRnJqu0tBTu7u5o66IikQjXr1/H4MGDjZsYY53gb6jMZLm6umL8+PEwMzODmZkZxo8fz8WUmTQuqMykvfHGGxCJRDAzM8Mbb7xh7HQYeyI+5GcmraqqCk5OTgCAsrIyODg4GDkjxp6ABJKenk4A+MEPfvCjXz3S09OFKoMk+O370tPThQ7JjGjRokUIDQ2Fn5+f0XL49ttvIRKJMHnyZKPl8CRbtmwBALz//vtGzoR116JFiwSNJ3hBXbhwodAhmREtWrQIfn5+Rv1cp0+fDgBQKpVGy+FJ9u/fD4D7fn9k8gWVMaGZaiFl7HF8lZ8xxgTCBZUxxgTCBZUxxgTCBZUxxgTyTBbUt956C0qlEiKRCHl5ecZOx2AxMTEYMWIEVCoVpFIpvLy88Le//Q319fXGTq1Xjh49CisrK3z11VfGTsUkHT9+HBEREcjMzIRarYZIJIJIJOrwl2EBAQFQKpUYMGAAvL298cMPPxghY8O0trZiy5Yt8Pf3b7fO0L7+j3/8A2PHjoVSqYS7uzvefPNNlJeXAwAOHz6MhIQEtLS0PJX3YxChBrS2Dew3Ffv27SMAlJuba+xUDDZlyhRKSUmhu3fvUm1tLaWnp5NYLKY//OEPRssJAgx8PnLkCKlUKjp8+LBAWZmW+fPn0/z583v03KioKJo5cybV1tbqlnl6etJzzz1HAOjIkSPtnpOdnU2zZ8/ucb5Pw9WrV2nixIkEgEaPHt1uvSF9PS0tjQBQQkIC1dTUUG5uLqnVavLx8aGmpiYiItq6dStNmTKFqqure5SnEP1bL55Qgbig9t6MGTOoublZb9nChQsJAN28edMoOQnd4YxBo9GQn59fn8XvaUHduHEjDR06lLRard5yT09P2rt3L5mZmdGgQYOopqZGb72pF9S8vDyaN28e7dmzh3x8fDosqIb09ZdffpkGDhxIra2tum0++eQTAkCnT5/WLQsJCSE/Pz9dke0Oofv3M3nID6BfTpFx5MiRdjN52tnZAQA0Go0xUnom7Nq1CxUVFcZOQ8+1a9fw4YcfYv369ZDJZO3W+/v7IzQ0FLdv38bq1auNkGHPjR49GpmZmVi8eDGkUmmH2xjS10tLS+Hs7Ky3L7u6ugKA3nxj0dHRyMvLw9atWwV9Hz3xTBRUIkJiYiKef/55SKVSWFlZYc2aNXrbtLS0ICoqCm5ubpDL5XjhhRd0P5Pdvn07LCwsoFAocOjQIUyfPh0qlQouLi7Yt2+fLsapU6cwbtw4KBQKqFQqjBo1CrW1tV3G743bt29DLpfDw8Oj17GM4fTp03Bzc4NIJMInn3wCwLD2Tk5Ohkwmg4ODA5YvXw5nZ2fIZDL4+/vj3LlzAICQkBBIJBLdzVMA4J133oGFhQVEIhGqqqoQGhqKsLAwFBUVQSQSwcvLCwBw7NgxqFQqxMXFPeUWge79ERFmzZrV6TaxsbEYOnQoPv/8cxw/frzT7YgImzdvxvDhwyGVSmFjY4M5c+bg8uXLAAzv333Vhw31eF9Xq9Xt/hC2nT9Vq9W6ZTY2NpgyZQq2bt2qu3eu0Qj1VdeYh/yRkZEkEono448/purqatJoNJSSkqJ3yL969WqSSqV04MABqq6uprVr15KZmRmdP39eFwMAnThxgu7du0cVFRU0efJksrCwoMbGRqqvryeVSkUJCQmk1WqpvLyc5s2bR5WVlQbF74mGhgZSKpUUEhLS+0bqIQhwSFRaWkoAaNu2bbplXbU3EVFwcDBZWFhQQUEB3b9/n/Lz82ns2LGkVCp1h4WLFy8mR0dHvddLTEwkALrPJjAwkDw9PfW2OXLkCCmVSoqJienVeyPq2SG/Wq2mESNGdLjO09OTbty4QUREZ86cITMzMxo8eDDV19cTUftD/qioKJJIJLR7926qqamhCxcukK+vL9nZ2VF5eTkRGdbefdGHx48f3+Eh/+M66us5OTkkFospOTmZamtr6dKlSzR8+HB69dVX2z0/IiKiR6f4hOjfevGECmSsgqrRaEihUNArr7yit/zRc6harZYUCgUFBQXpPU8qldLbb79NRL92uEfPZ7UV5WvXrtGlS5c6vUhgSPyeiIyMpKFDh+pdsHja+rqgdtbeRA8LqpWVlV6s8+fPEwBav349EfW8oAqpuwW1vr6eRCIRzZw5s8P1jxZUIqKwsDACQCtXriQi/YKq0WjI0tJSr+8REX3//fcEQPcHo6v27qs+bGhB7ayvr1u3Tu/OUC4uLlRaWtru+V988QUBoC+//LJb+QldUPv9If+1a9eg0Wgwbdq0Tre5cuUKNBoNRo4cqVsml8vh5OSkOyzqiEQiAQA0NTVBrVbDwcEBS5YsQXR0NIqLi3sd/0kOHjyIjIwMfP3117+Z37I/2t6dGTNmDBQKRY/b1RRUVFSAiKBQKAzaPjY2Fs8//zxSUlJw+vRpvXX5+fmor6/HmDFj9JaPHTsWEolEd3qkI4+2d1/0YUN11tcjIyOxY8cOnDhxAvX19bh+/Tr8/f3h5+eH0tJSvRhtbXnnzp0+zbUr/b6g3rp1CwBgb2/f6TYNDQ0AgHXr1unG+IlEIpSUlBh8sUcul+PkyZOYNGkS4uLioFarERQUBK1WK0j8R6WlpWHTpk3IycnhKT86IJVKUVlZaew0euz+/fsA0OkFm8fJZDKkpqZCJBJh2bJl0Gq1unU1NTUAAEtLy3bPs7a2Rl1dnUGvIXQfNlRnff3nn39GQkIC/vrXv+L3v/89LCws4OHhgZ07d6KsrAyJiYl6ceRyOYBf29ZY+n1BbbtC+uDBg063aSu2W7ZsAT08zaF7nD171uDX8vb2xldffYWysjKEh4cjPT0dSUlJgsUHgG3btmHPnj04efIkBg4c2K3n/hY0NTWhpqYGLi4uxk6lx9p2/u4MSPfz88OqVatQWFiIDRs26JZbW1sDQIeFszvtJGQfNtST+nphYSFaWlraLVepVLC1tUV+fr7e8sbGRgC/tq2x9PuCOnLkSJiZmeHUqVOdbuPq6gqZTNarX02VlZWhoKAAwMPOt3HjRvj6+qKgoECQ+ESE8PBwXLx4EVlZWR1+42BATk4OiAgTJkwAAJibmz/xFIEpcnBwgEgkwr1797r1vA0bNmDYsGHIzc3VLRs5ciQsLS3x3//+V2/bc+fOobGxES+99JJBsYXow4YypK+3/SH4+eef9ZbX1dXhl19+0Q2fatPWlo6Ojn2UtWH6fUG1t7dHYGAgDhw4gF27dqG2thYXLlzAjh07dNvIZDK8+eab2LdvH7Zv347a2lq0tLTg1q1b7T6wzpSVlWH58uW4fPkyGhsbkZubi5KSEkyYMEGQ+AUFBfjoo4+wc+dOiMVivcMukUiEpKSkHrVPf9fa2orq6mo0NzfjwoULCA0NhZubG5YuXQoA8PLywi+//IKsrCw0NTWhsrJSb4wiANja2qKsrAzFxcWoq6tDU1MTsrOzjTZsSqFQQK1W605XGart0P/R8ZsymQxhYWE4ePAg9uzZg9raWly8eBErVqyAs7MzgoODDY7dVR8OCgqCo6Njr3/uakhf9/DwwMsvv4ydO3fi22+/hVarRWlpqe79/PnPf9aL2daWo0aN6lVuvSbU1S1jDpuqq6ujt956i5577jmytLSkSZMmUVRUlO6q4I8//kgPHjyg8PBwcnNzI3Nzc7K3t6fAwEDKz8+nlJQUUigUBICGDBlCRUVFtGPHDlKpVASA3N3d6ZtvviF/f3+ysbGhAQMG0MCBAykyMlL3a48nxTfExYsXnzjvTWJiYl82YafQy6ug27ZtIycnJwJACoWCZs2aZVB7X716lYKDg0ksFtOgQYPI3NycVCoVzZkzh4qKinTx7969Sy+//DLJZDLy8PCgd999l9asWUMAyMvLi27evEk//PADubu7k1wup0mTJlF5eTkdPXqUlEolxcbG9rqNejJsKiQkhMRiMWk0Gt2ygwcPkqenJwEgOzs73VX9x61Zs0Zv2FRrayslJibSkCFDSCwWk42NDc2dO5euXLlCRGRwe3fVh+fOnUsAKCoq6onv7ezZszRx4kRydnbW9V8nJyfy9/enU6dOGdzXq6qqKDQ0lLy8vEgqlZKlpSVNnDiR/vnPf7Z7zRkzZtCgQYP0flVliN7273bxhApkaj89ZcIQusN1R3BwMNna2hrltbujJwW1sLCQzM3Naffu3X2UlfBaWlpo8uTJtGvXLmOnoqeqqopkMhklJSV1+7lC9+9+f8jPnm0mdc1pOU0AACAASURBVCchAXl5eSEmJgYxMTH94m5iLS0tyMrKQl1dHYKCgoydjp7o6Gj4+PggJCTE2Kn0/3Oopu7y5cvtzhF19DC1Tsr6XkREBBYsWICgoKBuX6B62nJycpCZmYns7GyDx88+DZs3b0ZeXh6OHj0KsVhs7HS4oPa1YcOGtRuG0tEjLS3N2KmalLVr1yI1NRX37t2Dh4cHDhw4YOyU+kRcXBxCQkKwceNGY6fyRNOmTcPevXv17ptgbIcOHcKDBw+Qk5MDGxsbY6cDgGc9ZSYqPj4e8fHxxk7jqQgICEBAQICx0+h3Zs+ejdmzZxs7DT38DZUxxgTCBZUxxgTCBZUxxgTCBZUxxgQi+EWpjIwMoUMyI+urm2M8K9p+9sh9nwn+Syl+8IMf/OhPDyF/KSX4N1Qy9pwuJmDBggUAgP379xs5k94TiURIT0/HwoULjZ2KyXqWPu/fGqEn8+RzqIwxJhAuqIwxJhAuqIwxJhAuqIwxJhAuqIwxJhAuqIwxJhCjFdTMzEyo1ep29wWVSCRwcHDA1KlTkZiYiOrqamOlaPKSkpJ0E7599tlnxk6HCeT48eOIiIhot4+88cYb7bYNCAiAUqnEgAED4O3t3ev5nvpKU1MT4uPj4eXlBYlEAmtra4wcORLFxcUdbn///n0MGzYM69atAwAcPnwYCQkJJn/DcaMV1MDAQFy/fh2enp6wsrICEaG1tRUVFRXIyMiAh4cHwsPD4e3t3W5GR/bQ6tWrcebMGWOnwQT097//HcnJyVi7dq3ePvLcc89hz549+Ne//qW3/b///W/s378fM2fORH5+Pnx9fY2U+ZMtWrQIX375Jfbu3QuNRoOffvoJnp6enc5WEBkZiStXruj+P2vWLMhkMkybNg01NTVPK+1uM6lDfpFIBGtra0ydOhWpqanIyMjAnTt3MGPGDJO/ozkTllarhb+/f7+L3RubNm1CWloaMjIyoFQq9dYlJyfDzMwMwcHB/W5fSEtLQ1ZWFvbv34/x48fD3Nwczs7OOHToEEaOHNlu+zNnzuDSpUvtlr/33nsYPXo0XnvtNTQ3Nz+N1LvNpArq4+bPn4+lS5eioqKCD2l/Y3bt2oWKiop+F7unrl27hg8//BDr16+HTCZrt97f3x+hoaG4ffs2Vq9ebYQMe+7TTz+