21_generative_adversarial_networks_implementation.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyNXOLGFN/9idWQBFcqfsJFl",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/dlsyscourse/public_notebooks/blob/main/21_generative_adversarial_networks_implementation.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Mpn1ti5Urdsv"
      },
      "source": [
        "# Lecture 21: Generative adversarial networks implementation\n",
        "\n",
        "In this lecture, we are going to implement a version of generative adversarial training in needle.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qXysoqn-vZuF"
      },
      "source": [
        "## Prepare the codebase\n",
        "\n",
        "To get started, please clone a version of needle repo from the github. You should be able to use the needle repo after finishng HW2"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JjEIRTyr8ajf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1eae9650-aa90-4466-c7c6-c2b71ee07aec"
      },
      "source": [
        "# Code to set up the assignment\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "%cd /content/drive/MyDrive/\n",
        "!mkdir -p 10714f22\n",
        "%cd /content/drive/MyDrive/10714f23\n",
        "# comment out the following line if you run it for the second time\n",
        "# as you already have a local copy of code\n",
        "# !git clone https://github.com/myrepo/needle lecture21\n",
        "!ln -s /content/drive/MyDrive/10714f23/lecture21 /content/needle"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n",
            "/content/drive/MyDrive\n",
            "/content/drive/MyDrive/10714f23\n",
            "Cloning into 'lecture21'...\n",
            "remote: Enumerating objects: 210, done.\u001b[K\n",
            "remote: Counting objects: 100% (59/59), done.\u001b[K\n",
            "remote: Compressing objects: 100% (45/45), done.\u001b[K\n",
            "remote: Total 210 (delta 17), reused 43 (delta 10), pack-reused 151\u001b[K\n",
            "Receiving objects: 100% (210/210), 11.28 MiB | 16.43 MiB/s, done.\n",
            "Resolving deltas: 100% (91/91), done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DFxG3p3S1sBq"
      },
      "source": [
        "We can then run the following command to make the path to the package available in colab's environment as well as the PYTHONPATH."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bix8OXLuCOKt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "219c286f-57e7-41f5-bea8-54c11cd9ced9"
      },
      "source": [
        "%set_env PYTHONPATH /content/needle/python:/env/python\n",
        "import sys\n",
        "sys.path.append(\"/content/needle/python\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "env: PYTHONPATH=/content/needle/python:/env/python\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BBIuE2jc1DaU"
      },
      "source": [
        "## Components of a generative advesarial network\n",
        "\n",
        "There are two main components in a generative adversarial network\n",
        "- A generator $G$ that takes a random vector $z$ and maps it to a generated(fake) data $G(z)$.\n",
        "- A discriminator that attempts to tell the difference between the real dataset and the fake one.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T-1piPnmkMVg"
      },
      "source": [
        "![image.png](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_mBCkBtSa54H"
      },
      "source": [
        "import needle as ndl\n",
        "import numpy as np\n",
        "from needle import nn\n",
        "from matplotlib import pyplot as plt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cbT1kk_4k6HE"
      },
      "source": [
        "### Parpare the training dataset\n",
        "\n",
        "For demonstration purpose, we create our \"real\" dataset as a two dimensional gaussian distribution.\n",
        "\n",
        "\\begin{equation}\n",
        "X \\sim \\mathcal{N}(u, \\Sigma), \\Sigma = A^T A\n",
        "\\end{equation}"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qrIYM_PDbhFm"
      },
      "source": [
        "A = np.array([[1, 2], [-0.2, 0.5]])\n",
        "mu = np.array([2, 1])\n",
        "# total number of sample data to generated\n",
        "num_sample = 3200\n",
        "data = np.random.normal(0, 1, (num_sample, 2)) @ A + mu"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "3yDlolmEizvN",
        "outputId": "e10490ab-97b1-4d46-f70f-c071994dd1ac"
      },
      "source": [
        "plt.scatter(data[:,0], data[:,1], color=\"blue\", label=\"real data\")\n",
        "plt.legend()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7bcf9f5c64d0>"
            ]
          },
          "metadata": {},
          "execution_count": 4
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiMAAAGdCAYAAADAAnMpAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAABOoElEQVR4nO3dfXRU1fkv8O9kIAEkCSQZEJiBCPIrImoVKleQCuqVepWFjYgCegEtvgBCiGChWgNajAoiagGF/gpcIbwmSGuXWsVEaa0KWLq0VQoIJYS3ADoBxACTc//Yv5N5ycycc2bO28z5ftaaFTI5M2fPJHqe2ft5nu2SJEkCERERkUUyrB4AERERORuDESIiIrIUgxEiIiKyFIMRIiIishSDESIiIrIUgxEiIiKyFIMRIiIishSDESIiIrJUC6sHEE9jYyMOHTqE7OxsuFwuq4dDREREKkiShFOnTqFz587IyFCe97B1MHLo0CH4fD6rh0FEREQJqKmpgdfrVTzO1sFIdnY2APFicnJyLB4NERERqVFfXw+fz9d0HVdi62BEXprJyclhMEJERJRi1KZYMIGViIiILMVghIiIiCzFYISIiIgsZeucETUkScKFCxcQCASsHgpp4Ha70aJFC5ZsExFRagcj586dw+HDh/H9999bPRRKQJs2bdCpUydkZmZaPRQiIrJQygYjjY2N2LdvH9xuNzp37ozMzEx+yk4RkiTh3LlzqKurw759+9CzZ09VTXGIiCg9pWwwcu7cOTQ2NsLn86FNmzZWD4c0at26NVq2bIn//Oc/OHfuHFq1amX1kIiIyCIp/3GUn6hTF393REQEpPDMCBERESUuEAC2bgUOHwY6dQIGDQLcbmvGwmCEiIjIYSorgalTgYMHg/d5vcDLLwNFReaPh/PkaWT//v1wuVzYuXOnpscVFhZi4cKFhoyJiIjspbISGDEiPBABgNpacX9lpfljYjBCmq1YsQLt2rWzehhERKRRICBmRCSp+c/k+4qLxXFmYjAC8aZXVwNr1oivZv8Szp07Z+4JiYjIkbZubT4jEkqSgJoacZyZHB+MVFYChYXAkCHA6NHia2GhsdNUgwcPxuTJk1FcXIyCggIMHToUAPDll1/i1ltvRdu2bdGxY0fcd999OH78eNPj3nnnHVx//fVo164d8vPzcfvtt2Pv3r2azn3s2DEMGzYMrVu3xiWXXILVq1c3O2bBggW44oorcNFFF8Hn82HixIk4ffo0AKC6uhrjx4+H3++Hy+WCy+XC7NmzAQBvvPEG+vXrh+zsbFx88cUYPXo0jh07luC7REREejt8WN/j9OLoYMTKdbOVK1ciMzMTf/3rX/Haa6/hu+++w4033oirr74a27dvxzvvvIOjR49i5MiRTY85c+YMSkpKsH37dmzZsgUZGRn4+c9/jsbGRtXnHTduHGpqalBVVYWNGzdi8eLFzQKGjIwMvPLKK/jnP/+JlStX4oMPPsDjjz8OABgwYAAWLlyInJwcHD58GIcPH8b06dMBAOfPn8czzzyDf/zjH3jzzTexf/9+jBs3Lvk3i4iIdNGpk77H6UayMb/fLwGQ/H5/s5+dPXtW+te//iWdPXs2oee+cEGSvF5JEpNSzW8ulyT5fOI4vd1www3S1VdfHXbfM888I91yyy1h99XU1EgApF27dkV9nrq6OgmA9MUXX0iSJEn79u2TAEh///vfox6/a9cuCYD02WefNd331VdfSQCkl156KeZ4N2zYIOXn5zd9v3z5cik3NzfOKxS2bdsmAZBOnToV9efJ/g6JiEgb+drnchl77Yt3/Y7GsTMjVq+b9e3bN+z7f/zjH6iqqkLbtm2bbr169QKApqWY3bt3Y9SoUejevTtycnJQWFgIADhw4ICqc3711Vdo0aJF2Ll79erVLBn1/fffx0033YQuXbogOzsb9913H06cOKG4B9COHTswbNgwdO3aFdnZ2bjhhhs0jY+IiIzldovyXQCI3EFF/n7hQvP7jTg2GLF63eyiiy4K+/706dMYNmwYdu7cGXbbvXs3fvrTnwIAhg0bhpMnT2LZsmX49NNP8emnnwLQNwF2//79uP3223HllVeioqICO3bswKJFixTPc+bMGQwdOhQ5OTlYvXo1tm3bhk2bNuk+PiIiSk5REbBxI9ClS/j9Xq+434o+I45tema3dbNrrrkGFRUVKCwsRIsWzX8tJ06cwK5du7Bs2TIMGjQIAPCXv/xF0zl69eqFCxcuYMeOHfjJT34CANi1axe+++67pmN27NiBxsZGvPjii03t2tevXx/2PJmZmQhElBx9/fXXOHHiBJ577jn4fD4AwPbt2zWNj4iIzFFUBAwfbp8OrI6dGRk0SESBsTb6dbkAn08cZ4ZJkybh5MmTGDVqFLZt24a9e/fi3Xffxfjx4xEIBNC+fXvk5+dj6dKl2LNnDz744AOUlJRoOsePfvQj/OxnP8NDDz2ETz/9FDt27MAvfvELtG7duumYSy+9FOfPn8err76Kb775Bm+88QZee+21sOcpLCzE6dOnsWXLFhw/fhzff/89unbtiszMzKbH/eEPf8Azzzyjy3tDRET6c7uBwYOBUaPEV6sCEcDBwYjd1s06d+6Mv/71rwgEArjllltwxRVXoLi4GO3atUNGRgYyMjKwdu1a7NixA3369MG0adMwb948zedZvnw5OnfujBtuuAFFRUV48MEH0aFDh6afX3XVVViwYAGef/559OnTB6tXr0ZZWVnYcwwYMAAPP/ww7r77bng8HrzwwgvweDxYsWIFNmzYgN69e+O5557D/Pnzk35fiIgo/bkkKVofNnuor69Hbm4u/H4/cnJywn72ww8/YN++fbjkkkuS2n4+Wn9+n08EIlasmzmJXr9DIiKyl3jX72gcmzMis9u6GRERkdM4PhgBgutmREREZD5Dc0YCgQB+/etf45JLLkHr1q3Ro0cPPPPMM7DxyhARERGZzNCZkeeffx5LlizBypUrcfnll2P79u0YP348cnNzMWXKFCNPTURERCnC0GDk448/xvDhw3HbbbcBECWha9aswWeffabbOTjLkrr4uyMiIsDgZZoBAwZgy5Yt+Pe//w1AtDz/y1/+gltvvTXq8Q0NDaivrw+7xdKyZUsAUGxRTvYl/+7k3yURETmToTMjM2fORH19PXr16gW3241AIIC5c+dizJgxUY8vKyvDnDlzVD232+1Gu3btmnacbdOmDVyxOpiRrUiShO+//x7Hjh1Du3bt4GbpEhGRoxkajKxfvx6rV69GeXk5Lr/8cuzcuRPFxcXo3Lkzxo4d2+z4WbNmhXUVra+vb2otHs3FF18MAE0BCaWWdu3aNf0OiYjIuQxteubz+TBz5kxMmjSp6b7f/OY3WLVqFb7++mvFx6ttmhIIBHD+/HldxkzmaNmyJWdEiIjSlK2ann3//fdNm63J3G43GhsbdT2P2+3mhY2IiChFGRqMDBs2DHPnzkXXrl1x+eWX4+9//zsWLFiA+++/38jTEhERUQoxdJnm1KlT+PWvf41Nmzbh2LFj6Ny5M0aNGoWnnnoKmZmZio/XOs1DRERE1tN6/U7ZjfKIiIjInrRevw3tM0JERESkhMEIERERWYrBCBEREVnK0GoaIiIiAs6dAxYvBvbuBXr0ACZOBFTUcTgGgxEiIiIDPf44sGABEAgE75s+HSgpAV54wbpx2QmDESIiIoM8/jgwb17z+wOB4P0MSFjaS0REZIhz54A2bcJnRCK53cD336ffkg1Le4mIiGxg8eL4gQggfr54sTnjsTMGI0RERAbYu1ff49IZgxEiIiID9Oih73HpjDkjREQOFwgAW7cChw8DnToBgwaJXAa7Sma8So/V871gzghzRoiISIXKSqCwEBgyBBg9WnwtLBT321Ey41V6bDLPHQgA1dXAmjXiayAgAoySkviPKylJv0AkIZKN+f1+CYDk9/utHgoRUdqpqJAkl0uSgPCbyyVuFRVWjzBcMuNVeuyMGck9t9cb/jivN/iYGTMkye0O/7nbLe5PV1qv31ymISJyoEBAfOo/eDD6z10uwOsF9u2zx5JNMuNV89iMjNjLKfGeu7ISGDFChBiRjwGAjRuBoiLndWDVev1mMEJE5EDV1WIZQklVFTB4sNGjUZbMeNU+Vutzp1pAZybmjBARkaLDh/U9zmjJjFev1xD5PFu3xg5EADFbUlMjjqP4GIwQETlQp076Hme0ZMar12uIfJ5UC+jsjMEIEZEDDRoklhDk3IZILhfg84nj7CCZ8ap5bLxllFjPnWoBnZ0xGCEiciC3G3j5ZfHvyIu0/P3ChfbJdUhmvGoeW1Ii/q3luVMtoAOilyDbAYMRIiKHKioS1R5duoTf7/UGq0DsJJnxKj32hRdi/3zdOiAvr/kFPNUCOjv3lGE1DRGRw8XqOmrXzqxmdmA9fhyYNi08UdXrFUGIHPxUVgJTp4Yf4/OJQMQuAZ3aEmS9sLSXiIiSFu0CG3kRtiM9AygtF3C7Bm6ANSXIDEaIiCgpZn+KVkPNxV7PACoQADp2BE6ciP7zVOohYkVPGfYZISKihAUC4oIe7WOqfF9xsTmJj3Ky5bRpIgCJl+sgB1CRn/5ra8X9WvMi5s6NHYgAqdVDJBVKkBmMEBGlqUQqJ+zQyCsQAJ5+GujQQQQeCxcCdXXhx4QGGXoHUIFAMDFVSW2tPatTQqVCCXIL605NRERGSXTJwupP0ZWVwIMPxp+VAMKDjNxc9QGUmmWIrVuBkyfVjXfSJMDvD35vx7wauQS5tjZ6wCYvOVlZgsyZESKiNJPMkoWVn6LlcSsFIqFqasSMhBpGBFqhgQgg3vNEloWMlAolyAxGiIjSSLJLFgMGKF+U3G5xnJ7ijVtJY6O648wKtCTJvLwatezeU4bBCBFRGlGb8zF7dvQch48/Vr6IBgLiOD0pjTue2lp9O6GqCciU2DG5tagI2L9fVM2Ul4uv+/ZZH4gADEaIiNKK2iWG3/wmelWKXksZWpNna2vVnTealSuBgQNj50MAYhkCUDcmNQGZGnbcIM/tFnkzo0aJr3YpS2YwQkSURrQuMUTmkeixlJFI2/HIahmt1q2Lfr+8DAGoH5NeQQQ3yFOPwQgRURpR2rwtUmQeSbKbvyWaPOvxqBuvFnPmiGUIQNuY9AgiPB57bZBndwxGiIjSSLzKiVhCS1+TqbxIJnk2MrFSD7/7XWJj0hrQRbN4sX2WQFKB4cFIbW0t7r33XuTn56N169a44oorsH37dqNPS0TkWMOHiwTV9u21PU5enki08iKZhmlyAKCnmhrg1Ve1j0lNQBbPjBlixoXUMzQY+fbbbzFw4EC0bNkSb7/9Nv71r3/hxRdfRHut/4UQEZEqcr5GaWmwcVd2trrHhi5PJFJ5kUzyq9sNLFig7vFalJaqOy5yTPECsooKcYsMnjweYP164IUXEh+vUxnagfX555+Hz+fD8uXLm+675JJLjDwlEZFjxdrg7vTp+I+L1YFTrryIJtrGdckkv1ZWAiUl6h6vxZkz6o6LNqaiIjHLFGuDvng/I20M3bW3d+/eGDp0KA4ePIgPP/wQXbp0wcSJEzFhwoSoxzc0NKChoaHp+/r6evh8Pu7aS0SkQGmb+FgS2Yk3Vqv5BQtEQKHUdjxyp9tYQZQZUmn33VRiq117v/nmGyxZsgQ9e/bEu+++i0ceeQRTpkzBypUrox5fVlaG3NzcppvP5zNyeEREaUNt07CCgubfT50K5OWp660Rr1rm7rtF/wogeq6FJAF33inGKp8rmc6rerG6FToZPDOSmZmJfv364eOQVn1TpkzBtm3b8Le//a3Z8ZwZISJKzJo1on+GklWrRB7E5s3A6tXh/T2UNnlTmn2RZxkWLACmTQs/zu0OD3bkc+XliZ4fVsjPB5YutUcH0nRjq5mRTp06oXfv3mH3XXbZZThw4EDU47OyspCTkxN2IyIiZWrzNS6+GPjoIzEbENloTKkXiNpqmYKCYPJrcbH4WeSsy8GDYpZk82Z1444mI0MEM6E8HvVJq+vWMRCxC0MTWAcOHIhdu3aF3ffvf/8b3bp1M/K0RESOo7RNPCCqasaOjd16XZLE7EZxsUjOjFy60FIt43aLMSmVuP7+9+qeM5p164Cf/7x5EikA/Pd/K+euxErOVSNaAm+s3itMclVBMtBnn30mtWjRQpo7d660e/duafXq1VKbNm2kVatWqXq83++XAEh+v9/IYRIRpYWKCkkSl9/kb1VVzZ9/zhxtj33/fXXHFxRIkssV++cZGeHf+3zitSq9Fy5X8+eV71N6vNJze73hz+v1Nn9OtcelI63Xb0ODEUmSpD/+8Y9Snz59pKysLKlXr17S0qVLVT+WwQgRUXMXLogLfnm5+HrhQvD+/Hx9gpHy8vBzyhd3pcfl5wfH8+ST6s41YkT0wCFW4LJ2bfTXHylaMKAmkIkn1vsQGeSoPS5dab1+G5rAmiytCTBEROkuVlmt3smgVVXBZQwtZcP5+cDRo2Ip4te/FrsDK3nySeDKK4GJE4Hjx7WPNV7ibegySYcO4r5jxxJbMlGbwLtnD9Cjh/Jx6VxObKsEViIi0o/SJnTJJIPKom2Ep7ZsGABOnAi2Vld7oXW7RX+S0EAkQ8PVKV7irdy4LSsLGDcOuPlm9TsJR1KbwLt4ceJt8Z2KwQgRUQpQs+HbqlX6nCuy74baxNXQ4wMBkUSqpG1b4Omnm1+8GxvVn09eBHn4YeDcueY/T3Qn4Uhq34e9e/V9PidgMEJElALUfCpPZIkjVKyN8NSWDYcer3Y2JSNDv4ZndXWih0pocJHMTsKR1L4PPXqoO07r+5rOGIwQEaUAoz9Fjx8veoNEy7tQu6Nu6BKP2vHW12sapqLjx8NnO5LZSTiS/D7E2rlXfv0TJ6o7LnIvICdjMEJElAKM/BSdkQG89lrsHA+3WySIqiEv8Vj5qV+SgrMdyewkHCn0fYjW7h4Qrz8zU91x6Zq8mggGI0RENhcIiFtkt1G9NDYCIbt2JCQ/P3yJR2kWwWjybEcyOwlHU1QkXmeXLuH3Ry5xqT0uVCAAVFeL1v7V1eqWjtIFS3uJiELYrWNmtFJeI5SXBze5i6SmtNfrFcs8eu/Gm5GhLZk1VHk5MHKkGLvWnYSV6N2BNV7Jdiq2rNd6/WYwQkT0P+x2QdDjYq7W+++Li2S0i2Z1tbr+JaG9SWSVlcCECcDJk4mNKy8v8cfK45HfRyD8vZRnbWLNVJgl1u/ZLuNLBPuMEBElQK/yz1DJTLvHqwLRW06O2LNmyJDoPThi7WUT6b33mr/GoiJg/frEx5ZoIBKaIJrIkolZ9Kz2SWWcGSEix1PbWVPLVH4isyyhU/pHjwLTpml6GboK/VR+4ID6sXg8wJgxYqM9eXYlEAA6dhQN0czgckUPMuy2BAckN+tkZ5wZISLSSM/yTyCxWZbKShEQybMTVgYiQPin8vx89Y+rqxOVIkOGAO3bA7NniyDArI+9bjcwfXr0gE/uxjpqlPhqdSAC6Fvtk8oYjBCR4+l5QUhk2j1W8GI1OQhLdEbj1ClgzhzRZTXR5RatGhuB+fMTW1YLZVZli97VPqmKwQgROZ6eFwStsyzJ5IaYVTab7IZu58/rNxYleuRZRM5SJbKPjVpqG6mle4M0BiNE5Hh6XhC0zrJo2YQuUtu2iT1Oq1deSa0EymQ2ojMikTketY3U7LCkZCQGI0TkeHpeELTOsiSTC3D6dOKPdQKt761VlS12rvYxC4MRIiLod0HQOsvSoUPiY7ZvLaQ9aM2z0DuRWYuiItE0rqpKNGurqhLLY04IRACghdUDICKyi6IiUZKaTPmnPMsyYoQIPKI12ZJnWSorgSlTdH0JhGApdrxltWhlvlZXtsjVPk7EYISIKIQeFwR5liVan5GFC8XPzeyu6iRqltVi9YCZMEHdOdK9ssUKbHpGRJSEeI20Yv1MzV4vTpOXJwKJZBuj+XwiEIk1w6XUel1uP6/nPjZOxKZnREQmUSoBdbvFRbBTJ3FR3Lo1GKAwEAn3q18lH4jMmSMCBSD672XDBuUEVfnfTq5ssQKXaYiIEhDrE/bBg8Cdd4qqi/btgaVLw/d2ycsDbrzR1KHans8nWs4nw+UCfvc7oHdvsVNv5O+ltlbcH48kiYBozhxg2bLYS2ykPy7TEBFpxGUWfa1dCzzwAHDmTPLP5fGIlvTJKC8XgYvd9rFJJVqv35wZISLSyKxllshqnHR0993A5Mn6BCJA8oEIIIIPJ1e2WIHBCBGRRmZsWta2LdCuXXrPvrRqBaxbZ/Uowrnd+gQ0pA2DESIijcwo7Tx9GnjzTWDVKmDFCuPPl6wXXwS+/Vb8u107sXOukh9+0HcMGRlio7xkBAJitsbtZn6ImVhNQ0RpwaxdVgHlLqt6eeut1AhE8vKAq64CZs8GnnlGJO+a8f5EamwUOSNK3W/XrVPO/zCi7TvFxmCEiFKembusAuJCNmqU8fkcr79u7PPr5eRJ4Oabg++53IXWinyXMWPE13iluR06xA80jGz7TtExGCGilGb2LquBAPD008C8efo+bzRnzxp/Dj2FvudFRWJ2wWzDhyvvMWR123dqjqW9RJSy1JTYer1iAzI9yjLlvWRC+4ZQuNAupVu3ilkqs88rd7mNVZpbXa1uXFVVrKhJFEt7icgx1JTYHjwI/OIXwC23JNYvQr6obd4spvgpvtAlDjm3prbW2CWbaN1R45XmKo1LzUZ7pC8u0xBRylI7jb5ihXIuSbQE2NBcFK2ByLXXAk8+qe0x6eTw4WDuCJB4MmtGxFUqJ0ckzIYKXYJRI9642PbdGlymIaKUpXa6PZR8sVm/HigoEBfN3bubt23Pz09ur5TsbGDTJpHY6UQvvQR07Chmo+rqgJIS9T1T7rwTuOwyMbNx4oRoihba+0PeYbdnz+YbEGrpmhpt9155oz2W9SZH6/WbwQgRpaxk2rLLFy8jPfWUSHZ1msh+Hx6PmJkqLBRB3mOPxW4sFpr7sXlz/B12Q2dDogUWXq+YAYkXWGgNYEgd2wYjzz33HGbNmoWpU6diocr5TgYjRKSkslJ8krYjJ7Rz16qgADh+XPm4998Hxo2LH2h6POLnb72lPmghc2i9fpuSM7Jt2za8/vrruPLKK804HRE5SFGR2GXVjhiINKcmEAHEEpzSjFddnSjhffDB6O+1JInbgw8CW7awiZmdGR6MnD59GmPGjMGyZcvQvn17o09HRA70xBPNkxoptf3nP+qOO35cObfnxInwpmxkP4YHI5MmTcJtt92Gm1VkcTU0NKC+vj7sRkSkhtmtx8lYb7yh/3Ma1QiPkmdoMLJ27Vp8/vnnKCsrU3V8WVkZcnNzm24+n8/I4RFRmti6NbnKF3IGeSmH+87Yj2HBSE1NDaZOnYrVq1ejVatWqh4za9Ys+P3+pltNTY1RwyMiG9Brczsz23a3bi0SKyk1cd8ZezKsA+uOHTtw7NgxXHPNNU33BQIBfPTRR/jtb3+LhoYGuCPqp7KyspCVlWXUkIjIRhItxYymUyd9xxbP2bOiiZoZpcFkHO47Yy+GzYzcdNNN+OKLL7Bz586mW79+/TBmzBjs3LmzWSBCRM6h9+Z2cntvM/NGGIgAbduad67s7Ng/c7lE/5LIbq3xmBnAkjLDgpHs7Gz06dMn7HbRRRchPz8fffr0Meq0RGRzgYCYEYlVigloX9O3cst6Jzt92rxzTZ8ugo5Y7dunTAlvtBaPz8d9Z+yGe9MQkamUNrfjmn5qMKuU2uUSwcMTT4jmZV26hP9c3pemZ0/1z8l9Z+zH1F17q6urzTwdEdmQ2rV6LWv68mwLmWfqVKC01PjzSFIweCgqAoYPj96+Xe3lZc4cdmO1I1ODESIitWv1R4+KKhs1+4UozbaQvsaOBXr0UN/aXU9ut9hAL5KcN1RbG3u5zusVMyxkP9woj4hMJW9uF++iEVmpEqvKRt7krKIC+O1vDRsyhYjcBM8MPp/YOE9paUVOjAbC/7a4R435bLk3DRE5W2g/ka1bxfbyQOzql8jk1WhVNpWVIqgZMoSBiJnMDkQA9TlERUXx80oYiNgXl2mIyFCx+olMny6Ck9D7Y/XukD/lPvggkJsLfPstcNddxo6b7EVtDlG8vBKyLy7TEJFh5GnzWP+XWbsW6NhRXDSOHgWmTVP3vFYsFZC1qqqi54qQPXGZhohsIV4/Edno0cCHH4p/a9lbhoGIs3g87AuS7rhMQ0SGUFPh0tgIzJ5tynAohS1ezGWWdMeZESIyBPf+ID3MmBGskKH0xZkRItKNXGor54AQhXK51Lfs93iARYuYqOwUDEaISBfRqma4s2368PmAa64BNm9O/DnatAHOnFE+7sknxfIdl2acg8EIESUtVtUMA5HU17Yt8OabIoG0R4/knktNIAIAN93EQMRpmDNCRElRUzVDqWvGDBEcfPyxPi33YzW6k3/GHXWdicEIESWF+8Kkr/z84F4ueiUkKwWt3FHXmRiMEFFS1F6kiotF51VKHUuXBgMDtRscJsrtBtatY8t2p2IwQkRJUXuRGj4c2L9fdNIsLxdbuTM4sa/i4vDAQN4VV2mZJVGBgKigIWdiAisRNQktzVW7p4fS1u0ul/i5/FyhLb179wYmTgTq6nR9GaSD4cPDv3e7xc7JI0bELtFNNm+IvWmci8EIkYOFBh+7d4tp+dra4M+9XnEBijZ1HvrYCROA0tLmFyn5k3K0PIDKSmDkyPgXMC19KUgfocFjJHlXXKNKuI1eCiL74kZ5RA4VrS9IJDmYiNx+Pdpj8/PF19A9Znw+EYhE7qI6YIAoE411bpdLlJSeOpXQS6MExfp9Rzp3TrRo37tXBIuLFiV/Xq8X2LePyavpQuv1mzMjRA6ktJuuTJLEhaK4WAQUbnfsx548Kb6OHw9cdJEINiZOFE2yOnYMD1Ly8+NvjCdJDETMUFAAHD8e/N7rFcFjvEBETRCrRbzZM3IOzowQOYS8rFJbC0ybpj1Po6pKTN0XFqq/ELVtC5w+rXmoZAKPR/weP/5YfY6Q2iA2lvx8oFWr8KVAefaMVTTphTMjRNSMHp9mDx/W3lOEgYh9LV4MZGaGJxTHk2xzO5dL5CRFLtmpSZKm9MdghCjNJftpVnb0qPgUTekhQ6GxQ2RlVSCQeDCbny8CEXn2Q20ARM7BZRqiNBYIaFtWiYVVLeknP18EmNFmJaLNpOXlBfOC1MrLE8/zxBOc/XAaLtMQURO9WrUzEEk/J04Ac+cCTz0Vfr9SgrKSl14SCctcgiEtGIwQpTE2kaJ4Xn45fNYimbwQuTz30UcZgJB2DEaI0hibSFE8J08C1dUieDh8WCzbJDKTxvJcShaDEaI0ptSqnWjkyMRyQUIfo6Y/CVE8DEaI0pia/UTI2bQGIgCwfn1wNoW5IaQHBiNEaS7WfiJEWsl5IYMHM/ggfSlUmhNROigqAvbvF5UORIlgXggZicEIkQPIDaz27rV6JJSqvF7lDfSIEsVlGqIUEtkVc8CA5nuLAOHH1NUBJSVcoiHt2DOEzMJghChFROuK6XaLAEWWny++xtsRl0gJe4aQ2QxdpikrK8NPfvITZGdno0OHDrjjjjuwa9cuI09JlJbkrpiRsxuhgQggghAGIpQM5oaQFQwNRj788ENMmjQJn3zyCd577z2cP38et9xyC86cOWPkaYnSSrK7pRLF4/GEf8/cELKCocs077zzTtj3K1asQIcOHbBjxw789Kc/NfLURGlDr/1liCL5fMCePc3zjjgjQmYzNWfE7/cDAPLy8sw8LVFK4/4ypEWXLmKpRU0Au3AhkJkp+oYQWcm0YKSxsRHFxcUYOHAg+vTpE/WYhoYGNDQ0NH1fX19v1vCIbIv7y5AWr7wivkbbeVeWnw8sXcqlGLIP0/qMTJo0CV9++SXWrl0b85iysjLk5uY23Xw+n1nDI7JUICA2LFuzRnwNTUyV95chZ7j1VqCqCtiwIVgdpUZ2NlBRIQIMuetu5N9Nfj4wZ47YEI+BCNmJKcHI5MmT8dZbb6GqqgreOP9XnTVrFvx+f9OtpqbGjOERWaqyEigsBIYMAUaPFl8LC8X9gFi/nzDByhGSmd5+W+wXM2KECBp+9St1j2vZMvx7uetuVRVQXi6+Hj0KPPUUc0LIflySZFyOviRJePTRR7Fp0yZUV1ejZ8+emh5fX1+P3Nxc+P1+5OTkGDRKIuvIJbuR/xXK5ZVyVcPq1cC99yo/X6tWwA8/6D9OMpfPB+zbJ4KG6moRoKrhcrEShuxB6/Xb0JmRSZMmYdWqVSgvL0d2djaOHDmCI0eO4OzZs0aeliglxCvZle8rLhbH1dWpe87MTN2GRxaqqRFVVEBwmU4OUJXIfzNEqcTQYGTJkiXw+/0YPHgwOnXq1HRbt26dkaclSglKJbuSFLwoRfaCiIU53+mjtlZ8dbuBl19W12cm9G+GKJUYWk1j4AoQUcpTW7K7eTMwfLixYyH7KS4GWrcWSy7Dh4vkU7XddVkOTqmGu/YSWURtye7ChWKZhhU1znL8uMgnqqwUMx1a2vyzHJxSDYMRIouoLdl1uYDHHhM7qKrNG6D0UVwMbNqk/nifL7h7M1GqYDBCZBE5F0CJnAfQvj0wezbABsbOIf/uV6xQ/5h77mHpLqUeQ0t7k8XSXkpHgYCYdpf3Atm0Kdg1M568PNF/QtayJXD+vHHjpNTE8l6yA63Xb1P3piFyuspKUc4bWkWjtlImNBABGIhQbMXFIumVMySUKrhMQ2QSucFZZDnv8ePWjIfsz+VSH6zKWN5LqYjBCJGB5D1nVq8Gxo+P3+CMnKNVK+DFF4FVq8ReMS5X8+Rk+fvFi7U1PZOxvJdSCYMRIoOE7jlz771sSEZBP/wgKqRmzgT69BE5Hl26hB/j9Yr7R4xQ3/QsFMt7KZUwZ4TIALH2nCEKVVsr/k42bhSb2oUmNg8aFMz50NL0zOUSgQzLeymVMBgh0lm8PWeIQkmSCB4eegjYsQPIyAAGDw4PRADtTc8WLmTyKqUWLtMQ6UxpzxmiUJIkkpiffRb4zW+Am28GOnYUs2sytfkf+fks66XUxGCESGdMHKRknTgB3HlnMCBRm/+xbh0DEUpNDEaIdBQIAEePWj0KShdTp4q/KXnrgFgVNS6XaAM/eLCpwyPSDYMRIp3I1TPTplk9EkoXBw+KZb/QrQNilQAzT4RSGYMRIh3EamhGlCx52a+oKH4JMJdnKJWxmoYoSVqqZzIygDvuANq0EQ2vyDny84HWrbUHrKH5IkVFosw3VgkwUapiMEKUJC3VM42NwaREt1sEMuQM998PlJWJv5faWhHAKpXrRusX4nYzN4TSD4MRcoTInXLVfppU87hEq2cYiDjL/PnA//pfweWU1q1FxUw8L7/MWQ9yBuaMUNoLbcs+erT4WlgY3schmcex7TapVVwcDEKLioCKCrF8Eyk/X+xZ09Ag9jZi4ErpziVJ9u0TWV9fj9zcXPj9fuTk5Fg9HEpBsdqyyxUIsRL/tDwuEBBBCpNXSY2qqvBlFnkzxepq8X2LFsCyZWIpR+b1ilkSJqlSqtB6/WYwQmlLKUiQ9/DYty98KjyRx1VWKk+5EwFAeTkwalT0nyUaPBPZjdbrN5dpKG0pJZZKElBTI47T43HRptuJIu3eHf3+eFVZ8n2hyzxE6YTBCKUttYmlkcdpfZz8aVbLRmaU+vLywr/3ekVAGqtLqmz27Oj5SokGwUTpgMEIpS21iaWRx2l5HHfoda7160X+R3m5+Lp/P7B0qbq/hWgzHIkGz0TpgKW9lLbk/Txqa6NfIOTcj8g+Dloexx16nUneByZa2W1ODlBfH/uxoTMcoYmsiQbPROmAMyOUthLdzyP0cbHIj+OnVOdxuaL/3cjLdfECkVCRfztqN8OLDJ6J0gGDEUobconkmjXB3gyJ7udRVARMnx49UJk+Pfg4fkp1lvz86H83iSzXRf7tcDM8cjIu01BaqKwUF4PQJZPQ3gxa9/OorBQdMyMvLo2N4v5rrwUKCsRSTm4u4Pcb87rIXmIlKWtZrou1PAgEg+dof8sLF7Ksl9IX+4xQytO7N0MgAHTsGL86hvvKOJfHA7z0kphtk4PaNWtEl141XC7lv8lEty8gsgs2PSNHSbSxWTxPPw2Uluo2REpj8uxbXp7YLkCJxwO89hpnOCj9sekZOYrevRkCAeXkVSJZba2Ylauri598CohA5OBBBiJE0TAYoZSmd2+GrVuBkycTHw85izyv/NhjYukGiJ586nKJGZHMTHPHR5QqGIxQStO7NwNLdUkrefatoCCxyi0iYjUNpTi5N4NSJcPx4+qej6W6lKjDh8UGeFort4jIpJmRRYsWobCwEK1atUL//v3x2WefmXFacgC3G1iwQPm4kpJg9Uu0fiQypcZTlJ4KCprvNaOVHMi63aKz6qhRsbu0ElE4w4ORdevWoaSkBKWlpfj8889x1VVXYejQoTh27JjRpyaH8HiUj5GTWCsrRfXNkCGiFHPIEKBDB1FBEwgEG0/Zt8aM9OZyAa+/DixbFszv0MrtFkmsRJQYw4ORBQsWYMKECRg/fjx69+6N1157DW3atMHvf/97o09NDqE2z2PzZlH5ELmkc/KkKOXt2DH6bqqUvny+YD5HrG69Ph8wY0b8ICUQAO6+m38/RIkytM/IuXPn0KZNG2zcuBF33HFH0/1jx47Fd999h82bN4cd39DQgIaGhqbv6+vr4fP52GeE4qquVt/jQc2n17w8VtQ4xYYNIkANFavh2MaNwD33xG52l0hPG6J0Zas+I8ePH0cgEEDHjh3D7u/YsSOOHDnS7PiysjLk5uY23Xw+n5HDozShZoOxggL10+gMRJzB5QrPJZLFyvkoKIjfdVdrTxsiCrJVae+sWbPg9/ubbjU1NVYPiVKA0gZjkgT88IP54yJjJTtZqjV40LunDREFGRqMFBQUwO124+jRo2H3Hz16FBdffHGz47OyspCTkxN2I4pHroxpaABmzwY6dw7/uVwhcfq02SMjoz34oD6VT2qDB7172hBRkKHBSGZmJvr27YstW7Y03dfY2IgtW7bguuuuM/LUlOLild/KIitjSkvFhWnOHKC8HHj/faB1a5MHTqaZPx8YOFD8O9qMmFpqgwc1y4E+X/TdeIkoPsOXaUpKSrBs2TKsXLkSX331FR555BGcOXMG48ePN/rUlKKild8WFoZXKsg79UZWxtTWihmSrCyxfKN2W3dKTevWAe3bN+8R4vUC69frGzwoLQcCwMKFTF4lSohkgldffVXq2rWrlJmZKV177bXSJ598oupxfr9fAiD5/X6DR0h2UVEhSS6XJIkV/eDN5RK3igpJunBBkrze5seEHuvzSdKqVbGP4S39bnPmSFJ5uSRVVYm/EfnvKd5jKioS+xuN/Pvz+RJ7LqJ0pfX6bUo7+MmTJ2Py5MlmnIpSWCAATJ0q/vceSb6vuBjIzVW3Uy+bUDnLsmXA/v3Gz0wUFbHlO5HebFVNQ862davyskpNjcghUYP9Hpzl4EFg7tzg93JwG4vLJYLbeOW6sbDlO5G+GIyQbdTWqjuusVHdca+8ktiFhlJXaWkwt0gpuJVn0NgXhMh6DEbINtQuqxw8qFzSyU+qziXPdrAvCFHqYDBCtqFmwzsA+H//T0yPA7GrGjgj4lzybAf7ghClDgYjZBuRG5TFs2IF8H/+T/NgJCMDuP12XYdFKejwYfYFIUolDEbINgYNUh+Q1NUBf/pT8/yRQAB46y39x0appVMn9gUhSiUMRsg2Nm/mHjKUnMjZjqIisdtuZJDr9Yr7i4rMHyMRNWdKnxGiWNuyy+SOqtF6jOjN5QKmTxct49VW8JC18vOBEyfiHxNrtoN9QYjsj8EIGW7DBmDiROD48eB9Xq+YQi8qit/sTG8uFzB5MtC2rfHnImU+H3DPPcC8efGPW7JEtHyXe8y43cB//3d46a7XKwKRaLMdcl8QIrInlySZcQlITH19PXJzc+H3+7mDb4p6/PHYFxqXS0yV5+WJ/WfIGSZNEhvcyTMUANCxY+yZD5dL/I20ahU+k9Wli9i5t2dPznYQ2Y3W6zdzRsgwGzfG/8QrSaInBJdKnOW668I7l27dGn8JRpLEzyP/Tg4dCm6KyC6oRKmNwQgZIhAQSzNKtOwhk5WV3JjIHiKTSRNtOha6XxH7yhClNgYjZIitW9UHGR6Puo6qDQ36jI2s43YDAwaE35dM0zG2dCdKDwxGyBBaPu126RK7H4SMn3zTQyAAfPxx+H1KzcnUYEt3otTGYIQMofbTrscjPinn5QFTpgCReU7MA0g/kYFDvOZkarGlO1FqY2kvGUL+tBtv11QA+L//F+jRI/pxOTlAfb0x4yPrRAsc5OZkU6c2L9c9exY4eTJ66bfLJY5hS3ei1MaZETKE/Gk33ifd4cOBBQtiBywMRNJPvL1gioqA/fuBqirRkK6qSny/dKn4OVu6E6UvBiNkGPnTrtcbfr/HA6xdC+zYYU6jM7IPpcBBbk4WWvrLlu5E6Y9Nz8hw0VrBb93KRmepJCOj+aaEWrjdIgAdMSLx51DaUoCI7EPr9Zs5I2S4aK24N2+2ZCiUgNxcwO9P7jmKi5MLRAC2dCdKZ1ymoYQFAmKvkDVrxFe15beVlWK6XouCAo2DI93okRy6YIH4vRMRRcNghBJSWQkUFoqlltGjxdfCQuULTiAg9hPRwucDHnkk0ZFSsj79VJ/nYadUIoqFwQhpVlkpptwjq2Bqa8X9ckASbeakulp5K/hIDzwgPlmTNerqgOzs5J6DnVKJKB7mjJAmgYDoBREt7VmSRLml/Al42rTmu6xed536c7VtK/ajmT072VFTsv73/9ZnmYWdUokoGgYjpMnWrfEbmcmfgEeObP6z2lpRiqnW6dPiRtZ7/30RGCa7PxA7pRJRNAxGSBN+snUmPRrQ5eezUyoRRcecEdKEn2yJiEhvDEZIEz12WCVnOnGCCaxEFB2DEdIk3g6rDFBICZf5iCgaBiOkWay9Qrp0AUpLrRkTpQYu8xFRNAxGKGGR5b2SBFx+uUhUjCeDf3WG+tWvlJfSzN7TxeWKv2MvETkbLwukmdz0LLSHCAAcOgTcfTdw//3xH9+mjXFjI9ETRGkpLdlOqC5XMOhUWp6Tf660Yy8ROReDEdJEqekZIHZn3bBBfDoPJV+82DvEGKGzD7GW0rxe0ZROD0uXAhUVzc8RGXB4vWIsRUX6nJeI0o9hfUb279+PZ555Bh988AGOHDmCzp07495778UTTzyBzMxMo05LBlPb9KygANi7F1i8WHwtLGRLdzOEzj4UFQHDh4vf2eHDIl9j0CDxvdaNCiPNnh0MLiLPMWAA8PHH4efkjAgRxWNYMPL111+jsbERr7/+Oi699FJ8+eWXmDBhAs6cOYP58+cbdVoySCAgLjgVFeqO37wZuO+++IFLIlq1An74Qd/nTAe5ucD48UBenvhdyRd/txsYPDj82EGDxGxG5DKbFj17Bv8d7RyR3xMRxeOSpGgT7saYN28elixZgm+++UbV8fX19cjNzYXf70dOTo7Bo6NYKivF0ozegUUiXK7oS0ROFvmeeL0iZyTWskhlpdg5WeuGhaGqqhhwEFFsWq/fpuaM+P1+5OXlxfx5Q0MD6uvrw25knUAAePpp4M477RGItGzJQCSayPckcvfkUHLycaxAJD9f3GIlpbIqhoiMYFowsmfPHrz66qt46KGHYh5TVlaG3NzcppvP5zNreI4QCADV1cCaNeJrvIqKykqgWzd79Q05f97qEaQGOTiRd0+WxUs+lmVkAGPGRD+GVTFEZBTNwcjMmTPhcrni3r7++uuwx9TW1uJnP/sZ7rrrLkyYMCHmc8+aNQt+v7/pVlNTo/0VUVSVlSKJdMgQYPRo8bWwMP6n52RyCshaciJxaPt1peRjAKirA155RfybVTFEZBbNCayPPfYYxo0bF/eY7t27N/370KFDGDJkCAYMGIClS5fGfVxWVhaysrK0DokiyMmmcjVDXZ3o/xH5affgQbEEU1ERvMCo+fRMqSO0/brWVuyNjeJrcbGomGFVDBEZRXMw4vF44PF4VB1bW1uLIUOGoG/fvli+fDky2HozYZEBRqwLQ7RkU7c7fnDx4IPiYuN2q/v0TKkjtP261lbskiSWZioqgPnzGYgQkXEMiw5qa2sxePBgdO3aFfPnz0ddXR2OHDmCI0eOGHXKtKV2iUVeXokMJpS6bZ44AcydK/7NjcxSh9utLdE0kR2Xoy33aKUlV4mInMmwYOS9997Dnj17sGXLFni9XnTq1KnpRurFCjAiKyaSXV555RXxHPz1pI6SEvE1Vsv3yETTeDsuK0k0SNWSq0REzmVYMDJu3DhIkhT1Ruqoab0uV0wku7xy4oR4jkQ+PZO58vPF0skLL8Ru+R4r0bSoCJg+XftmhYkEqWoDaSIiwzqwUuLk4GLLFnWt1+VckmQdPhz89DxiBBuM2cmddwKXXSYajQ0erNzyPVZ+R2WlyP9Q+3t1uURwo7WviFIg7XIFE2OZi0JEDEZsJpFup/JFKFm7d4uv8iZrWsfB4MU4774LrFsXfuGOTGoeOTL+hV3rUl4yfUXU7mG0dSs7uRIRd+21lVjT2krkT8PJLq+UlooxBAJij5PnngNefFH8Ww0GIsY5fVrMlMkSycXQupSXTF8RtTN1TJgmIoAzI7aRSAJq6BR6vOUVtTMWLpco8330UeDQIe2vgcKNHCl6dWzcqM/zvfEGcMstwaA1Vhv4WAGE2gv/5MliWSiZviJqZ+qYME1EAGdGbEPrp9ZoU+jy8kq0hMaKCmDOnPjPKUkikZWBSPIee0wsq+jZrfT0aW1JzZHUXvjvvDM8LyURSjN13OOGiEIxGLEJrdPVsabQi4qA/fvFrqrl5eLrvn3i/tBt38kYOTnAhg0iSRTQ95P/9ddry8WIZGaAEK+MmHvcEFEkBiM2ofai9eST4QFGNG63+GQ7alT4J1xOiRvnmmtEALJ4MVBQEJyZUJPL0769cq5PRoZYPksmF8PsACHeTB33uCGiUC7Jxo0/6uvrkZubC7/fj5ycHKuHY6hAQCQg1tbG3jHV6xVBSKIXC6VzUOIuugg4cyb4vccjdr8dPjy4NxDQPJcHEBfmTz4B5s2L/fwzZoi+ItXVIllVSVVV7CqVaBVbPp8IRIwIENRuZUBE6UPr9ZvBiI3IiYlA7ItWsheLWOcg4xQUAGPHihySeAHA448DCxaE53u43aLT6gsviO/1CloZIBCRkRiMpDgzPrVWVgIPPyw+sZN5pk8HbrstfgBw7pxY6tm7F+jRA5g4EcjMDD/GjKCViCgZDEbSgBmfWlevBu69V9/nJGUbNgQDiWSYvdRCRKQFgxFSZcsW4OabrR5Fakuk46zHE2y7nywutRCRXWm9frOahigB994rWrRrVVcXvew2EbGqpoiIUg2DEYc6dszqEaS2Bx4AvvsusceyBToRUTi2g3eAaNP57DmSuLw8YMAAkWCaCL73REThGIykuWiJjl6vaNCVmwv4/daNLVU9+qioeNG6oSHAFuhERNEwGEljsTZUO3gQuOcea8aU6rKzgd/9TvT5SARboBMRNceckTSVyC7ApOzUqcQCkfx8sVkhy26JiJrjzEgKUlPSqXUX4Ehud/SdX50isr271wucPSt2NdYiL08EhU88wRkRIqJYGIxYJNEeEbFyQF5+OfxTdzIVGy+9JDp/bt0KjBwJnDyZ+HOlGrmd+p49wMcfB38/gYD2vixz5jAIISJSg8GIBaIFFF26AA8+CPTsGTs4iZcDcuedwPr1wF13ifuSqdjo2FG0IHe7nReIACKvIzMzfKO5NWvUP4/HA7z2GpdkiIjUYjBislgBRW0tUFoa/N7rFZumyR07O3QApkyJnwMyapS4oI4YEdy6PpGlmt27xVen9cPwemO3U9cS3L30EgMRIiIt2A7eRPKOq8nkcqghJ0o+/XR4gKOW1wvs3y+WadRsV5/KPB4RPHTpEn+p7Nw5cWx9vfJzVlWFz6oQETkN28HbWLJJpWoVF4vAp2fPxB5/8KAYqzy7Ii9fWMnlEhUp8caSSLw6eTIwZkz8duqVlaLBmZpAhH1EiIi0YzBiIrOWPWpqRDCRTN6IvJnbyy/rN65E5eUBGzcCS5eK7yMDEpdL3O6/X/tzKwVslZUiH0dNEOlysY8IEVEiGIyYyMw24LW1yc1syGMdPhyYPVsEBFbJ+J+/0qIiEZR06RL+c69X3D98uPbnjvc7CQREUrEaPp8YA3NFiIi0Y86IieSckdpa45uRZWcD06cDl10mynPVkktb9+0DNm9uXvXTvj3w4x8DO3cC336r96hjjwkIXuzPnRPt2PfuFcsnEyeK6hetOTlycnCsmYwtW9SV886fL5bGOCNCRCRovX6zmsZE8rLHiBHiAmtkQHLqlEhezc8Hbr8deOstdY+TJLHUsHlz9Kqfb78VCZpmkiTxfsm5MCUl4QHHiy8G+6zI76+a93bRovgBRHW1uvF99x0DESKiZHCZxmSxlhrUkhM51TpxQn0gAohGXcOH26+VvCSJXJiRI5vPfNTWigCksjL4/iq9RzNmBHuyJOurr/R5HiIip+IyjUVCO7Du3g0sW6a8vBC6XAEADz0EHD+u7nxqZ2JWrQLq6oBp09Q9r12ELi8BYlZjyRLgz38Ws0Qyj0cs8YwYEbwvVjdctcs0BQXAkSOcHSEikmm9fjMYsYnIC+Lx4yIgCA1QfL7wplyrVwP33qvvODweEYykqjlzmgd2BQXifRo+vHkvkXjt9YcPFzkyocFMLOwtQkQUxGAkjSjtX1Ndra0pWdu2wOnTug9TFy6XqJoxYnO+yARYWaxuuKHHb90qAkAl5eWiAy4REbHpWVpxu8Wn7VGjojflkkt31Zoxwx4NzGIpKQn2DNGTHGzICbCA+BorLyb0+NtvV3cOM8u2iYjSjSnBSENDA3784x/D5XJh586dZpzSEeTqHKWLt8sllnieeEJ82tcSwJhB7hPywgvJJffGIyfAbt0qvlfqhisfL48v1nssv7fsukpElDhTgpHHH38cnTt3NuNUjqNUPRK6E63bLY7fvx94//3EGpnpnaQ5Z44Yj7x8Io+vqgp48kl9zwUEu+Cq7YZ77FiwC220zq8Au64SESXL8GDk7bffxp///GfMnz/f6FM5VlERcPSouLBHBhjyrENoroTbLW4nT2o/l545HXPmAE891fxCLi9PzZ6troOs1yueSw15OUXtskqnTsqdX9l1lYgoOYYGI0ePHsWECRPwxhtvoE2bNorHNzQ0oL6+PuxG6rjd4sJ+7JiYVSgvF1/37Yt+sTRyn5yf/UzdcUr7woTujRMrIJFnVp54Qnn5KXQ5RalVfuTyS+iMjdJ7S0RE2hgWjEiShHHjxuHhhx9Gv379VD2mrKwMubm5TTefz2fU8NKWUtKrzMiEy6FD1R2nZgyxZiV8PqCiIjiz4nYrV7Pcc0/w/YgX6MRaflH73hIRkTaag5GZM2fC5XLFvX399dd49dVXcerUKcyaNUv1c8+aNQt+v7/pViNnEDpMICDKdtesEV+NKHdNZhO9WOTGY5dfHj8fRWvSp5pZiUBAvF/xrF0b/l5y+YWIyB409xmpq6vDiRMn4h7TvXt3jBw5En/84x/hCrnaBQIBuN1ujBkzBitXrlQ8lxP7jMRrwqX3xVHuswFob/0e2dFV/j4/X7Sgj/c4QP+LvdqeK9Gakyn1cyEiIm1s0/TswIEDYTkfhw4dwtChQ7Fx40b0798fXhX1pU4LRtQ04TIiIIkMfmKRZz5efLH5ZnVKQYgssousXtasAUaPVj6OzcmIiIxnm117u3btGvZ927ZtAQA9evRQFYg4jZomXA8/LJpwZWbqd96iItH2PHSfnNmzw88LhOdRFBWJm/yYDh2AcePinyc/H1i3zrhcCy3VMUREZC/swKqRUfkcSk24ALFnTJcuYjZDT6GJmU89pS6PIvQxbrfy2E+cAL74Qt9xh9JaHUNERPZhWjBSWFgISZLw4x//2KxT6m7DBuDii0VuwujR4mthoT7BgdpS2+PHxVKO3gFJKK1lrGrHPm2afu9XpESqY4iIyB44M6LS448DI0eKYCDUwYP6BAdalw9C91kxgpYyVi1j1+v9iobVMUREqYm79qqwcSNw113xj/H5xOxBop+8AwExa1Bbq76yJZlt69VUkKitMklk7Mm+X/GwOoaIyFrctVdngQAwcaLycaGbsCUidJlBrUS7qFZWiuAh3nKTmmOijV1t35Jk36942JyMiCi1MBhRsHWrSBxVI9kW6/Iyg8ej7vhEKkPk8uHIhNPa2uDyiZpjYo1dy467tbXax09EROmHwYgCLQGGHmWjRUUiCCgoiH2MmsqQaFU/asqHp05VPiZWvoqc+DppUuxxhVIb5BERUXpjMKJAbYDh8ehXNpqZCbz+ugg6EqkMibXEMndu/BJcSRI/Vzom3hKL2w1cd13sx4dSOwNERETpjcGIArl/hZJFi/TNTUi0MiTeEktpqX7jizdjpHapZu9efcZCRESpjdU0KsRq0y6bMQN44QVjzi0vt1RXi+8HD46dlClXtahp7Z6seJU8asfhcrHklogoHbGaxgDyLEXkDInHIxqhGRWIAMDmzaLV+m9+I2433xy7qkVNF9d45L1nku1kqqUyyOh+KUREZH8MRlSK1pX08OHgrrdG0FrVoiXZNlYuyssv69PJtKgImDMn/jFK+SdEROQMDEY0MLN/hZrKl8hZBbXJtnPmxM9F0auTac+e6o5LtiSaiIhSG3NGbKq6WlTBKAnN3VDqhCovw+zbJ77XqwOrnq+BiIhSn9brdwsTxpRWzGo1rna2IPQ4OVdjxAgReIQGJNGWWJQCAHkmKFFyJZJScMSddImInI3LNBpoaZGeLLVLLpHH2WmzOO6kS0REanCZRqVY5b3yRVXvC72WJZdYZb522SyuslLkv4Qm4vp8IhBhWS8RUfrRev1mMKKCUt8MpcAgUXIABERfckmlHh12Co6IiMhY7DNiAKX+HUaVqNppySWaaPvfxMKddImIKBYmsKqQSDKpXoqKgOHD7TerEG3pxesVOSJqgyTOlhAREcBgRJVEk0n1kmxVi95i5c/IzdjUzNroEcwQEVF6YM6ICskmk6YTPfJnzE4GJiIiczFnxAAsUQ1KNn8mkc6yRESU3hiMqJRMMqmWRE+7SzZ/xqpkYCIisi/mjGiQSDJpuuVGJJs/Y2UyMBER2RODEY20JJPqkehpN8m2eLc6GZiIiOyHyzQGSdfciGTzZ+RgJvKxoc/h83G/GiIiJ2EwYpB0zo1IJn+GycBERBSJwYhB0j03oqgI2L8fqKoCysvF13371C072b2zLBERmYs5IwZxQm5EMs3Y7NpZloiIzMdgxCDJJno6gd06yxIRkTW4TGMQ5kYQERGpw2DEQMyNICIiUsZlGoMxN4KIiCg+BiMmYG4EERFRbIYu0/zpT39C//790bp1a7Rv3x533HGHkacjIiKiFGTYzEhFRQUmTJiAZ599FjfeeCMuXLiAL7/80qjTpYxAgEs2REREoQwJRi5cuICpU6di3rx5eOCBB5ru7927txGnSxnptmkeERGRHgxZpvn8889RW1uLjIwMXH311ejUqRNuvfVWxZmRhoYG1NfXh93ShbxpXmSLeHnTvMpKa8ZFRERkNUOCkW+++QYAMHv2bDz55JN466230L59ewwePBgnT56M+biysjLk5uY23Xw+nxHDM126bppHRESkB03ByMyZM+FyueLevv76azQ2NgIAnnjiCdx5553o27cvli9fDpfLhQ0bNsR8/lmzZsHv9zfdampqknt1NpHOm+YRERElS1POyGOPPYZx48bFPaZ79+44/D+7v4XmiGRlZaF79+44cOBAzMdmZWUhKytLy5BSQrpvmkdERJQMTcGIx+OBx+NRPK5v377IysrCrl27cP311wMAzp8/j/3796Nbt26JjTSFOWHTPCIiokQZUk2Tk5ODhx9+GKWlpfD5fOjWrRvmzZsHALjrrruMOKWtcdM8IiKi2AzrMzJv3jy0aNEC9913H86ePYv+/fvjgw8+QPv27Y06pW3Jm+aNGCECj9CAhJvmERGR07kkKdpndXuor69Hbm4u/H4/cnJyrB5O0qL1GfH5RCDCPiNERJQutF6/Hbk3jVVdULlpHhERUXOOC0as7oLKTfOIiIjCGbpRnt2wCyoREZH9OCYYYRdUIiIie3JMMMIuqERERPbkmGCEXVCJiIjsyTHBCLugEhER2ZNjghG5C6rcZCySyyV6frALKhERkbkcE4zIXVCB5gEJu6ASERFZxzHBCCD6iGzcCHTpEn6/1yvuZxdUIiIi8zmu6Rm7oBIREdmL44IRgF1QiYiI7MRRyzRERERkPwxGiIiIyFIMRoiIiMhSDEaIiIjIUgxGiIiIyFIMRoiIiMhSDEaIiIjIUgxGiIiIyFIMRoiIiMhStu7AKkkSAKC+vt7ikRAREZFa8nVbvo4rsXUwcurUKQCAz+ezeCRERESk1alTp5Cbm6t4nEtSG7ZYoLGxEYcOHUJ2djZcLpfVw0lafX09fD4fampqkJOTY/VwTMXXztfO1+4cfO187dnZ2Th16hQ6d+6MjAzljBBbz4xkZGTA6/VaPQzd5eTkOO6PVMbXztfuNHztfO1OI792NTMiMiawEhERkaUYjBAREZGlGIyYKCsrC6WlpcjKyrJ6KKbja+drdxq+dr52p0nmtds6gZWIiIjSH2dGiIiIyFIMRoiIiMhSDEaIiIjIUgxGiIiIyFIMRiwyd+5cDBgwAG3atEG7du2sHo6hFi1ahMLCQrRq1Qr9+/fHZ599ZvWQTPHRRx9h2LBh6Ny5M1wuF958802rh2SKsrIy/OQnP0F2djY6dOiAO+64A7t27bJ6WKZYsmQJrrzyyqamT9dddx3efvttq4dlieeeew4ulwvFxcVWD8Vws2fPhsvlCrv16tXL6mGZpra2Fvfeey/y8/PRunVrXHHFFdi+fbum52AwYpFz587hrrvuwiOPPGL1UAy1bt06lJSUoLS0FJ9//jmuuuoqDB06FMeOHbN6aIY7c+YMrrrqKixatMjqoZjqww8/xKRJk/DJJ5/gvffew/nz53HLLbfgzJkzVg/NcF6vF8899xx27NiB7du348Ybb8Tw4cPxz3/+0+qhmWrbtm14/fXXceWVV1o9FNNcfvnlOHz4cNPtL3/5i9VDMsW3336LgQMHomXLlnj77bfxr3/9Cy+++CLat2+v7YkkstTy5cul3Nxcq4dhmGuvvVaaNGlS0/eBQEDq3LmzVFZWZuGozAdA2rRpk9XDsMSxY8ckANKHH35o9VAs0b59e+l3v/ud1cMwzalTp6SePXtK7733nnTDDTdIU6dOtXpIhistLZWuuuoqq4dhiV/+8pfS9ddfn/TzcGaEDHPu3Dns2LEDN998c9N9GRkZuPnmm/G3v/3NwpGRmfx+PwAgLy/P4pGYKxAIYO3atThz5gyuu+46q4djmkmTJuG2224L++/eCXbv3o3OnTuje/fuGDNmDA4cOGD1kEzxhz/8Af369cNdd92FDh064Oqrr8ayZcs0Pw+DETLM8ePHEQgE0LFjx7D7O3bsiCNHjlg0KjJTY2MjiouLMXDgQPTp08fq4Zjiiy++QNu2bZGVlYWHH34YmzZtQu/eva0elinWrl2Lzz//HGVlZVYPxVT9+/fHihUr8M4772DJkiXYt28fBg0ahFOnTlk9NMN98803WLJkCXr27Il3330XjzzyCKZMmYKVK1dqeh5b79qbambOnInnn38+7jFfffWVoxKbyNkmTZqEL7/80jHr5wDwox/9CDt37oTf78fGjRsxduxYfPjhh2kfkNTU1GDq1Kl477330KpVK6uHY6pbb7216d9XXnkl+vfvj27dumH9+vV44IEHLByZ8RobG9GvXz88++yzAICrr74aX375JV577TWMHTtW9fMwGNHRY489hnHjxsU9pnv37uYMxgYKCgrgdrtx9OjRsPuPHj2Kiy++2KJRkVkmT56Mt956Cx999BG8Xq/VwzFNZmYmLr30UgBA3759sW3bNrz88st4/fXXLR6ZsXbs2IFjx47hmmuuabovEAjgo48+wm9/+1s0NDTA7XZbOELztGvXDv/1X/+FPXv2WD0Uw3Xq1KlZoH3ZZZehoqJC0/MwGNGRx+OBx+Oxehi2kZmZib59+2LLli244447AIgoesuWLZg8ebK1gyPDSJKERx99FJs2bUJ1dTUuueQSq4dkqcbGRjQ0NFg9DMPddNNN+OKLL8LuGz9+PHr16oVf/vKXjglEAOD06dPYu3cv7rvvPquHYriBAwc2K93/97//jW7duml6HgYjFjlw4ABOnjyJAwcOIBAIYOfOnQCASy+9FG3btrV2cDoqKSnB2LFj0a9fP1x77bVYuHAhzpw5g/Hjx1s9NMOdPn067JPRvn37sHPnTuTl5aFr164WjsxYkyZNQnl5OTZv3ozs7Oym/KDc3Fy0bt3a4tEZa9asWbj11lvRtWtXnDp1CuXl5aiursa7775r9dAMl52d3Swv6KKLLkJ+fn7a5wtNnz4dw4YNQ7du3XDo0CGUlpbC7XZj1KhRVg/NcNOmTcOAAQPw7LPPYuTIkfjss8+wdOlSLF26VNsTJV/YQ4kYO3asBKDZraqqyuqh6e7VV1+VunbtKmVmZkrXXnut9Mknn1g9JFNUVVVF/R2PHTvW6qEZKtprBiAtX77c6qEZ7v7775e6desmZWZmSh6PR7rpppukP//5z1YPyzJOKe29++67pU6dOkmZmZlSly5dpLvvvlvas2eP1cMyzR//+EepT58+UlZWltSrVy9p6dKlmp/DJUmSpE98RERERKQdS3uJiIjIUgxGiIiIyFIMRoiIiMhSDEaIiIjIUgxGiIiIyFIMRoiIiMhSDEaIiIjIUgxGiIiIyFIMRoiIiMhSDEaIiIjIUgxGiIiIyFIMRoiIiMhS/x9moUZfQhiBPAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nz4nFUT8m0wO"
      },
      "source": [
        "Our goal is to create a generator that can generate a distribution that matches this distribution."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1InC1BAQnC6l"
      },
      "source": [
        "### Generator network $G$\n",
        "\n",
        "Now we are ready to build our generator network G, to keep things simple, we make generator an one layer linear neural network."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7MPePSoJm_YW"
      },
      "source": [
        "model_G = nn.Sequential(nn.Linear(2, 2))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ibevU5pCnStH"
      },
      "source": [
        "def sample_G(model_G, num_samples):\n",
        "    Z = ndl.Tensor(np.random.normal(0, 1, (num_samples, 2)).astype(\"float32\"))\n",
        "    fake_X = model_G(Z)\n",
        "    return fake_X.numpy()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2cifleRqj-j2"
      },
      "source": [
        "fake_data_init = sample_G(model_G, 3200)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "n-ZhutLVkGgU",
        "outputId": "6f788bb9-86e3-499d-8cbd-5532a5cfb583"
      },
      "source": [
        "plt.scatter(data[:,0], data[:,1], color=\"blue\", label=\"real data\")\n",
        "plt.scatter(fake_data_init[:,0], fake_data_init[:,1], color=\"red\", label=\"G(z) at init\")\n",
        "plt.legend()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7bcf9c0946a0>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KVNer3vnpDxm"
      },
      "source": [
        "At the initialization phase, we just randomly initialized the weight of $G$, as a result, it certainly does not match the training data. Our goal is to setup a generative adveserial training to get it to close to the training data."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MsI-zBsgp9tC"
      },
      "source": [
        "### Discriminator $D$\n",
        "\n",
        "Now let us build a discriminator network $D$ that classifies the real data from the fake one. Here we use a three layer neural network. Additionally, we make use of the Softmax loss to measure the classification likelihood. Because we are only classifying two classes. Softmax function becomes the sigmoid function for prediction.\n",
        "\n",
        "\\begin{equation}\n",
        "    \\frac{\\exp(x)} {\\exp(x) +\\exp(y)} =\\frac{1}{1 + exp(y-x)}\n",
        "\\end{equation}\n",
        "\n",
        "We simply reuse SoftmaxLoss here since this is readily available in our current set of homework iterations. Most implementation will use a binary classification closs instead (BCELoss)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oettjmbLp9a8"
      },
      "source": [
        "model_D = nn.Sequential(\n",
        "    nn.Linear(2, 20),\n",
        "    nn.ReLU(),\n",
        "    nn.Linear(20, 10),\n",
        "    nn.ReLU(),\n",
        "    nn.Linear(10, 2)\n",
        ")\n",
        "loss_D = nn.SoftmaxLoss()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AtcTUZertq3D"
      },
      "source": [
        "## Generative advesarial training\n",
        "\n",
        "A Generative adversarial training process iteratively update the generator $G$ and discriminator $D$ to play a \"minimax\" game.\n",
        "\n",
        "\\begin{equation}\n",
        "    \\min_D\\max_G\\{-E_{x\\sim Data} \\log D(x) - E_{z\\sim Noise} \\log(1- D(G(z))\\}\n",
        "\\end{equation}\n",
        "\n",
        "Note that however, in practice, the $G$ update step usually use an alternative objective function.\n",
        "\n",
        "\\begin{equation}\n",
        "    \\min_G \\{-E_{z\\sim{Noise}} \\log(D(G(z))\\}\n",
        "\\end{equation}\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mwzkIy2VsxZe"
      },
      "source": [
        "### Generator update\n",
        "\n",
        "Now we are ready to setup the generator update. In the generator update step, we need to optimize the following goal:\n",
        "\n",
        "\\begin{equation}\n",
        "    \\min_G \\{-E_{z\\sim{Noise}} \\log(D(G(z))\\}\n",
        "\\end{equation}\n",
        "\n",
        "Let us first setup an optimizer for G's parameters.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DFEqvNyJpEcP"
      },
      "source": [
        "opt_G = ndl.optim.Adam(model_G.parameters(), lr=0.01)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fCJtUQFUu-mo"
      },
      "source": [
        "\n",
        "![image.png](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fexOFSSZvjRu"
      },
      "source": [
        "To optimize the above loss function, we just need to generate a fake data $G(z)$, send it through the discriminator $D$ and compute the negative log-likelihood that the fake dataset is categorized as real. In another word, we will feed in $y= 1$ as label here.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NhPHESKKpCoA"
      },
      "source": [
        "def update_G(Z, model_G, model_D, loss_D, opt_G):\n",
        "    fake_X = model_G(Z)\n",
        "    fake_Y = model_D(fake_X)\n",
        "    batch_size = Z.shape[0]\n",
        "    ones = ndl.ones(batch_size, dtype=\"int32\")\n",
        "    loss = loss_D(fake_Y, ones)\n",
        "    loss.backward()\n",
        "    opt_G.step()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9Qsn3cFwv-f5"
      },
      "source": [
