tutorials/Vetorização_SSE_&_AVX.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [
        "Ti4HW0kce4eA",
        "DFNwQHsehY80",
        "4eWaxOxHlMMk",
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        "qwmVLGfppx0e",
        "c_CbCBqSr0kb",
        "eV7O3c6hsygc",
        "gwWlvUJsztjc"
      ],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/menotti/pp/blob/master/tutorials/Vetoriza%C3%A7%C3%A3o_SSE_%26_AVX.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Vetorização SSE & AVX"
      ],
      "metadata": {
        "id": "4jvCMPN-SD6j"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KqUZfer8R_9-",
        "outputId": "69b62ded-f1f7-4d18-a471-f8780bf99445"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'playground-41d1904q'...\n",
            "remote: Enumerating objects: 704, done.\u001b[K\n",
            "remote: Counting objects: 100% (704/704), done.\u001b[K\n",
            "remote: Compressing objects: 100% (333/333), done.\u001b[K\n",
            "remote: Total 704 (delta 375), reused 600 (delta 311), pack-reused 0\u001b[K\n",
            "Receiving objects: 100% (704/704), 1005.31 KiB | 6.09 MiB/s, done.\n",
            "Resolving deltas: 100% (375/375), done.\n",
            "title : \"Vetorização SSE & AVX \"\n",
            "cover: /images/cover.png\n",
            "plan:\n",
            "  - title: \"O que é SSE e AVX?\"\n",
            "    statement: markdowns/intro.md\n",
            "  - title: \"Pré-requisitos\"\n",
            "    statement: markdowns/prereq.md\n",
            "  - title: \"Autovetorização\"\n",
            "    statement: markdowns/autovector.md\n",
            "  - title: \"Uso de SSE e AVX\"\n",
            "    statement: markdowns/usage.md\n",
            "  - title: \"Primeiro código AVX: cálculo SQRT\"\n",
            "    statement: markdowns/sqrt.md\n",
            "  - title: \"Frameworks C++ SSE/AVX\"\n",
            "    statement: markdowns/frameworks.md\n",
            "  - title: \"Mascaramento e carga condicional\"\n",
            "    statement: markdowns/masking.md\n",
            "  - title: \"Controlando o fluxo de dados\"\n",
            "    statement: markdowns/dataflow.md\n",
            "  - title: \"Palavras finais\"\n",
            "    statement: markdowns/final.md\n",
            "projects:\n",
            "    avx:\n",
            "        root: /projects/avx\n",
            "        buildCommand: \"chmod +x prebuild.sh && ./prebuild.sh\"\n",
            "        runner:\n",
            "            name: techio/cmake-gcc-runner\n",
            "            version: 1.1.0-gcc-6.3\n"
          ]
        }
      ],
      "source": [
        "!git clone https://github.com/menotti/playground-41d1904q\n",
        "!cat playground-41d1904q/techio.yml"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        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AUJwYZg4pvYXQDLHsCKcPC25WL8X1I8n5xcrAV89rDYttc1S2sJyLSCXTW8OtIXPERA4n4t1wyXZ7vKv2mympJYdrM/lMLxQMq6IArk446orpbszhpA1xXxAKgSkjOGNaS5TAokMRYrX/1+GCuLHAQ3J7fRLGP3kQmo1pgNOGolAezzUkQx0JPztca0R8QEMYqorIB2Ayv+Z7qQMob48Tk5xO0IySBMXxHBZvyMHpjtyBpgTGFMIgnZBDARlXnLRrbuAj89R1XPsI/UCUHG5nYosWjr0smA5bhSjchpajg4JegDACU1I+eVsAKEV8UFUZCsVcnkzRT2n/djHs0ayYGmPy6F7oU1KoE4AKqBUSiG9EJCzuiehOqBSkCumnIQsNKfigb7MoSTFLDARHGX+ROdmw8smQtNj4cUj1sKh4FSfOdGjj7b7RtrEkaZ7tOoYLjIDWao/iaNMgyFw1ggPlBdhaqwlHVrwmN8iB0ACybEIzKIWg0XoabAovSxOL0vgfjlYYsaS1LIkvSxFLU9RK9L0ijS1MkOtzNGrwPL0yjzuV+Up2K+GgwIFtrpArclTa+Agj8fw7ooctSJLLcvSy7LMdIpenKAXJOh5UaJpwvRYmB0L8eNhx3jYOWZ5JuLeyZS+qBRZ0ZFa3ZdeM5hY2W9N95rzmqADpKGspy/l7k25exLO7pijO+7ojnGdMbYrhlDpiTK9UbqHDPp1BoArVCOAXKmHQLKgEdXC2ENhxCgbMLNcKeB0C1My2ZINFRO5Aq/bo2EoWXxgdNJHp3xMxsfmFa6osBXdUQu4uhP+RR39p2w99IYLz37klnMfu/2sh28+9vYrFpx/cnL9Qu9A0dG0+JrBF/183s+lND6lcXGVA8UT9fGWzKF88XKmwJseV8DrCQneiCwndDUT8OcssxgL19NWMxNpz0c7C1ZbFjqAgsNf2JFYD0f6Kk1xVMvgmMc+SfbkABhjm4Ol3DwrOnjF5TIEISyLMRXCYSmhyQk/uHuUKXFDBaUSUr1+0SW5eTd6nJn0MYAi+5kEMZcZYhGDAJN1IVE4r4OTACpuXvXwuuAwRU/YB6G9nPTDrcmWBtePSgiVFks8PnRCm4gIxRmbGVjAfktuhMeZIU5wOCSXS3WDUACHgkolouDwTsIQU9BjQ7500AfHYdWti1BKeD2YC/jllgv4K4adGfOFuwC/LDpZ1Q3gB6XCxX1Qa86s4SmFvOWwWAp6cwa4S2dA4nxuxg3ctaH1MeLOZmffAvkXbsfB0V4no3m4sOjKqGLZVBohrS2st1tGZ9QA4duA2FwXkooTkOZzYi3bcQP6mtnFIAdcGBo9cwAVBO5VdLCamzO9OHAXkx1JnzOjegp+MrYWkooBLzDG8jkgABfAV0LKFFbuTGroy3DhyYFX5hpeBtyIy8H5gOs+HCMqWXo9bjaTgfZEqDMV6Er6O+JqMwIZecB1miILTt8BJYNzcVAIH0vwLxic4+IZycWYXi4mcymFzZDhgbzuKJpgfNFwwkFG5yMSq7tpgScB/l8qHCgZuBfYteqCXADUgtPBSoLTkCH+gAYjpUAaktkaHJsyvGCAk2xAThuiJQNUUEY4iKSbk86szc2x9SJk4QYuCs6QzwOJ5INqyTJBz1VAKRqelI9PKo6k5kpoGAwFZSd0FjfPQ0PCAIKkYyf4CW4WyJSFg8F5ff6xLnWkXRpqikN192CF68/TwJL+GM55DOEoFuJkIkQDRSZDrfGoqTA936IXWPRCiwaKLA7TS8AsalmUWhanlqPRK5L0qhS9Mk2vAsvQqzO4XwMHWWpNhlqdo9bmweh1RXp9id5QptaXqQ0VMHpjjViV3linNtYo3DfozU1mS4djW49zR69jWzd3SCe7oY1bX2fX1ti1VXZNlVldoVeVmZVlZnmBnc6zS7Lskgy7MMUtTPELE86FKXFJQV/ZiK7vzW8drx+2sLFjUWnjZGLFYGhhhz5eQb0ykBX7kmJPXOiKuttjjo4oGNcRAWO7omwPWIztjbBk8obutNjOKN8Zd3TEne1xvhlhayHkCk7jo4IhFqDKxqxqofN+HPgi0/jIldmBMkBLVkO0pBUq5aMyPirvYwo+pqwxdYNtBp29mdCqoandRx9/+/49j9+x96m7dz92+zG37Z8658TkxkXSSNXdGXM1w85GwFk2HQXDkTX4tB+cFDQpiFYgkOQtiQ+KziAQRfSGRV9M0dL+QCEYrsaibelkdy7bXyqONKpj7fm+crItY2bDUkhx+FyMwDEuCBtpykFRuGcoJ/RGjnKxM8bhK7a5WOh7OMime4SwpKb9ZjEQrIaDlXCwFAoULbNgGdmgH3xEVPMGJIfs5tw8QgVSRlYRw2N4BcJtSJmYA4zQBeI4Jw8BHY4dSQ6QKU6VzKaA9gr7AFoaYDIf1NMBJaZ7QUb4yPCai0GPCVeOQ8kkffJvazq0tbcnP0nA7uJYwcHLOIiHixrCihDVxLjuS5paJuzPR/yFiJ63tHRIBrEC6CK5QAwIuWD6Nv9sFtpQtI/RZmAJHRtuHEfA3KwhsCGRjcrOhIphfjms1mNqzVJKQSntB54BoTm4ZSdcJ0m/xXJiUCxQOPbrNvKJO8a8gEOKkwt5XUmfVDKUelBvhs3OaKgnEe6Nmd0RvRmS836PJTtVN+flSS2Dn4IU5hgCfvZfOCb/wjlOhgaoA7SgUcUUJ7S0jOaE1IqmCB4NLr4clDN+T0Rx6h4WoAIlM3t5s87xQMoffb31Ltwdx4oepyFBdK/kQ3o1YjZiZjMW7EwGupP+zoTaFpFKppAE9Iqs7AQlQbj4cRf8l42kT3tdjC6wlsQmfQiVnM4VdAd0okrAUTX4isnnVD4igsqnPVDO5JptohCKwMHsdiBl2yB9aE6Ck1e8TtPniWhuFCi6SGahpFwAcAIGMgJI4wXBp0Ir4lisZYgPZpTW7IAn85eUChiodgnQLrqSujcfVCoRoy3hb8R8pYA7q/JpYnHFEZZBVuLcqofnnRgmEqjMoPET3IJTvfpYh2+44R2quQcqjt4i25ulehJUX4QaCNHDIXoUdAniZGY4C+RIGEDSGsKyRcniMLXEIlokDjihVyapVSlqVZpCfuSodTlqfQFtQ5HagHt6Y5HaWKI2AT/K1MYKvblKH1KjDqlTWxrUtjZ6Wzu9rYPZ1snAfnsXs6OLPqybPbzfc+y4dvJ08LRVgZNXqccukQ6fcm8fcR7S79jY7djQ6Vjfwa9r59a1MWsazOo6u6rGr6xyK6rc8jK/rORYWnRMFzzLSuqqtvDG3szW0dL2ebXDFlW3LihumsysG0mtHkgv600t7khO1hKjhfhwNtyXNLvjWnvU1x4R2iwX6ICmxbVbfCeOifHdMb437uxLunpTrp6UC6BSjzpqEa5qMeUQUw6y1SBXC7L1IFML0FWTrphU2aTsNWDADzIUNssVBoQLHBd1uqRTRY0qqFRJoUo+qqxSVR0kEdtpuYfyyU3zF+895ZS7rznniTvPefzOMx+6deu1F/efdnhkzULvSFXojrs6Io560FkNYHiVhwjLAHOm/I6EzscUPiLzYckZEt1hUbIkLaYG0kakGEq2J/L9+dporXN+x9Cy/pGlA50TbcXuXLgU9cV08Kqcl2PcEDPSlJuYh6Ggd0HUJsAejsm/B4wHXwO+3hMQ1ZQeqUdT3cl8X7rUl8l3o+W6s8lG3CqEDBD+IZ9DcdMenoLEnUAscFUUHmNG6BPnGPw7wzCPA3KHMJmTnU7NI5iiGJblmE9L65FKKNWMZNpjiXo4kPX7LJz8RHcJ1w99FTwm0AvMxVBuMIjyODACSDC4DDAOZztAbMFdQPq6IIRkX0LXcwGzYoWbiVhHBizSng1Vk0Y+IsdsqLgBQjjtgenbfMWMcCIBE+TBCHrtCVIbnAxkzQgO1ufmNDdveB0hyQ2qLmNoFSvQjAebMbNqqfmAJ6o6VA/rBX1mc9GGIhQUIBzuwi52qAUai67luwl43CxCJSC447KU19VqQG8LA0vCA4nIUDoymAx1R/Vq0JtQnUA1kZTS7OXxRI86IU07F1Jo9ruQPuxdLMgs3i/gwGBCcWf97pzfXTSkakitW/5mRK8ElZyJw19+gfXyWDKYJuGKbXYucC+QCxq64BZxW/SC22EZr4vXvZ6IKmdNfzUcaEaMjkSgJxXuzwT7U/7umFwNeABpQZH1ObE2Z0sAjaQ2a7PJzmIMlCXoOV3gLMmRUvmsxuf8BCoBVy3kboY89YArrzmjIgdQgUKAa0ao2G59JpG5NlfEtNDO46pLv9cVkl2W4owqngROO3mBJQQq3qQfSskTkqEvEL2IUIHPkrUqc6DylwxH+Rw8K7o4w+uMq558QKxYSiOmNmJSJeyCG8nqToA96FG4Qb+HgzgMoiXQZ0jf1qVCOi0efCKbb6jdO1B39pXZngzVGac6o7hwC3AyFKRRncyMceHUCEGITRHYA0hsWxqhlsXoFXFqRQJECbCEXpul1uWp9WAFemOJ3limN7XIgfDAgzoghN7WoLc16W1tzPZ2dnsnd2g3d1g3f3iP44g+x+G9ziP6nEcOuI4eEo+fNHYuTZy3ubTviPpFxxbPPTJx+iHB41crRy3x7ph0bRl2bOrjN/TyG7r5DR382nZubRu3po1f0+RX1XmESsWxtOxYWnIsqwgratqazuC6ntjGodSm0dzm8cKm8fLmifZDJjs3jPauHuhb3tW9qNkcL1aG0vm+eLIrEm0LBdtCWjMkNkPuRphvhLhGmGtabEeE7467e1PevozQlXS1xfi6xZYtthBicgEqH6CLCAzQK2wNuUJWiJk46WLrEttAshQMtmhyxQAHdKn4maqfqfmphk41NKoOBscm1RZkumOesUpmx7Kll55x/L3XnfnwbWc8fOvx99yw/NJzG8ds1peMugaLju6Yo91y1DG8AqXizPr5jJ9N6lyyNQ7GWBIXEh0Qt4a83ohXjfvMtD9cCiWaiUJvtjlW61vYObasb3BJV/toLdeTC5VjYhShwoM7s30WGkN5WcrLUV6eEnk8FjlKdOCBMGNenlfcYlA2k3qsHEq1x0o9qUpPutKTrfTkSl2ZTCMeK4YDKb8vrIDOwM8CRVzgJakD3AJoQRZzbZZbbgdwiMUluS5ghmBIUtinxNVgIZisRbONWL4ZT9cjkWJQhQ7sF3jJQQsgCwgX8UaIkUulMWVE1Iw5AIrgpiF9cPeQPmgpLaGHCsF8T6ZtvNo1r967sKNzfld1tD3TXQ6V42rCFEwJIMrY6LUTB+8zc7Wg23DYBJJFQ2LNgAepSQNUJBenuBwgtoKSEFVBmhglywLuNuOhimXmgrjuiECFOH1CFLuggOhQ+BJLSbCHWoBKgXwJ0vA05A0tOzjT44qKUkZVKyZIE7MnEh5KJMayyZFMFCKnRhhyxDBfcrSggnp0pi6wNqGKidmJz6IFiCi5ASoeyyeldbUcBJCYHbFwdzLam4r2pEJtUa0QBHkHIpJpEYukDNcGH4eLxPgAKoK0KEwfYhQSPSBdZlQXoNfDOzSvJ6z6skGzHgt3pK3BYnKill/QzC1qxsdK/o6YmPe7LJlVnFDRrY/bBWXnMlsdH0scqAOq1OtgNAF0vCutufJ+Z95wFfzuckBqRrSuuNEZg3ITYhJvKxW8d/JBBAZJxL4X2+xbgxfhXWQMIZmbg7gBggZQnJ6Y6iHrGgQAScovpHSRTKcLEZwT4n1kEBI0hK1r7Y/Dga10bWvRccZwfpHjJBemj1AxBVyOEfFVo2LZchdMT14Xspon6XNFACrYFwAqnBuFY2t6z07nE9y4rjzTmaQ7YlRHBKcK+oL0EAiUMDVmL8eamTWZb0MlRJMxLmpJiF5qoS1HnKA0WZOm1mbpDXkatMjGAqgQ+pAqBQjZQsixvUnvaAej0DroQzvZQzu5w7r4w7sdR/S6juz3HDXgOXpQOHbYe+ywdNyodMKE76R5wJL4nk3Vi4/s23/K8FVnDF1xRs8lJ9f3HJ09c7t10jr9mGXSYfOFbePIlc2DPKBlUy+7sZdb38uv7+HXdfNrgTFAlzZ+VYNfUXcurwJUfKvb9TXdgbU9wbU91tq+1OreyrrB9nX9PSs6BxY3hhbX++aV20dSlT6r2BnKtQUTDX+kpgcafq0JjSzgbAa5ZohvhkGycF0xd09a6ssL3SlPZ9zZFmVrFuCESvupjE7l7BkUky0H2EqAASPTLWizk/m2ain46bLBlk2mbnANg20aVKdOdfrpjgDO3HQFqW6L7Ut45jWLR61dfvmuo++86sR7bzj+7uu2XX/ZyO6TYltWCmNtXE+GbUewcZBR0c/kNS6jcSmNiWt0TGWjKmv5mJDIBr2OoNcd8AggVqI+NaUaOcKVtlhhINuYrDQnq5WRUq43G6nGtFTATdwl52UBKuguBZryttwxYQlHSzwtOREqtkcDA7/g5TlQKn5BjfgCad3Km8lyJFOLZ2qJdDWersaixXAwa2gxBdwxqI0DHt/GCWYE7h7SdMwkTgwAAInbrsHDQfDrlJwuxe3xe0GsgCgxU0YchFcplK5YsUIQkCkFRZfq4kWe9ZAsQJ2QxEn6HBKFZIEmOjHHud4fh7/c3oCsx7Vw3ix2pTpGSkPzm6MLOwcX9bTP78r1lCKVpJ4wcRDP5wSooEez02/dwkwRQS5gmD5AqzVsiMoJAGOP48kOXnU7Da8niKsn9HQgVLTCpXAwZ/pTfimsQC1A+WMRoVGtXAAkPp5SeEp1oMGxBIwEH2o7OPCnmDXrd7vCojchK0W/Wg8YHVa4N5EczWRHcqm+dLge8YGcDYqMxDOQLChF+KxdEZCUzLXSh72PoAvyxfSxCqDcnLrojah6IRjvTBVGisWxYn1etX1hozZRSfWkgzVLSelO08tKPOtmmNbFz8YNcAuQLCROrh/2MtQIVDG5fvDOyAaoMp7XBCGsqLlAqJ5M9BTyU21tK/p71430bRzrWNmfGs7r1ZAQ83Gqi4YWYvt3gN9sKUGykAtcP9wO3BS8Dlcykz4ZxAO9JblTipD3CzkwTSwYZiMS6YjH2uNWw9KymstwM5CaradbnyXVYeeCjZ8cYOIz4LQNoKJCFfikjKmVLL1saeUwrhcvhnyFoJwLiEk/gJk3BA5aETRUJ83Y1TcXinYWJH16luvEoCHhipKg7Enp3pLpLQflsuUrh6VSQCyCmUBcIaW4I7LTLzhkpwOaBLRMFI4o5sh4LwiuT3BrWlQ7Oi+6J4irhAEnYKNkje+kRc2zqAUWhVMmZNYdcDIN0iRMLQtTKyIUqJOVCWpNil6boQAnm4v0lgroD3ZHO3d4J3dkD3dkH3v0AHNsH3fsoOO4Yedxw45jh1zHDTlhf8yg+5hBz7HDwnHD4vEj0gljykkT2ilTxumLgmcty5y3sXHZUf1Xnzp+/e4FN52/8Ibz51977tgVZ/defEp9zzGZsw+N7zzEOnmDefxq/ehp+dB5wrYxz9ZR99YR99Yh15ZBtEP63Zv7XBt7net7XGu7XGs63KvaPSvbxJVt8oo2dUW7trxhLK3FllaLSyttS0rdiwq983O9U7m2oUSl28o3zUxDT1e1ZE2LNVSrTQ92BLSOgLcz6GwPudosR0fE0RX39CbBvD0p4Iq7M8k1ohROzhtUSadKflzZlTepAqiWAF0KsNUAVwcLoXYhk/lkDp8sAIOTKwZdN7lGgGsPsJ0m2x1ge8NMb4TBaa04N5qVlvW2nbR17ZXnHXHb/iNuvWL7TZcvv/Sc5gmHKUvH+cEK05Wk2yxInKlAgiqV89EpmY7LTEzmIjJ+IzIoMQEva3pxTiXg8QZdYtgtRQVfXNQyPqOoh6uBeEcs3pEMNxJmJeJLGx5LduhuXuY4L8V6KVqgsNugEaKAO4C+CvZxqLRcNic73JpH9ItyyKdamhbza3FDTxhmyjSSODDlMSXQ+xDDYj/BwagDRIF0WjiBPZqdPvFocAL0MXDKHpYTOF50OmRcTwxokUKyHlUCcRXMH1PhX7ffA+6eI0qrpSHI5UFS9AwRD9hcrpBhMVZwODWPHJaNpJYoW9WebM9otWek2jVar/SXko1kMGf5wrpbk3gJlcqB67ehhemDzXKLcAWhiKNtCBWISV34hRVW5O35G5fu9ZgiUESL+0EhKVFVDEpuvwCFCR6fRvVAnDLkAqE9uEjwxZqD0l2U5iRcIQXlIYOE6I9wlJIFGWQKbkv0JmU5r0PcHWiGY92xeGc0Ugub+YAU1yCGZaBAoBZsbtl3Ae4Y0tSdlN+FBrnAvyCMQFsQrjO4cFwQw4qRDWU70/XhUudktW9R29DS7uZkJduTjlQtPaU5wV1KWAUMSh8isKAVwfXbRFHt9Mke7gLQaHMFbsH2qh6OUz1CyKelA9FGsjBc7V01OG/7gumjphcdsXh803hjXjNSj8oxjdfceNmQi22QS6uInHjxkD4cAx1niwhcsxOVEKe4Qat5QCam/HJGB+GlZA2rGkk142CxagRKyWkKNDRLKBwcoSVjj3g7pC9AL4BkwSA7+BdehNNsrhAoOnRRSgbiXcX6ZEfX4p6upX3VBe3l+c3K/LbiWC3cFpdSmiMgANchTUAvVATGEFAdAgMNFQqK9vE0QBf2EgRDLbSg2oBW5OZ5RfDGdK0CSjGO1hYzm1G9HtHrlr8e9leCctaPUDEE6A4QinHY6eBe4OPYSFCBfZJbu0F1mTQIlH7yTcPRGY1iE2Ue4CRCkxVc1BKrhZPlYWplhF4Vp1cnqLUpal2W2lSgtpS5w9qk44dCZy3OnL++eOGW4kVbC/u25S/aVrx4e/mSHfXLD29ednj54q2Z8zeGd61WT10snzKlEtNPnW+evih81nRyz5rm5UeM3bBz6Z0XrH9g/9aHr9v+yA1bHrh2zZ37F91w4fAVu3su3dm46JTGBSfVzj+usOvQ+KnrwyeuNI+dNo5dbB6/xDppWfTklbGTV0ZPXGmdsCx03HTg6Pn64RPyIYMeQMvqdueKhntZ3T1dERaXpcVFfVEuujCdX5iszIs3phL1kVip30q1GZGSEsrLVkFO1NVMh5EbsFIjicRIKjgQl3qirq6Iq8NydFouUCpdMVcn7t2dCUd7jG1EGHDr+J0Vk66gASpwHoUYCBSQLFwNDMfEwGh7NAzeLQNU/FTNj2Kl3eS6AlxviOuPcENxdiTFjmfcC2vRbQvH95607ZbLQakceduVm66/eGLPaclta93zurn+AtuVZNotFnIv++mCSudkJi3TCZmLyXxExu9AhEQmILBBjzPodgdc3gAvgIWdQtQlJjxqVtLzSqgRDNRCvrwuZRQx7vOERZfucvk4h0SzIhCF9FJ0B6Tn2ESRZ/w+KokDwRQ4PlZgeRQTDqfiBrq4dY9gCILh9QZEISC4oX1rHgjAwZ/i+dA/8VPYizAR9Pg2TmaspVRQQ7QGMVw0iA9W4HClmezA3qK5vX5BMgTZ9Hr9XiCNQ3FxECMLqLRI4uTyyPWju4eLl12QOC27GNll+/3WOBtx+ujxcRDMjUooogRzwXQzlWmkkrVktJIIZkKqpXt0Cdf7zsgU2KNAmdUoMhimT7hCPD7cAkKFeDTbKbjg8rCsOAkH3GxAekyki1sX4KY4H6gZUFo0QsVDtfSirSE0Ht2x4Z7xyHYWJLa1oQJohPJR3Q5DwOm0qM+b0sBvqjlTzxlaShctn8v08goAdY47hrKCLMC/Q5qQMqQPhugifhOhAqdBeeJsgccvKVF/pBjJNBOVvnxtuFwfqeS605GaBb5YjmBowokcB3oUKxcuniZEYcn1k/RNMCfuIQtwnXALcAEzRQR3wcpOtwFVoAfLkWJ/oXtx5+S6kelt85fuWDi6cqgxWo1BbG6hUiH3bkORcMu+BcNNGx5yCwS90G5x5JY0JzcULIclDzgP+qSI6osrMuA8ZQQLoWglnKhGrGJIT+ouv4dA5aODtHCpkAVyi9AX8oKbAhjj9ROiOKFhOJ1+n56Nlgcao9ODy7cuWnno9PIdi5dsX7h424LRVQNdCxqJtqg77GHgwiBZJwUBk91WoQm1uGtD0eYioEWEoAqRAO2HdUPLl32pUKwzX5po1ue1VSfr1Yl6ZbJRHKvmBvKJ9oRZCHhjitPAGMsBEQy0TCgcKCLIAhoq5PsJbnQ3ECVEDYTpwTA9YqFGsUe9cFqeaBQgyhICFRzssuiVEZso1NokDUTZkKM3FeitFceR3eEzl3RcfujCW89Yc9+F6x+4ePU9F6y4+4KVd1+45r596x+8ZOsj+w9//Kptj3xq9b0XTd58Vv1TR0X2bAictSx45tLo7pXZ8zd0X3H0srv3HPnUdWe9cu9Fbz+x/73nL3/n+Qs/+8xZLz963FP3bH3wplV3XjX/xn2j1+wZ3H9m976TansOL+/eXttzaMdFRw3vP3nh9WevuOW81beet+q2vatuPX/5TXuW3bBrwVWnDl98THXXIdbxS+VDhtxrO5wras6lZfeigndBRluQCi+IJ6as7EQkOxJK9vmtpmJURK3g1QvecE1N90Sqk9na/FJhXiE+ng2MpH0DCU93xNlp8R1hB1h72NUZAXN2RHHpVyPMNkNsPcg1QkwdsGFPpQAzULjQJYMtmWzZ4ECy1Oxp/ABOt9QCdD1A1QyqblBtBtNhsF0BvjfsGIw6huP8WMq5oKyv7es4bdOaq8457p5rTrrvxuPuvuaQ6y4e3HVCeMMS10Qb159ne1JMO0Faxc8UNAaUSkZmEqhU2JjMRGXGAq54HWGP23J5wg5viBdCnBhxgMlJl15S4wOJ+tJax4qOwmQ+O5IO1fxKQhBDbo/u4CXwlTTdIsrMeMIsTg74fSJW0LdCqEWDH+e9rENkHT6HoLpF3S0a4J1dHsPtMQQwp+Z0KLxD5kFJ0NC+oR+CIVFIFrPJgt8nrp9whcSYMwE162Z4D+vwsk6Jd/l4j+rw6k6v5hI0t6B5nD4y8EXcPaZPein2IkjkwJAIOGJniyggKcDX2NzCCXYMBlkXy0EkK5FlYAFRial6Sgdf7Evqkr2Y2OfGKW5y18SXkVwIUSi5dfGz0MJbmEmfeExbrLCMhwU6guflCVeAhbzPAY4ex0NAo0BqOGRkyxSACqkFjMH5loxApw8eGTwaVArxaFBEhLtwDPfL+nDhMqd7EC0QkgdlIYTfH3IHyOSt4iQyhYxN2UoIsph1+pi47ZGJR5uFCsa54JHBQ+H3B+WwZqQCwXw4WIwECxEjG1QTftlSXYbAKDwjsowXWhFpQnYrakGRpB/woJkzWdhQQfdKhqeciHYexIopaUk91ogV+/MdU7X+Je2D0x3NkWKmGQukdY9fYKFNQuHb7h4OoCggC1sJIbc8lO6mADwKVArWBQhHkCmsG6oPn27g0qBZSlJQVizVZ6lKVNPjqh5TVCBxQOR8cDKRILNQwVKCLIgSQrQTrrSKiLRSHCuDG+F4RZQjwXg91z7SPm/16PLNi5ZsmLdo8/yFm6ZGlvb0TFSz7XGPhYOEkOyMHsW+gK1IcdC6m4aLh/KBskJuYRHBxWBEglUAUJHkmBlppisj9d6Fnf3TnSNrBhdtn7dw08TIyoFCXy5UDMlRxYkDDw4Ms1Du4EWinoZCU+BCP7mN7g0CUdAGyZfSbajYM/PzwwAVarGFRJm2qGUWvcKiV0UpsDVxal2S3pClNxfoLRXmsKZ+xsKuK49af/+Fxz9zw8nP33bcMzduffiqDfdfuv6+S7c+8qkjn7zmlOduOeulO89+5e6dL9157KdvXnv/p/quPSO3b0fmgs3Nyw9bcvuuE5676fL3nrj9q6/e/813Hv72Bw9+8/3bv/rW1R985pJ3XrzgjU/vefWJs1565ISn79724PVLb9o3ftWu4f2nL7hu9/q7LzvikWuPefyG4x+//vjHrjv+sWuPffSaox+92j4+7tGrj3zwig13XDi+/+T8zs2+7eOOte3c8iq/pOhcmPPOS+pT8dBYxBoOhPuMULdmdipGmxJo1yI9odRQojBRqCwoF+eVMlPF6HjeGMmIvXFXp+VoD+GjXJohvoHmqIf5ephDs4mCUAFjG4ANeAWwYVCIFvsxLa1FX0wFqcPWTPgI2wwyzQDTNFE4tht0l8l0B7l+yzEUd41nvUsasW3zJ847bsctl57y0E2nPnTzCfdcv/Gai7pPP9JcM9811mD7clx3im2PsgCzqskUASoKm/UxSZkhI2BoUZG1vA4LoSLGXFLc5Uu41Kyg5UR/Q+vbPLr/ies//eVnP/jBe5/97rtPvPfo4edsKg4n1ZTs0KAJ2k7Z9ghk1AI88sc8PnrMA1CBsBoiU4cE7p71KA7JdGtxXzDvj9aC4aJpZHQlLEp+t0sD7wkuG1z5jJLA9InHn5u4vbfFkO1u3BTtpjgBucWLnFvhvX6XFPDIYQCh4DU9QBfI1yFxDvDUXnDZxAXYRIHrBNFgJw6+3ufGPfp9B/Zh5BYE+DiBAUoIx7jB3Uu8U3OJlkdLiGZa9qdENSHKUdFtuh2KgwOPiddPfBm5C6JRMAtInPa5EF2oVwi3WmILh/4h0mTcAC3OIfBOEZ/P5tLcLt0NeTkBiqoHuSKjTEGWtIhCbgScsh0gt4gyoyRsqNgeDbiFjo9lJQcHoFIhWUjc5dZdHh1K3o3r+lQXOGIStBJfBr7FhgpUtO0uEVpudMctj0w8JrwLKYN7dcKnoIg4VsKnD3h0r9eUxIAsBHweUybPZPPwqovx4UcgKDnQilpQJAN3LagIiK4Dt2BDhaLt5WegRxWXw3B7I5Ka0UKVYLQRTnbE0p3xSC2sZXRvCJw+iC1CI9vshoQeH7JwUQFyFxjpQ3VAvZMARQBcAVQ4XEGO33UFnIOqFnCWzhDhXrymVzRFkNdQVoyXJ8ODM0TBLGy9SEoJlRboiblQIQNluLwNmofbqfp8lhErJYqdhXpftdZbrQ40ioONXE8pVU8YcR1qBCoa0N6CChYU0RCklBAqUEqklmnApA0Ve3IFl+G5XYbiSwQjtXS+L982URuY7l6wYXjR2sF5q/trwwWQjN6QDM0JcIK5kH7aUtWfPFRQplj0ID5fix61qDGAysxyr/lhHPuakSkUyJRVESAKvRZlCrU+TW3MU4cUqW01x7G9xUu3rHngwjNevuu81x85741HT3npvu1P3LLuoavWPrB/y2NXHfnUDWe8fPe+tx674r1nrvzg2b1vPnbMp29fctclPdee1n3NSavvu/DsV+66/SsvP/P9z33mp1//zE8+fP6HX3/oW1+4/Stv3/Tlt2B/19fevvvrb9/5lc9e+8HLu196ZMdDN6+6/VNr795/xGM3nfH8Pee+/MC5L99/xgt3n/LsbSc+ffOxT9543JM3nvLMLWc8f8fuF+/e9fwdpz1zyyH3XNZ/6cmhE1fym/uplVVqOk8vzPBTCWEiJo9b6khQGzS1Pl3r1Y1ef7g/aA1Y8dFUaiqfniom