Fr6+vQVM8a7VarFmzBlu3bu1wfXR0NPLy8jpdb2wmXVAB6OZfz87OBvBwsrCoqCi4ublBLpfjhRdeQHp6OgBg+/btsLCwgEKhwKFDhzB9+nSoVCq4uLhg3759upinTp3CuHHjoFAooFKpMGrUKNTW1nYZv6989NFHUCgUUCqVqKioQFhYGAYNGoQrV650O5+QkBBIJBK9qSreeecdWFhYQCQSoaqqqk/fCxFh8+bNGD58OKRSKWxsbDBnzhxcvnzZ4PxCQ0MRFhaGoqIiiEQieHl5ITk5GTKZDA4ODli+fDmcnZ0hk8ng7++Pc+fO9So2ABw7dgwqlQpxcXF92j6dSU5OBhFh1qxZnW4TGxuLoUOH4vPPP8fx48c73a6rz8DQ/USIfaGxsRHfffcdfHx8DNo+MjIS77zzDuzt7Ttcb2NjgylTpmDr1q2m+TN3oW4K0NNppD09PcnKyqrT9bW1tQSAXF1diYho9erVJJVK6cCBA1RdXU1r164lMzMzOn/+PBERRUZGEgA6ceIE3bt3jyoqKmjy5MlkYWFBjY2NVF9fTyqVihISEkir1VJ5eTnNmzePKisrDYpviJ5MK9yW93vvvUfbtm2jefPm0U8//dRlPoWFhQSAPv30U12sxYsXk6Ojo178xMREAqB7n4ZCN28eERUVRRKJhHbv3k01NTV04cIF8vX1JTs7OyovLzc4v8DAQPL09NTbJjg4mCwsLKigoIDu379P+fn5NHbsWFIqlXTz5s1exT5y5AgplUqKiYkx+L226cnn/Ti1Wk0jRozocJ2npyfduHGDiIjOnDlDZmZmNHjwYKqvryciouzsbJo9e7Zue0M+g672EyJh9oUbN24QAPLx8aGpU6eSk5MTSaVSGjZsGH3yySfU2tqq2/b06dM0a9YsIiKqrKwkABQZGdkuZkREBAGg3Nxcg/PoTHf7d1dM/huqUqmESCRCXV0d7t+/j+3bt2Pu3LkIDAyEtbU11q1bB7FYjNTUVL3n+fv7Q6VSwd7eHkFBQWhoaMDNmzdRXFyM2tpaeHt7QyaTwdHREZmZmbCzs+tW/L6yadMmrFy5EpmZmRg8eLDR8+kOrVaLzZs3Y968eViyZAmsrKwwatQofPbZZ6iqqsKOHTt6/Rrm5ua6b14jRozA9u3bUVdX1+v2mDFjBmpra/Hhhx/2OsfuamhowI0bN+Dp6dnltn5+fnj//fdRXFyMDz74oN367n4Gne0nQu0LbRed7O3tERcXh/z8fNy5cwdz5szBypUr8Y9//EOXd2hoKLZv395lzCFDhgAALl68aHAeT4vJF9SGhgYQEVQqFa5cuQKNRqN3Ilsul8PJyUl3ONMRiUQC4OHQDbVaDQcHByxZsgTR0dF6wzZ6Gr+vmFo+XcnPz0d9fT3GjBmjt3zs2LGQSCS6Q3MhjRkzBgqFwiTbw1AVFRUgIoOnZ46NjcXzzz+PlJQUnD59Wm9dbz6DR/cTofqeVCoFAHh7e8Pf3x+2trawsrLC+vXrYWVlpSvwa9euxV//+lcMGjSoy5ht7XTnzh2D83haTL6gXr16FcDD6ZgbGhoAAOvWrdMbu1pSUgKNRmNQPLlcjpMnT2LSpEmIi4uDWq1GUFAQtFqtIPGFZGr5dKVtOIulpWW7ddbW1qirq+uT15VKpaisrOyT2E/D/fv3AfxafLoik8mQmpoKkUiEZcuWQavV6tYJ9RkI1fecnZ0BoN25e4lEAnd3dxQVFeH06dO4ePEi3nrrLYNiyuVyAL+2mykx+YJ67NgxAMD06dN1J6q3bNnSbl777txV3tvbG1999RXKysoQHh6O9PR0JCUlCRZfKKaWT1esra0BoMOdtqamBi4uLoK/ZlNTU5/FflraCkR3Bq37+flh1apVKCwsxIYNG3TLhfoMhOp7lpaWGDJkCAoKCtqta25uhpWVFXbt2oUTJ07AzMxMV7jbXj8uLg4ikUhvLHpjYyOAX9vNlJh0QS0vL8eWLVvg4uKCZcuWwdXVFTKZDHl5eT2OWVZWpvtw7e3tsXHjRvj6+qKgoECQ+ELqaT7m5uZoamrqo6w6N3LkSFhaWrb7Ica5c+fQ2NiIl156SfD8cnJyQESYMGGC4LGflrZfu3V3fOmGDRswbNgw5Obm6pYZ+hl0Rch9YdGiRcj9/9q796ioyv1/4O8NM8wwAwODIuAF4uIlFTVSU9SyxYpzzOUFkaS0sk6FVqKhfBFQRATNg6HH0tPyaFTqQfCy0FI8aS08ebycWkISHhVRQULkInK/8/n94W/mNIfLDLBhRv281vIPn/3sZ3/mmWd/2Jdn9s7IwM2bN7VltbW1yMvLg6enJxITE9skbc0ZR2RkJIhI5xKGpp8cHBx6HJvYTCKhEhGqq6vR2tqq7czk5GRMmTIF5ubmSE1NhUqlglwux1tvvYWkpCTs3LkTlZWVaGlpQUFBAe7evWvQtgoLC7FkyRJcvXoVjY2NyMjIQF5eHiZNmiRK+2LqbjweHh64f/8+UlNT0dTUhJKSEuTl5fVJvCtXrsSRI0ewb98+VFZWIisrC0uXLoWTkxOCgoIMjs/Ozg6FhYW4ffs2qqqqtEmytbUV5eXlaG5uxuXLl7FixQo4Oztrp9d1t+20tDSjTZtSKBRwc3PTvpvKUJpTf3Nzc50yQ74DQ9rWN/YCAwPh4OCg9+euISEhcHFxweLFi5Gfn4+ysjKEhYWhrq6u3Rtr+mj6yZB5rX1OrOkCXZ02dezYMRozZgwpFAqysLAgMzMzAkCCIJCtrS1NnDiRYmJiqKysTGe9hoYGCgsLI2dnZ5JIJGRvb0/+/v6UnZ1NO3bsIIVCQQBo6NChlJubS7t27SKVSkUAyMXFhU6dOkXe3t6kVqvJ3NycBg4cSJGRkdTc3Ky3fUN1dRrN5s2bydLSUjs9bO/evQZ93k8++YQcHBwIACmVSpo3bx4REZWVldGLL75IcrmcXF1dadmyZRQaGkoAyMPDQzvFyBDo4rSS1tZWio+Pp6FDh5JUKiW1Wk1+fn507do1bR1D4rt06RK5uLiQpaUlTZ06lYqKiigoKIikUikNGjSIJBIJqVQqmjt3LuXm5va47RMnTpC1tTXFxsYa/Fk1xJg2FRwcTFKplGpra7VlR44cIXd3dwJA/fv3pw8//LDddUNDQ3WmTen7DgzZT65fv653X/Dz8yMAFBUVpffz3blzh1599VVSq9Ukk8lo4sSJlJaW1mH9zqZNzZw5kwYNGqQz5aq7ujq+9bYnVkPdnYf6OBJjBzMVYg+4nggKCiI7Oztjh9GGGN93Tk4OSSQSnT+mpq6lpYWmTZtGe/bs6bNtlpaWklwupy1btojSntjj2yRO+RkzlKk/bai7PDw8EBMTg5iYmA4fGGJKWlpakJqaiqqqKgQGBvbZdqOjozFu3DgEBwf32Ta7ghMqYyYiPDwcAQEBCAwMNPkHoKSnp+Pw4cNIS0szeP5sTyUkJCAzMxMnTpyAVCrtk212FSdU9kiIiIhAYmIiKioq4OrqikOHDhk7pF4RFxeH4OBgbNq0ydihdMrHxwf79+/XeW5Cbzp69CgaGhqQnp4OtVrdJ9vsDomxA2DMEBs3bsTGjRuNHUaf8PX1ha+vr7HDMClz5szBnDlzjB2GXnyEyhhjIuGEyhhjIuGEyhhjIuGEyhhjIhH9plRAQIDYTT5yLly4AODx6YutW7fi4MGDxg7DZD1u3zfrPuH//1qgx86fP4+EhAQxmmJMh+ZBwib52232yAsJCcHkyZNFaUu0hMpYb3nllVcAACkpKUaOhLHO8TVUxhgTCSdUxhgTCSdUxhgTCSdUxhgTCSdUxhgTCSdUxhgTCSdUxhgTCSdUxhgTCSdUxhgTCSdUxhgTCSdUxhgTCSdUxhgTCSdUxhgTCSdUxhgTCSdUxhgTCSdUxhgTCSdUxhgTCSdUxhgTCSdUxhgTCSdUxhgTCSdUxhgTCSdUxhgTCSdUxhgTCSdUxhgTCSdUxhgTCSdUxhgTCSdUxhgTCSdUxhgTCSdUxhgTCSdUxhgTCSdUxhgTCSdUxhgTCSdUxhgTiUBEZOwgGNP4+uuvkZCQgJaWFm1ZaWkpAKB///7aMnNzc4SEhOCNN97o8xgZ6wgnVGZSrl+/juHDhxtU99q1axg2bFgvR8SY4fiUn5mUYcOGYezYsRAEocM6giBg7NixnEyZyeGEykzOG2+8AXNz8w6XSyQSvPnmm30YEWOG4VN+ZnIKCwsxZMgQtLa2trtcEATcuXMHgwYN6uPIGOscH6EykzNw4EB4e3vDzKzt8DQzM8OUKVM4mTKTxAmVmaTXX3+93XJBEPjOPjNZfMrPTFJ5eTkcHBzQ1NSkUy6RSFBUVIR+/foZKTLGOsZHqMwkqdVqvPTSSzo3p8zNzfGHP/yBkykzWZxQmclatGiRzo0pIsKiRYuMGBFjneNTfmayamtr0a9fP9TX1wMA5HI5SktLoVQqjRwZY+3jI1RmshQKBfz8/CCVSiGVSuHn58fJlJk0TqjMpL322mtoampCU1MTXnvtNWOHw1inJPoqpKSk9EUcjLWrpaUFCoUCRITKykoej8yoXnnllU6X672G2tlvqhlj7Emi75aT3iNUAEhOTtabmRkzhCAIXR5PZ86cgSAIeP7553sxMtMREBAAADh48KCRI2EaKSkpWLBggd56BiVUxoxp2rRpxg6BMYNwQmUmr73f9DNminikMsaYSDihMsaYSDihMsaYSDihMsaYSJ6YhPrOO+/A2toagiAgMzPT2OEYbPPmzRgxYgQsLS2hVCoxYsQIrF27FpWVlcYOzWhOnDgBGxsbfPPNN8YOxSSdPn0a4eHhOHz4MNzc3CAIAgRBaPcZs76+vrC2toa5uTlGjRqFS5cuGSFi/ZqamrBx40Z4eHjAwsICtra2GD16NG7fvt1u/fr6eowYMQJr1qwBABw7dgybN2/WeZtub3hiEuru3bvxt7/9zdhhdNmPP/6Id999F/n5+bh37x42bNiAzZs3Y/78+cYOzWj4eT4dW7duHbZv346IiAj4+/vj5s2bcHd3R79+/bBv3z4cP35cp/53332HgwcPYtasWcjOzoaXl5eRIu/cggUL8PXXX2P//v2ora3Ff/7zH7i7u6O6urrd+pGRkbh27Zr2/7Nnz4ZcLoePjw8ePHjQa3E+MQn1UWVhYYEPPvgA9vb2sLKyQkBAAObOnYtTp07h7t27xg7PKGbOnImKigrMmjXLKNuvq6uDt7e3UbbdmY8//hgHDhxASkoKrK2tdZZt374dZmZmCAoKQkVFhZEi7J4DBw4gNTUVBw8exHPPPQeJRAInJyccPXoUo0ePblP/3Llz+PXXX9uUL1++HGPHjsXLL7+M5ubmXon1iUqoj+LPaI8cOQK5XK5TpnmfUkd/nVnv2rNnD4qLi40dho4