        "### Discriminator update\n",
        "\n",
        "Now, let us also setup the discriminator update step. The discriminator step optimizes the following objective:\n",
        "\n",
        "\\begin{equation}\n",
        "    \\min_D\\{-E_{x\\sim Data} \\log D(x) - E_{z\\sim Noise} \\log(1- D(G(z))\\}\n",
        "\\end{equation}\n",
        "\n",
        "Let us first setup an optimizer to learn $D$'s parameters."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WOf4qlOvv-JV"
      },
      "source": [
        "opt_D = ndl.optim.Adam(model_D.parameters(), lr=0.01)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WfBh-6Adwnw9"
      },
      "source": [
        "![image.png](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c3gqNBcnxCF0"
      },
      "source": [
        "The discriminator loss is also a normal classification loss, by labeling the generated data as $y=0$(fake) and real data as $y=1$(real). Importantly, we also do not need to propagate gradient back to the generator in discriminator update, so we will use the detach function to stop the gradient propagation.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H4RwEfQ4nluL"
      },
      "source": [
        "def update_D(X, Z, model_G, model_D, loss_D, opt_D):\n",
        "    fake_X = model_G(Z).detach()\n",
        "    fake_Y = model_D(fake_X)\n",
        "    real_Y = model_D(X)\n",
        "    assert X.shape[0] == Z.shape[0]\n",
        "    batch_size = X.shape[0]\n",
        "    ones = ndl.ones(batch_size, dtype=\"int32\")\n",
        "    zeros = ndl.zeros(batch_size, dtype=\"int32\")\n",
        "    loss = loss_D(real_Y, ones) + loss_D(fake_Y, zeros)\n",
        "    loss.backward()\n",
        "    opt_D.step()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S-0El6XyyPNe"
      },
      "source": [
        "## Putting it together\n",
        "Now we can put it together, to summarize, the generative adverserial training cycles through the following steps:\n",
        "- The discriminator update step\n",
        "- Generator update step"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EeF1zzALyO83"
      },
      "source": [
        "def train_gan(data, batch_size, num_epochs):\n",
        "    assert data.shape[0] % batch_size == 0\n",
        "    for epoch in range(num_epochs):\n",
        "        begin = (batch_size * epoch) % data.shape[0]\n",
        "        X = data[begin: begin+batch_size, :]\n",
        "        Z = np.random.normal(0, 1, (batch_size, 2))\n",
        "        X = ndl.Tensor(X, dtype=\"float32\")\n",
        "        Z = ndl.Tensor(Z, dtype=\"float32\")\n",
        "        update_D(X, Z, model_G, model_D, loss_D, opt_D)\n",
        "        update_G(Z, model_G, model_D, loss_D, opt_G)\n",
        "\n",
        "train_gan(data, 32, 2000)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KCqCAuzazp8B"
      },
      "source": [
        "We can plot the generated data of the trained generator after a number of iterations. As we can see, the generated dataset $G(z)$ after get closer to the real data after training."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "2gWdtbKEs1vW",
        "outputId": "fbe2833e-65d8-44f1-ec91-9b7b41d39d37"
      },
      "source": [
        "fake_data_trained = sample_G(model_G, 3200)\n",
        "\n",
        "plt.scatter(data[:,0], data[:,1], color=\"blue\", label=\"real data\")\n",
        "plt.scatter(fake_data_init[:,0], fake_data_init[:,1], color=\"red\", label=\"G(z) at init\")\n",
        "plt.scatter(fake_data_trained[:,0], fake_data_trained[:,1], color=\"pink\", label=\"G(z) trained\")\n",
        "\n",
        "plt.legend()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7bcf78d230a0>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAi8AAAGdCAYAAADaPpOnAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAACZ+UlEQVR4nO2dd3wUdfrHP7NLEgikZ0NJIRBQUKqiCBoJiuKdBQlFAUU4D+QEJDQVRaqKhRIOzoI/DzwRkBLE0zsLMcEoNlBERTmJIMWQhABJaEnYnd8fXybZMuU7u7M1z/v1WpbMfmfmu7O7M8885fMIoiiKIAiCIAiCCBJM/p4AQRAEQRCEHsh4IQiCIAgiqCDjhSAIgiCIoIKMF4IgCIIgggoyXgiCIAiCCCrIeCEIgiAIIqgg44UgCIIgiKCCjBeCIAiCIIKKJv6egNHYbDb88ccfiIqKgiAI/p4OQRAEQRAciKKI6upqtGnTBiaTum8l5IyXP/74A6mpqf6eBkEQBEEQbnDkyBGkpKSojgk54yUqKgoAe/PR0dF+ng1BEARBEDxUVVUhNTW1/jquRsgZL1KoKDo6mowXgiAIgggyeFI+KGGXIAiCIIiggowXgiAIgiCCCjJeCIIgCIIIKkIu54UHURRx8eJFWK1Wf0+F8DNhYWEwm83+ngZBEAShg0ZnvNTW1qKkpATnzp3z91SIAEAQBKSkpKBFixb+ngpBEATBSaMyXmw2Gw4ePAiz2Yw2bdogPDychOwaMaIoory8HEePHkXHjh3JA0MQBBEkNCrjpba2FjabDampqYiMjPT3dIgAwGKx4NChQ6irqyPjhSAIIkholAm7WrLDROOBPG8EQRDBR6PyvBAEQRAE4T5WK1BUBJSUAK1bA5mZgD+c1mS8EARBEAShSV4eMGUKcPRow7KUFGD5ciA727dzofhJI+fQoUMQBAF79uzRtV56ejpyc3O9MieCIAgisMjLA4YOdTRcAODYMbY8L8+38yHjhfAJa9asQWxsrL+nQRAEQejEamUeF1F0fU1alpPDxvkKMl7cxGoFCguB9evZs6/17mpra327Q4IgCKJRUlTk6nGxRxSBI0fYOF9Bxosb5OUB6elA//7AyJHsOT3du26zrKwsTJo0CTk5OUhMTMTAgQMBAD/++CP+9Kc/oUWLFmjZsiXuv/9+nDhxon69Dz74ADfccANiY2ORkJCAO+64A8XFxbr2XVZWhjvvvBPNmjVDu3bt8NZbb7mMWbp0Kbp27YrmzZsjNTUVDz/8MM6cOQMAKCwsxNixY1FZWQlBECAIAubNmwcAePPNN9GrVy9ERUWhVatWGDlyJMrKytw8SgRBEITRlJQYO84IyHjRiT/jfm+88QbCw8Px+eef45VXXsHp06dx0003oWfPnti1axc++OADlJaWYvjw4fXrnD17FtOmTcOuXbuQn58Pk8mEwYMHw2azce93zJgxOHLkCAoKCrB582a89NJLLgaGyWTC3//+d/z0009444038Mknn+DRRx8FAPTt2xe5ubmIjo5GSUkJSkpKMGPGDABAXV0dFi5ciO+//x7vvPMODh06hDFjxnh+sAiCIAhDaN3a2HGGIIYYlZWVIgCxsrLS5bXz58+L+/btE8+fP+/Wti9eFMWUFFFkTjLXhyCIYmoqG2c0/fr1E3v27OmwbOHCheKtt97qsOzIkSMiAHH//v2y2ykvLxcBiD/88IMoiqJ48OBBEYD43XffyY7fv3+/CED8+uuv65f9/PPPIgBx2bJlivPdtGmTmJCQUP/36tWrxZiYGJV3yPjmm29EAGJ1dbXmWCPw9DtBEAQR6kjXPkHw7rVP7frtDHledODvuN/VV1/t8Pf333+PgoICtGjRov7RqVMnAKgPDf36668YMWIE2rdvj+joaKSnpwMADh8+zLXPn3/+GU2aNHHYd6dOnVySb7dv346bb74ZycnJiIqKwv3334+KigrNHlK7d+/GnXfeibS0NERFRaFfv3665kcQBEF4F7OZlUMDgLOup/R3bq5v9V7IeNGBv+N+zZs3d/j7zJkzuPPOO7Fnzx6Hx6+//oobb7wRAHDnnXfi5MmTeO211/DVV1/hq6++AmBswu+hQ4dwxx13oFu3btiyZQt2796Nf/zjH5r7OXv2LAYOHIjo6Gi89dZb+Oabb7B161bD50cQBEF4RnY2sHkzkJzsuDwlhS33tc4LidTpINDifldddRW2bNmC9PR0NGni+lFWVFRg//79eO2115CZmQkA+Oyzz3Tto1OnTrh48SJ2796Na665BgCwf/9+nD59un7M7t27YbPZsGTJkvrWCxs3bnTYTnh4OKxOJVm//PILKioq8NxzzyE1NRUAsGvXLl3zIwiCIHxDdjYwaFBgKOyS50UHmZnMylRqhyMIQGoqG+cLJk6ciJMnT2LEiBH45ptvUFxcjA8//BBjx46F1WpFXFwcEhISsGrVKhw4cACffPIJpk2bpmsfl19+OW677TY89NBD+Oqrr7B792789a9/RbNmzerHdOjQAXV1dVixYgV+++03vPnmm3jllVcctpOeno4zZ84gPz8fJ06cwLlz55CWlobw8PD69d59910sXLjQkGNDEARBGI/ZDGRlASNGsGd/9bMl40UHgRb3a9OmDT7//HNYrVbceuut6Nq1K3JychAbGwuTyQSTyYQNGzZg9+7d6NKlC6ZOnYoXX3xR935Wr16NNm3aoF+/fsjOzsb48eORlJRU/3r37t2xdOlSPP/88+jSpQveeustLFq0yGEbffv2xYQJE3DPPffAYrHghRdegMViwZo1a7Bp0yZcccUVeO6557B48WKPjwtBEAQR2giiKKeZF7xUVVUhJiYGlZWViI6OdnjtwoULOHjwINq1a4emTZu6vQ+5/g6pqcxw8XXcj/AMo74TBEEQhGeoXb+doZwXNwikuB9BEARBNDbIeHETKe5HEARBEIRvoZwXgiAIgiCCCjJeCIIgCIIIKsh4IQiCIAgiqCDjhSAIgiCIoIKMF4IgCIIgggoyXgiCIAiCCCqoVJogCIIgAhVRBCqrgdo6IDwMiIlS7lHTiCDPS4iSn5+Pzp07uzRDlOPEiRNISkrCUXvJYC+Tnp6O3Nxc7vGFhYUQBMGhISRBEERIU34K+HIv8P3/gJ8Psucv97LljRwyXtzFagUKC4H169kzh5HgKcePH8eUKVPQoUMHNG3aFC1btsT111+Pl19+GefOnXMY++ijj2L27Nkwc8j+JiYmYvTo0Zg7d67Hc8zKykJOTo7muG+++Qbjx4/n3m7fvn1RUlKCmJgYAMCaNWsQGxvr5iwJgiACnPJTwL5i5nGxp7aOLW/kBoxXjZf09HQIguDymDhxouz4NWvWuIwNyH4zeXlAejrQvz8wciR7Tk9ny73Eb7/9hp49e+Kjjz7Cs88+i++++w5ffPEFHn30Ubz33nvYvn17/djPPvsMxcXFGDJkCPf2x44di7feegsnT570xvRdsFgsiIyM5B4fHh6OVq1aQSB3KUEQoY4oAgcOq48pPszGNVK8arx88803KCkpqX98/PHHAIBhw4YprhMdHe2wzu+//+7NKeonLw8YOtSxKyMAHDvGlnvJgHn44YfRpEkT7Nq1C8OHD0fnzp3Rvn17DBo0CO+//z7uvPPO+rEbNmzALbfc4mD4KRmSEldeeSXatGmDrVu3Ks6hoqICI0aMQHJyMiIjI9G1a1esX7++/vUxY8Zgx44dWL58ef32Dx06JLst57CRIAj4v//7PwwePBiRkZHo2LEj3n333frX7cNGhYWFGDt2LCorK+v3M2/ePB1HkyAIIoCRclzUqKlj4xopXjVeLBYLWrVqVf947733kJGRgX79+imuIwiCwzotW7b05hT1YbWydtJy1q60LCfH8BBSRUUFPvroI0ycOBHNmzeXHWNviBQVFaFXr14Or9sbkkePHsV1112HzMxMhzHXXnstioqKFOdx4cIFXH311Xj//ffx448/Yvz48bj//vvx9ddfAwCWL1+OPn36YNy4cfX7Sk1N5X6f8+fPx/Dhw7F37178+c9/xqhRo2Q9QX379kVubq6DoTtjxgzu/RAEQQQ0WoaL2jhRBE5XAWUV7DlEvTM+y3mpra3F2rVr8Ze//EXV9X/mzBm0bdsWqampGDRoEH766SfV7dbU1KCqqsrh4TWKilw9LvaIInDkCBtnIAcOHIAoirj88ssdlicmJqJFixZo0aIFHnvssfrlv//+O9q0aeMw1t6QfOGFF1BSUoItW7Y4jGnTpo2qpys5ORkzZsxAjx490L59e0yePBm33XYbNm7cCACIiYlBeHg4IiMj6/fFk3MjMWbMGIwYMQIdOnTAs88+izNnztQbRvaEh4cjJibGwdBt0aIF934IgiACmvAwt8bVlZxC9fbGkeDrM+PlnXfewenTpzFmzBjFMZdffjn++c9/Ytu2bVi7di1sNhv69u2rWgWzaNEixMTE1D/03OnrpqTE2HEe8vXXX2PPnj248sorUVNTU7/8/PnzirlCq1atwuuvv453330XFovF4bVmzZq5JP7aY7VasXDhQnTt2hXx8fFo0aIFPvzwQxw+rBGb5aRbt271/2/evDmio6NRVlZmyLYJgiCChpgobQMm4lLZ9CX+tfQUzPuL0TzM0Rsj1oRmgq/PjJfXX38df/rTn1w8Avb06dMHo0ePRo8ePdCvXz/k5eXBYrHg1VdfVVxn1qxZqKysrH8cOXLEG9NntG5t7DhOOnToAEEQsH//fofl7du3R4cOHdCsWTOH5YmJiTh1yvWLWlBQgMmTJ+Nf//qXg6EgcfLkSReDxp4XX3wRy5cvx2OPPYaCggLs2bMHAwcORG1trZvvzJGwMMcfqyAIsNlshmybIAgiaBAEoEOa+piMtHq9l8ceFdE/ld1EmpwCG4JwKXIUYgm+PjFefv/9d2zfvh1//etfda0XFhaGnj174sCBA4pjIiIiEB0d7fDwGpmZQEqKskCQIACpqWycgSQkJOCWW27BypUrcfbsWc3xPXv2xL59+xyWHThwAEOHDsUTTzyB7Oxs2fV+/PFH9OzZU3G7n3/+OQYNGoT77rsP3bt3R/v27fG///3PYUx4eDiXtoyn+Go/BEEQfsESB1yR4eqBiQhjyy1xAIDaWmBXfjVSk+pcDBcJQUDIJfj6xHhZvXo1kpKScPvtt+taz2q14ocffkBrgz0ZbmM2A8uXs/87GzDS37m5bJzBvPTSS7h48SJ69eqFt99+Gz///DP279+PtWvX4pdffnHILRk4cCA+++yz+r/Pnz+PO++8Ez179sT48eNx/Pjx+ofEuXPnsHv3btx6662Kc+jYsSM+/vhj7Ny5Ez///DMeeughlJaWOoxJT0/HV199hUOHDuHEiRNe85ykp6fjzJkzyM/Px4kTJ1TDXQRBEF7FW0myljjgum5A98uAzu3Yc+9u9YYLALz0EpAU60GCb5DidePFZrNh9erVeOCBB9CkiWM3gtGjR2PWrFn1fy9YsAAfffQRfvvtN3z77be477778Pvvv+v22HiV7Gxg82YgOdlxeUoKW67g1fCUjIwMfPfddxgwYABmzZqF7t27o1evXlixYgVmzJiBhQsX1o8dNWoUfvrpp/owU2lpKX755Rfk5+ejTZs2aN26df1DYtu2bUhLS3OpQLJn9uzZuOqqqzBw4EBkZWWhVatWuPvuux3GzJgxA2azGVdccQUsFoth+TDO9O3bFxMmTMA999wDi8WCF154wSv7IQiCUEWPCq47Ro4gALHRQFICe3a6cS4uBkpOupfgG8wIoujdINhHH32EgQMHYv/+/bjsssscXsvKykJ6ejrWrFkDAJg6dSry8vJw/PhxxMXF4eqrr8bTTz+tGspwpqqqCjExMaisrHQJIV24cAEHDx5Eu3btPBe/s1pZVVFJCctxycz0isfFXWbOnImqqirVfCF7rrvuOjzyyCMYOXKkl2cWWBj6nSAIIuDw6qlaUsF1Qrz0z2enMmCNi2P7PHmKCc/ZeT/E8DD8VJOGH0riHOamZ865ucD06SIObdiL5MQ6mGRcEjYbcPZiGKIGdAvovkhq129nvG68+BqfGS8BzunTp/HSSy/h8ccfh0nu22zHiRMn8M9//hMzZ85sdAq2jek7QRCNjbw8Js1lX7CaksKi/x47yUWReVgUQjE2G3C0PAztRnTDX+46jVVTil3sBpsNgAAMnZOBrUVxSEkBRoxgXWd451xbC0RGAnf1PYXNC4oBEQ4GjLQP6+UZCGsd57qBAIKMFzJeCE7oO0EQoYkkhu58hZMMCI+j/KerWIhIg5umXoY3Zh1EskU+odbeyLHZ5G8e5eZs753Ztg14+21gcOYpLJ98GKlJDQbV4dIwFB5Nw+hpgW24APqMlyaqrxIEQRBEkKElhi4ITAx90CAPQkicya9ZPascjAlnTCYgrWUdMrtVY8ce+Qu285y3bXP1KLVoAWz7PA7bPo9FZrdqtI6vQ9npMPS6OQrPvxB6HnUyXgiCIIiQQo8YelaWmzvhTX7ljG20jnc1cEwmsd4QKTkZhqK9UXjmGQHz5rkaZmfPsmVjxgho0SIaGRnAww8D4eF8+w82yHghCIIgQgqfiKFLKrgaOS+Fe6Iw54HjsmMc5uJUMSQXAjpSFobZq9Mgiq4hIMk7k58PHDwYUPUjXsFnCrsEQRAE4Qt8IoauooIrJcnmrEzDju+jcaQsDEqSVzYby0sp2tsg9T84kyXfJlscDaPkxDqsfrQYgzPlpf691F4vICHjhSAIgggpfCaGrqCCe7Q8rL6CyGYTMGVFGiDAxYCxN3KkZF2TScTyyYchwFXq32QCIAK5kw7DZFKOR3mjvZ7VChQWskqowkL2tz8h44UgCIIIKXwqhp4YC3RqB6S1ws/nWuHmqR3RfmQ3bC1qCO2881kchs3NwAVR2ciReGJUCVKT6hQNL/sEXyWMFqXPywPS04H+/YGRI9lzejpb7i/IeCEIgiBCDiUx9ORkYN48oKbGAA+CpK6793/A4ePoHHkc/158CGPvPO0wLCUFGDUlDpH9G6T+P628DJkzHI2ccYNPYcFf/uDatVyCrzfa60kl584J0MeOseX+MmDIeAlR8vPz0blzZ67mhSdOnEBSUhKOqqXne4F58+ahR48eXt/PmjVrEBsb6/X9EAQRWGRnA4cOAQUFwLp1wPz5LC9k7lwDPAiSuq5Twm6kuQ6rcorxQ8FJrFvH9n3w4CV9Fjup/xvvisZvB4X6uRUUiHh15mFuAVznBF9vtNfTKjkHWPm2P0JIZLy4i7cacalw/PhxTJkyBR06dEDTpk3RsmVLXH/99Xj55ZddGhM++uijmD17tkPDRiUSExMxevRozJ07V3Wc0UbAjBkzkJ+fb9j2CIIgnDGbWTl0RATzuBw75vi6Wx4EUWRS/woIALoIv2HEgJPIymqQ/G/IGRFhraiCuaICWT2qMOJeEVk9qiFwaMfYRJbgu/OnKIfl3mivp6fk3NdQqbQ7lLv2qEB4GMs8t7iWsBnBb7/9huuvvx6xsbF49tln0bVrV0REROCHH37AqlWrkJycjLvuugsA8Nlnn6G4uBhDhgzh3v7YsWNx9dVX48UXX0R8fLxHc62trUU4h7hAixYt0KJFC4/2RRAEoYXhonWV1VwideK+3yBcISCvKK5eVE4qgTb/6HT94Lx2CGAJvnV1ApYtA1q29F57PZ+UnLsJeV70ouAqRG0dWy7XSdQAHn74YTRp0gS7du3C8OHD0blzZ7Rv3x6DBg3C+++/jzvvvLN+7IYNG3DLLbc4yN2np6dDEASXh8SVV16JNm3aYOvWrbL7LywsxNixY1FZWVm/7rx58+q3vXDhQowePRrR0dEYP348AOCxxx7DZZddhsjISLRv3x5PPfUU6uoajptz2GjMmDG4++67sXjxYrRu3RoJCQmYOHGiwzo1NTWYMWMGkpOT0bx5c/Tu3RuFhYUOc12zZg3S0tIQGRmJwYMHo6KiQvfxJggieNCqhDHUgyCKwKkqvomJwKndhzF8uFhvuMiVQKO2DjhWxrXJOatb1+fJFBd7ty+wT0rO3YSMFz1ouAoBAMWHDQ8hVVRU4KOPPsLEiRPRvHlz2TH2hkhRURF69erl8Po333yDkpISlJSU4OjRo7juuuuQ6ZTVde2116JI4dfbt29f5ObmIjo6un47M2bMqH998eLF6N69O7777js89dRTAICoqCisWbMG+/btw/Lly/Haa69h2bJlqu+1oKAAxcXFKCgowBtvvIE1a9bUdx0HgEmTJuGLL77Ahg0bsHfvXgwbNgy33XYbfv31VwDAV199hQcffBCTJk3Cnj170L9/fzz99NOq+yQIInjhqYQxzIMgJege1hadA5hHJ65ZHW7oWl1fAg24lkDbo3T1YHowTfDs2jb1y1au9G7lj89Kzt2AjBc98LgKa+rYOAM5cOAARFHE5Zdf7rA8MTGxPvTy2GOP1S///fff0aZNG4exFosFrVq1QqtWrfDCCy+gpKQEW7ZscRjTpk0b/P7777JzCA8PR0xMDARBqN+OfcjnpptuwvTp05GRkYGMjAwAwOzZs9G3b1+kp6fjzjvvxIwZM7Bx40bV9xoXF4eVK1eiU6dOuOOOO3D77bfX58UcPnwYq1evxqZNm5CZmYmMjAzMmDEDN9xwA1avXg0AWL58OW677TY8+uijuOyyy/DII49g4MCBqvskCCI44a2EMcSDoOR159luPCttTk2Sb87ogKimB9NWtnmjtyp/fFpyrhMyXvTA+6V148vtDl9//TX27NmDK6+8EjU1NfXLz58/r9ghedWqVXj99dfx7rvvwmKxOLzWrFkzl8RfXpw9PQDw9ttv4/rrr683dGbPno3Dh9U9V1deeaVDknHr1q1RVsbcqT/88AOsVisuu+yyeqOtRYsW2LFjB4qLiwEAP//8M3r37u2wzT59+rj1ngiCCFz0VMJkZgIJCerbS0hQ8SDweN1VKDkZJlvaLMeR87E4Ve1oDcjpwThPD/BO5Y9Sybk3EoT1QAm7euBtxMU7jpMOHTpAEATs37/fYXn79u0BMKPDnsTERJw65Zp7U1BQgMmTJ2P9+vXo1q2by+snT550MWh4cQ5nffHFFxg1ahTmz5+PgQMHIiYmBhs2bMCSJUtUtxMW5lz+J8B26TbkzJkzMJvN2L17t0sVFSX+EkTjgjePZcUK1qDQIzgTdOU4e16ASRBReorvupAWebr+/xWVZuRuScKza9vIelzsMaTZpALZ2SyZuaiIhda8mWfDCxkvetBoxAUAiAhj4wwkISEBt9xyC1auXInJkycr5r1I9OzZE/v27XNYduDAAQwdOhRPPPEEshVM5R9//BFZKt/68PBwLt0YANi5cyfatm2LJ598sn6ZUkiKl549e8JqtaKsrMwlX0eic+fO+OqrrxyWffnllx7tlyCIwMM+P0Wu+7J0sZ86FXjmGaCiQn1cRYXjhd9qbbhYd29dhys49Vecad5MxCfLfsWRsiY4UWlGfJSVyfw7IYJVEtkTF2XF/LElgCjg2bdaaxowgPcqf6SS80CBjBc9SI249hUrj8lIU85u8oCXXnoJ119/PXr16oV58+ahW7duMJlM+Oabb/DLL7/g6quvrh87cOBAvPHGG/V/nz9/HnfeeSd69uyJ8ePH4/jxhmSzVq1aAQDOnTuH3bt349lnn1WcQ3p6Os6cOYP8/Hx0794dkZGRiIyMlB3bsWNHHD58GBs2bMA111yD999/X7GSiZfLLrsMo0aNwujRo7FkyRL07NkT5eXlyM/PR7du3XD77bfjkUcewfXXX4/Fixdj0KBB+PDDD/HBBx94tF+CIAIPKT9FqfvylBVp9WGWEyf4xkkX/rw81Jc2A0C/HmEozPVsvsmJFwGBGSg2GxwMmPrQl1wvIwALH/wD4+8sd5irEv6o/PEHlPOiF4VGXIgIY8u9pPOSkZGB7777DgMGDMCsWbPQvXt39OrVCytWrMCMGTOwcOHC+rGjRo3CTz/9VB9mKi0txS+//IL8/Hy0adMGrVu3rn9IbNu2DWlpaYoeDYBVHE2YMAH33HMPLBYLXnjhBcWxd911F6ZOnYpJkyahR48e2LlzZ30VkiesXr0ao0ePxvTp03H55Zfj7rvvxjfffIO0NNbd9brrrsNrr72G5cuXo3v37vjoo48we/Zsj/dLEERgkZkJ/PVu5e7Lmxc0dF9W69JsP651a/kk4KK9UawztAeFpJIhUnnWhGMnXNVxte55ky2Oc5VDNW8nxBBE0QfSsD6kqqoKMTExqKysRHR0tMNrFy5cwMGDB9GuXTvFhFZuRLEhDhp+KVTkBY+Lu8ycORNVVVV49dVXucZfd911eOSRRzBy5EgvzyywMPQ7QRCE7xBFnCvYi6Ym+Qoem40lumaM6orf1v2AZIv6uBtndsP/fhXQsYOIdgmOoaVB15/GqhmHkBhjTDZs2SkTVuS1woFjEejc9jzmPMBXei3Ntd2IbrIhpPnzgTlzDJmiX1C7fjtDYSN3kXpUBChPPvkkXnrpJdhsNpjkAqx2nDhxAtnZ2RgxYoSPZkcQBOEGdjeN339Th+4tlPMPpe7LE+8ucwgVKY3714pq/G+nFZ8tcQwtnag0IyHaqqi/4g6JMTbM/8sfGDonA4V7oriNF/uO0jv2OF5/EhIAuxTDkIeMlxAlNjYWTzzxBNfYxMREPProo16eEUEQhAc4tWXpzllgmNG6RnsQgBuvOAXxaDngVHCZEM28LXJeG6m1gF5MJuZFeXX6IVyo1Z+9IVd2vWqVf6t/fA0ZLwRBEERgIwnEuYFg4vOZiEfLZQ0RNePEk0wBkwmwxFohivpDUfYdpVNTmVCcv/RW/AUZLwRBEETgoiEQp+X9mDT4BC5amedELoJus/ElzHoLvfsVw5pg3rKogNFb8ReN0ngJsRxlwgPou0AQAY6GQJzcxd/ZoDGZWBWy83LJcAkmhKR4ZHUIskl7gUZVKi2pt7orgU+EHrW1tQDgothLEESA4IayrbNBYhKYCJzVqWdQ+WmzX70ubpEY6+8ZBASNyvNiNpsRGxtb3ysnMjLSoRsz0biw2WwoLy9HZGQkmjRpVD8Fgggs1KQnONutrNyaiEmDTyi+bhIAkxmY92YKho4Mw6TpYUhOrMNbsw8a8Q58ghgehh175ENG9orAjSGc1OjO2JKirGTAEI0bk8mEtLQ0MmIJwl84VREBYAZLhzQm+qnRlkXSPvnipyhV40Vi/8EwvF2QgB17gP49Kw16E95HFIHxL6Th/95pOFelpDR0fbZXBLZ/LVQTeRud8SIIAlq3bo2kpCTU1fmm+zMRuISHh2vq4BAE4SWUqohq69hySbW8QxrEfcWATM4KBCBnZRpOVvO5GVrG1+F/+1knIbny50DknDUM989PQ96njgrux44BQ4bIr3PsGFMK9mfnZ2/S6IwXCbPZTHkOBEEQ/kKjiggAUHwYSIxFXlEc/vtmBuaMdBSQO1oehmn/SMXJajOSE2tgtbHwkJojNXfSURw7UYq642no0eGsQW8GOF8DNIswbHP12JJbYuzDMQhvUod+Paocmkmq1RtIyck5OawjdKhd7hpVewCCIAgiQDhdBXz/P81hn1Zehqy7oyGKrl2hE2MuYtnEI6oKunJIHpvNhbEY3v+0e/N3wl3BOi1qxSYIFy7W/+3cTJKHgoLA6githJ7rN/nLCYIgCN/DWUW0cW1dvYfBZhOwY080NnySgPgoKzbO+82l2SIPUqS4X/cq3ev6mjBcdPjbuZkkD1K37FCCjBeCIAjC93BWEf34q+s4k0nE8sks5ORu3opJAFrG25gXxgCM9LrYbKjvpeRS9n3pqr1qxiH071kJE4eCcOvWxs0tUCDjhSAIgvA9MVFAE/VEjAtWM4r2Rrksz+xWjdQk+S7RuvFCqMfTZIzy02bVaZkEIDHGik+W/YpDG/YqemEEgbUPyMz0bD6BCBkvBEEQRECiVAgo15jQ7X14wXjxxAtTdsqMdfkJ3OOlMFL2jY4GjDSH3Fz2XFgIrF/Pnq362ykFHGS8EARBEL6nshq4qH4VDResyOpR7bLcvjFhqJEYY8WUofw6ZFLrg79POewQQkpJYWXSAJCeDvTvD4wcyZ7T04G8PEOn7XPIeCEIgiDcwmr14I6eM2H39utOO/xtMokwCSIqKk0eh2cCEZMJgNiQ88KDIADJCXXYnV+NdetYddHBS8LBQ4c6itcBDRowwWzAUKk0QRAEoZu8PPdUXSUZe2tFFW5O1C6VLj3VBG2GdIfNJmBw5iksn3xYd2l0o6FzOyCJhZysVuZhcTZcJASBfV4HDwaOBgyVShMEQRBeIy/PvTv6vLyGEMatw6NQdkr7qtky7iIyu1VjcOYpbF5Q7FZptF4C5pY+OYm7KguAw9iiImXDBWDv8cgRNi4YIeOFIAiC4MZqZR4XuQu8tCwnxzWE5Gzw2GwC1n7Ml5jaJqHW49JoXrwlNucWibHAdd2Abpdpu0ciLjW0vASvtkuwasB41XiZN28eBEFweHTq1El1nU2bNqFTp05o2rQpunbtiv/85z/enCJBEAShA3fu6JUMnlNn+OIVfbucMa40WgM9hoso6vfS8I4Xw5qgcE8U1m8QUPh9NKwd09VXyEhzmDyvtkuwasB43fNy5ZVXoqSkpP7x2WefKY7duXMnRowYgQcffBDfffcd7r77btx999348ccfvT1NgiAIggN37ujlDB6TScT4O05o9ue5aAVXt2h/4I6HhnedxeuT0L+/0FAh1CsOX1ZnuIaRIsIaGljakZnJclqU9hfsGjBeN16aNGmCVq1a1T8SExMVxy5fvhy33XYbZs6cic6dO2PhwoW46qqrsHLlSm9PkyAIguDAnTt6OYNHEppTu5gLAmB24yoVMDkrHvDdz+EOfx87BvS9Kw55Jd2A7pex5NzulwG9u7kYLgCLMi1fzv7vfIztNWACJVlXL143Xn799Ve0adMG7du3x6hRo3D4sHIX0S+++AIDBgxwWDZw4EB88cUXiuvU1NSgqqrK4UEQBEF4B3fu6OUMHl6hOb3eDclw8ZUBIwjuhY+0SIpz7GnUkE8kwBoVzaqKYqNVD1B2NtN6SU52XC5pwKhVhQU6XjVeevfujTVr1uCDDz7Ayy+/jIMHDyIzMxPV1a6iQwBw/PhxtGzZ0mFZy5Ytcfz4ccV9LFq0CDExMfWP1NRUQ98DQRAE0YA7d/RyBo+3hOYEoeHhK6T9GdUnCQDKT7seH3cqhLKzgUOHmPaLvQZMMBsugJeNlz/96U8YNmwYunXrhoEDB+I///kPTp8+jY0bNxq2j1mzZqGysrL+ceTIEcO2TRAEQbii945ezuAp2huFI2Vhhl7w/Y2RBtOxE8rGnd4KIbMZyMoCRoxgz8EaKrLHp6XSsbGxuOyyy3DgwAHZ11u1aoXS0lKHZaWlpWjVqpXiNiMiIhAdHe3wIAiCIDxAFIHTVUBZBXuWiYnovaN3NnhsNgFTVqQBBnss/IkRxotNBA6Xhsk2pJQI1gohI2niy52dOXMGxcXFuP/++2Vf79OnD/Lz85GTk1O/7OOPP0afPn18NEOCIIhGTvkp4MBhR/n+8DCgQ5pLYqh0R6+EpKZbUsIuuIMGAXfcAbz0ElBcDLRrF4eHcjOw6C+HkBgTAt0CPcRmAyAAOSvTYLO5WkKSKm6wVggZiVeNlxkzZuDOO+9E27Zt8ccff2Du3Lkwm80YMWIEAGD06NFITk7GokWLAABTpkxBv379sGTJEtx+++3YsGEDdu3ahVWrVnlzmgRBeILzFSozMzT80o2R8lPAvmLX5bV1bLlMSa4SeXnA1BwR7RKq0Tq+DiUnw/Ddb1EABNjXVZhMsZgzSggscTg/cbS8CXJWtsXWItdjHAoVQkbiVePl6NGjGDFiBCoqKmCxWHDDDTfgyy+/hMViAQAcPnwYJrue53379sW6deswe/ZsPPHEE+jYsSPeeecddOnSxZvTJAjCXdxtcEMEHqLIPC5qFB9mqq9yVoYosk7RtXX49MswrPv7RXy25IhDH6IjZWGYsiIN2z6PRWY3ZtS0jK9DatJF1+01EqSI3JzVrfHs2jayHheA/axyc+lnJUGNGQmCcA9J7935FCJd2HhrMclzExicrgK+126UiO6XsRJde2RCTdLXwt7OsdnY3xVVZgoTXUIUgROVZrTK7iFruEyaBAwZ0jh+FtSYkSAI7+Jugxtn7Dv11UuJpit39iO8Ry1nw0PncVKoSWZ9ZweN5GhPiDbecAnW23BBACyxVmR2k5cQGTIkdCqEjISMF4Ig9GNEy1p3WxMT3oG3e7H9OJVQk5qInVLUiRfnsaGQL6Mk2lde7uOJBAlkvBAEoR9PW9Ya5bkhjCMmCmJ4mLoR4dS5WMpxMQI9xoeSOF6gICnu2nQYZEqifdOn089ADjJeCILQj6cta43w3BCGkrdVwPgX0iBCRXfFqXOxUYZLKCII4OqCbbOp67rQz0Aen+q8EAQRIkh678eOyXtPtAQpPPXcEIbSkHsdh4qKDCyffNihSuicNQyRXV11XrhDTY0MXk+QKEJV10WCfgaukPFCEIR+JL33oUMbOtNJ8AhSeOq5IQzDOYK3tSjOoZRZ0mfJyxNcE0djooCwJkCdMaXOoZC7ooc6NMG9c9pi2+ex6Nejqv54F+2NcjBm6GfgChkvBEG4h6T3LqfzoiVI4annhjAMuQiezSZgxx7HUtUBA2QkfAQBaJME/P6HIXOR7OBQMWBU30uYGeZruiEhoRKHNuyV1cN557M4+hkoQDovBEF4hrs6LVKsApD33PDqxBg9r0bG+vWsSp0HybiYMoVVtFssQM/kClwhHDRsPqFkvACu70cKFQlXZLC/fyqGCMf8GKlNwLC5GRg1Ja7RCNPpuX6T8UIQhP+QU+hNTfVcSpSUf7kpLGTyOnpo0sSGiXeXIaN1DQSTiEmDT2iuU3lGQFSkCBNnmUioGTESh0vDsHB9Gv50fyyyW+9VTHq22YALYhgi+3cLzQMhAxkvZLwQRPBgtIfEKOXfRoLVCiQlASdP8o1/7qEjmD68FE3sPiI5NV3CkepzJgx6MgM7vo+GKAro170KBbluKhqHKKSwSxBE4GK1stv99evZM8AkREeM8FxKlPRjdGM2s0PGw3MPHcGj95bCrHDlCK1bYXkkOf+bpnbEfc+kY2sRn2ERFWmDTRRgs7EmlK0UROlcoHJ0Wch4IQjCd3i7HQDpx7jFk08CLVqoj2nSxIbpw0sBKIvENQbPi/QeY1vY8NbHiRg6tyOqz/JdSu1VdJVE6VygcnRZyHghCF/j7HloLF4AX7QDIP0YPkSRNWIsq2DPoohmzUT061GFe2+qQL8eVTCZHN0oE+8uQxOzuux/YyE+yorNC4oxOPMUAH4lXXuDpWhvFI6UhSkLAgKuisZEPVQqTRC+pLEmkvKEcyZMAM6fB5KT3c97If0YbWQ6QFtFM/b9Hxw6PUvluluLmDBdRusan081UDGZWEJt7qTDOH3GjJgWahYIo/Sk2UFF12YTMGVFGjYvKIbNBvlEZmdFY6Ie8rwQhK9ozI0ItcI5AOtAd999noWSJP0YNfdAamqjEM6QdfApdIAOg9Wl03NyYp2Dd6G4JMI3Ew8STCYgrWUdsnpWcY1/a3uCi4ru1qI4DJ2TgWMnnEJDEWHAFRmuisZEPWS8EIQvaOyJpHrDNO4adJLyL6CcmKGm/BsiyKUWtW8n4twPyh2gnQ+XyQQIAF6dfggmk4h/vJOEi9bGkZSrC87j8e7OWNnlW4vikH5vN2TlXIadp9qx6qLe3chw0YCMF4LwBY09kfTXX/WN98Sgk5R/k5Mdl6ekNIoyaSUHX/uEakSa9VWuCAJgibXiyfuPIbNrNb7aFwmADBh7Cveo566oNV40mVie0fAsVqdeGxvPyqIpVKQJ5bwQhC9ozImkVivw2mv617M36LKy9K2bnQ0MGtToFHbVHHzcpbkyzH3gOMym4x7MLPSw2YCj5WHY8X20Yu6KpJQr13hxcOYplwaYYngYUC7TAJNwgYwXgvAFjTmRlCffRQ13DTqzWb/R40yQtRhQO9TcpbkyKOm6NFacjRIpd8XZGDlaHoaclQ1JzxKDM09h84Jil+0KtXUsJ4nyXTQh44UgfEGwNSI08qLtqTfJXwZdEFaG2R9qk0l06Az9+Y8tUFFpRkJMiOZV+ZCKKjMeWpJeb5SYTCJOVpvx+KoUWGLrcO/9TdA0Ohx5P0Rha5Gjx8VkErF8Mss9MilFh4oPA4mxFD5SgYwXgvAFUiLp0KEN3e0klBJJ/XXXb/RF213jw58GnVKLASmROEBzZ6RDLReSOFIWho++icaIAaf8NLvQwCYC52tN2PZ5LAD5Y33OGobIK9PQNVPA6jWO9yyZ3aodxspSUwdUVjeatgDuQM5AgvAVehJJva1EqySUp5TtefSo++XcWuXLcjgbdEYK+2ltK0grw6xW9hj9JxaSSLY4XiCTE+twz82ncPY83c17gkkA0pLqMHfMMcwefUz2WDcV6iDuK4b55CmX4rfW1BbAEKgxI0Go4Q3vh9Y2vd1YUMmzsmwZMHWqen5Kaipw8KD+YyC9J8DV6ySKQEICUFHhuB+ps7SRniClbS1dClgs7DMpLWXHQYuCAs9zagxCelt//CHi0Ia9SLbUyYYkbDbgfC0QeUmyhaISnqPU/VoUAaFpGNC7G/K2CvVfu349qlBIDRlloa7SZLwQRuCPnAerlXlYlAwIKZTijgEBqBtGvKcCdy/acsdTMlKUKoOMNOSUtuUu69axZpJ+xv5t8V4YlS64no4lZLhkhEj3LMdLRNzdei8ihDooHtYIZvQ0tgNPxgsZL4SneNv7oURhIQsRaeGOAaFlGPGydi0wapR76+rxZBlpyBn13u3x1PNigFfP+W3de1MF1s856P6cCAANP3tDbIfO7YCkBMdlktKxEo202kjP9ZsSdgnCGa2cB0FgOQ+DBun3fmhdsLypB+NpybJEebn76+opX9Yj7Ke1TaPeO2BMIrFBXj3nt+VJOTTRgGS0GOJ1kusKbYljBopTjylEhLF+Ro3QcNELGS8E4YyRF017eC5YvJU5SUn8+5UwSgDPYmlIei0sZMuystjDk3wgZ8Pu2DG+9Xjel1Hv3YgWAwZWMjm/LalTcXJinWyjP8UGgIR3UOsKbYlj5dCV1cyACb80tpGFityFvsYE4cy2bXzj9FwQeZsy8lbmPPCA/sofo/RSiouBli2BAQOAp59mjwED2DJ3q6Hkqqt4kmYBvvdl1Hv3tMWAgZVMVivLLXbAZsOqFVUQBBE2m+j8EpSTLAg59KSCyWKJV/8tCwJLyk1KoLYAOqGcF4KwJy8PGDKEb+yyZeyCbXTuhlJljhxbtvBfSKV5KAnlAQ1lyUo4VwV5OifA/SRid3Je1N67EryfMw8G5TTJOfEGIw/LMQWpOApk9gdmPAHExDqsV33OhKhIhSY8hNuohpcaaf6KO+i5fpPnhWg88Op78GA2M88AjwaL3qaMSnowcowfz683otVxWRCAadPkWwzLLVNiyhT+OfF4IuTQG75Re+9q+0hNBSZPZlVFnobFAENymuSceIORh80YimTYLYyOgfO9afNmZLgYjaYtXHyYOll6ATJeiNBAyzDhEX3Tk9TpvH3n8I897lywsrOBNWu016moAJ55hm/70nbVhPJeeEH+9eRkYN48ba8LwI4hb3ds3mOemCg/Xz0eHj1GoRG5LXJ42ONKztYzwYrlmAJAZCd0kwmYPB2ACMHJUDNdcmbRtdQ4NO16SS2XMBRK2CWCH61EWN4ESU+SOtWqkNy9YJWV8a33978DTz6pfpF1ToYtLgZ27lSuenI+VqIIfPMN33wA46umcnOZ0eGpWKBct+kTJ1zF+VJSGkTyjMTDHldytl4milioSKJbDyCppeIUDK2kaeS88UEcHriNo90CqeUaDhkvRHCjZZhs3MguTDxlz54mdSpVIbl7weKdT0WFeuWTmnHnLLKmdDz/+IO/+gcwvot2crJxarZy5dqDB/umj5Q7Pa7skLP1WsNpYXyi6yBCFb2GnM3GOka/8WEin/EiVy5NeASFjYjghSdf4uGH+fNN3OnBI4fzRV4r1wSQv2BlZgLx8Xz7VPJg8FY5Ae7nnziTnMy2xdOHSOuYS3knnjZn1AorSgaNp7ktPD2YNEJ31kHZiptonWRFPxTiXqxHPxTCBCtK4GQAnjzBNVXyujSg13CBAOSsTMOO76NxpCxM/eehVi5NuA0ZL0TwwpMIyyuoVlLCZ2TwkJPjmvuipymjhNnMn0As58HQW5ZrlJDbhQusdJonmdldw04P3m5y6c5+srOBQ4dYVdG6dbBuL0Dh6oOYWpSNVq0UNpGXh35j0lGI/liPkShEfxxCOhJRjiOmNNh6XA3cdCs7bmWllNjiJY6Wh2HonAxsLYqDzSZgyoo09RUy0shS9AJUKk0EL+vXszO8EdiXpir14FmyhFXj8JTbCoK8UaJXEt5qZWW6SomyauXCestyjTyeznME1BNs1foe6SkFdz6227Yp9zNS+oy0tin3eXnQTkLurTtvYrDIqokA0UGqxQYByMzCmcnzEJ0U2fDC2bNAZCREwCVpl3CflVsTMWVFW9hsjsf0h4JT6BJBarmeQu0BiMYBb75EYiK7+PPmm8gldUoXLbNZPl9BDrnkXT3y+NL4VavUL4xKngm9SbOe5vwoacTwtFQYNAiIieFX7HU2KpSSbs+fV/6cRFF9TrwS/h60k+DpFSmIVuRiCkSpmsgOU2YWxAXPwyUoEckMGTJcjOWLn6IcDBfp9NE5Mw4wxZJarg+hsBERvPDmS7z0UsPfzq8D8hd/pRwIKfzjXLrrjLN2iydkZwMzZrjO0WRiy5U8B3qTZqXjqRfpOKrltkjHY8UK5TJ2e8XeMWOUlY7lwjPDhrm6Lo4e1S7tPnKkwWCyz1dZsICJFfLkCunV8blEbS0wYYK2DSxVE7mcrC+VRAsyJdG6dHkIbo6daEi8dTl9kFquTyHjhQheePMlhg3Tn2+iRnY2U13lwYieOnl5wOLF8qJ6ixcr527oTYY1m12rj5wZNMjVwElJYZ4FHqZOdcwD0ZNQrDbeE4YPBx591NEgmjtXfqxcrpAbZeF5eezrqJaSZTKJ6NejCkNuqgZ6XOXalOi+sawkWqDTuLexicDh0jAU7W3wcXnaKYLwDMp5ITxHbx6H0fDmS9TWMi9McTGQkcEqkcLD3dunQTLvmsdOb2sBZ5RaDcjlYmjtC2DH9cABV42YoiK+42G/b6mMnfe98czPl0ifLe93Yf58YM4c5OUBw4eLuKFrNVrH16HkJLso2ocjBmeewvLJh5GaZJdDUVYKrFgCFBUw+f8Fz9PdvQ+QqovGvpiB0dPiUFbmn9NcY0DP9durxsuiRYuQl5eHX375Bc2aNUPfvn3x/PPP4/LLL1dcZ82aNRg7dqzDsoiICFy4cIFrn2S8+BjevABvo2UEGD3P2lqWV6AWKjGbgXPnlA0knjnxXhjV+u+oGXf2uT2lpXzNEOUMMr29gwSBhd54qsH0Ggm+Yt065qnife+CAOvGzZjwVn/MGelomBwpC8OUFWnYWhSHwZmnsHlBMQCmiCsh2mwsPLR6FXDvfUCzSDJefMDh0jDkrEzDfTlx5GXxMgGTsLtjxw5MnDgR11xzDS5evIgnnngCt956K/bt24fmzZsrrhcdHY39+/fX/01JZwEKr3KtL1BLhPXGPHfu1O7fY7WycXLzMlr1197ocDaAlBKQt21zz5MhNyc18TU59JaxK+3Xn0i5QtJ752joaV2zHq/OSIPA6oDqlycn1mHzgmIMn9seyyYdAeBouACAYDJBFEUIf3nIqHdAOMHE55pgzHPt0TKOecUOnYzC0mUCGS4Bhk/DRuXl5UhKSsKOHTtw4403yo5Zs2YNcnJycPr0abf2QZ4XH+FpOMNXeGuevGXF0t25u3PSE45xZsMG4J575F/jKXNRQi0UplX36w6B5nlR+s4sWKCcKwOwnJWtH0KMjpG9IbPZgPLTZrSM52xqSRiKKAIiUK/hkpAAvP22Mf04CT4Ctqt0ZWUlACBeQzX0zJkzaNu2LVJTUzFo0CD89NNPimNrampQVVXl8CB8gJsVFj7HW/P0RP6ed07z5jFDx13V33vvBWbOdF2uVtqrhUrfHQAN4mu8Cc2JifwJxXoVkL3hsVWrUOvYUX3dUWOBmFhFT7LJBDJc/MiJSjOGzsnAO5/FQRCYQsHNN5PhEqj4zHix2WzIycnB9ddfjy5duiiOu/zyy/HPf/4T27Ztw9q1a2Gz2dC3b18cVTjZL1q0CDExMfWP1NRUb70Fwh6jG+95C2/N0xNZe959Pf00Kx+WtErcuRgvXswqaSSsVlau7K5nZNw414RiZy17sxmYPNn4Mna16jI5UlJYkqyRqJWYqBm0JhMwVKOSi/AbGwtj0Sq7B7YWxSEtVUThO1XIvqECOF1FSsUBis9E6iZOnIgff/wRn332meq4Pn36oE+fPvV/9+3bF507d8arr76KhQsXuoyfNWsWpk2bVv93VVUVGTC+wOjGe97CW/P0pMGe3n2dPMme4+O1dUvkWLqUGULvved5SMfeu6CUcDxuHBs3bhzzHqkdn+zshjYIPF2dJZ0dJQVki8UxrwcAXnuNP5HYHmme8+ax6rTycrb9+PgGI80etQac3XowET4iIFlXkIR//UtA9+RTuDLiMITaOuDnSy+GhwEdSCk30PBJzsukSZOwbds2fPrpp2jXrp3u9YcNG4YmTZpg/fr1mmMp58VH8FRYJCSwCpZAyHnR6ujsbm6OO7L2eitzpHkmJwNr1gBlZfyVQRJjxgBvvOH5XaSUf8KbM5OQwJ7tjS774yNViR071mAcJCfztU3gLc9XKhe3n+Nf/sK8R3IG0c8/M0NVMiIB5Uo1pX3ddCsw5xnl90P4BSnP6PewNFzbpQb4/Q/lwVdkkAHjZQKmVFoURUyePBlbt25FYWEhOmrFhGWwWq248sor8ec//xlLly7VHE/Giw/Jy9OusNiyRVlq3xuo9bcBtLVOjNqn1vvTuqAqIRkPejVPmjdn/W48ISWF