CH05_FLYNN_ML4DD.ipynb

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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q1ai0Iyr3_xX"
      },
      "source": [
        "# 📚 Chapter 5: Classification: Cytochrome P450 Inhibition"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "*This notebook contains the code examples in chapter 5. For readability, the chapter notebooks only contain runnable code blocks and section titles. They omit the rest of the material in the book, i.e., text paragraphs, figures (unless generated as part of one of the code blocks), equations, and pseudocode. I recommend reading the notebooks side-by-side with the book!*\n",
        "\n",
        "You can work through this notebook locally as well as via Google Colab:\n",
        "<a target=\"_blank\" href=\"https://colab.research.google.com/github/nrflynn2/ml-drug-discovery/blob/main/CH05_FLYNN_ML4DD.ipynb\">\n",
        "  <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n",
        "</a>\n",
        "\n",
        "This chapter covers\n",
        "- How to use machine learning to model metabolism via Cytochrome P450 inhibition\n",
        "- Fundamental classification models such as logistic regression and decision trees\n",
        "- Ensemble learning with bagging and random forests\n",
        "- Different evaluation methods for classification problems\n",
        "- Additional methods for model interpretation and applicability domain assessment"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## ⚙️ Part 0: Installation and Setup\n",
        "\n",
        "**❗️NOTE:** After setting up a local environment, install the required packages via:\n",
        "`!pip install -r https://raw.githubusercontent.com/nrflynn2/ml-drug-discovery/main/requirements.txt --quiet`\n",
        "\n",
        "**❗️NOTE:** If running notebook via Colab, be sure to install RDKit via: `!pip install rdkit`"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Import and Setup Packages "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6DRXXWV5hyqS"
      },
      "outputs": [],
      "source": [
        "import math\n",
        "from pathlib import Path\n",
        "from zipfile import ZipFile\n",
        "from tempfile import TemporaryDirectory\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import scipy\n",
        "from rdkit.Chem import PandasTools\n",
        "from sklearn import metrics\n",
        "from tdc.single_pred import HTS\n",
        "from tqdm.auto import tqdm"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zutIaX5mYA5S"
      },
      "outputs": [],
      "source": [
        "import matplotlib as mpl\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "RANDOM_SEED = 42\n",
        "np.random.seed(RANDOM_SEED)\n",
        "\n",
        "colors = [\"#A20025\", \"#6C8EBF\"]\n",
        "sns.set_palette(sns.color_palette(colors))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WQPmByFsYJTW"
      },
      "outputs": [],
      "source": [
        "from rdkit.Chem import Draw\n",
        "d2d = Draw.MolDraw2DSVG(-1, -1)\n",
        "dopts = d2d.drawOptions()\n",
        "dopts.useBWAtomPalette()\n",
        "dopts.setHighlightColour((.635,.0,.145,.4))\n",
        "dopts.baseFontSize = 1.0\n",
        "dopts.additionalAtomLabelPadding = 0.15"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QvhV6gC5fDdB"
      },
      "source": [
        "## 1️⃣ Binary Classification of CYP3A4 Inhibition"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fqY52OT7KY9n"
      },
      "source": [
        "### Logistic Regression in Practice"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tJgnO1HVkUpn",
        "outputId": "29db516f-38aa-4058-d942-dcb038c8f01d"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Downloading...\n",
            "100%|██████████| 746k/746k [00:00<00:00, 9.66MiB/s]\n",
            "Loading...\n",
            "Done!\n",
            "100%|██████████| 12328/12328 [00:14<00:00, 859.70it/s]\n"
          ]
        }
      ],
      "source": [
        "from tdc.single_pred import ADME\n",
        "data = ADME(name = 'CYP23A4_Veith')\n",
        "splits = data.get_split('scaffold')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "abKVYnLQkeXf"
      },
      "outputs": [],
      "source": [
        "train_df, valid_df, test_df = splits['train'], splits['valid'], splits['test']"
      ]
    },
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              "     Drug_ID  \\\n",
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              "\n",
              "                                                                                                                                                                   Drug  \\\n",
              "0  NC(=O)C1=CN([C@@H]2O[C@@H](COP(=O)([O-])OP(=O)([O-])OC[C@@H]3O[C@H](n4cnc5c(N)ncnc54)[C@@H](OP(=O)([O-])[O-])[C@@H]3O)[C@H](O)[C@@H]2O)C=CC1.[Na+].[Na+].[Na+].[Na+]   \n",
              "1                                                                                                              NS(=O)(=O)c1cccc2c1c([N+](=O)[O-])cc1nc([O-])c([O-])nc12   \n",
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              "\n",
              "   Y  \n",
              "0  0  \n",
              "1  0  \n",
              "2  0  \n",
              "3  0  \n",
              "4  1  "
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "with pd.option_context('display.max_colwidth', None):\n",
        "  display(train_df.head())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dVnyBIW2GJ-E"
      },
      "outputs": [],
      "source": [
        "from rdkit import DataStructs\n",
        "from rdkit.Chem import AllChem\n",
        "\n",
        "def compute_fingerprint(mol, r, nBits) -> np.ndarray:\n",
        "    \"\"\"Featurize Rdkit Mol into Morgan Fingerprint bit vectors.\n",
        "\n",
        "     Args:\n",
        "         mol: an Rdkit Mol object (representing a molecule).\n",
        "\n",
        "     Returns:\n",
        "         numpy.ndarray: A 1-D numpy array of the featurized molecule.\n",
        "    \"\"\"\n",
        "    fp = AllChem.GetMorganFingerprintAsBitVect(mol, r, nBits=nBits)\n",
        "    arr = np.zeros((1,), dtype=np.int8)\n",
        "    DataStructs.ConvertToNumpyArray(fp, arr)\n",
        "    return arr"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QQew79C-Wbnm"
      },
      "outputs": [],
      "source": [
        "def process_df(df):\n",
        "  df = df.rename(columns={'Y':'Inhibitor'})\n",
        "  PandasTools.AddMoleculeColumnToFrame(df, \"Drug\", \"Molecule\")\n",
        "  df = df.dropna()\n",
        "  fingerprints = np.stack([compute_fingerprint(mol, 2, 2048) for mol in df.Molecule])\n",
        "  df['fingerprints'] = df['Molecule'].apply(compute_fingerprint, r=2, nBits=2048)\n",
        "\n",
        "  return df, fingerprints"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DraIuGV5XoFj"
      },
      "outputs": [],
      "source": [
        "%%capture\n",
        "\n",
        "train_df, train_fingerprints = process_df(train_df)\n",
        "val_df, val_fingerprints = process_df(valid_df)\n",
        "test_df, test_fingerprints = process_df(test_df)"
      ]
    },
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            ],
            "text/plain": [
              "LogisticRegression()"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.metrics import accuracy_score, precision_score, recall_score\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "lr_model = LogisticRegression()\n",
        "lr_model.fit(train_fingerprints, train_df.Inhibitor)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FduwgWAaXXQC"
      },
      "outputs": [],
      "source": [
        "val_preds = lr_model.predict(val_fingerprints)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JZxMZbAcyGIv"
      },
      "outputs": [],
      "source": [
        "val_proba_inhibitor = lr_model.predict_proba(val_fingerprints)[:, 1]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-CR92L7WKvmu",
        "outputId": "03a34332-f5d7-41b4-d64a-ba4510e235ea"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy: 0.78\n"
          ]
        }
      ],
      "source": [
        "print(f\"Accuracy: {accuracy_score(val_df.Inhibitor, val_preds):.2f}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JtDm38zOLbNS",
        "outputId": "feca6291-a434-4a4d-96c9-caec7bfb356f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Precision: 0.76\n"
          ]
        }
      ],
      "source": [
        "print(f\"Precision: {precision_score(val_df.Inhibitor, val_preds):.2f}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f2AuZNLwLnif",
        "outputId": "9395ef4e-9b1c-4f90-d1a5-2e7492c14d70"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Recall: 0.74\n"
          ]
        }
      ],
      "source": [
        "print(f\"Recall: {recall_score(val_df.Inhibitor, val_preds):.2f}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kyDGKGlQfNaM"
      },
      "source": [
        "### Miscalibration: Questioning Probabilistic Output Assumptions"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QV_sJEFlfTkd"
      },
      "source": [
        "**Code for Figures 5.2 and 5.3. Reliability diagrams for logistic regressor and its variants that have been calibrated post-training with platt scaling and isotonic regression**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sUElFOpfX5oh"
      },
      "outputs": [],
      "source": [
        "def r2(x, y):\n",
        "    return scipy.stats.pearsonr(x, y)[0] ** 2, scipy.stats.pearsonr(x, y)[1]\n",
        "\n",
        "def binomial_errors(n1, n0, ci = 0.95, eps=1e-6):\n",
        "    b = n0; a = n1\n",
        "    p = a / (a+b + eps)\n",
        "    pad = (1. - ci) / 2.\n",
        "    B = scipy.stats.beta(a+0.5,b+0.5)\n",
        "\n",
        "    low = -B.ppf(pad) + p\n",
        "    high = B.ppf(1-pad) - p\n",
        "    return np.array([low, high]).T\n",
        "\n",
        "def reliability_diagram(y, t, nbins = 20, ax=None, bar_scale = .7, pad=0.02, hist_format = {}):\n",
        "    \"\"\"Given model outputs and targets, plot a Reliability Diagram.\n",
        "    95% confidence intervals computed correctly using beta distribution.\n",
        "    Missing targets (np.nan) are ignored.\n",
        "    Targets must be on intervale [0,1], but can be fractional.\n",
        "    \"\"\"\n",
        "    t = np.asarray(t)\n",
        "    y = np.asarray(y)\n",
        "\n",
        "    bins = np.linspace(0.0, 1.0, nbins+1, endpoint=True)\n",
        "\n",
        "    n1, bin_edge = np.histogram(y, bins, weights=t)\n",
        "    n0, bin_edge = np.histogram(y, bins, weights=1.0-t)\n",
        "    p  = n1.astype(np.float) / (n1 + n0)\n",
        "\n",
        "    bin_center = (bin_edge[:-1] + bin_edge[1:]) / 2\n",
        "    width = 0.5 / nbins * bar_scale\n",
        "    yerr = binomial_errors(n1, n0).T\n",
        "\n",
        "    ax = plt.gca()\n",
        "    hist_fmt = dict(edgecolor=\"white\", linewidth=0)\n",
        "    hist_fmt.update(hist_format)\n",
        "    ax1 = ax.twinx()\n",
        "\n",
        "    #ax1.bar(bin_center - width/2, n1, bottom=n0, width = width*2, align=\"center\", color=\"blue\",label=\"positives\", **hist_fmt)\n",
        "    n1_density, _ = np.histogram(y, bins, weights=t, density=True)\n",
        "    n0_density, _ = np.histogram(y, bins, weights=1.0-t, density=True)\n",
        "\n",
        "    ax1.bar(bin_center + width/2, n1_density, width = width, align=\"center\", label=\"Inhibitor\", **hist_fmt)\n",
        "    ax1.bar(bin_center - width/2, n0_density, width = width, align=\"center\", label=\"Noninhibitor\", alpha=0.4 , **hist_fmt)\n",
        "\n",
        "    ax.plot([0,1],[0,1],'k:')\n",
        "    try:\n",
        "      ax.errorbar(bin_center, p, yerr, fmt='k.-')\n",
        "    except:\n",
        "      print(\"Unable to plot error bars\")\n",
        "\n",
        "    ax.set_ylabel('Relative Frequency')\n",
        "    ax.set_xlabel('Confidence')\n",
        "    ax.set_xlim(-pad, 1+pad)\n",
        "    ax.set_ylim(-pad,1+pad)\n",
        "\n",
        "    lim1 = ax1.get_ylim()\n",
        "    pad1 = (lim1[1] - lim1[0]) * pad\n",
        "    ax1.set_ylim(lim1[0] - pad1, lim1[1] + pad1)\n",
        "    ax1.set_ylabel('Density')\n",
        "\n",
        "    mask = ~np.isnan(p)\n",
        "    p_mask = p[mask]\n",
        "    bc_mask = bin_center[mask]\n",
        "    corr, pval = r2(p_mask,bc_mask)\n",
        "    rmse = math.sqrt(metrics.mean_squared_error(bc_mask, p_mask))\n",
        "\n",
        "    brier_score = ((y - t)**2).mean()\n",
        "\n",
        "    TEXT = \"\"\"RMSCE = %0.2f, BS = %0.3f\"\"\" % (rmse, brier_score)\n",
        "    ax1.annotate(TEXT, xy=(0.5,0), xytext=(.45,.8), ha=\"right\", va=\"top\",textcoords=\"axes fraction\")\n",
        "    return ax, ax1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "0M5LeXudZYyo",
        "outputId": "a36afdf0-34bc-4a9b-d6ab-bb83af192653"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "ax, ax1 = reliability_diagram(val_proba_inhibitor, val_df.Inhibitor, nbins=10)\n",
        "plt.tight_layout()\n",
        "plt.xticks(fontsize=14)\n",
        "plt.yticks(fontsize=14)\n",
        "ax1.tick_params(labelsize=14)\n",
        "plt.legend(loc=(0.1, 0.83), prop={'size': 12.00}, fancybox=True, framealpha=1.0)\n",
        "plt.savefig(\"figures/ch05/lr_model_reliability_diagram.pdf\", bbox_inches='tight', dpi=600)\n",
        "plt.savefig(\"figures/ch05/lr_model_reliability_diagram.svg\", bbox_inches='tight', dpi=600)\n",
        "plt.savefig(\"figures/ch05/lr_model_reliability_diagram.png\", bbox_inches='tight', dpi=600);"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NCvnD-FZIVh7"
      },
      "outputs": [],
      "source": [
        "from sklearn.calibration import CalibratedClassifierCV\n",
        "\n",
        "lr_ps_cal = CalibratedClassifierCV(estimator=lr_model, method='sigmoid', cv='prefit')\n",
        "lr_ps_cal.fit(val_fingerprints, val_df.Inhibitor)\n",
        "val_proba_inhibitor_ps_cal = lr_ps_cal.predict_proba(val_fingerprints)[:, 1]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EuZQc9Y_JBTg"
      },
      "outputs": [],
      "source": [
        "lr_ir_cal = CalibratedClassifierCV(estimator=lr_model, method='isotonic', cv='prefit')\n",
        "lr_ir_cal.fit(val_fingerprints, val_df.Inhibitor)\n",
        "val_proba_inhibitor_ir_cal = lr_ir_cal.predict_proba(val_fingerprints)[:, 1]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "MB-FAv8QIthA",
        "outputId": "93ea9ba5-e086-48aa-db00-836ba286ccc9"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "ax, ax1 = reliability_diagram(val_proba_inhibitor_ps_cal, val_df.Inhibitor, nbins=10)\n",
        "plt.tight_layout()\n",
        "plt.xticks(fontsize=14)\n",
        "plt.yticks(fontsize=14)\n",
        "ax1.tick_params(labelsize=14)\n",
        "plt.legend(loc=(0.1, 0.83), prop={'size': 12.00},fancybox=True, framealpha=1.0)\n",
        "plt.savefig(\"figures/ch05/lr_model_platt_reliability_diagram.pdf\", bbox_inches='tight', dpi=600)\n",
        "plt.savefig(\"figures/ch05/lr_model_platt_reliability_diagram.svg\", bbox_inches='tight', dpi=600)\n",
        "plt.savefig(\"figures/ch05/lr_model_platt_reliability_diagram.png\", bbox_inches='tight', dpi=600);"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 504
        },
        "id": "tImTdx73JBTw",
        "outputId": "7556aac7-c9de-4147-842b-3dc621bf8ab9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Unable to plot error bars\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "ax, ax1 = reliability_diagram(val_proba_inhibitor_ir_cal, val_df.Inhibitor, nbins=10)\n",
        "plt.tight_layout()\n",
        "plt.xticks(fontsize=14)\n",
        "plt.yticks(fontsize=14)\n",
        "ax1.tick_params(labelsize=14)\n",
        "plt.legend(loc=(0.1, 0.83), prop={'size': 12.00},fancybox=True, framealpha=1.0)\n",
        "plt.savefig(\"figures/ch05/lr_model_isotonic_reliability_diagram.pdf\", bbox_inches='tight', dpi=600)\n",
        "plt.savefig(\"figures/ch05/lr_model_isotonic_reliability_diagram.svg\", bbox_inches='tight', dpi=600)\n",
        "plt.savefig(\"figures/ch05/lr_model_isotonic_reliability_diagram.png\", bbox_inches='tight', dpi=600);"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "obTo6rfqfk0K"
      },
      "source": [
        "**Code for Figure 5.5. PR and ROC curves for the logistic regressor**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 326
        },
        "id": "WnQErqrSZywW",
        "outputId": "24388365-3b29-4335-c7e7-13d19ce175f5"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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c3d1zjbt161YoFAocOHAA9vb26NatGxwdHaFQKODn54fLly8jISEhexqngohEIhw/fhxDhw7Fzp07cfLkSXTr1g329vaQyWTw8fGBh4cH0tPTMWbMmOx+n3/+OS5cuIBevXph5MiR0NbWxsWLF2FoaJhjDZDiGjt2LNatW4fvv/8+++f/JZVKcfjwYfTs2RMdOnRAp06d4OrqCoFAgHfv3uHGjRswMTHBixcvSpyDiKiyYFGDiIiKpW/fvmjatCk8PDxw5cqVHG9Y5Kdz587w8/PDunXrcObMGRw8eBAJCQkwMDCAq6sr/vzzT0ycODFHny1btsDU1BQHDx7E2rVrUadOHWzatAnW1tYlLmoAH28gNm7ciPT0dIwaNSrf+W+zii9//vknLl++jNjYWJiYmMDe3h6LFy/G6NGjS5yBiIiIiKii0tTUxPz58zFr1iwsWbIk+w1oAGjZsiVevHiBP//8EydPnsSOHTuQmpoKS0tLjBgxArNnz0aTJk3yHFdLSwv79+/HpEmTsG3bNty6dSv7LQpbW1sMHz4cU6ZMQfPmzYuU09jYGJcuXcLRo0exZ88e3LhxA8eOHYNIJELNmjUxdepUTJ8+PceDSt26dcOhQ4fwww8/YPfu3TA2NsbQoUPx888/w8XFpcTnrGXLlqhduzb8/PxgY2ODjh075tmuWbNmePLkCX7//XecPXsWnp6ekEqlqFatGgYMGICRI0eWOAMRUWUiUBQ2uSAREREREREREREREVE5wDU1iIiIiIiIiIiIiIioQmBRg4iIiIiIiIiIiIiIKgQWNYiIiIiIiIiIiIiIqEJgUYOIiIiIiIiIiIiIiCoEFjWIiIiIiIiIiIiIiKhCYFGDiIiIiIiIiIiIiIgqBJG6A5QHcrkcISEh0NPTg0AgUHccIiIiIiK1USgUSEhIgLW1NYRCPgNVUrzHICIiIiJSzf0FixoAQkJCYGtrq+4YRERERETlRlBQEGxsbNQdo8LiPQYRERER0f8o8/6CRQ0Aenp6AD6eWH19fTWnISIiIiJSn/j4eNja2mZfI1PJ8B6DiIiIiEg19xcsagDZr4Pr6+vzhoOIiIiICOCUSaXEewwiIiIiov9R5v0FJ8klIiIiIiIiIiIiIqIKgUUNIiIiIiIiIiIiIiKqEFjUICIiIiIiIiIiIiKiCoFFDSIiIiIiIiIiIiIiqhBY1CAiIiIiIiIiIiIiogqBRQ0iIiIiIiIqUx07doRAIFB3jGyLFy+GQCCAh4eHSsavUaMGatSooZKxlUnVvy47duyAQCDAjh07itynopw7IiIiKjssahAREREREVUhV69exfDhw2FrawupVApjY2O0bdsWq1atQmpqqlKOoeoigbqMHz8eAoGgyD+K8+U9lT8F/dqOHz++WGPJ5XL89ddfcHV1hZaWFszMzDBy5Ei8efNGKcfOKkgV9KO4fx737t2L5s2bQ0dHB0ZGRujTpw+8vLzybLtnzx5MmzYNTZs2hVQq5e9/IiJSKZG6AxAREREREZHqZWRk4NNPP8WmTZugo6ODnj17wsHBAXFxcbhw4QI+//xzbNiwAWfOnIGDg4NKs+zatQvJyckqPYYqDBgwINdbAx4eHrh27Rr69++Phg0b5tj3359TxWNnZ5dnEaG4v7bTpk3Dli1bUK9ePcyePRshISE4dOgQLly4gDt37qB27dpKOfYXX3wBXV3dPPcV542Xn376CQsXLoSdnR2mT5+OhIQEHDhwAK1bt8bly5fRpk2bHO0XLlyId+/ewdTUFFZWVnj37l2Rj0VERFRcLGoQERERERFVAfPnz8emTZvQrFkzHDt2DNWqVcvel5mZiR9++AE//PADevToAS8vL+jr66ssS/Xq1VU2tioNGDAAAwYMyLFt8eLFuHbtGgYMGFDsp/ep/KtRowYWL15cqjGuXr2KLVu2oH379rh48SIkEgkAYNSoUejVqxdmzpyJ8+fPK+XY8+bNg6WlZany+vn5YfHixXB0dMS9e/dgYGAAAHB3d0fLli0xZcoUeHt7Qyj83+QfW7ZsQe3atWFnZ4dly5Zh/vz5pcpARERUEE4/RUREREREVMm9evUKK1euhLGxMU6dOpWjoAEAGhoaWLJkCUaNGoXXr19j+fLlOfZnrWsQGxuLadOmwdLSEpqammjUqBH279+fo23Hjh2xZMkSAICbm1v21Df/fko8r7Ub/r3ewqlTp9CiRQtoa2ujWrVq+O677yCXywEAO3fuRIMGDaClpYXq1avj999/z/V5Q0JCsGjRIrRs2RLm5uaQSqWoUaMG3N3dERERUeLzWFqJiYmYM2cOrK2tIZVKUb9+fRw+fDhXu6xprt68eYMVK1bA2dkZUqk0R9EkIiICn332GRwcHCCVSmFqaorBgwfD29s713h+fn6YMGEC7O3ts6cca9CgAebOnQuFQpGrfXp6OhYvXowaNWpAKpXC0dER69aty/MzJSUlYdGiRahbty40NTVhbGyM3r17w9PTs1jn5sSJE2jWrBm0tLRgYWGBKVOmICYmplhjlFebN28GACxdujS7oAEAPXv2RMeOHXHhwgUEBgaqK14u27dvR0ZGBr799tvsggbw8Q2RkSNHwtfXFzdv3szRp0uXLrCzsyvrqEREVEXxTQ0iIiIiIqJKbufOnZDL5Zg6dSosLCzybffdd99h37592LZtG3744Ycc+2QyGbp06YLExESMHTsWSUlJOHToEEaNGoXIyEjMmjULALK/eL927RrGjRuXXcwwNDQsUtZjx47hwoULGDBgANq0aYMzZ87gxx9/hEKhgIGBAX788Uf0798fHTt2xJEjR/DVV1/BwsICn3zySfYY169fx4oVK9C5c2e0aNECYrEYjx49wvr163H+/Hl4eXnl+LK2LKSnp6Nbt26IiYnB4MGDkZycjAMHDmDYsGE4d+4cunXrlqvPrFmzcOfOHfTu3Rt9+/aFubk5AOD169fo2LEj3r9/j27dumHAgAGIiIjAkSNHcP78eVy+fBktWrQA8LHA07x5cyQlJaF3794YPnw4kpKS4Ofnh3Xr1mH58uUQiXJ+NTBy5Ejcu3cPPXv2hIaGBg4dOoRPP/0UYrEYU6ZMyW6XmpqKTp064d69e2jcuDHmzp2L8PBwHDx4EOfPn8f+/fsxdOjQQs/Nrl27MG7cOOjr62Ps2LEwNDTE6dOn0aVLF8hkshyFAAAICAiAvb097OzsEBAQUNxfimKJjY3Fpk2bEBkZCWNjY7Rp0waurq7FGsPDwwM6Ojq5pmwCgO7du2dPYTZ27FilH7skstbeyOv3ZPfu3bFjxw5cu3YN7du3V3kWIiKivLCoQUREREREVMndunULANC5c+cC29WtWxfW1tYIDg5GUFAQbG1ts/eFhoaidu3auHXrVvaXzAsWLECjRo3w5ZdfYtCgQahWrRrGjx+PgIAAXLt2DePHj0fHjh2LlfWff/6Bp6cnmjVrBgBYsmQJHBwcsGrVKujr6+PRo0eoWbMmgI9T7Tg4OGD58uU5ihqdOnVCWFhYrrUFsr48X7NmDb799tti5SqtkJAQNGvWDB4eHjmmH+rSpQtWrlyZ5xfIT58+xaNHj3JN1/XJJ58gNDQU586dQ/fu3bO3L1y4EE2bNsWUKVPw9OlTAMCRI0cQGxuLP/74A3PmzMkxTnR0dK6CBgC8f/8e3t7e2VOQzZkzBy4uLlixYkWOosZvv/2Ge/fuYfTo0di9e3f22zezZ89Gy5YtMXXqVPTo0QN6enr5npf4+HjMmjULOjo6uH//PhwdHQF8XNOhS5cuCA0NVesbAE+ePMG0adNybOvRowd27tyZXWQqSFJSEkJDQ+Hi4gINDY1c+7PW0vDz81PKsZcvX57nmhqampr45ptvCs2blUVXVzfPaawKyktERFRWOP0UERERERFRJRcWFgYAOYoU+clqExoammvfzz//nOOpeRsbG8yZMwdpaWk4cOCAUrKOGTMmu6ABAHp6eujTpw+Sk5MxY8aM7IJGVta2bdvCx8cHGRkZ2dvNzc3z/GJ37Nix0NfXx6VLl5SStbhWrVqV4/x17twZdnZ2uH//fp7tv/zyy1wFjUePHuHWrVsYN25cjoIGADg6OmLKlCl49uxZrmmotLS0co1vbGyc53F/+eWXHGuq1KlTB23atMHLly+RkJCQvX3nzp0Qi8VYtmxZjunEGjVqhHHjxiE2NhbHjx/P8xhZjh8/jvj4eEycODG7oAEAYrEYP/30U559qlWrBl9fX1y+fLnAsUvriy++wK1btxAZGYn4+HjcunULPXv2xLlz59CnTx9kZmYWOkZcXBwA5PtmUNZ5zmpX2mOvWLECS5YsyfVj2bJlRf7ccXFxxc5LRERUlvimBhERERERERVKJBKhVatWuba3a9cOwMcv25WhYcOGubZZWVkVuC8zMxPh4eE51go5evQoNm7cCC8vL8TExOT4EjgkJEQpWYvD0NAQ9vb2ubbb2Njg9u3befZp3rx5rm137twBAISHh+e5iPSLFy+y/+vi4oK+ffti/vz5+PTTT3H58mX06NEDHTp0yFEc+q8mTZrkmRP4OCWSnp4e4uPj8ebNGzg5OWXv+zc3Nzds3rwZjx8/zjWt0r89efIEwP9+H/1bq1at8nyTRCwWo27duvmOqSz/XVumVatWOH36NDp16oRr167hxIkTGDRoULk6dmhoaKELhT9+/DhXsalGjRpc6J6IiCoMFjWIiIiIiIgqOUtLS7x48QJBQUGoU6dOgW2DgoIA/K+QkMXU1BRCYe6X/bPW6FDWk9v/fkMgS9YX2wXtS09Pz962YsUKzJs3D2ZmZujWrRtsbGyy31T4448/kJaWppSsxZHfk+8ikSh7EfT/ymv9k+joaADAmTNncObMmXyPl5SUBODjl9V37tzB4sWLcfbsWRw6dAjAx6nGfvjhhzzXvCjoPGcVh+Lj4/PNCPzv909Wu/xk/b7JazolDQ0NmJiYFNi/rAmFQkyZMgXXrl2Dp6dnoUWNrF/3/P58ZJ2foqzxUtxj5+fx48dYsmRJjm0dOnTILmoYGBgoJS8REZGqlLvpp65fv46+ffvC2toaAoGg0FdVgY+LWDVu3BhSqRQODg7YsWOHynMSEREREVHFwHsMoHXr1gBQ6HQ9L168QEhICKpVq5ZrqqrIyMg8v3wPDw8HUH6+5MzIyMDSpUthZWUFb29v7N27F7/++isWL16MRYsWQSaTqTtikf17SqcsWQWHv/76CwqFIt8f48aNy+7j4uKCw4cPIzo6Grdv38b333+PsLAwDB8+HJ6eniXKlpUj69f/v7KmPMurQPJvWb9vIiIicu3LzMxEVFRUifKpkqmpKYD/FY4KoqOjAysrK7x9+zbPKaOy1qbIWqtCmcfOz/jx43P9fslaHDwrS2JiYvavYWnyEhERqUK5K2okJSWhQYMGWLt2bZHav337Fr1794abmxseP36MuXPnYvLkyTh//ryKkxIRERERUUXAe4yPC0sLhUJs3rwZHz58yLdd1hoGEydOzLUvIyMjz2mSbty4AeDjOgpZshZELsqaA8oWGRmJuLg4tGrVKtfT/w8ePEBKSkqZZ1KmFi1aAEC+U1YVRCwWo2XLlliyZAn+/PNPKBQKnD59ukQ59PX1UbNmTfj7+yM4ODjX/qwvyfOaMuzfGjRoAOB/v4/+7fbt2znWSikv7t69C+DjWzBF0aFDByQlJeVZQMr6e6V9+/YqOXZJdOjQAQBw4cKFXPuy8ma1ISIiUodyN/1Uz5490bNnzyK337BhA+zt7bFixQoAgJOTE27evIlVq1blWjStvHr41W8I97hb4v5CkQgmLRqg3leToW2V+5VdIiIiIqKqrCreY/xXnTp1MGfOHKxatQp9+/bFsWPHckwvJZfL8dNPP2HPnj2oVasW5s2bl+c4CxYswMWLF7MXu37//j1Wr14NqVSKESNGZLfLWoA6ayqrsmRubg4tLS14eXkhOTkZ2traAICYmBjMmjWrzPMoW/PmzdGiRQvs378f/fr1w/Dhw3Psl8vluHHjRvaXzg8fPkTt2rVzvTGR9YaFpqZmibOMGzcOixYtwvz587Fz587sN0uePn2KHTt2wMDAAAMGDChwjP79+0NfXx/btm3DzJkzsxcLT09Px8KFC/Psk56ejtevX0MsFqNWrVolzl+QZ8+eoW7duhCLxTm237p1C7/++ivEYnGuqbsiIyMRGRkJU1PT7DcqAGDq1Kk4cOAAvvvuuxx/fv755x94eHigW7dusLOzK9WxlWnChAlYvnw5fvrpJ/Tv3z/7bZrHjx9j//79cHJyQtu2bVV2fCIiUr64l2/gu2oHoh/5AApFmR47OTO98EbFVO6KGsV1+/ZtdOnSJce27t27Y+7cueoJVAIJ/u8Qdf9Zqcb4cPsRQs5dR2+v4xBplfyilIiIiIioqqsM9xj/lpiYCF1dXfz222+Ii4vDtm3bULt2bfTu3Ru1atVCfHw8Lly4AD8/P9SuXRtnz57Nc8ogKysrJCUloX79+ujbty+SkpJw6NAhREVF4c8//8yxSLebmxsEAgEWLFiA58+fw8DAAIaGhpg5c6bKP69QKIS7uztWrFiBBg0aoG/fvoiPj8c///wDOzs7WFtbqzyDqu3fvx9ubm4YMWIE/vjjDzRu3BhaWloIDAzE7du38eHDB6SmpgIAdu/ejY0bN6J9+/aoVasW9PX14ePjg7Nnz8LY2BgTJkwocY6vvvoKZ86cwe7du+Hr64vOnTsjIiICBw8eREZGBjZv3gw9Pb0CxzAwMMCff/6J8ePHo1mzZhgxYgQMDAxw+vRpaGlp5VrbBQCCg4Ph5OQEOzs7BAQElDh/QVasWIEzZ86gbdu2sLW1hVgsxvPnz3HhwgUIBAKsXbs2V0FlzZo1WLJkCRYtWpRjEXc3NzdMnjwZW7ZsQePGjdG7d2+Ehobi4MGDMDY2xl9//VXqY2dZvnw5dHV189zXo0cPtGzZstDP7ujoiMWLF2PhwoVo0KABBg8ejISEBBw4cAAAsHnz5lzr62zZsgU3b94E8LEok7Ut642dtm3bYvLkyYUem4ioqlIoFEgOCkVmWtGmyYy6/wxeX/+O5OBwaEglBbbNTC37tcT+LQXKf3O3whc1wsLCci1MZmFhgfj4eKSkpGQvBvdvaWlpORaGK2zhsooi/sUbRNx4AOtufGKCiIiIiKikKss9RkhICP755x/4+vrik08+Qf369bF161aMHDkSmzZtws2bN3Hs2DHo6OjAyckJ06dPx4wZM/L8fAAgkUhw8eJFfPPNN