CIFAR100_with_Conv2D_superclasses.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "# Links:\n",
        "\n",
        "# https://keras.io/api/datasets/cifar100/\n",
        "# https://www.cs.toronto.edu/~kriz/cifar.html"
      ],
      "metadata": {
        "id": "hF7dul3U5cyT"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GqMIq82F3hn3",
        "outputId": "a44d3b1c-db8b-40af-b6ac-4a593dede80b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2.15.0\n"
          ]
        }
      ],
      "source": [
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "print(tf.__version__)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cifar100 = tf.keras.datasets.cifar100\n",
        "\n",
        "(train_images, train_labels), (test_images, test_labels) = cifar100.load_data(label_mode=\"coarse\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oSKcvpB94zh0",
        "outputId": "a3f01fee-2a27-4e4f-c078-c3551dce9245"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz\n",
            "169001437/169001437 [==============================] - 3s 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class_names = ['aquatic_mammals', 'fish', 'flowers', 'food_containers', 'fruit_and_vegetables',\n",
        "               'household_electrical_devices', 'household_furniture', 'insects',\n",
        "               'large_carnivores', 'large_man-made_outdoor_things',\n",
        "               'large_natural_outdoor_scenes', 'large_omnivores_and_herbivores',\n",
        "               'medium_mammals', 'non-insect_invertebrates', 'people', 'reptiles',\n",
        "               'small_mammals', 'trees', 'vehicles_1', 'vehicles_2']"
      ],
      "metadata": {
        "id": "-KxDZgHr5kgd"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_images.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZzO8M2ov65zZ",
        "outputId": "d7d870e0-0277-47bf-adba-6423630e1ecf"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(50000, 32, 32, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(train_labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8hK4bLBM6-Eo",
        "outputId": "83dc0272-3682-4ced-e9e7-ed6f175b57ab"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "50000"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_labels"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SUrrNuB-7ATz",
        "outputId": "d3d04cea-53c2-4990-9c7e-bee03b988a3d"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([11, 15,  4, ...,  8,  7,  1])"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_images.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sFay3nlL7Bp_",
        "outputId": "a863d089-17e9-4b4e-ae67-a733dd3a3154"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10000, 32, 32, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(test_images)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0BmNQ1Ug7EhF",
        "outputId": "112482e0-9343-4797-a905-b8059f2a655b"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10000"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure()\n",
        "plt.imshow(train_images[0])\n",
        "plt.colorbar()\n",
        "plt.grid(False)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "1Ak7kBuy7G1n",
        "outputId": "f03d4f93-f33f-42ef-e3f8-3d00a61108ea"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_labels = np.asarray(train_labels).reshape(-1)\n",
        "\n",
        "train_labels"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yhgP7SG67TTz",
        "outputId": "6b9e4a2c-1785-4749-d74d-47f842bc4687"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([11, 15,  4, ...,  8,  7,  1])"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(15, 15))\n",
        "for i in range(25):\n",
        "  plt.subplot(5,5,i+1)\n",
        "  plt.xticks([])\n",
        "  plt.yticks([])\n",
        "  plt.grid(False)\n",
        "  plt.imshow(train_images[i], cmap='viridis')\n",
        "  plt.xlabel(class_names[train_labels[i]])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 854
        },
        "id": "QEnEJ6OZ7b92",
        "outputId": "c3d33945-5349-43e7-d925-edea6059df43"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1500 with 25 Axes>"
            ],
            "image/png": 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8DNYL+06C9dxJZ+F6to+20b66MRWhVCzS98qR4MlDEU3lImkpJAGoOIXHtpnZDf/0T7B+3vMvgPXJKdzHbrvtZtrG+Oh+WJ8awut1243fgfVUjqdirD0B9/HzNr4E1p2Ht/vw4G7aRkfXQlhPZ/Gcoxy85wZPqYtJfSNpIyyxjC6HxpWZGWmDJUn5LFExpic5DyctRWy9WCJbwL8HT3HDx02P/FteQNbVzMzn+XWkjhNqKjV8TmLGU6aS5Pjvs8S5mIQ8FnkTkbbpvo2JzmneH7Pts8dawvcs0XSsSpBtyhLDzGLSE8lxMEUSwryQ95mbv/Z1XP/Gv8F6sowTqHzjiY4hGUehI1FaEUm1i/k39JrD57/O8Pq6Okm5JImBZmZ+hL9HcRQn3hX246TpwS0P0Da23PNTWB8bwOm5r3v3/4J1r6OXtuH5+HsEPP4Sl2OSF4OoOb0uJkJxjlm2vNtSTXNmdzdOokt2koQ6M/My+LxpooL7fSKPl9W/jF+vmIeXVS6T/MIQJ+clDK+rmZnV8XlpOpHFTVSnYN3x8DqrVXEfG6rgc+gDw7iNTMSvBabJJXI9hbf7dAmvcKHKE+8tjfeHI3O28/H7FyzG6YNmZgcGRmB9klwHseS5IO6c6LAA5SNPkNVvOomIiIiIiIiISMvpppOIiIiIiIiIiLScbjqJiIiIiIiIiEjL6aaTiIiIiIiIiIi0nG46iYiIiIiIiIhIy/3y6XVzNDnET+G0jPzCJbA+vBenP5SH99I22lL4ie2TZbxRNt91B6wXu1fSNm688U78mSn8dP52vx/Xu3EqgZlZoYIf2795N07d2l/AT7XfO4KTvczMrv/SF/FnHsSJWMU998J6W1igbaSzOEWhUsAJcyvzOPXBX3QcbaPsNfarRIGvjzwzGrxlZi7CyQ3VSgnWPZKi8uQTj9E2hnbhNMvvDuJ6Io3v048cOEDbqJIknJSP0yHvvuMWWE+n+GRbmsRj74zzXwDru8n3/s+vf4W28br/+U5YX0zS61iyWVwamTwD54G4LpIcQhIKn1oOKbPQN5JcFLcnWfgZD0UhyWcxbdTZ9yApUz5JVEokebpWkMTL8hL4M2ENJ+cWivz46IUkqSvE22SqgJNoBoaGaRs9fUthfenS5bAeBOR7x07abIeQ8tFMBb6L/3mO8zzP/Ob0OpLeFSTi0utwX2apgyzt7uAefo77sx/+ANajCk68c+SSok4SZ83MjKTXsbRFzyMJeREed2ZmqST+7gny7+4hm1JJypSZWS0iSXg1kkZGxkoiJuWyPI3PZb9/w7dh/ZQLNsL66S+6lLYRRSTGi3x1lrzJUhTNzBJNc0vzz3OZZ+6wc81EgI8F7SmeOByRcTo6OY7fTybLbIpfxpdIinutjD/T3taDF+ThJDozs2SqG9b9oAPWowif93qOp76FpE9OkmuBcmUa1icqo7SNKjkEOx8n/VUq+NovTOJ1MjPrzi/Ay4rw9TxLyyxV+VnRxDherxr5jEeSU11s+nHjZ1gCHqLfdBIRERERERERkZbTTScREREREREREWk53XQSEREREREREZGW000nERERERERERFpOd10EhERERERERGRltNNJxERERERERERaTmetXikWFLe0UThtnBZLoG/2uJTT4f12vQ4rG/fvYW2URzF8cTVNI6X3Lr1cVgv5HnEYqKGN8rkCI5+nOjF8eWZlf20jckxHOX80K79sD5cJfGgnZ20jd3bNsH63aM4jvL4PhyrmUryaMbxCn6tfSHeH4MDe2C9I0diQ80s1dPb8LOXwDG5cmQ8EotsZjY9hWNEb/zuN2E96eOIz/vu+wVtY7KIY1rr0zhq1iMR1izx3MzMORI9TiKFC1M4FtlP8211YM9uWL/z5u/D+l133g7rO7Zspm2EV+OYbO5oDgLSKi4m2n62IfMsEjeK6fgRiRA3I3WyvnHfwwvwa8kMPkalSIR1gpwvPNUIbqMc4VjmYvkArA+PbaNNlKZGYN0nx7QCiU4vlHnMcXsHHo+1Wjus1+t4G/oVfGx+Ct6ObPsmsnh/+MkMbaFuzfPpbHvzMeZCc65x3Hikj/kkzvqp13A9kcAvsAT73Zsfo20M79oF6x7pZix+2yPR32ZmPjlOpA3PLV1J3MbiTn7etrCnC9bzWXxuOF3C56W79uOxbWY2RMbkdAUfNyNy7sNHsJnv4W1VmsDnSps3PQLrp1y4kbbhpfG4dyGOrWf7POHzecILGj+TjOnnc83kWNGSycb+PDWJ9333GI6vNzPrIX0ySQZX6Mi4jvjYymW6Yb1rwTJYz5L3T0zxa1RL4u+4cNEiWK/U8LaanMDHQDOzhYu7YD3djutPDuA5rVTh12t10r+rlUn8fkeWleXnRJOkPyTIUPGT+Lg5NoqvT8zMRg7i7WuOHIMD3H/CiJ/vNx+XYk7RDv/skb9VRERERERERETkyOimk4iIiIiIiIiItJxuOomIiIiIiIiISMvpppOIiIiIiIiIiLScbjqJiIiIiIiIiEjL/dLpdSR0w9xRBBd5s3kEupkZSXIwM/NI2k4yjVNRlp77PLygmKCWwfvvhPVlS5bD+shB/FT7h+5+gLaRTeDUgL52nDBx0Qvw9zjv9JNoG5/93OdgfaqEn17PtqGr4wQNM7NiAT9RP728F9Yjh9NDDgzhJAEzs0Q3Tkvw2hbA+qZHt8P6xH08wat/zZqGnysVniIg/40lUHkxY/jgAZye+N1v/jusZ0my4XSRpzBUyGthHSdTeAFeXxeTXheRW/tBHaeU+BGud2fytI3JcZz88a2vfhm/f/ggXlDIs3MKJE2QYvN5zD6X2YtIf3ExyXKzPdQ60i/KJZzgZmZWmMR90iOpKNkcTpJKpvCxzswsmcbpZ14GJ7IEKXbKww/0ITmZYceo0McbtxiO0zZ2Dz2EPzOJPxOSuaOzaylto+LjBNtiFR8f2zK47sf8W2WlgPvDFOknPklay5NjuZlZsr1pveKiQ+ciLzo8/o0kVsWlu0aOpYnhvlyv4XOVh+7i6a71KZy2lGYpdeRAmCHJsmZm7WToHb+oD9YvPOU4WF+9eCFto6cdHzt7unDi3dg0/t73PcbPDe99fCusP7YHn8dMkvCrMG5yJi/Vynhh9QJJHYsZM4mA7Fsy7hOkL5BTpaeW1fQRMg3MSZlMypJNqWIlknZYJtdQZmaD+3H6uUeSPvMdeD5uz+LEOTOzJcvWwHo+j6+7Bvbj88LJmO/hkXPobpLa7JL4OH9glJ9fptpxKnv/ypWwHuzfC+sdeX4uUavjfThUHIB1lpwbHJau+jR1PO4iklmZJLF25SKfI6plPPASAV6WY3mZsSeJzZ+Jy9xsNI+GuoiIiIiIiIiIzBe66SQiIiIiIiIiIi2nm04iIiIiIiIiItJyuukkIiIiIiIiIiItp5tOIiIiIiIiIiLScrNKr4siZ1FTKhy7axXFPPm8XMUpGiny1P6AJHj4FhePgF+rk/iH7aP4qf1jJKnNzKxywimwfvJZF8J6bfcorH/tezfRNmolnKLxqpdcBOu/+YoXw/oT256kbQwV8BP1qyR9IEkSSlIJ/tT+9gzejm1dOJFhooa/d9sinMBjZuayHbC+dxinIoQlnOxRHecJebd855HGZcSkfcl/O5r0ul07t8H6NElqK5M0iXqNJ1OVSAqUq+JkID+J56juTp4sN03GsEciWxJpvL5+in+PYgX35YPjODUqSfptGPFUmzGy3Tl2DFB6XSs5sp2d43MTfY2MB6vi/lUfH6RtTAzugvWQnDQsXLYM1tOZLtqG1XBqU42kM0VZnLTjxxzn/QAn56QCvF4BSQ9bvJAnnR48iJOMDozhY1Glgr9f1vFTOs/Hx+dUCu+QVI6kD5GkNTOzyOFjbbW0D9YrI3h+Gh3aSdvoW3ZGw89TUzxBca5qngHZeKTJQmbmaKoi/sz4KD7/3PnEE7yNGu5nPulmPjl+5BL8e6zswcfOF56Kk7decPbJsL40Jr2uLYNTLvMk1a5M5sH2PjwXmJlFCfzdDxbwmCgO4/MCEkpoZmY+SYqrkUNtRObBDEn9NDMzch1Gpg9L0HM4fg3YfElHvtaclEwenl5XZumBbMeY2dQETvRmfW9Ndgmsd3TyxNKuLnxMzeXbYT308XEwDHh/CVL4MzWHd2obSYxMtXXRNmoOtz9FwhlHR3ES3fIlOLXPzKw9j/vxwYP43kAY4eO5q8Wd35LEajKGihP4fGVsmHxxM/M9Mtfl8dw1XcTnGDGHefObVnc2acjzaKiLiIiIiIiIiMh8oZtOIiIiIiIiIiLScrrpJCIiIiIiIiIiLaebTiIiIiIiIiIi0nK66SQiIiIiIiIiIi03q/S6Sq1qlVrj09QzqRR872SRJ4rcec/dsN6Rx0kSZ5x8Gqy3Z3O0jTDEERD7hgdg/dY7cILcjt27aRuVEn6yfHrJKlivT+En6g/twik/ZmbTJJll7arlsJ4wnFYxPsET2aoRjqWok4SrqIjTOHzH07WCDO4nI6NjsH5gCCcGZFM8PaStE6cJ5LvwZ9pJ2l42wR/Fv7yvq+HnWj20TfTdcggbj8UiT2HY/PjDsF4q4dSPRAL3sWxMUksiwH08Sea1VDYL6yRg08zMurpxqmLCw/2sTBJnJkgKnplZe28nrPsBnqOqZZKc4/O+v30HTjk6/hQ8P/d099FlydFxBjKBSHRIfHodTtsxkj5Vr+DjUGkKp66ZmZULOO0wkcPH7YD1vZholEoR9+MoiT8TRXibeGV+KhSGOJ2nXidxTiR9KmeLaRtnrvgNWD9x4fNhvVTAcyAJ1DEzs/YaTiyyCt4mxRRJnAv5PFQu4P5QKeG+UCUpfJUiP5dIjS1q+HlqGm+LucoPfPODpgMGSS9yMf8u7AW4z0YkNcpL4uNgroOfR7POXDe8f7LkQJgjxzozsyyZp2pl3JmL0ySZkoxTM7MMSevKdOLPFMfxeWmlxM+jO0kC5uoFeNxPFw/A+kiBj68iSwEmybadfTgpLEmSc814onCSRBbSzOqYZOKwKQG7Po8CbcvlkoVh47aYnsbnsTHByTZdxMfgKrmO6u7BKXX9/atoG1mS6J0i57ednfg8crLAz9OrITnWknGdbcPX+d19OMnczCxBEhU9krY3NY2vtYdJkrmZ2fLlOOnPMzw/drXjtkulCdqGT07JSlP4GFYgx/nJMX7My7Xh9cq14X5VqbG5IKbzNiWneiRJFdFvOomIiIiIiIiISMvpppOIiIiIiIiIiLScbjqJiIiIiIiIiEjL6aaTiIiIiIiIiIi0nG46iYiIiIiIiIhIy+mmk4iIiIiIiIiItBzPCQa8RGBeU8z85DSOUr7nwfvpcnYP4ijedApHui7owdHbJ65aS9uYmMQRvQ8+eAesD+58DNb37z5I2xgaw9/9wYd/BuvnLlsH62sW86jIsR4cedrZ1w/rewb2w/rg4ABtozCF42G78jgevjCNYycnx0ZpG2sW4jjKfAZ3wWIW18M6yaM2s7CAv0fo45jbancvXlACR/KamXV2Nm6Tao2vz/zHY45ZijlLyT2wdwes33Hrj2kb9SLuZ9kMjlwNWZxwmob6WobESyc9/JmIzJjlKu8HKbJNCiUce+pn8DzI4lPNzOo5EnNMxldQxXGoRRJza2Z23x23wPqCrm5Yv/Q3XgPrXkwb7BWP7Cc7iqjlmCTnuc+5wwZfGOKtFkV8HnMOxzVbhOulMh6LYxP8+DgxiV/LJ/C8Ww/x+lbLvL+w1xyZl8sTOI68TCKWzcymR/Br5RF8XKmRcR1EPII45eE5jU20FdLG1CiPa65Vcex1ZgGe6/Krcax2ZkFMlLKP92GlQLYhiQ03R7aHmXW7pvVlc8MclcukLJNp3LbpBN4Hcd/MI8dnNr/1knPJM889m7bx8E9+COthGe9nL4mPXck0Ppc0M0u34/P7/dP4+/1s02ZYHx4dp22cswGfe7eN4i386GbcxuM78LWLmdlEBe/D5StXw7pHzmMe3Y7PlczM9ozj8e0C3HZXZwess/721LLwNmHnMQnS4Tyf996w6TNhclaXosdUoVC0RNP2q1bY8RTPoWZmUYjn0aXLT4D11WtwH24n+9jMjJzGWoacEycCvE75LP8exXIV1pNk96eTuG0v4tcb1Qpuo0a2+8hBfP1fK+FjoJnZ8qV4u9cq+Lsnu3CfrYT4XMnMLCLnV5MjeFxPTOBzjETA90e6C+/DZBKfKyXJjqqU+TVNvV5p+JmdgyLz62gtIiIiIiIiIiLzgm46iYiIiIiIiIhIy+mmk4iIiIiIiIiItJxuOomIiIiIiIiISMvpppOIiIiIiIiIiLTcrCIDwkpoYaUxteLOu38B33vfow/R5axdh5PMBvbgJ7h/+7s3w/orXkYSA8xs+87HcX0PTobwgwysjw7xdJ59e3fCeiY8B9ZPXbUK1t/+5tfTNsbJ0+vXdnXC+sAATtd44mGczmdmNjUyDOudvThlKKzjbdUW8wD7pd3tsO58nErgRXhhgc8TDoIAp2jUa7ifFKfH8XISPBkgjBqf6B8ZT4ia7/iWNvNIYsnEGO5Ld9+GU+ruvPE/aBtdPQthPZ/HaWkhSYZwLHbFzNoDnKoTBHhqdBl8n96PiURLkWXVKxVYD7J4fJWmeHrdZH0c1r0iTo3KJ0gCVRvv+7WJIVh/7L47Yf3ciy6F9eE9PEmzd8kSWO/uwglHEUssjE2om8fxdS586r/mGhCXEsi2gCPbs17F/ahSxgmuZmb1Ov5MIsBt+GR9yyQh18ysMonHfJWk1JSn8PG0MM4TZwoHcfv1g+OwXiLHlXqFHyvCKt4jlRI+PpZJel2tzFP4WJphQOa03GM5WO9a0UXbyHS3wXpIelxIjvOez8do38LGfV4pzK8E2UwyYdmmtK5kAu+DuDHMTt5ZPZ3Ar6xcuYK2kUzi40GljPtfOoWPK20dPGGrTpKYxst4nujpxm1s37mNtpGq4nF/0ir83cd3H4D17hw+9zAzGy7heaJQwnPLkg6c9FdZyNsolvD43l/C5xKjg/h7BCx+2Mx8knjms7FKDrZBzBhu/nWHmKC7Oae3r8+STeM3l8VzayaFr3vMzDJt+Pz2hFPOxe324veHET5GmJlNT+Nr6mwSL6sti+fvhd18/LLE9KkxfO0cVvE4efKJrbSNzo4uWI+ak0z/y4FBfB1c78bnkWZmVTyErF7F8+bYCE7CnRjh9wy6yHX75Bien+o1PE67e3i/qtbwHMESi2t13H+qMYncntd8Dqr0OhEREREREREROYZ000lERERERERERFpON51ERERERERERKTldNNJRERERERERERaTjedRERERERERESk5WaVXjddmDSvKT3sJ7fdBN/bu4Q/Jb5CElZ2PYmfgt/c5iG/eAinJpmZPULS8zzylQO2KRLkkfZmdtElG2B9YXcPrNeL+Cnxp5x4Im3DHxuD9b0/wol+WZKoc1k7TiswM1t8wmmwfu/wIKxvzuL0kFXL+mkbCzJ4+5bLONmjHuKn4UckoczMLCCJXOkETiirFnHbqSxO7TEz85ONiSOef+RP7Z9/Zp/ytXvnk7D+s5/eCuv1Kt+fO3ftgnWWWJFO49S3TEwiWz6J9zVLr0uRxJl0kqTBmVmhhFMu6hm8fdPtOCmEpeCZmWV9nDoyugfPH8UKTh/q6szTNlI1PBeOjePEwh9+6yuwvnML7iNmZq/5n2+F9W6SOuKRFJ6YcB6atjMvROFT/z2NC0nKCEkrMzNzEd5AUQ3PZ9Uy3vflIk9UJCFIliFpVY4kMJXHcRKdmVn5IH6tOEaSpCbwnF+a5Al55Sk8fqsTuD49iduoVHjKUI0krFZJwmWlipcVhXyf+yQiKlHH80pIjsHhNG8jncfb0SPJaT7pC8kcbyM6oXGbREV+jjYXJS2ypNe4bVPkHNeLyZBNkX8yDjy83xKk3kaSoczM/DQ+PvpkTHSQpNilnfi4aWa2fBFOdOrpwsfB1cvwuezQDj6G9+3ZDutLOvFxO49PJWzxYnxub2bWt3QprHsemWsreH0zMYnIe/bhBNkSmW1r03g/eXV+3pUwvE0cTUllyYv8OOtbFPvzXLZwYb+l043zlvPxOOnM8evgri6cnNi3eDWsL+jGbTzy+L20jdHxEVhf1NkF63tJWnuVnPuZmW17/GFYL5Fzif5+PE4G9vJU4+oC3F99kq7J0mv9Tj6fFgr4uM0SIysh3iYDB/A5t5lZ78KVsN63iFw/NKfE/ZfA8XOJaBKv79g4O4fDbbDzBTMzZ03tk2MLXO4Rv1NEREREREREROQI6aaTiIiIiIiIiIi0nG46iYiIiIiIiIhIy+mmk4iIiIiIiIiItJxuOomIiIiIiIiISMvNKr0umUtZsq0xhaKzB6cd7duH0yLMzB7a9Ais79qG0xz6l+H0sd7Fk7SNKMKJPmOjuI0kSQ9ZtSYm9W1JO6yXKiSJpoyfOB+W+JPoSzv3wXpxJ06Wm5jAT87PduGEEDOzc1Ysg/X+NP5+HSM4ZSDRjRO0zMyiJN4fLsRP7fdISl1Yw0/mNzPzWEBKhJM9PJLuVK/wNlJ+07Ji0oLmC8cSwGI+wxLA9u/bC+ssTSKKCT3wfNwGu1PuJ1haCt9HLHQu14bja5rnv0OqZZ7iNVkahfXOLjx3tvfiNiok6dHMzNXw+EqT5L4wjaf+qQJPCpsYw/Pt8d14jnzwrjtgfXQYbw8zs6F9OLFw1doTYH2KpJElYtIE2/I4FWk+cO7w8eocSRmrxySAhST9jKRJ1kn/ipskEj5JqavgDxWGcP8qHyRtm1l5CI+7whhJoiEpddUCH7+lafyZaZJKWSSJflWSOGdmFpLjCEu1q9fJ8TQmttH38MwZkZQpzyPHTceTjMIiXt8gwMsKEuSg3c3/PdSrB7E/z3VJ3x12vpkkXzcuZzNBzll9Mig9kj626vh1tI0XXPJSWL//xu/Aes7Hffz4Pp4ge/76xbDe24nPJ8dIovPusQO0jc52fLzzMmRMlvCYT0U8merkRTjZrq2NJMuO450+2M0TZFcvwsfaNUvWwvrLLr0MrxNJ+jUzq5KTsoB0RtbfyOnbU5pei33vHJPvbrNMprE/p7K4D7clFtHltGfxa2053F+SJCk7GZP8l0vgcZdN4mvq+7bcB+uR8WNwWMfHgwxpY/mS5bBeWM/TJ5NpkjLZgbfVoj6cpL5sGU6PM+NpbRE5DqaSuO3I4+nnQQrPEVkfX2ubkeTcaT7X1Up4XxUnST8h1+BBzK8kBU2J8CzpFtFvOomIiIiIiIiISMvpppOIiIiIiIiIiLScbjqJiIiIiIiIiEjL6aaTiIiIiIiIiIi0nG46iYiIiIiIiIhIy+mmk4iIiIiIiIiItBzOESXufWir5ZriP0NHonADvugdT+6A9X37cGRivnsBrIdhN21jagrHFo+N4jZWr1gG6wsX4IhSM7O9e7fCendiHNaTJ+MIycQEjynf8+CjsP7oJI5S/N5j+P0TUZm20ZXBEY8vPvFsWL8whSMv9xzYSdsIOnEsYz2Hs1JrFby+LuKx0y7Cfa5OlhWGJN6ZRJCbmUWJxjZcTCz5fEFjtmNibMdHh2H9iccegfVEAu//QkzSZhThbZsgab+JLP4emTyPa27vwvHE2RweExH+GhZ6fGPVp3CfzXXh9Uq1ke/RxdsoTuA2qh6OjvczOO41n+VxzdNTeGcdGCFRt3XctgV8p9/38ztgvaMXz8MFMs+vXHMcbaMt30Ffm/vcf/33tAqZr1jdzCwKcR8LyZgLSF52koxrM7MqmXdL43ifVWt4neojPK65ehC3USbH+UoBHzfLRX4MLhTwsqZD/D1qdXxcqdf594hITHkY4v3B9lM8Mn+QuONalfQFcrgwMzNyeGbng34a/7unV+d9NwyC2J/nuqR5lmraF0myb/yYgzD71gE5FnkR3nHtC3ppG69/5+/CempyCNbLj2+C9VxMpHYviUNfsaAL1v0a7mTLF+HYejOzJSvwd1y9bjWsDw3sg/UsiUk3M+tow98jmSBj1eGo+UTMtdOaE06E9eNf/DJYP2PjubBeTsXNH6wv4h7H+mgQ+zsNjf0hrp/PNaPTg5aqNe7rJe34+jGd6aLLyWXbYT2bxfs/8vDxY3H/StpGZwmPlaSP9+Xa1Wtg3ZHjv5nZuhTu90EyDeuL+vH1Y3sX3h5mZpUauVYjbZ/7vIthffHCJbQNL4kvLNaSY7B5+FwiqvFzCRfifVsq4HOJYhmfQ+dihlbg4zYCH3+PVAovLGTXhWaWTDReJ/heaGZ4zmym33QSEREREREREZGW000nERERERERERFpOd10EhERERERERGRltNNJxERERERERERaTnddBIRERERERERkZabVXrdzt2PWjbbmMCWSOAnnC/s7aPL8QwnWWSy+In6l158OayvOwk/ad/MLKzcj9erB6/v8v4VsL6ghz9Rf81ynCSxYgF+Qn5AbvFNDOyibYyQlJAnDT/Nv/2002C9XpqkbYyPTsD6f+x6DNZPXtgP66s9nFZgZmb78RP9S534ifqujpM96nWeXhfVcJJBaHifF8s4lSjTxpM9Utnm7zj/0+t8H3fMifFR+pnvffsbsL71cZxeVyzg/VkLY+57e3i/9S3AY7Kzj6SSpfg055GXqh5e3zJJTxwv8G1VS+I+ku7A/dVL4vmxTMb8U+3jMVz28Pq2ZXFSRy7LU4Y6luFxXzCcqjI+hBMO+/r4sWHX9m2w/ugDeD43H2/Drm6eyNTZ3dh+pYL39VwUubpFrnF7RxFLRYuJhvTwuGOJkWGE+161yufjwiROZKmTNJhkhaQgTvLvUR0j43QcL6tA0usKZZ44UyJpWVWHt0lIkuhoSmjMa46EBjmSUBYXLMfmU48cw9hWr5LvZ2YWeiTN2OE+yoK9EjEptX5TfF7zz3Nd4DwLmnYsOY02i0lFZUdOj/Ul8v66x/fn8hPXwvp5l10K63cdHIT1oZh0yKFJ/FpqGB/TJifx2O4jCadmZtkkTmWdHsUdsLN9KX7/FJ4/zMy2794N68k0HhNDYziZarjMzymXbsBpdKdfjtO6qnmS6BxzbAhIRC9LmPNY8lzcsGye2NhENwft3jVqyWTjdl29Ap97Luji5yH5DL5eSifxXJkmicp9CZwGZ2bmRSSJkFwP5shFarKjk7aRbcPrRU4xzCfHm0yGp0xHJD3PT+LPLF+Nr827uxfQNhIkvc5IQl61jM/5vTKfI1iKaxTifT4xjq//Sywm1szqJPmVnWOw/cTSTs3MqtXG1OAwJp20mX7TSUREREREREREWk43nUREREREREREpOV000lERERERERERFpON51ERERERERERKTldNNJRERERERERERablbpdf0rypZra6x19+En19dI4ouZ2eUvPwfWR0ZwikUig592H5ecc8YZJ8N6maRoDew+COsb1uPlmJmtXbUS1scP4mSAwf0DsD66Zy9twz8Ot/GCF10E62WS5jQ5zdND6iQs49EtD8P67i04YWphTIpMh0+eqE+ekO+TRBWPJjWZOfJF6mS1qjWcPpQIeZJGvV5q+vnIn9o/V42O4L5/y40/pJ954Bd3wXpI0gWTWTzVFCPeL/0U3g9di3F6XaYdp088umU7bSMKSdKPw32pRFIVK8UyrJuZ9fXjVJ1MWxbWp6dxOs/wwXHaxsgITsxwpC+HDs9RQZ3vj5RPJgqSOpLI4f1RjDk2OJKEd+DATvJ+nABz1895Il3UFNfBtvdc5Jw7LIWEJc5FJCXOzCwM8dwXsUQ2kk4SOZzMZGY2RcbE2CROpcrXcH/JlPAxzcwsLOC+VC7i5LxSidQrfPyWybaqkQSoiKXExCSRORL1xI6oR5PXRpdF1jck/epo2mCJfiGZZ1N1ntTpNyUssfTV+YSG17VwWT7pfyzhysysRtKsTrv8Mtx2As/fj9/0A9rGpgGceDc1Pgbr0yM40SmdwcdTM7OougzWXYUlLuP+OjyC19XMrFLHx5F8Zxes75vA26pr3QbaxtlXvRbWsyvx96uS75GM+KVfgszpPDGTnUPFpHU2zQfNP89lxWHfEonGcTGxbxy+N9XNk8xSdXLMqeDzKRfieibH+32W/F7J0J4tsF4eOQDrYUy6oE/O+Ts78Xp55Jw0QyM8zYIUHqehj/txNt8G616Cn0tUq/ic0YX4+yV8vL4BSYk1Mwt8vKwUSSzMpPBYrJd4G5UyPnbW6+TamSUZxyTklSqN1w/sOgqZ/0drERERERERERGZc3TTSUREREREREREWk43nUREREREREREpOV000lERERERERERFpON51ERERERERERKTlZpVed+f9P7Z0pvEjdZIYtmLVArqcDReeBOu7tu+Hdd/D6W6j0yO0jSjET32fIokRI5M4zekXm3DSjpnZ5u04RWvfPrysDEnIWZfupW34bUtgff8ETpm6857bYT0uZC2ZxikDE9PDsF5N4m07keHJAIkAf6ZoeJuw5JwgEZO6QV6rkaf2++Sp/UGCJzKVK40JB7VfgfS6XTufgPXbYhJnKhWc1FILcb+MfJKWleEpYwEJ5YgyOClhkqTHTEzjxCozs67ODlhvTkg6JJfEaZ3VPE9VTPo4daRO0sUGB3Ca4L5deDw+1UYPrC9YsBh/wMPJFFHEU6OmyDgqHSTpb1U8NrKZmEymLJ5Ddg/uhHVXw++vkrQ1M7NMuvEztSqft+Yc557674jeyrcBS6mrVfH4rVZwf/FJWqqZmSPJMgdIAtTQKG5jsd9F20iQoV0kKbWlMv5ALeTjt05eY+l1NIksJr2OYalvUcy+ZTyShUZXi6SaOZJEa8aTFJmAtBHFpFk1JxB5MecEcxIYw7xvxCSAHUVKIuKTFCYzs9DHy/IX9sH6ea/F6WqJLEuJM3voa1+D9dwU7ksZD6dcVgo8eXWxw8fzjhw+/rMx39WBj7NmZmECL2v/OE4w2zGO2zjzFWfRNrJrVsN6icwHOfK9U7G/b0ASs9h8R9qOYhLPmvtuXNLdXLOsM2epZOOc48ZxouLEzsfocpLdeVjvWbkW1tMpPM95Sb4v/TI+N6se3I3rI/ia2kvxhLxiaRzWF+QWwXoqh/tFkOXXXVVy/ThdxXNEGp9yWzrF+1mxgs8NXA1fz1uE61E0RduolPBr5TKue4bnDjItm5lZEOBzshRJA6+x9GOSIG9mls42LismKPkw+k0nERERERERERFpOd10EhERERERERGRltNNJxERERERERERaTnddBIRERERERERkZbTTScREREREREREWk53XQSEREREREREZGWm1XW7Oo1PZbNNcVN13HM8cLFPEp5cnoXrE8VRmE9kcBxq7UQR6eamU1M4TjDWh1HJvYsWwDryfQEbSPI4DjDlevwvbwoxPX2RDtt4/Y7Hof1R5/Yh5fV3gXrHomvNjMrV3G89Mg43h+Rw8ty3TxOdmpsDNZLVRxTyWJ/UymShRnzWqlchvVECvdR3+f3YutN8ZJRNH+iXs3MQitbaI3b6fGt98H3Fqo4PtXMrEAihTu6cGxwmezn8hTeN2Zm5WncL4tlPOfku/B80N3TRttY0o/HfXcPHpO+h6NbDw7jaFozs4MjOE53chLPLfv24rHS23kcbeP1V78N1s88C8cvB6SLF4p4TjMzO3hwGNaLRbxvSyTCev8gnrueah/P2zkSub2gZyGsn3H2ubSN/qWNsdOTkyQSdw6KoppFUa2xGNbge31yrDMz82o443bs4EFY371zO6wHzevy9PbJHD46hfvY6CCebwoB75NdVdyRvRC3XSbnKyUyn5mZVUkkeN1mF01vMe9nxzt2eDmaiHHeOolC93AbMUno5kiWs5/A9aSPj9lehh/nE+nGuSBR5/tuLvI8j+7vw8Tt51l2Adqkz8ewkf3GNrmfwNHqy048gzZxV/pmWP/ZYzhu/pR+fJ55wvJVtI2exd34hRQ+budJ5nq6i5/jbt2Fj2uP7sLH/2r/8bjtlath3cwsItHxbaQvdER4bEc+7zzlgLxGEtTJ9GiOtG1mFjX16+af57LzN+Qsm268doiK+LjijR+gy0njUxrLkOO5V8fnw14Qc+yaGoT1ysE9sF6fwOdyYZpfo4Y1fJ63eiE+H48S+HsEaTx3mJm1ZbpgvUomokyK9Pv6FG3Dd+QcvjYOy9NT+2F9YmIvbcML8LaqkW0YRnhbTU/zfV4u4e8ekPsobPya8Xs4ab+z4eewHpkZv1fydPpNJxERERERERERaTnddBIRERERERERkZbTTScREREREREREWk53XQSEREREREREZGW000nERERERERERFpuVml15156nHW1t74RPrpafzU9cce20SXMzqO05nWnXQKrLfncSJWXB7L0DBOQ6hV8WemxvFT7ScLOLHJzKy3ZzGp46SM6TK+x5cJumgbiRxODWCJASkvD+u5PE/w8kl63vgwTjjo6l8F690p3p0mRrfCeuTh1Ic0SQ9hiUhmZvU6Tn2o1XAbbdkcrD/1JH6sLd/41P5aPTIz3J/nooPDg1YpN6Y0PPzovfC9qTxPL3jNb74V1k84YR1udxQnIW5/AvcLM7Nbb/0BXtYQThvrXdAJ66kUTnwxM9u3B6eLjI3i+aBawWkSY2O4bmaWa8PjvlzGn1myaBWsv+nq99M2zjgDp9TNVl/MaytXrG1JG2GIk9PMzOosuYUM+2SA55y4tM7DY5/mT3JOFNUsat5GZHuGZZ5KtXfXbli/++e3wfqBgZ2wvmZlP20jHeC0FD+J55XkItz7/DxJnjKzEhune3ESXpUktdZqPA2mRlKYaqRTsmSyuMQyjySmeobnrqMJe2LNB97sEq5YQp2ZmSON+An8PdJduI90rlpC28h1N54Phsn59m+nnh127kp2qOPRQhaSz0SOpCezTuP4PBFEeFkJh8dwWCZJj3V+LpHtXQrru8InYH0LOf539bDrBLPjUiQ5upckYPv4e+wbGKdtbN2LzyWGS3i7n3v2BbC+4jieUhuQY1W3h79fG+k/xZj0ugp5jYRZWsDGPIvINbOw6XskYt4715x8XMLyucbziwfveRK+d3qsiy5njGzPMjl1SRdxOlgmz1PcK+QarjKJr1m8DE6Q2zyAx6KZWZ4cH6f34+vK7AJ8PA8zPMmaTfE+SeG1Cl5WmSRfm5mVJsdhfXoUp9Ht24u3SbXCrwd7FuLjXTnA23B8DK/v5CS/3kgk8WdSKbKtqnid8jl+3dTedD+hVgvtAfruRvNnpIuIiIiIiIiIyLyhm04iIiIiIiIiItJyuukkIiIiIiIiIiItp5tOIiIiIiIiIiLScrrpJCIiIiIiIiIiLTer9LqJwojVvcYnnfuGn3w+OcFTNzZvxolw2578KawvW4FTbU7bwNOUVpDPZH2ccOFCnMAQ1nnSUiqJn/TvkaCOHEmx6M/x73HGBpyw1tfZA+t33nYnrE+MjdM26uQ7Du8bgnXX1gvr4Qkx6VZk+yYyuO10Am/EUqFIm4hCnECUyuB7q4HhPlot8X1uzUERPPRoTtq7b5/lm5MMPZxScOUrf4su59IXXQHrQQLPB6tX4OWceep5tI2TTzoN1m+57XuwPjKxBdZTAR6nZmbDYzj9anoc79iApKWtOx4nb5qZFco4uW9sZD+sL1m0HNZXrMD1OM7F9GWIJ1PFv4bejue7IODLCQKcWsnhse1i4r0OTxGb5fc6huphZPWmtLqpKdyH7/vZXXQ5d9+BU+r279sB6+1ZPEcs6cEJNWZmqXa8Xbs6cZJqvq8L1hctXUnbqJHvvsfH6bmjuwfIgniCl8fSAQ3XfZZEF5Nex46PHpmbPZJWFdsGGRIkoMw89m+SpG0zMz+J58d0Nz7vWnLaibB+0sUX0jayixrTj+q5WZ3GHnO1KLRaU5/yI5IAFpOs6SKSeEfSCJ1P+lIYs/0cSTAiCXkFMh6rMSl8F7/ySlg/9aT1sL7r/rthfeAgTpkyM7vjvs2w3kkSnSKfnBPHpEYdJOcMlQin6h44gLdVZbJA2+jtwkllAdm+viPn3aRuZpZmqYhk3NM0y7ik6aafebbh3OPVatYcur18OU4N31vCKeNmZtt2P45fGMVJePn9+DiYSvH0OivgxLtaAffjnR5e37t37KRNLCEptbkqXtbClfg8NtXBzyW8NnwOXyHXcEPkejeM+AXb1Di+LzEyvA/WyyU8fvuX8QzoPAnYHBrDaXuBj0fGwj48p5iZrV6Lz2XYPY6xIbz/Fi/i1w7ducbPlCt1+z59dyP9ppOIiIiIiIiIiLScbjqJiIiIiIiIiEjL6aaTiIiIiIiIiIi0nG46iYiIiIiIiIhIy+mmk4iIiIiIiIiItNysYj9ySd9yqcb7VC7CT49/3vln0eWsXYtTKZ7ctRPWh4ZxKsX4yDRtI5PET2Q/UMJPqO/qwo+Vb2/nT9R3Sfw0+KlJnBjQ07YM1hcsXEDbmFqOn9p/z89/Dusj4wdhPSL7KY5HQhF6evALPUu76LIK5PZmkiRipEhaEkvEMjMrkaQI5+PP1EmSQdymKja1UQtnv12PpUUL+g/r0298/Tvhe48/jieyeYZTxlzI9g+ue0b2s5mdesq5sL548RJYv/5rn4T1sZFJ2sZxq0+C9UsuehWs95CEreNPPJ628cCm+2D9i1/+c1h3VoX1coUnNzIs5Wrumm2SHOlXcSle81hpavqwg/Z3vv2f8L03fu8HdDmuipNXli3GqajVGp5bB/YfoG1YAu+DTBs+NgcJfKwLYqZYdgZQ7cVpQqVJfOyqO55qE1RICiOZ+31yAEnE9G2fvEYCysxYWhVJzjPjY4K3gV/wE7yNtm6cTLjypONg/aTzzob1vpX4XMnMzCUajxku4MeQuajuIqs17T+PHDfj+kxA/s04IKFDyTpJyEvxc6pUhqQ6V/B4KRdx8lqigyfILlyyENZPPXkdrNcvxOcFO+7jaZ2Dj+BjcHViENbTDidAtSf4ZBSm8WtjU3ibDAzhJM2REXyNYmbWt7Qb1llqJEuWC2LSXZPkpZAsKzyK311oHrHzaQTv2DZluXTj9q6QyPJkFx+/qTo+z3t4825Yr+3DKch+rou24ZHjRMrhPnmAnGOODI/RNmoe7ntBHXek3Pg4Xk5MwmUqi4/biQy+DqmV8ZmBi3BKnJlZVMevJQzPBX1d+ByjM2au8wO8z7PkfkWGzDcdnbxfnbYSJ9sVpvCBYYxcU59wHE9FzFcax3yxfORzwHy7IhERERERERERkXlAN51ERERERERERKTldNNJRERERERERERaTjedRERERERERESk5XTTSUREREREREREWk43nUREREREREREpOVw1iHhB6H5QWNUqk/yNTs6cYSkmVnf4qWwvv4UHIVeLuO45igi2bBmNngQR6EOTRzE9Ukc/by4fwFto7MTRwpGPo5rnK7he3wj5V/QNvaN4rj3Rx67E9YrZfz9Mhkef8i0deJ9u7wHd5uJKRz1aWbmd+H2u5J9sB6R2Pi4SOi6w/1hegrvj8AnQa0BbyNsSqrkPXBuWrZ0jXV0dBzRe0PHYzkdidn0WIQ9rcds6zqOC13Qh+O0z9rwfFh/4onHaRvL1y6H9csufwn9zGyde9YLYf0X994M6xMTI2RJRxEszPYh37UtRBrhac2zXpQZi9mNa2T+/ltLvVa1erXSUBsZxnN+LeSzU3tbDtarJEq7WMYR6TaGj81mZmXDEc/pNI4HXtCHjxGZOj4GmpnVSlOwHtXx+ibacJxxOub4WC/jY1G1iOOloxJ+f4LMZ2Y85t6f5VjxPN6GH+D5I0jh43kqj/dTW187baNnKT5fau/HMc71EMdRF0Z5RHcmv7CxUMPLmKuiemhRvXGHV3y8D8oen6gTpHNkyEdSAX5/eeeTtI1bv/OfsJ5L4T5wzqWXwLq3sIe2kU7iftmRwXNU9wknwfoJxx9H2xjedSasb771R7A++uhDsJ6q8fEVVnE/LA7jeTBVwfNaexLHwJuZpUO8rfwErtfJfG4Rn1gS5NgZRvi7+6SPBjHn6n7TMTgxj47JhWLKorDx+vaxnfh6M5Ep0+Wcse5EWF81hY9dN973GKyPem20DZfNw3omgftqvYzXNyyR47+ZjSbwa2UP9/tgBJ/fJmL6S3sWf8ckOaYlA7xOuQxvoy2Dl5VI4M8E7Hgacwwml02WcLiNBLmm7ca71czM2hN43iyR+akDv90WL+T9yoYaz3E8Mjcg82eki4iIiIiIiIjIvKGbTiIiIiIiIiIi0nK66SQiIiIiIiIiIi2nm04iIiIiIiIiItJyuukkIiIiIiIiIiItN6v0um37n7TsVONHOrtwikW6yhNnOjL4qejd7XhZGfLEed94ysPC7l5YTyZwes3k1DCsBzEJXpPj47B+YBg/nX/iwC5Y39a3ibaxrPMMWL/6tTgR6+F78LKqVZyoY2bW1d0N65Uk3lZufALWH3kMJ36Yma1agB+339uGU03qBZx8MBLyFIWOZBesO5KuMT2Bk48yOf7U/lxH4/eo1SMzY2ljc5FnzVFgjiSc+LEJZ/hF/pHZx6UFwew+k8vivlSt8HvrHZ1ds2rDOZzSEMakTGVJMtaZp10E61/76vWwXizwpDDqOUmpm6WWrtNc/ILPnkw6aZlM43Hv4oufB9+bzfJ+v3v7NlgvkqTPVIrEpTh+DB4dwf01ncZzeEdHBdbN4wlAyQB/Jk2SuvJtJJEtz+f8iIz5KbKt2Dasl8n3M7NaFW+TgCTe+SQsJohJXk2Q1MA0ia9p68HnY/mY6Jx0B57rynV8rB07uBfWU3mcdmdm1tO/tuFnV5tfGbJhNbKw2rgDI7JDXUx8YZqkPSVreNzt3oTPDe/9x3+kbez54U9hvberH9bP7sTn3euuuoK2UUrjy5Buki6cI2l3lSRPoFy24XRY78njc9w7RwuwPji+mbbh5fB6kdMSW9m/CNbdyDht4+DWHbC+Yj1O7kuk8TrVynzMpMjJDE3II2l3PkvOs8PPOePeO9dUfM/8ppPjuo/nq4MH+Zw/PYmvyc5Zh8fWyCROqb13L0/63F/F12pTFby9uxL4eN7fi68RzcwGpvHcXgjwuGbjd2HfQlg3M6tN46RYdk7cRu5LFGMS8ljab5pFzkX4+7VV+PlKipyv+iRNspckyC5bzvdHjdyziJK4XqjhuW7vJE+FbT7PKMWcdzTTbzqJiIiIiIiIiEjL6aaTiIiIiIiIiIi0nG46iYiIiIiIiIhIy+mmk4iIiIiIiIiItJxuOomIiIiIiIiISMvNKr1uYnrSKq7xI+U6flJ7Oo2fmm9mVmvHT/qfmsapL2Y42SOX5Ykz+RxOAMiQFJ4FnR2wXiNJIGZmE1M4oW/vtgFYT/h4cz90YA9tYw8J5DghtR7We8i2XbJwCW3Dj3ByTjmHn3Y/khyC9aWGEwPMzLIJvF7ZNvyZsIi/eC3kT9SvknQglgxUnGbpSvx7dHcvbmyzVjcznEo4X3gk3e8olzbL98ellrDXcBthHd9Dn54kUU9mtnrliTHtg5bJtsJ5HPESJH1zdBingUQk4eLX269Xel0U1S1qmgN7+/B8te6k1XQ5HW24x46P4NTQeh3Pu4mA9/yIpSCRVKr2dnw8D2LayKbxGOpow/VMhiRidfA53yPtd3Xjz5TL+JyoXOFJRlXyGavhYxdLu2Hb1swsRbZVhiR4ZdpwPZtN0jbSJC0rSVLY6lU815UK/PzRa+pXzT/PdQWLLGw6p81E+BiVr/C0XrcZJ5k9cuMPYH3nrTfh5ezGSZZmZudkcHqSlXCa1PD998D6hv9xCW0jtXgBrCdI4HLg4ZQpL8H7wSS5Tsn09MF697ITYL1W4mmd5Qruy8t78bbqy+FrkU0/uZ22sX8cX3Ms3YCvB0678GxYX9TF0yF7yXVVooa3b5LMj0GWz0XNvXo+/fbDcKlk6aZjsJ/F4ySY5ilj5RK+Hkz14u180bmnwPrSE/gcccdmnA66ewT31XwCr9OyBTxZrjiA567RGh7ALklSN9P8uFLFQ8iiBElkq+NtUq/x68cMuRuSSpJjl4+/XzrFv0f/Ihxl2ZnDx9qAzFsRuWY3M9u8/QCs9yxaDOvTdbwN736YH4NTTal6lZjjVLP5NNZFRERERERERGSe0E0nERERERERERFpOd10EhERERERERGRltNNJxERERERERERaTnddBIRERERERERkZabVXrdkoVrLJdvTG+o1/GT6/2A388qlfBT34fGC7A+OTUM68tX4qexm5kVSVJLeQq3kc/jJIne3l7aRjKZg/U1K3ECUC6PE9me3M5THtIJnCTh9+Pt3rUIp/BNT+O0AjOzIMSpOmtPPg7Wo804PaRWJ1F7ZpZJ420V+vh79Obx+xNJvq3GDo7AuhfhZIliiSQypUlii5n5QeOQIasvRywmeYgGk+EXikWWsIXHhJnZmtU49YXD6+t5fL7bvw+nWX7tK/8G6+kETr9Y0IeTduTXR6VUslTTsbVUwMe0TIrPY/3Ll8H6wn6cUpPwyDgNeWpJhSRcVUjKKEuGTJO0GzOzBEmpC3vxcTMkSWfJFE6PeWq9SDpTDh+jmDDCx00zs1qVRHWxlBqHDzyOpKCZmfkkZSpJjneJFD49DEjCkZlZMkHaSJJlkTbiQimdpZt+JttujopCZ1HYuJ/S5TH43qE7eZLZjn+/AS//gUdhfTFJGApiztWDFB4vJKTQSoP7YX10ACcqmZn1LsZzjvNwXy5F+DhfLvBkquIU3r7lSZwGN0HOiUdizj9zvatg/ZzF+BpiST9JzuvgiWdjU3hOHZgYh/V923Ey4RCZ08zMTjkOJ/clJ/E4m3hiN6wvXo+vH8zMgvVLG3+Ombfmmm37JizZ1A9Yt+jK85TAaXL9sW8Cj9MsSTv0Yo7BbT6e2xfn8JiPQpzoPTaBE8vNzALSfofHjhN4XxcLeCyamSXJhDNdwOPUL+NxkohJuPQDfNDJkPPx8hTeVgdDfi6xoKsL18n5ilXxNW29yLdVmlyUdpHLoOGD+LzksQfHaRteU/9h94EQ/aaTiIiIiIiIiIi0nG46iYiIiIiIiIhIy+mmk4iIiIiIiIiItJxuOomIiIiIiIiISMvpppOIiIiIiIiIiLScbjqJiIiIiIiIiEjL8exboFovWqLeGPOYTuN4wLZsF11OWMcRi8UJHHPYlsNxiWENxyWbmY0WcURqhkT0ejgV0SKfxxwXq9OwvnAxzibMkYjlxYt7aBv1ELdfiXBcY28PjmEtTeD3m5llkjiKM8jhz2SGM7Ce3c+j6f0IR1uGhuO+/QD3q2xbF22jWMCRrskMjnMM3TCsRx6P3i3VG6Mqq3XeP+RIxGRjE46knt599z2wvnrViXRZCxcsnmXjpB7zNYaGcIz01q1bYb1/yVJYTybJJCW/NgI/sMBvPB7mSZRyKoXjks3MKnUyxzkSv1zHc2ulOEXbmJ4kMcdkEIUkejmV4v82lkzhcwDfx9vEObysIODbygx/Dz/AdRpB7/FJwkX4OMK+OVuSi4ke90j7fgK34uhkx7Ft4vu4bS+J207E9F3XtD+af57rMpNjlg0bx9/U7T+F7933jX+ny0k+uR3W20gXCMi5r5ExYWbmEni/RSGeD6ISPmcc3jdI26j3dMN6vg33gUoNt12r8PO2FFnfLhLF/ryXbYT1iakybePgJP7unZ34vDhBri2SSX5d09XfC+tLaktgvRbh/Tc5ha+1zMwqIT5X71uKr1MqQwdg/aFvfZe20XZrZ8PPhTK/Pplrdg5MHzbHL+zD/WjxskV8OUMjsD40jQdwZyfue0/unqBtDIzg/ZzL4OurxX34uLl/4iBtI4rwuMtn22E9DPBxPizyseXn8Rjq7MDru7Adb8MO/LXNzKw9h+fHhYvwdXuxiLdtrcQbqUzi8yXXgde3pwt/77zPrwVyWTyfZrrxdftUF56HlnTz+XR4rOn+Sp2fdzTTbzqJiIiIiIiIiEjL6aaTiIiIiIiIiIi0nG46iYiIiIiIiIhIy+mmk4iIiIiIiIiItJxuOomIiIiIiIiISMvNKr2uWBoz5zd+pB7hhJOpaZxoYGYWePhp8J6H0xE623G9WORtJBP46e5eAqcMFMo4iW5qYBLWzcymp0lyD9kmjiRJBEmeahNFJN2N5NeERZxkkAj40+ULRZxWMVXF6QpeZxuut/EEisJBkjji8JPz64bXqVLi+6Pm8NP29w7ug/X9Q6OwvmAJTx9wxcbkhVrtyJ/aL8js0+u2b98G63v37IX117zmKrqsRBJPgY5E5Hne7O/TOx8va0E/Ts479fQNsM6SuuTXh+/75vuNfTCdwakkGR/XzczqDs9bLsLJMtUSPj56LO3OzCKSkFev4nqliud83+enKQFJXkun8XcPSOqLx+Jrzcw8MkeQZK+4lDraBKn7JJlt9i3EtOHNri9EMQl5NLmPJOE5smtTKb4/mjfvUWzuY2r0xz+2TLoxma32jR/A9/YP4uRTM7M62dbFJEnUJfvTY0mWZhaQf5dOkjGZIueZLuTJVBPjQ7AeVnEfSJAUvnTA+0yKnGPXDH/3iIyJTC++djEzyyTwZyplfA6//fEnYD2MSUQ+6/wLYD0gYzJJ5sFEAieLmZmVyziVq5TE5/BLLz4N1tszfN5++J+/3fBzsYbn/rmoWAstaLrGK1bwNcNYmSeAHizgvpep48+MkWui3RO8v0zX8GtteVw/blkXXqc2fpyfquAU8GUrcdJikVw3F4bxOYaZWTmJkywX5vHxcfUCPG8t7OTn0H0deKykc+S7L8LzzaIFy2gbgwPkPkMJb5ODQ3jeDNK8Xy3txYl3k1PkGFzFfeGyF59A29i7r/HeQLlSt1t/PkDf/3T6TScREREREREREWk53XQSEREREREREZGW000nERERERERERFpOd10EhERERERERGRltNNJxERERERERERablZpdfVSu2WaEp/KUzj5Iko5E+7r1ZxwlrKx09RH9uB0xQmCziVzMzslFPxk9cn9uNENp8k1MQltRhJo9uxHa9XOoWTL7p6eFpaZze+L9jZRZ7CX8VJGZkcT92YmMZPyC8WcVqFK+F9W07y9JCa4SfqRzWcGFAL8D6vJXh6XbGG0+ie3L0H1qdI6kPXMpyUYGZW96tNPyu97rnW3p6H9fde815YX7VyFV2WI+mJHo1EwnVH0pnMzFasXAnrH/rjP4L1VSvWwHo6zful/HqoWmhVa+qzJJE1iElkCxxOwok8PB4CkiYWxCUqknQtH6+uBQEeWx5JfzQzCxL4uJ1M4bES0CS8mH9/80iCHGn76NLr8GfYWh1NYJvP1oskGUYh/t4s2dPMLAjItmIJeSy9Ls3PiQ4L9CP9aa5yP77FXND4xRdOjsP3JnL8FH2SpLV1kNP69gLeb2Wy/83MCiFO2Apr+BwwrOBzyXyGzxPpPE5DTpLrATZPGDmWm5mlyHwQkXP4cg1/b493fUuSNMt683z9XxYsWADrhQI+hzfjiaBdnfj82iPn5HFnrUWyvv4ETt6qkfmg/dzjaRuntL2m4eepwrTZDZ+NWau5I5N2FgSN37lUxPts3/5BuhzP4YkrCvGsP7gPJ1mOTfE5oh7h43wug+fX45Z0wvqatThp2czMT++E9bZOfJ4+PYn7cDXBx++2MXxNvaCjC9aPJ+vbmeXnROEUSfEkyYRtebytaiG+v/HUZ8i+8vE+37MDX9MemOLfo1jH92QmSRrogXG8nLNW4OsWM7Na0zGDpSEj+k0nERERERERERFpOd10EhERERERERGRltNNJxERERERERERaTnddBIRERERERERkZbTTScREREREREREWm5WaXX7R+YtnS28SMs/SGVxIkUZmb7BnGCXLWKn9SeSOCnx3d148SGp9o4AOuBz1JicBu5JH4Cv5lZJoVfS6TxU/A3b9sM60vK/HskDuL0gWQSPy0+n2uH9bY2/KR9M7NSCSeOBCncRuhwglw+s4y2EfrkafulEiyP1fH+8xZO0TZGp3G/mprG36Ps8D3XVWeup22cckbjE/3LpZr96IffpO+X1lu0CCdTsHq8mDiaFunu6ptVXYRJZPOWyDUed3ySLOeH/PDukVQbx5KL0jitykvg+dvMzAJ8DuCncL1ew2mpPou7M7OApDMlSKpeQJYVhjw5h2XFsfWKCXfjyGdmm17HUvDMeKheRJJn6DaJ+YIeWWGanEfS7pIZfr7iNaUiNv881/UdHLVsU9/xDZ8zJrI89a3Xx68l6nj/JNJkf5LkJDOzkCVjkj7gk+sBjyRymZkFEX7Ni8j6kunAj/kejoykgFxbRHWyTnWe0pQ3PBdNhPj8OtfbDetd/YtoG4cllx5aFonV80I8pwYxCZvtbXiblIr4mFGpkiRDHpht6ROXN/xcneLn9XPN0v52SyYbO+E0SfZLJflxpUbSQUcL+Jg6MYZT0cKQXz+SLmkHD+KEs634EtVOu/BC2kQ6iZMh92zfAeudefy91yzD48HMbO2JuE+esn4VrPd143OMAyTJ3MysSvpxNoPXa/sTY7C+bd8u2kZ3B75ncPLJXbC+ejUeRMkkv9bZuns7rC9YuhbWd43i6+bvfO8XtI1K2LitajWl14mIiIiIiIiIyDGkm04iIiIiIiIiItJyuukkIiIiIiIiIiItp5tOIiIiIiIiIiLScrrpJCIiIiIiIiIiLaebTiIiIiIiIiIi0nI8UxnYsWO/JdONcYee4ai89jyP0Jscw/e6pqZwvOdJpyyB9VUre2kbewd24vVqx/GHroYjR3NtPI4yncTxh6tW4DjSnp4MrJfLRdrG+DiOyZwYw9vX7+mCdVfjsdO+j9dronAQ1qthAdbHJ4ZpGx0FHP2YdrgvlH3cRjrF75NOTOFtUijgz3QuxZHDmQV8W4X5xvjb0MdRx/NJFB153OVc5kiMsxcTD3wUrRzFJ/Bnnpv1lWZx0dpzXSa3wDK59oaaRyLvPRJfbmYWxcTeQ+T92Q4eCZ3vwXNjVCf1EH8PNn7MzIyNFX92Yyh+DiTtkybYuGb1/1oD3ITDMc4eWae4qYOvF96HjvSruDmQzmlsY5FT0ES6HdbNzJp31Xw7fIXlSQu9xjmoTrZ1IsLnKGZmnWl8ThWSfTAd4DYqjneaZALHoScDnMXe3o3PyXMZfI5pFjMfh3i9QhJtnsiSfHgzcyHp+xGuB4bPAb2YbeWzfRiQ651qGdZDfvpp2QTejpVaBdYDMu6CmInCBXhM1nO4L6YyOM4+X4/5ItXGbRVV5885z7LFbZZKNW6jsTTex12dfXQ5m7fth/U6mV/7enpgfXKcz8fZnjZYD+vTsH7f5r2wvrd2L23joR34GtWq+Lp23YpFsN573GLaxsnr8Pc4cHAE1u94CH+PwtgQbeOEFfg+w+o1uF708XifeIL3+2yuC9aHDuJ5dnoMj61sJ36/mZnXhtsoOzw/lut4XO/bj+cUM7NMvnEeqtf5eWCz+Xv2LSIiIiIiIiIic5ZuOomIiIiIiIiISMvpppOIiIiIiIiIiLScbjqJiIiIiIiIiEjLHdGDxA89HLJWOfxhUexB4tUEftgfW46ZWb1KHnRYwssqFflDnCsl3EYywMtydfwwtlKKtxEl8IPPSxH+TJmsb6XCt1WljL9HtTy7beXHPPzTJw9drVRJ2+SBrz5ZVzOzSgV/d1fB9z2r5OGmhp+9aGZmtRpeL/bQ3Ig8RLJa5vujeR+WS0/9HP+Q2GPv0PpNTk4e9poeJH5szLf1/VXR/ODaQ2NiLo/hQ+s2NXX4A0CP5YPEo5DP+WENz6NRqAeJgzXATZDj4Fx9kDjrJ3RjsQeJ1/gXqXuNDzGdmp7+r6bn7vg1++/1K4PtyrY1G9tmZskI7zf2IPESWRZan5n2yUt10l9TJCRgusjDcvxp/FBjC/H5tSMP8K3GPMw2QR6O7Xv4ob+OLMuLmSfYg8SnCzgUp1DD368W8z3YnMrW9+geJI63SZk88D1FHuzu4i4vq439ZD6M4UPrVgXXRdUauVYi28aMP3yZ1slgjDkE02W5Ol5WjV0Tke9nZlZnK0CO52xZJXKNaGZWKOKxUiyTa21yTc2uac3MiuQz07NsO25bVUh/KFXImCP3SixmW5XJd0zQ9cXrVCd95KnXwqafn3rvkYxfzx3Bu/bu3WvLly9/xoWJ/Lras2ePLVu27FivBqUxLBJvLo9hjV+ReHN5/JppDIs8k7k8hjV+ReIdyfg9optOURTZwMCAtbe361/hRZ7GOWdTU1O2ZMmSOR3FrjEsgs2HMazxK4LNh/FrpjEswsyHMazxK4LNZvwe0U0nERERERERERGR2Zibt5RFRERERERERGRe000nERERERERERFpOd10EhERERERERGRltNNJxERERERERERaTnddGrypje9yV75ylce69WYk6699lrbsGHDEb332dyOR7Lsiy66yK655ppnpX2RVvnSl75kXV1dMz/PZoyJPJucc/a7v/u71tPTY57n2YMPPnhUy7n11lvN8zwbHx9v6fq1wq/TeGuea5Bfp+0hMhue59m3v/3tY70aIr/Sms87urq6jvhaTtd9c1/iWK/AXPNXf/VXpkC/+e+b3/ymJZPJY70aIjNWrVpl11xzTcNB8aqrrrKXvexlx26lRIgf/vCH9qUvfcluvfVWW7NmjfX19R3Vci688EIbHBy0zs5OM3vq5sc111wzJ29CzQfXXnutffvb3z7qm4AiIiJzUfN5h+/7ls1mj/VqSYvM6qaTc87CMLRE4lf3XtWhE+NnU61W0w2RoxCGoXmed0Tv7enpeZbXRvvx10W1WrVUKvWsLDubzeqAKnPS9u3brb+/3y688EL4+pGOi1QqZYsXL2716omIiMivkGc675D5zT/77LOtvb3dFi9ebK973etsaGho5sVDvxb/gx/8wM466yxLp9N2xx132NTUlF199dXW1tZm/f399ulPf/qwX2urVCr2v//3/7alS5daW1ubnXfeeXbrrbce8YrdcMMNdvLJJ1s6nbZVq1bZJz/5yYbXV61aZX/yJ39ib3jDGyyfz9vKlSvtO9/5jg0PD9uVV15p+XzeTjvtNLv33ntnPnPo18t/9KMf2fr16y2fz9tLXvISGxwcnHnP0/906x/+4R9syZIlFkVRQ9tXXnmlvfnNb575+fOf/7ytXbvWUqmUnXjiifblL3+54f2e59nnP/95+43f+A1ra2uzj3/842Zm9h//8R925plnWiaTsTVr1thHP/pRq9frZvbUDb5rr73WVqxYYel02pYsWWLvec97jmjbffnLX7Yj2a8333yznX322ZbL5ezCCy+0LVu2NCznz//8z23RokXW3t5ub3nLW6xcLh9R+0/3l3/5l9bf32+9vb32+7//+1ar1WZee6Y+cmh/fec737GTTjrJ0um07d69e+b1j370o7ZgwQLr6Oiwt7/97VatVmdee3p//NCHPmTnnXfeYet2+umn23XXXWdmZlEU2XXXXWfLli2zdDptGzZssB/+8Icz7925c6d5nmdf/epXbePGjZbJZOz66683M7MvfOELtn79estkMrZu3Tr727/925nPVatVe9e73mX9/f2WyWRs5cqV9md/9mez3o7y3LnooovsXe96l11zzTXW19dnl19+uT3yyCP20pe+1PL5vC1atMhe//rX28GDBw/7zLve9S7r7Oy0vr4+++M//uOZ35q86KKLbNeuXfa//tf/Ms/zZm6eHsmfvKh/yXPtTW96k7373e+23bt3m+d5tmrVKjguDs2LT/+tm/HxcfM8b2Yuf/qf19166632P//n/7SJiYmZcXDttdc+4/oc62PajTfeaJlM5rDfznrve99rF1988czPd9xxh73gBS+wbDZry5cvt/e85z1WKBRmXh8cHLSXv/zlls1mbfXq1faVr3zFVq1aZZ/5zGcatt9b3/rWmWPbxRdfbJs2bTKzp+aLj370o7Zp06aZ7felL33JzMw+9alP2amnnmptbW22fPlye+c732nT09OHfZdvf/vbdvzxx1smk7HLL7/c9uzZE/vdNf/IsfRMx9YjudY4kuuJj33sY/bbv/3b1tbWZkuXLrXPfe5zseu1Z88ee+1rX2tdXV3W09NjV155pe3cubOVX13k1wo773j6vYW//du/nTl+LVq0yF796lc3LCOKIvvABz5gPT09tnjx4iM6v5Dn0Pe//323fft29/Of/9xdcMEF7qUvfak75JZbbnFm5k477TR34403um3btrmRkRH31re+1a1cudLddNNN7uGHH3avetWrXHt7u3vve98789m3vvWt7sILL3S33Xab27Ztm/vEJz7h0um027p1q3sm9957r/N931133XVuy5Yt7otf/KLLZrPui1/84sx7Vq5c6Xp6etzf/d3fua1bt7p3vOMdrqOjw73kJS9xX/va19yWLVvcK1/5Srd+/XoXRZFzzrkvfvGLLplMuksvvdTdc8897r777nPr1693r3vd62aW+8Y3vtFdeeWVzjnnRkdHXSqVcjfddNPM6yMjIw21b37zmy6ZTLrPfe5zbsuWLe6Tn/ykC4LA/eQnP5n5jJm5hQsXun/+539227dvd7t27XK33Xab6+jocF/60pfc9u3b3Y033uhWrVrlrr32Wuecc1//+tddR0eH+/73v+927drl7r77bvcP//APz7jtnHPun/7pn9yR7NfzzjvP3Xrrre7RRx91L3jBC9yFF144856vfvWrLp1Ouy984Qtu8+bN7v/7//4/197e7k4//fQjWoc3vvGNrqOjw7397W93jz/+uPvP//xPl8vlGr7DM/WRQ/vrwgsvdHfeeafbvHmzKxQK7o1vfKPL5/Puqquuco888oj77ne/6xYsWOA+9KEPzSx748aNM/3xkUcecWbmtm3bNvP6odoTTzzhnHPuU5/6lOvo6HD/9m//5jZv3uw+8IEPuGQyObMuO3bscGbmVq1a5W644Qb35JNPuoGBAfev//qvrr+/f6Z2ww03uJ6eHvelL33JOefcJz7xCbd8+XJ32223uZ07d7rbb7/dfeUrXzmibSjHxsaNG10+n3fvf//73ebNm91dd93lFixY4P7wD//QPf744+7+++93l112mXvRi1502Gfe+973us2bN7t//dd/bejvIyMjbtmyZe66665zg4ODbnBw0Dn3VB/v7OycWc5HPvKRhjGm/iXHwvj4uLvuuuvcsmXL3ODgoBsaGjpsXGzevHlmXnzggQdmPjs2NubMzN1yyy3Ouf8+3oyNjblKpeI+85nPuI6OjplxMDU19Yzrc6yPafV63S1atMh94QtfoLVt27a5trY29+lPf9pt3brV3Xnnne6MM85wb3rTm2Y+c+mll7oNGza4u+66y913331u48aNLpvNuk9/+tMN77niiivcPffc47Zu3ere9773ud7eXjcyMuKKxaJ73/ve504++eSZ7VcsFp1zzn360592P/nJT9yOHTvczTff7E488UT3jne8Y2a5h46nZ599tvvZz37m7r33Xnfuuec2bCPNPzLXPNOx9ZnOI4/0eqK9vd392Z/9mduyZYv767/+axcEgbvxxhtn3mNm7lvf+pZzzrlqterWr1/v3vzmN7uHHnrIPfbYY+51r3udO/HEE12lUnnOto3IrxJ23nHoWu6ee+5xQRC4r3zlK27nzp3u/vvvd3/1V3818/mNGze6jo4Od+2117qtW7e6//f//p/zPK9hHMuxZU//4Z577nFmNnMSeOhE7tvf/vbMeyYnJ10ymXRf//rXZ2rj4+Mul8vNdIxdu3a5IAjcvn37Ghq75JJL3B/+4R8+40q97nWvc5dddllD7f3vf7876aSTZn5euXKl+53f+Z2ZnwcHB52ZuT/+4z+eqf385z93ZtZwgdd88+Fzn/ucW7Ro0czPT7/p5JxzV155pXvzm9888/Pf//3fuyVLlrgwDJ1zzl144YXubW97W8O6vuY1r3Eve9nLZn42M3fNNdccti3+9E//tKH25S9/2fX39zvnnPvkJz/pTjjhBFetVtEmmhW2X59+M+173/ueMzNXKpWcc85dcMEF7p3vfGfDcs4777xZ3XRauXKlq9frM7XXvOY17qqrrnLOHVkfObS/HnzwwcOW3dPT4wqFwkzt85//vMvn8zP75ekTlXPOnX766e66666b+fkP//AP3XnnnTfz85IlS9zHP/7xhnbOOeecmW1w6OLqM5/5TMN71q5de9hJ9sc+9jF3wQUXOOece/e73+0uvvjimRufMvdt3LjRnXHGGTM/f+xjH3MvfvGLG96zZ88eZ2Zuy5YtM595+g1u55z74Ac/6NavXz/z88qVKxsuLp175ptO6l9yrHz60592K1eunPm5eVw452Z908m5w/v80TgWx7T3vve97uKLL575+Uc/+pFLp9Mz3+stb3mL+93f/d2Gz9x+++3O931XKpXc448/7szM3XPPPTOvP/HEE87MZuaF22+/3XV0dLhyudywnLVr17q///u/d84dPkcwX//6111vb+/Mz4eOp3fddddM7dA63X333XDZmn/kWIs7th7JeeSRXk+85CUvaXjPVVdd1XBj++k3nb785S+7E088sWGdKpWKy2az7kc/+tEv94VFfo2h845D13I33HCD6+jocJOTk/CzGzdudM9//vMbauecc4774Ac/+GytrsySf8UVV9iKFSusvb3dNm7caGbW8CdMZmZnn332zP8/+eSTVqvV7Nxzz52pdXZ22oknnjjz88MPP2xhGNoJJ5xg+Xx+5r+f/vSntn379mf87avHH3/cnve85zXUnve859kTTzxhYRjO1E477bSZ/1+0aJGZmZ166qmH1Z7+a/i5XM7Wrl0783N/f3/D682uvvpqu+GGG6xSqZiZ2fXXX2+/9Vu/Zb7vx67r448/3lB7+jY0M9u0aZNdd911DdvnbW97mw0ODlqxWLTXvOY1ViqVbM2aNfa2t73NvvWtb8386d0zue++++xI9uvTt19/f7+Z/fe2evzxxw/7k7QLLrjgiNo/5OSTT7YgCBraOLT8I+0jqVSqYT0POf300y2XyzWs2/T0NP1Tgauvvtq+8pWvmNlTf7r4b//2b3b11Vebmdnk5KQNDAzMej8WCgXbvn27veUtb2n4Dn/yJ38y8x3e9KY32YMPPmgnnniivec977Ebb7zxmTecHHNnnXXWzP9v2rTJbrnlloZ9vG7dOjOzhr56/vnnNzxz7IILLjhszpoN9S+Za54+Lp5Lc+GYdvXVV9utt95qAwMDZvbUucDLX/7ymT+P3bRpk33pS19qGKuXX365RVFkO3bssC1btlgikbAzzzxzZpnHHXecdXd3z/y8adMmm56ett7e3obl7Nix4xnPnW666Sa75JJLbOnSpdbe3m6vf/3rbWRkxIrF4sx7EomEnXPOOTM/r1u3zrq6ug47zplp/pG5gx1bj+Q88kivJ5rnggsuuACOC7Onxum2bdusvb19ps2enh4rl8tHdI0jIrN32WWX2cqVK23NmjX2+te/3q6//vqG45uZHXa9+EzX+PLcSnR0dNj1119vCxYssN27d9vll1/e8GwcM7O2trZZLXR6etqCILD77ruv4aaDmVk+n/+lV/qQpz/E+dABCdWe/kym5gc/e54Xm1Z3xRVXmHPOvve979k555xjt99+u33605+e9bo2b8Pp6Wn76Ec/ar/5m7952HszmYwtX77ctmzZYjfddJP9+Mc/tne+8532iU98wn7605/GPry6UCjY5Zdfbpdffvkz7tdn2la/LLStDy3/SPtINps94oeHx/nt3/5t++AHP2j333+/lUol27Nnj1111VWzXs7T9+Oh52X84z/+42EXM4e+05lnnmk7duywH/zgB3bTTTfZa1/7Wrv00kvtG9/4xi/xbeTZ1ryfr7jiCvu///f/Hva+Qxe2zwb1L5lrmo9jh/7x5enH0Kc/t68V5sox7ZxzzrG1a9fav//7v9s73vEO+9a3vjXzPCWzp8br7/3e78FnL65YscK2bt36jG1MT09bf38/fP5l3LPfdu7caa94xSvsHe94h3384x+3np4eu+OOO+wtb3mLVavVhn+gOVKaf2Sue66uNVC7Z5111sxzPZ9uwYIFz1q7Ir/O2tvb7f7777dbb73VbrzxRvvwhz9s1157rd1zzz0zx8e460459hJ//ud/bsuXLzcza3joNrNmzRpLJpN2zz332IoVK8zMbGJiwrZu3WovfOELzczsjDPOsDAMbWhoyF7wghfMeqXWr19vd955Z0PtzjvvtBNOOOGwA8uzLZPJ2G/+5m/a9ddfb9u2bbMTTzyx4V8qD63rG9/4xoZ1Pemkk2KXe+aZZ9qWLVvsuOOOo+/JZrN2xRVX2BVXXGG///u/b+vWrbOHH364of1mmzdvtpGREZvtfm22fv16u/vuu+0Nb3jDTO2uu+6a9XKYX7aPbNq0yUql0kzy11133WX5fH7mOzdbtmyZbdy40a6//norlUp22WWX2cKFC83MrKOjw5YsWWJ33nnnzL+gmz21H5/+G33NFi1aZEuWLLEnn3xy5remkI6ODrvqqqvsqquusle/+tX2kpe8xEZHR5+ThD355Z155pl2ww032KpVq2KTO+++++6Gn++66y47/vjjZ+asVCo1q996Uv+Sue7QBdbg4KCdccYZZmYNDxVHZjsO5tIx7eqrr7brr7/eli1bZr7v28tf/vKZ184880x77LHH6DH9xBNPtHq9bg888MDMb4xt27bNxsbGGpaxf/9+SyQStmrVKrgctP3uu+8+i6LIPvnJT87cCPza17522Gfr9brde++9M8e1LVu22Pj4uK1fv/6w92r+kbmCHVuP5DzySK8nmueCu+66C44Ls6fG6Ve/+lVbuHChdXR0HM1XEpGjkEgk7NJLL7VLL73UPvKRj1hXV5f95Cc/gb/AIXNP4rOf/ay9/e1vt0ceecQ+9rGPPeMH2tvb7Y1vfKO9//3vt56eHlu4cKF95CMfMd/3Z/5l8YQTTrCrr77a3vCGN9gnP/lJO+OMM2x4eNhuvvlmO+200xpO1JD3ve99ds4559jHPvYxu+qqq+znP/+5/c3f/E1Daspz6eqrr7ZXvOIV9uijj9rv/M7vNLz2/ve/31772tfaGWecYZdeeqn953/+p33zm9+0m266KXaZH/7wh+0Vr3iFrVixwl796leb7/u2adMme+SRR+xP/uRP7Etf+pKFYWjnnXee5XI5+9d//VfLZrO2cuXK2OWuWLHCUqmUzXa/Nnvve99rb3rTm+zss8+25z3veXb99dfbo48+amvWrJn1spBfto9Uq1V7y1veYn/0R39kO3futI985CP2rne9a+aEG7n66qvtIx/5iFWr1cN+W+3973+/feQjH7G1a9fahg0b7Itf/KI9+OCD8F+ynu6jH/2ovec977HOzk57yUteYpVKxe69914bGxuzP/iDP7BPfepT1t/fb2eccYb5vm9f//rXbfHixc+YWCZzx+///u/bP/7jP9pv//Zvz6RibNu2zf793//dvvCFL8ycuO7evdv+4A/+wH7v937P7r//fvvsZz/bkJKzatUqu+222+y3fuu3LJ1OW19f3zO2rf4lc1k2m7Xzzz/f/vzP/9xWr15tQ0ND9kd/9Eexn1m1apVNT0/bzTffPPNn0nG/iTOXjmlXX321XXvttfbxj3/cXv3qV1s6nZ557YMf/KCdf/759q53vcve+ta3Wltbmz322GP24x//2P7mb/7G1q1bZ5deeqn97u/+rn3+85+3ZDJp73vf+xp+m/fSSy+1Cy64wF75ylfaX/zFX9gJJ5xgAwMD9r3vfc9e9apX2dlnn22rVq2yHTt22IMPPmjLli2z9vZ2O+6446xWq9lnP/tZu+KKK+zOO++0v/u7vzts/ZPJpL373e+2v/7rv7ZEImHvete77Pzzz6f/uKL5R+YCdmw9kvPII72euPPOO+0v/uIv7JWvfKX9+Mc/tq9//ev2ve99D67P1VdfbZ/4xCfsyiuvnEk93rVrl33zm9+0D3zgA7Zs2bLnYrOI/Fr57ne/a08++aS98IUvtO7ubvv+979vURQ1PN5H5rhVq1a5dDrtLrjgAved73yn4aGgzQ8APWRyctK97nWvc7lczi1evNh96lOfcueee677P//n/8y8p1qtug9/+MNu1apVLplMuv7+fveqV73KPfTQQ0f0sKlvfOMb7qSTTnLJZNKtWLHCfeITn2h4HT2U1572oD/nDn/IKXqA6be+9S1n9t/PU29+kLhzzoVh6Pr7+52Zue3btx+2rn/7t3/r1qxZ45LJpDvhhBPcv/zLv8Su1yE//OEP3YUXXuiy2azr6Ohw55577kwix7e+9S133nnnuY6ODtfW1ubOP//8hoekxvnKV77iZrtfH3jgAWdmbseOHTO1j3/8466vr8/l83n3xje+0X3gAx+Y1YPEm7fje9/7Xrdx48aZn5+pj7AHzh5a9oc//GHX29vr8vm8e9vb3tbw8NXmB4k799QDbtPptMvlcoclJoVh6K699lq3dOlSl0wm3emnn+5+8IMfzLyOHph7yPXXX+82bNjgUqmU6+7udi984QvdN7/5Teecc//wD//gNmzY4Nra2lxHR4e75JJL3P33338EW1COFdR3tm7d6l71qle5rq4ul81m3bp169w111wz8yDRjRs3une+853u7W9/u+vo6HDd3d3uQx/6UMODRn/+85+70047zaXT6Zk555keJO6c+pccG3EP9Hy6xx57zF1wwQUum826DRs2uBtvvDH2QeLOOff2t7/d9fb2OjNzH/nIR55xXebCMe2Qc88915lZQ0LtIb/4xS/cZZdd5vL5vGtra3OnnXZaQ0DFwMCAe+lLX+rS6bRbuXKl+8pXvuIWLlzo/u7v/m7mPZOTk+7d7363W7JkiUsmk2758uXu6quvdrt373bOOVcul93/+B//w3V1dTkzm0nh+tSnPuX6+/tdNpt1l19+ufuXf/kX+AD3G264wa1Zs8al02l36aWXul27ds20rflH5ppnOrYeybXGkVxPfPSjH3Wvec1rZq5rnp6K5dzh5/GDg4PuDW94g+vr63PpdNqtWbPGve1tb3MTExPP3sYQ+RUXd95x++23u40bN7ru7m6XzWbdaaed5r761a/C9x5y5ZVXuje+8Y3P/orLEfGci3mg0REqFAq2dOlS++QnP2lvectbftnFiYjMKxdddJFt2LDBPvOZzxzrVRGReWLv3r22fPnymYeAi0ij5+LYumrVKrvmmmvsmmuuedbaEBH5dccfUBLjgQcesM2bN9u5555rExMTdt1115mZ2ZVXXtnSlRMRERH5VfCTn/zEpqen7dRTT7XBwUH7wAc+YKtWrZp5HqaIiIjIryL+AJxn8Jd/+Zd2+umn26WXXmqFQsFuv/32I3pGiZnZS1/60oZ406f/96d/+qdHu0q/Fm6//Xa67Z7NtI5mcetw++23P2frISIi89ev0zGtVqvZhz70ITv55JPtVa96lS1YsMBuvfXW2ERaERERkfmuJX9eN1v79u2zUqkEX+vp6VHqSYxSqWT79u2jr8el4bXStm3b6GtLly6dSZUTERFhdEwTERER+dV2TG46iYiIiIiIiIjIr7aj/vM6ERERERERERERRjedRERERERERESk5XTTSUREREREREREWk43nUREREREREREpOV000lERERERERERFpON51ERERERERERKTldNNJRERERERERERaTjedRERERERERESk5XTTSUREREREREREWk43nUREREREREREpOV000lERERERERERFpON51ERERERERERKTldNNJRERERERERERaTjedRERERERERESk5XTTSUREREREREREWk43nUREREREREREpOUSR/KmKIpsYGDA2tvbzfO8Z3udROYN55xNTU3ZkiVLzPfn7j1cjWERbD6MYY1fEWw+jF8zjWERZj6MYY1fEWw24/eIbjoNDAzY8uXLW7JyIr+K9uzZY8uWLTvWq0FpDIvEm8tjWONXJN5cHr9mGsMiz2Quj2GNX5F4RzJ+j+imU3t7u5mZffWz77VcNt3wmmcR/EwQcyM4DKqw7gK8Or614Tb8FG3DTwSwHjnchrMCrrsSbcNCB8vpCv7yYQG3US5N0CbKpUmyrClY9yenYb1aCmkbYbkO61GthtdpGrdRGMJ1M7PKAbysUgFvw7EI3y0dT/GOVc7jfb6ffOQA+d6lGq6bmYX1xvWNnLPh6crMGJmrDq3fKRvOtCBo3E5JD4+JsI73mZlZ5PBG9QzXs90deEGpJG3DN9w3ggj35YnhUbycuH+VIi85H7+QTKdh3U/gedDMrDxVxp8x3F/N4e/tBeT9ZlZ3eJskU3h9jWwTR8a8mZmF/DviJsg86PhyIsPtLztuPax3L8DjbveuJ2gbftP2rdfqdueP75zTY/jQuv3z575guWyu4bUw5HP7bOlfcGeBbSo8fM2RcR332mz3R1wb9DNkhaNoduM9DlsWa5uVzeywuatUKtnvv+9dc3r8mv33GH7XJ6+3dNMYTibwMTjF5m8zSyfxZzJpfEzN0vfzNtpS+By7PYuX1Z7BbXe08UuNns48bjuDP0OPgjF9pkbO6QplfC0yVcbH7GKFnxtOVyr4MyXcRpksqxRzDC5VyDl5DR8D6qReq+J1inutTo4zVbK+NbI9zMxqTeeWlXLJPn/te+f0GD60bnv27LGODnI+K/IcOLB/gL52049+COs7d+2C9Vdc+SpYP/30DUe8PpOTk7Z8+fIjGr9HdNPp0IlPLpu2tlwrbjqRCx560ymD2ziqm074oOgMT6gu5gKJ3nQi3y+M8EEmsJjvQdY3rJNtlcb1RMx1SRiRk06yb70kviEUBfzX6jxyEU/uXVianGynYk7CQ9JGgnwmIO+Pu0nB1neuX6wdWr8gCCxoGmcJn0wDMRcw3ixvOiUS5OZS8ihuOoW4nzV/r5nltPCmU4JcHMTddGq+yTfzGTb9sgtQMqeZmbloduvLbjpFsRetrbnpZDFzKjueJJN4jkylcT1BLrDMDr/pNNP2HB7D/30Mzlkup5tOc8Kvyk0n8pn5ctPpv8tzu+8eWr90NmfpbOM/pLKbTumYG0JpcuzMsptOKdxGNqaNHHmtjdx0ymdx2+1t/Djf0XHsbjoFKXLzhRxv/FTMP8qQz3gBvvkSJPE6eVXehpfAr3lVvCx20ylg52Mxr9XruA2frJPv8/MVn9yomstj+NC6dXR06KaTHFNF8ksnZmbZbBbW2bEkn8fz79H08SMZv3Pzj2dFRERERERERGRe000nERERERERERFpuSP687pDkuPTlmz6m+KwTv4Ei/3JjpmlQvJrpSH+VddaSJ4H5PjfV1er+G+y62O4jcjDv66WyPJfdc134mdNTbfj717x8O//Jtv430G2dS6A9TDIwXrZx3+KmIr5k5ZqFf/6ryN/250t4mc35Q/i50+Zmbl9w7A+vnsvrKe3H4T1xP5x2saOKfz8rf3kT8G2k74bxvwZUfOfCR7NnzMcS4lE8rA/ucqQXwsvkl+n/q8FwXI6g3+1M5XB/XLh0n7aRHES96fRgUFY99muiP2NT/KnGiSBIUWecZHp5H+iwJ7dYGXcz1j6A1snM/7nzPS5SuRPsqKY5zax1lkb/M/raBOWIH8ywv5MkNXpn+2YWdT058TNP89l6XTaMunGsVQnx9M4s95n8ktr5bHiuTjuPBd/XndUmv/0eZ512YRFlmg6xwjI4x1Y3cwsQZ7FGLBHXpA5kf1Js5mZI+fYtTpeL3asS/p8jmJPZahW8LEglcAfcDF/Zlwl53rFCj7HLZLnEZVj/vStRv7ELSTzcxSRZZHrnac+RP7EjewnR5716Fzc8zrxaxFtg+3buD/7bn6thfPDMdCqP4+ej2JmD1hlj82In8hn+WfmLVnKs9HK7D4zMYGf9/yD732XtjC0fz+sX/SiF8H6ggV9sB53jvHL9Gv9ppOIiIiIiIiIiLScbjqJiIiIiIiIiEjL6aaTiIiIiIiIiIi0nG46iYiIiIiIiIhIy+mmk4iIiIiIiIiItNys0uvu++L3LdMUNTFWJukPMU8+90mSRKKGn4g+XcXLGq/gFDUzMz+NkxOOW7MQ1tedsw7Wu1YcR9vILFgO666zC9bTKZzgFTqciGVmVq7j+4LVCskMKOPkCa+G0/zMzBLlAv5MCSf6FQsjsD49NkrbqE/hp/BXSRJJuh+n8y3N837VTdJc6iRVb2KYpKNF/Mn8LmrsV85cbEbHnBM6s6YUxUQWbzff5/0yTOD9kGrDKW51koTY1oYTIM3Mxg/iBMOApLj5QQDrcWmELGiCBE2aR+Y1P+BTaZDC2yQkCZskbNGimDRBNirCCG93Y4ltMSlTjiRWzD5JKyYVozmZ6hnaqJM0oVohJp2n6aV6XErjHJNIJiyRbOxrPot/Ogq/Dmk788mxTLxrZdutTK9rniPq9Zi0rzkolQwsnWo8ViUS+NiVIvW411JJXE+Sc60EqZuZ+aR5FqTqkwjZKCbRiSXLeYb3a72OlxXFJDTXSCprJSTHFXJ8jOvHbLQE5LwkGeAzxzBmPmevRY6c+5BtEjneRhSRdEDyBUOyDVnfMTPzm1YrJph3Xvi1Pm7S5D789moFn/cWpsZpE34Cn0MHAU64TJKUaXbtYGY2OT4G68PDQ7BeKePvkc3ha1czs2UrVpLP5GF9cGAfrI8M4YQ6M7NLLrkE1s++4Pn0M8+leT7URURERERERERkLtJNJxERERERERERaTnddBIRERERERERkZbTTScREREREREREWk53XQSEREREREREZGW000nERERERERERFpOZ7zDYw8us/STTmIezwcyT2d5nHrKS8L614ZR4i6NI5OXXPmQtrGBS+9ANaXHX8Obru7D9bL6Q7aRs3DMYdWxd+vXsXfr1ybpG1UwyJeVliAda8wCuvhAI9YHD14ANbH9+7F9V3bYH1wGEdOmpn1hXibLG3H9XBFF6yvvAzHQZqZ5SOc0Tn8/e/B+qMTJVgvkRhOMzOvKc/eOWeTxWn6/rmmVq1Z1BRx69pw5GkynaHLSbfhbZRJ4c84D8fSezFRytkMXpaXx+OuWMBjIpHkc1Eyib9HNSJzEYmpjkjss5kdtr1n6iRTlkU/kzRqMzPz2GuzTPL1YyJlWVw0+0xEonSTMTHgfgKvcEDyl6MaXqdagceoe17jYS+qty4a/tmWTCQP67OtjKOP2//y3HNkDLH6XG17tsuKa6O5jyYT/Hg9F7Vl05bJNkaAsyjvVIp/t3QSz4mZFD6tz5D3J1N8Pk6RuTpJrhwS5P1+wNtgsxc5bJoL8SdCh88xzMzqZGF11v/IcjyWA29mgYf3oQvwZzx2LhEzn9NRQVYrYp8g5xhmZi4k5z4RXt8wwO+PophzibDxtbgoe/kltOi8MI5PxkSJnI8/dP8vYH3Htq20jSoZ80ECT0S5LL6uzJBrCjOzsZFhWB8fG4f1QpFcg3t8rjv1THz/YeMll8J6LovXN5/D38/MbPfuXbC+fOVKWF/UvxQviMxnvyyNdBERERERERERaTnddBIRERERERERkZbTTScREREREREREWk53XQSEREREREREZGW000nERERERERERFpuVml1z2Sb7Nk05PqB6v46eoJHGhgZmYLSKvrT8cpU2de3AXra87aSNvI9rwM1sOgG9braZwSEjl+X86VcEJS1Y3Deq2KU87qB3HinJlZYfc+WD8wiOvlcdx2MuLJHqVe/N3bVuJ0wGWrl8B6PuTbanobXt9qNAHrZ13xclh3C0+kbdS2Pw7rWR/3q+MXLYb137/i+bSN7nxj6kypUrW3f+pf6fvnmvMuON9SqcbvMD05At87MMT7ZVTDfb87hftAwSdJdDExGmEdTyIhSXcJAjyxxGV7+SRBpj3fjj9AUl8SMd/D1fDY80k6BEuv49E13GzToYKYlCGLSe6ByyLfz4tJqkmQWCSWMhjWeUod09zn4vrgXJNMpSyVapzPWple5/nP/rZo5fZ+LlLc4hKrWsXRlClSPpbpdXET0SzX92jaaJ43a+RYNFf15QLLtTXOswmSwpSJSa/LkddY4l06hc+D0pmYdFeyXkGA53CWZBWXikmXRT7DpiiWEmtm5jt8LhGwYy1JnDMXk7xK4mXZ+UrokW1Lo2jNUqSN5uuyQzxyDE7EXNcE5DWfpNG5EG8TR87fzMxc006s/xr++sPsj9r4E3FHJ4/9Xgk93BzFnE/SDrdvxtdjjz64Cdb3D+C0dDOzcZKE57FIZzKuO/I8jT5DrgXS6TSs18oVWPdj0rIfe/wRWG9rb4P1s886F9bXnHgybePuX9wJ6+MT47D+ghe+CNbXnrCOtvHLpBz/Gg51ERERERERERF5tummk4iIiIiIiIiItJxuOomIiIiIiIiISMvpppOIiIiIiIiIiLScbjqJiIiIiIiIiEjLzSq9bkulfFg6RcrDT4nfsIw/U/+Si3Bq2Pqzz4D1jiX4Ce5Rx/G0jWqqE9brdfyE+mqtCuuuWqZtVEZwutfozu2wPnHwAF5OFbdtxtNAlh23Cta7FyzCC3K8jYlwCtY7Kvjp/BmPpMUksrSNyvEnwPrugZ2wHnStgfVCvUjbKI7twfXREq5P4GWtXYj7jpnZisWN6QfTJbyN5qqX/MZvWFtbY1JCeeogfO+e/cN0OdseuB/WwwpOwpsex/0vJMkXZjyVI0XSbqIQp8TFJVBOT+C+nyjgcd/b24PXKcFThgKHv0kyg6ffagXPqUeTUhaRdCiWPhGb1EWWxRLPWOJJGPF97tdZ+7Orx/Ur1/QdWb+Zi5LJxGFJfuy7xu5L9hIZKnxZPNWGveJFJGlpdgFuLfVcJNQdjdmm1LmY5Cv6GikfTYqmR+Y6F83ye8R87+a5qzrP0usWdaSsra0xEYklJGVI4pyZWSZJ0pZSeFksGTRBUpvMzJKkjSRJaDqqccQ+Q/oAOwrWHN9WYR3P8XVyvKmTc4yQ1M34PMwORXWWOBeTR8bOJdhQZQFyXkxIrc9eS7IkNJJ2R9bVzCzRlKrnxSRfzwdsvmrlccUnB+cw5Nd21TI+j2XjN5Gafcr00BBOnXv0EZxSN0ES1ktF/j0mJ3F6Xa2Or+36enBKfVw6cz6PE+TqZO4YGcXX/34Cz79mZkszePtuuvdeWO/swN/jghe8kLaxfwgnxW/fvAXWPcPbpKu3j7axYAFOtj8S83uki4iIiIiIiIjInKSbTiIiIiIiIiIi0nK66SQiIiIiIiIiIi2nm04iIiIiIiIiItJyuukkIiIiIiIiIiItN6v0ut88b72lm5Iu0tO74XsveOVpdDn9p5+NX8idBMu11HJYDwP8tHkzs0plGtbLJVxPePjJ+QefeIy2sW/Tg7CeT3bA+qoT8PfrWLaUtuF34WU9OYyTxb7x0ztg/YH7fkHbmBwchPULDD9p/5I0ftp9bwdPfRs7cTWsJ0h9cPsuWM/343Q0M7NOfyesZyO8b1euxtu9vZunD5TqjWkQ5fr8Sq+794G7LNOUoNDd1Q7fu+aEE+lyTl+3DtbHR3BywgOPbYP1kQJPHiqlcCKc7/C0VenIwXro8WkuZPEuJDypLYvHRGdfL22jQNIpiyWcfpFgiTq1uDQyvMIsAyqVw98jm8Z1M7NigSSIhHgfJlJ4nohLdKmR5L4a208RbjuujSDR+JqLSWaZaxJBwhLBkR2247aBF5AUpABvf5Z2ONt0NbOY9Lpo9v8GNtt0oFamCc32u8e1zZY12zYinydcOpI0TJfF0qfi0utIChVLtaNtxyR1Bk3xWqkkTz2ai7JWt6w1zluJEG/UREwKZ+CTeaCG05ZcRJKbIj6feB45Bnt4vRIJvKwgJiHP91hkJi6zIMRazL+hhyS1KkrgRsIQ978w5NsqIp9h6Ve1Ol7fWkxCXo3Mw+RrWMLH+6kSM9VWyblEiqTUkssBq8TMRbWmzZhyPP13rnnooYctn8831E46CV/bpcg5rBlPa2WHiYMH9sP6ti38GnV8fAjWszl87bxsGb4e6+zm57ePP/oQrI+N4VTsYgkn6tFzPDNra8vD+o4d+PuVSPp0e55fo1aq+Fxy586dsP7kTnzvY+HiftrGYpJ+Hdbwd7/tp7fAet8inh530UUXw/o+sr67du2E9Z07n6RtKL1ORERERERERETmFN10EhERERERERGRltNNJxERERERERERaTnddBIRERERERERkZbTTScREREREREREWk53XQSEREREREREZGWO7Ls5f9yXp9vuWRjPuaiFz0fvrfzrBfT5Yy2rYD1INkN6yRB3KqVcdpGtTQB616II+53bd0M634BL8fM7NznXQDruaXHwXqmsw+vU51Hi/7w1jtg/R//3/Wwvv0J/D26O3BEpplZfzfe7skOHFOZ9HFG6sgAjvQ0M7t7315Y/8lP74L1s849A9YvPA7Hz5qZud0DsL6jVIL19WsWw3p7GkdnmplZrSnq2M2vuObAlS1oimodHy7C9/58ZJgup697GaxvOPUUWL9i7amwvnnHTtrGth7c/4oHcT+rpUgEcUz8diKBI20jEv3dSyJPk9kcbSPdhsdetYbnomp9Ci8nm6ZtlKu4H5KUY6vVcB9PZzK0jXxPF15WFX8Pj0S0TxcKtA0X4RWukdjpegVH4/ox8fSRa1wv52YXJX8sJRIJSySbDtvkq/okXvupF0m9eXI41ATrSLFIJrTDjXvsi8Q2gdsgLZtH+kV8y+RV9iG2TqQe38bs1texHG4zc3SrkPcfxffwyLzpkXHNvkgUM2cHTecfqWqKvncuqpSKlmgaT2GAT8VdnUeu1wO8rX1ST5JjXSbDjythiI8TtQQ+3iSTuA3WtplZkMDfPSDfwyPzWsLn/TIgfd/hU1mLyLiLYqZU0vWtRtomU60FMYcj0k3MJ3NqQAZYMuLx9FWHG/EdPgY7cmyOYs6NK+XpxvdW8Dn6XPSlL/6LpVKNc8673/Nu+N7160+gy/HIXD0yhM9vf/bTm2F9fOQAbcMnYyIkc/j2rVtgva2tk7YRkv3ve3hwNZ9/zSzHeMdPp7Ow3tnZC+tD+wdhfd9eXDczG8/h48jkFD5fbcvj65Pe7i7aRlgn5+lkf+za/iSsf+8736FtXHTRC2E9l8PXIcMje2B9H7lmNzM75xz60jPSbzqJiIiIiIiIiEjL6aaTiIiIiIiIiIi0nG46iYiIiIiIiIhIy+mmk4iIiIiIiIiItJxuOomIiIiIiIiISMvNKr3OymNmYeMT6b2zcUpdSBLqzMwyWfzE+ZqHEy7K4/jp8bUqT2BgYTt7dz0B67+4405Yf8PVv0PbaF++Ftanqvgp/B1p/HT86Qme+vYf3/46rO/cgVMGXvK802D9hWfhupnZyevOhPVCCa9XPo2fgh8N8bSzc0ji2GN3PwTr3/3pbbA+/AROnDMz8w5Ow7pLdMH62jVL8YJY0o6ZWXMiA0lomKuqY8PmNfXDVDtOVezO80S23TtwqsLoQdwH1q5dDeuLly6nbbzqN66E9eF9O2H91jt+CutPPonHvJlZOo2nwOlJkixXnoT1sIbrZmbZACesJbtxIketgNME8x08QaRAkgZ9kl4TBLjfloq4bTOzRAV/xk/gf7toy+Pv55O2zcwmR/EYLpfxNiwX8PtZwpEZSq+bXarXsZRIJg9LiGKJbL4X829KZBc4Ep3E2mglj6zvYWl9T8P6cXPC2SEs0S822W2W3YMtKwp5Ok+tjlPC6iQZiCXkxeXwzXYX0nERsz28gMXRza7xuDHZvG+TqfmVXlcsVqx5ALJ+nKjyJN2AzLsJkgaXSuLz5VrEE4GTNbKsFE68S9ZxG4mAJ5mx9WV1tq1YGpiZmSNpiBHpZyw9MS7oNAzJ9iVJsbUa3u7VWkzqWwWfl9BlVfH3q5LEWTOzyQl8LrP/4BCsj0/hhO/pKZzCa2Y2NdV43Gbfay5auHChZZpSfsfHxme9nMnxEVj/xV234/eP4ZS6bIrPraRLWoIkQ5bJfFMqjvM26nicVsq4T7LjUDLFzwvZMTWdwueYHR04ZbpKxqKZWTiFx10qjROdu0hKXX//ItoG21OlMm47TebArQ8/QtsYI9cChWl8rlwq4XP+ShWfc/+y9JtOIiIiIiIiIiLScrrpJCIiIiIiIiIiLaebTiIiIiIiIiIi0nK66SQiIiIiIiIiIi2nm04iIiIiIiIiItJys0qvS63tOyztaTKNE65cyBNFFudxWtaBIk5BYKkQcUktyQRu/+EHN8N6QJ5Qn+nupm0U6zjKIkjiFL7hg6Ow/sV/+Cfaxq4dO2D9NS+/BNav/o0LYT2b4dtqYD9+2v2CNWtg3UvibZI6aT1to8vhNv708lNh/Rv/egus/8uPcMqgmdlirx3WT0vjeoWktkQx8Tzz/S7t6g6zbFNX3z60F763XMYJEGZm3WRc+CFOQnjs0QdhffcgTvAwM3vBORtgfflq3C9/exlOzNy29XHaxj0P/QzWd+7YCuuRK8F6b44n/dXJvDZdx72pvQuneJQLPA3GI3MRS4rzItzH63SuNauVScIMGRTO4WWlYtKm2vN5WE+S7zFVwakfQUxyW3NgkTePRnUiERyW6sSS5eIS5xxLeiKpNjGH2llj8ytLqGnrwH3CzCybwcdt9t1ZKhpNibO48w+MJSfGbcIySW+anMBzB0v0i0ssZLucbZOjSXX0I/LdHUkNPIq2m9PLEsl5liBbr1qiKfEpwU7FY6YmlvboSHRzGOJ6tcIj2SLS91k/88m4i2p8DIVkvEQJfB7NurizmGi5WfYzdkxg2zxuWY4kU7LEuUpMYmGxiM8/2OxSKuH3b9u+nbaxfxAnTY+RuagW4u8RkiRDs8Pn+mqVJ/bNNUuWLrZstjE1beVqnMLsQv69Nj90L6wP7dsF6xly3sSSfc3MjIwtn8TXsmk38PmtgkIJ7//pKXwt4MjYCgLeRp2MlXwbPv47w0nPPju/MbPAcH+dnB6H9fauDlxv5+crRTK2pydxspyfwtskR9J5zcymx/A11XQJt1Eo4uuKRQt5UvwvY/6cbYuIiIiIiIiIyLyhm04iIiIiIiIiItJyuukkIiIiIiIiIiItp5tOIiIiIiIiIiLScrrpJCIiIiIiIiIiLTer9Loom7OoKb0uOYKf+F7u5cuZKuOn2rMEBpbAZI7nwfgRTrKIJnECw5pVS8iCeKqNF+FkgkQqDesPbN0G6z+4+TbaxpJFOEHs6t96JawnbQzW0534Kf9mZicsx6lzrh0/vX66jPdHkOUJXuUpnMhgEU7XeO2rL4P1PdOTtI1bbn4I1nMeTn24oEySJUiyl9nhCSVHk/BzLI0XClZuSlNZvRinMNzz0Ba6nAMl3AdOO/MUWO/MsuQN3F/NzG786Y9hfdVynF638XkvgPXLL305bWPD2afD+r3342SRTQ/cD+vj+/fQNsbHcLpIxeExWSzhhItqiafzBKwbhvgzIevjJPnIzCyZwfMaC0mr1/D3KBbZ/G9mEe5Xbb2LYD3XlCJzyGSRzxPmNbURcxyZa4IgOCy9izm69DpSP4pNxObGIIEXNkJSV4ZHhmgbbW1tsJ4hqXZHuu2eLiLnEiFJvAvJ+x2pm5lNTuL+2t6Bk1f9BD7W1mJSoLIpPFZ8knB0VOl1JN2rlel1iaaUo+af5zrP82LHZuN7+Wtsv7WqHqdC0hbZmEjENBGw9DqSnMe2Sa3O+z6di8h84DcfIw7VY1Kj2D5l84fPUgZjkjQHBgbwssj3KJNz3D17cWKxmVmJHJ9Ziqcj01pzyurTNW+TIGa7zjVLliw+7LizePEC+N4ntz5Gl7N7B04QzKZxamOdJBRXqjwlMJXF25Wd/1WruO/FTREFcj1/cBwfzzs68fVGIsWvUcM6biOdxn0sJMehwQP8XCJNJqkaOYceHj4I633t+PuZmS1ZthTW91TweCySxOjeTp4sXivj/lAq4uuQM844G9Y3bMD1X5Z+00lERERERERERFpON51ERERERERERKTldNNJRERERERERERaTjedRERERERERESk5XTTSUREREREREREWk43nUREREREREREpOVmlTUbToVWrzTGfGb2T+M3d+OoPzOz0elh/IKPoyJZ3Go9JseRfbEuwzHeS3txHHhYwbGPZmYeiwrN4pjjn/0Cx7AfGB+nbbz4RefA+qKFODIxTOG2J4tTtI21K8+C9cExHKu5Y/s2WI9iYspZHGVbx1pYPzB5F6y//uV4Xc3MHt+CY+s37cXr9aoJ3EcDEu9sZhZZ8zaZP3HrZmY9/e2WzTb29eEBvH3yXokuZ6SIX9t070OwfsE5Z8D6ggW4v5qZTdfxWB3cvw/Wv/yVf4H1S190MW3jrNNPg/WXveg3YP3U4/H3uPWWH9I2RsdwbPHOPXgeLJbwxBK4mL5G4ppp9DipkyRdMzPzEzh+N0HqrI24iO5ajcT/znJZXsy4TCQbjzNHGl8+F/iBb37TXOo8vG1ivxd7KYjpALNEY8pJf0km8fF/8xNbeBukK+UyOH6ZRaTHbSsWn862lGODKKaNeg3PdeUqnjvSKXw892P6fUcbnmu7u7th3ZH5htXNzHwSVU26qDmyvixm3szMTzR+xg/mz/g1e+q7NX8/9n1dzHZwEYmwZ3M+xePq2b4OQ9x2rYb7azJmzk+S+YC1EQR4WVXStplZvY6j4Nm4Z2M+kcBzlBnf7nXyPabJOdTOXbtpG7t349dqZP6IyP6rkXklTjKJr6p8Mt/F9cPmfTv7PnvsJJOJw7ZFpYT35bYtm+ly6uT8NiBjpVopwnrcsSuRwPusSvZ/rYbHiRmrmwUp9gLu92PjY7Cey7XRNrJt+DWPHG/CAG/byWl+HXxwaBDWe3o6YL1Ajl0PjZN7ImZ2ximnwvrSS/A1ypadO2E9oBvdLArxvrr4ONzGKadvgPVcjrdRLBZjf46j33QSEREREREREZGW000nERERERERERFpOd10EhERERERERGRltNNJxERERERERERaTnddBIRERERERERkZabVXpdbXrKaqnGVIfJBSTlocaTzLIefrq6I/fAghCvZo7Fx5lZgqS1tRt+snxvZxnWa+F+3oaHn+7OEgD2DeNl8W9htnQxTpY5uHcnrFcSOJVg4XEn0jaGRvF337MPJ/flfJwMVM/wp/a39eJ+0rP4BLxOFZxQ1tdboG2cf8EyWH/oepyodvcjT8D6C09bTtvwm3Y5CQqYs9q7OyyXa0yvcyFOYXjyib10ORk/C+vhFO4z9/7iHlg//6JLaBuLFnbBetrDbWzbMw7r/3HTjbSNweEhWD9vwwZY7+7C4/Gyy15O21izZh2s/+xuvE1+cR9OuRwexOtqxhNPeBoJE5M4U2fJcngG88nRJS6RiSUTsYQqlkrkYhJdmrdJSJYxJwXusIQ59lU9n28D+spzkOSXJMlQLOHs4NQ4XVbk4z6ZLuPOx1LiLCaRjSXFpFN4Dsy347SbtjxP6gzreEwMDB6A9TNOwambyZgkt7FxPG92duL1Yt87Lr1u9kjyYtxJUdKL/3mOQ+l1LL0rLumTJd6xOZGNepaiZRaTWEbWi30Pj4zTp9ZqdulnzvG0Pd5Ia/oIS4kz44lgExMTsL57H07LOjDEj/OVCk5crrL0uvBoUuFYGipL9JtdwuF8NzQ0ZLlcrqF2849/BN87PcL3JRvZbLsVSEJYwE60zCzr8DEqIomDbE4pl/E1oplZOofbWNCLE9YP7MfbZGpilLaRX7wEt53NwXo5xNuqrT1P2xjYj8fQ8Cg+br74RZfC+jg5ZpuZ3XzLT2H9Zf/jlbB+8aWXw3pAkn7NzCbIcb5Wx9tk1+7tsL5j5w7aRm/v0oafp6f5tX8z/aaTiIiIiIiIiIi0nG46iYiIiIiIiIhIy+mmk4iIiIiIiIiItJxuOomIiIiIiIiISMvpppOIiIiIiIiIiLTcrNLrsunAcqnGj7gaTlMIayW6nJrDT17P5bpgvb0HP6G+o8afmP7kw5tgPZvBT3DP5nDCRD3EKWpmZhVLw3qHvwLWFy1YCOtxd/5KZZyIkSVP4e/uaoP1/Xv491i8+iCsr16J17c0iff5tif4U/snB3F/yLfjNrqXHAfrlYM4cc7M7LRVOC2hrwOna/xs65Ow/tAunAxkZnbGSY3Jdl79KJJUjqFtT+ywTKZx/PX0LYDvzZFxZ2Y2cACPl64snlIKJZx+sWMv7zM+2bQL2/ALaxfiZLndE7i/mpndcdddsD49jVMSzzwV942FfbjvmZldeMHzYf2kk06B9dPOOB3WN933AG3jkYcehvW9e/G4r1ZxUkfsv0JEOFWF5uOQhA2WiGRmFlZxG5Uqnj+qVbxv4xK2oqZ0p2geRVAGicCCRFP/P4qQII+kObH60WD9grURkAHvk0RDM7NqiI+PVsX7tEBSVuokPc7MLJMm82Aer1eefL9ETOKMR9LAerrJMa2nD9Z9j3eG8Ql8nK+QMdTRjr93TPhkjNmlaEUx8XXNCZcs8XKu8n3P/KZkyVqNzK2O70+2H1homCP7IIhNuWSpemT+IMupx80r7IuQ44SL8NiOS+tMkj7CEvIishHDkPfLSgWf4xwcwWlSY+NjsF6POR5FZH1Zei1LI2Pf+ylsO842ZTBuomj+zNGk7B0bF154gbW3NyZ+3n0HTiXz4rYB2czs3JMlhGXSOD3OjKfOTRdYG7gel4Lse3icemSstGfxdfMISYkzMxsYwOexS5avhPUEmTs6OnCyrJlZTw8+1r748hfD+pvf/BZY/87Xb6Bt/PyO22F9z/5hWD/z+RthPZPh+zysr4b16elxWC+X8bl1IsD7ycysu6e34efJyUn63mbz62gtIiIiIiIiIiLzgm46iYiIiIiIiIhIy+mmk4iIiIiIiIiItJxuOomIiIiIiIiISMvpppOIiIiIiIiIiLTcrNLrxoYjqyQbUx36t+CnrvefRFJlzKzeQdInKvjJ+VMPbYH1RzfdRtvo7sBpCOvOWQ/rxcoevE6FUdqGS+G0HS88EdbXkCftm/EEtNFxnLY3eJBs97Z2WE/EhCgUxx+H9ZUn4fUdJOkamTxOzjMzy9TxehXJ99s/OAjr3/uXm2gbro77XDqDEwsODuE0iFsf2kHbWLVyccPPhTJOAZurtu8aslRTAuXqAE8D605bQ5czPY2T/4oHcapdRzoF62mPJ8s98hjulysWdML6qetIkoWP+5iZ2aNkfB8cw8kyv3jkIVjv6MD928xs45nnwvriBTiB6rIXXgTrZ5+2gbaxeTOeI2/6CR4v995/P6yXSKqJmVlIEkzqdVz3SaJOXD4aS60qlvA+rFbxmGcpP/NdEAQWBI3Hi/gkotlh++xosPWi6XXNqXz/JRmT+lb3yLEoSZLwSNvFAk/bZe1nsjhBhq9vTLpWCs+PSxYsgnW2rZqTGZ+O7g/y/uZ+NvP+owo4nF3yVVyKYnMfbWWffW44a94enoe3Q63Gz6PNkT4Q4e3hh/j9jsXEmpnP0toSeP/4Af4e9bg00RCvL+nilkjisRKy2D6LT6dEWPJaPSbFa7owBevDI/gcY2IKv79S4edErK/PdgywVDuzmLFHymy7h3EpfE1JuM0/z2VLl644LAVt12I8T2/fjK/TzHha6zQ5FrHEOZbga2YWJHG/mCDJiZOTuE/mcjzJuk4SNkukH4c01pafx+zai6/PJ4u4jYWL8P7IZfDcYWZ2+YtfAuvveOc7Yb2jHV+H5EkKnpnZyWedDevnP++FsN6Ww9cVced8QQpfz/X04G3SCkkyJyPz7WgtIiIiIiIiIiLzgG46iYiIiIiIiIhIy+mmk4iIiIiIiIiItJxuOomIiIiIiIiISMvpppOIiIiIiIiIiLScbjqJiIiIiIiIiEjL4Ww9Yv0Vr7Z8NtNQ23vPg/C9j//853Q5NR9HLBYewjHs0WPb8PsdjiI1M0tcejKs95z2IrysMl5WtTBI20hH3bBeKxyE9TUrV8F6LssjFh969AlYf/HLnwfrpQretquW4zj5/5+9O4+TrKrPx//UvnT1vk33rAyzIzMM+yKiAg4YETEKEaIQ0QQVicnPJUS/orglUVFjjEZNIBpMjOKKqCzK4kQQlAEGZt+Xnume3qu69jq/PybTmap+Pre7hoLp1uedl69X5lPVde5yzrmnLt33AYBDo3tpfaB3N63Hol20PqtzltlGNBChdT8ytP6bX/JI962be8029o7wqNExP48dDiBP609t4fsNALv2LSr7dzrLP2O6KkXCKFVEau7at4++d+Wqk83PueLKhbT+m/vX0vrO7f203lJvT0FBI4r3sSeepPW6Bh7runrxErONoWEeEevz83zgAwN8P5548imzjYE+Hk97wfnn0PqiufNovdUjivW8886l9WUnL6f1h9c+TOv3/fRnZhs7t/H5eWyMxy+bscwe0dZWFKzfx//7SMmMa7bbCFTGd9vps9OO3++fEI9tRVx7xc5bjuVnqv0sqx4M8LkgFAqZbeQcn9uDQf5ZYeOzYlE7Etrv53HAsWiU1gNmfLlHzLHxM/G6Olq39m8sO2a2kc/za5UVh22dJ78xNwLeUc7VtOH1ORP6f5Vx8cdbsVicMD8dy7izjlGpxOvOiDZ3HnOlr8T7mc/P64EgPxcBj/2LRPj4qm9oovWmJh5Vns3YfX9oaIjWrTFRNK5duYK93us5eJDWDx7ia9ZUiq9XC4WC2UY4zOevUJAfQ+sS7MUck8b8ZV3nrWPLXvN673RTKuZQKpZfd4aN/jWW5t9vACBgXO+sn8nm+LWuWOB1AIik+PeuVDJJ67ksbzsa5Z8DAMbSDD5/gNaN6Qn5vN3vfcZcNzTAv2vn0ilaD3qsJS677DJab6jn843V789/2QVmG9Z1vqOjk9atOb6W6zSL1zX4+bQ/s67WIiIiIiIiIiIyI+imk4iIiIiIiIiI1JxuOomIiIiIiIiISM3pppOIiIiIiIiIiNScbjqJiIiIiIiIiEjNVZVeF19xBuoS5WkqSxeupO/NZLLm5+Ty/MnyfcM/pPVDv+JJZuG4Hc0QPsgT2Ub3r6P1hm6exjWQs9soFPg+ZpI8xaKr6yW0Pnv+iWYbT2/l+/4/j6+n9dOMwLHWpiazDR/4a8/+5iFarwvwtJtEU5vZRiYao/Xd256h9c5Wfj/05g/8hdnGnd+/m9Yf/i1PFpvVWE/rizrs/RjuL093yORmTuoGAPjDQfgj5QkO2TGeXvPM07zvAcCrX3Uprf/xla+m9Yd+wdMse+zACszv5OdnaB/vGw8//Bta7/RIfVu95ARa3zPAk3DyWb7BQY8wh/Ub+FjtG+KpduefzZPoznzJCrONWIxP5W3Gvr/+NZfT+orFPO0OAH7wgx/Q+lPr1tH64ADfv1TOPunOiELJjIzw91uJJx6pPSVfeSpHaSbF1/l8E5JDKtO8pvYxtUu8s1hH1W+l14V4Hw4G7cQZf4Hvuz/Ak3OCxrGKhO2lUCDAk6ECRoJXyUwVqz5Kykq7CRj7N2qMk8Pt8+2Kxfi12drcFyM5x6uNyv5+LP3/eCoUChMSyqzzGQjY+2a9Vu358Xy38WK1KaMxj/FV38Cv881t7bSeqOfvTw6bTWB0lKfUZrN8DW+lqfUbKWUAsP/gAb5dKf59Z8xIr/NKcovFeGJmMWwkFh5Dep01nqz53OoLXgmylftY8Egum26ccxP2OWcky5WK9gnI53lSXMZIr7Pa8BuJhgCQydjpeYyViuo1p1jbm0nzc5o0rlEFj34fj/B9LBT48S1m+dgK+Oy1Xn09T4qt1uzu7qp/xlob+KxowBfBC3Wdn1lXaxERERERERERmRF000lERERERERERGpON51ERERERERERKTmdNNJRERERERERERqTjedRERERERERESk5qpKryu4APKuPGWjEOFJZsG6BvNzomim9ROuvoLW8wd7aX3gF/ebbfjS/En/6T6elhZt4E/HT8SWmm2MZvbQeim7idbb2hbR+gWvOMNsY/3GJ2j9V4/x/VizhqeHpfL2U/uTaf6U+lNW87S9LUaq2VjaTiw8sKef1pct48l9Q738af7bN/FUQgDY28cTSuoDPJ1nzUoe9XfSaaeYbfTvKU8oyeWPIR7kOBoeGEOoIkmmkOH3nnf5eszP2fjcOlp/1SVvovVLWufR+j0P8WRBANj/LO/789v5nON6eeLcj39yn9nGJWteSevdHTw5J7OXH5Nsqz3f9Q3xftl3aIDW7/k53959+3g6DgCcfw6fQ+a08e1KjvGkkFndc802/uyt19P6ut89Seu/XruW1n/35DqzjYFhfqxCRrpT0UxR8kjeqHjpxUjjqhW/3z8hXaiWSXS1PBbWJ1lthIyUukiYp8cBwFjeSKmz0sD8Rt3H64CdIFPtsfJKr7M+KhDk22W9f2jYjvBqbGzibQSsZaCxZnD2fpsJV+ZPVK/yuM+g4QvgcHpdZXqX1Tf8Prvv+4zI1GrT/Ky51eu1QNBKVeT1qEd6XV2cr88SDY18myIRWg+n7WNlsZLiRpL8OrR1+1bzs/r6D9H6WIZ/F8lkq0svA+xEuGjUSL8yfq/Aq4+EQkYamjHOrL5bTXpdvjCzUqArWfta8NgvK9XQSlQMG+fFK6HOOjdWvy+V+H4UC3a6YKHE2zc+ykxkDXuk1OaDvP2ikV5nzUNxI/3x8M/Y8yBjXf+tNMf//SFanmkJrM/HH86eioiIiIiIiIjIi0Y3nUREREREREREpOZ000lERERERERERGpON51ERERERERERKTmdNNJRERERERERERqrqr0Or9vYiqQddfKeSQXwHiifnIWT4ya+5YraT29h6fHAcBYkr/WlOJPwR8b2ELr9bMWmG1EojwZKlccovX06A5av+jC08w2Hn6EJ8ite4an133pa3fS+huueK3ZRsjPz0fMSAnJgb9/dhtPGwGAQIh3tec27aR1v+OJH//1QzuJbN1mnmx3Qks9rTcbSQb7e/vMNnwVaQl5Iz1huprVFUQkUn4u0ikjliRnpzA8u573v/kLT6X1FadcROuLFqfNNg5uXEfr+TT/mRNm1dH67zbzVBkA+OFPHqD1K173Gt7GvNm0Htxv95nMGO/LhQjf3oN799H6Y+vsvtY7xNNIXnr6Mlqf09FG687Z83ZTIx9H5513Hq0vW7KE1ledss5s44EHH6b1bVt5alDGx/tC0G9HWlXORYVCdaklx5Pf55uQdHIsySdW4pfPV8ucMYORIBcM8etNQyRhflQqmaT1AHgSTthY8gR89lLIGel1ZhKNkZzjFWpjpS2GjPS64RGeBpszErEAYP78BcZ2mTmDVZUBjyRFrx+i22QfrAnpdVV+9vGWzxcQrEhjKpWMFE6PsR0IVJdaaX2WZxvGWLXmj4Kx7s97fB8IhnjqXDBojUkjOS/I5w8A5uBLG2uJbTt28fr23WYTo0menlsoGuluxtoxm7MTz6xRkS/w41sy2g4ZxxwAYmbCFz/pJcfb8BrDlalqXsme000gEJiQdBYxvit5JcvlsnxdaE1nOaNfeB3n1Bjvk9Z2OeM8+DzSXeMJ/j04bFzPi8Z+5zzmcQfefirDUyathLySx3709Njp0NWYSWnIx4N+00lERERERERERGpON51ERERERERERKTmdNNJRERERERERERqTjedRERERERERESk5nTTSUREREREREREak43nUREREREREREpObsnGDC5/NNOQ7Q831GFGqoxGOOA8tfQuuz3vYGs4ld3/gKrWd28raDUSNytP5xs41Y8+m0HsnwCMnMyAitd3WvNNtYuYq/9owRWX/PT3kE/HO/W2+2cc5qfnwXzu+m9aZmHp2+89ltZhs+I5L6/rXraP03GzfR+qhxDAEgHOR9ri81ROu78jwedK7fjiCtbynfD3/uRYgYr6G6uiAi0fJxFovwfTi4y74nvb8nS+u/e/LXtN7YtpDWu2bNNts4/ayzaf2JB39M6wMpHp8+r73ObOPAMB/3z61/jtaXrz6JtzHH3o9sqkDr/SXeXwfCfKxEQvb5eG4rH3tDyWFaP23FIlo//eQlZhtmzHKA959EPZ8HzzzzDLONufPm0vr9999D679dy+e7QpFfSwAgmSuPzZ1JCbfsGuwVeW7j58w6Fj6fNc95HDwjytkZPxII8nOWiCfMJsKDxvXciHoPGtchvxHJDAAl46A4s+Pw/faKBQ+H+L4HQ3y7tm/nke4NjY1mGwkj2traLrsveA2Y6vqJFfftFQNe2f5Mi6jO5wsIBsuvCT4fH8OFAr8+AUDOGJPW+fQHeF9yRbsNhPhnBYxrl3Xa0nn7HBVK1lxkzGuO1712o5jncfOpsRSt9xzoo/XhYR5BDwDpNH+taGyYsdvI5fi2AkDWiJu3xoDVF0LGfAMAuXy8qp+ptm1g4vj2Gu/Tji94+H9HaW5t4e/13C3+4lhylNatcz+UzJgt7O89SOvhAD9n7S3NtF4CX0cCQCga5W0Y1y5ryeAP2H0yk+fjdH8fH6eJOF/zl6zFB4B1TzxB6y89/3xab2lrN9qwT7p/hl2rXgj6TScREREREREREak53XQSEREREREREZGa000nERERERERERGpOd10EhERERERERGRmtNNJxERERERERERqbmq0utQChz+31ECjicUeD20v2Tc6opYqRTGE987L3il2Ua0wLdr473fpvW6QSMxorPfbKMQ2E3r9fFVtJ52vI1c3k7EeMOrX0PrbXX8Sf8NkQitRz1S1n7y0MO0/rNf8CSycJifj3jUTh8YS/O0s8EUT2QoGIlMfr/99P+SkeaSzPP0sKcOHKD1iy7kqWkAsOnpdWX/zhqpKNNVulBCqSK1zxfhYyUf4McNAAJFPnXs2beH1n/z5IO0vmzFRWYbC5YupvV9W2fR+lPP8AS3iEca4aqXrKD1tiWn0Pr9P/8F/5xTTzbbOHEBT2Rr7OfJG8kRnjIVTDSZbeRG9tP6WIqPlwcf4wkpI2n7WJ22vIvWt294ktYff4InAI4M2wmUJ5zIU2AWL5rH297Ik1NSY/a4LAyX992CR6LJdOP3+eGvSHU6lvQu62d8Pitx6BiOUZXbFTTSterqeJoSAASC/Gf8RnqdlYjldQytdEDncS1iih5pTtYn7d7N1xjJJE/qXLyQz5mAx34YaTvHlgpXm7HklchY+dqxpTceP4VCAYVCZXqdMR69VtLGvFX52eOfZfTXgt/+GlAy1k4BIyk4HOHjLh41kk8BFEs83c3aj2CYrzOLRXt85Y11WsDoO01NTbQeifA0MABIpfg1NZPh6WLFIj+3eWO/AfuYWElxQSMp3KoD9nwQMb5bHEt6XWWin7VfM0XI6JMB45oG2HP44OAQrR/s499Fdx04ZLaRM45rYwNPhB0dS9N62KO/1DfyNVsoxPuLudzymIeccV0ZHOapegUjMbLeIwl3y6aNtH7/fT+n9Sve8EZaDwbtuU70m04iIiIiIiIiIvIC0E0nERERERERERGpOd10EhERERERERGRmtNNJxERERERERERqTnddBIRERERERERkZqrKr0uGIghFIiV1bJZ/pR4r9tZzki8y4N/Vsj4sGC4yWyje80f03ouyNvYvflbtB4e5okUAFAMbKb1dKCR/0C0lZZTaZ5iBQBd886g9WuuvobWBzc+Q+uBMTsdImmkT2zuGaD1g8M8RcGleEoHADPJyEwyKBnb65GIkYjw7tzdzNPAFiJF67t/8COzjQ1bd5X9O28kfUxXsWgBkYrAr0KCp0Ml2uzpIZ7haTCuwOv79uyk9cHkQ2YbqxbxlLoly3iS2b4DvF+ms3afiXTyZLm5S0+h9dKPf0rrP73nXrONl796Da2vWnISrXcPP0vrOSOdEQB6K0/q/woH+b6n0ry+t48nqgBAPvUErT/2wA9ofcOGfbReKtkJV7v28H1cefJ5tD6YjtH6mNE/AcBVJDIVZ1Jyjg8TAsKOLWXshZ+3rO1y1qRvlOvq6sw2rBQmq20r6awyEfBoVkpd0dhe65LgdZr27+fpk3vG+DXqFefz8ZBI2Ok8xRLfsOOZ/mb3Ebt/+it+pvLf010+n5+QalWZ5vV/dfsaHAxYfdxKpuT1nEffLxmJZVZKHXy87nU+rX0vGGuJQLC6FDUAyOV4SnJqjCdHW6ljDQ18LQkAqRQfq2NjfF1spdRZx8PrNatuJch5nY9slidNW6zP8kqvq/yZgpGSOD05VF4/8xnev6zURABIp3m/OGCk1J28+jRa/7Oz+bUAAJ5++ila//Hdd9P6gJGcF4vZCbLOmD+s63wmx4+JzyPpb82ll9L6qJEMuXPHDlr3SriEcX381SOP0PqS5Tz5evUpp5pN1DYpdmbSbzqJiIiIiIiIiEjN6aaTiIiIiIiIiIjUnG46iYiIiIiIiIhIzemmk4iIiIiIiIiI1JxuOomIiIiIiIiISM1VlV43MjyKUqH86ev1cZ6alPfZaQQ+I6bGDDIzno5f9EjdSIVCtN7xKp5qh6ZDtDyw8wdmG/7sCK278HZaDwb4k+vzGZ5cAwCZZp6eF/O30PpwH0/QKObsZLkTly2m9dNWr6L1ex/8Ba17Bbn5HO8PUTMlpInWly2abbZx6gn8mMz387Z9W/fS+uj658w2GqPlyYS5FyEFqpYKYz4EKmKXckaSWT5v71shzMdedyNPuciM8PG1az9PjwGABfW8/YSfJ4U0NhopGul6s42MkaSWOrSb1hfO40lavkNhs41kPx/Dv9vE54nGlg5ab07z/QaAFj9PcYuE+fkYHEvTetRvzxNjQ0O0vmcPT7n0G/FefiPhCAAG+vg5T47y7S3meBv9B+3UmHDFpaEwk9LrXmBeqUbH8GlGnZ+zkpEeE4/b6XWRME+yyhX5WLFSqbzS6wpGaqSVwutg1D2Cc6z0mmXLl9N6RwefI7ySr+Cxj9ONZ5pP5WszLPknm81O2D8rQTCft89ZwF9dEpJVDwf5WhkAfGYCGb/eBQK8njJSGAEgneZze85KufLz+sGDB8w2hoxr19DQIK0nkzzF1atfxmL8Gmx9Vu4YEtuqTYqz3m/Ng4A9h1ipdlbbXnPRhPS6GXQNLhZyKBbKry+DA3wNlM3bSYD9g7zvxeqbaP3t73g3ra9Y8RKzjUsu5cnoQ0P8u+sTT/CE4liUf88HAFe0ro/8/VnjXLe0NZttXPJHf0Tr3fPn0/rffervaD1lzDUAEDLmwdFhfqzuu5cnVi9auMhso94j/fIPxcxZhYiIiIiIiIiIyIyhm04iIiIiIiIiIlJzuukkIiIiIiIiIiI1p5tOIiIiIiIiIiJSc7rpJCIiIiIiIiIiNaebTiIiIiIiIiIiUnPBat48NJycEKOey/BIyOZOHl8PAKUSjzz1+404UiNOthS0Yz9dwIiNdTw+ffbp19J6MchjUAGgf989fLvSe42f4PHOAeexH6nZ/GdaLqD1tpVn0Hpf7z6zjdauTlo/72U82vLZ59bTut9nx4PO7Wij9dUvWUbrc2Y10XpDnR15GUz10Hp6E69njPjM6Cknm22sufiKsn+PZTP4j3/4lPn+6aa3xyEcLh/DmcIYf3PAzviON/J40WiQj9WAMbYbEnGzjTnNPF40CD6GX3pOK61v2c8jiwFg70EeW3to3yZa7+zg2+Svs+eJM885h2/Xzp20vqfnIK0vXrTEbGNWmp+rsBFPn29povVQ1Iq5B9wYj0DO54x+YpzzsMdVJ5Xmc04xy9sIGZvrzKhvwFcRHT/DEtcJ65y98DtmRXIffs14wWdFLBt9OGxHulsx5ZmRDG/Diha31h6w99Gs+6y+Z/83vkWLeMzyonlzab1U5G34fVUt6aYxu1/NdNlsFr6KSafy3/9Xtz/HWBbDb/2QUS+E7Lj6YpG/VnD8WlA0xoTXTDQ4NETrbR18fVYs8HVmT4+17gb27D9A672H+PV/JMnbLuT5fgNAMMDnqXCIr/tTjs9RpZJX37eOZLV1W8GItLe2q1jkx6RQsI9V5fi22pyOUskUAhWDr7/vEH3v0PCw+TkHDvbS+uVvvJrWFy9bQesFY4wCQEtLO62//ooraH3frh20XsrnzDYS8Tpaj8V4vTTAx1xnV5fZRkcn/4566dx5tJ7P8XsM//qVfzHbSKVStN7Uzr9XPPPU07T+uyd+a7ZxwStfYb72h0K/6SQiIiIiIiIiIjWnm04iIiIiIiIiIlJzuukkIiIiIiIiIiI1p5tOIiIiIiIiIiJSc7rpJCIiIiIiIiIiNVdV1ElhaAiFbPlT7A8ZiTOBgH0/q6G1kX++40/hd1YAg0dCUcj4Ib8RDFGM8pSYuae/2WwjWM9TtA5s+y6t+/I8lSrgce8vc2gjbzvMk6z8nR203tacMNtIGckES1cspfV3vfNttB4N2el1TRF+bsPgyWmF4T20nhq2E0pGsjyxoHkJ34+TLz6d1mMLV5lt+Jq7y7cnlQJmUHpdMV9CEeXjJpfiKSMBn50+kvbxxMV9bpTWW5uitF4XGzLbGBvjiSCz5y2g9QbwJLykkWQBAFkj9S0R5WMi3sLnrj0H+LYCwHPP8TG8bBVPI8lvfJbWd+/mySIAkGicResteZ6QszAYpvVgnZ2kOTTMLxelEv+Z5iaeXtLcyOsAsGk7H/d+I6qpIca3qS5kz6m5fPlcVDLSd6Yj54pwFclRpVLttt9nJMtZSh7XYDPZzlgzlIz3l0p2Ok80xPuxNXUVg0a6lkd6ncVvrDGKBb5/TXV2Umd3K79u+0tG4piRJOX3233BSjuzktNqqdo2vFIRgcrja/fB6SidTk8YN9bx8VpHm2nPZmIZP6aFsD2+8gV+7bT6nxW85jWvhIZ4wlf/oT5aT2d4sty+Hn7tAIDtu3bzzxrja9acMYYzeXstkcnwa611PgIBfu3yeaa+GamVfn4Ntsa8F2tOt9Pr+PutVDvWhtd7p5uB/j7kc+V9cO9e3r+27+B1AKhv5slyr7nscloPGSfTmV+QbaecehqtL1vG08T377TXno0J/t0yZ/SLvJEAOW/+CWYb0ShfM1qJt1dc8ce0HvTbtzzuvP1rtF5I8/TrgX4+b/3GI73uZUZ63YwPUK6CftNJRERERERERERqTjedRERERERERESk5nTTSUREREREREREak43nUREREREREREpOZ000lERERERERERGquqvS67OgoAoXypIt5L3kJfW9PT6/5OdFYjG9MPd+cvJGmEDYSGwAgaKS+FAO87oORPBHhCXUAMOslV9J6KGik2m36Ia1nU/wp+AAQDPIEj5GhLbQeqOf7l8/wVDEAKGX40/kDef4zJ53Az182lTLbGBvlyX3psX5aDwV4KtHsxS8z22jq5ol+ic6FtF40zlPGDuGDq0jwyFpxLdNUUwMQrji0zhorBfue9MgoT6AoGWlwuQxPfWlYaI+v3TvX88/K8z7jK0ZofWjITpxZvZzPXzFj3D3zHG97x859Zhs7tvPX/D7e0VaeylMVn96w1Wxj/8EeWm9dzPu+GzbmA48UvjojRezE9mb+/sqO9r+aEjzJEAASc3gK39w4n+uLIX5u64N23y2g/LMKhZk0hh2mmt7lnQBmfHqVP+KVXme+ZtQLed6/Mka6KgAUc/xnUiP8mpaO8DEXifNrmhfr+FpptIkgn58AAMZ+ZEp8XRIKhWg96LGiCwSs5Cu+vdWno3ml1FXXsbzC7iqT0KpNXDzestmJfdA6bkGPE2rtt9UvrfNspUkCgK/KuSWfN9Lgsnbf9/n4dq1/dgOtj4yO0Pr+/fvNNvJGGl06y+eWdIbXs0aaHwDkyHkFgJyReGePO/vaZfUT62eOZWxYaajWdG5dM7wS6QoV3yNnUnrd3t27UFeRRLpnL0/VTo5ZiYbA2254O60vWrSY/4BxoL2SQa2z39zaQutLly+n9ZFD9rowYqzzBg/xtXLYeP9SI2XcW3W5b5e8+lLztd4Du2j9/rvvpvVsmn/f3ecxD2WN+SYa4cfk95F+00lERERERERERGpON51ERERERERERKTmdNNJRERERERERERqTjedRERERERERESk5nTTSUREREREREREaq6q9Lq5p5+FRCJRVvNFeCpFo89Oq9izhz/pf8HC2bQeivEnu1cmiZW9ZjzR30rd8Dnj/SX7vpxzPPGmbfmf0HpdfSet737mv8w2Rsd4ylS4hT85318YovVCyk4fcGn+M/4sb7tY4Okh8EiBamyZT+sNC8+g9VgbT3AIJrrMNnzGPdRsjidjFLO87oOdlhQMlSdfhUJVDaHjLhYCKoMSfI38vcMpO1GkWOTnOpPjKWM5Hx8rvcN2GkxzjLef38Hnj6Eh4/2FOK0DQFOcvxbp5GlS+ewYrQ8ODZhtlAr8mKz71Vpaj9c30fo5Z55qtvGrtY/R+uatPPGua/4iWm8v2Sle8QxP5Xj1GStpvaW1g9YjdXZ6XcZIB8o4nkbWPStN6y7L3w8Auw6W9xMjPGla8vt9E5KKrPQar1QbS7WJd15t2NvFD3gwwMdJ2Gcnu7S08OTEvn5+vRsc5UmxXil80Sjvr3UVa6Ej2ltaab2rxb52WQlA1aaaBYx03sOfVV360bH0n1p9llc/rPysY9nO4ymfz095H7xSvapNrwsY48sZaWWAnWBYKPCfsdpOeSQbD47w9WTMSLnOZHgiWN8h+xqcMdLokil+/RhL87qV7AbY5yqf423njWPoNRdZrOPuPJIJLdZ+FIv8s0rG9zCv/ZjJ6XXbt22f0DcHh3kfXraCpyMDwKWvfjWtW3OB+d3VbAFmfF0owNNPOzr5d9TGRuNLAoCicZ4HBvh4bO/i3/Pnzp1rtmGp9liFQvZa4pUXvorWH/+fR2l9x26eGJ030irlsBm03BYRERERERERkZlCN51ERERERERERKTmdNNJRERERERERERqTjedRERERERERESk5nTTSUREREREREREak43nUREREREREREpOaqyntPlwB/RbJl2IgHrA/zSEYASGZ5HPm+rbtpffayE/kHBex7ZkaKJ5zPiinlcbIBr0Pk+M9kjdjxhrkvo/U5RpQtAGzf/iyt+3N8B/0BI+o1z485AIQjPPo52thC64FYPX9/PY+KBoBghEdbI8DbzhoRqpkij58FAL/xWsiIFg4ZXbRknFcAE+LKK/893UV9QKQiZTSS4PtQcF7x23xcjA7x+NShYd4vB/uzZhtdrbyNjkbjZwJ83HV28/4KAIEA7zMDI3y8zDuhg9bPXnHQbGPdZh7TfvDQIK3/8mc/pvVc0qPvF/hrPVueovVIXROtzzllpdnG/AiPtA2W+Lldfsoq/kEle8zs3L2T1g8e6qX1A/u20vr5Z7abbbRvKo83zuYKuJen4k47Pt/h/x3NmoOqiZ0/VlYMOwCUjLhuv7FZPiP8OeyxqdFEnNabW/m1KJUeo3WPQ4VgkM9DkQiPX45H+TZF/BGzDWsXzZhs4/z5jGud12vVxk57qVW/8vqcytdq1eaLJZvNTji21j4EPNa41e62z2dc54Me6+gSX4dZ497slx77UTDG5PDIKN8mY14pWot+HJ7jmbEMX0vkcvx7jXP8eABAocDbyGZ5G/mC8V3EY9xZY9IZx8Sqeykaa++Ctb3GcS+V7LYrj5XV5nQ0NDSITKZ8zVPI831dcZK9nmpqMr4TGV6MeS4e42voYtE+l/39/bQ+PMzXvedfeDGtNzXbx6P662D1x6qto7OqeqHI19b1Cf6dFgBC1pfOPyAz6xuziIiIiIiIiIjMCLrpJCIiIiIiIiIiNaebTiIiIiIiIiIiUnO66SQiIiIiIiIiIjWnm04iIiIiIiIiIlJzVaXXzelsRX19eRKUy2foe3c+y1PXACCbTdH6/HmL+Q8YKVptbXZCUSHP05x8If4Ufp+RAORzXk+b5wkefsfTKrI+/lT71uWXmS20zT+b1jNDPM2p4OPb6wtGzTbCcSPdK8jTeZyx315hN1ayR9FIuLBCePyoM9sIGIlq1mdZ6RpF2EkNlakto6M87WG6yhSKcP6K9B8jfSQUsqcHZ9yvjsT4sTOGPBJGEiIAwEjOGRjl9aZG3nZrqz2Gxwo8OWfJ4qW0nmjkx6R3F0/eBIBkiqe7Pb2D14cHRmj96V/fbbbhL/IxuXc/TwAKBIzUrxb7fOTrePpWIcuPYTbKPyvgkSzy7NNP0PqurTylbmgnP+5tMaPDAVi2qLHs32MZnlQ0PflQmXVWywSXqrfGow2/kZZVbXqd32M3/D7e70MR3lebmprsD6sV69pV8khkM/PrjCbMNB/7Z6pPO5tZqXAzRS6Xm3IyoFd6nZ2cW10aodf4yoX59S4c5mtDK+mx+hxEIG8kY+eMNDivY1oo8M8KGDufM74/5I3PObxdxs8Y+2Gl1FWT3Dj+WR7JfdW+3zqMdt1K1Jt6Ct+xJGUeL8V8EUV/+feZUMj6rnScf6+jyil8wQkn0HprK08yB4A9u3bSejrD7w0ErcRbr7Rdr0mqRg4eMlL4RpK0Xhfn30Wt+QkAisb34ED4DyfVTr/pJCIiIiIiIiIiNaebTiIiIiIiIiIiUnO66SQiIiIiIiIiIjWnm04iIiIiIiIiIlJzuukkIiIiIiIiIiI1V1V63ay5rWhoaCirJUf5k90XJ1abn9PUzJ+E7zPugZUcT6XySvawEu+SSZ4Y5cCfKu8V4FLI86ftNzUZqXrGhxUKHmkVdQ20HuhaSOtW7ppXOo6VGZDJ8DSOsPGkfa/zUSzyxDEzecnYqkDQfsr/0ABP6opEeZJRNGakD3oNi4oUhZERnjQ2Xe3ryyIUKj8XTQ38mMaiHqlvvGsgHOF9oD7E0xMXdjebbcDxMXmgf5DWIxEjUceKLwSQzPOkiZE0P68tc06k9eWnnWK3kXuG1kdT/CDuPGAkPXqErHW18vHVM8DH146NG2g9aBxDAHjsEE/MTI7wpLjlp59K622dHWYbmx77Fa0P7N1P660x3q/GgvbBGk4Nl/07m+fHezry+/0TUquOZ0rdsbRtBtFYqXZe6XXGh5n5o8eQklTtvpt1V7v/xmcfdzt59dgyxKTWrDQzplDwSq+rzZgsGSmxAFAo8rVQocD7mbVNXioTgY+wExr5MclmeVoWAIyN8YTVdJrXMxn+PSGb80ivy/HruZWSbLFTCe1zW236m9f7re2tVX2mi4VDiFask7o6u+h7n37mKfNzduzcSesnLFhA6/Z48EpFrU5X1yxe7+Z1ANi+YzutF4zv83f9kKcwn3vBK8w2Fi7k33er7fdex2r79h20PjpqfBdo4fcxDhzoM9vYt5+vY09YMJ/Wj+WcT3f6TScREREREREREak53XQSEREREREREZGa000nERERERERERGpOd10EhERERERERGRmtNNJxERERERERERqbmq0uuY/n7+ZPdf/88682fOPIOnGh08yNORgkZiWTZrRGgBZnrdgR6+vcEQf0r8CSd0m03s23uI1kOROK0PDPK247E6sw2f33o6P993K0CuULATmpYv58kA+/cf5G0E+LHt6rZTqazElt6DxjEM8cQ5V7Kf2j86ytPrTj1jOa3H4gneRtFO3fD5edLKTJHNBVFy5cN+eJT3seaYnV43nOWpb6EQ72cNTXyqaWji6WMAUMjyPtNc4OMlGODbmzdSZQBg6YqX0HpLN0+pC0cbab1rEa8DwKK+flrv28/rqQxPgxvL2GO45OOpc2cs4emXj2zkc9GuLZvNNnJpfhwLBZ5+tH0T/6zUMN8/ABge5K+VSryP5nO87i94pFxmy38mV5jZqV61TJarVdvH9llG3etnjLpxiTJ/wqsNs3dYL5iJM1797IU/V8cQ3CcvgFwuNyGV6FjGcK1SFb3S66xEOCtxLhjk13mrDgDhML92WW2k0zxZzlr/ef2MtS61vltkPdYSVkJzLdPrasVrm6zELKtufVY1CXluBiXd+YIh+Cu/k/p4Xx0Y5knLALBz105at9Lrjol1CoxpZXBwiNaHPFK665t4AnV9hp/TE5cs4++v52tVL7Vcf3R28O+v9fV8bT/YP0zrBw4eMNv4zW9+Q+tWet3vI/2mk4iIiIiIiIiI1JxuOomIiIiIiIiISM3pppOIiIiIiIiIiNScbjqJiIiIiIiIiEjN6aaTiIiIiIiIiIjUnG46iYiIiIiIiIhIzdk5plNUV8fjyxedeIL5M5EIj7OOxXh8ut/P32/FaANANsMj3Ts6Wmg9EORxpy2tTWYbwWCE1odHedsHe3l8ZjFsR9b6HI+dbGiI03okzKM7/T77/mIkwvejo72V1lNjPNbcirgFgKQZZ8vPYTTKz3kyaUfWNjc3GZ/F98/qPy9GZO3xksu5CVG2mSSPHR1L8DhhAIjylGOUjAjTVJGPiV2H+s02XMaIeC4WaP1AL48wjfHTDwBYWs/jXnMuQeuH9h3kbTTweRAA4o08crW1jc93C0Z5vwzxIQEAmN3F54NZcaOPB/mxffQ5O3a6aERr+/y8jf59e2l97GCv2Ua+xM9twrhSZWFsU8mei8L+8p9xzp5/pfasq7Z9NbcZXQ9WWvexBCzXKpTZGddyz7bNxq0X7KNYy3jpWvGKVf99lc9PvK5a58ZrLVL9z/D3e81/JWPOLxZ5PZPJVPV+AAgG+eRurUtLJT6OrLa9Xkun07SezfG1T9Fo22u7LC9G37fa8Grb2o9qt9freFR+1kyaBta85nLU19eX1Q709tH3huJ8XQYAixcupHXrOB/L/G0dVuuTBgeHaP3Q0IjZRt4Z84qPr8HmzZ9P660tfC1++MOsC3rtrmnLl6+g9csuv4LWf/XwI7Re8Nvb1DWrs/oN+z3z+/vtWkREREREREREjhvddBIRERERERERkZrTTScREREREREREak53XQSEREREREREZGam9KDxI882GxkZOLDxEZH+QPGkqmk+XmjozH+M0n+M34/30yvBwdaDxKH4w8ItB4kPmo+ABtIJvlDCFMp3nY6zR/A7fd5PPzTzx/GFwjwB+4W8tU/SHx0lD/QeCw1RuupMV6PGA//BuxzmzL6ifXQvFTKfpB4Ps/bZ/0WAPIF3q+8jpWv4iFxRz57uj8Q9cj2FQoT+02pwI91Nsf7GAAUjH5Z8vGfCQZ5PRO0H1bussYDCo0HiVvbm87YbVhjOOj4WIUxJoxDeLiNlPEQU2O7rIeYeuQmmJ81ZjxpOWO0kSf944hCgc+RJeNxlc54XGXB4791FIwHieeNNvzGQywDXo+l9pe3cWSfp/MYPrJt7HrExjTw4jw4ejo+nBqo/gGqL4Zj6V61fJD4dHQsY67ywdNHxsR0Hr+A9zV4pj1I3H4/XxcUi1N/qPQR1n5YbVjzoNdr1gPOzboeJD71tj0fJM7bnM5j+Mi2se8yqRRfLwY9joH1vQRG4MQxPUi8yudvW9/Tslnj+zSAXI5/J2OBCYD98H7zeMBjTqvh+iNnbK/1fdc6Jl4PErf6ibXvtXyo/Aupmu/BPjeFd+3duxdz5859/lsm8ntqz549mDNnzvHeDJPGsIi36TyGNX5FvE3n8QtoDItMZjqPYY1fEW9TGb9TuulUKpWwf/9+1NfXT7s7bCLHk3MOo6Oj6O7u9vyvksebxrAINxPGsMavCDcTxi+gMSximQljWONXhKtm/E7pppOIiIiIiIiIiEg1puctZRERERERERERmdF000lERERERERERGpON51ERERERERERKTmdNNJRERERERERERqbso3nV7+8pfjPe95zwu4Kc/Pzp074fP5sG7duuf1OQsWLMDnP/95z/f4fD784Ac/eF7tHHHdddfhda97XU0+q1p33HEHmpqaavZ5Dz74IHw+H4aGhqbF9gDTv9/KzDHZuK+2/6tvyh8C5xz+/M//HC0tLTW5Rlt+X8bTVNYgMjP8vvTJ6eiFWC/Wis779Kdz5O14fjed7j7ykY/glFNOmdJ7X8jjOJXPnm79PHi8N0Bq67rrrsPQ0NCUbopdddVVePWrX/3Cb9Rx9L3vfQ+hUOh4b4b8ATj33HPR09ODxsbG470pE9x0001Yu3Yt1q9fj+XLl79gX/xFKv3sZz/DHXfcgQcffBALFy5EW1vb8d6kF4XP58P3v//9qhecjz/+OOrq6l6YjRJ5gRxrf58pHnzwQbziFa/A4ODgtL3ZJVIrX/jCF6Bw+5lvun0H1k2nP1D5fB6xWAyxWOx4b8oLqqWl5XhvgvyBCIfDmDVr1vHeDNNb3/pWPPbYY3j66aeP96bIH5Bt27ahq6sL55577vHelBmhvb39BW8jl8shHA6/4O3IsXPOoVgsIhj8w1qmq29OjY7T8fOHMDZfjP94ms/np9UNkZmiWCzC5/NN6b0vxnfgas5jVc90KpVKeP/734+WlhbMmjULH/nIR8Zf2717Ny6//HIkEgk0NDTgyiuvxMGDB8dfZ78G9p73vAcvf/nLx//93e9+FyeffDJisRhaW1tx0UUXIZVKjb/+9a9/HcuXL0c0GsWyZcvwz//8zxO2cfv27XjFK16BeDyOVatW4de//nXZ63fddRdOOukkRCIRLFiwAJ/97Gc993nLli142ctehmg0ihUrVuC+++6bwpH6P3v27MGVV16JpqYmtLS04PLLL8fOnTvN95dKJXzqU5/CCSecgFgshlWrVuG73/1u2XueffZZvOY1r0FDQwPq6+tx/vnnY9u2bfjIRz6Cf//3f8cPf/hD+Hw++Hw+PPjgg+N/evjtb38bF1xwAaLRKO68807668k//vGPccYZZyAajaKtrQ1XXHHF+Gvf/OY3cfrpp6O+vh6zZs3C1Vdfjd7e3qqOx9HuuOMOzJs3D/F4HFdccQX6+/snvOeHP/whTj31VESjUSxcuBAf/ehHUSgUAABXX301rrrqqrL35/N5tLW14Rvf+AaAib9amM1m8YEPfABz585FJBLBokWL8K//+q/jr69fvx6XXnopEokEOjs78eY3vxmHDh0af32yPirT01e/+lV0d3ejVCqV1S+//HK89a1vBeDd1444dOgQrrjiCsTjcSxevBg/+tGPxl9jf163du1avPzlL0c8HkdzczPWrFmDwcFBuo3ZbBbvfe97MXv2bNTV1eGss87Cgw8+OP76rl27cNlll6G5uRl1dXU46aSTcM8990xp///xH/8R73rXu7Bw4cIpvV+kFq677jq8+93vxu7du+Hz+bBgwQJks1ncdNNN6OjoQDQaxUtf+lI8/vjjZT/30EMP4cwzz0QkEkFXVxf+5m/+pmwsplIpvOUtb0EikUBXV9ek1/FKk10HJmv/5S9/OW666SZzPbRgwQIAwBVXXDG+38DhG3CXX345Ojs7kUgkcMYZZ+D+++8v27bKP6/z+Xz4+te/bs47wOTXrZe//OW48cYb8Z73vAdtbW1Ys2YNnHP4yEc+gnnz5iESiaC7uxs33XRTVcdRpm6y9dOR68dPf/pTnHbaaYhEIvjVr36F0dFRXHPNNairq0NXVxc+97nP0XWN17XDy5F14M9//nMsX74ciUQCl1xyCXp6esbf8/jjj+Piiy9GW1sbGhsbccEFF+B3v/vd+OtWf5/Kup/1TQC47bbbcPLJJ6Ourg5z587FO9/5TiSTySntE/PlL38ZJ554IsLhMJYuXYpvfvOb46+xx3MMDQ2VraFf8YpXAACam5vh8/lw3XXXAZjaXDQ4OIi3vOUtaG5uRjwex6WXXootW7aUvWey7yYLFizAxz72MbzlLW9BQ0MD/vzP/9xzf3O5HG688UZ0dXUhGo1i/vz5+NSnPlW2f3/xF3+Bzs5ORKNRvOQlL8Hdd989/vqvfvUrnH/++YjFYpg7dy5uuummsrXuggUL8MlPfhJvfetbUV9fj3nz5uGrX/1q2TZM9v3nwQcfxJlnnom6ujo0NTXhvPPOw65duzz364UwXccmMLV+8fGPf3y8D86fPx8/+tGP0NfXN/6dfOXKlXjiiSfGf2YqY/7osTuV9TPgPcaAw9eyL3/5y3jta1+Luro6fOITnwDgvfZ+PtepqZ7XBx54AKeffjri8TjOPfdcbNq0qexz/u7v/g6dnZ2or6/H9ddfj0wmM6X2j/aZz3wGXV1daG1txbve9S7k8/nx1ybrI0fO149+9COsWLECkUgEu3fvHn/9ox/9KNrb29HQ0IAbbrgBuVxu/LWj++Pf/u3f4qyzzpqwbatWrcKtt94K4PA9iFtvvRVz5sxBJBLBKaecgp/97Gfj77XuJwDe92iOzEdwU3TBBRe4hoYG95GPfMRt3rzZ/fu//7vz+Xzu3nvvdcVi0Z1yyinupS99qXviiSfco48+6k477TR3wQUXjP/8tdde6y6//PKyz/zLv/zL8ffs37/fBYNBd9ttt7kdO3a4p59+2n3pS19yo6Ojzjnn/uM//sN1dXW5u+66y23fvt3dddddrqWlxd1xxx3OOed27NjhALhly5a5u+++223atMm94Q1vcPPnz3f5fN4559wTTzzh/H6/u/XWW92mTZvc7bff7mKxmLv99tvHt2n+/Pnuc5/7nHPOuWKx6F7ykpe4Cy+80K1bt8499NBDbvXq1Q6A+/73vz/pMcvlcm758uXurW99q3v66afdc889566++mq3dOlSl81m6XH5+Mc/7pYtW+Z+9rOfuW3btrnbb7/dRSIR9+CDDzrnnNu7d69raWlxr3/9693jjz/uNm3a5P7t3/7Nbdy40Y2Ojrorr7zSXXLJJa6np8f19PS4bDY7fmwWLFgwfvz279/vbr/9dtfY2Dje9t133+0CgYD78Ic/7J577jm3bt0698lPfnL89X/9139199xzj9u2bZv79a9/7c455xx36aWXjr/+y1/+0gFwg4ODkx6bRx991Pn9fvf3f//3btOmTe4LX/iCa2pqKtuehx9+2DU0NLg77rjDbdu2zd17771uwYIF7iMf+cj49sZisfE+4pxzP/7xj10sFnMjIyPOucP99i//8i/HX7/yyivd3Llz3fe+9z23bds2d//997v/+q//cs45Nzg46Nrb293NN9/sNmzY4H73u9+5iy++2L3iFa9wzk3eR2X6GhgYcOFw2N1///3jtf7+/vHaZH3NOecAuDlz5rhvfetbbsuWLe6mm25yiUTC9ff3O+cm9v8nn3zSRSIR9453vMOtW7fOrV+/3n3xi190fX19zrmJffNtb3ubO/fcc93DDz/stm7d6j796U+7SCTiNm/e7Jxz7o/+6I/cxRdf7J5++mm3bds29+Mf/9g99NBDVR2HW265xa1ateoYjqBI9YaGhtytt97q5syZ43p6elxvb6+76aabXHd3t7vnnnvcs88+66699lrX3Nw8Po727t3r4vG4e+c73+k2bNjgvv/977u2tjZ3yy23jH/uO97xDjdv3jx3//33u6efftq95jWvcfX19WXjyYvXdWAq7Xuth5xzrre31wFwt99++/h+O+fcunXr3Fe+8hX3zDPPuM2bN7sPfehDLhqNul27do1/9tFrEOcmn3cmu24d2d5EIuHe9773uY0bN7qNGze673znO66hocHdc889bteuXe6xxx5zX/3qV6d8bmVyR8/xU10/rVy50t17771u69atrr+/373tbW9z8+fPd/fff7975pln3BVXXDGhr0927fBy++23u1Ao5C666CL3+OOPu9/+9rdu+fLl7uqrrx5/zwMPPOC++c1vug0bNrjnnnvOXX/99a6zs3N8nWX198nW/UeOUWXfdM65z33uc+4Xv/iF27Fjh3vggQfc0qVL3Tve8Y6y7T56vejle9/7nguFQu5LX/qS27Rpk/vsZz/rAoGA+8UvfuGc+7/vD08++eT4zwwODjoA7pe//KUrFArurrvucgDcpk2bXE9PjxsaGnLOTW0ueu1rX+uWL1/uHn74Ybdu3Tq3Zs0at2jRIpfL5ZxzU/9u0tDQ4D7zmc+4rVu3uq1bt3ru86c//Wk3d+5c9/DDD7udO3e6Rx55xH3rW99yzh3+bnP22We7k046yd17773j64l77rnHOefc1q1bXV1dnfvc5z7nNm/e7NauXetWr17trrvuurLtaWlpcV/60pfcli1b3Kc+9Snn9/vHz99k33/y+bxrbGx0733ve93WrVvdc8895+64446yufCFNBPG5lT7RUtLi/vKV77iNm/e7N7xjne4hoYGd8kll7j//u//dps2bXKve93r3PLly12pVHLOTW3MHz12J1s/Ozf5GHPu8LWso6PD/du//Zvbtm2b27Vr16Rr7+dznZrqeT3rrLPcgw8+6J599ll3/vnnu3PPPXf8Pd/+9rddJBJxX//6193GjRvdBz/4QVdfXz/ldfS1117rGhoa3A033OA2bNjgfvzjH7t4PF62D5P1kSPn69xzz3Vr1651GzdudKlUyl177bUukUi4q666yq1fv97dfffdrr293f3t3/7t+Gcf3c/Xr1/vAJTNHUdqW7Zscc45d9ttt7mGhgb3n//5n27jxo3u/e9/vwuFQuPbYt1PmOwezZH5qKqbTi996UvLameccYb7wAc+4O69914XCATc7t27x1979tlnHQD3m9/8ZvzAe118fvvb3zoAbufOnbT9E088cXzCPOJjH/uYO+ecc8oOxNe//vUJ27BhwwbnnHNXX321u/jii8s+433ve59bsWLF+L+PXvD9/Oc/d8Fg0O3bt2/89Z/+9KdTvun0zW9+0y1dunR8oDvnXDabdbFYzP385z93zpUfl0wm4+LxuPuf//mfss+5/vrr3Zve9CbnnHM333yzO+GEE8YvVpXYcT5ybD7/+c+X1Ssv2uecc4675pprJt2vIx5//HEHYPymSzU3nd70pje5V7/61WW1q666qmx7LrzwwrKbXs4dPqZdXV3OOefy+bxra2tz3/jGN8o+96qrrhr/99EDbtOmTQ6Au+++++g2fexjH3OvetWrymp79uwZX2hM1kdlerv88svdW9/61vF//8u//Ivr7u52xWJx0r7m3OEL5oc+9KHxfyeTSQfA/fSnP3XOTez/b3rTm9x5551nbs/RfXPXrl0uEAiUzTXOHR4DN998s3POuZNPPrnsJtix0E0nebF97nOfc/Pnz3fOHR4zoVDI3XnnneOv53I5193d7f7hH/7BOefc3/7t3064bn7pS19yiUTCFYtFNzo66sLhsPvv//7v8df7+/tdLBab0k2nya4Dk7XvnPd66IiprhNOOukk98UvfnH83+ymk9e8M9l168j2rl69uuw9n/3sZ92SJUvMtYQ8f5X/YeFo1vrpBz/4wfh7RkZGXCgUct/5znfGa0NDQy4ej1d17fBy++23T/gi8qUvfcl1dnaaP1MsFl19fb378Y9/PF5j/X2qN50q+ybzne98x7W2tpZt91RvOp177rnu7W9/e1ntjW984/gadLKbTs7x9e1U5qLNmzc7AG7t2rXj7zl06JCLxWLjPzfV7yave93rprS/zjn37ne/273yla8sm8eO+PnPf+78fv/4/FDp+uuvd3/+539eVnvkkUec3+936XR6fHv+9E//dPz1UqnkOjo63Je//GXn3OTff/r7+x2A8f+g/mKbCWNzqv3i6PPQ09PjALj/9//+33jt17/+tQPgenp6nHNTG/OVY9dr/ezc5GPMucNzxHve854Jx8Jr7V3L65R1Xo++mfaTn/zEARjv5+ecc4575zvfWfY5Z511VlU3nebPn+8KhcJ47Y1vfOP499Sp9JEj52vdunUTPrulpcWlUqnx2pe//OUJa5Wj+/mqVavcrbfeOv7vm2++2Z111lnj/+7u7naf+MQnyto544wzxo+BdT9hsns0R+ajqv68buXKlWX/7urqQm9vLzZs2IC5c+di7ty546+tWLECTU1N2LBhw5Q+e9WqVbjwwgtx8skn441vfCO+9rWvjf8ZSiqVwrZt23D99dcjkUiM/+/jH/84tm3bZm5jV1cXAIz/Ot2GDRtw3nnnlb3/vPPOw5YtW1AsFids05H96u7uHq+dc845U9ofAHjqqaewdetW1NfXj29zS0sLMpnMhO0GgK1bt2JsbAwXX3xx2X5+4xvfGH//unXrcP755x/T38Gefvrpnq+vW7cOF154ofn6b3/7W1x22WWYN28e6uvrccEFFwBA2a/5TdWGDRsm/Jpf5bF96qmncOutt5Ydi7e//e3o6enB2NgYgsEgrrzyyvFf7UulUvjhD3+Ia665xty/QCAwvt2VnnrqKfzyl78sa2/ZsmUADv9ZhFcflenvmmuuwV133YVsNgsAuPPOO/Enf/In8Pv9k/a1I46eX+rq6tDQ0GD+ielk4+lozzzzDIrFIpYsWVK2DQ899ND42L/pppvw8Y9/HOeddx5uueUWPZtJZpxt27Yhn8+XXYdDoRDOPPPM8bXChg0bcM4555Q9s+C8885DMpnE3r17sW3bNuRyubLrR0tLC5YuXTqlbZjsOjBZ+0dY6yEvyWQS733ve7F8+XI0NTUhkUhgw4YNk15Dveadya5bR5x22mlln/nGN74R6XQaCxcuxNvf/nZ8//vfn/DnxFI7U10/Hb1O2759O/L5PM4888zxWmNjY1lfn8q1YzLxeBwnnnji+L8r+/LBgwfx9re/HYsXL0ZjYyMaGhqQTCaPae3HVPZNALj//vtx4YUXYvbs2aivr8eb3/xm9Pf3l12Pp8pa+0/1+4llKnPRhg0bEAwGy97T2tqKpUuXls15U/luMtka/mjXXXcd1q1bh6VLl+Kmm27CvffeO/7aunXrMGfOHCxZsoT+7FNPPYU77rijrD+tWbMGpVIJO3bsGH/f0fOSz+fDrFmzyuYlr+8/LS0tuO6667BmzRpcdtll+MIXvlD2510vpuk6NqfaL44+D52dnQCAk08+eULt6DE92Ziv5LV+9trWyjFW2YcnW3s/n+vUVM/rZPcNJvuuOpmTTjoJgUCgrI0jnz/VPhIOhyesOYDD907i8XjZtiWTSezZs4duyzXXXINvfetbAA7/6eJ//ud/jn9nHhkZwf79+6s+j1O5R3NkPqrqKWiVNzp8Pt+Ev/G0+P3+CU/CP/pvGgOBAO677z78z//8D+6991588YtfxAc/+EE89thj4wf0a1/72oSTf/SJrNzGI4vGqW5jrSWTSZx22mnjN0WOxh4WeuTv1X/yk59g9uzZZa9FIhEAeF4P/p4sEcfrs1OpFNasWYM1a9bgzjvvRHt7O3bv3o01a9aU/f1oLSWTSXz0ox/F61//+gmvRaNRAIcH0AUXXIDe3l7cd999iMViuOSSS+jnTXbskskkLrvsMvz93//9hNe6uro8++gJJ5xwDHsoL6bLLrsMzjn85Cc/wRlnnIFHHnkEn/vc5wBMra8B1c2B1YzVZDKJQCCA3/72txPmtEQiAQB429vehjVr1uAnP/kJ7r33XnzqU5/CZz/7Wbz73e+ecjsif+hqFZ5xLOuh9773vbjvvvvwmc98BosWLUIsFsMb3vCGSa+hXm1Ndt06ovL6P3fuXGzatAn3338/7rvvPrzzne/Epz/9aTz00EN6uGuNVbN+qja5cCrXjsmw/nX0ev3aa69Ff38/vvCFL2D+/PmIRCI455xzJu23k637j6jc5507d+I1r3kN3vGOd+ATn/gEWlpa8Ktf/QrXX389crlc2ZesWjjyxfnobWXbebxV0zdOPfVU7NixAz/96U9x//3348orr8RFF12E7373u1NaC//FX/wFfXbOvHnzxv//yealyb7/3H777bjpppvws5/9DN/+9rfxoQ99CPfddx/OPvvsKe/n8zXdx+ZUsO+9k30XnmzMV/JaP1ej8hhOtvY+1utUNef1hb5vMNk4mUoficViU354uJc3velN+MAHPoDf/e53SKfT2LNnz4RnI0/F0efxyL0Lr3s0R+ajmjx6f/ny5dizZw/27Nkz/ttOzz33HIaGhrBixQoAhyeZ9evXl/3cunXrJpzs8847D+eddx4+/OEPY/78+fj+97+Pv/7rv0Z3dze2b99u/hbLVLdz7dq1ZbW1a9diyZIlE0720fvV09Mzvnh79NFHp9zeqaeeim9/+9vo6OhAQ0PDpO8/+gFh1n+FXblyJf793//dfFp8OBymv7U1FStXrsQDDzyAP/uzP5vw2saNG9Hf34+/+7u/Gz/HRz+YrlrLly/HY489VlarPLannnoqNm3ahEWLFpmfc+6552Lu3Ln49re/jZ/+9Kd44xvfaE5EJ598MkqlEh566CFcdNFFE14/9dRTcdddd2HBggVmKoVXH5XpLRqN4vWvfz3uvPNObN26FUuXLsWpp54KYGp9rVpHxtNHP/rRSd+7evVqFItF9Pb24vzzzzffN3fuXNxwww244YYbcPPNN+NrX/uabjrJjHHkIaNr167F/PnzARz+cvf444+PP+xy+fLluOuuu+CcG19krV27FvX19ZgzZw5aWloQCoXw2GOPjX8BGhwcxObNm83r5tEmuw5M1v5UhUKhCdfitWvX4rrrrhsP6Egmk57BIlMxleuWJRaL4bLLLsNll12Gd73rXVi2bBmeeeaZ8XlRauNY108LFy5EKBTC448/Pt7Xh4eHsXnzZrzsZS8DMPVrx/Oxdu1a/PM//zNe/epXAzj8gOijH1QP8P4+lXU/89vf/halUgmf/exnx28I/fd///cxb/+Rtf+1115btk9Hfz8BgJ6eHqxevXp8O492JCnu6H088cQTJ52Lli9fjkKhgMcee2w8wbO/vx+bNm0ab7/a7yZT1dDQgKuuugpXXXUV3vCGN+CSSy7BwMAAVq5cib1792Lz5s30t51OPfVUPPfcc89rPTTV7z+rV6/G6tWrcfPNN+Occ87Bt771rRf1ptN0HpsvVL84Fl7r56O31RpjlqmsvY/lOlWr76xHvqu+5S1vGa9Vcx9gMs+3jzz11FNIp9PjN5IfffRRJBKJsr88O9qcOXNwwQUX4M4770Q6ncbFF1+Mjo4OAIfni+7ubqxdu7ZsLbV27dqy3+ir1NnZOaV7NA0NDbW56XTRRRfh5JNPxjXXXIPPf/7zKBQKeOc734kLLrhg/FewXvnKV+LTn/40vvGNb+Ccc87Bf/zHf2D9+vXjE/xjjz2GBx54AK961avQ0dGBxx57DH19fVi+fDmAw09nv+mmm9DY2IhLLrkE2WwWTzzxBAYHB6f8hf//+//+P5xxxhn42Mc+hquuugq//vWv8U//9E80Be/Ifi1ZsgTXXnstPv3pT2NkZAQf/OAHp3xcrrnmGnz605/G5ZdfPv40+F27duF73/se3v/+909YwNbX1+O9730v/uqv/gqlUgkvfelLMTw8jLVr16KhoQHXXnstbrzxRnzxi1/En/zJn+Dmm29GY2MjHn30UZx55plYunQpFixYgJ///OfYtGkTWltbq4q9vOWWW3DhhRfixBNPxJ/8yZ+gUCjgnnvuwQc+8AHMmzcP4XAYX/ziF3HDDTdg/fr1+NjHPjblz65000034bzzzsNnPvMZXH755fj5z39e9oR8APjwhz+M17zmNZg3bx7e8IY3jP8Z1Pr16/Hxj398/H1XX301vvKVr2Dz5s345S9/aba5YMECXHvttXjrW9+Kf/zHf8SqVauwa9cu9Pb24sorr8S73vUufO1rX8Ob3vSm8VSirVu34r/+67/w9a9/HU888YRnH5Xp75prrsFrXvMaPPvss/jTP/3T8fpU+1o1br75Zpx88sl45zvfiRtuuAHhcBi//OUv8cY3vhFtbW1l712yZAmuueYavOUtb8FnP/tZrF69Gn19fXjggQewcuVK/NEf/RHe85734NJLL8WSJUswODiIX/7yl1Pue1u3bkUymcSBAweQTqfHF9QrVqxQ7LK8aOrq6vCOd7wD73vf+9DS0oJ58+bhH/7hHzA2Nobrr78eAPDOd74Tn//85/Hud78bN954IzZt2oRbbrkFf/3Xfw2/349EIoHrr78e73vf+9Da2oqOjg588IMfHP9yOpnJrgOTtT9VCxYswAMPPIDzzjsPkUgEzc3NWLx4Mb73ve/hsssug8/nw//7f//vef8X1cmuW9aXkzvuuAPFYhFnnXUW4vE4/uM//gOxWGz8ZqDUzrGun+rr63HttdeOj5eOjg7ccsst8Pv94zdEp3LteL4WL148ngQ1MjKC973vfRN+W4b198nW/ZZFixYhn8/ji1/8Ii677DKsXbsWX/nKV455+9/3vvfhyiuvxOrVq3HRRRfhxz/+Mb73ve+NJ0fGYjGcffbZ+Lu/+zuccMIJ6O3txYc+9KGyz5g/fz58Ph/uvvtuvPrVr0YsFpvSXLR48WJcfvnlePvb345/+Zd/QX19Pf7mb/4Gs2fPxuWXXw6g+u8mU3Hbbbehq6sLq1evht/vx3e+8x3MmjULTU1NuOCCC/Cyl70Mf/zHf4zbbrsNixYtwsaNG+Hz+XDJJZfgAx/4AM4++2zceOONeNvb3oa6ujo899xzuO+++/BP//RPU2p/su8/+XweX/3qV/Ha174W3d3d2LRpE7Zs2VL25f7FMJ3H5gvRL54Pa/0MTD7GLJOtvY/1OlWr76x/+Zd/ieuuuw6nn346zjvvPNx555149tlna5YE/Xz7SC6Xw/XXX48PfehD2LlzJ2655RbceOONnmuVa665BrfccgtyudyE31Z73/veh1tuuQUnnngiTjnlFNx+++1Yt24d/Y3Fo012j+bIfFTVg8QrH7p2+eWXu2uvvdY5d/hhWK997WtdXV2dq6+vd2984xvdgQMHyt7/4Q9/2HV2drrGxkb3V3/1V+7GG28cf6Dgc88959asWePa29tdJBJxS5YsKXu4pnPO3Xnnne6UU05x4XDYNTc3u5e97GXue9/7XtnDrbweBOicc9/97nfdihUrXCgUcvPmzXOf/vSny9qofIjnpk2b3Etf+lIXDofdkiVL3M9+9rMpPyDUucMPdXvLW97i2traXCQScQsXLnRvf/vb3fDwsHNu4sPaSqWS+/znP++WLl3qQqGQa29vd2vWrClLqXrqqafcq171KhePx119fb07//zz3bZt25xzh1NELr74YpdIJMb3nR0b5/iDGO+6667xY9zW1uZe//rXj7/2rW99yy1YsMBFIhF3zjnnuB/96Edln1vNg8SdO5wsMGfOHBeLxdxll13mPvOZz0zYnp/97Gfu3HPPdbFYzDU0NLgzzzxzQnLBc8895wC4+fPnT3hoYmW/TafT7q/+6q9cV1eXC4fDbtGiRe7f/u3fxl/fvHmzu+KKK1xTU5OLxWJu2bJl7j3veY8rlUpT6qMyvRWLRdfV1eUAjI+ZIybra2zcNzY2jieJsP7/4IMPunPPPddFIhHX1NTk1qxZM/56Zd/M5XLuwx/+sFuwYIELhUKuq6vLXXHFFe7pp592zjl34403uhNPPNFFIhHX3t7u3vzmN7tDhw5Nab8vuOACB2DC/3bs2DGlnxc5Vkc/SNy5w3Pwu9/97vFr4nnnnTceOHLEgw8+6M444wwXDofdrFmz3Ac+8IHxFFrnDj/A90//9E9dPB53nZ2d7h/+4R88HwxbabLrwGTtT7Yecs65H/3oR27RokUuGAyO7/+OHTvcK17xCheLxdzcuXPdP/3TP034LPYgca95xznv65a1vd///vfdWWed5RoaGlxdXZ07++yzyx6oKs/f0cf9WNdPIyMj7uqrr3bxeNzNmjXL3Xbbbe7MM890f/M3fzP+nsmuHV7YOvD73/++w1FfD373u9+5008/3UWjUbd48WL3ne98Z0I/Zf3dOe91f+UxOtptt93murq6XCwWc2vWrHHf+MY3yo5PNQ8Sd865f/7nf3YLFy50oVDILVmypCyAxrnD68hzzjnHxWIxd8opp7h77713wveHW2+91c2aNcv5fL7xsT6VuWhgYMC9+c1vdo2NjeP7U5leVu13k8l89atfdaeccoqrq6tzDQ0N7sILL3S/+93vxl/v7+93f/Znf+ZaW1tdNBp1L3nJS9zdd989/vpvfvOb8e8SdXV1buXKlWUPGGbbs2rVqrKUT6/vPwcOHHCve93rxufg+fPnuw9/+MPjD0B+oc2EsencsfWLymtG5XfAqYx5FgLgtX52bvIxZn139lp7P5/r1LGc1yeffHLC2vgTn/iEa2trc4lEwl177bXu/e9/f1UPEp8sTGGyPmLNdUc++8Mf/rBrbW11iUTCvf3tb3eZTGb8PWx+HRwcdJFIxMXj8Qnp68Vi0X3kIx9xs2fPdqFQyK1atWo8tMQ5fq/lCK97NEfmI59zHn/EKSIiIiIix10qlcLs2bPx2c9+dvy3A0Xk+NPYFPFWkz+vExERERGR2nnyySexceNGnHnmmRgeHsatt94KAON/miUix4fGpkh1pv6AApngk5/8ZFk84NH/u/TSS4/35h1Xl156qXlsPvnJTx7vzRP5vXHDDTeYY+2GG2443psn8qJ65JFHzPFQy8QgkRfLZz7zGaxatQoXXXQRUqkUHnnkkQnPBbT8IazFTjrpJHMfJ3sWyUyl7x/Tg8bm8TFdrvNe2/DII4+8aNsxU+jP656HgYEBDAwM0NdisRhmz579Im/R9LFv3z6k02n6WktLC1paWl7kLRL5/dTb24uRkRH6WkNDw3gyhcgfgnQ6jX379pmv1zKhUmS6+0NYi+3atQv5fJ6+1tnZifr6+hd5i154+v4x8/0hjM0XynS5zm/dutV8bfbs2RMCF/7Q6aaTiIiIiIiIiIjUnP68TkREREREREREak43nUREREREREREpOZ000lERERERERERGpON51ERERERERERKTmdNNJRERERERERERqTjedRERERERERESk5nTTSUREREREREREak43nUREREREREREpOZ000lERERERERERGpON51ERERERERERKTmdNNJRERERERERERqTjedRERERERERESk5nTTSUREREREREREak43nUREREREREREpOZ000lERERERERERGpON51ERERERERERKTmglN5U6lUwv79+1FfXw+fz/dCb5PIjOGcw+joKLq7u+H3T997uBrDItxMGMMavyLcTBi/gMawiGUmjGGNXxGumvE7pZtO+/fvx9y5c2uycSK/j/bs2YM5c+Yc780waQyLeJvOY1jjV8TbdB6/gMawyGSm8xjW+BXxNpXxO6WbTvX19QCAS9/3bwhF4mWvDQ8O0p/J5XPm5xXB7xJHozFaD0fCtD46mjTbyGSytF5f10jrDo7XHa8ffo3Xs9kCraeSKVpvaGww2wiFQrQej/C7iStXLKX1xia7jVwuT+vFAj+GwyO9tL5lwwazjUyK7/uc2bNpPdHUzj8nkzbbOLB7O61vePJxWu/ft4/WI0Y/BICWzu6yf5cKeez67V3jY2S6OrJ9u3fvRkNDeV/Qf7WRPySVc/rIyAjmzZs3rcfwkW37zROPIZFIlL0WDvPrY6lUqrqdfCFD62NjfP4eS/FrHQCkx/h1pVDkbcTrorS+q2fAbGMsy1/rG+TXop4Du2ndOoYAEAu9gtZ94VfSet7toPXM4H+YbQTwJK2n8vxYOcfXBc7Z57zATwdCwTitZzJFWg8E7GWjP8CvJbkcX0sUjI0aS42ZbeTy5Z9VyJfw4I/6pvX4Bf5vDO/atWvCNdgaq8f72uy1/q0ZYx//UFclXsfc+i2CpPHdYmh4lNa7uzrMNqw+90L2hZGREZxwwgnTegwf2bZv3HEb4vHy7wjWeQkGvM4lP85W3efnnxUw5lwACFT5W2PWdOPzGI1Bn7HvPr69QWM/gkG7jUiQtxEL85+JRyO0Hkp0mm24OH/NGecjmOVjy5fm90QAIJ/n47TymnZEscCvwQWPpV2O/wiyJf5C0bj2lIp2I8WKU5hMpfHKS2+c0vid0k2nI5NQKBJHKFq+QAlG+KKo5AvYn2d04GCEL36Cxk2noHV0AQRKxkAw2qjlTaeC4wupQJifxGCYb9Ph1/jiMmTcdIrEE7QejdudwR80vhwUeNsRY+AEI/bNmmCen6twlO97OFZH6yWPyS8U5l9a/AG+Hz4/7/5WHQD8Qd4Xj/ficDJHtq+hoUE3neQPmjWnT+dxcGTbEonEhAt7bW868bnSXCD77JtOfh//D0+FIl8b1CX49SMe52sMAHAB/h8hohm+6IxE+f553XSKhPh2+cL8mup3/NrljHUMAATAj0nez+vOWXW7D1vfP0JB/llW9wkEq7/pBPB+4vPzRkJ5+8tSyXgU6XQev4D3NVg3nUj5hW95WjqWm05+Y56wvjdW9r+jHY+bTpO1PR0c2bZ4PIa6qd50Ch7LTSdjfjPmykDAniutm07WUX5RbjoZN+JCNbzpVBczbjrV8+/HAOCM78j2TSdjHRm0f+Emb/yHn1zeGL/GTSfj6/ThzzJeC1d506lYxU2nI6YyfqfnH8+KiIiIiIiIiMiMpptOIiIiIiIiIiJSc1P687ojMtk8iij//bBgiP8aWzJl/0p8PMF/xc36RcS8+btk9q9y1dXxNiJR/idYo0n+fKjm5mazjWzG2EcfP6zpNH+/9ffYAFCX4L+qn8/wX+HbuZs/p6jT408R29raaD1inNvWAP8zgZWn2H8muGXjM7S+azd//kVnmv+Na3vHLLONExavoPVYI9+/Z3/zG1rfvek5s42De/eW/duV7D8vmY5KpdKEX+UPBKw/1XgRfrV+hqnlr3/r+E5dtb/y73WeKvv/sfwZ2vFSzBZRDJfP5T7jT5p8Hn9qZf26fMl4WIDf+JOqXXv5/A0APfsO0HomZzyL0c+vacPG86QAwBfg15x0jl9rC4FhWm9tOtlso5jiD48tGn2vnj+OEM5nP48wNWQ8V8HXROuF9BBvo2D/aj8cv26PjfFnU4yO8ucqRaN8TQIA0Rg/H8kUP+cp49lNY2P2sSoWy6+5hfzMmkf9fv+EP6GZroldMkMY17us8fc8PuNPhgAgYPypnvk8kRqsiYIef7I73YTSIwhV/Om49SduAa9nOhnXbetP4qxnIYU8pg77+VBG28apDHj0F2utFTL+vC5UMupefxpu/Dl5MMi/o5aC/Pt/MdRktgGf8ed1jh/gfHGEf076oNlEboxfB9M5ft3OGd/bj+XP6/LG8bX+jM5rXVz5Z/yZlH29rqQrnYiIiIiIiIiI1JxuOomIiIiIiIiISM3pppOIiIiIiIiIiNScbjqJiIiIiIiIiEjN6aaTiIiIiIiIiIjUXFWRAaXi4f8drVjgT6JvbeWJYQAQifEEuaERniyTz3skshjCYZ7UUizyR7sXCzyFrL//kNlG3EhqsZ7m39LSQutRI1HvcPv9tB402jjUz4/hgYMDZhuzZnXS+orlPA0uHG2g9YSzn3a/4iU8HWjvbn4Md2zcRuuppP2U/HmLltF6x5zFtB6r48mEDU38PAHApicfK/u3R+DCtOTz+WqSwFbLFLdqVZv6dizpZFYb1md5bZM151iJLVaaoPU5gJ1+ZH3W8Tx/x1vlvs+kY9Hbd2BC2lhnRwd9b8G4pgF2f00b6WfpAu97W7ftMtsYG+PJZMUSn8MzuSFadwEj7Q5ALsfTa8IR3u8b6o25IGfPEaESPybO8eS+gOP7FwrY1/lsnp+rYpCn8PmNVKQxj9RgGCk8KPF6scj32yNYzkwHtqbgsRRPqc1m7bmuvr48magQKAGw+8h045ybMP6OZ5KpZ9vWdbDKILNjmmOtz/JIrf594NUTrGSzvDHX7z/QR+uzu/g1AwDisRitW2ufWlw/Z1KC7ODwKLK58lRAa/3lM9ZfXj9j1YM+foyslDjAI/HO2KxwkJ/LiBVrByBktOEz2giG+Gf5w3Yb0Qg/JpGYkWps7IezdhyAL8j3w18yrqmOX3NKWfu7dm6Yfz9PjfFzm87ybfK4PCJvDKVqR5jXdcFVzFLZMY91RwX9ppOIiIiIiIiIiNScbjqJiIiIiIiIiEjN6aaTiIiIiIiIiIjUnG46iYiIiIiIiIhIzemmk4iIiIiIiIiI1JxuOomIiIiIiIiISM3xzG5DLptHCeVRkWa+pxXPCyBXETc5mWEjZrCxsdH8mUKBtxEKVhchHo/FzTb8fv5ZfiNPNlER9XtELsejiQGgvr6e1vfs3k3r2RyPTg0G7FPt6x2k9UBwG63Pn9dF6y2NTWYbcHy7Fi5cTuuJMP+s9c9tNpvYsXMfrc+au4DWG5p5bOzKM88z26hvKD8fhVwGD+/8hfl+qT1rrFoR8cWinS+az/N5IpVK0frQ0FBV2wQA6TTPGE8k+HwQNWLHrToAhEKhqn4mHA6bnyXT13DqAAoovybFs7zvefVJa6zkivxnckV+/QiE7et8sMhfc0Ue3jvcz6/zzQ32dT4WHKP1jmbeduf8LK37cvxaBwCFkcdpPWdco3KOb6/fIwz9YIbHLJcifO7yl+poPRLmdQBI8YRn5LJ8/WGtb5xH+LIVfR4wPisY4POQM+YzAPD5QhX/njlx68DhcVk5Nq146mOJozejro2PckW7Xw4OjdD60OgorUci/Hw2JOx1tHUtikUi/AeO4ZhYannca8Y+HaZcns/nT2/cSuuLF84zP6suzs+VdUxqcayO6/Gu0oGhPKKZ8uuL38+33++x8FBMOwABAABJREFUXz7j0hkw6iHjo4LWDwAIGO1b9aAxl1p1AAj5+WvhIG8j4ucdPBaw20hE+GfVRw/Rel3vXt52wwazDX+Y9/uS8f0hP8q/N48l+ZoEAMay/FyNZXg9nefHKudxybNeKxrXZmdcGJzH7yRVrmVSab6u4j8rIiIiIiIiIiJSY7rpJCIiIiIiIiIiNaebTiIiIiIiIiIiUnO66SQiIiIiIiIiIjWnm04iIiIiIiIiIlJz1aXX5Qso+crTnlyJP9k9GLTTR0LGa1aCQUcHTxnzinnI5/h2WekqVoKGlTAFAMkkT7jKZDJVte332/f+rPCD1jZ+THzGfcRMmm8TABQdb2TPPp4G19u3h9ZPPmmF2UZnSyut5zL8qfdtXTy55ORok9nG7j08seDAPl5v6+ik9fp4g9nGiStOKft3LpPCw+a7//BUmwZjJu3ATtiyfsYaR9msnawwMMBTo6yf6enpqaptwJ5brOS8ZJLHTLW1tZltWO1bP2PNqV7no1ozKZFmpugZ3IJYtjyRcChrJLVY6U+w+3fRSJ11Pt6HC76DZhulIL/mROK87W4jPWZ++xyzjURiE627zA5aP3HOflpvjveabeSH+Vqid4wfkwOHeHpdIMy3CQAaQvy4b+vnyZf7D/A0uFzaHnMpHkSGwSGeRNbewdc+kai9bBwd5Y1ks3ztUyrx7TWWlQCAXKZ8jioYCT/Tl8NUI8qOZT42f8JYfxaKdpr0L9c+SuvPbeQpwta1rnuWtYYHLrnwAlqPd8Zo3VpHe6nVtcjrfLwYbVgRhIMj/LvIrv083WvnXj4PAsDsrna+XUbP8lmxiL+nBkZziOTK9zkY5HOiEWp3+DUj9c1vpLv5zH5hNxI01oXG5QYho20jiO7wawH+YtioB328DY8gXMSD/IJQFzS+z/fxRNagz4hwBRB0xkXH+H6cNeo545oGAHnjtbwxpRWMiMOCx+8L5a3dMOrW939rWwEgXyj/sLTxPZ7RbzqJiIiIiIiIiEjN6aaTiIiIiIiIiIjUnG46iYiIiIiIiIhIzemmk4iIiIiIiIiI1JxuOomIiIiIiIiISM1VlV4XCYcQjJSnUxSNBKZggKerAEA+x3/GCqVobORpYrkcf0I9AIwM89QGgP9MXV0drafTPD0GsNMqfMYT56PRKK1bKVaAR6peHU/ISaf5U+RLJTsR4+BBntwTT/D0kNE0TxXbueeA2UZylCcZdRkJcqEYPx8NHsEeC4wEiUOH+mjdZ0TkhIP8mANAMFbeF/2w+/nvs2pTdazEmWLRjikaHeWpSvuMVEUrQcSqA3ZCnpX8ZaVZWomVgL2PIyM86WloaIjWrVQ7wJ4L29t5Eo3V9ty5c802rGNi7Z+1TV7znZXCF4vxucieg+3kjcq+W8vEvhfaaL4X+Xz5eRgZ5NsfCNj9Pl+wzoGVqMOPZ8gOd0WdEZ4XjvJ+kYjzOX9Wwxazje7u39F6YIQfk4bgMK0Hs/Z13vn5NdVfx9tojPMUvrrYoNlGNmMkyDzBr0XbtvJtGhmx59N0kqcGj47wvtDSaiThZuzxOzbG58FikR+rjJFql0nZYzJc0a+KherTzI4vHyoTp44t+IwfI2s6s+bWwVGefAYA67fwxMV9B/iaKmTMOUNJ+/p4ztljtN5kfE+wEsECHgmy1jXKum7HjbnImgdrySsJ17pW7dnHU0RHU/zYPrNxq9nGqScvp/Vo2E4k/0OyYetOhELlx6KlpZm+Nxi2r8GhEP/uYM0F1rj2Wr5Y38ODRhcLGv07GLD7ZDBkpNcZPxLw8fk6ZKTdAQCMtWQmy+tRo+3uFvv7Wtf8Llq3kvsGevj35v3G2gMAssZLxuURML5fFj1+XyhrXA/zRf5dp2Sk16Wz/P0AkCuW/0zWOA+MftNJRERERERERERqTjedRERERERERESk5nTTSUREREREREREak43nUREREREREREpOZ000lERERERERERGquqvQ6H4rwoTwFYlYXT0caHuYpMQCQSvG0jEBlLMmR9yd5skzA44n64TBPimtstFLf7HQNSz7Pn9hupe1ZiRiHDllJe3Z6Xsl43L2VZBD2SJ6wXsvmeOJH0EiSSmbsFJkDm3haxt59+2n99NUn0Xp9g0dckpGKEAzN4m83Hs4fcB4pKBWpM84jIUom55Vel83yhKbeXp4aYSXLdXbyhEQAE1JIjrDmKCuRzUqmBOw0uu3bt9O6ldrX0tJitmGl2uzdu5fWH330UVq/6KKLzDashE8rmdBKBvRKHbX2I24kgs2ePZvWGxr4HDzTBaNBhKLlc47PmPd8sOd8nzFtZbNDtO5K/PiHPeLr/GE+TgNhfq0NgM8FbcFfm210gI8hBPg1