Fisher_Linear_Discriminant_Analysis.ipynb

{
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  "metadata": {
    "colab": {
      "name": "Fisher Linear Discriminant Analysis",
      "provenance": [],
      "mount_file_id": "1dNTOWnHPzvYssDJvsDzjFs_1i3eavtHw",
      "authorship_tag": "ABX9TyOZY0Gz7mp9vvtHukT67v9+",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/gpriya32/Linear-Discriminant-Analysis/blob/master/Fisher_Linear_Discriminant_Analysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N1nTHBWDx5Kx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "c91d62d4-03a7-4fc7-cb4b-5a7f66fe853e"
      },
      "source": [
        "pd.read_csv('/content/drive/My Drive/a1_data/a1_d1.csv')"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>-0.598282822</th>\n",
              "      <th>1.02353385</th>\n",
              "      <th>1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.115929</td>\n",
              "      <td>0.512127</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.169815</td>\n",
              "      <td>-1.072747</td>\n",
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              "    <tr>\n",
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              "      <td>0.631926</td>\n",
              "      <td>-0.255663</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
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              "      <td>1.198445</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
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              "      <td>-0.507286</td>\n",
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              "      <th>995</th>\n",
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              "    <tr>\n",
              "      <th>998</th>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>999 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     -0.598282822  1.02353385  1\n",
              "0        1.115929    0.512127  1\n",
              "1        1.169815   -1.072747  0\n",
              "2        0.631926   -0.255663  0\n",
              "3        0.052532    1.198445  1\n",
              "4        1.947437    0.875587  1\n",
              "..            ...         ... ..\n",
              "994     -1.226337   -0.507286  0\n",
              "995      0.107578    0.634166  1\n",
              "996     -0.317336   -0.357282  0\n",
              "997     -0.295127    0.746446  1\n",
              "998     -0.883061    0.287511  1\n",
              "\n",
              "[999 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dEMcmiq5qDby",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 550
        },
        "outputId": "b90befe6-eb64-464c-bdff-648b9217bbd1"
      },
      "source": [
        "import numpy as np \n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import csv  \n",
        "from scipy.stats import norm\n",
        "import scipy.stats as stats\n",
        "import math\n",
        "\n",
        "\n",
        "\n",
        "def normdist(mu, sigma, col):\n",
        "\tx = np.linspace(mu - 4*sigma, mu + 4*sigma, 100)\n",
        "\tplt.plot(x, stats.norm.pdf(x, mu, sigma), color=col)\n",
        "\t\n",
        "def solve(m1,m2,std1,std2):\n",
        "\ta = 1/(2*std1**2) - 1/(2*std2**2)\n",
        "\tb = m2/(std2**2) - m1/(std1**2)\n",
        "\tc = m1**2 /(2*std1**2) - m2**2 / (2*std2**2) - np.log(std2/std1)\n",
        "\treturn np.roots([a,b,c])\n",
        "\n",
        "\n",
        "def plotline(poi):\t\n",
        "\ty = np.linspace(-5,5)\n",
        "\tplt.plot([poi]*len(y), y, color='green')\t\n",
        "\n",
        "\n",
        "def accuracy(poi, proj0,proj1):\n",
        "\tcount =0\n",
        "\tfor point in proj0:\n",
        "\t\tif point > poi:\n",
        "\t\t\tcount=count + 1\n",
        "\t\t\t\t\t\t\t\n",
        "\tfor point in proj1:\n",
        "\t\tif point < poi:\n",
        "\t\t\tcount =count +1\t\t\t\n",
        "\tprint(\"Accuracy is : \" , (count/len(class0+class1)*100),\"%\")\t\t\n",
        "\n",
        "\n",
        "def f_measure(poi, proj0, proj1):\n",
        "    \n",
        "    true_positives = 0\n",
        "    false_positives = 0\n",
        "    total_positives = 0\n",
        "\n",
        "    for i in proj1 :\n",
        "        if i<poi:\n",
        "           true_positives += 1\n",
        "    \n",
        "    for i in proj0:\n",
        "    \tif i<poi:\n",
        "    \t\tfalse_positives+=1\n",
        "\n",
        "    total_positives=len(proj1)\n",
        "    \n",
        "\t\n",
        "    precision = true_positives / (false_positives + true_positives)\n",
        "    recall = true_positives / total_positives\n",
        "\n",
        "    print(\"F-score is \", (2*precision*recall)/(precision+recall))\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "def load_dataset(data_file):\n",
        "    with open(data_file) as csv_file:\n",
        "        dataset = list(csv.reader(csv_file, delimiter=','))\n",
        "    return dataset\n",
        "\n",
        "dataset = load_dataset(\"/content/drive/My Drive/a1_data/a1_d1.csv\")\n",
        "class0=[]\n",
        "class1=[]\n",
        "for data_point in dataset:\n",
        "\tif data_point[-1]=='0':\n",
        "\t\tdata_point = data_point[:-1]\n",
        "\t\tclass0.append(data_point)\n",
        "\telse:\n",
        "\t\tdata_point = data_point[:-1]\n",
        "\t\tclass1.append(data_point)\t\n",
        "\n",
        "\n",
        "\n",
        "c0=np.array(class0).astype(np.float)\n",
        "c1=np.array(class1).astype(np.float)\n",
        "\n",
        "mean0=np.mean(c0, axis=0)\n",
        "mean1=np.mean(c1, axis=0)\n",
        "\n",
        "\n",
        "s0=(len(class0)-1)*np.cov(c0, rowvar=0).astype(np.float)\n",
        "s1=(len(class1)-1)*np.cov(c1, rowvar=0).astype(np.float)\n",
        "\n",
        "S=s0+s1\n",
        "\n",
        "S_inv = np.linalg.inv(S).astype(np.float)\n",
        "\n",
        "v=np.matmul(S_inv, mean0-mean1).astype(np.float)\n",
        "\n",
        "y0=np.matmul(np.transpose(v), np.transpose(c0)).astype(np.float)\n",
        "y1=np.matmul(np.transpose(v), np.transpose(c1)).astype(np.float)\n",
        "\n",
        "plt.scatter(c0[:,0], c0[:,1] ,color=\"red\", label='Class0')\n",
        "plt.scatter(c1[:,0], c1[:,1],color=\"blue\", label='Class1')\n",
        "x = np.linspace(-0.05,0.05)\n",
        "y = v[1]/v[0]*x\n",
        "plt.plot(x, y, color='green')\n",
        "plt.legend()\n",
        "plt.show()\n",
        "\n",
        "normdist(np.mean(y0),np.std(y0), 'red')\n",
        "normdist(np.mean(y1),np.std(y1), 'blue')\n",
        "result = solve(np.mean(y0),np.mean(y1),np.std(y0),np.std(y1))\n",
        "plotline(result[1])\n",
        "plt.scatter(y0,len(y0)*[0], color='red', label='Class0') \n",
        "plt.scatter(y1,len(y1)*[0], color='blue', label='Class1')\n",
        "plt.legend()\n",
        "plt.show()\n",
        "\n",
        "# print(\"Threshold value: \", result[1])\n",
        "accuracy(result[1], y0,y1)\n",
        "f_measure(result[1], y0, y1)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Accuracy is :  99.3 %\n",
            "F-score is  0.9929929929929929\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q7JnT08CikFr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "2e89511e-c5a2-424d-e5b7-3c7c61b31387"
      },
      "source": [
        "pd.read_csv('/content/drive/My Drive/a1_data/a1_d2.csv')"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>-6.672418458</th>\n",
              "      <th>-1.206198345</th>\n",
              "      <th>-1.081049865</th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.675598</td>\n",
              "      <td>0.614994</td>\n",
              "      <td>-0.971600</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-4.039058</td>\n",
              "      <td>0.335102</td>\n",
              "      <td>0.544618</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.793526</td>\n",
              "      <td>-0.235277</td>\n",
              "      <td>0.551771</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3.820273</td>\n",
              "      <td>-0.274691</td>\n",
              "      <td>0.454743</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-4.914811</td>\n",
              "      <td>1.232170</td>\n",
              "      <td>-0.663065</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>994</th>\n",
              "      <td>-3.680139</td>\n",
              "      <td>0.966962</td>\n",
              "      <td>-0.904337</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>995</th>\n",
              "      <td>-4.063900</td>\n",
              "      <td>0.802611</td>\n",
              "      <td>1.023708</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>996</th>\n",
              "      <td>-0.814430</td>\n",
              "      <td>-0.693945</td>\n",
              "      <td>0.876776</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>997</th>\n",
              "      <td>-0.325122</td>\n",
              "      <td>-0.759024</td>\n",
              "      <td>1.299772</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>998</th>\n",
              "      <td>-1.503431</td>\n",
              "      <td>-0.269458</td>\n",
              "      <td>-1.124390</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>999 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     -6.672418458  -1.206198345  -1.081049865  0\n",
              "0        1.675598      0.614994     -0.971600  0\n",
              "1       -4.039058      0.335102      0.544618  1\n",
              "2        0.793526     -0.235277      0.551771  1\n",
              "3        3.820273     -0.274691      0.454743  1\n",
              "4       -4.914811      1.232170     -0.663065  0\n",
              "..            ...           ...           ... ..\n",
              "994     -3.680139      0.966962     -0.904337  0\n",
              "995     -4.063900      0.802611      1.023708  1\n",
              "996     -0.814430     -0.693945      0.876776  1\n",
              "997     -0.325122     -0.759024      1.299772  1\n",
              "998     -1.503431     -0.269458     -1.124390  0\n",
              "\n",
              "[999 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m-ZuSxh8i4qL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 552
        },
        "outputId": "ab02e1f8-9906-4224-a635-59444fde4fe9"
      },
      "source": [
        "import numpy as np \n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import csv  \n",
        "from scipy.stats import norm\n",
        "import scipy.stats as stats\n",
        "import math\n",
        "\n",
        "\n",
        "def normdist(mu, sigma, col):\n",
        "\tx = np.linspace(mu - 4*sigma, mu + 4*sigma, 100)\n",
        "\tplt.plot(x, stats.norm.pdf(x, mu, sigma), color=col)\n",
        "\t\n",
        "def solve(m1,m2,std1,std2):\n",
        "\ta = 1/(2*std1**2) - 1/(2*std2**2)\n",
        "\tb = m2/(std2**2) - m1/(std1**2)\n",
        "\tc = m1**2 /(2*std1**2) - m2**2 / (2*std2**2) - np.log(std2/std1)\n",
        "\treturn np.roots([a,b,c])\n",
        "\n",
        "\n",
        "def plotline(poi):\t\n",
        "\ty = np.linspace(-5,5)\n",
        "\tplt.plot([poi]*len(y), y, color='green')\t\n",
        "\n",
        "\n",
        "def accuracy(poi, proj0,proj1):\n",
        "\tcount =0\n",
        "\tfor point in proj0:\n",