9i9ezdiY2NRt25d/PXXXxg5cmSOts7Ozti+fTtWrFiBv/76C2lpabCzsyuTogYA/PLLLzA2NsaOHTuwbt06WFhYYOTIkVi8eDFcXFzKJIMq2dvb49GjR1i5ciWOHz+O7du3Q0NDA1ZWVmjfvj2GDBmS3XbkyJFITU2Fp6cn7t27h7S0NNjY2GDGjBn48ssvUb169RLn0NTUxJUrV/Drr7/i4MGDWLVqFbS1tdGhQwcsWLCgyE/zjxs3DgYGBvjxxx+xc+dOGBgYoF+/fvjtt99yTGtWlvr374/Y2Fg8efIEFy9ehEwmg6WlJUaMGIG5c+eiefPmxRpv48aNcHV1xaZNm7B69Wro6upi4MCB+Omnn3IVKEpz7Kw3zPJiaGhYpKIGAHz77beoUaMG/vjjD6xfvx4SiQTt2rXD0qVL0bhx41ztb968iZ07d+bY5unpmWPKLRY1iIhyS4uKgfeyTfBZvrXEY6i7aFEQbyRBDxpKH1egUJTx+ybFIBAIcOzYsQJfV3V0dMSECRMwf/787G1nz55F7969kZycnOcF+eLFi7FkyZJc2+Pi4gpdxEwVPAZ9iqBjF5UyVosNP8Bx2ojCGxIRERER5SE+Ph4GBgZquzZWtapyjwEAhw4dwo4dOyAWi2FpaYkpU6agadOmJRora/5+VT0VT0RERFQUycFhCP7nOpKDw9UdpdRebz+KpHe516WqLC4gFnvwAQ6Qwh9pSr0urvBvalhaWmbPBZolPDwc+vr6+T5hNH/+fHz++efZP4+Pj4etra1KcxIRERERUcVQWe4x3Nzc8OLFCzx69AhRUVFYv349Jk6ciDZt2qg1FxEREdG/RT/ywdu9J5EYUPAX/NGPfJD4puzX66KSaQQdHEYUbP6/qKFMFb6o0apVK5w9ezbHtosXL6JVq1b59pFKpZBKpaqOVmQGdWsitU3u1zcLkpmc+nFhFyIiIiIiUqrKcI8BAGZmZpg1axY2btyIu3fvIiEhAZs2bUJqaio6deqUvagzERERUUllyv63BkTYpVt48dduJLwOKvJ1Rvyrt6qKRoUwK+b30YVJyUyHb0IUGhtafhwfwHZZA4iFGvC4e1Spxyp3RY3ExET4+/tn//zt27d4/PgxjI2NUb16dcyfPx/BwcHYtWsXAGD69OlYs2YNvvrqK0ycOBFXrlzBoUOHcObMGXV9hGJr9PMXxe4T9/INTtbtoYI0RERERESVS1W8x8hiZGQEd3d3iMVi3Lx5EwKBANu3b0daWhp69uzJwgYREREhIyUV8S/eQCGXF7mP/7bDeLVunwpTUXFoV7OATd9OhbaTmBjCpm8nmLVooNTjh4aGonXr1ggNDYWXlxecnZ2z98XHxwMGlbyo8eDBA7i5uWX/POsV7nHjxmHHjh0IDQ1FYGBg9n57e3ucOXMGn332GVavXg0bGxts2bIF3bt3L/PsRERERERU/lT1ewx9fX1Mnz4dEokEV69ehUAgwO7du5GamooBAwZAKBQWOgbX0iAiIqr4FAoF4nxfI+rBM0D+cZnl579vQZyPfyE9qSzZDe0Ji47Ni9RWbKAHS7cW0La2UHGqgllaWsLZ2RkKheJjEUPFyvVC4WWlIi6GmN+bGlwonIiIiIhKoyJeG5dH5fE8pqamYufOnbhw4QIkEgnkcjkGDhyIIUOGQCQqd8+7ERERUQllpsnw/tQVRN57CkVmJgBAIVfgxR871BuMCtR230rYDe0BYQW5Lrtz5w6aNGkCsVgMAIiIiICWlhb09PRytFPFdXHFOENERERERERUKpqampg4cSIkEgnOnj0LkUiEI0eOIDU1FaNHj86+ISUiIqLyIz0pGS/+3IUPnl7ITJMV2l6RkYlwj7tlkEy1tG2t8t+pUEDbxhJ1Z3+Car06lF0oFRFKxBBpaao7RrEsWrQIP/zwA5YsWYLvv/8eAGBubl5mx2dRg4iIiIiIqIoQi8UYO3YsNDU1cfz4cYhEIpw6dQppaWkYN24cNDUr1g01ERFRRZOekIjM1PyLEymhEXj45W+IfuQDAEj7EF1W0ZRO09wEJk1ditxez8EONcf2h0lTVxWmImWoW7cuACA8PBwKhaLM12ljUYOIiIiIiKgKEYlEGD58OCQSCQ4fPgyxWIzz589DJpNh0qRJ0NbWVndEIiKiSiVTJsO7v8/Bc8w8dUdRGbuhPaFb0wbA/xat1q1ho+ZUpCzJyckICwtDzZo1AQAjRoyAo6MjmjRpopY8LGoQERERERFVMUKhEIMGDYKmpib2798PhUKBK1euQCaTYcqUKeVmHRAiIqKKIDHgPcKu3kV6fGKufRHX7yPw6AU1pCqZxr99BS3rok8jpGVlBvPWjaGhKVVhKlKn58+fY+DAgRCLxXj48CE0NTUhEAjUVtAAWNQgIiIiIiKqkgQCAXr16gWpVIrdu3dDoVDg5s2bSE9Px7Rp02BkZKTuiEREROVO6OVbCPnnOmSx8QCAwKMXIYuJU3Oq4jN0cUSLDUsg1tMBAEiMDaFjY6nmVFQeWVpaIj4+HiKRCG/evIGzs7O6I7GoQUREREREVFUJBAJ06dIFmpqa2L59O+Li4nD37l3IZDLMmDEDZmZm6o5IRESkdgq5HC/X7sH92T+qOwoAwKBe7SK1k+jrwqp7W7jMnwahWJxjX1mvgUAVS1BQEGxtbQEAJiYmOHXqFGrXrg1DQ0P1Bvt/LGoQERERERFVcW3btoVUKsWWLVsQGRmJR48eYfXq1XB3d4e1tbW64xEREalMSkQUbk+cj5DzNyEU5/1VaWZKahmnyqnGiN7Q0NaCZcfmsB/dDwKhUK15qPJSKBT4+uuvsWrVKnh4eKBNmzYAgGbNmqk5WU4sahARERERERGaNWsGqVSKjRs3IjQ0FM+fP88ubNjZ2ak7HhERUaHkmZmIfuiN5ODwQtvKYuJxe9KCHNsyMzJUFS0X3Zq2cJg8NN/9Ii1NmLdvBpPG9cosE5FAIEBkZCQyMjLwzz//ZBc1yhsWNYiIiIiIiAgAUL9+fcyaNQvr169HYGAgXr16hT///BMzZsyAg4ODuuMRERHlkBIeieCz15AcFIqU0A94tWG/uiMVqt5XU+AyfxokhvrqjkIEAEhNTUVmZiZ0dD6ur7Jy5UoMHjwYvXv3VnOy/LGoQURERERERNnq1q2LOXPmYN26dXj9+jXevHmTXdhwcnJSdzwiIqpk4nz98Wb3CcT7vStePx9/xPn4qyhV8Yj1dCBPz4CuvQ0a/fIFjBrm/vdSKBFDy9KMa1lQueLl5YWxY8eiQ4cOWLduHQDA0NCwXBc0ABY1iIiIiIiI6D9q1qyZXdh48eIF3r9/j7/++gtTp05Fw4YN1R2PiIgqGHl6evb/h170xIs1e5D4Ogjxr96qMVXpOM4YBZf506Bja6XuKEQlFhsbCx8fH0RFReGnn36CkZGRuiMVCYsaVVByaAQkBnoQaWupOwoREREREZVTtra2mD17NtavX4+nT58iPDwca9euxZQpU9C8eXN1xyMionIoPTEJ8a8Csn/uv+VvvFq/T32BSqjWhMEQSsS5tgs0hDBp6gq7Id0h1tNVQzKi0ktJSYGW1sfvhTt16oQdO3agT58+FaagAbCoUaUkBYXi7oxFCD7jAQgEcJwxEs3+/A5CDQ11RyMiIiIionLIysoqu7Dh5eWFmJgYbNiwAWlpaWjXrp264xERkQopFArE+fgj2us5FHJ5IY2BR/NXICXsQ9mEUwGRjja6XNwOk2auEIr4lSlVPjKZDD/++CN27dqFx48fw9DQEAAwbtw49QYrAf4JrSI+3H0Cj/4zkBoe+XGDQoFX6/bBpIkLHCYOUW84IiIiIiIqt0xNTTFr1ixs3LgRd+7cQWJiIjZv3ozU1FR06dKFc4MTEVVCCrkctyd/i9fbj6g7SonZ9OsM5y8mFt5QABi6OEJqZKD6UERqlJGRgQMHDuDdu3fYv38/ZsyYoe5IJcaiRhXw7vA53BwzD/I0Wa59IRdusqhBREREREQFMjQ0hLu7OyQSCa5fvw6BQIAdO3YgNTUVffr0YWGDiKgMZaSk4uVfuxFx8yEy8/iuRxlCL9xUybhFpW1jWfTGCgU0LUxRZ+YY2A7oAgCQGOrz3yYiAJmZmdD4/1l6tLW1sXPnTrx//x5Dhw5Vc7LSYVGjknt35DxujPgMiszMPPdnJCaXcSIiIiIiIqqI9PT0MG3aNEgkEly5cgUCgQB79+5FWloaBg0aBKFQqO6IRESVWnpCIhLfheC0ax91R1EqTXMTGDepB53q1qg5ph/M2zZVdySiSuHJkycYP348vv/+ewwcOBAA0KpVKzWnUg4WNSqxoJOXCyxolAcKhQKy6FiIDfW5tgcRERERUTmnra2NyZMnQywW4/z585BIJDh06BDS0tIwfPhwiDgHORFRscnT0xH9yAcZSSl57o/1foX7s38s41SqYTe0J3TtbQAA2jYWsOnXGbp21dSciqhyOnToEB4/foyFCxeif//+leoBFF5xVlJhHndxfchsKDIylDJecmgEnv+2BaEXPaFf2w4Nls6FkYtjicdLeh8Gv00H4b/1b6SEREDL2hwtNvwA276dlJKXiIiIiIhUQyqVYsKECdDU1MSpU6cgEolw7NgxpKWlYcyYMZBIJOqOSERUISS8CcSzpevwesdRdUdRiaarF0Jq/HGdCi1rc5i3bgwNTamaUxFVbgqFInvqte+//x4pKSn4+uuvK1VBA2BRo1JKeB2Ia4NnQZ6eXuqxFAoF/Lf+jYfzfkV6XAIAIO65HyJuPEA/33PQNDMu1lgRNx/gxR87EXTico43SFJCInBj2BwMfOcBLXOTUucmIiIiIiLVEYvFGD16NKRSKY4ePQqxWIwzZ85AJpNh/Pjx0NTUVHdEIqJyS6FQ4OboLxCw/7S6oxSLVdc2aPz7V4W207IwhZalWRkkIqIsGRkZ+PXXX/Hs2TPs378fAoEAUqkUK1euVHc0lWBRo5LJTE3DtUGfQhYdW+qxkkMjcGvc1wi96JlrX1pULEIu3ETN0f0KHUchl+P9GQ88X7YJH2555dsuMzUNH24+QPVB3UuVm4iIiIiIVE9DQwNDhw7NnoJKLBbjwoULSEtLw+TJk6Gjo6PuiERUhQSfu47XO44iKSBY3VEKFXn3idLHNHB2UPqYWaTGBrDp1xnOX0yEoJI97U1UWbx69QqLFy9GRkYGpk2bBjc3N3VHUikWNSqZh/N+VcqUU0GnruD2xPlIi4zJt01KcHiBYygUCgQeOY+nS9Yg1vtVkY6bzoXLiYiIiIgqDKFQiAEDBkBTUxP79u2DQqGAh4cH0tPTMWXKFBgYGKg7IhFVAcFnPXC173Qo5HJ1Rylzeg526PfiHNcpJarinJ2d8dtvv8HMzAwdO3ZUdxyVY1GjksmvoGE3tCeiHnoj8U1Qwf3lcjz+7g94/7yhVDlCL9/Co2+WI+qBd6nGISIiIiKi8k0gEKBHjx7Q1NTEzp07IZfL4enpCZlMhunTp8PYuOhT1hIRlYT/1sNVqqBhUK82HCYOhkX7ZjBp6qruOESkBj4+Ppg7dy62bdsGGxsbAMBnn32m5lRlh0WNKsDQtQ5a71iG0w0KnipKFp8IzzHz8P7UlRIfK/a5Hx589nOeU1YREREREVHlJBAI4ObmBqlUim3btiEmJgb379+HTCaDu7s7zM3N1R2RiCqxyPvP1B1BaerO/gSWXVrnuU9DUwrTFg0g0dct41REVN64u7vj2rVr+OKLL3Dw4EF1xylzLGpUchraWmh/cBVE2loFtksJ+4BL3SYi9tnLEh0nPSkZz5auhc+K7UqZ/kodFAoFBAKBumMQEREREVVYrVu3hlQqxebNmxEREYEnT55g9erVcHd3R7Vq1dQdj4gqoYTXgUgOClV3jFJzO70R1Xp15PcSRFQkGzZswPz58yvtQuCFYVGjkmv0yxcwcCp4sajEd8G41GU8EvzflegYQaeu4P7MH5AUGFJoW53q1nCeNxHGjevhfNuRJTqesqRERCHw8DkEHDyLmEc+kBgZoMHSOaj1yUC15iIiIiIiqsiaNGkCiUSCjRs3IiQkBD4+PtmFjRo1aqg7HhFVAkEnL8Nv4wEkvAlC/Is3+bYza9WoDFMVn0AsglnLhnCeNwmaZpyqj4jylpmZiT/++AMGBgaYPHkyAKBu3bo4duyYmpOpD4salZhpy4ao8+noAtsk+L/D+bYjkfw+LM/9QrEYLgum4emSNbn2pScm49akBXi97XChWXTsqqH+opmoOaYfhGIxoh/7FO1DKFladCwCj15AwMGzCL9yJ8ecm+kJSbg1/hvoO9rDrGVDteQjIiIiIqoMXF1dMXv2bKxfvx4BAQHw8/PLLmzUrl1b3fGIqAIL/ucarg38tNA1NGwHdEHHY+vKKBURkeocOnQI8+bNg46ODnr27Mm3X8GiRqXW/K/vINTQKLBN/Mu3+e7TNDdBh6NroFvTNs+ixrOlawvNIDU1gutCdzhOHwkNqaTw0CqQKZMh+IwH/LcdQci5GwVPj6VQIPjsNRY1iIiIiIhKydHREbNnz8a6devg7++Pd+/eYfXq1ZgxYwbq1aun7nhEVIHIMzIQdf8Z4v0CcGvc10XqU62Pm4pTERGVjeHDh2P//v3o378/rK2t1R2nXGBRo5KqObY/TJq6lri/ft2a6HJ+G3SqWyM5NKLY/QUiEZw/nwCXb2eUegGr1MhopIRFwrBe7WLNLRn73A/+2w7jze4TSPsQXeR+stj4ksQkIiIiIqL/sLe3x5w5c7Bu3Tr4+voiJCQEa9aswdSpU9GoUfmeFoaI1CslIgrvT1xC5P1n8N98qNj9HSYOUUEqIiLV8/Pzw5o1a7By5UpoaGhAKBTixIkTXHPnX1jUqIQEGhqov2hWifsbN3JG5/PbSjyfo3m7pmi+bjGMXBxLnAEAIm554cmiPxF26RYAQL+OPbpc3AEdW6t8+2TKZAg8fB4v1+zBh9uPSnV8IiIiIiIqPRsbG8yZMwfr16/HkydPEBERgbVr12Ly5Mlo2bKluuMRUTn0/vRVXO07rUR9hVIJunns4Zd/RFQhpaamol27dggPD4e9vT3mzp0LAPw77T9Y1KiE7Ef3hV6t6iXqa9qiATqf3waJgV6x+4r1dND0j29Ra8LgUv1B+3D7EZ4s/guhF27m2B7/8i3uui9Gp1Mbc/VJCfuAVxsPwG/DAaSEfSjxsYmIiIiISPksLCwwa9YsbNiwAQ8ePEBcXBw2btyI1NRUdOjQgTfqRFWcQqGALDoWAPB06Tq8WL2z2GNY92gHPQc71PxkAEyb1VdyQiKisqGpqYkffvgBhw4dwsCBA9Udp9xiUaMScv5ycon6GTg7oNPZzSUqaJi1bow2e36Hnr1tiY6dpbC5MSOu38/x81gffzxfthEBB85Cnp5erGMZN66HaK/nhbZTKBT4cPsRAvafRpyPP/Qc7OAyfxp0a9gU63hERERERFWZiYkJZs6ciU2bNuHWrVtITk7G1q1bkZaWhm7durGwQVRFeX3zO57/urlUYzRYOhf1F7orKRERUdmRy+VYu3Yt3Nzc4OLiAgCYMmUKpkyZwmujArCoUUEJhMJ89+U37ZMsLiHfPtq2Vuh8fhukxobFOtbHqa5mwmX+NAhFqv/tlJmSBgCIeugN7583IPDohWL117W3Qa3xg1BjZB/o166BPSInKDIz82wb/+ot3u49iTd7TiLxTVD29rArdxB2+TZ6PzoOsV7p1gshIiIiIqpKDAwMMGPGDIjFYly/fh0CgQA7d+5EWloa+vTpA2EB9x5EVLnIMzNx2KpNsdbAzEUggN3QHqhXwoc7iYjUbdGiRfjxxx/RpEkT3L59G2KxmMWMImBRo4LStbeBxMgAspi4HNs7HF2Tbx+JgV6eFwsaWprodHojdGws8+ynaW4CnerWSAoMybFdamqE9n//CcuOLUrwCUpGnp6Oyz0mIeT8jSL30dDShN2Q7qg1cQgs2jcrsEgDAK/W7kXk3SeIuvc03zYJrwMR5nEPtn07FTkHAMgzMpAUGAINLU1oW5kXqy/lTZ6RgfT4REiMDPiXPhEREVEFoKuri2nTpkEqleLSpUuQSCTYu3cvUlNTMXjwYGhoaKg7IhEpSWpkNN4dPIuYpy+hUChy7Huz8xjksuLNuAAANv06o8HiWYAA0KlunefDmUREFcWMGTOwc+dOTJw4kddAxcCiRgUlFInQ8KfPcM99cfa2GqP6wnZA13z7iHS08tzectNSGNWvm28/gUCAhj99hlvjv8l+q8G4kTM6HFsLXbtqJfsApVDUgoZ+3ZqoO3MM7Mf0L9aUWgq5vMCCRpZ3h87CpnfHQosk8vR0hF29i3eHzyHo2EWkRcZAIBSi9rQRaL52UZX+Ij5TJkPs05dIfPseWtUsYNaqUYHnIzM1DTHPXiL6kQ+ivXwQ/cgHsU9fIjM1DSIdbbTctBT2o/qW4ScgIiIiopLQ0tLCxIkTIZFIcPbsWUgkEvz9999IS0vDiBEjIBaL1R2RiEopLSoGF9qPRpzv61KPVWv8ILTc8hMEQmGVvocmooovICAAN2/exJgxYwAA1tbW8PPzg1QqVXOyioVFjQqszoxRMGnqgoibD2Ho4gjLTi0L/MfdqktrxDx5kXOMmWNQc0z/Qo9Vc0x/GLrUxgdPL+jYVYN1z/YQlsfqoUAAmz5uqDNrLKy6tFbpxc7bPScR9cAb3Tz2QMvCNMe+TJkMYZdu4d3h8wg6finXGzUKuRyv1u+DebumsB/ZR2UZVU0Wn4jIO4/xwfMhIjy9EHX/GTLTZKg+uDtabliSY3ouhUKBxDdBiLz75OOPe08R/cgH8jRZdpsaI3qj7d4VEAiFkMUlIOax78cCxv8XMeJ8X+c7XVhGUjJujv4CQccvISMpGWlRsdBzsEPj376EtrWFys8FERERERWPVCrFuHHjIJVKcfLkSYhEIpw4cQIymQxjxozhzT1RBffu73NKKWi03r4MtcYPUkIiIiL1evv2LerXr4/U1FQ4OzujcePGAMBrnhIQKP77/l8VFB8fDwMDA8TFxUFfX1/dcVQmIyUVN0Z+hvcnr0AgEMDx09FouuIbCNXwFFRySDiOVGuX5z6BSIQ67qOQEh6JdwfPFmk8DU0pHCYPhdNn46FXs3qRcxS0pkZR1Rw3EG12/Ap5ZibCr97Bmz0nEXT8EtILWMMkS+1pI9Byww+lOn5ZUSgUSAoMwQdPL0R4PsQHTy/EPnsFhVyeZ3ub/p3hOHU4Iu89ReS9p4i69xRpUbGFHkdsoAdNcxMk+AUoJbd2NQsM8L8EDc3C/4GQxSUg9tlLxDz9/x9PXiD+5VtIDPVQc2x/1F80q9A3c4iIiCq6qnJtrGo8j0WXmZmJo0eP4siRIxAIBJDJZOjSpQvGjx8PLa283zYnovIrKSgU0Y994dFveon6O7qPgpaVOXRsLWHdo32uhwiJiCqyoUOHIiwsDDt27ECtWrXUHadMqOK6mG9qVCEiLU24HV+PtKgYiHS0i/Qlr6poW1vArHVjfLjllWO7Tf/OaPLbV9B3tMe9mUsKHUespwPHT0fD6bMJ0DI3KXYOgUij1EWNNzuPQcvCFG/3nkRycHix+mampJbq2KqkUCiQ4BeAsKt3Ee5xFxE3HhTr870/cRnvT1wu9nHT4xKKVBAqquTgcIR53EW1Hu2zt8kzM5Hg/w6x/ypexDx9iaR3wXmOIYuJw9Mf1kKsrwvnLyYpLRsRERERARoaGhg8eDCkUikOHDgAsViMixcvIi0tDZMnT4aurm7hgxCRWmWmyRB6yRNX+0wr1TgNlsxG/e9nKikVEZF6KRQK7Nu3D4MHD4ampiYAYPv27dDS0uL6GaXEokYVJDUxUncEAIDbyfW4N2spIm4+hFH9Oqg7Zxysu7bJ3q9VwELaEkN9OH0xEXVnjoHEsOQVPpMmLrkKK1nMWjdGzU8GwLxtE5xy6V3gOM9/21ziDFkUCgUi7z3F+xOXkBgQDNOWDVF7yjCItDRLPXZRj5/4NuhjEePqXYRdvYOUkIgyObaqvTv0D5LehSDa6/nHdTi8/UpUUAo8dhHOX0zKfmsl5ulLyGXpMKznAIO6eVfX5RkZSPALQMyzV4j19kNKaAR0bK1QZ9ZYSI0MSvvRiIiIiCoFoVCIvn37QiqVYu/evZDL5bh27RpkMhmmTZsGAwNeNxGVR6kfovFqw348+X51kfuIdLRzbdOyNof9qD5wXeiuzHhERGo1fvx47Nq1C0+ePMFvv/0GAHxYQ0k4/RT4anh5FeP9Cv80G4zM1LTsbRpamnCaOw7OX05WyhfCUQ+e4YLbJ8hITAIASE0MUXPcQNSePBQGTg7Z7U7W64U4H/9SH++/ao7tjzozx+Dd3+fw7u9zud4UsBvaE+0Pfbw4TIuKQdDJK4h+6A2Rng4cp48s9ULtqR+iEXLhJkIveiL86l0kBYaUaryqwLxtE8Q8fYn0+MTsbQINDTRftwjVerRHzLOXiPX2Q+yzV4j1foU439eQy9LzHKvx718j+X0Y0iJjoFfLFk5fTIJEn/+4ERGRevHaWDl4HktGoVDgxo0b2L59OxITE5Geno5mzZph+vTpMDEp/pvZRKQasT7+8Pr6dwSfvlqsfhZuLdHtyi4VpSIiKl9OnTqFYcOG4ZdffsHcuXPVHUdtVHFdzKIGeMNRnoVevgXvZZuQGhYJq66t4fzlZGgX8AZHSchi4xF60RNSUyOYtW4MDakkV5s4X39cHzYXsd6vij2+lpU5qg/qCv/tR5GZnFLs/i7fzkDk7UcIv3Y/x1RZWpZm6Hn/CHRsLIs8ljwjA5F3HiP43A2EnLuBaK/ngJr+CtC0MEVqeGTRGgsEMKhbE0aNnGHcyBnGjZ1h3NAJ7w6fw93pi9T2GZTNoF5t9H5wVK1TwxEREfHaWDl4Hkvnzp072Lp1K6KjoyGXy+Hq6opPP/0UFhYW6o5GVGVlymSIf/kWr7cfge+qHSUao9f9IzBp6qrcYERE5cT79+/x4cMHNGrUKHtbWFgYLC2L/t1dZcSihorwhoOKQqFQ4KLbWIRfu1doW01zE9QY0Rt2w3rCrFUjCIRCHDRpDll0rFIz2Y/ph9Y7foWwgHn4koJCEXLuOkLO3UDopVs53jAoDe1qFjBr0xhmbZpAkZGBh18sy7ethrYWTJrUg2mLBjBtXh+mLRpA29YKb3Ydw63x3+RoKxAKYeDsAJNmrjBpUg/GjevBsH4diPN4RRkAwjzuIuLmQyjS0yHUlOLxgpVK+Xzq0umfLTnW/yAiIiprvDZWDp7H0nv06BE2bdqE8PBwKBQK1K1bF59++ilsbGzUHY2o0lMoFEh4HYioe0+hyMzEq40H8MEz76mbi6r3o+MwbuispIREROWLp6cnevfuDRMTEzx9+hQ6OjrqjlRucKFwIjUSCASw7Nwq36KGhqYUtgO6oOYnA2DVtQ2EItX/8Xq75yQibnqh05lNMHT+OF2WQqFA9CMfvD9xCUEnLiPmyQulHMvQxRHm7ZvCvE0TmLVpDJ3q1hAIBNn7NbQ08XjhH5BFx0K/jj3MWjeGacsGMG3RAIb1aud5PmqNGwRD1zoIOXcdIh1tmDSpB6NGzvkWMPJi2bEFLDu2AACkxcThycI/oJDLi/XZRLo6MKpfB0b168Dw///7+LvVCL96p1jjKEPccz8WNYiIiIgANGrUCDNnzsTGjRvx/v17+Pr6YvXq1XB3d4e9vb264xFVWgqFAl5f/w6f37eUahyJsSFa71gGvVrVYeBUK8f9IxFRZePq6gp9fX2YmZkhOjqaRQ0V45sa4FNUVHRpUTE413Yk4l+8yd5m0aE5an4yANWH9ChwPQRVvKmRxdDFEU1WfIOgE5fw/uQVJL8PK/WY+nVrwtKtJSzdWsC8Q3NomRc+h3HWXyfqvFi93GMSQs7fyHe/rr0NjBo6wahB3Y8/6teBbg0bCITCHO2e/7YZXl//ruq4uTRZ/jWcv5hU5sclIiLKwmtj5eB5VB4/Pz+sW7cOAQEBAAA7OzvMmDEDderUUW8wokoqyus5zjYZWOL+jjNGofHvXxXrYTUioopGoVDg7t27aNmyZfa2N2/eoHr16hCVwYPOFQmnn1IR3nBQcWQkpyDo5GUo0jNg1rox9GpVL1K/M00GflzDopzSq1UdFp1awtKtJSw6Nlf62iVlJT0hEfdm/oDIe08B4P/X4Kj3cR2ORs5FXmA+JSIKV3pNQfRD7xzbtW2tYOBUC6EXbhbYXyASQb+OPYxcHWHo4ogPt7wQfPZaocdlUYOIiNSN18bKwfOoXO/evcO6devw8uVLCIVCWFlZYfr06XB15dz8RCWV8CYQj79dhQ93nkCRnp69PTk4vNhjCYRCtNm7HPYj+igzIhFRuZSZmYnBgwfjxIkTuHDhArp27aruSOUap58iKgdE2lolulCrM3MMbk+cn3MsXR3Y9HWD3dAeMGnqiuO1ukD+r4tJVRLpaMOyU0tY92gH6+7tilycKe/Eerpos/O3Uo+jZW6C7tf3IuzqHaRGREOvVnUYujpmF0Xi/QJwe9ICxPm+hlhPB4YujjB0qQ1D1zowdKkN/Tr20JDkXHQ+5PwNhF6+jcyUVGhamOLJd3+UOicRERFRVWBnZ4c5c+Zg7dq18PHxQVhYGNauXYspU6agSZMm6o5HVOFkJKfgQsexSA4KLfVYzdcuQh330UpIRURUMWhoaMDW1hZisRivX79mUUMN+KYG+BQVlZ3AYxcQsP8MxPo6sO7eDtX6uEGkpZm9/83u47g7YzEykpIhEAph3qE5qg/qiuoDu+LJoj/hv/VwqY5vVL8OrHu0h1X3tjBv0wQaUknhnUglkoPDcMQm99oZfFODiIjUjdfGysHzqBoRERFYt24dnjx5Ag0NDRgaGmLixIlo3bq1uqMRlXsKhQKxz/3w4ZYX3p+6iuDTV0s8Vv3vP4VxU1dYtG8GiYGeElMSEZVPYWFhkEqlMDIyAgAkJSUhICAA9erVU3Oy8o9vahBVcNUHdkP1gd3y3V9z7ADY9OuMxDdB0LGzhtTYMHtf01ULkBoZg/cnLhf5eBqaUlh2aQ3b/p1RrVcHaFtblCY+EREREZFamZubY/bs2diwYQPu37+PuLg4bNq0CampqXBzc+NCxET5kGdk4KRTTyT4vyvxGD3vHYbU1OjjmoT8s0ZEVcjp06cxbtw49O7dG7t27QIA6OjosKChRixqEJUzEgM9GDdyzrVdrKcLt+PrEXjsAq4Nmplvf6mpEWz6uMGmf2dYdW3DxdkqmPTEZGQkp0CkraXuKERERETlkrGxMWbOnIlNmzbB09MTKSkp2LZtG9LS0tCjRw9+2UpVUnpSMnx+24zwGw8hT03LsU+hUCDyzuNSjd9gyWyYNqtfqjGIiCoqU1NTxMbGwtvbG4mJidDV1VV3pCqPRQ2iCsamX2fYDuyKoGMXs7fp1LBB9UFdYTugC8xaN4ZQQ0ONCak0ni7+C08X/wVD1zroeGxtoWudpEXHIubJC0Q/8kHMY19EP34BgQAwa90YTVbOzzG9GREREVFloa+vjxkzZkAikcDDwwMCgQC7d+9Gamoq+vfvD6FQqO6IRGVGoVDAo/8MhF2+XeqxTJrXh65dteyfiw10Yd29HaoP7l7qsYmIKpKQkBBYW1sDAFq2bInz58+jQ4cOEIvFak5GAIsaRBWOUEMDHQ7/hbCrd5ASEgEDZwcYN67HJ9IqmdhnL3F9+Fz0fnAUwMcblaTAkI+Fi0c+iH7si5hHvkgKDMmzf8zTl0gJj0THo2vLMjYRERFRmdHR0cHUqVMhkUhw8eJFSCQSHDhwADKZDEOGDIEGH/ShKsJ/699KKWjoO9qj193SreNIRFTRxcXFYfLkybh27RqeP38OMzMzAECXLl3UnIz+jUUNogpIIBTCqjMXQ6zsoh964+bYeUgOjkDMY1/IYuKK1T/4jAfSE5Mg1tVRUUIiIiIi9dLU1MTEiRMhlUpx5swZiMViHD58GKmpqRg1ahSfpqRKL+DQWdyZsrDU4xi6OKL94T+VkIiIqGLT0tLCy5cvERMTg2vXrmHIkCHqjkR5YFGDiEgNRHq6EAiFUMjlBbZ7u+dkiY8hl6UjJSwSSWkhiH7si8zkFBjUqw3z1o3/1yY9HekJSZAYGfBtHyIiIqqQJBIJxo4dC4lEghMnTkAkEuHkyZNIS0vDuHHjIJVK1R2RqFgUCgXeHTqL0Eu3kJGYnG+7jJRUvD9xucTH6XHrIDTNTSDS1YaWhWmJxyEiquhiY2NhYPDxexGJRII9e/YgIyMDjRs3LrwzqQWLGkREaiDR14V5u6YIv3ZPpcc55dwL8vT0nMc2NoRtv06IefICsc/9IJelQ2Koj9Y7f4Vtv84qzUNERESkCiKRCCNHjoSmpiYOHz4MsViMc+fOQSaTYeLEidDW1lZ3RKI8ydPT8eznDQg5dwPp8YkAgDgff6WMre9oD4mRfo5tGtpasHRrgXpfToaGJgt+RETHjx/HtGnTsHz5cowdOxYAUL9+fTWnosKwqEFEpCZt963AjeFz8eH2YygyM1VyjP8WNABAFh2L1zuO5twWGw+P/jPQescy2A3tiZhnrxDzxBcxj18g1vsVhFIJrLq0Rr0vJyM9MRliPR2+2UFERETlilAoxKBBgyCVSrF//36IxWJcvnwZ6enpmDx5MvT09NQdkQgAkPguGLLYeADArfHfIOaxr9KPUX/xLDRYNEvp4xIRVTbPnz9HREQENm/ejDFjxvC7jgpCoFAoFOoOoW7x8fEwMDBAXFwc9PX1C+9ARKRE8sxMBJ++Co8B7kXuo6GlCaMGdWHc0AlGDZ0Q6/0KL9fsUWHKnIRSCZy/mIiGS+dCIBSW2XGJiEj1eG2sHDyP6qNQKHDp0iXs2bMHKSkpSE9PR8uWLTFt2jQYGhqqOx5VYQlvAnG173SlvYmRH7uhPdH+0GqVHoOIqCJLTU2FpqYmACAjIwPr16/H1KlTOWWliqjiuphFDfCGg4jULzM1DefajEC01/Nc+6SmRjBu5AzjRs4waugE40ZO0KtdA0INjew2flv/xp3J35ZlZACAlqUZBgVdg1DEF/+IiCoLXhsrB8+j+t28eRPbtm1DfHw8MjMz0bhxY8yYMQOmplw7gNRjn5YrMlPTVHoM3Zq2GOB3kQ8eERHlITo6GrNnz8aHDx9w7tw5vpVRRlRxXcxvoYiIygENTSm6Xt6Jl+v2IuF1EHRsLGDc1BUmjZ2hZW1R6D+01l3blFHSnFLCPuDxwlVovOxLtRyfiIiIKD9t27aFVCrF5s2bERUVBS8vL6xevRru7u6wsrJSdzyqAhQKBQIOnsH7U1cRsO9UqcaSGBtCQyLOd79QUwrzto3R+LevWNAgIsrHhw8fcOTIEchkMty/fx/NmzdXdyQqIb6pAT5FRUSVg//2I7g77fvsdTR07W1g1NAJQccu5m4sEABK+utfr1Z1DPC/VGg7hUKBlJBwxDx5gegnLxDz/z9k0XHQc7BDw58+g1nrRtCQSJSSi4iISobXxsrB81h+PHnyBBs3bkRYWBgUCgXq1KkDd3d3VK9eXd3RqJLzXrYRj+avKHY/qZkxjBs6QSgWwbRlQ9SdNRYSQ/49QkRUEpmZmdD410wXu3fvhqOjI1q0aKHGVFULp59SEd5wEFFlkZGcguSQCGiaGuW48Qk5fwOxz/2goaUJ40bOMHR1hIZEjL2Seko5bu1pIxDn+xoSAz3UGNUH1Qd2Q5yPf44CRuzTF0iLii10rOqDu6PNrt8g0tZSSjYiIioeXhsrB89j+eLr64v169cjKCgIAFCzZk24u7ujVq1aak5GlZVCLscejbrF7uc8b9LHty04JQoRUamdPn0an332Gc6dO8d/89WIRQ0V4Q0HEVVVmTIZHsz5Ce8On0N6fCIMnB3+twB5g7qI9wvAk+//RGp4ZJlna75uMdI+RH8sxmhKYd29HeyG9URKWCQ0zYyhIeUbHUREqsBrY+XgeSx/Xr9+jXXr1uHNmzcQCASwtbXF9OnT4eTkpO5oVMkkvA7EudbDkRoRVWjbBktmQ8fOGgKRCKbN60PPwY4FDSIiJVAoFOjcuTOuXr2K8ePHY/v27eqOVGWxqKEivOEgIvr4D35+N1CZMhmEYjGO2rZHcnB4GSfLm9Nn49H4969zLJguz8yEQCjM83NkJKcgLToOWpamXNiciKgAvDZWDp7H8ikwMBBr167Fq1evIBAIYGVlhWnTpqF+/frqjkaVyPkOoxFx/X6h7druWwn7kX3KIBERUdXx7+823r59i40bN2LRokXQ0uJsEOrCooaK8IaDiKhobk9dCP/Nh9QdI1uNUX1h3NAJMU9fIPbZK8T5voZCroC2tTnqfTMVKaEfEPvsJWK9/ZDwOjB7HRELt5awH90XNn07QcvcRM2fgoiofOG1sXLwPJZfoaGhWLduHby9vSEUCmFiYoIpU6agWbNm6o5GFZRCocCbnccQeOwiUkIjEHX/WYHta4zqi/rfucOgLqdCISJSlri4OHzxxReoW7cu5s2bp+449C8saqgIbziIiIrm5do9uDfzB3XHUCqxgR7qzhoL53mTkBISjlhvP2Qkp8CwXm2YNHVVdzwiojLHa2Pl4Hks3z58+IANGzbAy8sLGhoa0NfXx8SJE9G2bVt1R6MKRJ6RgUfzV8Bn+dYi9+nmsQcWHZqrMBURUdW0e/dufPLJJ9DS0kJgYCBMTU3VHYn+H4saKsIbDiKiokkJ+4BzbUYg8U1QsfppmpvAqEHdHD/0alWH97JNeLv3ZLHHKyv6dWuiz+OTEIg+TnH176musshi4xH73A8J/u8gMTaEedsmkBoZlHVUIiKl4bWxcvA8ln8xMTHYuHEj7t69C7FYDC0tLYwdOxadO3fmmgZUJPc/+xkv/thR5PZOn41HkxXz+fuLiEgFFAoF3N3dMWrUKLRr107dcehfWNRQEd5wEBEVXVpUDPw2H0JK6Afo2FnDqKETou49xauNB5EaEQW9mrYfCxf162QXMLQszQocM/7VW5yo072MPkHp1PtmKjJT0hDn44/Y535ICYnI1abN7t9Rc0x/NaQjIio9XhsrB89jxZCQkIDNmzfjxo0bkEgkEIlEGDVqFHr16sUvnquglPBIhJy/UeQ15B4vWFnksesvmokGi2eXNBoREf3HpUuXsGbNGvz9998Qi8XqjkMFYFFDRXjDQUSkfumJSXjx124kBQR/fLOjfh0kB4fj1fr9SA6JQEZikrojFkvrHctg069zdvEj7rk/Yn38kRwYCrGBLmwHdoWOrRXSE5Jg4FQLlh1b5DuWQqFA8vswxPn4IykoFNrVLGDZqRU0pJIy/EREVFXw2lg5eB4rjuTkZGzbtg1XrlyBRCKBQCDAkCFDMHDgQAiFQnXHozIQ9eAZHn/3B0LO3VDJ+FpW5ujtdazQB32IiKhoEhMTUaNGDURFRWHlypX47LPP1B2JCsCihorwhoOIqPx7d+Q8rg+dnb3Yd1GJdLSRkZSsolTKNTjkJjISkhDn+/p/P3z8EffibZ5Fnc7ntsKgXm2khEZAx9aKN8pEpBS8NlYOnseKJS0tDTt37sSFCxcgFouRmZmJgQMHYujQoRCJROqORyoQcOgs3uw8huCz11Qyvl7tGpCaGMK4sTOc5o6Hfu0aKjkOEVFVtX//fnh6emLZsmXQ1dVVdxwqAIsaKsIbDiKiiiH4rAcCDpxBWkw8DOrWhFH9OjB0dYSBkwMUmZl4tfHAxzc9LExg6OIIQ1dH6NpVg0AoRPQTX0TefQJFRiYS3wTBZ8U2dX8cpXOeNwmNf/0SAj5VSkSlwGtj5eB5rHjS09Oxd+9enDlzBiKRCOnp6ejduzdGjx4NiYRvR1Z0aTFxSA4KBQAEHDwL7583qOQ4UhND9H95HlITI5WMT0RUFSUmJuKbb77B+PHj0bRpU3XHoWJiUUNFeMNBRFT1yOIT8WDOj4i8/wwCgQCGLrUhi4lHyHnVTDtQVrSrWaDxb1/BflRfdUchogqK18bKwfNYMWVmZuLvv//GsWPHIBQKIZPJ0K1bN4wbNw6amprqjkclkCmTwfOTrxD49zko5HKVH6/HrYMwa9VI5cchIqpK5syZgz///BMuLi54/PgxNDQ01B2JikEV18V8j5aIiKokib4uWm9flmt7WlQMLnYZj5jHvhBoaECRmZlnf01zExi61IZ+3Zp4tW6fquMWWXJwOG6O/gLJweGo9+VkdcchIiKqUDQ0NDBs2DBIJBIcPnwYYrEY58+fh0wmw8SJE6Gjo6PuiFREyaEReH/qCu5O+75MjqdlbY4GP8xhQYOISAW+++473LlzBz///DMLGgSAb2oA4FNURESUm0KhgCIjAwKRCO9PXkbcizcQ6+nAsF5tGNRzgKapcXbbeL8AnHDspsa0uena22DA68sQCATqjkJEFQyvjZWD57FiUygU+Oeff7Bv3z6kp6dDJpOhTZs2mDp1Kn89K4B4/3e40H40UkIjSjxGy80/wrZ/l6I1FgBSEyNedxERKcn169dx//59fPHFF9nbFAoF/56toPimBhERURkRCAQQiMUAANv+XWDbP/+2+rVrwO30RtybuRTJgSHQNDeBQT0HGDg7wLBebRj+///H+73D+xOXIItNgFAqwYs/dhScQSSCnkN1GDo7QLemLXyWby1y/sS37yGXpUNDyjnAiYiIiksgEKBnz56QSqXYvXs35HI5PD09kZ6ejunTp8PIiOsllBeBR8/j5dq9iPd7l70ta+2M4qozcwwcJg2BcUNnZcUjIqJi8vX1RceOHQEArVu3RqtWrQCABQ3KgW9qgE9RERGR8ijk8iIv1K1QKODz+xa8P+OBjKQU6Nexh4FTLRg614KBUy3oOdhB+P+FlSxhHnfh8/sWpEZEIeqBd4Hj69etCU0zY4j1dGDTvzMcp44o8ecioqqD18bKwfNYeXh6emLbtm2Ii4tDZmYmGjZsiBkzZsDc3Fzd0aq8kIueuNxtQqnHMW3ZEJ3PbYXEQE8JqYiIqLQmTJgADQ0NrFixAgYGBuqOQ6XEhcJVhDccRERUESWHRsBjgDui7j0tUvsmy7+G8xeTAACZaTIINIQQivjSJhHlxGtj5eB5rFwePHiALVu24MOHD5DL5ahXrx7c3d1hbW2t7mhVUmaaDOEed3G5x6QS9Tdv3wy1xg8CAOjXsYdp8/q8JiIiUpPk5GQsX74cn3/+OXR1dQEAmZmZXDujEmFRQ0V4w0FERBWVQqHA3enfw2/TwSK117GrBqFEjAS/gOxtXS7tgFXn1ipKSEQVDa+NlYPnsfJ59uwZNmzYgNDQUMjlctSpUwfu7u6ws7NTd7QqRRYbj0tdxxf6xmpetG0s4ThjJFzmT+c0JkRE5UT37t1x4cIFuLu7Y+3ateqOQyqgiuvios2PQUREROWSQCCAVdc2RW6f9C44R0EDAC51GQ/vXzcpORkREVHl4urqitmzZ6N69eoQCAR49eoVVq9eDX9/f3VHq1Jebdhf7IKGQCRCt+t7MSjwGlwXzGBBg4ioHPnqq69QrVo19O7dW91RqALhmxrgU1RERFSxpScm4Z/mQxDn+7pU47h8OwNSYwPE+fgj9rk/Yp6+RGZyClzmT0P9RbO46DhRFcFrY+Xgeay83rx5g3Xr1uH169cQCASwsbHB9OnT4ezMxaXLgsdAdwQdv1Rgm+qDu2f/v25NW9iP6sPFv4mIyom7d+8iNTUVHTp0yN6WmpoKTU1NNaYiVeL0UyrCGw4iIqroMpJTcLnnZERcv6+yYzRZOR/On5V+MU4iKt94bawcPI+VW1BQENatW4cXL15AKBTC3Nwc06ZNQ8OGDdUdrVJTKBTYI6xTYJuhH+5A09S4jBIREVFxnD59Gv3794eVlRW8vb1haGio7khUBjj9FBEREeVJpK2F7tf2Ykj4bXT3PIAul3dCYmyo1GM8/PwXPP7uD6WOSUREVBHZ2tpizpw5cHV1hVwuR0REBNauXYu7d++qO1qllZ6YhBvD5+a7X692DYxMfsqCBhFROdapUyfUqlULbm5u6o5CFRzf1ACfoiIiosopPTEJHgPcEXb5dvY2sZ4O0hOSSjymSFcHwyLvcioqokqM18bKwfNYNURGRmL9+vXw8vKCSCSCnp4exo8fj3bt2nHdhhJKCgrFmz0nEOvtl71NFhWLkPM38u2jXc0Cg9/nv5+IiNQjLS0Nx44dw4gRI7K3xcbG8g2NKqbKvKmxdu1a1KhRA5qammjRogXu3btXYPs//vgDderUgZaWFmxtbfHZZ58hNTW1jNISERGVT2JdHXS9tBMj4r3Q59lpDAq6juFxXhireAWpWcmeYsxITEKC/zskBYYgOTQCCrlcyamJiFSD9xikCqamppg1axZatmyJjIwMJCYmYsuWLbh48SL4/GDxJb0Pw7k2I/B4wUoE7DuV/aOgggYAWHZuVUYJiYioqGQyGZo3b46RI0fi+PHj2dtZ0CBlKHdFjYMHD+Lzzz/HokWL4OXlhQYNGqB79+6IiIjIs/2+ffvwzTffYNGiRfD19cXWrVtx8OBBLFiwoIyTExERlU9iPV0YuThCx8Yy+6nRAa8uQEOrZAuxnXLpjaN2HXHEui1OOvVEvF+AEtMSESkf7zFIlQwNDfHpp5+iQ4cOyMjIgEwmw44dO3D69GnIWfwvltfbDiM5KLTY/VwXuqsgDRERlYZEIkGvXr1gbm4OsVis7jhUyZS76adatGiBZs2aYc2aNQAAuVwOW1tbzJo1C998802u9jNnzoSvry8uX76cve2LL77A3bt3cfPmzSIdk6+GExFRVfXh7hOEX72DjKQUGDjVgoFzLejXqYnM5BQcMm1R5HFGJj+FqIAiSUZKKtITkqBpZszpOIjKucp4bcx7DCoLKSkp2LZtGy5fvgypVAqFQoHBgwdj0KBB0NDQUHe8ckuhUCD2uR+uDZqJhBI8KFHn09FovmaR8oMREVGxeXl5oVq1arCwsADwcfqphIQEmJqaqjkZqZMqrotFShlFSWQyGR4+fIj58+dnbxMKhejSpQtu376dZ5/WrVtjz549uHfvHpo3b443b97g7NmzGDt2bFnFJiIiqrDMWjSAWYsGubaLtDTRcvOPuDNlYZHG2a9dHwBgUK824p5/nAO79rQRSAn9gLjnfkh4EwT8/3MUAg0NWHVpDaNGTjBv0wT6dWtC38FOSZ+IiCgn3mNQWdHS0sLkyZMhlUrxzz//QCKR4O+//0ZaWhpGjBgBkahc3X6XC2kxcThk3KzE/R0mDUHTVXyDioioPNi8eTNmzJiB/v374/DhwxAIBJBKpZBKpeqORpVQubqqioyMRGZmZnY1L4uFhQVevHiRZ59Ro0YhMjISbdu2hUKhQEZGBqZPn17gq+FpaWlIS0vL/nl8fLxyPgAREVElItbTKXafrIIGAPhtPJBnG0VmJkLO30DI+Rt4/q/tvR+fgHEDp2Ifk4ioILzHoLIklUoxbtw4SKVSnDx5EiKRCMePH0daWhrGjh0LiUSi7ohqlRQUCr9NBxH92BfpcQmIuPGgSP00zU2y/19DU4oao/qgwZLZ0Kji55OIqDxp1qwZBAIBNDQ0IJPJWMwglSpXRY2S8PDwwM8//4x169ahRYsW8Pf3x5w5c7B06VJ89913efb55ZdfsGTJkjJOSkREVLFYdWkNgYYGFJmZZXK8Mw37w7JzK3Q6vQkamrwAJiL14T0GlYZYLMaoUaMglUpx5MgRiMVinD17FjKZDOPHj4eWlpa6I6pFwptAnG02BLLo2GL1s+nXGW4n1qsmFBERlZhMJsPz58/RqFEjAEDDhg3x9OlTODnxQTVSvXK1poZMJoO2tjYOHz6MAQMGZG8fN24cYmNjceLEiVx92rVrh5YtW+L333/P3rZnzx5MnToViYmJEApzr4We11NUtra2nO+WiIjoP96fvoqbY+YhPS4BEAgg0tZCRlKySo+pZW2Owe9vcO0NIjWpbGtB8B6D1EUul+PkyZM4dOgQ5HI5ZDIZOnTogMmTJ0NHp/hvQ1Y06QmJiPX2Q8DBs3ixemeJxtCyNENXj90wqFNTyemIiKg0QkND0atXL7x9+xbPnz9HtWrV1B2JyjFV3F/kvhpXI4lEgiZNmuRYkE8ul+Py5cto1apVnn2Sk5Nz3VRkLcKWX71GKpVCX18/xw8iIiLKzaaPG4bHPMCQsFsYmfQEIxMfo8ftQyo9ZkpIBC60H4WkoFBkpslUeiwiqvx4j0HqIhQK0b9/f3zyyScQi8UQi8Xw8PDAunXrEBcXp+54KuW9bCMOGjfHudbDS1zQaLPrN/T3u8CCBhFROWRmZgaJRAINDQ34+fkV3oFIycrd9FOff/45xo0bh6ZNm6J58+b4448/kJSUhAkTJgAAPvnkE1SrVg2//PILAKBv375YuXIlGjVqlP1q+HfffYe+fftm33gQERFRyQkEAmhZmGb/3KxlQ4yRv4Tvym14OO/XAvuK9XSgkCuK/XZHxM2HOFq9Q/bPtatZwNDVEa23L4OWpVnxPgARVXm8xyB1EQgE6N69OzQ1NbFz507I5XLcunULMpkM06dPh4mJSeGDVADpSckIPHIeUQ+88eHWI0Q/9C7xWP39LkLfwU6J6YiISBn8/f1Rq1YtCAQCiEQi7N+/H9ra2rC0tFR3NKqCyl1RY/jw4fjw4QO+//57hIWFoWHDhjh37lz2wn6BgYE5nppauHAhBAIBFi5ciODgYJiZmaFv37746aef1PURiIiIKj2BQADnLybB6bMJSAn7gMyUNOjUqIb4F28Q5/saIl1tGNarDW0bSwgEAigUCqRFxiA1Igrh1+8j9ulLvNqwv8jHSw4OR3JwOA5btYHT5xNg1qoRYp/7Ic7HH7HefpDL0tF83WJYdW4FQR7TwhBR1cZ7DFIngUCAjh07QiqVYuvWrYiJicGDBw/w119/YcaMGbkWsa9oMlPTcKXXFERcv1+i/nq1a0BiqIcaI3rD6bMJnH6SiKgcWrlyJb755hv8+eefmD59OgCgZk2+SUfqU67W1FCXyjZvMBERUUXxZNFqPP1hrdLGM2nmiq6Xd0Ksp6u0MYmqGl4bKwfPI+XFy8sLmzdvRnh4OBQKBZydneHu7l5h5yKP8nqOs00GlqivUCqB24n1sO7eTsmpiIhI2VavXo25c+di1KhR2Lt3r7rjUAWjiutiFjXAGw4iIiJ1uj11Ifw3K3edjuExD5AWFQtNS1OIdbSVOjZRZcdrY+XgeaT8eHt7Y8OGDQgJCYFCoUDt2rXh7u6OGjVqqDtascT6+ONUvV7F7ld9UDdYuLWAdY/2nGaKiKicysjIQExMDMzMPk79K5fL8c8//6BXr158o46KjUUNFeENBxERkfooFAo8W7oWTxb9qdLjCDQ0UHf2WGiam8CkqQuSgsKQmZoG/Tr20LIw/TiVlY8/4nxfI+bJC2Qmp6LGiN5IjYxBWlQMjBvUhcu3M6Ahkag0J5G68dpYOXgeqSCvXr3C+vXrERAQAACoUaMGZsyYAUdHR/UGK4RCLkeE50NcGzwLaR+ii9xPQ1OKTv9sgVnLhtDQlKowIRERlZafnx9Gjx4NiUSCa9eucT0xKjUWNVSENxxERETqlxL2AXdnLEJKWCQi7zxWd5x8NfhhDhwmDoaWlTnX76BKidfGysHzSIUJCAjAunXr4OfnB4FAAGtra0yfPh0uLi7qjpYnhVyO25O/xevtR4rUXigWQ2piiGp93dBs9UKItDRVnJCIiJQhICAArq6u0NDQwK1bt+Ds7KzuSFTBsaihIrzhICIiKn8UcjkykpJxqn4/JAW8V3ecPNUY0RvN1y6C1NhQ3VGIlIbXxsrB80hFERwcjHXr1sHHxwcCgQDm5uaYOnUqGjdurO5ouQQeu4Brg2YWqW3T1QvhNPsTFSciIiJliY+Pz3G9cu7cObi6ulbYNZ+ofGFRQ0V4w0FERFR+yTMz8WL1TkQ98AYEAhjWc4BhvdowqFcb4R53cWfKQnVHRPvDf8FucHd1xyBSCl4bKwfPIxVVeHg41q9fjydPnkBDQwOGhoaYNGkSWrVqpe5o2dKTknFAt2GR2na+sB3WXduoNhARESmFXC7HqlWrsHTpUr6VQSqjiutikVJGISIiIlIRoYYGnD+fmOc+fQc7CIRC