tutorial-LSGAN-LHCb_RICH.ipynb

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      "source": [
        "# Parameterizing the LHCb RICH system using the pidgan `LSGAN` algorithm\n",
        "\n",
        "**Author:** [mbarbetti](https://github.com/mbarbetti)\n",
        "\n",
        "**Date created:** 16/10/2023\n",
        "\n",
        "**Last modified:** 07/11/2023"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yzrL35_aMf4X"
      },
      "source": [
        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mbarbetti/pidgan-notebooks/blob/main/tutorial-LSGAN-LHCb_RICH.ipynb)\n",
        "[![Open In Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/mbarbetti/pidgan-notebooks/blob/main/tutorial-LSGAN-LHCb_RICH.ipynb)\n",
        "[![Open in GitHub](https://img.shields.io/badge/Open_in_GitHub-blue?style=flat&logo=github&logoColor=white&labelColor=555)](https://github.com/mbarbetti/pidgan-notebooks/blob/main/tutorial-LSGAN-LHCb_RICH.ipynb)\n",
        "\n",
        "**Description:** This tutorial demonstrates how to parameterize the high-level response of the LHCb RICH system using a Generative Adversarial Network (GAN) [[1](https://arxiv.org/abs/1406.2661)]. The code is written using the [pidgan](https://github.com/mbarbetti/pidgan) package that relies on TensorFlow and Keras as backends."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HvTVEAa7i5xJ"
      },
      "source": [
        "## Introduction"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "opu773xWi5xK"
      },
      "source": [
        "### What is LHCb?\n",
        "\n",
        "The [**LHCb experiment**](https://lhcb-outreach.web.cern.ch) has been originally designed to study rare decays of particles containing $b$ and $c$ quarks produced at the Large Hadron Collider (LHC). The LHCb detector, shown in the background of the following photo, is a single-arm forward spectrometer covering the pseudorapidity range of $2 < \\eta < 5$. The detector includes:\n",
        "\n",
        "- **Tracking system** - used for high-precision measurements of the momentum of charged particles and the position of the primary vertices\n",
        "- **Particle Identification (PID) system** - used to distiguish different species of traversing particles (i.e., muons, pions, kaons, protons)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5If4oPxki5xK"
      },
      "source": [
        "<div align=\"center\">\n",
        "  <img src=\"https://raw.githubusercontent.com/mbarbetti/pidgan-notebooks/main/.github/images/lhcb.jpeg\" width=\"800\"/>\n",
        "</div>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZTEAA_GpgXXM"
      },
      "source": [
        "#### The RICH detectors\n",
        "\n",
        "The LHCb PID system counts two ring-imaging Cherenkov ([RICH](https://lhcb-outreach.web.cern.ch/detector/rich-detectors/)) detectors whose response allows to separate different types of charged hadrons (e.g., pions, kaons, protons) using the [Cherenkov radiation](https://en.wikipedia.org/wiki/Cherenkov_radiation) of the traversing particles. The size of the circles produced by the Cherenkov radiation (shown on the left) within the RICH detector is related to the mass of the emitting particle. Reconstructing such circles allows to distinguish particles accordingly to their species (shown on the right)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UvTtCmaBhqQ_"
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      "source": [
        "<div align=\"center\">\n",
        "  <img src=\"https://raw.githubusercontent.com/mbarbetti/pidgan-notebooks/main/.github/images/rich-response.png\" width=\"300\"/>\n",
        "  <img src=\"https://raw.githubusercontent.com/mbarbetti/pidgan-notebooks/main/.github/images/rich-performance.png\" width=\"400\"/>\n",
        "</div>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LPnfIVMoi5xK"
      },
      "source": [
        "### What are GANs?\n",
        "\n",
        "Generative Adversarial Networks [[1][1]] are a powerful class of _generative models_ based on the simultaneous training of two neural networks:\n",
        "\n",
        "*  **Discriminator network** ($D$) - trained by a classification task to separate the generator output from the reference dataset\n",
        "* **Generator network** ($G$) - trained by a simulation task to reproduce the reference dataset trying to fake the discriminator\n",
        "\n",
        "The goal is that $D$ optimally discriminates on the origin of the two samples, and simultaneously the training procedure for $G$ is to maximize the _probability_ of $D$ making a mistake. This framework corresponds to a **minimax two-player game** [[1][1]].\n",
        "\n",
        "Traditional GAN systems suffer from many issues, particularly during the training phase:\n",
        "\n",
        "* the generator may _collapse_ producing only a single sample or a small family of very similar samples (**mode collapse**)\n",
        "* the two players may _oscillate_ during training rather than converging to the [**Nash equilibrium**](https://en.wikipedia.org/wiki/Nash_equilibrium)\n",
        "* if _imbalance_ between the two players occurs, then the system is incapable of learning at all\n",
        "\n",
        "All these drawbacks result from the [**vanishing gradient problem**](https://en.wikipedia.org/wiki/Vanishing_gradient_problem), namely the lack of information for the update of the $G$ parameters. This is due to the saturation of the $D$ that is so good in distinguishing the origin of the two samples that no errors remain to the $G$ to improve the generated space. To fix the problem, one can add _continuous noise_ to both $D$ and $G$, allowing to learn thanks to a non-zero gradient [[2][2]]. Alternatively, we need to change the loss function originally proposed in Ref. [[1][1]], moving to a _distance measurement_ robust against the vanishing gradient problem. To this end, the authors of Ref. [[3][3]] propose the **least squares** loss function and define the so-called **LSGAN algorithm** .\n",
        "\n",
        "[1]: https://arxiv.org/abs/1406.2661\n",
        "[2]: https://arxiv.org/abs/1701.04862\n",
        "[3]: https://arxiv.org/abs/1611.04076"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z_Hs8F8pi5xK"
      },
      "source": [
        "<div align=\"center\">\n",
        "  <img src=\"https://raw.githubusercontent.com/mbarbetti/pidgan-notebooks/main/.github/images/gan-scheme.png\" width=\"800\"/>\n",
        "</div>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fmUghh5Ii5xK"
      },
      "source": [
        "#### Mathematical details\n",
        "\n",
        "The generator $G(z)$, fed by elements $z$ sampled according to a known distribution $p_z$ (typically gaussian), maps the **latent space** $\\mathcal{Z}$ to the reference dataset $\\mathcal{X}$, inducing a distribution $p_\\rm{gen}$ trained to match with the target distribution $p_\\rm{ref}$. The discriminator $D(x)$ outputs a single scalar, readable as the **probability** that $x$ comes from the reference dataset rather than $G$. Hence, the optimization problem corresponds to train $D$ to maximize the probability of correct labelling, and simultaneously training $G$ to generate fake samples as real as possible, moving them toward the decision boundary and penalizing what lie in a long way on the correct side of the decision boundary.\n",
        "\n",
        "The _minimax game_ of the **LSGAN algorithm** can be written as follows:\n",
        "\n",
        "<center>$\\begin{cases}\n",
        "\\displaystyle{\\min_D} \\, \\mathcal{L}_{\\rm{LSGAN}} (\\theta_d, \\theta_g; a, b) = \\frac{1}{2} \\mathbb{E}_{x \\sim p_{\\rm{ref}}} \\left[ \\left ( D_{\\theta_d}(x) - b \\right )^2 \\right ] + \\frac{1}{2} \\mathbb{E}_{z \\sim p_{\\rm{z}}} \\left[ \\left ( D_{\\theta_d}(G_{\\theta_g}(z)) - a \\right )^2 \\right ]\\\\\n",
        "\\displaystyle{\\min_G} \\, \\mathcal{L}_{\\rm{LSGAN}} (\\theta_d, \\theta_g; c) = \\frac{1}{2} \\mathbb{E}_{x \\sim p_{\\rm{ref}}} \\left[ \\left ( D_{\\theta_d}(x) - c \\right )^2 \\right ] + \\frac{1}{2} \\mathbb{E}_{z \\sim p_{\\rm{z}}} \\left[ \\left ( D_{\\theta_d}(G_{\\theta_g}(z)) - c \\right )^2 \\right ]\n",
        "\\end{cases}$</center>\n",
        "\n",
        "where $a$ and $b$ are the labels for fake data and real data for the discriminator, while $c$ denotes the value that $G$ wants $D$ to believe for fake data. If we set $b - c = 1$ and $b - a = 2$, solving the LSGAN minimax game yields to minimize the Pearson $\\chi^2$ divergence between $p_{\\rm{ref}} + p_{\\rm{gen}}$ and $2 p_{\\rm{gen}}$. For example, by setting $a = -1$, $b = 1$, and $c = 0$, we satisfy the above condition:\n",
        "\n",
        "<center>$\\begin{cases}\n",
        "\\displaystyle{\\min_D} \\, \\mathcal{L}_{\\rm{LSGAN}} (\\theta_d, \\theta_g) = \\frac{1}{2} \\mathbb{E}_{x \\sim p_{\\rm{ref}}} \\left[ \\left ( D_{\\theta_d}(x) - 1 \\right )^2 \\right ] + \\frac{1}{2} \\mathbb{E}_{z \\sim p_{\\rm{z}}} \\left[ \\left ( D_{\\theta_d}(G_{\\theta_g}(z)) + 1 \\right )^2 \\right ]\\\\\n",
        "\\displaystyle{\\min_G} \\, \\mathcal{L}_{\\rm{LSGAN}} (\\theta_d, \\theta_g) = \\frac{1}{2} \\mathbb{E}_{z \\sim p_{\\rm{z}}} \\left[ \\left ( D_{\\theta_d}(G_{\\theta_g}(z)) \\right )^2 \\right ]\n",
        "\\end{cases}$</center>\n",
        "\n",
        "Another method is to make $G$ generate samples as real as possible by setting $c = b$. For example, by using $a = 0$ and $b = c = 1$, we get the following objective functions:\n",
        "\n",
        "<center>$\\begin{cases}\n",
        "\\displaystyle{\\min_D} \\, \\mathcal{L}_{\\rm{LSGAN}} (\\theta_d, \\theta_g) = \\frac{1}{2} \\mathbb{E}_{x \\sim p_{\\rm{ref}}} \\left[ \\left ( D_{\\theta_d}(x) - 1 \\right )^2 \\right ] + \\frac{1}{2} \\mathbb{E}_{z \\sim p_{\\rm{z}}} \\left[ \\left ( D_{\\theta_d}(G_{\\theta_g}(z)) \\right )^2 \\right ]\\\\\n",
        "\\displaystyle{\\min_G} \\, \\mathcal{L}_{\\rm{LSGAN}} (\\theta_d, \\theta_g) = \\frac{1}{2} \\mathbb{E}_{z \\sim p_{\\rm{z}}} \\left[ \\left ( D_{\\theta_d}(G_{\\theta_g}(z)) - 1 \\right )^2 \\right ]\n",
        "\\end{cases}$</center>\n",
        "\n",
        "In practice, both the minimax games suggested by Ref. [[3][3]] show similar performance.\n",
        "\n",
        "[3]: https://arxiv.org/abs/1611.04076"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JnwMxPJUi5xK"
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      "source": [
        "#### Using input conditions\n",
        "\n",
        "Feeding the generator with additional information besides the latent space sample allows to **conditionate** its output. In particular, it's sufficient to concatenate the conditional features $x$ to the random noise $z$ passed as input to $G$ to make it able to take into account this information [[4][4]]: $y_{\\rm{gen}}(x) = G(x, z)$. Obviously, to preserve the capability to learn through the minimax game, also the discriminator must be fed by the additional features $x$, simply concatenating them to either elements of the reference sample or of the generated space [[4][4]]: $D(x, y)$ with $y \\in \\{y_{\\rm{ref}}, y_{\\rm{gen}}\\}$.\n",
        "\n",
        "[4]: https://arxiv.org/abs/1411.1784"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TvpIdCnSi5xL"
      },
      "source": [
        "## Tutorial\n",
        "\n",
        "The aim of this tutorial is to build a parameterization of the LHCb RICH detectors. Since we expect that the high-level response of the RICH system only depends on the kinematics of the traversing particles and the detector occupancy, the former can be parameterized training a **GAN model** properly conditioned [[4][4], [5][5]]. The pidgan package provides several GAN [`algorithms`](https://github.com/mbarbetti/pidgan/tree/main/src/pidgan/algorithms) implementing some of the most used tricks presented in the literature to stablize their training [[2][2], [3][3], [6][6]]. The pidgan [`players`](https://github.com/mbarbetti/pidgan/tree/main/src/pidgan/players), namely the generator and discriminator networks, are designed to be trained taking conditions as input. In this notebook we will see how to build a model for the RICH system using the pidgan [`LSGAN`](https://github.com/mbarbetti/pidgan/blob/main/src/pidgan/algorithms/LSGAN.py) algorithm.\n",
        "\n",
        "[2]: https://arxiv.org/abs/1701.04862\n",
        "[3]: https://arxiv.org/abs/1611.04076\n",
        "[4]: https://arxiv.org/abs/1411.1784\n",
        "[5]: https://arxiv.org/abs/1905.11825\n",
        "[6]: https://arxiv.org/abs/1606.03498"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4ohrh3dQi5xL"
      },
      "source": [
        "### Install\n",
        "\n",
        "The first step is to install the [pidgan](https://github.com/mbarbetti/pidgan) package and some other ones that are typically needed in machine learning applications to High Energy Physics (i.e., uproot, scipy, scikit-learn, matplotlib)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "r8glZROOi5xL"
      },
      "outputs": [],
      "source": [
        "%%capture\n",
        "!pip install pidgan[hep]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Kmb7g7HZi5xL"
      },
      "source": [
        "Now, let's verify the correct installation of pidgan printing its version:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "CWg67WFhi5xL",
        "outputId": "e1517725-0d5b-42ca-e843-10a826690ba3"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'0.0.6'"
            ]
          },
          "execution_count": 2,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import pidgan\n",
        "\n",
        "pidgan.__version__"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C8lt6bjDi5xM"
      },
      "source": [
        "In case you're running this notebook on a machine equipped with a GPU, let's also verify the correct installation of TensorFlow:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xGoxRYbGi5xM",
        "outputId": "b07ae82b-f9d1-4e8e-a7b8-164c0325877a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import tensorflow as tf\n",
        "\n",
        "tf.config.list_physical_devices(\"GPU\")  # outputs a non-empty list in case of GPU equipped"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Uslhx_PjvOwk"
      },
      "source": [
        "Finally, we just have to import all the modules that we will use in the following code cells:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "twM1ba1PvgVl"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from datetime import datetime\n",
        "from tensorflow import keras\n",
        "from sklearn.utils import shuffle\n",
        "from sklearn.preprocessing import QuantileTransformer"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Cz9PhSN8wP2h"
      },
      "source": [
        "### Data loading\n",
        "\n",
        "The data needed for this tutorial is provided through a CSV file downloadable from the [pidgan-notebooks](https://github.com/mbarbetti/pidgan-notebooks) repository.\n",
        "\n",
        "**Disclaimer:** The data results from a toy model inspired to the LHCb RICH system combining its high-level response when traversed by a set of long-lived charged particles, namely muons, pions, kaons and protons.\n",
        "\n",
        "The CSV file contains 250000 instances, 4 input features and 4 output features. Let's import the data as a pandas [`DataFrame`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html):"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "QfnPXl161n2f",
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      },
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              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-95611191-67c7-462e-9b2a-b7d25d70973c button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-95611191-67c7-462e-9b2a-b7d25d70973c');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-0ccee0d8-f81b-4247-8ac6-f26aa4e75b0c\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0ccee0d8-f81b-4247-8ac6-f26aa4e75b0c')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-0ccee0d8-f81b-4247-8ac6-f26aa4e75b0c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                   P            ETA        nTracks    trackcharge  \\\n",
              "count  250000.000000  250000.000000  250000.000000  250000.000000   \n",
              "mean    19112.579988       3.362231     180.468471       0.021152   \n",
              "std     22610.408265       0.770202      91.626085       0.999778   \n",
              "min      3000.090800       1.800331       9.000999      -1.000000   \n",
              "25%      5970.525000       2.749856     116.000000      -1.000000   \n",
              "50%     10983.800000       3.340136     164.000000       1.000000   \n",
              "75%     22392.768000       3.949773     228.000000       1.000000   \n",
              "max    199837.420000       5.434650     739.993000       1.000000   \n",
              "\n",
              "            RichDLLe      RichDLLmu       RichDLLk       RichDLLp  \n",
              "count  250000.000000  250000.000000  250000.000000  250000.000000  \n",
              "mean       -8.959810      -1.853031      -3.614234      -3.626358  \n",
              "std        16.090133      11.512878      28.799922      28.006328  \n",
              "min      -140.804340    -110.494200    -149.289830    -149.591670  \n",
              "25%       -12.259203      -3.784543     -16.768035     -18.402633  \n",
              "50%        -3.499332      -0.634627       1.748902       1.705883  \n",
              "75%        -0.179516       0.651420      14.155344      15.179315  \n",
              "max        58.021923     106.868935     120.245310     110.364006  "
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "url = \"https://raw.githubusercontent.com/mbarbetti/pidgan-notebooks/main/data/pidgan-toy-data.csv\"\n",
        "data = pd.read_csv(url)\n",
        "\n",
        "data.describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rhaU0lWcZ-6L"
      },
      "source": [
        "We expect that the response of the RICH detectors only depends on the **kinematics** of the traversing particles and the **detector occupancy**. Hence, to build the aimed parameterization, we will conditionate the generator training using the following information:\n",
        "* `data.P` - momentum in GeV/$c$ of the traversing parrticles\n",
        "* `data.ETA` - pseudorapidity of the traversing particles\n",
        "* `data.nTracks` - total number of reconstructed tracks describing the detector occupancy\n",
        "* `data.trackcharge` - charge of the traversing particles (boolean value)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 353
        },
        "id": "j99DTVou5KdZ",
        "outputId": "55eb3f8f-fc6a-4ead-fa9d-624c14a6cffa"
      },
      "outputs": [
        {
          "data": {
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SkiK5vnPnDiIiImBhYQEbG5s30DsiIiKi98e4ceOgUCjE68WLF+P27dtYsmSJmGZpadkQXSMiemWMSCaialWOAklPT1e5LwgCzMzMoKamhiFDhjRAD9+8Y8eOITw8HI8fP27orlTL0NAQMTExmD9/vsq97OxsTJw4Ee3bt4eOjg50dHTQqVMnTJgwAefPn69zWx4eHtDR0UFhYWGNefz8/KClpYW8vDwxrby8HHK5HAsWLHjltgYPHoyYmBh4enrWuZ9ERET0fnN1dYW/vz/GjBmD6OhoTJkyBdnZ2UhMTGzorv2lnj59+pfV3bhxY2hqatarrJaWFrS0tN5wj4iIiIjeTw4ODvD39xd/pqam0NXVlaQ5ODg0dDeJiGrFhWQiqpW2tjbi4uJU0tPS0nD79m00bty4AXr11zh27BgiIiLe2oVk5USz6sJ9UlISOnfujJiYGCgUCixZsgTLli2Dq6srkpOTYWNjgxs3btSpLT8/PxQXFyMhIaHa+0VFRUhMTMTgwYPRokULMf3UqVN48OAB3N3dX7mtDh06wN/fH126dKlTH4mIiOh/T79+/QBUfER37949BAcH48MPP0Tjxo1hYmKCYcOGSc70BYC9e/fCyckJurq6kMlkcHd3R2ZmpiRPTWf/BgUFwcLCQpL2+PFjBAUFQV9fHwYGBggMDKxx/njw4EGxbQMDAwwbNgyXLl2S5AkPD4eamhouXrwIX19fNGvWDH369AEAnD9/HkFBQWjTpg20tbXRsmVLjBo1SvIhX+U6Ll++DC8vL+jp6aFFixaYPHkySkpKJHmrOyM5MzMT/fr1Q5MmTfDhhx/i22+/RXl5ucp4Kj+nw4cPo2fPngCA4OBg8SPU6OhofPPNN9DU1ERubq5KHWPHjoWBgYFKv4iIiIjeJcognFOnTsHT0xMymQyWlpZYt27da9WrpqaG8PBw7Ny5E3/729+gra0NS0tLJCcnAwAePnyIGTNmoEuXLpDJZNDT04NCocCxY8eqre/IkSMYOHAg9PX1oa+vDxcXF5VdZqrKzMyEoaEhhgwZgtLSUjH90qVLGDZsGIyMjKCrq4vOnTsjPDz8tcZLRG8/bm1NRLVyc3PD9u3b8cMPP0BD47//ZcTFxaF79+548OBBA/aOrl27Bm9vb5ibm+PAgQMwMTGR3I+MjMSPP/5Y5+0LPTw8IJPJEBcXh4CAAJX7iYmJePr0Kfz8/CTpycnJMDc3h7W1dd0HQ0RERPQS165dAwC0aNECI0aMQGZmJiZNmgQLCwvcv38fqampuHnzprj4GxMTg8DAQAwaNAiRkZEoKipCVFQU+vTpgzNnzqgsEr+MIAgYNmwY0tPTERISgo4dOyIhIQGBgYEqeffv3w9XV1e0adMG4eHhKC4uxvLly+Ho6IiMjAyVtj/55BO0a9cO3333HQRBAACkpqbi+vXrCA4ORsuWLZGZmYk1a9YgMzMTJ06cgJqamqQOLy8vWFhYYN68eThx4gR++OEHPHr0CBs3bqxxTPfu3cNHH32EsrIyzJo1C7q6ulizZg2aNGlS67Po2LEj5s6dizlz5mDs2LFwcnICAPTu3Rt9+vTB3LlzsXXrVkycOFEs8/z5c+zYsQMjRoyAtrZ2rfUTERERvQsCAgLQv39/REZGYv369RgzZgy6desGW1vbetd56tQpLFu2DGPHjkXr1q1x5swZ8WPJ69evY926dfD398cXX3yB/Px8rF27Fv3798eZM2fQoUMHsZ7du3fD09MTbdu2RWhoKIyMjHD48GGsWbMGAwcOrLbta9euYcCAAejatSt27Ngh7mTz/PlzuLq64vnz55g2bRqaN2+OrKws7Nq1i4vJRO87gYioGuvXrxcACNu3bxfU1NSE5ORk8d6zZ8+EZs2aCYsXLxbMzc0Fd3d3SdmMjAxh8ODBgkwmE3R1dYV+/foJx48fl+T55ptvBABCVlaW4OfnJ+jp6QmGhobC119/LZSXlws3b94UPDw8BJlMJhgbGwuLFi1S6ePt27eF4OBgwcjISNDS0hI6deokrFu3rtp2rl69KgQGBgr6+vqCnp6eEBQUJDx9+lQlX9Vfdna2EBgYKJibm6u0ryzzpsdVnZr6MHbsWAGAcOLEiVeqR+lVnl1gYKCgoaEh5OTkqJQfMmSIIJPJhKKiIkm6ra2tMH78eJX8//rXv4QRI0YIcrlcaNy4sWBlZSVERERI8iifXW5ubp3GQkRERO8f5Vx0//79Qm5urnDr1i1hy5YtQosWLYQmTZoIt2/fFgAICxcurLGOwsJCwcDAQPjss88k6ffu3RP09fUl6c7OzoKzs7NKHVXnYDt37hQACAsWLBDTysrKBCcnJwGAsH79ejHdxsZGMDIyEvLy8sS0c+fOCY0aNRICAgLENOUcyMfHR6X9qnMtQRCEzZs3CwCEI0eOqNTh4eEhyTt+/HgBgHDu3DkxzdzcXAgMDBSvp0yZIgAQTp48Kabdv39f0NfXF+fDSlWf0+nTp1XGreTg4CDY29tL0uLj4wUAwqFDh1TyExEREb1LlPPVGTNmiGk3b94U1NTUVN55Kbm7u1f7fq8yAEKjRo2EjIwMSXpZWZkgCIJQUFAgFBQUSO7duHFDUFNTE2bOnCnJ36pVK6F9+/ZCYWGhJP+dO3fEv7Ozs8V59a1btwRzc3Ohd+/ewpMnTyRlzp49KwAQ1q5dK0kvLS2tdTxE9O7j1tZEVCsLCws4ODhg8+bNYtrevXuRn58Pb29vlfyZmZlwcnLCuXPnEBoaitmzZyM7OxsuLi44efKkSv6RI0eivLwc8+fPh729Pb799lssXboUAwYMgKmpKSIjI9G2bVvMmDEDR44cEcvl5OSgV69e2L9/PyZOnIhly5ahbdu2GD16NJYuXarSjpeXFwoLCzFv3jx4eXkhOjoaERER4v2PP/4YPj4+AIAlS5YgJiYGMTExkMvl9Xpu9R1XXSUlJaFt27awt7d/5TKv+uz8/PxQVlaGbdu2Sco/fPgQv/76Kzw9PSWRKvfu3cOZM2fg5uYmyZ+QkIDevXsjMzMTM2fOxJIlS9C7d+8at9whIiIiUlIoFJDL5TAzM4O3tzeaNm2KhIQEGBoaQktLC4cPH8ajR4+qLZuamorHjx/Dx8cHDx48EH/q6uqwt7fHoUOH6tyf5ORkaGho4PPPPxfT1NXVMWnSJEm+u3fv4uzZswgKCkLz5s3F9C5dumDAgAHi1oSVhYSEqKRVnmuVlJTgwYMH6NWrFwAgIyNDJf+ECRMk18p+Vdde5TH16tULdnZ2YppcLlfZeaauAgICcPLkSTGKHABiY2NhZmYGZ2fn16qbiIiI6G3h6ekp/m1mZgZDQ0Pcvn37teocMGAAunXrJklTV1cHAMhkMshkMgBAWVkZ8vLyoKOjA0NDQ2RnZ4v5f//9d9y8eROTJk1C06ZNJXVV3dEQAHJzczFgwABoa2sjOTkZurq6kvvK66NHj+L58+dieuUdLIno/cR/5UT0Ur6+vggLC0NxcTGaNGmC2NhYODs744MPPlDJ+/XXX6O0tBTp6elo06YNgIqXSFZWVggNDUVaWpokv52dHVavXg2g4rw0CwsLTJ8+HfPmzcOXX34JAPDx8cEHH3yAn3/+GX379gUAfPXVV3jx4gX++OMP8YzekJAQ+Pj4IDw8HOPGjZO8eOvWrZvkjJK8vDysW7cOkZGRACpe6tna2mLz5s0YPnx4nbc5rKq+46qLgoIC3LlzB8OHD1e59/jxY5SVlYnXurq64vN41WfXr18/mJiYIC4uTrIl4fbt21FaWlrtttba2tri2YUAcOXKFfj7+8Pd3R1xcXHiFoaff/45nj17VucxExER0f+WlStXon379tDQ0ICxsTGsrKzEIzsiIyMxffp0GBsbo1evXhgyZAgCAgLQsmVLAMDVq1cBQDI3qUxPT6/O/blx4wZMTExUXsZZWVmp5KsuHajYEvrXX3/F06dPJS/oWrdurZL34cOHiIiIwJYtW3D//n3Jvfz8fJX87dq1k1xbWlqiUaNGKudGV+1rdR8lVtf3uhg5ciSmTJmC2NhYzJkzB/n5+UhKSsLUqVNVtuQmIiIielcp555KOjo6koXW+qi8PXVV5eXlWL58OVasWIHs7Gy8ePFCvFdSUiL+rVxU7tix4yu1uWjRIqirq0NTUxN5eXnQ19eX3G/bti3Gjh2LNWvWID4+Ho6Ojujfvz9GjRqFZs2a1WV4RPSOYUQyEb2Ul5cXiouLkZSUhMLCQiQlJcHX11cl34sXL5CSkoLhw4eLi8hAxVduvr6+SE9PR0FBgaTMmDFjxL/V1dXRo0cPCIKA0aNHi+kGBgawsrLC9evXAVScTffLL79g6NChEARBEmEyaNAg5Ofnq0RoVI3wcHJyQl5enkp/3pT6jKuulH2v+iITAFxcXCCXy8XfypUrAdTt2amrq8Pb2xvHjx+XvHyMi4uDsbEx+vfvL2kzOTkZH330kWQBf86cOdDW1kZ0dLTKOXiNGzeu17iJiIjof4ednR0UCgVcXFzQsWNHcREZAKZMmYIrV65g3rx50NbWxuzZs9GxY0ecOXMGQMVLNqDinOTU1FSVX2JiolhXTQublV/M/dWqO5PYy8sLa9euRUhICOLj45GSkoJ9+/YB+O/4atOQC7bNmjXDkCFDEBsbCwDYsWMHnj17Bn9//wbrExEREdGbVnl++qYYGBjUeC8yMhJTpkyBvb09YmNjxbmtoaEhBEGod5tWVlbIyMhAkyZNMGrUqGrrWr16NX7//Xd8+eWXePLkCWbMmAEHBwfJAjYRvX+4kExELyWXy6FQKBAXF4f4+Hi8ePECf//731Xy5ebmoqioqMbIi/Lycty6dUuS3qpVK8m1vr4+tLW1YWhoqJKu3LYwNzcXjx8/xpo1aySLpXK5HMHBwQCgErFRtR3ll3I1bYX4uuozrrpSbmPz5MkTlXurV69GamoqNm3aJEmv67NTRh3HxcUBAG7fvo3ffvsN3t7e4pY6AFBaWorU1FS4u7uLac+ePcPu3bvh7+9fr4gfIiIiopextLTE9OnTkZKSggsXLuD58+dYvHixeA8AjIyMoFAoVH4uLi5iPc2aNcPjx49V6ldGFiuZm5vj7t27KvOvrKwslXzVpQPA5cuXYWhoqLJdYFWPHj3CgQMHMGvWLERERMDT0xMDBgyQfLBZlTIKW+nf//43ysvLa91tx9zcXKVcTX2v6mUL1QEBAbhy5QpOnz6N2NhYdOvWDdbW1i+tl4iIiIiqt3XrVvTt2xebNm3CyJEjoVAo4OzsrDKXVe52c/HixVeqd9SoUejcuTOWLVuGtLQ0MSilKltbW/zjH//AkSNHsHjxYmRlZeHAgQOvNSYiertxIZmIXomvry/27t2LVatWwdXVtdYv4+qi8mJkbWkAxC/hlNEX/v7+1UaXpKamwtHRsU511qY+ESr1GVdd6evrw8TEBBcuXFC5Z29vD4VCofIc6vrsunfvjg4dOohnZG/evBmCIKhsa62MNq98PvL169dRVFSE7t2712t8RERERDUpKipSiXywtLSETCYTj88YNGgQ9PT08N1336G0tFSljtzcXEnZy5cvS9LOnTuHo0ePSsq4ubmhrKwMUVFRYtqLFy+wfPlyST4TExPY2Nhgw4YNkpd6Fy5cQEpKimTOVBPl3LHqXHHp0qU1lqn6wk/ZL1dX1xrLuLm54cSJEzh16pSYlpubK0YS10a5GF7dIryyXUNDQ0RGRiItLY3RyERERESvSbn9dGU//fST5Ig7oOKdnpmZGZYtW4bCwkLJvaoBOJX5+flhyJAhmDVrluTM5YKCApU2lIvVPCeZ6P3Gf+FE9Eo8PT0xbtw4nDhxAlu3bq02j1wuh46OTo2RF40aNYKZmdlr90Uul0Mmk+HFixdQKBSvXZ9STQvGrxqh0hDc3d3x008/4dSpU7Czs3tp/vo8Oz8/P8yePRvnz59HXFwc2rVrh549e0ry7NmzB506dZJEuxQXFwNo2C0ViYiI6P105coV9O/fH15eXujUqRM0NDSQkJCAnJwceHt7A6g4AzkqKgqffvopbG1t4e3tDblcjps3b2LPnj1wdHTEihUrAFREYHz//fcYNGgQRo8ejfv372PVqlWwtraWHIUydOhQODo6YtasWfjPf/6DTp06IT4+vtrzihcuXAhXV1c4ODhg9OjRKC4uxvLly6Gvr4/w8PCXjlFPTw99+/bFggULUFpaClNTU6SkpEhe6FWVnZ0NDw8PDB48GMePH8emTZvg6+uLrl271lgmNDQUMTExGDx4MCZPngxdXV2sWbMG5ubmOH/+fK19tLS0hIGBAVatWgWZTAZdXV3Y29uLLxU1NTXh7e2NFStWQF1dHT4+Pi8dNxERERHVzMPDA+Hh4QgJCYGtrS3OnDmDxMRElV0Q1dXVsXLlSnh6eqJHjx4IDAyEkZER0tPT8fTpU2zfvr3GNpTz4NGjR+PAgQNQU1PDwYMHMWnSJHzyySewsrJCXl4eli9fjlatWqF3795/9bCJqAExIpmIXknTpk0RFRWF8PBwDB06tNo86urqGDhwIBITEyVn6ubk5CAuLg59+vR5I1scq6urY8SIEfjll1+qjcatHElSFzVFVFhaWiI/P1/yIu3u3btISEioVztvUmhoKHR0dDBq1Cjk5OSo3K8awVKfZ6eMPp4zZw7Onj2rEo0MVJyPXHlbawBo27YtAGD//v0q+auLCiIiIiJ6VWZmZvDx8cHhw4cRFhaGsLAwFBQUYNu2bRgxYoSYz9fXFwcOHICpqSkWLlyIyZMnY8uWLbCxsRGP9QAqjmHZuHEj8vPzMW3aNOzatQsxMTGwtbWVtNuoUSPs2rULfn5+2LRpE7766iuYmppiw4YNKn1UKBTYt28fWrRogTlz5mDRokXo1asXjh49Ki60vkxcXBwGDRqElStXIiwsDJqamti7d2+N+bdu3YrGjRtj1qxZ2LNnDyZOnIh169bV2oaJiQkOHTqELl26YP78+Vi6dCkCAgIwefLkl/ZPU1MTGzZsgLq6OkJCQuDj44O0tDRJnoCAAABA//79YWJi8gqjJiIiIqKahIWFYebMmdi9ezemTJmCS5cuYd++fdDX11fJO3ToUBw4cACtWrXC/PnzMW3aNGRnZ2PMmDG1tmFqaopFixbh0KFD4k48Xbt2hUKhQEJCAiZPnozly5fDyckJqamp4vF7RPR+UhNe5wR2InpvRUdHIzg4GKdPn0aPHj1qzGdhYYHOnTsjKSkJAJCZmQl7e3sYGBhg/Pjx0NDQwOrVq/Hnn38iLS0N9vb2AIDw8HBEREQgNzdX8sVcUFAQduzYoXLunIuLCx48eCAufubk5MDe3h65ubn47LPP0KlTJzx8+BAZGRnYv38/Hj58WGs7yvFlZ2eLUbSnT5+GnZ0d3Nzc4O3tDU1NTQwdOhQlJSUwNzeHsbExvvjiCxQVFSEqKgpyuRwZGRmSxdrXHVdNgoKCcPjwYckCvVJiYiJ8fHygpaUFPz8/dO3aFYIgIDs7G3Fxcfjzzz8RGxsrRue86rOrzNHREceOHQNQcfaecpEYqIh8adOmDQ4fPgxnZ2dJueDgYERHR2PkyJFwcXFBYWEhDh06BHd3d0yYMEGSt6ZnR0RERES1e5vnUefOnYONjQ02btyITz/9tKG7Q0RERERERHXAra2J6I2ytrbGb7/9hrCwMMybNw/l5eWwt7fHpk2bxEXkN8HY2BinTp3C3LlzER8fjx9//BEtWrSAtbU1IiMj61Vnz5498c9//hOrVq3Cvn37UF5eLi40JyQkYNq0aQgNDUXr1q0xb948XL16FRkZGW9sTPU1bNgw/PHHH1i8eDFSUlLw888/Q01NDebm5nB3d0dISIhkO8P6PDs/Pz8cO3YMdnZ2kkVkoCIaWV9fX+U8ZqDinD5TU1Ns27YNO3fuRPPmzdG7d28MHjz4zT4EIiIiInorrV27Fk2bNsXHH3/c0F0hIiIiIiKiOmJEMhHROyAoKAgHDx5ERkYGNDQ0YGBg0NBdErm5uaFp06bYtm1bvcqXlJTgyZMnWLBgARYuXPhWRtIQERERvc3exojk3bt34+LFi5g9ezYmTpyI77//vqG7RERERERERHXEiGQionfErVu3IJfLYW1t/dKtsP8/ubi4wMnJqd7lV61ahalTp77BHhERERFRQ5s0aRJycnLg5uaGiIiIhu4OERERERER1QMjkomI3gEXL17EnTt3AABNmzZFr169GrhHb86tW7eQlZUlXjs7O0NTU7MBe0RERERERERERERERFxIJiIiIiIiIiIiIiIiIiIiiUYN3QEiIiIiIiIiIiIiIiIiInq7cCGZiIiIiIiIiIiIiIiIiIgkuJBMREREREREREREREREREQSXEgmIiIiIiIiIiIiIiIiIiIJLiQTEREREREREREREREREZEEF5KJiIiIiIiIiIiIiIiIiEiCC8lERERERERERERERERERCTBhWQiIiIiIiIiIiIiIiIiIpLgQjIREREREREREREREREREUn8HwIxxAspLtY8AAAAAElFTkSuQmCC",
            "text/plain": [
              "<Figure size 2400x500 with 3 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(24, 5))\n",
        "\n",
        "plt.subplot(1, 3, 1)\n",
        "plt.xlabel(\"Momentum [GeV/$c$]\", fontsize=12)\n",
        "plt.ylabel(\"Candidates\", fontsize=12)\n",
        "plt.hist(data[\"P\"]/1e3, bins=np.linspace(0.0, 200.0, 101), color=\"#3288bd\", label=\"toy data\")\n",
        "plt.legend(loc=\"upper right\", fontsize=10)\n",
        "plt.yscale(\"log\")\n",
        "\n",
        "plt.subplot(1, 3, 2)\n",
        "plt.xlabel(\"Pseudorapidity\", fontsize=12)\n",
        "plt.ylabel(\"Candidates\", fontsize=12)\n",
        "plt.hist(data[\"ETA\"], bins=np.linspace(1.5, 5.5, 101), color=\"#3288bd\", label=\"toy data\")\n",
        "plt.legend(loc=\"upper right\", fontsize=10)\n",
        "\n",
        "plt.subplot(1, 3, 3)\n",
        "plt.xlabel(\"$\\mathtt{nTracks}$\", fontsize=12)\n",
        "plt.ylabel(\"Candidates\", fontsize=12)\n",
        "plt.hist(data[\"nTracks\"], bins=np.linspace(0.0, 750.0, 101), color=\"#3288bd\", label=\"toy data\")\n",
        "plt.legend(loc=\"upper right\", fontsize=10)\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yV9PdrDtb5gK"
      },
      "source": [
        "The high-level response of the RICH detectors describes how good the reconstructed circles match with a specific particle hypothesis in terms of **differential log-likelihood** (DLL). We aim to build a model able to reproduce the following output features:\n",
        "* `data.RichDLLe` - RICH DLL between the electron and pion tracks\n",
        "* `data.RichDLLmu` - RICH DLL between the muon and pion tracks\n",
        "* `data.RichDLLk` - RICH DLL between the kaon and pion tracks\n",
        "* `data.RichDLLp` - RICH DLL between the proton and pion tracks"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 863
        },
        "id": "2r8zWzbI7A2V",
        "outputId": "09d50cd6-b850-414e-e0e7-700ac5328760"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1600x1000 with 4 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(16, 10))\n",
        "\n",
        "plt.subplot(2, 2, 1)\n",
        "plt.xlabel(\"RICH differential log-likelihood $(e - \\pi)$\", fontsize=12)\n",
        "plt.ylabel(\"Candidates\", fontsize=12)\n",
        "plt.hist(data[\"RichDLLe\"], bins=np.linspace(-150.0, 150.0, 101), color=\"#3288bd\", label=\"toy data\")\n",
        "plt.legend(loc=\"upper right\", fontsize=10)\n",
        "plt.yscale(\"log\")\n",
        "\n",
        "plt.subplot(2, 2, 2)\n",
        "plt.xlabel(\"RICH differential log-likelihood $(\\mu - \\pi)$\", fontsize=12)\n",
        "plt.ylabel(\"Candidates\", fontsize=12)\n",
        "plt.hist(data[\"RichDLLmu\"], bins=np.linspace(-150.0, 150.0, 101), color=\"#3288bd\", label=\"toy data\")\n",
        "plt.legend(loc=\"upper right\", fontsize=10)\n",
        "plt.yscale(\"log\")\n",
        "\n",
        "plt.subplot(2, 2, 3)\n",
        "plt.xlabel(\"RICH differential log-likelihood $(K - \\pi)$\", fontsize=12)\n",
        "plt.ylabel(\"Candidates\", fontsize=12)\n",
        "plt.hist(data[\"RichDLLk\"], bins=np.linspace(-150.0, 150.0, 101), color=\"#3288bd\", label=\"toy data\")\n",
        "plt.legend(loc=\"upper right\", fontsize=10)\n",
        "plt.yscale(\"log\")\n",
        "\n",
        "plt.subplot(2, 2, 4)\n",
        "plt.xlabel(\"RICH differential log-likelihood $(p - \\pi)$\", fontsize=12)\n",
        "plt.ylabel(\"Candidates\", fontsize=12)\n",
        "plt.hist(data[\"RichDLLp\"], bins=np.linspace(-150.0, 150.0, 101), color=\"#3288bd\", label=\"toy data\")\n",
        "plt.legend(loc=\"upper right\", fontsize=10)\n",
        "plt.yscale(\"log\")\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vDfCdUkIi5xM"
      },
      "source": [
        "### Data preprocessing\n",
        "\n",
        "As usual in a machine learning application, the data sample is split into **training** and **test set**."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xinGo4AJ9OFu",
        "outputId": "85b61189-8091-4ece-be5c-b00f5a90e22d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "x_train shape: (150000, 4) \t x_test shape: (100000, 4)\n",
            "y_train shape: (150000, 4) \t y_test shape: (100000, 4)\n"
          ]
        }
      ],
      "source": [
        "data = shuffle(data)\n",
        "\n",
        "x_vars = [\"P\", \"ETA\", \"nTracks\", \"trackcharge\"]\n",
        "x = data[x_vars].values\n",
        "\n",
        "y_vars = [\"RichDLLe\", \"RichDLLmu\", \"RichDLLk\", \"RichDLLp\"]\n",
        "y = data[y_vars].values\n",
        "\n",
        "x_train, x_test = x[:150000], x[150000:]\n",
        "y_train, y_test = y[:150000], y[150000:]\n",
        "\n",
        "print(f\"x_train shape: {x_train.shape} \\t x_test shape: {x_test.shape}\")\n",
        "print(f\"y_train shape: {y_train.shape} \\t y_test shape: {y_test.shape}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HcGuX9oZ_HHN"
      },
      "source": [
        "To normalize the different ranges and orders of magnitude of input $x$ and output $y$ features highlighted by the previous histograms, we will use the scikit-learn [`QuantileTransformer`](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.QuantileTransformer.html) using a Gaussian as output distribution.\n",
        "\n",
        "Since one of the $x$ features is boolean (`data.trackcharge`), the latter should be kept unchanged. Let's preprocess the rest of the conditional information and verify the results with the [`DataFrame`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html) `describe()` method:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "BElR-YUR_0df",
        "outputId": "83604b66-4e85-441f-a75f-9f37f32f6cfe"
      },
      "outputs": [
        {
          "data": {
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>P</th>\n",
              "      <th>ETA</th>\n",
              "      <th>nTracks</th>\n",
              "      <th>trackcharge</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>150000.000000</td>\n",
              "      <td>150000.000000</td>\n",
              "      <td>150000.000000</td>\n",
              "      <td>150000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>-0.000884</td>\n",
              "      <td>0.008388</td>\n",
              "      <td>-0.011304</td>\n",
              "      <td>0.021547</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.989034</td>\n",
              "      <td>0.989918</td>\n",
              "      <td>1.004566</td>\n",
              "      <td>0.999771</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-5.199338</td>\n",
              "      <td>-5.199338</td>\n",
              "      <td>-5.199338</td>\n",
              "      <td>-1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>-0.665788</td>\n",
              "      <td>-0.659946</td>\n",
              "      <td>-0.681594</td>\n",
              "      <td>-1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>-0.004356</td>\n",
              "      <td>0.001409</td>\n",
              "      <td>-0.022584</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.670441</td>\n",
              "      <td>0.685286</td>\n",
              "      <td>0.659602</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>5.199338</td>\n",
              "      <td>5.199338</td>\n",
              "      <td>5.199338</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
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              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-d6217335-64ed-49ff-b810-d1db81b382f3 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-d6217335-64ed-49ff-b810-d1db81b382f3');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-2edc9781-8ca4-4c46-a31e-978b7da8fceb\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-2edc9781-8ca4-4c46-a31e-978b7da8fceb')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-2edc9781-8ca4-4c46-a31e-978b7da8fceb button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                   P            ETA        nTracks    trackcharge\n",
              "count  150000.000000  150000.000000  150000.000000  150000.000000\n",
              "mean       -0.000884       0.008388      -0.011304       0.021547\n",
              "std         0.989034       0.989918       1.004566       0.999771\n",
              "min        -5.199338      -5.199338      -5.199338      -1.000000\n",
              "25%        -0.665788      -0.659946      -0.681594      -1.000000\n",
              "50%        -0.004356       0.001409      -0.022584       1.000000\n",
              "75%         0.670441       0.685286       0.659602       1.000000\n",
              "max         5.199338       5.199338       5.199338       1.000000"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "x_scaler = QuantileTransformer(output_distribution=\"normal\")\n",
        "x_train_prep = x_scaler.fit_transform(x_train[:, :3])\n",
        "x_train_prep = np.c_[x_train_prep, x_train[:, 3]]\n",
        "\n",
        "pd.DataFrame(x_train_prep, columns=x_vars).describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PY6OOsx6BhQP"
      },
      "source": [
        "Let's preprocess also the $y$ features checking what obtained:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "VvfBZUjvB4oT",
        "outputId": "0375afab-00a7-42d8-f781-fb857198bad2"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-1b4c29f8-dd67-4329-9da4-a99ee945dacb\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>RichDLLe</th>\n",
              "      <th>RichDLLmu</th>\n",
              "      <th>RichDLLk</th>\n",
              "      <th>RichDLLp</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>150000.000000</td>\n",
              "      <td>150000.000000</td>\n",
              "      <td>150000.000000</td>\n",
              "      <td>150000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>-0.000817</td>\n",
              "      <td>-0.002380</td>\n",
              "      <td>-0.007552</td>\n",
              "      <td>-0.004656</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.006061</td>\n",
              "      <td>0.999470</td>\n",
              "      <td>0.989299</td>\n",
              "      <td>0.992517</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-5.199338</td>\n",
              "      <td>-5.199338</td>\n",
              "      <td>-5.199338</td>\n",
              "      <td>-5.199338</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>-0.678816</td>\n",
              "      <td>-0.662993</td>\n",
              "      <td>-0.681961</td>\n",
              "      <td>-0.686749</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>-0.001912</td>\n",
              "      <td>-0.003867</td>\n",
              "      <td>-0.009469</td>\n",
              "      <td>-0.007685</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.676172</td>\n",
              "      <td>0.665580</td>\n",
              "      <td>0.647560</td>\n",
              "      <td>0.654464</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>5.199338</td>\n",
              "      <td>5.199338</td>\n",
              "      <td>5.199338</td>\n",
              "      <td>5.199338</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1b4c29f8-dd67-4329-9da4-a99ee945dacb')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-1b4c29f8-dd67-4329-9da4-a99ee945dacb button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-1b4c29f8-dd67-4329-9da4-a99ee945dacb');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-eccd8c65-889e-4cde-8bd0-f04cd52da659\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-eccd8c65-889e-4cde-8bd0-f04cd52da659')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-eccd8c65-889e-4cde-8bd0-f04cd52da659 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "            RichDLLe      RichDLLmu       RichDLLk       RichDLLp\n",
              "count  150000.000000  150000.000000  150000.000000  150000.000000\n",
              "mean       -0.000817      -0.002380      -0.007552      -0.004656\n",
              "std         1.006061       0.999470       0.989299       0.992517\n",
              "min        -5.199338      -5.199338      -5.199338      -5.199338\n",
              "25%        -0.678816      -0.662993      -0.681961      -0.686749\n",
              "50%        -0.001912      -0.003867      -0.009469      -0.007685\n",
              "75%         0.676172       0.665580       0.647560       0.654464\n",
              "max         5.199338       5.199338       5.199338       5.199338"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_scaler = QuantileTransformer(output_distribution=\"normal\")\n",
        "y_train_prep = y_scaler.fit_transform(y_train)\n",
        "\n",
        "pd.DataFrame(y_train_prep, columns=y_vars).describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GVNTdWLPi5xM"
      },
      "source": [
        "### Model definition\n",
        "\n",
        "We are finally ready to define the **LSGAN model** to be used to parameterize the RICH response. GAN algorithms rely on the competition (namely the minimax game) between the generator and discriminator networks. The latters are provided by the [pidgan](https://github.com/mbarbetti/pidgan) package through the [`players`](https://github.com/mbarbetti/pidgan/tree/main/src/pidgan/players) module that implements the two networks customizing the TensorFlow [`Model`](https://www.tensorflow.org/api_docs/python/tf/keras/Model) class."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gvx8Dkbz5o9Q"
      },
      "source": [
        "#### The generator\n",
        "\n",
        "The **generator player** can be implemented with the high-level [`Generator`](https://github.com/mbarbetti/pidgan/blob/main/src/pidgan/players/generators/Generator.py) class that provides the neural network model wrapping the TensorFlow [Sequential API](https://www.tensorflow.org/guide/keras/sequential_model). Before to initialize a `Generator` instance, let's have a look at its arguments:\n",
        "\n",
        "* **`output_dim`** (_int_) - Dimensionality of the generator output space.\n",
        "* **`latent_dim`** (_int_) - Dimensionality of the latent space.\n",
        "* **`num_hidden_layers`** (_int_, default=5) - Number of the hidden layers passed to the [`tf.keras.Sequential`](https://www.tensorflow.org/api_docs/python/tf/keras/Sequential) model. Each hidden layer has a LeakyReLU as activation function (see [`tf.keras.layers.LeakyReLU`](https://www.tensorflow.org/api_docs/python/tf/keras/layers/LeakyReLU)).\n",
        "* **`mlp_hidden_units`** (_int_ or _array_like_, default=128) - Number of neuron units per each hidden layer. It can also be passed as an array of unit numbers having length equal to the number of hidden layers.\n",
        "* **`mlp_dropout_rates`** (_float_ or _array_like_, default=0.0) - Fraction of the input neurons to drop per each hidden layer. It can also be passed as an array of float numbers having length equal to the number of hidden layers.\n",
        "* **`output_activation`** (_activation_like_ or _None_, default=None) - Activation function to use in the generator output layer (see [`tf.keras.activations`](https://www.tensorflow.org/api_docs/python/tf/keras/activations)). If None, no activation is applied (i.e., \"linear\" activation: $a(x) = x$).\n",
        "* **`name`** (_str_ of _None_, default=None) - The name of the generator model.\n",
        "* **`dtype`** (_dtype_ or _None_, default=None) - The dtype of the generator layer computations and weights."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0th7xpVKCGN0"
      },
      "outputs": [],
      "source": [
        "from pidgan.players.generators import Generator\n",
        "\n",
        "generator = Generator(\n",
        "    output_dim=y.shape[-1],\n",
        "    latent_dim=64,\n",
