dl2_bnn_tut1_students_with_answers.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/emanbuc/uvadlc_notebooks/blob/master/dl2_bnn_tut1_students_with_answers.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8afrg8L7ACoE"
      },
      "source": [
        "# Tutorial 1: Bayesian Neural Networks with Pyro\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Filled notebook:**\n",
        "- Latest version (V04/23): this notebook\n",
        "\n",
        "**Empty notebook**:\n",
        "- Latest version (V04/23): [![Open In Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1NQNMdKaE9RncuWgO_vM2k3qywV76Byfh#scrollTo=5wlbUXQ5mxts)  \n",
        "\n",
        "Visit also the DL2 tutorial [Github repo](https://github.com/phlippe/uvadlc_notebooks) and associated [Docs page](https://uvadlc-notebooks.readthedocs.io/en/latest/).\n",
        "\n",
        "**Authors:**\n",
        "Ilze Amanda Auzina, Leonard Bereska, Alexander Timans and Eric Nalisnick"
      ],
      "metadata": {
        "id": "X2qU64isq2u5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "-------------------------------------------------"
      ],
      "metadata": {
        "id": "09IERPsBq9uk"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Bayesian Neural Networks\n",
        "\n",
        "A Bayesian neural network is a probabilistic model that allows us to estimate uncertainty in predictions by representing the weights and biases of the network as probability distributions rather than fixed values. This allows us to *incorporate prior knowledge* about the weights and biases into the model, and *update our beliefs* about them as we observe data.\n",
        "\n",
        "Mathematically, a Bayesian neural network can be represented as follows:\n",
        "\n",
        "Given a set of input data $x$, we want to predict the corresponding output $y$. The neural network represents this relationship as a function $f(x, \\theta)$, where $\\theta$ are the weights and biases of the network. In a Bayesian neural network, we represent the weights and biases as probability distributions, so $f(x, \\theta)$ becomes a probability distribution over possible outputs:\n",
        "\n",
        "$$ p(y|x, \\mathcal{D}) = \\int p(y|x, \\theta)p(\\theta|\\mathcal{D}) d\\theta $$\n",
        "\n",
        "where $p(y|x, \\theta)$ is the likelihood function, which gives the probability of observing $y$ given $x$ and $\\theta$, and $p(\\theta|\\mathcal{D})$ is the posterior distribution over the weights and biases given the observed data $\\mathcal{D}$.\n",
        "\n",
        "To make predictions, we use the posterior predictive distribution:\n",
        "\n",
        "$$ p(y^*|x^*, \\mathcal{D}) = \\int p(y^*|x^*, \\theta)p(\\theta|\\mathcal{D}) d\\theta $$\n",
        "\n",
        "where $x^*$ is a new input and $y^*$ is the corresponding predicted output.\n",
        "\n",
        "To estimate the (intractable) posterior distribution $p(\\theta|\\mathcal{D})$, we can use either Markov Chain Monte Carlo (MCMC) or Variational Inference (VI)."
      ],
      "metadata": {
        "id": "5wlbUXQ5mxts"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Simulate data\n",
        "Let's generate noisy observations from a sinusoidal function."
      ],
      "metadata": {
        "id": "rtNNqRdIOmqK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Set random seed for reproducibility\n",
        "np.random.seed(45)\n",
        "\n",
        "# Generate data\n",
        "x_obs = np.hstack([np.linspace(-0.2, 0.2, 500), np.linspace(0.6, 1, 500)])\n",
        "noise = 0.02 * np.random.randn(x_obs.shape[0])\n",
        "y_obs = x_obs + 0.3 * np.sin(2 * np.pi * (x_obs + noise)) + 0.3 * np.sin(4 * np.pi * (x_obs + noise)) + noise\n",
        "\n",
        "x_true = np.linspace(-0.5, 1.5, 1000)\n",
        "y_true = x_true + 0.3 * np.sin(2 * np.pi * x_true) + 0.3 * np.sin(4 * np.pi * x_true)\n",
        "\n",
        "# Set plot limits and labels\n",
        "xlims = [-0.5, 1.5]\n",
        "ylims = [-1.5, 2.5]\n",
        "\n",
        "# Create plot\n",
        "fig, ax = plt.subplots(figsize=(10, 5))\n",
        "ax.plot(x_true, y_true, 'b-', linewidth=3, label=\"True function\")\n",
        "ax.plot(x_obs, y_obs, 'ko', markersize=4, label=\"Observations\")\n",
        "ax.set_xlim(xlims)\n",
        "ax.set_ylim(ylims)\n",
        "ax.set_xlabel(\"X\", fontsize=30)\n",
        "ax.set_ylabel(\"Y\", fontsize=30)\n",
        "ax.legend(loc=4, fontsize=15, frameon=False)\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 498
        },
        "id": "NIkB27gArnmm",
        "outputId": "bf3c0320-a72e-45ef-c0b7-5f7982f4cc43"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_obs"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1WSk2XtHxCri",
        "outputId": "4ee9c5c9-88b5-47d8-bd53-3e69f4e3feae"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-0.66242115, -0.66974679, -0.65165146, -0.65913055, -0.62026169,\n",
              "       -0.55570868, -0.67492512, -0.64163922, -0.67841848, -0.68531657,\n",
              "       -0.67176093, -0.67817064, -0.62545235, -0.68548043, -0.61675782,\n",
              "       -0.66467905, -0.64984771, -0.67827779, -0.67631692, -0.63408696,\n",
              "       -0.66706469, -0.64316954, -0.66859431, -0.6850391 , -0.64214281,\n",
              "       -0.67444713, -0.67517823, -0.68609915, -0.65323627, -0.64539645,\n",
              "       -0.67864661, -0.68185513, -0.67097707, -0.67922567, -0.68439503,\n",
              "       -0.67424449, -0.68115066, -0.68578543, -0.68353112, -0.68408465,\n",
              "       -0.68581304, -0.68242059, -0.68027745, -0.6810558 , -0.68378658,\n",
              "       -0.68629233, -0.67281688, -0.68631415, -0.66421936, -0.67677075,\n",
              "       -0.68610087, -0.63440854, -0.68617233, -0.68475431, -0.67918212,\n",
              "       -0.68270382, -0.66278363, -0.67806662, -0.68011684, -0.67856723,\n",
              "       -0.68115358, -0.68616532, -0.67407328, -0.63814931, -0.67495896,\n",
              "       -0.67215114, -0.66526671, -0.67878657, -0.68427674, -0.66338807,\n",
              "       -0.60869958, -0.64818663, -0.67487568, -0.61886187, -0.63053692,\n",
              "       -0.61260647, -0.68430517, -0.65559193, -0.56583624, -0.56826623,\n",
              "       -0.66123906, -0.67479227, -0.67867702, -0.65679407, -0.65653913,\n",
              "       -0.63294639, -0.65766013, -0.67069661, -0.65657309, -0.65372086,\n",
              "       -0.66270628, -0.68259393, -0.6487788 , -0.66076983, -0.65136168,\n",
              "       -0.67194515, -0.6000641 , -0.61047246, -0.68623242, -0.56662849,\n",
              "       -0.61919368, -0.59736933, -0.58382286, -0.57189695, -0.66652566,\n",
              "       -0.50299485, -0.64598104, -0.62672299, -0.55327059, -0.67423541,\n",
              "       -0.49387259, -0.6338238 , -0.62618517, -0.37847583, -0.66622884,\n",
              "       -0.67128469, -0.55963024, -0.64098182, -0.63612458, -0.59153718,\n",
              "       -0.55313964, -0.58875922, -0.52641311, -0.64597547, -0.44504489,\n",
              "       -0.659497  , -0.54789587, -0.3920944 , -0.58941111, -0.4637522 ,\n",
              "       -0.61672439, -0.63073914, -0.65020543, -0.57510807, -0.37600464,\n",
              "       -0.50208162, -0.46365918, -0.19860668, -0.49976046, -0.53922509,\n",
              "       -0.49222913, -0.41510516, -0.50373353, -0.47241301, -0.52277352,\n",
              "       -0.50290177, -0.33953675, -0.52884683, -0.45471347, -0.49509048,\n",
              "       -0.45752371, -0.54962326, -0.45016253, -0.46776725, -0.3648145 ,\n",
              "       -0.48527901, -0.34325722, -0.29374179, -0.40520832, -0.42759079,\n",
              "       -0.41039338, -0.34143553, -0.43988138, -0.28969767, -0.29920763,\n",
              "       -0.40337993, -0.45371955, -0.258919  , -0.49595097, -0.45958788,\n",
              "       -0.50723497, -0.44605333, -0.25698974, -0.32272225, -0.3564159 ,\n",
              "       -0.31972727, -0.21477377, -0.41106067, -0.30600505, -0.29691358,\n",
              "       -0.3723122 , -0.43319754, -0.49044059, -0.48127547, -0.3897411 ,\n",
              "       -0.24560945, -0.27122265, -0.10945982, -0.35165715, -0.2024416 ,\n",
              "       -0.41055179, -0.18996701, -0.28545433, -0.14250686, -0.27936219,\n",
              "       -0.53142404, -0.16690777, -0.17270673, -0.45288303, -0.37650555,\n",
              "       -0.17628062, -0.1833717 , -0.02467132, -0.07559024, -0.23702144,\n",
              "       -0.41662746, -0.25257493, -0.23461225, -0.21110984, -0.01286906,\n",
              "       -0.32034346, -0.08209993,  0.08061031, -0.27213183, -0.26065831,\n",
              "       -0.06903   , -0.02205204, -0.20309506, -0.14900737, -0.31978772,\n",
              "       -0.05626441, -0.22664398, -0.13766321, -0.17513794, -0.0222083 ,\n",
              "       -0.19201064, -0.24278196, -0.12545001,  0.03097346, -0.1321975 ,\n",
              "        0.13718833, -0.01053528, -0.13087204, -0.09592184, -0.01740464,\n",
              "        0.08561662,  0.21345558,  0.08970594,  0.05839135,  0.04967042,\n",
              "        0.10700235, -0.21378043,  0.09112823, -0.0405704 ,  0.12724113,\n",
              "        0.17910486,  0.03554225, -0.17384113, -0.11705532, -0.16645197,\n",
              "        0.17036383,  0.01164846, -0.2271116 , -0.0522735 , -0.18623123,\n",
              "        0.05687591,  0.22966186,  0.41886073,  0.13697729, -0.04593128,\n",
              "       -0.08232785, -0.09890229,  0.1994425 ,  0.09503   ,  0.03570126,\n",
              "        0.0293558 , -0.03518221, -0.07473123,  0.07035466,  0.14565963,\n",
              "       -0.01873078,  0.03790478,  0.23812734,  0.0483912 ,  0.19002461,\n",
              "       -0.17261658, -0.09993167,  0.08130471,  0.28640099,  0.11997718,\n",
              "        0.30277155,  0.2518469 ,  0.30806533,  0.44909169,  0.28625228,\n",
              "        0.14344914,  0.21520401,  0.24798093,  0.22974809,  0.32163726,\n",
              "        0.24461908,  0.31709575,  0.19769555,  0.31263143,  0.16168207,\n",
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              "        0.33035522,  0.32428791,  0.31455564,  0.38785327,  0.31562091,\n",
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              "        0.31510966,  0.31889238,  0.33926854,  0.31598903,  0.31845977,\n",
              "        0.32080136,  0.33917011,  0.33182683,  0.31370724,  0.32094138,\n",
              "        0.31389048,  0.39339858,  0.32628078,  0.32527362,  0.31497802,\n",
              "        0.34036235,  0.31674587,  0.32322498,  0.3495086 ,  0.32580377,\n",
              "        0.33610589,  0.34193331,  0.34448097,  0.31676411,  0.31369182,\n",
              "        0.34762627,  0.35849662,  0.42567567,  0.31403844,  0.33106445,\n",
              "        0.34225237,  0.32463001,  0.40485527,  0.36265691,  0.31385574,\n",
              "        0.35708162,  0.33818836,  0.31707432,  0.31863302,  0.33849077,\n",
              "        0.34480986,  0.31752033,  0.38170439,  0.38847732,  0.4515725 ,\n",
              "        0.58794645,  0.3398248 ,  0.58321392,  0.38020654,  0.45796924,\n",
              "        0.35298334,  0.3739849 ,  0.39498728,  0.35812797,  0.43049268,\n",
              "        0.34352881,  0.33381996,  0.34989663,  0.33844371,  0.37305635,\n",
              "        0.43162635,  0.31455751,  0.4278716 ,  0.56942669,  0.39960502,\n",
              "        0.36880964,  0.38410339,  0.36525115,  0.4783578 ,  0.52411509,\n",
              "        0.54725639,  0.45466745,  0.46588167,  0.46187076,  0.39535756,\n",
              "        0.47929273,  0.39341812,  0.60121797,  0.67877704,  0.51283031,\n",
              "        0.41311978,  0.4639026 ,  0.4360229 ,  0.40682002,  0.38038595,\n",
              "        0.44741575,  0.53835679,  0.45909495,  0.41890448,  0.46014689,\n",
              "        0.63892386,  0.60123144,  0.42727349,  0.44694059,  0.61382753,\n",
              "        0.50668124,  0.50468293,  0.43488096,  0.53432579,  0.39886923,\n",
              "        0.64719801,  0.449388  ,  0.5509919 ,  0.4893232 ,  0.49484923,\n",
              "        0.50795395,  0.67275695,  0.49219755,  0.5353099 ,  0.46597413,\n",
              "        0.64799276,  0.47443627,  0.51578681,  0.61979641,  0.79577681,\n",
              "        0.51964467,  0.41870351,  0.45103285,  0.53351495,  0.49618522,\n",
              "        0.61143123,  0.58599673,  0.44811074,  0.5782541 ,  0.70230526,\n",
              "        0.64072437,  0.7430259 ,  0.68322391,  0.70729474,  0.6135986 ,\n",
              "        0.62222463,  0.44427781,  0.79352473,  0.63013187,  0.66695268,\n",
              "        0.72443642,  0.89627693,  0.68752248,  0.6175827 ,  0.81176957,\n",
              "        0.83406695,  0.77950786,  0.87866016,  0.6659272 ,  0.65696975,\n",
              "        0.68551507,  0.86809425,  0.58523323,  0.85961338,  1.02989069,\n",
              "        0.95343255,  0.71735916,  0.68225023,  0.78090601,  0.94319571,\n",
              "        0.87732328,  0.79231068,  0.97644832,  0.73210191,  0.7717727 ,\n",
              "        0.76607355,  0.71389929,  0.8261344 ,  0.74896062,  0.75606196,\n",
              "        0.93686945,  0.91206542,  0.80929105,  0.81969165,  0.71657802,\n",
              "        0.75194491,  0.78950904,  0.56261909,  0.92189827,  1.03890482,\n",
              "        0.97463713,  0.95935345,  0.96228901,  0.9969861 ,  0.83374407,\n",
              "        1.00668357,  0.69708722,  0.93026631,  0.8194796 ,  0.86844626,\n",
              "        0.97316022,  0.92343906,  0.6782437 ,  1.19562213,  0.98836988,\n",
              "        0.90619228,  1.0024816 ,  0.96910958,  0.94211984,  1.04823014,\n",
              "        0.89097945,  0.79098267,  0.94451576,  0.88117903,  0.82037399])"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_true"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HRYyYgNTxGyU",
        "outputId": "a243cc23-95bb-4bb1-d7e4-58b8d12334ee"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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              "       -0.56017091, -0.55263831, -0.54489966, -0.53695713, -0.528813  ,\n",
              "       -0.52046966, -0.5119296 , -0.50319546, -0.49426996, -0.48515593,\n",
              "       -0.47585632, -0.46637418, -0.45671267, -0.44687505, -0.43686468,\n",
              "       -0.42668502, -0.41633963, -0.40583216, -0.39516636, -0.38434606,\n",
              "       -0.37337519, -0.36225777, -0.35099788, -0.33959972, -0.32806754,\n",
              "       -0.31640567, -0.30461854, -0.29271062, -0.28068647, -0.26855071,\n",
              "       -0.25630803, -0.24396317, -0.23152094, -0.21898621, -0.20636388,\n",
              "       -0.19365894, -0.18087638, -0.16802129, -0.15509874, -0.1421139 ,\n",
              "       -0.12907193, -0.11597805, -0.1028375 , -0.08965554, -0.07643746,\n",
              "       -0.06318859, -0.04991425, -0.03661978, -0.02331055, -0.00999191,\n",
              "        0.00333075,  0.01665207,  0.02996668,  0.0432692 ,  0.05655427,\n",
              "        0.06981655,  0.08305068,  0.09625136,  0.10941328,  0.12253115,\n",
              "        0.13559973,  0.14861379,  0.16156812,  0.17445758,  0.18727705,\n",
              "        0.20002142,  0.21268568,  0.22526483,  0.23775392,  0.25014807,\n",
              "        0.26244244,  0.27463225,  0.28671279,  0.2986794 ,  0.31052749,\n",
              "        0.32225254,  0.33385011,  0.34531582,  0.35664537,  0.36783454,\n",
              "        0.3788792 ,  0.38977528,  0.40051882,  0.41110593,  0.42153282,\n",
              "        0.4317958 ,  0.44189125,  0.45181567,  0.46156565,  0.47113787,\n",
              "        0.48052913,  0.48973633,  0.49875646,  0.50758663,  0.51622406,\n",
              "        0.52466608,  0.53291012,  0.54095374,  0.5487946 ,  0.5564305 ,\n",
              "        0.56385931,  0.57107907,  0.57808791,  0.58488409,  0.59146598,\n",
              "        0.59783208,  0.60398101,  0.60991151,  0.61562247,  0.62111285,\n",
              "        0.62638179,  0.63142852,  0.63625241,  0.64085295,  0.64522975,\n",
              "        0.64938257,  0.65331126,  0.65701583,  0.66049638,  0.66375317,\n",
              "        0.66678655,  0.66959703,  0.67218522,  0.67455186,  0.67669781,\n",
              "        0.67862405,  0.68033168,  0.68182194,  0.68309615,  0.6841558 ,\n",
              "        0.68500244,  0.68563778,  0.68606363,  0.68628191,  0.68629465,\n",
              "        0.68610399,  0.6857122 ,  0.68512163,  0.68433475,  0.68335413,\n",
              "        0.68218245,  0.68082247,  0.67927708,  0.67754924,  0.67564202,\n",
              "        0.67355858,  0.67130216,  0.66887609,  0.66628382,  0.66352883,\n",
              "        0.66061472,  0.65754516,  0.65432389,  0.65095473,  0.64744158,\n",
              "        0.64378839,  0.63999919,  0.63607807,  0.63202919,  0.62785677,\n",
              "        0.62356505,  0.61915838,  0.61464113,  0.6100177 ,  0.60529257,\n",
              "        0.60047025,  0.59555528,  0.59055225,  0.58546576,  0.58030048,\n",
              "        0.57506106,  0.56975222,  0.56437866,  0.55894513,  0.55345639,\n",
              "        0.54791719,  0.54233232,  0.53670655,  0.53104467,  0.52535147,\n",
              "        0.51963172,  0.51389022,  0.50813171,  0.50236097,  0.49658274,\n",
              "        0.49080173,  0.48502265,  0.47925019,  0.47348899,  0.46774368,\n",
              "        0.46201886,  0.45631907,  0.45064884,  0.44501265,  0.43941494,\n",
              "        0.43386008,  0.42835242,  0.42289625,  0.4174958 ,  0.41215524,\n",
              "        0.40687871,  0.40167024,  0.39653383,  0.39147341,  0.38649282,\n",
              "        0.38159586,  0.37678622,  0.37206755,  0.3674434 ,  0.36291724,\n",
              "        0.35849246,  0.35417237,  0.34996019,  0.34585904,  0.34187198,\n",
              "        0.33800196,  0.33425181,  0.33062431,  0.32712212,  0.3237478 ,\n",
              "        0.32050382,  0.31739253,  0.31441619,  0.31157697,  0.30887691,\n",
              "        0.30631795,  0.30390192,  0.30163056,  0.29950548,  0.29752819,\n",
              "        0.29570008,  0.29402243,  0.29249643,  0.29112311,  0.28990344,\n",
              "        0.28883824,  0.28792822,  0.28717399,  0.28657603,  0.28613472,\n",
              "        0.28585032,  0.28572296,  0.28575267,  0.28593936,  0.28628285,\n",
              "        0.2867828 ,  0.28743879,  0.28825028,  0.28921662,  0.29033703,\n",
              "        0.29161065,  0.29303648,  0.29461342,  0.29634028,  0.29821574,\n",
              "        0.30023838,  0.30240667,  0.30471898,  0.30717359,  0.30976866,\n",
              "        0.31250226,  0.31537235,  0.3183768 ,  0.32151339,  0.32477981,\n",
              "        0.32817363,  0.33169237,  0.33533342,  0.33909412,  0.3429717 ,\n",
              "        0.34696331,  0.35106604,  0.35527688,  0.35959275,  0.3640105 ,\n",
              "        0.36852691,  0.37313867,  0.37784243,  0.38263475,  0.38751216,\n",
              "        0.3924711 ,  0.39750797,  0.4026191 ,  0.40780078,  0.41304925,\n",
              "        0.41836071,  0.42373128,  0.42915709,  0.43463419,  0.44015861,\n",
              "        0.44572636,  0.4513334 ,  0.45697566,  0.46264906,  0.4683495 ,\n",
              "        0.47407284,  0.47981495,  0.48557167,  0.49133882,  0.49711224,\n",
              "        0.50288776,  0.50866118,  0.51442833,  0.52018505,  0.52592716,\n",
              "        0.5316505 ,  0.53735094,  0.54302434,  0.5486666 ,  0.55427364,\n",
              "        0.55984139,  0.56536581,  0.57084291,  0.57626872,  0.58163929,\n",
              "        0.58695075,  0.59219922,  0.5973809 ,  0.60249203,  0.6075289 ,\n",
              "        0.61248784,  0.61736525,  0.62215757,  0.62686133,  0.63147309,\n",
              "        0.6359895 ,  0.64040725,  0.64472312,  0.64893396,  0.65303669,\n",
              "        0.6570283 ,  0.66090588,  0.66466658,  0.66830763,  0.67182637,\n",
              "        0.67522019,  0.67848661,  0.6816232 ,  0.68462765,  0.68749774,\n",
              "        0.69023134,  0.69282641,  0.69528102,  0.69759333,  0.69976162,\n",
              "        0.70178426,  0.70365972,  0.70538658,  0.70696352,  0.70838935,\n",
              "        0.70966297,  0.71078338,  0.71174972,  0.71256121,  0.7132172 ,\n",
              "        0.71371715,  0.71406064,  0.71424733,  0.71427704,  0.71414968,\n",
              "        0.71386528,  0.71342397,  0.71282601,  0.71207178,  0.71116176,\n",
              "        0.71009656,  0.70887689,  0.70750357,  0.70597757,  0.70429992,\n",
              "        0.70247181,  0.70049452,  0.69836944,  0.69609808,  0.69368205,\n",
              "        0.69112309,  0.68842303,  0.68558381,  0.68260747,  0.67949618,\n",
              "        0.6762522 ,  0.67287788,  0.66937569,  0.66574819,  0.66199804,\n",
              "        0.65812802,  0.65414096,  0.65003981,  0.64582763,  0.64150754,\n",
              "        0.63708276,  0.6325566 ,  0.62793245,  0.62321378,  0.61840414,\n",
              "        0.61350718,  0.60852659,  0.60346617,  0.59832976,  0.59312129,\n",
              "        0.58784476,  0.5825042 ,  0.57710375,  0.57164758,  0.56613992,\n",
              "        0.56058506,  0.55498735,  0.54935116,  0.54368093,  0.53798114,\n",
              "        0.53225632,  0.52651101,  0.52074981,  0.51497735,  0.50919827,\n",
              "        0.50341726,  0.49763903,  0.49186829,  0.48610978,  0.48036828,\n",
              "        0.47464853,  0.46895533,  0.46329345,  0.45766768,  0.45208281,\n",
              "        0.44654361,  0.44105487,  0.43562134,  0.43024778,  0.42493894,\n",
              "        0.41969952,  0.41453424,  0.40944775,  0.40444472,  0.39952975,\n",
              "        0.39470743,  0.3899823 ,  0.38535887,  0.38084162,  0.37643495,\n",
              "        0.37214323,  0.36797081,  0.36392193,  0.36000081,  0.35621161,\n",
              "        0.35255842,  0.34904527,  0.34567611,  0.34245484,  0.33938528,\n",
              "        0.33647117,  0.33371618,  0.33112391,  0.32869784,  0.32644142,\n",
              "        0.32435798,  0.32245076,  0.32072292,  0.31917753,  0.31781755,\n",
              "        0.31664587,  0.31566525,  0.31487837,  0.3142878 ,  0.31389601,\n",
              "        0.31370535,  0.31371809,  0.31393637,  0.31436222,  0.31499756,\n",
              "        0.3158442 ,  0.31690385,  0.31817806,  0.31966832,  0.32137595,\n",
              "        0.32330219,  0.32544814,  0.32781478,  0.33040297,  0.33321345,\n",
              "        0.33624683,  0.33950362,  0.34298417,  0.34668874,  0.35061743,\n",
              "        0.35477025,  0.35914705,  0.36374759,  0.36857148,  0.37361821,\n",
              "        0.37888715,  0.38437753,  0.39008849,  0.39601899,  0.40216792,\n",
              "        0.40853402,  0.41511591,  0.42191209,  0.42892093,  0.43614069,\n",
              "        0.4435695 ,  0.4512054 ,  0.45904626,  0.46708988,  0.47533392,\n",
              "        0.48377594,  0.49241337,  0.50124354,  0.51026367,  0.51947087,\n",
              "        0.52886213,  0.53843435,  0.54818433,  0.55810875,  0.5682042 ,\n",
              "        0.57846718,  0.58889407,  0.59948118,  0.61022472,  0.6211208 ,\n",
              "        0.63216546,  0.64335463,  0.65468418,  0.66614989,  0.67774746,\n",
              "        0.68947251,  0.7013206 ,  0.71328721,  0.72536775,  0.73755756,\n",
              "        0.74985193,  0.76224608,  0.77473517,  0.78731432,  0.79997858,\n",
              "        0.81272295,  0.82554242,  0.83843188,  0.85138621,  0.86440027,\n",
              "        0.87746885,  0.89058672,  0.90374864,  0.91694932,  0.93018345,\n",
              "        0.94344573,  0.9567308 ,  0.97003332,  0.98334793,  0.99666925,\n",
              "        1.00999191,  1.02331055,  1.03661978,  1.04991425,  1.06318859,\n",
              "        1.07643746,  1.08965554,  1.1028375 ,  1.11597805,  1.12907193,\n",
              "        1.1421139 ,  1.15509874,  1.16802129,  1.18087638,  1.19365894,\n",
              "        1.20636388,  1.21898621,  1.23152094,  1.24396317,  1.25630803,\n",
              "        1.26855071,  1.28068647,  1.29271062,  1.30461854,  1.31640567,\n",
              "        1.32806754,  1.33959972,  1.35099788,  1.36225777,  1.37337519,\n",
              "        1.38434606,  1.39516636,  1.40583216,  1.41633963,  1.42668502,\n",
              "        1.43686468,  1.44687505,  1.45671267,  1.46637418,  1.47585632,\n",
              "        1.48515593,  1.49426996,  1.50319546,  1.5119296 ,  1.52046966,\n",
              "        1.528813  ,  1.53695713,  1.54489966,  1.55263831,  1.56017091,\n",
              "        1.56749544,  1.57460997,  1.58151269,  1.58820192,  1.59467609,\n",
              "        1.60093377,  1.60697364,  1.6127945 ,  1.61839529,  1.62377505,\n",
              "        1.62893297,  1.63386835,  1.63858062,  1.64306933,  1.64733417,\n",
              "        1.65137493,  1.65519155,  1.65878409,  1.66215272,  1.66529776,\n",
              "        1.66821962,  1.67091887,  1.67339619,  1.67565236,  1.67768832,\n",
              "        1.67950512,  1.6811039 ,  1.68248596,  1.6836527 ,  1.68460564,\n",
              "        1.68534642,  1.68587678,  1.68619859,  1.68631384,  1.68622461,\n",
              "        1.6859331 ,  1.68544161,  1.68475257,  1.68386849,  1.682792  ,\n",
              "        1.68152582,  1.68007277,  1.67843578,  1.67661786,  1.67462213,\n",
              "        1.67245178,  1.67011012,  1.66760052,  1.66492644,  1.66209144,\n",
              "        1.65909914,  1.65595325,  1.65265756,  1.64921591,  1.64563224,\n",
              "        1.64191053,  1.63805486,  1.63406934,  1.62995816,  1.62572555,\n",
              "        1.62137582,  1.6169133 ,  1.61234241,  1.60766757,  1.60289328,\n",
              "        1.59802406,  1.59306449,  1.58801915,  1.58289268,  1.57768974,\n",
              "        1.57241502,  1.56707323,  1.5616691 ,  1.55620737,  1.5506928 ,\n",
              "        1.54513017,  1.53952425,  1.53387982,  1.52820168,  1.52249461,\n",
              "        1.51676339,  1.51101279,  1.50524758,  1.4994725 ,  1.49369228,\n",
              "        1.48791166,  1.4821353 ,  1.47636789,  1.47061406,  1.46487842,\n",
              "        1.45916555,  1.45347998,  1.44782622,  1.44220871,  1.43663188,\n",
              "        1.43110008,  1.42561763,  1.42018879,  1.41481778,  1.40950872,\n",
              "        1.40426571,  1.39909278,  1.39399388,  1.3889729 ,  1.38403365,\n",
              "        1.3791799 ,  1.3744153 ,  1.36974344,  1.36516786,  1.36069197,\n",
              "        1.35631912,  1.35205259,  1.34789554,  1.34385107,  1.33992216,\n",
              "        1.33611172,  1.33242256,  1.32885739,  1.32541882,  1.32210937,\n",
              "        1.31893144,  1.31588735,  1.31297931,  1.31020942,  1.30757967,\n",
              "        1.30509196,  1.30274805,  1.30054964,  1.29849827,  1.2965954 ,\n",
              "        1.29484237,  1.29324041,  1.29179062,  1.29049402,  1.28935148,\n",
              "        1.28836379,  1.2875316 ,  1.28685545,  1.28633578,  1.2859729 ,\n",
              "        1.285767  ,  1.28571818,  1.2858264 ,  1.28609152,  1.28651329,\n",
              "        1.28709132,  1.28782514,  1.28871414,  1.28975762,  1.29095475,\n",
              "        1.2923046 ,  1.29380613,  1.29545819,  1.29725952,  1.29920875,\n",
              "        1.30130441,  1.30354493,  1.30592861,  1.30845369,  1.31111827,\n",
              "        1.31392037,  1.31685791,  1.31992872,  1.32313052,  1.32646095,\n",
              "        1.32991755,  1.33349777,  1.33719899,  1.34101847,  1.34495343,\n",
              "        1.34900097,  1.35315814,  1.35742189,  1.36178909,  1.36625658,\n",
              "        1.37082108,  1.37547926,  1.38022774,  1.38506304,  1.38998167,\n",
              "        1.39498002,  1.40005448,  1.40520136,  1.41041691,  1.41569735,\n",
              "        1.42103884,  1.42643752,  1.43188947,  1.43739073,  1.44293732,\n",
              "        1.44852522,  1.45415038,  1.45980873,  1.46549616,  1.47120857,\n",
              "        1.47694181,  1.48269174,  1.4884542 ,  1.49422501,  1.5       ])"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r1VPB-FCdHzo"
      },
      "source": [
        "## Getting started with Pyro"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0s1zQEID65Ta"
      },
      "source": [
        "Let's install Pyro now.  You may have to restart the runtime after this step."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "rh_UZO_UEyyw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "524a0e54-43a9-4678-c137-adecbd6ca5d5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: pyro-ppl in /usr/local/lib/python3.10/dist-packages (1.8.6)\n",
            "Requirement already satisfied: numpy>=1.7 in /usr/local/lib/python3.10/dist-packages (from pyro-ppl) (1.23.5)\n",
            "Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.10/dist-packages (from pyro-ppl) (3.3.0)\n",
            "Requirement already satisfied: pyro-api>=0.1.1 in /usr/local/lib/python3.10/dist-packages (from pyro-ppl) (0.1.2)\n",
            "Requirement already satisfied: torch>=1.11.0 in /usr/local/lib/python3.10/dist-packages (from pyro-ppl) (2.0.1+cu118)\n",
            "Requirement already satisfied: tqdm>=4.36 in /usr/local/lib/python3.10/dist-packages (from pyro-ppl) (4.65.0)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch>=1.11.0->pyro-ppl) (3.12.2)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from torch>=1.11.0->pyro-ppl) (4.7.1)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=1.11.0->pyro-ppl) (1.11.1)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.11.0->pyro-ppl) (3.1)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.11.0->pyro-ppl) (3.1.2)\n",
            "Requirement already satisfied: triton==2.0.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.11.0->pyro-ppl) (2.0.0)\n",
            "Requirement already satisfied: cmake in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch>=1.11.0->pyro-ppl) (3.25.2)\n",
            "Requirement already satisfied: lit in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch>=1.11.0->pyro-ppl) (16.0.6)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.11.0->pyro-ppl) (2.1.3)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.11.0->pyro-ppl) (1.3.0)\n"
          ]
        }
      ],
      "source": [
        "!pip install pyro-ppl"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W7q2kdFwMJyY"
      },
      "source": [
        "## Bayesian Neural Network with Gaussian Prior and Likelihood\n",
        "Our first Bayesian neural network employs a Gaussian prior on the weights and a Gaussian likelihood function for the data. The network is a shallow neural network with one hidden layer.\n",
        "\n",
        "To be specific, we use the following prior on the weights $\\theta$:\n",
        "\n",
        "$p(\\theta) = \\mathcal{N}(\\mathbf{0}, 10\\cdot\\mathbb{I}),$ where $\\mathbb{I}$ is the identity matrix.\n",
        "\n",
        "To train the network, we define a likelihood function comparing the predicted outputs of the network with the actual data points:\n",
        "\n",
        "$p(y_i| x_i, \\theta) = \\mathcal{N}\\big(NN_{\\theta}(x_i), \\sigma^2\\big)$, with prior $\\sigma \\sim \\Gamma(1,1)$.\n",
        "\n",
        "Here, $y_i$ represents the actual output for the $i$-th data point, $x_i$ represents the input for that data point, $\\sigma$ is the standard deviation parameter for the normal distribution and $NN_{\\theta}$ is the shallow neural network parameterized by $\\theta$.\n",
        "\n",
        "Note that we use $\\sigma^2$ instead of $\\sigma$ in the likelihood function because we use a Gaussian prior on $\\sigma$ when performing variational inference and then want to avoid negative values for the standard deviation."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pyro\n",
        "import pyro.distributions as dist\n",
        "from pyro.nn import PyroModule, PyroSample\n",
        "import torch.nn as nn\n",
        "\n",
        "\n",
        "class MyFirstBNN(PyroModule):\n",
        "    def __init__(self, in_dim=1, out_dim=1, hid_dim=5, prior_scale=10.):\n",
        "        super().__init__()\n",
        "\n",
        "        self.activation = nn.Tanh()  # or nn.ReLU()\n",
        "        self.layer1 = PyroModule[nn.Linear](in_dim, hid_dim)  # Input to hidden layer\n",
        "        self.layer2 = PyroModule[nn.Linear](hid_dim, out_dim)  # Hidden to output layer\n",
        "\n",
        "        # Set layer parameters as random variables\n",
        "        self.layer1.weight = PyroSample(dist.Normal(0., prior_scale).expand([hid_dim, in_dim]).to_event(2))\n",
        "        self.layer1.bias = PyroSample(dist.Normal(0., prior_scale).expand([hid_dim]).to_event(1))\n",
        "        self.layer2.weight = PyroSample(dist.Normal(0., prior_scale).expand([out_dim, hid_dim]).to_event(2))\n",
        "        self.layer2.bias = PyroSample(dist.Normal(0., prior_scale).expand([out_dim]).to_event(1))\n",
        "\n",
        "    def forward(self, x, y=None):\n",
        "        x = x.reshape(-1, 1)\n",
        "        x = self.activation(self.layer1(x))\n",
        "        mu = self.layer2(x).squeeze()\n",
        "        sigma = pyro.sample(\"sigma\", dist.Gamma(.5, 1))  # Infer the response noise\n",
        "\n",
        "        # Sampling model\n",
        "        with pyro.plate(\"data\", x.shape[0]):\n",
        "            obs = pyro.sample(\"obs\", dist.Normal(mu, sigma * sigma), obs=y)\n",
        "        return mu"
      ],
      "metadata": {
        "id": "CKnbkVoz_iFU"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Define and run Markov chain Monte Carlo sampler\n",
        "\n",
        "To begin with, we can use MCMC to compute an *unbiased estimate* of $p(y|x, \\mathcal{D}) = \\mathbb{E}_{\\theta \\sim p(\\theta|\\mathcal{D})}\\big[p(y|x,\\theta)\\big]$ through Monte Carlo sampling. Specifically, we can approximate $\\mathbb{E}_{\\theta \\sim p(\\theta|\\mathcal{D})}\\big[p(y|x,\\theta)\\big]$ as follows:\n",
        "$$\\mathbb{E}_{\\theta \\sim p(\\theta|\\mathcal{D})}\\big[p(y|x,\\theta)\\big] \\approx \\frac{1}{N} \\sum_{i=1}^{N} p(y|x,\\theta_{i}),$$\n",
        "where $\\theta_{i} \\sim p(\\theta_i|\\mathcal{D}) \\propto p(\\mathcal{D}|\\theta)p(\\theta)$ are samples drawn from the posterior distribution. Because the normalizing constant is intractable, we require MCMC methods like Hamiltonian Monte Carlo to draw samples from the non-normalized posterior.\n",
        "\n",
        "Here, we use the No-U-Turn ([NUTS](https://arxiv.org/abs/1111.4246)) kernel."