5bMA/PuWvC7z5zNvjP3x8WWZvdy+EhKARx4BnnyyQUfGueXAhAny3i61747cvvrdBMx/3tj3RHiEzaZTiDgiDOjdjcrTvUjAGC8PP/ww1q1bh23btjlou8TExKBZs2YAgNGjRyM5ORmLFi0CACxYsADXXXcdOnTogNOnT+PFF1/EO++8g927d+OKK67Q3CcZLz6Ep2lgfDzQtCm7s5bw5QVK2hfgeeM/o3GnMse+eonHeDSSSyJruqt+5LxbWp6b+fMbjAo11AxHZ1HChx4CvvpKn7dGQ7dFzmtntQJFhVbUfVKEi0dK0CS1NcL69UG/MwVAfAJJPwQI4qVqdV2fRvfLmPQ/4RUCxnhR+pGuXr0aY8aMAQBkZWUhPT0da9asAQBMnToVeXl5OH78OOLi4nD11Vfj6aefRs+ePbn2ScaLD3G3dFVJ3dUT7wxPV19feoB4kd53fj7LS9HCuUz57bdZVZEaJtMlmVAPkQwnd7tP6/UaaRm5asbql1+yXB97LR6zmcn633GH+ndAr1dr+3ZWlqIwJYkX+v0XM+YlkvESIDgq7XDSuR3rXUR4hYAxXvwBGS8+xN2LGOB417ptm2fhg2DRnFGjtpblepw4If+62nuYOZNVFSlxzz3MyPEUT/VWJONHz/qCoByacVenxh6575ne9xcfD7z2GvKQrTmldaO3Y8RfYt2dLeFvyPPiVQJW54UIMTypJJK0TJ55Rl9jPjn0arnIlff6k7w8FtZQM1wAFuICXOf+4ovMgHFu3CcIzHD517+09VG0jDqp7NtqBXbs4HhTMkjfF71l6fZNEAHPdGqckfue6Z3fyZMQhw7Ff8fnQRCt6IdC3Iv16IdCmOD43bpv7c04UtYENltI3TM2DiLCgJgo7XGETyDjhXAfvaJhcixfLn8RkuveqwTvxebYMaaCmpTU0D24f3/HLse+hqdLsqQtAjh2Praf+3XXuRqTosg8LhkZDXkycnoqgsBCKWrcey/zkLVty0qH9eCseaPH6JUTEdQyVvVg/z2rrWUG4b59bmwHeLZiPA6hLQrRH+sxEoXoj0NIx2A0fLdsNgFTVrQFBMGQSB7hQzLSKFk3gKCwEeEZ7lbN6EGrMzOvm79ZMyb45oxRVUc82Of2JCWxEma1C7HFwl5/7z3lnB7eRogzZsiXAy9dqlxVI5GQ4J7GjH0H6cREx/etp1TcPlH5rbeA++7TPxctEhOVvV9KmExMwyU+ETh5Ara9e2Cys0psl7IqhmIztqLhu8W6Rv+O1KSL9cukQ0HXxwAjIowZLlQm7XUCpjEj0QhQEg1LSWGGwsmTyhco3guvlmdFTRzMHjnDBWhQr83JYUm9PHkxzgnGffuyJoxqiaDuVBeVl7P9KIVJ9BiMGzawyhvneT7zjLZh4o7hArDP5d57mSaNc5mynrnbe2t4GzrqRa/hktkfmDwdSGpZv8hUVgqsWAIUFbC/IcIGAbnIwTYMgg0N34lmEY7vn4wW43FXmBptWwORTZlAXUwUfTgBCBkvhOeodS2WvDJy8F68tMIMejsaK81FCk+oeXkAeSNE0gmRcE4E9STBtLDQ8zCJ9P6cu1zX1qon+7rL7NmsAqe8nOXdOL9ve8E3NaREZfs2CxaLcfN0l8z+wILnwWpW7Ei0sOVzHnMwYNJwBJkowg5kYXDmKWxZUOz7OTdG9Noc5GUJGijnhTAGs5ldFEeMaGjDmp3Nci7crfBR6w/kjOQBSk52XO6cxKpFSYl6Qq9SjopzXo59IqiRCaaeYu/Fystjx6u62rjtS5/ZvHnMG/Xww557jJzbLBT74cI/ahQQFcVMFZOJeVwgAoLT98tkYu9t0jSX715rlMBkErF88mEAdDPvC7gOcVgToFM6qyTq3Y0MlyCBPC+Ed7FY3Kvm0eoPJIezB0ivhD4A/Pqra9m15EUZNIjfCLEPRcXEuOc5kbwOWVl8GjA8SF4so0qNnRFF9plt28byaPSGYpyZM8cxDykvD5g717Nt8uCUy4L16wEby2ARu/WAYBcqkl23ZSu2/p5v6xe3RCn6datEalKdt2dP6KFjWzJYghAyXgjvwlsJFB/v2jvGHfVbyQMEsAuOHlq0YB4D5wu65EWZN0+fESKFagoL9c3Dfn2pJYbz8XEHi6Wh3NmbnqAvv2ShKCO2L1ci7W1kcllgl8sixCfybcdpXK5pOiqv+gPAcOPmSvDTxAxctPs+UYgoqCHjhfAuvGWxGzcyw8NI9Vu9OjSSy98ZyYvy4ouezUcvLVpoVwHpYdQodkwXLDCu1FiOpUuNM4x++YU9W63AihXenTfAl8tyktObdPJEg4rrJYMoRs1jQ3iXK9qz33FtHSXihgBkvBDeRasSyD404o6xotZWQNo3zwWvaVOgqkr5dVEEzpzRPz+A5X7wzsOeM2fc36cccXG+CbsYKfq3eTPQpw/w00/G5ubIoZXLYrOxXJZRg1mTy8hI+YufKALnzgKCCUKTJsCIB4C/POTduRPqRIQxZVwyVkIGStglvItUCQTIC6QB+vJa7MnLUxZtk/bN0YkcAHDhgv798zJ6dINGiVE0bap/nXnzgPHjjZ2HL/jyS+8bLgDLUUlq6Wq4SEi5LCPHMMNFCUEAmrcAlr0MfPgZ8OAEne2LCcMhgbmQg4wXwvsoVQJJyrHuCMNJHZWdvRlHj7LleXns8be/qW8nIYE9vMmJEywHZOZM4/bljrElisaFoEIR3lyWkaP5jRG91W6EsUSEAVdkUF5LCEIKu4GApx2VgwWj3qfVCrRsqX4hjoriu1t3R1XVXVJSgP/9D5g4kSnG1tT4Zr8EHz2uAnJf9fcsCCNITgISYymvJcgghd1gQk7wTE9H5WDCvhLIEwoLtT0IvGEGXxkuAPuMW7b0TQiE0M/ePayqKNEi7zEh/f6ARwwPg9CBKogaA+TT9CdKgmd6Oio3RtwtPQ4EgslwSU0FtmxhjxYt/D0b72OzsXJoQYBL10QyXAIWUQSWbkzCTTkd8P1Xp4DtHwRGt3jCq5Dx4i/UtDb0dFQmPMdi0XdRSuTMjQhWmjYF5s8HDh5s8P4ZWfUUyBQVsHLoE079kyjhNiARReCe+e3w2Uu78MaerugxNUC6xRNeh8JG/qKoSL10Vk+vHWdCMYfG/j3FGegSlroqD9chHPbWW0B4OJvLr7/6RvHVl9TUsMqkLl2YqnAwVih5QlEB8PmOBoXdtu2AB/7q71kRMggCcPnpLzEfQ+GizSN5sH3RLZ7wOWS8+Ate5VnecRLBlkPDY2i5041ZEpzTykf/61+BwYPZCW7cOD4V24oKx9LnLl30zy+QsW9tsHdv46xQstkapP17XEXGSwCTE78WgOgaRnCnWzwRNFDYyF/wqr/qUYkNthwaLZ0WaYzce9Ji+nT2rOXqnzuX7RNgKr88lJY6hvOys4FDh4D77tM3R6MxsixX8vw9/7xx2wxW9u4BKk8HRmNNwoWEk/9TvpDZe7CJkIKMF38hqb8qXVz1dFQGfJdDo9ZxWQ88hpa7PXgSEoDrrpPXlpFD2uepU+qficTUqfLx9O7d9c3TaNasAQoKgLVrjcvL8aZ4H0F4gCgCVacvMuNSC70ebCLgIePFXxitPKsnh8ZdeDwlPPAaWoWF7oViKiqYUJ3VyjwiWhd0aZ/TpvEr8tobWXl5rAR65kz9c7UnLMyz9Q8eZPlRycm+LQEPdbr1AGJiKWE3AImKMQPX99MeqLfPGRHwkPHiT4xUnvVWDo2EmqdkyBDmjeD1xPAaWp6WRI8YAWzdyndBl/b500+sg7MWksEzfjx7/0bkhdTVebb+4sXs+Bt1l+mpMRUq8CrvEj5FEABRFHBx8kyISjd5ej3YRNBACbv+JjubJZN5Wh3kjRwaCR5PSW4ue/AkB/vKhWu1AsOGAZs2Ad9/z7fO/Pn82w80uf3qatY52ig8NaZCBd4u0oTPMZkAU5IF6NoD+P5bx3OUXg92KFZphjDUHiBUsFpZCEere/PBg/p/kIWFLESkhy1blA0Y3u1t3w6MGeN5FY/Z3Hj0ctjtqL9nEVo0acIaLJpMFDoKVKpPAH8b53iuSE1lhguPBzvYqjRDFD3XbwobhQre7N7sjqdk/Hhlg4EnWdliAY4fZ+XLntJYDBeADBcjMZlYmfToB9nvhgyXwOWGvg35bevWsWd7kUU1gq1KkwBAnpfQQ+4OQs8diBzueF4AFoKZM0f+NemEAWhfcOPjgXPnqPKF8B2Z/YHJ04Gklv6eCaFFWBMgIwWICNffiFHyWCt5dz3xWBO60XP9JuMlFDE6dqsVklIiIYFpoijt2x3xOYLwNpn9gQXPAxABgZzTQUV4GKCnMSPvjVlBgTFNZQlVKGzU2JG6N48YwZ7VDBce3Ra1kJQaFRXqpdmSuJvR2iQEoRcpRHTzQGDa4yDDJfCw8dw31dYB+4qB8lN8G/V2lSbhNejX2ZjRo9uiVNathdaPXjK0SJuE8AWSkXLTrezZZGKelg3vArmvAk89DcTFk+ESYPxrZTma3noVPq28DOjUDmii4UkuPsznJfZmlSbhVahUurEi5Zw4/8DVmpnZl3X/4x9sjBa//so3H7qzIbyNXB5L5WkgOgYuTf2IwMBmA8rLsCavBS6KJtw3KRoHv6uC+aJGEn5NHVBZDcRqpA5IxQNaVZqkExNw0O1FY8STVgKSp2TDBpbTosW8eXzZ+nRnQ3gTKY/FYnFcHh3DnsnTEnjYbBAFAeUr38AOW2a9juT+Hzj1h/bs1a409GaVJuFV6BfbGDGilYDZDKxapb0vUeTrqZSZyadsSxB6MZmYx0Uuj0UQqAQ6QBHLyyDOeQwPFY2ADQ3GQ0kFp/Lz1CmOYXCl/D4jlc4Jn0Fho8aIUUlq2dmsHHruXPVxkiGklq2/dStw+jTfvAhCD916UMlzsPDG/wG/HwROnsCRvRXIsS3FVjgaD+aEKFZVVKvggbkUasLePezmaehQYMYMZrTIidANGsRunJ57DigvZ9655GRS2A1wyHhpjLiTpKZUfp2RwbetI0eUX8vLYzL+BOENqDdR0PDEL6Nw6Iuj+AOtUYRMB49LQ/qJAJxMY1VFzthsgABg5VL2f4kXX3QdK/VlS0hwbPMhGTVkuAQ0ZLw0RvQmqalJZ5eX8+1z0iSgWTNWDm1vAAFs23poTHL/hOekpPp7BoQGNhtwtDwMzW67Ghu+ZGE81TZFljjgigzgwGFHD0x5GTNcigq0dyrtwLk/mVrRAhEwkPHSGJGS1IYOde2F43yW0KpKmjyZb59VVa7elZQUJv+vR6RuyRLgyivZtqqr+dcjGicmE3DHYPb91ZPbonc84TY2EYAAPL0hDS9vEnBlF/l7JReRcEsckBgL5G0DXnqZNdDcu8fR4+IO0mefk8NCSuSBCUhIYbcxo9VKgEc6OzGR3/viKQkJzHtDirwELz2uYvothM8QRRGCDsOv7FQTPJybhpGPxNcbJ7pEwt1tX8IDKev6FD3Xb/K8NGbsdVvkzhI8VUlSgpsvDBhn9y5BOGMysQTd+ER2J56Y5O8ZNTp4DBd7x1ZS3EX8a84RRHYVADBZf0mRgQutMLgnkP5UwELGS2NH7SzB+8MdNYqFobzlxDOZgLg4Ml4IdeRE6E6d9N98CEWc7ZtI8yVZ/ysy+PsSSWiFwT05L5H+VMBCOi+BBk+vIV/ti/eHe8cdLLnNWQBML0p3bHPmkOFCqKMkQhcTyy5eoRUdD114Zf2dUdNqmTlT//YEgYXQSVk3YPGJ8fKPf/wD6enpaNq0KXr37o2vv/5adfymTZvQqVMnNG3aFF27dsV//vMfX0zT/+jpNeSLfUnuWC038L33shDT2rV8qrtKODdmTE0FtmwBLrvM/W0SoY+aCJ3J1HAxJAMm8JFk/d3BvtHrunXs+eBB4Lrr9G2HlHWDAq8n7L799tsYPXo0XnnlFfTu3Ru5ubnYtGkT9u/fj6Qk13j0zp07ceONN2LRokW44447sG7dOjz//PP49ttv0aVLF839BW3CrlJVj/RDMrJsT8++pLEA38nfZHI/23/tWnbn5Jx/k58PDBigf3uSyzghATh5ki5eoQpvUi5VEHkfI45x53ZAktNNkK4MXqf11IoOAFfpBfuiBcKn6Ll+e9146d27N6655hqsXLkSAGCz2ZCamorJkyfj8ccfdxl/zz334OzZs3jvvffql1133XXo0aMHXnnlFc39BaXxwvMDs1jY6+Hh3t2XpPFy8GDDySEvD3jkEZYQ503mz2chImfcNV5SUoBly4B9+7RVgIng5aZbgTnP+HsWBNAQojN54NTvfpljQ0U1nSktA4O3EmnZMqBlS32GEWE4eq7fXg0b1dbWYvfu3Rhgd+ExmUwYMGAAvvjiC9l1vvjiC4fxADBw4EDF8TU1NaiqqnJ4BB1aVT0Aq+ZJSfE8hOROX6NBg4CpUz3bLw/OTRylnJx33nFvew8+yOZNhktoc/KEv2dASJw9C1Qrn4M1b5UjwoCYqIa/Jc+v8zlL0pnSOh/yFh20bAmMGMGKF8hwCQq8arycOHECVqsVLVs69hVp2bIljh8/LrvO8ePHdY1ftGgRYmJi6h+pqUGopsn7Aysv5/vBGrEvaZyUGzNjhvv71IPUxNE+J+eS10438+eTJkyoYTKxMNFNt7Jnk4kl5ZLicmAQGck6dZ+pBkTX8LEgAKr2S0ZaQ9jJamUeFzmLR1qm1fTVnVYoRFAQ9KXSs2bNwrRp0+r/rqqqCj4DRu8PR0v5US0+rOfHrJQb4y7NmgHnzyu/Lnl9xo0D1qyhHBXCEblS6MrT7GKphM3mWQiD0IeUIN0iSnGIbEZMRBgzXOzLpPV4iZXkHvS2QiGCBq/+qhMTE2E2m1FaWuqwvLS0FK1atZJdp1WrVrrGR0REIDo62uERdGRmupb4KSEX1rFHq4pIa19SiWDfvsp3Pe7CG3pavZoMF8IRpVJoyXBRTBKl75HP4U3YTWvFknO7Xwb07uaq76LXSyyHpAEjNy+qKgpqvGq8hIeH4+qrr0Z+fn79MpvNhvz8fPTp00d2nT59+jiMB4CPP/5YcXxIYDYD48frW0fuB8sTH962DbhwQX6b9j/mnTuNC7lIBtFNNxmzPaJxoVYKLQjqF0sTXZQClrhoVlUUGy3/GRoV8lHTgKHmi0GL18NG06ZNwwMPPIBevXrh2muvRW5uLs6ePYuxY8cCAEaPHo3k5GQsWrQIADBlyhT069cPS5Yswe23344NGzZg165dWLVqlben6l86dtQ33vkHqxUfFgRmIKmVDMfHA6tWsR/z+vX65qOEvUGUmem7VgJE6NCth2OoiAhqRACCc2KuHEaGfLRaoRBBh9eNl3vuuQfl5eWYM2cOjh8/jh49euCDDz6oT8o9fPgwTHYx6b59+2LdunWYPXs2nnjiCXTs2BHvvPMOl8ZLUKMn70VO+ZEnPqylUtusGfuB652PGomJrH3Ajz8y44oMF0Iv8YnaY4igoF4Gxj4xVwkt2X9AX8hHV8MkItChrtKBgqS/otVcTBDkXZ3r17McF0+RuqjyzkeN6GggGEvXicCCOkOHDnKJuVrI6byQkFxIEjA6L4QO1BLLJBISlGO0RnlK8vMbSg/HjfMscZYMF0INubJnOfbuAcpKlZWbQ+v+K3RJayWfmKuFkux/drZve8ERAQV5XgINubuM+Hi27Mkn1cujPfWUSEj9iZTCTBYL0Ls38N57xndxJUIHk4nlq8QnMiG5vXsaDBC5sueyUmDFEqCowHVbUrWRs3orSf4HD23bAOltjNueJ8q7REASUO0BfE3QGy+A+308lPoQGdnjZ/78BiNK7uRBCbkEoG6cAMwQca4estnYd3XOY8oGjPM2ieDiigz9nhc5fNkLjvAZZLwEu/HiCWrxYUBfk0Vn5PoeORtax44B993n0VsgghzJS6JknFRVsnwo57JnaUx5GTBikHyYSPLmZN4EDLnHW++A0ItcMq0cEWEsdOSJt8yd/mxEUEA5L94gWGKravFhJb0DXuQE8qQMfqkviLvbJkIDNU0WSX01JlbecJHGtGzFDBQ5bDZgz7dA0ScGTprwCJsNgAh88pG2UVJTB1RWe7Y/d/qzESEHGS88aKnWBhrOBoX93Ydk3Mye7f721RQtJW0GykNonEiaLGrGCQ+ZN3mWxEt4DRdnfXkZMOdxoIKzQWZtnWcTMEJ5lwh6yHjRwtOupoGE5D3auLEhKdcd1CqbeKqmiNDFKE2WIfew8ugt/wX6ySgzX98PiIigvkU+RBRFiCLwt5Xt8enJ9sCy54Cch1iIDwCGcUo1hId5NhFqtkiAcl7UCaXYqlwujNmsP/yVkACUlmq/X7n9EaEPryaLniohUQQ2vAm89g/m2el746ULpUxoivAaZy+G4aA5DZ0z42AuKmQeaIAZkBveZcn6Wp+HkTkvWsq7wXBeJhygnBejCJXYqpL3yJ28nYoK1h/JfhtyuUBSeGrZMjcnTQQlWuEcmw04fYr9X8990733A1s/ZIbR8FGXehrR6Us3SsdcFF3DQaLIHm0sQPfL0PymbuiSFcfsAfuQjFao0B4eZV0tqNkiATJe1AmF2KpazyMJ5xOAmiteEICcHLZdrVwgsxloSWWtjQqbjZVDC4KrAWOzAQKAJYuAf77KfxGTmi9KHaQJY7l0bhCqKh2XNw0HruwAdGzr2jzRPiTDGypMTjKmTBqgZosEGS+q+CO2anRVk5b3CGgwbOLjgTFj1JMgJW/TM8/w5QJR3LnxUVTAtFpOOOn9SImdRQXAsSP6t0s5VJ4jdwxPnQLmPg4MHgjkPARbRQnQpQNw4RSw/QP585B9Yv5JzkTdxFj11/We+9QqK4mQh3Je1PB1bNUbipF6eh7pUceNj2eCd0rbkY4LYJzyLxFcqCnsUr8i/3L2DNC8RcPfdurGB8fMR7vtrymfh0SRlTt/thN4ajbw017gra1AokXZayvluths8gKcpJZLgETqjBWpU1OtBYxzUXpLMbKwsCGxztdITR6VjiHReJGSPNUueIT3cE6YttkgCgLOz5mLZkX/hYt/Rhr77vtAQhvHcueKE8BH/2F5Sc7tGySuyGAeNzkDZcQIYPFiUsslKGHXUHwRW1XLS5GWSXkmevGn7oqUC+SpOB4ReqjlxhDeQ0rClctzE0U0m/QwBDnjQxSBzCygRaKrTktCIjNczpyCi9UTEdZguCiFmV980TvnPiKkIc8LL+72G+KB1zsieTL0zk/Jq+NtHngAuP9+oKyMzam0FLj3Xt/OgXAftbCPUVC/osAj5yGmYmwPTzl0RBhwbVeg6gwzcMLDgJgo9p1Rk5zgQe3cR4QMeq7fTXw0p+BHUq31Bp5WNWnFiyXPx0MPASc4k+uM4I032EOCSheDB6XGiu9tBY4eMc6YKSoAPt/RYCTd2B/IGuDZNgnPkKseksqh1aipY4ZLrNNFh6doQAutc6Q3by6JgISMl0DAk6qmTZuA4cNdl0tVP1JoKzsbOHsWGD1aez8tWgBnzvDNSQ/k+g0O7Bsr2mNJAv4yoeFvuyRPj5D6FQHMKCLjxb/IVQ/xlkPLSf8bISWhdo6kZN9GCeW8BAJaeSmCwDpDZ2Y6Lt+8mSW7ySEXL66o4JtP374sP0VtPvHxfNsiggu1xorO34dECzNyMg1MCKeeRd5FLXRsswGlx9ln4AxvObSc9L8ncglK5z6JUGrfQuiCjJdAwB3FyLw8YNgwdW+GswKwxcI3n48+Ai5ckE/sk8qpz53j2xYRXOhRS5W6RE+a1lBhYjKxMuibblVvrKiEzQbkf0iaLkagJO+gNFYAsHKpvOHIY1RGXMpxccbdogEttVxvFjoQAQ8ZL4GCnqom6UfLi+S21VPtI2m4OHtYpL8vXODfFhE86G2saDIBLVsxoyezP0vqzH0VmPMMe97wrqNnRs64MZmAnlcDYx8C5i1ilSuE5+hJ0LfZgHmzlEOA9tVhSttVkv7XujkTBGDmTHaus0erojNU2rcQbkE5L4FEdjYwaJB24pneBDjJbSvdAfGsK3ldmjUDtm9nFUNJSax6iAhdeMMDztg3S7RHCi3NeYz97ZwEXHkaaBIGNG/u3n4JYzCb2WehhqScvOA5ONRER4Qxw0VN+l+6OZPLTcnNZa8vWqQv6TYU2rcQbkPGS6DBU9Wk58eYksI8NevXsxPCsmUNCb5ad2aiyE40ZjPLrVmwgE4EoY4UHtArHnfLnyCbJ2Mysbv26bOAmFi4GDfUr8h76A3Z8Xjd9u0FrNVAj6vZeUUqh+YJCWndnOmt6PRH+xYiYCCdl2BEj2puQoJjoq6kaLl+Pb/3Zt06ICICGDJE91SJIESqNlJSS7XHZgNOn+ZL4JbLoSICBzl9FyUCoZrH1+1bCK9DCruhDk8CnHTRca4wOnqUSXEvXcq8MDwkJenLsSGCG6XGis4XCKlL9PYP+LZLhktgYl9lJAjshsc5/8QZpWoeoxvLquFOoQMRMpDxEoyo/WglYmOV1xdFYPp04OGHtY2ghARgzx59OTaxscC//sVyZbZvB2bP5l+XCAyKCoB772J34wueBF5/hXWFtkfqEr1zh3/mSLjijiNdEFiVkdQ64P77gTVrWNWhkkdNrponL495Qvr3Z81g+/dnf3uzXNkX7VuIgITCRsGMkjhTly7ABxx3w8uWAb//zu5O9HSU1mLTJiAujt15AezO7tln3duWxQKUl2uPI7yPUrsAarIYONisgEmnp2HjW8BLueymyN5TEhMDVFZqr19QwKoTvdFYlhdS2A0JqKt0oBkv3vxhSds+dgx4/XV2InEH5xOXOwgCcNddwGef8QviKZGQALz9NnD8OHDffZ5ti/A+SnkyRuW5UL6MNqII2aRpLX7YBUyZ6L4w4Nq1wOOPK3tnKfeE4IRyXgIJb7tSzWZ21zNhgvuGC9BguDRr5v42RBHYts0zw0XSfVi1Crj5ZqBVK/e3RXgXe82W6kpg3uOueTJGGBwHi9lzaN1neQe9hgsAdO3lqsejh/Jy0lshfA6VSnsTpW7Ozn2HPN2HkVVA588bty13sNd9AJgXhwg8lBo3rlzKErwnTTduX+0yjNtWsCLlo8iF5aTQnR5D0dmTZa/Ho6dXVWoqv3I3ySwQBkLGi7fgka4eP57FlbOy3HOn6lXaDVQeeAC45RZ2B2exsCRBq5V5cebN8/fsCGeUGjcmWoD5zwGb1vllWiHNkd+B1LYNhoqEVPGlF2dDR9LjmTSNdfnmDSHdey+/cjfprRAGQsaLt+BRwa2oAAYMcF8zwYhW84HA2bOuMfOUFP97gQhXmjQBpj3O/q8kSHfLn3w/r1AnLZ09Oxsd5WXAe+8AD05wXkM/9q0eePVeFi8Grr2W/V619FaUmisShBtQzou30OMidbcDqj/csN6oJtm82dUIO3rU86Rfwlgy+wOb3gfi4pVDFCYTe/3UScpRMQIpXCS37J+vACMGAW+t5qsK4kVvf6tp05huFEB6K4TPIOPFW+hxkbrbAdUfblh3KxKI4EYKFanpB9mz51uqDvIU6byg1Nn99rvZ3zYbsGW9cfvV099KSsa1WEhvhfApZLy4A4+KpN428O5k5OvZx5//DCTqvKOyh/Q7Gi8mE0vO1VOGe/iQN2fUOJAq7+SwD/EAwNrVrLGikrdLUtEtK1W+AbFX2tVLSQkzUA4dYlWP69ax54MHWT8jX6nuEo0GuiLphbf0mUcFVw49oSCefZhMrN38++8Dr77q3t1wTAx5XBoz3XqwqiIew0W6AO7Z5fVpEWgI8dhswOJnG/5vj5TU+947wD+WsnOA0piVS9mNDm8FkYTkBZaaK44YwZ63bfO96i7RKCDjRQ9S6bNzfoZSzoqSdLUaekNBSvto0QIYM4Ylvb7wguNY574lkhiQUrz6llv0zcmea64hl3Gww5sDIZXfvv8O8MP36nf5hDHYh3iUelKZTMzwfHACMHEasOFN1zFSq4fPCoHvv2fnuIICJkCn5rEVBFYuLZeMq/d86Ywv+yQRQQcp7PIidTB1R0VS+hEOH84E5fSuzzs/LRVfezVeqSw5OZmN3bbNtdVAQgJ79iRxNjERmDwZmDvX/W0Q/qXHVUDuq/rWKSsF8j8E7r2frzt1Y0VNOVgp5wVgRmF5GUvYdTYQTSbgvrHA2Idc17fZ2N9zHweqTru2epBYtoz9bs3mBiPEfk7227XPabE/x+TkACcU8me0zndKrU/83cma8CrUHsAbxkthIXN5alFQwNylcug5CeiBx3DhORnYb+fXX5nGilFfj+hooKrK/fWN7L1E6EOrd5HcRVa6SG54E/jzXUBMrE+mGlLInSOAhhDPnMflBeWkz8tikQ/1qRk+9tifH+TOH6mpjoKScmO0kDtfKol7+qpPEuE3qD2AN+DNRVEb540OqDw5ODzuW3vDJSkJeO01Y40FTwwXgAwXf2KzASuWyOdKKHkHJCPnriFAdAx9fu4gGezOx1wK8Sgp4WrlKDkn+yphf35QSsa1N1zkzjFaOJ8vecQ99VZlEiEJeV54McLzImFUo0aeO5RBg7TDXfHxrKeRO4J30dFAXR0JyjUGMvsDM54gL4o/WLmEhZzlQjzO3HQrMOcZ7W0ueBL45CP1MTzhbK2QuhrO50sjz7NE0OF3z8uhQ4fw4IMPol27dmjWrBkyMjIwd+5c1NbWqq6XlZUFQRAcHhMmGKAcaQRaZclqiWvOOGfke9IaQOsOpbBQu2laRYV7J57Zs9kJ9bXX9K9LBB9XdGFeFMLniCdPMkNjz7faSdC8Oi0843gkHNxR+lY6Xxrh4TYaTxKHKenYa3ilPcAvv/wCm82GV199FR06dMCPP/6IcePG4ezZs1i8eLHquuPGjcOCBQvq/46MjPTGFPUjlSUPHeqaf+EPFUmtE4Z00vnkE+/N4eab2fvVU02lhCAw78+5c55vizCeG29iybeEXxCSU2WXi5ceDnehe/cAZaUQEy0QlBo5lpfp03NRMxb0GhJq50veaktfCXR6kjhMScdexWdhoxdffBEvv/wyfvvtN8UxWVlZ6NGjB3Jzc93ej9fCRhI8iWu+YP16luOiRVQUUF1t7L6dXcmS21iptwkR3JhMwJb/Mtl/wjOcq4vUqo3sxwDA3m+AuCjgq68AUcQBoSOe+EdrLMEMpMLpRiazP8QFz1/SFVRI9j34K+uttmaN9rzVwjS8oR4JtfOl1rnE06pMPXiSOExJx24RkNVGs2fPxgcffIBdu5TFq7KysvDTTz9BFEW0atUKd955J5566ilV70tNTQ1qamrq/66qqkJqaqr3jBfAuJwVT9B7wjAKpR+f0o+VCH7cKZUm5A0VwD2hSJsNqKsFbrqONcdEw/3LEGzGJgxjm7ZfJ7M/U0ZOamk3Bxtw6jgQF90QsvHUWOAxOBITWfm1JM2gdr70VlWmHjyVxnB33UaO33NenDlw4ABWrFiBhx56SHXcyJEjsXbtWhQUFGDWrFl48803cd9996mus2jRIsTExNQ/UlPl3auGYkTOiqfobT9gFLGxrIR60CDH5e4I8hHBgd5GfQRD6bf5z1eALW/r25bJBEQ0BW6+tb6SsHVrwAQrlmEqRDgZLgCrRrr3Log5D7Hk3MpSoN81wOC7Gs5bairdcuEduRwOnm288gowahTf+dIbVZl64Q3Ly+UCebIuwY0u4+Xxxx93Sah1fvzyyy8O6xw7dgy33XYbhg0bhnHjxqluf/z48Rg4cCC6du2KUaNG4V//+he2bt2K4uJixXVmzZqFysrK+seRI0f0vKXgxf6EwYsnvY0kTp1ignNyEt/Z2cDvvwPz53u+H8L7mEzMq3LTrexZSUhOT6M+gLxvStg3VPys0L1tNAkDhgwB8vKQmQlkJxYhFUeVT+Q2G4Q937Jk37880JDsa2+ExMcDb7+tbSyoyTIYbXBolWZ7G08ShwMx6TgE0ZWwO336dIwZM0Z1TPv27ev//8cff6B///7o27cvVq1apXtyvXv3BsA8NxkZGbJjIiIiEBERoXvbIYF0whg3Tlm5157cXHZyKSkBSkuBqVPd3/fRo8y1K5Vj24fRnnwS6NJFv2AV4TvkQgplpUzPxVk/ZO8e4NRJfTkvPLkcjRFJYwUiO95Kwn9KSIbklCkwDxqEmfeVALmc61ZUMIOlslI+kXTZMnaDIxcOVwoLS1owkoHifC7wJKQuebj9gSeJw4GWdByieC3n5dixY+jfvz+uvvpqrF27FmY3vsCff/45brjhBnz//ffo1q0b1zpeT9gNRObN4/N22CfdWa1Ay5aeSf8DrIVA06bsJCYhZdQ7n8hKS4FJk5QlwwnfkNkfWPA8XLpES6q4cx5zNWBuvAmY/xz7P49RQsaLOgueZPpIC57na58gp4pbcOkz0pP7dslroyuRtLHlcHjaziWQko6DDL/nvBw7dgxZWVlIS0vD4sWLUV5ejuPHj+P48eMOYzp16oSvv/4aAFBcXIyFCxdi9+7dOHToEN59912MHj0aN954I7fh0ijJy9M2XOQ0FfLyPDdcALYNe8MFaLgb27bNMTfonnuA48cbGr7FkGaIzzGZmMfF2XCRXhNFYNI014upKLIydl6DhAwXdU6eUG6k6HzBs+/4bK/xUlJSn/vGfQf60Uf61WsbUw6HFBobMEDdcAGUpTH05hERbuEVnZePP/4YBw4cwIEDB5Di1MFYcvTU1dVh//79OHdJ1yM8PBzbt29Hbm4uzp49i9TUVAwZMgSzZ8/2xhTdJxAqjeznMmUK31j7H8vmzcyg8BbSXXdODvO+2B8fe1fwhg3Ae+95bx6EK5J0vBL20vF7vmXL7D01BB9KnidnjZWiAuDzHex4xycCKanAHYMdP6PyMma4OHvDfv21/kIpDBnCNy812QR7I8Q+XNNYcjh4KyZTUrSlMaSQvlx4zteyGiGKV4yXMWPGaObGpKenwz5ilZqaih07dnhjOsYRaKJDvMqW8+Y5Jt0NG+bVaQFQPhFKWK3Al196fx6EI7yVQ9I4NU8NDzZb4+woLSXnyjVUdPag2GwNhiIArF3dYMyotQOYO5fllmVnAxs3Avfeq66+26IFcOaM9tydjZDGkMOhplguER/PjjNvhanROUCEA14xXkIS3oQ1X8J7p9OxI3vW46kxCqU5FhVR7os/0Csdr+WpUcJmA6oqG3cfJGfPi5IHxRlnY0Zt+5J3c9gw9rfajcnMmczg0cLZCJFkGbRyOHhaoyjhb482z43gyZMN5eW8+DPpOMRphLdEbhConU713hG504PEU1q3lteG2LbNt/MgGJek4xXv0G02oPR4Q1jDHY0XycOwZBGwab2bEw0htrwN5DzEkm21DBc9OOeaDB0KbNnCDAl7UlLY8scf177wms1A376uy7yZw6FWgu0rGktoLIQg44WHQE1Y09ss0sgfXkqKdjjAZALKylxPTC1bspMd4XtsNlYOLQiuBoxcWEOvxgvAPAxzHmcX6p0BHgr2BUWf8DVUdBf737WcPsqhQ2z5zp3aN1hWKxvnjLeE4ySPtvP5VfJo+8qAaQyhsRCDjBceAtUq13tHZMQPLy4O2L6d9UPROhnbbKzCyPnEZESVE+E+SlUu9kaHxI972QWNR1FBFNnjleVAdSUTvxME5ukJduE6dw2PkyeZF4tXENAdnH/XSgrgvOcnJa+o0cJxgeTRlm4ElVDqgk34Dcp54SGQrXI9We1asWseTp1iJ0N/uU9NJu/dwTYmnKtc7BNDTaaG5fHx/OEAyWCe86yjMV15mj0Hs/ZLwcfAe+8AzywGmkXyN1PMfR64vh+/IKBe9FxQec9Pb70FLF6sXAZsVA6HHo+2t/NGzGZm7L34ovIYKm8OKMh44cEXCWuewJvVLnlqhg5tqIZwB2kf3mToUPa+kpLY32VlnqsCE47IJYbKKe/qxfnCHnVJbOrsWVbxEoioGVaiCHTtDjwzB1g0v0FYTsuA2fAmGydXZp5oYcvlBAF5EQTlC6pcAmxmJlPQ1UqULy/3jcEQSB7tvDxmsCkxYwaVNwcYFDbiIRhEh3ibRSrFrlNT+XsSSSfDeB1y8XqZOJG9l5tvZo8RI1iuDOE9JD0Xi8XY7Urid9aLxm7XSNQMEUEAki5p30ght/Iy5fGnTgLzHgde+4d7goA8JCQo55ooJcBu2wZoNLqtR81gsFqB/HzgqafYIz/fvdBOoHi0tcqkBYFpUvm6IINQhTwvvISS6JCSpwYAXnuNz8NkNrNjwVN6qQdBYHeHx46xyiRpXkVFwL59xu6LaEBLz8XTkI/JFPxl01LllXPI7VQFAIH1frIPv/W4Sr8goBYtWrCS5yeflL9B0ZJ0mDePbz9KBkNeHjB+vGPe2tNPM2Nq1Sp958FA8WgHUviK4IaMFz0YJTrkb00DQDl2LYWVlLD3MD35JPD3vxubgCuKzG0t3SEmJLBnSvL1Llp6LkblqlRWAlFRgSFcJ10wed+bfeUVjxaLXkFAHs6cYcJ0SqEitQRYQWA3J8nJri09nJELLeXlsd5IclRUsNe2bOE3YNTC2L70aAdS+IrgJgDOIEEGb3hGCW9rGshpqughO5vFd+XyZZzjvmYzu9vyZhJmRQUZLr7AHT0XdzCbA8NwAYDyUhbe0aqGEkWgzE77hhe9goC8jB8v/7vm8SAcPQr89a/a+5g2zXEfvAKXU6boO+d4qwRbD4ESviJ0ESBnkUaCtzUNjDCMpMQ15xOQzcaWO29LOvl42mTRmx3AE310YQ501Mp19V5ApbJoXmw2Nr55pL79eAPRBrz+CnDvIGDHJ6zqB5B9P/UtTFYs1V/lxisI2OkyfdutqGA3Js7wegZ4jAtn3SpegcujR/XrXRldgq0XvXpZREBAxouv8LamgZJhdPQoc+cuWMAnUKVnjpKXp6YG+Nvf3Ju3RFWVZ+srYbEADzzgnW0HE5n9gQ3vArmvAnOeYc8b3mXLAe0LrTOCwO9xs9kaxrrTH8loNm0A3nwdovRepSTcqkqXoULlaWDxM44VQQkJfMnqvIKAn30GtGmj7z3IGS9GewbsjSE9IROtkJQcnnq0PSEYCjIIFwLgTNJI8KZKL09Tsblztb0wvHMsLGTGUFJSg5fnuef0z9sXXHMNsGSJv2fhX5SqiKRy3cz+6hdaTzl3Vp+x4w56vECXVH/PoZnj8poax79PnQSWPgeMGOboFSgtZQ36eOARBDx6FLj1Vv75K8HrQeBNOrU3hvQYRlOnelcZ19PQuByBEL4idCGIop5ffeBTVVWFmJgYVFZWItqboQi9rF/PLvJarFvH7j70UFjIjAgeBMHxx2ifPLxvH6sc0IK3O20g4ImeTShgMjEPi8Ui7/Ww2diFdMQg9n8jdF7sEUXg/Dkgsrkx21Pi/DkmHqdFdRUw6BbAZsNmDMZQbG0w7pwrreo9RheArEshA+n3smULsHIl//zshf/kOkUPGqSv39f27UxCwBnJAwvIJ8Bu3sz2lZ6uXeVz8GCDt8FqZevwhI7s92X0RT8vT77ic/lyY/YVCMUUjRg912/yvPgKbyaF6c2Cl0I/zjkyPIYLEDyGC9C4DRegoYpIKVxjX64LME/AvXcBU//GKoOUvDC8x1UQ3DdcpLwapX2JIlPvnTqBeUh4+OC9+vc0GFvVS8QlLZaIaPZs/3vRY7gADdVJn3wk3+dIj+GSkKDsPVHyICQnNxgT7oRJ7NfRwlvS/r7og+TP8BWhCzJefIU3k8L0GDxS6OeZZ+RPBN4gJYWdcNXee6AqrwY77pTr2mzAd7tYrodSGMkXMv/2XjO5nBGIrHO1KAKpaXzb/LyhUaQZ4DPuauuAf//Hd78XLVat0r6oOht8zn+7EybJzmYeJ0m+QGv/RjarDaQ+SERAQMaLr/BmUpiWYSTH8uW+80osXcpOuID8exfF4PLmBBPuluuaTKy54qb1Db2J/IHJxL4jznMoLwM2rGUKtbmvAqMvlf+qeWlKncqdTSag5zV881i71v9evPh4bR0VyTvhnDT7xx+u3gl3qnyys1nOzxNP8M3ZKG0Ub+YMEkEJidT5Em+p9NqLPfFy8qS+fSQkuKe3IgjA9OnspCj33pOTgfPn1bdtNtMdlbtIVUSJFnl9FSnnxf6iLpf3cuok8PF/gRZRwJ/v8vasXVm5FKgob8gZiYkF5smEiiRj2N5Itq/skTw4enN7fv2fp+/AfeLj2e9GSVVXgkekLieH5bxI2+FptCiXB3LLLcCzz2rP3agKKBKSI5wg48XXGKXSK7fdzZuBRx5RL1UUBHYy5DFEZs8GrriCzdFqBQYM0D8v+zsiuffOs10yXNxHqiJa8HxDx2j716SLOsC0X/r2A4aNgEsjwZhYYBhHwrm3qChvULSVkpABPm9jeRl7j1K5s32CrhZSwrFg0u5oHhEBhIcD1dXa2+Vh9myWlMt7fvCGzL1SguzSpb6V9ichOcIJChuFEtnZwO+/KzdYlE70jzzCtz2pIWJWFnvoDU3ZI90ROSfElak0uLOHcmLcR6tcF2jQgBk+8lJZs0zyqj8QRWa8/ri3YVl9nopKDhXAQpFT/8YqqSTDRauHk9y2IpsDy1521MWRo6bGOMMFYDcOepJGjfZOqCXI3nNPQ1WkL7RRSEiOcIKMF1/j7fYAZjMwZw6LjaekOL4mJeM9+aT+E4Fazg4PSndEvHdKenNivNnxOhiRqohyHgIWPMmeRwxir/F2kva2VovSPs1moEu3hmW8SciSwWvvLdFK0FXDXhdHDjUFY3dQ+m0o6ZwY6Z3gSZDdsIHp3fhCG4WE5AgnyHjxJb4o9ZNQS8Zz90SgVKGgdkHTuiPiuaPiqW6wZ/585tHh7aDbWHAu1wX0eSH0olXqrAd7g0VPK4N5zzkaG570cJJKpydNczVMtBSM9aL0m1G7+THSO8EbgkpM9J20PwnJEXaQ8eIr/FHqp6ZZ4O6JQM4oevtt+btynjsiHkOKN8xlMrE7wTlz2HYv09kzJpSR8wp44oXgQRCA3w8Zsy17g2XvHpZAzENUlKO3RG8PJ2ecdXEAPgVjvcj9ZrRufrZtM847oScE5UttFH/3QSICBkrY9RXeSKbzFHeTh+UqFMxm96uotKqwBg0CVqwATmhceGw2xwtIsCbvaamx6kWusqasFCjcrm87kpGtJ3T09RdAXBxL+HUDm02EUF4Kwb4aymYDcp9vqDZSm4+UZDtpGvD5DpzYexjxZWUQEhMheBLWkTw4WiJ3dvvm/gznz3f9zfBWEilV9emtaDQ6BGVkgQJPhRQR8pDx4isCtdTPqBOBmiHEc/LSMqTuu4+dfLWwP36SG12pIiIQUTI0VixxbA6oZ3sLnnddbknSVz1k31zRuWpJjWE6W104IQiAYF/iLLHjE2DDm8C992tv5JK3ZGq3fOzdY0X+imfkq6+cS6zVkDw4kvdKY9/o1qMhVKdGSgrLSXNGz82PERWNWr8d3moib8v5E40WChv5Ck/vZLzRjEwPPPuXcx/rSVBWcz8PGsQ3T+n4SQbT0KHBZbgYGX4wmYAZl8TElMIINhufR6C8jFUsyVUtyWGzXfqO6DAInGlihnCqRNloe3UFMPdx7mTu9vHVuCuqULn6iuc42GyOYnd6FIznzmXeEUD+8xAEdlGXMzL03vx4GsoxIkHWlzl+RKODPC++wpM7GX/fvbi7f+nk5fx+pZOXniQ7PcdPbr6BLnRnMgGPzoah4YfuV6mHa6QLpigqaMAIwKZ1wM5PHUNXn+8A7hsL3HM/0Fymb5G0rqfVNhet2hftTz9hSsDLXtHc3OST84DqS96PogL2PuzDcz/uZVVNktaN80VbTuxOj4KxJDmQmckf1pGM8H37+PZjZKjUE1FNdwTzCEIH1FXal/B0fHU+ISgZAN7s3GrE/rW60Mp1rrVfV87lzXP8AOX5iiJwzTXAN99ovm0H4uP1KxLrpcdVrEJFi5yH+MIPADBvEZDFISxYuB24oqtj+KP0uKOwmz1KXZglTp8CPvoPMHwU3zyVEEUWMpM6XishidZpqQhrbceebe8D0YmO70/umOjZ9yOPAMuWseU8oVQ5I1wJtd+Tp7iTs1JYyNfpvqCA8leIevRcv8nz4kv03sn4++7Fk/27m6Cs5eWRO37x8ezCcMcdQEaG+nx/+onnnTcwZAjw5Zf61rEnOhqoqtIe504DRTVMJqDXdXxjfz/EtF94koS1RN5sNibW9sVnnhsvgsCXL8KrIqwn6XnSBGDRImDxUvVjYrMBH/4buO9B7X2/9RaweDH7rWjlmindNMjhbZ0Td/LiAjXHjwgZKOfF1+gp9fN3MzJP9u/OyYsnRj5oELBmDTMqoqLY6xUVLJ8gJUV7vufO8c1L4oYb1NstqJGQAJSXA9u3aysEu9tAUYluPfhViffsctWAUbrQ83RhbtkKwCWviSdVUhI8BpuWirDeZOcjR9hnx3NMRgwDnpunve/ycu3fqtUK5OcD48bx52oFos4JyfkTXoY8L/6A907G33cvnuxf78mLx8szfrx676ZyjkRSPaSm8inPyiEIrJN2eDj7vLWSSt1poKgGr4fm7Fng++/4xgJAAufxiE9s8IboqeKRg9dgu5THYuvWE9Y2bRH2xyFXb4meMvSDB/nyrLKygPfeYwrGWttW+03pCRMBwP33A23bNuTSBBJGVSsRhALkeQlk/H334sn+9ap98nh5Kirc94LoRRCYG95ZxI8HsxmYMYN5iQoLgedlSpWdkUIfgiAfmtAb+uC94G94U593JC6Of5zkDSnX6F+l5GFwruzhwGYTIez5FmG9u7t6S/Sq4P797/z9ewYN4vNeKf2mlLyOarz5JvD006yxqZEtRoyA5PwJL0PGSyDj72Zknuxf78krkGLfCQkNbvjMTCaBrgerFXjxRaBlS5a0+MEHfOsZGfqQPDlKhokossTat1bzbxMATp/mG9fy0kXavqfS008B/3kXqKyUn489buaqCCkpELbI9O9ytwydt3+P9FtRnJjKb0XN68hLIJYfk5w/4UWo2ijQcadCKZD2L+cKT011TVDmrU7wNmPGAP/3f453hFOn8gnkGYVRCrvSBVsUXcXYAGYo6c0F4a2KOnkSGPon5aRf+/cXEwtMnMZf7STH3Xez75l9JYz03TWZgPXbmOGilGSsVo1UUMC2y1MdJPdbAdjvRem3YtR335sVR55gtMIuEbLouX6T8RIM8BoAgbZ/6aR17BjLR7FY2F2Y3MlLKq32txruunUNoQIJXxlWUVFAdbXmMPHSg8ttKqfYq9cwsMNmMkPY8h8IcRxdu/WUdZtMON2tH6rHP4rU4dcBPbsDR4/wfxe2bwduvtl1eV4e8Moq4Mmn3Z+v3HdCibw8lptVUeG4PCGB5UDJ/V6MNo6l8mMyGoggg0qlQw0j5L59vX+1kme59aQw09ChDZos/kAtf8fbhtW0aayvjQLSnhdjBu7FBqSCQ4RPTozNg15JR22tcXZfLTpfzzFYRwfnf9rux7g9r2NtrBkjEgDkLmvwYmiRkKCcsJqdDfTNBPb/rr0dpfnqzSlzNlyAS54oGWFGqxVYu1bf9rUoKfG/sCVBeBnKeQkWfNm51dP9uysLrhYjT0jgn2uLFvqrW3jyd7xpuMTHA089BWzZovheTyABQ7AFj+JFpOMQslCALx+5VG5/7pyy4SOTSCp5b3gRAVQiGoJJQOcrwvlW4kwaFgEMQD4A4NdfLy2UvgtqeSQSq1apfx+bRnDNQ3a+gqDdEFRCyl2RQ6lzfFER3/ajo5lGDA+//kqy/ETIQ8YLYSxaJc+A6wncHjkdnEOH2AWKl5kz2TOvAcNT/ZCd3dCXxhu89hrbd3Y2UFoKbN+On7OfwL9wHxbiCdyE7WiFUmwFu2u2wYxrZ2bhuuUjGkIES5dy70649OD1vwgAYlCFlG4WgCdkdPIkd5WQACANR5GJIrz2mt1Xw/67kJPjmjidksKMPa3Q5Z5vAWude1VNoggMH853wXdHF4k3Uf3GG9kx0EqgT0lhvxV3f38EESSQ8RIM+Lspox6MENaT8/JkZwObNml7nFJTWaWJnAcnNZUZNs5387zVD7zNIfWQkOB6ATabgZtvRuctz6DFljexKuUZFOBm2MDeu8XCDsULL1wa/+ijzNskV8WjwSlwGCJ2CLyhoO0f6A5NtUYJjh51+mpI34Vly4Djx12NWrXPbNMmoFUrlq8071KnZnfL0Hku+O7oIvGGpL76ij1rVfCNG6cuJ+BtYUuC8BGU8xLoBFvs2pvCekOHMgNu+HDlMX/9K3tWy9NZtKhheVISG19WxgxDtVweI3NfoqPZxfXmm1UNMs10o0cfZWXZbjIcG2BDGG5CPp4CR1Irr37Mzh2651ICdiFX/Grokal3Pi5SGbpz8nJ5mXbyslIrC2fc0UWSSvG1QkeSOq9Wi5GaGr45BJI0AUG4gdc8L+np6RAEweHx3HPPqa5z4cIFTJw4EQkJCWjRogWGDBmC0tJSb00x8PFmS3lveXOMEtZTmt+wYcxToZQLMXcu2/bmzcp5OtLyiAhWGj1gADByJLtDVxP7UtOu0UtVVYP6rgaK6Ua1tbpCRXIIECEC+AWdUCnEaK+gpR/jjrAcBBxGKorA8o081lzcvFneoHPWnDl9HGhSC/z0Pd92tS74enWRpGqgXr307V+txYi/hS0JwleIXqJt27biggULxJKSkvrHmTNnVNeZMGGCmJqaKubn54u7du0Sr7vuOrFv37669ltZWSkCECsrKz2Zvv+5eFEUU1JEkd33uT4EQRRTU9k4vWzZ4rrtlBS23Kh5CwLfvC9eFMWCAlFct449X7woP7/kZFGcP79hXE0N+1vp+ACiOHOm+jGQm6MgsIfasZCbnzuPdeu0j2VBgWhdu078blmBuH7txfpDJIqiKC5b5vEcTiBe/3qZ/UWx4GtR/OQrUSz8puHxyVeiWPAVe51zW1YIohWCOBhbPPpKOxwzi4X/vWzZIorbt/ONLShQ/Izqv5cbNzZ8h9S+V+58h+T2LzcfPb8/gggg9Fy/vWq8LFu2jHv86dOnxbCwMHHTpk31y37++WcRgPjFF19wbydkjJeCAuNOaPZ4ctHWuw93TuAJCXzvOyWFb+zGja7z4zUMa2pcDSv7bdi/tmkT/9x5PjuZY3MYKeJgbGmwMydN0m94uBgPbq6b2V8UN77naLy8/W9Nw8Xm9PfvSK03XDS/fnKGrjO8vxvpER8vih99xIxjvRd8pZuAmTNdl6emOn7vlfalZ/9q3x2e3x9BBBgBY7y0bNlSjI+PF3v06CG+8MILYl1dneL4/Px8EYB46tQph+VpaWni0qVLufcbMsbLunV8Jzatu3d7vOnNcUbuxO7JCdzdh8Xi+n54L3DOd/Ba3qmLF0XxiSf4tp2QoHyct2yRvdBLXorsSxf7PWOWuX1cbDLb1/0wmUSxx1WieNOt7Nlk4t73qhY5Yj8UiCZcFE24KA6zFIhf5agYJbzeQt7fjdznIf0GnH8Tchd8rZuATZvkDS2t36DS9vQaHFq/P4IIQALCeFmyZIlYUFAgfv/99+LLL78sxsbGilOnTlUc/9Zbb4nh4eEuy6+55hrx0UcfVVzvwoULYmVlZf3jyJEj3G8+oPGG58Vb3hwllO6U9Z7APX04v5+cHM+2N3++suHBe4znz1c+ZgkJioaFFYL4O1JFMy6K7ZJrRJvZrHv+bntbAG4DReuCbEtJFQu2XxQ/fWSjWBOT6Lj9WwaK4r//K4qnKkXRZtPnLdTrebHfFuDqPZO74HtyE6B3fp4YHDyeKoIIILxmvDz22GMiANXHzz//LLvu66+/LjZp0kS8cOGC7OvuGi9z586VnUfQGy/eiF17w5vjDu5eYNx92L+fixdFMTHR820mJ8tfVLQ+N+kCqfS5aeXxXHpIXovj/e9RHztokMuFthw6w1v2j40bDcm1EQFRvMdp7nKhqJ17RPHuocrbkMuhctcwFgS27vbtxoSm5G4CeH+DkyaRwUE0OvQYL7qqjaZPn46ff/5Z9dG+fXvZdXv37o2LFy/i0KFDsq+3atUKtbW1OO3Utba0tBStWrVSnNOsWbNQWVlZ/zhy5IietxS4eKOlfKBUIvi6TNP+/fAqmmqhVPHFU5GkpAhrtTasq8Fd2IZDSEfLgrflB5jNTNPmnXccKlM+nV+AiQkK66hhLwjXtStTBPaUt99uUPlV6vpcUwtMeVS567MoOuqW2B9/vYgiq+wzm9WVpD2RA+D9bQ0Z4h8lbYIIEnTpvFgsFlicTy6c7NmzByaTCUmSroYTV199NcLCwpCfn48hQ4YAAPbv34/Dhw+jT58+ituNiIhARASn/HewoaXpoFfnRUunRFLolJPINxJflmk6vx+jDaecHCbEYn+Ryc4G3n4bePhhR0MpJYWJiNXUyGvKFBUxZVoOpiJXWd5/zBjg1VdZKTbgoI9yI4DrO22GeA97SbHgOyUFWLOGCcNJTTV//BF45BF1ETSdCADr+jx5OgDRteuzILDy60nTWI8mpRJt+881O5u1Spg7171JaX1HPLkJKC/XXk+pTQVBEA14w/Wzc+dOcdmyZeKePXvE4uJice3ataLFYhFHjx5dP+bo0aPi5ZdfLn711Vf1yyZMmCCmpaWJn3zyibhr1y6xT58+Yp8+fXTtO2QSdu0xMnYdCJUIPKEVox5Sfop0/IwKeaiFB+SSJaOjXfMpnBNOOUMKFyEo561ohRN5wyobN7pVzlshxItPYa54GCmiFfKfr0s+T4+rHENFSo8eV/F/Bp6Ej7RyvtwN6fLOya7ikiAaE35P2N29e7fYu3dvMSYmRmzatKnYuXNn8dlnn3XIdzl48KAIQCywO1GcP39efPjhh8W4uDgxMjJSHDx4sFhSUqJr3yFpvOhFy9gJhEoENSMK0F92rPTIyXF9r1pJrmazPsPKPqdGTxWVs8FoZC6Q0gWYdx/33KPvGCQkiN8t2S6acFEERHEwttRXR9mPs0JwNV5uupXPeLnpVn5DQe07pvZ58OaRuXMT4OuEeYIIMvxuvPiTRm+88JaUBkIlgpoRZT+/+fNdx+kRIuN9SBeemTP1Xbili407d/v2F0yNO3obIFaiBdd2f5q9Tv4jtffuqJU6u+EV2z67wGHRYGwRD8PxePyOVPH7oU5JyZ54XowSFHTH86j3JiBQEuYJIkDRc/0WRFEU/Ri1MpyqqirExMSgsrIS0dHR/p6Ob5HaCTh/pFLiKE/zQV8jSaTLNu5RGde3L5CRoZ6/YzKptz0wmx1fT01tyCWS6yklt4+UFCbNbjazPJb+ComlWhQUsLwU6TMEHN4X+5+AQ2Pnod3quZqby0IBilOyXFtgSXPM7O/a56esFFixRL3Pjwr7Zq/DlU+PcFhmghWZKEJrlKAErVGETORvB/pmt0RYVUVDzsuGd4FEC/u/MzYb60E0YpBjzovFArzyivZ3WvruHDvGck4OHWKy+vb5J/afvR54v78A//dD+i4QRCND1/Xb66aUj2m0nhdfCtAFClqhJ57HsmXK3qeLF5VLl+Xu1N0VSHO+29bySKl4ZyQdGBMuyjsTLl4UxT/dyeT9C+Tk/b/WJe9v/7i4vYAvFWTTFleRPHdaDqxdq++74nxMExNZWNFXnkeS7icIVbxWKk0EMEVF6l4CUXQsKQ0FpGqs5GTH5SkprAqIh5YtlctizWZgzhz5RpApKa6eLE+qqOzXtW+8t3YtsGwZ64QtlScrlGLbLtUO5SAXNpjrHTc5OXYOJkEApj4GyFX2mEzsezJpmrwHRA2LBeasTO3q/iVWmHMegQCnSqdLXZ/FE07VOOVlwJzH5b1Bzp+7EkoNTisq2LE8edI3JcnekD8giEYKGS+hgifaE8GMUofduDi+9XkMDrUuvvZodRWWw7nTsITZzC6qjz8OTJ0K3HdfQ9drQNZoO4oUDMVmbEXDvBxs1vJTwJd7gfAIV8NFwmQCWrYCelyt732MGgWYzar25ObNQPbPzyiXWhcVQLjU9blu8fNAzgQWKnI2XJSOmRxWKwv/yYUWZa07L6N5gAIsrEsQAYounRcigAkUATp/YKdjAoDdac+bp76OXk0b530ojVm+nN3lC4L8BdN5DoD83bZS/pIkjrd5M3DoEPLnFeGfTx+DBeUogwUnEQ8TrLDBcXvmU6eAfcWab7Oe++8HvtvFP75tW2D9eqB1a2QPysSgQWbXVJBtedraKzYbsOdbhGXdCLy/xfV1vR4KPR5JX+WZZGczbSDeXBmCIFyghN1QwWpld+VaAnRScmmoIh0HtQuWhKQYazRyyb4JCey5oqJhmVKSqNZ7sPssf3xmG2LmTkEqGsYeQQqmYHm9B8ZkEnE2fy+aCnX87yGsCXCyBPjbeO1j6Zz4nJICl0xhPZ8LwLxbJ0+6Hke9ibXr1wMjR2qPW7eOhQ8JgvAbeq7f5HkJFdTu+htTPF3rTlti/nzvueiV7qyl+WndbT/zDJ+34JlncOW8eRCdtHaTcQybMRRDsRnvCNkYcnO1PsMFAOouAlEWYNce4NV/qHtMnEMu9t4h6Rjzfi5AQ0jIbPbcQ6HXI6mneoggCL9BnpdQQ+6u390y0GAk2O+08/JYXxse4uMV2wnYIOAoUtAeB7Hz36dxbdRB9+YTEQb07gZs3er6vXL2uNjj7Onj/VwAYz1itbVsHkqy/Pbz3LZNvhWHS705QRDeQM/1mxJ2Qw3e5NJQJZhzf6TkUl5U+iCZICINR/DJ/CJce0OY+3OqqQMqq12/V8uWqSe5Ole38R5vIz1ieXlMC0jNcAGYYb9tm3xFklIDToIg/AqFjUIRnuTSUCVQmk+6g57QSkKCY/6MAjd2LAFiooDwMKBWZ+hIQm49jn0DaKhu0/pcAPb6k0+6N0dnlBKenfeXm8tCU+npyhVJgiDfgJMgCL9BnhcitAhmLQ09ZeyPPMI3rnVr9r47pLk3J4AZPnl57ALfvz8L/zz9NP/+Ae3PRRDY60Z8Lmrl0RIWC3DgAPPyNEaNJIIIcsh4IUKPYNXS0BNaefJJVU0ZEQLOJqSi0JrJojuWOOCKDGaI6CEiDPhku3xIRQ05LRZffS6FhdpzLS8Hdu5k/2+sGkkEEcRQ2IgITYJRS0NPaEWlukxS2r2/IhdbB5jtck7jgMRYlsNSW3cplHQR+Pk35Tm1SwGG3aGtWWOPmofL259LXh4wbhzfWMkYCeY8KYJopFC1EUEEEgqNGRWba8pUlx1GKnKQW6/zotmXs/wUcOCwY27LxTrAfBGw1gIDBuh7D/6qbuPJc7FHaoBIGkkEERDouX6T8UIQgYbecnerFdbCIkwZXoIfT7LOzc4Ku5rXX1EE/v0f1kvp1/8Be/cwtVuVcmwHZs8GrrjCfx4uPSJ4cgdDr9FIEIThkPFCxgsR7OgUSyssZLm0WkjOBhf0ei24N+wjeA8AwAwSOWOksWskEYSfIYVdggh2dJa7e5RzylOdo0SglJ7zHoCEBGDVKnljJBjzpAiikULGC0GEAB7lnOrRl7EnkErPeQ/A228DN9+s/Hpj1kgiiCCCSqUJIgSQCpUUKqdlK5fr4fVaxMc7/h1Ipee8B4AME4IICcjzQhAhgEd9OXm9Fhs3sg0EYkiFGpMSRKOCEnYJQi8Gdh42uomxWzmnoVQqTEm3BBG0ULURGS+Et5C7OLrZedjATTnglkEUSqXC7lqERluSBEHogowXMl4Ib6BUTuzGBd7ATRlHY/ZaeMuSJAiCGzJeyHghjEZLBE1HaMXATRlPY/Q+BKQlSRCNDz3Xb6o2IggeDOw8HNBNjKVS4REj2HOoGy5qGjfSspwcNo4giICBjBeC4MHAzsPUxDiACGhLkiAIJch4IQgeDOw8TE2MAwiyJAkiKCHjhSB48EgFzmubIjyFLEmCCErIeCEIgOU0FBYC69ezZ+ccB0kEDXC1OnSKoBm4KcJTyJIkiKCEjBeCyMtj5T/9+wMjR7Ln9HS23J7sbFZ5kpzsuNwNmXwDN0V4AlmSBBGUUKk00bhxp0w2gBV2CTdpzBo3BBEgkM4LGS8EDwEtuEL4HLIkCcKv6Ll+U2NGovGip0yWuhGHPpLGDUEQAQ/lvBCNFyqTJQiCCErIeCEaL1QmSxAEEZSQ8UI0XqhMliAIIigh44VovFCZLEEQRFBCxgvRuCHBFYIgiKCDqo0IIjsbGDSIymQJgiCCBDJeCAKgMlmCIIggwitho8LCQgiCIPv45ptvFNfLyspyGT9hwgRvTJEgCIIgiCDFK56Xvn37osRJG+Opp55Cfn4+evXqpbruuHHjsGDBgvq/IyMjvTFFgiAIgiCCFK8YL+Hh4WjVqlX933V1ddi2bRsmT54MQaks9RKRkZEO6xIEQRAEQdjjk2qjd999FxUVFRg7dqzm2LfeeguJiYno0qULZs2ahXPnzvlghgRBEARBBAs+Sdh9/fXXMXDgQKSkpKiOGzlyJNq2bYs2bdpg7969eOyxx7B//37k5eUprlNTU4Oampr6v6uqqgybN0EQBEEQgYcu4+Xxxx/H888/rzrm559/RqdOner/Pnr0KD788ENs3LhRc/vjx4+v/3/Xrl3RunVr3HzzzSguLkZGRobsOosWLcL8+fM53wFBEB5BnZcJgggABFEURd7B5eXlqKioUB3Tvn17hIeH1/+9cOFCrFixAseOHUNYWJiuyZ09exYtWrTABx98gIEDB8qOkfO8pKamcrXUJghCB3l5wJQpjp24U1KYSjGJ+REE4SFVVVWIiYnhun7r8rxYLBZYLBbu8aIoYvXq1Rg9erRuwwUA9uzZAwBordIYLyIiAhEREbq3TRCEDvLygKFDAed7nWPH2HJSIyYIwod4NWH3k08+wcGDB/HXv/7V5bVjx46hU6dO+PrrrwEAxcXFWLhwIXbv3o1Dhw7h3XffxejRo3HjjTeiW7du3pwmQRBqWK3M4yLnpJWW5eSwcQRBED7Aq8bL66+/jr59+zrkwEjU1dVh//799dVE4eHh2L59O2699VZ06tQJ06dPx5AhQ/Dvf//bm1MkCEKLoiLHUJEzoggcOcLGEQRB+ACvVhutW7dO8bX09HTYp9ukpqZix44d3pwOQRDu4CQ46fE4giAID6Gu0gRBqKOSc+bWOIIgCA8h44UgCHUyM1lVkZI6tiAAqalsHEEQhA8g44UgCHXMZlYODbgaMNLfubmk90IQhM8g44UgCG2ys1k5dHKy4/KUFCqTJgjC5/ikPQBBECFAdjYwaBAp7BIE4XfIeCEIgh+zGcjK8vcsCIJo5FDYiCAIgiCIoIKMF4IgCIIgggoyXgiCIAiCCCrIeCEIgiAIIqgg44UgCIIgiKCCjBeCIAiCIIIKMl4IgiAIgggqyHghCIIgCCKoIOOFIAiCIIigIuQUdkVRBABUVVX5eSYEQRAEQfAiXbel67gaIWe8VFdXAwBSU1P9PBOCIAiCIPRSXV2NmJgY1TGCyGPiBBE2mw1//PEHoqKiIAiCv6fjN6qqqpCamoojR44gOjra39MJeuh4GgsdT2Oh42ksdDyNhfd4iqKI6upqtGnTBiaTelZLyHleTCYTUlJS/D2NgCE6Opp+fAZCx9NY6HgaCx1PY6HjaSw8x1PL4yJBCbsEQRAEQQQVZLwQBEEQBBFUkPESokRERGDu3LmIiIjw91RCAjqexkLH01joeBoLHU9j8cbxDLmEXYIgCIIgQhvyvBAEQRAEEVSQ8UIQBEEQRFBBxgtBEARBEEEFGS8EQRAEQQQVZLw0ImpqatCjRw8IgoA9e/b4ezpByaFDh/Dggw+iXbt2aNasGTIyMjB37lzU1tb6e2pBwz/+8Q+kp6ejadOm6N27N77++mt/TyloWbRoEa655hpERUUhKSkJd999N/bv3+/vaYUEzz33HARBQE5Ojr+nEtQcO3YM9913HxISEtCsWTN07doVu3bt8ni7ZLw0Ih599FG0adPG39MIan755RfYbDa8+uqr+Omnn7Bs2TK88soreOKJJ/w9taDg7bffxrRp0zB37lx8++236N69OwYOHIiysjJ/Ty0o2bFjByZOnIgvv/wSH3/8Merq6nDrrbfi7Nmz/p5aUPPNN9/g1VdfRbdu3fw9laDm1KlTuP766xEWFob//ve/2LdvH5YsWYK4uDjPNy4SjYL//Oc/YqdOncSffvpJBCB+9913/p5SyPDCCy+I7dq18/c0goJrr71WnDhxYv3fVqtVbNOmjbho0SI/zip0KCsrEwGIO3bs8PdUgpbq6mqxY8eO4scffyz269dPnDJlir+nFLQ89thj4g033OCVbZPnpRFQWlqKcePG4c0330RkZKS/pxNyVFZWIj4+3t/TCHhqa2uxe/duDBgwoH6ZyWTCgAED8MUXX/hxZqFDZWUlAND30QMmTpyI22+/3eF7SrjHu+++i169emHYsGFISkpCz5498dprrxmybTJeQhxRFDFmzBhMmDABvXr18vd0Qo4DBw5gxYoVeOihh/w9lYDnxIkTsFqtaNmypcPyli1b4vjx436aVehgs9mQk5OD66+/Hl26dPH3dIKSDRs24Ntvv8WiRYv8PZWQ4LfffsPLL7+Mjh074sMPP8Tf/vY3PPLII3jjjTc83jYZL0HK448/DkEQVB+//PILVqxYgerqasyaNcvfUw5oeI+nPceOHcNtt92GYcOGYdy4cX6aOUEwJk6ciB9//BEbNmzw91SCkiNHjmDKlCl466230LRpU39PJySw2Wy46qqr8Oyzz6Jnz54YP348xo0bh1deecXjbTcxYH6EH5g+fTrGjBmjOqZ9+/b45JNP8MUXX7j0lOjVqxdGjRpliAUcCvAeT4k//vgD/fv3R9++fbFq1Sovzy40SExMhNlsRmlpqcPy0tJStGrVyk+zCg0mTZqE9957D59++ilSUlL8PZ2gZPfu3SgrK8NVV11Vv8xqteLTTz/FypUrUVNTA7PZ7McZBh+tW7fGFVdc4bCsc+fO2LJli8fbJuMlSLFYLLBYLJrj/v73v+Ppp5+u//uPP/7AwIED8fbbb6N3797enGJQwXs8AeZx6d+/P66++mqsXr0aJhM5MHkIDw/H1Vdfjfz8fNx9990A2J1Zfn4+Jk2a5N/JBSmiKGLy5MnYunUrCgsL0a5dO39PKWi5+eab8cMPPzgsGzt2LDp16oTHHnuMDBc3uP76611K9//3v/+hbdu2Hm+bjJcQJy0tzeHvFi1aAAAyMjLoDs0Njh07hqysLLRt2xaLFy9GeXl5/WvkPdBm2rRpeOCBB9CrVy9ce+21yM3NxdmzZzF27Fh/Ty0omThxItatW4dt27YhKiqqPncoJiYGzZo18/PsgouoqCiXXKHmzZsjISGBcojcZOrUqejbty+effZZDB8+HF9//TVWrVpliLeajBeC0MHHH3+MAwcO4MCBAy7Gn0gN2jW55557UF5ejjlz5uD48ePo0aMHPvjgA5ckXoKPl19+GQCQlZXlsHz16tWaYVCC8DbXXHMNtm7dilmzZmHBggVo164dcnNzMWrUKI+3LYh0xiUIgiAIIoigYD1BEARBEEEFGS8EQRAEQQQVZLwQBEEQBBFUkPFCEARBEERQQcYLQRAEQRBBBRkvBEEQBEEEFWS8EARBEAQRVJDxQhAEQRBEUEHGC0EQBEEQQQUZLwRBEARBBBVkvBAEQRAEEVSQ8UIQBEEQRFDx/2PCBry8rXt/AAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "prU6W4Hd19pj"
      },
      "source": [
        "### Inspect the trained generator\n",
        "\n",
        "We can compare the weight/bias of trained generator $G$ to the parameters we use to genrate the dataset. Importantly, we need to compare the covariance $\\Sigma= A^T A$ here instead of the transformation matrix."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jcnu6eCgtR0Y"
      },
      "source": [
        "gA, gmu = model_G.parameters()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZwnY5BWTtdP1",
        "outputId": "8c16a97b-81ad-426f-a30d-caf734d3798a"
      },
      "source": [
        "A.T @A"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1.04, 1.9 ],\n",
              "       [1.9 , 4.25]])"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EV8e1ov5tlkM",
        "outputId": "f18d159b-7ffd-47ab-d4c2-be82ee3be643"
      },
      "source": [
        "gA = gA.numpy()\n",
        "gA.T @ gA"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1.0487837, 1.9050138],\n",
              "       [1.9050138, 4.3002977]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QwYq79Pkt1FV",
        "outputId": "3538e67c-521d-4a10-ab6f-1ad8fff85083"
      },
      "source": [
        "A"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 1. ,  2. ],\n",
              "       [-0.2,  0.5]])"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Htjcsj4vt2L-",
        "outputId": "e71f0c21-1a0b-460f-cf9e-1f4f4d79f109"
      },
      "source": [
        "gA"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-0.27900007,  0.375084  ],\n",
              "       [-0.9853642 , -2.0395122 ]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7K_sWQEI2Wlk"
      },
      "source": [
        "We can also compare the mean"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "00oNrAke2bc4",
        "outputId": "60ba63d3-81ec-497d-ceda-36d708a2c318"
      },
      "source": [
        "gmu, mu"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(needle.Tensor([[2.082873   0.87884533]]), array([2, 1]))"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0HKfa2d_0EtO"
      },
      "source": [
        "## Modularizing GAN \"Loss\"\n",
        "\n",
        "We can modularize GAN step as in a similar way as loss function. The following codeblock shows one way to do so."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L0EolNkR_XeQ"
      },
      "source": [
        "class GANLoss:\n",
        "    def __init__(self, model_D, opt_D):\n",
        "        self.model_D = model_D\n",
        "        self.opt_D = opt_D\n",
        "        self.loss_D = nn.SoftmaxLoss()\n",
        "\n",
        "    def _update_D(self, real_X, fake_X):\n",
        "        real_Y = self.model_D(real_X)\n",
        "        fake_Y = self.model_D(fake_X.detach())\n",
        "        batch_size = real_X.shape[0]\n",
        "        ones = ndl.ones(batch_size, dtype=\"int32\")\n",
        "        zeros = ndl.zeros(batch_size, dtype=\"int32\")\n",
        "        loss = self.loss_D(real_Y, ones) + self.loss_D(fake_Y, zeros)\n",
        "        loss.backward()\n",
        "        self.opt_D.step()\n",
        "\n",
        "    def forward(self, fake_X, real_X):\n",
        "        self._update_D(real_X, fake_X)\n",
        "        fake_Y = self.model_D(fake_X)\n",
        "        batch_size = real_X.shape[0]\n",
        "        ones = ndl.ones(batch_size, dtype=\"int32\")\n",
        "        loss = self.loss_D(fake_Y, ones)\n",
        "        return loss\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yXQZw5NwAosQ"
      },
      "source": [
        "model_G = nn.Sequential(nn.Linear(2, 2))\n",
        "opt_G = ndl.optim.Adam(model_G.parameters(), lr = 0.01)\n",
        "\n",
        "model_D = nn.Sequential(\n",
        "    nn.Linear(2, 20),\n",
        "    nn.ReLU(),\n",
        "    nn.Linear(20, 10),\n",
        "    nn.ReLU(),\n",
        "    nn.Linear(10, 2)\n",
        ")\n",
        "opt_D = ndl.optim.Adam(model_D.parameters(), lr=0.01)\n",
        "gan_loss = GANLoss(model_D, opt_D)\n",
        "\n",
        "\n",
        "def train_gan(data, batch_size, num_epochs):\n",
        "    assert data.shape[0] % batch_size == 0\n",
        "    for epoch in range(num_epochs):\n",
        "        begin = (batch_size * epoch) % data.shape[0]\n",
        "        X = data[begin: begin+batch_size, :]\n",
        "        Z = np.random.normal(0, 1, (batch_size, 2))\n",
        "        X = ndl.Tensor(X, dtype=\"float32\")\n",
        "        Z = ndl.Tensor(Z, dtype=\"float32\")\n",
        "        fake_X = model_G(Z)\n",
        "        loss = gan_loss.forward(fake_X, X)\n",
        "        loss.backward()\n",
        "        opt_G.step()\n",
        "\n",
        "train_gan(data, 32, 2000)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "eYNLN08VxzzL",
        "outputId": "0271a86f-d09c-4068-fa0a-8bc40220b6b9"
      },
      "source": [
        "fake_data_trained = sample_G(model_G, 3200)\n",
        "\n",
        "plt.scatter(data[:,0], data[:,1], color=\"blue\", label=\"real data\")\n",
        "plt.scatter(fake_data_init[:,0], fake_data_init[:,1], color=\"red\", label=\"G(z) at init\")\n",
        "plt.scatter(fake_data_trained[:,0], fake_data_trained[:,1], color=\"pink\", label=\"G(z) trained\")\n",
        "\n",
        "plt.legend()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7bcf78fb8700>"
            ]
          },
          "metadata": {},
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}