        "\t\tif point > poi:\n",
        "\t\t\tcount=count + 1\n",
        "\n",
        "\tfor point in proj1:\n",
        "\t\tif point <poi:\n",
        "\t\t\tcount =count +1\t\t\t\n",
        "\tprint(\"Accuracy is : \" , (count/len(class0+class1)*100))\t\t\n",
        "\n",
        "def f_measure(poi, proj0, proj1):\n",
        "    true_positives = 0\n",
        "    false_positives = 0\n",
        "    total_positives = 0\n",
        "    for i in proj1 :\n",
        "        if i<poi:\n",
        "           true_positives += 1\n",
        "    \n",
        "    for i in proj0:\n",
        "    \tif i<poi:\n",
        "    \t\tfalse_positives+=1\n",
        "\n",
        "    total_positives=len(proj1)\n",
        "    precision = true_positives / (false_positives + true_positives)\n",
        "    recall = true_positives / total_positives\n",
        "    print(\"F-score is \", (2*precision*recall)/(precision+recall))\n",
        "\n",
        "\n",
        "def load_dataset(data_file):\n",
        "    with open(data_file) as csv_file:\n",
        "        dataset = list(csv.reader(csv_file, delimiter=','))\n",
        "    return dataset\n",
        "\n",
        "dataset = load_dataset(\"/content/drive/My Drive/a1_data/a1_d2.csv\")\n",
        "class0=[]\n",
        "class1=[]\n",
        "for data_point in dataset:\n",
        "\tif data_point[-1]=='0':\n",
        "\t\tdata_point = data_point[:-1]\n",
        "\t\tclass0.append(data_point)\n",
        "\telse:\n",
        "\t\tdata_point = data_point[:-1]\n",
        "\t\tclass1.append(data_point)\t\n",
        "\n",
        "\n",
        "\n",
        "c0=np.array(class0).astype(np.float)\n",
        "c1=np.array(class1).astype(np.float)\n",
        "\n",
        "mean0=np.mean(c0, axis=0)\n",
        "mean1=np.mean(c1, axis=0)\n",
        "\n",
        "\n",
        "s0=(len(class0)-1)*np.cov(c0, rowvar=0).astype(np.float)\n",
        "s1=(len(class1)-1)*np.cov(c1, rowvar=0).astype(np.float)\n",
        "\n",
        "S=s0+s1\n",
        "\n",
        "S_inv = np.linalg.inv(S).astype(np.float)\n",
        "\n",
        "w=np.matmul(S_inv, mean0-mean1).astype(np.float)\n",
        "\n",
        "y0=np.matmul(np.transpose(w), np.transpose(c0)).astype(np.float)\n",
        "y1=np.matmul(np.transpose(w), np.transpose(c1)).astype(np.float)\n",
        "\n",
        "ax = plt.axes(projection='3d')\n",
        "ax.scatter3D(c0[:,0], c0[:,1], c0[:,2] ,color=\"red\", label='class0')\n",
        "ax.scatter3D(c1[:,0], c1[:,1], c1[:,2] ,color=\"blue\", label='class1')\n",
        "plt.show()\n",
        "\n",
        "normdist(np.mean(y0),np.std(y0), 'red')\n",
        "normdist(np.mean(y1),np.std(y1), 'blue')\n",
        "result = solve(np.mean(y0),np.mean(y1),np.std(y0),np.std(y1))\n",
        "plotline(result[1])\n",
        "plt.scatter(y0,len(y0)*[0], color='red', label='Class0') \n",
        "plt.scatter(y1,len(y1)*[0], color='blue', label='Class1')\n",
        "plt.legend()\n",
        "plt.show()\n",
        "\n",
        "accuracy(result[1], y0,y1)\n",
        "f_measure(result[1], y0,y1)\n",
        "print(\"Threshold value: \",result[1])\n",
        "\n"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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9d6H2h+0WQzpmW/BMJ+OBgYFpZByNRgmFQvh8vtR+A4FAhorlQkSRdLMgX0ODruuYpplVZjVXLoJN2AuB9Am+l1xyCatXr856zPMh3XSyraysZOvWrbhcLhKJBGVlZdNuwe0ROaZp0t7eTiKRSFV9dlXs8/kuKD1wPsi1Qp3LfTcyMkI8HufEiRMp9102K3QuOF/ys3yRD4HnQsbDw8MMDg4SDofp6enhn/7pnwiFQjz66KNs2rSJdevW5dxq6Onp4ZZbbmFoaAhFUfj4xz/OHXfckfEYIQR33HEHu3fvxuv1cv/997N169bcX4BJFEk3DbbsyzTNvAwNmqZN85Cn39rPloswHxWCjWg0SkdHB4FAIGOC70wohHTtiv/FF19Mka19Sz1T2yC96hscHGTNmjW4XC4Mw0hVfWfOnKGrq2uas8wm5IXqTXZ1KXznOw4GB1W2bLHYtm3hU7gKge2+sz8DK1euBOTFOj2BLB/33YVa6S6EeiGdjJ1OJ2vWrAFgy5YtbN++nZtvvplkMskPf/hDqqqq+Pu///uc9qvrOt/85jfZunUrwWCQbdu28ba3vY1169alHvPkk0/S0tJCS0sLe/bs4VOf+hR79uzJ+xyKpMv8DQ3p1Wp6tZneR51t20KrTsuyOHr0KMFgkObmZtauXZvTMRcydaK9vR3TNNmxY0fe/WfI7Onquk5ZWdk0q+ZMt5P2Qos0Mph5k4rfD3/91y6iUQWfT/D00xrHjy/n6qvt5wVFgUL8IAvVp55K3g6Hg4qKimmVWi7uu1gstmDHtdDthYUy3Uw9P1VVaWhoQFEUbr/99rwvhEuWLGHJkiUAlJaWsnbtWvr6+jJI9/HHH+eWW25BURR27dqF3+9nYGAgtV2ueF2Trk220WiU1tZW1q5dW1CFoGkakUiE3t5eJiYmcqo207fNt9INh8O0t7cTjUZZtWrVtHHpcyEX0s3WRjh48GDBiyq5LKTN5Cyze8WhUIhEIsGBAwcQQmRM8fX5fDNKtk6fVgmFFOrq5PPX1wtOnChjYgIefNDBCy/oKIrg+usNbrrJIN/CtZBKd2hI4ZFHdEZHFTZvNtmxI7cLSS7uu1AoRGtrK0CGZrYQN9lCV82FXLCzIdtx2Z+v+a4ZdHZ2cvDgQXbu3Jnx876+voxFwMbGRvr6+oqkmwumhoarqsrExERBb5Z9i3zmzBlWrVrFunXr8tpPPqQbCoVoa2sjFouxYsUKJiYmqKmpKSgWcrbRO/Y8tYqKiow2wmzEmcsxFJrjmy5BGh4e5vLLL0/pZ0OhUKrqsyVb6e0JqRLwYJoghKxok0lQVcHu3TrPP6/R2GhhWfDIIw4aGgRXXZX7RbCQ9sLEBHz1q05CIQWvV/DQQw56ekp497vD+b48wPT+p33h93g8GZrZdDdZes6CTcYzZfMuZKW7kK2Kqa2nWCw2zaGWL0KhEDfccAP33nvvOQvNeV2RbrYJDfaffG/x03MRKioqqKqqygjuzhW5kG4wGKStrY1EIsGKFStSGQytra0FSb+yne9Usp2amQuzk3UuzzkTfv97jZ/8RCeRUHjrWw1uvNFgrlNK18+mw1YJ2KaF3t5eIpE49fWrOH26AodDRVU13vWuPo4fX0VFhUBVQVWlRvfECYWrrsr9vAoh3dOnVfx+haVL5Wvp8wleeMHLu9+9sItfs2lm00Nvzpw5QyQSyeq+W0gXWTaiXMh9+f3+eRFlMpnkhhtu4IMf/CDvf//7p/2+oaGBnp6e1P97e3sLSvl7XZBuNkND+hclny/N+Pg4bW1tGbkI3d3dBfdlNU2blh1qY2Jigra2NgzDSJHt1G3zJd1YDDo6PIyMqFx2GZSVzU22Ngq5OKVvm42wjx5V+cd/lH0+IeDBB3U8HsF11xW2uDiTSmDzZoOnn47R359gyZIJqqq62L3bRV9fFUJYaJqDcNhNTU1+z1sI6U4tKE0TFEWgqudHpztb6M1U910oFOLll19ekNl357rSnY8bTQjBRz/6UdauXcudd96Z9THXXXcd9913HzfffDN79uyhvLw879YCvMZJdz7jcNIhhODMmTO0t7fjdDqn5SLYOZ6FINtCWiAQSPXkVqxYMeMHyVY+5Lo4EY3CD36gcuxYJW63i6efjvDGNx5n+fKSWcnWxmzthVxCuLNtu3+/Sk+PgmkqqKogkVB4/HFHzqQbjcoLSXn5dDJLh8ejc/31OuAGyti3b4C/+qt6vvQlnYEBC8MwaWjwU1t7mr17jdTtt00ys80pSz9vIaCvTyGZhKVLBdkKu3XrLJqaLLq6VFwueQ7vetcEmrZwvdNCLelT3Xf79u1j27ZtM86+y8d9dz5It1BjxP/8z//w4IMPsnHjRjZv3gzA3XffTXd3NwCf/OQnufbaa9m9ezcrV67E6/Xygx/8oKDnes2Rbq6Ghpm2zfwCyTE1HR0dlJSUsH79+qy5CJqmEYvFCjredMlYehW9cuXKOT9A+Uq/Dh1S6O9XqKkJEY2OEolUMja2hWuvze2LMF9TRTbSDYdhYkKlvl7u1++Hnp7cLoxPPqnx05/KC84ll1h85jMJ8il0Fi0SfO1rSX70I53OTifr11ewceNWSkvP3n6HQqGMOWUejydDX5x+ToYB3/iGkxdf1FAUeUxf+Up82jG53XDXXQmeeUbjzBmFDRssFi8OoijnTlsbj0uVhs9H3guF6W66qbPvZnLfTSVjWyp4LknX7/cXXOleeeWVc7bOFEXhO9/5TkH7T8drhnTnO6HBJhS78hwcHKSrq4vy8vI5cxFsc0Qh0DSNcDjMvn370HWdVatW5dyXykduJoSgt9fP8HCEmhqDRYsW4XBUIY1jue1jtkp3dFQhHJYVZzZD0Ezvw6ZNFiUlAr9fQVFA0yRZzYVTp1R+9CMHHo+sLk+cULj/fgef+Ux+dxyPPqrz4os6FRWC55/XaGlRueuuxLTb78OHVfbsUTDNBJs3B6itlXKhSCTC/v37KSkpYf/+Op57rnaywlXp6FD4z/90cMcd04+ppISMar6720JVF+brmF48CAFPPaXxs585EEJW2bfdlmCKYa4g5Ou+i0QitLa2ZpBxoe67izXsBl4DpGvLvoaGhigtLU29ifm+kXYC0ujoKD09PSxatCjltpoL2cwRuRz32NgYp06dIplMsnXr1lmjHLMhF2NF+gJZeXkNVVUrcDgCGIZKIKCwc2fuletMPd29e02efDKEw6Hhcrn44z9WufTS6dtmI+z16y2uuspgdFTB6QQhFN7xjrlfy95ehZ4eBctSUBRBKKTQ1aVSXy945zsNcrluxePwu9/pLFtmoaryYtHTo9LRobJmzdnzPH5c5bHHdGpqBJbl5re/9XDrrYvYtEmwb98+tmzZQjgc5okndCBBMBjDskxM08GBA4K+vrGsRoZYDJ54QuPkSQ1dr+C9700yZXZnwbA//8ePqzz4oIPGRoHTCceOqfz4xw4+/vHC2mG5YKa++t69e2loaEgpftJn3+XrvjMMY1o77GIIu4GLmHSnGhoGBgZwOBwF6UhN0yQej7N//36WLFnC9u3b87Kl5lPpCiFSubkej4eVK1cyMDCQN+HC7Lf7mWRbnurZLl6s8LOfqQSDKm9/u8WuXbmrEaaqFxKJBEeOdPOzn+msXFmCEHGCQT8PPGDxrncNUFbmTd2GW5aVcaydnQqtrSolJfAXf5Hk17/W8fsVNm0y+OM/nvu1DIXgzBmFmhpBLKbQ26ugqnDvvU4efVTn3/89llHNRaPQ0SFvuZcvNxFCLmDJ1yrz76nt0CNHVCorRWp/0SicPKnS1CR3oGkaZWVlbN6s8dxzTioqvCQS8jHDwyZf/rLOjTf2UVMj1S62dvaXv6zn1CkfS5ZYdHc7+f73vXz1q3JyxUIgFoNvfcvBSy9puN2yyr3kEosTJ3JrYyx0QJGiKFlNMclkMlUZ5+q+m6nSLWRh63zjoiPdmSY0OByOvKtNwzDo6upicHAQVVVZu3btNIVALshF9iWEYGRkhPb2dnw+Hxs3bsTr9RKNRunr68v7Oe3nnU36lU62NjZuFFRWTmCaJsuW5XePaVeryWSSzs5OhoeHKSlpprm5ntpaGeJeV6fQ16ewYcMShAjT1RWjtTVCPB4hEDiBy+Wip6eGBx5onJzoqrNuneBv/iaR8wTeaBRefFEjkYD2dpVwGHQdKioEdXWCnh6VF17QuPZa+Z4EAvBP/+RkaEi2MCxLZ2BgLeXlbixLsG+fhsMhcDhg61aL5cszX1OXS5BIqIAkoWRSYJpw4IBKS4uPyy6T04Pf/GaTo0cNnn1Wp71dVu5XXw3RqI9HHlnD178eo6JCGhnOnAlz9KiDRYvGCASSOJ1J+vt9vPRSkA0bHFmT2vLFAw/onDyp4XKBxyM4elTFMARveENuBcJCz0ebicQdDkfWQaSzue/s4ZR2lazrerG9cK5gmibJZHLahAY7mT4XpOfLNjY2smvXLk6fPl3wlX229kI6CZaVlbFp06YMbel8Am/S2wtzke3U7QpRWwgh6Onpwe/3pyzOiYTKwYMKgQCUlsrebnW1wOtVOXiwnN/8ZhGqCsPD5Vx3ncr27S6+9z0HpaUGTmdkMs/BxSOPDLBzp0j1CB0OD5GISmkp0zS7v/udht+vsGKFwDAEhw9LFUBFhb1IJKtfG88+q9HVpeDxyOrvpZd06uocrF9vsXu3dIW5XOBwCD74weQ0O/Ab3mBx+rRUWdifkZ/8RCcUUvD7m3nlFSdf/nICrxc+85kk73ufwe23u3C5ZOZDQ4MgHIa+PpXKSjmJobbWRXm5i6qqEhwOGB0dJRotobzcJBz2T0tqS1+8S4+EnA2vvKLR1GSh6yqjowqGAdGowi235PY9Waj4Tyisap7NfXfs2DEABgYGCIVC3H777am7yJGRETZu3MiWLVtyep6PfOQjPPHEE9TW1nL06NFpv3/++ed573vfy/LlywF4//vfz5e+9KW8z8fGRUe69rz7qXA4HHMSSSwWo7Ozk7GxsWm5CPNdDJu6rZ0zO5f+dT6BN/a29vPMRbbp2+WjQjAMg+7ubkZGRqivr8943dxuePe7LZ56CgYGFBYvFsTj8IlPuGhrU3nHOwwaGwVg8Pzz5WzfrpFMulm0SOBwSCVIIqFQW+ugvNxPKBRiz54QDzxQRzSq4fEoVFSoRCIu1q+HK65QGRpSqKiA2lqT48dVqqoswmGVsjKB3y+rzm3b7IsRHD2qcfCgisOhTKoloLpatifGxmRFunatRSIB//iPLq6/PpIh9aqtFXz0o0na21U0DZ56SicSkWTqcMQ5daqC3/xG5/rrpYXYNKG1VUtVxAcPKqxebeLznSUeXYfLLzf5j/9woCjgdHp529sstmypRFXPksxMCgE7EjJd1ja1JbZkiaC3V+XSSy2WLIH+fpWPfjTJ4sW5EWCh0rOZ9rUQBG677zRNo7GxMdVO/MMf/sCHP/xhrrnmGkZHR/nJT36SM+n+2Z/9Gbfffju33HLLjI+56qqreOKJJ+Z9/HARku5M0HV9RtKNRCJ0dHQwMTExY+RhPpXyVKSTrq186OzspKqqKsNGO9e2+UAIkcp7qKmpyYlsbeRKuvaU4L6+PhoaGli8eDF1dXXTvog1NXDTTQamKfjZzxx885tONE0wMqLwwANOPvGJBA7HWU3tFVcYPPOMzuLFgmhUBs2sX++guroal6uaX/7STV2dwO0WPPmkRiwmWLMmzO7dKq+8cgnveMcpenoaWL4cNm1ysmiRG6/XordXpbxc8Jd/mcCy4BvfcOD3K/z+9xqGASUlAsuC4WEVVZULaUKQams4HJIwo1FZtSeT8PDDOnv3atTWCm69NUlDg+Chh5QUgRqGiqoKhofPvh5PPKFTVWXR3y9JOh5n0vJ79jH9/QqHD2usW2cRDsOZMxabNhmoamaZPZNCwI6EDIfDjI6OZvRBY7EY/f39/OmflnLyZDVDQyqWBTt2mFx3Xe6f8XOdpTsfTJWfqapKJBLhPe95D4vzXI28+uqr6ezsXLBjmwuvGdJ1OByp2zEboVAoFQyzfPnyWXMR5kO6dq+zt7eX7u5uFi1axLZt23Ja1MtXZZHeRlBVlaVLl9Lc3FzQ8U5FMgnBIG0+3ZoAACAASURBVHg8FsPDPfT09GRkAJ86dWpWslZVeOghB16vNAVEo3LB6/hxldpanbIyk7IyjQ9/WNp89+7VqKiAW29NsGSJwDDgscd0jhxRqKpSqKy0sCwVpxPKykqoqYHW1nLe/vZyHA6TF17QMIwEq1f38Ud/NEhJiVygMs0yvve9GsrLBfX1EAhIfbKt//X5LLq6vLS26gSD0v4bj4Pfr7B2rYXPJ8n4C19w8uSTOmVlgpISuaD2z/8cY8sWk5/8xEFrK3R1VSGERmWl4JZbDNxuiERku2LVKgvDkBeaxkaLoSGF2lr5uh87pqIoglWr5P/b26McOlTKm9+c23toR0JO7YNGo9FUML4QfXzyk210dLjxeHQ2blTw+2fPWkjHhZq7ANlbH+eyp/viiy9y2WWXUV9fzz333MP69esL3tdFR7qzkaZd6abbZ5ubm1NZBbNB1/VppJ0L7PE74XCYSCSSt/IhV2Tr2Y6OjhZUJWerdHt64N//XWVwMEQsFuCDH1R585szpwTnkhQ2Pg5dXSqmSSpkprNTobnZ5LrrQkAVY2Oy77tqlUVtraChQR7Lc89pHDmiousKkYigo0PD7weXSxK6ZUlJWWmpxq236vzpnwLoeDxLEaIxZWg4dCjJ2FgQCBEIwMjIcgxDo6TEIpFQGB9XAA+qKvu4IyMKfr/KihUmN9yQQAj4m79x8oMfOBEChobkIl0yKTh6VGPbNpOvf91Jd7esmFetsujuVvnhD3U+9jGDN73JYPduHRA4nYKhIZXf/15j3z6d97/f4JZbEpw+rTIyolBfL1AUMAwFh2P+agFd13E6nak0rDVr4MorZVKbHQ40NjZGOCzDdWaz916oubw2pn6nk8nkgowWmoqtW7fS1dWFz+dj9+7dXH/99bS0tBS8v4uOdGeCw+EgHA5z4MABYHb7bDbkW+mapklvby+9vb3U1dVRUlLCqlWr8j7uuTDbAlmhC2JTSTeRsLj33igTEwEWL3bjci3h2Wc1rrjCJF3JNlfKWHc36Lq0wCaTknB1Xd62nzmj89vf6hw96uLoUZX+ftlr9PkEBw5o/J//E+ehhxwkk7B8ucX//I+GfQ1MJmVlWFMDV101TGWllAWl+1XS8wSamhR8PicNDVUYhkAIqU4Ih5VJmdjZuWTxuIKmSYNGdbVgzx6NYFDhRz9yYJqS7IWQC4R+P/z0pxp+vwOXS1BTY2EYSSYmnNTUWBw/rgEGu3ZZ/PVfx/nud2V748wZhdJSQWmp4Kc/1fnd7zTWrZP96DNnFFavtohGFd78ZgPIJKZwWDrJzi4Szo5sRJkefJOetWAP15xq77UdZbYueyFGCC1k2E02nMv5e+kSt2uvvZbbbruN0dHRjNcyH1x0pDv1jbdzEVpbW4lGo2zbtq2gpKFcSTe9z7l48eLUrffw8PCCDhe0Lcjt7e0zLpAVGoBuk66tbz56tJdAYA3r1i1B13Xa2+GFF1Ta2hTe8x6L//W/LHR97l7wwIBKQ4NFPK4wOKjidAoMQ2FwUCES8fHcc07cbpXxcYXKSkFfn8oVVxh0dan8+Z+7OXJEm5QEysWtRYtkNZxIyDyDz38+QWVlLzCzFvOppzQeeMBBT4/C3r0qW7dak/kG2Qwz8v+mqRCNmnR0RIlEEuze7SEalSE0ppm5zUMPOVFVKcHSNCknSyYVAgGFZcvsjA9497tN3vQmk7/7OyeDgyoVFRCJyPZNLKawdSu89a0mR49qbNliUl3dz/LlTannEQJ++lOdxx+XX9F16yw++9m5nWT5VKcz2XttR1l/fz/RaJSTJ0+m5rmlqyiyTa2YCQtZ6c5GsOdi/t7g4CB1dXUoisLevXuxLCsjtS1fXHSkC2edUem5CGvWrOH06dMFR7vNRbqGYdDT00N/f/+Ms87mI7GxCTsXsrVRqPJBURTC4TAvvvgiVVVVvPGNm3nhBQ+xmKysfvc7edtcUyP41a+kHOumm6xpla5lkbr9dzqhqkqu1ldXCxTFQgjo6FBIJlUmJlwEgzoNDfIxmiZJLRKRcrPxcZXly+XiUzQqlQXNzWZqpV3X4aqrTCaVQllx/LjKf/yH7ClPTEhyHx9X+exn4/z937unVYpnT0VB0zS6u0vp6