      ],
      "metadata": {
        "id": "7d14qstiA2Mq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from pyro.infer import MCMC, NUTS\n",
        "\n",
        "model = MyFirstBNN()\n",
        "\n",
        "# Set Pyro random seed\n",
        "pyro.set_rng_seed(45)\n",
        "\n",
        "# Define Hamiltonian Monte Carlo (HMC) kernel\n",
        "# NUTS = \"No-U-Turn Sampler\" (https://arxiv.org/abs/1111.4246), gives HMC an adaptive step size\n",
        "nuts_kernel = NUTS(model, jit_compile=False)  # jit_compile=True is faster but requires PyTorch 1.6+\n",
        "\n",
        "# Define MCMC sampler, get 50 posterior samples\n",
        "mcmc = MCMC(nuts_kernel, num_samples=50)\n",
        "\n",
        "# Convert data to PyTorch tensors\n",
        "x_train = torch.from_numpy(x_obs).float()\n",
        "y_train = torch.from_numpy(y_obs).float()\n",
        "\n",
        "# Run MCMC\n",
        "mcmc.run(x_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UUeVvklgDDy-",
        "outputId": "a7886d40-02de-4fc1-f1d4-80c922756469",
        "pycharm": {
          "is_executing": true
        }
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Sample: 100%|██████████| 100/100 [06:34,  3.94s/it, step size=7.53e-04, acc. prob=0.924]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BM-TzBWgM99Q"
      },
      "source": [
        "We calculate and plot the predictive distribution."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "SIG1pZkxansO",
        "pycharm": {
          "is_executing": true
        }
      },
      "outputs": [],
      "source": [
        "from pyro.infer import Predictive\n",
        "\n",
        "predictive = Predictive(model=model, posterior_samples=mcmc.get_samples())\n",
        "x_test = torch.linspace(xlims[0], xlims[1], 3000)\n",
        "preds = predictive(x_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "yJRQ9WKfMqst",
        "pycharm": {
          "is_executing": true
        }
      },
      "outputs": [],
      "source": [
        "def plot_predictions(preds):\n",
        "    y_pred = preds['obs'].T.detach().numpy().mean(axis=1)\n",
        "    y_std = preds['obs'].T.detach().numpy().std(axis=1)\n",
        "\n",
        "    fig, ax = plt.subplots(figsize=(10, 5))\n",
        "    xlims = [-0.5, 1.5]\n",
        "    ylims = [-1.5, 2.5]\n",
        "    plt.xlim(xlims)\n",
        "    plt.ylim(ylims)\n",
        "    plt.xlabel(\"X\", fontsize=30)\n",
        "    plt.ylabel(\"Y\", fontsize=30)\n",
        "\n",
        "    ax.plot(x_true, y_true, 'b-', linewidth=3, label=\"true function\")\n",
        "    ax.plot(x_obs, y_obs, 'ko', markersize=4, label=\"observations\")\n",
        "    ax.plot(x_obs, y_obs, 'ko', markersize=3)\n",
        "    ax.plot(x_test, y_pred, '-', linewidth=3, color=\"#408765\", label=\"predictive mean\")\n",
        "    ax.fill_between(x_test, y_pred - 2 * y_std, y_pred + 2 * y_std, alpha=0.6, color='#86cfac', zorder=5)\n",
        "\n",
        "    plt.legend(loc=4, fontsize=15, frameon=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plot_predictions(preds)"
      ],
      "metadata": {
        "pycharm": {
          "is_executing": true
        },
        "id": "xSKKJ86RlzBp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 498
        },
        "outputId": "4791d6c8-af48-47c7-ae44-7a4d06d80520"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o7RjGbEy6_2K"
      },
      "source": [
        "## Exercise 1: Deep Bayesian Neural Network\n",
        "\n",
        "We can define a deep Bayesian neural network in a similar fashion, with Gaussian priors on the weights:\n",
        "\n",
        "$p(\\theta) = \\mathcal{N}(\\mathbf{0}, 5\\cdot\\mathbb{I})$.\n",
        "\n",
        "The likelihood function is also Gaussian:\n",
        "\n",
        "$p(y_i| x_i, \\theta) = \\mathcal{N}\\big(NN_{\\theta}(x_i), \\sigma^2\\big)$, with $\\sigma \\sim \\Gamma(0.5,1)$.\n",
        "\n",
        "> Implement the deep Bayesian neural network and run MCMC to obtain posterior samples.\n",
        "> Compute and plot the predictive distribution.\n",
        "> Use the following network architecture: Number of hidden layers: 5, Number of hidden units per layer: 10, Activation function: Tanh, Prior scale: 5."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class BNN(PyroModule):\n",
        "    def __init__(self, in_dim=1, out_dim=1, hid_dim=10, n_hid_layers=5, prior_scale=5.):\n",
        "        super().__init__()\n",
        "\n",
        "        self.activation = nn.Tanh()  # could also be ReLU or LeakyReLU\n",
        "        assert in_dim > 0 and out_dim > 0 and hid_dim > 0 and n_hid_layers > 0  # make sure the dimensions are valid\n",
        "\n",
        "        # Define the layer sizes and the PyroModule layer list\n",
        "        self.layer_sizes = [in_dim] + n_hid_layers * [hid_dim] + [out_dim]\n",
        "        layer_list = [PyroModule[nn.Linear](self.layer_sizes[idx - 1], self.layer_sizes[idx]) for idx in\n",
        "                      range(1, len(self.layer_sizes))]\n",
        "        self.layers = PyroModule[torch.nn.ModuleList](layer_list)\n",
        "\n",
        "        for layer_idx, layer in enumerate(self.layers):\n",
        "            layer.weight = PyroSample(dist.Normal(0., prior_scale * np.sqrt(2 / self.layer_sizes[layer_idx])).expand(\n",
        "                [self.layer_sizes[layer_idx + 1], self.layer_sizes[layer_idx]]).to_event(2))\n",
        "            layer.bias = PyroSample(dist.Normal(0., prior_scale).expand([self.layer_sizes[layer_idx + 1]]).to_event(1))\n",
        "\n",
        "    def forward(self, x, y=None):\n",
        "        x = x.reshape(-1, 1)\n",
        "        x = self.activation(self.layers[0](x))  # input --> hidden\n",
        "        for layer in self.layers[1:-1]:\n",
        "            x = self.activation(layer(x))  # hidden --> hidden\n",
        "        mu = self.layers[-1](x).squeeze()  # hidden --> output\n",
        "        sigma = pyro.sample(\"sigma\", dist.Gamma(.5, 1))  # infer the response noise\n",
        "\n",
        "        with pyro.plate(\"data\", x.shape[0]):\n",
        "            obs = pyro.sample(\"obs\", dist.Normal(mu, sigma * sigma), obs=y)\n",
        "        return mu"
      ],
      "metadata": {
        "id": "pnjVP4wZGSNw",
        "pycharm": {
          "is_executing": true
        }
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YQ1qofIiNSjM"
      },
      "source": [
        "### Train the deep BNN with MCMC..."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "vbcKlUJd549g",
        "pycharm": {
          "is_executing": true
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ea4965d0-125a-4a0f-c9d5-b20aea4f620f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Sample: 100%|██████████| 100/100 [14:51,  8.92s/it, step size=1.11e-03, acc. prob=0.836]\n"
          ]
        }
      ],
      "source": [
        "# define model and data\n",
        "model = BNN(hid_dim=10, n_hid_layers=5, prior_scale=5.)\n",
        "\n",
        "# define MCMC sampler\n",
        "nuts_kernel = NUTS(model, jit_compile=False)\n",
        "mcmc = MCMC(nuts_kernel, num_samples=50)\n",
        "mcmc.run(x_train, y_train)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5ZMsM58lNgEo"
      },
      "source": [
        "Compute predictive distribution..."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "R0Xtrtqz7uel",
        "pycharm": {
          "is_executing": true
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 498
        },
        "outputId": "c95c6836-48c4-43c3-81aa-6fa9244768a1"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "predictive = Predictive(model=model, posterior_samples=mcmc.get_samples())\n",
        "preds = predictive(x_test)\n",
        "plot_predictions(preds)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Train BNNs with mean-field variational inference\n",
        "\n",
        "We will now move on to variational inference. Since the normalized posterior probability density $p(\\theta|\\mathcal{D})$ is intractable, we approximate it with a tractable parametrized density $q_{\\phi}(\\theta)$ in a family of probability densities $\\mathcal{Q}$. The variational parameters are denoted by $\\phi$ and the variational density is called the \"guide\" in Pyro. The goal is to find the variational probability density that best approximates the posterior by minimizing the KL divergence $$KL\\big(q_{\\phi}(\\theta)||p(\\theta|\\mathcal{D})\\big)$$ with respect to the variational parameters.\n",
        "However, directly minimizing the KL divergence is not tractable because we assume that the posterior density is intractable. To solve this, we use Bayes theorem to obtain\n",
        "$$\n",
        "\\log p(\\mathcal{D}|\\theta) = KL\\big(q_{\\phi}(\\theta)||p(\\theta|\\mathcal{D})\\big) + ELBO(q_{\\phi}(\\theta)),\n",
        "$$\n",
        "where $ELBO(q_{\\phi}(\\theta))$ is the *Evidence Lower Bound*, given by\n",
        "$$\n",
        "ELBO(q_{\\phi}(\\theta)) = \\mathbb{E}_{\\theta \\sim q_{\\phi}(\\theta)}\\big[\\log p(y|x,\\theta) \\big] - KL\\big(q_{\\phi}(\\theta) || p(\\theta) \\big).\n",
        "$$\n",
        "By maximizing the ELBO, we indirectly minimize the KL divergence between the variational probability density and the posterior density."
      ],
      "metadata": {
        "id": "xs1kQn7tHvO2"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0EeqtO-SN0kl"
      },
      "source": [
        "Set up for stochastic variational inference with the variational density $q_{\\phi}(\\theta)$ by using a normal probability density with a diagonal covariance matrix:"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from pyro.infer import SVI, Trace_ELBO\n",
        "from pyro.infer.autoguide import AutoDiagonalNormal\n",
        "from tqdm.auto import trange\n",
        "pyro.clear_param_store()\n",
        "\n",
        "model = BNN(hid_dim=10, n_hid_layers=5, prior_scale=5.)\n",
        "mean_field_guide = AutoDiagonalNormal(model)\n",
        "optimizer = pyro.optim.Adam({\"lr\": 0.01})\n",
        "\n",
        "svi = SVI(model, mean_field_guide, optimizer, loss=Trace_ELBO())\n",
        "pyro.clear_param_store()\n",
        "\n",
        "num_epochs = 25000\n",
        "progress_bar = trange(num_epochs)\n",
        "\n",
        "for epoch in progress_bar:\n",
        "    loss = svi.step(x_train, y_train)\n",
        "    progress_bar.set_postfix(loss=f\"{loss / x_train.shape[0]:.3f}\")"
      ],
      "metadata": {
        "id": "2bFbuA5fJmFl",
        "pycharm": {
          "is_executing": true
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "672108aa85994c5fb31c8b9e692af768",
            "7f382d839eee430cbd8f90c4acbd95b5",
            "e0fd2ad00ad24897b4d4137a27ec40ea",
            "0ee7a6b6fa8d4554ae86ed7271d5f4c3",
            "f22d6e33a7b049e6b60c511c2067fa72",
            "fb638353dceb4d5196da08d05d85747b",
            "903e406733bb42c88a6e58cd5d425820",
            "c0f1ebf3989548de8bed94c8e2151a22",
            "ed09ae00f9f24655aad97a56e9009506",
            "195bfd755fe7407b8a6fb4781c027dc5",
            "adbe578f451a4d09ad9cfd55b4ea627b"
          ]
        },
        "outputId": "e3381bba-0cb4-4f48-dbd9-a4693233aba3"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/25000 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "672108aa85994c5fb31c8b9e692af768"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vefi5wv-OFfI"
      },
      "source": [
        "As before, we compute the predictive distribution sampling from the trained variational density."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "R28wesFt3DjZ",
        "pycharm": {
          "is_executing": true
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 498
        },
        "outputId": "46c835d6-8ce1-4f10-e594-a40cd9ee3466"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "predictive = Predictive(model, guide=mean_field_guide, num_samples=500)\n",
        "preds = predictive(x_test)\n",
        "plot_predictions(preds)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Exercise 2: Bayesian updating with variational inference"
      ],
      "metadata": {
        "id": "APlZryo9nxBg"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IY8G3rLrR2mK"
      },
      "source": [
        "What happens if we obtain new data points, denoted as $\\mathcal{D}'$, after performing variational inference using the observations $\\mathcal{D}$?"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Generate new observations\n",
        "x_new = np.linspace(0.2, 0.6, 100)\n",
        "noise = 0.02 * np.random.randn(x_new.shape[0])\n",
        "y_new = x_new + 0.3 * np.sin(2 * np.pi * (x_new + noise)) + 0.3 * np.sin(4 * np.pi * (x_new + noise)) + noise\n",
        "\n",
        "# Generate true function\n",
        "x_true = np.linspace(-0.5, 1.5, 1000)\n",
        "y_true = x_true + 0.3 * np.sin(2 * np.pi * x_true) + 0.3 * np.sin(4 * np.pi * x_true)\n",
        "\n",
        "# Set axis limits and labels\n",
        "plt.xlim(xlims)\n",
        "plt.ylim(ylims)\n",
        "plt.xlabel(\"X\", fontsize=30)\n",
        "plt.ylabel(\"Y\", fontsize=30)\n",
        "\n",
        "# Plot all datasets\n",
        "plt.plot(x_true, y_true, 'b-', linewidth=3, label=\"True function\")\n",
        "plt.plot(x_new, y_new, 'ko', markersize=4, label=\"New observations\", c=\"r\")\n",
        "plt.plot(x_obs, y_obs, 'ko', markersize=4, label=\"Old observations\")\n",
        "plt.legend(loc=4, fontsize=15, frameon=False)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "vZ-qsDisMUSq",
        "pycharm": {
          "is_executing": true
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 518
        },
        "outputId": "d4a9f34d-102e-4121-a3b5-e517056d27f7"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-13-68b431b95167>:18: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"ko\" (-> color='k'). The keyword argument will take precedence.\n",
            "  plt.plot(x_new, y_new, 'ko', markersize=4, label=\"New observations\", c=\"r\")\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Bayesian update\n",
        "\n",
        "How can we perform a Bayesian update on the model using variational inference when new observations become available?\n",
        "\n",
        "We can use the previously calculated posterior probability density as the new prior and update the posterior with the new observations. Specifically, the updated posterior probability density is given by:\n",
        "\n",
        "$$\n",
        "p(\\theta|\\mathcal{D}') = \\frac{p(\\mathcal{D}'|\\theta)q_{\\phi}(\\theta)}{\\int p(\\mathcal{D}'|\\theta)q_{\\phi}(\\theta)}\n",
        "$$\n",
        "\n",
        "Note that we want to update our model using only the new observations $\\mathcal{D}'$, relying on the fact that the variational density used as our new prior carries the necessary information on the old observations $\\mathcal{D}$.\n",
        "\n",
        "\n",
        "### Implementation in Pyro\n",
        "\n",
        "To implement this in Pyro, we can extract the variational parameters (mean and standard deviation) from the `guide` and use them to initialize the prior in a new model that is similar to the original model used for variational inference.\n",
        "\n",
        "From the Gaussian `guide` we can extract the variational parameters (mean and standard deviation) as:\n",
        "\n",
        "```python\n",
        "mu = guide.get_posterior().mean\n",
        "sigma = guide.get_posterior().stddev\n",
        "```\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "r7_9erXVMy6i"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Exercise 2.1 Learn a model on the old observations\n",
        "First, as before, we define a model using Gaussian prior $\\mathcal{N}(\\mathbf{0}, 10\\cdot \\mathbb{I})$.\n",
        "\n",
        "> Train a model `MyFirstBNN` on the old observations $\\mathcal{D}$ using variational inference with `AutoDiagonalNormal()` as guide."
      ],
      "metadata": {
        "collapsed": false,
        "id": "ge3x8I-AlzBq"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/10000 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "328fadd8a3b6406caa7e66bd9850eb25"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from pyro.optim import Adam\n",
        "pyro.set_rng_seed(45)\n",
        "pyro.clear_param_store()\n",
        "\n",
        "model = MyFirstBNN()\n",
        "guide = AutoDiagonalNormal(model)\n",
        "optim = Adam({\"lr\": 0.03})\n",
        "svi = pyro.infer.SVI(model, guide, optim, loss=Trace_ELBO())\n",
        "\n",
        "num_iterations = 10000\n",
        "progress_bar = trange(num_iterations)\n",
        "\n",
        "for j in progress_bar:\n",
        "    loss = svi.step(x_train, y_train)\n",
        "    progress_bar.set_description(\"[iteration %04d] loss: %.4f\" % (j + 1, loss / len(x_train)))"
      ],
      "metadata": {
        "pycharm": {
          "is_executing": true
        },
        "id": "_UdLKlYxlzBq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "328fadd8a3b6406caa7e66bd9850eb25",
            "627b196cb7f4408b9905ea6f2e3d9f75",
            "f4f823e0061447ffa1c70ef3b1ef45ff",
            "48467a2a0f1442da8e20ba8719b98cfc",
            "43e35fe20dcb4d19aeef3627fe1d47e7",
            "ac41a2d59412486c9249a295354d6004",
            "5e6e53e08ca34df49ed1ceded2067390",
            "f9ef0f0a89f94a23990f29fa6099b5e4",
            "88f2fdf2cf114c55a1d50897b5095d14",
            "75c5d83b155d4e7ab2d4375c474fe37a",
            "f49156f15d1c478e99e31fbd968e9269"
          ]
        },
        "outputId": "5382d401-be7e-4237-9574-bca60e9e0918"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "predictive = Predictive(model, guide=guide, num_samples=5000)\n",
        "preds = predictive(x_test)\n",
        "plot_predictions(preds)"
      ],
      "metadata": {
        "pycharm": {
          "is_executing": true
        },
        "id": "z4Vo5waZlzBr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 498
        },
        "outputId": "c76e993b-97cf-42b3-8a0b-d1d920939826"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Next, we can extract the variational parameters (mean and standard deviation) from the `guide` and use them to initialize the prior in a new model that is similar to the original model used for variational inference."
      ],
      "metadata": {
        "collapsed": false,
        "id": "hfmv1MRglzBr"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "outputs": [],
      "source": [
        "# Extract variational parameters from guide\n",
        "mu = guide.get_posterior().mean.detach()\n",
        "stddev = guide.get_posterior().stddev.detach()"
      ],
      "metadata": {
        "pycharm": {
          "is_executing": true
        },
        "id": "A3t5vnxelzBr"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "AutoDiagonalNormal.loc Parameter containing:\n",
            "tensor([ 1.9129,  0.5943, -0.9050, -3.9051, -1.6747,  0.5833,  0.6641, -2.7249,\n",
            "         4.0177,  1.8829,  4.9077, -5.7872, -2.4847, -1.6129,  1.9661, -1.7780,\n",
            "        -1.0692], requires_grad=True)\n",
            "AutoDiagonalNormal.scale tensor([0.0136, 0.0047, 0.3125, 0.0250, 0.0042, 0.0065, 0.0070, 0.0714, 0.0228,\n",
            "        0.0043, 0.0185, 0.0210, 0.0179, 0.0145, 0.0182, 0.0179, 0.0709],\n",
            "       grad_fn=<SoftplusBackward0>)\n"
          ]
        }
      ],
      "source": [
        "for name, value in pyro.get_param_store().items():\n",
        "    print(name, pyro.param(name))"
      ],
      "metadata": {
        "pycharm": {
          "is_executing": true
        },
        "id": "KIGo4ulplzBr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e095267e-45bb-4fbd-c54c-2d4ea883362d"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Exercise 2.2 Initialize a second model with the variational parameters\n",
        "> Define a new model similar to `MyFirstBNN(PyroModule)`, that takes the variational parameters and uses them to initialize the prior."
      ],
      "metadata": {
        "id": "oocfregiR04Y"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "outputs": [],
      "source": [
        "class UpdatedBNN(PyroModule):\n",
        "    def __init__(self, mu, stddev, in_dim=1, out_dim=1, hid_dim=5):\n",
        "        super().__init__()\n",
        "        self.mu = mu\n",
        "        self.stddev = stddev\n",
        "\n",
        "        self.activation = nn.Tanh()\n",
        "        self.layer1 = PyroModule[nn.Linear](in_dim, hid_dim)\n",
        "        self.layer2 = PyroModule[nn.Linear](hid_dim, out_dim)\n",
        "\n",
        "        self.layer1.weight = PyroSample(dist.Normal(self.mu[0:5].unsqueeze(1), self.stddev[0:5].unsqueeze(1)).to_event(2))\n",
        "        self.layer1.bias = PyroSample(dist.Normal(self.mu[5:10], self.stddev[5:10]).to_event(1))\n",
        "        self.layer2.weight = PyroSample(dist.Normal(self.mu[10:15].unsqueeze(0), self.stddev[10:15].unsqueeze(0)).to_event(2))\n",
        "        self.layer2.bias = PyroSample(dist.Normal(self.mu[15:16], self.stddev[15:16]).to_event(1))\n",
        "        # 17th parameter is parameter sigma from the Gamma distribution\n",
        "\n",
        "    def forward(self, x, y=None):\n",
        "        x = x.reshape(-1, 1)\n",
        "        x = self.activation(self.layer1(x))\n",
        "        mu = self.layer2(x).squeeze()\n",
        "        sigma = pyro.sample(\"sigma\", dist.Gamma(.5, 1))\n",
        "\n",
        "        with pyro.plate(\"data\", x.shape[0]):\n",
        "            obs = pyro.sample(\"obs\", dist.Normal(mu, sigma * sigma), obs=y)\n",
        "        return mu"
      ],
      "metadata": {
        "pycharm": {
          "is_executing": true
        },
        "id": "SlX7AwEJlzBr"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Exercise 2.3 Perform variational inference on the new model\n",
        "> Then perform variational inference on this new model using the new observations and plot the predictive distribution.\n",
        "> What do you observe? How does the predictive distribution compare to the one obtained in Exercise 2.1?"