3J60oIxT5XR9yCyIDfTQ8/ZBGDg5qDULERFVLBYWFpg9ezY2bNiA+/fvIy4uDps2bUJqaio6duyo1gVZk96HIfLOY9wcM6/QtprmJuh8fiuMG/ILMSKiikIgEODq1auIi4vDtm3bsHz5cnVHIioSvqkBPkVFRERU0YVeuY3HC1Yi+X0YNM1NEP3IR21ZWu/8FUKxGEmBIdC2NoftgC4Q6+mqLQ9RcfHaWDl4Hqm4sooZt27dgkQigUQiwejRo9G9e3e1FDa8l23Eo/kritS244n1qNarA4QiPjdJRFTeyeVyyOVyiP7/7+yQkBD8888/mDhxoloL6VR5cfopFeENBxERUeWkkMuR4P8OCa8DEfvsFZICQwAAurWqI/iMBxQZmdCpUQ2Gzg4wcHaAgXMtKDLliHnyAorMTOjXrYnzbUchIzGpVDn6vTgHgzo1lfGRiFSO18bKwfNIJZGUlIQtW7bg2rVrkEgkEAqFGDZsGPr16wehUFgmGdITk+D11e94tX5foW0tO7VE18u7yiAVEREpg7+/PyZMmIAuXbpg0aJF6o5DVQSLGirCGw4iIiLKj0Iuh9+WQ4h++Bx+mw6WeJzuN/fDvE0TJSYjUg1eGysHzyOVVEpKCnbu3ImLFy9CIpFAoVBg0KBBGDx4MDQ0NFR67MR3wThWw63I7Udn+EKo4kxERKQ8Bw8exIgRI2BkZIS3b9/CwMBA3ZGoClDFdXHZPOpBREREVEEJhEI4Th2BlhuXYqziFcZkvoC+o32xxznfdiSCTlxSQUIiIqpMtLS0MHHiRPTu3Rvp6ekQCAT4+++/sW/fPqSnp+dom5aWBk9PT9y/f7/Ux4245VWsgobbqY0saBARVQByuTz7/4cNG4affvoJXl5eLGhQhcaiBhEREVExCIRC9HtxDq22/VLsvh4D3PFm70kVpCIiospEIpHgk08+wcCBA5GZmQmRSITjx49j586dSEtLQ3JyMq5evYrFixdj5cqV2LJlCxISEkp0rPhXb/Fy7R6cbzOiyH06X9gOmz5FL4AQEVHZk8vlWLNmDVq0aIHU1FQAHxcGX7BgAWrUqKHecESlxFW8iIiIiIpJIBDAYcJgiPV0cH3o7GL19RwzD1Zd20DL3ERF6YiIqDIQiUQYMWIEpFIpDh8+DIlEgrNnzyI+Ph7BwcEICAiAlpYW7OzsEBISgqCgIDg7OxfrGMVZDFzTwhTdru6Cft1aXEiWiKgCiIuLw48//ojw8HBs374dM2bMUHckIqVhUYOIiIiohOyG9ECHY2vxdvcJpEXFQq+2HUQ62nixemeB/Q5btAIAtNr6MxwmDimLqEREVAFpaGhg8ODByMzMxMmTJ2FkZISrV6/C2NgYdevWhVgsBgAEBAQgMDCwSEWN9KRk+K7Yhnd/n0Os96si5XD+YiIa/DAHIm2tUn0eIiIqO0ZGRtiyZQvevXuHadOmqTsOkVKxqEFERERUCtUHdEX1AV1zbHOZPw2HLVsX2vf2pAUIPHYRLdYugk51a1VFJCKiCiosLAzXr1/HtWvXkJycDGtra9jY2OR6U0IsFsPf37/Q8ZLeh+GobfsiH18oFmNU6jMIhJy5moiovAsICMDUqVPx3XffoV27dgCAPn36qDkVkWqwqEFERESkZFoWphiV8gz7tFwLbRt8+iqOnr4K1+8+RYMlszmlBxERISMjA/v27cONGzcQGRkJU1NTuLq6QphPcUFfXx8vX76ETCaDRCLJsS89KRlR958hIykZV/sU70ndUWne/HeJiKiC+P3333Hx4kWEh4fj8ePH/PubKjU+bkFERESkAhqaUgwJu1Xk9s+WrsUeYR0cr90VEZ4PVZiMiIgqgqdPnyIuLg5CoRAaGhqQy+X5tjUwMEBsbCyCg4Ozt8U8fYGLXcfjgG5DXHQbW6yChp6DHXo/PsEvxIiIKpCff/4Zw4YNw5EjR/j3N1V6AoVCoVB3CHWLj4+HgYEB4uLioK+vr+44REREVIl4DHRH0PFLJerr+t2nqD6oKwzq1oKGplTJyYjyxmtj5eB5pNJKSUmBr68vHj58iIcPHyIyMhJCoRDm5uYwNjbO8daGQqGAt7c3Zs+aheqhibg56vMSHVPX3gbdru+Djo2lsj4GERGpgEKhwJYtW/Dq1Sv8/vvv6o5DVCBVXBezqAHecBAREZHqZMpk8P5lI6K9fPD+5OUSjWHcuB7cTm2AtrWFktMR5cZrY+XgeSRliouLg7e3N+7duwdvb2/ExMRAU1MTFhYW0NfXhzAjE+lHPVDnRUSJxjdv2wQWHVvAZf40LgZORFQBeHl5oUmTJgAAT09PtG5d+Hp+ROrCooaK8IaDiIiIyoIsLgHn2oxA3HO/EvV3+nwCMlPSYNLMFVJTI5g0dYG2lbmSU1JVx2tj5eB5JFUJDw/Hs2fPcPv2bfj7+yM+Ph7tHoTA7H1MicYbmfSEhQwiogroyy+/hKWlJebOnQsNDQ11xyHKF4saKsIbDiIiIiorsrgE+G06CK+vflPKeM3+XIi6sz5RylhEAK+NlYXnkVRNoVDg3bt3eHzdEwnjlhS7v4aWJtodWAXbfp1VkI6IiJQpODgY3377LVatWgUjIyN1xyEqFlVcF4uUMgoRERERFYnEQA/1vpwMh8lDcWvc1wi9fBuZySklHu/+7B9xf/aPcHQfBcfpI2HkWkeJaYmIqLwSCATQjYiD4fFbSChin4Y/fw6Tpq4QaUlh3MQFIi1NlWYkIqLSUygUGDRoEO7duweRSIQtW7aoOxKR2vFNDfApKiIiIlIfhUIBKBRICfuAI9XaKWVMm76d0Grbz9A0NVbKeFS18NpYOXgeSZUUcjnONOqPmKcvC21r3rYJmq9dBKP6dcsgGRERqcKtW7cwb948bN26FU5OTuqOQ1QsnH5KRXjDQUREROVF0IlLeLLoT8Q8eVGqcXSqW6P/qwvQkEqUlIyqCl4bKwfPI6lCRnIKvL76DS/X7i20rX7dmuh17zDEerplkIyIiJRFoVBgz549MDAwQL9+/XJsFwgEakxGVDKquC4WKmUUIiIiIlIK2/5d0OfxSbTesaxU4yQFhmCfpgueLPkLfIaFiKjiSw4Jx36dBkUqaABAsz++ZUGDiKgC2rNnDz755BNMnjwZkZGR2dtZ0CD6H66pQURERFQO1Ro3CLXGDUKsjz8S3wQhNSIKqRFReDR/RbHGebr4L2QkpaDJb1+pKCkREala9BNfnGnYv8jtJUYGMGvbRIWJiIhIVYYNG4bVq1dj0KBBMDQ0VHcconKJ00+Br4YTERFRxeK/7TBuT1pQrD7W3dvB+avJsOrUSkWpqLLgtbFy8DySsoRfu4cLHccUub1JM1e02PADTBrXU2EqIiJSlrCwMOzatQtffvll9tsYGRkZEIn4LDpVDqq4LuafDiIiIqIKxmHiENiP6YfkoDD4bT6I579uLrRPyPkbCDl/A87zJqHJ71+XQUoiIioNhVyOk/V6If7FmyK17+t9BnoOdlxLiYioAklOTkbjxo0RGhqKatWqYfTo0QDAggZRIbimBhEREVEFpCGRQK9WdTRe9iVGy57DwKlWkfr5LN+KD3efcJ0NIqJy7v7cn4pU0Giy/GuMSHgEw3q1WdAgIqpgtLW14e7ujoYNG8LFxUXdcYgqDE4/Bb4aTkRERBWfLDYez35cB58V24rUXiASYeCby9CxtVJxMqpoeG2sHDyPVBqZaTLs0yz8y63RGb4QamiUQSIiIlKWQ4cOoVWrVrC1tQXwcaophUIBsVis5mREqqGK62K+qUFERERUCUgM9dFk+TcYLXtepPaKjAwcrd4BezVdcLhaW5xpMhABh86qOCURERVF8D/XCm0zLPIuCxpERBXMkiVLMHz4cEyZMiX7zWmRSMSCBlExsahBREREVIkIxWIMCb8N4yZFe31dniZDSkgEor2e48bwuQi95KnihEREVJibo+flu8+4iQt63T8CqYlRGSYiIiJlGDFiBPT09NCiRQtkZmaqOw5RhcWiBhEREVElo2Vugm7X9sDt1MZi9w08ckEFiYiIqKhSI6ORmZyS5z6XBdPR+8FRmDR1LeNURERUEpGRkbhw4X/X13Xq1EFAQACWLFnCxcCJSoF/eoiIiIgqIbGONmz6uKHHrYM413p4kfu92rAfhvXrQKSlCbG+DmKf+yPW+xUyklNh6FIb9b/7FCJtLRUmJyKq2uRpsnz32Y/qW4ZJiIioNF6/fo3WrVsjMTERz549Q82aNQEAxsbGak5GVPGxqEFERERUiZm1aoQxmS8QcOgsbo78vEh97rkvznN78OmreL5sEwDAtGVDVOvdEa4LpkMg5Mu/RETKkhgQnOd2TQtTGNarXcZpiIiopOzt7eHk5ITIyEgkJyerOw5RpcI7UCIiIqJKTiAUwn5EH4xKeQbbAV2UMmbkncd48t0feLxwlVLGIyKij26M+CzP7fW+nFTGSYiIqLiuXr2avVaGUCjEwYMH8fDhQ7i4FG29OyIqGhY1iIiIiKoIDU0pOh5bh5HJT9Hf7yIs3FqWekzvXzYi3i+g9OGIiAiyuAQkvw/Lc59ALC7jNEREVByffvopOnXqhD/++CN7m4WFBaRSqfpCEVVSLGoQERERVTEiLU3oO9ih3ryJShnvhGM3XHAbi5hnL5FZwFzwRERUsEfzV+S7z6xlgzJMQkRExdW4cWMIhULEx8erOwpRpSdQKBQKdYdQt/j4eBgYGCAuLg76+vrqjkNERERUZoJOXoZH/xlKG8+oQV24ndoIHVsrpY1JZYvXxsrB80jFlRoZjb/N8n+Dboz8JQQCQRkmIiKigsTExCAmJiZ7AXCFQgEfHx/Uq1dPzcmIyhdVXBdzoXAiIiKiKsy2X2eMSHyM96euICX0A+Ke+yEzVQZ9xxowdHWEXu0a8N96GC/+2FGk8WKevMBFt7Hodm0PtKtZqjY8EVElcrbJoHz3td23kgUNIqJy5O7duxg0aBAsLS1x584diMViCAQCFjSIygiLGkRERERVnFhHG/Yj+uS7v9mqBWi2agFe7ziKWxO+KXS8hNeBOGLTHs3XfI86n45RZlQiokop+KwHkgJD8t1v1bV1GaYhIqLCVK9eHSkpKUhMTERwcDBq1Kih7khEVQrX1CAiIiKiIrHp1wkGzg5Fbn9v5g+42HU80pOSVZiKiKji8/zkq3z3NV+7CJqmxmWYhoiI8vLmzZvs/7eyssKFCxfw+PFjFjSI1IBFDSIiIiIqEqmxIXrcOoimf3xb5D5hl27hgG5DJAWFqjAZEVHFlBYVg8s9JyEtKjbfNrWnDCu7QERElEt6ejomT56MOnXqwMvLK3t706ZNoaWlpcZkRFUXixpEREREVGQSAz04zRmHsYpX6HB0TZH7+azcrsJUREQVS0pEFA4aN8Mh0xYIOXcj33a9H5+AUCwuw2RERPRfYrEYCQkJyMzMhIeHh7rjEBFY1CAiIiKiEqo+sBsGvfOAtk3hC4JHXLtXBomIiMq/uJdvcNiiFWQxcQW203Owg3EDpzJKRURE/xYfH4+UlJTsn69ZswYeHh74/PPP1ZiKiLKwqEFEREREJaZT3Ro97x1Gw58LvsGTp2eUUSKqyB49eoSvvvoK/fr1Q5cuXbK3v3v3DocOHUJ0dLQa0xEpx61xXxepXZPlRWtHRETK5eHhAVdXV3z//ffZ28zMzNC+fXs1piKif2NRg4iIiIhKRdvKHK7zp2OM/CVcF7rn206hUJRhKqpovvrqKzRt2hTLly/H6dOncfXq1ex9CoUCo0aNwu7du9WYkKj0MmUyRN59Umg7k6YusOzSugwSERHRfyUmJiIwMBDHjx9HcnKyuuMQUR5Y1CAiIiIipRAIBGi4dC5Mmrrk2hfr/Qp7hHWwW+CIRwtXITNNpoaEVF5t374dy5cvR58+ffD06VPMnz8/x/4aNWqgefPmOHnypJoSEimHIlNeaJumqxagy8UdEOtol0EiIiICPhYysvTp0we7du3Co0ePoK3Nv4uJyiMWNYiIiIhIqcSG+gXu9/5pPY5W74BYH3/E+vgjPSGxwPZU+a1btw5OTk44cuQIXFxcIJFIcrWpW7cu/Pz81JCOSDmSAkOwX7t+vvu7eezB6HQfOM0dD0khf48SEZFyJCUlYebMmWjYsGGOwsbYsWOhq6urxmREVBCRugMQERERUdWTGhGFU/V65dimZWkGgYYQZm0ao+WmHyEx0FNTOiprPj4+mDJlCkSi/G9PLCwsEBERUYapiJQnxvsVTrv2yXe/y/xpsOjQvAwTERERAGRmZuLUqVMIDAzE6dOnMWLECHVHIqIiYFGDiIiIiJSqpMWIlLAPAIB3h/7Bu0P/QGJkAENXRzhMGoJanwxUZkQqZ0QiEWSygqckCwkJ4ROTVGEVVNAAAKGYt+ZERGUlPT0dYrEYAKCvr49du3ZBJpOha9euak5GREXF6aeIiIiISKmse7ZXyjiymDhEXL+PW+O+hu8fO7jQeCXm6uqKK1euIDMzM8/9ycnJuHTpEpo0aVLGyYhK79XGA4W2MW3ZUPVBiIgIN27cgJOTE86fP5+9rUOHDixoEFUwLGoQERERkVLVGj8I9b6aAqE097oIJfXgs59x2LI1IjwfKm1MKj8mTpyIV69eYfr06UhLS8uxLz4+HuPHj0dYWBimTJmipoRExadQKBD73A93p39fYLsao/rCqmubMkpFRFS1HT58GK9fv8aSJUv4wAxRBSZQ8E8w4uPjYWBggLi4OOjrc0E2IiIiImVQyOVIT0iCWF8XPr9vgdfXvytl3NHpPhAWsPYClY66ro1HjRqFAwcOQFdXF4aGhggODkaTJk3g6+uLpKQkjB8/Htu2bSuzPKXFe4yqSxaXgICDZ3B3WsHFDADo9fAYjBs6QSDk84ZERKqiUCggEAgAfFwY/Mcff8Q333wDAwMDNScjqhpUcV3MogZ4w0FERERUFl6u2wv/bUeQFhULAEgKeF+icay6tUXnc1uzb05JudR5bbx582asWbMG3t7e2U9POjk5Yfbs2Zg2bVqZZikt3mNUTW/3n8bNUZ8Xqe2A15egV7O6ihMREVVdKSkpWLhwISIjI7Fz5051xyGqsljUUBHecBARERGVPYVCAVl0LBRyBTLTZLg56nNE3HhQpL497x2GabP6Kk5YNZWHa+OUlBTExMRAX1+/wi4OXh7OI5WtO9O/h18R1s8AAKfPxqPpygUqTkREVLXdv38fLVu2hFwux8OHD9G4cWN1RyKqklRxXcx3XImIiIhILQQCAaQmRtA0M4aOjSW6X9+HsYpX6HZtLwSFTC8Vdvl2GaWksnD9+nUEBgZm/1xLSwvW1tY5ChpBQUG4fv26OuIRFcp/2+EiFzQAoMHSuaoLQ0REAIBmzZph2bJlOHPmDAsaRJUMixpEREREVK5YtG+Gbh67YdO3U75tUsIiyzARqZqbmxt27NhRYJtdu3bBzc2tbAIRFUNGcgpuTyraWxciHW2MiPeCWEdbxamIiKqeO3fuoF27doiIiMje9uWXX6JXr15qTEVEqsCiBhERERGVO+ZtmsDt5AbU+2pKnvtfrN6J3QLH7B/Ha3fFmz0nyjglKUtRZsSVy+VcR4XKJc9Pviq0jXEjZ/R/eR4jEh5BrFcxp1QjIirPFAoFPv30U9y8eRMLFy5UdxwiUjEWNYiIiIio3HL5dkaR2iX4v4Pn2C8ReoXTUlVWfn5+MDAwUHcMohyiH/sg8Mj5fPfXHNsfo9K80dvrOPQd7VmYIyJSEYFAgK1bt2LcuHFYtmyZuuMQkYoVPFkxEREREVEF8v7EZVh1aqXuGFQEEydOzPHz48ePIyAgIFe7zMzM7PU0evbsWUbpiIrm8ber8t3nOH0kWqxfUoZpiIiqjrS0NCxZsgTOzs4YM2YMAKBhw4aFTmdJRJUDixpEREREVG6JdbWhaWGK1PCiraHx4s9dEIpFaPTLFxCKxSpOR6Xx7y8dBAIBHj9+jMePH+fZViAQoFmzZlj1f+zdeXxM1//H8fdM9ojEEhJiiT322im1K13tVFVU6aKLltrV2qKllBalFNUqrQZVqlpaqva1FbXv+y4hZL+/P/wyX2kWiSRzs7yej0cej+acM/e+Z+Q733vnM+ecT5L+ABkww9Ud+5Lsqz7xwctSAQAezrx58zR+/HjlyZNHTz75pPLly2d2JAB2RFEDAAAAmZbFalXpnh0UPG5mih/z76S5+nfSXLkX8ZVL/jwq80pnlXu9awamxMM4ceKEpHtrYJcsWVLvvPOO3n777QTjHBwclDdvXuXKlcveEYEHig2PSLS90bLpcvLgbxYAMkqvXr20atUq9ejRg4IGkANR1AAAAECm9sgHfeVexFeX/timm/8eVcj+Iyl63J2zF3Xn7EVtf2O0HHO5qVT3dhmcFKlRvHhx23/PmzdP1apVi9cGZHa3T51T1K2wRPuKtWlh5zQAkL3t3r1bc+fO1WeffSaLxSJHR0f99NNPZscCYBKLYRiG2SHMFhoaKi8vL4WEhMjT09PsOAAAAEiB35oF6uLvW1M83rNcCXn4F1H1jwcpb6WyGZgsa+PaOH3wOmZ/Gzr20ekffknQXjKwjep/NcGERACQPYWEhKho0aK6deuWZs+erV69epkdCUAqZMR1MTM1AAAAkCWVfb1rqooaoYdOKPTQCZ1fs1FP7lyq/DUqZWA6PIzw8HDt2LFD58+fV0RE4sv6BAYG2jkVkJBhGIkWNCTJ0d3NzmkAIHvz8vLS6NGjtWXLFrVu3drsOAAyAWZqiG9RAQAAZFUXft+i3598WbERkal+bC7/Iqo//0P5NKqdAcmyLrOujadPn67hw4crJCQk0X7DMGSxWBQTE2O3TGnBPUb2dfvUOS3zb5Jk/2OLP5F/56fsmAgAspeoqCh9+OGH6tKli0qXLi3pf9cBALKejLgutqbLUQAAAAATFGpaT13Dg1Vn1hhZHFM3CTns5Fn92vgFfW0pq92DJir6zt0MSokHWbp0qd566y0VLVpUH3/8sQzDUOvWrTVu3Di1atVKhmGoffv2mjt3rtlRkYNFhtzSr026JVvQkKTiHZ+wUyIAyJ7eeecdjRgxQj169FBsbKwkUdAAEA9FDQAAAGR5ZV95Tp1v7tSzB1ar4sCXU/34/RNma1Guqtre533FREbq5v4jOvPjWl3etEvRd8MzIDHuN2XKFBUsWFBbtmxR3759JUmPPPKIBg0apFWrVumbb77R8uXL2Ugcptoz+GNdWr8t2TGPfNBXFiu32QCQFgMGDFDx4sX1xhtvUMwAkCj21AAAAEC24JTLXV4BpVR5+OsKOXhcZ1esS/UxDn32tQ599nW8tnw1Kqnpylly8y2QXlHxH//88486deokd3d3W9v9y0w9//zz+uqrrzRmzBg1btzYhISAdPGPB+/h49+FZacAILWCg4P1999/q2vXrpIkf39/HTlyRE5OTiYnA5BZUdQAAABAtuLkkUuNl03XnbMXFX75mhzc3fRTxScf+njXdwXr4LRvVO2DvumYEveLiopSgQL/Kxq5ubnp5s2b8cZUrVpVX3zxhZ2TISf7d/JcHZjylWLC721aH3HlerLjW235XrlLFrNHNADINvbt26caNWrIarWqevXqKl++vCRR0ACQLIoaAAAAyHYsVqtyFSusXMUKS5K6Rh/Qrn7jdfDTBQ91vOCxnyt47Ocq8mwz1ZkxUu5+vukZN8crXLiwLly4YPu9ePHi2rNnT7wxp06dkmMq900BHkZsdLQWOlVI1WM6XNwsNx/vDEoEANlXpUqV1KJFCzk4OChPnjxmxwGQRbDYJwAAALI9q4ODak19T92Mw+p4eas8y5Z4qOOcXbFOQUUaas1jXXT86+W6e+lqOifNmWrVqqXdu3fbfm/VqpU2bdqk8ePHa//+/Zo1a5aWLl2qWrVqmZgSOUFkyK1UFTQ8A0rqhdhDFDQAIIWio6M1Z84cRUVFSbq3Afj333+vH3/8UYUKFTI5HYCsgqIGAAAAchTXAvnU+tAaPX93n0r37PBQx7j81y5tChyoH3wf1bbeI2UYRjqnzFk6duyoiIgInTx5UpI0ZMgQFSlSRO+9956qVKmi3r17y8PDQxMmTDA3KLK9f0Z/lqrx/s89xSa2AJAKrVu31ssvv6xx48bZ2nLlysV7KYBUsRjcgSk0NFReXl4KCQmRp6en2XEAAABgR2Gnzyv4wy90+PNv03Qcv6ebyOrkKJ9GtRXQJ9B2cx4VdkehB44p/Mp1eZQoIq+AUukRO8NklmvjGzduaM6cOTp+/LiKFy+ubt26yc/Pz7Q8qZVZXkekzqqa7XR9V3CKxroVKqiWmxYpd4miGZwKALKPxYsX67XXXtNnn32mbt26mR0HgB1kxHUxRQ1xwwEAAID/OTZ/qTb3GJx+B7RYpPsuuSsM6KXqHw3ItN9I5No4ffA6Zk0rKj2lkP1HEu0r1KK+XAvmlyR5VSilEs8/Iw//IvaMBwBZzqFDhxQREaEqVapIkgzD0LVr1+TtzbJ9QE6REdfF7LQHAAAA3Kdk97YKv3pDh2cs1O0TZ9N+wP98h+jfiXNUosvTylctdRsR53QbN27U8OHDtX79erOjIBuKjYnRte3/JFnQ6Hhlq1y989k5FQBkbatXr1a7du1UsmRJ7dq1S66urrJYLBQ0AKQZe2oAAAAA97FYLKrYv6faHv9d3YzDan9u40NvLJ6Ui+u3pevxsrrr168rNDQ00b6tW7eqRYsWaty4sTZu3GjnZMgJQg4d10LH8vrl0c6J9vs2f5SCBgA8hFq1asnT01N+fn66deuW2XEAZCMUNQAAAIBkuBf2UetDa9Ql7G89tfdH5a9dJc3HjI2MSodkWd93332nkiVLqkCBAsqbN6+qVq2qzZs3S5IuXLigdu3aqX79+lq3bp2qV6+uFStWmJwY2U3o0VNaEdAq2TFWJxY4AICUiI2N1YYNG2y/e3t7a9u2bVqzZo0KFChgYjIA2Q1FDQAAACAFHN3dlK9qeT257Qc1WDTZ7DhZ3rp169SlSxedPHlS7u7ucnd31759+/Tkk09q+/btqlmzppYvX65q1arpxx9/1I4dO/TUU0+ZHRvZzIFJcx84pkDdRzI+CABkceHh4WrcuLGaNGkSb2alv79/pt1HDEDWRVEDAAAASKUSzz2tdmf+VNPVc9R09RzVnjFKrj7x14d2cHVRvuoVTUqY+U2ZMkVWq1WLFy/WrVu3dOvWLS1cuFC3bt1SixYtdOPGDc2ZM0c7d+7UM888Y3ZcZFPXdgU/cEy5N1+wQxIAyNpcXV1VunRp5cqVS+fPnzc7DoBsLlMWNaZPny5/f3+5urqqTp062r59e7Ljb968qTfeeEOFChWSi4uLypYtq59//tlOaQEAAJAT5SriK79WDeXXqqHK9X5eHS9uVpc7/+iF2EPqZhzW83f3qenPs82OmWnFzbzo1KmTra1Lly566qmndPv2bc2cOVMvvfRSup2PewzcLyYyUufXbNS1HfuSHOOSP4+eC90tl3x57BcMALKQ48ePx9sr45NPPtG+ffvUuXPiexQBQHrJdEWN7777Tv369dPIkSO1e/duVa1aVS1bttTly5cTHR8ZGakWLVro5MmT+uGHH3To0CHNnj1bfn5+dk4OAACAnM7RzZUlFlLo2rVrqlgx4UyWuLY2bdqk27m4x8D9wq9e1+pa7bWuVc8kx3S4sEmdrm6XU24POyYDgKxj4cKFqlKligYNGmRr8/Lykr+/v3mhAOQYmW7Hs8mTJ+vll19Wjx49JEkzZ87UqlWrNHfuXA0ePDjB+Llz5+r69evavHmznJycJIk3UAAAACCTi4mJkaura4L2uDZPT890Oxf3GLjfvx9/qRv/HEqy37dpXbn5sqEtACSnUKFCCgsL08GDBxUZGSlnZ2ezIwHIQTLVTI3IyEjt2rVLzZs3t7VZrVY1b95cW7ZsSfQxK1asUL169fTGG2/Ix8dHlSpV0rhx4xQTE5PkeSIiIhQaGhrvBwAAAED2wz0GJCk2JkZ3L1/T3cvXtP+j5JeFK/BodTulAoCsIzY2VqdOnbL93rRpU/3xxx9au3YtBQ0AdpepZmpcvXpVMTEx8vHxidfu4+OjgwcPJvqY48eP6/fff1fXrl31888/6+jRo3r99dcVFRWlkSNHJvqY8ePHa/To0emeHwAAAEDKTZs2TYsXL47XdvXqVUlShQoVEoy3WCzav39/qs7BPUbOZhiG9g6fosPTFyryZsoKTRXeTb+9XAAgO7h06ZK6du2qAwcOKDg4WHnz5pUkNW7c2NxgAHKsTFXUeBixsbEqWLCgvvjiCzk4OKhGjRo6d+6cJk6cmOQNx5AhQ9SvXz/b76GhoSpatKi9IgMAAADQvYJDXBHjv5IqONgD9xjZQ/Sdu9oUOFCng9akaLxX+VJ6as+PcnDhG8cAcL/cuXPr9OnTunHjhnbu3KkWLVqYHQlADpepihre3t5ycHDQpUuX4rVfunRJvr6+iT6mUKFCcnJykoODg62tfPnyunjxYpJr+rm4uMjFxSV9wwMAAABIsdjYWLuch3uMnCn86nWtbdY92b0z7lesQys1WvJpBqcCgKzj2rVryp8/vyTJ3d1d3333nXLnzq3SpUubnAwAMtmeGs7OzqpRo4bWrVtna4uNjdW6detUr169RB9Tv359HT16NN5N0eHDh1WoUCHW9AMAAECmFDxupm4dO212jByBe4yc6fCMb1Nc0JCkSoNezsA0AJC1zJs3TyVKlNDSpUttbdWqVaOgASDTyFRFDUnq16+fZs+era+++koHDhxQ7969FRYWph49ekiSAgMDNWTIENv43r176/r163r77bd1+PBhrVq1SuPGjdMbb7xh1lMAAAAAkhUVelvLSzfX15ayun3qnNlxsj3uMXKev0emfNZF01VfKH/NyhmYBgCylqNHj+rWrVtasGCB2VEAIFGZavkpSercubOuXLmiESNG6OLFi3rkkUf0yy+/2Db2O336tKzW/9ViihYtqjVr1qhv376qUqWK/Pz89Pbbb2vQoEFmPQUAAAAgxTa0f0tP7Vz64IF4aNxj5ByGYWhF+VbJjnFwd1OV4a/Lo0QRFXq8gVzyetkpHQBkToZh6O7du3J3d5ckjRgxQv7+/nrppZdMTgYAibMYhmGk9SDbt2/Xjh07dPPmTcXExCQ8icWi4cOHp/U0GSY0NFReXl4KCQmRp6en2XEAAACQTUTduq3v8tSU8YD9I9qd3qBcRQvZKVXyuDZOH7yO5jj21VJtfnFwkv2lXuqgujNHy+rkZMdUAJB5nTt3Ti+//LJcXFy0dOlSWSwWsyMByGYy4ro4TTM1rl+/rjZt2mjTpk1KrjaS2YsaAAAAQEZwyu2hAo9W0+W/diU7bmmxRpKkOrPGqOwrz9kjGpAtnf91U5J9RVo306NfjrNjGgDI/K5evarffvtNDg4OOnTokAICAsyOBAAPlKaiRr9+/fTXX3+pcePG6t69u4oUKSJHx0y3ohUAAABgmvpfT9SaBl1059ylB47d9uoIWR0dVfqlDnZIBmQ/0bfCkuyrPPQ1OyYBgMwrOjra9vld1apVNWfOHNWuXZuCBoAsI00ViJUrV6p27dpat24d09MAAACARHj4F1H7sxsVcSNEu979UMfmBSU7/sS3P1HUANJZszVz5V27qtkxAMBUhmHom2++0YgRI7Rx40YVKVJEktS9e3eTkwFA6lgfPCRpd+/eVcOGDSloAAAAAA/gktdLNT5+8EbTd85etEMaIOdw9fFW4ccbmB0DAEwXGxur6dOn6+TJk5o8ebLZcQDgoaWpqPHII4/o5MmT6RQFAAAAyN5c8uVRi98XmB0j01q2bJk6deqkKlWqqHTp0rb2gwcPasKECTp37pyJ6ZBVOXm4mx0BAExjGIZtH1wHBwfNnz9fY8eO1YQJE0xOBgAPL01FjZEjR2rFihXaunVreuUBAAAAsjXfJnXVNfqAqr7/jtlRMo3Y2Fh17txZHTp0UFBQkI4fP64TJ07Y+vPmzathw4ZpwQIKQkje9b0HzI4AAJnGpUuX1K5dO82cOdPWFhAQoKFDh7InLoAsLU3vYBcvXtRTTz2lRo0aqWvXrqpevbo8PT0THRsYGJiWUwEAAADZhtXBQVXee11XNu/W+dV/mh3HdJ988omWLFmi1157TR9++KEmT56s999/39bv4+Ojxx57TKtWrdKQIUNMTIrM7J/3p+vOmQtmxwCATCMoKEjLly/Xhg0b1K1bN3l4eJgdCQDSRZqKGi+++KIsFosMw9D8+fM1f/78BPtrGIYhi8VCUQMAAABIgdBDJ/Rb8+6qN2esPPyLmB3HLubPn69atWppxowZkpTonn2lS5fWqlWr7B0NWcSlDdv194ipZscAgEzltddeU3BwsF599VUKGgCylTQVNebNm5deOQAAAAD8v4vrtmhNw65qd/IPWaxpWjE2Szh69KjeeOONZMfkz59f165ds1MiZDXHFixPsi9X8cL2CwIAJlqyZIkWLlyooKAgOTg4yGq12r4wAADZSZqKGt27d0+vHAAAAADuc+fMBV3buU/etauaHSXDubm5KSQkJNkxp06dUp48eewTCFlK5M1QHZv7Q5L9pXq0t2MaADDHlStX1LNnT926dUvz589Xz549zY4EABmGXYEAAAAAk7j5eCfbf2b52hxR1KhWrZrWrFmj8PBwubq6Jui/fv26fvnlFzVs2NCEdMjsjn65JMm+0r06quQLre2YBgDMUaBAAU2ZMkUnT55Ut27dzI4DABkqXeaynzx5UmPHjlXHjh3VsmVLdezYUWPHjtXJkyfT4/AAAABAtlS03ePJ9gePn6XDMxfZKY15+vTpo7Nnz6p9+/Y6e/ZsvL5jx46pbdu2CgkJUZ8+fUxKiMzs6vZ/kuyrN3usHZMAgP1cu3ZNgYGBCg4OtrW99NJLGjNmjJydnU1MBgAZL80zNaZOnaqBAwcqOjpahmHY2oOCgjRmzBhNmDBBb7/9dlpPAwAAAGQ7RZ9pqkfnfajNPQYnOWZb75G68c8h1Zkxyn7B7Kx169YaNGiQPvroIxUvXly5cuWSJBUsWFDXrl2TYRgaPny4mjZtanJSZEZGTGyi7RUG9LJzEgCwn3fffVdff/21jhw5os2bN8tisZgdCQDsJk0zNVauXKm+ffvKy8tLH3zwgTZv3qwTJ05oy5YtGjdunLy8vNSvXz+tWrUqvfICAAAA2UqpF9up3an1yY45/Pm3+tpSVpGht+0TygTjx4/XmjVr9PTTT8vd3V0ODg6KjY1Vq1attHr1ao0ePdrsiMhiHhnDl+sAZF/jxo1TgwYN9Nlnn1HQAJDjWIz7p1ekUtOmTfXPP/9o7969KlKkSIL+M2fOqFq1aqpatarWrVuXpqAZKTQ0VF5eXgoJCZGnp6fZcQAAAJAD/da8uy6u25LsmIKP1VTLP7/N0BxcG6cPXkf7uHXstJaXbp6g3b1oIbU/vcGERACQMX788UcdP35cffv2NTsKAKRKRlwXp2mmxu7du9W5c+dECxqSVLRoUXXq1Em7du1Ky2kAAACAbK/JT7MeOObyxp0KO3PBDmns6+jRo2ZHQBZ0dF5QogUNAMhutm7dqjZt2mjAgAHau3ev2XEAwHRpKmpERkba1rtNioeHhyIjI9NyGgAAACDbc3RzVdeof+XklTvZcXfOXrRTIvspW7as6tevr5kzZ+r69etmx0EWcG33fm15aYjZMQDALurWravnnntO/fv3V0BAgNlxAMB0aSpqlC1bVj/99JOio6MT7Y+OjtbKlStVtmzZtJwGAAAAyBGsjo7qfGOnSvfskOSYXx7trKiwO3ZMlfGefvpp7dy5U2+88YYKFy6stm3baunSpXw5Ckk69d3Pyfa7+/nYKQkApL+bN29qyJAhCg8Pt7UtXLhQH374oVxdXU1MBgCZQ5qKGoGBgTp06JBatmyZYImpnTt36oknntChQ4fUvXv3NIUEAAAAcgqLxaJ6c8ap8vA3khyzpEBd3b18zY6pMtaKFSt0/vx5TZ06VVWrVtWPP/6ojh07ytfXV6+++qo2btxodkRkMg9ahq1E12fslAQA0pdhGGrRooU+/PBDjRgxwtZutabpIzwAyFbS9I749ttv69lnn9Uff/yh2rVrK3fu3CpVqpRy586tOnXqaN26dXr22Wf19ttvp1deAAAAIEfI90jSy0vE3A3XiW9+tGOajJc/f369+eab2rZtmw4fPqxhw4Ypb968mj17tho3bqySJUtq+PDhZsdEFlDrs+Eq98YLZscAgIdisVg0fPhwlSlTRq1btzY7DgBkShbDMIy0HmTBggX66quvtHfvXoWGhsrT01PVqlVT9+7d1a1bt/TImaEyYgd2AAAAIC3unLuooCINkx3zzP6fladC6XQ9b2a7Nt68ebMWLFigefPmKTo6WjExMWZHSpHM9jpmNxuf76eTi1YmaO9mHDYhDQCkzZo1a+Th4aH69evb2iIjI+Xs7GxiKgBIHxlxXZwuc9cCAwO1bt06Xbt2TVFRUbp27ZrWrl2bJQoaAAAAQGbk7uerigNfTnbMTxWf1PFsNmPjfkeOHNGaNWu0du1aRUVFmR0HmUhiBQ1XH28TkgBA2nz99ddq1aqVAgMDFRYWZmunoAEASWNBPgAAACCTqv7RANX69L1kx2zqNiBb7a9x9epVffbZZ6pTp44CAgL0/vvv68aNG+ytAZtzqzeYHQEA0k3r1q1VvHhxPfPMM7JYLGbHAYAswdHsAAAAAACSFvBWoI588b1uBie9rM4PPvVU5Nlmarhkqhyy4Dc7w8PDtXz5cn3zzTf67bffFBUVJWdnZ7Vu3VrdunXTU089xTdWYXN0blCi7RYHvrMHIPO7deuWVqxYoa5du0qSPD09FRwcLA8PD5OTAUDWkaqihtVqldVq1b///quyZcvKarWmqIpssVgUHR390CEBAACAnMy/y1PaOyz5vQLOrlinb10qqdPVbXLJn9dOydKHj4+Pbt++LcMwVK9ePXXr1k2dO3dW3rxZ63kg451bvUGnf/gl0b78NSvbOQ0ApM7t27dVtWpVnThxQgUKFNDjjz8uSRQ0ACCVUlXUaNiwoSwWi9zd3eP9DgAAACDjVBz0iiKvh+jfSXMfOPZ77zpqc3StcpcqZodk6aNAgQLq16+fXnjhBZUqVcrsOMikQg4d1+9PJr3PTJWRb9oxDQCknoeHh5566imtXLnS9tkaACD1LIZhGGaHMFtG7MAOAAAApLewMxe0tFijFI2tPWOUyvV+PtXn4No4ffA6pr+/R3+mf0Z9lmifi3dedbqyzc6JAODB1q9fr6pVq9pmH4aFhckwDGZnAMgxMuK6mEVHAQAAgCwiV9FCqvfluBSN3f76KEXdup3BiQD7uRl8JMm+gD6BdkwCACnz0UcfqUmTJnr77bdtbbly5aKgAQBplCEbhZ88eVK//fabXF1d1bZtW96sAQAAgHRS+qUOKt7pCe0eMEGHZy5Kduxiz+qZcimqMWPGyGKx6I033lC+fPk0ZsyYFD3OYrFo+PDhGZwOmVFkyK0k99JwL+KrysN62zkRADxYo0aN5ODgIHd3d8XExMjBwcHsSACQLaRp+alx48Zp9uzZ2r17t20a3fr16/X000/r7t27kqRSpUpp69atypcvX/okzgBMDQcAAEBW9M/70/X3iKkPHPf0vpXKW6lsio5pj2tjq9Uqi8WiAwcOqGzZsrJaUzaB3GKxKCYmJkMypTfuMdLXH2166+yP6xLt62YctnMaAEhcWFiYDh06pOrVq9vajh07xn5RAHK0jLguTtNMjeXLl8vf399W0JCkQYMGKTY2VqNHj9bFixc1Y8YMTZkyJcXfvgIAAACQMpWH9VZ02B3t/2h2suOOzf1BNScPtVOqB/vjjz8kScWKFYv3O5CYm/uPJFnQ8ChRxM5pACBxx44dU8uWLRUSEqL9+/erYMGCkkRBAwAyQJpmahQsWFAdO3bU9OnTJUnnz59XkSJF9O6772rixImSpBYtWujixYvat29f+iTOAHyLCgAAAFlZZOhtfedVPdkxXaP+ldXxwd9p4to4ffA6pg/DMPSNtVyS/eXe6qban7IkGQDzRUZGqmbNmrpx44aWL1+uGjVqmB0JADKFTLdReGhoqPLkyWP7/c8//5TFYtEzzzxja6tevbpOnz6dltMAAAAASIazp4dqTBqc7JiFThUUfeeunRKlzoIFC/TPP/8kOyY4OFgLFiywUyJkFudWrU+2v8aEgfYJAgCJ2L9/v+K+K+zs7KylS5cqODiYggYAZLA0FTV8fHx06tQp2++//fabXFxcVKdOHVtbeHi4LBZLWk4DAAAA4AEq9HtJzX+bl+yYRbmq6tquYDslSrkXX3xRy5cvT3bMjz/+qB49etgnEDKNi79vSbKva/QBObi62DENAPzP0KFDVblyZX3zzTe2ttKlS8vLy8vEVACQM6SpqFGrVi39+OOPWrlypdauXavvvvtOTZo0kYvL/y4sT5w4ocKFC6c5KAAAAIDkFWpeX8U6tEp2zM812yni+k37BEpHMTExKd5QHNlHdFjis4v8nmwkq4ODndMAwP94eHjIMAzt2rXL7CgAkOOk6a5g6NChio6OVuvWrdWyZUuFh4dr6ND/bUAYERGhP//8M97MDQAAAAAZp9SLbR845vyav+yQJH3t2bNH+fLlMzsGMokak4eYHQFADhMeHq4rV67Yfh84cKB+//13TZkyxbxQAJBDPXinwGRUr15dW7du1ddffy1J6tSpk2rXrm3r37Nnj5o0aaLnn38+bSkBAAAApEiRp5qo1qfvaUefD5Ic89fz/eTbtK7cfLztmCy+pk2bxvt9/vz5Wr9+fYJxMTExOnv2rE6ePKlOnTrZKR0yA8MwdOSL7xLt8ypX0s5pAORkf//9t5577jkVLVpUa9askcVikaOjo5o0aWJ2NADIkSxG3I5GOVhG7MAOAAAAmOnq9r+1uk7HZMc8s/9n5alQOl6bva6N719KymKxKKnbEqvVqnz58qlp06aaOnWqfHx8MixTeuIeI+3O/LhW69u8nqDdwd1Nz4f9bUIiADnV4cOHVbVqVeXJk0dbt25V8eLFzY4EAFlGRlwXsygtAAAAkA15166q1ofWJDsmeNxMO6VJKDY21vZjGIZGjRoVry3uJzo6WpcvX9bixYuzTEED6ePShu2Jtju4ONs5CYCc6MaNG7b/Llu2rJYuXar9+/dT0ACATCBVy08tWLBAktS2bVvlzp3b9ntKBAYGpi4ZAAAAgDTJXcZf7n4+unPuUqL9JxauUP0FE2QxeQPuP/74Q/7+/qZmQOYTczci0faCDWrYOQmAnCQqKkojR47U9OnTtXv3bpUqVUqS9MQTT5icDAAQJ1VFjRdffFEWi0V169ZV7ty5bb8nxzAMWSwWihoAAACAnVksFj0yrp82dx+U5Jgdfd5X7Wkj7ZgqoUaNGpl6fmROt0+dS7S91qfv2TkJgJzEwcFBW7ZsUWhoqL7//nsNGTLE7EgAgP9IVVFj7ty5slgsKlSokCRp3rx5GRIKAAAAQPooFdhWznk8tb5170T7D01fqCqj3pKrdz67ZWIGOJJjxMZq6yvv6fzqPxP0WRwd5eFfxIRUALKzyMhIOTg4yMHBQVarVXPnztXu3bvVvn17s6MBABLBRuFiEz8AAABkf1teeU9HZ3+faJ+TV251vr5DFqvVLtfGVqtVFotFBw4cUNmyZW2/JyduBnhMTEyGZEpv3GM8vNPLftWGdm8m2ufg6qLn7+6zcyIA2dmePXvUvXt3devWTQMGDDA7DgBkOxlxXZyqmRoAAAAAsqYSzz+TZFEjKuSWru7YpwJ1qtolCzPAkZyDn32TZJ+Dm6sdkwDICfbs2aN9+/bps88+01tvvSVXV95nACCzS1NRY9OmTQoKCtLAgQPl6+uboP/ChQuaOHGiOnXqpLp166blVAAAAADSwLdxHRVr31Kng9Yk2n/ryEm7FTVefPHFeL93797dLudF5nfr2Gld+mNrkv0+jWrZMQ2A7ComJkYODg6SpB49eujy5ct66aWXKGgAQBZhTcuDJ0+erJ9++inRgoYkFSpUSCtXrtQnn3ySltMAAAAASAf15ow1OwKQrDPLf0uyz6tCadWeNsKOaQBkN1FRUXr//fdVv359RUVFSZIsFosGDx6sggULmpwOAJBSaSpq7NixQw0aNEh2TMOGDbV1a9LftAEAAABgH855PPXo/A/NjpHAmTNn9Pvvv+vOnTu2ttjYWH300UeqX7++mjdvrlWrVpmYEPZy899jibZbrFY9E7xK7n6Jf6EOAFLi+vXrmjJlirZt26agoCCz4wAAHlKaihqXL1+Wn59fsmN8fX11+fLltJwGAIBUOXnypCwWS7wfZ2dnFS1aVM8//7z++eefeONHjRqVYLy7u7sqVaqkYcOGKTQ09KGz/Pnnn7ZjLlmyJMlxcRnmz5//0GMOHz6st956SxUrVpSnp6dcXFxUtGhRdejQQUFBQYqNjX3o5xEnIiJCY8aMUZkyZeTq6qrChQvrlVdeSfX/1xuGoaVLl6pJkyYqVKiQ3N3dVa5cOb366qs6fvx4gvF79+7V0KFD1bJlSxUoUEAWi0WNGzdO8viHDh3Syy+/rGrVqqlAgQJycXGRv7+/nn76aa1bty61TxvIVvJULmd2hASGDx+ujh07ysnJydY2duxYDRkyRFu2bNHvv/+uNm3aaMeOHSamREa7uf+Ijs39IdG+2jNGPXAzeQBIjGEYtv/28fHRF198oYULF6pz586pOk5i9xhOTk7y8/NTp06dtHPnzmQfHxISovfff1+1atVSnjx55OrqqhIlSqh79+7avXv3A8+/bt06Pf/88/L395ebm5ty5cql8uXL69VXX9W2bdtS9Vyke69L6dKlZbFY9NRTTz3weSd37f2gMXfu3NHUqVPVpEkTFShQQE5OTsqXL58aNGigDz/8UFeuXEl1/sSsWbNGjRo1Uu7cueXp6akmTZo81LX/kSNH1KNHD5UpU0Zubm7y8/NTixYttGLFigRjE/u7uP9n1KhRCR7z559/qn///mrSpIm8vLxksVgSLM0JIHlp2lMjT548On36dLJjTp06JQ8Pj7ScBgCAh1KqVCm98MILkqTbt29r69atWrRokZYuXap169apfv368ca3b99elSpVkiRdunRJP//8s8aNG6eVK1dq+/btcnFxSXWGL7/8UtK9ae1z585Vx44d0/isEjdp0iQNGjRIsbGxatCggVq0aCF3d3edOXNGa9euVVBQkF566SVbnocRGxur1q1ba82aNapbt67at2+vI0eOaM6cOVq3bp22bt2qAgUKpOhY/fv31+TJk1WoUCG1adNGnp6e+vvvvzV79mwtWrRImzdvtv1bSNLy5cs1fvx4OTs7q2zZsrp69Wqyx9+3b5+WLl2qevXq6dFHH5Wnp6fOnTunH3/8UatWrdIHH3ygYcOGPfRrASB9bdq0Sc2bN7cVNQzD0LRp0xQQEKBff/1VFy9eVPPmzTVx4kR9/33im50ja7t76ap+qpT0h2plX33OjmkAZBfBwcHq1auXpk2bppo1a0q6d82fFvffY4SFhWnXrl1asmSJli9frrVr16phw4YJHrNjxw49++yzunjxoipVqqTAwEC5u7vrwIEDWrx4sb7++muNHDlSI0eOTPDYu3fv6qWXXtLixYvl7u6u5s2bq2zZspLufalp4cKF+uKLL7RgwQJ169Ytxc9j/fr1OnbsmCwWi9asWaPz58+rcOHCD/mqJO3vv/9W69atderUKRUvXlzPPvusfHx8FBoaqq1bt2rIkCEaP368zp8/r1y5cj30eb755ht169ZNBQoUsBUIvvvuO7Vo0ULff/+9OnTokKLjbNu2TU2aNFFUVJSeffZZtW/fXpcvX9bSpUvVunVrjRo1KtF/p6pVq6pNmzYJ2hMr9MydO1dfffWV3N3dVaxYsTR9iQ7IsYw0aN26teHh4WGcPn060f5Tp04ZHh4exrPPPpuW02S4kJAQQ5IREhJidhQAQDo4ceKEIclo2bJlgr5hw4YZkoxGjRrZ2kaOHGlIMhYtWhRv7N27d42qVasakoy5c+emOkdISIjh7u5uVKlSxWjRooVhtVqT/P/MuAzz5s1L8nhJjZk1a5YhyfD39zd27dqV4HFRUVHGnDlzjO7du6f6Odxv7ty5hiSjS5cuRmxsrK39888/NyQZr7zySoqOc+HCBcNqtRrFixc3bt68Ga9v8uTJhiSjR48e8dqDg4ONXbt2GZGRkcaFCxcS/Bv+V3h4eLyMcc6dO2cULFjQcHJyMm7cuJGivEB2c3VXsLFAZRL8HPt6uWEY5lwbe3p6GgMGDLD9vnv3bsNisRgzZsywtXXv3t3w9/e3W6a04h4jdX55rEuif5cLVMb4vmBds+MByKJeeOEFQ5LRoEGDNB8ruXuM8ePHG5KMhg0bJug7deqUkS9fPsNqtRqff/55gv6DBw8apUqVMiQZ06dPT9DfpUsXQ5LRokUL4+LFiwn6b9y4YQwcOND49NNPU/V8unbtakgy+vfvb0gyxo4dm+i4uOed3LV3UmPOnDljFCxY0LBarcakSZOM6OjoBI/dvXu38eijj6bp2vz69etGnjx5DG9vb+PMmTPxzu/t7W14e3sboaGhKTrWE088YUgyli9fHq/95MmTRu7cuQ03NzcjPDzc1h733FNzr7Vjxw4jODjYiI6ONrZs2ZLqxwNZTUZcF6dp+al+/frpzp07ql+/vhYsWKALFy5Iki5cuKCvvvpK9evX1927d/Xuu++m5TQAAKSbt956S5JStISJq6urunbtKknatWtXqs+1aNEi3blzR4GBgQoMDFRsbGyyy0s9jJs3b2rAgAFydnbWqlWrVL169QRjHB0d1bNnT82aNStN55o9e7Ykafz48fGWAHn11VdVsmRJLVy4UHfv3n3gcU6ePKnY2FjVr19fXl5e8fqefvppSUowBb1ixYqqXr16vKVpkuPi4pLoMiWFCxe2bQx56tSpFB0LQMaLjY2Nt0Te+vXrZbFY1LRpU1ubn5+fLl68aEY8ZLBru/fr8sakl20p0rqZHdMAyE4++eQTBQYG6rvvvsvQ8/Ts2VNS4vcMQ4cO1fXr1zVkyBC99tprCfrLlSunH3/8UU5OThoyZIhCQkJsfX/88YcWLVqksmXLavny5fLx8Unw+Dx58uijjz7SK6+8kuK8N2/eVFBQkCpVqqQxY8Yod+7cmjt3brylutLDsGHDdPnyZQ0dOlT9+vWTg4NDgjHVqlXThg0b5Onp+dDnWbJkiW7evKm33npLRYoUsbUXKVJEb775pq5evaply5al6FjHjx+XxWLRE088Ea+9ePHiqly5su7evavbt28/dFZJqlmzpipWrJjo6wEgZdJU1GjYsKEmT56s8+fPq0ePHipSpIgcHR1VpEgRvfTSS7p48aKmTp2a6NQ7AADMlNp1uR0dU79i45dffikHBwd17dpV7dq1k4eHh+bNm5euNws//PCDQkND1aFDB1WoUCHZsf9dPitundeUCA8P17Zt21SuXDkVL148wXFatGihsLCwB64lLEllypSRs7OzNm3alGCq9cqVKyVJzZplzAdY165d07Zt2+Tu7q6SJUtmyDmArGpTtwH65/3p6f6BRkoUK1ZM27dvt/2+fPlyFSpUSOXK/W//j4sXLypPnjx2z4aMd371n8n21/50uJ2SAMjKoqOj9dFHH8VbYtTb21tfffVVhiyrlJj/3jOEhYXp+++/l6urq/r375/k4ypWrKh27dopNDQ03j58cUvH9u/fX+7u7smeOzVL5X777bcKDw9XYGCg3Nzc1KFDBx07dkwbNmxI8TEe5M6dO1q8eLHc3NySfe7SvdfNav3fR5QvvvjiA/cbvN/69eslSY8//niCvpYtW0pSip9bpUqVZBiGVq9eHa/99OnT2rdvn6pWrar8+fMneNz58+c1ffp0jRs3Tl9++aWOHTuWovMBeDhp2lNDkt5++201adJEM2fO1I4dOxQSEqI8efKodu3aeu211+Kthw0AgNlmzJghSapdu/YDx4aHh2vhwoWSpAYNGqTqPPv27dOOHTvUsmVL+fr6SpLatWunBQsW6Pfff0+3D+03bdokSfG+zZwRjh07ptjYWJUpUybR/rj2I0eO6LHHHkv2WPnz59eHH36od999VwEBAWrdurVtT43ff/9dr7/+ut588810yX348GF9++23iomJ0fnz57VixQrdvHlTM2fOVO7cudPlHEB28veIqYp0TdmMqPTUvn17jR07Vh06dJCrq6v++uuvBO8D//77L8XIbCryZtJrib8Qe4gNwgGkyF9//aXBgwfLYrHoueeeU+XKle127jlz5khKeM+wc+dORUVFqXbt2g8szDdr1kzfffedtmzZol69eknKuGv9L7/8Ular1TYrvVu3bpo3b56+/PLLZDcET40dO3YoMjJSDRo0SDA7O70dOXJEkhK9V7n/PiUlPvjgA23atEkdOnTQs88+q7Jly9r21ChVqlSSs35+++03/fbbb7bfLRaLunbtqpkzZ6ZprxAAiUtzUUOSqlSpYvuQCACAzOLo0aMaNWqUpHvfktq2bZs2btwoV1dXjR07NsH4H374QQcPHpQkXb58WatWrdLp06fVtm1btWvXLlXnjvtWVWBgoK0tMDBQCxYs0JdffpluRY24pVjun2adUgcOHEjx2Lhp8EndkMRNF79/unxy+vbtKz8/P/Xq1UszZ860tTdo0EDPP//8Q82MSczhw4c1evRo2+9xs2XiNncEcqIHfUC8e+BHdkryP/3799evv/6qpUuXSrp3fxH3/i1Jp06d0vbt2zV48GC7Z4N5mq6eQ0EDQIo1btxYffr0UbVq1TL0C7b/vcfYtWuX/vjjD/n4+GjixInxxsZdqxctWvSBx40bE7e0+/2Pf5hr/aTs3btXu3fvVosWLWwzWBo3bqxixYopKChI06ZNS5ciRFqyjx8/XoMHD1ahQoVSND65e5XU3qcEBARo69at6tixo+26RLr3xawePXqoVKlS8ca7u7tr+PDhatOmjUqVKqXY2Fjt3r1bw4YN0zfffKM7d+4oKCgoRecGkHLp84kBAACZ0LFjx2wfaDs5OcnHx0fPP/+8Bg8enOg3t4KCghJccHbs2FHfffddqj5UiYiI0DfffKPcuXOrbdu2tvYmTZqoaNGiWrZsmW7cuKG8efM+5DNLHwEBAaade8yYMfrggw80ZswYvfDCC8qTJ4/27t2rvn37qnHjxgoKCtKzzz6b5vM8/fTTMgxDkZGROnnypGbPnq3AwEBt375dn376aTo8EyDr8QwoKQc3V8XcDTc7io2np6e2bt2q4OBgSVL58uUTrDO9dOlS1axZ04x4MEmh5o+aHQFAJnbo0CGNHDlSc+bMkYeHhyRp6tSpGX7e++8x4vj6+mrjxo0qXbp0hp8/reJmldz/5SuLxaIXXnhB48aN07fffqvevXubFU+SVKhQoRQXNNLb9u3b1aZNG1WuXFm7du1SQECALl68qGnTpuntt9/WX3/9pe+//942vmDBghozZky8YzRr1kz16tVT9erVtXTpUu3evTvRvQ8BPLw07akh3Vuz8JNPPlHt2rXl6ekZ75uVe/fu1euvv67Dhw+n9TQAAKRay5YtZRiG7UPtM2fOaOHChUlORV+0aJEMw1BUVJSCg4PVqlUrLVmyRCNGjEjVeZcvX65r166pQ4cOcnNzs7XHTfEODw/Xt99+G+8xcWvI3r9R7n/F9d2/3mzc0lbnzp1LVcbUivvWU1LfcIrbGyMl3+pau3atRo4cqTfffFODBw9WkSJF5OHhoQYNGuinn36Sk5OT3n333fQLL8nZ2Vlly5bVxIkT1bt3b3322WcJ1skFcgpHN1dVHNjL7BiJqlSpkipVqpSgoFG8eHG1bt1afn5+JiUDAGQmsbGxatOmjb777jsNH27ffXfuv8e4fPmyJk6cqMuXL+vZZ59NsIF03LX6mTNnHnjcuDH3f5if3tf6ccvrenh4JJiJHlfkmDt3brz2zH6fIiV/r5Ka+5SoqCg999xzslqtWrZsmapXr27bi2/y5Mlq06aNlixZYlsWLDnu7u7q1q2bJKVoPIDUSVNR4+7du2rSpIn69++vU6dOydPTM97GgiVKlNC8efO0YMGCNAcFAMBeHB0dVbFiRS1btkylS5fW2LFjtXv37hQ/Pm7pqXnz5tk24477+fDDD+ONiRN3kX3t2rUkj3v16tV4YyWpfv36kqR169alON/DKFmypKxWa5Jr0Sa3ju1/xRUTmjRpkqDP19dXAQEBOnr0aIKbwvQSt4Fg3IaCQE5UdVQfNf15ttkxErVp0yZNnz5d48eP1/Tp0/kgAACQgNVq1bRp09SyZUv17dvXtBwFChRQ//79NXToUB04cEDvvfdevP6aNWvKyclJu3bteuDyR3HX8/Xq1bO1pfe1/tKlS3Xz5k3dvn1buXLlinefEjeLe+fOnfrnn39sj3nY+5RatWrJ2dlZO3futBUWMkpy+2ak5j7l4MGDOnHihOrUqZPoxuxx9y979uxJUS5vb29J95YpA5C+0lTUGDdunDZt2qTx48fr4sWLto2M4nh5ealRo0Zas2ZNmkICAGAGV1dXffzxxzIMI8XruJ86dUrr1q2Tj4+PevbsmehPiRIltGfPnngXw3GzR7Zs2ZLkseP6qlSpYmvr0KGDPD09FRQUZNsPJCkREREpeg6JcXNzU+3atXXo0CGdOnUqXp9hGPrtt9+UK1euFC0NExkZKUm6cuVKov1XrlyR1WqVk1PGbFZ8/vx5Scqw4wNZhd8TjdR83Vdmx7DZvHmzypUrp4YNG6pPnz4aNmyY+vTpo4YNGyogICDZ90cAQPYWGxurqVOnxvt8qVmzZlq9erWKFStmYrJ7hg4dqsKFC2vGjBk6efKkrT1Xrlzq2LGjwsPDNWnSpCQff+DAAS1btky5c+dWhw4dbO09e/aUJE2aNEl3795NNkNKrvXjvljVsWPHRO9TWrZsGW+cdO+zvaJFi+rw4cNJFjYSu09xd3fXc889p7t37yb73KV7q8AkNxPkQRo1aiRJ+vXXXxP0xf3NxI1JTkruUyTJxcUlRbm2bdsmSfL390/ReACpYKRBmTJljKZNm9p+HzVqlGG1WuON6d27t1GwYMG0nCbDhYSEGJKMkJAQs6MAANLBiRMnDElGy5YtUzR+5MiRhiRj0aJFifZXr17dkGT8+eefKT7WiBEjkhwza9YsQ5Lxxhtv2NqioqIMf39/w8HBwVi7dm2Cx8ydO9eQZDRo0CDJ45UsWdLYs2dPgv7o6Ghj/vz5xosvvhiv/cCBA8aBAwce+Jz+m6FLly5GbGysrf3zzz83JBmvvPJKvPGRkZHGgQMHjKNHj8ZrX7RokSHJqFixonHz5s14fXHHql+/fpI5Lly4YEgyGjVqlOSYnTt3xssY5+TJk0bRokUNScZff/2V3NMFcoxNPQYbC1TG9jNLJe1+bRwcHGzkypXLsFgsxuOPP26MHz/emD9/vvHhhx8aLVu2NCwWi5E7d25j//79dsuUVtxjpNzyco/H+xuM+4mJijI7GoBMYurUqYYkw8/Pz7T31QfdY8RlfOmll+K1nzx50sibN6/h4OBgzJ49O8HjDh8+bJQpU8aQZEyfPj1Bf5cuXQxJRqtWrYxLly4l6A8JCTGGDBlifPrpp8nmP378uGGxWAx/f/9Er5MNwzBu3rxpuLm5Gfny5TPCw8Nt7e+9954hyejRo0eCx545c8bw8/MzHBwcjEOHDiXoK1CggOHg4GBMnTrViImJSXDOv//+26hfv75x48YNW9v58+eNAwcOJLhXSMr169cNLy8vw9vb2zhz5ky883t7exve3t5GaGhovMecOnXKOHDggBEWFmZrCw8PNzw9PQ2r1WqsWbMm3vjTp08bBQoUMCwWS7znuXv37kRfz6CgIMNqtRp58+ZN9nls2bLFkGR07949Rc8VyIoy4ro4TUUNFxcXY+DAgbbfEytqDBw40HBxcUnLaTIcNxwAkL2kd1FjxYoVhiSjcePGyR4nJibGKFasmGGxWIzjx48nOS7uZiFPnjzG3bt3be1//vmn4eHhYVitVuPJJ580Bg8ebAwYMMBo1KiRIcnw8fExDh48mOgxJ06caDg4OBgWi8Vo1KiR8c477xhDhgwxAgMDDT8/P0OS0atXr3iPkWSk5vsNMTExRsuWLQ1JRt26dY1BgwYZ7du3NywWi1GiRAnj8uXL8cbH/TsUL148Xnt0dLTRsGFDQ5JRsGBBo1evXkb//v2Npk2bGpIMNzc3Y9u2bfEec+DAAaN79+5G9+7djU6dOtlej7i2/94ENGrUyPDz8zPatWtnvPPOO0a/fv2M1q1bG87OzoYko3///il+3kB2F3EjxPSiRqdOnQwnJydj9erVifavXr3acHJyMjp37my3TGnFPUbK3Nh/JNGCBkUNAPcLCwszqlWrZnz++edJfiCf0R50j3H37l2jcOHChqOjY4Iv9WzZssUoWLCgIcmoUqWK0adPH2Pw4MG261OLxWKMGjUq0ePeuXPHeO655wxJhru7u9GmTRtj4MCBxoABA4w2bdoYuXPnNiQZX3/9dbL54woTI0eOTHbc888/b0gyFi9eHC9D3bp1DUlGQECA8dZbbxnDhg0zXnjhBSN37tyGxWIxJk+enOjx9uzZYxQvXtyQZPj7+xs9e/Y0hg4darz55ptG7dq1DYvFYnh5eRm3b9+2PaZ79+6GJGPevHnJZr3f119/bUgyChQoYLz55pvGm2++aStCfP/99wnGx91j/fHHH/Ha474wZrVajWeffdYYOHCgERgYaHh4eBiSjHfffTfBcYoUKWJ07NjR6Nu3r9GnTx+jQYMGhiTDxcXF+PHHHxOce+PGjbZ7mCeffNKQZJQqVcrW9t9zAFldpitqeHt7x/vWZ2JFjfbt2xtFihRJy2kyHDccAJC9pHdRwzAMo2bNmoYkY926dUmOWbNmzQNnEMTp2rWrIclYuHBhvPYjR44Yr7zyilGyZEnDxcXFcHNzMwICAox+/foZFy5cSPaYBw8eNN58802jQoUKhoeHh+Hk5GT4+fkZbdq0MX744YcEN4CpLWoYxr1vL40aNcooVaqU4ezsbPj6+hq9evUyLl68mGBsUkWNuOOMHz/eqFatmuHu7m44Ojoafn5+xgsvvGD8+++/Ccb/8ccftrxJ/dxvyZIlRseOHY2SJUsauXLlsr0W7dq1M3755ZdUPWcgJzj29XJTixoFCxY0unTpkuyYLl26ZPoZ4PfjHiNlgifOoagBIIFjx44ZH374Yby2xL7lb08pucf47LPPDElGt27dEvRdv37dGDVqlFG9enXD09PTcHZ2NooVK2YEBgYaO3fufOD5f/vtN6NLly5G8eLFDVdXV8PV1dUoU6aM0atXrwRfCPqvmJgYo0iRIg/88lXceSQZLVq0iNceHh5uTJo0yahdu7bh6elpODo6Gr6+vkabNm2M33//PdljhoWFGVOmTDEaNWpkeHt7G46OjkaePHmMevXqGWPHjjWuXr0ab/zDFDUM496XIB577DEjV65choeHh9GoUSPjt99+S3RsUkUNwzCMX3/91XjqqacMb29vw8HBwfDy8jIaNmxofPPNNwnGzp4922jVqpVRtGhRw83NzXBxcTFKlixp9OrVK8lZ8fPmzUv2viax+ycgK8uI62KLYdy3s3cqPfPMM9qyZYuOHj2qPHnyaPTo0RozZoxiYmIkSWfOnFG5cuXUtm1bLVy48GFPk+FCQ0Pl5eWl8+fPy9fXVxaLRdK9tfSioqLk6OgYb728uA1+3NzcZLXe25YkKipKkZGRcnBwkKur60ONvXPnjgzDkKurqxwcHCTdW1cwIiJCVqtVbm5uDzX27t27io2NlYuLixwdHSVJMTExCg8PT9VYi8USb6Ok8PBwxcTEyNnZ2bYueWrGxsbG2taEzJUrl21sRESEoqOj5eTkJGdn51SPNQxDd+7ckXRvDcf//numZmxK/u3T4+8ksX/P9Pg7ifv3TOvfyX//PdP6d5LUv2da/07u//dM699JUv+eD/t3wnsE7xG8R/Ae8d+xvEfwHpHasRn5HnFy1e/a0OZ1Ocuqu4rRqzqukJAQeXp6yh5cXV317rvvauzYsUmOGTZsmCZNmqTw8HC7ZEqruHsMe76OWcmd85d0eOZi7Xt/eqL9nuVKqPVB9mYEcqIbN27I399foaGhWrp0qdq2bWt2JABAGmTIdXFaKiIbNmwwrFarUb16deOvv/4y3nvvPcNqtRphYWHG2rVrjfLlyxtOTk4pqjibKa5aJCne0hkffPBBost1uLu7G5KMEydO2No++eQTQ5Lx/PPPxxvr7e1tSDKCg4NtbV988YUhyWjdunW8sXHT8bZv325r++abbwxJRvPmzeONrVChQoKK8rJlywxJxqOPPhpvbNy3i1euXGlr+/XXXw1JRtWqVeONjatU3z8176+//jIkGaVLl443Nm6K3P2V8z179hiSjMKFC8cb26FDB0OSMW3aNFvb4cOHDUmGl5dXvLFxFfkJEybY2s6ePWtIMhwdHeONff311xNMn7xx44bt3zMyMtLW3r9//wRLjkRGRtrG3r9+Y9y3tl9//fV453N0dDQkGWfPnrW1TZgwIdH1D728vAxJxuHDh21t06ZNMyQZHTp0iDe2cOHChqR4a+HHVe6ffPLJeGNLly6dYC3477//PtFvh1etWtWQZPz666+2tpUrVxqSjJo1a8Yb++ijjxqSjGXLltna4r4ZXaFChXhjmzdvbkiK9y2F7du3J/qNgtatWxuSjC+++MLWFhwcbEgyvL29442Nm+b6ySef2Nrivg3j7u4eb2yvXr0MScYHH3xga7t8+XKi39h+++23DUnG0KFDbW23b9+2jb1/muvQoUMNScbbb78d7xi8R9zDe8Q9vEfcw3vE//AecQ/vEfdk1feIALmZNlOjRIkS8fbqS0yzZs2MEiVK2ClR2jFTI2nn125KcnZG3M+J71aZHROAiQYPHmw0atQowTJOAICsJyOui+997eshNWzYUNOmTdO+ffvUsGFDjRs3TpKUO3duPf744zp69KhmzJihGjVqpOU0AAAAALKxZ599VuvXr9fw4cMTzMQIDw/XyJEj9ccff6h169YmJUR6CTt7UWubv/jAcUXbNM/4MAAyhdjYWM2ePVuXLl2ytY0ZM0a///67SpUqZWIyAEBmlablp+IcOHBAM2fO1LZt23T9+nV5enqqTp06ev3111WxYsX0yJmhWH6KZSOSGpuVlo1gaRmxtAzvESkay3sE7xGpHct7BO8R9/97Ztf3CLOXn7p27Zrq1KmjEydOKH/+/Kpdu7Z8fHx06dIl7dixQ1euXFHJkiW1fft25cuXzy6Z0orlpxJ3eOYibes9Mtkxrj7e6nBhk+1/UwCytz59+uizzz5T27ZtFRQUxP/2ASCbyYjr4jQVNf788095enrqkUceSZcwZuGGAwAAADnZzX+P6vCMe3vg3Y4MV7PZH9r92vjq1asaOHCgFi9eHG+2hqurq7p06aKPPvpI3t7edsuTVtxjJBQbE6OFjuUfOK7ioJdV/cMBdkgEIDPYu3evGjVqpDFjxqhPnz4UNQAgm8l0RQ0HBwe9+uqrmjFjRrqEMQs3HAAAAMA9Zl8bR0VF6eDBgwoNDZWnp6cCAgJsM3SyErNfx8zowtpNWtuiR5L9BR6trqJtmqvCuy/JYk3TSskAMrEzZ87o4MGDatGiha0t7j0fAJD9ZMR1sWNaHlywYMF409kBAAAAIKVu3Lihzz77TDt27JDFYlGdOnX0xhtvqHLlymZHQwa4uv2fJPueDw+Wg4uzHdMAMMPff/+thg0bymKxaP/+/fLz85MkChoAgFRJU1GjRYsWWr9+vQzDYHogAAAAgBS7du2aatWqpVOnTilu8viqVas0f/58bdu2LcvsnYGUM6JjEm3PX7MSBQ0gh6hYsaICAgLk4OCgiIgIs+MAALKoNM3p/fDDD3Xt2jW98sorun79enplAgAAAJDNjRs3TidPnlTjxo21ePFiLV68WI0bN9axY8f04Ycfmh0PGSA2KjrR9voLJtg5CQB7MQxDq1atshWvHR0d9dNPP2njxo0qWbKkyekAAFlVmvbUaNq0qa5du6bg4GA5OzurRIkS8vHxSTBrw2KxaN26dWkOm1FY7xYAAAC4x17XxuXLl1dMTIwOHDggBwcHSVJMTIwqVKggBwcH/fvvvxl2bnvgHiO+8CvXtaRg3UT7Ot/YKec8vEZAdmMYhjp37qwlS5bo888/12uvvWZ2JACACTLdnhrr16+3/XdERIQOHjyogwcPJhjH0lQAAAAA7nfq1Cn17NnTVtCQJAcHB7Vs2VJz5swxMRnSW1TYnSQLGgCyL4vFovr16+vHH39UZGSk2XEAANlImooasbGx6ZUDAAAAQA4SHh6uAgUKJGjPnz8/66xnMxd+25Rsv4Obq52SAMhoFy5cUEREhPz9/SVJb731lp566imVLl3a3GAAgGzlofbU2LJli5o2barcuXPLy8tLLVq00Pbt29M7GwAAAAAgiwv592iSfYVa1GeTcCCbWLt2rSpWrKgXXnhBMTExkiSr1UpBAwCQ7lI9U2Pfvn1q1qyZwsPDbW3r1q3T5s2btX37dlWsWDFdAwIAAADInv766y9NmDAhQZskTZw4UYlt/zdw4EC7ZEP6iY1IetmZxxZNtmMSABmpTJkyioqKUnh4uK5du6aCBQuaHQkAkE2leqPwrl27atGiRRo2bJjeeustSdL06dP1/vvv64UXXtCCBQsyJGhGYhM/AAAA4B57XRtbramfNG6xWGzf/s3suMe4JyYyUt+6VEq0r/3ZP+Xu52vnRADSi2EYOnTokAICAmxtu3fvVuXKleXk5GRiMgBAZpIpNgrfuHGjGjRooPfff9/WNnr0aK1fv14bNmxIl1AAAAAAsrd58+aZHQF2cGnDjiT7rM4sOwVkVXfu3FG3bt20cuVK7d6927ZqR/Xq1U1OBgDICVJd1Lh06ZKee+65BO116tTRtm3b0iUUAAAAgOyte/fuZkeAHVzfvT/JPkcPdzsmAZCe3NzcFBERodjYWO3YsYOlyAEAdpXqokZUVJQ8PDwStOfKlUtRUVHpEgoAAAAAkHUZhqHre/7V4ZmLE+3PX7uKHN1c7ZwKQFpcuXJFXl5ecnZ2lsVi0RdffKErV66oatWqZkcDAOQwqV/I1k6mT58uf39/ubq6qk6dOtq+fXuKHrd48WJZLBa1adMmYwMCAAAAyDK4v7AfIzZW23qP1M812irs5NlExzT4eqKdUwFIixUrVqhChQoaN26cra1w4cIUNAAApkj1TA1J+uabb7R169Z4bUePHpUkPfnkkwnGWywWrVq1KsXH/+6779SvXz/NnDlTderU0ZQpU9SyZUsdOnRIBQsWTPJxJ0+eVP/+/fXYY4+l+FwAAAAAsjfuL+zr6o59OjIr8RkacZy8ctspDYD0cPfuXV29elUrV67Ue++9J0fHh/o4CQCAdGExDMNIzQOs1tRP7rBYLIqJiUnx+Dp16qhWrVqaNm2aJCk2NlZFixbVW2+9pcGDByf6mJiYGDVs2FAvvfSSNm7cqJs3b2r58uUpOl9G7MAOAAAAZEXZ8drY3vcXUvZ8HVPq4GcLtKPPB0n2W6xWdb65U065Ey5rDCDzuHnzpvLkySPp3pJyixcvVvv27eXs7GxuMABAlpIR18WpLq2fOHEiXU6clMjISO3atUtDhgyxtVmtVjVv3lxbtmxJ8nFjxoxRwYIF1bNnT23cuDFDMwIAAADIGri/sL/Y6OS/0Fbg0WoUNIBM7Pr163rjjTf0999/a/fu3XJ1dZXFYlGXLl3MjgYAgKSHKGoUL148I3LYXL16VTExMfLx8YnX7uPjo4MHDyb6mL/++ktffvml9u7dm6JzREREKCIiwvZ7aGjoQ+cFAAAAkHnZ4/5C4h4jpfLXqqwG3042OwaAZBiGofXr1+vy5ctav369WrVqZXYkAADiyfKLIN66dUvdunXT7Nmz5e3tnaLHjB8/XqNHj87gZAAAAACymoe5v5C4x0iJJ3ctU/7qFc2OASAR4eHhcnV1lSTlz59f33zzjTw9PVWrVi2TkwEAkFCmK2p4e3vLwcFBly5ditd+6dIl+fr6Jhh/7NgxnTx5Us8884ytLTY2VpLk6OioQ4cOqVSpUvEeM2TIEPXr18/2e2hoqIoWLZqeTwMAAABAKkVGRmrt2rU6ePCgwsLCNHz4cEn3PmwLDQ2Vt7d3qvf4s8f9hcQ9Rkq4FshndgQAifjpp5/02muvadGiRWrYsKEkqVmzZianAgAgaanf9TuDOTs7q0aNGlq3bp2tLTY2VuvWrVO9evUSjA8ICNC+ffu0d+9e28+zzz6rJk2aaO/evYneSLi4uMjT0zPeDwAAAADzrFixQsWKFdMzzzyj/v37a9SoUba+f/75R4UKFdLixYtTfVx73F9I3GMAyLp+/PFHnT9/XhMmTDA7CgAAKZLpZmpIUr9+/dS9e3fVrFlTtWvX1pQpUxQWFqYePXpIkgIDA+Xn56fx48fL1dVVlSpVivf4PHnySFKCdgAAAACZz6ZNm9ShQwcVKlRIU6dO1datW7Vo0SJbf+3atVW6dGkFBQXp+eefT/Xxub8AgPhiYmLk4OAgSZo0aZKKFSumgQMHmpwKAICUyZRFjc6dO+vKlSsaMWKELl68qEceeUS//PKLbXO/06dPp3raOQAAAIDM6f3331eePHm0a9cueXt769q1awnG1KxZU9u2bXuo43N/AQD33Lx5U3379pWLi4tmzpwpSfLy8tKIESNMTgYAQMpZDMMwzA5httDQUHl5eSkkJIRp4gAAAMjRzLg2zps3rzp06KDZs2dLkkaPHq0xY8YoJibGNmbw4MGaNm2abt++bZdMaZVT7zEirt/Uj+VaKuLqjQR97U5vUK6ihUxIBSDOhg0b1LhxY1mtVh06dEilS5c2OxIAIJvLiOtivo4EAAAAwFQREREPvMG5efMmsykyuei74Vrb/MVECxoAzHP/d1kbNWqk8ePH688//6SgAQDIsjLl8lMAAAAAco6SJUtqx44dyY7ZsmWLAgIC7JQIKRETHqHrfx+UERUlSTr/6yZd3/OvyakA3O/XX3/VyJEj9fPPPytv3ryS7s18AwAgK+OrTgAAAABM1b59e23atEnz5s1LtP/jjz9WcHCwOnfubOdkSMrxb37Ud3lr6pe6HbXmsee15rHnte/96UmOt7o4y8U7rx0TAoiOjlafPn20detWjRs3zuw4AACkG2ZqAAAAADDVgAEDFBQUpF69eunbb79VRESEJGngwIHasmWLNm/erEceeURvvvmmyUkhSXfOX9KmwIFSKrZn9H/uKTm6uWZgKgD/5ejoqHnz5mnx4sUaNWqU2XEAAEg3FDUAAAAAmMrDw0MbN27Um2++qe+//962QfjHH38si8WiTp06acaMGXJxcTE5KSTp8l+7UlXQKPVSB9X5fFTGBQIgSbp165YGDBigFi1aqH379pKkevXqqV69eiYnAwAgfVHUAAAAAGC6vHnzauHChfr000+1Y8cOXb9+XZ6enqpVq5Z8fHzMjof7pGbfjFz+RVRvzlhZLJYMTARAkmbMmKFZs2Zp2bJleuKJJ+Tu7m52JAAAMgRFDQAAAACZRv78+dWqVSuzYyAJ0Xfuav+HX6RorKNHLtX/6iMKGoCdvPPOO9q0aZP69u1LQQMAkK1R1AAAAAAApMjljTuT7Ksy4g3lqRIgSXLy9FCBR6vJKRcfrAIZZf369QoKCtKnn34qi8UiFxcXrVixwuxYAABkOIoaAAAAAEzVtGnTFI2zWCxat25dBqdBcu5euppkX8XBr7IZOGAnFy9eVKtWrRQREaEGDRqoc+fOZkcCAMBuKGoAAAAAMNX69euT7bdYLDIMg2WMMjG/pxpT0ADsyNfXVyNGjNCpU6f0xBNPmB0HAAC7spodAAAAAEDOFhsbm+jPzZs39fvvv6tOnTrq0KGDIiMjzY6KJDzy/jtmRwCytTt37mjgwIE6c+aMrW3IkCGaNWuWPD09TUwGAID9UdQAAAAAkCl5enqqcePGWrNmjbZv366xY8eaHQlJYRINkKF69eqliRMn6uWXX5ZhGJLE7DUAQI5FUQMAAABAppY7d2498cQTmjdvntlRAMAUI0aMUJkyZdS3b1+KGQCAHI89NQAAAABkelarVRcuXDA7Ro4XffuO2RGAHGHz5s06deqUunTpIkkKCAjQgQMH5ODgYHIyAADMR1EDAAAAQKZ2/PhxLVmyRP7+/mZHydFiY2K0/Y3RZscAsr1Nmzbpsccek5ubm+rUqaOSJUtKEgUNAAD+H0UNAAAAAKZ66aWXEm2Pjo7WuXPn9NdffykqKkpjxoyxczLc7/ru/WZHAHKEevXqqVGjRipevLjy5ctndhwAADIdihoAAAAATDV//vxk+8uVK6d3331XvXr1sk8gJCr00Ikk+9z9fO2YBMhewsPDNWfOHPXu3VsODg6yWq1avXq1XF1dzY4GAECmRFEDAAAAgKlOnEj8w3Kr1ao8efIod+7cdk6E1HDzLSDXAnybHHgYhmGoSZMm2rp1qyIiIvTuu+9KEgUNAACSQVEDAAAAgKksFoucnZ3l68u3/TOryJuh2jt8aqJ9j30/xb5hgGzEYrGoV69eOnnypMqWLWt2HAAAsgSr2QEAAAAA5GwlSpTQ0KFDzY6BJMTGxGhty5cUdvJsov2OudztnAjI2nbu3Kn9+/+3R81LL72kAwcO6JlnnjExFQAAWQdFDQAAAACmyps3r/Lnz292DCTh+u79urb9H7NjANnCokWLVLduXQUGBioqKkrSvdkaefLkMTcYAABZCEUNAAAAAKZ67LHHtG3bNrNjIAm3jp1OutNiUa5ihewXBsjimjRpIi8vL5UpU0Z37941Ow4AAFkSRQ0AAAAApho/frz++ecfjRkzRtHR0WbHQSoUa/e4XL3ZJBxISmRkpNatWyfDMCRJvr6++ueff7R48WJ5enqanA4AgKyJjcIBAAAAmGrChAmqXLmyRo8erVmzZqlq1ary8fGRxWKJN85isejLL780KSX+y+/JRmqwcJLZMYBMKzQ0VI899pj279+vbdu2qUaNGpIkPz8/k5MBAJC1UdQAAAAAYHcODg4aNWqUhg8frvnz59vaL1y4oAsXLiT6GIoamUuVkW/KwcXZ7BhApuXp6amAgACdP39eV65cMTsOAADZBkUNAAAAAHZnGIZtOZYTJ06YnAYA0se+fftUunRpubm5SZJmzJihmJgYFSxY0ORkAABkHxQ1AAAAAJiqePHiZkcAgDT77LPP1K9fP73zzjuaOHGiJCl//vwmpwIAIPtho3AAAAAAAIA0KlGihKKjo3XixAnFxsaaHQcAgGyLmRoAAAAATPHfjcABICuJjo7WmTNnVKJECUnS008/rc2bN6tu3bq8vwEAkIGYqQEAAADAFKNGjZKDg0OKfxwd+U4WgMzh5MmTqlevnpo1a6bbt2/b2uvVq0dBAwCADMZdAQAAAABTeHp6Kk+ePGbHwAPcOnzS7AhAppMvXz5dvnxZoaGhCg4OVt26dc2OBABAjkFRAwAAAIAp+vbtqxEjRpgdA8nYN36m/h75qdkxgEzh4sWL8vX1lXSvKBsUFKTChQurcOHCJicDACBnYfkpAAAAAEACd85f0t6hk82OAWQKH3/8sUqUKKE1a9bY2mrWrElBAwAAE1DUAAAAAAAkcHX7P8n2O+XOZackgPnOnDmj8PBwLVmyxOwoAADkeCw/BQAAAABI4PLGnUn2uRfxlWdAKTumAewrJiZG4eHhypXrXvFu3LhxqlOnjrp06WJyMgAAwEwNAAAAAEA80Xfu6sDkeUn2N1k5SxaLxY6JAPs5cuSIGjZsqFdffdXWlitXLj3//PP83QMAkAkwUwMAAACA3cXGxpodAcm4smVPkn2tti5Rvqrl7ZgGsK8bN25o69at2rdvn86ePasiRYqYHQkAANyHmRoAAAAAgHiiQm4l2Ze3Sjk7JgHsIzIy0vbftWvX1ty5cxUcHExBAwCATIiiBgAAAAAgRYp3flKObq5mxwDSTWxsrKZOnaqyZcvq8uXLtvbu3burWLFiJiYDAABJoagBAAAAAEiRgDdfMDsCkK6io6M1d+5cnTp1SrNmzTI7DgAASAH21AAAAAAAADlGbGysLBaLLBaLnJ2dNX/+fG3dulWvvfaa2dEAAEAKMFMDAAAAAADkCMePH1ezZs309ddf29qqVaum3r17y2KxmJgMAACkFEUNAAAAAACQIyxZskTr16/X0KFD420ODgAAsg6WnwIAAAAAADnCu+++q7Nnz6pv375ydnY2Ow4AAHgIzNQAAAAAAADZjmEYmjVrltq1ayfDMCRJjo6O+uyzz1SyZEmT0wEAgIdFUQMAAAAAEM+/k+aZHQFIszNnzqhv375atmyZgoKCzI4DAADSCctPAQAAAABs7l6+piubd5sdA0izYsWKadKkSYqIiFDbtm3NjgMAANIJMzUAAAAAADbXtv+TZJ9j7lx2TAKkzpkzZ9S2bVsdOXLE1ta7d2+98847cnBwMDEZAABIT8zUAAAAAADYxEZHJ9mXp1JZOyYBUqdPnz5avny5bt++rd9++83sOAAAIINQ1AAAAAAAPFCNjwfJyrfdkYl98sknCgsL02effWZ2FAAAkIEoagAAAAAAHih/zcpmRwBsDMPQV199pbCwML3xxhuSJH9/f/36668mJwMAABmNogYAAAAAAMhSfv31V/Xo0UMuLi5q0aKFypZlaTQAAHIKihoAAAAAACBLefzxx9W2bVvVrl1bJUuWNDsOAACwI4oaAAAAAAAgU7tw4YImTpyojz76SE5OTrJYLAoKCpLFYjE7GgAAsDOKGgAAAAAAm2Nzg8yOAMQTExOjRo0a6ciRI/Ly8tLIkSMliYIGAAA5lNXsAAAAAACAzCHiRojO/vS72TGAeBwcHDRmzBjVqFFD7du3NzsOAAAwGUUNAAAAAIAk6eq2v5Psc3B3tWMS5GSGYWjRokXasWOHra1z587aunWrKlWqZGIyAACQGVDUAAAAAABIkmIjo5Lsy/dIeTsmQU722Wef6fnnn1f37t0VHh4u6d5SU46OrKANAAAoagAAAAAAHqDKiDdkdXIyOwZyiK5du6po0aLq3LmzHBwczI4DAAAyGb7mAAAAAABIlk/jOmZHQDZ25coV/fLLL+rWrZskKX/+/Dp06JDc3NxMTgYAADIjihoAAAAAAMAUV69eVcWKFXX16lX5+/vrsccekyQKGgAAIEksPwUAAAAAAEzh7e2tZ555RpUqVVLu3LnNjgMAALIAZmoAAAAAAAC7+emnn9S4cWNbEWPq1KlycnKSi4uLyckAAEBWwEwNAAAAAABgFwMGDNCzzz6rgQMH2to8PDwoaAAAgBSjqAEAAAAAAOziiSeekKOjo/LlyyfDMMyOAwAAsiCWnwIAAAAAABni+vXrOnHihGrUqCFJatq0qY4dO6ZixYqZnAwAAGRVFDUAAAAAAEC627dvn1q2bCmLxaLg4GDlzZtXkihoAACANGH5KQAAAAAAkO5KlSolDw8P5c6dW5cuXTI7DgAAyCaYqQEAAAAAANLF7t27Vb16dUmSu7u7fv75Z/n5+cnNzc3kZAAAILtgpgYAAAAAAEgTwzD0yiuvqEaNGgoKCrK1ly5dmoIGAABIVxQ1AAAAAABAmlgsFhUoUEAWi0UHDhwwOw4AAMjGWH4KAAAAAACkWmhoqCIjI+Xt7S1JGjFihFq3bq3atWubnAwAAGRnzNQAAAAAAACpsmnTJlWqVEm9evWSYRiSJBcXFwoaAAAgw1HUAAAAAAAAqZI7d25dvHhR+/bt05UrV8yOAwAAchCKGgAAAAAA4IEuX75s++8qVarop59+0j///KOCBQuamAoAAOQ0FDUAAAAAAECS7t69q969e6ts2bI6c+aMrb1ly5bKlSuXickAAEBORFEDAAAAAAAkydnZWXv27FFISIh+/vlns+MAAIAcztHsAAAAAAAAIHMJCwuTm5ubrFarHBwcNH/+fF24cEFNmjQxOxoAAMjhmKkBAAAAAJAkGdHRZkdAJrBhwwZVrlxZs2bNsrUFBARQ0AAAAJkCRQ0AAAAAgCRpc48hZkdAJvDPP//oxIkTmjZtmmJiYsyOAwAAEE+mLWpMnz5d/v7+cnV1VZ06dbR9+/Ykx86ePVuPPfaY8ubNq7x586p58+bJjgcAAACQs3B/8WCXNmxXVOhts2PAJFFRUbb/fuONNzRx4kRt2bJFDg4OJqYCAABIKFMWNb777jv169dPI0eO1O7du1W1alW1bNlSly9fTnT8+vXr1aVLF/3xxx/asmWLihYtqscff1znzp2zc3IAAAAAmQ33Fw8WGxOjXxu/kGS/m6+3HdPAnu7cuaN33nlHTZs2tc3KsFqt6t+/vzw9PU1OBwAAkJDFMAzD7BD/VadOHdWqVUvTpk2TJMXGxqpo0aJ66623NHjw4Ac+PiYmRnnz5tW0adMUGBj4wPGhoaHy8vJSSEgIF20AAADI0bLjtbG97y+krPc6Xtu5Tz/Xap9k/wuxh2SxWOyYCPZy+vRpVapUSbdu3dIvv/yili1bmh0JAABkIxlxXeyYLkdJR5GRkdq1a5eGDPnfWq5Wq1XNmzfXli1bUnSMO3fuKCoqSvny5Uu0PyIiQhEREbbfQ0ND0xYaAAAAQKZkj/sLKevfY4RfuZ5k39P7VlLQyGYMw7D9mxYrVkxffPGFvLy8KGgAAIAsIdMtP3X16lXFxMTIx8cnXruPj48uXryYomMMGjRIhQsXVvPmzRPtHz9+vLy8vGw/RYsWTXNuAAAAAJmPPe4vpOx7j+FVvpTyViprdgykoy1btqhq1aoKDg62tT333HN64oknTEwFAACQcpmuqJFWH374oRYvXqxly5bJ1dU10TFDhgxRSEiI7efMmTN2TgkAAAAgK0jJ/YWU9e8xQg+dSLS95pRhdk6CjPbRRx9p37598WYvAQAAZCWZbvkpb29vOTg46NKlS/HaL126JF9f32Qf+/HHH+vDDz/U2rVrVaVKlSTHubi4yMXFJV3yAgAAAMi87HF/IWXte4zYmBjt7Dsu8U6Wncp2ZsyYoUKFCmncuCT+zQEAADK5TDdTw9nZWTVq1NC6detsbbGxsVq3bp3q1auX5OMmTJig999/X7/88otq1qxpj6gAAAAAMjnuLx7sxp5/k+yzWClqZGXh4eEaNGiQRo8ebWsrXLiwPv/8c+XNm9fEZAAAAA8v083UkKR+/fqpe/fuqlmzpmrXrq0pU6YoLCxMPXr0kCQFBgbKz89P48ePl3Rv+uyIESP07bffyt/f37Y2roeHhzw8PEx7HgAAAADMx/1F8iKu3UyyL2/VAPsFQbr79ddfNWHCBDk6Oqp79+7y9/c3OxIAAECaZcqiRufOnXXlyhWNGDFCFy9e1COPPKJffvnFtrnf6dOnZbX+b5LJ559/rsjISHXo0CHecUaOHKlRo0bZMzoAAACATIb7i4eTr1oFuXrnMzsG0uDZZ5/V66+/rscff5yCBgAAyDYshmEYZocwW2hoqLy8vBQSEiJPT0+z4wAAAACm4do4fWSl1/HMj2u1vs3rCdqbrp4jv1YNTUiEh7Vz506NHz9eCxcuTHZjewAAAHvJiOviTLenBgAAAADAPs6u/CPRgoYkWdgkPEuJjIxUu3bttHTpUttSagAAANkRRQ0AAAAAyIFiwiP0Z+d3zI6BdOLs7Kzp06erc+fOeuutt8yOAwAAkGEoagAAAABADnRtV7Bi7txNst/B1cWOaZBakZGRGjlypDZs2GBre+aZZ7R48WJ5e3ubmAwAACBjUdQAAAAAgBwoJjwiyT6rs5Py1axkxzRIrfHjx2vMmDF66aWXFB4ebnYcAAAAu6GoAQAAAACIp8G3k+WUy93sGEhG3759Vb16dY0fP55NwQEAQI7iaHYAAAAAAEDm8djiT1S8fUuzY+A//vnnH61evVqDBg2SJHl6emrnzp1s6A4AAHIcihoAAAAAABtHD2ZoZDbnzp1T7dq1FRERoUceeUQtW94rOlHQAAAAORFFDQAAAAAAMjE/Pz+9+uqrOnXqlKpWrWp2HAAAAFNR1AAAAAAAIBOJjo7Wp59+qhdffFH58uWTJE2aNEkODg7MzgAAADkeG4UDAAAAAJCJdO/eXe+++67eeecdW5ujoyMFDQAAAFHUAAAAAIAc6ejcILMjIAlvvfWW8ufPr+bNm5sdBQAAINNh+SkAAAAAyGEiboTo5Lc/mR0D/+/AgQM6d+6crYhRt25dnTp1Srly5TI5GQAAQOZDUQMAAAAAcpgVAa2S7HPy4IN0e/rrr7/UvHlz5c6dW/v371fBggUliYIGAABAEihqAAAAAEAOcufcRYVfvpZkf/7aVeyYBrVr11a5cuXk5+en2NhYs+MAAABkehQ1AAAAACAHuXX0dJJ9NacMk6Obqx3T5DwxMTH68ccf1bZtW1ksFjk7O2vdunXKnz8/G4EDAACkABuFAwAAAAAkSQF9As2OkK3FxsaqRYsWat++vb755htbu7e3NwUNAACAFKKoAQAAAADQo/M+5IP1DGa1Wm37ZwAAAODhsPwUAAAAAEDOeT3NjpAtHT16VC4uLipatKgkaeDAgerWrZvtdwAAAKQOMzUAAAAAIIe4vvdf/dr4BbNj5BhBQUGqUqWKevbsKcMwJEmOjo4UNAAAANKAogYAAAAA5ABGbKx+f/IVs2PkKJUrV5ZhGIqOjlZoaKjZcQAAALIFihoAAAAAkAPcDD6suxcuJ9lvcXSwY5rsKTY2Vvv27bP9XrZsWW3btk1r166Vl5eXickAAACyD4oaAAAAAJADRN0KS7Y/f60qdkqSPd28eVPNmjVT3bp1dezYMVt7lSpVZLVy6w0AAJBeuLICAAAAgByu2vh35VYwv9kxsjRPz/9ttH7/bA0AAACkL0ezAwAAAAAAzPPIB31VafCrZsfIks6ePatChQrJwcFBVqtV8+fPV3R0tEqVKmV2NAAAgGyLmRoAAAAAkIN5lithdoQs6auvvlL58uX1ySef2NqKFy9OQQMAACCDUdQAAAAAACCVYmJidPv2ba1Zs0aGYZgdBwAAIMdg+SkAAAAAAB7AMAxdv35d+fPf23ukR48e8vLyUps2bWSxWExOBwAAkHMwUwMAAAAAgGScP39eTzzxhFq0aKGoqChJksViUfv27eXg4GByOgAAgJyFogYAAAAAAMmwWq3asWOH/v33X+3cudPsOAAAADkay08BAAAAAPAfd+7ckbu7uyTJ19dXixYtUrFixRQQEGByMgAAgJyNmRoAAAAAAPw/wzD01VdfqXjx4vFmZTz++OMUNAAAADIBihoAAAAAAPw/i8WiX375RVevXtWnn35qdhwAAAD8B8tPAQAAAAByNMMwFBMTI0fHe7fI06ZNU61atdSnTx+TkwEAAOC/mKkBAAAAAMixLl68qDZt2mjgwIG2tvz586tfv362IgcAAAAyD4oaAAAAAJADGLGxZkfIlPbs2aMVK1bo888/14ULF8yOAwAAgAegqAEAAAAAOcC23qPMjpBpGIZh++8nnnhCY8eO1fbt21WoUCETUwEAACAlKGoAAAAAQDYXce2GQvYfMTuG6QzD0OLFi1W7dm3dvn3b1j506FBVrlzZxGQAAABIKYoaAAAAAJDNhR4+mWSfu5+P/YKYLDw8XIMHD9bOnTs1depUs+MAAADgIbDrGQAAAADkYPlr5ZwZCm5ubvryyy/1119/xdsYHAAAAFkHMzUAAAAAIIeqMXmIrI7Z97tuV65cUadOnbR69WpbW7NmzTRy5Eg5OTmZmAwAAAAPK/tevQIAAAAAFHkzVFtfGZ5oX3ZfemrKlClasmSJduzYocOHD1PIAAAAyAaYqQEAAAAA2dj6Nq/rZvBhs2OYYtiwYWrbtq2CgoIoaAAAAGQTFDUAAAAAIJu6c+6iLm3YbnYMu1m2bJnefvtt2+/u7u5aunSpqlevbmIqAAAApCeWnwIAAACAbOruhSvJ9uepWMZOSTLesWPH1KFDB8XGxqply5Z68sknzY4EAACADEBRAwAAAAByIL+nGsurQmmzY6SbUqVKaejQoYqOjlazZs3MjgMAAIAMwvJTAAAAAJDDFGxYS42CpslisZgd5aFdv35dvXv31qVLl2xt77//vsaPHy8XFxcTkwEAACAjMVMDAAAAAHKYsq8+JwcXZ7NjpMkLL7yg1atX68qVK/rhhx/MjgMAAAA7oagBAAAAAMhyxo8fr7Nnz6p///5mRwEAAIAdUdQAAAAAAGR6P//8s8LCwtSxY0dJUtWqVbV3715ZrayqDAAAkJNQ1AAAAAAAZGqrVq3S008/LS8vL9WrV09FihSRJAoaAAAAORBFDQAAAABAptayZUvVrVtX9erVU758+cyOAwAAABNR1AAAAAAAZCqhoaGaM2eO+vbtK4vFIkdHR23YsEHOzll7c3MAAACkHUUNAAAAAECmER0drTp16ujgwYNyd3fXa6+9JkkUNAAAACCJogYAAAAAIBNxdHRU7969NXXqVFWoUMHsOAAA2EVMTIyioqLMjgGkmpOTkxwcHOx6TooaAAAAAJBNRYbcMjtCiqxbt07FihVTmTJlJElvvvmmevbsqVy5cpmcDACAjGUYhi5evKibN2+aHQV4aHny5JGvr68sFotdzkdRAwAAAACyqQ3t3jQ7wgPNmDFDb7zxhurXr68NGzbIwcFBVquVggYAIEeIK2gULFhQ7u7udvtQGEgPhmHozp07unz5siSpUKFCdjkvRQ0AAAAAyIbuXLisqNDbZsd4oKeeekpDhgxR1apVFRUVZfflCwAAMEtMTIytoJE/f36z4wAPxc3NTZJ0+fJlFSxY0C7XchQ1AAAAACAbunvhSpJ9XhVL2zFJfLdv39aWLVvUokULSVLx4sV15MgRFSxY0LRMAACYIW4PDXd3d5OTAGkT9zdsry+oWDP8DAAAAACATCVv5XKmnPfSpUuqWrWqnn76ae3fv9/WTkEDAJCTseQUsjp7/w1T1AAAAACAHKTu7A9ksZpzK1iwYEGVL19ePj4+bIgKAACS5O/vrylTpjz04+fPn688efKkWx5kLhQ1AAAAACAHcXB1sev5tm3bpsjISEn3vsU3d+5cBQcHq379+nbNAQAA0seLL76oNm3aZOg5duzYoVdeeSVFYxMrgHTu3FmHDx9+6PPPnz9fFotFFotFVqtVhQoVUufOnXX69OmHPibSD0UNAAAAAECGGD16tOrVq6dx48bZ2goWLChPT08TUwEAgMyuQIECadprxM3NLc3LW3p6eurChQs6d+6cgoKCdOjQIXXs2DFNx0yJuL1WMouYmBjFxsaaHSMeihoAAAAAgAwREBAgwzB06dIlGYZhdhwAADKtLS8P0+p6nUz92fLysHR5Lhs2bFDt2rXl4uKiQoUKafDgwYqOjrb137p1S127dlWuXLlUqFAhffLJJ2rcuLHeeecd25j7Z18YhqFRo0apWLFicnFxUeHChdWnTx9JUuPGjXXq1Cn17dvXNrNCSnz5qZ9++km1atWSq6urvL291bZt22Sfh8Vika+vrwoVKqRHH31UPXv21Pbt2xUaGmob8+OPP6p69epydXVVyZIlNXr06HjP9eDBg2rQoIFcXV1VoUIFrV27VhaLRcuXL5cknTx5UhaLRd99950aNWokV1dXLVy4UJI0Z84clS9fXq6urgoICNCMGTNsx42MjNSbb76pQoUKydXVVcWLF9f48eMf+HpJ0o0bNxQYGKi8efPK3d1dTzzxhI4cOWLrj3vtVqxYoQoVKsjFxSXTzVBxNDsAAAAAACB7uHv3ri5cuKCSJUtKkjp16qTSpUurRo0aJicDACBzuxl8RFe37jU7RpqdO3dOTz75pF588UUtWLBABw8e1MsvvyxXV1eNGjVKktSvXz9t2rRJK1askI+Pj0aMGKHdu3frkUceSfSYQUFB+uSTT7R48WJVrFhRFy9e1N9//y1JWrp0qapWrapXXnlFL7/8cpK5Vq1apbZt22rYsGFasGCBIiMj9fPPP6f4eV2+fFnLli2Tg4ODHBwcJEkbN25UYGCgPv30Uz322GM6duyYbcmskSNHKiYmRm3atFGxYsW0bds23bp1S++++26ixx88eLAmTZqkatWq2QobI0aM0LRp01StWjXt2bNHL7/8snLlyqXu3bvr008/1YoVK/T999+rWLFiOnPmjM6cOfPA10u6t3zYkSNHtGLFCnl6emrQoEF68skn9e+//8rJyUmSdOfOHX300UeaM2eO8ufPn+ZZL+mNogYAAAAAZEN3L1y26/n+/fdftW3bVlarVXv27JGrq6ssFgsFDQAAcpAZM2aoaNGimjZtmiwWiwICAnT+/HkNGjRII0aMUFhYmL766it9++23atasmSRp3rx5Kly4cJLHPH36tHx9fdW8eXM5OTmpWLFiql27tiQpX758cnBwUO7cueXr65vkMcaOHavnnntOo0ePtrVVrVo12ecSEhIiDw8PGYahO3fuSJL69OmjXLlySbq3zObgwYPVvXt3SVLJkiX1/vvva+DAgRo5cqR+++03HTt2TOvXr7dlGzt2rFq0aJHgXO+8847atWtn+33kyJGaNGmSra1EiRL6999/NWvWLHXv3l2nT59WmTJl1KBBA1ksFhUvXjxFr1dcMWPTpk169NFHJUkLFy5U0aJFtXz5ctvyWlFRUZoxY8YDXyOzsPwUAAAAAGRDfzzzml3P5+Pjo9DQUIWEhOjo0aN2PTcAAMgcDhw4oHr16tmWgZKk+vXr6/bt2zp79qyOHz+uqKgo24fskuTl5aVy5colecyOHTvq7t27KlmypF5++WUtW7Ys3hJPKbF3715bESWlcufOrb1792rnzp2aNGmSqlevrrFjx9r6//77b40ZM0YeHh62n5dfflkXLlzQnTt3dOjQIRUtWjReseX+532/mjVr2v47LCxMx44dU8+ePeMd+4MPPtCxY8ck3ZttsXfvXpUrV059+vTRr7/+ant8cq/XgQMH5OjoqDp16tjG58+fX+XKldOBAwdsbc7OzqpSpUqqXi97YqYGAAAAAGQzYWcuSEntYXHfhwxpdebMGRUtWlTSvRviFStWqHTp0sqbN2+6nQMAAORsRYsW1aFDh7R27Vr99ttvev311zVx4kRt2LDBtlzSg7i5uaX6vFarVaVLl5YklS9fXseOHVPv3r319ddfS5Ju376t0aNHx5thEcfV1TVV54qb/RF3XEmaPXt2vOKDJNvSV9WrV9eJEye0evVqrV27Vp06dVLz5s31ww8/JPt6pZSbm1u8wlRmQ1EDAAAAALKZiGs3kuzLV61Cmo9vGIaGDBmiSZMmad26dWrYsKEkqVatWmk+NgAAOVGeSmXMjpAuGcqXL6+goCAZhmH7UHzTpk3KnTu3ihQporx588rJyUk7duxQsWLFJN1b5unw4cO264nEuLm56ZlnntEzzzyjN954QwEBAdq3b5+qV68uZ2dnxcTEJJurSpUqWrdunXr06PHQz23w4MEqVaqU+vbtq+rVq6t69eo6dOiQrfDxX+XKldOZM2d06dIl+fj4SJJ27NjxwPP4+PiocOHCOn78uLp27ZrkOE9PT3Xu3FmdO3dWhw4d1KpVK12/fl358uVL8vUqX768oqOjtW3bNtvyU9euXdOhQ4dUoULarxHthaIGAAAAAOQgXuVLpfkYFotF165dU3R0tFavXp3shxAAAODB6s0e++BBmUhISIj27t0bry1//vx6/fXXNWXKFL311lt68803dejQIY0cOVL9+vWT1WpV7ty51b17dw0YMED58uVTwYIFNXLkSFmt1iRnBsyfP18xMTGqU6eO3N3d9c0338jNzc22j4S/v7/+/PNPPffcc3JxcZG3t3eCY4wcOVLNmjVTqVKl9Nxzzyk6Olo///yzBg0alOLnXLRoUbVt21YjRozQypUrNWLECD399NMqVqyYOnToIKvVqr///lvBwcH64IMP1KJFC5UqVUrdu3fXhAkTdOvWLb333nuS9MBZEKNHj1afPn3k5eWlVq1aKSIiQjt37tSNGzfUr18/TZ48WYUKFVK1atVktVq1ZMkS+fr6Kk+ePMm+Xvnz51fr1q318ssva9asWcqdO7cGDx4sPz8/tW7dOsWvhdnYUwMAAAAAcoh6c8c/9FICERERCgsLs/0+adIk/fTTTxo/fnx6xQMAAFnE+vXrVa1atXg/o0ePlp+fn37++Wdt375dVatW1WuvvaaePXvaPsyXpMmTJ6tevXp6+umn1bx5c9WvX1/ly5dPcsmmPHnyaPbs2apfv76qVKmitWvX6qefflL+/PklSWPGjNHJkydVqlQpFShQINF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            "text/plain": [
