{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YqKnl2Yfem2u"
      },
      "outputs": [],
      "source": [
        "import json\n",
        "import os\n",
        "kaggle_json_data = {\"username\":\"xxxx\",\"key\":\"xxxx\"}\n",
        "os.mkdir(\"/root/.kaggle/\")\n",
        "with open(\"/root/.kaggle/kaggle.json\", \"w\") as file:\n",
        "    json.dump(kaggle_json_data, file)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5TJ4RIptfeRi",
        "outputId": "cd0cdd20-3827-4f11-f540-8085f97a3e42"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Warning: Your Kaggle API key is readable by other users on this system! To fix this, you can run 'chmod 600 /root/.kaggle/kaggle.json'\n"
          ]
        }
      ],
      "source": [
        "import xml.dom.minidom\n",
        "from IPython import display\n",
        "from pathlib import Path\n",
        "import kaggle\n",
        "import cv2\n",
        "from matplotlib import pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RgR9fC3tgNr1"
      },
      "outputs": [],
      "source": [
        "ds_path = Path('./legos')\n",
        "ds_kaggle_path = ds_path/\"/content/legos/dataset_20210629145407_top_600\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PjjqYOMfese4"
      },
      "outputs": [],
      "source": [
        "kaggle.api.dataset_download_files('dreamfactor/biggest-lego-dataset-600-parts', path=ds_path, unzip=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7MO44zWFex5e"
      },
      "outputs": [],
      "source": [
        "ANNOTATIONS = ds_kaggle_path/\"annotations\"\n",
        "IMAGES = ds_kaggle_path/\"images\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "obmBqARVic1r"
      },
      "source": [
        "## Show data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Oe9vaEyDletn"
      },
      "outputs": [],
      "source": [
        "example_i = 0"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xZui1NfbfHIB",
        "outputId": "00da9057-cf88-43fb-93fc-18b1f514df5f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<?xml version=\"1.0\" ?><annotation>\n",
            "    <folder>BrickRay</folder>\n",
            "    <filename>2059d05c-daa2-11eb-b1a3-3497f683a169.jpg</filename>\n",
            "    <size>\n",
            "        <width>600</width>\n",
            "        <height>600</height>\n",
            "        <depth>3</depth>\n",
            "    </size>\n",
            "    <object>\n",
            "        <name>91988</name>\n",
            "        <difficult>0</difficult>\n",
            "        <bndbox>\n",
            "            <xmin>285</xmin>\n",
            "            <ymin>0</ymin>\n",
            "            <xmax>353</xmax>\n",
            "            <ymax>363</ymax>\n",
            "        </bndbox>\n",
            "    </object>\n",
            "    <object>\n",
            "        <name>24866</name>\n",
            "        <difficult>0</difficult>\n",
            "        <bndbox>\n",
            "            <xmin>289</xmin>\n",
            "            <ymin>559</ymin>\n",
            "            <xmax>326</xmax>\n",
            "            <ymax>600</ymax>\n",
            "        </bndbox>\n",
            "    </object>\n",
            "    <object>\n",
            "        <name>3622</name>\n",
            "        <difficult>0</difficult>\n",
            "        <bndbox>\n",
            "            <xmin>301</xmin>\n",
            "            <ymin>90</ymin>\n",
            "            <xmax>333</xmax>\n",
            "            <ymax>187</ymax>\n",
            "        </bndbox>\n",
            "    </object>\n",
            "    <object>\n",
            "        <name>15303</name>\n",
            "        <difficult>0</difficult>\n",
            "        <bndbox>\n",
            "            <xmin>154</xmin>\n",
            "            <ymin>487</ymin>\n",
            "            <xmax>445</xmax>\n",
            "            <ymax>516</ymax>\n",
            "        </bndbox>\n",
            "    </object>\n",
            "</annotation>\n"
          ]
        }
      ],
      "source": [
        "EXAMPLE_IMG = ANNOTATIONS/(os.listdir(ANNOTATIONS))[example_i]\n",
        "dom = xml.dom.minidom.parse(str(EXAMPLE_IMG))\n",
        "print(dom.toprettyxml(newl='', indent=''))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 453
        },
        "id": "aD6RTO90fLGI",
        "outputId": "3ded0204-4cc3-448b-8dea-59000bc4180e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7eacc6cc73d0>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "img = cv2.imread(str(IMAGES/(str(EXAMPLE_IMG.stem)+\".jpg\")))\n",
        "img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
        "plt.imshow(img)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bGGweNtCfOSM"
      },
      "source": [
        "## Expected outcome"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 453
        },
        "id": "OXFDwnW1iims",
        "outputId": "9ca2d565-4c0b-4092-e84d-041902d0f422"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7eacc280e230>"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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FY1/vv4KZtYK+zAuE+H4/bZ1vWQFWZ5m+PlO9pZna924rAcirDxTVJ1U1VBDihd7U/c1K2q/7voJL9edWcqQVpOezeluZZI6rIlvBeMGBbqbWQqIAaabPnBD5SxykAP7CX/gL/9jw3u++7qbexshJCNYWDuyNKnGu2oJEmQR1kC58ynTojZmEV1bXmg6eeog9k9Ebs1nBAEbLpGN0BHvYtVGi8NtZGYQymOYOrQ5Ae0JhqxmdGYx4l3k5F2ymKq9WFzp8lRmt71f2uwChGQVvUj+P4xZ4/Xszr0OFK5u+MlPzyupErhgRvMZB5lQVRxAtueHslWlqA+uQeQ8X5FTVlyjQKGvkCnSr+iGVoXVzSGPWm4+saskM86bsNKx6CHnBJ8qFtfD17NGBZo3YKw+P5Axt7MccfBcnXj+74ewBvXVBp28wY3JOwcgjAu+d+/3Gre186Bv33umZ9Ah6gFfzOFvSe6eZEdV7y1URruxpXTFx2rVeMifWobV2VddPaKV+3oyIqT7fPHh7e+VxvrLvnZeXO/vtzta26/AH3W8FhTqYTUmbAeN88N3Pf8rv/P3f4tP9A3vvtLazu8IYCT2q0k0lUq01WnN6Vx/KzVWpjMEck4zgjLiSp8Twpq5lWEBa9VyskhMtxtXQn1UhrnW0gs4KVDHjHwhS8Kyarttblci6zOqg9arKq/rIUPDzSrm6G/TCUgthMNPBa30hARU0HVrvQi1s9WRVaRvvqyO9Trv8ExYR4fsQ5fc+Z1XQrCRbb57WOmauIJUKUuc5mXNyxmSMcfWCV2DIFGRuqGpLX1222lPtfbVb9zyVXBaiD0UyoQJvNxF0zN8RLt71tNYzeR90jScfYP4+vah1/SDYff+o6yfbjdM2TnQQrgDidcIHQRiMyhkFHRWefWXv+vtHwmbJvcFRUE2rB2EY0RrXeg/hxhZcB4nYLVqGWpxTpIzmWGWb6p0Y3p7BIa//Wxssr6opUZXQbVmDFCOpFpSvrDOVXfdVSbgWtZrT+e535xNCfIfzr9zVq/LRa9fXvUEmw+DNgpaDXk10x9ndBHHEu0OlDkAq67yyyKYA7Ov+6yNdsInYiWtv6FlNh+nGw4w3gs8MvpsHr+fBnEO/w2AzV79k3ws6XFmfYLFzDs451RdkEvmEMI8YvE3oMenR6r0rsbi1zse9s+839m1nb53NnQ74nDSD3kSu0OEgVuKtN5qrLwlJxtS7ybEeOFCEFXsWs+vf16ZvUWmoAwERg3OcjHFwHAfHOLjddvrHTm87W1NU9lh9G64sPa2IBDZ5nA/ejoPXx4Mvn39O24wf/eQTmTv9dqv+rC7H2Fqj3+5486vaP2MyMmlWxA6l4AQueBkuVq1nVGFd8C79OvhWkFgHmvuzEjLeQVNQh2/1QX9XxfS+cvoenFWEoyx23ZM4UESGEIFghQSr5+LmWr9m9GYXWaTyx2I01v0pWNZWFZHrldDvpBAfqlrMxa6t/VtVT04lhhEiAs05L2Yvax/R8SYILV3v6ObOjC6m6NBaH3NyhmDDGeoBKiF6wse63yvm5lUNkwtdsqtFYuZY60p0myBPd1cC4va957FOlcXwXZAlgFUSMv2PQJD6RId+44zByBD9vBqyOp+NaYYtjMCSrJu6moE59TMPA2eoMvJGhGjm5lXq16HfV25UC38FrEQYnbvTbcMzaU0Z5BiTGYs9pGy4taogeG7GK9+uBTvRpuxY9c1qkyzKqtk7aKKyZPfaOEULNfWRZvXLJkmGPoPV4fc8zKp+qtdzFu4IZySPYga2cNKTkZMTo4fuzVZYfePdPUObvXkrpk8Wbr4qKb2HqMoqTESFkZPB5DA4MnmL5DWC1/Pky/ngbRyMUA/Pm3PfNvbWuXln72KXEUoZ5kyOPHnEybQg973gr8pxI6tn5OzbRjfHItjMBdEucDUCm3GRUnptVsc4I8mW6sdlknNWlWnXejOSiHe5tVVB62D5ZEb6RaQpckAFLghlxynYFBMRads23d9sbP5kTIkJqGRCB6Uq7jOGErgMsOT24YVvfvSNKsl+J62x3bcCGuyCu3rv9b71hkZoA80iICgYOta0HsR+syIC5LW23cRKNRSMoaBcVy3TruQrn4lVhMgNBc+p+n73/tZJyKrmnxXMrArWisFZALXyMRPC4StoVYVnwNZUIW290XuvxDRKqqHiYyVEFWneESmsYJwiLVVFnCurLCjwksWk2Ippzu5OtCCmE9GesNqFdujZLshvPe+O0Xvj3hsjghna90cMxgzOMTmLEBOp3pAgQopElKy342Za403Vcm+SxPhVHxYkWJ9d5+Tz7MhVpdUaSmqtF6lCqMcfgSD1K7bT2870RrTkzOCR8Ij695hEjrrpCCoprDZtsWId7yJbnJa8EvScpHeCxg70qP5Lhm56c2baVbVkVTrdvPRXRcZwHW/N32cvRm86pm9NPaIVoAIxagaCDxcH72FGWGNzZSGtskZVCFM/h/GIJNI4LHlY8mqTLzl5MHm4IJhBLdB3TfwFAywoYmV5DSfdxHqbQ7R5M94kcmE4nGY0S/Xw3Li503unAzaVqap6EkxzfdbSaARZrDxl5741ZiZHJq8J346DL3PwFpO3c3CcowgqCq63bZcuqbRcmxmNVK/KHGqjvphj+13VSd90uL2DhggxjbygSCKqSqYo1KFavCrGBX8xC8LMCkQB5lN30yorz1UBTLHK6hA+o2G2YT25teAenY/3Rub5zNhNbEeRYJzWVdXPcfLx9gIFeWcInmner0NtwV/rQNN/b2yhpnduxm2/F1mHqv8bQeMdcYzeNxqNPCe+KZMWhKwKrdUBpvcR1yHoXjC1GREDd5GKcibe+vU9kwVdKWasXmfOKDYsqHc5Yd3nghGvooWqvqcClVfcaoi8ErOSQ/SeGko4m2XBfHlBcwtCX/3g3pqkK0DOUxD2qm5Cz9ZrbcT6HJnv3mcF2lRFTYbuXfVvpYekEokiyBQULkjwSbDItZZSRJiWFMGiMPJpF/M3mlCXLUVIObfJoxnbODlmXEElIi6dm7uz9y7Y1xeTuV19qYxnf3BGCNLPeSUAa217ZQnvEb3AnmsTyF9mdt9/U9eHbeO27dr8TA6msokGJ9AIMk/e5qkAsyJ36oB3M6z3YuWo9/CIweZNolIqaavI38IuKMgr0MSMq0JICgNXysAZo6jWEycLzzduvb/L/OqqxcaF6wo6FKtHgcLGwup1ggyeAflMBdgRycPg4fBGKEBlVuN9VWBBQxVCN2crWrQwdictOad0QBINJ33fdGhY481hMnnLZCs4csN4cSOaMqRRIs/e/Am2ltgwzZk+iRS5JN2IYicORHR4y+TLOPn2OHgdJ8c5GENC5903ejO25rxsN27e+LBtvLTO3hybwRwDX8dXVc9uz14lBS/NdTBkq0Nn9RXn1adoFD2+dUAHWNbviDk5j5OYYni1beNu0kdtlYREPnuE71lrMZ9SgMaCfCqVxSrrLPiqfqx5p1nSVr+rjtiRQ4e2uWAte7e2ruO3Mu7edQD3exFtAobWtFQXqcBTOXNrxashrqrBq/I0VzW5susIrdXVB7FKzd0FT3nBU80b06opb1xrXIdmkogpl6vqMLs0jrofkhksaDhyXsJgz3VXKBy18n+TZq2hHqJlPPVtxWh7ohoV+CkJyRx1SI8rEKt4KvkDRXVfe6xgLp0LRYJ4p/NqViQG8nmgU9VuVaDa5oKfW534iaq+lgG9XzrB+e7115nlte88k2mBe6dj7K1zjIKNK8nt5vTe2LvE4723C7aLCM7zJGZyjqEKbZZ4N5IRp57VE2Gt58UlTUgDrNWO1L2b58kvcv2gg9RmcDMnLHUoZt3UyjS01yVqzKK3ShMj2iwlGqUWsHrFzpHJd+PkyMGGhKC92Fm9ytgFr6ULOrByMvBc4jy0GF0L7NbVcJauSVlKZjLP8cSdFx6fIlisLCffabWWMj/NOM04unMEPHLyGXjL4EHwiOAwVYcDbWSnemSZErda48Vch7v7RWM/Z3Bk8nmePMbAvXRirZPmvGXyFur06cgJCZyz85GNTwY3FLwaSS/s3lz09DRRyGcG5wxRgqd6HGc4D5JjnLyd+jNGYDgv3bn1jft242Vv7K7/3sz0B6ctrNe6nkexOFefIKMYleUe0F1Mz26q7sYsQWnm1UPr76DWRmXJJih5mJh4u286sHtT/8YFM8dyNakkxW/tCa/2Lrh5TmaEDk57UrO9YB1VAdUzqKB2CUcXRGUKRKsK/t0MsasvMKeCYwT4BpUEWQUHTDoiXwQUg4zBDKP1dkXLnFlrv2Guz6QESv2cJX5eh7SIBXrN90LY5/uZ1wF+VUYmWPiCHfPJNEsK/ruqmsCJIsNo3TW0psUwU1DwVH+st0a3XgSHJ1Xa3r2/UZrGtQfzQjfeQcVwOUZcwaiezfXPi5L4DK7LzUGw5VNG0uzZ+1rJlCo/u+6x/mqRSlxnTP1AVjKqboSquDEnFron00X3F/Iht5M0E0zfWpF+dMbMqb1yHAeP4+Q81euSxqwSH1vP6cr8qm1A9akX64KFXlf/K5jzj0CQ8jHZAPPOLM1LZHDEJGMQAWfBNEGSVV0apRuIgIIBaDpyI+ERkzMGb2ls3kqw2IrNpwUUBWO11Sey1V+qN6e6nO6AO9NFkzaMUc1LB4mLm5hUEYIOz2L+GbWoIy9Md4SgtokR7hzeOLPxFsaXPHiNlH0PgvaufNoUxLsZH7c7H9vGR+t8NOcjIgI0lxh2tuAw49sIXs+Dk+RtDh5VsZ0z9H11EE0EO7yO4HMMvj0Pbua0UP+se/V2vOBDU9A7QzDijLxYVmElgF6QEc7Njd07L33jZdu57zsvvbFXAtDgstqxqc/p1cw3U2Vr5XSQlUhgLt1SwRlGQnMJhdOubLCVi4TrYcjVYgUxkmYJuxd8usBMwWmlBYdKLnBn21r1S4x965h1yOA4H/gMzHZVDAtOqqzcrekDFqDvWdntEh/X97EgZ6vq+wp6RaCZq4+3Maf2wpjSz4nY0JgpI2g19lWV3Pad1nvR/CvZcVU3fQU0hUesUAYPsWnFLPMKGq5+a2lytP2U8y9Ifl3pT72ULeVykZQmS7dUkgazWt+C9zpi2OnZKZuUID0vIsjWJB6/ROeLYBALnnsHrRWkNo2L2CDORVzfO2MqcFYFGBHPnpNuTwXn9z2zqKD0hOmaFVOw7oVnVq98Bd2VECwZyPrfky3otgKZ4G8Fh7jOJcPY20b2Yl6qwac2wpzMMXicp4Trxynd4ZxiaxY78grUBnGR0XROXYGqPmNmFpmmDNky+Ycrq/7B6wcdpPpq5Lnp0LZkpHNaqMowY07RLZeAqOHkUBdHH17YtzQI85lNoZs/M3icRwU2schawRR9uT6YVbVQi82e1ZabQeuXjqHoCnR7h8gsWKA26XST1ZE7zTcWPT0imaaMZAKxOcOMM5O3GXw5Jm9MpsPq/rDwd2vsZQ30o23jU9v4xjrfpHNbFOpjEp5MV5XW0vnod06ShwffHg/ecnK6MUXNEEOO0nhkMob6YG+lP+v2pPaqYZ4My4L4uHJSq+pxs2THq9oUDLmo/rI2ktvBlsk289rgVkHKq+rJ2ohyn1gN8ayAlhfeDsAcuBWbryMGaGbh8bA1CVehQQzBHiuDb5IYUM98FuwRBc+sP9YEkeZc7L5kI+hujK3j0bEWItQwK6NXkGle+iaWqLuSG+/MynSfXn51aNmCqp5uDK11WpO42r1jVow8YMbjckTBG1vfVq6OWycm9M3pTf28xfJbFc57OFFv3uVQwEro7CmCDSsmrP6+ueHbRl6BQeSnC+Oswy8pbRBFoDDYehcJIhNPZ0MHejer/VkkibXfQpBlW9ULJhgqq7cbaK9lMKrCveCtDKZZSTzyemuTem9wBc9Z5JbkmagsIklxheqzAUQxYt8xCK8gpn3Vrakabar6vdbF2le+GKfrdtWZYrkIDn7ZPQWl9UrEEjS79mbMyXEePB4Hb+MUSzBCLia9Q7l95BLc10d4n5wvhxEdbXVyelYRUMxGg7R3PeHf4/pBB6lE2pdR7Lw3T74j+c4m31kd2Pnk7ncrUgN+ORyQwgey2CkLoBhjLgnTRXl1d0bOqxJrteAs4Vb2ML139m1TVs8TputApw7JeHe4wjuzx1y9WM7zVEbqTrbGMDiL/HCEKprIk1nv6RgnR06xG6/DGzYXiWF34+6dl7bxicbHdF4w9jDaGcjiUFDjDPkI3mxjunpfrzG4e6rHFcoorbXL7NYo3HlTxhZDBIcVtFdeNSsbjdqJixXW0Gb5YJ0t6+AyvyBUBdpypeBJ/154g5KG0mzlOteExTevImSmYDjEzLKYz0BksLmea9920cy9iajiVlKC9QGpA23WOmQdWcyhvsoJzDEuJtxiYY4og1XgZQ46epZbb7ht7E0HSFaC01qrQ+6Zl64cSOiKiCqvr68XbPSkci+HiufX9LqtDjK9t32/MTL48uW1KM1W1Hld59TB28MYI9j3Xc+zsngZJeumL1LSs0FudTDqAJ1xGXJVlVVwZv07LOp8XHCVmGjPpOsigtR9lKhYSetmEtp3r2QyhaNYJSZpShPNnBkpZGBEkVomY6qaGjmr75L1NT3XZea8KsYsmHUSTHehNlbEqt91L7LW5hLEChIL1lG/GMfX1+ubVgfaJ7Q5VfGmXUhOb608FE3aRbOCAd/11wBpIDve8sqQWzNaL91oJDMHIwZnnE/ZBgpuqrSaHHBsJcLJM+RKTwZca1yfQedDgODpBQu+W2O/1/WDDlJfMjly8hqTN5JXCz4z+ZLBW1GvA7CwK5tfh+Hu0rt48XvDlEWc6+Dprfz6KutPZVTLsXymcbyrlh4xZBNDcqOgv4JrluPzqgpWkPN3D2rMoQBVgrlzDGVNtw1ykm4cMXmNk8c5OS4owa5gSsoE102eanvr3Frn3jov3tm90XFeEnpCm0BYBXKxEXHoZVOjk0ULck+4WTK8MzyxLiukZkZTfS9H9ghGDPJWcoCinIr9VBow43KoaCu7Lkjo5mr0Xk4gXvf4HWpicEEeK6m4mHB57XIdrF4Br8gTuxnMIScOd7beufXOByiLH2Nr/aqSBQLrtFDFXkEYiiCw3lEFj43SCr2Dj/J52I15KlkCfhTBHA9aqrqkWJItdViJrq3PmhWoVuCJEBGoIR2eWXKeD9zveDH83utWrgMhK2K/Q1rMRC+XSFj9v6xKxTDcO80645xIoz7om/wjJRCnmG3qLa3Pu6jzCoY6AFd1tpK+9f7AyPDqBcUFlS1I2WvPLFamkpfECWkJQS4w7uzNnzA8ixX4dN9fFcCZyTGCYwyOMUoqMp+aQgodq5pwSSSWhk+BdUnoZdcliDSqmlyZRP172iVWhqW3qh7lguNKcK1KXPcvqoq8+lgzy81kTUzw0nTKfLpXgtVrb6mKLEDQjCXYB9RPRLAn7kQDb4H7pPWUF+aijde6DiYz7Hf5a9YZQvXo3a6ev12lVlxwtOC+Z+L1e10/6CD12DpvDb7M5EsO0ZYJHhkXm80rG7aEnsbujZsLOurWZO+DMt1HJnsaS8oiKyEtshnByRQUsP6eWsDVnxlzckTy5fG4rFAAPawZmKkBumjOvjLcorcKYlBTP0wb0M9JDrDmMjotB/W5oJIFP2J4GN07m8lL7m4bL3R2GjecLVtZM1Xp7VqEowL4BUbOEETCMyPqptEk63DsfSdz4iksXKiSMs3TnplW2LPBKwekd8LN1CFi0lNWbJksOxar4PaEr2z1ZnV4U3j4SiZi1OEsrZHuTRSBQ83i3Yyt79yaDrPeGntzXtIuZyhv7V2VUHo1gjm0JmZVvfMiLFCwy6oKXdTm1sELbgMJadmuiudPfPMNeWx8mYPvHq+8nQc+ZM1zQVrUocJ71tgKOnEdZDNGjZyY9frtggatMusrfc8F0dhVlq3XOMZk88aY45nJd69+oeDHtCXRVfXdWnkFpstJQE1FqOdDBdUx51VRu0kIbvkkBFQhL+grKxkoqImrCiwtnmlcjGXQM2mRbFBC6+rp5GrvrwrqKe4+5uBtBo8ZHHPymOOd+bMgQKoKmhPi0noJQViJQ1qWIfX6sPV58pKucjUQUiSVeGf/1N73dlLuG2YSRGf9Tz9dkHIJkKN8Apd7i7uxYRz1XLtrhE5vq89VzjjrzKn1erEZ85n89dbZtmB4SD4zqp9UB5plicOfy6leIy/vx8WsXYQSTxGMLJ+o0fcypd/j+kEHqc8llPtiyZtJ9HlW5qUGKaJButF9Y0vj7o27OfeiY3rha2MKUlC2RGHJqaytmopnJMdUpiXz0uBEmV9W1TZXX2sRMhZKcS3y0LiOyiTlTl4ZcjVn88q+ApuUiNBVZqP3510BdnONgWhp3Js+22Lu3WkaHzL0Gq2gL6nInYcFD5KHJ5k6CD3lyaaKM3HmUyNWPZTuJp8ycx1W3WpcigJxt/IHROzGxfjJVBLvwdUXcOzKWmcGjzkqQJftCssdvQ7KVP+w18akgtSswL1y5UV/3VK2Rbs79954aU1roIJTX49ocgWGcwxGBG9jcszBWVDdIrSM1VgnrwRjJTvuZTzbVJ1IZ6MeifVWJA0dDj95+YD3zod58mHvfDlPPuwH3Q9m6qCitEi6lrPC9/fBl9fPvL5+Yd9uLJaYslWuQ3f1QvUp9ZX363NG8PrlCyPB+n716xJRjO81j6q1lQmPJ8znC2G4sCl6X7Dh0vYELTuztHPPKpg6v4VkAFe/JVJGrSVdfh6q662vJlKE+plNSdhao5eXXayqp55fBo9x8pjJYwYjIVurIJAsa5mogDYdESKocTM8X++Crur5UD8uwuGTyFB/u9prVz/MKjJr7RXwZ6un83xmtvq+JT7OiAupUMImSPWxWMYVqLvbU5RuJif76l/hFUCVbShIpWD1aI3TOtNkAkD1KxMH1/tYSIP0T/UMqx0Sy0Wkksq0fDIkL+LJU47xe10/6CD1W8cr2U1zkt5l1ESUS4NBxDXbZjPjbo2brcF9cg7w7tA25ibfsYhywq6M99IibM7YNIjvzORtnLzNwdt5VHASJu1t4etcjhWRsqRZQtpFJ7Y6eMYs1X7NDgIgi0zgyzNN+paMKdcD4N4699bY3Hmh09NL8Atbvm/A6jVnJKcJjnlk8MYzsBNS1Hczbk1N6GaQCE5I9NryVgusN2bKhJamxWooKYiYKiIWOcG4SBTdZOjZAKoikamuWFuzTi6vKjMR7yUjmdUfMSvodB1G1chV5pnFiEp2czaHe+987J0PW+NDew4iXN5nr7P6fXNyzMnrOThmcKQU+xpxYkxzBeTqNyztj6d6SR7Gnsl2RB0Ski7sbuy9sW9+Nd1ja+x0Xppzv934Zp7cM9RQLv3NRCSCppPtqa0pCPpxvPLd528xT1pf9HarZ7Ag0Ooavk+Z61ABiJj81u/8Fl/evvDpRz/Wuud6bNwt2eKk985tc7qXZ5256OY1R2hVldNKM6PTCWoPTUM9i6KjWxbpoBI093J4DBFIIq1EyhNba6b6TMyotUqxR+2aY6XEI5/9HdNYmTMmxxi8nSefHyePkN0X3quJn1dFcMk/LMvLMq8+kI6V8uhc98meFdQss2Cr9XURIprreV5hTixIke5W3VQ/409IbvXgFozg7vi+0aNLn1dw3ASWq8UiCY0ZnERp/ZxbIRJu6qmvAk1+fkAZYKcnO5NIVckSYc8rcIflk1pvCvD2fm1dUPM6k599uRXYL5/N3+f6QQepz6GGaNRhTHCJ8qTQzxrHIBhCbuXQUoHL7WnkGZHcrOM9aWwaUgdX9jUzCJzDjWxi1b26cx+dV2uivRc0o+GF0mK01qoxrB7GGOPKpHBlmOccaqjbEpzyxOszlf23TvdeDghB99R04Na4NTkGbDhzJCPHsw/yrgRfUM0wLbIzJoN3SL2hflKCW69+jlzgDVNPIk8c40zwqUxywTtXEze1sC58PrVYW8hWqJVrxoLy3FRJddfPjTrgrA7VK0nIWdmsc1iQ5nT0HCMFw6alek4RNe+ocW/Ox9vOh+Z86E1to1AwfEwxEX9+nnz7ePBlTg7gnMFIK8aWa+6SN7J1sb3WwWHSWRUfWYnFDFH6Cewc9Ezu3bm1xt6f5IGfz0n6TT2wOdgs2MywtpHzrYg9gnou3gC6l2dMPn/5lu+++47jePDy8oHWOr3fWDO7zJLenTEkRljiTGzN/BFN/bd/+7f4zd/6e2z7dnmwba1dcOuvffjAvd3YeuPjywfIvLzq0uxyX4mq3EaI6i49nD7rSXkzzskYYhdKCK/E7hxRyUgQpq+vislLx2UpApKFKna3p3NEp6b/VgWxiCezepNnqsp4HcnblFh88GTBGmWGm1WtZw3wK6jVLApCLT1XlQnr90A+E0+XddhCqN/3BA3ZfmUFwIiVXJdNVe39Sgl1bmSKfRj6nU9Gn0G2i3ZBcukCqaQkq/JPvbDgPHtqGFtITH/hxsXe22jM2HWeZcPzhBgyaa6qMuwd4FdVuVXvafUPF+V+2Vo9+5RPAfjvd/2gg9Qt1bQfotDRmyCifevsfacb3F0BilmRfE45LbjL0uRc1NMki1auJitc/+cKPpNi3w1tQJ9JTwlXWx2UzTS+QxRY9b18qwzYA9Ho1MwfVv53laGsHkFUTrX2woesAXrZSuuhgHtz4+46/BpiFR2I9RYp66D12lrBC3N2RiSt7IhGHShrtImw/MoqvYneHylLJTMsAp9lftukw8gY3FrnZlbWRI6Vyj2rqmktwWcp5vneIhVzKjgSjqi5XGMQGDO1UZ3UoeEyBQ4zjkRTgo16PpOutJ5mzos5n/rGp75zczEsPfT7jph8dz746ePgd87Bt+fgc0xOc8Jc2bWZTDV7u/qbdMGUixRhlqJk+4Iz/BI4OuoNHSRjDt7KVSEz+e3vvnAavPSND+68eMOtkxRpo6kujDDGOC+IZIyDt7c3Igdmxv3+wv12p/vONQsI2RVFTBkXG0SM6z0/joPjcfL5y3d8+fKZ1o2XD3f2myYBf8jbdbj+iR/9mJuJtbptm8Tg+65qwfR8Vr9mJRRrrMooItKxTE/7qIAkWvfEOGaUgHswDY4YkIZZstV+WGhAKYRE/KHgLJwNL/hZSZmtsT04YyZnDFXJUX1lVu/ZrvUX1+eoimbhqmYiEGU+pR2UXqh6lsA7Fl9pgerQ1n2JixQ1C9ZUn9BE1pjzXWUbF0Fm/fei66+qbDmuX5+zIE/FmSdrTq9RfawKwDMl2ZlL4pDVG0zXtIGaz7f78uurNgNGnGWiXMiG3mIFRMvrfQpKFFnFFq5r75mmPCUgv8/1gw5SP2k7zTqzKKti+TR229Q0NM36ATXtxBhSAzmG3ILV9JSLtWxGIOKsA1tCWytL+rWQlqq8Fey0lQfcyg6cwoXHgJBpraMKqJm8sWx6BSmISi+sCdudGVK712LYTZvSc9kiKUv3rAbtpKjDT/3HTMEhWGW4lfQoyxYkkqZDZJTK2dDh44gCr8PsQZhjvTObfrmHNqeZ00nuzbhtN16KlLJlERQWs4giOmReEObKCIE67PTaj0zepui/Z0gfNoEz4ZhTtNghVt0MNZoXY3DGEEHCnBd3PrbONw1+1JsqKDMsFLSP1OH4+Tj42dsrPxvw5o1zazzM9FzqQPJMfAZtasLuSibMnpNoLwdpLYQ6LL02K5rIm4nxtAz62ePgGMZLHxybk7vR9qW1k8B4ZumkatT7rHsjNp7Tdmmo9n1XkKJVr24BUTwPvLRy3Dg4z0eRLU66Gx8/fsP9/sK23TDf8MuyRnPAetuUmHTBfHEOussXcHO/4L5FXV8uDBSacNR6ejpyF9sxg2MOHsP48iZ49W06I8rBW8ujqgtVTYld2r9OVVpWrRWXxVbvXntplrt+CdEjnoHKwLxdFZQO4qfGJ1f1elVXVSnZ83Bd7YAVBJ7rwK9BhousoSBoYO17h3Uu6CyXS44VyUE/9wTStd56b5djDTMw11pb87/MnsHy2mJU7zaXuYEcU9aelGu+4P313rarH5rM6ZwIPbEpZw8vvRRL71TieFaYfxf0zfIKSos4NP8o9KR+0ne2/c6ibdqiiE3I46C12rAYrUFLbfo5gxxDzfttu2iZaaKCh2vIXE7hsDq+ddJ7FPmh3K33pce4FpaQ7eUGTIil1+uBd4O9Du4ta47VDHAvc1L1V2YmkXZVb4v55Ga0rr5VGJyuRd6di/Ze60o4f72nXo1h9xrGmLUBZnArNpxHshezbyMZbpzWOS2hcQ0v62FsU3TuD25848aHtnEz42bq++3eCjJa2ejKCvNi5FFQgLJAZf4fENy3DoZRkKV6gJPXefL2OHhk420+BZQjU0MozfjojR9vOz/Zb3zTgg97Z2/t0qec1XccFQjPmZjfyig1L8jDo8SXVX15bTa5YJeOq55lLFqx2zU2vZlXz21BT3a5vSfGw1U1HWMwirV871xiTdChliaT1wWf9L6Souemd+/kyOu/I5fbxDpo1PNpGTRaGc/Cjz79iK13jQzxRmt30jbMj/r98HoOMk58Tu6muVuZSW9a30K7dHivYXft/VyvauZvLrsoqrpcFdeIzjk73+ydx5w8At6Og7fj5O08OMvWB2DzJpPfBRlfvRArmLBhNfctKhFbZKiJ4EeJUxcpQTRyJafzebhX+q9t/N5DUDCovi4URijvmoyNqn17MlLliKFqO9qTTJEF47UF3dXfmNk1my1jQGm3yCSbiCLZqh1Reqr3WsT317o377BHvWfnXXAumn8FUdVOK4guJKhePcAPyWCWoBqXk8yC+3IF7ut+1CaxZzJaGfPveb6v6wcdpF7MeenbZYaZKKOR3mGw6pqIIOZZPlUgrr7L5qUEqYkzc+Kt0fuNEUPZXzWFM6OqmWrU9jqIy3alpdhJagaLjk5SC1QVRWsueMzFtptT+Hq67HUWlZSC1cIkpDyWSPBdpn75B5vVrKZWnPiCyt7pXHRo+LUxR90jWS4pK79lwZQRIgP0ncOzKpuTc556HeBu8LI1Pnjjm+Z8gwSUz2nFMhDFlNWOUN/nMacslXjazIjYYtxaZzeTWewyLE1VDiND4zu2ziM3Xred1zP59vWNL+NkmoYPusFLa/ykbfzK1vnYGvetiWm2KMN4ZZQwwpgpWM8p6jgSNsvJZEkUhNIqTdBcqCspqsNZiUEKVqZkCs51bMysCL8SbYPTOmEdm4MZg26TX/lw464Hq//3ovvGoHnj44ePjDWuvNZDpoKCbX71P9ffP7P+50DAW3da26D0g9o3JWqwTmRjLnPVTP7u6ysvXWNBej7YttI4Dfk+vR84r4pHQbbX8FGv3dmqT6bK+inBaK3jZty3XUlDBMd+41Hzrt7eHjWmg6K4a5977S9I0otE4610g/kMJnUwLhunZbFUNB+9QuaTlYdedzE3F4K1nvOymIp6L6L+xzufRLu+f1UN18+/P5gzNZHZ7EoAeV+NZZEuSmAdoT07zySn0KNM3ddrpeVzXXC9ZFaQz2vtLRaoSqzluKNAaK6M1Is81s1VtbrYqVs7cV55HIL+JoNEHqlkWSfZAhZWcC8ZgPn1d/Pd+/y9rh90kMqYxFgsMmUjM4qT3/s1YTbMLrhBTyhpm5rEczUsrzJdG9bTrjlO26YguGEX423zxoZEhG1BWZVRJupvLYjgopqjUpw8Vc2l2HQeyd43NjcsxoWrJ8mtO29pDFfmq8wtn/9OE/MsjZaT5cq80OE1jPGcBws7PokLj3avQ9g0ZmNHxALpmCSCbGacuuHcm/OBxqfW1e8x5xOC9rZa0Ev0+hqDtxF8OU9++njw8/PgOEe5LxQ7LMUmfOmdl9vOT7YbP9429iYoR04CTrj+3Ej9XRtENCYPjpi0Lo3Mx2bcm0gY2OARMEbD/Ukdnq1zzpTuLbvgEm9kK9q0sCuaeT2TgioyaNav7LRZyQYAXC4lIg4ohWgtqpr3gm/5HiPsdIN9g1HQ4/kg7F6wTV4HZ66KrpKOklpjtl4viz21fPy4qq5rr+SCzeR70l3CSiKJGHVwJ0mj2cY6/mcmf/v1lY93TSK+bzudU5VY/c5eh90iyegYLqcQ1+kr/VjtAeTnuJUQdVvroJKbW9dcrHuTV+NjuzFHME5BndNnMQqXH2NcPY7elORdGqmZl9xhiVuba5KwDkydEeYLylvQHTq83wWdzLw89dZ4kmcA+n5weO/ycbl9VCCb8XSTuKYmF2R4adtiQfvPwOthT6o/kOkSu9uz7/UO5b2CnQJ66QcLSvam5GBL8PDLBHn54KzK7KKom7E72H4rfSl8sVeJoSkd2apc/Rn0dVdDFdrFAizG6cJTf5/rhx2kSGYMIsb1gS/DTSgftGc24kjTRAZzHGSVv5X0Fg6rjCvmZHdjs86txnZsWbOcIuixrI4QXbs1tv1Ob3Z5hMHKhJ9Yd0QJjUtjdeyi03oiiudcU4arXG5d7LIspl5BgCIUJG+nxhXsvbGFBpn11sGcEaOcDsSoojZXNglWN286iE0Q1bUoI0sUGnhMerEht965G3zyzjcuUsIHc16slWtE6dEDPufkp+fJtxF8exz8zjj4kkm4aMUa4ugQA4vJdop+/XYMXlvjw9b5Zr8VKYTK9sSg1JTXYL837taZp0SiN3O2ZuDBYacqpVF9PwRn9f0GOF9m8LMx+XYmnzM4t03Bq4KMDp0BUW3jpFhKYjBe2i104KRpiN6s3kMtUCon19pEGp0FhDxSyUjrrp5bzEuEviBF6gAjqCz8CepkJnPklaFr7fN0EzC79sPaG4ASutr6MwVnzzmlCWTNXqqQYsax7bQmo+XZJEPYumE1zkFxIWQJFatZbhCBLfr4quqr4m0zsTixORSs0tj7xr33Qhu0PrZ+Y/Ot2GHqI2vyrLh5EtsOjU2JKR9GCm7zBenVvcBUJVdFFgj9COxKCt87mL93NL9Mem2hZysJiGLzroqpfmP+Lvit1oLxrF4Ne47X+F1VxfsAqEADeKtziquaX33rRY54Jtv6nVbV2ZJDrArWl8BXOXtVN1GOEVzCbNHbdTa0JnRBdl3GvjVe3x68DhkMPActau26u9oYVqSTUsOvAstz8otcP+gg1bbOdttlTl3N9NZkTlrFFTnHlV22ch4f5cOlzGmVtADGfrsp03o8dDhnsrPIC6IWL5q1BKLqd3Rf1PMS9hWdtDXNUFHGnWSWFrvgvDCwsks5T5k5Ps7BY56CuFAwtgqOQisbUSSLUfDeme/gBddSG8XsG5nQ2jMjs3z2PNAh3qqCnBQBMaNMNYFUP68H7M25m3EP50OT5qwXLDBDbMLPc/CzefL3j4PfGSdfgM/Aw/TM2kUlcUFd58FMQYFjHrydzo8yydb5pqosL/gPlC12V3BMdnrTZt+QAe3m+qwjgp89Hnz3ePBlDqY72/0Drd845uQtBq8EbxhnjUw5KklY4zsoynEU43NH1fdy8V4Hghcub11ZKvWMaRJh63wO6P06vLJJZxYB6VlTo5vE3VFuHQua9ufhaFZavnreM0pIsJST+DULidR6kL6nDsbaP4kYnjEVPFc1pmw9ryO2bzvQGBPa1sCdGYYXezDcMBrpRQKqw9ILxsOWv7Mq7OUoEaNmds2p9TWD2xhyvd9k6yVdVI172TrWjLY5PhbtPWjDOC2Yx9QYjqrSPY1ba5cB8zBZXw3TmIphVsltXhn+s8/H94IUFOvOnkHreu6rH+fv4NWCXVfvbQUQN65ZdJnJeeZzPVyBjKstUccSFBS3ekj6yhL/w5oWrOcbF6PO6mu2gtOzVqqXFOP5OWkhLz2UFYT67NNKJ9ZdwzW37my9sR0HjzFkTDsmOQbL31CO++0Z5N8hBBZ/BILUmGp+r2YtWA3tg75pVo63dm1LJaRGj/VQUtircdnaMAc5kjYHW8IHMz4059O2s2fQyWtsx978qrIWNg5os1aWZOh3vWeygc6T5qJ9AiJLbI3pxuMWvM3BaQpSn0pY+jYGb5Gc5jxCm3uYyAM5gihyxFjvpzW8d3oT9NlbL6ujQU5pbQQTqNOyucZ/eKnDQVWDJdicxeirYO9cFk/0CsTemASv5+S7Mfn5GPxsBo/eOForB2ttk8139tZgLrjyxApW/IIqu9sMmk98Wg1903sykh4L4nK6bSTB7p2baZT6RCPvhZ4br+a8ATFOLOUbNpsxfGNGDTPEuLVUsxjTfKdqhAzmNSZBbvRVpaRgpsWuFFX5mR0napqLmaJxGwvlOFIB2uIkximW5ep7FB4kJqFfvYYliFy6m23byFP6u/W1TPnvPS1wVvK0+h3vqoEwrBkeyZgnkXq2+W7Qy60y7pf7XZWsKWIqzWhPsg5PfRY5r14xlfBYyQdE0Z6Er0GTqr48kkHwmEkfqrB2c/bu7CWzwEL99q6K39Mkxo9Oy6z5ZaVtyqiKwMAb0TbGVj3bGhQx3sHj369l3rEi61LVuJLbZ3W6RqJkPSMdRbb4XBV0vOQKVeyg48LNr2eyHBnql+EWTxj2Ga30jFmMwRU0awVXxWqVdFjquRcVooy1/aqwSLnHZwmwVUEChAhoZmCqhqnxLOttOOpv9nbnQwQz7pwZHKcq3YildxOkSe2LucTHg1/o+kEHqeMxsO0s3BstjJnkTGllKEolK/NRxdIrKoklpD6VFooGz8kLbHB352Nr/Ng3Prpz90a3fJo3vutLzHL9xqwO/VqJ1aydFMzgypItExsig1IBqzcnTM7EvXWG2BbYlKbky3HwZQxez8ljBt/N4DDnQNNsM5N0wR/ppgqqDgYnRYe/9B7y/1qMrKxFOBZs5GK3ZcQFkTRES23vD8+CMsWeMh4IxnqEqhLQ/V+09JaSBdxn0lNBYEcjxCOmXCKaHOXFpJZprZI5QUsZwRYa2e5V4fma92U15DJSllL5zimaJ6Q2cdJVFdC9TIjXQSSPMS+tkplx9201HnTfqF6BLfznqRFKXMGorGQmOiyr9LrW7xyDPFVNtnFoVIc7mU5EEQHq2TyRH7vEmhHj6sU8HifbtrMgQq/Kax2gGmK4YMBn7wooL8bOOUeRbjR8wqAIDRuOeobmbfEINGjUnMxxZfwNOa4s01JDvRNjKCC6qq+ZfslCpAULTZiOuCBBZ2ittMbLJmeV3SkXlDV+Iy+jVbaNZQac5WCexeTrzXihMaMXeWCQc16OHhLUckF+6zxYgu11/59elMtRQrLbVRnFYl4szcfaAatvNMfVj1a1sXqBldi+q+oCK1hyVSG1BqhquYw1V3DxRb6o3yllTvW2nCcL2dTfuuIUCNmwp75rTZN+X/mUu/CyKKw1GOuApe2dbs6+P3tsa09pL4wLlUjyj8Zk3uaqEmJ5e6Ronq0ikENVBGoAN19PdM1+Kp1uCqZoE/oM7i6W2cve+Ng3Pm4bL64m+lVDp3ovI6c0HaN0Eu7MKQPLkamqqjJh9X8aC/BqJvHwNTwvhg5Xz/LXUhXoyOViu8nW5+iTcyafvfOK8fMIvmTyyBq/7aqmTlPpL5soJObNJjFhZXxS64t0QCQxz4IvEmbpvVJ/v7ugl5iD8C6/PJVQ0lxRc67MII0eyR3YZ0IXjbVlsKVxY9LrEMGthLM6hO7ufOgbH/vG3ZqEwMtGCQlyBxr5QQWiZTtjTUHTI9gs+WYT0eEeky0k9H0dk0dOjdRuHXwN16uGri9IlisBcrwyy6qcioMjOn0QU/KFYwzcBIv23jHvV2W1kqW1x7+5f8DZiLdJn8nNxZ5MYNZhot7De5ZeQT0hSnnkIDM4jjcyk32/60C18lFz3vVLKlFjGcQ+DyqzznFMDSFsgooXNnDGZOudo5y3IwWZj6nX2Lc1FqIgifrHOryIJCeqXnJViQvYMqjpvjDlk9jr8JtylHibwedxcMvgQzNe9o3bLtG+zym3fMtLuiFDYBkag/aTmyYffKTYhMcgj0MCZ6KmzSoAqAIVXEUhLllJZ1a1osKgEpFZFRJArMq1nlaVTIYO9zWHTXZcz9cWC9frmRR70p74TKAKfVa1Jd2RX+deXsmFEr81QXoFcI2bkRRHaAqXpZjb092iHldBlGuelirhuYgfFchkY6X1b7X/5E2p36HK97k3VxBVlasWxS9y/aCD1MutcdubHCdM7JHu/vQ3M6/+ypP5tGA9GTDqQeaYWE52E7X607bx0p373rlvmw7x2uQjq3k7J8eQW8RIZegTwUgHwVvInPQowWJSbhits7eN3Tt7UehvzdldOp+OdEt9YbZRjDJzum9MT6Y1jjP52G/8fEzsHMzzwecYPIBsGweCpMyk4WllOmYk+0xyBi2THWH4exO12pvVIDTndvUE9D3d0HDEgDHg4YB1tqWmNSUIN+988MnsG3eSbK2smZRzXuPeTb6Jwr5VQe7lorF701ygSBkBR17MJStmmkVCW2PvYXljGbLFeilvvA+988jJpzH5bpx88eDLODlqXeRM0iRFmJkwqy/TKmq4MUMwWhB1OMBTDyOWldMEEVL+hjPYbDlmx2UPs7LdfQS3CNq2c/cP/PFdgVVFqj0PsEpy3uP6l+WMOec5SjzL9XfvGWlcSdzz5Fwef+Y6IqtwYMxJt+3yVUvkb9db13SBc1SQCt4eD1prvLCxtU6C5mKlFYFFrzFjXuM3ZkxWX0y08kI8fMO81xG9SBiCpOdMbE76GLw1+Dhv/IgbDbiZ0dt2MViPcXKufnDvNazS5VyOcbOGtcDahvUN9wf5+iYkpH7uIqJcZwdcVSgLoYzvUc+vvsuC+bKkCrY0b1WR8aySCLt0VvC0gloQ4Hta++KEyvJtwbWLVCMbJ7LA9FaJm/K/y2i2v5sybRQCXe2O57X0a+ojHjE5zpNHMXPHnKq2jZLwdBaBxd01dw17+nQislaz0u4lBfgnx/mL4X0/6CD1qRu3zZmpnkIml+XIYvP1lT0WTXa5D4MEmmRiMbhl8KO+8alv/Oh+Z2uiZ7euykP6DbkmH+Mse//JmcroT5Px7DQ40nkYGuOegkXSlhPFxEfQbdDDufngpTsvDp+688mNTzgf0tgiaCyjVRnKdtcC3xtMF3Qxy+j2d86TIGlNVjHL4okMbkjouiP7nb1t9NRr3ppza05rsFXG44jZ2ItqPOcsqOK8YJKzqiyrqce4NFYvrdFebnzYOiOXi3VeJrFOOYTbGjPANepbTEP1oDxWNkhlsQVBrEzVo+CHkhKsAFWQZGs6MD/2ziT58Ra8jZ3XOfgyN46EE5RI0DnH5Iwys22tJgeLnReoIm6tgkMxpXId3FMQ6a23izjjZDXxFchynIxxXhjL7fHgj9nGNx8+8KHtfNMGW06xnqYyfOtl7ruy23XYmZVrw+Dnnz8z5+C2elDXHy9N1ZJeaN1fASzy8on77st3/PRnP2O/3di2F86zfOwTvjsOHuG0vpXlUMf7psOvd8KdWWNcAn3ekWBTyEDW158OA1VLuT6TAWeqk+q9EWlkkymsZCKrie+8ZXKOyfHtZ95a40cvdz7ddnzbqrJNcpzMMTgPOWW01tnMi6Fah3hPbNP63pqxPw5ez5NjnAUTatFlKHqXJA4zeUjO+h5py7ySZH8XWCTSbTW4dLFsL/ZnMd8yY3GsWH6bVKW0khVDQQeqCrGVj8W1v9slo+FCAQwFps1byTb8MoWwItmklaZv9YtSwvlRZ57cQAaPKVupEVFj4L2i67ymN6x1Jza/kKxuGoezebskCGo9wDiOX+ic/2EHqQzu1XfJBJpzlutE82IWlcbFfLsWnuAuHeCiuW58yMkfu7/wsYlybiac+jjPYq6cvM3gNWqUeyJbIzOGCZ6Jyuhn0UW7b9y6oLY1JG1VNhHGI+EImV1+GcHncfLFnddm/Lh3PtVYCViQVhm3GmQzBsnHzcncmLFzYnwegxirMVxGu+bcUWVyb52XvdUkYa9RH6pglGFpoziGZ1FOeo20N82rGXW/B1Zq+Ar+KTZPc+fFjXtv75qxohBH9Xi8CCdeRI2+ekEsCmxcGy4LK1d2VyMKkPfeMPS+8slE3E3JyQYXTLshofGLOZ9a42QXJJpKPl4nHK1x1nyhacZJyil7KiD3VmywZf0SYtn1UOXVyiX7Oc4cPI6nGHhOMucFM/+J+84/0V74eN/YfPDiD9o8NPSRgnlC/M6LHLScEAwej1d+9rOf8ni88vLy8i5TXwSKFZBSmXbBb1FebQvefH37wt/5O3+HNa4eS7ZNWikq4IxzsHsv9wvTlIDtflUPJwUTjcm+uT67qyG/KN70/uzLLdR8qqexbJQ0RmMwzpMxRvlsyurKuvo3c04FofPgEckxkw/7xr7f2feNLQbH2xuPty88jgeZg6117reNvW8S7Bu8uNFvG7cuCPFxHrydJ+cpf8EzQ074U/6WMsstH72KBJffpVcv1PRwM4JZzImoJG+W/sqsxPcGOYZIC7GGqsZ1i6g9bxQShOGViKlAfoqpvZ53nINFtGje2W87t965e6sAVROf/X1Fps8Sqep2TiFECk6TM8uIunc9p0qUI+WWs0avUMHPqm8r44NkSyOa11DRRZ2Cy07p97l+0EHqV8x5KU3iTJglFBUhYmJujFlD4Jp/vxFYm0TzVhrfeONl79x8Y8M458nrOJV1j5O3c3AYhDdpfdC/0/pV3s9SbWcJCFdpv8ZIXBizqH2yaZmTifGGTF/PSDH4MjjS+dSNl2bsBr38vS74hmTvjY+tY/2F27bzdg7OcV6zfrZt475t3LYKSs3YWtGSrztZmHhmjefRzVnMJyjL/xnMMGYWm28mD0vcZo2vbnTKsSK5poK6gYVrhEmJ/tbh6dXnsshLEL1w7gDSNRrjJJgm9wpVPgpSh1lpjMp+COgZcr+I5OjGrXqA3dQn6iT3zAv/PzI5Qr5u59AheWby5Ry85uQtJltkaTwavW2XLklnvTpJDS97LPndPT8X5Sjf5VRevYA/+aMf88fsI801vK+9S5zClzOGoC8u7ZaGJ57nwXfffcd5nuy3jW1rly/jc8SDAkXEU/wKJQKP4HxM3l4f/PSnv83b45VP33xiq/5S78++ipnT+i7IzF1TmWswpCBvHYxKRCb7VCC+7TehAM2rCozrPQiinBdkuQgiMav/UUFAMCHqL1kjTIzS6MZ5Hrydg9f5yqdz8M2HG/etcdtufHA5wnz75Y0vbydvb5/58gXu23YZ5fZ9p3fXBGuHo8OxGcfZOMasADV5DOf0wTG01sY6oLMO65jSaoWSUS9oPaC8FldgnUV06bSKQGnPGXFpC4ath0dZlxkaplw2TLbQFewKEKvaau4Qk27GvdjH9xUgTIStZiKfaGJ1JTG1/1UdSuy/pBZrNE3kEniX5i80lsRX32n10dahkYLR5Z5jF6xMfa9vv9g5/wMPUp2XYlglyqilpLlanRymXgKzFkU1MEEapr6JSfbBNXLZEMnimMHbefLdefA6JtnbNQCxmV5ujVxv5vh0QTSTq0muuUt5Hcze3jVDy80hq/SfiQaNTf3uMYLXmLyl8yODF+DenL0wZr2mkUw6xj2MvjlnOpchVHP2bdegs+bXokzTiMARy8RVkOSYguTmGi0SEnoCF901KjM0q4OnzDF7b+wZtKme096cPcWA1CC6ZLPGc7wB4OUGgEvxX/CJRlbLi+1M9fe+jJPHDB5DTtaPFIvwIDkpRwyvUQ2RElincTunhh32nZfWecHeBVFt6lsmW5dh7WzG7NLzvG2NMzbeLjsnVV5Z1HTpVJ7DGxtWwdq47xtbjVDpJqZkd9G9u6ti/Mlt40P4pXkCSFNP1dylXUs16M8YmkcWk3OoyjBP9lvndtvZbzvdnlN/zZaYvQJTluv4FD14xuTty4PXL2+c442Xl539tnG734Q8VFdbiZxso7o3bOvqKAwFk3Ex5MRwNZMweDm6gA6kbEpqolhl8tHTWTzmCebyJ0RTn1srgk6RfLIIEVnOG7RGsukQncl5nhyv8Gl0Pm5dQWr/wEfbSXvly/yOL5+/4/P4Ob05Ly8vvLy8sN1uClrNuVmTX2UPRgWoETVj7JycY/J5nDymKq0Fic0oAfTq9bnjvZG4pv2WyFmyFxMhYWb5JT41XYXcqbdkFG28IO+CnctGc6kMAb/Qkm5Jc/1sN69hh+WKU3/Mnw7qWnB1Vq7+bDFpReqCaOs8y3oOVu1NWcOtICp2oF5PAuPAzcqv0dm6HEs0TVkBM3/Bc/6HHaReXvj04QOrHwXKwme5BgTJgRbTwssnXLOAPJMtZM+ymV2VxSywLMyk8u5G229ijrkRqKqI6jZbbbDdNCNn5KwqZI0seGLqq75OkhsQ7jwSvnt74zE01fKtDus1FoMRRBMD6/0htIaOWSLKtoO10OJsmrO0914ss5UxSadwVCn/5TzFdpvJCNHIBUuq8vFSmLMYb1SfZkEY9ZqdZMSUO7XDiXNM9cX21iQJcGX2zawarHFBX566r4KrxUA7JnyZB1/m5OePg8/n4ESY+WG6b6cZ4e05WTUmzZJHGjYnfg7uM7lN40NP7j65u7Nh3FH1t3UxyxTENVbeMD5uvSycyrF7Cv6ZLKq57oa75i95bb69yWJqVUzdZR7sZoQ/hdTOZKZmiREnxrxYcYJ2agRLm7SQ4/iYop333rndbnIndxnMOk9dj0Z8W1VTomR7jYWfMTmOk+M8aM358Y9/zMvLXRBe63jbaOeSDxj32w1vN1X/1RvUhN2U+0YrBmPvsiaqxK1X/zdD1lfhHVLGpNt2I/PkGKPIA8Yc0vb0yvJJJXZefY73lj+B7HeOMRkVNB7nG4/eeWyds1i5rW18/Ojcts59bzxev/B4feO7777l8+dv2badl5cP3G+bZmRVIro1p0/ZJ916Y/bgnJN9dlVY4+Q4V58maxyIoPJJkOmElSdn0xidmYsWn6SVrCKkh1JvXK4qZPmDLmVNavTNqv3XqA5VvNslpu9eM9ma9pglMgCoZPnaazyRY6szU01PVWytDAfSID1FaMmBTWnPZtbfLZi24GRVxU/NaTdNfNjKraRNwZOk9GyXF+nvc/2gg9TeVNK/v9pqeBf8cMazbF2l6wi7JuE2R4SEjMo0qqdgGn1+6w3PRmtr8qsOcgPGanDWyTJN00fDvJqH1Gyj1Q6wpwah4KY5RbsdqNcjQea4elyPefBah8GdJLuV8WUJILO0TIhi2q3j5mXu6Zc1zBrcOGbymMnbOXiLyXfn4EgFy2gKQNAK4mlcSifLC7JcWZA4KuUC3/xiKUWqEmwpF/buk92Uoe7e2JuC7FZNYQo+yOWukHLBSAuOUL/uywzezK6q6XDnyFRfzJtYRlaItxnhQAvOs/GIwB6D9jjZSO6mHtynbee+d15y4+YlBEWQaMPYc5FsjOjLJkcQTdhzMqu7VyB65yrgWTN6iorrWV5mUatIVZnlVJU0TpoN7l1Wt2UeJ/YVTu9GtBu3PogUK0qjYZ5zk1pzlojlcuA26V/MNCdor4SjtY2t32nF4MyQubL7TtJYE1rX2oXkPAejWGipV6dZK4pxr8RIsNFy3yeiHDGcZtKljVlsxDBiGsep3u+Y6kVtDtvWeLnfeLltSmhWEjqfSWd9QmibXBMyGY+T18fB43bnDOPTTTD+3pzb1jg/vHA83jiPg+PxYIzB59fveDw6276zbzutRvQsjY9leVymJA63TEY0VZIRPM5ZiczUnK4xGEsOksjXzpPoDcnAdBZdXn2lC+vlOOKm8T9rQrKVdszwi63X3KtSLwspL/3Tel5ZFCN3rDR7RF4BxKWdgJLABOUI6YbFclmRvtHdaLRrKvX1Z85iwmp9uBnbtnHbd7ayimt1Psj/UueH2qOrZPj9rx90kPru9VXwSD1Uee913HsFG1O1glcF1KTvCNOIZ+oBmJzMVELrMG+mAEVoemV3L7d0UbvdG7PBLCX5zPIQmzVQbTUTsWshrR7VrKpjGLLFAWidvtVnma7gNocyTyvhadqF32tkueqYiFGqcZMq3MpEsvDuc0aNzhbO/mVMjkj1c2iwNdn58HRaz5Cd0oyqQjOuflHryjatFYU1XQ4OZc+z3ME7ds1iGq7nMFxZppiOwju88Gqgmq5FViiY5TEmpxnT5MiRVY2qQ2TMMI5zXhDq5k6W/1v2m3D2VF/nLSevGfRz8nDnfib3DO6mQPrinRdXg7+ZXSM61sA2b716CMqOl9CxrWq54JPnKJKqYE2N6QEXIeRtTrmmhGC8W80FilzWNQXKpBHZqmJvZArMV/6z6v8i5ZQXX9bBsYSUS8xrhTHtm5F3vZcYoWorgwjHvLP3myCmTI7zwNMvSvnqKQjdmcRRPdJ9p28b5miKNHkJfgUTl4tBwDynAn4aYwTHUfTmORUfm7HFZFQvs69kK4SErP6Jh97PrGPvCHhMYSjmDWfSrKky6p3mxtYb835jjBfO8+T17UEEjHTmOWgz8DbpF9GjaOEOrXRp6h3q4O27CEIznaMbY2x1iK8ZU8XMTCsSxZKxwBgnmVMOK61shnpXJdVqr1eVRS6BdcFxbTmKLIBmQfMlq8iC0DNYVlV6n7U+qwWxhjsqkIFnSWCa0fzZOolK/MfymIyoPrzWd3Onb13oTUHgXMSK9SoFc4KcQn6B6wcdpP7e6xc+mFIH9yrp2dkiC/dfqvSVC2rDbGb0tnDYZaXylBaClbhVflzTBD81l6g2Up5noyqgaSiY6FGKnjoU7EC9plnViRLNpbou6E5I2JVdkUkMZTPe5HSxm18DDYPCl8tZgWrY5wUPSKRLajbTMSafx+DzUWQQo+YaOaM1DTS8wCsWk5U0OBfRJJ5wnXtNenWBCGIrKlvzNdxsqmZoSuQlao4aYuemwwoxC7tS7tpUCqpfRvDz4+S3j4NvY/IFYzaX9qt1Aqsqo1T6Rf0NdaBXnVkYeQmqi8p9KhzzFkk7RYroNrm58SEnP/bJT9rGj7sEDNKqCU71KfHJalSL5VQqFqP6i6L1LvKHmQmijOAthsSmmfx/v3uljam1E5Nf3Q4+baWiMd2fWDKJOiwjl/BTFe2aY/Wcg7RcHtbThOUSXkU9zRqZz0GI3pI15VdMWLuqNZBLRNZ8BTMRR3pvV7+EMdWTC41M6a1pWnLE1U8hQ5Bn5uUCQlNaGAGb90qIBtYGe1NgUecpazYXDJd1VxTL8yzG7TwFJXmEqPgxyceDmY3hnU/bxocu+HQF30FyujH2jSOcs8bXSJM32epcUIW67qc0ezE0VHShBan6UwnS1iqxWIzT0reVIDdikQ6KzJIS3Paqnvpi2parxRLlMpeXYFxEBYNnwl0Js6EEalDz1rKmW1c1JQKDkk6fklUooaq1tkwkKNi1kJRa4AStklm7CCK/ewxJVvW3HNgxiolYkKA7TvCLXD/oIPWda+hf4Qrsc3I7TrYR6oNYYaNe4t3CZa/+QEFliV0moLJZ08iK5lk9CWX1A5nAYmomr0aYm7D8zVUGtzBaD46hnk14BZAy+LRU5dBMNOklGrRS9N8S9oAXb7xQdG43YdQOUKyaOhltLU5btZp+V0yRCl7n5OfHwXfnyZecPJoClHpNa0P4MzhlgVKmgGLWyKo6vSkjnwkxtGFkr6TnIPaQDrqsClPebYLmZrZiAIWqBucSQJY/BkcmjwxeSV4teXN4IELFrIAkKFMH+az3OytbnQkWzpFBs2oILwjMO0kymlh9c04yBjMHbQ4+BvwTfWNsN2zbsH3nVgFhbUg9AUpTEoysZ1yH1Vn6uXMIBkoT5HhG8KhJsZnwX39+YEcI77fGx4+z1kflnPXL9Jgq2sfqI1RWO4cCiymgWc2Esep71neqeqtgZ1Wp1ZlFVEXuDhETz1YsPAW+t9cHvTthxsuHD0IrqtfVe8faEmmmqOKhyvMcsufS7K5KdqCSnVpHNtmjsXf9t3viPmRiasZWEFYUrKQAIrh8QlXWTX2SqURymgT1GYN5Dk6GfCpj48VLHmDaTEdOvszJdzF41HwsN1XofbbqsaoikAREHoMUyUD+eGXoWvtJcNs7kXQFKiWYDesFIeesFV89yEIHyLj+ST07zYOKOkNWAMtrXwo8ewqAj5icYzz1nGNWz0xsyUg9I6faBJWYe6ETzbTKsIJ1XXAk9iRu6NxSAkci/WSsXv364FZu6iVkL5ZqY4mYf//rBx2kTi9DSxfUcaAR4j4HfRgttciXmE3zjkrL8u6AB1UxEs4tzzHBWW39HYjWbnZN7V00UnxNXO3VBK+hhZWxRFZD1AxnwXU6yHeMe09mOnM2PIM94TaDj+Z8bM6HDvfu7N00Nr6w/opGrIxZG0SVxEz1PN5m8OU8+O5x8JrB2ZzTlWm1InOsPsLaKEnWAakMtrnX1NOCE2YUXKDP0Dcto4jQULTedVDFgDEF3xGsuVqCZirQRRIW15wer4mjHkou7l33dEbW8w3SiwGYSztVTVj/XdZDi767XN8NUceR/igIqtgizThOmQq/ZfJw59w6001MMuOysRmRnDl5zMHrGLzWP6M7NAWkt6kgpfPJ6/01Cb4LInx1sQA9nMdxckSA7+QsY9vkSiCsnvVFujBVXGGlgVrJybvLrT2ZWXA5e2uicLv6Fjm9oGr1PdKWvqrCYSQxBn2/MY9TB/i20dOK5Wg6NK1g9dDhGi4vyjOD6TVxGVGZZ5EM5NgysHQ5E/Tap9UX9Fz9mCTnYIx5jepYE3a9bVCmWVni+pjJY5wcI4jptFSlvFljdz30ReKJoVE2b+ep+XSoN9ndpX2rKkMGw3kNwlS++M5SCKrNIp3i2pa58N36Ll+J2rv+ZFSGuIKTdFFyrai6/UlKKESFgsmtqpRjyuXmnEPQ/pi8lTPOgvqXVkvgUlHZhz7TdvW1hG7oj8T9LTWrb1X0qg6LKJJPo+olOL+EveYX4rM0VaCRR79olPpBB6lojdF8EeZk4jqXrqDsfjIZroNlbwpWt9aBEvku8VkIspgFa7hNeonNenO2tjFm09TfjGtG0bAiJKQskVrKbsgz2EzstVgHjStI9UWQWIpxnZ6YZdkKGffW+dg7L72xdblIuFXWBhoBsTLu60CR3kJ1pfofJ3LDyM0x27Amp/I17dZrwSwhrfaKAtXTEULjJq4ZO7GwZbtGBKzVq7kxs4gjtbmtXXBA1GKPUryLgaYM0ouK39y4bZ1PlhpfMQY2Jq9VXqTBidwWLrqs1X0wQZ2rWZurwjNjARat9COB2JYaYa738yngjrPfdnzreFezPwumHXPySLnU//w46s+Db8+T3Daydw7gNfT9vUSUZos4oPsLYC8fsHEjzwd72+k3q4BTt/Sqi9cfBdxL52dy1Xh7O7V22hptnvW9FdCWxVHBe5eOqjLg1hqRrVwmgvRYp1gFSt2/3hovL580koaq2kIZvFUAzGKhjREFG1bV6cbbXAM9lfTMDEkY1vsRwl7VvL42I5nnLHHvqaA2g2lxCbzPMneec5RGCFoEMQ5GJs06L9GZBlkHvGfiIZp2S4RijJM8h/RA3rBNIyl6welaPEUaijKTpXqwVSmu6vMpQowLcVlJpdVBnQV9rW+xzOrVrn3+3lrJi3lomK9gsfpcMM7J23nwOs/SrgVnCJU40zhw9XW9FSN37Vk5owiOLW0fkpHcWuNWQmpNBIh6j1yIEHCJ67FFLqsqfhUEK/FtC3IvQ+r4I2Aw60306vXISYiuyikXHXQuAWNePZFAmPjeZPIq+/+uxVEP/zoe7Jm5hCf07dJOLMufsyCcUWxCldPBOeXinCiLFdNF2LXPvMbIiwEmuGUzTSbdTRY7vcnGRdVNECmBcuRyUebdIl79Cn0tmyi05+oFNbvcyicKiucQ/blVf2cZ72r6sAJUqwC1cPw1Aloq+9JNUVTneEKoF9beBR/NGJznyTkfRNuwW2CbAnEvZpp7slvDm5xA7vvg5Rzsbwc/O04sJJq+e2OUpUtaY6BqykzajK1Gl79lcMyTDPV9+raJwedlTAyQSY9By8YHM37UOy+uqbCtKu3rvpaI90ROIZ8z+XnCd1bspzSmuYYDltLe68waagxcQWSMQc4dCzmisyeLMuFU5mV2bf5aSRWIVD211lXdj0EiAoR7Y7H+IkLz1SLq+5cuSx99DaRrvfN2PgTdhsxy6xuwreHWoTXCytLKy/2/jGh1Rmmm1hyQOGPMYrPqdVpzjnOQOVgu23iRb5ajfNql6QqDGIMY46qmwwzfGluxFRP1XOdQcNUj8hpcasxRAc4bc0yyy2PQUi4kYY2PvZMz2Jrc8d07W9/Ybje23lm+lAmcWWfJnMxTAxhnlHt8PL32sLgq+ItxV+eJrUqD0ldF9YOiJBmZlx1aeYfgPutttGs9ZkjYfYzJ43Hy5XjwZZ4K4ohoFDhBEZvI+oOSg4Ik11k3Y0K1HLa2tJtK/pcjjJe2s61nxtJeanVKnlGkkYKUqb6b5mhxncdrgvXvd/2gg1R3VSXnakDypHibmTK0pXZff0oJtBXOs+U6jO2dSK4otIWrGlqnOmxE9W1VuSnoyaNu5OScjfsMXiKUuRSUQIwSyCnwdBctVipwwRDd9HfN3glv6/BdAfccgxHSOk3EqvLWaJbci9ln+FXSZ7GSl3V+lljCV6MzNagwY6qpXFquZgXbVDW0NplagHLwoILXEaey2KxNWHOE3KSfEc21dExpkJNpyTEGPYIWjbZvos+nNtCealpv3tm6sd2U8bY5eX2ICt1aqdmbiDLHlNHnvu9ie7rx9nhwjAfjOFjhpmVirbLDVMW442wmK6pffXnhx1vjU2/sVlY0JWZe2fBEZIgzIftGdjiRGLT1nb0goV4VXYuoCbbPe3kLo4/QeJk09taxOAUX10HmbiVireoIrj6GEjNFwMd4cPNdMN7626q23hvSrms5PSwxZhY1+jhPjYwpMkiSvI2D221n2xrDNNV6zZDy6ifiNZWgWJ+RSc4J5mzdJf5Mre/jPKQznFGuDWKRYsaIGrOTq5EfK8oxL6wJrLRpi2moXpRjKa2exaR19ZZfTJVzN9OYHLJ8IUWu+abf2a3xqUxy011OMn0TREsxXWfyOKWLOsfU3KQxOI6Dcw7OIovMWOw/u4KUG+XG/tzb3VyapKQmDlCMWEorJnKBWyEzVbm2+m+KGRwzZcg75K8ppKOLjFltBxFWyvi4FtJCF+StmBJbj3wGFXsSX/xiHPZKZp9zqbLOqUUGkXPO061GSUw9zwt6lz3XL3TO/0Lf9Ut6veDs1AyhEstlKlv1YqWkKXCt/sREzspmgauDK+cJy6oirMp3KejJIITXlHXL01Jojc6eWMFNawrvUwuxbF6WktusxI7eaA2ar82uh65TbNGYK3uccJ6l7i8Sh1B/rYKWQTZl6i39EkKq+ppYqqT31HiM3ZvgjKJzbyaL/SXyI1MegdXrWRDjqpLWtQg/9a7rwLASd5ZoMZKj5li11thap99gJ9XTKHJFVKbc8Etf1GW9wNY6N2/swAvGz/KVz0Nary9jcBaxYhj43jnzYGsdb02wpSvL7wa7wd2MLSVRsBkQg49mvPTGj/adnyzfRHvHoqz/Lffrcao/EqHBmhvGCIgwtkkNx3Q21kEj/Vfrq+dp/GTfufkNj8Y5glvZI4E8AXHKNucJ3z1ZfOsdTb68feY4H9xv++VEDVoDUbKJ5q2q3llWSepptu7FQxkcx4PjGPSXvcxgFcj2lx1zZ+SUuWztrZWB93CtxjF4xJRLBJR+j4sUc3Ojp7P5xqRxMBhZezJXBj70u4vV/qxjE1uUa/NLsCp4F5oJwmZG6d00UfjjbvzKfuPHtzs/3m98cMPOE3JeFPZujd6ND9suclQEJ8bbOHmbb7yOk7fz5HFO3k4dxqMqwfXeBZ/HZQQgcspT2OpQFhtD5KKmhPVm/SIVWKzETP2wDXlhbg7dpoyUA5kWF7HEvNE354ZhvdGL/h6pGW0D8FNJvM2JzyzYc5XS6hXNLPVnuyy5lbQvdmnBvlvfnj17jFg+f+dZfcJ5kc2odX5lZfmsIqle4C9y/aCDVI/JDbi1ViwiVRh5NQYpf7ClE4gLX/cSmnXTYah/rpv6zFVFE9VryX2hCbc1UzVBDWFb0Aw156XgpkV31blS2WyuhniyBtBV2nqx6qifVlnOBa3MFGvPWvU4FtsHse2shfpLpsC5uXFzI7qcwYmpiZhNvyxMlFcznnTmggepw+3p85dX78mKtJKgzHlBUtd9D87zrGoUJpOtBzRpVVb/KEL2QseYWDa2CmT+3rEhs1ys73zYNn603/jpefKzx0F7Cz6HUPbWBN1anAI1svHiZdK7a7F3jJ1ky9JxuQ7YT73xcdv4dNv40J19wbIgk9SYdYjKlmjOgaO5Udkay0FqR5q7HY0j0SEddAPE7bqe14dNbic5FJRbq35DVuVbqYrYVYLpNEfK6uvBd9/9nM+fv6X3xpyDVm7kejbPn5eDCAVFa2rugmSSyXff/Zyf/eynvLx8KHrys+pqQMbkeLyqYmqbHASaEIAN9VfHPIFk69Ubm+o3RjmpgOjbd9c926wxTU4nj3kKLl9rygtiVYOKNePJ15qoXlKWwW+LYpJG4HOy1bP51BrftMan3rnVsrfqiRWWj0YyGtY0kPFk8uU4+OmXL3x7PPju7Y3X8+RxDs6pPWqt4fsOvZUQvhFZqE0kWbyJdS6k8DQyhhLgVP97FDNQdp4uZwqE2vSQPkpjfCiLLZHBWsSllzI3ttuNvm3cYtae0nl4juDNTnpMHiPlgQlXlQ3GdMiUkHhrvXqbhpXusVkZJ/RO70WESEGNYw6+PA5eHw9BxMu19h254zn36vuU8/yjUEltJDezSyjZTW7dRdysxd7IUHBw1sHtfOide9u49c5+ldWK9AiCFjmBjbVdvb1n1bCedBU/tdISHZRwMVyeU1Dr4cZTV5O1CakfXZWKFfPIcbIl/d7pY2hyKbCYdSWlq7xGsVHO4iJCvGxJ5o1tBo8I+pDwNKrEn0VOCIvy51PgyAq4fdsqoC8qaVyBlqnffY3TIJ+U5nWwrEY1FUhDLvJWPYqG2I+rV9ZCQlz1FfQzArA0jHLfOy+tcx+dD73x0oKfPx68WZJdAwwxLpilo93troZ1B2lp2nKAl23Ly01ee3tz0Z4zLir4zMmwopXXe7U6oG/Ch0hPbuJeXkPm+iKWWKO5JukWlxhIzjggwC3IluCzgkPWvRzEKTGq9fpcTf2oOU8+f/6W3/npb5OZfLh9opc4csF6qphU8WVG9VYhF23eFAi+fPnM3/pb/zVjnHz65hNBUL9Oa/NxsO9dB/OUP9ytfC7JKGarCBC9dfUDY9L3G81ajXlJ9c1qXURl5hLmyoHCUQ8zilLfrGuvXXqr573xUF9XFcikRbBvXTOTItgxbk37/Ef3m8acFzFhaazCgN6JmLxhHGZ8OSbfvX7h56+vfPv2JmPpCKY3xuZMn1UtTGyegD6n18yk5TQzS+eYtaeeMV96ydpKV0JqBSFPhIwQU1T3SDbXe7u1xuZyounU/WI8E2/EwmsrKBjMJh/Ll2act61Gt0uCQlVUM4S6GLVXvVW7YxkCRwWrJY+QFvScJ8dZZJaU3MbXuVTnsrnEwnL3WX1HrStrfwR0Uo3Ac5CLSiObiWLp5SWMtaKQd3d2d+6tcW+9DFuX4eKTwptUgGlNJosFnVkr3QMlbsWwCXyvpC19BPkuQC2fKj2gGVXRsVocdi3ihfFrVJI9q6vKUOXhvSjCYIQa0ZGwGplZVFI3yIbfnG0G2zglnh2yYgqTOHaxABNZOy28ejHf5pzg8rnz1q7xCiDdSnr1DMhqcEclCFb3szZrRDW1vKrS521T0OVi0oleXN+Oqkm3pmTCjdY7t0w+tU98ub/wlsEsWr+hNaCNVnc5y2m8gkd3ESw0eTefP2fzel7qlbxzgTbwokjfoj2V/admSZnXXDOTLk7riCs4LCH2KhAenMwAzykXAoJ09Qa8mHE5JmcOznNRzeVS8N3nbznPBxHBx48fuIS8a10Y74xrkxnqA+VcxBe5dHz+/B2/9Vu/xdvbK7/yx36CkAA5bWg9wjfbpjYFyf2+EalMfumuolH9y8rSx3FVTmsNezOab08I8jhFLa+AoimvjZEacRH5rKSioNlMafIyJ3nqnnkEHpOX5nzaOi+b8dIaH2rkztaMW2/KH08Z1iopylr7yZec/CySn8/J6+Nd5ZQQfaP1zrZtRCbHPHkcb8RxXHB+nhrOKe2VXaSCKP9QrccCSFfyuZAE1sTbwZihIBcSeLfSQg1vhHVmGLtrCOXuZeVW59dlOp2rl+kXxN23xsv2svLpq0+17NmWhCNTTjx59QPV4iCSmWp9LJTKvfLBpn7z3u4s95VcC8e+55ui8+4CY+xyuvj9rh90kNpCQ+WkFxE+2igD2SH35KtELtrx3prs+stNIKCEvUsVTpknaujaNeY5KOx2CSPjyjyeIa4OYysGUDXaF/MHqsRdC8nsOrwqxVXvBnmiteQiEphT2ZJfZbM0FVpcyhDVqLaiTS1Pry3h5s4H7xzTeGnFQqNo6gmvY3C4gtWo93N5h2XCHAxxAkvrpYAtWmoU/KfPNFeQeUdYacBmyc17UVuN3ZLdiwZcxqtm1ZNJyPmkr2q5P+FIDVc0bn3jE1sd8JUUSGUo7WvadVBfWhd3vWeVAkQGTpE84tmLmzE5Q9ZFoKrcmzL05s6esDPoZ/DIqEpp+Qeu8RSLmkwF4pVQIWF0No5j0mMgZVBXBmsSVsLJnCejyCkGHOeDx/GKW2O/fWDfP7L1WzHkXOgBivAZifXGGAfnPDgfp5rrGZzn5Nuff0fOg1/58U/4eP8Gb52t7dzzuap/HBOjMcN4yeTIYDzeyoDZNAbEnsY3b3MQUy4eVv1BkVwEhS/jZ6v0a/PGzZS0hBmPKdf3UUShC92IgPOEOVQ9ZWpd7xuf9p2PvXMzTQu4VaO/hWy5NLBz1DRdMdzeMvkyg9/6/JnffDv5bmQN90uNoykCUF+jSUz9W/kQbuo1I1KTR8KCVp1Lu+nNSkC0GghPtuO6v80gWiU3saoXlWStUBv1vqI0h3KCaVZ9z2Lg9oIELbNkMHlVWL4kMEVymBnlCTjLHb8GOc7AskbsrL3SjR5eo0hqf4D0g1Xlppf+MblcZZ6aqQrS8ZQzrLP3F7l+0EHq3mTJvzDfmVo0Yyp7au7lPKFG617N1nXzs9guR7kmSNORV+Ui+O37orUnbVKLccFqz8aV/m7pQy5mXvWlckGDZEFheS30zHza7hf7x3Oxl1Z1oNK7X+SNcspAUzhZsF99zSrg7gDmDG986Bo1MlKL/Yzk80SjQebUWHUT7p7rd6yqKIIRJytlWpi1cHWJL6Wr1z1cOqtbianvvXPvIkG0XJOHs753vfsCMUP3KlcwJ1jec56qyoxiQtWRrkN6BQa7mJ7LmsYqGYhKPJTNmg6cat5HkWZGPfdEhIduTkYWnOfVwE623Xg9a2xD1goatSZZkKF+bkkzDWnhmm2E9RKaiugimx3d1L1vuCecmly7elIfP34sB+w77WL1VZpaPT9LBUXPLK9HMe9yDB6PN9kRbY1Pn36Fl/snmu+4i3CyqZRh88Z/94/9CjHFeBvuPJAW7ECu8J+PVx283fHWdSg24zylY+ubBNkR87JyyotuLYPn1b9LlFQ+QmJc5lkGs0IHbO8wnY3kpUns/WnbZRbsrl5NrcEM0elzhkTAIebbRJ6bb+XE8jtvD/7+z1/5PKR7DG0ssWwNxuPEjqOmSRu7NfZbv9aJ2HxclaU3x/vSFaZINouuvzoC7sUiLlfzJh2apAPtqUmyGgFTyECG2HMRoqnPGAyXpZLIOgXzZmAzrkDorNYDgM4y4okUHFPeiQpSIm+sydaLbdyq8MnUnlGm5TQvCD9m+fjb5TpxnQEVqS6hPaui+v2vH3SQ+uZ+48P9BjyblCu4XCVvZeBO2XmsG102KytjXiOhl6uAzmDdzqwbrsrInz2pqhpU3q7cBwrkvQ4nsMsgtl1HFJduIYoRsyoTDc0z3DSC2gtd08OWa8YyvW31fqiFvErqmU8hndd7dTNuLbmFRJPSciWnJfcdHrPx5i5D1wyOnJyJUiCr+r4J1ltkEJIasz6W5FyQXGqOzd6c3Tu3bSsn6s7e2tX0VsCOuu8Sg55pl85tZX0rB1iQVrFrLwJG1ZWyzbHSGVFkEBVSBVkJipzxdM1QpoqqLwyNWRDrLShPwimNmNvyJFSys+8722bsu0SmqrxGySJQUuNWB5ldM8bIZD8PWpELtohq6nM1tA1Vb7ttNHdmDKLowWJdOma9ApRXFQqkpjL7whVTolQzNdqjbdz2u0gNMyswbbj16hs4bT4Pk3/i5c7W7tjWOVKV9lvKyeQxJ587vJ2DaXDMB3tJID50J2Zgx6vewwX/1OFmTmtFaEmIORjzZI6TPk4sJy/uJdlQ434v5+8bEl1vronLOwpQNsuiCKEjI2QRNMpbM13vbZjxmJPv3h68ngO8sW1VadRe9njHzmuqqPber4R0zslRMHKQLBmMbJJsLcuC2bKS4sXJrTPJVtVcCczVu/EnzT/fpTb+RGAuH8Cio5spUez5XMsahyOaf/kYsWZHUbKKaYUSmBMtSwJRp+qsTe5Nfd3636q0Ok54MSJLk3kxH2v5WR2GaVnJ17Usf6HrBx2k7lvnZevPqieTJXYECj+tTVGZw9JMzVwTJiViG1QWXItoNRavaH9lVqouggXrKTPP1b8pQZ/lOgD1UJb9kNf/qKw9y0HB2jpWtaCfBb9YTcUfxpYti0FPf7qrpxrJnitL0RyjzeVL6AWYCSoeBSPWQku4bRuxwcmuEeo5ec3JY5yXi3pkYL2TXfVchFwfouimzaA342Ybt+bcvXNv/Z0dlfpAAicTTHZDURFEFHtqoNwo0XJcE09FxNBDudKPdyy0unkAV8/v+kKtj2XHMwrGlc7NyK7no1lIhnkvcbIgkZWNdrKo8iF2aI2n2LZ2zdgasSqHuKi4qkAng3GRSD7E5EZqrHo2ug0dJ1leg6VjyzQaW/VtnmzTinUs66Or/1ljaLLGtkTKBb2lYOTwDfetTgm/iD1WzvmgtVPLno9m3Fuj986sxCxaY9qdkcmXMTRqfEw+H2+EuYZ3nicTLqcVRhF6Vt8s5NbQau2k137qG0kTg7VmPDVzrZ0UK7NjePVwvNh9NmuCcQKWtZd1szTeRjDsGGs8ugZo4s52U70S1SiNVMLTErYud28RO4w5TvVpM9iMYlbGU4+YQajwZTnXuxtnTemV8ewKGFrP6rsteKz0k1Y19Sq/9ElUnTRV9RUWmbl8TKKqLNHYvVAmW0gOz9eBlRApUNL8SuKpeWGydaPeh1/Bb/V8cWdaMQCRE8cZ8goUG3n5+cX3ApTe6uAXuX7QQSry3WwdKF+zJ4VXWo9qVhfeusYgD7isjNKfDcS80u7KpuBiqF3KWGA98dXUr196QXBhT5Ye9ddXE/FdgJLNiILZ8mUzo2ZUPfFfNTOfp9NSc9uaCEqSc+L5xLm7t2KsdXrTQR+s164I3eq9FfyyoyGFH2h8zODc+tOkMuIaOhhVr0c3zLp+h8lh/la0792cffVWMupTqioSEWFlusp2j6n5UY9j1EiVuJwJVmKw4Ky2rH6WEHEF58yr4szMyyizUD7mNXFVM6tGIuPNrtdbxIqGsVdw7Yutl8ZW68Nn4GNy61Fec4Jfe4O9dyIqjDa9RzP1biKneoVm/Hc+feQeH8oNwXjxL1L8v6um3Jqy/0XUSRE9cmXKdQjl0rOo/NKzXhVmqk+pKl/BqJVWT83ycUHGq4c437kBmBnzOLn1jb1bVbPy+AvgJ7dO3mGQvJ0vMtIdQz2O2ovq6a7Ewa5nZwWDWv1+MsCfxKCsjWgVfPM8L1ZpZl69lAFinkZ+zyFmeeKRz+BD1dm9dW5956y04ZFwhDSIvXqZPSmLreoVzafQdiaa8xXBZKLxShL4Lqf7S6pi2lfTUxOgo7RwWZ58q0IqwS7r7LHFcq0zKQqDMdQ7aupRB7r/IAiVamsso2H1vYtEliXCpc6hWEMbZ3lB6qxpU70to3z8XIGpVRUH1e4wLjJIb8ZO42yz5n7NStziKhAu95rflV/+o64fdJB6G5N2Dlbp/J6Kua5cho/xtEU6Q83ftxGEy9TygvEW3KdTkdZqCF1SVkI8D9k6fJo1lj5fsUpN/IslgwLjKsuyTGifsJSC40pivN4HF3yZTw86EAMOrk3KKt+d4rVWtTWTnoIkdroa2GZX1o0V/FnBT/ZM9d8GG0a2xnTn7BsjgyM1cmKa3odEfuqddDRaoaHD/CmOXgFS4SaYNUIkeJ1DjXLgGIMDky8feY0CWeLAGZUXB88sbGWvPBl9crzXhyuDDd33gjYewHDnCCUphydHiVwtQvetNuYau721xq13Ngv2LmeMhpy+92Zs6VUx6jn1mme1enI6MLpIGgXX/OR+4zZuPBKm7/h8rapcz/yLv/JT/21VQzwP7CQuIo4+mFW1tP5z0XioamtNgYV/8u2PVxN9jf62YqLFVYmZLTNdHerfRvKyelvH5L71YkrqazdULc1MXrwxvZHbTpaeZlXaS2yfIUsodRJ5V8nVvKViJc5ZTf0xiCn907IQmizHlafdjjzFF4Rezg+x4KuV7CgItNQze2liK86ipYuoUc8s6zmCGHdVuiohVs9m+RCOlNP7kvDGOwG8WTGDi2Qk7Z1eR9OolxWZDvq2oPoLC6gea64j5Nlr1X/6dc6sHqilyBzenhmvEe/o5UZYPM/HOXiETI6jemxt/Slpwd4K+sw1CknuF/Csltd+syYocHrWeCMlJRnP/vuIXyxK/aCD1GuV+XrAqMlX02VVsVALQRt2mjESHgSvkTxM4wcsYVl9sBgtJpbYqM1ynCePMheN1WAE9m3nw/3O1lqd7Vl9EgUuQS+1Aa3cMXAoi5SVvco3UA8tC8MWU+ydpqMWqhaOyuxla0O95rIyWlCyzUnPpGewxbwGPPbenwsqYPeOE3QT8y2vBeTsrlI+rBOWHIEyrsLNZfmzHCuepFPVAslKDkeW5iiSxwgeM3irKm2gquoEzqq5sg7NszD/UbCpEsWqTGZUj6jsqorUkIUr9GIzZZSZqVmx36RlOSM5pjMbRG08r4r6LLaoTHgFeb50LxPieXmsbVOmwps79+q57aYZSZuvNCSueWArGVEghHRnpBc9XH93cPK//Gf+1/yffuX//N/YfrnFzl/6m/8z/se/+Weqif70EXwf9C6qPErG/taXNz605EcpV4a3Y7KZc9tFJGAMejNyppwgckkussxrTb25tnqESfS8ql1l2pN1LAsuNzkkRMAU7BYm4gcpl4hlgZYJY07p5102aecsHz1N5VNv2A15Zapf+lKOJJnqp4wZbKWFs3D2vrM3wWYKuHHZKa3EA5T8PCo52lpjtM62ksus/Wx6j5puM8tLsuBKUyInvaFd6EkUPAZcKMpysRDS9j4dp86HqOSi9KJVCYK+37FrnzJSATF0Bq6BlArkIkPIhNrpFtWTNUGuWUEKQfweq2e+vArr/ZpdiJS3fjFOs5LdX+T6QQepRyR96hDpKOsx/HJ91sMU1TJNsM7bDL49Tn52nLxmQt+lg6jJnR3TwL4pz70xJ2+PN17fHrzG4PM4VL0U7v/x5YVvXj7y8nKriZqiRuTQRh1DpqpQsGHpdloxDq9sqwJq703MIABLjnHyOA9hzlYQxb7j9szsFm06vQK2t6oAwXyTUNY1et3TaAEt1BtZ2rEM3UMzUeDTZmHq9X6rWs3K+p7Du8uLqywXrHoOmvdTWRMa13CExMgaP71c2lO096tnA2maLbSavK2VdRVyClH8MfWtxpCMwJ8Tkhe8JsZhq/H25WmX8jmzIlQUiViD44pq3ksEsm0dQ6Jj9Z78CUHO1GeeMr3dkEj8PoJ7b3zojb0Zs8FesFEhcdc1SoQ6TO7qFlkb0jht8H/58W9wi53/wZf/fhFv4H1X4R9+5dWzEkCoPz/vP+f/8fL/5P/6k/87/6Pf/DNPaJBnFTPnfPa16mCbwN9+O/jgznfp3B4nt9b4sN9EwXdnJ9iqmm9hZe2UEEGz82lAuip2BDuNODGcWf0LvWevvSBhbPdG7jXrbOl0YlV5qzKnYFWr70nCgqN6zOrZBJ7t8tFrGFbuJ17v5YyaujuG1nGkBMzbdvVCrytCMGolj1trdFf/r7elFczrac0MphnnNIah3lchFou9twTzFkIOYkFkaI+v6jhqL1LJmBfLmKuHGZex78yacuCCBteYEfWYrApE7ebmjd2fHpvq3c+LXJFmV8CbdfZ4yr1l9c4WWauvj2eLuiZ48sq1E8b8IxCkrG3QNjX3QKV31hTRujG5Mvc5eT0H350nv/3ljd/68sbPzoPoG/