      ],
      "metadata": {
        "collapsed": false,
        "id": "RocPwY8NlzBr"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1000 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c02f159723754f358719c13a44ea4912"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "x_train_new = torch.from_numpy(x_new).float()\n",
        "y_train_new = torch.from_numpy(y_new).float()\n",
        "\n",
        "pyro.clear_param_store()\n",
        "new_model = UpdatedBNN(mu, stddev)\n",
        "new_guide = AutoDiagonalNormal(new_model)\n",
        "optim = Adam({\"lr\": 0.01})\n",
        "svi = pyro.infer.SVI(new_model, new_guide, optim, loss=Trace_ELBO())\n",
        "\n",
        "num_iterations = 1000\n",
        "progress_bar = trange(num_iterations)\n",
        "\n",
        "for j in progress_bar:\n",
        "    loss = svi.step(x_train_new, y_train_new)\n",
        "    progress_bar.set_description(\"[iteration %04d] loss: %.4f\" % (j + 1, loss / len(x_train)))\n"
      ],
      "metadata": {
        "pycharm": {
          "is_executing": true
        },
        "id": "Rxh4Ku4SlzBr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "c02f159723754f358719c13a44ea4912",
            "0192d97868664d9ca76cafc00d1c1842",
            "7fb86d994e2747d5ac87e334727984a8",
            "48ca7c4658a4495fa012d72ac69edd43",
            "8321ace7a49649f9957d77c4edbe6a11",
            "821a0a7bb2344908b107f6de3afa1c74",
            "e1a5a9e4023d4a10a6091b8a900d9eb8",
            "fc2e5db1dada427c9ada6781f35b73ea",
            "99a393a527e14ab1b7fd6681857354c8",
            "be392e0738544c80b2fc1780415d4d0c",
            "4790424630c64b3b9b252b6cf6e71ec8"
          ]
        },
        "outputId": "bcf6f575-04e8-4d6f-9447-82950d2fb041"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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RX6Z9SYSbbtSpCYcZdIo4voepGwC8a+mNGDM9bXoNm6nYwKzama8B+XyeLVu28OEPf5hf+7Vfm/JxR44cGfWP1tTUVI3hCSGEEEKIKvrsZ8cOCL/1LZjGreGU9RQzdBczaJpG2Xc5lunkWKYTX/kMOiVc5WF7LgW3hAJ0TcdTHiHdojFSMzJzqFBoBEGipmk4vkfBLRE2LGzPpeTZtMYaRqWAKqWCQFFBxAxxy/oYRzpKnNQK5DMh4nVlojUOmqHozDrseklj0/oQoZhLybUJGxamrvPE2dcIGxbXJVuwdIOkFSNuRdCGxiKm51//dWauI0HhBN71rnfxrne9a9rHNTU1UVtbW/kBCSGEEEKIGfE//kewVvBiug7f+EblAkJ3aIatp5jh9b6TZOw8Zd/F0o2htEwfT/kYmk7UCGFoOoNOkcZIElM3sIZm5y6madpIQDjM0g1SoTgAESNEktiYx0WM0Khta1aEqa9XvPhMhHRnCc/RCcddNE2RLmrsO5pncQssb46Sd0uUfbA9F0PT6SsPYukmlm6iCBITG8I1JKwIpmawLNFI3IpU5ht5DSoU4A/+YOx1rNUgQWEVbN26lXK5zPXXX8+f/MmfcNttt427b7lcplwuj3yezWZnYohCCCGEEGIc3/0ufOITo7dpWtC7773vvfrz550SBwdO01UYGErldLF9l3Q5T204jqd8NE3D0gxawrWjZthqw/GrH8AUaZrGolSUX/ol2L8vyp49UEiHR14vpEN0vQXnGizuvjtGNAo5p0TOKZF3y2iaRo0VpejaaFqw/jCYxdTpLA6wtWEFcTNCTzHD0kTjjH1dc93evfAbvxH0k5wpEhRW0OLFi/nKV77CTTfdRLlc5mtf+xp33303L730EjfeeOOYxzz66KM88sgjMzxSIYQQQggxlmefhf/r/7q8cftXvxrcqF8JT/ns6+3ANEzO5Hrwhwq75JwSyVCUrF0cms2LUTMHC7ZoGmzZCkvb4KldQR9DgHLeAuBMHr79bbjrLli+PEJiaAbQUz6DdpG6cJyMXRhZf2jpBj4+z3e9iaZpDJc4aYgkiZqhsYawIHge/MVfwGc+A647s9eWQjNTpGnapIVmxnLXXXexbNky/vEf/3HM18eaKWxra5NCM0IIIYQQM+zECdi+Hfr7R29/5JFgfeFUDfcGjBgWvaUsL/UcpeiWCekW/eUcoPCVIm6FydpFFsfqRgJEXZvbRVpcF156CQ4dHPv19RvglpvBtMY53vcoeQ4ZO4+GRsS08JUiYUXR0ViaaCCkm0SMEK3xemJmeOwTXWNOnw4KFz399KWvZAEpNDPv7dixg+eee27c18PhMOHwwvhhF0IIIYSYqwYH4Vd+5fKA8GMfgz/+4+mdq7eU5cULb2FoOkXPJl3Oo2saviqSsCKEjCBiihjWyFq/mUwLvRqmCbfdBsuWwc9/DsXC6NcPvwHnz8M73gGLFo1xvG6Q0A0SVgTP9+kvD1LyHMqeg67plH0HpRQxM8yb6bNsqGtjeWLRSHXTa9E3vwm/9VuQyVz+2g03wGuvVX8MEhRW2euvv87ixYtnexhCCCGEEGIcvg///t/DoUOjt7/73UHBmakUzewrDZJ1Ctiey4VihrxbQkMjYxdYHKsbKRhzrQQ3bW3w6/8Onn4GTp0c/VomDd//Ptx0E2zdOv73z9B1FkVTI+mjtu/SU8yiUOTdMqam83pfB2+mz6KjETIs2uINrEotnvMzqlORycDv/i6MlVCo6/Dww0EfzMYZWG4pQeEEcrkcx44dG/m8o6OD119/nfr6epYtW8bDDz/MuXPn+Id/+AcAvvSlL9He3s7GjRsplUp87Wtf46mnnuKJJ56YrS9BCCGEEEJM4rOfhf/9v0dv27gxqDRqTuFu2VM+z3UdRtc0ip4NQMG18ZXP4lgdpm5gcm0EgxeLROGB++HNI7D7+dHr4JQPe16GM6fhnndATc345xkupBM2LJYmGkbaavSVBukrDaJrGlEzRNyMcDh9jkQoytH0edbVLqU5VlvdL7JK/u3f4KMfhbNnL39t+fIgULzjDpipGpQSFE7glVdeGdWM/qGHHgLggx/8II8//jidnZ2cPn165HXbtvmDP/gDzp07RywWY/Pmzfz0pz8ds6G9EEIIIYSYfd/6Fvz5n4/eVl8PP/gBjLeEy/ZcHN8lbed5ra8DHY2CW8bxXcq+i6FpNISTmLp+TcxoTUiDdetg8WJ46inouTD65a4u+M534NZbYO3aYP9JTznUVmNRNAUEazQvFDP0uYOEDJPXezvIOgUKbpmbFq2iMZKk5DnzokhNNhu0mvja18Z+/Td+A778ZUilZnZcUmhmjslms6RS1V9MKoQQQgix0L35ZpDimM+/vc0w4IkngjVx4/nxmb34SlF0bQpuUDCw7DnEzDCpUAxD0xdko3bfh1dfhddevbx6K0DrUrjzzolnDSeSd0oMOkUAHN8jaoZoCNegAIVidXIxteE4zdHaK/4aqumJJ4LZwTNnLn8tlYK//mt4//tHb5+p2EBmCoUQQgghxIJTKMCv//rogBDgv//38QNC23M5nD6L7bkU3DJF1x7pK1gfTlwz6wWvlK4HQfbSpfCzn8HgJamP587Ct78FO3YG6bnTjZvjVoT4UIEaXdM4m+8joxXQNCi6NkopPOXTHK3lhsbrgKCYz2zr64P//J/h7/5u7NfvvTeYOVy+fGbHdTGZKZxjZKZQCCGEEKL6PvxheOyxy7d97WtvBytKKXpLg6RCMfb3n6SzMEDeKVH2HcqeS1M0RXgOBB1zkW3D7t3w1jgN2Jub4c67oK7uKq7huXQX0xiajkKhazphw8TUDGqsKArFu9puxFM+uqZjzHAqr1Lw+OPwqU9Bb+/lrycSQV/C//Afxg+QZyo2kKBwjpGgUAghhBCiuh5/HD70odHbNm0K+u9Fo0EwmHNKvN7fMdROQqfsOfSVB9GAVChG3IwsyBTR6Tp9Cp599vIZWQBNh82b4MZtYF1lbO0pH9f36CsN4imfuBVBR6MhUoPje+jAtkWraIrOzGK9gwfhd34HxutM9853Bg8gVqyY+DwSFC5QEhQKIYQQQlTPoUNBg/pi8e1tiQS88kpQCOVI+hwnBrvxlcL2HXJOCV8pyp7DomiSsG5JMDhNth0E3IffGPv1WAxuvhlWrWJKhWgmknNKRAyLglum5DkoFCHdxNINIkaI+nCC7YtWEzKqs4qutxf+7M+CViYXV2MdFo8Hs4O/9VtTS5+VoHCBkqBQCCGEEKI6SqUgIDx4cPT2f/5neN/7FGfzfbzaewJQFFybnFsCYEms7tqvIjoDOs8HfQ2zYzRpB2hqhp07YPGSyl3T9T36yoM4vkdtKI6vfJKhGLe3rGegnKM13lCRtNJSCf7yL4NKtmM1oQd4z3vgS18K1lxOlQSFC5QEhUIIIYQQ1fHJTwaFZC72O//R55Of7wLg0MBpHN9j0C6iUMTMMDVWdMEXkKkk1w0qlO7fF1QrHcvSNtixo7JN20uuTdrO4ymfqBmiLpTAUz6NkSTralupCyeu6Ly2DX//90EweOrU2Pu0t8Nf/RX84i9O//wSFC5QEhQKIYQQQlTeE0/AAw+M3nbjdpc/+eY+lO7heB5Fz6bs2cStKLqmkZB1g1WTyQQN78dqzzBs+QrYsgVaWip3Xdf3yLklXN/DVz61oTi6prMy2ULEsGiI1JCwopPOHpZKQTXR//pfx25ADxAKwX/6T/D//D9BiuyVkKBwgZKgUAghhBCisnp7g0IyXV1vb4tE4Md7OumKHMNVHkXXRtNgUSRFSDclGJwJKphde+GFoKn7eJqbYfOWoGWDXqEs3oJbpq80OFQ9VlEbiqOhoes6Bhrr69pYUdN02XHnzsHf/A189avQ3T3++d//fvj85ycvJDMZ6VMohBBCCCHEVVIKPvaxiwNCxepbuvnYZ8+RTXgMFovEzDBL4w0AEgzOJC2YDWxrgzePwN69UCxcvlt3Nzz5BMTisHYNrF0HVxsfxcwwXtgnY+dJWXH6yzkUYGgaSSvGwf5TIy1HPEfnX/8V/vEf4fvfB88b/7x33hkUktm+/erGN9NkpnCOkZlCIYQQQojK+bu/g499TGFYPobl03Z9P+/+2Gm231nAUR4pK0bEDM32MAXBesODB2Hf61AuT7zvokWwoh3aV0BtLVdUtVQphQJ0TSPvlNA1nfxQaqmuDAo9NZw5A4deC/Ozv1/JYE903HPdeSf88R8HrSYq+VxB0kcXKAkKhRBCCCEq4/RpuP56WHtvB23X92OGPGrqXW67y8HVy9SG49RY49/oi9nhOPDmm3BgP+Ryk+8fjwcVSxe3BMVp6urAnE7fQwWFIvT0wIULir6sg5HK0ns6TixlU8yGGOyN8MRfbSQ/EObiCPTee4Ng8M47p/1lTokEhQuUBIVCCCGEEFdPKfjFX3bp1s6x4sZeahcXsMIeazbaLKoNUReOS1XROc734cQJOHAAei5M79iaZNB/MhaFaAwMI1iPqGlB0GnbwWzkYDZYz+g4o4/XDZ+G5Tl8VydeVybTHSXbEwUFXYcb2Ny4nI/8Tontm6v7UEHWFAohhBBCCHEF3sqc54cvdRK5Aba0FNB0qGksUVcLzXUhGiLy4H0+0PWgof2qVdDfH8weHj0K5dLkxw5mg48r5Xs6PSeCn5NcX5jG5TkWLXFY1KCx4t+7JCJ9dCmfsreVkB6EVPN5PaoEhUIIIYQQ4ppRcMu8cu4Mr+6HeJ1PoqGMbih0pbNpeZJoWJrQz0f19XDrrXDzzdB5HjpOwsmTUMhX75qaHrTDWNZm0dIeJhTx8ZXPoDdIrqgRNkzeGDjD+cIADeEEO5vWzNvAUIJCIYQQQghxTVBK8bPzB3n1gE2i0SWccMn1hilkQtx9u0UsMj9v2MXbdB1alwYft98G6TSc74TOzmBNYDYLXOHiONMKCtg0NQVtMJYsCXoNBt5uNKiUwvE9+ss5Oga7KXo2Jc/mx2deZWtDO63x+qv8KmeeBIVCCCGEEGLeUkpxOH0WXynSdp7Dx0vYeolcf5zeUwk8x2DtWli2bLZHKipOg9q64GPDhmCT6waBYiYTtLcoFKFYBN8DXwXrFC0LwqEg4IvFIZWEZCooWDOViT5N0wgZJrXhODmniO25uL6P7bm81ncCU9MJGSZ14URVv/xKkqBQCCGEEELMS0opnuk6RF8ph6ZppHM259NFuo7U4pSC29x4HG65ZZYHKmaMaQYVSBsbq3+tiGER1k36yzmKno3tuxS9Mi/3HEXTNG5ouG7ezBpKUCiEEEIIIeYVX/mcyfXxxsBpCp5N1i7iKo+jh2J0HGjg4pYBd94JofDsjVVc2zRNoyFSg1IKV3l4vk/GLuAqj5cuvEXcDFMbjrO5fgWO75IMxSY/6SyQoFAIIYQQQswrL154i/5yjoxdIO+UCBkmXk8dRw+MLiKzdi20SdqomAGapmFpJqamyDoFLN2g6JUpuGUG7Bx9pUE85bM8sYh1tUsJGXMrDJPyS0IIIea0dDnP052HKHnO5DuPo7uY5l9P78VTfgVHJoSopO9+97usX7+eUCjE+vXr+e53vzvqdaUU/eVB9ved5EIxQ28pi6d8lsYbaLBqef750be1kQjcLGmjYoZpmkZTtJb6cA0aGiHdpODaFNwyXcU0p3I97Dq/n319J5lL7eLnVogqhBDimlX2HMKGddn2PReO4SmP+nACXdcpujZ1oThl36WrMIDtu2TsAj87f4CbGlcRNS0uFLPUWFESVoSj2U7CusmJbBembhAxgqbUIcPiXL6PuBmhr5TF9j2KbpmEFTQaLro2PaUMhmawOFaHPk/LiAtxLfjud7/Le97znpHP33zzTd7znvfwv/7X/+LXfu3XADg+2MWh/tP4KPpLOaJmiNpQHE3TePVVyA2OPufNt0BY0kbFLBouNGPoOulyHjVUDKk+nOBYphNf+WxuWEFfaZD6cAJTN2ZtrJqaSyGqIJvNkkqlyGQyJJPSWFUIMT8ppegY7GZxrB5T1zk8cJaz+b6RVT5LE414KqjU1l1Mo2kaZc/B1HRc5WNqBoauU3JtomaY/vIgtaE4ru9hGSZ5p0TMDKNrwT5hwyTv2hhDgV3IMPGUouzZmJqBrum4ymNdqpX6SA2O7/LGwBls38PQdDbWtXE0c56l8UZaYrUopXil5xhra1tJhmLEzQhRM4RSat72oBJiLlu/fj1vvvnmmNufe+1lzuZ6OT7YRU8xi6ZppKwYNaHgAc/AAHznO3BxIsDiJfDLv8TFSwuFmFWu7wHQWRggbFgoFFEjRMwMnlwsTyxic8OKy46bqdhAZgqFEEJMi698Bp0Sw42gSq7Dq73HWVu7FMd36SoO0BBOcjTbyZHMeZRSlH0HCAI/Q9M4mjlPyXNQKGzPw9R1Sp6NoRko5aNrOmHDouCWKXkOJc/mQtHB1E2Uo3B9j5LnoKPhKo+wb5FzSuiahoaGqeu4vo9CoWs6hqbj+i5Hs534mXPoms6gUySsW9i+y5vps6TtAnm3zNFsJ6am4ymfw+lzuL6LpQd/Ln0UWxvaWRpvwPZc9vV30FPMctOiVSyKBH+sM3aBmlAUQ5MVGkJM1fHjxy/bZkVClGM6z3UdJucUKXvOyKy+Pvz7peDZZ0cHhLoOd9yOBIRiThmeBWyN15O2CxiazqBTIu+WiRohzub7KHo29eEaVqcWz/z4ZvyKQggh5rX9/ac4OXgBUzfQCELDQbvIvr4OFEGaaG9pkEGniGdGcXyXkmejazplz0HXNHwVBGsh3cDHp+x5LI7VD80WBn84y75DxAhR9Mo0RWrRNCi4NhHDGpllVCg0FZxvabwBH4U3FAyGhgK5Qac4dK04WaeAIghsw7pF2XewPZf+ci4IPl0bH5+IEcLUDPrcQXRNI2KE8JWPq3xe6z3Bgb6TeATBqVKKly68xZJYPd3FNApFwopy1+KNs/VPJMS8s3LlylEzhRvfsZO1d9zIosUt5Jwiru/RFE29HQwOeest6Oocfa7NW4K+dULMRbqmUz+UVpoKxcjaBbJOUD236Nn0lrK0xuuJGiEUM5fQKemjc4ykjwohZpvtuZwv9LMkVs9bmfMsiQfB2vl8PxeKaWzfI23niBghbM/FIwjQQoYZpFcOzdR5SuH4Lr5SJEMxooaFpZt4yh/1pN9XPo7vjbneUCmFj6rarNuZXC8JKxIEiUaIhBVBKcW5Qh+g0RytxfM9yp5D2XdwfR9N09A1baj8uI+p6UTNMFHDImsXCRkmlm6ytaGd2nCMC8UMpwZ7WFHTRFuikbBhySyiEJe4eE3hTb96L22bVrFoRStrNqwnmUqyOFZ32e9NqQTf+mbw32E1NfDr7w161Qkxn/QUMwCkQnE0LfhvzinRaqW4YcnqqscGEhTOMRIUCiFmy/CfgwP9pzg+2EVkKLUSgtnAglvG0g0GnRKu76FpYGpBYZeEFZnVBfJXavhrvnSdoKd8NLhsViJrF7B9l9pQfOQG1fZdsnYBbyi41TUNUzdIWtEgHRZFwS0T0k3iZhhP+RiazuaGFcTMMKlQDA1N1iqKBS3nlPj2z37EMwf24OlQU5vkuo1rWduyHF8prDHeX555Bt48PHrbL7wLlkkLCjEPeb7PoFMk75awdJP6cIKBco64HuLfrb5d1hQKIYSoPF+pkcDnSPocp3M9+CiCdX82feVB4mZkaA2gTmnov55uUBuKETFC10QQM97XMN5M3lhNh8OGxaJoCnh7ZrPo2vSVcyPncoeCxZLn4CqPhBllf/+poe06YcPkuppmIkaIU7keti9ahaZp+MofWc8oxLXs550HqV3dyt0r6rF9F0PTaR5KFzXG+DXt6ro8IGxvl4BQzF+GrlMbjpMKxegs9NNbyuL4Hr4/M62U5C+NEEIsIL7yKXkOT3ceGglggsbPFjm7iK5pFDybRZEU6XKehkiS7uIAqVCMmqFWDtdCMFgtmqZhoJGwIsSHKsoFoTaUPIeiZxPWLAbsHCHDpLeUxRqaVXytrwMAXdN44uzrKMBTHjc0XDfSZmM+zsYKMRGlFMeynZRcm/5yjppQlIZIDbbnXjZTP8z34blnR2+zLLj1thkYsBBVpmkaLbF6/KGskrO53hm5rgSFQghxjSh7DiHdvCxoO5I+R9G1WZpoYHf3mxiawUA5h6HpFNxysGbPLaNrGkXXZXGsDlM3aI7VAtAaawAkGJyu4e/X8HctaoaImiEAIqZFd3GApBWj5DkM2EH/quEiODEzjOMHVVlf7+sI1jEOnWldbSsraprGvWEWYq7rGOzmzYGzQPDQJOsUyNrFkfceYOR3ZSwHD0B//+htN90E8Xi1RizEzArW3Qe/CyFtZsI1CQqFEOIa8eS5fbREa7lp0SrKnsOZfC9vpc9TcG1c5XIm30t3MUNINyl7DgAtsVrMoXVv461rk2Cw8iJGiKXxRgBqhgryaFoQ9mloZJ0CId0k55bQeTv404CDA6c52H+aVDhOSDcJ6QY3Nq4cmVmUdFMxlxVdm9f7Oii5QSXi4XW4bYnGKR2fy8Err4ze1tAAG6+vwmCFWEDkL4cQQsxDru/hKT9ogKsUaTtP2XPoLAzwbNcbZOwCJdceacdQ9hwKrk1jpAYdHUs3RgV7mjT0mjWaphG6pPJq3VC58oQVYaCcBxSaplF0bVzlU3DLZJ0ipq6joXOhlAWCtaKmpvNA2w0z/WUIMannug4z6BTJ2sWRB1P14QRxKzLlc+zeDa570QYN7rgj6E0ohLhyEhQKIcQ8tLv7CDmnyJraJZzO9QbBgu/jK5uugoPre+TcIrWhBKauE9aTeMqXNWnzjKkbLIq+XW2up5ih5NlDzY/zKAVZJ0/YCFp9QFAx9alz+/FR1IUTXFfTzJH0eTbULR2zUI4QM6GzMEBPKUPeKRM1QzQNFWeajlMn4WTH6G3r10NTc2XGKMRCJkGhEELMIwW3jFKKnFPE9l0O9Z/GHkq/GnQKQBAUeMpjSax+1Lqz4abwYv5qiNTg+v5Q8+MagJGCNYam4ytF1Azh+i6255IpFzif78f1PQbsHK2xevrLOe5eIrl2ovpeuvAWeadM3ArTXUwzUM4TM8MjRaumw3Xg+edHb4tEYceOCg1WiAVOgkIhhJgnlFI83XkQ2xsOAos4vjdUoERRY0WJmWGyToFUKCWFSK5BuqYTMkb/u8bMMCHdHOodqZF3SqTL+eD/3XJQEVXTqCPB6XwvJdfh5QtH8ZRHe00z9eEaCm4ZUzdITCONT4jx+MrncPocF4oZfOVzJt9L2XOmnCra0dHBnpf3kM1mSCZTbN+xnQsX2snlRu93y80QDlfpixBigZGgUAgh5iilFEXPJmaGyTlFTuV6GXRKKKVwfY+ia9MQqcHxPaJGiJARvKUPzyCJhcPUjZHU4LBhUc/on4GCW6a3nEVDI6SbnMn34iuf7mKGsG7iodDRWJVsYXVqCXm3TMSwJN1YTEvHYDeO7xHSTY5mzpO28xiaPma66FiBX3t7Ox0dHTz5xBMj+6XTAzz5xBNo2v1A+8j2xUtg9eqZ+sqEuPZJUCiEEHPUCxeOMFDOoaPhKI+8U8bHx/d9DN1gabxBKoOKKYmZYWJmGKUU5/J9+Ch8pfCVP1KtNKSbvJE+y/FsFx4KQ9N555LNIw8bhJhIXynLwf7TIz9T/eUc2f40Z86f5eT+IyRrkpMGfvfdfz97Xt4z5vmV2sNwUKjrQXEZqY8lROXIO70QQswRru+NpHxm7Dy9pSyDThFfKYChJ/AGDZGkBIPiimiaRmMkyaBTJGJa6OhomsZAOYfju+TdEnHz7fS+fzv7Goam0xyrJW6GWZZYREg3UYAls4gLnut7nMr1YGg6B/tPkbELI0FhOp3m2R9NL/DbsyeYORzb29u3bIXa2sp9HUIICQon9Mwzz/CFL3yBvXv30tnZyfe+9z0efPDBCY/5+c9/zkMPPcShQ4doa2vjM5/5DL/5m785I+MVQsxfxzKdvJk5h4420sA855QouQ6e8tE0jaXxhtkeprgGRMwQkUsag8fNMJ7yUUDeKVHybMKGRWdhgBorStGzMTWDjmw3PsEEzQNtN2DIutUFK2sX2NNzjAvFDAkrQncxja8UuqZR9l0OvfjamMdNFPilBwaIx+Pk8/kxXg3ST2uScIN0XBGi4iQonEA+n2fLli18+MMf5td+7dcm3b+jo4N3v/vd/PZv/zb/9E//xK5du/joRz/K4sWLeeCBB2ZgxEKI+eh4tovD6bOUPBt/qGhMwbVxfY/WeD0aGsHtuhDVoWnaSHXa2nAciAOQCsW5UEyTc0tYuoGh6Ti+R9KK8rPzB4ibYeJmhI31yzA0nTO5XiJGiIhhUROafoVJMfc5vsvxbDdH0mdxlc+gU0QNtT+JmWHKnoMGpPv6xjw+mwnWEKbTA2O+PnZACLAdgNtvA1PuXoWoOPm1msC73vUu3vWud015/6985Su0t7fzxS9+EYD169fz3HPP8d/+23+ToFAIMUIphe27HM10cjrXg+N72L5DzimNBIW+UrQlGkeOMWTxjJgFuqYRNkximoGGhhp6OJG287jKp48cvvI5O7ROUQ3NFIHGfa1bZD3iNaSvNEiNFeWN9Bk6st1kh9riNESC6rURI5h9DhsWwLiBXzKVYvv27aPWFE5uK9BO+3XQtuzqvxYhxOXk3bqCXnjhBe69995R2x544AE++clPjntMuVymXC6PfJ7NZqs1PCHEHOArnyfOvo6vFK7ygkIymo6uadSFE7i+N6qSpBCzre6SarbDDzXybhnP97B9F4Wi6NrUhKIopQgZFk+e24cO3LF4A47v0VvKsjLZIq1S5pmz+T4ydoGjmfOEdBNP+fSXc3jKH3lwFTMv7wuxfcfYgZ9j2wDcd//90wgMT2FZO7n11iv+MoQQk5CgsIK6urpobm4eta25uZlsNkuxWCQavTyV5tFHH+WRRx6ZqSEKIWZR0bV5va8Dx/fIuyVQUPZcgpoxGkkrRtiyZnuYQkxI0zTChjUyI1R0bfrKWepCCdJ2Hl8FlUvDhoWmaTzT+Qae8gEYKOdYV7uUuBWR9YhznO259Jdz7Ok5ioZGzilhaPpIMaJUKDbh8e3t7dx3//3sfv75USmh+Xx+pODM1GW4aTvE41f4xYhJ+cqnZNvEwtKrdKGSoHCWPfzwwzz00EMjn2ezWdra2mZxREKISlNKkbELvJU5z9l8L57vk3NLgMbiWB2mbuApX26SxbwUNUO0GkF7lLgVCfpoKp+CWyJrF/GGqur6ysfxXToLA4QMC6UUmqZxZ8sGYmaYs/k++ss5NtQtHWmTIWaWUooBO8ervScouGWUUvSXcoR0E9t3CRsmS2L1U65+3N7ePlRp9PJ1gruff37K49L0KBs3Tnn3BUspRdEpY7sOALbr4CvF3o43Rtala0BNJEYsFKUhkaJnMM2Fwf4xzxc2LRbV1LFucTuZ4iBFu0xzskECx2uUvOtWUEtLC93d3