              "<Figure size 1600x600 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(16, 6))\n",
        "fig.subplots_adjust(left=0.0625, right=0.95, wspace=0.1)\n",
        "\n",
        "fpr, tpr, thresholds = metrics.roc_curve(val_df.Inhibitor, val_proba_inhibitor)\n",
        "roc_curve_auc = metrics.auc(fpr, tpr)\n",
        "optimal_idx = np.argmax(tpr - fpr)\n",
        "optimal_threshold = thresholds[optimal_idx]\n",
        "\n",
        "precision, recall, _ = metrics.precision_recall_curve(val_df.Inhibitor, val_proba_inhibitor)\n",
        "pr_curve_auc = metrics.auc(recall, precision)\n",
        "ax[0].plot(recall, precision, lw=4)\n",
        "\n",
        "ax[0].axis([-0.05, 1.0, -0.05, 1.05])\n",
        "skew = sum(val_df.Inhibitor) / len(val_df.Inhibitor)\n",
        "ax[0].axhline(y=skew, color='black', linestyle='dotted')\n",
        "ax[0].annotate(\"PR AUC: {:.3f}\".format(pr_curve_auc), (0.5, 0.45), size=14)\n",
        "\n",
        "ax[0].set_ylabel('Precision', fontsize=14)\n",
        "ax[0].set_xlabel('Recall', fontsize=14)\n",
        "ax[0].set_title(\"PR Curve\", size=14)\n",
        "\n",
        "ax[1].plot(fpr, tpr, label=\"Logistic Regressor\", linewidth=4)\n",
        "ax[1].plot(np.linspace(0.0, 1.0, 100), np.linspace(0.0, 1.0, 100), label='Random Estimator', color='black', linestyle='dotted')\n",
        "\n",
        "ax[1].arrow(0, 1, fpr[optimal_idx], -1*(1-tpr[optimal_idx]), color=\"black\", alpha=0.5, length_includes_head=True, head_width=0.02, head_length=0.02, shape='full', width=0.003)\n",
        "ax[1].annotate(\"Optimal Threshold: {:10.2E}\".format(optimal_threshold), (0., 1.), size=14)\n",
        "ax[1].annotate(\"ROC AUC: {:.3f}\".format(roc_curve_auc), (0.5, 0.45), size=14)\n",
        "\n",
        "ax[1].axis([-0.05, 1.0, -0.05, 1.05])\n",
        "ax[1].legend()\n",
        "\n",
        "ax[1].set_ylabel('True Positive Rate', fontsize=14)\n",
        "ax[1].set_xlabel('False Positive Rate', fontsize=14)\n",
        "ax[1].set_title(\"ROC Curve\", size=14)\n",
        "\n",
        "fig.tight_layout()\n",
        "fig.savefig('figures/ch05/roc_and_pr_curves.png', bbox_inches='tight', dpi=600)\n",
        "fig.savefig('figures/ch05/roc_and_pr_curves.pdf', bbox_inches='tight', dpi=600);"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WE0ZMjLkVzGi"
      },
      "source": [
        "## 2️⃣ Tree-based Models"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bh2-W2hefrqx"
      },
      "source": [
        "### Decision Trees"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GnHt4ccudV27"
      },
      "outputs": [],
      "source": [
        "from rdkit.Chem import Descriptors\n",
        "\n",
        "RDKIT_DESCRIPTORS = {\n",
        "  'MolWt' : Descriptors.MolWt,\n",
        "  'logP' : Descriptors.MolLogP,\n",
        "}\n",
        "\n",
        "for desc, func in RDKIT_DESCRIPTORS.items():\n",
        "  train_df[desc] = train_df[\"Molecule\"].apply(func)\n",
        "  val_df[desc] = val_df[\"Molecule\"].apply(func)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "4_xOXOPcG517",
        "outputId": "768d965f-88ed-4d47-8165-eef91b123c43"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: /~https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(max_depth=2)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(max_depth=2)</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "DecisionTreeClassifier(max_depth=2)"
            ]
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.tree import DecisionTreeClassifier, export_graphviz, plot_tree\n",
        "\n",
        "tree_clf = DecisionTreeClassifier(max_depth=2)\n",
        "tree_clf.fit(train_df[['MolWt', 'logP']], train_df.Inhibitor)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SS8TJJdXf7KE"
      },
      "source": [
        "**Code for Figure 5.6. Visualization of basic two-descriptor decision tree**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "id": "GNQRy1PPG-yh",
        "outputId": "f4638ed6-2bf7-4312-afd0-904e50507175"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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+XOqrfoBN+zzYuPcIh1bPUm/bOGcsw6ctYuL81ZS3KYqVZV50FIofiksIkTBJBoRIYvlyZefi7hV4nrrIweNnGD1rKed3LuffVZvxDQjk2IYFGOjr0X30NMLDI9TvM9D7Yt0BhXbsdQgU316H4P+pVCo2zRlHvlzZ4+w7un4+py5dw+v0Jewad2HX4skUL1wgVpnE9AzMX7uNmwfXAZAtUwacbEvhff4q5W2syJUtM01rVUFbW5vMGdJT1a4M567eVicDOw8f5+/Zy9i/fHqsWwilrAqxb/l0ACIiIsnj0JCiBfL+0M9CCJEwebRQiCT2/PVbFNra1HWpxJQh3Xnn68cH/wD8AoLImikDBvp6vPB5yx6Pk4k+xobdh4mKiiY4JJStBzxxsi0dp0wtZzumLV1H9Mck4p2vP4+fvyIwOIS3vn5ULleSkT3bY10wHzfuPYrz/k89A/H9iy8RAMibMxuHvM8CMTMrnrhwFeuC+QBoVqsqB4/H7AsOCeXEhWuUKBKTgOzzOsXgyfPZvXiK+rbCJ6/ffh5oOHXJOiqVtaFAnhw/+iMTQiRAegaESGI37j5k5MzFAERHKxnQuRXZM2fkr9YNadVvDGXqtidH1kw4VSiV6GMUyp8bp1Y91AMI43uUcOrQHgyftojyDTqhpaWFvp4uU4b0QEdHhxa9RxEWHoFSqaSkVUHquzokOpYvLZ44mH4T5jB75SYiI6NpUtOZ6g62APRs24Tuo6dTuk47AFrWq05Vu7IAdBk+GV1dXVr2Ga2ua9+K6WQwT8eyTbvZuPcI0dHRlC5WmEXjByVJrEKIz2RtAiE+0tTaBD9K03MJ/C5kbQIhPpPbBEIIIUQaJ7cJhPjFHFw1S9MhCCF+M9IzIIQQQqRxkgwIkcSqte3DPq9TGo2h87BJFHBuQr8J/wLw5MVrTIpXxbZBJ/W/937+ALzweUv1dn3JUr429k26xKrn2NnLWJR2U7+ncrNu6n1Xbt3HuVUPLEq70eKLgX/fcvD4Wco36Ihtg06UrtOO0bOW8v9Dl95+8CNP5Yax6u04ZGKs+I2sq7DH4wQAb9770qDrEMrV70jJ2m3pPXYmUVExT1HMW7MV6+qt4pybEOIzuU0gxG9qQKeWdGvVQP3a3NQk3kmETIwMGdmjPYHBwYyfuzLO/qIF8nBi86I42zNlMGfyoL+4evs+R05d+O647EoX59SWxSgUCiIjo3Bu3ZNyJYpSu4q9ukzvsbOoXtmWwJAQ9bZlk4ap/3/19n1qtO+Pa6VyAExZ/B+F8+dh+8JJREZGUaV1T3YcOkbjGs50b9OI4oULfHXOBCGE9AwI8VWTFq5h4KR56tfv/fzJaVeP4JBQPE9dwLFFdyo07EzZeh3YtM8j3jr+v5fgy9ev3r6nRZ/RVGrWjbL1OjBl0drkPaGvSGdqQqWyJTA2NPyh92XPnJHyNlbofzFZ0vcwMTZE8XEGwbCICCIjItHW/vxRtH73ITJnTE/lcjZfq4KVW/fRrHZV9bG1tLQICg5BqVQSHhFJZGQU2bNk/KG4hEjLpGdAiK9oWbcajs3/4p8BXdHRUbBxzxFqOdthbGRISatCeKz9F4VCgc+7D9g16YKrfTnSpzP97vo7DfmHod3aUKmsDRERkdTo0J8yxYuon73/5Nb9x7QbOD7eOqrYlVUvYPQtAUEh2DfpgkqlonGNKvTt0Oy7Vv679/g5FRv9iY6Ogi4t6tG6vtt3HS8hF67foeuIKdx/8pzOzeuqp2x++eYdc1Zt5uCq2Ww/eDTe90ZERLJp7xH2Lpuu3ja0axta9BlNPsfGBIeE0rVVA+xKF//pOIVIKyQZEOIrcmfPQlHLvLgfO03tKvas3eHOlCHdAXjn60eXEVN48OQ5OgoFvv6B3H38DFsbq++qOzgklGPnLvPug596W2BIKLcfPImTDBS1zPvDawT8v6yZLLjvuYnMGdLz5r0vjbsPJ306U9o3rpXg+0paFeSex0bSmZrw+PkranYcQPbMmahiV+an4ilTrDDndizjna8/LXqP4sSFq1Qqa0P3UdOY0L8LJsZf76XYdcSbXNmyYFPUUr1t2wEvihXKz/7l0wkMDqFelyFsO3CUhtUdfypOIdIKSQaESECb+m6s3XmAvDmz4R8YjH2ZEgD0GjOTWlXs2PjvWLS0tCheo02sdQY+0VEoUCqV6tdhH8soVSq00MJ700J0dRNuhknRM6Cvp0fmDDFd6pkzpKd5bRdOXbz+zWTAzMRY/f+8ObNRp4o9py5d/+lk4JOM6dNRrbItW92PUqmsDWcu36Tr/alATMIUGh5B/S5D2LFokvo9q7fv54+GNWLVs+C/7SwcPwhtbW3SmZpQu4o9x85ekmRAiO8kYwaESEA918qcOH+V2Ss30aaBm7pb3T8wiDzZs6KlpcWRk+d58PRFvO8vkDsHF67fAWK+1K/ejpntztTYCPsyxZm2dJ267LNXb2LNw//Jp56B+P597y2CN+99iYyMAiAkNIy9nicpUbTAN94VM67h00j/D34BHDl5Xr2eQELOXb1Fjfb94t137/EzdYIUHBLKweNnKFYoZv2Cl6d3cefwBu4c3sA/A7tRrXL5WInA89dvOXHhGs1ru8Sq88s1ESIiIvE8dQGrj2siCCG+TXoGhEiAkaEBtavYs3q7O7cOfv7iHtfvT3qPncX4uSspU7wIxQvlj/f9/To2p1Xfv9l9xJuSVgWxKVpQvW/FlBEMmjyPsvU6AGBibMSi8YPImski3rp+xsmL1xg3ZwUKhTaRkdHUcKpAt5YxTxpER0dTyKU5ERGR+AcGU8C5Ce0b1WJEj3bsOHiMJRt2oqujQ1R0NK3qVadO1UpAzOOKVVr3JDQ0nLCICAo4N2Fkj3a0a1SLpy99MDTQjzeW3UdOsHaHOzo6CqKjldR1qfzNHopP/ttxgJpOFeOMzZg6pDs9x8ykbL0OREcrcbQtSYfGtX/iJyZE2iJrEwjx0a+yNsH36DxsEqWtC8d6tDAl9ZvwL43cnLEvkzoG8R07e5mhUxfEekRS1iYQ4jPpGRDiN2RmYsz8tdu49/gZM4b3SvHja+KYXzNvzVaWbtxNruyZNR2KEKmWJANC/IamD+up6RBSje5tGtG9TSNNhyFEqiYDCIX4SYVdmnPj3iNNhxHLkxevMbRypteYmbG25bSr91P1Xrh+56tPNvz/8b92rJdv3lG9XV/1a0MrZ4KCQ+Mta9ugE6Fh4QDMWb2FN+99ExG1EOJbJBkQ4jdlYmTI7iPePPzKkw6JUaZYYVZOHfFTdWTPnJEDK2d+uyBwZvtS9UDEuau38PaLeRm+16c1CoQQXyfJgBDf6fTlG1Rp3ZPyDTpSvkFHDp84F6fMrBWbsG/alQoNO+PYojtXbsUMTgsJDaNln78pVbsd5Rt0pFXfvwG4++gpji26U75BR8rUbc/M5RuTLF59PV16t2/G3/8uj3f/weNnqdCwM+Xqd6RG+348eBKTNBw7exnbBp3oMXo65erHnOvtB0/U+z4t+PPp6n/0rKVUaNiZEjX/4MSFa7GOEd+++HoNZi7fgG2DTpSo+UesmQc/9RpMXriWV2/e07LPaGwbdOLW/ccEBYfy57DJlKnbnjJ12zN92Xr1+6q17cOAf+bi0PwvGncf/pM/SSF+fzJmQIjv8MEvgOY9R7L+37FULFWM6OhoAoJC4pRrVa8afdo3BcDj5AV6jpnBsQ3zOXTiHIHBwVzasxIAX/9AABat30ltZzsG/tkq1vb/16LPaB4+if8K32v9vK8+xte1ZX1K1PyDK7fuY25mot7+5r0vHYdM5OCqWRS1zMvKrXtpN2g8xzfGLOZz88FjFk0YzNwx/Zm8cC2TFq6Jt0fgvV8AtiWtGdOnE+t3H2LEjEV4/jf3m/v+n0KhzZntS7n76CnOLXtiX6YEmTOkV+8f3LU1y7fsYd2sMVh/nD9g+PRFKFVKzu9cTmBwCE4tulOsYH6qO9gC8Pj5KzzWzkFHRxHvMYUQn0kyIMR3OHPlBkUs81KxVDEAFApFvOsQXLpxlymL/8PXPwBtbW3uPX4GQInCBbj94Cl9xs2icrmS6rn4K5UtwfDpiwkJC8exfEkcbUvFe/z1s8YkKm4DfT2GdmvD6FlLmT2qj3r7uau3KF64AEUt8wLwR4Ma9B47m8DgmASnUN5clLSKmROhvE1R9nqdjLd+EyNDajpVBMDWxoqhUxZ8177/17ZRzZjj5stNSauCnL1yM9YqhvHxPHWBqUN7oqWlhZmJMU1rVcXj1AV1MtC0VlVJBIT4TpIMCJFEIiIiadlnNIfWzKaUVSFevnlHAacmAOTLlZ2Lu1fgeeoiB4+fYfSspZzfuZwG1RyxLWnNkRPnmbZ0Pau27WfFlLjd2ontGQD4o4Ebs1Zs5MSFq999Lgb6n1ciVCgUREfHf99dX083VrmoL8oltC8lmBj92CqMQqRlkgwI8R1sbay5fX8qpy/foEJJa/Vtgi97B8IiIoiKjiZn1pjn2Rev36ne9/z1WyzSmVLXpRIu9mXJ79SYD/4BhIaGky9XNto0cKNAnhx0GT4l3uMntmcAYr6IR/fqyLBpC9XbyttY0XXEFO48fErh/LlZu+MANkUtMTU2SvRxfsaa7e4M6dqG+4+fc+XWPcrHs+CTmYkx/oFB6tfOFcuwaus+7EoXIygklM37PPhnYLeUDFuI34YkA0J8BwtzM9b/O5bBk+cTEhqGtpYW/wzsFmvBHjMTY0b2bE+lpl3JYG5Gg2qfF8m5cfchI2cuBiA6WsmAzq3InjkjUxatZcOew+jp6qKlpcW0oT2SJf6G1R2Zvmw9QR9vA2SyMGfZpGG0GzieqOhoMqZPx4rJmhtoFxUVTYWGnQkJC2fO3/1ijRf45K/WDekyfApGBvqsnDqCoV3/oO+E2erpnFvUrUa1yuVTOnQhfgsyHbEQH/1O0xGLb5PpiIX4TB4tFOIjXV1dwiMiNR2GSCFh4RHo6up+u6AQaYAkA0J8ZF2sGN7nryKdZb8/lUqF9/mrFCueOhZSEkLT5DaBEB+5u7tTo0YNGrk50aa+GxbmZmhpaWk6LJGEVCoVH/wCWLPDna3uXuzfvx83NzdNhyWExkkyIMQXVq5cyejRo3j69JmmQxHJKHfuXIwdO462bdtqOhQhUgVJBoT4PyqVikePHhEUFPTtwj8hMjKSQYMGcezYMaZPn46Tk1OyHi+18vLyon///jg4ODBlypRkv49vYmJCvnz5pNdHiC9IMiCEBkRERNC0aVP279/P1q1bqV27tqZD0qg9e/bQqFEjatasycaNG9HT0/v2m4QQSUaSASFSWEREBE2aNMHd3Z3t27dTs2ZNTYeUKuzdu5eGDRtSo0YNNm3aJAmBEClIkgEhUlB4eDhNmjTh4MGDbN++nRo1amg6pFRl3759NGjQgOrVq7N582b09b8+zbIQIulIMiBECgkPD6dRo0YcPnyYHTt2yCj2r3B3d6d+/fq4urqyZcsWSQiESAGSDAiRAsLCwmjUqBEeHh7s3LmTatWqaTqkVO3AgQPUq1cPFxcXtm7dKgmBEMlMkgEhkllYWBgNGzbE09OTXbt24erqqumQfgkHDx6kXr16VKlSha1bt2JgYKDpkIT4bUkyIEQyCgsLo0GDBnh5ebF7925cXFw0HdIv5dChQ9StWxcnJye2b98uCYEQyUSSASGSSWhoKPXr1+f48ePs3r2bqlWrajqkX9Lhw4epU6cOjo6O7NixQxICIZKBJANCJIPQ0FDq1auHt7c3e/bsoUqVKpoO6Zd25MgR6tSpQ+XKldmxYweGhoaaDkmI34okA0IksZCQEOrVq8fJkyfZu3dvmp1ZMKl5enpSq1YtKlWqxM6dOyUhECIJSTIgRBIKCQmhbt26nDp1in379uHo6KjpkH4rXl5e1KpVCzs7O3bu3ImRkZGmQxLityDJgBBJJCQkhDp16nDmzBn27duHg4ODpkP6LR09epSaNWtSoUIFdu/eLQmBEElAkgEhkkBwcDC1a9fm3Llz7N+/n8qVK2s6pN/asWPHqFmzJuXLl2f37t0YGxtrOiQhfmmSDAjxk4KDg6lVqxYXLlxg//79VKpUSdMhpQnHjx+nRo0alCtXjj179khCIMRPkGRAiJ8QFBRErVq1uHjxIu7u7tjb22s6pDTF29ubGjVqULp0afbu3YuJiYmmQxLilyTJgBCJFBQURM2aNbl8+TLu7u7Y2dlpOqQ06eTJk7i5uVGyZEn27dsnCYEQiSDJgBCJEBgYSM2aNbly5QoHDhygYsWKmg4pTTt16hTVq1fHxsaGffv2YWpqqumQhPilSDIgxA8KCAigRo0aXL9+nQMHDlChQgVNhySA06dPU716dYoXL87+/fslIRDiB0gyIMQPCAgIwM3NjZs3b3Lw4EHKly+v6ZDEF86cOUO1atUoVqwY+/fvx8zMTNMhCfFLkGRAiO/k7++Pm5sbt27d4tChQ5QrV07TIYl4nD17lmrVqmFlZYW7u7skBEJ8B0kGhPgO/v7+VK9enTt37nDo0CHKli2r6ZBEAs6dO4erqytFixbF3d2ddOnSaTokIVI1bU0HIERq5+fnR7Vq1bh79y6HDx+WROAXUK5cOQ4fPszt27epXr06/v7+mg5JiFRNegaESMCnROD+/fscPnyY0qVLazok8QMuXLiAq6srBQsW5MCBA5ibm2s6JCFSJUkGhPgKX19fqlWrxsOHDzl8+DClSpXSdEgiES5evIiLiwsFChTg4MGDpE+fXtMhCZHqSDIgRDx8fX1xdXXl0aNHHDlyhJIlS2o6JPETLl26hIuLC/ny5ePQoUOSEAjxf2TMgBD/58OHD7i4uPD48WM8PDwkEfgNlCpViiNHjvDo0SNcXFz48OGDpkMSIlWRngEhvvD+/XtcXFx4/vw5R44coUSJEpoOSSShK1euULVqVXLnzs3hw4exsLDQdEhCpArSMyDER18mAh4eHpII/IZsbGzw8PDg2bNnVK1alffv32s6JCFSBUkGhADevXtH1apVefHiBZ6enhQvXlzTIYlkUqJECTw8PHj+/DlVq1bl3bt3mg5JCI2T2wQizXv79i1Vq1bFx8cHDw8PrK2tNR2SSAHXr1+nSpUqZMuWjSNHjpAxY0ZNhySExkjPgEjTPiUCb968wdPTUxKBNKRYsWJ4enry+vVrqlSpwtu3bzUdkhAaI8mASLPevHlDlSpV1ImAlZWVpkMSKcza2hpPT89YfwtCpEVym0CkST4+PlSpUoUPHz7g6elJkSJFNB2S0KBbt27h7OxMxowZ8fDwIHPmzJoOSYgUJT0DIs35lAj4+vri5eUliYCgaNGieHl58f79e5ydnfHx8dF0SEKkKEkGRJry+vVrnJ2d8fX1xdPTk8KFC2s6JJFKFClSBC8vL3x9fXF2dub169eaDkmIFCPJgEgzXr16hbOzM/7+/nh5eUkiIOIoXLgwXl5e+Pv74+zszKtXrzQdkhApQsYMiDThUyIQFBSEp6cnBQsW1HRIIhW7d+8ezs7OmJiY4OnpSbZs2TQdkhDJSnoGxG/v5cuXODk5ERwcjJeXlyQC4psKFiyIl5cXQUFBODk58fLlS02HJESykmRA/NZevHiBk5MToaGheHl5YWlpqemQxC/C0tISLy8vQkJCcHJy4sWLF5oOSYhkI8mA+G09f/4cJycnwsPD8fLyokCBApoOSfxiPiUEYWFhODs7S0IgfluSDIjf0rNnz3ByciIiIgIvLy/y58+v6ZDEL6pAgQJ4eXkRHh6Ok5MTz58/13RIQiQ5SQbEb+dTIhAVFYWXlxf58uXTdEjiF5c/f368vLyIjIzEycmJZ8+eaTokIZKUJAPit/L06VOcnJxQKpWSCIgklS9fPry8vIiKisLJyYmnT59qOiQhkowkA+K38eTJE5ycnFCpVHh5eZE3b15NhyR+M3nz5uXo0aMolUqcnJx48uSJpkMSIklIMiB+C48fP8bJyQkALy8v8uTJo9mAxG8rT548eHl5AUhCIH4bkgyIX96nREBbW5ujR4+SO3duTYckfnOfEgJtbW2cnJx4/PixpkMS4qdIMiB+aY8ePcLR0REdHR28vLzIlSuXpkMSaUTu3LljJQSPHj3SdEhCJJokA+KX9fDhQxwdHdHT05NEQGhErly5OHr0KDo6Ojg5OfHw4UNNhyREokgyIH5JDx48wMnJCQMDA7y8vMiZM6emQxJpVM6cOTl69Ch6enqSEIhfliQD4pcQGBiIUqkE4P79+zg5OWFoaIinpyc5cuTQcHQircuRIwdeXl4YGBjg6OjIgwcPAFAqlQQGBmo4OiG+TZIBkeoplUpKlSrFggULuHfvHk5OThgbG0siIFKVTwmBkZERjo6O3L9/nwULFlC6dGl1IitEaiXJgEj1Tpw4wYMHD8iQIQNOTk6Ympri6elJ9uzZNR2aELFkz54dLy8vTExMcHJywsLCgvv373Py5ElNhyZEgrRUKpVK00EIkZCePXuyefNmtLS0MDc3x8PDQ9aXF6nay5cvqVq1Kv7+/kRHR9OsWTP+/fdfTYclxFdJz4BI1aKjo9m4cSOBgYHo6upibW1NkSJFmDZtmqZDEyJe06ZNo2jRolhbW6NQKAgKCmLDhg1ER0drOjQhvkpH0wEIkZCtW7fy9u1bAEJCQjAzM6Nfv360bdtWw5EJEb+2bdsSHBzM5s2b1SschoSEsHXrVpo2barh6ISIn/QMiFQtNDQUCwsLBg8ezM2bN7l+/TqjR48mU6ZMmg5NiHhlypSJ0aNHc/36dW7evMngwYOxsLAgNDRU06EJ8VUyZkAIIYRI46RnQAghhEjjZMzADzh69ChTJk/m+PFjhIaFazoc8RMMDfSpXNmBQYMH4+joqOlwhIacOXOGSZMm4enpSXBwsKbDEcnA2NgYZ2dnhgwZgq2trabDSbXkNsF38vb2xqVqVQpnNaV2yRykM9RFS0tL02GJRFCpVPiHRrLn8gvuvA7kiIcH9vb2mg5LpLDLly9TqVIlcubOS80GTUlvYSFt+jejUqnw/fCBfds38fzpY7y9vSlZsqSmw0qVJBn4Tk2bNOH6qSMcHOSCno5C0+GIJBARFY3rlMOUsHNh46ZNmg5HpLA///yT/QcO4X7yCoZGRpoORySj0JAQ3CqWoGaN6ixatEjT4aRKMmbgO506eQJX66ySCPxG9HQUuFpn5eQJb02HIjTg5KlTOLpUl0QgDTA0MsLBpTonZCbIr5Jk4DuFhYVhaqir6TBEEjM10CUsLEzTYQgNCAsLw8TUTNNhiBRiYmombT0BkgxoUJbuGwgOi/zpOpwm7Md5ojtV/3Hn2O3XSRTd9+m24hQlhu347nM5cdeHbD02sszrrnpbg1lHKDdqN1UmulNlojvrT31eAvbvbZcoO2o3Wbpv4NZLvySPX24Ri+/lYGNJhaK5Ys0kuGXdKgpY6LJ6ybxvvr9lnap4HNjL44f3KZE7PVFRUUDMfe1yhbLzz6jB6rL7d26lWU0n9TEe3b8bX5XJqkvrRtSqXJo6jmVpVtOJm9cuq/c52FjialuM2g5lqO1QhmNHDqr3bV67ghr2JantUIZ6VWw5dyrhnrfT3kcpmFFf/TNUKpX89UdTXMpbU6tyaf5o4MaTRw/U5SeOHIRjyYIUsNDlzs3r330+Mh4kYfI0wW9gb38XjA102X/lOV2Wn+TGpAZoa3/fH/6HoHAsTPQTfeyWdvkZ27gUxYbs+GbZoLBIxu24QhXruOsKTGhSmmrF465AWKNETjo7F6LejCOJjlGIpJI5azaOeRzE2bUGANvWr8baptQP1ZE3vyWmZuZcv3yBkmVtuXf7Jjlz5+HcyePqMmdOHMXW3lF9DIsMGclnWeiHjhMcFISunh56eno/9L5Pps1fjqlZOgAO7dvFkJ6d2eV1Tr1/7ooNFLYqFus9vh/eM35Yf46cv0XGzFk4vH83I/r+xYHTV+M9RlBgIFP+Hoqji1us7Q1btKFK9Vpoa2uzesk8hvftxtodMQlHtVp1adelJ81rOSfqvET8pGcglbj05D01px3CacJ+ak47xKUn79X7lnndpcLfe6g++SBT9lyj6KBt8dbhWDQrH4Ij+BCc8GOPIRFRbD//hNYLjuE29WCCZb+lcuEsZDI1+K6yo7ZeortLUTIYf/+Hk61lJnKkN05seEIkqUYt/mDbutUAPH38kJCQ4FhfiMFBQQzq3hE3u5K42ZVk0b/xr6FRoZIDZ04cA+DsyWNUr92AyMgIAgMCADjtfYwKlZ3Y8t9Krl2+wNghfantUIYTXgknxZGRkRxx30Ovjq1ws7MhwM830ef6KREACAzwR0v7218XKpUKFSqCggIBCPD3I2v2ry8zPmHEADr37E/6DBnU27S1tXGpUQftj8crVa4CL549Ve8vW6ES2XPm+uHzEQmTnoFUICIqmo5LTjCrdXkcimTl6O3XdFxygtN/1+KeTyD/HrzFkaHVyWhqwPDNF79az64LT8lubkjGeL6co6KVHLvtw9Zzjzn78B1VrLLRw7UItgU+T+s7bNMFTt9/G2/dyzvbkzeTaaLP8ciNlwSGRlKndC4OXX8RZ//Y7VeYsOsq1jnMGVnfhmzmMqhLpD4VKjny37JF+Pv5sm39Gho0a831K5/b5NxpE1CqlOw/cYmgwECaVK9M4aLFcHJ1+796nNi3cytdeg/kjPcx2nfrxasXzzh/2psSpcvx9PEDSpergJ2DM9s2rKFTj35UqV4r3phUKhXnT59g5+Z1HPc4RDm7SjRq8QczFq1CRyfmI37hrCns2bYx3vePnjybchUrxbtvaK8/Oe55GJVKxYrNe2Lt69flD1QqFWUr2DNg5HjM0pljkSEj42fMp55TeUzTmaNSKvlv9+F46/Y65E5QQAA16jXC4+De+H/gwJol83Fxq/3V/SJpSDKQCtz3CURXoY1DkawAOBbJiq5Cm/s+gZy8+4aq1tnUX/AtK+Zj27nHsd5fa/phtLW0yGRmwMouleM9huvkg7wPCmNikzLMbmOLjiJulj+xaZmkPbGP/EMiGL/zKpt6OsW7f27bCuRIb0y0UsnsA7f4c9lJdvd3SZZYhPg5WtSs35g92zayZ/smNrsfi5UMnDh6hFH/zEBLSwtTMzNqN2rGyaNH4iQDtpUcGT+sP1FRUVy/cpESpcvx6sVzTnsfJSw0FJvS5TAwNPyuiLq2bsRp76MMGzeFkRNnoG8Q92Kga59BdO0z6IfP9p9/FwOwfeNaJo0ewvJNuwHYsNeT7DlzER4ezvhh/fh7UC9mLFpNYEAAq5fMZ/uRU+QvWJi92zfTrU1j9nlfinXPPsDfj6ljh7Fqm3uCx1/07zQe3L3N2p2Hfjh28WMkGfgNfBozkJCZrcqz+exjxu64zJ7Lz2hQNg/ORWM/KplcPQO3X/rj4x9KjSkxtyTeB0Vw8PpL/EIi6F+zmPo2gEJbmz+dCzFt33WUStV3j3sQIiU1bNGGRq72lKtYmfQWGb79hnjkzpsfM/P07Ny8jtx586Ojo0N5u8osnTeD8LBQ9XiB7zFo9ES2b1zL0nkz8Tiwl9oNm1HVrTZGxp9vryW2Z+CTBs1aM7xvN3w/vCe9RQZ1N72+vj6tO3blz5YNAfD2PIRZOnPyFywMQK0GTRjUoyMf3r8jQ8bPvZB3b13njc9rGrrYAeD74R0e7nvx9/Wl56ARAKxaPJfdW9azduchefwzBUgykApYZjElMlqJ910fKhXKwvE7PkRFK7HMYopKpWLe4Vu8Dwong4k+G888TtQxSuaxoGQeC8Y2Ksmx2z5sO/+EYRsv4FA0KzNblQeSr2fA1jITNyc3UL/utfo0Nrkt6OhUiKhoJR+CI8hsFnM1s+38E4pmTyeJgEi1cufNT7/hY7EpUy7OPnvHqmxas4IytvYEBwWxZ9smho6dHG89tpUcmD9jEo1a/AFApixZCQkKxuuQO5PmLFGXMzE1IzDA/6vxFChUhAEjx9N/xDgunDnJri3rmTp2OKXKVWD8jPmYmpn9cM9AcFAQ/n6+6i/9I+57ME9vgXl6C0KCg4mOjsLULB0qlYrd2zZRtLgNADnz5OXG1Uu8e/uGjJkyc+q4FyamZlhkyBir/rIVKnHu7kv164HdO1C8ZBn+6NwdgHUrF7Nh1VLW7jyEeXqL745bJJ4kA6mAno6CZZ3tGb75IiHhURjp67C0sz16Ogqsc6anu0tRak07hKmBLpULZ/mp+Q4U2to4W2XD2SoboRFRHLj28ttvSkD7xd7qwY52Y/dRJHs6NvZwAqDKRHfW/eVIVvOvd3eGRylpveAoEVFKVEC2dIYs6mCn3j9s0wX2XXnOm4AwmvzrhYWxHsdG1vypmIX4WS3adY53e48Bw/l7cC9q2Mc8YdCgWSscXarHW7ZCJSe2b1hLebvPt/ZK21Zkx6b/KFX28xz6zdt2YuLIQSyZM52hY6dg71Q13vq0tLQoW8GeshXsiYyM5LjHQSBxE8yGhgTTs31zQkKCUSgUpEtvweJ129HS0uLdWx+6t21KdHQ0ymglloWLMnbqHACKlyzDnz3707J2VXT1dNHT12feyg3qWwS1HcqwbONusmTL/tVjBwUGMqp/D3LkykPbhjG3V/T09Nl2OGbCoDFD+nBw9w7evnnNHw3dSJ8+A+6nriTqPMVnMh3xd8qUwYIulXPRq5pVih87KCwSk4+3Aabuvcajt0HMb1cxxeP4Hf178CaLjj/j7fsPmg5FpDDLggVxqdWAQaMnajoUkQKmjBnG4b3buX/vnqZDSZWkZ+AXMH7nFc4+eEdktJLcGU2Y3iJu96QQQgiRWJIM/AImNSur6RCEEEL8xmTSoV9MlYnuhEZEfbNcy3lHefw2MMmOO2P/dcqP3k350buZsT/+KUAf+ATQYJYH9mP34jB+P73XnFHH6uMfSrXJB6gy0R3HCfvptPQEfiER6vc+fR9Ey3lHsRuzl8rj9vHfyQfxHkOI31lthzKEhYZ+s1yHpnViTdH7s+ZMnYBz6cI4ly7MnKkTvll+cI9OFLDQJTgoSL1twczJuNmVxNW2GAP+ak94+OfJz54/fUyHpnVwKW9N9Qol2LRmeZLFLpKG9Az8YjyGuX27ELCu+/c/mvQtp+69YdfFZxwdHjMFa42ph6homZmKBTPHKqero83YRqUonis9SqWKLitOsuDIbfrVKIaFiT47+lbFSC/mT27klovM2H+DsY1KoVKpaL/Ym/41i1HTJicqlYp3QQnPoijE72jPsQvfVe7T8/5J4ezJ4+zfuYX9Jy4D0NDVDlt7h1gDG790xH1PnHn+j3scYvfWDWw7dAJDIyOG9enKigWz6dpnECqVim5tGtNz0Eiq1aqHSqXi/bv4H2EWmiM9A6nQnkvPsB+7l6r/uDPL/UasRYC+/H/ZkbuYvOcaNacdouzIXbEW/yk7cleSLeyz8+JTmtrmw1BPB0M9HZra5mPnxadxyuXOYELxXOkB0NbWonSeDDz7EAKArkJbnQhEK5UEh0fx6enBY7d9MNHXpaZNTiBmVPT3TnEsxK/Gfdc2XG2LUcexLPNnTIp1hf3l/x1sLJk58W8aV6uEg41lrMWQHGwsf2iRnoTs3b6JBs3aYGBoiIGhIQ2atWHv9k3xlvX98J5/p4xj+ITY0yzfun6VchUrYWRsjJaWFk4ubuzcvA6AE15HMDYxpVqtekBM+86YKXOcuoVmSc9AKvMmIIwB68+xb4Ar+TObstDjToLlQyOi2DfAlafvg3Ac707zCvkSnIDIPySCBrM84t1XOFs6FrSP+5TC8w8h2H3RC5AjvRGn7r/5ZlzrTz1kWD2bWNurTHTnhW8IVjnSsbqLAwB3XvuT3kSPTktP8OhtIPkymTKmUUlZk0D8dt698WF4325sOehNvgIFWT5/VoLlQ0ND2HLQm+dPH1PDviSNWrTF2MTkq+UD/P1oWSf+Rw8LFrFi5uI1cba/fP4s1iRH2XPm4typ43HKAYwe2Is+Q0bHWrcAoFjJ0mxcs4wP799hls6cvTu28PLjegL37tzE3CIDPdo158mj++TJZ8mw8VNlfYFURpKBVObi4/cUz5We/JljZvtrWTEfo7de+mr5+mXyADFX5eZGurz0C6Vg1q8nA+mM9L77VkNiRUUr6bL8FPaFs+BWIvYiJR7D3IiMVjJ800VWed+nh2tRlEoV3nfesH+gKwWzmrHwyG16rj7Dtt5VkjVOIVLa5QtnsS5RinwFCgLQpHV7JowY+NXytRs2BSBn7ryYpUvP65fPKVCoyFfLm6Uz/+5bDT9q7/bN6Orp4lwt7jwfdg7OtO7YlXaNaqCnb4CdQxW8P66LoIyO5tQxT7YdOkGBQkVYNm8mA7t34D+ZYjhVkWTgF6ev+/lOj0JbiyilMsHyiekZyGlhxPOP3f0AL3xDyJE+/ulBo5VKuq08hbmRLhOblI63jK5Cm6YV8tJ/3Tl6uBYlR3pjSuROT8GsZgA0Kp+XyXuSpgtUiF+Zvv7n22UKhYKoqIQHDyemZyB7zlyxVgV8+fwZ2XLkjFPuzImjnD7mhYONpXqbm50NyzftpmARK9p37UX7rr2AmMTBsnDRj/XnpljJ0uokpl7TVsyaNCbB8xApT5KBVKZ03gz0WevL47eB5M1kysbTj5O0/sT0DNQplYvhmy/S3iHmQ2DTmUfxTl2sVKrotfoMCm0tZrYuH2uQ0QvfYMwN9TA20EWpVLH30nOKZo/paqxinY0Ju67g4x9KlnSGeN58hXVO88SfpBCpVMky5Rl8tRNPHj0gT74CbF2/OknrT0zPQI16jRk7pA9tOnUDYPvGNYyePDtOubHT5jJ22lz16wIWurifvKK+bfHW5zWZsmTF38+XRbOn0GNgzBoDji5uTB03gjevX5E5azaOHTlAEesSiT1FkUwkGUhlMpsZMKV5WVrOP4ahngLXYtnRVWhjqKe5X5V9oSzUKpkTh/H7AWhqm1c9hsD96gsOXHvBzFblOXLzFVvOPaFI9nS4TopZlKh8gYxMalaW+z6B/L3tMiqVCqUKiuU0Z0KTmITCWF+HiU3K0GLeUVRAemM9/m1jG28sQvzKMmbOwrjp8+jYrC6GhoY4V6uFrq6uRhfiqVDJkeq1G+BmFzO+p0Gz1tjax4znObx/N0f271avXpiQPxq6oVKqiIyKpE2nbuoBg0bGxoyeNIsOTWujUqkwt8jAlHnLku+ERKLIdMTfKSWnI/5y+uH1px6y7uRDWdI3mch0xGmXpqYjDgoMxMQ0ZkzQlv9WsmntCjbtP5qiMaRFMh1xwqRnIBVa4nWX3RefEaVUkd5Ij+mtZPphIX4XqxbPZf/OLURFRWOePj0TZy3UdEhCSDKQGvV1s6avm7WmwxBCJIPu/YfSvf9QTYchRCwy6ZAQQgiRxknPwG+uwawjdKtahGrFc3y7cDKY6X6DnReeotDWQqWCXtWtqF8mNwAhEVH0XnOGq0990VFoMbpBSXWcQzae5/gdH/R0FBjr6zC+cWlK5rFQn9PzDyGYfhxX0dm5EC0q5tfI+QmhSS3rVKVTj35UqV5LI8cf2L0DJ708SJ8hAxDzZMKnXg+lUsmsf8awd8dm9PT0yJ4zN8s27gJiJl/q37Udz589wcDAgAkzF1CybOxBw1vXr2ZQ944sWb9DY+eXlkgyIJJVR8eC6lser/1CsR+3F6eiWTE30mPB4duYGuhyZkxtHr4JpO6MI5z5OzPGBrpUscrGuMal0VVoc/DaC/5cfoKzY+qo653QpLTGEhwhxGdd+gzkj87d42xfsWA2j+7fxf3kFXR1dXn3xke9b+rY4ZSzq8yqAfs5f9qbfl3bceTcTfXjyK9ePGf9yiWUKitPFaUUSQZSQEhEFL1Wn+HOK390FNpYZjZlSSd73viH0mXFKYLCIgmPjMalWHZGNSgJwNS917jnE0hQaCQP3gRSInd6elWzYvS2Szz/EEwtm1yMbhhTtsGsI1jnTM+5h+/wC46gbulcDP+/aYABAkMjGbXtEjdf+BEeGY19oSyMbVQShbY20/ZeZ/uFJ+jrKNDSgm29q5DOSO+nz93M8HMdweGRaKGFUhnzAMvOC0/5948KAOTPbIpNHguO3HxF3dK5Y33Rl82XkVd+oSiVKrS1Yy+QIoSmhYaEMPCvDty7fRMdXR3yWxZmzor1vPV5Te/OrQkKDCAiLBynajUYMmYSALMnjeXBvTsEBQbw6ME9itmUpmufgUwcOYiXz55SrXZ9ho6dDMRc/RctZsPFc6fw8/WlVv3GDBg5Pk4cgQEBTBwxgNs3rxEeFkaFSk4MnzANhULBv5PHsXvbRvT19dHS0uK/XYcxS2eerD+XZfNnsWGvJ7q6MT14GTNnUe/bt3MLRy/fB6BshUro6elz7dJ5SpSOGSw9vG83RkyYxpQxw5I1RvGZJAMpwOvmawLDIjk+MmYaz09L95oZ6bG2a2WMDXSJjFbSbK4XHjdeUcU6GwBXn37g4OBqGOvr4DLpION3XmH9X45EKVWUG7WbNpUKqKctvvvKn739XQiPjKbm9MOUy58xzpXzqG2XsLPMxMxW5VEqVXRbeYp1px5Ru2ROFnne4erEehjq6RAUFomBriLOeRy9/Zox2y7He45NK+Sja5XC8e5bdfw+iz3v8MI3hFmtbbEw0QfguW8IOS0+P1+dM70RL31D4rx/+dF7uFhnj5UIjN1+hQm7rmKdw5yR9W3IZq6557RF2nbM4yBBgQEcOH0VAH8/XyBmAqAl63ZgbGJCZGQk7RrX5OjhAzi6VAfg+pWL7PQ4jZGxCXWdyjF1zHCWb9pDdFQUjqUK0rxtJ/W0xffv3GKz+3HCw8JoXL0SpctXjNN1PnHEAMrbO/DPv4tRKpX0/fMPNq9dgVvdhixfMJvTt55hYGhIUGAgBoaGcc7jhNcR/hk1KN5zbNi8DR3+6hPvvuXzZ7N+5RJy5yvAwJHjsSxclMAAf3w/vGffji0c3LMDbW1tuvQZhGvNuvh+eI9KpcIiQ0Z1Hdlz5uLVi+eUKF2O/5YvpGARqzi3DUTykmQgBVjlNOfe6wCGbDyPXcHMuBTLDkC0UsWY7Vc49+gdKpWKNwFhXH/hq04GnItmU19ZW2VPh3XO9OjrKtAHLLOY8vhtkDoZaGqbDx2FNjoKbeqXyY333TdxkoEDV19w6fF7FhyJWfwoNDKa7OZGmBnqki+TCT1Xn8GxaFaqFcuunufgS45FsiZqXYO2lS1pW9mSmy/8+GvlKRwKZ1EnBN+y/fwTtp1/wo6+n6dYndu2AjnSGxOtVDL7wC3+XHZS5mEQGlO0WAnu373N6IE9sbV3VM/dHx0dzaTRg7l49hQqlYq3b3y4df2KOhlwqOKqXvCniHVxiljboK+vD/r65LcsxNNHD9XJQMMWbdDR0UHHxITaDZtx6phnnGTgsPserlw8x7J5MwEIDQ0la/YcmJqlI0/+Agzo1p5Kzi5UqV5LPc/Bl+ydqv7w7IX9h48jc9ZsaGtrs23DGto3qY3XpbtER0cTER6OUqlk2+GTPH54n2Y1nShU1DrBHolnTx6xcc1yNu2TeRdSmiQDKSBvRhOOjajBsTs+eNx4xcRdV/EaXoNFHrfxC41g/0BXDHQV9F93lvDIaPX7/n/dAX2dH1uHID4ru1Qmb8a4q57tG+DK2Yfv8L7jg+vkA6zv7oR1DvNYZRLbM/CJVQ5zsqYz5OS9N9QulYuc6WPWPMj4cbni574h2Bf6vDrivsvP+Wf3Vbb0ciaz2ec52j+tZqjQ1uZP50JM23ddbiEIjcmdNz/uJ69w8pgHRw+7M238SPZ7X2LZ/Fn4+/mx7dBJ9A0MGNanK+FhYer36X2x7oC2QoG+wecEWaFQEB2d8DoEcahULFy7ldx54w6m3XrwBBfOnOTUMU/qVbFlxeY9caYETkzPQNbsOWKVmTB8AK9fPidHrjwYm5hQv2lLAPLmt8S6RCluXr1MjXqNAPjw/p26d+DTegiXzp3G59VLqlUoDsDbN68Z0utPBo4cT5PW7X/s5yF+iCQDKeClbwjmxnrUtMmJU9Gs2AzbiV9wBP4hkWQxM8BAV8ErvxDcr76gXWXLb1cYj63nnlC/TG7Co5TsuviMoXWKxylTrXh25hy8yZTmZVFoa/M+KJygsEgymOgTHB6FXcHM2BXMzPlH77j90i9OMpCYnoE7r/wpnC3m6ufJuyCuPfelULaYBYnqlM7Fau/7lMxTnodvArn85D2LPi6UdPDaC0Ztu8TmHk7kzvA5eYmKVvIhOEKdHGw7/4Si2dNJIiA05tWL55int6BarXpUdnalolVu/Hw/EOjvR+YsWdE3MOD1yxcc3r+bVu27JOoYOzato1aDpkSEh7Nvx2b6jxgXp0xVt9osnDWFcdPnoVAo+PD+HcFBgaS3yEhIcBC29g7Y2jtw6dxp7t66EScZSEzPwOuXL9QJwbEjB9FWKMiSLeZ17YbNOHr4AC3adebd2zfcvnGVQkVjBhPXqNeIdSsW02PAMM6f9iY8LJRiJctQonQ56jZuoa5f009LpCWSDKSAWy/9GL8z5n5itFJFr2pWZDU3pJNTITotO4HD+P1kNzekcuEs36jp6yyzmFJr+mH1AML4RtqPb1yasduv4DzxAFpaoK+jzbhGMSP2OyzxJiwyGqUKSuRKT62SSbPW+LS919UDJxXaWkxoUoZCWWOSg79citJ7zRlsR+9Boa3FtBbl1Lcn+qw9i65Cm45LT6jr2tLLGX1dBa0XHCUiSokKyJbOkEUd7JIkViES487N60wdGzPQLTpaSbe+g8mSLTttu/SgR/vmuNmVJGv2HNg5JH5J7gIFC9PErbJ6AGF8X44jJs5g8t9DqFW5DFpaoKevz8iJ09HR0aV726aEhcUMwrW2KUn12g0SHcuXBnbvwLs3Pmhra2Niasbi/7ah83Hp4gEjxzO4RydWLZ6Hlhb0HzFOvXLhoFET6de1Lds2rMHAwIBpC1agrS3T3miSrE3wnVJybYIfpem5BH5lsjZB2qWptQl+lFwdJw1ZmyBhkooJIYQQaZzcJvgNbO9T9duFhBC/pHW7j2g6BJEGSM+AEEIIkcZJMqABDWYd4eC1FxqNodfq05QcvpNhm2JGD597+I5a0w5Redw+Ko/bx5htl/lyOMn1Z77Um3FEvf/IjZcAvPILocEsDyz7b6Xa5AOxjrH/ynNcJx3AYfx+HMbtY8Hh2z8U47vAMKyHbKfjEm/1Nq9br6g2+QC5em/i722XYpVff+ohThP2U2WiO44T9rPE86563xLPu9iO3hMnRiF+Vss6VfE4sFejMQzs3gF767yMGdIHgAtnTtK4emWqVyhB9Qol+GfUYHV7Pu19FOscZtR2KENthzI0dPk8APfo4QPUqlya2g5lcKtow7TxI9XvCw4Kon+3dtSwL4mrbTGWzJnxXbElVCfAnKkTcC5dGOfShZkzdUKs9578+CikW0Ub3CracOv6FQC2rFtFybwZ1efQtU1j9XtWLpqDc5ki1Ksikxb9CLlNkIb1dC1KR6dCAJgY6DDnjwrkz2xKeGQ0jf/1ZMvZxzSxzUdweBTtl3izoH1FyubLSFS0koDQSACM9XUYXLsYgWFRTN17LVb9mc0MWdPVgazmhgSERuA66SCl8lpQwTJznFjiM2TjBVyssxMUFqnelieDCTNalmf35Wex5mQAqF0yF80r5ENLS