bN25g7dq1WL9+fZvxAgDe3t5YsWIFfvvtN6xatcoIEXbfX//6V3h5ecHT01Nv3bq6OoSGhmLbtm3tLo+OjkZmZmaHy3vqsU2oRIT4+HgMHz4cMpkMNjY2CA0N1anT0tKCqKgoODs7w9LSEmPGjEFycjIAYOfOnVAqlVAoFDh69ChmzJgBlUqFwYMHIykpSdvGmTNnMHHiRCgUCqhUKnh6emqvb3bWfk/k5OTA1tYWLi4uPW7rUXP27Fk4OztDEAR89tlnAAz7rrZv3w65XI4BAwZgyZIlcHJyglwuh7e3Ny5evAgACA4OhoWFBRwdHbXb++CDD6BUKiEIAkpLS7FixQqsXLkSubm5EAQBHh4eAICTJ09CpVIhLi6uj3sE2s9HRJg9e3aHdWJjYzFs2DDs3r0bp0+f7rAeESEhIQFPP/00ZDIZ1Go15s6di6tXrwIwfN8QY/w3NjbiwoULGDdunEH1IyMjtWd07VGr1XjhhRewbdu23rl0RHoAoOTkZH3VTE5kZCQJgkCffPIJlZeXU21tLe3YsYMAUEZGBhERrVq1imQyGR06dIjKy8spIiKCzMzM6KefftK2AYC+//57qqiooOLiYpo2bRoplUpqbGyk6upqUqlUtHnzZqqrq6OioiKaN28elZSUGNR+VzQ2NlJBQQF9+umnJJPJaO/eveJ1Vh8SYzzduXOHANCnn36qLdP3XRERBQUFkVKppCtXrlB9fT1lZ2fThAkTyNramvLz84mIaOHCheTg4KCzvfj4eAKg/V79/f3J3d1dp863335L1tbWFBMT06PPRkQ0f/58mj9/fpfWcXNzo5EjR7a7zN3dnW7dukVEROfOnSMzMzN66qmnqLq6moiI0tLSaM6cOdr6UVFRZGFhQXv37qUHDx7Q5cuXycvLi/r3709FRUVEZFh/izH+b926RQBo3LhxNH36dHJ0dCSZTEYjRoygzz77jFpbW7V1z549S7NnzyYiopKSEgJAkZGRbdoMDw/XyQOGSE5OJgPSJT2WCbW2tpYUCgW99NJLOuVJSUnajqyrqyOFQkGBgYE668lkMnr//feJ6L+Dpq6uTltHk5Rv3LhBv/76KwGgb7/9tk0MhrTfFQ4ODgSA+vXrR3/5y1+0g/ZR09sJtaPviuhhQrWxsdFp66effiIAtH79eiLqfkIVU1cTanV1NQmCQLNmzWp3+e8TKhHRypUrCQB9+OGHRKSbUGtra8nKykpn3BIR/fvf/yYA2j8Y+vpbrPGflZVFAOill16if/3rX1RWVkYPHjyg1atXEwDat2+ftu3x48dTQUEBEXWeUL/44gsCQF9//bXBcRiaUB/LU/4bN26gtrYWPj4+Hda5du0aamtrdS5qW1pawtHRUXtq0x4LCwsAD6dxuLm5YcCAAVi0aBGio6N1pnB0t/2O3LlzB8XFxfj73/+Or776Cs8884zJXcczNb//rjoyfvx4KBSKbn0npqK4uBhEBIVCYVD92NhYDB8+HDt27MDZs2d1lmVnZ6O6uhrjx4/XKZ8wYQIsLCy0l0fa8/v+Fmv8y2QyAMCoUaPg7e0NOzs72NjYYP369bCxscGuXbsAABEREXjvvfe09xc6o+mne/fuGRyHoR7LhFpQUAAAHV5HAYCamhoAwJo1a7Tz9ARBQF5eHmpraw3ajqWlJX744QdMnToVcXFxcHNzQ2BgIOrq6kRp//ekUins7e3h6+uLAwcOIDs7Gxs3buxyO6wtmUyGkpISY4fRbZp3bmmSjz5yuRyJiYkQBAFvv/026urqtMs0U4qsrKzarGdra4uqqiqDtiHW+HdycgIAlJaW6pRbWFjAxcUFubm5OHv2LLKysvDOO+8Y1KalpSWA//abmB7LhKq5y9nQ0NBhHU2y3bp1K+jhpQ/tv/Pnzxu8rVGjRuGbb75BYWEhwsLCkJycjC1btojWfns8PDxgbm6O7OzsHrXDHh5NPXjwAIMHDzZ2KN2mSRBdmbQ+efJkhISEICcnBxs2bNCW29raAkC7ibMr/STW+LeyssLQoUNx5cqVNsuam5thY2ODPXv24Pvvv4eZmZk2cWu2HxcXB0EQ8PPPP2vXa2xsBPDffhPTY5lQR48eDTMzM5w5c6bDOkOGDIFcLu/Rr6YKCwu1X7S9vT02bdoELy8vXLlyRZT2y8rK2n2PUk5ODlpaWjBkyJBut80eSk9PBxFh0qRJAACJRNLpJQJTNGDAAAiC0OX5pRs2bMCIESOQkZGhLRs9ejSsrKx0EhAAXLx4EY2NjXj22WcNaluM8a+xYMECZGRk4ObNm9qy2tpa5OXlwdPTE4mJiW2StuaMIzIyEkSkcwlD008ODg49ju1/PZYJ1d7eHv7+/jh06BD27NmDyspKXL58WXu9BXh4FPvWW28hKSkJO3fuRGVlJVpaWlBQUGDwhPnCwkIsWbIEV69eRWNjIzIyMpCXl4dJkyaJ0r5SqcR3332HH374AZWVlWhqakJGRgbefPNNKJVKhISEdKt/nmStra0oLy9Hc3MzLl++jBUrVsDZ2RmLFy8G8PDo//79+0hNTUVTUxNKSkqQl5en04adnR0KCwtx+/ZtVFVVoampCWlpaUabNqVQKODm5qa91GUozam/ubm5TtnKlStx5MgR7Nu3D5WVlcjKysLSpUvh5OSEoKAgg9vWN/4DAwPh4OCg9+euISEhcHFxweLFi5Gfn4+ysjKEhYWhrq4Oq1ev7tJnBv57SdCQea1dpu+uFR7Bu/xERFVVVfTOO+9Qv379yMrKiqZOnUpRUVEEgAYPHky//PILNTQ0UFhYGDk7O5NEIiF7e3vy9/en7Oxs2rFjBykUCgJAQ4cOpdzcXNq1axepVCoCQC4uLnTq1Cny9vYmtVpN5ubmNHDgQIqMjKTm5mYiok7bN9Ts2bPJ1dWVrKysSCaTkbu7OwUGBlJWVlZvdV2v6ul4+vTTT8nR0ZEAkEKhoNmzZxv0XV2/fp2CgoJIKpXSoEGDSCKRkEqlorlz51Jubq62/bKyMnrxxRdJLpeTq6srLVu2jEJDQwkAeXh4UH5+Pl26dIlcXFzI0tKSpk6dSkVFRXTixAmytram2NjYHvdRd6ZNBQcHk1QqpdraWm3ZkSNHyN3dnQBQ//79tXf1/1doaKjOtKnW1laKj4+noUOHklQqJbVaTX5+fnTt2jUiIoP7W9/49/PzIwAUFRWl9/PduXOHXn31VVKr1SSTyWjixImUlpbWYf3O7vLPnDmTBg0apDPlSp8netoUM13GHE9BQUFkZ2dnlG13RXcSak5ODkkkkkdqfnJLSwtNmzaN9uzZ02fbLC0tJblcTlu2bOnSek/0tCnGOtLbTxsyFg8PD8TExCAmJuaR+ElyS0sLUlNTUVVVhcDAwD7bbnR0NMaNG4fg4OBeaZ8TqhFcvXpVZypJR//6cqCxR194eDgCAgIQGBho8g9ASU9Px+HDh5GWlmbw/NmeSkhIQGZmJk6cOAGpVNor2+CEagQjRoxoc1eyvX8HDhwwdqiPjYiICCQmJqKiogKurq44dOiQsUPqFXFxcQgODsamTZuMHUqnfHx8sH//fp3nJvSmo0ePoqGhAenp6VCr1b22HX7rKXsibNy48Yn5IYSvry98fX2NHYZJmTNnDubMmdPr2+EjVMYYEwknVMYYEwknVMYYEwknVMYYE4lA1PljqwVBwKRJkx7ph0cw03Ho0CEeT3pcuHABALTPF2DGV1BQgAsXLuh9yj8foRqooKDgsZ1qwxgTh0FHqMnJyXjllVf6KiaTlJKSggULFvArjHuIx5N+AQEBAICDBw8aORKmYej+z0eojDEmEk6ojDEmEk6ojDEmEk6ojDEmEk6ojDEmElET6uHDh+Hm5tbmMXQWFhYYMGAApk+fjvj4eJSXl4u52cdaQ0MDli9fDkdHRygUCpw8edLYITETd/r0aYSHh7fZH19//fU2dX19fWFtbQ1zc3OMGjVK7+tIjKm1tRVbt26Ft7d3u8vPnj2LKVOmQKFQwMnJCWFhYdoXdR47dgybN2/u9efhippQ/f39cfPmTbi7u8PGxgZEhNbWVhQXFyMlJQWurq4ICwvDqFGj2rwEjLXvk08+wcmTJ3H16lVs27btkXh4MDOedevWYfv27YiIiNDZH/v164d9+/bh+PHjOvW/++47HDx4ELNmzUJ2dja8vLyMFHnncnJy8PzzzyMkJKTd11BnZ2fD19cXPj4+KCkpwZEjR/DFF19g6dKlAIDZs2dDLpfDx8dH+6rs3tDrp/yCIMDW1hbTp09HYmIiUlJScO/ePcycOdPkH4JrClJTUzF+/HjY2trivffew/z581FXV9fhX2nWvt7sM1P5Pj7++GMcOHAAKSkp7LlbvwAADPxJREFUsLa21lm2fft2mJmZISgo6JHb73755ResXr0aS5cuxbhx49qts2HDBjg6OmL9+vVQKpWYPHkywsLC8OWXX+Lq1asAgOXLl2Ps2LF4+eWX0dzc3Cux9vk11Pnz52Px4sUoLi7G559/3tebf+QUFBS0ebr4nj17UFxcbKSIHk292Wem8H3cuHEDa9euxfr16yGXy9ss9/b2xooVK/Dbb79h1apVRoiw+8aOHYvDhw9j4cKFkMlkbZY3Nzfj+PHjeOGFFyAIgrZ8xowZICIcPXpUWxYdHY3MzExs27atV2I1yk0pzSt709LSADx8v0xUVBScnZ1haWmJMWPGIDk5GQCwc+dOKJVKKBQKHD16FDNmzIBKpcLgwYORlJSkbfPMmTOYOHEiFAoFVCoVPD09UVlZqbf93tRZTESEhIQEPP3005DJZFCr1Zg7d672r+mpU6fg4eGBu3fv4quvvoIgCLCyssKKFSuwcuVK5ObmQhAEeHh4YNu2bVAqlTAzM8Ozzz4LBwcHSKVSKJVKeHl5Ydq0adr3pNva2uL//u//dOL88ccfMXLkSNjY2EAul8PT0xP/+Mc/AABffvklrKysIAgC1Go1UlNT8fPPP8PFxQXm5uZ47bXXerUP9fVTcHAwLCwsdJ78/sEHH0CpVEIQBJSWlrbbZ9u3b4dcLseAAQOwZMkSODk5QS6Xw9vbGxcvXuxR2wBw8uTJPn2t9Pbt20FEmD17dod1YmNjMWzYMOzevRunT5/usJ6+Pjd0n+yr/e7mzZuorq6Gs7OzTrm7uzsA4PLly9oytVqNF154Adu2beudXz3qe4sfuvGWSnd3d7KxselweWVlJQGgIUOGEBHRqlWrSCaT0aFDh6i8vJwiIiLIzMyMfvrpJyIiioyMJAD0/fffU0VFBRUXF9O0adNIqVRSY2MjVVdXk0qlos2bN1NdXR0VFRXRvHnzqKSkxKD2DWHoWw819MUUFRVFFhYWtHfvXnrw4AFdvnyZvLy8qH///lRUVKRtx8HBgd58802dtv39/cnd3V2nbN26dQSALl68SDU1NVRaWkp//OMfCQAdP36cSkpKqKamhoKDgwkAZWZmatc9ePAgRUdH0/3796msrIwmTZpE/fr10y6/cuUKKRQKnTjCw8Np9+7dBveHRlfHkyH9tHDhQnJwcNBZLz4+ngBo+7u9PgsKCiKlUklXrlyh+vp6ys7OpgkTJpC1tTXl5+f3qO1vv/2WrK2tKSYmxuDPqtGdt566ubnRyJEj213m7u5Ot27dIiKic+fOkZmZGT311FNUXV1NRERpaWk6r5E2pM/17ZNE4ux3/+u5556jsWPH6pSdOXOGAFB8fHyb+paWluTj46NTFh4eTgAoIyPD4O2a9FtPra2tIQgCqqqqUF9fj507d8LPzw/+/v6wtbXFmjVrIJVKkZiYqLOet7c3VCoV7O3tERgYiJqaGuTn5+P27duorKzEqFGjIJfL4eDggMOHD6N///5dal9MncVUV1eHhIQEzJs3D4sWLYKNjQ08PT3x+eefo7S0FLt27er2dkeOHAmFQoF+/frh1VdfBQA4Ozujf//+UCgUWLRoEQBojzaAh5dh1q1bB7VaDTs7O8yePRtlZWUoKSkBADz99NPYunUrvvrqK+zfvx9JSUloaGjAn/70px70kH692U8aEolEeyQ2cuRI7Ny5E1VVVT0eGzNnzkRlZSXWrl3b4xj1qampwa1bt7RHZJ2ZPHkyPvroI9y+fRurV69us7yrfd7RPtmX+53mTr65uXmbZVKpFHV1dTplQ4cOBQBkZWWJGgdgpFP+mpoaEBFUKhWuXbuG2tpajB49Wrvc0tISjo6OOjv9/7KwsAAANDU1wc3NDQMGDMCiRYsQHR2N27dva+t1t/2e6iym7OxsVFdXY/z48TrrTJgwARYWFtpTzp7S9NHvL8Brrsc2NTV1uJ6mzu+nmGhuiC1ZsgQpKSn485//LEqMnemrfvq98ePHQ6FQ9OrYEFtxcTGIyOC3h8bGxmL48OHYsWMHzp49q7OsJ33++32yL/c7zTXj9m40NTY2wtLSUqdM00/37t0TNQ7ASAn1+vXrAB6+/bOmpgYAsGbNGp25q3l5ee1Oj2iPpaUlfvjhB0ydOhVxcXFwc3NDYGAg6urqRGm/OzqLSTNtw8rKqs16tra2qKqq6rW42nP8+HFMnz4d9vb2kMlkba6xasTFxaG6urrPbsAYq59kMpn26PxRUF9fDwDt3rBpj1wuR2JiIgRBwNtvv61zBCdWn/flfqe5xq25P6FRW1uL+vp6ODk56ZRrEqym38RklISqmZw+Y8YM2NvbAwC2bt3a5jXK58+fN7jNUaNG4ZtvvkFhYSHCwsKQnJyMLVu2iNZ+d3QUk62tLQC0OzgfPHjQpw9fzs/Ph5+fHxwdHXHx4kVUVFRg8+bNbeo1NTVh+fLlSEhIwPnz5xEbG9vrsRmjn5qamvr8O+gpTYLoyqT1yZMnIyQkBDk5OdiwYYO2XKw+78v9ztXVFdbW1sjLy9Mpv3HjBgBgzJgxOuWNjY0A0ObIVQx9nlCLioqwdetWDB48GG+//bb27nNmZma32ywsLMSVK1cAPPwiN23aBC8vL1y5ckWU9sWOafTo0bCysmrz44aLFy+isbERzz77bJ/FmZWVhaamJrz//vtwc3ODXC7XmXqisWzZMrz77rv46KOPEBISgg0bNvT6HyRD+0kikXR6CaMr0tPTQUTap+WL2XZvGTBgAARB6PL80g0bNmDEiBHIyMjQlok1Nvtyv5NIJHj55Zfxz3/+E62trdrytLQ0CILQZuaDpp8cHBxEj6XXEioRobq6Gq2trSAilJSUIDk5GVOmTIG5uTlSU1OhUqkgl8vx1ltvISkpCTt37kRlZSVaWlpQUFCAu3fvGrStwsJCLFmyBFevXkVjYyMyMjKQl5eHSZMmidJ+d+iLaeXKlThy5Aj27duHyspKZGVlYenSpXByckJQUFCnbdvZ2aGwsBC3b99GVVVVj3Z4zVST06dPo76+Hjk5OW2uk+3YsQODBg3CvHnzADx8x/3IkSOxcOHCNqdZYjK0nzw8PHD//n2kpqaiqakJJSUlbY5WOuqz1tZWlJeXo7m5GZcvX8aKFSvg7OysndrX3bbT0tL6bNqUQqGAm5sbCgoKurSe5tT/9zdzejo2f9+Ovv0uMDAQDg4Oovzcde3atbh37x7WrVuHmpoanD9/HvHx8Vi8eDGGDx+uU1fTT56enj3ebhv6pgGgC9Ncjh07RmPGjCGFQkEWFhZkZmZGAEgQBLK1taWJEydSTEwMlZWV6azX0NBAYWFh5OzsTBKJhOzt7cnf35+ys7Npx44dpFAoCAANHTqUcnNzadeuXaRSqQgAubi40KlTp8jb25vUajWZm5vTwIEDKTIykpqbm/W2b6iuTpu6fft2pzG1trZSfHw8DR06lKRSKanVavLz86Nr165p13/mmWcIAEkkEvLy8qJDhw4REdGlS5fIxcWFLC0taerUqRQeHq7to6eeeop+/PFH+vjjj8nGxoYAkIODA+3fv58OHDhADg4OBIDUajUlJSUREVFYWBjZ2dmRra0tBQQE0GeffUYAyN3dncaNG0eCIJCdnR2dO3eOiIg++ugj7XdrY2NDP//8s8H90pXxZEg/ERGVlZXRiy++SHK5nFxdXWnZsmUUGhpKAMjDw4Py8/Pb9FlRUREFBQWRVCqlQYMGkUQiIZVKRXPnzqXc3Nwet33ixAmytram2NhYgz+rRnemTQUHB5NUKqXa2lpt2ZEjR8jd3Z0AUP/+/enDDz9sd93Q0FCdaVP6+tyQffL69et69zs/Pz8CQFFRUZ1+tvPnz9OUKVPIycmJABAAcnR0JG9vbzpz5oy23pkzZ2jixIkkk8nIycmJQkNDqb6+vk17M2fOpEGDBlFra6vB/Wvo/t8r81AfR11NqKx9pjSegoKCyM7OzthhtNGdhJqTk0MSiYT27t3bS1GJr6WlhaZNm0Z79uzps22WlpaSXC6nLVu2dGk9k56Hypip6O2nD/UVDw8PxMTEICYm5pF4gE5LSwtSU1NRVVWFwMDAPttudHQ0xo0bh+Dg4F5pnxMqY4+J8PBwBAQEIDAw0OQfgJKeno7Dhw8jLS3N4PmzPZWQkIDMzEycOHGizfMxxMIJlT2RIiIikJiYiIqKCri6uj42rwiPi4tDcHAwNm3aZOxQOuXj44P9+/frPCehNx09ehQNDQ1IT0+HWq3ute1Ieq1lxkzYxo0bsXHjRmOH0St8fX3h6+tr7DBMypw5czBnzpxe3w4foTLGmEg4oTLGmEg4oTLGmEg4oTLGmEg4oTLGmEgEos7fA9DegzIYY+xJpCdd6p821RfvXmKMsceB3iNUxhhjhuFrqIwxJhJOqIwxJhJOqIwxJhIJgIPGDoIxxh4H/w8zOb4+V7tqJgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(\n",
        "    optimizer = 'adam',\n",
        "    loss = 'sparse_categorical_crossentropy',\n",
        "    metrics = ['accuracy']\n",
        ")"
      ],
      "metadata": {
        "id": "dYNPvv-9IlVH"
      },
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(x_train_norm,\n",
        "          y_train,\n",
        "          epochs = 30,\n",
        "          batch_size = 32,\n",
        "          validation_data = (x_test_norm, y_test),\n",
        "          shuffle = True\n",
        "          )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cl7eFEEaIlSk",
        "outputId": "95d6615b-7363-4168-e6e3-0fa36a6c82c6"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/30\n",
            "1875/1875 [==============================] - 17s 9ms/step - loss: 0.7143 - accuracy: 0.7494 - val_loss: 0.6131 - val_accuracy: 0.7989\n",
            "Epoch 2/30\n",
            "1875/1875 [==============================] - 17s 9ms/step - loss: 0.5709 - accuracy: 0.8134 - val_loss: 0.5823 - val_accuracy: 0.8098\n",
            "Epoch 3/30\n",
            "1875/1875 [==============================] - 16s 8ms/step - loss: 0.5278 - accuracy: 0.8309 - val_loss: 0.5486 - val_accuracy: 0.8252\n",
            "Epoch 4/30\n",
            "1875/1875 [==============================] - 15s 8ms/step - loss: 0.5039 - accuracy: 0.8401 - val_loss: 0.5204 - val_accuracy: 0.8361\n",
            "Epoch 5/30\n",
            "1875/1875 [==============================] - 15s 8ms/step - loss: 0.4898 - accuracy: 0.8449 - val_loss: 0.5220 - val_accuracy: 0.8368\n",
            "Epoch 6/30\n",
            "1875/1875 [==============================] - 14s 7ms/step - loss: 0.4823 - accuracy: 0.8470 - val_loss: 0.5525 - val_accuracy: 0.8216\n",
            "Epoch 7/30\n",
            "1875/1875 [==============================] - 15s 8ms/step - loss: 0.4771 - accuracy: 0.8490 - val_loss: 0.5000 - val_accuracy: 0.8445\n",
            "Epoch 8/30\n",