v9zt6qEogF9UmYevWqUswgazu9lfmjAa3xJYLX11cJQ7OqhilboHPIDV2HbSuNjrzT2js4anl99b7J3bkYRsu8Mgpm2LaN++1OM9Po6ZALdfPGvsk94bbJlLVRkFPa5cQNFM05q8+S7BnsJvukPkNTgG0BF/Zu7EZczDqvChCWA4WORAn61Gw9QyV9pBiEGr42Skchd+Y0VT4ac6sMTvOJhNtbGQRejeRrRInh3mnbc+DjDPUHYjkbpILZ5jI5XbCfzykWZaRGNETQqlcm4892+R9GBmfWyG+kNxOip0x6Vk9QVddkhBT8kcE+k5sHoykR2BOwuDbpl8eDMV0MSk7uRlUX1zAFfu34J/lf/b//CtnvT/ju+7ugDsrKtktQG3XAWAqS+b998xv8+j/zP2dpwJwlNq974s7j8WAJ0MdcGXnyO8fJW2v8fAwxNntjb6/cts6tNz5uNQJn22gZ3HrDQxCpFWuxrUP5gjvVS9EzrgY/C4pe60laxAVnq5ehibcjJZ04ayLAgvpGhqYSWDCbARKjhlWilUoEWukR11qnqQr6ch5ymSidpUfgY2jUuZXNU6zKc173q1cSNCvAaV9oH1fGeRlTZy4XhqKYW4mno4JPnTmzWL9XUpR57TUnr0pKNWsFgistWUG1kkmrBL4q6MshhyDnrPl4C0mSKH2myBIzlRB7axdsKYKOZBoz5LXeTIQWwY5c0G7W/RL6mlf1e55/BIgTX94G1ocgl9UoLL72EsGN1birhTwNcuuwawT2bJ1ojdM0VntpqJr5pdtYeLbNLKNa43a/05pds2xGNcVnBEeIAh5tYzq43VkzVPAm5DaqfPbleCEoZGHCWf580wx8YwWAGcZ5nMSMy2YmQxv/1iWM7VuXlU6mKrOrT6cT6chDPSeT/ureGxvwcWt04N47H7eNlxp50Y2nv1iuCcAAlfHVhgQtxnOKtHJMQQZhMn89UF9pXelLLqDNtfWu0SZ1ckZlziJ7yIlCB9skC7KxXF07HYTaLoI9V3Uoo8zSYRncWjE36wtbBFbEiXVqr0w4SqSZViO4vXRSmyjdnon1jTgPYgZHGhQEOfuCiSCrOJQuTteodXDMoHuo0b+q43VV1p3rQFoDl1Z/M5/dTLdFPBeGK0LrZAnS1wtm6QmvaczV3zjPUxVr79daiUy+fRycmyZR92a0qUP5o7+w5eTno2AfTFXk3vm47dzQfLUO2Am7txoTH9WLaujpeh2YClKNGkiZsnOaUeazEYxK+l5n8jazqm8FpxXaR8HDmhtlMEVW0VDAk7url+SueVz3rWH9hftt5/PxkHHx49CIj7lGyhvWRLhSK6EIBvVc5px1PCfLRFpjO1TrrSqwredQa36RctwgLS5ntYZmfEWtwdkqQMUzKfRVnUZh+xeQ/Kz4Vv9TSWZW62GxQZ909JxTbfko3WlVpVi5TizWY6y9VdrSOaXPaiZHnNJDeq3jiOQ4B2PUUMZRiWoE4/xvaZ7Uf/6f/+f8lb/yV/iN3/gN/vbf/tv8J//Jf8Kf+3N/7vr7zOQv/+W/zH/0H/1H/PSnP+Vf/pf/Zf7aX/tr/LP/7D97fc9v//Zv8xf/4l/kP/1P/1PcnX/9X//X+at/9a/y6dOnf6z38rd+56f8aEwZg+47bbOiVou7P1Pq8vOiHBtk49adj3fHq2FPHeK3vrFv22WqSgqGWRWVXRBxCQ/nVFZu0At7pxu3rJ7Q6q3wbnrsYgcWbKWsvKAtF4SwDELNDN9v+p5y4l5Z2WoWRzyDVdT7PFIjQsgkjqHJmVUVzJrOmVDaE1nHtEwtNuDDy41f+XDnj9063+w7L9bKkDWvxrpV4NLt8DK1VOX0evl5VRVqxmiN0zUu/pqzsyiypX7XYb4I2kCNrb8o+YsRSV6Kdi8Sbp33GqxYuiQLOV0055qJhana6gsOxJjueBcOFKnndc7a+2t/mtbWGreRKXq8FlziWyuvRyVCp2nEgtDjlRmvHlktgf7CZjs+DzxfRUFO9eK+ZxlVrvCrd+nlaJ4sRut6U/pe9xSETDXzW2MhKwYlU3ByPnt7VvdlxgrIsVL0/x95fxer25aVhcJPa72P8c651t61i/KkqiSC4cJEiH5ikEBFY1AJiFyocMOJUfyJJKSKBEnUYPCLoB6+EBMNBvXGgCZy44WaoCEiRo1aopJ4oRgSTzghJ1KFAlW79l5zvu/ovbfv4nla72Ouvatql58cvx1eWLXXmvP9GW8fvbefpz3tabgdB24Hm3L3suP+cgdsFfU9r6KY4eHxhvb4iGgHvDW875VX0J9VvBIFF2OmS+McQDUU3bXe6HRGkKreI3iOjJmqVUNrdOI0cA0PtxuuR8MtwMy8k8TTVfPNhnyN3lbGr3PaA2WwxrnD2LuXtRUDkYR9wyu14qEYbkfBw+2Ghxth6hxDkWca7qoV05jzVlAVwkWcAQIxSCgavfNeArAhNlwn6xjyNTreE5YMY18iEJTgkrZjCE7lfzSS/TTJwOTQuDW4iU3n14Mkh4FFtgGU+egzR3ESlUx2awR6bxiNfWyjNZJnDMBWsW0XXOqGu42TjylKzPEmPng+jw5EHziOwE3/fSePz9lJvfnmm/hNv+k34Y/+0T+Kb/iGb3jL77/v+74P3//934+/9bf+Fr7oi74If/bP/ll87dd+LX7qp34Kd3d3AIA/8Af+AH7u534OP/ZjP4bjOPBH/sgfwbd8y7fgh3/4hz+nazFzbKXgbt/x/O6Cu1KYUQEAAr3bTC1j2KR7bzA8Kxu2wjoVZLCqVAQ4tZPRW0FgT0JFJSW4j4GjHTjaQdHSWjkc7XzQ1bXuIlD0wX4fE4vQS057Veqcvw8a4ORnEYnI6BiyRYIzg2l1pv1rSrHP9SFhYeDxuMFi4LjeODEggGEUtXzsgXGj3EyxwF1veOO4oj2/YxRbK56BA+3KW3o3bMJC3Qy3YC3gCjXzeSVEl0zA03dKNlOSJIhZiQmU8CEwxUfzO+WIEZ7DBQNBivFE4FiHKop4kyHHCLvANOBtHyF2MqPHIwIBThDOYm+tq4cNAqU85YAG5/jwnjIbLyrO9wBu6v0a3dC8gwmJ6nR9cEx59sWE1mIQks3o2C0nns5LmNH8lIHi6iBDcTc2n16vN7T0Y+B+G2LmZZRvFmjtwJsPb2LbNriGVOZj2you96+g1A1l31FrRdkqumqH5XIHLxuub74JgJJbj7eGi3FGVl68l9XCQQPs80tNmTAnpGtWcLte8dA6br3j4XrgsR00biNwHYRhRyX5CZaTELKOM1aP2wgp6kOKIoKiY6DVga2wVlUc2CoDgLviaJfAi+OGN68HHm4N13ZgDOolhnI+kjEKIUoFIJ4Zdnbz+QBGYTgVq5cR4PPIFMaJwZu/DfU3DWxiEY5TtmQuNRxjdj2nM8hCFLfZdK4yE7KbCu7wEpwTtXf46IgeaKOhHwzszVYQ09qB4/aIdtxYux2DjdW1YtupnvKsVtzVDZscdwTQC2DVgEFyUG/AzfJb/DKN6vi6r/s6fN3Xfd3b/i4i8Ff+yl/Bd33Xd+H3/t7fCwD423/7b+MDH/gA/v7f//v4pm/6Jvzn//yf8aM/+qP4d//u3+G3/JbfAgD4q3/1r+L3/J7fg7/0l/4SPv/zP/8dX8sHX32G9736HHeVTb1bbnQ5JgvqBBPZHvBuqJa1JRPbTxGXjMTcAjqknEdlojMzTunOGUddUSL7qNSKZ4YhaIoG1acBH6LlZGMqmxfV2KvPSbyXdoXZVBrz7B0hc0pEAfXEhHFO0sS0dFpGkLTg4BycjKS5JhLFjAHbCzw6UoQVxUQ0aZSbMUaE1Q2iAAk+YB/IMEos3cbAwxi4Dnb1c1w35VV8ZBc8AEFNSJzc05YthiIiM5isfyU0iLkWZnW+NntPhur2hoxOHT7UjwJHG03Mu433SyymQ9F29sup9UjEDJMmGwMAd5cTBHzXWkjtoGCghFoDOg1jA/vY8j0BOqk9VsZ1OkkTHtK/FA/YqkvkfkiUJ4QAqmYD4zoeo8GbFAcARuIeaHHwu3XC1f/1v/4cHm83vPbae7GNMfukzOikAODWDrLXWsOIncxVAHfmOK43jKPhzg0PD48ot4q7AdzdkQW4m5PCzIIOoUPXiAvQMWVduY2B23HDdXS82ToejoOtD2Fs9K0bV8W4Tk0jbFgvsdk8Co1bmVqYYv76CIzB5uM22FJwKXXWbFKabCtESPa64347cG0HXhwFR+rdZVZl6WgCMMGvFjxXmR4NjgNakqsLDltOiu9VSobZYt0F579t1ZA1pJ7DDdMZquDqcl6z308ZXgT3gEEiBiKfhIOiyfuG6+MVjy8ehYRI/0W9Vvw6rGXDDKUW7HXD/X7Bs7LhYgVlOLwHdQuFSJgVWK0YKLhDQRvc9x3MAN/J439qTepnfuZn8LGPfQxf/dVfPX/22muv4Su+4ivw0Y9+FN/0Td+Ej370o3jve987HRQAfPVXfzXcHT/xEz+B3//7f/9b3vd6veJ6vc5/v/766wCA/+3+Du+9u6j5kovI6II3hQdbNzxStdmEmQoWhAzhbDIJqadDRWaAKfsQlsseGa91FqFNzYgjITWxCYdS8jZW97bejmm8AkuDTdZN6AYj1ERoa6PNOoFlNG6wWvRdh+jVNqPuADPJEob9suPoDXfbhluk7MmCGj0CxQeiNXgM3BUD1ADcEByNLgeSci0GNRwGx1W3CDz2gTdvDW8cDVcYUHf4vlPzDDZp4jn4z82waQR675QWemxt6pxRJNdXdNlTrDdm/dCTxGKuXgTexx6Brl4rmMZjO7M7mOkQFnRkIZ5NqDeRJB41aZXCqByXUQoHvxXBwV1kAioiMHjZjLOOcryEmaGDit1F8jwAcG0H9tHgZcyxJOmAbeJ9IRYbdO9jIgKAnE6A932cHJgbs2YVxJNifrQDL25vohZH7w2Pj1f80i/+Ej7xiU/gPa+9F3Aaf8+6nbFuAwO2uuFyuSxyhdQiegTqtmPbL7DjhmKUZXo8DmwI2NgQIlO4soMRwbXaNtU1DdE7jui4De6jN44Db7aGx8H+vPCCUnZCk9kRNQaNeOvs+SHeN8VMq0JLCEY159np3qem5tDgy96AWh3VK2EsYzZxVx2bX/AsKp5JTaS1jtZ41o8+pvo/DFPgN4JQXvQgbAyDh+yMnBMJBouZStOjhmQeUMGDJO8h2JZiiQkGK+cFNu+LOVtdENQvpJ+k0wlzuEbXZIRDFIjsQCuO+njFi+sVx3HD7dpoHzSkschWVi+4lIpdjMAa7MuEBTzZuaQg6qwWwDuVdyrr3JkgfLbH/1Qn9bGPfQwA8IEPfODJzz/wgQ/M333sYx/D+9///qcXUSve9773zee8/Pje7/1efPd3f/dbfv7MHTtANd/MMobN+k8bGmkcgzUi3URXeu+FDY1xilBDcBRVjH1G8ISdMJv0JtRimI15RXWRIkXjLIJTd43D3bp6kAKxog2IFisNL3cDxiCd3djbAUXTfM8F/YReHOqLoKI7HSfhLWBDwdEaanG0HthR5vwxbuqknw/4JsKFB3b1crmR1p+BQEA1JcFeCeNw/ozhaIE3XjziF1884hoGv1xQd+r3XS4X7Ps+x1dwnDYDiNvthofrDW+8eCQl28hmvL/cLeZky6GDBw9jrdRUKyzaminrgRhFwYwQAREuQjUPZWfuuF0PXFXraKpzBIyab3qNFcrzuAGl8ti01nC9cizFVgj57tuOSzHsngMPSQGuMSiqa31ClA/XA/u4AaUDW4PXoUJfqIaQ2SIjXgZfhOjmzxVJI9T8HARJ++j45Oufwm10PLu/sMYE4MXDC/zs//1/UR+vVjw+XnF9fMT9/TNK/yRB47TvzQx39/ewuqHsmxxKoFTD6Ad7fxqn7MbtClwf8TAGHg3od3fw97yCrV4UFCjgiGDduItNa0ZBY0lwvfFwxS8+vIlPPjziRe+IUmFeUbzLKTtH6ZQNe2XNJ3oj7FYGwtjHZmosSiX8GB3hA8Or4tchOHZQnPUIVAxsddHV3Rkwbs62goGBUcdsUemDf7811X9BAzyEWISnfQEAjslgS3ugwQk7BnA7+mwD4LnTPR7KntrBOmvhVIetUlHGcp/JsUKIxRgdMU4ZVwBRC6z7VEPHICqwlYK6VTx/fk849XbDm2++iV/6pU/g8eEB1oLTi++eYd92TUYmk3g3tWiQ7sx9AN7bFjxPj7eGh9aYFXcOfZzQ42d5vCvYfd/5nd+J7/iO75j/fv311/EFX/AFZNpF4qxi84X+ZDOaiAINY0JqliFrhBz9mKKpQKh7Gtrc7JegwZNumufgiGS6qe4Bzwl94MuzkU3EhOD4j5niK+pNJXU6KUVOnmPG5cSc0XBkDcKSYk+jldJAs5aRmZg4oB7svt8iMNqYUJqBTK1aCqoVPb8jeqPSAlxKEmQisWjfGNkxcFJzY0FYwaUCl61hLweK3RC943a94doH3mgN5Xrj6AMQcqg5BVSw2yGGY9aRrHc8PD7Oxs/WGq6PV9yOA1Y55K44o8EtmYyJiQ8WsXMUQUrAPJ1rJUegoMQ3KksAhmfqHxsD0xh1AOiebAz4TniqgUoW3hrsGPBB1qkP4GKOOweelYJX9zL3KyNORfa9A7N8k/1ls2CD9Vf2hw1b5JqQ5tEkmfSGT3zyE7jerqj7zkxdzud2u+GTr7+OrTgiBm63K549f4bnz17B/f0damVWm8+PMfCp11/HixcN9e4OZb9on5EI0PuBzQxQ71kZHXdSW7m/u0O9bLBCaBSF686m0goOPWzofSAHEbJ1o+HFiwd84vXX8QtvvoFPXm+w7QL46vLpLdgzeLnD3eUO+7ahFmZAozfWPKvnaqo1oSurdmAjPD9CSuYeOJT9OoB66P5BDdtFJCMnqzSz9pSsamXgKI5bb3i8HrCjow9MbcjZGxhswThax+124NY6rrcbg4XbjTOyzDT5mK0QhgDagdE4ZmXfKu4lrl3vLkh19FBmu5xc2pkxWYH96HSSghpdtsVKEcWckH7ZNlxeeRXP6o7b9coZZzBs2zZ7BsmCriiFhojOtiO6PdFHvLaG68Ga3iG7zKT3fwHc98EPfhAA8PGPfxy/+lf/6vnzj3/84/jSL/3S+Zyf//mff/K61hp+8Rd/cb7+5cflcsHlcnnrLzKjUKFiIAvMKkAH59x0IzU53FRkVaQoeu4YSbVctQ83ILRhMJS2A4hiZ5uBZNyFBXr2DCF/z89KGmjJwnhRQbKtiDihr4RxkjKeYz08a1aQ2kMM4FTzmmMmgJnFjVg01RpZawHKtgrYKU5aLKVO2FcSQfpwGYICkTJHZEkaFiQabcAKs6LdHc+3Hcc9n3cPRm+9VHTN55maa70ToTc6OSuGzYG7wKxLBSD2Uzpex93dHfbLPXKQZC1Um8hO99D1x2AdsdYC3zJ7yygyH2QH+jlrlCmspSKGpHpax20MXJ2aeLZVHBh4cX3EtXeSUMaYjZbeOZ3WQYNR3bFvFZetIin8W9lgw0S1JyXcFPp7PUGcmUKpRuHuc/1CQZWB4p7X2xXXhwe8ePMF9sszXPadoy6ENNRa8fz5c0bgBmZQdUMtG0qhCnothQGg9sft4YbtsmPgBu88r1utGK2z9rEV+MZgzjvFaO+L49ndHd972wi3xtAZAKwYEAYvyhjVn5hBn2cPYfDcGKRq3skIfLwd6GEo5QGXyz3u7+9xd7lg9AaMTqeyVVQHPAZ6ozo95cCGehapAh/hvIetC87WrKVOuHJzUwPzjufVcVFzN1tHHGFDUK9YsDXQfUMTmnNtHS9aw4s33sCnHh7weBy4tsbSbhha4+yseZtL4Zl00ro3B7yC5IsYkujin815Jk0ljJK2AuxRIszG9oGh9pYxgjqDUs5JI+QS0i1ZU4fhftvxbLsTZArMQbKzty0AczSQgXl0Bh10TAd/poniTASS8Uyh4nfy+J/qpL7oi74IH/zgB/HjP/7j0ym9/vrr+Imf+Al867d+KwDgQx/6ED7xiU/gJ3/yJ/FlX/ZlAIB/+k//KcYY+Iqv+IrP6fNS42yKsMJUFFx8/j4EjjhZdbTBqwM+sxpmFnI6ajA0w2yCtVLmIDNAxjkWLMKf8QaOyP6fxVgqqp8YJOUy6BhCzomoXRbDU6iWm5jOIOmkUn6AOBLCBOiknFmjqNoAYQzCc5gNiSPZYgOkxUKNdjYIkYhosZmrt6VAGplICjgdC5DQVASvf3cOBbyrjucX1mdQ2YsWZePn91UfbI3RVG/rAHGsuWNkplpTqVs1MWc0PZRR1pIaecL9jY2SSRvPtTRlrDbXdjWJAuBrktUXtgIDBTlXDDwadQXDSAzZLjuuo1G9XQV7C9oFdEcFG0YvQZbodmJqbqWgdKNgaojwYcnQyhNsLD5DkT9/Qq281ud02N4b1Umuj3AYLncXXC47trLNEQsAndT9/XNmUiNwuVw0r6yilA3FK9wKYhxzD99f7nC5u8cojv1yB1zAnjaxa3PUfAHg7UAdgfvieL5V3FXNEjPjZGceAI4dUYZbqhQdEIjoqAY8v+z4vOevoHjB3fWGwwqGFQwv6GF47INjQxozsDePA2/cCF2OPrRf2VJRRkwJLbOYDqo6CQftOFgWEM08eif7sx2494L7WvB8r3jl7g63reK+cuTMVlyNwWQuVnOUfcdWt6lL14NO6v5Scdkcz+82XI+Gx+MGwFHgVAwfav4N7tN9kyMsZIuWzJYDIjzRGVXVY91y6CiAYH03xWJD+8VGsGesD4QR0m7o0tIbsMYJvOZkKbOPq2KvlSWRbBPpA7CurL7jGhxN0yRCTbHaNsd3zGkAUxaMweA7fXzOTuqNN97Af/kv/2X++2d+5mfwH/7Df8D73vc+fOEXfiG+/du/HX/hL/wF/Lpf9+smBf3zP//zZy/VF3/xF+N3/+7fjT/+x/84/sbf+Bs4jgMf+chH8E3f9E2fE7MPECdiNqRhasmlBebkyUhSFNPydFqZNRmNwBzjADoqG31pwPlSYJggTMIvsPmPFMqc/5YhFm8IqQ8YCKomGyEXC2hODF+rZG9mbOZlGtLM+gCsm24pYaJrZzVW16f6e7J0bFY6CFfIOVewGZPYNqP/zTAFaTGCzYZ6qfIwZbLB9Qad8V4Nz/aKjoEbSEU/wH6YMYJN0cpaqg8gHL2okXU6QINb1Vj2dFJDESdn5LAWlnXCzDg1INLXuiDyesdc1+IZEJBunQoIid27MdLvAWbgRTI9YP9XA7P055eKFoR5qDbhQO9wpsvMmODYEbh34M6X+6kypFAg4x5zX6mMNskL3GW2mlQFTTLQUG2nACgUA72UHXu5cOQIUsqVX9EG36fWDQCzt1o2noMRaqSFAjfH3X7HrG/fmBnVqrEsRRNgB8bBvhmy+Bz3nnOeDN46zApq9ZmNF6OSQdFn5L2HOy7VEZed/WxecdkbHkfg2gM3GBoMXgK7AbDCDLI1HE1DFWOISAMySgevb8QNo3cc0cjOHWxGHSNwHSTtjBR/HoEyJNEUAxYdiBsHRrrjbmMT8K5hqltIrkt7dy9pY8iOfBYFr9xt6K89ZxmidXSRPZqEdPn5eb4YkrjOdLatTeaykA2q2RNJ4Kwsm+zfGJpfFRAiQBLXGMQPhqtgp16/Pri39U/kgNNbO6bqTKJNiRe1TrmzdPItKHfWB1XOh5kyQ8dSvCg6b+/Mzn/OTurf//t/j9/xO37H/HfWir75m78ZP/RDP4Q/9af+FN588018y7d8Cz7xiU/gt/2234Yf/dEfnT1SAPB3/s7fwUc+8hH8rt/1u2Yz7/d///d/rpcy5/BhHmFDKoBlI7vr5nKGECEb9ghUpM4ckIwxp3gsWOAVwrIcgM02vpn5TKsNTNZRqrqwL4gHs2aNKTeZMT3v872eOpz5HcHsYKRD0PfLXqiUvYe+Y/bMCNUQjKjDY7lV8udOxpoyjgKK4VbDZOBMORucoJi81nTIJpV3yDEHIZOCQJUILoawcAAWYuSBLCEvvCpCfOlI0qm6RGUJFfRiGI3G3T2VpsV2woBJESI9/Kz96Y/NTIX6hhnk5O8DbMR0y0GFWm+QsecB1EF+FRI1dUfzXaMJBvF9ZWmcl0T9vt0M9zWDEZW1igPGiNm8i9DiKxDJvQRbWRajDvaAgWy5um2ovlEaywu8JwmnzMxXGwa1bKibo9Ztqqso3yQEFybSDi9j39jkbnVTdG8SIuZ64NZwIW0OJQZ2E+XcOLaljEAdAQ8XdCdFGCERox1I3Qw6P8ceBXe1IHadlaMjommPd1zMcbFCCMkIi3V3xGXD0Si348XUhgL02yGFg5jBCEINuq2xMb+c2KtjYINRkxGhqdepyM8+Kz/ILhwjywyqZxeH2RBBi1Tw0FoZKpXW7zb0wyXquqEHJZ5663IyA2OkLYjZZA7ZA7PTGRFkPowDQ4924Dg6bofGhgTrql3N/mM0TMkpMwW7dFqBHJKo8zyELCHl0HjeExEaiWQN1n4H6JhyInQG2amqsyj4koJ6B4/P2Ul91Vd91RMj+vLDzPA93/M9+J7v+Z5P+5z3ve99n3Pj7ts+vMC9zmjDnTRpspDpbCpiCrhWFQibbjz0O5iysogpW+KKcs0WWSGEjScFlGhXTDp1RuFFfQuJFRfLG5yZmMBJE7liZn+xspRM/061pNwMoR2fA+2gjKt1MotM8B6cs18SCjWQfLB5laNR/QQG6421JyPMtymy30vhyApR412ROVUj1q0IAzoMFh2lFlwAhHM+Tx2BPQp6J8zqnvORGKUy2xV9PsqEAgMKBBBTu7CaY3CeiKDHmSvCvRLmFM5OfUR1++ueZ00sWYoJCdIBeMY7vBUDagcIAE1sMeccJoAsLzUDewQ2IyycXS4YIsxMJ+Uos5ERAA4UuwBe4X6okGxZftNj7TnAZpRLeruhBgcCDq+MikqVM+am5tgYTHZfqRVeBQ05ENFnT5R75dgOZXf52a/sO4Bg9nB/Rykcc9LvzdjkC6DUAh8D96Xg4lRw2MxwqRV7pQBrNccYDa11jB5k446BoIw2ipMl6Ykv+3WSDvrgnuumrAHUiOzBHjcOIQ1EtUlu0iYDakWMFb2bzlE7DtxuN2Uc6rlqByw41r7C8KxueLbvHOBZBqFATZUeY6Bb4JrTuKOxNFAcLkbcbIweibpwYjHCEZ4B5UAJqmxEDPRgs+3oQxqMtEVECjK4tuUc+sB1kD33eLvhejtwvR3oTWQfK7M9p8dg025P7UGfSM5EcTKxBQOKqudk7XcKzWoWWUAaiq0pezCUUrnc7jAJb3tAzYurQf+zPd4V7L5P+wgVmi2yG4JQj5LlLXxmGlRBVoPmkKMwNs+WrDWFyAOWtSSBWsqiMqMA6GxGFpdtAmgniCmmowlI+BSg0UCOwlip84hFIsjPhww1IqOVU79MH4LrIi8IOTQRMPVSKcKKEOnCpsPmJKX8PNWsAGRfqWHMURI1Ha2cLU8jpkM1MHoiU4iyN2y3BNANjo4yCPtBRefMSRM+DWDWjfjWCcfq+wgQMjDVI9subalN9p/r4CapgqU8QoDp6FPOKYYhVQK4Hfi9cnovi+qL1EB69pjwI7Res20haS8zu6WD2pwOanex5mxl4rUYPDQFOMZ0PgsUnLf0yV8p9CvSxdx3AwDVD7yYBJehFU5H7jCkPh/7grgeUi8AM2TWVPI7DNxd7lGrY1wfqDoSAZej2cwnTftSCmw0FCM9eqsFd/uGrRYgOucbxVKTD0XjIz/PWex3Y//f0LX0GBidY2rCgOEa/TJIIS9SY7BCiD7VG6AMPIOJDDA58sOwRcFm26y1Rh8YvaomC1xKxX3dcLdV9sfZwOjMvIbqXMjvEKzHxOhAYw25HEU1btmCzOLA+mee5XRkEyFJQE373xK/VPCZDqqNgVtvuN4oGfXixnYKahyqDuRVzD0GMDFcDEM6tzk4VkEztQQ1xDMVQIqjVA43TSULM9a0vVXU1mG3Kz8vTjCfexbPOZByQPWw/G6f/fGudlJNqbcppUbIwwsSYUHRAGsYjVnTULaU7ibAOpGLNnvuE1lRNA3+U/F9vnr5CJlQSyhtmZnMnfKlhIx9vnY+b0JLTyG/rG24mHzpGJbqAjexy4mx/k7niISKICclY51Ti83U3BVrLlfRulVFTMXTQdGh4ck3W//M/ikyx5bBtpFAn4J9pkiAMd89wwaZBc5aTUKgqDPiz141N59rkw73PA4CaagsV+zsFnOdMO/b0H3nAdJCKPpN2NFdjmEQGqU6QMzoMxSRa/Xl6JmZ7tU1ToYLVt04lycwx7g8gSe1trPWOdc590us+w9gRNbVaIzZn0P46Uz7Z1eCKYZKognHqhsGaiVRJm9tDeDiYvwZG5ZjDGx3G6IP1BgY7cBeCu42km0u7nh+t3MvOYOworU7pHLRhyCuhIINqDNbIAS61cD9vhHlwLkVgBp81+MqjUayd2uhAyTVPNsPBiBYr0swFpDcmAKIVJqoW8FW7nAnYkgGaCs7LejF0JvDujOD6FTo6MPQpb2ZepluVKJPJzUGm2O5NZ2sYbHuEopOGjmzGy5g+CIARe5nI4R36w0PtytePFyngnsEEO4kpZQNXjcygQejRUNQt1o1cWCJ4GbNHqYeL2ddxUqB1YKcxgsE3NkKMAabswGjPqJOxZQ9yzM1g4fTfz/L493tpKSyXUs2xc4YHG7QvKaAGRWrrS21hMjQHVlABwBTJ3hWKfU7ZTbFIBqlXnrqZUnzlzBgMrjSNBIKODm5mDZnPtLRjWCdIW/icqjrT0bz+QfAzNzm90una+u7DEg81E0ilB0GyccYldG3UrE7sCmLItst5pUsvYrMTvKnAehg5wiCYoR5jqNP2G3IUIQK5jEPJaE5S+dvNPpdMFvCoYtZuSj72ShKZeoyHVbe2tQCJAzEv/tpfcMg43Kq6yFrdssZhgWKEVZMFeg0QMl28JADc44MT6rw5qtBE1jBzDJadPJcRzut8dvvg5fvv2u9EIzykxh0DqtcRBQ2nbOo9nZ7KSJlkcjWLINqAbVUjjUZnQMdjVmMb47n93e4SKLsrqrnLwbnGLnBnFnD8JXlHQ0a58Iax7XfYAZc9p2ZSC145lQof8+zuyl9RQHjgevtQqrzwXH1MDYD9wBGY+RujU7K9CeD0eLUN6yFWd/uCibMcanbLCNk/1Yf1AtsQ5T2o8/6T1Mtm6gI1dnz/E0ijq49IgWWIaq/DHnnuiLGVEfPWWqBZYtoWzKrcqEB/Myjqe8shQiCAWteT05s6BGI4nBXS4QJ5VEmSAJNSOZN/WBm6jXV2Qw62uKs2V0iW0w4462NSUNTFnuCW7GC7M/2eFc7qevRcNMYagQL/gCQQ9HmpMiqQV44YJ2MGUbwisqHDrkvhYk06mlA09jnhEwqF6SxNsknLZiKB8GmrclIYoqBWkrbPL1RjGRO2VJeUxqRMeb7P6W/P/13RjpIphaS8kknUHVAMkLdCg9ndaPsvuoJWfBMGDCzhKT9j2kC5Y5d9G0Ittn4nocNVGNXfusNPQzdBHuqxuBhq36vTC9hIZOqPddjZRw5e8rc0GJMWCRCNa50qE8McGafgoPHQB98TcClCReznypwymxCwVBopIpovWTQ2azfbcVnP8vm/DkNQV/eRs4y8cczJf5Jxp6ZFEjPny/FWw+6exqCmHJcSUle959KHWOM2VCcTr21gyzBbHI2w92lYtxuclKEA0sBNhuIOHBXHHfbjrutkGpdKwdHjpjvn43UPdj4fDsaHm8HDqk2wCjsOlqDGSRwaux1KmxuzXEqZhzBwzE8d7gd7IE6jobH6w1XO9D7wK2pTlVIKhjVMUaZ61TUZFy8YC8cWVO9UH9OhpqTpClye+0N106Y7HYcuN1IJe899B0Ii2XDOJMg3qhiRgeg9hAGcaqZZn/cJMbQIQSY1oSksOAcLR9mar517KVibDtu24109QqEBorMpt0YwHCYxyT6rM2sjAkBNjuKXhjMqlPercepz6pw3JBrnHwZKpF4gRe+Jpoc4VgO6Rj9CTqSjfSf7fGudlJ9cPFa51j0sFNhGRloMAK/FIehopbA0UmdZHQkj++KdpzsJkbdlMlPeMZALTlMGjtz8Aj2iKTLYl9N/gNTAghjOTXDUwP4dhBOQmWJrSfksIzt2biFRG1XVvW0rjHzNNJyDRPeqwB2N0aSqhlt7qiqw7muhyw9n03DhMdUrNbBs0HHmI69QJu3SqevOo7G5snWeZD7qUbHSjK/bs6TCqgG52U6KBaXO/og827ViWxlGeChnuuJhANp9OWj6ZxmMe5pRejcn4XI/js6No5tTAeezokNpJdiJAsUOqlsyn66f0kJJnlF6yyCSuoT5v0dMzM8fx/MSBmA6nLcl2YF7kDrMZW09fRZQHc3iXyqoN5p3MzWdUYM9Nsj7uodtlpx3B7IJqwVVgcuteLVux13W1XmyDpb0Vr24LC83oJNrLcDt9bw+HijEwmg94GjXee+BWLCb27ArqGKtVL1IXtsigz73cURsaHVhn3b0A5ObG5Hl67eaR1laAFmaWaUSaslsDlHTLiRQdrGYAN363joB65Hx2MLtB7ze1yPxprZ0IlIJwjWdyTJj1SVYZ2TQQ4zKk50NnA45eaOPYkXJxQgJChghdebdWRzx6VUxN0zGCp2ESZuQ2NMIqaoMHlBLp1OKdicUZG5lwY/X5kwM7CODtLL2QI4ACtw2QjTG8Ro+i6EuYf2eQhSTSdF8tSvACellUGsf5yczHokVfwiIcQ7GI7elaYPMYMEgXnCR6z/eKbWaTlP2YrLGeVAOQpn0vgxiueBz4I+03SfG3TVWzDf90wWMF131pamkfJMN+Jk94bw6lwawVPzOSur8hiC99IxKdp3VyZA52W6joTXSMPn2vTpqLLoPfMCEkqMEGHGbKVokm04RTwVJExtxbGaoNPppkTNHBt+gq9ydHji92sfAKsGJ0JKZiIJ+c27aStAyIWb/w5AsjKr5sMFIGvTKAljPLSbEc6rzv6yS4Ukm1IomK9lcKP9IgVwdEKuAosEi5wzqdwSLx3qmLt+7R2RZfL/xmhviVh7P9RL5jRc5nh8fMQbb7yB589foaJF7jcYSQNmKNFJxe4DuxueXTbcbTuebRsbk4tjU1GdSQARhsfHK25H43Ts2xVvvHjA0Yc0ErlDuu7NcuYdZTjhr8cx1To2SXiR4Xbe/zp3KMy8fEPsguYE1Q0NQM2AIwMbQmrMdHtbcmqPR8PjcXAQYm+6ZkKVzQti03lWPya/gshJljRu1pzidI7mVwzW1Y9O9l5xYC+YrRrMsrhPXfe3dbL8GE/zXm+loNxdsO077o6GN958IMPvoGNlsNeF3LjOMMutI88VFtlnBvgmIoXZPIeDyCnbSCwQclScEpB2CSJohYI3ZorGTOBk197Z413tpDJ1JqMJkqgX/0wUUxozbkAaEB6c6sSgWxto3dEhFWLBgCa2XeYfCXEhCQGyHBa54Eksl3GDoEThz7CU7THAit4j5vtOA5s1JOR72slgyajmLn9ipBQVW5I3lE1MmIqfUWDTmGzu2FxZk+jq3Mb89Pzf/PSOHN6n+VhaL6IG6nwPTEr3sE6WkIxm6GB5qSg1FUFkQLp01EKYftffAQQKo7gx5joXMwwrC66QvSI78NTNllmoHp9+PEAh3JKZs6WRXoFJkgBSWDeduoMCvQmPFgN2Db0jXRfaQ8lRnDcbPRgw7T6wKR0PUCfyKSx43vfJAFzOKw2gae/DCLu21lGqWhWAST2HsVbkBtxuj/jv//2/AQCeP38FpVTUU9ZWg6r4d/uOZ5cLEGxNuLtcJHDqomQP9B4wFHipJEZEx4vbgYfrgU89PuLhdlNQaIBzjA7MgcqG98zKoSDISgFi4KF3vDiuE81IQk8a6pzJ5VgpskFQmbG1Io0+8cx5opiBDkKI1+sNtzZwPQ68eb1x6OFxoygqcJqPxkoulRgC4zjQ+qCsVC2nc8eeqYUKuLJfTG81M7wIwmSt4+Ym6L1iLxxMyr3dJ7MwxmBLgVO53VzOC3e42ytePDyi2E02Tk3KpYLkb6ntTOcEzGZ3nLLZWqaT8ghgdDKla0GMImIF9/pmwIjKQZtqA8l1MKO0Wu7cFS5+9se72kklwLYQrsxK1GgmbJiss1Bzo7TZzFArs4gxqqAX4uOtDxxB5WE6Jop5Pk1ZY9ah3JDEVrhMUZdxI5W9nBxGGj2bd8nUCHlOAGeR1FSPT2d3hgNxvtFkI5qiGDqlLMjb/G8tjotUlKvRyJJCHKLuL5iJ0TQNT4CimD0w4Y1OwJxRkg1JCQFuARN1zm1MQ5LXQPaWzUbnKAaUQMSK4LvqFz1CVOPAzaQKMhgFZszH+74YRDQGTCs5rTUN9GpJAHA6nCCsodEM877knpL1L2aog20KpSTzC6hG0VHWpCZqCA0sWK0B5hix+ndmlH802BaoG282E6ITDCMnh8hahwKRs79TGk0HS6LJ0RqJHb6+a+8NA13afzccx4EXb77Acdzwnve8xiJ9jAmpmQGv7DsuXjkB++5Ojena95NsklJEXPvrceDoAy9uB148HvzvwTEcVnfVRgodtb7zENMtRCDhfQmQS2CwYHA3bl2zjQiTWoAzoWqZ9yNTFiIZpFiXk2PPMgCPN+ntx9Hw8HjD43HgjesNj40zp26DgRIjlMI9BZIKOgy3frBWqLlqPcb8LJgyltkYfrJTmU2owXlIVqgHtfWaGxt6QftCdIP1sqYAfAOlrkphPa0UBuI3kUDuLxeJ2Kr+N+jg+ojl6IVyIAr3VLYDpISU7BpErECQun5IPhcatRMgUaTWik1B8dQKZOQP66qrmq36yGd5vKudlJ2jdGTJJ51Tn9EJggXUEgzzU2TUjY2XkTBNMI1v3lF7xwGNasAyasxWUgEh1Icih4Jk1mX7LMQWZJYn/GnWHpBHJgP+l1Lg2SNhySh8qR4lbDuzqzRcpoNrEKXcuUbVl9Yah8HJwE4Hkm6N8T4PS0zHfZPGWANHoc9gUNmlJ3TiNumyFApVo6B+VqLM52R2GWRyqz2AKUtCrukYL8HGz9Zjth+wppeD/mwGJzlDiuQAOYnRxa7LXqrsvQKSivvU+at24T6j9ztP1XifRrIYJMKbMKnJQa94MbPyJEUEgIfrDePRMG43FG94Fpgq/bVspyBGO2ve53PVLPemqReO7rr3jk+9+cast6aTevHiBT72sf+KvVadFYZXd/f32LYNpbDRulzKdNLvub+HdxMsXHDZNzHVRFuemYscaU8nGDhuDa0NXI8GrxX7trFDz03zyTRTCYzqTcK6R6e2H0AyhYuS7gCbnweVKm5HwzhuuJaOcbfjUpnplOxGhejYqTcUMfuAEmEBRKQaQO+B2zHweOu49WBTbfYKWcHhmD1R2VjvUhiZ0NmIGTkbgGxS4I90lj13BTMxqB4WZsheanMSilrwfrqFzivX6xgDo93QRqeWotiAW3G4USWEfWaDqutXNvpSK1DBrnNeWLYoQN+JwQLYVK06M4aYmaMpZefzhmUt0zU+pwhRMtYbO5mFPUW6g9/tV0QmtZWicd3ScwgVjkcAjVTK7LTPZsfhZLKFcQNnZhVmsCEoajhqp2p17zmMbMgwsoZCZhwziexbYIMlZVTKApwIHfZVJ+BejUSq+NAGZ4S1HFE6gXQ0ySzLgXpnoFH9yYI3aFTplHLSKAekVUsnEnAh4MXoKNzKFKrtMBzRcR0D185hgNkBNJSxJHnCEhIYgRKYEIENggtuRnq5k0BUTf1pIh8U4dkzWjTmpVwHNu+24RgeaM4srqeRG5r0GeyjyaGQXUK2YefjEEvMdBoyUE4JLqWRHJTHfh2Xs91KxQ6prrvzIA9KQEHOIeeB5b0xY8M44d/ALecnReDh1tGOht4a3uPKRopLqDbzsITvToFS7wpsfBrbQMzZZrCBT774JD7xxut4/sprlEbSGlxvj3jzxRvw51Q/9+K4v3uGy36Hy+VOCusxe4kAZjOX/YJSK8kPx4EYA7UUjCbVggBG0EAlZb+jU2LJHHe14tEwC/JDCh+31gjdBkQIKBxXgYoR7GtqESiDBq540fk9nZvBfXn0QK3AkBPaisPHQLsCbRxUrEiUYDSqw2d27TaVGQKVZ6joDDpgRbqJrs8bTaQdanMu1IPrz0Bx1aJnr5UXBtDQqBVlvnQDyYjUfYejm+m8U10jFPgUoS9ItGg0RHHEqKLXU3IsArgUMhv7VtDGRrp8LEe71Q1Urxkzi4rAHAhJu0T1ktENEQUjmgKcIfUWCDEqHJHiFV4rbj2A42BWr6kTSQTp7X/BqI7/px9e2MeSdNk5dnwMov8GLLXdFYGSdi5ditERHkB23JxYM9UN3VdnPKEnRvC9dzTd065xC1nw3ATdZfSUqgRPiQ6A6HhyLOo/iMzEMDO1zGxCWRKA6WSKKV23hBeYtaQTqKXMqb+lZOkUMwtzYfZcQ2Uuo+PoNKi3zubAayNbaAomCvIY4ObM2CBp1F2fSSo6mYJhARvGKNeADhWMjUrVEIRUiotsQLq5W47p4vh1T9gFipVHTCcwRYIjBTW55jLxK+M0aMQF946LNOOCIw12Ut/A1LibFa9T5DyV4Z2UZo6fyBstZYAepDG3NrfBrUuJINmfuucsc+a+hTJ3zAGXDJwOpAqG6b4FgOO44Y2H1/GpT30SpW6QGODsJ3v27B6f93mfp142qpqPHrjsdzDL/qmqLJULfD0a2vEoiS9Ha23Vgrzism90HLDZq2QSn922DQMNV43QSHJRqNC+l50MQPPZ4AsjLJn3AIPtHtEHzAeaFaR4814rm1YxqDnpKU7MmxxGpCSMIzXa6IQNC2neukW8f/onWZpl3v/EcFt0oR0BDFdtT6w9kSeorF6mNFcGaOfBqqMQvpxos4JcHsFIpE07FtOW5Hy0c90azlpPE3rUe6CUSphdOWpO6wY0ebj6qoOpTSF6e0tmU7Q2aXsCSY4A5ayiTT3AAB2P+coS3al9mNODAdpNiB073iHe9652UjmKPCG27NRf4xcYwXB5BmY1YG58bYYYkPLckzQ9GWbVmIZz8J+glSiCvE50dlGpE6LKiCTJF7kL+C96JFsWFMkAwumPy3NFvoXxQFOuqEgdQDW2kygo85BUviD8B2H9s6g7Yb7ciHTERxukCR8Dt3YQ9rCYmHw6Ko8MApKNNta1I2E3raU5x7ADGFnvUGQ6QGqrhWjxfXA+nhxuegrPOTtaw8whq+f1P6X+R8IvL0NjWNk18j6LdTShNK27Zaai4Ccm1JOMLt3D03VCcFaAkXULNoFee8PD0ZDwcYeapWuB1wLzNtsIFpyHBd8HpupIz9rCkWw19kI9Pj7g8fYCgcB+ueByuSOlGbmvnZORw1Drhn3bYTs1MEvhHwRnRqVRfbgeuL54E/vdZUJZWym47BdANRIrjrJR3gmGOS/KSsBawIL3NIlJ1Qg495zRKbh1sjULNRyH6qa9a6gibNLETdT+YQOb8WxWw1SKcAMHU1bnID7BkwiT8KyiQmO2uldH3wta7yKxKBNmhAeXEy4wtOqTNERkIZsyILhuRaMkEok9ODoQzJDG6AuFmV3eJ5p8ZC8g6fYMWjmQtBnltjb1FmaQ2HuHtaw3ZfP4sj0ZcE8z2Ps8JzkxHLpeQO0eWaOCjnae7TAYUseS5yxGCA4kPEr9ZE20dkdrJPN064h2jtg//eNd7aQ2saugBldDarZhDXqb0W5Gpgs7TXjNtPCmAyY0nsCWzYoUN19gOoLIzvlaFjsnkjWTn4sn1OqshWBuXDmhfD6yYkHHmL0N2fxmMtwp05/jDwwcb71CsHR+OeMoxSplS81EQdYGjpWJHL0Tw1b2NEDyh5eiiFRG1BNqsiUvBGCOSfGYDnFkpGUmCDAdbmab+d9gHxc6isWsadViE5qFVixEB8+IP8kmpyt5mrlOX7UaZhPKOMtNxemFkazDzobRPtpk3qVqwTQErsjdVrP4MUhlvnau52Mkv89gdZNETUb0+fcz40pvmNeuz5lpd6HhO1pDNOaWl7s77HtFLZfJXMu3K16A4dgvO0rZ0PvAvldQLgxox6r/wHhG3nx8xKvP34M+OupF+m8hqNeMeyRuGBi42zlaHAFBzORV97Zh9Bt7d1qjwKsPbHXDZoXzh1pnzdLovMcIWOWYlHa0KQproKSUIaQ9R/3Nzcsis3g6BTw9l4PSRDECW1QMT2NuuGwVa9pxYDxccRwdxwFYVe+SlwmnJVzG805bkEHyhPdCezEWktMBpEJlnO57ChznOYmxejS7MnWMUP9TRxQHQvp4CjgJP6/eRWpFjrmvhjKo3F8j1gHJUR5ABnB+CrLx9HVDTMBSJbSSDjttT5BtCBOhhshUc6C5o3e2XryTx7vaSVUnqyr9fASjloRBzjp853g6lSRgaQwyTTk5Mj6RjDD9NM4MsPmQg8mQP4DQRWVmlGM2ZrQzEs4DnpjFU5STqZN5ZhQZ6QtCM5sRm1nWgGJ+x3Uwu2ijVHwm3FU0JhyQu0AkL1FwTFLBIXJKeEFOBiYpwWYElplYZn2mPA4a1DYCU0A0M1le56Bzcq63m5p/EWJMkg1EJXeH9xz2lnUfCAJdgQZi9WbZOXudGZ7uWSznxo7+DCIwWaEcvSEYU062jWNmYrVUGDhROARPZuTdjbJdt97w0A88Hg1HDxyZFQPKLCVsmo7pjPOcHrMlSHUVK2RC8iYwUg0n3AVBLo4N1eocvQHwu5GBtaufzYGgAoU760F5RgwA3PH8+XPs245t43ibEMNwtK4RMIB7QQzDofEVxcjyutt37DVQrWArFUfveLjd0KHMZhyIMMougQFZH0MZmVhvSKdjU3uvhBqGiwHOOlg6KAwGFAE1CqvuN6RuEX1gFO7NWgsQhm1TaOqcYdZHY6YarBtGY50sAMK62v/I2vSIec88DwQjJ2SfXpIJ0t6YYPq83ed+wGzmznIFMy420MIZIXSQdNJ13ggXLuM0M728xtOjz21G+Dgp8l12I0J09zwvqv2Ovn4GGFxBXOppMoYyjTbJgJFjgDoIpfpWMIrD/leMj/9/+mFdU1BT8FBp0WySNTXUqSuaRncBH5lszF6nWI5g3mpb4kWzxmRp9CCNvRV9n2tLAFYfCIMpjd1wNR4uyrwuCbMHJubHzzpIPs31mpk4WDbOKjqLmJ3i0fvKBCSwWco2vydyYwt+6FJkpowOneNQM16AkThnKgbOdj/XY6IW+ZUyUwxMx7a0UvQ9RPjwERghpXVFzBxXHxxjD9XaUv09M1Jk9rtglnTqqy/pKcSXNyQjQNJy2StDozY0PTVHDmhMgft03Ctw4O+sMMscY6DZ0OA3DoW7qrbXTpFpyICk487FHBGzAZdrYNNJUVUlpxazRlbKBnhFVI3m0FQ1KlkrZFDAtu87XnnlPZOoEgOzwTUJK9u24XrcZmB3d9mwl4J931bg1wfq3QUAp+NiSPnFDcetE74drI8Uc9xtG4rTAV0ulVnl7YrbcaAHR5lwQ6unp9sSxY2YQeWAo0smqHre9azJcr+H2XS2x8HR7O3ouN0OQlGhFgejqK9VR5TQwWLQcHe3qemVlddra2i3BtTKUQs6D1N9ZYrD+sycCCuuLC6z92EMaqcKeqz9kGd1UtbV/uLKbMyHdgTFXeGccWVB5znjbKy9FDmLKq8lSRihvseugEzZ/8iWgBmOrTO+GNNPfx4jeyLZxD4GUJwNyjySQ/1U6mP1jOY/++Nd7aQKBPdNr6L5KglHqJ/gSYaSDgyKwhNyiwSu+DzWS/TzUxaDSNICn5kDyBxZv9AjHZlltM/xGSJnnmAqSfJg5VTzGgyYjD6pRaTRSofI19l8bW7EjMytFPhYsByssxfGkrKczkqOI3MlZWxDrwxlSIoDGOHKyA5bNa+8evOYEF5GA2foINc1H8w4df35XcScK8oUC4Ch/26zqZrfv6tZ8LwXkNkyFvyaGVeEst78fKWoBtaI4Ib7es/v7UXZWxqLc5oTgpbX90BmkqDW4FYrusZOJK0aCNHWKeVFNqaDlJPcDaeAPPRNg9BSybsTPvdLKCCK+XINPjzBoEC2ChS4M4uYlOPoMEu5nPW4HQeiMfgqpU7I20FywbZRpbwfRniNlo6ff9Bhbjsz9/2yUc+xd2xb9nLxvLbbAbijMaXRYMSAFc4/o+JCQwTfyyXtM3pfOplR+W9z9N6xbRvcx6zZxlhOoJOfDbOBW9M8LHO4d1y2qsytzT+344oYjbW3unFQJWwiIzzoorrbAu6TgUoDP2SDJOr7xJnEmvulNheb+01/Ers1ZTfR4OB+DYgAlE8/BVEJ8zXJJR1ShydrNlmwWDBuBkXTFqVUFy3QQqFMGaChBNS/GIjo2EogooicxX3uGDOQ/xUxqgOqCcGo7M1oOCVQTgSGEEkCmQ3lVKR0YIAAJRpXPTMQSOLb3FRKZ3P8ePEUfUzjbTinQbnFUhA1DXXqh2V6P4uZ6ltZIoxphBRxm+m78C4L+gYmtCWnd4beLOBeUJ3sKBpjm/Ad03ST0gAjqFpYpxl9ILo+wx2Ihpzo6Yk9zo9/Cke6QZFkZjTptNMp05ifv29u/nRaE4YDm3pZbwhw6KMgQrGsBgKpcg85x7A4HQaFIedAUOlktg+UWrHr5UPfJ91OBOZohTi93JSpR19xJ6eHs35hlWKimxU8HoccnWGbGbCen6lpZpx6TKczb6vyahWu1xXmri5yy4rUM/oF0DoFfgElBaUyuo2C6+MV+17Q+jEh2BGBWyM54eFTD+wRqxWsNXTVCwu2bZvZNoy1uHa9zaCqDULldVSybmG4bBsu+45Qnao7iQm9SHBVsOu8lhHMrtLIu7EmVVNlhtnlkedC97Vuhj1INonW0VqnkK7WpAfHuRcEtkK5peKGrVJct48dkzDjxlldScQybVIjVMxYUhC25dovOaZUuM8AFJGqOMsOeHFUqdKwzaEjQirroqNjtj2sc4ZgtSsb0EO4L1sEDCMajn7g8SDL9DjaylI9GaSrsSXjyzyrYZI2ymvNfakgiE5OxJQcBQMxkF22wNfGPjfTf6bHu9pJdQRuckyts+6S2CoLh4DyHr2CFmeocJ7MFeL0BnNi3SsqBjIFiGi6F4RQiqium4qoSWbI0tRLweuExjKimfTPiLWBBzc+FZrVvOgpnLrMUBpVzFQ+FgQ5LbQppc6sS0w8l4PMvhtkppejzOuko7NGxZ6YozfEcMEugzRb2FyTTGBY3VKGOZ0knsCgAydHrGtIAz2zKIAbX30sfQQV4LXOSQBJei6AJ05qtunGuUaGJxkcg03+3U83K69rZszKHJg1Foxsjj0dsmRAsV+MahpmjuHgAEAPXLzi4mWSRe7rBo+NpQYc+m/um1OWactRTXgV50xSe24AUH1gTvCaCIAo0b4UODLoihjA4P1vrWlG1qItX4+B4YGyVYRXHJ2IQxsk5OC44dK7RnIUkl32Hf04JpzZjobiVBDPEesA1CLhhCyNzLoWPAdJuea56YIj2ePYYyA066s4C/xNUFm2bph6fzIjKaVwPyXBBXQCIyEw9VYWtV26GZ7d36FuGy7393j+vOHaDrVoSH0lBooCjRD1OpTtpl4gx3OMeT6z9YHEk0X2SnFhZuwSGsigc1a2FVSFT4JJknfKtEUGif4JUeL5OY6G68H66K0n3Z9kkOwTS8iPmMKYzMQMhNe2l01SdJ6a3HkOW9pVYx8dFW40lNazTvcrwEnd+oC1hqM1djVDYxZORj0hvbSQGUVPySRlNxbs4emSS0kzRgPwtAhoYN+LD8NhXc4ppXJECffMLOJkbqCNw8+eBcuknJ5AljxURfRNSwOufdozE8rsbORhlPDmKYp+uhcMUXJcwckUag042JAjNhyDYzZ6w2NrkvEhLThHbMAMVgQ/WRaNY65hZp4QtAiEjEs6iEkbYP0Z8eSi0mDP/hfdhHP2FbqvNqM6wRSGmanx3fizEVhZbWZVJ/UJyyxRe2iE+pkGJtHjXPCmqkHWAtdPI2iQNzVjtzBs+3rtfa2wYE8Nv5cc/Ai4j6f7Jq9/3kWuKZeHa87BnULa9Bi9y3FkBMw1yWm17iKAeMzsoveAbSU3IqxsMN8oblpcWRMmdFaK4zCItUlEggFHBgB0oK2x3llLQYUSkBFTBqmrb2w2ghuN50iGafFZ2MdQ1tUZQJHkooAv+HPe65g9SkQ+6NBmyV4ZeGYplPq0uTFqKdgrp/I+vwdu7UDrrG9dGyfi3uI2eyjJHGdN2AY48HPo37ITrlHu2S/W8/5pn1sAJuab67pTvDVsTOr9pOKrMZjGPyR+nNAmkaXb0XA9rhyVM6g+Dy9sLlYf2UqdCJDHoIPkJ68xRnayh4r7Z/BgQ8Ezli4nCcSBPLxmVfWzpaH4mR7vaif15uOBw2/UdqNF4mIpeivsypVjGDMqTpbNUGo7ky3jaI6MsHJzTCMrKncaQW6+NcCsDMCdelecbjsmTd5N/RJIOKDCEFJNGCjRZ+OfGwU6S2Gh2pXajyBe3HtSnCXNMqiCkZEzgGm8RjaMmk0JFIDGdKgXqUBIwVBjreA+MrIMbRRcjsL5Xb3jGDE/L8LpQHV44sS665bjvlXYtsxuVz0t70UAGGpkzO+QYKkb1SAKWMcx2DwciYu7HFfWJmadL98bMQ3c0h8kUaOPRTRJVuiTSC+dYlDtnPOybGU9QaNcpFSQ+D4A9RWtkQwlnTeA59uG4hfYcBR74FpEV4355JCCueEJX5kZKJdTTlF1kKIspXVFxb1jDXI07HXX3srgjaMf6NxInGiYmxVNTnTb71DqxmL7cVD+xgsOYzbTe9AwC/pkLZHkjrVYHN/QWsfmlc2pGGR6jeA4e2dPHMZAsUK1BGVHMQJHO3A9mubJrcGDCfsnq7aPhVowiExWK9ewi6nGs87G9giQyRea8h2ixcsJlOIYxXFxw30veHTDowMPtwPj8Yqu3p/euxrMMfvYCE9nuaAIoWDtVrwCThgfnUoeUAtGLCTEikvJIuG4rrwdypYNpv0T+dmjU5EiBjU6B9sdR5DwMYVmM2MX9hBmk9lq6Axe064gIeegg4MQk+JsPwldbxK4wAy+K2xELATksz3e1U7q1joZYeYqxNk8uXmUXQ5lljFNkY4RW5Znmz8DoIVXIVIGPoUSZ/1IhiHFF30EmmmsuXPDFwNGKRi1aCqrzUgRYCGxRqehVykcgLIhFie5wZnxNRnXJqmYLjbeVFjOCCcdlWEa2lIKCgprd50OsRj1/JCsIFvrkVEnjONNqhuKRmzcOvXzDjnHzEoQCXOd1hGMoryf2XBlZT04Q7NAzuJKQ34Wj00JqlkLmBlU1u/0d0WUFPZMg6UR38Fm5ZxFxkL/gtG4HG+FIRLGG67alS31DiV7CKSkTWaHeoyxst75c0JeNRzp0ULN0JyZdabnLqBnZdunYOT0kxnRxumTbDHI3B2tH3LGmy6P8mHX61VZkoYkKiCrtaJ4xfXxCgSw7TuiVEkKUYWBzMhATp7dap1XR6q5hIaBGbAZ2Chvkf103EvVKJg6i/ByngnRP1wPvPHwgIfrTXI7IUke0fIte7iofZcfOsxw7SzqmzK3nmNSokm1wqWP5xg20DshqqgF6Ks3rtSKvVbsXnCpGy71wMUrHq43XK83XBPqyzqlUYKJJ51lhZEwIaEQ7iXRxd0GiTXGsSSl5nBEIxEhHUQUOarTPohAoMOCyI5vrN+ZA3YDRjQKxeqzecKGoLs8/0vLL1PzhL+RZ0GbK9l+GRibGzDyXhex4vkZEJKU6NU7ebyrnZQbNyb8BHvov2d68Bn7nPWd08/n4p6cVUbTCZPkYxb1dejG/FxtOGVrQ2QKFwxixnEOWb8BGFOGMF0WojM+Tsyc19eCUEYbUoNQ/a1nE/PMFpg5nllzOV2WUzppTWI0WAS73t3ZFGhiekGbMQKQgjmcEjClOrYR2HUNt9Zx64wGh6KlGEHDgIWaGARHCu5Ig5+eyLCK9KnBNzuykiSgZxXzmSXlZ7BPx5EUat4n3svWsxGXdYQhZfURNp0UiR0KHMbKU+h4T47WgFGADRyZzT0oooj7Sd1kYoinZk9BzJYNkRzpXUejgdA6kJyiQXHr3SYs+aRO9vIZz/pA0Fm5sWYL1WZys1sBIhoCxt6odsMv/eIvATBcLqSVVzXzIiDB2APbtiOnCiTbKyJglaoD1MJbBfWjdww3Nc5rerUcko0g7TvFYAMwDIxhivxXBRbaR10yV10NrUdn4Abpd4ZrKq6lXSVTM891Fzx5tLZqRPkRQR26EoOTAYSApK5gi04Vi8GZWoR8iTaUS8FeKu63HbdLw8P1isfjYFN8bwzmxjm46CLWMBghCsE9ZlqHItRjK3XKhCXknfuT+oGG0WPuuQTVWZtN+nqh45Z6eWtjSpIZxMjL+F5MMQYUp+AfzOgNRJdCGfKgsUN4hpIxoVVDrH2NMVnBlnH0yU59pse72kmtdJqZj586r003LSNSLgwXJR3PmBtHZihhk1zkjH7PjkuFfB8TbMFUY89MBDxUA0ALwDoLssMNtcSUclrPXsY2HZRhUUFhjh40stcuFYiZebgyQoDVXp/GZTpfXWlKrfgkRdCIZ32sFM1I0nRZZoUZPRFSKB5sou7KrlpHsY7WsbDuKMpmTveD2AQhUwgWfEKXXnWBkVGWJbRna9MjVc1Vv7HUr8sAIPtAWNg/JFk16xYQzGCCe1XDNIg15k8zwfMk14jAIWikJHzDb8NppbbUQCZaZguabcFsLnfO7XZD7Wx0tELWZe7IqUqSy6ObOEDI8+0yqUkJBiYl+Xa7Yd+31S/Gggczi9FwvT7iE5/4JF688QKvvfZ5k9aNU72AsdMQKcjRW9eeC5Rtm/uzK7N2LzhG5x4oLucDjEYD5kH5oqFonPvKUMvGndk7oeJSFPjwOo7O2koHUOqOHQWbcd9nIDY0QDBDPGZTSWYARgkgCvvDRNCgLXCt75itAs00f017r3hBtUAvdBZZf7YR2CCncnFcasVV63Fr6o8bHe3gWPvRBY0HsBfKQ81abh9wB52TzkiqtmcmnRnliED0jn4cklsikSJUdyua4AsxhvdSYRuAYfBouEWDjYE2GmZF3FyjO1xmZNXCp63SPaPCxqn2J5sYMaRfmXtXcL11QZ0MeuMdctDf3U5qytvMhFW9Oadsx1dj55kpR8eT84UUqWYaapivz8cqXgKzuChDEsjoPyGbFe0cHexnMGBoqmDCkB5prjJ11/vrpnOyaSiLGnMcdGZLcYqueF0GFJnNiEnfXvOM6FBNTQqRWcUI9Ogow3D4wDZYk9qKSTE9r1uRvLMDp5ggQzPcrONodAgjGizFpWw1XJphGhBew0BmLITKAjWkIK8PK5BTNBEKQLq2BwvEKSZKJ28zfcuZOX0oO4MhJEo7AoBgxCT9zcGUnpku98zohG20fMgye04STpQ05zxVjf62udYJ6YCZ3mlkNrOZOtc29xkhpfLEAyVkF4LFXAFAKGPj/ydphs9vrXFHisIMAG++eBP/7b/9PLu4mrqow3H/7B53dxdF3oKbke/dcbc/Q6kFqTZiZmj9wHG7wesGr5QkGgcFdDNY6oL6aMBUIx1D95ZBXGKmtaaxTiaiI+A4+oFb63h4eMSLxxuzqTCYF9RSKc3U+rQFS2Q4ZlO66Sz14Fm0WuFqJeC4+BCSkJA4ob82JFxrVK9oGKiDahe9SP4IWR/K+8OJ180AAx3ZiIKxb1JuUM/hZPMqoIugZBSCmaxsjeV+jSXrNEZHbw1NVPLeG4MovV8xx143DkVUEA0vQKVz4PQIZ/AW0h4NTXjoZA9m8KgDg0Bep+xBkOASY8CkbCOXj2iYvVCZfbk5xwSpbpzaFZ/t8a52UrOOAUX7ETNiB2Qk5kI89dpnZ7WcFH83x6GfMqpzppVvl0oVAGbkitMn9gBv3sR5qfZdlCEknLTSHkzobmlkBWG1o6kQbpxWmtjvyVCZKcJRsdLm+6+om+AZ+yc4VVR95YrMLEgsqGOgD2DHhq3amVtCJ1vA3isL7F5wtQPXCFwHR5qEJrUySDgZXAUAJoIAxH4sBn6fU11pzqIKFo9hzCI8dBCd6hOuez2zlzCMLnhIjr21gXDnPJzTvVzZdTo6TLHZiJAslRorI2BR+N10PyOozh1gkXuA9PhSNUhOAUkMsYJPbM+ECufOMXlmYwaysvJx2qchCrWCkEyT3ZATeWHA9bjiU2++zqy2rH3ajoY333wTdzu7wQyOu8tOqnV1OWSDlzoz8M2Y+SA6ai2o24YXj4+oGpkxoVQhEgODKuOa31WKM4OM3D/pEGJuf5ihN2ZfCYVRTJdXCRno4yr9P3Og7AxIxSQLpEEshAyhjHQQOQBkNLW/AqbmaEJcCJ8iJmFkCJYQzdwkBgySnTYvlMkSNBc9+5h4nQYXiYP7JuE6uKHK1tjIUC73rVCeCKBlm0gGcYSnE35m1sP34/RiZqAAxMAdgHVUGJUf5PTMDFvdULyilsK5XWPgGCSgJBoyZv0IU1VDh5dnJwPqWHlsnuUJ6UIjeqyQRJZi2Hm++q+ATCqQNZlYWQ4SD00jAgBnau6J/IBYDunJZllO6uyo5h++YEY35xoQP017Xv+am2pmfJBzWJ7RIEMMFfL1RjHYaHgkU8vYy+TSi1v0WUbqY3RtJEw6NLHkBXMmow5IGjvX0gBAzL0+eJ0DLKrvxTXoz3KaAOdG6eA6KopxdEIysSjaqpqYalVFhzBGNgWtBXPBJyUdVSaIXRNBbT29WM568pPD4NiOULaJ6PNnTZkPAsx0ZsYENWxL8y/6zHbmgZs7i+tWSsUsdnuZxo77oMy9k5l4inF6ACXWe1+2ikuthGMie7vEynopIOKbpmQV19JTKRwDDmZwHQN9NPzSL/0SHq43PLt/5cmerlvF5XKHTdNc9+2C+/vncFDPz6fGX8Lkhr0WWHSMg2rn7TZwv1fAmJmYay4UpNB+NO0H7fgA17syeAiRBNLgmZeFYsBmkNEOZafuaCIe3d3dwa43vLgdiLjhpgyxC3mw7BUqdarPJDEngnPWwmUD+upXnHdYOnxjkOF3aLxINaos1sKsflgKxQJI4o32Wu8d7fY4SUUdcWIVrsCIIcKkS+latGd7o7OTHZkuWAFvmCOKCDxe4UVTxRuZnAOBY9DZlEFGX9H5d89Bh459FKlvDPRR2WYycjowv0sfxA+IHojc0/uMrRlaJZmDC0KbQM3GWglf7qVMBwUA46V6/6d7vKudFCL1xlw1jOkZkFpemfYDeFJbOD9ezpRyzPjbPaaYhIwvN835mgQBhbIWReLmOMnxC4rQ62c6L8iJnyMYM3IwPY3kUMe5DQ1+82XME7qDnAPXQcrh8/3tlFwZOKkYSBo1+7aEdzegjwN9OKIUhPp8E8JaNThGvftu2HCirsbqR6PRlQhmJA1bht0xyS+GE2zHleK4bhXfM5PjrzPsdV3+6fDLiJdgRusOqZKoTcCXsyABrJyCiTQIK7vOfiyAGWjImEB6j/napZwHrMIh/1uUbeU3u2wV+6gY7ZjONW/LOe6x3CfGemXWENEbjX9xwj0jcDse8akXr+PFwwvc3T1TY3hFLTzqW91wudzhsm3Ytp09UmYovhTAeT9tXnvBwOYG3zbADLd2Q5dxLduGrNEOjWlPGjWzIqaRIaabB1lme6nYa4HHgEBE5EdSmikkd0SnWczx/O4y6f9eKm4jSTBqIYD6Ametk/BrZgNJw0/yDBuGQ/dJdR4Fvpm5YSRTss9zThkiIElQpLCn4BmdcBPrtmlQKNKBFo26iVD/V+5yzH7JMXKKbczPDN2LzJwj1msBQtnp9JpEeM053n6MgEeqxRMSnoNI61LJj9gEh8e0Jbd24Gj8BiQaSV6pMQNLxfak1qc94ZggquBvtUj3MpMHneOXgvtP93hXO6lMoZdvyoMuZkowjbeVsNBA2vLgluH5S4+3YwXSWLz9a3ITwQw2hmoXgpAyxfUUKIUK16s4XpVlpUFKZzWJATbUnBeIwYF3OGUEYRx8Nps0jYVe6JAmXm9TUodXbWCUyx+FokKO9GD/FRDRkWKYxXi4y9xokAM8Lx6/8xDERadv0yGnc6Kj05qdot10NrIVKl2d0i5b97oHqbpQjSIhXosOt4KtGgYo7Iqea5j3VHfObTYWZk0BwBS3nrhHAEAXQcIAd2XitBwjmF3SKi5Ho914OqBcu1oqHI5VplpZ9csHeAZQxtlFrS1R0AAj73ajvlzrV9S9Yr9s2EqVAvrZgRcUr0gQNzNDogKYdb7c9/tWsHthQb1uKEW1JgNGawBojAzMqCIh8tFJ588MNoZIOYSa9lK4jyJgcUzHMDPoIJKQ9dVqjvvLBbVuKK2hNCoocLaWoL3MSMBsepluDekc7DmwwezINJK+z/qoZaqOUtUD2Aais+ZqFqihZmBLqG8IGeH19sb3CRTUTTJQUo/J85+zpA7R9mcwN6TUHpiwZO7VDB7z+REiK5yQhOwlA+hsKLdFAtkYhu4xtQzZx8b1TpIGFHBl0LTXwuyKR4ytBr3juN1w3Nqse7qxpcfdpwoGxwkteG+VTj63x7vaSaVRSu8x/RASLrP1exkyIKQxdepkiXyujGCc+lnmU3IEhGGyzs7XIrgiX5BBNCMMRu7VGWW6JaSnSA1QI6Z6JzyVlMnQqQHULsgMGeV0ZpJeZoZmiVHrOj17WTKTEU68hh7SCeb345JZ2txJGR4AJxQPSGTCspwk2G6cKM7K2GR8TfWvdXeWQx8ZXcSKCXk4MK8hpipITHiBpIFEyTV6XEYqMxWHYXNgdDCq7w12dGRj7cwajEaMNRNbe0LXeM6np6MH1djNafh66xPC8hioAC5g02eRQ5u0/hn/GjNAGMIawjowbEKK50duKxJuOEguNNp8SNk+UQJ3x7P9GbYtHRR7oZIbZlaw1ztp9qkIHoYcQ5/fc4w2P5+AZsdWN84v84rWG0otrH/ARe8PlGqTPu9OOZxpSKuckhu2WtjDY4R3RwPacQBmaJIR4lmg7qSZYd8L7OgolfDZdhzY3XGMjupUgOhBentT5tCaGlxTSUakiAwofagtdRCC9rQLBiqmKxDzAilqcE/keRpdmdbMmunk7+/uFfwo85bTZG9jR+ui0o/A0bpEbPm7Q+oZiQiY+8xsSbzoc+xI3ven5YiVqbCnTztOawNBcSRYrEDSk0Xoi1GZWo1xcvJjOI5iaFuVXuVQsCpik7vCn1MgG5jnLbO1p6fr0z/e1U4K4TJoSbuOuRCJF0/jeEqbk6I8ZA0jMOnrtPeuyD6Ju3RwKSKLjHS1+EpDpi/sek6xHEVNg1mLtKueAEtZQ1O2Z6ZoMjLIRzXHXakoAKpxymsPzCjawGvbXBvA+L4c0GcTvkj3xY9JmCqL1+nAMNl8jI35PWbuqYxrnNY21CCJ9DnQMbCia8w63lPG5JOHMtSkiJvifEbgmIxEEmSyG2TGEHT286DQEfUxUC2wuTI9FysQEBOJQxyHGjgIp64AJZsbZx3S6CQARdKi9I957bxhKdhJy3Ze88gNxPcICAq9IsBpppm0rfYIgHdi7RcHmXRW6ACr+1RPML+AjdqO6hVAQVfzKsBRK5ftbjpc10rPWVgKkvK8GCAncqUirXG44N1F4152NtAehzIZr5gtG/BJrU6mKRCTDm/emeXJeddKqOqQ3h1GIAabh2sprEEWk2I8FSuOWtBGx23bpEt34IoD1hO26pkAayV5HyY0lyodbuhDcKruY6nCNyyhQVcggrXXE9cWkcWNa1xUf5ns0hHo6GqFUK9SZGO8mvJHiPBj6NLwG8oDW5QZzA5QZJqsVW3/ETMghjJiM8OwZBmKpYskNQzYSCYpFcxTqYKBIPd8iRw8SnQmADa0V0evmb6x+ZjHj46Q9kx7TEzBuepGclH/NCWVlx/vbicl5wBgZgfn7GcYJhkhjcU5W4IOZRonGprMQ/hGJobW+qyEAZXtWJIwMA9nampVN2xFdG7nn5SsYaHcZuMrL3gZy/P1Vk0Z9iJ2YNLRRbXFSasvjRklgJYrMSzHhJc2x4zb6cEnKzDT9u2UBc46ktZ0ZaTre7CPgw2QPR3qyGjzaaawisjnC1rfnfL+Wm3L76KgYN7bUK8TkFGdKYvegobfIQZfp4nMWiUHrxmi8AjPcR+gIkQgD9v6blAAk7W1Yo5kjLPQv9oe+E5aN7z1kRDnhJFhc1zD+UlmIkPqXRzgSPPCOkf2znBLpkJ3Ycc/bPbSmTsulzvBeqlgYDiOA2MM7Pu+amO6MXWr2PYL7i53yLaNnK1lWq/NAmW7oPU+CTqWe0pnq+v+L0mtJa4cIpccrZHo0gaHKg4acTefauvsnwqxCzusA7XuuNxtuDsoAsvxE5R06qfAKLPyhBF5DdxPIyHMdLJu3Cl2Qg2ChIs18ynH2jADKVpT1pXYsN1y0nU/1KZBMtJUxVCw7NLSw8DU7IssIUwMFBodw/3du2sfZyNOrMOkQJY/InJhciqm71XGwBBdfkl90QMXyV25+ppMNzRFe6GgOEVtJ1rg6j8LEOZX6SWwWl5a77gdt7c5EW99vKudlClyiBhTH+7spJJusIwq/1OtsK4AMBLWn2RkpXFZytgLbnqqIG0zlT3Xr2oBNl9jsHdlUpNybNAoam3QxJBH5hFi5DF0QtbAqDLNZsjixIcTK2ZHfT+l1tM+TGguHW1gFelTYmhtKsxO9AqpF5fsvsdTJwV9tqLhQCzKt+SThjLLLgLIzHhzDdcKzzVmtrCgCxZhBX+oWD05KEFmXLhkkfK1Gf3B4V5Jne4VtTXcjjb7pRLGCBm1AATTmLKz3FOhGh3v1bnQj+DnEH+n2jOniWROmCKdQILGuddaD/TeYEa6c6g28gS795fbH2waTsRToo9lRK/17asqPo9AawdqrfPfSU/OoZhvIQ6ZY9t2xBjU60Mq/rOvp5ZC5qcDl1LnZN3MMqhS4cpSTI20ybykqKuXoibhioJ+EoqlM+DsJhnBWrlOaj+AAVY4Y2yrG+6jIhUmcrqyNplUWsBr6F39RSZGH0sBSwcTE4VI6a8+1Gw+qOOZY0RyPlc60H5Q2aK1jms7cDsabsehHq9YwY+dAhIzsFdvBWnkboy5X+J0H5coQfaL8rtZ7teT2Zt7eNoBGoaBYO3wbAf0mjxL7nRE5HsokBYi4MrozxCjx5o4zC+qnkxJlLXecBwHbtdfAU4qjeeZwZfD/BL2y/ie7POVbVkacj0nTu8x09231AZW8Tt/Ikt3MrxqQDWQ2eI+IT4TV3Wy0DKrU2sEjUOXYVufukiBauANFqrPjb5TMkaGTo3gCJtJPiWGbEWQuRZL9iQ3PyGGvRq2Iu0wf2qYc6W0B8U465RuyubjE9MqROIwSyjrpbpe+uqEJRUpGiAoZMyDWZyOc0EYzB7K4PoUOdxkMaXzKHK4mwzpFJmFWIjILn6NyQ6ujSZCqBh/zoh8QiyuA02AjZJELocfMeY8M5wT5/z8oPp8SDAYvp42YuDNF2+qnDqjL6y/JNlgTSH2EB5ASikQ2djLV9Ral5OakbJP1latFVHWtxzBDKd6ag3KqQ8DeqOqiwOtBeq2acT4csa9d4Q5OgQNSqYqsBh0yXorW4XVgJeKWht7f0Qu4JrxGnlz1EagDGG1LdAmVDdULBmtANCb9lMEMBqiZ9dpBmdsBZljf1QHjiQ4tDYdlyNVHQQNGvdZ6x0dgaN33DpnN7UxqHauOiUyOOIiYU0rSM+yyg/keixm8iLR6KkJ3Qfo9HSOQ9+Bn8YzNcH37P0DHVPuT8bLkzHEoLRnUKiDkFmVZTa+TeiZwaHeaAY6MSFsjqAX3Hmav/aZHu9qJ3VXHXtxyc5g9UOcItz8T0bq3Ae2zvvJmYX+nYYwI5BZFMWCMCIoxa+YSBuBxqUgsJlhz56fExw4IpPygFl5koWdtlCmCdqy5/+KySMHG8NRSS5CB+fxXBudxIhkEWVNQ+9/snVJpEjHzK5w1jm2YnNWllt2ACnC07oyU2Lm1HooazJ05NyeJGYQQ3cZ/smwjPxPIHypdUxh0ACGL5ZdmLPeIwd1ro2N0z4Yg/XA2ZMjWKtWqhRwxEOXLFSAPDlNfw06xRYdfY4e4MnPy07Hzboja0QuNlmOFDEd1JBRBwgL5RqaS4LGNhiOWRPASYNwjIE3XryxonptjZG4PzB7zogWp3Hg/uqDNaY3/I35uWlYgRWN59+7qI95PhIye7aXCZk6nJTyweGBcKMsDyiThcjxJrxPQySGY1BrbyjLgfF3Lsq5m6H1ga1W1L2glE3OcJBFOIYaoxVEyMlnXbeUwvsjeLQUBXOWmQhUewKOWLOe2tGR2atZYYtHsGk8sygEGX5n9ZGklCdzj3uczunF7Yrr7cDRGm69yQYZn5sO2WyN6RknhyiI74nVyUP/BHsQJBk8nxGO7B3FWEofUxlltmTJbWXwK0Qj1NifzMGn9sEgkJzv7zFZxylvZlaIIJjS/Mza0i5HOnvR79+Zj3p3O6nLVnDZWDjtPVWtE+tVE2kgU4oZ9QN5q/2UtcRsCM7xHNMhcQdg1b258Am50MBDKgiO3UEDnxhwxDSgOV57BIinZyZgmLhuuD3p2E6jA+BUw7Cpj2duiKlW7thKoVp6DBytn6K0AEDDCFs9S4jBuopqUHMmVl6X1stsRXIBp4p0MGO6tSWk2WEq7vo8aOP0p5idDuG5PoB5FEzYmykzDlf05z5HrKS7n2KasIlsjegY4aj61oRlXMxAnx63KGAZgnhoQKkW0LpPKDij3hCWv3mZwp/sM8o9k/dd6+XgXB7d56lYn5kjTGyqmNlTF+w7H9Ij5D6xeR0zTrXT+hlraZSFaspoODYcIMTVWsO2bai1TsTA3XG9XmdUXkTJNzeqngNwTcUtxXOiCNjyYGid49dHZlBWdY+1FyIzfmaut9ZV/4EgTjp7uKFFILXqihngTo3A4L0ercF6qB0jHWrH6JzcWyuDjdEC5iIhab+4gYK4GWiaZi+pGh0B+HCYFCs6MtBgMFYsrQWkiuKEQHUuONuOZ+EYHdd+oB0U801hVgj6T38z91gsIhQiBXHHDJ4DANxh2cs4xkRBQgFK7i0yVflBM0ieEFwGrJitJfkICJLNcHnCmgy6JuFk8Iy6815ilj4YRJ7lougQV42ttZxU3PFOHu9uJ1Ud93tB747mpHVe0dRIx2Xu/Zw2KE/1U38RMnVekct5SmwWltOZne/zmv/EelFNSMkoI1PMV3NepHqzRF0V8Wu7z76jnK5ZPd0o4No2djogGAMeUv4Gadkjo7O6RE2Po0/1Z2Zhy0nliIRAmb0x6axy+BrUPX6GPnMuUwiKaCNFXNlP0Q0S9cwFk5GXAUzodUZvJ2hgvWSmWPP+uducIBpj0b6Jgy/h1ZCTaKMhtCakxvINzYYMVii6NHCmkgIbpEZaiCE2tSCU0aTzzgBDeyPOdUcFMSPXgkrkywFxLlJJRlWc1+TkoKAMPI0KlI3qHjyFpPmcbOakgxiwWJqVnvqR+bpp5ALX6xX7TqVzykfxmm7twO47Whuoe8HRugICR9kuCImbwgrcKx2aMagYea8MqFZxPFIhvBszmzCgHayD2YDgsqFzwKDLwzQaZSxSRhisDXSwuX0MYHQyX48Ws8nUiupGolXPHiIEkIFFqZg8uSDDrg459m56dkcZmA53JjfRlYHjSV17DOXmTggTSOYq79IAYeVUhz8PbMyU56kaxiK6DI2sMazsOvfGzNxP+yOD7xVg8gX8dwY/67CxLUI1CGVcBWyYR5Im9CL2eg1Yp26flVXnPkOwZmBmiqxP9Zm1f7bHu9pJbQ7c1YLugV4cfQTqcNy6IvvOND6xTy/MlmwwBVqMMD4ygsneHOFuyj+02NmH45gOajoWkSHIcsFM8QcYbXY5qFtj3LZgyYGuLCwbEYcMi0VQewvLWSZWHbHET91FKHZg0onNUTYgUBBRn9Ln47xpk12mq41QOrOghdMxyMWa15JZwQjNFBIEMVOwmeE8dTwLljgdqJPRXfDs/IGcU8g4jJm5FH3vFiIt6Du1EMsTNCLUCEynmFE2KdtaBOQ01/CEqsrJGCwoJPLiIqPVECoTmXLrLeWce8yMDQCO44APYK/BZljtu97bU0aaIeNaANSSy3uWzjwNminTQcnJu5mfZTbBfZT3PqHtx8dHPD4+IiLw/PlzMff0fBECTJlRHwwIotMBlFLgtQJBRqh7YQZiBZYIAgZurXM4qbvgv6F1VivACERvqMVQJFZazETgKQoy1LBdOQU4G71H5/XkOIpci9Fi1aQCJ9zktMfGGpWSiAPrZWP2GOb9H0FmHlmOhqy5rbqWzkaV0zUDBjMo0wypONWm81zQuTHbJEM173Y+p8/vBMj2nAg1WSfjnlvB05lwsf5+WgAjMWJmcGYgY1yjNc4BO0KTdwemWrwQAO+aymyQarspW8xcSnCgU0XkLPb92R7vaifFfhCod8LQHewhGYNQTOuIaE8M8ugdpVANYXp66DACQCQEEWK/iJWmmkNmFVUd1akmUUwNnmBUPPraHIQ6Eo9num9wWMnELrMYMJrWzTWsGpSwL4Vv+r2RrprpvSHUhDf0+xClms5VXhMZSp1nxSSlOmFJAKQvO1T7UvETmgtkpnoAJnSXbLmkGp97b87RG5BRX36PzEYYPDzZu/OsrkIzDX12YOW981mfyusvil7pSJjPdXpSmGUWbFnimnDJzKlDDL1Y4NrsWdbl50iNyXoKIGsk5yw8s7CZuYAZaGkdNXn22gtmjn60aXQIYefYcJvR7/mxsqpQnQiAFTEI+4QLSR9ng2Y6noeHB/z3//7fMcbAK6+8woGDp16pKpXzdOTFHO1GxKKEw8uY8GCSfIqa6MnS1jiYUO1y8DzACuslpTKDKIZ+3BhQDKqlD0uoVDVcw1SZhw1YB/rorJNZzMZhAKJ6E34+M3fXpO1TxjqSiSlb0bOXcqxaDbJJ+cR+yfVfN2L2hC3W3ZmBZ1gjKpajy3N0djBdbRskbqx7bkKDApjw43kPZPCRP+tnqTN7YgK0jqljuTKnyY4ICF7U6fKBUH9q9pHGCDQLuA14z+neeY/4nqT1M9jZtop97Di2XwGZ1AS1EWqwE45dHAUVl8Kpma13jbqg0u8YbWUK6vU497XMMdKKmil6ymi9iEwwMxva1VkEJThgc2pu9gxlYlGLoaBIcw0ae5HsHB42/lt4rtks5J4zmcjPLzluJGSQE+rg87K2NnuK0lHhacQ1se+MGpUBYchJBeEZUt5pNBhJZV2KnfItOFeJAwBpRLhZ8eTzV2c860OBdJTrsDnTVUa1eap0WcQeeHDSefPQmKa+nv7PuFdCBfzRmpxU7hWf15TeZ0abypqmsy8r+l1Tk+dNWdeqWsCYl2cyQj6XIvtSGApRQaQYSRhLhSOAaNNgnHH81vskYsCBGF39LHSzfTBAY6Ol3LUDpQKlUk/vjTce8anX38Dj4wNefeVVXLYdjmwEzqg9mPVtu+oyBVYKTPd+kJOt+zUbasigbB3XG8e9v3m9yhhrWqvzvnjBDFiKVTkb3ugIQmCjkV1nW2BDUa2qzLU3iKF4HCRDBPPsI4J/70sp3aUCwYCQmWSMPuF7XqQG/vnJoOs8rYf2aGbWAGBsfs2BiZ2HRE6CNyDkHYoCR64Fg06PuTCAWIIrAFoBHjU9VyCRtuJMurB5hhmcD5FV7MmLVO+cC2lP4uHpu/jVwMYOXYdaFUZ0ZYBcMI7wAQP7YO2bc70AK1TR3yKw/Urok8q6Qa6ioyNpyUyFDZsHegFGFNyG4+iO1p8y/c79OqYNOlN30JiU4ijFpmimmcLELsfUGVV3GeTUdDNz3jCztWkUZdr8wJiGdGZ2qe7N386NoUvGlHtS9oEnTigEJ1va2Wk48h3Phy3hKzrEpLamspstWAJyLpnM5eFBzENBp00YLUU/1zwrn9fgp8NCEpWMXaynujK4zJtOX17XnLqFotoapnFz+KSCzwhZn82on9NQ+SLHHH+XB5QXvsKCvBdaK1LiDTZsrotWEwYQ1tKiuUFyO5jN4QaSLzbR8oGhKDfVIpLdGEAcchpY6wA9V/JINBY31XlcUDavhO0PKVgItHFFexy4XQ88Pl4BGN773tfwyrNXUEvFVjasKTQxv1OMQCkba30xWPcULDcA1ABKtWVUsfqLKEZRlFH0yZ4FBqKzObhJk9K8ovWb9ka2ZRBSGpvYasnyDIPbwAix/cJwDOAYQGtqiZCKBUCprtES/lrnzKVMkvXLrFEHCEeOYfDqHGCqvqV87dwwQUZggeMOO6DM8Wh9ZpBDe3o2x1ga/yRKAQOaTOxSBAkyZacEEtI2qf1AQZNAyicZYt43KHCDMiBgBTMZXNFerCASkf2KmEiNzYPOfrkWrEdG7+gdiGzyzvL/2TTpbBbt+1rXYM3P9HhXO6keq5vckkOux4RbwIilg/TPrQy0cWJZATpQvNHFGJsudV9F5tPJLKOAFKXU/1JFR3BgTcw1N+M89TBRy1K+CDNJihSd4PNCr5ZDOvdIQFHR/NIZQZ1GQXDrZyS4ojBP1h13MF+fbC6ZHPY8eHqQxTxKZ6fmWcRADceliqreOU8KCDUk27y2PCgG9mzl1a/11EUp9eqRzZNjNjRyoq8ifMGkNIZJN4PClMoZbzJAeS8CAyE8POdGIRvBT9HkZEIBa5/kfdS94x+jrBKePh8mhxKLAgxF//lU3zY6H92P4kaevgGp/g3jITXV4njLxtofScHW4acIaLLlHKNnVr4OR+9NiLBpbMeObbtgrzthOn1GGrCHN95EvWNzxfX2gn1PseqIGuuINjpq5e+2bYPDsBUgqoKVYItE0+uhAKipnyFGR4+OEZ1j23uHFQ1KPGkNMuEe02hSYFa2wMnUG+ZoaBjudBIjAE8VCGZqU+DZDGHUCazG3sucHXVOk20ab5/9R4hV5crs34cCDeP9GM5zXUCh5pxllY/FHM02ipjnIu3YuR41z9Pp9YhJxzidpdyyqQZ/IlXodxlAzraY/MVQdKVDmud46Z6eWglGQ09nNVyDUIGi8T2sE2jN9cmlVGx1wzt5vKud1CHWnJ8MIHC+P6eIQgfcPWnJjvQPAaBTKljEBwP8VDicxvy0SbAcIQCmsiZ4ajov3di8knk5DRk79YyCIibTLWst+fApkgtkg97ciKbvrexqwlunLCuhS8jpllikj+l0cp2sgFDHOctZh6ZrBIOHS13Bcb9VbMVxbR2348BNqhOhAjMloLJ+Y9OAZyE+lHHSBs0RaggsVmRPBuAQaaVknifHlxmoolHWC6FeJpuHmiw7OS1fTjyDjgAQXYbhdG/GXJ/83RpbsAzIOvCpYjEbl00Ua1vw4JCjTNdNJ8T7ZAlDw3AR/NdVPM8hjCOov5Y2i77SqfZuAEawThqLOu1m2LeLsllJHPlGqAmukR1FkCm/7yv3FzbN94Z2tFMvVaCUio7BGkQpeLzesNUNx9FY+yoOv2yoW8G1D/ReqDZwNA0VZCbVomMcTdC4w7YNZQT2UmBesXuZorTVqOpioezCAsMBDAn+umFYQWvO3ix3dFTWeDzXjgFPknaaSBhFQ0iqQsl0VGZEVGbbhu42WaVi4Rank3fDGAd3g2praE02YCnazHOpcwYF1QnXd25MxDxLJ88DCfBmjJptCGNd2/lsp6N7st+xGIn5ePp3mSwF7MUS3lvObkT2eXGOnaFIUYLrV/WeISpnSk9BAeY7ebyrnVQy5ywNrvtk5mVDWjqJFHoMZ+TH9RHkoIOcMNLEaFULyggqo/H8e/qB/GNTBYLEjCHlh0nZjtwESUJI46+Nk5TTdLgnB5RyQElVXcVzm1mBWz3BcXrv3LwAILJACenNecJuQEJwekeUU1OpAWJhgRFRrKKwu2Evhr1u2ErBrRR22R9NXeWKup1E+oxc8/uY7qO+rByTa41i3qPiiYUrkDCNkYdIKxy5h2qOzcusCRRkv5ccoqDRoT0xHT5Oe0X3ivfynHXHakS2xaiLvF+z+UV9L329N89lEzTJz3lsDd4HLiVQitGgnZqf87ElKQGEiywDD93rEQlBKlnzbK5MAxGsu4H7aKs7ZgXdSIwAfEbRafjzlKEPtHFD7zoHeQYK+5c4kNI1o4nNvx4OoHOmVzYtV+6bVgyX4nNI3w0NPRxluMZhGKw37LViL45SHfvGOtRWioIQIGKggYFHREOPgYfHGx6OA9cRuPWOx+PAmKuHKePjaj0YGt8i/AQWSxXBQohCDLHRUqcPGgEC3TMGQYm+1FLZp+amcJTLWY1ixrmy2dpwInXzSp12zcc42Y4T2zOzWGSvGubZeDuzHy//++SYznWukYGPft77el7WG9PBY/7cAM3XgliSCT0++SwF/F1KI3DHO+3mfXc7KbNT5E3PnSXM0Gbr6lkgu45GBtkLlMBprIwh4a9k+rkM+xyHMeGoNGJj1kzIxwjBieqqjmTDxDRYY6SRSWOQXG2+Bjgbg5X1QI5iQDAcn4gkIYQ8Xg4tC1GVOxJDz1qIo5hUkHXYHE/JI3wMNvaWdCjKPrIYHCyQ7hvVqffC/q5t0KA84sDRSKjoo6NpzUhFdfXvkLJMJy0QJzJSH2Lf5UDCwYgyAjXogIrx74RoSDpgtJ1ThJNEIlgQmJJNrXXSudNR2apzNrHEAtBh1R5RMESii9YVzHw8Wxv0XTh2JCGlmGuaB/jaOmoPbCX3nPbiGQFQlgmQ5DFhPwURfVB8FWBvHp0h4coMeFyCpLmv3LdVxwiw3iIqOWHAVCihLbrdbrgrzxgsaHqvl8psuBRse5njL0Ikmiruh41GGFPHpiCw7RWoBWOQkHF1Tl9rW8HRBrxUICgrtm+a+LwVhHpxiJx0klKKz2Aygs20D9crHvvAEQPXPgBjjYSF/JgyUAzMCnrvqMMUJBI27rM+ypyec9SYYQHZlqKzLFtUTJOEa8W+bUioprQGg+FofO90VMx+ee7Ym5fUJ8x7Zcax8cnaO9O219/iSa+hPnYhJfr3bBF/6T0y0yLtPBEHPnm17FFz7/zgXjKOBNL6Za9iwrKs6xrVuXT/yP5dY0Y+2+Nd7aQ6Uq9M8bgKc3wkpMO0uQXZfRR31IhpX4U7vgM3nrvPUeer70jOIZ2JovCuaPqI7KZn8bafjEDqaeUn5WC17HVImBARMGeDHwDkXKZuZIlpN2Fypuc3lQOZnzkgoReuk2CQfAXZNpkZsuaGNLaK0LhLB0oPeCdsk4PMMvvLnXy0gAuGgjFYuNtYQ2h94Ho0PBzHhAb6aOgq8LMHJyVWUjBUdyObUAMICZImxFcHUD3YQyOVjVSfL2JIQtlGZsetDxzRcVMh++iNUaGaPQ2sj0RkwyYNxHRWY7AmJjgm61kGOoJQahgRGNkHMlbvDZd1GYFra9ii4E73zQUxzugZwEN5wL/9vJ+kawvBrZAjC/btFLGsVlLP6+pim5oB/+fz/4u7Tw236aTm7slMAMuZrd0FUFevSINvrQuMs6SYRWhwpLFpnSNeQvcFKKY9oqDJDbjbttnD1oejb3IqCOylUqMxAtWCqbP2d7Yb5P9msEZpJKc6gpQ+VI7Suoca20m+SAUST4s8INgwoViuwdBSsEeqrB4gBS8DGucRA7035DDWWh1jUMG9SWWBnVDQPDxTwDaIBEGZrJAFAMt5AtPB5j0yYKIVQ1lz7o2e9TJlMosUsRzgcrl5/pfEbCrCJ7zXUrcwLVkGPiGHJZJYOaEBgUR5eC+qGVsLYjVmf7bHu9pJZf9RsZjRLQDCSaQ9wXoOTgtiw9rkTQ2JyYFywSTlNEEzB44Ba1AXM6SO3roclJwVFCOHSe1BxVEDAJ8bfmZUcqDmutYZKZ16GjSkcciYnMeqP4FzZ8q+sGIAkmHJ56RjBIa5DgQP2dAbsnn47IBokNEbtlGwVceuOTnuVY6NDK+ER84qK9ULSuUU1rJX7GPg1huut6aRDFrD3qmk7XVmq0mrSObTucG6wFGd87M2ZU7MDn1mhgyEx6xntdbkpIbGhwSaMg7CpWwSTXaUVY5FNxklBCh2atnXEvNgZg1pOleE7veAjaeK4jk+RZ6NQxndYXBmicrKzAqe93v8t8sv4P/9xf+f/5/PCsCo/ZXxCuE+YErr9N7T3sKgybapzm2Gul9AiZ0yHVRvlPqBVB7YsH5wjRDU33MiANVIS47esHll4zWArVTWKgqdkqsp2oyF95Qcu2wFo3USRZyDA9MgJ+yc2UAtBZdtR1gHvANjoCGDPiD7fyJSf3Ioy5whHP//fI5OxjxCvZPaO8kCN/0cEUAfp15BOs4KYIsNI4KSUDmN14tkhxhoMTAaiMFAaYCSZd1ka0410PPgTuoHVgwbs/Y0xkD0PpmKM8OK0yBEU/Z/ypjyyS4lmgFoj9hLezkU2OQsK5twO2XV0gkxmPGi5QcDKG/HO9q372onlVpxuamy9sCHWGSCgbjrhfMKa7WMNCwp5uqs17A6Rs/SOxttjlSG/mTRH17pcxQFrx0RE+rJLCB/tNhePt+HsAHZSS1OAoyCijJ6PEfm+WDEJPFLyx4j0JHYqsuYmSA1m5eYG3NCelAElEG0oIh+C/QSEp7l8LvZ/4NQd/pqGh2dQKi5465WVAT2UXAplYogrWtNea+KggbY1NCemQHrQDGzz1KkMl/KomxrVfoItE7nfmig3NFlVJ2TJ81iGqKIwBGYrLkcdQ8AVhYDifOLfE5E5Xo6Sl3Pz9sSyKUIQV2WWMt8vxDs4yK8TFhmBO7jDt/zf/4p/IvX/s3ciwsGzBEW634j99P5cTKygOFuXPC//9+/H7Vuc11777jFDcdxYJdjXhuKj8feUJWBJVvu8bjhaDKGpj0cuceAA2MK8Hrv6iMMFOv8mRn2KvgONuvAZkBvDQeAvVR4dODGMe1jDBF2CmB0hrNmYiYCg4YiemAHaePNMHukhvp8KHKa9eoxb01XzXDoBhqgsSsZV6jtIaCgIskHYyIShP6W0+QcuAHLYNSuaI+NQ0l7zEDJS5l6gJnTFnOgbvAoc3LvqrmvGmZqeBancglAuzXSwcaqJWWD8nR0yCBX+9YgizSAkbU4myNdVp1p1cbcl25o9cJaovYdkWCF8fr8HrRx7+TxrnZSo7Me5IJnuq3NBGAaAFiIiuwoY8yNOAkRTkPjpcqIs6Z3aMzzcRy4KdU1MXgyijFP2ncSQHWDFSznTRR6NXuQ0rBQDTqvN6GpNJQQFCgiAdaByLlWfGEe1HRIUpd+MhLkZHfilMbnFeuAzdrWyFIzNe5cz+8daE4iwyEpqKoNWQu7k9J4+CDENnonndgMmxnpp8XRd0ggVAYu1QkAaQpiOv9Zn0vJlWqCIF1NswOjpxo7FbdzFEafjZRaZam/unFUOcd4s69mRs0JR4w+nRF/cboOUdifwnkiByRNPGuaYNY8ryOkeThWdJo1B3dDWMFveOPX4//1yS8BbCl5R2SNLXC9PeI4bhPuG4KppyjxyHlaC9YOwT4BzDrUttE5Hccxh9zNPRmB6+2GF62hxQtsd88xAng8OKp9ACh1QxudNGvVkxwdNnL0fIgOD4zesFdG2uXW4Riqofe5roT6CvpxoISGbR6gRmA2qqvJm03oaw3z3mX/Yk7VNRF3zHi2BwKwmMowLYgIxEhjOmB9zECGtSPA9oIpxFyK1Bgki6SMf+4XrMzZjGM9LvvOBnwz3I6G660jetd3kJRTBsoKslO3KPfHWdPvLHmUtizPewbdU8B5dI2gWYE2mZ+gEG8ah4gnZ8XVrzXVI1IJQ3Uw3g4Fi+ZrvpgZa1wjHT/nd7XOWvDtdn2LTX+7x7vaSVG9jf0qPVT0Swgm6c9QR7iFFJFZbB/ZU2MmwVU6iRaBow8cbeDaObRshGhTxgwi3DhKIhZVOsVIMzKcZAtgRWYyD4n4DMvIJRsifabQgKKoeBrnTIrydFJ6zyDE6EaDzbVQGp7WXtH8muA6/duESxgdAQiOu55ACPN5ZieC0RoGStiEAptU2IuMtxVHrQNEQcSaipy9xB4mFcgAGIavb7qgM8jo6TonPAf1zQgv7wO3Nua9a6dMl8oBpiyFFOOE5zps1jaHr+w75oHMSb9i8bkkYrhgJ5JcFt7HclKW8kCmrxireA3gFgO7uWAiGkfWmh0Wnc7bigKeyKSMmWUpqKXgeiXTMxs/eWtpmKGZW9SEXMV3SIoHIybSYNuON283HEfDtm8zgIkAPvnmA2JsQKl46G8gvLBvad/hVjDM0K5X4DpFIgABAABJREFUHH3AjdldFRzM5liRWIqjBycAm9SJY6T8E69lqw6PjkdrcPUsTaczAjkdgJOBtXd07qoZ9uq43oBqpDz33pACu5klRCID0vhjVhpyNjmmY8Cia9zKUHZBGHJXcLZVTj4e3dH7gRiNZzqE1mg358ktAMpWsG+OzQ3X48B1azgOajUejY5eZTt49lnpO5sC3DXsUBTwycIbMA8UaePBkpkbiOjqDcNJAOE0XJQ9C3LIClydQ1uzJl+CobhZljYSEubeNpeCixul6mR7sx2DCMdQTbjhdvsVIIvUkYQCGuBh2ZJ2Mut5P7DSXjM1Omb0odd30VYfjgOPGlQG4wyiHB2QTKok/6RTmcrVipSfCtKSbskhe0MQF1iTQJJHWQSfkbwlJXiQ+ZGIjr6Tz+7ljHAChoxeMKOkdGYLxptBkox/RvlrsnFEAGUAIi67nHM6MWRjrbLH1huSxNukBs/XrKwy+wK71mlMzTJdv3FkwplZlhccooAnHMlIcUiWRzWn3vF4a7gpi+p5oEs2LtOA9wCnqmqN83em652uP1IrkG5hrktvylQEPymiR5xGEGT0DAN8IKW3EBRalb9RJhLTCYXRiVfBnwGge6EBGOs7ABzc53U7OV4arzkiIbNnw5M6AgCpivj8XY4Z2bcND48PAFaNIhDoCWuWiiisZ3qtqNuGQ2zAsl+AYEbQe58BSXdG1tn6YFs99dkQKmy9UStvDOzFSIgpVF6sReT5xvtNWN/go9NJ5B1Sn91mjmf3F3ituPUOPwaODmbMCXeB2c9UYgiCgDBlQmPAMbA5sFWfo2s2wVhbJSmoVg3xLMBxdByHBAYaERg6lAyqcvYUGYV7cdxfNlyPA8dBFuzD7cZJAr3xZFkXNL+ypXTKCNVpx7JxhlWTRSw1njGDJ2U+08YsRm8q9MzOQZ3NHqvuFFLXtxAc71lOAbZSsClwKCUds0hco6H1A6NpcnfPWvE7g/veGb1Cj+/93u/Fl3/5l+PVV1/F+9//fvy+3/f78NM//dNPnvP4+IgPf/jD+FW/6lfhlVdewTd+4zfi4x//+JPn/OzP/iy+/uu/Hs+ePcP73/9+/Mk/+Sfn5NDP5UE4p69RCsoMskdqnG7u/KPXmrH5jqk3ob3rccPD7YrH2w231igk44U1Jy+zfpRy/VPyhe84I7WMnNcMnaSAi52jz5Xd0pRY1k2O3iYJ4xjEbdtgl346uJ6fOTd/Ra07tlqwbxX7VrBVEyRBWjgPi3DmdF46nNWAzUhEuBTHXS243zY83zc8u+x4dnfB/b7hslVcav4puNSKvZCiPoVUI6QA0N9yqGaWmZmmHG0X468NKtfnhOHJAkZmEJiyNZyh9RRe62p47dG1L7qMU8KjCyrj4LyG6CyuO5j1UKqo4rJt2EoVa5AEEo9AiYHNgGqBAv7x0adh8+gKDkbmcXLqHalknddRa1nvj+wdK0/IJ0BmuwVm/JPH1q3MCaerNvGUpuzqbXlCYbaEgNfzTBlG7w3HcVO/DA3Ys2fP8Oqrr+Lu/g53O/dZrSyqb9VhHti3gvvLBZddc6qqBgEGBxket4ZrGmGxXxsMwwvCC5o5bhF4GMCL4XgxDC+64c0WePPoeDEC1zDcwtHMOeAzDA2a+AvCYqVuuL+/x/Nnz/Ds/h7P7i64VEdFoCLg0eGjo4yOioHdDXfFcVcMd9VwKcBmAxcLXBx4tlW8dn+PX/XqK/jfXnsV733lOV65v8P9ZaNRhpxcCK5uA3E0XB8ecVyv6LcDoxFma62hHQd6O4AIVDfcX3a8cn+HV+4vePX+Hq/cXfBs37EX07U2/ukHfHR4NBR0lBinPx0FA8UGqpEJuTmwF06K2AqwF8Olms5twS4S1F4K9lrYOuK0FalTWk0lBHRgNCAG97wZduf5v6sbLtuOy1ZJlimmUjjh09Ybbu3A7Th4/9tB+4DgjLh38PicMql//s//OT784Q/jy7/8y9Faw5/5M38GX/M1X4Of+qmfwvPnzwEAf+JP/An8w3/4D/F3/+7fxWuvvYaPfOQj+IZv+Ab8q3/1rwCwmPf1X//1+OAHP4h//a//NX7u534Of+gP/SFs24b/4//4Pz6Xy6EzUN/BUBpKo7awU8uI/ZSJMA1+6iRuveHxaGxCFXSW8isD7aQ1thpgn0SotkgR6QQiEat0JhpdkF3obYwJBQ6xzmYUJABwBjaICUUReqJhq4XpOOnhkJwLEIWGdTbe6o36UO+DoiE6qaXO4K4iNCDqqM91C6ZwNJRRZm6W8GDmHAZK/NSM5qNMg82aX6zG0Qjc+kEn0zvGgJQKKtzFDHMXjdlnBJh9JQFBHtnJbwvHJG+BCt05PkDLqwx4zMAlJglBQURmQgYMohZc/yxOQ/fE2atHhWlHt4Le9dmWdT0dWgSS3AJoqq8gWQvmwtQwdMx5VBgYg+5doKIgF8dx3HC93lCK4XK56HOGalCEtFd/XNZtVJgfZ+CR7/n4+IDemyS9FjJQRFDpg5mge0HdNxxtYKsbp2On+gsMiMKieLjgJUbyDo7UMEtxHF7X8IrYSAXvZjiCEHX0Bu8BGx0bDBWscbHtgGSEmnsDCY+TiODu2EtF2V2MM+DxOODKcg4Z0WFqNpdCxG6O7a5id6cR3hSwKEu1hI3VqN0gEoOYdNEamY8aONkxMEYDDsx6UarQ52Tm4pw5tbmjjQ23gxDg0Zr0B1kHzlpQ9kEaBlwkhNBqFtDhVOmMZnAy/4xAj1WXCpxq85bQe4CqzzHtWQY65lVnQOo1OXYDMdGWzIoJpTa0dqj22+bG54DLXwbixI/+6I8++fcP/dAP4f3vfz9+8id/Er/9t/92fPKTn8Tf/Jt/Ez/8wz+M3/k7fycA4Ad/8AfxxV/8xfg3/+bf4Cu/8ivxj//xP8ZP/dRP4Z/8k3+CD3zgA/jSL/1S/Pk//+fxp//0n8af+3N/Dvu+v/MLMp+ZU4tY6sYTvmNUS/l+THYKyQ58i6HmQ2YtyVQSJAUIxmvwcA1em+ZrQmiYGK9KLIgZrebGzGZgRLZjnFJ4UOy0gJNJmQWuMd7nRuCE3xbVemAUR4XDasmqBgzOcew9Fh6PAvPAZiuFdtV2yoT0AKQTCcBORnVuZiSXg0cjvz3HmCQsmkg83zOZT5Pua2sEBWtKPJi31mESoNxKwWWrcKuEzLSEs0lRbzUc2EIqIvLts0/MXPULn8baVPujoZEBtcVYsliqH3kgzXJUBZawRO4AL0hV6hFFDZ4rg8zvOYIirBk/VgtKVGltQhAI2yFkwj00/wpIuS0GBh1vvPEpjNGx77t+5zNjBxjMjNFRfJvOisMsY54BNnwPHMeBT33qk3ov1mfSMG21oFjhiBsEUOhML7nfBk+Ls1BId9qk8GI8U0OkkYRN54kRXDsyqzLDrXHK7nFrKAigH6gAajCbJexG8eitqv0goc7RUbzofpG1W71iL0BVdtG643YAR48pYGyCDvet4v7ugr0U3NV0TjrjCXHrzEKZfAvAB89M4dQ/lMvSaATkqEUeyn1BBXXWFgsALxwBtJcdcdmninzrDKIJJw+xjTti+KSlE1mhAeKkBh5YA6bqDCaw4af7cO7hlKvLksE8T3xhUQki23UyYEzmatpasl8bckwMZ4sZfKO7MTNm2u2XIZN6+fHJT34SAPC+970PAPCTP/mTOI4DX/3VXz2f8+t//a/HF37hF+KjH/0ovvIrvxIf/ehH8Rt/42/EBz7wgfmcr/3ar8W3fuu34j/9p/+E3/ybf/NbPud6veJ6XUyQ119/HYAKh0iDgFlbOPP5Vf6dkaTPRljpfA2N8BiMfr04C4SCqvo0CmrYU9R8rvUkfKUf6OYlpMIHn36CvcwQhZG0UfiPYqXCzHNjAKlRsBwh73nWxoTtmsNbILwwRQ9lLrl7bV1jiZjadkA2Wup6pcCQH3euEPG6nddmK67PrGz1k+SXtrxY3R5fawMxy1yOXI4EhUXsfKP52ZaZnS0Ha46ubKZgTE3CEvx5gKrXs8FT9zXrQ5yoSiLMyzHduUcm/8u2gZgMTWZKpqzD1x6AItJIia6hXjo2emcmfl8cd1Zg8TgPvJeC1hpGbxLgVP+VxWTAjdHxqdc/iU+9/knUyh4kg5pMJ7uL19JaR2i8DMzY/Ktr5+0OtKPhYx/7r3h85GTeWiuO834OANFQasVl3yRiG+AYVu3RYP3i1g54BPZCctFUV4HxnB1Lw67P33ETxhjo4JSC23FwvIYB0aguMSCDFQPSM9UY+yDFewyhCQOGJqq9oSI4YmUr2ApriUd1jNjmOB0SDliXu1TV0EA0IFVLzClzFJ1n+In0WpCwgMosoWPpG6bdmT1dmdGPgWg8vyaZsomWuFF9vbCn6G4U1XOy7hlTHWWMNZhxWMy9RFORZQ4RHSzJPqvuuRrBtdd1ZhM6zkATavZOHchUNJkZ2qCcVGsclzIEs7MXNANFIRXuuP1yj+oYY+Dbv/3b8Vt/62/Fb/gNvwEA8LGPfQz7vuO9733vk+d+4AMfwMc+9rH5nLODyt/n797u8b3f+7347u/+7rf8nIQFQnLhGsSllDXJDIwGX35hqLN/zAJvlsgJ51EvbRix7HQQES8RJtQIaJKtz9dnr8h0lCJgmNgDxHwrOugwZpE50rlkxJM0ZptOIQkIDija10ZpgQMdQ9JATMU9fQSSFmsWuOha55Rhfjt4DBoAZNbmT9bO1AScjjYPYKoPnLvYeXBzuZdj1hIR9oAK8jVQqoahae4PRszoeBafZVihazZLmRtNLgbhpiLnFIBEXM+9LSQhjNM95OWnJuCiX+e1J4UXAmE9jbyuY1LMT9+fa6CVHRz5PYID+NKA3NeCu1FIKJgmg2/c+8C4PiBKUyYItNZwO654ePECb77xKbgZLpfnKEUF7FIByynCqeIx0Psh6SOfaENEoB0c1fGLv/gL+IVf+AW8+uqrk47uVlZgF4OsO2cdI0Bm57ZvAEy1h64mcSESg8bJSlX2ARxmeJCRbb3DRw7kA4MUiHnaO6rR4BYzlLsLLlvBBmaBhPeGZKDogPJejWB92WTEDUBxGtdqQKmOGMBemTFm0y2MTbOprOIBIGE8nTXGe+mURatGsC2lbiLJkNyxFaEc0gaMfP5IpgMwGa9yUufgLREYB4dUohSMugKe9X6q6Yqh2IUM5BbOACEGx7emnZqQe2ZBvvbt3MfcxLI/TgJFoieyaUNNyZnhhRr7s2BBsoir2dgnojROa/nZHv/DTurDH/4w/uN//I/4l//yX/6PvsU7fnznd34nvuM7vmP++/XXX8cXfMEXAJAhUrYQIHvLkZ49qeeaqntq+MybnQX12WsAenuYJIDMEepR6drPsxCdfT3RkJL9LFT7k8gECU3F0gFMI+3B0fGZEWXvSQBAUdqtzv2zhl/i75jGU8V0MXSoQKzvLuIElMZXWgNkM6LZcnrroc/0lUlMPM0Wzp39WvbyhhPcl6RHByap5BToYwKomq5cSxIvxlSbcJYr9DhnZoJIjStWYgCuvjnX4fT5QoYb5qoNrRlEBiruUFB0ZXy5p8KzuZPvModdYrEWDUCO2j47VH43unwEoJZiGIBLcWwweFeLqamip8bMdr2xl8QAxMDtdsPRrui9Y9sq7u84ZgNmgvSSLpxBUketVB1v7eAeliRQO264XW/4pV/6BI6j4T3veRX398+xbRsn82Ld98u2Matwx2bs73rl7qJVA8Jt1ky2siGCIrOAzaDm6AMV4LiOg5NcQ/Bkj5iSQD3YS3ccN+oCVjbKb1vBZhxOSIp4hwUdS5GzGkEWbW9DYqYHHIEutpmHiEtG4znkTDh5uiGRjmzylaHgmB9kpndi6AIKjgegRl1OTwa8OjzGbLqdwVGsek0EGXNzbwYUJIIUeFN2btlikKgBocQMAskobGLNLVFnIBGhwFDgZjqb/H6xAth56umEss6UeBSQLTM2221CpLAUku6d5AoAS7WnFJRaSBST7RiCk1NN/7M9/oec1Ec+8hH8yI/8CP7Fv/gX+DW/5tfMn3/wgx/E7XbDJz7xiSfZ1Mc//nF88IMfnM/5t//23z55v2T/5XNeflwul1kYPj9SPgZ2aogdYxIDAKjvxPAUehuZG027vNpls8DIOtFMiWfElVkIMw0BiZDeJ3Ik+ZOxzoJrklSAvD5jw2s6mpmhGAkLU10BULTMq/RzwyY/QbIskVD5vIaiTCKjFoOmCJ9GRgCYGHSkdpcBWTxPCZiXGWGwZYTW2oYcCNct5zSxNqHATc5qQmphkxaQjnHkKVSWExgYhpXpCLaYIa7TCMzoL79t7/O7J3Rp8zMc4doFuT6WBzXfh9eebMQFGWf2c/o8Oe6wmCjdZDLilK3qURQpz2DjnI26o24V/dbYKDsaRnS4O7Ztk+r8TrFYc40BIRUmAykoKo/gGrTW0doV7bihHQdutwP7peKVV19B8Q3bdkEpG/UUT8FJdaeckdQ9SmWmUQonOsOY9bbobOXWno0QO8wNuztuHtis4jCg5XRQyXVZqXJuDffFEHd1CpoGOnK68BBDMvvdGKEX5MwsF3xrwYwpRrIqA8MM9UQiAmhDdKomcjGe7GdAcA1Sq7Mn1IYkJtHO9NmaoX03zzq/68AgW1gIAutSJT9kOQ6kbRJUOKnheexWTZ3BLkhoAmtz/RREJtxsGHRu+R5ZmzwFXHyMFVxHZqcpfhBojc3ymQEmMznhPQNYY8s/tax+UYhg01mrGscvg5OKCHzbt30b/t7f+3v4Z//sn+GLvuiLnvz+y77sy7BtG378x38c3/iN3wgA+Omf/mn87M/+LD70oQ8BAD70oQ/hL/7Fv4if//mfx/vf/34AwI/92I/hPe95D77kS77kc7mcE0XZiOuOkGaUzZsjPqTKIxlVLChqpbdKa5PRl7ITxsbOoaxgKPJwaVGlXhwGDakry0mzNNcOCQGBUTxibvrM6LIuTomRIvp4QcUg3dUWnTgvfhrKCI66jpjO0jJzwxAUqguI1AE8NQYZpjMx0Kkmm2/d//WdqIjBHh7ICNLW29wr6agSrhwW60Ckg0J+hAOFhBKTCOdMFLFIAcMxxTuTvRcWUijP+5QF8Ryvng42ZtZIB0qHl3UtWyd/bTId+AweZs+T1sH1PrMwDQYLFByFpI/4pUMOL784a4FLsio3ShJtto1znm69Y4yCy+UO0N7m5xaUss3sP1d0vVV+Z0bGtTpC1OlHQCQlh3uFW0GtO6oyoZH2TAFVMkExOrYLazmk1nZBiGSWhjKNkv2BglSH1oIqPxXN04kV5FRl94JRFWkbcDtUWwyno+4d7uwvY+1YSEnCsIKHw0N4rqF3XodDxApJAuVn9tZINlAGqts47QcJNpIjQsJ8UlJRRtFGBgFNWfo6VJbbKdbZjhmkqqJtI63DzIDZh9dntuNyGJkJTcKCU2+SQXN+tM19kk70FKvN6zCk/Vgy3WT18RpGBJl6MaZKR2tdcmZ06Nn7mWQxtyUvV6sUL4Dp0HrvZC32X6ZM6sMf/jB++Id/GP/gH/wDvPrqq7OG9Nprr+H+/h6vvfYa/tgf+2P4ju/4Drzvfe/De97zHnzbt30bPvShD+Erv/IrAQBf8zVfgy/5ki/BH/yDfxDf933fh4997GP4ru/6Lnz4wx9+22zpMz0SIuONC8ADMTgynI6JUWw2jro2SjK/ZuQObvAeXf1NjIqSIAAoCjYo7cbsFyigygMjZ+e/3abB4IsZxWXExMudHhZ5+dn3w2xHKtIWEuhc2eCqidjpU1IlYUVF3HinfFGHI2QhM9U/P8p8tdIePP09v87pewBYo+zXWoWyH+BEKtFaJOwIZLaW4JJL6p+F60kYWR+3Dt5c2+xPCzLLDBQ5ZYAvUkEyELO2lI6Y61hxxuDzK8sVaR9x3fgudgpCcoU4yE1fD5kZ5t9N16+MWY/J0ETQUYJRsYEagSzGV9wVm2wpRRO6b4uxeN5zFnnvM3fkMS9e4XVDR8P2yv00vG519jQlXDhGZjGMfK2QBOER6Edn9tID1SvCpCiO1Zw8Bs+Hic4/EHy+UI9+yqSGgqbeO+CGbjRqdXMZwEItxjT2tXKe1Ei2HJmFoytIrC65o9B+9tW8PAxWtykrZEJVaEeYnbcxEHMWGA1xS3huBpiS3FIf4wjWEUP2hDdBxl+RiZudtD21H0ZHDm3lc1TPC0xShMVYA53lTXL/LuFq1Vbh09FCDouN2OfsK2bsOS3KiggVWAjhCN2Dkd9TGZ7QlUnKgHFPJLNSgQ+Ctm60VPkIzh/rT47CZ3x8Tk7qr//1vw4A+Kqv+qonP//BH/xB/OE//IcBAH/5L/9luDu+8Ru/EdfrFV/7tV+Lv/bX/tp8bikFP/IjP4Jv/dZvxYc+9CE8f/4c3/zN34zv+Z7v+VwuBcDqv4gJ/QA4ZUkA132AUFDeuIGYkYjD1adCLzF18WRMy6QuM/FQuYoZVBoIpdyuiHXSo+c1LCrCvPY4NQOfsOs0RFRkz1oUuOnVC5YuFtBmTcP70k3PTCajKuQrdN20/6ds77RwZ/2xlx9PGkOf/OL8Huu/T7LIfG2cXxhyiGMa9ZDJ43eIJ99n1g9PkOH552ROkpWVRopR8LxMTCecUWmsaPa8HtNNTzjwHAKs60/7OfH9aQTe+p4zC1MWFTO6hoYFBufDeqjHDADqhF/P2fSZzRq5jLa+YwzAUKZR4vBK7X2XKoF67maCblT/ON0dPFyveP7sGcgypAGmUC9p3lS01zgJY6tDJClCwd1WHMOSZXta43SMzqy2AIhSmEGpyT8dV24VwsgcqJh1xYbjtLc4SHIoO5oBmeC2vC1jJAmA2VrvfQ2rTMjMqDfJ4IhOg8hqYPTGc5pI3rwnb2+BVzuJ9oJEhi2VWCCblcGksfG5t7eqkLs7FwaZ9SEvgLs+g6tzMJzfO3idIwZSGokOVYH5WPslSR3uPJXnQJY1Y64hSR+u3ilNFRi0bb11HIL5UlHd8fb25eXH5wz3fbbH3d0dfuAHfgA/8AM/8Gmf82t/7a/FP/pH/+hz+ei3fRTPuVBqjFRR9HxXnhCoX75ZmYfHTJCRpANzm2M7ki1HKb2YdSe9q6CQVftaJAPMjO7kS/TjleEEymos1QHL+g8zvXEyfGmcnn6/t7s3Zydy3uBP63Of/vX53Jef/5leM43wWxOwl94YT+38+sBl8LGcYTr+dEYvf/75upajOGVsOvCf7fuef/d2NPR1mW/j0M4/O/81g4VYwcW85rdZhszAAsxQp0Ta6V683b3UV37Ld3A1Zffe30KFpoHha87zfZJoExG4Hje8crnDrR1Uk0h4Fcxa23jaSB+RgxlXf9o4fd4YqgmOmMxNgO0fEUspZl5TBIVz5yHKfi8hJa7aoRwW1eHF6KvyHKcEJhDAWPp9vYsQIXV7jJzbtdpWctox30dtJ4OEjz4Sgcl7QuM+P1dnPRvjZ0YfsXqYsJzik32gSQMzwB1rbUbkeofauE7vC4h4hJl5nfcPex75uUua64TNuFpNpHfqZvCh+pdhNp8T6qbVNK1V8TIz1yUs29GOQ/UrCemOXwHafdWlwm0aCXByUnOT67nlbOBjHaiM3IsNqi3LVSWfPyV/ktEFj9PNxOqXkkGcafNLUc8TozUP26lnQtngebbN/A4h2ObkwJYRyv85vf/JWb7snF427Of/vpPHy0by5dfO6PAlox04UdA/03ufjMnLj5gp2Uuf9zbXYXaOChLXfzvqKzOJ8/udHV6+18uO6vz9Xn4u//K231DwDL8nYZT0Qi9/uacO0b0s4zRWlH6+jic+8pTtngMiQypOJEiDld2evk9MUbhTQMbDxZESvBI6mrqyy6f3Xe+j89IHJwms61/f+ZyB8z348SFiUVH7RCqLFLepYJ7fi1enunSImGDpYHJSMuHLWz/QW189Rup/nOLL+R3PGc6sca81LnlvdG3plBhaLvIDRmBYX3spgCSNnL9v7tm1j4RoKPAaSVQ67/uIaXeSMj/O/z7bqJllJXNw2bMsfUDtGLAcP+L8GUj+mBJQwaA90Yjcsox1MujuGOgU0TZKQQ0xbKP/MihO/P/bw0FsNSfGblIIjUjVYj4mvCV7MNkxFlNJOeBASUUEiWjOP6Ia24qKMto9oUZPIr2QsV3H8KXIWz+SgM7JwKzY+om9eykLOxumPN9vC8HhrVnU2xnhT2eU833Pf/9MTm2+19sY6fPrVr+uve1zX/6uZ1jv7bKYc8alO6gbc/5sdoqMHA0yv+fpLn2a9flM2efbZlz2ds7/5Uj5lHWf7iHF/BUoMX2fGdEYT7Oh8zWer//89zECpVQqVwBcnaJeKVHTgafOL/FBM6oD0AgHEB2bV21zQ9mWUO3bBUDzO0G1lpccYmttZnBjDHitMFsjXhJGcmS2Jk3BoY0/MtJfAakp6zIz9CAzMgD0zuGXfVBHb5yuuY+h1hCf38UlWjtHY2irpKM1uLKVZLJqTI0p7hFRAMngtawDz4Whgxnrfo3TvczgYbnv9TD3t+wv7n1JXsWi76zMdP1xH5P5a8Za9Koi83ys/j9B0wHUrHkZJHkFwAitrsA85j6Gq3dxK6iwuZcB4PqZY9b5eFc7Kc6XIejtvrTk8tDngnkaq4gnkkhpIJIubiClVftJNi7rWUEGzclxZOTGrfG0B2s6GpyyMGVCMd9ApPTTRnxLHjGjq6fVjSeO18/P+/SO5HPJnJ5E4Z/ldW8xkPqy5/d42RFOo2y5FE8zlXOGenZQcbq2l53HNFJ46+/OEbwnjPbkc+MEJfI+x3w/RroMcmLupfl70z6wfN9YDiivF9C91jeZxurtDfvaYT4/g/9dunqtkUxRalLiHW93i87jxhO+yjXctp2DD2+3CcOcs8HQ2vB3HWaO1g/UsgFSuTh/zss9OhFsRjWAI2TM1FCr/eFAUr9dNdiezNqIJxR+cmPKNJxVCvEA5rW7IKh05ICGlraGo6fOoyBBY1YJN2xYrSaz1o3FjMzPoKN1cPB3phPaAyPtC91DEaGK6IAIJRLuTabu+XalY8w9cYZlE9ojWSaz/WWFcn8CS/1lmGncDokR6yzbpNAz0GemNnsW8/35TO1VYwChumDv8zSeVE4AE5tvwrbaa9lOEwNSjze8U+bEu9pJcfLnYhZNcD0C56m7S+lAkIARz6aTkNvQwXIoJLNVz/B0Vi+nzWYno5AkCL7jvGnIvXN6LU7RN5+NVVvCfN58Z2HH6edo9HIzp6vExLzf7vFOnM3bPc6Q4fn1bweBnf/+FtLFy45zZg6YFjgdLzH4gA17kjHksZjGHysJW4fKZqYLpPTT6vPKQ8pDtNYzTgfmSQY7f7j+m/sKL3+f6XjePgtd7/P093kHQ4boM2WXs36jCLiPhoJNr+UU5FyDRA3m59hb3zR/7u44pM7NesHJS4eaabcLSuFn9jhgUqUg7OVzMmwEdRGzUT7fsyZFLQzDMxNhdhjB6+3REX1MeC/hqrNjTSjXcK4DyfA7poPqvU8JIJruRZ1e9TJXGxPh1H6ebp2CtacG1VKK9uSY9bIkdKSTCKm2rLU1hGZaQc70tJvfkl0+Caiy5jXfLxOwOAUu6SufnsN0HhzpsZzUeWhq7v2uNTIEfKJNwSEQspOe2dJsUMYMmPIz85sxYHD1R44pVh2+nFrxT2+vzo93t5NKTr7OtOcNiXQ6TxlLi/Z8isrxVkNiT242HdQSTM3nLGO1Ntf5XeL0zHWQYJklsH8Bp8//dLARTtd9PgywxXzLzOXJFbxNRvV2DurljONlDDsffoIYPp2TWm/6ma8j32+qN5wcDw3Q04bEeYhfCjjy851h6sximNEl7Cv83U/CmgEVyGMamqTzftrv9BnW8Uw6OBubT/uayNDivDe4q14e1aF3xdpT+onUDhIym6PAlemt1731+7x8L2utOI4rWm8yxCvIKoVEh9ZuqHWfLFbAxDoDrFbYUEv8ZG9hrnPYQhDm/SokHxC5EwFgiCKge5RNr+m4Z9G9N5GmbH6XYSn9o/OlfqacNrDuU+ow6hTHqh1NR4jlCBNai+lgntxG/Tc/80SEwHIWE0I8K6CcAlVkcPX/Ze9fYi1Zuv0u9DciInPOudaqqr2/7fO40sHuYNk+Fkcgd87XRYCFLITEadDCbtCyDA0sWZYlN/yQAdFBNIxbFnLHHVuiw0PmIUEDbGFZWLLMlVv3yuLec873fXvvqlprzUdmRgwaY0Rk5FxzVdUHorHvd7O0aq2ZMzMjMh7jPf5DIKRoIdoIKzTaeo9ezW37XKqPcV0zVuVZLHnfx7WotDDCJrhVn1nXmEFLuYYbpCV41/fq+9Qf/T4NVXiQYtGkNVaiEdDPHz9sJhW9EqyssEFFOnXVmZX9rpkr9VDoSp3nTsoAaWCmFfVXqIvXiIBS1XFbxFU6X59d/9JVg6tScmWinfTzKe2mmjvAXiHTMbKutc+Z466ltf641pKuz1em1BzVN67pntaG+haDqkRqZVLdeGkt5LZlUPbUVXeqmlNFtKg4ZK3ChN9TsnppbEOG74WKilLQBvLqvT43Ttfj097timFtxmCzBPv1oi491bXiBF97rXl7W02PWKOlbG5idHOK2yz7d9pU6O3eqUavNb9UrRggwm7cMUYrsGg4gpXYBttXVheepVj0XwyrX1J93mpAAp4U2l5bqrOeFqFW/YnVdLauSbsvu68qiDqwQ+w0Ft+XFaWE0rUv7jdax9FlFDPlVcZSp0m2mnwFl17n2+dVjJmY8KtufpfN3tzOd2NhtapMY4g0rSU2umXMPm/m+jVTfGVSW01LaRKDrOZumrBc6eR6b9tn2fajgxE2K1BDX1fbsxuTH2z8fZbzWJlV7ujzL4C5L6VIiuL4XYJqplaYbKUwiqEVBNbcAKlAhwCrjabxDlNgbKlWoaDZUNW+a5Aoijs+V0m/N+m9pqXUBOJrJnVLO6l1lMAmv/6t/l8zBbAlPtfazq2+9Av+uo/Nzn/DSftCw+kPW6UvGFz/Xi0ct5c6napVc8eGkNb/rt6vPT/U+7VVSdZSzNSggkhZcRU3TJvWdt/Pa+Z66+/XxYq1f9dEZDOGlcip2++zImLFNW0B9cypks6VGOacmee5BfeIpO0cl1pbaiU812ZqkZdzXkpBhtB6WZaFeZkYhsHhkJLlDRXvVzITo4W4RwsW6Ci0CRG6zq8PnqJudnfzJVWbtfwoqeY479+y1H1G279Ng+wIONBAbBfXlnvhSgsGxotaMm/dv9QcK2kINW1NqbaqCbQxW02DjduW1c3QC8lVqKprogm4rJ9r+YtyY61cmwT7tXVL8NSeTlwJe3U16dXf9ha94BQIEo3Zz9lMqW3f1ns9F008craUNRpU/JnVitG9d1v7X3D8sJlUqH6pGt4cKdmjdDS4o7Y6KFfp3vZKzXtQlzYNniS4ilzV2coe6qRXX0tELAMepdVtaTuQrcQOXDODNnn1+f5TGRx0i8+JStHVQFSlr9Wbsvq1+vfsc7caU9JqemmiVmurAkeurOMq6ovXtYuNdncl5W18CY2QKtX8szlqmHa27yra2jWjrf3dai8CQSFXJ3fnyPWCe9eMqD67P9p49MStf+d63ZVdXVufQhN6DP1cW+2ierehOph0LzqgLKjO2Lb0eQtiWFwI1ACGoGhZuFyOzPPEMIyoJlacCztyWbAI1gp1U9evC1woNRCjlMLxeKSUbHBMbW7hMk0UryekKSAhWqJ0cux5MeKuIbbcpxgTOS8e/ZWaf6hK8CHFzfoA9yVpaAjguUX32RhWhpSz1elIUoFQq1Zhgui8FKY5O3PSNXm0JuZHm5usaj4rX2/qJl/FmYYLPxbwIk4n+jwum3sr94EzuD54RahM1VdrWxPqwAGthLq3u1lj3b5S7f3fHZPYMC9/D/WE9rL1tQbEq4SvWKe2p8xXVLW5mlxd+6DqgRtZm8Bu+yZQQ9h7mlBU23lDYTFrQHCNq6G0/yIwKUP6Do0QO7VBxbP1ixBS2CwoxSelqto4Vxex3AhdaSRtE1l7G23A4ZGk32iqjWj2k9arwvVoTMnvrQyznqsN91Kv1k2l6wJ2wADrl/9XiXm1rV9rMS+UbNUO+oeVeennpZ1bxF3c3LEBwe2Yb332Wlmtaq5U0b4961p6fM1XtOllm5+wbqh+DK8kzjquXLX34t07htYz4lt9Ul3RA+p1RUtjuNZnXwVqpkgRCxwwqMLowLvStB1r37wu83Lh/YfviCG1QoVFDQ1glZyzE9AahVcJqDEoVZyAwzRfeH5+MvDacdwIJiEmdmnvxNuw7FQCWQvjkFCyrztjeA0Z3iGTQEgxdoCsLihcjXMzZQULyAiIlZ5QA2KuAkAIwWCvfNizm42qPoQLFOrLq5o/G1MUf24TX12ArYyi1OCRqrUFhykqhGht39JOStXgr4Q22hy6KNvuMYH41lq7tmys67dd8WId1+9Nc4VqS9wIi3WcOmF5faKNX7ixR/yF2g3rK0jbu5t942PfaHOw9681uKwI5C+Auc8ATjzcF7dxqm2SECIlrJy8hmt2sn7TRkz6qLbeVSO61nygEkPlak3Z0REv+7hdgBttSnBw5e1ivH5wW+jXiwPa5lpNaLK575p0VsLTb672/FvnuuO173otaaM5hrC5b8Ns1l6+aIfr8fDrRG73q13bvTfefoMC6ojg9fusn9e2+2PTpqyRoqr9d+t6sPUj9axL3S55V0GqPi6ImYq796zVTENMDGloJhitTF0Lp/OJDx++Z55n7t7db8ammjBFDDGloihUX5L5Ciq8ViEm4XK5eNHDE+M42Dj0rx0C0zyzH3d+b2rM2oQk6fbHKqSIgLpmhQhSTHNYgxZ8Tro5U9UWxr6WtCgtwqzi/G1yQdRCu3O2cjp1X4To5rOyXVPFMQv7o5Wf8O9rAULbYDYHBlMlrcJ3b6ZrpTe6fvfCVb/2rs3K15aKes2XmsPq0YTXuha7ce6l36b5dIJba7MUB27u9tRVn17uxdU0XK+rtHZjYpSKyv+6cHfr+MEzKQ9ZcMIoHoq+MprVFFYHrHPUNy3FzRW6yrn9AIqbWtSZGVVKoYuuq4uuu6v+ttQarUt+vaJTNGqbNyWqri+N6NeWm8a0aXx7Xyf1XUt/nztqn25FJfWfN76QzdvXkPCVQPfMqi+E6FP2op9XtL210Q9euepD0886wSAGd3xrP/auRV+ZWm4eqrSigv6SAmutqf4dNuCEwtbQV+91Yak7F4KgS+Z8PjGFM4MHlxgyQmaaLhyPT0zTxDiOpjSGQErV3Ec3XzYK8zIxzxMxRSvpgWkn02TlOn7yuz/heDzy7t07xnEwLL4uhaAU5W5/sLDu4mXhSyGFyHmeLIAjrAQydv5GMx/b+OduTmuk3PV8V4FylbYzPRGtYL0KrS6beP6SCmjuTGg+Hz2TKJUIyzZ3TL1QqskMbtoVWJE3VkZXn/lyedwW9K4r8l7fc+vo120vXF5fszmnleApnc60ajNg4f7F8TFvaEx9Cz0Nke6c5++2Ppbmk78W9l4KmZVJ3WLMrx0/bCYlFX7fpXcxU586FRK14meUCkYPdeCCRC+1bE7dEKsq6lf5BIbqxPYkOyGuUCq6BhageKa5T4iyWbC2zreLtGput47N4rvSCExC9/O1jeCZ5t2z663X/pXr49oceN3+tbmyX2TX2qIRR5eUkLXScK9NXr17e0YVMK6YlFZucDU+11JauNVOJ1VWZaRKesborb9Fbkt+fVtagVVbJ7bMcqun2x917qsD/kWYe8e5qmk2pUhRmC4nppJBrV7PsixM04UQAuM4stvtCSGuY1774AJXZWBSzD81ny+GuJBnL4S4cDqduVwm3ry9Z3/Yt6KH2s13dv9enYRpXhiHkSXbuZKL1XHS1Q9RcdlEhKUiL3TvfYvwbs1avSa1rq2apyRd0ms/372Unj0/KITV3FeBKvp9YcgIVuuoCTtq4KvVNFoZVdDY9bWacbuyHn1e3yu/v0RL2ghcV/vz1nMrg7ElZX2tDLWi5VTfVlulHV15IbxeCZ0KrVwIsAp7V0J2f7tWP17Teo1hxmh5eDH+AmhSjUH4UbWLCn5ZZ0GlKj7dwEutQWOAl6LVkdwvnjWB0J5vZoWNvCFCbq2t15mfy9utk28740WYdH2X1re+zUocZb0+dwuwHqUUL8fe+6O23/fPvzWWG5PcFfe8ZnCfMr1ZOoBjjF0/F9bMdh+tDaGCzYYxH8U2aOHW8QIXUG0OMp0EWzdlU/MqQoG0Mb2pydY+rY+4/b0LNfaO6/sgteJRNVH73yEYVNFmaO3+IQnCjmW+sMwLFuYr7PcHBz6OxlCC1ZzqexZCtDf30hspmQnPyn0s/rdXUkX55ptvCCGw3+0Qr1GlaX1eGgemZbYADBESgUUhKp68W6yApBZCjFaOoo41yjRPLeChMYtciMkK/vXBLwEhL0boe9NQ/akguWlIKGsVWvF9FUIgOHq6KK2cj6JembaT/sN2z61LR933BSKhJRvXar7qe7qFWasFYVTaoM7UWlCN2Gov2jFml5qK0OqP2bPcH1aFKfH1+soGuF5/9e8eMb6UrS+3z9v6FLNswrWPSumIq+pKp/p93Fwm2pFbl9jqLq7CRjWbfu74QTMpgrQaTKo0E0v7X2rggFZXsX+mXdcK8alsoHCgLoDrfITOSStQfWLr9ayWH+9XhSnppbyehazSjy9Ke1gj2NXepS7dtY1SV6Ezw9gFmq33vTQXaN+ueLPdhq39qNUGbi3mlfBLf9f276rlfpnA5Jvrpb1adfuQWyaVm+dY56F9d9WZpglJ1e5eho2vZgoQwub715i7APRSaz+3mz4XVsRjYyq2RgspJVIUckqtZIWZnu0aCwgwLSGGRH2Var6ygoKZIGYOrGvXkn8jh4MRoeARkFaVN2xSDhSY55nd7gBYxJ2EgDja+CK2s2pwQs4LKUZqrXJBSOOOvBhChWlURsCXpQvOqMgggAQDdq4ApL2/p2kvORtpL+6j0go5hGsPgoRAUVhKdi3DiXUHLdXaFwz8FEE1gBce1Dbm0lIbqpmSjTZVNoKchKsIOdY180kTW11X7uuqjKvjFpt5vnX096haXmXLSbxqq/W362tjjqus0eiNCq00yupbc62qPaMyRxPX63UNEEGt1lj6RWBSRTEV3Ye75NXO7VaH9rcd1cQjVLu8rl/59TUK0PUhdVQCP9umVtYH9xL2tSYAHqzVTWJ9fiOCm7uvnoM20d9QmrsFKh0RvKGJVSnvhRbVaRIv7qmtd5fZpS9NFM2ngK6F9ipDZc3TuA7hWInFdrOW8jKR90ucq69foW3GpP2WzXd1yFvfu3ftHdzrm/TRc/XdV02tmRlZN2wlvle8dm18FRuaECVVUMAAXmMYm9DUru2YaiUifZ9FxBIwvVp1iuoaWKFkmOaJIY0WNi5rzp4ZTtc1E2MipkQpFnYeghXv05I9D8v2UkqphYeLuEldLSKuVOHQpXORQNGlG19p7atUs5kFVZimtWpBbTYUKKshH7UnGHBqdN9UxeHrkpydYG5Nc85sPQqthnxrLv7MuGE218f1WrkWXORFe7q5t7/vlpYjL67fmkZb+1qFZmk0pp63FbLugiZIXj2j0szX9pV6eHnDhOSK3lnewTqH3ufUYUgGF0S+5PhBM6laMbOWgy/FKoiCK5eyEier9+KFwWrVzG7PV3DQWstUqXZzu2jLFOp9vhiUbun6wujWsrr6HuhDyrcE+lNagTZFeV04q6+s3xz64hlatpuqSkm3jg0T0pfnbzGMVVPQ7vP2/v5ic+KGdmHdSNX/sGHCV317zRS57UvXAWWF3ME2+s176zhXZ/6r7+s5L85fbd4rAdj62EIVLHwoQl0r0moKt5Vmj+6ZjzGKitBfF1jpNnz/k71InxXllCbVqlamWefcQZglEoKSohCCoY4Lq2mtF3oEE44ul4mUkq1jsTputWpTjMlMYv42S87EGCjZSnMENeYYRMwc6Ca6mDry48SxjnmtuF0r4Ypuk9qrEBXEfF422OpaZbBIQtXG6KpFwTScmh/Wz3Ul3asG0lJUXDP1ZumREkJYCzNCt+9067ethPkmA+oZr//dMzSpDb9y1HsqNFhoxR7X9+udItY/37UvaE3XWN2jV203H3x3bzXzXfer0mXAUUI6TMFXNMHr4wfNpAqFuZRtkbQafCeCOgBkf0gt2LZhUNIyqW0hregULZpPV2nYDn+AbrUicAap67OrdKzgORhgJhtnh500uV2N9b412IAr88DKLHXVYDrpCN1+ltBt9Hb/+l4dq3yxOOv7bE+sjKpJu516Un0FdVxkJQVt3K6ZVHtiZz7wHUWVEJsWV+f9ql+6dgETuLuQ4E5MbCPv41qxH6vk+4Ix6tpAk0Y7Cf/WXFYfXM0VWYdOVmLR/pPWRimlRbMZkVs1p3WTexshEKKNbS7a9akGspggplphapQhJfddBOdLwRFbVhN2fY4EYV4WxmFo/a8BCSKQOh9WFQrni6FUGEOKLZioB4utMGR1/ut3Nax7qXPgA9X2kBYr0dP5rSxM3ZAPen/RxuymBSnV31SDgkA9LF2VZqqzGk3BIYDcFN1Brl0LC8AmQOTWdf35ul5eM6X3JtdqibklRK0mOltjAUPUKQ7QvNIAf5/KKH0dN+Ggk3U3WlrXt9Uzqy2fLOua9mOJ3J6w68DYdX/nYlXUK9Na5oUvOX7QTGoqmejS2mai/Gi22XbUKLtOwqzfSE9oTOupyn0QJy7iJh7/Mf9QleK2m7Q+sz9X+7dlZ9fSekf6/P7ectsT5qrZ1Y2roi+ehsgmUMDMcL3zqhLL9Zk2dgAvATVfHFXk7JllfYv6rLr+u77UTVIUSsFqPHUYdX27Fky9Snq1v9ebdu3S+i6KSZVE9x6KbGBnqpTaBzqs0nNvsrSf3J/o2qt5yes+d8e9i/yGQ2dYcu0odeikId2DIgVPqKTV97HN7p+cuNfXCMG0KC3GoCpDq0zMfDimDcRgAQdd5c/2DsGLGhrxljY4IrAsmTiMZJQyTQxpYBwHAmGFIPOfGCOaPdDAhbJ5XqxKrqwrPpeMQZnVnDZtyb8qtJpuTcPNXZ4fmM9UlYDVu4oiLFooni9VAA2rkNSsslLRPnxe1W0VbW2stORa49GyCkl17VVG2Y9lH5nYmOgVkvu1RnUtHH3qGggt/6uUqjHiq6XSPWno/6sfW1pxx1oYsWqarx1NoPQBa64QMWGhitulmpYlEKKlfKQAOUDOoaUgVCaZfxE0KfXCaKYJVZmWNuh9SPbmPl1LOd+ScvrfPeOi6j51IamuExTUqqx6J6TaI+rkKo1Ibsn+SyZQ26oEYiPidFrb6tY1RlGoDKGjmN399b54ZTvQRhwAN6mI6Lqrr8Zuu3E7uWDDHNa325oj1yinNQjk0+aPrZbqT7q65tZRRyf4B7tOGi9tcqq2Wbr5vI1E/BpDZCvp6ovvFV3juOz961VVURDd9NOIt2tKrD6NWoYixLCCsBYTPj63ls3MZau212KBDXHsxxAcMUJNmxhTMsJcCtXI9yIwQLcmNdVCzmtfAp1Po16Tbc/aGi4eCFHzb9akaPUxWtQK8dU1ttklrW5U6YIY6jyaz63Shz7yt1+q23FzBuFVf3vz8y2zVTXv9f7gPoDpxTjryyCcdh0rkHJ9Ts/82k+954WmtU3i79fnLUZcr11tPXWnrAJoGxdoZUAQsWKUDlUnIhCj+5880bpn4F+YK/WDZlLUgVTb3H34r3bnlX7z+0JUXfUYvXaIajMJbZ5Bk3XXhUMlhi61Nam4Pao9w3YXnjy6MrRbjKpJc63V9agbplLbtug6jal7+I0nv5Si16PDm1HlFgPYXC/SFCnpvq/mg95M1kpat42yjnovZV4fvSmt37gvtJmrTe7Nbtrs29owR9E2Lv34rN/TIkD74XvBpKCtifZ2bf3o5lzzea58rD7Jmb9rnrUESUfYjABmVGnE/tb49cjomzF15paSgdLO89zMbSLSYYOZxpODklIkBFqV3hiEGCJLWSykPFpYfDV59UmkPTOsplqcefWmy1L3Zb23Rux289G+a2PfqUi+LZr2IjXdXxujtTGXtbpt3Z+yMVZfMQFpQso1S7rFNOp7XofR1/P92rkW0q6/q+vnen5fVGi+2tN1THuB5BZDvXXYnKwPXdtYDX4VOaQGUnQjQoX58qchsjLPKO6bfEW4vD5+4EyKVjirEbJ1hXWMqJO6N2euTEXdmm/Mq0mGNEpc7xWRtdyya07ihsJVTncTYUecKrPiM5PUNrW9YNv0WjcjdcE4MZHtJrBXuBEm3W3kzXBefRbWBbk5X0uLd2PVS1f1dzO/NkK/mi9qEEslvv29r0mm299b1n1L++nNJdcE4vraPtlblRf3mNCyRj693PCrYGCpJDZyWufav+/jWGpE2TWaYq89V61B2KLSxxjJs+U6Jddsamh6fae+/3112f6oIeelFOZ5InuEbImr6bWUgqS+TAsUKczLwqQLGpTk0XSq2vZk2zKyio498ax92/SpMTN3xPt8pZgo9FV/Sx2cJgiu+cadNudCQNFi2Jy24jYaU93nVH5V1+gmmTh0l2810FsMajufWyGrv+5TjKOnPbXd6+f11/YAA69ZIK7bvKVFrYJlNRGuDKoOwrVAakK7TYcgzffdxrMYLW4FLlXJyy+AT6o31V2rz03S0pfXt+/p6ecaskk3Mc1VYJ+6yTbzWnCC0tFhWiitN6Bdf14sx9rWDSLb/xZtjXULpFt84pGJnYLWGJhuF1798pbm0T3OkWNeMrLNONMFddRzV/NRdQhY/Xh1od7qQ8+sXuvfpq83Nn8/dtd9ukU0KtPTzUbf3tvexYnjtYnQGNg6R/bcyv4q8+sUJ5FuZFxbag68GqWa/V3sU69JBDGnde5N3jfmYR0LM/dttRf7bhgGljw37axCPVUBySLYCuOYLInY4MEBsaRcH5yARf61QJgb/dCrPdGbGQ081s+xmjLXygTdc9qzQdWCO6IKMRbCstj54hh+NWdN7TNqCcWtP+4722DwtfUjTbDiGvX+WrC7Fgi7+ei1nr6d6+e9MAWizaT2ck678VU1QJRrAasb576dzwmpK3PqVqm68Fq/Kh5V2vrryC+luFBKExiKr9XKqJZfBCaVQiB5hBHgUn1H0D5B224tov64JnCqNZrLF4iYc7s14lrV2qxLvyIdnMjau8o8VnnziqH217Fd/LeksEaAodnh6RfdDWHtU9JUNdXZe0v3mtvxqmNyS3q77udrEuetTXerrdeO1zY8fPo5fZ9zFaO789fvcE1w+udcX7dKvmbqUDzibCvSotrKG9qzqsm4E4z6fjVhKNjPsiwMQ6QUUFnLWaDXTvr+HTri0Y1PzpllmRmGoc23gvl9R0uMDclKwOdiJd5jimYOzNlMW5jvgav5CNESi/txNeikayGkilfWn5IzgjDnvJnjJo3n7IVOxcvPa8MyLFgUX2Vomi24IM/LZs6yw6GJCMt17SkwCdH9hcH3eb8XbwlDt9ZFv3bqT79m+kTqXvM1gk+7tgolfRsVqq0J1+37VW7pzdzt2TeYbOsTdQ307+gMqu7j7KXpxSr3KoLG0IQmE70ct1Kt4KE9o0JOff74QTOpGBMh1dwPcUfcSlCBljtx67iW3l+TxNfzShBFa5WpjhiZEcFV3Foq23WqVpNmw4hMGhep27LzV7DyF1Wt2S2rVmQfqDrKKrubRBskNA1wswQrUeo0JDO30Z7gr2XPcRNXpd/1ncU7IFUju2Ji18Skon3U/wvaIv7WNm9rT7c0rNp7ExBfChPXR8XrazJB24C6kRJvtbMdvhvMnNUMt+n3JtS/ejq2gkpw5AaciKL6oi/WVtVaajtGtEqppeOr2a7v51qnuxJgkZdo3Os8WcmOnLMl5S5zuy4OCQFHrVByLozDQF5KQ1NJKTUiaEUPV+Za946iTduzRNyAal7LbwCqhehMobSVoxQvMFgJZim+49p669ehmUNTDXt2QSGXCqNkBVMbo3Too9L55a4ZeD1yN37X2ILXx6cY2Keu3TCKymVke91rTLKUbeCEdPPQH5UpflKj8iVbg48KSt/7IAIpeTqARYTaT2glSNb2Xobsf6kQ+oNmUtU3orBG6mwk1a20+BqRgZdS5fU94hRZKrupgJpN46jErteQVsLeOqcdGeonqmNK9USTeFRbBNLmeie2rTKpC3zd47bKZHu3aipZL7x+715rrHv/FsPon9EYdv1xqavDBGjXiXitJFbH/2vHlyzmW5pmY+3dZlWj2GtHqEyMtik3z7zq+fWzoeYLOS3w9dCLsL1GFfq7pXkz1xY6Rrfpa69x+3E+n+gjt7h6jq5cmTXIvg9NX6/NpTBNUzPraRhaW6UUSlAirmWIBVoEsSKjpJVZ1He39diXsrCBXqMyhYoG0bQB1yrrGKYY0bq2S9wwhWCwfx7p6FqEE14JqzYlOVuOkwuFGQPMXZYO8Nbj2GKw6sA2p9aPfhwbM3Rm32MR3jqqhlOvqcErfZDQ5zA1wYQUCd1c3IiKs745w9wI2ryQ2+2ZqxXhloVDRJzZabdf65JwAduFhCDSMak1gK1vq2AmS/zHwJpvvu6L4wfNpK4H95Zk0Yi5rBKszZvSkBu78Vo5fEcUGq3uQ91r5VXXEVqS35aQ0D2X7ql14q6/v3WPrF14wTg35oX+3hvPrJ8LuUH5aPeuq4bWjJpU85Dd3z+tSsk+hKUDyaRjWGw1lSrB9UyqJ8p9pNqto51fO/uCQX1Kc37NfLdO8/W79kRI2lqqzG+r9dVxrMytJxbanW8nfZxcY/Aw7RSjlWfviOL6DtaPy+XC6XQmxj44Y/ueViVXidG2eUUyF+mfa87sUjLv379nvzeQWToGXQWwZcmkFNyMNhpDS0LEzHx9KHslli3YR2soeadd6Uqwm+kLmmZ1S5vupssZxRpJCqvg2qrwIhSxGl1ZC1JYK0/ripBeiWutv1WZwmvmutpG37/rNXXNwPq/N/iIHc26ZhY227wQYq+F6vquIYglHt5o89b9nzT3aW29CmbdOOtKY1rKCzSa+JIBFuhBF2SlpZ87ftBMas4LSy1gppW7v0Lg6qasG0Z7tVY75hHcLwBVBKnaUICG2FyhVVYm2G82Wad2nT974vXEbIjc5sJ1UXQ0qBH+V/peEz5Vq2ll25xQzXjruLRvZHtdvzua8qHrua4LzbGrVcqSPojjpQa2NrLtXEMKYA1FbgT/Svi6adq71phZmXlPRPv7VRWtwK5+Uy89Nwd627TixPClM79eZgqOrtpbvbVydSrjssOSmt2ZrOt4Nw2t9bUwLxPvP3xHzjPjeGgEygIieq3UnieCY+yZj6bUOfQ9oRS+/fZbTucTd3cHmsnOn7EyF3uXaiqr/igp0IIctJq4A7ma8vzdN0JCsXwx6cLrKwBqwEPHO6YWO99zZYLZ541g/uHSzaex1RWV3mpNQcnaAoyKr9vadtNSZItQshFYfe56n1Go/t9ubRcfh5amUtdBk+q0zWmbrRsMqvvS19v2VCP6wUW+YmuggrvW561m0Rtak+/9vq/4mNVk8yZq1qUs3b6Wjl76umr5jx1aB2xRU1L6Mvbzg2ZSS1Hm3BEAN0VsfBdaiVslfD0BsvN9WlMIELRKSOt5kYq9585TARGvcOoOIBFzHlopnVXC3Ur/q5YRmm2uZ1SrRN8WgnT3FpeIZSuVGyFZI8kqgwmyNaXZAglXfdpyi5XY9ppK/91KbCxzvdMJpb9zbfPW0ZHrNg6VD4iE9v2WyN/o55W0ura3MpL63S0TjYhAMT+KhKsUghqN2F1bmcILqblyUbURWBHz135uysc7RM8aRGObeJpPLPnCMAyuEdhY5DxzmU58/Pie8/nM3d0dKZnWJVjRQyFicHViGG66cJkm0mBJlUqwfD4xzLl5nvnZz37KT3/2U+7v79EAYUjm/PYxDGqE3ZDLLUBColALsAdN7doqxOUlG6BoZU5F29oN7li35N012q5qXhVIKnhtOHUGUE12GoQlQ8b+zrmmagSvceUoHJVviNWe01IYYiCExLIUNKjjHi6NuQYpXqS0ZxqVZgTXGVcBKqMt36oXftaE+cokfH+qIh4MYULdS+tPb01YNaZrBlP3SN07xiiiC3pBVmGv7RFlA4a87h2LKm0m8WKmz7oPggtQDTIMLEnXzZcpVDeIDXatOK5qQkGpieYbnyksnzHz1+MHzaSAVf10Lem2ZKsNaLWHxGlHWDUfFXcQVqJTD5fcbHHmllG9/tCYTpV+ew2pl7K3+sDmNdqnyqBs4fgPYIXD1kS5Rr8F9LrPwAuGfeN8fzRVvb3HS8lr89nbvX6J3hxxbQa5xVw+5XfaMJXN5vo0oxKXSG5pTtftrn6xNYqzsqaaeW/fXhMP2fxWXTdr9Tg10wds5qcnIoabF0gSUc1M08Q0Xfw6k9jnZeJ0eqKUzOFw4HA4eImO4BrUalarfQpBWJbC6TStRDIOlFI4n888PT3x+PjImzdvOBwOjONo7+yMsQ56ipGQBu+fUW0jvrFpq9AhjXfHhgCLSftajPEV1Gps+fgHL2TaM4kK4ZOLsmRl9oCIJV+XaXdGUFZfGKW0mmEihisXNBDI5pfyxOSK92eE3Nd3k3VkFTaDE2EXxrQifogho9VovChhXYNtHEyorecFfYG6sDHf13WvSnVNNKaJmPmyrjssYMLSx+pa7BS3KkDVVSzdmncrSB/VCGsSxDVN6fdAaEnf2r68Fbnok4t0cxvyL0CpjusJvWU2a5JryzGq0nV7SNOuGinqtJSqdbW/HaqluMmnmSkCLdLpug/tmbUeTX12Fdm8hX7BtXaLETBjWG6njytTXWF6pEO+ezFS9Wr7fcXEbx9bx/otAt++u/W+3fefYkC3rrvF3K+1n74v17+37V2x/xvrBdayLp38uyG+vkBsHq8k377ttR3dbm4Rx4jbal/imqwrqAhWpFBVmWcr776WrVCGYSSlwG63I4RkZjx1dMONBrlGUw3DgE7KZZpZ8sycj5zPFzfpiZeNHxmGkRBMMyvJ1pxJw4Us2ZhUjFTQ5SGNiK5CSPXpAJsy4ZXQtjFX3K9lDCmEFeHcCmauPrXsodVZrXT9nL2EvRbmnK0USBUU1YULVUq2mlmBagZd94fgTDEWghihnPPqtzIGXDzYQ1Z6oFXY0M1G6yvz1rku3TXtUgezFWdwqG6qSV+v0XVt1TXi2omCUoNFXOtq5QNWQNlesLLmKiOy3KZaY6ru32ZarVWPO6FXBDNxl1fyuKjtr+HwItJcJ+JWCrgScr/g+GEzqc652h+bl3+NQG4GmSbx1s/rd92AaqdxKQ03DKE5GdtTtZfwOk1D15+VAVbkimr6WU2R1SwoUksMSNMae+J8rVn1b7BhsvLy/a6vrhJbuXVNu182AtS1BvupBXhL0n6p0WyvbRrelRZ0a4OvfdlGMH2KWVbitRV9sdyPShzQzTqp/brxgi6A3GCaV12opqP1S5vrlAZEgpfBKO5LypSyQ8QRp2XoNCnpxmMd/xCsWGMtC3+ZziynI/v9zk2JxkwshHwwM1uIVCt6E6JKIc8LQRIxBSukGKLvibrWa55W1QAMEcYw+MSjujzoQCDF4CY5DyppY2nvUNQYx1Ks7EZlTkULc6kICzhQaTXXmYnRXIIrlxCX+muicQyBGAZKyeQle8VQM1sVUTQrlJp4jhf78z16ta+r9ng7HL3bJKy7sZ7podXWO9a9VQGK8SrLjVo1xuXMRz2fypl2q19XhXDXXl3dNIZbVqsRdey8fWOGufVMghmUqUFPXeBHLhm68iW90LIVCkOLxszZx/0Ljh80k7olzV5z+Hp8lpnB1Ua/cZ3gqrSr/9UG619mp0sVp6rpLpt+XTPQrWTeo3FXYcyY4rY2zca8xVZCf+0dG8GX7efr6xrz6xjfKlGt2l6lYNctX5tb69/XPpzr41pz+tS73GrrWsJbR/y25tOIp7jAUZ3sfXt1DirxZ/tOX6Iprm3LRopQ1Y6Z1UCZyqwjZgKM5LwQgpCLEEJxBhVwmFZ3xVYiVhodqm0aozJGshsPBNeGcvEyNAR/d3uuaoe8r8p0OUOI3MWBZZpAzfS35MmZgjMnJ6JarrRN9xVHESRZuH4pBUpuVoJ6VAm8ln1fSm5144xwKkthLVgqayCU+RP9nWNqycDJ8wZVTQiKPs+5ZARnlj7vImsfYDETY0V5p5hNL7BGpjmjbFq3bsW+W1aIZg5z5uniVLunF2qF4EgX0oXzV0YfVvSWqzVX0citYUzZNj2ztbYxW7d/NQp0SzfMYhTae1amXHxeKmJHXYc1QKJy21KUoouH/y+UnJl/EUp1fPZQNoXiPnVcE7v69+ZeX8VNI7nSHtoluqpK5gC2jy+I9I0FnHuNrpOmmtblzyndoqid630n0v5bjxq5VJHOvSNbwvmZ8emPTzGUz433da7Hp8yFr/md+vtualxCK+t9Pa91A9VhBZfIr9qqrMkIRp88urZ/i7lKHWdZ38G0027zewtVEy5F/J7ihM8IvyQzWQ1p7MYCRKJd02BptmH4G19QSJjzvaAaKWp4eDRzXXSDcV176/sc9jtC8Yq8OVMWM4+lmCzQws1A2SvtVmJfmVapzLmCRiKMw7CB+qlt5ZyZF4sKvMwzWQ0RYp4XFEhpIITqjF/nSitzYRV2hKpJ6pptUorlXrH6CCuz64vxxehCYylEFUelkMYUq4JTy2S0sVYXUK0jLnO8jKzzmb8pWl4LVNdo6mvUrpUnEbEij4qgpaZ2yHYtakRqoRhPeRCnZWYq1bYPtmA1LjSx+vWqyTaomYKhClX926xanqoFLC2eW1bD/Kf8/2dSJr0Fkwk/ReBev/0lA1pn74ZmdHXG9oU6JjCNadVmk0SXCK8jd9RWlu2yDj2k9ue6b/bMSgDNOlDNgt37NAKEBzvU7327r5zL/u80hS+1H/fHz3PfLY3k1r2fel4vqbZzCLXuTu1Tb6qoV2VYK7FezWn1a/YmqRda20aDEyBgBY9qCzf6Lm6n17rR68a2Eh1NoleLtFo1plVKbkSmMYBV67utmVp0G+rRc6qb8enHpB4pBu7v7l2rCyjFIMm8XlT09mOKV9K+9cMSY6W6hYFa06iGb6/FAk2DssjdjFXdzTVq19+3EkykFteTZurbaLceb1/HXlVZcvHAIye+Epq/uuacGYNThkFJWgv2ZbQEjwZkfT/NJmx2pr4q+PQCTFFPhnahKTTtZ103r633ayFNcE3XBScL9vI0Bh8kofRb31Zw9d91JnU19bRd01uNQqsnceX7txtdGJENCezfuWdQxQNdctFWfXvbw9eP/59jUhvJjC4yqykMtplf6g7iC4u2ymyv6woyq5v5sKd056Td3GlVr/Sz7+P1IqyEsqrffe2kLaOsSwiXySpBovGfW3y4PkPRNZhD6vtvb3hNMt88T18jiK8LAq+ZBPs2P3d8ziTY9tuVtnX9d10WK66erBK2E5g2M1eS/+0+VXGgi4yq470xo2x63k9rYz7FJXjB8pykPXt9v9BUdTchBUBrDl/ttq6gsRJc2+8Iz2Y+3LTlfRxSNCYoyv4wWmi598XT8oAuZ4hVGGjMwCFztpGW9l1Bm7CWS2HOypKz9dejx4IYxERldEGpL9rQJhqDcUBapYvmreYphewRgM2/KIYD2giwVEboa8MhmkRAYjDtoVpC8P0p25SQyhibwNSt7VrIsa6r1/ZYI/RU03S1nvj6QLu/jfkEkVWjbM2ujChUqlTH0V62RTbWsuWyoSf9wjRYKXXhoaZnSF0ssuZa1YCSWpV38/4ixF+E6L56fI6Atuvq/+vstbPtc4cYIU6U6FRhrcEL/t0q21RZe30k7ez617X/aGWK6/V6RbB6VNJrH5PQEXzvhyHQaP8qr5oV6vNuQRNdj+ktRnTrmlvHawzl5zl/i4HeOjYbne273VorG3gtrbhwXeXS9ebGWT7JqPDbei1WxOtR9VJs+6/rd29KDISwtr8Ck9p1KxyWA7XSh/i+0i9w05590JKbBL1h4mG9wfw4AkGIAsMQ1z0RhRASqoV5Xlai6VJ+L/g1tPaG7JDJWVlKbj6apeCpFM5UOo2yeY6cSGroBBvwwIE6tlsNpCoMtTpsdl+KeMTdskwkL4EilUkFC1bSbOklAhQVcghIXpOuSw2S0k6DkDXSsQqvlbFA59H9xDqizhHaJM6KQ4lqe5bJRcGZMitdquOk1Ry6PrR4hKYxszXQwRZaTxuv9qI/r3gRSvF+9abS7GDAPZBuKcUSr1mFousaZ68dP3gmVSXcJul2EkCnPrWjJy43pe5+V7mE0vhYK8+u6+KhEtW+U1smtJIhO4r4wvBLq8nHnKSr/6kRumvpisooXQIOrYPrvVzdd6X8++hQ7dJm47eT66LsiIKfWRMsrzeZXX/NpPujH8uqkXT7hq7nm/fdPOMTzKs3M5gZqL5QlTUq09FKrX2qq8bTmT39/bR7JtrBNq0dfNGfK/HHniuVGfb973HzVwa1ajarD6CiSZh2ZTk+0vqvW2Z8Y8xemIfdlBNCZJ4nlNVvp7rKagqcLxN5joy7HSk6ofGE4Ca/KQxDIufSNLsqTdd3zgp5WRAMqLW4T2rObjJTYckFaX6kdf9uYHRUkdJpq5UxlJoMLJ0GFVBZkboRCw/PqhSP4FM1oFyN2iIFjUlZdKcIpJDaXg5Bqcm1ImaGNQVEG3KIWqfbOsulzpXNegzVH+qm5w0fWAXgIhC9gKBZdLW59mokcE1aVqr/202MFCdF7icMdd2v9zYxWzrBwpm/+PitW9Yaquuk7QVZ12kNPy+eHlCaxrgV+Dp577PHD5pJ1eJyW5N6R1ExBPHPmY76RaLd/+vCkXWRUB2uW8bTqq/79r8m9JX49X4il4P8EovMMrq1zp5qF0nkR4McaajYa2ROlY1evN/mkPac2udVyl6d57060BPcyqh7x2+Fl1lVQ92061k3nRZ5tUKlMmDZYgBuiPbLd3mhAfQMy7/PRV/cq1pNZ9bf2j+qUNFJ6Ne7aZUdOrv85vlV6l4Z2mrv1+4pakSnCiRsTWQbTYDVb1VjQE1C9RBoaMKRQXtBKYubc6UxuNp2JTDBkVQvlwulZGJck2rrUSSQVVgWZQoZZQKUNAwUhCjdOgigWjzU25iRgDHWvDKmdTAFkdh2lZnpLBghiqASCGVNLtbqg4oRWp0p800Fh2vKFZpM6jqvfpvioeTaGGnNQSsKsw12E2RiFGI2Q2sU9ys7SPAQvU2CPTvIRmMoBXJjXGaC9Mmx92oEv4bLr0MSpZqb+93iBEjr2nVNSmn7tAq+dS9VIbiW8KjPrf0gyGb/XPuTpPtu9VXJJpCj7TNovqeixVIFimMkegK0vAhi+zRdrscPmkmJI+q+9soCG4J3y0TzOQbWT5LLtjYxlag2eJNOC7sCnrx19K1WyX91Nm7bvu5PdRBXaduep036v3XPtd+rvZO8NEHeuu76nlvf9S/3KY3omqlsvguywVXsGdRr77I16/SEHWCNsHvRzY5Jr1rxy77fYlSvvVv9u1TBp/XXfq4R4UP3Ptfvuu1DaJMbQmAcR47H4wrWG9Yggi3h2UJ8VWm3XldKIaVIKYnTyRKHY4zUenSKRdfFFJhyZtDEPGcrPy8WUZZ1DZaIMVqYsUtthtuWm5ZxHR1p0l/1HBszLmLENWtxk5A0ZAdicLVhjW69Lj/Sl2xvwURXhBi26BilGGHVpfatsCzm+4oiXrtO2vjbffa7Jl9bbSsj0ouXSqmmd4vfWk1iNdekQBufOt7Uvlf/XWVIfkFbIU2N3e4XI0MvBbcbi2ozHv3vUtawhp4+vWirzmHX52ruq2ZGEAuJ15ftfMnxg2ZS18cXao9f9qzPmJWqvlDque6aF8RG2XZOzRJQi6dtGdT24mb28UYUE6h6P0tpG7Lv7FYHauYboSvC2HfJGFXv/IaXjKAngr3ktRmzOjg3nl+fdUszMv9KPw7b9ur1n2Kg1+cqzuJmaHppsdpwun6+uLa+0ivCzk0maScaoWza0FXEX/V79f25FgTsUbXNampZ7flWv0kbeoJdb76mZtp5RQip7cZo2H7zPBvNievqkRibJeB4PpNCYNyNlCXjtiFCMCIdcqHkTEypGw8bwUItb2EgzlXC7wW9XN/dCV8dJy3lilBah+rnypjq0Zg2tFyi1+aq96esGkKg5Oz1qKCUQPaE+hjWqGGArKvvKQSrAmyRmsWw65yZ1DInVAHK92POWxpgaBWlMSZR7aSM9b0rW68L+DXi/xrien/0a/maHrUgj36vsmVw7fmyXV+VqYaGtvEFzPPq+MEzqc+99LWU+knt5hXi96nrXiuQZuoyzlg6ybaut04C2jKo9V3qTws3Nl3/ZV8qoYOVFrmQtXndSrBUXyzoZq78xBi297q65iVBfcksTKulkwhfC5rYfr7WDj41L32dnqr5Vqa++sPst7bB6ZzYrNdtmG99zo219pqGWIlIjVwz00cGz4HajNUrhGM9KpPpr7P3nJfJ0CIUarSDSGfKXcXh1q75kvoqubRrl2UGUUpcTZXTvFBCZjfumknUIIsyaQi0gAEJHgYdGh4cgkUABiAEcimGcenCV4yBnM3P1sq95GX7HtJpxmFFdjC0jXWvVHNTPx9Vw+n3aF+X6jV6YHwhNJOYEelsmmBRsr+D+MWhVU9Y108gmTBaVvT9JiB1UY5BAqWsNapQdd5fKX4nKFWd04nLKgR0q+Uz+2T9ar2/BuNsGVTZfK79bcy4ChPGsW6CBNTGXmNqX3L8sJnUDVX19mWdBnRFSK7//tzgvaZhvRj8ShQUr02zyrNmMtStBN8Wwo0oO25I/DcYDQ7h8qkxkavzphXKhpn1mkbf3peMRcsfke31q0x8xcg/s2hfMP5PfL7W7EwCX4lTE0g3TfnWr/3ovnxNe3tpUrti0u3J3TiKE71mr5G2fo1PvXz+VsAyn2XPpIrOzPPF/UjRy2Loi3GgPd+ke5Pyt2Y/3NTW6ij3+0HENCPXKkOM4OHOIQ4QLFIsiJgPSrVBllXmUddEGtJm3Kx2k0U+EkA0bNao1n0kgoT1fGXCfT2q63pW0ZE1rr+v97X369bSpiDhsqwEeMNYsICLtnogUKjl3+2c5WPRze2mWrOswqKFjivZVavgPrbef2RTVCXPum7FH9PZUCpdqRpbY7CFaobXJs2u3xePsNS+jV6Apm+bDXMSMfmoIalfrePrca6/bxVvvHX8oJlUc3x32kP35fbaV5jYtdRxa2BfPKOdl6adSL1evSPdrRZM4ZngsmUI11KKsnX6XjOKF1pN50+pkXfe2Wbu064/dZ29GA+R9l6vMYzXmEojHHWRSz9GLxlNf+41Le1TzOtT/bs6Qa3zdd2PT62H19qoptBrre7ltWv+ShCBCsrqBbgrO7SAibXgoR3h6hmOriC5fbZS6AuXy3nD0KzrSg3SUFUvz4HxiO5aQ0dfK8ciBmi7uDMqjvv2NrlYKYtlyQwpsJQMpRCHgXnJxLBqBN66v5+PabB8r5JLW//Vf7WO+Sos1ITTpu10e7y2QygNsaKeb8zF77t28Ne5re1WQlm/26CAF0VCNMHP/1WzcWVM7Xq8ZIcqwYWiGLFxqv3qBY4NbWBlghgjq8EhPm1tvVTkmcq0+6fYvttaZfqUhSo8Nqbfmd7761frgQvSIlv6VsfHGVRjVKwCQ1+Wfhtx2gVu8PoevD5+0EwKfKg/pfzcIEy3NISNeecG42ptdRujSeVVsqnSzqZ5yy3pI/TUqUZ/6TWhu950r5vYdAW39X60jd21t3n+LUZQ9LOL5nXTyEsiff39l5plr6+9vuZTmu5NzfiGKeT62g0TZbsGKvHoz33qPYDN2Nv8C2Amtqwvpcc6vzlnlmXZJMT2WpGqIsHMTk/PH7lMZw77u/Y+MW77k/NCXaBGdKrvxcxKMXp1JM1M08zj42PTBiqzAhiHEdXc3sx8TgNBAov7fV4QedgQvcpsDVrJhIecbQ80/DcHPVWPeDQE8zYx648LXbXNPi+ntlffQbtxbHPcEeb6+foaV3RRdVBU9Shax8tzjmKCi0AFn16KgbyKl0fXylUcEDelalExsNsNk3XBRkVQB+ZdGfGq0djHG+/DFgasZ1S3GFE/V/U5/SFdSaA6BXVCblkZqiba+wcbOjtYqQ7kRTufO37QTGrVPF8hat3/8FLlvD5/i5Burr3FuG5MVn9Nkzo+0f9bZ+uzLKTVzr8Ib7+WyK76tpoatppdlbRX5vu69nD9jv3x2mKvku+t5/Xj9qUa2wtz2o35utWnKl321Wo/t0FUu0q8bAnA9Tt86nlVfqkwD1K1yy7htAGFdpLn5XLxshlDt6bMf1Q0gy48Pz/y/fc/I6UBpHTYfX1/LLR6nosXT9yWzogxbpjjt99+y/PzM2/evPF1W/ttb2JaidYB6JjSNm/mmgH0Y9ZHIMIKhXT9d/8u1/f073jd1vWasvlfmXyvZYnIBr289HOslalmVnCnGilck1idi+GQrVVGRU1HLrXkR9VM3Jy6eHVasKriWtdGlXG1Mbaac9Vz6LWX2k6va7Tzf96Yh36O+nPX5us2DKIrL1TX4qoQp1DhvPrxr1pihbsi9CbD7tk/B6P6QTOpLzluLdxPEcd6z6cljNu+sGviWU0X+AJqi6FqPfrymcW1os3GE/+e7T3X7cYq/tnJVQOoC7ne/oK4m7TdP+tTGs2njpUpf5pBtXf7FIFhO75f0p/rZ/bv1WufPVuXKsFqJ/3yf55BtbG3RD5qxWSvQrY2rdWUtY5HzpnT6cSyLKQUqX4spbAsE6fzEx8+vEe1sNvtNkSmN12BtGi9y6UQYyHFoTGdOh6X6czPfvYTvv32W/b7g7epHj3o/fLUucNhT8ASgFIIEIJVvO2i27YmxS2WXtXegKap9fuxlmGPlUlRCSuNQDZoJ5+4ymz6NnMj/muu3C0B65rBtTmVfu2skE0axEp4iBNrX0bi2g+qjWERYmNShoFZQMMagVv9x3VvlrXcRluLmwbKarqptOPqfhufre/tmlbVdXK9hvvr2vpHX3xuzP6KQQFm7nRXhUU5smmvv+dTdPj6+IEzKZ+lenQEGta5XBlCXd318+ZJtEWBcg1X35oIq6ZwrbHUjyZk1bwqu6DWN+wheGoSX0+oakWXFTEaQj+ZTuOkf8HuHWq4at2c9fzmTZQOSr8N14Y4978/xxyC0Fkf7J4eUqjv5rXUJttv143RPf9F6zcYdn3OWlUZtI1jlYP7nLA+WGL9XyhrIbr+p210Np/tXAe7pDYWDbpIQLXGNPY49fVttQkxIQq73chlOvP0/GHdxGJRdznbT4yB+/t7K1IoCZEtArWNsYWppwSXy5l5nlbtySPkno9Hnh4frRT9/YHdzsp4hBTRzpdQ5sJuDEiZXehamYdEweIi7F0rkxAHdy0l2/VBLKij+S0qUy6UYppibHtGDcUAYdG1wq6omc5N0MNNjTX4wCsDYHuoJZZeLR4D/QgGGOtEMzoSSCnqycigUggxoCV7OQ0DBiihW5F1eqqU420bEde2KusaqfO55Gx+J4xOVKZUgzyuiXiNs6w+on7p9yJUpSkv0mLomJB2eaX9Pr/BrI1PO7VxwasieghV4xOQ0hJ/xQlTo5FdG9U39ovFpCoB6phDVXvbJU0aXCNkOgG7Hf4V/fS/bI9uIjqe2Knjdl59sdbn1LBRu361Ndc2186svqsrc9g1U+0Jvf9eUbO39vZr9b6Oi7CaI7eRXlsGdUsTrc9Zka1dwlRt79fezPdWxVqrEnMNh2/v42N57bWpEvWLc6iHl9eWxBBGhLU8hF9dGVHd4lWjRWgJ4YKxstDmqZo71lLq0j2xfVc3qJtjM4IGJdQqqdaEtd7loIVA8ycaEYKYAjsZECnkvLDkhXmesZBxZb+3Eu8xJGJKzqRCy0Op76aO5BCjWOFEPXOZLizLTKEwzTPPz0diiDy8uyfFgRgHht1gAQNtuIU8w0yxPqSAaiKUQAmgZSaFaGHZy+JrMBBiLXZYUTJszS05g9AYZo2Gq+gNZiXSpvGLroJLKwcvhsQeIhYV6HNVGVjNMYrB6hoVL5RowsuWyK9z6kBRXjRxhWQSolgRyFLUC99qK1ux3ZersKneUjUXWjK2wQUpHq3pGnajK1ohxzoirus6V7D2Ubp4bxeKcZk9GBRxx3he0A4R+vIim+oJsFqA2jyYoNX6WwVrldY3oFVN4EaUH9ic1wjQOoZfcvywmZQfTbX/hOT/Oa2gJ7yNkHbfVSK6uVtpi6qXROoy7U0vfnlTf+03TSp9zcT4KdPkLTPk9fE5f9LnpJnrsb1+tlm0anirb0oRNJj58bpHvcmzEtTWj7r3vmTt+phXq4iIOfabwFJJT0UDr/fQS3uVyBWKlFXLqgJiSI1xtrcXDxmn9tfyY+r3btAjeUhuyRkNFtlZUNTTEWAVjIy3SOtbCNHLxA/EZSYlR13QWlwwEkKkFkZcu1b/lnXkRRnHcS0XHgJzXogF7u8fGIedn0/+257Z1cujSOK8ZJJmkNFNfEakSs6UGLyAYFjHVC30wYdzjW5zfErLrYpOsF2jwCPbWEwscKpZdNXAVASJYLW3usCKZiZzgopVJJbgiOltCbyy7ktpsGeGAuGajtLWRZCqda3njZH6uqibQMz6oapmBam+PFVEixeU1FZzap2/uo5eMiqjMy7EXQWo1Bl3sCx/N9k8p67r6ksqFFo0ILKu4br2KwPWLa2pWp9vBS9BJOv+d5ipHjy20TdViIEYnAbGX4DKvPX4eX0nL6TyjkF98vu64IQmaTQpvmoHnWSzYVLqJQJ8wm0ypS2CTzGqL33fT43Dp5jztXYE2yTl1+7zN2+b1b+x+6XT1LRi43l7gOGlVcYvTbv91FTKi7+r+AirWa8j0UEMtw1pNnswwM4ga//NFFNa/2pgMXWjSqBmuCirWVelal++NkRWf4qWhhitTi1VHX3b+1FNQutYBq+xFFt02jAISYduzBWD1/GfCvtUO9Se5204FmQMCY1VyxkYx30j8BZ1l0zjcQ1MqATEwt334479foScGVMy4Iy5EIfAPC/EaAgTS84MQ3JhpRJe4x6GwVelciNa6poFWile1SpYBTjcnyGgwUrIS7TghEq8/WJnZKFST9uHPpd9YKWt73WsbNpc8y2KFCtJUbI68a1ztGon4n3ttfH2nXQmd2mnm9aUc4UdWtNGjAF0uVh1/6zLy/rn2rtFGZbWlgmNq8WoMmyLsFv9QZl1faz0as0ba2u2bD/35kipwpq4NkWlAzbevX+0Xh82VoRAXma+5PhBMylRk5J6W+i1hlEx9to9r2kF9fqrc3Xy6aT/Ssx6c5s4Z6qO6cqkgCYBVebUMuC/WG24kpo+w6SutcLrd35Nu+q/u3ak9g7x/mgYdf3z/XdztFIJ+oa1uAbRn1i10Bc9a7v06h3qczYP82coZKlO79p3+6kgCOC+lKqZURmPdgRDWpmBrGW1tFRfk1bfkvkDrBC7dTk5uV+KVoFz8w7runXBpl7k5iBU3EJY86Xwp9fxqmx+MwVtUEMILlcpMQxYJd9KdPzKJJRsT1Es3y52lXnf3B1AY5uVRQu7NFLIEAVS4pLN15UJ5keqQpf7eFAI0jnbBavcJ2aiNb+P+YnaENQpLR5Q4NFmFqLu/ikMW1Ar5FCx8O/Q1q5H4/kYNDFG4srUVIG8agkdNxOfX4d/bvhzda2Ezd4SVh+5sCHmrrEg0vx1qwCr22s6DWZdK+uYGLj6WudMa5RlUErofVz+W3AhzfpdAxxgFZQ2K6ciiHSaGFWIc3XM3x5VJbqJtEYw0miutgV/yzr1SYm0O17CG3zi+Gt/7a/xG7/xG7x9+5a3b9/y4x//mP/6v/6v2/fn85k/9af+FN988w0PDw/81m/9Fr/7u7+7ecY//af/lD/2x/4Yd3d3/PIv/zJ/5s/8mU1Oxs91dNK8iy1Usv+l5H+dBG1SFMV/twl1FG9ZifenTGtN0u6lkPIyZ+ElwXqN+UB1vtRIK3Gczf4HL0y3fq590XZ/Pyh9/yrMya2fW9/V+/vfdS7Ex62nmnp9nQhbZG4/ymrCs24379l6rv4tWCBKcEkZK6ZWxBiDydkZxOCILFxbKWSUTPGf7HktoUBUA8KM2I9hOUDQgpYFNEPOSLHkXDQbGyoZzQt5mcjzDHOGXAypG8uJiZUgt7HqCFhjGpXpGHBpjInK9oJ4r6StyBfzZHO1RVOw8bLzQRKBRAyD+aGCPT8GK1UfRNjMiMLxvPB4WThlOGrgqQjHmDingY8a+NmsfLvAtwt8V+C7Inyb/e8sfF+E7wl8p5EPkngMkUcCz8CJwiUULpI56sSzzpwDzCEwiZB9LoNjC0YRBomI1ZUnFCWUsv5oIeFCR87okil5jZpb2fr2J3TrvI4j3Zqr2lGQ0GZQi+EUqpu4amK2CUiO6qG2Hguu5RBQLDJSgpk7dTPi/aZeKxIIBdFs60+z36VAwQIq3GeI2jVmtKRtjyDtHWNIZjKW2AJv1v3mwj++95z5BO9DdOzCEMxEKGFLhJqG34TIrXDe/9TS8587fi5N6td+7df4D//D/5Df//t/P6rK3/gbf4N//V//1/lf/9f/lT/8h/8w/96/9+/xX/6X/yV/62/9Ld69e8e/8+/8O/wb/8a/wf/0P/1PgEXc/LE/9sf41V/9Vf7n//l/5rd/+7f543/8jzMMA//+v//v/zxdaUcdgE8dm21XpUfpaKhyvUyuru0ypWW9v37X9+W6oWoffgHQKCsx+qypr2c2/uxbmtF1X6vkU00eG4lPt/f1xy1G9KmjbvLmZ2pn/ZdUu72yeS3/js250CSn68CQ1hcn7FWLaxKl4lWM1TUTIXZaJS4/JhfBe5t/n+uySnjaoswa8qCuGpaFerkTvUV9+btmay2XjAaMEBQnLFp7U/d24DosuK7rDQpAZz6pT7A/u/SGTlBaBYNqrjb/Wc5LSxLtteRVCAstCrUofHtWFlE0L+Zfi5lwPiJR0QjPC8yLIxo4yS05t5pTrXIwShKIKCkIYxASyhgD0U1tAIMWUoyEYppokEREiRLNV1VMCKpwSzlbdFmKreCKaRfOPBpEE4LGdXxzLpRcuqq36zqrSA1V4l/H3tdmiMQaGw9YxVpt+0wkOpafz6U4hiA1P06af83ASLZaRz+HeNBKXeSdPtJtJDUtPLuWVB+nOLP0y/2hxcuTVI1JfIkbk/L21TQjg9oqjWbaTFema+vet/d2n3Y0wZrdaqF9PuKnjp+LSf1r/9q/tvn8V/7KX+Gv/bW/xt/7e3+PX/u1X+Ov//W/zt/8m3+Tf/Ff/BcB+M/+s/+MP/SH/hB/7+/9PX7zN3+T/+a/+W/43/63/43/7r/77/iVX/kV/vl//p/nL//lv8yf/bN/lr/wF/4C4zj+PN0BOkLDS6LbbNvc+N4Z0zqerwcHbD/bzev5SgDWIgz9YxrBudFPY1BbdIG+3V7Lu+5Z/6xbPrXXxsRuFtb18jJKsH/+p/62d2iDsobP19/Cioos7rvpeFd7qQ7HsDL1yjxwBtv3F9ZoOZMXbRO26qN+ayA0009zSqt46L80IqSqzDG0jkntoKylHszkZwSr+qBqOHZliHiBvIARKLQg7tpRKl7cWnKjEqYqbVbh49qGLyI3GNbWCLLO4/ocwUXirmJsv3Y3NYG6dpugIcJSlP/3d4/IeM8kmSwgMhHDSEhAVFRGIzqOmKFGibEweDeTlhnVwhADSYQkMERhFyMxrObW4OeHlBgCDCFwGEcSwhgCCUyLrcRSizOGghZBS2XCrt2IacZNEGwjRDMdUqxMh02r0ms2EoJH+nUmsLaGA+KJ1s0ZUFyIkdVMVdQCJqQWAawYgLFDX6fXwvo0CrV1VLKvaGtZwdIE1KPlEKJCqmZEX5eW3FuZp/dHhCyh1eNTqknUAj2q/iZSiK6t1Qyv0kbH63uprMxILOG81Gf486smV0po72Rj9GWGvP/TPqmcM3/rb/0tnp+f+fGPf8w/+Af/gHme+Zf+pX+pXfMH/+Af5Pf+3t/L3/27f5ff/M3f5O/+3b/LP/fP/XP8yq/8Srvmj/7RP8qf/JN/kn/8j/8x/8K/8C/cbOtyuXC5XNrnjx8/AlvCfovJ3PJF3WJk13+/pj3YIvdnXJtFOtVslWBpARJNv+ikYZHVZPPzHNdM43Vt7mX/7ZqX5249/9aYXo+X6Q6laSa4NGnRWtLyhvTGK67Z7NKJYdI5oZ2IS+nakkZAG7EBk1Tr5nWNoBAoXhOlNCcwQDAzjFYAiGI5N45OYZVX61hYPxTbeEmUGKIXkMOL2VXCZjdlESTWd/ey3k3LWk04Zt+v62YrxPQmvJ5p1fwfC0mvWko/dz5usq4/MGIbpUYJbrHtjLHY9TU0vFiEAwqcpTBdTjAKWZUUArvg1WtR7napkc+cC3le0GxlKirIalYhDFabyXJTFRalLOavW3JmnieWspBLJoRAGiJpiNzfHdjHyCElDjGSENewzFwVIxAKsyoheCBGUPdPFQ/bliZ4VE2pmudMvpBG0FVouRXVR6jiAQiej6WuFQfUogf9R3wStMyrDIZpfkbwM1qsjEopYoymMj0x85lFc9pcruU+Ylv7gvlIBTOFWjBJ5/9VvPrwCjnVqgUDGquh0PaWGSSEEBQr5uXaEwVLqFjLhgTvQWkddNrWv0PVWj0Fo1Y/KLqlHV96/NxM6h/9o3/Ej3/8Y87nMw8PD/zn//l/zq//+q/zD//hP2QcR7766qvN9b/yK7/C7/zO7wDwO7/zOxsGVb+v3712/Af/wX/AX/yLf/HF+RgiyevWwFarqp8/dWw0jmqq0vp3fQjr99pNRveM2tZrvhrt7tu0393f3/vSd9Nk2xf9vz4+x1TcekHfoes2P3Xc1sz8O9WrUiRuLmv8x5hO02pqL+p/um7UW/PYSiGwzpGqRXvNWC2iJdegB8glMxVhnjPTvLAsZsJZsj2hxRYKaKy2fdyOv0Z8VWKWBO6SsEsD4zCQWiE3z61ST4NyYiguUa9oQtLGxT6rawIrU5Gream/e9/gsiwefJPRLiel1UXySQlugrTvA2ugzipQ1VBrEwK1KXpNexXYjUbs79/eUVQZwo5dHBnHwG4Q3u52jDGZdpMLeckEDa3eVB2jmGRFVCjCkpXzPDMXZZozpzBynGeeponjtKBZ0Wnh/XRklyL7GDjExCiRMcJ+CIwpEb2rEZxxmnYWA6SSiS1IoaI8dD5TH3/TTAQhNBRy6t7roKyy2jpTCsnHSLp1RGVyVI2pzp0Nbwo1KMUS/BcRqmbUz0md9+a7DpHaZRBKERZVLrkw58xcMmUpkG0/lFzIlYlozaEMhBiJ0fLRpP6oEkTxuowINY8xO5BAaQzYjDDuEC/GysycWAVUGqNcD2PqWhOX/d3+bzH3AfyBP/AH+If/8B/y4cMH/vbf/tv8iT/xJ/gf/8f/8ed9zM91/Lk/9+f403/6T7fPHz9+5J/5Z/6ZrYnMJ6FHAt5whg3xXGVZ6b+ju8CPLeNZHfhNommPv8UwKoGBK9ZnEbc9A+lvum5b1u9f1QZ99q97UWWvNkatia4dtp/r29W+tP77zR378FDhauwQiN1jhEYAtuNbmlOWukl1/bs+u5qnzMRWuyVkE8KZcmHKmUvJnEphyjAVmLJynjLH88zzJTNNS9NAgipDSuyHxC4lxhQN9HTJZK3lr838op5zUk21SWAfIkMaGIeRlKJXbFV2KbAbA2MUy69Kg0etXSgSCCRSqSjZnUBSQ+NdOLqlFa/muVXbUS0ORhvXNVGDawC0+rka16T60/r5FhFSSszzzDxPuJGAquYGEX7PwwO78cAwWODAkHaMQ2Q3mHZzH12zzNXkFkgSKbMRtDqPIeiaEqDB5rEEpgzzkpnzyFyE01w4TRNTWTiXzHleOM0Lz3PmQ5nJCkMQ9kNiSINFL4oyRmEXYZ+UQ4TDELgT4RDXCEItGRx+qCzZIgMB4iog1cCUquHWqDXxwITQxtkStqUGLNVSJ1oQiYhC0aUJmMG101hriqkyVNMcVbN3TUVLlwrhYQv+/VxgKfB8nvlwPHOcZy45s8yZkt335Qjs616y9mNMDMm01GFIDIMlfA8RxiiMIhAUiULQYIzItc7Q0Q7T60x7nj1IxNDbiwFqs64hX0mbxOtSBYUvOH5uJjWOI//sP/vPAvBH/sgf4e///b/Pf/Kf/Cf8m//mv8k0Tbx//36jTf3u7/4uv/qrvwrAr/7qr/K//C//y+Z5NfqvXnPr2O127Ha7F+d7x/fKBGyjW4a5XXe98a/9Pz1l3/iCeHn/+qAto1r9Cp0E1DHKa81rlXCvni3CdSZ2V1nmk2ryzW8aA6b17dZ1K69fF/b63C13l811lbjSJG+ux13NVKbeUCOSWt8PNwV0vqjaWHC/k/p1BaalcF4Kx8vC4+nI4/nEx/OF41w4ZWXSyFyEaYF5EZbZbOpjCjwcBg67HV+/ueOrw8ghCUGURQtzWZiWzJQzszOrDORi0mrOhcc5M1/OFL00BPAhBR72Iw+HgYf9wH6wyq1DVJIoEiNDGJE8s8wr7ajmRWP8da2sTHrV9Ktvqo6W55mUxWsR9bb9ep+A1vM2+lvNrE0NIIzjyLxMTPPFTH4W50wQ4evdAw+HPbuwEFRIQ2LcifmMVAllJgaDfCpNCMlocOGlqEeBrc5yI+nCkgt3KaC7QC6u6Wui6MhclOdp4vGy8GGa+e505nEpnDKccubxcuKSn1mKjUoKymEI3I/C21F4cxh4GyP3MZgPTAuxZGMsDVTWB6I4EXb/TSC4tmUmSldK7P1kXaKKuKBRPBghUwRK9gmT6IzLTIMtYCWvtMvMlhZZmcXgnop6YIJrwVmFOWcul8LxPHO8ZJ7OM++PZ07LYuvV7ddBsdGt2rxHHvbguiEGUooM48AwRsYhchgThyQchugMC5ILWIqNG6UTe9TCz5XYcq+EQHCG1vawr7PVZ2o0I35hcPn/5TypUgqXy4U/8kf+CMMw8N//9/89v/VbvwXAP/kn/4R/+k//KT/+8Y8B+PGPf8xf+St/hZ/85Cf88i//MgD/7X/73/L27Vt+/dd//eduu2cMVx6im76m+ven/Fj9dX3k0832O2bWSoR/gXRwzWg+xxC/xKxXucfrLOznedan+7INtsC5dQXArAqPXI2h9bHmJuHflVqNVRwex3MwsjOlJXuekWJmjTlzvpiGdLrMHM9nLi5JXrJymQvnJTMTmDMog22dkBjHgfu7He/eHPjR2z1f7wN3oTC4U7doZC6FpXhCbwhkMfy4eVmYcuFxnjleFi7TwnnKTEtmKcrTlLkU4eNF2cnMDpCgDEkZdsphiBwUlnk1c7zQersxu6UT99pPcJNozjPE4UYpmH7+lFpmopqetbsOIKZEGgaen8/GTDxyEVVkvpDGSMwzh7hDcmbIgR3RQvOD3IwW1FopOOHacEEJzadjSyI2gh+DZSMJM4IwqpDEgivuBuFNCpwLnJfM47JwLAuPpwuXOXPJC+dp5jgplxMch8CHY2LvZsJdChxi4BBgH2EnwhAjgxgyStRIVLH0BGUNVPF/psQGYhvcVbgy5lT9nKZPZU+ctlhGtQg+VWMc7sSqvrGiXeCBOOMTtdB1hUWV81x4Pk48Hc88HSeO58xlMT8cITEOgSTKEAspRKIAWiw1wsO/52U2q4KXDZmnhTlnwhRtrcbAYRy43+847Eb2g2urwXEVy4JQiBoQFyiCQmgh8SsMWM2lEreCWIma1WcMvMBVfO34uZjUn/tzf45/9V/9V/m9v/f38vj4yN/8m3+T/+F/+B/4O3/n7/Du3Tv+7X/73+ZP/+k/zY9+9CPevn3Lv/vv/rv8+Mc/5jd/8zcB+Ff+lX+FX//1X+ff+rf+Lf6j/+g/4nd+53f483/+z/On/tSfuqkpfe7YbIpOMu+/f628e/3+S9q4de0LrenGgH+Jc/C1574w6dG93ktlh6qZVAbxJe1/KkDi9ZuuBIIGnvdSEFiJrjRTgaiZoEy6FFrQsgSyKllgQplKYVrUTT+m4SzLwuz+mJwFkcjd/YF36a1FkUnkkgvPl8zH05kPTyeOE0yzRVaZ0acQgxIkE1iIkhkBVQsq2AU130Sw6CeLhIroGMnApezdNKXGMKeFy2xEKsSBXJTL5cTxfOHDZWZaFtJwYh+fObg5el4UhnV+1nV8zZy2mlXVoqrU3kyYISAxbbRhLdViYMzGLML1794M5EJWh92oqh36A4SwoMvEmECW2QISopX/iAhZCzkXQoyomE+kL7EhEsjkNdKPyiSNEUQHyC05AwtBPDIvQ8iFoShvQuDhPrU4s4sqF5TzkjmeJ47nicfzhdO0cJpnllk5nSfey0IOaoEXQ+IuRR6GyH2Auyjcp2CMrHgemmX/E3RuJrP2T3EmpdTItIKrWN6v4swrh8Xes4iPu8FBacmUvFCWGc1KrkDEwXzsuLkxu5a3lMJlUZ6mwvvTxPvHM8dz9pD/wG4cOewTh/3A/Rg4DEpKZn6tDKrkbIEpSyYvyjwXLrMJWFNZmBZjVtO8cJ6V8yLc5cBuF9lnYT9EdlEYJZJi1TYLms3cH1ugBZ6UtQpBlZFnApXl1+P/Fib1k5/8hD/+x/84v/3bv827d+/4jd/4Df7O3/k7/Mv/8r8MwH/8H//HhBD4rd/6LS6XC3/0j/5R/tP/9D9t98cY+S/+i/+CP/kn/yQ//vGPub+/50/8iT/BX/pLf+nn6UY7au2U1az0Uv68DrOF1eTXH9ff19+vManre19a7V6aGP/PHnKj6avq8/+XjxeaY11lzoAaH9IrpqQ4