aO2dXd3k0wmx5wlBAiHw4TDl6ddCCHmP6UU/3b2tZFUUdvzSNt5QGNRJDky0wJIQCjmtYuDBE3TsDSDVChOjRWlrxysOwwbFr2lQSzdIGSYOL5HjRXl552H0AF3aDaxxorQGElSY0Wl9UqVKaU4lu2kPlxDf3mQI5nzI+nBg04RSzOoDccZtAssjtVN+9+jo6PjCgrKjDXQIqdOddDe3j75vguEr3x6B9MM5LOcG+ghbxendJwCsqUC2VKBruzYxYCGlV2HswMXODtwYWTbwXPHR+2zonEJ7YtaiVohDFn2MK9JUFhBt9xyCz/+8Y9HbXvyySe55ZZbZmlEQojZ0F1Mcz7fT29pkJxTJucW8Xyf0NBTdkDWVYkFQdd0FkWSI58nrChpO0/cDA9V2S3jKR9LNyi5DqaucyzbxeH0OVpitfi+Yk3tkklTFcX02Z7LrvP7KThlIqaFrxR95UE838dTPjEzTH0kWE861prByVzaoP5qKOWPpJwuxMDQ931O9JzjcGfHbA/lMid7z3Oy9/yobZuXrmZ5QwvIQ515RYLCCeRyOY4dOzbyeUdHB6+//jr19fUsW7aMhx9+mHPnzvEP//APAPz2b/82f/VXf8WnPvUpPvzhD/PUU0/xrW99ix/96Eez9SUIIWZA0bU5OHCaGxraGXSKvHzhKAAFt0zeLZMKxYgYlgSCYsHTNY36cAIIZqlyqsSgXSSYv9AwdQMdHUd5+HmfomdzvtBHMhRjdWrJSK/OnFPijYEz3Nh4nRRlmgZfKdyhNc0vdh9hwM5TdG10R0PXdFzfozlaW5Hv6XgN6q/qnHv2LJigMFMY5NiFs5xP98z2UKZt/9mj7D97dOTzhniK1S3LWJSoDTZIsDgnSVA4gVdeeYV77rln5PPhtX8f/OAHefzxx+ns7OT06dMjr7e3t/OjH/2I3//93+e///f/ztKlS/na174m7SiEuMadzffSVRjgyWIGR3mUPZey51BwyzREaq7oKbsQ1zpN02i8aBaxu5Cm7DkYmk7Rs8k5RXRNZ3AonTFrFzmd6xlq3eJTdG16SlnuXryRuBXBV8GqKUnFDgLuvFsmYUUoe8G6shOD3ZwcvIA3lMruKY+CW6YunEAphaHrhHWrYim74zWov6pzZip/zjlBKdLFHGf7u+m4ZNbtWtCXz9B3/MDI56aus23FBuriSSxpWzNnaEop6Yg8h2SzWVKpFJlMhmQyOfkBQohZVXJtftZ5kJxTxNQMMk4Bx3exdJOmSErWRAkxRcO3I5qm4SsFKEqeM1T8JI+OhqUbKCAVijFoB03TF0WSrKhppqswgI/ijpYNmLq+oGfm0+U8T3ceImqGcHwPRTBDWHRtbN/BG+qHWhdKEDVDVRnDt775rXHXE16p2ro63vve91b0nLNpIJ/ltVNvkrdLVbtGPBRB13Vakg2YhklDIoXjeTieQ1OyfuR3CuD4hbN09J7D9xWrm9uIWGEOnD2K7blVGx/AiobFrFvSLgHiOLpzA3xow71Vjw3kuy+EEFfozfQ5zuZ7R9JEPd/H1A1aYw0SDAoxTRf/zuiaBmgjs+xRM4wG5NwSru/RX85hajqmbtBv58j2FzE0HU/5QX9ETWNdbSsXihniZoSEFcHSTcKGOWp28lrg+h57e4+zNtXK3t7j2L5LjRUl5xRxh2YDh2/qFQpfqZH+gpXW0dHBnpf3kM1mxi2wdzV2bN9e8XPOtJJT5tDZ45zP9FbkfMvqW1jZtJR4ODr0QMUHBbo+9Yciw795q5rbWNU8ugL+krpFoz53PY+iXeLswAWOXThztcMH4GRfJyf7OgEIGRbbVqyjsaauIucWUydBoRBCTJPju3i+z+GBM0TMEFm7gON7LInXo6NJQChEhelDv1M1VhBoXDyr2FUYIO+XhyqaupQ8h5Busq/vJK7voWkapqZj6cHr9y3dCgTBVNrOsyzx9k2v7bl0FQdGbZtrMnYBXwXFYApumf39p/B8j+5CGg8f1/dGCvj0lwbRNZ26cBxDC9YJmtMIFqbj0sIy06ouOolwJMJdd97Jinm6ntB2HY51n+FUXyeu713Vuda1rKCxppZkND5mtU9d09+O8qrANAxqonHWR9tZv6R95HdxuB2G7/t0Zno50nXqis5vew4vXJRqum3FepakGmUd4gyQoFAIIabodK6HiBFiT89RdDQKnk3OLeEpn6VxmR0UYqZc/LvWHK0d2eYpnwvFDINOkZJno2katudiaDoxM4ymwa5z+7F9l6gRouy7nBzsYXliEc3RFD87fwAfhaWbJKy3e7HZnounfJqiqasatxpK27w0tdVTPiXXJmKGLlsTeSzTyVuZYJ3Zxrpl7O09hqEZ6JpGybOD1hG6iVIKHzVSPdTQNJZWaUZwLOMVlrEsC8/38b0rD4bKpRLd3d3zMijMFvM8fWTvFR+/bfk66hMpItbcXJs+/LuoaRrxcPDQpiYaZ03LcgZLeQZLBXzf50x/N7259LTPv/fkYYa/e9vbN9CSbJAAsUokKBRCiCkoew77+k7iK5+Ca+OqYH1OUzRFQ7hGAkIhZsnFv3uGprM49nbaWcEtE44E/UBzTpGS5zDoFDE0g5xWwleKvFOiuzhA2LBwfA9d03i+6zBhwxqZofSHZkNMTScRilIXStAcTbEomkIphad8HN8jbJgU3DKFofeGUePQLV7tO05faZBfaLuRk4MXaI7WUvTK7LlwDNt3MXWD1anFJMwoA+UcJwe78VEMlPNYusH+/pP0lXOAImKE8HyfuBnBUz4R0wrWCipFzAxjVGlGcDzjFZZxHKci59+3bx9Nzc3zovqo7/scPHuMU/1d0z52c9tqWmub0DTmfd+/mkicmkgcgKX1zQB4vscb506MpItOx56ON0b+/8bl62itXSQBYgVJoZk5RgrNCDG3eMrneLaLgXKOE9luip6NQpEwIySld5oQ80pvKUvMDBMzw3QW+vGUIm6GKbrBrKLreyPFWUK6iaHpuMojbFgopXB8j4gRQtMgpJuYmo4PI+mchqZjajq272HpBkvjDcSsMIf6z2DpBt7QfpZu4isfjeA9xlU+Jc/GGjqnhoZCBZVWUSNrAi3doD6cIGRYI4HmTAd/46lGYZlLzelCM0rRne3n5Y5DV3T42pblrGletqCCHKUU+88c5fQVBM8Xu2PNDdTGaio0qrlHCs0IIcQs6y1lefnCUWzfHSpyUSRpxaixojIzKMQ8dHGRmcWx+pH/rw0FLRwihkV3MT3ywCddDtbFDQdl3lDwNtz6QqmguJTr++ha0GcRBbbvEjfDvOWW0TSNQac4ci1zaG2fpRuUPAd/KCgEhVLlYDmYpo3M+EUMk4ZwDYam46NG0kvnWqub7Tu2T7NZvQ6kABuY2vrDudqSwvEcnnrjFWxverOim1pXsmJRa5VGNfdpmsaWZWvY3LaansEBPN8jVyryZtfJaZ3n2bdeY/3idlY0LsaUCqZXTL5zQggxhjfTZzma6cT2XYquTdmzaYykiBjWbA9NCFFhmqaNrCFsjTeMbK+xoiPpobqmoaHhKm+k0XtIN8m5JSzdpOCWcHwPSzfxPJ+cG7QZUEpRE4oR0t++5eorZyl7OvWRBEXXJmWEiBhv9wj0lI+GNpK+OsyoZgWRq9Te3k48Hp9SgZlIpI5SaXjG72+nfI1k6urWdFZS0S5xqreTo9OowLm2eTmt9U14vk8yGq/i6OYXTdNoSr79kGZ1yzJcz2XfmaOcT/dM6RyHOzs43NkBQMwKc/OqzSNrHMXUSFAohBAX8ZVPXynHoYEzOL5L3imjUCyJ1cvsoBALUFC99O21XZYW3DoZRjBjN1wR9eIHRqlQbKhFTZAKeul7R6vRMFJwJmJc3ifw0mIz88WqVavYt2/fpPuVSmnga0AwQzpVc6Ulxcne8xw4e2zK+7fWNrF1+ZoF3TtzukzDZNuK9WxjPZ7v8dLxg/TlpzZTXHDKPHV4D7qmc/PKTTTEkwsqLfdKSVAohBBDsnaBZ7sOo5RP1i7gKZ8aK4qlGxIQCiGmzND0kaBxLNrQrOO15tSp01PcUwHTr0Y620UwlFK8dPwAPVOsohkxQ9y3cacEJFfJ0A1uXb0Fz/foyvTxxrkTlFx70uN85bP7WPCQYtuK9SypnbutZuYCCQqFEIKgMuGzXYfJ2gUKbgnLMGkKp+TJrhBCTFFmnAqklbJnz55ZqT5admzeOH+CswMXprT/ptaVLG9cIg8TK8zQDVrrmmitawJg/+m3plzhNWhtcZi2+mY2tq7E0g0J1i8hQaEQYkFTSnGhlOHVnuPk3RI5t0TrUAEK+YMuhBCjdXR0sOflPWSzGZLJFNt3BCmde17eg/L9ql57pgvNDOSzvHT8II7vTmn/B66/hZAp685nyua21bTULuJY92mKTpmCXZr0mDP93Zzp70ZD4xc23YppzO+2H5UkQaEQYkEa7sbzSu9xzuX7yNoFbN+lxopIMCiEEGPo6OgYVWE0nR6YZsXRqzNThWYKdoldb7w85f1vWbmJxpq6yXcUlaVpNCXraEoG33tf+fzbgRdw/clTkxWKfz3wPADb2zfQnGxY8H/7JSgUQiw4tuey69w+FOD4Lhk7T8yMUGvERxWUEEII8bY9L++Z1evPRKGZV08e5twUK16uaV7GmpblCz6YmCt0Teddm28DwHYdjnad5kTvuUmP29PxBgCLU43cuHwd+hzp/TnTJCgUQiw4XcUB0naBkGHSXx5ERx8pRy+EEGJs2SqvGdR1HX+cFFRN11lRpfWErudx/MJZ3uo+NaX97994M2Hr8qqxYu4ImRYbl65kQ+t1PH/0dQYKg5Me05np5Uf7n2NFw2I2LV214NYcSlAohFgQSq7Nkcw5POVzPt9Pzi1heDqNkeSo/mFCCCHGlkymSKcHqnLu2ro63vve9/Ktb35rzGukqpQ6WrTL/PSNl6a078pFS9mwpH3BBQvzmaZp3L7mBlzPxfU8npzCv/XJvk5O9nUCcOuqLTQk5k5/zGpamPOjQogFZ1//SY5nuzmR7SZjF0iYEZqjKSJGSCqMCiHEFAwXlamG5cuXT3iNaqSOnhu4MKWAsL1xCb+05Q42tF4nAeE8ZRomkVCYX956J7et2jLl43Yf28fP3tjDYDFfxdHNDZoarrYg5oRsNksqlSKTyZBMJmd7OELMey9fOEpfKYvte6TtPI7vYuoGjeEkxgJdNyCEEFeqo6ODPXv2kMlkKlptdHim8OJrZDMZkqkUO7Zvr2zqqFI8f2wf/fnspLvuvO56mpL1lbu2mDMGS3lePHZgSj0PATQ07l63jUQkVuWRjdadG+BDG+6temwgOVNCiGtWVyFNdzGNp3xKXvCmvyRWL0UBhBDiCrW3t9Pe3j5umieAbhj4vgIVBWzAmfS8F7ebGL5GNZzsPc+Bs8cm3CdsWrxzww4MTZeZwWtYTSTOfdffjFKK3Uf30V+Y+CGBQvGzN18B4I41N1Abq5mJYc4YCQqFENesV3qPYfsuGbuAr3xaYnUSEApxlTo6OvjZUz/DdSe/0Z8uTdfZvGkTO2++edIxXNorbzaami9kExWd8b3hlgBTT7mLxqo7+9I7OMALxw9Mut+9G3YSDYWrOhYxt2iaxm1rtuJ6Hl2ZXl47fWTSY5596zUAtrStZkntIkxj/odUkj46x0j6qBCVUXRt/vXMXmzfw1c+yVAseOorhJgWx4HubujshGPHXmQwu6/q17Qsi7vvuYf29nY6OjrY/fzz5PNBgBGORCiXLm9Sfd/990tgOIMmmim8Epquo0FVgvyn3niZ/CSNzXe0b6Q51VCxa4r5rS+XZvex/VPef0vbGhbXNmAZVsXHMlPpoxIUzjESFApx5QpuGR2Nl3uOkrbz5JwSRdemJVY7K8VkhmczMtkMmqahfB9N11FKEYtGcT1v9M2tpsHQW7JlWTiuO/L5pDQNTdNIyayJqIB0Gk6ehFMn4UIPKB+gA5i5RuVXora2Tn7+Z8hLL77Ivn3Ve0BwtUG+47kc7z7D0QtnJt33XZtuwzSkR60YzfM9urP97Dv9Fq7vTX7AkO3tG2lJ1lcs9ViCwgVKgkIhrozre/zb2dfRNY1Bp0jGLhDSTVKhGOEqPLm7OH0tGo0CUCwWR55yAzz5xOzdQMusiZiuQh7eOgpvvQVjTwB9C6hOO4JKk5//6bmSdNxKzxReKh6P8xv//t9f0bF9g2l2H598lqeppo5tKzZIQCgm1ZXpY0/HoSnvv6R2EduWr6tIYCiFZoQQYooc3+Xn5w9RcEu4vs+gU2RJvL5q6aIdHR2jAr7htDaAdHqAJ594AsuqfCA6Hbuff15uisXkFJw5C4cOwZnTk01MV7dxeSXt2bNHfv6n6NL3s+H3sIsD67GCxmo3ss/n83R0dEz737Ez3cMrJw9PuE9DPMX26zZUJdVPXJtaUg388tY78X2fF48foC8/8c//+XQP59M9vGP9duLh6AyN8upIUCiEmPcOD5wlbefI2AViZpjFsbqqrh/c8/KeSfdxnMoX4ZiOiwNVIS7leXD8GOzbDwP9k++v6aDrKTx3fswUXlzJUkxsvPez4cB6vKAxHo9f9fuMputomnZRYZqxxzAVSil6BgcmDQjv27iTiCWFZMSV0XWdW1dvAaXYd+Yop/u7Jtz/qcPB79fO666nMVGLPodbYUlQKISY187m+ziT7yNtF6gLxYlbkapfs9pPyCtGAVJsVVzE8+DwYXj9NSgUJt43FoMV7bB8OSxugTNnt89qSvR0JFOp2R7CvDHe+1k2k6Gjo4Ndu3aN+XqhWLyq695///2sGCPovHQMk/F8j9dOHaEz0zvhfotTjdzUvuGKxirEZTSNLW2rWVrfzO5jk6+tfenEwZH/v25RKxuWXDfnqqFLUCiEmJeydoFXe0+Qc4oUPJuwYREzZ+bpbzQanQczcQm++13YvAVWrpRWWwud78PRt2DvXsjlxt/PsuC6lbB2LTQ3j/65aW9vDwoljdGwXNN1YnPo96JYLPLVv/kbIFibdutttwGMqmI6vH2hp5kmk6kx1wb6vj/hQ4CraVxfW1c30oy+vb193FnHiYJ7pRSvnjzM+UmCQYC71m4jGY1f8XiFGJOm0ZBI8e7NtzOQz5K3i+w7c3TSw070nONEzznuXHMDqTnU61CCQiHEvFNwyzzfdZhBt4Ttudi+Q124ZlpP3a6mz5k7TqrT3HIrvb3w1K4gELjhBli1CuZw5oqoktOn4YUXIJMef5/aWti8GVauCgLD8YwX+KVSKd773vcCwe/WM888M2bbiJly8bXz+fyYwc3w9oVelGb7jpmfAd6xffuoz2+97bYxx3DpfsMG8lmeO/r6pNe5bdUW6hMyayyqS9d1GmpqaaCW+nhqpMH9ZJ556zVuXrmJRTV1VR7h1Ej10TlGqo8KMbGy5/DkuX2UXYd+exDQqA8npjVLOF660lg3h5cGj8uXL6tqGfbKuR8Y/bWkauHmnUE6oKSVXvsyGdi9OyggM57FS2DLFmhrm3w2eaI0v+FUwLGO2bVr17hrxuKJBIVCAaXU1NuvVJhuGGy6/npOnTp9RQ+JrgUdHR3s2bOH9EB114zW1tWxY/v2cX9W9uzZQzaTIZlKjbtfrlSY0k33A9ffQsiUQjJi5uVKBU72nqej9/y0jrtvw04iocvvZaQlxQIlQaEQ43src55jmU76yoMUXZvGSJKwYU67B+F4pdTj8TiWFZqHAeBY6oD3jvnKkla4+WZobJzZEYmZ4Tjw6qtwYH+QNjqWxYth+w5oaZn6ecf9vUkk+I3f+I1xjxsvmNyydSs7d+6cMNicTQtlBvHiB1/+VaSETma8BwdTpZTipeMH6MmlJ9wvGYmzc+X1UkxGzBln+rp5/cyRKe1766otNFwyuz0nW1IcOnSIjRs3VmssQggxppJr87POg6AUg05Q3KAtceURzXiFFYK0uCA1Lp0eqGoPrmrT9Az19dA3xnKb8+fgu98N1o3t3AmR6tfmETPk9Gl49lnIj7NusHER7NgBS1uZ9mzxeL83xUkq1rS3t3Pf/fePOws0lWq+s2EhtLWYiYBcNwzufec7rzwgVIqTfZ0cOHtswt3ioQjvWL9dFlCLOaetoZlFyVpePfnmpK0sdh/bR0uygc1tqwlboRkaYWBaQeFNN93EH//xH/Of//N/xpBGn0KIGXIs20XOKVHybMqeQ0v06vLvxyusMJdoQ4v/rrR4RyqV4j2/BqdOw95XoPfS4FDBkTfh1Em45RZYvZo5nVKqlEIpha7reL7HYKlAMhIfs7y35wepioamBwVYNY3ewTT5cpHmZP2o9JxsMfjeJiMx1NCxpjH/ltuXirD7BTg2To2DaCxIHV61+srvmcf7vZlKpc/29vZxA6y5Ws13vre16OjomLSwzkwE5L7n8fLLe1BwRUH2no436Mr2TbiPtJkQc13ECo+0sujM9PHKyTfG3bcr20fXoT7qYknWtCxHaTOT1Dmt9FF9qJ/MDTfcwGOPPcamTZuqObYFSdJHhRit4JY50H+KQwNnSFpRYmb4qss4z9V0tWGXpuMNr7XJZDJTrvh3caqWUnDsGLz88vgzSK2tcPsdMNOV/AfyWQ6cPUammKOtvhnfVxTsYDZY13R8pRgoZGd2UEBTsp5iuUQiEqMmEiMSCpMIx0hF43MraFRw/Dg8/zyMVddF02HTJrjxRghd5UPn8X5vrjYtcLy01NlWW1c3UjxnvpnoPe7itNiv/e3fVjVldKJrT+ZCdoCXThyYcB9D07l73U3EwpLuIOaXQrnIoXPH6cpO3ig2Ho3xF/f91txaUzgcFCqlCIVC/NEf/RF/9Ed/hGnOoT+Q85wEhUK8rb+c47muN9DQ6CsN0hyrrdi5Ly1q4Nj2jJfTtyxrzCb3E91kj1WMQcGUCjS4Lhw4EPSoG+OyGAbcdFPQxqKSGVi5cpG3Ok/heA4NiVr68xm6p/CHcD5oSTawuLYR0zBpSCSxjJkrbFEoBKmip06O/XrrUrjttqCyaKVMtRjIdM85Fx/SzOeWFRMF2hevnWaCxvHVcHGgPVYF6GXLl3F+oHdK669uW72V+rjcJ4n5z/FcXj5xkP782A9A53RQCEEqj6ZpbN68mccee4ytW7dWa4wLigSFQkDeKXF8sJvTuR4GyrkgbVDTaYhUtp/PxTclVig04yX0NV3n3nvvrfhN9mTy+WBm6WTH2K83N8Pd90xv1lApRaaYw/FcTvd10pNN4/huZQY8j13fupKBfJaaSJxlDS0VWyNysgOefgbKY/zIhsJw6y2wZg1zOiX4YhcHm3OtD+h8LDhTjRlATdNQxEDB8NrrMfcbp5clBPeRH/3Yx8Z8EKCZBi3b1kw6jpgV4Z71N42ZOi7EfOX5Hj/e//yYr81UUDitKb5du3bx0Y9+lI6OjpEZw3379rFz504+9alP8dnPfhZrogZHQggxBT/vPITre+ScIkXXpsaKkgrFKnqNS29KZqOnmqZpE661qpZ4HO6/H06eDILDS1NKu7vhO98J1qBt2DjGrKFS+ErhKZ9j3Wc4duHMTA193jl47vjQ//XwZtfJke3rWlawpG4RCohZ4Snf4DpO0GbiyJtjv76iHW6/HWKV/XWpukt/D/72b//2qpqjV9J8LDhzJeumNV0ndUn2wcUp68EcwnAwGGe8wFADUrV1E64/vWwto8aUAkJpMyGuVYZu8O7Nt3P4fAcnes/NyhimFRTec889HDx4kIcffpi/+qu/GtnuOA6f//zn+cEPfsBjjz3Gtm3bKj5QIcTCcL7Qj+u7XChlCRsWS+MNV72GcCxzoeLhbHcEWrEiWEv4yh44cJChGYCA5wYBY8dJuPsuSNRA32Ca3cf3z9Jory1vdp0cFSRCECiWXRvP91m7ePllhTO6u+Gpp2BwjAyjaAxuvw3ar6vioGdQag4Vg5qPBWeupCG98n2WL1vGyxeldIbGzaAYv+JsMpVi+/axrz/cjP7i4kLRxhS1K5dMOLZUNMEda26oyt8CIeYKXdfZuHQlG1qv43y6h9dOvclM3iVccZ/C3bt385GPfIQjR46MzBoCmKbJH/zBH/DII48QutpV7QuQpI+KhUopxb6+Dk7ne+kpZmmI1BCu4vqsmS6wMJa5VMiiqwt+/jPIXhRwGJECiZZzhJMZjOjEbQeuVm00QSISIxWtYXFtA0opSo5NLBwZCY5cz8N2bWKhyKjpS1/5lGx7VLEJX/now7mTl9xI+spHQ8PzffpyaQxdp2CXSUSiWLqJ47tcyPRzur+bsmtX9eueTCqSYEVjK6dOKd7aX4dnX15hsf06uOOOa6u1yFxaZ1hbV8f27dsvW/8212cPL6s+mkiAUlVPzR1eEz3R+tNvf/c7OCGNmqWLJj3f9a0raW9cIq0mxIJUKBfZf/44f3rXB+fWmsJLlctlPve5z/HFL35x5OZqeK3hunXr+PrXv87OnTsrNtjZ8OUvf5kvfOELdHV1sWXLFv7yL/+SHTt2jLnv448/zoc+9KFR28LhMKVppKVJUCgWqu5immc736Dsu8SMEBGzug+V5kLFQ03XSc2hG0zHUTy3t5dc5HBVzh+1wqSiCUpOmaZkPcsbF8+fMvJDfyrTxSDXtuzYhEyLM31ddGZ6sb2ZXT85eHY5zkALt+wMs2aOtxO5UmMFFd3d3ezbt29Gx7Fl61b2vf76Zdvn41rDagbbU+1HeKq3k/1nx+mdcpGNS67juqallRqeEPPWTDWvv6qgcNjevXv58Ic/zIEDB0bNGhqGwSc/+Un+y3/5L4TD8+QP/0W++c1v8oEPfICvfOUr7Ny5ky996Ut8+9vf5siRIzQ1NV22/+OPP87v/d7vceTI21WzNE2jubl5yteUoFAsRBeKGU7lLnCw/zS+UrTEatG1Ky8iMFZVu0tv3mZqJmLL1q2cOnWKTCYTvD/6/phpo7N1g6mU4lj3mctSGa9UzIqw/boNJCNxbM/F0HUMfeH0tc2Xi9iug6ZpnO3vZrCUp2jb5IfabFTDokQt29rXo1QQrNZEYtf0rMpwsJgeqPJDHU0beRgwntraujnzUGeqqvXeN1xEZjxKKfafOcrp/q4Jz1MXS7K9fcOMN+4WYq6aV0EhgOu6/Pmf/zmPPvroSIn14VnD1atX8/Wvf51bb721EpeaMTt37mT79u0j6yd936etrY3f/d3f5dOf/vRl+z/++ON88pOfJJ1OX/E1JSgUC8mgU+TZzjewfRdt6POGyNX93I93wzNW0PXSiy9WddZhrNYS481QznQq6dn+bl47PXnZ96nY0raa1rqmBRX8XTGlONFzjkPnT1T1Mres3ExjInVNB4f/9D//55ypUjrfZg2rUchnovewcwMXePXUONWRLnLj8nW01l3+0F2IhWymgsKKNRg0TZPPfe5zvOc97+FDH/oQe/fuHZk1fOutt7jzzju59dZbr6inoaZp7Nq1q1JDnRLbttm7dy8PP/zwyDZ9qHz8Cy+8MO5xuVyO5cuX4/s+N954I5///OfZuHHjuPuXy2XK5fLI59nszDdpFmK2vNB9hKxToODaKOWzKHr1ndPHKyBzaQXBjo6OqgaEtXV1Y6ZRXVxgYdT2KhazGJ696sz0cqavG9sbo0nhFOXOt1Hsa8Irh0nW6Nxzj05TQwUHe63TNK5rWnpZWpzreaQLWY6cuUB/sReMq+sd98IlBYFqowk2ta2mNlbZti6zqVis3uzrdO1+/vl5teYwVoW2H8uXLx+9QSnydok9HYcYLE2+Jvkd67cTD0crOiYhxNRVvOv89ddfz0svvcQXvvAFHnnkkZGAx/d9nn9+7P4bExmebZxpvb29eJ53Wepnc3Mzb7459tOutWvX8vWvf53NmzeTyWT4i7/4C2699VYOHTrE0qVj58U/+uijPPLIIxUfvxBzXW8pS94tkbWLJKwICSuKVYGZpomCrovTSqs9gzJcZe9S45WKT06nKeAU+crn54f3XlXqoqWbrGtezeHX6jlzavS/TyYNP/gB3HgD3HAjSNuwq6AMjuyv49DBOmDt8EZiTZ2k2o9d9enTxRzPvvXayOchw2Tt4hUsTjXO2zS9K2m7UC1BgBUEWen0AE8+8QTxeJxbb7ttzgWHHR0dVZlhPXXq1EgdiXy5yFOHJ6/w3JysZ3Pb6vmztliIa1jF0kcvVSqV+M3f/E2+9a1vXVVQNxwUet7VPTWdrvPnz9Pa2sru3bu55ZZbRrZ/6lOf4umnn+all16a9ByO47B+/Xre//7382d/9mdj7jPWTGFbW5ukj4pr2oVihpd7jtJfGqQunMCsYNrheOmZ8Xi8aqlmw/29ptKAfrz01rFSTa9UppBj/9mjpAuDV3yOe9bdRCIcfTt4VnD4MLzwArhj1FRZ1AT33AO1tVd8yQWrrw+e2gVjLZGLROGuu2D5crBdh6Ndp6vSw2rloqUsb1xM/JLKrnPZXKpQOpH77r8fYNZmEi9dY+04dlXeC3Vd56Mf/Sj9+SzPH5s8C2Pb8nUskVRRISY179YUXuz555/nIx/5CEePjq4udaWXmo2g0LZtYrEY3/nOd3jwwQdHtn/wgx8knU7zgx/8YErn+fVf/3VM0+Sf//mfp7S/rCkU17rOwgCv9h5n0ClScm2aY3VXdJ7xismMd6MYjkSq1qB+uusBJyrVfqVypQKvnHxjSmlaY1m/uJ1oKExDIjXhU/tMBn72FFy4cPlrhgm33AwbNnBNVsOsNKXgwH54+WUYa3lX2zK4+26ITpBRp5SiZ3CAV0++ieNXrgLqktpFXN+6cs7PInZ0dPDTn/50zjS6H8t47z3VXod4aUuKqtI1Glcvw6qNTbrropo6rm9dSSIy+b5CiHm4phCC/P5Pf/rTfPnLXx5pUTG8rnDDhg38/u///hWtKZwNoVCIbdu2sWvXrpGg0Pd9du3axSc+8YkpncPzPA4cOMAv/uIvVnGkQswPnu/zXPdh0uU8tu+Qc0o0XmFRmUsDv+F0reGbrC1btly2XrBaASGMnyo6nvb29ordDJacMse6z9DRe37axy6pbWTT0tWEzKn3g0yl4Ff+D3j9ddi7F9RF9+KeC889B6dOwV13Q0zu+caVy8HPfw7nx5j0m05wrWkaTcl6fmHzraAU2VKes/0XON5z9qrGdz7dw/l0z8jnNyxby+LaxjlXTKi9vZ177713xh8ETcd4Y7h0nXMlzeQsariuhvo1U2sdIesGhZi7Khah/fznP+ejH/0oHR0dIymfSikMw+AP//AP+dznPjfvmtk/9NBDfPCDH+Smm25ix44dfOlLXyKfz4/0IvzABz5Aa2srjz76KAB/+qd/ys0338yqVatIp9N84Qtf4NSpU3z0ox+dzS9DiDnhJ2dfw/YcBuw8OtAQSV