            "1875/1875 [==============================] - 15s 8ms/step - loss: 0.4726 - accuracy: 0.8508 - val_loss: 0.5280 - val_accuracy: 0.8337\n",
            "Epoch 9/30\n",
            "1875/1875 [==============================] - 14s 7ms/step - loss: 0.4692 - accuracy: 0.8528 - val_loss: 0.5114 - val_accuracy: 0.8406\n",
            "Epoch 10/30\n",
            "1875/1875 [==============================] - 14s 8ms/step - loss: 0.4648 - accuracy: 0.8542 - val_loss: 0.5210 - val_accuracy: 0.8339\n",
            "Epoch 11/30\n",
            "1875/1875 [==============================] - 15s 8ms/step - loss: 0.4650 - accuracy: 0.8541 - val_loss: 0.4939 - val_accuracy: 0.8410\n",
            "Epoch 12/30\n",
            "1875/1875 [==============================] - 15s 8ms/step - loss: 0.4617 - accuracy: 0.8556 - val_loss: 0.5035 - val_accuracy: 0.8439\n",
            "Epoch 13/30\n",
            "1875/1875 [==============================] - 15s 8ms/step - loss: 0.4611 - accuracy: 0.8551 - val_loss: 0.4866 - val_accuracy: 0.8508\n",
            "Epoch 14/30\n",
            "1875/1875 [==============================] - 14s 8ms/step - loss: 0.4572 - accuracy: 0.8564 - val_loss: 0.5115 - val_accuracy: 0.8390\n",
            "Epoch 15/30\n",
            "1875/1875 [==============================] - 15s 8ms/step - loss: 0.4571 - accuracy: 0.8576 - val_loss: 0.5197 - val_accuracy: 0.8381\n",
            "Epoch 16/30\n",
            "1875/1875 [==============================] - 15s 8ms/step - loss: 0.4579 - accuracy: 0.8568 - val_loss: 0.4839 - val_accuracy: 0.8511\n",
            "Epoch 17/30\n",
            "1875/1875 [==============================] - 15s 8ms/step - loss: 0.4566 - accuracy: 0.8581 - val_loss: 0.5277 - val_accuracy: 0.8293\n",
            "Epoch 18/30\n",
            "1875/1875 [==============================] - 14s 8ms/step - loss: 0.4545 - accuracy: 0.8572 - val_loss: 0.4820 - val_accuracy: 0.8466\n",
            "Epoch 19/30\n",
            "1875/1875 [==============================] - 14s 8ms/step - loss: 0.4515 - accuracy: 0.8601 - val_loss: 0.4951 - val_accuracy: 0.8440\n",
            "Epoch 20/30\n",
            "1875/1875 [==============================] - 15s 8ms/step - loss: 0.4531 - accuracy: 0.8597 - val_loss: 0.4861 - val_accuracy: 0.8494\n",
            "Epoch 21/30\n",
            "1875/1875 [==============================] - 15s 8ms/step - loss: 0.4529 - accuracy: 0.8594 - val_loss: 0.4833 - val_accuracy: 0.8521\n",
            "Epoch 22/30\n",
            "1875/1875 [==============================] - 15s 8ms/step - loss: 0.4489 - accuracy: 0.8608 - val_loss: 0.4898 - val_accuracy: 0.8429\n",
            "Epoch 23/30\n",
            "1875/1875 [==============================] - 15s 8ms/step - loss: 0.4512 - accuracy: 0.8586 - val_loss: 0.4986 - val_accuracy: 0.8445\n",
            "Epoch 24/30\n",
            "1875/1875 [==============================] - 14s 8ms/step - loss: 0.4505 - accuracy: 0.8586 - val_loss: 0.4825 - val_accuracy: 0.8486\n",
            "Epoch 25/30\n",
            "1875/1875 [==============================] - 14s 7ms/step - loss: 0.4495 - accuracy: 0.8597 - val_loss: 0.4992 - val_accuracy: 0.8408\n",
            "Epoch 26/30\n",
            "1875/1875 [==============================] - 15s 8ms/step - loss: 0.4513 - accuracy: 0.8598 - val_loss: 0.5105 - val_accuracy: 0.8359\n",
            "Epoch 27/30\n",
            "1875/1875 [==============================] - 14s 8ms/step - loss: 0.4493 - accuracy: 0.8609 - val_loss: 0.5200 - val_accuracy: 0.8398\n",
            "Epoch 28/30\n",
            "1875/1875 [==============================] - 15s 8ms/step - loss: 0.4521 - accuracy: 0.8587 - val_loss: 0.4900 - val_accuracy: 0.8439\n",
            "Epoch 29/30\n",
            "1875/1875 [==============================] - 14s 8ms/step - loss: 0.4488 - accuracy: 0.8605 - val_loss: 0.4893 - val_accuracy: 0.8471\n",
            "Epoch 30/30\n",
            "1875/1875 [==============================] - 14s 8ms/step - loss: 0.4489 - accuracy: 0.8598 - val_loss: 0.4829 - val_accuracy: 0.8535\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fa0511e5ab0>"
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "loss, accuracy = model.evaluate(x_test, y_test)\n",
        "print(\n",
        "    f'Ошибка модели: {loss}',\n",
        "    f'Точность {accuracy}',\n",
        "    sep = '\\n'\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LceDmSKWhiPu",
        "outputId": "09804621-d912-4e7c-e9f3-679b2f86ddcf"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 2s 6ms/step - loss: 58.6129 - accuracy: 0.6714\n",
            "Ошибка модели: 58.61289978027344\n",
            "Точность 0.6714000105857849\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.imshow(x_train[1], cmap = 'gray_r')"
      ],
      "metadata": {
        "id": "cJntspPTIlKo",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "outputId": "97fe4fff-c092-4aa7-c3a1-254f7d6d720d"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fa05125b130>"
            ]
          },
          "metadata": {},
          "execution_count": 48