4ugsEgcx+/HrlBmrHOY09m5EFY50jFm++Wf+IkJkXp9uUaAiakZU+cvJ1+BgoSHh9OmfjV2bPqPBs1aA2BZuCg7Pc7EqaNsBXt2eZ1DoVAQGRlJ0xoOlCxTHpcadVgwcxJ6unrs875EaEgITdwqU7aCHaXKVUgwroTqPHvyOPt3bmH/icsANHS1w9begfJ2lXn98gVDenZm5Za95C9YmLDQUCIjP38W2DtWZd6qjXGO165LT4pYl/jqnAkiftIz8BNm7L/ByC0X1a8/BIVTdNA2gsOjOHb7NTWnHaLqPzFXqdvPP4m3jv/vJfjytY9/KB2XeFN9ykEcJ+xnlvuNZDuXotnN1bMZ6usqKJYrPc8+xEwNvO38E2wLZKJsvpgJQnQU2uoZBM0M9ahgmRkjvbjTF5fJl4Gs5obqcgWzmvH8Q9zphuOz5exjMpkaUNEyU6zt+TKbUixXenTimVfA1FBXvdBJSEQ0kUolMvuA+F5zp01k/LD+6te+H95T1jIrIcHBnDjqQeNqlajjWJYa9iXZvTXulxDE7SX48vWb16/o3rYZDVwqUsO+JPNnTEq2cylsVUw9e6G+vj5WJUry4tnTb77P2MQEhSKmLYeHhREZEaF+5O/W9atUrlINLS0tjIyNKW/vwM4t63+qzr3bN9GgWRsMDA0xMDSkQbM27N2+CYC1yxfSoFlr8heMmczMwNAQUzOzH/xJiO8lPQM/oaltXmpMPcToBiXRUWiz7fwTqhXPgbG+DiVyW7C7X1UU2tq8CQij2uQDOFtlw/wHFv/psfo0/dysqVgwMxFRMVfrpfJkwLFo1ljl7rzyp9uKU/HW4Vgkq3pBo+/1NjCMvZef8V83RyBm3QMdhRYt5x3ltX8oJXKn5++GpX7oXO69DuDi4/dMa1Hum2Vf+4WyyOMOO/pUYfelZz8Uu/vVF0zceYXH74IYVs8Gq/+bOEmIr2nYvDUNXe0ZMnYyOjo67NqynqputTEyNqaYTSk27j+KQqHg3Rsf6jnb4lC1GunM0393/QO6tafHwOGUt6tMREQEbepXo0SpslRyjj2V9r3bN+n7Z5t467B3clEvYPS93r19w4Hd21m6Yad62+P796jrVA4dHV1adexKoxZ/qPddvXSeIT078/jhfVq176KeXrmYTWn279qGa616BAb4c9zjEPktC31XDF+r8+XzZ9jaO6rLZc+Zi3OnjgMx6zHkzJWHVvVcCfDzpUIlJwaMmhAzZTNw9uQxajuUwcTUjC69B6rrFIkjycBPyGlhTOFs6Th84xVuJXKw8fQjxjYqBcD7wDD6rL3CwzdB6Ghr4RccwX2fAPXV9bcEh0dx8u4b3geGq7cFhUdy93VAnGSgcLZ0iVozID5BYZH8sfA43aoUoXiumA+6aKUK7zs+7B3gSiZTA0Ztu8TorZeY3eb77sn5+IfSdtFxJjUro+4pSEj/dWcZWd8G43jWR/gWtxI5cCuRg+cfgmm32BsX62xYZpGrCfFt2XPmpmARK7wO7celRh22rl/NiAnTAfjw7i2De3bm8YP76Ogo8PP7wMN7d77ZRf5JSHAwZ04c5cP7t+ptwUFB3L97K04yULCI1Q/PBPg1QYGBdGnZgI5/9cW6RMxnk3WJUnhff4SpWTqePXnEHw3cyJotB/ZOMU8llShVln3el/jw/h3d2zbl3ClvyttVpmufQUwaPZj6VWyxyJgJW3sHPrx/911xfK3OhCijo7lw9iSrt7mjb2BI385tWDx7Kj0HjaBK9VrUbtAUA0NDbly9RIemdfhv5yEsCxf9uR9YGibJwE9qXiEfm848IncGYwJCI6nwsVt70IbzVC+RgxWdK6GlpUXFMXvj3OOGmPn1lV9M+xQWGbPegEqlQktLiwODq6GrSPhuTlL1DIRERNF6wTGcimalm0sR9facFsbYF8pClnQxX+QNy+ahz9qz31Xn28AwmvzrSXfXItQtnfu73nP+0Xvu/BdTf3B4FKGR0bScd5R13R2/8c7PcloYUyqPBYeuvZRkQHy3Ri3+YNv6NeTKk5fAgADKVawEwMgBPajqVpsFqzejpaVF1XJWhIeHx3m/QkcH5Rdrhnxai0CpVKKlpcX2I6fVS/p+TVL1DISGhNC5RT0qObvSqUdf9fYvu9pz5cmHa826XDhzUp0MfGKRISOOLm7s27mF8naVMTQyYszUOer9owb0+OEv3/+vM3vOXLFuX3xaowBiegmKlyyjXsypZv3GbN+4Vl3PJ9YlSlGmvB1XLp6TZOAnyJiBn1SzZE5O33/LwiO3afZx8BpAQGgkuSyM0dLS4uit1zx6Gxjv+/NlMuHyk/dAzJf6jecxy5+aGOhiWyAjcw7eVJd94RvMG//QOHV86hmI79/3JgJhkdG0WXCMMvkyMLh27HUN6pbOxaXH79UD+TxvvoqzbkF8PgSF03SOFx0cC9LKrkCsfRcfv6fRbI9433dnakPOj6vL+XF1Gd2gJFWtsn1XInD3tb/6/++Dwjlx9w1Fc6T75vuE+KR67QacO3WcpfNm0qjFH5/bs78fOXPnRUtLC2/Pwzx5eD/e9+fJV4CrF88DMV/qNz+OfjcxNaVsxUosnDVFXfbl82e89Xkdp45PPQPx/fveRCA8LIzOLetTsqwtfYf9HWvfm9ev1KP5/Xw/cNzzEEWL2wDw6P5ddTITEhzM0cPuFC5aDIDAgADCQmM+f27fuMrBvTtp3aErAFcunKV1/WrxxpJQnTXqNWb7xjWEhYYSFhrK9o1rqFm/CQB1GjXn1HFPIiIiUKlUHPc4SJFiMespvH75eZzVi2dPuHzhDEWs467HIr6f9Az8JCM9HaqXyMGGU484N7a2evuIejYM3nieqXuvUzKPBVbZzeN9f3fXonReeoL9V19QIld6iuX6fA9yQbuKjNx6CccJ+wEw0ddhVmtbMqf7dlf7j1p38iEn773FNzgCz5vuQMxCQn3drMlpYUyPakWpOe0w2lqQO4MJ01rG3PuPViopM3I3EVExTxiUHL6TVnb5GVirOHMO3uLhm0BWez9gtfcDADo7F6JFxfw8/xCMgd6P//mduf+WLitOEhgWiUoFOy48ZWar8jhbZWON9wOO3nqNjkIbFdDBsSBORbMl2c9I/P4MjYxwqVGHLetWcfTy52lrB42ayKiBPZk9aQzFS5X96hfPn70G0KN9cw7t24m1TSmsi5dU75u5aDXjhw+ghn3MNmMTUybPWUKmLFnjretnbFq7nDPeR/H78J7jHgeBmC/e7v2H4r57G+uWL0ZHV4eoqGgaNm+Da826ABzat5tt61ej0FEQHa2kWq16NPujIwDPnjykZ4eW6Ch00DfQZ8ai1WTJFrMc+4tnTzEwiP9zKaE6K1RypHrtBrjZxSQjDZq1xtbeAYAytnY4urhRx6Es2gptrG1K8VffIQCsXbaAw/t2o9CJGZjYf8Q49W0QkTiyNsF3Ss1rEyRGr9WnscltoX60MKUN23SBeqVzY/t/TwskpxN3fRiz/TIHB1dXb5O1CdKuX2Vtgu8xsHsHipcso360MKWNGdKHWvUbU7ZCJY0c//+d9j7KP6MGxXp8UtYmSJj0DKRRpoZ6LD16jwdvApnYtEyKHz+lj7nE8y6rve+TI71Rih5XiJRgapqOVYvn8ejBPUZPmpXix9fEMb9m5aI5rF+xhGw5k2bl1bRCkoE0akKT0poOIUV1di5EZ2fN9IIIkdxGTZqp6RBSjXZdetKuS09Nh/HLkQGEyaDsyF3ceumn6TBiefo+iCzdNzBo/flY24oO2vZT9V5+8uGrTzL8//G/dqzXfqE0mPV5MGGW7hsI/mLWwS9VmehOaEQUAIs87vA2MCwRUQuRMhxsLLlz87qmw4jl+dPHlLX8vnEKBSx0CQ4KindfbYcy6gGFCZ1nh6Z1ePIoZszQlnWreHT/brzlhGZJMpCGGOvrsP/qcx5/5cmGxCiZx4IF7Sv+VB1ZzQ3Z3qfKd5X1GOaG4ceBh0s87/AuEclAVLTy24WEEAnac+wCBobfHsy8fNNu8uSLeZpo2/rVPHrw4/fso6Kifvg94sfIbYKfcO7hO8Zuv0xQeMwf6ugGNnFGry84fJsdF54SpVSir6tgSrOyFMuVnpCIKHqtPsOdV/7oKLSxzGzKkk723PcJoNeaM4RGRBOtVNG8Qj7++uKZ/5+hr6PNX1WL8M/uayzqYBdnv8eNV0zYdYVopYoMJvpMa1GOfJlNOXHXh5FbLlE6bwbOP3qHlpYWizpUpFDWdLEG5T19H0T1yQdpU8mSIzdeEhoRzcxW5WMNEpy462qcfZ/ed2tKQ3W5eYdv4371BWGR0QyrW4LapWLu/2XpvoGH0xux2Osur/3D6LT0BPo6Cha0r0jO9EYM23yRS09iBgM2tc1LD9eY544bzDqCdc70XHj0nvRGej80Z4EQ33Lx7CkmjR5CcFBMoj1kzGQqV3GNVWbp3Jns2b6R6Kgo9PUNGDt9LlbFSxIaEsLAvzpw7/ZNdHR1yG9ZmDkr1vPw3h0Gde9IaGgI0dFKGrX4g849+yV57AUsdOk/YhwH9+7A78MHhoyZhFvdz21x1eK58e4rYKHL1ae+GJuYALBz8zq8vQ4TGBBA+6491YMZHWwsWbJ+B9cunefa5QuMHdKXGRNGMXTsFCpUdmLy30M5diTmiQeHqtUY/Pc/KBQKBnbvgI5Ch4f37xIcFJhkEzGJ+EkykEi+weG0X+zNij8rUS5/RqKVSgLD4mavTWzzqifwOXr7NQM3nGf/QFe8br4mMCyS4yNjptD0C4kAYMWx+1QvnoPe1a1ibf9/HZd48+ht/N13ewe4qK+e/197x4LYj9nL9We+mBl9nvzkbWAYPVafZnufKhTOlo7/Tj6g28pTuA+KeXb4zit/ZrexZVrLcsx0v8HM/Tfj7RH4EBxB2XwZGFa3BFvOPmbczivs6e/yzX3/T6GthccwN+77BFB7+mFsLTORydRAvb+vmzX/nXjA0k72FP342Oa4HZdRqlQcHe5GUFgUtaYdomj2dFS1jnn86cm7IHb3q4rONyZxEuJH+Pl+oNsfTZi/ahNlbO2Ijo4mKDAgTrkGzVurJ/854XWEkf26s/XQCY55HCQoMIADp68C4O8XM9fI2mULqVqjDt36Do61/f91b9uMJ4/in/dgywHv77p6NzE1ZceR05w/fYJeHVvGSgYS2vel92/fsMvzLO/e+FDHqRzl7SpTxLqEen/jVu3YtmENnXr0o0r1WgD8t3wht65fYZdXzCRjHZrWZsOqJbT6OH/BzetXWL/bAyNj42+eg/g5kgwk0vlH7ymUzYxy+WNmwlJoa8c7V//Vp77MPnAT35AItLXg4ZuYKwernObcex3AkI3nsSuYGZdiMV9YFS0zMXbHZUIjorAvlIVKheJf4W9Z58Q9wmOgq6BvDWsm7rrKpOafR/RffPweqxzmFM4WM0lPiwr5GbLxgnqioQJZzNTTE5fJm4GD117GW7+xvg7ViucAoGy+DPy97fJ37ft/Le3yA2D58bgXHr3HrUSOBM/t2G0fxjcujZaWFqaGutQvm4djt33UyUDDsnkkERBJ7tK501gWLkoZ25jeNoVCEe+aBdcvX2TBzEn4+fqira2l7i4vWqwE9+/eZvTAntjaO6rn2C9vV5nJfw8lNCSECpWdqFjZKd7jx7dy34+q3bAZAKXKVcDn1UvCw8LQNzD45r4vNWndHoCMmbPg5FqD095HYyUD8Tnh5UGjFn+gpxfz2dm4ZVsO7tmpTgZq1G0kiUAKkWQgGUVERdNx6Ql29q1CidwWvPYLxWZ4zGIheTOacGxEDY7d8cHjxism7rqK1/Aa1C6Vi7L5MuJ16xVzDt5k/amHzG8X9wo8sT0DAC0q5mPBkducuf/2q2X+n4Hu5y9RhbYW0cr477vr63wup/1/5RLalxKM9eXPXWhGREQEPdo3Y/0eD4rZlMbn1UvsrPMAkDtvftxPXuHkMQ+OHnZn2viR7Pe+hFvdhpQqV4HjnodYNGsKW/5bwYxFq+PUnRQ9A/r6MV/un1YXjIqKQv879iU3SQRSjnw6JlLZfBm4+yqAcw/fxbpN8GXvQHikkmilkuwfn21fcfzzwJmXviGYG+tR0yYnTkWzYjNsJ37BEYRGRJEnownNK+Ynf2ZTen9lDYDE9gxATC/GkNolGLPjsnpbmbwZ6PvCj3uvAyiY1YyNZx5RLGd6TBKxWFBSWH/qEf1qWPPwTSDXn/lSJl+GOGVMDHQJDP381IFDkSysO/WQ8gUyEhwexY4LT/m7QckUjFqkRaXKVeD+nVtcPHuK0uUrqm8TfNk7EB4WRlRUFNlyxIx9Wbt8oXrfqxfPMU9vQbVa9ajs7EpFq9z4+X4gNDSE3Hnz07hlW/Lmt2Rwz87xHj8pegaSwtb1qylbwZ73795y9LA77br0iFPGxNSMwIDP04bbO1Vh2/o11GrQFIBt69fgVrdBisUsPpNkIJHSG+uz4s9KjN52iZDwKLS1tBjdsCSORT4/smNqqMug2sVxm3KQ9Mb66kFwALde+jF+Z8w9wmilil7VrMhqbsgs9xtsO/cEXR1ttLS0GN84eabYrFM6F3MP3VI/wpfR1IC5f1Sg28pTREUryWCiz/y237ciW3KIViqp+o87oRHRTG1RLtZ4gU86ORWi99qzGOrGDCDs52bN0E0XcJwQM51yk/J5qWIt0xGL5GWe3oL5qzYxccRAQkKC0dbWZujYKbEW/jE1M6PP0L9pULUi5hYW1KjbSL3vzs3rTB07DIDoaCXd+g4mS7bszJ8xiV2b16Orp4uWlhYjJ85I8XP7ERYZMlLXuTyBAQF07TOIwlZxp2xu3rYTE0cOYsmc6QwdO4XmbTvz5OED6jjGTG/uUMWVZn90SunQBTId8Xf73aYjFjFkOuK063eajlh8m0xHnDAZTfWdtLW1iYqWvOl3ExWtQltbmkFapK2tLc+vpyFRUVHS1hMgP5nvlDdvXm688NN0GCKJ3XjhR758+TQdhtCAvHnzcuvjEsPi93fr+hVp6wmQMQPfqVmLlgwaOJClXndpWDYP6Yx00UJL02GJRFChwj8kkm3nn7D/ygumTO2j6ZCEBjRv1oyOHTuyaPZUGrVsS3qLDGhpSZv+nahUKnw/vGfrulWcPOrBsmXLNB1SqiVjBr5TdHQ0nTp1YuXKlZoORSSh9u3bs2TJEvVjUyLtUKlU9O7dm7lz5yIfg783LS0tevTowezZsyXh+wpJBn7Q8+fP8fb2JiQkRNOhxCswMJBBgwbh4uJCo0aNvv2GZLBlyxaOHDnClClTMDU11UgM32JkZESlSpXImTOnpkMRGvb69WuOHTtG0FcW5ElNpH3/OBMTExwcHMia9fsWZ0qrJBn4zQwaNIiFCxfy6NEjMmSI+2x+Snj//j158+blr7/+YvLkyRqJQYjfkbRvkVxkAOFv5M2bN8ybN49evXpp7IMCIEOGDPTq1Yu5c+fy5s0bjcUhxO9E2rdITpIM/EamTJmCjo4O/fol/cpmP6p///4oFAqmTp2q6VCE+C1I+xbJSZKB34SPjw/z58+nd+/eWFhYaDocLCws6N27N/PmzZOrByF+krRvkdwkGfhNTJkyBT09Pfr27avpUNT69euHrq4uU6ZM0XQoQvzSpH2L5CbJwG/g9evXLFiwgD59+pA+fdylUzUlffr09OnTh/nz5/P69WtNhyPEL0nat0gJkgz8BiZPnoyenh59+vTRdChx9OnTBz09Pbl6ECKRpH2LlCDJwC/u1atXLFy4kL59+2Jubq7pcOL4dPWwYMECXr16pelwhPilSPsWKUWSgV/cpEmTMDAwSJVXDZ/06dMHfX19eSZZiB8k7VukFEkGfmEvX75k0aJF9OvXj3Tp0mk6nK8yNzenX79+LFq0SK4ehPhO0r5FSpJk4Bc2adIkjIyM6N27t6ZD+abevXtjYGDApEmTNB2KEL8Ead8iJUky8It68eIFixcvpn///piZmWk6nG9Kly4d/fv3Z9GiRbx48ULT4QiRqkn7FilNkoFf1D///IOxsTE9e/bUdCjfrVevXhgZGcnVgxDfIO1bpDRJBn5Bz549Y8mSJb/MVcMnZmZm9O/fn8WLF/P8+XNNhyNEqiTtW2iCJAO/oEmTJmFqavpLXTV80rNnT0xMTOTqQYivkPYtNEGSgV/Ms2fPWLp0KQMGDPgl1hL/f2ZmZgwYMIAlS5bw7NkzTYcjRKoi7VtoipZKpVJpOgjx/bp168aWLVt49OgRJiYmmg4nUQIDA8mXLx9NmzZl/vz5mg5HiFRD2rfQFOkZ+IU8efKEZcuWMXDgwF/2gwLA1NSUgQMHsnTpUp4+farpcIRIFaR9C02SnoFfSJcuXdi2bdsvfdXwSVBQEPny5aNRo0YsXLhQ0+EIoXHSvoUmSc/AL+Lx48csX76cQYMG/fIfFAAmJiYMHDiQ5cuX8+TJE02HI4RGSfsWmiY9A7+IP//8k507d/Lw4UOMjY01HU6SCA4OJl++fDRo0IBFixZpOhwhNEbat9A06Rn4BTx69IgVK1YwaNCg3+aDAsDY2JhBgwaxfPlyHj9+rOlwhNAIad8iNZCegV9Ap06d2LNnDw8fPsTIyEjT4SSp4OBg8ufPT926dVmyZImmwxEixUn7FqmB9Aykcg8fPmTlypUMHjz4t/uggJirh8GDB7Ny5UoePXqk6XCESFHSvkVqIT0DqVyHDh3Yt2/fb3nV8ElISAj58+enVq1aLFu2TNPhCJFipH2L1EJ6BlKx+/fvs3r1aoYMGfLbflAAGBkZMXjwYFatWsWDBw80HY4QKULat0hNpGcgFWvfvj0HDhzgwYMHGBoaajqcZBUaGkr+/PmpUaMGy5cv13Q4QiQ7ad8iNZGegVTq/v37rFmzhiFDhvz2HxQAhoaGDBkyhNWrV3P//n1NhyNEspL2LVIb6RlIpdq2bcuhQ4d4+PAhBgYGmg4nRYSGhlKgQAGqVavGypUrNR2OEMlG2vdKTYcj/o/0DKRCd+/eZe3atQwdOjTNfFDA56uHNWvWcO/ePU2HI0SykPYt7Ts1kp6BVKhNmzZ4enpy//79NPVhARAWFkaBAgWoWrUqq1ev1nQ4QiQ5ad/SvlMj6RlIZe7cucO6devS3FXDJwYGBgwdOpT//vuPu3fvajocIZKUtG9p36mV9AykMq1bt+bo0aPcv38ffX19TYejEWFhYVhaWuLs7MyaNWs0HY4QSUbat7Tv1Ep6BlKR27dvs379eoYNG5ZmPygg5uph2LBhrFu3jjt37mg6HCGShLTvGNK+UyfpGUhFWrZsibe3N/fu3UvTHxYA4eHhWFpa4uDgwH///afpcIT4adK+P5P2nfpIz0AqcfPmTTZs2JDmrxo+0dfXZ9iwYaxfv55bt25pOhwhfoq079ikfac+0jOQSjRv3pxTp05x79499PT0NB1OqhAeHk7BggWxt7dn/fr1mg5HiEST9h2XtO/URXoGUoEbN26wadMmhg8fLh8UX9DX12f48OFs3LiRmzdvajocIRJF2nf8pH2nLtIzkAo0a9aMM2fOcPfuXfmw+D8REREUKlSIChUqsGHDBk2HI8QPk/b9ddK+Uw/pGdCw69evs3nzZkaMGCEfFPHQ09Nj+PDhbNq0iRs3bmg6HCF+iLTvhEn7Tj2kZ0DDmjRpwvnz57l79y66urqaDidVioiIoHDhwpQrV45NmzZpOhwhvpu072+T9p06SM+ABl29epUtW7YwYsQI+aBIwKerh82bN3Pt2jVNhyPEd5H2/X2kfacO0jOgQY0bN+bSpUvcvn1bPiy+ITIyksKFC1OmTBk2b96s6XCE+CZp399P2rfmSc+Ahly5coWtW7fKVcN30tXVZcSIEWzZsoWrV69qOhwhEiTt+8dI+9Y86RnQkIYNG3L16lVu376Njo6OpsP5JURGRlKkSBFKlizJ1q1bNR2OEF8l7fvHSfvWLOkZ0IDLly+zfft2Ro4cKR8UP0BXV5eRI0eybds2Ll++rOlwhIiXtO/EkfatWdIzoAH169fnxo0b3Lp1Sz4sflBUVBRFihShePHibN++XdPhCBGHtO/Ek/atOdIzkMIuXrzIzp075aohkXR0dBg5ciQ7duzg0qVLmg5HiFikff8cad+aIz0DKaxevXrcunWLmzdvyodFIkVFRWFlZYWVlRU7duzQdDhCqEn7/nnSvjVDegZS0IULF9i1axejRo2SD4qf8OnqYefOnVy8eFHT4QgBSPtOKtK+NUN6BlJQnTp1uHfvHjdu3EChUGg6nF9aVFQU1tbWFC5cmF27dmk6HCGkfSchad8pT3oGUsi5c+fYs2cPo0aNkg+KJKCjo8OoUaPYvXs358+f13Q4Io2T9p20pH2nPOkZSCG1atXi4cOHXL9+XT4skkh0dDTW1tZYWlqyZ88eTYcj0jBp30lP2nfKkp6BFHDmzBn27dsnVw1JTKFQMGrUKPbu3cvZs2c1HY5Io6R9Jw9p3ylLegaSSXBwMMHBwWTOnJmaNWvy+PFjrl27Jh8WSSw6OprixYuTL18+9u7dy5s3bzA2NsbY2FjToYnfmLTvlCHtO+VIz0AymTp1KvXq1eP06dPs37+f0aNHywdFMvh09bBv3z7OnDlDvXr1mDp1qqbDEr85ad8pQ9p3ypFkIJn4+/sTGBjI33//jZWVFc7Ozvj4+Gg6rN+Oj48Pzs7OWFlZ8ffffxMQEEBAQICmwxK/OWnfKUPad8qRZCCZqFQqQkJCOHDgALVq1aJIkSKMGDFC02H9dkaMGEHRokWpVasW7u7uhIaGIne+RHKT9p0ypH2nHJkZI5moVCp8fHzIkCEDU6dOpW7dukyePFnTYf12Jk+ezJs3b5g6dSoZMmTAx8dHPixEspP2nTKkfacc6RlIJi9fviQkJAQ/Pz9mzpzJjh07sLCw0HRYvx0LCwt27NjBjBkz8PPzIyQkhJcvX2o6LPGbk/adMqR9pxxJBpKJjo4ORkZGnDhxgj59+qClpaXpkH5bWlpa9O3bF29vb4yMjGQgl0h20r5TjrTvlCGPFiYjlUolHxIpTH7mIqXI31rKk5958pFkQAghhEjj5DaBEEIIkcYl6mmCo0ePMmXyJI4fP05oWFhSxyRSkKGBAZUrV2bQ4CE4OjrGWybm9z1Zft+/gc+/78Gxft9Hjx5lypTJHD/uTWhoqAYjFMnF0NCQypUrMWjQYD58+MDs2f9y7vw5IsLDNR2aSAZ6+vqUK1uO3r170aBBg2+W/+HbBN7e3rhUrUrhzIbUKpaJdAY6ILdwfk0q8A+LYu/1t9x5E8oRDw/s7e1jFfH29sbFpSpFcmSgboXCpDMyQG7Z/ZpUKvAPCWPX6bvcfvGOI0dift8xv2MXrArlp2ENV9KlM5Um/ZtRAf7+gWzbf4hrt+4RHR2NVdlKlHJww8BIpvb9HYWFBHPpmDs3z3uzceNGmjRpkmD5H04GmjZpwrUTBznQoxx6OnKX4XcQEaWk2txz2FSqzsZNm2Lta9q0CdfPHsfzn/bo6coo3t9BRGQ0TkOWU6KCIxs3bqJp06bcvHqZ03vXo6enq+nwRDKKiIgkSwkHLEtWZOjC7Whry2f470ypVDKpW0P0IgI4e/ZMgmV/+C/h1ElvXItYSCLwG9HT0ca1sAUnTxyPs+/UiZO4lS4gicBvRE9XgVtpS056nwDg1KmT1HJxkEQgDdDW1iIkJARb13qSCKQB2tralKtah3PnzhIVFZVw2R+tPCwsHFN9mbjwd2NqoENYWNx7h2HhYZga6msgIpGcTI30CAuPGf8RFhaGmYl0FacF4RERABgam2o4EpFSjExiftfh3xgb8tukhi6zThEaGf3Ncq2WX+Tx+5AkO+6Mww+pMNmbCpO9mXH44VfL/bX+GiXHHyXb4EMEh0d9976UktrvETsMWEJoeOQ3yzWduIFHr32T7LhTtxyndI95lO4xj6lb4vacACiVKqoNW0nlAUuoPGAJjcev5+kbPwDO3HmOw4Al6n9Wf87GadDSOHX0mL8biyYTCAqNSLLYE6KVyn/j5dyaftdg1bptu/Pg8bMkO+7E2YsoUqkWRSrVYuLsRd8s/+eAUejntiEo+PNnypS5yyjl0pDizvXo2HcE4eExv9Pw8Ahqt+lGdhtHstvEP1g3rRrUqCIRYd8euPpPt4a8fvr1z9kftXXhJHq5FaeXW3G2Lpz0zfILRnSjWTETwkKC1NvevHjCP90a0qd2KfrVLYPH1lUAREaEM7FLfTpVyk2nSrmTLOYf931t/be5xD/cp+J3lfuvQ+kkO+aph77sueaDZ7+YY9eae5aK+dNTMX/6OGVblsvB2DqFKT7u6A/tEzGOTev8XeU2DWueZMc8efMpO0/d5sT0PwFwHbYSe6s82FnFbtja2lpsGd4cM2MDABbuPcuIVYdZPbAxtoVzxoq99ZTN2BbJFev97ufvpvov55R2zn3TtwsBu1bNS7JjHj9zga17D3Hp8FYAKtVtTeUKZalsWybe8nsOecWZAOfQsZNs3LUf711rMTI05K/BY/l36RoGdu+IQqFN3z/bktHCnBotuyRZ3L+DKVtPfVe5oQu2Jdkxb5735vSB7UzbcRaA4S2cKFq2ElZlK8Vb/oLXvji/b5VKxfTeLWjcbSjlqtZBpVIR8OEtANraCmq3641Z+gyM71Q7yeJOLr9Uz8Ceaz5UmnYC19mnme3xKNaV9Jf/LzfpOFMO3qf2vLOUm3Sc5SefqusoN+k4t18HxVv/j9p19TVNSmfDUFeBoa6CJqWzsevq63jLVrK0IKOJ3g/vS0t2nb6Nbe+FOA5cyoxtJ2JdKX/5f5u/5jJxw1GqDVuJzV9zWbL/nLoOm7/mcvPpmySJZ/vJmzRzLI6hvi6G+ro0cyzO9pM34y37KREACAwNj3eWtLf+wXheeUgzh2LqbR8CQ5iy+TgT2rokScy/ku37DlPcuR7lazRl8tylsa6wv/x/IbsajJk+D4f6bShkV4P5K9er6yhkV4Mbd+4lSTybdx+gVaM6GBoYYGhgQKtGddi8+0C8Zd/7+jFh1iKmjBwQa/vVm3exL18aYyMjtLS0qO5sz/od+4CYKYyrVq5AOrO02UV/5tAO+tYpxeDGdmxfPDXWFfaX/+9RzYpNc8cxolUVelSzwn3dQnUdPapZ8fTejSSJ55T7VhzqtkTPwBA9A0Mc6rbklPvWeMsG+r1ny/x/+GPQP7G2XzvliYGxCeWq1gFipk5OlyEzAAodHUpUdMbINF2SxJvcfpmegbeB4Qzados93cuRP6Mxi44/SbB8aISSPd3L8+xDKE4zT9GsTHaMExjr4B8aSaNF5+PdVyiLCfNbFI+z/YVfGHb5Py9OksPcgNOPkq6LOi154xdE30X7ODixHQWyWTB/T8IjX0MjIjk4sR1P3/hh328xLZxsMDH8ekLlHxxGndFr4t1XJFcmFveuH2f783f+2FvnUb/OmdGMUzefxin3SdOJG7j68DUWZkZsHdEizv4NR6/hbJOfzOYm6m0Dlx5gSFOHWMlEWuDz9j1/DRnLsZ1rKJgvD7OXxv+7+SQkNIxjO9bw+NkLSrs24o8m9TAxNvpqeT//AFybdYp3n1WhAqz6958425+9eIVjhbLq17mzZ8X7zIV46+g9YiIj+3WL88VeunhRlq/fyrsPvpibmbJlz0GevniV4LmlBX7vfFj8dy/Gr/MgWx5L9q6em2D58NBQxv/nwZsXTxhQvzxO9VtjYGTy1fLBAX6MaV8j3n05LYvSa/LyONvfvXqOVbnK6tcZs+Xk1oUT8daxbHw/mnQfHueL/fmDW5ims2BGv9b4PH1Iltz5+WPgJDJmy5ng+aVGv0wycPGZP8Wzm5I/Y8xApxZls/P3nrtfLV+vZBYAclkYYm6ow0v/cApm/vrppjPU/e5bDSLpXbj3khL5slIgW0xy1drZhhGrDn+1fEN7KwByZzYnnYkBLz8EUChHxq+WT2ds8N23GhJr07DmKJUqZm4/wfSt3kzrHPvDaZ3nFUa1dFa/3n7yJno6CqqVKZiscaVGZy9do2SxohTMF5NstWtan0Fjp321fNM6bgDkzZWD9OnMeP7KhyKW+b5a3jyd2XffavhRW3YfQE9Xl5pVHeLsc7a3pesfzanVuisG+vo425dH55g8iXP/2nnyFbUhWx5LAJwbtGH1lCFfLW9XozEAmXPkwcTMnPevX5Ajf+Gvljc2M//uWw0/6pT7VnR09Sjt6BZnn1Kp5PrZY0xY50mO/IXZs2oO84f/yajl+5IlluT0yyQDP8pA53MD1NbWIlqZ8HQKiekZyGFuwHPfz4NeXviFkd08bV3haYq+7uc/XYW2NlHRygTLJ6ZnIGfGdDx7669+/fxdADkymiV4HG1tLVpXKUm5XgtiJQPn7r7ALygU19KW6m0nbjzh2PXH2Pz1+SrJrt8iNg1rTpFcmRI8TlpjYPC510ehUBAdnfBA28T0DOTKkY0nX1zFP335mpzZs8Ypd/T0ebxOnqWQ3effbymXhuxaNY+ihQrQs2MrenZsBcQkDkUL5k/45EQcuvqfn2DS/o7fd2J6BjJmy8m7l58Hn7579ZwMWXPEKXfj3HFunD1Kj2pW6m3965Vj6MJtZMyWk/xWJdWJSuXazdg0d3zCJ5dK/TLJQOlc6ej78iaP34eQN4MRmy4mbddbYnoG6hTPwohdd2hnFzMgbPPFV4yv9/XsVXxdmYLZuTr/NY9e+5Iva3rWH72WpPUnpmegXsWiDFl+kE7VYwaQbTx6jckdqsUp984/GC0tLTKYxXRb7zx9i6K5Y3+Z/+dxmaYOxdFRfB6mM61zjVgJg0WTCZyc0SXB2x2/i/KlivPngFE8ePyMAnlzsWbLriStPzE9A41qudJv9GS6tW0GwH9bdzNzbNyr1zkThjNnwnD1a/3cNlw6vE192+L1m3dkzZwRX78Api5YzrDeMljQsnhZHt26wuunD8maOz9Hd/6XpPUnpmegQrUGrPxnINVbxAwQPrZrHe2Hxe2d6jRyFp1GzlK/blbMhOk7z2FgZELGbLlZP+tvfN++Jn2mrFz2PkyewnEvHH8Fv0wykMlUn8kNitJ6+SUM9RS4FMmIrkILQw1OhmNXwIKaxTLjNCPmj7BJ6WzqMQQHbr7h4M23TG9sDUCH1Ve4/CzmKrPStJMUzmLChk6lv7kvrchsbsL0P2vQbOIGDPV1qVbGEl2FNkb6mpsIp5J1HmrbFsau32IAmjkWV48h2H/uLvvP3+XfbrV54xdM93m7iYxWolKpyJPZnEW96qnrCQ2PZPvJWxz6p50mTiNVypIpA3MnjqBeu+4YGRpQs6oDuro6GBlqrmfNsWI56rtVpWTVhgC0blQHh49jCHYf9GLvYS8WTvn7m/XUbNUFpVJJZFQU3do2p171Kup9drVb8uKVD77+AeQv70o1J/vvqvNXZ54xC51GzWLSX43QNzCktKMbCh1d9Ay+Pu4juVmXd6C8S1361y8HgEOdFuoxBOc993Lecx9dxyb8tIqBkTHth03jn64NUKlUmJpb8NeEzwMehzZz4IPPC4IC/OhWtRA29q7frFNTfng64kwZLPizfAZ6On/9fl1yCQqPwuTjIMAN516w7vxLdnUrl+Jx/I7meD5i8dn3vH3/Idb2TBkz8Fd1G/o0sEv2GAJDw9UTHP3neYW1Ry6zf3zbZD9uWjRr+0nmH7jC23fvyZQpI306tmJg944pGkNgUDCmHyc7WrVpBys3bMdz26oUjSGtCQ4JwaJIRXpNWYF9zYTnqk9qocGB6smOPLevwXPbasauOZSiMaRFJ/Zt5t9B7QkKCsLY+OuTi/0yPQMAS088Zc9VH6KUKsyNdJnWsKimQxJJaPG+c+w8dYsopYr0JgbM6lpL0yGJZDRvxTq27j1EVFQUFubpmD95tKZDEslo/9oFnD64nejoKEzSpefPv+doOiTxhV8qGehTJT99qshgnN9V/0aV6N8o/gk/xO9nSM/ODOmZvE94iNSjYZdBNOwySNNhiK/4pZKBn9Fw0Xm6OeTBtahmRmlvufiK+Ucfc/dNMGPrFKKD3edZ7JouucCH4JgJdaKUKu74BHOkTwWsspny4G0wA7fdIiA0kvAoFfVssjDAtQAAszwesuuKDwptLVQqFT2d81HPJu7o57Sozug19Khbgeoaemxv07Fr/LvzNHeev2ViO1c61/h8O2vgUneOXXuMnq4CYwM9/mnvSqkC2WO9f73XVbrP2836IU2pXqYgHwJD6DpnF49f+6Kro6BANgtm/FmDjOlkTQEA16Yd6fPnH9Ry0cw0v71GTMTzxBn09fQwMTJi+t+DKGNjrY7t6YtXmJnGPCffo0NL2jatz3tfP9r3Gc7DJ8/Q09XFMl9u5v0zkkwZLDh1/jK9RkxU1//23QeyZM7AmX0bNXJ+qc2Ydm7UbtebMk7xP0GQ3LYtmsIp961oKxSoVCrqd+qvfhwyPDSEBSO68vDmZRQ6Clr3n6iOc1yn2gT6vgcgOjqK5/dvMWXrafIULpbgvpSQZpIBTSuW3ZQFLYsz1+txnH2bOn+e7nT/jTdMPnAfq2wx99bG7btH7eKZ6WCXm+DwKBxnnKJqkYyUypWODhVzqXtKXgeEUXnaSRwLZsDcSFaf07RiebOwtE99Zu+IO8LZpVQBJrZzRVdHwYEL9+g4czsX53ZX73/xPoCVhy5StuDnx5y00KJXvYpU+jiAcdTqI4z5z5M5f6X+aU7TgupO9kwfPRBdXV32Hj5Kq+6DuO29V71/xpjBcRIVLS0t+ndth2PFmERxyIQZjJg0m0VTx1CxbMlYT0M07tQHu3KlUuZkxDe5teyi7uX48OYV/eqUpoRdVUzSpWf3ytkYmpjx7/6rvHpyn9F/VOPf/VcxMDJh5NI96jrOHdnNhjlj1V/2Ce1LCSmaDIRERNN703Xu+ASjq9CiQEZjFrcuwZvAcLqtu0ZgeBThkUpcimZkZM1CAEw79ID7b4IJDI/i4bsQSuQwo4dTXsbsvctz3zBqFsvMqFoxZRsuOo91NlPOP/HDLzSSOiWyMMwt7pVhYFgUf++5w81XQYRHKbErkJ4xtQuj0NZi+qEHbL/yGgMdbbS0tNjyZxnSGf78l2uRrDFXBdrfmIJ+w7kXNC/7xZeAFgSExTxjGxqpREsLMhrHPHpm9kVcweHRaKGF8sfGgyarkPBI/pq7i9vP3qGro41l9gys6NcQH98gOs/eQWBIOGGRUVQrbcmYNlUBmLTpGPdevCcwNJwHLz9gkz8rfRrYMXLVYZ6986d2+SKM/SOmbJ3RayiWNwvn7jzHNyiM+nZFGfnFpD6fBISEM2LVIW4+eUNYZDSVrPMwoa0LCoU2kzcfY5v3TfR1FWhpabHr79akS4LZAK1yx0xJqh3PtMRf9laUK5SDl+8DUSpVaH/84+i7aB8T2rkyZq2Hulx6U0N1IgBQtlAOVhyMf3Y8TQkJDaVj35HcvPsAXV0dCuXPy7oFU3n95h1/9BxMQGAwYeHh1KjiwD/D+wIwbsYC7jx4REBQMPcePqF08aIM/KsDg8dP58nzV9SvUZVJw/sBMVfYJawKc/rCZT74BdC4djXGDe4VJ46AwCAGjZvGtVt3CQuPwLFiOaaOGoBCoWD8zIVs3LUfA319tLTg4IalmKdLeO6I7/HlF32FMja8eO2DUqlMcJlgC/N06kQAwLZUCRatifs45Jt37zl87BTz/hn503EmpfDQEOYN/5Pn92+h0NEle76C9J2+Br93Pswe2I7Q4EAiw8Mo5eBG6/4xz95vnjeBF4/uEhocyKvH98lvVZJ6nfqzZupQ3r18RnmXurQeMAGIufrPU6QEdy+fIcjfl4rVG9Kiz99x4ggJCmD1lKE8vXudyPAwrMs78MegSWgrFGyeP5GT+zejq2eAlpYWo5bvw9jM/KfP/cuZCMNCgkBLC5UqZq6TU+5b+WtCzBNI2fJYUsC6NJeOH6Ri9Yax6vDcvgbnBn/EW39C+5JLiiYDXnffExgWzbH+MSPT/UJiVqEzM9BhdbuSGOvrEBmtpMWyi3jceUeVwjEzyl19EYB7T1uM9XWo9u9pJuy/z38dShOtVFF+0nFa2+ZQz0x4900Qu/8qR3iUktrzz1Euj3mcWwN/77lDhfzpmd7YGqVSRfcN11h/7gW1imdhsfdTLo9wwFBXQVB4FAY6cRvzsXvvGbs3/tkPm5TJTpfKeeLd9y1vAsM5dv+D+nFEgLF1CtN25WVWnXqOX2gko2oWIpeFoXr/qtPPWOL9lJd+YcxsYo2Fcep5Rt3j8gMCQyM4PSvmOWu/oJgJmtIZG7BucFNMDPWIjIqm8fj1HL70AJdSMbc/rjx8hcekDhgb6OE0eBlj/vNk0/DmREUrKdV9Hm1dS6lnKrzz/B3uE9oRFhFF9eErKV84Z5xbAyNWHcLeKg//dquNUqniz393sNbzCnVti7Bgz1luLe6Nob4ugaHhGOrFTfy8rj5i1Or4Z0Ns7lSCv2rbJvpntMT9PNVKW6oTgeUHLlAkZ6ZYvQL/T6lUseLgBdzKFkr0cZPDoaMnCQgK4orHdgB8/QIAMDczZdvyOZgYGxEZGUntNt044HWC6k72AFy8dotTe9ZhYmyEbc3mDJ80m12r5hMVHUVh+5p0bNlIPVPhrXsPOLp9NWHhETjUb0OFMjZxrrgHjZtGZdsyLJzyN0qlkra9hrJy4w4a1HTh32VreXL+MIYGBgQGBWNoEHd57iPHTzNkwox4z7F14zr07tQmwZ/DgpUbqFGlcqxEYOjEmYyc/C8lrAoxYWgfcmTNEus9SqWSRWs2Uds17m2OtVv34OJQkSyZMiR43JR25cRhQoMCmbErJikN8o+Zit3INB2D523GwMiEqMhIJnapx2XvQ5Ss5ArAo5uX+WfjMQyMTBjcxJ51M0cxdOF2oqOi6OlmTdUm7dUzFb54cJtxa48QGRHGiFZVKFTSNs6tgdVThmJVthJdx85DqVQyZ3AHPLevxta1HvvWzGOR5330DAwJDQ5ET9+Q/3f1lCdrpw2L9xwd67Wi1h894t13aONS9q2dx/vXL+g6dj6m5jG/n3evnpMp++fFyDJky8n71y9ivdfvnQ/XTnvG+5hhQvuSU4omA9bZTLj3JpihO25hl9+CqkVivuyVKhi77x7nn/ihUsV8Kd54GahOBpwKZVRfBRfNaop1NhP0P35JF8hkzOP3oepkoGmZ7OgotNFRaFPPJgve9z/ESQYO3HzLpWcBLDoWs75BaKSSbOkMMDPQIW8GQ3ptvI5jwQy4Fs2kfpTxSw4FMyTL1MWbL7zCuVDGWIsWrTnznMals/GXY158AsJptOg8NjnNKJ07JjNtWyEXbSvk4tarQLpvuE5lS4tUkxAUy5uFu8/fMXCpO/bWeaj2cfa9aKWS0WuOcPbu85jft18Q1x/7qJOBKjb51XP1W+fOjHXeLOjr6qCvC5bZM/Dota86GWjhVAIdhTYmhno0tLfi2LXHcZIB9/P3uHj/FfN2nwYgNCKK7BZmmBnpkz9rerrN3YWzTX6ql7FUP9r4JacS+ZJlKuOtJ26w9fgN9oyN+XJ54uPHmiOX2Tcu4SuCwcsPYGygR2e3sgmWS2nFixbmzv1H9BoxEccKZalRNeaZ7WhlNEMmzOD0hSuoVCp83r7j6o3b6mTA1bGieo7/4kUKUtyqMPr6euijR6H8eXn45Lk6GWjTuC46OjqY6OjQtK4bXifPxkkG9hzy4tzl68xaEjPjZEhoKDmyZSGdqQkF8uSiQ58RuDhUpGZVB/WjjV+qWrlCoqcy3rRrPxt27uPI5hXqbctnTSBX9qxER0czZd4yWv81KM4jlH1GTcLE2Ii/2sVd02L1ph2MG9w7UfEkpzyFi/Pi4R2Wje+LVbnKlHaIma5XGR3N2mnDuXP5DKhU+L334fHtq+pkwMa+qvrKOk+hYuQpXBxdPX109fTJnrcgPs8eqZMBh3qtUOjooNAxwc6tMTfOHo2TDFzw2suDa+fZs+pfACLCQsmQJQdGJunImis/c4d1xsauKqUda6gfbfxSiYrOiZrK2LVZJ1ybdeLp3evMGdyR4hWd1QnBtxzdtQ4bexfMLOKOYUtoX3JK0WQgTwYjjvavyPH7H/C4/Y5/3O/h0bciC489wT8kkr3dy2Ogq2DA1puER32eXlb/i6tzhTbo6375+ttTDf8/FbDiDxvyZIg74cXe7uU5+8SPE/d9qf7vadZ1LK2+f/9JcvUMbDj/klG1Yn+RLTvxjDODY0bYZzHTx97SgtOPfNXJwCdFs5mSxUyfkw99qV089lWHpuTNkp6TM//k2LXHHL70gPHrPPGe/ifz95zBLziMQxPbY6CnQ5+FewmL/DzdqL5e7KmGDb6YWCrm953w1MP/T6WCtYMakzdL3KWlD05sz5nbzzh2/TFVBi9n8/DmWOeJ/fNLjp6BPWduM2G9FztGtVIvXHTu7nNefQikQt9FQEyS1GvBXka2dKJ1lZIAjFx9mAevPrB+SFN1b0JqkT9PTi4d3obHiTMc8PRm5JQ5XDy4hdlL1uDnH4D3zrUYGOjTbfBYwsIj1O8z+GLqWYVCgYF+7KmHo6ISnor2/6lUsHnJLPLnibtYzPGdazh5/jJeJ85SsVYLdq+ZT/GisXtYEtszsNP9CKOmzMV9/eJYV/G5Pk5prFAo6NGhFeNmLox1C2Hw+Oncf/SE7cvnxLmtcObiVXz9AqhRJfU9ZZMlVz6m7zzH9dNeXPI+yIbZY5i6/Qx7V88hKMCPCeu90NM3YPHfPYgMD1O/T1fv8204bYUi9tTD2gqif/j3rWLAvxvIkivu3Dfj13ly59Iprp85ytCmlRi6cEec+/CJ7Rn4JHehYqTPnI2b545j61qfjNly8vblM/WX+ftXz7EuH3sdC6/ta2jdf0K89SW0LzmlaDLw0i8McyNdalhnxrFgBkpNOIZfaOT/2jvruKiyNo5/hxm6MUFUxEQQTDAxsbu71t61de3ubl+7a3WxdXVVVMRObEBMEFG6a2beP0YHEaRE3V3O9/Phj7nn3HvOnTPc+5znPOf3EBGXSH4jbXQ0pQSEx3H68Qd6Vc1e1ifXOwG0si9AglzJsfuBjG9UIlWdRjb5WHXhJQva2CDVkBAcnUB0vBwzfU2i4+VUtzajurUZt16H8fRdVCpj4Ht4Bm6+DCMyLlHtDflEETNd3LyC6FjJgqj4JK6/CKXhR0+HV2AUpQuoXiSvQ2J5+DaSUvn/OdHl/sERmBro0syxNHUdrCk7YAWhUbGER8dTwNQAHS0Zb4Mj+OuWD30aZk9xcb/7A9pUL0t8YhKHrz5hcpc6qeo0rlyS5YeusKR/E6RSDYIjYoiKTcDMSJfouARq2Balhm1Rbnr78+T1h1TGQE57Bk7f9mHy9rMcnNKVIvlN1Mfb17Kjfa3kB9WXOyJm7TmP5/N37JvQKUVuhn8KfgGBmJkY0apRPVycq2FVxYWQsHDCIiIpmD8fOjra+L8L5PiZ8wzo3jFbbew5dIIOLRoRn5DAn8f/ZsbY1A/q5i61WbR2C6vnTkIqlRIUEkpkVDR5zUyJio7BuWplnKtW5todTx55PUtlDGTHM3Di7EXGzlrMyd3rsSqcvMSTlJREcGi42jj448hf2JUpoX7pT1mwkrsPHnN422q0tVN79Lb9cZiubZsjk/3zxjv4nT8GxqZUqd8C++r1GVSvJFHhoURHhmOaryBa2jqEBL7lltuJr+aJyAiP4/uo3rgdiQnxXD19kM7DUmtRVK7TjCObl9Bvygo0pFIiQoOIi47C0DQPcTHRlK1Si7JVauHjeYM3zx6nMgay4xnw832CZXGVzs17v5e8fOJJIesygErm+OyBLRS3q0jAq2f4PrzNsEXJniKvu9eIjYqgQq3U0ubplX1vfugv7Mm7KOaeUuUelyuUDK1rRUEjHX6pUYQBu+5TZ+kVzI11qFXCLIMrfZ0S+fVpsfamOoAwra2EM1uWZtZJH+ovv4pEIkFLKmFmi9LINCT02+VJXKIChVJJuUJGNLXLn+2+fM6hewHMOuFDWGwipx9/YPX5l+ztV1H9Mt936y0dKlkg/WK2t7yDLZOPPGX9pVckypW0diigXl5ZcuY5XoFRaEolaGhImN2yNKUKfD3N54/m8ev3zNx9HlCN98g21TE3M2Rg0yr0WeJK9VEbsDAzxNnOKtttlCyUl8aTtqkDCNPaSji3jwvTd7pRa+wmJIC2ppS5vRsik2nQa7ErcQlJKJRKHIoVpLlTmWz35XNcPR4xdec5wqPjOHnLm+WHr+I6uQtlCufjtzXH0ZJp0HtJcu70w9O6YWb4dWnWJ28+sOzQFUqYm9F40jYAiuY3YefvP1ZFLj0ePvVh8vwVAMjlcn4f0heLgvn5tU9Xug4eQ4UGbSlkXoC6NbIfY1G6eDFqt+mpDiBMayvh4mm/M2HuMio36oBEIkFbS4vFHyP9Ow8cRWxcPAqFggp2NrRuXD/bffmcAWOmoaUpo8ugMepjp/ZuQEdbi9a9fyMhMRGlUolFwfzsXL0QgMdez1i4ZjMlrYtSu41KadOqsAUHNi4HIDYujj+Pn+bSkV050sec5rXPI/YsmwqAQiGndb/RmOU3p0m3wSwb1YPRrauQp0Ah7KrWyXYbFsVKMaV7fXUAYVpbCXuNX8DuJZMZ264qEokETU1teo1fgFSmydKR3UiIi0WhVFDMpjyODVpmuy+fc2DtXHXgpIZUSu8Ji7Asrnp2tOgzgrWTBzKsiT0aUin9p69KsTxx4fAunFt2RUOaWko/vbLvzb9KjjgjfraWwL+Zf4IccVb52VoC/2b+CXLEWeVnawn8F/iZcsRZ5WdrCfxXyKwc8df3vQgEAoFAIMgV/PMWor6BgwP/WdHVgu/LsRnpb/ES/Lc4s3/zz+6C4Acybdupn92FXIXwDAgEAoFAkMsRxoBAIBAIBLmcn7ZM8E8I9hu+/yGXfEJoapef2a3KEJ+koPf2e3j6qZTTHk+rk6L+m5BYxh9+wqvgWDQ0JAyqVZSujoU4fj+Q5W7P1fXehsdTtZgpW3o6ALDy/AsO3g0gSaGkYmFjFrUrm0I7IS0y6svfjz8w86Q3coUS+0JGLOtgi55WcgSqUqmk06Y7PHwbqT735sswJhx+wqOAKJ7NrIt+GoJKP4p/QvDfr6uPceHBC1o4lWZ+30YALDt0mQOXHiGXK6hU0oJlA5uirSnjXWgkXefvJ0muQK5QUrJQHpYPbIqJgW66ZemR0XmnbnkzbacbSXIFDtYFWf1rC/S0NdPt5+v3YXRfeAAvvyDOL/xFLYv8I/gnBPj1GzUFN49rtGpcn2UzxxMfn0C7fsO5c/8xAG89L6Y6R6lU0qTrQO4/9lKXH/37PHOXryc+IQGlUkmvTq0ZOUAV8R8VHcOwSXO49+gpiUlJ9OncllEDe2XYN89HTxk+eR73Hj2lcd2a7Fu/JEX53BXr2XHgKAA9O7Rk4vCBGbYXExvL4HEzuf/YC6VSiW3pEqxbOB1DA30OnjjDjCVr+RAckuZ9fyv/hAC/tZMG8uDaeRzrt6TPxMUkJsSzaGgnnj+6A8Amj9fqutktA7h94SS7lkxCniTHumx5Bs9eh7bu13f/ZOaahzYuxuPEHyjkSZQoV4UB01ehqaXSXHjv/4rNs0cS+OYFGhoaNO81jHrtenHt70McXL9QfY2QQH/KVKrBmBV7ee//isXDOuPn+5T5BzwoUtKWrJDrPQO/1bVidivVlhCpBAY7F02ROOgTSqWSvjs96e5kicfYGlwcVY2GZVWGTHP7ApwdUU39V8hEh7blVUIjF7yDOXzvHSd/c+LS6OpoSjXYcOlVhv1Kry/R8UmMcX3Mjt7lufp7TQy0paxzf5mizpYrb7A0SamxX8XK5LsoJ/6bGdG6mtoQcPN8jqvHY87M7c215QPRlEn53/EbAOQx1OP4zJ64L+7P5aUDsMhjxKI/PTIsS4/0zouKTWDEupPsGd+R26uHYKCrxeqj1zLsZ5H8Jrgv7k9Bs9RKa7mFMUP6smzmeACkUg1GDujFX3vWf7X+2m17KWppnuJYwXx5OLhlJXfPHuTioR1s2HkAj+uqh/qC1ZvQ0tLk9t9/cu3EXvYcPM71O/cz7Fe+PGYsnDqaRVPHpCq7dP02rifOcPesK3fPuuJ64gyXrt/OsL1Nu11JSEjkzhlX