Hp0/5waRrdqW/TLJs+oSLQHXzRlnCs/zzPFiGsvxsnC6TCjmdB5TYLcb2I8j+yGxH0b2MZpZIkSWAqel8HQ58OF44P1R+fB85ng5k3XhdJr5Pk4E3VHuEsshsgx2fwxGLFtIheKoJnYmYsnAh51Xjd1FlhxYspJVKBK5TJknGfguC0/PyvcfL+RlZgwnYjHz4WSRGw5XszIGbabZmwbZbo2qgc86rp+V/g5eRt5GWSloO9dn+q/PV61mGK8r5QJAjJG8mAEnSODt/o4dgV0UBhkhJiS55lBgDubnMSBWC5ZZAI2ORCDCUjziq5kELSlUEUNK94hkKQHNICGhoaCpOFKFAhkpM4MIBwnkXGAnlGHHdIic8p7nKfM8ZU5z4fF45tvLkY/zhcs8M50mHhE+DolDEA4p8rBL3I8DdyFwF2GMiTFWkGGlFjAMaqNamZSqRRFGX7tIQMUY0QJcipjPdFKmpbAsFsGYc2aaz8yXM3menH4pKSXHa4zElMyMmwYKgcuiPM/wYYb3E5wXECL7IXG42/HVIfHmkLgfhX30HDPM5FiyojmYplcGS9coYgxqKVxy5rRkjtPE8+nCvGTOl4U5X0hnSCmwH5ObAgP7FBjE3rvmgQ0aHDEEqPluYknM1WSpaunz6gtPxKJgv+T4uZjUX//rf/2T3+/3e/7qX/2r/NW/+ldfveb3/b7fx3/1X/1XP0+zrx/OsWuEkvlbqgRvBPOWye6aaYisVUX742aUXndP++3t3br32v/1pRrLtq2teah99wkz5CdDy/3/dkXVwNYWaSywEc711OZcM9uVFg69mpLK1ft3YeMSbMOImCmvKMfLzHenE9+fzkxzNqlZIcaB/Zi434/c7xK7MTFGN28gDCgjhSBKDsKbJHy9G/nlh4GnJfD+eOG7x0e+fzry4fjM++eJp/Mz7+/3fP32jnd3e+5T5n5MjCIkbGMMtZJtdm1DQYonNbpMOKbAEjFnNsK8FIqoB3BEMgNFlFmVKS9ciiVRBh87RJEAZbGyFmtIee+b6jWdggQrv1FKxoBU14z+yqhEDB/OIJAKLa9KzdQTQq9VCTnPfHx89DpWg6NbmKO+XDJxGEz0TQOS9pzzhIhFTs5BWdQMqwUhDQNzNvOvFitNIcE1CcXGUx3eJ8MQE6iS50wMES3RAgDUkmJjiAQKKSjkTBRHmRALgBBVxggPQ+TtLjCVgfOiPN8PfHMa+XA883S+cLwsPJ9nTueFx5yJwQJdDvuRh33mYT/y5jBwT2AvgSEmBgrkxYhwyZZzp+KRc+avLBrI0cLCl2IBPc9ZeD5lno4Tz6eF82VhyplcMvNyYV4u5GXxyDqIKTIMVk6FKMQ0kHY7YkzkrJyXhecFThpYxPLMYgwcxsDbQ+DNoBykkFQJiwd7eL5YNSW2MiRJWWJgGYSZxKUox3nkcRx4Pk+cLjPTfOF8Nt1nSIH9fuThsONuP7IfA0NQQ6H3xGrz39l8tGR8zNph6rhrWM78Bcihj7J6/fhBY/dV+2aTQqFTJ1dC2yTUTvvoo+4snv/z5rFrf1bfjy85rkPU67nPMS77esv0PuUv+xKtTbr/V/dxx41e8em9bEdbvR0DO3X/k4ckrgzerjWZOFhAApaFccmF42Xi6Xjh/fHCcSqkYeBu3DEOicM4cBgid0NgF82RTbB5i7m4ucE0HgGiBAYxP8Q+Kvcxcj/c8XCI7J8i708XnueF6QIfHwsPy8Kv3MM7gbsQOKTAQQJRM0nFTIViOUEDkVIwjDqxyLRzKZyycFoyj6fCx+eZp9MEubALEGIy9GxNDHnZ1BRrEYwo8zxTSiYNiSaB1WkRz/+Rwvl85vn5icqMUkqEkNykV9eMMad5mRhSwrDpTPoNIXqINa6NJU6XE0/PTwYOq6UhhyswEflYhEmFUGCeZs66cL5cmKfMSQsaAjFlhjQgsZCL5+6gLMti+VhZGzxYKyvu4d1DGsg5O8ZhIA7JCGQUDoP5k+4T7InuM8sGWRWCraaSoSyMpTCEgTEJ90H4Zthz2Y08nSfeHxe+TReeZ+W4mO/yuMw8Pc785OnC4ZD4+s0db/eJhzFyH8VSDmJiACLRamUVZSmZRWHJlv6wFGVBOS0mbH08LXz/4ZkPTydO7sMsFUYomG+OYMDBGRuTaZqB2bWPiZAmMzsjFA0siyIxkQSSWkXmSCGh9tk1cxEzPorTNNn4IixyNYkyByUSSEEY4sBuSNwf9jweLzw+PfN0vnC5ZPKSLadLhYyQZSQGRaSQl4nlklnmGc3ZhQhhCJEYkkFvpcFglKIJWNEBm5cvA0H/YTOpdqzee7bT0f/10iSn9f/K4G5oWNsggZfll2FrFrxmYtfP6L8zc8vLZ/XX2LnyglHcYlCvBYv8vMeXBm9U4mq4hqEhOzSUl8agqkAh7bf5dwpzyTyfZz4eTxwvC1mD2dcPB+52xpz2MTAGi96KWMSSakI1di6xmkToGg8GuRPIjIOyiyN3+4H9Yc/hOPGz48TjVHiahUmUFCayZvJ+T4yRMQgLNdGzJqoqqoEpFy4ZzggfzjOPU+ZxWni8zJxns74PKfL/+HrPIDCkkZASZZk45QtDDCxtTD3iKkDRzDLNIDtSSv5i1V9l4czn05nv339HycrhcNcw5qoZTzrBAFGWZTJiFg1/R1XJpUYLKnGMPD595H////zviATu93skRjPXYT6F/+/ljMjMLgckLjzNC5Nknh6fCCWhYWQYRtDMMPhdrvVVtO95WVhmY5S45aIW/7N9cCGXQhaYpUA0hhDdLPfVfuSrIfHNfuTNMPAw7Hw9FKQsoJNj2ym6LIbSTkBL4D5F7g877tPI27s9Z42cVHiaFp6nmcfjiffnRz6ejpymCz9LgTeHHV/fH3gYI2+GxF0SxpAIshBCoXjAxxKUJSuXrJxm94U+Hvn+w5njeWKeFyQGDvtITJEQIA2B/Ti6qdZqTOU8My8Tc16Yc2HOVom5YKH6lsUUSSINML7kzOUCz0GJ+wApMoRkY0/Nc8oe4bdaUUQcALmaXxViKOxCRGKkAPNi+WvzbLXHVGeymunyPC3EoKguloA9zczTRFmWhnU5xkQK0fEnB2IIpMGFJAnEYOblLzl+8EyqEU+RrgDe/8VnvXLuFmNYJeH+8/b6n9fE97Ifr7/VTc3ulTZFpO/qZ49PMboayi6scEjSobrXQBaLVFqjeoo7UM38pRznzIfTxMfTRFZhGAbudoOZXYbILsIYrAbPmoAprbJpDBX004ijFhAVkyQNoMiEiyjEKG77t40jTxe+e544HWd+uhTOUyKX0fJbBuGQgkV/ifk/LyinUjhOC8/TwtNceDzNPJ1nc9bnwrDb8XY/8PU+8c1hx9tBiGFAQ2RZIlOJjMl8NoaJFpsQklLicjlxOj0b8kAMxJRMI5onzuczHz68Z1kKX737ilD9TRqI0cq+V1zBmsi7LMrp9MwwJCNOMVqbKPOy8Pj4kW+//RZQ7u72WFX4QHF0iqyF//377whpJMWCysAsgSKWK7OPkTG6r3BIDClZwmoIiBbysliRQlXTkoph5l2WzJwt0mzOhcs0MS9mFZpK4XyemIugQXi6LDyfM+9j4PGQ+fqgfL1LvB0s6CE5+sIQFNHFy2w4XFE0X1EKmYMUYoJFYJHAMSfOS+TjXtk9Trx/XjhPC8/HmfOl8PG4sBsib3aJh33ifkjcD5ExWVkTARbNXDTzPE18eD7x3YcnPnx44nKZQWAYI28f9rx5c8cYI0lgjDZW0WGNDPVjYlomlpK5LAvnuXBZCtPsUatLYFoKc7Fgi7wsnPLMzyY4n4SPu4F3dzse9uZDGqKB8aYQEFmsJIpmz18KzResWORdS/NQkGD7cBghzYWlWADHfMpMk3I5L7aLy0wuM+LVi6MIg+cOopbLtcwLslitt3CueWBubs6/IExKqcS341BXhPjzZjy3n16drM+tGpdqi0Wjam23HN19ntQtE996h3T9vc2gLHKxpsQC2rdfGXT3KFca1+Zeapj0X1116kuYfNOQaoNao8dyY0rVxKD1fRwt2dFwmBaL2nu+LJyXwqzBfE+7xN0ucJcChwSjqGtE0picSCAWk6IrGpiLziAe7uvYA4uauUTUcpYOCm8DLGNi2SvTeeLjvPB4CpwuC/N84TILb3cDhzEyBEuCzGrJwcfzzPN54jIvTLOCGDjqV2NkFwfuDqNJ7fuBH43C27CAzixBIRU0WkFEGxzzKVWTcwg1X+lizCpb6YpSjEmpKg8PbyilMO6M0QY8Mq44fK7I6sMM5vhWCufLscEf5WLBK5fLmaenJ3JerADiOJKSVf2tJci1KNPHI8SJ+/uR+/s7ht0dKUXuxpEdkf3eNIQhGgE3EFmswJ/sCEiD5SlFmXJhUTMHzrnYWC7ZI9kKT/PC07RwysoxF87O1KYp8zw98+1p4k2E33O/s3DpITGEyIASizJIYBgGUjJtLLs/b0yBAWXJM1O+kFTZR2W3yxzCHd/c3fH+dOH75wsfzxMfjjNFL3wbhcMYebMf+WqfTCMfR0KAeZ44nS48PZ95/3jk+XRhAtIucNgNPNzv+Orhjrd3ew7J+jgqbpqzQpEFhSFS5EARM4NPWbksyvky83QpvD/OPJ0njpeZ05IpJaMEC0OfC8+XicfLzCE9c78fOewSd6Ot4TEm4mhCzFIKJdt6yY5OMaswq3LOE6c5W9DSyXx4cy4QojE2VfICOlc09MQuJvYj3I1rcvBaDdnqp2W1Aq9azO84zRPTNLNM0xdQmx84k2qZy1RmoVvLnvOtNbK3UvPOOSUN1KRlQCsutYsR4Fq/pvpfasFDPDCjBk5UZqL+3FK02YWtHe2YjDropLWu9X9d76/Jg9l9PbbxrUyE16ez4APEwzkdKUGra9LeK7iWGZTm+EVZzQABgyyieo3svvrWdUyqc09RL6ERVt8T5neybrjj3t+pJiSqoyBcsvI8KR/OmeeLcskgYWA37NgPA7tkEr+lkxj6cy1SGMCw9YKPtr+DCFanpyaNlpq5YcJCUBi8DEeQyLATRgJ7Fr47wrfnieNl4f3zzOPlzDjuScNgGflB0JyZ54XLVNBFGQvcx8BXu4E3u8T9GBiCSZK7wSLExqGQzI5H0hMSFpC1fKLNnJXKsCkPSKrzFsh5YV4sAixG81+Mw86YdAyIWMkFK43AGhxRN4FaMcNhCG01XC4XpuliQlQuvH14C0BKicPuQLCkJgM/9fH+0cMdw3jg7i7x9uHA3f7A3bjnMJj5KQwRLZkhQJLi8ES2V6LD+aC4H6KwuG9DQ2TOhVwSqrCQKYsyTZmTCzEfJzOnPl1mnqfMJcPzXHiaCj+bCynN7IaRw2CJqG8H4c0g3KfCQTMxWpCD6MIgxRKQJTNqZlZlUdgPcJ8Gvj5E3ibhIQZ+FoWfPZ14uiinBZ6mhccl8v4yc3+ZeXMPwxCYzhem04nz+cxpOhND4Ju3DzyMFjl4d5c4DIFRCjsppFLD2E3AUtyc7IJlAeZcGBEOEZZ94GEIvDskTvPI42nm8Xjh6XTmcp6Yp2xwYJPwOGeeZebxODCOo0W+7gbGMTGOkRQdvigX5nlhdu0/q2lKkwsJlyVzmWeWRUGj40+WVuU4UhijMaaHw8i7nfCwMwbVEoiNDFhRnKKOoGEmvvMlcTqdOB5/AZgUzkd6TWFlNqsNdk1iWn9rf8bNYI6n7M82AmjWpbqYVv1lTVD1a51xqV+D+mMdO2vTA117INKZ5irvrAEJ7mMwSKBqV/bcL0wjycEQjWdVIDaQzNo+RY2QUOtiaauTVPNvBXs5ywURWtE3Z5SV0dtXxRAE1MYqiBhwqZrQYPER2hCms6gzUIMzmkvmUgrPufAxF55KoRDMTxOFGThrgMUYzEggUewd3LolWpmoRagFNzVIjWQyT4X3x3HX3CQURdgBIQopRu7HA18fAm9OA9+7n+rjrBynGV2UcImEYPA3SZWBwN2YeBMSv7RPfLNPvN0ldtGRQrz8xCCZHIRiIVDEbPO3lFXtLdQk0V4VNo1SJCIyWXQUvSk4bupG1WCVFtHaXa9qALSiARnEnxnY7XaoKvO8EPx6kUiUtMIs+XoNIfBL7x4IYeTt2z33u8jDbmSfkpnXyC0cOQWgLKQgRExK12zJqiGIBTeoGq6f759EbsAYMUYWFgqBJcEyDkxl4FTgmOH9tPDxkvlwnnic3eR6nimnBSiMonxzN/ImFb7ZR74+jOyjkDDmBJanZOvFNmUMlj03CiyixENgF0d2Q2CXIt8eJz5cMk+XhdNkQMQXChexqsn5PLOcZ3SydzuMA+/u7/jmMHK/i+xGSLoQPZQ9ZhNOmgVAXZTSOsdCKErJhik4hsgwBO7GwLRLPIyBrw+R4zlxOl14fDrxeLxwPFu+U9HMPCvhogiT1eGKBn0Uo1h6l+fYLYuhqORcC00Gsph1II07xiFAEfKiqGTytIAqMQr7QXjYj7y72/F2hMMgFrafF8SDOrQK2sHopwa1fMPdjvkucD4I/4TPHz9oJiUqrSgemPR5ZXij0QC/4oV1rvOXaLvGI/80YphcWm9vzxPwKC3xtbYyGqhFEoMTolVDsmevOSNG+NUZlC0Qe45D2wgGOqK4KUC4YGGoE+bXmcrCvBTmJZrUks0eHyWQgtnAh2hZ4xHz8Qy+QYMTeW3AmJYUu5rQNsbNTmt1sM4iUFwSxHJgrFCdlRQsbVwXYxzB0TkGQXVEgzuJFeY8M10y48UwxHZeymGXImOKRIKZDNQSFCnZIu5SYIcYbIsqwuIrwZNyK9PEcjWWooAl696NybS2XeKwX/juOCHHM8+Lcilghd6wEPgEP0rCm5R4k4S3MfAmRfZBCFqTysWgaaSOqaOcyWCRUXlFWChV85QVDaJafkMI7nRew3SrS85u6bRcb9vKwK8mYwnBzELdPUMam7YcY9oIcF1BWpqVNghfH/bsx3vGpBxiZIfAvJAjjElIy0IKgVCEnE2js+i+NaG7rLYCWysWz88gAYoBokouhGx4hDEE9mJh3felMCF8pSNPc+bpnHiaM+9PM4+nC8+XmefzhWma+ThPXIKS73fMJfLVzjQrcXgttXh/Xyf2fHOhFCKFXVTYRyQlxnHkcJg4PF/4/unE8zRzmeGsmWU+G6rDPJEvE6ksjD4nAc+jUhvTQjaoIvW5Jphw2mNDUn87w4/RU8/V88gWhgJ3QRhH4WHYsdyNPN3t+PbjM++fTjxdAuc5UzLut1K3Nk1mcRBjMCHV6NFqDQoUikcdKmmMHA579ruRUpPWTxOn+dJgl4ImSwEJAanWF7H9FhBPyDaTbtUYHWSRFIVRRtLuZdrPreMHzaRMacaZRmgbqzdSVUdyNao1TYeqQGmVaRrzauY/0TUYoAUj2MRm1L0JoRGWTRCFePVKMQJWQ4Ltu5Uprf4d93b5QrYtY+toyRbeukjggnDOcFoWC6GdZy4XiyKal2gLs2QDPo2BMUWTCofI4JhcD44gvU+BUSwwwBJX7V0rTp5J5maq1DYO/p6emS8lNAanqEnFyTa/hAaCRBUYFtTyidTgXmaFS1Y0zwTNLARiNh1pF910lhIpRSQGz4yfWdzGnUTNaZ8g6Ax5IYg4OrZpB4ZkoQ6vpORiZS4sT2Yga2AuUHIgL1AWoeTCks1cl4aB3W7kMAYekvJmjNyHwI7Sog2jWlVfBTPHRihajCCWqqX6Oeo6W+tD4Wuh0+VdqxHPkaroh03dtvHtwotrjtUGo02CQewU9RIkrmmWQvCSEMtSPPpvZZbFfXwBeBMtBFyXiaALQ7CE3kUzl3lhGAaLVgyR2XX2qVhdqZgiqCfdqzEDcbIVFSSrVdJF0MVylywAsBb3W7zNwCFGHiJcxsSlDDzdj3x4jnz/fOZbKTyWwjzPPJdCXgrPp8zlTWK5t2TUMQRyrFXrc7UMGzKDl66PBPYxEUNkNwzc7RJvPBDmu8cjv/00c56VZc7M7mANJTAmR4XfDaQhMYwjIQpIpoqkIQYiFtDQV881IbVUAwoVQUc9ECZIJnjIfRIrPyMS0DFyGPcMu8h4tyN8OLN8OHLOM1rMmCxeKLKI0YWiAiV6MU9bg8H9ruLXJVHDXiwLgjIEpURljkpZZsqiLGSmITBPMzlZEIYVlzTWau9hgM7VDRKkUkvTsLQrJ/+p4wfNpHKwH9u1FXalmk3sl6C+OVbzn1ARyKW7cA2JQFcz14rmK6gutsm0SqsVuNNNN9DOrdD1HvigpTGxUqpmVcmRmwUJDYZn0cCkhTnDqSSe58zj5cJzLjwvxR2mC8s8oUv2+j3F/RPaSm0Hr2MQgm2SIQXepWgmq/3Au8PA/ZAYZa2LU/H0LNw+MCOGMJ6tpMKSS9tI8zIxZaWUhVIyIpgN3H0zuzSSYkSC6VKXYiCw56UwTcYQBg2EhmgQ0VBaefbz+cRZTSvIIgb+umRmGclqDHaMhgaxzGfyfCEI7EYLi9ZsJrWsxcFi1SLXQkAlEmKmuNR5nhcezzNPSzFiG42JI4VSJqagnELmYTfwbjfw9Rh5lyIPQdi7/zKqQogsIsxuznOsVhZfJ3XNxS7Zua4PMw1H16oskaQvcBgkucykVN/fdZBO0148F6qZkUJyTLql7QQRGIbanj0rdNIxwKiFUZW7+zuyLgiWsDrHQA5wVrhMCwSYNHLxCDVV0HKhFAslj+7PHaJFTe5i5C4lRiCUbGClmm2PFQdlFSWRGbBoxCiBUQwd4mEfeJf2fL0b+Ho38tPhiffPZz48PnM5LSzTxHyGj8+GR/fmbs+b/cghCqMGRs8XEteEi1rIti6ZWGAfRlKI7PaBN2nPfbS5/f44M00W9DFlZVmEWYRFI0tIlDigIUH0vCjUTNEuzTajixq4bKmOhmB9KLkGRrhJEjOwGLOLrVjnogsLsBsjOx1J5wUVYV4WKzBYzMQXQmw5ZYZ+bhaWIDgSbjaah9PJktHnJy7nVcvOSyGSQSxi87hMIIUQBYmJkEZkSETPI4xStf3oJoDs0EkV0Wb1XX3u+EEzKaGa+twBU7WSqj25X8Q0Lb26W6mmPb+pM/15aqqjIjRHiBoaMLpeU+/J6iYvWUMOlKZRGwEoluEfxe23GjpfTjDfEsJFheOSeb7MHOfM4wIfLwvP5wvHZaEa4RKBEdeWdsIQXXsLFQUCZi2c54lzXphnM/08YYTi7cOeb/KBX3o48CZG9iHQLJhaWIhMWc1RfVn4cJodH222GOeGbG3JlXm5IKpW+2ccuN8fuNtbzSGGyCzCeVaeLwvPl8LxYv3ZBbiLiXd7K/1gASSG37Ysi2GPFWUuhUGERSKTj1fAivCZUGZMOufMeYFznl1m8dAEieRssEnHojyXhUnMDBndl7cUIZNsjDUyL4Ypd2HhfZ6BzG4IPIyJHx32fLMfeTtEDlrYFWWHEb0cQXduuqGaejznpUU+1vW6Eq76ufa6CTstWMfX/o0o0Bf3+7lS/aINX8sFqVJWc6KaTy3XyrGxPtcCBEJQYgqgydZngeNUeC4zT5NHaBZh1uBlVLJrTwUti+1BZ9hmwhXe7nfcD4m7IbHDTLtJAlFNG45YFFndT9GFUKkm1WJ5c+MuspOR+/Ed3+0Hvo3K4/OJ8zzzfIanRdmflPtz4e1d4d0+8dUYOYgyqgV8IIEgAyIFZs8SU4EyI77H4l2kyMhdCnx4nvlYMguFKc88nxVlgEFI48D9bmAQc0AIBpGUavmTKJ7QnG15Oo3Aq+MmEUiJql1XeqbVD+y3WMSreo0rQUNAYjQNeXGtXhTqeAZhPwTevRl5e2dh6uabtoKTJZeGXJKzMmtxwGVHQBf7WYIy58zTdKE8w6KJuey4L8VqrcVoCbtiaBpaFgfgtfdTxWnTdg2/dvygmZSByJv/RGuZVWdYwSWTRXpz3/ZoWhE4E/Iqqu77qbEKvrSoCjJiDKB4LaAaRSYVIJKVUBiBcJVfrEDbEOrisF5k1IMK4FSUp2Xh+/OZ98cLT+eJ02IytURhl2BMiX0U7lPkTRq5T9F8BdGS5BCLaFqK8jzPFp46TTxdzpymzGlRznPm8nzhTOFSlF867Hm327FLAQnCPMPxvPDx+cz748T3xwvHxRJwFwGJikTMsVvt7HEkCJQgnIpwPk18PxvY6JISkxg+37Qo06wsS2FUQZLZyQ/DwLtdYgxqeU26mq7MTKde2LB4eQYjXiEGQgwo9+RSDDBzKWTNSPLimCREE+ep8N1p5tuzoUKcijJJYPRoQlGIJROLIUjLYhIzDoWjSdxEmXmeL/zseWYnsFdlXDIjYjWqxgg78wEMEhhEPTzXiDhYfNf2WM3U4pp+lJ6JrWa813P2Vh8V0BDH7fHiwRa+3lFKVvcRrebdLY6llY4YhsBpmclBeF7gqcBPn4+8v1x4mgOzj1MRNzFVfydet0nUBTslyEwS5f3pwj4Zosg+RnZp4JAC91HYp8SImo+qFCN6QQzEVj0AAoilMKI8DFhkKDvuwwMfBuHxdOZnl8CHGT4cJ54umadz5ng3Mt+PvNsFHqKA+w2NiRcIavO/iqgmkKbA7zl4knieyJfCJWbOWKj281QoRyUNibtkATAkL8XBYjh6MYCEZvGoNADXZHNlWPRui9Aq79o02h43CVEs+XcRkIFxf2DJkKdsOUoYSnkKplUdBuXrg/DNG7N0pGBS5jIFyMXNyy7gAudskX6XyTA0z1PiNM+cLhNzzpYAfc48PSfuDnfcHw483B2MqYvJsoKV9EjiQnAQVIolbX/B8YNmUtOsTEtNKrXQYosewjZepktKga1Nnw1wq6isuTwSyZKYsjK7E7Jk9bLP2YjmYpqJQeSYKafdT2eicQk2hcgQo1cTtUkLjeAY4brkwmkpfJgWvn2eeX80BAMpC/td4s1+x5v9wNv9yNshcRcDhxBIrgkktRBRQs03j8y7yPlutPDU+cDHy8T3F3h/OnLWhe9PC0u5kEvkkhOHEUJUztPC9x/OfP/xiQ/PFx7PE2m3ZzzseHe3Z78fSIOYBKyQPPlUnKhOOfN0mTguVlbjeBbOaiHIhejmCCMDAzAX851EKeyjNpBMG7/gRNpt2cU2HiU3p7MVnhML2tDRQ/eVjJdZJ4ImTjOMu5FwnrnEwHK+mPhRDIJnJ3AIkZ1EpCin84XnaSZLQGOkhNBQ0i8qnKeMLAspF4ZsTGogEKKicTYYGLWglSEZk5p/I8Ng0ErXYMbiRL5nVNVIYFLu0nAmr9FPXsvL2+InquP7GfHMDnpatapqJiw9Aamm1lJ4nmamcOCnz2d+8nzmw3liKSMhJsY0cLfbm6CjCwm14IKcmXUhRw8E0EJGmfPCUeHx+eIBF8JOhPsottbHyF4Ke1EOyQpLDo352kIo2Z39IgwJZB/ZxzvejInj5Y7dsSCPZz4+W52w949HM5Pnwnw/sOwHckgMwfLdgmaX+B1yKQJqe3qMgXtRtATmXeSyD1xyYMqRy2LRpNOcOU+Zp2kmDQYNNERL6E6jaf5afYZY+LlXNLH5VQvuqRGsNfxWsIi/xV0FRS08KGtkyTBdMvMkaIkEGUyAUwyNg+w0yAUmIqNalGt04WeI2bD43KSoKkwKc0kUHZiLWjmcJfN4uvDx+cTz6czz84nLNDNfJj4+nhmHgd04sksGBTbEwLgb2A0DQwxNEEKEZf4FSOb9/pJZRsuFMBOaqbRRKkovq23d/UECHqJu0rH4VxYHEMkEJrW6NY+nmcfzzGXKTLNXu8QI4zTNVpK8JiYiZjgOBtmvnnWtDmS5S5H9MHC3GznsRsZksPcpBtJguTKzmn3/uGROkzLlgMrI3S7xS2/v+D1vDrwdA3exsBdPdO18SdRs7qJICKRoUl9KgTTAne7ZTwl5msmM5AVOi/KYBTkrlzyzmxbSIJyXwoe58FSEkwRKSuz2O949HPjRu70VDBzcr1IM6HUMvgkV5jLwPCee5sLTNPFhVj64eWhalMu8YKgoyjjAPMC8DJQBMz1hZtFqBsG1JsE2dSoOFWSTS/0LsWBDddPrXIIHghQWycwRhgS7pNwPwnG2/KkocD8K78bAuyFwP0RSEJ5OI98/nfl4XnielbOaQKIF0wCHSIgwlMKeaAzbAzqKmC9Mil3/dDqbvyE7k/HY62sw4peAyPbbfK4J1YXs2forA9qa+NZ7K5gs7fsKVhtjJMbI5XJp9ZMq42v4gq6RlyUzUziWwsfzicc5clwCl2LXH6IVzfvqELgfAg+7O/ZBGATMgFvIkpm1cMqFYy58uCw8XjIflpnnpTBngZxJWrgfFt4Oga9H4Uf7SBmt/ETEcwTVogdVokVSqlddBkKKxMOe3bgj7JQQI98G+P544cPxwuPxzDRNXM47Lm8OzG/uOCTYiZnQA4kkoauILa7sWO7XECJjjIzJkDbGwdDflwV0KVzOE++DTXrQHXFnlpPoAKxzngmSTUtGiVg0R8ZK1WgNxsJ4VKCaSi1EvFTLSzYT+HQpHJ9njkcrG7PMkGelzBa4E3RGYzGflEZCHpEiLjwVN5VXuDH3TwmMEhliaGgUBzXElP0QuB8Tx8Oep3Hk42nH5TKzzEYrL+eTa6CGdD8kq+VmaQjmlQ0hwC+CT+r/9d17HhY1CR4v2rWz+P4xJfZD5OC1jCLmB1oTREMrd6HOypYSOBV4XBYep8xPPz7z/eMzp/Pi4I/WhgGAOj6dGFiqSrDMbFfnLVJPIYzkYrD4p2Xi6bIQeHZVWBmGwH6/Iw2jSUcl8HSeeZ4Kmci4G3mzD3z9cMfXh5H7mNnpQqKWc7Y8KRXBSi6w2rjJOPiCby7hgPDVPjHpSM6RPBWyJk4k8iyEXAgLBs2SQceRMY28CcLX93u+OiS+OQTe7QqHWKw8tgipKINCKk40Y2SOifMOznnkSRPfT4WPx4mPzxPv84VnzQSEVGxTTVPhMghjFLSI5VvUMthqqBMpRFK0MhGBZHNaNWR/eXVpXTMEDIduUvPPNRim42zwLkvhzTDydjfwzd3IN4fIuxEOwUqDHO9HPjzs+Onjhd/5eObbs5mAShD2eyui+DAE3qTAPYW9Zka16qQaZ2M2xfp7vEwUVcYhccbK4fUM6VoLuhUUEYJBIC3L3JhNCGF1rN/wTcHLAIv+c0oJVeVyubTn55zbvUtWhjGw5MyiwnFa+PA0cV7gMhfTGJIyjvBwH/lqCDzEwn2KBnwKtl41k0PgwsBTEd7eBb6bCsMxE0+ZjydD9DjOVtTvdLkwD8IQdhxSMPNttGg1Q/SO7uelmamqEziEwC5GvoqZyM5qLT2NiAgfTxPP55lzLQczFd7trLbU3Rg5RCHFwZeTh1OpBQdJCaQQGNLMOCR2aWGMgVnMP1RyZjqdKRrQPCF5gl0ip0CJkZ0Ay0IMxWtHCXNeKEUoYsJuWX0MZn00b5ft8UiNc6BgASuX88LpeOL4nJmyRaayiNE4LCgkBbjfjzzsR8aUXFuyApQSjIGZHG/BNoaD6xV91YLCDHPPoJ7uUuKy2/FmHNmfFp5OF56fj0znmWU29HWlav+WoA10CCAg+gugSf30nPkYJw8eWdilxMOhsN8P7HewX5RDMGyxQeAQhR3FEI1D9dt4MmyBY1E+LvCz08z7s6EPHC8ZNBMdJ+xuTOx3I/vR1FfDjpMWiQNWlyiIh3UEcdPgYlFpiyEXnC8XzqcLzwDjDoYDZdiRJXGZlZmAVIY4AiEjTAwUBjGHa6lF7ByVoXj4bnTpS0s1j6lVHS1K0sBhF/hRSpTzwpQnjsUAN4/ukCtzpiwFXRZ2Cd4dBn50SHxzl/h6jDwEZV+UoazEM4p4NnoNZcmG0CyB+xR4I4W3UTjGgfdR+EnJfKeZ05Qpc+FJCvIUmTTxIxl5UEg5Izmz5IWlWC7O/ThyKJFDDO5IdwcyUJ2IQSFkvGR5ZhZhLqAlcF4yH48nPj6duEwLSQJfjYlfu4v86JB4dwgcQmGQgpAZgxHfdBBkNiPq45SZJXA37PhqH3m3E74eA28Q7iQRtebaJSLqJXaEhZElTwwRzoBnPrf1XJlK1Wb60irVNK1YLlSIBsmT0uDBGDWhuri+aUdN8rVNYmZtdX/UCn5sOVnn84VpnhhlwAEuLPBjPzJaFBJnTQb8KpBKZi8RNMKijBLZSeBuiNyFwkHMXxSW3PxgWpRdEqvnVJQ7sVyz+zjxXci8T4Hvn4VyKVzmwiVGTnPmOQsHscJ7IEhcc9OKVgHFA2i8XplqZpTMuz3shpGd5wr+9DHxux+OPJ0vnJ9njuXEx51p0F8dEt/cDYxukWlpIuql1IMiJXMI8JCEUxSOMXBy02wB5nmhiPCcMzItlF1i3iXKfkcZK8ahuDZo/iaLDjYkDgJevVl8iRQmckO7KRj6+umSeT5lTueZ6TKR50zJhmuJWq5eFGGIicMO3j7s+PrdHQ+7HWMUC2xALI0k2E4CrQYZSw1QNxsoqBazUHklgiBmtrjoYCgii1DK7MKykhLsUmEclSGJYWIuuQlXJS/85Avo/A+aSb057Im7HdO8GGJvNhPd81KQk6FNP8TEfhh4Mw6wGwjRJGQVc1JOqlZPZVGe5sxPHk/87uMzp2LRMvf399zvrODXg/uBDPYmMERIUgFWV2ITnEiYhOsSaM5MXolzzsVKn8+zwb0sylHhNC3MWO6QYpreZZqZkhXTW6KHKHsxOfOHq2Wzoya9qWmGGsyXU6GjavhI8A2exGrCRIftWbIyF1tARugzgxTux5Fv7kZ+6WBYdG+SsCd4fpAQKotwUwidJC8qbYEFyYQESSJCJJcdBPj28cTjaeLjWbnozKyJGO+Q3cBODR1gLhboEYKhU+hADVVxE5B4TRuMGLsPqxRz1l+AcxGesvLR0QMu8wKlsB8jb3eRHx0i70blIcKIESIj5IGYIvGQCOwJu5lvnyeeF6AszKcjiyQ0WCHAQCQFU6ijb3Rxs05WC+SxIfLkUl0ZRhWfew3Ifoqb/DwB1IsULsviUFAKxUKN3YHhkqxuwteF0HG8ygQtIjTGyG43cjzOVPTytqadoD0MA+eLcjckcsmUOTNPmWmGcy4cY+IpBsYcGfeJ/RDMRO4h02bGWAhaGBAOrvLHENk9RB5SZPecCSVwLIFlNr/zx3NhCDOHQdiFYOZ9xfxRlfl6QIi6L0m0sHgV4UGEkAQOEZV7NIw8TYVTtgiz95MJj8sgxoAS3O+iMUHVNg7itbwCym4wbL67RRlniLGYKUsrkRfOMyyXifNx4rhLnA8LX93veLMfySRSFMun85SP7GvE9qy/DxUFxnzYAXMhzFk4l8A5FybzXBDE8vZKlWjEhYw0sLtL7O/uOdzdsRsjQ9UQHUigJZxqdYf4W7fxdYEnGOhVCTYe2bElDcBWzY+3S8Qo3O8Db+4Th50wRIPLqmtBRMjzwv/zC+j8D5pJ/b6v70h395znzDQVnk8Xni4XjqeJyXOF5t2eN8U26BgigwZiCkSxInUnCo9L5vE88+F44sPTiWWauD/seXN/z9s7g+vfxcBeYKfmDLYcF0hSURikEYSaTW7TbMReU0AjHv0UyA93nFV4nGY+XiY+nBe+O008zzOoVTJVNRyvWWAOkSkIM0JSiNEx9AQIFu6pmKRaVK28DpZUu2C5VxmLTDsXsU3qvrZ5LpSsLT9D1BIbCYVhJ+x15K4o9yVwyGK2dXGTi1HgnkeDVMiolfCK2gYaJTAOZiq7U+E5L5x1piwzmcWAKD3ULMREiqYhzGlAVZlD4GhWekowwNBBC1EtHLmZb2NgKspxFj7Omfdz5rt54dvjmQ/nTCGy3yXe7ne83Q1WN0jUfCjed4N98nySIVLCjjIk6+NpMV/bcWE6Ccv9yGUYebsbeTNE7oJD72CmFEJEi5Wud1aEaqHm428RJHwYW66+Mx4XAuqVLYy55uK0nMG6/tyEqOvZ1ZIkrSFLVyhICMzLBKF0Jd6V4/nMMBrxutvt+SYeOOwKsmTmS2ZZCqfLhZ+dJ8rpwHS/g3d3cDewFzikwXynUgm5+T4isAuBkAJ3KbEPCnkhzxE5Cx/V8umel8hdFqYs5CUj6hVxXVCzIISCBPH8HNtnBBfYnLgfUmDZJ04a+eqSOfo+OC8Lp6UQy8IoC+/uEnOBVBN/AcQRGRQ0BMIQGQjsS2CcYcwwBQsOsehDIRdlmk+c5onj8cL790+8O+x4OOx4c3/g4W7P/mCRkIgwa2HKlodYcjExTFwQVHvZnDPFo1fPl8zpUjhPs2nQUoghI9mrH4uaADIkZBjJYWDGtB4R08xEzDcanSFLXULg4e3a4Luy51JZioEyZ+V4yTw/nzmfZ5ZpQVQ8eCLxsI+82Q8cdoEhGDSVuEYmYhHEX3L8oJnUL98F9veG73WZhQ+7geE58n6emZaZOQiPvhc1Z8I0o0QmAlLgIrgDd+b905Hj8YSqcn+356u3D/zo4Z43w8A+QBIvLOZJaXhyXk3slVYuQiwhD6uVU6XkKGJJe8UCLAx/LTEcrNrsw35hPwS+P018PBeOU+Gc3UEaYFqiRRGphXIWwZMAjYxlhVyloZoUTGApxphmteKCpznzcVY+ni2/5TibVJa9+FzEzDqGg1co00I+TxCjAa56zljx5NOCRcbhhKCmlLk4T60fFNWJvYhV0xXT4sR/CmLEblGeZmVICvsduzRY/xmsxEMppFw4BeFBAweBA8JOjPhlxBCkS+ZxmvnuLHz7fOH7y8SHeeGsxhwedgNf3+/40X7k651BHo1BiZrXea1Gs2BZ9w/BxvQyRpY5c54Kx2ni46LM08R5P3K5v0cPA7sBC6pAHRzXGNSWmZvUqxRPgAxeu6pSCfexuNmpwnCpawkGeRRXU5rW5N/V34Rk3PW+arnqJkDpkogpLGVmLpmgHsFolyLJwqWHCITCV7uB+2EgTLMVAvz+zGO28fjpVDg+Tyw5MBfh3SGSh8jOaz+FYCUetDgYstj60XmGaSIsCzJNMM+2fyR6ufrk5lOan4saaBKcUSloyRTNINaeq7Gm+VOYxYBbD2Ni3I1mKicwL7OXUi+W85Wt0rBoIbipbbUUWO7WmAK7MbEbI+MYGYqlSqgYbBC+5rNa0vsyz5aWEZ4sj/D+wP3dSBoHQozMmpkWB311LTHGyJBiS8dYloWcM1p8nyvEFHi423E3RsoyefFIjwQUCwPPWjheFuT5zDQKu1CIwSwqo8W+uqleG00paiV1lmJFLOeCVRYu5ou8zJnj8cz5dCLnbBaEGNjv4M1d4jAG7gfLg7QiiEZTTGhq6ttnjx80k7qPcBdhFmECIgMxRWSeOT8/8lQyT2TO85kc9paMWKxeTXaQ08dl4fvnM8fjTNDCw27kfr/jq/uRt2PgPmQGLaSiZF3MOSqChDrYGNMRkzxQw61DlOB2WHEtR1yMNeTuQiiFfUw2uQnkkCy0OhfKXDynyDS9sByZniMfdpH7MTIkM3uMo9XwiRLI7vtQLLflUgqn2RItLy79HKeFp8VglbJEC2NFyFlbdGQIBdFIxIoQHufM47xwlxMSYBdxn5t6QqIVPDStw+FVSnEUdKgDFTQQonut1BJbpxy4lMisBl/0uAzoCU4U3gkMatLyXILV1rlciCj7MfBuD18Ngbch8BACSQyu6nFe+P584bvnC7/7uPD+OPO8LFwojIcdbw8HfnQ/8isPe340Bt4MgTF6tJUaw0ieKV9hraJa5Nu9FH7pMBLjQAkXNAQepwvPS0amwriHh2L1oiQGR04wwinBfJhNYxLLydEMpcwQB6+NVZmJm5kqZAUAtnbPZysfbwmz2fmZrHxIDUlDokNzSQA182+NBlxNjUZIn56fWJaJ3WGgIlYqpjloECgLoyhRMg/jwOHrN7y9h2E88rPvn/n4PHFelKdL4bffW6mRH73d8SN2vNknBwu20MusBoYqROZZOOfM03nh/dPMx8cLl8tMoDiYKYYknoIl9woEMVOmCkiDnbJ9pV7iJHjwS9WopAiDFMeCNCT5mMTD2DFhKBsyetY6/N4eRmCTmIEbzPR3Nwj3Q+A5BaZoILWIF/hDSLs7wt2Izhf0fGE+n8mXM09Pjxyfn/k+JqIz4YxX/C0rEklKiTHB4PTGwHptH40RdmOyCMOYrI9i6/c8L5wuF6Z5cWHE6oqdjk+WDjEIu2Fkv9uxHwwyqgWWufk+q0UPWsh+9kKM2TXE7NaXQopiIeaDIe7f7UbudgM7xwwdXFATzdV7jIIDv33++EEzKVGxRL8CA8L9ENExMV3gqDvmy4WPjmowZUPf3msgYYlqz8vM07zwPGfOquyixScHh5wHD/P1pD5xVG9QoktvZq8tloS3CR0NjWDjoadBogFdZltIRlxMch9FuB9g3pmpYSliCa/zwnnK5HnmUZVRlSFAFGVMkbvDHbtxZyURcM1ryVaSfVo4zQslJkqIaAgGxy/BVP4oECJZQdQQJJZSiBIJBIomjnMmLwsLF5YgHPeJrw+JuxhJWY3ZuIcoOD5cFEe5BjRYyY6SYc7G8L6b4KenzE+PMx8XeNbELEIR4ZyF52Phwzzz/SQM8YJ4H6dlZprOgHIYAu/PM8+HHacxcU4GxVRU+HZa+N2nme+eLrx/ylyWQgnCYbfjq7sD37zZ83vuBn75kHiXYO/wRHg+nIiQPfKqmrwqysEhQNwlJFowRpbAIvDhdOKsgTOBKQ4O2WVF7sqyOMxNMQ27rpH6bBEz42Rl2Ccq+v6Km+gal1r49ePzE0/HZ3b7HcUZuUWvGqoJwRLSl5IhGuQUVWPDnN+ldJGgKJfJStJbmDDNdK1FOU6ZfSXsWS16Fk/32EXi73nD1/d7fufbJ373/ZH3xzPvHyeeLvDxsuPjdMfDYeA+DQbHA0Y88TpdU7Y6RtPMaS6cciKMgbeHwmGArw/Km5TZ4T5UcQxEVTOX5lrd2H3B0dDeSzEzdqgao5dAR23/BoGcF/ebGZZfilYok2KQPyGY/zVg11dkkoABrd5hARfzYbA5nGeW5f8g719iLVuStH7wZ+6+1tr7nBMR92bWI2k1THpSIEBAD7pyCgikrhk16AEChBih+iMeEiohMeANYoLUQkgIIXqEkOghDHhNkHgIBBMELQbdLUFLlZWVee+NOOfsvdZyd7P/wMzX3nEzq+om/eKqdlXkjTiP/VjL3c3ss8++r7q5Yck8nWbOeWHmgawd3Xe2y4XtemXfGq06e86SizF39UBEJHw5uyP1lJUkKRTNC6UUlx2bMqeSgsTl8B4pU1XZ9rOPenSjtc5lXfnw/My6Vq5bIqfGVHZy8UHpG/tzkJG8u27mqi+Eqj3hgJwlcz4VpmlhmnIQQrxXP5fkepbEiIyvpjgb73rIX+HxtQ5SpoM84De0JGEyv0DnZeJRjVp3UheffzChSGYqE0mMk2VW3RwrF8+e3ABM2BTW7uwYs4Qmc/jHT60b3TXEP128c3RhXG6fIbQIJJPIon0Ov5v7wBzCoyHOmLJPupt5D8DUqF2oVpDA9FLIzWQaU76SUwWEva3eK5KEpeQunkCaIS2JPCUCvAARV8NwHr3/u4N2owWsWVXJCdZkbOxceuf5cWHngU/niTMTYX/kMIE5ixD/BO4nY663d+3wXI3368r3LjvfvzQ+NGNVocbGMlXX0VNDm9JbD1XvDJJ8AFQd1lq7wxfr2nme3OL7vCw0E95fK59dlfe7U8+neeLp5M6l33ya+fHHhXczvCvCg7giPMndZjXURLDucNKAzeJPSb7OKLAtic0KLzXzfPXq9drDtC8nlu52jQMivcEc8RDvNQyixF4rcGWeJg8S0SA4ekYC7z984Puffc+rgJwPyw4vivxQVTucvNj3nXkWxNzoTpK77UpyYzrJsO8bv/iL36HWnYeHxziEY84PcT0+mZC687TMTDJRa/MBdTqfnDKPpzNLScxz4vGlcKmN676ybVc+f195fhFO2UcGSsrU7hLNW1V2NWqwbCUXylJ4XIR358S7BT6ZEw/JOEv0HcUOCN91CNORZNwIKHZApIqL6G6qbA1e18blsjtVvHaKeeB9N2XenbNXASXmDKUfZ4eQsObvIWO+9ouwLYVqyengF+OiDWnNCSEIb5bFK89yYpYH+v5ADWt594Py80ePNGIEYQ8WWaBkZ+mlnD1YyU3RI9kYojGn9uIMyodcaKdMa0rrxjonHopwWatrZ1YndG11p5vbkHQLlQ0REhMluypFFo2B9DhDU2IqhdPsflWl5HDl9SrXekNQkh7L+KO6SQYE/RUeX+sgpYGgqnibuJkbqhkumZQtsUhx2KaBJkPnmMFJLoWzFKFIRXtjV+VD32lbZ98623lmO008TImFdAwmpiBNJAxE42bIwYA5egkEHVhGGz6a2BFcu2Q0Jx8gJvlBvq18/rLyxfPK81q57h1kQpJ7DKUSva9eUW1uJ6E5VJnxId6wR8+T0zxtZOro0ZpwFpKSUiGVyd9fSOiYKiHMTDOFBOvWeX3eeXmuvDx33k4Tj8XV1X0DuW10js51s86uzeetWndmXTVe986HMLAbZo6n4orsUyp+HSN4u5I47nljUHF2UYuvXVpnv1beY8zJ5Y+6Cbsaa1dqM5IkHs8nvvn2gR97KPzkY+GTSThJYwlpHZddjI2fD8zD4SMds3UhcaWQYpj6TVbWGb6Y4LNsbNp4uV6YtLK0BdR4k42HBFPoDNrdxrToe5l1Sio0lJfXF6ap+CBqKEAA1Fp5fv7A+w9fUHLidDpRih8iKfk27qFmbkZk3JnrdaX17q67eT4qtOG1dXl95Re/8wtcLhc+efcJ8zQzlRkdjr9m/OIXF+pT4ZunBW0+2H0SJRfvL2btbiH/yYl3jzMvtfH+9cLzdWPdV2qraO9YbT5YnbOTH5JDolPOPuuXMtM8O+PynHh7Ep6KccaYVCnWEa0BZfq9ERtalTfofegPpuSJjRm0WDfXWnn/fOX5eWe7bqDGkgpPU+KbbxZ+/GHm3ZI5T4lJNLzXLNRs1NEHNdAGKVFMmJPrEc45kXA5rba7pt+WoGWFNDHPJ04JZMIFWWVxM0D13tyolrv1Q0cPOAxLc84MsNGh9tt+JfZMH3AdbpKaBaZiaIJTmnicEtujublhbWy1snd3Rq69eW8agESW7I7HRaI6EqYkh0XHlEKqyjUMHNIjEul434xqXdTRmvjSTaT7V398rYOUETp7ZjTcgnzrxnXrXHefs9j3jjW8nEcopdIin92Al7WybYq15HbKVak7LodzVd49KE+nzMOUWcQ4S0gAiSssTKmQQuZFsMhqPCASulyCZ7AxEucLsmR2FaqKqz7sO59fd773fOWz5ysva6MpkMpxcDr841XbJIVMivNU2FZgnZyCm0fmruRspOzDmGX26fgkPsvRWrDAUsOi6asGrXcPpBqEEIXeoVZje9748NnOlNyO+1Qyy5xY5sktNXKGZOzdlZLX3qhm7CZsGpWceRpxLj5D9e7sg7RvzxMeLj3INVPW2rmEO+pVYQc2c5uEujdarbzuG6/msxlIIpUJzZO79/ZOyX7/3s2JdwXeJIdNx/4xwzdWjr740HCTULhAQB227EPOCuWclKcivD1NvHs888VW2feNL9pOqg2tlX4qpNOElEwWxQe9/ZFIrjoRkN88Tah1Wqtc1+sxUKumrNfV7eWXiWWamefZIZogusBQpBjVukN201RYt5Xr9ULKBUFiGLjxennh/fsvaHvl3bt3nJYTU5nJUmhDx1KV73zxQmVhr8rTnFj7xqfnTBVjmSfPlnujlMzp7C6y3zifaXpmqz4029pwZnW1czWH4bqBSYbkRpW5JOYCp9lpy5PBpKH7ZgJUbn5Qo4K0sDXhEGtWU2rTMDAQVBK7GR+ulQ+vK5dro9dGRijJB4/fLoVPHmYespMmBsx3dFbNUAsOqWRUEmYOddfeqSGE3NT3kWiK2SEcamyNFEomg94+1GnGnJTGSIVlh8xlnB9GAHAtDniiP25o8h6vyugAB3PUNzHukuyDuPOceZj8zOyaaTp7rykkl0aMGq/hSIYzVcuYjyJsiu5IJEe8ierb/5tiT0YPKpK+A136tdCT0tBu6xi7GZcOz2vzXsTWuIQWXsLx6X1bWftGelZSyjRJrN3Yag+GqdCietjorFvn5bJxKq4efMrGY0mc5omHZeJpmThNiSJ4wMCwbviudQFWCdjLb5Qv6C5OLb+uysva+Ozllc9eNz6/Vq4d9uabSoprip0m86x19gz7tGQeF8/0TJ0C/Pz+yvvPhX3rNAzV6stelNNp5u2biceHiYfTiVMpR6ZWW2Ovjd6N1n0zbXujdtirsm+VunV6M8Q5Hby4lIMbnMnOJK5EXpJn7ykLVdR19DL0wNp77+TkkNlDEb6xLPz408SPh9LDYxEybvexq7ErXKbEsyjPu3HpwhW4NFgRLlnc/0lczsrCsXYuTkBA1c3jst+jglF6Y0kuF2U4wcHwwVTfcz6xAn7PxIL3FAHLg4w3gKckPEjik/PCKwlNG5+/vFK7V4xzqpxL5hGhmGHNDnbTeAFnEgb0JsL5dGavESCs+zySds4PC9M0uRli8r6EW2rI8VyHq64kP6BIDm2nzJpWLq+v1NZZt83hzd5YloW3b9+wTAulFJfOyZk2ki2Dzy8rjVdep8Qn587nwMvTzDfezjyqcRbhNE3uxttWJlGSKFISNhXsPFG7sXfozWFCMQ0mYyRF4B5kokhWTzDwQCQ+uxHhN/nnRIHsLNfQ1ZQUg7GqcRYm3wvmzLRLU172xuve3cW2d5IIi2ROyTgVOBVxYpDeqgFX8vA6uCMxdmF09V73ujderxvXdaWpyzUhE5ITqSyU6USZi89qEaLI0Sdzr7EgfqBxUDhjcyjARxblBKyA+odqvQTRRsWTHhMgjC6TBaOWG8zsSQoHMuAO1p6gm8aIQvTtVDoSJCkXQrXxZNGjBzPvWWEhghx/7G5QfeydYb8zYG+XmfvVH1/rILWrf4CNzPvWeF93vnfd+P7ryropjURNXvlYBDOtvvjmycjJfPL9NCGLzzW03tj3Rq1K3TfevzReJJHFG4KnRXg8nXjzYHzyJDwtmdMkLCVHfwG0uVy+xGZN4oGqkZwCft08m7vsfPF84XVrXDts6sEp58zDnDgvmTfnE28DfnhYZs6zQ5RLcckf04m9Nr7XVvImfK5K27tvlFwoRVjmiYck/Nhp4htvJt7OwSJUwzSjvdCaYpJRUgxGG5dr5fnlymWtXDenGG9N2JoExbWiNIdZW6HIAnvYmZdEz4qf8Tn6O0qOaf1Pp8xPLspPnBOfLvCUjVOCbJ2EExSqCA8iPJJ5M8HahVWNl135rDrEqjgM2MXzMsmZJhZ6ZE7dzXk6tM52dZaj4f3Ggg8zi4WthNhhA28mMesYZAQUS9khZUtodrHON6r82ARtscD7jZXCBy2cyJwAUeVBjcnCFRXo0oAcTesUEJUzpYokWE6eSGiPysjJHDlnTKHkCbBjiNcDHTEXJCQp3oS3xkM5Mz1ktq1yXp58jiYOPw98Tn/PueCGkHdzVmnhtaVIiF5QMu+18EWtvJs2PnmceTy79M5pcZuGkh3+UXVzvyyJKQmnydWxfagbMHXUQQTV5hbjGbq2kDzKmHRKTiR1N2HRRM+eAGnvqIwhWicfkJML0KZE75VNjedd+fyqfLbDh+iDiXXmJDxNxpugZZfo6ShG690r+6gKLMCRuBuhtSc0dXKRH/KJlGYkGWVOLEvmdCosc6Kk7ooNyg2edODB75ndOpZ2kHYI3Wo5CDzjvaB22GGIhh/WOBwlCBjDc4XRhTDf9+LEBTc4PDphxx8hAk0fPebb8/rsGccackFkfI8LMTrAwF8ZUkuD5WzH2rvrz/4Kj691kFop9FZ4qZXP153Ptp0vrhuvm1soC8oixpLcPuFUJseOS2YpTt3MgxYcpfveGq/XK88vKx8+XHitxl4bu0JPQtsT+2VlfVVenysPp8TDuXCas1tC5III5O7iiilDSp7p7Gp8can80ucvfPb+lXXbWbeGpQnyTDnPTNPEeUp8+nTi3cPE2/PEu5R5zBIq0EoJOqzgxny7Cenxgb3vQT1v7P2o1V0VQDo8TpwMnkxDqNM8fmSXXrKU0PAD6oarRH96dlpuh70p6+5qDeteWfed19r4sHWuO2w1sbUQVQ1mkJCOPpOoUrJxzok3c+btKfNuSTwWWKT5EK2Nfp+4BUh2K+1FoaqwK7wpsOzGQ+o8dONZOhtCTRxVqkv+CZo71175rLrbKqnQzJW2Tym5IGlyIV/DqxbMvFEeBwWOsgI+P2ZxGCoEa9NCisoh1tqVGuKnZWuQlX0SPkmFJykfbc2ehgakYFJo4pm/TAEDdmXSSH0xCINIw+Jw4/ieROofx0DIQoX8UVdKKlh24VC3mmkQ+onDV8rUsZnR5wrg0w0TwS1eyFxeK8/XK+9K5/PXhWXeePfmgacH4fE0uewO3ecCQ07HIa7wherN94V28lGyBGykwmSFroqpK0iYxrCpT5ATKacfgHhwT5KpTZFSSN1oJrQkPO87v/S68r3nne+97rxWaAqlFB5PE29PpxBuTj6DJaGcMMguwc5yFZgeh7ow5rR83s8vlsOHIDHMLaJOOkijNx391qjOGIPx2HBXP1xzDTlEbkVhMLPukDUAr6wOnCYUKhhVlHz0sxCBaoi7qoXtiQWj2caTct8/PQbIx++PW0aswUAhRr/sBv9F4LWP38Mh1/UVHl/rIPXdK4hWPlw3vriuPO8re5wq8zzxMGeeJuO8TJynmYepcC7lkPx3tfToQ+Dy91Uz63TidZn4Yp74fL7y4WXlsu5OF22wWmNfjefnlVyM08PEcsrMU2aeF+ZJmIs3dAlVhqbGpvB82fj8w8Z1N7oWeilInsnTwrKcOJ8Kb5fMT7w78+k581CUN+rN40Jj0pgvUMIvC1LKfPL0RF06NQmvXdlXpdod8WA3dOvo5ooWOVn01PwaeEh3SdoWgNeUIU3R9zLv97Se6DpR9UQz4/0ufOdV+f7LxufXxhdbpzelmfqBZImkbt2QcavwjDdeT2XymZWkrkmIRUUU8Jq4sGbGlSB6gqbGyWDJiU8m4bIkNoOWJlaB19r54rLxuvrg8r6/su2V12vhdTnxxWnh3Vz4dCm8KcZD9vkbhwpdUdvrSYuBZidfpJCpSUPQF58Dq2J+vcQzbCeMdMwS+1rZtXPpwvOp8L7AJzh7ESBbgmiE+8PVFMqh3ecsPCfj6AGbmFloNEpYdhNs0wgwctNgUwXEmYsp+0HpeoIa8j0BF5n/fslOFah3UEwWoe+VnoxcJozCpuKqB2vlszVxWhLz9ZXHh8rjaeI0Zd6cZgopWIDKOQtT8okjMe8Ximr0A32/uJqEOwgnjQXsH8KhPNE4uN3zqVs4+iIH1KYd9g7rXnlf4XsrfPdD43svISxcnRCRS2EpE0/LibfnwtMkzLjiSBpJgHi/21+Rj2aJ1IY+Z6AHxOC2+SC/NqXXHW2za+qJW3FMcccdTvWyytUe7BYQ5b56uYULuQs8cWwdBIQUvbgjuYqKxWcn76qYu+dL0ee28frR1xO7f33hcO61o9Y71ocEE/E+Cg05KbMRmMf7TEdkc4mlX/3xtQ5S//fPXkmzZ/eX1mmWvcSeM28fznz6sPCNqbvzZ86ckvvVTGKhynxvOze8XIQ+J7Zp5pO58I3HhQ+XyofXKx9eNl6vjW13+ZLePGPwuaRgv1ilNMjdRSBr796PUa9OnA4KucyUKUp3yUiZSNkzXm2G7jtWgsmT/b85IIHxnj2B0ThghRlhmZOTJLrQuovdztk4zcbDPHEq2a3koyHqg6axgAwEt513JQg5hhiDuM5chG6JSqJaYhWYOuTaSWqUZJSeoHWsGvSEdj/cU9iXaIPehR49LstDOcBujVdi44YIJuIBbs6wYCxZqRO0U6JJQnNhy5n3VTkvic9eO+8vlct2ZquZdVN6U162ne/nzkPZeCzwUBKn7KKop3niNHkSU8TIKUeg4jazFLCaJm9yNxKrCLt0qmQsF1S8/2Im1FV46Y3vr5XHBG/aytbdWv2zz1859T2qnoSPHtxtfjxQp+zwqql7QfkwtEHzQJJSikZ38AcjyN1T0Xtk6RquAKNvIGFmNLJdxft65uUNCXhaCtvqBoQqXiFqFlKZqZbYy8KHZsw5wctGue6c58Lj7NXxeZo4FXGLmVI45cQ5J6d+y0zG9fWmlGim7NYDInI0wiWb/PDsd4PxMlZlyg5Rq7CbcF07r+vOy2Xll14bn23Gh3XneVd29fs258RpdtXzt+eZx1ncxwy79YLicLWAUI/D9dh/EgQEZ8+2Vr3P0ttRHVlNWG8kzSDDtDJ7UWQWYJxwhBUJ8sc45APmG0nKKKWGuo0HqlGC2S2pgTBl9Z8dCjEjklhAetw9p8b9d7kuO+JOgAjHmePQvf/rtlw/4q1yLKj7RyRPI87+mmD3/cLzlflhois0c3fWlIRcMqdz4Xx2QdiHklgkMZsy050xhCEaHlEBm2RxrTtNQkGZZ2EpC0+nibePE99/uvJLL1fseWNfXV6/LAUeJtJDZnpYyFnozdg6bHVn292KGYMpZ5Z54t3jg1t+BDurhnRR6wa90Xbl+bnBLvSHCTsVWhgczpgHKxx+aKKs5jbzL6uxrjUM5Bx6nKfMm3Pmx54Kn75deDhlSvJDoBPQFhbsQ7nV8Dg0A2MLuUKESmZFeN6Nl63y3cvOL14qHy47W/fNXESQmD0TXHKptYbRXJiyw/Pc+bAaD6dQm87Ggr+uqyp6tXCnlRkb0RXsl6wUCw8ejJr8YCjJpXdS9uCeeaTYglARg9aMi3aue+Nzaz5WIMbbSXiYJx5PC+fTzJTDBUlHJhoyMerzbWTPxBGXnro04/mq7FVCikrdDVMmVBKrVi5SeamVHiKb37uunFql9xaDmilkffRIHEw7GtR6waGyIawq4tdCzai1Mk9DO9It79Vcv7L3fhwKrp1WQsTUYTJwFt9UCr02768sDrflXPjf/tg7Lq/QtpXLdcdSRvvmSU0BzWBT4kIjhbHd67bx+d5cuaNdeTzPZNtZcvKZoZx5mtwW43Ga3DQyh8kgjSJODEhqUd0ZGn5Kzcwregsfpwpr88TgeXUbjvcvF55fL7y/NF4bVPP+UcoO/T8WeDsn3gZacZqyyz5piwA/kqQxEgEDj3MIVGjmEHTtGpYZK72HBXzOLqWWfN9PxR2sU7vZUyRJhwoNo9oZVVCsEYsNoMeeF4Zwlt/LeK+MkHoHxwWk5jN25uom43sDBhR/XdXb69y+7htPj58ee/D+OWyUd8SLE4vt+Lm7SHar8sx+bQSpLeajnKRQSNmVtiWbS/vQIzMQmraoQpTpxjXGSIEte+XQ1bDmrJ8iiSUnN1GbjDotfMg+xJdKQrqQzyfmh8LpYeZ0msGMi+1c951dA/tOiYd55s0y8cnDiTcn741NybPjaom1w8vVLZpbrbxcVtarsdUH1g5PS+bNlDghTBY9kPj8W6t8uFz57Nl4/1zZdqWpb8jTPPH2cebtY+K8wDzpYZMuh4zOWJASlP7ItCKDbDjUt5lwrYkP1fj+a+d7Lyvva+OlN9cJU2UxN1nMk9Pwe+7sYqxmbNUPmq3DZ68V44XXdePH3574xuPEw1x4KJ0lKSRnuDWNiXyye+5YHBApObzTPHBU6zz3je+vje9dOl/siZcqaC+ksgTkC+cFsrRjnqVWn5HbdGfdNr7YX0kv2V/bhGaJqhlN2ZMIsXCxdVkZ8TdEa8bWOq/7Tk8WSgVC7gDJ/YSAzUavQdimB7KByXRg/a0rOc2uTdE6KblnRiqZ3ho1zA5dpNOh2K4NcuKiUHtnnmZad0di2w2CNt27P6f3gSaauuJJykKrG/NUqNvGVDLr+uJBTISfeHemfPJIXXc+PF/cc21vochgpHOhnE7s6u63e2vU5vuuNweNXi47ng1WPpTOnBKnyeHmKd+cnU8FT0AwcldOkplC3kgQqjZXUyCzKm5I2pV177x/vXLdKq/bzsvr1SHDkCwrdEoyzueFt+cT706JT5eJd5PLYp0mQfA5rhGc0qhOLEj94t5PTp5xAeOtu7/WJXq0ZpCLSxbNOejmAR2mIL2I32xPAmwkEA4vj4BDMEhHAj3GdV15LUSGowii6y242I3wIpGYpCROQBk6jXargEag8OpbnTCkd0LZNuDV0I8cceeu+tNQ5b9zifSfOYLQrVIfr6/mycZXeXytg5RkgewHmvsNCjl79jLkiNTclNDBlPgjhP5eYYBmFpbdFgrg4/JJ8uYnZmgWdCrYMiM9e7/ltGAl08jszU/4Wj0jKQmWk4txvltm3s6Fd6fC05xc6iRmmRR3A75OEy+r8OHV+GITajPeXxpbU17nzPUETyVxSjDnYKYRlHUmGrBpD8sNQ6hkce2vkp3NZD1ovOYLL0fz18y3RVcJx1MLNk6QKLryWo331873L43vvax8ca1sAlWUGZ9mf1MynzycOC2u/7Y25eVS+d6HlQ8vFvbxwsvWuGw7X7wkPmyV99uZT9+c+PGnzBvxqXbPVjs1BD/3rk7qUHGYqRr7trPtlbVWXnrjtcOHnrjqxE5BJ+W0CKdz5tOHzDcfss/BCKSutFpDxXuNyndna8pa3QLhorCa0DVTnd/rvcbWKa2R1Uh7I2lHEHdMToJMQlE3kZRkSAHKxFzekHPimq/8n/8Pf5F0NypyKFuMLvz9Wh+wD4DgvQv5UuN6wID3PzvS4bsse9DU/d96/DeJHIHJMK7LhYd1IYlwnhKfnN/yjbePITBa4xBU5qVQykRTZW2d61bpJL+GuwuR7nundWHvDa2d1SrXHcgxE6fdqzDr7pArMJNZJBiaeEXTuw+dNmA1c+q0wd66j05UNyjtvZFTYpbGqQjzUjgtE08PC+8eFt4sPvJwTjDhlYZ6ow+Xz3RR1UFBHwiWJqEJbGZcFF6rcamuCqPRb0lR4U4lk5OAagzTjwm2cepwV4WExmKQckY/TKL/OFbDfeUUTaKb7M7Ri/r44VWR3rWN5D6WHP20YQ80IpG/R6/bPlZKudVtNmDAQXpBPnqvFpDgEADGOFyge/81MCfl0iSGiB8QkiJQpSFtL/iksy+InJxvdni+EJc/DodRPpuE31QSGsJmxlUba1dUMlJmZPHn1eJ6eG3tzrIDzDqnJMzTzENOvJ0zb+eJxyycszFbJTWXHEp4IFwkO25/gsUKk515XiuvW+O5wuWauFwaj8vkPbY5s3TPzFROMD+QHgyuBltImyQXHxXtiPo1GTT5W74GmG+wZkKTRMUhpN41ZFOUy1a5rJ0vLiufv7rD8K6GZJ8f+2RZ+PHHM58uhW88zjycFxTh2pQPl8qSjInO+5edaw0LdoS6KfsXF15r51WFnk5cTkLRYFtWZavGujdnFjZl2ztrVbatU7eK9pjST4KWQk8+gyI0ZGpIzkgqzrhKhEOxspSgmxdBl0e6PlC7q89fu/GhCR924UODlyqszSs2IuMUU6bU3Y7eEqcps8wFKd5nyzajzYDKsiSW08x5Xvj25/9H/vnD3+P/9e7/wQ/g9r/C46v85K+WnH61V/PjSRB+8v/2v+fltcGyYbNxngqPp0J5mCk55HJQ5mnySlydBVq7q35ct8Z1qx74d+VaxauPamwoV+te5QmszaFQQkUhm7hCekBow67CBnx1OLz6/S7iCdzDqTDlmTknFuk8zcLj+cTjeeZhLpynzJygJLd40T6Gg73asZRdnsjGwe4li2L0VKjA2hMvtfP5ZeNlNyoZyTPad1rzynYqM8s8ex9R+Cj5JaA8vb8hQWIYlVzG2X0a3mC3X7Vbr+j4VYkc5UvVCzf47+PHXVfLLFidH/figtyIq+bbR2vLkfcR7NJHAXf83n2AMrVoe8TX+NXX6nh8rYNUwWX7x0XNoa8lVrAGllyxOWmOgVsLZlNYTEQJfYhtalRVA04i0USoktjTRJeGiKsrS+pRfcQItSrahUxmTp3zlHhaCm+nzNsp8xh+VJM4tjyG8Nwmz8kJSwwUlgUmJubkU9nvd+HajbXuvNTOPE1M82ATzi6DNE286uqu0cU/WRGCCkxIkjiMVeVG8zZCXkoSWx+ahcqlGi+1sW07617Zt0rvPsW/N2clTQKPsysMfPPxzI+/eeDtLJyzMpdKM1gQlkVInywsxThPxvc/7Lzsxh7qG6sKulb6+1euvbLMTgbQEMbcQyTTySdejXV1sUtVb65P0+ySTNmVQOauXOtG3RR6Yt8nXupM3mbqJNQMbws8ZXHoVRwS6SmxqrJq4qEJ82akraOvu/cVu3/2UhwKfpDM27LwNPmA8nkplOI6MaYzVRWznTk3TiVzzgt/5Lt/gv/T938/0OnqvbLevRfk1Yz3i7p2SK5np2Ra1xhYhdrcb2hvTny5bhtbhdetsbXOtTY282H2irDbMMdL7LtDgapO+sniBBGtjb5tJLMY0YCn7YFfmJ5597DzMBfenk98cl54s0wseK8FU3JziaEc97zhahw1wzYnVCZnuPbJfb5aZ1Xjwx5zZV15vl7Zu9GaUpsPMjdtYax3o6+XXPAr4knaJImlZE4psSQ4F5/HmhNMopymxLlkluwmnCUQDOuOrdTe0d4Zc2gW61JSvqEvkdi2JB5wu/FaG19cN96/Xtlbx1L2syfEhOe5cD6dKGUMXXu2fPR+NXpAX2rNGHekAhsVc1THd1+3+5P+OPHl9mtfagvdiutodYxeVlSLHngCDlRlMPok4MVbC8mCdOPvYeh1+tuxWwDSMRPllb9+HJGDSPHVzvmv7aNYg7aBRtO5i1tMZCNboiDMQmjuGcXCxbaFIkTKUfkMI7a4eeFv1IFqXlk0Mirepu+60/vuTCd1zHcqbuY34861b+fM01R4KomHFAEKKFG7D7aYLxZ8mDQYbE+zMJWJqYSZ4Zz4sCvrtvGiHV07uU/kWiibktIOCLV5tWEE1VjEfXTULRGcxODQk4gcY3vVPAi+1s7Lrry/7nxYO+83pdbqXlw2KOuZ+ZRga5QsfOvdmW89LXzycOKhCKcMyRTRTkHIaWJeMsv0wNPjiU/fPPDpy85331/5/vOV163SqtGaUFvnZd+j1+FN/qbD7yiyNPx7JSmn2dUlTqXweF7cojogyutWeX3tbNUPlX6prLtRXyvXpXAt0E4ZHmaHWbLP8oD3JNQ6tXa22qi1o71SVDmLUGZ4PM08nQpPRXgzCW/mxCI+BxYIj1sdRDY5iTkUaBXRzDf6t3wmK+HqH606pNxH30DDlyhBV1frNjnkg7q5/A7JG/i6GG0SthnW1lhVuTTl+68XXrurrzy3neqFNuvuoqO5gXVDNJFVsNYp5v022zvNdj6rG6/XnULn3cPCh/PMNx7PfPp45vHhRM495hCLk5Facwkcc2eBNDlJx4DFQMk8dl93n/aZ2qE2qE8PrHtj7xp9rcbeKqpGbe78OuXMXCZyctFVMU+9nL0LpyxOdRd1hqbcZMyydiTWsnsudbqANpcPGoemigd+lytyco5KchNAhLU13q+Nz182vnhZ+XDdsd4pphSEkkoEqIXTaSYnQyV6NAHd+VychtQTjAAWf/OVHlElMXyoB5mb+AkC4gsI12xwiwKR82B4X0lZQG4DPToCS/xuCthRJOTLjtg3Ulr76PllRDmzMTh4C4aHpqCMed4fATe4O+f/B37nf5rHQ3LV3rb3Y8I8LzDlzsPZnTjn7OKeRYO63cElRYgUwm+Oa4h5FdXFTTr2buwoq8Jlq7xsG6+1su+KWXLRSclMWThl4SmJa4BNhYfkGfpJEiciQEHovg1w1pvJPtgtDEqw4j2kN0uiyEypcForzznzUhOXZmz+hmlbux1qDVCnexeEefLNOqeQsJVQN1bfLN0cW3+pyhe18/1L57NL5/PrztadlDEl4TwXzslYrDLRmcWYF2fCffPNmW+eEkuOBKB7cByyLSZGTq6fN4nw8Kbw7iHz6aPw+UX4/oeNz18qH65eoW0vjVzkmN8gJyy5aMWUjSUnHqfC0zLxtBT3/5oLD1OJ2Te/rdteuZ4Lz5u7Lr/UzrUZa9147Tt7gq0WNhEuSXgrykLCVNh74qUpz9WFOFHlsbh311MRnubC0+x6gOfsAWjJMQMWG79nUKvHAZEoLhIqjZQJNYVh2aJkcRixZKL34lp40EPmx1NjwYeluxqEBp7i7qsUqNZR82rl2o2feDvx3Dvvt53368rz6vY1L6a0LGxqtKiyc5pc97G7QoSk7JCvZJq6Zt56Nb5/feXxeeWbn3Z+rBs/9pB5wu+TtgaqTDmya+1HX6apK00MZYRzzExZFmROriX5MNOCVNBaDyuNW//CCQ2+HlxnI2Aq7a61Jy5XlfAh+nQMAfsoQxcP8JZCvLd3UgR+l7rzM2VIHw1AS5IrlqxmvK6dzz9c+ex552V3ZXWfK3Zb+cflzNODW2mUkuOs6bgXcTg2i7cVujhB4UYu8Apm2AN53TPGMOLgs3vVBg6YzuJ5ZPQq5T443YWHMcg74LgRIGWcTBLKKxEu4/0f1+Ko1uyIT0QAVbu91tFjZZx5iVs36wcKyF/28bUOUo8PZzYK/bJ7n0lv0FUzd2i95uSeSeK04BQCrYkQYDSnX5M981UTl/TvndWU1268duVld/rw2pS9q9O8Y8EVdYjhTU68myfezhNLsAOLWShE2LG4VBxjHzfwKL+BUYabGDkLj+cFJmUuPtPx1ISXKjzvyuuutFB9NvMB0zE17q65Ri6ZFHAgOdMFtq5UVS7hgvu8d75Yd5435VL9QJhMfJ5lSjzNKSbyTyzJnNKfM1Nx9805aUizaCRUclcp+mJM1pkFyPgQ5fzIJ28fefu4cvr8Ap+vfLh6011xRWxJbhxZSmKZE2+XwrvTzKePJ97M8DgFrIO7JufA8s3gtBQep8zjA3zSjEtTPuyVL9ad172yt8b71mlr41USH2bhPHlmXqty7Z3X2thbIyfjYc485szb2Z/3nJMPIIv6LJn60G/G1REs5ruEFH0TcBlin2NCXE0iWT9U38e1Em+OuJ6k/4OUcvyeswZt8mOsH+xHx/1LKPyX5Lbtj5PyFvjGg/JaN57XnQ975f1l57p2PpTGuguXa6f3zjRNVFO2VikGkiY3C7UJM6VZY9uNrXfqFxc+XHY+PM584+0T754Sc5lJ1iMp9JWdSZgoFvJHLh+koXTgckyiypy9n5uTD1f70IInKj3rrYeCK6WMuTK/ziUg90pSh+Wzul8V4rp241Ts3e+DiR/kFkmA4fC976Mw6AwJrNqVtnUuVXn/4cr79xdeLruTpQI2kwTTXHh6c+btY2GZpkMYFpODADEOchfZHe9r9GviucxnIo/4cZx6Euy+dFDKbxCbuBD1SIN/uaaP3PWb7sqbj2b0Au78ciA5HJ/j7zqQjrs/9z87XuL4Grcg92siSM2nB8wye0tY9xmDliZeG9gWkMcOU05Hlj0lHxrM4bApAtlwSEV94VyqH1AX9SB1NWVT1wp0yaCYpFYjSWcRn8d6LJnHFDRxnKmT7CaqqHQY80OjHD82T9y4uHMiIWsixlNg6u9OE1fLPG/GZ9fKLz2vSBNeu0OTOfpPyZL3oSahSXbmYBPyLghukf28VT5cK++3xrXB3j3AzCXxZll4mDJPOXmVUoSHAucsLsCZXLB1xKGRJ/nFPOrE2yI07wFk7NBCS5KYpkQ/z+wNrs3YbWOr9Qjmgz47FYmhy4lvnCa++TDxZsK11iQqOOu3vU78RUIPcBbmOVGWmbxkyjbxYW1cWuPzKnzeO9OUmHOlSKgFaMe0ItZ4LIm3pfA4ebA+JXwYXPuRFHRzWroFVTqPZDUq9gg1AeN5xS7iTrOaFCxH5usfIOVQ2I4L6Rveg79qHKaSUGfe+AHRGEJUwSLzaz2J26E8ysSbJHzjNHE9n7hW+Ox55bOXyvOkPF8qapWeNV5TSTl7wExTeKv1kMiqfNiE6668bjvffXnm6WHj3eOJ8+xakykZU0kshO9VMkqW6E84LCgac0UCqFHEwa0UG1MiaqcevLhQHfd9FAdyDDofkyURBLoonXCmJebb8H6ZBgNWzYf4FXxQOOHjLCm7cLG5x9y6NZ5fNz67Vj7/cOXldWWviqSJMvn5MotwPs88PZ558zAFA9cO9YXjfWGYukVLhK1bkYT/TNLbrNbYS4NSfniNhc3HsCqBL80e/ZAYNYb27Yd9k48DVUreixpf+3JAGv8ev3c/n+W/nz4OWnxchX3Vx9c6SF226tbx5uZ2vSlNlErjuXcKsGQPTllAkjHnTMKiXyLMpbiRVwrxSHPL9WvrXHtnM6EClkpo2w2TQ+9hlJRZJpddOpXEUoorq9lN9NJ1s4PKzr2CwThSxo31xXwv1QTGHLpkncQT4STbOn1S6J1mHWtBjT5wbaGTuTSQa+e6d07FRTwve+W6Ny57Z2vmMKkIj3PmzXnmzWni7eLQ1mlKLFm8WsF1A4sMuaBQYPCpo8ONt0f258oGhqgh2lDcar6kTCKTMc7JeHsufNJOXLsGldjFPRXDqqssZHNK8kkaD9Y4qVG0kfB762IV4YMFh6JzwiWYHHpMkCcoE7109rXxUo1rVXoFs8oUzfZZlIdivJ18Pu3TOfFQ4CTesxHF9Q5iQ5oQJAmvHJzezw23D7hOSXE4Blst3pfFhKbZkKXxHohGcx+Rm8ae4KZy4tCuWSgXlAxBLjDTGAANXbfuArpThkcRKoW9CE8p8cnJ+Ox15/nRK4XL5tqMqoluSu07aI7egpGWE2U5e8/QjA8dPlw6n2+VX/hiY5ng8TwxFeF8mjmdFuY5ufVGyYFACAknNCVTnxfrnTnB0G8UkbguFv1gDrjTtPvQdsoo7qrrlZH/XXvMGcUoBZIc5RqVhnliZ5Zdrojo7Vl8r+M26c3llV5eVz68bHz/cuXDunPdg1Iu5ijHUnizFD55WHj3uPA4J3LIaknEVEKXcSg/uJ5LvxUz48BXZYgej1ERG4oTflgwoDpPeMeYr587/lS/QhCQrxYszG6BcXhbjeB0/+/jae8C5JcHdb/8OoOk8lUeX+sg9cXLFWaf8NfoX1SF7sM0JBFe9pv1O5Htog6x5JQ4TTPLXMjZD+um5qW9QiMHVTo5jdncPdYzWjuCgpMPoKVET67CnePATEGDT0dGJVHO38voO6wjZkffaGTPgpEsEWYUTEmQ3GFRCpkpK7MIz7twbWE/rT5TpA20J65bR+iU5A148M2exXiYvHf3OGfeLBOPp9l7LXPiVJwdOSchi5G6G8VDaL0hQfPPAXeMA/tI/2OThVxLeCcRh6fhs2JTEU6Lq4TMmw+ZVvV5teFOLCiTGDPGZI3UDaz5Bk0jnEemamGpkDxrTPF2ZnGq857hVBLzNFHUSR69BjW/N1Qa8yw8LjPfOE18ko23WZjFEGsk0gHXdgtV7OhhNPHPlFOg8APfIQ5d8UNoDPQ2cxUFjZtiIqHt5+tBo6IuIkcFRgLRcLoaEFF4EjXaMa8jkRiNdTb8kSZJzCIskpiWxHmCp/PMLpkPa+X95cr75wvvrxtbdYad3zpBpglIbvOgcfik7PbiJXHdVy7aed82UoLptVKmjVIgFx8ByJI4z5kMLEk45cycEkWckbek6Ivl6M2MfeBgOQQblj7szDV6K76bzJyMUrJrIya/sJH5R2KVsn8mXL5sV092qxrb3rlunsy9Xncua+Vy9QH9y14ddUiuGDNP8DALb5fCN55OvDnPPJTEOcuxBxyO06OSug84ZgGoHNXW8SkCgLmdF2DRTrLD/ueA1INAgZrfC/9iFPJfDkR3GF887iuiI4DYx7/7w6qnj571Luh8GfqDW9D7UR9f6yC1VpiK363EYaAc1UuwwMp8aH8lnLWlAatUFWrtvBqQPRN3DTXfzmZy9Bit+0LzNtZNqYEEL3v1Ybac6Mmz8IwyJVc5nsylgjzYuGleEg8+vlYtsmGHMjJEZhhMw+7imQkX3ZxEeTPDNE3MS+Hx1Pj8tfH9V+Hl0li33bXMUqdqcrZcdow5pxQ9FeMhKW9LULGnxHkSlsmv6VSEnHrAh57VBUgVqF7QR1OKmarucCm+wbzR6xWFEA1VIbLHoU/mN8yl/Y2jpxb6Y4qSJLsnVMreI0i3oWwszPPwSlAZQcMP9B6wIiYR6FyctygsIiy4Hl1qndIj+5bOXIynaeLT08Knc+GNKI/JEK206GUahAioWzaMqUYnMnQm8+xd414OblRXwcg0NXaMGh5IXd28Ts17q0nScS0KkaSYO9QOWVqxSKzBxVilMw0bYTxZyePaq7kQq/l1k8HWSpCSVwN7goe58DTPPM2d09XhvJfViTRVE3sNV1yS9zslg2XKlBExcjlh1qltR9VJBmzq1U5xmC+JkMT3x3kqPC4Lp+xW5G+mwiPGlAq5W6w/whGWW5KjID2Fp5IfqCOhwYzC5GadgIYbQFcnK+yqqCQ34+yuxXltxnU3Xq8rL+vGZa28bDHYPQITuIdaFk5z4VRcg/BN9ErfnRceirhCRvS21HqczrdEaiiom9ih9GBDTNeGxNEtUR1KE2JgyR2HR0ITszOhL+hbcmg3jl7Vl+OC3P3sL/ewW9T/ofCeP88Pjzg/GJwi6ZKPv/9VIb+vdZCykBfJoTAhQMoOicyTz3AsxRu/InbMoji1tVFDgLHhMzgmMfAqKTBj80qha1iqR2aLMCxU1KLq2hvVrnzYNuZkZGuRASdmnG1XIDJGh/hyQIZZhCnLIQ3jPWHvc3gAcKiiiFCSHzZSCgnP2LpMVN143vTwAfIFlumWUCmknMgxW3WehXdz4tMZPs2NN7nzIMaU3U9oVEZ5VD6AkGN4cAhdxkYIqrUGhHJIuRxQoASZxQKCdEjFjSqdObmpsjW3sG6moXTtcMjo5iiZRmKXzCoO1ZZY9YozMjWUvd2hVI4gAh7wfN7GEw46WGvQdqgN2aNnNkv0FxMPOXMO4V6HjuLwgNvsCDfohOghIH4od/WrpjJMDMUPejXWGr3OJOzaqL0fFGVw5G4MpWftDrNGL9BHKhKT+UhDCug6h0itRn/R1CHw0f/yPRPSojI02bxvOieNoGekyZieJqZT5lKNt3Xhdeu8f165irF3hyFL8QQi2WDcpbCzF3o/se47XX3mqfXEpjtGonUnLLUMW2tcNgu/tsQpCw9ZOC+Tw/TmckZLSSwlxQA1TOJa4iWg6nFPLMw8rfnB6jN1t4pprTtVlWrG2ip772y1ct2MtSrXdeO676yxHjXuuYsge3/vvBTenhce5+JD+pPwmAeZxhM6Wgsk4V5VwS3iRUAJenbs8aMPG1t3tAAMAdWDqSc4O3GEh0MOCRgCtgcV6464dK9A8gN11YCszX4g8HyZEHH4Wt39zpcfH6mjHF/DSSgfPfcP/OoPfXytg1RvO8lOPJaJ81Q4z4V5TmFj7kZuy5TdkFB8slxVWWvjujuxwntPxibJN99xQ2Kxd25BCnN16JRIxb2iNCc0+YHbtsqL+rCg9d0zOskxr5V8VkOEKeVwsk3H5pxzcSHK7NBk18ZeK007TR2ymLOwTN5Hm+cJNW/qXiys1bvSrAd05JVMD5YV5kEn5UyZEmUulCWRsiKpeSUjPg8y0j6zfODnt0wo+1xNBAJC30vH90fWKGG3HtvOHUBDCd6g4VYiGw6ragLJE6kY0iqpOB3cFPYKr2vliwyJCSmOwS9xMIagH3AXRLhtRvMjgW7ed9ibuipCrdRa6bWGdqiRUqFkz4ZTSGSpGPWQy7JgpHmXfui7GRyK6YUhPRVwi7pkblXhpXU+rI0PwbJ7bY1qPrBKcvv0lH0NTzmSBVVEvfOXzNXGH6aZU85MSZiTw2UzRFAMtp+49cmY+Pez0GnYMb3imf7w0BIJONAgJ2SaOM3Gde/MuZEM5q3zuvkYhojbZEyzJzDzlHg6nzifFsw6+76jrbNeV9a6OxtV3T3ALIcrQGfddw+sav7Z58w0Fegd0+YqITmxTIUs3qOcUiYpPtqQMzKCtLpFfas95s98ELh3Za/VA6a63ftaK93M91nrQXv3QXHFySM5OSEpZ5c5OmVXhX/7sPD25AFqRn0sAz3sXTQIIRo2FkZYtNzyvqis4n/vYL6hz3eEnxEYbjsxmIK3aukGFd4FlFv69HHg+CFBhy/93JBKUv04SI2f8af54UHtyz93e9xKuB8GB/5yj691kDpP3qj89NFFWx/nwjK5g67EgpmlMeVGxmWRTIR9KlxPmavBpXVem/Hc4bI31tpd5shcwLWLebNVzGf9kiHZvDrL4pbQxRubVn1xVnNYR2O4bcxojJI9iW8yMfGZppSZcw5ldD+Aq/ogoytYu1X4lGHOiWnqTKVjceiurfN62XheV7cQSQMu6EHv7dANa0ZdG6/doGbaVljnxGuBh+z9gTlpaB8OTH3Mb3hRCWOdmSvOiwzUPxrEcUAyoI3YcLGHukTVI5lqPuzaksOaeYLplCgKkOlV0W7sTXm5dswaJopMmTwbTIkZh3IOT5vY5OCbpGtD8Wp37z60vHVlrZ3rvrPVnb139wbKrrtH8T6XH1WJJkYzBVFn7fm5cTj4HkKkvjyclGMSHDvxStuEqwqfr41fet344rLzYatc6u4XrriI7CzuXFu7eQ9M3WKkV7c7RzsFYymbJy3zxHmZOU0zp+KCpmONiUR4jpvhCg3BjjOvcxE/hDGC4p6johIwpRSvBpNkkhTmRUkvV67iosKtb957pTu5Ynaiz5wSZZkQK/SnRG0LW3tg3Xf/PYN127msG+vmyiZ177TWuYiRxlDzMczdgXpkHhIVS86e8IkM117o3YOS9htM1Xuntx49rJiHHL1BikPaApZBUiQa0pmzcJ4zj+eJh3nmcZk5Te7GfS4wWaMQCuXqZJ+o47mlTP73NGSQomBOY3+MBOsIHL5XXMPPWwpi3pEbAc+j1RHijmTygNS4JWn3oWK0KX4Y5PYDxIf41iBJfPnn7x+/XH/qPpCN//1hdPVf6fG1DlJPTzPvnhY+eZi9z1LEoTaxEPx0JloOamuOCmGSUIgAlmnipImpGqdSeN1bzEMZtcWBm3EMPxx2U8xVpeKpVspjeqNA0qPHktShF3TQN73hjXSqApqQIEWM6fgcBmoqGmKvTk2W3qGOMr6SJGHmM1JNlVYbvVffJGFl75GhO7xl0NVFWd/TeEnwfUk+iFx8QPZxdluT0+SzSUgMPJpnVCNIDZO3lIyibj7ndKiGtu5/8Dmt0Vcjef/CcvYB3ZyowLV2XqryYe/O1ozArHqDQDCHbKoKWzMuVblmmEtQrmVMyVvAWRa9HZcWar2zK6wdrhVeaud561y2zt6UZoZFsFFxeLLhFcd+Bzr6znfSgEMunUBtwpnXe27gFUs1aOqB8XmvfLY1vnfZ+XytvOzd1Shycphsch+wh/PCaS5eXacE2rEGtfocU+s7tdZgoBpFlakr09TdAj55vym5ARPZjAkX1J0Drkrmg7CjnyXhLDuUAYYPUQmfKkuZskwsM0zXinWfN7zsjb252oLgUktbz9QKy1w454mMkotgmumWqS0HWSCx7o3ruvG6bly3meu6s9UWZJJE60als4c325CAOowCGa7InvWLuMSUhiq4mSelI3CkXBwCbs0JBoGJ5uxqMUvJTGU5YPc5wZSMpfgeWfJw9M4UWsxA3ijmw7PJk5X47xE0bkiDjspbgGO9fAmHG/YwMvqZ7g02AhxHDLn/pbHnPz4n7f4+j0Tuh0By6b6XFXvvUA00O1gPx2/9SgHrnkTx0R/7wSbZr/L4egepx5nHh4nTJExZD1hLTCjh7prEYRwTDpkPi6ommWPw2TrnkmICPFEpNGveJEaR7EwgKeIU9uw9BmcQe6aa8BkQyTFoZ7OTDTSCVvPMTrsEIUNQ8+dJOlyC0xGkTLI779IhhSupeoW2V3NjRO1Y91kLMcg5R3Y2XsOgxybq0HpzB1kZQ4RBz8VVKabkjrdT9ioypRCObNHviUpUskNdOQnSO1mrY/Btx2qlt+aeSAhIhlwgTUieyPNMniasONxzrTvX2mgYe21eNbR+6HzlGOYtOblOXMpx8MPchS4xToAHbJ9rcSWN2o3WjL0ZmxqbCluD2oVr9f8yem1xUBNCmLvCaokJwbpwSgVU2AGHVG5EEtSCVp8c1sP34a7GtcFLg++9Vr73euX9Wt0xOWXyPIz3CufJh7WX0ARccmISyBpyVnHwdl24bpW1uhJ4E1eNWJv33LbWqSbUYKpMuNL3m7m4+OqUOefMnAvSmw9A048gJSmRB2SrfjxO6hboJWdkScAMJbF+qJANaz7/1zCufeV5VR6WJ5IkFiwClRNYqnaSCTlBn6HliXVJrHVmqwvXvbE315C8bM0TkhaBKofCTGuIdTelvAsG1t0frlk9DsSCOw1LyM1YMpidZFFKJot4JTrDaRLmqRx9Yh+d6P7vNOB6JZlbvYgRfdhbNe1xwnDLCzuu6ajoCDQFswPSU7VIJCM4yFCNuJs5lBwMZThECGKdjVkmi7/ffOBuFdGRZMU3vkxBH0Hro34TIeeU4rC7ewqI4PlDoELix0egG885IOhxdupHz/bLP77WQepxmt3hU8bQm0X24ZBSDvbLuCC3zMNimt3N0HaVQ+9svddrMxvrzm+sgGS53QCzI1NKgZuXVML5Nw76wdiJgOF9lkQPDJ5YVFmyw2fJ51F6vOcWTd4eSuV7Veg7a60+R5Edly/Fsfmm7q9TW6NVX8w5+ieDrqzWx6Sww3jdicsFY0uedbsjKvTavImEVxmWBosyhGtVnbZdd7RuaK1oq55kpYzkArlgJWMpGGHZzQJVJIajOd7PsM92er9EDy1EW0umqvC8VXoz1t3JMVlSKDO4nFVVZVd3T67a3YPIgvkXjrk9G1oUspGCJCMitNa4WnM34+K0aB8adQjIs9DYeDYo9v4Z9FhhRke51M6HrfFh63x+2XneKk0S8zIznxbePsw8zYmHnDhnWJJR8GRhHkGqxzyN+HyV5UwvMx3YTNgwtmasrfPcKp/pymXdeVk3WvMk6Vwy9TxxnhK9nABnuk3JvOrCoSq3qQlTB8k+xhGfyBO+xJzMe2DiIj9JjUyidVebTxoVWFOmOXMqyRUgIKjREnNtjjRIyDtNS+a8LDyp0dR7rVsP89DoIbY+NDY98DnMdlNo0N58LXaHeDXkmVJK7m4dc0oikVgEVJhzPnQyvSrzgMdIKnt3RMKGM7YdCYpXaPmAfjWS0h7qIsYdCSLmLG30vjXgP7sLUHeHvnHX8xGOEywdQcbHQVIExShRj0D5VeaQfiUChPehx+D57TlHtTh+//7rx3v/EpxoUdne+lJfLUDB1zxIDfjCcyWXP3EM3g8TzAkODCjjKHwTzdx7ae3GS+28bMalNy6hcNzVN+U4hYxQmug9sh0LLDx7Y7eIs+PAKedoUMlvBoXOHI3MTjME7DLmEkRSDHZmlMTWO6/XnX1zbcIW7ESrlWzdpe1K4nyaOJ8WplLYauNaO+tWEWlYM1BhUFmVsEWXMZ7PsdGLCHOG05yZS3HYpDbcg9DB+m5KI5LGmNmordI2h++a3ThKGvCepIIm0BSac6ru3ZMykia30QhiNeIV4hgQ6uquq8Ugm9t2QOcZh3VzcqKB5Lh+Sfz6iauFD3q30/ChRDWcyUfzfK2NurviuqpQBS57ZZkzJRUsC2rCkpyheQ+V+SEeB1skR9XgtSkfauX9tfJ+7Txvla36we12FzOfPpx4N8GCskhnxkjWyaqhkRi+X+br1uKQRYQeFVtLmV3gKq6k0rvRamdPySWM2o5ZIhWjpUzWiaQVSiGVRLWxTnE7BWt+j0zpPT5psmDNef8km7NWJxNyU+q2Q1JnkKqwpIkiPtI8ZpkEsOZ2GmN2bIwn+CHuVzKVRLFO1hiS9ZrFFfG1Obki0I9iTmNPAcn6/KPSez2gtVzuZxq9DwVyq2oIUV8ZB61D1yqKSqfTUfoRHI9HoGqeZ7qMk2o69lgVRx38ZwNWTH49LQXr1MxdUS3ewx0Md2g1BnLm12gMIvsb8CN/8FeJ/XzPEoyB2zG3GI/70PXlwHJfUR0f8ks/NzTd74PhD/a2Pq7Qbq8x+tPHu/5VH1/rIJWCXeVDpImuLeokjb4OaA8QVwa26vnH3jtrU15r5XmrPG84rKDQY75lQEC3mQXvjRx4a9TQPnSaCaCNQZYQoCQL6rj4YWCxKbM3RTNCSjg8EAu74v0Xa0rdG9vV5zVaq253DywZ5rm4R9GpcDpNJCmk5JtENTtjKY38a0ASoXExYAARUipMeeI0Z28Sn2Ye5swszQ8kXCUCEWrMQ3UdGwuqdjcP3DbavrPvO7X7YdeIwxvxAGbEIKggeaZMC0ihdWjdD0PrQb9PFv04oxUlNTmu9xAIlqwOR8T1QyCV4hVcXFvBrRymDI9z4d3pxJwzrTt54sPrlS+0oq370HHONOB1q2CdNmXaXFBJnCS8uo50g1svSxyW3Q02hIsKLyq8qrCmQi8OGzVttL7Ru89GlSnHHFQnWfM1k3zGKYtn8hIVncN+3LQCe6IAp+SZ+t4zrU0+ZrFXNjVqWHZoFmxrfogkh4SzKTNKtkFU8GsvGO5q7ffDh72CICPKJN6fOU0ztlevJPeOLa6WQFesqcs2jQpII8BqKE4ErOWWDj6D6LGquZK+eJUsCGkOTcTuQ+1ZO2lAcfiQQkr4bGJP3jeOQ1/HgStCJzmJwm7KCdb7oVLuoya3gNRF6aJYCl3KoyLwGKN4D0wU2qEyM5K1u2PYHNYb0DLJvGeYBQ3Yelwfh8hifftI1BF4cqx7s6hybYgLxEiEjPf1sSLEvdjs+PqvRl4wDeLKHXFC7v73V/rdgywhcvTjYfhq3YqFr/L4WgepXTuNMS8jCK6wDM7wAfWNNeSCIqp3Nao5g2prytq8X1HVKdNqY2H3MYkZuLY/1xAI9cw2TNvU7SbAIYFRIanJDXs2Z2sx0GMpHEKsI0AZvO6V123lZa08v268rD5vItrd7bcUHk4TTw8nt6iYMqUk1q5s1rG+s+8b21aRYGWlnLEw6hO9yeYIAR8U77WlnN3+ekq8KXDK3pfJOHOoW3wmA+sezBsJLIMtWHeGVu2N2tTnngxMihM9zF1+q0KzjMnE3o3LpfK67tTWsOoW510MKQPYMGIgboBuLuKaQg47jA7HoUGvuNpAJ4lxSkLJmXPyP0sM0U5lQqfGNSt7aiEDZE72GAf16C8kryLGgKmZeFUYPU/yaI6Dz5FJQFdgqZCW4odO8cTKxLDsWnIp5LMkQRcnGJqIQ1xoqLvbYXnRwB1tEbq559QsxkMR9iWztcJ1zawkWlMu18regeKeZSkZeXJIsdZGajWGzFP0yH2WzfuuDvmYGc28um16OxxT8XHjVIScSkg5JVrvNAtImID7siuBi4hX5dpuw9kSgdCG5bnDkAesagraj6qJSNhuD0dPsvmwr5sj5kM6iugzFZInsDgyoJJQ67TulPQsY+ZOQ57Ijuf3Y+ROIcQiobVwnTYndqjcDvFB7hnD7pEzIxrXLztkN+SyDtTHy3MkFGuI38/mye7tY4/z5BYUf4Byzi1weF/rhweoj+G5LwUyu7U+7Es/f/8Y1vX3/ybgUR1UxjiPv