bFv96fuXZBMgtttYZoqhiHbJ5YlcxaWLxfcckvmXYfPBytXyv52PA4jI2rq3J97TufNbzZoaLDYudMiEIhlkOW+fSo//7lOY6Mk92PHVH70I51PfGL2wmAhWgLpjrKSkhIaGxuBs4t36Qlk6VMr0u29U78HC5m7kI3AY7FYwa2FD3zgAzz//POMjo7S2NjIV77yldQd5Cc/+UkeeeQRvvvd76LrOh6Ph5/85CfzG9FU8JavIgYGBlK323Yugp2VWyhmIt10A0VDQ8OMs85sFUIhHyybPO0ks3Ml/bIJvaWlBcuy2LlzZ+qDeuut1mQCmUIiAdu32w4swd69CjfdlNle8PvhvvtUurqkaeC977V4y1uSXH+9wU9/qhMOK0SjkthcLkFdnUE8rjE4qLJqlUVfn4IQcj9lZYJoVOpolyyxCIcV4nHpzBobkzKvt7/dmObUisXgN7/ROHFCY3AQwmGFkRGFsTGVZFK2HwIB+OEPnbzhDQYnT6rE41ImBrIitY/PsiSJ6rqCwyF/J/X3AlAmHytfm5ISY5JwFZYvn+B970vyoQ+ZlJb6mPqVuummJI88onP0qFQQaBpUVgpUVXDokMrVV5vU1Qn8/sy7pM5OFYeDlHStqkpw8qRsX8yGbKRru+PCYWhsFKkc37kwdV/ZFu+mTq2w1zfsoHSbiCORyIKN6lnohLGHHnpo1t/ffvvt3H777QXtOxsuStK1LGsaKc33tmKqTjeZTNLV1cXQ0FDKQDEbodoGiXwX0eyLxd69e+fMs832nLmQru2Ga2tro7y8nPXr19PZ2ZlRGVx6qeBv/9bk8ccVHn5YS422iUSk3hMySf7BB6VZYOlSQSIh+OEPk5SV+bnuOjdvfKOH559X+Yd/cJNMwsSEgqrqlJaa9PdrnDqlEg7LhaNAQOHrX4/z5S+7aG9XCIclsS1bZvHHf2wQCklFwVvfOr2i/9WvNI4d0zh+XKWzUyUUEoyPq1iW7KFOTMjnGB1VkO5QeSFwuQSmKU0DDoegvFwqHGSQuazgJw1P2C0Im6CFUKirk5VuebnF5z9/kq1bq/H7w/T2tqVmuqXfhj/8sMUdd3g4ckROqRgZURkZYVLelsTjkZ/pUEjl0Ud1enpUhJAXFXvaxcSEwtatc9/VTCVKw4CHHtJpa1NTkrgbbzTYsCE3yeBcRDnb1N5YLJZavBsdHcUwDAYHB1NjluzXaLY4yGy4mMNu4CIl3cbGxnlFDGaDTZrpbrVcBkvayNdckd5GME2TDRs25P2hmau9YDt02tra8Pl8bN68GY/HQywWy/r6+XzwvvcJDh8WdHbKL4HHAx/60Nk8YrvSbW1VqKmRUy0CAT+GUUZXVwKvt4euLvjGNy5jYsIAHMTjMDamo+tSexuJ2HZuWdH9xV+4+cu/TPDNbzopKRGsXi1YvtwiEFD43OeyLxSaJhw/rlFaKhgcVHG7Bd3dKvG4rbWVlXNlpewrCyFlWytXymO3rCTJpIt4XMHjkS2FcFgS3dQbHk2TLQLZ45WtgvJy+M53ooRCAlVdSm2tYPXqszPdbLKxpzK8+91e2tvXMj6u4XZrLF+u8Y1vJFLtk2QSvvY1D93dGj6fTF2zLFKZEjU1Iicn2dQWV0eHQkeHyvLl8nmiUWnu2LAhMdMu0l7jwvuw6QubNTU1CCHdhosWLUpN9piYmMiIg0wnYulKdGQl42ykOzExUXCle75xUZLubCh0MSuZTBKNRtm/f39eZGsj16SxqT3bzZs3c/r06YJWdmdrL9iLix6PZ5r1eLbtvF74yldMDh2SFeeaNTKd6qtfVTlypI41a2J88YuC8vIgx49Hqa9XqaurJx5XWbfOxcaNdRw+rBOPS7mVpgksSyEeVzAMQUVFgmhULkYZhiSbgQGFxx7T2bjRpKFBLrD5/TIQx8aJEypPP61x5MgKjh3TaG6WRBmNKsRiMDiokkgoqbAa05QZCaGQgsslOH1aSsYcDnlMLpeFwyFYsULg9cp+bTisMjZ29jll5oKCELJirq4WrFlj8bGPJdm+3SQctnjwwQaqq3WEUHjTm6RywVYKlJdX88orKhMTChs3mnz721GeeEIhHk9w+eWDhEJ+DhyQi1NdXQrt7RZLl8r3p6xM0Nen8JGPJBgcVGlsFJSUzN0WmFrpynD4s793OmVLJxcspGTM7unOFgdp94unJpBNXbybKUu3SLrnEDORqt1XzYfA0q3Bqqqya9eugicCz1V1TiVbO6O3UBVCtu3Gx8dpbW3F6XSyYcOGrKHrc/WC3W7YuVN+wRMJuPFGnVOnFHTdxf79Lg4dGuPOO0cIhVYRDEpL7FvekmD9egtQUlKxRELF45GknUxaGIaK2y3fG6m5BSFMTBPC4RAdHR6OHFFRVYVYTGp5R0dln/Zzn3Ny+rTK+Hgjjz0mf9fYKE0N/f0KoZDcp6pKUrULNDkJScrYRka0VBXr8bhobpb9zUsusWhtVTjbuwUQk/uTZLdkiZwW/NGPGqnq9KGHHDgcUuHwu99p/OIXOjfdlOC226T54/vfd3DsmDp5/jof+IDKXXdZ2NMrJibgoYdUWltNYrF+wmEZhCMLB5WRkVK+9z2Bw6GiaTrPPqvxxS8mmG2g7lTrbn29hdstJW9er2BoSOGKK85/4M1c30s7qS1bApl91zA4OEgoFErZoC3LwufzEQgEGB0dLbYXXg3Y+Qu5kG4sFqO9vR2/38/y5ctZvXo1L774YsFX9pnaC7ORrY1CVQjp2/n9flpbW9E0jTVr1swaFZnPAtyBAwpHjyoIYRGJWPh8SU6dquXSS6u4+27o7DR54QWpu338cZ1rrzW5+mqT//xPwcCAYGJCXiSXLk0Sj6vE4zpOp0IkIm/XhdDweAS9vT5GR+XF1OGwKCtLoutxfvzjAC+/XMmJEy6SSQWQKopYTKGnB8bHFUpLz5onJFnKW3OXS5oTKioE3d1aRtsgGtWYmLCIRgVXXGHy0EMugkHSSFcStTQ4wD33xLjmmrOV5pEjKnv2aFiWh5YWjVBIIRJReOABB263lIOdOKHS3Cy3icel227XrkRKHfEP/+Dk1Cl1Uj5XhRA+IpESPB4IBi1cLpOSkhgeTwjDSPLyyx4eeWSUq64yZ+yHTq1Oy8rg1luT/Nd/yZCga64xueaaV4d0C9mXw+GgsrIyI/Smp6cH0zQpLS0lHA7zb//2bzzzzDPE43EOHTrEhg0buPPOO3OKS50r7EYIwR133MHu3bvxer3cf//9bN26Ne/zSMdFSbrzsfJGo1Ha29tTY6rXrl07bURPIe2Jqe2FXMg2fdtCSFfTNBKJBAcOHEBRlJynTuTiLLNx9GiMM2dKcLkEmqYzOurA55Ok5nTCI49oHD6s4PNJmVNHh8pnP5vkDW8waWmRs888HotoVOOtbw2yZ08FliUr0YoKQSwmhf+xmKxwLUuhtlbBNDV03cGjj3o4csQ5SbgKmibNDMmk1LsqitwPSKWCYShoGjidMqpRCIWhIWVan1ZR5ELbH/2RyQ9+oDMwoKIoUw0I8jgDAYXbbvPwzncaXHWVSWmp4Ne/djA6KujsrGB0VFazcsFL5f77nVx9dSwjHc3hkMdmKxiOHVP5xS/0lA64tlbKw971LoOJCYWVKy1+/WsdXffh8chb8Xgcysp0PJ4xAoEAfX190/SzsVhs2uJXXZ3ggx/MX9lzoTrSTNPE6/WyaNEiFi1axD333MO3vvUtmpub2bVrF0eOHMlZPjZX2M2TTz5JS0sLLS0t7Nmzh0996lPs2bNnXsd/UZLuTJgtaSwSidDe3k4oFKK5uTlrDoNN2oVIW2zizIdsp26bD4LBIC0tLUQiEdavX59XP8s+bzsboapqenxiKBSipaWFY8dq8XhKCIf1FFE3Nso/fX1w9KjCsmUCIaCiwuLllzX6+w36+lRuvtng5EmNcFjeJvb0ONi508TjkW2LV16RI23OnJH9Y9OU5GgYsnI9fVpnYuIsMQGYpswscDhMDEOSZzQaQwgdcEw6yyySSZXmZot9+3SmvrRyLJCgtBT+7/91EgyeHW2TeS2SrZJkUs5te+ABJz/7mWDRIukuGxuDM2dkrq/bLdUIUganUF4uXW79/QonTyq0taksWSJjKLdssXjgAdmW8XikVvn48XIGBxVuvz3BG98o70ICAYVf/EJnyRLZRtE0hc2bndTV1VFXV5c6ynT9bCAQIJFIMDQ0lApLt3uoueQtpONcD5Kcz76y5S5UVVVx6aWXcunUsSWzYK6wm8cff5xbbrkFRVHYtWsXfr8c0zSfsPTXFOlmq3TD4TBtbW1Eo1Gam5tZv379nJVyoaQ7OjpKX1/f5BSBuck2fdtcSTcUCtHa2koymWTlypXEYrGCFhD27avkq191YBiC2lr42tcMli0jNccqFouxcuVKFi+uBmRalsxSECxbJggG4fvfV3n5ZYWODoUNG8zUiBxNkz1dTYOtW+WUhiNHpP7WPtRIBIaGVLxeQSBwtmq1LOjuVidlXUxzhNm6WbdbpbRUVrOW5cGypDLC4RAEgwJNSzA4mMDr9eDzWYyMOIjH1RSput3m5Hy62e9q0rswyaSMoZyYUKiqEixZYiJEkv5+z2S4uzyHJUssysvh9tuTfOpTLk6fVqmqEui64K67XPzzP8dob5daYr9fnXwOhVhMcMcdbt7wBgNVlQtz111n8Ic/aPh88PGPJ1i2bPodSrp+1jRNnE5JzPF4PGNxKhKJcOxYGf39ldTXa7zznYLy8pklWxdCe2GmfWVTL5yLnm5fX18qxwKkcqqvr+/1R7ozkWb69IhQKERbWxvxeJwVK1bkHHpTyKyz4eFhWltb0XWdLVu25Ey2NnJZSAuHw7S2thKPx1m5cmVBEy5sdHfDj3+8jKYmuVjU1ga33abyta8dIRIJsmLFCqqrq1EUhaoqSTylpQIhLExTMDys8f3vS4lWU5NcZf/DH1RWroQrr5Ri/5tvTvLAAw5GR6UKwSZVIVTWr7c4dUolEpHqA9k6OKtJtd+mmTogy5dbqZaCvBhII8dttyW44w4X/f0q4GB01E1FhcWKFQmiUUluyaSC02lSVxdhYsKLYeSjUCFlovD75UKhZcnFNFklC6qqBDfdZODzwXPPqRw5ohKNSv2xLV/bs0cjEJAnaRee9rl3dqokkzqXXGJx+LCTT30qybe/Hc/5GO2WQHregm1ZfeABnfvuc2IYArB47rkJPvvZUyQSZ/N505UCC9legIWz6C60OeJ846Ik3Zmg6zrBYJBXXnmFZDKZItt8ts+VdNPbCGVlZaxcuZKJiYm8CRdmX0iLRCK0tbURiURSZDvfD293t7xtDofhyBEFwzBpaYH//M9mvv51N5p2dv/19VBfL1BVME0LhyNBSYmHo0elMaK+HlpaoKtLcPnlBtu3Wxw8qLJzp0l7u8I//IOLsTEF09QRQi58HTwonWHZSNW+xa+okD1X0xQIkXm+Pp+guVleMPr6FD70oSQtLSr33ONI9Wbtffn9Ki+/7CUala0Lr1dgGBqjox4ikfwqL/t4ZVC5dMpZlgoo1NdbeL0Cjwfe8haDU6dUfvELB7qupJQafr90ux0+rLJ0qaCnR+qC7X1PTMjHjo4qrFsHui74xS90/uRP5GfSMGB4WOZSuN0yKMgw5ETlmhp5cDMRZTIJ993npKzM7nWrnDhRiWVtZscOa5rFt6Ojg1AoxJEjR6ZZfAsh4oUMpJmJdCuzjaGeJxoaGujp6Un9v7e3l4aGhnnt86Ik3WykEwgE6OnpIZlMsnHjxoLegFy0tlPJ1m4jBAIBxsbG8n5O+3kTiUyxejQapa2tjVAolFF5Hjmi0NIib2/zGSqZjro6ucB0+LCFECa6ruFyaRw8WMbBgwbbt5/d75VXWjQ1yWkNDodc8Lr5Zovf/EbhwAGFykpoahJMTAh+8QsnP/+5tPVeeaXB6dNaigDttC5FgXhcQddFqsLNhpERhUWLpBwrkZDbu1xyAkVvr47TKWMhW1oU7rrLRThMygSRDlv+BUxKt+TPAgEHtttsLui6iWFoGIbA5Trbe3Y4mNTxyup31y6LwUEpH1u9WqAogvp6i6EhLXUxcTql8mHTJhkHWVcnOHZMkpiiMHlxUxgaUikvt1K99vFxuOceJ7296mSOg3w/VFWGs3/jG3EuuUTMSLqxmG1zJuO5IhFl8hynW3z37t3LmjVrCIVChEKhVCSkECI1QqhQV9l8cD5J97rrruO+++7j5ptvZs+ePZSXl897+OVFSbrp8Pv9tLW1oSgK9fX1RKPRgl/82VxlM5GtjfnMOkvf1payBQIBVqxYkdGDfuYZhYcfVvF6IR5Xeflli+3b8/ugy0WIbq65Js7992/E49FQVdi0Sc4VCwQyH19RAf/xHwY//alKf3+C0tIR9u9fyv79MpZQ1wVer9SAVlVZVFXJJK0XXtAnV/RlpXpWinWWfO0+cTaYptTn2qSj67YtWScWU5iYSCcykZVwbUxMpOcHTE8Om/s1k9MhFEWStmUJfD4DUCcHVkpzxt69sHSpHOdeXS3P2+2WvVxVla0Ny4IjRzQsS+GSS0x6e23lQ5LKStl2kCltACqf/rRsLfzwhw56e1UaGgQnTqi0tckFuUWLZB7w97/v4O/+LjFj6JLPB5ddZnHokJyuEQ5Lx97atTN/ZtMtvumpWnYk5EwjhKa6yhYyfc9+/qkXlng8XtBd5lxhN9deey27d+9m5cqVeL1efvCDH8z7+C9a0h0fH6etrQ1N07j00kspKysjEAgQDAYL3me29kJ6bsFsC2TznbEWj8c5ceJESjecLmVLJuFHP1K5914tVeEuWSJob1eor8/tg2ZZFt3d3fT29tLY2Mj73jdGKCR45RWFpiaRUgJceul05qquhk9/2iIQiPDXf+1mcFC6nOSCmpSEBYMWNTVxDEPD59MYGFB4wxtMRkd1RkbkrbFNtiArRLd7OgH6fJIQVJWMtC+7opSvNanXRVXl6v9cd6826Zvm3I/NsjVlZXLSsGFIa24ioadeMyEUdN2gtRXc7ghr1gxRW+vkbW+r4vvf9+J2y2PVNCZzdeXdgM8HDz8c5f/9PwcPP2zi89laYhns/t73mrzlLfIz1dEhF+POnjepvGGPR0wGs88810xR4JvfjPF3f+filVdUVq60+NKXEhSSUJgeCTmTimJkZISOjo7UwnQ8HmdgYCBFyPPtFU+VeRaKucJuFEXhO9/5TsH7z4aLknSFEPT397N69eoMAXQuwylnQ/r0iVzJ1kahlW4ikaC3t5fR0VHWrVvHmjVrplUF3/ueymOPqcRiMDSk8OtfK7zvfVZqCOJslYRlWfT399PV1cXixYvZtUtOg+jv7+fuuw2+9jWdl19WqagQfOUrJk1NWXczuS8ZKmMYkkAcDnA4DFQ1iqq6iERUFCVBLCaIRDR27jzNmjUV/OpXtezf72J8XJ+8rZX22nhcoaREWmuvuMJg924HIyPKZGWoZFTBmnaWgO3nt2/F54KqSqK2LFkR5wObVC1LVvBynDksWWJy+rS8S2hsNNF1GUn5yU8aNDeD3z/O2rU9fPCDbv7931dhGBrDwy5AZfFia7JVI+8k/uqvYgwMBHjhhaVUVAg+/GGDjRtlElsgIB+zfLnF/v0aDQ3Sjmy775JJ2Se3Z5/NtvhVWQnf+lZui3KFENlMKWTBYJBTp05NygZ7prUo7Ko41xbFbEH6FwMuStJVVZUNGzZMe/HnM+fM3j4SiTA8PJwz2drINXvBRiKRoLOzk9HR0VS1kK1XJIQM5m5okCvkQ0Oywjl2TGHtWkF9ffZbSjugvLOzk+rqanbs2JEhhVNV2TP8xjfMyepo7skEDofCsmVREgk5bcLvjyOERkWFl7e+VdDSojI8LHMVvvSlBG9721JCoRBXXtnBs8+afPGL6/H5THRdwTA0wmGVf/mXMO95j2w//NEfmXzxiy5aWrRpbYf0l/bs6J7cXmvLgkWLpKHCdr3NhLPONhsKmiYr+lBI/t7phP5+bVLNoOD3y0XFxYsF73ynk9LSs+/j5s2werXFvffKGW1Op8H4eJRQyOKGG0bo7Ezgdrv58IcH2bSplsnoWvnMytn2y4c+lGRgQKGvT6owbrwxSXe3zHW44YYkf/qnc5NuPlio/SiKksqhbUq7oqe3KILBIAMDA7O2KGxkKzDsgZwXCy5K0p0J8yFd+4rc29tLIpHIS2cLksRyqQ7SIyOXLVvGrl27iMVinD59Ouvj5XhuWV02NcmV564uhVWrBHfcIRUC6RrI9Aq9oqKCTZu28Z3veHn8cRWPBz7/eYN3vlOkrMCapmUYI44dU7jnHo3RUZmp+9nPntXfKorC2942QDJpMjhYxeBgGVVVCm9/u8n//t8GiUSScFhOzn3mGY0bbqhCVav4+MeTLFnix+lUUFXnZMVpoWkWLtdR9u+Pcvx4HQ89tIyGhhh+v5uhoZmUBdLmK2MYxZw6WxsDA1LX63bL3vVUOJ2y+rYJ1+cTqYhHu5q2LAUQk/m3ciJwNCoIBjVaWxVuuSVBuhHKXgC8/HKVqioHS5YoBAI6Q0NlvOUtST72sQSRSIiJiQmECBKLneb3v/exeLGKYXhobHTg86mATmUlfPWrCYaG5NDLRYtE1ovkhUa6kN3MkN6imPrYbC0Kp9OZqoanHl8gECh4eMGrgYuWdLNZWXMlvnSkk5Tb7aaysnJekz5nQnoY+tKlSzNSzObKXvjoRy3uvVdLLQJdc43grruks6uz8+zoHTtZLD3G8Zvf1Lj/fpXSUrkC/rnP6dTUGFnzF4aG4AtfkCRcXg7//d+ypfG1Wfge3gAAIABJREFUr8kEqJMnT2JZAe66Sy4q7Nlj8qtfqSSTCt/9rsY732ly5ZUWjz2mc/fdLoSQsq9PfMLFtm1lOByy9yiEghAqtbWCK67YSFmZ4MEHdSoqTNzuOKtXRxkbq8TlsojHNZJJNaOq1XWZhxCN5nc7aSeOpcPhkD1WW+WQSCipx6ZXvbb6wrIUQqGz0yAMQ0XXpRvtt7/VWbpU8IEPJLnrLifHjsmYxne8w+D4cZVLLhGT1augpUVHVUuoqyuhrKycxx4r4amnVhAMQl1dkptvHmbDhl6OHg1hmmbGAEmv1wd4yKa+WCiyfLXGr88WlB4KhfD7/SnruxCCl19+mZMnT2KaJh0dHSxbtiyv83/qqae44447ME2Tj33sY3zhC1/I+P3999/P5z//+ZRM7Pbbb+djH/tYzvvPeo7z2voiRraerWVZ85rymQ2madLd3U1/f/+MYehz9YOvvVYOcLznHo2hISgtlUE0V14pK9bx8XF6e3txOp1s3LiRkpIS4nH4l39Rue8+SViVlfK2OBKR43je9KbppHv6tJRn2V2O+nrB3r2CV145SjweZtmyZYAc1BcKwe7dcjXd4ZAE9NRTOpddlpzMFJAJYdLWq/DCCx5UVfZHS0oElZWyXfL2t3u48kqTSETB7dYpK3Ph8YDbrVJSomAYCsmkdKHZ0i9ZfUrytazcF8bshTSnU2Casm9s90XDYVDVdBKzd2pXuZP/m3xILCZ/ZlnyZ7KKhmee0Tl+XJoiliwRhELw4x87Jvct8Pul+87O/QU4cEDl0UfraWoSLF4MAwMOursX86EPVU0e99lA8FAoxNDQUGqAZLpsayENDReSGy1dReF0OonH46xbtw7LsqisrGRiYoJXXnmFO++8k66uLr797W9z1VVX5XRcn/70p/ntb39LY2Mjl19+Oddddx3r1q3LeNxNN93EfffdV/DxT8VFS7qzNc1nW1iabYEsHo8XrECYivQBlvX19bNOnshlEe73v5cV5ZYtYrL61Lj7bj9+vx/DMDKSxSwL/vIvNf77v1WCQfkFTyRIBVmXl4tpdwWGASUlTJKYdJ+NjoZJJk2WLKmltraGWCzBI48EefppbTIwBmyHpK0ZjUYlqU5MyH3KW3KwyUvXmZS8yV6o3y/48Y9lRa1pCo2NFs3NFpddZjI6KseUp88yU1ULl0smgCmKwOUSk0YLZfK1tKvQ7O+/XCeVv3O75TFKk8NZArVTxrKZBB0OQVUVlJSYHDyoIYSUtY2NScdZaamgs1Olulqk7hikoEZw+LBCMCh7sj6f4PnnNa67zuTUKY1EQo4+SiYlgb/88tnPiqIoRKMe+vtLaGqqYfly+fP0DFr7VjwYDHL06NEUGft8PjweT95EfK7bC/PZl101q6rKpZdeyrZt2wiHw3zrW9/Ka1979+5l5cqVNDc3A3DzzTfz+OOPTyPdhcbCefwuEMxkqbV1tnv27GFkZITNmzezfv36aVrb+SzEKYqCYRh0d3fz0ksvpeaQLV++fNYPXS5tkf37FRYvFpMLOUkmJoK88MIg5eXlrFq1KkPF0dMDL76osmiRlHVpGkxMyPZBfT28//1Wqr3Q3w8f+IDOtm0OPvc5naVLTVpbY5w8GSIcdvHlL5dSV1eLoig8/bSDp5+uZWxMutpeeUWhs1OS1cgIk24neR7BoJpK1ZLneHZaxPg4jI0xaZBgcjS6gmXJgJhYjEmhP6lBjjYsy96vgtOpUFEh/3Y4ZF9WUeRQzFwQiShpzjgFVZWzy2ZDZSX87d/GUVWFmhoTt1uqSIJBGTf5mc8k8HrPtj7sWWo33pjEsmQfuLHRYtUqi3vucZFIyOMeGnIyNqYQCim0tKj4/Wcr4Z//XGftWh/XXutlzRpfakKwnUFbX1/PqlWr2Lp1K16vl9WrV1NeXk40GqWzs5MDBw6wb98+jh8/Tnd3N2NjY9PMOFPxarUX5sJCGiNmylWYip///Ods2rSJG2+8McOdVigu2kp3Jtiyr6kLS+3t7ZSWlqYGWWbDfAwOlmWRTCZ56aWXWLJkybRpwfNFdTWMjZmoaoBk0sTlqmTbtktxu49Nu8jIL7ok30BAQdelm+uWWyz+5E/kwpiiKJimxV/8hU5Hh0JNjcDvT7Bnj8Gdd05QV1fN6tVKhm73+ed1ysritLdLKZNlSXLp7YWGBsGb35zk+uu9HD06/csqLyo2CSuTmt1MV5phyHbESy9pVFYaqcGWU6+hstcqtcpuN1xzjcF73mPwV3/l4swZJUdZ2NkK2q6S5TrB7Ft9+MMJjh1TaWlRAYuyMoNk0kEiAX/yJwl27LD4yEfk6KGeHhlHuXOnwY4dgl/+UqS0tkLIhLdwWFbNPp9JIqFNjn6XjrP773ewdavJJz/pzqjcP/5xN1ddFSKbwz09c6G6ujr1c8uyMsJvurq6Uqv+6VWxraFdaNLNd3bgTJhpVM+5Wkh7z3vewwc+8AFcLhf/+q//yq233sqzzz47r31etKQ7V1LY1DbCbGQ71z5ng2VZKWkWwGWXXZZTeHI+iMVivOtdfdx7bx0ORym67uDKKwU7dpi0tWVW9uPjcgbY8LAyGSAuf+5wKPzXfykcOKCzerXgxht1Av+fvTcPjKsu278/Z5k1M9mTJk26Jk23tLTQ0oJFCgiyKO7Iw09AsYooILyg4oagKFUBhQf94SPog/IiAlKLsmqFsrfQUtrSJXuapVknmcxk9nO+7x/fOZNJMpMmafGxvs/9D3Qyc7Y5c537e9/XfV1+QWOjQl5elEAggstlB9xUVzuSgt2jEUjT4MABLw6HXJr7/QqKIvjBDwwUxeQ3v9FpaFBTHN5oVIKjNX5rmkraqKv19/Hnm0go/O1veupvo4FQpBpXZ54Z57XXbLz7rsbTT8tJNSs7nlqMnKu8XuMXgFI5TfDWWxqmKQcS+vpUAgE5UWaaCocOaRhGnAsvNFiwIMLBgxoFBSannmoyNCRryUNDcjrM51OSwj0y0503L8LQkE48LrnAug6//KUNv9/O2KRU16G1VaWwcPJuI6qqpmzV08NqUAWDwRSH1nq/pZzn8XhwOBzT5sEmEolpTYtlikxZs9/vH0VHm2xMRlchfRJvw4YNfP3rX5/yfsbGcQu62ULXdXp6eujp6RnVxT/Wkc6DLSoqYvXq1ezfv/+YErRjsRhNTU34fD7Wrp3PH/6QQ329isdjcMIJIjkgMMJ8aG+X7IT9+6UYi6LIH6iVLcZiCjNnCg4cUHjyySI+85k+4nEXoZBKXp4XIaRNd7aV2oknmvzhD07y8mT2XFAgp8EGBqQmb2/vyJJ6RHRGasZa01uWxXkoxIQZaSQyYhiZLjRjtxvEYjqmqbB5s50lS0z8flnOmG4oioKqKhQWGoTDko87PgR5eSb792t8+MMJFMWkr09LDU7U1Jj09Cjs369SW2uyeLGguVmwaZONl14SXHllnJ/9LMott9hpa1OZO9fkvPPiPPusRllZjKIiKbZut8satSw5qBQXy+2mRzwu6YPHIux2O4WFhaOEoUzTpK2tLTXma4mlW35l6VnxZLLhY11eSPf7AykFsHz58ilva/Xq1dTX19Pc3ExFRQWPPPIIDz/88Kj3pGvnPvnkkyxevHj6B5+M4xZ0x4Kbldn29PQcsYxwpDhSI667u5umpiYKCws56aSTUir1R1OeSI94PE5LSws9PT3MnTuXhQsXpo7HskO3Ip369ZvfaASDkqWgaSMCK9KkkZS7gcsV48ABQTA4wG23zeG221wMDMj3X3ihybJl43/QnZ2ytmgYCQ4flkAzNCTB0xpUaGyU6mHxuNWwGhl99ful+LhhjHfbzRa6Lrm4qiozy3hc1k0LC00SCQWfT2Fw8MhDHUcKISQDIx5XCQYzb8wwFAYGFHJyYnR2+igoUPF6CxECli+HsjIJmtHkwNcDD9i47TZ7suEnjTf/9rcQH/iAwfPPkyxROKiuNohGCzjrrG6Kirw0NGgsW5bg5Zc1VFXgdsu6fGurvB42G/znf0YoKjp2ql1jQ1VVdF0nPz9/VOaX7lfW2dlJMBjENE1cLtcoMB47WfZei6FPV0tX13XuvfdePvjBD2IYBldccQVLly7l5ptvZtWqVVx44YXcc889PPnkk+i6TmFhIf/93/991Odw3IKuFek1W4/HQ0VFRapjO51IHxoYu590R4gTTzwxRdS24mj0FxRFSY0EHz58eByXN1ukNw67uxXcbpmBOhyyPmqBgK5DZWWUnh4//f0OTjlFYc6cOaxapVNbm2DHDoVAAJYtk+yIt95S+NWvNEIhKCuTal5NTZLZYI2gRiIK4bDgmWcEOTmHef31UjRNQ1G0lM5CSYkUgMnNNQkGlSkt/U880cDvVzhwQEUICbiGoRAMqikn4ZYWZQzVK9v1HT1KnB4ej6WRm307UmBdpaDARihUnOTOGkmGRpDGRgWXS0FVO6ivz+H22+diGCRNJAU+n8Ldd9vZv18eu8Mhj//ddzXsdsETTxRzxhlw8cUx6uo0CgqsZqP8Pk1TcM45CW66Kc6MGZkB91jKJ2aqw2byKxNCjJos6+rqIhwOj5osC4fDx+zYsjXSpitgfv7553P++eePeu373/9+6v9vv/12br/99mltO1sct6CbDoIejydlM97W1nbUo8DpFBchBH19fTQ2NuL1elm5cuU4sLViuuwHqwm3ffv2rFzebJFeXjj5ZJOHHlIpLwe/XwKlxwO1tXEqKnqor8/B4ylg5UqNiy7qxjTlMs3lEjz/vEYgAM89J6UC29sV8vOlTc3LL1uAB0LYkzKLUvO2sDDCCy/0sG6dA7/fSSymomki+V4Fn2/EndfrTRAMSuNGIGtWCZIJEY0qHDqkkpsLCxcavPaanrIUt+qcfX2yhuxyiQmHJYTInmG73YKenuwPN7td8oENQ44R67pGcbFKZWUURTHJzc2nttZkxYoot95axe7dNnw+Wb8Oh6XKmBAKbW1xNM1Od7dGV5fcns0mdRU8HpPhYSm7edllMYaG4AtfcFJfL3nWK1aY/OAHMcY4l485x2On5jXZ7FRRFNxuN263m9LS0tTr6ZNloVCI+vp6TNPE4XAcFZ3tnynr+F7FcQu6sViM3t7eFNhaYbPZUs2A6YQFuna7nf7+fhobG8nJyRm3n0wx1fJCuhgNwMqVKzNapk8U6aB7ySUmAwPw3HMapaXw5S+HqK2twzACVFVVE4u5icehosKgrY1UhvzwwyqxmEiJ3bz8siwRzJwpqWCWjY6uj6iFxWICj2eIcNjO6aeXUVsr66oyoxzRmFVVOQUWi8nPFhZGKC8P093tZnjYMU6g3IqyMkmrCgYl73fHDi3FfBjbWMrJEbhcEqQn62SvKDLzstkkcGe/viJ1Loois87DhxXa2xU+8AE59jx3rsmllyb43vfctLUpSVEchXBY8oWtceC6OpNYLEJ3tx3T1EkkJPD6fCpLlsRxOJyEQnK/ubnw0EMR6uvlNV2wwORIZdFjya092m2lT5Z1d3ezfPnylJqe1bjr7e0lFAqhKMooLzePx5OV7ZCtvPC/oPtPCKfTmXFc91iI3vT39/Puu+/idDqpra2dNBBOtrwghKCrq4vm5uaUGM277747rSxF07SUMprNBtddZ3LllRFaW5vx+fqZNauK0tLF47atKEoKdPv7FdKrMS6XJW4tgcniyrrdEAwKhBBJHV0Pa9bAJZcYHD5s4nJJRoMEKIGmSSqaqsrSxeCgjUjEjs/nwu02kiO4cYJBlWhUI32sta9P8lXnzjXZskUjHs9O5zKMEcCfbAghXXJ9PjUrUGuaHCgJBgU+n0ZOjmxuWfXqN990c9ppw7S0OHnrLVmzlk0wKC42aWtTU/XoRYtMgsEcVFXgdKo4HNL+Xd6qcdrbY/T0+DjttF7a20doXEuWTP4n+q/s3ivLTpnpbIZhEAqFRtHZYrFYKitOF8DJBLqhUOg9aZa/V3Hcgu5kfNKmGgMDA/T19REKhViyZAmeidZyGSIdADPFWDGa9CbckfQXskX65xKJBC0trWzZEiaRmMWJJy4kTe503Ocs0D35ZMFDD6m4XCK15C0sFLS2SjAOBGSJIB43yMkxKCnR+dKXBB/+sMmsWTID/PWv9WTZQA48yLqlSDbDpAatw2FgmlLeMRLRqKw0iEZ1dF3Wo8HySJOCMk1NUT74wUaeeWYpIJL83PHfezQqs91oVEk2xCZz3SR7IpuDhaJIWto990gr9RtvdFJXJ0sZsjkGgYBGb6+OyyVFyw1DXrfubmvYY0SCcmgI2tvVZEMQVqwQVFUJnnlGx+93UVfnoLhYQdN0FKWf7u5uGhsbMQwDp9M5bkme6f7/VwXdI5U9NE0bR2dL11sYHh5O0dmGh4fZv38/Ho8nyTWXDtzH0svtvY7jFnQhs+hNuibuZGNwcJCGhgY0TaO4uJjS0tIpAy7ImydimV6Nif7+furr67PS2KbLfLBAt7W1lfb2dv7yl2Vs2lSUbGQpfPGLBjfcMH67TU0OXnvNQXm5yvr1JsEgPP+8SlOTQn+/1HcIh+HSSyOEwz1s25ZDe3sBTmeEq6/O4XOfM1Nlhx07FF56SWXtWmnRIwW1BR/6UIInnrCl2A6xmCWmI5fsp51m0NmpYrcLXnoJhoetH458v93u4LLLKvnxjwFMolEVyw04PbxewS23xLjjDjuHD4/+8UmdhfG6u5Yn2YwZgvb28dfV6ZTjvj6fwlNP2SgosOrUI27Hssarc/31Cc46y+C11zT27pWOGn19kvalqhJ0Gxs1HA6YP1/Q0qKwa5fO0JBsxK1YEUraADm5774CHn/cmXacmXUXNE0bR9/6VyovHG1kc63Yvn07c+bMIRgMsmvXLu68807a2to488wzWb58OZ/61KdYt27dpPZxJLGbaDTKZZddxo4dOygqKuKPf/wjc+fOPepzO65BN1NMpbwwNDREfX09iqJQU1NDbm5uSkruWO17YGCAhoaGUWI0mWI6oCuESIndzJkzh8rKtWza5CInR6Rccv/rvzT+z/8xKCsb+dzOnQq33lqCqiYoKVF48UWdH/0owYknCi6/XKeiAlTVRIgYjz2W4NlndW691cuDDwruv9/kySdVZs4UnH224LbbNLZuVWlogPZ2nTVrDLxe6RD8wQ8m+POfbRQWyu59NCozxFhMApeiwKJFJp/7XJxPf9pFS8tIzVhVJZiGQjYuuCDBpk22cWUAj8ekqChGbm6UpUt3cP31JXzrW0sRQkXXRdJ5Qo7eRiIiNdXldJqYpvxbKCTLDBYX1m6XughutyAvT3DjjU7WrEmwcqXJvn0KPp9sbMXjMoO/5pp+DKOIO++0M2eO4KyzYgwPK9xxh4OZM00aGlR6emRTbf58E69XsGiRoKtL4eSTZZ3Wbrds5aG/f7TDhqIouFwuXC4XJSUlqXNPb1R1dXURDAaJx+Mpta1s9K3JxrHMdI9lKIqSOrdzzz2Xs846i7PPPpvHH3+cPXv2THowaTJiNw888AAFBQU0NDTwyCOP8I1vfIM//vGPR30O/3agOxn3iEAgQENDA6ZpUl1dPUpG7mhqwunAmQ7o6WI0k/nskSK9TOF2uykpKaG6upq9e+Vy2fqtWFNUfr/UbZDHBZdeqtPVpaOqdoqKFGprBVu3qpimoLsbursNXK4o5eUaQ0MePB4Hf/yjyi9+oaGqCkNDCjffrNPSYrB1qxy4ANi/X2HbNpWFCwWrVxvs3aths8mptIICgd8vMAyNykrBJz8ZZ8ECkzVrpMHkb38b4bzzXIRCSpLeZrJokcnevSof/3iCzZv1ZLY6kukqiorLZae2VmP16tUMD5vMnx+nq0vD5UpgmoKhIRsbNnSwYoXgK1+ZnWQhKElHCOm64HRKjdyvfz3KGWcY/OQnUgchFJIKZ1u36nR1ycEJSxQHpEnnN74xg5NPlv5jBw9KgZ6rr47xX/8lM+OlS03mz4dXXtFSPm9S8AYuuSTOnj0OgkEFt1vyjk87zZhUbTqTBKLf76e9vZ2cnJxxwuAWUHm93kkNNRwr0D2WjArTNMdtyxoBLioqYv369ZPe1mTEbjZv3swtt9wCwCc/+UmuvvrqY3I+xzXoTlVTNxgM0tDQQDwep7q6OmPHU9d1wpb51BRD13UikQi7du0ikUiwYMGCUT+KiSKbUM/Y8Pl81NfXk5OTw4oVK1Ki6CCbPh4P+P1SMSwYJOXWa8U990iBcmmJIzPQpibo6hL84Q8mgYBA11UikRwSCcHSpZKg/9xzapLPapKTI4ciXntN2o+rqqCyUk6L9fer3HhjjJdf1njuOT0pgahQXGxSVhagrMzLQw+FiUTgz3/WeeQRndpak/e/3+ArX4nR2CgpYnl5grY2lbfe0njmGT1Vq03n2waDUk+4pMSkvl5j/nyVggKdWAx8PgdCwNy5JldeacMwAlx2WSfPPJPHoUM5SWaFZD14PHD77VE+8QmDp57S2LzZlqz5itRIs88naXOW9Y/lKjw0pPPWW4LKSpG0VZdDFN/+dpQf/MBBICDfd+ONMZ56ysbAgPwevvWtKCedZPLDH0b5wQ9MAgE7p59ucMMNEwvRZAsh4IUX7Bw4MJP3va+YdetGwDuRSKTKE9ZQgxBi3FBD+qjvv6JEZCa1sulydDOJ3Wzbti3re6yHXH9//6gm4HTiuAbdycbw8DCNjY1EIhGqq6tHjTyOjelmuqFQiIaGBgYGBli5cuWE+8gUR2qkWZmzqqosXbo0VXM2TTP1uZwc+P3vE1xzjU5Tk0J1teA//zMxiplQX6/g9cp6puUZ1tws+NnPTAIBG5WV0NMjl9ChENxzTwJFkbzZWMxy55Vi6rNmmXR2akSjlr25wjnnJFi0yOQnP7HjcEh33I4OlZ4elYICwY9+FKGpSeXjH3emrHPKygTr1hnU1ppJYBMMDUl5xFdf1fB6BfPmCfbv11KNNJtNAs3ChSY9PSpf/KKTX/9aWgn19srjz88XPPhghLKyXCCX730PWlsdNDZa4vGS+maacQYH9/Pccwm+9rUTkiplSnIAQ2bFlvauNVFnPdctSll9vUplpYFpymNbu9bkwQcjdHYqFBcLZs4UfOELcbq7pbW8hROrV5v8/Oct5ObmTvvHLAR8+9sOnnjCSTwOv/udxhVXxLjxRrnis6bL0sFJCJFiDGQa9Y1GowwPD0961DdbvNfTaH6/f9KJzb9KHNege6Q0PxQK0djYSCgUoqqqiqKioiN+Zqqgm26ZPmfOHBKJxJQBF7KXFyxieSwWo6amZtwNNtYBoqZG8Nxz8VF1wfRYscJk2zad4uIEfr/UTvB6DWbO1GlslA2g5cstWhhUVcnPfelLJldeqdLf7yAUklSwa6812bYNfvELDcMQLF5s8NWvyprmwYNqinamKAqzZiW4/vp6qqqWcO65zhSwAzQ3K3R1KezZIw0bP/lJg+pqk5NPNrjoIheqCiUl0N8v6OuTRo1DQwpz55o4HHJQY2gIfv5zOzYbnHqqweCgVEJ7c0uQhbkBwu5CXn3Txe7dWlL+UfKJEwmVaNTOhRcu5J13DHRdo6QkTk+PnnS4gKVL/dTUmFRWOnn1VVfK7kfTBCUlJoGANKVsblY4+WSDmTNls+z22x20tiosWWLyrW/FKC4WeDzjV2FHu2RtbFTYvFnH5UrgdJpomsoDD9j57GcTFBdnN3HM5Ohrjfr29vbS0dExzkQynUc7mWM+llq62cRupgO6kxG7sd5TWVlJIpHA7/ePaupNN45r0M0WQgj27t1LMBikqqqK4uLiSd/UkwXddDGa+fPns3jx4pRQyHTCIo5bEYlEaGxsJBAIsGDBgqxfdibbHciuR3DVVSa7d0fZulVgtwu8Xp2VK+XYbnu7lBrs7gaPR+H660ceAkuWCH7/+zgPPthGTc18zj5byk1eeKHJeeeZhMMmmhbBZtPZsUMlGpUZmK6TzD41SksjGIYsCUjnhxGVsUQCSktlNr1vn8pVV8ks7VOfSnD//TbcbkFJicBuj/D5zys88IADq0xuKYv5/dJE8sABlUQcIkMxHr0/yifM5/j9npOocy4jELBseEaoYvPmGRQXC4qKdAIBDSE0PB45qRaJwKJFsk8we7afrq44nZ1O/H47OTkJVFVmsRs2xFm0SHDCCXKy7MorpVW9ywWvvaZxww0OfvObCJnw52iX8oGAPG9r4MNiVwQC8juaSlijvna7PSXuYplIBgIBBgYGaGtrIxaLZZWFTI9/hpbudMoLkxG7ufDCC3nwwQc55ZRTePzxxznzzDOPSX36uAbdsRfAyjrD4TBz585l6dKlU75IRxpwSCQSNDc3ZxSjmS7XFkYy3Xg8TlNTE/39/cyfP58lS5YckeM4mVowQDgcpqGhgS99Kcy1185iaMjPXXfVMjQkJ6BqawX19bBmjclnPmNyxhmjs6S5c+G88/pYuXLWKPfVUMhPXV0dsVgMIQR79lSSmzsHIaQjQm7uSA1Z06C83KS3V8U0R6bL4nGF5maVefNM/P6R873iijgej2DLFp2CApN16/ZywQUL8XpV/u//taUy+gsvTFBZaXLXXXbicQX/oCAetXNAK+aJQ6tp7HAwr7qVRYsW8uabKoYxAg51dRobN9qSou8mjY1qaoz5llui1Naq2O12brnFy8yZggULoKcnTkuLyUc+0sM55/Tg8QwBOo2NHt56q5QDB9zY7ZIdoSiy/NDXp2TUTTjaTLe6WtbZfT4Vh8MgElEoL5d15mMR2Uwks8lCut1uvF4vHo/nPXONsGK6oDsZsZvPf/7zXHrppamS5COPPHJMzuO4Bl0rotEozc3NqawzGo2Sn58/7QmvTJmuxYWdSIzmaJ+C/f39KZfgBQsWTCr7mQzQW0Du8/morq6muLiYYDBIS0sft9xicN11On19Mlu8+GKT226TluyZIr15GQ6Hqa+vJxKJ4nItQlGFzWyYAAAgAElEQVSczJtn4nbHePBBFVWN4/Ek8Ps1li4dIhIJ09XVxfe+V8DllxcyNDTCRNA0aGuT02Ff+cpIM0lV4eKLE1x8sfxOduyQ4ikbNsRZscKgoUH6tK1bZxCLwUMP2di5UwrkaIpJr9/J/32mmvdVd0EkwllnJdi1y0Y8LjNej0eWGh5/3MaiRSYf+pBBb69JICAz9Pe/X7IoOjtljddacMyYoRKJxPjoR+2sXbsodZ37+oZ55RU3iYSJwyHr4dGo5O5GIj5isZxxI65Hm+l6vfC734W55hpoa7OzbJnJnXdGeK9dybPJQlq1Yp/Ph8/nS9WH07Nit9s95XPOBLqDg4PjygKTjSOJ3TidTh577LFpbXuiOK5BNxaLUV9fT39//6iss7+/f8oDElaMLS9YJYO2tjYqKiqmJEYzmTBNk/b2dlpaWnA4HFPefiYGhxXppphz586lpqYm9WCwxoBXrhT88Y9xDh5UyM2Vk1ITPTtUVSUWi6UeclVV1dx33wz+9CcpRejxCH71K5Wf/SzB97/vYHDQwVlnGdx8s42mJo3BwRhudyPf/34j3/nOCmIRVdLKUInHVSorTS6/XLpGhEJSpzf9eBRFgVAIJRpl1bJcTjrJwQsvaPz3f9uYM0fWgXfvViWUm1Jo5mB7Lv+xbDd1QzW0b5MDGppmkpcndRLC4RFGRCwmyxwlJdKSSHJo5aSZ0ykZEx6P/AwISktHHnjv/uUwX7+1kEM+B4mYSlh1pChzK1cGicX6ePfdFrRDh8gJh7GXluJYunTa92p6LFgg+PWv29B1PaX/Ot3IRM2abKSbZQJ0dXURi8WYMWNGKivu7+8nlBSZSNdc8Hq9o1ZQYyOb7sJ74d79XsZxD7oej2dcVng0XFuLcpYuRlNWVsbatWuPqf2OJYLe3NzMjBkzqK2tpbOzc8qAnunHIYSgo6OD1tbWrKaY6bXgsjJSPN6Jwqrtvf3228yrqKBm4UJefiefxx/XyM8fGd/9znccPPJIhPXrQ6nlv2HY+fnP57Brl+zMvW9tjPPnHeTlunLy1SCGruNzFbFy5SHuuMPGY49VACq5uQonniioqjL5j/+Ik1Nfj+vhh1GFwHS5+UHimzzxj+KU3OSiRWaSXqagqAo6CXQVPvfZGLdvLcTbITjllDAvv+wkGJTZq90uBxTmzzfZ+aYU20HTOOkkg7lzpViPwwE33xzl1lsd9PTIc/rCFw5TVCSX3NEd+/j6TcWgRZnljhIJF5Ew3VRVCRwO+Pa3dXmfvvIK+uuvkxCCeDSKr7WVoZoa9u3bN65G6vF4pnQ/GIaRGis/mngvxokzTZdlsxDKpkSWSCTGKfwNDQ39L3vhnxlerzejzOJkBiSyhRAi5XVmOUJM1d/pSCLofX19NDQ0kJ+fn9p+MBg8agF0a2iioaGBoqIiTj755KyZQ7YGXLbtdnV10dTUBMBJDgfe+++HeJzOttMx4x9GVXWEkEvd5uaRH6x1GbZs0dm6tZSaGjka+8rTIc6Pv0mdto7BeA4irrKqpoc5c2azcaMdlytBKGRy6JBOR0ec2bMjvPFSjJ+ozxGrmYU9L4/DHYI/P2FStNBE1eTgwoEDKpWVIlkOULE7bFx9VQTX2adSelChdrnB8HCYwsIwW7YUYrNJNkRgSOHlP/uJDUXRvV18e0MnntPPYP166dbh9QruvTfCww+H6emRtK/DhwMoiszo+rfsIcp5FOXI0kiV6KctolO7IM7lnzVYtkyHUAj9uecQlZVoNhuaYVDR0ED/ggXMXr4cu93O8PBwarDBEgm3mANWnTQbc+BfkVs7kT9aJgshS3MhEAikWBThcDilsxCLxVK0NpvNdlRauv9TcVyD7pF80qYS6VNehmGwdu3aaWUNVmMr0007MDBAfX09LpdrnP7C0bpOWNt2u90ZBdbHxmRB1+fzUVdXR25uLqtXr+bgtm04H3lECr+63cwf7EEdGiJRVJByHV66dPx29+1TsdnMVPfeHhniD4ffT3VeL6udDXzK/RfedJ3FLbd8gqEhBQUVhMCtR4gHNaqrXHQ0CBpcM7GFwxiBAK2BMkhESUQjqHY7mqah6/CLX4TZssVGe7vCqaca9PSofPWrelIIx8TjgaVLh0HkcrghzJ56NzPcAcpFB2ahk45QOcMv/JWr/t8zaEpa6IRCkpHwj3+EUlb2nZ0jq4Oi3Bi6YhCO67hsCZxEmal28T3XA5Q+FSJh/xjmnDmS0mE9CGWdA6LRlFvD2Ckza3URDAZHMQfsdvuoZbnL5fqXBN1M9joTRbrmwlglsr1792Kz2ejt7aW+vp6rrroKRVF44IEHOP300znppJNSE2aTDZ/Px6c//WlaWlqYO3cujz76aMahKU3TWLZsGQCzZ8/mySefnNJ+0uO4Bt1sYbPZsgrPZIr+/n4aGhpSU15vv/32tN1LrUZc+k0bCASoq6tDVdWs6mXTBV1LJLq5uXlKymhHAt1gMMjBgwdRVXWUZoQ9EJAcreQPae2yYa5s2cov2j5CYFglFIJYTOXSS53cfnUzc+u3oDQ1MbvuROh/H2KWk7hi552uMvLNfmLovD1UhT18Gk+0nYWBggIowsRAI2LY8RpDaH1hNGcRNl1lRl4emttNSd8QZd5h2vwzcLpiBINQVBRDVZv49KdzcLu9XH31DHbu1FFVOdDR0GCwfr3KwQMedr00TInSR2R4Nm1BJ+5cD0UuOcnVxQxaOhzYXCNec4oCu3drzJsnH+jpKxrnOe/jR69v4puvf5TwsAZh+Nb7/0ZJTT4i6kJ/7DHi116LmDkTpbMTUVKC4vMh8vKI5eZmTSDSmQPpfNp0XVqrRmo1rEKhUAqQp1MS+1dUK9M0DVVVKS8vTyUrb775Jueddx7r1q3j4MGDNDU18Y1vfGNK2924cSNnnXUWN910Exs3bmTjxo38WCosjQqXy8WuXbuO+jzg3xR0J6s0Njg4SH19PXa7fZRurpUpT1TUzxbp4Jk+2LBgwYIJl0FTBd1IJEJDQwPDw8PY7XZOPPHEKR1nNtBN325NTc24p77p9RKKabyyexaDcQ/L8lpZX93Gr3sU/J0jbhH79gi+8kWNvyx5Htc7b/GJ+OO8HP0hdTtPZihvNppdY6mrE2d8GJcyzEuRNWguO2oU7JpB3FBQhECgUJoTpKNFMP90jdyVS1B3bUfx+XA6HNz3G/j+7zTq620sX27y7W+b5ObOJBgM8sYbft56qwS7PYymKdjtKi0tGuvWBah/06QQgcerUhSP0Bt00hN0Y3dH0VST1Z59UknMHPGbM00oKRnJbtNBV8yfz9of6Gx6fQcdAzlUvPMsxSuSXXWHAxQFZWCA+Gc+g/7kk6itrYjKSuIf+QjGoUMTg1wkIjt8Xm+qXpOpRrpv3z4KCgowTXOUNGT6uK/X6z2is+97oaV7LGJsI01VVUKhEJ/61KemvY/Nmzfz4osvAnD55Zezfv36jKB7LOO4Bt3plheOJEZjcXWnA7qWdkNLSwtDQ0MpitaRYrLL/XQeb3V1NSUlJbz++utT5nqOZT1Y/OPe3l6qqqrGc5yFgGiUsLuQGw/fSNM7cTkCqyyE8jLiCQVdV1LDEEbUoHvYTs++AWZ5vTh1nTv7vknjgo+yc8Xn+K+XlmITJZhhN7GEiivhJO6wM9ApsOkmqpCliMXFXazL28Oik5xceEkPrb25hL71LezRKKKggDK3m1+ujaadmQrkkpubS1+fisNhx+mUE3aGYYIwUV5+mvyOWUQiC8lxxJjj7SEUKyeR0Mkze/j20j8xb2Ued340wg3ftCWvF5x7rsEppxhpl2T0NRezZ5M3ezZ5gO2uN2BgQIpfJOeGRV4e5OaS+MxnRn0XorU163en/eMf6H/9K5gmZlUV8c9+FiYQT7LqvunHmO5h1tnZmRr3td47drDhvdZLmG6MPS7r/j2arLy7uzvF9igrK6O7uzvj+yKRCKtWrULXdW666SY++tGPTnufxzXoQmbKVLZGmiV4k0gkqK6uzpp5Tpf9EI/HCQQCvPvuu9TU1LB48XjHhonOY6JIp3+N5fFOVEc+0v4sylpbWxuzZs1i7dq14/nHe/ag/eIX0NvLYHMlTf7/h9lzVURxCWFcvPy6jdJSkRybld9FNK7gSJjk2sOpVFG1Qa2+n6pTW3ltcAl7ds9Cd8QwbfDlqxMsXRnnyis1Dh7QcdrCnFq8n+sWPcO6Wc0ooRDcr0JvLzafD/HhD6P09SEKC8lmHLZokcns2SZNTWrSakjnxPwGTq37B0VFeeyuu4q+mAehKcyztXL7hx+m6NRSbGW1NC9dytmFQf66TGHvXhtlZYLTTx+tADbRgy5x8cXYHnwQOjrANEmcey4iC5Ur23Jeqa9H37QJUVkJNhtKczP6pk0kLrts0tvJ5mFm3atjBxtycnJSv6l4PD6txCM9juVEWtbrdITfzgc+8AG6urrGvf7DH/5w3Haybau1tZWKigqampo488wzWbZsGVXWjPwU47gH3UwxFjTTNRiqq6uPOD89VdBNH5xwOBwsWLBglPbp0YQQgs7OTlpaWigvL89K/5pqdpLO0igpKWHNmjWZfxy9vWh33YVwOFD6+jAGi1CHh2DAlJSsmXPIz5e0qoICQX+/ZBEkFJ1LaneSGwyh+P2YqooirXNRltZw+2lh/vQnO9u368yaJVj7fsHMmSYvvhgikYDQQBR3h4FdOwvlt79FSOkxoqpKwaOPomzaJBV+7HZi112HWVs77tDtdnjooTA//rGDujqVE+b0863hnyIKS1mxbTMPu97k2ch6FNXGefo/mH3QJOFYTqS4mJ6KCjrCYSKRYaqqRNL01DuKTzohS2XmTGLXXSfrttKiedx71L170R99lJq6OmwXXIBxySWkzwmr3d2SB2cBX0kJakND1u90KrVYm82WcbBheHiYjo4OQqEQe/bsSdG0JuNckSneS13eyfKJ//73v2f924wZMzh8+DDl5eUcPnx41IMpPawBjPnz57N+/Xrefvvt//+C7kSZbroYjbXMn8yXNFlX3/Qs0eLDtrS0HDX1C0ZTywoLCyekrk2F/gWyll1XV0cikWDNmjUTMh2Uzk5SvummyeKyXlx1w/QlynC39tIbKOTSi3RMm4Mnn9RwOqV+Q0GBwp+GL2BGZYxLXHeiNDej5edjzpqFsn073SsuZNMmjWBQoa4O3nhD4847w8ybJ0sUuSUOKFmCaRgoQ0OIpMSekkigNTRgrlyJmDkTAgHsd99N5N57Ze10TBQWwo9/LMsPStNh7HdGie85gBYIMC8PviweAJsNoemYrmq0zk5cBQXMfvllyq+5JnnacsoqEAjQ399PS0sLiUSCWCxGW1sbBQUFmQXDXS5ElmkppaUF2x13oDY14Q0GcRw8SOLgQWI/+EGqbivy82UhOUlCVgYGMOfNy/pdHW0DzKJw5ebm4vF4qKysRAhBNBpNZcVjnSusEkU2NbL3EnQDgcCkRcuzhaWvcNNNN/Hggw/ykY98ZNx7BgYGcLvdOBwO+vr6ePXVV/n6178+7X0e96CbKRKJBKFQiJ07d6bEaKZS7zxSpptuLFlSUjKKD3u01C8YAcVM1LJMMVn9hVAoRF1dHYZhsHjxYvbs2XNEapnwekdUaUyTGXSzsfyn/CpwGb5wDp+c8WeuOPwm+kfO5YazSvjg11cjUMjJgUjE5KcN51G7dDsrzjkHtawMFAXHvn08uec8olGVWTMTEI3S5bPz2GM6139pANvzz6N2diKqqjA+8AGMmhrUpiZEeTm2oSF5XFaG5vVCRweK34+wspThYdmostkQ8+enskdRWYmoqMD28suYNhtaKCQBVyrgSAGKaFRm9R0dqWswdsoK5D3w9ttv4/F4RgmGp9dKvV5v1nFX9eBB1KYmsNuJ5+YiHA60F19EravDXLhQXu7FixH5+Wh//Su4XBgnnEDik5/M+l0dS8qYtepJN5Mc61xhsSeyqZF5vd5jJmKe6dwGBwePmqN70003cdFFF/HAAw8wZ84cHn30UQDeeust7rvvPu6//37279/PlVdemUpubrrpplFi51ON4x50079QaczYQnd3N6qqcsopp0zrC88GumMHG9KNJa04GtA1DIO3334b0zRZvHjxpJ/iR9JfiMViNDY24vf7J1Qsyxjz5mGefz7qU09JwPT7mZtjsDH+DfyratGdTmxb3sR8dyfD5YsZbplHUZWH4d4AQtPIycknkrMAtdwYmZRQVUJDYBdRtHf3QyyGM+Ih+q6NnPseRjlwANPjQdmxA6W1lfDll+P49a/RmpvBMEjMny+/VyGkRzzJhwOg9PZiv+02FJ9PNp9OOIHY9dfLJbrdTuyaa+Ctt9CbmtAMAwYGUGIxREEBSns7SrJBZ65Zg9Lfj7p3LygKxgknQBp/VlEUVFWluLh4ZAXi86H84Q8kWlsJzpnDoVNPZTiRSMkoWoMAOTk5aC4XSigkyybBoFxN2O1SgT4Z+qZNaO+8I88tHpeligkekscKdCfTH8ik0ZuuuzAwMMChQ4cIhULs2rVrVFbsdrun/Ls8lmI36VFUVMSWLVvGvb5q1Sruv/9+AE499VT27NlzVPtJj+MedGF0k8kSo3njjTem/YTNRDmzhg+cTicnnHBCVsK35R4xlbBkHKPRKIsXL56ymHW28kJ6rXnevHksWrRoytdEAPGLLkKsWiWBLBLB9dRT4PPhmD0b/vEPhMNBPB7HDHaQP9TEwNu55OfEiAkH5NqpXJGP0rYLUV6O0taG+s47nOl9mJf2n0PAraE4CwhpDs7u/g1a/B1EdbUExNxcHM88g/7WW6hNTQzn5NB9xhk4TjkF9333oW7bhur3Y1ZVYbvzTqLXX4/joYdklur1InJyUHfuRHvlFYwzzpAn5PXSd/PN5N9+O45du+SDILl0VwYGIB7HdvgwidZWtK1bJfMgEkEvLyf605+OKN4wppEWieD40Y8k6Hs85LzyCsWJBPFrr8VI1koDgQBdXV0EknYSKwoKcLW2ottsiJwcRHX1SLNNCPSHHpIPguQstZlIoO3di3HKKZm/q2PkimsYxrS2k2lFsH37dhYvXpxiT/T29hIKhUa913oQTdRw+3cRMId/A9Dt6enhwIED4zQGLEGX6dw86ZY9gUCA+vp6gElln5OtB8N4mtbw8DC5ublTPt6x5YX05ls27YUjhaU/YW1XmT8/pWhuqCr6b34DsRhqJALBIFpfH7a2Nn6R+DLX2O7DRwUKCW4o+BldsxaTiORQsHcvee+8gzF/Pqc5dvHNxDb+ELkckTuDz6/czum+7Sj7GlF37JBsBSEQponpcpEQApfdTm0gQPSkk6C4GHXbNkR+PmZhIeru3ahPPIH64osoTU2g6/LYVBXbr3+NyMvDTHKZjRkz5N/CYVl3dTrlA0VaFSMAtaMDpaUFkZMDmoZSV4fYuJHYT3866hopigLhMNrmzWgvvii35fViVlejvfkm8e5u7Pv2YTcM8pYvRyRLB0IIIr/6FdxxB9G6OkJFRbS///1EfT48sRhep5M59fXS/dLthkQCta1NiuS+x3Gs/dEycYoNw0iVJyxjTYtTnE5lszjF71Wm+z8Rxz3o5ubmZuy8WyWC6UyWWaC7e/duotHoEQcb0mMy5QXTNDl06BAdHR3Mnj07RdPq6OiYemkiHsfR1YWIxyEvj76kh1pBQcGkdCPGcU2TYCuESP1tbHZsnnoqiaEhtGefJa4o2Hp6UHQdh6KwxNzHU0WX0X3mp8krtePpPkSi+DTiXi/RnBwiPT3EAgFyXn+dc+J9nBt+glhJNUp/Ofo778guXDwul9SBAKaioBgGTkvgvbcX+403opgmyvAwSiiEevgwxrJlON59F7W3F9XnkyZmiYQcStB1nBdfjLF0KbGrrsK7cyeeV19FMQwIBuUQhrVsdzohGpXbD4Ukt9btRigK+osvEu/tldNkXV2UPfkkji1bsO3YgfbqqzA0JAWTZsxA8fkwKytxfPe7qP39CAC3m+httyHmzkVRFFzz58Mvf8k7L7/MSaecwkJNS+kOhNraCOXlYevrw4xE0JJZbDAnB0cmalhDA0Wvv44WiWCsWSNLFdOMqdIPs8VE4K1p2riR53ROcbqFkM1mQ9d1TNMkGAym6uTHo9gN/BuArsvlyphZThd0o9EobW1t+Hw+li9fPimLn0z7zRTpymKWcln6TTnlevDQEOovf0npvn1oikJbZSUDH//4hOWP9LDKEpqmpRgghmGkwDbrKkFR8K9bR11pKTXd3ZTu3UtS2AChqjjCfirsvYioC2VwENsvf4lN03APDIDPhxqPQzRK0sIBe309ZksLkZwcNLsdDTAjERRdRzMMlHB4pKOfZC8IlwvFNGVNNB5Hq6sjsWYNamenBM3YiCav1tqKcDjQX3kFfft2XOGwpK+ln1KSp0ooBE4nxsyZqENDI5qPpomZlweDgyhC4LjxRoo7O7FHIqgNDfJYHA5Ze+3rkw+OmhqU/n7M2bPl9W5qwr5xI/Hrr081ywBMpxN0HYWRSTPy8nCsXInS2ws+HwlFIZyXR5uq4t+xAyBVJy48cIDC++6jYmgI29ataEuWEPvud6cNvNMtL2TazlS545k4xbFYjPb2doaGhmhtbSUUCnH33Xfj8/morKxk69atnHDCCdPKeh977DFuueUW9u/fz/bt21m1alXG9z377LN89atfxTAMNmzYwE033TTlfVlx3INuNkCcqtJYPB6npaWF3t5eysvLEUJMyygwE3CmN+AmykCnCrrqU09htrUxmJuLME0quruZHQ5LXuhkPp8EXeu/Fu9xIpK4VX+ORCIsWLCA4uZm2f2XVgso8ThieBj6+lBKS+XfSkslp7aiAvX55+XfLOZARQVKezt6KIQejWIigVtxOCRwWuCYfCikjipZ51QGBxF2O6KwUIJiKCTBJg10MU2UcBhhs8HwsMxiM5yb0DSUREKuGuJxzKoqCcaahllZCcXFiPJy9Oefh8OHU6wHxaJ12WwI05QPIMNAGRxE0TRob0dtaUE5fBhl3z7UfftIvP/9UF6OcLmwl5WNPxibjfhVV+G47jrU9nY0QF21ipqTT5bHk1Yntt1/P/1AtLCQhMOBe8cOglu3YjvttCOO/GaKY1VeyFQSmE7Y7XacTid2u53KykoAfvWrX3HrrbeiKApPPPEEt99+O88888yUz7W2tpYnnniCK6+8Mut7DMPgK1/5Cn/729+orKxk9erVXHjhhdNmMBz3oJstJjvgkN6Es5b6sVgMn8837f2mA6ffL61sHA7HEelfU7HeicfjBHbvJhoK4SgqwuV0YguFEN3dTNakxaqVpf87201rGAaH9u0j/OabVMyeTd5pp6FoGiIel5+DFLVMiUZheJj4DTdgl3YGEpBmzcJcvBhlcBD17belgdfhw3KZD5iqippIyONPJEayzDE8bBMktUtVUUxTZoqGge2++yRQZ2tkJpkEKYqYxYCwQtdTwwjmrFmE7r4b149/jHrgAEp3N0ZlJcru3WiPPoq2ezf5ioKqaYhYTIr0pIvfGwbE46jvvCMbdYEAwjRREgmUjg7s992HsW4doqCAqkAAZdWqEcqb9X0cOoTweEisWwd2O2p9Pdqzz2JccMEoWUSnpiFKS+nx+cjNy8P0+wn5fHQdPEg0EiG/pYW8UAh7ZSX2tWtxezwTgtM/o7ww1RirhWKNLX/84x/nrLPOmvZ2LR+4iWL79u1UV1enFMwuvvhiNm/e/L+gOzaOlOmapklHRweHDh0a12w6GhF0q5E2PDxMfX09hmGwcOHCSTXIJlsPbmtro729nUULFlDh9+O32xGGAaEQIrmUnSisum2ey0XrPfeQ39uLOm8eyoc/jLe0dNTNbZVEDr/9Nivuuw9XKIQiBGZNDfG77pLGaV1do6hOQgi0f/wDZcMGlIYGCYQOB2LfPswVK4h985s4r7gCOjshEkEoCkLTUK0HjqpiLlmCtmvXCEc4LRRIuVoqpokSDEIwiFAUDJsNNRaTHX8k+8KCF0WIVIacPNDR29W0FBCrioJeVYWYOxftL3+Rr/397+hbtqDE46AoEkQzAbyug8eD2tsrXS5APjxUVdaPYzEUXUdtayO+eDFaby/qjh0Y5503ajPqvn1S7DcvTx5rTg7qvn0YF1ww6n3GqaeivfACiqZhCwTA46Fs/XpmzJiB/qc/oTz9NHEhMKJR+nbs4N0zzkAdM9yQLpj+P1VemCgySUT+sxppHR0dzEoO5wBUVlaybdu2aW/vuAfdqYreCCHo7u6mqamJ4uLijELfR8O1tUjje/bsmTIndqL9ph93aWmpbB6edBIkEtjeeANhmogPfxiRXpOKxVDvuw/1hRcgP5/Edddh1NbKJplpUvPcc2g7dhB3uTDq6xmqr+ediy4inhTO1nWdwcFBShWFUzZulCOoXi+iuhp1/360TZtIfO5z2HfvRgQCcpkNMnM1DLQXXwRdRySvgeL3Y65cie2xx4ideCLxujpy9u8flyUqhoG6Z884UEz9PXlu1l/VNGAWgOlyoUQimcsISYpYSjYsPZIlDxwOjLPPxn7HHdh++lNZOlEUhGGgxOMopolht6OnlzDG7kNRpOCNaWLOnIna0zNS8jBNhAVG1jFmOFdRVgbbt6NEIjLbDgZl7T0USklrAsQ3bMC029GefRZRUkJ8wwbEjBng88nsf2gIm92OsWwZcxobKbviCuKFhRkF03NycgiHw/j9frxe77QlTuHYit1k2pbf78+ofTs2JtJeyDSB9l7HcQ+62cJms42yNBdCpHRzc3NzMw42WDEdfm86/UvTNNasWTPl7WQbchgYGKCurg6PxzP6uHUd84tfZHD9esKxGPPS9QdME/Wuu9CeeEJObw0MoF97LYnf/hZmz0YdHER76y3EnDnoioJeWkppezsFM2cSKCjg4MGDRCIRihwOZn0851oAACAASURBVH73uxgNDRi6LrPIfftkRt3RgXnJJcTr6rB/73vZLgyKpSGgKOg//zmB2loGS0ooB5Sx9VeQFK14XH4mDSDTs1bG/H8qDAMlGsX0eNDCYUgkEIqSAljTZpOUMMPAyuWExdVVVURODubChZjnnovj3HNHgbSSdj9p2QA3eQwoimQ/eL0QjWLm56P29CAMA1FSIhttTidKd7d8SEWjqLt2YS5fjuUKmvjIR9C2bkV74QVwODBLS1HCYfSHHiLxxS+O7M/pJPK5z1G3ejUrV65Mvaw/9hhqczOioEC6VrzyCkZtLSRVxjIJpodCIfx+Pz6fj0OHDqXsc9Kn7MaNO2e9DO+t/fpk2QsTaS9MJioqKmhra0v9u729fdpmmPBvALoTZbqWcpKlm+twOFi+fPmUlOyPFOnLfUula7qDGWMz3WAwSH19PUIIli5dmlWgXMnLw0jjb6pPP412110ou3bJ+f2SEkReHkpXF9rOnSMOBmPPRQgaGxvpc7tZMGcOBaqK/dpr5VSUYUAkgllYiIhGifl81DkcDL/9NmWVlcwXIjMIjlwohK7DwAA577yDp7QU7cCB8cdh1VuTn5EnqIz8N0v2m7oWJDPlQEBmhYqSysAhM1gqQmBoGsJuJ15YyOAll6A6HJTGYpIlMQVurFAUKW4zZw5meTnC7ZbNuJ4ehNuN8Hgwy8sxamowZ81C9PRgDgyg//3vYBgYa9eSuPxyea5eL4mPfhT18GFEebmkryUSaLt2MXYNl6kOq23fjllYKMs7djtiaAgMQ2bBGcIaWLDZbCxYsECezxjtha6urtS481jthbElifdSSxckh/6fQRlbvXo19fX1NDc3U1FRwSOPPMLDDz887e0d96AL2UVvLP0FIKNu7tFEum/YjBkzsqt0TSE0TSMejxONRmlsbCQQCLBgwYJRSlCZIn0iTdm3D23jRgm2LpekODU2IhYtkj9kq5FXVISxahXatm2YXi/h3l76ysrwOhwsuu8+lEOHZJ3Wbke4XLKpNTCAFgiApiEuuoiaa64hGouhbNo0MeCOXDQUTUPz+yWtKlOkA631nR4BaLPuawqf1WIxEieeiKrreJ56CvOhhzDicbTJTheqqqxbL1pE/JprEMuXI/Lzsd18M7S1oQaDUn3H6ZQUs1CImKYRbWwkUVNDYsYMWTd+4w3M9eshWZvXCgvltFpBQYqtYc6cOW73GeuwXi/mvHmo/f0oQ0Pg8RD/1KemRCXLpr0Qj8dTU2bp0pDpU2axWOyIuiGTjUygeywafps2beKaa66ht7eXCy64gBUrVvDcc8/R2dnJhg0bePrpp9F1nXvvvZcPfvCDGIbBFVdccVQOxP8WoDs2LPuawcFBVq5cOam6z9hIp1ONDYv+lZeXNy3jyomit7eXjo4OqqqqJi3UMypDPnBA1ncdDpg/H2X/fpS+PpTDhxFz5sil5U9+gigrI3HppQwWFhLYuRP7OedQ+ulP47r6aggEEBUVqO3tEA5j1tTIeq7djsjNJf7FL2Jcd53UFNi8Gdvvfje5k1MUybmdTKSDZRKAlemA7xRCf/NNtPJyKZAeiaCEw5N7mCgKif/4D9T6ekRODrbf/574V7+KWLyY+N13o/32tyhtbfJ7SNa7qa8nKgTe/n48CxciNE025pKeadb3aZxwAuKEE7Dt3i0fdg4H8c99btwhZLpX45/9LI5bb5VTdS4XZmUlxjnnHIMrJZOagoKCUb8twzBS7r7d3d309fWhKAq9vb2jyhPZjDUnirGlirFJ1nTjYx/7GB/72MfGvT5z5kyefvrp1L/PP/98zj///GOyz38L0LUy3Wg0SlNTE4ODg8yePRshxLQAFzIPV1j0L7vdPmGZYtQIskV9muAmsyzTGxsbcbvdnHLKKVPqHlsPCMMwUPLy0ORGETNmSEqXECSuuw66urDdcw/C4cAIhRh68kk6v/Md5n/605LP2dmJMjCQWn6KsjLUgwfB7cZcsQLl0CESV1yB8eUvSwBtbsZ2551SRNzvTw1IZAurWTa2NitANtycTgl0mja6zisVyCUQa9qI1OSxDsNA6e6W2r9JGthkIu5yEd6xg3hZGfZEAvvQENrNN2MsX44yYwZqeztKb68coEgk5Lk4HORGIlBYiNi9WzbpOjuhsBDbnDnYnE7JndZ1El/7Gon9+yEUwpw/X2a9FlUv+SDL2biRJS+8gKu0lNh112GcfjrmihVE7rhDlodcLoz3vU8qqU0QE4JZOIztwQdRDxzArK6WLhZpJS9N08jNzU0xdVRVpaioCKfTSSAQwO/3097eTjQaTdmsT0UEZ+zk5NjXjpf4twBdy8Kmt7c3JexiGAbt7e3T3mY66FpeZ/F4fFL0L03TMAYHsf3sZ6jbtyPcboxrr0VYoivJsIYm6uvrKSoqYtGiRQwMDEyZrmONRPb19ZG7Zg36qaeivv66BCiPh9gddyBqanB86EMkSksJhsOYmkbhwAAFpomZbMwJ6wdk1QArKhA9PVIIxuEgsWEDxpVXph4gSkeHzL5KSmQZIxCYGBCtYYIxLyvISTNleBgzuVxUkxxcQJYcLDB4rwDXinicqZKltLw8coqLEcPDaLt3y7qpouDfsIHGm2+mdu9evC4Xqt+PME1UIWSdta1N6uPa7Wh//rNkTng86L/8JYmvfQ1VVeW9oOuQbJBZI9rp/3XefTfKli1E8vMxDQP7rbcS/sUvYPFiRFUViSmIbWfVKxECx7e+hfbmmwi3G23HDtS9e4nee688vgxhlQSsKbNMxprpIjiWRm+6CI5VPhj7MAgGg8e0XPjPjH8L0G1qasLhcIyymjlaXVtLf6G1tXWUCPpkP6vffbcE3PJyiEbRfvxjjMpKRLJBkT40sXLlSlwuF4ODg1M65tSPzumksrJyRGD7Qx+iZOVKPID9hBPwzJ+PEothDg8zrGl48vJw2O0o0Sjx9P3l5hL/ylew3Xuv/LdpEv/2tzE+8QkJtGPYHmLWLAmImka8qgr1wAFZA1WUcWO21vayhZKkWWljPieSn8tE/RK6LlkOY6+LzZbx9VSoKiI/X4rcHIMwly9HAfRt20YGREyTsp07yTFNDJeLvupq8uvqsA8MJD8ks16luVlO1c2fL0HXMNAffhjj4x9HZADL9Psb5IhsfMsWDK8Xt9eLoqryAbh3L7Hq6lGfm3C0OxnZml/K4cNoO3dKGpuiIHJz0Q4ckKJAafsZu61sfY5MIjgW3TIQCNDR0UEwGATA7XanBpYs1w6/3z8tcah/hfi3AF0rs02Po1l2JBIJAoEAPp+PBQsWTFkSUdM01B075ISRqoLLhdLfj1Jfz3BFRdasebIPilEKYEKgv/QSc559FtxuEhdfjHnSSYRCIYaGhugZGmLf9u1EIhGWrFxJ2c6dkhoVjcKMGZhjJnLMD32I2JIlKB0dsjxRU5P9OObMIXTDDSg/+hFCCJz5+VJTdqK6bTo7YWxkG4QYU98VQMLlQo9Gx70fVYWiIsxQSGonZDpujwdz7VrUV19FSRvqACSQTyWrtttRDx0i/s1voj3//MjwhcOBEg7T+fLLqBdcwPzHH0dZvBjl1Vflfizwi8cRfX2Ec3IgmdnpioLZ14cyQYZqKckdOnSINaWl5AQCMuMUAkVV0QoKsNlscrS7vh7973/HVBQSZ5+NOXeuHGDRtNR9fURTymz3/wS/i6myF7Jp9AYCgdRKrqWlhc2bN/P6668TjUbZ/P+19+XhUZVn+/c5c2ZJMpnsAZKQjaysIQkIrVurLLUWxU8FscUF69KK+AMpKpUCKtSF1qoVqBuoX4VKFduKVT8RECRhUUAg+77vyUxmn3Pe3x9n3pMzk0kyExICce7r4iLLmbNl5jnP+zz3c98ff4ysrCzEx8f7/Jn3VnchMTERwcHB4t+G43DixAmfjtPrOi/o1aMMcvsdtVqNpKQkjPU0Fz8AFAoF+PBwKLq7xYkiZ5Cs6uxE7enTomaBh6x5oKDbS26RYaD48ksoN28WqUkOB1R5ebC99hqCkpJgsVjE4YboaCQkJMCenY3u996D4uRJGEJCUDNvHpRlZdDpdJJNi0qlAklOBomPB5uXB6asDAgMBHvoEOBwiBnYtGnS+HRTfDxSPvgAY776Cuyrr4oqXQqFSOD3FFydgwGM+3UO1BihfF0nK4FzBglCSx3O1ztUKljj4+H40Y+ge/31HiGbnpssbn/ggCjt6AYpQ/f2A2yziUMoRUU9jAtCpFHk2K4ucKtWwZGQANXKlT0lFkHoWZZzHAJbW+EIDYVgMMDGMPjOYIAtL09aatN/quZmWI8eRXVLC4Qf/QgzZsyA4sknwaxeLU4GEgJhyhSQa68Vg0RJCVSrV4slDwD4/HOYX3wRfFKS9H4CIL3vbDabxyYyGTsW/OzZUBw+LNberVbw2dkgiYl93pqhGI5gWRYajQaBgYFIcyYAWVlZ+PDDD/HBBx/g5MmTePPNN7F582afGQXe6C5QfPXVV4PSYvGEURF0+3vCeWMXIp/2oiaNNTU1PvmOycFxHAwPPICI554DGhpgMRrRMmEC2B//GLP6eSL3pb3Qn9yiYs8ekYCv04kNqro62D/9FN/Png2FQoGpU6dKtB2VSgU4fb8CAUQTInl/tbe3i6UJkwlJ//kPYvfsEe1sQkJEUn9gIKBUgnv/fTT+5S8oCA3FuHHjMHPmTLH2+NZb4rKe48SGWlCQqCTmiRdLXRKc6l1eQb6dc8wWLCsGVTkP1+GAEBeHyltuQebOnWAAKKxWUfPAOboLvX7guq0P3XGmqQmKf/xDLFl0dPS8NjAQ2v/+F7a5c8GYTD2Blt4ThwMkOBhCejrYqipwHR0gY8eCvPgisrOyJFEbvV6P5uZmNHzxBZK3bAFrsyFZqQRz8CDsW7eCTJ8O29tvgzl7FoA4Pqx87DEIkyaJtkcMAzjF0ZnGRqg/+QSOlStlt1aQjlVWVoaIiAjwPC8FYvp+M69fD9UHH4AtKIAwYQIcd97pYqTpjqHScHCni7EsK1lZbdy4cdD79UZ3YTgwKoJuX6DNsP5spNva2lBSUtJrSu1C9RcsKSmoe/pptB45grDx4zFm3jwoBqCW9aVQ1q/cotPeHBDf5FazGfXNzUhKShpwLp3ayAQFBUkZPfvWW6JXl8UCXq2GoqlJpKCZzbAHB4MxGqF56SXk7tgBpbO+BwBEpxPZD1arGHRZVsxo3cVlnP8Tlap3tusNOA7C9OlgGhrE5b98HwwDRqNBUGUlJj/7LBRGo2vQH2iAY5BgjEagoQHGMWMQaDRKY8lk/HgQhgF7+rRYs+3oEFckgHiPCBEfPqGhcMyZA8eaNT3Ov93dUG3fDs25cwhLSUHTokVQ7N4NjUYDLi4ODiftrOHNN1H/4x9Do9EgOCkJSU8/DU15OZjAQHAnToh/BxnPmygUHlkmNTU1aG5uRnp6OkJDQz027KBQwLx4seRQwTgHT/qrEw8Fu2CkBcwZhsHcuXPBMAweeOAB3C+fBhwERnXQVSqVfQZdvV6P4uJicByHKVOmICgoyOX3cvcIX2Gz2VBUVISoqCik3nef1zxe+Riwp1KCpzcwf8cd4Natg0Wvh91shjokBPHLlolljUFAcewY2KAgMAoFOLW6J2gSInbGrVYEnz6N7ptvRsfUqTDceScSN2wQeaRWqxg0nA0hOvrrvKCe/xUKsQxhMvl+gg4H2BMnxP3IP+wqlTj4IQhAczMU586BBAeLmScNguhjdLg/eDEFB6cegoZhwKjVohC7QiE5+ZLoaPBJSeL70Cm9yZjNIIAoSZmbC8fDD/cEXEGAatUqMN99ByEgAPYTJxBw9Ch0HAc2JATgOCg4DkxgIJLDwxE/axYsFgssp05BUVICQ3CwWB4ICEBgY6MYJCE+1Bm73YWr29nZiaKiIkRHR2PGjBlSAHVv2Imn5SEQAy6JgrcNO1/Ql+6CN0F3KHQXDh8+jNjYWDQ3N2POnDnIyMjA1Vdf7d3Je8CoCLr9jQLb7XaXqRiTyYTS0lJYrVakpaX1OUY4mEzXYDCguLgYVqsVMTExSOmjq9sXWJaVMtuBgi3gbKakpqJt6VIkFBRAN3YsyK23SnblPoHnxSk0jUYsEzAM4HBAcH5QCcdBY7GILIisLATHx0NbWoqo5ctFa3DBaRPO83CEhIAzGEQGgSchF51OnJAaZPlG2qe7PKPTcYJpbRUbbjabywjwoHIuZ3DX/+xn0H3ySZ+bMYIActVVIPn5Yh2c44DaWiApCeyhQ+C2bAFUKjBwSlMqFIBSCccjj4C/5x6wX3wh0sZYFsL114M5cwYWnQ5Wmw0B48YhrK0N/HXXgfnqK1EX2GYTfdOmTxddKAICEBgRAZVaDbVOJ3mqCTodmpYtg+rwYdgdDrQuXAgEBCCopgYdHR2w2WyYPHlyr6TDE7wJxPRro9EIQRBgs9k8Nux8QV+6C4n91JMpLlR3AYCksxAdHY2FCxfi2LFj/qDbF+SBkzridnZ2Supf/S19fAm6FosFpaWlMBqNSE9Ph9Fo9ElAHXDNbM+cOQOdToeQkBAEBwd7pN1QPYng4GCk3nknVCoVBhnCgLY2qJYtA1tWJqp3cRxsY8eCra4Gq1ZDuOoqsEajyCuNjhbFbhgGTEgIuNOnRXsap4EjeB5KZ12Tduilu6xUigI4HR1i6WGw50vhFnBhs4ni6BAbbKyPBqF9gddqwVVV9Z31Ot9Hyv/9X7H8kZEhWsMDohHn4cNio89qBXE4wJrN4jCIWg3lM8+AMRig+PRTcXRbEIA//xmWtjYIAQHQ6XSingQh4JcsAYKDRZZEQADsa9eCyARuSHIyhMmTwZ46JY4bWyzAtdci6t57gWXLAAAhdjtqampQXl6OgIAACIKAs2fPujTraKfeG7gHYkIIqqqq0NjYiPT0dMmVxD0rlovlDxSIR7K8QB8ewcHBMBqN+Pzzz7Fu3boL2ueoCLr9uUdQp4PGxkafHHHdxcg9wd1YctKkSWAYRlzqefmBd88SrrjiConu1dTUhNLSUvA8D61WC51OB7VajcbGRsmmvS8RnD4OJo4Fd3RASEsDnLP0yqefBltcDISEgHc44GhtRft110H3wgtQzJwp6TUo/vEPcH/7m7Q7xmQSR0xtNlFQx2p1yV7lWSYYBgLHiXVcQnyq5xKG8TwCLP8Z1c+lgb6/LNqbkgHdDoDCYECgs0klZ1HQc6Pb8QoFWABMRQWE7GzR3LK6uufc3Y7LOF2BuRdegJCTAyEoCAaDAQzLQhsXB85gEKl9djv4K64ASU+HIzMTjt/9zvP5chzsW7ZA8d57YMvKIEyaBH7xYun8LBYLioqKoFAoMHv2bKnsRU0i9Xo96uvrJZlHd+ZEf70RQBxYKCgokNxR3AP3QOWJvgIxz/O9SnR6vf6Cg643ugtNTU3SmLDD4cCSJUswf/78CzouM8AM8/AOuw8RCCGwuXXJacbY2dmJxMRExMfH+7S0MZlMKCoqcpHKk++7trYW1dXViI+PR1xcnMu+W1tb0dbWhnSZD1Zf5y1vkvX1MBAEAV1dXaioqIBer4dKpZIUoejY5YDZCSHg1q8H989/iktfhQK211+HkJ0N9Zw5IG1tsAIgggCNxQLhzjvheOop131YLFCuWydmUiwLYfx48AsWiFQoQKyfms29ApP4S0YMnoIgOj64/94TWBYkOBhk0iQwJ064+J4N9LpBly682Y8z+BCtVgzu9OFjNksykjadDuqODrFs42nf8gDMcXCEh6N12jQEabUI7OgAf8MNQEQE2PPnIaSkgF+0qNdwircghKC2thZ1dXVeazzL7YD0ej0MBoMkJE7fb8HBwVCr1RAEARUVFWhra/PKMdv9OPJVnns8YhgGlZWVCAkJcRHc+dWvfoUXXnhBUkO7BNFnZjfqMl05/UutVmP8+PFe1X7c4am8QAhBc3MzysrKEBUVhVmzZnlc+g+Gb9tfwK2rq0NtbS3i4+Mx3VnDox+Krq4uNDQ0oLi4GIQQl0AsdwNg8/PBffihuOxlWcBohPI3v4HlT3+CPjQU6vJycBER4BQKkYPp6c2s0cC+ebNYs+R5sSOvVsM6ZQrY06eheP11KA4edGkigWXRsnAhIj/6SAy0A02LAeADAyFwHKw6HZRWK0hDA/oLN70y4YECrvOhQKKjxay7vb3Hi00eDOX7oQ9VQYCQlCQ2wqKixGNXVooPHIYRG488DzXPi9fa1/tAdr5EEKBob0ekyQTWYhFpZDffDBIbiwsdejYYDCgsLERoaKjH7LMvyO2AYpyqZkRGMezo6EBVVRXMZjPsdjt0Oh3i4+PBcZxXNE35cYC+68RtbW1obW1FdHS0S8muqanpsrRfB0ZJ0KVod9qPa7VaZGdno6urCwYftFDlcA+6tMsbFBSE7OxsaKhldx+v9RR0fQm2VJehrKwMkZGRmDFjRi+uIv1QUFCLar1ej7q6OunatVotYr7/HmN4HgzLio9giwVsRQWEX/8agQwDLixMLA/wPISf/hT8rbeK7g87dkBx5AiEmBg4li8H09YGzulF5liyBMLVV4MkJoJPTBT5r06nA8p46I6LQ0BnJxATI3btGxoGFMZRmExQAFDSibKWln6370t9jISGijVed2oZHbJobe3NM+0r+6bUPJYF09kJBAWBaW4WWR1Tp0Jx6JDksUYCA8XmYx8rK0GrFaUeAclBghEENN9wAwJ0OijnzoXiAkSyAXFJXlFRgfb2dp+zz74gpxhGRkairKwMVOvZbrfDYDCgsbERZrMZKpVKen/qdDqvBG0oWJaF3W5HcXEx7HY7cnJypPKCxWLBn/70J2mA6XLEqAi6hBCcPn0agiC4dGIH09CioEwCk8mE4uJi8DyPiRMnevXmpT5p8vPzNtgCYnZSUlIClUqFrKysfgO8+znLVZ6AnnqdJTkZDp6Ho6sLBEBQfT0cGg2UUVHgeB6MxQLrG28AYWFiBsuy4DZsALd7N8BxYB0OKL74AmxDgzRtpfjPf2B94w0IzhoXv2QJ2P/+F9zeveJ1syyC9HqQtraecoOziQRCLli8RoBTLEepFG3d3cBYLOKQBg28FPIxX6fLAxQKUeQHEJkBTmaEVBsmBIQ6FGu1UomGqa2F4tgx5wkJInvCWav1lOUSjQbW2bOh+eILsUHmXA0QjQbmm25Ck9UKQ0MD+NpaaSlPl/MD1VQp2tvbUVxcjJiYGMyYMWPIlbgotz0uLg5paWnS/t1t02lpQi5oI68Ra7VajyW/lpYWlJaWIjExEWPHjpX2f+rUKaxYsQILFixARUWF1/fjUsOoCLoMwyAjI6PXk4/ydAcDm80Gi8WCM2fODNrrTE6hGahuC0ASLzeZTEhNTR0SVXyFQiHaslx3HRy//z24Z58Vg6ZKBUdSEhw2GyyCAM5kQoXBAE1MDHRGI4LUajHgBgdL2SBbUSFycWVqZMotW2B1Bl2rw4HO0FBEBwSAcXJ94XAAer24PKc0MYVC/N5dwtFHsBwHEhAAMnMm8OWXvTewWMTz7XMHbE8AVipBlEo4VCoozeYeOU5nuYBERoLExQHOUgIBRENMu13kRNvtIjOjrU0M3nFxovBMe3uPyzAhoopaQYF4b5yrAbAshF/8AjFJSaDy5IQQaRqtpaUFZWVlvWqqdGybwmazoaSkBDabDdOmTRsyAXEKu92OoqIiOByOAZMBlUqFiIiIXoI2BoMBBoMB1dXVLsLnOp0OAQEBqK+vByEE2dnZ0ufZarXi+eefx4EDB/Dmm29i6tSpQ3pdFxujIugCkOgvcgyGa8vzPKqqqtDQ0DBorzN63H4nyTwcs7m5GcnJyYiKihrS7EQQBFRVVaEpNRUTPvsMkSYTyN13Q8VxUAUGiqLlERGImDgRepsNVVVVMHZ1YZbVCsJxUOr1onqYU0FMAuXyCqJlUX19PbJ1OrBKZY+uACFAaCisf/mL6ExbUgL2zJmeMoPTcQE2m0gj8zIIE60WjM0GMn68uMzvc8M+Sg8M09PpcHJ6HUqlSNtqbXXJjIlWC/vy5VA9+6w00OHy1zEaQaKiRPEck0mcRIuPB1NSAjiVzAjDiMFbp4OGZUESE8XzttvFpuaWLS7nxzCMJHPYcymuY9tVVVWw2WzQaDSSvGdSUhJiYmKGdDiB9kkqKiqQnJyM6OjoQb0/OY7zKHze3d2N+vp6lJaWQqVSQaFQoKioCHv37kV0dDTeeecd3H777Th06NBlm93KoVi/fn1/v+/3l5cSPHU+AdE+2RsTOaradPbsWeh0OkyePBkNDQ0YP368T28wmtnW19ejtrYWBoNBYlYo6dCBbNvGxkacO3cOISEhmDhxIrRa7ZAFXNr4O3fuHIKDg8X9h4WBiYoCycwE98UXYrYWFAT7yy9Dk5KCkJAQREdHIy4+Hmx7O9T794sWPXT5Tultzoy148EH8a0gugdPmjQJqpQUMUM2GsUsl+Pg2LABwk9+AjJ2rNhw0mjAVFVJS2s4HKIh5A03iKPENlvPVJs7nPqykjCNwwEkJICpqvLupjg/tIJTIEhSMWMYCGPHikMdNDt20sEYtRrs2bNiFutpf4SAUalED7SxY0WKnTPLZNrbwTuzZiY4GMJDD4nsD7W6x3iT40Qa2ACBkmEYqFQqaLVaREREICYmBuHh4WhqagIV7O/s7ERVVRVaW1thNBrhcDjAsiw4jhvU+8piseDcuXOwWq2YMmWKyBsewoTA4XBIteHp06e7PDT27NmDzz77DGq1Gt9//z3a2tpwzTXXDNmxhxkb+vrFqMl0Pb0RvM10qZA45RfSJRttiHnjfeZet83NzXWRpauurkZ3d7dUd1UoFGhtbUVYWJhLo2CoQKfjAgICMH369F6lF+HKK2H56ivRv6u4WLTziY8XTRXpNg88AOb118XmkVIpGiMajXDExMDOsij/6U9Rk5GBQIaB3W5Hc3MzdNHRqJ4bqAAAIABJREFUIJ98AuX27WCMRjhuuw3CddeB/fZbqG6/HTCbRfaC1QohIUH82kmzEhISwGo0IBoNiCCge9w4BH//fU9tlXJi1WqpbMB0dQFVVT0BzMkDdgF1ngDEh4dSKWajpaUgCgVYh0Osv5pMsNAxW0EA56S/kTFjwBUXe7zPJDJSyliFK66AEB8P7pNPgKYmdCcno2rtWiQ1NkJNCPhbboEwYwbYI0egyMvrUTkLCoJy3TrYN23y+u8rCAKqq6ulIQR59khdVCjVq66uDhaLBSqVyqU0ERAQ0G8jl7JmfC2veQvKMpowYYJLPfi7777Do48+ittuuw3vvfceOI6TaJOjAaOCpwtAWs6745tvvsGPfvQjj6+h+gvU/dTdfufbb7/FxIkT+61d+dok0+v1KCoqksaTrVari81JSEhIvx+GgUDrwmazGWlpaf03/lpboV68WFqek9BQ2HbtAqHGh01N0Pz4x2LWRqlUVitKn3sO9bGxSE1NRVhYmNS51uv10Ov1MJlM4DhO+nDrdDqE3Xcf2MOHgcBAMZNtbQUJDxd920wmMejp9WKtWRBATCYwoaHgGAbo7BR1bru6xOaYO1gWJCJCPD+7XQyylF1gs4n1WQ+NNkGjAUOIGPgFAfyMGXA89xxUDzwAgWXFCb/ubjDt7eAsFs8jzZGRIGlpsO7YAdWaNWCPHwevUMBhNkNITgb27RP1GGRgioqgufZasayh0Yg6FFYrrAcPirXgAdDV1YWioiJERkYiMTHR61ICdfWlwdhsNrv8nahbg8lkQmFhIbRaLVJSUobM0Vd+HoWFhVAoFEhPT5dKBhaLBZs3b8bRo0exffv2CzJ/vAQwunm6vsJsNqOkpOSC9Bd8bZJRSyHqQiF3+LXb7VLAam5uhslkkrIS+k+j0fS7f3nmk5SU5FXdjfvb38QM15klMW1t4P78Z9hfeEHcIDoawjXXgD1wQFx+2+3Qx8YC06ZhRlKStH+lUonw8PB+rymtvh7BgiAK1igUYJ3OFTAYAJaF49Zbwb75Jux2OxQsC2VwMJiuLlgOHoTijTfA/e//9iz75WAY0WU3MBD2DRtE8fXISPC33gr2k0+gXrECcNKzer2UZcV9OoOp4vx5CAcOwPbXv4LbuhWs3Q4hNxeKbdtE5kJnp8vrCQDBZELhE09A29SEuOPHYVapAIaBOiICbFMTrAUFIFlZrge220F0uh6BG0B8MAxAb3Q4HNK4+aRJk7zSS5CDujXIdWEpy8BgMKC5uRldXV1wOByIjIxEcHAwTCaTR3v1wYCW0yorK5GSkuIy7HD8+HGsXLkSixYtwoEDBy7YWftSxqi5Mm80dWnga2trk/7og9FfGFBuUQY6vVZXV4eEhAQXig2FUqns1emly0M6mmmxWKDRaFwCsVqtBiEELS0tkhW8LwR4pr5e4okCEOUWGxpkGzCwvfYa7Fu2wHr4MPgJExD4hz8gzgsxZ/drUtx/P7i1ayHwPAjPwxEQgPI774R9wgQImZmwVVZiqsMBVUCAqMlrMkFISgJJSgKZMqVHscxZipAQGSlm5iYTSFIS+Ntu67n3//M/sAYFQblmjehm7A53/q7JBG7nTljWrIHtxz8Wf9baCm7bNrEkodWKNXDp9oj2QpEAatraEG2ziaPOLAu7zQaFwwGT1QqN27AAmTABCAkR+ccajdigjI4GSUrq835SGlV8fDzS09OHrK5KWQYqlQrNzc2IjY3F+PHjJducqqoqqSwm5932RffqC1arFQUFBVAqlcjNzZWyW7PZjE2bNuH48eN47733Rkzj9mJi1ATdvqBQKGC321FfXy8FvtTUVK/eMBfCt5UHw+joaMycOdOnZZparUZUVJSUDcjrdJ2dnaiurobFYoHdbodGo0FCQgIiIyN9OoZw5ZVQfPUVCJ19t9nguOoq6fdWqxWlpaWwzJuH9EceQYgvOg9u4JcsASwWcG+9BUahgGPFCsTdfDPKCgrQ1NmJ0OnTUXvXXYjbsQNQKCDodGj74x8RYDZD29wMpqWl96SZQiFOlXV3A2o1BDd7G0II6rOyoL/7bkx95hnX0oRaDX7hQnC7donfOyldTF2d6MBAHUMiI2HbuhWq3/ym9/EJAWw2BK1ciYC33wY3fz40+/eDMAwIz8OUkYESlQqm/HyJoyrxqHfvhurRR8EWF0OYOhX2v/xFDMBuoEtxlmVdaFRDBZ7nJQdt2sgF0GvlwvO8VJqoqamR/Mso3asvoRxCCBoaGlBVVYW0tDSXxCI/Px+PPfYYlixZgv3794/q7FaOUVPTFQSh1yAEIQRHjx6FIAgYO3YsEhMTffrDlpWVISgoCGPGjPG5bltSUgKNRoMJEyZ4PdzgLWw2m7TMTEhIgCAIUlZst9sRFBTkkhH3ec2CAG7LFnDvvAMQAsett8Lx+99DYFmpVDEcFDYAML36KsLWrQPL8xCmToX1gw/EQNfRAXtzMzq1WugtFui7ujBt0SIE1de7vJ6oVCBTpoBpbgYZOxa2l15yWcYbjUYUFRVBo9EgJTkZQatXQ7F3r+jfplLB+sYbIKGh0Myf3xNMGQYIDoZ1714IM2e6nrDRCG7rVnDPPScpmQHOD0hwMPi774Z940Yo3n4b7JkzIGlpcPz614CzT+Cp7u0eiAMDA6VkQK6X4Ispqi/o6OhAUVERYmJifGbpAD10L3pdcqEcuhKrdQ55pKamSu9Ds9mMZ555Bt9++y3+9re/DahRcpmiz5s5aoNuR0eHpG07adKkQXVfKysrwbIsxlGrkwGCLZV4pLXiobaI9qZuKyfV03/yD4JHcRwadFgWra2tKC0tlXzVhrqJYjKZUPfhh5j86KNgKSdXoYCQmwvrF1/02p6pr4dmypSeIQdCQAB0TZmC81u3QhcUBF1YmPQhl4+/pqWl9cznEwLm3Dkw3d2i9GJoKNDVhYBJk0QWAR2EUKlg/vZbQNZNl5081DfeKIrv0LKEMzPkZ82C7eOPfboX8kBsMBhgNBqhUCigVqslFS15o2mo4HA4UFJSArPZjMzMzCEdoqCj6NXV1WhtbZXEmVQqFf71r38hMjIS7733Hu655x488sgjQ/7+uoQw+htpNPh0d3ej2EnvmTRpEqqrq31uAtAmmVarRWFhIRobGxESEuLS1JKD53lUVlZKEo+RkZFDmhn6UreVk+qpUIlcHKe+vt5Fk4FeF8MwKC0tBcdxPo0eewt6j1pbW5F75gxYucAMz4M9edLzCwVBrDU7RbsBUWsh6MorkZmZCb1ej66uLtTU1Ehj36GhoUhKSnINJgwDMnmyaxYREgLrG29Aff/9Uq3Y9vLLngMuABOAU5s2IWPjRoTm54NRKiVxHyEnx+d74t6A5HkeladOQff885jY0AD9pEn49tZbwWg0LhnxhTS2aG04ISHBZ5drb0BLUoGBgbjqqqsk37/6+npUVFRg3759CAwMxM6dO2G1WvH4448P6fEvB4yaTNdut+P7779Hd3c30tLSJN5iSUmJRPj3Bp7kFmkttaurC3q9HlarFQFOgWm73Y62tjaMHz8esbGxQzoJBPTwbTUaDVJSUoaspkeXhh0dHWhoaIDZbIZGo0GYM2sMCQlBUFCQ64fSZAJTVAQEB4vNIC8+sPSBUVZWhtjYWCQUFED961+L47HOoQQwDEh0NCxlZb13IAhQ3XabqF7mzHZJcDAQFweYzXDccgsMjz2GQucDIz4+HhaLRcrybTab9Lei/3pxojs7wdTUgMTGikyIXqcgSBODaWlpCNNooF60SHpQCLm5sO7aJQ1EuN0Ar+5Te3s7Ss6exezly6GurRUpbhoN+Kuvhun992FwChkZDAaXxpa3gdhms6GwsBAAkJ6ePuS1YXk5RM4bpiW+1atX45577sFvf/tbKRB3dna61I1HGUZ/eYHnedTX1/eqP1ZUVECtVktZX1/wtUnW2NiIsrIycBwnWVbTWip1fLiQpROt25pMJqSlpbmI2AwF5A0O+sCQN0v0er203NXpdIhob0fsPfeANZkAhwOOBQtgf+21fqeoaF1VrVYjNTUVKpUKmtxcwGIB09TUMxygVsO6ezeE66/3vKPubnDPPw/FyZMQwsLAffllj10Ny6J2wQIonn/e4weYEAKz2exyXTabrZeYTF/DKbRMRcstUmAjRJyCYxjJSUMOprAQ6nvuAVNeDhIbC+ubb7q4PFBQNS2bzYbJ7e3Q/fKX4qgx5UUrlTCfPdvT2HNCrmNA66meGAYMw0g0LfchhKGCyWRCQUEBgoODMWHCBOl9bzQasWHDBpw/fx5/+9vffLavuswx+oMuIC5t3FFbWwue55GQkODxNb4ONxiNRpSUlIBlWaSkpEgDFbSWSrNhg8EAQoj0AaCZ40CZMK3bNjQ0XNCce3/o6upCcXExdDodkpOT+60ZUr5txIIFUBUW9tjsKBRo27QJ3OLFvTjE1FGjo6PDta4qCNCkpwP0AdLdDZhMsP/ud+Cd1vADQblxI7iXXgJxTikxhICJioLFmcV5AxqI5XVvu92OwMBAhKjViKirgzIkBGUaDWx2OzIyMnyre1qt0OTkiIwLtVrk5Wq1sBw/LhmGyjmrSUlJGDNmDBSHD0O9aJE4Qk2DrkoF85kzkoV6f3B/aBoMBmkSLS4uDmFhYT5TvfoDIUTS3MjIyJD+zoQQHD58GGvWrMF9992Hhx56aDTXbvvC6K/pAmLAdH+IcBznMRj7GmxtNhvKy8thMBiQmpraS0BZXkulWg90Cd/V1SXxHeXTZ3KdUfe6ra8UM29A621Wq9Vrqx/Kt9XU1Yk1TCoAbrWCFBejsLAQFosFarUaOp0OgiCgpaUFCQkJSElJcb2nLAth1iyw+fliA0qpBIKDJWlIb2BXq8ECACFQOJ0diNsk4UBgGAaBgYEIDAyUrOcJIbCUlEB3ww3iaDHPI3HyZJS/9BKamppc5RX1eqhWrwZ7+DBIdDRsf/qTSxbLVFaK8o60Lq5SiXXr4mIIM2fCbDajoKAAGo3GhbMq5OaKY8UWizi2rFZDmDGjV5bbFxQKBUJDQxESEiLRuqZMmQKO43pRveRTaMHBwT4HYqPRiIKCAoSEhLj0GLq7u/GHP/wBxcXF+Oc//4kJbjS+4YTFYsHVV18Nq9UKh8OBW2+9FRs29CmBMGIYVZmuzWbrFXTb2trQ0tKCjIwMAL5PkskzT3d9z8HA4XBImUhXV5dEHaJ14uEoJcivYcKECd5RwGw2cG+8Aeb8eZCsLCh27wZ7+nSPFi7LwrZ1K/gFC0AIkTRcGYZxcWHuVUttb4dqxQqw+fkgoaGwb94M4brrvL6G9vPnMWv5ciicgREajXgeN998wfeJ+9nPwB05AtbJTCABAeheuxZNixZJGSTP88havx4hJ0+K95BhAK0W1kOHxJowALS0ICArq0cPwinhaNq/H1UqlUe9BAktLVA++STYkhIIM2fCvn69RDvzBtSnLDQ0FMnJyR4f3DQjlpcmAPQqTXh6LTWebGpqQkZGhjTNSQjB119/jccffxz3338/HnzwwSHvbwwEutrUarWw2+248sor8Ze//AWzZs26qOfhxA8706UDDr5MklGFroqKiiHNPDmOkzrWtG7b3d2N+Ph4acxT3qij7ILB0obkFDCvr0EQoL79drBHj4rNqz17wP/4xyDR0WIW6HDAcfvt4H/xC2nKT6/XY9KkSdIDQ76Eb29vR2VlZQ+H+OmnoQsORrCX10XrqlFRUciaPx+2I0fAvf02GL0e/C9+AaEPbQ1vQQcE0s+flwIuADBmMzROMXAKYjQi8JtvXAYlHISgbvduOG69VQxa4eGwP/oolC+/LImkG2+7DSc6OxEREYGZM2f2/d6LioL99dd9vgZBECR2SEZGRr8PbpoRy1drcnPK2traXsMPlOFSVFQkCUPRazAYDFi3bh3Ky8uxd+9eJA7CHmsoQFebgFgWs9vtQ16aGwqMqqDrCTTr4nne61JCV1cXSkpKEBgY6FGh60LhXrfNzMzsJflosVjQ1dWFtrY2VFRUSALWcupafwHUaDSiuLh4UBQw5tw5sMeOiQHDaZuuOHwYliNHxCCs00GIi5MacZ7Gm/tawlOn4+aWFpSVl4PneQQFBUnXJW9AWq1WlJSUwG63Y+rUqT111bFj4XjiCV9ueZ+gD6WYmBhw06eDfPWVJBtJAgJ6Nb+4fft6xMeddVfOYkFEQgJaFQo0NjaipKQEwlVXYWxsLELr69EZEYGWSZNcJr6GEl1dXSgsLER0dDRyc3MHlWFKYvcyHRJ5eaywsBBGoxEajQZWqxX5+fkSRW/jxo146KGHsHXr1oue3bqD53nk5OSgtLQUv/3tb3HFFVeM6Pl4wqgqL9jtdhchczo6m5eXh/DwcOh0OoSGhvbZ0DKbzSgtLYXdbkdaWtqQf0Dc67bx8fFeZ8/yoQfq/Ua5xDRgabVayZm1o6NDUgHzFezx41AvXOgykACVCpaDB0FSUyWlNG8acQOBcojlzR+6ErFYLIiPj/fZydkbWCwWic+dlpYmPpQaGqCZO1dUJ3M4wF9/PWx//7uLjxr32mtQ/v73InuCfnY4DuaWFrF2K7uumpoaVFVVSQL78sZqf0t4b8HzPMrKyqDX65GZmemzAI43MBgMKCgokBTN6M8OHjyIV155BcXFxRg3bhyys7OxYsUKj+7ZI4HOzk4sXLgQr7zyCiZPnjwSp/DDYC/I3RrkTTJCiPTEpjUs2tCirILGxka0t7dLww1DDep7plarh4xvK9fr7erqQkdHB2w2G0JCQhATE4OQkBCfDAElmM3QzJjRI37DMCATJsBw4ABKKythNBqRnp4+5BN3QE/WRpuS3d3d6O7uBsMwQzIgIO+4exyvtdvBFBcDAQGiAI3bvWOPHYP6llvEoOssRfBXXukyjWa1WlFUVATAlRMrNw6lDxgAvaYFvbkuWkOPjY1FXFzckC+j+7JVJ4TgwIEDePLJJ/Hwww9j2bJlcDgcOHv2LMaMGeOVYcDFwsaNGxEYGIjHHntsJA7/wwi6drsdDqd9zEBNMrvdjs7OTtTV1aG9vR0cx0lZI80ch0JY3GazoaysDEajcViaZIArBSw+Pt6FDmU0Gl1kIkNCQqBWqwf8kDJ1dVA+8ogkyFK1Zg0qjcYhaSZ6Aq1vWywWpKen98ra5HSorq4uiUMcHBws/b0GesDo9XoUFhYiPDwcSUlJg84yFe++C9UTT4jC5dOnw/ruu0BUlCT8XVNT00u6sC/Ia6nuTS15RkwDsZzXm5mZOeSTg0DPfYqKinLhJuv1evz+979HXV0dtm/fjvj4+CE/9oWgpaUFSqUSoaGhMJvNmDt3LtasWYMbb7xxJE7nhxF0V69eDa1Wi9zcXOTk5CA4OLjPD2FbWxtKS0tdPoBWq1XKhru6uqTGDw3Evgw8yH3DKA9zuEYuqdZDX+UQm80mXZder5dkIuX14b4eMFQwOywsDElJSUOuBEVtkqqrq32+T+4iMkajEUql0uUBo9FowPO81LDMyMgYmrKRIIhTY84stru7G4WFhdKAwIXcJ/kDhrIMWJaFQqGA0WjE+PHjfSpNeQtBEFBeXo6Ojg4XSiEhBPv378fatWuxYsUK3HPPPSNeu/WEM2fO4K677pL6N7fffjvWrVs3Uqfzwwi6RUVFyMvLQ35+Pr799ltxymfyZOTk5GDGjBmYNGkSTp8+DYPBgMjISKSkpPRLepcPPNA6KgApu/I0KksIQWtrK8rKyoZNNEYufDMYFTDaqJNT1+ROszRYlZeXw2q1esw8hwIGgwGFhYXQ6XQXHKgobDaby9CDwWCA3W5HWFiYVHLxJtP3FvJl+ECsgcGCatEKgoDQ0FCYTKZBjQL3B1rWGTt2LOLj46X709XVhSeffBLNzc3Ytm0bxo8fP5SX1i9qamqwdOlSNDU1gWEY3H///VixYsVFO/4F4ocRdN1hsVhw6tQp5OXl4euvv8ahQ4eg0+kwd+5czJo1C7m5ua6jnV6AZiHy+jDNrtRqNZqbm6HRaJCamjosSz+qY+BrI24gyB8w9fX10Ov1UKvVEtk+JCRkyKaZ7HY7ysrK0N3dPWy1YZPJhKKiIqhUql4lF6vVKgnCX0gpiUoj0kA11NmffBWQmpraq/5MR4HlmT714JMH4v4eMH014wgh+OKLL7Bu3TqsXLkSS5cuvejZbUNDAxoaGpCdnQ2DwYCcnBzs3bsXEydOvKjnMUj8MIMuhc1mw5w5c7B8+XJce+21OHHiBPLy8nD8+HFUVVUhLi4OM2bMQE5ODnJzcxEWFuZTJkQ1BoxGoyQvOFQ8W/kxKAVsuAI65cNGREQgyWnH4z5W6uuHWg756GtCQgLGjRs3LA0gF3EaD+wN90zfkx5Df38zu92OkpISWCyWIZdGpKB6BkFBQUhJSfF6FSAfvpHr9nqaguzs7ERhYWEvPd3Ozk488cQTaG9vx7Zt2y6Z5thNN92Ehx9+GHPmzBnpU/EGP+ygC/RY9riDksrz8/ORn5+PEydOwGAwIDMzUwrC06ZN8xjk+qrbynm2NCPmeV7qUtP6sDeZg8PhkJT9XXQMhhAWiwUlJSVwOBxIT0/vZdDpfj7yTJ+aULpLX7rf6+7ubhQVFSEwMBApKSlDrhEL9CNO4wXkHGL6j3KI5cyC1tZWVFRUDFtDkRAicbjlegYXAk+1bzo4EB8fj8jISMkM9bPPPsP69evx2GOP4Ze//OUlU7utrKzE1VdfjbNnzw5LCWcY4A+6voDKRNJAfObMGXAch+zsbOnfqVOnwHGcVKIYaJkv17Sl9WF51uhO7/KkAjYcWSGtDdPx4MGA1lFpIJb7uWm1WnR0dECv1yM9Pb1PE9ALgc1mQ0lJCWw2m+/iNP1AEASYTCaJjtfS0gKGYRAREYHQ0NAh4drKQWvctGk5HCIxlGo2duxYaLVaGAwGtLe3Y9myZRLFctWqVZg3bx6Sk5OH/PiDQXd3N6655hqsXbsWt9xyy0ifjrfwB90LASEEBoMBJ06cwL/+9S+8//77iIiIQFxcHLKyspCbm4vc3FyfGQruWSOld6nVanR1dSE0NBRpaWnDkhW2tbWhpKRkWJp9NNOnqwClUgmWZb23EfLhOLTmOVyKbHQ109DQIK00KMWrq6urF8XLWzU592OUl5ejvb3dhRM7lKAj5iaTyaUkQgjBp59+ig0bNuA3v/kNEhMT8d1338FsNuPpp58e8vPwFXa7HTfeeCPmzZuHlStXjvTp+AJ/0B0KCIKAu+++G8uXL0dubi7q6+ulbPjYsWNobW1FamqqRFnLzs72aTiBmhCazWbodDpYLBZYrVYXVsGFBiuz2ewyiTVc9UjaxKI6unT5LqeuCYLg0n33Re1qKClafcFbXq+7pCJlFnhT+6Z11eFqxgE9D9jx48cjJiZGOo/29nasWbMGZrMZf/3rXyVbqksFhBDcddddCA8Px0svvTTSp+Mr/EH3YoDneRQUFCA/Px/Hjx/Ht99+C57nMXXqVCkbzszM7BUg+qOAyWuNNGDJg5W3rALaYGpqakJqauqgPOO8uX4qupKenj5gPVI+oUWzRjp5Rh8w7sFK7oE2XBQteUd/sLxe94aWO4c4KCgItbW1UubZXx19sHA4HJJPoHyQghCCTz75BE8//TSefPJJLF68+JIUhjl8+DCuuuoqTJkyRXp/b9q0CTfccMMIn5lX8AfdkQBV2jp58iSOHTuG/Px8FBYWIiQkROIOt7a2orGxEb/85S+9poDRYCUXTKeZFaV30cYI0EMzG85sih5j3LhxGD9+/KCPwfN8L+lLjuMklauWlhbExsa6cEmHEq2trVJWONR1dCoI39DQgObmZnAc5+I2QmmHQ3FMeh3uLJG2tjasXr0aDocDf/3rXzFmzJgLPpa3uPfee/Gf//wH0dHROHv27EU77gjBH3QvFdDhiQ8//BAvvvgiWJaFVqvFuHHjpGw4OzsbISEhPteH5dkwDVY2mw0qlQrp6enDkhXScgXDMD3CMUMM2mCy2+1Qq9Ww2WxQq9VSoAoJCbngkW2r1Yri4mIIgoD09PRhuQ6bzYbi4mLwPI+MjAyo1WrJf899WlDeYPXl2uiYsN1uR2ZmpqT7QAjBv/71L2zatAlr167FokWLLnp2e+jQIWi1WixdutQfdPuBP+gOE15++WXk5ubiRz/6EQRBQGlpqVQbPnnyJEwmEyZNmiQF4smTJ3stkkOX+c3NzRgzZoyUPdpstl4+boOthV6MckVf4jRUPU7+kKE8Wzl1zZtrkzfjhstDjBCCpqYmVFRUIDk5ud/s8kI4xNTp132curW1FatWrQLDMHj11VeH5Rq9RWVlJW688UZ/0O0H/qA7QrDZbDh16pQUiM+ePQuNRoPp06dLgTg5OdllGe/uvBsXF9fr9+7NrMH4uFHa0ZgxY3zmw3oLX8Vp3GvfBoOhFzfand5lNBolRbPhasZZLBYUFBRApVINmolCy1Tyv5vD4ZAeoEFBQWhwKsJlZGRImTEhBHv37sUf//hHPPXUU7jttttGvHbrD7r+oHvZgBCCzs5OHD9+XGrUlZeXIzY2FtnZ2YiJicG+ffvw1FNPuSwrB4JcqNqT7CXVYaBW9HQJPlzMB0ptGgpxGk9SitRdwGazwWQyYeLEicMycCK3JB+OlQAd266trUVjYyOUSiUUCgW0Wi0OHTqE2NhY7N69GxqNBq+88sqgOdhDDX/Q9QfdyxqCIKCwsBBPPPEETp48iYyMDDQ3NyM9PV1q1E2bNs2lqeYNaMOHBmKz2QxCCOx2O+Li4jB+/Pghkb2Ug9ojlZeXD9uIMCA2kgoLCxEYGAiFQiGNycrLEr7eL3dQ08bg4GCkpKQMy5CDzWZDYWEhGIZBeno6VCqVpK+8YcMGfP3117DZbAgLC8PMmTPxyiuvjHiWC/iDLvADsOsZzWBZFoGBgbjhhhvw4YcfQqFQwOETdABzAAATK0lEQVRw4Ny5c8jLy8P777+PNWvWgGEYZGVlSWPN6enp/QYC6gAcERGBzs5OFBUVSRNYBoMBZ86cgd1u7zXWPNjgIuf15uTkDHlAB1z1EqZPn+5C0aIPGb1ej8bGRpjNZsndWM4qGAhy3Qe5aeNQQl4fdq9Bt7S0YNWqVQgICMDBgwcRGRkpjXhfCgHXDxGXZab7wQcfYP369SgoKMCxY8eQm5sr/W7z5s148803oVAo8PLLL2PevHm9Xl9RUYHFixejra0NOTk5ePfdd4flg34pgLpmnDx5UipLUFGbnJwc5OTkYObMmb10BOhordVqRUZGRi8e6WBkL93hjTjNUFw/zaB90Utwl76UN7PoQ0Zen6U1aGprMxx1bjo8o1AokJ6e3mPdLgj45z//iRdffBEbN27EzTfffEkG2TvuuAMHDhxAa2srxowZgw0bNmDZsmUjfVrDhdFVXigoKADLsnjggQfw4osvSkH3/PnzuOOOO3Ds2DHU19fj+uuvR3Fxca8M7Pbbb8ctt9yCxYsX48EHH8S0adPw0EMPjcSljAio2texY8cktbXGxkakpKRg+vTpaGlpgcPhwJo1a3warZXLXlJ3B6VS6ULtojzUCxGn8RYWiwWFhYVQKpXSZNxg4amZxfM8AgMDYbfbYbPZXNyQhxJydTZ3icfGxkasXLkSwcHBeOmll4aFRdIX/vvf/2LFihXgeR733XcfHn/88Yt27MsAoyvoUlx77bUuQXfz5s0AgCecTrHz5s3D+vXrMXv2bOk1hBBERUWhsbERHMfh6NGjWL9+PT777LOLfwGXEHiex0cffYQnnngCERERYFkWFosFU6ZMkerDEydO9Ln77i6GYzabwfM8WJZFUlISoqKihlxbQk41S0tLQ3h4+JDun4LWhyn1zj3bv1BhcaCH/aBWq5GamuqS3f7jH//An//8ZzzzzDNYsGDBRc1ueZ5HWloavvjiC0ka9f33379ctG4vBn4YNd26ujrMmjVL+j4uLg51dXUu27S1tSE0NFSiB3na5ocIhUIBlmXx8ccfSx8ci8WC7777Dnl5eXjllVdw7tw5aLVaqTacm5s74ISbSqVCZGQkIiIi0NDQgMrKSiQmJoLjOOj1etTV1Q1a9tITqHtteHg4ZsyYMSxNLHl9ODs724XFIddhqHSaeMr1bN2nBfsC5Q/X1NT0Yj80NjZixYoVCA8Px8GDB4ftodIfjh07hpSUFEmJbPHixS7vHT/6xiUbdK+//no0Njb2+vmzzz6Lm266aciPt2jRIsnBtbOzE6GhoTh16lSv7RITE6WmEcdxOHHixJCfy0jBXTZPo9Fg9uzZ0kqBEIK2tjYcP34ceXl52LVrF6qrqxEfHy+J/OTk5PQSgafiNFqtFjNnzpQeeFRgRS57WVtbO6DspSfwPC/pDk+cOHFofNA8oLm5GWVlZX0yLBQKBUJDQ11oaPJGXXNzM0wmk9Sok5ddKMxmMwoKChAQEIDc3FzpfgmCgF27duHll1/Gpk2b8POf/3zEard1dXUu1j1xcXHIz88fkXO53HDJBt3/+7//8/k1sbGxqKmpkb6vra3tpXpPO/IOhwMcx0nb7N69W9pm1apV/Xaev/rqq2Gxab/UwTAMIiMj8bOf/Qw/+9nPAPR4hOXn52P//v14/vnn0d3djYkTJ2LKlCk4c+YMpk6dinvvvbfPeif1+goODkZcXBwAV9lL6qZMXY1po44GKqqiFRsbi9zc3GEJRHJbdV8ZFnI2iHx/tORSW1sLq9UqZczd3d1IS0tzYSY0NDRgxYoViIqKwsGDB4el6ejHxcElG3QHgwULFmDJkiVYuXIl6uvrUVJSgpkzZ7pswzAMfvKTn2DPnj1YvHgxdu7c6ZI5E0Lwj3/8A/v377/Yp39ZgmVZTJgwARMmTMCSJUsAiHXcbdu24YUXXkBqair27NmDjz/+GNOnT5fqwykpKf2WEDiOQ1hYmEtwkY/+1tbWwmKxSA/P5ORkREZGDouTAxWT99ZW3Ruo1WpER0dLgdVkMuHs2bPgOA6RkZGoqqrCmTNn8NprryEiIgJ5eXl44YUXsHDhwkuCmeBNguOHZ1yWjbSPPvoIy5cvR0tLC0JDQ5GVlSU1wp599lm89dZb4DgOL730kpSR3XDDDXjjjTcQExOD8vJyLF68GO3t7Zg+fTree+89KWs6dOgQVq5c2WfZICkpSVo+P/DAA7j//vsvzkVfZnj99dfx85//HDExMSCEQK/X48SJE9JYMzXXlNeHvWVK0EBYWVmJ8ePHg+O4C5K97AvyZf5wWQzJm37u9jzV1dVYtWoVHA4HYmJiUFBQgIkTJ+Ktt94a8vPwFQ6HA2lpafjyyy8RGxuLGTNm4O9//zsmTZo00qd2qWB0shd8hTd14oceeggpKSlYtWqVx33U1dUhNjYWzc3NmDNnDl555RVcffXVLtusX78er7/+upQV9aUB+kOm3BBCUFdXh/z8fIm21tbWhrS0NKk+TIcY5IGY6iX0ZdjoSfbS10bWxWI/mEwmnD9/XrKgp00/QRDw3nvvYevWrXjuuecwb9486XwFQbhkfMv27duHRx99FDzP495778XatWtH+pQuJfiDrjdwOByIjY3FyZMnpdpif1i/fj20Wi0ee+wxr34uh59y0xs8z+P8+fNSNvzdd9+BEIKpU6ciKysLp0+fxoQJE3D33Xf7pJdAjRnlspfyiTO5fGJ3dzcKCgoQGhqK5OTkYWE/UPPJxsbGXpNrtbW1eOSRRzB+/Hi8+OKLwzLV5sdFwQ+DMnah+L//+z9kZGT0GXCNRqO0fDUajfj888+xbt26QR3LT7npDYVCgSlTpmDKlCm47777JOWwnTt34o9//CPi4uJw4sQJfPbZZxJTYsaMGQOKjSuVSoSHh7tkrHTirLOzE9XV1bDZbCCEgOd5JCUlYdy4ccMScI1GI86fP4+wsDDMmDFDyloFQcA777yD7du344UXXsCcOXNGrHbb38SnHxcOf9CVYdeuXbjjjjtcflZfX4/77rsP+/btQ1NTExYuXAhAzIqXLFmC+fPne9zXq6++infeeQe5ubnYsmVLr26zn3IzMBiGQVBQEBwOB/bv34+UlBRJvpJ60+3cuRP19fVISkpyEYGnThN9QaPRQKPRIDo6Gl1dXRK3V6vVQq/Xo76+flCyl32BWjI1NTUhMzPThclRU1OD5cuXIzk5GV9//fWIW4xPnjwZH374IR544IERPY/RCn95YZDorz48a9YsqZP+1FNPoaGhoVfzY8+ePfjvf/+LN954AwDw7rvvIj8/H6+++qq0zerVq/Hvf/8bKpUKEyZMwNtvv+1xWT2aucPegIrA5+XlSSLwFoullwi8O82L53kXGcmgoKBev/dW9rI/dHd34/z584iIiEBSUpJLdrtjxw68/vrr2LJlC6677rpLgplA4T7x6YdP8JcXhhre8oh//etf48Ybb+z1c28oN3PmzMHmzZvBcRzWrFmDzZs347nnnvN4nB8qdxgQaWtpaWlIS0vD0qVLAYj0MioCv337dkkEPjs7G7m5uTAajSgrK8NDDz2EtLQ0j8GOSj7K66py2cuGhgYXex06+kuDuyAIklGnu7V6dXU1Hn74YaSlpeHIkSPDNszhx6UHf9AdBjQ0NEjTVh999BEmT57ca5sZM2agpKQEFRUViI2Nxa5du/D3v//dZZu5c+dKX8+aNQt79uwZ3hMfRVCr1bjiiitwxRVXAOgRgf/yyy+xadMmdHR0IDw8HOfPn5ey4ZycHERERAxYH5YPOsjtddrb21FZWSl5uRmNRkRERGD69Okumglvvvkm3n77bWzZsgU//elPRyS7vdgTn370wB90hwG/+93vcOrUKTAMg8TERGzfvh2Aa32Y4zi8+uqrmDdvnkS56Y/j+NZbb2HRokUef8cwDObOnevnDvcDhmEQFhYGk8mExx9/HLfddpvEIsjLy8PXX3+NP/3pT+jq6kJGRkYvEfj+9hsQEICAgACMGTMGgiCgvLwcra2tiI2NhcViwbfffosPPvgAdXV1qKqqQmZmJg4ePDiizITBTHz6MTTw13RHGN5kHM8++yxOnDiBDz/80GNW1Bd3eCAesNVqxdKlS3Hy5ElERERg9+7dSExMHJbrvFxgt9slEfjjx4/j1KlTYFlWmqbLzc1FWlqaR2aDXq9HQUEBxowZ4yIExPM8tm3bhr1792LatGno6OhAQUEBXn755V4c70sJ/pruBcHP071csWPHDmzfvh1ffvllLyFxT6Ac4f/3//7fgDzg1157DWfOnMG2bduwa9cufPTRRy4aFH64isDTQFxcXIyoqCgpCE+ePBk7duzA/PnzMXXqVJeGXEVFBZYvX44pU6Zg06ZNLr8jhFxSjTOK/iY+/fAa/U/f9PPPjxHEp59+SjIzM0lzc3Of23R3dxO9Xi99PXv2bPLpp5+Sb775hsydO1fabtOmTWTTpk0ur507dy755ptvCCGE2O12EhERQQRBGIYrGV0QBIHU1dWRjz76iPzqV78ioaGhZNasWeTmm28mTz/9NPn0009JfX092bJlC8nKyiIHDx7039cfHvqMq5fGPKEfHvHwww/DYDBgzpw5yMrKwoMPPghArA3TseKmpiZceeWVmDZtGmbOnImf//znmD9/vkcesLtusHwbjuMQEhKCtra2i3R1ly8YhkFMTAx+8pOfoKOjA8ePH8fhw4fx7LPPIjY2Fnv37sXs2bORl5eHI0eO4Oqrrx6xjHb16tXIyMjA1KlTsXDhQnR2do7IefjRA38j7RJGaWmpx59Tu3UASE5OxunTp4f1PGpqarB06VI0NTWBYRjcf//9WLFihcs2Bw4cwE033YSkpCQAojbvYKf1LheEhITg3//+t/T9xIkTMXHiRNxzzz2XTOnAF9qhHxcH/qA7SuEND5huExcXB4fDga6uLo8eWxzHYcuWLcjOzobBYEBOTg7mzJnTa2T5qquuwn/+85/huaDLDJdCwAX8tMNLEf7ywiiFnAdss9mwa9cuLFiwwGWbBQsWYOfOnQDECbm+OKPjxo1DdnY2ANH/KzMz029xdBnirbfekqRO/Rg5+DPdUYq+eMDr1q1Dbm4uFixYgGXLluFXv/oVUlJSEB4ejl27dg2438rKSnz33XfS0IEcR48exbRp0xATE4MXX3zRr616keAt7ZDjONx5550X+/T8cIOfMuaH1+ju7sY111yDtWvX9vJT0+v1YFkWWq0W+/btw4oVK1BSUuJxPwNpRRBCsGLFCuzbtw+BgYHYsWOHlGn74Tt8pR36MSTwU8b8uDDYbDYyd+5csmXLFq+2T0hIIC0tLT7/jhBCPvnkEzJ//nwiCAI5evQomTlz5qDO2Q/vaId+DAv8lDE/Bg9CCJYtW4bMzEysXLnS4zaNjY0gzlXTsWPHIAiCx6acN/j444+xdOlSMAyDWbNmobOzEw0NDYM+/x8y+qId+jFy8Nd0/RgQR44cwbvvvospU6YgKysLgGhBVF1dDQB48MEHsWfPHmzduhUcxyEgIAC7du3qs4M/kFZEXxxjKiLkh/foi3box8jBH3T9GBBXXnmllMX2hYcffhgPP/ywV/s7fPiwi1ZERkbGoDQIioqKXESAysvLsXHjRjz66KPSz0YLf/ipp57Cxx9/DJZlER0djR07diAmJmakT8uPQcBfXvDjooPyhaOjo7Fw4UIcO3as1++9sfdOT0/HqVOncOrUKZw8eRKBgYGSs4ccV111lbTd5RhwAXGy7MyZMzh16hRuvPFGbNy4caRPyY9Bwh90/bioMBqNMBgM0teff/55L73hBQsW4J133gEhBHl5eQgJCRmwtPDll19iwoQJSEhIGLZzH0nILXyMRuMlM3zhh+/wlxf8uKjoy2du27ZtAMT68A033IB9+/YhJSUFgYGBePvttwfcryd/O4rRwh9eu3Yt3nnnHYSEhOCrr74a6dPxY5Dw83T9uOxhs9kQExODc+fOYcyYMS6/84U/PNLw1s1h8+bNsFgs2LBhw8U8PT98w6D1dP3w45IHwzA3AfgtIWSuF9tWAsglhLQ6v38LwI0Amgkhk50/CwewG0AigEoAtxNCOjzs6y4Av3d++wwhZOcFX4wXYBgmHsA+er5+XF7w13T9GA24A8D7nn7BMMxYxlkAZRhmJsT3vFy/cgeA+W4vexzAl4SQVABfOr933284gD8AuALATAB/YBgm7MIuo28wDJMq+/YmAIXDdSw/hhf+oOvHZQ2GYYIAzAHwoexnDzIMQ6cAbgVwlmGY0wBeBrCYyJZ3hJBDANrddnsTAJq17gRws4dDzwPwBSGk3ZkFf4HewXso8UeGYc4yDHMGwFwAKwZ6gR+XJvyNND8uaxBCjAAi3H62Tfb1qwBe9XG3YwghdASuEcAYD9vEAqiRfV/r/NmwgBDyP8O1bz8uLvyZrh9+9ANnVuxvfPgxZPAHXT/86I0mhmHGAYDz/2YP29QBGC/7Ps75Mz/86Bf+oOuHH73xLwB3Ob++C8DHHrb5DMBchmHCnA20uc6f+eFHv/j/j/nCmrzNjK0AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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FpFhEirdu3RpDGMaY0EhGoveWKEyjQVOxJPqmQH/g9865fsCXHFZN45xzgKvtyc65yc65IudcUW5ubgxhGGNCo6wM2rXTRbwTqVMnK9E3UClQ6pybF7n/Epr4N4tIR4DI9ZbYQjTGpI1ErRV7uM6drY6+IZxzm4ANItIjsmkYsAx4HRgb2TYWeC2mCI0x6aOsLLE9bjxpVqJvGuPzrwemi0gmsAb4Pvrl8WcRuRJYD1wU42sYY9LFxo0wcGDiX8cbHeucTnQWcjEleufcIqColoeGxXJcY0waqqqCLVuSV3VTVQXbt0NOTuJfz2c2MtYYEwxeL5hkVd1A2lTfWKI3xgRDMgZLedJs0JQlemNMMCRj+gOPleiNMcYHXtJNRoneG5Blid4YY5KorAwyM5PTOJqZqQOzLNEbY0wSeX3ok9XdsXNnS/TGGJNUZWXJqbbxWKI3xpgks0SfMJbojTH+c86fRL9tG+zZk7zX9IklemOM/3bsgF27kp/oIS360luiN8b4z6tCyctL3mt6r5UG1TeW6I0x/ktmH3qP91qW6I0xJgmsRJ9QluiNMf7bsEGvkzH9gee443RZQe+1Q8wSvTHGf6Wl0L69jlhNFhEt1ZeWJu81fWKJ3hjjv9LS5FbbeCzRG2NMkpSWQn5+8l83P98SvTHGJIWfJfrycti3L/mvnUSW6I0x/tq5UwdM+ZXoq6th06bkv3YSxbo4uDGNsm4dvPgivPsuLFwIe/fq9sJC+PrX4dxz4eyzIcOKIOnDj66VHu81/fpFkST272SSYuFCuOgi6NYNbrsNPvpIE/v558OIEdoB4rHHYPhw6N0bnnwS9u/3O2qTFF73Rj8SrdcuEPIullaiNwm1Zw/ccw88/LB2W/7xj+Haa6FLlyP3rayEl16C//1fuOoqTfZPPQUnn5z0sE0yeUm2tg9FoqVJorcSvUmY1athwAB46CH4/vdh7Vp48MG6/5+bN4dLL4XiYpg+XZ/frx888URy4zZJVlKiP+mSOf2B5/jjddBUSUnyXzuJLNGbhHj7bfja17SNa8YMLZ23bt2w54rAd78LS5fCWWfB+PFa3VNdndiYjU82bIAOHZI7WMojoiUPK9Eb0zh//zsMGwZt28K8eVrvHo327eFvf9Oqnocfhssus3r7UCop8acPvSc/P/QlequjN3E1axaMHg2nnAL//CdkZ8d2vKZNtZE2Px9uv12rd6ZMsV45obJhg7bA+6VLF1i0yL/XT4KY/11EpImILBSRv0XudxWReSKyWkReEBEffo8ZP7zzDlxwAXzlK/FJ8h4RmDBBG3WnToXrr9cFiUwIOBeMEv3mzdobIKTiUS66AVhe4/5DwCTn3InAZ8CVcXgNE3Br1sDIkfo/M2tW/JJ8TT/9qfba+d3v4NFH439844PPPtOVpfzocePxXjvEUyHElOhFJA84D3gycl+AocBLkV2mARfG8hom+D7/HL71LW0s/dvfoF27xLyOiPbaGT0abr0V/vGPxLyOSSKvbtzvEj3A+vX+xZBgsZboHwVuA7z+ENnADuecN3FEKVBrnykRGS8ixSJSvHXr1hjDMH6prtZG0pUrtQ989+6Jfb2MDJg2Dfr0gYsvho8/TuzrmQTzkmtBgX8xeK8d4gbZqBO9iJwPbHHOzY/m+c65yc65IudcUW5ubrRhGJ9NmgR//asOcho6NDmveeyx8Npr2hvvoot0UJZJUevW6bWfiT4/X38uerGEUCwl+tOBC0RkHfA8WmXzK6C1iHi9efKA8K/TlabmzdNG0lGjtIE0mfLz4emnYfFiuPnm5L62iaP16+GYYyAnx78YMjOhY0eruqmNc+5251yec64QuAT4l3PuUmAO8N+R3cYCr8UcpQmcigq45BKdnmTKFC0QJduIEdo4+/vfa7WRSUHr1+uMdn58gGoqLLRE30g/AW4WkdVonf2UBLyG8dmNN2qV5nPPNXzEayI88AAUFcHVV4d+ptlwWr/e32obT0GBJfr6OOfmOufOj9xe45wb5Jw70Tn3HedceDunpqnXX9f+7LffrtMc+KlZM63C2blTJ0Kz/vUpZt264CT6kpLQDr228YWmUbZu1YTap4/2aw+Cnj1h4kTt2jl1qt/RmAb78kvYvj04iX7fPl1tKoQs0ZtGufFGHePy9NP+zEFVlxtugDPPhJtugo0b/Y7GNEgQetx4Cgv1OqQ9byzRmwZ74w149lm44w746lf9juZQGRk6nXFlJfzoR35HYxpk7Vq9PuEEf+MA6NpVr72YQsYSvWmQL77QBs+ePbVuPoi6d9f5cF59FV55xe9oTL2ClOi9XxWW6E06u/tunQpkyhSdQTKobrlFFyu57jrtAmoCbM0aaNECgjBgMitLFz5Zs8bvSBLCEr2p14IF8JvfwDXXwGmn+R3N0TVtqlU4W7bAXXf5HY05qrVrtTTvdx96T9euVqI36Wn/fq2yyc3VPuupYMAALdH/9re6LKEJqDVrDtaNB0HXrlaiN+lp8mT44AOdy8bPgVGNdd99ukLV1VeHtmt0anPuYIk+KE44AcrKQjkvvSV6U6ctW7ThddgwGDPG72ga5/jjdc76+fPh8cf9jsYcYds2HeUWtBK9c6EcIWuJ3tRpwgRdE+Kxx4JTjdoYF12kM2reeacO9DIB8sknen3iif7GUZMXixdbiFiiN7V67z344x91ANJJJ/kdTXRE9Etq587gdglNW6tX63UQE70XW4hYojdH2L9fGzM7d9ZulamsZ08dzTtlCrz/vt/RmANWr9ZRbt6I1CBo1w5atrREb9LD5MmwcCH88pf6uU91P/0pdOqkI2atYTYgVq/WtVqDNChDREv1luhN2G3frv3PzzpL67jDoFUrePhhbZh96im/ozGAJtMgVdt4LNGbdHDnnTqi9De/Sc0G2LqMGQNnnKF19Z9+6nc0JrCJvnt37fa5b1/9+6YQS/TmgAULtNrm+uuhVy+/o4kvEf3y+uyz1G93SHmffqo/HYOY6E88EaqqQtfF0hK9AaC6Wuuwc3N1YrAw6tNHp3H4wx90rVnjk5Ur9bpHD3/jqI0XkxdjSFiiNwA884x2qXzoIR1sFFb33gtt2+qXmq1G5RNL9Elnid5QUQG33abLAl5+ud/RJFbbtroa1dtv69z6xgcrVugakEEaFevJydEPyYoVfkcSV5boDT/7mU538Nhj2rU57H7wAxg4EH78Y/j8c7+jSUMrV0K3bjrVaBD16GElehMuS5bAr3+t68AOGOB3NMmRkaFfaps2aVWOSbKVK4NZbeOxRG/CxDkdAXv88fDzn/sdTXINGgTjxunEZ0uW+B1NGqmq0q6VQU70J52kpYAdO/yOJG4s0aexZ5+FN9/UOuvsbL+jSb6f/1y/5KxhNolWrdJk37u335HUzetbvHSpv3HEkSX6NFVRAbfeqnXVV17pdzT+yMnRZP/vf1vDbNIsW6bXQR6o4cXmxRoClujT1F13webN8LvfQZMmfkfjn3Hj9Mvu5ptD9Us9uJYu1dFrQZ4StaBA17K1Ej2ISL6IzBGRZSKyVERuiGxvKyIzRWRV5LpN/MI18VBcrAn+uuugqMjvaPzVpIkOoNq2Tad/MAm2dKmu5NSihd+R1C0jA04+2RJ9xD7gFufcycDXgOtE5GRgAjDbOdcdmB25bwLCWwO2XTu4/36/owmG/v112off/x7+8x+/owm5pUuDXW3j6dXLEj2Ac67cObcgcvsLYDnQGRgJTIvsNg24MNYgTfw89pjO4jhpUrhHwDbWvfdCx44wfry2FZoE2LNHuy2ecorfkdTvlFOgvDw0S5PFpY5eRAqBfsA8oL1zrjzy0CagfTxew8Ru3TqtnvjmN+Hii/2OJliOO06/BBcv1oXQTQIsXao/Kfv29TuS+nkxhmRSpJgTvYi0BF4GbnTOHTLO0DnngFo7ronIeBEpFpHirSH51gwy53RCL9AqijBNQRwv3/42jBqlk7qtWuV3NCG0aJFep0Ki79NHr72YU1xMiV5EmqFJfrpz7pXI5s0i0jHyeEdgS23Pdc5Nds4VOeeKcnNzYwnDNMCzz8KMGfDAA9qpwNTuN7/RRY/Gj9cZPU0cLVqkS5adcILfkdQvJ0fX0kz3RC8iAkwBljvnav7YfR0YG7k9Fngt+vBMPJSXa2Pjqafq4CBTt06ddDWquXO1N46Jo8WL4atfTZ0Jlfr2tUQPnA58DxgqIosilxHAg8A5IrIKODty3/jEOe1ls2sXTJ2a3n3mG2rcODj3XJ3Rc80av6MJif37dWWbfv38jqTh+vWD5cvhyy/9jiRmUU8f55x7G6irpndYtMc18TV9Orz+OjzySLDHqASJCDz5pI7S/8EP4F//Sp1CaGCtWKEJc9AgvyNpuEGDtP5u4UL4r//yO5qY2Mc3xEpKtKrm61+HG2/0O5rUkp+vXVD//W+d+MzE6IMP9HrgQH/jaAwvVi/2FGaJPqT274fvfU+v//Qnq7KJxve/DyNH6oLiIell558PPoBWrYI9a+XhOnSAvDxL9Ca4Hn5YZ6Z87LHU6OQQRF4VTtu28N3vwu7dfkeUwubN0wUPUq0ObNAgjT3Fpdi7bhrivffg7rvhoovCvzRgouXkwLRpOpHhTTf5HU2K2rlTe6+cfrrfkTTe17+uLfLl5fXvG2CW6ENm2zZN8Pn58PjjNjAqHs49F37yE30/bTrjKMybp3WIqdig6cX8zjv+xhEjS/QhUl2tJfgtW+DFF6F1a78jCo/774czztCBVMuX+x1Ninn7bS1xnHaa35E0Xr9+cMwxeg4pzBJ9iNx7L/zjH9pLJF3Wf02Wpk3hued0dt1Ro3ThFtNAb76pA6VScRa9zEwdafjmm35HEhNL9CHx8svws5/B2LE6QMrEX+fO8Oc/65Knl16qtRGmHrt3a7XH0KF+RxK9oUO1jWHbNr8jiZol+hD48ENN8KeeqsP2rV4+cYYMgV/9Cv7+d12ly9TjnXegshLOPtvvSKJ39tk6xHzOHL8jiZol+hS3YQOMGKG/il99FbKy/I4o/K65Bn74Q3jwQXjiCb+jCbhZs6BZMxg82O9IojdwoM5jPXOm35FELeopEIz/duzQueW/+ALeeksXzjCJJ6KzXJaUaDVZx45w/vl+RxVQb7yh3SpbtvQ7kug1bQrDhmkDmHMp+ZPZSvQp6ssv4YIL4OOPtST/1a/6HVF6adZM6+v799furCneKSMx1q6Fjz7SD2qqu+ACKC1N2dksLdGnoN279XP3zjs6vUEqt3OlspYtta4+P19/Wb3/vt8RBcxf/6rXYUj0552nJfnXUnPWdUv0KWb3bl0Jac4cnXbYlgT0V7t2Ortl+/YwfLgtLn6Il17SRba7dfM7ktjl5moV1Msv+x1JVCzRp5CKCk0m//d/OgfL977nd0QGtNvlnDmQna1VubNn+x1RAJSUaMPRmDF+RxI/Y8bAkiVaHZViLNGniE2b4MwztXrgued0nnQTHPn5Wk9fWKi9oFK04Bc/3lwRYUr03/mOTgP7zDN+R9JoluhTwMKF2sNr1Sqt9rTqmmDq1EkHUBYVaU6YOFE7aaSd6mr9yXnGGeGaOjU3V+vqp06FvXv9jqZRLNEH3PPPa9WgiDa+Dh/ud0TmaNq00a7jY8bAHXfoCNqdO/2OKslmzYJPPtEBB2FzzTU6mdQrr/gdSaNYog+oXbt0Aq0xY3RepQ8+0LWKTfAdc4z+up84Ub+oi4pStldedB55RFunR43yO5L4O/dcbVz+5S9T6ueaJfoAev99TQ5PPgkTJsDcufp/Y1KHiP7tZs/WAW2nngoPPQT79vkdWYL95z86gvTmm6F5c7+jib+MDJ2zurhYe0WkCEv0AVJRATfcoGsd7NwJ//ynlgqbNfM7MhOts87SZQjPP18T/6BBmiOC6sYZN3LjjCgXGHZOk2B2djirbTyXXw5duugakykys50l+gDYt08nI+veXYfWX3stLF0K55zjd2QmHnJytBfOyy/rQkUDB+okdKWlfkd2pEWbFrFoU5T1TC+9pD8/771X14cNq+bNdaKjhQvhqaf8jqZBLNH7aO9emDIFTjpJC0A9e2pp77HHwv1/kq5GjYKVK7Vk/8ILcOKJ8KMf6cR0KW/LFrjuOswqGd8AAAohSURBVJ0TYvx4v6NJvEsu0Z9rt9wC69f7HU29LNH7YNMmuO8+7Xk2bpyuBPWXv2hhqH9/v6MziXTccVodt2KFDnh7/HH9HFxyifbDT6H2vYOqqrTP7xdf6AK7TdNgrkSRg6X5UaMCv3K8Jfok+fxzHeh03nmQlwc//amODn/jDe1RM3JkSk6KZ6JUWKhTHH/yiZbqZ8zQbucnnaSFgI8/9jvCBqqqgssu01LK5MnQu7ffESVPYSFMn65VOKNGwZ49fkdUJ0v0CVJdrV3qJk3Svu85OfDd7+oiIbfcoj/h//lPnQzLEnz66tJFPyNlZdrLqlMnLQT06KFJ/9ZbdXbcL77wO9JabN6sH+A//1m7VKbjnBzf+pZ+Y8+YoVU5JSV+R1Q751zcL8A3gJXAamBCffsPGDDApbLKSueWLnXu+eedu+AC57KynNMf4Qcvxx3n3F13OXf11c5lZBzcnpnpXPPmR+5f26Xm8+ySWpeMDOeuuca5Z55xLju77r9tdrZzrVvr7WbNaj/Wscc6d/fdzn36aXw/x88841zzH57puOJMV1Cg94/YoaBAg2jTxrmWLfXDO3XqkY+nw4c1M1P/YCL6XnjbW7d27okn6n+zCwr0ubW+2Q0DFDtXf04W3Td+RKQJ8DFwDlAKfACMcc4tq+s5RUVFrjiAfc4qK3Vxjx07dLnIrVu1fn3TJm1AKymBNWtg3TotwdcnI6Nh+xnTUNnZ2qhbUKC/Djp1gg4ddFbNnBxo21bbgFq2PPovx+nTtQ1110VDdMPUubRoobUxl46p1vUTJ0w4dOh/RoY2ONx2W40D7Ero+aaU00/XlWlOPVUbYpo00e21vVcH3uxLG/USIjLfOVdU734JSPSnAfc454ZH7t8O4JybWNdzok30c+fqfOA1v2arqw+97N+v1/v26e2qqoOXvXv1smePXnbv1gU9du7Uy9Gms+jYUf+xunbVbpFf+Yp+3svLG30axkSlTRtd9GT1ai10lJRo4aQ2IprsW7WCY4/VvJKVpZfmzWHRv3fQoXIda664kgyqKZr6CFnsppV8SRupoEn1XjKoRnBkUH3gthzXitw7f8htvytMid4nSVWzZJeZqTPfdegACxbU3nhbUKClxkZoaKJPRPN4Z6Bmh7FS4NTDdxKR8cB4gC5dukT1QgsXwm9/qx9iEX1fRfSL07vfpIleN22qt5s100tm5sHrFi30n6ZFCx2+7v1DtGqlpaHWrbV0lJNzsLRU2yCmyy+P6jSMicqOHTr+wuOcbisv11+f27frpaJCt+/cqXX9u3drYdIr4FRU6BdEFZnsoymODJbTkyqasc81pVlWM/bvrsQhVJOBQ9hPExyC+1zo9ie4Lah1036qrtYkNX++tq6XlGi7Rl09dBL4HvrWD8o5NxmYDFqij+YYN92kl6Do0sUKNSZ5Di8fiWiBpU2bxh+rsLA1y9e3Bo4BoJxOQI1CZmHv2j/cBQXw0TootA//EQoKdIKqwyepKiys/b2KssDbEInodVMG5Ne4nxfZFnoPPKC/CuqSYX2cTJxkZurnLV5q++y2aFHjNerbob4Pf7o55M07TL1vdgI0pMW2MRf0V8IaoCuQCSwGeh3tOane66ammo3p2dkHG+W9hvVrrrFeN+l4aUyvG+8z490Gve/tm50ddSeNej+7Dep1U1dPkXTudVPbP3t9b3Yq97oBEJERwKNAE+Ap59xRv6qC2uvGmHQzZOoQAOZeMdfXOEzD+NkYi3PuDeCNRBzbGGNM41itsTHGhJwlemOMCTlL9MYYE3JpMJ+oMaah+nawhYnDyBK9MeaAR7/xqN8hmASwqhtjjAk5S/TGGBNyluiNMSbkLNEbY0zIWaI3xpiQs0RvjDEhZ4neGGNCzhK9McaEXEKmKW50ECJbgfqWp8kBtiUhnKCy80/f80/ncwc7/6Odf4FzLre+AwQi0TeEiBQ3ZN7lsLLzT9/zT+dzBzv/eJy/Vd0YY0zIWaI3xpiQS6VEP9nvAHxm55++0vncwc4/5vNPmTp6Y4wx0UmlEr0xxpgoWKI3xpiQC1SiF5G2IjJTRFZFrtvUsd/YyD6rRGRsje2ZIjJZRD4WkRUiMjp50ccu1vOv8fjrIrIk8RHHVyznLyItROTvkb/7UhF5MLnRR0dEviEiK0VktYhMqOXx5iLyQuTxeSJSWOOx2yPbV4rI8GTGHS/Rnr+InCMi80Xko8j10GTHHqtY/vaRx7uIyE4RubXeF3POBeYC/AKYELk9AXioln3aAmsi120it9tEHvsZcH/kdgaQ4/c5JfP8I4+PAp4Flvh9Psk8f6AFcFZkn0zgLeCbfp9TPefbBPgEOCES82Lg5MP2uRb4Q+T2JcALkdsnR/ZvDnSNHKeJ3+eUxPPvB3SK3O4NlPl9Psk69xqPvwS8CNxa3+sFqkQPjASmRW5PAy6sZZ/hwEzn3KfOuc+AmcA3Io/9AJgI4Jyrds6l2mi6mM5fRFoCNwP3JyHWRIj6/J1zu5xzcwCcc3uBBUBeEmKOxSBgtXNuTSTm59H3oKaa78lLwDARkcj2551zlc65tcDqyPFSSdTn75xb6JzbGNm+FDhGRJonJer4iOVvj4hcCKxFz71eQUv07Z1z5ZHbm4D2tezTGdhQ434p0FlEWkfu3yciC0TkRRGp7flBFvX5R27fB/wS2JWwCBMr1vMHIPJZ+BYwOxFBxlG951JzH+fcPqACyG7gc4MulvOvaTSwwDlXmaA4EyHqc48U6H6C1mA0SNIXBxeRWUCHWh66s+Yd55wTkcb0/WyKluDedc7dLCI3A48A34s62ARI1PmLSF+gm3PupsPr8oIkgX9/7/hNgeeAXzvn1kQXpUkVItILeAg41+9YkugeYJJzbmekgF+vpCd659zZdT0mIptFpKNzrlxEOgJbatmtDBhS434eMBfYjpZkX4lsfxG4Mh4xx1MCz/80oEhE1qF/13YiMtc5N4QASeD5eyYDq5xzj8Yh3EQrA/Jr3M+LbKttn9LIl9jx6Ge9Ic8NuljOHxHJA14FLnfOfZL4cOMqlnM/FfhvEfkF0BqoFpE9zrnH6nw1vxslDmtceJhDG+N+Ucs+bdG6qTaRy1qgbeSx54GhkdtXAC/6fU7JPP8a+xSSmo2xsf797wdeBjL8PpcGnm9TtDG5Kwcb5Hodts91HNog9+fI7V4c2hi7htRrjI3l/FtH9h/l93kk+9wP2+ceGtAY6/sJHxZ0NlqvugqYVeMfuAh4ssZ+P0Abn1YD36+xvQB4E/gwcpwufp9TMs+/xuOpmuijPn+0ROSA5cCiyGWc3+fUgHMeAXyM9sC4M7LtXuCCyO0s9NfpauA/wAk1nntn5HkrCXgPo3ifP3AX8GWNv/UioJ3f55Osv32NYzQo0dsUCMYYE3JB63VjjDEmzizRG2NMyFmiN8aYkLNEb4wxIWeJ3hhjQs4SvTHGhJwlemOMCbn/D+JVYcFoMTn/AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Accuracy is :  100.0\n",
            "F-score is  1.0\n",
            "Threshold value:  0.008813863129467877\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y8jjZMzMjrKw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}