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaAAAAGdCAYAAABU0qcqAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAgOElEQVR4nO3dfWyV9f3G8ast7aFIe2op9GEULAjieNpkUvGB4WgoXWIEyeJTMnAGoitGRKfppqLbkk62OaNhuGQbzEwUjQKRLGwKtsytsFBAxoYNNN3AlZaJtgf6THv//iB2v0oBv1/b8zkt71dyJ/Scc/X+9u7dXr05p5/GBUEQCACAKIu3XgAA4NJEAQEATFBAAAATFBAAwAQFBAAwQQEBAExQQAAAExQQAMDEEOsFfFZXV5dqa2uVkpKiuLg46+UAABwFQaBTp04pJydH8fHnv86JuQKqra1Vbm6u9TIAAF/QsWPHNHr06PPeH3MFlJKSIunswlNTU41XY8tnStJgvGqsqqpyzjzyyCNe+1q4cKFzZtq0ac6ZpKQk58yQIe5frocOHXLOSNLWrVudM1dccYVz5sEHH3TOpKWlOWcQXZFIRLm5ud3fz8+n3wpozZo1+ulPf6q6ujpNnz5dL7zwgmbOnHnR3KffQFNTUykgCkiSNHz4cOeMzzdrSUpOTnbO+KwvWgU0bNgw54wkJSYmOmdCoZBzxudr/FL/vjCQXOz7Ub+8CGHjxo1auXKlVq1apb1792r69OkqLCzUiRMn+mN3AIABqF8K6Nlnn9XSpUt1zz336Mtf/rJefPFFDRs2TL/97W/7Y3cAgAGozwuovb1dlZWVKigo+N9O4uNVUFCgioqKcx7f1tamSCTSYwMADH59XkAfffSROjs7lZmZ2eP2zMxM1dXVnfP40tJShcPh7o1XwAHApcH8F1FLSkrU2NjYvR07dsx6SQCAKOjzV8FlZGQoISFB9fX1PW6vr69XVlbWOY8PhUJer54BAAxsfX4FlJSUpBkzZmj79u3dt3V1dWn79u2aNWtWX+8OADBA9cvvAa1cuVKLFy/W1772Nc2cOVPPPfecmpqadM899/TH7gAAA1C/FNDtt9+u//73v3ryySdVV1enr3zlK9q2bds5L0wAAFy64gKfX7fvR5FIROFwWI2NjTH7G8+DbULBvn37vHIbN250zrzxxhvOmYSEBOfM6dOnnTOS1NLS4pz5+OOPvfYVyyZOnOicudDQyfP54IMPnDO9PZd8MYWFhc4ZSXr44YedM1OnTvXa12Dyeb+Pm78KDgBwaaKAAAAmKCAAgAkKCABgggICAJiggAAAJiggAIAJCggAYIICAgCYoIAAACYoIACACQoIAGCCYaQxLBKJOGe+/e1vO2fef/9954zkN5R1+PDhzpnk5GTnzJAhfoPefQafnjlzxjnT2NjonBk2bJhzxufjkWJ7eG5ra6tzxmfIrCS1t7c7Z2688UbnzO9//3vnTCxjGCkAIKZRQAAAExQQAMAEBQQAMEEBAQBMUEAAABMUEADABAUEADBBAQEATFBAAAATFBAAwAQFBAAwQQEBAEz4jQxGVCxcuNA5c/ToUedMZmamc0bym5jc2dnpnPGd6OzDZ30+U8FHjBjhnPFZm68YG5Lfg8909KFDh3rty+cc//Of/+ycOXTokHPm6quvds7EGq6AAAAmKCAAgAkKCABgggICAJiggAAAJiggAIAJCggAYIICAgCYoIAAACYoIACACQoIAGCCAgIAmGAYaZRUVlY6Z3wGi2ZkZDhnzpw545zx1dLS4pz5z3/+E5X9SFJXV5dzZsgQ9y8jn8Gi8fHR+3mxvb3dOZOYmOicSUlJcc6MHj3aOePzOfLl83n69a9/7Zz5+c9/7pyJNVwBAQBMUEAAABMUEADABAUEADBBAQEATFBAAAATFBAAwAQFBAAwQQEBAExQQAAAExQQAMAEBQQAMMEw0ih59913nTNtbW3OmdbWVueM75BLn8GdoVDIObN69WrnTHZ2tnNGknJzc50ztbW1zhmf9fkcb58BoZLfMNLTp087Z/bu3eucef75550zI0eOdM5IUkdHh3PG5+vpjTfecM4wjBQAAE8UEADARJ8X0FNPPaW4uLge26RJk/p6NwCAAa5fngOaPHmy3nnnnf/tJIp/DAoAMDD0SzMMGTJEWVlZ/fGuAQCDRL88B3T48GHl5ORo3Lhxuvvuuy/4p6Xb2toUiUR6bACAwa/PCyg/P1/r16/Xtm3btHbtWtXU1Oimm27SqVOnen18aWmpwuFw9+bzMlgAwMDT5wVUVFSkb33rW5o2bZoKCwv1hz/8QQ0NDXrttdd6fXxJSYkaGxu7t2PHjvX1kgAAMajfXx2QlpamiRMn6siRI73eHwqFvH45EQAwsPX77wGdPn1a1dXV3r+ZDgAYnPq8gB555BGVl5frX//6l/76179q4cKFSkhI0J133tnXuwIADGB9/l9wH374oe68806dPHlSI0eO1I033qhdu3Z5z2ICAAxOcUEQBNaL+P8ikYjC4bAaGxuVmppqvZw+c9111zln6uvrnTM+xywpKck5I/kNnwyHw86ZXbt2OWf+9Kc/OWeksz9AufrOd77jnPnVr37lnJk8ebJzxmc4rSR1dnY6Z0aNGuWc+epXv+qcmTBhgnNm+PDhzhnJ7/j5DID94IMPnDMHDx50zkjSxIkTvXIuPu/3cWbBAQBMUEAAABMUEADABAUEADBBAQEATFBAAAATFBAAwAQFBAAwQQEBAExQQAAAExQQAMAEBQQAMNHvf5AOZ73//vvOGZ8/T+4zRLKtrc0546uxsTEq+yksLPTK+QytPHTokHPmZz/7mXNm4cKFzpm33nrLOSNJZ86ccc74DBbdu3evc2bIEPdvW83Nzc4ZSYqPd/8Z3Sfj87VeUVHhnJGiM4z08+IKCABgggICAJiggAAAJiggAIAJCggAYIICAgCYoIAAACYoIACACQoIAGCCAgIAmKCAAAAmKCAAgAkKCABggmnYHv7+9787Z0aOHOmcSUhIcM74TMP2yUhSS0uLcyY9Pd1rX67+8Y9/eOVCoZBz5vjx486ZH/zgB86ZIAicM4mJic4Z3335Tmd2lZ2d7Zypra312pfP12BcXJxzJjk52Tmzc+dO54wkLV682CvXH7gCAgCYoIAAACYoIACACQoIAGCCAgIAmKCAAAAmKCAAgAkKCABgggICAJiggAAAJiggAIAJCggAYIJhpB6eeeYZ54zP4M7LLrvMOTNkiPuntLm52TkjSUOHDnXO+AzH3LNnj3Pm5MmTzhlJ+vjjj50zHR0dzpn6+nrnjM+x8/kcSVJ7e7tzpqGhwTmzceNG58wnn3zinPEZ9in5fUw++/I5hyorK50zsYYrIACACQoIAGCCAgIAmKCAAAAmKCAAgAkKCABgggICAJiggAAAJiggAIAJCggAYIICAgCYoIAAACYYRurh+uuvd874DJ88cuSIc6axsdE54zuMdMKECc6Z+Hj3n3ny8/OdMwkJCc4ZyW99Ppmuri7njM/AyiAInDOS31Dbzs5O50xqaqpzZuLEic6ZpqYm54zk93nyOeY5OTnOmQULFjhnYg1XQAAAExQQAMCEcwHt3LlTt9xyi3JychQXF6fNmzf3uD8IAj355JPKzs5WcnKyCgoKdPjw4b5aLwBgkHAuoKamJk2fPl1r1qzp9f7Vq1fr+eef14svvqjdu3frsssuU2FhoVpbW7/wYgEAg4fzM41FRUUqKirq9b4gCPTcc8/p8ccf16233ipJeumll5SZmanNmzfrjjvu+GKrBQAMGn36HFBNTY3q6upUUFDQfVs4HFZ+fr4qKip6zbS1tSkSifTYAACDX58WUF1dnSQpMzOzx+2ZmZnd931WaWmpwuFw95abm9uXSwIAxCjzV8GVlJSosbGxezt27Jj1kgAAUdCnBZSVlSXp3F+6rK+v777vs0KhkFJTU3tsAIDBr08LKC8vT1lZWdq+fXv3bZFIRLt379asWbP6clcAgAHO+VVwp0+f7jEipqamRvv371d6errGjBmjFStW6Mc//rEmTJigvLw8PfHEE8rJyRkUYyMAAH3HuYD27Nmjm2++ufvtlStXSpIWL16s9evX69FHH1VTU5OWLVumhoYG3Xjjjdq2bZuGDh3ad6sGAAx4cYHvtMJ+EolEFA6H1djYeMk/H/TJJ584Z3ymTqxdu9Y5I0llZWXOmTFjxjhnfAaspqWlOWckqb293TnjM7Ay1vl8W/A5Dj4/mPqcD1OmTHHOSNKGDRu8cpe6z/t93PxVcACASxMFBAAwQQEBAExQQAAAExQQAMAEBQQAMEEBAQBMUEAAABMUEADABAUEADBBAQEATFBAAAATFBAAwITzn2NA9Fx++eXOmZkzZzpnQqGQc0aSduzY4ZyJi4tzzrS1tTlnmpqanDOSdObMGedMfHx0fo7zmVDtO+ze52Py+TwlJiY6Z1pbW50z119/vXMG/Y8rIACACQoIAGCCAgIAmKCAAAAmKCAAgAkKCABgggICAJiggAAAJiggAIAJCggAYIICAgCYoIAAACYYRholPkMhOzo6nDNJSUnOGZ8BoZKUkpLinOns7HTOJCQkOGd8PyYfPp/baK4vlnV1dUVlP2lpaVHZj+R3jvsMfx0M5xBXQAAAExQQAMAEBQQAMEEBAQBMUEAAABMUEADABAUEADBBAQEATFBAAAATFBAAwAQFBAAwQQEBAEwwjDRKfAYHJiYm9sNKzjV+/HivXGpqqnPmzJkzzhmfAau+fD5PDCM9y+fz1N7e3g8rOVc4HI7KfiS/Aas+A3cHA66AAAAmKCAAgAkKCABgggICAJiggAAAJiggAIAJCggAYIICAgCYoIAAACYoIACACQoIAGCCAgIAmGAYaQyL1lDD5ORk54wkhUIh50xra6tzxmcoa0dHh3NGit5gUZ/9+GR8ziFfQ4cOdc40Nzc7Z3yOw6U67DPWcQUEADBBAQEATDgX0M6dO3XLLbcoJydHcXFx2rx5c4/7lyxZori4uB7b/Pnz+2q9AIBBwrmAmpqaNH36dK1Zs+a8j5k/f76OHz/evb3yyitfaJEAgMHH+UUIRUVFKioquuBjQqGQsrKyvBcFABj8+uU5oLKyMo0aNUpXXXWV7r//fp08efK8j21ra1MkEumxAQAGvz4voPnz5+ull17S9u3b9cwzz6i8vFxFRUXq7Ozs9fGlpaUKh8PdW25ubl8vCQAQg/r894DuuOOO7n9PnTpV06ZN0/jx41VWVqa5c+ee8/iSkhKtXLmy++1IJEIJAcAloN9fhj1u3DhlZGToyJEjvd4fCoWUmpraYwMADH79XkAffvihTp48qezs7P7eFQBgAHH+L7jTp0/3uJqpqanR/v37lZ6ervT0dD399NNatGiRsrKyVF1drUcffVRXXnmlCgsL+3ThAICBzbmA9uzZo5tvvrn77U+fv1m8eLHWrl2rAwcO6He/+50aGhqUk5OjefPm6Uc/+pHX3DAAwODlXEBz5sy54DDAP/7xj19oQfgfnyGXPuLj/f4n1ifn8zFFa9inL5/1RWtIqO9xiNbx8zmHzveK2r7ej69ofd0OBsyCAwCYoIAAACYoIACACQoIAGCCAgIAmKCAAAAmKCAAgAkKCABgggICAJiggAAAJiggAIAJCggAYIICAgCY6PM/yY1LR21trXMmLS3NOeMz/diXzxToaE7ejmU+xyExMTEq+zlz5oxzBv2PKyAAgAkKCABgggICAJiggAAAJiggAIAJCggAYIICAgCYoIAAACYoIACACQoIAGCCAgIAmKCAAAAmGEYaw+Li4qyXcEEJCQlR2U97e7tzJj7e72eraA0j9cn4nA++g1J99uXzeQqFQs4Zn7VFcxhprH/dxhKugAAAJiggAIAJCggAYIICAgCYoIAAACYoIACACQoIAGCCAgIAmKCAAAAmKCAAgAkKCABgggICAJhgGCm8+QyS7Orqcs74DD312Y/kN8Q0WsMxExMTnTO+gzE7Ozujsq8hQ6LzLaihoSEq+4EbroAAACYoIACACQoIAGCCAgIAmKCAAAAmKCAAgAkKCABgggICAJiggAAAJiggAIAJCggAYIICAgCYYBgpvPkMCY2WIAi8cr7DO135DPuM1uBOye84+Bxzn/34DGVtaWlxzviK1jk0GHAFBAAwQQEBAEw4FVBpaamuvfZapaSkaNSoUVqwYIGqqqp6PKa1tVXFxcUaMWKEhg8frkWLFqm+vr5PFw0AGPicCqi8vFzFxcXatWuX3n77bXV0dGjevHlqamrqfsxDDz2kt956S6+//rrKy8tVW1ur2267rc8XDgAY2Jye1dy2bVuPt9evX69Ro0apsrJSs2fPVmNjo37zm99ow4YN+sY3viFJWrduna6++mrt2rVL1113Xd+tHAAwoH2h54AaGxslSenp6ZKkyspKdXR0qKCgoPsxkyZN0pgxY1RRUdHr+2hra1MkEumxAQAGP+8C6urq0ooVK3TDDTdoypQpkqS6ujolJSUpLS2tx2MzMzNVV1fX6/spLS1VOBzu3nJzc32XBAAYQLwLqLi4WAcPHtSrr776hRZQUlKixsbG7u3YsWNf6P0BAAYGr99sW758ubZu3aqdO3dq9OjR3bdnZWWpvb1dDQ0NPa6C6uvrlZWV1ev7CoVCCoVCPssAAAxgTldAQRBo+fLl2rRpk3bs2KG8vLwe98+YMUOJiYnavn17921VVVU6evSoZs2a1TcrBgAMCk5XQMXFxdqwYYO2bNmilJSU7ud1wuGwkpOTFQ6Hde+992rlypVKT09XamqqHnjgAc2aNYtXwAEAenAqoLVr10qS5syZ0+P2devWacmSJZKkX/ziF4qPj9eiRYvU1tamwsJC/fKXv+yTxQIABg+nAvo8wwaHDh2qNWvWaM2aNd6LwsDgM1AzWmJ9IKTvsNRo8Tl+XV1dUdmPzxDc5uZm5wz6H7PgAAAmKCAAgAkKCABgggICAJiggAAAJiggAIAJCggAYIICAgCYoIAAACYoIACACQoIAGCCAgIAmKCAAAAmvP4iKqIj1ic6+/CZmBzrojXZOprTx33OPZ/j4HM+DBni/m0rlie3X8q4AgIAmKCAAAAmKCAAgAkKCABgggICAJiggAAAJiggAIAJCggAYIICAgCYoIAAACYoIACACQoIAGCCYaQxzGe4YzQHmCYlJTlnWlpa+mElfSc+3v1nMp+BmgkJCVHZj8/H4ytaA0x9jl2sD3K9VHEFBAAwQQEBAExQQAAAExQQAMAEBQQAMEEBAQBMUEAAABMUEADABAUEADBBAQEATFBAAAATFBAAwATDSBFV0Rrc6TPkUvJbX7QyPoNFfY+DD58hnD7HwUc0h5Hi8+MKCABgggICAJiggAAAJiggAIAJCggAYIICAgCYoIAAACYoIACACQoIAGCCAgIAmKCAAAAmKCAAgAmGkcYwn+GO0ZSTk+OcOXz4sHNmyBD309RncKdvrr29PSr78TkffM8hn2Pe0dHhta9oiOYw0lj/uo0lXAEBAExQQAAAE04FVFpaqmuvvVYpKSkaNWqUFixYoKqqqh6PmTNnjuLi4nps9913X58uGgAw8DkVUHl5uYqLi7Vr1y69/fbb6ujo0Lx589TU1NTjcUuXLtXx48e7t9WrV/fpogEAA5/TM43btm3r8fb69es1atQoVVZWavbs2d23Dxs2TFlZWX2zQgDAoPSFngNqbGyUJKWnp/e4/eWXX1ZGRoamTJmikpISNTc3n/d9tLW1KRKJ9NgAAIOf98uwu7q6tGLFCt1www2aMmVK9+133XWXxo4dq5ycHB04cECPPfaYqqqq9Oabb/b6fkpLS/X000/7LgMAMEB5F1BxcbEOHjyo9957r8fty5Yt6/731KlTlZ2drblz56q6ulrjx48/5/2UlJRo5cqV3W9HIhHl5ub6LgsAMEB4FdDy5cu1detW7dy5U6NHj77gY/Pz8yVJR44c6bWAQqGQQqGQzzIAAAOYUwEFQaAHHnhAmzZtUllZmfLy8i6a2b9/vyQpOzvba4EAgMHJqYCKi4u1YcMGbdmyRSkpKaqrq5MkhcNhJScnq7q6Whs2bNA3v/lNjRgxQgcOHNBDDz2k2bNna9q0af3yAQAABianAlq7dq2ks79s+v+tW7dOS5YsUVJSkt555x0999xzampqUm5urhYtWqTHH3+8zxYMABgcnP8L7kJyc3NVXl7+hRYEALg0MA0b3hoaGpwzp0+fds74TFk+efKkc0bym5rc1dXlnInlydGS3zRsn2N3sRcx9aalpcU5U11d7Zzx5XM++E5vH+guzY8aAGCOAgIAmKCAAAAmKCAAgAkKCABgggICAJiggAAAJiggAIAJCggAYIICAgCYoIAAACYoIACACYaRxrCLTR/vTVxcXD+spHfXXHONc2by5MnOmbS0NOdMNId9+gyfHD58uHPG53Prcw5JfsNIfQZqJiYmOmd8huDOnDnTOePrUh0s6oMjBQAwQQEBAExQQAAAExQQAMAEBQQAMEEBAQBMUEAAABMUEADABAUEADBBAQEATFBAAAATMTcL7tPZVZFIxHgl9mJ9FlxbW5tzpr29PSr7ifVZcD6z1gbjLLjOzk7njM/50Nzc7JyR+D7k69PjdrHzL+YK6NSpU5Kk3Nxc45UAAL6IU6dOKRwOn/f+uMD3R6R+0tXVpdraWqWkpJzzE18kElFubq6OHTum1NRUoxXa4zicxXE4i+NwFsfhrFg4DkEQ6NSpU8rJybnglXHMXQHFx8dr9OjRF3xMamrqJX2CfYrjcBbH4SyOw1kch7Osj8OFrnw+xYsQAAAmKCAAgIkBVUChUEirVq1SKBSyXoopjsNZHIezOA5ncRzOGkjHIeZehAAAuDQMqCsgAMDgQQEBAExQQAAAExQQAMDEgCmgNWvW6IorrtDQoUOVn5+vv/3tb9ZLirqnnnpKcXFxPbZJkyZZL6vf7dy5U7fccotycnIUFxenzZs397g/CAI9+eSTys7OVnJysgoKCnT48GGbxfajix2HJUuWnHN+zJ8/32ax/aS0tFTXXnutUlJSNGrUKC1YsEBVVVU9HtPa2qri4mKNGDFCw4cP16JFi1RfX2+04v7xeY7DnDlzzjkf7rvvPqMV925AFNDGjRu1cuVKrVq1Snv37tX06dNVWFioEydOWC8t6iZPnqzjx493b++99571kvpdU1OTpk+frjVr1vR6/+rVq/X888/rxRdf1O7du3XZZZepsLBQra2tUV5p/7rYcZCk+fPn9zg/XnnllSiusP+Vl5eruLhYu3bt0ttvv62Ojg7NmzdPTU1N3Y956KGH9NZbb+n1119XeXm5amtrddtttxmuuu99nuMgSUuXLu1xPqxevdpoxecRDAAzZ84MiouLu9/u7OwMcnJygtLSUsNVRd+qVauC6dOnWy/DlKRg06ZN3W93dXUFWVlZwU9/+tPu2xoaGoJQKBS88sorBiuMjs8ehyAIgsWLFwe33nqryXqsnDhxIpAUlJeXB0Fw9nOfmJgYvP76692POXToUCApqKiosFpmv/vscQiCIPj6178ePPjgg3aL+hxi/gqovb1dlZWVKigo6L4tPj5eBQUFqqioMFyZjcOHDysnJ0fjxo3T3XffraNHj1ovyVRNTY3q6up6nB/hcFj5+fmX5PlRVlamUaNG6aqrrtL999+vkydPWi+pXzU2NkqS0tPTJUmVlZXq6OjocT5MmjRJY8aMGdTnw2ePw6defvllZWRkaMqUKSopKfH+sxT9JeaGkX7WRx99pM7OTmVmZva4PTMzUx988IHRqmzk5+dr/fr1uuqqq3T8+HE9/fTTuummm3Tw4EGlpKRYL89EXV2dJPV6fnx636Vi/vz5uu2225SXl6fq6mp9//vfV1FRkSoqKpSQkGC9vD7X1dWlFStW6IYbbtCUKVMknT0fkpKSlJaW1uOxg/l86O04SNJdd92lsWPHKicnRwcOHNBjjz2mqqoqvfnmm4ar7SnmCwj/U1RU1P3vadOmKT8/X2PHjtVrr72me++913BliAV33HFH97+nTp2qadOmafz48SorK9PcuXMNV9Y/iouLdfDgwUviedALOd9xWLZsWfe/p06dquzsbM2dO1fV1dUaP358tJfZq5j/L7iMjAwlJCSc8yqW+vp6ZWVlGa0qNqSlpWnixIk6cuSI9VLMfHoOcH6ca9y4ccrIyBiU58fy5cu1detWvfvuuz3+fEtWVpba29vV0NDQ4/GD9Xw433HoTX5+viTF1PkQ8wWUlJSkGTNmaPv27d23dXV1afv27Zo1a5bhyuydPn1a1dXVys7Otl6Kmby8PGVlZfU4PyKRiHbv3n3Jnx8ffvihTp48OajOjyAItHz5cm3atEk7duxQXl5ej/tnzJihxMTEHudDVVWVjh49OqjOh4sdh97s379fkmLrfLB+FcTn8eqrrwahUChYv3598M9//jNYtmxZkJaWFtTV1VkvLaoefvjhoKysLKipqQn+8pe/BAUFBUFGRkZw4sQJ66X1q1OnTgX79u0L9u3bF0gKnn322WDfvn3Bv//97yAIguAnP/lJkJaWFmzZsiU4cOBAcOuttwZ5eXlBS0uL8cr71oWOw6lTp4JHHnkkqKioCGpqaoJ33nknuOaaa4IJEyYEra2t1kvvM/fff38QDoeDsrKy4Pjx491bc3Nz92Puu+++YMyYMcGOHTuCPXv2BLNmzQpmzZpluOq+d7HjcOTIkeCHP/xhsGfPnqCmpibYsmVLMG7cuGD27NnGK+9pQBRQEATBCy+8EIwZMyZISkoKZs6cGezatct6SVF3++23B9nZ2UFSUlLwpS99Kbj99tuDI0eOWC+r37377ruBpHO2xYsXB0Fw9qXYTzzxRJCZmRmEQqFg7ty5QVVVle2i+8GFjkNzc3Mwb968YOTIkUFiYmIwduzYYOnSpYPuh7TePn5Jwbp167of09LSEnz3u98NLr/88mDYsGHBwoULg+PHj9stuh9c7DgcPXo0mD17dpCenh6EQqHgyiuvDL73ve8FjY2Ntgv/DP4cAwDARMw/BwQAGJwoIACACQoIAGCCAgIAmKCAAAAmKCAAgAkKCABgggICAJiggAAAJiggAIAJCggAYIICAgCY+D/mH5algQN60QAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\n",