        "    num_hidden_layers=5,\n",
        "    mlp_hidden_units=128,\n",
        "    mlp_dropout_rates=0.0,\n",
        "    output_activation=None,\n",
        "    name=\"generator\"\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "luH3v1XVEBlc",
        "outputId": "976b55e5-17e3-4121-a7f1-ed6b4f65cc9f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"generator_seq\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_0 (Dense)             (None, 128)               8832      \n",
            "                                                                 \n",
            " leaky_relu_0 (LeakyReLU)    (None, 128)               0         \n",
            "                                                                 \n",
            " dropout_0 (Dropout)         (None, 128)               0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 128)               16512     \n",
            "                                                                 \n",
            " leaky_relu_1 (LeakyReLU)    (None, 128)               0         \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 128)               0         \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 128)               16512     \n",
            "                                                                 \n",
            " leaky_relu_2 (LeakyReLU)    (None, 128)               0         \n",
            "                                                                 \n",
            " dropout_2 (Dropout)         (None, 128)               0         \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 128)               16512     \n",
            "                                                                 \n",
            " leaky_relu_3 (LeakyReLU)    (None, 128)               0         \n",
            "                                                                 \n",
            " dropout_3 (Dropout)         (None, 128)               0         \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 128)               16512     \n",
            "                                                                 \n",
            " leaky_relu_4 (LeakyReLU)    (None, 128)               0         \n",
            "                                                                 \n",
            " dropout_4 (Dropout)         (None, 128)               0         \n",
            "                                                                 \n",
            " dense_out (Dense)           (None, 4)                 516       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 75396 (294.52 KB)\n",
            "Trainable params: 75396 (294.52 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "_ = generator(keras.Input(shape=x.shape[1:]))\n",
        "generator.summary()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-MkE6GA14-hz"
      },
      "source": [
        "#### The discriminator\n",
        "\n",
        "The **discriminator player** can be implemented with the high-level [`Discriminator`](https://github.com/mbarbetti/pidgan/blob/main/src/pidgan/players/discriminators/Discriminator.py) class that provides the neural network model wrapping the TensorFlow [Sequential API](https://www.tensorflow.org/guide/keras/sequential_model). Again, before to initialize a `Discriminator` instance, let's have a look to its arguments:\n",
        "\n",
        "* **`output_dim`** (_int_) - Dimensionality of the discriminator output space.\n",
        "* **`num_hidden_layers`** (_int_, default=5) - Number of the hidden layers passed to the [`tf.keras.Sequential`](https://www.tensorflow.org/api_docs/python/tf/keras/Sequential) model. Each hidden layer has a LeakyReLU as activation function (see [`tf.keras.layers.LeakyReLU`](https://www.tensorflow.org/api_docs/python/tf/keras/layers/LeakyReLU)).\n",
        "* **`mlp_hidden_units`** (_int_ or _array_like_, default=128) - Number of neuron units per each hidden layer. It can also be passed as an array of unit numbers having length equal to the number of hidden layers.\n",
        "* **`mlp_dropout_rates`** (_float_ or _array_like_, default=0.0) - Fraction of the input neurons to drop per each hidden layer. It can also be passed as an array of float numbers having length equal to the number of hidden layers.\n",
        "* **`output_activation`** (_activation_like_ or _None_, default=\"sigmoid\") - Activation function to use in the dicriminator output layer (see [`tf.keras.activations`](https://www.tensorflow.org/api_docs/python/tf/keras/activations)). If None, no activation is applied (i.e., \"linear\" activation: $a(x) = x$).\n",
        "* **`name`** (_str_ of _None_, default=None) - The name of the discriminator model.\n",
        "* **`dtype`** (_dtype_ or _None_, default=None) - The dtype of the discriminator layer computations and weights."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ufj8Xi4fC_Ot"
      },
      "outputs": [],
      "source": [
        "from pidgan.players.discriminators import Discriminator\n",
        "\n",
        "discriminator = Discriminator(\n",
        "    output_dim=1,\n",
        "    num_hidden_layers=5,\n",
        "    mlp_hidden_units=128,\n",
        "    mlp_dropout_rates=0.0,\n",
        "    output_activation=\"sigmoid\",\n",
        "    name=\"discriminator\"\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nfqi9DHvETlY",
        "outputId": "2a530009-e077-4744-ad43-5aa7a6ded246"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"discriminator_seq\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_0 (Dense)             (None, 128)               1152      \n",
            "                                                                 \n",
            " leaky_relu_0 (LeakyReLU)    (None, 128)               0         \n",
            "                                                                 \n",
            " dropout_0 (Dropout)         (None, 128)               0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 128)               16512     \n",
            "                                                                 \n",
            " leaky_relu_1 (LeakyReLU)    (None, 128)               0         \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 128)               0         \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 128)               16512     \n",
            "                                                                 \n",
            " leaky_relu_2 (LeakyReLU)    (None, 128)               0         \n",
            "                                                                 \n",
            " dropout_2 (Dropout)         (None, 128)               0         \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 128)               16512     \n",
            "                                                                 \n",
            " leaky_relu_3 (LeakyReLU)    (None, 128)               0         \n",
            "                                                                 \n",
            " dropout_3 (Dropout)         (None, 128)               0         \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 128)               16512     \n",
            "                                                                 \n",
            " leaky_relu_4 (LeakyReLU)    (None, 128)               0         \n",
            "                                                                 \n",
            " dropout_4 (Dropout)         (None, 128)               0         \n",
            "                                                                 \n",
            " dense_out (Dense)           (None, 1)                 129       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 67329 (263.00 KB)\n",
            "Trainable params: 67329 (263.00 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "_ = discriminator((keras.Input(shape=x.shape[1:]), keras.Input(shape=y.shape[1:])))\n",
        "discriminator.summary()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IpQQWkPS5htN"
      },
      "source": [
        "#### The LSGAN algorithm\n",
        "\n",
        "The **LSGAN algorithm** [[3][3]] can be implemented with the high-level [`LSGAN`](https://github.com/mbarbetti/pidgan/blob/main/src/pidgan/algorithms/LSGAN.py) class that defines the training procedure of the two players customizing the TensorFlow [`Model`](https://www.tensorflow.org/api_docs/python/tf/keras/Model) class and, in particular, its `fit()` method. Once again, before to initialize a `LSGAN` instance, let's have a look at its arguments:\n",
        "\n",
        "* **`generator`** (_generator_like_) - The generator network. It must be passed as a pidgan [`Generator`](https://github.com/mbarbetti/pidgan/blob/main/src/pidgan/players/generators/Generator.py) instance.\n",
        "* **`discriminator`** (_discriminator_like_) - The discriminator network. It must be passed as a pidgan [`Discriminator`](https://github.com/mbarbetti/pidgan/blob/main/src/pidgan/players/discriminators/Discriminator.py) instance.\n",
        "* **`minimize_pearson_chi2`** (_bool_, default=False) - If True, the loss function $\\mathcal{L}_{\\rm{LSGAN}}(a, b, c)$ results from setting $a = -1$, $b = 1$ and $c = 0$. When False is passed, $a = 0$ and $b = c = 1$ are the chosen option, as suggested in Ref. [[3][3]].\n",
        "* **`injected_noise_stddev`** (_float_, default=0.0) - When different from zero, it represents the standard deviation of the gaussian noise added to the discriminator input to further stabilize the training procedure following what described in Ref. [[2][2]].\n",
        "* **`feature_matching_penalty`** (_float_, default=0.0) - Importance of the regularization term added to the generator training to force the hidden state of the discriminator to match between reference and generated examples. Its design follows from what proposed in Ref. [[6][6]].\n",
        "* **`referee`** (_classifier_like_ or _None_, default=None) - When passed it enables the training of a third independent player: the referee network. It can be used to assess the quality of the generator output during the training. If passed, it must be a pidgan [`Classifier`](https://github.com/mbarbetti/pidgan/blob/main/src/pidgan/players/classifiers/Classifier.py) instance.\n",
        "* **`name`** (_str_ or _None_, default=\"LSGAN\") - The name of the LSGAN model.\n",
        "* **`dtype`** (_dtype_ or _None_, default=None) - The dtype of the LSGAN model computations.\n",
        "\n",
        "[2]: https://arxiv.org/abs/1701.04862\n",
        "[3]: https://arxiv.org/abs/1611.04076\n",
        "[6]: https://arxiv.org/abs/1606.03498"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qgW6y0Ru1vPp"
      },
      "source": [
        "To not further complicate this tutorial, we avoid to use the referee network in this case, even if its initialization is straightforward:\n",
        "\n",
        "```python\n",
        "from pidgan.players.classifiers import Classifier\n",
        "\n",
        "referee = Classifier(\n",
        "    num_hidden_layers=5,\n",
        "    mlp_hidden_units=128,\n",
        "    dropout_rate=0.0,\n",
        "    name=\"referee\"\n",
        ")\n",
        "```\n",
        "\n",
        "It's time to initialize a [`LSGAN`](https://github.com/mbarbetti/pidgan/blob/main/src/pidgan/algorithms/LSGAN.py) instance!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SBEOKmeEDWC4"
      },
      "outputs": [],
      "source": [
        "from pidgan.algorithms import LSGAN\n",
        "\n",
        "gan = LSGAN(\n",
        "    generator=generator,\n",
        "    discriminator=discriminator,\n",
        "    minimize_pearson_chi2=False,\n",
        "    injected_noise_stddev=0.0,\n",
        "    feature_matching_penalty=0.0,\n",
        "    referee=None,\n",
        "    name=\"LSGAN\",\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZMrE8bs952KB"
      },
      "source": [
        "#### Model compilation\n",
        "\n",
        "To finalize the [`LSGAN`](https://github.com/mbarbetti/pidgan/blob/main/src/pidgan/algorithms/LSGAN.py) model preparation before to run the training procedure, we need to execute the `compile()` method. Differently from the TensorFlow `compile()` method, the [pidgan](https://github.com/mbarbetti/pidgan) one doesn't require to pass the loss function since it is encoded in the customized `gan.fit()` method. The arguments of the `gan.compile()` method follows:\n",
        "\n",
        "* **`metrics`** (_list_ or _None_, default=None) - If not None, list of metrics to be evaluated by the LSGAN model during training and testing. Each of this can be a string (name of a built-in pidgan function) or a [`pidgan.metrics`](https://github.com/mbarbetti/pidgan/tree/main/src/pidgan/metrics) instance.\n",
        "* **`generator_optimizer`** (_optimizer_like_, default=\"rmsprop\") - String (name of optimizer) or optimizer instance for the generator network (see [`tf.keras.optimizers`](https://www.tensorflow.org/api_docs/python/tf/keras/optimizers)).\n",
        "* **`discriminator_optimizer`** (_optimizer_like_, default=\"rmsprop\") - String (name of optimizer) or optimizer instance for the discriminator network. (see [`tf.keras.optimizers`](https://www.tensorflow.org/api_docs/python/tf/keras/optimizers)).\n",
        "* **`generator_upds_per_batch`** (_int_, default=1) - Number of the generator weights updates per batch of data.\n",
        "* **`discriminator_upds_per_batch`** (_int_, default=1) - Number of the discriminator weights updates per batch of data.\n",
        "* **`referee_optimizer`** (_optimizer_like_ or _None_, default=None) - If not None, string (name of optimizer) or optimizer instance for the referee network (see [`tf.keras.optimizers`](https://www.tensorflow.org/api_docs/python/tf/keras/optimizers)).\n",
        "* **`referee_upds_per_batch`** (_int_ or _None_, default=None) - If not None, number of the referee weights updates per batch of data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kHL20AxZFNOq"
      },
      "outputs": [],
      "source": [
        "gan.compile(\n",
        "    metrics=[\"accuracy\", \"bce\"],\n",
        "    generator_optimizer=keras.optimizers.RMSprop(learning_rate=0.002),\n",
        "    discriminator_optimizer=keras.optimizers.RMSprop(learning_rate=0.001),\n",
        "    generator_upds_per_batch=1,\n",
        "    discriminator_upds_per_batch=1,\n",
        "    referee_optimizer=None,\n",
        "    referee_upds_per_batch=None,\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0htLzsSri5xM"
      },
      "source": [
        "### Training procedure\n",
        "\n",
        "The [pidgan](https://github.com/mbarbetti/pidgan) package provides several learning rate scheduling strategies through the [`callbacks.schedulers`](https://github.com/mbarbetti/pidgan/tree/main/src/pidgan/callbacks/schedulers) module. The pidgan schedulers are designed as [custom TensorFlow callbacks](https://www.tensorflow.org/guide/keras/writing_your_own_callbacks) that also allow to trace the learning rate value of each passed optimizer during the training. In this tutorial, we will use an exponential decay schedule both for the generator and discriminator networks:\n",
        "\n",
        "<center>$\\eta(t) = \\eta_0 \\cdot e^{- \\alpha \\cdot t / \\tau}$</center>\n",
        "\n",
        "where $\\eta(t)$ indicates the learning rate value at a specific training step $t$, $\\eta_0$ the initial learning rate, $\\alpha$ the decay rate and $\\tau$ the decay step."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "C0-PqvTDGd1J"
      },
      "outputs": [],
      "source": [
        "from pidgan.callbacks.schedulers import LearnRateExpDecay\n",
        "\n",
        "schedules = list()\n",
        "\n",
        "g_lr_sched = LearnRateExpDecay(\n",
        "    gan.generator_optimizer,\n",
        "    decay_rate=0.1,\n",
        "    decay_steps=50000,\n",
        "    min_learning_rate=1e-6,\n",
        "    verbose=True,\n",
        "    key=\"g_lr\",\n",
        ")\n",
        "schedules.append(g_lr_sched)\n",
        "\n",
        "d_lr_sched = LearnRateExpDecay(\n",
        "    gan.discriminator_optimizer,\n",
        "    decay_rate=0.1,\n",
        "    decay_steps=75000,\n",
        "    min_learning_rate=1e-6,\n",
        "    verbose=True,\n",
        "    key=\"d_lr\",\n",
        ")\n",
        "schedules.append(d_lr_sched)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0hDNGvV_FI0S"
      },
      "source": [
        "Calling `gan.fit()` allows to perform the **minimax two-player game**, training the generator to reproduce the reference space $y$ on the basis of the conditions $x$ thanks to the discriminator feedbacks. The arguments taken by the pidgan `fit()` method are the same ones traditionally passed to a TensorFlow [`Model`](https://www.tensorflow.org/api_docs/python/tf/keras/Model) instance. The schedulers are passed through the `callbacks` argument and, since their verbosity has been enabled (`verbose` and `key` passed), the learning rates of both the generator and discriminator are printed during the training procedure.\n",
        "\n",
        "**Note:** Training the [`LSGAN`](https://github.com/mbarbetti/pidgan/blob/main/src/pidgan/algorithms/LSGAN.py) model on Colab using the hyperparameters chosen for this tutorial (i.e., batch-size, epochs, updates per batch) takes about 15 minutes with no-GPU equipped or less than 10 minutes enabling the T4-powered runtime."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N1mGZ3_FHMNg",
        "outputId": "56a31e30-bbb1-420c-9395-f8e304bb1285"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Training procedure completed in 0:16:26.859855\n"
          ]
        }
      ],
      "source": [
        "start = datetime.now()\n",
        "\n",
        "train = gan.fit(\n",
        "    x=x_train_prep,\n",
        "    y=y_train_prep,\n",
        "    batch_size=256,\n",
        "    epochs=100,\n",
        "    validation_split=0.3,\n",
        "    callbacks=schedules,\n",
        "    verbose=1,\n",
        ")\n",
        "\n",
        "stop = datetime.now()\n",
        "\n",
        "print(f\"Training procedure completed in {stop - start}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GomKgX8QLeQM"
      },
      "source": [
        "The following code cells report the **learning** and **metric curves** for the training just finished."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z97AGJog5-jN"
      },
      "source": [
        "#### Learning curves"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 492
        },
        "id": "-jdGN4aFIhdn",
        "outputId": "dbc6b173-fb93-4076-8852-bc7c6d4c006b"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1600x500 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(16, 5), dpi=100)\n",
        "\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.title(\"Learning curves\", fontsize=14)\n",
        "plt.xlabel(\"Training epochs\", fontsize=12)\n",
        "plt.ylabel(\"Loss\", fontsize=12)\n",
        "plt.plot(train.history[\"g_loss\"], color=\"#3288bd\", label=\"generator\")\n",
        "plt.plot(train.history[\"d_loss\"], color=\"#fc8d59\", label=\"discriminator\")\n",
        "plt.legend(loc=\"upper right\", fontsize=10)\n",
        "\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.title(\"Learning rate scheduling\", fontsize=14)\n",
        "plt.xlabel(\"Training epochs\", fontsize=12)\n",
        "plt.ylabel(\"Learning rate\", fontsize=12)\n",
        "plt.plot(train.history[\"g_lr\"], color=\"#3288bd\", label=\"generator\")\n",
        "plt.plot(train.history[\"d_lr\"], color=\"#fc8d59\", label=\"discriminator\")\n",
        "plt.legend(loc=\"upper right\", fontsize=10)\n",
        "plt.yscale(\"log\")\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 492
        },
        "id": "2id4-AK5J-WW",
        "outputId": "dfa6662f-1dfa-4e2a-859d-feab267b59fe"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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9vT1CQkIQExNTbIfJBQsWiIHEnTt3YsSIERrnU1JSAADr1q0r9R5ZWVkaE0dtdYrUP+yKFv5W93/w4EEcPHiw1P6LsrOzK7Ew+dChQ/H333+jcePGGDZsGOzs7KCvr4+0tDSsXr0aeXl5pT5LURkZGZBKpVprj9rb20MikWgUwS56riKkpKSgoKBAa9BZrehn8/vvv2PYsGEwNTWFt7c3XFxcYGxsLBYAj4qK0trH08Zb1q/ns/ah7qdoH+rP087OrlxjLSo9PR0AyvSPjPIqbTzvvPMOli5dit9++02cAP7666/iObXU1FQIgoCHDx8+09f6m2++wfbt2/H5558DKPyMx40bh2+++QbGxsbP9VxERETloQ60Pa1uu4uLC86ePYsFCxbg0KFD2L17NwCgadOm+PLLL/Hmm28CKN/P8vLOw0qb75R2rugmPc/av7qf/86nOJ/lfFaN81miisGAJlEN1KVLFwQGBiIgIAB9+vR5pmvVP7SvX7+ukSFZUdT9r127FlOnTi3zdSUFMy9cuIC///4b3t7eOHjwoEYx8LNnz2oteP608alUKiQmJhabkCQkJEAQBK0Tm+fdBbLo/SUSCZKSksrUfsGCBTA0NERwcDAaNWqkcW7nzp0lXldR460I6s8zISFB6/n4+Pgy96XeIODhw4dwcXEpta06o7hoBoCaeiKpTWmfXcuWLdG6dWscOHAA6enp0NfXx969e9GkSRONrAf1M7dr1w4XL14sdZxqxsbG+Oqrr/DVV18hIiICAQEBWL9+PVavXo2cnBz8+OOPZeqHiIioIql3Vy5Ldl/Lli3xxx9/ID8/H8HBwfjnn3+wZs0aDBs2DE5OTujatavGz/Kyqk7zmvLgfJbz2aI4nyWqGNKnNyGi6mbMmDGQSqX46aefyjyRUFPvLh4UFFQZQ6vw/tXL4l977bViOxueOnVK6zUymazE38q2adMGwJPJeVHqY9qWIFUUDw8PJCcn4+7du2VqHx4ejmbNmhWb/MXGxuLevXuVMcQKp97N8MyZM8XOZWdni0t4ykK99OjIkSNPbWtlZQVA+z+Y1Eu1yuOdd95Bbm4u/vjjD+zduxeZmZl4++23NdqYmZmhWbNmCAkJKbbkrCxcXV0xfvx4nDhxAqampti/f3+5x0tERFRed+7cwe7du2FgYIBBgwaV+Tp9fX106tQJCxcuxJo1ayAIAg4cOAAAaN++PaRSKQICAsqcCVnTcT7L+WxRnM8SVQwGNIlqoMaNG2PmzJlISEhAv379EBYWprWdth8848aNg5mZGT7//HPcvHmz2Pns7GyxDmZ5dOzYER4eHtixYwd27dpV7LxKpcKJEyfK3F+9evUAAP/++6/G8Zs3b2LRokVar7G2tkZSUhJyc3OLnRszZgwAYOHChRpLcdLT08WlFOo2lWH69OkAgPHjxyM5ObnY+bi4OISEhIjv69Wrh7CwMI3f+ubm5mLKlCk15h8B9erVQ9euXXHlypVi3xPLli0TyxSUxZgxY2BqaooVK1ZorQ1VdLKn/g3z1q1boVKpxONBQUHYtm3bMz7FEyNHjoRMJsOvv/6KX3/9FRKJpNgEECj8WmdnZ2PixInFSiwAQEREBCIjIwEAiYmJuHHjRrE2qampyMvLg6GhYbnHS0REVB6nT5+Gt7c38vLyMHv27Kcujw0ODta6zFk9h1H/LLO3t8eQIUMQHh6udRlrQkKC1my0mozzWc5ni+J8lqhicMk5UQ319ddfQ6FQYOXKlWjatCl69OgBd3d3GBsbIyEhAdeuXcP58+dhamqq8RtaW1tb7NixA2+++Sbc3d3h4+ODpk2bIi8vD5GRkThx4gS6dOkCPz+/co9tx44d6N27N4YPH45Vq1ahbdu2MDIyQnR0NIKCgpCYmKh1cqZNx44d0bFjR+zevRuxsbHo1KkToqOjsX//frz22mv4448/il3Tp08fXLx4Ef369UP37t0hl8vRo0cP8TVt2jSsXbsWLVu2xJAhQyAIAvbs2YMHDx5g+vTp6NGjR7mf/Wl8fHwwd+5c+Pr6omHDhvDx8UG9evWQnJyMsLAwnDp1Cl999RWaNWsGAJg2bRqmTZuGNm3aYOjQoSgoKMDRo0chCALc3d2f6bfBurR27Vr06NEDo0aNwp49e9CwYUNcunQJZ8+eRY8ePXDy5Mlim05pY2dnh61bt2L48OHo2LEj3njjDTRp0gRJSUk4d+4cXFxc8NdffwEAOnXqhK5du+L48ePo3LkzevTogaioKOzbtw/9+/fH3r17y/UsDg4O8PT0xJEjRyCVStGtWzety4UmT56Ms2fPYsuWLTh9+jQ8PT3h5OSE+Ph43L59G+fOncP27dvh4uKChw8fok2bNnB3d4ebmxtq166N5ORk7Nu3D/n5+fj000/LNVYiIqKnCQsLE+uvKxQKJCQk4Pz587h+/TpkMhm++OILzJ8//6n9/Prrr/jxxx/Ro0cPNGjQAObm5rh16xYOHToEa2trjBs3Tmz7/fff48aNG/j6669x6NAh9OnTB4Ig4M6dOzhy5Aji4+PFZbkvAs5nOZ8tivNZogqik73ViajCXLp0SZg0aZLQtGlTwdTUVNDX1xfs7e2FPn36CMuWLRPi4+O1Xnf79m1hwoQJQr169QS5XC5YWVkJrVq1EqZPny6cP39ebBcRESEAEMaMGaO1HwBCz549ix1PSUkRvvjiC6Fly5aCkZGRYGpqKjRq1EgYOXKk8Oeff2q0rVevnlCvXr0SnzEhIUEYP3684OTkJBgaGgqtWrUS1q1bJ9y7d0/r2B49eiRMnDhRcHR0FGQymQBAmD9/vkabTZs2CR06dBCMjY0FY2NjoUOHDsKmTZuK3TsgIEDr9WXRs2dPoaS/Zo8ePSr0799fsLW1FfT19QUHBwehc+fOgq+vrxAdHS22U6lUwvr164UWLVoIhoaGgoODgzBhwgQhISFBa//z588XAAgBAQHPPKYxY8YIAISIiAjx2ObNmwUAwubNmzXalvR1F4SSv56XL18WvL29BVNTU8HMzEzo16+fcP36deH1118XAAipqala+9Pm8uXLwltvvSXY29sL+vr6gqOjo9CvXz/hwIEDGu2SkpKE0aNHC9bW1oKRkZHQqVMn4fDhw1qf62nf60X99ttvAgABgPDjjz+W2nbXrl2Cp6enYGVlJejr6wu1a9cWevXqJaxYsUJITEwUBEEQUlNThQULFgg9evQQHB0dBblcLjg5OQk+Pj7CP//8U+bPhYiIqKzUP/eKvoyMjARHR0ehd+/ewty5c4WwsDCt12qbH509e1aYPHmy0LJlS8HS0lIwMjISGjVqJEydOlWIiooq1kd6erowd+5coWnTpoKBgYFgYWEhtG7dWpg3b56gUCjEdqXNOQRB+9ympPmLIJQ+V6rMuRDns5zPauuL81mi8pMIgiBUTqiUiIiodEqlEg0aNEBOTs4zFVMnIiIiIqoOOJ8l0g3W0CQiokpXUFCgdQOrxYsXIyoqCgMHDqz6QRERERERlRHns0TVCzM0iYio0qWlpcHe3h59+/ZF48aNkZ+fj3PnzuHChQtwdHREcHAwHB0ddT1MIiIiIiKtOJ8lql4Y0CQiokqnUCjw0Ucf4fjx44iJiUFubi4cHR3Rr18/zJ0796k7pxIRERER6RLns0TVCwOaREREREREREREVGOwhiYRERERERERERHVGAxoEhERERERERERUY2hp+sBvChUKhViYmJgZmYGiUSi6+EQERERPRNBEPDo0SM4OTlBKuXvvGsizkeJiIiopivrnJQBzQoSExMDZ2dnXQ+DiIiI6Lncv38fderU0fUwqBw4HyUiIqIXxdPmpAxoVhAzMzMAhR+4ubm5jkdDRERE9GwyMjLg7Owszmmo5uF8lIiIiGq6ss5JGdCsIOplPebm5pxAEhERUY3Fpco1F+ejRERE9KJ42pyUBZKIiIiI6KWybt06uLi4wNDQEB4eHjh//nyJbTds2IDu3bvDysoKVlZW8PT0LNZ+7NixkEgkGi8fHx/xfGBgYLHz6teFCxcAAJGRkVrPnz17tnI+BCIiIqIajAFNIiIiInpp7Nq1CzNmzMD8+fNx6dIluLu7w9vbGwkJCVrbBwYGYsSIEQgICEBQUBCcnZ3h5eWFhw8farTz8fFBbGys+NqxY4d4rkuXLhrnYmNj8e6778LV1RXt27fX6OfYsWMa7dq1a1fxHwIRERFRDccl50RERET00li5ciUmTpyIcePGAQDWr1+PgwcPYtOmTZg9e3ax9tu2bdN4v3HjRuzZswf+/v4YPXq0eNzAwAAODg5a7ymXyzXO5efnY9++fZg2bVqx5VS1atUqsR8iIiIiKsSAJhERUQ0kCAIKCgqgVCp1PRSqQfT19SGTyXQ9DJ1RKBQIDg7GnDlzxGNSqRSenp4ICgoqUx/Z2dnIz8+HtbW1xvHAwEDY2dnBysoKffr0wVdffYVatWpp7WP//v1ITk4Wg6pFvfHGG8jNzUXjxo0xc+ZMvPHGGyWOJS8vD3l5eeL7jIyMMj0DERERUU3HgCYREVENo1AoEBsbi+zsbF0PhWoYiUSCOnXqwNTUVNdD0YmkpCQolUrY29trHLe3t8ft27fL1MesWbPg5OQET09P8ZiPjw8GDx4MV1dXhIeH43//+x/69euHoKAgrQHkn3/+Gd7e3qhTp454zNTUFCtWrEDXrl0hlUqxZ88eDBw4EH/99VeJQc1FixZh4cKFZRo3ERER0YuEAU0iIqIaRKVSISIiAjKZDE5OTpDL5dyVmspEEAQkJibiwYMHaNSo0UudqVleixcvxs6dOxEYGAhDQ0Px+PDhw8U/t2rVCm5ubmjQoAECAwPxyiuvaPTx4MEDHD58GLt379Y4bmNjgxkzZojvO3TogJiYGCxbtqzEgOacOXM0rsnIyICzs/NzPSMRERFRTcCAJhERUQ2iUCigUqng7OwMY2NjXQ+HahhbW1tERkYiPz//pQxo2tjYQCaTIT4+XuN4fHz8U+tWLl++HIsXL8axY8fg5uZWatv69evDxsYGYWFhxQKamzdvRq1atUpdSq7m4eGBo0ePlnjewMAABgYGT+2HiIiI6EXDXc6JiIhqIKmUP8Lp2b3s2bxyuRzt2rWDv7+/eEylUsHf3x+dO3cu8bqlS5fC19cXfn5+xXYl1+bBgwdITk6Go6OjxnFBELB582aMHj0a+vr6T+3nypUrxfogIiIiImZoEhEREdFLZMaMGRgzZgzat2+Pjh07YtWqVcjKyhI36Bk9ejRq166NRYsWAQCWLFmCefPmYfv27XBxcUFcXByAwpqXpqamyMzMxMKFCzFkyBA4ODggPDwcM2fORMOGDeHt7a1x7+PHjyMiIgLvvvtusXFt2bIFcrkcbdq0AQD8+eef2LRpEzZu3FiZHwcRERFRjcSAJhERERG9NIYNG4bExETMmzcPcXFxaN26Nfz8/MSNgqKjozUyoH/44QcoFAoMHTpUo5/58+djwYIFkMlkuHbtGrZs2YK0tDQ4OTnBy8sLvr6+xZaD//zzz+jSpQuaNm2qdWy+vr6IioqCnp4emjZtil27dhW7LxEREREBEkEQBF0P4kWQkZEBCwsLpKenw9zcXNfDISKiF1Rubi4iIiLg6uqqsSnJy8jFxQUfffQRPvroozK1DwwMRO/evZGamgpLS8tKHVt1Vdr3D+cyNR+/hkRERFTTlXU+wwJcREREVCV69epV5uBjWVy4cAGTJk0qc/suXbogNjYWFhYWFTaGylDRnxMRERER0YuGS86JiIio2hAEAUqlEnp6T5+i2NraPlPfcrn8qTtZExERERFR9ccMzRokZtFJhHr+gtQ/b+l6KEREVI0IggBltkInr7JWrhk7dixOnDiB1atXQyKRQCKRIDIyEoGBgZBIJPjnn3/Qrl07GBgY4N9//0V4eDgGDBgAe3t7mJqaokOHDjh27JhGny4uLli1apX4XiKRYOPGjRg0aBCMjY3RqFEj7N+/XzyvvldaWhoA4JdffoGlpSUOHz6MZs2awdTUFD4+PoiNjRWvKSgowPTp02FpaYlatWph1qxZGDNmDAYOHFjis0ZFRaF///6wsrKCiYkJWrRogUOHDonnb9y4gX79+sHU1BT29vZ45513kJSUVOrnRFRd3Eg8jw+PDcTScx/peihERET0EmOGZg2SH/sIuaFJKEjK1vVQiIioGlHl5ONGkzU6uXfL0OmQGcuf2m716tW4c+cOWrZsiS+//BJAYYalOlg3e/ZsLF++HPXr14eVlRXu37+PV199FV9//TUMDAywdetW9O/fH6Ghoahbt26J91m4cCGWLl2KZcuWYe3atRg1ahSioqJgbW2ttX12djaWL1+OX3/9FVKpFG+//TY+/fRTbNu2DUDhDtfbtm3D5s2b0axZM6xevRp//fUXevfuXeIYPvjgAygUCpw8eRImJia4desWTE1NAQBpaWno06cP3n33XXz77bfIycnBrFmz8NZbb+H48eMlfk5E1UW+Kh8R6SFQCUpdD4WIiIheYgxo1iASfRkAQJXPCSQREdUsFhYWkMvlMDY21rrs+8svv0Tfvn3F99bW1nB3dxff+/r6Yu/evdi/fz+mTp1a4n3Gjh2LESNGAAC++eYbrFmzBufPn4ePj4/W9vn5+Vi/fj0aNGgAAJg6daoYSASAtWvXYs6cORg0aBAA4LvvvtPIttQmOjoaQ4YMQatWrQAA9evXF8999913aNOmDb755hvx2KZNm+Ds7Iw7d+6gcePGpX5ORLpmrF8YnM8pyNLxSIiIiOhlVi0DmuvWrcOyZcsQFxcHd3d3rF27Fh07dtTadsOGDdi6dStu3LgBAGjXrh2++eYbjfYSiUTrtUuXLsVnn30GoHDZWlRUlMb5RYsWYfbs2RXxSBVCKi8MaAoMaBIRURFSI320DJ2us3tXhPbt22u8z8zMxIIFC3Dw4EHExsaioKAAOTk5iI6OLrUfNzc38c8mJiYwNzdHQkJCie2NjY3FYCYAODo6iu3T09MRHx+vMaeQyWRo164dVCpViX1Onz4dU6ZMwZEjR+Dp6YkhQ4aI47p69SoCAgLEjM2iwsPD0bhx41Kfj0jXjPUKv3ez8zN1PBIiIiJ6mVW7gOauXbswY8YMrF+/Hh4eHli1ahW8vb0RGhoKOzu7Yu0DAwMxYsQIdOnSBYaGhliyZAm8vLxw8+ZN1K5dGwA0amEBwD///IMJEyZgyJAhGse//PJLTJw4UXxvZmZWCU9YfhL9wpKngqLkf0QREdHLRyKRlGnZd3VmYmKi8f7TTz/F0aNHsXz5cjRs2BBGRkYYOnQoFApFqf3o62sGWCUSSanBR23ty1oXtCTvvvsuvL29cfDgQRw5cgSLFi3CihUrMG3aNGRmZqJ///5YsmRJsescHR2f675EVUGdoZldkAlBEEpMHCAiIiKqTNVuU6CVK1di4sSJGDduHJo3b47169fD2NgYmzZt0tp+27ZteP/999G6dWs0bdoUGzduhEqlgr+/v9jGwcFB47Vv3z707t1bYwkYUBjALNruv/+40jUJMzSJiKgGk8vlUCrL9jPs9OnTGDt2LAYNGoRWrVrBwcGhyjfHsbCwgL29PS5cuCAeUyqVuHTp0lOvdXZ2xnvvvYc///wTn3zyCTZs2AAAaNu2LW7evAkXFxc0bNhQ46WedzzL50RU1Yz0Cr9PVYISCmWujkdDREREL6tqFdBUKBQIDg6Gp6eneEwqlcLT0xNBQUFl6iM7Oxv5+fklFv+Pj4/HwYMHMWHChGLnFi9ejFq1aqFNmzZYtmwZCgoKSrxPXl4eMjIyNF6VTV1DkwFNIiKqiVxcXHDu3DlERkYiKSmp1MzJRo0a4c8//8SVK1dw9epVjBw5stT2lWXatGlYtGgR9u3bh9DQUHz44YdITU0tNSvto48+wuHDhxEREYFLly4hICAAzZo1A1C4YVBKSgpGjBiBCxcuIDw8HIcPH8a4cePEIOazfE5EVc1QzxgSFH7/Zxdw2TkRERHpRrUKaCYlJUGpVMLe3l7juL29PeLi4srUx6xZs+Dk5KQRFC1qy5YtMDMzw+DBgzWOT58+HTt37kRAQAAmT56Mb775BjNnzizxPosWLYKFhYX4cnZ2LtP4noeYoalgQJOIiGqeTz/9FDKZDM2bN4etrW2p9TBXrlwJKysrdOnSBf3794e3tzfatm1bhaMtNGvWLIwYMQKjR49G586dYWpqCm9vbxgaGpZ4jVKpxAcffIBmzZrBx8cHjRs3xvfffw8AcHJywunTp6FUKuHl5YVWrVrho48+gqWlJaTSwmnZs3xORFVNKpGKWZqso0lERES6IhGet1BUBYqJiUHt2rVx5swZdO7cWTw+c+ZMnDhxAufOnSv1+sWLF2Pp0qUIDAzU2BSgqKZNm6Jv375Yu3ZtqX1t2rQJkydPRmZmJgwMDIqdz8vLQ15envg+IyMDzs7OSE9Ph7m5eal9l1f8d+cQt+QUrIe3gvMy70q5BxERVW+5ubmIiIiAq6trqUE1qhwqlQrNmjXDW2+9BV9fX10P55mV9v2TkZEBCwuLSp3LUOWqqq/h+EM9kZQThxV9/kAjq1aVdh8iIiJ6+ZR1PlOtNgWysbGBTCZDfHy8xvH4+Hg4ODiUeu3y5cuxePFiHDt2rMRg5qlTpxAaGopdu3Y9dSweHh4oKChAZGQkmjRpUuy8gYGB1kBnZRI3BeKScyIioioRFRWFI0eOoGfPnsjLy8N3332HiIgIjBw5UtdDI9IZI+50TkRERDpWrZacy+VytGvXTmNDH/UGP0UzNv9r6dKl8PX1hZ+fH9q3b19iu59//hnt2rWDu7v7U8dy5coVSKVSrTur6wqXnBMREVUtqVSKX375BR06dEDXrl1x/fp1HDt2TKyJSfQyUu90npOfpeOREBER0cuqWmVoAsCMGTMwZswYtG/fHh07dsSqVauQlZWFcePGAQBGjx6N2rVrY9GiRQCAJUuWYN68edi+fTtcXFzEWpumpqYwNTUV+83IyMDvv/+OFStWFLtnUFAQzp07h969e8PMzAxBQUH4+OOP8fbbb8PKyqoKnrpspOKmQNwcgIiIqCo4Ozvj9OnTuh4GUbVirM7Q5KZAREREpCPVLqA5bNgwJCYmYt68eYiLi0Pr1q3h5+cnbhQUHR0tFs0HgB9++AEKhQJDhw7V6Gf+/PlYsGCB+H7nzp0QBAEjRowodk8DAwPs3LkTCxYsQF5eHlxdXfHxxx9jxowZlfOQ5aRecq7iknMiIiIi0hEjfW4KRERERLpV7QKaADB16lRMnTpV67nAwECN95GRkWXqc9KkSZg0aZLWc23btsXZs2efZYg6wSXnRERERKRr6iXnzNAkIiIiXalWNTSpdBJ9BjSJiIiISLfUS85zmKFJREREOsKAZg0iBjS55JyIiIiIdMRY3wwAMzSJiIhIdxjQrEEk8sIvFzcFIiIiIiJdETcFYoYmERER6QgDmjUIl5wTERERka6pa2jmMEOTiIiIdIQBzRqES86JiOhl5+LiglWrVonvJRIJ/vrrrxLbR0ZGQiKR4MqVK89134rqh+hFYCzucp6l45EQERHRy4oBzRpE3OWcAU0iIiIAQGxsLPr161ehfY4dOxYDBw7UOObs7IzY2Fi0bNmyQu9V0Z4W4CWqCEZ63OWciIiIdEtP1wOgspPoF8afVQrW0CQiIgIABweHKrmPTCarsnsRVXfqJeesoUlERES6wgzNGkTKDE0iItJCEATkFmTr5CUIQpnG+NNPP8HJyQkqleYv5QYMGIDx48cDAMLDwzFgwADY29vD1NQUHTp0wLFjx0rt978ZiefPn0ebNm1gaGiI9u3b4/LlyxrtlUolJkyYAFdXVxgZGaFJkyZYvXq1eH7BggXYsmUL9u3bB4lEAolEgsDAQK1Lzk+cOIGOHTvCwMAAjo6OmD17NgoKCsTzvXr1wvTp0zFz5kxYW1vDwcEBCxYsKPV5AgMD0bFjR5iYmMDS0hJdu3ZFVFSUeH7fvn1o27YtDA0NUb9+fSxcuFC8p4uLCwBg0KBBkEgk4nuiiqbeFIg1NImIiEhXmKFZg7CGJhERaZOnzMFb+9ro5N67B1yGoZ7xU9u9+eabmDZtGgICAvDKK68AAFJSUuDn54dDhw4BADIzM/Hqq6/i66+/hoGBAbZu3Yr+/fsjNDQUdevWfeo9MjMz8frrr6Nv37747bffEBERgQ8//FCjjUqlQp06dfD777+jVq1aOHPmDCZNmgRHR0e89dZb+PTTTxESEoKMjAxs3rwZAGBtbY2YmBiNfh4+fIhXX30VY8eOxdatW3H79m1MnDgRhoaGGkHLLVu2YMaMGTh37hyCgoIwduxYdO3aFX379i02/oKCAgwcOBATJ07Ejh07oFAocP78eUgkEgDAqVOnMHr0aKxZswbdu3dHeHg4Jk2aBACYP38+Lly4ADs7O2zevBk+Pj6QyWRP/cyIyoMZmkRERKRrDGjWIGINTS45JyKiGsbKygr9+vXD9u3bxYDmH3/8ARsbG/Tu3RsA4O7uDnd3d/EaX19f7N27F/v378fUqVOfeo/t27dDpVLh559/hqGhIVq0aIEHDx5gypQpYht9fX0sXLhQfO/q6oqgoCDs3r0bb731FkxNTWFkZIS8vLxSl5h///33cHZ2xnfffQeJRIKmTZsiJiYGs2bNwrx58yCVFi6CcXNzw/z58wEAjRo1wnfffQd/f3+tAc2MjAykp6fj9ddfR4MGDQAAzZo1E88vXLgQs2fPxpgxYwAA9evXh6+vL2bOnIn58+fD1tYWAGBpacnl8VSp1DU081UK5CsV0JfJdTwiIiIietkwoFmDMEOTiIi0MZAZYfeAy09vWEn3LqtRo0Zh4sSJ+P7772FgYIBt27Zh+PDhYvAvMzMTCxYswMGDBxEbG4uCggLk5OQgOjq6TP2HhITAzc0NhoaG4rHOnTsXa7du3Tps2rQJ0dHRyMnJgUKhQOvWrcv8HOp7de7cWcyeBICuXbsiMzMTDx48EDNK3dzcNK5zdHREQkKC1j6tra0xduxYeHt7o2/fvvD09MRbb70FR0dHAMDVq1dx+vRpfP311+I1SqUSubm5yM7OhrHx0zNliSqC0eNdzoHCZef6MmsdjoaIiIheRgxo1iDqTYGgEiAoVZDIWAKViIgK60iWZdm3rvXv3x+CIODgwYPo0KEDTp06hW+//VY8/+mnn+Lo0aNYvnw5GjZsCCMjIwwdOhQKhaLCxrBz5058+umnWLFiBTp37gwzMzMsW7YM586dq7B7FKWvr6/xXiKRFKsjWtTmzZsxffp0+Pn5YdeuXfjiiy9w9OhRdOrUCZmZmVi4cCEGDx5c7LqiQVyiyiaTyGAoM0auMhvZ+VkwN2BAk4iIiKoWA5o1iHrJOQAICiUkRgxoEhFRzWFoaIjBgwdj27ZtCAsLQ5MmTdC2bVvx/OnTpzF27FgMGjQIQGHGZmRkZJn7b9asGX799Vfk5uaKAb6zZ89qtDl9+jS6dOmC999/XzwWHh6u0UYul0OpLH01RLNmzbBnzx4IgiBmaZ4+fRpmZmaoU6dOmcesTZs2bdCmTRvMmTMHnTt3xvbt29GpUye0bdsWoaGhaNiwYYnX6uvrP3XsRBXBWN+kMKDJjYGIiIhIBxgRq0HUS84BQMhnHU0iIqp5Ro0ahYMHD2LTpk0YNWqUxrlGjRrhzz//xJUrV3D16lWMHDmy1GzG/xo5ciQkEgkmTpyIW7du4dChQ1i+fHmxe1y8eBGHDx/GnTt3MHfuXFy4cEGjjYuLC65du4bQ0FAkJSUhPz+/2L3ef/993L9/H9OmTcPt27exb98+zJ8/HzNmzBCX0D+riIgIzJkzB0FBQYiKisKRI0dw9+5dsY7mvHnzsHXrVixcuBA3b95ESEgIdu7ciS+++EJj7P7+/oiLi0Nqamq5xkFUFuo6mtwYiIiIiHSBAc0aRFxyDtbRJCKimqlPnz6wtrZGaGgoRo4cqXFu5cqVsLKyQpcuXdC/f394e3trZHA+jampKf7++29cv34dbdq0weeff44lS5ZotJk8eTIGDx6MYcOGwcPDA8nJyRrZmgAwceJENGnSBO3bt4etrS1Onz5d7F61a9fGoUOHcP78ebi7u+O9997DhAkTNIKLz8rY2Bi3b9/GkCFD0LhxY0yaNAkffPABJk+eDADw9vbGgQMHcOTIEXTo0AGdOnXCt99+i3r16ol9rFixAkePHoWzszPatGlT7rEQPY16p/McZmgSERGRDkgEQRB0PYgXQUZGBiwsLJCeng5zc/NKu8+1+ish5KvQ/Pxk6DuaVdp9iIioesrNzUVERARcXV1ZN5GeWWnfP1U1l6HKU5Vfw7knx+JqYhA+6bAcPev2r9R7ERER0cujrPMZZmjWMOpl5yoFMzSJiIiISDeMHmdosoYmERER6QIDmjWMOqDJJedEREREpCvqJeesoUlERES6wIBmDSORF37JuCkQEREREemKsR5raBIREZHuMKBZw4gZmlxyTkREREQ6YqxvAgDIzs/S8UiIiIjoZcSAZg3DJedERAQA3NOPyoPfN1RRjPRYQ5OIiIh0hwHNGubJknMGNImIXkb6+voAgOzsbB2PhGoihUIBAJDJZDoeCdV0rKFJREREuqSn6wHQs3my5Jw1NImIXkYymQyWlpZISEgAABgbG0Mikeh4VFQTqFQqJCYmwtjYGHp6nALS8xFraDKgSURERDrA2WwNI5VzyTkR0cvOwcEBAMSgJlFZSaVS1K1b96UPgq9btw7Lli1DXFwc3N3dsXbtWnTs2FFr2w0bNmDr1q24ceMGAKBdu3b45ptvNNqPHTsWW7Zs0bjO29sbfn5+4nsXFxdERUVptFm0aBFmz54tvr927Ro++OADXLhwAba2tpg2bRpmzpz53M9bGcQMTS45JyIiIh1gQLOGUWdoqhjQJCJ6aUkkEjg6OsLOzg75+fm6Hg7VIHK5HFLpy11xaNeuXZgxYwbWr18PDw8PrFq1Ct7e3ggNDYWdnV2x9oGBgRgxYgS6dOkCQ0NDLFmyBF5eXrh58yZq164ttvPx8cHmzZvF9wYGBsX6+vLLLzFx4kTxvZmZmfjnjIwMeHl5wdPTE+vXr8f169cxfvx4WFpaYtKkSRX1+BVGrKHJDE0iIiLSAQY0axiJnEvOiYiokEwmYy1Eome0cuVKTJw4EePGjQMArF+/HgcPHsSmTZs0siXVtm3bpvF+48aN2LNnD/z9/TF69GjxuIGBgZg9XRIzM7MS22zbtg0KhQKbNm2CXC5HixYtcOXKFaxcubJaBjTVGZo5zNAkIiIiHXi5f0VfA0n0uSkQERERUXkoFAoEBwfD09NTPCaVSuHp6YmgoKAy9ZGdnY38/HxYW1trHA8MDISdnR2aNGmCKVOmIDk5udi1ixcvRq1atdCmTRssW7YMBQUF4rmgoCD06NEDcrlcPKbOHE1NTdU6lry8PGRkZGi8qoqxmKGZVWX3JCIiIlJjhmYNI24KxIAmERER0TNJSkqCUqmEvb29xnF7e3vcvn27TH3MmjULTk5OGkFRHx8fDB48GK6urggPD8f//vc/9OvXD0FBQWIW9fTp09G2bVtYW1vjzJkzmDNnDmJjY7Fy5UoAQFxcHFxdXYuNS33Oysqq2FgWLVqEhQsXlv0DqEDG+iYAgFxlNpSCEjIJs8WJiIio6jCgWcM8WXLOgCYRERFRVVq8eDF27tyJwMBAGBoaiseHDx8u/rlVq1Zwc3NDgwYNEBgYiFdeeQUAMGPGDLGNm5sb5HI5Jk+ejEWLFmmtt1kWc+bM0eg3IyMDzs7O5errWalraAJATn4WTOXmVXJfIiIiIoBLzmucJxmarKFJRERE9CxsbGwgk8kQHx+vcTw+Pv6p9S+XL1+OxYsX48iRI3Bzcyu1bf369WFjY4OwsLAS23h4eKCgoACRkZEAAAcHB63jUp/TxsDAAObm5hqvqqIvk0NfWrg8nnU0iYiIqKoxoFnDsIYmERERUfnI5XK0a9cO/v7+4jGVSgV/f3907ty5xOuWLl0KX19f+Pn5oX379k+9z4MHD5CcnAxHR8cS21y5cgVSqVTcWb1z5844efIk8vPzxTZHjx5FkyZNtC43rw7UGwNxp3MiIiKqagxo1jBcck5ERERUfjNmzMCGDRuwZcsWhISEYMqUKcjKyhJ3PR89ejTmzJkjtl+yZAnmzp2LTZs2wcXFBXFxcYiLi0NmZmEQLzMzE5999hnOnj2LyMhI+Pv7Y8CAAWjYsCG8vb0BFG74s2rVKly9ehX37t3Dtm3b8PHHH+Ptt98Wg5UjR46EXC7HhAkTcPPmTezatQurV6/WWFJe3aiXnWczQ5OIiIiqGGto1jDqJecqBjSJiIiIntmwYcOQmJiIefPmIS4uDq1bt4afn5+4AU90dDSk0ie/8//hhx+gUCgwdOhQjX7mz5+PBQsWQCaT4dq1a9iyZQvS0tLg5OQELy8v+Pr6irUxDQwMsHPnTixYsAB5eXlwdXXFxx9/rBGstLCwwJEjR/DBBx+gXbt2sLGxwbx58zBp0qQq+FTKhxmaREREpCsMaNYwUi45JyIiInouU6dOxdSpU7WeCwwM1HivrnFZEiMjIxw+fLjUNm3btsXZs2efOi43NzecOnXqqe2qC+PHGZqsoUlERERVjUvOa5gnS865KRARERER6Y6xvgkAIDs/S8cjISIiopcNA5o1zJNdzpmhSURERES6I9bQ5JJzIiIiqmIMaNYwDGgSERERUXUg1tDkknMiIiKqYgxo1jASOWtoEhEREZHuiTU0maFJREREVYwBzRpGzNBkDU0iIiIi0iEjZmgSERGRjjCgWcOImwIxQ5OIiIiIdMj4KTU0C1T5rK9JRERElaJaBjTXrVsHFxcXGBoawsPDA+fPny+x7YYNG9C9e3dYWVnBysoKnp6exdpLJBKtr2XLloltUlJSMGrUKJibm8PS0hITJkxAZmb1m4CxhiYRERERVQdPq6H5+cnRmPBPb2TlP6rKYREREdFLoNoFNHft2oUZM2Zg/vz5uHTpEtzd3eHt7Y2EhASt7QMDAzFixAgEBAQgKCgIzs7O8PLywsOHD8U2sbGxGq9NmzZBIpFgyJAhYptRo0bh5s2bOHr0KA4cOICTJ09i0qRJlf68z0r6OENTxSXnRERERKRDT2poZhU7l69UICT5ErLyMxCbGVXVQyMiIqIXXLULaK5cuRITJ07EuHHj0Lx5c6xfvx7GxsbYtGmT1vbbtm3D+++/j9atW6Np06bYuHEjVCoV/P39xTYODg4ar3379qF3796oX78+ACAkJAR+fn7YuHEjPDw80K1bN6xduxY7d+5ETExMlTx3WUn0uSkQEREREemekb4JAO0ZmonZT+bQmYqMKhsTERERvRyqVUBToVAgODgYnp6e4jGpVApPT08EBQWVqY/s7Gzk5+fD2tpa6/n4+HgcPHgQEyZMEI8FBQXB0tIS7du3F495enpCKpXi3LlzWvvJy8tDRkaGxqsqcMk5EREREVUH4pJzLXUy47MfiH/OzE+rqiERERHRS6JaBTSTkpKgVCphb2+vcdze3h5xcXFl6mPWrFlwcnLSCIoWtWXLFpiZmWHw4MHisbi4ONjZ2Wm009PTg7W1dYn3XbRoESwsLMSXs7Nzmcb3vMRNgRQMaBIRERGR7pjomQEAcrRkaMZnPSn/xAxNIiIiqmjVKqD5vBYvXoydO3di7969MDQ01Npm06ZNGDVqVInny2rOnDlIT08XX/fv33+u/srqSYYma2gSERERke4YFcnQFARB41zRDM1HivQqHRcRERG9+PR0PYCibGxsIJPJEB8fr3E8Pj4eDg4OpV67fPlyLF68GMeOHYObm5vWNqdOnUJoaCh27dqlcdzBwaHYpkMFBQVISUkp8b4GBgYwMDB42iNVONbQJCIiIqLqQL3kXICAXGU2jPRMxHMJRTM08xnQJCIioopVrTI05XI52rVrp7Ghj3qDn86dO5d43dKlS+Hr6ws/Pz+NOpj/9fPPP6Ndu3Zwd3fXON65c2ekpaUhODhYPHb8+HGoVCp4eHg8xxNVPC45JyIiIqLqQC41gExSmB/x3zqaRTM0s7jknIiIiCpYtQpoAsCMGTOwYcMGbNmyBSEhIZgyZQqysrIwbtw4AMDo0aMxZ84csf2SJUswd+5cbNq0CS4uLoiLi0NcXBwyMzUnVRkZGfj999/x7rvvFrtns2bN4OPjg4kTJ+L8+fM4ffo0pk6diuHDh8PJyalyH/gZiUvOGdAkIiIiIh2SSCRilmbOfwKaRTM0ueSciIiIKlq1WnIOAMOGDUNiYiLmzZuHuLg4tG7dGn5+fuJGQdHR0ZBKn8Rhf/jhBygUCgwdOlSjn/nz52PBggXi+507d0IQBIwYMULrfbdt24apU6filVdegVQqxZAhQ7BmzZqKf8DnxCXnRERERFRdGOmZ4JEiDdkFWeKxPGUu0vKSxPdcck5EREQVrdoFNAFg6tSpmDp1qtZzgYGBGu8jIyPL1OekSZMwadKkEs9bW1tj+/btZR2izkgfLzlXcVMgIiIiItIx4yIbA6kVzc4EgExmaBIREVEFq3ZLzql0XHJORERERNWFsd7jgGbBI/FYwuP6mdLH/9TIzGcNTSIiIqpYDGjWMGJAk0vOiYiIiEjHtGVoxmcVBjRrm9UHAGQq0qp8XERERPRiY0CzhpHIH3/JlAIEJZedExEREZHuGOlpCWhmFy45b2DVHACQU5CNAlV+1Q+OiIiIXlgMaNYw6gxNABBYR5OIiIiIdEjM0CwonqHpatFMPJaV/whEREREFYUBzRpGIi8a0OSycyIiIiLSHWM9EwBATtFNgR5naDqa1oWJvhkA4BGXnRMREVEFYkCzhtHM0GRAk4iIiIh0x0jM0MwSj6l3Obc3rgMTfXMAQKaCGwMRERFRxWFAs4aRSCWAXuGXTVBwyTkRERER6Y7xf2po5hRkIUORCgCwM6kNU7kFACBTka6bARIREdELiQHNGkiq/zigyQxNIiIiItIhdQ3NnMc1NNXZmab6FjDRN4OZ/uOAZj4DmkRERFRxGNCsgdR1NBnQJCIiIiJdEjcFepyhqd4QyN6kDgDARK5ecs6AJhEREVUcBjRrIHUdTZWCAU0iIiIi0h0jPc1dztUbAtkZ1wYAmMktATBDk4iIiCoWA5o1kDqgKeSzhiYRERER6U5JGZp2JoUBTVNuCkRERESVgAHNGkjCGppEREREVA2oNwXKebzLeXz2kx3OAYibAj3iknMiIiKqQAxo1kBiDc08BjSJiIiISHeKZmgKgiAuObdXZ2g+Dmhmcck5ERERVSAGNGugJ0vOGdAkIiIiIt0x0jMBACiFAihUecU2BTJjhiYRERFVAgY0ayAuOSciIiKi6sBQzxgSSAAAidkxyMovrJVpa+wEADBhDU0iIiKqBAxo1kDiknMFNwUiIiIiIt2RSqRilmZkeigAwMLAWjymXnLOXc6JiIioIjGgWQOpl5yrmKFJRERERDqmrqOpDmjaGdcWz5npPw5ocsk5ERERVSAGNGsg1tAkIiIiourCSE8d0LwN4En9TOBJhma+SoE8ZW7VD46IiIheSAxo1kBSubqGJpecExEREZFuPcnQvANAM0PTSM8EUknhL+OZpUlEREQVhQHNGkjM0FQwQ5OIiIiIdMv4cYZmQvZDAJoBTYlEAlNxYyAGNImIiKhiMKBZA4mbAnHJORERERHpmJG+icb7okvOAcDs8bLzRwxoEhERUQVhQLMGYg1NIiIiIqou1EvO1eyLZGgCgAl3OiciIqIKxoBmDSRmaCpYQ5OIiIjoWa1btw4uLi4wNDSEh4cHzp8/X2LbDRs2oHv37rCysoKVlRU8PT2LtR87diwkEonGy8fHRzwfGRmJCRMmwNXVFUZGRmjQoAHmz58PhUKh0ea/fUgkEpw9e7biP4AKpl5yrmZrohnQ5JJzIiIiqmh6uh4APTuJvnpTIGZoEhERET2LXbt2YcaMGVi/fj08PDywatUqeHt7IzQ0FHZ2dsXaBwYGYsSIEejSpQsMDQ2xZMkSeHl54ebNm6hd+0ngzsfHB5s3bxbfGxgYiH++ffs2VCoVfvzxRzRs2BA3btzAxIkTkZWVheXLl2vc79ixY2jRooX4vlatWhX5+JWiaIamlaEtDGSGGufVS84zFRlVOi4iIiJ6cTGgWQNxyTkRERFR+axcuRITJ07EuHHjAADr16/HwYMHsWnTJsyePbtY+23btmm837hxI/bs2QN/f3+MHj1aPG5gYAAHBwet9/Tx8dHI2Kxfvz5CQ0Pxww8/FAto1qpVq8R+qiujIhma9sZ1ip030eeScyIiIqpYXHJeAz1Zcs6AJhEREVFZKRQKBAcHw9PTUzwmlUrh6emJoKCgMvWRnZ2N/Px8WFtbaxwPDAyEnZ0dmjRpgilTpiA5ObnUftLT04v1AQBvvPEG7Ozs0K1bN+zfv7/UPvLy8pCRkaHx0oWiGZp2/1luDhTN0GRAk4iIiCoGA5o1kPTxknNVPmtoEhEREZVVUlISlEol7O3tNY7b29sjLi6uTH3MmjULTk5OGkFRHx8fbN26Ff7+/liyZAlOnDiBfv36QanU/svnsLAwrF27FpMnTxaPmZqaYsWKFfj9999x8OBBdOvWDQMHDiw1qLlo0SJYWFiIL2dn5zI9Q0UzfkqGpqm4KRCXnBMREVHF4JLzGohLzomIiIiq3uLFi7Fz504EBgbC0PBJncjhw4eLf27VqhXc3NzQoEEDBAYG4pVXXtHo4+HDh/Dx8cGbb76JiRMnisdtbGwwY8YM8X2HDh0QExODZcuW4Y033tA6njlz5mhck5GRoZOgprG+ifhnbRma6oDmI0VaVQ2JiIiIXnDM0KyBxCXneQxoEhEREZWVjY0NZDIZ4uPjNY7Hx8c/tW7l8uXLsXjxYhw5cgRubm6ltq1fvz5sbGwQFhamcTwmJga9e/dGly5d8NNPPz11vB4eHsX6KMrAwADm5uYaL10oWkPTzlhLQPPxLudZ3BSIiIiIKggDmjUQMzSJiIiInp1cLke7du3g7+8vHlOpVPD390fnzp1LvG7p0qXw9fWFn58f2rdv/9T7PHjwAMnJyXB0dBSPPXz4EL169UK7du2wefNmSKVPn4ZfuXJFo4/qqmgNTXuTkpecP2INTSIiIqogXHJeA0ke19BkQJOIiIjo2cyYMQNjxoxB+/bt0bFjR6xatQpZWVniruejR49G7dq1sWjRIgDAkiVLMG/ePGzfvh0uLi5irU1TU1OYmpoiMzMTCxcuxJAhQ+Dg4IDw8HDMnDkTDRs2hLe3N4Anwcx69eph+fLlSExMFMejzgzdsmUL5HI52rRpAwD4888/sWnTJmzcuLHKPpvyMnmcgSmFFLZGxQOw4qZA3OWciIiIKggDmjXQk13OuSkQERER0bMYNmwYEhMTMW/ePMTFxaF169bw8/MTNwqKjo7WyJ784YcfoFAoMHToUI1+5s+fjwULFkAmk+HatWvYsmUL0tLS4OTkBC8vL/j6+sLAwAAAcPToUYSFhSEsLAx16mhmMAqCIP7Z19cXUVFR0NPTQ9OmTbFr165i962ObIwc0L/BO7AysoO+TF7svEmRJecqQQWphIvEiIiI6PlIhKKzKCq3jIwMWFhYID09vdLrF6X8cRP3P/4HZj1dUP+36j/JJSIiouqvKucyVDmq69cwT5mLN/9yBwDseOMiTPTNdDwiIiIiqq7KOp/hr0drINbQJCIiIqKawkBmCLm0MFs1k3U0iYiIqAIwoFkDSeSFXzZVPpecExEREVH1p94YiAFNIiIiqggMaNZAUnWGpoIZmkRERERU/YkBzfwMHY+EiIiIXgQMaNZAXHJORERERDWJ6eONgZihSURERBWhXAHNK1euYMeOHRrHDh8+jB49esDDwwOrV6+ukMGRduIu5wxoEhER0QuK880XizpD8xEDmkRERFQByhXQnDlzJnbt2iW+j4iIwKBBgxAREQEAmDFjBn766aeKGSEVIwY0FayhSURERC8mzjdfLOqAZlY+A5pERET0/MoV0Lx69Sq6desmvt+6dStkMhkuX76Mc+fOYejQoVi/fn25BrRu3Tq4uLjA0NAQHh4eOH/+fIltN2zYgO7du8PKygpWVlbw9PTU2j4kJARvvPEGLCwsYGJigg4dOiA6Olo836tXL0gkEo3Xe++9V67xVwWJfuGXjRmaRERE9KKqzPkmVT0zfWZoEhERUcUpV0AzPT0dtWrVEt8fOnQIffv2hY2NDQCgb9++CAsLe+Z+d+3ahRkzZmD+/Pm4dOkS3N3d4e3tjYSEBK3tAwMDMWLECAQEBCAoKAjOzs7w8vLCw4cPxTbh4eHo1q0bmjZtisDAQFy7dg1z586FoaGhRl8TJ05EbGys+Fq6dOkzj7+qsIYmERERvegqa75JumEiV9fQ5KZARERE9Pz0ynORo6MjQkJCAACxsbEIDg7GuHHjxPOZmZmQSp89Vrpy5UpMnDhR7Gv9+vU4ePAgNm3ahNmzZxdrv23bNo33GzduxJ49e+Dv74/Ro0cDAD7//HO8+uqrGgHKBg0aFOvL2NgYDg4OzzxmXeCScyIiInrRVdZ8k3TDTG4JAMjkknMiIiKqAOWaBQ4YMABr167F9OnTMXDgQBgYGGDQoEHi+atXr6J+/frP1KdCoUBwcDA8PT2fDE4qhaenJ4KCgsrUR3Z2NvLz82FtbQ0AUKlUOHjwIBo3bgxvb2/Y2dnBw8MDf/31V7Frt23bBhsbG7Rs2RJz5sxBdnZ2qffKy8tDRkaGxquqcMk5ERERvegqY75JusNdzomIiKgilSug+dVXX2Hw4MH49ddfkZCQgF9++QX29vYAgIyMDPzxxx/w8vJ6pj6TkpKgVCrFftTs7e0RFxdXpj5mzZoFJycnMSiakJCAzMxMLF68GD4+Pjhy5AgGDRqEwYMH48SJE+J1I0eOxG+//YaAgADMmTMHv/76K95+++1S77Vo0SJYWFiIL2dn52d63uchfbzkXKVgQJOIiIheTJUx3yTdMVUvOc/nknMiIiJ6fuVacm5qalpsuXfRcw8ePICxsfFzDexZLV68GDt37kRgYKBYH1OlKlySPWDAAHz88ccAgNatW+PMmTNYv349evbsCQCYNGmS2E+rVq3g6OiIV155BeHh4VqXpwPAnDlzMGPGDPF9RkZGlQU11UvOUaCCoBIgkUqq5L5EREREVaU6zjep/EzVS86ZoUlEREQVoEILDykUCuTk5MDCwgL6+vrPdK2NjQ1kMhni4+M1jsfHxz+1tuXy5cuxePFiHDlyBG5ubhp96unpoXnz5hrtmzVrprHL+X95eHgAQKmF5g0MDGBubq7xqirqTYEALjsnIiKil8vzzDdJd7jknIiIiCpSuQKaO3fuFDMe1RYuXAhTU1NYWlpi0KBByMzMfKY+5XI52rVrB39/f/GYSqWCv78/OnfuXOJ1S5cuha+vL/z8/NC+fftifXbo0AGhoaEax+/cuYN69eqV2OeVK1cAFBajr47UNTQBQMjnxkBERET04qmM+SbpjpncAgCQXZAJpapAx6MhIiKimq5cAc0VK1YgKytLfH/mzBksXLgQ3t7e+Pjjj+Hn54evv/76mfudMWMGNmzYgC1btiAkJARTpkxBVlaWuKPl6NGjMWfOHLH9kiVLMHfuXGzatAkuLi6Ii4tDXFycxuT2s88+w65du7BhwwaEhYXhu+++w99//433338fABAeHg5fX18EBwcjMjIS+/fvx+jRo9GjRw+NbM/qRFxyDmZoEhER0YupsuabpBsm+k9WM2WxjiYRERE9p3LV0AwPD8eYMWPE99u3b4eDgwP27t0LPT09qFQq7NmzB4sWLXqmfocNG4bExETMmzcPcXFxaN26Nfz8/MQC8NHR0ZBKn8Rgf/jhBygUCgwdOlSjn/nz52PBggUAgEGDBmH9+vVYtGgRpk+fjiZNmmDPnj3o1q0bgMIszmPHjmHVqlXIysqCs7MzhgwZgi+++KI8H02VkMikgFQCqAQI3BiIiIiIXkCVNd8k3ZBJ9WCsZ4rsgkw8UqTD3MBa10MiIiKiGqxcAc28vDxx4x0AOHLkCPr16wc9vcLumjdvju+//75cA5o6dSqmTp2q9VxgYKDG+8jIyDL1OX78eIwfP17rOWdnZ40dz2sKib4MQl4BMzSJiIjohVSZ803SDVO5BbILMrnTORERET23ci05d3V1xbFjxwAAFy9eRFhYGHx8fMTz8fHxMDU1rZgRklYSeeGXjjU0iYiI6EXE+eaLx+QZNga6k3INk/28cCTi98oeFhEREdVA5QpoTp48Gbt374abmxu8vLxQp04dvP766+L506dPo0WLFhU2SCpOvdM5l5wTERHRi4jzzRePemOgpwU0C1T5WBP8P8RmReFIJAOaREREVFy5lpxPmzYNhoaGOHToENq1a4dZs2bByMgIAJCSkoK4uDi89957FTpQ0qQOaKq45JyIiIheQJxvvnhM1QHNpyw5/ztsK6Iz7gIAItNDoRSUkElkpV5DRERU0fIKcuB75j24WDTBu+7/0/Vw6D/KFdAEgIkTJ2LixInFjltbW+PixYvPNSh6OqnB4wxNLjknIiKiFxTnmy8WU/3CgOYjRVqJbRKzY7Hj1nfie4UyFzGPIuFs3qCyh0dERKThUvy/uJZ4FjcSz2NE82kw0TfT9ZCoiHIHNNVu3bqFqKgoAEC9evXQvHnz5x4UPR2XnBMREdHLgvPNF4OpvLCGZpai5AzNjVe/Qa4yG81rtUOBKh93Uq/hXnoIA5pERFTlriScBgCooMKtpGB0cOyl2wGRhnLV0ASAffv2oUGDBmjVqhVef/11vP7662jVqhUaNmyI/fv3V+QYSQuJvnpTIAY0iYiI6MXE+eaLRV1D81G+9hqaF2NPICjmCKQSGd5rMx/1LQsD1xFpIVU2RiIiIrUr8WfEP99IPKfDkZA25QpoHjp0CEOGDAEAfPPNN9i7dy/27t2Lb775BoIgYPDgwfDz86vQgZImMUOTAU0iIiJ6AXG++eIx0S95U6A8ZS5+uuoLAHij4Ri4WDQRA5r3GNAkIqIqFp/1ALFZUeL760nndTga0qZcS859fX3h5uaGU6dOwcTERDz+xhtvYOrUqejWrRsWLlwIHx+fChsoaZLI1UvOWUOTiIiIXjycb7541EvOtW0KtCf0J8Rl3UctI3uMaD4VAFDfshkAICI9BIIgQCKRVN1giahUcZn3se3WavRwfg0dHHvrejhEFe5KQmF2pqNJPcRmReFe6i1k5T9iHc1qpFwZmteuXcOYMWM0JpdqJiYmGDt2LK5du/bcg6OScck5ERERvcg433zxmMktARTP