Kj/E55SxUfvalU4O8Cbq+XViziy+9skV7TWRtV5pivBzaAW2ZY1EVgAoGik8dQ28beuTxkbsVJtCgTdSCPK0zKzj6Tx+H08SAoBSIVfx75mVXlcsFidc96w51O+305asJDWfkWpnpSpv383XYABwaJCft6Z6fi0I+Hl/LcFOptp7gM8Tg0N8vCycN4fWWxrrzTYGhvh+bNvNk3BXr1hC69GInWxsBVBZSarWOR8a4dd/r8/ad5CnCRYKfBRv32UnTR88xOe7BXP4mLRSx48tkXH6S9THJ6SK+/x8TvRKxrUSPa1xbQUnGsMaABAwEuHMhDyjEWNaP/ya0ceKVkJegO93wW9/lxoY4GPi0MAQrbfGeeOzuhaYbSw9+0JaD/t4iub2Jx+m9cGddkptsbrQYBRLxvnI29e8kpGKaAatGx8V8uhYlYl+gdDUr8H6TScREREREREREak53XQSEREREREREZGa000nERERERERERGpOd10EhERERERERGRmtNNJxERERERERERqTnddBIRERERERERkZqrKu89EAohUBExPjzMo0j9HlHyeSNi0RkZ9sE6/lmZDI9eBoBIJELrTU1NxmfxCNHhYTvm2Gojn+e5iCNGFKoV2w4A/f39tN7SzH/Gb8RO1tfbcbJWFHo6y6PNB4z92LuXx88CQCTE729a8b5btu2g9QXz55ptNLW00fqwEb2bH+Px2c4jjhK+gPe/hbLigb3G8MgIn1us/rpw4UJaz2b5eQaA3bt307o1hq3xaM0FACbEYx9hjXtrP7za2LZtG61b892GDRto/Qc/+IHZRizGY+it/Wtr4+PR2j8AiMfjtG71n0Kh+qjiyte83jv9+P73f//HikV2Jfsa7PPz4xkI8rGSyySNz7HbCBV5vHghzd+fK/LrTV2MzwMAEDrE931wYIzXe/m5zqbs/egf5HNUoonP/bEAv9ZlnX1d8UX4GJrf1crbCPO2ezP8GAJAqcjPbclYd2WL/ERlcvzYAoDPx+eouiZ+TApFox/m7TVRIFy+/igUph7XPB0EAgEEAoGKGn+v156VjGPnM6azbIH3jU2bt5htpMf4Wq+js4nWc8Z8PJq1ssKBHTv5NXheJ79+FLs6+Qd5zOOFHN/3/T09tL78xAVGE/YZKRrXKOuaZq0xHv3N42YbjY1NtD6a4mPSOT6vDAwOmW1s2baT1hfM6TZ+wjomfG6e6RLxKMKR8mubzxh01rX58GvG73wYP+Iz615t8HrIqPv9/LOM8v/+DN+PSJD/UMjH32+8HQDQOreJ1leewL/XtsT5Dp6wdLHZRsjH17e5rLV+5+O6MWbfMwgHjXVsie98rsjr+Yj9vdO6HhaM2z35Au+7xlQOAChVjPlQFb+/pN90EhERERERERGRmtNNJxERERERERERqTnddBIRERERERERkZrTTScREREREREREak53XQSEREREREREZGaqyq9Lpsrougrf5J7LMbTSgolO0miuYUnsrS0tND6/v37ad1KTQLspKdDhw7RemWayBFeyQDhME/nsX7G+qhczk72mDVrFv8s8O1NGSkWx5KuZSUvRI3knMFB+6n9cLz9cIgfw56D/Dzt7zloNrF06VJanz9/Pq3n/DyVKGkkMgLAhLA9v9LrpsJKCPMaX4lEgtat1JdUKkXrVgoeYKdTWol3Q0NDtO6VwpdM8uQva86xUu1GR3mSkFcb7e3ttH722WfT+qOPPmq2YR1faz9yRmKQV5Jmayu/NlifZaXazZ4922yjcr7zOq7TDUu+go+Ph1LJTm0slXg0SSjIz03Bx68rRdhJZpEST5lsb+bv76zfy9seta+P+/r4fzc71MuXNju38u0dHrTXEmM5Pne1tPFxms/wa10wav83vjmL+LHyGek1w/t4slxu2L4euQDfj1KAz13Dw3y8NzbY+1Eq8Ot5cSxK6/kU76P+Up/ZRjBUOZ/PpPRJYOuOXUhUzIEtjY30veGwneIXi/N+Fgryvr/+KZ5YusEjvS4W4p8VMtZtrsT7UgR2FNKefXzcd7Y10fr+AzxpetMWez9aWvikEzFiuQrGmjiX9JpT+bWo3ljHbNvH9+OQkZALAM7x7e0b4GucgJEsZu0fAGzdzbfrAmPdFTbWK7+vCbJ+P1AZPOczEtkCHrFv1itBI9UuaEy7ZgqeB+eM8Wj04UDQvq7ErTnCSMj1GYmKIStSD0DciLaLWMcqaCTLp43oXAAHD/D09RB4v08b6+G6kEcMn3FfZMw47hFj/JYK9vi1WvcZc4eVPuk3zh8AVIbtlQL29kz43Cm/U0REREREREREZIp000lERERERERERGpON51ERERERERERKTmdNNJRERERERERERqTjedRERERERERESk5qpKrys6B19FykDGSF5zHul18XiM1q0EKCsdyUpsAoCgkeBhpbhZbbe1tZltWOl1o6P8CfnV7h8AFAr8+FrJclYKRF+fnQZjpUk5I+GiPs6TaGKRTrONTI6nJQwb5zBvpCU4I/kAAPbu48l2pRL/rLldXbTeZKQrAsBoxfYWS3ay0u8zq59ZSYhjYzw1yqoDwMDAQFU/YyWc1dXxZCjATofcvXs3rVtpd9EoHxOAncLnleLGHDBSewA7BdD6mZe//OW07pUIevAgH19WIqiVMmgdW8DuV4UCnz+str3SOis/y+s6Mt344Iev4r8VlZxxDfZI9XLg59kPfm0OGfNxtsQTXAAg7OdptHV+3o9mNW+j9dKwnThzoIf/d7Mtm/i1dnCArwuKRTslzAV43xsa5vXnnuLzU1uHnWrTOYe/VswP0XoixI9JOsrXJABQ18rPx4gRvNnablznY/Y1r++AlbzJ+2KwyM9HV5u9JupeWJ70lsuW8Ot77cSv6eYfvnTbhLVj+yy+FumafYL5OauWrKD1kxcvoPUde3bR+pBxTQOAqHEawkaaVGaMj4l81r7ODxnJkVu3baf1vbv30PpOj+vKCfPn0nosGqf1ngN8jhoaGjTbCBkpXjHjWvT0M8/S+sYNG802umZ303pfL7/OuwJfE3nka2HvPp7i1dvHr7VzunnfLTk7/ary0jST0uucy6NUkQQWMFKsnfNI+iwax6cyGux/FY0kvIDxXfdwG3xsWdf/oJWWZm0rYCbeWWl7PmtdYqzfASAX5D8T8fF6Os23KeOzrxMdPt6/E2HeRj7Dr8GFnJ1waW1XsmD0H+P75WjWPlZ5x6/B2Zyx5jPuJXh9t6085VmP7ZnQ3pTfKSIiIiIiIiIiMkW66SQiIiIiIiIiIjWnm04iIiIiIiIiIlJzuukkIiIiIiIiIiI1p5tOIiIiIiIiIiJSc1Wl1wVCEQRC5UkM6TRPpQiG7DSYrMdT6hkrmamzo8P8mbCRGGGlHYWM7S0ZT+YHgKLxRP+gkfQzOsr320q3AgC/mYrAn6hv1buMpDYASKettD1eb25uoPWhkVGzjZiRIJbK8CfkD47wRJXGevtYZbL86fxbtvPUlt07edrJ6aeuNttobm0vb3PMTsmajkql0oQ+XW0S3ZHPYUZHeR+wUtS8UhX7+3nShDVeRkZGaH3RokVmG/E4T6+x5hxrHHV381QZAGhp4alR1ly0ZcsWWrcS3ACgoYGPyViMp5E9/PDDtG6l+QHASSedROs9PT20vnXrVlr3Souzzrk1P2ezPCnEq1+1t5ePYSvpdDoKhcMIVSRf5Yq8X1ipJABQLBlj28+v54EoT6kL5O05AiUjxcU4/74h3kZ21E5R6T3A2x/oNxJyfHy8+0N2WlrRz6+DzhkJXhl+PsY8QhJHBvm+x+r5vl92KU++3N1vJ2L2DPO5YF8v7//xsJEynBoy2xgZ5Ns7ZqT2WOlDHa32dfWV55fPp2NjRfzbF2dOet2W7U8iULFG3LyLJwXWt9pJuk8+9TStrzxxIa1nUzx5Leaz1+OhAJ939+zgc3sJ/PwHPcZXoMT7X+9+HqsYCfG+Efb4T+gHe/bynwlZaZ18m4JGQh0AlIxkZSv1dWCA99lY1O77W7byhM98mk8u8TD/LK8wsswIv3YOG2tyK73OSq8FgJ6DvWX/ttaN01EqnUPlZc8fsL6P2Z9jBECiLsYTSH3gY6hopJId3gBeLhlrhkCIDyKrbQDIl/hnFY01tDPq1jYBgN8Y3MEw/yy/n89p/pydtlvK8PkxZSTLJY2UukLe3o+isV4qGOlvKePcFjzOR8nHf8Yaj4UMf7/XvQ/4ys9HNmvvcyX9ppOIiIiIiIiIiNScbjqJiIiIiIiIiEjN6aaTiIiIiIiIiIjUnG46iYiIiIiIiIhIzemmk4iIiIiIiIiI1JxuOomIiIiIiIiISM1VlRUdCIURCJXHOUaM+MN8zo7LLFh5nUb8daHAI/2am3l0+uGf4e0XjVjGdNqIig7Y0YThMI+2dCWeUzl/3jxaz3tEoVtRolkjYjEY5BG3cHb8YShoRXHyyNqBPn6sYg08Gh4A0kb0YyTCt7ejcS6tjwzz2FYA6OvjEbSJBO8ng0l+bH+7jkcRA8DKlSvL/p0dmzlRr8Dh/uSrGLNW7Lzfb9+Ttvr+sHF+UikeVZrL2XHNVhvWmOjp6aF1r+jP2bNn03o8zqPV6+t5JHnQiFgG7DnEilLevn07rSeNqHkA6O3tpfU5c+bQurW91vkD7PNhnUNrv63P8WrfOodWG1u2bDHbKBbL5yKrb05HAZ8fAX/5Pg8PDhjvtaO3CwV+3Q4bx9M5/lnJMX4tAIBYnMeUx+v38zaG+HkYPWSP394D/LoyPMj3zxfi1+axYtpsI1/k82NTHb92JYxY9UzanuuGBngbrcYSZ/UKY+2xzc7o3tvPj284yBsZ6ufX/8yYHdFdMGKZFyyM0rovz7fJ5+x5qDle3h/CHmubacmlAVc+zjKjxhwatNeGB4v8Z36+exOtn76AR9u7nJHdDgC+Blres3Mrref9fBxFIvb1MRrl46hknNeAn1+bO9v4tRwA6uuaab1gZNr3HdhN617XrmKR931rRDrw414XM9bwAA7183mioY4fkwZj7eslHOafFQrxa0PWuP7/6tEnzDaeeHpd2b8zaXv+nW5SqRxyufKzGgzztbI9GwNh43gWRvm8a30V9Vp7Vq51xj/LWts7Xs/leL8DgHCQ92O/31hLGN/B4xH7+0Y0wMdE2M+PcNw4HwVn35cYSfPj7oxjODTIj8lw0u7LqRyf08byfD8yBV7PFu2elTNeyxvfwYvG+72+N/kC5T+T87jfU0m/6SQiIiIiIiIiIjWnm04iIiIiIiIiIlJzuukkIiIiIiIiIiI1p5tOIiIiIiIiIiJSc7rpJCIiIiIiIiIiNVdVep3zHf7f0QJBnubg89kpD4UCT7zJpPnT4KOROlofHLVTh8JB/kT2RJynqPgTMf45YfsQpVNGcpkzUp6MxDkzSQBAbow/Ud8IO0FTIz9WmVE7DSYa5O3H/Pxp94kmnuCVrOwcR3El/nT7jjqecFAyEgOa6vh5AoBDfTzB61ByhNbbFyyh9ZTHsHjsifJEjkLWTm+ajvr6+pCuSArJZHgfq6vjfQkAIhGeZpU30osqE/OOiEb5ePT6mf37efqV9VkDAzzdCwAGBwdpPRaz+xljpdoBwL59+6pq2zruXvthJbAVjGTMri6eZOSVXrd7N0/0sdq20g9DITudxxlpQlZCS7UJh8DEPmodo+nIBwcfylNFkqM8tbOQt+fjQoEfz6Cf113RmPPH7FSbWMcBow3ej3ODfM4fG7Kvj8N815EZ4+PXhXh/GcrY83jBSHfJpfj2htt5/47aYYJIDfPrYP2oMVb8fJ7NjdjnfPAQ7+dZx9OqUqP8mPg8ro+nnT6f1s883/iBHL9mJ3vstV3UV75dRZ9XRtT0Uypl4SuV9+mAkXhopUwCwFiSn89Ckl8HSwWeLhzw2de6iLH+DRtJUz29fK2VL9nJq2HjemAGbJX4WMnn7dSoWc08ObouxtPdCml+bS4W7ATKRIzPLSVjXTyW4XOnx1cOzGrgk0jOSLkKlfj5KPnsRuoCfHsDASMRzEgjT2fsRKtNmzeX/dsrxXi6GUmNIljRZ0M53oeDxhoWADJGtl3OWEOXjOMcj9sXlqDx3a6QNxJIjanUK30yaHzXttZs1jEZzdjXeWud0VnP9z0Sb6T1sN8+VpEoH7+hIP+ZjDF+x7J2v/dljbRWIykubHSfQMC+5gXBrwsuxo+v8VV7QkLj0UoVv6/k87iHUUm/6SQiIiIiIiIiIjWnm04iIiIiIiIiIlJzuukkIiIiIiIiIiI1p5tOIiIiIiIiIiJSc7rpJCIiIiIiIiIiNVdVel0+OQSXL39ie6yuib7XBeyEIn/ASKzyWakU/MnukbB9z8wP/kh2n3GfbdHCRbTulRg1NMhTtIJhvh+ZHH+qvZUKAQDBCH+ifmucJ474Sny/Q0H7fIwV+PFFopO3YSR1uRE7+apQ4k/CT5V4MkDA2CQ7CwKIxHkSSdh4CP/I8BCt19XzzwEAly5Pzink7MSU6SiXy01IChkb4ylFIyM8+QQAOjo6qmq3ZKQzWGl3gJ3uZiWctba20rpXkpn1mleKG2NtK2CnA1p1K5HNSgwEgGyWJ2lY22XtdyDA5y4AaGhooPVkkicTFY1YDK/EQiu5r6mpidatY2Il5wETk3JmUnLO3NYi6hLlx7XOSGTZua/H/JwN23g9EGyi9XCYH6OS306pTeX4du3q4X0sV+LXqJRHKlXBCM/LZXgbBSsRy+M/vznjWjSW41ejrJEM6BHmhGSaN5It8OP7zHqeUPPQ43bizK6DfCcbuvhBbG7lCUBz5p5gtnHCiUZqYG4jrXc08IOyuIkn6gGAGyuf01x6ZqXXjQ7nJqz5IjE+V/qDRrQQgKCfz6NNCZ5SN5bn/TJkT/lmOnUoyufpsTRfS5RgDCIAmRQf31byVi7P+37eIzUq6OP9yWd854hG+FzkPNLrnPEzQSv1zdhen7FWBgDn423UGd+FCnkj/TprT3jtTfycH+o9ROtPrFtH6wd695pt9B7cWfZvr3XgdJPPFyaGvBlr0oJ18YD9XaZknP+S0UYyZSevWunrAR8//0YTQNbej6zxWrHAk9qsNabzSCGti/Cf8Rd4v/EbCXnhqP09eG/PEK3njCm4aMxDxRwfcwCQshLv8sa5zfDG0x5r1qxxD8DqP9ksf79H10WwYn2dN44Fo990EhERERERERGRmtNNJxERERERERERqTnddBIRERERERERkZrTTScREREREREREak53XQSEREREREREZGaqyq9rm/3JgRC5Yk0s+Yupu+ta7DTrXxWkpqfb44Df0J9bsxO1yo4/jORaDOt79/XR+vBkH2I/EZqUKZopO0FjdQejwQ0q/muOv7E+YSR2re3z07XGi3wFJRiPU+vGRrlT+Bv99n7kc/yc5UPz6X1QIinD+RzRlwRgERLF3+hxPtCNm0kl+3fZbZRFy9PQXEzKPkKALZs2YJ4xT5YKXVWihpgp4OFQnxsW+lqXqklfX18TFrba6XweSWyWWlpwSAfeFYKX39/v9nGoUM89cVSb6RDep2PRIInLqbTfEz29vbSeizG06cA+1xZ22Uddyu1D7D71dDQEK1byXPWeQImbq91jKaj7tY9qK8v3/55bTw9MOQfMj9ny05+nA8N8nPZ1savdfFGu0/6wjxFqy/Nt6uukW9TKWZnloZD/GeyWd7HXJyP62jY7vdpIwHKH+LHxAipRShm/ze+eh68iYARpLrXuJxv3m6nnSVL/FwV64ZoPRXm+53cYbeRaOfHZO5c3kfD4HN8xGNpOrCv/ACPzbD0uuRwHv6KRLNinvfxkrPXF7kgf62uka/bevuHaP1Qn33tamrm52dgiCdj5oz1WaFg9xkracpKRLIStrI5e85PjvG+HAvzNUNzM19L5sbsMTxq9MNQiNd9QT7nFCdmo/3fa0bCZ9yIew4F+fombCWIA/Abv4vw26efpvW7H/g5rfcP7DbbGEuV95OiR/+Ybvzu8P+O5qzt90hks14JGN8Tg8Y48VrrWMe16LPS3XkbRY9411LRSC4zvnclQjxJMl+wj9Whfv5dbWSIX1f6Rvh4n9XO50YAaEjw7YqFjeu8sSwZGLPPx0CKz4+pLN/3jDGnZT3S4vKOn9tggG9wwM/rziMqvrL9QkHpdSIiIiIiIiIichzpppOIiIiIiIiIiNScbjqJiIiIiIiIiEjN6aaTiIiIiIiIiIjUnG46iYiIiIiIiIhIzemmk4iIiIiIiIiI1JydS0u01EURDJdHbW5/jsdonrh8lfk50RaeDxz08c0JRnhMaCZvRxMeOsSjXts7eBx5NMrvv2XSdmRtNMojFhsbeBupYR6d7hsbMNtob+Kxqme08H1f2MjjHbfE7FP99H4eQd9n7HpLA29jSYh/DgCkY/wcbszwuPK+IV73hXgELAA4P9/HfJ7HVDbE+bEd7TMyrwEkk+URu8X8zIlbBw7H3ufz5fsXjfL43L1795qfMzw8TOt1dfz8xGL8WEcivC8BQDLJ41DHxnjMcX8/j35OpVJmG62tfC6yIkCt/cvl7HkineZ9xGrD2j+/3/5vBKGQEaUc53OU1baz8qgBjI7y2FrrHFqf5TOif71+JpvlYzgcNmLgi3YE88hI+TyVyfB43emo5H6DkiuPVI6E+HWle5Z9LuuiPJb5qb38WjSW5XPELB+PFgcAf7GJ1k+Yz+vN7XwMpTODZhvNbXxsuz28PjrC55SmuH1difv59dw5PoaKGT5+g5U520eJxfjY9gV531z+khZaf2aLvSba2ctfyxhjpWBcBgf6eH8DgPnDfB7qXsjn/0bwbUpusefs0QPl88eYETc9XQVDRfgD5ducyxlR2gd4HQDCUT73tUT4/JrP83GU9pj/xvbvovWRFL/WprN8DOdz9nwcNuaiUJjvRzLJ24hFG8w2Eg18vPiM7xwjxvpm9/YdZhsNjXwOyeT5QIrG+HVzLMnnDwBYtGgxrQeMIVAycs8jxnoMADJpfm4fX/8grW/fuY23XbT7rr/ilHtdr6eb9FgKwWB5vwkEeT/yG3H0h/GTFijwa0EkzNsIeDSRy/G+ly/yedfn420HjfUlADjHz13I2LCsMdflc/x6CgAlx7c3X+LH5MAgH0N9w3wNCwCN9Xyt3N2UoPV0lh/b3lH7u0DGWMfay25+PgJ8ygQA+Hz8RVfkjRSM4571+E6TR/m5tb5PMPpNJxERERERERERqTnddBIRERERERERkZrTTScREREREREREak53XQSEREREREREZGa000nERERERERERGpuarS61716j9CNF7+JPcf/fBu+t6dm9abnzPnxBNpvaFtFq0HwFMeAiGeqAMAbbO6+c8YT/rPZHhaSiBoPyY+FuPpISUjvaYZRupGkadFAMDLZs2h9XO7+f3CuiJPKJk/z04fOLGBp25sPMif9D+rlR/EFQ38eABAwc/bn2MkxT1V5Mfw2V47yShXx5OUokYy0cAATzhsbJttthGMlCccFLJj2Gy+e/pJJBITEs0q0+yOaG5uNj/HSivo6emhdSvhbPZs+1hb7VttW214JeT19vbSerXpfO3t7WYb1s9YqXZW3UpwA4D6ep6wVSrx1A+r7pUsZyXkWaz9sNISAfvcWuk2VqKf17ZWftZMSs7xlfbBVyo/R8U0T59qjDaan9OaMBKu0vw6ODTIz4svyOdQAMgl+LxSTPGU0/Qc3l8Sw3aaaLiZX1Pb+GUTfbv4GOrvt9NXmiJ8mRQ39q+zk78/aqSNAcDIKB+PxQRff/Qe5NubTHmknRnXQX+Mzx3pLN+mnJEkBAC7d/L6Q/fy/ThtYROtdxTs/UhXJBNlsg7AzBnDmUxhQqpVLM77TChop4zN7VzE67N4PTu8h9bzWTspsM5Y47Y28evdWJqv23p77XkileTjKF5nXCeMxCpXssdXfUMbrTc38GSqgYM8ubev315/xo00uqSRmBkNG+nJxtoDAHyVsW//K++M1LEMX9+EPMZXBvy1PiMdeCzFvyeEg3aqpM9fPn85jzllukmNJBGoiA8z1xte0XLGS1aSmZXgVvJIHDbXeUbj1noqELB/P8Xvs9rndb+xxgyF7NsRfqPfl4wEWWuL/CX7WB0yEu8O9A7xzzKSIWNxPqcA9j6WSkbKYMlIAy3Y/Spv7GMxZ6TXGd//rL4DAKGK1Ggr9ZDRbzqJiIiIiIiIiEjN6aaTiIiIiIiIiIjUnG46iYiIiIiIiIhIzemmk4iIiIiIiIiI1JxuOomIiIiIiIiISM1VlV7XMfcExOoaymqvf/0f0/c+dP+95uds32XkfRX5U9TrmngSXTDG01gAYG73QlrPDA/R+r79+2m95JFs1NDA04Hi4E/aX9DA0y1Omcv3DwBOb+dPkG8CT8SwUlxyowNmG6153sbL5lmJH0Y6T46nWABAzkjLOK2VJ3g0R4zUFiNdAQC2Zfh+DCd5WlLeSEX0hxtoHQCCkcpEFTvdcDrau3cvYrHyY2ul13klmVV+xhGtra20biWvjYzwcwMAzkjlsLZrYID38dFRu19arMS7XI6nSVhJbYCdLFeZgDJZPRy203kyGZ5gZiXnVZt2B2BC6uER1jFJJHiKh1cbVpKcdUwaGvhY7eriSZbss6zElukoc6iAYLq8/6eM8KlAnZ3gt2T2XFpvruNpMPv6ebJLKGSnwfjzRopKmo/faJTX59nhk6hv532vy0i7SRpjKHvIvs43Rvj82DaL719rB98PX8Berxwa5X1wYz+fN5/ayK//Jb997YpE+ZgfTQ/RejrL5zSXt695h/bxOeKxX/FO2pDn52/WfH4dAYDUaPm5SuccAN4/p6PkQG7CNSw/Zqzz2u3O7wvwvlEw1tHBOD+m8zwGWGVK0RHFAh8v2RzfppJH2lLfCE9Fs64T4SgfKzGP+a65qYXWW5v4eCkYadZds3nCNgDUNTTRes5Ik2po4um8+QIfEwAQr+NjOF/g59wIg0WpYH1/AGAklYWMUxgI8P0LhjwSJQvlP+Mr2teR6Safy6FUsYawEsDgt/u9tb61+n3B6EfBoN3vrVQ9nzGFO6PtvNWRAASCRnJikH9PLBhrPJ+RmggA9VH+WXUxnnCZN77TZrJ2Em4wzNtorLfWsfyYFHL2d4GkMbSTxhq6YCyVAx5rVr/xu0TWOI2ErSRD+/tGZfSi34piZJ875XeKiIiIiIiIiIhMkW46iYiIiIiIiIhIzemmk4iIiIiIiIiI1JxuOomIiIiIiIiISM3pppOIiIiIiIiIiNRcVel1fvjh95Xfp+romkPf+6rLLjM/5+GHebLd00+tp/WGLH9KfMfcJWYb6VGezlAf4U9ZD4T54/wLxlPwAcBf5Ikpy4x0t1UdPCVkRbOdatMZGOYv+HgqwagRGJEcs9Ndwkb6QUsDf3p9NMrrJfDkGgDw5XmCmG+kh9Y7gjwd7cw5PDEQAAZ38X6SKvDEj6LjaQlW0hoA5HLl6QfF3MxJzQEOJ2ZUpmaMGX3DK1nOSg1ra+OJh1bymVdyWH8/T7WxkuKsdDcrEQ0AGht5f+ro6KD1wcFBWh8eNsYp7AQRK/XNSmqzPgew0+us497U1ETrXkl/VuKddUys93ulIiaTfN629sNqw0rOAyaec6v/T0cHNzokK6bG/JDRX5rsa1dDG79GNcR5fdMenvrSCbtPFgtGeo2Pn7NChJ+zQr09thDj/d5f5MekfR5PsYp22ak2sQAfE7EmPq/4I7w+wIcJAMCFeYJtczdP51lipAwOjNlLume399J62kjIy6T5scWYnXZ2MD9E6/Pn8rluwUn8s9JDRiQjgMGR8n3PGCmJ01V9UxT+ilSrnJHo6BGEhKGRIVpvbjTm0DC/1jm/x3qnyK/P1pxphXX5jYQrwE7AdOB93G8kU8ai9vozaqQhpzN838PGZy1a+hKzjYjRRns3byMY5nNnotlObgyE+XZl8vx8pBz/LOe354n6ID/uY6kttJ43Ui6LRpo0ABSz5a95JdpON4VCccIa2lrHBj3Sra11kNW/rSHkN9IGAQDG2rdoxKKVSlYqpX19zBvnzmdMBn5jvyNG2h0AjBk7n4jy60pd3Ejt89vXiqKR0Dc4wL8HBQNGCm/YPh9WimcuV12SoTU3Hn6Rn8OCM+59BI3+ZsVVHt6A8s820kwZ/aaTiIiIiIiIiIjUnG46iYiIiIiIiIhIzemmk4iIiIiIiIiI1JxuOomIiIiIiIiISM3pppOIiIiIiIiIiNScbjqJiIiIiIiIiEjN2RmFxKGeXYjGy2ONO2afSN8bb+8yP+cVa15F67EYjzn89a+fonXnEdNXv2gprR8q8ejHRH0drZ8+r9NsI96/i9bPqedRwwsbeSxi3G/vR8nH7wsWS/yzkikenRqI8v0DgJZ2HqsaNmLKcwW+TfmiRxR6ikcgDw/203rWiNus89nx2e2B2bQ+EO2g9dEk36b6aNhso1Aq3y4rhnK68vv9E+JdKyPkjygYEaIA0N/Pz9vgIM8Ftz6rpYVHmANA0CNClTl48CCtZzJG9DeAuNHHEwke317tNgFANssjk/v6+qpqI5nkUdheYjEe49zU1ETrgYAd8ZsyxrD1WUUrrteoA0AkwiPUo1EeHW/1K69zPmfOnLJ/e+3zdLNvYwCxSPmck+3j/aVYZ+etzztvgNZnzeLHojvJrxGtbfZ1pWjEAzt+mYe1tSmfHYUeCfBxmvPzfo8wvxb4Q3zuAICgEVOeD/N+7EvwGOeocR0CANTPoeVwB1+vzO3mR6tu1I623jXMt6u0h8cy50aNGOdRPp8BQDTIj3s2xfvogT4+TotJvo4BgLGx8u3N5u0Y7Omort4/Iea8qYn34/pYu/05DQ20Hg7zMZlO82O6bcs2sw1nxLen07z/xY35e3iUrwsAIBDhbYRCvM+EA7yPhQP2ui05Okrr2Sy/pgZ9fEykU/acGo/zfc/mrPmAX9PGMnYbeeM7TyTCj0nBx49tX6893w0ake+Fypz0/5XL8PmgmOPH8PBr5f92xveZ6cjnO/y/oznHt79oRN4D9rrDqvsqG52kbQAolYxrlPFZoQC/OAdj9rq3BHsf+TYZx8pjXTiW4WMomeZ9LzDC989r/R4yvnMGjV/NCQf5eSp5fCUcyxjnymgjFOIfVgrYvy9kHd+CNcbyxmeN2TtSyJevu73OXSX9ppOIiIiIiIiIiNScbjqJiIiIiIiIiEjN6aaTiIiIiIiIiIjUnG46iYiIiIiIiIhIzemmk4iIiIiIiIiI1FxVUUy/efwRhCpSEl75Kp5qE21sNj+nIc5TNy698GL+/hh///33/cJsY4uRUtc8bwWtL2riyRen19kpKvPAn5zfEuJPtY8Yt/hKzn76/5jxVPiCkWIRjvFEjPo6OwEoaKS1FQt8/5yRGJAZtZPlRkZGaH333h5aLxiJDEGj7wBAFDw9pM5I1ZvfZqS2BfkxBIDRTPlT+33FmXXfNhqNTkhNWbBgAX3v1q1bzc+xEsushLPt27fTutUvAKCzkydHWqkvOSMlxivJzErha27m85eVlmalxAF2ep21vVYboZAR+wVMSCQ8wkoTtN7vlYRi7Yd1rMbG+NxpfQ5gpwlaqSNWP6xMqDta5bkqeSTMTDcDBx2iFd2gaIQdDSU9UhuHhmh92cpZtH7ITFexj116jM8FVsjUWIbP0zuTdn8ZNKLw6kp87qhLzKf1kMc1OBzn7Q+mh2g9YwS5hBL2HJE2knOSKX4OQ/4DvB60E0cXzKmn9b2b+b6nBvgaKpaw56GEschpMS7bUSP9NZ21+27R7zz/Pd1ls0X4A+XH3Bflfcznt/tlPMRT6vJjxrEr8Pqsdr4OAoCDh/iaLm3M7aU8v6b5fHa6UdS4dkZj/FoQM5La4lF7fJWKvC+Xiny8OKNPHTyw12yjuYmfj+EhvsaZN38urQ/18bENAJEA365YmM93wyP8+r9jzya7DWOd0T9wiNYLWSP9Mm0naZaK5fvhte6YbgKB4ISEOXvz7f0qFvlxs9LH7GNkp4wZIXWIxvhFOBw2Ls4e10e/sY/W9lp1r3VYyfgenDPqmRzve2kj3R0ABvP8s/xGgmc8wtekzuOcO2Pd7TOu/zkrWc7jnFusvpAz9rtYsM9HKFy+7yWjLzMz6xuziIiIiIiIiIjMCLrpJCIiIiIiIiIiNaebTiIiIiIiIiIiUnO66SQiIiIiIiIiIjWnm04iIiIiIiIiIlJzVaXXzV0wF5EJiRL8qeUBI5UEAIolnj7lD/In5591zstovc4jMeqB+3my3cDmJ2i9yUi1aRqyD1FbiSd7RII8DcTvM1JfjCfaA0C+xI9jyfiRcIQf21DYTpzJ5PmT/gtpHotUSiZpfaCvz2zjqWfW03rOCNs5ecXJtB6J8iQBAHDg+z7LSPQ7kOR9d8OBAbONSKA8ASgww27b1tfXT0gIs1IjWlpazM8pGqkR+/fvp/VFixbRupmWAWDISNjatWsXrUciPL3QSj4Dqt93K53POh4AkDfGl6Wxkc8fXols1n6Mjo7SupWc57MiLjzasNL2Zs+eTeuVyS/PZ7va29ur2iYA2LNnT9m/08Y8Nx2lx3xwofJjEW3gY6hQsPvdwKiRsMIDzuALG+e+ZB/naIxPjqEAH4+RAr+eh/12mqjPx8d8AB20Ho/wtF2/sxPySuEhWi8aSV3DRrKXr8THIgAU/DzhKpDh5ykKftyzOTtBdn77alqvv/BMWt+1j8+zgZA9R7TU831cvWwfrc9p42Ovb8Re2yW6yvtPMFcCYF+zp5tcrgh/RVqR38/Hqt9jgZFO8T6TGuH9srOLJ1O2NSXMNoLGXJ3L8vPmjFSlorOvj1aalTV/hRt4FOIsY/8AoKGB72OxwOeibIaP4YbGJrONdJb/TKKJp7tG4jztLhK3z8eQkYTXP8z7f98gT5xLJe15IhfgqYEjg3xsF3NW+pXHOS/9fqXX1ZJ1LKw1kNf61owsM8ZpOlNdyuPhT+Lba81d4ZBx/TfW7wAQMPeR70cqzcdiKmXPp2GjvxbyfN/zRkJeJGR/pykYSXFJK2Hbx/tZwFhDAYAzkgYrx9wRxRLfJq80QZesGL9VJEDPsK/MIiIiIiIiIiIyE+imk4iIiIiIiIiI1JxuOomIiIiIiIiISM3pppOIiIiIiIiIiNScbjqJiIiIiIiIiEjNVZVet/DEpYjVlcfbxGJG3I3H0+4Lfp6kVvTxe2DxIH+6+hknLzPbmOXnT6/f9tu1tL6ykafX1AfsVIWC40+pjxhP7ff5+BPeSx7JDcUCf80Veb3k+BP1s8aT6wEgbyQQZTP8mAweGqT1J5582mwjZyR4nX3u+bQ+u4MnX5WyvG0ACIZ5XxyL8f7W3s/3z5e3kym2pcuPY95IbpiuFi9ejESiPB3lmWeeoe9tMFJiAKCrq4vWrQQKK8Et5pFA2WekIVqJbH4jBbKtrc1sozLJ74hVq1bRupVeZ20rAGzcuJHWe3t7aX3p0qW0vnLlSrMNKz0vaSRNPvnkk7R+LElu1s9Y53zZMo95exZPILJSDqNRO9nMUnkOrWM0HZUQRqkisSWT58cml7cTRQo5fpx7dvLkq5FBfj2tC/EEJgDwl/h2NTfzuaOrsZvWE3GeOAcAAcfn6kKK130FPkc4n53UOTzK+/GhQX5d8dXx9CmfRxJZKcTH0FiK1/uTvB4O2uMharw2p72T1rvaeYrmUKbHbKOjnc+nrc3GWmJoL63nSvY1uHtZed9NZ4qYUel1qQJ8Fel1ISO5OR63E51iYb58z2X4tSDRwMdR1EpOAtDZyMd3bg6fP6wks6xHkma2yOecSIzve2sTTyxtbuEpcQBQV8fXGbksX7+Fwrw+lrb7Zf8hvr1hY126r+cArcfjfNwBQCHP07oG+nfSejrVT+vRgJ2wFQjyMVwqGHOtcc49gk3hr0xVm0HLaJ/PNyFJziv1t1pmkp/RhJU+BgDFovGd01wa8Ea8ds8ZJ89nNFI0OobXeiUY5K+FQrxPWvNpLGp3NGcdR5+R+mak3WWtSHYARugsgiG+lrC+N2fTdtquxfp+VDLuDXj16Qnjt4r+r990EhERERERERGRmtNNJxERERERERERqTnddBIRERERERERkZrTTScREREREREREak53XQSEREREREREZGa000nERERERERERGpOZ65aojF2hGLlceo+5wRG2xlAwIAeNRg0PiZUIpHizeO7TNbmD+bxwOfFj+Z1uNhHk0YKtr7UQjxiNRC2IhlNvbPipkFgFKWvxYwkh8D4NGS+Zwdi2vllSZH+c/8z9Ob+fvH7GP1R5dcTOudHTx6N5PmMbolj/0oGZGbwSL/mTmO1wsNdnx2X7Z8yOT81UdXHk/19fWory+P8O3o6KDvLdm5qmhp4ccokeBx4RkjljmbtY9fPM6jeyMRPu6sqNmGhgZaB4DmZh6zXHmMJtsmrzaGhoZofffu3bTe3c2j47u6eNQ8YMebJpNJWj9wgMc1W+cJsPejUODjvqeHR6tb7weARYsW0brVr0JG1GwsxiOygYnRsalUynzvdDOWdShWXBJymTR9bzFux2K3tZ9I6+nMKK03jfHo7fbEbLONWfXzaL27eQ6th338nJXydkx5rsD7a8BY2qSN64dxqQMAbNnKx9C2nbw+ezE/7t0n2lHoQSP6uWislUphPv9m83w8AMC+vXx757Xx/t/aZMxpUT43AkDRWK+kU3zuKhmR0Plgn9lGKV7eF0s++zo1HYX8Afj85fN1JMivaeGAvTasS/D9ntXMz9vsRr4mPljkYxsAnLGeXNrayX/AWDPU1fNrGgDE6ox+FuCflQMfrLPaWs02WhrbaT2bGaP1lPGdI9VhXyvGCrz9Ypqfw/Zmfk0LR+1rF4r8mIT9fB0TCfLxNTJor7tGjOth0IiOD0d4v4rG7XHZ0FA+T5WKJezeNjPW0s45c61ZDWvNZn12Nse/E1WuZ8rbsF6zIu55217764wxXznH/V+db1PJ6NsAkC/xflyqXAwdqZd43Ws/jNNh/oy1tdaaFACiUf79wTrueeP+Q9pjnZ4z+om17raPldnExH23uhOh33QSEREREREREZGa000nERERERERERGpOd10EhERERERERGRmtNNJxERERERERERqTnddBIRERERERERkZqrKr0u6K9D0F9XVisZT1f3evJ52EgNi2cGaL0pxVOeFpQO2Y0YqRTDUZ60EAvyDfbl7USFYIAnzhT8vJ4v8KfE+43UNQAIGEFPfuMA+3xGG8bT8Q//EC9v3bKd1vf0jdD6Gy5/rdlE9+w2Wk8leVpS0dimnJFWAADJ1CCtuzGe2hM2EkpiPjuhpDmyrOzf2ecfYnHcWekXVkocYCc0BIN8Soka4y6d5slbgJ220NbG+5L1WUWPPmMlrG3fzvt+tWlwALBz505aHx3lff8Xv/gFra9fv95sw0rPsxIIR0b4GPZK3rBSMazPsvrCjh07zDY2btxI69Z+WMkiXokula/ljQSt6ShbKKJywvbF+LGJNNrpdaF6fl2b08aTLNs6l9N63MdToQAgVuKJSr6cke6a4/tRKNjXx0KRn/89e/fS+sbNW2m9rsVOg9nbw9t/6mk+32zYxdPA5u/h4wcAVqzmiX4tXTwpLmeMh/27+FgEgNH9fH2VMoLIlszjCYeJDjvdNRjgrxVy/JrqCxrnts5e2xVT5cl2RSvKd5pq62hEIFA+BhL1dfS9gyN8TQMAwRgfwy0dfP05GuBpz8Ml3i8AIFXi1+CRIj+fISNpMjNkf9Woy/M2Ygl+Ldqyn6cnJ+I8DQ4AAo7Phc5YG+QyvF5M2cmNLsPPVcH4DlEM8HM+kjlotuE3Fut+I3072sjbDifsRNDAIT4/JxJ8rh8Z48ckmeXfwQAgUJFsViyWANgpitNJqeTgq0jyM74KerK+IztnJZDz60fRI/XNWs8FAnw8Wol6VroaADgz/cxK56s+bdRMkDPatnYj6LG+rTynk7HXmHaUW9FI4bNSBq0Wwh7rWxivhSJWX+Df8wrG/QoAyOXK5+yS0utEREREREREROR40k0nERERERERERGpOd10EhERERERERGRmtNNJxERERERERERqTnddBIRERERERERkZqrKr0OyP/v//6Pz8/TEfIB+35WLM9TX5qSPEmqy8dTngJFO3UoXeBPoq8L83rQOBRF8NQtAIDj+2iEbsFXMJ7mb2zr4df4k/5L4I0UA8YT50N2vEIpwLcr0cj3/dWvOIvW53XZqTZjYzztJG8crHSK95HhUTtl6NDQMK1bT9YPZHnayFDRPlZDgVll/85l7fS16SibzU5ItLASK+rqeLoKAKRS/Hxms3w+sN5vpcF5vTY8zM+z9f6xMTtFxUq8q7Zu7TdgJ29YySL9/TzFZWDAThkKGPEpVt3ilV4Xj8dp3do/61hZKXher1ltWPtnp7DM7PS6dDGHYsW+hfx8X7s8UsZy4GMl5OcJRYFSE637CnbCZd5I1Qn6+TUq64zxW7DHb28/T+R64tlnaH3nTp5YuuSkLrONlJEumze6mB+8T27cYCeyheN8rj21fRatw8evm4m4vR/7BrfQ+vpenorZt5NfH1s65pptNM5qovXWLr6+am7jqWKhCJ9rACASLZ8jSs4B4GvE6aitvRvBYHkfSRaM60rBvj62+oxE4BGeGjmSMdLu4kYfA+DP8e2KgPfXSJyvGTNpe87PZnhfTiZ5226UtzHYa68N1+14mNabmnjyWlsrP7YD/fb3mnyGj3t/gKfq7djGr+fFkr2WKBmxzqPDfI5MGynewbC9LrBSx4pGUlhDfROtR8N20l9lElvBSN2ejrLZzIQ1hJX0XDLSHwF7TWPxGaloResLJ4CSkc5oJa9Z6yaP5ZTna0y1+334Z4y2/fyFSIRfV7wS6qrdLisV22vtaSkU+Bq0YCR7OiO9FsCEZNT/2zBe9htr6GDA/i4QDpW375WgOKG9Kb9TRERERERERERkinTTSUREREREREREak43nUREREREREREpOZ000lERERERERERGpON51ERERERERERKTmqkqvK6KAYkVqSsDxVIr6nJ04Uz/GU1waSyO0HsjwetJI4wAAF+BpAnXgT5wvGQlypaLXfTkjOclIBrB4vb9kbG8B/GnxRpARQn47rSJgtH/yimX8/SGeHlLySBkqFXiySNZItRsd5sk5vUayFwDs7+P9asBINTs4wusjzk6DGJi9qOzfBSPdZbry+/0TkivmzuVpRF7JclbCmleKG+OVZJbJ8D4zOMj7hrW9XttkJVAkEjxxJhaL0XrJI03CSs+zUvisNLVo1E7StFLnrO21jvvQ0JDZhsU6VlYayLGkxVk/49V/LJXn6ljSVI6XWKNDOFS+vYkWnhJU39hqfs7gAO9LQSPJtMNILvQH7ITLkTFj/gjzOd9FeAKZi9rjt7GOj7sTcjzBazjH2ygF7H7U0sXXEufPWkrrnbP5eBhN2ula/iDfDyudMWikIp0wf6HZRmeMp5T95sFf0PrWbXtpveEQP38A0J3roPWRHD8m+w7xuXzZ4m6zDX+kPFks74oAnjLfP93sP3gA/oqEoZERfo2IRHkKEwC4wkFaH4rxfhbz834cb7cTDxsjfG7Zs48nTSci/DznjOQzAOZ/+i4a19SQsf48eIAfDwDoO8RTK3M5fqxSSd7Hcx7fawrWGsC4vGSy/JqWz9lrCef4a7mckaQFvi7IZu35Lm+kgueM+kiKz6mRsP29xrnyz6om/ep4KxaLE9YMVlKvlaJ2GO8YZlKbmSxnp6VZa5ti0f6OU20bxxDWVjUrbc/vqy6Fr+TRz6x0Rot1nvzWl3AAPmN7rTnCPOdWQh3sY2XNp9Z3IK9VcaliHqr8txf9ppOIiIiIiIiIiNScbjqJiIiIiIiIiEjN6aaTiIiIiIiIiIjUnG46iYiIiIiIiIhIzemmk4iIiIiIiIiI1JxuOomIiIiIiIiISM0Fq3lz6X//r+wDMjxatHF4s/k5LW6E1gNGnHE2zaPFnREHCgCBAI+HtSMkeWygFT8IAKEgP3xFI2zQClL0G58DAM5I3AwEjQjJCG8l4HF/MWS8FgjyxvM+6xjaUej5LI+aHUsN0fr+Q4do/ckNm8w2ntuxh3/WoNFGmsdRN3Y3mW0sWNpY9m9fzu6D09Gvf/1rxGKxstqBAwfoe8fG7Hjgytj5Izo7O2k9FOLHqaeHRy8DQCbD44wjET62R0b4vHLwoB2lnM/zPmtFlVsqj+nRrO21Ynat/ba2FbCPr9WGVbeOoZfhYT4/R6M82rqxsZHWAftYpVL8OmNtbzabNdvI5crjoguF6uKDj6eFJ8cQjZbP8aHQHPreUGKR+Tml6AJa95V45PnwAO8vPf27zDZGC0O03rWggdbrmvi1qwA+HgCgFOTbtXjlLFpvm9VE64MDfWYbsYZWWo82tNB6MMLXEvmcHUIcDvPxW3CDtL57J5+zN+6y112hNG/DX+JjbjTJ2/aFeR0AZs1dSuvdC+bS+p4evr3JJN8mAIjE4mX/LvjzAJ4y3z/dpJLZCetKY0mF7Jg9jw0Ya9ZSHX//SJGPL3/GjgoPxvh52NPP11rdjR203t7ExxAADOX4OsNMMDdesNaYAODy/Dimk/z64fJ8XimV7GuFHRpuHHfjJ6LGmh8AikVjHV/Hfyhj7Pdoku8fAPz/7N15mFxVnT/+9721V3V1V2/pdJJOOgsJYQ07YTEoYMSRQfyO8BUUEEQHQcRnRH6MjiCMMH5VcBnHGVzC4oYCKqCyStjFsAXIvi9k7/TeXdu9n98fmZSp6s/npjsW0pH363l8HnNqOXc559xzD9X3Xcjr84xcNm+U6+NzPG7f14Qi5cckKMp+tIlEIkP6r3X/mDeOJQCEA+77NNac23GsO0vAesm6D7bKg+qoluD90F8zNhdeUX/Bd+x2VvT0vm1tlusa9whi32tb22vNDMwZQ9DpGOG5stqV1Rb0Lxn+e/lLJyIiIiIiIiIiqjouOhERERERERERUdVx0YmIiIiIiIiIiKqOi05ERERERERERFR1XHQiIiIiIiIiIqKqG9Hj88MoIizlT3jPb9dTLGoG7VSbGPSUusG8no6QNJ7yL46dMOX7empAwXh4vYiRJGEkMAFAKKzXL0Yakrj6U+WdESZlAUAooq8XOmEjWS5np26YjcDVP+MZT7Xv6+8z6+ju0c/5lk69/Pml69XyJ15YZNaxpVv/rmJIT/ZoaNETjmZOHm/WEQ+VJ6QUXDsFZDR64YUXEI2WH4/16/VjbSWiAUAqpUfk7Ny5Uy23EhKsVDLATgSx+qRVxw4jCREYeSqHlWZppcEB9nG0ttfab+v9ANDXp/e9kSZTBCXkjXR7rfRDa1uD6rDOk1VemVAXVEfQcR1tasbXIpEob4N+Vk8GK+bHmd/TvUU/btu3bVLL17+5Wi3vET1FDQDGTNZT6qL9eupbztGT8+IpO8ksFNHH9qLobc9J6GlVsTq73afqkmp5NKVv70DOSHcL2dfgvJEyFQnpqZiupx/btcuWmHV0rO7S6xB9BmCl6krM/m+VWzr0fXfj+jEc06xfa8Mxu/8WKuZRxbc+WKmq+rv6h4xbjjE3DEwpGjDSxHr1tuwbc8BOI90XAIohfWwfk9Hbn+Pp7Xjz9jfNOjr69HlbYUCvOxyy2l/AwTLiAV3juFvX+aDTYSZTjSQFCoAXkASVyxv3FsYkfsBIcR0YHP71cTdz34335wfsMbUyfVtGkH71dhvJfCHovdb8yExqs+ow5l+7vmxExaagJmzPzcxPqKVWXwxiptcZx8Qq30U/V64x3tjnyb6fd43PmOXWMQk8IUbdVtqeIehYVbbrkYxx/KUTERERERERERFVHRediIiIiIiIiIio6rjoREREREREREREVcdFJyIiIiIiIiIiqjouOhERERERERERUdWNKL3O6e2C45c/0by4Zbn6XonrKTEAMCh60pOE9M3xjKeuBwVPOdCfvF709Kesi5EyEJhUYITRjDStoujbT4kXY1nQNeoQI6HEC0ilgmPsu6NvV95IKOkNSPBas0NvD396eala/vqKjWr5xMkzzDqOaG5SyzONelrSYdOnquXNNWPMOl7akS37dy5qJxKNRs3NzYjFytOgrHS1dDptfo+VIGe1fSvpIah/FY0USCspznp/e3u7WUfYSmgy9sPa76CkP+sz1vZadVeetz1ZSSjWsbLOR1BihZXoE7RdI1WZrLibdZ6s/evq6jLryFUk+gQl9o02vjTCl/Lr4bbt+nvf3KCnUgLAmxu71fIdHV1qeWefngCZmWT/d6s6X+8TvYN6WlUypY83rmv3reyg3u6LBb1d5I3ro2PFPwEIu3r/TcXq1XKvoPehgqdvKwAUCvprucGsWh5L6n3OiQbMJSJ6O88aiVg1GT1xzonZ52PRYj3lcNlyvS0efKh+DT70sElmHQOD5f03m91/kq8AoLW5cchY2tuv94lI2D7W4ZD+Wjiqz5f9ojGfC0j6jBn1hzz9+rFjpz6uODF9XAeAqJHQWFOn70c0pvfVoLmEde0KOXq5ayRKu67d1qz7Eceow0y/CmjOBWPO4It+brv69HFloF+fw++q3ziOIwwXs9obMDSJzfd9dO20U21HExEZMkez2t6+JOOa6cVmfp1NPCuJcGQn0zHuBXd/m/4ZvTxkpMH5/r6k140whTlgP8LGWOc4RuqbcXMedGyt7R1p8mXQ2odvrHFYXGdk6bWAcnyNNQT1e4f9TiIiIiIiIiIiomHiohMREREREREREVUdF52IiIiIiIiIiKjquOhERERERERERERVx0UnIiIiIiIiIiKquhGl1/ndO+AXytNUIr1vqu/NRvVECgAohPQ0mLjxtPSiq5dHrJQFAK4VLQfjSfRBj4M3WCkD9lfpLxQ8OwFNjC9L+PoT6v28/tR+LyghL2wkWRX177KSdnoG9KQdAHj+9ZVq+cLVW9TyOUcfqZa/6zi9HAAyNXqiTyyiH6uEkbrVuc1Oczl8ysSyfw8Y6UmjVXNz85A0tZqaGvW9+5Jklkql1HIrhcFKHwPsdDcrVc+qIyidx9oPK3nDEjLSboJesxI29iW9rra2Vi239t067kFpKyNNYrES5xIJ+9pgnQ/rmFipgXV1dWYdle2qMs1uNFv6RhaxWHl7WrZohfrejh325b2vX28X2bw+hvdm9fTReGvGrCOZ1seCdEY/Nynj/RKQjNLfb6TX5fSxSwr6MRnotduAn9eTxQb7OtTyNzduVstr6/Q0OADINOlj2trNS9TyREpPAzvo6AlmHTta9HF+65s71fIJbePV8sYmvRwAtm/vUss3GEmKGzfpKbVjx2fMOkID5ecql92/EmRPP+2kISmdLy98VX1ve5ud4jelXU/+s+ZtnnE9zRtzRsAe862EMzPh1EglBYCQcZ2wkvsiRvpVEDtRemTJW0YQHQD7Om/ODYw6XCstC4BvpJEVjHOeK+jjfNA5N4+UMSWy5ole0Fyi4nzkcnn81//cab5/NPE8b9jpdUFGmnK+Lwl5Viq7dZJHmj4NjHwObQxD+8Sqw3X18oiR7AkArpVkGdTpFfvSFkbOPh92KqZ10o3vMY4hMDRlOuhesRJ/6URERERERERERFXHRSciIiIiIiIiIqo6LjoREREREREREVHVcdGJiIiIiIiIiIiqjotORERERERERERUdVx0IiIiIiIiIiKiqrMzlRW5gW64Up53GMkbkcWeHe/tGxGLUSOG1TEiCB0z3NOOWfaMz1jR20BAHKWxvZ4Ra2ptbdG34watz2Stuo0oVC9gP2BFUnv6d2UH9HjppctWmVV0bNmilv/DnBPV8rnHzlLLU9ZpAlAY1KOtUdC3V3w9ontwwPgeANv7y2MkB7N69Pxodeqpp6Kmpjw6e4txboJiMK0Y4LARf2y9PyheNJvV49tXrNAj4vv6+tTyynjPPcVi+jiVSultI5/XI4gHBvRxMOi1XE5vl1bdVjkA1NbWquXW9m7dulUtN+OdA77LOudWlG5QVPBIY4GteNigY5VMlkfXDw7uP314wZ+2Ixwq3+fubr0ND2bt/6Y0mNP7li96nnHB0ctramvUcgBobG7WP5PWz40b1tvegDWuAxgcNPZDL8b6FfpY17lNHzsAYPwk/aLT3at/16JXl6vl7ZPbzDomHjRGLd/UsU0tbxoTV8tjCXtK13ZQk1p+yDHT1fKIq4+bkVBSLQeASVP1cz7zsFa1vFDUx0DPs/vkpo2byr8jP/y45tFg+apVQ8bMnZ096ntPnm23mZkzD1TLi8Y4bQVgBwW3iz+yWHcrZdsP+B47mNu4Fhnz1VBAxLdVS9G63hhzn6DjEY7ofc+63hWK+vw64HYArqMfEzHm90FR95YRR74bVVj3RwBQ9MqvJ4PGXG800o6PdZyDjuVI5zrmHMisIeDVETaLoDnbSI10LgfYc0nrI1Z5QJOEb4yEjjHeWNsbdN80UkHz8ZEacRsN2I3K7woa3yvxl05ERERERERERFR1XHQiIiIiIiIiIqKq46ITERERERERERFVHRediIiIiIiIiIio6ob1IPHdDxHLDg59IG7ReBjugPGATwBwi8ZaV1F/aJbnGg8tDngEovVQQc/TyyMh4wGqYb0cGPkD38wHicvIHyQeMR8krj9EMvBB4tb5MB4knjPObdaoGwAKRf04Wp/pMx7uazzndlcdVpvz9TYaFn2/+wMeajgo5duV/d8H8lbzQXtvhd3bpz1su7+/X/3MaH2QuPXgZ+v9QefGes16cKH1MG2r7qDXrO8a6bEF7CCEkW7v2/0gceu1kY61QftR+RDE3cdiNPfh3dvmeUO30TOPjf191oMffeMYiKOXFwv2GJHL6tePSFgvD/lWW9Xfv6sO/YJgPUjceuh0sWgfrELemBsY++4bQSJBxyqf0+so5vXvKuT073LEfhirY1yDcyHjgcbGHMo33r+rfn27rLZQNB6m7Pn2hb7yHO7+92juv8Bftq9YHHqMrPCZXM6eUw0Y10FrDrhvDxIPeFFR3QeJG/NSYywyLjeBtXjWOLgvDxIv8kHiewp8kHhF/85mc//7mdHbh3dv2/70IPFqHc99CX8Z6THZl7Zqf0R/IbgK6zMjK6+ut77uoH5qq3yQuP+/37X39ubIMN61ceNGtLXZKRpE73QbNmzAhAkT3u7NMLEPEwUbzX2Y/Zco2GjuvwD7MNHejOY+zP5LFGw4/XdYi06+72PTpk1Ip9N/o9U9ov2DiKC3txfjxo0zf9kxGrAPE+n2hz7M/kuk2x/6L8A+TGTZH/ow+y+RbiT9d1iLTkRERERERERERCMxOpeUiYiIiIiIiIhov8ZFJyIiIiIiIiIiqjouOhERERERERERUdVx0YmIiIiIiIiIiKpu2ItOp5xyCq666qq3cFP+OmvXroXjOHj11Vf/qu9pb2/Ht771rcD3OI6D3/zmN39VPbtddNFF+OAHP1iV7xqp22+/HZlMpmrfN3/+fDiOg66urlGxPcDob7dERFR9IoJPfvKTaGhogOM4yGQyvBZQVXBeMbpdf/31mDVr1tu9GfvkrZgHV8tw5vj787HXsK+/dUZzW+d5f2vwl05/Z0ayiHXuuedi+fLlb+0Gvc3uu+8+3HjjjW/3ZtDfGV6QiEa3hx56CLfffjsefPBBbN68GYcccsjbvUlE9A41nP+gPZrs6xzn85//PB5//PHqbxC95ar5g4rR6K/9YQT99cJv9wbQ26NQKCCRSCCRSLzdm/KWamhoeLs3gd6BRASe5yEc5hBL9HZYtWoVWltbccIJJwDAqOuL+Xwe0Wj07d4M+hvjtYHebm/l2FNTU4Oampq35Lv3N+/Uvs5r2/C8E4/TiH7p5Ps+vvCFL6ChoQFjx47F9ddfX3pt/fr1OOuss1BTU4Pa2lqcc8452Lp1a+l17Rc4V111FU455ZTSv++55x4ceuihSCQSaGxsxGmnnYb+/v7S6z/84Q8xc+ZMxONxHHjggfiv//qvIdu4evVqvPvd70YymcThhx+O559/vuz1e++9FwcffDBisRja29vxzW9+M3CfV6xYgXe9612Ix+M46KCD8Oijjw7jSP3Fhg0bcM455yCTyaChoQFnnXUW1q5da77f933cfPPNmDx5MhKJBA4//HDcc889Ze9ZtGgRPvCBD6C2thbpdBonn3wyVq1aheuvvx533HEHfvvb38JxHDiOg/nz55f+9PDuu+/GnDlzEI/H8dOf/lT9aeMDDzyAY445BvF4HE1NTTj77LNLr9111104+uijkU6nMXbsWJx33nnYtm3biI7Hnm6//XZMnDgRyWQSZ599Njo6Ooa857e//S2OPPJIxONxTJkyBV/5yldQLBYBAOeddx7OPffcsvcXCgU0NTXhzjvvBDD0v9bkcjlcc801aGtrQywWw7Rp0/CjH/2o9Pobb7yBM844AzU1NWhpacHHPvYx7Nixo/T63too/f276KKL8OSTT+Lb3/52qZ/dfvvtcBwHf/jDH3DUUUchFovhmWeeGVZ/Zpsjqq6LLroIn/nMZ7B+/Xo4joP29vYh7+ns7MQFF1yA+vp6JJNJnHHGGVixYgWAXTcLzc3NZX111qxZaG1tLf37mWeeQSwWw8DAAACgq6sLn/jEJ9Dc3Iza2lq85z3vwcKFC0vv3/1nJz/84Q8xefJkxONxAOzf+7u9zYt2/9f1ymtDb28vzj//fKRSKbS2tuLWW29V5yuf//znMX78eKRSKRx33HGYP3/+sLZr9/zuwQcfxIwZM5BMJvFP//RPGBgYwB133IH29nbU19fjyiuvhOd5I96fxx9/HEcffTSSySROOOEELFu2LHB7dt8D3HTTTWhpaUEmk8ENN9yAYrGIq6++Gg0NDZgwYQLmzZtX9rlrrrkG06dPRzKZxJQpU/Bv//ZvKBQKZe/5j//4D7S0tCCdTuOSSy5BNpsdUv9w7h8sr7/+Ot7znveU+ugnP/lJ9PX1lV7XfhX0wQ9+EBdddFHp9XXr1uFzn/tcac6w23Dmwd///vcxdepURKNRzJgxA3fddVfZ63u7/7LGHos2x9nzvuWll14yz33ln9ftPu/f+MY30NraisbGRlx++eVl53Dz5s34h3/4ByQSCUyePBk/+9nPyn4ZJiK4/vrrMXHiRMRiMYwbNw5XXnll4D68FUZ7X3/44Ycxc+ZM1NTU4H3vex82b95ces+CBQtw+umno6mpCXV1dZgzZw5efvnl0uu7r5Fnn3122TVzOPfup5xyCq644gpcddVVaGpqwty5cwEAt9xyCw499FCkUim0tbXh05/+dFm/GamgfqA9Yqerq6vsPvjd7343AKC+vh6O45T6Z39/Py644ALU1NSgtbVVXRcImi/strf1hfb2dtx444244IILUFtbi09+8pOB+5vP53HFFVegtbUV8XgckyZNws0331y2f5/61KfQ0tKCeDyOQw45BA8++GDp9WeeeQYnn3wyEokE2tracOWVV5bNLdrb23HTTTfh4osvRjqdxsSJE3HbbbeVbcPe1jDmz5+PY489FqlUCplMBieeeCLWrVtn75QM05w5c6S2tlauv/56Wb58udxxxx3iOI488sgj4nmezJo1S0466SR58cUX5U9/+pMcddRRMmfOnNLnL7zwQjnrrLPKvvOzn/1s6T2bNm2ScDgst9xyi6xZs0Zee+01+d73vie9vb0iIvKTn/xEWltb5d5775XVq1fLvffeKw0NDXL77beLiMiaNWsEgBx44IHy4IMPyrJly+Sf/umfZNKkSVIoFERE5MUXXxTXdeWGG26QZcuWybx58ySRSMi8efNK2zRp0iS59dZbRUTE8zw55JBD5NRTT5VXX31VnnzySTniiCMEgPz617/e6zHL5/Myc+ZMufjii+W1116TxYsXy3nnnSczZsyQXC6nHpd///d/lwMPPFAeeughWbVqlcybN09isZjMnz9fREQ2btwoDQ0N8qEPfUgWLFggy5Ytkx//+MeydOlS6e3tlXPOOUfe9773yebNm2Xz5s2Sy+VKx6a9vb10/DZt2iTz5s2Turq6Ut0PPvighEIh+fKXvyyLFy+WV199VW666abS6z/60Y/k97//vaxatUqef/55mT17tpxxxhml15944gkBIJ2dnXs9Nn/605/EdV352te+JsuWLZNvf/vbkslkyrbnqaeektraWrn99ttl1apV8sgjj0h7e7tcf/31pe1NJBKlNiIi8sADD0gikZCenh4R2dVuP/vZz5ZeP+ecc6StrU3uu+8+WbVqlTz22GPyi1/8QkREOjs7pbm5Wa699lpZsmSJvPzyy3L66afLu9/9bhHZexuld4auri6ZPXu2XHrppaV+9thjjwkAOeyww+SRRx6RlStXSkdHx177M9scUfV1dXXJDTfcIBMmTJDNmzfLtm3bhlwL/vEf/1FmzpwpTz31lLz66qsyd+5cmTZtmuTzeRER+dCHPiSXX365iIjs3LlTotGo1NXVyZIlS0Rk17X6xBNPLH3faaedJmeeeaYsWLBAli9fLv/yL/8ijY2N0tHRISIi1113naRSKXnf+94nL7/8sixcuJD9ez+1Z1sa7ryo8trwiU98QiZNmiSPPfaYvP7663L22WdLOp0ua6Of+MQn5IQTTpCnnnpKVq5cKV//+tclFovJ8uXL97qN8+bNk0gkIqeffrq8/PLL8uSTT0pjY6O8973vlXPOOUcWLVokDzzwgESj0dIcaCT7c9xxx8n8+fNl0aJFcvLJJ8sJJ5wQuD0XXnihpNNpufzyy2Xp0qXyox/9SADI3Llz5atf/aosX75cbrzxRolEIrJhw4bS52688UZ59tlnZc2aNXL//fdLS0uLfO1rXyu9fvfdd0ssFpMf/vCHsnTpUvniF78o6XRaDj/88NJ79nb/EKSvr09aW1vlQx/6kLz++uvy+OOPy+TJk+XCCy8svadybBEROeuss0rv6ejokAkTJsgNN9xQmjOIDG8efN9990kkEpHvfe97smzZMvnmN78poVBI/vjHP4qIDOv+Sxt7gmhznGKxOKxzf91115Ud+wsvvFBqa2vln//5n2XJkiXywAMPSDKZlNtuu630ntNOO01mzZolf/rTn+Sll16SOXPmSCKRKN2L/epXv5La2lr5/e9/L+vWrZMXXnih7PNvpf2pr5922mmyYMECeemll2TmzJly3nnnld7z+OOPy1133SVLliyRxYsXyyWXXCItLS2le6Vt27YJAJk3b17pmimy93v33ceopqZGrr76alm6dKksXbpURERuvfVW+eMf/yhr1qyRxx9/XGbMmCGXXXZZ2Xbv2daD7K0f7L7PfeWVV0qf6ezsFADyxBNPSLFYlHvvvVcAyLJly2Tz5s3S1dUlIiKXXXaZTJw4UR577DF57bXX5AMf+MCQ87O3+cJw1xdqa2vlG9/4hqxcuVJWrlwZuM9f//rXpa2tTZ566ilZu3atPP300/Kzn/1MRHb1++OPP14OPvhgeeSRR2TVqlXywAMPyO9//3sREVm5cqWkUim59dZbZfny5fLss8/KEUccIRdddFHZ9jQ0NMj3vvc9WbFihdx8883ium7p/O1tDaNQKEhdXZ18/vOfl5UrV8rixYvl9ttvl3Xr1pn7NKJFp5NOOqms7JhjjpFrrrlGHnnkEQmFQrJ+/frSa4sWLRIA8uc//1lE9t5wX3rpJQEga9euVeufOnVq6WDvduONN8rs2bNF5C8N7oc//OGQbdg9STzvvPPk9NNPL/uOq6++Wg466KDSv/dcdHr44YclHA7Lm2++WXr9D3/4w7AXne666y6ZMWOG+L5fKsvlcpJIJOThhx8WkfLjks1mJZlMynPPPVf2PZdccol85CMfERGRa6+9ViZPnlxq6JW047z72HzrW98qK6/s8LNnz5bzzz9/r/u124IFCwRAaYI8kkWnj3zkI/L+97+/rOzcc88t255TTz21bNFLZNcxbW1tFRGRQqEgTU1Ncuedd5Z977nnnlv6954XjGXLlgkAefTRR9VtuvHGG+W9731vWdmGDRtKg9Te2ii9c1ROMne3/d/85jelsuH0Z7Y5orfGrbfeKpMmTSr9e88+u3z5cgEgzz77bOn1HTt2SCKRkF/+8pciIvKd73xHDj74YBER+c1vfiPHHXecnHXWWfL9739fRHbdKP3rv/6riIg8/fTTUltbK9lstmwbpk6dKv/zP/8jIrtuxiKRSGkyL7L3eQ+NTtoiw27WvGjPa0NPT49EIhH51a9+VSrr6uqSZDJZ+t5169ZJKBQqm3+K7JoXXXvttXvdxnnz5gmAshubT33qU5JMJssWNefOnSuf+tSnzO+x9uexxx4rved3v/udAJDBwUHzey688EKZNGmSeJ5XKpsxY4acfPLJpX8Xi0VJpVLy85//3Pyer3/963LUUUeV/j179mz59Kc/Xfae4447rmzhY2/3D0Fuu+02qa+vl76+vlLZ7373O3FdV7Zs2SIie190Eim/t9htOPPgE044QS699NKy93z4wx8ufW4491/a2LM32j4N59xri06TJk2SYrFYtv275+lLliwRALJgwYLS6ytWrBAApeP1zW9+U6ZPn27e97yV9te+/r3vfU9aWlrMz3ieJ+l0Wh544IFSmXZvO9xFpyOOOGKv2/mrX/1KGhsby7Z7uItOe+sHe1t0EtHvUXt7eyUajZau+yK7FokTicSI5gvDXV/44Ac/OKz9FRH5zGc+I+95z3vK1hB2e/jhh8V1XVm2bJn62UsuuUQ++clPlpU9/fTT4rpuqa9OmjRJPvrRj5Ze931fxowZU5rj7G0No6OjQwCU/iP6cIzoz+sOO+ywsn+3trZi27ZtWLJkCdra2tDW1lZ67aCDDkImk8GSJUuG9d2HH344Tj31VBx66KH48Ic/jB/84Afo7OwEsOunb6tWrcIll1xS+nvhmpoa/Pu//ztWrVplbuPun8Lv/vnjkiVLcOKJJ5a9/8QTT8SKFSvKfl682+79GjduXKls9uzZw9ofAFi4cCFWrlyJdDpd2uaGhgZks9kh2w0AK1euxMDAAE4//fSy/bzzzjtL73/11Vdx8sknIxKJDHs7djv66KMDX3/11Vdx6qmnmq+/9NJLOPPMMzFx4kSk02nMmTMHwK6f9o7UkiVLcNxxx5WVVR7bhQsX4oYbbig7Fpdeeik2b96MgYEBhMNhnHPOOfjpT38KYFc7+e1vf4vzzz/f3L9QKFTa7koLFy7EE088UVbfgQceCGDX80GC2igRUN7HhtOf2eaI/vaWLFmCcDhcdg1qbGzEjBkzSnOWOXPmYPHixdi+fTuefPJJnHLKKTjllFMwf/58FAoFPPfcc6U/MVi4cCH6+vrQ2NhY1pfXrFlTdq2fNGkSmpubS/9m/97/DXdetOe1YfXq1SgUCjj22GNLZXV1dZgxY0bp36+//jo8z8P06dPL2tSTTz6pzh81yWQSU6dOLf27paUF7e3tZc/caWlpKfsToeHujzXXXr9+fdn23nTTTaX3HXzwwXDdv9x2tLS04NBDDy39OxQKobGxsWx77r77bpx44okYO3Ysampq8KUvfalsW/Y2lxzJ/YNmyZIlOPzww5FKpUplJ554Inzf3+ufFA7nu/c2D7buW3aPU8O9/6oce/4aQfdZmoMPPhihUKjsM7vfv2zZMoTDYRx55JGl16dNm4b6+vrSvz/84Q9jcHAQU6ZMwaWXXopf//rXpcds/C3tT319z2MMAFu3bsWll16KAw44AHV1daitrUVfX98+3b9pjjrqqCFljz32GE499VSMHz8e6XQaH/vYx9DR0VH6k/SR2Fs/2FerVq1CPp8v64cNDQ1l52c484Xhri/s7T58TxdddBFeffVVzJgxA1deeSUeeeSR0muvvvoqJkyYgOnTp6ufXbhwIW6//fay9jR37lz4vo81a9aU3rdnX3YcB2PHji21m72tYTQ0NOCiiy7C3LlzceaZZ+Lb3/522Z90akb0dLPKhQ7HceD7/rA+67ouRKSsbM+/6Q2FQnj00Ufx3HPP4ZFHHsF3v/tdfPGLX8QLL7yAZDIJAPjBD34wZIDecyCr3Mbdfzc93G2str6+Phx11FGlRZE9aYP/7r91/d3vfofx48eXvRaLxQDgr3rw954XTU3Qd/f392Pu3LmYO3cufvrTn6K5uRnr16/H3Llzkc/n93mbgvT19eErX/kKPvShDw15bfffpJ9//vmYM2cOtm3bhkcffRSJRALve9/71O/b27Hr6+vDmWeeia997WtDXmttbQ1so5MnT96HPaS/N3v2seH0Z7Y5otHp0EMPRUNDA5588kk8+eST+OpXv4qxY8fia1/7GhYsWIBCoVB6SHlfXx9aW1vVZ3Ds+dzEymsw+/f+bSTzor3Nvyr19fUhFArhpZdeGjLPHe6DmrU5e9A8fiT7Y821J0yYUPZclT3DXEa6Pc8//zzOP/98fOUrX8HcuXNRV1eHX/ziF3t9Fuuedl+Hh3P/sK/2dn8zGoy0/QUZ6X3WX3PvCABtbW1YtmwZHnvsMTz66KP49Kc/ja9//et48skn9+k/wO+L/bGv79kmL7zwQnR0dODb3/42Jk2ahFgshtmzZ+/1/m24bbtyn9euXYsPfOADuOyyy/DVr34VDQ0NeOaZZ3DJJZcgn8+X7uurZfdi9p7bOtr6IDCytnHkkUdizZo1+MMf/oDHHnsM55xzDk477TTcc889w7qf/dSnPqU++2zixIml/x/UN4ezhjFv3jxceeWVeOihh3D33XfjS1/6Eh599FEcf/zx6naN6JdOlpkzZ2LDhg3YsGFDqWzx4sXo6urCQQcdVNrAyhWwPS9MwK6dPfHEE/GVr3wFr7zyCqLRKH7961+jpaUF48aNw+rVqzFt2rSy/41kYjZz5kw8++yzZWXPPvsspk+frl58du/Xntv9pz/9adj1HXnkkVixYgXGjBkzZLvr6uqGvP+ggw5CLBbD+vXrh7x/93/FOOyww/D000+bnSkajaq/2hqOww47zIw6Xbp0KTo6OvAf//EfOPnkk3HggQf+VQ8RnzlzJl544YWysspje+SRR2LZsmVDjsW0adNKA8wJJ5yAtrY23H333fjpT3+KD3/4w+ZF6NBDD4Xv+3jyySfV14888kgsWrQI7e3tQ+rbPVBYbZTeWYbTz4bTn9nmiP72Zs6ciWKxWHYN6ujowLJly0pzFsdxcPLJJ+O3v/0tFi1ahJNOOgmHHXYYcrkc/ud//gdHH310qY8eeeSR2LJlC8Lh8JB+3NTUFLgt7N/7r32dF02ZMgWRSAQLFiwolXV3d2P58uWlfx9xxBHwPA/btm0b0qbGjh07qvZnT5V94K9JEH7uuecwadIkfPGLX8TRRx+NAw44YMhDavc2l/xr7x9mzpyJhQsXlj2A99lnn4XruqVfQ1Te33iehzfeeKPse7Q5w3DmwdZ9y+5xajj3X/vir7mXGIkZM2agWCzilVdeKZWtXLlyyC8+E4kEzjzzTHznO9/B/Pnz8fzzz+P1119/y7dvt/29rz/77LO48sor8f73v7/0sOs9A2uAXQsQled8OPfumpdeegm+7+Ob3/wmjj/+eEyfPh2bNm3a5+3fWz/YvQiy57ZWbufupLg993Hq1KmIRCJl/bCzs7Ps/AxnvjDS9YXhqq2txbnnnosf/OAHuPvuu3Hvvfdi586dOOyww7Bx48ay7dzTkUceicWLF6v3z8NNzBvuGsYRRxyBa6+9Fs899xwOOeQQ/OxnPzO/syqLTqeddhoOPfRQnH/++Xj55Zfx5z//GRdccAHmzJlT+inZe97zHrz44ou48847sWLFClx33XVlg/ILL7yAm266CS+++CLWr1+P++67D9u3b8fMmTMBAF/5yldw88034zvf+Q6WL1+O119/HfPmzcMtt9wy7O38l3/5Fzz++OO48cYbsXz5ctxxxx34z//8T3z+858392v69Om48MILsXDhQjz99NP44he/OOz6zj//fDQ1NeGss87C008/jTVr1mD+/Pm48sorsXHjxiHvT6fT+PznP4/Pfe5zuOOOO7Bq1Sq8/PLL+O53v4s77rgDAHDFFVegp6cH//f//l+8+OKLWLFiBe66667Sz3zb29vx2muvYdmyZdixY8eIVnqvu+46/PznP8d1112HJUuW4PXXXy/9AmPixImIRqP47ne/i9WrV+P+++/HjTfeOOzvrrR7ZfQb3/gGVqxYgf/8z//EQw89VPaeL3/5y7jzzjvxla98BYsWLcKSJUvwi1/8Al/60pfK3nfeeefhv//7v/Hoo4+af1oH7Do2F154IS6++GL85je/KZ2PX/7ylwCAyy+/HDt37sRHPvIRLFiwAKtWrcLDDz+Mj3/84/A8b69tlN452tvb8cILL2Dt2rXYsWOH+l/thtOf2eaI/vYOOOAAnHXWWbj00kvxzDPPYOHChfjoRz+K8ePH46yzziq975RTTsHPf/5zzJo1CzU1NXBdF+9617vw05/+tOzPtE877TTMnj0bH/zgB/HII49g7dq1eO655/DFL34RL774orkd7N/7t32dF6XTaVx44YW4+uqr8cQTT2DRokW45JJL4Lpu6Zcj06dPx/nnn48LLrgA9913H9asWYM///nPuPnmm/G73/1uVO3PW+WAAw7A+vXr8Ytf/AKrVq3Cd77znSELsp/97Gfx4x//GPPmzcPy5ctx3XXXYdGiRWXv+WvuH84//3zE43FceOGFeOONN/DEE0/gM5/5DD72sY+hpaUFwK77m9/97nf43e9+h6VLl+Kyyy5DV1dX2fe0t7fjqaeewptvvlm62R/OPPjqq6/G7bffju9///tYsWIFbrnlFtx3332l+5bh3H/ti+HMcarhwAMPxGmnnYZPfvKT+POf/4xXXnkFn/zkJ5FIJEp94fbbb8ePfvQjvPHGG1i9ejV+8pOfIJFIYNKkSW/JNmn2975+wAEH4K677sKSJUvwwgsv4Pzzzx/ya5n29nY8/vjj2LJlS2nRb2/37pZp06ahUCiUjtddd92F//7v/97n7d9bP0gkEjj++OPxH//xH1iyZAmefPLJIfeKkyZNguM4ePDBB7F9+3b09fWhpqYGl1xyCa6++mr88Y9/xBtvvIGLLrqo7M+AhzNfGOn6wnDccsst+PnPf46lS5di+fLl+NWvfoWxY8cik8lgzpw5eNe73oX/83/+Dx599NHSL6J2jx/XXHMNnnvuOVxxxRV49dVXsWLFCvz2t7/FFVdcMez697aGsWbNGlx77bV4/vnnsW7dOjzyyCNYsWJF8PxluA9/2tuD8tatWyf/+I//KKlUStLptHz4wx8uPWRvty9/+cvS0tIidXV18rnPfU6uuOKK0sPIFi9eLHPnzpXm5maJxWIyffp0+e53v1v2+Z/+9Kcya9YsiUajUl9fL+9617vkvvvuE5HhPURMROSee+6Rgw46SCKRiEycOFG+/vWvl9VR+bC/ZcuWyUknnSTRaFSmT58uDz300LAfJC4isnnzZrngggukqalJYrGYTJkyRS699FLp7u4WkaEPafN9X771rW/JjBkzJBKJSHNzs8ydO1eefPLJ0nsWLlwo733veyWZTEo6nZaTTz5ZVq1aJSK7EghOP/10qampKe27dmxE9Ie43XvvvaVj3NTUJB/60IdKr/3sZz+T9vZ2icViMnv2bLn//vvLvnckDxIX2ZUEMWHCBEkkEnLmmWfKN77xjSHb89BDD8kJJ5wgiURCamtr5dhjjx2SWrF48WIBIJMmTRrywLXKdjs4OCif+9znpLW1VaLRqEybNk1+/OMfl15fvny5nH322ZLJZCSRSMiBBx4oV111lfi+P6w2Su8My5Ytk+OPP14SiUQp8UNr+8Ppz2xzRNUX9CBxkV2JdB/72Mekrq5OEomEzJ07d0hS0CuvvCIA5Jprrin7XgDy0EMPlb23p6dHPvOZz8i4ceMkEolIW1ubnH/++aUH/FY+YFdkePMeGn32bEv7Oi/q6emR8847T5LJpIwdO1ZuueUWOfbYY+X/+//+v9J78vm8fPnLX5b29naJRCLS2toqZ599trz22mt73UZtfqe1wco56L7sz+5+smbNGnN7tAcSa/cVlXPwq6++WhobG6WmpkbOPfdcufXWW4fs11e/+lVpamqSmpoaufDCC+ULX/jCkP0Mun/Ym9dee03e/e53Szwel4aGBrn00kvLHsaez+flsssuk4aGBhkzZozcfPPNQx4k/vzzz8thhx0msVhM9rz1Gs48+L/+679kypQpEolEZPr06WXhOSJ7v//SzvveVM5x1qxZM6xzrz1IfG8Pot60aZOcccYZEovFZNKkSfKzn/1MxowZI//93/8tIiK//vWv5bjjjpPa2lpJpVJy/PHHlz3M/K20v/b1X//612Xt7OWXX5ajjz5a4vG4HHDAAfKrX/1qSF+7//77Zdq0aRIOh8uunUH37pXHaE+33HKLtLa2lq6vd955Z9nxGcmDxEX23g8WL14ss2fPlkQiIbNmzZJHHnlkyBrADTfcIGPHjhXHcUr9s7e3Vz760Y9KMpmUlpYW+X//7//t03xhpOsLe3PbbbfJrFmzJJVKSW1trZx66qny8ssvl17v6OiQj3/849LY2CjxeFwOOeQQefDBB0uv//nPfy6tB6RSKTnssMPkq1/9auD2HH744XLdddeV/h20hrFlyxb54Ac/WLqfnjRpknz5y18uC4uo5IhU/LEmERERERH9TfT392P8+PH45je/iUsuueTt3hyit83GjRvR1tZWehD13xv2dXqnGtGDxImIiIiIaN+98sorWLp0KY499lh0d3fjhhtuAICyP+8keif44x//iL6+Phx66KHYvHkzvvCFL6C9vR3vete73u5Nqwr2daJdqvJMp3eqm266qSyOcM//nXHGGW/35r2tzjjjDPPY7BmhS0RERPRO841vfAOHH344TjvtNPT39+Ppp5/e64Pnd+McqzreifP49evXm/tcU1OD9evX/023p1Ao4F//9V9x8MEH4+yzz0ZzczPmz5//N0um+1tgXw928MEHm/uopaf9PXgnjj3887q/ws6dO7Fz5071tUQiMSQm/Z3kzTffxODgoPpaQ0PDX5VoQkRERPROxTlWdbwT5/HFYhFr1641X29vb0c4zD+EGS3eCX193bp1ZvBVS0sL0un033iL3nrvxLGHi05ERERERERERFR1/PM6IiIiIiIiIiKqOi46ERERERERERFR1XHRiYiIiIiIiIiIqo6LTkREREREREREVHVcdCIiIiIiIiIioqrjohMREREREREREVUdF52IiIiIiIiIiKjquOhERERERERERERVx0UnIiIiIiIiIiKqOi46ERERERERERFR1XHRiYiIiIiIiIiIqo6LTkREREREREREVHVcdCIiIiIiIiIioqrjohMREREREREREVUdF52IiIiIiIiIiKjquOhERERERERERERVFx7Om3zfx6ZNm5BOp+E4zlu9TUT7DRFBb28vxo0bB9cdvWu47MNEuv2hD7P/Eun2h/4LsA8TWfaHPsz+S6QbSf8d1qLTpk2b0NbWVpWNI/p7tGHDBkyYMOHt3gwT+zBRsNHch9l/iYKN5v4LsA8T7c1o7sPsv0TBhtN/h7XolE6nAQDT28cjVLGK1VgXVz8zZ/Zx5vcddMBUtfy1Ra+r5U/8+TW1vLc3a9bRPzColounv98N6SvX4ai9ol1bk1bLx4+pV8untOkn4+gjZ5l1zH9+gVreVFejlq9cu14tf2XxarOOfFE/KK6xmu9CL49G7ObUWJdSy9vGjVHLGzL6sY1G9e8BgJNPPl4tX7lquVr+4ONPq+VbtveadRTzhbJ/+76Pjds7Sn1ktNq9fZMmTxiyEp3N6v0oHLbP58HTD1DLjz/qcLV84sTJavnTC35n1uEjr5Z39xbV8u07evQvcsWsY8rUsWp5Z+d2tTydblDL87l+s47t2zvU8lR9RC0f19ailk9oP8KsY/Obm/UX+jrV4t5+/diGa4wBEkDIag6i/5eNkBvTy0P62AUAg/36sQIKamkkqY8fvmtfG+IV25XPFfDjW/8wqvvw7m37z//8VyQS5dfcgqfvazypX4cAwDf6RNHXxz43NKCWS9HuWw6SernRjorFnFGHfV58z1fLPV8/JgK93I1EzTpE9P2Ao+97NKL3B6+gj1sAAF/vd+GQ/l2Oo5eLBJ0P6zPGJvl6n3NcfewAgLAxB/D10wQYcwmIPq8EAKn4ssHBHD776f8Y1f0X+EsfnnPKu4ZcW7ds3aJ+Zty4ceb3RaN6m7XmYVGjjff02POd2mRCLff69WttYVD/rkRNrVnHQE5vHF2d3Wr5jPbxavk/vf89Zh0h6I18w45tankv9G2a/9xzZh09nfocIJMIqeWzxuvlHQv1uQcAdHXpdWwp6Nf5hkZ9vpxpM8Y0AOu29qnl3YP6fMVz9eu5NUYBgFSMnUXPx58XrhvVfXj3tn3j+88ikSjfZ98Yv4NY464YY6VvvOD79phvveIZ7duqw/wiAGIM4dbVrmh8wCoHAN+8TugbVnmN2M0LqMN6zTfmt+Lr/dc6hADg+3odYtTt7EMdnnHgrWtwQfTrRUH0und9V/n2FnK9uOebRw6r/w5r0Wn3TwlDrotQxSQoHNI3LB6zJ3HJhD6hiEX1Qc2qo3IBbE/WT7ysju66+kkPGeUAhhyL3SLGzbq1f9bxAIBoRP9MzJhwWHUH/eTNNW5ARrroFHQ+rHMYMSZIMWO/o8YxBIBkQp8gWW1xX9qVb7w22n9uu3v7XNeFW9FurbYR1Gas8xaP6QsN1rkJOp++cWE0mgbCYWOQDFh0ikb1/YhE9O+y3i/GwA0AYeO7zDpixrFN6Md212eM8bZg9KOCfmzDsYDxbsSLTnrdoZB9zr2iWYlaGonp3+W79iQwZmzXaO7Du7ctkYgjmSy/XuSNXU0YN4yAvehUMBYaQiG9kuBFJ/26Zi06WWsyUrSvj+aik2dMRo2FouBFJ6P+v8GiU+RtXHTyjAmvEzAnsq4L9qKTcY0ZwaJTabtGcf8F/rJ94XB4yOJcyJwf2WOl9Zp1DqxrbVAd1vyzaP0HxoK14GVfHwvG+bSu59Y2pQLGO2vRKRnXr6lFc+4RcJ03ttecK0X198eMPg8AUeuewzP+Q7DxH9NjxhgFAJGw8R/grbHInBMPf9HpL58ZvX34L9fgGiSS5TfXvm+N7fb+jHjRybhG7Muik9W+97dFJ+t69/YuOgXsh7XoZJRXc9HJmCrBMe5dnBEsOpU+M4z+Ozr/eJaIiIiIiIiIiPZrXHQiIiIiIiIiIqKqG9af1+3mOLv+t6fatP632l2dXeb3dOzUn9uRz+vPCggZP1t1An66mC/qfyZg/ew+ZP1Npfk3Jfaf5NXV6H8v3WA8h2nQ+Pt4AHDE+puDEf7EPOBXb55n/GzT+Eml9ednfjjgZ8HGnwM2NzWq5a0NdWp5Z7f+bBHA3o9cVn9WiP2nXfZvSf2Kn21W/nu0yxfycL3y82T1u6B984yf+iYDfuKuyeXsZ+/U1Bs/884az/eJ6du7afMOs450rX6urZ/Kr1v7plpek7b/PCef1/twT5e+f5GY/lyMzm77eRKRkP5nAjHj58fbjWeIJLL2ftQ06v17cEA/h0XjT/tCsH+26xX150nUNkzRtylxjFq+eo3+HEAAaG2oaP85+xk1o01fdhM8p/xcFx39+YWRgGfvGM0FfkF/xklR9Gu2J/rYCgBhR58buMafVgwW9eeVdO/ImHUM9ultMpzSnwcTiunHJBnSrzcAEIno9Xuif5fnWD/t188TADjGnzVGEsZzuRy9b4nxPQAgxp9UFIr6fhR9Y3utB2MCcMPGc7xgjAWufr2QgDqy2fL9KAa089Got693yDOdCgX9vFnzJgDIZDJqeS6rn7e+Pn1sHRy022Xc+JMqa6aXTOnPEKqty5h1bF61Vi0PGX8CHHb062nIeLYdYM9X+vr0cQLG4wDcgD9lqonpfxI6cbz+DKpoWr8O7sjp12YAKCT0ZzcdOEsfJ4qD+vx6wxb7GZS+qz9TMlOv923P6KuO8fw8APArPhP0l8ejTSg09DEz+/b7DePPtoyvcowh0TGuN4B9J2M+Zsa4Dw561IZn3CeIUbv5F+BBf/pmHCvrzwHF+nO8wD93tP7U2/gu6zFTxvwGwNAFlL18l3Xjbj0DCoD5OBHH+Izx17SBf8PnVax9+OHh3wfzl05ERERERERERFR1XHQiIiIiIiIiIqKq46ITERERERERERFVHRediIiIiIiIiIio6rjoREREREREREREVTei9Lr+voEhT7HPGqkb1lPaAaC7S0+M6B3Uk3AcR095yBXs1JLBQT05wQivgxvS6whK8Orv05PUenv1lBDX0df4ikU7qcX39FgHz0jRMALcUCzYdeSM9DJrRdJKr3MDUhSyRh2OkaiTyaTV8q4ePdkLAPr7utTywUH9PGX79fK+PjvZo1hx3H0zimF0EhFI5TYbXTUosaLfOEb9A3a6oCYcNmK0AKxfu04tzxaMccLV02Oam40EKABFT2+XkYiR1FLU2+vWLdvNOiJhPdEnL/rwu2qlniAWNpK3ACAZ1+uoS+gJYs31rWr564vXmnXEG/S2Xt+gH/edO/VxPubofXvXdulpO5mkvn9btjytlocCUkrikfJj4gQkEo06LoYMzJGofu1KpfXzAgCxpJHc2acnqfb26df5fD5grDTSa/ycfk0rGNeoHVvstMOFL29SyycfqCfFTpmRUcujUbu9JJL6duWL+n6Ip6eBRaN2mlMI+vGNp4w0UCMxyvPtGCjP0+vwjfRDcfXtlYBUGzein/NoVG+LcaO8aBxDAPAr+nbR37/S6wYGBhCqmG9a88zK9+1pMKufH2teYyXLhoISmo35zUC/3u9T9fr1JlGj90cAaGjQr8+b1q9Xy3s69TRaV+zkxpyR0Bi2EpfD+vgYde1jNWHCWLW8oa5JLX/upZfUcjdqJwAfe/IEtfzQ4/WEvF/eqc9LBn37fMRS+jkMhfV9j1mxavbpgFOR8FUI2fcno43r7vrfnqzbxCFz7fJPjaxiKxUtKC3NSiA37kWtxLnATTXmT1aqnnVXIQG/gTG3y9h33/gqI9ht12es4HdrfmjsX1CwnPUZa73EcY00QStpL6hq6z5P9DmDG3DOh6QJusOPn+QvnYiIiIiIiIiIqOq46ERERERERERERFXHRSciIiIiIiIiIqo6LjoREREREREREVHVcdGJiIiIiIiIiIiqjotORERERERERERUdXb+pyISjSNUmRXpGDGacT1yHAAyjXqE6AG1elz25JlHquUrV60167j31/er5f29vWq5E46q5YW8nfuZSujRphMPOEgtHzf1QLV87Fg9ahUAjojWqeU1cT1qePyUmXodk6ebdfzmtw+o5Z2dnWp5OKKf81zRPlbuDv27JKwfwzETp+l1ODGzjohxTMIxvY50Wo+GzdTb8Y8bt5TH2QdHoo4+vl9A5Vqz6+qxzG5lX99DPKmfh3hM7/cxo8001dvxwD2d+nb5Yb3uuoYGtXxSmx4nDAD5vB4Rv2atHsUei+l19w3YcegwYk+zffpnEsaxPerwQ80qGuvb1PI1q9ap5Sccd7hanqoZY9axYecKtdwN6/thnfPm+oxZR8bok5GIPj7v3L5ULS8U7fOx0U+Vvze//8Q1i8QgUt4+YhG9D2Uy48zviSf1ftrXp0fVD/Zu0cv77bh6V/Tzbw2Z4VCzWu5nU2o5AGS7u9Vyb6fxXV16VHis1r6uhLJ6/w0XjdhpI69ZAqKtPejXnHx+ZFHvEhBz7Hv6Zxzo103XmB4G7Ydf1I+Vb0SiF70BtTwfMO/yvfJzKPtP9wUAFAoF+BUZ68Wifv537Nhhfo9jZGDnBvVjOnnKFLW8udke87dsXK+W19fr19pUSm9LvX19Zh1uWL/O9xpz9fhkfb4cCtnzFd8YpmIJfTyI1tar5RPbJpl1bF2vH6ueDn2MiiT1Onb26O8HgMbJ+ri9Zecqve6sPq6F4nrdAOAbWemOMU90xChH2qyjckrkOsOPXH+7CTwIKgYdxxqE7LFSxMiwD6hZ4zgBgz70OhzR+4pvXJyD7nOsXuca22ttrRtwXbHqF+v4WsfEsccIa3utOqxtssblwNeMewTX0a+DQU3HOreOb3yoOLLzBABuxVn3Xft6PfSzREREREREREREVcZFJyIiIiIiIiIiqjouOhERERERERERUdVx0YmIiIiIiIiIiKqOi05ERERERERERFR1I0qvu/mmG5CsSL1Jp/RkmVTc/upUjZ7A4BoJcqFwRC13QnbijO/oiQo/+vEdeh0h/Qnu+YKdzvMPZ56pll9/01fV8ohr1FG0nxN/RM56TS8PGU/t9wLWF62kwVu//V21XD8bQKFgJ1AcNP0Atfz8iz+pv//gQ9TywUE9XQkAHF9PrDrmXXqay4c+mlPLu3v6zTq++rX/V/bvQqGIRx973Hz/aFMsCtyKdBLX0fudHxCFFI3q/TuV0McDK8ls6hQ9ZQoAmhr187lolZ4sV1ev1zGm0WqxwNo1+md6duppDOGwns5TE8+YdbhRvV9MmjBBLZ9xoJ7cGI3bSX+S1+s/9AA9paamVu/zRx4/2ayjdmOHWr5uzUa1fPpUPXW0sUbfbwDYvn27Wr5jx2a1fLBfHw/CIX38B4CuneXjQbEQlNMxyjjOrv/tIVWTUd8aC0go8oyk0b5evc917tSPs5cPSLiM6K/Fonrba204WC33e+xzefwx+neNb9D7qZfX08D8roA4mLg+psWNa2oRRiKbGxCzZgxR9jVVn5c4sOdELvRxPmqcD8eYHhY9e06UzetjhOfrSWSe6O0t22/PH+Oh8lTGkKNfx0erZDKJUMX41N+vzzkq37enlDH37jZSh31Pb39BGVri63PWeELvX7G43v42btCvEYCdLhxL6uU1tRm13HPt63zR6ntGcl44aqTaRe1rsO/pY+qE1la1fO22LrW8pkVPcAWARK3eL7ycng5YNJKpHDNtbVe6omYgq/d78fTj7hppmcDQsK5CwD3QaCPiQyojM4NiQ+1vMsqNxLmAdLeRMr/KSFcLDOk2PmMEsu3jL12MBDljR/YlVNxKqbOOu2vczwe1hJB5fI26xUiFCziIvpFS51gnxIjCcwOuDH7FsfKMxEv9e4mIiIiIiIiIiKqMi05ERERERERERFR1XHQiIiIiIiIiIqKq46ITERERERERERFVHRediIiIiIiIiIio6kaUXjdlajtqaspTHRzrSelBT9o3nqJuPcG9kNef4N44psms4oTZx6vlv/jlr9TyfiOZIRqQHnLErMPUcr+gJ/1sMpKZ0plGs45UXE+yyPt6+oTn6Uku0aidanPSySeq5d/77/9Wy7N5ve5IyF7DbB+np5Q1ZurUciM0BZGInVDiOHpzbqhp0Os2ntofi+opPwAw973vLft3Npvdr9LrfC8CSMV5slImAhIJvLyeqpTN6m1/2w49Neq1RavNOrp79ESWLdv0vrpx7Ta1fMFzG8w63EhaLZ8yWU9Ym32M0eeLdptZvmaZ/hkjldMXvQ8vX7HFrGPTaj396OiD9f2YPP0otby3Q0/eAgAp6mNh+0Q9bc9Kv9ix9U2zjmRMTw0aO2WmWp5J6GNnsWAnb8Ri5ecql8vjqd/b7XA0CYUEoXD54JhM6UlSfkCEyzYjDXDtulVq+WCf3hejIbvdO2H9OpGM6+fYdfRr1OCAnsYFAAMDelvqCxtJls16f+/b0mPWsbOg94m2ie1qeSqhHxPXt8fTnJECGI7rn3Ghj7+hsH0N9oz0ukhYn2OEHL1dQfS2AABeUU+vK/hG+qGR9VPM23O7CePLEzb7Y3ai7WgUjUYRrmiflf/erbu72/yenh69zY4bP04tHzNmjFqeyxkJSQCSRkJe1JiHDRgpfLW1er8DACeiJ8INFvTxI1arp3L6RrIsABSMdtbRox/fN3fqaXsC+35gyrQpanlfZ5davnXbWrW8vsVOyEsm9Hl0X48+Rjmip0b6xvwNALIFff7RNaiXZ42vchx7rl45N/CMZMXRSTA0Tc1KPrPnIY7xGd9oq9Y3BVRh3lObMwPz/QHpZNZnrA0zKncC5ivWa0bovHlQfAS1M+McGil11mF3nYDf8nj6cTT3PCBl0uIa1fvGTbUjxpgWsB9+xd77YTvRthJ/6URERERERERERFXHRSciIiIiIiIiIqo6LjoREREREREREVHVcdGJiIiIiIiIiIiqjotORERERERERERUdSNKryvmCyjmK59SbqTXWY9QB2A9QN4xvkuM1BevYCcwtIzRUx5qa/W0tJ5+PRkqmgxI5zGeLL9pvZ6W5RmPqB8YsFOpJrQl1XJxjGNS1CvJO/axqm/IqOXppF73YE5P/EjE9fcDZmAhOrbriWPWM/sH++00l1SNnsKTrNXT6+IxPSUkIAwCrS3lCTADA/tXcs6uI1veRnwjxS8Ssdt+2EgKzA7oyUaZTEYtX7/JPp/LV69Vyxtq9D4cCenns7ZWT+0BgM5eve/Fkvp35WSrWu4iY9axbccmtXzdFj3NsjGtp3sdM+Mgs47ZUw9RyweM5A3XSM57c42ePgUAHTv1hIrGZn2s96Dv92DWrAK1tXoS3rFHHamWb9qgb+/KNevNOsa0tpRvT9AGjTJFr4Bisfx4D2b1FCuvw76urN+wVi3v6db7b21CT59Kx+wxIhbWx5W4kTK1ZMlStfyVF+1zWV+rt+/ekN6HxjTo14KikboFAFu3630+HNOvN20T9OS1mG8neOUH9WOVM5J702P0K6QTslNkfNHnDNmc8V2+3i98sdPOzLqN8KPBAX3sqI2NN79rTGP5ONgXs8/daFQsFIekSmWz+rHOD5lv/0VDg57iljIS53qNBMr6jN4nACDXr3+mUNTnk15B396g+4HtRrKtG9HTLAuV6bv/a+UGPZETAPoG9e3d0a0nsnnGLHB8q54MCADFXv1652X1BLkDZ7Tp22TciwBAT6e+Hzs36X0yP2CcD0dvIwAgOX3frbY46Bn3FiH79jJSkdboGfczo5HrKPczxk1DYLKc9RnjUIidcTbiOjzjDssxrhFW0t6uSow5prW8YIXaBaS7umbSnz4WWIc96Bg6zsiS+8R4f9CxMoYuGIcdMOowywNYSYbmT4+sBQsAvl+RXhcafsoef+lERERERERERERVx0UnIiIiIiIiIiKqOi46ERERERERERFR1XHRiYiIiIiIiIiIqo6LTkREREREREREVHVcdCIiIiIiIiIioqqzMy0VLpRVKivRb58SMI3Iwn34rrEtY/XyZj0eduMmPW41EdPjnQEAnr5hyRo9SrlpTKtavmrFMrOKQlGPdI1E9Mhz8xga2woADXUZtbw+o0fTb+vUY+6TCT1mftdrerS2GCd3oE+Pmc0O2vHIvpEPmqrVz7mdZmpHRdbWlp/bUHhEXehtF474cN3y/fOKIfW9XsGOwSxkjajhoh7dG4sn9fcHnAU3rreNgw6ZoL9fjD4RsvvwWCPtt6dvp1qeN47VYI9+PADAi+jtKV2jt51IZ4daHt/4ullHql3frpUFPRo5tUM/VjOnN5p1vLld7/crVqxVy1vG6NHx/Tk72nrVm0vU8ta1Lfp3dettZPOOjWYd/cWesn/ncnYs+Wgz0D8A3y9vtLFu/b8d+UYsMgB0bN2ulmeS+rWrPq3332jAhd4r6jHwXlHvD5s3b1XLV69abdZx6Ex9LOgb0OseHNTPtR8wyegf0K9Ff17wglruYpZa3j6+2azDKernsHenPkA5Yf0YxjL2Oc97A2p5oaDX4Xv62Ow4dh0+9O/KFfTtzQ7q+90ycYxZRzpZMa54AXO0Uajo5SAoH6/dsH4cojFrngekkvp+e1l9jtSb169RNSl9ngcABSM2OxHR24ZrZMQn0xmzjmRe/8ysWUeq5fF0vVq+ctMOs45ozJh/uPp1s9uYf4a32tf59ib9fLRMGa+Wv77kDbW8Lxc0buvtpKNPP4dOQj9/EcduVzFXPx8J41iFjLEzGvCThmiovI6i52GF/fZRxYWPEMr32TGug47RH4KIcdx837ovse9XxJhfi2PcJxrf5YQC6jAunY5Rhy9G3cb7d9VhfJdxLXKNr7LKAcA3foNjfcR3jHljwJzIN86H9Qmz+QQ2K2sdxSg32pvVFoCh43wxZI9ZQz477HcSERERERERERENExediIiIiIiIiIio6rjoREREREREREREVcdFJyIiIiIiIiIiqjouOhERERERERERUdWNKHrL8QVOxRPQzSeij/yh/QEPZNef7e4VjegpAJlMRi2f1t6mlr+0cJFanojGzDpScT2tzcvryTndO/XEoEJ+0KxDxEiW8Y3EIivhwLePVV1NWi0f06yn7Sxbs14trzHSVAAgEdWbmnXOxdhex7XXSQcH9eMYNhLmxEhR8AKS/tLp8mPlBmzPaOS67pBtLpipGPZxqE3rbb82lVHLHaMdtxiJLwDQP6gnaQ1k+9TyaKRGLR83PiAJqVZvG7m+jFoeMxJcxLUT0A6s1xPkYq6RxGh0ihrpNOuIGmPI0Yccp5bH9S6PYj4gHXKrfs637uxRy1ua29Vyx9WThACgAH3svP+Rh9TytnEHqOWNTXp6KQBEKtIMXbETiUab7p5uZPPlyUNOWE+MjOiBobs+o38ENQm9rcZd/csirj1GDOb1SgpGktnAgN6Ge3r0/r7rM3q/yxjjkxvR+3vMSFcFgHETxqnl0S49Da6nS++noTZ7HIoa18dspz5uDr6pH6sm157SFRx9ez3RywF9rMsbKWgA4Pn6a/39enm2V0/RymbMKuAgEvjv0W5gcBChUPmxTdfq17pYzG6X9Rl9EHfyep8Q4zgVi3bykGfMDfIFYwDx9e/q2G4ny7muPseeNLFdLS8W9f1zIwFzCeOYFIyExryRwrt1m55qCwDSrdeRcvRraq5bHycaE8bFGcC6NV1q+bYefTwouNY5t+8HPGNOG4nq1+2wMY+ujdp3dLGKlMtCQBscbcKuIFwRg+bb+WPm91j3H1ZqWGXqdOn95vzdnsGHjIRCa5tglQMQ/TJhptT5Aalodh1WEp6+hyHjsAeE1EJ8K+lP/5BvTKKshDoAKBp9yxiG7OMecD4sVruytlcCkhelYj9c17geKPavO2YiIiIiIiIiItovcNGJiIiIiIiIiIiqjotORERERERERERUdVx0IiIiIiIiIiKiquOiExERERERERERVd2I0usA+d//DeetI08jsJ52D+Mp6kFbUpPSkxamTW5Xy8PGg9pjMfsQNTQ2quWDA3pSy8qVK9TylJEeBwCOEUFkBawZAQfwffux/dGEnvoxoW28/oE/v6QW16b0xCAAiESMhBlje2NxPUUpEpDm0tvbq5Y7RuKYZxwTz0hgAYBkTfk+2qkVo1OxKGYKRqWgdyWS+vlpbK5Xy5MpvR9lGuzzmenW20xnr54g0z5JT4fK1OnJQAAwZoy+XbUTWtTyHWu26F+00061iXtdanm3sVmbjUSnMOxjtWTlm2r5sQcbSXF5fcB7acESs45V6/R9z/brdezcrtcRCjeYdXhOl1o+cZJ+sHJZPRmotna6WUdzpjzB1Eq9HI36BgdQ8Mvbhzj69aa+Tu+jAOAaQ5xT1M+ZY6SluiEjugZAKKRfVwpGHVZyTrFoj7FeUa+/u0dPQSwYoU2NDXpSKwBkmvU6mrL6lxUHutXy7ICdDAlPPyFhI0Gur9c4Jnn7GuyE9IQZgZF2ZjSSgUE9rRIACsYBFk8fu9yCkThWtBPpUhXpXgHBvKNTKApU9Jv6Rr39hYzUVwBwjXlx0ehHfYN6+/N6usw6rBS3VFK/blqhdtmsnXg4ddpktdxKHfaNE54PSIEe6NMTMB0j3c3z9DqC9iMXMRKljWOYMe4firATszd3dqjl27Z1qeUDWb1/BTQrM2Erb6R7RYxkMSstCwAiTsUGOPtTep2PcEVqa9GKRQuYRFfr3sFxAioxU870c2kmy+1DGr31i5Zq3jOJse++dQ9X2e724Ib07bLasTUtKQYcLMcYs0ccXhfQsMzvstqJlfQXlF5XMRa4xhxUw186ERERERERERFR1XHRiYiIiIiIiIiIqo6LTkREREREREREVHVcdCIiIiIiIiIioqrjohMREREREREREVXdiNLril4RxWLF09/FeBp7wJPPLVZ6zTDz8spYqTqZ+oxaHg4ZyQxR+xAljQSvlvFtajnC+hqf9T0AIGYqghVTpz+d3yva6RCRqJ5w0dqqJ3hZZzaTtvcjHtVTQgpG3EnLuAlqebGopxIBQG2tnpxmRf15lW35f1ntEACiFSl80cgIAyDfZuJr/clKk7AThJyw/lqmKaOWh+N6f4wn7eTG/n49jWbSVD1VcXyjXvekGiPBDUCT6ElT6xcvV8udrH6s6ieOM+tY36/3+4Kjt51o9w697oBUm2Vb9X609Kf3qOUhXz+2tY32+YjG9J4/oU3vq93deppkf/+AWUe6Qd/H1Jg6tdz19ESmrVvXmXWgItkkKJFotAmFwwhVjDnWNSKftcfKfFZPLPNC+vH3jOSigGA5+KL3+crkk90aG5vU8lSyxqwjHtPba3+/nla1ebOe/jSmWa8bAGJJPRHOh5EGBr09uQEpQ76nn49IWP9MQvRrdtYOlkOqIaOW53L6Mekv6MdwYMBOCXONMa02pfdf39P3I5POmHVUzpeKAXOb0SgUiSNUMT91Q/pxizj2fxfutxLZjBnzviRTiau/GDcSl10jyawubKcq1tTY/VvdJmN+5hkJkADgG6+5xvENh/Ty7d1dZh0xI132kGn6/YBndNZszp5T1tbq2zW4YpNa3t+vjyt+wP2Zb4xTRSORPGokE7c02ym19RXjWq6w//RhxxU4FQnQjpGEHXQvYfXTgC6vC6gDvnFPbXzEtbZpH+7nzVQ0a5v24U7f+oTv6vd28UhAiruR7lo07h8Hc/o9UC5gHLIS4YxlFIhxEK1juPtTI/mMddytugFAKhqpG5AKWIm/dCIiIiIiIiIioqrjohMREREREREREVUdF52IiIiIiIiIiKjquOhERERERERERERVx0UnIiIiIiIiIiKqOi46ERERERERERFR1Y0o790rFpWYeStWb+QRi2Yso1UeEBUpRoRlKpVSy6NRPe40HtMjfQHADVkx8HoE7LgJk9TyoFhNT/QoQs83IgqN+Gzft2McXeP4NjXUq+XxiL7fdWk7mj4WNiK3i3pMZTimR+yKa6+TpkL6uRIjjtUv6Mcw6HxE3fIuE3FH1IVGARdD1pqNuMtwWO8TAJA0+tHOrk61vKtXj2/fuN6Oq49GW/Vy1zjPnW/q5QNdZh2o17+rcfzBanlnQo+KfnPLNrOKSFqPVs3m9HZWW6f3rw6jvQLA9v4danlctqvl7z39KLV83GQ93hkAcgU9Kr2rb0Atd/P6sR3osa8Nq9asUMs3rO5WyydNGq+WhwO6ZX19+TkcHNTPz2gUi8UQi5Vvbzyqt6NCXo/LBoCCMSZ6xthX9IyYcseOIIbo7TgSianlIVdvR8Wife3q6NDbxcSJ49Ryz9cbhuPYY10qVTei7Sp4+n6EAxplxNNfi0X04x4K68ewY1uvWYcb0a/nCOvX7b6eDrW8aMytACAeN44v9LbgF/Q26hXttpvPlV8z8gHtfDTKD2aHzB23b9PH6fEtjeb37DA+09ikR9XHE/p5zuX1azMAZI1jmzfGCTeqz9visK9dqaS+XVbyeHHIPcguvT09Zh3ZrN4no8ZY5BnzaNex54aRuH696+zpU8s7Nq1Xy7u67PEuUav34WSyVi13jXHNdwPuz1x938MhvfxdJx6hlx9/mFnF6tcXlv17MJsHfvuSvU2jSMgpIOSU37eIMYcWN+Ae1SzXz411XyKwx2PHuF0yNyvg3qdarGMi/sjrFuMoGrfmCIXsdm/NfTzPOL6i91M34HyEjV20tso8TQHjkMU67gVjoLXa4a7XyhtWGMO/BvOXTkREREREREREVHVcdCIiIiIiIiIioqrjohMREREREREREVUdF52IiIiIiIiIiKjquOhERERERERERERVN6LoLV8EfsUT3q2UODNxDgG5dsZnHCNpIeip/XlPTxOIGwkTVnksZqfahFwjkcU4JkXjKfhBaWni6eluPV071fLBfj2lI12jp1sAQDylJ3hkMnpqTzyuv7+2JiC9zki8E8946r11rIr2sfKN9AOzjRrv943EQAAIRyKB/x7tfPGVgAoj6iGgf23doScbrVmzTi2fMH6yWt5Yr6fBAUAoYiTIDOjlY1NdanmsVk+NBAB/lp6wsm39arV84pjD1fKiu9msY+HSP+l1b9WTpvq36ylxWwMCImIhvR22NI7R35/W+3Cu0G/W0d+jb4CV1pHP6ftRKOh1A8CsWXqq3qatetLPjh16MtCRR8w060ilyo9VwKVq1AmFQghVRrOInj7lefrxB4BUUr+uRY3hzPf1OgpGXwSAmmSTXkc4o5b3dukJkOPG6QmFAOAZ5653UG8XY8fqqXaO0X8AIBrWX4tG9GOYyBiJY0X92gwAxYJ+nXeNdMC6Gqv/6ulhADDYo6fU1DTp1/l0TG8/uaKeGAjYSbjWtbZgpNd1dm4x69iydWXZv/v67DFrNOrt3Aa3IoV36gEz1Pf2BySyxcP6dTtdo59PzzHmYFbEFYCUkcScyxvJjTl9nEhG7FuNgQG9X4jo22W9v3/AbgfJhD6/9437BN9I9Ksz5soAIEU9iXfdmg1qec+OrWp5KqWn9gLAYJ/eJ2uTeppwwkjnixrzcQBwjZS6RFz/zKFT9bndYGeXWce2zeXJi9m8Pv6NRg4KcCrSulzHSBwMSBnzrZQ66/3WfUxQ4pwxHoesuq2g+IDEUkvQnmv2aR5mJMhZSXhWYjQAeFaKm3XYob8/FLT2YRzHkFGJlc5XuQ4zHI6xXVbbtdrnLhXrQEEpxpX1DfudREREREREREREw8RFJyIiIiIiIiIiqjouOhERERERERERUdVx0YmIiIiIiIiIiKqOi05ERERERERERFR1I0uv82XIE/StJ+pbT0rfFyFH30wnIBmgWNRTKcIhfZ2tJqWnP8RjdlqFmapnP+7eeL+9H7kBPUVj2wY9ESNrJHvk6o1EHQDpOj2hpLZGPyY1KT2lLpXUE0IAIBLWj7tnpNdYT/l3A45V0UgfsJ70byYD+HaShlPRFvej4KtdHAzZaF/0/mV0FQB2+smktglqeXu7niDnRTeZdWzYulwtf/MNPfVlpxEgMlhrD3PN3Xob2LZdT0/K1G1Xy/N6cA0AoGONnkCUMtr+hh16ql3cSKIDANdIMOs1xo+1G/X9GOfp7weAsOjJWOGofuC9sN47unrsZKrJ0/R0sZbWWWr5o489qpZnc3aSkbixin/b+zzauHCGJM8UjdQkx7WTV2MJ4zVXb5OFol7ue3YKkmckjb65uVMtH+jRx91a4/oEAFu366mRXWv1NjZuXEYtjxiJrABQNPY9ZSRGNTfqdWwzEhgBYPsOPbnPMa4wxYKeYtXcZF/nPejb2zegj09hT09BC4Xt8dR39GMVcfT2VpvW5wwDA3o6KgBs3Lio7N/9/XZK42gUFQ9uxRzRNVKKs1l7bBrXpqc6OkaqcjSkn7dYwk4dztQ3qOWbtujXj6VLV6jlUyfaCZS1aX2szlsJeQV9vAsHtMuolVod0Sc5G9focwwvayc3Dvj6dkVdfT/GNuspdaFQvV1H1pgvO1bd+vhRY+w3AIRdfdwOGSlXrz3/glo+0G8fq3zFfuQKxuRtFHLgwalILqv8925BGWMjzXd3rDTpgPtgewOs8299ICBh3Si3989KarON9Lt83zgfAfePrnEcfaty46uC1iXM5D4rvc6o2wk6H9b9rnlqrfUK+6hXftdI0g35SyciIiIiIiIiIqo6LjoREREREREREVHVcdGJiIiIiIiIiIiqjotORERERERERERUdVx0IiIiIiIiIiKiqhthep1flfS6kSbbWU9j9zz7ielWAlnMSFRKG4lskWjErMPaD884JtaxCopAyxuxWIWsntiS7deTrxIpOwHISndLGml0mRr9WFmpdgDguPr6ppXOUijo6YPmMYTdTqzP2O3KTtKoTA2Qop10Nxo5IWDIqTAOaWNjrfk9U9ua1fKxY/WEtYKRZBV37HaZ6teTGJtr9fOzo6An5KSNdD4AGJM3Um2i+n5s2qSnYu3Ybie1jGvU9zHfp7fxeFLfXjdkpzQZgUVI1+qJVQ70DwQmiBgJlB2dO9XyZELf70yjvk0A4Ib0c+uIfqxCxjVg+1Y7/WrqtOkV3z2iy+Dbalf4ZPkFwxrHIlajAJD3jJQ6Y+xzw/rxj0TtY1cQ/VrU2an366yRDNXbo6fdAUBHh55e19iQUcvDIeOYBMxJ3LCevJap08fH2nq9PG8kzgHA6tWr9M0Svc8VjP5w4NQpZh3J9Fi1fMmKZWp512a9jmxeP68A0DhGT7xzika7Mvp7oai3EQDYvHVd2b8HBwOiQ0ehpnQcoYpo2MJgn/pe17H/u3CmQU8qtFpyZ5fevzYtXmrWMW58m1q+cbOe7jZhgp5S6/v6eAMAvb16ewqFjIRNa7wL23P1gUG978Wj+jWqs0NPk2xO2+Nd2Eh3Syf1ZMyIkcqdHbDnlPmcPl/2Hb085OrXx6gxHweAiDX/zevHvaNLH5/dgPMRrkhStK7jo5GWXgfoY2XQna6dJmak15kpY/aszUo/FSNtz54ABtUxss/Y+XhBs0+LfkxCjpWQF5T6ZrVB477SSqO3XgDg2CfdKDbOX8A5t5j3wVZTMGP7htYvxvVdw186ERERERERERFR1XHRiYiIiIiIiIiIqo6LTkREREREREREVHVcdCIiIiIiIiIioqrjohMREREREREREVUdF52IiIiIiIiIiKjqRpQV7Xse/Io4Td+IGXQCIogt1mdCRmywV9RjKgHAN77LNaIJa5J6dHokbB8iz4gWNct9K5swIGLRiL0uGMc9V9SjaWMxI34WgG8cx6TxmfpaPWY2FbMjUq3ozmxOjzsuFIwY5IBjZR1f34iwFF/fb9+z29WQs2FEj49WoRBQmZYbCuttLJPRo78BoFDUz49nRHwirx+nlBF5CgATU3okdJOj99VlXWm1PBrS2ysANNfrMeLZnB7ZvW7terW8b9sWs46mjF7/ikE9wrp+TL1avv5NO9o6kxmjltekE2p50Uhl7u3Xo7sBIFOnH/eevn69DiPWvbvLPudHZY5Syye2tqjlm9ZMUMt3dPeYdeRy5Tufy9sR1aON+AKpiL51jbE1GdfPPWAFDQM9ffpxKwzq5zgZssf8REw/ro2Nen/YuVPvD7199n8ba2zQ+3xLkz521NbodWez9jgecvXj29jcpJZbc49YUt9WAHDCet/auX2HWp6s0Y+7cTkFAAwW9Gj6sa2T1fLWcdPV8qef/Z1Zhzeoz5eicX0ukSvoces5X4+4BwDpKm8P2cH96xrcXF+LcMU1d1Onfm6ax7aa3xN19flO1hjDY6If0wmNSbOOptqYWl5fd4BaPpDXtymXtedUA9msWp5MGG0pqrf9gQG7zVjXqAbj+tjSrF+Dk2H72tWY1Mcc15iX9ulTDBi3OwCAUESfq4VC+nkKh/XySOUkcM/PWNHqxtQ7HtKPoevo2woAgvJjEhL7vaONgyIclLdnx9+HOYRxPMW4ntv3PvY9Eaz5uPlV+gsjv5u3N9cJ2l7724wvs77LasN2/zVfG+ExkYB7VBh1iHGPatZi74Zdv7V/vtH3/ICBqLIKc/uH4i+diIiIiIiIiIio6rjoREREREREREREVcdFJyIiIiIiIiIiqjouOhERERERERERUdVx0YmIiIiIiIiIiKpuROl1KOYghfInnfvGU+WDnmXuGekIjpESs2nTRrV8R2eXWcekKVPVciupLW6kQoiVOAdgzZrVannrxEn6B6ynyhvHA7CTOsa06qlNdWk9ISdkpOABwOply9TysJHCk65JqeWJiL2G6RuRHKtWr1HLDzmqQy3P1GfMOhzrkf5WYgD0c+sYqXYAkKtIYctn7cSU0cj3fVT2zro6PX3ECdnnc/Wbelrb4V16AtW4uoyxPXZaWrhZT2Qbn2hQy7es07cpnLOHuWy/npxTU6OnpR0wUU+Z2lboMutorBuvlr+xZpFaXjvWGIs22ElhRWNcyw3qqUg+9D7s7dSPBwAMDugJLX1GWlIqrSfneEX76mANt46j79+48Rm1vKOny6xj8+byRLDsYEDk1ygjng/xysczx0hwiUf14w8AjnFd6ejqUst7evVjFI7a1xW3Rn8tbAwrnpG8aqauAEgYCauZOiNhtUZP6rLmHgDghPTxQ0L6/nlG4owTthNk3Yi+Xds7utTy5rCeLNrda4+n9c16ulZNpk4tX7jwNb0OY4wHgPFj2tXysKufw4Kjt8NCwYj2AhD2ytticV+So95GjgMENLcyTQ12gmwmrh/TUFQfD5rq9espCnb6X68xNnZ7etvvN5LoonH9ugkA+YI+tsesZGxXbzMScNeRSOjXu5q4/l11U/RrdjggrTPp6+NE52Z9XuJE9P0Lh+z9cKEfR99IKQsZY5fn2w0w7+n1u64xfhn3NbGQXUe4Yt+9wn7Uh6W46397FhlJvUGsRG9r1m0F0TlBaWnGPZwYX2bdz+8L++yPfM3A/iZ9wihmvFtA7JuVeGfMr6xjGLQnnowwwd6Ksgw65eZ3VakcGHqomF5HRERERERERERvJy46ERERERERERFR1XHRiYiIiIiIiIiIqo6LTkREREREREREVHVcdCIiIiIiIiIioqobUXrdQ488hli0PMHAMxImigGpbwM5PeEia6RobFynp9dNslLiAHz84x9Xy30jKS5kROr4nr0f9/zqV2r55i16WkVzk54eEwnbiRjW9g5k9VSRgV49WWbtKj0lDgByeT2B7X2nn6qWx4zkIyspAwCyBf04PvvCs2r5QFFPBjjyyCPNOkJWAqHxYP1iTm9v/T2dZh2b3txU9u9sbv9JvgIAeBiSfFBfpycndfXYSUghRz/Xxc4dankWehpRf0BqSceOrWr5ViOZLN6gp9plavUkKwDo69a/a0deT30b06OPRfmd2806lm3R21m+oLedQk4/H4ODdvJGMqY38hrHqKNoJIuJfgwBoCaub1e9cXx9I9GlMWOnqnme3uaiST1NsGViq1q+doveDgFgx/by60/OGEtHJfGHprlZ1+Ci3bfEuK6IkUqVjOjtIh7RU6EAwBU97ShsRHcV83o/CUpLy2T0dK8xjUZSm5G8Kk7Af39z9bFu0Lh++EbaTW7AbmedXf1q+UBWryOW0FOsuo3rPwDUNujX4FVr9PTahW88p5aPbbLH00ydcXyN63/B19/vGuMvAFSeqqBTNxpt2roDoYo+UNM8Vn1vT9dO83ukWU+dTSf1A9Lb8aZaHpTWO1DQ2/KOXv18Zgv63DAv+rUDACJhvS0XjLnBoKOPXbmguZijj2sRI2FtbJN+vcnU6eMKAGxYpqdZh42Qq3RYP7aFgDRr38gEKxaNFC/POLfGeQWAYk7/Lt+Y38fi+vXcCUjMLlTMDSr/Par5BcAvH5cdc/vtwUmslFPzA1aKmlmFzUhFc/chQ06s7TLTxKtXh5lGZyXeBtRtpZ+PPPRt5Al5ZhyddZ7M5DzAakFWwJxrrNVYc8Rdr1Vsl5XKp9U37HcSERERERERERENExediIiIiIiIiIio6rjoREREREREREREVcdFJyIiIiIiIiIiqjouOhERERERERERUdWNKL3uR7+4D65T8WT0yn//L9cNemq/wXggu5V28+bGTWo5AMw4YJpa3mykq/ie/qT2gtgJQAtff0Mtf2bBy2p5IqanPETC9mkIhfQECN94FH3EOOyRgIfdj21pUsu3Hn6IWh42tjcoraJjR4davuHNzWr5G3feqZbfc++9Zh2hsF6/a2yXY8QPxK24EQDxWHnSSjEg3XA0cp0Q3Iq4n7CRSjIQkLYUyejHdMeb69Xy3Db9ODlpswps3bFBLV+/Xf8ub6vetzeE6s06kq6eOuJ6enJeuFmvY3NWT+QEgB1G6lx/0Ui/6ujSvyggZajGCMA8bfZRavmSDXoq0usr9X4KAPlBvf6mFr1yz9ePSdjopwCwcbOepNU9sE4tH8zqaXddvV1mHelUZQLR/pOc4/ghOH758XOMy/hg/8jTnNKxOrU8ntY7ar6gJ58CwJYNegpo1xY9yTKV0FPRpk87wKzD2A00Neppe7XGfoQCrl39/fr25jbr7TuZ1JO6dm63k8g2b9X7XSyuJ3tZyUA7O+06BvN631q9YbFaXlurTyaam/XEQABIp/TtLeb07c16+jmvSertEACKlV8VkJQ8GsVqahEKlR/bmrqM/t6QPTbFXH3+0t+pt6XeLv2alqrRU/AAoOgbCVtGKlp+QN+mbV12InB9g54Ul0zo/ShrJDoGJVC6ZsqV3nYirj5XbzJSMQFgrb/SeEWv2/X0e4tQQGoUrNSqgn4+ikZqpF+w6xjM6d9VMMZIMW5fwjF9LACAxtbye45szr7PGm3EcSBD7oONeyLY1xWrtVpJbcatNiQgcc5KyAvIyNsHI0t9g5Hu6uxDcp419IsV1RZQh2MeE/0zvpnaZ7PqsBLvxBifggIAPc9IrzMuJUOS6Ep12JV4FXvp+cNPgOYvnYiIiIiIiIiIqOq46ERERERERERERFXHRSciIiIiIiIiIqo6LjoREREREREREVHVcdGJiIiIiIiIiIiqjotORERERERERERUdUbYpa43W4Bj5TZWcN2A+NKQ/h0RI0o7HIqq5d0DenQqADz8+BNq+UnHHqKWW9GSvQGx8XnoUeGDeT1KeSDXr9cdEE1oHcdoRK+7NqFHvdY2Zsw66mv12OKdW95Uy8Mh/TxljdhWANi+s0stzxmRl1kjxrF3Z7dZh29EzVptNmKUj2mw45ontJbH+xaK+0/c+i4uKteafSMWORyyh4eQEeW5Y6ce15yL6HXUit63AWC8q5/PVI3e77ca0eZ5T+8TACBJve231+oxztmYfkzW9Gw068g7+n74RgTx4KARQx8Qk9pUp8dLp0P6sYpAL+/s3GbWMTioRyAna1r1D7hGHd12H35jyStqeTiq73xLix5hPdCjj48AgObytpjbn+Ka82n4TnmfCbv6eSl4dlyzdf1wi/px7u3Tr2mdnV1mHdu27lDLd27Xz384rI8FJ7/rJLOOxqYGtbwmnVLLo1G9jnzAtStr9Mf+7j61fKBfb/fr1m4y69iwXh8/xjbpcfadnfpY19GlH3MAGMiuUcsbGtJq+fhx49TysGf3rS5ju3LGXM0r6mNjyNfHZQDIVbRRLxvQ10ejaAKomEOJo/fVulr9/AOAFPU+2bNzp1o+2K+342LBmIQBCMX1+sXTx4mwcZEKu/bFa7C3Ry336/S+7Rj3D0HR8XljTi5GjnjBOCb5vH2sBgb04xsyxrWia8yvjHksADii77vj6NvlGnMoRO37uFhKb4vpGn2c6M0Z9zsRe/7YmK44txH7Xm606R+MQZzya65nNAsn4HcdAuPcOEY7Nu4Tg+4f4ej1i7Fd1j2UiN0mfeNebYTLCxDf3g9ru3zPmrvpdQeNEb5n7btx3PfhWHlGHUax1ULM7wHs82ENK47xXaGQXUf3QPk8I2eMARr+0omIiIiIiIiIiKqOi05ERERERERERFR1XHQiIiIiIiIiIqKq46ITERERERERERFVHRediIiIiIiIiIio6kb0eHntKe4jfbI7AHiir3V5RSMCwEiSiMftVKq16/WkmEyNnvSTN57g3mckYgFANq8/Od+30gTMB+fbSRLWYSwax8qqOxqQJNHSVK+Wh6xn5/t63f1GegcAdPbqx3HQOLeekWTgG+kdQa+FjASHkJGWGI3Z7aplTPmxyhttYLQScSEV/a9ljJ4+1t3VZX5PKKSft5apk9Xytgl6+c4uPWkHABYvWq+WJ40knLSRLBfJNJl1TJ12oFre3aGPHwvXLFLLt/XoCTwAEIno6TXNdWPV8pjRV6NxPaEOAAYH9f61faueWNU3oCdpzjx8gllHbkBP+tm2VU+8CxmJNOLaCUD9A/qY05DQ06y2bdePe2+HnVISDpUfx/2pD+cHQ3D88nGrYKRP+kZqIgCEXD2VdbBfbxddXfpxHghId81l9frzBb28f0Cvoy5Ta9Zx0kknquVdPXpCXpcxphUCEhUt3cYxWbVSH7c2b9aTPQGgu1sfB5PxjFpeZyXO7rTHoa3bu9TycLhdLd9u9K10Wk+xAoCNm4zkNGNuEA/p19qCmdsDFJ3ytLpcfv9JvgKAnC8IVSZUGSnF2YBUoL5efb8HjT7Z0aGfg94Bu10iop+fxvFtannKGKdrAs5RV48+5mQH9fJEjZ5M6VkRYgD6B/SkSddI6HVCeiKi1YcAIGskf0aNlLoB0eefvpHOt2vD9HbS7+nn3A/p25SoscfUdFpPb84bac85Yw7nJuxk4h3ZygRZ862jzpr1vYjHy9uab6Q5WsnQACCw5h36PMtOqQtqL1bSo3EPbvQhKz1u13YZ5da+G9sUWIdxP2ilT1pHJCi9zkzPM5K6XaM8KE3QXBexjomRFL8vvxeyEu8c32qH9rWno6d8jpMfwTWYv3QiIiIiIiIiIqKq46ITERERERERERFVHRediIiIiIiIiIio6rjoREREREREREREVcdFJyIiIiIiIiIiqroRpdcVi0U4FU9ZD3pSu8nT17rCRspDrqg/od4z0iIAIBLSkxM2be5Sy3uNpIxs1n6C++Cg/louZzzJXeyn81uKxr6HjLQTeHrdMSOpDQC2jmlUyxua9NSvvpz+tPuOHju9bnOHkTLVrx/DQlGvo1Cwk6ZGmqRYCOvHMBaQHtLRXb4fhYJ+fkYrxx0agFIwEgTFSCkEAM/T010yzc1qecHV359u1FNwAGDCjKPU8vxAr1q+o1fvw46RUAMAnQX9u1ZueVMt39qrt+OkvnsAANfT20idkfSzs6tTLe/p1csBIGJswBubtqvlUqfXXczrKT8AEI3qSUapGv34bt2+VS2vq28w64hE9IS+Yt5I+hF9nLfSWQAgHC8fD3wjDXE0ymYLQ1Jhcv16m8wb7W4XY0w0YoT6jXE6m7VTS6JGamM0qpdbabQdHXa6VsxIGs1kMmq5df0ISs4pGsdxwEhk2/imnnw5OGAfq0hU70NiJLJ6RvLVYNY+5z29+vau36inT/Yayb0tLS1mHYNZvY6tm42ES2PO54Tta3AoWj5G7G/X4GgsMSQ51zPylrIBqUD9jn6sfWPsEyMpsDdrJzQP9OnjQR/0a1E4rr/fNcYCAEgZ/d43krSslKm+Pv36DwC+lX7l6P1IXH2bdhp9CACcaEIt7+rWj1Vn3kgKC0qzNl4rGulpmXo9mbqY0FO8AWBbl57k2TZ1mlrePm68Wr59mz4OAsCm7eWJd4X9KIFSJASpGH+ttO2ikdQKADDanuuOLN3NMRINAfueyNpez0icq0y8Lt8u677LascjS9TbVb91fI33G/cuZnoc7HB5x7hvd4xPBK6JGNdz8zPG3NQNmrNWJqOW6jD2w/gqL/BYRSr+Pfy1Df7SiYiIiIiIiIiIqo6LTkREREREREREVHVcdCIiIiIiIiIioqrjohMREREREREREVUdF52IiIiIiIiIiKjqRpRed+oJxyEcLv9In5Hu1mMkuwDAxg16MlRPj54+4TnGU/CNdAsA6O7TU5hWbtAf1Z4v6OkJvthpaQM5I1XPeHK+9RT8IFZilBFwgGJRr2Prji6zjgULl6nlW7brn+ns0RO/tmy3U4ZWbdislvcO6sfXTh+wk2o8I8EjHjeSU1w9PSKbt8/TsrVbKuq02+Bo5El+SBLFxo3r1fem4nYkWzSTVssdYx2730h9C4X0xBcA+IfT5qrl2ax+fn76u/vV8pSRcAUAnVv0dnncUYeo5XMzJ6rlnmcnznR26fu+aJE+DvYP6GPqibOnm3V079yplh8wY4JanoeeMtTTr5cDgGOkrcQTejsZGBirlvf26+MHACRiRlqXp48TyWRKLd8YkIYTr0hPc41EkdHIyxdRrOhjxRFehwCgYCSyWUfCNVLGEHB97DfSz6yEs1hMH6c3bthg1rFtm56KNm6CnqjU1dWllluJegDgGulAA0Y/tdK1XGseAyAW1ftWwmjf/UYSXmeXnT5pJRP1Gd9VEP27+nN2co4YiT4dnfp35fN6O7SaGwDEE+XHpFjcv67BmUwG4Uj5Dq5Zu0Z974w2PX0MAAaNFGEX+vEIJ/XrYDhlX4NhXGs7jetEQvQT1xBwDfbM1GFjP8J6HbV1dWYdsZj+mQHjGG7ZrifOZQt2CjTCeiLctk49DW7HgF63E7bHomhMryOR0tNoYaRiDnr2dT5sJOF29Xap5dJjtJEd+tgMAPGKsd4LOKyjTTLmIh6vuAYXjHS1gP2ywsFCRvuG0U+s9DjATmTLG2Nm2JgLuVZaOoCicQ/kGLMJ3+jXnmffdznGtdOzkv6Kerk1hwXsBDnfSpA1rqd+wH6MlGPUERCwPiSd/C9Gtn8I2QmXMa888d6BPhfS8JdORERERERERERUdVx0IiIiIiIiIiKiquOiExERERERERERVR0XnYiIiIiIiIiIqOq46ERERERERERERFXHRSciIiIiIiIiIqq6gGDaoa781IVIJcvjVX0jFjGetuNLX319iVr+re98Ty3v7dUjR4856nCzjsnt7Wp5X48e3bt5ix7vuXLtOrOO3k36Z6zYYMeIZDzttNPMOj549gfV8pgRq1kwosKlaEdbm1Gc0KMfrfTMdev1CHgA6L3/d2r5zpV6VLCVCBkxIjIB4IQTj1XLP/zhD6vl9Rk9jjgesSNrE4ny1wYGBvDhj37cfP9o4zgxOBWZmt1deoR585QG83vGtDSq5VYkuS96VGltyl733rZ8s1reY8SC10X0WOaDDppq1pHP6WPLmBZ936NRvY5YpNasI12nxxm//MoytTxuxEu3jp9g1tG5fbtafsiMg9TySEQ/H0VHj0sGADFyzIvG2GLF7Bb9AbOOHR1b1PJIRK97/Vp9DF6z+Hmzjmxv+b5bse2jkesMjcuNhPXjHDPaKgB4ou9zLqdH3xYK+jmOxuz2YkX3CvQo3kJBj/HevmOHWcf69evV8sYmfXwKhYx2X7TbQHe3PkasW6vX3devt+9oZOTHqq9fH+u6u3vUcl/suOZUWh+HBrP6nKGrT6871tNv1mFdncPGNbVvQP+ufFG/JgFAqqb8XAVFbY9GA9kBhIvl49mYsS3qezMZ/ZwBQH5nr1qeNdpMNJFUy2PphFoOALms/l2RlP5dqVp93p+uS5t1eL7eJ1O1+jU1b4xFvmtHx+fy+mdWGn24JqYfk/rm8WYdflGvP1aj7/vYer1PePZuIBTWx5Bo3Io31+PpXdfuM82N+thZ1L8KYsS6D/Z0mnVsqXitaH35KJTPD8KtuA/xjO13gn7X4esn2ivox1N8/Zz5RjkAFI2xseDr2yvGvWAoZC8VVB6LUh1F/briGdsbdA0299HT+3XlHGk3x759NNugGFc1T/Ryx7wK2msDYrQF1zPaT8B9sBvSj5UYY0HROOf5gOtqwQv+dxD+0omIiIiIiIiIiKqOi05ERERERERERFR1XHQiIiIiIiIiIqKq46ITERERERERERFVHRediIiIiIiIiIio6kaUXhePhhGPln/E9/QnzqfCdgTDP8x9j1q+ZsVitXz1cj3t7r3vnm3WcfDBh6jl+ayezrNlq56UNf+5l8w6fvHAo2r5QFY/JvUZPcXi/P+rp6sBwPvf/361fNBIKAmH9SQQMdIKdr1mJBx4+mes9IGgtKRt2/SUqRUrV6nl4ZC+Hjq2SU+cA4CLP3quWv4PH/iAWt7ZqacSxRNWEgjgVyQs9BoJP6NVXSaOUMWxzQ/qqVHRmN2HUzV68srq1Xoa4QEHHKh/T9JOClzxxlK1PNelJzfNOkZPqfPEPkd9fpdaXuzSE6jq68eo5QVjmwAgb/XVmD4WpV09GSibtdNgxrXpST+9g1vV8q0b9EQwz9HrBgDX1RN9+vv1pKlcTk8W8Qr6fgMAHP27ao0ko0Wv6+2tttG+tI0ZX96/c9mAZM9RxnE8OE75uGwE+yEWD0giNNJojFBUZLP6eXEDopZqE/r1LpvV+3yxqI+7cTOZCdi4caNaPnZcq1qeTuvbFI3a41Bvr95/fSNxpmCkIcaj9n7EYnr9oYh+Hezt15PL3LCdahMzrmtmeo2RPuQYCTwAkC/onwkZDStnpO1aSYYAEK1I3vT3s/S6jp1dQ1IUx00Yq77XSpkEgD4j5aovp/f7eqMtJQPapTj6+Ymlm9TyRF1GLQ/Vpcw6UjHjmuMYg5HR76zkLQCoq9evH1Fj8CwaaXd+1B5TYzX6Pk4Y26yWW/2rr0/v2wCQzen9YiCrz1cSxrltatTbGwD4RirXwIA+Dm7bqs8xolF7LjGmqXy7rHTU0WhgwINXeT9ltEmjywEAPCMd3BpDrZTOoES2XN4YR43PWInDnnGfv6t+/cv6s3rdnpWWbtxvAoBvfMbx9e2KuEZqY8CKhxjtHr41Dul1BF2NxEyX1Y97wZhfFQoBSX+OtV16ecFob77o5QDQny2/37G+Q8NfOhERERERERERUdVx0YmIiIiIiIiIiKqOi05ERERERERERFR1XHQiIiIiIiIiIqKq46ITERERERERERFV3YjS63KDeYSd8tQNz9dTB8TpN78nldGfan/U4Qep5S11empSa3OLWUdlythu2ayewJAf1NOnPOP9AAArLcN4mn/MihkKeGr/diNVb9UyPdnrICO1DyE74sBKJrBSYTzjyflOQHpIxErVMz5jpfDVpe0UlM7t+rHasFpPyFuzbp1a3jzOTvZorm8s+3d2wG7no9H/OfeIIalWBSO9q7tjp/k9a9YtV8vHt+oJcjkjnWRHn13Hym16MtnUZj0NJlynt/FIzG778UG9jUeNPlns1ZNlomF9jAKAutYatfyY42eq5c8+ox/blrF68hYAHHLwDLU8m9e3t2mq/n6E7BQv1/hvFK6rfyY7qI8TPV0dZh2NjXVq+YIX3lDL125eqZafc8G7zTrqx5SPwwP9dlLWaBONhhGtSJANR/SEInHs8Riu/lpdWv8uK3Guz0h2A4DaGr29xqL62G6l8NQ36G0CAAaMpKcN69ar5c3NevpkyEjzA4BEzJh/tI5TyweN9NrGRjt5tb5BHyN8T2+bPT36fsdidrpWNKr335gRsSQxPY02H5CcI0bazkC/nq5lpdQa07dddVQM2QHTjlEpl83BrdjvwUH9+AwEJAVm80YakehtuZC3UoftMX/MGH2O7SX0eVjY6Cv5gJTLN42+Wlen9/uWFr3fNTQ0mHXk8npiat+gnspppVy2TNDrBoDsoF5H0Zhfe0by1s5eOwm3t0dPXK5MJN5tIKfv344ddkJeLqsnUYWNBMqxY/X5cixmpyL2dJcn8eaNtMDRKJsvQpzy7bXuN8Oufq0DAM/T97nfSCL0jdRw67wAdruw2p79PfZ+WIl3rhWRZw0FVnocANcxrlGOfkx8MRLcgi4s0PfR+ox1v2sdDwAomtdOfd+Lvl5uJcsDQBFGIqyxVmNtk8CeF/cNlo8fRabXERERERERERHR24mLTkREREREREREVHVcdCIiIiIiIiIioqrjohMREREREREREVUdF52IiIiIiIiIiKjqRpRe5/nFIU+9F+OJ8/Zz6AFrrWti2yS1PBbWE1lqjHQLAPCNJ+QXi/rT7q39CHpqP4wn6lsxPBHjqfYS8CT67q5OtXz71k1q+eCUKWp5okZPxwEAI6TOjIXxfCPZq2jvRzyRVMsd41i5RupDKql/DwD0devJHju2b1fLkwk9acWxGg8AqUiQqPz3aDfr8HYkU+WpRF0depqYl2syv2dJjZ4UODigp6K8sXqhWu6G7DSJmTPa1fKpM/TUt6yn150IGOVaWvU0KzHSvRI1eiJLKKwnPQFAxHjtAGP82rFTTy9ZsOAVs44Jk/TvSqX19umG9TrESP0AAKuph8J6ykVtvd63awPSyJYu0tvVHx5+Xi0/+TS9LRx+xHizjkiovN/39ekJP6NRsiaFeEWqWNxIDxwYtJM1E3H9M/G4fq01QnDQ36MnNu36jH7+Uym9bs+4DtYk7b7lQP/MquV6AmR/j562N3Fiu1lHTaLW+MxEtdw3rpvWtQ4AHCM5xyvqqTCppH49dwLSkqz0o5wx1hWN8ycBc6J8QT8f+by+H7GYPjj7YidfuRUJm4L96xo8ffoBCEfK+1kqpbdxp3+b+T1WSlI0ovcvX/T0okTAnCrfr4+NRUfv94Wkft46e+xEttZWfaweP14vr63NqOXxuJ0gu237VrU8ldJT+Ky+snrVarOOTEbfLqvbL3rjdbU8mbDbfsJ4rWOnPoebatwPuLDrqDOOby43spTXjp12MnG4YgwJhex5x2gzMDiIyttI30hnDgqQtRLWrGQ5617Uum4Cdtuz369/IKgO6zUxbiyLxrEKWjWwxrqikQAYCu9LpKn+GWss8CtjVEsvBLVlvY6Ckf6WzxnJlwHz9CKMthi2jq+x3wHJqdF4+fXKDQ2/ofGXTkREREREREREVHVcdCIiIiIiIiIioqrjohMREREREREREVUdF52IiIiIiIiIiKjquOhERERERERERERVx0UnIiIiIiIiIiKquoAw8aGKXg6FYvk6lfh6VF7Ms2OOfdE/k2lsVstDIStW16wCubwe6RoxYlUbmlrU8ilTp5l1xCJ/UssHc0bcelSPsnUC4g/7ujvVcteIOe7s0qNTg2JxpWhFXhrlxvvNTHUA9bV6xHM4rJ/bREyP7m6otffDK+rxkjXptFq+fNkKtTwa1s8TAKC2Mit1/4prHsx6cNzy85fN6RGb6ZR+zgDg2BOnquWer8e65rJ6HU1N9WYdNUZftWLSkyEjRhx6uwCAsKO3v7yvR5jmPP27nIDByI3odbgRfRw84rhJavn2nXZ89q9++ZRaftzsg9XySVP1bUqm7XFboJ+PgawRn53Xz9Nrr+hjGgA89PDjavmMg1vV8jP+cbZa7hrXDADIe+VtND+yy+DbKpGIIV4RV5sw4oTh2O0+ndDH0UydHiEeNdpwT+eAWUdvvx6xnc9bMcf62BH038asbtfd162WR7r0sb15jH79B4BUWj9WbVE90r1o7N+aNWvMOvqNGGnPGIdcVz9WdXUZu47+PrU8FtOPiRjHPVSw5ysFYy7jG2O2G9LrsOYFABByK14LmD+NRtt27Bja1rfoY+jkJr0/AkA0op+3wQG9zTgxvc24RhQ7ALRNbFPLuwv6Zzp37lDLZx4806xjbIs+ti9fvlItj0b16+CkSe1mHUlj/hs15pnZrD52iRWTDmD16tVqed64F6mt1eel1vsB4M2Nm9XyQw/Vr/OZTJ1anu23+8zmzXod7e36vMTa756eHrMO1y2f+xSM8W806ssOIFox7w8Z16FIwDzEMVLmrbHPN+41ssb8CwBE9A2LxfV27xpzCcfa2F2VqMWeMVe2yoPq8Ixul83rY12oqG9TKGzP0x1jHmjcVqLo633IM+6bAXsfxTi3TkgvD8HeD+sV35gPijGXEGOdBgAqXwp46xD8pRMREREREREREVUdF52IiIiIiIiIiKjquOhERERERERERERVx0UnIiIiIiIiIiKqOi46ERERERERERFR1Y0otsfzivC88pQB39MfW+4HpDkJ9M+4YT05KVFTq5YXrcfKA3Aj+tP5o0aCXKRJX39zo3aCV6ZGT8To7RtUy5PxuFouxtP8AWDjhnUjKq/J6GlgY1vGmXWY6XXG0/nN9LqA/bDS66JGUkNNUk/KqkvqxxAA8jkrcUR//+TJ7Wr5lg0bzToa68rTQPIDdnLEaDR2fC1SNeXHsKXVapd2JEHcSL8qGm1gcEA/NxEjgQcAdnZvUctrUnobr0noST9Joy0BgBhNtr+7Vy3P5vS+3dDYYNYxkNVTXKx26bj6Rp30ngPNOp57Uk+Qefqp19Xyzh49eaup1T7nUWMsXLF0p1r+2st6P9rZpb8fAI6fM1ktn/u+k9XygtFGoxE79SkULr+GhYyUvdEoHA4hXJF8Jb4+BiUSdhJhnZGcVJsy+orRWBsz+rUZAAqFLrXcukqEw/o4lMvZ1xWnaKW+6O2iq0ffpoF8v1lHQ6ZJLXd79bEgboxpjhXBA0CMuUzYSA1sbtaTfq3xFwCKRupcOKwnEEatpC477AxJR9931zgfvb36cS9YcwwArlM+t/MCjutoFA6HEKqY99Rn9OuHI3obA4BiwUg2MtJ3fV+/BmdzegIUAGzr3q6WF2L6+HHI4Yeo5dGwPh8HgLXr9Lns2LF6omQyqV+Hgu4HckbKVTant0urH+3stJNXs1m9LTc16+PH9u16SlzaSFsGgFmHH66Wp4x7ESuxsq/bnrda7WH9+rVqea+VFBq1by/9ij4bFI422rih0NB0XCN9zDUTWQEY46tvpr6NPC3NyjIrFoz7dmObgtJErUTH3gF9Dm2ljFeOiXsyU988ff+skDrfmnTDPu75gv4ZsY5tUHqdtfbhGuVG6isc+5pXtMb5gt7nnbA+djhhu1NW1j6SKzB/6URERERERERERFXHRSciIiIiIiIiIqo6LjoREREREREREVHVcdGJiIiIiIiIiIiqjotORERERERERERUdSNKr/P9oQ/pFzOCyf4ex3jquxifcVw9ASAS8HT1cGW6wG4xPSHHejr/GCONBQDGNmXU8o3b9ISLaFxPExoctBNKJkyaqJaPn9imlnd360lZ+bydVmE90d9Kr/ONpAY/4Kn9tWk9cSQW11NNYgn9PCWMhDIAEGO7OrbrCSz9A3raiDh2CkrBzwf+e7QLxx2E4+X9pjCgt/F00k5kK3hW6pCehJQ20gtjETv9KmIkN1nJeVkjJS5fkbi5p5Crt79wXG/LOaOOrn67Dvh6HSHXSI1wjH4UttvaqR84QC3fuUPv99Z4191rp/PkPf27aur18ePI4/WEvImTjzXraGo1kr+M4XzQ6MPRqJ3cls+VJ/oUsvtPAqWIA6m4UHYbSYvJgKTPkKsf52hET6+LRPT2nTDGaQCIx/Xv6hm0UrT0cdcX+7+NOUbqSziqfyZsJHtt324nKoZcfR97u/SxYHBAHwOtJDoAiCf0Y5U20gELRf189PXqdQOAV7SSe/RjaKUJhQOyaorGGGGllLoh/f1SsK/BlQlLMqLsnLefGw4hVJFqtXOnPkcZ02pfg3u69GQy678lm4HSAdFhnpFMlUrr87DK/dptIGCOO2bMGLU8XaOnuLmu3i57evRxEADCEX27xozVUyA3b9aT5YKScMeN0xOirVS7/n69r9bXZ8w6rPsqa37vGYlcVhrYLnpD2bJVTxNOJo0k44A0wXS6fFzL5wPmT6NMLB5DNFY+v8gP6O27ULD3S8RIozOOm2/cj7khOyHPOs0hoz+4vj525IxkcADo7dX7XV9OH5/E0duXNV/Y9Zp+DY4Y1yjHuCbkC/YcOm/MP6xmHDKu565rz1esVEzr2uxAbz+eb7crz8gH9q3yon5uQ46dOOpWNKzKfwfhL52IiIiIiIiIiKjquOhERERERERERERVx0UnIiIiIiIiIiKqOi46ERERERERERFR1XHRiYiIiIiIiIiIqm5E6XVeIQ8vXL5OVZmk85dyO8nMN16rTCX5y3cZT3a3kvNgpzM4jrXOpr8/HpBWMaZJTxaJhtfp3xXTE1xiMTtpqW2inl6XSOmJETt27FDLPW/kCS9ixZ0YxVa6AmAnXNQYxzdpJP1Fo/YT9fNGwkJtrZ6CYqXe1KTtxJjK5AUriWG08nMh+BXJFS70Y93T12F+T/fgerXcSiwZE9PTFiMhu+1byVg9PXrSTyRppEmIPcx5RiyaG9X7aktLk1oei9j70d2j94u8pyeexKN60l/QsRLobb9tap1ed1Y/T20TDzTr2Lptk1re0KBvV0uLnkrU3dNl1rFi1SK1fMrkQ9XyXLFbLfc67ZQSryIZq79//0mvg4R2/a+yTNHbYyeZ9Sb09pKI621vMK+3YSdkp7u6RmLZwE499W3ASH2rMZJPATtNqmgk3oUjenlvn90Gamr014rW9dFI8AoZYwoARCL6dc26bvf2GMlAAZcj10gBrqvNqOWDOeOYZO2+VSzoSUbhiD6/8ozEWyshFwCcirla5b9HO3EEfsW8oW28nnxWn7bbzNYt1vVZb+OFgtGHA9KWwkZCdFeXnnIaj+ltbGr7VLsO47r25pv69Sad1q9pra2tZh2r1ixXyzdv3aiWb9+uz6PTaX0uCQD5vD6m9vTo16hG4/4hX7CTwpYv17c3FNLPU8y45xjTNMGsIxTS28PgoD4+p4x7kaC5+tatW8v+XQxIqxxtivlBuBX91/f0MbFYsMcx37j/sO4pPGNMDEoJhPVdxvzPy1sJZwH32sb4m4zp7cL6qYtjtDsAKBSNOXRRv0b5Rmqgla4JAGKtGRi3D77o/TToGlyEvh8F0bfX941zG3Df6bj6a75Vh3H5dALS6L2Ke+1iQCpgJf7SiYiIiIiIiIiIqo6LTkREREREREREVHVcdCIiIiIiIiIioqrjohMREREREREREVUdF52IiIiIiIiIiKjquOhERERERERERERVZ2eJK4r5LIoVcXyeEZdYNCIkAaCQ11/zjazBohF/6ARlE5r07XWNuMagGjJ1enRrIqZHhUaN+NlYzI5CjyeMSGgj/taKP7aOIQB4nh7LKEZMpWdEdDp5O7rYioS24lZTcf39bsAZKRjbZcVOT506TS3vH9AjbgEgO1gec5/L6rH3o1UinkAyHi8ri0b0Ntax0963utomtdyK5Y5G4mp5LG5HQoc8I/ZcUmp5MaoPZ4U+eyza3LFFLY8m9T4ZC+ntL2JEsQNAnaN/V7+jx726RuSp4wREyub1/l3M6t/lit6/Ktv3nmJR/buinh4jPdijn9t0Qh83AWD6FD1aOxarVcszcb3u7q6dZh35fHncvBV9OxqNb5uMZMU1Id+QUd+by9vnsrG+RS3PZPTjHK3Ty5P19rGrbdYjtuua9Kj3vv4+tTwasceIdK2+XVYcNYw+NGH8RLOO+owebe77+rhSZ5yPMePGmXVYMeXi6X0ua1x33IAxwjMikItGec4YU6z4agDY1tGpljuuvl19/T1quefb1/mQWz525fN5rF69znz/aDNu3AREKuLk3QF9vPJ9+1j7Rtuw5kGuEUufy9nXx1BEn59NaJ2glqdT+lzizTc3mXVYc4NQSJ9LWPPV7m573pZM6tsFRz++0ah+fbT66a76u9Ty2lr9GpWp18cua64MALGYPhZ2Gdc719Xn5Na2AkAqoW9vU1OjWl5To8/Hcjn72lBXcT0pBNyfjDZdfV2I5MvbbEiMe5+Accwaj33jHsf39WuaHxBt7zv6d3mi96Gwo/e5SNS+BjvGvjuOvr1iXJsLxv7tes1aA9A/Y10GCwHjKRx9P8L6IYFX0OfvBc9uy1b9ed+4pw7p2xQ0DvlFfTy31mTckD7eFD37ft4bLO/bxeLw+y9/6URERERERERERFXHRSciIiIiIiIiIqo6LjoREREREREREVHVcdGJiIiIiIiIiIiqblgPEpf/fWD3YHbog+GsB4nHB+yHmPb196vl5oPEjQdavt0PEs8bD2Y098N4AOfAoP5AMgDo69OPlfVwbDHrruKDxI2HmEcD6hgwHlBs7Yf1cNN8wAMHjWfmoX9Af6Ct1Q6t9wNAvuLhiP3/286t4z5a7N6+/r6hbc14/rb63t38kP6QSOtB4sWo0b+MB1oD9oPEC1l9u6wHiXsBDxIf6Nf3o2iczqLxIPFQ2F6/d8Xo955xDIv6d1nHFgAKBeuhndY5NLZX7P2wHgxq7B7CIf3hkwXjQYcA0D9onI+CPn6EC/r5CGq72Yo6dreB0dyHS9dgZRzNG9ePvPGgS8Aej6NGHxowHko+oMwJdhs0titrtCPrgcZ+wMNYI0b9I32QuHU8ACAa1a8HYjxI3Pou63gAI3+QeC6nf1dQ2ID1wNmicQ3OGdfaoAeJ542QGOtB4nljbhf8IPHy13Y/hHg091/gL9unPTTZesh33niQLGDPkazT4xrnOeh8FqG/Zj34OZ83wjwC523GA8ND+vaGw3obC3pwdd4YW6z2VzTOR9B+FI0HuFt1WNsUNN5Z31Uwttd6kLi49n7kQ1Yd1jk33m+UA0PHnP2hD/+l/w5tZ/6+PEjcejD42/ggcTEeJG7eXKGKDxI3HgoOAAXrQdvWg8SN8sDWZTxI3Nosr2A9sDug3RsPErfKjUMI8QOu88ZD163tco063KAHiVfc6+/+93D6ryPDeNfGjRvR1ta21y8jeqfasGEDJkzQE11GA/ZhomCjuQ+z/xIFG839F2AfJtqb0dyH2X+Jgg2n/w5r0cn3fWzatAnpdBqOsRpI9E4kIujt7cW4cePgGv81dzRgHybS7Q99mP2XSLc/9F+AfZjIsj/0YfZfIt1I+u+wFp2IiIiIiIiIiIhGYnQuKRMRERERERER0X6Ni05ERERERERERFR1XHQiIiIiIiIiIqKq46ITERERERERERFV3bAXnU455RRcddVVb+Gm/HXWrl0Lx3Hw6quv/lXf097ejm9961uB73EcB7/5zW/+qnp2u+iii/DBD36wKt81UrfffjsymUzVvm/+/PlwHAddXV2jYnuA0d9uaf+xt34/0vbPtkl/z9i+gw1nrkH0TnL99ddj1qxZpX+/nfPj0aqa9x+0d7yOvXXeinu+auF5f2vwl05/Z0ZykT733HOxfPnyt3aD3mb33Xcfbrzxxrd7M+gd4IQTTsDmzZtRV1f3dm/KEFdeeSWOOuooxGKxskk9Eb09FixYgE9+8pNv92YQERH93S9o/rU/jKC/Hhed3qEKhQISiQTGjBnzdm/KW6qhoQHpdPrt3gx6B4hGoxg7diwcx3m7N0V18cUX49xzz327N4Nor0QExWLx7d6MfVIoFIb1vubmZiSTybdsO/bnY0hEtL97p47B+Xz+7d6E/cI78TiNaNHJ93184QtfQENDA8aOHYvrr7++9Nr69etx1llnoaamBrW1tTjnnHOwdevW0uvaL3CuuuoqnHLKKaV/33PPPTj00EORSCTQ2NiI0047Df39/aXXf/jDH2LmzJmIx+M48MAD8V//9V9DtnH16tV497vfjWQyicMPPxzPP/982ev33nsvDj74YMRiMbS3t+Ob3/xm4D6vWLEC73rXuxCPx3HQQQfh0UcfHcaR+osNGzbgnHPOQSaTQUNDA8466yysXbvWfL/v+7j55psxefJkJBIJHH744bjnnnvK3rNo0SJ84AMfQG1tLdLpNE4++WSsWrUK119/Pe644w789re/heM4cBwH8+fPL/3p4d133405c+YgHo/jpz/9qfrTxgceeADHHHMM4vE4mpqacPbZZ5deu+uuu3D00UcjnU5j7NixOO+887Bt27YRHY893X777Zg4cSKSySTOPvtsdHR0DHnPb3/7Wxx55JGIx+OYMmUKvvKVr5QG8fPOO2/ITXShUEBTUxPuvPNOAEN/IpnL5XDNNdegra0NsVgM06ZNw49+9KPS62+88QbOOOMM1NTUoKWlBR/72MewY8eO0ut7a6M0Ot12220YN24cfN8vKz/rrLNw8cUXAwhua7vt2LEDZ599NpLJJA444ADcf//9pde0/4ry7LPP4pRTTkEymUR9fT3mzp2Lzs5OdRtzuRw+//nPY/z48UilUjjuuOMwf/780uvr1q3DmWeeifr6eqRSKRx88MH4/e9/P6z9/853voPLL78cU6ZMGdb7iappb9eO3X3nD3/4Q+kXec888wx6e3tx/vnnI5VKobW1Fbfeeqs6pgf1m70J6qMPPfQQTjrpJGQyGTQ2NuIDH/gAVq1aVfqsdW3dPd/5xje+gdbWVjQ2NuLyyy8vW5Da88/rhnMty+VyuPLKKzFmzBjE43GcdNJJWLBgwV6P4d7mFJ2dnTj//PPR3NyMRCKBAw44APPmzRv28aO/X9Z8Z3f7vummm9DS0oJMJoMbbrgBxWIRV199NRoaGjBhwoQh7eiaa67B9OnTkUwmMWXKFPzbv/3bsBdp9+aUU07BZz7zGVx11VWor69HS0sLfvCDH6C/vx8f//jHkU6nMW3aNPzhD38ofcbzPFxyySWlvjFjxgx8+9vfLvvefdnX3ePCL3/5S5x88slIJBI45phjsHz5cixYsABHH300ampqcMYZZ2D79u2lzy1YsACnn346mpqaUFdXhzlz5uDll1829zmfz+OKK65Aa2sr4vE4Jk2ahJtvvrkqx5OGGq3Xsd33cg8//DBmzpyJmpoavO9978PmzZtL79lb22pvbwcAnH322XAcp/Tv4dy7n3LKKbjiiitw1VVXoampCXPnzgUA3HLLLTj00EORSqXQ1taGT3/60+jr6xvWPmm+//3vY+rUqYhGo5gxYwbuuuuu0mvaI3a6urrK7oPf/e53AwDq6+vhOA4uuugiAEB/fz8uuOAC1NTUoLW1VV0X6OzsxAUXXID6+nokk0mcccYZWLFiRdl79ra+0N7ejhtvvBEXXHABamtr9/pL5731766uLnzqU59CS0sL4vE4DjnkEDz44IOl15955pnS+NPW1oYrr7yy7H61vb0dN910Ey6++GKk02lMnDgRt912W9k27G0NY/78+Tj22GORSqWQyWRw4oknYt26dfZOyTDNmTNHamtr5frrr5fly5fLHXfcIY7jyCOPPCKe58msWbPkpJNOkhdffFH+9Kc/yVFHHSVz5swpff7CCy+Us846q+w7P/vZz5bes2nTJgmHw3LLLbfImjVr5LXXXpPvfe970tvbKyIiP/nJT6S1tVXuvfdeWb16tdx7773S0NAgt99+u4iIrFmzRgDIgQceKA8++KAsW7ZM/umf/kkmTZokhUJBRERefPFFcV1XbrjhBlm2bJnMmzdPEomEzJs3r7RNkyZNkltvvVVERDzPk0MOOUROPfVUefXVV+XJJ5+UI444QgDIr3/9670es3w+LzNnzpSLL75YXnvtNVm8eLGcd955MmPGDMnlcupx+fd//3c58MAD5aGHHpJVq1bJvHnzJBaLyfz580VEZOPGjdLQ0CAf+tCHZMGCBbJs2TL58Y9/LEuXLpXe3l4555xz5H3ve59s3rxZNm/eLLlcrnRs2tvbS8dv06ZNMm/ePKmrqyvV/eCDD0ooFJIvf/nLsnjxYnn11VflpptuKr3+ox/9SH7/+9/LqlWr5Pnnn5fZs2fLGWecUXr9iSeeEADS2dm512Pzpz/9SVzXla997WuybNky+fa3vy2ZTKZse5566impra2V22+/XVatWiWPPPKItLe3y/XXX1/a3kQiUWojIiIPPPCAJBIJ6enpEZFd7fazn/1s6fVzzjlH2tra5L777pNVq1bJY489Jr/4xS9ERKSzs1Oam5vl2muvlSVLlsjLL78sp59+urz73e8Wkb23URq9du7cKdFoVB577LFSWUdHR6lsb21NRASATJgwQX72s5/JihUr5Morr5Samhrp6OgQkaHt/5VXXpFYLCaXXXaZvPrqq/LGG2/Id7/7Xdm+fbuIDG2bn/jEJ+SEE06Qp556SlauXClf//rXJRaLyfLly0VE5B/+4R/k9NNPl9dee01WrVolDzzwgDz55JMjOg7XXXedHH744ftwBIlGZs/2Pdxrx2GHHSaPPPKIrFy5Ujo6OuQTn/iETJo0SR577DF5/fXX5eyzz5Z0Oj2ifhNkb330nnvukXvvvVdWrFghr7zyipx55ply6KGHiud5IiLmtfXCCy+U2tpa+ed//mdZsmSJPPDAA5JMJuW2224r1b3nXGM417Irr7xSxo0bJ7///e9l0aJFcuGFF0p9ff2Q8afyGO5tTnH55ZfLrFmzZMGCBbJmzRp59NFH5f777x/h2aa/N0HznQsvvFDS6bRcfvnlsnTpUvnRj34kAGTu3Lny1a9+VZYvXy433nijRCIR2bBhQ+k7b7zxRnn22WdlzZo1cv/990tLS4t87WtfK71eeX3S7hssc+bMkXQ6LTfeeGOp/lAoJGeccYbcdtttsnz5crnsssuksbFR+vv7RWTXHP3LX/6yLFiwQFavXi0/+clPJJlMyt133122DSPd1z3vRx566CFZvHixHH/88XLUUUfJKaecIs8884y8/PLLMm3aNPnnf/7nUl2PP/643HXXXbJkyRJZvHixXHLJJdLS0lIaA0Sk7P7j61//urS1tclTTz0la9eulaefflp+9rOfDfsc097tD9exefPmSSQSkdNOO00WLFggL730ksycOVPOO++80nv21ra2bdsmAGTevHmyefNm2bZtm4js/d599zGqqamRq6++WpYuXSpLly4VEZFbb71V/vjHP8qaNWvk8ccflxkzZshll11Wtt173vMFue+++yQSicj3vvc9WbZsmXzzm9+UUCgkf/zjH0XkL33ulVdeKX2ms7NTAMgTTzwhxWJR7r33XgEgy5Ytk82bN0tXV5eIiFx22WUyceJEeeyxx+S1116TD3zgA0POzz/+4z/KzJkz5amnnpJXX31V5s6dK9OmTZN8Pi8iw19fqK2tlW984xuycuVKWblyZeA+B/Vvz/Pk+OOPl4MPPlgeeeSR0j3B73//exERWblypaRSKbn11ltl+fLl8uyzz8oRRxwhF110Udn2NDQ0yPe+9z1ZsWKF3HzzzeK6bun87W0No1AoSF1dnXz+85+XlStXyuLFi+X222+XdevWmfs0okWnk046qazsmGOOkWuuuUYeeeQRCYVCsn79+tJrixYtEgDy5z//WUT23nBfeuklASBr165V6586deqQwfTGG2+U2bNni8hfGtwPf/jDIduwZMkSERE577zz5PTTTy/7jquvvloOOuig0r/3nAg+/PDDEg6H5c033yy9/oc//GHYi0533XWXzJgxQ3zfL5XlcjlJJBLy8MMPi0j5cclms5JMJuW5554r+55LLrlEPvKRj4iIyLXXXiuTJ08uNfRK2nHefWy+9a1vlZVXdvjZs2fL+eefv9f92m3BggUCoDRRHsmi00c+8hF5//vfX1Z27rnnlm3PqaeeWrboJbLrmLa2toqISKFQkKamJrnzzjvLvvfcc88t/XvPC8ayZcsEgDz66KPqNt14443y3ve+t6xsw4YNpUFqb22URrezzjpLLr744tK//+d//kfGjRsnnuftta2J7JrsfelLXyr9u6+vTwDIH/7wBxEZ2v4/8pGPyIknnmhuz55tc926dRIKhcrGGpFdfeDaa68VEZFDDz20bBFsX3DRif5WKhdV92RdO37zm9+U3tPT0yORSER+9atf/f/t3Xl8XGW9P/DPOWf2ZJI0adqkW7pv0J0CpUARigWvWEUBpWLhIveiIFev4vJTQUBBRVBEvSp6WRTZLiDKjshaoBSwZWnpki7pkjbNvs16zvP7o7dzO5Pv9zTBUVL4vF8vXi/6zGSeM+c82zyZnE+urL293cRisQH1Gz8H66OF9u7dawCYN954wxijz63Lly83dXV1JpvN5srOOOOMvLnpwLXGweay7u5uEwwGze233557PJ1OmxEjRpgf/vCHxhj5HPZnTXHaaaeZ8847r9/ngN4f/NY7+9v3/s1XY4yZMmWKOe6443L/zmazpqSkxNxxxx1qHddee62ZN29e7t9/76bTgZ9R9td/zjnn5MoaGxsNAPPiiy+qr3PRRReZj3/843nHMND3Kn0eueOOOwwA8+STT+bKrrnmGjNlyhT1WFzXNfF43Pz5z3/OlR34+eMLX/iCOfHEE/M+Y1BxHQrz2M0332wA5G1i/PznPzfDhw9Xf+ZgbWu//m46zZkz56DHec8995iqqqq84+7vptMxxxxjLrjggryyM844I/c58mCbTsbIn1G7urpMKBQyd999d66spaXFRKPR3PXZsGGDAWBWrFiRe05zc7OJRqO5n+vv/sJHP/rRfr1fY/z792OPPWZs2zbr168Xf/b88883//Zv/5ZX9txzzxnbtk0ikcgdz6c//enc457nmWHDhpn/+q//MsYcfA+jpaXFAMj9Aqs/BvTndTNnzsz7d21tLZqamrBu3TqMHj0ao0ePzj02ffp0VFRUYN26df167VmzZuGkk07CjBkzcMYZZ+Cmm27KfcW9p6cH9fX1OP/881FaWpr777vf/W7eV90Lj7G2thYAcl9/XLduHRYuXJj3/IULF2Ljxo1wXbfPMe1/XyNGjMiVLViwoF/vBwDWrFmDTZs2IR6P5465srISyWSyz3EDwKZNm9Db24uTTz45733edtttueevXr0axx13HILBYL+PY78jjjjC9/HVq1fjpJNOUh9/9dVXcdppp2HMmDGIx+NYtGgRgH1/WjlQ69atw1FHHZVXVnhu16xZgyuvvDLvXFxwwQVobGxEb28vAoEAzjzzTNx+++0A9rWTBx54AMuWLVPfn+M4ueMutGbNGjz11FN59U2dOhUAUF9f79tGafBbtmwZ7r33XqRSKQDA7bffjk9+8pOwbfugbW2/A8eXkpISlJWVqX9ierD+dKA33ngDruti8uTJecfwzDPP5Pr+JZdcgu9+97tYuHAhLr/8crz++uvv9FQQ/VP1d+44cI7avHkzMpkMjjzyyFxZeXk5pkyZkvt3f/qNn4P10Y0bN+JTn/oUxo8fj7KystyfHPgd936HHXYYHMfJ/Xv/eklysLmsvr4emUwmb/0SDAZx5JFH9lljHXgs/VlTfO5zn8Odd96J2bNn46tf/SpeeOEF9XzQ+8fB1juHHXYYbPv/PkIMHz4cM2bMyP3bcRxUVVXltfm77roLCxcuRE1NDUpLS/Gtb33rHa0fNQfOz/vrP/CYhg8fDgB5x/Tzn/8c8+bNQ3V1NUpLS/HrX/+6zzG9k/daeDz76y48ngN/Zs+ePbjgggswadIklJeXo6ysDN3d3eo5Ovfcc7F69WpMmTIFl1xyCR5//HGfs0N/r8E6jwFALBbDhAkTcv8unG8G2rYGat68eX3K/vKXv+Ckk07CyJEjEY/Hcc4556ClpSVvTd1f2uf3/u4xaOrr65FOp/M+j1ZWVuZdn3Xr1iEQCOQ9p6qqClOmTMnV39/9hYN9Dj+QX/9evXo1Ro0ahcmTJ4s/u2bNGtxyyy157WnJkiXwPA9btmzJPe/AMcqyLNTU1OTazcH2MCorK3HuuediyZIlOO2003DDDTfk/UmnJNDvdw/02eiwLKvPPVI0tm3DGJNXduDfcjuOgyeeeAIvvPACHn/8cdx444345je/iZUrV+ZutnnTTTf12ag4cFFXeIz7b+jb32Mstu7ubsybNy+3kDxQdXW1+HwAeOihhzBy5Mi8x8LhMAAgGo2+4+MpKSnxfdzvtXt6erBkyRIsWbIEt99+O6qrq9HQ0IAlS5b8w26G1t3djSuuuAKnn356n8cikQiAfRsJixYtQlNTE5544glEo1Gccsop4usd7Nx1d3fjtNNOww9+8IM+j9XW1vq20XHjxr2Dd0j/TKeddhqMMXjooYcwf/58PPfcc/jxj38MoH9tDRjYGDiQvtrd3Q3HcfDqq6/2GdNKS0sBAJ/97GexZMkSPPTQQ3j88cdxzTXX4LrrrsMXvvCFftdD9M82kLnjYHNUof70Gz8H66OnnXYa6urqcNNNN+XuCXf44Yf367gHul4ayFzm58Bj6c+a4tRTT8W2bdvw8MMP44knnsBJJ52Eiy66CD/60Y8GXDe9d/itdwC5ffu1+RdffBHLli3DFVdcgSVLlqC8vBx33nnnQe+rOhAHO6bCzwR33nknvvKVr+C6667DggULEI/Hce211+beY39fd39ZYf+W6i4sO/Bnli9fjpaWFtxwww2oq6tDOBzGggUL1DX23LlzsWXLFjzyyCP4y1/+gjPPPBOLFy/ucx9Y+vsN5nkMkNvogZ+5B9q29jvYZ/f9Ct/z1q1b8eEPfxif+9zn8L3vfQ+VlZV4/vnncf755yOdThc9RGP/pvCBx1qs+8UV00Dahl//7s/n2X//93/HJZdc0uexMWPG5P7fbxzrzx7GzTffjEsuuQSPPvoo7rrrLnzrW9/CE088gaOPPlo8rgFtOmmmTZuG7du3Y/v27blvO61duxbt7e2YPn167gDffPPNvJ9bvXp1nwF44cKFWLhwIS677DLU1dXh/vvvx3/+539ixIgR2Lx5s/otlv4e54oVK/LKVqxYgcmTJ/fp6Ae+r8bGxty3pl566aV+1zd37lzcddddGDZsGMrKyg76/OnTpyMcDqOhoUH9Ns7MmTNx6623IpPJiN92CoVC4re2+mPmzJl48skncd555/V57O2330ZLSwu+//3v567xK6+88o7qAfad28KJvfDczp07F+vXr8fEiRPV1znmmGMwevRo3HXXXXjkkUdwxhlnqN8CmzFjBjzPwzPPPIPFixf3eXzu3Lm49957MXbsWAQCctfwa6M0uEUiEZx++um4/fbbsWnTJkyZMgVz584F0L+2NlD7+9MVV1xx0OfOmTMHruuiqakJxx13nPq80aNH48ILL8SFF16Ib3zjG7jpppu46USD2judO8aPH49gMIhVq1blFkkdHR3YsGEDjj/+eAD97zcavz7a0tKC9evX46abbsq99vPPPz/gOvrLby7bf/PUFStWoK6uDsC+BfWqVavybkZbqD9rCmDf+mz58uVYvnw5jjvuOFx66aXcdCJ1vfNOvPDCC6irq8M3v/nNXJnvDWf/CVasWIFjjjkGn//853Nl/f1myT/qeH7xi1/gQx/6EIB9N/E9MMhGUlZWhrPOOgtnnXUWPvGJT+CUU05Ba2srKisr/xmH/L4xmOex/uhP2woGg30+P/bns7vk1Vdfhed5uO6663IbQnffffc7Pv79n9+XL1+e954O3GMAgMbGRsyZMyd3nAcKhUIAkPceJ0yYgGAwiJUrV+auT1tbGzZs2JCbM6dNm4ZsNouVK1fimGOOAfB/64P99Q90f6G/tP49c+ZM7NixAxs2bBC/7TR37lysXbv27/pM0989jDlz5mDOnDn4xje+gQULFuAPf/jDP3bTafHixZgxYwaWLVuGn/zkJ8hms/j85z+PRYsW5b5KduKJJ+Laa6/FbbfdhgULFuD3v/893nzzzVzjWLlyJZ588kl88IMfxLBhw7By5Urs3bsX06ZNAwBcccUVuOSSS1BeXo5TTjkFqVQKr7zyCtra2vr9gf/LX/4y5s+fj6uuugpnnXUWXnzxRfzsZz8TU/D2v6/Jkydj+fLluPbaa9HZ2Zk3YR7MsmXLcO2112Lp0qW48sorMWrUKGzbtg333XcfvvrVr2LUqFF5z4/H4/jKV76CL33pS/A8D8ceeyw6OjqwYsUKlJWVYfny5bj44otx44034pOf/CS+8Y1voLy8HC+99BKOPPJITJkyBWPHjsVjjz2G9evXo6qqCuXl5f0+3ssvvxwnnXQSJkyYgE9+8pPIZrN4+OGH8bWvfQ1jxoxBKBTCjTfeiAsvvBBvvvkmrrrqqn6/dqFLLrkECxcuxI9+9CMsXboUjz32GB599NG851x22WX48Ic/jDFjxuATn/hE7s+g3nzzTXz3u9/NPe/ss8/GL3/5S2zYsAFPPfWUWufYsWOxfPly/Ou//it++tOfYtasWdi2bRuamppw5pln4qKLLsJNN92ET33qU7mUxk2bNuHOO+/Eb37zG7zyyiu+bZQGv2XLluHDH/4w3nrrLXz605/Olfe3rQ3EN77xDcyYMQOf//znceGFFyIUCuGpp57CGWecgaFDh+Y9d/LkyVi2bBk+85nP4LrrrsOcOXOwd+9ePPnkk5g5cyb+5V/+BV/84hdx6qmnYvLkyWhra8NTTz3V77a3adMmdHd3Y/fu3UgkErnJePr06bmJmOgf4Z3OHfF4HMuXL88lRA0bNgyXX345bNvOfWugP/3Gj18fraysRFVVFX7961+jtrYWDQ0N+PrXv16Uc6LR5rKSkhJ87nOfy52LMWPG4Ic//CF6e3tx/vnnq6/XnzXFZZddhnnz5uGwww5DKpXCgw8+yDmNfNfk7+RPuydNmoSGhgbceeedmD9/Ph566KF3vIFVLJMmTcJtt92Gxx57DOPGjcPvfvc7rFq16l375vqkSZNyCWmdnZ249NJLfb/RcP3116O2thZz5syBbdu45557UFNT0yeVmv5+g3ke64/+tK2xY8fiySefxMKFCxEOhzFkyJCDfnbXTJw4EZlMBjfeeCNOO+00rFixAr/85S/f8fFfeumlOPPMMzFnzhwsXrwYf/7zn3HffffhL3/5C4B931o++uij8f3vfx/jxo1DU1MTvvWtb+W9Rl1dHSzLwoMPPogPfehDiEajKC0txfnnn49LL70UVVVVGDZsGL75zW/m/TntpEmTsHTpUlxwwQX41a9+hXg8jq9//esYOXIkli5dCmDg+wv94de/Fy1ahOOPPx4f//jHcf3112PixIl4++23YVkWTjnlFHzta1/D0UcfjYsvvhif/exnUVJSgrVr1+KJJ57Az372s37Vf7A9jEwmg1//+tf4yEc+ghEjRmD9+vXYuHEjPvOZz+gv2t+bP0k3U1u6dKlZvny5MWbfjdA+8pGPmJKSEhOPx80ZZ5xhdu/enff8yy67zAwfPtyUl5ebL33pS+biiy/O3Yxs7dq1ZsmSJaa6utqEw2EzefJkc+ONN+b9/O23325mz55tQqGQGTJkiDn++OPNfffdZ4zp303EjNmXRjN9+nQTDAbNmDFjzLXXXptXx4E39zRm382njz32WBMKhczkyZPNo48+2u8biRuz78aFn/nMZ8zQoUNNOBw248ePNxdccIHp6OgwxvS9SZvneeYnP/mJmTJligkGg6a6utosWbIkL6VqzZo15oMf/KCJxWImHo+b4447ztTX1xtj9iUQnHzyyaa0tDT33qVzY4x8E7d77703d46HDh1qTj/99Nxjf/jDH8zYsWNNOBw2CxYsMH/605/yXncgNxI3Zl8SxKhRo0w0GjWnnXaa+dGPftTneB599FFzzDHHmGg0asrKysyRRx6ZlwBkzL62A8DU1dX1ueFaYbtNJBLmS1/6kqmtrTWhUMhMnDjR/Pd//3fu8Q0bNpiPfexjpqKiwkSjUTN16lTzxS9+0Xie1682SoOb67qmtrbWAMj1mf0O1takfl9eXp5Lp5Da/9NPP22OOeYYEw6HTUVFhVmyZEnu8cK2uT9JZ+zYsSYYDJra2lrzsY99zLz++uvGGGMuvvhiM2HCBBMOh011dbU555xzTHNzc7/e96JFiwyAPv9t2bKlXz9PNFAHtu93Ond0dnaas88+28RiMVNTU2Ouv/56c+SRR5qvf/3rueccrN8cjF8ffeKJJ8y0adNMOBw2M2fONE8//XTeOKDNrf25+WrhWsMY/7kskUiYL3zhC7m1xMKFC3NBLX7n8GBriquuuspMmzbNRKNRU1lZaZYuXWo2b97cr3NH711+6x2pfUufEQrb+KWXXmqqqqpMaWmpOeuss8yPf/zjvDXf33sj8YPVb0z+PJ5MJs25555rysvLTUVFhfnc5z5nvv71rx/0GA5WlzQuSP2zcA3+2muvmSOOOMJEIhEzadIkc8899/R5Dwce/69//Wsze/ZsU1JSYsrKysxJJ51kXnvtNf8TRQNyKMxj0me5+++/3xz4Eb8/betPf/qTmThxogkEAqauri5X7vfZvfAcHej66683tbW1JhqNmiVLlpjbbrst7/wM5Ebixhjzi1/8wowfP94Eg0EzefLkvOANY/aNWQsWLDDRaNTMnj3bPP744332AK688kpTU1NjLMvK7V90dXWZT3/60yYWi5nhw4ebH/7wh33eU2trqznnnHNMeXl57v0UJgsOdH/hYA7Wv1taWsx5551nqqqqTCQSMYcffrh58MEHc4+//PLLuf2AkpISM3PmTPO9733P93hmzZplLr/88ty//fYwdu/ebT760Y/mPk/X1dWZyy67LC90oZBlTMEfaxIRERENMj09PRg5ciSuu+4632/4EBERDUacx+j9qih/XkdERERUTH/729/w9ttv48gjj0RHRweuvPJKAMh9pZ2IiGgw4zxGtI998KeQ5uqrr86LIzzwv1NPPfXdPrx31amnnqqem6uvvvrdPjyi94wLL7xQ7WsXXnjhu314RH+XH/3oR5g1axYWL16Mnp4ePPfcc33uiabhPER06GtoaFD7cWlpadFi34n+UTiP+TvssMPU9yilp70XvB/3EPjndX+H1tZWtLa2io9Fo9E+EcXvJzt37kQikRAfq6ysZLIGUZE0NTWhs7NTfKysrAzDhg37Jx8R0eDAeYjo0JfNZrF161b1cb/EYaJD3fthHtu2bRsymYz42PDhwxGPx//JR/SP937cQ+CmExERERERERERFR3/vI6IiIiIiIiIiIqOm05ERERERERERFR03HQiIiIiIiIiIqKi46YTEREREREREREVHTediIiIiIiIiIio6LjpRERERERERERERcdNJyIiIiIiIiIiKjpuOhERERERERERUdFx04mIiIiIiIiIiIqOm05ERERERERERFR03HQiIiIiIiIiIqKi46YTEREREREREREVHTediIiIiIiIiIio6LjpRERERERERERERcdNJyIiIiIiIiIiKjpuOhERERERERERUdEF+vMkz/Owa9cuxONxWJb1jz4mokOGMQZdXV0YMWIEbHvw7uGyDxPJDoU+zP5LJDsU+i/APkykORT6MPsvkWwg/bdfm067du3C6NGji3JwRO9F27dvx6hRo97tw1CxDxP5G8x9mP2XyN9g7r8A+zDRwQzmPsz+S+SvP/23X5tO8XgcAPDD71+JaCSS91jGc8WfCYciYjkAuK78M7btiOWWlxHLs55aBQKRmFgeVHbhMqmkWF5eKr8OAPQk5Z9JZeQDi8VKxXLXTal1ZNJpsdwo5zDkKOfQGLWOZDYh/4wtNw8nIL8/xwqrdVgB+bHe3l75teS3gYATVOtIprrE8mw2K5Z7StuF8ekWBW00mUzi8u9ck+sjg9X+47tm0ecQKbgWdlA+p15Yv55eUO7fRvkFkIH8gIHeLstjcv2OpVw3ZfyIBkJqHWGlfstSypU6sp7SYAEElN+K2Y4ygBm5z0d8frtmAnKbbUrJr7U7Ife73mS3WkcsKl/zeLxWLA9CPu8hrZEAsBy5ryaNPAe4ynl39GYFy+S3n0Q6gUt+8+VB3Yf3H9tV1/4QkWg07zHbkc+z5zPmB0urxHLLlc+zk2mXX8j0qHUkIc+d2UC5WG47crvoWfuoWkfYludOp3y4WB605P5QYrWpdWQjZfIDPe1icahrvVhuVdepdbiVS8Tyrs6dYnnJnkfkOpyoWA4ApqRSLO+pWCSWd3sV8vOT8jkEgHTXDrHcseW26JSPE8s9S18/dtv566h0ohc3f3n5oO6/wP/14e3bt6OsLL9NucpiNptV5joAmYzcV1MZeV3ak5TXedp6HID6jQ5tKjJGXl9nM3od7R0dYnlrS4tYnlDW3Z4yR/zvEfg81pdRxk6/3+JXVsr9q6KiQiwPKuuud3JcnieXG0++UNqaGAC6u+U1wNChQ8XySRMnieX6iqhvu+rs7MTo0aMHdR/ef2zfv/bnwhwsn/9wWL/G0yZPEcvHjx0jlkei8jxv+60LlSWAq3z2Safl8g2bt6l1JFPyWnLMiJFieXWV3E/8viCj9TtH+6Coruvfv99Q6+yU+3VLW6dY7vnMC2Xxkrx/d3V1Yc6cmf3qv/3adNp/oaKRCKIFnS3gyYNXOKQvfga+6SQfpv+mk1y/tukUsOXGGIvp78PTPkwqmzLaa2Wzem8LKJ1qoJtOts8HECurfcAe6KaTvlC0lU0nY5TXUj6A+G06WbayOZmR26g28PptOmmbDoN9MNt/fJFAGNH+bjopG0t+jxVz0ymqbFwHLGXBpLTXd7LpZGubTkr/yvhsOgUHvOkkv9Y72XSKKUu/qCvX7Skb/AAQUa5HNCyPayHIff6dbDrZykacttkXGMCmU658EPfhXP+NRvvMwe9o0ykmbwjpm07KL0Y8fRK2ILeLTFCuWxvz3bBP/1X6UCAit72gcomjll5HVlnsw5XHzXBGmSOi+tyV1dYGGfl9RCNKHY4+d5mIMs5Hlbo9pdzSxzpLOV5100mp27P0dVfaVtruIO6/wP8dX1lZ2T900ymZVj6cBuW28c/YdMr4bDpllfqTyuaSpazh3+1Np5KSErG8tFT+ZfNg3XTS6tDeR2Fb3m8gm04HKx8M8ufg/DFI3XRSxlxg4Ofz3dx00o4VAJyA3Ce0DQjt/XHT6R9NPodp5fO/37wQj8vtoT/nd3D+8SwRERERERERER3SuOlERERERERERERF168/r9svnUnBCRR8fUrZtvJ8vnavsQPK17+Vr/wFfL6e6ilfV0668t+1R0Pa14Llr/gCgIH2ZxraV2Dl52tfTwSAVEL+ynBY+ZOKgPJnTEHl/QGA9s3nhHKfq6DyFerupPw3+AAQUe6xBVf7GrP8NX2/L+9pbU77Ey7Xk/9kJ+jzJwpWQRu1Hf0riINRaSSGWMGfxvUq58Gz9D7ck5b/jjuVkP9uOBySr2dJRP/artsjtz/tnk6BiPKVWp9Wo/0ZjqX8HU5G6Ua28jV2ACiDPE4lbOXPPpU/P8om9HupbGtuFMtf214vlq9u2CiWt/S0qnXES+RrdezME8TyudPnieWWz5+vGlf5urTyMymliaZ9/rSs8KvXCeVPfAcjx02gcMgxjtyGlaEVAGArX522PHl+tF1lHvT5MyjXkv/cxCh/wqfcChG2z5/qG1e+p5SXbeMaPGwAAHQjSURBVFeOST4p2bD+p8SB5B657rB83yjTIY+BmWyFWkeyTb4XUmmTfO8m7c8HMj5LOhMdL9ftyPdkS/c2i+Wect8mAIi68r2x7OgIuVw57z5/VYZIwbxk2YdO/wWA1X97o8+fq2zdKt8zRfsTM0D/E6msUf5EWVlnhn3afiSi/Im78qd6jvIn7n4rN+19ZDLyfOcqfxruOH6/Q5ePS/0zOuW1/NafXV3ymijpM29L/D47aY9pf0rtKefK788EUyn5T6m1tvB+U1s9HLFY/txWViHPdY17GtTXSaXkuUvrD8mEdksEtQr1/mfbG+V7BXYn5Pm/cVeTWseelt1i+dbtm8XyMTXyvQ1Hj9Rv0l5SIq8B4nG5TYaVP8nX+gOgjx/vZpqidvsZvz+71v5Ue8vW7WL5U8+/JJYHbH1/ZfLECXn/7unR7+1ZiN90IiIiIiIiIiKiouOmExERERERERERFR03nYiIiIiIiIiIqOi46UREREREREREREXHTSciIiIiIiIiIiq6AaXXZbMZZDL56UKxkrj4XMvScx6iUSWxREmAcpR0jUDQJ03ByK+VVtLgMhkltcfnDu7Gk5OWAkoKn+vKqQRdHR1qHbGwkuYE7X0od7X3SWjKZuXUjXBITtHyIB+TZ7rUOoyRfyYUlFMJAo78/LQrJ2sAgG3LP+MoSXhZ5bVCYZ9rXvA+Akqdg5UpicCE8vtNzCjpamm5jQFAslduM6GkXG6UJMSAT3KOB7nNppR0DzXtLqKnXyGqpNooKTyultbp6P2r05b7ZNhS+oQlV/Jqw1tqHXeufFws39q6Syxv7pZT6rRETgBwlOye9g6536cgp/aMHianlwDAsLIasTyqpByGlRSPrF8CUGGBMi4PRl42Ca/gcK1SORksGJbnZgAIBuS2Z2eUNCdPmW8cJZUUgK2M+epcm5VT34IhfZliK0lxtiWP7ZaSUOOVz1brcNrlpB+3Uh67THiMXJ7tVOsItz4mlgcy8tqgMyb3ITukp4EmepT1hyu/PystH280LSdlAkBAu1QhbZyX+2k6o/fJcLTgPb6L6ULvxPe+dzWCBcnLHcoaMKCeUD1VSWvjTkie54PKehUAgkpKnZYcrR2vo6zn/B7TyrXPFo6jf+awbfkx9Rwqdfh8rFGT5bS0PS2B0vP02FEt2Uz7bJFOy3PwlClT1DqmTp0qlmvnytbOlVrDoa21ox2JgvM6drw8B9cMm6O+jnZttMSycFj+HJNM6GOllqj4+pvrxPI9zfJ6MZ3S14XdSnLZniZ5nti+Xa5j/F49Ie+wKZPF8tKYPNeuW7dFLG9tkdNVAWDevMPE8pISZZ5Xuqnf3of2Q1qP11Lq9u7W30dXl3w9tm2TkxQbd2kJi/rc4xYkXCaT8ppOcmjN1kREREREREREdEjgphMRERERERERERUdN52IiIiIiIiIiKjouOlERERERERERERFx00nIiIiIiIiIiIqOm46ERERERERERFR0emZeALHdvpE2WvxnrYShQsAmbQc4+goUeEWQsrryJGTABAKKZHQShR6IFgilhuf+FLHyHGGmYwSL+3Kzw8E9Nj4kBJz29PRIpZHA/I5zPhEEHuQ67AcOaIzm5bfXzw+TK1DC5G0LLmdZAtzwfc/35bbAqDH+BolpjLoyNfcP0E9/0HvEIpbB4B2N4lUwSFXhuVzGs3o0Z/x0jKx3AvL/aupXY74tLJytDkABMNyv7CD8nFFPLntB5S2BAC2cv0M5PEjbOQ6Qq6+f58t1cYcufztrZvE8l+99JBax9pdG5RH5DHHVhKsA/qwrZwRYOseOZ72jof+IJYPKalU6/jo4k+I5QvnHCuWB5Py66Rdve1mCscDJWJ8MHKdCFwnmlfmBWLic4OWz/SuTWtKXLNRTqel1A0AVlaen7V5HtkusdjOynMdAAR75VhmZJU5LRwVy5HYrdaRjoyW6+7cKFddMV0sDyfr1Tq8rLwmSgfjct0huTztDFXrcI08ngYz8thsZ/eI5SFXOecArNhY+YGgPMd4kMffwnXmgdyCMc1WxrjBqqlpDxwnv28mEv2PnN7PUc6RrayDoPQ729bHP+0xrVzr2n4p4lrEuFqH9mJ+degPDYi2ljzYY8Wqw1U+Q3ieVi73r2HD9LW6Vn9x3t2h7+XXXkIonP/ZqKdHjqk/dsEx6utUVlWJ5bbSr7NZ+Qps36GPx9t2bRfL67fI68Wuzg6x3HX1hWEqKc/zobA8DiV6usXy9rZWtY54TP4smkzI5/2vT68Sy4dV6/PjmLoRYnkkIvcVbXxSuuI+yuddZdmFHQ3yumTN62+oVbQq57GzS15Hucrao7W5U60jaOW/yVRKWYgLDp3VNhERERERERERHTK46UREREREREREREXHTSciIiIiIiIiIio6bjoREREREREREVHRcdOJiIiIiIiIiIiKbkDpdZZlYFn5d9C3jZxk5hfkkEzIiVVhJanNGCXJzFFSOgBkMtpbk3MstJQ6z/OJc1LS6yxbPi5bOSfREjlFDQAiSrJYJqmk3aTkZIBYVE8ZspWUo7Qnn6twWE4SCGqpKT60O+cbJSvDQG5v+8jXKpmUk2ECATklIhLSz1UylZ/UYGlxLYNUb1cPvGB+fwqm5esfUZIeASCrpDCUV1aI5cNL5HPa3KInU6U75cdG18gJFGVxOVGvs1NOuACAdLfcXwK23PYDUTn9KhLW24yWndeopEn8YcUjYvnaPXKqHQA4IWVcyyjjmquU+7TnrNIessoc0JzYK5a39urXfH3DW2L5jElTxPKakiFiea+S9AKgz+SU8UkoHWyMHYVn57fBoDJWBlyfRBFtWvPkn/GU308Zn/biKKlillFSZ5W6g4kdah1eUk5Ys5JKqm6pnJwYTOnpdW7pBLm8d6tch90sPx9y4hwAZLULUjpGLPYqThDLExn5fAAAUvJcG4U8Pjpdcjqf5ehZYGo6YEAeOzxlDg4aPb2uMKnLUtZhg1UsVoJAwfkIKkmmWloZACSTcn9xs/KM4wSUdalPtpun1O8qacglpfI8GI7oqcMxZU51lDZjacdr+STLKet4T/mgoq37s0oyNQCklcfSGXm8yyrXSY0Khf7etXLtfftSzomllQ+8hkOahQgs5H8GcpW01PrNDerr9CaUJDWlGbe1y+umdiUZGgA2NWwWy5tb5TkqlZDbqjYO7HtMbseuK/ffVEJJmQ7I8xMAvLjqBbG8uVn+mVRKbve9ST2R7bW/yeNQNDJfLC8vVxJkk3padjYrH1dGSfHeUC+nDG5v1JNwO9rbxfLuLjkduKdD/gxkW/o1bytIyEun9RTyPq/b72cSERERERERERH1EzediIiIiIiIiIio6LjpRERERERERERERcdNJyIiIiIiIiIiKjpuOhERERERERERUdENKL3OtgJ9ks6yGTlBIxDS97PCSsKJFiJUmJiXe52AT26CI9efdeW7xxslmSHr6mkV2bSS9GNrd6iXn9+d0O/aXxavFsuVtwFbSd0ySkIdADhKKpXtKXevt+Q6Uin9DvbZrJbgoaTXKe/PLyHPUZJwtGaSzcjvrycr380fALyCRqqlDgxWXmcPvEB+m85WyP2xZHSV+jpNOxvF8mRWTkKylGsT00OKUBaQk3BKHTkJJ6Dsods+W+t2UH7QS8mJh0kliQbD5URHAIgq/SugxJRYyvgRVtorALhKKoalvHklYBPG6CfLDsrv0dGOSxknbJ/Es5VvyCkl08bKCWJ1R58klmdTeoKIW9CHg4dQep1nAvBMfntylPOs9bl9r6Mky6Xk9m3UBqNWobY9R4lxDdjyWOp0ymMNAGS75UQW18hjmtu7Xa67TEkSAmAF5ccykfFieTgtJ+G5JSPVOgLpv4nl2fA4+QeC8rUt9ZmPvKAy13bJKZNeQk7CC1TKfREA7KA8ZpuAPHZ4jvw+kj6BdE7BWs32SS0bjCx733/5hcq61CetN6pMnuPHThTLu7vkuXn7dj0dUltPzp0zVyw/+uijxPLy8nK1jspKJVEyJM/zrpKW1dXVrtbRm5DXdGllvNOSATM+6XWdyljUWpD0tF9zs5xGtnu33B8BoL1dTt9KJeT1ijZAWz5znvaYlnL4fkuvO2bhBxCL5SeOT5tYJz63rb1DfR0t2S6blsfpjk65DSeSejpz4y557uxSxoJMSm7fts/3U2xlvaoMHUi7yufHjD53bd3RLpa7Wfm44mXyfBPv0N/Htgb5enS0y/36sMOniuVjR8mJswCQUdalu5t2ieU7dsjrlW4ldRsAHFueF2xPXsN5yvo9ENDHiEwyv41m0srnIuk4+v1MIiIiIiIiIiKifuKmExERERERERERFR03nYiIiIiIiIiIqOi46UREREREREREREXHTSciIiIiIiIiIiq6AaXXwXL2/XeArLJv5UCJHwPgKQ/FSpSECyVZLmDpEScpJd4tFI7Ix5RV7uDusy9nKacvGCkRyzOufFf5rh4teQLo6JRTBkqV5BLLksuVt7fvZ5QLElHSQ9Ku8mJKEs2+41LOVVC+Hl1dcmJAyOgpYVkl8iYYkuuwlVQxD3qKgtsnWeLQSq9DTydQkP4WrZLbqx3Wc0mqY/J1sNNKImCvnLYwJCrXDQCxsgqxPFMq/0yiTU4K6U3r16i8Ki6W20k5jaFESXRMd8lJNADQq/SL4dXyePeBoxaI5dub5YQLAEgqx9XWpZwTJZ3P02IjAWTT8s9oI6SaduMTNtXR3SaW72yWUzxcyO877Ohtt89xaTErg5ATDMEpSBH0lIQiLZUUACxPSfdT0kTtcIVYbnzmeUtJ5LKgJN5m28XyTEZOogMAq1xOkIEy36Q2vCyWG/tNtY5grTx+mLCcLJtSOoRdXqHW4UTl9KNgqZx455TKdXsROT0OANC9USxOdsljs6MkFtqWngzp2HL6kbHk8ozSdm1lzQcAhV3bJwB1UIpEwggE8s9tT4+83qmq0hNk//X85WL5gvny/HHXnXeL5ffee69ax2HTp4vls2YdLj//sGli+dFHH63WoSVHO1pSsfL8PY071TpefOl5sdxV0mgnjB8rltfW1qp19PTKa/WObjlxrkvpd9sb9Pfx/PMvieXr1q0Ty9W0Z5/+pV2PQ2eW/MeafdgklJbmrxuHKuvI6io5mREAQgF5jnrpJXmO8lwlAdwn1bhXGVe8jDyGJ7qVtbKnX/1YTPlMnZbn/64eeS7o6PFJZ1YS78JhJdleS3HP6O2+uVlOmWxrk/tpKiUfb6Jb/zzf1SmPBRs2ycl5I0bIybnzZ8vrBQDYunmzWB70msVyLa2yU/kMDgApL3+daLT1pIDfdCIiIiIiIiIioqLjphMRERERERERERUdN52IiIiIiIiIiKjouOlERERERERERERFx00nIiIiIiIiIiIqOm46ERERERERERFR0emZyhI7CNj5cesRJY7eKNHLAOBm5ejHRFKO6ItE5RjgjE+MI4LycaWUyEQl3RmW0fflklk5MjGTlt97Wolht5W4cwAIK4mnnhK3GbLlOHmTkGOqAcAKyZHUXb1KfKYrv5YV1K9HtESODnX0Ey8WJxN6NGMkUiqWZ5Xj1cMz9esRdPLbQ9Y+tPZta8tLEA3k9+GAEnve0dSkvs6o0nKxvKZ8iFjes3uPWJ4M621mtyNft2yX3I9GlMrXP2brMeKpXrmOeIncjyKOHNDdkdZjUp9c+5pYvicgR8eGh8jxu/MOm6nWMbRyuFi+Zed2sXzVm38Ty/d2ybGxAJBV2omt9FVL+Z1GyNOnncNHjRfL502Uo7ttS67DtpVxBUBhyq7yEoOS5TiwC9ugJfchy5PbFwAYZe60lLHSNsr5LMyvP/AhVx5HswF5bk6UyDHsqVnD1DpibXLfCiTluavTGy3XvX6LWsdQS+7zzjCtjcn9IWTNV+swE08Ty734GLE8a8lzNoJyxDIAWBE57t2pUcbHHjm63evZqtaBsDwveI48NntGaT+ePMYDgLHzr4eB3s4Ho4ATQLBg/eZ5cluaPGWy+jofPPmDYnlFmXwNKsrLxPKxY+U2BgBl5fJctG2b3F+OPW6hWJ7NKGtJAB0dHWJ5TJmDsxn5eu9U5joAWLt2rVi+WYkXHzlypFh+5JFHqXXUjJDn4CGV8vWIRuU+XF5eodbR0yt/tti8uV4sN8oiV2tv+35GXxkTUFURQzye3zajEXmOiITlcgCYM2SSWD5+vDxOv/rKm2J5ff0OtY6ScJVY3t7aI5YnE/KcPa5uglqHttbauUs+rqAjf6gN2Xq78wLyIi3gyD/T0S4fU0WJvvbMBOQ5J1Imr1faWveK5Zs26e9DW2oeMW+OWD57lrzuffMNef0OAK0tbWJ5OBQWy4dUVIjl6ZT+Obi84PNfKqWP74UOoeU2EREREREREREdKrjpRERERERERERERcdNJyIiIiIiIiIiKjpuOhERERERERERUdFx04mIiIiIiIiIiIpuQOl1jh2GY+ffAT0UlO9EnzF6ooiWnKAEQyGVlO+0b1lKtBsApOW7qTuQ01IcJYXHduQEDQAIK8krPQk5YcJVUjcsoyeyZXvkBLF4lZxKYCnXw4TlO9cDgKukD1iOfPf6QFhLidPfB5S0pGRGTgzo6pKvuTF6SpilJOF5rlx3MCSfq2xWTx8Ih/KTfrLuobVvO3p4BUqC+W2hoblZfK4xcmoDAATK5fbnVclJSOUlI8TyqKunHuxSkmUCvfL1qZg9Sj6mlE+KV7OSxKikZXQpfSUT1NvBm9s2ieVPbJQTKCIl8jmM2Pp4N7xSPr9jxtSJ5RPHjBPLezbpKZcpZZxys/L5dZW+PayyRq3js59aLpbPmTxLLO/pleuO+nTLcEGqmu2TVjnYGJOBKZhbPSW9zhj9fWmPeVllfM12i8UBW04lBQDPlSf0tJHbcSIjJydGMnqKZrBFTtEyNXLqS+zEs8TyPbf9RK3D3toulldH5EROK6okyEbkMRMAEJYTi7Ip+Xq4Rkl3U9oCACAgjyvpbItY7kTkJDxTIo8pAOApyX1eRn4f2aw8/pugXxpOQbvyST0ajBzHgVOw2NUuW1lcTo/zk1bWvq6SZrljZ4P6WlklafqkxSeJ5YmEPE5s375NrSMSldcZgZR8Uppb5Pa6fYeeXjdihDw/tre3i+WrV68Wy19Z9Ypax9LTPyKWz5h9mFje1d0plgcD+lq9uloeQyLRkFje0y3P537pdX6P0b7PqYXh4Y4jLzgSCT2FM6pcs9oaOQH6AyceKZbH40qSKYDGPfL8GGmTx5XdTXKb3LLNJyEvJtefdZWE9YA8/w8p0xdte1vk+SOhJC2OnST392lTJ6p17GqUx8GG7fK4EovJ41ZvUk7jBIBYRO7bdRPkzy7PPfe8WN7Wqtcx/TB5rZzolvcldjfuEsuH1+ifN1pa88fggaygD61PzEREREREREREdEjgphMRERERERERERUdN52IiIiIiIiIiKjouOlERERERERERERFx00nIiIiIiIiIiIqugGl19m2BdvOT5RwPeW+5bb+0vG4nO6SScl3V9cSXEIhfc8sEJUT1mwlcc5z5OPV6gaAgCPf3b00rKQJhOQ717vQ07VSQTl9ImPLqQhhJb1u17p6tY7mPbvFciVwDvER8p32a0fLiVgAkErK11YJE0QkLCft9CrpKACQdeU6As7AEmBiUf35tp2fOpPN+qQFDUKl8RhKQ/mpC5PK5b7SltDbfrpXvg5dXXIbt8vkdlkBOcEDAKYMkdOTOkLycbX1tonlqU6l7QGIKJGZjV3ya61tklM8IhVyewWAcZPHi+XDdmwQy3d1t4vl7ZY+3u3q7BLLd7TKyV8VSipSiZIsAgBRJT0vFpcTzIYPrZZfxyedxw3I12PV66+J5YGUnLRz5NTJah2hgoS2wn8PZk6mF04gP7HFSyrXTElRA/T0OktLRWtfLxZ70WFqHSklzTSlpLg6WXmuC6TkVDsAyLbKCXKRMfPF8rLxcnlD+Vi1DrP7bbE8Ht4p133EB8Xy3pCe2uil5PHU8uQU14yS+hYM63OX62npk/K4knHk53u2fj0sW177GFse511lkdGbklOUACCRyO/zKZ95ajCybBuWnX/OLSW+LhzRE2S1yDstCS+ZkttSU1OjWsXw4fIcPHSonLB1//33iuULFhyt1vGhf/kXsby7S+4TK1e+IJb/8Y9/Uus48kg5+SuVkvvR3r17B1QOAG+/vU4sHzFGHiM7O+Q5u0JJBgaAYFCeH0NKEnOvJb8/rb35MUpS2PtNc2srkgVzWDIpn+dMSs/1GjVSng+GVMrr8WRSnh93NcpzIAB4rjwej6gZK5bXDJM/2xWmleXVv0v+/JhVUo0DynddyuP6nkF5qfxau/bI5Y27t4rlzc16iqartG+tbzW3ymNBql6fj4JB+T2ur5ePa/pkOfnyA8cfp9YxceIYsbyzQ55TEz2Hi+VdHfJ8AQCr31id/xoDmIP5TSciIiIiIiIiIio6bjoREREREREREVHRcdOJiIiIiIiIiIiKjptORERERERERERUdNx0IiIiIiIiIiKiohtQep2xUjB9EpTkl7BsOWUBAFxPvkt8Srk7f1S5e3w0qid7dLTJiRw9yl34y0aOFcs9n205x8jpLr2dHWJ5W4uciBUt15OvhgyV00MSPfKd6Hvb5WSZRJuecLBly2axPBaTUxRaWuX3MW6iT2JUQE6ZymTkFI1MVm4/JTH5fACAgZy8FFQSuWwlUcevW7he4b8PrfQ6p6QETji/35SF5PPQ3q0nBXZ3y8krUaW/RCLKdYvo5y+rJDR2uPJxubvlBIWAHg4JR0n4auqR+/Dzr70slu9K6olOcw+fKZbXDa8Vy8u65WMqVcYCAGhT0lNsZawdNVxO1GlokvspAOxSUi6HDpHTdkbXyuks9Vu2qnX85vf/LZbXxCrE8nMWf0gsD3nyXAIAbrKz4N+HTvqVHQjBDuT3VzehJH35pXAqqY2A3F6c5jViuVezUK0jGJTnj2xGPt+OLXdUs0uenwD0HZD3C8jvo/Da71c2UU7jAoDWl+QxSgnXgldSJ5anW7aqdcCSX8xR2rExcrllj1SrcDNye8h48tih5S4Zn0WRq/weM62slWDLa59srz5o97TmJ/2klbFvsLIsS0gPk8+bm9HTr1r2ysmkqRI5saqiQm7jWrIbAAwZIv9MOCwnkB5//CKxfNQoORULACIR+XhbWuS1equyho/H5fEGAEJBeY2jpbhZSqzymNFj1TqGVQ8Xy9ta5LVBMCifw4yS7gkAniu3h4Aynmshdbat92Etpc5V6n6/eWzFvYgUfPYsseRrXzNUTywNheSLs3uPnIpmKZ9LRtbKaWUA0Csv05FKy8lkkyaPFcvXrZOTGQHgqW55Tt29Rxmf0sqcbcrVOmpr5DVxIiWPBdu3y8my5eV6WnZpidyHkr3KZ1dXS7vT66hV1vwTJ0wQy489+iixvKxUTybe2SjvfdTUymv+6mHy54pN6/R1VzRS0BaNvt9TiN90IiIiIiIiIiKiouOmExERERERERERFR03nYiIiIiIiIiIqOi46UREREREREREREXHTSciIiIiIiIiIio6bjoREREREREREVHR6dnwEtfrG1HsyVG4gZAcBwoAHR1yHHlQif3sTckRy7alH75nRcTyXbvkGMdgVo5FLK+T448BIKVE9XZ1alHzckSm6VXihAF0tciZl5UBOU4eSgyrFQ6qdVRXyVGVRonrXVO/VX6d1WvVOg6fLUdCOgH5uExKvrahkByvCwDptBzdadnyOUml5OtnTK9aR2HMrfGU6zBIrd++HbGC9xANyuc6qMSnA0A4Jl+33oR87py9bWJ5bKQeVV5SKUd8V7bJfaKnQ667N6FHandG5J9JKGNOOCOfk+6OdrWOp199USwvCcttua5SjjYdHtEjZbuCcsxyOiO3z9rKKrG8ubNZr0OJ2W3skCN+W96Ux/nuDiXHF8CEEXL879Tx8jg8rqZSLC/Rhzukg/njcNBVcqUHIRMbChPLj5kPdu8Rn2tZev+FkX/fZLSM7YzchzJ+UblK9DY8ua06STn+2G3Q55Xw6JHyA8paovv5/5brWCf3UQBIp5V5e+hEsTwblM+t1VWv1uHZch1Zo6yVjNyvja1fc8+TxxvPldcfWSOviVxLv+bKS8FT1lfZZLv8Oj3ymAIAkVj++Ggp52KwshwHVp/2Kc/BkXBMLAeAbFruk61JeU4bO3acWD5x4mS1DqP04XhcnotmzpwtlsdiesR3OiVfv1QqKZZXDZXXDMcfv1Ctw3HkNeDGjZvE8s5Oea4bPVpexwJAeVyei5549C9iufb5ZehQef4HANuWf8ZVYugta+DfK3Bded2fzeqfU95Pdna8gXAqf4FRVy5H25eXTVdfJxaT+3ZjozwPBh15/B5WXabWEbDlPr+xXm73GzdtEcuNp4/5h0+bIpbbljzPt7TInwW6evR1ekT53DemRn7vkUCFWJ7KyP0aALo6WsXyiiGlYrmdkedsv3VXb4/8GXXt22vEcldZv08cJ59zACgbIrerMXUjxHI7KL+PqmHyZwQAKCvJH4MDlr7f06e+fj+TiIiIiIiIiIion7jpRERERERERERERcdNJyIiIiIiIiIiKjpuOhERERERERERUdFx04mIiIiIiIiIiIpuQOl16ZQLx85PMIiWyHdK9zw5TQGAGnGS8eS7vlsBJWmnVL9jejgs33F+Z7d8h/yt2+S7x09qk9NjAGDUxMPE8l4j32m/sWO3WD5/1ly1jrVvy8k9VpV83nvTcvJEw179rv29SlKH2ynfzT/uyNfP6ZFTlAAglZVTP0qDIbE8HJbTEizoCRpK6AaSCTkFxbLlHwiH5eRDqQ4toGmwaty5GxEnP3lj9DA5DaakTE/O2bVXScxKy301oyTq9CqJaAAQj8nXIaZslYeVlIlomZ542NIqJ2kMLUzp/F9LRsqJVcNL9Tqe2PqWWN7QIadcZhNyu2wLtqt1GCjpNUoCYVOLfP129+p1KKcErb3ytQ0qiXrasQKA58jvPRSVf6a0XG4jgag8rgCA7eQnL2VC+nMHGwumzzU1Rj7PnpISBwCWMtdqaVUZJQ0WKb3/ZpR+amXlZEhX6SeWkoIDAFa5kn5p5LErs0dOkFOWGACAmglyFGLpmBr5BzrWyeUpOTkXAGw1EU4ut6NKSm1EfyNGuSBOQp7ng66cnON5ykQLAEaen20l0ccOySlomWFy6hIAdKEgOSegpx4NRrbjwC5Ir7OUZOOgkiwLAOGw3AaSSbl/DR8+XCwfNkxPS2tultNMa2trxXJPGVccJU0SAJqVtYSWch0Oy+O1VjcAdHXKqVGtbVpaq/xao0YpaZkAJk2aJJY//cxfxfK/vfayWO6X0BwMKvOdkgLtBOXzro3zgJ5ep5W/39SUTUYkmn8dJo+TPwuOHCX3OQAoL5OT16JRee4qKZGv/YaNm9U66rftFMvTGXk9FQrIY0qdT7tPJ+W+VTusWiy3lTmtp1tLfQfSKfmclJfLiZE1NfK6pGGHWgWiMXkuioTl5M14qdznLCWJFgDCSop8d498vC//TR4jmtvlzy0AsOTkxWJ5SP2sLZdX1+jpdaPqRuf9u7tHv3aF+E0nIiIiIiIiIiIqOm46ERERERERERFR0XHTiYiIiIiIiIiIio6bTkREREREREREVHTcdCIiIiIiIiIioqIbUHodHAdwCn7Ell/Cy8rJJwDQpSRGxALya8VicipVtkNPzgkqP/Ohj35ELH/6kcfE8i3129Q6SkZMFsvrN28Uy4+cLyccJDv11LeMkhhRMlxOd9m7Y5dYHq2uU+sYO368WN745iqxfFKZnKIQNl1qHY6SzpJW0vZSKTlxLhbVU1AcW74Lv2XJSQ1ZT069SelNF+FQfuKEXyrLYOQlMvAKDjnZIV+3HktPK0mn5ESp7na5T6a75HSD2C593ztVKffhWFVcLHcKx6b/1dnertbhKsc1qkJObnCU8a7SJ9mlYrKcvPFms5xmFU3L7XVoRE+12QO5v+xKydc2XCofU1RJQgOAYK98riKeki7aI79W2OdXHdVGHidKM3KnDEWU/heRU0IAwLXzf8b1SdMbdNyskP4qt71MUp8fA0pCUlB7rUCFWJ619KSWTFpuk4Gs3I6slq3yMcXl/g4AwZCcAJTslRN1bEe+1pWnfkatIxqTk556Nz8hlrstclvN9MjnAwC8pPxYebU81wbHKOlurn7NSxvl5D67c6tcHpfHCKtUTzJy46PF8mzpWLE8HZHXHlZYT32KpvPnbSshp7UNVgHHRqCf6wbLp3+5Soqg9jNaYNns2bPVOrq65PkjpCR+JpVr8eILL6h1PPmk3I/iSnrutOnyultLagWAZmWudRx5MjrhA8eL5YcfPlWtI6GkJFcOkVO8HEdOCstm9WtulGvuOMraV3sdn/Q63+RxwrCK0YjG8ufPsaPkdO6SEjn5DNBTzoYOlRNZu5SU8WRCv14hZbGVdeXycWPGiOURJeEMAGpqRsjHlZSPK5WS215np/75sTshf1YLR+XPCMmkvMYoKdX7VtUQee7avnO7WO4pcc4TxsltAQDGjJTPb1OLnGy/u1X+PN/R3aLW8eyK58TyZEo+h0fMmyOW19TqqaZVQ/M/H4Ui/U+A5jediIiIiIiIiIio6LjpRERERERERERERcdNJyIiIiIiIiIiKjpuOhERERERERERUdFx04mIiIiIiIiIiIpuQOl18bJyxGL5iRJaYkO3z53oW5vkO7W3K4Ee6bT8wOgRNWodEyfLCQCdPXKqzbgR8p3aWwJ6ysjW7VvE8qFK6lZHh3xH/aiSiAUAQ4dUiOXVtaPE8m4lfMoO6SkKXlZORYhVVYrls+fKKXw767eqdXTtku/CH66Wz3tQCZ9yfZI1LEu+Vo4jJxZYtpwMZIyeRNbT257374SSOjRYhSyDkJWfHtHe0iY+t6fHJx0yLrfxUEw+d25STk5wXL3tlyiXIbtHPt72Trl/BbJ6UkuFkpjZ1bJXLO/Mytd7mzKuAECHJ6dZjausEMtrjdwuhygJNQCwx8h19CpJWluU99eqvD8ACCoJFUOichKapSRTlvuMdydMmyWWn3jEfLHcVS5tc7d+PZxIfmpQj60n9g02BhZMYSaRJf/uKKKUA4AJyG0MQblfGyW5MNOxw6cOeRAPJOS2ByXVzo7rqY0mLD8WSHWI5RXT54rlkVmL1Tq6dstzV2LzXWJ5qkse65QhcN9r9cjXKlwpp7iFQ3J54rW/qXV4KflaOcPl13Kq5Xke5XICz74Dq5XLg3IdJiCv00xAX6/0FIznabswzXFwCwRCCAbzx1Itcc7zSRnTwtpsW25LmYw8HldUVKhVjB4tJzrt2CG3pcohQ8XyJ598Uq3j7nvuFsunT58ilteOkNtSIKCPdzt2yinUQ4aUi+VTp8p1p5RETgBo72gVyyur5DZeqiTIdvqkchs1mVBZFxslWZbpde+Y62XgevlrGO10RiNyQiEA2MrnEk9JKAwrCXKzZ8lpjgBw1JHTxfK33toslr/x+nr5mPok5v6ftnZ5Pd7R1S6Wt7TK6fVpn9jwgC1/tuvtkSfVUEA+79Gwvi+xZ4+cIp/olevQPiemfN5HSam8Xmmul/dEehPyWGDbeh/dskVOEG1plq/TbuV9f+BYOcETABzkrx8zqf6PGfymExERERERERERFR03nYiIiIiIiIiIqOi46UREREREREREREXHTSciIiIiIiIiIio6bjoREREREREREVHRcdOJiIiIiIiIiIiKTs+uFnR19iCbyY/GCwbk2Mem3Q3q63Qk5EjBPU1ylKIWR7l2wya1judfeVMsP+nEhWL56NoqsdxKyjHOAJDpkWMkrbAcOx0prRDLe33i1tOuHMvY0iZHQmddJT47Kh8TAKR7lYhH5X0MnXKUWF5WM0GtY9XK58TyYLUcJ5vpUeKzY5VqHbYS0R0MyuWukaPSHSVyGAAM8o8rq1yfwSqU7kLYKej2SvSnldLfWzItx4g6kKMzo8o5tXzOX6JLbgNZS/4Zk5GvZ8jIcasAEMzIfTitRMSmQ/Jg9EaPHJcMAH/dVS+WByJybP2ocJlYPsSW2zEAdKXkmNSOrHydYiE5frcrq1+PTlceJ5qV814akyOh40pMNQC80bJTLO/+m9yuMo7crhxbnpcA4LRTTs37d48nH/9gZIzXJx476MjxwF62V30dG3H5gVCF/Pxx8ryZ3KHP85FRI8VyKylH91qe3FatkBzDDgBGiUa2e5Q6yuW49cY2fQ5ObN4qlqfa5DGirEbuW2E9pRzJTXK/SytJxN7ww8VyZ+gxah2hptfE8kBAmf8j8txsheVzCABuVL7mWUce09yAvMbwlDEFAKyCSPfCfw920WgEoYLx11bmx94evQ9rsfeWpY99kogyDwFAMpkUyzdvliPXQ1OVOW3UaLWOeFwei6qq5DW59r537Nih1gHIPzO8plos7+mRo9WzPvNjNiv3o2HDKsTyE086Tixv8xmLOjvkNdHOXXLkejIjH5N2DgHAPcTWtP9s5SVDEStY22SVtVEqo/dfNyH3Uy/bI5Z3dcltcugwuZ8AQDAof8TfunWrWP7q314Wyxsbd6l1tLXL7TWblcfw3l55TLGNPm45ttxek8r+Q7BE+Szos4Z2Pblv1QyvFcvnzJollnd26P33zbfWiOUdHe1iua3s0JTE9K0b48mPRSPyHNPVIa+VdmzbrtZx2OFT8+uE/hmrEL/pRERERERERERERcdNJyIiIiIiIiIiKjpuOhERERERERERUdFx04mIiIiIiIiIiIqOm05ERERERERERFR0A0qvKysrRSyaf9f+VEq+0/4en7vdp5SwkVMKUoX2GzVSTktJJuXEJgBYu1ZO1bnn/kfE8jDkO+1/8Pij1ToqYyViebRCTlgri8l3zu/taVfr6OqWEwu2bpMTsYZWyUk/XlJPUZi/8Fix/L675DrWb5Hval8e1tMHmnZslX+mWk4GKKkcJpY7wahahxOQ91AzGSWdzZKfHwgoyU4AIuH8LmM8vQ0ORuVhIFYQNJDx5GQI4ympRoDaiYNKE4gqqREBpW4AaM/KKRe9cjgUwkbZQ/f0vfUeyAfcBvm9b+2WEy52KUl7AJD15DqSCbntrO2Rx9SUktoHACYov8chtnyyJgfl1KiMq/evUER+bJgyDnYoyUeNbXrSX3dCHqf++vpqsTyjJC+ecrycDAQATji/Azhu/5M33m2u2fffgZyQnAZod8vjNwC4Kfn6OyE5sSxYN0ksr66V2yoAZDq3yQ/sbBKLtfAay9Gvj6X0X6Ok9vRAPlcZJe0OAJyUfLyV4+RzGBteI9fRrZ8rbJVTGzu27xHLh+yVz214zJFqFW71J8VyKymvldIBuS2YVLtah2PJ591x5FSzjLIWzKTksWPfa4UK/i2nCA5W4XAE4XB+6qKnpG32KOMhAKXlA46yromUyusav/S6v70mJx5m0/J6ORaV0yQrq/TE0jlz5BSoeFzuX83Ncsp1h5LsBgCWJa8/duxoFMunTztMLM8oabcAsLe5RSwfMUIeDw6bKo+p6kAIoLVd/jzwxF+fFcsTe+Vz5SejJEcy1W6fCWMnoLQ0v21u2/mG+Nxtu3zOmSevzXratMhSuV9PnTZdrSIUkcfjqVOniOVa07vjrjvUOpqa5TnKKGuzdFJeW8ci8joSAALKEiCl7GBEI8pnOFdPOq0sk8fBUFge09LKGDhihPyZFgDCYfkaJpWUw96UnFLf3i6XA4AFuV25ymetkg75/b3x9itqHU3t+euV3t7+fw7mN52IiIiIiIiIiKjouOlERERERERERERFx00nIiIiIiIiIiIqOm46ERERERERERFR0XHTiYiIiIiIiIiIim5A6XWpVAJ2wTZVV2e7+NwhPmkVdeMniuWWks60ddsmsbyiokytY8IEOXll7KSPi+V/fewvYvn6TUoCD4DZxy0Wy21Hvkv87t1yUsae3Xqa06ix48TyrVvkZKLuNrmOoRUVah2ekjJQXiWnboSUKIFhw+TEOQCYPlVOA+nskNM4qsdMFctdV08P6eqW00NCoZhYHnDkPddeJT0MQJ94h0xaT18bjDKWjXRhuk1AHgYsJXUNAEqM3AbKCgeIXL1yG2szcgIEAASUPfHKjJI6qCQAJQP6+6jvlVMg3uqQU18a0nLKRKNPmylVtvZLlXMVU9LgWlw9TbBb6RdZpXxPUn7fCSWRAwDGjh4jlp80e45YvnHXbrH8z8+tUOvoUMYDy9KizeTitzfKcwYA3P6H2/P+nfJJJBpsjGfgFaSQeEE5mcmJ6uOxt1dOpTJDZ4jlanCRJY+tABAIyfNzqlWeo7TULc/V26SXkFPnLOWAM7acUGO5ev+NjJbPY0nF8WK5iVaI5c7ONWodGSOn1wU65FQY07xFfqERcioRAHhpOVU3HVLmeSPXnS2RxwEAyLopsdxAvh5GSRx1fBIL0wUpTt4h9rvTaEmsb3qdMo519+rt0lPGrVBITi8qjcrzirYuBYCVL74klo8cOVIsT6XkNjN1qrzmB4Dm5tli+XPPPSeWa6nVsZicdgcAO3bIc1FLi7xmHFolJ2ZXVcl9CAAiSvrW3iZ5jBpbKydNa+uYfY/JbV1LptJ4np7ipaXX+f3M+0l3bydg549nTc1y+8p6+tqivVP+3LdrW7tYXlUyQixvUlITAcB15fbygRMWiuV7dsup852derqr9rk9m5XbkTYXeEZf32aVhGHXlce6biUZOugTVFxaKve7UFB+rVYltc9vvdLcIl/zTiX9WttL0FJiAcDRksKVBE9TGIf8v7R0dwCIhvPHWqO0M8mhNVsTEREREREREdEhgZtORERERERERERUdNx0IiIiIiIiIiKiouOmExERERERERERFR03nYiIiIiIiIiIqOgGlF4Hy+z77wCJpJzmVFUpp8cB6JOAt18qIyd1xEvkVAgbelpFOtWp1C2nq0xUktdeXCmn/ABA7YTtYnlFhZzo06wkZdSNmaDWMXveMWJ5SUR+77Yrpz+NmyCn4AHAjh3y+xilJJQ8/fTTYvnoWj0tadvb68Tyyho5kaG8XU4GCATktAIAMJDvwm9rDc7IaQUu5CQBAAg4+akzlqMnFQxGzUkPESc/hcSxlfOmJYYBiClJCJaSXpSy5cQK1yelKKokVpQaedgKKCkM7Z58TACwukVO63ilR07raFWSNxxLT3aZOVxOvDlhiNy/hkXiYnl9u55yubNDHjtfT7eL5ZuVcssnBSe7rUEs36uMa50pObklqTcrOAX9a79hcTkJrTwmPz/bJc9LAOAVpPN42UMnvc4zaXhefvs3njwmerFq9XXsHjmV1euS5wInUiWWZ31+bWXb8rUxGa2vyA3DUlJwAADd8jxhlY2Sn6/E14S75fcNABHlZ+wxJ4vlbqJJfiF7vVqHq51IJRnIUhLK7Iw+RjhheRzKOnJ6XVpJCYWtLxvdASbkeEq6ZkpJKAMAL5i/9vHMoZUgG41EEYnkpyhq6ZzdXfJ6DgBSaTntaWilkrCmjLvPr3heraO+Xk5JnjBRXrNmlUS9IUP0zwO1tbViuZai1t2trc/0waitTZ7PtTpaW+V+FIvpaZ2lZXJi9/Zt8tiyx5bHwaohevK3lkbreco4obQrV40j1c+JGWA/83u+mkZ7CNi9qxWxWDKvzDby57GWtjfV12nvlNdNKeUat7TL81Bnh/55JZ2Wr/Mf7pDb5Mb6DWJ5MqmPQzUj5L6d6JXX3V2d8mu5aX0dpqWWZ7JyHbaSKm6H9Lmrp1du9xlPTnrW0nnjpfq+BJSlT3OLnH6YymrrWP1zUzgsJ/SmlfmiYaecnGs78usAQCjUnvfvhJIWKL5uv59JRERERERERETUT9x0IiIiIiIiIiKiouOmExERERERERERFR03nYiIiIiIiIiIqOi46UREREREREREREXHTSciIiIiIiIiIio6PT9Q0N3VjmwmPyrSICk+N1Kiv3TQUeI6LXkPLGDL8YCe0WPKjSc/FgzIMc5llcPF8oQS9QsAa1Y+J5ZPnzJWLI8qEYsI6fGHmzfKkZsZJUKyd68cAR8weux01YgRYvnbb70qlochn5M/3HG3WkdTixxZu/QjH5HrcOQ63GynWkcwIMdIA/K5crNyG7Ghx8misM35tMHByPKsPnG1aU8+170h/b11ZeT4TcfIkdlJV7s2etu3lVjdmK2UK3mknZbeh7el5cjVva48rmWVsSgM+X0DQIeRx8JGI49Fu9rk+NG32+SIXQBIK78/6MnK7z2lpNMGtFxtAG298nF1JuRzNTYcF8tnVCpx9gCikNvJ9JoxYnlZqfz8rNHjdyfE8iPiezNp/FJ99uCS7WhENp0fZRuokuPLLaV9AUCg8jCx3LRvFss9ZT71C742wahYbpfWiOVuhxzP7qW02GDA7ZHbpBcfJ5aHHGW8CevvJJCU49MzaTleOpCQ46gTvXvUOjK9cv1VJ8yW66gcKZYHQ3qku/F6lMrlMdBT1mNeRo/PdoNyfLalzDHGkfuv5chjCgDAKRhP7QEtY9918XgJIpH8vuEo7bK1TV43AUBPtxyVrsVXr17zslje3aVHrs+cOVMsDxReg/81apTcLtetX6fWsWuXvGatqKgQy21l/q+qqlTr8Dz5Zzo62sXybdu2ieVjxsjzkJ+eHvn8rtsjjxOjR8rrcUAfU7NZeb4rXOvtp43nAJDJ6J8VBkKr+1BXGi1DSawkr2xv20bxuXsa9TVb2155Xkun5Gszolq+9sGAvvZs79grlm/fIY/hAeW19M9WQG+3PK8Eg/JrxaIRsbzX1ef5bFb+vJFOy3NUKCiPT8bobTKVlOco25HrDoflul1PX3vGy+U18chaeU3c2LxTLNc+/wOArcwl6bR8fpMpub+3t7erdcQjzfmvkfSZrwvwm05ERERERERERFR03HQiIiIiIiIiIqKi46YTEREREREREREVHTediIiIiIiIiIio6LjpRERERERERERERTeg2I9QKIxwuCARx5Lvxm5b+t3MjXLn9aAjp+0Yo6VP6Xdwzxr5Du6ukj5VOrxKLK9Wkt0AoKtDTlL7058fFssnT5gkl2fktBEA6AjLd/rf2NAklscy8jEl41vUOp58RL5DfmODnHyws1W+tp0+d7D3lP3Nl1e9JpYfe/Qcsby8rEQsB4CsklDiaWF0yvODjpwSAQCuksJzqMh4aTgFKW92QL42pUq6HwCks3LiQYeSDul48lDT7pOq2B2Q64+n5Ovm2HLCRtonYytjydc66sn9riIop0ONjOhtJpuS+8WTu+T0q56M3Ma8jJ7u0aM0cuPK5UHl1w0ZnzTGsJJmNW3IULH8uHI5pWxyvEKtI2LJ9dc6SmKhMnRmlFQsAAhuz0+U6XGLk9bzzxBufQuRSP57c8NymwwEx6uv42ljXFRLgNLS6/TfWxmtLcXlOdVLySkxmZScrgboSS0BJTESWjKpsvYAADdaLZaHuuSEK7tXTuMyCfn9AcDojy4Vy4ceNU/+gd0viMWeU6HWEVQSx4yrpPMF5CQ6ZPX0OicgrwdTyrzpKumhns+YbRUkEFm+GYqDj7SOtpV1aVenfq5f+9vfxPL16+SkuDfeXCuWawl1ADBjxgyx3AloqbPytWhsbFTr2LFjh1iuJcVpSX+zZ89V69i8eatYriXn9fTIiVxlZUoCNYCsK493YWUNv+Vted3tKWsrACgZIo9FKZ8xUqJ/pvJJKn2PptENVMO27YhG8+fPt+vla7l7r95/WxrlVMNwUP6MszMtzzclkQq1jkRKXiDtaZKPNxiU101a0jcAdHbI72NIpbyWCDpyfxg9Wk6QB4CWvc1ieUpJTs4qfc5x9D0DLfE+kZD7lm3L87nn8xmxq1v+fB4rKRfLy5V+3dounw8AcLPyGqckKs/NsZC8LnDT+vtoatmd9++BjD/8phMRERERERERERUdN52IiIiIiIiIiKjouOlERERERERERERFx00nIiIiIiIiIiIqOm46ERERERERERFR0Q0ovc4Y0yf1wHhKSpzxSR8Jy3tdjqOU23K56xMkFozIKRNKqB26OtrF8mG1+h31T/vYx8XyB+6/XyxftfJVsfz1jVvVOsJh+RKVlsvJMjMmTxHLt7e0iOUA8MzKt8XyUES+2z2UFIvxE/W0pJ6EnLy1uV5O1duyVU5XOGK+nlCSUhJHgkrSSlZJZFIbCQCYgrQEnxSQwajJ7UG44P2FlPcwLqgnOlWElD7pyckrYVuuI+mz7d2rXJ6AMk5kg0o6lM81qhkit/FMhZzuVaMkQAwtlRPEAGBjq9yWn98jJ1DuVn4X0B1Uq4CVlhMr4mH5h7Q0kp0+6XUZpb809cgJLW8pKUOpZLtax4lVcuLJnAo50cV4SuKZksgB9G0/XdlDpw9bQ0bDiuT3y1DbRvG52YCeqGhVDRPLg0r6mGfk1BfXZ6y0lLZkDZFTqbJZZc2Q0BOAbE/pFEoClA25vVienmrjpOUEIE9JXrPSct3dbXLKDwCEhyqJlbbc7sMZ+bVco58rOPLaQEsaDmTlOTujJOQCgBeU07UySmhgtrdNrltJvgSApJvfTvyS7gYjJxCAE8gfn2xbfg9pJdERABoatovlJ33gBLG8pnakWD60Wr5mgJ5mFSuR57us0u+0ZCgA6O2V29n4cePE8kxGrmP92/I6FgDeWisn+tXWykmahx9+uFiuJnICaGmVUyBbWuW1d2ub3PaVpgAAiPTK7UFLj7JseR7UEuoO9thA+CXkHcpJeE2tWxCJ5KejdextF5+b7ND7r63MqY4tD5aukrK7fWe9Wkenkn6ZVvqQ1hddJQUZgBZYidZWeX1bW1Mnls+dfYRaxcsvvyiW7+yS318yKb8Pv/6rfdbOpuU3uH2HfN4TCb3d9/bIr1VWJn+eDwTk9U17mzzWAMDI6fLYVTdyrFi+dt1bYnlPQv48DQDxeP460VE+30n4TSciIiIiIiIiIio6bjoREREREREREVHRcdOJiIiIiIiIiIiKjptORERERERERERUdNx0IiIiIiIiIiKiohtQel0qnYRVsE0VVlKCfG58DhOS73SeTst3+s968h3f7YC+Z2aUZADPVX7GyOUdXXriTMDIx3v2WUvF8mOOO0Esf/XVl9U6Nry2UizfvklOLKrfskMsdz09fSChRsvIyQATJ8rpQ6d/4l/UOuKlchLaA396VCx/7fXVYvn0WYepdXhKclpaSTKwlXQ2v6SGdCb/tdJpOflnsNrQ1YxAQRqkY+Tz5kb1RLYPDakVy6sCcl91jZwAFXT11KiwI1+fbEROdLCU9Lqw8bmew+V0HoTkuksC8vP3KumMAJC25eMKDpcTml5rbRfLd7lyQg0ATK+WUzYnDpXLW9rlce2+ZjlpDwC2Ku9xR0ruA4kOOeWqvFJPBLWVdMBImZziFUwrCVshPR3HDeTPP6nMoZNe54arkI3kp9JFlPZtdsuJLwDg2sfID5TLqXaBjDKG2j7pdUr/NZVyu8/a8rU3PXryKlLy+BFU5jsH8jiEjFIOwO2Vk2JMWu5DVm+7WJ5s1vuvY8nXypk9XSzPlMp9KOCTxOhpaa2OnPQLTz7vdiAilgMALLk9eEranqXMF5aljMsATEHylTnE0usi4RAiBQmURklPDEf0BMqtu+R0qJfXyEltY5U0uHC5Pj8aJaW2W5k/3Iy88C9V0u4A4Ii5ciKxlujX2N4ulu9sbFTrCClrhmiJPEZ1dMnJchOHT1TrCITlftGwY49Y3qkMB6lW/cOT0yWv1VJKem1USa/1C6jT1r+WNcB58tDqlv22vWEbQqH88amrU54j0kqiNgCElFR2x1LS65Q5Levz2a6jS16DlZTI6ylPGYcSPfr8WHguDng1sbSzU+5bXd3tah1hZT2utVVtLyEQ9ImAVrZDXGVObW2Wz63rblBrsCEnhba0yXNtJiuvrSsqqtQ6YOT3sXa9PC/YAfn5EWUuB4C9LfnXUDvfYn39fiYREREREREREVE/cdOJiIiIiIiIiIiKjptORERERERERERUdNx0IiIiIiIiIiKiouOmExERERERERERFR03nYiIiIiIiIiIqOjkrDyFcdMwbv4+VcCRXyKkRK0CQFCJ6AOUKHQletkzekyfpUQ5e0q8dCQsR9MOKS9X6zBKNKGbkWMnRw+vEMtrTv2gWsesyaPF8r17lYhOyPGOkYh+qVc8u1Isnzptilg+bfo0sTwala8TAMCS4zP/5dQlYvmbb64Xy7VzCwCekeNktaRXx5HbWyqrR1tbBe298N+D3Zude2EXRE4HlK7amupQX2daaalYvldJ2axVKqmI6DG8LWk5RrTKyNHqpWm5jcV8kn7doJzrmzbyD1lKdHxJUo6QBoBZlfIY0pWQ23Jpq9yOPzt6glrH4VVDxHItMrcrIsc7vxzW+7CrxDKHlbF2lyu/v5fb5KhvAIAnn9/6lByzG3Pl/hcN6dG4E8ry209PVh9TBps0HNjIP9+BaI34XDcpzxEAYDbeLz9QPVUs9irkuSAUH6bW4Shjo4kPFcutKrl9p7bL4wAAeMplLlHWEvDksd125WhiADDKvBJ028XyVLccL53ymbtGV8lrGTulzPNDjpSfn9mp1gFlbeDZ8lhgInVyeVruowAAVz5XTkC5Hkrdns+Ync3kXytXiZUerKKxGGKxWF5ZLCqvPwufd6CelNyeXnr1b2L56rfkyOyKigq1jiFD5HlFi0kPO/KFC/t8HihV1hJpZVzOKuvutM8w7npy/XZAnu9CEfm8r99Qr9Zh2fI5qR01RiyfMmO2WO44elR5R4e8JtuxfYdYnnWVdYxaA2CMvI5KJuVxLZHoEss9T68lHM7v99lDaA7etq0JgYLPsIleeUx0s/I5AwDtY7BlyWu2lLL+8nyuZlBZBwWDcnl3l3y8fm1Sm1c0GeXzVVurvl6ZOXOWWL55y2ax3PXkNuwpa1IASCfl96G9d+PK/b1Hmf8BYM6skcpxyZ9pOpX1e6xEnxcadm4Ry5MZ+bhGDJX3GLq79c9/bV35fT6bled9Cb/pRERERERERERERcdNJyIiIiIiIiIiKjpuOhERERERERERUdFx04mIiIiIiIiIiIqOm05ERERERERERFR0A0uvMx5MQfpbV1eP+NxgQL/bvatEk3iQ74Aei8rpFjA+dSg31PeUO+1HQ3KKyszDDlPrgCXXn8wod3I3csqKa+uXYfSEiWL5mPHy+3BdOQVn955GtY7qYXKa0FFHKwk5QTnxwyiJAQDgeXJqQERJIjn+2OPkF3L0fdJ0Sj6/nqskGSjtJxTWkwGCBSlslq0nFQxGzW4GVkHahWXL59RSkt0A4OGuZrG8rVNOMplTJie4LRwnJ28BQEunnGYxbUiFWF4Rla9bVBsMoHZhZJQUHktJdhkSlccPAGjpldM6krvkFLcKWz7v08vL1DoqIbf9biOnX4wdJp/3inZ9LBozrFYsP7pSTl763W45UedtpY0AQJNyrVY1y/1sr5LoUhXTU/jOmTI779+9Gb8sn8HFWDaMld9ftaQWlMhJKQBgp+U0wMzmR+TnZx8Ty625/67W4Q2XU1GckHxtolNPEMt717+m1uEG5GvnKSmMTkZuL8bVx3E7KydTeml5ru3aKycZlZXoc1e4Sh4fbUtZMyipW1ZATsoCADhyQg6Ccv81lpxw5Cpz9r4H5eM1trwm8tQ69LVEb1d+200lDq05OBgM9kmP8pQ+3NMjr6/92Mp87rpy2+9VkrcAYNeuXQOq21HW/bbPus2y5D4cVRL9tPeR7NGTqUJKVFjjLnkcjETlNpVO6cnGASXRL1Yq97tJkyaJ5dr1A/T2kFHGos62drHcU9J5AUB7aNOmTWK568nXIxopUesYNSp/buju1tN/B5uOjk44he1JOQfptN5ejPJ5V1tj9vRoY6i+fimrkOcVLb0upMybGWUOBPT2GlT6Q2mZlhipJw6PHTteLK8eWiWW79mzWyxPpvRzlcnI7yOmJD1Ded893XJfBICdOzeK5WNGzxfL01llzG7Tk/6qh8rr9GxWPq5de+R1esInkbvwg5Nr+p8+yW86ERERERERERFR0XHTiYiIiIiIiIiIio6bTkREREREREREVHTcdCIiIiIiIiIioqLjphMRERERERERERXdgNLrIuESRAvu5K6lIASDegJY1u0Qy20lMcpAvjN6VgmJA4BQSH5r2ax2l3W5vDCtLO+4PDmZIKLctb+nV06esB39rv2WkmSVTinHG5TrbuvQE17GT5KTAQpTknJ1Z+V0hZDyvgEgoyR4aakmnpLgkOjV76jvZuTknIByDmHkZAB4eipipiBNIJNUXmOQcmH1TYtR0kqaevUUhr8m94jlSSWZcn1Sbn9rs3pyTlTpq890yNf56GFyutu/1AxR6xihpCc5SrKhUcaoTFAfJ7oz8vuYOm6YWB5UEmr2Vut9uGZkhVhenZbfX1lQPlfWVj01alSVPKYeNU1+rZpj5PTLR19+U61jwZQ6sXza+BFieWdSbgvREn1qmz45P92jM5EG/ucZ9fmDiQcHHpyCMmVOC8nJLgBgl08Wy52UMkc1rhTLs6/+Ua0j8IEL5QeUhJzw+FlieaZUbhMAkG6V07VGamlZaXm8sY2eamNc+fy6yrnqbZbbZEW1Pj+iTE6QDShLAwdyP01Z+lgXMNoYpfQVZWxEUO9blnLAtiunaNpKcm9KWWMAgIeC5Det/Q9SwUCwzzqtt1cb2/WUMY1R1uSOI89pASXZze8x7bWyWvSZq6+ptPS6lLI20NKyQiE57W5fHfLPJJT007SSwqi9bwDwlGTMTIf8eUd73360nxlWLa8l3JSSeOaTNJ1R0rdbWlrEcqOMRWXxSrWO2tr8+VxLJByMRo8e1Sf9bY+SDp5M6Ws242mffZTPwcq119KnASCZkOeo5rSSiqqMu35NNRSW5zXHkX+ot1vu17t2bVfr2L6jQSwvj1eI5Xua5HVB1vPZNIDct9MZ+XOQMqTA8/STtb1hp1xzSN4vSSZKxfKKcn1tV6Gck3Ub3hDLOzvlBE/tMzgABAvGWk/53CfhN52IiIiIiIiIiKjouOlERERERERERERFx00nIiIiIiIiIiIqOm46ERERERERERFR0XHTiYiIiIiIiIiIim5A6XWZTBaOk58UUlKqpNT5JLK5bkQstyz5DuiekmzgaeljANJKqpHnym/ZteS72gcsPa3CVerPKDd911I34JMM0N0j3+k/FJbPu3ZGpk2fqdaRznTJ5UpCXkCpxSjpcQBgK9fWUdJrtDQL29bvkh+Kyuekt1tO3XCUFB4LegqK5+bXn1WSDQYvg77hGPI5Tfkk56Rc5TEl5mK3K3eK1uZ2tQ5tcHKNnMjxVqdcPi6k761Xl8oJK11KsogdlPtEKK2fq8q4nEAxZ4jcXkcaOekxPlweNwEgUi6/Vo8yIPQqKTxeaYlaR2yo3C+88nKxfM7IWrF8qDpKASOGV4jlo8bWiOWuI18PLbwMABDIr991Dp3kHGM5MAVzkqslh2hJUgA8Jy6WByunyD9gySk8gYY1ah3Jvz0klkcXfFR+rVI5ea3smKVqHbsf+b38gKUkminJMsbT24CWUtuzV543bSWkLjZCT5xx4vJjri2PHY4tv7+MX4iM9t6V1C2j9K2MTyKTHZTHKNeWEwtdJUUro6YMA6FI/msZn3XgYGRZfRNka2rl8a1m+HD1dbQkZm3ttGPHDrG8p1dPkNVSzrS1rEnLfcUv/kp7LduW5yhbS3T2SfHS6rCUxbe6VvehvUN7gCl1WvogoF9bo8wBYSUp1C+9TnssqKRTB5TEynHjxqp1jB+fn5jd2SmnWw5GzS1NfVIdEwk5VdvxWYi4yvo6m5Y/i2rJiVoCOKD3Fe0aW8rnq4BPins2K38GCgXltWTalZ+/bec2tQ5P+X5M2JHryCiJ0Z6SRAsAQeXzY1Y53oDyCcWx/fY+5Ndqb98tlvf2yGvunT5Jf5VV8meaRFJer2Rcub3VDhut1lFSUpH373S6/5+D+U0nIiIiIiIiIiIqOm46ERERERERERFR0XHTiYiIiIiIiIiIio6bTkREREREREREVHTcdCIiIiIiIiIioqLjphMRERERERERERWdlkouPzkcQjCSH5uZTslxq46jR3KGw3IMsAU5ztAoEeKRkH74WpysMfJxeUpksqtETgKAbeT6s54cH2gHlSxlyyeuWSlPJOSI0fJ4hVjueXLcJgBYkGMZDeSIRS3R1XH0qMhoVH7vqVRSLHeNXF4YNXwgLQZUi7A0aSU+0/GJhC6I0rXt/kdFDh5+udoDfVY+7epoMcAp9ScAJXwZRmkDvUrUbE+Pfo26I3Lfe7a1SSyvceT3Mbtcj0OHJx9XON0mlo9UhgnTK/dHANjQ1iOWv9ktn8VERK6kqVM+VgB4JdEslrfulOeAEU6DWD5jiBxzCwBuk9yHG9bJ1yMbkttCxqddNSTzz1WvEhk/GBlv338Hyqbka6+kBgMASuPyHBwskWNyQ8rzvbgczwsA3tqnxfLsajma2J6zWCwfeoRcDgCBtNwuLGW8yVpKPHtGH/ONMkcl2+VxpaJWfn+hIfoY4cTict3BCrHctuS1h+fp7T6jLMk8ZXyCMp56Gbm9AUDGyOc3qfYxuQ43o4/ZwWj+efSgr20Go0g0gmg0kleWVd5vc/Ne9XXC4bBYrq2RtPKAPfDfPWvraEtZNdg+6zZb+RlLqcP25OO1lL4NAEb5/bqlvHcPA58TLO13+MrxamsiN+v3eUA5V9ox6QsytY5QSF4bRCMRsXxE7Qix/PDDZ6h1RKP5nzkyGX3dMdjs2bMHjpM/5mgfE4NBfWwKBOQx3LLkfp1RJvSMMk4DgFE+E2l9RW1fUf2zXTAgv8fxYw8Tyzu7O8Tyjk55fQkAs2bMEstffPE5sTyVkte9JTF5bgb0NqiNXbGo/Fqepy+8kml5rby3SR7nKyqHiuWWI39m31e/vJYJBOT3oY1DwYB+zTs78j+7DKT/8ptORERERERERERUdNx0IiIiIiIiIiKiouOmExERERERERERFR03nYiIiIiIiIiIqOi46UREREREREREREU3oPS6UDiAULjgR7Ly3fGNkh4H6Hd3t5S74GeVG6M7jn74EeUu9RnlxRIJ5Y7vITlJAACM8hYtS74bfEY5J5atp1V4rvxYMiEnWUWUpLaskrQHANGYnEzkenJqT9aVX8vYcroFACTT3WK5pcQ+uGn5fRujp6CElaSIUFC+ho6S+eFoKYMAEgV36Xe9d5Lx9t6lJSH4/ID6kHqljdw2SivkBKh6Vx+LJtlyW347JacJpcLyPv2R1XoiW9bRxjslYRNK3Up/BIASZQyZqpzFbFIufzahZQYCzd1yHy7vkRM5RkfKxPKqgJ4gYve0i+VpZc5IWfI1366kkQLA7Vs35f07M9A2+y4ybhrGLRjnlPMc6JXTEQEgEJbba6BEfi3Xq5ZfyNGvZXi6fF7bXn1ILDdv/00sL581T62jYpycnJTukt+7ZZTktaTe7lPt8s84EbkPRavkc2v5Jf1FasVy4wyRX8uS53nHJ3Ur7crzmuXKa6JMVn7fwbB+zbX52crKY5dRkn4LE+oOlI2U5xd4A1rGvusiQQeRgrVKqlceW1ua9qivU5igtZ82BzvOwFPfNFpal6WkF8L1S69Ts9e0SsRiLakLACwtqFhZM9rKufJbr2jHm1ViI7W0ZVdZ8wN64pnjKCmuyrnS1t2Anoo4dOgwsXzu3PlieVWVT6LvIczz3D6f8Wzts6vP5+B0Whn7lM8fkYjyWVS/lEin5LE9qaxvtT6UVBJcAaCiQn7v7R2tYnlnt5y8HrD1z12bN28Uy7uUNWlYSWf2y+TWrlUoKPe5jJI4qo3L+15LnjtTGfnzfDIpnyttDASAjr1yOmAyLV/DYFj+3N7V3aLW4br59fu180L8phMRERERERERERUdN52IiIiIiIiIiKjouOlERERERERERERFx00nIiIiIiIiIiIqOm46ERERERERERFR0Q0o9qOzrRXpRP4d9EvjcnqQUdKGAD0tJRSR76JuXPmu/WmfG6aHo/Ld6zNJue5YTE7tyfjctT9oy3t2WSXRL1Iiv79USk/d0O6cHwvJyRCuclK01AsASPTKKQOO8jOepSVoyIk6AGCU9Dxj5Pce0BKulOQyALAs+bXUhLysnD4AJeUHAKLRwnaiv2f6+2j5DLYlX89upfy+Jj2FYeiQkfID5XJqVKJcbhtNI/WUy4yScmWXKD+jpEOlfBLZktvlxIxgmzwe7NmyVz4mnySUCcOHiuWLyuXy0l75Cjb6JBm9qST9bOiUEzl2p+V0jy5bTxB5JZV/Hr1DKL3O9TJwC9K6nJDcXgI+v1IySspU2pPHMy3hyomO1usYJl/nqgVyulvX80+J5YlHnlXriM0/Qn6geqZ8TJ1yGlg2pS8mEi1yQk5Jufw+7Jg8N1ulctIeACAsn0fPqZDLld8XWkryJaCnmqVcObnPSsmJOqZE7u8AoAVvZZW0JCuizPPhCrWOwlblM2QNSlWVlYgXrJtHjpDbxsaNcmoTAHhKKqurJAkpIdCwlXWs32NaQpP9TtaGnnwFS0rk/lVSIqct+yVTJZPyOj6Tkc9KQHkffo3N05KMjVyHpaUn+1wPNbRKKdeStANK2hoAlJeXi+UzZshj6qhRo5S6D7We2T+hSKBv+1fGVr9ERVtZbCV6lST1gFzHkCF6KmpvSk4WzrbL6yZP6w8+i4lMRp4/tjasF8sjymdzbS8BALbvaRDL7aB8XCUlUbG8t0tJr4XeXj1lmySVls9VSUz/rG1D7neJpDxx2kH5mo8dU6HWEaiRH3t741qxXNvjsJVkb6DveJrN6p9N+rxuv59JRERERERERETUT9x0IiIiIiIiIiKiouOmExERERERERERFR03nYiIiIiIiIiIqOi46UREREREREREREU3oPS6kLPvvwOpgQ0+6XVBR75LfDYl3wU/qIQgBHzCEZJKSh0sOUXDDshJUiYpJwkAQDor3/XdceTX6uySE5hKS+S0CABwlToCyvuwbDktwTP69cik5CSDTEZOGUh7SjJgRq8jGpGPV0v6A+S24LdNqqaHqOVykoDnk5DnFKZUHELJV+8VWgrT9k4lbckn1eaerXIqRsCRfybYIg86r+yR0+AAIGnJY5ESFAZE5Eae1INQkGyV+56WcrWjWe7zbfpwh6FKKtKOFvmHsmm5H2WU5DRAT51rTMqpIz1ZZZzwGSjSBQ9p7WkwMnYJPDs/mSWrpIypCaAA0t1tYrltywmrWtiRpSTnAYBdOlYsN0q6W/lHJojlmbdWqHWkm94Uy8NhJSUmIS95Mq6+FHKGyMfraGE7ZbVyeck4tQ4vVC2WuwE5qctVUnCsjJxEu+9n5AS5ZFuTWG52viSWZ6dXqHV4jny8XlBOE7Ii8rnNKmMNAHgFqV+ukrg0WNXV1aGsLD8F9+yzzxaf+9JL8jUAgJ07d4rlu3btEsvb29vFci3ZDQBSyppcuz56wKpPIpuSGqWlqLlKRGIopKcO9/bKKV7ae49E5DW87ZOKqqe1KclmSnKzX+qbto7Xlqzaa0Wjcn8EgFmzZonl06ZNE8u1JEO/efVQTrZzMy5QcOm0JDqjffYAkHXl69+bkNtqdaWcqDx9yly1jldWy3OnrcUgKim1GZ+oeFdJ6Bto28uk9eTVcESebCvL5Hlz7x45pbazXf4MDgCRsLz2UZMhlQdcT5+PtKQ/21I+iyqfj1tbdqt1lJbJdYQiyjikdMV0Rv8wUDU0f92npYBK+E0nIiIiIiIiIiIqOm46ERERERERERFR0XHTiYiIiIiIiIiIio6bTkREREREREREVHTcdCIiIiIiIiIioqLjphMRERERERERERWdnhMsPTkYQjCUHyWaycgxh0EtYxlAJiFH8YWUmGMnKJcHbH3PzEDOAUwpx5tWToXjaLnmgKXEZGrv3UvJuaYmq8cNOo78Hq2AEkFsaXWoWbZwHDkqUnt/0dISsbynW4/eTWfkuMa0ElnrOPLxxkrkGF0A6OqQY+ttJZo2FlLiqC09HrS7Oz+SOuETN0z/GFoIbcqT26sDPUr5hRYlYlxpA46rxKSqNfg/JlLeoNyK9wkp8bSeEo2btOTxzlLGTQBo6ZbHzrXKOdGO1zctWYns1ZKHLfX3JnpUsVsY5ewT7TzY2LEKONH8uFpLSRpOJ9rU17HalchdJcY5VF4rv05Qn4NtKyaXB+U4chOsFMsjR49T67C6GuTXysjjclSpIxiR46gBwNn+lFju9cqxzM5wOXLci49V68gG5Eho48jzvGuUfmLL5xwAvFSXWB6z5bEuNWSEWJ5sfFOtIxMbJZZHKseI5a7SsV0lGh4AsiZ/fZVR2uxgFQgEEAjkj78f+MAHxOcuWLBAfZ2uLvl6dnZ2iuUdHXJceKdSDgCdSh1a3V2dPWJ5d7dcDgC9vXJEvFHG5XRaXi9rrwMACeUzR0+PfFxpJb7d9VlHZ7Lyz3hKW/aUdanRJjvoc6etzJvxuDyuzJ49W61j/vz5YrkWda+xfCf6Q1cm7cK186+R4yjrL1dvL9qyo7SkQiwvKysTy3c37VTraG9rF8tDIeVzrTw1I9Grj8faG7FseY3Z1SXPzSVGX6fHy+XjrawcJh+SctpbmprUOrTxRnt/WWUsCLr6noFR5u2s0udNSj7v3T36WNedks9vWqk7lZbraGqWP08DQGlF/nzuKp8zJPymExERERERERERFR03nYiIiIiIiIiIqOi46UREREREREREREXHTSciIiIiIiIiIiq6ft1IfP8NtpLJVJ/Hssr9o7LKTagBwHPlvS7XU24YnpXLs8pNtgHA2PLPJFJ93wMAuMp90iyj3yjaUu5WllFuJJ5KyTdAzGonEYDlyI8FHPnSua58w0S/GyCm0/I50W4knoV8o7REQj9XTkA+3oHeSNyy9ZvNaTf11m4kbrnKOfS5kXii4Fzt7xPqTegGicF+fMWgvUfjc1Np/bwor6Xda9DvuHweGwjfOtT3rr3WwI9K+xm9XH0h31rE0gG/mE8lfW4kvr948PaR/ceWSvS9gaSdlsd8yyfkwBLmcgBASP4ZLyTfuNLz+b2VG5AfC9jy2G4rt54PQL9ppqXMOSajzGlZ+flZTz6HAOAklRsEJ+X53Elo51avIxtQbqbsyOdEu5F4Wmgf+yWVmylr7SSlvG899gTI2EqbU45Lu194xqf/Ft5IfH+fGMz9F/i/45Nu9O0qNxxO+vTh7u7uopRrN9MG9Jtzazfm1sr93kdKufGtfiNxudFo69h9jyltWQkWymTkVu5/I3H5Z9QbiWs3D/Zpx+qNxJW1eiYjr3G16wToN6L/R94YfH+dg7kP7z82z5PGZPncGPG5+19PLtfGgmxWbkdaWwW0Y9XrGOjrAFAbpVaHpdx02rdvZbQ+P8D+6/M+1OPVrq3yvrM+51Z7TL8e8vFqNzH/3yOT61AanFq3EkoG9D2/+69Pf/qvZfrxrB07dmD06NEHfTGi96vt27dj1Cg5uWcwYB8m8jeY+zD7L5G/wdx/AfZhooMZzH2Y/ZfIX3/6b782nTzPw65duxCPx9+zMZhE74QxBl1dXRgxYgRse/D+tSr7MJHsUOjD7L9EskOh/wLsw0SaQ6EPs/8SyQbSf/u16URERERERERERDQQg3NLmYiIiIiIiIiIDmncdCIiIiIiIiIioqLjphMRERERERERERUdN52IiIiIiIiIiKjouOlURN/5zncwe/bs3L/PPfdcfPSjH33XjmcwsiwLf/zjH9/twyAiovcwYwz+7d/+DZWVlbAsCxUVFfjiF7/4bh8WvU+dcMIJg7r9bd26FZZlYfXq1X/X64wdOxY/+clPfJ9TzHXgu7nOvuWWW1BRUVG013v66adhWRba29sHxfEAg7/dEtGhg5tOREQDxIUY0eD26KOP4pZbbsGDDz6IxsZGHH744e/2IRHRIDeQTayzzjoLGzZs+Mce0Lvsvvvuw1VXXfVuHwa9x3AN/f4UeLcPgIjovcYYA9d1EQhwiCV6N9TX16O2thbHHHMMAAy6vphOpxEKhd7twyCidyCTySAajSIajb7bh/IPVVlZ+W4fAr0PcQ393vSe/qbT//zP/2DGjBmIRqOoqqrC4sWL0dPTk/tNxtVXX43hw4ejoqICV155JbLZLC699FJUVlZi1KhRuPnmm/Ne72tf+xomT56MWCyG8ePH49vf/jYymUxRjvWEE07AF77wBXzxi1/EkCFDMHz4cNx0003o6enBeeedh3g8jokTJ+KRRx7J/Yzrujj//PMxbtw4RKNRTJkyBTfccEPe676T97r/a9Z33303jjvuOESjUcyfPx8bNmzAqlWrcMQRR6C0tBSnnnoq9u7dm/u5VatW4eSTT8bQoUNRXl6ORYsW4bXXXlPfczqdxsUXX4za2lpEIhHU1dXhmmuuKcr5JPpHOffcc/HMM8/ghhtugGVZsCwLt9xyCyzLwiOPPIJ58+YhHA7j+eefh+d5uOaaa3J9dNasWfif//mfvNd78803ceqpp6K0tBTDhw/HOeecg+bm5tzj2jhGRLJzzz0XX/jCF9DQ0ADLsjB27Ng+z2lra8NnPvMZDBkyBLFYDKeeeio2btwIYN+Ct7q6Oq+vzp49G7W1tbl/P//88wiHw+jt7QUAtLe347Of/Syqq6tRVlaGE088EWvWrMk9f/+f3//mN7/BuHHjEIlEALB/v594noevfvWrqKysRE1NDb7zne/kHmtoaMDSpUtRWlqKsrIynHnmmdizZ0/ucekbOF/84hdxwgkn5P59sLb0m9/8BtOmTUMkEsHUqVPxi1/8os8xbt68GR/4wAcQi8Uwa9YsvPjii3mP33vvvTjssMMQDocxduxYXHfddb7veePGjTj++OMRiUQwffp0PPHEE/04U/9n+/btOPPMM1FRUYHKykosXboUW7duVZ/fnzn3rbfewoc//GGUlZUhHo/juOOOQ319Pb7zne/g1ltvxQMPPJCb259++uncmviuu+7CokWLEIlEcPvtt4t/zvbnP/8Z8+fPRyQSwdChQ/Gxj30s99jvfvc7HHHEEYjH46ipqcHZZ5+NpqamAZ2PA91yyy0YM2YMYrEYPvaxj6GlpaXPcx544AHMnTsXkUgE48ePxxVXXIFsNgsAOPvss3HWWWflPT+TyWDo0KG47bbbAPT9RkoqlcLXvvY1jB49GuFwGBMnTsRvf/vb3ONcz9DBcA39Pmbeo3bt2mUCgYC5/vrrzZYtW8zrr79ufv7zn5uuri6zfPlyE4/HzUUXXWTefvtt89vf/tYAMEuWLDHf+973zIYNG8xVV11lgsGg2b59e+41r7rqKrNixQqzZcsW86c//ckMHz7c/OAHP8g9fvnll5tZs2bl/r18+XKzdOnSfh3vokWLTDweN1dddVWufsdxzKmnnmp+/etfmw0bNpjPfe5zpqqqyvT09BhjjEmn0+ayyy4zq1atMps3bza///3vTSwWM3fddVfeMQz0vW7ZssUAMFOnTjWPPvqoWbt2rTn66KPNvHnzzAknnGCef/5589prr5mJEyeaCy+8MFfXk08+aX73u9+ZdevWmbVr15rzzz/fDB8+3HR2duaeA8Dcf//9xhhjrr32WjN69Gjz7LPPmq1bt5rnnnvO/OEPf+j3NSZ6N7S3t5sFCxaYCy64wDQ2NprGxkbzl7/8xQAwM2fONI8//rjZtGmTaWlpMd/97ndz/ai+vt7cfPPNJhwOm6efftoYY0xbW5uprq423/jGN8y6devMa6+9Zk4++WTzgQ98wBjjP44Rkay9vd1ceeWVZtSoUaaxsdE0NTWZRYsWmf/4j//IPecjH/mImTZtmnn22WfN6tWrzZIlS8zEiRNNOp02xhhz+umnm4suusgYY0xra6sJhUKmvLzcrFu3zhhjzHe/+12zcOHC3OstXrzYnHbaaWbVqlVmw4YN5stf/rKpqqoyLS0txph964OSkhJzyimnmNdee82sWbOG/ft9ZNGiRaasrMx85zvfMRs2bDC33nqrsSzLPP7448Z1XTN79mxz7LHHmldeecW89NJLZt68eWbRokW5n5fWk//xH/+Re87B2tLvf/97U1tba+69916zefNmc++995rKykpzyy23GGPy130PPvigWb9+vfnEJz5h6urqTCaTMcYY88orrxjbts2VV15p1q9fb26++WYTjUbNzTffnDumuro68+Mf/9gYY4zruubwww83J510klm9erV55plnzJw5c/LWgX7S6bSZNm2a+dd//Vfz+uuvm7Vr15qzzz7bTJkyxaRSKfG8HGzO3bFjh6msrDSnn366WbVqlVm/fr357//+b/P222+brq4uc+aZZ5pTTjklN7enUqncuRk7dmzu/O3atcvcfPPNpry8PFf3gw8+aBzHMZdddplZu3atWb16tbn66qtzj//2t781Dz/8sKmvrzcvvviiWbBggTn11FNzjz/11FMGgGlrazvouXnppZeMbdvmBz/4gVm/fr254YYbTEVFRd7xPPvss6asrMzccsstpr6+3jz++ONm7Nix5jvf+U7ueKPRaN548+c//9lEo9Hcur1w3DzzzDPN6NGjzX333Wfq6+vNX/7yF3PnnXcaY7ieof7hGvr96z276fTqq68aAGbr1q19Hlu+fLmpq6szruvmyqZMmWKOO+643L+z2awpKSkxd9xxh1rHtddea+bNm5f799+76XTsscf2qf+cc87JlTU2NhoA5sUXX1Rf56KLLjIf//jH845hoO91/wT7m9/8JvecO+64wwAwTz75ZK7smmuuMVOmTFGPxXVdE4/HzZ///Odc2YGLjS984QvmxBNPNJ7nqa9BNBgVLsT2Lxb/+Mc/5sqSyaSJxWLmhRdeyPvZ888/33zqU58yxuzbyP7gBz+Y9/j27dsNALN+/XrfcYyIdD/+8Y9NXV1d7t8H9tkNGzYYAGbFihW5x5ubm000GjV33323McaYn/70p+awww4zxhjzxz/+0Rx11FFm6dKl5r/+67+MMfs2mf7f//t/xhhjnnvuOVNWVmaSyWTeMUyYMMH86le/MsbsWx8Eg0HT1NSUe5z9+/2jcI1njDHz5883X/va18zjjz9uHMcxDQ0NucfeeustA8C8/PLLxpiDbzodrC1NmDChzy/1rrrqKrNgwQJjjLzu238M+zdazz77bHPyySfnvcall15qpk+fnvv3gZtOjz32mAkEAmbnzp25xx955JF+bzr97ne/M1OmTMlbI6ZSKRONRs1jjz1mjMk/L/2Zc7/xjW+YcePG5TaXC0nnef+5+clPfpJXXrjptGDBArNs2bKDvq/9Vq1aZQDkPgAPZNPpU5/6lPnQhz6UV3bWWWflHc9JJ52Ut+llzL5zWltba4wxJpPJmKFDh5rbbrst73XPOuus3L8PHDfXr19vAJgnnnhCPCauZ6i/uIZ+f3rP/nndrFmzcNJJJ2HGjBk444wzcNNNN6GtrS33+GGHHQbb/r+3P3z4cMyYMSP3b8dxUFVVlffV17vuugsLFy5ETU0NSktL8a1vfQsNDQ1FO+aZM2f2qf/AYxo+fDgA5B3Tz3/+c8ybNw/V1dUoLS3Fr3/96z7H9E7ea+Hx7K+78HgO/Jk9e/bgggsuwKRJk1BeXo6ysjJ0d3er5+jcc8/F6tWrMWXKFFxyySV4/PHHfc4O0eB3xBFH5P5/06ZN6O3txcknn4zS0tLcf7fddhvq6+sBAGvWrMFTTz2V9/jUqVMB7LsnzcHGMSIauHXr1iEQCOCoo47KlVVVVWHKlClYt24dAGDRokVYu3Yt9u7di2eeeQYnnHACTjjhBDz99NPIZDJ44YUXcn/atGbNGnR3d6OqqiqvL2/ZsiXX1wGgrq4O1dXVuX+zf7+/HLimAoDa2lo0NTVh3bp1GD16NEaPHp17bPr06aioqMi1x4Pxa0s9PT2or6/H+eefn9c+v/vd7+a1z8Jj3P/npPvXeevWrcPChQvznr9w4UJs3LgRruv2Oab972vEiBG5sgULFvTr/QD7+tWmTZsQj8dzx1xZWYlkMtnnuIH+zbmrV6/Gcccdh2Aw2O/j2O/A+V2yevVqnHTSSerjr776Kk477TSMGTMG8XgcixYtAoB39Dli3bp1eeMX0PfcrlmzBldeeWXeubjgggvQ2NiI3t5eBAIBnHnmmbj99tsB7GsnDzzwAJYtW6a+P8dxcsddiOsZ+ntxDf3e9p69Q5fjOHjiiSfwwgsv4PHHH8eNN96Ib37zm1i5ciUA9JlwLMsSyzzPAwC8+OKLWLZsGa644gosWbIE5eXluPPOOw/69+wDcbBjsiwLAHLHdOedd+IrX/kKrrvuOixYsADxeBzXXntt7j3293X3l+1/Xenn9tddWHbgzyxfvhwtLS244YYbUFdXh3A4jAULFiCdTovvd+7cudiyZQseeeQR/OUvf8GZZ56JxYsX9/l7XaJDRUlJSe7/u7u7AQAPPfQQRo4cmfe8cDice85pp52GH/zgB31eq7a21nccGzdu3D/wnRC9v82YMQOVlZV45pln8Mwzz+B73/seampq8IMf/ACrVq1CJpPJ3aS8u7sbtbW1ePrpp/u8zoH3fDlwfAD81yns3+89/Vl3aWzbhjEmr+zAe4r6taVYLAYAuOmmm/psVDiOox5j4Zrzn627uxvz5s3LbYoc6MDN2wOfD/jPuX/Pjb8L+28hv9fu6enBkiVLsGTJEtx+++2orq5GQ0MDlixZoq6R/17d3d244oorcPrpp/d5bP895ZYtW4ZFixahqakJTzzxBKLRKE455RTx9Q527rieob8X19Dvbe/ZTSdg34S5cOFCLFy4EJdddhnq6upw//33v6PXeuGFF1BXV4dvfvObubJt27YV61DfkRUrVuCYY47B5z//+VyZ9Nuff+bx/OIXv8CHPvQhAPtuAHngzdwkZWVlOOuss3DWWWfhE5/4BE455RS0trYyMYMGtVAoJP5m90DTp09HOBxGQ0OD+pvBuXPn4t5778XYsWPVlA5tHPvP//zPv/t9EL0fTZs2DdlsFitXrsxtHLW0tGD9+vWYPn06gH397rjjjsMDDzyAt956C8ceeyxisRhSqRR+9atf4YgjjsgtkOfOnYvdu3cjEAiINy33w/5N06ZNw/bt27F9+/bct53Wrl2L9vb2XHusrq7Gm2++mfdzq1ev7rNJpLWlESNGYPPmzeq3WPp7nCtWrMgrW7FiBSZPntxn8+rA99XY2Jj71tRLL73U7/rmzp2Lu+66C8OGDUNZWdlBn9+fOXfmzJm49dZbkclkxG879Wdu18ycORNPPvkkzjvvvD6Pvf3222hpacH3v//93DV+5ZVX3lE9wL5zW/gL5sJzO3fuXKxfvx4TJ05UX+eYY47B6NGjcdddd+GRRx7BGWecoX4LbMaMGfA8D8888wwWL17c53GuZ6i/uIZ+f3rP/nndypUrcfXVV+OVV15BQ0MD7rvvPuzduxfTpk17R683adIkNDQ04M4770R9fT1++tOfvuMNrGKZNGkSXnnlFTz22GPYsGEDvv3tb2PVqlXv6vH87ne/w7p167By5UosW7bM9zcj119/Pe644w68/fbb2LBhA+655x7U1NT0SQMhGmzGjh2LlStXYuvWrWhubhZ/ExyPx/GVr3wFX/rSl3Drrbeivr4er732Gm688UbceuutAICLLroIra2t+NSnPoVVq1ahvr4ejz32GM477zy4rlv0cYyI9s1VS5cuxQUXXIDnn38ea9aswac//WmMHDkSS5cuzT3vhBNOwB133IHZs2ejtLQUtm3j+OOPx+233563CF68eDEWLFiAj370o3j88cexdetWvPDCC/jmN7/p+8GS/ZuAfe1nxowZWLZsGV577TW8/PLL+MxnPoNFixbl/tzkxBNPxCuvvILbbrsNGzduxOWXX563CXWwtnTFFVfgmmuuwU9/+lNs2LABb7zxBm6++WZcf/31/T7OL3/5y3jyySdx1VVXYcOGDbj11lvxs5/9DF/5ylfU9zV58mQsX74ca9aswXPPPZf3i9uDWbZsGYYOHYqlS5fiueeew5YtW/D000/jkksuwY4dO/o8vz9z7sUXX4zOzk588pOfxCuvvIKNGzfid7/7HdavXw9g39z++uuvY/369Whubh5QQvXll1+OO+64A5dffjnWrVuHN954I/cNjDFjxiAUCuHGG2/E5s2b8ac//QlXXXVVv1+70CWXXIJHH30UP/rRj7Bx40b87Gc/w6OPPpr3nMsuuwy33XYbrrjiCrz11ltYt24d7rzzTnzrW9/Ke97ZZ5+NX/7yl3jiiSd8NyXHjh2L5cuX41//9V/xxz/+MXc97r77bgBcz1D/cQ39/vSe3XQqKyvDs88+iw996EOYPHkyvvWtb+G6667Dqaee+o5e7yMf+Qi+9KUv4eKLL8bs2bPxwgsv4Nvf/naRj3pg/v3f/x2nn346zjrrLBx11FFoaWnJ+9bTP9tvf/tbtLW1Ye7cuTjnnHNwySWXYNiwYerz4/E4fvjDH+KII47A/PnzsXXrVjz88MN5958iGoy+8pWvwHEcTJ8+Pfc1eclVV12Fb3/727jmmmswbdo0nHLKKXjooYdyX+sdMWIEVqxYAdd18cEPfhAzZszAF7/4RVRUVMC27aKPY0S0z80334x58+bhwx/+MBYsWABjDB5++OG83/IvWrQIruvmxdKfcMIJfcosy8LDDz+M448/Hueddx4mT56MT37yk9i2bVvufogS9m8C9rWfBx54AEOGDMHxxx+PxYsXY/z48bjrrrtyz1myZAm+/e1v46tf/Srmz5+Prq4ufOYzn8k9frC29NnPfha/+c1vcPPNN2PGjBlYtGgRbrnllgH9icncuXNx9913484778Thhx+Oyy67DFdeeSXOPfdc8fm2beP+++9HIpHAkUceic9+9rP43ve+1+/6YrEYnn32WYwZMwann346pk2bhvPPPx/JZFL95tPB5tyqqir89a9/RXd3NxYtWoR58+bhpptuyvX7Cy64AFOmTMERRxyB6urqPt/s8nPCCSfgnnvuwZ/+9CfMnj0bJ554Il5++WUA+76pdsstt+Cee+7B9OnT8f3vfx8/+tGP+v3ahY4++mjcdNNNuOGGGzBr1iw8/vjjfTaTlixZggcffBCPP/445s+fj6OPPho//vGPUVdXl/e8ZcuWYe3atRg5cmSfe3YV+q//+i984hOfwOc//3lMnToVF1xwQS5+nusZ6i+uod+fLFP4R+JERERERERERER/J36lhIiIiIiIiIiIio6bTv8EDQ0NeXGOhf+9k7hUIiIiIqJD0dVXX62ui9/vf/5y6qmnqufm6quvfrcPj4howPjndf8E2WwWW7duVR/3u+s+EREREdF7SWtrK1pbW8XHotFon5j095OdO3cikUiIj1VWVjLhmYgOOdx0IiIiIiIiIiKiouOf1xERERERERERUdFx04mIiIiIiIiIiIqOm05ERERERERERFR03HQiIiIiIiIiIqKi46YTEREREREREREVHTediIiIiIiIiIio6LjpRERERERERERERff/AdmK8oovYmsrAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# build the model\n",
        "\n",
        "model = tf.keras.Sequential([\n",
        "    tf.keras.layers.Conv2D(filters=32, kernel_size=3, padding=\"same\", activation=\"relu\", input_shape=[32, 32, 3]),\n",
        "    tf.keras.layers.Conv2D(filters=32, kernel_size=3, padding=\"same\", activation=\"relu\"),\n",
        "    tf.keras.layers.MaxPooling2D(pool_size=2, strides=2, padding=\"valid\"),\n",
        "    tf.keras.layers.Conv2D(filters=64, kernel_size=3, padding=\"same\", activation=\"relu\"),\n",
        "    tf.keras.layers.Conv2D(filters=64, kernel_size=3, padding=\"same\", activation=\"relu\"),\n",
        "    tf.keras.layers.MaxPooling2D(pool_size=2, strides=2, padding=\"valid\"),\n",
        "    tf.keras.layers.Flatten(),\n",
        "    tf.keras.layers.Dropout(0.5, noise_shape=None, seed=None),\n",
        "    tf.keras.layers.Dense(units=128, activation='relu'),\n",
        "    tf.keras.layers.Dense(units=100, activation='softmax')\n",
        "])\n",
        "\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JF-xPsJs73XA",
        "outputId": "41c0fa08-9cb3-40df-897e-8b20298a8476"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d (Conv2D)             (None, 32, 32, 32)        896       \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 32, 32, 32)        9248      \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2  (None, 16, 16, 32)        0         \n",
            " D)                                                              \n",
            "                                                                 \n",
            " conv2d_2 (Conv2D)           (None, 16, 16, 64)        18496     \n",
            "                                                                 \n",
            " conv2d_3 (Conv2D)           (None, 16, 16, 64)        36928     \n",
            "                                                                 \n",
            " max_pooling2d_1 (MaxPoolin  (None, 8, 8, 64)          0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 4096)              0         \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 4096)              0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 128)               524416    \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 100)               12900     \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 602884 (2.30 MB)\n",
            "Trainable params: 602884 (2.30 MB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(optimizer='adam',\n",
        "              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
        "              metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "EkbzWHcz-w4U"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(train_images, train_labels, epochs=120)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zm4cUs6C-_7x",
        "outputId": "15157925-4fd0-44bf-84e7-3536e303e790"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/120\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/backend.py:5727: UserWarning: \"`sparse_categorical_crossentropy` received `from_logits=True`, but the `output` argument was produced by a Softmax activation and thus does not represent logits. Was this intended?\n",
            "  output, from_logits = _get_logits(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1563/1563 [==============================] - 15s 6ms/step - loss: 2.5783 - accuracy: 0.2424\n",
            "Epoch 2/120\n",
            "1563/1563 [==============================] - 8s 5ms/step - loss: 2.1184 - accuracy: 0.3486\n",
            "Epoch 3/120\n",
            "1563/1563 [==============================] - 7s 4ms/step - loss: 1.9536 - accuracy: 0.3943\n",
            "Epoch 4/120\n",
            "1563/1563 [==============================] - 7s 5ms/step - loss: 1.8481 - accuracy: 0.4255\n",
            "Epoch 5/120\n",
            "1563/1563 [==============================] - 7s 4ms/step - loss: 1.7716 - accuracy: 0.4475\n",
            "Epoch 6/120\n",
            "1563/1563 [==============================] - 7s 5ms/step - loss: 1.6951 - accuracy: 0.4711\n",
            "Epoch 7/120\n",
            "1563/1563 [==============================] - 8s 5ms/step - loss: 1.6347 - accuracy: 0.4863\n",
            "Epoch 8/120\n",
            "1563/1563 [==============================] - 7s 4ms/step - loss: 1.5911 - accuracy: 0.4968\n",
            "Epoch 9/120\n",
            "1563/1563 [==============================] - 7s 5ms/step - loss: 1.5479 - accuracy: 0.5129\n",
            "Epoch 10/120\n",
            "1563/1563 [==============================] - 7s 4ms/step - loss: 1.5105 - accuracy: 0.5252\n",
            "Epoch 11/120\n",
            "1563/1563 [==============================] - 7s 5ms/step - loss: 1.4639 - accuracy: 0.5353\n",
            "Epoch 12/120\n",
            "1563/1563 [==============================] - 8s 5ms/step - loss: 1.4425 - accuracy: 0.5454\n",
            "Epoch 13/120\n",
            "1563/1563 [==============================] - 7s 4ms/step - loss: 1.4113 - accuracy: 0.5521\n",
            "Epoch 14/120\n",
            "1563/1563 [==============================] - 8s 5ms/step - loss: 1.3818 - accuracy: 0.5617\n",
            "Epoch 15/120\n",
            "1563/1563 [==============================] - 7s 5ms/step - loss: 1.3572 - accuracy: 0.5703\n",
            "Epoch 16/120\n",
            "1563/1563 [==============================] - 8s 5ms/step - loss: 1.3332 - accuracy: 0.5779\n",
            "Epoch 17/120\n",
            "1563/1563 [==============================] - 8s 5ms/step - loss: 1.3176 - accuracy: 0.5846\n",
            "Epoch 18/120\n",
            "1563/1563 [==============================] - 7s 5ms/step - loss: 1.2895 - accuracy: 0.5889\n",
            "Epoch 19/120\n",
            "1563/1563 [==============================] - 7s 5ms/step - loss: 1.2780 - accuracy: 0.5932\n",
            "Epoch 20/120\n",
            "1563/1563 [==============================] - 7s 5ms/step - loss: 1.2619 - accuracy: 0.5975\n",
            "Epoch 21/120\n",
            "1563/1563 [==============================] - 7s 4ms/step - loss: 1.2513 - accuracy: 0.6007\n",
            "Epoch 22/120\n",
            "1563/1563 [==============================] - 8s 5ms/step - loss: 1.2274 - accuracy: 0.6088\n",
            "Epoch 23/120\n",
            "1563/1563 [==============================] - 7s 4ms/step - loss: 1.2219 - accuracy: 0.6122\n",
            "Epoch 24/120\n",
            "1563/1563 [==============================] - 7s 5ms/step - loss: 1.2025 - accuracy: 0.6202\n",
            "Epoch 25/120\n",
            "1563/1563 [==============================] - 7s 5ms/step - loss: 1.1883 - accuracy: 0.6196\n",
            "Epoch 26/120\n",
            "1563/1563 [==============================] - 7s 4ms/step - loss: 1.1828 - accuracy: 0.6235\n",
            "Epoch 27/120\n",
            "1563/1563 [==============================] - 7s 5ms/step - loss: 1.1704 - accuracy: 0.6272\n",
            "Epoch 28/120\n",
            "1563/1563 [==============================] - 7s 5ms/step - loss: 1.1622 - accuracy: 0.6293\n",
            "Epoch 29/120\n",
            "1563/1563 [==============================] - 7s 5ms/step - loss: 1.1613 - accuracy: 0.6310\n",
            "Epoch 30/120\n",
            "1563/1563 [==============================] - 8s 5ms/step - loss: 1.1352 - accuracy: 0.6354\n",
            "Epoch 31/120\n",
            "1563/1563 [==============================] - 7s 4ms/step - loss: 1.1293 - accuracy: 0.6401\n",
            "Epoch 32/120\n",
            "1563/1563 [==============================] - 7s 5ms/step - loss: 1.1259 - accuracy: 0.6411\n",
            "Epoch 33/120\n",
            "1563/1563 [==============================] - 7s 4ms/step - loss: 1.0987 - accuracy: 0.6483\n",
            "Epoch 34/120\n",
            "1563/1563 [==============================] - 7s 5ms/step - loss: 1.1162 - accuracy: 0.6437\n",
            "Epoch 35/120\n",
            "1563/1563 [==============================] - 8s 5ms/step - loss: 1.1040 - accuracy: 0.6488\n",
            "Epoch 36/120\n",
            "1563/1563 [==============================] - 7s 4ms/step - loss: 1.0907 - accuracy: 0.6494\n",
            "Epoch 37/120\n",
            "1563/1563 [==============================] - 7s 5ms/step - loss: 1.0801 - accuracy: 0.6537\n",
            "Epoch 38/120\n",
            "1563/1563 [==============================] - 7s 5ms/step - loss: 1.0876 - accuracy: 0.6537\n",
            "Epoch 39/120\n",
            "1563/1563 [==============================] - 7s 4ms/step - loss: 1.0684 - accuracy: 0.6582\n",
            "Epoch 40/120\n",
            "1563/1563 [==============================] - 8s 5ms/step - loss: 1.0777 - accuracy: 0.6569\n",
            "Epoch 41/120\n",
            "1563/1563 [==============================] - 7s 4ms/step - loss: 1.0734 - accuracy: 0.6607\n",
            "Epoch 42/120\n",
            "1563/1563 [==============================] - 7s 5ms/step - loss: 1.0482 - accuracy: 0.6673\n",
            "Epoch 43/120\n",
            "1563/1563 [==============================] - 7s 5ms/step - loss: 1.0421 - accuracy: 0.6691\n",
            "Epoch 44/120\n",
            "1563/1563 [==============================] - 7s 5ms/step - loss: 1.0341 - accuracy: 0.6700\n",
            "Epoch 45/120\n",
            "1563/1563 [==============================] - 8s 5ms/step - loss: 1.0222 - accuracy: 0.6763\n",
            "Epoch 46/120\n",
            "1563/1563 [==============================] - 7s 5ms/step - loss: 1.0254 - accuracy: 0.6739\n",
            "Epoch 47/120\n",
            "1563/1563 [==============================] - 7s 5ms/step - loss: 1.0183 - accuracy: 0.6769\n",
            "Epoch 48/120\n",
            "1563/1563 [==============================] - 8s 5ms/step - loss: 1.0238 - accuracy: 0.6745\n",
            "Epoch 49/120\n",
            "1563/1563 [==============================] - 8s 5ms/step - loss: 1.0194 - accuracy: 0.6757\n",
            "Epoch 50/120\n",
            "1563/1563 [==============================] - 7s 5ms/step - loss: 1.0099 - accuracy: 0.6795\n",
            "Epoch 51/120\n",
            "1563/1563 [==============================] - 7s 5ms/step - loss: 1.0023 - accuracy: 0.6814\n",
            "Epoch 52/120\n",
            "1563/1563 [==============================] - 7s 4ms/step - loss: 0.9975 - accuracy: 0.6827\n",
            "Epoch 53/120\n",
            "1563/1563 [==============================] - 7s 5ms/step - loss: 0.9984 - accuracy: 0.6830\n",
            "Epoch 54/120\n",
            "1563/1563 [==============================] - 8s 5ms/step - loss: 0.9870 - accuracy: 0.6864\n",
            "Epoch 55/120\n",
            "1563/1563 [==============================] - 7s 4ms/step - loss: 0.9885 - accuracy: 0.6883\n",
            "Epoch 56/120\n",
            "1563/1563 [==============================] - 7s 5ms/step - loss: 0.9862 - accuracy: 0.6873\n",
            "Epoch 57/120\n",
            "1563/1563 [==============================] - 7s 4ms/step - loss: 0.9831 - accuracy: 0.6895\n",
            "Epoch 58/120\n",
            "1563/1563 [==============================] - 7s 5ms/step - loss: 0.9717 - accuracy: 0.6907\n",
            "Epoch 59/120\n",
            "1563/1563 [==============================] - 8s 5ms/step - loss: 0.9722 - accuracy: 0.6904\n",
            "Epoch 60/120\n",
            "1563/1563 [==============================] - 7s 4ms/step - loss: 0.9816 - accuracy: 0.6872\n",
            "Epoch 61/120\n",
            "1563/1563 [==============================] - 7s 5ms/step - loss: 0.9786 - accuracy: 0.6897\n",
            "Epoch 62/120\n",
            "1563/1563 [==============================] - 7s 5ms/step - loss: 0.9631 - accuracy: 0.6942\n",
            "Epoch 63/120\n",
            "1563/1563 [==============================] - 8s 5ms/step - loss: 0.9627 - accuracy: 0.6944\n",
            "Epoch 64/120\n",
            "1563/1563 [==============================] - 8s 5ms/step - loss: 0.9648 - accuracy: 0.6943\n",
            "Epoch 65/120\n",
            "1563/1563 [==============================] - 7s 4ms/step - loss: 0.9633 - accuracy: 0.6948\n",
            "Epoch 66/120\n",
            "1563/1563 [==============================] - 7s 5ms/step - loss: 0.9616 - accuracy: 0.6960\n",
            "Epoch 67/120\n",
            "1563/1563 [==============================] - 7s 5ms/step - loss: 0.9404 - accuracy: 0.7028\n",
            "Epoch 68/120\n",
            "1563/1563 [==============================] - 8s 5ms/step - loss: 0.9637 - accuracy: 0.6976\n",
            "Epoch 69/120\n",
            "1563/1563 [==============================] - 7s 5ms/step - loss: 0.9455 - accuracy: 0.7003\n",
            "Epoch 70/120\n",
            "1563/1563 [==============================] - 7s 5ms/step - loss: 0.9676 - accuracy: 0.6940\n",
            "Epoch 71/120\n",
            "1563/1563 [==============================] - 7s 5ms/step - loss: 0.9323 - accuracy: 0.7024\n",
            "Epoch 72/120\n",
            "1563/1563 [==============================] - 7s 5ms/step - loss: 0.9343 - accuracy: 0.7055\n",
            "Epoch 73/120\n",
            "1563/1563 [==============================] - 8s 5ms/step - loss: 0.9533 - accuracy: 0.6988\n",
            "Epoch 74/120\n",
            "1563/1563 [==============================] - 7s 5ms/step - loss: 0.9389 - accuracy: 0.7040\n",
            "Epoch 75/120\n",
            "1563/1563 [==============================] - 7s 5ms/step - loss: 0.9189 - accuracy: 0.7079\n",
            "Epoch 76/120\n",
            "1563/1563 [==============================] - 7s 4ms/step - loss: 0.9434 - accuracy: 0.7016\n",
            "Epoch 77/120\n",
            "1563/1563 [==============================] - 8s 5ms/step - loss: 0.9306 - accuracy: 0.7073\n",
            "Epoch 78/120\n",
            "1563/1563 [==============================] - 8s 5ms/step - loss: 0.9447 - accuracy: 0.7007\n",
            "Epoch 79/120\n",
            "1563/1563 [==============================] - 7s 4ms/step - loss: 0.9250 - accuracy: 0.7079\n",
            "Epoch 80/120\n",
            "1563/1563 [==============================] - 8s 5ms/step - loss: 0.9357 - accuracy: 0.7065\n",
            "Epoch 81/120\n",
            "1563/1563 [==============================] - 7s 4ms/step - loss: 0.9301 - accuracy: 0.7065\n",
            "Epoch 82/120\n",
            "1563/1563 [==============================] - 8s 5ms/step - loss: 0.9258 - accuracy: 0.7080\n",
            "Epoch 83/120\n",
            "1563/1563 [==============================] - 7s 5ms/step - loss: 0.9043 - accuracy: 0.7107\n",
            "Epoch 84/120\n",
            "1563/1563 [==============================] - 7s 4ms/step - loss: 0.9373 - accuracy: 0.7054\n",
            "Epoch 85/120\n",
            "1563/1563 [==============================] - 7s 5ms/step - loss: 0.9012 - accuracy: 0.7157\n",
            "Epoch 86/120\n",
            "1563/1563 [==============================] - 7s 5ms/step - loss: 0.9036 - accuracy: 0.7147\n",
            "Epoch 87/120\n",
            "1563/1563 [==============================] - 8s 5ms/step - loss: 0.9140 - accuracy: 0.7104\n",
            "Epoch 88/120\n",
            "1563/1563 [==============================] - 7s 5ms/step - loss: 0.9107 - accuracy: 0.7128\n",
            "Epoch 89/120\n",
            "1563/1563 [==============================] - 7s 4ms/step - loss: 0.9158 - accuracy: 0.7102\n",
            "Epoch 90/120\n",
            "1563/1563 [==============================] - 7s 5ms/step - loss: 0.9120 - accuracy: 0.7116\n",
            "Epoch 91/120\n",
            "1563/1563 [==============================] - 8s 5ms/step - loss: 0.9004 - accuracy: 0.7162\n",
            "Epoch 92/120\n",
            "1563/1563 [==============================] - 8s 5ms/step - loss: 0.9099 - accuracy: 0.7129\n",
            "Epoch 93/120\n",
            "1563/1563 [==============================] - 7s 5ms/step - loss: 0.8932 - accuracy: 0.7199\n",
            "Epoch 94/120\n",
            "1563/1563 [==============================] - 7s 4ms/step - loss: 0.8975 - accuracy: 0.7169\n",
            "Epoch 95/120\n",
            "1563/1563 [==============================] - 7s 5ms/step - loss: 0.9005 - accuracy: 0.7201\n",
            "Epoch 96/120\n",
            "1563/1563 [==============================] - 9s 6ms/step - loss: 0.9117 - accuracy: 0.7137\n",
            "Epoch 97/120\n",
            "1563/1563 [==============================] - 7s 5ms/step - loss: 0.8840 - accuracy: 0.7214\n",
            "Epoch 98/120\n",
            "1563/1563 [==============================] - 7s 5ms/step - loss: 0.8910 - accuracy: 0.7181\n",
            "Epoch 99/120\n",
            "1563/1563 [==============================] - 7s 5ms/step - loss: 0.9044 - accuracy: 0.7154\n",
            "Epoch 100/120\n",
            "1563/1563 [==============================] - 7s 5ms/step - loss: 0.8940 - accuracy: 0.7183\n",
            "Epoch 101/120\n",
            "1563/1563 [==============================] - 9s 6ms/step - loss: 0.8974 - accuracy: 0.7194\n",
            "Epoch 102/120\n",
            "1563/1563 [==============================] - 7s 4ms/step - loss: 0.8799 - accuracy: 0.7243\n",
            "Epoch 103/120\n",
            "1563/1563 [==============================] - 7s 5ms/step - loss: 0.8921 - accuracy: 0.7224\n",
            "Epoch 104/120\n",
            "1563/1563 [==============================] - 7s 5ms/step - loss: 0.8741 - accuracy: 0.7283\n",
            "Epoch 105/120\n",
            "1563/1563 [==============================] - 8s 5ms/step - loss: 0.8755 - accuracy: 0.7251\n",
            "Epoch 106/120\n",
            "1563/1563 [==============================] - 8s 5ms/step - loss: 0.9030 - accuracy: 0.7155\n",
            "Epoch 107/120\n",
            "1563/1563 [==============================] - 7s 5ms/step - loss: 0.8780 - accuracy: 0.7218\n",
            "Epoch 108/120\n",
            "1563/1563 [==============================] - 7s 5ms/step - loss: 0.8817 - accuracy: 0.7248\n",
            "Epoch 109/120\n",
            "1563/1563 [==============================] - 8s 5ms/step - loss: 0.8894 - accuracy: 0.7223\n",
            "Epoch 110/120\n",
            "1563/1563 [==============================] - 8s 5ms/step - loss: 0.8858 - accuracy: 0.7220\n",
            "Epoch 111/120\n",
            "1563/1563 [==============================] - 7s 5ms/step - loss: 0.8923 - accuracy: 0.7228\n",
            "Epoch 112/120\n",
            "1563/1563 [==============================] - 8s 5ms/step - loss: 0.8795 - accuracy: 0.7243\n",
            "Epoch 113/120\n",
            "1563/1563 [==============================] - 7s 5ms/step - loss: 0.8614 - accuracy: 0.7294\n",
            "Epoch 114/120\n",
            "1563/1563 [==============================] - 7s 5ms/step - loss: 0.8660 - accuracy: 0.7284\n",
            "Epoch 115/120\n",
            "1563/1563 [==============================] - 8s 5ms/step - loss: 0.8697 - accuracy: 0.7294\n",
            "Epoch 116/120\n",
            "1563/1563 [==============================] - 7s 4ms/step - loss: 0.8653 - accuracy: 0.7322\n",
            "Epoch 117/120\n",
            "1563/1563 [==============================] - 8s 5ms/step - loss: 0.8803 - accuracy: 0.7248\n",
            "Epoch 118/120\n",
            "1563/1563 [==============================] - 7s 5ms/step - loss: 0.8621 - accuracy: 0.7310\n",
            "Epoch 119/120\n",
            "1563/1563 [==============================] - 9s 6ms/step - loss: 0.8809 - accuracy: 0.7249\n",
            "Epoch 120/120\n",
            "1563/1563 [==============================] - 8s 5ms/step - loss: 0.8651 - accuracy: 0.7269\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7bbce73500d0>"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(class_names)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Nvtqn7J-_HBG",
        "outputId": "5d8ca94d-a007-45be-f163-a1eae96d6d35"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "20"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_loss, test_acc = model.evaluate(test_images, test_labels, verbose=2)\n",
        "\n",
        "print('\\Test accuracy:', test_acc)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S-X6F3d9EBrC",
        "outputId": "4b19ddd1-f367-441e-b67f-8bf602e5e21a"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/backend.py:5727: UserWarning: \"`sparse_categorical_crossentropy` received `from_logits=True`, but the `output` argument was produced by a Softmax activation and thus does not represent logits. Was this intended?\n",
            "  output, from_logits = _get_logits(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 - 1s - loss: 1.8084 - accuracy: 0.5102 - 915ms/epoch - 3ms/step\n",
            "\\Test accuracy: 0.510200023651123\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "probability_model = tf.keras.Sequential([model, tf.keras.layers.Softmax()])"
      ],
      "metadata": {
        "id": "HlehRvm8EPs_"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = probability_model.predict(test_images)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KBwrEY6wEeU3",
        "outputId": "2b919eeb-2673-4b55-c2b6-cc647e582da1"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 1s 3ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictions[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "urOri3LbEqP7",
        "outputId": "2039ae06-cd07-41b2-c8d0-e4e30e418e8e"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.0173343 , 0.00999934, 0.00988791, 0.00988652, 0.00989779,\n",
              "       0.00997428, 0.00994426, 0.01001028, 0.01042761, 0.01093732,\n",
              "       0.0099452 , 0.01002205, 0.01004562, 0.01038895, 0.0103038 ,\n",
              "       0.01028658, 0.01011176, 0.00991589, 0.01006094, 0.01013122,\n",
              "       0.00988111, 0.00988111, 0.00988111, 0.00988111, 0.00988111,\n",
              "       0.00988111, 0.00988111, 0.00988111, 0.00988111, 0.00988111,\n",
              "       0.00988111, 0.00988111, 0.00988111, 0.00988111, 0.00988111,\n",
              "       0.00988111, 0.00988111, 0.00988111, 0.00988111, 0.00988111,\n",
              "       0.00988111, 0.00988111, 0.00988111, 0.00988111, 0.00988111,\n",
              "       0.00988111, 0.00988111, 0.00988111, 0.00988111, 0.00988111,\n",
              "       0.00988111, 0.00988111, 0.00988111, 0.00988111, 0.00988111,\n",
              "       0.00988111, 0.00988111, 0.00988111, 0.00988111, 0.00988111,\n",
              "       0.00988111, 0.00988111, 0.00988111, 0.00988111, 0.00988111,\n",
              "       0.00988111, 0.00988111, 0.00988111, 0.00988111, 0.00988111,\n",
              "       0.00988111, 0.00988111, 0.00988111, 0.00988111, 0.00988111,\n",
              "       0.00988111, 0.00988111, 0.00988111, 0.00988111, 0.00988111,\n",
              "       0.00988111, 0.00988111, 0.00988111, 0.00988111, 0.00988111,\n",
              "       0.00988111, 0.00988111, 0.00988111, 0.00988111, 0.00988111,\n",
              "       0.00988111, 0.00988111, 0.00988111, 0.00988111, 0.00988111,\n",
              "       0.00988111, 0.00988111, 0.00988111, 0.00988111, 0.00988111],\n",
              "      dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.argmax(predictions[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gZo-8qvfExrO",
        "outputId": "62df6a72-1dc1-4afa-d376-f315a44e73a6"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_labels[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WU0KOXaDE0wE",
        "outputId": "3b24f080-1ae4-4f69-d9c1-bfc5eafc6a65"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([10])"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "vXhI63C1E5Y7"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}