7p49iFyuUOc3aNvMhSPbV2fiG/t30+qXUfSZuBhQiRY17z2cyZ8l/flEdsviYqJYP+03fl99QJVwSN+QY9tWZNiv9K7pefkcV04eYM6eCyw9egeZphYndqjGSqlUsmR4Fxq078Py43dZcuSW2uCq2rANC12vqv/yFLSkZjOVZkf+QkVZ6HoVs/zmqdrLDN9sDCw795ypx7zUn0OiEyg74wIxCXIuPQum+ZobuKy4Rt1lVzl8712a12i7/hZnnnxI83NgRDz9dnrSZNV16i67ygq3F9/a5a8ik2rgXDIPxjqpZ8zuPiHoa0tpYquaaUkkkhSywZ+47x/B2/A4taHwOCASp2Im6GmpEuHUK5OHg1/5HjLbFzevYBwsjdQSzD2qWnLEM1Bd/jwomiOe7/itrlWm7vtbWOzqwcRtZ9SfQyJjKNF3KdFxCVx88IKGE7dRe+wmaozagOvlR2leo8W0nZy+7ZPm53ehkfRa7EqD8VuoMWoDSw9e/m738vBlINVsCqOvo4VEIqFBheIcuPQQAE2ZVD0rl8sVRMclqBUA0ytLj/TOO3vPl/LFzdWyy30aVuTQlccZ9jMnmLdyA2NmLFJ/Dg4Nw8KhNtExMbh5XMe5dQ8cm3Skoks79h/9K81ruHT8hRNnL6b5OSDwA50HjqZGi65UdGnHgtWbcqzvXyKTyahfq6pa7vhLfF684sDR04wZ0jfFcccK9lgUVP2vGxsZUqZEMV77vwXg/hNvXJyrI5FI0NfTo5ZTJfYePpHq2l9iUTA/jhXs0xQXOnDsNN3atUBXRwddHR26tWvBgWOnM2xPIpEQExdHYmISiYlJRMfGYmmeNQVS1/UL2L5gnPpzZFgw/WoWIS4mmgfXzjO5Wz3Gta/OmDaOXD55IM1rzOjdmNsX/krzc+iHdywd2Y2JnWszpo0jhzYsSvMaOYFUJsO+Wt0UiYS+tezupb8pbltRLZHs0vEXrp5yTVUvK9d85fWAMpWqo6Onj0QioXxNFzxOqNJTP7h6Hh19A6rUbwGoxtg4T2oP3/PH9wgO9Kdy3WYZ9iUzfLOfuENFc5qtucHUpiWRSTU4dO8djcrmQ09LSjkLI44MroJUQ8KHyHgarbxOnVJZS7E77I+HjKhvTTVrUxKSFHTYeJvylkbULpVSA9orMIpf9z5I8xrOJfOoMxtmF+/30ZjqadJ/lycvg2OxyqPL9OalKfTF7Hvfzbe0LW+O1sdlAPtCRuy+4U9wdALGOjKOeQbiFxr7TX3xD4vF0jS5XUsTXd6Gq+Q+FQolo/98zNzWNmimkzEtp+jsXA6XiVuZ2aM+MqkGf3o8onHlkujraOFQrCB/zeqJVKrB+7Ao6o7bQn0H6wzd558zeNUxxravSfWyRUhIlNN65m4qFDenroN1inpP33xgwIrDaV6jjr21OtNhepS3NmfnuXsER8RgrK/D4StPeBMUnqKO85iN+AVFYFs0P7vHdcx0WXqkdZ5fUDiF8yU/RCzzGuMfHJHpfn4L3dq1oFbL7syfNBKZTMa+wydp7lIbfT09KtjZcN51G1KplMAPwVRr1hkX5xqYmmQ+81/fkZOZOHwAtZwqkZCQSOMu/alkb0sD55ReqyfevvQcNiHNa9SvVVWdzTC7KBQKBv8+gxWzJ6CZjsLf02cvuHH3gTprYMVyNhw8eYaWjeoSHhnFGfcrlLK2+qa+vPEPoHbV5N1QRSwK4vHRM5Bee/27tef6HU8KV1Rl63SpXZ3OrZtmqe3aLbsyqUsduo+eg1Qmw+PEfirVaYaOnj7FbMozc8cZNKRSwoICmdCpFg41GmBgnFra+2usmdCftoPGUbZyTZISE5j1SzOK21XCvnq9FPX8fJ+w8ve+aV7Dvlo9dTbDH01QgB95P0s8lDeNxENZxdq2PG6u24gIDULf0ISrpw8S9PYNoPoeDI3NWDqqO4Gvn1OgiDU9x84nr3lKVd4Lh3ZQs1knZJo5k4vmm40BS1NdShcw4JxXEI3K5ueP22+Z0bw0AMHRCYz88xEvgmKQaUgIjU3E90M0lYqaZOraMQlyrjwPJTj6qfpYVLwcnw/RqYyB0gUMvqsLXK5QcvlZKCd+c6Rkfn3Wub9i+P6H/Dkg+R84PknBoXsBuH62xbFmCTN6VytM50130JFpULOEGTKf76cp/z/3V1SzNsXOwpA3Id9mdGQGy3zGlCmcjzN3ntGkSin2nr/PnN6qhCRBETEMXXsc33ehyDQkhEXF4vM2hCqlvp6R73Oi4xK4/PgVwVui1cei4hLw9g9OZQyUKZzvmyWDnctZ8UujSrSbvQdtTRnO5ayQeaY0qNwX9ycxSc74LX+z7cwdhrWqlqmy9MjqeZnp57dQpJA5NqWK85ebBy0a1mHngaMsmjYWgA8hIQwYO5VnL14jk8kICYvA+/lLnCraZ+ra0TExuF+7RVBIqPpYZFQ0T589T2UM2JQqnu2EQZlh6frt1KpaCQfbMrx8k/bDPSDwA+37DWfl7IlqT8HYIX0ZP2cZ1Zp3Ja+ZKc5VK6e4n5wmvfbOeaiWjl7dOgdAz2HjWbp+e6ZiGD6R17wwliVsuHvpNJXrNuPikd30/H0+ABGhQaybMpiA175IpTKiwkN5+9KHUg6Ombp2XEw0j29dImJeUPKx6Cj8nz9NZQxYFrfJVsKgfyN2TnVo2Lk/cwa0QktLB7uqtbl/RfU6VigUPLzhzpw95ylkXZrj21exdtIApm45qT4/MSEejxMHmLY1bc9cdsiRCLKOlSzYfzuAIqa6RMYlUbWYCQDjDz2lYdl8bOnhgEQiocaiyykSEKk7oSFB8ZkQYnyiqo5CqUQigb+GOqEpTf9h9709A5YmOthbGlLyo/Z/+4rmLDrjm6LOXw/fU8RMN1Uug/41i9C/ZhEAjnq+o+Q3SgYXMtHlsm/yw8cvLBYLY5Wn4NqLUB4HRHHgTgByuZLw2ESqzL+E24hqGKax5JATdKljz96L9yma34SImHiq2ais6DEbT9G4ckl2jG2PRCKhyrD/EZ+YOgmJTKqB4rNkU5+SFimUSiTAufl90ZSlL8+ZE54BgEHNHBnUTPWgO3TlMaUt86aqoymT0rmOPSPWnUj14k6vLD2+PM8yrzEeD5NjS/yCwimUJ3n2nZl+fgs9O7Rkl+tRihUpRHhkFDUdVbkjhk6cQ3OXOuzfsAyJRIJt7RbExcenOl8qlaYc0491FArV//SVY7vR1EzfQ/i9PQMe12/z8KkPu12Pk5SURGh4BKWqN+HW6QMYGRrwPiiYJl0HMHpQb9o1T9aK19PVZeXs5MQ2QyfNwaZk8W/qS+FC5rzyD1B/fv32HZYfExyl197GXQfo3q4FOh9TMXdo3ohdB49nyRgAqNOqOxeP7CZ/oaLEREZgU0mVUXLzrBFUqtOU0Sv2IpFIGNGsfIqkQ5/QkMlQKpOf7QkJqvFWHZMwd587sgzG+5/qGchrbsnjG+7qz0EBfuQpmLkJTXo07fErTXv8CsDVU65qOeO85pZYly1PIWvVpLpW807sXz07xbk3zh6lgKVVqjwL30KOvB2a2uVn2nEv1l16RcdKFkgkqplveFwihU11kEgkXPQO5kVwTJrnW+XR5d6bCBqVzY9XYBSPAiIBMNCW4WRlyqrzLxnVQDUT9A+LQ1MqIb9hylSz39szUK9MXuae8iEwIp4CRtq4eQVha57ypb73lj9dqqT+kbyPjCe/oTZhMYmsupB8LwA1F1/mQP9KmBvrpDrva9QtnYeJR57yPCga67z67LzmR0t71Trhzj4V1PXehMTSaNV1bo6vldXbzRLNncowadtZ1hy7Tpe69snjHx1HkfzGSCQSzns+5/m7kDTPL1bQlDu+b2lSpRRP33zg4UtV/IOhrjbVbIqw/PAVxrZX3YNfUASaUg0KmKb87nPCMwAQGBpFAVMDwqJiWfFFuyb6OhjoaqFQKDl27ak6Uj+9MgCn4es4NLUrFnlSutLTO69+eWvGbT6Nb0AIxc3N2Pr3HVpXt8mwnzlF6yb1GTtzMcs37KBnh5bJYxoRSdHCqv/xs+5X8X35Js3zi1sV5vb9h7RoWIcn3r54PlbFFRka6FOjSkUWrd2ijpZ/8/YdmjIZBfOnNGi+t2fg8Lbk4LqXb/yp3rwr3ldUM63g0DCadhvE4N5d6NO5bYrzIiKj0NSUoaujw4Mn3hw95cbVE3sBuHnvAZPnr+T0vo1Z6ku7Zi6MmraAwb06AbDb9Zg6CDK99qyKFOKM+xXaNW+IUqnk74uXsS2VdcPEsUFLti8cx/Htq6jdupt6vKMjw8lXqCgSiYT7V9x499o3zfMLFrbG9+FtKtdthp/vE149VQU46uobYlOpOkc2L6HdINX9BAX4IdPUxCRvytiGf6pnoHxNF7bOGU3Aq2eYFy3Bmf2bqdoo+TcxskUFpmw6gVkBiyxdNywoEJO8BYgKD+Xw5qW0//j9lK/ZkL3LpxP64R2m+Qpyz+MsRUuXS3HuhUM7qdM2Z0XXcsQY0NOS0rhsPvbdeqtOtwswqXFJJhx+wuIzvjhYGlO2YNoz4l9rW9F/931OPf5AOQtD7CySZ9Zrutgx7ZgXdZepfiQGWlKWdrBNZQzkFI1XXScgPI7w2EQqznGnbuk8LGmv2rY3u1UZum25gxIw1dNkecfkrRv+YXHcfBnGhm6p3aWdNt1BoVCSpFDSp3phdRBiUFQCoTGJX42h+FpfDLRlLGprQ8+t95ArldhZGDKrpdX3+DoyhZ62Jk2qlGTP+fvcW/Or+vjUbnUZu+kU8/9wp0IJC2y/ss1tWKtq9FlykJM3vHGwLkg5q4LqsvXDWjFp+xlqjNoAgIGuFquGNE9lDOQUbWftQaFUkiRX0K9xZZo5qqzzZ2+DmbLjLEqlymNRzqoA8/s2zLAsKDyakMhYTNOIk0jvPENdbZYNbEKXeX8gVygpV6wA81pUzbCfOYWeri4tGtZh+/4jeF1Odk/OHj+cYZPnMmvp/6hsb5sq498nRg/qQ9fBYzh6+jwV7Gwob5ucAGr7ynmMnbmIii7tVPdqoM/6RTNSGQM5RfXmXfEPCCQ0PAJrRxca1qnBuoXT0z1n0dot+Dx/xabdf7Jp958A/Na3K706tubFaz+6DhmLTCpDR0eLbSvnqpcQXvsFoKuT9rPp5Rt/6rXrTUxsHHHxCVg7ujBl1GD6dG5L7WpVaN24PuXrq14y3du1wPljDEF67U0eMYgh42dSoYHqPPuypRk/NOtGsbauHpXrNufC4Z2sPp0c6Nt1xEw2zx7JgTVzKG5XiSKl0p6Jtuw7kmWje3DT7TjFbMpjZeOgLhu6YAvbF4xjTBuVJ0tXz5BBs9amMgZyigmdnAkJ9CcqIozB9UvhUMOFQTPXZLtMV9+Q/tNXseDXDijkcqxs7OndW7W9LyLkA5FhIV+NoUivvdn9W6BUKJAnJdGoywB1wKCOnj59Ji5m3qA2KJVKDE3MGDJnnfqaQQF+eN27xoglO3L0e/tPJSrKKsP3P8TB0oi+1Yv8lPZPPAzEOzCakfWtM66cg5iPO5NKZ+DfmKjoW/l19THKFy9I/yZVvlsbx64/xcsviDHtfmw+eochq9k7vuNXdQb+jYmKMkO/UVOoaF+WIb27/JT2R06dT/vmDanhmL2U3DnNJ4/HJ52Bf1OiosywdtJArG0r0LjroJ/S/vUzR/B7/pR2A8dlXPkH8VvDsvy+5oBaZyCziYr+U7kJsoqhjozNl9/w/EOMWmvgR9LMrgDNcm7JJ0M+iQ7lM9BSuwFzM4Z62mz46xa+ASFqrYGcpoVTGVpkP2NvlvkkOpSUJEfzJ6RB/dkYGRqwZusefJ6/UrvZfyQ/o82vcfDEGWYvX0f+vNlPCf9PR8/AiFO71xHw8plaa+BH4uTSCida/fB20+KT6FBSUiIyWeZ37H3iu+49qzL/Ek/fRX3PJrLMm5BYzMedYdyhJ8xuWYbLY2swsFZRys648E3XvecXzpCvBDB+2f7X2noXEUe79bfUn83HnSE6PnXAHUCD5VeJTZQDsOHSK4KiEjJs+5Po0P0ptVOoFeYUDkNW8/j1+xy/7rfw+n0YZh3mMHrDXymOlei7lPl9G3Jz5eBsGQJ3fd9+NWDxy/ZL9F2aZllASCQtpyfnqjfrMIeo2LTH0XnMRmLjEwH434kbfAiPTrPeJ9GhRxuGU7JQnjTr5BSlqjfhkZdPxhV/IMP6defZi9ckJiX/37x844+Fw7epIt72fESvrwQzfk56bb19956GnZK9L9pFHIiKTjuOqkrjjsTGqQL1Vm7axfug4Cz3uW0zF+6cceXeuUNZPvdb+a1hWV77pK0rkpP0nrCI5SfuZcoQeO//in41M+cF7mRnQFxM2u+u39tVIyFOtVMrvfucN7gt716rlGkvHN7F25ff93/lk+jQOjcfLIplPWA+VyoQ6mtJOfXoPS+/EtCYHcpbGrO2S7mMK6ZDQSOdFNsS0+PsiGroaqpe6Bsvv86UMfAlSfLUOzv+ixjoaHHyphcv3uXc9q8KxS3YMLz1N13D3MyQo9O7Z6qu++L+6H4UKFqXjjGQHrlmvPX1OHb6/FeDG7NDJQdbtq+c903XsCiYn7//yFzmxZun9qOrowoqXr1ld6rlu8yQlJT2RELwbSx0vYqWTsZaKRP+d5CCRVRLwBcP7yLg5bMstyX/gWOYI8sEt16FMfOkj3oWO6VpKep8oQOwzv0lhz0DkSuUaMs0mN/GBjsLQ2IS5Azf/xCvwGg0pRKK59VnQ3d7nn2IZsT+R8QmypErlHSqZMHg2lY50V20ZBoMdi7KgtPP+F/X1AF/bl5BzDv1DLlCSR59LRa2taFYXj2u+IYw9ZgXFQobc/t1OBIJ/K9LOUoVMOCKbwgzTnhzelhVdRR/DydL3LyCiE2Qs6R9WZyKJQeZzDv1LFXZp/M+z0Ow9uIrTj9+T1yigvGNS9C8nCro5tO6/8bLr1Uqjbs80ZFpsKZLOSxNdJh0xIt7fiohmg4VLfi1juq7a7v+Frbmhtx5HY6JnozdfbO+tnnDy49pO88RFacyQGb0qE+9L/b9rz52jUOXH5MkV6CtKWNJ/8aUK1aQmPhEhqw+ytM3QWjKNChhkYeto9ri4x/Mr2uOEZuQiFyhpEsde4a2rJpW81lGS1PKry2rMmffBTaNaJOq/OxdX2btOY9coSSvkR5LBzTB2twMj0evmLj1byqVLMRNb38kEtg0og2lLfPi8egVU3ecxW3BL7x+H0a98Vvo3aACZ+76EhufyMrBzalqkyxUMnvP+VRln857tiV5i9yqo1f566Y3cQlJTO5al5ZVVctXZh3m8HrHWNafvMG7kEj6LDmItqaUDcNbY5nXmHFbTnPXV7U1rXPtcuptjS2m7cTOqgC3fPwxNdBl/8TOWf7+rt32ZMKcpUR+nMXOmzQSF+eUMSTLNmznwNHTJMmT0NHWZtWcSTjYliEmNpZfRk7hsbcvmpoySllbsed/i/DyfUn/0VOIiY1DLpfTo0OrLG+H+xraWlqMGNiT6YtXs3P1glTlpy9cZsqCFcjlCvKambJm/hRKWBXh4tWbjJmxiCrl7bh+5z4SiYSdqxdgU9Kai1dvMn72Uq6e2Kteh+/XrT2nzl8iJjaO9Qunp4gTmLpwVaqyL9fvAZau28axMxeIjYtj1u/DaNO0geoeijgQ/OQqq7fs5m3gezoPGoOOtjY7Vs6jcCFzRk6dxy1P1Yy0W7sWjBncB1Cp49mXLc2Nu/cxNTHm6PY1OfKdfg3ve9fZtWQysdGqHWDdR8/BoUbKbbzHtq3kyl9/opAnoamtQ78py7EqY098bAxrJg3A79kTpDJNLIqVZOSSnbx94c3ayYOIj41BoZBTp1V3WvQZnuN972RnQOdh07hx7hhR4SF0Hz0bJ5fW6vK/dv0vzbJOdgZsv/EOHT1VELPH8T+4f9WNmMgImvYYoo5f+LR27/vwDr6P7rJt/lj+WDWT7mPmYufozO6lU7h3WaXeWr6GC91GzUJDKmXtpIFoSGUEvPQmNjrqh+2w+GZjIDQmkb47PNncw4EqVibIFUoi41JbM+0rWjDI2QoAd59gxh18wonfHLngHUxknBz30aqHS1iMyhW67eobGpbNx7CPgYqfjn9Jv52eX53hH/vVUT17/pLe1QpTc/EVHr6NTCH5GxSVwNA/HnJwYGVKFzBgzw1/ft33gJO/qRZ+vQKjWdbBlkXtyrLc7TnL3V6k6REIjUmkclFjJjQugevdAOb85cPRIY4Zln2JVEPlBXj2IZqWa29StZhpChnkEfWs2X3Dn03dHSjzcbfG7JM+KJRKzo+sRlS8nOZrblCmoAH1y6iitV+HxHBkcGVkGWg3pEVoZCw9F/3J9rHtcSptiVyuIDI29T7zzs7l+O1j9PuF+y8YtfEvzsztg9s9XyJjE7i2XLWtLCxK5W7bfPo2TaqUZGSbGimOf0mvxa68+MoWxdNzeqtnz1/Sr1FlHIf/jwcv3mGsn7yN80N4NINXHeXYjO6UKZyPnefuMWDlEc7OUz1cn/oFsfrXFiwb2JQlrh4scfVI0yMQEhlLldKWTO5alwOXHjJ9txunZvfKsOxLpBoauC/uj49/MI0nb6eaTWHyGScH/YxuV5Md5+6xdXRbdXDg9F1uKJVKLi/pT2RsAo0mbcOmSD5cKqjkU18FhvHXrF7ZGu+QsHA6DhjJH+uXUq1yeeRyORGRqb0S3du1UCfyOXfpGr9NnM2lI7s4c/EKEVFReLqpXNWhYSoVxfU7/qB5gzr8/tsvKY5/SeeBo/F9lfYM3/3wDvXs+UsG9+xMubqt8Hz0NIUU8fugYPqOmMTZ/ZuxKVWcrfsO0nvYBDyO7gbgsbcvGxfPYO38qcxftZH5qzam6REIDg3DqaI9M38fyt5DJ5g0bwUXDm3PsOxLpFINbp7aj5fvS+q06UkNxwrkz5s8kRo/tD9b9h5k37rF2JZWJfWaOHcZCoWSO2dciYyKxrl1D+zKlKRxXVWQ6ovXfpx33YYsHVXFnCAqPITFw7swevkeSleoikIuJyYq9TjWbtmFFr2HAXD/6nk2zhzOnD3n8bx8ltioSJYevf3xeirP3d/7NlKpTlPa9B+T4viXLB3ZTe2G/5LZu90yNXvXNTBk3h/uPL1zleVjeqYwBtIr+5zw4A/M3+9BWFAg4zvUwKZSzRT7/+u26YH7kd0pkjr9vW8jL73us+CASl593qA2nD2whYadVTtBXj29z7Rtp9DR+3rAX07zzb+W26/CKFVAnypWJgBINSRpbpW77x/ByvMvCItJREMi4XmQ6gVua26Az/toJhx+QnVrM/ULq2oxU2af9CE2QU6N4mbUKJ721o1NPRzSPJ4ROppSRtYvxrxTPsxvnbx/+87rcGzNDSn9URioc2ULJhx+QtRHr0fxfHqUK6TaL16piDFnHgelvjiqpYhPGRkrFTFmxnHvTJV9ySfdghL59ClnYcjt12E0Kpt+Jjr3Z8HMalEaiUSCoY6M1uULculZsPq7bVPePFsvBoCb3n6UtsyLU2mVNKZUqpGmvPC95+9YdugyoVFxaEgk+Aao1jztrArg7RfE2E2nqGFblIYVVS+s6mWLMH3XOWLiE6lla0Utu6Jptr99TLts9VtHS8aY9jWZtfcCi/s1Vh+/7eOPnVV+yhRWjUe3ug6M3XRKbeCUsMiDfTHVVsfKpQpx6nba634GOlrqDIyVSxZiyvazmSr7ku71VL/nkoVU7d7y9qdJlfTX/y7ef8G8Pg2RSCQY6WnTrqYtF++/VBsD7WrZZnu8r932xKakNdUqlwdUYkJpSQ/fefCEhas3ERIWjoaGBj4vVIJJ5WxK4/XsBcMmz6V21co0qa/SQ6jlVIkJc5cRExdL7WpVqFM9bWP4y8x+mUVHR5sJwwYwZeGqFII9N+4+oJxNKWw+7sXv1bE1wybPJTJKZeCUsi5KeTvV88Cxgn2K/AqfY6Cvp87O6FjBnnGzl2Sq7Et6d1Z5qkoXt6K8nQ3X7zygRcM66d6bm8d1lkz/XTXehgZ0atUEN49ramOgc+sm390QAPC+dwPL4mUoXUFl9GtIpWlusXv++B6HNi4iKjwUDYkGAa9U7vKipcvh/9yLzbNHUrZKLSo6q/4vbSrXYPeSKSTExWDr6IytY9oxGKOW7f7me6jepD0ApRwcCX0fQEJ8HFraOhmWfU7dtj0BMMlbgArOjXl80z1DMaAH185Tp1V3tZRwndY9uHHuqNoYcGrY+ocaAvCDdhMkJCnov+s+hwZVxr6QEe8i4qgw5xIARfPocXF0NS49C8HtaRDzTvngNrIazcsVoHJRYy56h7Dqwgv23vJnTefUM/DsegYAOlcuxP/cX3HtZebXkj9PPSyVSEhSpL0Oq5WqnjJTZT8Cfe3vG2WekCinzxJXjs/sgYO1OQEhkdgOXAmAVQFTriwbgPuDl5y968vsPefxWDKAllXLUKVUIc57Pmf54SvsPu/J+mGpo3Sz6xkA6FbHgTVHr3P1SebXknU++/1INTSQf2XdXStFvZS/i/TKfgQGOjmjXf41EhIS6TJoNOcObKVCORvevntPMUeVJLV1UUvunj2I2+XrnD7vwZSFq7jz95+0adoAp4r2nL10lUVrt7B9/2G2rUg9A8+uZwCgV8dWLNuwncs37mT6XnS0kzUCpFINkpLkadbT1tL6ar30yn4E+nrpp9b9kSQlJrB0ZHembz+NddnyhLwPYHA9lWFcoHAxlhy5ycNrF7jr8Tf7Vsxg0aHrOLm0pqSDE/evnOPIpqWcP7iToQtSx1rkhGfg08td4+POG4U8KVNl35sfbQhADhgDlYqa4P3nY269CqNy0eRlgs+9A/FJqnztnyRzt131U5e9DYvDRE+TJrb5qV0yDxXmuBMWm0hseDxFzXTpVNmCYnn1GHkg7YjN7HoGQPVgHt+wBDNPJs/MKxYx5tGfkfi8j6Zkfn32336LnYURBto/ZxfmvltvGVnfmudB0Tx4G0mlIiap6hhqy4j4bGnGuUQe9t58i6OVCdEJco54vvtmOeZPVClliZffCW54+eH42TLB596BuMQkkhQKtXzultO31WX+wRGYGujSzLE0dR2sKTtgBaFRscTGJ2JVwJSudR2wNjdj6NrUaT8h+54BUD2YJ3auzbSd59THKpcsxNCXJ/D2D6JUobzsvXifcsUKYKj7fUStMmLPhfuMaVcT34AQHrx8R+U08jgY6moTEZO8NFPbvhg73e7hVMaSqLgEDl5+zMwemZNfzoiqlRwY9Ptzrt32pGolB/Uywefegbj4eJLkciwtVPEsn1LoAvgFBGJmYkSrRvVwca6GVRUXQsLCiYmLw7qIJT07tKKEVRH6j5maZvvZ9QyAyosxY8xvjJ+bvJvDqaI9A8dO5+mzF5QpUYydfx6lvG0ZDA1+/MMXYMf+I0wYNgCfF6/wfPQUp4qpJzyGBvqERyRHtter6cS2Pw5RvUoFoqJj2H/0FPMnj/6R3QagVHlH/KY+xfvedUqVd1IvE3zuHUiIj0MhTyLvR/nev/dtUJcFv/PHwNiUKvVbYF+9PoPqlSQqPJSEuBjyWxajTuvuFCxSnHVTBqfZfk54BnKCi0d2U6ZiNSJCPnD30mmadEuteaBrYJRiCaVc1bpcPLKbao3bqa/h5PJztyh+8xvOVE+TzT0cmH7cm5gEORoSmNqsFM4lk9e9DHVkjHUpTpPV1zHV01QHwQE8eRfF3FMq16tcoWRoXSsKGumwwu0FB+8GqGfRM1vkrMLaJ5rbF2D1xZdExavWqPMaaLGqkx1D9j5QBxCu7vwDxQC+QK5Q4rLiGrEJcha2tUkzbfIvNQoz8sAjdDVVAYQj6xdj4pGnatXG9hXNqVc6Z9TdTA112T62PZO3nyUmPhENCczs2YA69skiVEZ62kzoVJv6E7ZiZqBLy2rJGg6PX79n5u7z6nsb2aY65maGLD14mQOXHqIlkyKRwNw+LjnS3y9pVc2GlUeuqoMf8xrr87+hLRmw4jBJclUA4fqhP++fMkmuoPbYTcTGJ7J0QNMU8QKfGNC0MkPXHkdXS8aG4a0Z064m4zafpsZolQRuJ2c7GlT4Nq38T5iZGPPH+qX8Pmsx0TGxaGhoMH/SKOrXSg7uNDI0YOqoIdRo0Q0zE2PaNk0eu4dPfZg8X5X7XS6X8/uQvlgUzM+C1ZvYe+gEWlqaSCQSlkz/PqItbZu5sPh/W4mKUnkP8+UxY8vyOfQaNp6kJDl5zUzZumLud2k7MyQlyXFs0pGY2DjWzJuSIl7gE7/26Ur/MVPR09Vlx8p5TBw+kBFT5qkVHLu1bU6jOjV+dNcxMDZj9PI97Fg0gfiYaCQaGnQfMxf7anXVdfQMjOjw22Qmdq6NgYlZinX31z6P2LNMZQQqFHJa9xuNWX5zDm1YhMeJP1QudImEXuMX/uhbyxKGJnkY37EmMZERtO43Ok2Vxvrt+7Bz8USObV1O9zFzadChL+/ePGdcB1WsnEONBtRv3+dHdz0FuVqBUJBMblQgzM38VxUIBenzX1MgFGRMZhUIsxVV9GNXuAU/gvTGVClG/D/Hl2OatSmB4N+PGPDcQ+bGOsvGgJGRIR8isy5wI/hn8z4yHqPPtmB9wsjQkPdhWRe4EfyzCQyNxshQte5vZGREYDYU7gT/PnS0tdHQ0CAs+MPP7orgBxEW9B6ZTIaubvoBlVk2Bho0bMyJR0F8iEy9t1zw7+RDZDwnHgXj0qhJqrIGDRtx7Lo378P+WbLSguzzPiyKYze8cWmkkmFu0MCFQ3+dJfCDMAj+6wSFhKGlpcWFQzuIixFG/n+duJho3I/spl69+mhopP+6z3LMwLNnz3CuWYOoiDBqFTfBWEeGyHnz70SphPC4JC75hmFgZIK7x2VKlCiRos6zZ89wrlWT6MhwnO2sMNbTFuP9L0WphPCYeNwfvkTf0Bj3Sx6UKFFCNcbOtYiOiqJeTSdMjAxFIqv/GEqlkrCISNw8rqOppUVsbDxauvqUdXRGW/fn7KQQfF/iY6N5fMOdxPgYzp09i5NT+hnTsmwMALx+/ZrNmzdzyf0iMdHRYk35X4oECXr6+tRyrs0vv/xCkSJpJ/FIHm93YmKixQLzvxWJBD09fWo5O6cab/UYX7pETLSYMf4X0dPXp1atWvzyyy9ERUWxdetWrl+/QVy88PL+F9HR1sbJyZE+ffpQtmzZDOtnyxgQCAQCgUDw3yFXZi0UCAQCgUCQjDAGBAKBQCDI5QhjQCAQCASCXI4wBgQCgUAgyOUIY0AgEAgEglyOMAYEAoFAIMjlCGNAIBAIBIJcjjAGBAKBQCDI5QhjQCAQCASCXI4wBgQCgUAgyOUIY0AgEAgEglyOMAYEAoFAIMjlCGNAIBAIBIJcjjAGBAKBQCDI5QhjQCAQCASCXI4wBgQCgUAgyOUIY0AgEAgEglyOMAYEAoFAIMjlCGNAIBAIBIJcjjAGBAKBQCDI5QhjQCAQCASCXI4wBgQCgUAgyOUIY0AgEAgEglyOMAYEAoFAIMjlCGNAIBAIBIJcjjAGBAKBQCDI5QhjQCAQCASCXI4wBgQCgUAgyOUIY0AgEAgEglyOMAYEAoFAIMjlCGNAIBAIBIJcjjAGBAKBQCDI5QhjQCAQCASCXI4wBgQCgUAgyOUIY0AgEAgEglyOMAYEAoFAIMjlCGNAIBAIBIJcjjAGBAKBQCDI5QhjQCAQCASCXI4wBgQCgUAgyOUIY0AgEAgEglyOMAYEAoFAIMjlCGNAIBAIBIJcjjAGBAKBQCDI5fwf9FaNRsRC3aAAAAAASUVORK5CYII=",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plot_tree(tree_clf, feature_names=['MolWt', 'logP'], class_names=['Noninhibitor', 'Inhibitor'], rounded=True, filled=True);"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lvPHvBR-kMIJ"
      },
      "outputs": [],
      "source": [
        "export_graphviz(\n",
        "  tree_clf,\n",
        "  out_file='tree.dot',\n",
        "  feature_names=['MolWt', 'logP'],\n",
        "  class_names=['Noninhibitor', 'Inhibitor'],\n",
        "  rounded=True,\n",
        "  filled=True\n",
        ");"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ugMlR_iBkdy7"
      },
      "outputs": [],
      "source": [
        "!dot -Tpng tree.dot -o toy_DT.png"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7Rd1RD1ikstq"
      },
      "outputs": [],
      "source": [
        "!dot -Tpdf tree.dot -o toy_DT.pdf"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "g6DBCZ6phYeX"
      },
      "outputs": [],
      "source": [
        "val_df['Predicted Class'] = tree_clf.predict(val_df[['MolWt', 'logP']])\n",
        "val_df['Probability Estimate'] = tree_clf.predict_proba(val_df[['MolWt', 'logP']])[:,1]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9Up0D_EKdqv_"
      },
      "outputs": [],
      "source": [
        "val_sample = pd.concat([\n",
        "  val_df[(val_df['Predicted Class'] == 1)][:10],\n",
        "  val_df[(val_df['Predicted Class'] == 1)][11:16],\n",
        "])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 537
        },
        "id": "j9I9HrfUgAKV",
        "outputId": "10c18827-58dd-4001-823b-d8c67cea9c7d"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-03fe78b2-e926-4978-87b9-5b421452e2ae\" 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>Drug</th>\n",
              "      <th>MolWt</th>\n",
              "      <th>logP</th>\n",
              "      <th>Predicted Class</th>\n",
              "      <th>Probability Estimate</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>COc1ccc(C(=O)N2CCC[C@@]3(CCN(Cc4ccccc4)C3)C2)cc1</td>\n",
              "      <td>364.489</td>\n",
              "      <td>3.82350</td>\n",
              "      <td>1</td>\n",
              "      <td>0.639259</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>COc1ccccc1CN1CC[C@@]2(CCCN(C(=O)c3cccc(F)c3)C2)C1</td>\n",
              "      <td>382.479</td>\n",
              "      <td>3.96260</td>\n",
              "      <td>1</td>\n",
              "      <td>0.639259</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>N#Cc1ccc(CN2CC[C@@]3(CCCN(C(=O)c4cc(C(F)(F)F)c...</td>\n",
              "      <td>495.467</td>\n",
              "      <td>5.72418</td>\n",
              "      <td>1</td>\n",
              "      <td>0.639259</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>O=C(c1cc(C(F)(F)F)cc(C(F)(F)F)c1)N1CCC[C@@]2(C...</td>\n",
              "      <td>470.457</td>\n",
              "      <td>5.85250</td>\n",
              "      <td>1</td>\n",
              "      <td>0.639259</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>COc1ccc(C(=O)N2CCC[C@@]3(CCN(Cc4ccccc4OC)C3)C2...</td>\n",
              "      <td>394.515</td>\n",
              "      <td>3.83210</td>\n",
              "      <td>1</td>\n",
              "      <td>0.639259</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>N#Cc1ccc(CN2CC[C@@]3(CCCN(C(=O)c4cccc(F)c4)C3)...</td>\n",
              "      <td>377.463</td>\n",
              "      <td>3.82568</td>\n",
              "      <td>1</td>\n",
              "      <td>0.639259</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>COc1ccc(C(=O)N2CCC[C@@]3(CCN(Cc4cc(C(F)(F)F)cc...</td>\n",
              "      <td>500.483</td>\n",
              "      <td>5.86110</td>\n",
              "      <td>1</td>\n",
              "      <td>0.639259</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>COc1ccc(C(=O)N2CCC[C@@]3(CCN(Cc4ccc(C#N)cc4)C3...</td>\n",
              "      <td>389.499</td>\n",
              "      <td>3.69518</td>\n",
              "      <td>1</td>\n",
              "      <td>0.639259</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>COc1ccccc1CN1CC[C@@]2(CCCN(C(=O)c3cc(C(F)(F)F)...</td>\n",
              "      <td>500.483</td>\n",
              "      <td>5.86110</td>\n",
              "      <td>1</td>\n",
              "      <td>0.639259</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>COc1cccc(CN2C(=O)N(C(C)C)C(N(O)C(=O)NC(C)C)C2(...</td>\n",
              "      <td>422.526</td>\n",
              "      <td>3.26370</td>\n",
              "      <td>1</td>\n",
              "      <td>0.639259</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Cc1ccc(SCC(=O)Nc2cccc(NC(=O)c3ccco3)c2)cc1</td>\n",
              "      <td>366.442</td>\n",
              "      <td>4.57112</td>\n",
              "      <td>1</td>\n",
              "      <td>0.639259</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>CCOC(=O)c1cnn(-c2nc(-c3cccc(F)c3)cs2)c1C(F)(F)F</td>\n",
              "      <td>385.342</td>\n",
              "      <td>4.33040</td>\n",
              "      <td>1</td>\n",
              "      <td>0.639259</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>CCOC(=O)c1cnn(-c2nc(-c3ccc(F)cc3)cs2)c1C(F)(F)F</td>\n",
              "      <td>385.342</td>\n",
              "      <td>4.33040</td>\n",
              "      <td>1</td>\n",
              "      <td>0.639259</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>Cc1ccc(OCC(=O)N/N=C\\c2c(C)n(C)c3ccccc23)c([N+]...</td>\n",
              "      <td>380.404</td>\n",
              "      <td>3.23244</td>\n",
              "      <td>1</td>\n",
              "      <td>0.639259</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>Cc1ccc(CNC(=O)Cc2csc(Nc3nc(=S)[nH]c4ccccc34)n2...</td>\n",
              "      <td>421.551</td>\n",
              "      <td>4.65981</td>\n",
              "      <td>1</td>\n",
              "      <td>0.639259</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-03fe78b2-e926-4978-87b9-5b421452e2ae')\"\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",
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              "  </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-03fe78b2-e926-4978-87b9-5b421452e2ae 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-03fe78b2-e926-4978-87b9-5b421452e2ae');\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-7c53f6d6-7510-4e17-98f8-5487a1b7c2b1\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-7c53f6d6-7510-4e17-98f8-5487a1b7c2b1')\"\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-7c53f6d6-7510-4e17-98f8-5487a1b7c2b1 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                                                 Drug    MolWt     logP  \\\n",
              "0    COc1ccc(C(=O)N2CCC[C@@]3(CCN(Cc4ccccc4)C3)C2)cc1  364.489  3.82350   \n",
              "1   COc1ccccc1CN1CC[C@@]2(CCCN(C(=O)c3cccc(F)c3)C2)C1  382.479  3.96260   \n",
              "3   N#Cc1ccc(CN2CC[C@@]3(CCCN(C(=O)c4cc(C(F)(F)F)c...  495.467  5.72418   \n",
              "4   O=C(c1cc(C(F)(F)F)cc(C(F)(F)F)c1)N1CCC[C@@]2(C...  470.457  5.85250   \n",
              "5   COc1ccc(C(=O)N2CCC[C@@]3(CCN(Cc4ccccc4OC)C3)C2...  394.515  3.83210   \n",
              "6   N#Cc1ccc(CN2CC[C@@]3(CCCN(C(=O)c4cccc(F)c4)C3)...  377.463  3.82568   \n",
              "7   COc1ccc(C(=O)N2CCC[C@@]3(CCN(Cc4cc(C(F)(F)F)cc...  500.483  5.86110   \n",
              "8   COc1ccc(C(=O)N2CCC[C@@]3(CCN(Cc4ccc(C#N)cc4)C3...  389.499  3.69518   \n",
              "9   COc1ccccc1CN1CC[C@@]2(CCCN(C(=O)c3cc(C(F)(F)F)...  500.483  5.86110   \n",
              "12  COc1cccc(CN2C(=O)N(C(C)C)C(N(O)C(=O)NC(C)C)C2(...  422.526  3.26370   \n",
              "17         Cc1ccc(SCC(=O)Nc2cccc(NC(=O)c3ccco3)c2)cc1  366.442  4.57112   \n",
              "21    CCOC(=O)c1cnn(-c2nc(-c3cccc(F)c3)cs2)c1C(F)(F)F  385.342  4.33040   \n",
              "22    CCOC(=O)c1cnn(-c2nc(-c3ccc(F)cc3)cs2)c1C(F)(F)F  385.342  4.33040   \n",
              "24  Cc1ccc(OCC(=O)N/N=C\\c2c(C)n(C)c3ccccc23)c([N+]...  380.404  3.23244   \n",
              "25  Cc1ccc(CNC(=O)Cc2csc(Nc3nc(=S)[nH]c4ccccc34)n2...  421.551  4.65981   \n",
              "\n",
              "    Predicted Class  Probability Estimate  \n",
              "0                 1              0.639259  \n",
              "1                 1              0.639259  \n",
              "3                 1              0.639259  \n",
              "4                 1              0.639259  \n",
              "5                 1              0.639259  \n",
              "6                 1              0.639259  \n",
              "7                 1              0.639259  \n",
              "8                 1              0.639259  \n",
              "9                 1              0.639259  \n",
              "12                1              0.639259  \n",
              "17                1              0.639259  \n",
              "21                1              0.639259  \n",
              "22                1              0.639259  \n",
              "24                1              0.639259  \n",
              "25                1              0.639259  "
            ]
          },
          "execution_count": 32,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "val_sample[['Drug', 'MolWt', 'logP', 'Predicted Class', 'Probability Estimate']]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cc5IIhqCgH1_"
      },
      "source": [
        "### Dealing with Data Set Imbalance"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1kkGNkFLItrz"
      },
      "outputs": [],
      "source": [
        "def pr_auc_score(clf_, X, y):\n",
        "  clf_proba = clf_.predict_proba(X)[:,1]\n",
        "  clf_precision, clf_recall, _ = metrics.precision_recall_curve(y, clf_proba)\n",
        "  return metrics.auc(clf_recall, clf_precision)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "g1z4CcPUeR9w"
      },
      "outputs": [],
      "source": [
        "inhibitors = train_df[train_df.Inhibitor == 1]\n",
        "noninhibitors = train_df[train_df.Inhibitor == 0]\n",
        "# Downsample noninhibitor class subset to same size as inhibitor class subset\n",
        "train_df_downsampled = pd.concat([inhibitors, noninhibitors.sample(len(inhibitors))])\n",