55XDE0V9OCv2JBxtAAmTA6sYrG1lEB8mS1QILguG40qAqwY5oldaaGkEmDp5lIu9qriEkyj2g1tQtcA5bQG2PcB1BNnOAN1lUCAfe4JMv8UORB1jWXlZR/HY1auv3H3aQzfufe7+8z99786Hbtl43cVtpx/uXzXuHKuxA1mmJ4lQaYug9IGrKqhsVmLTABWJiUlMVGIiABWRjwjuiMcb9XhjTjXnDdf13Ehq657tr3z77R/+wy/+/h9++Zvf/OLXv/n5r/7xZ5//4VdWn36oUgw5Am5W5nFsxzbsq3M0iu3uW/YRqPAi65JZQeGlkBhri284eeOuK3deeee+PdectfKI5ZnurJrQ3abIKy4Ik1u+ctYXzIXKbBY4ZEGggu6bcAvFEOeSHaIhqEkjVEunB+qZkQ6ro6ilAxBdulSXU+QdAsuB0xSIU4O8IJ25iR/gygxU8BZwRBvEFguf9fIO1SGE3WZeTXda7ZOl+ngh05cMVwxAixBwORUQE1AyxGPCZ4lMaY2tYeK2F0OuQNZ2hGinD7cAWSB9Rd6tuMWQpCaUQN7Q0qoc93nDotvvhsQ5hDrecmvgC4toroyYhcqMUrFLEtBFiAKuxKECSyDS9yB3TZdsuES/y625cbkR3jJWGToycGeYC3GXtkfGGJ+4M3D6QBd4BT2mDRWEEAN60cNyUMg+lxvEnN+NY5smDm+6/R4IGuD6QY+ysl0+M4m3oEJGdeDKgwIacsuGCgQZpKJJLkAjDhSt3+kG3Wx5pIRXy/q0nKznfWpOEeOSM+ThNEDjjL6xbwGysG8B0oQYPwjoInSBTKFqoA2TdR+0B4iCYpElSz9wxS2gXXF5VCcIX7eKKwxxQgv8OyqDGZmCxUsqAnhpo9HvoAwiiT4GFZxZAckCQg3KAZ+y6k+oVtZv5Y1AzvSlyGLi4Mz3hEDiQ/pYCzZUZkIHAhU6KCBaZrhrL9wgBpfEMaLLqUresKaljVA5nO3Ods9vDCyod0+UCl1x2ZKA7kgUAAlcOakI7BEiS2MWnyxU+sNAFFQqQ6hU6JnvyeO3GheRsa/pCI2LhkGmRBAqq6MUQGVdEge+DinSWyv0joZ4ynj/dSce/enr93720Uvee2bvO8+e+Mqj6x67ed5dl43ffuGiey7d+Nh1p33mgSu+8PLt33j31q++dem7z574/D2rH9g/evOepXdftPNlJMpLP/rS+7/67vu//uFnf/n953/yzQe+/aW7Pvz8A9/50jM/+torP/3w1Z9+84Ufff2er717wevPHPn4HVsevPGIx245+8UHLnnz8Sve/TQkeM4bTxz//L07nrxl6+M3HPbUzSe/cPdFn33sqg8+ffV7z1zw2kPHP3XLwhvOTe7a4tgxQq2t4zfwF6eoBXF2XtQxGfVMhqUJywdoGQ75R8LmeCQwETMnkoGpTHAqb0wW5JGsszvGNAJUTaMrKlNRmDKYxpSIgTIoajiTkdfovMrkNabgR8xUDA6FiG2tkS5QEjiDUtTB6KIOp8ErbDPAtQf5riDXHWR7TKY3gPUyEGWGEvx4zr9moPesHYfdeslFz9579WtPXPXaExc8c98Rd1w1dM4J1qaF3vntzpEiP5jl+1JcV4xvD+O0TdHP53wAFTYpswmZRa542YiXtwRnRBBiXintDVT0VG+8d83A5Y/d8O1f/uiXf//zX/zyx7/8FdgPfvWrH3/zH359/gN3pocaYlR1QAxlqxAwDPNthICvnzX7lQMeGdylQ2RBQMgBIVK0Nh6/+YEX7n38hXvveuj6ux+5/rp7rzjmvBMyvTUhpDhUjz1Xj70IPgtZ/DlUwDB9e/iL9GcI6BAqLC87xbASbmR61i3Ztm/3SVdfsue+W4+6/MLm9HyjnPYEfE78thcoFRRbGKChx59198iSA+abhQrkgn2PEzgQEC7VLQUFqx5addSKGx++5snPPvrCl1686Zlblh+1ON2b9MUkp+ZkgLvIFVI+kAW4IQicUaagUfYBZAF38VFogcpxKi7BFP250MiWBcfsO+b8O/bsvXvvylPWFEbz/ozmNkGscDj2eMAjQ/nMCfPBY9o2427wNCxPjsb5fxJ9+wUpKGpRnxH3GTFJt0QlJIgmiCEgFvgjVCoIFVumHHBn9sARIYoNFXgF0od3sQrQ/bFuxuHlHJID+CGGvLIl+KKSFJHkiCiFQTK63TrIL86h8rREbgGuH6sYoGKnT2TKAagQbrU4R2rZQ7s03mM6PEGnGBN8GS+YP+/Tc7KS9oopaMwuV9DJ+yF9uAu48RmoQAqzWSBU7OGjGai0JlRwHJLIFGwhOPwo81AmHt0h6ryo8V6Nd6mgYDgW5zBmPL4NFahEKAq7lOCyIWWAClQB1AteyezwFzFQdQLDyJxDd3lDghITtIQgRT3ukMsRcLJ+Jwttb1amIFTm1AKOrblp000HPQgVqJEDUIEsCLpwnJNlJB66qjskSDHZl1bNihlvj8QaQSOtuA2MHnBmjvRNHE0l3YHGWyBzNp/gRg8GacAJPv2XQAUf7AhQsej5EZAp+KiVJRY1bdHLCVHIwBcNRFmfojYiVKhtFfbITmv38mX3nL/rtfuu+cLzN33ltSu+8Jldn31q+9O3rXrwqul7Ll/xwJXbn75115tPXvXF12766ttXff7Vc9948qhn7lh27+WTt+/d8tjVl7335BPf++Ddv//OV3770y/+08/e+tWPnv/ptx/5/tce+f5Xn/nxh2/84ntAms/95sev/uw7d3/tvT2vPrnt0ds2P3TDcU/fvufVh/e//cy1n39l/+c+c97bzx3/4oNbn7xl8+M37Xj61p2vPvypzz1/21dfu+Mrr3/q7WdOe+7OJbdfmN6zgz9ynNrcwAe9rMjgg8UWxun5MWZehJ2K8JOWcyrinrKEeVFxXkKalxKnUsJ40jkUZ3vCTJtJ11S66mOqClfTwPia7qjprqrfXdVdZc1R0viiyuYUOutjsgqTVekCIEdnywZbM8HR4zBXPYATLbUATuAjWoiV/XTNYBomihXgSk+Q7QuygxY7FOVG4555xfT2qUUXHXfKfVde/crDd7zz3J3vvnD9a0/tfuLOrTfuG959TOHI5YlDpmLrRqOrByPLuoML2rTRktiTdDeCjrzGpn2gVNioxEZE2hKYsMcBYiUuKVkl1Axk+tO960aufuaBb/zyFz/9xd/97Bc/AK784hc//NHf/+StH3z/mMv35oZrUkp3mQIDjpi4S2IQcYNzITixj21rLZ1Cvw8enJd4wRD8Cb0y0H7O9Zc+8foTDz5798Mv3f/oi/fd9/y9F951ZefyeUYu5jEVTnbjB22ioMcBooDNunvIyzYyYk48MnRsxg0yhXf7xWAp3rl84virLrrg3hsvvPeGfffdfPkjd+++65auNct9GQv8KegAhMqM08cxKJLyrNNHvz8LFXtaBTyyrVF8Tq8lxttiW8454vM///Af/+OXv/m3n/3mj3//8z/+6v0fvr9tzzF6yQS/D06hFYATmYIddSb9mbvAETYyrTIHKuCRJYfXFK1SdNPp2979/mc//Pmb3/7Jm9//+3c/+P6zlz9+WduSTikucYrTHqFqOZqWL7NlhAsD8ICtVD4a5kNFePEbDPgM/5DPyATSbanKQL5trNoYKafaY0ZGA+0CN8iCc5l1x7a7hEKAW9AgACejRkAUU8As0COT68csUPxB2Tpk3qUDUSSzGMkOFOvz22vzu9MDlWAtoqVU4LHHD0E6zqnMSd92lyR9SByIglmAx7SzmBOdeFmP4fIEXULE40vJoVo42hGL9qZj/Sm9Zqp5zZcWBcvpNDlOgyqGLMDpw56g1/bIcNlQPqhU7PSh3ZL0cQ0hOmWcOYO2JHDQYp2qy+N3SYYHgiHRcIumW9AdDh9Od+GcECZOPL59F7MeGUtpDlTsIoJzbLHipFCViizoLbfh8kbcSsKjp7xSDG7K5Q45nQbPazzvgywgZTsLSB+bE6740py04aEDLoQKiBW8hZZehGRtqMABNFdW5R0BlyvsFpOAW1HKSEpOBPRiRgEO2AySlxVQ4uP8GVwhtCUoamj2UBGf4DZDFAKVUVAq9oSKRS2I4GO7ZpXKzNiXDRV6Q5relKe2FKgdVeexfbXLtu948qr9Hzx194dv3v3hu9d96fVdrz9+xNO3HfLodZsfuXb7kzed+vIDF73z3JWffxW8/57PfvqY5+5b/eA1U7deMHXbnmOfvfWWr3zmlZ99/Qv/+OOv/vPPv/hPP3/zlz989kfffOx7X33qB1/7zM+/88FvfvTl3/7sc//wk5d++q1bv/L26S89suaBa1fcu//Qx2/Y+eI957/+2N43n97zxlNnvPLo8c/dc9RTtx7+5M3Hf/r2sz/z4Kfef/bWr75+21dev/zd50549q55t+6NnLuFOWqY2tKkNpSQKyuz+Myx6Ti1KEoBQedbaPMi9LwYMxVjp+L0eIQesnBpXJefbtOYhsq2aa52Q+6LGkOp8HDS6o9HeiNWdzjYHtCbhq+mCxXNWfRxeeCKj874KKALePaiH+fncVgMDYfCcDTMpMsELSUdZ/KrfuRKh8l1B7i+IDcY5oYjzrGkvKReOnrpistOOeOBa6575ZH733/p4c9/5t53X7z+M0/sffru4+666pBrz1tx8amLzz9+wa4jpk7fNnr8hu7t07ml/YGhstiM8jmdict0VKTDXgzWgi425HHFRDmjGlUz2R3vWt57xL7Tb3vj+Xd/+N1v/exH3/j5T7740x88/Y2vnf/A3cOHrEj1ldWM32N6WfDswIyPQIVwZZYoSB30xdhXSRN3KC4pJBtJM9NW23jaidc/ff8jbzz50GuP3v/Ko/e99tTO6y7uWTYvUstKEd2pCageoJVjQ8cFlGgzvhiNQKV1DvQ30l0hC5Ap3rCa7CwuPGzD+Xdff+Xjt1///IO3v/jYrc89dtGDd00ctjnaUZZjhlNzg/BvEQud/kc10AHJMmf4iwyM8F5cdOTPBHtWTd7/5bd++Z//+ts//vQf/vTb3/7nr3/3x5/+/X/+67UvPpgaLUkR0aGSQTxiH4NKa04FhAsEyFBEZNSFTKuwDHmAhxjWyyOdt7/2+G/++K1f/uMXf/XPX/zZP3/l737z+c/9+oM1OzeGqyEnLjaF6wei/BlUcKqADO+A6wTXAPnCu61SwgNWdnlMWUuY2c7C0NKhRZvmbThm1cbjVi4/dHHXgo5gMSQEvRCe4/DmLFSwCmx3OQsVMqcy6/GRQGA046GBKB7d44tqia7S+jOOu/bpux56/dEn3nnq+idvWn/aIYWRipYxPYYHssAvwaBh2SJUwF3a7thWKnYWB5QKcZoehpU5Jzj3uKoWQlZ3vnfTwkMuPHXXbRfvueeyLRccObhlItoXUwqSEHFyOkNJZGkZQmXW4xMxB6VkGw5P2dAiRUSGjxgXzQHdUZIKYkhRU2agGLYqlpk2NHzWteTSPThOCDVre/yPKBX7Lgjdwez0oXZa6ZNpKieFQ7Ui59CguXq1pBIqaLGaGSprakaS4l6P5XIZvFODCAD00Byo2IOcZOyLCQl0yAtQQUmkQeviUXZDfblx2glHIH28w+/yWl5fUlKLsl5WtIKsFWU1LypJjxh2uP2sS2E4LwW1hh+E64fmak+ofNJQieDPk+CPlFjUGHmY/CT41gi1IEotbE2ozEKFXh2j1yaotYkWVLYWqcNqvlPGx2469fRX7rjlq6888O137vzwncs/ePmUFx/Y+tiNGx66ZuNDVx/59C27X3vs0vde2P+5Vy5+76VTXnls9UPXj95y/uANZyy/d995bz7y6Hc/+Ozff+dLv/3Zl3/7i/d+/ZOXfvLtJ773lYe/86Vnfvi1N3/53S/+00++8tu/e/83P3n+x9+84cufPfbT982/9eLRm/YsvevibY9ed/wzdwAwjnvmzqOevOWwJ67f8eg1hz52zRFPXH/K83dc8Nbj133h5eu/8JkL33rmsMduGbjmbOOMtdTh/dTWBrWxTK0rUGty1OostSJJLUtQS2PUNEEL2MIovSDCzLOY8RAzHGT6DbpLp9v9js6AbyCanF+qTDfal7Z1Tze6F5Tbx9LVwVi+N5TsMCNthgloqajuss+Rl5mshEuwQCtkFDarsQUbLQZXBeFi4hR91WCqfrqCRKEqOhzQDT/TbrDAld4wNxhzjqbkRbXcYQsXXHDMUbddctHTd970+lN3vP3sHZ999obPPHHJp+8/44Ebj7/tU4ded8EhV+xet+/UFecet+DUHX2HLi+sGAqONXydaWcxyCZ8lAVEIZGg4aRNlyMsCHGfWvBH2yO1qfrg+rGVJ207+5Zr7nr1xSfef3f/g3eecNnu6WO3ZIeaRsHyRlSn7mWAH14SZaPHJ055VqnYe0COrVTctrvknIpbDvr0qB7OxSrDQ2tOOfG0G/afdNXFJ+6/6IiLzp06dENlpDNcSCoR063LJH0SYttQwVHvmSzQPgYV4BbHeh1ORZAtf6KeH1o9fdjes/feeuWVD95yzaN3XPP4fcdddsHA6ul0V9mfDnlMiQW3bjt0sFb69l1gyjNGvsHQ8piYBS9CiCeH85H+tQsf+/qXf/Ef//Fvf/qXP/zpn//wH//8b3/67d/96fc3vfJIx7IeNaXyyC0c1geiILQg+vuoTAFDeTS7+gtLiaxXVr1yWM901XfdevWH//Ctf/3jh7/93Rd/+8+f/6c/fucz3319/Qkbos2oz8Ip1ta0Tcvjt0ZFwJHRNldmoXLgFkBs8bwqCGHdzMaqg+3z1i3aceqhR5915AnnHH3MriOXbF/avajPzIfcfg8u7MZP2VBpBbBzlIrt8UEJQaERPIPjRqXF8pJTCPiCpcTiYw+/953XXvve5z/7vQ/e/t4Hb3z3/fvee3rF6dujfWWPpfI+8s1BO33bHf85VOxbgNdbSgXn9lkf7wzIvkI8P29wxRnH3fTSM29/99tf/9H3v/6jH3zxB9955kuvHnX5yamRoicu8H6OkQlXIH3glu3xIUEbKqBU0Ok7MVOEFpxDQhN7DaHk9OiymYx2jHRNrZl3yIlbdpy5Y8vOHX0rR0OlmBRWXSoWEY1EsaFCsoDrRCVBspgLFXgdlSWkT9EuJAoLkk5xuAIeKaVF+3MTW+dvOXvr6lPWdK7qifUn1bLujXtdJlTx7CDkx6ECRKHCABWoCNBD+G0bnA4hVcYCFL2sQ3O4gi5vQg62RbOThY5VHcWpbHl+Nj0cjnZqWl7whHinAkoFZA0UEZbS7Cz9J6xUWkSZgQo1EaGmotQUgcqiKFEq9jfnASoWvZrM0q9PUBvT1CEAlRJ7eHvs3OXrHrjgwrceue1rr97+9c9e8YVXT/3M4xsevWnx3ZfPv2Pf0nsvPxSg8sYTl777wmXvv3jh288f9/wD8Fb/dWdN3bLryKduuObzzz/7oy+/9+sffv4ffvrer3784o+//fC3v3T7196758MPACpv/fJ7X/rHn375tz9/7zc/+fQPv3HN51874sk7xm88r+3yE9r3nzB8w9kLbr9w+d2Xrb7vU2vu27/m/v2r77t85b2Xr3/4yqOevnn3a4986v3nL3v72Z0vPLD+/ivrl58knbyEOqwHv+cPUNlQxAfJrAOupKmVKWp5Em1ZktAl1hIuE2F8vkAffhvR2WWKvaHEZL6+uNo1XetfXBteUuufKnQOJ2t9VqkrkOvwJ9v0SEML1VSlonmKKodLsGQmJTEpmcEHDyt0VufyflxYXDH5igl0IaNhJl0zqBrQxY9P+qrrVJuf6QzyvZZzKOqZzAXX9HedsG75xaccduO+nfdff86jt+957I7dD91y6l3XH33z5duv3rv5U+esu/iMVeeftOTsoydO2NK5dXFqcY8OSqWe4DIGHZOhOVIhGypuaECs3+0MCmLCp+b8ZjUc6UqVp9pH1k9NH7r6sLOOXX7k2rEN86vDtWDJEmOK0y+wPhdtYwNcD3h29Miwh855wOm3Tpj1mGQZrlv3iuARLN3MxzN97c2FE93T8/qWTvUsGauNdcebOSMZEPHR8V7ycfBW0Btt3TOb8qzB62QoHNLHGJzHb94pgjeg+NNWtrcxtHLRiqO2bdl5wo6zTl1/wlEjqxZXhjuj1bQWNQRDYhUXdiEwcA3IxY9CxXb6cDB7Cx7yzUfJ4w36gsXI4KrJvbdd+8HPfvyLf//3f/zTv//q3//tp3/4ly/84y/23HvNwOYFai6AC2chTbhCLCLwaJgsA56UsKRFF3ix9b16zAIfuym6XJqoRs1Ue2Fi4+K9d1799Jdee+MHH7zz48+/9M23dt1y8eDasVgj4YvpLl1goNiJEjrgLm2PTAxHSFQihtClgvvGKmAgfV3yRQPhYrI61D29fd1x55121v7d51y5e+flpx9z3gmja+fHm2klAlVAlk7Zn8VScnwEKthyPPivDS1wqWi4isEpuYWAGm4Ud+w778Vvfu7d737w1nfee/M7777xgy8+8vW3D/vU2bnJLl82AK2IwbojH4QsoAogKVsJ2cN3ATdtQ0WGtkTEHFyGxPGayxPzh3pqPeuXnHr9/le/9eG3vw/bd7773W9/77vf+cb3vv3pL705ceQquWQ6gi5aheibQJFUNMlidsky8fvojm0lhNcPeEAN4eVAo2gRqznUv2r7ul2X7nrytYfffP/p1956/Jk37j/i7G2xtiSAk4caJJywgUouD6BCvpsCrEKu21CBWyClhFkQJeGhOZ/DE5SManRg04KLH7313R9/7bv/9MPv/PYHb/74g089c+WiE5ZYPUl3XGAUwnVbCcHH7YoGqAQEGohiidiRATBQSpAvRg+kFlAvcm7TI6dVq6e48YKzHv3ya+/8/OsvffvVp79833XPXHDY3tU9a6pKUQAxByczCC1SC5ACpA9JgUD8JDdbqdg2GgGo0FNRen4Uh78W4giYvZiYWmFRK4lYWROj1sVnoeI8qqt8ySE7Ht9/2btPXP/Fly9//4XjXnhw6q79pctPTV14RObCw2qXH7/4rn0nPn/3BZ998rJ3nrvk7edPe/HhdQ9cPXHzOavu2Xfmy/fc/pVXX/rJ19//9Q/f+/WPXv277z/+va/f+KV3L3vvMzd86bOPf+/Lb/ziO5//xx9/+bd/9+6vfvT4d750yTvPb33kpuHrzileeHj6/M3Z8w/JXbijePGR5YuPrlx6bPnS4wqXHlvdf/LQzXs2P37jma8/dt5nnzrrpQcPf+ymeTedm957qPPYSWpHB7UFoFLBEbD1BWotESurMqhXlifw4cpAlCUxamEMhwFHQlRfgOry8+2G0Bnw90cLE8XmvFLngnLvVLlnqtIcTJU7rGLDyDb0dE1J1Xyxms+qKUZN8ZUVZ15hM8ASiU5KdIJ8vz2hMEmVzWhszo9P/cLvrBi4lqxhMjWDqQNd/AAVuq6BZGE7Anx3yNkfFyYK5oq+3I4lXSdtGd91/JILdy696IylF+xcdM4pE2ccO3Ty4b3Hbe06ckPbjjWNLcuLGxamlo2Yo02xI8MXw/gzLTjw1SIKNnrNRevIFc708CHBGZU8KUUpGlYzku6I5XsSuc54rBYMFk0xKnNwPvQQ8IO2K7QPwNDvE0MGwL9gJLpsuRv0aHAaLTo4n9uheR2m7InqcjpsFOOBUjxYipv5qBT1e6CvaiIjeigBHDp8ZA5UiKM/YHaOkJE9dkSgwspup+b1hFQlFY42coX+RmW8qzk1UB/vy/Q0g+WMLx4QgopDE2hceUWu075s+BfMXpeFZpOSKBW4C/StHDhB8CNuU9QzgVRfacmRG65+7O6H33311e9++MKX3n7wpfsvf+jaqcNWxnpLngjcBa5IJumTQBuzOHDxqFpmv19JShKfziLgj4y5NdEXUgNZK9NdHFo3f+UJmw8999ijLjj+sHOPnty8oDnRjNZiclRFp4xXSAaOYD8bINvuEvZQU7a7hItHrwe3AFx3gQpUokYoH8v2NMY2LNt02pHHXXj6GfvPOfXSnYfvOnZk9bxEI6PHdbcugNIiVUDQDsf2qMgsVLDlwE3BCS2ZAsYKvNPnFsOaVc8uPnzLlY/e8fKX33j/W+++/+33X/3mF2546en1u44vT3QYubAQlEAN4DNU4PIgBWgtkJRqL80CqNjffLRlBFwGxCW4UhZulrOh0lFoLpnccemlL37tG1/79je/+a0PP/zWh9/85je+/s0P3/j654+/YlesL+eJiqwGJUDSh4wQKmQWHUoJDJ0+eE8UkaSUoBXhLTAeFFteUwkU0kOrlh2/9+y7nn3wza+8+N6Xnn33Sy+//u6Dz73zyOTmKb0QdvtFXGCG31Mhq5yxlCALcs0IFTJXj3MqUErkFojHR255WV51+ZJGYbz3zJuu+sqvf/mLf/nd7/7ld//rX//1X/7w+7/715/e8fodvZv7A42QM+zG5Qxz9SKpBVQqNlRCXjyGXGYWleBUipfhFd4ZlpRKYuyIrW/8/O9/95//9u///r/+4z//+C9/+tWv/+Obb/348Z3Xbe9dV/YmOc5HMcIcqMClfuJQYYZj9HCMGopRsB+N0ZNxMGoqRs1Dx4q/moW/hhKhl7VGwBhc+hWnNqSoQ3L01pLnmN6O/Ycf8cRVe15/aO8bj534wj2L770if+mJwbPXB89YHj5zWeKc9X1Xnbrj0WvOe+2Ry95+5rK3P332K48c8sgNS+7Yt+6By3e9ct+dX3395R9//e1ffv/tv//BZ3723ce/+9Vbv/LudZ9/4+6vv/fsD7/+1s+/+7lf/+h9HBP71u1fffvsVx5dc++VPftPzZ2/Lbl7TeysldaZy4M7lwV3LvWfPq2dPh3ctaZy6ZHzbz33yKduPuOVB0578YHDHr9pye37mpccr5++kjl8kNrWoDZVUanYI2Dr8tQaAhVbrLR++ytOzY+hbhsIUd0Bus1wNA2xPeDvDiX6ooWhVHk4WxnMpTviVsUwcz4jI5oZIVyQ4lVfoqFFmrpZV+WK6iooTBqUCn6znYqjAVfouI+O+ZgkvKWxeT9XNLhygCePNObqABiDafiBKPYzJemmn20PObqjzsG0OF5SF3YElw1aq8ajayajqyes5WOBRYP6ZLcy2uEbrssDVbm/LHZn3e0pZ9Xi834mpVBRCTWK7XSgLYJfgJ6sksETiID8bsb0MEEBF4YlJHdSEjM+Ka1447IDPgiuBPwU0uJjRJljsy+CwTkHDDoVun48Bzqe5qb9Av7IsSnypoRmiJzmJSNCLsoLRjLCNG2nPytQ5tgswJBA5IOyh1a8rF90hH3umIZPe00HvZmgkAq440FHSOcNiVGBKG5UQvBBmyi2zXH6rSW/8KKdPrINR7FYxc3pojOkiJlQpL/atnbeijMOP+GKs8++ae8RF588//BVsb6SkDQ5v5fB4TVy8WDQUeemPGvkFdRDYF4HIzmBuDx5tL4c1/ylcKQtkx6qVud1NRf3NOZ35IaKsbaEljbcIZnT3OhtwQugI5h1xzNEsSOG2RgcPT4WPhFz+HO5WjII0K1P9Y+uXzx9+PrlR6xbdvgakCmV4bZgzpLC+KxoFq7Krjv0+HatEW7huiYS4+PX6WfSB8eKM2e8Q3EJAUlNmumu8vCGpYeff8Z5t1x9wW3XnX7lvvUnH963dDDeSGgJvxeflghiEf0gGnp8IiPsdVMgVsDp243T9vh4C8gefGpWyKfkIsmh9tUnHHPzM89++t333vrgc+9+DuyD1z744N5Xnt1yznHp4aKYghgISt4eJJwJw1tZ2B6fqAq4BXgLvCpO/rEMcFHxyCE9XE52zBs/4twzr7jvuvuee+DZt5557q2nXnjn6RueuGl04/xoLe7D38B3kntvFS9eKrwCaR64hRmlYkPFzkJ0OPyCmg5VJwcvuOP2b/z6H375+9//8+//+V/+9Xf/+q//+ts//P5zP/vKtnN35IdKUkpmNfggGSGEFCB9jB6cB5RKCJUK5tKCCn7jhJE4h+4UE7rVV9t80UVf+Oc//PN//v5f/uOf//AffwSu/OaPP/3ZH75650uXbT5lPNomOXSyYsLOwi6iTxwq/EiaH0nBngMbS3OTaXYyzUymmKkEvSBJL4awPUItjeDqrxVRarXVUirrE/TGDL2lKBzTU9u3ZcWde7Y8+KlND1257J5Lu649M37+ocEz14V2rgzvXBE/e11931Fr7r5k5wv3Xfj6Y3tefujkT9+z6YFrF9920Wp48fl7bvr8y89898uv/uTbr//02y//6MPHv/2le7767h1fefuhb37+uR98/dWffPPVH3/rme99DV45/7Wntz1808T1e+oXHp3ZvTl+5urI6cuDpy3znzqtnrzQd/IC7bTFqT3r+648dultuzc/cOmhj16z9ZFrl9xxcdunTg6fvcFxzBi1rY06pEJtJkQBpYJipQhoodeV6JU5elmGWpKiFqWoBUlqMkmNJajBKNVjUW0BRyPgbcNnf4XbQ9GOcLQtFKoG9LwmpSQx4RUTHl9GDFW1VEco1RmJtoeMhiFWNEceyCFxoFQSEhWX6BixKJiMFpNpVC1+NmdwRZOrBBy1AF8PcE2TcEWnaipV00G70PUAoqUz6u5JuvpS7v6sqz/n7M06utJ8R5xrRJhqmK0EuVKIKwX4gp/LaVxG5ZIyB9ooQqbowfVAv8IwCrrZHLOfLQEGvQJCLXBPQU+r4UI7a8V0swbefI4hRWYOZo9bT2MkfgGMeGdMB7oBJIgGKXsoFRIHThD3DSnbKdhm+2V0+rN+f2ZmwuYKOmVyGvwLbymYJv7AZVhiI2gc7EMibQi0JlA+T+uD8BHbm7fSnPHyc9x9y+zTyJk4cqW6acPLRclvMhYD3kZc6y5Ehht6e9qTD3NRnQ7IlEZyQWjZaRINBBcGtwlv6V40zUupMzeOt0YMjjU3awrOmM+T0cSKIdcDajNkdkeDPUm9GZNypjPqY0zB9iPE18+Up60hoL5wYIeU7aw7Q7FI6gvQBVwBMWcpSiYUrKeSfdXyRHd1sqc01pHqKQdKMSGscrhQgnAdy98OwO0sbKjMemQiwiB99JX2nA35WqXucQdEMe7Xy4loTyU32lWe6q5MdGf7K8FyREro3hA+ypCBNO2G1MqCrP6Cy25lQdonODh4Cy4eR1DRsdKyA4Dqjqj+SrI8f3DZsYeffe3Vl99/381PP3nbM09e+ch9W885ubl4QCkFnSEvi6VEmGFzC0qjpbeIYfsn6WMRkbtwscBF+0kzWtxMtpVH1yw7fPeJp3/q/PNuvvriO6/ffcNVq0/YURnrCOXDUtDH+9zk2ogWxxshNQJQQaXiRK7gLczUgg0VD0KF171S3MgNdWw549T733jnW//wT3//+9//9ve/++2//stP/vW3T375tU27dpRGSmpKATyAspm5fgIV3cUEvQiV1kS9PfME7QfaG4CHZRTeZbillJYabjts367nvv2Nn/zbH37/pz/84U//+Pv/z//6+Z9+9c3ffeOmJy5df/xUrE0Tgvj9RxsqNEROsoOGZgO18AluwmhBGMkJwznnYNY5lOaHETD8aIobS7FTSQbc66IYtTRKLY/iEyRXRag1UYTKugS9Pk1tLjgP7wyfuaTt8iMGrj559IazR64/p/vKMyr7jk3t3ho7c13s9FWJ09cUz9kyedVZ2+6/5rjHbzv2sVu3P3D98lsvnX/tnukb9x7x0HUXvPLorZ979dFvfO7Jb37h0Q8/d+9X37njS2/d+oU3bv/im/d8+a174Phzr1/x5gtnffqhLXdfN3XVOV0XHFfevTV1xrr4qSvDJy8NnLTYf8JC5bj58rHz/KcsSZ67of3SYyau3bnolt2Lbj1/9IbdlYuPMXaucRw1Tm3vorbUkSib8Ndc+C1N3+F9gaPHAoePq1sHhbUdrqVlx5KCY1HBuajoWlBwTZWco3lnfwofKd+0PI2grx7QG0GjHjAqhl7U1IKq5H1yTvYVfHrFH6wHrEYo3AiCjFWrAQ9ZzkslRDYhMnGRBouCIVSoSMtAstiqhU3jXAuPX23xM1WDrfnZms7UNaqqUBWNKmsUeZ2rB/lmmG+PONqijjaLb4TxWS8Vk87rVB5/3YtJ+tikBDjhE0gUNiqzlsSERToIXJkZ/rJ71+weDXoFCbXAguCnZqBi93AMwIkjAMNI8/9opEvbvQJ9B4Sf6DrRwKXCMTgXTNY+ExK3fdmM00cjnt128bbTh85sWwsw5Bz7XejD0McMMtkL+JydPYLs4C304OR86IGzqbU+SIAEhi/OpG//2/oIuQZ4BcAApRfxUjGRToL09NkLtamgSJkSpYtIC8gLDW4QsgZ4CAgSP5hIGeQcMIAcvgUnk1zsk+FMv0ADBSFBSDansnliWYWG7CyhhQ1w61BN6NlJxUFVYhDgpkLEguQEeAudJqmv2eoA+ioe1vDyYdmV1LzZgFSypILlzQRcMY0LSLRiE5EMP8IH4a7tIoXsMNSwY3DiN8F7tsQQiRgwaACn5qBBz2kCH5QcUcWZVF0pzZ32C2m/O6G6IjIfEFldADaTC7PbEhl1sVsFNDz7Xuzhr1b6dlBCBrIkjlEcnOFxWLInaZjtxerC0dENy1ceu23NSYdNbl+ZH29XS2GH5WVAS9nPUYb0weNj24NcyGXbkRNeP2kJOItOPD4+ltTBeF2c4vEEFDkeDFay5Yn+4Q3LJ7auHdm8sn16MtFT8+cjXkvhFQ/ImhaNbKi0snDMlJK9ugyaE2k50IbhHDIIzKou0FsGiKHpifWnn3D5o4889aWvv/q9H7z43W/d+Npzi0/Znh6pGYWQF5+T5gIIkVKCFksuGCfqBcAJA1DBbwuRBoB1DRGPi9FdnAGJC96kZvWV1p5+6JWP3/bCN774hV///Dv/9ruv/tPPXvzOe1c+deOa09eXJgpGQfEEQJIi3WkANkCFRA809JdPcKuunaqsnUgu6VeGqq7OtKMTf0DX0RPj+2LsUIwZi1HzovQiHAQ7ABUQK+sT+I36TTlma8V5ZLd6yvzEOesblx7Tu//k/stO7rrwuNKu7ZnTNmZPWVc8bV39rM2DFxy95Mpd62++eNNtl6+96eKlV5+7aP+ZK67ZvenGC49/4Nrznr3/iteevuHtF69/56Vr3nnpyrdeuOLNZ/e/8cz+V5/e98JjZz917zH337T2xkvmXX5m//nHdO3eUd25IXfSqthx08GjF5hHTOmHTcg7xsQdo/LhE+aJ09ldmxt7j2jbd2xp75GhszZ6j5vPHNpHbW2jNteoTTiVQm8scVuakRPnVXau6TjrkLZTN2WPWOZfNywu6XDPq3imysL8ijy/5puqeocL3l78uRRPW8xTDYvVoFwJKGBlUynrStlQyppa0bWKoVWDejWklQNqJSCXTE/Oz+MjUpAlTNwLXEEHAUQBllgzBlCx0PDBXPi79CqT1picxhY1rqxxFR2MrWhMRaXLCj54uKDSRY0uweu4hIwHKxtcyY/rlfMajQCTqJiXjgpMRGAtL2dJ+GN2+ChJiQ2K2BzB8/rJjCu0IfCSti6ZNfDFtlMGlW2Ba/ZSBsTXNgaIk20F+7An1mr6pPXPNftdOB8+BWG+5qF1cKYz6YDBu+jvbPDMEAU/SLriXzSfCzwXemHwgGAHAAB70sEwZidfdwBDfBKogCOzP2Xn20rBNry2PzPi8eHA/oh9Jlw28BXSDwsUuPioYNMFSwkyMgk2UItAbxeRIrYu8UvIEtibMh7AHo7t04ArBwz+JeUMpQ1pJmXgCv7oZ1qm4l4qApmCuSnLgwYHswgJ2W/Nvg5OjXAF3A24UfBEqJaIsIAbATFkePiQl7dER1TmIz4W1xFBvjMLulpn2nE9iTAMEnoHnSQ7SNye8yAGft8+H1wqRsqwJ3UNbwEegh46LDD4jShAOxEfUIBQ2nA99gchC8xlJqCx3TEY5IjcImeiU4YsgD0ceXAA6mnadNIhgY35XFnoYhGtFjebaaUScaZ0BlovfFaFW2DxI/ZVQQqQ4yxR0OBiyDXAuwgeAgbAgwfbHsCV9Yt8yOeM+oVMWMxbUjbsTZqukMr5vSzcAjRR+AieTz6LACZKArKGu4BKwQVmUAtkqBlbEdnbtwOuP+B1xhSxEAr3VfOLRjrWLh45bO3wjpWlxT1GW0xM4Y9LMoaHhpPndgfSvGnotqTP0mBwQDCMx/jNFThwc2HBlfKJ5WBkrLHw5G1n3XXlVc/df9sbT1/29C3HXXvmspPXFhfUtZrpiUu8381AytABpRZRqICAA2uf4Lb29GMXHHVIY9VCc6jL3cg5KlGuEuZrYa49zHSFqP4QPRai54fpxRYuLF4ZoVbPiJX1SWpjmt5SpLdWuUPbleMnU2etqZ6ztX72