5xc/rJiskcO3b1a7CmIhyJcNedd1Ys7XM6egYHePH4gWkfd+uqLTQkrm79oq7DjTdC21J46mfB2sKLnTkD3/423HlH0FhdXETBsWPw/PNw0cqBEQ2N8M53QO2VTKBrGslogg2tCTa0Xke+HKwldTx31HrCK/Ha6SMj1WobE7WsbFpKIhwjFo5c1Xkrob29nfvuv3/c2fe5mmZayeJSY6WIVptVE6Nxw/LJdwQ2LV3FisYlVR6REOJqXHVQmM/n+cM//EO++tWvXjY7uGnTJh577DFuvPHGqx7obHjf+95HT08Pn/3sZ+nq6mLr1q385Cc/GSk+c/r0afSLKisMDAzwsY99jK6uLurq6ti2bRu7d+9mw4YNs/UlCDEnHM10UnTLFD2b2lCc6BQa00/Ua3CyYjIzVaa+XCpRlUXZE+jPZaa0Xudi9bEkO1duwjQqO8uzqAne8x546UU4dGj0a+USPPkkrF4Dt90G8+yZYFUUCvDss3Dq5BgvarBlC2y/CSo1GXfxjMwvb70TAM/3ePP8yatal9ibS9ObSwMQMUPEwhEaErW0Ny6ZtXTTiWbfLw4aq97bcBoqVVxqrMyJaoo2pqhdObUAb+uytSyZgzPMQojLXdWawieffJL/8B/+A6dPnx41O2iaJg8//DCf+cxnsKwrmwlYqGRNobjWnBq8wKt9J+gtDaIBzdHaSQvLTNZrcKJef6jq3xRdes2q9xdUimMXznK4s2Nah929bhs1kXiVBjXamTPw9M+DwOdSiURQhGbxQp0oUHD0GOweZ3YwPvT9WTIL3x/bdXjj3AnODHRX9Ly3rNxMY01tRc9ZCeO9d8yGShWXmqmvKd5ST3J58+Q7AtnTFyh2D0zYzF4IMTVzek1hNpvloYce4rHHHhspHjMcEG7dupXHHnuMLVu2VHSgQoj5pew5PHVuP3m3zICdpymSwlXelCqNTpYeun3H9jGDxh3bt/PTn/70qsc+HdXsL+h6HscunOFo9+kpH1Mbq6EhnmJVc9u01gperbY2+He/HsyEdVzSlz2Xg//fD2HzZti+HSo8YTmnFfJDs4Onxn595Sq4/XYIz1JF/pBpsXX5WlpqG+nLpamJxDjWfYa8fXVr8Yb7JN60Yj1Nyfq3l5TM8ozReO8d1bZl61ZOnTpVkeJSl2ZRZMbJnKgEI2yRWNxArHlq+czF3gzp48H65tq6KysiJoSYHdMOCn/84x/zW7/1W5w/f37U7KBlWXzmM5/h05/+9LwpJiOEqJ7OwgAZu0DOLdEaC24KQ1N8y5mswft4a4gUzHg5/Wr0FwTYf+Yop/o6p7z/fRt2EgnNbq+vSATuuzeYFXvuORi1rEnB/n1w9gzceSc0TW3CYd7yfXjjDdiz55Lvw5BoFG6/A+ZKC7uWVAMtqQYAljUsHtk+kM/y/NHXrzhN+pWThy/b9q5Nt1U8lXmqxnrvcOzqtGi42LGjRykWi0Eq/FUGhDORKmqEQzRtXTmtY3oPncTJvd0TdboFuIQQs2ta6aMf+MAH+Kd/+qfLZge3bdvGY489xvXXX1+1gS4Ukj4q5ruCW+bV3hO4vsfxbBctsVp0bXpdzSdKDx0vVXO2iklUsr+g7/s8f/R10sXclPYPGSb3X3/LVfWCrZZcDn72M+gcpyjq2nWwc0fQh+9ac6Ebnn0O+nrHfn3lqmCdZWT2a7RMm+O59GQH6Og9R38+e9Xna07Ws3XZ2hmd1b7UbLx3XNzYfqL105eqZqqopuvEFtWSXDG9JzY9+48T1i2sUKiibXaEEIE52adQ1/WRmw+lFOFwmM997nP84R/+IcZCygeqIgkKxXymlOL1vg5O5i6AgpLnUBue/pq2K2nwPlPranTDAKUqfuPzxrkT02ojMB9KuysFBw4M9eEbo9VsOAzbd8D69fOmX/qEisVgZvDNyyfHgGB28I47YMU1cq+cLebZe/IwufKV9cYcz5a21bTVt8zow46///u/n5X2FWO1zxnevvPmmy/b/rW//duRon6VoplGEAwum14j+dy5XgbPBm1LKvlwTFRO0bWxdANd0/GUj6HpaEDOKZFzS6RCMXJOiYhhETYsPOVj6eZIJxxN09Av6osz6BSJmeEpLQMRlTOn1xQqpdi5cydf//rXWb9+faXHJISYp35y9lV8BelyAVd5LLrCPoSTlZgfy3gpp5qmkaqtJZPJVKTB9b3vfGdFb37Kjs0Th16c8v73rLtp3jR91rRgHeHSpUHD+76+0a+Xy/Dcs3D4MOzcGew3H7kO7D8A+14Hxxl7nzVr4eab5+fs4HiS0Tj3rL8JCO4LBksFDpw9etUziPvOHGXfmaNA0Etz45KVmIZZ1ZRTx65+C4exHDh4cMztw4HipYFhMpka8+GXpuvTfn9LLmsmvrh+Wsf4tkvuaCeWZZHPZKitq5NZwVmilEIB+lDWHoCvFINOkbLnEDFD5J0SCoUGmLqJ7bsopYiZYUK6Sbqcx9R1bM/F8T1s32F4qkih0DUd1/cwNA1PKcKGxaBTJG5GCBkmOadI1AxTY83tB5RiaqYdFIbDYf70T/+Uhx56aFQ7BiHEwtZfHqTkOfhK4SqXpfHGqzrfdBu8j3ezpJTixhu389KLkM/vAsaYsppAPJGgWChUbmZQKc6le+joOc9AYWo3z6ub2qiLJ0cKdsw39fXwq78GBw8EDe8vDZz6euHHP4LWVtixExYtmp1xTpfvw1tvwSuvBAVlxlJXD3fcDi2Lx379WqFpGslonNtWbwWChx1dmT72nz16Vec9n+7lfPrtPNyIGaIl1UB9IsXi2sZpp6aPZ7z3j2rzvfHfj/bt20dTc/Oo98HxCuVMNSC0EhFSyxdjJab/dKL3jVM4gwWZFZwj+sqDlDyb+nANfaVBNE1DAyJGCF3TsD2HhBXB1A0cz6UmFMP2HMq+S8wMow/9LdHQ8JWPqRsUXRt/aEbRHtqv7DlEzTA9pQxJK4qh6fSWB9E0SFhR8k6JrF0gFYpjaDpRMzRqmZmYP6aVPnr77bfz9a9/nTVr1lRzTAuapI+K+ajglvnZ+YPk3RJF1yZuholbMzsl8tKLL46ZhgVgmnFcd3qFJHTDqPisIMCx7jNTbi3REE9x66rN10Zu5ZB8Hl58AY4fH3+fFe2wdcvcLUbjeXDkTXh9H+QGx97HtOCmm+D660Genwb68xn2nT5a0XTTiBli+3UbqY3VXNV55mqD+1EzgJoG0+giphk6ZjRMpDZBovXKHtLljndheMhawVnSX85hajqWbmL7Djo6jnLJO+WgeJtuENItQoYZpImioYCSZxMzw2gEgaKPor2mmbgZwfZdmqJJPKUI6QYJK0p/eZCoEeblnqPknbfTqLc0tFP2HGzf4Wimk6gZoi6coLMwgKnpLI4FM80nB7sZsPN4vo9CYWg6ju+haZC0YmTsArXhOAkzuC+QYHF65uSawuFqo6J6JCgU803JtXmtr4POwgBlzxn5IzBT7xUdHR3sfv75ilcP1HW9oj22fOWz7/RbnB24MOm+YdPi9tVbic3xNYNX49w5eP55mGhyZvFi2LIVlrUBc+BPT6kUBIP7D0BxvLhGg9Wrg9YbicSMDm9eUUpx4OyxaVXYnYrl9S34SlF0yixvXMziVOOU34uq9V4yE0I1MTRdo5wtYEXDNGxYjmZc2dMI33bpf+sMTr4ks4KzyFM+3YX00Do/Y2QdX0g30TWNmBnG9l3a4o0sidfTEq3F8T0O9J+iq5jm3W3bRn72PeWjo03pdyFIF9VH7auUouQ5RAwLTdPwfB/joqddZ/N97OvroOQ6GLpOWDfJOMGbZM4pURtKkHdLQ/cIGs3RFIamV2ym/1o3J4NCUX0SFIr55PmuwyNtJ0qujad8mqK1I2kp1VaJp/u6YYyZwlXJpvQFu8SuN16edL94KMI967cvmIdvw+mXe18JZhDHk0zBunWwdm1QrGVGKejqDtY9njgezBKOZ2lbsDayoWHmhnctsF0nqFbcfYaTFQ4Sh7XWNtGUqmfJFFJOOzo62LNnD+mBudHkfjJ1a9qI1F39Ewi97FM420umf0BmBWeB63sM2HkSZoSiVx4pEmNqBjWh4I3P0AxSoRiGppEKxcnYeW5atBrrosIvru/hK0XImNn2cEcznRzNnGdRJMnWxus4ke3iVK6HsudgaDpl38H1fXRNo680iK5p+Eph6gb14QSGpmMM/W4ulL+BUyVB4QIlQaGYL3yl+NHpV3CVR08xSzIUIz5DVckq+URfu2iR/sUq8YTc8Vx+fvgVSu7kRSzuWHPDVafAzVeuC4cOweuvBcVnxqPpQTGa9nZYsaKKRVsU9PbBiRNw7Nj4KaLDmltg27b5WyhnLnI9j1y5wPNH9+GrylbbvFgqkmBNyzKakvWX1UmYUymlGljxCJquY0bCWPEIsabaqz5tsTfD4JkeNmzYwC07L692KqpLDRWGydoF0DRSoSDV0tINGsI1eMonrJusqGmmPpygKZqaswGTUgrH9y4LRgtuGaUUpwZ7UBqczfUy6BTxlE/MDFNwbbJOAQ1QQNiwqA8FDzkMyb0HJChcsCQoFPNF0bX58em9lDybZCg2YyWqZ+JGbcvWrezcufOKj/d9nzfOn6Cjd5wmfRe5cfk6WuumVwr+WuU4wYzcgf0TzxxCECA2LYIlS4KPRU0QCl3ZdZWCTAYuXAjSWs+enSA99CKtS+HGG4I017mQ3nqtcoemZ3tzA/TnstNq3XI1wmaIwQv9pE914dkOViwCho6TKwR3r1fJiITwbAf8oZNpWvBz5CvMaJjUihZCyepUGr640XwlsyLE5JRSFD2bshfMkCvA1HRChknCigYplgQpliuTLdSFE9dUC4iy53Cg/xTn8/2sqGniXL6Pku/iK5+CGzwVHJ7tTIViRMzQyAziQiVB4QIlQaGY65RSvJE+w8nBHgbKOXzlsyiamrHrz0Q/wpFehJM0kh6Lr3x+tO+5Ke37zg07iIWuoR4FFeJ7cPQY7N8H08ngq6mB+gZI1kAsDvEYWCEwdNAN8Nwg8LQdyOcgOwjZLPT3gzPFjgS6AdddFxSQaZJYftb4vk/JsXnpxMGK90m8Kr4ie7aHSG2iagHdlSinc/ieT757AGdw9Per0uunxdgKbnmkSqip6Zi6MdIeIm6GaUssYk1qCZ2FfiJmiBorSswMz/awq6I0FBje0NCOoemczfdxaOA0ru+N9FR0fJdBJ1iHGDYs4maYqBlakOsQ53SfQiHEwmX7Liey3QyUc2iaNqMBIUBmnH6ElTS8xjCdHuDJJ54Yt5H0pd7qOsWRrlOT7rekdhFb2tZUte/afKYbwfrBtWuC9XxvvhlUK/XciY8bHAw+qiFVG4xp3VqIXLv1f+YNXdeJhSMjfRIBUIrz6V5O93XSk0vP0sC0aTeBrwY7WyB3vo9yJjfpvsnUzL6HL0S255Au5wkbFnWhOK4K1tZZukFDpIadi9aMpIUur5n9n59qixgW2xetGvm8LdHI0njDyHIOV3kc6D/NmVwPPgodjQE7z4CdQyNIs9XQZrzK+bVOgkIhxJSVPYeXLhwl55SwfZfW+MxX1NA0rRKZW9MyVr+wi+XKRX52eM+k57ll5SYaa+oqPbxrlwYtLcHHrbfC6dPQ0RH8d7IAsRJqksGs4KpV0FCPpIjOdZrGkrpFLKl7u9Gl7/t0ZnrpG0xzqr9rFgdXXbnzfSjfxys7lPoHx+xbuGXrVva9/vpl23ds3z4DI1xYgjYOLiXXoeQFaQiLosEMj2WYNFhRNtS1kQrFZ6ww21w3HBRrmoalmWyoXUrZc9hY18bJwQsY+V7KnkPeKZF3y4R1i3P5PlKhOAkJDitCgkIhxJQ9eW4feadE0SuzZKg/0Uybfsa7CVx9BLFnz54xg8IL2QFeOnFg0uN/cfNtGNfQupCZFgoFwdmqVUFhmq4uOH8+WAPY2wuVqEUSiwcpoa2t0LY0qHoq5jdd12mta6K1ronNy9ZQdmwM3cB2bQ53nuR8ugcI2sCUXWeWRzt1ufN95Lv68Z3J39vCkQh33XknK9rbaWpqYs+ePdJ3sEp85dNbylL2XAxNJ26FiZpxNDQaw0mWJRbRGq+/ptYIVkvEDHFL81oANjesYHPDCg4PnOF4titYY6hpaBoU3BK+8rF0k5Bu4hP8v5g++a4JIaZkf99Jiq5NwS2PNKydaR0dU2v6PtrUA8Lx2lNA0Lz5YunCIM++9dqk59zevoGW1JU1jhZjM82g0udwtU/Pg3Qa+vtgIB0UqSnkoVAA1wte9zwwDAhZwTrDaDRYg1hTA6kULGoMgkJxbQtbQTUi04iybcV6trF+1Oslp0zBLmHpJgW7xOunj2DPxLT0OBriKdYtXkHYCmEZJiHTCl7YemXna29vn9YaaTE1SinO5vuAoHpm2LCImSESVpSEFeG6mmaWxOrnbOXQ+WJ9XRurUovR0Tlf6OdA/yk0C3rLgxTccpCWi0bEsKgNJ2QWdpokKBRCTOrUYA/Hsl1k7PyMzRB2dHSw5+U9ZLMZkskUy5cvY9++fVW7nq7rvPOd7xy3sunF624yUwgI1zQvY+3iFZUcohiHYQS9AaU/oLhaEStMxAqKe9RE4zyw6dbROyiF47v4vuJMfze+8mlIpNA1nZ7BgTHXFNeEY2xovY5TvZ10ZftGtodNizvX3oipmxi6juO5QdCnVFCJVMx5ru+Rd8soFHErgq986kJxdE1H1zTubd0is4IVNjwL2JZopC3RiO257Dq3D0cFFUtd3yPnlsjYeRzfo8aKYOmm/DtMgQSFQogJ9ZUGebXvBINOkeZo7Yw86by07UQ6PVD1iqPRaJT29na2bNkyZvC5Y/t2Sk6Z54/uo2CXJjzXDcvXsVTaTAhx7dE0LMMCA1Y1t416qS6eZE3L8nEPbUpO/EBtZBZQAsJ5wfFdeooZQMNTPkvi9RiazvV1y2hLSHbITAkZJu9ato2CW0ZD4+fnD+Apn5xToj6SoK8UVB9riqaAoPKrzNiOTYJCIcSEXrzwFgW3RKgKT9ounQ0cbv+w5+XJi7ZUWj6fp6Ojg50330xTc/Nl624i9UmePPTShOdY27yclc1LZe2gEEJcowpumdxQq4RF0SSe72PoBsvijWxuWDHbw1uwhtt3WIZJXAVtPgzdoCmaouw5DJRz2H6wPKQ5Woslf6cvI0GhEGJM/eVBXup+i5xTpOQ6LI5VtmrmWLOBTz7xBPfdfz/ZGWg7MZY9e4Jg9OJA9aabbqLLG6Sn48yEx163qJU1i8efJRBCCDF/eb5P3i0x6BRJWBGiZoi2eCMXihmao7Vsqpf3/7nglqa15NwSNVYUUzPIuUVyTok9PcfIO8F64b5SloZIDa7vEzEsmTkcIkGhEOIyru/xfNeb5JwSju+SDMUq/qY53mzgnj17SCZTVU8XHUsmnb4sUH319BHCteNXIKmPJVndsowmaTUhhBDXFKUU5wr9xMwQRdcmaoRoiCSJGBab6pezJFa3IJupz2VxKzKqf2G9UUN9uIa0XeDU4IWgP6Rr0FPMomkaru8F/SPD8QVftXRhf/VCiMsopXi5J+hFmHNLLK1SL8LxZgPTAwPE47NTBvLidhdmNMSizSsn3H/ndddPuk5ICCHE/FT0bEzNoOw51IbiRAyLtbVLWZVskdmleWZT3TJWJVuIGiHeypznWLYTgKxdBKC7mKEpkiJkLNzQaOF+5UKIyyil2Nt7nJ5ilqJnV7XS6ESzgfl8vmrXnYrUihZizRPP/L1r022YhqxJEEKIa41SClf55J0SqVAMT3nEzTA3NF43YwXXRGVpmjay7nBtbStra1sZKOfY3f0mngrSSPvLQVGaxkgST/mEDWs2hzzjJCgUQox4vvtN+ss5+suDhHSzqj1+tu/YPm77h1mjaSSXNU0YEG5Y0s7KprZxXxdCCDH/+MpHQyPrFMg7ZTzlB1VElQItxDuWbFpwQcK1ri6c4NbmdRzsP41CETEsCp5NZ2EAU9dJWrFRqajXOgkKhRAAlD2HjF2gr5QFoCFSU9Xrtbe3c9/997Nnzx7SA1ezflAD1KR7XeqyRvUa1K9pm3D94DvX7yAWXjh/IIQQYiFQStFVSKNQhHWLllgdRbfMqmQLq1NLUErJ7OA1qi6c4I7FG0Y+3939Jo7nErPCDDoliqUscTOCqeu4vk/UDM3iaKtLgkIhBAA/PbefQaeA43tVW0d4sYvbUVwWoE3LlQWFmzZtYt/rrwMQSsZoWD9+5biQYfLODTslXVQIIa4hSil8pUjbeSKmRdKK0RhJsqVhxcg6QkACwgVkx6LVpMt5VqeWkLHznMr1kHUKuL6HrumEDBPjGi0uJEGhEAtc0bU5nu2i5Nlk7SKLY3VV/wN4aTuKq+NPa+94IsFtt97KivZ2mpqaePXIQUItqXH3v3vtNmqis1P4RgghROW5vocCBp0C/lCBsZgRYV1tK6tTS4LPh9afiYXF1A3e1XbjyOdra1t5tvMNMnYBU9fpKgyQDMWIGqGK926ebRIUCrGAnc71cLD/NGk7T9G1WRyrm5E3udloTn9xMDgsazjjBoQtyQZuat8gT4iFEOIaEqSKDqBrOqau0xStxVPBw8VqL5sQ88PFf/drrCi3tazjmc43MDWdVChOdzFNzikRN8OEDYuQbl4T9woSFAqxQLm+x5vpcxTcMjmnxJJYPYY+MykRM9GcXtN1UqkUO7ZvHxUIAvi+z4/2PzfusfFQlO3Xbaz2EIUQQswg1/foLAwQNqygubmuc/fi6wnpJqfzPdSFErM9RDEHJa0Ya2tbWZ5YhKd8XrrwFkXXJmsXSNt5bM+lJVZHwS0NrT+cnzOIEhQKsUA9cfZ1fBQZu0BtKD5jASFM3I7iaumGwb3vfOdlgeAwX/k8d/T1cY+PhyLcte7GcV8XQggxvxTcMn2lQUKGSdQMUWNFWVHTxNpU60jhkPaa5lkepZirNE1jzVBaMcA9SzbhK8VPz+2j5NnknTJdhQEs3WDQKRI1QjREkrM44isjQaEQC1C6nMdVHulysLg+McMll6vZjmKigLA708fLHYfGPXbjkpVct2gJXANpIEIIIQLpcp64GSZihogZIZpitWypX3FNpPyJ2aFrGncvvp5TuR5O53qImBa256JQlDyHnFMkYUVne5jTIkGhEAuMr3ye7XqDdDlPyXNoizZW9XoXVxlNJlNs37Gd9vZ2tmzZwr59+yY8VtN1lD+1QjKTzRBOFhBKM3ohhLi2FN0yvUMzhLXhBL7y+YW2GyUYFBURMkxWpxazOrWYjsFuDg+cxVM+SkHJcyh7LnErgo5GyJj7IdfcH6EQoqKOZToZdIpYuln1RfWXVhlNpwd48oknuO/++zl16vSEx2q6Pq1mExMFhCd7z3Pg7LExX0uEoty1fhv6NVpiWgghFpqsXSBjF4gYFg2RGnylMDSdpBWVgFBURXtNMy3ROjJ2nld6j1P2HFzfI2sX8JRPUzSFjjanf/4kKBRiAck5JY5kzuP4Hosiyaq/OY1XZXTPnj2TFptRvj/lgDCeSIwZEE5WUKYmEuPudTdN8SpCCCHmOttzyToFmqJBZemmaIpBu8g9SzbNi9kaMX9FzRBRM8Q7lmziTK6XE9kuIoZFyXPoLWWxPZcaK0oyFEXBnOt3KL8dQiwQPcUsu7vfJOcUKXvOjDytGi/wy2YyFS02UywULt+o1IQBIcBtq7dW5PpCCCFm13AxGUs3qDGjhA2LbY0rR4JDIWZKzAyztraV1ng9x7NddBXTJP0oabuA7TtcKNoooDmamlNZShIUCrEAdBfTvNpznJ5ShrgVoS1R3XWEw8YL/KLRKI5jV+46qcv/6L904uC4+9+x5gZqY9KPSggh5jvbc7F9l4FyjrgVJmFGsHST62qaJSAUsyphRdnS0M4W4ES2mzfSZ1Aqgq5plDyH/nIuqP6u6ZQ8Z6QS7myRoFCIa1zBLbO35zh5t0zMDFMbis/YtcerMprP5yt6nR3bt4/8v+/7/Ov+5xkv+fS21VslIBRCiHnO8318fHpLWUzdoC4cJ6QH1bTfsWTTbA9PiFFW1CwibFgkrDBJK8Zz3Yd5K32e/nKOsudgaDqWnprVHocSFApxjXu68xAFt0zaztMQntlgqL29nfvuvz9YQ5jJkEylcGy7okGhpusj6wlP9Xay/+zRcfd94PpbCJlWxa4thBBi5tlDsyy+UjRGatA0neZoCsf3uL5u2Zwu5iEWJl3TaY3Xj3y+qW45ju9Rcm2Kno3re1woZlgUTWHNUmA4dxJZ56gvf/nLrFixgkgkws6dO3n55Zcn3P/b3/4269atIxKJsGnTJn784x/P0EiFuNzZXC8l1yZjF6gLxWclNaG9vZ33vve9fPRjH+O9730vxWKxoudPDaWOHjhzdMKA8J3rd0hAKIQQ85xSiu5ihmQoRn0kwbbGldzY0M6ORau5vWU9teGZy4YR4krVhuO8Y8km7lq8kTtaNhAzwySsCH2lLDmnRNlzZnxMEhRO4Jvf/CYPPfQQn/vc53j11VfZsmULDzzwABcuXBhz/927d/P+97+fj3zkI7z22ms8+OCDPPjggxw8OP7aJiGqxfZc9vYeJ+MUqAlFic9wg/rxJJOVXeOxY/t29pw4yMm+zjFfb0zU8u4ttxMLz42vXwghxPTZnkvZczib7yNuhYkZIa6vW0ZrvIGliUaZHRTzUtyK0BRN8cDSG2iOpoiZEQpumf5yjs7CAK7vYSt3RsaiKaWmWvV9wdm5cyfbt2/nr/7qr4BgrVJbWxu/+7u/y6c//enL9n/f+95HPp/nhz/84ci2m2++ma1bt/KVr3xlzGuUy2XK5fLI59lslra2NjKZDMlkssJfkVgouotpXuk5zqBdQMGsPTkdblyfyWbQNA2lFKFQiHKpVJHzb9m6lYYVSzjc2THm64sStdy8anNFriWEEGJ2lDyb/lIOhaLGitEQSXB783ois1yYQ4hKUkrxw9OvYGo6Jd/FVz795UF0X+OjG++vemwgawrHYds2e/fu5eGHHx7Zpus69957Ly+88MKYx7zwwgs89NBDo7Y98MADfP/73x/3Oo8++iiPPPJIRcYsBEB5qB9OupzDUR5NkZmvvtbR0cHu558ftXZw+OlTpQJCgGhdzbgBYXvjEq5fuqpi1xJCCDHz+suDFFyblmgtvvIxdZN7W7fM9rCEqDhN03jHkk1EzRCncj0cHjjLokiKol25+6aJSProOHp7e/E8j+bm5lHbm5ub6erqGvOYrq6uae0P8PDDD5PJZEY+zpw5c/WDFwuW7bnsOref07leCp5NUyQ14yk1HR0dPPnEE5WtMDrG1xCpT3Kq0Dvm7utaVkhAKEaxPRdP+UHFQuWPbM87JXJOZde5CiGuXMEt4/k+OadETzGD63s0RZLUh2u4e8kmfrHtxtkeohBVE7ci6JpOe00z72q7kRorStSYmRlxmSmcZeFwmHA4PNvDENeIsudQ9h1c3yNiWLOyxmLPy3sqe0JNg4uy3DVdp27NUsKpsVNiFyVqWd2yrLJjEFWllMJXCkO//DllwS2TtQssiqbQ0VAoNDQGyrmRNivJUBQNjZJno2s6Zc/B9l3ChkXMCJOx8zi+F1yL4Fq6plEfTpCx84DGQDmPqRtEDIuEFaWrMICmQW0oTtQIo1CYuoHju/SWsjRHa9HQZB2TEBWklCJdzuEpRcKKYGg6cSvCippm1tcuJWTIbatYODRN467FG0lnMzNyPfntGkdjYyOGYdDd3T1qe3d3Ny0tLWMe09LSMq39hagk23N5tusNCq5N2XNojtbOyjiylX7zuiQgbNm+dtxdW5INbL9uY2WvL6pKKcWFUgbH92iN1VP0bPpKg0QMaySQCxkm5/P9QxPGwdpUUzcIGxamrnOhGPzMGZqOrxQhw8TUDFzfo9fJEDFChK0QBbdEzAwaB2toZJ0iIcNC1zTCukXeLeMrRXcxTdyKYGgaZc9loJxH0zQMTcNTCks3OJfvx9JN6sJxPOVT9hw0NCKGRcQMoZSi6NlEjZAEjkJMUdlzCBshLN0gGYrhK8Wm+mUsSyya7aEJMStM3SCkz0y4JkHhOEKhENu2bWPXrl08+OCDQFBoZteuXXziE58Y85hbbrmFXbt28clPfnJk25NPPsktt9wyAyMWC1XBDQoVnc330VvKUvYd6sM16LN0I5pMpkinB6py7qYt14372sYl13Fd09KqXFdcnUtnApVS9JSyKBS256JrGjVmhLP5PjRNoy4cZ9ApUROKogExM8IAuSC4M0wc38XQDExdR9d0okYYHzWSFhrSTXRNh5FZweC6MTNEYyRJfTjBsWwXcTOMq3xM3WBFTRMnst1ogON7WLqBqzxc30fTgkbZlm7iE/w3bobRNZ2+0iAKRW0oTs4tMegUaY7W0l1MEzJMyp5DwS0TMULErQhh3UQBg06RrF0gYUWoDQWz3hI8ioWq5NoM2HlAkQrFiZth7l58PaZuzGozbyEWEgkKJ/DQQw/xwQ9+kJtuuokdO3bwpS99iXw+z4c+9CEAPvCBD9Da2sqjjz4KwO/93u9x11138cUvfpF3v/vd/Mu//AuvvPIKX/3qV2fzyxDXuJ+fD1qeOL6HpmksjTfO6ni279jOk088MaV9NV0HFUOp3KT71rQ1oYfG7jO4vX0jLamGaY1TVI+nfPJOiagZoq80OCrIiplh8k6ZkGFiaDo1VhRD10mYEfShoDFuRogMraFQgKkH+62tbaU1Xo/tuVhDT08jZgjH9ziX7yNtB+tY2+INNESSuL4XpH1qBsWh1NKIEfwMrahpQkMjbedZFEmiaRrrUq3YflD2PhmK0THYTWd+gLSdRwEaQeDmKzXy/7qmU3JtomYYUzfoK2XpKWVYFEmiazqDTpG6cIKcU6K/NIiPQimFpmlEzdBIiX1N02iJ1mIMBbASIIprmVKKrFMkZoZxfY9Bp4Ch6dSGYiyvaWJz/fKRhzlCiJkhQeEE3ve+99HT08NnP/tZurq62Lp1Kz/5yU9GismcPn165CYG4NZbb+Ub3/gGn/nMZ/ijP/ojVq9ezfe//32uv/762foSxALgKR/bdyl5DjVWdLaHMz0KlPoNoAMYJ5DUNVLLW4g11Y75sgSEc8dwH7G0nSdkmGTsAg2RmpEZsZBu0V8eJBWKETPD+EqRCsXQNI3ti1bx9NADjjsXbxwJngAGnRIH+0+xNrUkCJYueTZgDc30XeriGYaYOXrtdngoOGyKvl2dV9P+/+3deXhU1fnA8e+dfTLJTFYSlgBhF9mRTZFNNkWRalUUVFyw7ku1Lq1VEVtb9Ve32lqVCq0KxX1XVEAUARHZBWQJ+xLInsnsc35/DBkZZgJJCJkMeT/PMw/Muefee+7cTDLvnHPeo2HWG8Pb8lKyyUvJDrejKlALqiAaGkEUawq3s6+ymDOyOrCvshi9pgstvaI30DUtF/fhBYi3lO7DrwLoNR3egJ/KgAe7MQlv0E+Fz02SwUSBq5Tg4Ty9Vr0Jm8EcnkOlETq3QsmHZZHw3Id70Mt9Lqx6E3aTDYOmo3dmO1rZ5Pe5EPEg6xQ2MmVlZTgcDlmnUNRIsaeCr/et56C7DIvOSIYlJe49DHP/N7cWw0dtgAkoBazo9RBULlTwcHZITSO7T0d0htjDh9zbCjijd1/y8vLqoeWiNpRSFHudmHUGgijcAR+BYACT3oBZFxrmWeFzk2q24Q340Ov0h+f8BdFpOs7M7ozNYEn4oWFKKfwqgPGIOR87Kw7SzOKIWENNKYUn6Mek07Os4GcKPRVkWewUucvxqyB6TYcr4EUpRUAFCSqFJ+gLD68NhgNTSDUlYzOY4/5eF6IuPAEfRZ4Kkg6/P6p+Dwxs1ok0c3KcWydE49NQsYEEhY2MBIWiNpYV/Mzaoh1kmFPitohv1QL1ZWWl2O0OSstKfwnq6mDU6NFAaD5uZtc2GJNj934eWLmZoNcf3kcCw5OnKlBRKPZXloQDEnfAh07TSDZY8AT9pB5ODDGseTf8KsABVyl5Kc1w+t3oNT22w0Msq4ZPipD9lcW4Az5cAS97nUWYdAYq/G78wQC+I4bAVvhcoIE/GEpsk2K0Yjw856rc5yLVZEv4IFucmpRSFHrKMekMOP0ezDoDaeZkgqjQIvSHEzQJIaI1VGwgw0eFSFCVfg/7KotJNlrjGhAeOX+wdglmNH5Z0v4XX331FSiFo21OzIBQBYIcWLkZFfgl8Fy+fLkEhSdBVTC431WMDh1BFFkWO+7DSz44TEkopWhvz2FfZTEDszuhlCLFFLpvVd/6Vw3HrCIBYaScpLTw/09LDSVLCgSDOP1u9lcWs99VQpfUVvxwcAsQmj9c6fcQJIhfQZGrArvJSoGr5PBrq6FDI9NqD89RFCIeqn6HlPlcGA9/YZFitGDSGWidnEVLWzqp5tjLCwkhGpYEhUIkoEAwyMK96yj2VJBhiV+P8omtSRh7kEIwEMCalVrtHMLCDTsiAkKAstKGWcPnVFe1PERQKdLNyRxyl4Wze5r1Bow6A82sDko8znBQ2MnRghZJ6fTOrD4zrKg9vU6H3ZSE3ZREp9SWAJyb2weFYsmBnynxVpBlcWA1mNhcug+rwXR4eC54Al5MegNF7nIyLXacfg86TQvPqzx6jqQQ9cl/eCkZvwpw0FWGTtPQazqyLHZOS8tlt7MQT8BHO3t21FxfIUT8SFAoRIJx+70s2LeOIk8FDlNS+NvXeDjWmoQ2mw2Xy4XVagVNw1VZidVqxemspLqAECApKxVHu+Yxtzn3F+FzuqPK7Q5HjNqipgLBIEWe8sNz14Jo6DjoLiPTkoJRC80FbJ2cRXNbGunmFLaU7qPIU0H/Zh3j3fQmRTu8vuJZOV3CGV2VUmRbUzHrjRx0l/JzyV7MegNmnYH9/hJ2OwtJMVpxB7wUeSpQSqHTNBRg1hlIieNIA3Fqqlo3NEgQu8kaWk9U02PWm2hvz6G9XdZuFqIxkqBQiASztOBnijzlVPo9ZFhS4tqWY61J6HQ6I+b6LVu6lNWrVx/zeLbsNOxtY39gKN91kIp9hTG39e/XrxatFlWqEpqUeZ0YdQa8QT9JBjNmnRGDTk+39NY0sziwGS0R+3VwxA7aRcOp+jJI07RwBlWHKQm338fOioP0yGiLu2AzBp2eZlYHB1ylBFQQVCgpUJLBTKnXyUF32eH1IDXsRiuapuELhrKkxmutU5F4quYJhxJJadhNSVT6PdgMFuxGKz0z8sKZdIUQjZO8Q4VIIAddpRxylxEIBml+xDykeDnemoTfLV5MXl4e+fn5xw0IzanJ1QaEheu3k2S0MHrUKBShOYRlpaXYHQ769+tHW5lPWGuVfg/FnopwYhKHyYZfBTHpDGjAWTldcJhkrk+iOT0tly6pLTHo9PTM8OEw2ciwpOAL+in3udlSupcSbyUWvTG8tEbVB/rdztCXLgadHoOmw2a0oKGhO7xUx5HrMwpRJaCC7KsspmoESLLBSpLehEVvpFdGOzItKVHzioUQjY8EhUIkiPzyA6wv3kWp10kza2qjyDKYl5fHqNGjqw0MnU4ny5YuZceOncc8jjk1mfTOuTG3OXwGLph0Vfh5fn7+L6NP1bEGooojufxeSr1ONMB/eAhhqsmGWW9EobAbrVT6PfTMzKO5NU0++CcoTdMwaKHfDe2OGKZn1BlINyfTv1knAMp9Ln48tBWj3hBac1EFCaggSQYTGhqF7nLKvJUEVDCcEkoBejSaJaXiOdzbKITT58ZutOIPBkgyWjBoOvo36xT3kSxCiNqRoFCIBOALBlhXtJMSrxNN0zWKgLBKXl4eqalp1Q4jXb169TEDDJM9qdqAsGDVVvb7/ORmNg/3OB6d7fSLefNkSYpjqPC5KPFWYtTpSTXZ0Ot0KBX6dt9hSqJfVgeMOgMmvUEyVTYhKUYrQ3JOx+l3Y9Yb2V5ewM+lexnYrBPfF2wm3ZKCUdMRUEG8wQC+YGi9RE/Az15nUfhnJclgxh8MUOFz4zAlyZcJp7CqTKI6TYfL78GsN1LoKccb8NPKlkGWxU5uchZ+FSBd1hsUIuFIUChEIxdQQXZWHKTE68Tl956UYaNHrzXYr3+/WgVZbdq0PuZyFNUth5rcMouUVpkxtx34cTNBX2gdwqrAr7psp7IkRSRf0E9Ahday8wb8ZFnsGHV6Wialc1paLr5ggD3OQjo5WsiH+CZM0zSSjaHlQzrYm9MiKR2b0cKIlj1wB3z4gwG2lu3noLsUvWbBr4JY9aGkRKlmG8UeJyVeJ0GlSDFacAW8WPUmAirYqL64EvXD6fdQ4nVi0HQowO+pIMOSgjIoMi0p9JPEU0IkNAkKhWjkVhfms8dZBEBucuwA6kScaO9bTeYLxmJJt1cbEB5csy0cEFZZvnx5tdlOZUmKUAZRnaZR5nPh9Lkx6PQYdXpMOgPtUrI5Pb11uHfHrDfS+fAyB0JAKECsSihk1hvDc8DSzcmUeJ2kmZPxBf18tWcNmZbQ+odpZhtBpajwubHqTRS4S1EqdKxsa2p4KQKRuIJKUeQpxxPwEVQKuykJm8FM8PAQ9NzkTDLMKTJUVIhTgASFQjRyu52FHHSVkmY+OX9069L7dmTPYl3m9OmMBtI6xg5KCjfsxO/yRJWXlZZWm+20qS9JEVRBCtwl+INBNA2aJ6WHE4R4Aj46p7aUD+eiTjRNI+3wUECjzsCYVr1xB3zoNI0DrhLWF+8KLVtyeHgyaASCAQ65SwkohcOUhFlnxB3w4g74SDcnSy9iAlBKUen3UHQ4GZVRZ8B+ePmS09NySTXZsJuS4t1MIUQ9kqBQiEaqxONkZ8VBPAEfdpMN60laS6y2vW9H9yzWlt5kpFnvDrHPuaMAb5kz5ja7w0G/frGznTbVJSkq/R58wQDlPhfJBgt+XQCbwYLDmMTA7E54A35cAa9k/hP1RtO08O+i1slZZFtT2V9ZTLY1lUPuMjxBP5tK9pCMFafPjS/oxxPwoZTCoOko9JSTbU0NzU0jNHQ5iJIvLRoJl9+LO+AleHguaarZRiAYxGowYdYbGduqtww5F+IUJUGhEI3UkoJNeAI+nH4PWZaT1xNW29636noWa0LT66oNCIs27cZTUl7tvlVLT4waPbpJL0lRNT+z0FOOLxgaYpt2OIsohJYT6JWZh1FnwKgzRK0xKER9MuuNtElpBkCrw8Pbc22ZlHorWVu0A0/Ai92URKm3Ek3TKHCVsOfw0heK0M+z7nBvpFlvxBPwYdEb0UmQ2KBcfi+Vfg+Vfk846M+y2DEcTjak1+kZ3qK7BIRCnMIkKBSiEXL5vfiDAQ66y3AYk07qItLVrTVYXe9bdT2LNeFoG3vR89Lt+48ZENqSk8OBX15eXpNNKnPQVUpQqXCmUKPBjN2YRG5yJmXeStqkNCPdnCxLBYi4MukNZFntjGjZPaK80F3G4gMbCapQz2BABXH5vQQJUuFzU+Qpx6QzUuSpwKQzkGK0YNAZMGi6cBIbCUrqT0AFD/fk+sPzkKuGiFr1Js5p2QODTk+xpwKrwYxFRhwIcUqToFCIRkYpxd7KIoo9FaQYraSYrCf1fHk17H2rmkcYDAZrfxJNI7NrG4zJsa+l8kD1mUsBXJWVtT/nKUIphTfop9hTQVApDDo9CkWqKZmACjA45zSZ2yMSQro5hX5ZHdFpGilGK06fhx8ObSGgggSCgXDvoC/ox314lIQnUIFZb8Qb8FNEOWmm0PxGs94ocxPrwBf0oxRUBjxU+NyYdHo0QkOC00w29Do9zZPS6ORoEX5902R5CSGaBAkKhWhklh/czL7KYvSaDkcDfdg/Xu/bic4jzOjSutqA8MDKzcfdvykmklFKEUQd/lnQ0NBINdtQSmHRG+lgz6HYUyEBoUgYmqZFLKmTZDAztlVv9lYWsa3sAD0z2rLLeQi9pie/bD8BFG6/lzKfiySDGZ2mUeF3A1DsdWI3WrGbknD5vWgaWPQnZ951ovEGfBh1BjRNQymF7/A6k6XeX75csxkt2I1WkgxmAioYSiKU21uG7QrRhElQKEQjUuguZ2dFIcXeCrKtjScQqvM8Qk2jef8u1W4uWL2VoNePptPhcDgoLS1FxeiJbGqJZIIqyEF3Gf5ggCSDCYvehA4Ni8GEhkZeSjNZUkKcEjRNo6Utg5a2DABON7UGoEtqSw65y1hbuAOLwYRSCk3TwkGMhsYhdxkAFb5QoGjQ6cmy2HEHfLgCHsw6I5qmYdIZTvlexaqkU2a9kUJ32eG1I62U+1yhpWn0RpIM5vB8QbPeSK4tk+ZJqTj9HjLMKRIQCtHESVAoak0pKC2FAwfA7QavF/x+sFjAbg890tNBf2r/Da53e5xFrC7Mxx3wkm11YNQ1nrdnaR3nEWZ1r773sWjjTgJuLwAOh4NLL70UODxMtYkmkgnNsfJQ5nWhO9yrEjyctdFuSqKTowWZFnu8mylEg8i02Bnesju+oJ9CdwWaBqkmG+U+F/nlBeFhpkkGMxaDkQqfm/2uEoIqSKrJhtPvwR8MEFBB0sw2lIJkowUFaJDw8xOrkk4BFHnKMWh6yryVOExJ+IMBNC10vSlGa/h6u6blYtGbInpsM+LQdiFE49N4PnWKRkcp2LwZvv8e1q4NPTZtgr17Q8HgsRiN0KYNtGsHnTtDv37Qvz907Ag6+TIypjWF+RR6ynEHvKRrjWMOR35+Pt8tXhyz9+54jEkWDNbYCU8KVm4h4PWFnx/ZE9gUE8kED68J5gv68QUDpJuTMeoNdEtrjVFvoLk1LeE/wApRV0adgZyk1PBzs95IqbeSAlcJqWYbOkJD7fe7SlAoPAEfbVOasb+yOJxRs8zrwmIwsbeyCAXoNR0mnYFko4ViTwVZVkdo2YwjehSDKgiH1/tsDCp8Lgw6PQZNj17Tsc9VTFAplFIkGcyY9QbcAR92o5Usq4N9lcXoDgeCSXozdlPSSVvaSAiR+CQoFBEOHYIPPoAvv4SFC2Hfvrodx+eDLVtCjyOnoqWlwahRMHYsjBkDLVrUS7MTnsvvxX84E19LW0ajCABOZB6hwWoms5pewoNr8wl4feEho02pJzAWpRR7nIWY9AaCSpFmTibVlMTwFt2Pv7MQTVReSjPSzDbSzSnhskq/B2/Aj9PvoUVSGods5Wwq3UORpxylQKdp6NAw6PQ4/W5sBjMH3aWYdEb2OovQaVq4J1Kn6fAG/SilyLI6cPk92AwWFIrg4Xm9Nf097QmEvgAzHZ7nV0UphSI058+kM4TWGzVa8Ab9mHQG9leWoB2eT5xkMOEN+PEEQ/MCq4aHWvRG/CpI0uGh5Wa9keEtumMxmCjylKOhSaIYIUSNaOrI8Qci7srKysJzq+z2hhkmVlwMc+bAW2/B119DINAgpwVg8GC44gq45BLIzGy48zYmQaX4fPdKijzl+INBmjWSuYRz/zc35vqFNdF8wGkxy8t2HMC5vwhNp2PkyJFNrkewilKKgAqiUBR7nCgUDpMNk05PstFK/6yO8o2+EPVkZ8VBdJqONYXbCaggBp2eoFKcnpbLT8W78B/OfooGTp8HCA3l1ms69JqOcp8Lq96EO+hDh0YolAOjpscb9GMzWtChYdIb0NDQaxqH3OWhekqh03SH91HoNT2ZFjtOn5tKv4eACqLTtPAyHUFC/2qEEudomoZVHwrwUoyhnj6n341JZ6BNchYOk439rmLapeTgDfoo9lTQM6Np/l4V4lTVULGBBIWNTEPdeKVCAeArr4SCQY+nbsfR68FkAoMBKivrHlAaDDBhAtxyCwwdCo2go6xBBIJB5u1eSYm3kgq/mxZJaY1msv8rL79c++UnjpFYJuj1R2UaHTV6dJMMDEu9lTh9bkBhM1pINlqxGcz0zWxPstHSaH4GhDiVrCvaiTvgpXdGO4IEw/O2/cEA5T4XJV4nhe5yDrpK8QYD4WGjvqAfAFfAG95Hh45Kvzvc82fVm3EHvOG1/4w6A2lmG06fB6NOj18FAA13ILQGrVFnIN2cjNPnxqQ3ElQKvwpgPbwWYBCFRW+ig705lX4Pu52FtE7OpL09h6/3radHeptwch4hxKlNgsIm6mTfeJ8P5s6Fp56CVatqtk9ODpxxBnTvHnq0bQvNm4fKLZZf6ikVmmtYXAw7d8K2baHhoz/+GJqXWNOhqKefDnfdBVdeGQo4T2Vri3awtmgH/mCADEvjyP5WtR5hXXoJk1tmktIqK6q8fNdBKvYeiipPTUsLJ5hpCsq8lVgNZg64Ssg0p6DTNEx6I4OadSbVbIt384QQhILE7eUFZFhScAd8HHCVsNdZhCvgpU1yFgWuUjRNC885tBstOMzJ7KkI/Y5z+j1YDCbaJGexrewABp0OjV8CTL1Ojx6NHhl5rC/eQSAYxGG2UeypYFjzbhR6ytlRfpBu6a1l6KcQQoLCpupk3XiXC156Cf72t1DAdixJSb/M+Rs2LJQcpj567nbtgi++gE8/Df1bepyElq1bw333wbXXRgafpwp3wMcXu1dx0F0WkQkunk5kHqGteQb21s2iyit2H6J8z8GY++h0Oq6fOrVO50s0ARWkwFWCPxgkyWAmw5JC6+QsMs0pZB+RREMI0fjsrDjIT8W7GNOq9+Ehnzqq/iweOU/QG/CzpWwf7ew5WPRGij0V6A9nD3YHfOyrLKJlUgYmfajHMagUGqAIJZKRdUeFEEeToLCJqu8b7/PBq6/Co4/Cnj3V1zMa4cIL4fLLQwFh0kn+u+TxwOefwxtvhBLbuFzV123eHB58EKZODbXzVLF4/wa2lu3HrDc2mg8CdZ1HaE5NIb1zq6hyn9PNoZ+2QzD2r5mm0lPoC/rZX1lCitGK1WDCqjdxTssep/zaaUIIIYQ4MQ0VFMZ/rJo4KZQKDRM9/XT4zW+qDwg7dgz1Hu7ZA2++CRdddPIDQgCzGcaPDyW42bcPnn0WOnWKXXffvtBcw+7dQwHkqfA1RqG7nAJ3WSh9eCMJCAHK6rAeod5iihkQBiq9HFqXX21ACKf+ovTegJ9iTwUHXCVkWR3YTUmYdAZGt+olAaEQQgghGg3pKWxk6uPbgDVr4NZb4Ztvqq8zaBDce28oMGss6wYGg6HlK/70J/j22+rrDR0aCiJ79my4ttUnl9/L/L1rKHJXYDdZMelPfvdn1TzBsrJS7HYH/fr3Cyd4OXIbmkawFtmCNJ2OnH6dY247uHYbZi00RMrlcmG1WkHTcFVWnvKL0iul8AR9FLrLMej0pBitdLA3p4M9B03TSDLEXr9RCCGEEOJIMny0iTqRG19SAg89BC+8EAqwYjnnHHjkkdBSEI1VVWbU6dNh/vzYdfR6uOOO0LWkpMSu0xg5fW6+3LMaT9BPqddJK9vJX4ejunmCPXv2ZMuWLTidzjofO71La8yO6AQph9bn46twh583pSyjnoCPQ+6yw+uD2dBpOlJNNgbnnCa9g0IIIYSoFRk+KmpMKZg5MzT88vnnYweE/fqFFqT/8svGHRBCKKnNsGHw1VewcCH07RtdJxAIDXvt2hXefTdxhpR+vW89FYfXp8qxNkxymeXfL49Zvnr16hMKCDNObxszICzfVRAREAIsXx67DaeSQDBIqbeSQ+4yDDo9mZYULHoTZp1BAkIhhBBCNGoSFCa47dtDWUKvuQYOxkjw2LZtaB3CZctCvYSJZujQ0HIWr78eykZ6tN27Q/Mgx48P/b8x21F+EFfAizvgJdua2mBBQl3mCR6PLScDU7I1qtxbVknF3sKo8pLiYvLz8+u9HY3JPlcxTp8bi95EhjmFlrYMxuT25tzWfSUgFEIIIUSjJkFhggoGQ8NEu3ULLe9wNIslNLTyp5/g4osTezF4nQ6uuAI2bYJp00JJao720Ueh12LWrMbZa1jhc7P84GZKvU7SLSev6z8Wu91Rr8czO5Kxt4leeiLo9VO4YUe1+30xb94pGRgqpQgEgxh1elJMVhymJHpl5NE3sz36RrDupBBCCCHE8cgnlgT088+h4ZW33gqxRv9NmBAKBh9+GKzRnTkJy2IJzZlcuxZGjozeXloKU6aEeg337m3w5h3TT8U7cQW8ZFvTsDRAYpkj9etffxk+NZ2O9C65MbcdXHf8gO9UG0bqCwbY7SzkgKsEs85Ikt5Ee3sOucmZEWuXCSGEEEI0ZhIUJpBgEJ57LpR1M1Zm0ZYt4cMPQ3PsTuWcHh07hrKUzp4N2dnR26t6DV9/vXH0Gu6rLGZHxUGUUujiECjk5eUxavRoUtPS0Ol0pKal1Snr57EyjRas2kLQ5z/uMcpK638oa7wUuEo55C4jxWgly2In2WiheVI6p6VGL88hhBBCCNGYSfbRRqa6DEN798K114YWfI9l6lR48klw1O9IwUavuBjuvBP+85/Y2y+6CF56CTIyGrRZAASVotBdxtqiHex0HqKZxdEoeo+qy0Z6PM0HnBazvGjTbjwl5TU6xqmwWH2Jx4kn4EPTNDItKWhoaJpGXko2nRwt4hL4CyGEEOLUJNlHRdi770KPHrEDwry8UJbOl15qegEhQFpaaB7he+/F7jV8553Qa/fVVw3eNPZXFvPdgY2UeJ3oNF2jCAih+mykx2JJj/1LyO/21jgghMRfrN4X9OP0uwmoIKkmGz0z8jivdV/Oze1Dl9SWEhAKIYQQIiFJUNiIlZfDddeFersKoxM6cvvtofl1I0Y0fNsamwsvhPXr4fLLo7ft3Ruag/i734HH03Btcvo9lHorKfRU4DAmNdyJj6O22UitmQ7SOraMKg94fBxcu61Gx9Dp9YwePTphF6sPqiBl3koOuEqxGkw4TEm0smXQMik93k0TQgghhDhhEhQ2Ut9/D716wb//Hb2tRYtQxtFnnwVb9DJxTVZGBrzxRmgJjljDRZ96CgYOhA0bTn5bij0VrCveCUArWwYmveHkn7SGapON1GA1kdq+RVS5CgQpWLUFgpGjz6vtDVUqYQNCpRR7nEW4Az7STDZSTcmkmZPpk9mu0fT+CiGEEEKcCAkKq1FUVMSkSZOw2+2kpqZy3XXXUVFRccx9hg0bhqZpEY8bb7yxTucfMwa2xeiEufhiWLMmdvZNEVL1Go0aFb1t1Sro0wf++c+Tm4Rml7OQUq+TVHPyyTtJDeXn5zP3f3N55eWXmfu/ubRpE2PBx2pk9Wgfs7xg9daY5dVNUbYn6NjmSr+H3c5CrHoTGZYUbAYzg3NOY0SL7hIQCiGEEOKUIUFhNSZNmsT69ev54osv+Oijj1i0aBE33HDDcfebOnUq+/btCz+eeOKJOp0/GIx8npwMM2fCm2/GJ2lKomnRAj77DP72NzCZIre53XDzzaEhpwcP1v+5N5fu5eeSPZh1RoxxXrR82dKlfDFvHiUlxQSDQUpKilm9ejVmi+W4+zrymscsL9myt0aZRo+UiHMJvQE/he5yTHoDqWYbp6W2YmiLbjhMSRIQCiGEqDcrVqxg9OjRpKamhjsVtm/fHu9m1dnMmTPRNI1HHnkk3k0RtSBBYQwbNmzgs88+45VXXmHAgAEMHjyY559/njlz5rD3OAvgJSUlkZOTE34cL0uQx+OhrKws4nG0M8+E1avh6qsTexH6hqbTwV13hYbidu0avf3DD6F791DwWF/KfS5+Kt5NsdeJwxTfeYT5+fmsXr065jaD/tjBqi0nnaRmqVHlnpIKXIXHn5N45PIXiTiXsNzr4oCrhAxLCqkmGwadnvb2HJIM5ng3TQghBLB9+3Y0TWPYsGHxbsoJKS8vZ/z48Xz55Zf06dOHq666iquvvprk5PiPNKrOlClT0DSNhQsXxrspoh5JUBjDkiVLSE1N5YwzzgiXjRw5Ep1Ox7Jly4657+uvv05mZibdunXjgQceoLKy8pj1H3/8cRwOR/iRm/vLwuB6PUyfDl9/De3andg1NWU9e8IPP8Ctt0ZvO3AAzj0XbrsNjnOrjsvpc7No30+UeJ1kWexx7006VpZRp9OJpov99tcMeuxtYqRyBYo27arRuctKS7HbHfTr1y/hAkJvwIfT78ZmNNMmOYszsjowJCfGtwpCCCHECVq+fDl79+5l8uTJzJ8/n1mzZjFz5kwyMzPj3bQ6+9WvfsWGDRu4NdYHL9FoSVAYw/79+2nWrFlEmcFgID09nf3791e73xVXXMFrr73GggULeOCBB/jvf//L5MmTj3muBx54gNLS0vBj167Qh+527eC77+DBB8HQeHKUJCyrFZ5/Hj7+GI66tQD8/e/Qty+sWFH3cyw+sJFCdxk6TcOsN9b9QPXkeFlG1dFjlAE0jZy+nWLW37d8Y43PXTVU9Yt588jPz6/xfvHmCwYocJeRbk7BYbLRNS2XVrYMbMbjD7cVQgghamv37t0AtDuFvv13OBx06dIloQPbpqhJBYX3339/VCKYox8bN9b8g+/RbrjhBsaMGUP37t2ZNGkS//nPf3j33XfZujV2Ug4As9mM3W6PeAB88w3071/npohqnHdeKAnNeedFb9u4MZSd9E9/An/tpsxR7nXhCfhwB3xkWk7ewqK1UZsso1Wa9+8Ss/zQuvyoTKM1tXx57ddFbGhKKYrc5RS4Ssg0p9A5tQXjcvvKcFEhhGiEHnnkEfIOj0L5+uuvIz7HTZkyJVxP0zTatm2L1+vl0UcfpUuXLpjNZiZMmBA+jqZpzJw5M+Z52rZtW+2onw0bNjBlyhRyc3Mxm81kZ2czceJE1q9fX6NrqBr+evXVVwMwbdq0qGuoS/sWLlwYPkZRURE33XQTzZs3x2w2061bN/4dK639Ybt27eL222+nU6dOWK1W0tPTOeOMM5g2bVp4epOmacyaNQuA4cOHR7z2VfMgjzWnsLKykunTp9OtWzesVisOh4MhQ4YwZ86c417jK6+8Qo8ePbBareTk5PCb3/yGkpKSaq9H1E6T6oO6++67I35ZxNKuXTtycnIoKCiIKPf7/RQVFZGTk1Pj8w0YMACALVu20L597CyO1WnEQ8kTXnY2fPRRKAPpPfeAy/XLNr8/1Dv7ySfw3//WfNjuuuKdFLhKYwaE+fn5LP9+OWVlh4dU9u8X/mN2MvXr348v5s2rcf3sPrF7CMu2H8DndNOmbVt2xJj4bktOxlVZSTBWzyOhoaSNmdPnpsTrxKI3kZOURoukNDo7WsZ9+K8QQtREMBh7LeNEkJERmv9fW7169eLiiy/m7bffJjs7m7Fjx4a3DR48OKJuMBhkwoQJLFq0iKFDh9KjRw8yTjBj33vvvcfEiRPxeDz06tWLgQMHsmvXLubOncuHH37Ip59+ypAhQ455jOTkZK6++mq2bNnC4sWL6dmzJ7169Yp5DXVRUlLCoEGDqKio4Oyzz+bQoUMsWrSI6667jmAwyPXXXx9R/5tvvmH8+PGUlJTQtm1bLrjgAlwuFxs3buSRRx7hwgsvpFevXlx99dV8++23bN26lTFjxkR8Lj7ePMjy8nKGDx/OihUryMrK4vzzz8fpdDJ//ny++eYblixZwrPPPhtz33vvvZdnn32WYcOG0aFDBxYvXsxLL73Ehg0bwl8MiBPTpILCrKwssrKyjltv0KBBlJSUsGLFCvr27QvA/PnzCQaD4UCvJlatWgVA8+axsziK+NG0UAbSESPgyitDcw6P9N13obmITz8N11137AQ/e5xFHHCVYDNasBoiU53m5+dHBGZVQypHjR59woFhfn4+3y1ejNPpBMBms3HmWWeRl5cX3lZTBqsZnTE6+UzZjgKcB4oAKC0tZdTo0Sxfvjw0Z9DhoP8Rcwbn/m8uJSXFUcdozMtRKKUo97kw6gykm5MZlN2ZVJNN/rgIIRJGYWHsaRGJoKAAavCxLMqECRPo1asXb7/9Nl26dKm2Jw1CvV9ms5lNmzbRsmXLujf2sO3btzN58mSMRiMfffQRI49YI+yzzz5j/PjxTJ48mS1btmA6Ov35ETIzM5k5cyYzZ85k8eLFTJgwoV6zdb7//vtMnDiRmTNnYjaHRr289957/OpXv2L69OkRQWFRUREXX3wxJSUlPPnkk/z2t79Fd0S0vmTJElq0CK1ZPHPmTKZMmcLWrVu5//77a5Xo5/e//z0rVqxg+PDhvP/++6SkpACwceNGhg4dynPPPceoUaM4//zzo/b973//y5o1a+jcuTMAhw4dYtCgQXzzzTcsWLCAESNG1Po1EpGa1PDRmjrttNMYO3YsU6dO5fvvv2fx4sXceuutTJw4Mfym2LNnD126dOH7778HYOvWrUyfPp0VK1awfft2PvjgA6666iqGDBlCjx494nk54hi6dPll7ubR31ZWVMDUqTB6NFQ3Le6gq5QfD22lwufGEmMeYXXJXk50SGVVsFkVEEIoecwX8+aFl6E4ctuxaHodWT1id4k69//y9XNZaSl5eXlceumlXD91KpdeemlEEpl+/WMvO9FYl6MIBIPsqyzGpDeSYUmhT2Y70szJEhAKIcQp5vHHH6+XgBDgmWeewel08vjjj0cEhABjx47lpptuYteuXXz88cf1cr66stvt/P3vfw8HhBAKprt168bOnTsjlrx45ZVXOHjwIGPHjuWee+6JCAgh1FlydK6N2nI6ncyYMQOdTsc//vGPcEAI0KVLFx588EGAansKp0+fHg4IIRRUV60FvmjRohNqmwiRoLAar7/+Ol26dOGcc87hvPPOY/Dgwbz00kvh7T6fj02bNoWzi5pMJr788ktGjx5Nly5duPvuu7n44ov58MMP43UJooaMxlCW12+/hVijfL/8Erp1g+eeg0AgVKaU4pC7jB8ObqHUW4k/GIiZXKa6ZC8nOqTyWJlF165bV+Pj6M1Gcs7oHHPb/uWbIp4fr8cvLy+PUaNHN/rlKJRSlHkrOeguI92cjMOUxJhWvchJSot304QQQtQzTdO44IIL6u148w6P/rnoootibj/77LMBwp0G8dK3b9+Yw2Q7dQpNFdm3b1+47MsvvwTgN7/5zUlrz4oVK3C5XPTp04cuXaLzF1x55ZUALF68OOZ0lNGjR0eVxboWUXdNavhobaSnp/PGG29Uu71t27Yo9UvijdzcXL7++uuGaJo4SQYNglWrQmsbvvJK5LbKSrjjDpgzB156CRxtC1ldtJ1iTwUuv5dWttjzE+x2x0kZUnmszKLBqsi1Bpr16hCz/NC6/KjspDXp8cvLy2uQ+ZInosLvpsLnRq/TYdIbODe3DzpNvh8TQohTUbNmzSJ6y05UVQ/b8XoeDx06VG/nrItWrVrFLK/qofN4POGyqsz3tc1/URtV63y3bds25vbU1FQcDgelpaUUFxdHBbSxrifWtYi6k6BQiCMkJ8PLL8OECXDjjXA4U3TYkiXQqxfc+/dSugyrxIefVraMaoccVpfs5USGVObn51O3PKCRHHmx57pW7C1k2FlDqp07mMhcfi+lXifJBisWvZE+me0lIBRCJLSMjNDcvER0gvleasRiqfuSQrF6rKrKqrKGVqc2OSjqqroEb0DUENBEcKzpG4l4PYlGgkIhYhg3Dtavh/vugxdfjNym6QNs2l+Md5OblsmpNO947DloNpstPL/PbLFg0Ov58ssv65SJ9OjENXVlTLKQ1Cw1qjzo9aOv8CVEj19tKKXwBv2Uep2kmpKxGcz0yWxPTlJqvJsmhBAnRKerW7IWQTgRTEVFRdS2QCAQc23qVq1asXXrVv7v//7vhLOYnoz21UVubi4bN25k69atdO/evV6OebSqnBw7duyIub20tJSSkhKsVitpaTKdIx4k7BaiGnZ7aNmKhQuhY8dfys//3WqSUr0U77OwaIGBt9+GnTvg6O67WMlgPG43TqczYnH3ZUuXxjx/fn4+c/83l1defpm5/5sbXtrihGkamd2jA76A28uBlZtp06bNiZ+jEXH5vex2FlLoLsOoM2AzmDktrZUEhEIIkcCqAiZ/bRcWPkJVdviff/45atuCBQvw+XxR5aNGjQLg3XffrfN5a6ou7auLqoQ5R+bOOJa6vPZ9+/bFarWyYsUKNm/eHLX9tddeA+Css86SXsE4kVddiOMYOhRWr4aHHoLMVpVY7V7sWS7KC0NDUooK4bPP4P334fCQeeDYyWCOtHr1al761794/bXXyD+c5rQqoCwpKY4IIGPNT6wVTaN5NYllDq4LnXtLjF/Wiarc56LQU4ZZb8RhspFstHBWThfyUrLj3TQhhBAnIDMzE6PRyNatWwnUYi79karWEnzttdcisnHm5+dz++23x9zn7rvvxmq1cs899/DOO+9Ebfd4PLz11lvsPnr+SQO1ry6uv/56MjMz+fTTT3nmmWcicmYALF26NGL97qpev02bIhPSHYvNZuPaa68lGAxyyy23RHxh/vPPP/PYY48B1Ot1idqRoFCIGrBa4Y7fl/PcZz+R286Hz6PH54ocfX3gAHz0Ibz3HuRvO3YymFiqlpSotx7BGGzN0kAXPdy1ZOteVCAYbsepQClFicdJkt5MitGKzWCmmdWB3ZgU76YJIYQ4QSaTibFjx7J//3569uzJVVddxfXXX8+rr75a42O0b9+eq666iuLiYnr16sX48eMZOXIk3bt3p1u3bjFHznTo0IHZs2fj8/m4+OKL6dixI+PHj+fyyy9nyJAhZGRkcMkll9RLopm6tK8u0tPTefPNN0lJSeGuu+6iffv2XHbZZYwfP56OHTsyaNCgcKIYgAsuuABN07jnnnuYMGEC119/Pddffz2FhYXHOEtoaZC+ffvyxRdf0K5dOy699FLGjRtHz5492b9/P7fffnu9ZooVtSNBoRA19OOhbbgNpXTt4adv5xTS0mPXKzgAX3wBQVX9orXH8t3ixZTWMqCsCVvzDOxto3vIgv4ArkP1f754qlqU3qQ3YNGbMOkNDG3RjTOzu8g6hEIIcYp45ZVXuPLKKyksLOSNN95gxowZtc4E//LLL3P//fdjt9v5/PPP2b59Ow888ACzZ8+udp8LL7yQNWvWcPPNN6NpGl988QUff/wxBQUFXHDBBcydO5euXbue6OXVuX11MWzYMFavXs2NN96IUor33nuPxYsX43A4ePTRRyMyk/bt25fXXnuNrl27Mm/ePGbMmMGMGTMoLy8/5jlSUlL4+uuvmTZtGpmZmXzwwQd88803nHHGGbzxxhvVrlEoGoamju4jFnFVVlYWTslrt9vj3Rxx2F5nEUsKNuEN+jHrjFgNJpSCLZvhhxVQXnb0HvnAiSeEqS86k4Hs3h1jbtv3/YaI+ZCaTsfUqVMbqGX1z+X34g8GqAx4SDclY9TpSTUnM7BZJwkIhRBCCJFQGio2kOyjQhxHpd/DikNbKfNWkmVxoD88AVrToGOn0IL3GzfBqpXwS4KwxXFrbyy2ZrEzeRWs3BKVICeR+YMBSrxOAipAjjWNNslZtErOIN2cEu+mCSGEEEI0WhIUCnEcKw9to9znwq8C4YDwSDo9dO0KXbqE5hKuXg2HDjWeeXlJWakkt8yMKi/csJOANzpzmcPhaIhmnRQFrhLspiTMOiMtk9LpkdE23k0SQgghhGj0ZE6hEMewtWw/+10lOP0esq2px6yr00H7DnDRRQ3TtprQdDrsbaLnEbqLyvGWxQ5c+/frd7KbdVKUeSvR6/SYdEYuaNOPPlntj7+TEEIIIYSQnkIhquMJ+FhXtJNCdzktbOnotRp+h6JFLlgfNxrk9ItefsJf6aF4c3SqbFtyMmedeSZtE2zReqUUBe5SNCDZYCHFZJW5g0IIIYQQtSBBoRDV2OU8RKnXSfOktJoHhIededZZfDEvvolmqluPsOjnXeH/azodDoeD/v36JVwwWOWQuwyL3oRe02E1mOiX1SHeTRJCCCGESCgSFAoRQ6Xfw8aSPeg0DYNOX+v98/LyGDV6NMuXL6ekpAQaOMmvIckcGs96lJKfdxP0BUhNS0voQLDKIXcZfhUky2jFGwxwZnYXUozWeDdLCCGEECKhSFAoxFG8AT9f7F5Nhd+NRV+3tQYhFBjmHQ66li1dyuo1ayKDw6ohjnUMGDWdDpPJhNfrRdM0VDCIptOhlCKre7uo+pnJDi64dEidztUYFbrL8QR8NE9KA03DZjSTZk6Od7OEEEIIIRKOBIVCHGVvZRGVfg8ASQZzvRxzwMCBDBg4sF6OdTzbCnazfu+2qPJB7Xs0yPlPNqUUhZ5yfMEADlMSOk3H6Fa9aj3EVwghhBBChMinKCGOUOJxsrpwO5V+D2kmW8IlLPl5/46YAeE5p/X/pWcywZV4neg0HSlGC0kGM70z2klAKIQQQghxAqSnUIjDKnwuvtn/E4WeMjIt9oQLCAsrStm0f0dUeWaygySzJQ4tqn/lXhdKKZKMZjo5WtLalkmKSeYQCiGEEEKcCAkKhQAqfG4W7fuJYk8FyUYrJn3ivTX2FB2IWT7wFBg26g8GCKogTr+bLIudDo7mdEltFe9mCSGEEEKcEhLvk68QJ8HSgk24Al7cAR8ZlpR4N6fWDpYXs6Nof1T5gHbdEq7HM5b9lcVomkaK0crA7M5kWuzxbpIQQgghxClDJuKIJq/S78Ht91Lhc5FqsqFLsPlpJZXlLN26Nqo8Ny2bZvb0OLSofh1ylYGmYdQZaGFLJ8OceEG7EEIIIURjlliffoWoZwWuUlYXbqfQU4En4K+3bKMNRSnFki1rosrbZjSnV5vYi9cnCqUU3oCPIEGSDCbSzcl0sDc/JXo+hRBCNLyZM2eiaRqPPPJIvJuScOS1O/VJUCiaLG/Az/cHN1PoKUcDWtky0MdY8L0xW7p1Lf5gIKIsKzmVbq06xKlF9afY66TAXYbNYCHNlEy/rI6hNQmFEEIIUa+mTJmCpmksXLgw3k0RcSJzCkWTtbV8Py6/h0q/l1RTUsL1QK3YvoFDFSVR5X3anpZw1xKLL+jHYUoi02JnWItu8W6OEEII0WT96le/YuDAgWRmZsa7KeIkkaBQNElBFWR7eQEVPjf+YACT3hjvJtVKpcfN3pKDUeVntO2KyZBY13K0oApS4CrFrwIkGeyc3bxrvJskhBBCNGkOhwOHwxHvZoiTKLHGyglRT9YU7cAT8OELBmiVnFjfeiml+HbzqqhyhzWZ5qmJdS1HC6gge5xFmPQGsiwO0kw2WZheCCHEMS1ZsoQLL7yQrKwszGYzbdu25eabb2bv3r3V7rNp0yYuvvhiMjIysNlsnHXWWXzyyScx665bt47JkyfTrl07LBYLWVlZ9OrVizvvvJN9+/ZF1d+wYQNTpkwhNzcXs9lMdnY2EydOZP369VF1j5yr9/PPPzNx4kSys7PR6XS899579OjRA03T2LhxY8y2FRYWYjKZyM7Oxu/3A+B2u5kxYwYXXngh7dq1w2q1kpqaypAhQ5gzZ07UMTRNY9asWQAMHz4cTdPCj+3bt0e182iVlZVMnz6dbt26YbVacTgc1Z4LoG3btuERTa+88go9evTAarWSk5PDb37zG0pKSqL2qaio4PHHH6dnz544HA6Sk5Np3749l1xyCZ9//nnM84jakU9boklRSuH0udlZfpBSbyVWgyneTaq1tbs34/F7o8oHd+rV8I2pR96An73OImxGM+mmZE5Pa80ZWYk/N1IIIRLRO++8Q8+ePbFarfTs2ZN33nkn3k2K6bXXXuPss8/mgw8+oHPnzlx00UWYzWb++c9/0qdPn5jB1NatWxkwYAArV65k9OjRnHHGGSxZsoTzzz+fV199NaLuihUr6NevH6+//jopKSlceOGFDBw4EJ/Px7PPPsumTZsi6r/33nv07t2bWbNmkZmZyfjx48nLy2Pu3Ln079+fRYsWxbyOTZs20a9fP77//nuGDx/OqFGjMBqNTJo0CYDXX3895n5vvvkmPp+Pyy67DIMhNABw+/btXH/99fzwww+0bduWCy+8kF69erF06VIuv/zyqMDu6quvpn379gCMGTOGq6++OvxITk4+5utfXl7OkCFDeOihhygoKOD888/nrLPO4vvvv+fyyy/njjvuqHbfe++9l1tuuYXmzZtz7rnnopTipZdeYvz48SilwvUCgQAjR47k97//PXv37mXYsGGMGzeOnJwcPvnkE2bPnn3MNoqakeGjoknZUrafzaV7cQd9aJBwyxuUuSrYURh7PcJEW0rjSEopijzlGHR6HCYbQ1t0S7hMsEIIcap45513uPjii9E0DaUUa9eu5eKLL+btt9/moosuinfzwnbt2sUNN9wAwPvvv8/48eMBCAaD3H333TzzzDNceeWVLF++PGK/1157jauuuooZM2aEA6mPPvqICRMmcOuttzJ69GhatmwJwHPPPYfb7eapp57i7rvvjjjOxo0bI4ZUbt++ncmTJ2M0Gvnoo48YOXJkeNtnn33G+PHjmTx5Mlu2bMFkivxSes6cOdx6660888wz6PX6cHmPHj144IEHmD17NtOnT496DaqCxargESArK4svvviCc845JyLHQH5+PiNGjGD69OlMmTKFtm3bAqFewClTprB161buv/9+hg0bdoxXPdLvf/97VqxYwfDhw3n//fdJSUkJvzZDhw7lueeeY9SoUZx//vlR+/73v/9lzZo1dO4cypZ+6NAhBg0axDfffMOCBQsYMWIEAIsWLWLZsmX069ePRYsWYbFYwscoKytj8+bNNW6vqF7ifooUog72VhbhDvio9HuwJ1hyGaUUX2/6Maq8fbNWCb8eYbnPRZLBTJrJRro5WQJCIYSIo2nTpoUDQgj9/dE0jUcffTTOLYv0yiuv4HK5uPTSS8MBIYBOp+Mvf/kLLVq04IcffmDx4sUR+yUnJ/PMM8+EA0KA888/n1//+tdUVlZG9BYePBiav39kgFelS5cuNG/ePPz8mWeewel08vjjj0fVHzt2LDfddBO7du3i448/jjpWVlYWf/3rXyMCQoDc3FyGDBnC1q1bWbp0acS2HTt2sHjxYjp06MCAAQPC5RkZGYwcOTLqM05eXh5/+MMfCAaDfPjhh1FtqC2n08mMGTPQ6XT84x//CAeEEHptHnzwQQCeffbZmPtPnz49HBACZGZmcuONNwJE9KhW3YOzzjorIiAEsNvt9O3b94SvRUhQKJoQd8BHmbeSYk8F/mAAc4Ill1m3e0vM8k7ZbRq4JfWrwufG6XdjN1qx6I30kyGjQggRVz///HPE8D0IBYZHD5WMt2+++QaI7CWrYjabueSSSyLqVRk9ejRpadFLHF1++eVR9asCjltuuYWFCxeG5+3FMm/ePIBqe1PPPvtsAL7//vuobSNHjiQpKSnmflXX98Ybb0SUv/HGGyilYl4/wLfffstjjz3GTTfdxDXXXMOUKVN48803Aeqld23FihW4XC769OlDly5dorZfeeWVACxevJhgMBi1ffTo0VFlnTp1AoiYq9mrVy90Oh2vvvoqL7/8MoWFhSfcdhFNho+KJmOvs4hynwuT3oDdGPsXb2Pl9LjYXhg9mX1Mt0EYjvpWMZEEVZAyr5M0cwo9MvJoaUvsHk8hhDgVdOrUibVr10YEhpqmRfTqNAZViWSqhkEerap8z549EeVt2sT+MrWq/pEJan73u9/x7bffsnDhQoYPH05ycjKDBg1i3LhxTJkyJWr4KBAeelqdQ4cORZW1bt262vq//vWvue222/jf//7H008/He5NjDV0FKC0tJSLLrqI+fPnV3vM8vLyY7axJo73+qempuJwOCgtLaW4uJiMjIyI7a1atYrap6q30ePxhMs6derEE088wQMPPMANN9zAjTfeSLdu3TjnnHOYMmUKPXr0OOFrEdJTKJqIcp+LtUXb8Qb9ZJhTMOkT5/sQr9/H/A3Lo8pbpjZL6OUngirI3spi0szJWPRGWsjC9EII0Sg8/PDD4SGjQHgo6cMPPxznltVOfUwRsdvtzJ8/n2+++YZ7772Xrl27Mn/+fO688046d+4c0eNW1Rt2ZKKWWI8jh3pWOXpY5JHS0tI477zzKCgo4MsvvwRg9erVrF+/nn79+tGxY8eI+vfddx/z589n6NChLFy4kEOHDuH3+1FKhTN1Ht0TfLIc6x7odDUPQ+6++262bt3Kc889x7hx49i5cydPP/00vXr1qnZ4qqidxPlkLEQdBZVi4d51OP0eTDpDQs0jBFi5M/Zwnd5tGtc3trWhlGKPs4g0s41sayoDmnVKuPsihBCnqosuuoi3336bRx99lE2bNtG5c2cefvhhfvWrX8W7aRFatGjBpk2b2LFjB6effnrU9up67nbs2BHzeFXlLVq0iCjXNI3BgwczePBgAAoKCrjzzjuZPXs2f/jDH5g7dy4Q6vnaunUr//d//xfVK3aiJk2axLvvvsvrr7/OmDFjwr2EkydPjqr77rvvotfr+eCDD7Db7RHbtm3bVm9tqnqdqns9S0tLKSkpwWq1xhyuW1u5ubncdttt3Hbbbfj9fubMmcM111zDvffey1VXXVUv52jKpKdQnPLWFOZT6ffgCwZINdni3ZxayT+4h4KyoqjyUacPSNggKqiC7HYWYtEbSTKYOTO7CwZd4g6BFUKIU9FFF13EqlWrcLlcrFq1qtEFhPDLHL1YSxJ4vd7w/LmqelXmzZsXcy28qnX1qoK/6jRr1iy8rMO6devC5aNGjQJCQVl9O//883E4HLz33ns4nU5mz56NXq/nsssui6pbXFyM3W6PCgiBcAB7tKpsqMeaM3m0vn37YrVaWbFiRcw5iq+99hoQShBTm17BmjAYDEyePJl+/frh9XolA2k9kKBQnNKCKkh+xUEqfC7SzLaECqS8fh/r9myNKu+Z2wmLMXGzc7oDPtLMNrKsDvpldUyoeyKEEKLxuO6667BarcyZMycio2cwGOT3v/89e/bsoW/fvpx11lkR+1VUVPDb3/42IgD69NNPmTt3LlarlWuuuSZc/uKLL5Kfnx917qqF7nNzc8Nld999N1arlXvuuSfmuo4ej4e33nqL3bt31/pazWYzv/71rykvL+eee+5h9+7djBw5kuzs7Ki6nTp1ori4mP/9738R5U8//TQLFiyIefyqXr/aJBOy2Wxce+21BINBbrnlFpxOZ3jbzz//zGOPPQbA7bffXuNjxrJgwQK+/PLLqGQ1+fn5bNiwAU3TYs5PFLUjw0fFKW1z6T4q/W5STEkYdYn1474gxjzCthnNaZ2RE4fW1I+gClLkqSDTksLYVr0lIBRCCFFnrVu35l//+hdTpkzhggsu4KyzziI3N5cff/yRTZs2kZ2dHe6tOtKkSZN45513WLhwIQMGDGDfvn0sWrQIpRTPPfdcRIDx4osvctNNN9G1a1dOO+00DAYDGzduZPXq1VgsFh566KFw3Q4dOjB79myuuOIKLr74Yjp06MBpp52GzWZjz549/PjjjzidTlauXFmnIGbSpEnMmDGDF198EYg9dBTggQceYPLkyUycOJEXXniBVq1asXr1ajZu3Mhdd93F008/HbXPBRdcwKOPPso999zDF198QWZmJgB//etfjzkU9vHHH2fp0qV88cUXtGvXjqFDh+J0Opk/fz5ut5vbb7+dCy64oNbXeqTVq1dz1113kZWVRd++fcnIyODgwYN8/fXXeDwebrvttqghv6L2pKdQnLKCSrGpdA9KQYrRGu/m1Mru4gK8geghHN1aJe5yDS6/l72VxSTpTVh0RgkIhRBCnLArr7ySb775hvPPP58NGzbw1ltv4XK5uOmmm1ixYkXMpRI6dOjAkiVL6NGjB59//jnff/89AwcO5MMPP+T666+PqDt9+nSuvfZaNE3jq6++4sMPP8TlcnH99dezatWqqF7ICy+8kDVr1nDzzTejaRpffPEFH3/8MQUFBVxwwQXMnTuXrl271ulahw4dGg4mk5KSmDBhQsx6kyZN4uOPP2bgwIGsWrWKTz/9lBYtWjB//vyI9RyP1LdvX1577TW6du3KvHnzmDFjBjNmzDhultKUlBS+/vprpk2bRmZmJh988AHffPMNZ5xxBm+88Ua9JIE5//zzefDBB+nUqROrV6/mzTffZP369QwePJi3335bEs3UE001VPohUSNlZWXh9L2xxoKLmjnoKmP5wc2U+1wowGFKnCUo3D4PX6xfFlXeM7dTQvcSFrhKSTm8FuHIlj0TKgOsEEIIIUQ8NFRsID2F1fjTn/7EmWeeSVJSEqmpqTXaRynFQw89RPPmzbFarYwcOVImvsbJ9wc3U+gpp9Rbic2QWPPvthXsiVneOj163kAi8AR87Ko4hDfox2owMaxFNwkIhRBCCCEaEQkKq+H1ernkkku46aabarzPE088wXPPPceLL77IsmXLsNlsjBkzBrfbfRJbKo7m8ntx+T0EVJBWtoyEymxZUlnO1oPRE9DP6dofEnS4ZYnHSZLBTJbFTre01iQlWJAuhBBCCHGqk6/rqzFt2jQAZs6cWaP6SimeeeYZHnzwQS688EIA/vOf/5Cdnc17773HxIkTT1ZTxRH2VRaz8tA2XAEvWRZ7Qs1b8wX8fPPzyqjy1uk5JJmqX9S2MQsEg/hVAIchiU6OFuQmZ8a7SUIIIYQQ4ijSU1hP8vPz2b9/PyNHjgyXORwOBgwYwJIlS6rdz+PxUFZWFvEQdbez4iAH3aV4Aj50WuL8eLu9Hj5b+11UeY49nZ6tO8WhRScuqBR7K4tINydj1OnpnNry+DsJIYQQQogGlzifmhu5/fv3A0StF5OdnR3eFsvjjz+Ow+EIP45c70bUjsvvZbezEIPOQDOrI97NqZUlW9dGlVmNZnq3OS0OrTlxARVkb2UhdpMVs95I25Rs9AkUpAshhBBCNCVN6lPa/fffj6Zpx3xs3LixQdv0wAMPUFpaGn7s2rWrQc9/qvAF/SzctxZ3wIfdaE2oNQm9fh8Vnsqo8tNbtsOgT5z5kEcqcleQabbjMNnolZFHt/TW8W6SEEIIIYSoRuJ8cq4Hd999N1OmTDlmnXbt2tXp2Dk5oaUCDhw4QPPmzcPlBw4coFevXtXuZzabMZsl8caJ2l5ewCFXOd6gnzSTLd7NqbFAMMDn66KHFyeZLDR3JN78u4AK4vS50TRonZJF38z28W6SEEIIIYQ4jiYVFGZlZZGVlXVSjp2Xl0dOTg5fffVVOAgsKytj2bJltcpgKmpvV8UhfirejV8FSLekJFRymR+3x+6ZPqdr/wZuyYkLKsVeZxEmvYE0UzId7c2Pv5MQQgghhIi7JjV8tDZ27tzJqlWr2LlzJ4FAgFWrVrFq1SoqKirCdbp06cK7774LgKZp3HnnnTz22GN88MEHrF27lquuuooWLVowYcKEOF3Fqc8XDPDjoW0UeytIN6dg0Rvj3aQaCwQD7C8rjCp3WJLj0JoTV+KtwKDTYzcmYdIbSDFa490kIYQQQghRA02qp7A2HnroIWbNmhV+3rt3bwAWLFjAsGHDANi0aROlpaXhOvfeey9Op5MbbriBkpISBg8ezGeffYbFkpjLCSSCb/f/RKXfg1FnSKwF0ZXikzWLY27qntuhgRtz4gIqSKXfQ6bZjllvpLOjZUL12AohhBBCNGWaUkrFuxHiF2VlZTgcDkpLS7Hb7fFuTqPmCfj4fPdK9leW0NKWgS6BgpBD5cUxM46O7T4IYwL1dlYp9lSg0zRSTTYGZXfBbrRKUCiEEEIIcYIaKjZIoK4VIX6hlGJr2X5KvZU0szoSKiBUSrFy56ao8n55pydkQBhQQTwBH1lWByNb9sSgS8yMqUIIIYQQTZUEhSIh7ag4yJqi7Rg1PeYEC6Q27M3H7fNGlDmsyeQ4MuLUorrzBvwUeyvIsjoY1KyzBIRCCCGEEAlIEs2IhHPQVcqaou14Aj4c5sRZfgJg477tbD24O6JMA/q2TbxF6j0BHwXuUsw6I10cLcmwpMS7SUIIIYQQog4kKBQJpdznYmnBzxS6y3Ek0HqEACWV5Ww+sDOq/PSW7bGZEytTp1IKp99NitGCw5RE86S0eDdJCCGEaHQWLlyIpmlR62Q/8sgjaJrGzJkzT+r5NU2jbdu2J/Uc4tQgQaFIGEopFu5dR7GnApvBknDDRr/bvDqqrEtOW/KyWsahNSdmt7MQpSDFaGVwzmnYjJJhVwghhGhI1QWcQtSFzCkUCaPS78EV8IbWwDMlVs/aqp2bCKhgVHkiBoTlPhdGnR67ycp5uX0ly6gQQghRS7feeisTJ06kefPmJ/U8GzZswGhMrC/RRXxIUCgSxo6Kg3gCPtLNibW4e0llObuKDkSV92jVEYM+sRKz+IJ+3H4vdlMSRp1BAkIhhBCiDjIzM8nMzDzp5+nSpctJP4c4NcjwUZEQ/MEAm0v3odd06LTE+bFVSvHNzyujyjtlt6ZN5sn9drC+KaUodFeQYrJi0hnISLDgXAghxKlj+/btaJrGsGHDKCsr44477iA3NxeLxcJpp53G008/TTAYPUKnbdu2aJqGUornn3+enj17kpSURK9evcJ1/H4///znPxk0aBB2ux2r1UqvXr145pln8Pv9Mduzfv16JkyYQFpaGikpKZx99tl89tln1bb/WHMKfT4fL774IoMHDyY1NRWr1UqHDh245pprWLFiBQBTpkxh+PDhAMyaNQtN08KPRx55JHyso+cUvvPOO2iaxmWXXVZt2+6++240TeO5556LKK+srOTxxx+nd+/eJCcnk5yczMCBA5k1a1a1x4pl5syZ4XZu3bqVSy+9lMzMTOx2O+eeey4//fQTELoPf/7zn+nUqRMWi4UOHTrwwgsvVHvcXbt2ceutt9K+fXssFgvp6emcf/75fPfdd1F1lVLMnj2biRMn0qlTJ2w2GykpKfTv359//OMfMX92jrxna9euZfz48aSlpWGz2Rg6dGjM8yQS6SkUjZ7b72XB3rWUep2kJVggsnrnzzHLOzdv27ANOUFKKTxBHxa9kVZJGVT43bSz58S7WUII0eQFVZAylzPezagTu9V2wl/0ejweRowYwdatWxkxYgRer5evvvqK3/72t6xevbraRC433ngjr776KkOHDuW0007D6w0tFeVyuRg3bhwLFiwgPT2dgQMHYrFYWLZsGXfddRcLFizg3XffRaf7pd0//PADw4cPp6Kigm7dutGtWzc2b97Meeedx0033VSr63E6nZx33nksWrQIm80WDgy3b9/O66+/jsPhoG/fvgwePJj9+/fz+eef0759ewYPHhw+xpEB7tHGjRuHw+Hgww8/pKKiguTkyM9VwWCQOXPmoNfrmThxYri8oKCAUaNGsWbNGnJychg6dChKKb777jumTJnCDz/8wPPPP1+ra83Pz6d///5kZ2czcuRIfvrpJz777DNWrFjBmjVruPHGG1m4cCHDhw+nXbt2LFiwgFtvvRWTycTUqVMjjrVkyRLGjRtHcXExnTt3Zty4cRw8eJDPP/+czz77jNdffz0iEPZ4PFxxxRVkZGTQtWtX+vTpQ2FhId999x233HIL33//fbU/Oz/88AO33HIL7du3Z8yYMWzcuJFFixZxzjnnsHz5crp161ar16GxkKBQNGpKKebvXUuZrxKdpkuodfCCwSC7iqOHjQ5q3z0OrTkxpd5KKv0eMiwpnJHVQYaNCiFEI1HmcvKr5+6OdzPq5N3b/4/UpBNbzmjp0qX06NGDzZs3h4djbt26lSFDhjBr1iwmTJjAhAkTovZ75513WLlyJaeffnpE+T333MOCBQu47LLL+Ne//oXD4QCgvLyciRMn8sEHH/DSSy9x4403AqHPKVdffTUVFRU89NBDTJs2LXysf/zjH9xyyy21up477riDRYsWMWTIEN566y2ysrLC2w4cOMD27dsBuP766+nQoQOff/45gwcPrnEWU7PZzK9//WtmzJjBu+++y5VXXhmxfcGCBezdu5exY8fSrFmzcPk111zDmjVruOOOO/jrX/+K2WwOt+n888/n73//O+PGjWPs2LE1vtb//Oc/3H///fz5z38O995ee+21zJw5k3POOQedTsfmzZvDr8FXX33FyJEj+dOf/hQRFJaVlXHxxRdTVlbGa6+9xqRJk8LbfvjhB0aPHs3111/PiBEjwscyGAy8++67jBs3LmLO5cGDBznvvPOYNWsW1157LUOGDIlq9wsvvMCzzz7L7bffHi676667eOaZZ3jiiSf4z3/+U+PXoDFJnHF4oknaVn6ACr8bl9+bWHMJleLjNd/G3JSZnNqwbTlBvmCACr+bVLMNs17mEQohhGhcnnrqqYj5ee3bt+ePf/wjAH//