0Ix5FIk/Qn8CAEx0/xxGeoVf83oWjSGFFOl5KUjJTajSsRJR6Q5H7MKJ+3/D98x7WH1xDrLyH+l6SEQV6kp8Yf3MnnX7w8nURayjSdVHuQKahoaGSElJKfF8SkoKDA0Nyz0oejpuCkREREQvMs43Xzym+o8zNIsENPMKcrDs/AwUqPLR1r47Ojt5iecMZIaobVYfAJedE1U3DzMjxD/7R/2JaUf7a9QbrM6WnfsYUw5745EiTddDoWpKKShxLfEsAKC1XRe0tOkAgHU0q5tyBTT79OmD1atXIygoqNi5c+fOYc2aNfD09HzuwVHJ1EvOVQxoEhER0QuI880Xj6lcs4amIAhYE/w5wtNuwlxuhffbLCy2rLzosnMiqj4ePooEAAxvNhUOJnWRlBOLef+Oww+XFyCnIEu3gytFam4iTj04hIeZkTgR/beuh0PV1L20W3ikSIORngkaW7uhpW1HAKyjWd2Uq4bm0qVL0blzZ3Tr1g0dO3ZEkyZNAAChoaE4f/487OzssGTJkgodKGmSclMgIiIieoFxvvniUQc0Fao85Clz8XfYVpx6cBAyiR5md1oDO5Paxa6pb9kMJ+7/zQxNompEKSjFzVL61BuIwY0n4Jfry3Ho3jb8c28HsvMz8UnH5ToepXbquogAcDxqL15v+I4OR0PVlTrbuJWtB/Sk+mhpUxjQvJd6C9n5mTDWN9Xl8OixcmVourq64tq1a5g+fTpSU1Oxa9cu7Nq1C6mpqfjwww9x9epVuLi4VPBQqagnNTS5KRARERG9eDjffPEY65lCKin8pXxg9H78emMlgMK6mersl/9ytSjM0GRAk6hqbLj6Nd4/0g8ZeakltknMjkGBKh96Un3YGjvBUM8Y77WZhzmdvgMAnIvxR4Eqv6qG/EyKLosPS7uJqPQ7OhwNVVdXHwe+W9t1BQDYGDvA0aQe62hWM+UKaAKAnZ0dvv32W9y+fRs5OTnIycnB7du3sXLlStjZ2VXkGEmLJ7ucM0OTiIiIXkycb75YJBKJuDvsD5cXQIAAH9dheLXByBKvqW/ZFAAQlxWN7PzMKhkn0csqpyAL/4TvwINH98T6gdrEPF5u7mhSD7LHv6QAAA+nV2Amt0SuMhthqTcqe7jPTBAEXE0o3OillpE9gMIsTaKi8gpycCu5MGjZ2r6LeLyVuOycdTSri3IHNEm3JFxyTkREREQ1jKl+4bJzlaBEc5v2mNj6i1LbmxtYw8bIAQAQmX670sdH9DK7nnAOBUJhZmV0xt0S28VkRgIAapu5ahyXSqRoZesBAKUGRHXl/qMwpOQmQi41wPhWswEAgff/hlJVoNNxBUbvxwz/wbibck2n46BCN5MuokCVDxsjR9Q2ffI9rl5JcCORdTSrizLV0Bw/fvwzdyyRSPDzzz8/83VUNk+WnDOgSURERDUf55svBzO5BWKzAFtjJ8z2WAN9qfyp19S3bIaknDjcSwtBc5v2VTBKopfTpfhT4p+j0ksOaD5UBzRNXYqdc7P1wJmHh3E94Rzeajqloof4XC7HF2ZntrDpgE61PWEmt0RqbiKuJJxGO4eeOhmTSlDh15vfIjE7Br5npmB5791a6wlT1VHXWW1t30Vjozp1Hc3w1Juso1lNlCmgefz48WI7Dj7Ns7anZ/NkyTlraBIREVHNx/nmy6F3vYFQqPLwUfslsDSsVaZrXC2a4XxsAO5xp3OiSlU0oBmdUXJtSfWScyczl2LnWtl2AgCEJF9CvlIBfdnTf2lRVa4WCVTpS+Xo6dwfB8J/xfGov3QW0LyZdAGJ2TEAgLS8JPieeQ9Leu2olGBZRl4qknLiYGFgDUuDWpBJn4SDlKoCRD8Kw92U6whLvY64rAd4q+kUtLTtUOHjqO6uPC5LoK6fqWZj7AAHk7qIy4rGraRgtHfUzfcMPVGmgGZkZGQlD4OeFZecExER0YuE882Xw2sNRuG1BqOe6RpXy8KNgSLSuOScqLLEZEYhLus+pJBCBRViM6ORp8yFgcywWNuHmREAtGdo1jGrDytDW6TmJiI05UqJG35VtXylAtcfLxVubV8YqOpTbyAOhP+KszHHkKnIgKncvMrHFRC9DwDQwaE3wtNuICrjDpae+whzu6zXCDiWR1puMm4knceNxAu4mXQBUUWC1BJIYG5gBStDW8ilBojMuAOFMlfjekFQoaXtL881hpomNTcJkemhAAB3u87Fzrey7Yi4rGhcTzrHgGY1wBqaNRQDmkRERET0Mqj/OKAZlXGn2u6cTFTTXYorzM5sYdseZnJLqKDCg0f3irXLU+aKGYVOpq7FzkskkmpZR/N2ymXkKXNgaWADF/MmAIAGli1Q17wR8lUK/PvgnyofU15BDk4/8AMADG7yLr7osh4GMiNcij+FDVe/hiAIYltBEHA39Tr+CP0JQQ+PIl+l0NqnUlDi9AM/fBYwDKMPdsHScx/h0L1tYjDTwsAaUokMAgSk56UgMj0Ud1KvQaHMhZGeCVrZesDHdRiAwixbhTKvkj+F6kWdxVvfsjksDKyLnVcH6G8mXqjScZF2zxfyJ52Rqpec53PJORERERG9uOyN68BE3wxZ+Y9w/1E4XC2a6npIROUSmnIVdsZOsDK01fVQirkc/y8AoK19dwgCcCPpPKLT76CBZXONdnGZ0RAgwETfTGvABwDcbDvh5P0DuJZwFiObT6/0sZfFlfjCQJW7XWexXIlEIkGfugPxy41lOB69Fz71h1XpmM7G+iOnIAt2xrXRrFZbSCVSfNJhORadnYpD97bDydQFzW3a4fQDP/z74B/EZz8Qr7UwsEYv5zfg6TIE9SwaI0+ZC//IP/HX3c2Iy4oW29Uzb4yWth3R0qYDWth0gKVhLSgFJR7lpSE1LxGpOYnIKchCPfNGcDJzhVQihSAIOB97HCm5iQhJvqQ1U/FFJdbPtOui9by6jmZYGutoVgcMaNZQEnlhcq1KwQxNIiIiInpxSSQSuFo0w42k84hIu82AJlWJwxG7kZKTgGHN3odU8vwLG/ff3YKN176Bq0UzrHplb7WqAZyvVIjZlG3suyMxOw43ks4jSstO5+oNgZxMXUp8BrfHdTTvpFxDbkE2DPWMK2fgz0Csi2ivWRexV903sPXGCtxOvoyYR5Fa64JWlsCowuXmvesOEL/HOtX2xNhWn2Hz9aXYeO0bjfYGMiO42XZCeNoNpOQmYl/YL9gX9gsaWLZAYnYMMhSpAAAzuSVeazAK/eqP0Bo8l0lksDSsBUvDWlr/PpVIJHCz64zA6P24lnD2pQloCoLwpM7qf+pnqtkaO4p1NEOSg3VWe5UKccl5DcUl50RERET0snC1LPxH9720W5V+r7is+1j470T8eMUXUeklb4xClUsQBPx281v8fHURVELVrkp7kBGO7y/Nw46QtWLm4vP498Eh/HxtEQAgIj0Ed1KvPXefFelWcjDylDmwMrSFq0VT1LNoBACI1hLQjCkS0CyJvUkd2Bo7oUDIR0jyJa1t8pWKEpdNV7RHijSEpd4AUDzzztrITgxyHo/+q0rGAwCpuYni91bvugM0zg1sNB7ej5d9y2WG6FrbBzM9VuHX189gbtf1+LlfIOZ2+RGdnbwgk+ghPO0mMhSpsDeug0nuc/FzvwCMbD79uTKB3W0Lg5hXE4PK3UdN8+DRPSTnxENfKkczm7Yltmv1eNm5uiYr6Q4DmjUUA5pERERE5bNu3Tq4uLjA0NAQHh4eOH++5H+UbNiwAd27d4eVlRWsrKzg6elZrP3YsWMhkUg0Xj4+PhptUlJSMGrUKJibm8PS0hITJkxAZmamRptr166he/fuMDQ0hLOzM5YuXVpxD13D1bd4vDFQJe90nluQjW+CPkBw/EkcDP8N0471x6zAEQiI+gt5/9kwgypXcNxJ7L69HvvCfsH5mONVeu8/7myAgML6hf/c2/FcfV1LOIuVF2ZCgABTfQsAwNGIP557jBVJvbt5G/tukEgkqGdeGNDUmqH56PGGQKVkMkokEjFL81riuWLnFco8fBrwFsYd7IE7KZUf3L2WcBYCBDibNUQtI/ti5/vUGwSgcIOeqgqen4g+ABVUaGrdplhWqEQiwfttFmJFnz/w2+tBmNVpNbrV6Sdmusqkeujg2AtzOq/F5ldP4oO2vpjdaS3Wex/G6w3frpCMWLfHWZlhKdeRlf/oufurCS7EBgAAWth00LoZlpq6juaNSg5oJmbH4scrvth395dKvU9NxoBmDSVR19BUsIYmERERUVnt2rULM2bMwPz583Hp0iW4u7vD29sbCQkJWtsHBgZixIgRCAgIQFBQEJydneHl5YWHDx9qtPPx8UFsbKz42rFDMwgyatQo3Lx5E0ePHsWBAwdw8uRJTJo0STyfkZEBLy8v1KtXD8HBwVi2bBkWLFiAn376qeI/hBqo/uM6fvfSbmtslPEssvMzEZpytcTrBUHAd5fmIjI9FBYGtdDZyQtSiQwhyZfw7cVZGHewBw6Fbyv3M1QFlaBCRl5qhfaZp8wtczC3vF+b/1IKSmy5sVx8v+v29xXW99PEZz1AYPR+8f3F2EBxExxtcguycebhEaTmJhY7F5keim+CPkCBKh+dnbwwq9NqAMCpBweRW5BdYp9nHh7Gbze/RUZeynM8SdmpNwRqY98NAFD3cUAzMTsG2fmav3h5kqFZfEOgotQbA11PKL4x0L67vyAiPQQZilTM/3e8mD1ZWcS6iPba6yJ6OL0CE30zJGbH4LqWAGxlUO9u3qvuG1rPSyQSNLJq9dTgpKVhLXi7voUutb2ee1f0omyNHeFk6gIVVFX2mejamZgjAIBOTp6ltlPX0bybeh2bry1BpiKjQseRp8zFzpB1eP9IPxwM/w0/X1uE4LiTFXqPF0WFBTQFQcDx48fxzz//4NGjlyOCr0sS/cIvHTM0iYiI6GVREfPNlStXYuLEiRg3bhyaN2+O9evXw9jYGJs2bdLaftu2bXj//ffRunVrNG3aFBs3boRKpYK/v79GOwMDAzg4OIgvKysr8VxISAj8/PywceNGeHh4oFu3bli7di127tyJmJgY8T4KhQKbNm1CixYtMHz4cEyfPh0rV64s13O+aOqY14eeRB9Z+RlIyH749AuKEAQBAdH7MPmwFz4LeAuLzk7VmnG0P2wLTt4/AJlED7M8VmNO57X4uV8ARjX/ELbGTsjMT8ePV3xxL61ys0TLK1+pwNxTY/HOgc7YfmsNlMLz/zshU5GBKYe98d5hL607XqupBBV+vOKLdw50xoGw3547+BgYtQ9RGXdgom8OQ5kxwtNu4mLciefqs6z23vkZKkEJd7suaGXrARVUOByxq8T2qy7OxuKz0zDuYA/MOzUe/lF7kZ2ficTsGCz4911kF2Siea12mNFxGdxsO8HBpC5yCrLE3a3/Ky7rPpaf+wS7b6/H+0dexYnovys1mJucE4+ojDuQQII2j5djm8otxEzG/y47Vwc0a5ey5Bx4EtAMS72h8f9bam4S/ghdDwCwMXJEVv4jzDs1vlL/v7oS/7h+Zgl1EQ1khujp3B8A8HfY1kobh1pkeigi0kOgJ9FHd+dXK/1+5SVm2WoJSqtdjj+N28mXq2pIlSYxOwZ3Uq5CAgk61+5baltbY0e8Um8wBAjYe3cTJh/ui7/Dtj53CQVBEHDm4WF8cORVbL+1BnnKHFga2AAAfri8oNRfgrysyhXQ/Pzzz9G7d2/xvSAI8PLyQt++ffHaa6+hVatWCA8Pr7BBUnHiknNuCkREREQvoMqYbyoUCgQHB8PT80n2hVQqhaenJ4KCylYnLDs7G/n5+bC21tzdNzAwEHZ2dmjSpAmmTJmC5ORk8VxQUBAsLS3Rvn178ZinpyekUinOnTsntunRowfkcrnYxtvbG6GhoUhN1Z5xl5eXh4yMDI3Xi0pfKkfdx3X9ItJul/m6+xnh+OLUaHx7YSbS8wq/JmdjjmGG/xBEpD/p51rCWWy+XrjEf4LbbLS07QAAqGVkj2HN3sdPPsfQtbYPBAjYcPXrKssWLCtBELDu8jxcTzwHAQJ2hqyD7+nJz52tuT/sFyTlxCE5Jx5fnBwtLjcuSikosSb4fzgY/hsyFKn46aovFp6eqDVjsSzylLnYdqswk/Gtpu+hX/3hAKomSzMlJwFHI/8Q7/1q/ZEAgCMRf2gNVtxMuogzDw8DAFRQ4UrCaay+OBujD3TBpwFvISU3Ac5mDfFFlx9gIDOERCKBp8sQAMDRqD1ax7D91hoUCPmQSmTIUKRixYVP8eXpSUjIerZAflmp6zg2smoF8yK7ltfVsuw8U5GO9MdZo46m9Urt19bYEY4m9aCCCreSLorHt91cjZyCbDSyaoXv+h5AU+s2yMxPx9xTYzX+n6wosZnRiM9+AD2Jvvj/tTavN3wHQOGy45hHkc90jwJVPrbfWoOjkX+U6XtUnZ3ZwbE3zOSWz3SvqqTOaL2aoP3nY0Tabcz/dzy+ODkGabnJWtvomiAI8I/ai3WX5pX69+GZh4XZmc1t2pep9uj0dt9gXpef4GzWEI8Uadhw9WtMO9ofgdH7EZMZBaWq4JnGeT8jHHNPjcXis9ORkP0QNkYO+LTjSqz3PgxbYyckZD/EjpDvnqnPl0G5App79uxBx44dxfd//PEH/P398dVXX+HAgQNQKpVYsGBBRY2RtHiy5JwBTSIiInrxVMZ8MykpCUqlEvb2mjXU7O3tERcXV6Y+Zs2aBScnJ42gqI+PD7Zu3Qp/f38sWbIEJ06cQL9+/aBUFs7T4uLiYGdnp9GPnp4erK2txfvGxcVpHZf6nDaLFi2ChYWF+HJ2di7TM9RU6t1475WhjmZeQQ623liJD48NwPXE85DLDPFOixlY3HM7bI2dEJsVhc8ChiEg6i8kZsdg6bmPoBKU6F13AF5r8Hax/mQSGca7zYJcZoibSRfEIJY2l+JOYdvN1cWW6lamP+9swPGovZBKZBjUeALkMkNcij+FGceHlHs5b0ZeCvbd3QwAsDCohZTcRHx+crRGsEepKsCqC7PEe/u4DodcaoBL8acw7Wh/nH14TGvfpWWPHgjbiqScONgaO+G1Bm9jYOMJkEsNcCflqrhTdVlciA3E7BOj8NWZ97Dj1ne4EBuAlBztpSXU9oX9gnyVAk1rtUFLm47wcHoF1oa2SMtLKvYsKkGFTdcWAwC8XYfhR++jGNl8OmqbukKhykNqbiJqGdljQbeNMJVbiNe9Um8QpJDiVtLFYgHiiPTbOBH9NwBgcc/tGNX8Q+hJ9REcfxJTj76OnSHf4UDYb9h9ez22XF+OHy4vwHfBcxGbGV3mz+W/itbPLKqeeWMAQHSRjbFiMqMAANaGtjDWN31q3252mnU0I9Jv49jjgPEEt9kw1jfF/G4b0NjKDY8UaZh7cqzWjYieh/p7pkmt1jDSMymxXR2z+mjv0AsCBOx/xizNwOj92BmyDmuDP8eqi7OhUOaV2FYpKHHicUmD/24GVN20svWABBLcfxSm9f+dv+4WrmxQqPJKzWIuiSAIiMu6X2m/qHjw6B4+Pzkaqy/OxuGIXdh9+4cS2wY9Dmh2qe1Vpr4lEgnaO/bEGs99eL/NQlgY1EJMZiRWXvgM7x32wtC/3DHlsDd8T7+HzdeX4m7qda3PWfRn1bXEs5BLDTCs6fv43usf9HB+Dcb6pniv9TwAhaUaqusKAV0pV0Dz4cOHaNiwofj+zz//RPPmzTFnzhy8+uqrmDJlCgIDAytqjKQFNwUiIiKiF1l1nG8uXrwYO3fuxN69e2Fo+GTDgOHDh+ONN95Aq1atMHDgQBw4cAAXLlyo9PHNmTMH6enp4uv+/fuVej9dq29ZuDFQWXY6/yroffwR+iMKhHx0dOyNdX0P4s2mk9Hcph2+7fMn2th3g0KZi28vzsKM40OQoUhFfcvmeL/tl5BIJFr7tDV2wuDG7wIANl9fqjVocTn+X/ieeQ+7bn+PuafGlpoRFJkeWqad1HMKsjD35FjMOzVe6wYqZx8ew9YbhaUJJrp/jnGtZmJZ711wNKmHhOyHmBU4Akcifi/1HtrsubMROQXZqG/ZHGs896OueSOk5Cbg81OjEZMZhQJVPlZe+Awn7v8NmUQPMz2+xfttF2LlK3vgatEMGYpUfHP2A3x7YRZ+u7kKK85/gpkBwzH6QFcM+bMlvgmaWqxGZEZeKv4ILawbO6r5h5DLDGBlaAOfx1maO0PWPTX4IQgC/gzdiK/OvIdbSRdxPjYAO0LWwvfMexh7qDvGHuyOnSHfFQuqPlKkwe/xBkBvNnkPEokEelJ9eLm+BaD45kCn7h/E3dTrMNIzxsjm0+FoWhfDm32A773+wco+ezC65SdY1GMbbI0dNa6rZWSPtg7dAQDHIjWzNH+7sQoCBHSr0w9Na7XGsGbvY43nfrSw6YBcZTa231qLn6764reb32LPnQ34594OHIncja+D3kdeQc5Tv6b/pRSUuBJfWF9SPSY1bRsDPcwsDMA+rX6mmnrZ+bWEsxAEAZuuLoYKKnSt7YPmNoUZ6yb6ZljQ7Wc0tGyBDEUqPj85BnFZFfd3mfr5/ru7uTYDGo0FAPhH/YlMRXqZ+hcEQWPTloDov/D5ydElBs+vJQQhJTcRZnJLtHPsUaZ76IqZ3FKsX3wtUXPZeVJ2HE7ePyi+/+fedhSo8p+p/603VmCSn6eYFV1R8pUK7Lj1HaYfewM3ks5DT6IPADga+YfWXzSl5CQgJPkSAKCzU9kCmmoyqR586g/Hj95HMKzp+3CxaAK5zBBKoQAPMyNxIS4Ae+/8jE+OD8WH/gNwIOxXPFKkASjMBv7g6GsaP6u+8zqIUS0+1Kid2sGxN7rW9oFKUOK7S19USDmRF0W5App6enrIyyv8AS4IAvz9/TV2crS3t0dSUlLFjJC0UtfQVOVzUyAiIiJ68VTGfNPGxgYymQzx8fEax+Pj4+Hg4FDqtcuXL8fixYtx5MgRuLm5ldq2fv36sLGxQVhYGADAwcGh2KZDBQUFSElJEe/r4OCgdVzqc9oYGBjA3Nxc4/Uia2DZAgAQknyp1Fpl0Rl3cTXhDGQSPfyv8zp83ukHGAcXIHLiPtzq9COSJh3He8HjMdjkHUggQXpeCszklpjTaS30cqTIi0xD9uVYKB5mFAueDWn8LmyMHJCQ/RB/3dmE/LhM5NxORE5IIkIun8HiM9OgFAoggQR3U69jzolRSMrWzLAVBAF+93bh0+Nv4mD4b/A98x5yCrJKfJ5fb3yLq4lBuJJwGp8GvIkV5z9FYnYsACA87RZWXPgUAgS8Wn8UXmswCkBhNuuKPn/Aw/EV5KsU+O7SF/CP2lvmzzolJwEHH2+A9Hbzj2BlaAPf7r/A2ayBuPx88dlpOPXgEPQk+pjVaTW61PYGULhUeXnv3Rjc+F1IIEFA9F/YffsHnLh/ALdTLiMtLwkqqHA25iimHxugUZ/v99AfkZX/CK4WTdGzbn/x+KDGE6AvlSMk+VKpG5TkKxVYE/w//HJjGQQI8HJ5E++6/Q+96r4BZ7MGkECClNwEbL+1FvNPjUdq7pO/Qw6E/Yacgmy4WjRFe4ee4nEvl7cglchwI+m8mD2Yp8zF1puFQeQhTSbDytBGbC+RSNDQqiWGNpkEB1PtWdOeLkMBAMej/xKXpt5KuogLcQGQSmQY1fxDsW0ds/r4usdWTG37FTo4FAY2+roMxYCGYzG82VRYGdoiOuMufrzqW9qXFPczwottbnQ35Toy89Nhom+Oxlaaf6+pSzwUDWiqs3OdnlI/U00d0IxID0FA9D5cTQyCnlQfY1p9qtHOVG6Ohd03wdWiKdLzkrHs3MfPXY8QKAxSX0ssXC7d2l57/cyi3Gw7wcWiCfKUOTgcsbtM97iScBpRGXdgKDPGLI/VMNW3QGjKFXwSMFQjOzo5Jx4B0fuw7WZhOYXudV6DvlReUrfVhjrL9r/Lzv8O3wqlUICmtdrAytAWKbmJOF1K5vp/hafexN47PwNAifVky+NWUjA+9B+AHSFrUaDKRzv7Hvje+x/UMauPnIKsYr9EAICzMUchQEAT69awMS59LlASY31TjGrxIdZ47sfuAZfxc79A+Hb/Be+1no8ezq9DXypHZHoofrr6FcYe7I4Zx4fA98x7j5eXO+J/ndfhiy7r4WCi/e+Mie6fw0TfDGGpN6r95nRVqVzbYLVs2RK//fYbRo0ahb179yI5ORmvvfaaeD4qKgo2Njal9EDPSypnhiYRERG9uCpjvimXy9GuXTv4+/tj4MCBACBu8DN16tQSr1u6dCm+/vprHD58WKMOZkkePHiA5ORkODoWZmZ17twZaWlpCA4ORrt27QAAx48fh0qlgoeHh9jm888/R35+PvT1H2eTHD2KJk2aaGww9DJrUqs1rA3tkJKbgAuxAWIA7b8Cogrr07Wz7ob6+81x+7efoYhKE8/nP3yER8cj0AaGMG3mg+C+Yehxth3iZ/yBuFzNumdSUzkMG9WCQaNaMGxcC0KeEq9leGJLs9+w+/J3cPw6AeYZJki3yMRPH+9HjmU2XO86ot/ezvht8mHcRzg+3T8Ys02+hktzd2QrM/Fj1CIEZRwHAMggQ0L2Q2y5vhzvtZlf7FluxV/EwfDfAAAtUprjpvUtnLj/N85EH4a34QCcUQQgT8hBs6xm6LvBDXcfbIMqOx8GjWrBqJktPmj2Mewc7fF37Hb8GboRfeoOLDEDtag/Qn+CQpmLprXaoJ1DYRaZlaENvuqxBZ+fHI0Hj+4hKScOelJ9zOm0Fh0ce2tcry+TY2yrz9DeoScOR+yCkZ4pHEycYW9SBw4mzshX5WNN8Bw8eHQPc0+NxZtN30OfeoPEZx3T8lPIJDKxv1pG9ujr8iYO3duGXbfXiUGWotLzUrAoaCpuJQdDCinedf8crzUYpfG8OQVZOPPwCH68/CWuJZ7Fx/4D8WnHb1Hfspm4GcybTSdrXGNj7ICOjn1wNuYo/rm3E5Nbz8XfYVuRmB0DGyNHMavvWXRw7AULA2uk5iYiOO4kOjj2xpYbKwAAfV2GoraZZgakVCKFl+ub8HJ9s1hfLW06YN6pcTgWuQetbDqid72Bxdrsu/sLfr62CADQxLo1utXph251+onLzd3tOhfbIVsdAE7PS0ZabjIsDWs92RDIzKVMz2llaANns4a4/ygM6y7NBQC80XCM1qCNmdwSn3f+Hh/5D8Ld1OvYcn053nX/X5nuo01KTgLm/zsBWfmPYGPkiIZWLZ96jUQiwRsNx4g1YQc0Ggs9qX6p1/z1uCxDX5ch6FrHB64WTfFV0BQ8eHQPswNHomsdH4SmXBU/OzVPl8Hlfraq1NquC/be+RlXE4IgCAIkEgmy8zNx+F7hEvM3m0xGWOpN7AhZiwNhv6Kn8+tP7VMpKPH95flQoTA562bSBSiUeZDLDMo9TqWgxO6QH7ArZB1UUMHSwAYT3T9Htzr9IJFI0L/haPxweQEOhP+K1xq+rfH3izoQW9LPlGcllUhha+wIW2NHuNt1xqsNRuKRIg0nog/gaOTviEi/jbDUG5BJ9PBGozEY3uyDUsshAIC1kR3GtPwU31+ej99ufotOTn2LZX+/jMoV0Jw3bx769+8vTiK7du2qUbT94MGD6NCh5IK79Py4KRARERG9yCprvjljxgyMGTMG7du3R8eOHbFq1SpkZWVh3LhxAIDRo0ejdu3aWLSo8B//S5Yswbx587B9+3a4uLiI9SxNTU1hamqKzMxMLFy4EEOGDIGDgwPCw8Mxc+ZMNGzYEN7ehf84atasGXx8fDBx4kSsX78e+fn5mDp1KoYPHw4nJycAwMiRI7Fw4UJMmDABs2bNwo0bN7B69Wp8++235f8QXzAyiQx96g3CH6E/4ljkn1r/8VmgLEBA2F8AgAbfyhF76SQAQGomh9WQFrDo2wC5d5ORHRyDrEsxaBRSB41C6gAABBQGMyWGetCzMkJ+YhZUmQpkX45F9uVY8R4NYAjnD+1w3zUBR/tfwICAPvhtzBFkWGbBLtEa7+wfAHkyMHFVf/wy5RCS7ZKxMPlj+HzqgeM+wUi1eQSpUgKvvzvCIaYWfnn/EA7d245GfrXQtrknjNs4IDckCUmHb2FFo28g2Aloe64xBu3oiofOjeE34CwiG8bhQF7hMnKbeEsMWtUBmTlh4hhzQ5OQfiAUAOBmoI/DX+rjPsJwfPEPcK/TBUbNbGHQwBoFydnIu5eKvHspyAtPRV5kKlJN0vHPa9sBKdAvuDcSrp2Dnp0JDFytYFrfCr7dfsH80xMQn/UAszutRjuHnhBUApTpuVBl50Pf3hQSvcLVZC1tO6Kl7ZNauCqFEnnhKcgLS8aXtVZju9kmHIvZi923f8D+u1tQoMqHu21ntLHvhoLkbOSEJEJqIodxawcMaTIRRyJ243riedxMuogWj5csJ+fE43riOfx2cxUSsh/CRN8MMz1WFasJCQBGeiZ4pd4gNLFyw+JzHyI64y4+P/EO6sfVRaZjOhz16qCzlu+rfvVH4GzMURyP2osBDcfi99uFu3S/0/JjGMgMi7V/Gn2pHL3qDsC+u5txNPIPSCRShCRfglxqgOHNPnimvtzsOmFYsw+wI2Qtfri8EI2sWqGOeQMAhUGeTdcWa+zcHZpyBaEpV7Dp2mLIH4+9rX33Yv0a6hnD3sQZcVnRiM64C0vDWnj4OChX1gxN9fjuPwpDvkoBCwNrvNn0vRLb2pnUxkftF+OroCnYH7YFLW06olNtT61tVYIKEki0Bujjsx5g7qlxiMuKhrWhLRZ026ARwCpND+fXseXGCiTlxOHMwyPo4fxaiW2j0u/gcvy/kECC/g3HAACczFywrPdurDj/CS7GnRA3AJJCivpWzdHK1gMdHHqXKcBaHTSv1Q56Un0k5cQiNjMKTmYuOByxG9kFmahjVh/tHHqioVUr/B66HqEpV3An5RoaW5e+isHv3k7cTb0OYz1T6MvkSM9LQUjyJbjbddbaXhAE/HNvB/Sk+uhS2xumcs3VCEnZcVh54TPcSDoPAOhddyAmuv9Po3Zt77oD8OuNbxGXdR8XYwPh4fQKgMJfhNxMvAAAT93d/HmYyS3xesO38VqDUQhPu4lbScFwt+uMehaNy9yHl+tbCIjeh5DkS/jxykJ83vmHMv2Cqrwuxp6Avkxe4telOihXQLNv3764dOkSjh49CktLSwwbNkw8l5qaih49emDAgOpd4LamUy85Z4YmERERvYgqa745bNgwJCYmYt68eYiLi0Pr1q3h5+cnbsATHR0NqfRJVaYffvgBCoUCQ4cO1ehn/vz5WLBgAWQyGa5du4YtW7YgLS0NTk5O8PLygq+vLwwMnmSbbNu2DVOnTsUrr7wCqVSKIUOGYM2aNeJ5CwsLHDlyBB988AHatWsHGxsbzJs3D5MmTXrmZ6yu8iJSkbr3FvIi02DWwwUWXg0hMy85I0edDVSUZ73B+CP0R1yKO4nknHjUMir8uuXHZyLl95s4f/oAUoYmwjBbjsZX68DIzR613mkNyzeaQGZcuLzTrIcLMKEwUzY/LhO5oUmQmsmhV8sYerbGYjuVQglFRCpy7yYj904Scu+mQGogg0HDWpjg0gwL8j7BlQ53kdFXH3FpybA2tMWi0bth+54ThHwlcu8ko+41TyzLWoAHZg+w5+1AAIBVmjlG/P0a6iXXAZQqdDgdgQtdQ7Cl1maYzXgEA0VhRph/v4tItEuFaaYxRuhPgNUcR9gm56BFXA9cTr6I/U39UCArwJTQSXAa6Qp5bXPo1zGH1EAPuXeSkHMrEbkhiUBYClpfaIjz3UJwNHs/bP6XXerX6dCwk1BKlah/xwnW3z9CHP7VOC81lWNq/TehtJZC7/tI3Ey+hYLkbED5eHm+nhTy2uaQ17OEQT0L6NmZQBGRhpyQROSFJUMoUjKrJ2xg5/k6/vQ+hlwUjuuVXW1w6+P1KEh4sgxf7mIJ67daoneb13E0fi82X1uC+pbNcD3xnBhkAwBHk3qY2+UHOEqdkbLnJjL/jYLM0ggGLpYwcLGCvJ4l9O1NYHZahal73sZ2m99wpcNdhDsWbnbTaWt9RP2xFw6fdoOx+5Olp+52neFk6oKYzEh8cWoMcgqy0NCqJXo6P1kW/6z6ugzFvrubcSEuEA8ehQMo3Glb/T39LN5qNgU3ky7gWuJZLDn3EZb3KQx2f3thpriB1bhWM9HTuT/OPDyMUw8OIST5EvKUOZBAojWgCQB1zRuKAc1Wth7ikvPazxDQbGXrIWbejmw+HSb6ZqW27+jUBwMajcO+u5uxOngOXC2bwt6kjng+LTcZW24sR0DUX6hj3gA+rsPRu94Asd/7GeGY9+84JOfEw964Dny7/1Li0n9t5DIDvFp/JHaErMX+u7+ge51XSwwa7Q/bAgDo5NRX4x4m+mb4vMsP2H/3F6TkJqKlTQe0sOlQLBBXExjoGaGpdRvcSDqPq4lBsDOpjb8fP/fARuMhlUhhZWiD7nVeQ0D0X/g7bAs+6biixP5SchLw6+Oav6NbzkBoytXCcgQJQSUGzoLjTmL9lYUAgB+vfAkPp1fQu+5AtLHviuC4k1gT/D88UqTBSM8Y77VZoHWzJUM9Y3i5vok/72zE/rAtYkDzbMwxqKBCQ8sWJS73rkjqkhTlCWhLJVJ80PZLfHRsEM7HBmD9lYWY3HoepJJyVZIs1aHw7Vh/ZSFkEj385HOs2maDSoTK2lLqJZORkQELCwukp6dXSf2inFsJuOO9FXp2JmgRPKXS70dEREQvtqqey1DFq45fw4KUbKTtD0Xq3lvIvhSrcU5iIINZL1dY9m8K814uyLufjpyrcci+Fo/sq3HIDU2CnpUhDOpbw6C+VeHL1Rq+WZ/hjnALAxMG4ZUb3aCIfYTsSzGAUsCekYG40vEuuid0wwddv4axW/nqoZXFqouzcfxxXUojPWN803MbGjzeQKOorPxH+OpMYcCps5MXprX7WiOwkRGXgI/ODEGSkIDOIe3w6oZ2SGqVg7Vjt0ElUWFWh9XoWtenWL9AYQbe0zLPVHkFCLsVjE/vjYZUkGLukU9hdKUA+bGPIDXWf/zZFn7Gaa65mC1MhQoqzE6cC5ekOlCm5UIR8wiKyFQoHmQApf3rUU8KFJRe419qbgDDhtbIj89E/sNHAIBU6wwcfe0iat+3QdfAx9ldEkBezxIFiVlQZRVuNpJaKxOrPt8FlfTJPSSQoL5lc7jbdEbfOE8U7HuA9KPhEP5TPkAbAQJuDIvH3k5+sM2xxaQFr0KmKAxeWfg0gtWbLSDkFUCZkYd/8v/CbrPt4rUf3/0YjTIbPvk8pBJIpJIn/9WTQmqkB5mxHFJTOaTG+pCayCF3MoNBfStIjfTxWcAwhKZcAVAYBPvJ5xjM5JaaY1SqkHXuAdIOhCIvPAVGbg4w7ewMkw61ITN78guB1NxEfHhsINLyktCr7huIz3qAkORL0JPq46P2S4plGiZmx+JszFHYGDmWmJn2641v8Xvoeni7DsPwZh9g3KEekEKK3wddLXP9x+z8TEw71h+2xk74uvuWYkvbtclXKTDnxNu4k3IVja3csKjXNkglMhy+twu/3vwWWfkZGu0NZEbo7vwqWtt1xY9XvsQjRRqczRriy+6byhUgTstNxoR/eiFfpcCSXjvQrFbbYm1Sc5Pw7j+9S23zotgZsg7bb61Bl9re6OTkiZUXPoOlgQ029jsuLhMPS72BGceHQCbRw8Z+x0v83Jed+xinHhxCYys3LOm9Eyei/8aqi7PQ0KolVvYpXt8SAFZfnAP/qD9hpGeMnIInv5Axk1uKG+w0sGyBzzquhFMp5RASs2Mw0c8TKkGJ1a/sg6tlU8w/NQGXE/7F6JafYGiTmvELxGORe7A2+HMIEPBKvcGY2u6rMmcgl0Vg9H58e2EmhMd/ub3V9D283eLjCuu/LMo6nylXhqba2bNnERAQgISEBLz//vto1KgRsrOzcfv2bTRu3BimpqbP0z2VgkvOiYiI6GXA+WbNFbPoJBJ/uvgkwCWVwKxHPRi1sEP6kTDk3U1BxuEwZBwOK7GPgsRsFCRmI+vcA/FYi442uDMSOIXjaLu/FiQoDEDpdbJFSIfC3ZFff+sDGNeqvGAmAIxuMQNBD48gT5mLmR6rtQYzgcJA1Vc9tiAuMxpOpi7Fsr3MHezwYbelmHtqLIKaBcP7zBQcvL0KqjQVOjn1LTGYCaBM/4iVGuihcRsPtHrkgeuJ5xDyYQbebvExVDn5kBjqaYxn2/lPobqvQnuHXugy5O1ifanyCqCITkdeRCqUj/IKs1ptjKFvYwKZtREkelLkxz2CIiodeVFpUESlIT8hCwZ1LWDYzBZGzW2hX9tcvGdBag5ybyci52Yi6od0gdRFH0ZLbWHU1BYGTWpBZiyHMkuB9IN3kLL7BnDuAbz2d8BN9wjUibKD610nuNxzgImeGSAASZn+4lgN6lvB4tXGEBRK5EWmQRGZiryodAh5BdCzM4HVkOawfqslWjeshaEFXwIAJN0ViF91Bql/3kK6312k+z3ZEKeBsQx6C2QokCvR7Fo9WG/KRDKuPPXz10oCyOuYo3UfF4S2KeyjX/4ACKdTkW2VA5mVERQPMpB+IBTpfndRkPQkiJN55j4S118AZBIYt7IvDGzWMobUSB8Tjd/Hcn1fBEbvBwAYFRhhwvm3YbcmHtfur4K8jjksX28Mi9ebwLapI/o3HF3qMJ9sDHRHrAFpZ1KnzMFMRewjPNp9A5/9PRpSSwM8ehQOC59GYkmCkuhL5ZjZ8Vt86D8Qd1KvYc3FOXiYGSlusuNq0Qzj3WbhfkY4/CJ2IjrjLo5F7hE3fGlo1RILum6EuUH56g9bGtZCr7pv4GjkH9h39xetwcp/7m1HvkqBxtbuaGrdplz3qSnc7Tpj+601uJ54DrGZhdnMrzd8W6PmZUOrlmhWqy1Cki/B795OjGrxYbF+LsWdwqkHhyCFFFPaLoRMIhOzMsNTb+KRIq1YQD9fpcDZmGMAgC+6rIexvhkCov7CifsHkJ6XDAAY0HAsRrf8BPqy0r8vbY2d0KW2F/598A/2h23B/9k77/g2yvuPv0/T8t52nDixM8ggkyzCSAgEEqAFyqbsUkYp0JK20LSFwq+lhNm0lELZo0DYZSeEkDCzJ9nLiRPH25aXtnS/P053kmzJlmXZjpPn/XrpZfvudPdo+rnPfb7fz8/G3q2lt5/UyXTz3mRW0UUYdEYWrP09Sw+8h8vr4M7JD3fY7zUaVh1eyoK1v9dCknbWbWRxyVtcOuLWLvU47S5iEjRdLheXX345H3zwgVYK8uMf/5hhw4ah0+k466yzuPPOO/njH/8Y7/EK/GiCpig5FwgEAoFAcBQi5pt9G9ntpeZZRcy0jM4l48JRpJ8/EmOuEnyQf/epOHbUYP1wB9aPduI6YEWXaiZxbB6J4/KxjMvHMjIXb4ND6fFYUuf/Wc9kXTafeFZSm9uA7f4CRuVMxHJ8Lt8bvsG5xkF+0sAeERgyLbk8fvq7uH0uitKGt7utXtK3CXoJZlzuNOYUX86ikoU8sPpW3D4XScYUbh5/T9zGe+6QK/mhehWLS97mshG/xGgJPfnfVrOWrw9+DMCVo+4Iuw+d2UDCsCwShmVFPI6pIBVTQSrJ0zou3zRkWEieNpDkaQMjbqNPMpF56WgyLx2Ns6Se3Le2cNaqMjxVLbgrmvHZ3fhQErENuUlknD+C9AtGYhmT10Y8ln0ynhobBr/4qpJgSFR+KUpk4IJzyL11CpVPrMKxqwZ9qhl9ipmMNDPn1jayLnMdV6b9gpw7cpAkQD2GLCN7Ze2n7PEhOzx4W1z4ml34bG68TU5cpQ2K6/VgI0MXppFalITRrWfkowZKXOEdavr0BNLmDCNxfD62jRU0rziI64AV28YKbBsrtO1SgRlzJrB8znrS6pO4+j9zyKsAJ3UAOPfWUfmPlVT+YyXm47JI/9FwEsflK8+hzY2vxY3P7lZMMzqJFLMDsuBA9Q52Lf8OdB2Xm/tcXhqX7KHuzS00fbUffAFL74FVhzEOSCXnZyeQefmYEIdpa9R+mg+suJWv/O/LJGMKV476NWdmXkDz5yUMLjiFs0+9jJ3Nm/hs30K+K1vE8dmTmXfiEyQau3ax67yh17Jk/zusLFvC4ab9Ic4/p9fBp3sVt+4Fw67r1j6GRwLDMsZgMSTR5LLS5LJi1luYU3x5m+1+PPRqRdAsWcilI34RIjA6vQ6tbPzHQ6/RLgBlWfK04Kgfqle16Y38Q9UqWtyNpJmzGJU9Cb2kZ0j6KK4b8zs2V63EYkzqlDv2vKHX8u2hz/j64McUpgzBK3soShverrPzSOS0gedh0ifw6Kq5fHPoU1xeJ3dNXYBBZ6SsaR+bq1fxQ/UqDjXtY3D6KMbnTmNc7klkWnIj7nNT1QoeXvVrfLKXmQMv4LaJf+HmRWdRYy/n20OfcXqYwLHeJiZB85577uHjjz/mqaeeYubMmQwfHvgHnpCQwCWXXMIHH3wgJpjdiGRWBc32SzoEAoFAIBAI+iJivtm3cR6wIrt96JKMDPv06jYn/JIkYRmZg2VkDvl3nYK3TnGlSbq2wkBwL0OVU9fuZOmB91g3bhsnTbwSgGXfKOEbpw86v8cEhvZEys5y3Zjfsb7yG6psZQBcP+bumMplIzG13xlkW/KpsVfwXdkiTht4nrauqqWMB1fejozMzIHnMyTj+LgdN56YizPod3dov0dvswt3pSJsWkbmIOkju/8knaSJ6u2RcFw2g55oGwZzA+dwQ+eHHYIsy3jr7Dj21OHcW8df9kzEXdmCfooHT70Db50dT70dncVI2uyhpJ17HCknD9QMLVlXjgPAdbiRlpWHsG2qwNvsQra78dk9nNcykFFLxjJQV0T6BQWYB6VjGpSOqX8Ktk2VWD/aQdNX+3HuqqXy8e/bH6vei+5hCbvezroNS2ACZB1OQfb62jzPnno7NS9voPalDXhq7drypKkDyLx0NK6DDdS8shH3oUYO/99yKh7/nowLRpJ8UiFJkwdgzG8rQE4tOINLR9zC2zv+w2kDz+O6MXehW17Pnj++qvVY1SUZSZlexPVnXMutp/2JhNy0uHz+B6Udx/jck9lY9R13fHEeMwddwHlDr6UwdQjLSz+k0VVPTmIB0/qQsy9WDDojo7OnsKZiGaA4BMO5X08sOFP7jvn60CecPvACDjbtYVPVClaULaGi5SDZlnyuGHV7yP3G507jYNMeNlZ+10bQVHvATis4M8SRbtAZOSE/fO/X9hieOZ5hGWPYXf8D/926AICT+vfN1/Ck/mfxh2lP8uDK21hVvpS5X15Ek6ueOkd1yHYHGnexrPR/AAxMHca43Gn0Ty4mMyGX9IRsMhNyqLGX88D3ysW0EwtmccfEB9DrDJw9+Ape3fo4H+99lZkDe+5/a7TEJGi+8cYb/OIXv+Cmm26itra2zfqRI0fy9ttvd3lwgshooUAub9iG6QKBQCAQCAR9GTHf7Ns4diuvmXlIZofzVEmSMGQldmr/s4ouYumB9/j20Kf8fNwfsLmb2Vy1AoDTCvtmOGmiMZk7Jv6N+777OeNypnFm0cUd36kT6HUG5gy+nP9uXcAne1/TBE2Hx8YDK26lwVlHcdpIfjHhvrget7vRJ5vQJ2f29jCiRn2/J2clkjx1AFmET4Tu6BzPVJCK6cJRZFzYttXB0Aj3MQ/OJOMnI/E2OGhYspeGT3birmhGl2hCl2hAl2RCZzEq5hmvjOz1kevIpSKpkl3HK+0czO9Z2fXsKxT86TRSZhThOtxI9TPrqHtjMz6b0uvUkJtE5iWjybxsNObigPCVe+sU6t/fTvWza3HuqaP2v5uo/e8m5fEMTCNpcn+Spgwg+aSBmAYpwuRVx9/JpSNuRdfgpew3X2L9cIe2vc/hwVPVQsNnu2n4TGkPkDA8m8SJBSRO6EfSCf0wD80Ke6EkGm4Zfy+Prp7LHutWFpe8yeKSN5mUP4NDTSUA/HjINVH1BD0aGJt7ImsqlqFDx/lDrwu7jUFn5JwhV/LKlsd4+YdHeWXLY9QHiWs6dNw8/t427tnxeSfz0d5X2ej/Dlfx+NysOLwEgJMHRG690RkkSeK8odfy2Jrf4pGV9+tJ/eOz795gUr8Z3HvyM/z1+19woHEXoLRsGJE1gTE5UylKHc7O+k1sqvyOvdZtlDbuprRxd8T9jcs9id9N+bv2vj6r+BIWbv8Xe+q3sLNuEyOyxvfEw4qamD59VVVVjBkzJuJ6vV6PzdZ+ep6ga+j8V+isGU08tuo3nD/8eoZlRH5NBAKBQCAQCPoSYr7Zt3HuUgTNhOMilyZ3hVFZE+mXNIjylgN8f2gxDc46fPgYlTWxU4nGRxpjc0/k5XO/xWJI6hbDwplFl7Bw25PsrNvInvotDE4fxYK1v6ekYQdp5iz+dNK/A+XXgl6lOw0r+rQEMi8+nsyLO3biDl21mIpDn+EyKeJPTksOjh017LvqHSyjc7HvqNH65CaMyiH31qmkn3tc2D6ZOouRrJ+OJfPyMTR9tZ+mZftoWVOGfVs1rtIGXKUN1L+7DQDjgFRSTh5I8smDQJY5/H/LFOenTiL3F5PJ+/VJSCY99h8qafxyH41f7MW+uRLHzhocO2uoe32zcsxUM8a8JGSnF5/Ti+z0ILu8SCY95mI1bEz5aeyXorQKcHrxubwkOj3cI/+NAyMr+LThbVYdXsraiq8AsBiSOLM4vhcdjmROGXA2n+x9jRMLZrX7HXtW0SUs3PYvrM4aAEw6MyOzJzIudxqT809jUNpxbe5zfPZk9JKBipZSKloOamnjW6rX0OSykmrKYHT25Lg9lpMGzObFHx6mzlHFgJTBDEyNdAmgbzAudxrzT3udDZXfMjxzHMMzx4f0uzyx/ywY/RsanfVsrl7J1pq11NjKqXfWUO+opt5RjcfnZnzuyfxh2r9CWgWkmTM5tfBcvjzwPp/s/e/RIWgWFhayY8eOiOu/++47hg7t22+KIx3JpAiam0/Yy9dla0gwJjJsohA0BQKBQCAQHB2I+WbfRnVottdrsStIksQZRRfy361/Z8mBd2lxKanHM4/AHl+dpXUoRjzJSMjm5AFz+OrgR3yy9zVyEgv4vmwxBsnIH6b9i5zEgm47tqBvMjB1GPCZ9veJz9yC95n91L68AfuWKgCSTyok5xdTSJnRNvQqHJJOInVmMakzlZYN3kYnLesP07KmTCmj33AY96FG6t7cQt2bW7T7JQzPpvCxOSFtKBLH5ZM4Lp/8O0/CXd2CbX05LesPY1t/GPumCnyNTpyNzraDsLmxbSjHtqG84/ECPykcx+zTT+S7CetZZ1zNRSNvIsmY0uF9uwvZJ+O12vHU2PDU2jEPyYyqnUKsZFnyeGbOkg63SzVn8IdpT7KrfhOjsiYxImtCh2EyicZkhmeOY1vtOjZVrSC/WBE0vy9bBCil7PF0whp1Ji4afiPPbnqAM4suidt+e5Mh6aMiBtOppJozOGXA2Zwy4OyQ5bIsY/e0ROw7+6MhV/Hlgff57tAifjb2bjIScuI27q4S07vipz/9KY8//jgXXXQRxx2nKOzqF9ezzz7LW2+9xfz582Ma0JNPPskjjzxCRUUF48aN44knnmDKlClht3322Wd55ZVX2LJF+ZKbOHEif/vb39psv337du6++26++uorPB4Po0aN4t1332XgQKX5tMPh4De/+Q0LFy7E6XQye/Zs/v3vf5OXF7+eNfFG7aHiMnkAcHrt7W0uEAgEAoFA0KfozvmmoPtx7FFCSMzDsrvtGKcPvIDXt/6DbTVrAeUk9eQ+XDrYU5w75Eq+OvgRy0s/xCsr5xK3nnB/p4I1BMcOg1IDjjqTPoHc/EHo/lxM9jXjsX6wg5QZRSRO6NelY+hTzaSeVkzqaX6B0+aiZVUZzd8doPm7UlyHm8i+ehy5t5+IzhxZwjDmJCl9R2crF7tkjw/Hzhq8jQ4kswGd2YBk0iOZ9fha3DhL6pWwsX11OEvq8VQ2I5n825j06MwGfE4P9q1VuA42Yni5kRkv92cGPyHpRDMNP99D6qzBYfu2ym4vLWvK8Lm8JE0sCBuAJPtkbJsqaPhsF47t1ZgK00g4LpuE47IwD8vCkJ2Ip9qGY0c19u3VOHbU4NhVg7uiGU+dXXPGAkhmA7m3Tib3F1PQWbqedt0VTsg/tdP9LcflnuQXNL9ndvGleGVvULn57A7u3Xl+NORqJvebSV7igLjvu68hSVK7IVpDM0YzInMCO+o2sLjkTS4feVsPjq59YhI0//jHP7Jy5UqmT5/OyJEjkSSJO++8k7q6Og4dOsQ555zDnXfe2en9vvnmm8ydO5enn36aqVOnsmDBAmbPns3OnTvJzW2bxrR8+XKuuOIKTjrpJBISEnjooYc466yz2Lp1K/379wdg7969nHLKKdxwww3cf//9pKamsnXrVhISErT93HnnnXzyySe8/fbbpKWlcdttt3HhhRfy3XffxfL09Ax6CSTwGJWUc7fP3csDEggEAoFAIIgf3TXfFHQ/steH0y9odpdDEyA7MZ8JeaewrvJrAKb0O51kU2q3He9oYXjmeIakH89e61YAzh96HbOKLurlUQmOVAalDtN+L0gehE5SxDtzcQZ5v57WLcfUJ5pCHJyxIhl0WI6PnOpsGRmd08zb4qJldRnN35fS/F0p9i2VtKw8RMvKQ5gGpZPz8xPIuGQ0kk6iafl+GhbtpnHpXrwNfmeoXsIyOo/kaYUknzgAncVIw6LdNCzag7u8KfL4EwzIDk+7Y9OnJaBLMuI+3ETl31dQ99YWCu6dSdrZw2JuWyD7ZOxbq0gYloUuoWd6hI7LncYb259gU9UKfLKPbTVraXDWkWxMY0zO1LgfT5IkrbRd0DHnDr2KHas3sGjfQi4afhNGnanjO/UAkizLcix3lGWZ1157jXfeeYfdu3fj8/kYMmQIl156KVdf3TbJMBqmTp3K5MmT+de//gWAz+ejsLCQ22+/nd///vcd3t/r9ZKRkcG//vUvrrnmGgAuv/xyjEYjr776atj7NDQ0kJOTw+uvv87FFys9MHbs2MHIkSNZsWIFJ554YlRjb2xsJC0tjYaGBlJTe2YitXno3/no3K9ZNX0bk/Nncs/JT/fIcQUCgUAgEBx99MZcpiO6Y755NHOkvIbO/VZ2nPocktnAmJ13tJs63VW+PfQZD6/6NQD3nPQ0k/vN7LZjHU18ffATHl09l4l50/nTSU8dM8Emgs7jlb1c9r8JuHxOTuo/m9+f+M/eHlKv4ypvoualDdS9tkkTLXUpJmS3L0SANGRZ0CWZcJU2RNyXLslI6hlDSJo6APfhRhy7anHsqsVVagUZ0EmYi9JJGJlDwohsLMNzMA5IxZidiD4rEZ1JjyzLNHy6i8N/WY67TBFIk08eSP5dp2AZnYfOpI94/NY491s5+LtFilhblM6AB88k5ZRBsTxNncLjc3PlR1Oxe1r4++nvsWT/u3y67zXOGHQhv5r0YLcfX9A+bp+Ln392OvWOan475XGmF57brceLdj7T6f9cdrudP/7xj8ycOZOrrrqKq666qksDVXG5XKxbt4558+Zpy3Q6HbNmzWLFihXt3DOAzWbD7XaTmamk3Pl8Pj755BPuuusuZs+ezYYNGyguLmbevHlccMEFAKxbtw63282sWbO0/YwYMYKBAwe2K2g6nU6czkAvjsbGxs4+5C4jGfWaQ9MjHJoCgUAgEAiOErprvinoGbSE86GZ3SpmAkztdwaDUo9DJ+mYkHdKtx7raGJ64bkUp42gIHmQEDMF7aKX9BSmDmWvdSv9k7vmmDxaMPVLoWDedPLuOJH6t7dS/fw6XPutyrrCVFJnDyNtzjCSJhUg6XW4DjfSsvIQzSsO0rzyIL5mFykzi0k7+zhSTh0U1gXps7txlzdh7JfSYQm5JEmknzuc1NMHU/Xv1VQ9tZrm70rZc/7rYNCRMDQTy6hcEkZmYxmTT9LEgjbHlL0+al7cQPlD32iirGu/lX1XvE3GRaMouOc0DFmdCwyT3V5chxq10n5vowPz0CwsI3MwF2eEBEcZdEbG5ExhdfkyNlR9x4rDnwNwSpzSzQVdw6gzMbv4MhZu/xcf73212wXNaOn0fy+LxcJ//vMfRo1qv+FoZ6mpqcHr9bbpW5mXl9duQ/hg7r77bgoKCjRxsqqqiubmZubPn89f//pXHnroIRYtWsSFF17IsmXLmDFjBhUVFZhMJtLT09sct6KiIuKxHnzwQe6///7OPcg4Ixn1eAxqybmrV8ciEAgEAoFAEC+6a74p6Bmcu5V024Shmd1+LKPexD9nfSjcujFQmDqkt4cg6COMy53GXutWRufEL2n6aECfZCL7uglkXTOelrVl6JNNJIzMafN9ZCpIxXThKDIujP5/ms5ixDy4c9+hOouR/N+cTMYlx1Mx/xsav9qPr9Gp9N7cUaNtJyUYSD6pkBR/31LZJ3Pwt4uwrT0MKO7OgntPo/aNH6h9eQP1726jcek+Cv40g8RJ/XHuqcO5rw6H/6evyQU6CXQSkv+nt9GJ62BDSJ/PYCSzXukXOjwbc1E6pkHpjEwfzWqW8eHul7E6a0gypjA2t3vaGgg6z5ziy3h7x9PsqN3A3vqtDMk4vreHFFsPzYkTJ2pBPEcK8+fPZ+HChSxfvlzrj+nzKR+e888/X+uxNH78eL7//nuefvppZsyYEfPx5s2bx9y5c7W/GxsbKSzs2R4MklEX1ENTCJoCgUAgEAiOHo7E+aYgOro74bw1QswUCLqXa0b/hvOGXkumJXI/ymMZSSeRPOXICZcxD0xn0L9/jCzLuMsasW+vwbGtCvuOalpWl+GpaqHpyxKavizhMCgx7rJS/l7wp9PIvHIskiQx4C9nkHHhKA7d/TmO7dUc/O3iTo9FSjBgLs7AXJyOPsWMY3ctjh01+Gxu7D9UYv+hUts2Na8e5oHVqYivx5cOp/bx1ZgHZ2IqzkDSSXjq7XitDiXh3erAkG4hZUYR5uKM+Dx5gohkWnI5ZcAcml2NSFL3Vl9ES0yC5oIFCzjnnHMYPXo01113HQZD18sUsrOz0ev1VFZWhiyvrKwkPz+/3fs++uijzJ8/ny+++IKxY8eG7NNgMLS5uj9y5Ei+/fZbAPLz83G5XFit1hCXZkfHNZvNmM1tk8p6EskUcGiKknOBQCAQCARHE90x3xT0DI7d/oTz43pG0BQIBN2LTtIJMbMPIkkSpgFpmAakkXam4siWZRnHjhqaviqhafl+WlYfQnb7SD51EIUPn4VpQFrIPpIm9OO4T66i+vl1VP5jJXh9mIdmYR6SiXlIBglDMtFnWMAnI3tlkGXwyUgWI+bBGRjzkhXXZhCyT8ZV2oBjezWOXTU4D1hxlTZQcCCJFGsiTek2AIZ9nEnl1pVRPVbToHRSTisi9bRikk4sRJ98ZITWHG38etJDR1SbkphGct1116HT6bj55pu544476N+/PxaLJWQbSZLYtGlT1Ps0mUxMnDiRpUuXav0tfT4fS5cu5bbbIsfCP/zwwzzwwAMsXryYSZMmtdnn5MmT2blzZ8jyXbt2MWiQ0th24sSJGI1Gli5dykUXKel+O3fupLS0lGnTjmx7c3APTeHQFAgEAoFAcDTRHfNNQfcjyzLOHnZoCgQCgSA6JEnCMjIHy8gccm+ZgrfFhaeqBVNRekS3u2TUk3vLFHJunASS1Eag7PQY/EFH5qJ00s4eFrJu0qoSlh36gAQsnHLB5fjGteDcV6f0KJUk9BkJGNIT0GdY0Kcl4NpfT8uaMlwHrNS+vJHalzcCYMhJVMTcwjRMhamYh2aR/qPhnU5tl2UZX4sbr9WOt8mFt9GJr8mJt8kJSBjzkjDkJWPMTUKXbDrqKwaOJDETYhQ0MzMzycrKYvjw4XEdzNy5c7n22muZNGkSU6ZMYcGCBbS0tHD99dcDcM0119C/f38efFBJuXrooYe49957ef311ykqKtJ6XiYnJ5OcnAzA7373Oy677DKmT5/OzJkzWbRoER999BHLly8HIC0tjRtuuIG5c+eSmZlJamoqt99+O9OmTYs64by30Bl1eAxKw163VwiaAoFAIBAIjh66a74p6F7ch5vw2dxIRh3mQem9PRyBQCAQtIM+yYS+ODo3Y3eHvAFMH3Quyw59wOmDf0L/i06O6j7eZhfN35fStLyEpuUluA424qm24am2YdtQrm1X+Y8V9P/LGaSeFj7cyralkrrXN+M61Iinxoan1oan1o7s9ITdvjU6iwHToHRSzxhM2uxhWMbld1n8FbRPTIKmKgbGm8suu4zq6mruvfdeKioqGD9+PIsWLdKCgkpLS9HpAh+ip556CpfLxcUXXxyynz//+c/cd999APzkJz/h6aef5sEHH+SOO+5g+PDhvPvuu5xySiAF8e9//zs6nY6LLroIp9PJ7Nmz+fe//90tjzGeSKaglHNZlJwLBAKBQCA4euiu+aage3Hs8iecF2cgGfW9PBqBQCAQ9CUm5s/gP7OXkJ3YftvBYPTJJtLOGkraWUORZRmv1YHrUCOugw3areGz3bj2Wym5+l3Szj2Ogj/PxNQvBVmWafpqP9X/WUPzt6URjyGZ9OhTzehSzOhTlZvs8eGpasFd1YyvyYXP7tECmKqeXI0xP5nU2UNJm60k3neUVu9zedGZuv//Zk8dpyeQZFmWe3sQRwONjY2kpaXR0NBAampqjxxz93mv8dCZT1BVUE+6OZtXfvRdjxxXIBAIBALB0UdvzGUE8eVIeA2rn1nL4b8sJ+3c4yh6+rxeGYNAIBAIBMF4m5xUPP49NS+uB6+MLtFI1lXjaPrmAI7t1cpGeon0H48g+eSBGLMTMWQlYshORJ9lQZ/YvovVa3PhqWzBtrmShkW7aVq2D19LwHQmmfQkjssnaeoAkqYOIHFcPq4DVmwby7FtrMC2oRznvnoSJxUw8PGzuy3kqO7tLRya9wWpZxRT+Ogc9Cm9mwsTiWjnM10qgHe73ezYsYOGhgYtUTyY6dOnd2X3gg4QPTQFAoFAIBAc7Yj5Zt+ipxPOBQKBQCDoCH2Kmf5/nknmpaM59McvsK0po/qZtQDoEo1kXjGWnJ+f0CYUKer9Jyql++biDDLOH4HP4aH5uwM0LNpD45f78FS10LKmjJY1ZfCvVRH3Y1t7mF1zXqHgvplkXj4mrj05GxbvUZLqfTINn+7GsauW4ucvwDw4M27H6GliEjR9Ph/z5s3j3//+NzabLeJ2Xq835oEJOkYyi5RzgUAgEAgERydivtk3UQVNsxA0BQKBQHCEYRmZw9B3Lqf+7S3UvbWVlJnFZF01DkN6QlyPo0swkHrGEFLPGIIsy7j2W2ledYiWVYdoWX0IV2kD+vQEEif0I3F8PokT+mHMT6Hsz1/SsuIgh+76nMYv9lH48FkYshIBcFe10LK2jJbVZXjqbBiyEjHmJmHITsSQnaQELUVwdjavOMiBX34EPpnU2UOxb6rAuaeOXT/6L4P+eS6ps4bE9fH3FDEJmn/729945JFHuPnmmznllFO4+uqreeihh0hPT+ff//43kiTx8MMPx3usglZIRp1waAoEAoFAIDgqEfPNvocsyzj3CIemQCAQCI5cJJ1E5mVjyLxsTM8cT5IwF2dgLs4g63LlmN5GJ7qUtqnoQxZeSvUza6l4+BsaP9/Dzg2HSZlRRMvaw0rSewekzhpC/l2nYBmZoy2zbamk5Ib3kZ1eUs8aStHT5+Gps3Pglg9pWVNGyc/eJ3/uyeTecWKfCzGKKabqpZde4tJLL+Wpp55izpw5AEycOJEbb7yRVatWIUkSX375ZVwHKmiLUnKuJG75ZC9eWTgUBAKBQCAQHB2I+Wbfw1PVgrfBCToJ8+Du6f8lEAgEAkFfR59qDltOLukkcm+ZzLCPryLhuCw81Tbq39mmiJkSJIzMIeua8fT743RybplMxkWjSJ5eRMKoHNBLNH6xl12zX6b015/iLLXiLKmn5Op38TW5SJo6gEH/OhfJoMOYm8TghZeSdc14kKHise/Ydc6rVCz4HtvmCmRf34jaicmheejQIe666y4AzGaliajD4QDAZDJx1VVX8fjjj/O3v/0tTsMUhEMyBkrOATxeF3qDpRdHJBAIBAKBQBAfxHyz76GVmxelozN3qVW/QCAQCATHLJZRuQz7+CpqXtmI1+ogaUp/kk4oQJ8WuTTesbeOike/peHjXdS/uw3rhzvQpyXgqbGRcHwuxS/8JCRpXWfSM+CBWVjG5FH2py9wbK3CsbWKyse+x5CbROoZg8m4cBTJJxb2xEOOiZhmGllZWTQ3NwOQnJxMamoq+/btC9mmvr6+66MTtItsAp8+oJy7fW7MCEFTIBAIBAJB30fMN/seon+mQCAQCATxQWcxknvz5Ki3TxiSSdFT52G7pYLyh76h+ZsDeGpsmAalM/jVi9Cnhk80z7p8DKlnDKbpy300Lt1H09f78VS1UPfGD9S98QM5N0+i392nIhn18XpocSMmQXPChAmsWbNG+3vmzJksWLCACRMm4PP5+Oc//8m4cePiNkhBeDzm0KRP0UdTIBAIBALB0YKYb/Y9nLv8/TOHCkFTIBAIBILeIHFcPkNev4Smbw/QtKyE7J+dgDEnqd37GHOStL6iPqeHllWHqP/fdurf3kr1f9bSsqaMQU/+KOYU+O4iph6aN910E06nE6fTCcADDzyA1Wpl+vTpzJgxg8bGRh577LG4DlTQFm8rQdMjBE2BQCAQCARHCWK+2fdQHZoJxwlBUyAQCASC3iTllEEU3HMapv6pnbqfzmwgZXoRAx8/m6Jnz0eXasa2vpxdc16lYcnebhptbMTk0DzvvPM477zztL9HjRrF3r17Wb58OXq9npNOOonMzMy4DVIQnrYOTXcvjUQgEAgEAoEgvoj5Zt9DTTgXJecCgUAgEPR90uYM47hRORy49WPsmyrY/7P3yblxIvm/n47O1Psl6HHr1p2Wlsb5558fr90JosBjDE01Fw5NgUAgEAgERzNivnnk4qm14am1KymsQ4XQLBAIBALB0YB5YDpD37uC8r99Tc3z66h+dh0JI3PIvGR0bw8tNkGztLQ0qu0GDhwYy+4FUeIxtXJoeoWgKRAIBAKB4OhAzDf7Fmq5uakwLSRFVSAQCAQCQd9GZ9LT/76ZJJ84AOunu8i46PjeHhIQo6BZVFSEJEkdbuf1ejvcRhA7HlPo8ytKzgUCgUAgEBwtiPlm30IknAsEAoFAcHSTNmcYaXOG9fYwNGISNF944YU2E0yv18v+/ft55ZVXyM3N5Ze//GVcBiiIjMcgSs4FAoFAIBAcnYj5Zt/CqQYCiXJzgUAgEAgEPUBMguZ1110Xcd3dd9/N1KlTaWhoiHVMgihp3UPTLQRNgUAgEAgERwlivtm30BLOhUNTIBAIBAJBD6CL9w6TkpK4/vrr+fvf/x7vXQta0TYUSJScCwQCgUAgOPoR880jD8cuf8n5cdm9PBKBQCAQCATHAnEXNAF8Ph8VFRXdsWtBEB69cGgKBIK+jSzLPL/pQRZuf7K3hyIQCPoYYr555OBtdOKpagFEyblAIBAIBIKeIaaS80g0Njby9ddf88gjjzBhwoR47loQBo/BE/K3SDkXCAR9jVp7JR/seQmAmQPPJy9pQO8OSCBoB1mWowqpEXQvYr555OGx2gHQWQzoU8y9PBqBQCAQCATHAjEJmjqdLuKEXpZlBg4cyL///e8uDUzQMW6DG+Sgv4VDUyAQ9DFa3I3a7ysPf8H5w67rvcEIBO1QY6vgruWXcWbRJVwx6rbeHs4xgZhv9h1kp1I1JJni6pUQCAQCgUAgiEhMs4577723zQRTkiQyMjIYMmQIZ511FgaDmNB0N269F4JMmqKHpkAg6GvYPS3a7ysOLxGCpuCIZXvtemrsFaw4/LkQNHsIMd/sO8guv6Bp1vfySAQCgUAgEBwrxDQLvO++++I8DEEsePSeEEFTODQFAkFfI1jQ3F6zjnpHDRkJIlBCcOTR5LIC0OwSqdo9hZhv9h18TmVCqksQArNAIBAIBIKeoVtCgQQ9g1sX6sgUPTQFAkFfI1jQlJFZdXhpL45GEG9KG/dwqGlfbw8jLjS7FSGzSQiaAkEbZL+gKRyaAoFAIBAIeoqYLqP+7Gc/6/R9JEni+eefj+Vwggh4dKGhQB5ZlJwLBIK+hc3dHPL3ysNLmDP4sl4ajSCe2NzN/G7ZJeglIy//6FuMOlNvD6lLqEKm02vH5XVi0ovgk+5GzDf7Dj6H6KEpEAgEAoGgZ4lp1rFs2TJsNhvV1dUAZGRkAFBfXw9ATk4OiYmJIfcRqaDxxy0Jh6ZAIOjbqA7N4rSRlDRsZ3PVSppdjSSbUnt5ZIKusr9hB3aPDYDqlsMUpBT17oC6SHCpeZPLSpYlrxdHc2zQnfPNJ598kkceeYSKigrGjRvHE088wZQpU8Ju++yzz/LKK6+wZcsWACZOnMjf/va3iNvfcsst/Oc//+Hvf/87v/71rwFYvnw5M2fODLv96tWrmTx5Mvv376e4uLjN+hUrVnDiiSdG9bh6C7WHpk44NAUCgUAgEPQQMZWcf/LJJxiNRv7whz9QVVVFbW0ttbW1VFVVMW/ePEwmE59++iklJSXabd++o6Pk7EiitUNT9NAUCAR9DVXQHJYxmsKUIXhkN2srlvfuoARxYZ91h/Z7RcvBXhxJfGhy1Wu/iz6aPUN3zTfffPNN5s6dy5///GfWr1/PuHHjmD17NlVVVWG3X758OVdccQXLli1jxYoVFBYWctZZZ1FWVtZm2/fff5+VK1dSUFAQsvykk06ivLw85Pbzn/+c4uJiJk2aFLLtF198EbLdxIkTO/Gs9Q4+reRcODQFAoFAIBD0DDEJmrfffjtnn302f/3rX8nODoQ3ZGdn88ADDzBnzhxuv/32uA1SEB7VoSnJyt8i5VwgEPQ11JJzizGZaf3PBGDl4S96c0iCOFHSsF37vaKltBdHEh+aQhyaQtDsCbprvvn4449z4403cv311zNq1CiefvppEhMTeeGFF8Ju/9prr3Hrrbcyfvx4RowYwXPPPYfP52Pp0tCev2VlZdx+++289tprGI3GkHUmk4n8/HztlpWVxQcffMD111/fxlWalZUVsm3rfR2JqD00hUNTIBAIBAJBTxGToLly5UpOOOGEiOsnTJjAypUrYx6UIDrcKI5Mk1PpSyYcmgKBoK+hOjQthiSmFZwFwLqKr3F67L05LEEcKLEGC5qHenEk8aF1ybmg++mO+abL5WLdunXMmjVLW6bT6Zg1axYrVqyIah82mw23201mZqa2zOfzcfXVV/O73/2O448/vsN9fPjhh9TW1nL99de3WXfeeeeRm5vLKaecwocfftjufpxOJ42NjSG33kB2+ntoCoemQCAQCASCHiImQTMzM5PPPvss4vpPP/2U9PT0WMckiBLVoZngEIKmQCDomwQLmoPTR5Gb2B+n186Gyu96eWSCruDxuTnQuFv7++hzaFq77TjNrkZe/OFhDjTs6rZj9BW6Y75ZU1OD1+slLy+0B2peXh4VFRVR7ePuu++moKAgRBR96KGHMBgM3HHHHVHt4/nnn2f27NkMGDBAW5acnMxjjz3G22+/zSeffMIpp5zCBRdc0K6o+eCDD5KWlqbdCgsLozp+vFFLznUm4dAUCAQCgUDQM8QkaN588818/PHHnH/++XzxxRfs37+f/fv3s2TJEs477zw+++wzbrnllniPVdAKN35B0+4XNEUokEAg6GPY3QFBU5IkTixQBIIVh5f05rAEXaSsqSTkIlt5c98WNGVZDnFodmcPzUUlC3l/1/O8s/M/3XaMvsKRON+cP38+Cxcu5P333ychIQGAdevW8Y9//IOXXnopqlCiQ4cOsXjxYm644YaQ5dnZ2cydO5epU6cyefJk5s+fz1VXXcUjjzwScV/z5s2joaFBux082Dv9atVQIOHQFAgEAoFA0FPENOv405/+hNPp5JFHHuHjjz8O3aHBwO9//3v+9Kc/xWWAgsi4ZeVk0exQeiuJHpoCgaCvYfMoPTQTjckATOt/Fh/ueZnV5V/i8bkx6I783nGCtuzz989MN2djddZQ2XIIWZajTqA+0nB4bXjkwP/Y7nRobqtZ6z+G6NPZHfPN7Oxs9Ho9lZWVIcsrKyvJz89v976PPvoo8+fP54svvmDs2LHa8m+++YaqqioGDhyoLfN6vfzmN79hwYIF7N+/P2Q/L774IllZWZx33nkdjnfq1KksWRL5Ao/ZbMZsNne4n+5Gc2gmCEFTIBAIBAJBzxDzrOMvf/kLv/rVr/jiiy84cOAAAIMGDWLWrFkhjdsF3YfL30MzwSZKzgUCQd8kuOQcYETWBNLMWTQ4a/mhejUT8k7uzeEJYqTEn3A+pd/pLNn/Ng6vDauzloyEvjk/aO3I7C6x0Sf72F67AQh8No514j3fNJlMTJw4kaVLl3LBBRcAaAE/t912W8T7PfzwwzzwwAMsXry4TSr51VdfHVJ+DjB79myuvvrqNj0yZVnmxRdf5Jprrokq7Gfjxo3069cvykfXe8gO1aEpSs4FAoFAIBD0DF26jJqdnc3ll18er7EIOknAoSkETYFA0DcJLjkH0Et6TiyYxeKSN1lR9rkQNPsoaiDQcZlj2VD1LdW2w1S2HOyzgmZrAbPZ3T2C5sHGPbS4lVAXh8fWLcfoi8R7vjl37lyuvfZaJk2axJQpU1iwYAEtLS2a+HjNNdfQv39/HnzwQUDpj3nvvffy+uuvU1RUpPXaTE5OJjk5maysLLKyskKOYTQayc/PZ/jw4SHLv/zyS0pKSvj5z3/eZlwvv/wyJpOJCRMmAPDee+/xwgsv8Nxzz8XtsXcXqkNTEj00BQKBQCAQ9BAx9dAsLS3l22+/DVm2adMmrrnmGi677DL+97//xWNsgg5w+ZxAIBRIlJwLBEcf7+18jnd2PtPbw+g27K1KzgGtj+aq8qX4ZF+vjEsQO7IsayXng9NHkp+khJ6U9+FgoNYl5k2u+m45zrbaddrvwqHZffPNyy67jEcffZR7772X8ePHs3HjRhYtWqQFBZWWllJeXq5t/9RTT+Fyubj44ovp16+fdnv00Uc7feznn3+ek046iREjRoRd/5e//IWJEycydepUPvjgA958882wSehHGmoPTZ3ooSkQCAQCgaCHiGnWcccdd9Dc3MwXX3wBKH2HZs6cicvlIiUlhXfeeYe3336bCy+8MK6DFYSiOjLVHprCoSkQHF3YPS28tEUJgzir6BJSzRm9PKL407rkHGBs7olYDInUO6o51LSXganDemt4xxR7rdvYWPkt5w+7vku9S2vtlTS5rOgkPQNTh5GfNJAfqldT2dI7YSXxoKdKzrfXrtd+twuHZrfON2+77baIJebLly8P+bt1D8xoiHSf119/PeJ9rr32Wq699tpOH+tIQFYdmkLQFAgEAoFA0EPE5NBcvXo1Z555pvb3K6+8gt1uZ9OmTZSVlXHGGWfEdNVa0DncqkPTn3LuESnnAsFRRaMz4AKrtpe3s2XfxCf7NNEmWNA06kz0Ty4G+n46dl/ixc0P8fKWx1hX8XWX9lPid2cWpgzBpDeTn6QEpZQ3911BUxUwMxNyge5LOd9WIxyawYj5Zt/B51QdmqLkXCAQCAQCQc8Qk6BZV1dHbm6u9vfHH3/MjBkzGDJkCDqdjgsvvJAdO3bEbZCC8Lj8AmaC1kNTlJwLBEcTwaJJte1wL46kewjuERhccg6Q6y9Trmw51KNjOpapd9QAUGUr69J+9vn7ZxanKyW1+UmFAFT0cMl5ta2csqaSuOxL7ZlZkFwEQGM3pJzX2itDnnuX14FX9sb9OH0JMd/sOwiHpkAgEAgEgp4mJkEzJydHS5q0Wq2sXLmS2bNna+s9Hg8ejyc+IxRERO2hadZ6aAqHpkBwNBFc1lpjO/ocmqoDTS8ZMOpMIetUEawvlyn3NdQwmjpHdZf2U9KgCEzFaSMByE/u+dfSJ/v4zZcX84vP5/Dwql93WdhUe2j2S1bcpi6vA6fX0dVhhrDd3z+zMGWotuxYDwYS882+gwgFEggEAoFA0NPEdBl11qxZ/POf/yQ1NZXly5fj8/m44IILtPXbtm2jsLAwXmMUhEGWZdxev6BpFw5NgeBoJDiI5Gh0aNrdSiCQxZCEJEkh6/JUh6ZNODR7imZV0LRXdWk/qkNzcLpf0PSL03WOapweO2aDpUv7j4ZGZz1Wp+I4/fbQZ3xf9jlnFl3M5SN/SZYlr9P7U93SuYn90Ul6fLKXZlcDZktC3Ma8rUbpnzkudxplzSX4ZC92dwtJxpS4HaOvIeabfQcRCiQQCAQCgaCnicmhOX/+fEaOHMlvf/tbPv/8cx599FGKi5V+Z06nk7feeoszzjgjrgMVhOKR3cjIQHDKuXBoCgRHEyGC5lHYQ1MLBDImtVkXKFMWgmZP4Pa6cPkdh/VdcGja3M1aaXlx2nAAUkzpJBlTgZ4TqOudymNINCQzOX8mPtnL4pI3uXnRmbyy5fFOl3KrbukUUzoppjT/Mmtcx6wGAo3KPkHrKes4xvtoivlm30HtoSklCIemQCAQCASCniGmy6h5eXl89913NDQ0YLFYMJkCpYI+n4+lS5eKK+bdjDsoACiQci4cmgLB0USoQ/PoEzRtfrEm0ZDcZl1eYqCHpizLbRycgvjS4m7Sfu+KoLnfX26ebckn1ZypLc9PKmSvdSvlzaU9klpv9fcDzUks4J6Tn2ZbzVpe3vIY22vX887O/zApfwajsidGvb/mIEEz2ZhGg7MursFANnczJX5n68isiVgMibS4G4/5YCAx3+w7yA6l5Fw4NAUCgUAgEPQUMTk0VdLS0kImlwAWi4Vx48aRmZkZ4V6CeODyl5tDUCiQSDkXCI4qjvZQILvb79A0tHVo5iQVICHh9NqxOmt7emjHHGr/TIA6R+wl5/usof0zVQKO257po6n2AVVTyUdlT2L+jNe1cQU/3mhQLy4oDs10/7L4CZo76zbhw0duYn+yLHnaZ8J+jPfQVBHzzSMfWfTQFAgEAoFA0MN0SdDsLp588kmKiopISEhg6tSprF69OuK2zz77LKeeeioZGRlkZGQwa9asNttfd911SJIUcpszZ07INkVFRW22mT9/frc8vnjg9gcCGTx6DB69f5kQNAWCo4kmd0AwqXdUH3Wfcbsn0EOzNUadiSxLPiCSznuCYIdmk8sa8wWykobQhHOVfH+YTk8Jmla/oJmekK0tkyRJ60fp9No7tT815TzFlBYkaFq7PlA/gXJzxTUaEDSPbYemoO/gEz00BQKBQCAQ9DBHnKD55ptvMnfuXP785z+zfv16xo0bx+zZs6mqCu8YWb58OVdccQXLli1jxYoVFBYWctZZZ1FWVhay3Zw5cygvL9dub7zxRpt9/d///V/INrfffnu3PMZ4oDo0DT4Deo/yMnplDz7Z15vDEggEcaTJadV+l5GptVf23mC6AVs7PTTh6Eo6L28updFZ39vDiEhrx2KsLs2SVoFAKppDs7k0pv12lnp/yXlGkKAJYNYrIT5OT+cSylU3ZrIpjeRu6KG5vUZJOB+ZdQIACYZEQKScC/oOstpD0ywcmgKBQCAQCHqGI07QfPzxx7nxxhu5/vrrGTVqFE8//TSJiYm88MILYbd/7bXXuPXWWxk/fjwjRozgueee0/oqBWM2m8nPz9duGRkZbfaVkpISsk1SUviTbFCa0Tc2NobcehKX36Fp9BrQewKTR4/ooykQ9AjrK77h072vd+sxmtzWkL9rbBXderyeRgsFCuPQhKMn6dzqqOWXn5/DPd9c39tDiUhrQTOWPpoen5sDjbuB3i85V8efkZATslxNWHd0wqHp9Dq0wKTgUKB49dD0+jzsrNsEwKgs4dAU9E18TtFDUyAQCAQCQc9yRAmaLpeLdevWMWvWLG2ZTqdj1qxZrFixIqp92Gw23G53m55Ky5cvJzc3l+HDh/OLX/yC2tq2Pdnmz59PVlYWEyZM4JFHHsHj8UQ8zoMPPkhaWpp26+mm9Go5oMFn1ErOQZSdCwQ9gU/28cjquTy98X521W3utuOogolRp/SOq7YfXX001R6aiR0Imj0lgnUXFS2leGQ3Bxp3dTpdu6dodjWF/F0Xg6BZ1lSC2+fCYkjSXjsVzW1rO9Qjz0HAodlK0NQrgqbTE72gqX4OdZIeiyEp7g7NkoYdOLw2koypFKYOBQIOTSFoCvoKWg9NIWgKBAKBQCDoIWISNL/++muqqyOf7NTU1PD11193er81NTV4vV7y8vJClufl5VFREZ0z6e6776agoCBEFJ0zZw6vvPIKS5cu5aGHHuKrr77i7LPPxusNnFTdcccdLFy4kGXLlnHzzTfzt7/9jbvuuivicebNm0dDQ4N2O3iwZ0+41ZJzo8+I3ht4GYVDUyDofipaDmqOtq01a7vtOKpgMsifCn20JZ3b1B6aEUrONYdmH++h2exS3is+2Uujs66XRxOeFneo27DO3vmS831q/8y0Eeik0OlFdmI/DJIRj88d0747S72/ZD6zjaCplJx3xqEZCARKQ5IkUuMcCrRNKzefoD1vIhRIobvmm4L44xMl5wKBQCAQCHqYmATNmTNnsmTJkojrly5dysyZM2MeVKzMnz+fhQsX8v7775OQkKAtv/zyyznvvPMYM2YMF1xwAR9//DFr1qxh+fLl2jZz587ltNNOY+zYsdxyyy089thjPPHEEzidzjBHUkrYU1NTQ249iRoKZPQZkJDQy/5gIJF0LhB0O/us27Tft9eu65ZjyLKsCSaD00cBUHOUJZ0HSs6Tw64P9NDs24JmSIJ4D4h5sdDsDnVoxlJyXqImnLfqnwmgl/TkJvUHFMdqd6M6NNNb9dBUnY+dCQVSHZrJRsWZmewXNJtbtYSIFTUQaKS/3BzAIhyawJE73xSEInt94FF6uOuEoCkQCAQCgaCHiEnQlGW53fVOpxO9vvMTmuzsbPR6PZWVocEXlZWV5Ofnt3vfRx99lPnz5/P5558zduzYdrcdPHgw2dnZ7NmzJ+I2U6dOxePxsH///qjH35M4/f28jLIRAAPKT1FyLhB0P/v8wSegiBEdfSfGgs3TjM9fmluUpiRGH20OTbXkPGIPzUTFoVljK+/T7vPmYEEzxrCd7qbF7yJV2xvEMk41EKg4bUTY9arjtrybg4GcHrvm/m1bct75UCD1woKabq720GwMCu2KFVmWtYsiaiAQBFzLx3ooUHfNNwXxRXYFKp5EyblAIBAIBIKeIupZR2lpaYi4t2PHjrBlPlarlf/85z8MGjSo04MxmUxMnDiRpUuXcsEFFwBoAT+33XZbxPs9/PDDPPDAAyxevJhJkyZ1eJxDhw5RW1tLv379Im6zceNGdDodubm5nX4cPYHqxFQFTaNswCmJknOBoCcIdmg2OOsobz5AQUpRXI+hlrma9An09++72n6UCZodpJxnJORg0plx+ZxU28rplzywJ4cXN4LDY2qPUIdmi9+h2T+lmP0NOzstaMqyrJWcD/E7ilvTL2kgG+h+x63qzjTpE0hs5f5VQ4E649BUP4tq78wUzaHZ9ZLzStsh6hzVGCQjwzLHaMtV1/Kx6NDsifmmIL74HIGe8yIUSCAQCAQCQU8R9azjxRdf5P7770eSJCRJ4oEHHuCBBx5os50sy+j1ev7zn//ENKC5c+dy7bXXMmnSJKZMmcKCBQtoaWnh+uuVdNhrrrmG/v378+CDDwLw0EMPce+99/L6669TVFSk9dpMTk4mOTmZ5uZm7r//fi666CLy8/PZu3cvd911F0OHDmX27NkArFixglWrVjFz5kxSUlJYsWIFd955J1dddVXYNPQjAS3l3O/M1MvKSykcmgJB96M6NJOMKbS4m9hWuy7ugqYqgqWa0smxKBdfqm1lyLKMJElxPVZvobroWotOKpIkkZc0gINNe6lsOdR3Bc0gh2Yspdw9gVoWX5gylP0NOzs9zlp7JU0uK3rJoAXbtCZPSzrvXodmvdOfcG7ObvNZSdB3XtBs1hyaasm5GgrUdUFzu79/5pCM4zX3KARK4x3HoKDZU/NNQfyQ/f0z0UtIhiMqb1QgEAgEAsFRTNSC5qWXXsro0aORZZlLL72UO+64g1NPPTVkG0mSSEpKYvz48W2CfaLlsssuo7q6mnvvvZeKigrGjx/PokWLtP2Vlpai0wUmS0899RQul4uLL744ZD9//vOfue+++9Dr9WzevJmXX34Zq9VKQUEBZ511Fn/5y18wm82A0g9z4cKF3HfffTidToqLi7nzzjuZO3duTI+hJ3CroUAo5YEGIWgKBD1Cnb0Kq7MGHTpOG3g+n+z9L9tr1zOr6KK4HifYFZadqAiado+NFncTyaae7dnbXXRUcg6KCHawaW+fTjoPdmgesSXnfodmYepgoPO9PksbdwNQkFyESW8Ou02/JEWQ7u7XMlLCOSiuTQBHJ1LOm/xOzNYOTZfXgdPrCBEiO8uu+h8AGJE1IWR5IBTo2BM0e2q+KYgfPpfi0NSZRPm/QCAQCASCniNqQXPkyJGMHKk0+n/xxReZPn06xcXF3TKo2267LWKJeXCQD9Bhj0uLxcLixYvb3eaEE05g5cqVnRlir+NqLWj6lJdSlJwLeppvDn6K2+fi9EEXxG2fPtnXJiX5SEF1Z/ZPKWZC7smaoBlvmoJcYQmGRFJM6TS5rFTbDx89gmYHJecQlHRu67vBQH0hFCjYoQnQ6KrH7XNpPTU7Qn191CCncOQnqw7N7hY0/Q7NMIJmV0KBVIdmoiEZnaTHJ3tpdjVgtsQuaFr94mtuYkHI8kAo0LHXQ7Mn55uC+CBrCeei3FwgEAgEAkHPEZNicO2114adXO7bt4/t27eHuYcg3mgp55I/FCiKlPP3dj7H8tIPu39wgmOGipaDPLp6LgvW3s2O2g1x2Wd5cylXfXQi/93697jsL97sa1D6Zw5OH6m5qg417aPRWRfX46gOzRST0vYixy941BxFwUD2DkrOITjpvC87NPtAKJDfoZmfPBCD//+KKrZFQ1VLGRAQoMOhrmtyWUOek3gTEDSz26zTQoG8nQkFsgKQbEwHFHdgilZ2bo19oATaESQbQy9SaA5N97Hn0AxGzDf7Bj6n4tAUgqZAIBAIBIKeJCZB84knnuDyyy8PWXb99dczbNgwRo8ezaRJk6iqOjJP2o4WXGookOrQ9LZfcl5rr+SlLY/wz7V/OCZL2ATdwxf730VGSaF9fdsTcdnnD9WraHY3sPLw0rjsL96oDs3B6aNINWcwIEUp0d0eJ0FXJSCiKMJJoI/m0SFoyrIcXcm5P+m8T5ecu4/8knPNhWhMI90vBNZ1oo+m6tBsT9C0GJJINyv77k6B2tpOyblZ7aHZiZLz1g5NCHwuuypotrhCy9lVEjSH5rH9/1rMN/sGqkNTZxYl5wKBQCAQCHqOmATNZ599NqRn0eLFi3n55Ze56aabeOKJJ9i3bx/3339/3AYpaIsWCqRTS879Ds0IgqbqvvHIbrbXxL88VnDs4fV5WHrgPe3vjVXfsa1mbZf3W2077P+pBOAcaagJ54P9Sc6jsiYCxL3sXBNRzOlAwKF5tCSdu7wOfPiADkrOk/0l592cjN2dBLsRGxy1eH2edrbueVxep/a/I8mUSqYlF+hcebz6+qgCdCRUx215NwYD1bVbch5Lynlb0VH9XHY1GEh1aCZFcGg6jsGS82DEfLNvIGsOTSFoCgQCgUAg6Dliqg05cOCA1t8I4K233qK4uJinnnoKgIqKCl599dX4jFAQFjUUyCQpgqbeo0wiI/XQDD55+6FmNSfknxp2O4EgWtZXfkutvZIUUzqT+83kywPv8/q2J/jr9Je7tF9VsFMCcBrbOJd6kxZ3k+YULE4bAcDIrBP4fP/bbKtdF9djaSXnqkPTHwxU4xd8+zpqwrmERII+MeJ2qkDW5LJiczeTaIxcnn6kEtxD04cPq7OWLMuRE2Sijk9CwmJIItMvBNZ3wk2qCpq5Sf3b3S4/uZAddRu62aHZXsm5Imh2JhRIddiqYUAAKf7y8+auCpp+sTuSQ9PhsSHLcpu09mMFMd/sG/g0h6YoORcIBAKBQNBzxOTQbO2a+vzzzzn77LO1v4uKiqioqOjayATtooYCmVNTANDZlNckkkPT6Qn0C9tSvbqbRyeIB26vi0/3vn7Elhgv2f82AKcPvIArR92BQTKyuXpll99f1bayoN+PrMdeYt0BQLalH6lmpbflyKwTANhTv0X7XMaD1q4wVdA80p6TWAkuN29PrEk0JpPq7yMarUtTlmVKrDvi+nrEitfn0cqG1YTteJSdl1h38Ksvzmdt+Vdd3leLX1RLNKagk3RkJvgdmlGWnNs9LTS66oH2S84B8v1J5+XN3SdotufQNMfg0GxW2z8El5xrPTTrYx0mPtmnicmRemj68OHqRL/Pow0x3+wbyKKHpkAgEAgEgl4gJkHzuOOO4/333weU8p/Dhw+HTDAPHTpEenp6XAYoCI8qXCZmpwMgNShXxyOFAgWfvO2u/+GY78vVF3hzx5M8vfF+3tgen96U8aTeUc2a8uUAnFl8MTmJBZxZfDEAr237Z5dKxYMFu+ojzI0YKDcPOIb6JQ8izZyFx+dmT/2WuB1LdX6pYl622kPzKCk5jybhXEUVyaLto7m2Yjm/Wno+L2ye3+G2y0s/5MsD/4tqv7EQ7M7sn6yEm8Qj6fyDPS9R0rCDLw682+V9NftbkiQZlQtkqhAYrfCqCs3JxjRtH5Ho7pAnn6w4YCFSD81AKFA031Nun0tLGg/uoZnqd2t2peTc5m7WehBHcmgC2I7h/9divtk38IkemgKBQCAQCHqBmATN3/72tyxZsoSMjAx+/OMfM3LkSGbPnq2t//LLLxk/fny8xigIg+o8suSkg15C71AcTpFLzgMOD5/sZVtNfMtjBeH5bN8bPLr6N3yy9zUONu6NWuhzeZ0s2vcmEEgPPpL48sD/8MoehmeOZ2DqMAAuGX4LBp2RrTVr2Fy9Mqb9+mQfNbaA2+ZIE+/2NQQCgVQkSdJcmtvjWHauOr8CDk2lh2atvfKI68EYC5qg2U4gkEpABIvOoVnSsBOAPdat7W7X7GpkwZq7WbD27rj3QA0+BiiPU3XZ1ncibCccPtnHugrFmRmP1PuWVn0cVSGw3h7dOLX+mR24M6H7e2g2uaz4ZC8SEmnmzDbr1ZJzn+yN+P8yGPX1k5BIDBJr1c9lV0rO1VJ2k86MSW8OWaeTdForhmO5j6aYb/YNhENTIBAIBAJBbxDTzOPyyy8nKyuLTz/9lPT0dG699VYMBmVXdXV1ZGZmcvXVV8d1oIJQ3P5QILM5EfNgGb1H518e3qHZumRtS/VqJuZP795BHuN4fG6e3fgAHtnN1wc/BhShYGzOVKYWzOLk/nMiltp+c/ATrYSzwVnXY2OOBlmW+WL/OwCcWXSxtjw7MZ/ZxZfxyd7/8sa2Jxibc2Kn+77VO6rxyAGRoS84NEEpO195eElcRbGmVsnK6QnZ6CUDXtlDnaNaE8f6Kja30kMz0dBxT8zcTjo01ZTrjt4/VbYyLZjo5R8e5cEZr8W9V6Ea+pJsSgsq5e6aQ3N3/Q/a90JNHER/NTQu2aQIdlmWzo2zyt8mojOCZo2tHLfPpQXbxQt1zKnmDAw6Y5v1aigQKJULRn37x28Oav2glwLuM/Vz2diFlPOWCP0zVSzGJBxe2zFdUSHmm30D2aU4NCWTcGgKBAKBQCDoOWK+lHrmmWdy5plntlmemZnJe++9F+YegniiOjRNejMJwy0YPO2nnKsOTR06fPj4oUb00exuKloO4pHdGHUmRmVNZHvteuod1Xx18GO+Ovgxt53wV84qvqTN/WRZ5uO9/9X+7kqPtu5gW+06ypr3YzEkcmrhOSHrLh5+E5+XvMW22nVsrPqeCXknd2rfrftDHkn9It1eFwcb9wIwJMihCTAqW0063xCXAA+f7AuknPtLW/WSnixLHlW2Mqpth/u8oBmLQ7PKFp1DU3VA1juqcXtdEUWrqqB+rdtq17GmfBlTCk6P6hjRoglixlQyLf5S7i6WnK8pX6b9Xu+o6bIw2Nah2bkemoGE8/YDgZR952DWW3B67VS3HKYgpSiGEUdGFbPTzW0DgQAMOqN2YcDhsXcYOqaGcyUbQ7dL9n8uu+bQbF/QDDg0j11BE8R8sy/g8zs0RSiQQCAQCASCniSmknNB7xMQNE1Yhud0nHLuT3QdnjUeUAJMVIeUoHsoayoBYEDKEP4y/SVeP28ND0x/hTMGXQjAq1sf15xRweyo28Be61YkFFGs0WntUk/KePN5iRIGdOqAc9uIUVmWPM4efAUAr8fQS7PGHuqoq7YfOQ7NA4278MoeUkzpWj9LlcHpIzHpzDS5rJQ17evysWzuZs05GJysrJadH2ml+LHQqR6aiZ1zaNY7a7Tf23uu1HYOOv+/wpe3PIZX9kZ1jGgJiFapZCYoyeZddWiurQgEAcnIXRZI1bLq1iXnjc66qMqyO1NyLkkSud34Pq73C5qqGzYcCZ0IBmpu5ZRWSdF6aFpjGGXovlsHAqmon41j2aEp6BsESs6FQ1MgEAgEAkHPEbOguXnzZm688UYmTpzI0KFDGTx4cMhtyJAh8RynoBUuf8m5UWcmYXg2em/7JeeqQ7N/SjH5SYX4ZG9c+/0J2hIQNJUgEJPezJicqdx6wv30Ty6mwVnHW9v/3eZ+H+95FYDphecC4JHdYYXP3qDZ1ch3ZYsAmBVUbh7MRcNvxKRPYGfdxk6XYFe1KAJmv6RBQHz6A8aLfVZ//8y0kW0cmEadiWGZYwHYFoeyc1XoSNAnhrgLVVfmkfS8xIp6QaVTDs2WMnyyr8PtVZcetF92rq47o+hCko1pHGzaw7I4BwSpr2WSMVByXtsFAbLWXsk+6zYkJE1Uq7F3LeW5ddJ2mjkTnaRHRsbqqO3w/pV+52xuYseCJkCWJR9QHku8qfeLxekJ4R2aEBoM1BFN7kDJeTApWsq5NZZhAqFidzjUYKBjXdAU880jn0AokHBoCgQCgUAg6DliEjSXL1/OlClT+PjjjykoKGDfvn0MHjyYgoICDhw4QHJyMtOni/6M3Yk7uOR8RHag5NzjDLu96kQx6y2MzpkCwA/Vouy8OylrVgTN/n5BU8WoM3HD2HkAfLTnVQ437dfW1dor+b7scwB+ctzPtZLDxiOk7Pzrgx/j8joYmDqM4Znjwm6TkZDDyf3nALDC/1iiRXVsqSXcdfaqqBxiPUGk/pkqo7LUsvOOBc06exUPfH8r6yq+Dru+dSCQSo7F72w7CgTNQMl5xz00sxPz0aHD5XNGFagTvE17gmaVf11x2gguGXEzoDiLoxG6oqVZK+dOIdPfm7K9x1DSsIOl+9+LKNyq7sxhmWMpSjsO6HqvWfWCSZK/h6ZO0pHhFwTrO3CTyrLcKYcmKE5u6LoQGw7VoZnRjqBp8gcDRRO2E8mhGZdQoCCxOxyJBtWheeyGAon5Zt9AODQFAoFAIBD0BjEJmvfeey+DBw9m586dvPjiiwD84Q9/4Ntvv+X777/n0KFDXHrppXEdqCAUteTcqDNjGpiGwd8O1dkQ3smnnqCb9QmMyZkKwA/Vq3pgpH0bWZbZWrM