8rjax2knvedU2somSIuUlnIkUqEKoD5YvXkx2VRmimNRB9iQ5LDUt5ikcWcAzDoqhCMqVBDGD4trbvIqG9TCYKGs8MsskBVbyo7ZOVnakkO7fkB7/utYVxb54vnK599uLJWV1uv5q8xGEhPsw8Tvn04cV6KU+staNJ9895Q2zFreGlBZMdxMErMbOAXSLXTGuzitvatZ8/6AtOfgVkCA/di7GjUmrmQp+sNZrCE9kLT4gdsBGj3T3eWX1Vj7cbajNdmvFwre+6gm8NKPfybJocB3WjSE4MYtncx0eQN8VScYWlBRDEb8x1DVNRotbGLsSdhRUjzNJJr5lI4nYyqrsK+1Ypa8UMDw+WyoM+dObsaucOjhUZ1gWKRcVaQJQWdv1OkumVEZN1kIU+ZNA2mYxna6Jj18NFSVJNDpSS67a7cfRwYgRJY9AnFD12EowJLEgehCTnPUTkXWhPWaj5IvncWMVI3so4ZvhKtjYQ7UPioAeaMzK13WhIfiFVXY/DCqbNME24f4nuoqWLTRLfmgSaLOxvHhVdxdiPj+uF7tUdFkcRJM8ksgpMgOXnPyTyRqL2hgVSYOjSqmBugigRyEcBYv3VTR69tkGCTeJWrrR3sxt6DKcpNIWWse9cRiM+gseaQ6H37JpHY2yOJ9earhTI6gep4SOpmPoQuHOw/v9O+cHToZSJDo9rvTSQPWCQUyaHTNFRRGEhKQPTcAgzH178M1X0crEaQSz8EnvP+1C06StzH8TpfhhM9fxsw5lcLUl+t3vplHn/tr/01RIQ/8Sf+xPG1dV35uZ/7Ob75zW/y9PTEz/7sz/KLv/iLH/3ef/tv/42f+Zmf4eHhgZ/4iZ/gT//pP+2Wyz/i4/PXlV96/8L3ni88b53XBtcubJqoUmhSWBVWhb3j+lwImgotZbRMaJ7pkqN68s3WW3OX0tpC7sftKxzHtlDJttsQ6U1Iy58DYTevbF5rD4aS+fuQREuZngt7LqySebHE+wafXTvff9n5/qs32bduaMlMp5nHxxNv35AkNV4AAQAASURBVD7y6dsHPnl75t3DzNOSOWdhEacZFzrZnH2UBabsEGTXGn96yPb4gj4qgCSul9cae21s++7W7DWhLYMWZ3p5JPdN0sX14xTvTagwKUwq/qcnZkvxd2HWzqK7/5HOQzbmrCSraNuo+5W2r/S2gbkq+fmUeXycePs08/Zp5t2bmTdPM+/eLLx7M/Hpm5lvvp35iaeZn3ic+HVvFv43b0/8ujcL33qc+OZJeFwSORtb23i+vPLdDy9854sPfOf9M1+sG69d6TlxWiYezyfmeSKR0O5Dv+veeb5uvF83H8LdG1eFmiZazmguNJHDA2kMcSZVFnGZoXMR0EprK7W5msHaOmtVtt2oDVQDQJSFlBcgU3dj35VaYW9G64Jqommix3/NhuzvRCWx42y/rcWANSmyimjyB0pg5jDt3rt3haRg5KhwHRbTnLBSvFrLiSoJKzMNd/qtCire37WcbtcAJ0xbSi66m1LMxvmfpq780FRJJXuSkaKiFyFnT/mseT/LRv9qQNQ+/+FVvbhauaaClYLmjKZMl0SPGbcuHcvRTEtKp9JtR6ViqSJF43B3fzgkMwwmSQ7z+kiCP7dXYB6o99486bVBQQ8LIDXv2Q7mnkaFMbqZARk2gYrD4V08uR1kImfzeoLZwqXBooSzu+cAOXo9OkYI7MYi9sxZXGWlB4xnN5mjX8luYyA9acCPd4FFLOTR7mLN6GWNgP6DX789/+iZ/WqP/+FK6t//+3/P3/7bf5vf+lt/60df/5N/8k/yj//xP+Yf/sN/yLt37/hf/pf/hd/3+34f/+pf/SvAvVx+5md+hm9961v863/9r/mFX/gF/uAf/INM08Rf+St/5Ud6D8+vK11eOU+F19MSQ42F85Rd4QEcnruL9OCxpaXEbokNcX+l1pwA0HuIh0bJHc1izO3hwYKJFlnRwIPFrdmdBu2whIbOWhZna2lxVpvkjGSnYO/qbMJtVxe1XHfWrdNNgrShzNFof3ueeSrOBDuJ02FVB33Y3YdPJXGeJ/beqN0p2QcGbD7RL+LSTIZ/DIcAnTZee+O6uj/TQiFLxrJnvL69bjne0VSNRe62aBJq0650Lsd81ZicGnCAoL1Tt53turJfN2pt9HiPy2nm8WlhfpgpcwppK88fUwon3SzMkpmTU61LzgyNvBakiPdNednhZe1stbPXjZfLxmnaeXo4czovTHNxseF5Zq/9UKQY1aYkyAHbXc1ZfqNPMIm4FFDAcP4biiQjl848G3MzSg0ItDau5lRp6Y3UGn2egvXow92KhNSQ36NKOqryJM6ubGpxuCW6JWozVus8b8YXm/K8uSWLp34OYU3FmCbIaQxip6AleM+iRuLlfcocPdnQJiTTVVj3yrp3avPqwLUundhAzBOq+VxT6505F5oFW7R7xSJl1DbpsMzwrMni8wWM2juYw+wp+dxXP/pFPgSv6vM9owd1qxs4+jM+0xZ3x7x3ZVG9SByczhWMUiSaYwJRtQGSXV4pek59BJJoC8hB1orXHn0lohKxURfFwK3cThAltPcYM2r3yjd4hR0MChuJZTyJ2XgOi97exyiakA5W76jqTD7uu31ZGHZ8zfNXTzBM7gKKjc929yXuAth4HW7VmH+mfveZ8Ij5FR7/Q0Hq5eWF3//7fz9/5+/8Hf7SX/pLx9ffv3/P3/27f5e///f/Pr/zd/5OAP7e3/t7/Mbf+Bv5t//23/LTP/3T/NN/+k/5L//lv/DP//k/5yd/8if5bb/tt/EX/+Jf5Od//uf5c3/uzzHP81d+H10T297pTdlq43WrPkQ4F87zxCkX5hzMHiNotk5vaGJcuvG8blz2nT0m+p2G692MkUVIQHPgZIFxXDt65fRWu1swmNGt37LqAwpyAVWjU1un4pn6tnX2CuvaqSHLIrGYM8osbou95MRcEksG0R4LjcPzpyQ8QClcq5F3h+GahWKGOOSSSybnyQ+U1n0jVqXV6vTYfXdNO82oLrS50Can+g/mushtIVu4xzrW7plbwvt3FlCkBgyKJDo+Q3TZKy+vV15fV7a10qv5HEsxypKYzpnlIVPmRCkefkwlgpRXSIvASXBFbwmsPiW6ZZas5OqsvGzGM8Jl7d6jaR3wwdEHfEZpLoXTslAtoetGb9XTkDQhuaAkVjWojV07axLOSTilxMSgBuMpJooVr6jmBR6ZkJxco68b21Z53xuqldYn7DTTzSuAIt6DGV5kPfl8GBCb3Q+OzTR6qpXLVrnUxsveeA5jx2t1ItGUYVqE8zlzPgvLNDFNhalkJhVQX/s9GG+k5PbvCpIyKWX25q91XXcu18rlurGuK9u2BcXNXOFBjZKDrG8EAtHdaqM1Z0fmAHDEDhUWzBUxAFeF6C1Yp4L1RguU0A+55P5h5mrz0TY+ZJ0yRKI5qiPC5M/hYt+MLjPVY+ZILRinMPI5l1PS20BrVw3tyagYLQ77IEQceEqcA4Ydgre3g32c7vFaSQ5kwyB6WRwVx9D1ixISiWBht03ISJaJ72ByJIL+Jb19D46q6/Y+Po44Eu990OphwJsj2I2Ad/es0QL4iJ3Ilz7H3XsYjgBf5fE/FKR+7ud+jp/5mZ/hd//u3/1RkPoP/+E/UGvld//u33187ad+6qf4Db/hN/Bv/s2/4ad/+qf5N//m3/Bbfstv4Sd/8iePn/m9v/f38kf/6B/lP//n/8xv/+2//Qdeb9s23wzx+PDhg/8lJzQJuxm1NnaMtTfWVljVOM8wk44F21sP+M4bz1t37yiXxfHOwGDXKObzD9kb58CxEAaj083l5DiYQcJBVR0WS65aDhz9p2tXLrtTyfem7HunNV9Y6vuPpbgTa4DsTAWSuLEfJg5zDEVikQNzT1MhZygq5NJIqR8Y+9g2Wb1/N8++OBvROxAPeoNwckVBKm6vt9BSZs6DSRVVWfQNbFyxI4t0rfN7N1RFaOZ9gUriWpX3153310gSevdDa8qkJZNPmby4VFIq5tfUhq9Q9LkO6DIxNmoavTPcmuOUcAr5MpFyoaTGdWuu0daNfe3M2dDsgW+ZJyy7/l271COrrVXRalSBtVSWLNQpo0uOije53l+cUsl8PGIRH6QtGc6zC8vuu8Oqe+180Xe2NXFdZz6U7E7DJbOEUGvJ2W3c1UKotVO728mstbG15uoT287WfGaqjqsxubbfMgnLkjifCudT8WRHcF+wob0YPaRjqFl87GBUDdfauGzKy7Xyet24XDau60ZtrnRRSuG0FM6n5ZApMny4OCehNT0SCEEOIsjxZyiyhGCsk3JvB2SCg+iDjZ7xLWnyTQZjEQpOToml74xIs5s/lQYpBjmqioO4EEGihcyR2nDttpi3i/cchJKOkyskwA8MUrAnx3szOJJKGxJe4kndADq8T+dHiSU5emNi4cAsUQHigbhJuH+nABFH3GN00aOajGFcD5gjwKURawjJ4ePhZrAJHfT2oPl7QNa7mGbHe/8yFd3040oqMgYORRa86v4qjx85SP2Df/AP+I//8T/y7//9v/+B733nO99hnmc++eSTj77+kz/5k3znO985fuY+QI3vj+/9sMdf/at/lT//5//8D3zdCuiUnMVjPvjZRLzRWJXNKlKjmYjDS613encpobEAzfBGeh9yqIHv4lWCb6rRIIzBTwhyhgQ7KaMpxEfLBCrkbiAujeS0bc/c9lod2tojKFg4mE6ZeZpYltmbuN3nM+bsM1gudOqHZTLHqr2g78EWczbZ1hpNOzfri8gcAyJcJuM8e3Bp4uoMe3cCxPCJamrsHa7NlbYlvLWSjYohNkLyzAxy4PFjhsj111IoaHQ1mg7dPHhZG19cKi9V2RA0O7MtnZ0Fl8+FPDtrcRoq9hGU9QhSvlErXnGCzzGJOvzUe7igirEsGZldwLWUzLo16q60rbHl5nYVizvIzlPidBJ2K+zdg0PdOtKAbixL4XzKQKYkN3YhKTWIHXH0UXLiYZk5SeLUjK3BpXSuKfNixrpW9r2yNuUlrUH1h3kqPJwfmJfCVCZyTt7H2SrrVtlayDm17oKt3A5dslewJQtLyqGub8zFA96UvOL0gBqrfcyWJYs+jIOA1n19763xurUIUDvX687lutLVE7tqymk6uYXFeea0FLK4on1Sl/eqrbl7bQoCUqQ2vj7c3kS7hgsBrvLCGJB2JqAI0Qu+EXiQAS+PSkWOKk4g6NMeVBKMkuBWDY0kzYh/e1CuOth/vm7VnJXqrEr/OQnSSj+GuyM4yhj85qjK/KUHy1dugSo+nxzUPAkpJ2L2yvtlo6doXk46VKtGEw2dwTEOQehpfjx0a2IMUwNXhU8HAUpNDgZjgE6R9o7PEDDjUQR6YnMTeD/CEQPSPEgScY88GA8Kut+DW/X3Kz9+pCD13//7f+eP//E/zj/7Z/+M0+n0o/zq/1uPP/Nn/gx/6k/9qePfHz584Nf/+l+PlESanFEmI6iIL4BeK6KeUyJjwY6G4WBtCbmUQ4ooMzmzLH6m67A86FizYJg5hCVj9ZkxEb4dR+YXsxcxDNtaO2YFmnqQcjsF37BlyszLxGmZWKbCPGUyQts1hoojoGhMvUfA1e6T/Km5Hp7i0N51b66BFoHK5Y988Hea3G33VCKoJSLaN6xll2Ey6AkqxoYxi3saVR3GGzh8iLnS9wH7ybGJ6J7llgAPaoe1K5d15/VSeblWrteddfd5KkvJfaPmibJM5DI5tbkURu8f85a8jvkNzFU3zFl+jfF6rs7QTdm60snkXFjE6c8pGcKG1pVmnb3u9Css5jAYxYcz81RINHrttNrQzR2Qey9on6B1dEusWZjFez2lCGUSpkko04RMbn2Sxbsei/hmbbXRc6G3RmuVvfUDlikls2yVaS6UMlEm959at51td2p/91QUk1BUKL72lqW4YvfkGnMZO2R8ighWO7soTfzelZDispwje3aKuZpTxlXhunVerjvr1ni9XNn3nb1uTJNDxsv5xLJMPJ5nlz/yYSBf7wGHtdbIpRz9icitAULo1TN2T7j9M/X43bucnEFMyIFu9N6jNxWV2Ai8StxnfxzDuwOOsnsGHTEm4aQSd4WO+T/tEaR8nMJHVKKKGs8xKragqw/IV+6P4bhXN5ICBxIyFE3uwtDxPo+95dmgIwfq10Fw6FfxnyviVi4WTMUIfz9AWhhDMKp6VIUxXnw4ATtKYsd7AjgAo2Pt3XpdY8j8HlIcQXccjbfKyY5r8FUeP1KQ+g//4T/w3e9+l9/xO37H8bXeO//yX/5L/ubf/Jv8k3/yT9j3nS+++OKjauoXf/EX+da3vgXAt771Lf7dv/t3Hz3vYP+Nn/nyY1kWlmX5ga+nKSFRSVlsqjHDIHKXlY0FAYdEShHBLTdyNF0BPADRWhy2+MKOBajojaEj3uwUc0AMM8gOj6QWtiBdfbB1r3cL05ehSCKXQsmZeZ5YIkjNoVOX1Hzu5nhtx4edeqy3gDWEc72F4J45rbP3doirGkaeMlMRlimxFKGkkN1M3rPQUtDZSRg9MujVOr0bUjstuQwTeEabQuEhJTsGDP3kHBPpQpZCwQ/ka1Ne98br68r1ZWPbuo+6aMzgiDklOyUPTtNEKpNTrONg6qrUPua7XCXAkT0nmuSAPVJs5sGWQvy9ZsnMxd9ry50tO4V5bxUzZdfmJIop05ORSg5Fk2CLpYauFeud9bWxv668ivs7zdlcBuhhYnmYmEVc5DglJOXQaXQpKMlCmTNTK6gtCBnVdszmmMG1NVZTSjdSSzEw3Z0IE0y0Uiby7LY0rsaunJfJD5icmFLyKk8FeosDyy1UWiRROSWmMpNzOWjlhtCbV+S1dfa9c10r17UGBAalFKfvLzPzMjEtPlg+FF5aKEOM1DmVEgcbHPPwZl6NqSKEOG5ATW674WhBDxVzYQQb82HwkDFSzHX2iC3rtzxcCHxNqloQNG7wk5NzY1xhJJjRh2p4ktMs/i4SLM6A3eTu9SJY6VFNObvudtjfKgY/t2+iRvfntFu/2/FzKXpOFmWhaASoe6gU30cpSfiwOSHGcNLDIY48qji59ZecfKjHmYT5+ZZwiDFFVXgPyon4XKJXqMmV3w+Y1O5+7i5Ex+f/slTSV338SEHqd/2u38V/+k//6aOv/eE//If5qZ/6KX7+53+eX//rfz3TNPEv/sW/4Gd/9mcB+K//9b/y3/7bf+Pb3/42AN/+9rf5y3/5L/Pd736Xn/iJnwDgn/2zf8bbt2/5Tb/pN/1Ib57kcxOk4Obnj9qHR3MuHbeJA35KYR+dZBAf/OLl0QENC+ZxQKYIBIb4UNMBN3hJPqbVe6hPa/Nm9L7vMUsVLqLJN2DO2YNUySyz+0PlKflBm0BMKdm1/FQdZuw4fNVCmoiUmfIUA5L+fpoJTToWwTj37v2S7G67c3GChRysKiAU491+JJFiKLFKpotw6cq+VhhTIIlgozmrd0oJUdxnaixuEjkZpp26uwfR63VnWxv16mSXTEIOlQx1eaRUHOqJrL4ejKpOC1VzSJQcbswiNNzCPTNUnMfG8wDhLLYxgGwuiKrOsDPtNGr0B/w5BmMzJVjCEZiSsamg08y+Vba1UtedS1OyGaVAWQqbCHMQNSQIHiLeV0ric1+KoTkznRfyEs7RAf30MQxda8y2+TovJPI8EiQ39lyWmXlZKFOJdd1JaRzKnd4btTeseW8yAVOZSMXhNBHXBKwiwTSFrXp2PbTyau3se2OLPppGNSIBK6bs9PFpnshTQXFaezIfPM8lOYkrxqGab6qDVITe+kxJ3StKRILSzfGnGz7nNPouFpWh3EwAM+7Ei93mj5z6HfAWvh41oKbbvF8o30dlEfiDQ31w9G2basylWVQ147C5p1VbDKwOSFOOfXYIy9qt4rL734/nvdHz4vrco2Lx9zFAPKoriSTNJZrsqHb8V6LiG9v9qKgiuHJ7/xJV4vid4W6cRI594T35jE9ME/1suVl6mIVTAgfx4ga93n2Ur4b2/WhB6s2bN/zm3/ybP/ra4+Mj3/zmN4+v/5E/8kf4U3/qT/GNb3yDt2/f8sf+2B/j29/+Nj/90z8NwO/5Pb+H3/SbfhN/4A/8Af76X//rfOc73+HP/tk/y8/93M/90GrpV3rogPKGXEuJw9oGQBAqBHdldhJxdlFodbmczljUXrpL9gFCE3VbCfPDwwU347kIeGIESBGH3MTJAb2PZqzLDqXklgyDTltKOdQdUnZ8uEWWmLs7ekpkohqvUdX9l7okNOzgU56RlLHos23qgpw1TNwQC9dWcKTbjeS63vpEvXtPK8+ZLJms5odE13Az9SA2cPOUEqLuYpvV0OSbhJ6wZqH8bk4xr8215PbOvlb/fk8ucSSEmoeENXzAqSZODNhrwDp+cPSQYUoxuDlgOU3e5F0HVIsnE1nCFG/kgs0p73XvbPtGbRvNGj4pNLps/UhpprhXk/j9ytOETsZWCpsUVsusuqPVjfw0Ke1ayaJIhzw59Gbi14+kLvYb66XMxe014EZC6Q4V99bC+wjXfQxV+pKdmVmC6JFyYrC8jHzMwdUGq7nMV0tGxe/JZInUQ2cy5xtaAKhkNu1se2fbwyAwVOl7933k0LNbdUyTQ7G5+Gchx8Gu3Su4GH53pRWniau6rFbGQu2XY7g1CSRVkvVDcsuid9TVDT3H4TvdgWPea/X75/2iG9uO2I9OZ7cD/hLhUCk3C3fu+DPkrTqj8pCwoCeeNYhCg7w0Ag8DrLsd+i0qQIcCb6SjAeUxUAhuzwPxMQ/o8B50uwW22+sGwmOeyIbiEmmMf0Tidn823gctAiK8L+vG98VuhIgkciQVcvf7zmgebymCpsZ5YR+zDCXW/kfsw1/l8f9xxYm/8Tf+BiklfvZnf5Zt2/i9v/f38rf+1t86vp9z5h/9o3/EH/2jf5Rvf/vbPD4+8of+0B/iL/yFv/Cjv5gNmZK7xMH/h8HKEpUjKxosIomMA/PJdOuGiquFe0MzIr7gOZXhJInwOXLIQcb0YQQsb6KO6oqAvMpUPOglr5KGWsUtwLlLqFpzHyxz2niWG2zmE0YxMIqFHXnMzQdk0rtbGNS9ct1W6rZ5doo3TFWdIbTXmwEkkoLFGNm+uCJEN0NbJ5GwtlOrQzJjHiVn3wA9Pn9Ooau2d/resaqH0Glrza03doPao8YKkVHupq6SBymLoFmrb4iUJG7VDT8XcU3C1o1U/JD0e+wZcY+NlQQmfMPWmIystVL3yrbvdHNNhcHwTqKYVb/aoUo/5SFk7LNukhMLwmpCUZDo/9Xe2TdnXwqZpIWkiVx0PHk0p71CT0mYRZnUmHL2e8LNWmYOOLWI26ZPyc0Jp5TdkiUgp7HO1YzdhuqBH6w54GwwtDh5R5OrMLSuZPM+gSD02mjN2Gqjd7dSbwFXawstzJiL8tiTIYtLcs0Tksc8VQQWjN6UkjxIJFP2ZtAbYJ4AjIOvO2wt6n25AscBpubVc1dlqIwTn7ObRA/S5xGn5Gozwy/r8CQL9p5AKJrHFrWxx3G1h+j7jDXpptwO92lU5jfadIKA+QewJWOWSVPEHYvg6OcH5sOvg6TgSbIdwSCOnIA/Od6gtwfiZdWhzY+gs4hgaoakG51+VL1HQBuB8B4uNA7pKyKAalR6RJV5H6j8g4VixFEMjHP3yHmOquo+GCUskC0iGf0qhzyI/agA4f8Ejw8fPvDu3Tt+9h/8E+anJ0ZTUqLiIPli0hHN4UbZZhCYHJZLI/8JqQ6LReB/50aA6B2rDekjZYBRe98Gv7PDj4cGXvFKqdxXCYZpu2Pz+P31TFmdCBC4coqFo9Fv8tkM/3wOjY0wJrRa/aCpje1yoa17zI8MeK545j5lzrP3wlIa78uV1y0GjZs5k6xV2LadfXUbcPDA6Rbfk88qSQyg7pW2Nfrm8FJvzv7p6uxG2ztZHWYCbqKwyVyobooh5ykhxavOlPMB/8DtQB5N45QkyDNOdpDsFdUQFE0C2cLG3UUDaetGq9UdlHvzIDsX8uJGdznHbFfMk82h87eQmclMvUA36q7sl8p62Vmvm/ti6U6fhL4kagJKdig2l8ODaxhiiriq+yzO1CrJK7eCuEUHMCHMuXDOiSV5YpFHc1okDnDf+K27er/b1AgNYzNlN2M3NwyssR8sqnz3TYr70Y26N+oeZInrTtt3Wm1+veNgA5imiVwSecoss7NR57mAGNoqvVcS7hJQxHUixRTTduhalpRcSmuoMoxDs3dmceLHSOeP7N9w+DKIA0mgpExOEiLOKcw3x0D+gNjUoWj46ID3SvIWlNzB2fu9VWHvjS3sXnr8HKEpiHowsSAvmTaGFJsYPtxs3rO3UCJxtfaPD24ZJUicJqNXF+XSrbo5otRNHut2pkVAYPhe+Z88oDk0ksKA+m6h5YD84gf9+fT2fsYPDnbkwRg8YpN8BGP27qzO+/N2fN6U0tFuAdi3lf/r/+Vv8/79e96+fcsv9/haa/clU6R7c3oMwbnZmFdLU6g6gPdQ/N4bwxtmZA0Ei2hYWgz44SBcjIudEkKHgTqYEyDc1OyWSRKSJCCHdp0N2iwhXHnU7tG7SMTgb2Dyo4Qe7CYb2mPxcypIUMYN6M0DhLaGmEsiae8xG+HwoJrL6dRop2Zixuso9X279tD90gBAGNBGj6wv55gx6V49NqXvlbbu6N7RajFPIcd+E5OYu+hQIpgXCX8uh09QQ3fBmsvwSPKeHJG9f3ky3kSP0f80ZSR6Itrbcc8SEezV6dBGh2zkufAgswfaKSNTJDlmNOuu4xiHelKnhnvF50F/Fii5cFoS+nahqnK1zoXOasoq3ZXr9Xb4WhwYzgEOkkTy/lJNQktCEejJg5bPf/UQtQ3oDLCuBwTWjRg9cA+tAGbRBBXox/K9Kfpbcj+z2n32zqIn1mpzu5Jm1H2n73uYahLCysTsoAf2NBV68nW/tYYwhjYTzXpUboSCfsJUHDZPDqFX7WSEoZWp6rNUhlfiOYVPkzhtu1vHnaIGyOTreKjtF/PD2Pu/0UcxENWDcUvsWbObooiL2foeC85q7FPfnx4DvEXQ26iehrdUVBQaPTPzBCJItWS835cYCQbHge1Bs3/UnElxzgzU5/j6+BnJx+GvMpiCt/NMIonzuc94D5HED0zOdASmSJQPLDEWy1izdxjjMch7HBNjFks4ImAAVFnNxbcHuSsN8PWrzUV9+fH1DlJwWDYkomSPQHTIlARcNkp7GItYIkP0BUbQTMUGvhplfKQ33nNyCGbw3YdIogx4T31Wh2Nj+YZHQ/1XbplSSskhkyOr8oalpRSB7UYXdTaUhVCpV1lAiHH6YVdrdUJA16MSlDFgHFUTkrGUo+sSvB/1ZjKisTn6AV+mkkjVs58enjqOcAjddsf3JaENp8vviu069GIY9FlEotLJfrjNhTT73xFnZfZh6U04KCNYh9o7In6x/TCJwzaCv0L4TxkyhrJDjUDE+3a5ZAT3npK5kMwrtSkXpuzECC1yuL7WgMwsNpuIkzzcldXXWkpQJiBDlomO09CLKlkbog3ZKrX3sAyyo08nEaRMlZ4SvfnzaUk0AZ2MYm7fUFGqdNaemLtrMmgPTUmImZp0MMV6b0cvpQ0mn0bFhScElYCxWnevJ/MKQ/dwRg6r+gFBeeXqA+YpZydJzHOQJxxJqKG9qbhslZBifbYwpsT3SFi+tF6xw0upx4EsLk6UBljuRKMkyaW7Iuj0Xh16lGDcQiiXu0szASMWkQCU/UxQYnuaHsmqpGDH3iWjg/U2IDDDvC+n6kzegByLEK8R2naBIfo6zcfBnu4qJxuJTwSpnFMYffoZNYjGYniSMvo4B9Rox1lvR5IaMKje0cjF72kacB13uShBuWdUTP7+bt/TozIizqsjjH4EvDkMKsPZPs4xiR4hKYV/F1H53QetgGO/wuNrHaQeaCStqDm8IblAEBSITORgusDx3zQm4ssIBgItJt00KjHuMNPsmZnDyrcLOxqGauqnTL5BeEPN2ReHYMMNN3pROm4oHAO0Trn1o9dMDqkUQ/2m52E3Fm6q3ULi3x1YBw3KRTlj+DEYbqQcATDFIezCoED04eT4/BLQqeefXk2J3CpMC8KF66yZO/f27ooCMoA5OGCI5FVTOhfmhxNlKU6dnoq/gna0FcYM2xi0ri0a6OMAUe8bpVG9Dsklc8ae99WDNpF8bkmibzOGnSVnkkxececUgqFOmW7qPZlqPeBjovXogcrn0KCY09yT+SGfJWOSaSn73FJOTMnhqtLz4cGlYtTudHcbdGJVSBbnnvf5etC3D08y8X5LCZsLbc4uTTmH4ywOJWKH39lxHdXVKboqwxTSHYoNbUpv7TAh7HsNmSyF5pWfK0Bk8hTBPifvs6YgJXVFLbHXSimFphpzS77vRKC1TikJV+X36uIgMoxDOIWdBUI1Dd1CT80lZ3cDwFmtOx54RW5q7zl5fypHNeE9XYXW3P3X4YwD1TgGX82ZoxwVSFy3Ud0R0kjRGzO7iQgPGxc9EhcPPodTgjmjcPSezSyqSMLTM3QajzPqRsDgqBPTsecOmC2SXRXv6w0ljsHKEySUbm5rKEViK15aRRJtHz3feORSjs/oucXttU2H/p5F+I7zM74vQvS2idcbgsYepDyw6TH7+VUeX+sg9euWCebCpsaqxmb9UGIeFQnJIbR0KEFLZOIeLzojvRBX+U6GWCJ5ih4UVn+uUaxqkCk+nqq+/3uU6fccWjQgrghNKliJjKLfMX0YQ4JeWY3FYyngh+hFOJThC0VUScptyFV7JGYxEX8HN4x/gxxzGL0FjDdE1MRZexZQqER2lFKQTEwdQgxgJMVrerBzAq8dmGnyuYoCMoHMQprApNGsO6kjGTbLUUEm8wwydaCHbfroXQSEquMGymjExg4JWIaAcGT0EOKPGge92X3F7jI9LKxb8MMkDv011BM2EapkV2wIWrvrQoZiQQvmpXBUbjlnsuZQse4IMzVVeuuegcZhYogz6LrTtBPcwSqGSGVkQCIurpozpOzMP+w2pC7isFqLWZZBrLADxhUPRpHc9K4HTd20k6In1FRdDDb6diM4SUnHuIe3gJwdSiQC1fwgHNCYKZQI9KaeSY8+bzJjSt5PKmnIJSVaZPa9uUtvDwp4VfUeW1R3IyglE78n4kFQ4AaT3gGEqt3vTUBxRc0rkIOUcev9tn4TRE3JB2Vb67H3Ipk75rtGBeX7X8HXtkFPcSZFAncPlY3/eoDyRDNLIo9lPCrBuHYEwuP7O8SBfVHE2TBqv6MIOpJGP0uCgTyqMYskNX7Y95ev/9FDG8HToVKHTwlYVqInCnKnuBGbLYgo3lYJSSXc/dqD+/8XBWb/Z3n8795+AsuJ5979jzZetHNFqeJsIAfiYgHpXUACD0DEph2lbxzGOQtYdldRBPqAH7zxi95w9PjF448npiFnonKjusqYP4iqpfWjEhivPXKLEbRsfF0GBu3fHwt8QAESh8D4io3PiGdM2loE2thYWSjiA5YpiatijKoFIPuG9ngXmy+U0MlyQ0dG1pm8R2ShseewRNB2M0FX9OvcQiH+5vjqC9oVD3xzGz5/I0WOIUb/pmF6I8qkdNeUFW7EBHxDacLj1oBbsMNSXlT988cVPYLqQWBWavVeSxO/75Y7lgsUYZJQKjd3FR69vK7mQTkTlZXPsFhPZMtYisqu6a3iFjkaz7G8vBE/PrONfqSvtSTdVdNjmPsGp0TvM5KUcXBFou5VmuTwp4oAfWTNcWSO+zEJaXKIr0xuN49wHDCG0ZOriktKrL1S5smDvSk17nPOrj0oUUHmQCUIKnqKfpmIZ/y5hDa7X0xqq4BDv9teXU2+d1J2iF/wNdLMe1yjMiwpuwKDERJPt1GSIq7QMCUJQ0WLZRvDvRFIMl7Jqvksnos5R07kHU83FI0kx/ur6Rhw1QAaPFcNyHf0pMVfd0By3sd11KRIDpLFSFj8+2PGSyIJPAqggYpI5MhxfW+zaD3WTYpz5AZBHn35cQ7F/6VItjDzZN9wgeccXgMGQTONQMTBChxn4lDVGCzSe/WLsdZ/tcfXOkj9eJ7I88IbU14w3mvjfd15X1dee2PbOxWC3lyOA0iOPy6YqhgtgYodGRJxoI8hXWJRDiYLYpRyw529UBuBxaICA/CZnmOeIo4CixmkW8V7a0ymUCkYat9j06j1g3mToq+WBnzQAwf3lQORURu3YVIR7/6LBjyGV2Ua8GBvLQ6usEMnpvQZFabnbQM68KpNwnAvGs6hYp6QQ0dOJGFTwqLvoyoe0BhFj+eSLkdjYQ3A0e+TfIwjek9MfGbHSSueUOTkbCa/Ec4mU4hDgwNfl4CC9e4wOtq5dqNvj83Uo5nv8zK3aqSLUsWHbAVodDxHdwWSPqphSzdDTcyDeBJkcnhYelSNMowmfXbJuveGDgMUyx8lNN0UaSDi2awLrg7iga/5ceSMIJWSH6va6wHBgB1ByXBliFJyVExGmYtXg1PxmazIy4eNfZJBSw8F8hKsPRMkJ7Z1O0xMEoneG43Rt5QIMr4G+lgrRtjCD8auJ3oVdWUUg0FyqN0HgM0sqOzBzO3mlaY5REUwAvteEVUmSUw5Ubu/3xzJAONQH2QqU+Z0G5DVod1pXs03DS8ozGHUkbwO6NBcr1OjmjiqswHTxdkShwSiFuaT2dd0sCAHueugp8tdAGOk3eOOA92Off1lph3Jz5iUxz72a3NAfiIhPBDrY8CZUVkVCVLKUCiMAGr9Rjb5qMKLU0/SSBTdt+//b3NS/798TNIpyW/anDIPJN7kxLssvNbK875xMaNF6d7QMEnzqkMkR/LslYrAAYt0vYMCbTTQ5UZhHSSLOBdVNCoOZ92lyDiRgegkP2C73lUE5lnPCHTj4EgJS9G4zZkDAoyDT0yicuE47EfGrYyDbASoAUuMN+rHTOuVXnuI2PpztzFDMgJuGpIu47CwoHhrHFV6VAWpOEPO+pCpiuCWXeZoWiZSHHgSmXgLZpNye+9jUZv6ayWJ5zn6Cob2kK1KCdWE9qCv52BMHcldQCWRth6V6qh2xa9VwyFLD8bOzFQ7Wu0Oa0agqgDmKhpZYgOZxVyKIKkfJAYv3KMStyHxE+CLOLNRShzGoy4evapuaG5oC1JBwKx6t15GgjMEZn1NRhLDCGp6OyzEldVTKdF/8YPJxxCMKXpqJiBZmBJMpUSOYo4qaKiOmIUlhss9Fcn+eQZSoYJEv841MF20uBkhr+UPC8Zibd0PaFw9viTX/phSPqC4AeFrJBwtrmXt3ddlj+sy+rPcDlb/WofWyWZY9htk4pJaZbBr7xCKEj3glEc5Fmoh1unqbNvWOlU9QDW8/9e0+70OOnaPhEmjktNRFXWN/uTN0icfFZ75SMRBJe+3syiqm4Td9nas9EFx96p5uD7oUYWNpeDKNtn3ivkqv1Vmvu9LHqFGD3h0yolOvM807jdg3kqQMUkc71HG+xl3Y/SiJJHSVzO6/VoHKZJRkt/cWeCM8FQKn6TEmguvKfOMcenKtSsvrbL2YG9FduRDihyqDsY4DIwxLT3aK0QAMAKb5daHMiJrF2/Wp4CY+l1DxLpXObeMJajIg/EzDlEztDZfQL0j5ON1ZMwZGDc5JhnBr4d3j5f1hMikxfzGAcNF1TYgpvvBwCOoBUMJCQIBkT2FagDRuL6fgRiZ6DHHYd5/mOaZNPtQcwmVA1PzPlrAAUhg8xJYUByuB9QRGa51RZrL7gz8W5NAKWG3cVPw8MontlkwExGffxouscPH8ugjRoaOxCE1+mPBLtS4dz7U7T1QdFRrguEsSdck9E+gcSiPs/M4VITwxcoj7fTvRZAi5whQ43qnkE1qx8+Pa92bMyRHRu05gs8QacCaEqoeeS7+mWI9DFJAKSWgu4C8Y00chz/RfxK5wXMpNN9H8kYkT2rsW1RsDIZhJDdx2KYk7ObVTgpGH92DyJR9PUzZg0eP8Qt3MtB4Pf9aP5CGcBxoHeujd+T7NEdwLJE8uV27pyFjXScLeEyijy2D+u172hNLH+tQNfbWue7usuzVFOy9O6MzgtNQTEfugtSotnqnaXOo1TwAlJSZkiu+FIWcuKO6+1r1IMXx2SwICYNVl2Lfd4vgaQSFf6xV37tZGkORHridS3FNSg8OsDlaXyT5aIAQwXQYZ44T7tYSET0OzTg5LSrq8ZM4u/krPL7WQUpKZppKHMChiGwwYZxEeJgm3iBcs/HaO9/vyvteuZixQgSqsH4YQ60BD928a/DrOqqmNHpMgy4aB1Sk8I7V3iosuzuEh0vwWAw++xLEh4DQenc5HB1NyT6cRInKhsgNb4FlHBBOmIjXi+qQkXkyAkhw5/2Xj5XpAXKU+SkONcfbJWaGXNB1wJt2EBIkyY1lNjKr+2tS8tGAb3aDIHzz+oF4H6gUP1wGOBpdPDyZ9wFKalCeBR+UVd/ROhspFYYaSLzELQGI66FR48SNYDAR/a6YQ4kS9OY0IFiHowYRxszo2o55knEdHCKOzzGg0aNSzMd7GmWrHa8HY/5LkpGKkMSt1H1+Rm5Bw5/lgHJ678HW88PRxAsHE6IBFGsiCTqPIXA7YLIiPv8UywyzTg3rdrMgs0AciNETGddTzAMteJUrKcgHdiicYwQK4fCWdsMGjKsBUJvPMpWU2LuPJKR2V0WP69i9QjiWmnnFOuaURm+uSPZ5s1GpmB0VQBG3WPGBZkg23KVgqJUMVGOct928atursnfXo1yrsnb1QCByDAM3XJzWxvlAJMGSUOk3yaW7IDHuUYrZQAsWcEaCwk/sP1+vGj2nHmxFYbAdvVLpkYxrklgXUdkPNp4GPHqs/ej9+jvx64MTgApCEWUXJ6iUUfUlO4bMh5Zi8Y0c9/QeJeKgtx9/vsLjax2kNGR8Usw7FfEGbTGYs3A2eOjKpsYZgTy57XjzqmPtzZnnKYE5XJFioYAy5lzFUsxjRT0lHDdWzLMCHQfMkek4bHQkzRHJJALNgFREvXJIgc1Lc7iMFgOpejuQRlAYApfAx3hzcohvBEgkjBs/Chw+2wJ3ZXhkxH5mjvcsN124kZ9r9yG9OKAlgpwHYwlq+MCex/sVhyxjZkL7DSMflRskUo5XOeCa28YRs6gUDbQj/TaA6il4UPbN4Vg/EsZ7t+NzCgScZ7f9cVf9HQdvHOC3gzySFnU48ZDbAUjlGLQc0JmbxHmPaVg6aPQ5RMzvORJBMF7bPRKiD+K9ufFcXjhH9S6JPDlxZQx9ei9LaTUqLo2eWPeA4oYdg/mXnGU5qm1VUvTFypGqO6RVezsqptGP7Xi1pi2gKjPvGapb3ox1VcS9wLbqag1OKbcQcY3qqJonfWJA+Fgl9xw76Pm31XdUYKr+/dE7JrJ9i2joeywfic8k7klWzJhNWcTNKDNGweed5jTI3sTe9YrQBZ09EOyts3dja52tNi61savRkEOUVsVRGfcVDug9ejEpZL9EM5I6vbtK/ficrl5fAr0Qvx6BmhzJDko0gR396X7/R8ge21cOotItOHrfuTucHV5gDhET13f0lfzrCWNCmErxHlkErQlzJiYSsORQuvBqS8FnEsdautvvx56Dozr+1R5f6yD12jsLhJyMkfBeUB78glh8U0ALLDNTSsx7J7edl95ZzY4syI/ffAyiHUeWeDAywMKcT4JfarFBBhQ7FkWPcpwRfOSW0SMDXowqQgerz6sY17XNERg0ztGRuVrANwTs4i88YLeD6HAr1+6q7oACsj//R9UBHIvH4u+e/fcbxJlG8TUO3pElO3QkIWGUzKFG4vOlNCpL79aMzN9i81nIZEtg3Kp6ZOL3AcoO6qsnFhYbmSxYcU8tz87juo1AeRcQHYf3REHvsvPkE6THADSkUBaJ31U8wFg7iusk+VYtp3Hgekbr7KsbGxPsmIcRuZtLG9Wi9QPCGkHJp9QCfsSHcz05GsFgFBmKiqI5kpMc5IUODAWTkO/JKaHa7iCaTg5r9ym5ieN4Xko6KtAhLtpimHz0XbwSg6Tqiv3mQaX2qxs2tsHEy/Taj+TLHK5AJp9TU/WER5MfqBGmOQZKuUvIRoJgdhyEToDRQ3bJYq+IOdRXkrkGoxoTyoSQTZlSqNZjNxSB5LNtBns4IO9d2Xpnt861+cB0LYW996iY4rxICSwEqscJMmLf3f1ONkKikXI5UIMbjX0M9cda6HfirClYhWIoNeDQMQJye0lLOcYhBJPoN+YYGxAnMI0AP5KAmzZpqJBkn6trahT8nGvis4JZYEeZU6aoUcQool75jYH6e9agxvl8DPP+GghSv3h9hYczT1PmLMLU3aWymBywVBMX60SEnAspTaTUkZpJdSf3zhWjksJGvXm+MKoRxtkdaugxIxWgzR1yFrRNjG4B4dgIaBy21d3iMBswQEB5A1/23xFXaBBndQ2r7Ph/l4KSAR1GMLNbDwi4SZoMBs9RbeHVhHtF+msdVeDIou3IkscsmBdXHtBSMqRkrEdwHdCaEYzAoDRnJ4s4OUN8aPfo34yA5x9KTY8K44DjIrMbMOVhXZ3EA9NQTg/ixjEsjVdVLhwcenVE3L4bGUgjy2S4LXt/8n7THptePLjIXULAUeEMSv6tT8JdcB/Y+7AQcbqxHaoYgSURwiWY+IHpCbhiEjbu2p3lR/Pr2NoxNNujBxOr0assAcOTAR29MvEMOUcl4WytjGRjTimo2R7Mk6nr4eEHy5AwEouBYoy611s+JInWOiKZniefaRJX0WgKW4JNhLV311I86PcGoXEJzvJLyRXuW2+39Sgc1/reG8qZZd0hO+0UnHyhMffoid4xaEDCSQkFc/fi6L/52h8DvZ2tG9fauLbG1t1AczWXn9J4n5qmgygz9ARtBE8NgWePv/QeidZR7RNJS4yUHPf89rhBue1GrkiJqRRX5k+gOSO1HfYuxBDySCItNn6yYAITLMVEjNG0G6Iw4ETxsRYDUHftrnh/rEJoAyZKCCm4TmP3rxN9RhswpH+uEhs/JX6kx9c6SP0/P3zgUgqfzgufzDNvSuGxFC9Hx+xQNNhzMmYTnvCDNkshi5EbSOtcB10zgabbzMPg+nSiZxWLbsBzDh/FG4obnQ+wKALUyOaJzPyonCxM/XzD3cNyh0ICzgIbPyOYz3D5r98OTHPM/ai4BpEBOWASkIMsAg7pDEjoPtvzj6GHVqC/oaieLLj13qhh2J2M9zCozZ5h60AsD/q2hgxPhpvqgohHzIAcR8V0aCcSn7OUIEDY8fpDdSHlu8Qi3uuogsb7H5vQA3wcHoyglKJaHtk5B91X475KVNtIEGLkdt3G3NfI/od6AQA5XtMs3G4H7Zjj94eU0HFfSzDZ8AV0syCJe3IQfcTp6gSkGPeQ1smhgM4xBzUIP0rusKTEZMaiyikVluRmmyUgHrHujfiwnU8iDHq/ZU+UdCpugImvNw9Sbrv+v7L3N7G2bclVMDgi5lxr73Pvfe+lbew0IOyqxofBCARFw04JOgiwEEWjQF/DDeMGLQtoYJAQEhI2CCzRoWV6CFrIBV1AJTCiU2ALCX1SCShcwFcqC+G0wTjzvXfP2XutGRHVGBFzrX3fS2emKKS6Mtu++e49Z++118+c8TNixIhtHxi4wCMowDt2vEg6K3PchuDmjj0E+04JMZdUMAdgwhYJZqpHYFD9PzW5l0Qnyk0paa6E4VXIOalngBytnllciRdr1gMpOhzY3PBshpfheB6GF6MO45bIi3EjUm2/VStuEWNO2V4lgZGN1idmX6nCsIcp5hrQRuq5qB8IB0+czNxc4xbBZwESNgxEZ9wqPyPphsNRM2M6NesjkjBmwYkps16Y+y1RnUr4RxDqE5DhqnC2P7rS3pqDcpxZv4qH9ki07G1swpYcQcyhOF/v9V47qV/aNmwff4KP2ws+uV7xrU9P+HA1vG4Nr7Tl1M4UnwWgHrh2nSOz0QSxZUH0vmN3Y98Ku3Xp5FQRSHwbBQdUoVvmQ4WAqhYMr08QXTqHCslzSc/HEwc5gO+kkRMtWZhzdHxEkeUgIcUIzOwhCtY6Umoa4qNG42GkCE84AbNuQ3/GBXs+xll6n5fYUCzA+nl4QE4NeoTqSIHl5zMCtyRNeEynApVZg5lOLh1BwTAiMum0jKr1yOLy1s9XklTauxuhjMSEYeWEbxX0Qcilrl/slFUKr6sahOfnQykiOzMr1ntK7FNE04DxD585nbgqVSHCfLITJ40d9R105pb3Zeoi5rl0aXSSaQAgDt8Hx3UgphRQ9d0sIrgCuELwpnW80Y6LKJuERVOlXGedbWZ2UXgBDT66wH0H81GBi6bEk2Drqd2mDUNX2NopPTWcAxY98HYM3CyweWAzDkzcw1n38cCG4FBGOfrn9MgPskG9ESYMGkMpAz6DPt6XCsZEKFy8Lsscs74HHdTNDM/7wKf7jpsHXszxMmnmgS1JotDMUiJ7C5PtSUULJHSSMGkAiApOjr0rSZpg0zz3ZAsQ5Yji9BZlO5EPbqykwaeTNpsoRbG/JfeVoHonGXgi96lH1VrTVuUX8Lvy/X44Y4D3veVaKORH4dgHFUWaAQ2E+xZBki0OFmBIOi0nWWPM6P5Xf73XTurujk+2HXvseNk2fHq/4VuuT/jWV6/w0XrBNeGLacPkUCi4iuASgYsOrAL2bhgFJDmqHZONB8msRo5FLqLvmj8UNj4j9emYDkcmgllInfIhGcEXzXq+SuonapGdmDI4rqmOTdFcme+dp1Xff85KyrjiZNulNkTCemXiBYcCe92DePxcHeehQe90vp40ah/s/SFPgOfgSkMovVw4JvY+xTjz75I0Zs9sB4L8+ykMkIPOfh7dXeeik3BQhuaASQtGrUw5PGHLcpDzeKdnF5WoZqMwMKnXkJKqSYHUCngiAG3Hfa9MO2Ky1OoZiRAGgxywJ6dFp8E4BUHT54MEGc0MSiXSeAg6gIsAVwCvmuID7XiTCEQDGXH0P+kcK4ASgWjn9yehww1ourCXB2SRhbBn6bKupEGbZYZJA2wNGKLYI/Cy7yRXCPuoXmzH2zHwvG94uw+8WOAW3OsbkqGWzDGRYDavMifyKgLN/YAzg2uhg453UcWlNSyNDfPZOojNHM+74TYGnvcdb7cdL+a4BR0oyROUzTIHRHlX2BROUWMbdFKIA+KdLR0BBHLd189EoR2sfTIF5lrOvjKb7z32Fhml2dCb6ZvNpt1CQQJqtUFzfQiODA9I4QGZozPMSIpSACgJpukkkyBzWtcMfDO7D0/pNdb/NAIjs6Zqmu5JAmnZe6kZvH0jr/faSXlbsLUFY+y47TtewvESgaEcR/HhsuDD3nMkRW1WGnMLxbUJnpriqopVBbsK7sZGvcMJJcyVEUY1y1UkS7sf0wF8/isXWjrIKOC5jHtFW/VddkQyEZbO5zg+CRLVe3PMzDrM9DmLy3/X99T3VkZ0OoeC+rSaYiFTEaKOPY2+V93qFOKdnCAAGjLwVzqNbzr+eV45niSnFtfmkZJUQkbP5dg5YnhKxpSzsYR1eW7l4IBzenUmfJzP893O9+nETx+P/MKz06tgY25nLeVv1n+knkNlT5X9JElEk9n27jN0PzKscqhHqigwS0KJsG9L0hBXPUKQtRe3ZK8FFuG8qlUUVw1cBbhC8aY1PIXgSTlioetRvNck6XsackNMQgttWDlgQSRTkJk7VUCKGk1IVmaTdzS+fx8Db64L9laUdWAPxVtX3H3B293w8bbjk33HpwN4DsE9R4xY3Q4glegFoe0gTwj7nhYILqpYVbEmbNl7p0pFZqbbGLiZ4eN94Pl+x6fbzgnFTh1FF4VJqkp4kpO8nNQhbVazowCyIGstVTZFFyezrtSyCEmpJZkOLsCsjTVaT6Ch1mQhJ5FM0KN5orQv3R37qeYpIphifVIZemWiaSX0sJERmKhNftXpMzEltM7Z/ITEg0zTPXO4LoLFGfh0bVga/67ya4Q4AV0QfcUO4fyaAKVR7jtMNog0rC2wBtAj0KOK4wYJpqNXVbxaOj7wC9Actm0AYs6nmuKJ7xiy2cEdmMZI50ONI60vh6BHFjKPETajHYhMtYBc/khQDM0fm94CmA171c9V2RsNMY0icGRrOtnkSUs9O9oKv49Mf5IXZl1I5aFmViuXsNxnHfRnDH/Vj0RI+a97ozRiyI1a5xXIgXFy3MKHZuNy+gDCkGNSTvff6RBL4+9dh1THOTstALMfjL1OB329MszjeaXTO2VBFWUWa7IypjDBSWRhXgfms9GZDdJIEFPyYKCiWk4KWc/TqYAyjZ4Iehe4GNSN2UQEVjhWAE8qWEWxAHgKx5MIrhJ4BceT0nHxbNKhKisQww3mwD0cz2NgyyzKst7IHEkRNhgh1zmm8+RMqERlNRUJIusv4D5aexEKSJ2/CrCr4gMVfKCCj1vDJ+b41B2f7Due94G7j2wQNkRC1/xuOqkOTsFdhXv8qhxgufaezFmd6hBv7zs+tQ2f7gPPu+HTbcMG4diUvO+R1Oo5RiYVO8o2sL2A2S7XL1sjZqYbwTlpcvQpRZKGuCZSQAAxs1KgkBmg+ugqeARkEmxK5SEyowuwRcDD50AxrSwerF1BDmThUIjwuT7PQfH5FXON0yFqlHMsm0AHO+JQEOluKSDs6GFYNKeU4/O/493Xe+2kRBegLYAo3AybDbyEYjWgb45rM1xVIEtPdXriE2GMThYRPLWGN8uCDQKTHbt3uDnuXv0dgHYlG6bgj4wcZuNbGfb694yY+SJZImbWUpmUZ5R0LMk6RhrI0wKW1Aw7T7Z8N3srxW8KsOaCzp6ls3F1D0yp/DqnOpYUyYEQXenq1flV2l+sKsJZGTWeILM4/R/t7+RATTkiVTK6POsw5wyPzv+cDcVkIn7W2Z82VWUdM/EgmeHsNM/QXv17Opm8rvIph5Zj/aBGM7wTuKgcIW05fTALLWWD6qOu7Lskk6RpwmFFIU7qTT5zZtZU6q41p9DZesBaUbZCuEHcoOZY3LEg8Lo3vAJwAXBR4EkET0oUYYXgosccJwfrM5Yw2/PYcTOjzNi+4TYGdqfxHl7EGBrhipYFZHuxCZUkjLWT3r7k2JvujtboFHshFABWaQgzjCAk+dQWXFvDkzmug1N7LwF8MhwvY2B3w7BgJsfwnNJKqlhEcRWhE+6dP9PGQYTuuG07nvcNz9sdX93veDsG7mPgDsGWec8Uuq29r7zvczx7/Uo4b6tsRJNey4V7JpzTAIIoiA/L53bs8RkUZYCjcd6XMTN5yUxHM0iCSPaaxYlccUDZkc5fkQNiCU0c++Wk2/du0HYQlzCD0gruAOREhUCF1qcLosMCiRkWJFw0C3Q4llAOfPwGXu+3kxJSyhENQ3Z4OG4j0H3Hgo7n1fB2BJZGbDTAQr77QEhD145L63gVwAbBfTe8QHCPyI0I6v17wIWd9GWsDs2qMtyHwarYe74C07kBBws6kIX4+nQkQytrG3VMKYNb6bYUQeJ8N4iZV09SObMJ+1UwHmWcPy+KOV2FYLKe6qeTop7f924t6GhK1IeFHAhEForDBYgkrmTmxP6mNqPh/DIcM3Ae4b+CO89qC8f5nRKeukoPUCssM9a6wDjeN6oHZ379UbsKYNLVef/58WJyVaY53XI+w9bapNyLpioGKhKXzPQwR7VUjQ4Jp0XVFeI4LuZjzGyxfKMHYAOwHc0rig1cVXExw+u24JUCT73hSRUrqHdJTTbeHxOyuLYANgS+6sBX9x2f3F7w8X3D2zEIhQ1OZLbgM/VciC2JACqC67JAEnpsoBN8ksDrpwuaAGtTXBp7GC8RWIW1MgWnB3SnIW4BtKXgIsLyazgaBhoEtz1w83EQSiKSFKF4aopXoriK4qk3NCikCBBmeHu/45P7HS/bjk/GhpsbduRMLyioEJkOgwsbFHH2jDgqEy9DXgjBEazW82LGhYItuJN9gm78+8zcCbVVp+YhBow5LLGg4DMkfARap91dzqfWTNmPuQ+4ds/YXgWhD+c+F17M/REPn4nJ1p22q4IzyXDM2f6gQRRL7NeCdt8p2lbpkC5QOFy4yZ7HjnvvxLuzJ6Aayjwj0JZNaiuC0Z15UlmTz+8J50awGXVSYQ+DPiGoinLfWRj1gGshHQ4jswxkLShmwPRQoGTGU/0+R72Ch5W5YA7lA3l0EueFhYy0/JSViSTLKR6gSlXi76XdF2mgI41jHechszt9L+SAYSbDyJM6XPdssvpOGdR5k+W9e0hN60dlHEuS5+QEJmZ+bjyZNv9dB1+J5gHdQQ7mY10X31cRal0y/y0u8zkCUWcwYddi8OH0WeAkWVWnOCNXOuKqB0peH+OdmGw9I/+PDiEMDc4hiV3wBMXrpngtwJuueN0bLk3xpMm6qjYFyfHzEXhxwnrPZvgVC3yyD3wyDG8jcBPBbVmwaaeTGmAtJeErToDmvnqJIOJqRC66Oy4xcLnd6Tx7w6ul4ZUK2bi94aoKa4qn3hFJhcfOOs1sHEYA7Jcl3BUG2OB8qcz6rwJco+FJgde94UkaLr2jQzB2zqTax8DLtuH5dsfbsePuAzs4ht5E0lGdGLXhZDsCWb/J5zvH/5TRLiLNA757xJi5OrkIdAYa04kBJx3E47jzj+XIjWLZqc5G9Gp4nyLW9VJhM/pxOnSyuT4P0ez8MzOc2ni1rjM89SP4nB6xrjmK9HPE1ZYqNRIMEuCBPQIxfg04Kc87L6BysBcUJ4CpYAAUekQW+ZNWDpXZHY/gZtBwQgmt4SnYODmj1EjCQEb+qooapllJSRm5Sv8BzCyDX3Myu4JZT0BGPvVGQTqqUyuNk7s0ncqZ9l3/PYrrB6X9iINOUX/+oCI56OewFI87nBmgHCeGw2lNJ33OIk9w5OxZwmGYpeuU6Tkbf2Ljdd/KcedmsJgBQDV0YsKH6dT9NGpg5jQg9R4HNf/svM/nabn5zz9/uBPuALJHqJocz8eos0mDE+EY+z4/PwMEyPy8nAxbGaMpNnyi/xfpogg0Z8dG48k1oUaYbRUhtCfMpN4sCyG+rrioYG1szyhKvwdwB/AcgU8j8DZrP1+NwMf7ho+3DbsobFkwouotitizxw6EIfcghV7mGhSMYO9UE8VNF6jTifZ9x7rd8UoFX7he8NobXvcVrwDc444Owau+QlpmYb1DckJ0iw6VFV2p0XkdinuKuorzmp9U8EoFr7riIpwrpQBcAmYD9+2O+7bjZd/wvO24h8MUsFbXxwB40lgTr7XIfaAViBRh5Ag1yHRDsvROmQUc9cYJqZ3Snrk2Iwc3SoWocZAqyhloZKDnM5OtjTZnOmW7gKTEW9R6LbFcHJdXsGCNNeGpvbsPPkt0mOhF/b22QGDCoYQgMzDN85fwXyNOKouX5SC0d0rBKOe43C1wN8FuC0Yado5sBsjUO4Q2OxTXpeM1GkYzmA2IO/Y0KGTX6FTB5lceUdS7LxqTwuzZAT4NW6sRGtRDmw2AKLudcXgcRrmaOQFSiZ0J0ITEuDgrQivmoc+op7oAefrOhsHCrUTwGaZNLj6d18dIzwrayhqbNo4XByj+WsY1H8mDIz02YaB8FC84359/r8WMvP5zJFmX0oqIEjEJGXUNBzEhDU2tFz/f58NRUbXcj8zvc2RbqjNfwifT0KPgWqAK20WN1xLnkdTIQ8KdODX5Rl4MzySDoeq/k3numsy46UQ1JqNPQrCo5L+VLD4RLAhKggmwCLB2hYazJpNrJzKDujvwEo5PbOArOwkEn44dH5tjC8D7wqBPgJCGlC05jZ3JFVIKIFVLC0AWtjJABZtQ6WLzHRgb1Bxv3fByv+PN6HhzVXwQgjcN+GC9QD2wgAa/K3Dtjf194RA0qCxYBbg0wc12CuIGVSQuIriAZKlFMyAwg+0DY99wv99x2+64bRtu+449DNHYE0kdPgd6n8EVUQqfjLqpSgIqb8yMulhygVmjneSEKEyh4F3M7L16phgIvWtP2Igr9Y4IIA5a/4SKZ0ZzrK8ZPM/Aq2DKExpxzvQCU1XkoP5VwHk4t9o7cx+FPCAgEzmpzwfgeiAYcjakX+f1XjspzofZEVFK5jKZRBw0BoQrLAwmjXp5cSgHcGNTqWER0nNf9eyJAHWvzH0aBOLTgfn80kgpWGOoqgUNms1G2kqjy2iLFD20IqOTAQY4Pu+EGQo0O7eTPBGaI9YxszKcHBS/8jDGh6PI+6a9cpCToz295sI8IiMBZV0EmhCUz812nixcs35Ovq2+6eErtBwD8LBhkUSPmXn4cR00DvX8GBVrbbLaiMGaxgwq8uzn5+IYN8Hv4M+qqXPClSpliaaKdhfCajFKOPRwvprPuSBbrXqDkBpNByXTYEmyvxi3K8Q5m6g13kNzp5JDU1Lq83s8G4EhmDTr6uxvvWFVUq6vIPHgoqz/TMo+9WomS810If16M3wcgv/yPPBpOG4G3KVjD7Z0eJNJ3OAcqoA0kn8ia1Nc3zTYVSe0MKDTeLfKBKGQZYW1hud9Z39UOD69D7ztDR8osKvgDYAPpOEJQJOAUFscaIk+iKK5ZhMskRP3QJPAqsCazlrTiVDuaOC+b9hsYB8jJ1IbywFlqSWDsgCqXqySgqiGh960mglHQeG0FSFzoGAuoIwRKRmFOEhRAObIlKptk/RQ6IFnkNwqXSFpBnHoep6IJ/MrI2YmV2NGai/zQTFbnHZCc5KWCBRJLsu1WgQrnXuTTqgg7Uksqk1fyhUVXNYXz9QKyQj8xhzVe+2k3BxuA4DkxMx2ulnE86UWALgxOagtGFCADqYJFXxXabjHQAtHc0IHyIioa2PHO2iYRvhcVJbGKSqCQj6jkv05wpZp8dmQHoegI07GGo+1FOqynuosCa8UWiZSzoj/LViwoKNzFsOi6QEdIirbOeplgZjO5qjFHGK48LMzPKX6QmfgD9Fcqni/k4HqKcqcDgZgjaMyt8jo1Wo5HxttdsprHiNPpij5dLAOSEfV7eoQM1PKc601M1GTCPjw0+PiXzSzIdDdzN4QOkTjQI00Dprd+wFw/pO0tBNyGJTADJYQ7C9RVxq1WktBBycljJs1VyCo/u9kSdYIiiWzp0trWBFY0plVewIEM8M3F9wG8GKBr94H/tu24ePd8LHtGKKIlYEaA2rq2T1M9M0IfxinAodzVHwFFJZGqUgvs5YJOmFA0NqK277jvjtuY8fWHTcF7ttbfNt6QbSGSPiuq+KiHWqBfQRCFNIVblTcuBvzmpbMvp7PDB4YY2Afhtt2x+Yj5zwFoIreF3iXlNoSRONat0iqdTXM5vP2EzpR2Qi3EPcm51rlPZqpQyIdR+yZz/BwdrNtpNaHHN9xivdONi6f68Mv6y0JeU80JbOj6aCOzHxu5OOImG5Yao8fadK8pPk/+ZdKpQoCD6AkmWrNBHBCW34tOKlw7DlNFtlIKU1SXy7QQrF2Mvi6JEwwPUhMJ6UZtQhATa1xjFQnRfiAgSLrXlK9DgAQB5Zcx66GzQg75bzTf86ofv4uX2e6czmWYu+IYDqA8KNH4/jsYwo+j3f6+0zv87sfHCOONF3Tmc1eolzggVNvFjB/VxuhDHdlEZp1L2YUMs/jDOGdIcH533N2ds726nJmM+XjRuBSOEgM4UeTb2Vlj+O0D8i3XmWM6pEyki6SAedr9czaFDV3J50YHB4CS+ZSCMk5AUslawZFTetbj9pEIBA2prJFZEamrQHGuVWMEbKg72ym0k5HoBZYtOPaOlYIewMzwGJjJSHBzhiNg/rcJuT19nbDiw22oV770WOmAoQhLCMiZOPqoAFsaZgiAPHAfr9znXjMBu1inYoKR6xHySgJpHUIOMbj45c7dgGsG/y+Y1eFvHnN2XHI5mBtuLQFEhwcSM1BSQo91dgVkteeoqrB47sIpHcSplywaue+lKy/AomgxGy0LmHjErKtOmK1Z9QqmbYFwdoOkG0nzM5mIItj3xc95yAh0HHKqZ411+VpbdbePR8n4rTf37ErQO05P7ZQBqEVrMZpD2AGlgHg8VhRm0LwzmdjOqo4HyfXh76zz989w6/1eq+dVJzSzHLgNdZ6UU66vLZOdWdgNhpWs52X8GKks3I6K2ZoBNs1UxVRqiJQAiQg0lIOJR4MXEXdiJwbxdGahADnwtEZnQgErelh2M/XV+/PxU8JFkzjzogkY/1csAWjeWVNlc7nf4uyemRZB5voXcJAvLNBIJJCl/4QUVahdhp390ma8ITCuFk/66QeMrw6hxOV1d1n2nQuzNbfS3EB8nh/MZ/Dcf1F+aWTlZSiIVw1BW8zejxdNRA4sqZgbYOTXg29NSoaQNCC9ULtC7wt0wGRAcnPiYB1IdWUbspzyWZOj5iaZiOoEo6K+kVmHxNCZza/+ICisT4Tge48vy6kcIvnMDwRwNlgC4+EsA3b2HHft5wmG5DeEaCCRYVyJfU0s/7I4XwR2MeWE1MkvV9B28hIPnuplAbNBUDrXH+R6y9RhTDHMMf9tsN6gz5d8NoIg16TFKEQ9NYRbtgALFCU2nuWpFPRgYYxq0acDQXAVYFlQZNGkdnhaBIsC2Q2U0LK5aQqSKlnysbl8/7MzwBH4IEjeAPOVPUKimIGTEfdKu1JPK75z389Blbcf5kBnYNAYEKRyOxq1o8EfMaCU7AWn/MN7wTAZ6eWTuvkMxNZOUF9nwmoSV77Rl7vtZMSbdDWKYGmrAspHL1JzolRtAhgWEYMp9A4bZhkuFwmtLeU7nAnaSA3phibe63S5XhcIJOUUHBczgeq7IVGKZdrBVcyVzePc/YReU6k+PppA9T6PRZwzWwqWOshAqvFilNW5AMVJNmojXZkK1VrC83G1ZLmSedcNSG004zgxL2p53VyWHUeKpCEYz9v45WDqqGItXknPbbeAzxsnAoigIaWgwMPtYfTm0+3RAIccJnZYh591lV4j306Q8l/IxXc1Zwqzx54QuBJgCcFnvqCtajBQhdQ7V1NhH1MyIFxkmoR1ZQZrD6YZ8SfjnEbOwyU54HQQb0MZjt7RGanQfHkpri6J7tN0c8q9qqoAZ1Urcj1Wj1dS4cMAwcxMnvqXdl+kXvL0BE2WFsamoGzoy1rGkEGc5dlwRiDS0JZV6PDowOuDIR1sZybpIoBw3DHPgwhgrfueB3AJ2OwrtYkJ28jRVkHcM5EK9PRgGgjGsIKL0ZQef0ejpsH7slG5ITcQFUHo9Z/CFB6lRlQIvcxopw+95SNzCCKkjtrQPknaGdq5E7VcueaZuQ0ZaPMx1GzSgP/UMSqYC1i6t8VMlLB5MEqrMMU2iAoAd65P2VeDJEIpAOS+tyxRx/OoRzVtD3HrycP5LiId2zWwxt+1dd77aSAqq2kgXdPAUOgq2BtFJRscmj2Hc78iBmi0u1yXk2h6BAb84HaGLCmzIxAOODM/mL0I+ej4fR8H6IIOg3SSluTqflW+muH0kIt7oN9U+Mjavk0LVUCznKJU679Lp35qC891sGmQ8l/F83d0rLH6Trr3CcVvBag+byf0tuE46jekOfkNs/nXYjvvIDnqwrD7wQE0+HOe199SMxGdF7XwcaqDVtQTbH6MloAXatj7Uve42zfHay3KFgMlxw+18Jx7YrXveNNb3i9rHjKPiQqiGdWnyoMCIPEiqrjEGbme6pg7WlEdrMMTABfFuwq2CJSCNXwojt7fZQTYkWofXeJwBXA6oElHKsqlvweDOMQQxWYHT1FTQRP64JXAF574Haj0OgwY+tDV2xjAL0DSPguDTFRCd7DpVEPz4ZB+gKVRsekfE5NOzYfmZMB+8gJvzmjy7YNZpyPZe7ojQK0dxvYY2H/UrC/RpwZJuV9GnI65QwoQySndXPdj3Bsbrib4WXf8TyopvEyDFsQ9hxOR1W1Tq61VHgoqumx2h82t2hkb2+1fiTkHDiyFwCa8KGcspBiikIk+5kis2c8ft/cu2VmeE4TeZhBpDw4p/m5xvfVrDdU5pb7bH7/aV99zde7+zSv5yBA5UnKZ99/BMqeY4K+/uu9d1KzaJmMBdWirAp6ScKpJJZNIgWhvoxYwb4VKCmSIUdURMcVp+Nn5BJpAFH1KSBQ6gya2RTmQhBkjQdILJ7xX2l9lQ5YpDpByEF+iKhJljJZaTOb8JP0CbgZj9Ap4ca8R1p1oXOW9XAPH53G9Nln/PiUbdX1hQqiKTMTyHRoJIQYRDpq3Pr5e86iqufvfXBUCecec56SYZYblKME5JTp8USp4MBsbHaBnZydtGMjH8MBs8gezoZYpO5YF3TnpNUWMgVKVxE8LR2vtFPZoC90UI0U8JbZYMvou865XoFU6M6q9ynuxtqzyhW8ahPFnrDMfd9x14bdHS/3O8bSsNtAJKlgkT5Zf93z+J5Qog9Y5VARCKGe35t1wQ7B5o6XXXJIJUcpDAfa0uFwLL1hN67pkv+ZcBGYbQE5/dhxyGoJa1+sx7WUA+P3IMDRLc5JrpqswaaCngSG1lItRHnfKovc3bEZNfx4fcEQXhTDA1AqHIzICbtj4O19x9vh2ADcI1I8VmdzOUqCKo49HGWEMxgkwcFPBCUy7mbQlNJJ/Hs+VD+Coxkw5T4pt1C2vfrhKlOr/VdMQzqCCoox9/y7e/thfwGpLCEQpaxTBRqR15kx6UHaOmdvlRE+7NPDWde5zffN6zmjO5jOlbH/13WHAN5zJ3X00iRlMymSIlSVIAyGCYXNtBZcWFShCAx4anU5XB2heRNnx246D7dkVsnpIXFj0KAfdjwKEpiK4sczd6SjikPSB/mZc6RRSgr85UmevxZ5bszIRTH1FjITqujojE/X9NBzXai+GzhlVkAqdbcJA3CD6QP0WPDdnK81r4NRNxfskf2cv+Pd7OkzTL96vx6gw9zkGYw8bNSTY+UnzvJSRarQEyHg+HiTpEjbYG9NUIHkKtS8u2rHdeHMpYtSUXuqa0tl7IKWtRkVyYyCDmo475sha4sg3NLk3APHukbBhch8DgCaA2jMUjvY37RCsO8DO3KUiQd6jYgA5/poZh506mzV2HMVIhlwr1uH9cC9Cd6qYNsH7mNj31DLrD4U0YFlWeDG8w/4zFymiK+2DFR4c31QB3HEAJkbfPZdORgx3RvYdwUIWOer6QSXbOaVyOnO+cgHHMNGjqlABo61vpQj2VWwO0dt3Ixj31+G4WasZVlJpQlmf5dO5OKU4VfdqJCB/HdrZF4ihIMA8llKrllGorUuheoUFVRHMkRzU0/6UkmKZQP/sWdqwx1/D5zh7wyuJ11V6v8fgksGFS1HjWAG0fyVHA7rgHLqyyoy5eHKRiCd5UwbD3sSeVo15Xv+uvb8rwknJVkkTCbOdC4Vd8s5S2D9oQy5Z72FxVSDIbCLYYdhx2ABVXgcz6K8oXpdbIrFph84wQAHIaF6Z6Q8CD+N83wWrtdjUZwjpyJHSOLHlYGdFwg3RuHIfM2oBVwgGo+SPGfNuXeXiczFdTAY5yynWnV1oNO5PHxnMqCO7vLjvN4lc3ytwvC7zL+Hc1TJaPxouqXyOCCRjimQqiDHnVEhacRw6AK6UVlENGbP0YoUY0XgjXa8Vuq/XVXxtHaOHG+NNOcs5PN5Fp1cMUKyJpKZJQS7A3sg+/AKEj7g4QnBghmFgllE84Aa6emqOeYho/W+CK69J+kg5jMEKOTalCMTJJitjHBAmU21CKhz7VzC8aYpvu31FWiAvwyMVOHY9w2iK9wH3Io3w++yoBgtYTeaWo9kx8WR/asy4xPheWvLcSVpoFXY8NsWznl73TveiOKD1nFphE+R9808qGA+KL5b4iOKXKeg2KsH5ZQMmXU5Yb/bGNgCpLA3tiigpzisZkdkQs0zuEXZbqq6QxOZQXvMZMD7ozJB/1rR+WxrEeceS9tU+Ug5iNB3fhZl+ANzpMbch+VI+IeZXkZgJ+9WOdssqYvknjkFpjg5q4ezL6r7O+xh4CCH5FYLxHRAE/VIe1K2QOrUv4HXe+2kEDHVHDyNoQsA5dCxppJNsDIjbERlRWw4LDosmVSU/jd3Lu6EA0kw85RfqIckmcsD5ZxKMPQYuVHBRy2UaY4wF63o6ed8zXHWgclyq5oPIA/08ZaD26Ki8VPKL3LaYAUN5ve/m8k8YNGne+aWBBJJpY5iP2VGUM5JMsMEDic1nWwee0Iq8/E9rtLDacZ04hPyqwwzSDF+cHYV2aVjytCF19CUhixK+zAm/ITcywIg9h0B4Omy4grgNQKvgobyTW941RoWEVwaHV3LhmFC/XyeHqzljADuAxjGNbQPy3qSY0dgC6d4qaT2XtK83RytKTwMGoK1dyyt4RUET8hGdc+hcQCW1pmhCDB8HPUikALAACqz2Mhn5jWfSFNxhY7qGoGP1gX96QJZGkYM7PcNb43qK+YDvixolyCfIDFSD6CtKyI41qOJAtI4NsQpiVTLEiHQJFcUMiDu8H2Hjx1dBJeVEk5vloaP+orXIlhxFPPNPcM8Hs/GMTVawXEwZBEeVG+fO44RpTQFrCBLPseDjp1jULQxAxV6pJkNiICc/8p6uLfGKCZkkqaOiz7249z182zmz8/vp688+ggZcqSoclg6qblpZqBTB6PTsDzvw8FOF6lROre8hojJP3nYnyfHN51POks5jnZcxClQPfZ3zKARflg5eccW/Gqv99pJKQALErmHG5ZG2u0TBB/2Ba+146mxR6Qae+tmqUU2ydKYNTQg2OMSopyjE5EOKxc3AMiJxFBuqDqxLVLtO5uG6yHEqXaU30eIRw4aPbLzPMCFVY41z7fOlVEKN7pCZ1c8tGGOJk+46VgQRdQg/mABBHw6j8kyzI1akChnciMjIDlRzx0FV8yISHCigJdvkWSPYQ4YOlScAtpoyGaTZ2VAJ5adVK1R0khFIHIce2QLAO9L0unlAPckN9SM8Fjph+cobxVHd7q0Fo5VBU/u+EJr+FAVr4KTay85LG9N1ick4E1xi4zQB2cucfz4wMvOSH3fB/bdsO3sceJU2QZvwJ7rS5rA87xaU8Q+MqMTtPvA2hqehGy9tXesrafcEYWXXAjzhXY6Xg1S5BNlGEa2K9VYGg2SHuaya4OE4FXvCXkCQxfc+4KXbWDfN4QAt31QvSOdG6TD9oFFGvbtxpqMHxOO2SPlZN4Gs6W1dS6WhAY1qlsrsgEX+CACH4ngo77gqoKLCOn9btxHGaDBDGY7JhQfcUDczEFPtPtAbwvW1XAJxxCBycAQIjAODjolOsw5btI068bIaLFCH76HiUNBgNULWU7mlDFUwfPkqBwyUYo0JCeDL9PQ017wv2MGfuwTE2BKeeVqh+AIDqP2ZwbmOE5hOqR0g/y6rEPF2bZFHLT4+eGT/WOqiXiwNRMVfIQokfuvrg3cx9/I6712UmFGmqgImlOj7HVTfNA7PmgLXkPRQ9kzBTaWThmTYFOlZZYCB6QcF5tC4IN4Nw3eidVUUYBy00bCHy0XT0SkfU8Jm2Q4MRPIc8/PVs2ioo2JThezFumoUHUKAJr1noT6ABASrE0pOGALySgwIRVPJ4Y4KPFHyld7XI5rzCyk1DMKT6/Ikp8hxFLOurKKGSid7tnJhdBRzaiYfiyTNlSTYB1/RpStNkRg4vvne1U/hyBaTFgW2TsW00EDRcNdRHCB4kkUH2jDF/qCD4TZxave0fvR16QiGAjchuEFwPM+OGp8Nw7jM8PmTsaYBfZ9YOwJD4sAfZmwDQcr+lSA17yvtg+IcWihhmAVZH1mwdPCP9WgTqEENpZa9ZQJ2YcKUrUHBCqsfzUALdeywmHpzAomXZrizdJxWy4YrxtW7Xi2gbc+MLRhd2AgMGIHzBAwtBA6Rm3Ytzsh1ayN6AkC6klScXcsTQDjcMZVBE99wau+4KOl4cOueFoWtAAuqoAbtDW4UZrLEhWQ1gi3Dp8j6t0B7YS6zI+F0VVx6R2bBx1U0LkUPoHThF0aUBY9H4gFlT9kDcYzYILb0fgqDKwkeyHjRIyo15zuXE6gtsmEFelkZtN5/Tn1J07CWB7mUKXBw16T3NRFxDgjLWWTzt9dr1lDqn8fJiKPmcd959zrhB5Qq/zZI4Udj//+VV7vtZOyfUDbDhFKwbxSwUfLim9dF0IF2nEBeyvUQVFDB5BTRQHaKbhMxyUWwHBgRE30Stw1J2rSvrNwn8eoYiny3SHZXyIZd+TGrA1QxIam/YCxjDCGVoR1jr5m05vMIPhc1ykiQy2juUhFE9suIx9oUuyuSTuaDqAgGcmNGOB1RhWAU4IoEsfOXCnHBchp0XNg2wQoteSCMJHPia+LEpIK/t3jaESumxp+ZH11fVHeLI5MsH42Bz2eNokoi+Iz6gazlUWAiyjeQPA6ndRHfcFrcHzLpSmla4KwnkXgFoGPx4ZPfODj+4ZPXu54NsezDewiMAi0dfa4LQ3RGyJSOSPVMCxsQqQ158qd48Wr6VUzezZVhDTsAHZz3DDQh7EXsHesCW2XwKsCObuHqt3NOVtqyf4hda7ZIWRg0lH6rM9clxXf8uoNFhv4YFlxc+M4CxW8WOAl6zq2NNz3gRGCYQ64AfvAZk7oEcVuJht2WLL24Ggj7y+AD5cVHywr3iwrPlg6nhrraEVmGfuJYDSzfeYxNvvqavnneHc51E8agB6CDsUqDbtQq9NA9qIjz1c8gz0BxAA/2hgeiv/ceMy6ksGJSlgq2zvemI7m1LN2chTT8MvZgsz4rAxM6kfWYY+MBMKG7WJWximYnWPro8gOeOc8H+H++jOdhwAThnjIgo7z1WCfGea+qn1Zbi2mg/o8eP8beb3XTip2g7QdixKu+LZ+wbetF3zrZcWHCZOsUKhnFOJAZOHXnBRbRxogD9gAxk582SyAU78SjR3hualUncye2esAPGYh+cmasqsZmk0DLDgaXwE8dMB9biR0WkTZSxGltBBcyKVCURGVO2E1gPtEM8NCRvEhivBxLPp0HoyScgOWpE1IXgt/XwPY6rxKPbkcZ0WyEpXjHPflWKCnSC7wMNzw/L66Z+GRBXcer2DLSjn1BJdIQoCS15VVNp57AKtwdMUrCD4UwQd9wQeieILgCsGlYbLvAsCWo8affeCrtuMr24ZPxsCn+45NFJsAQ+mMWk/BLZeZNVpm0nSinRF3nWAAURVtFSgov9SaYIfCQKLD8xho++DgwN6wuuOClYzExg2tytE1Gsw2LNh87MMRQpixBRC9hG8JXfV0Wg2KV6pY2oI3vcEA3N1wj8AGxbPteLvvuLnh+X7HbQNe7I77tuHaGvYA0vTDlc9UqtEeAYVDd8ObteOVKr51veCDZcGTNrxeFlx70eAD+75RVipIOHEPWMaPdIA5kj4zElXFCBAKBkkVms+7C8eU7B5YW8o+mWM3rlctQkihGyiynCfBAKdsJVVjpDIcTDiw1lvBFNPB1Xp+x2nJaX8fmNwpsypbMANTPEJ5cdq3p82TSTUD5RnYVkD3Wafxme8tp1bf85Bsyfzf7PA7ruO0tysAfszTvrnXe+2kxHYsvuCVNHyogm9dV3zbesFHveOVsOA6azYQmCcbKUjwtKA47G6BzQKbA5sLNmcyZZrq6pkROYKMIG3UU4OmUGwacWgWtRPUqobWOB4WUL1Ewfk1gkkDPaKYNOaVncwrLsppnPyZzMAq9xBwggoYjB/pf9V/gFNK7zjO7wRl0gfLjLpEBNrbpORWDaDmGx3U9Eg8/3BILNgToqyfzecoR6+TVD2uMqSJLeDoq7CCEpmB1g0qB/xABsk2AvXc2MHeGU54Ba4OvOkdHzbFR+uKJwCXAJaExyDAbobNHJ9sG37l5Y6P9w2fhOEr+4ZNGnZpcG3MlHpmx61z7VigtB2r5aAIJb3pzE7rfH0Y3IERhkVbGmfCqXPYYXAAXzdDD46J763huiguTbEGxZIXEfTeoL6nzh8gTkZdhGPfDaGVwTfsbhAAa1/YiAwSIVQb3qiyViuCDR33uODT+x2fCvCsga03bGMB68OezcBHYGfDsCDQFYAZ1mXB695xQeALl5XOSRsuSnkykpi4nvagUoM7a2zDmUHtI1mkuS5a61QqB++T5/r2ANUpnMNMV224tA4LQpcIDkMtpyKIGZiUAxDJpuhToBgpgOwQyqll5gtEynVFOq8jm9T8QQUu570wHRiOwK+yIGmHbujcqxEPtedKkxicHm0i8s53zL4skYf/nvfjvHQc1//ue2erVr7xvKfj8azeCdzzPVNo9ld/vddO6iKsQX20LviWy4pvu674aF3w1IAlSpsskijAOtFuZFbdzXH3wN0MdwPeDseLGW7uc4z0iMhGP2ZOTSS73AmHiYDCtNPoj9yUKVp6rHIceyBgWfB3GbmucpOdtPhItuXHy/hPuqocKucZBnNtG6YBPFNKJ/MvX02ODVEkCCSNVHqjTD9wsObATcKNm6ZHMNWx6385l8myg+ZgHAWScoyvvSjNDK21x3lUOGBKBGYGNyNHZ90l8hpKdLccqIhAnRu3ZIjgFITtzoF5rzKTugqHBD61jkUFvSlEqHBwywzmK/cbfvl+x1fvG57heEbAe4e0BdpWOhWhph6gyF5jGlyrK0pquEjWO8sYgQzOEDLLIBhjoJTX3emQmwC9LTT84bgPh9qOJoaXHXi9Lrio4nVP5fQwqHSIOJpwoB3h5UBp+4QI9n0n2cID2+2Oy+UCdCG5JJLMkj1RIwLRGj66POFFFPcLuWRmlAIz0LGbO0QbRiqItwDJEm5TX3MVYBGhYrtS2b1V9gGfckIWOdVAuAansfYjQ2HzMAlQZyOvZpBwlK6nICdvZ+jpvqOGS3r29xGulKMnKv89A7jIhnStmm+ZdDojecdoc+sf7RgT6aj3REHeJ2Oe9x0tHZr4Z5h9ZVOK2FHfq9CH738AaaAHMfmd19kZTeeWjvAz2VABGKffHN+ZOVyu7XOo/c2+3msn9STAF9YF33a94NteXfHRwpHRFyVOWw8eykm7Di7C3Q17SqVsZnRYxmL4cEOxTlQEix6Ya/UoibMQrec0JzMYy4VjUrURnZJNkXR5qzpABM5I4WQJgnTv6j+YjmLSuRVVka7aE5sKi91Tm7gyMmREmcfK7K1gQ15XYvFOcoJUUxmAwwsLasyDptOo7OtwtjpZdCVFVdDluRAcOArIATzUnA61DUIHBVsWzDFFOaurP9/TsgGWNF1kFEsHtcjR2NsgWCPYDyWCV63h2jmV+dIbFuHt9aC+24vt+HS/4+Ntw8f7jrdu2FTgrUFaQ+sreltBs0cZrhwgwyJ3HCNj9NQrYs5ApDV9uD9NFE060FveI4WZzZHhFSq4B2JwWq2qYjMql4zWGXQsndBoOJ7aQhIRHAsAk51tF/kZR2C3DSU86wi0oAArCD5yfLoIlozGFwCLKHwp+HtBBKG4Lee8SamlDJs6jJqrHD7AnjCqxnN/4IQCsM7WAMgYMDvYbrVOPKhkIbmemgpCGkrMgqoW6dwFGGZU45AU4AUdp4/B2haCWUvTvJZ0P0Vzbwc9PHO2uUcO1IEZ1sxemGYc+6Vep8zp+FE5rcOR1f4qaFZO75XAbC+ZsKNI1o0P+xRzG3M9xNcYOviQKcnJ/byT9dVBuf8fPyvn9x9vfcjovpnXe+2kvqV3fPHpim9ZF3zLuuK1KtbEloEcAQ9GGeZchAPOuTgVkc2epYHADlGg9KuXkxSLe5EtuBBJW6fKACI480iyN0ZBGMzBFN31aCyMOPq6LHJMNOswnk3J5HZkxIuYtZ8oBkNuhlocjoKQiFt7eGZ6koadm6qgMMveFQGyVgBAGpOpvHfuHHsSQDqdjFbTcZ7xbumkqs9+FSlnS7qtZiZ7BFmJsae+2cHMOGjskyWtNWrB5o4otfCKcAuC8Yrpsmm3st+GA8/vvUHM55j1VYtizqK+JKQyBnvnXsbA87bjk23Ds2WGLTjgPZGZhbobgByzUZAK2IytaSDKyddQOSoTJCxsabRKKDfvY2uK3hpqxdoYzFiF6AAiODQxHKoOgaXY6oA3ResdwznSooGZpEiHOIkCw0DJJPPZfGsCqG3orWFXRRfF0hoaBL01aCSzMIOxyTJNQ+V94fUan61rgzRmWyp0LsMw2ZdFY97duG8zRvLcmRMNqdpyBmOSwaGgSqdJ3+8t9wR/djcGDWMYVhBq8nDsEHQj+7UguEhpJEj2JwEzyzGntiHXW2RPFdvsDzjbJySeOBihvjiM9ee9zhlUOZ5JaGiC2nEnv8hjy7G+i+ggFZnm8ervdJSfl/k8nkc5KJqaAxPxZMSeTuO0VvGZutrpiz6TS/2aIE588c1rfOfr1/ho6XjdMgJGTFFTLmTLxW2YAwbD0yB41igcXQ2XxsymBaebQhRo5SAysa1ZNTQ/Jxip6k806p76WIQb+WczSxgEGCKz879YeIbIBs+ASRbagXfgMy6+yBV0Jl5U7YfadTKdxQFH1pysg6VUzobXpkDobBCuSH+2esx7mvWavL459iNlkB5YQ8gIsIgUDxGWJizXZsYVQnkqcxybMUCMX2T6Zwdhl8hxEcd3MsNy0Fn2lkoGYKbpboANeGtAasKpCi5Lw9KUNZjMRN1T1NQddzPsHhhW60ASU1fsFthtp6yPtsNJJQmkmphFySKtY1f2WISXXgMDyxjnLTAbHGugAsv/w0SGBRJ1DxS3MNg2sCN4b5aGqwA9gFfrBdo74U4w4Ghu7NvbHL0v7LdDQHqHOMdfDHNYBncSwHW9YFEa8N4IqndlYICsF8GMAVBCUdwHxvliAPddsPbkGYxZBAcSgkoXVRcyd453n9kxeL3Jhqzm9iY6M7MavqgO7mFPiKt3UvsRE24Lu+Bl23k7HWRXNk0JphNTVao+zf+rNUnSAIkqoXQaMXzuuXJNB3mifveOcc/XHC8Sp73lj86j9haxi9xWZ2bwfEf+4JSB1j18tyZ2PodZC8MB8SMwiVEPdd93fI3mPs7IFv+9r/faSX3761f4wrrgw97Bki2b6kapK6R4q7lNKaAa6w7nDW8aWAWQrlhDMTQFJ5Wjx7Wxl+lQrWC/TEvYpkVptRU2PXMCysOUkwKbPXc3eADDA8OFbPeUq9nNKOoZSGosjfOezivSKZkPbi7V2SmvwnpOgFExtxLBLRTFNgCSJ+Jg7CAX+hTZPDYeULaQtThjupRwVNFcgWhpKDIbnLWyPIpLTEMEOTZb9Q6p8u81wr2elVb0GMHriIQs29GNn270SDBzWyGNiWYkeGZVam+A6gwAIjPfUpNJ658PMoMVod6ZaBarO+naJoy0PetJmnGqNvakBTw/T3WJisIZPDwSWaYTDt6ziko9AiqM1MMris91PZXlGUCV0xtmeB474APNDGgdT9pwWRY6bRF0DchwRDYqSwhazxlgqujBcRj7GITB3QAhBLoYnSodVptQniQMq2CvlErjMXzAwb1XKhQQqqE7dM6y2sWxIdVfUrIK0CRCgI4+ZBrK6g1rIElGhP1m6lRg76oMBntABoBWARqdzNUbbFmYfQ/DZoMTuJvw2Sv33ANjDTU9OlL3TyrxqgdJaB44VGPmTqutUQ6iDH081oFOgQrSYaAy9zzIIXUWM7uZnryytpOPqOyzlOjPrzPJ6XBAx9d9rVeRJyYEOyG/w4Y8XPe86NO/v87rvXZS37J2vF4aepMkMCRGnRGAOSGbEmJlg6FCFehCE76AxW6PINNOSfdN+fTpnNjICVT6zr4r1hjaITBDI5gW2wM5cl5JwmjghrSqWyl2o5PajYrO9+EwlancTG1Bx+6ctTPCsQeYaTng2XcjYM0ghEaVs2t0OoYyxg6gX5bHyAq5ttOIc1oRzS0VGUDBznT6V23MbhojJkZyPnF7K1ikDhzVP5FbPGWAjpEdzGotI9x23lhH/ghjZEEpoaLFxDF2Ylr4bBoFBHvWFw2afW2CpTVs4TDleHQLh4fCBJPNSaiwcRm0htY6mjNzGBHYI7NfpfTMZLHls3AU+xEIOXpnCnINLXjUp4OlhTraB1i/ESDrTbAxVeDdywoJtEtqElKU2ExhngHPZoAB0hwdhDfZCiHoIVikYVkb1k5q9n0M7BGwQQknZjd0hmPscDPcth0iwOWyYt3vuOqKVTmeQ4E5gXgkkaIQBkvG3zBmT9oaxknfL8BAzhpVUfbZp5hSSMHjOALowgZkc8YRFvBgU20LRTfFXSl31BrbHlWBBZLZacBFsKtg6R27A6YBD7adDADaFFEiz871PVVq0gjzaefqDDINFYDnKHoNhVs2JGujGryfHVc5k8pUgMQJctxHvq/uIx4dikbZpPrvidqeWWyhC2Qx5pe8A9F9PvR2zpKOvK2cV5GaBAeAWEzLgsARcbRYlaOtdz8U5L/26712UqsKmmb5Uo7RGdQoy8gOAoRms256e21oQkNfEkSE2zANDVp141JJQkPm8eZxgmoFLUUzy0jycSaWnQaBGyDhu8YIE9LmkDtzx7CVBhDCUQRG+GOD424D2xjY3LBHYEunaiJTSXn4AKfMOmGsVrJRABWrmSh4+GnYXsxzL9quBqGsMv7lhArz7yWVIoqhMTOXWnyaTaWeAqWem4Q+UKbDr04nRoXMvgzA7MwMOY4rOfzRPfURbX6vZMDBpudTPwkCYjVLLDOVAOnbCrTGZ8f7YAAym4Ln+4AWgTWACwS3pDDXFF3WSTrQ+rEJ0wi208bXvP9RhijXLwcWJsTHNBDls5EGb2ZZUQV8/luFUCcbPJkdSyuqdcKcmY3fw7GJ4BaOT8aOCIUvC556R3MiAK4k9KB36hgKMypzZf0NhLK2faBJ0NGbV5c87uMOFSXzUAVj34/17qzH7u6zf87BdWbB7LI1Oh1jQ2Guv2NNhmfGqwAsG52VsLlnH2Tk+hnhEANCe6INdBitUUasB5JpyOBlB5Xh2ZJSz4/PYe4ZVglQpVHC7YfwcuDU9S4xm2/nsxLWrVr+nWLYRyYUfoLR5MiqUEGyHtnjeY7dg295CDojyVF+/LvWaC6yd0kSn0uJP7/kXDo4mHvHIR8zu3MVSuQI/ubPfi1Q0FsWiT2QWnmZOcxem4AYSQSa1F9qqKX4aBp4qnXxRnsaUjv1OA3jXKIGoWpFyMTCw5OgEY6mmmkuuFZLD2tGPAmfKeVcACCUm2OADic0I/qE33ZfsEvg7o5tDNzHIE1+36fDMieseBPCiqpBiCkL5R7E9WlgBQPFQNIjAssshRRWBlvaWtZoMqOxHNaXsGn1bVTdrqJhOsKKgnNcRK5kqnPU0L98TwDwYyJvFc5rBhZQk4c5gXYaCeRIDEVmvLy+SIhMstajAKPIco61WUQQSQ6xMBgUJoSVCOECHUH9uAjItiPuO7PGJpDe8rky69Q0iOFORQnwRGsUeca02fydEXhCxJVx8lGQKl2DHC2ilHgwvVzh/tJmEEJnWwKsNJbSBHczvIVCfcB3IPSSwZxjyfthDlhXasRp1Rq5vpt03semkItyVlvvvMelKt8bHIrN6exNgW049kFtwz0MmwVhw/TGrbJsoawZKdSC7qkBmGNiCu5q2eftYtAhcGl4UTpYGbz2vOsYDuhuSTjwbOjPfsTsAewQrKG4ykHsQNbKBhfFdDxiwS0iJzp2OikV7i9eTmYWGVGcnU8FZNNMzzVBu4P53mzKT8iwgpfHOtDnZD7l2PJVWnkP75Vpoeaxzq+vR2aImbXxRVLXqdZcTrQyqcqyTg5qiud+DYbhu6/32knRtBddlZFKzmQjbANGLZ54dgaoCfFUospmSU8obCTExumo/J6KXHoI1OisqIEGdFF0IcAjWViu5l8oDQntWGSDKqDRgGIeAkBQ2LN1zLk2ZIAHLNhvY6rYVLA1xX0Y9s5Idkuo8D4GPpVU2A5miaTcp46aOxWj+XXw0GPWVjLM5kgO0OFLU8Sopkg6vB03/OIX/zeYDHhmSe8utWmOk9UOpJOTSNgLSWfmbxCkClMS6R1NwJR1Kic3G6TTyWjSzqWcVO2X6Rj5hXKK+JqyN+eqQpWD3vBKSZZZQIo11fM1h+UZPr3f8dUPXvDJNnCLwNYEoyuiMZPybPJ2SSq5V3sAZiNz3Yup3SaVIdXixDRsZ/kdA+uPvKzIUkmlh3iMwDMDk5OzgxuWoATRRRUfrJQgerWuNABpGAPUGiRq5LBBuK4UVSYNWyuAqP9yL1k22kIUuxvuu+OebR0jIpl7Rw+TCFX857y3DOZWYRuEVrQUzJyZ/QNhhu4CH4EbDJsbdDcSHBqPtTKhTIUYyR40OvshHJpoAGwHvvj/+l+gVU+MxpEewWczQjAyoCP6Up0ikkoQCe+LslWlnoWx5WRmNwm3TQeX970WhYgyPcygB/Wsjy3yuMfecVRn+veRNQmhUH/MjorL+C5h4htl233mfdxwRwY1f39yyhlgPbD/vsHve6+dVO3sooJ60OFYkI7sIBbtkX02AAuhXirUzEBGeNZ22N1uQtVhm705+WAs0ENT2LP+pEFDwVdIdlcaKwnWtgAISI9vnmMK4ISctHM9iQBhUMnoDUF5G7BmtkIo8pnFbQfhjfu+4+3tjlcAnsfAs+942XfcA7OxOFFRHlWqvM90vcY3FNMQSCa6jSlT5MGa2M99z/8V/8/v/dtc+f/z9T9f7/lLXPB/+mf/Z/wv/+L3o42Opnfc9h0yHGPswKi6TGbeALMRPaAvwtmWkCMDQhJ2MMsOiKon5feeHMOs7STUM+uWMwNj0HsmNRR0/GjmT87i4SIxM6pJi3/nfZ/pj3onwzp+R6hysk/reJ9xUMc5PfosOuaIQBSB5uu83nMnRYjBkJqwLrhbYE/22t0cmx1yNBzzwG54R+ToaRZQLTHTqk+xV8nnwx3OSI19Jsx8VuXQu645nj6x3oPJVrJJ6Zw0GwiT9ruEU/xTCBWS1k7B1UrxWT+j/mBTxaqdzlOqDwnYRfAKire74W0ILsOxOHCH48UcJhQCLTjKJZKm3if86aLs9SooFIAIHXjhy9EEX/3C/w6I49t+6Q+ijW9hr1C+Yv7PsRnnv6PcIn9Y5ebCtoFIRQA5RHbn+0/RZx32nc0wFdrzyGUYzt+H88YXQnodnKp7bVTL76pHbxVqYyNJODGz1wFS03nvJHX5YmbQhpnEPagMzLlWgcxQToYoMCG7ikBrvAoN3PHf49bGhJXrhyq5MNxRytk1/oPXSGHaS29zPEdFwwU38og+77sm5BqVcecFUcKJa2g4a6X3QZXxLVLCKCLrphSFBRLK9EO8VU4BYTVn94SRVYCuRWBiPxuTTrJ5PWFq9cAarDvdFfiVJ8EnYrB9R98d6zbwYXR82C9YHPj01Vfwc7/9X+HjX/8LuK6NJKBYckjkDneFGBt9ax23yYIVQBNWDU9IGbPV5WioP2XJhSq842xmjSmha0AeYsAD8sun4u8cG+VYEuJDPc/TS0/7Kpz3DufPyvz7+efnFyHymChBncu7ad7MloRoTalznK/9fA++3uu9dlIbFBKKuxluw3CzwN2B+/Ccgko5m5Ht5x5kGI2kpe/GbKqcFHH3NsdgFLziXk2srJySvssu/Krlcm2V2UgF5sSWpNEw1KgEdZ9zry4QLA1YNZ1fOEVCM7NqjVCkSjIYkz6qItP4iAr60rBAcYXglTZ8FI5nc9wRuJnjLVg72CKwBGnttu8JC2YNDMKZWKJoOf/J5TD4lb0BwLf84v+K/vKbZwaa5vZh0/Bm0CzpecGfnAfSKZ6hgckQQnL4ojTKytFlUyxYwwAIRfF3HKvBYrQf019nwMesW0AFgubAVRSvBTnUsM2m16V1zOZmsG6z3w13N2wSuIVjczqr3dkH56KQbLyVhCNlRtoBH2M6pVWUVOk44D1Fst24+CgXhQwghP0/1bODXJ9kSXLRBJywtAdiN/jYIWbzOl+1jicFPrxe8OH1SucsNLSS6suSlG7Rqh8CizasrWHsAy0Zbwz0Ap8OZvAvZng7Br5yu+EOwS0MN2PDLBpZtPsYsyfOzGBjAJZOKskGJTXWtAg2hgWCp85zWDKLUTjUHJvvEHcs5njlHeqCT151/LvvaEDb8fzJJ4hPX6AfP+PpreE3yiv8H54+wn/99v83fu63/ys4Ag2Opy7o6KlGQZe6YEAz8B0eGGHpTBTJP4erTq3B8MjaL+bzou0htvN5daUHll3ufZdi5JUze3RuD87kXcMoR8BYtddzsCfBgPxMwKhjfi0yxbvve3zP8cU0SynNNr0zDm1OOej2vya0+z7ZHc/3HZ/eN7zddrwdxs2x0zCbCJ7HzpuVfQysx8ScyAsh5DeL5Z6VKuED8Io+stjtTpp0z0r21I8LTFy/QsIQAI3OrzXFIhwXATOoB64QXIU9KtfWce0di3D4Wxc6QPFAk4HWc4ibe55nCYPSRagAl0WwLgs+kBUGwQ7B3T3vzY7bGHi7bXg7nGMfgk6LzYOOaD1ZSHTAHoYBRoYtIY+qS9w1sKtnoxRvkQCZOQIP4VUkHBrnaC3muwQHHBIq7IM6HbMK1MioU47fAFKOLJlGyUisjiWb3qk+oTl+Psksg0FBjwB8QMdOYmf2g02qcC6e5sGesSS4WBoCUv8X1jlAxibgDEok3W0A223L8Q6KVQENndci897kcMB0/ZpwtaZxdC8HjakfVz1gARb+ewjcBOGUZGrOmuoFgje94W1v+JXOcRlaIyoiUklC0UPwJIoFmXEKVSci52GZGbaxY3PHJ+548YGb7biF46UF2XLSsLdkdyoHk1pjZujZAzU0a03VHwFMLUa3kY6L9Pm3NtABrI3s3B6BqyMDAtalBwCI41M4XoJt9xoL9j7w8rTiPz9/BR//t1/B/fULrt/h8/t0bBBnILm0VPlYO56aYp3sWsPNjDbBHRGEviAkjHgGF1LB6cmYE1KnvqBlYDJlwcrKT6eQmUr2X+FznFMFOtP5SNVtj213zuLffb3buPt5v3uE9PJ38at/ll9bvz+y4887/jeWR73nTurLH7+FbJZ1GMeLB24QbCG4OyGsHciiZCY2opk9+Sy+Bzx7iEhaYHod01BIHQMAkm0ehasGkrFG3TZzUjZUKgvjEcIcXQjvxTCIOVTYqX/RhkszvF4C19YYzbdUswhgAdDNZltBGT4F5swdINCUOnAq1bcl2F3xhI7XDXiWwBManrQlLRm4RfC+mWH4yHtAuIrQZ6bmvOi54Mydgp5+kBKCbzkgKZUjEkwjDWAK1ZI2fqa+YjKoZsomdewkIuQcrCnsGYzoPTJaAwv4Wg26EbPAXbymEUAUQ1OYK2wqCM3+FARvcjiiRnnlCeqCFHdNRlbCaF4yVdUk3Elnl2BQUZnRXQweA007tibpgN/Z+LxCZpx5H+M0Eiac582R4o+fE+GkXt76hkDL+5Z0fA/qTmKgxY6WrE9+fWZOBkp2OZUpmOGnvJWl05ekj0OwK7B3hzey60IajxSA50wmPnPuEcKEWfv0dmjMWTonH3x2TrFYUg8d3QExg/qADsM6Ak+e4sHq8EWBtUN6x35puPXG3kdpiAGIAXtr+OoS+H/81/+EX/8bue66Bfo2UkGiEZLvDSsCmwoWrLgPxV02vEUQhbCcppBEJXhC5XkXRThi/pxBZPVq2hcmv7n+p/PxKWsGOY0h0SMLKedxVp/4mi85Nwgf6/gz9a3PeX3eeyoTq+/nz2TCuLUSPdd7IX/lVE+n8FlI8mu8vikn9WM/9mP48R//8Yeffc/3fA/+3b/7dwCA2+2GP/tn/yx+6qd+Cvf7HT/wAz+Av/k3/ya++MUvzvf//M//PH7kR34E/+yf/TO8efMGP/zDP4yf+ImfQO/fvL/8hXRSm7Au8+KBDYJdlYwcAfs+Mmrh2Pa8+brwAebiYQyKw73PG1gLIU6Q0kGx9kzRSy0BmeZy0CmtN5cUN7ClIShVcVHF3ho2YW3t5oGLOC5C5mBDYBXH4gUZpoOKEskkTRnZPxQGrK2jSYdEMEvQwALBujY8tQteOSfKvoRjjUA3jhB4tpyxlVCGN5CZkQodaMfCtH0D9jvEMdU3ij1V+ZEU7AagGItI41sb87j/GXGF4Lz1JGHYKQ8zN/WRbMxajfE97k5RV3DTz2dW36KH02IvkcFUUdVuuvfqL8vPZqaiSUl2OSJhZneVZWbGUxp91cMDipzCBmJQEcQFQFuOiFgw6x3zvs2MiTJbJUYrcWT7BwssqItXcDNYKyIDFhAJaOe4jchMUiOVxadcUY6jl8DQChwi6zSAOCal3qquIZn95P2LgmCNvVs1k6uYm1J7JAcZ1tqpAYKRWBWzZ4WYUNbIA2JknOIeWDbD6qBEkyp8EfhTgy4NhsDbfcPeFUOCTm53DHNsYZAW+IVf/i98fmNgPL8Aa87XikBvHdoUa2PGe2+CuwJrV9yGYQOHO+5sliOjWErnT7LXD9nacmQ/VbM8K1QUJIf67AOKIJ9hV1J1I9fWyfIH4mEt1HfO78njnf1D5M/eAT6O85rHKLj9/N7AAdgxbYs49kzM7J5f6HEgGg/n+nVe37Rn+G2/7bfhp3/6p48DnJzLn/kzfwb/8B/+Q/z9v//38dFHH+FP/ak/hT/6R/8o/vk//+cAWA/6w3/4D+M7v/M78S/+xb/AL/zCL+CP//E/jmVZ8Nf+2l/7Zk8Fv7QbVAdCG1wJbw2wiI3E2TMuTjeE08pIL4+K0Bkyl2moDVjPLwpiycZR1E0WdqaXYdDscvfU7PKIqR5dc5ekN37eyabbg9mLGDDEydgbjt404Q0O5+suWHtD04DEwNIEuw8sQWXrMQCoYASzrpaWfMn5V4sA1xZYh+NVb3gZAx/vezoZgdF2JmxCFmQPEDISNg/XwtIxYLZx2boeqhMVFIagBv4B2btVK/jYBpjq60eX6sTzkVlQlXgDNLoVv0U6BEaqekR+Uj6VEIhE4FQmnseqTFiEDnnK0nj1ix3ZNrL52ewgEwRwguAYVWs2vooq23zqOGFURPAd5iOle0hbh2SdCelwMhjSzOptNooDvmeRXmqr07DRntT3FJxUDekVGJAKIV7cV5yEYdtRKxD2gVFB/HCYrCOUgQvWmYISRCXJ5OnEIwLSWjonnqkH61GeChZFDS8VdJQKiAfhR1DloYg8qCbYHhAFxlXw4nntxm7H2J4Rd6IUJoE9RXF1bMDY4L7DxbFtd/jzWwBs/r3tN8QWaJ016dY6+tJTU5HTm61d8NpX3PcN995hwf7FWwjemuMlgHsAWwRiJJEmG5crEmnaIJ6kLIkp3gyUAY/5z3JcE6aodozU4BQ9ZzsHGhGV0Z2cgkDm+qYmaTJ5p6OI+XXn19nBBRO6dzxcMRjfYftmGaBsbjmsWofzd9/A65t2Ur13fOd3fudnfv7Vr34Vf+tv/S383b/7d/H7ft/vAwD87b/9t/Fbf+tvxc/+7M/i+7//+/GP//E/xr/9t/8WP/3TP40vfvGL+J2/83fir/yVv4I//+f/PH7sx34M67p+U+eySUfvC+nmkpg3kLRrTMdwLgieI40zzls1PElDPCe2ZwRYsNNxnHwzrckBR6lSjMhlGrFq6KvXTI1T1SHA8xzZHGv7wKocQtdEsIdgtIZVBWZFYafg6SqYi3lRsJnRqh5Cw6oQQAgDLg2AdsI8qlQtGAMwQ4jjeThupcZdjiKY/xXNHgCe/9uvID75CNIaliD01qZkj0HAYXmtd7iT6aWJ0z88i8ocQJZQb43nX3Bhvq8klGqmFHvgck02OgmZ7z2KubNQmw8wMv2S3DD1TI+aW5wst54yYd5nE0+dRj5J5OavcRdhA6111i2lQ5AK+nlPbRAq6pyZCViy20SQSNg8JzGyKilhmGvIqMtXWciEncFMif6e51VQUzEEJ2QtydbMR1zZasW4NWOsp3+eI1ZEETZwuh25mo/sqX7mKU4LIHvX/GARBhXBa64U3NABmueUHqLtZiYFLxbZQLZmwwXYMPKeKdBOxlKCJJDIYCGYtfnYKXxrjn3fyNoDg+e3z88QAK03tNawrCuFfZeO1jshxQb0teO6tKk4skXgHsCzBT7ZB57zz4YkZoH9iqj+xxMlvQKtCtx4a/x4nnxq09B7VOZSxI3DfqV3SjGDwzlNuOH0PdNZneLGMmW1qs+ZGLJ958zSO9vPYlnWQd4leHBtZz0eibZE/I8jTvz7f//v8Rt+w2/A9XrFl770JfzET/wEvuu7vgv/6l/9K+z7jt//+3//fO9v+S2/Bd/1Xd+Fn/mZn8H3f//342d+5mfw23/7b3+A/37gB34AP/IjP4J/82/+DX7X7/pdn/ud9/sd9/t9/vvjjz8GAHhTDO2IAEY2rjKKPaLfM+55xnMLPpqzYfLfpZ58fok/MnHmSIp3bvLEaCtSmPI+aRRO75k+QNgUyNlFlvWOzLgioR6lod89sLlDxaFhaHBce5uEh4hUt87F14K0dQVnDoVwjPdSzrRxuJs0hQyj2G7jiPSXYdiczEGymgaioD8A7e0N+skzdqUDLVQ6gg2kgRwwKVTvHtm8POnS6Wg8AhMzCLLIGD0TfpvZp2iqXKTjTYMbCOrVDTscWTqNcED7AeHUM2w5Ur02qzuHMtZEZUBT7YH6cmcFjZaitzUixMOnvItAYCmv1JqiIzDuG3zbeYkewJ1sPfVGByU2tc3ETw7cmQH21iCC1M0bcGMtEKdmYX73kfXTAEnSjAVhY0JKkX04M9IGJiw7YcakrnuMSVGv9bpAp9OShA2tHh7KqBJmpiOVY39pShhFJPROGLTgPn5JmzJfEUfGBw2uP3B+lIINypHOLmaEf9RbREl00dTFDDP4fUfcN4Q5siwGc8O27ejasp5JckPvHS0cLQxNG7MsOIk1uT7WIKP20snWfKWKTwF8Mgw3OLYMJhCAFIlEBJIMSTs1r5+dTv1sNsvPf9fz5vMsxiDXMoM4PwVhRbBhgMfP+uk+1dqZ9+wcQJ7/IO1Z1HsPmS+cjjOv9cH0xqxbz1+kDfpGXt+Uk/q+7/s+/J2/83fwPd/zPfiFX/gF/PiP/zh+7+/9vfjX//pf48tf/jLWdcUXvvCFh8988YtfxJe//GUAwJe//OUHB1W/r999rddP/MRPfKYWBgCbKHqOFjAie/NGW5D51rXNuxcZktRMlOqd4U+zGC0k9st8HzLbOR4qRypgPqhSE5gPOoKYc1Rfy/H5jL0TGsGUFvKshTjYd7XHmf3DJmN1RxuRESXHiL9aOsYCvOoL1vA5I2k2FjunnXIwHjLFz2I+gEohGwSLsGF4Mcdihmfn7CmJAdWABaEiAJCvfoL+Xz+GSMBbQmH7Dt93tNYYlYpApKXzT4fTCL8WhEVli+obohSMm80xCBnqM9N0Q+8dIYKNjBRmNRVogEPtztmaZa9abeoIZ00ma1WR0WG7dFS7gWSkOsaANjZaVyF4Qe2xmt7KAGlYoF9WPL15jUtTdHSMl2fc3z7znhTsHAC6olA4qptjrqFilqoQbru/3IF9g28bxr7z3kfdmyShREC1MVJP41zXJqokG2TkPdGFqsudMZ68Rh87Pv3KVzD+2y9n/SxfZmjrZTIey8R4Q5I1ODWgjJRqy3lSAe0dEjKn3voUlc11Xtekejg9qWnVkUFfZL2MO6Wl1FVUlliZYxpnZuIMjmzfWUPdBnBnRuU42Gs2PPskGfSULYlUbjE1LOgpfYbcMzkRIZmPvQle6YJXABZzvA3Hcxhe9oHdGdy6IFEA/kHB4OdngERgwEDlzASs13Rkj/F03rbKrOiYqoerni+HS/rhLE7BTh1vfptkNookCoGLmMl2zJ6pw9HyOyQqe4vcd1F3+3Su757857++KSf1h/7QH5p//x2/43fg+77v+/Dd3/3d+Ht/7+/h6enpmznUN/X6C3/hL+BHf/RH578//vhj/Kbf9JuwCRtvEVWryEgtF3aRJYAjaiS+fTCqjkJgTgyNcia8wedegkqRCyaqBVHReBmB+fOsNVShU1I4kh9gGTIQee7MegyYNY7aDns4WsoSSWTB2SnDdNsHntvAB6vhdQOurcF7n5uh5/VRf48wWlOZmmtIJ9uaoIVh7Q2rL1jcsYjhPgx3BO42MO535Bx0fPcHX8CH+68jnd4Ca+/QK8+tiWIMg4UDoVR5B/DJdsPz7QZo49wgTwXyyl9UsdkgMaTTubn57OkQd3hjJM9xG3Q8o0gVwSL4sq7ovWHbd0AYKWvWGWzs2X+maQD5YPc0ZmSKcexJy4wuhILAglOxHwlh9I5VSEHvHnjSDnu54e3YMO4b9vuGBsUIoK0L2nWFrAulhZae2ZNmZkGYbLvfsW87bB+wfaC5o50GEta9qqyHtdDM/k6K4jWnapIVcn2Oyv7yvrpTPZ2BxkAAaGPgoj3V7DPwc0Bu2wzomMVktddZP2qdEGchH2XU7/sO3wbasqI1xf1+R+s9jebJKIJZrVlOyK79poqaZk3Cy47YN6QVBfoCZHAUTVGzvLAsuHzwBhg7YhuQ+wbdBicBpwPmDLbDSanW95MA1fL63QcuSwebVDMj1aTmCzIQEyy9AZcVizgWIbLwYoa7jcykCP2h7iGQrEDMbCcgJ/mlDDgf05PjZ6gsJ21XMcASNiWH7Oh35DpAYbMJK/JvFWhXnWvCgCfUibmaJlnrCPLLzh5OsuxhQfE+T/xr9WB93uu/i4L+hS98Ab/5N/9m/If/8B/wB/7AH8C2bfjKV77ykE394i/+4qxhfed3fif+5b/8lw/H+MVf/MX5u6/1ulwuuFwun/m5Cft5yKYrx1Mir5XFyFx0ZwpnNSTOVPmd9527uquvJyptFnn4nZlNuBB4B9sFJgRJsCBSYTprNxX1BzC8REKzBhNUJbAI9kd5icQqwjmOewewjY1U5KUhFtaHsHRkXM4MTECRzBBIF4jwWJyfKlMdoQvQm6AvHQsEz2HoFtQlTBgHAH5dv+CLlyesLlh2RxcODYQ5bAzIlTTk59sdrsDLPvDSLvhKNEQoPn55i9tGpeyeRWj3gEHR1g7ZaWT2fUDSaEE6dtsBCM8xRx/weZbBa+j3Imp0qMbM5uABc66TpoLXr56wbVtuWGbIrdFBjTGg0mkYc6MGANNjDdCAG7R33LcBl2ds479AI/BhXpcbdfte7jf0ywU3N1w++oC9VgKEcpZSBLCPnQ2uOwkGpRFpERjFcEyjL61n9o2s0ymCMyBg4ZPQJMI62z4G1mVJBh2fYWvtqIEwVcRT73B37PcNAhIQKvsqo7ksC9wDYwzetwis64p93xJiAtYcXb9tG1Qb1usKeXWd+2pp6wNrk5qBNOg9BgRG7cgA3CzrcnSItu+IbYdvG4P11iHN0C8XBi8AfI41aegAbvcNug/IvkOGZb3vgCJvtxuWZcnPODq4Dji3q+UIFBrapTWsy8Jm53ASmRizcJZdA/zSIVgIuQvQd4PuO9SdYtIRqeavDwaD2XYxUmuczSMsSBV+f3AmQdplPvNHu4YoZl2x796B5/JfhQzNGmQ6KSRi0Ca2LCDpI2MIj3ePNG3fgyM6n1P9+QZe/11O6tNPP8V//I//ET/0Qz+E3/27fzeWZcE//af/FH/sj/0xAMDP/dzP4ed//ufxpS99CQDwpS99CX/1r/5V/NIv/RK+4zu+AwDwT/7JP8GHH36I7/3e7/2mv7+GDGr2pjC7yQeYdNYRY9aZ6gbVv486ExcYR2jbETFMaZOCRY7IB3jsgTgXqc9wU5Gd0wdMqI/HT0eHiqBYH7I4HKw5Zpo88ppEBEjmlIVjQBA7x3IbHFvwui8CrJF9VkoJoHCHD6BnTUZF0FGbjHWUFnFkbiMZs8FMpue6+lAU3xIcKa569Gu5CmRdqH5thtY77rc73n76FlsoYg+qWLtibQsMAtUODKOKhypgLGDf7xvW/pTPZMVuhg+ur7GPgc13rH1llJ7ZSqW3DY0Faiv5l8Dlek3Iy7BeVmz3G9oWaFvgg9evoLbj1XpBbx37vmG5XPHm6Qkc+x24rBfEGJCxpQJEOcesBX7Qc9gmZ42pB8YwXD94wn3fEG8U/emKT7cbhoC9PNvA/WVHX5Ys3F+wj4F9F/Ryugi8dYMvDcMM+zAMG+hrYM+5TiqKZVkQGIBwEKAqA51tyxHw+451JUQaKthtYN93uHnKHTGb6OmkPQKbeKpYpIM2x7ANIhssSTuiwp452WFjRxGBCEXS8Ys32MYBjBlvY+kddqfzCRH0nC68NoVtL1BRjN3QFz5jHwMtAHeD3TZm7LqQuHO5QFsHWiOzVRVNBZuxUTjMELcNcbsjto3kjzGYnQJwcwzboY0s1whF7JVdFlDLgHEblfkJommWEwIQZx9aBFVjIvDUGuvmJvCuME8lkhQMiCQczTpQGZbM6kh6wiRJmFkGFZy6QAhtWpOZ8TKmjmmrSH44HNacMYUDUp9ySsDRoD+dIv97VtoLJGM6JeHyjdP9FRJVMG5UkHP6ecT/AO2+P/fn/hz+yB/5I/ju7/5u/Of//J/xl/7SX0JrDT/4gz+Ijz76CH/iT/wJ/OiP/ii+9Vu/FR9++CH+9J/+0/jSl76E7//+7wcA/ME/+Afxvd/7vfihH/oh/PW//tfx5S9/GX/xL/5F/Mk/+Sc/N1P6ei8VFnWJnsVMW8/46Jkg8dBnA7zz7yNS+Ty5kPProch8cn7vFh4Z91VEcqgmFDQy0+w4JdwJATi4ViwNFaPYLNYz/2YhOkd171C89cA+DNdwbEaV7ysEqwAXb1ibwI2l7hCd40JIkY9JWxcVaEbZWBoaGtAbrg1YkrDw4bLio2UlnNPIwAojdDeGw91QytB7BDYFPrndYMKBcncNLNcnjGEYoZDeEaDiO4/Z0DNbWJcnRnLK7OXp9SsstqcgZ2B9esUMIptBe+uYQxiHobeG7X7Htu/oKhhvb6ChFJgFvvrVT/DhumJowJU6bWMMPO/PeHW9QEF9xUUber9A3LEuF+z3O+COy7JgjH2usaV3yHCsb15jd8fWFqxPT7htd3zL9RUioeAhBlsZ5W7KoEOaYrvf0fsys/TdAtCG+3bHp8/PkEXQescuO5a+INzRe0vFfce6LMxObUDfMAttvWPf7lQBt4G7b3Bd4CCkOWwj5VpYV1r6Qugu08iA4L5t0N6xbQO7Ew677TtuGrjtG+67UJVh2zHCoX3Jms6BTpCqLtjHxqb0pgwKlp6Z9EBAWQs0YcELCgzA4Rj3gFjD0haSaFoHooNd11RcVOkYYTPD9DEg2wbZdtZNbUBT3/IFmZnYwBhA6x1La7NWA68hm87a53BIB9QF0YJfa+mcZ93HUysRWJR6iSMElyawYKCEDGggC4qmWs3hRyknULL+mllyOSUSKgh/Fru4vEXwDafyRBnGtDUuBxT3aOhoKyuAP2lozsyngm9RtJyRp7kWaLiOLC2KgRj1MeE1lY1+JwP7Wq9vykn9p//0n/CDP/iD+OVf/mV8+7d/O37P7/k9+Nmf/Vl8+7d/OwDgb/yNvwFVxR/7Y3/soZm3Xq01/IN/8A/wIz/yI/jSl76E169f44d/+Ifxl//yX/5mTuM4nlYfTBbpE7YgKiEJtbDmE8kpr0hjYsFaFN7qTTkyHeARujtTR7UVdbqcXBbcUaSImEVtUaEmHnzCLwjqtNWJVEQzF46w8bjUGSon8/zd/BODhIEQvIAMwB2Sk0ZTLb0pIz3pbJEA52XVEGwoGzurRrOkz7wSFwOiQ7BiIAjpAXi1LnizLmitofUFNgb2fcBNYfstIaYdL9uGPTMABycLuyj60ikPA8x6EWOFgPaFBIils/t/5b9DgMty5X3UmFlMCZ8+tT6x+dvtztqVKEIXtItAM7PSxoh8N0dbLghz7NJhm2FdF+y7oSFwXYD97Qs0HF/4gL14Iwxvrle01tB7y36nHIkBipF6FgIGHLIqZCj22HF9tbLQrg0+nE2nThFWXV+TAamCfalMH1hfPyHMMTaDXFc8LxywuPuAvrpmn5GmA88+H2EvkvQLRIDN2CtnywWReoMvFmh9IRkjAmgLM6gxcF1WNCgu1BAjBbsvGH0FIHjBxqwx+PfnYbhrx00WvPiGl1DcxkDXFS6Kl21jvWi5Tt24u+9ce8EgatwNkozOPQxNV6xLm3U6iwWX3rDJW0BLB88h1sDmLTagC3KatguWZYWuK2zssNsdcduYjZhTM1MEL7m/zQcWIRF+HzuWdT325Ky3cM/GAAbIsCRDk/1xAKA53wulRQigq2KJwNIVa1nsMaZ9gDwa7apPRoCZkzmKAKJl8BIKJAEjg+JycFlSKEMmE5fjf/XkUIt3fHZXcbYxZZvy52f7JMBUKynHNqdQezpYL8d0zI87B/PfyOubclI/9VM/9av+/nq94id/8ifxkz/5k1/zPd/93d+Nf/SP/tE387Vf8yVIOm46DxUlK6weQMJvelYxEDlJ6gQiU9/TM52ZF4BZbyq1XzZO0rnM8eeRrDCpxkU6k+o3kSC7r3q2Sim5WlMKQ1Y96O1HYMKLmVFWYtSakWlrrLuEB242oOEYrhislsAai7qMfA4IAUQomHJXHwR4PyQVBjoouoreCLe5oWcm9dQXvF7X2byq2tAXxS029L7A7gMv+45tcJbQer1iv7O1OpRjw6GKYVy+qh0C5LBJJMNKsFwuCAHWqrE0UtH7khR1I8w5UfCUhLpcgYgLxAK2M6uTRXFdX2HfNsQY0KUnE1GmIPBNApFOMgK4j4FFFfH8gjfXC65d8WwBNMHalmzWdLT1grUJtvudz18VNxu4tiu6LtCuZEomu7ILA6slaygltTTGwDUNpBkNU186LOs3/bpiWRYMt1lnWPoCBHDfN7LjbGBZj/rPBx9esO87+uWC57dvcb1csCQl/JZ7BEJlg34lotGkZY12cF2GZWARuFw6rq3n+hNcQ7CPwH3f8bxuGADe3u+UJfPA2he4ALvz+oY5LtowzCGt4T4GmixY1wuGDaguCM8pvwK4Osw2NBeseoVoIGIgwuAW6LpAKJpIeNaZXejaAZCkYTsp6NgNiwGv0NDR8HHahAjCsz0Dxbp3iqyLeYBFhZob5aTFwzFU0TQHbOrB9hUEugquS6eGpzcsC4eobrtit8DwY5+7I/uc6HPdOTJo2I5ACts2LRnRQ50EnHNVKiSPr9zTckKaFICfGceR5YYjPK9Av2zmPFo6J6S9lFT2KRJPOWi+icFxZM078toAYKrYfAOv91q7T0AdskpIPDBZQJ7No7EfBcaC/ugwTv0LcvQQ1IM5iof5tszSEAdzygDoJGAcMN+EAKOkYPLZhM8MVyWdmCDZfDu6cFOdX1qOKvWQXKe7pc8S9jEJyHSkllsqmwfrCC4KwCGD6hyc4capsoxsA6ZAmHFTOlUNliaANE4IB0p5DQCnmi5JwQ1h1rZHRZLOmpkItjB44+RTyCXzTFJaW1shwyHS0JTCpUtmmtoba3BLPzr267kLwJMKLMs67zcZWow220pKuW3bHInBxx24PD1lP0w2IfclKd+sSUpLB5pOwEYADRjPG968WglhPm94fbmgK7CuC9xIAglVXK9XNmKbADaSUGLZtueQkBQjJf2eJJ5IqJLrqWlLh8Oaka4X2DCs2We2oJh8mpOSPaflkgHZlM/OhuHSFjzfqeb9wXKFu6EtzM77pUF7m7BywVZr64AZrssF2vs0StdLYzYCAK1jFcVmgU0HntaGV5eOEcClCV7MsJlhaQ33fYe4QeFYmiYjM6PwdYF5YFFB04VQXULQEYHdN6yNdbN1WeE+eA9DsNsOtx3NgLhtuCwdsQ3YAuja0EwQLzs3gzuuLrjugtepiEI7wn1rbmh+MGxFyI7VVImgS8og0pVrnMYCIo6ltyRSVLDMhnoyDRuWLrg69/PoHfswbAHWGcegyHPeaAOojJGZOokWyZJTyToxbT3rV3klkbp5OGTeSk4JyPJZtJwp55hc1Rml+2RFS+6XsosVfD/sQyD7LyPvZLZ3SLUH0FC9W3+XMlDfwOu9dlIwz+Y1ADjVhCq6ME8I68SMyWfxLtEBwINTKgrqQy0rbV1lVgFAs2FRpjYdJiEhHy9KKy4SC+aPdKbaR4Eyl0XhuJDTueWJZdRrBvgIBEYqTFT6yAzAghp80hVqAy2cDaQtchAjWWcLT3iqRdjsITvS+2p6bafz+d9+4/8d/59v/bl5r91J7Bjm2NzxfN/wvO94GTshidawmWWfTDZNtp6TWpNynxulWGwFYyDrJJqUc1KrJZ8rMhI8ot9JZPGj2NxSi61kjTzioKDjCChaZdnVRe8cI1Hk264c57H0TqOa2ZAk60s8sC49C6We9UebpYHytzX/qloUSoetKQkTonqIj55eM7oNNvjW8xmDGf+ydP4u1z57vUgkKcM7xk5CxQwAhNDniWBUdO9lXej88zx65zNrrWHb7tD8d0Rg28eUkbptG+77wG6GbfDnwz2zA4614DPWpFEL9tZRw0tL2mmiG7Uv6h44VVUsiVJiDjFDtA4Lx66B/uqCdlmBTz+G3jbovhM6DeAlBPYdz7mlSBjhNOKBthC+RlC1XBuVbWhyct0wIZ52QyRgxjWqeuqPzHaYLmSjUuFf4J2TGO4Jhe+jJWqTOzlyhpk27KNh2E6Gn9CBUdyWxAkvNC+daEPa/xNzk1kP/6gE2IZz2B4RpMBKjUepgPscrGdbjGTxIf9bje60ZVOADpyLVihUzGZgPu3HuXG/2uu9dlLhnrMSMpJI/bDDWQFVQ6qBhgdzj++Z2oMFe9Sx46Bi9k4NLzlBdpWxHc4vs5lT1lavyfQTycbVdE/kpqOG5R2fOWdLSZxIjJrxb0KAaZgKYNYcnc1BhKS6MhJztIhcFAIk/VyFskyigO8xZ+RIVC0sMrYTsrRU8eu2L0JC8H/7zX/v/+fP83++/ufrv/f1/Kv8zkHSzss7P7z+t9d0vOnEfQxI71ObMNwQwdYWwvqaNRcDytgDsJ11U5NINRequIcL0DsEAyJsuqazIHFkAefZWYoElF6iB2C9Y/iCPZnHFqW+bqkEk44q38+5cICB7wsvOybTXomQwCKSuM0UXMb8GQNlm0xpBnEJCWW5gcFrZp5A1jkysEtPG+BB2ahddawM4lUIt36d13vtpDixOY2pUrLbUfUdnammwKdzmnUkyIxCjzTlYPYVVf14Pwqjm0VDyaZYHqfN4uU5CwNwSPmcHGRlCHSkJHMUlCuVuuf5M/s64bmoomopJERmGEUlT5jupFzRRTinCmxObBHoSpJEyTKpJ0EjyKLzyFpYRoFNgP/1P/wJ/PqX34hoMTMSA899T3jnbo632x0v+8A9m6uHRMKRPrOpMRJnh04cgTi+JnTLWsiEmzx1FJ0RWmt63Ccwc55K0fXz1jFmW8FB+Z11mFSRb2RTZG0q0lEf84Eqs6rhk+GGdVnYOxKOy9I5GLB3PC0LFqH2XYsgXd0GqdqZuVd2UrI3lf2dpbZ6kiBs0AxUJsl1GrOW4hnxNmlUTgjKOVnObAJkCr8Ch3hy3YMjSffsB7LM7Ki+0JuSRRpxGkQI2DD0XoougTEIObI/aqeByyh/JNR930j0MXdENn2PysLy51v2z3m2mJSBQwFQuU5LWgkCdG3wfWdNKaj2LgIOQ0wYLGyk1Fj2/wQQe+A7/un/EboeDOBwz54pfbQRkvC+JIQHyaixFBsC0oogEDmjK1vyzRFmEPHMR2Q2+WtE1t4097iQ1AeiIR4NLh0jdRtJtqHDqsnH4Y6Bk8Nyz3p0wJB7uQJhQTrYRGlSrUezdjQDZE/K+8yEIs3gYTMFmpUIXlPLAB5BZycp3KwTm8lsThVuDb+Ir/96z52Uk1ItTGFJwyxgBkwaaj5U/s8ZDgLwkA2df37Ofo7MiLBTFQAVJ6pqPVqRWac609TjZAwiF+gRaSTEcnqQ84EiF4UXBBjz1zMNTzKDJPMutOR66GTUgbsAmgzGtbO2RY4UVcZbRn1u1c9AJ4FJ4ed1rvGEP/if/i8TKiPEx6mlb/cNn9w2fDwGvrJt+GTb8eIBb4pNKTLr7tkcmFg/FG6UmIl0uL3UApRaeCFIWJAG0vYdmgb0nLGO3cjozHtv4WS4Ner32WCNq177zrlFMxAAsCwdw6hIQLvjU/TWwyA5CsX3DV3YNNqb4tW64tIUHz69wkdPV3zQyY5cwtGDI8z3fcO2bwhHwo68r01bNq7yPDghutafHkhuwZhxsDz3MSaU6HncQGDbyFArBmWtqtaoxFEgQwUNCBIHADmCrKqzgEocYY5FhHPLEFwzYEtDz6m918uV15g1ETPHboPQVGuEsJLRuJnjPgzb4H/3MfByu+HltmMEhypaOSs92jxsGBTsJQulwVuUCiE9KAPGmlygQ9CMWYHAYdudZYIMevYxEAG0NeeL5eKXDIC0JXSbKhE9qe/IuWsQz+wqA9HWqHpVqUXB9cjastuEf8teNGEAJYKDqcfYh4iLBFyokrJmmcLCZ/ZUgYpn0FPZkydE6kailgdhVgvn5F/k8FdkYCZ1yjKD5VD2jVXRfzbuJhTubuja0FWpdZjEpTqG1vHkoJ9LZmI2HuvvX+v1fjup4Bh4sryydpEPCm4sYEZwcFhg9gUByIV1cgnlTArOk4PlM1I+hdnuI45a/VCo5tiMsCIXy7mx98Bu83+ERUYA0wDPCGTmAmQYHbAe3+uR6hQnZ4pWGtGHxE4TNg9CgIaGiwp2p8GA6ul7kMXUwX4Vo36zIcj0qvdls6QkVq3pON0MYx+47Rtu286ahBlMOGTSHDmqvmBGNoEW3VYQ6H0lpRVsIvXKeqKo+zkQDmnkT8/JjM8bfsCtqm2KWNoYiCIsnOqRELIteyOkyzEvCu0Ls1sbcAj6umJpCt/5/aoKG8wKpAnuImjS8DIMy7ZDDdToy2dIY0Pj11rDemmzeLyuK/uXRgnB8hxsskDJquOdZsNqCPvRDFUT5Aj7AUBUsTXF0hQmglGK6bxx6MuCsfO7QpVK+2NHBJ8JIJlZDkJP7my0FtYV1QKXpdWlsebhA0vviFQ2b426fUOAp8uFahqi0M5jDHcMdezasHfHcMc2Gu694bbugApuY8cww33sDAbTmTITzUBRMwNFogRO5mTVLZs2mA342OE+4Bkc7vuO2EeqZ4C9RnISoAYHiKJnINoamnaIdsJ+ZTOEUWCxUikbRpZf5fmShluFcF4RQmqMR0hlTpgZWiFr3Jf8uybyEhUg516X3EMcMZPwnh/1JE4himmDdgzsyUql04qHZl6RguRO1i7AY1d90smsdnMoqMBBZCGvNZ9VZWdUyh/065JBpPwPaOb9/7eXBbJ7m0BqK9XsvLNTuToN2UgNt7BsNj25nLMuWkF9Zsm6mbJHjKa4uQ94gPWshPcqDclziooE47HRVxJqIiwZU1CVASCNdkkVASkOCxIMWmswkak2bnCUVlkgpVmMXenDR3ZYBLoHLpL4dzLhkI68qsCVnFU9qrV2REeViZaXzb93VeximYkNZiLOyHj0TmydWAm0VR8PqlxI6MgM8J0NxQkXmAMQJy25VKMtEGGEt4I1tYpCXXySBEjE0Jy7k/OuJHvDMuPQGTU3suw6n/mEdLIjUiqggCASHlyWBbJQCzDCeB+FaiHDHBuA7sEgAdRZ7K1hWRbY4FrqS5+kF9WGy/WJkCAI4WhbYBHYhsHLgWbmujtFeC2oMLGbwZNRuSdZwnYa990y28qgq+d3hjm6dog7rKCHZBTCBloE7qBhWiSwNIGGYxHFPgyKwNoJc6/aMLaBpbfMuGrpcmK0q6ShJox53wO9KRYNmNP52tJwXzssLkDu1909mYBU7Y9cezaSwlz9OGbZ3J8kmJ3N1bsZvDcOPxyKiM4R7gKwYQ6wfWRmgdxHtCM9D1+jbtAbEmVFya/RqSXTMm3OSBq8CnmsHTiQ/Qz2PIPbmg01IruVnP1u7H+qPU3q+URt5AgsycITaE6sVo9J1gg3oMgRUvAIMNCwp3yWh2cT+EFSqUCg7BDOPy8Ex4vgFVBnOaAyqWqC5jXTSZlzvRRTVySwn6ZK/2qv99pJRVGypwa9TQz7cWwzI00bY0bbFc14QhvvMvzGGPxMOqgINjV6sNjZ9az9p8ReTzWninDeJVKcm9hK1qlIFxOarIwOFYllwVbl6IbPyb5sU+gUi60+q8K140j7RwSGCKVZXGFC6M1Dcs5L1g9ys8aJGFIQiJ/HDsghL2XunBqc9Ys68ZLXAfj3kdel2lHzhqoo2zOr44TXk/cC51KxppD3MOgw9mFQBZZFGQ1nPxppyTaVK4DIfSpQKIaNA+VDoLVl1hJramxrbAjV1iZVlr9nxiRZR1wvJNQsYMAUwX4hZTKApQkWpSyV5nDJ3hdmeaJAz3YHYWa7x05Kr3JkhRllrjZ33PcdKfeK27ZBloV1CrdkelkO3Axst42BVcpnITPXvBIolFp+O9mhXVm7q+fSNI1rQkocWQFcmqIFaeRNFTc3rK1jgPtjzkAbKXkUJPjEKeMWpfZiA7PFHXRCsiy4BpuVHaxTGahc4X5kH5I9Y4BALYBh03iOkbWwlROPGekzQIrmGMZgBtphMjDCYMK29uEVvPIeqjlac/S+UNMybb2C60oy6yGbT0lYKkJFBpdk1g22G8TcGiiyU9mxIisNZ1ZmwnuQIQ5K1qhqP0TDj38nYndMUJ6B7jvYT+0DERAATaaiIN1UZFlBwCpWudVM7TLFEz0hTnHM7wPASQmVMWWUp3noctDH1X/91/vtpAhuJnzGB1zZTTmFnoKZRRkfY6SxYwQ2axoThz2cC3AmWoBZVH13VB2JkIPUYTJTO7MDH84ZeDhG4daT7ilZhM1/M5SS+kCKiOa5CABQrDY8cixCRslxUvXK6LNS73J4hTvWdUdFexEz8OLnWasCKKHEc+NCra56RXbWLwtW46TgjkF1i7qfmS2OQZHYpj274f2AHWeW5TMTBeikixhSWz2mM5ZZ7Gdmxch3HxymJ/n8IWyo9AAbU/PY4TabhNl3l+unpvAGKe5sevZcc3nfEvbThEjKgLo7ZMnxFA5OVG5ZzG8U/3UALsBIaIayQEjTAGzDcdvuuA/HyxipukEq92YDNgyW0KCD7DJRZnojjfm+21SyKLJOAFia4vl2yxHygG83IAjTDTcSDlqDeClaABIDhoCEoXtgVe4t3bdUqWAzrLqjIzMJAREMkSQSgHtFD6ibSbJDFWwWz2wQjdqVKgFTR89eugjPnjygZZNQ7FXnOSjZHoFF2TQbRtms+74hwMkCd7fUW6SCxz4Gxk4lCBUB1CDY0deBPjqasvVAC/7rHSpt6lZGOjskEzjKwH+ONRZVQmaVwUi2cQQHt+7O0TQMYhMtmDCiHHFcnByUCFo6pZb7qc3v9ml/Ih1Yy50UHmxQLvQGByRXTrXaY2bweLKXh7Hgf61sUDrs2ehrg2tQWQO3R/f5NV/vtZM6DOnhUOao6XqDyMTaFRkhZSd9ZVEcAZ44cEUekrRrHA5p5MMlhGfQpJMWs5u8HS48B31LSZ/UghWAxV45FkKUo4JjDKcjSAabZLd4SibnZR0LhTY+t2ekWOhMqQWCBtiYi5mq3h0KzNoQz8EOREC4KMOPpuPqK2JmlVydmWkRtuwqWFvDpTesQaFNRoiYDMeQQJflcN602pnpaL7fZ9+UB7M67aS6WmZBhPgIv3rCcZoTes0KXi2Vh3KAzBS1tVlIbgtrDJzrRebUsi50xPXM8hlFGHoT5PQ9HsOClOWs2QTvOC5Nc64UYbK1NT5zVUJqENa0iJfitg1sw+HacB877ub5Z+BmhptxLHz1yKARsmGP2dGYoGB/DcfSC4ZENnMzw60s0n1HE8175Rg7SRNLBX5BSBHgemxggNcFuPQGMWOPXbY33PYNlxwSuIKTjrsK+/OAbGzNZRt06PvGUSot1efLyWgGg0tmwVULgac+IqhdyHXPZxLosEFNygEaeC4MhfuAOcM06Ir72LENYIdiwLGPnSSNZMzZMNY8k1G4mGHpK3rraDsdvraG3g3aWNNbWsfaOJWhCe9jC/C9MdP2xyC3KcQ7KBZr0+B70HnuTtXA6slSIQmqSgwSZwg+5bnSfvV8Zg2E7Uj24ppnDfgI8Kf6OQ7bJ3NFVVh9BNNlKOp/AyRClV3wYGM0Jv097+kgGampoi/JfvwGXu+1k1KRrEMBM1nNxZDpCLZseIwAPBWihx/FbxS7KReIqpIamnBW5U6BmA1+EU518qAxcFTvVEbH4HdFMJUHMJs1eWoOF5m6gWS8ccGZGVhsCsD4+FmKID2YKs0Ag3FN54jJ5AlU0TUXstAZKTxjMdYVEDg5dLrVWvyVSfWu08HOmhUwGxyrgOXBa+/a8GpdcYvATQQ3BG7jJKaJonwfwSV/ygJ+iE56ccuZQHLKCqU1RNBYIxpUFAiy9oiwFJyYhtvZtFjrIdLhtNS/E7Spt0kWI/UA3QedUyKXbowEiWR6Roek/EdOyTUJcNgu9fPW3nBdO65NcVWwNlVZIQA0RdMF8IHN6Xx2YVPty24cQR6BDY57APfgfTYJYFnogFL5gXAYA5QRTp08514wsLZBWEagsgBhOfQya2xAFtcFm6fag7INoXXFnrT/EDYub5lpdXcoqBKiEbjtA6sKVhH0CLThuK4dHcKMrCedPgJi7N1xq6Avm+Mln32y51rrc516Bm5c9JGgQc1j40calNmQB4ZR1mqPwOaB23DczHE3YDPB3ZD1PYogk0kYgFC9nHdEgWgIF5JSoshCDokdzRqaCpo51tawtI4FChMa1w4FtGEB97tOyIwlB7GAmGE4MCLZppqbOmtLhpJdc0wmcBSyIafaEAdBFnrRhJCqFKIjXMdVf5Rk7R0j4dPSZZbKt2X/Z3rXYhDW+7ivdGbzATIPLSG/6rOywTYMBIOe1Rpn3X0Dr/faSY0gnRPAUXvKBTv7ZbIYHmHT4VT20ARHoRSHusSEDCPmmPF5bDnqXbPpLvtwWs4tEpw6s7UhnIXaox7FyVLsf4lKXWZWYjk4MBEmVFoWCYOx7DSyfsIFxP1aPQ3pc8Mz5Q80J9ms5e8ab9JUHPDM2nh/AIB1r3BHjBwK51k7CsCVBePMLRndqmIR4No71oTaLsLNTV0vmfCHpoGsNFRyo9XoCEJukc47af9Cdeyi1Jo5RzOA9bzcNRREDc47ihwPIUlqkSRuMCpMhmLWgER6sjNjriMqV5DdyaAmJ9W6ASNxfyFBognnKC1LR+sCaYK+NCDFdJoyVDABDIEBTmo1CEwVu7Gv7DYG9jDc3fFigxFJwnTIBmwHa0AVlLV0LNVLVolq0fRb6/DhAGzWCpkZZ21QSBxBIJmSjc48DAGBGxDwCWsFYqoamPK/4o5ra7DesQqwqmKHYnfDKmDtq1E02PacAROGUT1EWdd030/7sGqcuR9rUnDGTbPPyI8/7oLhRD52D7yMgdsw3MtJeeDuVEaxAGu1rVOMWWJmUE0yrFNBKEkpkaxaVCYeAQ3S3zdzLM2x9oahikWF5AsA0juDKqV9kNzYEkRfGEg2aELW0h01IsodGD6mYzqXIrKd+LgnVe+RlEYr8HjSB4EiAAGYSEceOFmBhJ0Pe0W3VPXOgwLPgIHEqGwtqEwtm+kK5TEz+Dic1Nb6N+x83msn5RHHfCVk5Fh1qSQRtPlgj/dXMd/ioKLPrKEilHdmTgHMSMxY22DxPqZhh1cu57MBFBD4yNkv2VgpRRCo9Do3RFFGJSN00ZjGWSpj9Ixy0tAGUgU5szsBuKLzXnAw4sjhfED1Z3XtbMCrdF4wWYa1LsMdZjT6/J6MWEPQhUSFatSr4mxDoIti1YbXy4LNHZu9YM+eoJEVF0VqngkmNEkV69Q9Ax0Q0Ga0Rj/Oc2LXvmECu0LHZ5G9OxmJttZoQCMoRyOYjgrBabsI1ipojgBvMSGcshJa58pFNAvW2vO6M5NYNLA29qG1JtDO0fWMMeicIp22iebMLcfdDM/D8bwP3C3wbJVFAbEsEG1owaBqN6u+Z5SgMv131hQTAXABwmz2PPUGTjvOPgc3O3prHFjXlbWjplh6mzU8CAkSVVi/G1mKEex7q++tLFkciGEYEGDh0EeNgPSOUsMGBG1dJw3edxrgsQ9s930SZKuZXpvCnPOvdqOTkmPppvX03C+cgjzMcdt2bAhsAdzM8TyYtW5BTTxrDdYU5nrA8kWxDjLsai/XhGFtnfPA8hx3yZqeCjzJQfcxcBc6qVV5PgHBDsMqyswz2Y7SImt+DV0D4Z0BgLE1YgwG2GNssxncnc3nTThuR7QlKgNMkC7RCoMTJlTFLJz7ESwWvDp7RXNtzbIHMNdboPqxTv1W6dDMPcVwDaP2ZNrAgu2rEb8hYEF78Y283m8nRcyKlM2sfwA4cFORhEG4CDjviDda8UiK2Hc29c1JmFawV7KiZsITR0NbZRFpRCf7LY1mKQHMwqEQ6qvPomXwP9Nxnkspc9eLLKJUWE5G0WTIZKTlwfHVNaoElji3+3QiXYBFlVRiJa29ZX2LTs8nZMrNcODfUOU00yyyShpcRu8+DXpT4KKK18tCmGUfcAzc8mRJDkhmVmeEWtGyh877W1BDTLuWgp85dbEUPKZyg3OkvGaEiJaN18ZNW8xLZsH8vszh5r2uhmtNp8yNXdAGDVdBGApBV4WC19xFcFVFV4fCs7fsCPc16ysOYDfWG277wOaG533Hszmed8NmNKADgg10vJrrkKoEMgua0vrsxQGo5VfGpjJNyc+y16zhqCvkFOrWoEJB39h93iv2jPE5UXRWoMsCc/aGae8Y+87eqAhAmMW7kIo/nNdzXTpto/E+XpYOLKwnhQZggGpDWEbbOZIlKqsWodiwDexjO/UV8fsaBIhqIEX20gnZfEKixMvYSWUPYIhiQJg9KVJthcMI3UFty3CSq8qIZ+ahInAFnW0ZYUkwoLVJ8Yc7dgSacb9t4tj2DVdtrFVqw6otGbGZMaJsOuHQCipNcp5VwSpejpTojysQPUkVQjc9xV4TYlYgm5uJEkCJYAjigVk4Mb2yT9wVKDCn1FbodEvtBhP2r4+ds7MIJCWeyEshtSQh/RpwUofuXZT9OGCBbWe0IvJwx7iu+AAqypyHK1gvC7IRR5G9JvZWplNGrpygy0nHL7HyM+28HvmxDOKcgR+RTa6v4uZFRYy5iiXPr2pQs4OpssmK/lQgxsXZQ7A2xbIo1kXRWxa1leWvylKOkSQ01XVydOqPhrwgn6oBaWuQ4GiGRQNXUbxpDb4uEDO8HQ41w5abWHPheiRMWZsu5Lg+IZCo0nIkB6P1anblfpP0oXR2ZBRVwRbQtqTKOB4kh+jcuXEomjqyKZs1gCKtTDUezf4bT+gyAOmEUNeuuHTWny6qeFLFRTlWvYMODDHL0qh2g913bB64B/BiVS8hBOVNs22gfFIarq4cRhiA62myq8c07nVt5hxj0nKtq1bbAJU8PNcP6wgx79OerRpNlTXduT495eoya9NGKSRVwAXhVeHniBf3gG826ybaALsPDAc0nExAaVkHBFT42TlN13MN2FFDnQFUlMr2wQqt/sUIwnYuRxbrQor+yPtGnU8aeSD7rzLgcrN0upEBWOrhCbgmMpCVvAeiPCbFXiUZmKDocJCxtwHYm2M3xd4c1x64RMMFCiLRbaIqsFMJg+UeeOO0bAuDVVkCgi4NLRRd6PSYUWYjsYNOSsAAl9Fo/hvHHo9Im4Vs/yhLxVpytaWkD0cPxhwWCfUFpy9Y0uc9Aruda3ySgWGb/IGztNLXe73XTqqirbR0GckQQmM2kZ5LD+dA4sFRj0BmQryRtZl94vaeBr9SV0AmZFfYq8x0+dhETQ5Yr4bz0ZYEp5AmrltOiIKNbHfzOu98wF4pUxpKANMBIkVlGbenI/WYPRmE4ITj45VU4iY6+1xYC/KJR3OERDIKgekEy2FpZagRnHCqOW48p8eKHtN7zQzWG7B29BZoQ3Abhpsh9do29rpBZx3cc8FHbk52rjsim1cd/HmTHOwnRWWW2WlfIUFrKeZZg+iKmJEOXRNCdU+nW9JawY1ext4HZxH52AEjhNxUsYTgIoqLCl51Oqhra3gSxTUipyIrVq1OE8EA77mNARusj2zGTc0In44+nOoRISyCewa6EQEZg8oVQEauqWkoB0wtcjjkpvmzXI8iCpXg9N+m8H0c7RIJ60VmJRrIeqQldVgpE5VOJ7ceEMLZU61jt3Q2HhgIwA1DG4YEWggu7rjm0Ms195K0hU3C2z2hWMpmQbj/GhTeJPuIbAZxmgVZGmBFS8KFi6NbRxuDfTsZt0/KdBrJDOoZQGjlpZK9Pp5tLinjlZ8vkpSgEIiyt/y7i1DlxsvUMxizYRiaQYhI4gk58kYwA4o1f17yWOoCBSc5DzUUdZ918INRyGME7cmE7jm2ZZZFRI7+qkJQAojk7T8G7dn3mXfPEViQRI5yQAB249gURNWrInUGCQmOrGe7G2nn+b3ux3f9aq/32kn5tsPbcqQjABc4OS3TMcBqgSJJAzTfUvpVhUMLi/SesFacIliO4sh0uRajH71S1ZtVxmAu9gKv8xjsFUmjdepXYsHTZvrMSb4ZhaBgQTmYa5LQ5Ey/+NKMjZGEgS6KDuLAa9OM7DUzkjyHTJ7CAuKSCFdG6JqQkepBPc8FGhFoVZ8SCvREbpYFiqdlgapg7R3LtgM3YAlAzPBsA5EjHIrZVzTutnQy+TJSJlSVzyfxTQtS5vndueFUM5IGOIqADLgxSMawyjAl0xPUfQfO2mVwNnWagMyofePQvDEgblhUcVlXXCLwWhWveiOEI4LFgVUCF1WsQqbbIprMOyDQoE7VC3cKrt7GwBaEo5KiloxRqkwwytc0qKk1J6wDNKkAIgvkKlTjLsJWxCQFKaqHEOAgvIP635pwrEca6khotlhhDk/IjvCN28DSGsz3zPT5rLZ9J7vMkqruVMy42X1CXK8EiNaxKokwMQy9FZMTCX8jZY0yg53rG3AJtMjsIze1ZJAH5PyzRubmrsC4c5KwYp99YZp7UD3metZsPqpdJTn4seDPuZNP9xzTOAOHI1Su1+TyeGbEAmCT2vtI2JxB0qJcJyQgCQRk6olEQrKsZXpjxoaEcUsnj1cORE6IUrDpnAxozSnmh4M4YziebQpVY6/fPMJxMqHlnBg3986iQJSQLn0kdrPUbaRjHTZg0UGtUAZD9muBgh7DoCfv33KDTRmqCDQ/jFH5jlkkrPdXYHWKruoQhmCxXJlqa+K5bEqjw+DHMpqApELRCdiLR2X0fd9TDYDwIbOZhJHyM14ilOXk8hx3t+kIBYwCa80JwCzKHL4NSAyIUu181Z4G86DzsrZC9Azu0GQRtiD8YWEzMsNpE55fU6kaAJKhpa0BYVhUoEo9r6YNXRpufceqinU0tPsdNzfsaSQImwChkWgeC6yaMAuzR0+VCsuMKoU5lfdMlUV/FpwDiOwFyuxPu84otZpyyzBVvsMgwcngM4cMgw6DeOCpL3jqDR+uKz5YF7y5rFiVw/G6A6vQWfXpYmPCZZKOZYwdZoZt27Hf91ls3gE62czWmy4Akt6OjOKDwZUArE1IQYgxe9oCB7kG+TlmU0UayqzdCam1fK9qpEHjcQbYOyRQSGMNi2w8TF1MbVlPipHGk8X6pXd8um1wP9o2XgYd/OgN9/sNr7ribjuuorgIIUBpSU12I+QXlbVwrTLA1AyGCPEWnR7IIn9er2RgsJnhrmlkw9DQ5p4hO460cgarQNVOaRd0kidYn33sewICkQ3ptRfZj3y0PEAZdOxRMHNgCEfpRBisCdZUS6HzZdBARINOpo8du+h83rXvSqE8Qws+q2z4pZPSz0xzcLCPqZyzOTIYotOgyZS8Nhz2Z97lDMMLXUkDcRZCaEpFixxQghEK6w2ucszFknetyee/3msnVQtFkL1AUUySjIqTlty0EYP3QDQapmK0BBLCSuo3AGg70twjrqgguxbvAZ1Y8URxpM8UB01h1DgETYsx6EloUD3OQXCW0j+gBMkVMRc9yogADqOR8ZzgOVJd233WRBYVytm4oxEQOaCOou2asenPaoMKSJSr6AvJFj+YiWTr0CjVxhQX9GKDgzTeSycTqQVwbYq1NzyZ4bqu+GTbcBuGPRhZDQRZgEXF5V3Nx73nMy8oI7Knho+aDi2hjEbWHpuufdKybZAazvqJTEME57m7D9KAzaFuwHDK4UCx9o431xUfXFe8bh0fXlZcG1lcaoYWgQWaCgQpFaV1fjIJIQHMDG/fyYjawzGEE4V77ylZgxkle0JWKOMUKTXD3IzPxymGPNd2HIaDCgeYP+NztBnYFRpQwZS5wzUQDfAwfnUyTsnyJGwVbujaARdYBHpvMDe8bPe5znmWlJpyCMIG9hgYLnjVOrxpGjTQ4QedcS5UyioJGa81A83zRlYQgIIxFfleone9Ky5Lx9O6cPjiGFxrHpMMYEjmW0RtPQYvcRjdenBH3yA/UsEl4qTP2XJPiMOybkgHye8cIDV+NE0HJtmiQNu0oCA5OlpOxh5olqUGZc1rmGG3kQ6LgVbrDartkO6KgyDmcSBA/Dybnkcqb3gcxLIKkCvUFhx7q2WQR51EgGQwn0MMJUixb5M0lEGAAOaCFpIw9gFJ/2qv99pJUU0lYR8ELVVinkRIcg4OInsg8tdaAqJA1X0CwcwGOCA2d9KUUVR0Fge7ECt3Z3/DLLDWIsVBZeVMJKYDnnTmOZr5tImRqXtBB3NxBCCdI+KrFkR24SH9JAlhILJfJxxdQTaRCl4vgte946qCNWqmVLKhytlkHSCE6shzcnFwYUbBaZltVnQZAsQg5lyfsZrzlfdMtXGa7dJgXfFq7bh74IPrik9ud7LbxsDLtmHTmHI1hpSbASecKnwaCQ0hs8sqoiTU6banM3hXDdpnW0IMFtjT50JTKUJUodmoKsOxiqILCFO1jjfXCz68dLy5XPC0dDz1Jacesza6tpbZI/9UBhHCfpbdUnEaVM7wsOz5yhoDKhM+CD7lfGq9hHuSVHK5+3ENXhJTKVcESd1D8PzKUQtwKKNHZL1J2M+XAruER2n46/4xS6WsmGlG6NInR3lZFox953FzTzmyKb6zgOXCQZwRbOyNCNhQeO946h2tKUwCrQUaOjSMMGvQEHJ3aaK1BXX9f9t721jbruss+BljzrX3Pude33vtOrHjNk7TpiRqSyJIiXER8L6KRWkrKIgfVZUfFSBQIZVaqUIqIAj8SiUkJKhQ/yBa6f1BBIgUBG1FlLRpi9K0SWOatOAmJdRpieMkzvX9OnuvNecY/HjGmHufG7+JKfjjXu8ZOffaZ59z9tprzTnGeMYznscJG9oBR8ro1FvcsBZgFscKhp13Nv17Z+WpGhUaAFVoVBbI5y4EWt3z84rkIQO+YcisxdaALR1dUkGijWr9kNBQpGPpChMmKb5aQeA4UaWqiSA0NZ1oRtHBOgUcRQyGBpUecHyQUJTD0EUoniuB7qSCTFqlzN2wWxp2beF8E8giNE8rj/NnrYJKKtnPnrSQBg9BESZPKgmlOhB7VZziysWZPCSs7ucGtL7yuqODlAKANSC8X3qU3KrM5opUZgctbcvzsMVgB0EIJyWJQUUHZEL23L7iKCJkMcFGtpGsvrSLHxmveFQe3KrZSwriJ9KUcS9CG5VbVH+RJI/DtscQHKCkbjMSsGqSyCjFUYtg6o6VKE6r4qQAF1cVF6aKi6sacxrMdsbgJ3iYpoGkCj8fLTGHVWQEKAeiqZ7zMdhvzqwUk16aWL90qBf2x1RRXYJkIDhRwa1WcLYsOJsUZ71j2xsWo6hqc4eJjfvjIpEzgiZ/YfuAonEkIjJ9UrfHLB32g6BD+8yTZbRgVSbUgHEL+EidFEV1x+m0xipo9feerLBZrYbJoXSFtYZlRmTTmfdIMA75nlunCOwShwODBEk0c2+YnOK3JAuQrmIoMRypvN/wYJdFxjoGWCJ5CSjMLCE9iYpc4/72fPl4JhEwaw9dtT7msBxQHMhKRcIXyVi+NoWBSzzfqjqsbWqdkMbX9K3i9zUjaaTC4S0yap5z6KXAwGp8ckoLqQZBApF5ZxLnYXlvfP49RiYIezqKsdHfwAptXRWLcS5r6SFvJNMwzxRgn8hSqnWQA1KarCgSJhhwn0p8E5J8Rbh5DLQmBBiwSFZ+ao6dGLQ31F7YL66OKc6k4hGAPKplrUxs0Bm0rTNxC0YkiRP7jjgSGo0eZDfDvHRs24Jdb9gtC3YBNXfFnhABDPZwbnKNc6HAMJmgSqf0kghWAaVWFdREOMZJlzcs3MXFSUpxBKHlq687OkhliZ1UVVFKc1gcXF2oT8byBUhJevWQPkLCG5yD8MPmvB8MufEERmosDkbPQTDrwLlKikFqj+AeyvWYp1BrPE7ZfE0oLypBy8AVPwOOMdQbv4IzLvm7nf2DIsCmFGymgpOqOFkVnNSCVanUFBNm7B4PYpJGWkzTe5WAdyTYd/z5nEGJiu6AYTX+CEHWZgtyGFAccGVFg3BtrYLRhJVSsCoF96w32PWGs9Ywdw5d7lqjGZ45/W9YHo9BTjIwdahmLE4K7GKEQVqj1cPSOKRqnVV3QVaDhlWdMGnBSbIfJYgeqijmZOuVCVULLqwmXJwU61UlmypOSxI7eDiZGWqlkK1oWG4ExTktWzKpqaWipBoDFK4Vpa7QnIF0iYSnakFfbD+3lSMn2W9woFuQHkodv+eQlp60bY+ghAhUmaxlgtEj+UHsj3xuM5kas3PIfZBsWR8BrZSK3huf2pHIBMRohh5jHOYJMbH/0tRo7aIcmTARTODwuEvMCkUp6NYHxK6hCjh+VwRoD880xLBrmSas5h1WuxlTW0hYMfqghTAY9172nzIdyn+XSBDybDXfb+Goeiw35j7rHP1OjZkkFSIRbiSkNDMswpGDjkL2H/ica+xR0aTgC5IKq1mbSbQ7guhBOTcSmhwJs1GXctsXnC0ztq1RMsocVngfuwgJFAnnI3uqgZgASFZ0KqYUAVxZXQlKzF3KqB2ZSLFHHWyL/fF98PevtO7oIGVtQfUVAEIA7j4eMkgcsINW7JFZhOV0D90rYAyApnglYnAvJYaQQSVKez/IhgY0GB/44abuKbuj2A/ZZsspTtrso2X6kz2EweArJRhphzc3DtuYi7C2cJt6p9+PFkwA1hDK0wiZQyspnF9JE7moJrJ3lrYODvblKOiaunvRs8geg++zpBwY5d9BwsEBjg+TsJ0ApIRKgbO5eqKFMtiloDv9kxocc2yiuRslgpZGXyULuaPoV+k0UWfO08aeOPtOFTNmzAB2snB63zqhEBVMZUJxYFUrNtMam1Kwngqq0sCtKqG+CfwsBc5sXIAJdsCeC6pymHuZZwJBBYQpbEDYA4rPS4FSKyZ0rEUhPlHAFTxQEfMtHcTvTeKQyyrb8whQiMUhihiQDfLLgAc97GbAQJqahhpWM2k8CaWaO0QgRcfsX6119Ko4Z1VHRVYCdqy1oDfGg2UJSSOpsS8ymAWlHwIpVFVYuiP7td4XrD2kiVzjwHS4AhBFsfxM+Rky0Mf1qBISlEPHgr0ajahgUlqTrFYTVqsd1suCm/OMW7stbu1mvjfhyAnp4WwR5M/USCT21XImHBjXlvkm1DFNJZ4aH8GNbgmshjkYT3i1iYweZM51RcwbVW3xqCIzMQBZumS9kiAhWvYQLAQQR9p/zL1j7p1Byhp21jHDKUCMqLiiJWLYJ1OZgGomzoYxmtMRuo0BYxYAJkq7niiNRyU1RoOMAct9SDN9tXVHBym1zgMxmqrImR0gKGGsqrpFSYwymvHue1KCCoOPjQDEQCVCWIhVDeeX8mDPCgfAqKAoUeO0PI8DiWMczP56iJGWSGUVGnRMHUyZ5Mp5/F3gexHJ6JmJCZD9AwvCAwgPVAhqMawEqCbYyIRqDm30EAJiUDIy6WT5yMiWbQ8V+763c5hpN+vj9eIxGBwPnGUVynSX1VVAhd0cxUsw1zLD5NUWIGaVAJfCeSGlzcLSOxatmCuNFGcElBYMru7sJ7rS4qGjwNYTlmXCtne4nISVB1lnVRXraUIRDkNOWlCdPSURoCrZbkU485X2JJRtckj6N0k2rzvmtJtHQO3GalU9Ia5Q1bZQ3Y/DlBJDCGdXHUHOuo1ZN+82CA4WtiISJ2ImSFE4xdzXwZC65DxeslB9/F21Dm0/d6pOkPEIBMUPVPmooWHI50EkjSV5sb3N497zIIuEMPeAO1rv0QuOvpYUSE3GoMdeIQ2+l4IFjiVm6FCU0JL6qIIRfka28Pknfb7sA1VAoiqCtQiscOaoFMFUFaulYlVDAaIWzE6R2aX3sLYPkVrvkWcFIzLu796niYEnulKZBwOpiRhnkRsDQVYSkt9xiNR0A/RAlskx/i5qyFy2CAdj97heQdAZAlvkHyNAecdZX7CdCfNtzUaA6nHOxJ2NM2hfLCJ+BYv3CPruQwmF5Bl+KA4eqFqUs5KRIKgJE9XOYOYBK6q9AoLUuvAg7D0gBI9hsTFSzQ2b/agINxhKxwl3mWFKNW3fw2slKOLwoNoibOTjYfHI4iKXQuqdpXpDsqpS7ZyxL1iBWZ9kkQcGzu45e0WGFQ8i0qXHoWW0wy5u0avgIbYSx6lWXKwFF6riwsQZngmcEkdURey3+2D2ZcZL+i7lZBaL7EeYbffWQ8+PgS2FSosKnVWF/YEMwh5d7HxwJaj73eKA8ex3dLKWJorXchjbUcKTaoHDC1Wpey3o0wp9TembHCikXxK7h61wAzscVic6sEZlmgGxKhu/VfSgTxbD1qHSIEDMeE3D0oVHSwhpWkeHB67fMC9twC4TFKWyKm1ZgahgWULsVqJHAcI51hogGtWeonVSo5sDzQ2uZX9gCSVw6AR9gDyZo1tDTwFc0P68h6J78tayUk8bmoT1LJhsWY1C9qaWaWufgqIqAleSMfg8RhLGyXfs+5sBNfbOCzbAc0BWSvQ++V6ji4nF2bNgNSMDTls5Z+8g6XjL4GoJ/WEPR6VTQEp6SZwJKkAFB7BRCwQka9xzssHshl3r2CUUtgh2vZEB13vMLSb4lUEqUA/f96VyHw2JMUtauw5VhwHPm2MSHwSdmsmLG9xCwSKYg+OwSKwbJEqMCj0To0h+MmlZgiSxbQtuzlv2eaFYBESanIgJvbAwvjerNYzQFSMCHkw9I1ye9SI0hGWdSZ7W0LSM98iBbAMkdER7zJg9j3VHB6n7T0/gdcJZ5zDkErL8KBoW4Mn5J3mBTDWKm5r1wIsBmKEtDX3hU5QzFt0IqQA4yPhGBRxZg4AfP5/OzCb5zJIVxESJPYXUv3LPYk/HHJCAlZ8BAVEIkGZ8RYMi3oG2QKyhAFBvKPAB752WggtTxYVacKEoqlvmWHGtDvOONE8UhE5bVJvwpKD6eNJ7Czp3Pw/jeW6m8cPzgXbABVooxt/Mgk1FaEebjSBAqxVCVpZNeESjFjKazYRhCgddJfyYAnrsqb0Gh0vYbyT9X4P04TmvxM0jscmqOBl5kfn3HBzm7YN0KhakAG+P1yzW2EObZ8y9YbeQ+l1rhehEC3fE52usRmrhkGtrS8jv0ENqaR1NK+butJ93YR8OQodWo5Ykomo0IdU4CRkefOw8mMZ4RB6kmgOqnDHLJntRDVIRZ39ylkzA3l7e6EGtjltrcSr2cEq2IEpIwFStW1RM8XWwCuN8TGaBPpK35vRBmo1qHqahYahKIoUKNuLYqMBUUMVQEcVSIaFDBMEYw1DfyIDK51GHLBYhRAZJF0EHk7KlGba1YrvM2NaC7Txjbg0te8mWUmoYPSjup3gzEsmklgHZteiPeue8WFZO4oTHqlNLcwIJExW8L0NTM6K0pUVNoDJ5qqUeX8KOmn1IkUHU2S4zzuYZZ22OHlThXoqKUw4T9tzeWYlHOi1GJX8YESfunw64YGc0amyFVS39wSb+eMhAr6REm4Sl6P4c/Srrjg5SD56coteKW/NMrDUbgU4ar4ugCbFmOMUapbPp181Hb4n7JqGMZMvFF2KDdutkN+WBlhXbgPMkhDIbb+AgIvGGaOGBrEKh2HSRlRR6jKCwmurAt0dFFQ8fzd8aYC3moAyTG1ZFsVHBRSk4Cf+iFZzwX2RuhGiYCRXNWZfIYM0o3SSUnSG8EFliwFglZlAIOVlAVhjZZK3sQVCHjVlkB7Nskkq4+fKgBAxVFJMXKpSDGythQ4fE4KaMTa3xfpPVWOJ+lZF1J2wBeAnFCTPowQwLmX3RFwplEh0/k8+B5BmHOFiR0AYTnW6GZVlwNu9wc7vFEmaJpU5wE9QePQZ1SNmPFgQDN3orweq0jt4btsuCBQXNBR0FCwArBd0dc5sBURStQAzWmqQdPElDrBSzNyLj+WEFY6zszOFaMBWN/pIiuzzzzPtMliqVUSisbOPAIuVaxt973yuk5H9j5cxZHUf4o+HgvSITORtDsR7yOeoBqxvt03s4PyeBJ2FO15yTi/ciCIZb9GiMf++Hh4VkzzmGZd1QOt/L0hlsFJzlmjBhXQou1IqlsWp2Y1AmbMwkqIeqgrsNiCyf01RMac4zqLvt7717qNoL1lIwqWJTJ2xqoXGiJFrKfegR5DlOE5/ZoC0NzI+cmtzXgeZQmJf/zI1akQIHKiCoUUlzfzI4BbiXlVv8XCCQI8uWhw0UxkCRAbhjKX1o+CkEVmQka9ConpR0+OdZSN3ZQerV6w0wTbhVCrat0iiud9KWndDB7GzWuimZPPMcCYTEAFsEA/MIICQpOOjBw3lVDqsmbJFDtSLZCJaIWcyiektdO2ZTqqTFa2SjJGFEJAo8F5npe8xSHPQtVlLot+NBR28dFY4VnKZ6tYxNtdEkS3CQVz2JDqGe4R66ggj5FWZcA/aLzFBCBd6JBWA07pFDfaQdp2xMiQOVHld9+M0YMCBYUw42AwEnhKVEQ2SQvaOIRaUZw6rmmGo0giPbJyXa9xUEWB3QOj02GwujMUSZny8iy0zYxBxojZCKStCsfc94BEgoycSmd0q8tIU2Cj0sWHprcKNOouh+tgiIAdS4liqKzWoNX8J4sPK/t75gQcNsApQJXQq8hkXG4pSVAYLVFy5Clbi/m6PPIUulHSKkm3sjEUSjd6gIcdSAj5Z5jupWYlZK94l6ZMSt7f3UEvrLlckKac9UPocIaqUpo6QyuIcCQbgLq5bonyUxyeNwBMwJRjflvM3SHbNT325RQ4kZppKoBICQGxn3HLrvDzfbV4KVqX2gAAczi6pQN5jSTmUVz3KzOogCaV3TWguYvKOHz1o6V3MMALEfJj77bvBVJMARpDTuRYmkrAiJHYSg8/OMTElCz9NBSNtTlDnvCd2ZU84qUq5RFXGvcbiYuocGobndqNICrog9Et/n0bs8rHfcA3oM1ZccNQHvX/adU9x5/Bzkxst9EdJcz4+BfmcHqQsAaq04UcGuVrS1Y+5BWW603L7Z2AjtDpTOoNXgnD8Jb5rM+EQLpBYSFFI5uzITJotoGmxBFfYnui3QdHiNT13AUljB/UOTWT4AGtCbgSwfTzgFzMCGKnlnblZUsSqZ4/MBkkKJo00RKm+Pf0pIzCTNO3tfBxML7jFEGdbUiN5OQFnjATcfQTUbB6KKqRRIqYjiMQRmC1LYt3XSPbpR5qd7NFlFxnR7vo8SD3sRipYmBEd4kg3aCg4g547TCIx5OJCFxs1aJUgzshfA7Emxzs0siPvuaMHizKxQvA/oSGLTQghntfwaKKSaNhG1FHQzrMvEANANDQ0mBa2RuQV1Xl/0eVZCxYDtlWt475/6cXzp4udHFcJKJOu4KOmzgkycB/u/jmmUcZbkfQYufvb1eN3PvQOn8yVWGvGQ+cFMoEfgBYA0vEPArt1sKKPP87z/vZ5JFOe6qgSRIw5rVlmpXkBuE/LwRpYJvB63IHE4eOhJp01Ob2P2Swsr7o2WsH3IgzcSGUFAwj6G6s1zmDs+DCGpJhECDieH/FdrY/ZMRVFBZiXh9jimLUYMpmkM75t5ONQEiQP7Azl70Bb3c8gFjTMg4P1IGLLXg1HbjhyJlXgMaHfjcHMSbLLfQ2izxLkEOBQplK0q5xINsw70GESOPiK0jOp+H2Ey0UqCU3zwmVRn0Ip9NE1USym17mW5cn+5jxGIHj5n+dx9tXVHB6neFqxthRMRnEwrPrgeDJ2VYbcsOOsUuGzdcAZntWWOs95x1unYCY0sy2PSX5KOatBax6CuRN8FBzAIGTmBC5sHUSLsGawTKoJDOjOkSSvcDcUkxDkJNYiNliwEZJXVoqjitOJWanGJCiqIyU8CrCvZSZMINrWgOIadd8IDKimL0ghrZSkviMAUG2xkPxjCpZqQEYBaasCh+chGoI75JNGCxcka2rlxRqk36nTFmEQ3WkQM6nPQiosE97BzyLOooEJJHQ4yRlGBNAaIkNEdQQoCiC2Bx4PQn1mwJ3kYeg8VAERSkKaGCYmJjv7NSAsC7ktWXo9DdQpSgVY+dxbiq9Y7iimk8ffP1oBpzwyTSFYKDE++/uP4vYeewDSvUdpq9BWAgFLlQNT0YO2z9viqZ/mzf3WfzvClN30U9//6n8LJ730L2rJAwL5PrYW27EXRWw/5KB/Oq4j707wPBX8kW6tEgHEEfM2eTg2l9d4ogCtagDCTNFh4V2lUUTaubYiMOgCJdMo62kJJq1LqsNfYH/iAp7ZlJhPusJ5IA6udVGcZQ+ZAHNj8lmSYRiSI/kzOHVF3jrA8DwVXkhm8ANYDYrOMt/vPxcetkAQAMu+ASM5IIoJpul4b0ANizcom9mTS4Z1ZBs0VLV19dRAmEiDIQdoCnmurqNJWUYkt4vvB7m4MaCXJYUHesqTa4GC/BJKs+3NKjO2RdanYTBXracJUcnBfRm/Nwm4lE+DBanwe644OUttlh6mvKdWhac0gY0p/rYqNOGcuKquts9pw1g3Xlx1uzIZqMZhmJDmgcGBOjEoL3htMOYdgHhle7/DGfxSImRM+EFKiEZ9kBBgkMnA+/T0w80r7h2DBMWakmjHL/7VUBisY1lPhzXcyggo4t1OVvZ6p0BLCQwiVNFxFWxqqOiFMZx+gR8ZsEXzZ18hMku+z1EMleUVG7+4ItW6yAw0O04BOZ/pFLeBcxq4b9dJUGLycNtg9D43sFcX7qlrCcjusB8AHdFUCtoJTxBacEYEHa1CFcbQotBYeipGhjuoEwLl/iw0i4lhPE5W4IxOtqiEBE99mOfvGnhs6hY1rQL8UiA3BTlAtAs1hajAomjogjmlVo4dG/6mpskp520e+B2/49f8HNxqfTa0rXDy5gHtOL6E40JctzBoZjWXCtd0WXzy7hZ0qvNQYI5B9E7w3PPn//n/44lt+EQ5gOy/8jEnoRlvIuDNlVru0FmhMVA0HWa/FgLuGSKkXBmwPi5w8m5vtXYDTFDLn6tIZOpVgUtrKY15GkFVPh/QO9D7sUfokw18L5kPFnIlNiL3GoWySfS2N3g+ZqRbSWZADM1OQyThMITllOyD/Q0uaEr2wUfEhA8++QmJluj93k02cz56Lj6pXvI9+DGe9HB2doytjMspCY49SWqb7gdqO4SGJEmUqg4dTPkyDTSeCzTSxMi4FdSo4m3c4aw3btkRiFTB4nFGEPvkseTSOko2cfVu4Ic0+VQtWothMFZtpwrpOWGlBYREKOdxvQbN3C3NLewVUUrf6jMkWFChaSoskg0youbZCbCxRdBfszLDtHReWNW60mdPXveOmNSoWKB07u/ARMwdgHB5Vd6zMySBqBnTCUSXYarVUqAEqDggxYGg0tj2gjxg6FmWfqwJDY65AIUb7hakWrF2xFtpAnJSJvlACVCH1cyrUzRJhYNOc14hDQIBxuEKSiqvRX4seWGwyQKJXE7Mg8VrrKZsUGTxIP+0WnjHu2HZ6PVGUVLBrHbORxDL3BoOgwQ+qrviZFkaGkQ2rdBSQEs7qpoctu3J2KRhxRRU1DsFkYkqJZLyk5mAoLEQVuO/xcbPogEkcultiY/EQX5dKv63IttGN9GfvSN+vQTnuhqpRlURSkg7Nwx1XYjNmNVxIUU/x2dO6wX2rS1DfofSGzeYeXDi5B/N2wTwvyP6blgIsjlWfcLJUXNicYFqtsaoTtmdbXN2ecTDUKzR6f6KBNwcyw8HeDrQ2kjr3lK8qAw6DpAeaDKbssIkZVXfA24I4dGx81qOnp0FDBkafzg8ProB8PVTPk03J+UBFFcEkRAcmhNWM03wvWsGj32YQJpRVR7UOIPy4/OC5iOBqHnJQ2FfjEiKqJWp19wFdJUknWYLZG1XNFHQviMzdFQzTUfDuEzN4QOHWg5TFP1sgDS7RA3VDc+6zAOCYHDaWcBXhtSWGWjgvpfm/QG6kVJSpoFRlEGkLpu0OS+vsR3gqogCJKqcgArzEeyarMYO/CigaUAo2pWJV+c+6hFqLSKhq9AHxtdZY5fYO621Uc19t3dFB6mZfUOYds14XbLRy6M/54FBbqo4HzwvVwE9qxelqwmXfYO4dN3vDDXAaezEbgaw55XhSwl96R+2BKXegGBWbq3PotxrPg+4OQ8fiHSZkKlHqSMaQnWpjdlkk/J2EjVMl22dVaF+wcsdFrViDOmaTssor9BZAVUIYaNQw5HnEQz9psp59gNiEvRtQQHmoGCxuzlkzauDFHASCZWX0OlqMsyTNPOYv+PedhdGZGbqQcr7EPy1Ybzwa+X4sYTQnUlqUB0JSm9WceW6wqqgTFvT3bhBpUPdxYIxDpICNsFjLEioEOXg8oMo9pVqVreaVEP5TAKtKM76VFqwK7+1KCO1MOIQD2WUc7D0TSFguMANnhZ1ySd0dK1kHe0uQneNVqbhns4FBsZocZbXB2a0zLG3hrBLf8Xj/fjZD5wUPvupV/LnbHU61YH16gi/tdlj8QH19KijrNWyZY5hbOWfnPpibvfXR8D/sS2jJgfd975SBjffbO0LmiIGGpAoZZJocTE/3ZHiw1YzBiagR6dXUouRhrZGorSoNJenLBdK0DajC/Z42PDnCoVKIfiifseqEoJMw0CPAWiAY5o4uhB979ngIeEEPmIuiobAhNA/NURLKDwUCEmeMhgpEQv8MZFzdc4QjVOaNzMHsNWVilX3c7hZ9XEp9JQLRzen9BoR6fIUpYE7Gq7qimkJkPzi/coXrhLICplKxcsWirMA5WcI+fQdG0jJofQqiSzHrViJY10JWIqXFNOYPY4TEE7rls2GtwTqVX7KC2oPTX3nd0UHq+u4WrNAhdQVF14oVCNVUVWrFSWieVVKOS8jfbIzaUw0TTq3hHgWlQnrHWWu4tSw4C2r7rjW0bhSsNA4EbpTZdu2hxt4Nvlsg7piTnSSk0VohdNgje3ew4Q7kTEEltBXZ4qYUbEoqcCuqd6xDs6u68GAHM1NxQI0UeY0m9yHMkO6iyXYKMD7gMrCEl4KCEkPRNgYAWzcsPQIOPLTOaDOwdOMQq/mgQ0spkfEF/AeBB7vMpQTBgQEq+1wc3mSwsMD2NdlxAkiREDFlkKA1Sg+yBa8hDw7YPvhQ5YGXW2tUPXCa68W1m3m4ExvgHUVINqlLYPkCrGvFyVQxGXuAJzHrUSBYaYUI6F0F9lnoaVXjAEp1CWJEK8RQcVeUqge9PsXF9QYuFdjNuHHzFlzYR2i9Q6IK7r1zPs4Nl082mKxht93R6wqOk9UKswpuDNVwxL3g4DW/n5WMxWGdpBJ4Pk8HKhZhAT4+0xxKN49eVhsyS0ORX+IapUHqvhkvMYzuHSkGQfjHWqhyEJZTS3NOwYlWXFytcFELLtSCjSoH0y18l5Cd0WC1yT4JLNwuqCEzRYipwVqHKW0qujjluETGQDLA60a8J0UQnCIpWsJzSwFU4/1PNm5CnGm7o15orijRu+0UFt7rKNqARzMwpGLeIKZk8iSsotqwZTdel2ikfyRMVLAXJPE9RT2gP2ADxaQTJjjWmwKbjBT71mEGoh+9EV719LOLzzehUJRw+CZVfioTUQeADgzwUMEJuLPt+22ehAvBGIF4PuuODlK71jF1HnjWKYnfRbAKX2sxwAqlcGDxNLkDofOVjb1SKtbi2DVHV8VOFDfMcKYF24pBtqCCuGMSxUYLJXMA+Nwxb3eYb55ht9thiw45WaOsK6RSvZgyMCWYU4pay5g1WoWidhXFSsnSU+uDQl4A1IBAFIhKw8f1OCS4G6GmgT1tNt1L94w4h5awGHHExuYD3oTQ3bY1LAbc2M0korhjuyxkRopEoArbiSLcKEkBBwJCIm08vbkS7uQ2jAM3egQiACJgJEtSBNBSoUq4hocdMW0THj4NPByahMm9xoaKA7dLhWrBbKCiuzvMdOghujDYWrCcJDEx6wH3cQbtpE5YVWLvp2XBqkbFLo3qEoiGtYTMVWTArDwoWqPBfMTSMazd42BXUdRSsVlPuDl3bM+2mDbrqGgowTRpgTdmoJtaqU946xYcxurZDNJmnKhijlkcAAEjBYmmUduR6hQ2pJcAsCfa2U9l7sJD260jzSaQzLioDDmQzEPXc7DXUrUlILX4PirWx4Fl4aCUGFP0sErAg2st2JSCi9MaF6eJCiqq2EjM/yFJAwd9oeinALwZXUYOFqaRjuaKpobZhIPS2THRgr0lh+0PZiQLNxtNMmTOxIHWl71kVvZ2omJM1RI6egMWsN7S2iDfAEywBHJAfJFAWFixikc/LCTbqMLYYVGVJYO29Y4pclDOjYXbgUqMwCTtXVEVaKIwdSzo6KJo3VC7YYGyegvmcYIwyaotQuSDgYqJddWg8rtE71P2oy8IkoaTso7oB1La6RUQpKa6Rq0TGTZCE7bZnNpgWtEBFKdOVTFD7aSglkoIZZomIBp8GyX+bQDWSm23RQrmtmBX6O5aISjuZJ0JmXQ+k/J+pgDQsFvO4N4wbchMkyrQArg6at1nSKtKMsYqdNFWpWKqFBpdlQK0BoUBDXBrsVEii9OwEBAAChi5q3yockMBQGSxSZ2Gx6b2mCcSNo+zSrrVOnZmuLXMOFs6bs4LlsO+kyrdREMFwKVGDwOBYwdchICbJOYhhHN81nroygFwGT2blN/RomFvYAED8Zp8wHSFTqql0KguKidWa0HVtYBaEFR5UUJSQSJrHuQQw5CFQpJiotcGT3IKMBswt47JSFTZWcXaBYqOlZaRtBQQ5oDw2qTWcJblfdIgWZhT708CRo7bBI+qqYPw6nzzFk5PTxgwtcB2C/pCiSMRmiO23mAgxiwCeOc4xMo9PH2AvjTY0gjtdapWs3MGpDYTh0BZTVkwzCwI1eYGMeoipjuyBmRsCYmBh2EOumpYwUtWBwZW9FGZJLyXXkMenwdHMAQbpS3KxfUKJ0J4fqOC6rbviZToe0RgMnDAuUUFnZDzbE5DyW4x0NoDOqNvFMGFJFMk8wzxPMUWk+jrqsB1tNqivwYevNlviuCf6jNAmHn2FiSVBQYScFSEPmu1srea7NLR/2LypcJ9oOZQ7aNv1KWP6kQR1G7jszup88yxPdIgoXeY7GMLiNc87GmkoiqNCbv30JMMxnMEuiK0KyFhi1B5GfB39MEzYCPDbn5mEpUlq6w92f4rrzs6SFVM0Vdhxp4ZHWckOPDKQTmJKW/Sw4t1TNMEm7cQEUzTBG8Ls1Vhk/ZkmoAJMJvQ89w3C2IDTcXEHH3XMG9WmE8m3DyZsDld4eayw3Rxg7Ku8FoglU3d9WpiIBL2ngR7+f4iQEXYPgfFXaOTKZFVMUhFb4k4AKnyOaERHVoie3ySRYKtY9FDccAic2oi2JrjVmeAurEsOFsaWXlmOAvfne5k5vFJprArShmHHALqKoUYOI1cHSKF8EVWUR6Z/dDCQ7CplAdGgjeRhTILw4BDmM2VYDnpSMTYaQgzOlBI1B0xz+LBzKTQrZnzsBdF+nQJozccOtiKEJI9SDsWLA4sJth5w8q5WU+nQlklN0xBrGGWyEOgVMKWHeBzExmzzT1gMX4GS++4evMGnt0tOGsdp5cvYd7NaMsCheMLT38Bn/nd38PVZ66iimJ9ssFrXvu1ePVDD6CsCq1IROiGKuynlUjVpRsZn/FgGGxQrUXBSqbnwLfvKyQ4el94Hwsn1zySnkF9B4BAA3QI5baYOwoZr0g0OhDN8vi8jSw+MZoTqpPWv64FF6YVm/EiYwawCKCdveYiOfsTlUoE/8Ucs7OfvF0W+iYthtk5iLsPTtGP4kWPQ5+VNKIa4OOuEgdyHMFVhFV2nCmD9RbIhUmHIuSrjEPVS2tY+oKlNyxtgQOoU0WNQFvAIW+SnMJYNKDt2Ahxbwgtz42ixhwu93htR3GOrExCJ4RFHFMxVCM0ri4hryYRfEH1EoR1jLKvbiHNZQfQMOBBQWcCpO5Qk4EGDTsSRNIaWGEO/bs5k2nHOK/23bqvds7fwauY0q1LYt6nh+QIwGYfCFU5wDkYDxXlTuy1aH6AFtbNXByZiCym6GAyZRmeUIADWCogUlEqoBMwnUy4YDtg4vyOHVRu61qxrhU15HhKmaJfUFC0kv4Zmb/F70yVAI3rodwL50dMcRAEDqoox8GDWMZ0PpzzSksXbDtwZg3XWsO1ZcZNM+wg2HWgddBiOyonshKdemSQmKEIy3hNt14OO0rP/nOSfImgcVLfRu9CE2oJKMHjz1F1RHabDKC0tJeYM6PGHAbeX1ECp08jyMhKzYM0IqMZbcaKrvXcLLFho18ohQ34UqhpthiFXndm7AlJQ1XFmRlWKiEQyiHQSRUrEZwkDBXeSRQazSQiemHxvJ3tdvj81au42QFLdY3IPJ/53NP42K8/juvP3iQJKA6/Z65exW6Z8fDrH0bUm8Pcs5Rp8Mx86cDSIJNCcrxCS8ypgLRlEZQug4UFYFDQx4kTbzZtO+Bx39yxWq2QQrJSyqiw0pgzKwqHw/oCBUgzXxay9MAAtZkmnJSCTa3Y1Ip1pRGgIp1zMe6llLhfQu3H1gw7Y7J1azfj5m6HW7sZZ814oAciQAmzsMJAMvlKfloBRzGpSAHVJql1KFi7QpxJWkmyxCBQsDRzc7TF0ZaG1pcxVK7ulEQTWqWUUlBqRdGJPyPHKqAjyTLfq0UsS8NuR0HjZhH4cyAXAonh/+rAZB2zhkGhCqrlaEVBsUiKI1EuJaTaPNjGLuiuAdFH/2zAmdz7ac/B62YPaq/TJ/tEKBOaovBWoGLo1qIizx76V153dJCiXTd7Lt0avE5sxgIhrc8PaBebRgWkE5cC9w5zGRn9YoD2HuWsjIzGTfbzVzHvkcQXC6ykToR3ZBJMF9ZofQNDbNi4ybRdrphQUFwwSUUXOrMyoY7DFQB7OQU9oEgaG6aFRsx1eM4p7TXR8kFKJYuUMWKyx9e07rg1O271juttwbNtwbN9wY1uWAp141w07Oo5OAjww8tMOrCPoOdnZu2DkpuzVW5Aos7BqxpZ2SHbMNmXhjAdEMrSFCUt2rpBiqPWmPZPpQ549Di4kUlnZ1NXlJsmkxATBkoyLInto8hICtjTCufmwj9RgrgQ99xabFY3rAqw61tm/IVKHNXY61sD6CpY14pVKVCwWhAVYvPwgPcYsHdLx7Z1dC0koTRDsY622+FTT/w2dre22KxW6PMCccVms8F8tsPv/PancM+lS7j33ssAHMtuBw39QIufLVS0RTNnkBIDYhCaKgfM2g+7AxZEBw3IdhyBI3HIXhr3V+us5JKyPtWwr89kzgl1dfDQd+voywLtvGdkzSk204R7VitcqDWo59HbCFo2xy00YDfCWR4V0WwNZ23BrXnB9e0Zrm9n3NrN2AX5p7sjDQJ5sUysSiFjcySp7tR6BCi6C9LEkxKv3SDFgOrQWoPYU6JiSJ06RyuOpTQsjX1RKRWlL9DCmbRSK6py1KSWyv5rMEXdI/i4ReUU/k/LgnluYfcuQyme2qQIdwaSF2YjwWSSCFKKUPunPcmkvM9VK69LwCRKwhLeNc4u/omY3+IvIhtXJAaHJYNTaCOGRDSE94nHJJGVbgbtgWIk3P1V1h0dpDaToq5KNCL5obQQtGzu0XCPPNN42PTesHLDRK44pkpYIZ1WRQXVOUCrcfNqYM5mGNbHQUXbU30dtHWAYb0qY4AxpZJyHqhIgboyQAbEBA0XVncAe38dyUO8yPAjUhX0vhCqCWHRrqGSDRmVpPo+WHU4do29uV133FqArXXc7IZbBuxcMQNU4dbINGtF6qH1zhHVZhYwDkBqEcKCYx+gJLH6OKQyi0aIv44DYp+a8zOKezWU1iXIHjGdPolAWiipg8EM8Mg2YzrebADg7mwet6Add2egsqC1a/ZxHCGB5QO6IlR5fiN5VmQE8tGgHC9w4KwZandIYyW/AnBTHKfThE2p2KhiHbBr5MlojZkxkJYcQOuNh0wXTC546vNfwnbuuLBZYXftGZx96RksZztc/JpX4Wu+7vW46cAzV2/gypUrkD5HJVPRdjvYQi6+7G6h7G5ycDe0AEsw3oCgLQcNW7XGR6/7e1CSLGBk87mPxK55OAYgB75DUQSEOx088GVpQG+j0Y7uKAtnoqqQCHJBKi4WwcVVwT1T5T7rHZUTuUPNPvs1eZA3Y6W06x1n84zrt85wY7fFrZkM3dlosONAzAgWEiU8oFelkkONnieEg+WSQquxLyWg91kM1jr60mHV4FMFqhMCrGU/9A2huaNTA7ASe8AEGj6WEJbl6VEgLgNeRCRRDo4/9HmvFUm+hMIryUpd0qgwkj9QmEDEWKU658omF0zuJEc4R1xYcdFHbaWsphjvYtRBQObgGJjOxDKQhzyIPb4qB6/DHklAPPNMKJWzo82gh5nRV1h3dJCa1itMqwmt7+cgBDIa0MuyIP2bHGBJq4IFFF+dakVTwbY19hC0QI0Zm4IGHKSgIgYGo8z1wFpFaeQVUJzE/EWpOlx//SBZiBA0KqIOiVkIYHFDeC1zU3TOipgqqsVEjuS8iqEtnTNMQly6uY8+REcoWhjC/4mzTDeWBTdbx63FcNY6tubYwnEGQnsdEqV+VGfdxoGUI5g9pIWSMZgQI0V6GWzyfQIBF9ie6bP/LAICckenhAXS0fhcph54vEcPRbCHNYch3IA5qToAAMOqI7L17GtRiQMRoPY/x7Ji1VTKjoCie8fnpFN7wEY5E6MOzG6E1swwuWMRx3ZuOK0TLkwVVidIrUDlHJ+7j15nP2CUeeuoUHgXLLsZ02qDSQ2f/9T/RLtxHSqCW88Ar/q61+HipcvYzjt075jE8cwzX8SzN25it1tw49uv8YfPO2B7Rsq7E8YcbLpSEQrKUYHzYEytNVFKDbmAXfOAkDNQSTzj5sYhdg04MZKs4oAkbCoeiRr3kkwTSiHD9mSacLqecDoVnBTBShyiTAw4tI0histHbO+jlIy5eV6wXXY4m7e0T2kkS5hIEEsYnKRMSCFdMv5YKS0HfRoyfkugKTnAnYkqfcTEnMgLIu+KYOa59+PzKUVRjKzPghL7JO04kmCCMYZRko3rCrqzcCzAOuCN1Q5RYwJuHhPNhP1JKjEFEBVQt3B9cOppFpFw6WX1OkGwKQW9kOiV2qEa+wRIhDX2RQThlGGCIxiIufYBCpBInvPe+VBIN8Qz9TzWHR2kDIJmZPNQ3YBZVeudJXFnlTEYVocNw9ZQoxcloMTNBITcDUtkqjhwGl7HoBwfqqosizNYuXVomDBq32fkPfyh4hgOZgt7VAsEM/iel6DuBiUA1VMZWTCVgqnyvwHRjLboG0R1oCUqGqWqNQofchP2nrYG3GgNN5YZtxbHjXlGg6CVgiVgJjcemD0Dy8Fh1JsNNYLDYJKQ6uGfhwruh4Hg8O/n7mP8nBFYojLOV4mQbWQuCJL7Hus+eM2YNzkISLzfgY3nrsLei2q8N9n//fBnH77v7qkB54TIJPX8fFSXlJLhabr0NlQDSOU3bIxZrKqiByafowFAMCCjKY/oZ9rWsGyXyEcVuzMySC/fcxFYVbh3QG1cw4XTDVarCQCwLoqV0xNM3HioVcJLVTCgWjNAvEOgozI1oywSKfshA3XwOWXlfujXRTq+B1Ep+rtTgZpET5iV3Gq94sB0Vc6hlT2tuaDzc+zUvoSTfs2cJAKAc3anqDK51B59PoUbD8feqSDjIpAaz0HwsQnbJ10eo6dLEpNgEg5lU/9OMUYQDUyG3IetDdzQRYOOX8YMZL4cQTMvHoCY7IWmC5LxxkCQzgJeFGocv5haR437gxYzZR7wv2kwDjMtRDznoDyURnIZX2zOpKqZoaBjBYFrR1fFSjmzx159VNt6EKRUxxiCJFwf1d7YrFE58fNNZmNHa2Ratk4yljk9/J7PuqOD1HZugIGZQVBOd0HxXDpVvikdTzweXWKzldEs9R6DoTDKrYCqDqtQkSiBUadCeRoI9iil0zgPTs08EuAo0uJgpsfhO36PQWBGPbIzGHZxEzlAx40yacxLKfsdGy8s8UvZU19jc+QMokTmL3HwLUbViGbAjUY1+GstKikz3DJSys0FJiEO6r5XKT8gOQDRp4hKggPIKXUTGbX7kIs5DF5D3PMw+z6ovs5VODg4/IBzr9tDTNk/kr1q98Frz/VMcsMjBro9PiflwdzjkOkBAR4G1wzEhz+bzsvxOz2z14QzMKRkZtigme/McHOZcWuquLCesFESBNbThCV3dsBJIuwldW+YSsXFe+7B5bNb0JMJp/feh1tXr6Lvtrh47/24/9UPoF44werCCUQcS2s4vXCKzeYCNtOEz61WAICTacIFVSr/d4OjoKsBaKieqF4ZjXpHONFGBVOUlusZyH3AsPvsGN6pA+fU3KsQEiOcLsjrUqkegRjhkGDtKTUnN1MhaxXB0Yz7QS2+vezQHuojq04hY/Ym71P+Q9mujmYJP3V4reH1RiYoPcjIrvXo6yCStBZebFPe14C6SqlAoAw9EhJpHVUNEnN5wtYOnxUDtCAG0hWhHxaBPGboXAiiSG7m3HXBFi2UetJeSERpSTfnjJ9J3puDJBEg+zfguwIfHnLqgbyYhX18Y8JahWSVyhlB1RhGLvHDnRWeBSU/2XkdB0lj/LksnAdrrWGJXtrcmLi11niP+oE8zFdYd3SQ+tLNWzBVKiF0wl8NCN8d2iHQWiMagRIZmPdo2ubEP1AQihIiqB2YPPTCgr2lEiV+0I2ZibDpKpKmgMI5DiCIAHH4mmNuLdQbqNjQIkhtrWPXEusnfXlS6mFhDdRpPXxaRDjnggGhRTZWasxNsYoyESydXjy77rjZgavzghu94WZfsHXBrKB9NCKTJOozDoHDoALsmXcAD+sRAOJ1eXAdHuyHVc2gnD9HiX9OqDOGeg+D12Gw7AHlSjtfkR2+7lz1JkmPjZkglZgDGtSLgwrrfFAdkGb8fB4gcQjFrx6fX/xHz4xVee2zM1htveFWX3A6TThxw8YNZ42bNOZZuWkDARAHVpsJJ6cbNBW86a1vw1Of+Qy8LXjtN3wjrjzwABZVbE42NKtcOq49ew3r1YbPaCQRF6cVrqw3gCqaG/8sMSAtMkYTKOXFZGQORt7OjIaUzs+LRn5xj1sjtBsX4O5A7+jbLUQlZKUUG6Eh56YoTotiEqIDq6KYhM87z/OEkTG06xIipPzQ/j4kJJu6cIi+ZcLw6Z/F90XlD6mEzXL2KdRRkclKIgespjj+QWJS9nz4vYZQcoizoI5eZlD0o1IjtQFkGRt/ZjjusPKJ98yHWQdkN8TDJK1z2D+dVhWTdRRrQPhYdQSUlz9TdQgBZ7Agbhq/BlE1S5JCALGY2XODJ2yX9H6P5ztkk7olo9dD8onVc4/nNdsqBkdbSPiYlwXLsqAtC1qzcwzfV4TA7Bev30AXTti7CHrRkPVHlPQ5XhZVBzAawWSUBSOs8YGp0UCt7lhrZHsgy64K7S+KswoSo11GF/Jp1iHfU7Wyd6XC5q9QtoSDoQsMFpqAjgWhEWhpTR3N5VUZGVXa0ZtzRiIVHCQav8mnUQm1aBc0CHbdcGtpuLUYbixk8V1vC4OiY2Dz7jrICGZ+LricV3neVzW3/7dzBzm+HOI7/Pfe+zm4MIPdeP1Bryp/z+H3ABhSPLcHk8OgdVj9SCl7KDEz7aBaU0PPx6a+/foOq6tap/H+DiFBC+8kpPahx/xacd4zFW5YGLZuWLeGTa24Me/4/VGlCgRtabh5g2SIaZpw33334ur1G1hfvoxv+brXkoigBTKtaNFSJ6B3fOFzX8TNG7cgFxVYGpaF/k8rAe47PQl/J0GZCpoE6KkSPVPei9RabKJoveP6PA8yh4ugtQUQYgRDlgsSdg8Oaw1iC4oBJ+s1TlRwUgpO1xVrZZBalQkKKltQvSBozGGIB2ffGC6DfUuNRsJ9lK6K4ikGX2tRTF5Ry4KT1RrNwhnZDWiUKvNucGnwgPrgVAYXYWWBpFqPCpHPWI/76iKoec2eCWokR5Uoh8XZwj7RfkYwdgXHKcxHVjJIc4yVkfkQDXHx0N3jwPhGEJJlDbuu6GFLYt4pKuB8b/Hws4ebCAS7EHsSiO/3JsK3jp9pQqqpo5nnC5mtjFyRpCRzIiC9dC1OyadlafRTa2ES2jqk26iGVfdw/ldbd3SQ+tLZFqgTRRFjgLML4AfwhQScISU8kwzhGcQMg6oFpJ4vnZpfxR2LCHZo4dUUIo0iAf8xE5qUg4aEHjT0AmPwMXeUEZIoWlAnypgoDBppyr6Hw4ellgmrqVK0sdY9HT5mg+al7Sfhc6A2RE1LndDMqDvYDbcWw9WzLW62hptL/YjGAAAc4UlEQVRGosQiioVYGo3PhHCFBfEEB9XTYaBKkVFgD4UN6OW2/tBhcHquAJcrA1QGLAsKNw6qmaxsAEQze9+sz96AOFmEiZ3zlwVCcRCcDt9fBqkMkEMENQsmyMiQRUDoo/BzdjsfdHM4OQVMaZ5ZoVLhE7llPivm3tikNyYqu3gPZ9strl27hrYs+OIzz+Dqs1chAtx3771Yr1a4cOkSZmdmT1mbmAErpGhf+9KX8IXPfx6bk9M4vTt6SChNCpxW9jggPJhLrUPZgJ9bQRpfNvMYFO24Z1n4nuNri5Fzlsr4FpVLXa3iHna4ndBGptAtel0qNisKkK41hkoRKggI201PmSCOfLiXvflkPDN78o2MbF0oBhmDqIJNndBXMipCWIcK6MjdA8aUhSe2F5IxiJ+PXloGFZFQhYAFUxb73xsVlwoHvitIjGjgqAiM7si0sg84OQ5wyrVZnE1CKHr8XI1A2YLswWsvolhJRfeOpVcsVimk3AzoSWpymEkw9PgMuLNPnfHPzcZ18ONhUFZln7CEgWlMPPGZTmgz96QCQEg9xR5oRh5A7z3Ee30omLBRSYr+VBNxip5sewXAfVv3GOiL8lYrRTAjSnfJDIPT0ckqJWsr0klPiItpAYMQFYELHGcLBzeLKDaFtgEFpKyvEAeb87CnHxwrLLHA0rVAJ0A7iRALQKZrHHDSNRrshq6FGnGlhIeSDG2vvI7eOqpSraBGZkx3XEpCzWbYNcONecGzZzOuzTPljgSYhW60PbAqj0NV5SAYxGeaweq5ghCALzv0bydD3P7vt6/DAAXsKxNCjwEjHASohBS1sHnbstqLn2dubPaOt7rvk7mlConnlw4ceBHPju4RksPrjWrWehuHSEJ9vB9tWI/0YDdyOJKzc2pkhpkCvgja3GBtQWsNZzM36dIa/udnP4sbzz6LUgoefOABtLbg2Wev4uLpKdbTFEr5iuJRoaPDe8P1q1fxO7/9SaxqHQOobhgHgPeGvswcfSjgILl3FA/hX0QGLgBE0ZWmlQbFuiq6TcMuIgk143NENNqTCYc49LAfwKV5J3tOKRRcY56IbMF9AuNZESTyls9W7yNrT9jVo7rLyqBAMIXLQSsFbVJYKwwYba9u4T0TnA5FZaBD9HRMkP1eDvPvr4XsvrjvcXQgBqFNKVbbkDJPHmgK97l10tats2+NqNbGKIeRUOGRuKqwjyWIBMlpy2NTwdIrWk/7DJ4NRFuS9RdJGRT73nU+zzJ6shLBmMap1EpcF41zB0FB1+in+xhIZuzxQG1YNWWAymQtE0PV1CiN+46QsorWSFteAey+OVkoMeBHLDgeciS2HVVB1AFF6edjnjUwm5ZS+OH1ICM0p5cRrKMYh3EXF2wKpe5rDP/JSLM6rJG+voJiI4VkixItXmHQWq9WZD2ZwZoOheJmDpdC6EIpb6LuY77ALHyKjLvYPTK/7lAxmJNav106bmxnXN/NuL7jcOOZdbRa0JQKEt1ZOQCsNplwJSMRQw6lYA/D3c7Yu/3fzzHqbqtcAHzZ9yJ+T64Bz/WDHpbvvzfZecnKqzEwelhx3f67sh84DrvYPFllELrif1sOyBaHcF/22kawBEK4N2Z1eo8ZHgk4ZJ/p9zj8VIX6ghWU7eqcdVsa2U1aCu65eAkndcLlK5dxevECrl67ii98/vO4uVrhvvvuw3q9YXXHN4C27PD5zz+F//n7v4e+NKxPL2JVC6x1nO22Q6mD6t8L2O8XCjEbOJAahwUkYD9l4OYIBoOMV84WZdbcjYEo+xuqDFJUYvE9vNRjfAMMTnk/3CmB5Ea6Ptl77MbkDFviU/yd4eobz2rRUPAXsKckLJlFQvNyLUkwgzpV71etYltow7O47ElKvSFnf0Z14almkghCtgYikMT7zXJLdD+n1MEKyUCft2LYQ435T6iPyHhOfV+1I3ucJFdBGjxl3+AheRRWJUjFceM5Jh6tDRkVmo1ihsiRiKBoJbErPp/JHBtVnJTC2Snl74Ig9WJir/Bv3Tg4n5/TmJvLvc9NxKH8QiX/vThtjnUkuWO/Z7/SuqODlNcJqBWpsA0Ln5v8esAZdap7VYSiISLKg6cEx7KZo5ZgwwRjqER22MEqyJ1ZzyKGYoadNGw7e1gbpZbbyVThUlFrJYSAtGNTqBpqSNt4F5xAg1lI5WEU4u+rUjgcKAjR0462NPS2QIVBpoZWmkemv5hjVpo6DquRZcF22GkEwwtG4VXSfkKepUKV9GmzHIwOm2tkYMG+GYvzkFn+++1BKv80s8EGPOwf3c7qG72wqBrHRo7DgJBG9LGqQk0HDl6i75Q/K3/X7T2mQ0uJ26HH2+nzrbVh1nY7KYTwSChb6J4OPQJUa2woq6PWQqmckAmSQdPl+9wFrHZ6eoqT0xNsd2fobcF6mvDs1at45qmnsK4TNpsTAMCN68/i+vWruHb9KkqpuHLlMooCtswUtW3zUIMwNw5/g7C2CCt0D/vzUitKTSFe7pn8WGpULJJogxCl4KHNa6V5ZKpP+IANPQIdmNPv70skC70t/DuhCCzLDA9GWfbIHCSi8LMPuCoQB2pSAlL2MK+oQIz6jqtpwqZWbBv3wW4huWq3tAG3Uql+P39FiDDmjZAJh5D8IPsjNWIgk2MJVCYqfAfnH6VnWejUIsuI4ftALsjnGsiSjcdYh1pUGVlRwVGKYJoU615gXqHKwJWVPN2Fdch7dRyMtQioGVoLz6bwSavuWEHItJQglphTwSfFAiwtfALKA5XXs4ec+7RColUVyUSpNDCNRAbi5/aY+CuAOLFrhl457Z6uqMD+kNKkSnsKHxagsRlo0cBs1lCmChHCbh6T5QJBh6NKQYthtI6QgOl96ASqUhh2I4KLteLyyQl0M6G60TKk8wAsq4kZj3XAO1aIgUotmIoRg44GqsSD3R1wKSiIjD0qQy0KrdP4Z3EysW7MHdfmhutLx42l48wMiwhalNhUOgrdslAej2OX16w0XszP0A4y43yw+kHWlEsPPvPDzz+bxDkYm3pwh9+b1O/DweF+8DodgZKK3jg4RLPKKaXsNefi3w/hw/RSOgxEt7P4vNuogviNsrek6PvKaXwW0Tguo0FNxhSEcOUSTW0A6JSyJ9TTADOBOZ2iAR6AW2OTf7XbQs1QobBacXKyxtYWfOmZZ7DMM85u3URfZsLLpWBzssFmcwGr1RrSF8w3r6FOhIMAYNcbtr1jAgVfi/JZ6zN1/mpk5HwfrFCz0uzx2Ql4baVE01sIf/XeOY4QMFPCWUUktAoxYEQ/OJyp5NHjNef7egIPwtG+Gs7ni013js9SjxKAxyiJRx8x+tCTKlbThJUAp6WirWwooKdKResUam3WgnkGNDi6yb5YGnNCiL/00Seq0ddLJwCJKiajnjsZwd4RrGIAFlzTOLx1POfco8iKHDzsU6IJIVOl6xVWteKeEzLn5mWJYZcIHAPCdSzLXp8yE81SgKkidPxyzAahkch+u0sQkyyThBh6Du6hBZKDqKSokhE/X6KKivGCGv0oQvWVlV1WrvIKCFJelH2nsH9w+HhoeOymr0uCyEGjTGw5ByhTaPRg/ghwQiWR0FhxuPeYfPfwzwk6qjt2JW3hKR676EJdNy3hP7S/ORYbKpu1VeM2yJ640IX9p4S42rJwsA5l9FCY2ZKeftYW3Fw6bu52nLg3zokhlBoGtKNseh7CVxameAH6j7ZO/nk7dHc7jVxwvrIalZTv6eqmkXUdVFK8L9HYzcjjUcGNX54IF++xKoNtur9KvEb2eA03TJGRqYrIl8GDI+hGpXTYH8trOIT5RoWV/w6MWSE+IHQvFgmDu9E4DjPHTFD6Ph9Pm4kOskS3y4Jr125gU7m51+sVelujzwvuuXwZ89kZpqKYt7ew7GaoUjNQHJi3Wyy7MxQFNusLI5Cfbbc4W7aY24JJJ1StWFpAWqpQ79C+py1X2AjOtKyIgVkRlE6lhOTd87OMPoWw99JazJwFnDxsUNyC0ZWHnwdDk4zUzNCIYgRsdftTFihHzieVfLYTugb7N4iEaBKEKzT/vU/hhOt0mW4HgWpZForQ2kRoHdHDlINqXkB7GkFIpjHwqIZSQ8yZieU8Et9zEuHEhVp2QJAUNPQII+mJMQ/uzwhcYLBCBLaKQhuOaUKbVuGGzI8nmcAJW7bWOZfU9yK/qvRSy766gMQuMc6g5NiZRa8rUap8apN/y/0diBSyb6dD3KAGLFuVvVRVhRRSvnKPtOcYR3mudUcHKTNDj5tUS90/1jqmKr7s9akplwdlwlBAHq7EWyUO7MSge6fRWMIDnrCAMLiZKMoSkAEcW1WspeCkVGzWK0wWEinKA7cYyRyl+xiW5AMdA7Ei6CWy+ZjvSNZMdKrgYC9r1xp2fcF26YT4loXOuiKwAB1jt7GqiBId7mMDmNlw3siH3LHvG/Xez1caOIBvDkr40RtyD7QjCCKRmqZ31OH3H8KDA9fO+zUgov33DCbgIXU9Pr/x73FQ1lrPQY+3r3NV3cHXD+G9/FrCGvvK0MfvSptsj4MiUusRROk823FuW2aVGICwm+Pq9euQ1nBhs8aF0xOsphXk9CI20wrbUnG6WuFWLTjDTfTeMG+3uBGH6WoidNtm6vgBbMBTT4+zNYv2SFoE3husA9oi4Kugdu4jAR2oxRmkpoPnhhBOqM730I2UGFyPABiOlICwQtprOEbFMT7bbPiT9CJBNpBxKyIQRlKT/0+5nwaxA/goyh4Bg6uGxQjiee7S0YRJ3RqKLkpR32Lo0yrsNXpA7LaXBwMG/Iewm8hgmyw2iXMiA2X17M9EEICHCv0+USPDEgPOPhTEk4PPhpUsqxyEDBfiPtg0jfuVifmofpQVa/fOmS+Lz/PguQ4qJMdPLPaX09qkI8gyGZiwD5w03pUgnlC5px5WUVpRC8Vz01uO1xOsQafH1vNZd3aQ8pDqgYxB1LxZeSB/eSN/P3sDYH84ZpZfBKrsIZj1eEgEkDDpkhIadbFphRlEc8XWDDYvMHNstGCtBafThHtEsJoUKyhWCPgOMiRXJKo8RHZj3UKeqWCJRyQpwxqeTXlAthY2963h1jzjbA67ewea0GrCVTM9wnjkVPa9H81BWgwc+vb+DkCoEHJ+2DfVr/N7cgPGDTpXiWg5mN1wytbIwe9IWECw/5nP1V/K9/VcxIvD19wemIZSwm3fA+B8gMlqS3Wv4SdJle+AEmLSOGy7xCxWwFyZBMHZ90ioNiGhPLTz4F5CHFXmGaU70A1Xn72O7c0zTLWw0R0JlWiBNVYhGtV4351BSypyayhrh+SSGXbzDrVMVHCAR3skKhIRoAmkssdWrAfERofrIgEPmwcaEKK9ca+zms1DsINVAG+/7yWfhDBuwlqq+aeOqjvvmx4gH3AmgQwDPu6NewcSxsrnK1TSRcK5AD5YbA5W6DVQClMOKHmJXqXv2aQejLUWwXHAWxKBBNl765xXQiZkfB09ovbJjIZKUj5bmcBA9sSdfB7z1yDfdRAvC1L2Kqsr/q3K3jlBogIzJOtQwkQzfedCSzMRHeN8UxI6hq6e0yS0C1md/HlxPzIJyMBY8n1EtS0MUFNYmQyZJ2cgJI2+Y9cXnJ1t8XzWHRmkBhNsmUc/IA/Hc1CUDvnH/fckO0nYD2KGgIPhsswbAOkNCE0s8gyi75EBziy8YaiJNcfm7LsdWpkwC7Xx+jJjMynWteCkVqxVwqmWwQpmQTklZEQWFTdANxtCtFILzDpmAKVMMN1hAXBzmXHt7AzXzhpuBrV5644OhdYV1ZiFgdw5OTlUvQ+TqnHduWkODvT4AjfWweGfrK/cfCIHnB0Lj6HY3H5wb/Jg84OqKA/wwwopv5++O4pEsTUC9f5X7YOqx+GAUuAHmwTY+1q1gyTFI0v9sr6ayAjwmQT13vkz80ANirsDlLuJ798HYCOFOnJyVzJIuxn6zRm4Blx748exvfT7nP53EHYO29j9AYxRrfXekE6w8WZRaiWsUnm9N1/7ReAaYAuf4aXtAAi8b4NQBHqZVdqiiAimqaLWaR9gQefoFtDNVJW2FNgnKvMsYz9BwrHXfAzMp/AzEL3GYO8xGUx7B9nfe/cxQJoMsExeEiIELFANjypmX4SkT1MDIrkYNzTeM/aMtPHwRHJ1CAf3Rjp53Iv0kzMEHAcKO1vvo3fr8VxroYBaFveHCVS+znITwAfDch8MEILW8awjPPKwrzBFIkCJjM8rE8YemB9huvEmxkAvg1DMNrU+yGKLMTi1rDyDxecCBjaxcT35ewljOm1TRAAFSVg93odZPPMcbJ97w3Zp2LYdbm3P9nvtKyzxr/aKl+H67//9v+Mbv/EbX+q3cVzHdVzHdVz/h+szn/kMvu7rvu7/9+t3ZCV13333AQCefPJJXL58+SV+Ny/eunbtGl772tfiM5/5DC5duvRSv50Xbb0Sr/uVeM3AK/O6X4nXDLCCun79Oh566KGv+Lo7MkglHHT58uVX1E3NdenSpeN1v0LWK/GagVfmdb8Sr/n5FBnPjwN4XMd1XMd1XMf1EqxjkDqu4zqu4zqul+26I4PUer3Gu971LqzX65f6rbyo63jdr5zrfiVeM/DKvO5X4jX/76w7kt13XMd1XMd1XK+MdUdWUsd1XMd1XMf1yljHIHVcx3Vcx3VcL9t1DFLHdVzHdVzH9bJdxyB1XMd1XMd1XC/bdQxSx3Vcx3Vcx/WyXXdkkPpn/+yf4eu//uux2WzwyCOP4Fd/9Vdf6rf0B16/+Iu/iD/35/4cHnroIYgIfvqnf/rc190df//v/3285jWvwcnJCR577DF88pOfPPeaZ555Bu94xztw6dIlXLlyBX/1r/5V3Lhx40W8iv+99e53vxt/7I/9Mdxzzz149atfjb/wF/4CnnjiiXOv2W63eOc734mv+ZqvwcWLF/GX/tJfwuc+97lzr3nyySfx3d/93Tg9PcWrX/1q/K2/9bf2/jovw/UTP/ETePOb3zyUBR599FH87M/+7Pj63XjNt68f+7Efg4jgh3/4h8d/uxuv+x/8g39wTrlfRPCmN71pfP1uvOYXbPkdtt7znvf4arXyf/Ev/oX/5m/+pv+1v/bX/MqVK/65z33upX5rf6D1Mz/zM/53/+7f9X/7b/+tA/D3vve9577+Yz/2Y3758mX/6Z/+af8v/+W/+J//83/eX//61/vZ2dl4zZ/9s3/W3/KWt/iv/Mqv+C/90i/5G97wBv++7/u+F/lKnv/6ju/4Dv/Jn/xJ/8QnPuGPP/64f9d3fZc//PDDfuPGjfGaH/iBH/DXvva1/v73v98/8pGP+B//43/cv/3bv318vbXm3/qt3+qPPfaYf+xjH/Of+Zmf8fvvv9//9t/+2y/FJT2v9e///b/3//gf/6P/9m//tj/xxBP+d/7O3/FpmvwTn/iEu9+d13y4fvVXf9W//uu/3t/85jf7D/3QD43/fjde97ve9S7/lm/5Fv/sZz87/vn85z8/vn43XvMLte64IPW2t73N3/nOd45/7737Qw895O9+97tfwnf1f2fdHqTMzB988EH/R//oH43/dvXqVV+v1/4v/+W/dHf33/qt33IA/mu/9mvjNT/7sz/rIuK///u//6K99/+T9fTTTzsA/+AHP+juvMZpmvxf/+t/PV7zX//rf3UA/qEPfcjdGdxV1Z966qnxmp/4iZ/wS5cu+W63e3Ev4P9g3Xvvvf7P//k/v+uv+fr16/5N3/RN/r73vc//9J/+0yNI3a3X/a53vcvf8pa3POfX7tZrfqHWHQX3zfOMj370o3jsscfGf1NVPPbYY/jQhz70Er6zF2Z9+tOfxlNPPXXuei9fvoxHHnlkXO+HPvQhXLlyBd/2bd82XvPYY49BVfHhD3/4RX/Pf5D17LPPAtir23/0ox/FsiznrvtNb3oTHn744XPX/Yf/8B/GAw88MF7zHd/xHbh27Rp+8zd/80V893+w1XvHe97zHty8eROPPvroXX/N73znO/Hd3/3d564PuLvv9Sc/+Uk89NBD+IZv+Aa84x3vwJNPPgng7r7mF2LdUSroX/jCF9B7P3fjAOCBBx7Af/tv/+0lelcv3HrqqacA4DmvN7/21FNP4dWvfvW5r9dacd99943XvJyXmeGHf/iH8Sf+xJ/At37rtwLgNa1WK1y5cuXca2+/7uf6XPJrL9f18Y9/HI8++ii22y0uXryI9773vfjmb/5mPP7443ftNb/nPe/Br//6r+PXfu3Xvuxrd+u9fuSRR/BTP/VTeOMb34jPfvaz+If/8B/iT/7JP4lPfOITd+01v1DrjgpSx3X3rXe+8534xCc+gV/+5V9+qd/Ki7Le+MY34vHHH8ezzz6Lf/Nv/g2+//u/Hx/84Adf6rf1gq3PfOYz+KEf+iG8733vw2azeanfzou2vvM7v3P8/c1vfjMeeeQRvO51r8O/+lf/CicnJy/hO7vz1h0F991///0opXwZC+Zzn/scHnzwwZfoXb1wK6/pK13vgw8+iKeffvrc11treOaZZ172n8kP/uAP4j/8h/+An//5nz/nzPnggw9inmdcvXr13Otvv+7n+lzyay/XtVqt8IY3vAFvfetb8e53vxtvectb8E/+yT+5a6/5ox/9KJ5++mn80T/6R1FrRa0VH/zgB/FP/+k/Ra0VDzzwwF153bevK1eu4A/9oT+ET33qU3ftvX6h1h0VpFarFd761rfi/e9///hvZob3v//9ePTRR1/Cd/bCrNe//vV48MEHz13vtWvX8OEPf3hc76OPPoqrV6/iox/96HjNBz7wAZgZHnnkkRf9PT+f5e74wR/8Qbz3ve/FBz7wAbz+9a8/9/W3vvWtmKbp3HU/8cQTePLJJ89d98c//vFzAfp973sfLl26hG/+5m9+cS7k/8IyM+x2u7v2mt/+9rfj4x//OB5//PHxz7d927fhHe94x/j73Xjdt68bN27gd37nd/Ca17zmrr3XL9h6qZkb/7vrPe95j6/Xa/+pn/op/63f+i3/63/9r/uVK1fOsWDupHX9+nX/2Mc+5h/72MccgP/jf/yP/WMf+5j/7u/+rruTgn7lyhX/d//u3/lv/MZv+Pd8z/c8JwX9j/yRP+If/vCH/Zd/+Zf9m77pm17WFPS/8Tf+hl++fNl/4Rd+4RxF99atW+M1P/ADP+APP/ywf+ADH/CPfOQj/uijj/qjjz46vp4U3T/zZ/6MP/744/5zP/dz/qpXveplTdH90R/9Uf/gBz/on/70p/03fuM3/Ed/9EddRPw//af/5O535zU/1zpk97nfndf9Iz/yI/4Lv/AL/ulPf9r/83/+z/7YY4/5/fff708//bS7353X/EKtOy5Iubv/+I//uD/88MO+Wq38bW97m//Kr/zKS/2W/sDr53/+5x3Al/3z/d///e5OGvrf+3t/zx944AFfr9f+9re/3Z944olzP+OLX/yif9/3fZ9fvHjRL1265H/5L/9lv379+ktwNc9vPdf1AvCf/MmfHK85Ozvzv/k3/6bfe++9fnp66n/xL/5F/+xnP3vu5/yP//E//Du/8zv95OTE77//fv+RH/kRX5blRb6a57/+yl/5K/66173OV6uVv+pVr/K3v/3tI0C5353X/Fzr9iB1N173937v9/prXvMaX61W/rVf+7X+vd/7vf6pT31qfP1uvOYXah39pI7ruI7ruI7rZbvuqJ7UcR3XcR3Xcb2y1jFIHddxHddxHdfLdh2D1HEd13Ed13G9bNcxSB3XcR3XcR3Xy3Ydg9RxHddxHddxvWzXMUgd13Ed13Ed18t2HYPUcR3XcR3Xcb1s1zFIHddxHddxHdfLdh2D1HEd13Ed13G9bNcxSB3XcR3XcR3Xy3Ydg9RxHddxHddxvWzX/wJ+D92ILToO7gAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "img_marked = img.copy()\n",
        "\n",
        "for marker in dom.getElementsByTagName('object'):\n",
        "  bounding_box = marker.getElementsByTagName('bndbox')[0]\n",
        "  x1, y1 = int(bounding_box.getElementsByTagName('xmin')[0].firstChild.nodeValue), int(bounding_box.getElementsByTagName('ymin')[0].firstChild.nodeValue)\n",
        "  x2, y2 = int(bounding_box.getElementsByTagName('xmax')[0].firstChild.nodeValue), int(bounding_box.getElementsByTagName('ymax')[0].firstChild.nodeValue)\n",
        "  cv2.rectangle(img_marked, (x1, y1), (x2, y2), (0, 255, 0), 2)\n",
        "\n",
        "plt.imshow(img_marked)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pITdjHLyj4xV"
      },
      "source": [
        "## Dataset creation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "N38mfjVEFmKi"
      },
      "outputs": [],
      "source": [
        "!rm /content/legos/dataset_20210629145407_top_600/images/225122c6-db87-11eb-bf78-3497f683a169.jpg\n",
        "!rm /content/legos/dataset_20210629145407_top_600/annotations/225122c6-db87-11eb-bf78-3497f683a169.xml"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "an-CFdLES6Yf"
      },
      "source": [
        "### Convert XML to Pytorch TXT"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZBBtleS9TtyB"
      },
      "source": [
        "- https://blog.paperspace.com/train-yolov7-custom-data/\n",
        "- https://learnopencv.com/fine-tuning-yolov7-on-custom-dataset/"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FJSfgbjNhQsy"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "from IPython.display import Image  # for displaying images\n",
        "import os\n",
        "import random\n",
        "import shutil\n",
        "from sklearn.model_selection import train_test_split\n",
        "import xml.etree.ElementTree as ET\n",
        "from xml.dom import minidom\n",
        "from tqdm import tqdm\n",
        "from PIL import Image, ImageDraw\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "random.seed(108)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-xW2iu8_kVu5"
      },
      "outputs": [],
      "source": [
        "ANNOTATIONS_TXT = ds_kaggle_path/\"annotations_txt\"\n",
        "if not os.path.exists(ANNOTATIONS_TXT):\n",
        "    os.makedirs(ANNOTATIONS_TXT)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Lzoz-43IToQx"
      },
      "outputs": [],
      "source": [
        "# Function to get the data from XML Annotation\n",
        "def extract_info_from_xml(xml_file):\n",
        "    root = ET.parse(xml_file).getroot()\n",
        "\n",
        "    # Initialise the info dict\n",
        "    info_dict = {}\n",
        "    info_dict['bboxes'] = []\n",
        "\n",
        "    # Parse the XML Tree\n",
        "    for elem in root:\n",
        "        # Get the file name\n",
        "        if elem.tag == \"filename\":\n",
        "            info_dict['filename'] = elem.text\n",
        "\n",
        "        # Get the image size\n",
        "        elif elem.tag == \"size\":\n",
        "            image_size = []\n",
        "            for subelem in elem:\n",
        "                image_size.append(int(subelem.text))\n",
        "\n",
        "            info_dict['image_size'] = tuple(image_size)\n",
        "\n",
        "        # Get details of the bounding box\n",
        "        elif elem.tag == \"object\":\n",
        "            bbox = {}\n",
        "            for subelem in elem:\n",
        "                if subelem.tag == \"name\":\n",
        "                    # bbox[\"class\"] = subelem.text\n",
        "                    bbox[\"class\"] = \"lego\"\n",
        "\n",
        "                elif subelem.tag == \"bndbox\":\n",
        "                    for subsubelem in subelem:\n",
        "                        bbox[subsubelem.tag] = int(subsubelem.text)\n",
        "            info_dict['bboxes'].append(bbox)\n",
        "\n",
        "    return info_dict"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s57RSFBTgOEr",
        "outputId": "c1b9f1c8-3591-44fd-910b-0eb554bd9a95"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'bboxes': [{'class': 'lego', 'xmin': 285, 'ymin': 0, 'xmax': 353, 'ymax': 363}, {'class': 'lego', 'xmin': 289, 'ymin': 559, 'xmax': 326, 'ymax': 600}, {'class': 'lego', 'xmin': 301, 'ymin': 90, 'xmax': 333, 'ymax': 187}, {'class': 'lego', 'xmin': 154, 'ymin': 487, 'xmax': 445, 'ymax': 516}], 'filename': '2059d05c-daa2-11eb-b1a3-3497f683a169.jpg', 'image_size': (600, 600, 3)}\n"
          ]
        }
      ],
      "source": [
        "print(extract_info_from_xml(EXAMPLE_IMG))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_doESsMWgzPY"
      },
      "outputs": [],
      "source": [
        "# Dictionary that maps class names to IDs\n",
        "class_name_to_id_mapping = {\"lego\": 0}\n",
        "\n",
        "# Convert the info dict to the required yolo format and write it to disk\n",
        "def convert_to_yolov5(info_dict):\n",
        "    print_buffer = []\n",
        "\n",
        "    # For each bounding box\n",
        "    for b in info_dict[\"bboxes\"]:\n",
        "        try:\n",
        "            class_id = class_name_to_id_mapping[b[\"class\"]]\n",
        "        except KeyError:\n",
        "            print(\"Invalid Class. Must be one from \", class_name_to_id_mapping.keys())\n",
        "            continue\n",
        "\n",
        "        # Transform the bbox co-ordinates as per the format required by YOLO v5\n",
        "        b_center_x = (b[\"xmin\"] + b[\"xmax\"]) / 2\n",
        "        b_center_y = (b[\"ymin\"] + b[\"ymax\"]) / 2\n",
        "        b_width    = (b[\"xmax\"] - b[\"xmin\"])\n",
        "        b_height   = (b[\"ymax\"] - b[\"ymin\"])\n",
        "\n",
        "        # Normalise the co-ordinates by the dimensions of the image\n",
        "        image_w, image_h, image_c = info_dict[\"image_size\"]\n",
        "        b_center_x /= image_w\n",
        "        b_center_y /= image_h\n",
        "        b_width    /= image_w\n",
        "        b_height   /= image_h\n",
        "\n",
        "        #Write the bbox details to the file\n",
        "        print_buffer.append(\"{} {:.3f} {:.3f} {:.3f} {:.3f}\".format(class_id, b_center_x, b_center_y, b_width, b_height))\n",
        "\n",
        "    # Name of the file which we have to save\n",
        "    save_file_name = ANNOTATIONS_TXT/info_dict[\"filename\"].replace(\"jpg\", \"txt\")\n",
        "\n",
        "    # Save the annotation to disk\n",
        "    print(\"\\n\".join(print_buffer), file=open(save_file_name, \"w\"))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ISSuQBOQhC25",
        "outputId": "156bb76d-9942-4dd8-fc63-7237a5a4eca5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 167801/167801 [01:00<00:00, 2753.96it/s]\n"
          ]
        }
      ],
      "source": [
        "# Get the annotations\n",
        "annotations = [ANNOTATIONS/x for x in os.listdir(ANNOTATIONS) if x[-3:] == \"xml\"]\n",
        "annotations.sort()\n",
        "\n",
        "# Convert and save the annotations\n",
        "for ann in tqdm(annotations):\n",
        "    info_dict = extract_info_from_xml(ann)\n",
        "    if os.path.exists(IMAGES/info_dict[\"filename\"]):\n",
        "      convert_to_yolov5(info_dict)\n",
        "annotations = [ANNOTATIONS_TXT/x for x in os.listdir(ANNOTATIONS_TXT) if x[-3:] == \"txt\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-gwA9A6wpS6n"
      },
      "outputs": [],
      "source": [
        "# for file_name in annotations:\n",
        "#     os.remove(file_name)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eTxOcD3Rk-Tx",
        "outputId": "93f02744-8d00-4cdd-c36d-45af399c02cd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0 0.290 0.330 0.580 0.307\n",
            "0 0.683 0.555 0.627 0.483\n",
            "\n"
          ]
        }
      ],
      "source": [
        "with open(annotations[0], 'r') as file:\n",
        "    content = file.read()\n",
        "    print(content)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qSlQ4lTqj8xN"
      },
      "source": [
        "### Train, valid, test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XWGpnt7-qC1G",
        "outputId": "38d721bb-a2f7-433b-e470-1a71b38c21b7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(167799, 167799)"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "images = [IMAGES/x for x in os.listdir(IMAGES)]\n",
        "annotations = [ANNOTATIONS_TXT/x for x in os.listdir(ANNOTATIONS_TXT) if x[-3:] == \"txt\"]\n",
        "\n",
        "images.sort()\n",
        "annotations.sort()\n",
        "\n",
        "len(images), len(annotations)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1LLlIgVRlvFn"
      },
      "outputs": [],
      "source": [
        "# Split the dataset into train-valid-test splits\n",
        "train_images, val_images, train_annotations, val_annotations = train_test_split(images, annotations, test_size = 0.2, random_state = 1)\n",
        "val_images, test_images, val_annotations, test_annotations = train_test_split(val_images, val_annotations, test_size = 0.5, random_state = 1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p0412vCYrZ5K",
        "outputId": "c22a79f8-2e4f-4c8e-978a-a97511efa7b9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(PosixPath('/content/legos/dataset_20210629145407_top_600/images/4256da1e-da69-11eb-be21-3497f683a169.jpg'),\n",
              " PosixPath('/content/legos/dataset_20210629145407_top_600/images/ce18d9f4-e477-11eb-b6bd-b0c090bd3910.jpg'),\n",
              " PosixPath('/content/legos/dataset_20210629145407_top_600/images/99bcc124-da57-11eb-a621-3497f683a169.jpg'),\n",
              " PosixPath('/content/legos/dataset_20210629145407_top_600/annotations_txt/4256da1e-da69-11eb-be21-3497f683a169.txt'),\n",
              " PosixPath('/content/legos/dataset_20210629145407_top_600/annotations_txt/ce18d9f4-e477-11eb-b6bd-b0c090bd3910.txt'),\n",
              " PosixPath('/content/legos/dataset_20210629145407_top_600/annotations_txt/99bcc124-da57-11eb-a621-3497f683a169.txt'))"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "train_images[0], val_images[0], test_images[0], train_annotations[0], val_annotations[0], test_annotations[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LsNdIiSKmCbq"
      },
      "outputs": [],
      "source": [
        "YOLO = Path(\"./yolo\")\n",
        "YOLO_IMAGES = YOLO/\"images\"\n",
        "YOLO_ANNOTATIONS = YOLO/\"labels\"\n",
        "\n",
        "os.makedirs(YOLO_IMAGES/\"train\")\n",
        "os.makedirs(YOLO_IMAGES/\"val\")\n",
        "os.makedirs(YOLO_IMAGES/\"test\")\n",
        "os.makedirs(YOLO_ANNOTATIONS/\"train\")\n",
        "os.makedirs(YOLO_ANNOTATIONS/\"val\")\n",
        "os.makedirs(YOLO_ANNOTATIONS/\"test\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eTiE7wl-qLf_",
        "outputId": "2e4bda7d-684d-4151-b1a8-647ff222af71"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Moving images\n",
            "Moving annotations\n"
          ]
        }
      ],
      "source": [
        "#Utility function to move images\n",
        "def move_files_to_folder(list_of_files, destination_folder):\n",
        "    moved = 0\n",
        "    max = 1000\n",
        "    for f in list_of_files:\n",
        "      if moved >= max:\n",
        "        return\n",
        "      try:\n",
        "          shutil.move(f, destination_folder)\n",
        "          moved += 1\n",
        "      except:\n",
        "          print(f)\n",
        "          assert False\n",
        "\n",
        "# Move the splits into their folders\n",
        "print(\"Moving images\")\n",
        "move_files_to_folder(train_images, YOLO_IMAGES/\"train\")\n",
        "move_files_to_folder(val_images, YOLO_IMAGES/\"val\")\n",
        "move_files_to_folder(test_images, YOLO_IMAGES/\"test\")\n",
        "print(\"Moving annotations\")\n",
        "move_files_to_folder(train_annotations, YOLO_ANNOTATIONS/'train')\n",
        "move_files_to_folder(val_annotations, YOLO_ANNOTATIONS/'val')\n",
        "move_files_to_folder(test_annotations, YOLO_ANNOTATIONS/'test')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eXa1Ve9qq81G"
      },
      "source": [
        "## Training"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mYgZkc4CtqdY",
        "outputId": "d35af067-8f45-47e9-a767-e6325be6c423"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'yolov7'...\n",
            "remote: Enumerating objects: 1191, done.\u001b[K\n",
            "remote: Counting objects: 100% (6/6), done.\u001b[K\n",
            "remote: Compressing objects: 100% (4/4), done.\u001b[K\n",
            "remote: Total 1191 (delta 2), reused 6 (delta 2), pack-reused 1185\u001b[K\n",
            "Receiving objects: 100% (1191/1191), 74.23 MiB | 21.37 MiB/s, done.\n",
            "Resolving deltas: 100% (511/511), done.\n",
            "/content/yolov7\n",
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            "Collecting jedi>=0.16 (from ipython->-r requirements.txt (line 34))\n",
            "  Downloading jedi-0.18.2-py2.py3-none-any.whl (1.6 MB)\n",
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            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<1.1,>=0.5->tensorboard>=2.4.1->-r requirements.txt (line 17)) (3.2.2)\n",
            "Installing collected packages: jedi, thop\n",
            "Successfully installed jedi-0.18.2 thop-0.1.1.post2209072238\n"
          ]
        }
      ],
      "source": [
        "if not os.path.exists('yolov7'):\n",
        "    !git clone https://github.com/WongKinYiu/yolov7.git\n",
        "\n",
        "%cd yolov7\n",
        "\n",
        "!pip install -r requirements.txt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "659yPHkKt2Jx",
        "outputId": "24f8a169-161b-4617-a764-da91e49a2f2d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Writing data/lego.yaml\n"
          ]
        }
      ],
      "source": [
        "%%writefile data/lego.yaml\n",
        "train: ../yolo/images/train\n",
        "val: ../yolo/images/val\n",
        "test: ../yolo/images/test\n",
        "\n",
        "# Classes\n",
        "nc: 1  # number of classes\n",
        "names: ['lego']  # class names"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CpQRwf5guZ2P",
        "outputId": "667db1f0-d6fe-4134-8d6b-f9f7bf6301c2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2023-07-25 16:40:21--  https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-tiny.pt\n",
            "Resolving github.com (github.com)... 140.82.121.4\n",
            "Connecting to github.com (github.com)|140.82.121.4|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://objects.githubusercontent.com/github-production-release-asset-2e65be/511187726/ba7d01ee-125a-4134-8864-fa1abcbf94d5?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20230725%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20230725T164022Z&X-Amz-Expires=300&X-Amz-Signature=ee456e3605ebebb3f7b2b884cc0e28498ca9271a5eb274cbd1a9beb47756961b&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=511187726&response-content-disposition=attachment%3B%20filename%3Dyolov7-tiny.pt&response-content-type=application%2Foctet-stream [following]\n",
            "--2023-07-25 16:40:22--  https://objects.githubusercontent.com/github-production-release-asset-2e65be/511187726/ba7d01ee-125a-4134-8864-fa1abcbf94d5?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20230725%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20230725T164022Z&X-Amz-Expires=300&X-Amz-Signature=ee456e3605ebebb3f7b2b884cc0e28498ca9271a5eb274cbd1a9beb47756961b&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=511187726&response-content-disposition=attachment%3B%20filename%3Dyolov7-tiny.pt&response-content-type=application%2Foctet-stream\n",
            "Resolving objects.githubusercontent.com (objects.githubusercontent.com)... 185.199.110.133, 185.199.109.133, 185.199.111.133, ...\n",
            "Connecting to objects.githubusercontent.com (objects.githubusercontent.com)|185.199.110.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 12639769 (12M) [application/octet-stream]\n",
            "Saving to: ‘yolov7-tiny.pt’\n",
            "\n",
            "yolov7-tiny.pt      100%[===================>]  12.05M  49.2MB/s    in 0.2s    \n",
            "\n",
            "2023-07-25 16:40:22 (49.2 MB/s) - ‘yolov7-tiny.pt’ saved [12639769/12639769]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Download the Tiny model weights.\n",
        "!wget https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-tiny.pt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lmfjSAKFutia",
        "outputId": "84bba082-40a1-4881-e4a4-c2166b88409e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Writing cfg/training/yolov7_lego-tiny.yaml\n"
          ]
        }
      ],
      "source": [
        "%%writefile cfg/training/yolov7_lego-tiny.yaml\n",
        "# parameters\n",
        "nc: 1  # number of classes\n",
        "depth_multiple: 1.0  # model depth multiple\n",
        "width_multiple: 1.0  # layer channel multiple\n",
        "\n",
        "# anchors\n",
        "anchors:\n",
        "  - [10,13, 16,30, 33,23]  # P3/8\n",
        "  - [30,61, 62,45, 59,119]  # P4/16\n",
        "  - [116,90, 156,198, 373,326]  # P5/32\n",
        "\n",
        "# yolov7-tiny backbone\n",
        "backbone:\n",
        "  # [from, number, module, args] c2, k=1, s=1, p=None, g=1, act=True\n",
        "  [[-1, 1, Conv, [32, 3, 2, None, 1, nn.LeakyReLU(0.1)]],  # 0-P1/2\n",
        "\n",
        "  [-1, 1, Conv, [64, 3, 2, None, 1, nn.LeakyReLU(0.1)]],  # 1-P2/4\n",
        "\n",
        "  [-1, 1, Conv, [32, 1, 1, None, 1, nn.LeakyReLU(0.1)]],\n",
        "  [-2, 1, Conv, [32, 1, 1, None, 1, nn.LeakyReLU(0.1)]],\n",
        "  [-1, 1, Conv, [32, 3, 1, None, 1, nn.LeakyReLU(0.1)]],\n",
        "  [-1, 1, Conv, [32, 3, 1, None, 1, nn.LeakyReLU(0.1)]],\n",
        "  [[-1, -2, -3, -4], 1, Concat, [1]],\n",
        "  [-1, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]],  # 7\n",
        "\n",
        "  [-1, 1, MP, []],  # 8-P3/8\n",
        "  [-1, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]],\n",
        "  [-2, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]],\n",
        "  [-1, 1, Conv, [64, 3, 1, None, 1, nn.LeakyReLU(0.1)]],\n",
        "  [-1, 1, Conv, [64, 3, 1, None, 1, nn.LeakyReLU(0.1)]],\n",
        "  [[-1, -2, -3, -4], 1, Concat, [1]],\n",
        "  [-1, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]],  # 14\n",
        "\n",
        "  [-1, 1, MP, []],  # 15-P4/16\n",
        "  [-1, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]],\n",
        "  [-2, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]],\n",
        "  [-1, 1, Conv, [128, 3, 1, None, 1, nn.LeakyReLU(0.1)]],\n",
        "  [-1, 1, Conv, [128, 3, 1, None, 1, nn.LeakyReLU(0.1)]],\n",
        "  [[-1, -2, -3, -4], 1, Concat, [1]],\n",
        "  [-1, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]],  # 21\n",
        "\n",
        "  [-1, 1, MP, []],  # 22-P5/32\n",
        "  [-1, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]],\n",
        "  [-2, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]],\n",
        "  [-1, 1, Conv, [256, 3, 1, None, 1, nn.LeakyReLU(0.1)]],\n",
        "  [-1, 1, Conv, [256, 3, 1, None, 1, nn.LeakyReLU(0.1)]],\n",
        "  [[-1, -2, -3, -4], 1, Concat, [1]],\n",
        "  [-1, 1, Conv, [512, 1, 1, None, 1, nn.LeakyReLU(0.1)]],  # 28\n",
        "  ]\n",
        "\n",
        "# yolov7-tiny head\n",
        "head:\n",
        "  [[-1, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]],\n",
        "  [-2, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]],\n",
        "  [-1, 1, SP, [5]],\n",
        "  [-2, 1, SP, [9]],\n",
        "  [-3, 1, SP, [13]],\n",
        "  [[-1, -2, -3, -4], 1, Concat, [1]],\n",
        "  [-1, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]],\n",
        "  [[-1, -7], 1, Concat, [1]],\n",
        "  [-1, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]],  # 37\n",
        "\n",
        "  [-1, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]],\n",
        "  [-1, 1, nn.Upsample, [None, 2, 'nearest']],\n",
        "  [21, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # route backbone P4\n",
        "  [[-1, -2], 1, Concat, [1]],\n",
        "\n",
        "  [-1, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]],\n",
        "  [-2, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]],\n",
        "  [-1, 1, Conv, [64, 3, 1, None, 1, nn.LeakyReLU(0.1)]],\n",
        "  [-1, 1, Conv, [64, 3, 1, None, 1, nn.LeakyReLU(0.1)]],\n",
        "  [[-1, -2, -3, -4], 1, Concat, [1]],\n",
        "  [-1, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]],  # 47\n",
        "\n",
        "  [-1, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]],\n",
        "  [-1, 1, nn.Upsample, [None, 2, 'nearest']],\n",
        "  [14, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # route backbone P3\n",
        "  [[-1, -2], 1, Concat, [1]],\n",
        "\n",
        "  [-1, 1, Conv, [32, 1, 1, None, 1, nn.LeakyReLU(0.1)]],\n",
        "  [-2, 1, Conv, [32, 1, 1, None, 1, nn.LeakyReLU(0.1)]],\n",
        "  [-1, 1, Conv, [32, 3, 1, None, 1, nn.LeakyReLU(0.1)]],\n",
        "  [-1, 1, Conv, [32, 3, 1, None, 1, nn.LeakyReLU(0.1)]],\n",
        "  [[-1, -2, -3, -4], 1, Concat, [1]],\n",
        "  [-1, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]],  # 57\n",
        "\n",
        "  [-1, 1, Conv, [128, 3, 2, None, 1, nn.LeakyReLU(0.1)]],\n",
        "  [[-1, 47], 1, Concat, [1]],\n",
        "\n",
        "  [-1, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]],\n",
        "  [-2, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]],\n",
        "  [-1, 1, Conv, [64, 3, 1, None, 1, nn.LeakyReLU(0.1)]],\n",
        "  [-1, 1, Conv, [64, 3, 1, None, 1, nn.LeakyReLU(0.1)]],\n",
        "  [[-1, -2, -3, -4], 1, Concat, [1]],\n",
        "  [-1, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]],  # 65\n",
        "\n",
        "  [-1, 1, Conv, [256, 3, 2, None, 1, nn.LeakyReLU(0.1)]],\n",
        "  [[-1, 37], 1, Concat, [1]],\n",
        "\n",
        "  [-1, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]],\n",
        "  [-2, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]],\n",
        "  [-1, 1, Conv, [128, 3, 1, None, 1, nn.LeakyReLU(0.1)]],\n",
        "  [-1, 1, Conv, [128, 3, 1, None, 1, nn.LeakyReLU(0.1)]],\n",
        "  [[-1, -2, -3, -4], 1, Concat, [1]],\n",
        "  [-1, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]],  # 73\n",
        "\n",
        "  [57, 1, Conv, [128, 3, 1, None, 1, nn.LeakyReLU(0.1)]],\n",
        "  [65, 1, Conv, [256, 3, 1, None, 1, nn.LeakyReLU(0.1)]],\n",
        "  [73, 1, Conv, [512, 3, 1, None, 1, nn.LeakyReLU(0.1)]],\n",
        "\n",
        "  [[74,75,76], 1, IDetect, [nc, anchors]],   # Detect(P3, P4, P5)\n",
        "  ]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "saGvxvXru0Hf",
        "outputId": "803715d0-6ff9-4586-e0c6-a2c3320238d8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2023-07-25 16:40:26.489525: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
            "To enable the following instructions: AVX2 AVX512F FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
            "2023-07-25 16:40:28.704857: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
            "YOLOR 🚀 v0.1-126-g84932d7 torch 2.0.1+cu118 CUDA:0 (Tesla T4, 15101.8125MB)\n",
            "\n",
            "Namespace(weights='yolov7-tiny.pt', cfg='cfg/training/yolov7_lego-tiny.yaml', data='data/lego.yaml', hyp='data/hyp.scratch.tiny.yaml', epochs=100, batch_size=32, img_size=[640, 640], rect=False, resume=False, nosave=False, notest=False, noautoanchor=False, evolve=False, bucket='', cache_images=False, image_weights=False, device='0', multi_scale=False, single_cls=False, adam=False, sync_bn=False, local_rank=-1, workers=4, project='runs/train', entity=None, name='yolov7_tiny_legofixed_res', exist_ok=False, quad=False, linear_lr=False, label_smoothing=0.0, upload_dataset=False, bbox_interval=-1, save_period=-1, artifact_alias='latest', freeze=[0], v5_metric=False, world_size=1, global_rank=-1, save_dir='runs/train/yolov7_tiny_legofixed_res', total_batch_size=32)\n",
            "\u001b[34m\u001b[1mtensorboard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n",
            "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.05, copy_paste=0.0, paste_in=0.05, loss_ota=1\n",
            "\u001b[34m\u001b[1mwandb: \u001b[0mInstall Weights & Biases for YOLOR logging with 'pip install wandb' (recommended)\n",
            "\n",
            "                 from  n    params  module                                  arguments                     \n",
            "  0                -1  1       928  models.common.Conv                      [3, 32, 3, 2, None, 1, LeakyReLU(negative_slope=0.1)]\n",
            "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2, None, 1, LeakyReLU(negative_slope=0.1)]\n",
            "  2                -1  1      2112  models.common.Conv                      [64, 32, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
            "  3                -2  1      2112  models.common.Conv                      [64, 32, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
            "  4                -1  1      9280  models.common.Conv                      [32, 32, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
            "  5                -1  1      9280  models.common.Conv                      [32, 32, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
            "  6  [-1, -2, -3, -4]  1         0  models.common.Concat                    [1]                           \n",
            "  7                -1  1      8320  models.common.Conv                      [128, 64, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
            "  8                -1  1         0  models.common.MP                        []                            \n",
            "  9                -1  1      4224  models.common.Conv                      [64, 64, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
            " 10                -2  1      4224  models.common.Conv                      [64, 64, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
            " 11                -1  1     36992  models.common.Conv                      [64, 64, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
            " 12                -1  1     36992  models.common.Conv                      [64, 64, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
            " 13  [-1, -2, -3, -4]  1         0  models.common.Concat                    [1]                           \n",
            " 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
            " 15                -1  1         0  models.common.MP                        []                            \n",
            " 16                -1  1     16640  models.common.Conv                      [128, 128, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
            " 17                -2  1     16640  models.common.Conv                      [128, 128, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
            " 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
            " 19                -1  1    147712  models.common.Conv                      [128, 128, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
            " 20  [-1, -2, -3, -4]  1         0  models.common.Concat                    [1]                           \n",
            " 21                -1  1    131584  models.common.Conv                      [512, 256, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
            " 22                -1  1         0  models.common.MP                        []                            \n",
            " 23                -1  1     66048  models.common.Conv                      [256, 256, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
            " 24                -2  1     66048  models.common.Conv                      [256, 256, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
            " 25                -1  1    590336  models.common.Conv                      [256, 256, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
            " 26                -1  1    590336  models.common.Conv                      [256, 256, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
            " 27  [-1, -2, -3, -4]  1         0  models.common.Concat                    [1]                           \n",
            " 28                -1  1    525312  models.common.Conv                      [1024, 512, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
            " 29                -1  1    131584  models.common.Conv                      [512, 256, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
            " 30                -2  1    131584  models.common.Conv                      [512, 256, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
            " 31                -1  1         0  models.common.SP                        [5]                           \n",
            " 32                -2  1         0  models.common.SP                        [9]                           \n",
            " 33                -3  1         0  models.common.SP                        [13]                          \n",
            " 34  [-1, -2, -3, -4]  1         0  models.common.Concat                    [1]                           \n",
            " 35                -1  1    262656  models.common.Conv                      [1024, 256, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
            " 36          [-1, -7]  1         0  models.common.Concat                    [1]                           \n",
            " 37                -1  1    131584  models.common.Conv                      [512, 256, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
            " 38                -1  1     33024  models.common.Conv                      [256, 128, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
            " 39                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
            " 40                21  1     33024  models.common.Conv                      [256, 128, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
            " 41          [-1, -2]  1         0  models.common.Concat                    [1]                           \n",
            " 42                -1  1     16512  models.common.Conv                      [256, 64, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
            " 43                -2  1     16512  models.common.Conv                      [256, 64, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
            " 44                -1  1     36992  models.common.Conv                      [64, 64, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
            " 45                -1  1     36992  models.common.Conv                      [64, 64, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
            " 46  [-1, -2, -3, -4]  1         0  models.common.Concat                    [1]                           \n",
            " 47                -1  1     33024  models.common.Conv                      [256, 128, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
            " 48                -1  1      8320  models.common.Conv                      [128, 64, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
            " 49                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
            " 50                14  1      8320  models.common.Conv                      [128, 64, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
            " 51          [-1, -2]  1         0  models.common.Concat                    [1]                           \n",
            " 52                -1  1      4160  models.common.Conv                      [128, 32, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