+99j7nPfffdFBYQFBQW8/PLL5Obm8uqrr4YDQoCUlBRmzJiByWTin//8Z7h84cKF/PTTT7Rr146HHnoo4ng333wzAwYMqPF17N27l5kzZ2I2m/nPf/4TERACZGdn1+p41Zk8eTIAr7/+etS2qrIjA6tVq1bxySef0K9fP/72t7+FA8KqNr300ksAEa9LTbRr145HH300/LlC0zTuuusuAH766SeeeeaZiNfgnHPOoXfv3uzYsSMcHAP8+9//Zt++fdx5550R7QY444wz+OMf/0hFRQWvvfZauNxgMDBhwoSoJDxZWVk8/vjjALz//vsx233WWWdFBIQADz74IACLFi2qzUvQqEhPoWi09jqL2FCymxKPE7spKaGCkR2F+2OWn9fjLEig61BKcdBVSqopiSSDmYHNOsW7SUIIIURYeno6o0aNiiq//PLLuemmm/juu+8IBoMRwz0Bxo8fH7XPwoUL8fl8jB07Fqs1Ost5Tk4OHTt2ZO3atbhcLqxWK9988w0Av/71r9HHSB53+eWXs2zZshpdy8KFCwkEApx//vm0adOmRvvUxZAhQ2jVqhVfffUVBQUF4R5Bt9vN22+/jc1m41e/+lW4/rx58wCYMGFC1OsIhOcYfv/997Vqx7Bhw6KCsnbt2gFgNBoZNmxY1D7t2rVj5cqV7Nu3LzxXsqp9F110UczznH322QAx27dq1SrmzZvHjh07qKysRClFeXk5AJs3b455vNGjR0eVZWRkkJ6ezr59+2LukwgkKBSNUqG7nFWF+ZR4nCQZzDhMSfFuUo1VuCtZszv6F0nP3I7oE2j4K4SWAbEZLbRNyaZraitZj1AIIUSjUl3w5HA4SE1NpaSkhOLiYjIyMiK2t27dOmqfqt6nl19+mZdffvmY5y0qKqJly5bs3bv3mO2ozcLxu3btAkI9nSeTTqfj8ssv58knn+R///sft912GwAfffQRZWVlXHHFFdhstnD9qtflD3/4A3/4wx+qPa7b7a5VO1q2jF6Wq2qOY05OTswgu2q7x+OJat9ZZ511zPMdOnQo/H+v18uUKVOYPXt2tfWrgsOjtWrVKmZ5SkoKRUVFx2xDYyZBoWiUdjkPUeytoNLvoZnVcfwdGpEFG3+IKsu2p9M6I/GyjZZ6K8m0pHBGZvuE6qkVQoimwm618e7t/xfvZtSJ3Wo7fqWTxGKJ/pKzKuNkr1696Nmz5zH3P3IIZSKaPHkyTz75JG+88UY4KIw1dBR+eV0GDx5crwFrrF7Hmmw7WlX7fv3rX0cEs0c7cnmOv/3tb8yePZvu3bvzxBNP0KdPH9LS0jAajfz888907twZpdQJty2RSFAoGp3A4Te32++jlS0joYKRMldFzPK+bU9r4JacmIAKcshVit1kpaOjRULdAyGEaEp0mu6Ek7Uksp07d8YsLysro6SkBKvVSmpqao2OVdUDNHjw4Bpn0qxafH7Hjh0xt1dXHktubi4QSpRzsvXo0YNu3bqxdOlStm3bRlpaGp988glZWVlRwyOrXpcJEyZw992NL6lRq1at2LRpE/fffz99+/at0T7vvvsuALNnz46aW7pt27Z6b2MiODVDXZHQvtyzmh3lBVgNpoQKRoIqyNebfowq79AsN+GGjR5yl6HTdCQbLHRNy413c4QQQoiYCgsL+eqrr6LK58yZA8CgQYNiDkOMZfjw4ej1ej766CN8Pl+N9qmar/b222/HXNuuqh01MWzYMPR6PZ9//nl4KOmxmEwmgGrXTjyeqh7BN954g7feeguv18tll12GwRDZZ1Q1Z7MqkGps6tK+4uJiIPZQ0Llz59ZPwxKMBIWiUdntLMQd8FLsdZJsjJ7k3Zit3bUlZvlpLfIauCUnpsRTgYZGhsXOwOzO8W6OEEIIcUz33HMPhYWF4ef5+fk8+uijALVaEqJly5Zce+21bN++ncsvv5wDB6KXldqyZQtvv/12+PmwYcPo0qULW7du5bHHHouo+69//YslS5bU+PwtWrTgqquuwu12c/XVV0dcE4Syox6ZtKZFixYAbNq0qcbnONIVV1yBpmm88cYb1Q4dBRgwYACjRo1i8eLF3HLLLZSVlUXVWb16NZ999lmd2nGifvOb39CsWTOeeOIJXnrppajg3O/38/nnn7Nu3bpwWadOocR5L774YkTdt956K2GXlDhREhSKRsPpc7Py0DYqfG6UUhgTqHetoKyYnUXRGUdHnNYvDq2pO6UU3qCfTIudES26k5ZIy4AIIYRocgYOHIhOp6NDhw5cfPHFjB8/nm7durFnzx4mT55cbUbK6jz77LOMGjWKt99+O7wo/BVXXMGFF15Ix44d6dixI//973/D9XU6HTNnzsRms/Hwww/To0cPrrjiCvr3789NN93EzTffXOvzn3nmmSxYsIA2bdpw3nnnMXHiRAYNGkRubm5EYpS2bdvSo0cPfvjhB/r3788111zD9ddfzwcffFCjc7Vu3Zqzzz6bDRs28PXXX9O+fXsGDhwYs+5rr71G7969+cc//kGbNm0YPnw4kyZN4vzzz6d169b06tUrbkFhamoq77//Pg6Hg9/85je0bduW8847j0mTJnHOOeeQlZXF2LFj2bLlly/v7733XvR6Pffffz9nnHEGV1xxBf369eOSSy4JL4vR1EhQKBqNzWX7KPVW4g74yLamxrs5tbJ827qosty0bGzmxOrtPOguJdlo5czszlgNpng3RwghhDgms9nM/PnzueKKK1i6dCmff/45ubm5PPXUUzVe0P1IVquVTz/9lFmzZjFgwAA2bNjAW2+9xQ8//EBWVhbTpk3jiSeeiNhnwIABLFmyhAsuuICdO3fywQcfYDAY+PDDD7nkkktqdf6UlBQWLFjAs88+y+mnn84333zDBx98wMGDB5k0aRJXXXVVRP23336bCRMmsG3bNv7zn/8wY8YMfvwxeipLdY7sGYzVS1ilWbNmfPfddzz33HN07dqVlStX8tZbb7FmzRratWvHk08+yT333FOra61PAwcOZO3atdx7773Y7Xa+/vpr3nvvPXbs2MHQoUOZOXMmI0eODNcfMmQI3377LSNGjGDbtm189NFHmEwm3n777Vr1Lp9KNFVdah0RF2VlZTgcDkpLS7Hb7fFuToNQSjFv9yp8KsD+ymJSjFbsCbQExd7ig6zYsSGizGFJZkiXPnFqUe35gn4OuctJ0pvoldmOLqnRaaKFEEKIxmL79u3k5eUxdOhQFi5cGO/mCHHSNFRsINlHRdwtP7gFd8BLhd+DQadPqIAwqIKs3xOdJeyMvK5xaE3dHXSXoUOH3ZREZ0eLeDdHCCGEEEI0IAkKRdwVesop87pwB33YDImz7o9Sio9XfxtVbjYYSTInziLvB12l6NDIsCTT3p6TUBlfhRBCCCHEiZOgUMTV6sLtBJXCFfCSabFj0Rvj3aQa23Zwd8zyIZ0TZ9ioUgqFItNip09me1okpcW7SUIIIYQQooFJUCjiptLvYWvZPpw+Dw5TUkIlNvEF/Py0Nz+qfFD77liMidPbechdhlFnoHdmO1ra0uPdHCGEEKJG2rZti6TFEKL+SPZRETc7yg9S7nPjDnoTah4hSvHZ2u+iiu2WJDJTEqenzRvw41dBUoxWsixNI6mREEIIIYSIJj2FIi48AR8/l+7BqNOTmWAByfo922KWD+nct4FbcmLKfJXYjUmkm5MxJ9CwXSGEEEIIUb+kp1A0uIAKMn/PGkq9oaBEl0CJTZRSbDu0J6p8YPvuCZOgRSlFsacCpRRpZhsDmnWKd5OEEEIIIUQcSVAoGtz6op0c8pTjDfrR6xLrR3D1rp+jypKMZrISZNioUordzkJ8wQB2UxJn53TFoNPHu1lCCCGEECKOZPioaFAHXaVsLy/AE/DRzOqId3Nqxe3zsKvoQFT58K794tCauilwl5JstJBkMNM3sz0mvfwKEEIIIYRo6uQToWgwSil+Lt1Loaccq8GMUZc4P34ur5svf/o+qjwtyY5OS4zezqosbTaDBaNOL8llhBBCCCEEIEGhaCDegJ+1RTvYU1mESW8kzWSLd5NqZf6GH2KWn9WxZwO3pO58wQAmnYFWyRl0S2styWWEEEIIIQQgQaFoIF/sWUVQKZw+N9nW1IRJygJQ7nISVMGo8v55pyfMdXgDfg66S3GYbJyemisBoRBCCCGECJOgUJx0Tp8bd8BHuc+FpmkJE0hBaMjlwk0rosrzMluQ7ciIQ4vq5pC7lDRTMia9QQJCIYQQQggRQYJCcdLtqyzG6XOHg5JEMm/d0qgyu8VGt1Yd4tCauvEF/VgMJnKTM+mW3jqhgnIhhBBCCHHyJUaGDJGw/MEA+eUH0Gu6hAsID5WX4A34IsosBhNndewVnwbVgSfg44CrBKveRLf01iQZzPFukhBCCCGEaGQS61O6SDjzdq+i0u/BYjDFuym14g8EWLJ1TVR5h+xcDPrEWdevzFtJM4sDh8kmAaEQQgghhIhJgkJx0ngCPjyH5xJmJdiahFsLdsUsb5PZvIFbUnflPhd6TUf39DbkpWTHuzlCCCGEEKKRkuGj1fjTn/7EmWeeSVJSEqmpqTXaZ8qUKeFEKlWPsWPHntyGNlL55Qf4YvcqynwuHCYb+gRZyw9CvYQ/H9gZVT6gXbeEWpOwzFuJw2yjdXKWzCMUQgghhBDVkp7Cani9Xi655BIGDRrEjBkzarzf2LFjefXVV8PPzeamN2Sv0u9hffEuijwVGHR6rIk0dFQpPl27OKo42WSlmT09Dg2qPaUUhZ5ybAYLLZLSMegSZ7irEEIIIYRoeBIUVmPatGkAzJw5s1b7mc1mcnJyTkKLEoPL72VH+UGKPRW4Az6yE2yR+sKK0qgys8HI4M6949CaujnkLkOv02MxmDg9LTfezRFCCCGEEI2cBIX1bOHChTRr1oy0tDRGjBjBY489RkZG9evZeTwePB5P+HlpaSgoKSsrO+ltPRm+PrQBXzBAwB8g22SHQBBvwBvvZtWI2+dh8aZVUeXdctuhEuQ6vEE/wWCQFJ2ZrknN8ThdeHDFu1lCCCGEEKIOqmICpdRJPY8EhfVo7NixXHTRReTl5bF161Z+//vfc+6557JkyRL01WSsfPzxx8O9kkfKzZUeHiGEEEIIIQQUFhbicJy8xI2aOtlhZyNy//3389e//vWYdTZs2ECXLl3Cz2fOnMmdd95JSUlJrc+3bds22rdvz5dffsk555wTs87RPYUlJSW0adOGnTt3ntQbL6pXVlZGbm4uu3btwm63x7s5TZLcg/iTexBf8vrHn9yD+JN7EH9yD+KvtLSU1q1bU1xcXOPkl3XRpHoK7777bqZMmXLMOu3atau387Vr147MzEy2bNlSbVBoNptjJqNxOBzy5oszu90u9yDO5B7En9yD+JLXP/7kHsSf3IP4k3sQfzrdyc2A36SCwqysLLKyshrsfLt376awsJDmzRNnbTshhBBCCCFE05IYi67Fwc6dO1m1ahU7d+4kEAiwatUqVq1aRUVFRbhOly5dePfddwGoqKjgd7/7HUuXLmX79u189dVXXHjhhXTo0IExY8bE6zKEEEIIIYQQ4piaVE9hbTz00EPMmjUr/Lx379CSBAsWLGDYsGEAbNq0KZwtVK/Xs2bNGmbNmkVJSQktWrRg9OjRTJ8+vVZrFZrNZh5++OEmub5hYyH3IP7kHsSf3IP4ktc//uQexJ/cg/iTexB/DXUPmlSiGSGEEEIIIYQQkWT4qBBCCCGEEEI0YRIUCiGEEEIIIUQTJkGhEEIIIYQQQjRhEhQKIYQQQgghRBMmQWEDKyoqYtKkSdjtdlJTU7nuuusilrmIZdiwYWiaFvG48cYbI+rs3LmTcePGkZSURLNmzfjd736H3+8/mZeSsGp7D4qKirjtttvo3LkzVquV1q1bc/vtt4czz1Y5+h5pmsacOXNO9uUkhBdeeIG2bdtisVgYMGAA33///THrv/nmm3Tp0gWLxUL37t355JNPIrYrpXjooYdo3rw5VquVkSNHsnnz5pN5CQmvNvfg5Zdf5uyzzyYtLY20tDRGjhwZVX/KlClRP+9jx4492ZeR0GpzD2bOnBn1+loslog68j6ovdrcg1h/ezVNY9y4ceE68j6ouUWLFnHBBRfQokULNE3jvffeO+4+CxcupE+fPpjNZjp06MDMmTOj6tT270tTVtt78M477zBq1CiysrKw2+0MGjSIzz//PKLOI488EvUe6NKly0m8isRW23uwcOHCmL+H9u/fH1GvPt4HEhQ2sEmTJrF+/Xq++OILPvroIxYtWsQNN9xw3P2mTp3Kvn37wo8nnngivC0QCDBu3Di8Xi/fffcds2bNYubMmTz00EMn81ISVm3vwd69e9m7dy9PPfUU69atY+bMmXz22Wdcd911UXVfffXViPs0YcKEk3glieF///sfv/3tb3n44Yf58ccf6dmzJ2PGjKGgoCBm/e+++47LL7+c6667jpUrVzJhwgQmTJjAunXrwnWeeOIJnnvuOV588UWWLVuGzWZjzJgxuN3uhrqshFLbe7Bw4UIuv/xyFixYwJIlS8jNzWX06NHs2bMnot7YsWMjft5nz57dEJeTkGp7DwDsdnvE67tjx46I7fI+qJ3a3oN33nkn4vVft24der2eSy65JKKevA9qxul00rNnT1544YUa1c/Pz2fcuHEMHz6cVatWceedd3L99ddHBCV1eV81ZbW9B4sWLWLUqFF88sknrFixguHDh3PBBRewcuXKiHqnn356xHvg22+/PRnNPyXU9h5U2bRpU8Rr3KxZs/C2ensfKNFgfvrpJwWo5cuXh8s+/fRTpWma2rNnT7X7DR06VN1xxx3Vbv/kk0+UTqdT+/fvD5f985//VHa7XXk8nnpp+6mirvfgaHPnzlUmk0n5fL5wGaDefffd+mzuKaF///7qlltuCT8PBAKqRYsW6vHHH49Z/9JLL1Xjxo2LKBswYID6zW9+o5RSKhgMqpycHPXkk0+Gt5eUlCiz2axmz559Eq4g8dX2HhzN7/erlJQUNWvWrHDZ1VdfrS688ML6buopq7b34NVXX1UOh6Pa48n7oPZO9H3w9NNPq5SUFFVRUREuk/dB3dTk7+W9996rTj/99Iiyyy67TI0ZMyb8/ETvaVNW188sXbt2VdOmTQs/f/jhh1XPnj3rr2FNSE3uwYIFCxSgiouLq61TX+8D6SlsQEuWLCE1NZUzzjgjXDZy5Eh0Oh3Lli075r6vv/46mZmZdOvWjQceeIDKysqI43bv3p3s7Oxw2ZgxYygrK2P9+vX1fyEJ7ETuwZFKS0ux2+0YDIaI8ltuuYXMzEz69+/Pv//9b1QTXwbU6/WyYsUKRo4cGS7T6XSMHDmSJUuWxNxnyZIlEfUh9PNcVT8/P5/9+/dH1HE4HAwYMKDaYzZldbkHR6usrMTn85Genh5RvnDhQpo1a0bnzp256aabKCwsrNe2nyrqeg8qKipo06YNubm5XHjhhRG/z+V9UDv18T6YMWMGEydOxGazRZTL++DkON7fgvq4p6J2gsEg5eXlUX8LNm/eTIsWLWjXrh2TJk1i586dcWrhqatXr140b96cUaNGsXjx4nB5fb4PDMevIurL/v37I7p7AQwGA+np6VFjg490xRVX0KZNG1q0aMGaNWu477772LRpE++88074uEcGhED4+bGO2xTV9R4c6dChQ0yfPj1qyOmjjz7KiBEjSEpKYt68edx8881UVFRw++2311v7E82hQ4cIBAIxfz43btwYc5/qfp6r7k/Vv8eqI35Rl3twtPvuu48WLVpE/NEZO3YsF110EXl5eWzdupXf//73nHvuuSxZsgS9Xl+v15Do6nIPOnfuzL///W969OhBaWkpTz31FGeeeSbr16+nVatW8j6opRN9H3z//fesW7eOGTNmRJTL++Dkqe5vQVlZGS6Xi+Li4hP+3SZq56mnnqKiooJLL700XDZgwABmzpxJ586d2bdvH9OmTePss89m3bp1pKSkxLG1p4bmzZvz4osvcsYZZ+DxeHjllVcYNmwYy5Yto0+fPvXyN76KBIX14P777+evf/3rMets2LChzsc/Mvjo3r07zZs355xzzmHr1q20b9++zsc9lZzse1ClrKyMcePG0bVrVx555JGIbX/84x/D/+/duzdOp5Mnn3yySQeFIvH95S9/Yc6cOSxcuDAi0cnEiRPD/+/evTs9evSgffv2LFy4kHPOOSceTT2lDBo0iEGDBoWfn3nmmZx22mn861//Yvr06XFsWdM0Y8YMunfvTv/+/SPK5X0gmoo33niDadOm8f7770d8uX7uueeG/9+jRw8GDBhAmzZtmDt3bszcC6J2OnfuTOfOncPPzzzzTLZu3crTTz/Nf//733o9lwSF9eDuu+9mypQpx6zTrl07cnJyoiZ9+v1+ioqKyMnJqfH5BgwYAMCWLVto3749OTk5UVmGDhw4AFCr4yayhrgH5eXljB07lpSUFN59912MRuMx6w8YMIDp06fj8Xgwm801uo5TTWZmJnq9PvzzWOXAgQPVvt45OTnHrF/174EDB2jevHlEnV69etVj608NdbkHVZ566in+8pe/8OWXX9KjR49j1m3Xrh2ZmZls2bJFPgwf5UTuQRWj0Ujv3r3ZsmULIO+D2jqRe+B0OpkzZw6PPvrocc8j74P6U93fArvdjtVqRa/Xn/D7StTMnDlzuP7663nzzTejhvQeLTU1lU6dOoV/V4n6179//3Ayn/r4+1JF5hTWg6ysLLp06XLMh8lkYtCgQZSUlLBixYrwvvPnzycYDIYDvZpYtWoVQPiDwKBBg1i7dm1EsPPFF19gt9vp2rVr/VxkI3ey70FZWRmjR4/GZDLxwQcfRKWGj2XVqlWkpaU12YAQwGQy0bdvX7766qtwWTAY5KuvvoroBTnSoEGDIupD6Oe5qn5eXh45OTkRdcrKyli2bFm1x2zK6nIPIJTZcvr06Xz22WcRc3Crs3v3bgoLCyMCFBFS13twpEAgwNq1a8Ovr7wPaudE7sGbb76Jx+Nh8uTJxz2PvA/qz/H+FtTH+0oc3+zZs7nmmmuYPXt2xHIs1amoqGDr1q3yHjiJVq1aFX596/V9UKu0NOKEjR07VvXu3VstW7ZMffvtt6pjx47q8ssvD2/fvXu36ty5s1q2bJlSSqktW7aoRx99VP3www8qPz9fvf/++6pdu3ZqyJAh4X38fr/q1q2bGj16tFq1apX67LPPVFZWlnrggQca/PoSQW3vQWlpqRowYIDq3r272rJli9q3b1/44ff7lVJKffDBB+rll19Wa9euVZs3b1b/+Mc/VFJSknrooYfico2NyZw5c5TZbFYzZ85UP/30k7rhhhtUampqOFvulVdeqe6///5w/cWLFyuDwaCeeuoptWHDBvXwww8ro9Go1q5dG67zl7/8RaWmpqr3339frVmzRl144YUqLy9PuVyuBr++RFDbe/CXv/xFmUwm9dZbb0X8vJeXlyullCovL1f33HOPWrJkicrPz1dffvml6tOnj+rYsaNyu91xucbGrrb3YNq0aerzzz9XW7duVStWrFATJ05UFotFrV+/PlxH3ge1U9t7UGXw4MHqsssuiyqX90HtlJeXq5UrV6qVK1cqQP3tb39TK1euVDt27FBKKXX//ferK6+8Mlx/27ZtKikpSf3ud79TGzZsUC+88ILS6/Xqs88+C9c53j0VkWp7D15//XVlMBjUCy+8EPG3oKSkJFzn7rvvVgsXLlT5+flq8eLFauTIkSozM1MVFBQ0+PUlgtreg6efflq99957avPmzWrt2rXqjjvuUDqdTn355ZfhOvX1PpCgsIEVFhaqyy+/XCUnJyu73a6uueaa8ActpZTKz89XgFqwYIFSSqmdO3eqIUOGqPT0dGU2m1WHDh3U7373O1VaWhpx3O3bt6tzzz1XWa1WlZmZqe6+++6I5RLEL2p7D6rSAcd65OfnK6VCy1r06tVLJScnK5vNpnr27KlefPFFFQgE4nCFjc/zzz+vWrdurUwmk+rfv79aunRpeNvQoUPV1VdfHVF/7ty5qlOnTspkMqnTTz9dffzxxxHbg8Gg+uMf/6iys7OV2WxW55xzjtq0aVNDXErCqs09aNOmTcyf94cfflgppVRlZaUaPXq0ysrKUkajUbVp00ZNnTpVPogdR23uwZ133hmum52drc477zz1448/RhxP3ge1V9vfRRs3blSAmjdvXtSx5H1QO9X9La16za+++mo1dOjQqH169eqlTCaTateunXr11Vejjnuseyoi1fYeDB069Jj1lQotE9K8eXNlMplUy5Yt1WWXXaa2bNnSsBeWQGp7D/7617+q9u3bK4vFotLT09WwYcPU/Pnzo45bH+8DTakmnjNfCCGEEEIIIZowmVMohBBCCCGEEE2YBIVCCCGEEEII0YRJUCiEEEIIIYQQTZgEhUIIIYQQQgjRhElQKIQQQgghhBBNmASFQgghhBBCCNGESVAohBBCCCGEEE2YBIVCCCGEEEII0YRJUCiEEEIIIYQQTZgEhUIIIYQQQgjRhElQKIQQQjSAPXv24HA40DQt/Hj99dfrdKyVK1diNBrDx9HpdHzzzTf13GIhhBBNhQSFQgghRANo2bIlTz31VETZHXfcQUFBQa2O4/f7ufbaa/H7/eGym2++mbPPPrte2imEEKLpkaBQCCGEaCBTp05lxIgR4eeFhYXceuuttTrG448/zqpVq8LP27Rpw1/+8pf6aqIQQogmSFNKqXg3QgghhGgq8vPz6d69O06nM1z2zjvv8Ktf/eq4+65fv54+ffrg9XrDZfPmzWPUqFEnpa1CCCGaBukpFEIIIRpQXl4ef/rTnyLKbr75ZoqLi4+5XyAQ4Nprr40ICK+99loJCIUQQpwwCQqFEEKIBnbbbbdx5plnhp/v37+fO++885j7/N///R/ff/99+HmLFi3429/+drKaKIQQogmR4aNCCCFEHGzcuJHevXvjdrvDZZ9++iljx46Nqvvzzz/Ts2fPiLrvv/8+48ePb5C2CiGEOLVJT6EQQggRB126dOHhhx+OKLvhhhsoLy+PKFNKcd1110UEhJdffrkEhEIIIeqNBIVCCCFEnNxzzz306dMn/HzXrl3ce++9EXWef/55vv322/DzrKwsnnvuuQZroxBCiFOfDB8VQggh4mj16tX069cPn88HgKZpzJ8/n2HDhrFt2zZ69OgRkal0zpw5XHbZZfFqrhBCiFOQ9BQKIYQQcdSzZ0/uv//+8HOlFNdffz1Op5OpU6dGBIQTJkyQgFAIIUS9k55CIYQQIs68Xi99+vRh/fr14bKePXuyevXq8PO0tDTWr19P8+bN49FEIYQQpzDpKRRCCCHizGQy8e9//xu9Xh8uOzIgBPjb3/4mAaEQQoiTQoJCIYQQohHo379/tWsVjhkzhilTpjRoe4QQQjQdMnxUCCGEaCT2799Py5YtCQaD4TKbzcZPP/1E69at49gyIYQQpzLpKRRCCCEaiQcffDAiIARwuVzs27cvTi0SQgjRFEhPoRBCCNEIfPnll4waNSrmtm7duvHjjz9iNBobuFVCCCGaAukpFEIIIeKsoqKCqVOnRpQdGQCuW7eOP//5zw3dLCGEEE2EBIVCCCFEnD3wwANs3749/Lxv37689dZbEXX+/Oc/89NPPzVwy4QQQjQFMnxUCCGEiKNvv/2WIUOGUPXn2Gg0snz5cnr27MnkyZN5/fXXw3UHDhzI4sWL0enkO10hhBD1R/6qCCGEEHHidru57rrrOPL72fvuu4+ePXsC8Mwzz5CVlRXetnTpUp5//vkGb6cQQohTm/QUCiGEEHHyu9/9jqeeeir8vGvXrqxcuRKTyRQumz17NldccUX4uc1mY/369bRp06ZB2yqEEOLUJUGhEEIIEQfLly9n0KBBBAIBAHQ6HYsXL2bgwIFRdS+44AI++uij8PPRo0fz+eefN1hbhRBCnNpk+KgQQgjRwLxeL9dee204IAS44447YgaEAC+++CJ2uz38fN68ecyaNeukt1MIIUTTIEGhEEII0cAee+wx1q1bF37erl07HnvssWrrt2zZkieeeCKi7Le//S0FBQUnrY1CCCGaDhk+KoQQQjSg1atX069fP3w+X7jsq6++YsSIEcfcTynFiBEjWLhwYbjskksuYe7cuSerqUIIIZoI6SkUQgghGojf7+faa6+NCAinTp163IAQQNM0Xn75ZaxWa7jszTff5P333z8pbRVCCNF0SFAohBBCNJAnn3ySH3/8Mfy8ZcuWPPnkkzXev0OHDkybNi2i7Oabb6a0tLTe2iiEEKLpkeGjQgghRAPYuHEjvXr1wuPxhMs+/PBDzj///FodJxAIMHDgQH744Ydw2dSpU3nppZfqra1CCCGaFgkKhRBCiJMsGAwyePBglixZEi674ooreP311+t0vDVr1nDGGWeEh6Fqmsb8+fMZNmxYfTRXCCFEEyPDR4UQQoiT7LnnnosICLOysnj22WfrfLwePXpw3333hZ8rpZg6dSoul+uE2imEEKJpkp5CIYQQQgghhGjCpKdQCCGEEEIIIZowCQqFEEIIIYQQogmToFAIIYQQQgghmjAJCoUQQgghhBCiCZOgUAghhBBCCCGaMAkKhRBCCCGEEKIJk6BQCCGEEEIIIZowCQqFEEIIIYQQogmToFAIIYQQQgghmjAJCoUQQgghhBCiCZOgUAghhBBCCCGaMAkKhRBCCCGEEKIJk6BQCCGEEEIIIZowCQqFEEIIIYQQogmToFAIIYQQQgghmjAJCoUQQgghhBCiCZOgUAghhBBCCCGasP8HjX+FL8+XK04AAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "predictive = Predictive(new_model, guide=new_guide, num_samples=5000)\n",
        "preds = predictive(x_test)\n",
        "plot_predictions(preds)"
      ],
      "metadata": {
        "pycharm": {
          "is_executing": true
        },
        "id": "_AR1mp0IlzBr",
        "colab": {
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        },
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      }
    }
  ],
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