2JoFDdWj2Ty5us25SvxlMzJuOR3bzwg/zteWf7XsDr+xhVPYkBqePJNWcASiln0cCwWFA7fWJnFag9DtbcXhJp8rOVWFmWMZoDDojPnxdLqeNF6pDsziCoBlIOu9Y0Hx/1/OsKl/Ke7ueC7u+dSCQiubQPIJK8WOlMyXnBp2RbP9j7yjp3OEJDVGpatehqZSc5yQWcO6Qq8i29KPGXsEne/4b8T6dpSVMKFCDszaiUP+PtfP4x7p5fLH/3bDr15YvB2BS/gyt9UG8HJpJQaXPmpu0A0GzyWXVnu/cpI57aEJA0KztFkFTEYvVsvlwBErOo3Boqj00g1o/AKSalO9ml88ZswDekUNTdS/b3ceuQ1PMN/sGqkNTpJwLBAKBQCDoSWISNNevX88NN9xAamoqer1yNdbrVSYzU6dO5eabb+aee+6J3ygFbVBLy016M5JehzlDcdY46sP3xVR7aJr1FkZnKw7NvfVbRR/NDvhs3xvM++pK3t35bKfvG8mhqXLD2N+jlwysLl/GhspvcXtdLC55C4AfD1FSW9PMmcCRkXT+fdnn7LFuxSAZmTnwvHa3nZB3Mma9hSpbmeZsjAZV0MxNHKCJNUdCv0iv7KWkYQcAg1sFAqmMyBqPhMTh5v2aSyzsvnwevvKn3qs9HFujlrmmtBJRVEHzaHBo2jzRl5xDwP1X2UEfzdYl0tE4NHMT+2PSm7ly1B0AvL3zP11y3gWjJVkbU0k1Z6CXDBFLud0+FwcadgHw1o6n2rQQcXmdbKz6HoDJ/WYGBM0uvh9aXH6HZpCgqQqCHTli1XJzJewnIarjZXdrybnq0IwsaJr9Dk31/2J7qIJm64sLFkMSOn/qeZPTGsNIoUXroRkhFMhfcu7wHrsOTTHf7BvIWiiQcGgKBAKBQCDoOWISNA0GAykpioCWnp6O0Wikqirg4hg8eDDbtkUvYgg6j+bQ9PfXS8hUTkSdjREEzSCHZk5iP/KTBuLDx7Ya0UezPdQ04X3WrZ26X7OrQRMhC5KLwm4zIHUI5w65EoDnNj3IVwc/osFZS7YlnxMLZgEBF1Bvl5yXNu5mwZq7AfjxsGtI9QutkTAbLEzMV8oAVxxeEvVxVKEuJ7GfJt4FJ1H3FuXNB3B67Zj0CREF6mRTGgNThwGwyS86hWNj1XdY/aE1NfYKvD5Pm21UgaRNyXniUVRy7nedJUZRcg6BPpqVHSSd1ztDBbhIgqbDY9M+V+rzetqg8xmYOowWdyPv7HwmqnF1RHCSdXApd7hgoPLmA3hl5f1QZStj6f7QBOct1atxeu1kJuQyOG1kkGO3i4JmUFm8SmaUgqYqyudF2T8TuruHpurQjFxynqDvfChQa9FRkqRAH023NZahCodmFIj5Zt9AODQFAoFAIBD0BjEJmkOHDmX37t2AMqkfMWIE77//vrb+k08+IT8/Pz4jFITF7Q8FMunMAFhy0gFwNoU/8dF6aPpL7cZqZecruzSOaHqQ9VW8spdt/uCkzoZ4HPKXm2cm5JJojCzYXD7yl6SY0jnYtIenN/4fAHMGX4Fep5wUqMJhb5acN7saeOD7X+Lw2hibcyLXHD83qvupouyKsugETZu7mWa/MzEnseCIEu/UcvOitOHopcgOlGn9zwLgk72vRdxmWemH2u9e2RNW2Gr2CyStHZqqI6/Z3dBr7mq7p6XDsu9o9wPRlZxDwKFZ0cGxVYeeXlI+Q5HeP+ryREOyJijpJT3Xjv4NAB/ueZmnNtzHnvotnWqb0JrWSdZqKXe4VgoHGpT/q2rq+ls7ntIuXgGsqVgOwKT805AkKeBitnVDybkl8jiDCfTPjK7cHAIOze7podlxybnZ0IlQoAjtH5Rl6UDsfTSbOkg5D4QCHb3/ZztCzDf7BgGHphA0BQKBQCAQ9BwxCZrnnHMOb7zxBh6PMoGZO3cu7733HsOGDWPYsGF8+OGH3HzzzXEdqCAUV1AoEIAlL11Z3hL+xMcV5NAEAsFANbEHA22tWcsVH07mzTBJ3UcDJdbtmujS2RPvjsrNVZJNaVw56leA8hoZdSZmFwf6gQVKznvHoemVvTy2+reUtxwgN7E/d039uya2dsSk/NMwSEYONu3hUNO+DrdXXWZJxlQSjcnk+gXNmi4GnsQDLRAoLXz/TJWzB1+BQWdkZ91GdtRuaLPe5m5m5eEvACUcCqAyTNm56tBsLWgmGpM1F113iEHR8I+187hl8Wz21nfOtdyaQChQtIKm36HZoaCpCFpFacMBqLYfDhuwUxXUPzOYSfmnMbXfGXh8bj7b9wZzv7yIXy09n4/2vEJjDJ/D1i48TSgMI2SXNirCzfSBPyLbkk+NvYLPS94GlH6+Wv/MfjMAJSwJuu7QbG4VCgSQoQqvUZacR5twDoEemg3OWtxeVwdbh+KTfSwueYuKMK0HnB671sogmpLz6EKBrEDbHprKMjXpPDZBUys5DyOWQpBD8xhOORfzzb6B7PI7NE2i5FwgEAgEAkHPEZOgec8997Bp0yatn9G1117LK6+8wujRoxk3bhwvvPACd999d1wHKgggy7LWW82oOjT7+QMKXA58jrYlrIGSc+VETg0G2le/TUu47Sw7ajfglT2aa+hoY2vNWu33Bmdtm3527aEGAg1IGdzhtrOLL2VQqpJWPL3wR5qICQRCgXqp5Py1rf9gXeXXmPQJzJv2rw5LzYNJNqUyNvdEAE3Eaw+1NFgtow24z44ch2ak/pkqGQnZzCj8MQAf7H6pzfrvyxbj8jron1yspaKHK6EO9NBsK3QEnpfeEXp31/+AV/bwtb8PaKyoDtOoBc3E6HpoqoLmkPTj0aHD43NjDdPTtDqof2YwkiQxb9q/+MupLzG98EcYdSb2N+zk2U0P8LPPTmN3/Q9RjVeldZK1KrSFFzT3aGO/ZMQtALyz82mcXgcHm/ZSaTuEQWdkXO40IPBeaHJZo+oHGQ5ZlgPBRUFl1ZqTtINQoM4mnIMi1KuCfq2jc2XnKw8v4cn19/DkurZ9E+v9rRxM+oR2WxmYOxUK1I5D05ju38ba4X7C0RzGGRuMRXNoHruCpphv9g18ooemQCAQCASCXiAmQdNoNJKVlRWScnzVVVfx/vvv884773DdddfFa3yCMAQLa6pDM8EfCuTVe3HsaRs24Wzl0Myy5FGQXNSlPpqqyFbasDusA6qvs7VmTcjf9fb2nUrBtJdw3hq9zsDdUxfwoyFXc83o0HJurYdmHErO6+xVPL/pQf709bVR9a777tAi3tn5HwBuP+GvDOlAzAvHiWraednnHW6rCpeqM1MrOe/lUCBZljWH5pAICefBnD/sOkB5zK3dhMtKPwBg5qALNAEoXDBQsyt8KBD0bh9NWZY1cXC1v79srKgiTXstGYJRe2jW2ivbdfWp48tOzNfckOHeQ1WagF7QZp1O0jEudxq/nfIYL537DTeNu4f8pEJcXkfULRRUWlo7NNsRClWH5sDUYcwquoicxALqHNUs2rdQc2eOyZmqicBJxhRN9Ir1c+LyOrTE9Vh6aMYiaEqSpPXRrOtkH021nce22nVt3gdaubk5O2R+0hotFCiKsB21DUY4F2WKOV3ZJlZB0xXorxoO9XU+mlu7dISYb/YNZNFDUyAQCAQCQS8Qk6Cp4nQ6WbFiBR988AE1NZFTfQXxJbinmhoKZPQLm169F8fOtq+F6t4xBaXQqmnnP1SvimkcqivF4bUdEcEt8cQn+zSHpoRyItUZJ1G0JecqA1KHcNP4P7Upk4xHynmdvYpnNz3ATYtm8cGel9hcvZL1ld+0e5+KloP8Y+08AC4Y9jNmDPxxTMeeWnAGEhK763/o0FGoCkzZFlXQVJ2IZV3qYdhVGpx1NLrqkZAYmHZch9sXpQ1nXO5J+PDx8d7/asurbYfZUq20eDit8Mfk+nsOhvvsNGplrm2FjkAQTM87NG2eZu2CSllzCYeb9se8r86WnKeZMzHrLcjI7X7fBKdcZwe9h1qjLssNI2gGk2JK50dDr+Li4UpZ6866tq0EIuGVvZoDXnXhRepN6fI6KW8pBWBg6lCMOhOXjbgVgHd2PsP3ZYsBmJx/mnaf4D6aNTH20VRdgjp0Ia+F+l3U4KgNG1wFyvek+lp0JhQIgvtodk7QVBPd3T4Xu+s3h6yLJuEcgkKBPO07NL0+j/b6hbu4kGKMveTcJ/uCnLGRemiKknMVMd88svGJHpoCgUAgEAh6gZgFzX/+85/069ePU045hQsvvJDNm5UTi5qaGrKzs3nhhRfiNkhBKGogkA4dBskIgFGn/PQYfOEFTdWhaQgImmP8fTS3xCpo+vv8geLSPJo41LSXJpcVs97CsIwxANRG2bPQK3spbz4ARC9oRiJNCwXqfMl5vaOaZzf+lRsXncFHe17B5XNi8L9Pgl+7cGys/A6H18bQ9OO1kJRYyEjIZmS2UlrdUdl565LzHL9QY/fYYm6L0B4+2ceqw0s7DPRQU7NTzRmaw7kjzh96HQCfl7yllVYvL/0IGZnR2VPITeqvlTpXhSk5j86h2fM9NFuXbnel3YSa3BytoClJkubSDNc/UUV9vTLM2dpzHM7NqgrouUntC5oqI7LGA7Cr7oeIAl9r7EHBTa0dmq2dj2VNJfhkL0nGVG2b0wddQH5SIQ3OWnb5xbtJQYImoIm2sfbR1ARXU2qICy4tIQsdOnz4sDrbuv7Vx+D2udCh0/p5RovaRzPa71WVYHf51lbVBdEEAkFwKFD7Zfqq4AjhRcdAD01ru/sJh83djIwcsp/WCIemgphvHvkEHJqi5FwgEAgEAkHPEZOg+eKLL/LrX/+aOXPm8Pzzz4e4p7Kzszn99NNZuHBh3AYpCEV1aBr1Zu0E1ODvR+YxeHHsaCtoutSUc78zBQLBQPus22MSjIJP4vY37ur0/Y9ktlQr5eYjMsdrpZTRJp1XtxzG7XNh1JnClrN2Bq3k3NU5h2ajs57fLruUj/a+itvnYkTWBO4/5QXOGfxToOMTcLWdQFH6iKhDgCIxzV92vvJw+6W6asmsKkKZDRbt8Vd3gxvxg90v8cCKW3l92xPtbtfgUJ77VFP0/UNPyD+VASmDsXtaWLL/HWRZZrlWbn4+EHicYUOB/K9PWEHT0nsOzdbC1uryL2Paj9vrwiMrZc7RlpxDIEm7vWAgq+bSy9Y+f1Vh3MEBAT26dO4BKUNIMqbg9NrZ37AzqvuoJcVmvUXrGRmp5FwtNx+UOizoe93IZSN/qW1TmDKE/OTCkPsF3g+xCppqH8eUkOV6SU96QhYQuexcdWdmJ/bTLpZES6xJ58Hbt2kLogma2e3uQwsF6kDQVJ2XiYbksN+D6uczFoemWspu0pm11jGtsRiFQ7M755tPPvkkRUVFJCQkMHXqVFavjhyS+Oyzz3LqqaeSkZFBRkYGs2bNanf7W265BUmSWLBgQcjyoqIiJEkKuc2fPz9km82bN3PqqaeSkJBAYWEhDz/8cEyPrydRU85FyblAIBAIBIKeJCZB87HHHuP888/n9ddf58c/bluKOnHiRLZu7VoCriAy7lYJ5xBITPZ25NAMcphlWfLItvTDh087me4MwaJYLPc/klHLzY/PmUSm5iSKrjSyrFlJ9C5ILkIvdc2toIbw2D22kFYD7eGTfSxY+3uqbYfJSxzA/53yIg/NeIMJeScHnYBb291HpJTtWDixYBYAW6vXtls6r5aSqg5N5feCkHXxQpZlFpe8CdBhAnuDX0xOT4he0NRJOs4bei0AH+15hd31P3CwaS8mnZmT+88BAj0Ha+wVIY4/n+yjxS+EhQsiCZTi93wPTVUsVN1122rWaaJdZ7B7As7FBH8PyGjQks7DuFpBee6sDkV0TU/I0cS+1u0OPD63VvLdUcm5ik7ScVzmOAB21G2M6j6B/osBd59act7grAvph6wGAg1MHRqyj9MKf0xBchEAk/JntDmGlnQeo2O3xRVaEh+MJr5GuJgT6J8ZnSgcTFYnv1dVgoXb7bXr8cpe7W/1/ZkepaDZUcl5Uwcp5Or3YziX999W3MbcpRdG7Pfa4grtrRoOEQrUffPNN998k7lz5/LnP/+Z9evXM27cOGbPnk1VVfj3+vLly7niiitYtmwZK1asoLCwkLPOOouysrYXpN5//31WrlxJQUH475b/+7//o7y8XLvdfvvt2rrGxkbOOussBg0axLp163jkkUe47777eOaZZzr9GHsSn9+hKUKBBAKBQCAQ9CQxCZp79uzh7LPPjrg+MzOT2trwJWqCruP0l5yrIiaguWO8ei/uw014G0JP1NQTt2CHJgRKotUQm87QGCSKHWg4ehyasixrzp/jsyeTldC50kg1tKKr5eaguKb0kuJ4iLbs/IPdL7K2YjlGnYl50/7F+LyTNMeXegLeGKVDU3VIdoW8pAEMST8eH0qJdzi8Po/mvAp2tQbEu/i6EbfXruNw835ASbBvjwa/QJZmzurUMWYOPJ8UUzpVtjL+sfb3gNJTVHUkZiTkYNAZ8cneENeZzd2EDyVkK3wPTVXkrcAn+zjYuJd3dj7DXcsu5+qPp7GyrONE+VhRHZrDMsZSmDIUr+zpsB9rOGx+gcast3RK9A8knYcXNJtdDZrzM92cFVRyHvr+qbFX4MOHUWfq1Os6MmsCADtq10e1vRb6EpQenmJK11qFBJfwBwcCBaPXGfjN5EeYOfACLjjuhjbH0FLv4+zQBMjQ3KThHZrq65Dbyf6ZgBYK1BlB0+l1aBdjjDoTdk8L+607tPXqOFUhNhJqyrmjg1Cg9lo/QOSS84ONe1l5eAl7rFs52LQ3/L615z28WAqBkvNjWdDsrvnm448/zo033sj111/PqFGjePrpp0lMTIxYvv7aa69x6623Mn78eEaMGMFzzz2Hz+dj6dLQ/2llZWXcfvvtvPbaaxiN4V3LKSkp5Ofna7ekpEDbjddeew2Xy8ULL7zA8ccfz+WXX84dd9zB448/3unH2JMIh6ZAIBAIBILeICZBMz09vd2m7Nu2bSM/v3P9tATRE9ah6Q8H8poVIcSxKzDB9/jc2kl+cA9NCKRwl/nFnWiRZTnElVLWVKIl5fZ1yltKqXNUYZCMHJc5juzETjo0O5Fw3hGSJJFqVkTFhijKzrfXruflLY8BcOO4PzK4VSp3qj+Vt0OHZjslz7GgujQj9dGsc1Tjk70YJGNI/zvVYReuZLgrfHHgPe33jgRNtdy/MyXnoIgmZw++HEATNU4beL62XifpgvpoBlw+qthsMSSGXLRQyUzIRYcOj+zm5kVn8ssl5/DKlsfYUbeBBmcd81fdwVelH3VqrNGiOeDMWUzudxoAa2IoO1f7Z3am3BwCrtZIgqbVqYwvxZSOUW/SBPHW75/qoIRznRT9v8HhmaqguTGq7VWHZlKQC08n6ciwKO/x4DYWB/yCZmErhybAsMyx3Dn5obCl1IFQoK4Kmm2dghkdJJ2rTtlYHJrZMfTQrPW7UM16C2NypgIBNz2ANcqScy0UyNuBQ9NtBSI7NFMjON5XBLXXiPTcNbvaunfbjNPv0PT43CFu3mOJ7phvulwu1q1bx6xZs7RlOp2OWbNmsWLFiqj2YbPZcLvdZGYG/i/4fD6uvvpqfve733H88cdHvO/8+fPJyspiwoQJPPLII3g8AYf+ihUrmD59OiZT4Lt/9uzZ7Ny5k/r68Bc1nU4njY2NIbeeRjg0BQKBQCAQ9AYxCZrnnHMOzzzzDFartc26rVu38uyzz3Leeed1dWyCCGg9NHVhSs5NSn8p+47qNttDOIdmEdB5h6bN04xX9mj79MhuzfHW11HdmcMyx2DWJ5CZ0NmS8/g5NCGoj2YHSeeNznoeWTUXn+zl1AHnMrv4sjbbpPj31XEPTWvIsbvKtP5nAbCx6jstJCcYVWDKsuSFCEzZWgBO/Mqr7Z4Wvj34mfa31VmHT/ZF3N7q6HzJuco5g6/U3Hhp5iwm5J0csl4TNIP6aAaEjvSw+9TrDFoQTKXtEAbJyIS8U7hl/J+ZOfACfLKXx9f8jsUlb3V6vB2hOjTTE7KZ0m8mAOsqvok6JEdFLTmPNhBIpSNBs14TXBVBS3WztrgbQ953VUGCZmcYnjkOCYlK26GIQlUwAYdmqGjVOhjI6bFT6Q86au3Q7Ait5NxeHtJfMFqa/f2TwwlrWiK7o4OS8y44NOsc1VG/f1Qnc7Yln+OzJwOwrTYQDFTvjC7lXP0/6PK030Mz4NAML2iqQmfrkvOV0Qia7rbu3dYEt2M4VoOBumO+WVNTg9frJS8vL2R5Xl4eFRXRCex33303BQUFIaLoQw89hMFg4I477oh4vzvuuIOFCxeybNkybr75Zv72t79x1113aesrKirCjktdF44HH3yQtLQ07VZYWBh2u+5Edvkdmibh0BQIBAKBQNBzxCRo/vWvf8Xr9TJ69Gj+9Kc/IUkSL7/8MldddRWTJk0iNzeXe++9N95jFfhRU86DHZpaybnB79AM6qOpJrlKSG0cX6qLsLNipFr+bNZbKE4bAcCBoyTpXHX8jM5WQpO0Xm+OyqgEg7I4lpxDdEnnat/MGns5BclF/PKE/wtJLFbpbA9N1dHZVQpThtA/uRiPz83aiq/arK+OIDBpJedxDMD5/tBiHF6bJsL4ZG+7Seeqg7OzJeegCEIzBip9304beF6b4BQtGCjIoRmNO/amcX9iTvHl3DV1Af/98UruP+V5zhnyU3416UHOHnwFMjJPrr+HD3a/1Okxt0ewQ3N41gRSTOk0uxvYXruhU/tRS2hjFTSb3Q1he3fWayXHiqCVaEzWnIfBZefV/uc72v6ZKonGZE1wjMal2eIO34NRFdxUofBQ0z5kZFJNGaR38n2mupjtHltM4W4t/vd++B6aqkOzox6anRc00xKy0El6fLI3Yop6a2pDBM2JgHIBSpblkP6p0aacRxsKFEl0VD+jLp8Tp18crbaVs6d+i7ZNpHL9aByaRp1J+85QXc3HGkfifHP+/PksXLiQ999/n4QE5b20bt06/vGPf/DSSy+F/d+rMnfuXE477TTGjh3LLbfcwmOPPcYTTzyB0xldj+xwzJs3j4aGBu128ODBmPcVK8KhKRAIBAKBoDeISdAsKChg3bp1zJkzhzfffBNZlnn11Vf56KOPuOKKK1i5ciXZ2e2XfAlix+UPGVDLzCHg0PTolavkoYJmoH9m64l2gd+hWd58ICRcoSOCRRf1BP9AJ5POX9nyOH/8+pqYQkW6k63Vav/MSUDApeTxubXekpGwuZs1kSIeJedAoOS8HYdmcN/Mu6f+I2IpryZoOq3tirPq40yJk0NTkiSmFpwBwNqK5W3Wq/3/2gqa8Q8FWnLgXQDOKr5UEyraE1TUUn9VWO4sN43/E3dM/BtXjfpVm3W5SW1LzrXPVjvOrSkFp3PrCfdzyoCzQ15rnaTjlvF/5if+XovPb36Qt3Y8FdO4wxHs0NRLeibmTwc6X3Zuc8cmaFoMSdrrEC4YqD5MKExuGJdvrA5NgBFqH826jkVc9butdX/K1mE7wf0z2xNDwmE2WLTPdSxJ56oIGq6HZnuhQMF9b9Wwps6gl/Ta/qNNOq/2b5eVmM+wjLEYdSYanHWUNZfQ5LLilT1ISB1+VqMtOVdFx0gXdiyGJK3HsSp+rmrVVqNjh2ZkQVM9BnTc7/NopTvmm9nZ2ej1eiorQ6suKisrOyxff/TRR5k/fz6ff/45Y8eO1ZZ/8803VFVVMXDgQAwGAwaDgQMHDvCb3/yGoqKiiPubOnUqHo+H/fv3A5Cfnx92XOq6cJjNZlJTU0NuPYns8YFHuZguJQiHpkAgEAgEgp4jJkETIDc3l+eee466ujoqKyspLy+nvr6eF154gaSkJA4fjm/PO0EAl+rQDFNy7sGDjIxjR40mWLlUQbNV/0xQTuiNOhNun6tTwStNmuCVTlHacUDngoEanXW8t+s5fqhexf92h2/C3xtU28qptB1Ch04TLoKDQzoqO1edrmnmrHadN51B7d3YGKGHps3dzKtb/w7AjeP+RHH6iHb2lQ6AR3ZHDJrw+jxaX7149dAEmJR/GgDrK75pI54HHJr9Qpar7rM6e1VcerQebtrPtpq16NBx+qALNOGjvT6aDQ5V0Oy8QxMUQWJW0UVaEEkw4UqomztIVu4ISZK4bvTv+OkoJTn3v1sX8O/193Yo3kRDsEMT0MrOV5cv69R+1PdeorFzgia0HwwU6KEY1IfVL1oGi8bVLcr7TXXIdgZN0IzClRqprLh1KXekhPNoCfTR7HzSeXN7PTQtqpO0rShXY6/AJ3sx6kwdOiIjkd3JYKBgh6ZRb9JS57fVrNOey1RzRhsndGu0UKAOyrjViwvJxvSw6yVJCgQD+fttqv0z8/0ibyRBs6WD1hIqqqBpO0YdmhD/+abJZGLixIkhgT5qwM+0adMi3u/hhx/mL3/5C4sWLWLSpEkh666++mo2b97Mxo0btVtBQQG/+93vWLx4ccR9bty4EZ1OR26u8p0wbdo0vv76a9zuwP+7JUuWMHz4cDIy4nOBMd74XIGWEZJwaAoEAoFAIOhBYhY0g8nJySEvLw+dTtndggULeqWHz7FCuFCg4BM4r1HGa3XgqVJOgNRSOJO+raCpl/T0Sx4EdK6PZqDHYrBDM/qS89Xly/D5Ra2P9rwcdYJ3d7PNX24+OGNUiPNNKzvv4MQ73uXm0HHJeXlLKR6fmzRzJrOLL213X2aDRRPCI7lNVYEDIveOi4URWeNJMqbQ6Kpnd90PIesilZynJ2QrSeD4wrrEOsvSA+8DMCHvFLIseZqTr11BUy05j5NbNZi8MKFATR0kK0eDJElcPvI2fjZGSVdfVPImv1t2KYcawycuR4Msy1qPQvV5m5B3KnrJQFlzCYeb9ke9r1hLzqH9PpqBHooBx5b6ngpOAVcdmp0tOQcYkTkegD31WzoMalEvDLS+uJHVyvl4QHNoxiZo5gT10ewsmkPTFNmhaXXWtrkIEUg471ywUjBZnQwGChY0gZCyc2ur/qntYe6kQ7O970F1XZPTSqOzXmtZMqdYCQSL1H+0OcJ7ozWaQ/MY7aHZmnjNN+fOncuzzz7Lyy+/zPbt2/nFL35BS0sL119/PQDXXHMN8+bN07Z/6KGHuOeee3jhhRcoKiqioqKCiooKmpuV3rxZWVmMHj065GY0GsnPz2f48OGAEvizYMECNm3axL59+3jttde48847ueqqqzSx8qc//Skmk4kbbriBrVu38uabb/KPf/yDuXPndul5605kZ+C7QSd6aAoEAoFAIOhB4iJoCnqWsKFAQeXn+iHKialjh3KCFyg5bytoQmx9NAM9FjMY5HdoVrYcjPqkSw1NkJCwe2y8v+v5qI/dnaiBQGrghEpWQnQn3mog0IA4lZtDcMl5eAFSDZTJSxwQVblqSgdJ56pwmmRMRa+L38mJQaeE1wCsa9VHUy0Hbi0w6SSd5j6rjkGsCcYre/myVBE0zyi6EAiIxWrvvda4vS5s/gCbtITYHJrtoToEa20VmgM1ngnzFxx3Pfef8jxp5iz2N+zkzi8v0kTdzmL3tGhub9WhmWRMYbT/s9IZl6Ya0BOboKmIF2EFzXZLzhUR0yf7tJ6ssZScFyQXkWJKx+1zsc+6vd1tI4UCte6hGSg5P67T44GAQzOW8KyWdkqf081ZSEj4ZG+bUDK15D83hkAgFVWYjLrk3KYKmsrjVb+nt9as1V77aNyiWiiQ19FuIFhThB6owWjBQO4G1vgv1BWlDdecvKrQ2ppI743WqJ+RSI56QWxcdtllPProo9x7772MHz+ejRs3smjRIi2Ap7S0lPLywOfpqaeewuVycfHFF9OvXz/t9uijj0Z9TLPZzMKFC5kxYwbHH388DzzwAHfeeSfPPPOMtk1aWhqff/45JSUlTJw4kd/85jfce++93HTTTfF78HFGEzT1EpJBnFYIBAKBQCDoOcTMow+iuoJMYXpoAhiGKydYah9NNRSodcK5SkEMSefBokuaOZM0cxYyMgf9pZPtYfe0sKHyOwCuOv5OAD7e+9+IolJPorpr1P6ZKr3r0PQLmhFKzlV3X7TBHMF9NMMR3E4g3kzMnwHQJhhIc2ha+rW5jxYM1ImWCOHYWPkdtfZKUkzpTO2n9PNU3VyRemiqfUv1kiFsSW5XSU/Ixqgz4cOniTrxFDRBcaP+44z/MTbnRJxeO/9Y+3v+vuauTgsk6nNk0ieECJGT+p0GwJqK6AVNzaEZoddrewQcmm2DL9Ty3gxzcMl56PvH6qjB43OjQ6d9rjuDJEmMyIyu7Lw5giAWXHJu97Ron+GYS879jzEmh6Yrcg9Nvc6gtVpoXXYeCATqfNm+SmaU36sqtVoPTeV+wzPHo0NHla2MXXWbgegEzYSg9g+udlya0Tg0U7WgtQat3PzEgjODROvqsP2KI7032o5VSTp3CEEz7tx2220cOHAAp9PJqlWrmDp1qrZu+fLlvPTSS9rf+/fvR5blNrf77rsv4v7379/Pr3/9a+3vE044gZUrV2K1WrHb7Wzbto158+ZhNptD7jd27Fi++eYbHA4Hhw4d4u67747XQ+4WfE6l5FxnFu5MgUAgEAgEPYsQNPsgmkMzqORcJ+m0cALDccoJkn2HcgLakUNTdROWdcah2Up0KfI7i6IpO19X8TVun4t+SYO4ePhNDMsYg9Nr571dz0V9/O7A6qjlYJNSkjsqa2LIuuCk8/Y4pAqa8XRo+ntoNkVwaAZKP6MTFjpKOg9uJxBvTsg7FYC91q1auW2zq1FzQWYnhhM024a6xMIX+5UwoBmFP9YczekJ7Ts0VRE51ZwRc1lte+gkneYgVJ22gc9W/Mr9My253H/qC1w56lfo0LGs9AOe3fRAp/ahOs0yzNkhTuAp/U4HlD6G0QZ8dankXO2hGTYUSO2hGVxyrnwuVEFTFQ8zLXkd9lqMRLR9NAOhQKFiuFrK3eSyUuJ3eaabszU3dmdRLwTEVnIeuYcmBImvrVo+dCXhXKUzDk2n16G1yVDvl2hMZnDGKABWHP4cCH3tIxHcfqW9snOth2Y734XqumrbYTb6L9RNKzhTS4h3eR3a91swkd4brREOTcGRjuwXNCUhaAoEAoFAIOhhhKDZB3GHCQWCQB9N/RDlBKnpq/14GxxaD83ucmgCDEyLvo/mSn8K7In9ZyFJEj8ddQcAn+59LS59EmNlW63izhyUelwbYUETNG2RBU2f7NPK9uPp0Owo5bxKLf2M0inVoaDpF05jFVfaIyMhm2EZYwBYV/k1gFb+m2rK0NxIweRYuu7QbHTWs6pcCYCYVXSRtlx1n0XqodngFzpjTTiPhtykUIGuq6FAkdBLei4beSt3TnkEgK3Vazt1/+CE82D6JQ+kMGUIXtnD+spvotpXPHpoVrWUhTjf3D6X9p7OCFNyrgZLdaV/psqIrPEA7Kzb2O52gR6aoa9liild+77eWLUCiN2dCYELAdWdDAWSZVnroRnp/aYKc63DbdT3a1cETfV7tS4Kh6bq4jTrLSEhS8dnTQoZXzQOTZ2k0/5/RmqT4pN9tLjUcLR2emj6x/L1wU9w+ZzkJQ6gKG04ZoOFRIPiQK4P838t0nujNep3ol300BQcofj8Jec6EQgkEAgEAoGgh4n6cur69euj3qlIOO9eXFooUKjj0qgz4fTaMU/JxVOcgauknsN/WY7z1sgp5wD9k4sAxd3j9NjDpjG3RhW91H6Mg1SHZgdJ526vizX+XnvTCs4CFNfeiKwJ7KjdwLs7n+HG8X/q8PjdwZbq8P0zAbLUNN52HJq19kqcXjt6ydClk/zWqGJak8uKV/ail0JPGiqDemhGg+q8bIwgaMa75Lk1E/NnsLv+B9ZVfM2ZRRdT43deRupnqC6PxX2m8tXBj/D43BSnjWRw+khteccl56qgGf/+mSpaMJDm0Ox6KFB7qO7jKlsZXp8n6j6prRPOgzkhfzoHm/byQ/Uqphee2+G+utJDMyexHzp0uHxO6h3VmoNQTaPXS4YQR12aOQuDzojH56bWXkm136EZS/9MlWEZY9BJemrsFVTbyrWy9mCCBbHW5dySJJGZkEuVrYyNVYqrTw1Xi4VAWngFsiy36aXb4KzDoDO2GYfDa8Mre8KOMbBv5bG9v+t5RmRN0ITXqk5+77Q37poI4w4mEAiUF7LdqOxJfLDnJe3vaBPXzQYLLpczokPT5m7Ch9Jfs3VKfTDq/8CKllIATux/pja+jIQcbM3N1DmqGZA6RLtP8HtD9NBsi5hv9i1klyJoSiYhaAoEAoFAIOhZohY0J02aFFXgCNDhiYmgawRSzk0hy416E7iVlPPCR2ez9+KF1L25hYYzlNciUsl5qjmTFFM6TS4rh5sPUJw+osMxtC5LVoOBShvbFzQ3V6/A7mkhMyGH4zLHAsrJ/ZWjfsU931zHopI3+clxPyfbn9rbU3hlr1ayOD73pDbro+mhqTpc85MKYy5lDYcqbMnItLgaSA1yC8qyHINDU3FeRnJodregOTl/Bgu3/4sNld/i9rk0x1w4USh4eVWMDk2Pz80Hu18C4Myii0PWqWJxRIemX7jvCYemWgrd3c9/liUPo86E2+ei2l5OflJ0CcGRHJqg9Jz9YPeLWqhWR6jiTGIMPTQNOiPZif2ospVR0XJQEzTrndX+8WWFtAfQSTpyLAWUtxyg2nY4yKEZe+/HBEMixWkj2Gvdys66DWHfu3ZPS0AQC+PCUwVNtfdjVwTNLEseEhJun4sGZx3pQQFWVkctv1xyDhkJOTwx66OQ/81q/0y9ZIjo4D9v2LWsLv+SsuYSfvvlJfxy4l+YVnCmFmjUlYs3GZYcJCQ8PjeNrvp2P2fqhQ/14pLKqOzQ9iDRlJyD4vRswqr1mG6NemEhQZ8YErrXmtZi57SCWdrvmZYcyppLqHeGBgN19N4IxmI89gRNMd/sW2gl5wmi5FwgEAgEAkHPEvXs48UXX+zOcQg6gcsfCmSMUHLu8blInjKA7J+dQM3z66letBWmRy45ByW5d2fdRsqaS6ISNAOiiyKOFaYo7pM6RzWNzvqI5corypRy86kFs0JEh7E5J3J89mS21qzh7Z1P84sJ93U4hniypXo1tfZKkoypTMyf3ma9Kmi2uBtxeGxhS6PVhPN4lpsDfmdVKi3uRhqc9SGCZpPLqpUixq+HZveVnAMMyRhNmjmLBmct22vWBwKBIjk0tZLzsphOXpeXfkiVrYw0cxZnBpWbQ0Cciyxo9kDJuf91q7SV4ZW9UZeixopO0pGfVMjBpr2UN5dGL2i249BUXZ+HmvZhddSGCGrh6ErJOShBNFW2MipthxjFxJDxZZjbClo5if00QbOj91u0DM8cz17rVnbUbuSUAee0Wa+2DjDqTGEvJqlCrE9W3E1dKTk36IxkJGRT56imxl4e8vx/e+gzmlxWmlxWau2VIReLAv0zUyJ+rgakDGbBGf/j0dVz2Vy9ksdW/0Zz1yfoE7X/AbFg1JlIM2dhddZQY69oX9D0X0xq3Wc3zZxJYcoQrf9xtA5NNRhIbcnSmmatf2b7n8OUVm7g4f7+qspY/GK7PbRcX31vmHRmTPrQ/+OtsWihQMdOybmYb/YtRCiQQCAQCASC3iLq2ce1117bneMQTqdlpQAAgDJJREFUdAI1lbX1iZCadK6moOffdQqNS/bidCjiQSSHJigi3M66jVH30WztIks0JpObqIgMpY27GZ0zpc19vLKXVeWKoKmeEKsoLs07+MPXV7Ok5B0uHn5TlwWHzrC89EMAThlwdlg3TqIhmQR9Ig6vjVp7ZVjRsjsSzlXSzJl+QbOOQgKli1rASUJOhyfGKmqJZMSUc//y7nII6iQdE/On8+WB91lb8ZXmes2xhH+9VQHD7rHR4m4i2RR92rjX5+GtHU8DcOFxN7Rpp6CKc3aPLWy7BWsPlJznBpWc29xNyCh9IeMZCtSa/KSBHGza6y+TPTmq+7Tn0Ew1ZzAwdRiljbvZVruOk/qf1WabYLpScg6QmzgAWK0F0wDUq4JrGEFL/S6pCnJodiWdG2Bk1gQ+3fdaxGCg5g6EaTUYSKUrDk1QSsPrHNVU28oZmjFaW/71wY+13/c37IwgaLb/mUpPyOL+U1/gjW3/5K0dT2tu9rykAV12x2Vb8rA6a6i1VTAkfVTE7dSWE9lhkulHZU/qtKCpXuDryKHZ0ecw+Htyar8zQlqCqOJ6vSO0h2bgvdHxd9mxWHIu5pt9C9nhLzkXPTQFAoFAIBD0MCIUqA+ipZzrIgmabgD0iSYKH52D26RcPeewK+I+C/x9NA+HSTqXfTKOnTU0ryjF5/Li8jq1k8Dgk7mitPaTznfUbqDBWUeyMY3ROW37VI7OmcKIrAl4ZDcb/GmxPYHT6+D7ssUAnDbwvLDbSJLUYdl5WTcknKuobknVPakSSDiPvuwzVXNohk9N7+6SZ4BJfhfsuoqvghxz4UvOEwyJ2ljUAKFo+frgx1S0lJJqymDO4MvbrLcYkrTPTbg+mo09UHKuluzW2Ss1Uc5iSIpr24LW5CcrrsyK5tKo72N1RnZoglJ2DkRVdt6VknNAc5WGCpptE85VcoOSzuPRQxNguD8YaJ91e9g+jB31SFQdmqCIm50R6sOhCv/BvWYrmg+yoy4guO5v3Bk6Rn8gUJIpfP/MYPSSnquOv5N7TvqPVmbdL3lgl8YM0fUnhuAemm2/J9T3nkln1oJ4OkK9wOeI4NBsckcXzhUseE7rPytknSqu1jnCOzST2unNqZJwDDo0BX0L4dAUCAQCgUDQWxyRguaTTz5JUVERCQkJTJ06ldWrV0fc9tlnn+XUU08lIyODjIwMZs2a1Wb76667DkmSQm5z5swJ2aauro4rr7yS1NRU0tPTueGGG2hubu6Wx9dVtJRzfeSSc5XkaYXoRqcDYF9SirdZWedtdmHbUon1453Uvr6Z9G3KfUsP76B59SEal+2j4rHv2HvlO2wZ8y92znqJvZe+xd7L3qSuXBGVdJI+JEhioD8YaMc331D/wY6QBGKAFWWKq2dyv9MiijVq6bq1Vc+x7mRN+ZfYPS3kJvZnZNYJEbfTBM0IJ95qyfmAbnBopvrLOhtbJZ2rDs1o+2dCQKjsKBQooVKH1xZZBO8K43NPQSfpOdi0l30N24H2U6e1YCBb9MFAXtnLWzueAuCC434W1g0oSZLmOAwnaPZEyXm6OQuTzowPHyVW5bnoTjEZoF/SIADKWzohaDoiOzQhEKa1rWZdh/uKpuRc9sm4y5vw1NvxOT0h3yeqCBzOoRlO0FTF8pKGHVqLhq4KmnmJA0g3Z+OR3eyt39JmverCS4ogVAY7CYPdmZ56O43L9uHYVYPPH7YRDWprhuDPyNeHPgnZZn9DqKDZHKVDM5jJ/U7j72e8x/lDr+PykbdFfb9IZAUFGrWHWnLeuocmwPjck0kypnJ89uSoHaOqGztSKFBzlOFcOYn9/KXzmYzNmRayLsMSPiG+JQaHpu0YcmgK+hYiFEggEAgEAkFvccRdTn3zzTeZO3cuTz/9NFOnTmXBggXMnj2bnTt3kpub22b75cuXc8UVV3DSSSeRkJDAQw89xFlnncXWrVvp3z8g8syZMyekL5PZHCoGXnnllZSXl7NkyRLcbjfXX389N910E6+//nr3PdgYcXn9PTRbhwKpDk2vO2S5YWwGHAZ9rY9dc17BZ3fjqQo9OZL71cHdUNZYwp7b3kAi9KRQZzGAJGFbe5jSW16D65UTveCTx9xyRXQrqd5G6T0fU/vqRvr/9QwsI3KQZZmVhwPl5u6aFurf2oK32U32NeMx5iuuGrW0VxVPAJz76mj4fC8JwzJJPmkgOkv0zjXnfivWT3aiSzSSdcVYdGGa1i8v/QiA6YU/CunrKcsyzj11OHbWkDytsF2HptPr0JyG/VMGB5aX1GPfUY0xOwlDThKG3ET0iZEDJlrjqbXhc3lJQXHyNDhCRbfOJpxD9D00a3+xnJ1yCQV/nknaucfFNXgh2ZTKyKwT2FqzRmuhkN2eoGnpxz7rNu05joZvD31GWfN+UkzpnDP4pxG3SzNnUm073Oa5BSUdGiK7EuOBJEnkJvXnUNM+9li3Aj0gaPodmuWdcGg2ROnQLLFux+Zujui+9Po82mseSdB0lTdRcvW7OHYGXdjQSegSjZj6p5L2f8p7pdIWEDQDDtLIJed7rdsA5XumvRYc0SBJEiOyJrDy8BJ21G5klP/xq6iCWKSE7OCS80F+QdO+rYp9V76Dp8bvxjPoMA9Kxzw0E8vIHDIvH4Opf3gRTHVoVtaU4rE6MKQn8M1BRdA8qf9svi9b3EbQVEOBkowpNK8+RP1727BvrkQy6dFZDOgSDEgJRvRJRowFKZgK0zAVppExMI2fjb4bSd/5a6Kuw40c/N3n2LdUkjSpP5YzvGCGGlsHgqZfqDWtsrPzqZdJmtSf/LknYchKJD0hi5fO+RpDO+E97vImWtYdpmXdYWS3F9NsZQ4QueTcCkCCzYR9ezWy24vs8SF7fOhTzBiyEjFkWkg1ZzJ/xmtYjMnonGDfX4WzpB5niRXJVAtZUGdvVXKuuXfbvjdsmyuoeXkjjZ/vwdgvhZZZDTAE7Pamdp8fgaC3EA5NgUAgEAgEvcURN/t4/PHHufHGG7n++usBePrpp/nkk0944YUX+P3vf99m+9deey3k7+eee453332XpUuXcs0112jLzWYz+fnhk7O3b9/OokWLWLNmDZMmKSelTzzxBOeccw6PPvooBQU918sxGlyqQ7N1ybleEfrcvlBXnUvyC6AuA64DVm25PtOCuTgDQ4YFk7sASX4XR6ILz/EJJLuSSBybT+KEfiROLMAyIgfnASv7f/Y+jS2K6JLkUkrhvDYX5Q98jbRkF9wNVQVWJIuellWH2HX2q+TccALNP0unylaGSTKT/Vgz2z94RruqX/P8OnJvm0rOjZM0saTBWYun1kbF37+n9rXN4FESYSWznqSphaTOLCZlZjHmQelIhtCTak+tDetHO6l/fxu29QG3Us2LGyicfybJJwXKJBuddayr+BpQys19Dg/NKw/S9OU+Gpfuw1XaoD1Xyf+nnCyHEzRLrNuRkUk1ZZBqykD2+Kh6chUVC1ZoY1fRJRkxF2eQe/uJpJ09LKxQ6Km1cfivy6l/RxFgXD/aB7Ng39NfsukjJ5JeAiT2XP8NjAD3/B3sqn+V7OsmkH7eiHZPLFSxzO5pwe1zaUI4gM/ro8lhBQkSbQm4G5o58IuPSD51EAP+egbmwfFzKk7Kn6GVJ6sOJ9kn07K2jMYle/H53cRIYCn0QC4cWLkBp+4czMXtB5H4ZB9vbf83AOcPu67d0uZ0c3sOTUXQTO1GhyYoJdGHmvaxzy9ohitzlX0yPocb2eHB5/AgGfUYshNjEprzk5TPQGXLoaiClpweu+ZsbPjNSmzl39H/L2eQOD5Q/ptlySM/qZCKloNsr10fNlwLQnsBqgnOIcc6YGXfFW/hOtgYusIn42t24dhZg/32cvgD1Noq8PjcGHRGzQWX6XfFeawOJIMOfbJJEzTVAJ723MDRInt8DDEcx0qWsKf6Bxgeur4jF15WUMl5YepQWtYfpuSad/E2ODFkJ+Kzu/G1uHHurcO5t47GxXuo+vdqsq4cS+4vp2LMC7ynZZ9M4i7l90PrfmDbz/9N47kJHDhtFwbJyBUjb+P7ssUcatqH2+vSLoY1VinfZZ7Pytn7wsJOPX7JpCdp6gDS5gwjbfbQkPFEonHZPkp/9RneekVEbPx8D9RVwFVQ+s16Dr77ORnnjyRp6gAkXeA96fI6tQsttrvXgS0Bx7Zq6t/fRt4d08i+fgJmc2j/W1d5E41L9tKy8iAt6w7jPhwqCHpSq2BI+JJz2SdT8c0WyAL326Xs+vjlCE8C6DMsGLIsNDa5qK0Irepw59XDPKitKmPPxQtJmjKApBP6UW9SLkQlGZQKB5/TQ8Mnu6h5aQO2DYH/WV6rA6ejAn4FDfvL2X7KcySfOIABD88OeX4Egt5EdooemgKBQCAQCHqHI0rQdLlcrFu3jnnz5mnLdDods2bNYsWKFVHtw2az4Xa7ycwMFSCWL19Obm4uGRkZnH766fz1r38lK0sRz1asWEF6eromZgLMmjULnU7HqlWr+MlPftLmOE6nE6fTqf3d2NjYZpvuwu2NUHIuKSepnlaCppri2v/GaQy8/BTMxRmYi9LRp4U6lHI+e44qWxmJ/53GiFZuI4CEIZkM+/BKtj3wIADGPS4O3bOUpmUluA5Yydano5N1OBKc5Hx2Lo4Ht9G4eA/V/1nLsrrNcDIM3diPlrf3AJA4Pl9xfW4op+Lhb6l7YzOm3yviZNXefWy/7jlN1EqaMgDX4Ubchxpp/no/zV/vh/uXAYp7VJdiRp9iRmcxYN9RExARdRLJJw/EsbMGV0k9ey97i4xLR1PwpxkYMix8e2gRXtnDQLkY751b2PrtJ/hsAYerZNJjyErEXd6EvLAMLoaaxrYuwS1+YW5U9iRc+62U/vpTTUxNGJ6Nz+7GXdWiiFEtbuxbqjhw84ckTR1AwZ9nkjhGcX/KPpm6N3+g/IGv8TYoLjbJqCOpWXmtWpLs4PEh+9ui1qcp77u0cgv2XZUcnLuI8ge/Juua8WRdNQ5jdhJemwvbhgpa1hyiZXUZbmsL0s8kZEmm2dWglb7KHh+77nof38nKczf07rORqt1U/Xs1zd8cYOeZL5Nz82SyfjoGyWxAMuqRjDokox5PTQuO7dXYt9fg2FGNY3s1slcm44KRZF4xJqzYMSl/Bi9veRSALH0uFX/9GutHO3GXt3UiJZzmgAvg0NZt7Pj985iHZJJ6xmBSZw0haVIBkjH0ROr7ssUcbNpLkjGVWaYf07h0L65DjUhGxXkmJSjuM53FSKo+HWgraDo9dhxeRcTryKEpyzK+Jhfuiibc1Ta8dTY8dXa89Q6lZNruJvGEAlJnFod9LtQej3vrFQHb0mii6uk12DZVYN9cgbuiWbsAEIw+04JlRDYJI3JIGJGNZVQuljF5EcUO2SfT9NV+3F/sQJog4fDasDprIgap+OxuGr/cx94l38OpYHDrcX12CDcSey5eSP+/ziLr8jHa9qOyJ1HRcpCtNWs5IfsUWtYfBp+MLsmELtGIPtFIs04Rpow6U4iYDuDYU8u+K97GXdGMqSidIQsvxZibhM/uwWdz421xUXbPUuRv9mN0GXCbPFTbyumXPFArOU+xJVN652fUv6OIw7pEI/Qzw+2B46SWJ1D37laMuckYc5OQLAZktw/Z5dVuPpsbT50dT70db71d+b3Whru8CffhJtxVLeiGH4CbYdfGVWy/+1kso3JJOD4Hy6hcrEmKKy9SOXewQzPnQAr7bn4bn81N4qQCBr90IbpUM+6KZpy7a3HsqaNh0W5aVhyk5sUN1L7xA9nXTSDnZyfQ+NV+qp9eg9OzA+6EhoxmZLePFZ7lABz3wwB8i7aROCsRm97GijufIX9vGp4aG6WTV8BMMFUrF1rSzjmO1DOHICHhc3jwOdzKz0YnrrJGXAcbcB1sxFXWiOzy0vzNAZq/OUDZH78gcWIBaXOGkXJaEQnHZYe8B2WPj4pHv6PqyVUAWEbnkn/3qdi3VJG3Q/m+tSY0UPfaZupe24xpYBoZlxxP5kXHY8hLZudziyAPjC49id5Ecm4+gebvSrFvqaL8ga+o/e8m+v1xOqYBaTQu2UPjkr3Yt4S6ItFJWEblkDi+H9ZPdiFVuhXnY3Po943P4aH0159Sl3YQsiBJTsaQm4Sk1yEZdaDX4Wty4qmzg0/GW2fHWxcQRfXpCZgHZygXCz2NwDvYE51Y1+2nZZXiKD547ho4Exyv72XLTU+C14e3Qfm/Lhl1pJ07nMzLx+C12rFvWgF8hMvsxnXAii3RKMRMwRGF6tCUhENTIBAIBAJBD3NEzT5qamrwer3k5YWmmObl5bFjx46o9nH33XdTUFDArFmB5vxz5szhwgsvpLi4mL179/KHP/yBs88+mxUrVqDX66moqGhTzm4wGMjMzKSiInwZ3IMPPsj999/fyUcYHyKGAmkOzdCSc7VHWNrw/mQMGBlxv/2Ti6mylVHWvL9N+aSKPi2BhBuHw2aw2BKofUkJnDAWpDD40Tn0t3/NwaY9HE4qZ+JzF9C4dC875n/EpmHK6zdq22AyLhpF9rUTSJzQD1mWsf5vO4cf+BrXwUbsC8rgl1BXX4Gv2YVlTB4Ff5pB8kkDlRLw3bU0LiuhaXkJLavLFOHB7sFn94SU0VvG5JFx4SjSzxuBMTcJb6OT8oe+ofbVjdS/tYWmpXvJuPh4FmU+D3kw6oNcGpcrQqshL1kRy04fTPIpA5EMOir//j0p3xwAoGzTVpqkA6ScOkg73pZqpW9rcUkBu659GZ/dgy7VzIC/nkH6BSORJEkRvVrceKqaqX9/O1VPraFl1SF2n/sqGZeMJvPiUVQ88h0taxT3TsKoHAbMP4ukCf2oLnmPRetXIZ2Zw6g7bkb2ycg+H41rXwUfTHzieizLHNS+tAF3RTOVj31P1b9WYR6ahWNHNXhD+5km2EzYk5wceP170q44B3QSpbd/wuE1G+FkMJNAv6uVpPqMn4yi7N6lNC3fT9UTK6l6YmXE91BrKh77jop/rCBt9lCyrhpH8skD8TU6sW+rwrKlgYzEdOoTrCRu8VH9zFoAdCkm0mYPxTQwHWQZZBho0QOraB7kBoMO5946qvfWKffRSRgyLUoJaHYiuuwE/jv5cUiGqZ8dx8Fftu8885x3AE6Hig07cKU2YipQBCjVnWmUTHjX1FFfcwhvnR2PX6z01Nrx1LTgrmjGU9mMz+5p9zh1b/wAKGJOyszBpEwfhOzy4txvxdLkhHyweRSHl++LSsrf+SriviSjDtnjw1tnp/n7gzR/f1BbZ8hNIu2c40g/ZxhJUwYg6XV4rA7q39pCzasbce23ApA2KAlrVjO7l37H5HPOD3Fpug43UvPyRupe24y3wUH5oEo4FZLtSeTdMQ3Htmoav9jLod8txr6pgoL7T0dn0nN89iS+PPA+m7Z/xeRbUkMc4SqV+XXwezC7zbgONmAqVNyo9u3V7Pvp23hqbJiPy2LI65do4q/eqEefasYIFL/4E0pv/4T0umSq863s/uwb+l1yJVa/Q7P2ui/xlQbcej6bG/a6SW6w0JymCE8J3zs4+OFn7b5e0ZBfrbh7a3Kt2MrqcJU20LBICUUru3gVnALOt/dz4LmPSRiapYjOx+diHJBKkjGVguQimlrq8P7iB3Q2ieRTBlL0/AVaWwpTvxRM/VJImV5Ezs9OoOm7Uioe/gbb+nKqn15D9dOBAKaM/opruSnDzpClV7Ntw3sAjFkzmMZNe8gdksb+oTb2HdpM2mal37EjUblglD9rDMc/dmvU7Txkrw/nfiuNS/bSsGg3tnWHtVv5A1+hSzWTdEI/paR8bB5VT67WxLysa8ZTcM9p6BIMpJ5WzNimPPj8JZrynGRcPpqGj3fhKm2g8rHvqXzseww5iexN2wO3QboznRFLrsdcnIHsk6l/ZyvlD32D64CVAzd9GDpInUTixAJSZxSRNLk/lnH56JNM2hjMLyjO/Mr3N+AptmPIsOCpt1Nyw/+wrSnDcZPy3Ay9+2yOL74k7HPgqbfjqbHhqbWjsxi0igdtG1nG+L8HcPtcpPxtEpbVLuxbq3FkKd8VFptZE0ON/VLIumqccgEoJ+BcHjTDAosfxJMBxS9fiNzK8S8Q9DbqxTad6KEpEAgEAoGghzmiBM2uMn/+fBYuXMjy5ctJSAi4Dy+/PJBuPGbMGMaOHcuQIUNYvnw5Z5xxRkzHmjdvHnPnztX+bmxspLCwMPbBdwK1pNzUql+YQUs5b+XQ9PcIM+tDS/JaU5BSxIaqb7W07kg0e5Uy7Jz/b+++45uq9/+Bv0520zRtugctZU9ZLSDipjL0oihcUBCBqziwoIKK3KsM+SlDRBQHV/m6rnDxDnHLBge3DFnKHkKB0lJK6R5pk/P7Iz2nTZu2aZtmtK/n49EH5OTk5KSn49N33qNHB6jC9DDe3h7Rc2+D0qhF292dcCH/NFJzTyHG0B7rg/+BLdP+izKrGRpRg5Er58IUVVmmKggCTPd2h3FoR2S+uweZX9oGBxUaSxD31l0IuqernI0iCAJ0nUOh6xyK8Mf6QyyzwFJghiWvFNaKfy0FZmjbm6DrYJ+hqzRq0eaVJJju7YaLszeh5ORVnPjPNpx76SIEq4D+xdcjYlYfGJM6wK9HeI0S3KgXbkaPm8qAjE3I1efjj/H/hiYuEIJKAasaOPLwbkADmN7Mh7VYA8PgOMQuHy4Hx6TzVxo0UBqCETlrMILvvw7pi39GzpfHcO1fh3HtX7bBIgq9GpGzBiP0L/3kcvogP1uGYL6YB3WUrUwxp+QqSq0lECAgpks3qLtrEP5oInK+P4Ws1b+i6GAGSo7YspTUUQHwHxAD//4xgCjCv/Q/toDmJz9B/XYaNLGBKNqfjuIOtmC4UV/5+dO2M6Hdp6ORu+EUMhb/DPPFvJrZgioFdB2CoesaCl23MPh1C4MlpwRZnx1C0d405H53ErnfnYQyUCdnngJAxz9HYe/gHJhyAxE0sguC7u6KgFvb1eh12inbAGxfhQvh6fj6kxNom90GEYcMCP5WhCbDiuKcPBSVZ6KwsARn1ZdwyZAObbEa12/tDkGjhLa9CZq2QYBFhLXUVrItlpSj/GoR/HNtbwxcPnwSx55/H7ruYVD4qfGH5SQwGfDLVuOPp/5V6/eD3ddZoBaqcH+ogvVQmfygDPaDyqQDICD/l1QUH8pA8eFMFB/OtAsMq/vkApMrj+OvMCJwRCf49Y6EvncktPFBUPip5cxSQamAtbgMJaeuouS4LSu2+HgWig6mozyzEFc/PoCrHx+AKlQPfZ8o5P+SCrGkos+ZUWsLMOeakBNSgKP/9z2C/68YUS/eAojAldX7kPvDSTkIrm5jhGKs7WdLWNu2iJpyI0SriMy3diFj+U5c/ewQio9mIu7NuxD1ox9gAP6wnkRhWhZ0gf5Qheht2ZVFZbAWmlGqs32Nqa8Bx274AP4D28CY1B6Z7+yBJacEfj3D0X7NGKiC9Q4/xwqtCm3fHYnwjz/GFeTg5L+3oc2JYJR0s/2c02eo4Nc7Em0WDoG2SwjKLxeiLLMQEWk/okC01WVHte0Iw01tUX65wJY5XVoOQaOCoLFlHNt6SKqhCvaD0qSDyuRn+3+wHuooAzRRAVBHG6EM8cO7332DQuRB98kAhJ8MQPHRTBQfuYJiQ8XrTC1Hzo/2b8gpjFr4dQvDrPZP4Oq3x6AuEGC8owPavjvSYZ9fScDgOBi+HI/8bX8g/bWdKDmSCXWkAaGPJMA0/jqoNq9BuViG/X6/4qrqKvxU/vjT4jko+iEV8QGncA7pKBxnRPzjo6AK1UOTfwbIOoHQ3h0a1JtYUFZ8v3cIRvjj/VGWno/cTaeRu/E0ivZdgjWvFPk7ziF/x7nK12zQoM2SoTDd3dXuWFJv4lKUIOTVwWjz8hDk/nAK2f8+jIKd51F+pQgFPWyfy6j2neR2E4JCQPDYngi8qzMy392DK3//FYJSQMAt8TDe0QHG29tDFeL4a8ivWxjC/9QLyDqI4rw8nBn3L7RZMhQXnv4epX9cgzJQC2sfA2CuvWWAoFRAHeoPdWjtg60EQYBJF4bMojSIQ0MQ90BfAIBm9wHg4mHEP3k7Os8cBWtJOfTXRdRonwJU9pktsRTDcFu8XZ9nIm8gMkOTiIiIPMSrVh+hoaFQKpW4fNm+R+Hly5dr7X8pWbZsGRYvXowtW7agV69ede7bvn17hIaG4vTp0xgyZAgiIyORmWlfnlZeXo7s7Oxan1er1dYYLOQucoZmtZJzqXyzvJYMTU09QzBiDPEAgEsF5+rcL780BwAQ2rUdeuyfZndfW2Nn/IIf8OWpj/Dp4ddhhS2bpHNwb0zu+RxMYVHVDwcAUPprEPXcjVD9ORbY9zmK9CUIuKdjnaV1glppCzSY6g7UVuWfGINOPzyErI/2Y1e+rf/qdaYB6P+fafU8EmjT7zrge6AgsBgWhVXur3kxLhOlGjP8CrWIzA5H9IJbEDq5b71lgZoYI9quvAuhU/ri0vztKDqQDuOwjohZcHuNwR+OppxLE86D/cLlnniCWgnTPV1huqcrig6kw5yWB33fqBrHC9nWFlnXrsHcVoHy7UUov1IEhZ8Kxr/1A/LW1xhKIwgCgkZ0RtAIW2aXKIqARYRYZoG11AKFXu0wO8N0X3cUH7uCq2sO4dp/j8rBTE2sEX49IjA2vgcMmo0YPfMJxEV0qfF4SZyxI0L9IpFVnIG9V3ZgLwD0tH3oFH4osdbsgzfceB/6bngCmriafVYloigibW8AcGEXSqKtgEJAyVFbpl9ud9vn2lDoB007E9Th/lCF+EEVrJf75qmC9VBHGuSPuoJCUbgJZVcKkf/jOeRv+wMFuy9CGaiFtq0J7boHA9gm79v+8dsR3+meWo8FAAo/ta3Pba/Kn1HW0nIU7DyPnO9OIm/jaZRnFSFvyxkAgK5bGEIn9UHQvd2g1GvQYfcvOHPxArIjC1D4TRpO32M/AM1wQyxCH06AcUh7nE/9N3Cgst+ooBAQ8fQg+PWKQOr071C0Px3Hb1oNESIMC2yZkCUvxaDv/ePshmCJoojys9uBA1/bAjUCULj7opy9p+8Xhfafjq7RDqM6QaVAfEJfHPnjKK6F5OPcf/8HvAhoStXo8MqfEDy2p/z9p2yngbadCVG7O+DMRVtAs8uDd6DD8417M6u6toGdcTTrV2TG56LHLbfJ21U//wRknkabB65H1A39UHLyKoqPZ6H0ZBaseaW217wb8IMaQfd0RdwbI2q0TnD42gUBxiEdEHBbe5Sl5UEVYZC/94L9wpFZlIb/nvgAADAoZigCe8YisGcsep5Nx479O3A5OhuBN3UEABT9ZGup0JAp546oowIQOqkvQif1hVhuRfHRTBT+eglFv6ah8EA6NLGBiF0y1GHvW63KDwZ1IArKcnG1OANxxk4w3dcdpvu6w3wxF8WHM3E8YiPwxyaHE86l3x2RTw+CKDqfJeYfEQJkAeVGoOTYFZy+2/b7QB0TgPafjkbxuR9sAc1ahjo5SwpoZleZdC4NBQo0hcGvreN2DxI/VWVQtqS8qM5+wESeYC2pyNDUMUOTiIiI3MurApoajQYJCQnYunUrRo0aBQCwWq3YunUrkpOTa33c0qVL8corr2Djxo12fTBrc/HiRVy9ehVRFZmCgwYNQk5ODvbt24eEhAQAwLZt22C1WjFw4MCmvzAXK6tlKJBK4XgokNRDs74MzZiAdgBQb4ZmXsX0V6ODScxtjbaAlzRxuF/ETRjT5VH0CO3v1PCSkLaxUOxXwipakFd6DcF+4fU+RlJmMWPZnlkI0oVgQvenYNQ6Hhyj0CgR9mgiDm56CSgAbutUs0eqI4G6ECgEJaywIHLrKATmGiCWWXA073MgH+gW2Bfdf3oEmooMSmf594tGx6/GozyryK7UsCppKE1uabY8xCWzYsJzbRPO9X2joO/rOIAsfW6McxPR5s7uyNtyBuGP98fusL3Ar5UB1NoIggCoBAgqRb2ZXX7dwtDm/yUhas7NKD11FZp4E1RBlQGrLhhR5+MBW5bSqmGb8EfOUZzIPoRT137HyexDyCi8IAczVYIaRm0QjNpgxBja4f6E56BV130tBEFAWNt44AJQ2lmFHgeeQP4v5yGoFEgz/AJc2YiIfl3R7amH6z1HZ6jD/BE8pgeCx/Sw224quQp8t0S+7WgokDMUWhWMt9vaJYiLLShIuYCiA+nwHxQL//4xdt+D0UHtgItA+bhQBOt7IvtfhyFolDCN6obQhxPg160y0CJPENfZ9xI13t4enb99EOemfoWSE1nQtAlEN/8+2IsUpA3KtwtmArbPd6nW9oZLUIcYdNv9GHK+PIacb05AHR2AuBV3QmmofVJ1VRH+toz44sFaFJ63ZQiFBEUh5M7rHO4f5lf5vSD1LHWFeGMXHM36FefyTtptl4YChfXthPC7Kn+PWM0WlJ7JRvGRTBQfyYQ6TI+wx/o3eGK4oBDkcn1JqD4KmUVpOJt7DABwS+yfKs8z0PaGQdVJ54Vl0pTzpgU07c5LpagMtP+ln1OPCfGLQEFZLrKKbAFNiaZNIDRtAnHtgC3YHqav/Y1NQa1EQzpL6ip+H2pujYbqBwPKLxfAr2c42n18H9QRBuSftL1hVf3NnYaS+tPmVA1oltmObXDi865R6qCAAlZYUVxeyIAmeR1maBIREZGneN3qY+bMmZg0aRISExMxYMAArFixAoWFhfLU84ceeggxMTFYtMg2mGbJkiWYO3cu1q5di/j4eLnnpcFggMFgQEFBARYsWIDRo0cjMjISZ86cwfPPP4+OHTti2LBhAIBu3bph+PDhmDp1KlatWoWysjIkJyfj/vvv97oJ50D9GZpllmpTzisCoFpV3VlP0RUZmukF52ERLVAKjt9tz68IaAY4CHr1jRiMgVFDoFPpcW/nh9E+qPaenY4oBAUCtcG4VnIFOaVXGxTQ/P3KbqRcspWs77y4AVN6zcbtcaMcBlJP5xxGWsFZaJQ6DIq+w6njKwUlgnVhyCrOQH5wMaI72oK3p3aeAvKBPt1ubXAwUyIIQq3BTAAIrAhollnNKLEUwU/lj8uFtoBmuH/DgzMB2iAAtvYBIfdfJw92yT+12XZ/E/+Id0Tpr7Gbit1QGqUWXUP6omtIX3lbXmk2Csx5MGqD4a8OaNTE70CdFCy+ClWwXi6JLT7xI3ClZhCvOQRqg6FV+sntIRy9WdBQglqJgJvjEXBzvMP7Iw22SeeZlnTELhuOqDk3A0qFXbBZklNiG5gkZWhWpW1nQqdvJqD4cCb0fSLRN1WPvYdScCTrV4fPW1Rm6xPqp/KHJioA4U8MQPgTAxr8+iL8bYH83OhimP5+M7D7C5j0tWe7hVUJYoa5YMq5JD7Q9nMgNddxQLN6hp9Co4RfRVsGVAtsN1VYlQzGQG0IeoVdL9+OM3aEAAE5pVnIKbmKIF2IfI7+msb93HKVUL9IpOadRFax457VWcW2AWuOMjQbS6uyBTTL/Szo9M0E5P94DkF/6gKlQQNRtA1MAxr/5oIkuCKg6ShDs7Zy9qoEQYBOpUdReQFKyouadC5EzUEaCqTglHMiIiJyM68LaI4bNw5XrlzB3LlzkZGRgT59+mDDhg3yoKDz589DoajMZHnvvfdgNpsxZswYu+PMmzcP8+fPh1KpxG+//YZPPvkEOTk5iI6OxtChQ7Fw4UK7kvE1a9YgOTkZQ4YMgUKhwOjRo/HWW2+550U3kBSw1FYrIZfKjhuboRmmj4ZaoUGZ1YzMwjREVQQ8qsuXMzQdlw/+7YZ3638RdZADmhVTi511PNs2oEgBBfLM1/Dmry9gW+oXeKLPfLQxdoAoisguycQfOUfxwx//BAAMjBrSoIyXkIqy56vFtrYIFtGCoxWBm+vCGh6UcZZW6QeNQguztRS5pdkVAU1byXljss2kgKWUbSvJN1+zu9/bGbXBcvZqY0lBurzSa3aBfKm8P7CeCeeuIAgCwvUxuJBvG0zV1CCKM6L8bd/f6YXnAaDWfoNA7RmaEoWf2tafFUCPMFuW/PGrB2CxlkOpsP81U1xuG97V1EwzKaB5ufCi/LMiSFcz4CoJ09uC6XqVwalAkrMcZT4CQEGZ80ErVwmtkoV6U5s77T73OpUeUYa2uFRwDql5JxGkG9QsGZqNIQUqs0suO7w/q+LnbagrA5oVvz9Ly4uhiQqQ39QBbH2ny0VbJnFAE78Xpa/Ja1UCmnKw28lj+6n9UVReIH/vEHkTqae2oPG6PymIiIiohfPK1UdycnKtJeY7duywu33u3Lk6j+Xn54eNGzfW+5zBwcFYu3Ztvft5mkW0yH9oSRmZEqnkvLYemtUDoNUpBAWiDfFIzTuJSwVn6w1oSll+rmYLMJ1AbunVBj3u2FVbQPOR3n9FqaUE/zz2Nn6/sgczttyDbqH9cD7vlDy5WnJbXN19CquTBlhkV/yBfS7nOIrKC+CvDkB8UNe6HtokgiDAqA1GVnE68kqvIdI/FpelknN/xyXndZEClvnVApp5Ff1RayvXb4mMmiAIECBCRF7pNZgqAhA5ckDTPZ+LCP/KgGZAE/v2OSPSYCvZzjfnoMCcV2fgTQ4YOsjQrK6tsTP81UYUluXhj5yj6BRs39NYCspIw04aS/q6zzNfw6WCVACV5b2OdA7uBT+VHr3CBzXpeauTSqSzSzKRV3oNRq2pIsPPcYZmcwrVVwY0b65Sbi6JD+yCSwXncC73BHqFXY/CinP0r6c1Q3ML1dt+rmYVOQ5oXq3I3KwasG0qndIWwJd+P1aVX5GdqRLU9b4RWJ9gna3KQApoWkVrlc+7c4Fk6VyZoUneSCytCGgyQ5OIiIjcjOMyfUx5lXJyTW0l51UyNMutZbCItnKg+gKaQGUfzYt19NHMK23eLD6pvDqnAQFNi2jByeyDAIAeYf0xustUvH3Ht0iMvAXlYhl+v7IbuaXZUECBOGMn3Bp3N5L7/T8kRN7coHMLqfjjVCqN/P3KHgBAt5CEWkv0XUX6vEif/8wmZGhKJc3SgCdJXkWGpitKnn2FUqGSv5arBtGl/7sjQxOwv44Bbgii+qn85QBlRkWWZm2k70Vnyu8VggLdQ2y9iI9k7atxf9WS86bwVwfI103KzjbVkaFp0oXhk7t24oXrXZt5r1cb5GuXWtFHs8RSJP/cdWc5d7ShLQAg0j8WXYJ717hfKo8/l3sCxeWF8tA2d2QE1yVYZwtoXnVQcm62lMpvRElvKLmC1IJFavNQVdVy88a0sajKVK3kvDGfdz+1v/xYIm9TWXLulTkSRERE1IJx9eFjpH6YQM0MTUc9NKtmnziTaRJdz6Rzi2iRy+WaK6AZJGfIOR/QPJ97CsXltt6SUsZUpH8sXrrh7ziY+T9kFqUhPrAL4o2d5d5pjSGVRkol54ezbAHNnqH9G31MZ0kl/rlm22CgK0WXANQ+FKgutWVoVvZHDWrsafqkIF0o8szXbL0iK2IM7iw5B+wDms4MC3GFSEMsckqzkFF4Hh1NPWvdT8rQNDmRoQkAPUITsTdjO45k7cWozlPs7pMzNNVNC2gCQIQ+BvnmHJzNOQ6g/gxSnar2svqmiA/sgsyiNKTmnsR1YQPl7EyloJKz69yhT/hgTO39t1qHsMUbbVnk53JPyD/HVQp1jQFz7iaVkjvqoSn9rNUotC79uST9PiwprxnQrPw52PRAr9RDU8rQlIKlGoXWqTcZgcrgfxEDmuSFOBSIiIiIPIWrDx8jDQRSCqoavenkknOxsuRc6p+pgEK+vy71TTovNOdChAjANX/sORJUEUCSBpE449jV/QCALsF97DIlBUFA34jBLjs3ueS85DKsohVHKzLQmrN/pkQqA88rzca1kiswW0uhgAKhdUz+rU19Ac3WVHIOVGa/Vs3QrCw5b1qPTmdJw5381QE1vrebS5R/HI5fPYD0ggu17mO2lKKo3JZVWVePyqq6h9r6aB69ug9W0QqFUFkMUFly3vRpzeH+bXA654icDVlXyXlzig/sjD3p23CuYjCQPMVaY2xyhl9DCIKAkR0fqvV+qd/n+bxTcqa3v9q95+iI9DNMCl5WJZeb66Ncep7SG1sOMzTLXDMQCKj8mswtuQqLaGlUb1U/FUvOyXtZK3poKjQsOSciIiL3Ysm5j5ECmhqlpsZ9dWVoalU6p/4YjKknQ1MaIqNXGZwKkDaGHNAsdX4okFRyWnUCdnOQAppXizORmnsSBWW58FP5o31Q92Z9XsC+5DyzyFZuHqKPbNR1kPqfSiXmkuZuJ+CtpMw+KStYFMUqJefuCWi2NdrKgRvTQqCxpD656YWpte4jZWeqFGqne/51MHWHVumHfHMOLuafsbuvuMw1PTQBWxZ2VXWVnDcneTBQnm0wUKHcP9Ozw3aqC/ePgZ9KjzKrGaeu/Q4AMHi4fyZQmfleUJYrvwknuSJPOHdduTlQZSiQw5JzqQqh6QHNQG0wBAiwwoq80mw5Q9O/Ab1Vpe8V6XuHyJvIPTR1DGgSERGRezGg6WPKKkrO1Q5KBB1NOZf+WNM4OdhAytDMKs5wmA3ijpJkqU9f9QE+dTleMRCoWzMHNIMr/qjOKs7A71d2VzxnP7dk1ElZk7nmbFwuanz/TKCyfD3fnAtRtGXciqLYekvOq/VtLS4vlL+P3BXQjDV2wNwb3sdzA99wy/MBQGTFpPOMOjI05f6Z2hCnM+TUCo3cw/FI1q9290nZni4pOa82EMtTGZptjRWZj7mnYBWtckDM38O9KatTCAr5XI9k7QXg+QnngO0NMqk0v3qW5lV5wrnrBgIBVYYClTsaCpQDoGFBx9ooFSq5bUV2yZUqE86d/7zrKgKaJRYGNMn7sIcmEREReQoDmj6mMkOzZkDT0ZRzZyecSwI0QXIwS5ocXJU0RKa5JpwDlT0Lpcyw+lwruYKMwgsQIKCzg0EYriRlCZktJdidvhUA0NMN5eYAYNRIGZrZuFzY+AnnQGXA0ipaUFiWD8AW/JaCeK2u5Lwis0/KypSC6VqlX7P1XXQkMeoWtAlo77bnkwOadQwFqpxw3rBeoj3DbH1lf7zwrRw0BypLzvUuKDmv2j9WgOC24HN10Ya2UCs0KLEU4XLhxcqSZS8IFlYnDQY67EUBTUEQ5J+t1ftoZhXZMjSlPpuuIg0FKhfL7H5nApV9Ll3VVkUKtF8rviIHuw3M0KQWQixhD00iIiLyDAY0fYwU0HSYoelgyrlUvufMQCCJlKV5oVqpKFClx6Km+QJeUvlvbmk2rKK13v2PXz0IAIgzdoJ/M5dPapU6ORh4+Ir7BgIBVUvOc5o04RywBcSlrwnpmkrl5iqF2q2DTLxB9b6tuW7un+kp0RUl51nFGXYDxKqqnHDesHLuIW1HQ6PQ4mjWr9iTvk3e3lwl50atqdnaYNRHqVAh1tgRAJCae1J+k8DT08MdaVtRHi9lPrpzCntdwvTRAIDfrqTYbZcCnK4uOa/6M676136+2XU9NIHKwUDZJZlVJqg3JEPTdq7F7KFJXsgqlZxrWXJORERE7sWApo+RgpWOMjQdBTSlAKiUjeKMuADblPDzuadq3Cf1XGzOkuRAnS2IZBHL5fK8uhyvGAjU3OXmkmBdOABAhAit0q/O6dCuJJecl2bLPTQjmtBvsfpgoMpgdZDHh4S4W/WhQLlVyqxbsgCNSc6UlLJ+q5N62TZ02nuYPgp3d5oMAPj499fkLLhiF5ach+mjIcD2tVrfhPPmJvVAPZd3ojJo5QXZj9VJ/T4l3pChCQB3xI8BAPz3xGq5vydQGXgN07u25FylUENRsQSq3rdTyrB11e85k58toJlTmlU5FKgBn3c5Q5NTzskLiRwKRERERB7CgKaPkTM06xgKZF9y3vAMTakkMTXvZI378qoEvZqLWqGR/8h2ZtL5MXkgUL9mO6eqqpY+dgvp57assMCKrNg8c9NLzoGqAc1rFcfNqdjeusrNgcrsw5xqJefGFp6hKQgCIg1SH03HZefS92BDMzQBYEyXRxGoDUZawVlsOvsvAK4tOVcrNXLmnqf6Z0rkwUC5J+SglbcEC6uSAq8Sbwm63thmBG5sMwIWsRwr9s6Wsyaz5KFAri05FwRBfqOvpNpgoILmytAsvlI5FKgBx5YCmpxyTt5ILGXJOREREXkGA5o+RhoKpHFQci4F1hxNOXeU0VmbOKMtQzM1t2ZA0x09NIHKzDgpU642ZRYzTl87DKD5J5xLqpY+Sn0C3UEKrhWW5eNK0SUATZuILQWlpUCmlH1rbOZr643kDM2SqxUTzltHyTkARPlLk85rCWiWNq6HJgDo1QY80C0ZALD26EoUmPPksllXlJwDQHhFUN9TE84l8htBuScbVVbsLgaNUS7vBgB/LzlHQRDweJ95MOnCcCH/DD47/AbMllL5e9HVPTQBQCsNBqoW0Mx3cYZtkLaih2bVoUANytCUSs6ZoUneh0OBiIiIyFMY0PQxlRmatU85L3cw5bwhGZptK/4wv1x0scYfUPluyuILrJh0LgVTanMm5wjKrWUI1IbIgZnmFlw1oOmm/pmALVtIKpEsF8ugFFRN6isnBaWlILUcrG6NGZoVwTqztRTF5YVyIL01BDSlDM30ZsjQBICh7cYixtAOeeZr+OzICnm7K0rOgco+mo09P1eRMh/TC1KRXZIJwDszNAEg3lhZdt7cfYcbwqg1YUbCKwCAr05/jB3nvwZgewOvOdqcSMPyqk86d3XJebBfZUBTHgrUiAxNBjTJ24jlVsBiG/rGHppERETkbgxo+hi5J6aTPTSlP9ScnXIO2II4Uj+683mn7e6rDGgGOX28xqg+pKU2x67ays27hfR1W99HKYioUerQydTLLc8JAApBYZcZG+oXCaWi8RkRtfXQbO5r6410Kr08JCSn9GqVgGbL7qEJVGZo1jbpvCkZmoAtc3zydc8BADb88U8AgEJQOswyb4yh8X9Gz9ABuCV2pEuO11gmXRiMGhOssOJUtq0HpDcOBQLs+2h6W9A1IfIWDG83DgCw6uACALZy8+b4+S4N26meoVlQ8bPQVdfPVHUoUFnDsz+l82TJOXkbq7lc/j8DmkRERORuDGj6GClY6WjKuVRy3tQemkBllmb1snO3BzTrKTk/XhHQ7BrsnnJzAOgc3AsKKDAw6naHvUybk1FTmTEY7t/4cnOgrqFArS9DE7AfDJRbMfE9qDVkaFZkOGYUXnB4v/SmQlNKugdE3Y6eoQNghRWArX+mqwJU3UMT8Oot/0D7oG4uOV5jCYIg/9wssdgCT97Sn7I6+4Cm92RoSqb0mo1I/1j5d1mo3vXl5kDVDM3KgGa5tUxuixDg4oCmfYZmA0rO1czQJO8kVkw4BwCFhiXnRERE5F4MaPoYKUPT2SnnUg/Nhkw5B4C2Uh/NvOoBzYo+i80d0NTVn6EpiiKOywOB3BfQbBfYFe8P34LpFaWR7lS1BLopA4GAmgFNd0yw92byYKCSygzNlj4UCACiDG0B2KacW6zldveVWc1yRllTJr4LgoApvZ6Xb7uq3NzbVJ8gzgzNxvFT+ePpxCXyBPvm6J8JwOFQICngKECA3kXB3mBdOADb99OVYlv/4waVnCulHprM0CTvIpZU/M5QKSCo+CcFERERuRdXHz5GGgrkKENTDmhamtZDE6jM0Dyfe0reJopilUnYQQ06XkNJJe91DQW6XHQR10quQCWo0dHUs1nPp7pw/xi5DNCdjNrK7MmmDAQCag4FctfAJ29ln6FpG0TSlCCerwjxi4BaoYFFLMeVionSktwS2+dBIShhaOL3fCfTdXJZuKsGAnkbb50gXl20oS00FdmJVX+meJPuoQkY120agOZ7w8rRUCCp3FyvDoBScE0JrUaplQPHUtZpQwLJfmoDAFuGpiiKLjknIlewVmRoKlhuTkRERB7A+hAfU1eGpsOS80b00AQq/zBPzasMaBaXF8rHbu4/ggOdKDk/dnU/AKCDqUeDprj7sqrl4BFNDGhKw38qS86l7FvvDHA0NymIfq0kq8qU85Yf0FQICkT4t8HF/D+QUXBBLkEHqvbPDIZCaPr7X5N6PovLRRcxOGZ4k4/ljapnaHrLBPHqlAoVZiS8ivSCVLcNU2uM8d1nYGj82CYNP6uLo6FA+fJAINdm15p0ofKEc6BhGZrSm2dW0YIyq7nV/L4j7ydWTDgXOOGciIiIPIArEB9TOeW8Zu9GaVuZ1QxRFCEIQqMzNOOMHQHYAhq5pdkI1AbLgS+1QtPg4zVUZcl57VPO5f6Zbiw39zSXlpxLU87lknPbv6235Nz2ub1UcBYW0fZHWmuYcg4AUf5tcTH/D6QXpqIPbpC3X6v4/gvUumaCeKg+EktvXeeSY3mjOGNHCBAgQoQCCq/ORL059i5Pn4JTmqt/JlB1KFBlKXeBuWJoj8sDmmG4mP8HAOl3qPNvMlatBiguL2RAk7yG1VyRoalhhiYRERG5H0vOfYycoVlHybkIUQ7IyFPRG9hDU6fSy5la0mCgqgOBmnuiuJQZJ2XKOXK8yoTz1sK+5NxFPTQrSs3d1R/VW0kZmufzTgOwDa5x99AnT4k0VAwGKrCfdC5naOpafqaqK9h+btoyHv01RpdktVLzkTM0LVUyNCsCmgHqIJc+V3DFYCCg4cFSpaCU30QsLuNgIPIezNAkIiIiT+JfWz5G7qFZR8k5UFl23tgMTaBq2XnNgGZzk3oXlliKUOJgEEJRWYEcaO0S3KfZz8dbSENqVIIaJr+wevau51gV17HEUoSisgJ54IS39tRrblIQ/UL+GbvbrYFUdpxebdK5NJQryEUZmq2BVHbujcN2yJ4U0Cwpr9pDs7kyNMPl/zemt6qUpVlsYUCTvAd7aBIREZEnMaDpY5zJ0AQqJ51LmSeNKVFrG1gx6bxiMJBUkmx0w9AYP5W/PLTCUR/Nk9m/wQorwvUxzdZfzRtJWT4R/jFNHlihVwdAUfEj4FLBOQCAAgqXTfb1NVJ5ufRmQGspNweAKIMtoHkx/wysolXeLmVomnQMaDpLGqhm8NL+mVRJq3IwFKhMCmi69vqZqmRoNqa3qtS+gBma5E2YoUlERESexICmj5EClRoHpbBKhUoOUEmTzpuWoWnLNJIzNKUp2G4YGiMIgpylKWWJVXU65zAAoEtw72Y/F2/SPSQBozr9BX/pNafJx1IICjkLKa0ioGnQBLbaMtmgakG71hTQbGvsAoWgxMX8P7Dgl6ny9xwzNBuuR2giACDG0M7DZ0L1qbPkvBmGAkkM6oYf268i+OqoYoHIU8SKDE2BGZpERETkAa0zcuHDKocCOc64VFUZDAQ0fso5UJmheT7vJERRdGvJOVBZdi5liVX1R85RAED7oO5uORdvoVSo8Jdes9E/6laXHE+6lmn5ZwG03nJzwDbJu6rWVHIeqo/EUwmvQqPU4UDmL3h66z04fGUPe2g2Qu/wQXj99v9gWr8Fnj4VqofOQZCwoOL3nMHFPTSrZmg2JlgqZ2iWM0PTVd555x3Ex8dDp9Nh4MCB2LNnT637fvDBB7jppptgMplgMpmQlJRU5/6PP/44BEHAihUr5G3nzp3Dww8/jHbt2sHPzw8dOnTAvHnzYDab7fYRBKHGx65du1zyml3NWpGhqWCGJhEREXkAA5o+pqyOknMAUFf00azRQ1PV8AzNaEM8VIIaxeVFyCxKc3tAs67BQGeutc6ApqtJk86lkvPWOuEcAAyaICiqlPG3pgxNALit7Sgsv/0/iA3ogOySK3jxp0k4mf0bAGZoNlQn03VePeGcbKQ3+sxuyNAMdlEPTWZousbnn3+OmTNnYt68edi/fz969+6NYcOGITMz0+H+O3bswAMPPIDt27cjJSUFsbGxGDp0KNLS0mrsu379euzatQvR0dF2248fPw6r1Yq///3vOHLkCN544w2sWrUKf/3rX2scY8uWLUhPT5c/EhISXPPCXayy5JwZmkREROR+DGj6GHMdQ4EAQKWolqFpaXyGpkqhRkyArWzyfN4p5MlTsN2TxSdlheWU2GdoFpUVIL0wFQDQIaibW86lpZIGA12qyNBszQFNhaCwC2K2toAmAMQZO+H12/+D2+JGwQqr/IYIMzSpJdJVvNFnNxSoLA9AcwwFqtpDkxmanrZ8+XJMnToVU6ZMQffu3bFq1Sro9Xp8+OGHDvdfs2YNpk2bhj59+qBr165YvXo1rFYrtm7dardfWloapk+fjjVr1kCtVtvdN3z4cHz00UcYOnQo2rdvj7vvvhvPPvssvvjiixrPFxISgsjISPmj+rG8ReVQIGZoEhERkfsxoOlj6s/QtA9ompvQQxMA4isGXJzLPVnZQ9MNQ4GAyqyw6kOBzuYeBwCE+kXJU7+pcaR+qGkFLDkHKtscAEBgKw3i6VR6PNN/CZ5KWASNUgedUo9wfYynT4vI5aTfi3ZDgZopQ9NfHSD/3m5MhqafWgpoMkOzqcxmM/bt24ekpCR5m0KhQFJSElJSUpw6RlFREcrKyhAcXLkGsVqtmDhxIp577jn06NHDqePk5ubaHUNy9913Izw8HDfeeCO+/vrrOo9RWlqKvLw8uw934VAgIiIi8iSuQHyMWR4KVFuGZmXJuSiKTeqhCQBxRltA83zeSeTLGZpBjTpWQ0kZcrnVAppnrh0BALRndmaTSRmZ0h/JrTlDE7Dvmxmoad3B8iHx96FvxI0os5qhVxs8fTpELqdxOBQoB4DrMzQFQYBJF4bLRRcbdWw/pa3knBmaTZeVlQWLxYKIiAi77RERETh+/LhTx5g9ezaio6PtgqJLliyBSqXCjBkznDrG6dOnsXLlSixbtkzeZjAY8Prrr2Pw4MFQKBT473//i1GjRuHLL7/E3Xff7fA4ixYtwoIFnunZazVXDAXSsOSciIiI3I8BTR8jDQWqLaApZ2hazCi3lsEKK4DG9dAE7DM0pT+k3DYUqGIqbPUp53/kHgPA/pmuUD0j013Bam9lV3Kua90BTQAI9guvfyciH1W9L6VVtKLQXFFy3ohJ5PWJDojH5aKLjcp4rszQZEDT0xYvXox169Zhx44d0OlsQfF9+/bhzTffxP79+yEIQr3HSEtLw/Dhw/HnP/8ZU6dOlbeHhoZi5syZ8u3+/fvj0qVLeO2112oNaM6ZM8fuMXl5eYiNjW3sy2sQUR4KxIAmERERuR8Dmj6mrJ4emuoqU86rZp00PkPTNuk8Lf8slArbl4v7hgI5ztCUJpx3YECzyapfywA39Uf1VlIQHWCGJlFLp62WoVlUViC/CejqDE0ASO73/3Am5wh6hCY2+LG6ih6aHArUdKGhoVAqlbh8+bLd9suXLyMyMrLOxy5btgyLFy/Gli1b0KtXL3n7zz//jMzMTMTFxcnbLBYLZs2ahRUrVuDcuXPy9kuXLuG2227DDTfcgPfff7/e8x04cCA2b95c6/1arRZareM1YXOTemiy5JyIiIg8gT00fYy5nh6aVUvOpb5gSkElb2+ocH0M/FR6lIuVx3NXQNOkq9lD02wpxfm80wCYoekK1a+luwY+eaugKhmarb2fKFFLp6so4y6tGApUUGbrn6lV+tVaBdEUYfooXB+d5FQGX3UcCuQ6Go0GCQkJdgN9pAE/gwYNqvVxS5cuxcKFC7FhwwYkJtoHpSdOnIjffvsNBw8elD+io6Px3HPPYePGjfJ+aWlpuPXWW5GQkICPPvoICkX9y/CDBw8iKiqqEa+0+VVmaDKgSURERO7HFYiPKbPYemhKmZjVVR0KJGWdNOUPM0EQEGfsjBPZB223IcBf0/CBBo0hDWjJN+eg3FoGlUKN1LyTsIoWBGiCEOpXdyYF1a96ibm7Bj55K2kQVYAmqNFvAhCRb9CqpAzNYoii2GwDgVxBKo9nQNM1Zs6ciUmTJiExMREDBgzAihUrUFhYiClTpgAAHnroIcTExGDRokUAbP0x586di7Vr1yI+Ph4ZGRkAbD0vDQYDQkJCEBJiP0hOrVYjMjISXbp0AVAZzGzbti2WLVuGK1euyPtKmaGffPIJNBoN+vbtCwD44osv8OGHH2L16tXN+wlpJFHO0GTJOREREbkfA5o+RBRFmKUgpRNTzkubOOFc0rZKQNOgCYRScM/C1aAJgkJQwipakFuajRC/CPyRY+uf2SGoR6OyXMhezZLzIIf7tRZSyXnVaedE1DJJvxtFiCizmpFfEdD0b8QU8ubGDE3XGjduHK5cuYK5c+ciIyMDffr0wYYNG+RBQefPn7fLnnzvvfdgNpsxZswYu+PMmzcP8+fPd+o5N2/ejNOnT+P06dNo06aN3X2iKMr/X7hwIVJTU6FSqdC1a1d8/vnnNZ7XW1jZQ5OIiIg8iAFNH2IRy+X+Xs5MOW/qhHNJ28BO8v/dGfBSCAoEaoNxreQKckqvVgQ0bf0zOeHcNVhybu+6sIG4JfZPSIy81dOnQkTNrOrvxpLyoioZmkEeOqPa+bGHpsslJycjOTnZ4X07duywu121B6azqj9m8uTJmDx5cp2PmTRpEiZNmtTg5/IUkT00iYiIyIO4AvEhUv9MoI6hQFWmnMsZmo2ccC5pa+ws/9/dAS8poJlbMen8jBzQZP9MV6heYu6NpZbupFFqMWvA654+DSJyA6XC1l/a1nO6BPnmHADNMxCoqfxU/tAp9bVWZxB5gpU9NImIiMiDuALxIWVWs/x/KXBZXdUp51J5etMzNCsDmu7OXLH1NDyBnNKrsIgWnMs9AYATzl1FrdDAT6VHcXkR/NUB8iR7IqLWQKfUo8Cai9LyIhSU5QHwzjd2uob0wb9GHfD0aRDZkYYCsYcmEREReQKnnPsQKUNTpVBDITi+dFKgs9yFPTQDtcGVw1LcPDQmSGfrZZhTkoW0/LMwW0rgp9IjytDWrefRkgVUZN0GtPJycyJqfSoHA5XIJefemKFJ5I1Ec0XJuYZvhhIREZH7MaDpQ8oqApp1lZxJPTTLXNhDEwDaGm19NN2doRlYMZwlt/QqzuQcAQDEB3atNaBLDSdd0+oTz4mIWjrpDb9SS3FlybmaAU0iZ1gremhyKBARERF5AqNCPsRstQU0a+ufCdSSodnEHpoA0DNsAIDKwKa7SNOmc0qv2k04J9eRApreOAiDiKg5SQHNkvLiKkOBGNAkckZlyTkzNImIiMj9uALxIXKGZp0BzYoMTYsZpRX7uyJDc0yXRzEoZihiAzo0+VgNUTWgmVWcAYATzl1NztDUsuSciFqXqiXn+WUsOSdqCHkokI4ZmkREROR+DGj6EHPFUCBNLQOBAEAlZ2iWVemh2fSAplKhQpyxY5OP01BVe2hmFl0CwAnnriYFMt09wZ6IyNN0Sj0AoMRShEKzNBQoyINnROQ7xIqSc2ZoEhERkSd4Zcn5O++8g/j4eOh0OgwcOBB79uypdd8PPvgAN910E0wmE0wmE5KSkurc//HHH4cgCFixYoXd9vj4eAiCYPexePFiV70klzDLGZq1ByjVSqmHphmlFVPO69rf20k9NC/knUFhWR5UCjVije7NEm3pbo+7B73CrsctcSM9fSpERG4lveFXWl5S2UOTGZpETpF6aAoaZmgSERGR+3ldQPPzzz/HzJkzMW/ePOzfvx+9e/fGsGHDkJmZ6XD/HTt24IEHHsD27duRkpKC2NhYDB06FGlpaTX2Xb9+PXbt2oXo6GiHx3r55ZeRnp4uf0yfPt2lr62ppJJzZ3pollnLYC53zZRzT5Kmq5eLZQCAtsbO8msk1+gU3Av/7+ZP0NHU09OnQkTkVlKP6VJLZQ9Ng9royVMi8hlSD00FMzSJiIjIA7wuoLl8+XJMnToVU6ZMQffu3bFq1Sro9Xp8+OGHDvdfs2YNpk2bhj59+qBr165YvXo1rFYrtm7dardfWloapk+fjjVr1kCtVjs8VkBAACIjI+UPf39/l7++ppCGAtU95bzqUKCKKecqH87Q1AXb3Wb/TCIichVdxRt++eZr8u9YlpwTOadyKBAzNImIiMj9vCqgaTabsW/fPiQlJcnbFAoFkpKSkJKS4tQxioqKUFZWhuDgykCY1WrFxIkT8dxzz6FHj9onZC9evBghISHo27cvXnvtNZSXl9e6b2lpKfLy8uw+mltlhmbtGYryUCC7Hpq+m6GpVmjgXyVbhv0ziYjIVaSS86ziywAAhaCEn8q73swk8lZSyTkzNImIiMgTvGoFkpWVBYvFgoiICLvtEREROH78uFPHmD17NqKjo+2CokuWLIFKpcKMGTNqfdyMGTPQr18/BAcH43//+x/mzJmD9PR0LF++3OH+ixYtwoIFC5w6J1epHApUf4ZmmcUMq2hbaLpiKJAnBWlDUFhmCxh3YECTiIhcRKuyDQW6WpQBAAjQBEIQBE+eEpFPEMutgFUEwB6aRERE5BleFdBsqsWLF2PdunXYsWMHdDpbEG/fvn148803sX///jr/SJk5c6b8/169ekGj0eCxxx7DokWLoNXWDCDOmTPH7jF5eXmIjY114aupySwP+amrh6YtQ7PcWoZyq63vpK8HNAN1IUgrOAsBAuIDu3j6dIiIqIWozNC0BTQNag4EInKGtbSyikmha1F/ThAREZGP8KqS89DQUCiVSly+fNlu++XLlxEZGVnnY5ctW4bFixdj06ZN6NWrl7z9559/RmZmJuLi4qBSqaBSqZCamopZs2YhPj6+1uMNHDgQ5eXlOHfunMP7tVotjEaj3UdzMzszFEgpDQUyV5acq3y35BywZWgCQExAO+gqsmmIiIiaSmrJIgc0OeGcyClilYAmMzSJiIjIE7wqoKnRaJCQkGA30Eca8DNo0KBaH7d06VIsXLgQGzZsQGJiot19EydOxG+//YaDBw/KH9HR0XjuueewcePGWo958OBBKBQKhIeHN/2FuUiZPBSo9h6acsl51aFAPp6hKU06Z7k5ERG5kq7KlHPAVnJORPWT+mcKagUEpVf9OUFERESthNfViMycOROTJk1CYmIiBgwYgBUrVqCwsBBTpkwBADz00EOIiYnBokWLANj6Y86dOxdr165FfHw8MjIqsiwMBhgMBoSEhCAkJMTuOdRqNSIjI9Gli618OSUlBbt378Ztt92GgIAApKSk4JlnnsGDDz4Ik8nkxldfN7PF1kOzzgzNKgFNaYiQLw8FAoBBMXdgX8aPuC1ulKdPhYiIWpDqb/gxQ5PIOaIU0ORAICIiIvIQr1uFjBs3DleuXMHcuXORkZGBPn36YMOGDfKgoPPnz0OhqHwn+L333oPZbMaYMWPsjjNv3jzMnz/fqefUarVYt24d5s+fj9LSUrRr1w7PPPOMXY9MbyAFKOseClTZQ7OlZGj2Dh+ED0ZsrX9HIiKiBtBWa2MSwB6aRE6RSs5Zbk5ERESe4nUBTQBITk5GcnKyw/t27Nhhd7u2Hpd1qf6Yfv36YdeuXQ0+jruZrU700Kwy5VwqodP4eECTiIioOdTM0AzyzIkQ+RhpKJCCGZpERK2e1WqF2Wz29GmQD1Gr1VAqm/6mKFchPkQaClTnlPOqQ4HKpQxN3y45JyIiag7Vfz8aNM0/4I+oJagsOWeGJhFRa2Y2m3H27FlYrVZPnwr5mKCgIERGRkIQhEYfgwFNH1JmreihWedQILW8b+WUc2ZoEhERVScNBZKwhyaRc5ihSUREoigiPT0dSqUSsbGxdq0BiWojiiKKioqQmZkJAIiKimr0sbgK8SE3t7kL8YFd0D00odZ9pGBncVkBRIgAmKFJRETkSPXfjwHqIM+cCJGPEc0VGZrsoUlE1GqVl5ejqKgI0dHR0Ov19T+AqIKfn20NnpmZifDw8EaXnzOg6UMGRN+OAbi9zn2kgKbUbxOou0SdiIiotWKGJlHjSBmago5/ShARtVYWi+3NLY2m9gpSotpIQfCysrJGBzSZE9zCSCXn8m1BXWMbERER1RwKFMCAJpFTxBLbH7EK9tAkImr1mtIDkVovV3zdMKDZwlTvr8n+mURERI7VHArEgCaRM+QMTfbQJCIiIg9hQLOFkaacS6pnnxAREZGNptrvSIOaU86JnCH10GSGJhERtXbx8fFYsWKF0/vv2LEDgiAgJyen2c6ptWBAs4WpXl6u4UAgIiIihwRBkLM09SoDlApmmxE5Q5QyNDX8niEiIt9y66234umnn3bZ8fbu3YtHH33U6f1vuOEGpKenIzDQuyuDXP15ag5chbQwKsE+oMkMTSIiotrpVH4otRSz3JyoAaylzNAkIqKWSxRFWCwWqFT1h8zCwsIadGyNRoPIyMjGnhpVwQzNFkYQBLs+mgxoEhER1U7K0AzQBHn2RIh8iMgemkREVI0oirAUmT3yIYqiU+c4efJk/Pjjj3jzzTchCAIEQcC5c+fkMvAffvgBCQkJ0Gq1+OWXX3DmzBncc889iIiIgMFgQP/+/bFlyxa7Y1YvORcEAatXr8a9994LvV6PTp064euvv5bvr15y/vHHHyMoKAgbN25Et27dYDAYMHz4cKSnp8uPKS8vx4wZMxAUFISQkBDMnj0bkyZNwqhRo2p9rampqRg5ciRMJhP8/f3Ro0cPfP/99/L9hw8fxogRI2AwGBAREYGJEyciKyurzs+Tt+EqpAVSKzQos5oBcCgQERFRXaQ3/pihSeQ8aSgQMzSJiEhiLS7D4S5veeS5e56YAaVeU+9+b775Jk6ePImePXvi5ZdfBmDLsJSCdS+88AKWLVuG9u3bw2Qy4cKFC7jzzjvxyiuvQKvV4tNPP8XIkSNx4sQJxMXF1fo8CxYswNKlS/Haa69h5cqVmDBhAlJTUxEcHOxw/6KiIixbtgz/+Mc/oFAo8OCDD+LZZ5/FmjVrAABLlizBmjVr8NFHH6Fbt25488038eWXX+K2226r9RyefPJJmM1m/PTTT/D398fRo0dhMBgAADk5Obj99tvxyCOP4I033kBxcTFmz56NsWPHYtu2bbV+nrwNA5otUNU+mtUnuBIREVElrcr2e5IDgYicJw0FYg9NIiLyJYGBgdBoNNDr9Q7Lvl9++WXccccd8u3g4GD07t1bvr1w4UKsX78eX3/9NZKTk2t9nsmTJ+OBBx4AALz66qt46623sGfPHgwfPtzh/mVlZVi1ahU6dOgAAEhOTpYDiQCwcuVKzJkzB/feey8A4O2337bLtnTk/PnzGD16NK677joAQPv27eX73n77bfTt2xevvvqqvO3DDz9EbGwsTp48ic6dO9f5efIWXIW0QFUnnVef4EpERESVKkvOmaFJ5CyxooemoGOGJhER2Sj81Oh5YobHntsVEhMT7W4XFBRg/vz5+O6775Ceno7y8nIUFxfj/PnzdR6nV69e8v/9/f1hNBqRmZlZ6/56vV4OZgJAVFSUvH9ubi4uX76MAQMGyPcrlUokJCTAarXWeswZM2bgiSeewKZNm5CUlITRo0fL53Xo0CFs375dztis6syZM+jcuXOdr89bMKDZAtn30GSGJhERUW10Fb8nDeyhSeQ0a4lUcs4/JYiIyEYQBKfKvr2Zv7+/3e1nn30WmzdvxrJly9CxY0f4+flhzJgxMJvNdR5HrbYPsAqCUGfw0dH+zvYFrc0jjzyCYcOG4bvvvsOmTZuwaNEivP7665g+fToKCgowcuRILFmypMbjoqKimvS87sShQC2Qfck5MzSJiIhqw5JzoobjUCAiIvJVGo0GFovFqX137tyJyZMn495778V1112HyMhItw/HCQwMREREBPbu3Stvs1gs2L9/f72PjY2NxeOPP44vvvgCs2bNwgcffAAA6NevH44cOYL4+Hh07NjR7kMK6jbk8+QpDGi2QHYZmipmaBIREdXm1ti70TGoBwZE1d5UnYjsWSt6aHIoEBER+Zr4+Hjs3r0b586dQ1ZWVp2Zk506dcIXX3yBgwcP4tChQxg/fnyd+zeX6dOnY9GiRfjqq69w4sQJPPXUU7h27RoEQaj1MU8//TQ2btyIs2fPYv/+/di+fTu6desGwDYwKDs7Gw888AD27t2LM2fOYOPGjZgyZYocxGzI58lTGNBsgexLzpmhSUREVJvrY5KwfMgXaGPsUP/ORASgSoamhgFNIiLyLc8++yyUSiW6d++OsLCwOvthLl++HCaTCTfccANGjhyJYcOGoV+/fm48W5vZs2fjgQcewEMPPYRBgwbBYDBg2LBh0Olqj/dYLBY8+eST6NatG4YPH47OnTvj3XffBQBER0dj586dsFgsGDp0KK677jo8/fTTCAoKgkJhCxM25PPkKYLY1MJ8AgDk5eUhMDAQubm5MBo9W7b2wo7xOHp1HwBgYo+Z+HPXxzx6PkREROT9vGktQ43jrmt4euznKEy5gLh3/gTT3V2b7XmIiMh7lZSU4OzZs2jXrl2dgTVyPavVim7dumHs2LFYuHChp0+nUer6+nF2PcPGNy0QMzSJiIiIqLlIGZocCkRERNT8UlNTsWnTJtxyyy0oLS3F22+/jbNnz2L8+PGePjWPYsl5C6RWVgY0NQxoEhEREZELiRU9NAX20CQiImp2CoUCH3/8Mfr374/Bgwfj999/x5YtW+SemK0V31ZtgeymnKsY0CQiIiIi17HKGZoMaBIRETW32NhY7Ny509On4XWYodkC2Zecc8o5ERERUVXvvPMO4uPjodPpMHDgQOzZs6fWfT/44APcdNNNMJlMMJlMSEpKqnP/xx9/HIIgYMWKFXbbs7OzMWHCBBiNRgQFBeHhhx9GQUGB3T6//fYbbrrpJuh0OsTGxmLp0qVNep3NRSyVMjSZG0FERESewYBmC6RiD00iIiIihz7//HPMnDkT8+bNw/79+9G7d28MGzYMmZmZDvffsWMHHnjgAWzfvh0pKSmIjY3F0KFDkZaWVmPf9evXY9euXYiOjq5x34QJE3DkyBFs3rwZ3377LX766Sc8+uij8v15eXkYOnQo2rZti3379uG1117D/Pnz8f7777vuxbuItYQ9NImIiMizGNBsgZihSUREROTY8uXLMXXqVEyZMgXdu3fHqlWroNfr8eGHHzrcf82aNZg2bRr69OmDrl27YvXq1bBardi6davdfmlpaZg+fTrWrFkDtVptd9+xY8ewYcMGrF69GgMHDsSNN96IlStXYt26dbh06ZL8PGazGR9++CF69OiB+++/HzNmzMDy5cub5xPRBJUZmiw5JyIiIs9gQLMFYg9NIiIioprMZjP27duHpKQkeZtCoUBSUhJSUlKcOkZRURHKysoQHBwsb7NarZg4cSKee+459OjRo8ZjUlJSEBQUhMTERHlbUlISFAoFdu/eLe9z8803Q6OpfGN62LBhOHHiBK5du+bwXEpLS5GXl2f34Q6imRmaRERE5FkMaLZAVaecM0OTiIiIyCYrKwsWiwURERF22yMiIpCRkeHUMWbPno3o6Gi7oOiSJUugUqkwY8YMh4/JyMhAeHi43TaVSoXg4GD5eTMyMhyel3SfI4sWLUJgYKD8ERsb69RraApRFGGVMjQ1zNAkIiIiz2BAswVSs4cmERERkcstXrwY69atw/r166HT2dZY+/btw5tvvomPP/4YgiC49XzmzJmD3Nxc+ePChQvN/6TlVsAqAuBQICIiIvIcBjRbILuSc2ZoEhEREQEAQkNDoVQqcfnyZbvtly9fRmRkZJ2PXbZsGRYvXoxNmzahV69e8vaff/4ZmZmZiIuLg0qlgkqlQmpqKmbNmoX4+HgAQGRkZI2hQ+Xl5cjOzpafNzIy0uF5Sfc5otVqYTQa7T6am5SdCQAK9tAkIqJWKD4+HitWrJBvC4KAL7/8stb9z507B0EQcPDgwSY9r6uO01IwoNkC2WVosocmEREREQBAo9EgISHBbqCPNOBn0KBBtT5u6dKlWLhwITZs2GDXBxMAJk6ciN9++w0HDx6UP6Kjo/Hcc89h48aNAIBBgwYhJycH+/btkx+3bds2WK1WDBw4UN7np59+QllZmbzP5s2b0aVLF5hMJpe8fleQ+mcCzNAkIiICgPT0dIwYMcKlx5w8eTJGjRplty02Nhbp6eno2bOnS5/L1eoL8LoKVyEtUNWApoYl50RERESymTNnYtKkSUhMTMSAAQOwYsUKFBYWYsqUKQCAhx56CDExMVi0aBEAW3/MuXPnYu3atYiPj5f7WRoMBhgMBoSEhCAkJMTuOdRqNSIjI9GlSxcAQLdu3TB8+HBMnToVq1atQllZGZKTk3H//fcjOjoaADB+/HgsWLAADz/8MGbPno3Dhw/jzTffxBtvvOGuT41TqvbPFBTuLbEnIiLyRvVVebiKUql023P5AmZotkBSyblKoYZSYCkQERERkWTcuHFYtmwZ5s6diz59+uDgwYPYsGGDPIDn/PnzSE9Pl/d/7733YDabMWbMGERFRckfy5Yta9DzrlmzBl27dsWQIUNw55134sYbb8T7778v3x8YGIhNmzbh7NmzSEhIwKxZszB37lw8+uijrnnhLiKW2DI0ORCIiIiqEkURJeVFHvkQRdGpc3z//fcRHR0Nq9Vqt/2ee+7BX/7yFwDAmTNncM899yAiIgIGgwH9+/fHli1b6jxu9YzEPXv2oG/fvtDpdEhMTMSBAwfs9rdYLHj44YfRrl07+Pn5oUuXLnjzzTfl++fPn49PPvkEX331FQRBgCAI2LFjh8OS8x9//BEDBgyAVqtFVFQUXnjhBZSXV1ZT3HrrrZgxYwaef/55BAcHIzIyEvPnz6/z9ezYsQMDBgyAv78/goKCMHjwYKSmpsr3f/XVV+jXrx90Oh3at2+PBQsWyM8ptdu59957IQiCfLs5MEOzBZKmnLN/JhEREVFNycnJSE5Odnjfjh077G6fO3euwcd39Jjg4GCsXbu2zsf16tULP//8c4Ofz52spbY/WNg/k4iIqiq1FGPsV3098tz/uucAdCp9vfv9+c9/xvTp07F9+3YMGTIEAJCdnY0NGzbg+++/BwAUFBTgzjvvxCuvvAKtVotPP/0UI0eOxIkTJxAXF1fvcxQUFOBPf/oT7rjjDnz22Wc4e/YsnnrqKbt9rFYr2rRpg3//+98ICQnB//73Pzz66KOIiorC2LFj8eyzz+LYsWPIy8vDRx99BMC2jrh06ZLdcdLS0nDnnXdi8uTJ+PTTT3H8+HFMnToVOp3OLmj5ySefYObMmdi9ezdSUlIwefJkDB48GHfccUeN8y8vL8eoUaMwdepU/POf/4TZbMaePXvkwYc///wzHnroIbz11lu46aabcObMGfnN13nz5mHv3r0IDw/HRx99hOHDh0OpbL71AgOaLZBUcs4J50RERETkSqJUcs7+mURE5GNMJhNGjBiBtWvXygHN//znPwgNDcVtt90GAOjduzd69+4tP2bhwoVYv349vv7661rfDK1q7dq1sFqt+L//+z/odDr06NEDFy9exBNPPCHvo1arsWDBAvl2u3btkJKSgn/9618YO3YsDAYD/Pz8UFpaWmeJ+bvvvovY2Fi8/fbbEAQBXbt2xaVLlzB79mzMnTsXCoWtKLtXr16YN28eAKBTp054++23sXXrVocBzby8POTm5uJPf/oTOnToAMDWOkeyYMECvPDCC5g0aRIAoH379li4cCGef/55zJs3D2FhYQCAoKCgZi+P50qkBZJKzpmhSURERESuJJoZ0CQiopq0Sj/8654D9e/YTM/trAkTJmDq1Kl49913odVqsWbNGtx///1y8K+goADz58/Hd999h/T0dJSXl6O4uBjnz5936vjHjh1Dr169oNNVJpg5Gjz4zjvv4MMPP8T58+dRXFwMs9mMPn36OP06pOcaNGiQnD0JAIMHD0ZBQQEuXrwoZ5T26tXL7nFRUVHIzMx0eMzg4GBMnjwZw4YNwx133IGkpCSMHTsWUVFRAIBDhw5h586deOWVV+THWCwWlJSUoKioCHp9/ZmyrsKVSAskZ2hywjkRERERuZBccs4emkREVIUgCE6VfXvayJEjIYoivvvuO/Tv3x8///yz3QC+Z599Fps3b8ayZcvQsWNH+Pn5YcyYMTCbzS47h3Xr1uHZZ5/F66+/jkGDBiEgIACvvfYadu/e7bLnqEqtVtvdFgShRh/Rqj766CPMmDEDGzZswOeff44XX3wRmzdvxvXXX4+CggIsWLAA9913X43HVQ3iugMDmi1QlMEWhY82xHv2RIiIiIioRRErApoCe2gSEZEP0ul0uO+++7BmzRqcPn0aXbp0Qb9+/eT7d+7cicmTJ+Pee+8FYMvYbEg/7W7duuEf//gHSkpK5ADfrl277PbZuXMnbrjhBkybNk3edubMGbt9NBoNLBZLvc/13//+F6IoylmaO3fuREBAANq0aeP0OTvSt29f9O3bF3PmzMGgQYOwdu1aXH/99ejXrx9OnDiBjh071vpYtVpd77m7Aqect0DxgV2watgmPNN/qadPhYiIiIhaEH2/aHT4fCxiXh7i6VMhIiJqlAkTJuC7777Dhx9+iAkTJtjd16lTJ3zxxRc4ePAgDh06hPHjx9eZzVjd+PHjIQgCpk6diqNHj+L777/HsmXLajzHr7/+io0bN+LkyZN46aWXsHfvXrt94uPj8dtvv+HEiRPIyspCWVlZjeeaNm0aLly4gOnTp+P48eP46quvMG/ePMycOVMuoW+os2fPYs6cOUhJSUFqaio2bdqEU6dOyX00586di08//RQLFizAkSNHcOzYMaxbtw4vvvii3blv3boVGRkZuHbtWqPOwxkMaLZQ0Ya2HApERERERC6lMvnBcEMc/BOiPX0qREREjXL77bcjODgYJ06cwPjx4+3uW758OUwmE2644QaMHDkSw4YNs8vgrI/BYMA333yD33//HX379sXf/vY3LFmyxG6fxx57DPfddx/GjRuHgQMH4urVq3bZmgAwdepUdOnSBYmJiQgLC8POnTtrPFdMTAy+//577NmzB71798bjjz+Ohx9+2C642FB6vR7Hjx/H6NGj0blzZzz66KN48skn8dhjjwEAhg0bhm+//RabNm1C//79cf311+ONN95A27Zt5WO8/vrr2Lx5M2JjY9G3b99Gn0t9BFEUxWY7eiuSl5eHwMBA5Obmwmg0evp0iIiIiBqEaxnfx2tIRETuUlJSgrNnz6Jdu3Zu751Ivq+urx9n1zNemaH5zjvvID4+HjqdDgMHDsSePXtq3feDDz7ATTfdBJPJBJPJhKSkpDr3f/zxxyEIAlasWGG3PTs7GxMmTIDRaERQUBAefvhhFBQUuOolERERERERERERkQt4XUDz888/x8yZMzFv3jzs378fvXv3xrBhw2odKb9jxw488MAD2L59O1JSUhAbG4uhQ4ciLS2txr7r16/Hrl27EB1ds0RmwoQJOHLkCDZv3oxvv/0WP/30Ex599FGXvz4iIiIiIiIiIiJqPK8LaC5fvhxTp07FlClT0L17d6xatQp6vR4ffvihw/3XrFmDadOmoU+fPujatStWr14Nq9WKrVu32u2XlpaG6dOnY82aNTVG1h87dgwbNmzA6tWrMXDgQNx4441YuXIl1q1bh0uXLjXbayUiIiIiIiIiIqKG8aqAptlsxr59+5CUlCRvUygUSEpKQkpKilPHKCoqQllZGYKDg+VtVqsVEydOxHPPPYcePXrUeExKSgqCgoKQmJgob0tKSoJCocDu3bsdPk9paSny8vLsPoiIiIiIiIiIiKh5eVVAMysrCxaLBREREXbbIyIikJGR4dQxZs+ejejoaLug6JIlS6BSqTBjxgyHj8nIyEB4eLjdNpVKheDg4Fqfd9GiRQgMDJQ/YmNjnTo/IiIiIiIiIqKWgHOmqTFc8XXjVQHNplq8eDHWrVuH9evXy1OS9u3bhzfffBMff/wxBEFw2XPNmTMHubm58seFCxdcdmwiIiIiIiIiIm+lVCoB2CptiRqqqKgIAGq0hGwIlatOxhVCQ0OhVCpx+fJlu+2XL19GZGRknY9dtmwZFi9ejC1btqBXr17y9p9//hmZmZmIi4uTt1ksFsyaNQsrVqzAuXPnEBkZWWPoUHl5ObKzs2t9Xq1WC61W29CXSERERERERETk01QqFfR6Pa5cuQK1Wg2FokXly1EzEUURRUVFyMzMRFBQkBwYbwyvCmhqNBokJCRg69atGDVqFADIA36Sk5NrfdzSpUvxyiuvYOPGjXZ9MAFg4sSJduXnADBs2DBMnDgRU6ZMAQAMGjQIOTk52LdvHxISEgAA27Ztg9VqxcCBA134ComIiIiIiIiIfJsgCIiKisLZs2eRmprq6dMhHxMUFFRv4mJ9vCqgCQAzZ87EpEmTkJiYiAEDBmDFihUoLCyUg48PPfQQYmJisGjRIgC2/phz587F2rVrER8fL/e8NBgMMBgMCAkJQUhIiN1zqNVqREZGokuXLgCAbt26Yfjw4Zg6dSpWrVqFsrIyJCcn4/7770d0dLQbXz0RERERERERkffTaDTo1KkTy86pQdRqdZMyMyVeF9AcN24crly5grlz5yIjIwN9+vTBhg0b5EFB58+ft0tlfu+992A2mzFmzBi748ybNw/z5893+nnXrFmD5ORkDBkyBAqFAqNHj8Zbb73lktdERERERERERNTSKBQKeYYJkTsJIkdSuUReXh4CAwORm5sLo9Ho6dMhIiIiahCuZXwfryERERH5OmfXM+zaSkRERERERERERD6DAU0iIiIiIiIiIiLyGV7XQ9NXSZX7eXl5Hj4TIiIiooaT1jDsRuS7uB4lIiIiX+fsmpQBTRfJz88HAMTGxnr4TIiIiIgaLz8/H4GBgZ4+DWoErkeJiIiopahvTcqhQC5itVpx6dIlBAQEQBCEZnmOvLw8xMbG4sKFC2z07oV4fbwXr4134/XxXrw23s3V10cUReTn5yM6OhoKBbsS+SJ3rEcB/mzwZrw23o3Xx3vx2ng3Xh/v1RzXxtk1KTM0XUShUKBNmzZueS6j0chvYi/G6+O9eG28G6+P9+K18W6uvD7MzPRt7lyPAvzZ4M14bbwbr4/34rXxbrw+3svV18aZNSnfficiIiIiIiIiIiKfwYAmERERERERERER+QwGNH2IVqvFvHnzoNVqPX0q5ACvj/fitfFuvD7ei9fGu/H6kKfwa8978dp4N14f78Vr4914fbyXJ68NhwIRERERERERERGRz2CGJhEREREREREREfkMBjSJiIiIiIiIiIjIZzCgSURERERERERERD6DAU0iIiIiIiIiIiLyGQxo+pB33nkH8fHx0Ol0GDhwIPbs2ePpU2p1Fi1ahP79+yMgIADh4eEYNWoUTpw4YbdPSUkJnnzySYSEhMBgMGD06NG4fPmyh8649Vq8eDEEQcDTTz8tb+O18ay0tDQ8+OCDCAkJgZ+fH6677jr8+uuv8v2iKGLu3LmIioqCn58fkpKScOrUKQ+ecethsVjw0ksvoV27dvDz80OHDh2wcOFCVJ0byOvjHj/99BNGjhyJ6OhoCIKAL7/80u5+Z65DdnY2JkyYAKPRiKCgIDz88MMoKChw46uglozrUe/ANanv4JrUu3A96r24HvUuvrAmZUDTR3z++eeYOXMm5s2bh/3796N3794YNmwYMjMzPX1qrcqPP/6IJ598Ert27cLmzZtRVlaGoUOHorCwUN7nmWeewTfffIN///vf+PHHH3Hp0iXcd999Hjzr1mfv3r34+9//jl69etlt57XxnGvXrmHw4MFQq9X44YcfcPToUbz++uswmUzyPkuXLsVbb72FVatWYffu3fD398ewYcNQUlLiwTNvHZYsWYL33nsPb7/9No4dO4YlS5Zg6dKlWLlypbwPr497FBYWonfv3njnnXcc3u/MdZgwYQKOHDmCzZs349tvv8VPP/2ERx991F0vgVowrke9B9ekvoFrUu/C9ah343rUu/jEmlQknzBgwADxySeflG9bLBYxOjpaXLRokQfPijIzM0UA4o8//iiKoijm5OSIarVa/Pe//y3vc+zYMRGAmJKS4qnTbFXy8/PFTp06iZs3bxZvueUW8amnnhJFkdfG02bPni3eeOONtd5vtVrFyMhI8bXXXpO35eTkiFqtVvznP//pjlNs1e666y7xL3/5i922++67T5wwYYIoirw+ngJAXL9+vXzbmetw9OhREYC4d+9eeZ8ffvhBFARBTEtLc9u5U8vE9aj34prU+3BN6n24HvVuXI96L29dkzJD0weYzWbs27cPSUlJ8jaFQoGkpCSkpKR48MwoNzcXABAcHAwA2LdvH8rKyuyuVdeuXREXF8dr5SZPPvkk7rrrLrtrAPDaeNrXX3+NxMRE/PnPf0Z4eDj69u2LDz74QL7/7NmzyMjIsLs+gYGBGDhwIK+PG9xwww3YunUrTp48CQA4dOgQfvnlF4wYMQIAr4+3cOY6pKSkICgoCImJifI+SUlJUCgU2L17t9vPmVoOrke9G9ek3odrUu/D9ah343rUd3jLmlTlkqNQs8rKyoLFYkFERITd9oiICBw/ftxDZ0VWqxVPP/00Bg8ejJ49ewIAMjIyoNFoEBQUZLdvREQEMjIyPHCWrcu6deuwf/9+7N27t8Z9vDae9ccff+C9997DzJkz8de//hV79+7FjBkzoNFoMGnSJPkaOPo5x+vT/F544QXk5eWha9euUCqVsFgseOWVVzBhwgQA4PXxEs5ch4yMDISHh9vdr1KpEBwczGtFTcL1qPfimtT7cE3qnbge9W5cj/oOb1mTMqBJ1EhPPvkkDh8+jF9++cXTp0IALly4gKeeegqbN2+GTqfz9OlQNVarFYmJiXj11VcBAH379sXhw4exatUqTJo0ycNnR//617+wZs0arF27Fj169MDBgwfx9NNPIzo6mteHiMjLcU3qXbgm9V5cj3o3rkepoVhy7gNCQ0OhVCprTL67fPkyIiMjPXRWrVtycjK+/fZbbN++HW3atJG3R0ZGwmw2Iycnx25/Xqvmt2/fPmRmZqJfv35QqVRQqVT48ccf8dZbb0GlUiEiIoLXxoOioqLQvXt3u23dunXD+fPnAUC+Bvw55xnPPfccXnjhBdx///247rrrMHHiRDzzzDNYtGgRAF4fb+HMdYiMjKwxoKW8vBzZ2dm8VtQkXI96J65JvQ/XpN6L61HvxvWo7/CWNSkDmj5Ao9EgISEBW7dulbdZrVZs3boVgwYN8uCZtT6iKCI5ORnr16/Htm3b0K5dO7v7ExISoFar7a7ViRMncP78eV6rZjZkyBD8/vvvOHjwoPyRmJiICRMmyP/ntfGcwYMH48SJE3bbTp48ibZt2wIA2rVrh8jISLvrk5eXh927d/P6uEFRUREUCvslgVKphNVqBcDr4y2cuQ6DBg1CTk4O9u3bJ++zbds2WK1WDBw40O3nTC0H16PehWtS78U1qffietS7cT3qO7xmTeqS0ULU7NatWydqtVrx448/Fo8ePSo++uijYlBQkJiRkeHpU2tVnnjiCTEwMFDcsWOHmJ6eLn8UFRXJ+zz++ONiXFycuG3bNvHXX38VBw0aJA4aNMiDZ916VZ0oKYq8Np60Z88eUaVSia+88op46tQpcc2aNaJerxc/++wzeZ/FixeLQUFB4ldffSX+9ttv4j333CO2a9dOLC4u9uCZtw6TJk0SY2JixG+//VY8e/as+MUXX4ihoaHi888/L+/D6+Me+fn54oEDB8QDBw6IAMTly5eLBw4cEFNTU0VRdO46DB8+XOzbt6+4e/du8ZdffhE7deokPvDAA556SdSCcD3qPbgm9S1ck3oHrke9G9ej3sUX1qQMaPqQlStXinFxcaJGoxEHDBgg7tq1y9On1OoAcPjx0UcfyfsUFxeL06ZNE00mk6jX68V7771XTE9P99xJt2LVF4+8Np71zTffiD179hS1Wq3YtWtX8f3337e732q1ii+99JIYEREharVacciQIeKJEyc8dLatS15envjUU0+JcXFxok6nE9u3by/+7W9/E0tLS+V9eH3cY/v27Q5/z0yaNEkUReeuw9WrV8UHHnhANBgMotFoFKdMmSLm5+d74NVQS8T1qHfgmtS3cE3qPbge9V5cj3oXX1iTCqIoiq7J9SQiIiIiIiIiIiJqXuyhSURERERERERERD6DAU0iIiIiIiIiIiLyGQxoEhERERERERERkc9gQJOIiIiIiIiIiIh8BgOaRERERERERERE5DMY0CQiIiIiIiIiIiKfwYAmERERERERERER+QwGNImIiIiIiIiIiMhnMKBJRFTN5MmTER8f36jHzp8/H4IguPaEWohbb70VPXv29PRpEBEREXk9rkebB9ejRC0HA5pE5DMEQXDqY8eOHZ4+VSIiIiJqgbgeJSLyDoIoiqKnT4KIyBmfffaZ3e1PP/0Umzdvxj/+8Q+77XfccQciIiIa/TxlZWWwWq3QarUNfmx5eTnKy8uh0+ka/fwt1a233oqsrCwcPnzY06dCRERE1Chcj/o2rkeJWg6Vp0+AiMhZDz74oN3tXbt2YfPmzTW2V1dUVAS9Xu/086jV6kadHwCoVCqoVPzRSkRERNQScT1KROQdWHJORC2K1Bdn3759uPnmm6HX6/HXv/4VAPDVV1/hrrvuQnR0NLRaLTp06ICFCxfCYrHYHaN6z6Jz585BEAQsW7YM77//Pjp06ACtVov+/ftj7969do911LNIEAQkJyfjyy+/RM+ePaHVatGjRw9s2LChxvnv2LEDiYmJ0Ol06NChA/7+9783qA/S7t27MXz4cAQGBkKv1+OWW27Bzp07HZ7j8ePHMXbsWBiNRoSEhOCpp55CSUmJ3b7l5eVYuHCh/Jrj4+Px17/+FaWlpTWe+4cffsAtt9yCgIAAGI1G9O/fH2vXrq2x39GjR3HbbbdBr9cjJiYGS5curbHPypUr0aNHD+j1ephMJiQmJjo8FhEREZG34XqU61Eian5824aIWpyrV69ixIgRuP/++/Hggw/K5T4ff/wxDAYDZs6cCYPBgG3btmHu3LnIy8vDa6+9Vu9x165di/z8fDz22GMQBAFLly7Ffffdhz/++KPed9F/+eUXfPHFF5g2bRoCAgLw1ltvYfTo0Th//jxCQkIAAAcOHMDw4cMRFRWFBQsWwGKx4OWXX0ZYWJhTr3vbtm0YMWIEEhISMG/ePCgUCnz00Ue4/fbb8fPPP2PAgAF2+48dOxbx8fFYtGgRdu3ahbfeegvXrl3Dp59+Ku/zyCOP4JNPPsGYMWMwa9Ys7N69G4sWLcKxY8ewfv16eb+PP/4Yf/nLX9CjRw/MmTMHQUFBOHDgADZs2IDx48fL+127dg3Dhw/Hfffdh7Fjx+I///kPZs+ejeuuuw4jRowAAHzwwQeYMWMGxowZIy9qf/vtN+zevdvuWERERETeiutRrkeJqJmJREQ+6sknnxSr/xi75ZZbRADiqlWrauxfVFRUY9tjjz0m6vV6saSkRN42adIksW3btvLts2fPigDEkJAQMTs7W97+1VdfiQDEb775Rt42b968GucEQNRoNOLp06flbYcOHRIBiCtXrpS3jRw5UtTr9WJaWpq87dSpU6JKpapxzOqsVqvYqVMncdiwYaLVarV7ze3atRPvuOOOGud499132x1j2rRpIgDx0KFDoiiK4sGDB0UA4iOPPGK337PPPisCELdt2yaKoijm5OSIAQEB4sCBA8Xi4uIa5yWRrs2nn34qbystLRUjIyPF0aNHy9vuuecesUePHnW+XiIiIiJvwPVoJa5HicidWHJORC2OVqvFlClTamz38/OT/5+fn4+srCzcdNNNKCoqwvHjx+s97rhx42AymeTbN910EwDgjz/+qPexSUlJ6NChg3y7V69eMBqN8mMtFgu2bNmCUaNGITo6Wt6vY8eO8jvFdTl48CBOnTqF8ePH4+rVq8jKykJWVhYKCwsxZMgQ/PTTT7BarXaPefLJJ+1uT58+HQDw/fff2/07c+ZMu/1mzZoFAPjuu+8AAJs3b0Z+fj5eeOGFGs3nq5cmGQwGux5TGo0GAwYMsPscBgUF4eLFizXKp4iIiIh8BdejXI8SUfNiyTkRtTgxMTHQaDQ1th85cgQvvvgitm3bhry8PLv7cnNz6z1uXFyc3W1pMXnt2rUGP1Z6vPTYzMxMFBcXo2PHjjX2c7StulOnTgEAJk2aVOs+ubm5dgvgTp062d3foUMHKBQKnDt3DgCQmpoKhUJR4/kjIyMRFBSE1NRUAMCZM2cAAD179qz3PNu0aVNjUWkymfDbb7/Jt2fPno0tW7ZgwIAB6NixI4YOHYrx48dj8ODB9R6fiIiIyBtwPeoY16NE5CoMaBJRi1P1nW9JTk4ObrnlFhiNRrz88svo0KEDdDod9u/fj9mzZ9d4t9gRpVLpcLsois36WGdI5//aa6+hT58+DvcxGAx1HqO2Ru/ONoB3hjOfh27duuHEiRP49ttvsWHDBvz3v//Fu+++i7lz52LBggUuOxciIiKi5sL1aB+H+3A9SkSuwoAmEbUKO3bswNWrV/HFF1/g5ptvlrefPXvWg2dVKTw8HDqdDqdPn65xn6Nt1UnlQ0ajEUlJSU4956lTp9CuXTu757FarfJEzbZt28JqteLUqVPo1q2bvN/ly5eRk5ODtm3b2j334cOHnXr33hn+/v4YN24cxo0bB7PZjPvuuw+vvPIK5syZU6OMiIiIiMgXcD1aE9ejRNRY7KFJRK2C9E5s1XdezWYz3n33XU+dkh2lUomkpCR8+eWXuHTpkrz99OnT+OGHH+p9fEJCAjp06IBly5ahoKCgxv1Xrlypse2dd96xu71y5UoAkHsk3XnnnQCAFStW2O23fPlyAMBdd90FABg6dCgCAgKwaNEilJSU2O3bmHf8r169andbo9Gge/fuEEURZWVlDT4eERERkTfgepTrUSJyHWZoElGrcMMNN8BkMmHSpEmYMWMGBEHAP/7xD5eV2LjC/PnzsWnTJgwePBhPPPEELBYL3n77bfTs2RMHDx6s87EKhQKrV6/GiBEj0KNHD0yZMgUxMTFIS0vD9u3bYTQa8c0339g95uzZs7j77rsxfPhwpKSk4LPPPsP48ePRu3dvAEDv3r0xadIkvP/++3KJ1J49e/DJJ59g1KhRuO222wDY3oV/44038Mgjj6B///4YP348TCYTDh06hKKiInzyyScN+jwMHToUkZGRGDx4MCIiInDs2DG8/fbbuOuuuxAQENCgYxERERF5C65HuR4lItdhQJOIWoWQkBB8++23mDVrFl588UWYTCY8+OCDGDJkCIYNG+bp0wNge1f7hx9+wLPPPouXXnoJsbGxePnll3Hs2DGnpl7eeuutSElJwcKFC/H222+joKAAkZGRGDhwIB577LEa+3/++eeYO3cuXnjhBahUKiQnJ+O1116z22f16tVo3749Pv74Y6xfvx6RkZGYM2cO5s2bZ7ffww8/jPDwcCxevBgLFy6EWq1G165d8cwzzzT48/DYY49hzZo1WL58OQoKCtCmTRvMmDEDL774YoOPRUREROQtuB7lepSIXEcQventICIiqmHUqFE4cuSIPDmyqebPn48FCxbgypUrCA0NdckxiYiIiKjl4nqUiLwNe2gSEXmR4uJiu9unTp3C999/j1tvvdUzJ0RERERErQrXo0TkC1hyTkTkRdq3b4/Jkyejffv2SE1NxXvvvQeNRoPnn3/e06dGRERERK0A16NE5AsY0CQi8iLDhw/HP//5T2RkZECr1WLQoEF49dVX0alTJ0+fGhERERG1AlyPEpEvYA9NIiIiIiIiIiIi8hnsoUlEREREREREREQ+gwFNIiIiIiIiIiIi8hkMaBIREREREREREZHPYECTiIiIiIiIiIiIfAYDmkREREREREREROQzGNAkIiIiIiIiIiIin8GAJhEREREREREREfkMBjSJiIiIiIiIiIjIZ/x/zYtkpeF77LsAAAAASUVORK5CYII=",
            "text/plain": [
              "<Figure size 1600x500 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(16, 5), dpi=100)\n",
        "\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.title(\"Generator learning curves\", fontsize=14)\n",
        "plt.xlabel(\"Training epochs\", fontsize=12)\n",
        "plt.ylabel(gan.loss_name, fontsize=12)\n",
        "plt.plot(train.history[\"g_loss\"], color=\"#d01c8b\", label=\"training set\")\n",
        "plt.plot(train.history[\"val_g_loss\"], color=\"#4dac26\", label=\"validation set\")\n",
        "plt.legend(loc=\"upper right\", fontsize=10)\n",
        "\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.title(\"Discriminator learning curves\", fontsize=14)\n",
        "plt.xlabel(\"Training epochs\", fontsize=12)\n",
        "plt.ylabel(gan.loss_name, fontsize=12)\n",
        "plt.plot(train.history[\"d_loss\"], color=\"#d01c8b\", label=\"training set\")\n",
        "plt.plot(train.history[\"val_d_loss\"], color=\"#4dac26\", label=\"validation set\")\n",
        "plt.legend(loc=\"lower right\", fontsize=10)\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e5wv91XH6BDC"
      },
      "source": [
        "#### Metric curves"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 492
        },
        "id": "P8rgOZgpKjPj",
        "outputId": "e19083c1-397c-49be-d03b-629803ae2fc8"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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egSAIEAQBpaWlSlnM//73PwwZMgRGoxEbNmzA3r17cfHFFyM/Px8ZGRkYNmwYvvjiC7d9epbgCIKAV199FZdccgnS0tLQq1cvfPLJJ8r1niU4ixYtQk5ODj7//HP07dsXGRkZGD9+PI4cOaL8jM1mw+23346cnBx06NAB9957L6ZMmYKJEyf6va9lZWWYMGECcnNzkZ6ejn79+mHFihXK9du3b8f555+PjIwM5Ofn4/rrr8fRo0dbfZyIEoXYYsXucYux69yFEE22eC+HiIgSBI9HHXg8GlvMsEwyJlszlv32OgBg0im3IUWXFucVERFRNIktVmzv82xcbvvU3bdDm2Zoc7tnnnkGe/bswamnnopHHnkEgOOMtHzwc9999+HJJ59Ez549kZubiwMHDuCCCy7AP//5TxiNRrzxxhuYMGECdu/eje7du/u9nTlz5uDf//43nnjiCTz33HO49tprUVZWhry8PJ/bNzc348knn8Sbb74JjUaD6667DnfffTfefvttAMDjjz+Ot99+G6+//jr69u2LZ555Bh9//DHOOeccv2u49dZbYbFY8NVXXyE9PR07duxARkYGAKC2thbnnnsu/vKXv+Dpp59GS0sL7r33Xlx55ZX48ssv/T5ORAlDI8C0owoAIFnsQAo/KhAREY9H1Xg8Gjs8CkkyNtHq8/9ERETxkp2dDYPBgLS0NJ9lMI888gjOO+885fu8vDwMHDhQ+f7RRx/FRx99hE8++QQzZszweztTp07FNddcAwB47LHH8Oyzz2Lz5s0YP368z+2tVisWLFiAE088EQAwY8YM5cAMAJ577jnMmjULl1xyCQDg+eefdzs77cv+/ftx2WWXoX///gCAnj17Ktc9//zzGDx4MB577DHlsoULF6KoqAh79uxB7969W32ciOJNMGiV/4sWG7QwxnE1REREgePxKJSfby/HowxYJhm7ZFf+bxNZqkNE1N5pUvU4dfftcbvtSBg6dKjb942NjXj44YexfPlyHDlyBDabDS0tLdi/f3+r+xkwYIDy//T0dGRlZaGystLv9mlpacrBIQAUFBQo29fV1aGiogLDhw9XrtdqtRgyZAhEUfS7z9tvvx233HILVq1ahbFjx+Kyyy5T1vXzzz9j7dq1yhlutb1796J3796t3j+ieBMEAYJBC8lid2RYEhERgcejajwejR0GLJMMMyyJiI4vgiAEVAaTyNLT092+v/vuu7F69Wo8+eSTOOmkk5CamorLL78cFoul1f3o9e4HrIIgtHow52v7QPsg+fOXv/wF48aNw/Lly7Fq1SrMnTsXTz31FG677TY0NjZiwoQJePxx7z7TBQUFYd0uUawIeo0jYGlmwJKIiBx4POrC49HY4dCdJCO6ZVgyYElERInBYDDAbg8swPHNN99g6tSpuOSSS9C/f3906dIl5s2+s7OzkZ+fj++//165zG63Y+vWrW3+bFFREW6++WYsXboUf/vb3/DKK68AAE477TT8+uuvKC4uxkknneT2JR8kB/M4EcWDYHDkM0hWPk+JiCi58Hi0fR2PMmCZZOyiKmApMWBJRESJobi4GJs2bUJpaSmOHj3a6pnmXr16YenSpfjpp5/w888/Y9KkSa1uHy233XYb5s6di2XLlmH37t244447UFNTA0EQ/P7MnXfeic8//xwlJSXYunUr1q5di759+wJwNECvrq7GNddcg++//x579+7F559/jmnTpikHhcE8TkTxIPexFFkSTkRESYbHo+3reJQByySjDlIyw5KIiBLF3XffDa1Wi1NOOQWdOnVqtf/PvHnzkJubizPOOAMTJkzAuHHjcNppp8VwtQ733nsvrrnmGkyePBkjR45ERkYGxo0bh5SUFL8/Y7fbceutt6Jv374YP348evfujf/85z8AgMLCQnzzzTew2+344x//iP79++POO+9ETk4ONBrHIVcwjxNRPGiMjoAle1gSEVGy4fFo+zoeFaRwi+ePA/X19cjOzkZdXR2ysrLiupb99b9hxuoLAQBPn7sUJ+b2i+t6iIgockwmE0pKSnDCCSe0epBC0SGKIvr27Ysrr7wSjz76aLyXE5LWnkOJdDxDwYvV72/XOQth/r0aJ75/FTJGFkXtdoiIKHHxmDR+eDzqwqE7SUZdEm5lhiUREVHIysrKsGrVKowePRpmsxnPP/88SkpKMGnSpHgvjShuBD0zLImIiGKFx6P+sSQ8ydglm8//ExERUXA0Gg0WLVqEYcOGYdSoUdi2bRu++OILpQcQ0fFI7mHJoTtERETRx+NR/5hhmWRsok31f2ZYEhERhaqoqAjffPNNvJdBlFA0HLpDREQUMzwe9Y8ZlklGlBiwJCIiIqLoEOShO2YGLImIiCh+GLBMMnbJdfDIgCURERERRZJSEm5h6yEiIiKKHwYsk4x66A57WBIRERFRJGkMjo5RLAknIiKieGLAMsnYJVdWJaeEExEREVEkCXrHxwNOCSciIqJ4YsAyybhlWIrMsCQiIiKiyFF6WHJKOBEREcURA5ZJhj0siYiIiChaBGdJOIfuEBERUTwxYJlk7G5Twi1xXAkREVFkFRcXY/78+cr3giDg448/9rt9aWkpBEHATz/9FNbtRmo/RO2BPHSHPSyJiOh4xOPRxMGAZZJRl4HbWBJORETt2JEjR3D++edHdJ9Tp07FxIkT3S4rKirCkSNHcOqpp0b0tiKtrQNmokjQKFPCGbAkIiLi8ai7WB6P6mJyKxQxLAknIqLjRZcuXWJyO1qtNma3RZToBD0DlkRERDIej8YPMyyTjFtJuMQMSyKi9k6SJJhszXH5kiQpoDW+/PLLKCwshCiKbpdffPHF+POf/wwA2Lt3Ly6++GLk5+cjIyMDw4YNwxdffNHqfj3P4G7evBmDBw9GSkoKhg4dih9//NFte7vdjhtuuAEnnHACUlNT0adPHzzzzDPK9Q8//DAWL16MZcuWQRAECIKAdevW+SzBWb9+PYYPHw6j0YiCggLcd999sNlcf3fHjBmD22+/Hffccw/y8vLQpUsXPPzww63en3Xr1mH48OFIT09HTk4ORo0ahbKyMuX6ZcuW4bTTTkNKSgp69uyJOXPmKLdZXFwMALjkkksgCILyPVGkKUN3GLAkIiInHo9+rHzP49HYHY8ywzLJuJeEM8OSiKi9M9tbcOWywXG57fcv/hEpurQ2t7viiitw2223Ye3atfjDH/4AAKiursbKlSuxYsUKAEBjYyMuuOAC/POf/4TRaMQbb7yBCRMmYPfu3ejevXubt9HY2IgLL7wQ5513Ht566y2UlJTgjjvucNtGFEV069YNH3zwATp06IBvv/0WN954IwoKCnDllVfi7rvvxs6dO1FfX4/XX38dAJCXl4fDhw+77efQoUO44IILMHXqVLzxxhvYtWsXpk+fjpSUFLeDwMWLF2PmzJnYtGkTNm7ciKlTp2LUqFE477zzvNZvs9kwceJETJ8+He+++y4sFgs2b94MQRAAAF9//TUmT56MZ599FmeddRb27t2LG2+8EQAwe/ZsfP/99+jcuTNef/11jB8/Hlqtts3HjCgU7GFJRESeeDwK5ed5PBq741EGLJOMuiTczh6WRESUAHJzc3H++efjnXfeUQ4QP/zwQ3Ts2BHnnHMOAGDgwIEYOHCg8jOPPvooPvroI3zyySeYMWNGm7fxzjvvQBRFvPbaa0hJSUG/fv1w8OBB3HLLLco2er0ec+bMUb4/4YQTsHHjRrz//vu48sorkZGRgdTUVJjN5lZLbv7zn/+gqKgIzz//PARBwMknn4zDhw/j3nvvxUMPPQSNxlGgMmDAAMyePRsA0KtXLzz//PNYs2aNzwPE+vp61NXV4cILL8SJJ54IAOjbt69y/Zw5c3DfffdhypQpAICePXvi0UcfxT333IPZs2ejU6dOAICcnByWC1FUsYclERElIx6Ptr/j0YQLWL7wwgt44oknUF5ejoEDB+K5557D8OHD/W5fW1uLv//971i6dCmqq6vRo0cPzJ8/HxdccAEAR7qt+skCAH369MGuXbuiej+ixT3DklPCiYjaO6M2Fe9f/GPbG0bptgN17bXXYvr06fjPf/4Do9GIt99+G1dffbVyMNXY2IiHH34Yy5cvx5EjR2Cz2dDS0oL9+/cHtP+dO3diwIABSElJUS4bOXKk13YvvPACFi5ciP3796OlpQUWiwWDBg0K+H7ItzVy5EjlbDMAjBo1Co2NjTh48KByBn7AgAFuP1dQUIDKykqf+8zLy8PUqVMxbtw4nHfeeRg7diyuvPJKFBQUAAB+/vlnfPPNN/jnP/+p/IzdbofJZEJzczPS0trOLCCKBEEJWPLEOBEROfB41IHHo7E9Hk2ogOWSJUswc+ZMLFiwACNGjMD8+fMxbtw47N69G507d/ba3mKx4LzzzkPnzp3x4YcfomvXrigrK0NOTo7bdv369XPrS6DTJdTdDorb0B32sCQiavcEQQioDCbeJkyYAEmSsHz5cgwbNgxff/01nn76aeX6u+++G6tXr8aTTz6Jk046Campqbj88sthsUTu5Nt7772Hu+++G0899RRGjhyJzMxMPPHEE9i0aVPEbkNNr9e7fS8IglffJLXXX38dt99+O1auXIklS5bggQcewOrVq3H66aejsbERc+bMwaWXXur1c+qDYqJoE5hhSUREHng8Gjgej0ZOQkXu5s2bh+nTp2PatGkAgAULFmD58uVYuHAh7rvvPq/tFy5ciOrqanz77bfKL8lX00+dTtduyqfchu6whyURESWIlJQUXHrppXj77bfx+++/o0+fPjjttNOU67/55htMnToVl1xyCQDHGe7S0tKA99+3b1+8+eabMJlMygHTd99957bNN998gzPOOAN//etflcv27t3rto3BYIDd3nogpm/fvvjvf/8LSZKUs9rffPMNMjMz0a1bt4DX7MvgwYMxePBgzJo1CyNHjsQ777yD008/Haeddhp2796Nk046ye/P6vX6NtdOFC6lh6WZzzUiIkouPB4NTLIcjybMlHCLxYItW7Zg7NixymUajQZjx47Fxo0bff7MJ598gpEjR+LWW29Ffn4+Tj31VDz22GNeD95vv/2GwsJC9OzZE9dee22b6b5msxn19fVuX4lCXRLOHpZERJRIrr32WuVE47XXXut2Xa9evbB06VL89NNP+PnnnzFp0qRWz/56mjRpEgRBwPTp07Fjxw6sWLECTz75pNdt/PDDD/j888+xZ88ePPjgg/j+++/dtikuLsYvv/yC3bt34+jRo7BavU/+/fWvf8WBAwdw2223YdeuXVi2bBlmz56NmTNnKiVFwSopKcGsWbOwceNGlJWVYdWqVfjtt9+UvkEPPfQQ3njjDcyZMwe//vordu7ciffeew8PPPCA29rXrFmD8vJy1NTUhLQOorYoPSytDFgSEVHy4fGof8l2PJowAcujR4/CbrcjPz/f7fL8/HyUl5f7/Jl9+/bhww8/hN1ux4oVK/Dggw/iqaeewj/+8Q9lmxEjRmDRokVYuXIlXnzxRZSUlOCss85CQ0OD37XMnTsX2dnZyldRUVFk7mQEqEvCrcywJCKiBHLuueciLy8Pu3fvxqRJk9yumzdvHnJzc3HGGWdgwoQJGDdunNsZ77ZkZGTg008/xbZt2zB48GD8/e9/x+OPP+62zU033YRLL70UV111FUaMGIFjx465nd0GgOnTp6NPnz4YOnQoOnXqhG+++cbrtrp27YoVK1Zg8+bNGDhwIG6++WbccMMNbgdrwUpLS8OuXbtw2WWXoXfv3rjxxhtx66234qabbgIAjBs3Dp999hlWrVqFYcOG4fTTT8fTTz+NHj16KPt46qmnsHr1ahQVFWHw4PhM6qT2TzA4CrBYEk5ERMmIx6P+JdvxqCBJkhTVWwjQ4cOH0bVrV3z77bduTUvvuecerF+/3me9f+/evWEymVBSUqKMU583bx6eeOIJHDlyxOft1NbWokePHpg3bx5uuOEGn9uYzWaYzWbl+/r6ehQVFaGurg5ZWVnh3M2wvfXr03h/1wIAwNlFF+Lu4U/FdT1ERBQ58t+0E044gX0LKSStPYfq6+uRnZ2dEMczFLxY/f7q15agZPJ/kXpqZ/T+3+So3Q4RESUuHpNSOCJ1PJowPSw7duwIrVaLiooKt8srKir89p8sKCiAXq9XgpWAo86/vLwcFosFBoPB62dycnLQu3dv/P77737XYjQaYTQaQ7wn0WUT2cOSiIiIiKJDY3T2sGSGJREREcVRwpSEGwwGDBkyBGvWrFEuE0URa9as8TkmHnCMdP/999/deg7s2bMHBQUFPoOVgKOp6t69e5Wx7cnGbUq4GLlJVkREREREgt7Zw5JDd4iIiCiOEiZgCQAzZ87EK6+8gsWLF2Pnzp245ZZb0NTUpEwNnzx5MmbNmqVsf8stt6C6uhp33HEH9uzZg+XLl+Oxxx7Drbfeqmxz9913Y/369SgtLcW3336LSy65BFqtFtdcc03M718kiG4Zlhy6Q0RERESRI3DoDhERESWAhCkJB4CrrroKVVVVeOihh1BeXo5BgwZh5cqVyiCe/fv3u01DKioqwueff4677roLAwYMQNeuXXHHHXfg3nvvVbY5ePAgrrnmGhw7dgydOnXCmWeeie+++w6dOnWK+f2LBPcMS5aEExEREVHkKAFLloQTERFRHCVUwBIAZsyYgRkzZvi8bt26dV6XjRw5Et99953f/b333nuRWlpCcOthKTHDkoioPUqQeXiUhPjcoXCxhyUREcl4XEGhiNTzJqFKwqltdolDd4iI2iu9Xg8AaG5ujvNKKFnJzx35uUQULCXDkj0siYiOWzwmpXBE6ng04TIsqXWiqiTczh6WRETtilarRU5ODiorKwEAaWlpEAQhzquiZCBJEpqbm1FZWYmcnBxotdp4L4mSlDJ0x2KDJEl8DyIiOg7xmJRCEenjUQYsk4xbSTgzLImI2p0uXboAgHKASBSMnJwc5TlEFAo5wxISAJsI6Bn8JiI6HvGYlEIVqeNRBiyTDEvCiYjaN0EQUFBQgM6dO8Nq5fs8BU6v1zOzksImGF3PIdFqh5YBSyKi4xKPSSkUkTweZcAyyYicEk5EdFzQarUMPhFRzGkMro8HktkOpMVxMUREFHc8JqV44dCdJGMXVT0sOSWciIiIiCJI0GkAjaNPmcRJ4URERBQnDFgmGZvkyqq0MsOSiIiIiCJMmRTOgCURERHFCQOWSUYUOSWciIiIiKJHDliKDFgSERFRnDBgmWTs7GFJRERERFGkYYYlERERxRkDlkmGU8KJiIiIKJpYEk5ERETxxoBlklGXgdskKyRJiuNqiIiIiKi9YcCSiIiI4o0ByySjLgl3fB9+H0tJkrDr2I9osjaEvS8iIiIiSm4a9rAkIiKiOGPAMsl4BigjMXjn16Pf4551V2PBjw+HvS8iIiIiSm6uDEsOeCQiIqL4YMAyyXgGKK0R6GN5uLEMAFDVUh72voiIiIgouSkBSzMzLImIiCg+GLBMMtEoCW+2Njr2FYFsTSIiIiJKbkrA0sqAJREREcUHA5ZJxjOoGIlJ4c02OWDJqeNERERExzvBoAPAHpZEREQUPwxYJhnPDMtIBCxb5AxLiQelRERERMc7DaeEExERUZwxYJlkPEvAIxGwbLI1OPfFknAiIiKi451gcHxEYA9LIiIiihcGLJOMZ0l4RHtYRmBfRERERJTc5JJwZlgSERFRvDBgmWQ8y7YjMSW8xdoEIDLZmkRERESU3OShOyKH7hAREVGcMGCZZESPLMhITPZWhu4ww5KIiIjouMcelkRERBRvDFgmGbnPpF5jcH5vCXufckk4e1gSERERkWB0BizZw5KIiIjihAHLJCOXhBu1qQAiE2Rsdg7dETklnIiIiOi4JygZljyZTURERPHBgGUSkSRJCSoadSkAItN30pVhyR6WRERERADwwgsvoLi4GCkpKRgxYgQ2b97sd9sxY8ZAEASvrz/96U8+t7/55pshCALmz58fpdWHR9A7e1iyJJyIiIjihAHLJKLOgHRlWIYXZBQlES02x9CdSPTDJCIiIkp2S5YswcyZMzF79mxs3boVAwcOxLhx41BZWelz+6VLl+LIkSPK1/bt26HVanHFFVd4bfvRRx/hu+++Q2FhYbTvRsjYw5KIiIjijQHLJGJTDcUxaI1el4XCZGv2uX8iIiKi49W8efMwffp0TJs2DaeccgoWLFiAtLQ0LFy40Of2eXl56NKli/K1evVqpKWleQUsDx06hNtuuw1vv/029Hp9LO5KSJQelpwSTkRERHHCgGUSUWdARirDssnaoPxflOyQJCms/RERERElM4vFgi1btmDs2LHKZRqNBmPHjsXGjRsD2sdrr72Gq6++Gunp6cploiji+uuvx//93/+hX79+be7DbDajvr7e7StWBIMOAIfuEBERUfwwYJlE3EvCHT0s7WEGLFtsjW7f2yT2sSQiIqLj19GjR2G325Gfn+92eX5+PsrLy9v8+c2bN2P79u34y1/+4nb5448/Dp1Oh9tvvz2gdcydOxfZ2dnKV1FRUeB3IkxySTh7WBIREVG8MGCZRNQTwQ3OgKU1zIClPHBHJoqxPzCtMR1l/0wiIiJqF1577TX0798fw4cPVy7bsmULnnnmGSxatAiCIAS0n1mzZqGurk75OnDgQLSW7EXQOz4isIclERERxQsDlknE7uwxqRG00Gn0bpeFyjNgGes+lgcb9mHairMx7/v/i+ntEhEREfnSsWNHaLVaVFRUuF1eUVGBLl26tPqzTU1NeO+993DDDTe4Xf7111+jsrIS3bt3h06ng06nQ1lZGf72t7+huLjY576MRiOysrLcvmJFKQlnwJKIiIjihAHLJCKXhGsFLfTOgGW4PSybPUrCwy0xD9bBhn0QJTt+qdoU09slIiIi8sVgMGDIkCFYs2aNcpkoilizZg1GjhzZ6s9+8MEHMJvNuO6669wuv/766/HLL7/gp59+Ur4KCwvxf//3f/j888+jcj/CoQzdYcCSiIiI4kQX7wVQ4OzOcm2dRgetxvGrs4VZSu2VYRnj0mybaAEA1JmPodFSjwxD7LIHiIiIiHyZOXMmpkyZgqFDh2L48OGYP38+mpqaMG3aNADA5MmT0bVrV8ydO9ft51577TVMnDgRHTp0cLu8Q4cOXpfp9Xp06dIFffr0ie6dCQF7WBIREVG8MWCZROSBOBpBp5SEh51h6RGwDLfEPFhWu0X5/6HGEvTJGxjT2yciIiLydNVVV6GqqgoPPfQQysvLMWjQIKxcuVIZxLN//35oNO6FSrt378aGDRuwatWqeCw5ogQDMyyJiIgovhiwTCLqknCd4PjV2cOc6u1ZEh7rDEv10KCDDfsYsCQiIqKEMGPGDMyYMcPndevWrfO6rE+fPpAkKeD9l5aWhriy6GPAkoiIiOKNPSyTiLokXKcxAAg/w7LFM8Myxj0sraIqw7KhJKa3TURERETeXAHL2J7IJiIiIpIxYJlEXFPC1SXh4R1INtkaPG4jtmfS1QFXBiyJiIiI4k9QeliKcV4JERERHa8YsEwicnBSK2hVQ3ci28PSFuselqJ7D0siIiIiii8NS8KJiIgozhiwTCKiM5io1eigj9DQnRZrk9v39lj3sFQN3TncWBrzDE8iIiIicicYHCfGWRJORERE8cKAZRKxK0N3dNAKcoZleAeS3kN34tfD0iZaUdl0KKa3T0RERETuBCMzLImIiCi+GLBMIkpJuEbr6mEZ7pRwz6E7cSwJB9jHkoiIiCjeBL3jI4JkFSGJgU8+JyIiIooUBiyTiFISrhq6E+5U72aPoTvhZmwGS10SDgAHG/bF9PaJiIiIyJ3GWRIOMMuSiIiI4oMByyRiF+WScFeGpTVCQ3fSdBkAXEHRWLE5Myx1guP+cPAOERERUXzJU8IBQLIyYElERESxl3AByxdeeAHFxcVISUnBiBEjsHnz5la3r62txa233oqCggIYjUb07t0bK1asCGufiUrpYalx9bAMZ0iOKIlosTmG7mQacgDEIcPSGbAsyjoRAEvCiYiIiOLNLWDJDEsiIiKKg4QKWC5ZsgQzZ87E7NmzsXXrVgwcOBDjxo1DZWWlz+0tFgvOO+88lJaW4sMPP8Tu3bvxyiuvoGvXriHvM5HZnf0qtUJkpoSbbM3K/zONOc7biE/Asji7DwBmWBIRERHFm6ARlD6WIgOWREREFAcJFbCcN28epk+fjmnTpuGUU07BggULkJaWhoULF/rcfuHChaiursbHH3+MUaNGobi4GKNHj8bAgQND3mciU5eEazXOKeFhBBjlcnCdoEeqLs2xvzj1sJQDljWmKjRZG1r7ESIiIiKKMjnLUjIzYElERESxlzABS4vFgi1btmDs2LHKZRqNBmPHjsXGjRt9/swnn3yCkSNH4tZbb0V+fj5OPfVUPPbYY7Db7SHvEwDMZjPq6+vdvhKBXBKuUU8JDyPDUh64k6bPgFaIzBCfYMkZllmGXOSldALAsnAiIiKieBP0zoClJbYns4mIiIiABApYHj16FHa7Hfn5+W6X5+fno7y83OfP7Nu3Dx9++CHsdjtWrFiBBx98EE899RT+8Y9/hLxPAJg7dy6ys7OVr6KiojDvXWTI5do6Qa8MqQmnh6UycEefAV0EMjZDIQcsdVoDumaeAAA4xEnhRERERHGlZFhaxTivhIiIiI5HCROwDIUoiujcuTNefvllDBkyBFdddRX+/ve/Y8GCBWHtd9asWairq1O+Dhw4EKEVh0cOTmrdMiwtIe9PDlim6jJUQ3xiW/YjByz1GgO6ZvQEwD6WRERERPGmMToCluxhSURERPGgi/cCZB07doRWq0VFRYXb5RUVFejSpYvPnykoKIBer4dW65pk2LdvX5SXl8NisYS0TwAwGo0wGo1h3JvoUErCI9XD0ubKsJT3F+uhOza7KmDpzLA8yJJwIiIiorgSDI5jQ8nMknAiIiKKvYTJsDQYDBgyZAjWrFmjXCaKItasWYORI0f6/JlRo0bh999/hyi6SlX27NmDgoICGAyGkPaZyOQMS51GH5Ep4UpJuC4jIj0xQ2F13p5eY0A3pSScAUsiIiKieFJKwplhSURERHGQMAFLAJg5cyZeeeUVLF68GDt37sQtt9yCpqYmTJs2DQAwefJkzJo1S9n+lltuQXV1Ne644w7s2bMHy5cvx2OPPYZbb7014H0mEznDUitEaOiOqoelVnAclIbTEzMUSkm41lUSfrixVLmvRERERBR7gt7xMYEBSyIiIoqHhCkJB4CrrroKVVVVeOihh1BeXo5BgwZh5cqVytCc/fv3Q6NxxViLiorw+eef46677sKAAQPQtWtX3HHHHbj33nsD3mcykcu13UrCwxm6oyoJlwOH8Rq6o9cY0Cm9EHqNAVbRgqqmw+iSkRjDjoiIiIiON4LRcazJHpZEREQUDwkVsASAGTNmYMaMGT6vW7dunddlI0eOxHfffRfyPpOJMnRH0EUkw7JFVRLeZK13u41Ysap6WGoFLQozilFWvweHGvcxYElEREQUJxqWhBMREVEcJVRJOLVOLpPWaXTQCXrnZeFnWKbqM6DVhL+/UNiUHpaO2+fgHSIiIqL4Yw9LIiIiiicGLJOIK8NS3cPSEvL+mqwNAIB0fSZ0gs7tNmJFLgnXaQ0AwME7RERERAmAAUsiIiKKJwYsk4jSw1Kjgy4CPSxbrE0AgDRduqsnZhx7WAJA1wxnwLJxX0zXQUREREQugt4RsGQPSyIiIooHBiyTSMSnhKtKwuUAaCwzLO2SHaLzPikBy0zHpHBmWBIRERHFj8YoZ1jG9mQ2EREREcCAZVIRncFEnUYHrbOEW4KkBDKD1awauqMR5IzN0AOgwbLZXeXseq0csHRkWFabqpT1EREREXlasmQJTCZTvJfRbrlKwsU4r4SIiIiORwxYJhG5XFujmhIOhB5kbLY5elimqTMsY1gSblX135TvT7o+E7kpnQAAhxqZZUlERES+XXPNNejSpQtuuOEGrF27Nt7LaXdcAUtmWBIREVHsMWCZROyid0m44/LQDiSVDEt9ppKxGU5PzGC5BSwF1/2R+1gebGAfSyIiIvJtw4YNuPbaa/Hpp59i7Nix6N69O+677z5s37493ktrFzQG9rAkIiKi+GHAMonI2Y9ajU4ZkgOENilclES02OShOxlKAFSMZYal3TVwRxAE5fKunBROREREbTjjjDPwwgsv4PDhw1i2bBlGjRqF559/HgMHDsSgQYPw1FNP4ciRI/FeZtISDM72QwxYEhERURwwYJlERGXojg5aQQuN89cXSlakydas/D9NnxHXDEt54I5MmRTOgCURERG1QafT4cILL8S7776L8vJyLFq0CB06dMA999yD7t2747zzzsNbb70FiyX4E7zHM8HgOM6UzAxYEhERUewxYJlE5GCiVnCU6IQzKVwuB9cJeug1hrj2sJQH7si6OSeFsySciIiIgrF9+3Zs3rwZ27ZtgyRJOPnkk3Hs2DFMnjwZJ554IjZs2BDvJSYNOcOSJeFEREQUDwxYJhF1STgQZsBSNXBHEARonEHQhMiwdJaEH2ksgyRJMVsPERERJZ89e/Zg9uzZ6NWrF0aNGoX3338fkyZNwg8//IBt27Zh69at2Lx5M/Ly8nDzzTfHe7lJQ+5hKVkZsCQiIqLY07W9CSUKdUk44Apc2kLIinQN3MkA4Ap+hjrAJxRyD0udR8CyQ2o+AMAimtFkbUCGIStmayIiIqLk8Mwzz+Dtt9/Gli1bYDQaMWHCBMyfPx/jx4+HVqt123bo0KGYOXMmbrjhhjitNvm4poQzYElERESxx4BlElGmhGscB5D6CJSEp+oynPuUg5/B7ytU/jIsDVoj0vVZaLLWo9ZUxYAlERERebnrrrswatQoLFiwAFdeeSWys7Nb3X7o0KF48MEHY7S65CcYnQFL9rAkIiKiOGDAMonIwURXhqWcFRlKSbhHhqUg97CM3UGpzU8PSwDITemIJms9asxH0Q0nxmxNRERElBz27t2LE044IeDt+/Xrh379+kVxRe2LoHcELNnDkoiIiOKBAcskIsoZlvLQHcERsLSGkWGZ5pFhGduScMe6PTMsAUfA8mDDPlSbqmK2HiIiIkoe6mBlZWUlSktLAQDFxcXo3LlznFbVfrhKwmN3bEhEREQk49CdJCJnP7qG7oQ+2durh6UQerZmqPyVhANAjrETAKDWdDRm6yEiIqLksmbNGgwdOhQFBQUYOXIkRo4ciYKCAgwdOhRffPFFvJeX1FxDd8Q4r4SIiIiOR8ywTCKeJeHhTQl3D1jKfTFDGeATqtYClrkpHQEANcywJCIiIh8++ugjXHHFFcjPz8c999yD3r17AwB2796NN998E+effz7ef/99XHLJJXFeaXJSeliyJJyIiIjigAHLJOJZEq4MygmhjLvFsyRciENJuNLDUu91XW6KI8OyhhmWRERE5MMDDzyAU089FV9//TUyMzPdrrv//vtx5pln4oEHHmDAMkSCwXFsKJlZEk5ERESxx5LwJOIqCXf2sIxAhmWqXBIeRvAzVFa7xXnbvjIsWRJORERE/u3btw/Tpk3zClYCQFZWFm644QaUlJTEYWXtg1wSzqE7REREFA8MWCYRuVel1tlvUuk7KQUfsGyyNgAA0vWOg3xl4ngMS8LlQGurJeFmloQTERGRt5NPPhmVlZV+r6+oqFDKxCl4gt7xMYEl4URERBQPDFgmEblc2zPDMpQp4S3WJgBAmi7dsS8h9AE+oXKVhPsYusMelkREFCa7ZMfPlRuVk3TUvvz73//GggULsGzZMq/rPvroI7z00kt48skn47Cy9kEwOkvCGbAkIiKiOGAPyySilIQLcsAy9L6TniXh4fTDDFWrQ3ecU8LrzTWwiVYlOEtERBSozYe/xNzvZmD8CVfhr6c9Eu/lUIQ999xz6NSpEy699FIUFhbipJNOAgD8/vvvOHz4MHr37o1nn30Wzz77rPIzgiD4DHCSN0GZEs6AJREREcUeA5ZJxJVh6SwJD6eHZSIN3fERsMw05kAjaCFKdtSZq9EhNT9m6yJqb/bX/45jLeUYnH9mvJdCFFNVzYcBABXNh+K8EoqGX375BYIgoHv37gCA0tJSAIBOp0P37t1hMpmwbds2t58RBCHWy0xaSg9LMwOWREREFHsMWCYRzwxLOcgYSsCyxZlhmeaRYRlKP8xQ2ez+A5ZaQYscYx6qTVWoNR1lwJIoDP/49maUNx3AS+NWoyCje7yXQxQzVtEMADDZmuO8EooGOUBJ0aFkWLIknIiIiOKAPSyTiGvojiO46MqwDD4rUu7nleYcuqMT4lgS7qOHJQDkpnQGANRwUjhRyJqtjShvOgAAONiwN86rIYotsz05ApYH6/eivPFATKsciNoiBywhSpBsYnwXQ0RERMcdZlgmEX9Dd2xBZkWKkogWmzx0xyPDMg4BS3/9KTkpnCh8hxpLlP9XNLEslo4vVmfAsiXBA5YzVl8IESIWXfA18lI7x3s5SWf9+vVYvnw5ysrKAAA9evTAn/70J4wePTrOK0tuSsASgGixQavzfYKZiIiIKBoYsEwirpJwzwzL4AKW6kwTuSRc3pcIEaIkQiNEP/m2tR6WACeFE0XC4YZS5f9VUe7j983BlbCJVozuPiGqt0MUKEsSlITbRRtEOLLX/FUckG8WiwXXXHMNPv74Y0iShJycHABAbW0tnnrqKVxyySV49913oddzcF8oNKqApWQRgbQ4LoaIiIiOOywJTyJKhmWYAUt54I5O0CvBQnmfgKv0PNqsrfSwBFyTwlkSThS6Q42lyv+jOXik2dqIJzf/DU//cG9CB4fo+GJJgpJwOagKAAaNMY4rST5z5szBRx99hL/97W84cuQIqqurUV1djfLyctx9991YunQpHnmE0+FDptMAzhlF7GNJREREscaAZRJReljKJeEhTvZutsn9KzOUaZk6jSpgGaOycFdJuL8elsywJArXoQZXSXhlFAOWRxrLYJdsECU7TLaWqN0OUTCUgKW9GaKUmD345JN3AKBjhmVQ3nnnHUyZMgX//ve/kZ/vGs7XuXNnPP7445g8eTLefPPNOK4wuQmCAMHgOD6ULOyvSkRERLHFgGUSkUvCNYJHD8sQMyzlcnDAPcMyVoN32hq6I5eE1zLDkihkh1UZllXNh6N4O2XK/+XXNlG8Wewm5f9me2IG0uXXi0bQQito29ia1I4cOYIRI0b4vX7EiBEoLy+P4YraH8Hg+KjADEsiIiKKNQYsk4QkSRCdAUs5UCkPyrEFWcItByxTdaqApSb2JeG2NnpY5qWwJJwi6/1dC/DWr0/HexkxI0kSDquG7tSZq6NWGqse7hPsSRQ6/thEK9769Wlsr9oc1duxqsqtE7UsXM6wZDl48Lp164Z169b5vX79+vXo1q1b7BbUDsl9LEUzA5ZEREQUWwxYJgl1EFEbboalzTvDUiNooHE+HWIVbLDaHbfjf+iOI2BZyynhFAFWuwVv/zof7+9agAZLbbyXExM1piq02JqhgQYpWse0hMooZVmqMzmZYUlt+bnyW7y/awEWbns8qrdjUZVbJ+qk8LaqDci/KVOm4P3338fNN9+M3bt3w263QxRF7N69G7fccgs++OADTJ06Nd7LTGquknAGLImIiCi2OCU8Scjl4EAES8JVGZaAI8tSFC1KJme0tTUlXO5h2WJrRoutCam69Jisi9qnFlsTJEgAAJOtBZmGnPguKAbkrMfO6d2QoktFad1uVDUfQveskyJ+W+pp5AxYUlvkwHmduTqqt6MuCU/UDEt56I6/v4Xk3/3334+9e/fi5ZdfxiuvvAKNxnHiVRRFSJKEKVOm4P7774/zKpOb4MywlKwMWBIREVFsMWCZJNSDcOTybZ0QuR6WgGPwjlW0JEwPy1RdOlK0aTDZm1FjOorUDAYsKXTqYIU6iNGeHXIGEbtmFEOr0aG0bjcqmiI/eEeSJLdp5CwJp7Yca6kAADRGOdvZ4lYS3hTV2wqVXBLOgGXwtFotFi1ahJkzZ2LFihUoK3P00u3RowcuuOACDBgwIM4rTH6C0RmwZIYlERERxRgDlklCnfUoTweXJ3sH23PSV0k4AGidAVB7zErC5Q9per/b5KR0RHnTftSaqlCY0SMm66L2qUUVrJAnB7d3cpl2YWYxJMmRXRqNwTsNlho0WeuV79VTj4l8kQOWLbZmWEVL1IJ11qQoCXe8Hxm07GEZjObmZpx11lmYPn06br75ZgYno4Q9LImIiChe2MMySaizHsMtCW/xWxLu2G+wQ3xC1VZJOOAqC+fgHQqXOlhhPk4yLOWBO10zTkDntK4AgMrmyGdYqrMrAZaEU9uqWyqV/zda6lvZMjzmJCgJl4OqOmZYBiUtLQ0lJSUQBCHeS2nXBD0zLImIiCg+GLBMEnIWpUbQKgfnypTwIEu45QzLVK8MS2fGZoxLwnWtDBrI5aRwipDjsyTcGbDMPAGd0x0By4ooBCzV/SsBloRT246ZKpT/N1rqonY7STElnEN3QjZ+/Hh8/vnn8V5Gu6b0sLTE5tiQiIiISMaAZZKQS8LlcnAg9AzLJmsDAO8MS2V/McqwtAWUYSkHLDkpnMJzvJWE20QrypsOAgAKM4rROa0QQHRKwg83lrl9zwxLaos6w7Ihin0s1a/1hC8J17AkPFgPPvgg9uzZg+uvvx4bNmzAoUOHUF1d7fVFoXMN3RHjvBIiIiI63rCHZZKwi46ApVy2Dbh6PwZfEu4I3KTHPcPSse6ASsLNDFhSeNQDNyxi+8+wrGw6BLtkg0Gbgg6p+TBqUwA4gv9mu0n5PhLkaeQyBiypNWa7CY1WV1ZlNDMs1QFLkz1BA5YcuhOyfv36AQB27NiBd955x+92djvLmUOlcQ7dEVkSTkRERDHGgGWSsEmO4J5GlWEpD8mRrwuUv5LwUIf4hMoWwIe0HKMjYFnLknAK0/HWw1LuK9k1oxgaQYNMQw5SdWlosTWjqvkwumX2jNhtycN9NIIWomRnSTi1Sh64I2uwRidgKUkSrPbEnxJuYUl4yB566CH2sIwyJcPSzJJwIiIiii0GLJOEXBKuFVwZlqGWhDf7HboTWk/MUNhFG0Q4yota+5DGHpbhK6nbhfy0bl5T4Y836v51x8MUa7l/ZWFGMQBAEAR0SuuK/fW/RTRgKUoijjTuB+AY7nOg4ffj4vGl0FV7BCyjlWFpk6zK3xkggUvCmWEZsocffjjeS2j3OHSHiIiI4oU9LJOEXBIuZ0Gq/x9sCXeLM8PSM4AVy5JwdcloYFPCWRIeir01v+KOLy7G/B/ui/dS4s69h2X7z7BUJoRnFiuXyZPCK5oiN3inuqUSZnsLtIIOXTNPAODqT0vki2eGZbQClp6B88QduuPsYallD8tg/fnPf8amTZv8Xr9582b8+c9/juGK2h85w5Il4URERBRrCRmwfOGFF1BcXIyUlBSMGDECmzdv9rvtokWLIAiC21dKintvtqlTp3ptM378+GjfjYjyVRIeSIalKHk3SVeG7ugz3S5XMiyDLDEPReABS0eGZa35mM/7Qq2Ty4KPeAxFOR6ZjtuS8BOUy+SAZWUEJ4XL/Svz07shRZcKwNWflsgXr5LwKAUsPV/niRuwZIZlqBYtWoS9e/f6vb6kpASLFy+O4YraH43RcWzIDEsiIiKKtYQLWC5ZsgQzZ87E7NmzsXXrVgwcOBDjxo1DZWWl35/JysrCkSNHlK+yMu/gzPjx4922effdd6N5NyJOGbqjKgmXMyL9lXAv+20Rrvv0dGyvcgV8RUlUMs28poTHMsPSmfmigUYJlPqSbcyDAAGiZEe9uSbq62pv5PL/RP2gHkvqctDjYUq43FeyMFMdsJQnhUcuYKncTkaxEnBpD0N3bKIVC358BN8d+iLeS2l3qk2Ov+fy86UxSj0srR6v80QvCdcxYBlxhw8fRmpqaryXkdSUHpYMWBIREVGMJVwPy3nz5mH69OmYNm0aAGDBggVYvnw5Fi5ciPvu813WKggCunTp0up+jUZjm9skMtE5CEcd3FOmhPvJiNxa/jUarXV4YetDePa8T6DXGNwCV14l4crQnegflMoBDV0bQwZ0Gj2yjLmoM1ejxlyFnJQOUV9beyKX/yfqdNxYOp5KwltsTUoWW2FGD+XyzunOkvCIBiwdJ4i6ZhTD4ixtbQ8l4durvseKfW/jy7KP8ErHNcg25sV7Se2G/NwsyjoJ+2p3oMFSG5XbkZ+PspaEHbrDkvBgLFu2DMuWLVO+f/nll/HFF94nFmpra/HFF19g2LBhsVxeu8OAJREREcVLQgUsLRYLtmzZglmzZimXaTQajB07Fhs3bvT7c42NjejRowdEUcRpp52Gxx57DP369XPbZt26dejcuTNyc3Nx7rnn4h//+Ac6dPAd/DKbzTCbXR906uvrw7xn4bMrQ3dUU8KdAUu7n/LLFrvjw9mhxhIs//1tTOw9Tcm40wl6r/IzndD6/iLJVQKnb3PbHGNH1JmrHZPCs6O9svZF/oCeqJlFsWRyy7Bs3wFLOYiYZchFpiFHuVwpCY9gD8vDDaUAgMLMYmXQT3soCW9WBfv/u/sV/HnAvXFeUftxzOQIWPbI6o19tTui1sPSM5M6UTPNbRy6E5QdO3bggw8+AOA4Yb1p0yZs2bLFbRtBEJCeno6zzz4b8+bNi8cy2w32sCQiIqJ4SaiS8KNHj8JutyM/P9/t8vz8fJSXl/v8mT59+mDhwoVYtmwZ3nrrLYiiiDPOOAMHDx5Uthk/fjzeeOMNrFmzBo8//jjWr1+P888/H3a774OvuXPnIjs7W/kqKiqK3J0MkVz2rdV4Twn3FxxQfzh7b+fzqDEdRbPN0b8yVZ8OQRDctnf1sIzd0J1ASuA4KTx0coDaYjfFpNQ/kZnsruwqczsvCZeDiF1V5eCAK2BZY6qK2CRvebiPuiTc1g6mhJtV758r9r6N6hb/bUkoOPJj2SOrF4Do9bD0DlgmaoalM2DZRsUBOcyaNQsNDQ1oaGiAJEl47bXXlO/lr/r6ehw5cgSfffYZevfuHe8lJzUNp4QTERFRnCRUwDIUI0eOxOTJkzFo0CCMHj0aS5cuRadOnfDSSy8p21x99dW46KKL0L9/f0ycOBGfffYZvv/+e6xbt87nPmfNmoW6ujrl68CBAzG6N/4pJeHqoTtt9JyUA5bp+iw02xrx5q/zlABWusfAHce+ta3uL5KCySjhpPDQyVliAGCyt8RxJfHn1sNSbN8ZlodUQUS1bGMeDNoUSJBQ1XIk7NuxizaUNx1Ubst1EqUdBCxVWbgW0YwPd78cx9W0H6IkugKW2Y5AUpM1OlUMVq+S8MTMsOTQndCJoohJkybFexntmmCUA5bH90lPIiIiir2EClh27NgRWq0WFRXuE0QrKioC7j+p1+sxePBg/P7773636dmzJzp27Oh3G6PRiKysLLevePM1dKetKeHyh7Mpp94NAPii9L/4qfJbAECqx8AdQJVhGYuhO0F8QMtxBixrmWEZNDlADSRuOWSstFjVPSzbd4alXJrtmWEpCIIyeCcSZeEVzYdgl2wwaFPQITVfyRDz956UTEw2R4BfzkpdWfIeqprDD/Ie7+rNNUrf5e7ODMtGSx1ESYz4bcmtH1J1aQAS9z1QHg7EHpbhaWxsxIEDB7B//36vLwqdq4dl5F+jRERERK1JqIClwWDAkCFDsGbNGuUyURSxZs0ajBw5MqB92O12bNu2DQUFBX63OXjwII4dO9bqNolG6WGpUfewbL2EW/5wNij/DJzT/WIAwAe7FgDwHrgDuAKg9piUhDs+sAZSApeX0hkAUM0My6Cpg3SJOnAiVtSDh9p/D8tSAN4ZloCqj2UEBu8o/SszekAjaNrVlHCzMyN5UOczcGrH4bCJVnyw68U4ryr5yRPCs40dkGN09JGWIKHZ2hDx27I4M/mzDI6BSSZbMyRJivjthIsZlqEzmUyYNWsWOnfujOzsbBQXF+OEE07w+qLQcegOERERxUtCDd0BgJkzZ2LKlCkYOnQohg8fjvnz56OpqUmZGj558mR07doVc+fOBQA88sgjOP3003HSSSehtrYWTzzxBMrKyvCXv/wFgOOM+5w5c3DZZZehS5cu2Lt3L+655x6cdNJJGDduXNzuZ7DszowUdUm4PLBGlOywS3a37Eu7ZFc+cKfp0jH51L/hu8OrlazLNF8ZloKcYRmDoTtBlIQrGZZmBiyD5VYSnqDZRbGivv/mdhywlCRJCVh2y/T+oB7JgKVn6Xl7LAk36lJxbb87MGv9tVhd+l9c2mc6uqTHv69xspInhMsZuSnaNJjszWiw1CHDENmpavKJiWxjHiqaD0KECItohlGbEtHbCVcwfw/J3V//+lcsXrwYEydOxFlnnYXc3Nx4L6nd0ShDd1gSTkRERLGVcAHLq666ClVVVXjooYdQXl6OQYMGYeXKlcognv3790OjcSWG1tTUYPr06SgvL0dubi6GDBmCb7/9FqeccgoAQKvV4pdffsHixYtRW1uLwsJC/PGPf8Sjjz4KozF5yq98lYRrVRO27aINWq3rOrPN1a8wRZeOLK0RV578Vyze/iQAfxmWzp6YUvTPogeTUZJrlHtYsiQ8WOqsyuM5YClJknsPy3ZcEl5nrkaTtQECBHRJ7+51fef0CGZYemRyKhmW7WjojlGbin4dh2JQ51H4qfIbvL/rRdw+5LE4ry55KQHLFMff9AxDNkwtzWi01ALwfr6Gw+LsYakOhJpszQkXsJTXqWdJeNCWLl2Kv/zlL259yyPphRdewBNPPIHy8nIMHDgQzz33HIYPH+5z2zFjxmD9+vVel19wwQVYvnw5rFYrHnjgAaxYsQL79u1DdnY2xo4di3/9618oLCyMyvojgRmWREREFC8JF7AEgBkzZmDGjBk+r/MclPP000/j6aef9ruv1NRUfP7555FcXlz4KgnXqQKWNtHq1v9KDlRpBK0SRLjopClYXfohDjeW+gxYatsY4hNJSsBSq29jy9CmhNeYjiLH2MFrEvrxRt3DMlEHTsSCRTRDVAXi23NJuJz12Cmt0GdPPFeG5eGwb+twYxkAoKtHwLI99LCUMyxTdKkAgGv73YGfKr/Bl2Uf4/I+N6Ewo0c8l5e0qk1yhqWj1UeGIQtHW45EZVK43BsyRZcGozYVZnsLWmxNyDbmRfy2wsGS8NAJgoDTTjstKvtesmQJZs6ciQULFmDEiBGYP38+xo0bh927d6Nz585e2y9duhQWi+tkzbFjxzBw4EBcccUVAIDm5mZs3boVDz74IAYOHIiamhrccccduOiii/DDDz9E5T5EghKwNDNgSURERLEVUg/LTZs2RXod1Aa7rynhHgFLNTmbLlWXpgTt9FoD7hz6L/TtcBrGdL/I6za0SoZlDErCnR/QdEFMCW+y1geUGffOjmcxZfkofH1wRXiLbAdaWBIOwPu+t+cMS2XgTobvvm1KwDICQ3eUDMvMYgDtrSTckaVu1DoCln3yBmJolzEQJTve2/l8PJeW1OQMy7xUR4ZlpjP7scEa+YClWTXMJiWBB+/IGcmGAHo6k7uLL74YX3zxRVT2PW/ePEyfPh3Tpk3DKaecggULFiAtLQ0LFy70uX1eXh66dOmifK1evRppaWlKwDI7OxurV6/GlVdeiT59+uD000/H888/jy1btiT0YCDB4Dg2FJlhSURERDEWUsBy5MiR6N27Nx599FHs27cv0msiH+SsR61GVRIuaCHAEYz0HJQjfyiTP6TJTu4wGI+PeRd9O3hnJOiEGE4JD6JnV7o+SwmEtDUp/GD9Xnywy1Ea9uvR78NcZXKTJMkjw/L4Hbrjed/lEsz2SA4iek4Il8lTwqtbKsLKhDTbTahyZmkqGZba9jN0R54Sri4fvrrvrQCADQf/l5DDW5JBdYtj6E4HZ8AyQ58DAGiKRoal83Vu0BgTelJ4MCfwyN2DDz6Iffv24cYbb8SWLVtQVVWF6upqr69gWSwWbNmyBWPHjlUu02g0GDt2LDZu3BjQPl577TVcffXVSE9P97tNXV0dBEFATk6Oz+vNZjPq6+vdvmJN7mEpWRmwJCIiotgKKWD51ltvoVevXnj00UfRq1cvjBo1CgsWLAjpoJACI5eEa1Q9LAFXRpNn4EEO0KRo3QOWrVEyLGMQsLQFUQInCIJSFt7apHBJkvDKL48pwduqEEtem62N7SK4Z7I3Q4IrqJKIH9RjxStg2Y5LwlubEA44hljpNQaIEHG0pTzk2ylvdGQEpeuzkGlwDLpojyXhRmdJOAB0y+wJwHH/2nPQO5qOOUvC81IcJbVKhmUUApYWJcMyRTl5l4itMSyqTFAKTq9evfDjjz/i1VdfxfDhw9GlSxd06tTJ6ytYR48ehd1uV/qny/Lz81Fe3vb75ubNm7F9+3ZlAKQvJpMJ9957L6655hpkZWX53Gbu3LnIzs5WvoqKYj/wiz0siYiIKF5C6mE5adIkTJo0CUePHsV7772Hd955B3/9619x5513Yvz48bjuuutw0UUXwWBgtkCkyEFEdRm443sdrKLFKytS/lCWqvd/Zt+TPHTHJsWih6UjoKEPsAQuN6UTqpoPt5ph+f2RtfixYoPyfSg9+uyiDTNW/wkaQYuXxq92G3KUbNTZlYAjgHm8koO1AgRIkNp1wFIpCXeWaXvSCBp0TivEocZSVDYdCnni9SE5kzOjWGk70Z5LwgH34KXZ1pJww1uSgXpKOOAaiNMYxYClXmtI6JLwYE7gkbuHHnooIXtVv/baa+jfv7/fAT1WqxVXXnklJEnCiy++6Hc/s2bNwsyZM5Xv6+vrYx60FIwMWBIREVF8hDV0p2PHjsqAnL179+Kdd97B22+/jauuugrZ2dm4/PLLMXnyZJx55pmRWu9xy9XD0jPD0gCgudUeloHSCo5gQ0yH7gT4AU2ZFG72nWFpsZvx6i+Oyb2nF56H7w6vRlXzIUiSFNSHmXpLjZJ11mSpR5YxN+CfTTSeWYWJmFkUK/LrIdOQg3pLjdLbrr2xS3YccWY++suwBIBOaV0dAcswJoV79q8E2teUcF8l4VrnEDOraHEGNJP3/SEeLHYzGiy1AFwBy2j2sFQyFzVGpGodJ+9MCZg9z6E7oXv44Yejst+OHTtCq9WioqLC7fKKigp06dKl1Z9tamrCe++9h0ceecTn9XKwsqysDF9++aXf7EoAMBqNMBrjm3kr6B3HnSKH7hAREVGMhVQS7ktqairS0tKQkpKiBImWLVuG0aNHY9iwYdixY0ekbuq45K8kXB7CY5P8lYQHnmHpGrqTWCXhgGvwjr9J4ct+W4TypgPIS+mMW09zfEhosTWjMcgPweqgXjQyfmKpxTPD8jgOWMq/12xjBwCAKNnbRdmyp5qWKtgkK7SCDp2cvSp9icSkcDmTUx0Y1bWjknCLM8PSsw+wnHFpdgY0KXBy/0qDxogMvSNQmWHIAQA0OgOZkWQRHZnUyVISrmdJeNjq6upgt4cfWDMYDBgyZAjWrFmjXCaKItasWYORI0e2+rMffPABzGYzrrvuOq/r5GDlb7/9hi+++AIdOnQIe63RpmFJOBEREcVJWAHLhoYGvP766xg7dix69OiB+++/H8XFxfjwww9RXl6Ow4cPY8mSJaisrMS0adMitebjkjJ0R3BPivXXw9Lf0J3WJOrQHQDIcfawrPXRw/Joczne3+UoqZra//+QbcxTAlNVzUeCWpc6K7HRGvvm9pHU7JFJdHwHLB2PRbYxT7msPU4Kr7M4+ghnGXOhEfy/vXdOlwOWoWdYHmksA+AesGxPQ3eUHpYeZd8pzrJwk50By2Ap/StT85XM9wy9I7ssGj0srckyJZwZlmH54YcfMH78eKSlpaFDhw5Yv349AEcfyosvvhjr1q0Lab8zZ87EK6+8gsWLF2Pnzp245ZZb0NTUpBzPTp48GbNmzfL6uddeew0TJ070CkZarVZcfvnl+OGHH/D222/DbrejvLwc5eXlsFgS9z1T4NAdIiIiipOQSsKXLVuGt99+G5999hlMJhOGDRuG+fPn4+qrr/Y6QLv88stRU1ODW2+9NSILPl7JGZZyn0mZzs+gHDlAE1RJeAwzLJWpqAH3sHRkWPoaurNo+xMw21twcofBGF00AYBjEnKd+Rgqmw+hZ07fgNdlakcZlp49LNvDIKFQyb9XdYm/2W5Cmj4jXkuKijrTMQDugVlf5EnhVWEELNU9LGX6dtTD0lUSnup2ufy9iRmWQZMzLPNSOyuXRbeHpSsQqEwJT7BevqIkKiccOXQneN9++y3OPfdcdO3aFddddx1effVV5bqOHTuirq4OL730EsaMGRP0vq+66ipUVVXhoYceQnl5OQYNGoSVK1cqg3j2798Pjcb9xNDu3buxYcMGrFq1ymt/hw4dwieffAIAGDRokNt1a9euDWmNscAelkRERBQvIQUsL7nkEhQVFeGuu+7C5MmT0adPn1a3HzhwIK699tqQFkgOrgzLwKaEu3pYBlESrmRYRr+cM/gelnKGpXtJ+PaqzfjqwGcQIOCmgQ8qWTud0grxW822oCeFt1jbU4YlS8JlJiWAnw6DxgiLaG6Xg3fkDMtsQ1sBS0eGZUVTaAHLRks96syO4GhBRg/l8vZUEm5WSsI9ApbO783MsAyaMnAnxTV5OVMuCY/C+61FlSWbqCXh6tcKMyyDd//996Nv37747rvv0NDQ4BawBIBzzjkHixcvDnn/cp92X3xlbvbp0weSJPncvri42O91iUw9JTzYvuBERERE4QgpYPnll18GdSZ4+PDhficlUmDkrEeNR4al1k8Zd0soJeF+sjWjIeiAZaojYFljdgQs68zV+GDXAqzY9w4A4I8nXIkTc/sp24fao8+tJDwKPdViSe5hqRV0sEu24zrDUunpqkuDQZviDFi2v5LwenMNACCrjQzL/PRuAICjLeWwizYluzpQFU0HAAA5xo5uWarK0J0kz7C0S3blPniVhGsZsAyVXBIuD9wBVEN3LLURD4ZYRFdvyEQtCVe/DzFgGbzvv/8ec+fOhdFoRGNjo9f1Xbt2RXl5eRxW1n5o9K4T5ZLFDsEY1rxOIiIiooCFdNSRqGUr7ZlSEu7Rw1LvL8PSHnzA0lUSHv2yn2B7WCpTwk1VeHfH8/j4t9eUoOzATiMx5dS/uW3vKnkNI2DZTjIsc1M64mhLecJ9UI+lFlXGsUFrBKxonxmWZkeGZU4bAcvclE7QCXrYJCuqTZWtDujxpd7iCIzmpLi3AFFnfCdzJo56oI7Rc+iOjiXhoapu8Q5YyiXhNtEKs70lqL9ZbbGoeljK1QaJNiVcHdyXXz8UOL1eD1EU/V5/6NAhZGS0r9YfsSZnWALOsnAGLImIiChGQhq688ADD3j131EbPHgw5syZE+qayAdRLgnXeEwJb7MkPPihOzHNsAywh2WOs4elTbTi3Z3PocXWjJNy+mHOma/hkbNeVz70yjqmFQAIfqiIe4Zl7AOWT23+G2ZvuAGi5P8DWKDk8va8FEe/uHiUQtaajiVEebB6CJXBmTFnbpcBS0eZdlsZlhpBo7xGQikLlwekyOW8MvUJCJsU/997qNTPDYPGva+gkRmWIVNKwlU9LFO0adAJjr9jkR68owQsNcaol4TXmKrwzo5ncbQ5uGw+m6raIFkD/PF0+umn48MPP/R5XVNTE15//XWMHj06xqtqX9QZlexjSURERLEUUsDyww8/xPnnn+/3+gsuuABLliwJeVHkzSaXhPubEu4RHJCDVSnB9LD0s69okD+kBZpRYtAalcBb14xi3DviGTx17n8xOP9Mnx/y5JLwYDMs1VmIDTEuCbeLNqw/8Bl+rNiAGh/DhYIlZ1jKAy5inWFZ2XQI01acjX99d3tMb9cXdQBfLvGNddlyo6VeCdhEi5xh2dbQHQDo5AxYHm05EvTtyO0SvAKWqhMQchZ1rLz448N45JubIhLsN6sG7ni+v6QwwzJk8vM/T9XDUhCEqA3esYqxmxL+v33v4r2dL+CzvW8G9XPqLFAK3pw5c/DDDz/gT3/6E/73v/8BAH7++We8+uqrGDJkCKqqqvDggw/GeZXJTdAIgM7xcYEBSyIiIoqlkOo69u/fjxNPPNHv9SeccALKyspCXhR5s4uOg8RAh+602F1DRgIl7zsmGZZ2x3qD6dn19zP+g6rmwxhR8Ic2e+7JJeF15mqYbS1KGWdb1BmWTTEuCZf7rQHO/pOqsslQyFPC5eBArAOWZfV7YJds2Fe7M6a364t7D0tHYCDWJeH3rrsaVc1HsOhPX0dtOnmgPSwBIEPvCBI1WRuCvx1nwFLeh0x9AsIWw4CwJElYue89SJBQ0XQQBRndw9qfv4E7gLqH5fHbYiEUkiSh2uQ9JRxw9LGsNR+N+EkiVzAwBana6AYsa02O7OZg74NVOXnH/pWhGDFiBFasWIFbbrkFkydPBgD87W+OFjEnnngiVqxYgQEDBsRzie2CxqCFaBMhMmBJREREMRRSwDIjI6PVgGRJSQlSUlL8Xk/Bk4fu6DwCdf7KuE0hDN2Rg4ByNmc0BTt0BwB65fZHr9z+AW2brs9Cqi4dLbYmVDUfRrcs/wF2NfWH2ViXhFtVwxeaI9BnrcUzw9LeDFESoRFCSqwOmhzUinTWVCjce1jGviRckiQcbNgHCRJqTUejFrCsdZaEt9XDEgDSDZkAQgtYyr/TTGOO2+VaQQuNoIUo2WGNYSsAs70FEhzTdxut4T/fzMp0ae+ApVISbmt/LQWiqcFSq7zvq6eEA0C6IQtAZH53anLAUq81RL0kXM5oDzYgamWGZdjOPfdc7N69Gz/99BN+++03iKKIE088EUOGDGGZfYQIei0AKzMsiYiIKKZCilyMGTMGL730Eg4d8u59duDAAbz88ss455xzwl4cudjbKAn3DA6E1sPSsa9EnBIeLEEQQpoU7j50J7aBNouqhDYSE72bnW0BOqS4spli2XevwVwLwBEojffUaPXrQe5JGMsp4Ra7SQmoqTNpI63eWRIeSIZlus4RsGwOIWCp9LD0yLAEIj8pvM5c3WaZt1tmtCX4++NJfr54TggHVEN32MMyKPKE8CxDrlfv4kx9DoDInySSS8KNmhTlb6EpSpmx8uso2IBotP8WHk8GDRqEK664AldddRWGDh3KYGUECUZHBQ4DlkRERBRLIQUsH330UZjNZvTr1w9/+9vfsHDhQixcuBAzZ85E//79YbFY8Oijj0Z6rcc1fyXhrsne7kHGljAyLD33FQ2uoTvRm4oqTz4OOWAZ48xA95LwCAQsnRk/2cYOEOD44BbLsvBGa63r/3EYYKRmsrl6usajJFwd3IpW8NZqt6h+520HLNP0oWdYymWvnsOuAFWbigj0sCyp24Upn43Cf7bObnU7dZAoohmWPkrC5QzLWLdYSHa+JoTL5OdRpEvC5d+jXmtU+jlH6/cmt+AIOsNSDL49CvlXUVEBrVaLL7/8Mt5LaVfkSeEsCSciIqJYCqkkvE+fPvj6669x22234emnn3a77uyzz8azzz6Lvn37RmSB5CAHET17N+r99bBU9ewLlFxuHpseltHv2yX3sQxm8E5LgpSEy+Xc4ZCDnmn6DKTo0tBia0KLrRm5Ye85MPXODEsAaLLUIdc56T0e3HtYxr4kXB3EsEYps1MeuKMVdEjXZ7W5vVyWHkqGZaOfKeGAKsMyAsO7Sut2Q4SIfXU7Wt1O/fiGEoD1JGci+yoJl/taxroHarI71uLoX+krYJkZhaE7oiQqfxfV5daRyF73RX7emYLcv7psnSJDkqR4L6Hd0RiYYUlERESxF1LAEgAGDBiA9evX4+jRo9i3bx8AoGfPnujYMX5BifZMlOQMS/dfmdZHwNIu2ZUP02lBDd1x9rBsByXhgCvDsqoltAxLk70ZNtEa8CTzcKkz7yLRw1LOtkvTZyDVGbCMZVaYOluqIcbl9Z7ce1jGviTcLWAZpd6OdRa5HDw3oD6lGc6gZjgZlj4Dls7ASyQyLFucWWttZRybIpwZ7ZoS7qMkXMsp4aHwNSFc5sqwjNz7hPrEgEFrVKoTbKIVVtES8b898vstS8KpPRIYsCQiIqI4CDlgKevYsSODlDEgBxG1Go8p4YJ3wNKs+iCdEkzAMoYl4TalJDyaGZbB97D0DOg1WuqRk9Ihouvyx+KWYRmBoTvOYE+aLsP5PKgKOvsnHOrAUTxLwiVJcuthKQeh4lYSHoFAni9K/0pDYDm0ckm4XMoaDLnsOtNXSbgg99UN/37Kgfu2+g669bCMYIalrwx1uUw8lv1g2wN/E8IBV+A7kn2D1S02DBojRNWJJ7OtBXpDhAOWckl4kD0ylaE7Gg7docTlClhG//iQiIiISBZWwPLgwYP48ccfUVdXB1H0HoowefLkcHZPKkpJuNfQHe8go/zhXSNog8ra0CkZltGf7hubDMsCAMGWhLsH9BottTELWKoDPOH2sLSKFuUDu1wSDkRvQq4v9aoMy8YI96YLhnqCtLokPKYBS9XjHq2hO3JJeI4xsOerXBLeZA0umCxJkpIJ56uHpXwSIhKZpM0BZli6t3KI5JRw7wzLFC2H7oTiWIx7WMongDSCFlqNDlo4+qvaRCtabM0+n7uhkiRJOUEUfA/L6J+8O55kZ2fj9ddfxymnnBLvpbQr7GFJRERE8RBSwNJkMmHKlCn473//C1EUIQiC0jNIPZWRAcvIEf0M3fE1JVydTRbMlMyYDt2xx64k/FhLBeyizav/py9eGZZBBnPCoc6wbA6zh6U6wJOqS1cClrEduqPKsIzh4+hJDmYJEGDUpiol4ea4lYRHJ8OyLogJ4QCQHuLQnWZbo9KiorUelrZIZFiqgkCSJPl9P3PvYRn+c81ka62HpeO1ZGZJeFCUgKWvknBne4JI9rCU30+Nqv6Vqbp0NFhqI/4+2GJrggjR+f/Wn6v+1qlnhmVEpKSkYMqUKfFeRruj9LA0M2BJREREsRPSlPD7778fS5cuxT//+U+sW7cOkiRh8eLFWLVqFc4//3wMHDgQP//8c6TXelyzyz0sNZ49LL0H5ZhCmBAOqDMsY1ESHv3JqLkpnaDT6CFKduXDclvkQJ88ZTmWpcxWMXIl4XLA06BNgVajQ6o29gHLBtXQnUj2pguW6/WQCo2gcWVYivEqCY9uhmUgE8KB0EvC5Sw4dfBXTTmJYg8/w1IePiVCbLUEW/16iURwXBm643NKeIrbNhSYapP/DEtXSXjk3m/l91N1INB14iayrTHUzz9Rsgd1UoIZluFZs2YNnnjiCbfLFi5ciO7duyM/Px933XUX7HYG2cLFHpZEREQUDyEFLD/88ENMmzYN9957L/r16wcA6Nq1K8aOHYvPPvsMOTk5eOGFFyK60OOdzTlx17Mk3NeUcPnDU2oQ/SsB1wAfMcoZlpIkxeRDmkbQoGOqoyw8kD6WVrtFeZw7OX+uwVobtfV5cuthGWZJuPwcSNM5yn5jXRJutVvcerk1JUTA0vF6cPWwjF2GpTobL1oZlkoPS2NgPSwznAFLk73Z7YRHW1wDd3yX1CoZllLkMiyB1p+76uuaInCSwdTa0B0dh+4Ey2q3KAH1Dj56WGYoU8JrI3abclm/QfU7TNFG533QM0s5mICojUN3wvLwww+7nSDftm0bbrrpJnTq1AljxozBs88+iyeffDKOK2wfBIPj2FOyMmBJREREsRNSwLKyshLDhw8HAKSmOj68NTW5DtAvu+wyLF26NALLI5m/knCtMnRH1f8w1AxLTWwyLO2STekpGO0PaZ3lSeHNh9rcVp0l09HZ/zISwY9ARXLojjJwR+8esAx2IESoPHvRxXNKuJylJwcrXFPCY5dhqf59RitQWms+BiCYHpaZyv+DaUHQ2Er/SsD1mo7EcCH1ulp7TUS6JLzVoTtaeehO7LKVg3W0uRxryj6K2oCnYMkDd3QaPTJ9DIWSg98ttuaIBfSVYTZuJeHRyTRv9ghYNgfx/m2JQXuU9mznzp0YOnSo8v2bb76JrKwsfP3111iyZAmmT5+ON954I44rbB8EgxZfn/szHtc9rJwMICIiIoq2kAKW+fn5OHbM8eE4LS0Nubm52L17t3J9fX09TCYe0ESSv5JwufzSJnmXhKcGGbDUOIOhwWRbhUL9gTTaH9LkPpaBZFjKARGDxogsg7MkPIaBNvXjEm4Py2arnGHpyCqUs23DzdwMlGfAMpK96YIlB/BT9Y7HwBCPDEtV+XAkejv6Um+uARB4D0udRq88Fk2WwPtYyuX9vvpXyvsFIjN0Rx2kbC1gGemScEsAQ3fMNpPSuznRLNz2Lzzzw3347vAX8V4KANWE8JTOPns7pukzIcBxeaTacPgKBCqZ5hEONnu2VQgmIMqS8PA0NTUhKytL+X7lypUYP3480tIcv+thw4ahrKwsXstrNzQGLTaP2oFduh3YW7M93sshIiKi40RIAcsRI0Zgw4YNyvcTJkzAE088gbfffhtvvvkmnn76aZx++ukRWyS1PSXcV0l4ija4knA50CBChCh5T333Z9lvi3DvumsC/qCpzvrRRflDWue0rgACmxRuUgW2MgzyEIg4ZViGGViUA56pnhmWEe7d5o9nz8pYPo6ePHu6GpUMy1gO3Yl+SXiwPSwB1eAdW+ABy0alJDzH5/Vy4CUSgdkWdUl4K68JtwxLS0PYgcRAhu6IEKP2uwzX7zW/AnA9J+KttQnhgKNyID3Cg3d8BZ2jNXys2eZZEh5EwFLOBOXQnZAUFRXh+++/BwD8/vvv2L59O/74xz8q11dXV8No5GMbLsGghcXoOA61JEjmNhEREbV/IQUsb7/9dvTs2RNms+NA+9FHH0VOTg6uv/56TJkyBdnZ2Xj22WcjutDjnZz1qNX4nhIeiaE76mBooFmWkiThw90vY+exrdh5bGtAPyN/yNcIWq8S90jrHEKGZaouXSlRjOWwmIgO3ZFLwj17WMa4JFzOmvLMuIwlJRAtl4Rr5AzLGA7dUQUwovVhr94SfMBSfn54lrS2fju1AIAMvb+S8MhlWKpLa1sLAqlfLzbJGvZAnECG7rS1pkiTJAlzNkzHwxv+0uoJJbOtBRVNBwDE9jnemuoWV4alPxnKe25tRG7TIg/diUFJeJNHhmUwPTKZYRmea6+9Fi+//DIuuugijBs3Drm5ubj44ouV67ds2YLevXvHcYXtg2DQwmJwvKcn6okaIiIian90bW/i7cwzz8SZZ56pfF9UVISdO3di27Zt0Gq1OPnkk6HThbRr8kMpCRd8ByytERi6o1OVm9skK/Ro+wNUVfNh1Dl75wUa9LDGcMhAp6B6WLoCvenOYExTDEvCI9rDUsmwlEvCYzslXA46dEwrQFXz4YhO/w2WknGsk3tYylOeYzkl3PW4R+PDntVuUQZ/BJVh6cwk9hwa0ppGpSTcd8BSJ/ewjMD9dB+6E1hJOOC4P8GesFEzt1ISrtXooNPoYROtMX0ONVnrsaXiKwBAedMBFGb08LndwYZ9So9gixi7LOLWyBmI/p4z8nXlTZHLsPSVuSgP3gr3/dVTOCXhFh/TzClwf//732GxWLBixQp0794dixYtQk5ODgBHduW6detwxx13xHeR7YBkEGAzOI5DGbAkIiKiWAk6qtjc3IzrrrsOl112Ga699lrlco1Gg4EDB0Z0ceTiyrDUu10uZ0X6yrAMtoeluj+mXQxsEuSeml+U/wca9LDFcMiAqyT8CCRJ8tk/TaYO9MajJFz9IaDF2tjmelvjOSVcDl7HOmBZkN7dEbC01IV1f8Lh3cPS8byL25TwKNxuvcXRv1KjKq0NRLrOURLuGXBpTVs9LOUMy3BLwu2izS1TMtChO4Aj6OWv/DgQ8v58lYQDjgFOjWIdzDHMsFQH/UvrdvkNWO6v/035f6IM3ZHL+Vs7iSZn7Ebq5IbZx9CdqJWEhzEl3MqhO2HR6XT45z//iX/+859e1+Xl5aG8vDwOq2p/bCmuY0IGLImIiChWgi4JT0tLwxdffIHm5sSdkNoeuXpYumdYuoID6gzLCJSES4GVhP9W7QpYtgQ4KEbOBo1FCVyHtHwIEGARzW32c1Nn4kX6w3Mg1MEFEWJY5ZxyAMqzh2UwpYrhUAKWzqCKXbLFbEK5J6+ScHnojhjLKeHRzbCUn9tZhlxohMDf1uUp8sFM1g58Snh4JeGeAcpgApbhTgqXMydTfJSEA4BRF/ssXXVQubRut9/t9tf/rvw/UTIsXX+TWglYRrgkXG6xEY8p4SwJj799+/Zh586d8V5GuyH3rwRcJ52JiIiIoi2kHpZnnnkmNm7cGOm1UCtcJeHuSbFaZUq4KzggB4aCDVhqBA00zqeELcD+c3tqtin/9+zj5U8sS8L1GgNyUzoBaLss3JWZ6uph2RjD3oueGX/NYZQtykN3XBmWsS0Jl4NaHVLzlbYF8ZoU7gpEx3NKuLqHZeRvV27LEEw5OKAauhNESXhDG0N3XG0qwvtQ65n12XpJuOPxjdSkaYvd/9AdwDUp3BRmr8xgqIOwJbW7/G6nDlhGI5s3FIH8TXK950Z26I5BPXRHG50TN57v1cEN3WGGZTieffZZXH311W6XTZs2Db169cKpp56KoUOHorKyMk6raz/k/pVA4pwIISIiovYvpIDl888/j6+//hoPPPAADh48GOk1kQ9tDd1RBxhDLQl37N+7xLy1Nf1es135PtgeljqP8vZokcvC2xq8oy4JT49LSbj7h4CWIMp0PSlDd5QMS7kkPDZTwuXBLFmGHFe2apwmhXv3sHRkXMW0h2WUp4SHMiEccAUsgysJrwXgvx+ha0p4eBmWzbZgApaO63JSOgIIPzPaNSXcu4clABhjfAIAcEw/l5XUtRawdJWEJ0pgIZC/SRkRHnRm8REIjFZJuBzw1zgrIILJJmeGZXheffVV5Oe72j98/vnnWLx4MW688UY899xz2LdvH+bMmRPHFbYP6oBlorSaICIiovYvpMk4AwcOhM1mw9y5czF37lzodDoYje4N4wVBQF1dfDKq2iN/GZY6Hz0sA+kX5o9Oo4NVtMAWQEn4gYa9bj3mAs3SinVGSae0Quyq/rHNgKVJ3cPSGWSziGaY7Sa/gYtI8sy8C2cwhPwcSHM+B+JVEp5pyEWmIRu15qNxmxSuzpwFXEEoi90Us76a6gBJuL0dfQk1YJmmBCyDGLpjbb2HZaSG7nhlWFrbLgnvmNoFNaaqCJaE+w6wGeMwuKnJ5vodVTYfQpO1QQk4y0y2ZlQ0u04iJkqGZSBtSuTnU2OEBp1Z4lASnpfSCUdbyoN6n1XWyaE7ISkrK0Pfvn2V799//32ccMIJePHFFwEA5eXlePPNN+O1vHbDanAdE7KHJREREcVKSAHLyy67LC7DM45nSoalZ8DS15Rwu3sJbDC0gt7t9lqzR9W/Egg8S8tVEh6rDEt5UngbGZZWVyZemj4DAgRIkNBoqYMxNfoBS68MywiUhKfKJeHa+JSEZxpyXNmqcZoU7pndpS4RtYoWt4BG1NagCuxbopCd4gpYdgjq54ItCZckScmA89/DMjIl4Z49cf29HuySXTlx0jG1AL/VbAsrm9cqWpQevv5OVMilxbEcutPkcZ9K63ajX8ehbpcdaNjr9n00nmuhMAUQsIx0D0ufJeHylHB7hKeEO5+rean5ONpSHtT7bCyH0LVHkiS5fb9q1SpcfPHFyvfFxcUcvBMBFr0qw5IBSyIiIoqRkAKWixYtivAyqC3K0B2PknClhFvynhIebA9Lf/vz5zdn/8ou6UUobzrglgHUGqUkPEYlcJ2cAcvKNnpYtthdmXgaQYMMQzYaLLVostaHNXE4UJ7BhbAClsrQHfcMS5OtOSZZhQ3OqdWZhmxk6nMAJEIPS/eScMAR1IhJwDLKQ3fq5aE7xtygfi4tyIBls60RojPbu60My3BLwj2f//6CQOoJ7B2dr9NwMizNNlfWpLGNoTux7GHZ7PH+6itgqe5fCSCswV2R5Jnl7EumPrI9LK3KlPDol4TL77d5KZ2d+w/8vVvOsNTH4H2oPerduzc++ugj3Hzzzfj8889x+PBhnH/++cr1Bw8eRE5OTvwW2E6YVQHLaFQJEBEREfkSUsCSYk8uCdcEMCU8nB6WuiB6WMoZlgM7n4HykiUB91yM5dAdwNXDMtAMS/lDdbo+Cw2W2pgF2jz7zQXTV9CTHOxJ85gSLkKERTRHtcRdkiSlh2WmIQcZSj/Q4B7H3dU/Y03pf9ExrRBFmSeiKLMnumR0D7r3qWc5qk7QQwMNRIgw203IgO9MwUhyC1hGZehOqCXhjudHoCXhcvabUZvqN9DrmhIebkl4YFPC5cs10CA31REwCiebV87W1Aha6ATfzzV5GI86uBltcg9LOfPb1+AduX9lliEX9ZaahOlh6XnSwJeMCPcNtjj/zhg0rvc6+W9ia+0FQiEH/OUTW0FNCWeGZVjuvvtuTJo0Cbm5uWhqakLfvn0xbtw45fovv/wSgwYNit8C2wmz1gKIjv+zhyURERHFSkgByzfeeCOg7SZPnhzK7smDJElKVpNnsEbrI2AZSL8wf+SS87Z6WJptLSir3wMAGNT5DHxesiTgLK1Yl8B1TpczLAMduuN43DIMWUBT7IbFyIEsraCDXbKF18PSY0q4OlPMZGuOasDSbG9Rno8ZhmxXqWeQveme3/KA8hyTaQUdumX2xK2nPYKTOwwOaD+e2V2CIMCgTYHJ3hyTD152ye6WVRmVoTuW0ErCM/SOIFGgr11Xqb//IG+kp4Sn6zPRZG3w+3pQfr/6dNf9CeM1a1YN3PGXiaxk6gUxXCVccgZ7cfbJKKnbiVIfg3cOODMsT8zthx8rNiRMYEE90MyfjEj3sFRKwl2B9Wj93uSTdXLAMqgp4Ry6E5arr74aHTp0wIoVK5CTk4O//vWv0OkcxzHV1dXIy8vD9ddfH+dVJj+rKmBpYYYlERERxUhIAcupU6f6vU79AY8By8hQl2drhbanhAfy4dAfrTLEp/Vyzr21OyBKduSldEL3rF4AQulhGZsPaB1TCwA4ykSbrY1KVpkndeADgFLKHGygLVTy0J0sYy5qTFUhBywlSfKaEq4VtDBqU2G2t6DF1hR0Jl4w5Cw8naB3G2DUFESGZWndbpTV74FO0OPMovNxsH4vDjaUwGRvRln9HizZ+R/MPvOVgPbl6/Vg0BphsjfDIkY/Q05dsgx4D1eKhDpTmEN3bIG9duXfrb/+lYAr8BJ2wFLuC5iS32rAUp29lxGBfqltDdwBVEN3bLErCZczLE/tNMwRsKzfA7tkd/ubIJeEn5RzKn6s2JAwGZaBZP3LQfBGSx1ESYRG0IR1m/LrTO8jYBnJ4WNW0aI8zq6S8OADlhy6E7rzzjsP5513ntfleXl5WLp0aRxW1P6YdRbAeViYKCdCiIiIqP0LKWBZUlLidZndbkdpaSn+85//YP/+/Vi8eHHYiyMHuRwc8C4Jl6eEywFLu2RXMkvCKQm3tVESLvev7JU7wFVWamsMqD9irDNK0vQZyNBno9Fah8rmQyjO7uNzOyXwoVVlWCJ2vRflvlDZxjzUmKpCLgk32ZshwTGIQM6wBBwf1s32lqgP3pGHsmQacyAIghKIaAjicVx/4DMAwJAuZ2PmsCcAAKIkYm/Ndvxt7RX4sWIDakxVyE3p1Oa+fPV0NcRwyrPn4x2VHpZyhqUh2B6WjudHoBmWDQFkWOoj1cNS7guY2hkHGn73+7xVfr/aNKTLwfEIlIS3loWc4sxYjuWUcLls/6ScfjBoU2Cxm3CksQzdMnsCcLx/yX16T8ztByAxpoTbRKvynG+1JNz5u5Mgodna0GpQPBAWu68p4enO60xewd5Qqd+n5YBlaCXhsRlC1141NTVh/fr1KCsrAwD06NEDo0ePRnp68CduyZtF43ov4dAdIiIiipWQUhh69Ojh9dWzZ0+ce+65+PDDD9GpUyc8//zzkV7rcUvdT9KzJFz+Xs7CVGf8hDQlPMChO3L/yl55A5RJw6JkD6jULh49uzoFMCm8xSPDMl0pl41NSbhcZpVtyHOuJ7QMS/kDtAYatwm5qSEMnJDsIuzNwX04UbLwnAGIYKeEi5KIrw8sBwCM7j5BuVwjaNArbwD65A2CCBFfObdpa18mu3tJOAAYnUGMaGQ7evJ8vCM9sMAqWpSAY1aQGZbya9diNwX0IbRR6U3qPzCq9LCMUIalqy+gvwxLdUl4pnOdob9mTUpJuO+BO47r4lAS7nz9ZBhy0MOZ1V5at1u5/kC9Y0J4bkonJXAWi+d3W0xuf5P8Byz1WoNysiiYkxv+yFmP6sxF9e1HKjtWGXCmS0O68wRAMM8LDt0J33PPPYfCwkJMmDABt956K2699VZceOGFKCws5LFohJhVAUsO3SEiIqJYCa/myo8LL7wQS5Ysicauj0uBlIRbndlMygAKQRtSQNCVsdlWhqUjYNk7dwCM2lQl8zOQrECb5FhrLAOWnZVJ4a0FLN0HQ2QoJYoxClgqJeHhBSxblAnhGW7Zrq5yyMD3e3DWavw68D9o/qU84J9RJoQbcwC4ApeBZqruOrYVlc2HkKpLx7CCc7yuP6f7xQCAL8s+bnNfZtUU51QfGZaxmKLsOUnacxp8uOrNjsdbI2iDzkpTZ+AG8tqt9whG+6K8J4U9dMcZsExxBiytTZAkyWs7de/ZdIOcYRl6wEvJsPQzIRyIT0l4ozMona7PxAnZJwOAWx9LeeBO98yTlPVZE6AkXA7Y6wR9m+/5rvfc2rBv1+ojw9KgMULjPOwJp0ewmpz5mqrLUE4SBjrUR5KkmPd0bm/eeOMN3HHHHTj11FPxzjvv4KeffsJPP/2Ed999F/3798cdd9yBN998M97LTHpmgRmWREREsSBJUsziD8kgKgHLvXv3wmwO/YPSCy+8gOLiYqSkpGDEiBHYvHmz320XLVoEQRDcvlJS3Ev5JEnCQw89hIKCAqSmpmLs2LH47bffQl5frNnFVkrC5QxLZ4BR3SusrdJsX7QeGZu+1JurUd50AADQK68/BEFQAh+BBD3kQIYuDgHL1jMs3XsdukqZa6O7OCf5A3Z2uAFLjwnhsmD7t9nrTKj57w5IJhsqX/w+4NuXs6OynEM01L3pAiGXg4/s+kefZblndjsfOkHvHDyy2+t6NTlwIEBwyzaVs5liWRIu94eN9Ic9eUJ4liE36L5/Wo1OCeQGUhYeyNCdSJWEKz0snZO/RYg+A8yuknBXhmWLrdktMz0Y8nOi9ZLwNOe2sQtYNqsClsXOgGVJrev5L/ev7J51ktJuw5wQGZZtTwiXhdI+wh9XSbjr9ygIgmvwToRaYzSpfi/B7tsu2SA6J5lw6E5o5s2bh7PPPhtfffUVrrrqKgwYMAADBgzAVVddhfXr1+Oss87CU089Fe9lJj23gCV7WBIREUXNW7/Ox3WfjsCOoz/EeykJIaSA5VdffeXz65NPPsHdd9+NZ599Fueff35IC1qyZAlmzpyJ2bNnY+vWrRg4cCDGjRuHyspKvz+TlZWFI0eOKF9yDyPZv//9bzz77LNYsGABNm3ahPT0dIwbNw4mU+z6j4VDDh5qBK1XEFJdEi5JUlgDdwBVSXgrH/bl/pXdMnsqJaXB9MKLx1TUTmldAfgPWKp7f6bp4lUS7h6wDLWHZbMcsNS5ByxTtY77FeiH6bpVv0OyOILldf/bA8vhwB4Hz8Es8uTmQKb/2kQrNhz8HwBgdNGFPrfJMuZiaMEYAMDa/cta3Z+6f6X6tROPknD58Yh0X0E5YBnqICVl8E4Ar105kBSLoTtysFndp9RXsF15z9OnK69ZIPTXrfz7ar0kPMW5bWwClpIkqQJjWTghxxmw9JFhWZTVSymDToQeli127x6y/kRiaJJMGbrjcWIs2KCiKImtvk/IgfU0faYS/LdJ1oCe/+ptOHQnNLt378YVV1wBrda7H6lWq8UVV1yB3btbP7FFbTPD9RpIlGFeRETB8lWpQ5RodhzbAhEidlf/HO+lJISQApZjxozBOeec4/U1ceJEPPPMM7j88svx4osvhrSgefPmYfr06Zg2bRpOOeUULFiwAGlpaVi4cKHfnxEEAV26dFG+8vPzleskScL8+fPxwAMP4OKLL8aAAQPwxhtv4PDhw/j4449DWmM07Dr2I1775V9YXfqh13Wic+iOXK6tplVdZpOsPgeMBEMuObe1kmG5p9o1cEeWHkTQI9ZTwgEgN6UjAFdwx5P6w6tc1hfLknBREpWMtHAzLJWeanr3oLXrg3pg+639zPkhTyMAdgnH3gjsTdM1mCUHgKPnHuAIZqsHSPnyY8U3aLDUIsfYEQM6ne53O7ksfP3+T1vdZ4uP/pUAYNDEviRczh4TIYac/edLnfkYgDACljrHaze4DMscv9vIJ1HC7XMmB4LS9ZlKb0Nfrwn10B11xmhjgIOEPFmUKeGtBCxjPHTHMSTG8ZxJ02egR1ZvAMDRliPKCQL3DEtnQF40x/3gXA48BzIELkOfAyAyJeEW0XembGqQmeaPf3cHpiw/E/V+/nbI77dpugy3NgKBBETVmWo6ZliGJDs7G6WlpX6vLy0tRVZWlt/rKTBmuN7rWBJORMnol8rvMOnTYfjfvnfjvRSiVtWaqgAA1c5/j3chBSzXrl2LL7/80u1r7dq1+Pnnn3Hs2DG888476NChQ9D7tVgs2LJlC8aOHetaoEaDsWPHYuPGjX5/rrGxET169EBRUREuvvhi/Prrr8p1JSUlKC8vd9tndnY2RowY4XefZrMZ9fX1bl/RVlq3B8t+ex2bD3/pdZ3cT1Kr8c4gUE8WtYlW5UNYqAFLneBeYu6L0r8yzxWwVLK0bAEELOPQsyvL6BgS0lbAUivolHXFsiRc/QFAXmvIAUub6wO0mhzADOSDuq3WhIavHJnKXf7vTADAsXd+gdjSdplvgzKYJQeAK2sKAJraCP6uP/ApAOCsbucr2b6+DO0yGpmGHFSbKvFL5Xd+t1O3SFCLZQ9Ls3MN6iBfJDNUws2wlE82BBKw9Pzd+uIauhPmlHBVawPXc9f7NaHuYQnANSk8xLJi15Rw/wFLOYAaqbLitsgZhxpokKpLR4YhC52dWeNldXvQbG3E0ZYjAIDuWb3c+jaGW5ofLtdrsO2s/2iUhHsOs5FPSAX6u9t5bCuarPUo8dN+Qj5Jl6bPgF5jUAL2AQUsne/7WkEXkYnlx6M//elPeO655/Dee+95XbdkyRI8//zzmDBhgo+fpGCYJdffShtLwokoCf1U+S2arA147ee5ONxY1vYPEMWJHKisZcASQIgBy9GjR3t9nX322ejfv39YZ7KPHj0Ku93uliEJAPn5+Sgv9z30o0+fPli4cCGWLVuGt956C6Io4owzzsDBgwcBQPm5YPY5d+5cZGdnK19FRUUh36dAGXXOEkMfPdFcJeE+MixVQR27aPMboAmUvD9/H3IlSVLSk3vn9lcuT1dKwgPoYRmHknB5kI08EMaTPKhGXTqsDIuJQUm4unQz2+gI9odaEi7fF68elkEEWeo//w2wiUg5uSM63zIM+m5ZsNe0oGbZrjZ/tsFcC8AV1NJp9KqsN/+PZYutCZsOrwEAjO5+Uau3odcacFa3CwAAa/d/7H+fVrl/nkeGpdLDMvqlbS0+ApaR7AEmD90JdkK4LJjsaFfAMpAelpEZupOmy1CCXa1lWMpBTfn+hJphGdCUcOf7dax6WMqPRbohS3l/Ks7uA8BRFi5nV+aldEaGIcutvDje5ZvBlYQH1++2NfJrzLPUWsk0twd2QsjsXH+t6ajP69WZwABUz9XAMyw5cCd0//rXv9CzZ09ce+216Nq1K8aMGYMxY8aga9eumDRpEnr27Il//etf8V5m0lMHLJlhSUTJSD7OtYhmvLh1dtwrUIh8Mdmalc87NX6OPY83IQUsS0pK8Omnn/q9/tNPP221RCeSRo4cicmTJ2PQoEEYPXo0li5dik6dOuGll14KeZ+zZs1CXV2d8nXgwIEIrtg3OZjka+qsXBLuKwNDXRJuFa2uSdfa0HpY6uQeln5KwiuaDqLBUgudRq8MfgAcE1KBAKeEx6EkPMvgyFqsN9f6njTso3RYzgxsstRH/Y+anA2kgUYJBoWbYemZ0ZQaRO+22k8d2UQ5E06GoNWg45TBAICjC7e2+Vg0WGsBuAfo0gOYFL7p8BqY7S0oSO+BXqpguD9jnGXhGw+t9vtYyUEJz2CJMZYZls6gVpo+QxmaFckPfJHqYRlQSbg18JLwcIKykiS5Bd5Tlaw4XxmWrpJwwBX0CnVSuDJ0R9fK0B35/TpGJeFyP0511rQ8KdwRsHROCM/qBcD1OwBi06e1NcG0KVEyLMOY8g44nj/ya9vgkWEZTEm4JElKALvW2XrBk/y6SXWeIAqmR6YcTObAndB16tQJW7duxbx589C/f39UVFSgoqIC/fv3x9NPP40tW7agY8eO8V5m0jOJrmNTq40BSyJKPure5j9XbWyzDz5Fz4aD/8OOo1sitr/tVd/j8o8Htoty/1qT63iTAUuHkAKW8mAdf1544QXcd999Qe+3Y8eO0Gq1qKiocLu8oqICXbp0CWgfer0egwcPxu+/OzJO5J8LZp9GoxFZWVluX9GWovRE85Fh6ZwSrvNRIisIglsZt2e2UbDkAKh6MrnaHmc5eM/svm4fsgLJ0pJECY0b98NikYch6P1uG2nyxGqbZPVdVuqjz5o8wMMmWaOeSeX64GpsNZssEPJ98SwJD3RKuK2mBQ0bHKUSORc6euXlXd0fmlQdTDur0PRd6wF8V4alKwsvkEnh6/c7ToKM7n5hQBPu++QNRGFGMcz2Fmw8tNrnNi1+ylFdJeGxGLrjeO6kaNNUw1AiGbAMr4elnB3d1skGSZICG7oTgSnhZnuLMj05TZeheu76LwmXt1GGPFlCzbAMYOiO8/06ViXhysAdVXsFOcOytHYXDjgzLIuyTgTg+LuQKIN3gsn6l/vdhtvDUj192ztgGXhJuEU0Q4LjBI3fDEs5+1UOWLbSb9VTPPo5tyctLS2YOXMmVq9ejTvuuAMrV67Ezp07sXPnTqxcuRK33347UlL8n3igwKmPgSzMsCRqd2pMR/HMD7Ow69hP8V5K1MjHUnIf8Nd+meu3VRhFT1XzEfx7053413e3R2yfK0veg8VuwpdlH0dsn/FSY3aVgdeaoxuwXFXyAR7beGvMEjBCFVLAcuPGjTjvvPP8Xv+HP/wBX3/9ddD7NRgMGDJkCNasWaNcJooi1qxZg5EjRwa0D7vdjm3btqGgoAAAcMIJJ6BLly5u+6yvr8emTZsC3mcstDZ11iY5Pvj7KgkH1EMu1BmWIfawbCPD8rdqR8Cyl6p/JRDYpOEjj3+NvVe+j8bfyp23FbsPaUZdqhKA8PXHydd09VRduhLAjURPtdbIPaEMqoClTbSGFNhSMiw9S8KVUsjWP6jXrfwNsEtI6dcZxp6OIJguJwW5l/UDABxd+GOrP+8rC0/OVvWXOVVnrsaPld8AAM72Mx3ckyAIGOMsHfd3ltTfwA+DaihJtJlUAbVITdBWq7NEKsOy9dYHzbZGJds7kCnh4QwXkoNAGkELgzZFeU00W1spCZd7WMqZ0WFmWKYEMCXcbGuJSUmRa0K4d4bl/vrfUersryhnWAKqoHy8S8Jtvtsy+BKpHpbqExEGj6E7gZ64AdwrHvxlWLpaFzhLwvWBB0StfiaZU2BSU1Px0ksveZ2QpshTtyuK90kQIoq8DQdXYE3ZUizd82q8lxI18rHUlSffguLsPmiw1GLhL2wZEmvHWhxxgFrz0ZDbn6nZRRu2lH8FANhXuyOiSSHxUKPqW9lgqY3a/bHaLVj4y7/w3eEvWp0HkQhCCljW1NQgMzPT7/UZGRk4dsz3wX1bZs6ciVdeeQWLFy/Gzp07ccstt6CpqQnTpk0DAEyePBmzZs1Stn/kkUewatUq7Nu3D1u3bsV1112HsrIy/OUvfwHgCGrceeed+Mc//oFPPvkE27Ztw+TJk1FYWIiJEyeGtMZoMOrkEkP/GZb+mvIrfSdVU8JD7WEpB0X9ZUfJGZa9PUp2lR6WNt9vPM3bK1D10vcAAFONIzAS6zI4eZhNvY8+lurBEJYDdbAcqocgCEqgrTHMEsW2WFSZNuqgaYufx7M16t5/aq2V1aq5ysH7uF3ecZqjLLxu1e+wHPD9eKiz8NwCls7pv/4GoWw4+D+Ikh0n5fRDt8yera5PTQ5Y/lK5EUebvXvSmvz0z4vl0B35g16KLlXJKo5kEEnpYWmIbkm43L/SoE3xmryspi5HDjUwq25rIAhCYD0snc93V4ZlaL1nlaE7rUwJl59PIsSYDLVpVgKWrgzLLhndYdSmwiKasf2o4721e9ZJyvUG5/trMpWEK32DQ/zdydT32TMYGEzJtvrvsf8MS9fQHQBIDaJXsPz68MwCpcANGTIE27dvj/cy2j31iU72sCRqf+RqnaPNh+O8kuiRj3OzjLmYcdo/IEDA2v3L8GPFN3Fe2fFFfVL6WEv4Jxx3HvtRSbqwihaU1LU9byGReZaB+zthHq6fKr9VPm+FW9kUbSEFLLt3745vvvH/4v7666/RrVu3kBZ01VVX4cknn8RDDz2EQYMG4aeffsLKlSuVoTn79+/HkSNHlO1ramowffp09O3bFxdccAHq6+vx7bff4pRTTlG2ueeee3DbbbfhxhtvxLBhw9DY2IiVK1cmVKmQkrHjI2ApOrMdfZWEOy5XZ1iGOSW8lQxLSZKwr3YnAOAkj4BlaxmWkk3EwXtWAXZHJpLVGp+skmylj6V3wFIOhBjsBuz+42LsOutVHHvnFyX40dZ063DJH7ANWiO0Gp0SUGsOoSxcDnKm6TMgSRLqv9gLy8G6gDKLbMea0fjNfgBAzp/cA5YpvTsi46wegCjh6GLfWZZN1gYlC89nhqWfgOVXBz4D0PawHU9d0otwSsehkCApE8bV/L0ejHKGZYxLwvVRLAnPCTHDMkN57bYeHJfL+bNa6V8JuL+uQw3meQ6Oaq2M17MkXA7qtZUx6o9ZGbrj/++D+rq2MpYjQelhqXedKNQIGvTIdpQ1ye/XRZmugKX8XEuUgGVgJeHyoLPwThBZRdffGM/2EsH08jUFkGHZZJMDlo7fTTAZnPL7QCyrDdqb+fPn47333sOrr74Kmy20jG5qnV20ub2XW2NwkoaIYqve2c7pmKkyvguJIvlYKl2fid55A3DhidcBAF78cbbPGRIUHerg2NGWI/43DNAP5evcvt/jHA6crGo8JoNHa1L4t4c+V/4fiwHD4QgpYHnNNdfg3XffxbPPPgtRFJXL7XY7nnnmGSxZsgSTJk0KeVEzZsxAWVkZzGYzNm3ahBEjRijXrVu3DosWLVK+f/rpp5Vty8vLsXz5cgwePNhtf4Ig4JFHHkF5eTlMJhO++OIL9O7dO+T1RYNr6I53xpfdGQDyXxLuyor0l1EWKK2SYel94G8TXb0c81I7u13XWpbW0de3omVbBTRZRhhPzINNJzrXHbselgCQ2UqGpfzhUvi9BWKjBZJVxMF7V0HvPPETbsZPW1wfsB1BhnD6WMpls2n6dNT8dwdKpn2E/Xf+L6AP6nX/+w0QJaT2z4exOMfr+k5/Pg0AUP3eNtibvYNuclDLqE11yxpyBSK8H0dREvF7jSNDZliXMQHcQ3ejnSXkWyu+8rrO1GYPyxhkWCpB01RVSXhkgkhW0aI6YxxehmVbU8LlDMvWysEBx3uIAEFZXyjkQL2cJZwaTA9LJSs61AxLZ0l4KxmWOo1e6R3sq41HpDUqGZbulQ0nqAafdUjNV+474Mrai9RzLVTBtCmR2xrUmatD7uELuH6HnuXggKs0PZD9q4PR/voIyW0n0j0ClsEM3WGGZeimTp0KjUaDm266CVlZWejVqxcGDBjg9jVw4MB4LzOpmTxOpNskC6frErUz8onCWtPRdptF7Vmtcm2/O9ExtQvKmw7gg90L4rm044o6eeVoi3d1XLC+P7IOgKs36e7qn8LeZzx5BiyjMXjHJlqx6bCrXWK04xzhCilgOWvWLJxzzjm48847UVBQgLPPPhtnn302CgsLcdddd2H06NH4+9//Hum1tmvyh2ObZPXKSpKDh1qN75Jw+YOzTT10x0+/sObtFdh/5wq07PIdrZeDiL56z6mzPz2zj+TAQotHlpblQB3Kn9gAACj8+2hkjTsJNp0jAOsvw1JsseK3CW+hZNpHET0ozgogwxK7HevPmdjXscYyx1rraqNzdkOmzrAEVAFLHz372iKnd6cgFRVPfwsAaNpyGAa74/Fu7YN07aeONPqci/r4vD7z3J4w9MiBvc6Msps+gbnE/bFscAaDMz2CWhmtTAk/1lIBq2iBVtAhPz34zOyTchy9NQ82lHhd5+pN6rskPBZNhuWgh1EX+QxL+bmsEbRtBhL9kQMtcqaYP65S/9ZvRxCEsCeFy9mecj9A+d/WSsLTnNsoGZYhDt1RSsJb6WEJuErG/Q3k2l//Gz75bbFywikczX4Dlq7XaffMXm7X6WOYRdyaliAGweWldEaX9CKIkj2sEi2r3X+pdUoQJdvqjIs6U7XPv0fyCYM053u2cmLI3vZ7t41Dd8KWl5eHPn364Oyzz8aIESPQrVs3dOjQwe0rLy+0kznk4PlakSD57XNORMlJHpgpQUJNS3Q/88SDXbQpxyNyG7M0fQam9P8/AMC3h1bFbW3HmwZ1hqWPdl7BKG86gAMNv0MjaHFV31sAALud8zaSlWeAsiYKg3e2VW1yq2aKduu7cPlO2WuD0WjEqlWrsHjxYixduhR79+4FAAwfPhyXXXYZJk+eDI0mpFjocUv94dhka0GGwZV9KJeEawPIsPQ3ZAQAGr87gJJpH0FstEBssqL4lYu9ttG2UhIuB3e0gs4rO1LpYakKWEqShIP3r4bYYkP68G7Iu7o/GtaXwr6j9YBlw7pSNP/keANr3noE6UMKfW4XLKWHpY+hO/IBuaFZh/RhXdH92QuQ86feSPtmHQCg9KV1MN16HlL6dGz1Nuq/2Iuy25ej29w/Ivfik1vdVk3+gC1n4MmlsKH0sJSfA7avq2DZ73wDsokQyszOffr+oG6tbELjdwcBeJeDywSNgIJZZ6FsxnI0rCvF7j+8jo5/GYL820dCm2FAvfOPUKZH2XBrU8KPNDomkuend1Oef8Ho6ux5WWOqQrO1UXnsAHV2o2eGZeyCOXLQIzUKQ3fkAVJZhlxohNDec5Xs6DYCfHIJh9yP1JMkSjg4azUkqx360QZYRUvoJeFeGZb+A5aeGXxKD8sQ//jKGZO+svPUjNoUNFnr/ZYRvf7Lv7Gl4isYtCkY3/OqkNYia/LRwxIAilUZlur+lQCiMpE+FMH0sBQEASMK/oBlvy/CpsNrcEbXP4Z0mxbRf9uRUEvCbZIVjdY6r/c2Vw9LOcNSfq4GkGHp8b5PwVu3bl28l9Duya8VrV0Du9ZRIWMVLTGvkiGi6Kl3K9MtR+f0rvFbTBSoKwDV7XX6dRgKADjcWAar3cK/xzHgnmEZXkn4D0fWAwBO6TAEgzqfCQAob9qPOnN1yMNIQyFKIgQIXm2IQiGXgOemdEKNqcor4zISvjnoKAfXCXrH8W17zLAEAI1Gg2nTpuHTTz/Fjh07sGPHDnz66adKeQ4FR6fRQ+McquOZsaMM3fHbw9Lx5mqXbGix+57IWr92H/Zd91+IjY4PSPXrSnyW9OqcQVGfGZat9HZTykpVWVq1y3ahYV0pBIMW3R4/D4JGQPrwrkpJOI76DmbUrfxN+X/1km0+twmF/MblqyS8qaEWAGCw6JF/1xkQBAHZ43uhyzmOXp2N5lr8dum7sFa0HkCsWrgVYoMFhx9dB9EUeAaC5wfssErCnUHO5rf3AACEFMfvVNrp+N34+6Be9789gCghbVAXGIr8Z9Hl/KkP+qyagszRxZCsIqpe/B67Rr+G6g+2K2doPbP9WpsSftgZsCxI7x7oXXSTps9AbkonAMChhn0AgKath7HzrFdRvdXxvXcPy9iVhMuBC6M21RVEilCZrhywDOePcrqP164v/oLRsup3fkH1O7+g5oNfobU6/gaEXBIeYA9Lm2hVbsOzJLytIUL+mJUhSa0H2FJaGZQGuMpcNkbgrL3nYBdZsSrDssgjYCkfdMd7SriS9a9tO8MSAEYU/gGAoydRqFPm5de1r79VSo/JAHqPevYnrTO597EUJdEVXA+hJNzKDEtKAnJJeKrVdWKdk8KJ2hd11lskBqEkGvm4MkWb5naypUNqPtJ0GRAlOw41eldqUeSpEwrCLQn/vnwtAGBol9HIMGQpg1tj2cdyb+0OXP7RANyyajw+2PVS2K+fameAUm775G/oY6jsog3fHV4NADi961gAQFOCZ1iGFFmsrq7GL7/4T7fdtm0bamq8g0LknyAIrsE7Ns9+QXKGpZ+ScGXojsVnNkvtZ7tResPHkMw2ZI3tCUNRFiSTDQ3rSr32JZed23xkWKqnHXtK9+hhaatpweGHvwQAdL5tBFJO6uDYf7oBYqqjrM62zXuIgWixo+6Lva61f7rbZ2C1LZJN9LqstZLwml0HAAAZnTog40xX4CwnvwsAwFIoQKw3o/az3X5v015nQuNGx35sFY049m7gKenywb/Bo4dlcwgl4XJZvna/Fbr8DHS+ZRgAQNzmeDPy90HaNR287czQlF4dcMKbl+GE1y+BoTgHtsomHJi5Eoc+/QGAd1DLNSXc+wzOkSZnwDKjR5u360/XjBMAAAcbS1Dz8U7svXIJLKW1aG5y3J6u0r0s1zWQJPoBS3UATA5MRK4k3Jlh6cweDoXna9efxlZKwq2VTTg819VDVGhwPN7hTgmXMyyVIJPH60GdAZfqURLuK5s3EOZWgl1qcla8vx6W8sH/L1XfhX3mslFpFO+eYZmmz0Bxdh8IENA7b4DH+hzrj3dgwbPHaFv6djgNmYYcNFhqsfPY1pBuU86c1vsqCQ9mSrjH79Zz8I7J1gwJjr9nruB6EAFLu/9MUArMu+++i6lTp/q9ftq0aXj//fdjt6B2yCyfdJBSobHL/Yk5eIeoPWnwyLBsb1zDC91P/AqCgO5ZjpY6++t/j/m6jkeRKglvsTVhW9UmAMCwgnMAAH3yBgEAdscwYPlTxQbYJCsON5bizV/n4YYVY/Dwhr9gw8H/Bf05SJRE5VizZ45jgHSke1huP/o96i01yDTkYEShI2DZLjMs77rrLtx4441+r7/ppptw9913h7yo45UrY8c9iNJWSbhWKQm3eU1kPfbeNpTd+hkkq4ici05G8csXI/t8R1Pauv/95r0vQe5h6X0wKgdeDD56u8nZJTbRCovdjIpnvoPtWAuMvTug819HuG1rT3F8wLP87B2wbPx2P8R6M3Sd02Hong2x0eJzna2pXrIN23rPx7H33LMzs/wM3bFWNqHxsGMqXucxp7ilc8uZgvbejg//tcv3+L3d+i9LAJsIaB0/X/n8poCzLJUMS+cHbFdJeHABS6toUfZlNBmQP2MEMkY6ArDiFkdwyyZZvQJmtpoWNH1/CACQfYF7Lzx/BEFA1tgT0eeLqehyjyMNv2qb44+95yRp15TwWq/9HGl0TCUvDCdgmekIWO5Z/RX237YcktmOzHNOgDXTEbg+9sh3qF+7T9leLgk3x2RKuOskgl7reH1ZWvkDVve/3/DbhLfw+2XvofSWT3Fo9peoeGETqv/7K+z17uuVMyxzjB1CXp/na9cfuYTDV6/Mw4+shVhvdgxr6t0BWotG2WcoWpQelhlo+bUSRosjoOP5epC/1wl6Jegjr6/J2hBSD1xXJnlbPSwd7wmeAylkcsDULtm8JhgGS84M8OxhCQCzTn8ej561yC3bEnAF5WPRp7U1wZSEA46/Z/KB53eHvwjpNlvrYRlUSbjdM2DpftAoB/l1gl452RTUlHDR/zopME8//TSMRv+PX2pqKp5++ukYrqj9ManeE3U2x0nteLeaIKLIsdjNbtUi1e0ww9LfiV/A1VJnf31wnzcpNO7ZvKEHLH+u3AibaEWX9CIls7JPnmPIXiwDlpXNhwEAAzufgVM6DoUIEVsrvsa/N92Jf228Pah9NVhqITp738vH9TXmyJaEy5VfpxeORbbBUaHXLqeEf/nll7jooov8Xj9hwgR88UVoHzSOZ3JGjGcZmlIS3maGpdU17brEhAP3rsLB//scECXkTRqA7s9eAEGvRfb5joBU/Zq9EM3uATUl+Omrh6Wt7TI7AGgy1aF22U4AQOGss6ExuK9bLgm3bK3yCijIwcnscSch9wrHMJWa97f7vN/+VH/4KySriEMPrIHpN1dQ1F+GZdWCzTDrHQff2X2L3K6T++FZnEPRm78/BOsR35lodasca+/0lyHQF2bCVtmEY+8ElmXpd+iOnx6WRxf/iF8H/weVCzZDEl2PoToDLT0vD3nX9EfqwHxAK0A44PqA4Rn4afxmPyBKMPbuAEO34Ia3aIw65N92OjLO6oHmFMdzJMMrYOl/Srjcw7IgI7SScAAoNDp+trTkVwBAp5uH4YTXL4FY5Hg89bUCSqZ+hKpXt0CSpJiWhCsBG21qm0N36teWYN+tn6C8ZC+aNh9E3We7cXThVpT/62scuPN/OPB/K922V3pYhlESnqpLV6Z6t5Zl6cqwzHG7vGF9KWqX7QI0Arr96zx0ffhc5UNtU2lof2TlDEvtQSv2jH8D9f9xvI48n7e+gmFyUM8u2bzeS9siSZIqI7b1gKXcM9NXSbjZ1uJWir3x0Oqg1uGpSTnQ9g5YFmR0x4DOp3tdbohwv9RQyb8DX32V/RlR4CgL33R4TWhBZ9E5JVzjK8NSbi8QwJRwj6BjrUdJuPz+nKbPUE50pbQy0d4TS8LDt3v3bgwePNjv9QMHDsSuXbtiuKL2R6mu0adBKwcs2+kUYaLjkWcyQfvMsHT2Ajd4H0fJLXUO1O/1uo4iT10B1WxrVE7KB+sH53TwoV3GKMdgvZ0By9+qf4EoeVdbRkNVs6MP55ndzse/Rr+NBX/8HH868ToAwN7aIGMYJkcCVZYhFx1THVWekSwJt0t2bHSWg5/RdZzyegi1Ki1WQgpYVlVVoWNH/8NHOnTogMrKypAXdbySM3rkwKBMnjLrt4elM/PS2tSCFpPjDfnIDStR7QyWdbpxKLr96zwIWsevO+20Qug6p0NssDgCVT721dqUcKOPD/JaQasE2Y5u+R22o83QZqcgc3Sx17Y2wZF1JR0ywVLmeoFIdhH1nzsDluf3Rt7l/QABaPz2AMz7a33ed0+iyYbmHx1vHJLZhv13rIBocTx+WT56WFqrmnD0zZ9hMTrW5FkqkO7MDGwWmpDmHP5T97n3GTjRbEPDWkfvk5w/9UH+bY4AQuULmyC2tJ1l5vnBtbUp4dYjDTj8j/WwHW3GkX9+hX3Xfaj01mxqrHXsx6JF4W1nQGPUQZtmQGrfTtCKGujhCG57fhBv+KoUAJB5dnGba/Wny/+dieY0x3M3tcH9A7g8JdxiN7ll8YmSiCNNjudgQXpoGZa2mhZonneU4h/Lr0O3J8ah8O+jIWg1MEmO52zHMX0BUcLhOWtx6KEvXSXhfvr71a3ei9+vXALzPu8BTcGQJEnVSkE9dMf7dpt/PIKym5Zh46if8cScd3DguTQUPnwOOt86HLmXnwIIQN2K39Cy0xUEdPWwDL0kXCNolOdbUytn2Bp89LAUW6w4eL/jD1/HaYORNqALMs/qAUOa4z2i/M3vQwo4yQcv9u8d98/+nSNQ5B2wdA4ZU02gNmpToXNmigdb4mAVLUqJb1sZlnJA09fQnQaPP/xbK74OK9Ox2c/QndYkzJRwa3Al4QAwOH8UDBojKpoPoqzef1a7P1aPE0BqwWRAegaj/WVYqv9uyL06A8ngbK10nQIjSRJqa2v9Xl9TUwOrleXL4VAqd1IyXRmWDFgStRuelWfts4el8zhK5x2wdJWEM8MyFjyPkUMJkEuSpFQvDSsYo1zeI6sXjNpUNNsacbBhn+8fjrAqZ4Zlp9QCAEBhZjEuOmkKgOBbu8nBydyUTsp8hhrT0ZA+S/my69iPqDFVIV2fhQGdT1c+n7fLDMuCggL8+OOPfq/fsmULOnXqFPKijldKby2PrCC5rPL/2TvvMLmqgo3/7vSyvbcku+m9kEBCTyAQelVAehMUURTLh36IYvlQVAQFAREQRDpIDR0ChEAS0nvdzfZep7f7/XHn3Oltd0MW3Pd58ig7M3fuzNxyznvekrglXJmc1/7iTTySMog0BozknTWVCc9cQMXPF0fYnCWNUigD0Lc8cjIYagmPzPxT9itIvCSYyAv1T/uH2wHIPXUSkj5SXSnLsvp5dD4Ntk9DhKl9bRO+LifaXBNZi6owVOWSdbSinOt5flvc94yGY0MLstuPtsCMNs+Ec0sbbXetAkIKS5unTyVkOx5ci+zy4clVLgTRKiBBzgx4+sg7TbHSx7OF21bVE7ArmZHmOWXkXzATfWX6KstoC2Oy0p2WP36C7PJhqM5DY9Zh+/gAu05+jP5399H8n3UAGL1GCi6Yqb7GMk+5iBp8CmEWfozJsjwshKV1XjnescrvHXg3csHCos9CE7zchK/i9Lg68PhdaCTtoBoJ5YBM/U3Lyf5MWUXrrrSTf+EM9XHx/dX8z0mU33o8SND1zw3I+5W/x1NYyrJM868+wP5pA82/+TDjfQqHL+BVpf2RpTuRkz3Xvm72X/kiAaePjnnKPi23vErR1YdRfstxjP3zaeSdoVgD2v7ymfq6fo9oCR9aE55KzCdZ5QwRliEFbttfV+Op70NflkXZj45R/24uD1oMdrbS/2bmmUCqsnib8r8Gtz74d0fU84R6NXTeSpKkrhgmI2DjIZxkSp1hGVTExyUsewHINRZSYqnE7XeyoW1lRvsi4A/41M9pjVpQSYbhLngaDPwBn7ookInC0qSzMLf0KEBRWWYKcV7Ha3oPt4SnGgBG/7bRCsvohnAYLd35ojFv3jyeeuopPJ5YAs3tdvPkk08mVWCOIjVUwtKajdan3Mc9nkMbNTGKUYxi+CAKMwW+2grLxJbwFnv9aNzFQYZf9qtj8zyjIn7rdGTeFL6/dzvdrg5MWgszi45Q/67V6JiYr8x/v6jiHdF0XmKpUP+mloX6HRkpPXvCCMs8kxL35fY7B1XCGw+rGhWn3hHlJ6DXGNTINo/fNaKP/UERlueccw4PP/wwr7zySsxjL7/8Mo8++ijnnnvukHfuvw2JSncCqsIyviVci0IyOvWh18354AbG3XcGWUfFt9nmBsm3vrf3RRTUhNvLo5Eq201M2jrXKQRF3plTYp7jk0Pb1fq12FY1qP8t7OA5J01QiU5BunU/ty3C+pwIttXK9rKPGUfVnScD0P631dhWN6pEi4yMzdtP//v76Xp8o7JfecrrzVHt6sISbvP2qdmO9tWNeNsjLxz9bys2gtyTJiBpJDQGbUhl+bc1KVWWYlIvSAah2HFEZ/Ztb6fnOUVePvYvpzNp+WWYZpTg73ZSe9V/aHp6LQBWS24EWWw5TLmIGl2xxI97XzfepgEko5asRVVJ9zMVPEHCUv6kJ0IJqJE06iAhvB1O2MFLLJURrX3pov3e1Qx8UEu+PR8tOjyyW73x+WW/SlyY9VZKrj9cJf0crypq2HiqN/vaJjx1vQD0v7MPx9bBrzSH32BMOjO6OKU73lYb+y99Hn+3E/PsUvSHK4s9jQP72dwRIidLvqccT32v78K1W7mhiWBmcVNLuB87Ouh7Z19CgkaU28SzhNvXNbN94YP0dSjfg3avB9kXwLW7k44H1gBQ+asT0WaFSBeDWblG+HR+mn/9QVoq43CIFUmjU4dk0mFUCUt7xGcQ3280GaaetxkqLMXxoNPoEyraBYxJLOGCsMwx5LEoGGg9WFu4IywWwhLHEp4II0FhGZ4BaYq6tqbCEaotPPN4GY8/MREoCEUZOWUkhCBqCoKr3LEKS5EtGiKSM7KEqwtVo4TlYHHLLbewdetWlixZwquvvsr+/fvZv38/r7zyCosXL2bbtm3ccssth3o3v9QQ1ziLNRudPzje7Og9hHs0ilGMYjghxuWlFmUO0O1qjytc+TIjWbROgakEqz6bgOyn0fbFqPL+W2EPE62IjMbBEOSiHXxu6VGqe03gi8yxtHsH1LlTkaVc/Xu48yudBWyBnmBDeL6pCLPOqs5vhsMWHpADrGpW8iuPrlwGRM4rRrLKclCE5S9/+UumTJnCueeey2GHHcbll1/O5ZdfzmGHHca5557L5MmTuf3224d7X7/yUC2G/viWcE2CDEupX3ncVRhQn2cuTJ5DmLWwCm2+GX+PE9vqRvXv2iSWcDG5E2UT0RAkm907gDbfHJcsDSdqdD4ttlX1yLKMLMuh/MogmQqQe8okNNkGvI39agN3MtiDz7EuqiLv1MnkXzATZKj//nKw+VXp845bnqf2CkXRZj2iCrdWmdRH2xbDCzz0lTlY5paBDH1vhmwDckCm7+1g2cyyierf87+evsrSG2UNjGcJl2VZUfzJChlsnVeOaWIhk16+mKJr5wPg1igTi6y8SMWdNWhn1w8op3z4xXPgI4U0tB5RhcacOWkYDrtW2V+Lw0jrHz+JeEyVnYfdrJqHkF858Ek9rX9S3mPcr0+iIrsagCZbkIz0hZMlyu9acNEsABzL6wChgIxc+VIzU0V50j2fMViIiZ5Oo0en0asKWkFQ+/tc7L/8BbyN/Rhq8ql57Dw8UohgWr7vSfX/m6cWK6S5rCgbIZTHmkxhafu0nj1n/pu6q/9Dw81vxC2CEgM4RxRh6e20U3f9K3ia+3EYlM/See17bJt7H/svewHZGyBn6QRyTpkY8Tp90JJNkQFPQz8df/88xTcVCbEfJpeBip8vxigr18aA7I+w8YdKxiLJMGGdzlRhKdpwU9nBIXnpTrh9flHFSQCsbflgUCVEds+Auk+ZkPqqwnIQhGW3sz1la3w6EL+PVtJlrCI8vHwJEhJ7e7dl3CKpLgDFsVqH/7apbOHi/C21KtnGsQrLYDmULswSPgiFpW5UYTlonHrqqTz88MNs3bqVc845h0mTJjFp0iTOOecctm/fzkMPPcTpp59+qHfzSw2hNDbpzOilYAFae0+yl4xiFKP4EqE/qLAckzMRjaQlIPuHNTdvJMCeJFpHkiQ1x3K0KfzgQtjBLbosSiyKs25QhGVYfmU0RI7lF6GwFHbwbENeBIdg0BhVTiWTjM4QYakslOcZg7Zw99DPx93dm+lytmHWWZlbejSgRPqJOaB9BOdYDoqwzM3N5bPPPuPWW2/F6/Xy/PPP8/zzz+P1ernttttYs2bNsHnt/5sgJlLREx1BHiaarMqdyuTMV608btZZIyzg8SDpNOSePAGAvjdCFmeh4vTHKd0RE/NEk3mRC+I2ecg9bRKSLvbw8oVZYXUaA752O57aHpybWvG2DKCx6Mk+NpRlqDHryTtrKpC6fCfg9mFfp6jrso5UJpiVt5+AYWwu3sZ+Gm99F6tL2fe29btAK1F83QKqHzs3IfEhlFoB2Y/TZyf39KAyNcxK79jUiq/djibLoL4vkJHKMlS6E5VhGabSGVhRh+3jA0gGLWW3HBt6H6OOyl8soebx89EcrhBXFkOkddRQnYc234zBrYvdrmoHH3xLt7qtIFFjdprof3uvmicK4U3hsQrLTPMrva026m98DQIy+RfMpODCWWpTuMgsEZ9RI2lV8ibrmHHoK7PRdIVWjsMJHb/dQ++ruwCo+u1SJTfyzcjcyEygTvSCajxB2vj8Hvw2D/uv+g+uHR3oSqyMf+J89EXWiAWL1S3vRZA1pd87EoDeV3bi2tcdlmEZn7B0bGih9qr/IAfLtXqe386+i57F2xGp/hIrbOEklewPUP/d1/G12ZBnZBHQKtf0LH0O/j433mblfK38zYkx1xux2pl1ljIAbLt3NW1/+Qxfb3o2QttAL6CcBwVfn0Hx4qnqY+EkvjNBA3VIzTs4hWWqwh0IK91JYgnPMuQyregwco0F2Lx9bOtMn7gNuH3IspxUFZAMIXI8M4tHl7ON77xzGr9ceW1Gr4sHcQ6adJaU96Ro5JuKmFqoWHnXtGRmC09mCddIGvW3S0UqiuOhTBCW7viWcOuoJfyQ4sorr6ShoYFnn32WO+64gzvuuIPnnnuOxsZGrr766kO9e196uNTz2Kpe253tI3diM4pRjCIzhGJsClRHwVetKTxEWMaP1hmbrbjoGkZzLA8qBsIKPIssSqlMpk3hPa5O9vRsAWBB2fExjwuF5YG+3cNmpU4EUbgjyFcBSZKSxrslQrglXPnfouDfh94UvqrpLUARBIQv6Fv1g5szfZEYFGEJYLVauf3229myZQsOhwOHw8HatWuZMWMGF198MeXl5ak3MooIGBMqLBWyIVFLuNyuPN8T/MrTzQoTbeF9b+xR7daidMcXr3QnhSVcLRswe8g7c2rc53jD8jiz5imqP9uqBlWxmH3CeDSmSCumsIX3Lt+Nvz+xWsixsRXZ7UNXbME4QSFwtFkGxt5zGmgkev+zA+MB5XP65liY/MblVPx8MX5TgACKyi6a+DBoTepkcsDTq6o/bZ824OtSJqT9bwXVlUtq0Bgj9z3/6zPRV+UoKst/J1ZZhiauwhKufJfCDir7ArT8ZgUARVfOwzg2L2YbOUtqsF4zTXm9LvKGLEkSlsPK1SxAMZkOuH3YVyk5otnH1yTcv3TgD/hUcqViyWxAydsUiNcULgp3KrLSJyxlX4ADN76Gr9OBaWoRVb9RrKOCsGwaUBSW4XZhQZZIGomCC2ai94bOpfDzre/13QQcXgzVeRRcPDuUG3nPp2nvXzhcYYQNgF6rfP9ut5Pay1/AsbYJTY6R8f86X/1NharLoDESkP28VfuMuj3zjBJyTp6oFAjdt1L9vuMRls6dHey/7AUCdi9ZR4+l5tFz0eYacaxrZs+ZT+DcHsoZtepjLeFtf16FbWU9GrOOwj8qK3EGrYm5n9/ExJcupuyWY6n553kYKmNXq4ViTD+nkOzjq5FdPlr/sJIdCx+k6fYP8DQlvyk6ncp+FB87DY1ZT+H5M0Nkuyu0j86o71dAqHntGVvCk1/jwhG6XjsJePz4bSFiMHxAppW0auv1p01vp9yuLMt0P7OFbXP/xoHrXwkbZA+SsMxQYbmu9SPs3gH29GwZcsNiaCEo/fzKcIjv7bMMcyzV0p0ERKBq2/YnH0SK/Q8pLCODzx2+2AxL8Vk9AXdcp0Lc/Rwt3RkycnJyOP/88/nJT37CT37yE84//3yyszM7Z0YRH6HiODMGvXKsOrtGCctRjOKrgnBXSGGwmbjzq0ZYesTib/zywrGjCssvBDZ1fJyrtmBn6qJZ16r0C0zMm0GBuSTm8UJzKUXmMgIE2NuTWUt3puhwNAFQbInlvcxRc/l00OtWiMm8IFEp/nc4FM9iDnJU0A4uoM7PM5wzfZEYNGEpIMsy7777LldddRVlZWVcdNFFfPrpp1x88cXDsX//VVAVO1GlO8ks4QGnF7lDmfS48pXJUbptrFnHjEOTragchRJOG1Rx+uVYNaA7hSVcHxSfeAsUy3k8eMOyxYRl3Laqnt7lCmGZF8yJDIdlXjnGiQXILh+9r+1K+HnsnwXt4AurItQ81gWVlH53ofL/Pcp3Y71pBuZpyuqFIyJnMPK7kyQpgmgzjs3DPKsUArJKsva9HczePDnSFgtBleV3lPfufnpLwn1X22ITtIR3P7cV1+4utLkmSoKqzXgQZSXh2RkC1nnlKukjJuKOdc0EnArJa5palHC76SDcfltzwwmg02D7qA5b8HeJZwlXFZYZEJYtf1iJfXUjmiwD1Q+epdrYq7IiCUtXAvVdwddnokETKg8II3S6gyreggtmIklSWG7kbpw701/d8js8BNy+iIkehAjpng/3YA+SlROe/Drm6aEbrtunnGcnVp8HwNt1z0WU9JTepOxT07sbAeW6kGWIjIBw1/aw/5Ln8fe5sBxWTvXD55CzdAKTXrkEQ00+3qYB9p77FH1Bsl0M4IRirP+DWrXcp+r3J+OpVL6rbEMukk6DdX4Fpd9ZGKEoDodezcL1UPPouYz9y2mYphUTcHjp/Mc6dhzzD+p/sBxfT6w60dtpxxmMNig/Y67yvsdVY/Aq50bn6tDqd2JLuEJUZLpa6FIXZZIX7oQ/x+VzsO9rT7Nj0YO4dikDiuhGdWEL/6z53aQkoH/ATf13X6fhR28RsHnoe3svA7ae4GdKvyEcQsdaqpzGaIjc1IDsVz/HYJHoHEwXCysUwnJrx5qMLOpulQiM/zumq4J0qZZw5X7mCbgjVspF1qolwhKeFfP6RBhVWI7iywBX2GK13hDMWu8ZemTEKEYxipGBSMKyFMhc9TbSIRYYExOWoin8y09YZmJB/qIxEGykzzLkURRs1RalNeliS4cSizU/jrpS4IuyhQuFpWgID4cYGzozaArvdipzzQJVYRlqCh8KPH43bQ4lAnBW8RERj4X3dYxUDJqwXLduHTfffDOVlZWcfPLJPP7445x++umsXLmS1tZWHnnkkeHcz/8KJGqdTWYJt3/ejNajkHMObWaTQ41RR86JkbZwnSaJwjKF+ki7X5kkylMtce3gEDZB04bs031v78NT24Nk1JK9ZHzMayRJCpXvJLGFq8TYolgSpfSHRzPh2QupWKJcwPq9vepj4SogjRS736ECD+VEFrbw3td3497fjXtPN5JeQ84JsfuuPl8j4drViacx/sXAG5W5Jia9Tp8dv92j5kGWfv9IdHmJyRRxk4pWWAJY5lfEtC2Ht4NnatmMRn9wwGPRZWGpLqIwmBfZeMs7uGt71NIj8T3KskyLTVFYppthOfBhHR1/U4pexvxhGcbxIWWhqrC0RRGW2ijV7Jhcso4Zhy6oshSEjruuF/vqRtBI5H9NaRo3Ty1Wf+/2sHbuZOh9YzfbFzzA9vn30/yv1cF9UM4ZrU95T2ffgEpWWuaURbxenGfHjzmTfFMxPa4OPmsKFY9YZpeRfeJ47GbledmGvIjj1tMywL6Ln8PXbsc0rZiax85Ha1UIEeP4Aia9cglZx44j4PBSd+1L7Dn3SaTdyrYc3gE8Tf3U37QcZCi8dA75506PIeBSQSgsfQEvkl5L/rnTmfzW5dT863yyjh4LvgA9z29XyoYGIhWAHc9vwqdXFmnypypEtqTTqCR8+wc71Oe6EpXuqNmzg7OEG9OxhAff097RjWNDC/4+Nwe+8xoBpzfm+5pTciRmnZVuV7tqY4mGY2MLu095nN6Xd4JWQmPRg1+mZ68ywLBk0BAOoWtJJi3hsiyzuT10nA91RTeRZT9dVGbXUJU9Hp/sZV3rR2m/TnxmfQLlYro5k8JVkGsoUK8j4d+JXW0JD/02Oo1ezS1KtX2PP/l+jmIUIwFudeHNgsGkXBudPSNXiTGKUXyV4A14eGzrn1SS5mBAkEjZhryQ6u0rRljaPLH363AIhWWrrf6QlhUOFe/VvchFr8xnRX1sMfJIwEC4wtIyuGOtYUApuq3Jm5bwOVMK5gIHv3inI0i2FoU1hAsMxhIuyh3zgw3qKmHpHpolXESI6SS9KiAS+MopLPfv38+vf/1rpk6dyhFHHMHzzz/PJZdcwjPPPIMsy5x//vkceeSRQyY+/luR2BIebAmPo7C0fXIAjV/5GQUznon9LsIWLsuh0p047XChPL7YyXzA40faqUzO/DWJJ18hRYkey7xyJKNOzdfLPrY6omk4HPnnTQethGNdc9w8wYDHj/1zJfg2HmEpSRJZR44hN1u5AAy4Q4HxzrB8pnjIiiLa8oQtfFU9Xc8oBKp10Ri0OfE/ty7frJbe9L8Xv30ueuIafpHreHAtvnY7hnF5FF4+N+7roz+LOc4N2TKnDINHISwdfb2AQgACZB9fnXS76cAWZoMFKL3pSHQlVtz7utlz5hPomxVlmbhZ9bg6cPkdaNCoCqZU6Hh0PaAQacKuLSAIy05nKy6fI+y7iP1dFVu4cqy7vcpx3R1sX88+dhyG8pCVsDSosux9LdTOHQ+yP0DL7z7iwHWvEBjw4O9z07FCIdfkvQ763txD/2PbAPCbiEtWQuj8t+qzWVZzAQDL9z8Z8Zzs785m+XmKTb1YU4LtswbaH1hD3fUvs/uUx9USn/H//loMwa3LMzH+sfMouuYw0Eg4Pm/G84ZCirV9spO6617G3+PEPKuUil8sASIHGOlAKMbClaGSJJGzuIYJT1/AxJcuRltgxrm5jdor/6Pmu8qyTMt/1quvCf/trDl5APRsqVWjIcLPXb/dg3N7O942G1atcvxn3hKeviVcXAdtLaFcQ9euTpp/82EYYal8X3qtQc3ZibaFy7JM+wNr2XPuU3jq+9BX5TDx+YvUAq/eA8pvk7HCMpg15wkrOkuF+v49EU3Y0ZmNmSJRi3smWBhsWV+dgS1czQTWxL8mh5q8UygsVcLVTJ6pEIj8TuJlWEqSlHZT+KjCchRfBoQW/8wYzUGyv3/kKnhGMYqvElY2LOeFXX/nrrU/PmjN3UJwkGP86iosQ3ng8cdS+aZirPocAgRUp9aXEdu71gEht8xIg+Aqwslxp8+etotGlmX196nKThxlNqVAiSbb1b3poPaqiNKdkiSEZbqWcLffpX4PeVEZlkMVEPQHCcscY14MT/eVUlgeeeSRTJo0iXvvvZcTTzyRDz/8kPr6ev7whz9w2GGHHcx9/K9BqtKd+IRlPVq/8ncxOTdp4xNv8ZC9uBrJpMNT34dre4eqsIyXvaVO5uOoj2yfHMDQE7TY5iW2PIa3omqMOqwLQie4IE/jQV+apT7e8eDamMedm1uRXT60BWaMkwsTbkdk/fV5ukOvVSfVCQhLfWT2orEmH9P0YvDLavtx7rJYO3g4cpYq6stEhKWqsIzKsHT7nbQ+rHze8luORWOIn2MqIC6K8RSW2mwjFmuw+KalQ7HeblVyDLOOGb7CHZWkKcti8uuXYZlfgb/Pjftp5QYjiE2RX1moLaH9lx/T9MsPkL2JB2P+Phe2oCK06OrYa062IY8cQz4ATbY6lYyIR5bknjIJvV851nvXH0D2B+h5XiETCy6cGfFc8/QShTwKa+eOhq/bwf7LXqD9PkX9WfzN+dQ8ei6a2XkAaNt81H3zZfz7go3PC4rjkpUAnjDSbFnNhWgkLds611LXp8Qh9Lm7+b/en1A3sQWjU8+SOyax7+vP0PLbj+hbvgd/txPD2FwmPPV19MXxj2lJr6Xylycwfc31lP/vcWRlK99bf0c7zs1taHONjHvgTDVP1iZKZPR5cbcXDaEG9yYofLHOr2D8E19Dk23AvqaRum++rOSprm5koLVd/fzh1zxrtnJcuSU3vcHSK/EbSwdc7Fj4d3Yve5ztCx6g9xfK79D24Vb2XfgMdde9TMOP36L5Nyto++tndD+7Fb8jdt8ys4QHlUYDyvFc/lOlCKvr8Y30tinZT+GK1CMrQ7bw8MFT73920PLbD8EXIPf0yUx583KsCyqxHq4EePe1KNvKOMNSWMIzUFhubF8V8d9DHSCFiI7070nREDmW61o/THg8RcOTIhsyXUt4+D0vL7jaHUFYqhEckdfbdBWcIiIlEbE6ilGMBIRHOxizlHPZ63AQcCXPaB3FKEYxdGwOKiu7nG1s7VhzUN5jINgS/lXOsBQOtKwEYylJksJyLL+8xTt9wTFKm73xEO9JfAgBRJYhF5POos6x0yXIe1wdOH12NGiSFrZOyJuBVtLR4+rI2HKeCQRhWRyPsNRHxrulgijWMWiM6pg/3zg8lvA+lbCM7TwQRaWZ5v5/kUibsFy9ejXV1dX8/e9/55577uGYY445mPv1XwlTitIdjSay0MXf78axuU3N4hOrR/EUZYmgtRjIWaysUPS+uhONWroTm2EprLPxJvO9r+zCGLQbJ1tJCM+whFCbN1qJnJMmJN3Xkm8pmQs9L++MKe2wfSrs4FVJFb6C0OoPU1hGF6NEQ7Rbh2cv5p0eVPf5FHI2Xn5lOLKDdnHbqvq4RIlQQenVlvCwHDSvHdPUIrXwJxlUS3gCy4O1SLlQ2Tq6sH2sEIamGSUJia1M0K8Slvnq3/RlWUx45gIKL52D2aFMyjvW76Z/RS3bH1sOQM42LZ2PbqDz4XVqLmg89L2zD9kbwDS5ENOk+KR0ePFOMrJEY9JhNCl/73x/B7aV9XibB9Dmmsg5Kfa3LP1+qJ27+/ltDKw8gGNTK+7aHmyrG9l92r+wfXwAjVnH2PvOoOK2JeQsnUDOldMByKoqRptrxGBQznF/gq9blmU1w9KoM1FoLmVRUGH2xv6n6HC0cMuKi9nXu40cTR5X338mY+tK0ZcphH75z45jwrMXMuW9q+KW4URDX5pFybeOYPyPlQBmf7UB0/Rixt13ZkSx00CUejbldrUhS3giWGaVMv7x89GYdQx8WEf9ja/T9cQmXCblNdHHsFhQcJu8KrksfmPbP3fg73OhsepBI2FyKO9vD9iUUq839tD99BY6Hvyc1jtX0vDDN9m99J8MfHwg4j3CrY+pIBZuPDoflnnllNywkOLrFwDQ0xZUe4cpUg8rPQ69xkCzrY6GgVBGUudjGwAovn4B4+4/E22ucn3NOkJRHQ/0KIPPRISlY0ML3c9siVlBVi3hGVibwu3gkFph6R9w49rXnfDxRKVImWBywWyy9Lk4fDYa++Mv+EQjOmIjGuJYcqVQQIZcBZaQwjJs0Ciut1Zd5G+TLiEaikiJjXsZxShGCiJKd8zKdc+n86csTxvFKEYxdIRbwQ+WzTc8xqboK6iwDMgB1RFhSbL4+1XIsewLzm9HLmHZC4TmE0LRK7IgU0HEfpVYK9W5RjwYdWaqc5W5+sGyhfsCXrqdisgibulOhpZwMb7MMxWpXMZwtYQLIjvXGDt/jhZmjUSkTVjee++9lJeXc+6551JWVsb111/PBx98cFBltv9tEIodd3SGZdACIBq8BWyrGyEgY8hRJkcyym8RndmXCrlBa237/WtxfFgffM/0MywDbh99b+/F5FQuHEkJy0AkMZd72mQko5a8M6eiy09uw7TMKVPz7zoeWhfxmO0z5cKcqAREIMcYJCw9sZbwxApLIZUOncgi1xDAPLs0wkIcD6YpReircpDdfmyfNMQ8Lr4XobTRaw3q7+0xeim5cRGSJnXUglMtgYj/WbLLlZUae08PAx8pF/3hsINDmCXcmBfxd41RR9UdJ1F1gWKtHnD0UnvZCxzYrZBORf0FimIV6H4mcUZp3+uKqi78u49GOGGZiiwx5Sm/Wd/GejoeUpSyeedMjWmpB7DMLCVn6QQIyDT84A32f+M59pzxBDuPe5h9X3sab9MAhpp8Jr5yCflnTVVfJyZ6+bOrmb7+Bmr+7xQgsfLQF/CqjfXiPDttvFJg9sGBl7nlw4tpstVSZC7n90uf5sSnb2HamuuZvvZbVP/9bEq+fQRZR46J+xmSIcugfBf+MXqmvHVFzDERrZ5NhXiW8HiwLqik+uFzkQxa+t7cQ+/LO3GblNdEH8Pi/PSYvNhXN+Lc2UHPVoVwNLj0FHxjFjM2fYfZ+3/AlD9/Tfk8k02M/evpVP7mRMp+fAzF1y2g4MKZ6Cuz8TT0s//i52j40Zv4eoM5pkkWZaJh1CjP8Rp8FF6qZOOW/eRYzLNKcRqV7WTrQt+XRZ/FnJKjgFDrtXNHB471LaDTUHz94RGLLcZJhWjzTLj0wYgAQywBLfsD1F77Eg0/eov+dyPJvFDpTnqEpS/gZWunot6YUXQ4kHyAJMsytVe8yK4lj6hZuNFIVIqUCTSSRs056na1p3i2Ak+apTspLeH+MEu4MdYSHi/DMpPthzsORjE4nHrqqTz55JM4nckLjkYxeLiDx7FRa1Gv7T6dH0/9yLWPjeKrB1mWWdf60YguFBlutNkbaQ+2EAOsanorZo44VATkQESkk1BYdjnbvjJzfJfPoY6t442lBL4aCktlEbnT2RrXLXmoIeYTYkFfEH1daSp6hR28MiuxHVxgSrB452ARlt3OdgIE0Gn0cYlAS4aWcKGiFLmVENYS7u5KWtqZCoLIzo2jsIwnzBppSJuwvOGGG1i5ciX79u3j+9//Ph9//DEnnngilZWV3HbbbUiSNJpdOUSEFJbxS3e0mkg7sO0TZbJuqYo8STLNC8s7Y7Jigw3IdN2rEIHxSndUu6QUqVqxfXyAQL8bq1m5+DiS5FD4ojK7TJMKmbHu24y965S09rXk24rKsvupzWrDsOz14/hcuaFbFyYnLLPjKCyTWYdBaTKDyBPZNKEA0xTlIpKbQl0Jwfy+ExWV5cD7sSohbxwLo8mvTLYDE8zknZFaXQmJLYoCOWOVgYjDMRDKrzyuOq1tp4La/KaPT2qVn6DkibhyvGjzTAzMV/4+48rTqH7wbGUbH9XhaY5d4fH3u1VSRFW3xkFVlvIdN9lqUxLRpizlO/JIHvW7EOVO8VDxiyXkLJuIZX4FxsmF6MuyFEWfViL3jMlMfu1SzFOLI14Trt7VGLQYTMox5k2QKxh+7otjYVbxQsZkT8Dld9DhaKYyq4bfL36KyuwaDGNyU5Ll6UCsOCc6d8MtHOlAJSzTyE/MPnYc4+4/E7TK/UOeqFwHY222yu8oT1K+wz1n/BuHTdmviosOZ8ydy9AYdUhaDblFyu/gMrjIP2caRVfMo/R7i6j4+WLG/PEUprx7FYVXzgNJIcl3nfgofW/sUScC8XJ6o+HfqBzvXpOPvDOVY1Jj0DL23tNxWJXz2f1k5Ll+WKniTNjeqRDkXU9uBpRIiWiVs6SRsC6oxKkSuLHntP3zZnztyjHW9cTGiMdCpTvp2aj39GzB6bOTbchjXnA/kyks7Z81Yl/bBDI03f4Bsi92ICUIS6PfQOsfV9Jy50ra7ltN5z/X0/3sVnqX78bbkjq3KNSSmN4Kc8oMS21qBaQ/4FMVwkatOTRoDFNYOn3xFe3pWsJTWddHkRr79+/n0ksvpbS0lCuuuIJ33333KzPJHikIV1iKxWa/LoCnYeRObkbx1cNLex7l9k++yR2f3vhfc44LdeWUgrmUWCpx+uysaflgWN/D4bWpZF6OIZ8CcwmgjB0GwsQdX2YIB6JOo08awfJVUFiKrMKA7FcLYUYSVHI8OFcMRRCkqbAUhGWS/EqBg90U3i7s4OaKuKW9Yh6TqSVcqCpBIRglJAKyXyV7BwMRhZcb5oIU+EopLAVqamq49dZb2b59O2vXruWiiy5ixYoVyLLMDTfcwHXXXcdrr72Gy+VKvbFRREDNsIwmLIUlPFph+YmihjSPiyQsM7XfSVoNVX9YRvF1C9AGC3w8vbaIAYGvy4G9XTnYmy5/jU01d7Fl2l/YdtjfOHCTYu0tPkIh1ZIRliFFScgCp801IemTZzMKZB03DtOMEgIOL12PbVTeb0sbAYcXbb5ZJRETQawsRBCW3hQKywQrD5W/XUrBN2ZRdOW8tPZdEJb97+2PGWxFl+4EnF70/cpvYfnGRCRteqeqWHlO+FmCCkuPzoOvw4HGHJkjOhT0BzNwcqIUlgJCnecpgxmbvkPfBOVYqMitxlidh/XIMSBDz3PbYrf97j5kjx/jpIKkv3E8S3giIloo5EQjtWlaMeZZpQm3bazOo+Yf5zDppYuZ+t5VTF/7LWbtvInZtTdTff9ZcUuXoouqxGQvEYkkFH46Sa+eI5IkcdakKwElk+V3i5+Maz0YCoTdOFHodXShUiqIfffJiS3h4cg9eSLj7j0Dw9hcTOdUA7EEnTimpenKvspuHx6L8tuVnDgj4rnqzTdBHos2y0DVr09k4gvfwDihAF+7nbrrXqZ7tdI8aEijJdzxSh0AXksAjTl0PQuMMRDQKoN/2z3bcGwMDcKmFSrZq7u6N+JzuOl5cTsAhRfPjvse1oWVuM3KsRJPFRAeoTDwQS2extA1SpBgHn969+JN7UqJ06zihRQECcJkGZYdfw9lCbt3d6nkazjEYpDnvWba7vmM9r9+RuvvPqbp5+/T8MM3OXD9K2w/6iEOfPd1HJsS288KTMoEqjtdwlJtCY+vXEyHUAy/D5t0lqQKy2i7voihSGUDil7AG0Xm2LVrF6tXr+aqq67i7bffZtmyZVRVVfHjH/+YjRs3Hurd+0ogPMMyQmE5SliO4guCzdPPczsfAGBTx6d82vzOId6jLwaCsJxdvIjFY88E4IP6l4f1PQQJYtJa0GsN6DUGVS32VcmxDL9XJxNXqU3h9vqYeLYvA7x+T4Sab7hs4fetv43r3lwaMXceLAai3HiZttILS3g6hOWYHCVqrs0e62wcDnQ4RX5l/DlZppbwEGEZEr/oNHrVHToUW3h/kgzLr5TCMh7mz5/PXXfdRUNDgzpQfOaZZzjrrLMoKkpOHI0iFiFLeHTpTmxLuLfTjmunMpm01kSqugaTFyZJEuW3Hk/JZYrszWt30fSzd7FvaKH+B8vZfsSDOPuUyb/eowNfgIBNIb0C/W7QSJSepEy67UnsGtEZloPZz5JvKXbFjkfXE3B6Q/mVC6tS2qbFSe/yO9SbkbD9ZWIJF+835s5lauZcKmQdORaNWYe3ZQBXVNN5dOlO9zNbMTgUglp/TEla24fQRTFRhqVZrxwbbqNCJFmPHIPGmJl9OBHCm9/iQW1b9/aDBC02RSFcnqWEJouym+5ntyIHIgnd3qAdPC9FjmcmlnBhF/WZFXKp4IKZg1KJJ3uNSlgG90Ec94lsukLhZ9RFHlMnV3+dOxc/ze8XPxVXzj9UWHQhwjKeciE6cyYVMlFYCuSdMYVpn3wTZijHbiKbbaDaiHlWKdnHVxOoEpmvkb+xuPm6/I6kOZrWwyuZ/ObllHxnIQB925VrSSpLuKdlAM+HykDFo/VGfGfiu9L5dehdGhpvfU89nqtzp2DSWrB7B9i2/D0C/W4MY3MTll5Zj6jCZY6vsJRlmf63FMJSm2sEGbqe2qI+HiIs07OEi/zKOSVHxi2YCYdrb5diQZeg6Cplwab1T5+o7e3q84LXVnmnDcmgpfCKuRRcOJPcM6aQfUIN5pkl4AvQ+9IO9pzxBHu/9jR9b+5B9keqNVXC0pmuJTy5tT8dy7YgaTRo0GsMYbaczuB7uNVjy6LPxtfjpO3e1fi6HGlnWIYrLN0Hegl4Dk4D7Fcdhx9+OPfccw9NTU0sX76cE044gQcffJD58+czc+ZM7rzzThobR2ae15cB4Vmuo4TllwuyLA/JRjhS8NKeh7F5+9AE50GPbP5d2ve2VBiJtllQfjtBWM4qXsjiMWcBsL7tY9X2OxyItuhCiET6quRYhgjL5NnuecYisg15yMg0DqSXmQ0j5xjqj1LEDgdh2TRQy1u1z9Bqb1Bjg4YCdT4RFBYUBcm+Tkd6x1pjBpbwIrOy7R5XZ9puo0wgcjfjFe5AOGGZqSU8kkMTY/KhFO+Ia0a8OaQ4L9Jtaj8UGBJhqW5Eo2Hp0qX885//pK2tjaeeeooTTzxxODb9X4VUpTu6sNIde5CkM00rxpAVqQYabF6YJEmUfEOZfAa0Abqe2MTes/5Nz/PbkT1+vFZl0DPt35cyfc31TP3oGia/fQUTX7mEqSuupnBmdXD/nQlJAu8wKEryzpiCYWwu/m4n3c9uxf6Z8l1YF1WlfK1Fl4U2qFQVrXhCYZm4dCcPAPsQVx40Jp1KTETnzYWX7gQ8ftofWIPRpai2nKSfV+NM0hIOYTmAQcJyuOzgEPo+E9mGxd8Dsp8Wez1Onx0JiVKr8rvlnTYZTZYBT30f9tWh1TD/gJuBD5UbVG4SOzhAmXUMWkmHy+9QbQOJzgdB6FhOrSZn6fikdvDBIpSBZ4l4T18KhWU00SJJElML5x0066g1mGEZkP0xkRQwiAzLFErSZAhZ+ROU7uBi8vLLGP/E13AFBCEc+RuHh6qnugFrTDrKbzmW4m8djscQvNZ2J7ebdT+1Gb1LmTgF8Edc78QKZY45H22WEeemVrqfVbJZtRodkwuUhZ2Na98FoOCiWQkXWswzS3Fagvm2UUIH1/YOPA39SCYdFb9YouzX01uQvQrpJTIsvWm0hLt8DnZ2KeU/c0qOilswE46OfyjRITknTaTi54sxTizA3+2k7a+RpT32PmXwbHDrKfvJMVT9Zilj/ngK1fefyfjHzmfyG5czafll5J83HXQa7KuVxvhtc/7GvgufoflXH9Dz4nay+5V7XKaWcH2qlnB/YkIxtHhgRpKkkMLSpZC44TlqJp2Fpv99l9bff0zLHR+FFJxJtg9hjoOAjrqr/8OuJY/g3JYeKTuKWGg0GpYtW8a//vUv6uvr+drXvsb27du55ZZbqK6uZunSpbz++uuHeje/VPAGPOoYNNISPpphOZIRkAO8Vfssl712JLev/OaXmrTscXXw8p7HAPj+gt9RaC6l3dHES3seHfK2n95xHxe+Mp+9PYnz0w8VWu0NdDpb0Ul6phXOoypnAhPzZxKQ/XzcMHzXMWH7Dl+QFkUo6areRjpE+3Gi8kIBSZIYk62o8hrStIU/vOkOLn1tEa0HScWXCaKJ7OEgLJfvf1L9/w39+4a0Lb/sV+35Ym6dCTnu9XtoD36mdBSWucYC9BoDMnLaGZmZoDNJQziEhBfpWsJ73coYN88YKUTLV11PQ1FYKud5XIWlSliO3Hv6sBCW4TCZTFx44YW8/PLwStb/G6BawmNKd2It4QNBO3jW0WNjAvszzbAMh1BxytlaJL0GyaAl//zpTHrlEvzZyiTeUpiPvjwbY00+5mnFWOeVY6zJj1BEJQrFji7dGQwknYbibyptvB1//1zJUSN14Q4EsySjindSlu4E1VoDwyCVVm3hUTmWnjCFZc+L2/E2DWCSFdLKmWbAuCzLKVvCxURdJSyHqXAHYCB4octJoMIzak0qUb2nW7GPFlsqVBJOY9aTd7ZSWBNevtP/3n5ktx/jhAJMU5Mrt3UaPWVBAnRfr2K3TaWwNCwsoebR8+JauocKNcNPWMJTlNEIstCQRobicMKktajKhXgEn1DPppthqVrCk6gbEyHRMWzWx1orEpW6aCWtStrbE9jCo1H+P8cilyv7PfDkzhi1oIDsC9D15BYM7pANPJzk7RfkrimfspuVkp2WOz5Si32ELXyvtAu0UlKiXGPQ4slSrv/S9sjrQN8biuo4Z3ENeWdPQ1dkwddup+/doK1dVVimJo23d67DJ3sptlRQbh2rrub2ubtjJrreTrva0l583QIkvZaKW48HoPOR9bgP9KrfU09QsZo9tlS9ZkfDMquUsfecxvRV36TkO0egzTXh73NhW9VAx0PrqL9pOY7fKsrR7oH0Jk/RJWbRSNQSHvD4CbiV79sV1Rgf+k4UEldEn5h1Vrz7++h9bZfy+Ft7MWqU8zfVIFUokO1v1OHa3YW/342hKrkCZBTJsXLlSr71rW8xceJEnnvuOVVh+ac//YmOjg7OOussbrvttkO9m18ahBd8GHVmdSEkmcKyxVbPXWt/TG3fzi9kH0cRicb+ffzso8u4b/3P6ff0sKF9JZ80vnmod2vQeGbH/bj9TiYXzOH4MWdy5cwfA/D8zgeHTEKsbn4Pj9/F560fDseuDiuEunJy4RyMQUGLUFkOZ1u4GLOEj92/cgpLX3oKSwgv3kmPsFzbugK7d0B1qSSCy+fgmR1/O6jt3X1Rrpg2x9Dey+Vz8P6B/6j/3TAwNMIyfDwu5tZFYRmWqbJpW+z1BAhg1llU500yaCRNKCMzTQVnJlAzLIfJEi5ijwpM0YTlwVVYit/C6UvuSjuUGHbCchSDR+LSnVhLuMivzD56LFpNpKU3Wm2UCbRBoiGglZn2yTeZ/vm3GHv3aZjnluH2JbfZ6TR6lQRK1Ig1VEu4QMGFM9EWmPHU9xGwe9HmmjBFFZ4kQk5U8Y7TF6mCi8ZwhtHmnKAQlo71zfi6gnZJWVZLd3ToaP+bIrnPqSwN7l96FzqX36E2xSdWWAYJS5MPy+GVGCcMn704ZCvJS/gcodDb3aMQluXWsRGPC/Km9/XdKmGktoOfNjkty7ZYdROreAkzLAWhEzh4OTVqiYtOZFgmt+lm0lI9nJAkST1mohcbZFkOZc5kagkfhMLSkUAlHLrxO9Rti4zMeOeuyHxM97yVdBq0M/OU/9/iof6m5THRBKDkqfrabBjzrapaO3yRKTzvs+jKeRgnF+LvdtL6p0+AEGFZX9NG7kkT0ZfGP1dB+e6dhuDxuTHyc/S9qQymc06ZiMagVSMVup5Qzi3xG/hlX0q70qYOJb9ydvEiJEki11SgvtYWFfLd9fhGZLcfy9wyrEdUKp/1hPFkHTsO2eOn5Y6PAGi/fw0ul/JbVl66MGVch748m/JbjmP6+m8z6Y3LqPrDMgqvnIfl8Epy3YoqoqO1Ia2yBWEJT6RIjraEezvtNN/xEdtm38vuZY8TcPnU+7A4F4Ul3Olz4PY5wyZA2bTft5rgpRd/rwttmy9i+/EQkAPq8dv9t/UAlN50ZNoRI6MIYfv27fzsZz+jpqaG448/npdffpkrrriC9evXs3nzZn70ox9x0003sWnTJq655hruu+++Q73LXxqIa5tW0qHXGCIs4f5eF/6B2HvZB/Uvs6L+FZ7dcf8Xuq//7fD6PTy9416+997ZbO/8HJPWwtySowF4YtvdI3Yimgyttgbern0WgMtn3IwkSRw35gymFs7D5Xfw2NY/DnrbATmg2n4P9O0elv0dTmzpUAiw2cUL1b8dO+Z0NJKW3T2bM7IsJ4NwR2WH5c+HmsK/Gop/u0e5XycSc4QjVLyTuilclmVVhdpsq0v63Ldrn+Pf2+/hj2t+mHK7g8VwKyxX1L+K3TuAJkgXDVVhKcbHFl2WKmwQhKXT50jpiApvCE83wqvYklmpTyZQLeHm5ArLdFvCexNZwgVh6R4KYamQ2cks4TBybeGjhOUIgpgYKRacUJaVUFgKYtLT1I+nrhe0EtaFVREFNjC4DEsBYTv3yV705dno8hWixRfwqi1yxiSFFFa1bTi5wjJaFZopNGa9mp0GYE0jv1JAKCxFY1a6CkuHdyDidxkM9OXZmGaUgAz9K5QLr0/2qkSj450GPLU9aPNM5E5QyABHmoSl+M41klYljqMhPqM3K8DEZy8cVGZjIqiDniSkljVI/u7pVhRT5VmRhKVlXjnGSQXILh+9r+7Eb/fQ/4HyPaXblB6da5KIwBffkXuYcpDiwRllCU+psAyzoX7REDfW6JuVw2cjEDzuM20JT2R9Twa1OEof+btFr1SGq9fikdJq9mwGymiPRtlfY8BI/7v7aP9LaMU84PHj3NFBx0OKHTr/wlkqER1u/RX2qixDLpJeS+WvlHiUrsc34tzeziTLDKQA9BQNoLkoeYyF2+8kICnX3cDq0EDUXduDa1cn6DTknKjYlwq+oVjNbR/V4T7QG0HWeQMeup/fxo5j/kHPK7Gqp/D8SlB+P3Eeh+dYBpxeOoNlZ8XXLVCvH5IkUfHzxaCR6Ht9Nx2PrKf1rlVqVm52afqZ1hqDFsvMUgovmkXVr09k0ovfYOZdFwPQZ+yj6+nYcp9opGrfFseL0zlA0y/fZ8eRD9HxtzUE7F7c+7rpfWWnStQIdbRZZ1UVmz3uTvU4NWGm5z87lOfMUQbG7FKOzWSW8AjyoEXJMi28fG7KzzaKSMydO5dZs2Zx9913s2jRIl577TWampr405/+xNy5c2Oev2TJEnp6vhrNt18EXFFZ0MIdEwjeouKpLIUrZHPHZ0MeM40iPdg8/dz8/vk8uf2v+AJeFpQdz70nvcZPj/wrucYCWuwHeKfu+YSvf23vE/x21Q0RitqRgCd3/AWf7GVeyTHMLlkEKPeb6+bcioTEivpX1DiTTNHlbFUXpg70