        "# Upsample inhibitor class subset to same size as noninhibitor class subset\n",
        "train_df_upsampled = pd.concat([inhibitors.sample(len(noninhibitors), replace=True), noninhibitors])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dRq1Eh3deuvL",
        "outputId": "843ee1d6-a95e-4769-bd7a-410628c8d1ea"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "6956"
            ]
          },
          "execution_count": 35,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(train_df_downsampled)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zbwGI3TuIh78",
        "outputId": "912b149e-17c0-44fa-d0f1-90ce3852276c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "10302"
            ]
          },
          "execution_count": 36,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(train_df_upsampled)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XKM_gsDXb_bn"
      },
      "outputs": [],
      "source": [
        "tree_clf = DecisionTreeClassifier().fit(train_fingerprints, train_df.Inhibitor)\n",
        "down_sampled_tree_clf = DecisionTreeClassifier().fit(np.array(train_df_downsampled[\"fingerprints\"].values.tolist()), train_df_downsampled.Inhibitor)\n",
        "up_sampled_tree_clf = DecisionTreeClassifier().fit(np.array(train_df_upsampled[\"fingerprints\"].values.tolist()), train_df_upsampled.Inhibitor)\n",
        "wt_tree_clf = DecisionTreeClassifier(class_weight='balanced').fit(train_fingerprints, train_df.Inhibitor)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NTotN9l-KUH3",
        "outputId": "b4bc597e-de55-47ac-dba2-9626291f750b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "LR Validation PR AUC: 0.813\n",
            "DT Validation PR AUC: 0.724\n",
            "US DT Validation PR AUC: 0.721\n",
            "OS DT Validation PR AUC: 0.719\n",
            "Weighted DT Validation PR AUC: 0.736\n"
          ]
        }
      ],
      "source": [
        "print(f\"LR Validation PR AUC: {pr_auc_score(lr_model, val_fingerprints, val_df.Inhibitor):.3f}\")\n",
        "print(f\"DT Validation PR AUC: {pr_auc_score(tree_clf, val_fingerprints, val_df.Inhibitor):.3f}\")\n",
        "print(f\"US DT Validation PR AUC: {pr_auc_score(down_sampled_tree_clf, val_fingerprints, val_df.Inhibitor):.3f}\")\n",
        "print(f\"OS DT Validation PR AUC: {pr_auc_score(up_sampled_tree_clf, val_fingerprints, val_df.Inhibitor):.3f}\")\n",
        "print(f\"Weighted DT Validation PR AUC: {pr_auc_score(wt_tree_clf, val_fingerprints, val_df.Inhibitor):.3f}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9ohinqtT6Ls8",
        "outputId": "974ceb19-44e5-4fc7-9186-1650e8cbcbd7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "LR Train PR AUC: 0.9516638242935856\n",
            "DT Train PR AUC: 0.9999989681253082\n",
            "Weighted DT Train PR AUC: 0.9999989681253082\n"
          ]
        }
      ],
      "source": [
        "print(f\"LR Train PR AUC: {pr_auc_score(lr_model, train_fingerprints, train_df.Inhibitor)}\")\n",
        "print(f\"DT Train PR AUC: {pr_auc_score(tree_clf, train_fingerprints, train_df.Inhibitor)}\")\n",
        "print(f\"Weighted DT Train PR AUC: {pr_auc_score(wt_tree_clf, train_fingerprints, train_df.Inhibitor)}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6CF1iCYfgOPG"
      },
      "source": [
        "## 3️⃣ Ensemble Learning: A Preview"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Nh5Y9AOVgRo3"
      },
      "source": [
        "### Random Forests: A Strong Collective of Weak Decision Trees"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cGNZU6z0fbKG",
        "outputId": "8530bd41-dbec-4d2d-fd56-dacd64b2f841"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "RF Validation PR AUC: 0.837\n"
          ]
        }
      ],
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "rf_clf = RandomForestClassifier(n_estimators=500, oob_score=True, n_jobs=-1, random_state=RANDOM_SEED)\n",
        "rf_clf.fit(train_fingerprints, train_df.Inhibitor)\n",
        "print(f\"RF Validation PR AUC: {pr_auc_score(rf_clf, val_fingerprints, val_df.Inhibitor):.3f}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IuJ2xZdd4Ma0",
        "outputId": "5b7401e0-3b4e-4596-a211-33ef1ccd1112"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "RF OOB Accuracy: 0.802\n"
          ]
        }
      ],
      "source": [
        "print(f\"RF OOB Accuracy: {rf_clf.oob_score_:.3f}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KqcC255szoSx",
        "outputId": "89e0b6b3-d712-4dd6-84b3-55175c7abac0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "RF OOB PR AUC: 0.806\n"
          ]
        }
      ],
      "source": [
        "pred_train = np.argmax(rf_clf.oob_decision_function_, axis=1)\n",
        "clf_precision, clf_recall, _ = metrics.precision_recall_curve(train_df.Inhibitor, pred_train)\n",
        "print(f\"RF OOB PR AUC: {metrics.auc(clf_recall, clf_precision):.3f}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 92
        },
        "id": "WBPLaB0bu54K",
        "outputId": "3aaa9b4d-1641-414b-dfce-51f99082a432"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: /~https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(n_estimators=500, n_jobs=-1, oob_score=True,\n",
              "                       random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(n_estimators=500, n_jobs=-1, oob_score=True,\n",
              "                       random_state=42)</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "RandomForestClassifier(n_estimators=500, n_jobs=-1, oob_score=True,\n",
              "                       random_state=42)"
            ]
          },
          "execution_count": 43,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train_val_df = pd.concat([train_df, val_df])\n",
        "rf_clf.fit(np.array(train_val_df[\"fingerprints\"].values.tolist()), train_val_df.Inhibitor)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oMC0KyiwLtGF",
        "outputId": "e705c95a-daa5-4514-bcd7-8937b6b1d0c3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Random Forest Test PR AUC: 0.830\n"
          ]
        }
      ],
      "source": [
        "print(f\"Random Forest Test PR AUC: {pr_auc_score(rf_clf, test_fingerprints, test_df.Inhibitor):.3f}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h-sCF0UB73_l",
        "outputId": "c04b5c68-4765-4636-ef60-8861a4539edd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1. Fingerprint Bit 1750 : 0.01401\n",
            "2. Fingerprint Bit 1088 : 0.00986\n",
            "3. Fingerprint Bit 1603 : 0.00905\n",
            "4. Fingerprint Bit 1536 : 0.00899\n",
            "5. Fingerprint Bit 875 : 0.00876\n",
            "6. Fingerprint Bit 841 : 0.00729\n",
            "7. Fingerprint Bit 1152 : 0.00718\n",
            "8. Fingerprint Bit 802 : 0.00667\n",
            "9. Fingerprint Bit 781 : 0.00630\n",
            "10. Fingerprint Bit 1873 : 0.00623\n"
          ]
        }
      ],
      "source": [
        "bit_indices = np.argsort(rf_clf.feature_importances_)[::-1]\n",
        "for i in range(10):\n",
        "  print(f\"{i + 1}. Fingerprint Bit {bit_indices[i]} : {rf_clf.feature_importances_[bit_indices[i]]:.5f}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GtKpUxSygdLr"
      },
      "source": [
        "**Code for Figure 5.10. Similarity map for example validation set molecule**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3WBRgpodG6F6"
      },
      "outputs": [],
      "source": [
        "import io\n",
        "from PIL import Image\n",
        "from rdkit.Chem.Draw import SimilarityMaps as sm\n",
        "\n",
        "def show_png(data):\n",
        "  bio = io.BytesIO(data)\n",
        "  img = Image.open(bio)\n",
        "  return img\n",
        "\n",
        "def sm_predict_proba(fp, pred_func):\n",
        "  fp = np.array([list(fp)])\n",
        "  return pred_func(fp)[0][1]\n",
        "\n",
        "def sm_predict(fp, pred_func):\n",
        "  fp = np.array([list(fp)])\n",
        "  return pred_func(fp)[0]\n",
        "\n",
        "def similarity_map(mol, model):\n",
        "  d2d = Draw.MolDraw2DCairo(400, 400)\n",
        "  dopts = d2d.drawOptions()\n",
        "  dopts.useBWAtomPalette()\n",
        "\n",
        "  _, maxweight = sm.GetSimilarityMapForModel(\n",
        "    mol,\n",
        "    sm.GetMorganFingerprint,\n",
        "    lambda x: sm_predict_proba(x, model.predict_proba),\n",
        "    draw2d=d2d,\n",
        "  )\n",
        "\n",
        "  d2d.FinishDrawing()\n",
        "  return d2d, maxweight"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EdxgiLpyINDj",
        "outputId": "de3af217-e319-4663-ba8e-58bc07d1dfee"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Maximum Weight: 0.652\n"
          ]
        }
      ],
      "source": [
        "fig, maxweight = similarity_map(\n",
        "  val_df.Molecule.values[6],\n",
        "  rf_clf\n",
        ")\n",
        "print(f\"Maximum Weight: {maxweight:.3f}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6C4tC8_cH76t"
      },
      "outputs": [],
      "source": [
        "png_image = show_png(fig.GetDrawingText())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "oAQc7_JuMZKO",
        "outputId": "e56f4a96-1cca-4846-fd80-a0729e13c2e6"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=400x400>"
            ]
          },
          "execution_count": 141,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "png_image"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JI7cOeXjMa9R"
      },
      "outputs": [],
      "source": [
        "png_image.save(\"figures/ch05/similarity_map_example.png\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "80BfPptijGCj"
      },
      "outputs": [],
      "source": [
        "ad_threshold = 0.8\n",
        "ad = np.amax(rf_clf.predict_proba(test_fingerprints), axis=1) > ad_threshold"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CpTvqJYDNdu0",
        "outputId": "dae24966-1fd0-4697-a5a1-31b45831dca9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "In Applicability Domain PR AUC: 0.957\n"
          ]
        }
      ],
      "source": [
        "print(f\"In Applicability Domain PR AUC: {pr_auc_score(rf_clf, test_fingerprints[ad], test_df.Inhibitor[ad]):.3f}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wOVl1DOdNobw",
        "outputId": "519246ae-42ce-44d4-a9ec-c7b7ba845d21"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Out of Applicability Domain PR AUC: 0.713\n"
          ]
        }
      ],
      "source": [
        "print(f\"Out of Applicability Domain PR AUC: {pr_auc_score(rf_clf, test_fingerprints[~ad], test_df.Inhibitor[~ad]):.3f}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O0khHca5jZg8",
        "outputId": "fb085927-6e46-40ce-bad4-b96a9b42f6db"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Coverage of Test Set by Model Applicability Domain: 33.928%\n"
          ]
        }
      ],
      "source": [
        "print(f\"Coverage of Test Set by Model Applicability Domain: {sum(ad) / len(ad):.3%}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5_E3MbVmOWMb"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "def adversarial_validation(train, test):\n",
        "    train['label'] = 0\n",
        "    test['label'] = 1\n",
        "\n",
        "    tmp_df = pd.concat([train, test])\n",
        "    train, test = train_test_split(tmp_df)\n",
        "\n",
        "    adv_rf_clf = RandomForestClassifier()\n",
        "    adv_rf_clf.fit(np.array(train[\"fingerprints\"].values.tolist()), train.label)\n",
        "\n",
        "    pred = adv_rf_clf.predict(np.array(test[\"fingerprints\"].values.tolist()))\n",
        "    return pr_auc_score(adv_rf_clf, np.array(test[\"fingerprints\"].values.tolist()), test.label)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bwjFj45SR0Zb",
        "outputId": "0cebc8ae-d6cf-4475-d3e6-b0fa38b9a8a7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Adversarial Validation PR AUC: 0.504\n"
          ]
        }
      ],
      "source": [
        "print(f\"Adversarial Validation PR AUC: {adversarial_validation(train_val_df, test_df):.3f}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dvrw11oHgk_d"
      },
      "source": [
        "## 4️⃣ Multiclass & Multilabel Classification"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bO0ySa4OgowN"
      },
      "source": [
        "### Multilabel Classification"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pa8vF2sMbkCy",
        "outputId": "0a71ab53-c9b7-4b25-cf0a-6a8ae706fd3a"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Found local copy...\n",
            "Loading...\n",
            "Done!\n",
            "Found local copy...\n",
            "Loading...\n",
            "Done!\n",
            "Found local copy...\n",
            "Loading...\n",
            "Done!\n",
            "Found local copy...\n",
            "Loading...\n",
            "Done!\n",
            "Found local copy...\n",
            "Loading...\n",
            "Done!\n"
          ]
        }
      ],
      "source": [
        "from rdkit.Chem import MolToInchi, MolFromInchi\n",
        "def process_cyp_data(cyp):\n",
        "  df = ADME(name = f'{cyp}_Veith').get_data()\n",
        "  df = df.rename(columns={'Y':f'{cyp}'})\n",
        "  PandasTools.AddMoleculeColumnToFrame(df, \"Drug\", \"Molecule\")\n",
        "  df['Inchi'] = df.Molecule.apply(MolToInchi)\n",
        "  df = df.dropna()\n",
        "  return df\n",
        "\n",
        "cyps = ['CYP2C19', 'CYP2D6', 'CYP3A4', 'CYP1A2', 'CYP2C9']\n",
        "cyp_inhibitor_dfs = [process_cyp_data(cyp) for cyp in cyps]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "coxHPekJeOON"
      },
      "outputs": [],
      "source": [
        "cyp_inhibitor_df = cyp_inhibitor_dfs[0]\n",
        "for df in cyp_inhibitor_dfs[1:]:\n",
        "  cyp_inhibitor_df = cyp_inhibitor_df.merge(df, on='Inchi', how='inner')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zcfCucPlGXrY"
      },
      "outputs": [],
      "source": [
        "cyp_inhibitor_df = cyp_inhibitor_df[['Molecule', 'CYP2C19', 'CYP2D6', 'CYP3A4', 'CYP1A2', 'CYP2C9']]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BsmWLL6VGrxe"
      },
      "outputs": [],
      "source": [
        "cyp_inhibitor_df['fingerprints'] = df['Molecule'].apply(compute_fingerprint, r=2, nBits=2048)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RiCuLosqrOlL"
      },
      "outputs": [],
      "source": [
        "from skmultilearn.model_selection import IterativeStratification\n",
        "from sklearn.multioutput import MultiOutputClassifier\n",
        "from skmultilearn.model_selection import iterative_train_test_split\n",
        "\n",
        "X = cyp_inhibitor_df[\"fingerprints\"]\n",
        "y = cyp_inhibitor_df[cyps]\n",
        "\n",
        "stratifier = IterativeStratification(n_splits=2, order=2, sample_distribution_per_fold=[0.2, 0.8])\n",
        "train_indexes, test_indexes = next(stratifier.split(X, y))\n",
        "X_train, y_train = X.loc[train_indexes], y.loc[train_indexes]\n",
        "X_test, y_test = X.loc[test_indexes], y.loc[test_indexes]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZayFuXEZQtiV"
      },
      "outputs": [],
      "source": [
        "lr_clf = MultiOutputClassifier(LogisticRegression()).fit(np.array(X_train.values.tolist()), y_train)\n",
        "lr_y_pred = lr_clf.predict(np.array(X_test.values.tolist()))\n",
        "lr_y_pred_proba = lr_clf.predict_proba(np.array(X_test.values.tolist()))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yiXO4ieXCvDk"
      },
      "outputs": [],
      "source": [
        "rf_clf = RandomForestClassifier(n_estimators=500, n_jobs=-1).fit(np.array(X_train.values.tolist()), y_train)\n",
        "rf_y_pred = rf_clf.predict(np.array(X_test.values.tolist()))\n",
        "rf_y_pred_proba = rf_clf.predict_proba(np.array(X_test.values.tolist()))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WaC5VOW4gr5E"
      },
      "source": [
        "**Code for Figure 5.12. PR curves and AUC performance on our custom multilabel classification task**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 326
        },
        "id": "ZLKJBoAgD_N6",
        "outputId": "f4192403-1434-48a6-abc1-e3343b3c42b3"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1600x600 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from matplotlib import cm\n",
        "\n",
        "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(16, 6))\n",
        "fig.subplots_adjust(left=0.0625, right=0.95, wspace=0.1)\n",
        "cmap = cm.Blues(np.linspace(0.2,1,5))\n",
        "\n",
        "for idx, cyp_label in enumerate(cyps):\n",
        "  precision, recall, _ = metrics.precision_recall_curve(y_test[cyp_label], lr_y_pred_proba[idx][:,0])\n",
        "  pr_curve_auc = metrics.auc(recall, precision)\n",
        "  ax[0].plot(recall, precision, lw=4, label=f'{cyp_label}, PR AUC: {pr_curve_auc:.3f}, Skew: {sum(y_test[cyp_label])/len(y_test[cyp_label]):.3f}', color=cmap[idx])\n",
        "\n",
        "ax[0].axis([-0.05, 1.0, -0.05, 1.05])\n",
        "ax[0].set_ylabel('Precision', fontsize=16)\n",
        "ax[0].set_xlabel('Recall', fontsize=16)\n",
        "ax[0].set_title(\"LR PR Curve\", size=16)\n",
        "ax[0].legend()\n",
        "\n",
        "for idx, cyp_label in enumerate(cyps):\n",
        "  precision, recall, _ = metrics.precision_recall_curve(y_test[cyp_label], rf_y_pred_proba[idx][:,0])\n",
        "  pr_curve_auc = metrics.auc(recall, precision)\n",
        "  ax[1].plot(recall, precision, lw=4, label=f'{cyp_label}, PR AUC: {pr_curve_auc:.3f}, Skew: {sum(y_test[cyp_label])/len(y_test[cyp_label]):.3f}', color=cmap[idx])\n",
        "\n",
        "ax[1].axis([-0.05, 1.0, -0.05, 1.05])\n",
        "ax[1].set_xlabel('Recall', fontsize=16)\n",
        "ax[1].set_title(\"RF PR Curve\", size=16)\n",
        "ax[1].legend()\n",
        "\n",
        "fig.tight_layout()\n",
        "fig.savefig('figures/ch05/multilabel_pr_curves.png', bbox_inches='tight', dpi=600)\n",
        "fig.savefig('figures/ch05/multilabel_pr_curves.pdf', bbox_inches='tight', dpi=600);"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BCPpSQArTolD"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import (\n",
        "    classification_report,\n",
        "    coverage_error,\n",
        "    multilabel_confusion_matrix,\n",
        "    label_ranking_loss,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v9f9IherVZfa",
        "outputId": "ae2da10f-0b6d-475a-9c77-27b92f712f38"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "     CYP2C19       0.31      0.27      0.29       374\n",
            "      CYP2D6       0.00      0.00      0.00       194\n",
            "      CYP3A4       0.25      0.13      0.17       333\n",
            "      CYP1A2       0.24      0.18      0.21       368\n",
            "      CYP2C9       0.36      0.19      0.25       301\n",
            "\n",
            "   micro avg       0.29      0.17      0.21      1570\n",
            "   macro avg       0.23      0.15      0.18      1570\n",
            "weighted avg       0.25      0.17      0.20      1570\n",
            " samples avg       0.13      0.10      0.10      1570\n",
            "\n"
          ]
        }
      ],
      "source": [
        "print(classification_report(y_test, rf_y_pred, target_names=cyps))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "txstwyMNmFoR",
        "outputId": "08dda4ab-cab7-4576-81e2-6ae22755429c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "LR Coverage Error: 2.863\n",
            "RF Coverage Error: 2.862\n"
          ]
        }
      ],
      "source": [
        "print(f\"LR Coverage Error: {coverage_error(y_test, lr_y_pred):.3f}\")\n",
        "print(f\"RF Coverage Error: {coverage_error(y_test, rf_y_pred):.3f}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LGoyB-XoVzEw",
        "outputId": "62f0d376-4697-4996-c739-31c0175b5000"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "LR Ranking Loss: 0.481\n",
            "RF Ranking Loss: 0.476\n"
          ]
        }
      ],
      "source": [
        "print(f\"LR Ranking Loss: {label_ranking_loss(y_test, lr_y_pred):.3f}\")\n",
        "print(f\"RF Ranking Loss: {label_ranking_loss(y_test, rf_y_pred):.3f}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QZ2zltI6RF_i"
      },
      "outputs": [],
      "source": [
        "lr_ml_conf_mx = multilabel_confusion_matrix(y_test, lr_y_pred)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "2o0JmPtEYdHo",
        "outputId": "abf4daf7-bebc-4699-8105-8126c1f74954"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Text(0.5, 0.93, 'CYP Inhibitor Confusion Matrices')"
            ]
          },
          "execution_count": 110,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x1600 with 6 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from sklearn.metrics import ConfusionMatrixDisplay\n",
        "\n",
        "title_size = 16\n",
        "plt.rcParams.update({'font.size':16})\n",
        "display_labels = ['Noninhibitor', 'Inhibitor']\n",
        "colorbar = False\n",
        "cmap = \"Blues\"\n",
        "values_format = \".3f\"\n",
        "\n",
        "f, axes = plt.subplots(3, 2, figsize=(10, 16))\n",
        "\n",
        "axes[0, 0].set_title(\"Model 1\", size=title_size)\n",
        "ConfusionMatrixDisplay(confusion_matrix=lr_ml_conf_mx[0], display_labels=display_labels).plot(\n",
        "    include_values=True, cmap=cmap, ax=axes[0, 0], colorbar=colorbar, values_format=values_format)\n",
        "\n",
        "axes[0, 0].xaxis.set_ticklabels(['', ''])\n",
        "axes[0, 0].set_xlabel('')\n",
        "axes[0, 0].tick_params(axis='x', which='both', bottom=False, top=False, labelbottom=False)\n",
        "\n",
        "\n",
        "axes[0, 1].set_title(\"Model 2\", size=title_size)\n",
        "ConfusionMatrixDisplay(confusion_matrix=lr_ml_conf_mx[1], display_labels=display_labels).plot(\n",
        "    include_values=True, cmap=cmap, ax=axes[0, 1], colorbar=colorbar, values_format=values_format)\n",
        "\n",
        "axes[0, 1].xaxis.set_ticklabels(['', ''])\n",
        "axes[0, 1].yaxis.set_ticklabels(['', ''])\n",
        "axes[0, 1].set_xlabel('')\n",
        "axes[0, 1].set_ylabel('')\n",
        "axes[0, 1].tick_params(axis='both', which='both', bottom=False, left=False)\n",
        "\n",
        "axes[1, 0].set_title(\"Model 3\", size=title_size)\n",
        "ConfusionMatrixDisplay(confusion_matrix=lr_ml_conf_mx[2], display_labels=display_labels).plot(\n",
        "    include_values=True, cmap=cmap, ax=axes[1, 0], colorbar=colorbar, values_format=values_format)\n",
        "\n",
        "# Remove x-axis labels and ticks\n",
        "axes[1, 0].xaxis.set_ticklabels(['', ''])\n",
        "axes[1, 0].set_xlabel('')\n",
        "axes[1, 0].tick_params(axis='x', which='both', bottom=False)\n",
        "\n",
        "axes[1, 1].set_title(\"Model 4\", size=title_size)\n",
        "ConfusionMatrixDisplay(confusion_matrix=lr_ml_conf_mx[3], display_labels=display_labels).plot(\n",
        "    include_values=True, cmap=cmap, ax=axes[1, 1], colorbar=colorbar, values_format=values_format)\n",
        "\n",
        "axes[1, 1].xaxis.set_ticklabels(['', ''])\n",
        "axes[1, 1].yaxis.set_ticklabels(['', ''])\n",
        "axes[1, 1].set_xlabel('')\n",
        "axes[1, 1].set_ylabel('')\n",
        "axes[1, 1].tick_params(axis='both', which='both',bottom=False, left=False)\n",
        "\n",
        "axes[2, 0].set_title(\"Model 5\", size=title_size)\n",
        "ConfusionMatrixDisplay(confusion_matrix=lr_ml_conf_mx[4], display_labels=display_labels).plot(\n",
        "    include_values=True, cmap=cmap, ax=axes[2, 0], colorbar=colorbar, values_format=values_format)\n",
        "\n",
        "f.suptitle(\"CYP Inhibitor Confusion Matrices\", size=title_size, y=0.93)"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "authorship_tag": "ABX9TyPX6w+92vXlVjIerCnYHkMA",
      "include_colab_link": true,
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}