        "    y_train[1]\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SfnKhgo_XE46",
        "outputId": "0ff9ee6a-f740-4eec-cdbc-31db6aa1d117"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "numbers_to_display = 25\n",
        "num_cells = math.ceil(math.sqrt(numbers_to_display))\n",
        "plt.figure(figsize=(10,10))\n",
        "for i in range(numbers_to_display):\n",
        "    plt.subplot(num_cells, num_cells, i+1)\n",
        "    plt.xticks([])\n",
        "    plt.yticks([])\n",
        "    plt.grid(False)\n",
        "    plt.imshow(x_train[i], cmap=plt.cm.binary)\n",
        "    plt.xlabel(y_train[i])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 826
        },
        "id": "KMw2rq7ui28b",
        "outputId": "046b90c7-e34e-4ec2-f602-d6903634eef5"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 25 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.imshow(x_test[1], cmap = 'gray_r')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "EH1qLmKGc-4O",
        "outputId": "3a52c24a-7489-4edb-a041-865ded2122d7"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fa051259240>"
            ]
          },
          "metadata": {},
          "execution_count": 51
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictions_one_hot = model.predict([x_test])\n",
        "print('predictions_one_hot:', predictions_one_hot.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LGSdsSxjj11g",
        "outputId": "3787aee0-69e6-4bf6-cd90-67e08f434cc7"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 1s 2ms/step\n",
            "predictions_one_hot: (10000, 10)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = np.argmax(predictions_one_hot, axis=1)\n",
        "pd.DataFrame(predictions)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "J5A8gQRVkANZ",
        "outputId": "f1afcdb6-7329-46c8-faa5-d9f073ae74ef"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      0\n",
              "0     9\n",
              "1     4\n",
              "2     1\n",
              "3     1\n",
              "4     6\n",
              "...  ..\n",
              "9995  9\n",
              "9996  1\n",
              "9997  8\n",
              "9998  1\n",
              "9999  8\n",
              "\n",
              "[10000 rows x 1 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-72d4e6ec-cf3e-4937-8d48-00d050c34c08\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9995</th>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9996</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9997</th>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9998</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9999</th>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10000 rows × 1 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-72d4e6ec-cf3e-4937-8d48-00d050c34c08')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-72d4e6ec-cf3e-4937-8d48-00d050c34c08 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-72d4e6ec-cf3e-4937-8d48-00d050c34c08');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "numbers_to_display = 25\n",
        "num_cells = math.ceil(math.sqrt(numbers_to_display))\n",
        "plt.figure(figsize=(15, 15))\n",
        "\n",
        "for plot_index in range(numbers_to_display):\n",
        "    predicted_label = predictions[plot_index]\n",
        "    plt.xticks([])\n",
        "    plt.yticks([])\n",
        "    plt.grid(False)\n",
        "    color_map = 'Greens' if predicted_label == y_test[plot_index] else 'Reds'\n",
        "    plt.subplot(num_cells, num_cells, plot_index + 1)\n",
        "    plt.imshow(x_test[plot_index], cmap=color_map)\n",
        "    plt.xlabel(predicted_label)\n",
        "\n",
        "plt.subplots_adjust(hspace=1, wspace=0.5)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "k9zEr_VZkUJj",
        "outputId": "6d7303f9-829d-4802-9b43-ebea0bceceee"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-84-c0d4ca65887b>:11: MatplotlibDeprecationWarning: Auto-removal of overlapping axes is deprecated since 3.6 and will be removed two minor releases later; explicitly call ax.remove() as needed.\n",
            "  plt.subplot(num_cells, num_cells, plot_index + 1)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1500 with 25 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def check(label):\n",
        "\n",
        "    match label:\n",
        "        case 0 | 1 | 2 | 3 | 4 | 6:\n",
        "            print('Одежда')\n",
        "        case 5 | 7 | 9:\n",
        "            print('Обувь')\n",
        "        case 8:\n",
        "            print('Сумка')\n",
        "\n",
        "check(predictions[9])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GrKkcgkXlb2g",
        "outputId": "186cc6c0-4e1b-49db-84c8-0a0db2aca19f"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Обувь\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.save('drive/MyDrive/fashion_detects')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PPwbATsk9_Av",
        "outputId": "bc9ea753-3058-4013-e74d-01b2d203e372"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as _update_step_xla while saving (showing 1 of 1). These functions will not be directly callable after loading.\n"
          ]
        }
      ]
    }
  ]
}