jyzCMjy/cmbREerf801FzAuS0B/Wvzos7xUvo7zoq2YJFzbkNBSWYzJQWNq8feoCaXMwlz8RxDG2q3sjO7rWp9z2YCAIy4qsamBohKUsyyzf/28AThl/EQBNA/uHdE3vj5OXatSZ1WMvlaI3k8IdAZFjOdzHskJWD5/CMiAH1MihvGiFpXFolvDwMqZ4hKVWo1P3daQW74wSliMIxrBJd/igQZAFQs1jW6WoKy2zy9BmG9FFtYcPzRKubCtakRM+8Uqm/gqptOIz9GKSNhytqEVXzENjVvY3K438SgGhsBwIKixTtUkLhaWMnLQBPe33F7bwYI5leDFJ9z0KGVJ0zWFYzAr5m67C0hnWEJ6IiBSTDm/Ag18zfBMJr9+jHiOJLOEQUljuD9q1y4M3VQFJkii4YBag2MIVO7gPQ00+pmnpKWgrs8dH/HfiDEvlOE63RXkwcIeVFUA4keeNmyl1qBSWEN4UHqnkE4NZg9aU9n6JRRSvP3NFh1ocFfW7qaq4IFEpzluDxhijMofBhUiL77/8YqXYq/WuT6i99iV2nfgoW6bcw+6TH8O+RhkAFl48O1SUFnYMCTWqOA+yjx5L7hmTISDT9PP38b7TQkmLMmBomJR88CH2XeOXYL8TT4vy36IdPOvIMejylN/EOC6PrGAmbfdTmyPyG9tf2kzDzW/gOdBL44/fwl3Xqz7W7+5Rz0dBWAJqZmO4BaXnhe34u50YxuSQe2rkAoJ5WjEF31DO3eZfvA++AL784GNDUP0DlM9Qsmv9ugD77nwTd31vwucG5EDIEh51vMpeP87t7Xg+Ugauffua6Xx4PbLLh2VuGXnnTAOg858bIpqRgaDyNJRjKQhLfQfgC5B17DjKfqhMJAPblHMoWelOuMpa59NSfsuxaAzahM8fRXzk5eXx97//ndbWVp566ilOPfVUNJrEQ9uzzz6b2tratLd/3333UV1djclkYuHChaxZk7h0YPHixUiSFPPv9NNPV58jyzK33XYb5eXlmM1mli5dyp49qZU8hwqhaITIWJNAljLGiJdjKUigAU8vtb07vojd/K/Hx42vc6B/N9mGPH50xF38/KgHKbFWYtZZuXDqDYCS1xiPkFzZuJy/b/o1q1veY2P7qi961xOirm+XSspdNvMHEY9NzJ/JidXnAfDQpt8OKqMzXKHYbDswbCU+w4EmWy3drg70GgNTC+dFPLZ4bMgWno7jIBXiEZYFQcLyq2IJd4S1hKeCUFi22RtSEvjhJFgqhaVQBwL8Z/fDKfdjMBDOwUn5ylis19056EWIHV3rqevbhUFr4uLp30WvMeAJuOmwNw96/2wJ3FrhtvBkUBWWgyEsHcOrsLR5+1QXTVEqwjKNDEubp1d13YgceYGhWsJFBJ5G0iaMRYgoxh2BGCUsRxAMGiMSyiAw3BbuCwiFpTKZsYXlVwIxCsuhTA6FwlImsllQ2MF1kj7m/cIRIj3ikwTDkWGp7mu+mYrblmA9vJK8c6el/brEGZbxrQJ6rUElJmxp5uElff+liiJq4MNaZK8/YpAU2NuPrshC0ZWHqeRvuhmWqRrCIVJ9G52VOhSIAY8GTUThSTSEWlVkdkYrLAHyz58OWgnHhhbFaolSyJOuGjT6RpY4wzKzFuXBIER6mCPeE+LnO4ZsqF+8wlLcxKLV0aEBRnrqShguhWXkcWyJWqmMLjSKRpZqCU9/tVD8XkVLplJ42RxFCf3WXly7u8AXQJNtwLKggspfn4BxXF6IsPSFKyx7g+8f+r4qbl2MxqzDvqaR5l9+wLhaZTKwsye5MkRcR01eExIS9jVKXq8gLHNPmRTx/MJLFZVl9zNbkbzK9RrgwK/eBhm0uSYCDi8NN7+hNnFv6ViNjMzYnElqsDeEBkxCYSkHZDoe+lz5fq6Zj6SLHT6U/fBoNFnKb68rseILfgVDUf2Dct6IRsk+XR8NP3wzrl0fItXLeo0Bd20PTb98nz1n/Zst0/7C7mWP03f3RgBcRg/WhVWMf/LrTHzlEipvX4Jk1OLc0kb/HmVQbgo7FwWJ2+fuUidAuibl/lz63YVkHT0WTY4RXafy3SZbbArfz+zZleSekbxQbBSxcLlcnHfeeUyaNImcnPSyPy0WC+PGjUvruc888ww333wzv/jFL1i/fj1z5sxh2bJltLfHt0m++OKLtLS0qP+2bt2KVqvl61//uvqcO++8k7/85S888MADrF69GqvVyrJly3C5Dt7C2VAQagiPJCz9wWFmPIVluGthuAiwur5dvLn/6WEhaL6KEMTwydVf57gxp0eMl5bVXEiJpZIeVwev7H088nV9O7nn85+p/30wiikGiye2/RkZmaMrT2FifmzW82UzfoBZZ2VPzxY+OPBSxtsPJywDsn/I+XzDia0dysLI1MK5MU6BRRVLMesstDkah0WpF19hGcr9+yqcc2IslWx+IpBnLEy7KTw8F7HFdiApcS7UgaBkp4YTmMMFobCszK5W55BtjqZBbUuU7Rw35nRyjAXq3KphIL1sz3gY8PYCsW4tNWcyxfUnZAkfn/R54RDqx45hJizF9nKNhQnFHGrpjs+e8jwSZGS2IS9G1JU3RMJSHBc5hnw0UnzqbzCutC8So4TlCIIkSXGLd1RLuKRDluU4hGV0huUQFJZhZGS4ylIlUnTJFVaCZEikRByOlvBwFF46h4kvfgN9UfokbXSGpaqw1CfexmDIj0SwzClDV2gmMOBh4KM66n/1NgA6rxbr/EomvXIJujxTxmG9DlWZlpiw1GsMKomRTP2TKcT3YjXkJLwYQmy+ZUVW7MRRX2xVba6u7YoCLTdNOzhAVYzCMlGG5cFXWEaTauHHfTyi9FAqLBNZwuMNZlMh1BI+iNKdFBmW4rh1eCOtitEYisLSqDNT8csTKLvlWMp/eiw1/zyXaZ9dx8xt32XSfy6m6MrDgu+dWGEZ/n0ZKnMo+a5iZ/P3uhhbpwzOUk02hNrVIimf3b6mEW+rDcf64EDp5IkRz89dOkEhCjsd9L+9F52s3Bu8Wi8Fl8xm0uuXorHqsa9tUpu+N7Ur+ZVzihdFbEuUzIhMnf539+He34Mmx0jBhbPi7q++2ErFL5agL89m7D2nhbW4D42wBMg3KwHr9hIP9s8a6Xx4XdznecPOq/ZbP2LnkkfofHg9jg0tyG4/mhwjhdOqAfDkB5j4/EVkHzsOSZLQFVjIO1tZ/Or6RLFvhcczqN+Ju0s9To0OPZbDK7EuGoPGoCV36QS1kCnZNda2V5no6DxaKm5dPKzxHP8tMJlM3HLLLezateugbP+uu+7im9/8JldddRXTp0/ngQcewGKx8Mgjj8R9fkFBAWVlZeq/d955B4vFohKWsixz9913c+utt3L22Wcze/ZsHn/8cZqbm3nppZcOymcYKtzBe5gx6h7mD96i4hOWofHrxrZPhmU//rbhF/xtwy9GlAJwJKG2TzkHqvOmxjym1xq4ZMZNALy4+yH1nj7g6eX/Vn0Ht9+piiW6XSODsNzeuY41LR+gkbTqvkcj31Ssqkef2nFvxirLaDJqJOVYCjv4rLD8SgGjzsyiipOB9Mp31rd+zAMbbo9wc4Uj2hUCoZZwl9+Rdv7eSIZQjaVTuiNJUto5luEKVG/Ak1DFN+DpVUmjWcULkZF5ec8/09n1jNDvEcRUAaXBAtLB2MJ7XJ2sanwLgNPHXwIQ1p4+eGLfFudYg3CCPIXCckiW8OEmLJPbwSE0b5GRk0YEQYiMzDfFugnVlvAEGZZuvyspIZqscEcgK8Pc/y8ao4TlCIMxTvFOQJTuaHS4tnfgbbUhGXVY5yshrwejJRxQpcnK/ggiJbnyK6SwjH+D8w1T6c5QIE7YaIVlskm1RZc8mzMTSBqJ7CUKqVZ77ct0vqHYMfUaAxOfvwjDGGXlKV4rcjIkyv6Lhvic0Q25Q0F/GvmVEJsfU2qN3+wuCkQADOPyMM9I3QYnkGPIV238kLol3H0wCcsoS3i4OjlejuWhzLAM5c9GEnxq63UGCkvVEj4IhaUzYUt45EplooZwgcGsFoYrXDUGLaXfWUjJDQvJOXEChsqcGFIp3gJTIoK3+LoFGKqVv02vUCzn+3q3J7XriN8iy6R89/a1TfS9pQygLfMr0JdFfkeSXquSiS13fIR2QBnAWM+ooer/TsI4Lo+K25YA0PqHlbh2daqE5ewwOziE1IS97i5kr19VOxdeOgdtVuLrd+FFs5i+5nosR1ep3+dQLeEQGrBpLlGuGS2//xjXnq6Y59k7gtYXv0Tfk9vBL5N94njG3nsGUz+6hplbbmTa/RcC4JZdMfEnIhu5b5eiSgi/fqiqU1cnA/3KANDkNFD63YXqsZF7yiSMaRCWzQ8oRI5e0pN1RPqRJqOIxIwZM6irqxv27Xo8HtatW8fSpUvVv2k0GpYuXcqnn36a1jYefvhhLrroIqxW5fivra2ltbU1Ypu5ubksXLgw4Tbdbjf9/f0R/75IRJcSisUov1Ehh+IRluGLgNu71g1LLqKYHO7r3TbkbX3VEJADKtlWkxtLWAIcN+YMxuVMxu4d4IVdD+GX/fxx9Q9pczRSaqnirElXAiOjZMUv+/nHpt8CsHTceTGL0OE4feKlmHVW2h1NGasNBWEp7tUH+g7OwkemUPIrFYXlzKJYwhJgydizASUKINm8xOG18ae1P2L5/idZ3fJu3OeExiz56t9MOos6jv4q2MLFd2Q1pFZYQoicS5VjGZ2L2JTAFi6UgYXmUr4x7bsAvH/gP2pm4XAhnJgaCmH5du2z+GQvUwrmMiF/BgBjcoKE5RCUyPEcSABFajFO4mOt392tvr4yKk4sGdLZ9mAgFJYllsqEzzFqzWphUSpbeI9LufbGJyyVxfIBT2/MwsO+nm1c8soRPLrlzoTb7hcKS2N+zGOyLOPttGNyKEru1ve30PSL96m95iW1Q2IkYJSwHGEQyqrwCbBPVVhq6Q02JuecUIPGrA/+fThbwkPbCp/IiYlXKsIyVSNWyBKe2FZ+sJEtSnfc6ZXuQGqre6YQOZb4AlCuXCSMFmuEzTJRa3MiJFKmRUPNAkyx2pMJ0lXhhd+kiszlCZWEOUtq0BUr+5l7evp2cFBWR8NX3xJnWB5cS3hADoQpk83qviWzSx9ahWWQsPRFHuN7gwVJicjleAjP6swUiY5jcdz6ZR/egCclYSlawjNSWKpFK+l9/8Y4pTuJMno0Rh3j/no6OUvHM+O7Z1FgKsYv+9jTsyXh9m2CsMxRrlmunR10P6dM2HNPmRj3NYUXzwJJyZbT+ZQFqPybFqilZAXfmEX2khpkt59Ntz5Ni/0AGknLzOIjIraTKywojnYO3PAajvUtSEYdxVdG5mklQjhJMRwKywKTsmjhPcxM9vHVyG4/B779Kk2/eJ8DN77GvoufY9eyx9hxnmJ51Pm0WBdVMfE/32D8P88j/+ypGGvykTRShC0s+l5lmVmK5fBKPDrl/Iy0hIcUll0blEG7NSuP7MWh60324mqMKMePwxH/2LOtqqd3TR0ABvMXvzjxVcJvf/tbHnzwQd59N/5kfLDo7OzE7/dTWloa8ffS0lJaW1NPfNasWcPWrVu59tpr1b+J12WyzTvuuIPc3Fz135gx6V+HhwPuRJZwfZCwTJJhCco9YHtXfDV0upBlmT6XMl4bKaTSwYDX7xnUonirrR6X34FeY0g4kddKWi6feTMAr+39F/et/zkb2ldi1Jr52VH3UZ2juFi6RwBh+ca+p9jbuw2rPptLZnw/6XONWhNHVS4D4MM01IbhaAoSlosqlAWEuhFSvNMwsI9edycGjZEpBXPiPmdWyUKqssdj9w7w2r5/JdzWm/ufVsfniUph1PF7WEs4hFSWhyomQJZlXtz1D9a2fDDkbQm3ilWXHmEpFJap7M/R301LCsKyMquGGUULmJQ/C0/Ardquhwv9KmFZSKklSFg64hOWHr+b3332Pe5c/X22dqxVFXr+gI+3ap8B4LQJF6vPr8oeDsIyfsRUOsU4jcHvsNhSkZGoQ2x7wNM7rKViHUGrfbE5scJSkiRVRJRKqdyToCEclC4N4Y7scXTgru2h/4P9dDyynv88czeegJvPd7+bUGUZT2Hpru+l7Z5P2bXkUbbPux//e8qx3PrhVjofWU//23txbjv09wOBUcJyhEFM7MIHfMISrkFL3+vKYC288CBctaWRtENSL4oTAkLZmcr+CKtk8om8VRdfpSXgHcbSncEi2hIeIiyTKCxTWN0z3ocTx2NdVEXeOdMov18ZbEXn1GRsCVczWpITlmZVYXkICMsw5WO8/EoBSa+l4ueLsR5RRVGaBEk40iIsNYKwPDgKy/BzOPzYSkaUHtoMS+XctXkij/G1rSsAWFB2fNrbStWGngyJMizDSS+nz44zePNPmGGZocIyIAfUbNV0CTaTmmEZbglXrivxzgXL3HJqHj0P89RiphYqtvIdSRpO7cHM3CxrPoaafJDBuUkZ0OUumxT3NYaqXDUn12BUPoeP0O8gSRJj7lyGNtfEXqei7h6fNy0mjF4MmlrW7aDvzT1IRi3VD52Fvjy9Ab+4bmnQqOfaUFAQXHXucXcw5g/L0OYace3qpPOR9fS+vBPbxwcUB0LwvDKarEx49kKsC2JXv3Uavaqwtntir+lFV87DY1TufwYpdM8TCsseWzs9e4PlS/MnRCyoaMx68udWA0oLeTRkf4Dm33yIT6cQPgbDKGE5FNx7770UFBSwbNkyJk6cyEknncRZZ50V8e/ss8/+wvfr4YcfZtasWRxxxBGpn5wEP/3pT+nr61P/NTQ0DNMepodQ6U6kwtKnVZw/noa+mEmSuB7mBlXaQ7WF270DquOnbgTZdocbv171La5afmzGpGFt304AxuVMiltAJ7CgbDHTC+fjCbh5t+4FAL47/7fU5E4NlawcYkt4t7OdJ7b9GVAyKuNN3qNx/JgzAVjZ+GbaYw67d4DuYOOuIDxHiiV8a9AOPq3wsIR5/1pJy4XTvgPAS7sfjUt0u/0uXtrzqPrfrfb4145EY5ZQEcqhUVju793OP7f+gd+v/v6Q+wPEwrVYyE6FsWk2hQsLs1jMTKSwbAxamauyxyNJEudOvhqA5fv+PawkWm8GCsuNbZ+wquktVja+wc8+upTvv3cu79a9wMqmN+l0tpJjyOeYylPV54vvpLF/36BzTW1CYanPi/h7YRolT6odPCt9OzgocxsxB+sYRlu42Faiwh2BdOfyPUG7d74x9prn63CQE1CO3c/PvJedxz1M7eUv0vjL99hgULLlO52t1H9vOX577DVQzbAkl65/b2LveU+x8+h/0PrHT3Dv6wYJrJIytpdnWin+9uFU/vrEkLhqBGCUsBxhCE2AYy3hgRY77v09SAYtOUtDB5E+jLBM1hCdDiRJQhO0hQuiFNInUiwJbKUCYjARbWP/IiEk0QOeHrwBj6oC+yIVlhqznonPXcS4v55OwKr8XtEkbiaW8A1tK3l6x31AKLw4EUwHhbAUA57ktuHwx8utyYsP8s+dzsQXLsKQJkESDnFD00q6hCVRoZbwg6OwDFdJh7cV65KQeSOhJTxcYdk0UEuzrQ6dpGduydFpbyvUEp4ZYRmQA+pxGa2w1Epa9fqjEJbDq7AMJ64zVViGk9OJVpCjMU0lLBPb2MSKrFWfTdYRIeLNNKUIY02stUNgzJ9Ooebx87CUKc/xRP0O+rIsKn+7FJdZ+Xu23RozAM2VhBK9C8moo+bhc8lZkv7gJbxlezjyGYVNptvZjr48m+p/nEPhpXMo+c4RVPx8MWPuPpWax8+n4u+nAGA0mZO+r3pN98UeH3mnTsKXq7xWqg39tmJS0rFvPy6tcrwUTIu9jhUdrRTouALOmO+15bcf4tzSRiAn/nV/FJlh8+bNeL1exo4di9/vZ+/evWzZsiXmX6YoKipCq9XS1hZJ4LS1tVFWlvwea7fbefrpp7nmmmsi/i5el8k2jUYjOTk5Ef++SES7a1T1vMYPEgScPnxdkWMJcR87vHwxABvb07PQJ4JQDYFyTxqMcn+ko7Z3JxvbP8Hpc7C7e3Nmrw0SlvHyK8MhSRKXz/yh+t/nTr6G48YoDfaCMDjUCstHtvweh8/GxPyZLBt/UVqvmVWykAJTMTZvH+taP0rrNcIOXmAqUe/F3a52dfH9UGKzyK8siW8HFzim6lSqssdj8/bxalSZEsA7tc9HZN612mIJS2/Ao46loscs6ZBIBxOCYPX4Xayof3lI21IVlmmU7kDI/txmb0w6jhVkrsgabUmgYo1utz6y4mRKLVX0e3p478B/0tqnVPAFvOrnzDXmpyQsd3VvAhRLs0FrorZvB39Z9zP+tEa5RpxU8/UIwrw8axwaSYvDZ6PbNbjrRKLxcbHa5J245GkwDeGgXPeKzEqEXqbFO06fPSFpLbZVbKlIuo10m8J7gwsoecGxrru2h/b717DnnCfZvuB+zPUKZTdgUmIBTVOK6LnQwECucv66LB7alm9mzxlP4NzZEbHt7i5lX92P7qPxlnewr20CCbKOGcuYu05h5rbvMu7qYwHQHF9Exc+Op+jKeZinxdrTDxVGCcsRBqFgdEUoLBXC0vW5coHIPq4abXZIsRK+ojqU/EoBXdBi7o+jsDSlaQlPlGHpHQkZloZghqW7N+ICkjTDUiVihz98WhBmg1FYBuQAz+68n1+uvJYBTy8T82dy3uRrEj4fwizhw0hYCrVqTpJAXwiF+kL8wp3hgrihJSNLxPd9sDIsQ/Zic0QRkao+TKawPAQZlvEWGz5v/RCAGUULUip3wzFYS7jL50BGDu5P7PuFnxOuqGy1aKgKyzSLshIRzMlgUjMslX0JyIGElvBoTA9OknZ2bUhYFiAUllZ9NtbDQ4Rl7qnx1ZUCunwzOUvGq8e4NxB7rOWfPRXNYQop6fu0k53H/IPmX6/A/nkTfocH+50KyWPLcVLzz3PJPr466XtGI9XvkykKzEGFZXBQl7VoDFV3nET5LcdRfN0CCs6fQc6SGqSxohwkuaozUWYrKApvzTTlcd/6UMZUtls5JnvsHbhMyr0snmKj6FiFsPQYvDh3hwaOnf/aSMdDij02/1tzg/s5SlgOBXV1ddTW1ib9t39/8qbXeDAYDMyfP5/33ntP/VsgEOC9997jyCOPTPJKeO6553C73Vx66aURf6+pqaGsrCxim/39/axevTrlNg8V3KrCMoqwlD3oS5XzwdPQH/c1R5SfAEBt344hZbX1ukOv9cneg9Kue6jxfhhp0WKvz+i1dUHCMlF+ZTimF83nqlk/4bzJ13L5jJvVv4vIDZu3b9jGRC6fI6MF4Y1tq/io4TU0aLhh3u0RmfrJoJW0HDvmDAA+rH81rdcIwrIqezwWfZaaQ3eoVZYBOaA2hMcr3AlHuMry5T3/jFic9QY8vLj7IQBOGHcuAK1xjquBYP68Bk1MIY3a3Ow4NKrbcHLpjf1PD1rVJ8tySGGZJmGZZyzCpLUgIye0VMuyrFrCZ5copYVNA3VxnyviB1QxhUbH2cHc2Jf3/FOd5w8FYh6mQUOWIU8lLNsT7P/uIGH5tSnX8ehpH3LFzB+p9mmtpOPUmsgFA73GQLlVccYNtngnkRuvMJgz6fI7EooMwm31mSKUY5kZYfngxl9z4zunx10IEbnKJXEIS1mW6Xl5JztPfBRNvTJWTGUJF4pvzYoedp38GDuPe5iW//sIx7pmkCFXygPA+ou5zNp9E1PevZK6yyKv1Y5JAdx7u9lz5r/pfmYLjg0t1H37FZo/UH5rS68B0+RCyv/3OKatvp4JT11Awddnos02hrnSRkt3RpEGjNpYS7iY9Ds/U06O3NMjG5PDFWTDMTkUBKgvXGGploEkn8insk6HMiwP3SRN5LT4ZK+6SqTXGBIq8WD4LeHhEMRV9MQ1VYal3TvAHZ/eyBPb7kZG5uTqC/jd8U/GDewNx8GwhItilmQNZBDZEl5+EAnLSfkz0Uq6pLbzg90S7vTHL3MKkUjxSncOpcIydrFBZActCCpl0kWoJTwzhaUg57WSLi6Ro5LtXruaG5lokcaa4c1XXHMNWlPSpvtwGKNawh1eGwEU8jEVYVmTNw2j1ozN2xfTVipgV2MesrGGFbPknpKcsBRIpSI2LlMmaUafAU99Hx1//5y95z7Fttl/Q/tRr7IPuW6yjk58HiVCKgVsphATajGoS4REC0DRsKRSzVcHc2frXDg2tWL7rIGe73wMKCSuPEU57uJlBmflhdSvHW8qmaMDH9bR9HOFqCr78TEYFyoD6EN5LxxFctx888089NBDPPbYY+zYsYNvf/vb2O12rrrqKgAuv/xyfvrTn8a87uGHH+acc86hsLAw4u+SJPH973+f3/zmN7zyyits2bKFyy+/nIqKCs4555wv4iNljJjSneB12eN3qwWB0TmW4npYaqlSSbTNHZ8Neh/6whSWMHKyBocLvoCXFQ2h/MV4xFIyiIbwdAhLUJSVV876cYTYwarPVlW0Q80sdHhtPLHtz1z62pH8ZEV6KkmP380DG28H4LQJlzAxf2aKV0RicdAWvqbl/bRcFeGEJcC4YIbnoY4cqO/fQ7+nB6PWnNZ3cEzVqYzJnoDN28dre0NZlh8ceJlOZysFpmIunf59QPldo8cCgkCyGnJixj2HWmHZ4WxW/3/DwN5BZ+F6Am51Hp1OSzgo12pB+MVTpoIydhDj0FlFSvRHm6MxZqHeH/DRYlPO6XB14InV55Glz6XFfoAXd/2D9w+8xAu7HuLhTXfwpzU/5I39T2XwKaEv2BCebcxDI2nUDEu7dyAmGskv+9ndoyi5pxTMJduQx/lTvslDp7zLz496gN8e9zgl1tg4nVDxTnKrfCIIhWV06Y5Ra1LHzIlIxcE0hAecXtofWEt2qzIW7nSkfyzLssz2fcp966V3/4a7tkd9zBvwqIvn0QpL+7pm9p7zJPU3voZ7dxe6dmVO0PTv1Xg7IgVIsizj2NRKy+8/pm2X8p0Gnm7EtaMDtBJZx46j8rdLmb7meqqOU+LRbHlOJI2ELMt82vR2xPay/nqkkvHu8tHwo7fYc9a/6XttN3arMr+puWYJk9+9kpJvHRHjXrQGfxP7CG0JTxx2MopDAkFURFjCgysv/voB0IXyyQTCcyeHhbCMq7BMzxIespWmKN05hKoSo9aESWvB5XeoUvlUk+pUn2soELl5+miFZdAS7vI5kGU5QinYYqvn9k++SbOtDr3GwPVzb+Pkmq+n9X6mDLMx04GajxHWMhgP4S3hycjEoaLYUsFflr5CbpxGNAFB5gwmZzEdRJcVCIQmfPEs4Ycuw9KiEwSOcrNyeG1s61SyUQ4vW5zRtgT575d9BORA2gSgI6whPJ4yNlxhKdTRCRWWqiU8M8IyE7LYqEZ4KINWMfg3aS0piSidRs/kglls6VjDjq71aj5QOMJVAYbqPIqvX4DslzFNT8+mkSqn1a1XjsGKi45g3JKz6HtjD/3v7iMw4CHbqHx/fnzYvH0pCdhohEqRhkdhma8Slu0x18NwCDVpKsIylLcc/5ru0QXzlj066m9ajru2B5NJUXi4zB4cVjfY4yuBDVoTkiwhSzLtK3ZQesos6r79Cvhl8r82nZLvLmRfszLQHI58z1EoGBgYoK+vj0AgVrE8dmzm95sLL7yQjo4ObrvtNlpbW5k7dy5vvvmmWppTX1+PRhN5bdu1axcrV67k7bffjrdJfvKTn2C327nuuuvo7e3lmGOO4c0338Rk+uIXqdKBuLaZtCLDMrTgZhibi31tU0xTeHh52ZySo6jtU+zOwn6cKfrckerMA3274YvtHjqoWNf6UQQpmyhrMB5snj5V6VOdO2XQ+yBJEoXmUpptdXQ72wblgPEGPLy5/2me2fE3+oMxQft7t+P2u1LeV1/c/RDNtjryTcVcMuOmjN97fN50qrLH0ziwn0+b3mZp9flJn98UTVjmTmZt6wccOEhk+Kb2T9neuY4Lp92QdDwkxlzTC+enNU8SKss/rrmZl/f8kzMmXoZJa+b5XX8H4NzJ11JoLsWss+L02WmzN6qkE4TGLDlx7u+HOsNSKCzNOgtOn4M39z/FjKIFGW9HOFU0aDKaI5dnjeVA/+6ECwiCWMs25FGWNRaj1ozb76TN3hhBqrU5mvDJXgwaYwS5ZdZZOXX8N3hu1wP8a9tdMdv/sOE1FlWclFaOK4Q53YIuQqPOTJ6xiF53J232xgiSsGlgP06fHZPWEjH21Gp0HF6+JOF7jMmeyGe8O2iFZTIHUpGpjAFPL3XvraEgR4cm24g226A4SvP1KnFclSZh6Wnso+6bL+Pc2o5uWQ+cCrXvfEZv7UnknDBeLS6OB9nrp/6Wt+laoIiathg2su7UeymqGUfeWVNxLVHUtwaNCavPSkD24W230fq7j+l9VVlA0lj0FF+3AKv5U6CJrnX72PnwPyj59hFYDqug7+299L+1F2+LMs4f+K0yFi2dPpkx3zqWnJMmoMsPzQXzu4NFmMFyntq+nbQ5GjFoTUzKn8W2zrX06LuZ//j5tN+7mtY/fYKklcg7exreqW+CB8rmTkk4dh7pCstRwnKEIX7pjkJYavwaso8Ziy4v8savi8iwHE6FZWiVSKyYp7JKWlJMAkcCYQlKjqXLESIsU93EhIpmuDIswyEUltETV0HOyMi4/I4IUvXxrX+i2VZHsaWCWxb9hUn5s9J+PzHxGF6FpbhRJicsc40FSCg5qWUZtE4PBuGDsng42JZwVBGhVAAAuzZJREFUYTGOPraSqQ8PaYZlkOAT5+6GtpX4ZR+VWdVUZFdntK3w89sX8KYkjwTEgkCiBYRwEj9lhqU+lGGZjOAScCf4vZLBpLaEK69VA8VT5FcKTCs8TCUsl9VcEPO4I4ywlCSJilsXp71vEDrWPAlIeUH6WkzZ5J06mbxTJxNw+7CvbcJQmYN1y1PYvf30uroyJyxVhfFwKSwVktbjd+Hw2RJau4SCJHoBKBqpVPNCOWHw6JRQcqDitHnopKfwyT7VJhZvPyRJwqSz4PTbGahrYd/FzxEY8GA9ooqq352MJEkjIh7lq4L777+fu+66K6n12+8fnOXuxhtv5MYbb4z72IoVK2L+NmXKlKTWRUmS+NWvfsWvfvWrQe3PFw1XIkt4wIt+jHKNjSYsxTlo1JmZW3IUL+15hI1tq9K6DseDsIRrJR1+2XfIbbvDDZFhNzF/Jnt7tiZUdMVDXVBdWWypSPu+kwgFphKabXV0DSKf7pPGN3ls6x9VsrUyq4Y2eyM+2UuPqyPpeK/ZdoDndj4IwDWzf5q2bTcckiSxeOxZPLHtbj6sfzUlYSkUlpVhhCUcHEu4X/bzxzU/pM/dxfSi+cwpSRz/0BpU4lXnTk74nGgcXXUKz+y4j4aBfby291+UWqtotdeTY8hnWc0FSJJEmXUstX07aLU3RIyN+1WLbuzYXdhoB5tXOFR0Bon4syddzdM77uWTpre41v2/KZ1c0XCEOVXSXTwHKAvanxNFNAjlaZG5HI2koSJrHLV9O2m21UUQlsLKXJFdHfP+Z0+6gq2da3B4beSbisk3FZFnKuLTpndotdezvu1jTgxa+lMhXhN0qbWKXncnrfYGJuTPUP++s2sjoFxzkhV1RaMqRzlfBtMU7pf9qoAgy5BLwO3DsaEF26p6bKsaMMxxwnTY+/QK8j+NVHl3lQ/g+x8vBslIgak05XsNfFLPgW+/ir/HibbATKFBWfDutDVz4FuvoskykHfGFIqvW4BpUqQTwt/nou5br9C6cRfeo4IdIlqZzYfv48gVZpxb26l9thluhJwWI9um/jXyzSUouGAmZT8+Bn1pFkWfvwoHthKoMRL4xEvrHyNL6DQWPaYTq3Balfvm7LsujnstF8S1ICxXNb0FwGGlx5JjyGdb51o6Ha1IGonS7y0i7+ypaCx69MVWBl75BZA8ti1rhCssRy3hIwyCqAjPUxNKR01AQ+7psSuokRmWQ58c6jSxCst088hCttL4k0DfCJmkieIdMelM9b0d1AzLBDZ5o9aMJniKhr+vLMuqNeIHC+7MiKyEEKk9nArLUIZlCoWlIZfvHPZrvr/gd8OWbTdYGA6yJTzROZOsQfuQtoRHkfJqO3iS1dZECD+/M1Gwhiss48ESnmHpT5FhGbz5BmR/Wsf6YMhiVWEZfG1/gnyeRJhaECze6YxfvBNSWA6uaEONH0hgCY9nq9cYdWQfMw5jTX7YACm5DTseoq2kQ4VRZ1Ynsj1JiiHE+WzUJP8dxbZsCQZngsDOnV6FJsvAmD+dwrg/n05usClcOB/iWcIBzHrlc3uMPnztdgzVeVT/42w0RuX+GlLWjxKWQ8EDDzzAd77zHSZOnMhvfvMbZFnm+9//PrfccgtlZWXMmTOHhx9++FDv5pcWLl/kPSn8eNWMUa7H4YSlL+BVF7sNWiMzihag0+jpdLbQnKBBNxXERHxKwRyAg6aCOxToc3er0SuXTP8eAO2OpojxdzLUqvmVg1dXChSagyr2DC3hb+5/ht+vvolWewN5xiJumHc79570GgXB7fW6OpO+/sntf8Eb8DCn5CiOrTptcDsPHBfMsdzc8VlSW7sv4FUtukJhWR20hB/o3z3orMRE2NG5TlUJtzuakj5XtLSnKs8MR0Rj+J5HeWbH3wA4a9KV6v1XEMbRasFkijexDwOe3mFtsk4XooX5yIqTmJg/E1/Ay3t1L2a8HZtKWKafww5h31mCBYTO4DEmlKgi5qo5qngnWfZijrGA3y9+ir+e9Cq/OvYRfnD4nVw16yeqGn19miVSEConiyYsIbZ4R+RXimtqOPz9bvyO+OP2sdnBpvCBfdg+baDl9x/jPtCb1v7ZPf1qRr3j4V1sX/AA+77+DG1//hT76kZyu4Nz01lazLNLMY7PR1dsQTLp6MxXPltBo5W9pzxB1xOb4rZhy7JM+4Nr2X/xc/h7nJhnlTJ5+WXM+oUiCLDV+NFX5RCweeh+egu7TniU2mtewv65cl56GvvYc95T2FbW018eeczvuLSHqjtOIuuoMfQWKHOK3J7IYyrrqDFMWn4ZY/54iprxLPgF6yWTGfvX09XPVXDRLGoePZcZm75D7h+UDFSdRp9wvC/KeMT19NOmdwA4svKkuBmdxnF56Iut+AJeNcs/Gdmvjomj4gNGCkYVliMM0ZloAD6vclJq0JB7cqxqTB+RYTl0wlJYwn1xSndSTebTbQk/1JM0oQRstysXqdSE5cHMsFS+k2gVmiRJmPVW7N6BCMKlw9FMj6sDraRjUkFmZCUc3JbwVIQlkLZ1/WDDeLBbwgWhFkU+CiVt3NIdkWGZIiv2YECcu06vDb/sZ12wcGdB2fEZbyt8EcXr90Bi50UEnEGFZUISKI4lPNG5a9AY0Wn0anNiqsFq9MQ8HRijSndsaTaEC0wtnAtAi/0Ava4u8kyRK72ZBsVHQ69awuMf46m+wzxjEY0D+yNKL9JFaNvDtzCRbyrG7h2g29VBVQIFdaIFoGikWoQS97wx31vMzN8chaRVFo9yjYURk2FLgt9GuRd34DF40eaaqPnneRH2npDCctQSPhT89a9/ZdmyZbzxxht0dXXxv//7v5x++umccMIJ/OQnP2HBggV0dQ2+8OW/HYKoMEdlWAJIlcrxHJ5hGX6tMWrNGLTGoJJ8NRvbV2Xc8AohS/jskkVs71pHu6MJh9eWMQExEvFRw2v4ZR8T82Ywr/RY9BoD3oCHDmdLWi4UobCsTjO/MhkK1MzC9AnLPd2b+fumXwNw5oTLuHTmD9T7Sb6pmHZHk6oISoTaXoV0PXfS1YNS4AqUWccwrfAwdnSt56OG1zl38tVxn9dqb8Av+zBpLSrZVJldg07S4/TZaXc0qUTPcGBVWM5cqu9W2K9FfmS6CFdZ2r39WPXZnD7hEvXxsixBvkUSlmLsLrL9w2HRZanxWV3OtoydNkOB2+9SFyqKLeWcUnMR9/bcypu1z3DO5KszUkqGGsIzW/gVsVWJLOFdUb+VICSjS8FCat70r30Lyo7n2Z33s75tJf6ALy0VpBinRRCWwRzL6OKgXSphORcA175u+t/ZS/87+7F/3oTGqKXwqsMo+dbhEeMW8Rn63N1svuoxrHYTHX//nKKrD6P0u4vQ5iQez/TblN/T6DbQ+btPAdAVWcg6aixZR41hwngta5p3EDi1kMk/vyzitTs/vAc636aoMx/Xjg4af/oOzb/6AH1VDvqSLHQlVvTFVtwHeul/S8mCzP/adKr+7yQ0Zj3FA0qZUK+ln6mfXItjbTMdD31O/9t71X+Wwyvx1PXg63CgK83C9Nsx0PyCqhavte1k4EwTEy69kLXbOmDnCmqWLmTmD74HfhkkCW1W7LgzfN6Sf8408s+ZFvOcnm7lGplvKk54DQwXEDT276NhYC86Sc/hZUtY3fwuED+jUwiKJKSkKvwsvfKYw6fMAdMtPfuiMKqwHGGIZ9f1uZQBYNbMcnQFsZO/8AuZeP1QEJ4/J+BOczIvBpBOnz1u8+1IsoRD+gpLa6qChiFAZK7Fm7jGawrf2b0RgJq8qYOyDg93S7g34FG/l1SW8JEEQRD7ZV/aaoZMoBJgUY3fySzhnkHkKA4XxDEeIMCW9tX0ubux6LKYXjQ/421pJI2arZtJU3gqhaUpTB0sLMeJzl1JktQbcCIVXTgG09AubJLi+pioATERsgy5jM1RCnR2dm2IeTw00B4cYSmOcU+clnAIXVcSqSAFgZpKIRMPqRSwg4EoFEtmURMLAanOoWQt4RCWwWmwqmQlQJ4xklRO9NuIz511xVQmPHchpgmRK9sjZfHuy459+/Zx5plK4YZer1xzPB7lu83NzeXaa6/lb3/72yHbvy87RHmcMXg8h0cQCcLS29yP7FfGe+I6KiGp47y5JUcDsLEt0gqXLgRxUZk9Xi3f+qqoLN8/8BIAJ4w7TynKSFH0EY3aDBrCU6EgLCc4HfS7u7njs+/hC3hZVLGUa+f8b8T9OF2FvnhcqISGguOD5TsfNiRuCw8V7tSo5IBOo1fJmPr+PUPeD4GAHODT5vQJy+6geyBTwlIrabloWii64owJl0Xcm8pVe3PkcZXMFSJyTeGLz7HsDMuvtOpzOG7M6Vh0WbTa69nU/mlG2xLjyixDZuMoYQlvtTfEnc+KjE1x3Irc15ZoheUgymImFcwm25CH3duvkoupIBSWOSkUlg6vTT3G855ysvP4h9m1+BFafvsR9jWNEJAJOH10/G0NO45+iNa7V+G3eZC9fgYe2U5+j/I9dpT1YJpRguzx0/HAWnYe9w86/7UR2ad8V7Is4+tyYF/XTMcj69l6tVIKZbYbMIzNZczdpzL9828x7r4zKLxkDmUVyvfTGdYOL9CepYxBp519IhW3LcZQnUfA6cO9pxvbJ/X0/mcHHX//XCErdRoqf30CY+46Vc2pFAsTLr8Dh2+ArIVV1PzjHKa8fxUFF81CMmhxrG3C1+HANL2YSa9eQm+hcu8bnzdNLR4V1+tOl3I+lORUorUoOZvxyEqI5EUSocepXAOTlebmG5XHetydrGpW1JWzSxaRZcihyKIQsvEKi0QZU44xPykJKXL/IZT7OpIwqrAcYVBLd8Jbwr1e0ELuMePjvmbYMyzjlu4oahNTism8UJvIyDh99pjJnFCVJGvk/iIgiLW0MywPZulOklZblbD0hi50u4LEhlgZyxTD3RI+4O4FFAXwUDOUvkiE57G6/S4smuFVaySyhOvCWlajEVIyf/GWcIPWhE7S45O96mB/Xukxg15c0Gv1+HzeQVnCzSkVlo60LMdWfTa97s60br7DYQkfyFBhCUqOZX3/HnZ0rWNR5dKIx8T3MWhLuIgfiFPwBOHFOIkVlsDgFJbDbAmHUI5lsgmw+C1SZVimWoRyJSCw88IC8HUafULCUXxuw7JKzFWxg9CRsnj3ZUdubi4+nzJWycnJwWKx0NAQmpRnZ2fT2npoSiO+CogujxOLUT7Zi1yoQ9JrkL0BvK02DJU5IZeA1qySQXNLj+Jf2+5iS8fqtNVC4RCEZZ6xkHG5k+l2tXOgbw/TCg8bro95SFDbt5N9vdvQSXrVAlpmHUPjwP6gquuopK/3B3zU9ynEw3AQloUZKCyVXMYf0elsoSKrmpsW/C5GGRS6fyRe8PL43apdURCmQ8ExVafw0Kbfsr93O/X9e+OW2TVF5VcKVOdO5kD/bur6dictHskEu7s3R3yfyb7bgBwYNGEJcFTVMqbvX0CHo5kzJ14e8ViIfItWWPYCiRdZi8xlNNlqkzaFu/0uNrSuZFXzW8iyzHcO+9WQ7/sqGWiuUDOhl4w7h9f3PcGb+59mXunRaW9LLPyKjoV0UWwpRyNp8QY8dDvbYwh18VsK67xQoDZFRV8IxWVVHEt4ImglLfNKj+GjhtdY1/pRWsKBvqCSLpUlfE/nFmRk8nqzcd+jKLQlvQbrkWPJPWkCOUvH49zRSesfVuLa0UHbn1bR+cgGdEVm3Hu6Kbouj578AaRfT2HyMZcx8EEtzb9agXtfN00/e5f2f3yOzmrEXddDYCA09hyYFnTi5RUzdcXVSPpI8ixEusUea4L0HVM8ieJvLqDomvm493XjbbPha7fjbVf+1+/wUvC1GVgXRDacG3Vmsg15DHh66XC2qHNV08RCxvxhGWU/PJrOxzbgt3ko/59j0WYZ6GxTMlSLLRVMLZzHZ83vsKL+Fa6Y+UP1+Cy2xDapRyOe8CgaPe4gYWlMXLAkBAQev4sPgrnHR1aeDIQXZLXEZEVHlzElgk6jVxXVNm9/Wo7JLxKjhOUIgynKEu5p7FOJw7xj41vghrslXM2wDFdYpqn8MmiM6mDW4Y0tRhgpkzRxIooLiCjzSASr7uArLA1xvhOzsKKHEaVitW3qoAnLUHHJcKA/zFKSiU3jUCO85Mjjd6dlL/MGPGkfuyFLeOQ5aQhrWY3GoSzdkSQJiz6Lfk+PGuYsVhUHA4WYdWREWIrzMdFvEU7gp2M5LrKU02Sr5UD/HmYWH5H0vcX5kAlZHCrdiWwJjxdgnwjTCw/jrdpn2Nq5NuLvvoBX3e5grY9qTmsKhWVCwlIoLJNMOBNhuFvCIVwBlJiwTHY9DUdKS7hK1ETuf7jCMpny1Zyi3Gy0dGd4MHPmTDZtCilQFi1axP33389pp51GIBDgwQcfZPLk9AssRhGJUHlc6LooFqN8kg99RQ6eA7146vsUwlIl+kP3sPF508nS52Lz9rGnZwtTC+dltA99YVbH6pzJbGhb+ZVQWAq1zhEVJ6hj0nBVVyo02w7gCbgxaS2UBe2rQ0EmhOVT2//KxvZPMGrN/HTRX+NeC/PVBabE9w+h5tRrDINemAtHjrGA+WXHsqblAz6sf5XLZv4g5jkNUQ3hAuNyp0DDa8N6bH0aHEuJtuZkxF+/uwef7EVCSqq0SgStpOWO454AiCGPRbxAm72RgBxQx+pCcBCvJRygUM3GizwmXD4H61o/YlXTW6xtWaGOVUBxjlw/9+cZ7384OpyCLCpX/3ZKzYW8vu8JVre8R5ezLW1SV3WqGDI7vnQaPSWWClrtDbTa6+MQlqJ0RygsqwGFNHL7nBh1ZuzeAXX8FE2Qp8L80mP5qOE1Pm/9kMtm/oCAx4+3eQB/nyv0r9+N7A1grMmnN0h65YYRTcIS3u5oIiAHsK9qZNVLj8IiqKotxjS5kJKbjiRnSY3Sxh2EoSqXnBPH0/vaLtr+9Anu/T1KgU2+mfFT5rKHBtpyOpAkiZwTxpN97Dg6n9jEe8sf59nzH+HsZ45l7oDiHtJXZGMYl4fxTIWryC8rjyErIUT8djpbY0g3NQc0qFKVNBKmSYUxhTnJUGQuZ8DTS6ejNWaBR1+WRfn/HBvxt/Zg6VOJpYL5ZceRY8in193JhrZP1CzaYnM5qSDG18k6MMQ1MlkjvElnwayz4vTZabLVISGxsPzE4GdTvjunz4HdOxChloxXxpQIWYYcXE4H9hGYYzlKWI4wRGei9S3fQyBLkVcbC+NPjiRJUtsTh6N0R0iGIzIs07SEh5MeitUu8mQeKTa46JWGVFZ6QRh4/C58Ae+wKkQ9YuKaTGEZJBY8fjf7e3cAMCWYf5cpRM6psHoNFeJi+GWyg4NyrBo0RjwBd1rFO5+3fMj/ffodrp93W9xG52i440z0IEXpTgIb+RcFiz6bfk8PTp8dCYn5pccNelsiWzcjS7iaYZmgJTxO6U6ya96ckiPZ1L6KDW0rI/Kc4iFdFXk4VIWlTygsFfI+E6XxzOKFAOzr2YbdOxDXqjzoDMsUpTshwjKBJVwoZFyZKyxTqTcHg3xBWCYp3XGrivVUecuiZCpWfSvLskq8RB8PeWEr4MkUG6myggWJHE9ZP4r0cemll/LAAw/gdrsxGo3cfvvtLF26lLFjFQJHr9fzwgsvHOK9/PLCHbzOGsOuEXqNEScOfAEPhrG5eA70YlvdQNaRY+K6BLSSltkli1jV9BYb21dlRFj6Zb+qEMk1FiqkEpm3Obt9TnRaw4jJ5fIFvKyofwWAE8aeo/49VI6SmrAUdvBxuZOHZbG4MNi+2+1qT9rovqb5fZ7deT8ANx72a7VhOxrpWMLFY8my2zLF8WPOUgjLhle5ZMZNMd9NYyLCUhTvDFNTuCzLfBIkLE8d/w2e2vHXpGSwKNzJNRYOeo6R6DsstpSjlXQxakGxyJpozFKkktgKibSzewPv1r3AysblEbFSxZYKZhUv5P0D/+H1fU+wqGJp0jb0VAgp2CrUv43Lncz0wvls71rHO3XPc1GwaCgV7KpTJfH92rWvG9fuTryN/Xia+vE09ONtGSDvfAuteUpTePSit1ACGtd7aHj3TXwDLsyLTTgNLj69+K+UNhfQNL4DzoYcVzZdP11Jb44RfVk2+qocDBXZ6Ctz0BVakDSxv9ucgqOQkKjt28H6qx9G9/EAsitxfFXHT2uhFPp/sY4DFhnJoMUv+ZEWSXgDHjZe+0+0b3dTe00dADOnH83kX16BpIt/7ZA0EvlnTSXvtMn0vLQDT30vRVcdRnPva7y1/lUa+/eHnqvXUnDlHD4o3YrP6WffFXYu/P2VGMbkqrbsHXsfh02JHUiCdHP7nWztXMOs4Ng4gvTNQKUajWJLObV9O+LapuNBEJbFlkr0GgPHjzmDV/f9i/fr/6MWQoUfn4kgMvmTKSx71etgcpV5vqkYp03ZzvSiBaqoIFxB2ulsiSAsQ1EBqefoVn0Onc7WtGK0vmiMSDnUfffdR3V1NSaTiYULF7JmzZq0Xvf0008jSRLnnHNOxN+vvPJKJEmK+HfKKacchD0fOqJLd3qX7yagUVq1tJrEAy1hsRkOhaVWZFjGK91JowzEksRq5wsIS/jIUFgKpFJYhiuchrspPBNL+N6erfhkL3nGInXlLFMMd+lOKLT7y0VYQmZN4Tu61uOTvaxLs7UvZIlNQFhG2XQDckAlMQ6FwhIiB3STC+bElMBkAl0SYjYRnMISnk6GZRqW43nB7LQtHZ+lJE4H09Aeul4LhaWyKplIrRAPxZZyKrKqCRBgW0dIZSkG2SatJWMLpYC4prgTHN/OFKSimHD2DqolXORjDiNhaU5tCRfk7FAs4R6/S22zjD4ews+JZMrXVFnBvlGF5bDgqquuYvXq1RiNyu999NFHs23bNu666y7uueceNm/ezOmnn36I9/LLC/UaEbaoq+Yw+z3knaUoVdru+Qz7uuaEbpy56rV4dUbvP+DuVc/FHEOeSo5l0uZc17eLy147ij+sjlXbHSqsb/2YPncXucZCDisLKXtE0UdLVDlKPAxnQziErq++gFcd10Wj3d7Enz//CQCnT7iU48eemXB7eer9I4nCMrj4JBrFhwNHlC/Bosui3dHE5vbPIh6TZVm1hEcTltXBY6txYH9G45ZE2N+7nXZHE0atmZOqvwYojqREJXjdqsU4czt4Kmg1OpVcCbeFp7KEFwRJ7PVtK/nOO6fxPyu+wTt1z+P0OSixVHLu5Gv405Ln+Mcp7/P9Bb/jlJqLAPjruv8d0lypQyWLIkUvp4xXtv927bP4ZX9a20qWBe7rcVL/g+XsWvwIB657heZfraDz4fX0v70X55Y2crYqNMmBzZuithkqQx34ziq6n9lK//K9FLYo79FsP4DnQC+NQStzYUMW3c9speOhdTT/egUHrn+FPWf+m+2H3c+WSXezdc59bFvwADuO+ruSKXniozQe9iQVB5RzaGP/Z8guH5JJh74sC9OUIqxHVJFz0gRyTp6IcWIBjixlnKfb7KT3pR30PLuV/md2kNujjMEa92xH1kLzVOXcnrfstIRkZTgknYaCr82g7Oaj0eWbGRMsPGwY2BvxvE+b3qLFqSy01OsPYJpcpJKVEBofi2z5aBi0RhaULQbgFyuv4eOG5UBIXVlgKh5S0VpRUA3ZEScjMx46whSWACeMOxdQSrQ8fhcSkkqyJoPgF5IRlt0qYZlYYQmRkURHBe3gAqotPKp4JzOFZTD3fzTDMjWeeeYZbr75Zh544AEWLlzI3XffzbJly9i1axclJYlvaHV1dfzoRz/i2GOPjfv4KaecwqOPPqr+txjcjjQIpZ/b58DbasOxrpnAxYrCUmRLxoNeo8fjdw1PhmVwcuyTQxP80CA09fZDjdqxNyuv3xvc30OtsIwiLFNMqnUaPUatGbfficNrG9Zsh2SlO9FhvaFmtzmDXo0e7gxLVf2QIh9jJMKgNYIX3Akss+EQ6r/oQO1ECCm0Io+tRKU74aTpociwhEgCZjDt4OEQCstE+Ynx4EhhUQ6/8QsSPxlhWZM3jRxDPv2eHnZ1b2JG0YKEz43OaksHJlURH1m6k2mW6+zihTTb6tjc8RlHVJwAhNuYBqeuhORqXlmWwyIx4g8Chf15MBmW6RDKmaIwuPrck6QUQl0AStG+nSyX2BWWIR2TYRmusEwyeLakiN4QixOH2m3wVcT48eO56aabDvVufOkhy3Jc1X/4daXgwrnYPqqj99VdHPj2qzj+Xa08P4qwnJQ/E1DIw2TqvWgIO3i2IQ+tRseY7AloJC0Dnl66Xe1pkTvP7XwQl9/Bqqa32NT+6ZCUX8OF94IZZIvHnhWhpgvPGkz1PdUF27WHoyEclN8111hAn7ubLmd7RHlH+H7bvQNMyp/F1bP/J+n2VEt4kkgRsfg0HPmVAkadmcVjz2L5/id5s/Zp5paGskB73Z3YvQNo0FAeLEkRKDKXY9VnY/cO0DRQS/UQiWDRDj6/7DgKzaWqo6fb2a62doej6yASlqAU77Ta6yPUgqJ0J5FDSpAgguQ0as0cXXUKJ1V/jemF82OOz6tm/4QNbStpczTyyObfc+P8X6e9f7Isg19G9gXosCmW24JAUcR5cHTlKTyw4XY6na3s79nGpILZgEI8unZ2YppaFNFqDaFFyfDIAVmW6X1pJ02/fB9/txMksMwpQ1+Vi2FMDobKHLR5Zko+3AvsYP/qz6l/bzkVt5+ANtvAnifehxww240YAkaKrp6LsSafMVm7aKQD+XtjmVjwDda2/x1cMK56BmX/cyz+HifeVhue5n68TQN422zIHr+yD3Ewta6GpnEdNH7dzcX/dyXGSYVxrwn+gA/nf+4EYMrtZ2Oul5D9yoJOqXUlvexAvrKSgiOW0r/rIXSSnvF509P+bcIxJlshLDudrTi8Niz6LGRZ5vldD6nP6Xa10+vqiljgtakZ73kJt/0/C+/mT2t/zGfN7/CHNT+g3dGoXhsyKS2Kh2I13iA1Yenw2tRsXUH0j8+bzricyWpkRL6pKK3xWzqWcLGok5eCsAzPuFxUcVLEY0Xmcmr7dsZ8vowIy+A5Ij77SMKIIyzvuusuvvnNb3LVVVcB8MADD/D666/zyCOPcMstt8R9jd/v55JLLuH222/n448/pre3N+Y5RqORsrKhN9AdbAgFo9vvwl3bg4xMQCsUlol/LjHgGQ41iy5O6Y7aeJyG8ssqrHa+SOWKLMsqCXrICctohWUak2qLPgu33xnzuYYKMcGO952ELLDKhW5XsCF86iDt4DD8LeEi6HmkBfSmA7VFOQ2FpSB3Wu0NaU24VMImigATREq0TTdcBZdKHXawED6gG2rovFBYDmdLuDgf+t3dBAhE/C0eNJKGuaVH81HDa2xs+yQ5YZmBilxAnEuxpTt5aW8DlKa/N2ufYXNHSA0iBtmZBsWHI3R8xxLy3oCHQFChkLglPFS6kwnJAKnt5oNBqCU8scIyXat1MoWlOHcNGmOMhTRCYZmgHApC92JXglX1UJ7zyFw8/TLCZrPR09MTV3knLOKjSB/egEe9zpoiLOEhwlKSJKruXIZzWzvu/T00/+MzOAIMUUR/VfZ4JCQGPL30urtSKkkEwgt3QDmvK7LG0TiwnwN9u1OSO632Bj5pfEP978e2/pE/LXl+2OzHg0G/u4e1LR8AcMK4cyIeK7EqJQ5On51+T0/SSaaqsMwbHsISFEWdQli2xd2uaBc+puq0lOP48JK0RPePruDi03ASlgDLxl/I8v1Psrr5PXpcHeq9Q9jBS6xVMfcISZIYmzOJHV3rOdC3e0iEpSzLahb4kZUnBxu3y2ixH6DL1XZICMuyrDHQHmqgl2U5JYk0o/hwJuTOQK81sLT6fI6pOjXpQp1ZZ+WmBXfws48u4+26Zzmy8iTmlyWPFvK22mh/cC3dT28hYFPui00/2wElYLvhM/ZYXIz725kYq/PQaw3MLlnEZ83vsqF9FZMKZmP7rIG6617B3xOcp04owDKvHOv8CsyzSxnoV64hFkm5J3sa+mj82TsMrKgDwDSliKo7T8Z6WKy9d+bcc3nt8w/oKRqg5/HtDHxSj77Eyl7v5/AtyPPkMuXNyzFNUa5nNTvm8On2j+gs7MW6oJLOz3qgCSbMP5zSSQtjti97/XhbbQQcXgJeP3gDBLx+ZI8ffYkVY2Ej7624kO2mbegm5ia8bvV7FCW6hETVKfMj+IKqz6ex68AOnEcaqMtSiOeavKmDdnJlGXLJNxXT4+qgcWA/kwtms6FtJft7t2PUmsky5NDlbKO2bwfzTMeor0snMsmoM/M/i+7h0c2/55W9j/HY1j+p58NQ7OAQUlhGKxDjQagrrfoc9XiXJIkTxp3Do1sUYrgoDTs4hBavk5fuiAzL5Nm14vFJ+bNi1MdFCQjZUGzbl1thOaIs4R6Ph3Xr1rF0aagpVaPRsHTpUj799NOEr/vVr35FSUkJ11xzTcLnrFixgpKSEqZMmcK3v/1turoSK0bcbjf9/f0R/74oqIodv5OA06vawYGk2TtCfTk8lnBRuhOS27sTNKbGg5hgO6ImguHExaFWlUQPAtPJWROT0+jPNVSIiashzncSbgmXZZmdakN4ZqH18bZp9/anbalIBlG682XLsIRwS3gaCssgmeb2O5NaUgXUEpdoS3gChaVQshi0pkNWXiRInEJz6ZBbRxN9zmQIZVgmJyzDCatU1yRhC9/Q/knS54lrnCETS3jw+PHLPrwBT0p7VSLMKl4EKOojkRepqgKGoLBMRI5D5OAp0X0jN0gS+ALeuFmPyRAi7IefsHT67AkHf94kERvhEMe6yCUOR7L7XaTCMnWGpdMff2FotHRneOByufjpT39KSUkJubm5VFdXU1NTE/NvFJkjXB0cvvAmFqPEfVObZWDcA2chGXX071YmetGTYaPOrKoHBemVDkLqkNBCgZo1mEY5yst7/kmAAFMK5mLSWtjbs1Ulkg4VPml6E5/spSZ3asx91qg1qRP0ZLbwfne3Wlgjvo/hQKFZFJvFV7E3DOwDYGxO/BLQcIhrZbL7R3iG5XCiJncqUwrm4pd9vFv3ovr3RPmVAuK7rBti8U7DwF6abLXoNHoOD9pcxe8arwU5/O8HjbCMykd1+uxquWq2MQ8AX5eD/hW1tN23mrpvv0r9ic9y5dVHcdX/nsCc18ag70lN9M8sPoIzxl0KwN3v/JB1x9xN3fUv0/XUZjzNoePAXd9L40/fYcfRD9H5j3UqWSkj05en3N/zeqw4t7Sx54x/0f+B8tuJeIlN7Z/Q9fQW9l/8HP4eJ5qcYATOvm56nt9G40/fYc/pT9C1QTlmO25aweYJf2bHcQ8zsKIOyaCl7EdHM2n5ZXHJSoCKvGoAeqtdGMfn42uz4dzSxkCZsq8VU6epZCVAebB4pznYFK42hCdQB0p6LYYxuZimFGGZWYplXjlZR1SRfcw4TJOLmFQwixxDPg6fjZ1dGxN+5yKnMMuQGyNuCm8K3x106U0umJNwW+lAqCzF9eCF3X8HYFnNBUwrPAxQIhHCkW5kklbScu2cn3Ht7J8hIalEfqalRdEItZCnVli2R9nBBY4fcyaaIHWWTuEOhBxMTp897mKqLMv0Bcf94aWO8bCg7HiMWjPnTLo65rGQ5T3KEu4JLvqZRhWWw4bOzk78fj+lpZEX69LSUnbu3Bn3NStXruThhx9m48aNCbd7yimncN5551FTU8O+ffv42c9+xqmnnsqnn36KVhtLAt5xxx3cfvvtQ/osg4WYILl8DgI+DwFtQH1Mk4SwFBP5TLLTEkG0jodP4tRCijQm84naV8OJi0M9ScvS5yAhqdlI6RC9yRQ5Q4EnSeaaKiX32el0ttDtakcjaVV71WBQZCnHpLXg8jto7N+XMDA9XfR/JRSWqQlLZ9jx3GKvT5m5JGyl0QozoaiKbm4+lA3hAkJRd3jZ4iErUITqezAZlukoLEGxJ6UqUZhbqgxu93ZvYcDTm5BMdA3CEh5OaDm9NnVSlilhmWssoDp3CnV9u9jauZpjqk5TF0ayhtCcKq4p8VrChaXeoDUl/A4NWqNqket1dWZkdXceBEu4RZ+lXrt6nB2Ys2MXmsR5lLJ0J4wUd3gHIuyPoWbk2H3PNuahQUOAQPKW8BTRG8kWqkaRPm644QYee+wxzjnnHI499ljy879896GRCkHc6zWGiElwvMUo87Riqn57Imte3AKAti92YjbGOp4W+wHqOnYyK3uBkp+m1RBwePF1O/B3O/H1OPF1O9GXZpF19NiIhnCBcbmT+KTpTepSlKP0u7t5p+55AC6d8X22da7l6R338cS2P7OoYumgs4GHClG2s3js2XEfL7OOpcvZRpu9IaGbprZvl/rcoeS6RUNkFsYrh/EFvDQP1AEwJntiym3ptQa1Hb4nwf1DZFjmMfzn7Sk1F7KreyNv1z7L+VO+iUbSpCQsRY5lbft2Ak5vRAZfJhCk+LySY9TfRxCR3QmKd8R3IcqP0kXA6cXTPIC3ZQBv8wCSXkvWkWPQl0UeF+FxAxASGxgkIz1//pz+9/bj3BqfqPY09NN650pa71pF7kkTKLxkDllHj0UOyOAPIPsCyH4Z56ZWel7czvx3TXx6Yw5dJf28cvS7nPfkYvqWKwsVxsmFGKvz6X9vHwRty9bDKyn57kIs8yroD/Ti++AfSEgcvvz7NN/wBo71LdRe8SJlPzyauVcpFv/t7evY/7+vYfDqyT1jCmPvOoWAy4djQwv2dc041jfj2tWJy6pcp8xOA7JHEWlYF1ZR9fuTMU1ITuIIktceGKDi1bOxP7gTf7cT/bkSNLwf0xxeqRKWB/DLfpW4rMwaHNmmlbTMKz2GDxteZV3rh8wsPjzu85LZfgVh2e5oVEtepwyRsKzKnsDmjs9o6N/Lzq6NbOlYg07Sc86kq1nR8AorG99QC2IFhJo33XHkWZOuoNhSwZ/W/BBPwD3kiAZBMHY6WwnIgaTCkFCGaiRhWWAuYV7pMaxr+0hVw6eCmLcEZD8evytmIdzps6tj9FS27cPKjuXZszfEnZ8lUlj2Z6CwtAbLeuye4eU5hgMjirDMFAMDA1x22WU89NBDFBUltpdcdNFF6v+fNWsWs2fPZsKECaxYsYITTzwx5vk//elPufnmm9X/7u/vZ8yYWPn+wYDaOut34Xd5CWhChGWy1rhr5/yMPT1bBp1JEQ5R7iNW3iD9lnAgrOU2MWE5nC3bg4FWoyPLkKsqotJSWCb4XEOFN0nmWniGpbCD1+ROHVKLtFbSMiF/Bts617K3Z+swEJbpN5CNNGRSuhOu6Gq11Se1F4OSQwuxua+JSncGU/oy3DhzwmXoNQbOmHDpkLelH4Il3JxCYSkWGtI5bwvNpYzNmUR9/x42t3/G0VXxC9cyUZEL6CQ9GklLQPbTFdZcHd7Qly5mFy+irm8Xm9s/45iq00KW8EE2hEO4wjKWNHb6k+eFCuQZi4ItjV1UkVpVI3AwWsJBGTA22+rodnVQkV0d87j4rIYUi2JajU4lP20xhGX8OAdQrp85xgJ63Z1YkpS1pSo3E/t5qAvovux48cUXufbaa3nwwQcP9a585ZBo0UFcV3xRi1EFF85C16CMxb2fd9K87gM8jf3Kv4Y+TEf1wMmw6ZGXqH42dS6uZX4F7TfUAdEKS9EUvgtfjzMms05g+f4n8fhdTMibweziRUzKn8XyfU/SZKvj3QMvsqzmgjS+heFFq62BHV3rkZA4bkz8Mqgy6xi2da6lxZ5YYVnbKwp3hs8ODiFSLR5h2TxwAJ/sxRgwYlzvQT4mdUxIvqkoSFh2qGUd4ei2Ke/Tf+Mqdsp2so8eS9bR48haVIU2d2iLt8dUnco/Nv8fbY5GNrWvYk7OQupbFaK3XK7E1+NEY9IRcHhxbGzBvr4FXX0zLIZ9+zay5Yp70BVbMFTmqNmG2cfXkHXUmISf29tpp/uZrXyY+yKYYb7/CPx2D1qrIaXCUrSEFyRQWMqyjKe2B8fGVuwbWnBubMFd14u/N/741TixgKyjx5J99DhMM4rJa1PO4+buWtr+8hl7OjbCXDB162i7JxRHYxyfj3lGCeaZJZhnlGKcVIBtVQNd/96E4/Nm+t7YQ98byVXSOiQu+OAMHrjgKTYcsYel475G2dsGHBtacO/uwr1bOf+zjqum9LsLyVoUmmN394TssdbKAiY8eyHNv/yAric20frHT8jeNJ6CJfl0W3qom9DCsadcSOkPjkKSJDRmPTknjCfnhBBB6Hv9KXDB9H9eTo12ArI/gGFMYnt1OEw6i2p/bvO3MOnHisW5Z90KIFYNWxEkLPvcXdT17cIb8KDXGCi2pmcfjocFZccrhGXbR1wx60dxn9PvEfOwOIRlsKC1aaBWVTlOKZg76P2BkMK6cWAfL+xSVKTHjz2TIkuZek2KJixDkUnpL3wfWXkSd534Avt6tzM76EQaLArMJUhI+AJe+txdSVXdocKdWFLymjk/JWdnAaeNvzit9w2/fzp89pg5hiCbTVpLWgv8iY5bQchGX7szy7AMWsJHFZbJUVRUhFarpa0t8stua2uLmz+5b98+6urqOPPMUEtdIKAQfDqdjl27djFhQuwNcvz48RQVFbF37964hKXRaDxkpTyCrPD4XficbvxhCstkSqL5ZcelzAlJF9qoDEtZlsMm8+m0hAczLKOUiOoETdIfMstrOHIMeWGEZSZlQgfHEp5MYen02lU7wFBvNAAT82eyrXMte3q2cGL1eUPa1pfZEm7UpE9YhpdztNhTF++41NKdNC3hI0BhWWAu4eLp3x2WbSUrfEkE1RKeQmEpkK56b17J0dT372FD2yeJCcsMcnoFJEnCpDXj8NnUVU2LLmtQCzKzixfxyt7H2Bxs0Q0FxQ9DhmUShWVKwtJURJOtlp4kTa/xcDBKd0CZxDTb6hLGMoTKbFLfwy36LFx+R8w1PRV5nWcqVAjLJPmiqbKC083aHEVySJLEYYcddqh34yuJ0KJb1D0swaIbgHnZGNgNugHoeGZdxGOlrcoYob0stoFaY9ahLTCjK7CgzTVi/7wZx7pmGt/6HI4C64ByXfb1ushfo4yF6zt3s3nuXyk8fRqVv10aQVy6fU5e2/sEAOdNvhZJkrDos/j61G/x8OY7eGr7X1k89qwv/H77YcOrAMwuOTKh9Vc0hQvrbjyE8itDqiNfjxN/jxPZLyP7AxCQkf0yxuo8tNnJrzO2VfU4NrVirlIWA8MnvZ7mfnqe385nG16AM6CoIYe6m1/ANK2Y4msOI+/saWhM8aeTeaYiGgb2xb1eOza30tnZCBbI7rPgblWIrM5HN4BGwjyjBH1FNroCM7oCM9p8M7o8EwG3H/+AG3+/m0C/G7/dg2lSIfnnTsNQFSJDlPKds3l93xO89NZ96O/cSN1N26AAvN/dyLbaWOIwx+yBxdBXYMNl8mDqAF+HAzYqz+24fy3GiQUUXjaXgvOnq6Sqc3s7HQ+vp/elHXTmdNN0axMav0T+N5vZ6vgLhnF5yCe1wkxoWr+V9k/XoCuwoMk2ELB7CdjcdFqbQAO+B/ZR39eP7FayDANuHwGHF9fOTvx98ceqGqsefUU2+rJs/H0unFvacO/txr23m67HNgLgNnjhTrBLNmrvfY+2ce0wFywuE7lnTCbnhPFkL6lBXxQ7Jij42gwKvjYD584Oup/cTPcL2wn0x44rtPlm8s6cQv7505k9r5yta/r4uPF1mpbYOfaGK/H1OLF9Uo9rVyc5J47HMjfWVitanIW6TWPUUXXHSZjnlNF067sMvLOf8fmldB/VQ9u3DJSdd3Tc70RALITnFhRhyMp8MbnMOoYeVwettgYm5c8CQqRzdEu0RZ9FnrGIXnenmlNbnjUupRMoGeaVHo2ERF3fLjodrTGqTgh1CcQrPxUKSxGllGPIV5Wjg0VV0BK+rfNz7N4BJCTOn3wtgCqcarbV4fTZ1TFmqJQyL6P3GpszibE5k4a0v6AIpfJNxXS72ul0tCQlLNsTKCxBUWf/4PDfp/2+GkmDWWfB6XPg9NnJJ1Jg1xvMr0yHUEyGUEZni5oZ7Jf9qrI1HpkdjSxVYTnyMixHFGFpMBiYP38+7733Hueccw6gEJDvvfceN954Y8zzp06dypYtWyL+duuttzIwMMA999yTUBXZ2NhIV1cX5eXp5Q98kQgnN9wOR0SGZTJL+HBCJ1rCg4SlN+BRFU3pqL/UrMeocpoQMXdo1ZUCOcYCmoJy/USKrnAcNEu4mLjGUViaw8J6dw5D4Y6AuOnu7dk65G0JwnKoF9tDAUEWuNOxhIcpLFtsiScSAokIm4QKyzhtrF9mJJvUJoIg0RJmWEYp2tItdJlbehQv7/0nG9s/SRj+H4q9yIxgM+kUwlIMsjNtCBeYUXw4GjQ02+rocLSo9vIhKSyTlEqFFJDJP2+oKTx9wjIgB3D5D5LCUi3eSWBdy4D4t+qz6Xa1x1zTQ/EA8b+bfGMxdexK+lunawk/1PEoX3acffbZvPvuu1x//fWHele+cki86CbiPmLV8x5J+Vt2dRnF1y1AX5WDoSoXQ1UOWTmtPPfZB3ROdjBj+3cVO2gggMasj7HeettstN39KXbjO8q+/G0nu3/3L5zb2wkEAuh/p8Nr9NFd1I/21V3Y1jQx5o/LyFmsZMW9d+BF+j09lFgqOaryZPw2DxqTjlPHf4NX9j5Gh6OZ1/c+wXlTro38zPu68Xc7scwrVyzrGUIOyEia+AoYWZZVO/ixOSdj/7xJafINKHZa2RdAV2ShNCeYNZgkw7KuL9QQ7h9w03LnSroe2wCxTnw0Vj2Fl82l+JsL0JdEXo8dm1tp+d3H2D5WFmG90+rhemjesI3dv3sCjUWP/bMGkKH5ZKW5uVIzBo1Fj2tHBw0/eouW331M4WVzMM8qU+zB/gCyT7EKZ5uU62D0glfPf7ZTe8sbOP5PuV7Puf9yjO0abCsPYPukHvf+Hpxb2nBuiW+fjofWO1diXVhF/tdmkHfaZHxdDua9PJbXZ8Im0waO10+kr0Ahr4q7IserxolKUUvlvHIKdW/S5WvH8vZiJtgm4G3sx9PYh2tXJ72v7cK9t5vmX7xP6+8+Iu+sqXga+rCtCo0J95yifNaJnePJsRbgczjwHOjFuMYFM6Gju5GWuz+KeH+P3ofjD8q9IvCvRnrc8e9vklGHeVYJlrnlWOaWY5pSiKEiB022IWJc4+t1Yf+0gYFPlO/Tc6AXa34+2Q4rAxY7/m+UoC9VxgclsyZSfeNZaX3H5qnFVP7qRCp+vhi/zYOk1YBOQtJoghEPUsR+TMqfxceNr6uKYF2+mbwzpsAZie29Qt0WTQYWXjQL87RiDtzwKlPaJ/I5O9mZHT8uTv0eAl51LDLY6IQy61h2dK2PUDwLQr8oTo5hRXY1ve5OPm9ZASSOH0gXOcYCJhfMYVf3Rta3fczJNV+PeU5IRRcrHMk3FaPXGNQxx+SCOUOOfBqTo0RCiLHTooqTqAqqLvNNRRSYiul2dVDXt4tphYfhl/2DjkwaThRZyul2tdPhbGUSsxM+ryNBhuVgYdZlKYRlHIdmnyv424UVOg4GYgHME3Az4Okhx1jAgLtX5W/SiQwMKSxHCcuUuPnmm7niiitYsGABRxxxBHfffTd2u11tDb/88suprKzkjjvuwGQyMXNmZJZfXl4egPp3m83G7bffzvnnn09ZWRn79u3jJz/5CRMnTmTZsmVf6GdLB+G5W07ngGoJ10jaL6zVUBtUB/mDjd5CbQLpTQJD1un4hOVIscCFKwIzs4QPs8JSlC/EyTITN9g+d7d6AR0OhaUgLPf37VAtC4OBLMuhDMsvocIyE0u4w5uhwjIB6TGSFZbDCaEyTNcS7vV78AWvOYkGltHkb7pk2Iyiw9Fp9LQ7mmi21VEZJwA9ExV5OERJj1hxH2yOsFWfzcT8mezu2cyWjtXq8ZY1BMJSnySjNdTinVphCahlQOkg/J4xnC3hEGqTTUxYBhWWabRvW4P5oNExH6F4hvjHwjmTr8Kst3J4+ZKE207ZEq7u58i4H35Z8fOf/5wLLriA6667juuvv56xY8fGzSYvKPjyLagdaiTK9dUlUc+Lc6dw8RQqZi6OeGysPxeNpMXhG6BP10thduKsPn1pFlV3nETgzQfBDtYBs0pemScXUSFXcYA6pLunY7zViXt/D7WXvUDhFXMp/dkxvLTnUQBOaFzMvjOewrmlDV2xhfxzpvH1k67ib47f8vyuv3NyzQVYsNL/1h46H9+I/bNGQFGK5Z48gdzTJ5N19Dg0hthjKuD04tzRgWNTK87NrTg2teLe241xUiG5J00g56SJWOaWKaQOsHPvGppsteh9OnLPr2WvuynuZ/cu8cHZiUt3vAEPDf1K2UXhBiM7f/EovjblGqbJNiBpNUhaCbQaZF8Af7eTjgfW0vnoegounEXJtw5H9su0/mElva8ohI+k15C9ZDzFLmVy259tw7kppEC0LqrCfqpSojHt1BOYduVldD+1mc5HN+BtGaDtzwmKUc/pgMVQ99IqOkvnkn1cNV3/3kTHg58zkK+MpXWSnpL5U5AkibzTlIgiT8sAjg0t+Lois039PU4kow5tjlH5l21EMumwfVSH7dMG7Ksbsa9upOnWd5G9AYwBmbHfK6V+fBuf/KIR/MpY9chtP0P2BQh4FGGG1hK6DlevnEpXWzuN8gFmzToKZoWO04pfnkDPi9vpenwjrl2ddD8TXPTXKvtuuXoKW3t+CP2w9JTLmXHDRXg77bh2duKuXwu8i63CS/5505Xc1gEPGouerjKFIDD6jYy74Xg0Jh2SUYfGqEMyapEMOsWqPbUISZ9avKLLM5F76iRyT41UplV+sIKd3RuQv1mJ1mWCTZA9iDgnSa9NGMUQDtE0LxTB6aDDGamwDIdlThlTP7qGSncvT77xOvX9e+hytiVUK4fP1wbrVinPEmVFofOxM7hAXRTnfSuyxrG983N292wGht5uDTC/7Fh2dW/k89YP4xKWoWiu2PucRtJQYqmkyaZYt4eaXwnKQrbIpwU4f8o3Ix4fnzed7tYP2d+7g2mFh+HwDqjE2WAik4YLReYydrNJ/f0Sod2hXJuHj7BM3BTe606vcCcV9FqDqu7tcLaSYyxQowKyDXlpZTaL30aoMkcSRhxheeGFF9LR0cFtt91Ga2src+fO5c0331SLeOrr69Fo0l/11Gq1bN68mccee4ze3l4qKio4+eST+fWvf33IbN/JoJE0GLQmPH4XLpddVVjqpC/upxLvJRqkxaBVp9GndcCHMiwjT8yRpigJz1xMr3RHKEfjT0AHC0+SVltxkRMEWa6xYMhSflAsDuJmU9+3hwn5Mwa1HafPrv6uX84My8SW2XAE5ECUwjKx8kHg/9u77/g26vt/4K/T9pZHbMfOchbZCSQkhBkgkAClrLZQQhsobSkkBRraAi2E9YW0tKX8oJQUWkZZpQNoy0gLgbAaAoQMRibZw473kC1r3e8P6XM6LWtLJ/n1fDzyAMsn6eyzpM+97z3EVd3g7BRTxKE72e9hmUrxTglXl9xHej3qJb3y/jjQdsEshkJMqpyJzc0fYEPT+2EDlvYESsK9jy0CliLD0hrX/dWmVh+H7e2bsbn5A2WhnZIelmGOQcwBywQyLEVWoQQp6vCbeIkynkgl4f5hNrGVhAOhF6HsUbKdj645EUfXnDjgY0crCR+oFQjFbtw47wn5hg0b8Kc//Snidm63O1O7lDf8VQKB7xHGoCnhagNdeDPqTagrHokD3buwt3N7TNOQuyXva/Oo5V/F8L31KJozDOaRVoxbvwN79+xBY0UzTl11DQ7d8w5an9iA1ic34v0Dr6Hx3P0o7DFj3C+M6HN4A52u5l40P7oeNX/0oGb5EDSVN+Opp27Dyb8fD9cR3+e7ToK+xAx3ex/anv8Mbc9/Bl2pGcWz672lyB12uLv64e70/hee0JTG/u2tOLK9FUce+hCGygKUzGuAY18nXhr2d+AUYMLmkbA4TDCNKPNnpem8/+3/sh1F6+zAeUB7fzP2/mY1hn7zWBiri5WMzwPdu+CSnShwWmC7/gPv++xIK4atOAMlJ40M2BdZltH95i40PbgOvesPofXPG9H6zCZAkgCXB5CA8gsmoeaG42EeYUVlfxvw8h9hK7Gj7uGFkFqcKDm1AeaRVjS98SzQCYwoHQuD1YLqq2djyHdnouPVHWh7/lN4uvsBg84fMNVJKHV5g5ytbYdw8MHVAfumv9L7OVxRUB2SkGEaWgLT0Ng/+2qWzPGWrr+4Be0vfKH0SCw5rQFnz1yMle334iP3/wD4M94kgw56Q+j5yJjySVjf9A5e/fJZnDbygoC/ZX2xCVXfnoHKb02H7cOD6HhpC/TlBahYNBUfy2vx6KbvoM3eDJPegjl18wEAxqoiGE8swpg+C/Aq0GnuQv39CwLKhHub1wHvAFVldaj9xsAlzsmoLR6OrW0b0Gg7oLx+4+kpGK8G36CURts+9Dp7Yspy9A88CV8FKel1KC2swLjyqdjevhkbj/wPp4+8IOy2NqUvemHCQ7aUYUW+yqpeZ4+yXg3Xb1QM3lG+jjAhPB4za0/Bs188iE1H/hc2ySRa0KumaJgSsEx2QjjgbcUyvHQMtrR+gmlDjsP4isBsxYayifi48W3s9vWxFP0rCwyFWY0BiCB4pD6ygHdtJtaXVakKWBojByw7UxSwBLyDdzr6W9DSexhjrJOURINYE4r8U8KZYRmTpUuXhi0BB4A1a9YMeN8nnngi4OuCggL85z//SdGeZYZFX+ANWDp6lSnhYhBOJuiDSsLjDaRE62GpmYClOsNygOEJguhXlvoeltFLwoWjKmakJNNWkiSMLZ+CjUfex472TxMOWCpTBvWWlPeqywRzjBmWwWWdNmfXgBOnXR6nklkYqSTcFTJ0J78yLOPtYdmrWlgO1OO2wFAUd8AS8PYB2tz8ATYeeQ9fGRs6VMifYRlnSbgvg1aUhCdT7jJtyHH4x7ZHsPnIB8pV/aKkpoRHDizE2mMykQxLUdpvMRSmvDKgvMBXEt4XKcNSTAmPJcMyfJuPfndsv5uBFOhjG7qjlc/DXLV8+fKMVZ8MNpEuug10MSraenFE6Tgc6N6FfV07cUztSVH3QZzM1Uwah4o5/tLKkaXeTLy9XduhKzBi2F2no2z+GOy94TW8Nd07QOS4D6dhyPxJKD19DEpOGom+z4+g7W+foev1L3H634/Gs9/7L1ZbXsdxzXUwVRej8ptTUbFoOoxDimD78AA6XtmOzlU74DpiQ9cbu8Lun6GqEAXTa1E4rQaF02thHluJ3o2N6Hp9J7re2g1Xax/a//EF3DoPNn/VmxV56oQLMenDS0KmOAPePpQtz26Cxf5X2C392PaX1ei4f4P3m3oJOrMBG4/dAVwAVO+zQmfUY8gPZqPmh3PCTrSWJMn78582GrYP9qPpwXW+8m8ZJfNGYehNJ6NgcrWyfYmpHAadES6PE/Kp5Rjim4Tr9rhwsNsb8FBPCJeMepSfNwHl54Uf/nPUHiNWrX8fzmkWFO0cBtv6Q9AZdRh+31n44ui9wAcYsJdcPEx1pahZMgfV18xG//ZWSCY9zA3lqHfb8fQrf1CywaKV6J4z5jL8Z/ffsLdrOx7b/AtcffTtIdtIkoTiOcNQPGcYmmwH8MuNP8HHjW8D8A5eWXrMXSi3BPaqs5oroYMOHtmNTnsrKgr8v3cRQIkliJ8Mf/Btn3KxrCSN1VGl5gpUFtSgta8Jezq3YVLVzKj3EdlvQwoGDhbNqDneG7Bsen+AgGXyrXWCp6uLcvAiY0nYAOzQNAQsx1gno8xcgc7+Nuxs/wwTKwP7NiuVbhFac4k+lhKkkOBiok4dcR6abAfw7Sk3hHxvtHUiAGBXh/eCRaL9K1NNtBkInqSt1tLbCBkyTDpzSoKIgKpVXriScKWcPwUBy4Ja7Gz/THk/ERmWsZabiynhvc7uqJPUM02TAcvBzmwoABztsDtsytAdXQYzLIOH7sQbSCmKUDrtUjJKtHGCJjICddCFDRYGixSITZZSwjhAhqUwofLolD2vCFgm08eyO4fLwQF/SXh/lICluCqmk/SwmivRZj+Cwz37UFJhDbu9yNACwpWER8iwzLMelvGWhIsr1tH6yRYYipST2Hj6I86oORFPfvYbfNq8Di6PM2QwTqIBY7F9ixKwTDxbYVLlMTBIRrT0HVYCXUUJ9l0C1FPCQwOWIlM82sWacrMvYBlHhmWf0r8y9RcxREl4xKE7A2SsByuMELCMVAobD/WU8HB9U+PJBKXIbr/99mzvQt7yDyILP3QneEo44H/9RXofHVE6Dv87+B/s7doe9fmdbofy2gw+cRzly9za0+l/HMtJdfjijzIOfdkCE8xY/IvforzEHwwzDi1B6fwxcLX3ofqfn+I5+b9wWJwo/92JGLFwdkDZd/HxI1B8/AjU33U6bB8fhH1LM/QlZujLzNCXWrz/LS+AoSr0oox5pBXl502A7HTD9tFBdL+zF1tqd8BW0odSUznmXbA44mA2Q3kBapcch/o3xuDLzi9gO84IvOT7pluGp9eJ3TXeTK8RzpEY/9q3YTmqKuxjqUmShOK5I1A8dwT6Pj8Cj8ONoqNDM9gkSUKFpRpHeg+i1d6Eal/Assl2AE6PAya9Ja6JxxW+C0y2IQ6M/fslcPc4ALcH+jIL2nZ+5N3GUj3QQ8RNkqSA34lZb8GpI8/Dv3f+GQBQHyVgWW4ZgmXH3ovb3rsSr+16DtOr5+L4+tAWYh7Zg3/ueBzPfPEAHG47DDojvnbU9/G1o64K+76u1xlQXjAErX1NaO1rDAhYtvmCYOkOWA71VWgdtu1TAsXp7inYUDbBF7DcGlPA0l8SPvCciRnVJ+CvW1di45H/RQyu+IcXJn7hVwzBau1rgsPdrwouhw6/AcJkWKagJFwn6XBUxXR8ePgtbG/bHBKwjDYJWkwKH1YyJqlBjmoLR1+ChaMvCfs9MXhnT9d2uDxO9PgCltnsXwn4/6aaBygJV2f4pupi6EAl4eJ8JhVzINSDd7yPHXkYUziih6UMGb3O7oR78qeDdkKnpBAnSXZnn9LD0pBgKnsilAxL0cNSnLzFePKplNm5Aq8kaK8k3PsCLjAWxfSm5O9hGXqFJBkiwzLc7yX46l0qeo8Ioo/ljvZPo2wZWadyVS9XA5aRe/ypiWNeaCjG0GJvyZW6AXcwkWWik/QhJyYRh+4M8gxL0Yw6WsmQOkgZT8CyoWwCyswV6HP1YlvbppDv90cZtBKJCDCLq+7JLMjMhgIc5RuqJbJBksuw9AfHZTmwdFEpCY/y84oMy3imhEcqJU0Ffw/LKAFLXSz9liOUhEeZEh4L8XnpgSdsywmtfR4SBfMP3Ql8j/BfCAmTYRnlwtsI37CG/V07oz6/OAnXS4aQ90GRYdlk248+lw3vH1iFpf/9Cp7+8v8BAM4ZtyggWKlmKC9A7eWzlWxt54lFYXtUAoCkk1A8exiqFh+N8gu92ZpFx9bDMr4KxiEDrx0lox7Fx4/A0JtOwoZJ3kyjk4afEzFYqVbrC5Lorm3A1O3XYfLmJZj00Q8w7t3Lse0kb8DkjCt/EFOwMljB5OqwwUpBBM3Uk8L3dXuP17CS0XFNPFYy9H0XvPTFJmWytrjolKoMy4EsaLhY+f9YhqAcXXMiLvRNPX5w/c/RZDsQ8P3O/jbc8d738Pin98LhtmNK1Ww8cPo/cemkawe8CFVpEb/bwAoB8XW4EuNUqikS/Rj3K1lvJWZrWp+zocybbbe7c1vUbZ1ufzlutIDlUZUzYNEXorO/FXs7w18A8QcsEw/SlZrKUWAoggwZTbYDSnZe8FAgobZ4BCR43xes5qqU9WwUpdzbw6xfRSZdpHOxOXWno8IyBAtHXxz2+6lWUzQMBYYiuDxOHOjepZSElxizGwBTAnoDZFj6A5b1KXtekRgQLn4gyvmTHboDhGaQDjSMKRyj3qQk8vRobFI4Myw1SCz0+l29MOm9J5nxLBCSZQjJsIyvt5sonbY5uwMyS7RWAieyAmMNxKZtSrg7cqaNet90kl4JMqbC2HLvYKq9XTvQ7+pL6ORcmRAe49UbrYl16I4I7hQaizG0eAQ+b/kIh3siD94R21v0BSEnNJGH7uRZD8sIpe+RqIPCA1Fn7cVTsquTdJhRfTze3v8yNjS9h8lVs5TvuTxO5QJNvEN3xD6I+yd7RXLakOPwectHytfJlDKp37NdHmdAdrsyJTxKhqW6h2WkCevBxKCZdLSJECe3NmcX+t32kM8lERyMJZNfBEFsrqCScBFwjTN4raZ+P7W7ekP30x37fpLfnXfeCUmS8POf/xw6nQ533nln1PtIkoRbb701A3uXXyK1jTAMWBIuLryFD9qIQOP+7p1RS878mSflIe87VkslysyV6OxvxbLVFyn92azmKlw66Vqc0fC1qD9fVcFQtNmbo06MTVafy4YPDr0BAJg3IrZpzKJX+eGefQFT1D9v2oxudwdKTeWYWj07LfsrLgqpA5ZiyM/wkjFxPVa52ft+3dXfHlLZIAanqTMN02VE6VicOuJ8bG/bGFOWHwBcNvl6fNbyEba3bcJvPvwx7jnlKRh0Rmxp/QT3rrserX1NMOkt+P70W3DGqK/F9NlYWVALtG8O6aEnftcioJkuIluwpfewck6T6KDAWDVYvdnQop/hQMTvxaS3RC1VN+pMmDJkNj5uXIONR95XBvyoiZLwZAKWkiShtmgEdnduQaNtH1p9+xgpYGnWW1BVOBTNvYdSUg4ujC/3BiyDL7h7ZA+6+zsARD4Xqy9pwBPnvJeyfYlGJ+nQYJ2IL1o+xq6OLcpF4Wxn7IkgeHtfM9weV9i+pkdSPCEc8J/XhB26Y09lD0tfBqkoCR9gGFMkxcZStLntvqSJ5GdmpAoDlhokAhb9rj4YdFkoCQ/uYekSC9D4elh6ZDf63X3KYleZEq6RE7ThpWOggy7mdH1/5mjqApZujwtu2ft7DhfI1Uk6WPSFsLt7MarsqJQGAKoKapWJYrs7tyZUbu7vm2JN2X5lUqw9LP39FYtU/Wz2R9x+oKzkSINQ8i3DUpyYOOMtCY8jwzLe18OMmhPx9v6XsbHpfVw2+XrldnWGbbwB4+Djlezif1r1cXhuy4PK18kstNXBMIenP+DreKeEuzxO2JzdMWUL9MXYHzMRRcYSmHRmODz9aO9rRm2xf0Hl9rjg8Q2Li6eHZfBVb39mWeIXD/SSHmZ9Afrdfehz2ULKfZhhmZjbb78dkiThxhtvhMlkiqkknAHLxCgBywgl4Yn0sBxaPAIGyYg+Vy+aew8pvdXCUTJPIpzIjSodj03Na3GwZzfM+gJcMP5KXDD+OzFn3lcVDsX29s1RJ8aG45bd2N+1E/XFDVEvOnxw8HX0u/swtGgkxpfHFhgVGZaNQZUc7x14DQBwfP2ZCQ8RiUZkWIqAIgDs69oBwFvSH49Sczl0kt7bt7G/LaDkWQlYZiDDEgB+dOwv49reoDPix7N/g+vfOB9b2zbgmS8eQJmpAk9+9mu4ZRfqixtw43H/T2lPEAsle9XeFHC7+DpSECxVrOYq5Zxiny/LOd1luqPKvIHEPV3b4ZbdAybgKNltBbGV486oPh4fN67Bhqb3ccH4K0O+b3Mkn2EJeN+3dnduweGefWjpFeX7kY9VXfEob8AyBeXgwriKqZAg4UjvQXTYW2H1ZeT1ODrggTdWoKVqtzFlImD5hXLhPZ0DnmJRZq6EQTLCJTvRZj8SdhK9mBAe7nuJiqUkPCUBS5Fh2RucYRlHwNJUhjb7Ec1lWLIkXIPE4rDfY1dKwjOaYakTU8KDMixjPHkrMBRB5/vTUp8Iau0ErbZoOH6/YBV+NvehmLYvSsPQHfWCP9IJtsiAmlAxI2XPC/gH7wCJl4V3ijKEHO1hKf4W+6OVhCvBtCLU+U4kBsqwjFRK531OXyAvuCRcKaXLj4ClOIkL1+csnJgzLFUZgYVxlhzPqD4egPfvXZRDAf4Tcx10cb8/BZ/MJ9vAfnzFtIDJ2sVJlIQbJKNSmhQclI+1bNukNyvHJNY+luKx4z0+sZAkScnIUZ9QA4G9aGOZTh6pL7E9Qu++ePknhQcuUmVZVjKPtfJ5mCs8Hg/cbjdMJpPydbR/nBCeGHuE8u5YpoRHev0ZdEYl62hflLLwrijDCI4ftgAGnRFnjvo6Vi74Dy6d9MO42oSIk7vWASbGRvKPbY/g2je+im+9PBe//ehGfHz47YjtT9bs+zcAb3ZlrD3RalWlu4Lb48LaQ/8FAJww7Ky49zlWSsBSnWHZ7cuwLI0vw1In6fxZ+kFtRUTpb6p7WKZSbdFwLJ35fwC8x/yxT38Bt+zCScPOxm9O+3tcwUpAXW4f+Dfn72GZ3t+FJEnKRQJxcS/dAcuhxSNh0lvgcNsHXDcD6v6VsQWLjq45EQDwRcvHYd+P/BmWyZVlq1+PrTEMSBLtuyamcO5AkbFEaWewo32zcrtozVVkLI2p3USmiD6Wuzu3oMdXEp7toTs6Sacct0h9LJvTkGGplIQHtcpze1zKuUgqhu6IDNK2viPwyB5Vq4D4MiwBf1sqrWCGpQYpJeGyHR6d9488oz0sJe8bnghY2uMsCZckCQXGYticXeh1disnl1oLWAJAna8fYSzEFaI+V2/Uq4SxUi9wI/1eCg1FaEdzSvtXCuPKp+LjxjUJD94RQ3dKNHRVLx7ibzrcUBI1pSTcUKxkWB7uidzDUpTEhgt4RBy6444vk1nr4u5h6Yq/h2W8GXyVBTUYUToO+7p24OPDa3DqyPMBqH73BkvcTbZNQSfzyZa8GHUmTK6chQ1H3oNeMsQUeItEkiQY9WY43PaQxbyY5B3LCb7VUoXenh502Ftj6v8V6wTyRJVbhqDRtj9k8E4s76dqkS5Cxdu3OZICg7e/VvCkcLfsUjIiOHSHtKpfTAkPHhw3wNCdSIN61EaUjsPeru3Y17Udxw6dF3G7jijZIWeN/ibObPhGwmsxfz+z+AKWsizjv7v/BsB7AvrWvpfw1r6XUGwsw5y602HSm9Fhb0VHfws67K1KluQpI86N+TmG+tYZR2wHlfXm5uZ16HZ0oMxcgSlVx8a1z/Hwl4R7Lwi5ZTcO+ErCR6gmhMeq3DIEbfYjIX2Q23yPL3qJatWJw87CpiNr8Z/dz8MgGXHl9Jtx9uhLExrIEa4/qFt2Kz2Z093DEvBmC6qHXqU7YKmX9BhVdhS2t23C7o6tA64h1ANPYjGsZLQyhfyLlvWYUXN8wPfFZ3syrXWAwEnh4v1ioH28ZOISHF+/IO6AdjTjKqZhf/eX2Na2CccOPRWAOkNPW625GpRJ4VuV95RsD90BvJn1Tb0HlOB4sCNp6GGplIQ7Ay9edzk6IEOGBCklvWQrLNXQQQeX7ESHvSXuoTsAlCoqZlhSVEpJuNyfpSnh3sWf0sMygWwTpd+j6mqCyCjT0hWgeKhLCvpSNHhHBBH0kiFiec+ZDRdjYuUxOHboaSl5TrVkB+8oPSw19kEZK6WHpSdKD8swJeEd/S1h0/sBf2ZKuCnJ6t6O6kEo8faK1bp4p4TblAzLgQNolgR7WApz684AADz+6a+UEyi778TclECwODjDMhX9oKZVzwHgfc9JdkphpEnhsZaEA6GDE6JJZ0k44O9jGZxhqfSF1JkG7I0nRJwSLgI1SZSEA/7fbXDAMt7AKsXO5XJhy5Yt+Oijj9DTk9oBeYONXQncB2VYKj0sQ9/b1Rd/IhGDd6JlWMZSKpfMheOqQm+GZaQT10i2tW3Ekd6DKDAU4q6TnsA5Yy6D1VyFHmcnVu99Aa/teg5rD/0XW1o/wWHbXsiQMbPm5LgukFcW1iqli6293gDJ+wdWAQDm1qWvHBwILQlvth3ythTRmQYs4Y+kXAxu6/dfYHJ6HMr6sVLDGZbC96ffgquPvh2/Oe3vOGfMooQ/l/1Zvf6AZYe9BR7ZDZ2kVz5r00kM3gEACRKKUjQUZiANvrLw3Z1bB9xOlLLGGrCUJAkzqk8AAGw4EtqjsScFQ3cAf+/Pwz37ok4JB7zr39HWiTGtQ+IhWkpsb1NnWMafRZcJw0vHwCAZYXN24csO79CxbJeEA6Fl02oe2aMMrIn1bzAWkUrCxWdcqbk8JUlQep1BuQDU0tfo7wNtif1vo8g3GElkJ2sFMyw1SAQs+qEqCddlriQ8pIdlDAvQYOLFqc5cEanQuXqCZtSbYNSZ4PQ40OvsSUnzYKcycCfy7+SC8d/BBeO/k/RzhSNKwg9270avsydqdlswpYdljpaEi+ymWEvCC43FKDaVosRkRbejA409+8M2+h5oyrB4Tg88cMsuGHwZzY5BnmEpSkaKoryuEp0SLnxtwlX44NAb2Nu1Hf/v45uw/IRHlD69wcHHWAQH5VLxvjCz9hT8+bP7Ejo5DGbSmwAn4Ag6Dn1xDMbxl/S1xvScsU4gT1RlhEnhDqUcNbasxUiD1FJfEh4UsFS932ilp3OuefXVV/Hcc8/BYDDgW9/6Fk477TS89NJLWLp0KQ4f9p5wmEwm3HDDDfi///u/LO9tborU2kR5bw83JTyG4XFi8E70gKUvwzIF01PDUTIse+PLsHx3/ysAgDl18zG9ei6mV8/Fd6f/DF+0fIxPGt+FXmeA1VwFq6VS+e/QOIKVgDcQW1NUj4M9e3DYtg8VBdWqcvCFcT1WvCpUWYCyLGO/b0J4fUlDQoFSEYRTZ8SL8nCDZEy6jUomGPUmnDX6m0k/TvDvVpIkpRy83FKVkfZfQ1UBy2JTWUaes8GXabgnSsBSlOkOKYi9HHdGzQlYvfcFbGz6HxA0lzQdJeGi+jDd/UbDEZV2O9o3K0PLEulTmAlGnQkjysZhV8cXONC9C0D2h+4A