            " 53                -2  1      4160  models.common.Conv                      [128, 32, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
            " 54                -1  1      9280  models.common.Conv                      [32, 32, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
            " 55                -1  1      9280  models.common.Conv                      [32, 32, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
            " 56  [-1, -2, -3, -4]  1         0  models.common.Concat                    [1]                           \n",
            " 57                -1  1      8320  models.common.Conv                      [128, 64, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
            " 58                -1  1     73984  models.common.Conv                      [64, 128, 3, 2, None, 1, LeakyReLU(negative_slope=0.1)]\n",
            " 59          [-1, 47]  1         0  models.common.Concat                    [1]                           \n",
            " 60                -1  1     16512  models.common.Conv                      [256, 64, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
            " 61                -2  1     16512  models.common.Conv                      [256, 64, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
            " 62                -1  1     36992  models.common.Conv                      [64, 64, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
            " 63                -1  1     36992  models.common.Conv                      [64, 64, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
            " 64  [-1, -2, -3, -4]  1         0  models.common.Concat                    [1]                           \n",
            " 65                -1  1     33024  models.common.Conv                      [256, 128, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
            " 66                -1  1    295424  models.common.Conv                      [128, 256, 3, 2, None, 1, LeakyReLU(negative_slope=0.1)]\n",
            " 67          [-1, 37]  1         0  models.common.Concat                    [1]                           \n",
            " 68                -1  1     65792  models.common.Conv                      [512, 128, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
            " 69                -2  1     65792  models.common.Conv                      [512, 128, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
            " 70                -1  1    147712  models.common.Conv                      [128, 128, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
            " 71                -1  1    147712  models.common.Conv                      [128, 128, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
            " 72  [-1, -2, -3, -4]  1         0  models.common.Concat                    [1]                           \n",
            " 73                -1  1    131584  models.common.Conv                      [512, 256, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
            " 74                57  1     73984  models.common.Conv                      [64, 128, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
            " 75                65  1    295424  models.common.Conv                      [128, 256, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
            " 76                73  1   1180672  models.common.Conv                      [256, 512, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]\n",
            " 77      [74, 75, 76]  1     17132  models.yolo.IDetect                     [1, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
            "/usr/local/lib/python3.10/dist-packages/torch/functional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:3483.)\n",
            "  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]\n",
            "Model Summary: 263 layers, 6014988 parameters, 6014988 gradients, 13.2 GFLOPS\n",
            "\n",
            "Transferred 330/344 items from yolov7-tiny.pt\n",
            "Scaled weight_decay = 0.0005\n",
            "Optimizer groups: 58 .bias, 58 conv.weight, 61 other\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mScanning '../yolo/labels/train' images and labels... 1000 found, 0 missing, 0 empty, 0 corrupted: 100% 1000/1000 [00:01<00:00, 530.94it/s]\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: ../yolo/labels/train.cache\n",
            "\u001b[34m\u001b[1mval: \u001b[0mScanning '../yolo/labels/val' images and labels... 1000 found, 0 missing, 0 empty, 0 corrupted: 100% 1000/1000 [00:02<00:00, 471.27it/s]\n",
            "\u001b[34m\u001b[1mval: \u001b[0mNew cache created: ../yolo/labels/val.cache\n",
            "\n",
            "\u001b[34m\u001b[1mautoanchor: \u001b[0mAnalyzing anchors... anchors/target = 4.78, Best Possible Recall (BPR) = 0.9997\n",
            "Image sizes 640 train, 640 test\n",
            "Using 2 dataloader workers\n",
            "Logging results to runs/train/yolov7_tiny_legofixed_res\n",
            "Starting training for 100 epochs...\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "      0/99    0.545G   0.07584   0.02258         0   0.09842        64       640: 100% 32/32 [01:16<00:00,  2.38s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 16/16 [00:19<00:00,  1.23s/it]\n",
            "                 all        1000        3212       0.115       0.177      0.0646      0.0123\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "      1/99     4.96G   0.06328   0.01986         0   0.08314        26       640: 100% 32/32 [00:48<00:00,  1.53s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 16/16 [00:19<00:00,  1.19s/it]\n",
            "                 all        1000        3212       0.374       0.459        0.33      0.0984\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "      2/99     6.11G   0.05529   0.01803         0   0.07332        55       640: 100% 32/32 [00:47<00:00,  1.48s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 16/16 [00:15<00:00,  1.00it/s]\n",
            "                 all        1000        3212       0.585       0.548       0.535       0.167\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "      3/99     6.11G   0.05042   0.01637         0   0.06679        34       640: 100% 32/32 [00:48<00:00,  1.51s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 16/16 [00:16<00:00,  1.01s/it]\n",
            "                 all        1000        3212       0.462       0.604       0.496        0.16\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "      4/99     6.11G   0.04387   0.01678         0   0.06065        40       640: 100% 32/32 [00:47<00:00,  1.48s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 16/16 [00:15<00:00,  1.03it/s]\n",
            "                 all        1000        3212       0.686       0.731       0.748       0.318\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "      5/99     6.11G   0.04076   0.01626         0   0.05702        31       640: 100% 32/32 [00:48<00:00,  1.52s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 16/16 [00:15<00:00,  1.03it/s]\n",
            "                 all        1000        3212       0.815       0.793       0.858       0.524\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "      6/99     6.11G   0.03782   0.01591         0   0.05373        47       640: 100% 32/32 [00:50<00:00,  1.58s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 16/16 [00:15<00:00,  1.01it/s]\n",
            "                 all        1000        3212       0.862       0.821       0.895       0.539\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "      7/99     6.11G   0.03914   0.01615         0   0.05529        35       640: 100% 32/32 [00:48<00:00,  1.52s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 16/16 [00:15<00:00,  1.01it/s]\n",
            "                 all        1000        3212       0.781       0.785        0.84       0.454\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "      8/99     6.11G   0.04087    0.0156         0   0.05646        44       640: 100% 32/32 [00:47<00:00,  1.48s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 16/16 [00:15<00:00,  1.01it/s]\n",
            "                 all        1000        3212       0.861       0.676       0.769       0.526\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "      9/99     6.11G   0.03808   0.01543         0   0.05351        43       640: 100% 32/32 [00:47<00:00,  1.47s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 16/16 [00:16<00:00,  1.03s/it]\n",
            "                 all        1000        3212       0.742       0.746       0.807       0.516\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     10/99     6.11G   0.03835   0.01526         0   0.05362        42       640: 100% 32/32 [00:51<00:00,  1.59s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 16/16 [00:15<00:00,  1.03it/s]\n",
            "                 all        1000        3212       0.898       0.805       0.892       0.558\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     11/99     6.11G   0.03999   0.01578         0   0.05577        49       640: 100% 32/32 [00:46<00:00,  1.46s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 16/16 [00:15<00:00,  1.01it/s]\n",
            "                 all        1000        3212       0.877       0.753       0.854       0.595\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     12/99     6.11G   0.04559   0.01619         0   0.06178        33       640: 100% 32/32 [00:48<00:00,  1.52s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 16/16 [00:14<00:00,  1.09it/s]\n",
            "                 all        1000        3212       0.848        0.78        0.86       0.589\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     13/99     6.11G   0.04025   0.01575         0     0.056        48       640: 100% 32/32 [00:46<00:00,  1.46s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 16/16 [00:14<00:00,  1.07it/s]\n",
            "                 all        1000        3212        0.89       0.738       0.853       0.562\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     14/99     6.11G    0.0444   0.01679         0   0.06119        30       640: 100% 32/32 [00:48<00:00,  1.51s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 16/16 [00:15<00:00,  1.06it/s]\n",
            "                 all        1000        3212       0.289        0.18       0.151      0.0785\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     15/99     6.11G   0.04641   0.01696         0   0.06337        29       640: 100% 32/32 [00:47<00:00,  1.49s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 16/16 [00:16<00:00,  1.00s/it]\n",
            "                 all        1000        3212       0.741       0.556       0.652       0.377\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     16/99     6.12G   0.04704   0.01714         0   0.06418        39       640: 100% 32/32 [00:47<00:00,  1.49s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 16/16 [00:15<00:00,  1.01it/s]\n",
            "                 all        1000        3212       0.814       0.604        0.71       0.459\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     17/99     6.12G   0.04195   0.01658         0   0.05853        29       640: 100% 32/32 [00:46<00:00,  1.46s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 16/16 [00:16<00:00,  1.02s/it]\n",
            "                 all        1000        3212       0.676       0.368       0.418        0.27\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     18/99     6.12G   0.04595     0.018         0   0.06395        40       640: 100% 32/32 [00:51<00:00,  1.60s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 16/16 [00:16<00:00,  1.00s/it]\n",
            "                 all        1000        3212       0.663        0.46       0.528       0.324\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     19/99     6.12G   0.04717   0.01843         0    0.0656        32       640: 100% 32/32 [00:47<00:00,  1.48s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 16/16 [00:15<00:00,  1.01it/s]\n",
            "                 all        1000        3212       0.699       0.609       0.672       0.465\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     20/99     6.12G   0.04641   0.01884         0   0.06525        35       640: 100% 32/32 [00:48<00:00,  1.51s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 16/16 [00:15<00:00,  1.01it/s]\n",
            "                 all        1000        3212       0.773       0.547       0.651        0.42\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     21/99     6.12G   0.05045   0.02093         0   0.07138        57       640: 100% 32/32 [00:47<00:00,  1.47s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 16/16 [00:15<00:00,  1.02it/s]\n",
            "                 all        1000        3212        0.44       0.301       0.306       0.158\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     22/99     6.12G   0.05514   0.02023         0   0.07536        38       640: 100% 32/32 [00:50<00:00,  1.59s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 16/16 [00:15<00:00,  1.06it/s]\n",
            "                 all        1000        3212       0.702       0.498       0.568       0.354\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     23/99     6.12G   0.04782   0.01867         0   0.06649        33       640: 100% 32/32 [00:48<00:00,  1.51s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 16/16 [00:15<00:00,  1.05it/s]\n",
            "                 all        1000        3212       0.758       0.617         0.7       0.466\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     24/99     6.12G   0.04942   0.01907         0   0.06849        45       640: 100% 32/32 [00:47<00:00,  1.47s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 16/16 [00:15<00:00,  1.02it/s]\n",
            "                 all        1000        3212       0.538       0.405       0.407       0.245\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     25/99     6.12G   0.04196   0.02045         0   0.06242        38       640: 100% 32/32 [00:47<00:00,  1.49s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 16/16 [00:15<00:00,  1.05it/s]\n",
            "                 all        1000        3212       0.635       0.469       0.518       0.311\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     26/99     6.12G   0.03918    0.0197         0   0.05887        54       640: 100% 32/32 [00:47<00:00,  1.48s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 16/16 [00:15<00:00,  1.01it/s]\n",
            "                 all        1000        3212       0.552       0.353       0.395        0.24\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     27/99     6.12G   0.05032   0.02059         0   0.07091        41       640: 100% 32/32 [00:48<00:00,  1.51s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 16/16 [00:15<00:00,  1.06it/s]\n",
            "                 all        1000        3212       0.338       0.136       0.129      0.0618\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     28/99     6.12G    0.0482   0.02159         0   0.06979        71       640: 100% 32/32 [00:47<00:00,  1.49s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 16/16 [00:15<00:00,  1.04it/s]\n",
            "                 all        1000        3212        0.59       0.404       0.434       0.256\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     29/99     6.12G    0.0451   0.02049         0   0.06559        62       640: 100% 32/32 [00:47<00:00,  1.49s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 16/16 [00:16<00:00,  1.02s/it]\n",
            "                 all        1000        3212        0.72       0.597       0.666       0.454\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     30/99     6.12G   0.04167    0.0192         0   0.06087        34       640: 100% 32/32 [00:47<00:00,  1.48s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 16/16 [00:15<00:00,  1.00it/s]\n",
            "                 all        1000        3212       0.787       0.628       0.722       0.499\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     31/99     6.12G   0.04126   0.01933         0   0.06059        51       640: 100% 32/32 [00:46<00:00,  1.46s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 16/16 [00:15<00:00,  1.03it/s]\n",
            "                 all        1000        3212       0.745        0.68       0.751       0.518\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     32/99     6.12G   0.04797   0.01896         0   0.06693        41       640: 100% 32/32 [00:46<00:00,  1.46s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 16/16 [00:14<00:00,  1.14it/s]\n",
            "                 all        1000        3212       0.806       0.664       0.763       0.554\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     33/99     6.12G   0.03921   0.01872         0   0.05794        60       640: 100% 32/32 [00:46<00:00,  1.44s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 16/16 [00:14<00:00,  1.10it/s]\n",
            "                 all        1000        3212        0.78       0.575       0.678       0.479\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     34/99     6.12G   0.04822   0.01898         0    0.0672        46       640: 100% 32/32 [00:47<00:00,  1.49s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 16/16 [00:14<00:00,  1.07it/s]\n",
            "                 all        1000        3212       0.691       0.466        0.55       0.359\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     35/99     6.12G   0.04201   0.01861         0   0.06062        38       640: 100% 32/32 [00:47<00:00,  1.47s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 16/16 [00:15<00:00,  1.05it/s]\n",
            "                 all        1000        3212       0.819        0.66       0.769       0.549\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     36/99     6.12G    0.0419   0.01787         0   0.05978        41       640: 100% 32/32 [00:47<00:00,  1.47s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 16/16 [00:15<00:00,  1.04it/s]\n",
            "                 all        1000        3212       0.801       0.698       0.789       0.585\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     37/99     6.12G   0.04807   0.01794         0   0.06601        69       640: 100% 32/32 [00:47<00:00,  1.47s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 16/16 [00:15<00:00,  1.06it/s]\n",
            "                 all        1000        3212       0.845       0.671       0.779       0.569\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     38/99     6.12G   0.04825   0.01869         0   0.06694        38       640: 100% 32/32 [00:46<00:00,  1.45s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 16/16 [00:15<00:00,  1.02it/s]\n",
            "                 all        1000        3212       0.824       0.681       0.778       0.555\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     39/99     6.12G   0.04074   0.01737         0   0.05811        41       640: 100% 32/32 [00:47<00:00,  1.48s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 16/16 [00:15<00:00,  1.06it/s]\n",
            "                 all        1000        3212       0.803       0.736       0.813       0.594\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     40/99     6.12G   0.03863   0.01761         0   0.05624        57       640: 100% 32/32 [00:44<00:00,  1.38s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 16/16 [00:15<00:00,  1.00it/s]\n",
            "                 all        1000        3212       0.636       0.481       0.544       0.359\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     41/99     6.12G   0.04602   0.01959         0   0.06561        46       640: 100% 32/32 [00:44<00:00,  1.40s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 16/16 [00:15<00:00,  1.05it/s]\n",
            "                 all        1000        3212       0.765       0.564       0.644       0.434\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     42/99     6.12G   0.03685   0.01805         0    0.0549        24       640: 100% 32/32 [00:46<00:00,  1.44s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 16/16 [00:15<00:00,  1.04it/s]\n",
            "                 all        1000        3212       0.821       0.699       0.795       0.581\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     43/99     6.12G   0.04007   0.01812         0   0.05819        35       640: 100% 32/32 [00:46<00:00,  1.46s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 16/16 [00:16<00:00,  1.01s/it]\n",
            "                 all        1000        3212       0.845       0.733       0.836       0.633\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     44/99     6.12G   0.04391   0.01738         0   0.06129        37       640: 100% 32/32 [00:46<00:00,  1.45s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 16/16 [00:15<00:00,  1.05it/s]\n",
            "                 all        1000        3212        0.86       0.727       0.833        0.64\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     45/99     6.12G   0.04433   0.01737         0    0.0617        33       640: 100% 32/32 [00:46<00:00,  1.46s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 16/16 [00:15<00:00,  1.06it/s]\n",
            "                 all        1000        3212       0.823       0.749       0.837       0.633\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     46/99     6.12G   0.04426   0.01757         0   0.06183        53       640: 100% 32/32 [00:46<00:00,  1.46s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 16/16 [00:14<00:00,  1.07it/s]\n",
            "                 all        1000        3212       0.831       0.708       0.817       0.607\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     47/99     6.12G   0.04668   0.01767         0   0.06435        33       640: 100% 32/32 [00:47<00:00,  1.48s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 16/16 [00:13<00:00,  1.15it/s]\n",
            "                 all        1000        3212       0.827       0.739       0.835       0.629\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     48/99     6.12G   0.04632   0.01953         0   0.06585        47       640: 100% 32/32 [00:46<00:00,  1.44s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 16/16 [00:14<00:00,  1.09it/s]\n",
            "                 all        1000        3212       0.778       0.597       0.695       0.494\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     49/99     6.12G   0.05066   0.01813         0   0.06879        32       640: 100% 32/32 [00:44<00:00,  1.40s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 16/16 [00:14<00:00,  1.07it/s]\n",
            "                 all        1000        3212       0.841       0.698       0.808       0.598\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     50/99     6.12G   0.05145   0.01779         0   0.06924        37       640: 100% 32/32 [00:46<00:00,  1.45s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 16/16 [00:15<00:00,  1.01it/s]\n",
            "                 all        1000        3212       0.833       0.746       0.835       0.632\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     51/99     6.12G   0.04621   0.01744         0   0.06366        34       640: 100% 32/32 [00:45<00:00,  1.43s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 16/16 [00:15<00:00,  1.02it/s]\n",
            "                 all        1000        3212       0.824       0.651       0.759       0.574\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     52/99     6.12G   0.04361   0.01833         0   0.06194        48       640: 100% 32/32 [00:47<00:00,  1.48s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 16/16 [00:15<00:00,  1.00it/s]\n",
            "                 all        1000        3212        0.85       0.714       0.818        0.62\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     53/99     6.12G   0.05082   0.01728         0    0.0681        37       640: 100% 32/32 [00:47<00:00,  1.47s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 16/16 [00:15<00:00,  1.02it/s]\n",
            "                 all        1000        3212        0.88       0.733       0.849       0.655\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     54/99     6.12G   0.04604   0.01733         0   0.06336        62       640: 100% 32/32 [00:46<00:00,  1.44s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 16/16 [00:15<00:00,  1.03it/s]\n",
            "                 all        1000        3212       0.886        0.75       0.862       0.673\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     55/99     6.12G   0.04567   0.01777         0   0.06344        43       640: 100% 32/32 [00:46<00:00,  1.46s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 16/16 [00:15<00:00,  1.01it/s]\n",
            "                 all        1000        3212       0.862       0.786        0.87       0.685\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     56/99     6.12G   0.04991   0.01651         0   0.06642        39       640: 100% 32/32 [00:44<00:00,  1.40s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 16/16 [00:15<00:00,  1.04it/s]\n",
            "                 all        1000        3212       0.893       0.773        0.88         0.7\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     57/99     6.12G    0.0453   0.01653         0   0.06184        43       640: 100% 32/32 [00:44<00:00,  1.38s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 16/16 [00:15<00:00,  1.00it/s]\n",
            "                 all        1000        3212       0.863       0.801       0.883       0.703\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     58/99     6.12G   0.04037   0.01624         0   0.05661        36       640: 100% 32/32 [00:47<00:00,  1.47s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 16/16 [00:15<00:00,  1.05it/s]\n",
            "                 all        1000        3212       0.892       0.779       0.885         0.7\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     59/99     6.12G   0.04619   0.01642         0   0.06261        32       640: 100% 32/32 [00:46<00:00,  1.44s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 16/16 [00:16<00:00,  1.04s/it]\n",
            "                 all        1000        3212       0.902       0.769       0.884       0.707\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     60/99     6.12G   0.04282   0.01664         0   0.05946        42       640: 100% 32/32 [00:45<00:00,  1.43s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 16/16 [00:15<00:00,  1.01it/s]\n",
            "                 all        1000        3212       0.892       0.803       0.894       0.718\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     61/99     6.12G   0.04817   0.01665         0   0.06482        29       640: 100% 32/32 [00:46<00:00,  1.44s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 16/16 [00:14<00:00,  1.10it/s]\n",
            "                 all        1000        3212       0.888       0.808       0.895       0.723\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     62/99     6.12G   0.04383    0.0163         0   0.06013        36       640: 100% 32/32 [00:45<00:00,  1.41s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 16/16 [00:14<00:00,  1.08it/s]\n",
            "                 all        1000        3212       0.899       0.799       0.894       0.721\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     63/99     6.12G   0.04112   0.01618         0    0.0573        50       640: 100% 32/32 [00:45<00:00,  1.43s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 16/16 [00:15<00:00,  1.04it/s]\n",
            "                 all        1000        3212       0.902       0.815         0.9       0.724\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     64/99     6.12G   0.03991   0.01567         0   0.05558        36       640: 100% 32/32 [00:45<00:00,  1.43s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 16/16 [00:16<00:00,  1.01s/it]\n",
            "                 all        1000        3212         0.9       0.825       0.904       0.725\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     65/99     6.12G   0.03772   0.01613         0   0.05384        45       640: 100% 32/32 [00:46<00:00,  1.44s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 16/16 [00:16<00:00,  1.01s/it]\n",
            "                 all        1000        3212       0.896       0.828       0.907       0.728\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     66/99     6.12G   0.04105   0.01603         0   0.05708        41       640: 100% 32/32 [00:46<00:00,  1.46s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 16/16 [00:15<00:00,  1.03it/s]\n",
            "                 all        1000        3212        0.89       0.834       0.904       0.727\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     67/99     6.12G   0.04565   0.01625         0   0.06189        39       640: 100% 32/32 [00:47<00:00,  1.47s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 16/16 [00:14<00:00,  1.07it/s]\n",
            "                 all        1000        3212       0.917       0.804       0.903       0.729\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     68/99     6.12G   0.04109   0.01585         0   0.05694        37       640: 100% 32/32 [00:45<00:00,  1.41s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 16/16 [00:15<00:00,  1.04it/s]\n",
            "                 all        1000        3212       0.919       0.815       0.906       0.739\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     69/99     6.12G   0.03848    0.0157         0   0.05418        37       640: 100% 32/32 [00:44<00:00,  1.40s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 16/16 [00:15<00:00,  1.06it/s]\n",
            "                 all        1000        3212        0.91       0.835        0.91       0.742\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     70/99     6.12G   0.04158   0.01596         0   0.05754        53       640: 100% 32/32 [00:46<00:00,  1.46s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 16/16 [00:15<00:00,  1.04it/s]\n",
            "                 all        1000        3212       0.898       0.822       0.906       0.738\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     71/99     6.12G     0.039   0.01597         0   0.05497        37       640: 100% 32/32 [00:46<00:00,  1.44s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 16/16 [00:15<00:00,  1.04it/s]\n",
            "                 all        1000        3212       0.897       0.822       0.908       0.741\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     72/99     6.12G   0.04191   0.01598         0    0.0579        32       640: 100% 32/32 [00:46<00:00,  1.46s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 16/16 [00:15<00:00,  1.03it/s]\n",
            "                 all        1000        3212       0.911       0.836       0.913       0.744\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     73/99     6.12G   0.03879   0.01609         0   0.05488        31       640: 100% 32/32 [00:46<00:00,  1.44s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 16/16 [00:16<00:00,  1.00s/it]\n",
            "                 all        1000        3212       0.899       0.836       0.909       0.745\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     74/99     6.12G   0.03602   0.01605         0   0.05207        45       640: 100% 32/32 [00:45<00:00,  1.43s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 16/16 [00:15<00:00,  1.06it/s]\n",
            "                 all        1000        3212       0.889       0.793       0.893       0.727\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     75/99     6.12G   0.04168   0.01596         0   0.05765        57       640: 100% 32/32 [00:44<00:00,  1.40s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 16/16 [00:14<00:00,  1.08it/s]\n",
            "                 all        1000        3212       0.879        0.83       0.904       0.736\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     76/99     6.12G   0.04326   0.01618         0   0.05945        46       640: 100% 32/32 [00:46<00:00,  1.44s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 16/16 [00:14<00:00,  1.09it/s]\n",
            "                 all        1000        3212       0.902       0.824        0.91       0.745\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     77/99     6.12G   0.03482    0.0157         0   0.05052        44       640: 100% 32/32 [00:47<00:00,  1.47s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 16/16 [00:15<00:00,  1.04it/s]\n",
            "                 all        1000        3212       0.884       0.836       0.904       0.739\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     78/99     6.12G   0.03856   0.01584         0    0.0544        79       640: 100% 32/32 [00:45<00:00,  1.43s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 16/16 [00:15<00:00,  1.01it/s]\n",
            "                 all        1000        3212       0.905       0.827       0.909       0.747\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     79/99     6.12G   0.03529   0.01551         0    0.0508        33       640: 100% 32/32 [00:46<00:00,  1.45s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 16/16 [00:15<00:00,  1.00it/s]\n",
            "                 all        1000        3212       0.893       0.832       0.909       0.751\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     80/99     6.12G   0.04084   0.01605         0   0.05689        43       640: 100% 32/32 [00:44<00:00,  1.40s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 16/16 [00:15<00:00,  1.05it/s]\n",
            "                 all        1000        3212       0.907       0.831       0.914       0.754\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     81/99     6.12G   0.03395   0.01518         0   0.04913        35       640: 100% 32/32 [00:43<00:00,  1.34s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 16/16 [00:15<00:00,  1.01it/s]\n",
            "                 all        1000        3212       0.901       0.831       0.908       0.748\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     82/99     6.12G   0.03609   0.01591         0     0.052        57       640: 100% 32/32 [00:48<00:00,  1.50s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 16/16 [00:15<00:00,  1.06it/s]\n",
            "                 all        1000        3212        0.91       0.832       0.912       0.753\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     83/99     6.12G   0.03515   0.01621         0   0.05135        37       640: 100% 32/32 [00:45<00:00,  1.43s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 16/16 [00:15<00:00,  1.04it/s]\n",
            "                 all        1000        3212       0.915       0.824       0.912       0.755\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     84/99     6.12G   0.04195   0.01569         0   0.05764        34       640: 100% 32/32 [00:45<00:00,  1.42s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 16/16 [00:15<00:00,  1.05it/s]\n",
            "                 all        1000        3212       0.916        0.84       0.916       0.759\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     85/99     6.12G    0.0393   0.01649         0   0.05579        46       640: 100% 32/32 [00:46<00:00,  1.45s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 16/16 [00:15<00:00,  1.04it/s]\n",
            "                 all        1000        3212       0.876       0.808       0.891        0.73\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     86/99     6.12G   0.04183   0.01667         0    0.0585        39       640: 100% 32/32 [00:45<00:00,  1.44s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 16/16 [00:14<00:00,  1.14it/s]\n",
            "                 all        1000        3212       0.869       0.792       0.882       0.725\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     87/99     6.12G   0.03868   0.01619         0   0.05487        38       640: 100% 32/32 [00:44<00:00,  1.40s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 16/16 [00:15<00:00,  1.06it/s]\n",
            "                 all        1000        3212       0.893       0.814       0.897        0.74\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     88/99     6.12G   0.04012   0.01626         0   0.05638        43       640: 100% 32/32 [00:46<00:00,  1.44s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 16/16 [00:15<00:00,  1.04it/s]\n",
            "                 all        1000        3212       0.891       0.815       0.901       0.744\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     89/99     6.12G    0.0387   0.01567         0   0.05437        37       640: 100% 32/32 [00:46<00:00,  1.45s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 16/16 [00:14<00:00,  1.08it/s]\n",
            "                 all        1000        3212        0.91        0.82       0.908       0.752\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     90/99     6.12G   0.03941   0.01573         0   0.05514        41       640: 100% 32/32 [00:45<00:00,  1.42s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 16/16 [00:13<00:00,  1.15it/s]\n",
            "                 all        1000        3212       0.913       0.817       0.912       0.755\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     91/99     6.12G    0.0346   0.01524         0   0.04985        33       640: 100% 32/32 [00:43<00:00,  1.35s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 16/16 [00:15<00:00,  1.05it/s]\n",
            "                 all        1000        3212       0.906       0.834       0.915        0.76\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     92/99     6.12G   0.03961   0.01599         0    0.0556        40       640: 100% 32/32 [00:46<00:00,  1.44s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 16/16 [00:15<00:00,  1.04it/s]\n",
            "                 all        1000        3212       0.889       0.856       0.918       0.761\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     93/99     6.12G   0.03485   0.01583         0   0.05068        34       640: 100% 32/32 [00:45<00:00,  1.43s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 16/16 [00:15<00:00,  1.06it/s]\n",
            "                 all        1000        3212       0.891       0.849       0.916       0.762\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     94/99     6.12G    0.0367   0.01552         0   0.05223        48       640: 100% 32/32 [00:45<00:00,  1.43s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 16/16 [00:14<00:00,  1.09it/s]\n",
            "                 all        1000        3212       0.876       0.869       0.917       0.763\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     95/99     6.12G   0.03733   0.01518         0   0.05252        35       640: 100% 32/32 [00:45<00:00,  1.42s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 16/16 [00:14<00:00,  1.10it/s]\n",
            "                 all        1000        3212         0.9       0.854       0.918       0.763\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     96/99     6.12G   0.03763   0.01582         0   0.05345        42       640: 100% 32/32 [00:46<00:00,  1.44s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 16/16 [00:15<00:00,  1.07it/s]\n",
            "                 all        1000        3212       0.903       0.854       0.918       0.764\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     97/99     6.12G   0.03719   0.01501         0   0.05219        18       640: 100% 32/32 [00:46<00:00,  1.44s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 16/16 [00:15<00:00,  1.04it/s]\n",
            "                 all        1000        3212       0.898       0.856       0.917       0.765\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     98/99     6.12G   0.03399   0.01516         0   0.04914        38       640: 100% 32/32 [00:46<00:00,  1.46s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 16/16 [00:15<00:00,  1.02it/s]\n",
            "                 all        1000        3212       0.899       0.853       0.917       0.764\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     99/99     6.12G   0.03579   0.01568         0   0.05147        34       640: 100% 32/32 [00:46<00:00,  1.46s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 16/16 [00:24<00:00,  1.54s/it]\n",
            "                 all        1000        3212       0.902       0.849       0.917       0.762\n",
            "100 epochs completed in 1.771 hours.\n",
            "\n",
            "Optimizer stripped from runs/train/yolov7_tiny_legofixed_res/weights/last.pt, 12.3MB\n",
            "Optimizer stripped from runs/train/yolov7_tiny_legofixed_res/weights/best.pt, 12.3MB\n"
          ]
        }
      ],
      "source": [
        "!python train.py --epochs 100 --workers 4 --device 0 --batch-size 32 \\\n",
        "--data data/lego.yaml --img 640 640 --cfg cfg/training/yolov7_lego-tiny.yaml \\\n",
        "--weights 'yolov7-tiny.pt' --name yolov7_tiny_legofixed_res --hyp data/hyp.scratch.tiny.yaml"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "M7rpTmKJvXDx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f58c29df-99e9-4872-a005-8ae31923448c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Namespace(weights=['runs/train/yolov7_tiny_legofixed_res/weights/last.pt'], source='/content/inference/ugly2.jpg', img_size=640, conf_thres=0.8, iou_thres=0.45, device='', view_img=False, save_txt=False, save_conf=False, nosave=False, classes=None, agnostic_nms=False, augment=False, update=False, project='runs/detect', name='exp', exist_ok=False, no_trace=False)\n",
            "YOLOR 🚀 v0.1-126-g84932d7 torch 2.0.1+cu118 CUDA:0 (Tesla T4, 15101.8125MB)\n",
            "\n",
            "Fusing layers... \n",
            "IDetect.fuse\n",
            "/usr/local/lib/python3.10/dist-packages/torch/functional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:3483.)\n",
            "  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]\n",
            "Model Summary: 208 layers, 6007596 parameters, 0 gradients, 13.0 GFLOPS\n",
            " Convert model to Traced-model... \n",
            " traced_script_module saved! \n",
            " model is traced! \n",
            "\n",
            "8 legos, Done. (6.4ms) Inference, (2.1ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp12/ugly2.jpg\n",
            "Done. (0.519s)\n"
          ]
        }
      ],
      "source": [
        "!python3 detect.py --source /content/inference/ugly2.jpg --weights runs/train/yolov7_tiny_legofixed_res/weights/last.pt --conf 0.8\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "PGRVjyisWBQv"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}