/nYD4TLr2+3NcHmc0EE3YMl/vCKVhHdGaXuSiKqCoWjta0Jz76Gow5jCUTIsGbCkaJQhNXonPMqU8MwdKpEZFdzDMp6JqeJEsLWvCa/v+Tve2PMPbGn9BEBiQQatKDSWoLO/NWSqbKKUiba67JQFWi2VGFJYh+beQ/iy43NMHTInrvv7r+xpf8EZTsxTwoPKZ4cWj0R3WwcO2/aFD1gOMGVYHbB3ehzK683fKzY/MiwHOqkNR/RLiXYFNjBgGX9AzKy34Cdz7sOy1Rfhk6Z38c8dT2C0r3Ql1j69wY8npCpbYVTZUfjlvL+k5AqveG8JKQmPJ8PSLDIsm6Ns6RVrf8xElfvajASXhMf7fuofuhNUEj5AD9p4iPuHZlh6M9P0kiGj/anzxapVq/CVr3wFRqMRBQUFePrpp/HYY4/hyiuvxKRJk/D1r38dLpcL//nPf7BixQqMHDkS3/ve97K92zkn0ueYMaYp4dEzLPd37RywvY5/Snh6TsSVfl/2IzG3+XHLbrx30JvpeNKwc5Tb9ZIeU4fMiXsNNZCaouE42LMHjbb98MhuXzl4ZVrLwQH/BaF+dx9szm4lsDw8gXJwwD8pXF0SLi42lRcMSbqKIJeIIIj3d9uFYlOZUnqf7gnhgqgSAjJXousvDx54Unhzn7cctyqOtc/0IXMBeHslqofRAP7e6MWm5DIsqwqHKhnPgHfdFG+CRyqMLBsPk84Mm7Mbh3r2YFjJaH+fwjiCUpky2joRu3zZlYBGSsIHyLAUAfOKgpqUVoMWRCgJ70jDZ1xVQS22AdjTuS2hYUzFIsPSoa0eliwJ1yBRfuMwuVQZlpksCRcZlt43Zn9JeOyBFFFq98D6n+HB9T/HltZPoIMOs2pPSVu2YCaIPhTBU2UTJTJtstnHbKzVm2WZSB/LXC8JN+vDB3OC9Qb1VxRXqCM1EB+oh586o0qdaSWy/PIlwzLeknDRLyXakJnAHpaJBcRGlI7Dd6f/DADw1Gf34fOWjwEkVo6vDnIWGUtTFoCaUDkjJVd4RVDe6YkUsIwhw9KSWIZlukvC2/uCMyzjez8Vn1NOjyMgsB5pOnK8/CXhgYtU/34yuzIR9957L6ZMmYKmpiZ0dHTgyiuvxFVXXYUzzjgDGzZswG9/+1s8+OCD+OKLL3D00Udj5cqV2d7lnCReB5GG7gQHLF0epzLIY6DPsZri4TDpzHB4+tFkOxBxO+VkLk0n4lZLFfSSAR7ZHfJeEsnnzR+h3d6MYmNZSL+8VFP65vXsU5WDn5H28wGzoUDJSGuzNyU8cEcoD5NhqfSvzNCEcK0w6y1K0EaUhYsJ4ZnoXwn4J9ADmct4E1nVbfYjyjCtYLIs+zMs4xh4YrVUYqx1MgDgg0OvB3xPZFgWGpILWIqMZyEb2ZWAd109ptz7s4qycKVPoQbPw0aXTQz4WgsBS3GhKlyGZToG7gCRS8JFpnkqJoQLItgvyvDjHcak1QxLBiw1SBkEYnbCYxIZllmYEu4JzLCMJ/NL/cZZX9yAxVNuwJ/OXoPlJzyiTIjMRUVGEbDMjwxLABhXkVgfS7fsVsp4c6GkJ5yYMyyV4I4vYOkr71JP8FTzZ2iFnrTpJb1yUUDdAyzadNVcM1AWTjgiwzLaAlo9JTyZbO0FDRfj+PoFcMlO/HXL7wEk1rNQnX2khcVYMFOEoLwIRsSXYRnflPBEMmBjURGhh2W8F4DUf0vqsvD+AVo6xCNiSbhH9HNmwDIRn3/+OS6//HJYrVYAwLXXXgu73Y7LLrssIFvLYDBg0aJF2Lp14DJECi/S55g/ez784Dhg4PWiXtJjmC/4ta9zR8Tt0l0Srpf0ylDIlhj7WL574FUAwPH1Z6a9vZHom3ewZzfW+gIxJ6ZxOriafzjMEez3ZViKzNh4KT2QVRmW7b737opBFrAE/EONRMBEBC4zFQSrLqyDznf6n4qe27EoNPoHVu7u3BZ2mx5np/KeE+/v4qThXwEAvLnvpYDbxed6Kipf1Jmpop1ENvj7WG4CACUArMVKN1G9JGijh6X32IngpNqR3oMA4guYx6LQGKmHpffYpTRg6Xvt7Or4HED8gWxlSjiH7lA0Zt8JsMPoglzgDVRmsiRcXL11+66U+0t8Yj/5vHjCNbhk4lLcO+8v+P2Zr+Gio76f0n4Q2RJpSEOi4s0ISodEMyx7HJ2Q4Q2oa+FDKBEiONgfJWDZqxq6A/gneEbKsOyLkqEVrlw6lt5fuST+Hpax9Roq0Cc+JVxNkiQsPeYuDCmsU8omEvndq7Mytfg6UALHqr81l8epHBd10C6SeDMs0z0lvELpYRl+6E6sQX+9pFde0+oG46nKdhbB7EhDd3K1n3O2NTc3o6bGv56orvb+PahvU3/Pbh/4/Z3C8w/didDDMui9XVTj6CS90ps5kpGl4wAA+7rDByxlWc5IbzalPDCGgKXL48T/Dv4HAHDS8HOibJ080Tfv48NvK+Xgk4ektxxcEOXJrX2N2O+bEJ5wSbglXEm4CFhqf+BOqgUP3hH/rSzIzO/CoDMqWViZvMiqDN7pCH8BSWRXWs1VcZ8TnTL8K9BBh62tG3C4Z59yuy1FQ3eAwMzUysLsnc+O9/WxFAFLrfawBIBRZYEts0S5cTbVFA2DQWdEl6MdW1s3BHzPn2GZugnhQOSScP9QnNQHLEXbjXgD2SJpRCSRaAUDlhokrkw7TS7A4s0WyGiGpSR6WPpKwmPoSRSstng4Lp30Q0yoPDqv+tMURuh5ligRRDBmcXL6WF95QaNtvzJEJxZi22JjWc5Ofo+UfRasL6gkXCxcDtv2hd2+P0oPPxFEcqjKdBMZbqVl4m8i3qE70RbQgRmWyQXEik1luOHYXyvZBolkt6qzjzSZYenL3u73+IM26gBaLEFFccIpSjSj6XPHnr2ZCLE/3Y6OgECsErCMI2M9uI+ly+NU+lQlPyU8QsBSAxeqcp16XZFPawwtibeHpX+taI56TES23t4IGZZ2d69S+ZDK7JNg/myb6IN3Nh35H7odHbCaqzBlyOy07ZMgMizF+9Hx9Wdm7FxAZJ5ua9sEu7sXBsmo7E+8ysP0QFZ6WA7CDEv/FHZvoFIM3clUSTjgv+ie0YClVUwKD9/HUgSLEundXVFQjenV3hYNa/b9C4B34nNvKgOWqmFF2SoJB4DxFd4My92d29Dvtms6YFloLMbQIm9FmkVfqHx2ZFOhsRjzRnwVAPDSjscDvndE+RtMT0m4S3YGrFn9fZpTXxIuxNtSpYgZlhQrsTh0mlyQC7yHKKM9LH0TycWU8Eh9jAYj/1TZ1PSwVErCs3jiWmwqUz5QvvSlkMdC9K/UYhlCrMyqknBZliNuJ66KiR6m4vfV0ns47FAZ+wAl4UD4DJX+FE0m1grxM8bSw9Ije5QMt+IopTvig1+ClJLf1aSqmVg0+XoAiZW8qfdBCxMQg4mgmLp8U5SlGHTGmLL8/FPCWwZ8nQjpzrAsMVmVgLi6L5r//TT2RXFh0Hu6yCoD4hs0F060knBmWCZuz549+OSTT/DJJ59g82ZvL68dO3Yot4l/u3fvzvKe5iZZlgcoCRfvKcEBS+/2phjel0eIDMuu8AHLTrv3JNysL0jb+wgAVBXGnmH5zn5vOfiJw87KSOCwpnBYwNeZKgcH/Nl+G5veBwDUlYxK+MK0KAnvc/Uqnz2iZ2hFhrIKtaRSGfrhDVSK0vBMDd0BvMNbgNQHZgaiZFhGmBSeSP9KtXkjvUGot/b9E7Iso89lU6rAkp0SDviDvID/GGZDdWE9ysyVcMsu7O7YkpZJ06kkysJLzNbs7ojK+eOuAACsPfjfgIzcZILmA1EnV6gnhXekIdgcHEyP9+9CnMfYNNbDklPCNShg6I4v4SejJeFScA/L+DMs81VhintYOhPICEqHcRVTcdi2FzvaP8XRNSfGdB9lQniO9q8E/Bl1MmS4PM6IgY5e0cPSl91XZq5AgaEQfa5eNNn2K/24BCXIH+HELVwQKd9eZ4Y4poT3OnuUhWW0khHxGiw0Fqcss+rrE67CCfULUFM0LPrGQdQXcjLVDyoe4aaExzMhHPCfcDo8/eh19UTNVhAB/nT1sJQkCRWWahzpPYg2ezOqfc3wHQm8hpQMS5f3PV0EXXSSPunMcWVKuDswYCmORTYvVOW6W2+9FbfeemvAbddcc03IdrIsMwMzAQ63XXlPDv4ci55hGf31JwKWB3t2w+VxhrzW0jE9NZwhvgzLligZlg53vzLQ46ThZ6d1nwSzoQAVliFoszfDaq7CpKpZGXleAKjwBc+aer1DkYaXJDZwB/B+zpj1Beh396HD3oqC4iJVSTgzLMV/M9k26xsTrsZo6yQcX39mxp5TBCwPdO2C0+MIuWAnJoQPSbA/5Ny6M/CwvhCNtn3Y1rZR+X0adaaUVDOoS8KzmWEpSRKOqpiGDw+/hW1tmzQ//HS0dRLeP7hKE+XgwojScZhZczLWN72Df+18ElfNuBWyLCs9LFNdEq7XGZT3wD6nTTlWnb42S6msIhDD5NyyN4YT79+Fkpjl6IJH9kAnaSO3kQFLDVKmugZkWGZ+6I5LDhy6k2y2ST4oSnEPSyXTJssnrmOtU/DO/pfj6mMpSsJzOcNSvYjpd9sjByx92Vciw1KSJNQWjcTuzi04HDZg6X3NRArYBGdYujxO5cMlfzIsYy8JF+XgZn1B1Oy4+uIGnD36UgwrGZ38TqrUlYxK6H7q98ViDQYsTWGCC31xDNwBvAEIi74QdncvuvrbowYsRYAu0SnusagsqMGR3oNYtfsvGF8xDTpJ5w8EJlASLt7T1RPCkw10RZoSzgzL5Dz++OPRN6KkiCoBIPYp4fFUCQwprFPeUw737AuZQC1K5dJZDg74S8KjZVh+3Pg2+lw2DCmsw1EVM9K6T2q1RSPQZm/OaDk4EJr5ODzBgTuAd71UbqlCo20/2u3NGFo8QikJr8hgVqFWiEBaS18jep09yudxJgOWpeZynDby/Iw9H+B9zRcZS2FzdmF/15chA1lafBmWwSWtsbIYCjG3/ky8te8lvLXvn1g4+hIA/iqKZKkvaGczYAkA48q9ActPmt6FxzdvQqvJIzNqjsdTn98Xcryz7fzx38H6pnfwxp5/4NJJP4QEnXIxP9UZloB3Pdjv7gsYvNORhpJwvaRX1sdA4j0sPfCgz2VLSTuFVGDAUoNEVobD5IRsFj0sM5lh6ethKTIsxSI0T3rrJaPQ4H3h9rlSVBKukV5miUwKz4eScINkhA46eODxZWeFLxvpC8qwBLz9pXZ3bgk7eEcEbMwRApYi+1Ac/1inq+YSEXiMpSTcPyE8etmOJEn4wdG3JbdzKaQ+XlrsYWkM06fVnwEZe0CxxGyFvbcX3Y4ODMXAvcxEgC6dpZwXjL8S29ZuxJt7XwQA/HDm3UoAxZRASbi4KBGtnUM8xMJPPWwCUPcuZsAyEYsXL872LuQ9Ebg36S0hgbJwQ+OA+Pow6yQdRpSOxfb2zdjXtSNMwFJMvk1v1pC/JHzgDMt3978CwFuWncmMkwUN30Cvqwfnjv12xp4TCA2ejShNPMMS8Gb9NNr2o6O/BS6PUwlID+Yelq19TcrAnSJjaVo/L7VAkiQ0lE3AZy0fYnfn1pAAlr8kPPFg0akjzsNb+17Cu/tfxdw6b/ZocYoCLma9BfNHXeStqkrxBfN4icE7nzavA+BNptBCf8hwxpVPxWNnvY1yX6WOVkwbchwayiZgd+dWvLbrLzi2dh4Ab+A3Ha/FAkMROvpblJJwu6tXSQizpnDoDuANqIuAZbwZlma9BUadCU6PAz2OTs0ELLWR50kBzOoMS18cK5M9LA3KlPDgkvD8yPxKhihHTVUPS61k2oy2ToQOOrT2NaGt70j0OwDoyoOScEmSlIzmSIN33B6X8qFSqArwiH42jbb9IfexR5kSrpSE+45/PNNVc4UhjinhormzlkpGYqXOsNTilHCTzvd5ohrwlEiPSRGM7XYMPJjLI3uiZhinwnF183HD7N9AJ+nx5t4Xcf9HNyoXFuIZniSypkW/nn6lnUPy+z7CV0a5r2snPLJHud2fWa/NEwyigT7DImVYOuJcK4qsvb1h+lhmqiRcZFh22Fsiflb1OnvwUeMaAMDJw9I/HVzt1JHn44H5/0J9SUNGnze4n2KiE8IFMQ283d6CDl8ZpF4y5PQF70SJ7LxuRwcafYMbM5ldmU0NZUcBAPaEmRSebEk4AEytnoMKSzV6nJ14e/+/AaSmf6Vw7cx7cPfJT2X0nDycceXeJBOREJDuCzvJqiqszfrvLJgkSTh//HcAAK98+TQO9ewBAFSnqa+riB+IhAHxGWfyVTClkjpLOZHPUP+kcO30sWTAUoMChu6YMz8lXLypuDyBJeH50lsvGanuYamVDMsCQ5Fy8rC9fXNM99F635RYhZvYraZO3y/wBTcA1aTwcBmWrtiG7rhEwFLV+ytf+q3FVRLuy7AsiiHDUmsMOiN0vvdnLWZYmsJlWPqu8MaVYen72bocHQNup+59l+6MkZOGn42fzvkt9JIBb+//N17a/hiAZEvCU5dhObR4JEw6M/rdfQEXNrRyoYooEn+mcehrWHxmumVXQCA+3j7MI0u9gz/CDd4RF0TTXRJeZq6AUWeCDFnJdgv20eG34HDbUVc8CqOtk9K6P1pRZqlUPtd0kh51xaOSejyrb1J4u71Z6V9ZbqnSTH+0TCoylioX1ba3edfbmRy4k02jfJPCdwVNCnd5nEqyRFUSASO9pMcpI84FALyz/2UAqSsJ15JiU2lAlmeun4dly0nDzkZlQQ3a7c34x/ZHAQBDUty/UhDrbXFO2an6jEv1eZ+6ZUEiwexiZVJ4Z8r2KVmD75MiB4hyGqfRBY/vfEaXyYClFJRh6RJlPsywTH0Py/h7rqXL+IppAPwLqGiUHpY5nGEJBE4KD0d8uBh1poCMKJFhedi2L+Q+/umq4QNCSpDU7QjYPp+ymA2qKeHRJkuLD8WSHMywlCRJ+RvSZIalPrD9ABD/0B3A/7NFW8CoJ2Jn4u/5+PoFuHHO/4NBMiqlNuY4LgCJIHlwSXgqPu/0OoM/i6xzu3K7UyMXqogiGWhwnDrQrr4gFe/n2AjfayNcwLIzDb29wpEkSTW1OXxZ+PsHVwHwntzmywXFaPSSXinhHFo0IulscPFYHfYWVcBy8JWDA96/ORFQEOvtwTItfXSZtwx8T+e2gHVha18TZMgw6kxJB99OHXEeAP97k1ZKWlNNlIUDQNkgzFROBYPOiK+M+RYAKDMc0pVhKdbbYp0qBu6kI9hcpcpSLjPF//hiXZyqWEcqMGCpQWKxJ+sAh9mb7p3stNJ4iAxLty/QkI/BlEQp/c7yrIcl4G3iDAA7Yg1Y5kEPS8AfsOyPELAUgYzg4M7Q4pEAgCO2g0q/V0Hp4RfhNRNcUpdvE8KBwJNalzxwH0tRdlCswYBfLI6pOQnVhfVJDSZIl/BTwpMpCe8YcDt/KWlhxrJnjqufj5uOe0D5nIwn2OgvCfdNCVftfyqMEiVwnduU25hhSVrXP0BbB3XwSt3H0n9xO7bPsdHWSZAg4UD3LhyxHQz4Xocv+6TMkv7MoSFKH8vQwTuyLGNr60YAwDG1J6V9X7REZP2Jie7JEMHJNnuzkkknysQHo0pfgFJUNA2WkvDhpWOhk/TodnQoGc2yLCvBoqqCoUmvG0aVHaVMJAdSWxKuJeN9ZeFA+i/s5LMFDRcHfM4NSVtJuC/DMqgkPB3HTp1hmVBJuDG2BIVMYsBSg9QnW3azdzGYyZJwg+QvCXd4+pXyvnwKpiSqyJDaknB/pk32T1yP8l2t29H+aUCZVyQiw7IkxzMsi6KkvovgtGgHIFQW1MCoM8ElO9GsOtGRZVkpK40UOFF6WIqhO1G2z0VG1UWW4OEMwcTvvjhHF5Y/nXM//rDwdU02zff3S002w9IKAOjq7xhwOxGwVA+oyoTZdafh1uP/gDlDT8fJw78S8/2KlKE7vpLwFA7dAfxlr+qAZSLTzIkyqU8ZHBf6OlD3WU4mw9JqqcSUIbMBAO8deC3ge5maEg6oJ4WHZli29jWho78FOkmvZIcNFhW+INrw0uQHjPgzLJvR7psQPlgzLAEoWb2id/JgCVia9GallPnv2x7Brz+8AZe/ehJ+ue46AEB1UWrKcef5siwBoMiU/xmWWu9hqWXFplKcMerrytfpClhGKglPR4blkCLvz1BkLEkoO75YybBkD0sagF7Sw+D2Bg37jHbltow9v2rojgikAPkVTEmUeqJsLEG9aBy+xb5BA5k2I0rHwqwvQK+rBwe7d0fdXgQsc713ivig74owTMQ/UTkwYKmTdKgtGg4AaOzx96dTB/kjBYQGQ4alQfUhGW1SuBi6k4s9LAFviVcm36PjET7DUvxNpz7DUvTHTHUT8VgcXXMCfn787+Oa4FkYHLB0pbaiQGRY7u1SlYRz6A5pnFj7hXsdS5KkmhTuf18Rn2PxVIyc5Bti8+6BVwNuz9SUcEAVsAxTEi4yv0aUjh10a+AFDV/HxMpjMG/4V5N+LKsvYNne36IELAdLGXQ4wT0rB0sPSwBK9uOru57BO/tfRru9GUadCVOHzME3Jy5NyXOcMvwr0PlCHPlaEj6q7CiYfOu7RMp+ye/csd9W/l5qioal5TkKxAwM3xq5o78FgP+9MZUayibg7NGX4luTf5TQ/efWnYlLJ/0QEyuPSfGeJU5bI5tIYXIb4dK7YDd4F4C6TE4J9109d3vcygLUqDNp9oQ8k8QHnwwZdldvSNZdvJSScA1k2uh1Bowpn4wvWj7G9rZNGF46JuK2/W477L4MjFwvCRf7L5r8BxMfLuEyxmqLRmB/95c4bNuLGTgegD/DDIgc5Bcne+L497sj9wvLVXpJD52kh0d2Rx28I4buaHFoTa4Ll2GZSBZkacwBy/jLzbOpyPce3iNKwt2p3X8RsDzcsxd2Vy8shkIlyMOScNIqpbXDAJ9hTo8jqQxLADi+/gz8YeOd+LLjcxzs3o36kgZ4ZE/AQIJ0qxqgJHxnx6cAgLHlU9K+H1ozs/YUzKw9JSWPVW72ZlN22FuVUuDBXRIeFLAsrI2wZf45beQF+KzlQ9QUDce0IXMwdcgcHFUxI6WtsSoKqnF0zYlY3/ROUlPHtcygM2JsxVR80fLxoMnQTZeaomG45pg70WjbH9BOIJWUDEtn6NCdVNNJOvzg6NsSvv/c+jMwt/6MFO5R8hiw1CiT04heUx/69H2A7C/TzgRlSrjsVPp55VMgJRkmvQV6yQC37ILN2Z10wFIEEbTQwxIAjiqfhi9aPsa2tk04fdSFEbcT2ZUGyaj0gMtVYmiQ+JmCiR6W4X7Oob5J4V+2f473DryGT5vXYfORtQAGDvIblSCSL8PSlX8ZloB3QeVwxxCwzPGScC3zTwn3H4PESsK9r5NYe1jGk72ZTZEyLCP1n42X1VKJMnMlOvtbsb9rJ8ZVTIPTl3GshVYgROFEGz5l1JsAV1BJeAKfY6XmCsyoPh7rm97BuwdexSUTl8Dm7IJHdvu+n/4LogOVhO/wZViOtQ6+gGUqiV6kbtmFvb4hS4M7YBkYoBxMGZZH15yAx89+J+3Pc92sX+DDw2/ipOHnpP25suX702/Bh4dXY3bdadnelZx3ZsPXo2+UBH9JePqH7uQjloRrlNHpzXLs03kXjXpdJqeEe5/L7XH5S1VjbKKe7yRJUoKUfSkYvKP0MtNIwHKcb1L4jvZPB9zOP3DHmvNTM8V0va4IgRgR3AkXnK71TQr/756/4d511+O1Xc/hYM8eSJBw2sgLIj5naEl4/vWwBPw/Z9SScGXoDgOWqaYELMNkWMaTRSgGIsU8dCdHApaih22vs8c3ZC71n3kiy3K3r4+lU0OtQIjCUYbuRGjtEPwZBiSWYQlACSi8u/8VyLKsnMgVGUszkoWsZFj2BmZYqoeBjBuEGZapZNSZlAoKkclaXjCYe1j6A5QGyZjzlUpaZLVU4syGr2d0aG2mjbZOxCUTl+ZdskM+8scO0j90Jx8xw1KjjA5fD0t4T/50GcywNPgyLGXIygsrVdkm+aDIWIJuR4cyVTYZYrFv0siJqxi8s6dzG/rd9ogfgvkycAfw/wydEUrCRWA6XDbapKqZkCBBhoyRpeN95S3HYcqQYweceC0yq8QwmnzsYQmEP6kNR8mwZEl4yokelupec70JZFjmb0m4N8PSJTvh8PQrAVdzCntwjiodj01H/qf0sdRSKxCicOy+9whzhNex/33F/94u/q7jDfbPqTsdRp0J+7u/xJ6ubbA5vGsra4YyT0TJaJejPWDdc6T3ILodHTBIRoxKU5ngYFJuGRLw+TG4Myz9AcuKguqkJ2MTkbaJ9XavK/0l4fmIAUuNMvV7sxz7ZO/JU2aH7vivRomgnIkBS0VBCieFi8W+VjIsqwqGotwyBO32ZnzZ/jkmVc0Mu12X0hA/9wOWSg/LKEN3CsMEd0ZbJ+LRhath0ltgtcT+oWOIlGGZbwFLX2DWledTwrXMXxKuGrrjTKAk3Gz13tdlg9PjiJj5JHrbxvPY2WQxFCoXHXqd3cprMVVTwgF/hqWYFO7i0B3SOHuU14H4201FhmWRsQQza0/BB4dex7v7X8UY6yQAmcs8KTKWwqIvhN3di9beRtSVjAIAfNnxOQBgRNk4vlZToNxchX3wloPrJP2gLoW0WqqUHt+DefgQ0WDh72HpHdrblcYp4fmIl3Q0ymj3xpJ7Pd4TS0NGh+74n0sELFkS7icycmzOFJSEe7SVaSNJEsaVTwUwcFm4CO7lwxtt1B6WIhstQr/S6qL6uIKVgP94K0N3lN5f+XVhQAzwcg5QEu6W3cpgo4GyUikxwQOegMT6TBYZS5UpiiLAHI7IzMqVDEudpFNKdWzO7pRPCQeAkWXjAXgDlrIs+1uBaOR9nyhYtNeBf0q4OmCZeKXAycPPBuAtC890qZwkSUpZeLNq8A77V6aWehpuuaVqUGcV6iU9KizekvjB1L+SaLBSl4T3ODrggQdAfpxHZ8Lg/bTQMFmWYbB7D40H3sbjmSwJ16uCoyKLMN8CKckQbzqpybDUVg9LABjvKwvf3rYp4jYiuFeaByXh4sOiO0KGpRi6k8qMMSXz0BNUEp5nFwbCZeEEszm6lP8XFwModcJNCfcP3Yl9YJZO0qHI12N0oLLwRLI3s61QuQjVnfIp4QAwvHQsdNCh29GBdnuz8npg1hZpVbTXQSp7WALArNp5sOgL0dR7AB8fXgPA3186E8IN3hH9KwfjhPB0KLcMCfv/g1WFryycE56J8p9/6I5NuShXYrIGxFwoMgYsNUh2uGGyB/4BZ7QkXBUcFZlP7GHpV6Q6uU2W0stMQwHLo5SA5eaI23TmYUm4zdkdNrA20NCdRAVnvYkTvXxrveAfuhM5YCmy9QoMRXndHD1bwpWEJzoYR/Sx7OrviLhNXxoCfulWqGrzkeop4YA342xo8UgA3ixLETzOxEARokRE60Vr1Ivs+TBTwhO48GYxFCqTbtc3eScIZ3IYQfDgHQ7cSb1yVYZlBQOWqC6sAwAM8f2XiPKX0sPS2YMO32A59q+MHQOWGuTpdcLoDAxYZrIkXJIk6HwBUn9JeH4FUpIhsnF6U1ASrrWhO4A3m0CChKbeAxEH0ShTwk25n8quLnXtDhOI6R1g6E6ijErWW34P3REByIFKwv0TwlkOng7KcAxfkMwjexLuMymGIg1UEu7vR5o72bIiczSgJDzFn3mij+Xeru1wur2vB620AiEK1h8lcB/chxlQX3hL7HPspGFnB3yd0YBlUIZlo20/bM4uGHRGjCgbl7H9yGfqrMrBPHBH+PpRP8BXxnwL80Z8Ndu7QkRpJpJenB4HWu1NADghPB4MWGqQx+aAyREYoMxkSTjg72MppjXmWyAlGUWqfmfJcmiwJLzIWIJhJaMBRC4LF+XT+ZBhqZN0ykCRcIN3+nyB6cI4ymejCS6n609D3zwtiCfDkgN30sOfYek9BiK7EgAKjPEFLEtEhmWE9gkA0GZvBgCU59AJaZHBfxHKP3QntRmi6sE7/pJwZhSTNtmjvA5MYaaEJ3vh7Ziak1Ck+hywWjJ3QbSqIDDDUmRXNpRNYCZ0igSWhOfO50O6NFgn4PszbmEPO6JBQJ0g0NizHwD7V8aDAUsN8vQ6YewPPJHR6zJXEu59Pt/QH5c3KMeScL9CcXLrSkEPS42WBo6rmAYgclm4KAnNhx6WgD9TNFxGaTpKwoNP9vK2h2WYwQzBmGGZXiJg6ZZdcHtcyt+zDrq4M/xEwHKgHpYd9hYAgeV/Whc4dMdXCpvizzz/4J3tHLpDmideBxGH7uhDL0Y5kuhhKR5zbv0ZyteZzD4ZUhiYYbnTN3SQ/StTJ6AknJOxiWgQMeiMyjnRYdteAMywjAcDlhrksYWWhOsznWHpK+UUWYQmloQrUlkSrsUelgAwvnzggKUI7OXL1SGRKRpu8E56hu4E9v9KZliBlsVUEu4LfhUbGbBMB3VQ0uHp9w/cMRZBkqS4Hqs0SsBSlmW0+zIsrTkUsCxS3tO7lcyydJWE7+/eqZTkc+gOaZX4TCqIMnRHrGFkWfb3sEyiIuekYeco/5+dknBfhmXH5wAYsEwlq5k9LIlo8BIXx0WGpdXCgGWsGLDUIHevM6QkPNNTpESAVARrWBLuV5SigKUsy/7SQI1l2iiDd9o3wyN7Ar4ny7K/h2UelIQD/kxRMf1cTfSwTO3QncBBKPnawzJcFk4wf4YlS8LTwaAKijndqoBlAgF40TohUsDS5uxS3tNyaQqsuAjV7eiEw/datOhTWxJeXViPAkMhXB4nmnsPAWAPS9IuMXQnUuA+uK2JS3bCA8+A94nFtCFzMLxkDKzmKtQU1if8OPESk5ptzm7YnN34st0XsLQyYJkqxaYyGCTvRUz2sCSiwUasu0WGJYfuxI6z1DXI0+uEMThgmcEp4YB/yE+vGLqTZ5lfySj09X1LtoelS5V1prUMy5Fl42HSmWFzduFwz17UlzQo37M5u+GR3QD8JaK5rswXeO0MClg63Q7lOBWksoelfrD0sIwlw9IXsGQPy7TQS3oYJCNcshMOtyPhCeGAPws2UsCy3VcOXmQsyangu7gI1dHfotxmSXGGpU7SYWTpUdjatkG5jRmWpEUe2RM1cB88dEdkVwLJXXjT6wz41al/hQw5o8MeC43FKDKWwObsxqdH1qHX1QOTzozhpWMytg/5TifpcNrI87GncxtGlHKQERENLiJg6a9SZMAyVsyw1KBwQ3cyXRIePCU81SdvuaxQVT6YDIevfyWgvRNXg86I0eWTAIQO3unyvdEWGAo1F2hNlMgUDR4m0ufyZ9FGKo1LhD87JSjDMs96WBpiGLojgl/FeRL81iLx/uLw9KPPmXiGZamSYRl+Sni7MnAnd7IrAX/Asq3viHJbopOOByL6WAoGjfUuJgL85eBA5LVfyEU33330kkFpBZIoETzMNFEWvvbQ6wCABuvEpH8WCrR05v/h16f9TXNrXiKidCsIqtTLl7ZqmcCApQZpI8PSu0jzl4QzYCkoJeGu5ErCRTmwBEkpk9GSo8pFWfinAbcr5eCm/HmjLYlQEt7rC+6Y9QUpbcsgAr3+oTv5nmEZOWBpY0l42vn/3vrR5048w1K8TiJnWIqAZe70rwT8F6Ha7N6ApUlvgU5K/fJI9LEUWBJOWmTSmXHvvL/gzhMfjxi4FxfdXMGD43IoszqYmBT+0eG3ALB/JRERpU5hUKIAS8Jjx5JwDfL0OmFyBE8Jz04PSzEcIJcXoakmpoTbnN2QZTnuwRWCU0yK1ZsTfox08k8KD86wzK/+lcBAGZapnxAOhCmny4OTvXD8U8JjKQnn0J10EUGHZDMso00JFyXhVnOuZVh6X98iYJnqCeFCcMCSWUakRXqdARMqjx5wG1OEDMtMlnGnWpVvUniP05tBzoAlERGlSvC6u4xDd2LGDEsN8tgcYTIsMz0lPPD5WBLuJ4JXHtkdUDoVL1ESrtUsm/G+gOXujq1KNigAdDq8JeFiUE0+KPNli4pyd0Fk0aZyQjjgP+ZOtyOgX1i+ZViKTO2Bh+54Tw6ZYZk+JiVwnOTQHRGw7O+ALMsh32/3BfxybQKsyLAU/WoTyT6Nxcigvm1GloRTjlKmhAf1sMzli24iw1IYx4AlERGliHoWgkFnRGEKZyPkOwYsNcgdriRcl9mS8OCMznwLpCSjwFAEne+lEzx4p6W3EY9v/iV2tn8W9XFEObBJo1k2NYXDUGaugEt24o73v4u/bHkIm46sVSbcluZR741IGZaiJUJwGn+ylOnZsjMg6J1vPSz9fc4GyrAUAUtmWKaLUS+m0vuH7iTSk7XUF7B0yU4l8KnW7htaY82xkvDgfnnp+rwrNpUpffIAZlhS7lKqBILamqSj92umiAxLwPseUF8yOot7Q0RE+aTA6D+XtJqrNFldqVUsCdcgLQzdCe6pmMtXzVNNkiQUGIthc3ah19mDyoIa5Xt/3/YIXt31DP654wmcN+5yXDrp2oglUiLD0qjRDEtJkjC37gys2v08Pm3+EJ82fxjw/bwqCVf1sFSX+SvZaCkuCVdnVomSaCC3T/bCCS59D4cl4eknMnodbntSGZYmvQVGnQlOjwPdjo6QVgmiJDzXMiyLgibUp7OiYFTZeLT0HdZs72KiWBiD3tsdedDWRJ1hOcY6KeO944mIKH+p193WPEr6yQRNZlg+9NBDGDVqFCwWC+bMmYMPP/ww+p0A/OUvf4EkSTj//PMDbpdlGcuXL8fQoUNRUFCA+fPnY8eOHWnY89QIO3Qn0z0sgzMsWRIeINLgnc9avH+rHnjw4o7H8MM3zsWmI2vDPoa/h6V2s2yuPvoO3H/6S7hqxnKcPPwrAdlBo0rHD3DP3CImtTk9joDMMZFhmeqScPUxFyXRRp0p706QjEpJePgMS5fHqfTJZUl4+igZlh5VSbgx/r9pSZKUsvCeMJPCxdAda44FLIMDr+kMWI709bE06ky8uk45S6kSCO5hmcPVOENU65sx5ZOzuCdERJRv1CXgZRy4ExfNBSyff/55LFu2DLfddhs++eQTTJ8+HQsWLMCRI0cGvN+ePXvw4x//GCeddFLI9+6991488MADWLlyJdatW4eioiIsWLAAdrs9XT9GUsIO3clwICP4+XJ5EZoO4gS3V1US3tXfhn1d3kD49bN+iaqCWjTa9uPWdy/H//v45oBMOsA/JVyrGZaAN0Ax2joR54xZhB/P/g0eO3sN/nTWW/jVqX/FvJHnZXv3UsZsKFCyG9Vl4WkbuqPKrBKBn3x8jUXLsBSvCQmS0keQUs/fM7Ufdt/ftEWfWBBeBCy7wgzeydUp4RZ9IXSqzzyzPj09LAH/hR4tX6giisYYUhLuXc/kcoZlZaE/w5IDd4iIKJXUiQIcuBMfzQUs77vvPnzve9/DFVdcgUmTJmHlypUoLCzEY489FvE+brcbixYtwh133IHRowN7zsiyjPvvvx+33HILzjvvPEybNg1//vOfcejQIbz00kthH6+/vx9dXV0B/zJJC0N32MNyYOIqibqH5ect6wEAw0vG4rSR5+N3Z7yCs0cvggQJq/e+gEc33R3wGMrQHb12A5bhDCmsw1EV0/MuG1CUhXf3qwOWoodlagOWkiQpQSQRtMu3/pVA6EltsB5f0KvQWJJ3f09aIoJjDlUGcSIZlgBQarYCCJ0ULsrEgdwrCZckKeA1ns4My3EV0yBBgtWcW0FdIjWxblGmhLtyf0q4WW9BTeEw6KDDxIpjsr07RESUR9TVemUsCY+LpgKWDocD69evx/z585XbdDod5s+fj7Vrw5fVAsCdd96J6upqXHnllSHf2717NxobGwMes6ysDHPmzIn4mCtWrEBZWZnyb/jw4Un8VPHz9Lqg9+igV7UYzXRJeEgPyzwMpiRDZIOJkmHAXw4+Zcixvm2K8YOjl+Mnc34LAPi0+YOAxxClVFodujPYhBu8k66ScMAfRBIl4fl4UcAQpSS8xyn6V7IcPJ2U4IK7H32+oTuJTsIuEYH9oIBlh70VgPfiWrEvCzOXqLOo0/larCseidtP/BNunvu7tD0HUbr5p4R7L7z250EPSwBYfsIjuPOkx1FbnNl1PxER5Tf1hXErS8LjoqmAZUtLC9xuN2pqagJur6mpQWNjY9j7vPfee/jTn/6ERx99NOz3xf3iecybb74ZnZ2dyr/9+/fH+6MkxdPrDWSZJf/CL+Ml4UEB0nwbBpKsImNohuVnzR8BACZXHRuw7TE13jYFLX2N6FJl7zl8WWdaLgkfTMpM3qtdnf2hJeGpHroD+I+7vyQ8/15j/inhA5eEc0J4eon3b4fb38My0cn3oiS8WxXYB4AOVTm4TtLU0iIm6knhiQZzY3V0zQkYUTo2rc9BlE6RpoTn+ufY8NIxmFZ9XLZ3g4iI8kzglHAGLOORe2cVKt3d3fjWt76FRx99FFVVqSuvMpvNKC0tDfiXSW6bNxvJLPkDWRmfEq4KWJp0ZpZrBvFnWHoDlj2OLuzp3AoAmBIUsCw0FqO2yHu1frdvG8DfwzLXSsLzlSh1VQeVRYZlqkvCAVWGpSgJz8MMy+BJssG6nR0AGLBMN5MqG6rPmfiUcMB/rLr7OwJub1MG7uRmqbN6UrglD1+LRKkk3tv9Q3dEhiVfO0RERME4dCdxmY2CRVFVVQW9Xo+mpqaA25uamlBbWxuy/Zdffok9e/bg3HPPVW7zeDwAAIPBgG3btin3a2pqwtCh/gmATU1NmDFjRhp+iuR5en0BS30B4P1xoNdleuiO/08jl3sSpUtwwHJL63rIkFFXPAoVBdUh248qm4BG237s7tiK6dVzAQBOj/aH7gwmoodlQEm4r4dlov3+BiKCSKK0NtczU8IRJeGRApY2B0vCM0GZEu7uV6ayJ5pFWCoyLJ2BU8Lb7S0AgHJzbvWvFAJKwvmZRzQgf/a8d73qUHpY5t/nGBERUbICelhy6E5cNJVhaTKZMHPmTKxevVq5zePxYPXq1Zg7d27I9hMmTMCnn36KjRs3Kv+++tWv4tRTT8XGjRsxfPhwNDQ0oLa2NuAxu7q6sG7durCPqQVKSbjqpCmbQ3fyMZCSLFE+aPMFtD5r9vWvDMquFEZbJwBghqWWlYgelv1tym3+8tnUZ1iKkjqbM/+H7kTuYekNejHDMr3UU8KTzbBUSsKDMiw7+kVJeG4GLANKwtM4JZwoHwRnz4sMS7YPIiIiCsWS8MRpKsMSAJYtW4bFixdj1qxZmD17Nu6//37YbDZcccUVAIBvf/vbqK+vx4oVK2CxWDBlypSA+1utVgAIuP3666/H//3f/2HcuHFoaGjArbfeirq6Opx//vmZ+rFiJssyPL6ScHUGTOaH7qgDlsw2CSYCWCLD8vOWjwEAk6tmhd1+VJk3YLlHFbD0Z1hy6I4WiIlt6gxLEdwpTEcPS73IsMzfoTvRp4QzwzITRIZlv9sOu2/oTtIBy6ChO219/h6WuagwoIdl/r0WiVLJFLGHJV87REREwYqNpajwXdTnlPD4aC5gefHFF6O5uRnLly9HY2MjZsyYgVWrVilDc/bt2wedLr7E0J/+9Kew2Wz4/ve/j46ODpx44olYtWoVLBbtXQmW7S7AIwMALCZVwDKLQ3dYHheqSDUlvNfZg50dnwMApgyZHXb7Bl/Acn/Xl3B6HDDqTMrQHWZYaoNSEt4fpiQ8DVPCRdabyDLMx8yUaCXhIljLDMv0Eu8xNmcXPL4+I4lPCbcCALqCp4T3+0rCczbDUj0lPP9ei0SpZNDn55RwIiKidNDrDHjwjJcB+M+PKDaaKgkXli5dir1796K/vx/r1q3DnDlzlO+tWbMGTzzxRMT7PvHEE3jppZcCbpMkCXfeeScaGxtht9vxxhtvYPz48Wna++R4+vylkxaj/4RSl+GAZWCGJRegwdQ9LLe2boBHdqO6sB5DCuvCbl9dWI8iYwlcshMHunYB8JZnAv7sJ8quUnNoD0slwzINAUuRfZjXQ3f0A5eE21gSnhHib61T1e4g2YBlcIal0sMyZwOW/ixfc5qnhFPueOihhzBq1ChYLBbMmTMHH3744YDbd3R0YMmSJRg6dCjMZjPGjx+PV199Vfm+2+3GrbfeioaGBhQUFGDMmDG46667IMtyun+UlFLafbiDh+5wvUhERBROicmqrKMpdprLsBzsRDm4ZDbAYvQHMDIdiderni8fAynJEiXCNmePUg4eKbsS8AbNR5VNwOctH2FX5xY0WCcomQksCdcGkZ4vgjqyLKuG7qSjJNyXYSlKwvO4h2WkDEuWhGeGCCKIYLxFXwidlNj1SrHQsjm74Pa4lGz8dnselYTzM48APP/881i2bBlWrlyJOXPm4P7778eCBQuwbds2VFeHDtdzOBw444wzUF1djb///e+or6/H3r17lVZFAPDLX/4SDz/8MJ588klMnjwZH3/8Ma644gqUlZXh2muvzeBPlxzx3u6BB26Pyx+wZEUOERERpRADlhrjtnlP7HVFxoBAYTZLwtnPK5QIWPa6uvFZy8ADd4TRvoDlno6twEgO3dGaEl9JeI+jE27ZDZfbAY/sBpCeknBj8NCdPAySiAstrkgBS9/PzgzL9BLBcTFQKpmp9yWqY2VzdqHUXAFZllUBy1zNsPRflOBnHgHAfffdh+9973tKD/WVK1filVdewWOPPYabbropZPvHHnsMbW1t+N///gej0fveN2rUqIBt/ve//+G8887DOeeco3z/ueeei5q5qTUiex7wXpBSpoQzw5KIiIhSSJMl4YOZp88FANAXGQOuVGe6JFwdIOUCNFSRwZuN0+Powo62TwEAk6MELEf5JoXv6twCwB/EYYalNohAjAwZNkcnen0TwiVICZfPDsTkO+GT4S0FzMfXmT/DMsKUcF9ZMQOW6SX6pXb5JnsnE4DX6wxKD1/Rx9Lm7FKyaHM1YFlo8GdYsiScHA4H1q9fj/nz5yu36XQ6zJ8/H2vXrg17n3/961+YO3culixZgpqaGkyZMgX33HMP3G63ss3xxx+P1atXY/v27QCATZs24b333sNZZ50V9jH7+/vR1dUV8E8L1OsWp8ehKglnsJ+IiIhShxmWGuMRGZYFRlj03pMmvWSAJEkZ3Q+DjlPCByLKB92yN8BcWVCD2qLhA96nQZkUvg2yLHPojsYYdEYUGUthc3ahs79dCdpbDImXzw78fIGB6nx8nQX3OQvGkvDMUHqJyt7AcbIB+BKTFTZnt9LHUvSvLDKW5uz7GUvCSa2lpQVut1sZ+CjU1NRg69atYe+za9cuvPnmm1i0aBFeffVV7Ny5E9dccw2cTiduu+02AMBNN92Erq4uTJgwAXq9Hm63G3fffTcWLVoU9jFXrFiBO+64I7U/XAroJQMkSJAhw+l2sIclERERpQUzLDVG9LDUFZmUnnaZLgf3PqeqhyXL40IEl1ROqTo2alB5ROk46CQ9uh0daO1rgtPXw1JkP1H2iT6WXY529PkyLAvT0L8SCM2szcselvrAPmdq/W670seVGZbpFfwek2yLA2Xwji9jM9f7VwJQskaB5AO6NDh5PB5UV1fjkUcewcyZM3HxxRfj5z//OVauXKls89e//hXPPPMMnn32WXzyySd48skn8etf/xpPPvlk2Me8+eab0dnZqfzbv39/pn6cAUmSpLSacHj60e/2lYRzvUhEREQpxAxLjfH0hvaw1OsyH7BkhuXA9JIeBYYiJag1uSrywB3BpDdjWMlo7Ovagd2dW5QelpwSrh2lpnIcwh509bfDY/KW8RUa0hOwDM5Ey8fXmXpYmMvjDOiNK4YN6SR92n7H5BX8t1aQZEBOBJj9GZb5FbDMx9cixaeqqgp6vR5NTU0Btzc1NaG2tjbsfYYOHQqj0Qi93r9mmzhxIhobG+FwOGAymfCTn/wEN910Ey655BIAwNSpU7F3716sWLECixcvDnlMs9kMs1mbawSjzgSH2w6X24F+lzfD0sQMSyIiIkohZlhqjKfXl2FZaFJOmnRS5uPKekkdsOQCNBz1Ce6Uqlkx3UeUhe/u3KpklzHDUjtKzVYA3knhvU7fhPA0DNwBwmRY5uHrLLjPmZoYNlRkLMl4y4vBJviiSLJ/06W+DMuuoJLwckvo5ORcUWouh9VchcqCmqSGElF+MJlMmDlzJlavXq3c5vF4sHr1asydOzfsfU444QTs3LkTHo9HuW379u0YOnQoTCbve2Fvby90usClt16vD7hPrlD3KFYyLPPwc4yIiIiyhwFLjVFKwguNyqRSdbZjpjDDMjrR88xqrkJ9yeiY7iMClrs6tsDp6+unnrZJ2VUapiS8IF0l4YMgw1L0OQNCA5Yiw7LYyHLwdAvNsEy2JLwcgP8Ytvf7MizNuZthadAZ8bszXsYD8/+VlTYspD3Lli3Do48+iieffBJbtmzB1VdfDZvNpkwN//a3v42bb75Z2f7qq69GW1sbrrvuOmzfvh2vvPIK7rnnHixZskTZ5txzz8Xdd9+NV155BXv27MGLL76I++67DxdccEHGf75kibVLr6tHNTwu/z7HiIiIKHtYEq4x7oCScO9iMBsnT+qsTgt7EoUlehtOrpoVc4ZYg9U/eEdksebqkIp8VOoLxHT3tyuDYNJVrjwYelhKkgSDzginxwFX0KRwZeCOiQN30i04izsVQ3cAoNvRDkBdEp6bE8KFUnN5tneBNOTiiy9Gc3Mzli9fjsbGRsyYMQOrVq1SBvHs27cvIFty+PDh+M9//oMf/ehHmDZtGurr63HdddfhxhtvVLZ58MEHceutt+Kaa67BkSNHUFdXh6uuugrLly/P+M+XLPEZJi5cAFzPEBERUWoxYKkxoiRcX2iExeDt/5aNkvDADMv8C6SkgtVcCQCYOmROzPcZ5cuwPNyzV8nmM7IkXDNEwLLL0Q6ry3t801UeOhh6WALek1qnx6FkFAvdTl+GJQfupF3qMyytAMKVhOduhiVROEuXLsXSpUvDfm/NmjUht82dOxcffPBBxMcrKSnB/fffj/vvvz9Fe5g9SsDS197DIBkD+hYTERERJYsBS41Rl4QX+DK7snHFWj0cg1Mfw7ts8vUYbZ2I00ddGPN9yi1VKLcMQbu9GZ39rQBYEq4lIsOqs7/NPyU8bRmWgSd2+XphwKg3AS6WhGdT8EWRpAOWvl6vwUN3rDmeYUlEsRNrF/Feno9VAkRERJRdDFhqjH9KuAnjyqdgQcPFmBzjQJdUMkj+YEq+BlKSNaJ0HEaUjov7fg1lE5QTfIBDd7SkTNXDstfpC1imKcPSEDJ0Jz8vDIiMm+CScDF0hyXh6Rd80SvpknBj+CnhFQxYEg0awSXhXCsSERFRqjFgqTH+KeFG6HUGLDnmzqzsR0CGpT65k1sK1FA2AZ80vat8zQxL7RDDRLr629HrSu+U8JCS8DzNThHZfX2u3oDbRQ/LEpaEp13we0yybQ7UGZZOj0MJXLIknGjwCC4JN+XpRTciIiLKHk4J1xilJLwou0Esg6QuCc/PQEq2jPIN3hGYYakdoiS8q78dfb4My4JMDd3J05O9UWVHAQA2N68NuF0EuYpYEp52BsmoTGsHgAJ9anpYdjs60WFvVZ6j2Hc7EeU/ZlgSERFRujFgqTFuVYZlNul1/snk+RpIyZbRZRMDvuZUTe0QQ3fs7l509HsHiYhp8KmmPu4SpJAAZr6YW38GAOB/B/8bcDtLwjNHkiQYVX9vyWZYiteJw21Hk+0AAMBqqYRO4pKCaLAQmdvivZz9zomIiCjVeHahMR6br4dltgOWqh6WFgYsU6queGRAVqWRAUvNKDKWQO/LLhaBmHSVhKt7WJr1BZAkaYCtc9extadCLxmwr2sHDnTvUm4XJeEcupMZ6uynZP+mCwxFyutkX9cOAICV5eBEg0pwSbiZaxkiIiJKMQYsNUb0sNRnuyScU8LTRq8zYGTZeOXrfM2sy0WSJCll4W32IwAyk2GZz20Xik2lmFZ9HABg7cHXldtFSTh7WGaG+n0m2aE7kiSh2Hfc9nXtBMCBO0SDjbjYanOIgCXXikRERJRaDFhqjEczJeHqoTv5G0zJloYybx9Lg87IMkqNEeWuQmEGeljm+4ne3LozAQBrD/nLwkVWThFLwjNCHSBPRdZwqa9fpZJhaWbAkmgwEZ9h3b4eliauFYmIiCjFGCnRGK2UhKuH7nARmnqjfAFLDtzRHpFhKaSrJDwwYJnfr7Hj6uZDgoSd7Z+hufcQZFlWsnJYEp4ZRl1qA5YlQQHLigIGLIkGE39JuBi6k98X3oiIiCjzGLDUEFmW/RmWWS4J1+u8AVOT3sIMwDQY7ZsUbmG5veYEZ1gWpKkkXAwsAPI/YGm1VGJS1SwA3uE7/e4+uGTvex2H7mRGYIZlciXhgD9g2eVoBwBYzexhSTSYGH3rRJfH+16e759jRERElHmMRGmIbHcBsvf/dUXZzbAUGTjFxpKs7ke+mlB5DL4y5lu4dNJ12d4VChKcYZmuHpbqDEvTIAhcz63zTgtfe+h1pYTQIBlh0ScfPKPoRIDcpDMHtPxIVInZGvB1OXtYEg0q6otuAPudExERUeolf9ZCKeP2lYMDgK4guwHLocUjcPmUn6C+pCGr+5GvdJIO359xS7Z3g8JQZ1jqJH3ayvYDhu4MgsyUufVn4o+b78GWlvXKtPAiU2neTkfXGvF3nOzAHUFkWAoMWBINLoaggYGD4XOMiIiIMosBSw3x2Hzl4AUGSLrsn8RfeNR3s70LRBlXZq5Q/r/QUJy2gNpgGroDAEMKh2Jc+VTsaP8Uq/e+AAAoNrIcPFNEL+JU9WQNDViyJJxoMDGGBCzz/3OMiIiIMosl4Rqilf6VRINZiaokPF3l4ABgHGQZloA3yxIA1h70TgsPDnpR+oiM3gJjugKWzLAkGkzUVQLA4PkcIyIiosxhwFJDlIBllieEEw1m6pLwdE0IBwZfhiXg72Pp9HjbX3DgTuaIv7d0lIQXGUtDghdElN9CMizZw5KIiIhSjAFLDfH4elgyYEmUPeqhO6nKRgtHJ+mgl7xdOcyGwZGZUl/SgJGl45Wvi1gSnjFKhmWKgvClqoAly8GJBp/ggCUvWhAREVGqMWCpISwJJ8q+MlNgD8t0MvmmrA6WDEsAmFt/hvL/xaayLO7J4GLUpTZgWRIQsGQ5ONFgYwieEj6IPseIiIgoMxiw1BCWhBNlX4nZqvx/OkvCAf+U1cHU+0v0sQSAEgYsMybVGZYMWBINbqFDdwbP5xgRERFlBgOWGuJmSThR1pn1Flj03j5/6Ry6A/iDSIMpM2VU6VGoLRoBgCXhmSRaHVhTVL4dELA0syScaLDhlHAiIiJKN0O2d4D8RIalniXhRFlVai6Hvbc37RmW4oRvsPSwBABJkrBo8nX429aVmDP09GzvzqCxsOFiWPSFOHHYWSl5PKPeBIu+EHZ3LzMsiQahkCnhg+hzjIiIiDKDAUsNYUk4kTaUmstxpPdg2jMsRV/BwZaZcsrwr+CU4V/J9m4MKsWmMnxl7GUpfcwSsxX23l4O3SEahIy6wLXqYPscIyIiovRjSbiGcEo4kTaUmrzlswVpHrpj1A++HpaUP2qLhgEA6koasrwnRJRp7GFJRERE6cYMSw3x2MSUcAYsibJp6pA52HzkA4yvmJbW5xlZOg57OrZieOnYtD4PUTpcN/MX2Nu1HePL0/s6ISLt4ZRwIiIiSjcGLDXE0ydKwtnDkiibLjrqezh37LdDenSl2rUz78HlU3/KklrKSdVF9aguqs/2bhBRFoRkWLKHJREREaUYS8I1hBmWRNqR7mAlAOh1BgYriYgo5wQHLE0sCSciIqIUY8BSQ9zsYUlEREREGqe+qGfQGaGX9FncGyIiIspHDFhqiCgJ17MknIiIiIg0Sp1hyf6VRERElA4MWGoIS8KJiIiISOsCA5YsByciIqLUY8BSQzwsCSciIiIijVNPCWeGJREREaUDA5Ya4ukVGZYsCSciIiIibdJLeuh8fSs5IZyIiIjSgQFLDVEClsywJCIiIiINE2XhzLAkIiKidGDAUiNkj8yAJRERERHlBH/AkhmWRERElHqaDFg+9NBDGDVqFCwWC+bMmYMPP/ww4rYvvPACZs2aBavViqKiIsyYMQNPPfVUwDaXX345JEkK+Ldw4cJ0/xhxERPCAQ7dISIiIiJtM+mZYUlERETpY8j2DgR7/vnnsWzZMqxcuRJz5szB/fffjwULFmDbtm2orq4O2b6iogI///nPMWHCBJhMJrz88su44oorUF1djQULFijbLVy4EI8//rjytdlszsjPEyuRXQkJ0FkYsCQiIiIi7TKIDEv2sCQiIqI00FyG5X333Yfvfe97uOKKKzBp0iSsXLkShYWFeOyxx8JuP2/ePFxwwQWYOHEixowZg+uuuw7Tpk3De++9F7Cd2WxGbW2t8q+8vDwTP07MPDZ/Obikk7K8N0REREREkYmScBNLwomIiCgNNBWwdDgcWL9+PebPn6/cptPpMH/+fKxduzbq/WVZxurVq7Ft2zacfPLJAd9bs2YNqqurcdRRR+Hqq69Ga2trxMfp7+9HV1dXwL908/Q6ALB/JRERERFpn1HPHpZERESUPpoqCW9paYHb7UZNTU3A7TU1Ndi6dWvE+3V2dqK+vh79/f3Q6/X4/e9/jzPOOEP5/sKFC3HhhReioaEBX375JX72s5/hrLPOwtq1a6HX60Meb8WKFbjjjjtS94PFwD9wx5TR5yUiIiIiihenhBMREVE6aSpgmaiSkhJs3LgRPT09WL16NZYtW4bRo0dj3rx5AIBLLrlE2Xbq1KmYNm0axowZgzVr1uD0008Pebybb74Zy5YtU77u6urC8OHD0/ozKCXhHLhDRERERBrHKeFERESUTpoKWFZVVUGv16OpqSng9qamJtTW1ka8n06nw9ixYwEAM2bMwJYtW7BixQolYBls9OjRqKqqws6dO8MGLM1mc8aH8rhZEk5EREREOcKo966VzQZmWBIREVHqaaqHpclkwsyZM7F69WrlNo/Hg9WrV2Pu3LkxP47H40F/f3/E7x84cACtra0YOnRoUvubSv6hOywJJyIiIiJtY4YlERERpZOmMiwBYNmyZVi8eDFmzZqF2bNn4/7774fNZsMVV1wBAPj2t7+N+vp6rFixAoC33+SsWbMwZswY9Pf349VXX8VTTz2Fhx9+GADQ09ODO+64AxdddBFqa2vx5Zdf4qc//SnGjh2LBQsWZO3nDCZ6WOpZEk5EREREGldXPNL331HZ3REiIiLKS5oLWF588cVobm7G8uXL0djYiBkzZmDVqlXKIJ59+/ZBp/MnhtpsNlxzzTU4cOAACgoKMGHCBDz99NO4+OKLAQB6vR6bN2/Gk08+iY6ODtTV1eHMM8/EXXfdlfGy74GUnTkW5jEV0JdpZ5+IiIiIiMK5fOpPsLDhYtSXjM72rhAREVEekmRZlrO9E1rX1dWFsrIydHZ2orS0NNu7Q0RERBQ3rmdyG48fERER5bp41jOa6mFJREREREREREREgxsDlkRERERERERERKQZDFgSERERERERERGRZjBgSURERERERERERJrBgCURERERERERERFpBgOWREREREREREREpBkMWBIREREREREREZFmMGBJREREREREREREmsGAJREREREREREREWkGA5ZERERERERERESkGQxYEhERERERERERkWYwYElERERERERERESawYAlERERERERERERaQYDlkRERERERERERKQZhmzvQC6QZRkA0NXVleU9ISIiIkqMWMeIdQ3lFq5HiYiIKNfFsx5lwDIG3d3dAIDhw4dneU+IiIiIktPd3Y2ysrJs7wbFietRIiIiyhexrEclmZfZo/J4PDh06BBKSkogSVLanqerqwvDhw/H/v37UVpamrbnofjx2Ggbj4928dhoG4+PdqXj2MiyjO7ubtTV1UGnY1egXMP1KAE8PlrGY6NtPD7axWOjbak+PvGsR5lhGQOdTodhw4Zl7PlKS0v5QtUoHhtt4/HRLh4bbePx0a5UHxtmVuYurkdJjcdHu3hstI3HR7t4bLQtlccn1vUoL68TERERERERERGRZjBgSURERERERERERJrBgKWGmM1m3HbbbTCbzdneFQrCY6NtPD7axWOjbTw+2sVjQ9nCvz1t4/HRLh4bbePx0S4eG23L5vHh0B0iIiIiIiIiIiLSDGZYEhERERERERERkWYwYElERERERERERESawYAlERERERERERERaQYDlkRERERERERERKQZDFhqxEMPPYRRo0bBYrFgzpw5+PDDD7O9S4POihUrcOyxx6KkpATV1dU4//zzsW3btoBt7HY7lixZgsrKShQXF+Oiiy5CU1NTlvZ4cPvFL34BSZJw/fXXK7fx+GTPwYMHcdlll6GyshIFBQWYOnUqPv74Y+X7sixj+fLlGDp0KAoKCjB//nzs2LEji3s8eLjdbtx6661oaGhAQUEBxowZg7vuugvqmXs8Ppnzzjvv4Nxzz0VdXR0kScJLL70U8P1YjkVbWxsWLVqE0tJSWK1WXHnllejp6cngT0H5jGvS7OOaNHdwPao9XJNqE9ej2pIr61EGLDXg+eefx7Jly3Dbbbfhk08+wfTp07FgwQIcOXIk27s2qLz99ttYsmQJPvjgA7z++utwOp0488wzYbPZlG1+9KMf4d///jf+9re/4e2338ahQ4dw4YUXZnGvB6ePPvoIf/jDHzBt2rSA23l8sqO9vR0nnHACjEYjXnvtNXzxxRf4zW9+g/LycmWbe++9Fw888ABWrlyJdevWoaioCAsWLIDdbs/ing8Ov/zlL/Hwww/jd7/7HbZs2YJf/vKXuPfee/Hggw8q2/D4ZI7NZsP06dPx0EMPhf1+LMdi0aJF+Pzzz/H666/j5ZdfxjvvvIPvf//7mfoRKI9xTaoNXJPmBq5HtYdrUu3ielRbcmY9KlPWzZ49W16yZInytdvtluvq6uQVK1Zkca/oyJEjMgD57bfflmVZljs6OmSj0Sj/7W9/U7bZsmWLDEBeu3ZttnZz0Onu7pbHjRsnv/766/Ipp5wiX3fddbIs8/hk04033iifeOKJEb/v8Xjk2tpa+Ve/+pVyW0dHh2w2m+XnnnsuE7s4qJ1zzjnyd77znYDbLrzwQnnRokWyLPP4ZBMA+cUXX1S+juVYfPHFFzIA+aOPPlK2ee2112RJkuSDBw9mbN8pP3FNqk1ck2oP16PaxDWpdnE9ql1aXo8ywzLLHA4H1q9fj/nz5yu36XQ6zJ8/H2vXrs3inlFnZycAoKKiAgCwfv16OJ3OgGM1YcIEjBgxgscqg5YsWYJzzjkn4DgAPD7Z9K9//QuzZs3C17/+dVRXV+Poo4/Go48+qnx/9+7daGxsDDg2ZWVlmDNnDo9NBhx//PFYvXo1tm/fDgDYtGkT3nvvPZx11lkAeHy0JJZjsXbtWlitVsyaNUvZZv78+dDpdFi3bl3G95nyB9ek2sU1qfZwPapNXJNqF9ejuUNL61FDyh6JEtLS0gK3242ampqA22tqarB169Ys7RV5PB5cf/31OOGEEzBlyhQAQGNjI0wmE6xWa8C2NTU1aGxszMJeDj5/+ctf8Mknn+Cjjz4K+R6PT/bs2rULDz/8MJYtW4af/exn+Oijj3DttdfCZDJh8eLFyu8/3Pscj0363XTTTejq6sKECROg1+vhdrtx9913Y9GiRQDA46MhsRyLxsZGVFdXB3zfYDCgoqKCx4uSwjWpNnFNqj1cj2oX16TaxfVo7tDSepQBS6IwlixZgs8++wzvvfdetneFfPbv34/rrrsOr7/+OiwWS7Z3h1Q8Hg9mzZqFe+65BwBw9NFH47PPPsPKlSuxePHiLO8d/fWvf8UzzzyDZ599FpMnT8bGjRtx/fXXo66ujseHiEjjuCbVFq5HtY1rUu3iepQSwZLwLKuqqoJerw+ZHNfU1ITa2tos7dXgtnTpUrz88st46623MGzYMOX22tpaOBwOdHR0BGzPY5UZ69evx5EjR3DMMcfAYDDAYDDg7bffxgMPPACDwYCamhoenywZOnQoJk2aFHDbxIkTsW/fPgBQfv98n8uOn/zkJ7jppptwySWXYOrUqfjWt76FH/3oR1ixYgUAHh8tieVY1NbWhgxAcblcaGtr4/GipHBNqj1ck2oP16PaxjWpdnE9mju0tB5lwDLLTCYTZs6cidWrVyu3eTwerF69GnPnzs3ing0+sixj6dKlePHFF/Hmm2+ioaEh4PszZ86E0WgMOFbbtm3Dvn37eKwy4PTTT8enn36KjRs3Kv9mzZqFRYsWKf/P45MdJ5xwArZt2xZw2/bt2zFy5EgAQENDA2prawOOTVdXF9atW8djkwG9vb3Q6QI/7vV6PTweDwAeHy2J5VjMnTsXHR0dWL9+vbLNm2++CY/Hgzlz5mR8nyl/cE2qHVyTahfXo9rGNal2cT2aOzS1Hk3Z+B5K2F/+8hfZbDbLTzzxhPzFF1/I3//+92Wr1So3NjZme9cGlauvvlouKyuT16xZIx8+fFj519vbq2zzgx/8QB4xYoT85ptvyh9//LE8d+5cee7cuVnc68FNPZVRlnl8suXDDz+UDQaDfPfdd8s7duyQn3nmGbmwsFB++umnlW1+8YtfyFarVf7nP/8pb968WT7vvPPkhoYGua+vL4t7PjgsXrxYrq+vl19++WV59+7d8gsvvCBXVVXJP/3pT5VteHwyp7u7W96wYYO8YcMGGYB83333yRs2bJD37t0ry3Jsx2LhwoXy0UcfLa9bt05+77335HHjxsnf/OY3s/UjUR7hmlQbuCbNLVyPagfXpNrF9ai25Mp6lAFLjXjwwQflESNGyCaTSZ49e7b8wQcfZHuXBh0AYf89/vjjyjZ9fX3yNddcI5eXl8uFhYXyBRdcIB8+fDh7Oz3IBS8QeXyy59///rc8ZcoU2Ww2yxMmTJAfeeSRgO97PB751ltvlWtqamSz2Syffvrp8rZt27K0t4NLV1eXfN1118kjRoyQLRaLPHr0aPnnP/+53N/fr2zD45M5b731VtjPmsWLF8uyHNuxaG1tlb/5zW/KxcXFcmlpqXzFFVfI3d3dWfhpKB9xTZp9XJPmFq5HtYVrUm3ielRbcmU9KsmyLKcuX5OIiIiIiIiIiIgocexhSURERERERERERJrBgCURERERERERERFpBgOWREREREREREREpBkMWBIREREREREREZFmMGBJREREREREREREmsGAJREREREREREREWkGA5ZERERERERERESkGQxYEhERERERERERkWYwYElEg8rll1+OUaNGJXTf22+/HZIkpXaH8sS8efMwZcqUbO8GERERkeZxPZoeXI8S5RcGLIlIEyRJiunfmjVrsr2rRERERJSHuB4lItIOSZZlOds7QUT09NNPB3z95z//Ga+//jqeeuqpgNvPOOMM1NTUJPw8TqcTHo8HZrM57vu6XC64XC5YLJaEnz9fzZs3Dy0tLfjss8+yvStERERECeF6NLdxPUqUXwzZ3gEiIgC47LLLAr7+4IMP8Prrr4fcHqy3txeFhYUxP4/RaExo/wDAYDDAYODbJhEREVE+4nqUiEg7WBJORDlD9KVZv349Tj75ZBQWFuJnP/sZAOCf//wnzjnnHNTV1cFsNmPMmDG466674Ha7Ax4juGfQnj17IEkSfv3rX+ORRx7BmDFjYDabceyxx+Kjjz4KuG+4nkGSJGHp0qV46aWXMGXKFJjNZkyePBmrVq0K2f81a9Zg1qxZsFgsGDNmDP7whz/E1Ydo3bp1WLhwIcrKylBYWIhTTjkF77//fth93Lp1K77xjW+gtLQUlZWVuO6662C32wO2dblcuOuuu5SfedSoUfjZz36G/v7+kOd+7bXXcMopp6CkpASlpaU49thj8eyzz4Zs98UXX+DUU09FYWEh6uvrce+994Zs8+CDD2Ly5MkoLCxEeXk5Zs2aFfaxiIiIiLSG61GuR4koM3hphohySmtrK8466yxccskluOyyy5RynCeeeALFxcVYtmwZiouL8eabb2L58uXo6urCr371q6iP++yzz6K7uxtXXXUVJEnCvffeiwsvvBC7du2KehX8vffewwsvvIBrrrkGJSUleOCBB3DRRRdh3759qKysBABs2LABCxcuxNChQ3HHHXfA7XbjzjvvxJAhQ2L6ud98802cddZZmDlzJm677TbodDo8/vjjOO200/Duu+9i9uzZAdt/4xvfwKhRo7BixQp88MEHeOCBB9De3o4///nPyjbf/e538eSTT+JrX/sabrjhBqxbtw4rVqzAli1b8OKLLyrbPfHEE/jOd76DyZMn4+abb4bVasWGDRuwatUqXHrppcp27e3tWLhwIS688EJ84xvfwN///nfceOONmDp1Ks466ywAwKOPPoprr70WX/va15RF6+bNm7Fu3bqAxyIiIiLSKq5HuR4logyQiYg0aMmSJXLwW9Qpp5wiA5BXrlwZsn1vb2/IbVdddZVcWFgo2+125bbFixfLI0eOVL7evXu3DECurKyU29ralNv/+c9/ygDkf//738ptt912W8g+AZBNJpO8c+dO5bZNmzbJAOQHH3xQue3cc8+VCwsL5YMHDyq37dixQzYYDCGPGczj8cjjxo2TFyxYIHs8noCfuaGhQT7jjDNC9vGrX/1qwGNcc801MgB506ZNsizL8saNG2UA8ne/+92A7X784x/LAOQ333xTlmVZ7ujokEtKSuQ5c+bIfX19IfsliGPz5z//Wbmtv79frq2tlS+66CLltvPOO0+ePHnygD8vERERkRZwPerH9SgRZRpLwokop5jNZlxxxRUhtxcUFCj/393djZaWFpx00kno7e3F1q1boz7uxRdfjPLycuXrk046CQCwa9euqPedP38+xowZo3w9bdo0lJaWKvd1u9144403cP7556Ourk7ZbuzYscqV3oFs3LgRO3bswKWXXorW1la0tLSgpaUFNpsNp59+Ot555x14PJ6A+yxZsiTg6x/+8IcAgFdffTXgv8uWLQvY7oYbbgAAvPLKKwCA119/Hd3d3bjppptCmrsHlw4VFxcH9HgymUyYPXt2wO/QarXiwIEDIeVNRERERLmC61GuR4ko/VgSTkQ5pb6+HiaTKeT2zz//HLfccgvefPNNdHV1BXyvs7Mz6uOOGDEi4GuxWGxvb4/7vuL+4r5HjhxBX18fxo4dG7JduNuC7dixAwCwePHiiNt0dnYGLHDHjRsX8P0xY8ZAp9Nhz549AIC9e/dCp9OFPH9tbS2sViv27t0LAPjyyy8BAFOmTIm6n8OGDQtZNJaXl2Pz5s3K1zfeeCPeeOMNzJ49G2PHjsWZZ56JSy+9FCeccELUxyciIiLSAq5Hw+N6lIhSiQFLIsop6ivXQkdHB0455RSUlpbizjvvxJgxY2CxWPDJJ5/gxhtvDLnaG45erw97uyzLab1vLMT+/+pXv8KMGTPCblNcXDzgY0RqpB5rg/VYxPJ7mDhxIrZt24aXX34Zq1atwj/+8Q/8/ve/x/Lly3HHHXekbF+IiIiI0oXr0Rlht+F6lIhSiQFLIsp5a9asQWtrK1544QWcfPLJyu27d+/O4l75VVdXw2KxYOfOnSHfC3dbMFHeU1paivnz58f0nDt27EBDQ0PA83g8HmUi5ciRI+HxeLBjxw5MnDhR2a6pqQkdHR0YOXJkwHN/9tlnMV19j0VRUREuvvhiXHzxxXA4HLjwwgtx99134+abbw4p8yEiIiLKBVyPhuJ6lIiSwR6WRJTzxJVU9ZVTh8OB3//+99napQB6vR7z58/HSy+9hEOHDim379y5E6+99lrU+8+cORNjxozBr3/9a/T09IR8v7m5OeS2hx56KODrBx98EACUHkVnn302AOD+++8P2O6+++4DAJxzzjkAgDPPPBMlJSVYsWIF7HZ7wLaJXLFvbW0N+NpkMmHSpEmQZRlOpzPuxyMiIiLSAq5HuR4lotRihiUR5bzjjz8e5eXlWLx4Ma699lpIkoSnnnoqZSUwqXD77bfjv//9L0444QRcffXVcLvd+N3vfocpU6Zg48aNA95Xp9Phj3/8I8466yxMnjwZV1xxBerr63Hw4EG89dZbKC0txb///e+A++zevRtf/epXsXDhQqxduxZPP/00Lr30UkyfPh0AMH36dCxevBiPPPKIUsL04Ycf4sknn8T555+PU089FYD3Kvpvf/tbfPe738Wxxx6LSy+9FOXl5di0aRN6e3vx5JNPxvV7OPPMM1FbW4sTTjgBNTU12LJlC373u9/hnHPOQUlJSVyPRURERKQVXI9yPUpEqcWAJRHlvMrKSrz88su44YYbcMstt6C8vByXXXYZTj/9dCxYsCDbuwfAe1X6tddew49//GPceuutGD58OO68805s2bIlpqmR8+bNw9q1a3HXXXfhd7/7HXp6elBbW4s5c+bgqquuCtn++eefx/Lly3HTTTfBYDBg6dKl+NWvfhWwzR//+EeMHj0aTzzxBF588UXU1tbi5ptvxm233Raw3ZVXXonq6mr84he/wF133QWj0YgJEybgRz/6Udy/h6uuugrPPPMM7rvvPvT09GDYsGG49tprccstt8T9WERERERawfUo16NElFqSrKVLPkREg8z555+Pzz//XJm8mKzbb78dd9xxB5qbm1FVVZWSxyQiIiKi/MX1KBFpEXtYEhFlSF9fX8DXO3bswKuvvop58+ZlZ4eIiIiIaFDhepSIcgVLwomIMmT06NG4/PLLMXr0aOzduxcPP/wwTCYTfvrTn2Z714iIiIhoEOB6lIhyBQOWREQZsnDhQjz33HNobGyE2WzG3Llzcc8992DcuHHZ3jUiIiIiGgS4HiWiXMEelkRERERERERERKQZ7GFJREREREREREREmsGAJREREREREREREWkGA5ZERERERERERESkGQxYEhERERERERERkWYwYElERERERERERESawYAlERERERERERERaQYDlkRERERERERERKQZDFgSERERERERERGRZvx/+CeQX5XvVV8AAAAASUVORK5CYII=",
            "text/plain": [
              "<Figure size 1600x500 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(16, 5), dpi=100)\n",
        "\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.title(\"Metric curves\", fontsize=14)\n",
        "plt.xlabel(\"Training epochs\", fontsize=12)\n",
        "plt.ylabel(\"Accuracy\", fontsize=12)\n",
        "plt.plot(train.history[\"accuracy\"], color=\"#d01c8b\", label=\"training set\")\n",
        "plt.plot(train.history[\"val_accuracy\"], color=\"#4dac26\", label=\"validation set\")\n",
        "plt.legend(loc=\"upper right\", fontsize=10)\n",
        "\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.title(\"Metric curves\", fontsize=14)\n",
        "plt.xlabel(\"Training epochs\", fontsize=12)\n",
        "plt.ylabel(\"Binary cross-entropy\", fontsize=12)\n",
        "plt.plot(train.history[\"bce\"], color=\"#d01c8b\", label=\"training set\")\n",
        "plt.plot(train.history[\"val_bce\"], color=\"#4dac26\", label=\"validation set\")\n",
        "plt.legend(loc=\"upper right\", fontsize=10)\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6hdn_BRai5xM"
      },
      "source": [
        "### Validation plots\n",
        "\n",
        "Once trained the LSGAN system, the **generator model** can be used to parameterize the high-level response of the LHCb RICH detectors, namely the RICH differential log-likelihoods for different particle hypotheses. Let's have a look at the achieved performance on the **test set**!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HVid9HKfLzPW"
      },
      "outputs": [],
      "source": [
        "x_test_prep = x_scaler.transform(x_test[:, :3])  # input pre-processing\n",
        "x_test_prep = np.c_[x_test_prep, x_test[:, -1]]\n",
        "\n",
        "out_test = gan.generate(x_test_prep, seed=None)\n",
        "out_test_post = y_scaler.inverse_transform(out_test)  # output post-processing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CfMNEUEs6EHn"
      },
      "source": [
        "#### RICH differential log-likelihood $(e - \\pi)$"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 473
        },
        "id": "XM985aPPNPR3",
        "outputId": "96a3586e-cf49-4c8c-a200-9ecca3d023ec"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1600x500 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(16, 5), dpi=100)\n",
        "\n",
        "bins = np.linspace(-100.0, 50.0, 101)\n",
        "\n",
        "for i, scale in enumerate([\"linear\", \"log\"]):\n",
        "    plt.subplot(1, 2, i + 1)\n",
        "    plt.xlabel(\"RICH differential log-likelihood $(e - \\pi)$\", fontsize=12)\n",
        "    plt.ylabel(\"Candidates\", fontsize=12)\n",
        "    plt.hist(y_test[:, 0], bins=bins, color=\"#3288bd\", label=\"toy data\")\n",
        "    plt.hist(out_test_post[:, 0], bins=bins, histtype=\"step\", lw=2, color=\"#fc8d59\", label=\"LSGAN model\")\n",
        "    plt.legend(loc=\"upper left\", fontsize=10)\n",
        "    plt.yscale(scale)\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "df-bcHAW6lGH"
      },
      "source": [
        "#### RICH differential log-likelihood $(\\mu - \\pi)$"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 473
        },
        "id": "KeRJbgTMPl6g",
        "outputId": "4ba0d67b-90b8-4c1a-9bda-d284746e57c2"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1600x500 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(16, 5), dpi=100)\n",
        "\n",
        "bins = np.linspace(-75.0, 75.0, 101)\n",
        "\n",
        "for i, scale in enumerate([\"linear\", \"log\"]):\n",
        "    plt.subplot(1, 2, i + 1)\n",
        "    plt.xlabel(\"RICH differential log-likelihood $(\\mu - \\pi)$\", fontsize=12)\n",
        "    plt.ylabel(\"Candidates\", fontsize=12)\n",
        "    plt.hist(y_test[:, 1], bins=bins, color=\"#3288bd\", label=\"toy data\")\n",
        "    plt.hist(out_test_post[:, 1], bins=bins, histtype=\"step\", lw=2, color=\"#fc8d59\", label=\"LSGAN model\")\n",
        "    plt.legend(loc=\"upper left\", fontsize=10)\n",
        "    plt.yscale(scale)\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "36gOX6UI6nsL"
      },
      "source": [
        "#### RICH differential log-likelihood $(K - \\pi)$"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 479
        },
        "id": "rMh4nOK3PyEI",
        "outputId": "86152baf-5c7b-4d48-8ae2-361d9f0389a9"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1600x500 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(16, 5), dpi=100)\n",
        "\n",
        "bins = np.linspace(-150.0, 100.0, 101)\n",
        "\n",
        "for i, scale in enumerate([\"linear\", \"log\"]):\n",
        "    plt.subplot(1, 2, i + 1)\n",
        "    plt.xlabel(\"RICH differential log-likelihood $(K - \\pi)$\", fontsize=12)\n",
        "    plt.ylabel(\"Candidates\", fontsize=12)\n",
        "    plt.hist(y_test[:, 2], bins=bins, color=\"#3288bd\", label=\"toy data\")\n",
        "    plt.hist(out_test_post[:, 2], bins=bins, histtype=\"step\", lw=2, color=\"#fc8d59\", label=\"LSGAN model\")\n",
        "    plt.legend(loc=\"upper left\", fontsize=10)\n",
        "    plt.yscale(scale)\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7EW3FlfL6qJX"
      },
      "source": [
        "#### RICH differential log-likelihood $(p - \\pi)$"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 473
        },
        "id": "z3WQRODrQE7H",
        "outputId": "ad04f569-6721-45c9-9e3a-451b246d3ec8"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1600x500 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(16, 5), dpi=100)\n",
        "\n",
        "bins = np.linspace(-150.0, 100.0, 101)\n",
        "\n",
        "for i, scale in enumerate([\"linear\", \"log\"]):\n",
        "    plt.subplot(1, 2, i + 1)\n",
        "    plt.xlabel(\"RICH differential log-likelihood $(p - \\pi)$\", fontsize=12)\n",
        "    plt.ylabel(\"Candidates\", fontsize=12)\n",
        "    plt.hist(y_test[:, 3], bins=bins, color=\"#3288bd\", label=\"toy data\")\n",
        "    plt.hist(out_test_post[:, 3], bins=bins, histtype=\"step\", lw=2, color=\"#fc8d59\", label=\"LSGAN model\")\n",
        "    plt.legend(loc=\"upper left\", fontsize=10)\n",
        "    plt.yscale(scale)\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jOYaaPWMnJXD"
      },
      "source": [
        "### Model export\n",
        "\n",
        "The [pidgan](https://github.com/mbarbetti/pidgan) package provides the **generator model** with the `export_model()` to export it as a plain [`Sequential`](https://www.tensorflow.org/api_docs/python/tf/keras/Sequential) model to ease the **deployment** of the RICH parameterization through tools like [scikinC](https://github.com/landerlini/scikinC) or [keras2c](https://github.com/f0uriest/keras2c) [[7][7], [8][8]].\n",
        "\n",
        "[7]: https://pos.sissa.it/409/034\n",
        "[8]: https://doi.org/10.1016/j.engappai.2021.104182"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mxbF1xSaPlhf",
        "outputId": "5c15d760-252c-4fe1-c328-a5a16dad0581"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"
          ]
        }
      ],
      "source": [
        "keras.models.save_model(\n",
        "    gan.generator.export_model,  # TF Sequential model\n",
        "    filepath=\"saved_generator\",\n",
        "    save_format=\"tf\",\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IUflfVY7bm5z"
      },
      "source": [
        "Exporting the generator as a [TensorFlow SavedModel format](https://www.tensorflow.org/guide/saved_model) file allows to use **scikinC** to transpile the [`Sequential`](https://www.tensorflow.org/api_docs/python/tf/keras/Sequential) model to a C file as follows:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MxsMQAkhU2_S"
      },
      "outputs": [],
      "source": [
        "%%capture\n",
        "!scikinC saved_generator > generator.C"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dZ9f1NZAafov",
        "outputId": "d574f8a7-a3ff-4842-b066-646b625fafaf"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "-rw-r--r-- 1 root root 1.8M Oct 18 10:22 generator.C\n"
          ]
        }
      ],
      "source": [
        "!ls -lrth generator.C"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UKqBzg-Fi5xM"
      },
      "source": [
        "## References\n",
        "\n",
        "1. I.J. Goodfellow _et al._, \"Generative Adversarial Networks\", [arXiv:1406.2661][1]\n",
        "2. M. Arjovsky, L. Bottou, \"Towards Principled Methods for Training Generative Adversarial Networks\", [arXiv:1701.04862][2]\n",
        "3. X. Mao _et al._, \"Least Squares Generative Adversarial Networks\", [arXiv:1611.04076][3]\n",
        "4. M. Mirza, S. Osindero, \"Conditional Generative Adversarial Nets\", [arXiv:1411.1784][4]\n",
        "5. A. Maevskiy _et al._, \"Fast Data-Driven Simulation of Cherenkov Detectors Using Generative Adversarial Networks\", [arXiv:1905.11825][5]\n",
        "6. T. Salimans _et al._, \"Improved Techniques for Training GANs\", [arXiv:1606.03498][6]\n",
        "7. L. Anderlini, M. Barbetti, \"scikinC: a tool for deploying machine learning as binaries\", [PoS **CompTools2021** (2022) 034][7]\n",
        "8. R. Conlin _et al._, \"Keras2c: A library for converting Keras neural networks to real-time compatible C\", [Eng Appl Artif Intell **100** (2021) 104182][8]\n",
        "\n",
        "[1]: https://arxiv.org/abs/1406.2661\n",
        "[2]: https://arxiv.org/abs/1701.04862\n",
        "[3]: https://arxiv.org/abs/1611.04076\n",
        "[4]: https://arxiv.org/abs/1411.1784\n",
        "[5]: https://arxiv.org/abs/1905.11825\n",
        "[6]: https://arxiv.org/abs/1606.03498\n",
        "[7]: https://pos.sissa.it/409/034\n",
        "[8]: https://doi.org/10.1016/j.engappai.2021.104182"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1f26wCo4i5xN"
      },
      "source": [
        "## Credits\n",
        "This tutorial is based on the notebooks provided by the [mbarbetti/tf-gen-models](https://github.com/mbarbetti/tf-gen-models) repository."
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "gpuType": "T4",
      "provenance": [],
      "toc_visible": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python",
      "version": "3.9.16"
    },
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  },
  "nbformat": 4,
  "nbformat_minor": 0
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