ltrOzbWdh7Tt3NK7a/vouYdNnHfkvL3HLLnklFVXnLn+6nM2XrNn0zXnbr723O03nH/EzRcee/tlpz1ww65H7jjvyXv3PHXv7qfuPfOJu8587I6zH7397Edu2/nATafdf/0ZD1x3yj1XHH7L3i3Xn73hqtPnX3B08+R1icMXBbaO+zcP+zYOiuv6hHX97vX9zk2DwpYRYceY+9Bxx44hZns3fitlS5XaVKI2FnGwC3+AsiRu68mcuKTttHXtx69pP2Z1acfS8KphZVG7b6quTdWMeY3wwk5rfkdorGoOlvS+vNqZlpsJuRlTGlGtEfU3ImYTLBpoWoFmONCIBJoxsxE16lGtGvEVw56c4Uyp7pTiTvvcWdWV0RxpFfoDQ0BiQ4UOzxiiRaYiMm35mLhCpxQ2p+Fvmhb9HACmpMGeKSpM3v7iCxrGs4Af2zIqm1bYFDDMy8QEGhwNeJyAhzyTWGQhTgQzvIwhQJPFMRMwaJT2N84AJMgSghNLBOaBsTGQOD4GLgkjcdsPkh5iu2a7k8w1jOg/avCi6mZUj21sy1MDHgiTgCLQmTHOxTiO2MyQF+lI9Ox4F2RnH9jHB/z+rBGfBc7CxiQ4F3T0c6AyixY8mRy0UEH0BximOXP8lw18H/EUIShbIApBC0A3LGG5hUCv+KiAfMCAH3PNJgoIGjBEDoEK7Ft0IYIGKgJAFReRJVmZyslURqKSIgGYh4oJCJi4h4qTA/gXXrffsmFjuQlX7HsnwTJG5eCMcIqC8budpoC/Hh2R5KgkRSWP5XUEATNuGryhHb9DjN+yGd0AJQlpohF0gbtEvw/lMPMRkvgMYIjBv/BWS9zYhLPPgZNnxojgRXTBts2ciVAhr8wmDlxsKQwCHrhUcKAhwJWHiXq5hM+RUB0pDdyoIy4zcPvwcQOcO7mGjxgkQpoHAowc2ynDW3BVcM1IFxLfwB6O0clCUpBgy4Nj1WMLBy8/Ewah2SeTdOD8A8Vlx2fki5zQYNBIX7MNZaWHjnrZlOIoaELJEGuWWLUceZVJeBmoPjgfLhWStcMyGyp2s4dmDIVgkksKomrBoTAwnGgR6IhARz2QCJuSnEVT7S+UN8wbPHbNwlM3jR63Or+2Xx/POat+Ng3C100bThoum9AdhA4+vDksUqCSP8Ft++5T5h+2sTY9Fe7rkCppIRfg0zqX1Zmin676qU6DHgpQUwFqcQjn6gEqKFbAYtS6BE7Xb85Smwvs1prnsK7gcVOpk1emT1ydPWFN45SNY2dtW3Le4asuOm79padtuvz07VftOubmC06587Iz77vyvIevP++RG857+AbYX/DYLRc+fttFT9x28VN3gO178va9j92y5+Ebz3vohvMfvuHix2+98cX7b3z+nmuevuOqZ++84LEbT7r1wg2Xn9Z38qb01oWBtUPaqj55eZdnut2xuMEtqTPTNWZZjV5Zo9bUqPVV/LliGycbyJz82hy1NkOvKYgb2iOHjhWPnC7vWFLZuiixejK4qM9a0Jle3JNf2p+fHigvH66vHK0tG6wu6i8u6M5MtGdGm5nhenqolhqopgcq6YFyerCSGSxnBkrp/mKmHw7K2f5Ktr+aG6hm+8q5/kp+oFwYKOcHK6mBcqAjhWUbU1gLuoGEwx0HTKJCZAoODCoYkBOTmaTCpVWcGskhY9g87PGL+jT4nSTO0OA5cR+TIE9hiYPQkdmYyEa9jIUDrxjO6G5aB5XgQa0A/gtdqh19k2aK0TfEkoAWcGp21tBAJTbi4yCSBQtJDBAFgmiIktDz2i5+xvV/3OxuMGPQMXxuxv6iRmvvoSR3CyEHZl/IwSxUcOLd5gd+iqgQMlJk82MWBvBiiyhgGIZjb7TjRLhx+4vZ0If1uWAghnfxV/iBLv6vGyQFBWUrFYQKABhkhExZGAdQIQUN0BIEqIB8ARLLtI0TNPvAHgrDka4ZnNhoIUIH+AS9OgU48VEFBQ3QkpYoELhAkaSXSoMJVFqkMiKV8lIJYAxwBa4Brgpo50Ki2L4MoQLuG27fyfidDtPtDXrVsGRE5GBUDsakQExSw4IYcLsNF687cMgIDD8yx8tDSUKyyE4wcDrQSMiLGIaDgZcnwIAPoneeYwAPTIr49xZRyItwDG7dViRg6HmJ2SBErswkbgc9YPAvjvl4maDIWxIILEfM54jJjoTsTCqulIK/j5vycXFwlJAUXDnhClwSAmyuETLZNnvcIu6MhMJGa3OCxxfhUrEoyJ3CLaAmswUZadsAGPDINq7gZgGEdnEBVBDwJD7DvgzVKlIRomgxFvFQETcV89AJgUpB9CBRWZHOShApYuVCoIBoJAmix58TSGFvhWvGwiFkJenPGG15gbIMNANIOSXQGYHKi0xNc/Va0nDSmMiGFmSUkShb99F5EU6gom4arpOUNq5Bt7/+EhVpcCmf4DZ95Pbe1UvzYwORjrKeC6sp3Z1UIRZg0j666KMbKt3npyZM/NlggMoKW6xEUaysTaBY2ZCmN+TZjSVuQ929sUveOKhsHA1tnqweuXT0lHWrzjv80E+dftR155508wW779m//6lbb375gXveePzBt56+/61n7nnzqTtff+ruN5+++42n4fjezz5z95vP3PHaE7e88shNLz9012uPPfDZJx95+5nH3vn0nS8/eNNz99z0wn37Hr7+pOvPX7v3uO6jV2U2jEdW9YVXdBvTTc+8IjeeYccy9HiGmkxTCzPUsiy1Kkt+Hj+Hv/iyNkutyeDP5i9PUUvTzHROWFY3VvSHlw0FFvboY23BsWZ6XkdlSV/HiuGeNZP9a+ePbFg0uXl6/ubpBZunJ9cvnFgzf3TV1PCKiYGlowNLx9CmR3oXDXXP6+2a6uma19c9v29w0dDgwoGeqZ6eqd7uyd7O8c7O8a7Oie7KUDPVUzaqSU/S4C38ySwmIDIBL+yJT7c9kRedAhoOTbSmnZEWoEIAMAqRIz4wOiHRENWCT4EgBVoVjjaIDGhn2MO/8NkAiBLiCu1uYzdQ2/vPUgEOEC0QixG0wPlwAXgNIh2wvSFE1iKlgRH3DVQAL29/FgHwMYNQbs6/mD5kak+BEBPduGTL7aDcPBrgZBYqYLOfkgh7wGQyzPUxqLT2c1+ZAxXbsUJ54u3YnPgYNua8iAd/yezBK7C5L0JqUC8IkhmzlQqIubCPDqtUEKCiEKUC9UgmUXBMjNjs4JhtNlpmj+1kE+BZNPwN0JJOVXSqpFF5whXQK1mJyolU3ktMpLJeChwHAAZIY6sWVEvEQZPgmvhidIu07nIYTsHw+ExBNwUzKARC3kBQMAJuze9UdHyqlVvDp4PQGhlfAgcKH7SLEV2kTRQ7fShVkgUmTgy9vx37zwxqgau1+THXm9tQAf9rE6vldm1c2f6XVByYDRXEyUyDNLF3QGTDhyWHJTsjPlfU54z6gC7OuM8JgMFxPIkFeQ2XN3vjthJChpE9XMPHDIiC58zkZdPFHpGzx7LgauEELEy4BnIC3h0Z6UKWkFuzGQmFADkigwkd4XbAuaOIJC0kBtUqYvwHewSJBy3jobICVmhBoopyywrAGKJB4eNQFJA7XKTdcyF3OEbEgjRBddJiiQ0VDG4wR1A/TNJLZwW6INAVgW76mE6D7Q+5huPe0YSjL0g1ffh6ScDGA+yJ40cQeHCdUbxIOiO3ePCJbLnB3nh3M9IsBQtRPaUrCcUV93FRCZQUnZeohkL16dSkSS0K4W/Rr7Cn66OEKzH8av36NLM+y67LM6vz3LISv7DkXFBSlrZnN492H7VkwRlb1l104tbLdx59zbnn3HXFpQ/fdP2n77n/jafve/3pu1576s7Xnr7p5cdueunRW15+/OaXHoP9TS88et2zD97w/EO3vPzwPZ957N5XHrnrpYduee7+Tz1++577rtt5+/7jrr1w80WnjZ+4ubJ5QXr1YGplV3y6HlpYVqbyruEk2x/Fh2MOW9RkhFocwyXCq+zfOU5Tq1L4I5XLE9TSOLUwTk1GqeEY0xvnO+KORlRoS5g92fRQuTLZ0TE93LtyfHjt/HmbppdsWb5sy8rlh6xctmnF4vXTUyvnjy+bGl8xb3h6Ynjx2MCCkZ6pwY6x3q6x3o6x7u6J3r6Jvq7RruZQewNssK3aV6/3N0tdlWQtEy7EjVxMThhCVHWGZNYU0e/PNb9Awx7FAelUBvwrMkGJDctsBH8ThU34UJSgLpGYmIiaF0NI0qPISCtrCsTskS6BsRdZQW8BZz0rEdCV294cXrRdOVEAs+E/OFxFoFQvGhyA4QIq4ujthb+zUgOHsOba7OvE7MRhDwb/Qo42Tsj3ImfQQj5lp9nCkgvxY2fXUirE7GuzQTKXK8hFcEnEPYWhQADSQGgoQzK9oc2wAc6cJcTc479oswCYdf1QHRB4QicEPQGdEA6wV0vEZCJTlNZIVwsYAJI5KXzMAFGQIMAvCnJEpfN+/CXpmok/qFMhDxut+Kki4UqBCJeyRJW8VFGkijZaCFcg6gS9Ap6LxBaEK8Q1Q7SLvhWfUej0OwXNJWpOWXNqmkvXXbrm0DTep3CSjxVk1u3jHArP4M98kSfstqBCRr3A6bdmdIjHtP0mosuGCnIL3bEKjpg4XPTIEOYTPiGlyBdEIPC33TR4YfggpGy5ZnAywyrbd2ODJ14eGi0Ukd9DQ3fwC7i+wBT5gMgHJWdYclkyGP6CIWiXsMgGBQZbvhsfh2U7XzBIB5oE/AsGThm886yhjwZokexIF2u5b0AFGtyOTR3kBGp9SA0+YvMG74Ws5AZcwe0jeKC0Z0oGbgcKirh4JD1UTRLYD/oSgAH60ovRQA7rjobqK3rpkojVWvFRJYAKiR6APVFQHqQesShIadjRDBlLoAGfKHoIVEJenLSHTOEjoNXSXhpAVfLSVZFuinS7THWoVI+f7g/QfQGq2091gDyQ6JpIwzkFUEigWuDCfHRapkEzAVFynyhU/LmMlombOSuYMfWkIsUkp+XlIl4GpHfBR7dp9IBBT4XwyZL4VRWASpRaSX5SBbiyLkGvSzFr0+zqNLMszS7K8JMZfjTjnijoi9uSqwab2xYPHLN2/KQty3cdu+XiM4+5+oLzH7j5mmcfvubZRy9/5qF9Tz5wwSP3Xvz4/fsev++Ch+46975bz7zj+tNvu3rXPddf9ODNlz188yX3XX/mjZccdsmu1btPmn/yEWPHbh04cmPzkOX5VePxpb3x6Y70dD0yLxuYSPuGk67eMNtlUD0GNRCgxgPUQpBWcLVxanWSWhmnlsfwi41LyYNYJgE8QSh0quFnSiqbV1wFv68eDXak432l9HAjP95dmertmR4bXj5vdPm8kaVTo0unBheNd00Odk8OdY73t412N4Y6q/3t+c5atr2aba/kO6r5jnK+vZRpFhLVXKiYNvPJYD4ZyifD2biRCOmxoBIxpLAiWT6vpfKmxKCLgf6A/QfbOjZxcoDtfs4B0MUE7SKidrHsR0Pic+xRlISwU7WaIATsuhtA0jINzE2jugfFAM4aHD04cQ5/CnDWbMUAdEFXDnQBNWMb0Rn2AezxXRclgNmJEGsR4q8ZSXx2b89t4r8s7l3kHCTKDI1aXCHX2VI5ROjYVLOtRZEWV3De3iYKdjzs20AUxpJp8P5kAKo1h4HRKEHFX2TJrCKBkp89mDX7FZvx0JkBJygOoPOTkY2ghBMqIVAnvtYasI99vGWkEuFK7H8NMtgYlems5qgEvc241JX0dsXc7RbXDNAV4IpO1cA0qq6iNcF8VE2iqhJVBq4Q1QLuCfwU+AUcB4PUyNAKkGB2Rt10sobTiY8H5j0KL8i8W+ZcIuvyMi6RmMQ4JcYhs7zCMfoMVHBgytVaC0Ag3fKVkDLswdkhA1rQQt8KHtaO4ltjR7YuId4ZHPGsfIFjgAqgqJXsHMMESQPGNgwNnjRjjIQwnqBVN6MJnN/rMPFXxcSwosRUX0yRo4oQlpymwJnQ1AEJBAaz2MC9HXaAkXj/gJHBz1bPIvvWCeSyISnUH8Rs6iCWZkSMDRVApn2OrVFsrrSgMqNUgPRQNSnYA0ugvmS0IokMwIAlUJVQoRWoUKJX4F0AD0JFPIBbJC7syTCaPZJmqxM4hrAJ9sAYXIvopTPACYEue6maSDVFqkNC65SoLpnukWnYdxJrk6kGZk0DyYo+pEgO9j7atryvxYNPZFNjli8aUOO6kfSpMa9oefiQhwWoJKEF++gOlR4y6HlhajqCPtqGyooovSpGr46BWGHWJdk1KXZFglmUYCfj3FCM6487BzLCcEEfb0QW9WVXTZTWLawdsqzziI3zzjh+y/6LTrz1+uNvvn7H1Vds+tRlW/ZfesR1Vx59w9WHXrl/3b4Ll+45Z+meXesv3bvjiouPvHLfIRfumj71mL5D19fXLy2vnJ9fOp5eMhRb0BuebAbGS9a8YmJBMTye9A9aUk+Y7/Az7RrVpVH9BkJlgS2tYkgUQMuyGD7aa0kEn5U5GqL6TApOLtu/5S46kj5XSheyQV8xppRT/kYh1FVNDXQ0pobbpkabEyO1kYHKUF+xvzvf05Htaiab1WilECpkzWzSzCQMsHRcT0aVWEgKm4Kpu/yKU5MdmuxURacquVTJo8teXRT9HjkoiCHJYXgZ7DOIgQPTHrah+yMxuL23D3QI3ECC4JgAA4AJeVkcbgao4PQ7fsMWQypECO1z4dNBWob/th45BV4bYfCXuALWUi2EJS2i2KSZVRtOcuYsUQgk5ibltA/gLSJK5r7154Yfn+HTrMppcYVkakMFRNJHuEL0E0qoGb0CbgjHhQXWEjj8hooP0Eujoydm65WPjWX9BZspeVLOH38RoAV9GDo84CQORsQKdG9gQ0BCw1ykA6oIDBOBfGcHvuzgAIcWUdmkNWclLHUk9d6svzej9qbkrqSnK8a1WVQjSDUMqqFRTQ3bZ6dOdelUp0q1EaspZIk5OAXwRBBgSui88JLAl0GYTLw2cgX1CoNTJk6nxjsV3imznMSwXor10gzsRRr+5WWG93GsyuE8hOHAOQkAEkKFjFPZTh/d5YzBMaQPUEGuQMnPQAVwItpLoWagAnt43eYN7DH8J0oFsURSxqSIAVTgFXjLHvtCFw9VDx+EvRNaL6e4XJpHNLxqQDItXziuhOO+YEzWLFEKeNy6m1edNPh68PjwEZzJs7O2jzERNIyNZhoVzlLY0LI1CgES9B1b4uAYGhwQuqAUgxPs25khCg58kXmUWa6gUiE3BcLOHvtCmQJQEXHoEolCnHgJIgMf1VCxcqEq61CPhDSgMDIgGsjMma07oXXZ1pqPIQiBxoZowUkUNMBJAses6LzMlGS6ItF1iW5KVBsSBRHSJQNX6G6Z6fa1oNIu002ZrsPJCq4mhasiRoMUto8/wU1JRnwxU4nKetStRJzeoJMDqEQ9NJRLRaa7NXrUpBdY6I6XRenlMXpFjF5JbHWcXhNnVsf5VSl2aYyZF2OHLbYnxHdFnN0JT09G6iuqg3VzrCsy1RtfMJxbtXDw2B3Lzzl9w749q/funnf6KZOnnrTq/N3b91+841OXHHLpvmXn7lp4+ilLd+1cds7ORaefMHnC0f2Hba2vX55fNpVdPJKa15+Y6rHG2s3BqjGY8w8mgyOx4LBl9AfkDt3TpnF1H9PwUZ0K1adRYzo136Smw4hAEFhLLXzezMIQNS9ITQapAYPq0LCLghRL4ZJfPibzUcUR1VxRvysecKcjaiUX6WqkejpSPZ2xjma4Xo3Uq/G2eqxetSpFI5v2xaNSNKzEI1oypsUjvkjISMSy1fLQxNiSFcu2Hbpj66Hbt+5AW71uTbO7XQtpou6RDadiesSA4PS7GBUMuIKqglYxLmshBGzGaeLjPXAUqNUT8It4mgASh/GTAS57pAu0DogSiM7gNOxC6JppMMkFFkxGC43K8NTY2k3rt+zYtmX7VtsWTS9OF7MED8TAp4M3R1c+Byo2V8DX207fhgqeP4cowBLbHHOOP2azLLHNztEmCkKFJGtngZJlhis4Akbmcmyi+Gyu2HuiV8jaFTYkOqOSKya5Ej4HroPw0ThhTlZk2TNDraGwGY8PZise2wAbH/l3VtPYe7LkAfozsCQJRp6MgFBpDUrMDKTMCJHZ9GHfwgnBEpwJDiKucDnDWbfEzqTSkwGoqL1pX1/a25N0dEapZpBq6ogTUCcQG/XoGB71+VFSA1o64C1wSSrhikoVwRcoZJ2Y2JpfAY9mgfe3uYJigjYcjJ9nNY5TOVZhWR/DyAzufSz8y6gsrXI0yBQDDKDiIFBxUmEn4cqsEVah3ySG3CJosYe/wINjeyNrqOAA/p0lCtjsnDacDM7a5orNEtsdo0cmx+Cjbc0B5yOlUPqwMu/0OQTFKWsuzfAYQW/Q8oYiYiAs6iFBMdxekGKqg/fxNC4jJkNwtmayZ9QxkIKk7EZFbFYH2x0KxT3k++c4IRS0eaOSMT2UX/ZsCuEKnNaCin0LRM+ByIDyh1qAusC1FSKVAyFCKqsOdWrQbSbbEaQ7DKxleKUCNQhQwRn7lrX0DWlpIFxiEhlonVnCA+0HGh6uO/fSgKuCTAOoIMIAPoGWbcpUh0x1+dC6fVSPj+ol+x4fDf/Cix0+qh1OA7YpFH5FQQGczNonDBUtEVGifiUiqpZDDrNCkGeDLibioeH2aj66V6fHg/RC8MsgU2ItqICtijGrkCgsQGVFnF0cZccsph9/uNDRFna1R4XOtNSTV/sqxlBbaLQrOtWfXTbVf9SWqdOOXXjGyVOnn9x/7FFDxx2zYs/uzZfu23b5pYdcevGKc3ct3XXa8t07l+3aOb3z5KnjjujZsq62alFxejy7YDANUJnsDg7V/f0FvT9t9IWD/YberfjaZaHmdVS8bMXL1ERUeT0KNQJQCdBLyJAd6BU4WBRG7TIVokZM7KhNhSphdTIxXD7BR2SH5XNaitPSnRG/kAyb1XykrRZvb8Tbm5FGLVgpRevVZLMer1dj1Uq4WPCnk9lm47Cjj77rnru/853v/Pu///v/5/+0/eQnP3n8icePPvbIanvJoToZXNJHOIHmoe0hHWzuM6s+iMjAB6fP+no4RvGBZ9KqwABgNIFtjXQBfvD8ckd951lnvPTSSz//+c9bGf/17X/9r//11a9+9fobrl+6crkv5CfefA5R5kLlAFE+qkIQGyzlYHH/fzT7I3YitiFObJtB1wGuwC3PLgOzzXb9gBMUdqwpuCxZSmpqxu/PG1rO8CZ1V1RlASqmjyy7sg24QqbHbWzMJTf820p55l8bJ3BgQ8IgoxARCVmSkHGBVsKHvd0eiwhL2Ntby67IMofZYS4cY8F5MnwRF3f5mIzOFQN8NexuRIWOhNidEntSQm8KIjCmw6LaA1Q7kEOhulSqF3DipwZNaihIDQVwOBeENaClw0+1kWGxqoouydYr4IzAf0EIaOuVFlScrdVQfp7yc4zG0RogpGWUBsbhtAcQpTX2NQcquJYM0DKHK/jKjNlqAzFAZCLGMTNQAZ9uDxMdIAqRC+Ca0QWTT6ER5qHNYAYRCM6dzPnPgIGWePx1E5n3yJzXx0uqw6e5VL8b6KIabll3iSrwhncrPP6cs49nfA781gWqHIIlMPuqZg1fJMKFdCJQIbTfAzEZtCIGxC6wH8QKMoYQBeEBZhcROYCSbL34V6ZVACq4CkvAaXlQHqAjwVnX/Qy4xJ6Iozfi6os4eyy+M0i3GTjCiZ7dHhYj0/VAiFaFSvbCP+QHTuMRg3+hlrMSSBMcQCr56KpCA04aQBSFaoNgmli3gt6vR6H7VKpPhT3dp9E9EKNAu1KQKzZUIC4hXEGDi7TtE9yUWMgXUXyWRwmxUoBxmywfcLIAFSiXpo8e8DNTYXox4CROr2gZs7Jl7Ko4tyrOL4uyCyxmJET3mEzT4OshdzMmdmak7rzSW/EP1APDHeHxnszSqYEjty0685Slu8+cPO3k4ROOnTzlxJXn79m6//JDr75qy/7LN126b91Fe9deeP6q886ZPnvn4BHb29Ytry1fUFg8BlBJTfVHxjoDg1WtN6v1xoL9wWCPqnd4pbrHVXRyRTdT9tI10IAAQpUa81Pzg+S5/VFct7bQohaQH0ieCGFfhTCwDooPmc9EBS4icpbsCPucYQ3NAt9k6aVsoFKymvVwo2aUikaxYNWqUbBqedHqVfsuu+yDz32u5Zj/R9sHH3wA0oFTBVrBr/XN2iw50GyQ4CPf7ch9NoRHh4u8gc/ikwrdYkhfu3nD7Xfc8V8ByV/bADB333N3+0D3DMDIxdiZ4vAXgUprauQjSkUPGD/4G28AY7weQgVa9/Km1xuW/QktljNz9Ui5PV5pgkwMR/KmntAFS+GDABUyKuWHAwIYXUJg2NP+NqiQVXOOwRAqZKjd3oOBCgFsAEVAo6QUKqtSGZVOKgAJKkIMF4CRcBK0SIAYLrUgB2HQND4qoVEZncr6mYLJlYJcJeSsRZzNqKM9yndG6DZQJybipFOnuok0gfY5HKTGQtRYBJ8+NxamhkMtroBeAaiAEwG/YA+CFUioCz5oFioRkAIIlZ27z2iV3Se66UWLDPrDDZJ1JVBECrRGx3PPPdc6Y87Walj/hQ2jfhQHwCFw/fiVeFriOIlziOwpp578hz/84Y9//ON//Md//Od//mfrA3/7TU7qhMozhizB/fvvvw89Ba7nT3/6U+vU/+32d3/3d9JwShpOCv0xVw9UephqBqi6QdnPI29oqD4BDyggEBhYoTggZgsXcpAhC/+grss+HLyqAU5UGuROG+BEodqBFsgMhEePhhQZ0JkhPz3opwb84MDpfj8Nqrdbo3EcFTjkQxrhGgEw4IqKRAG6fIKbHDWksCSHnIpJew3GobOcSaCSE/Fyhw1mnkUvmYEKzk+gUkGirEiwK+Pcihi32GInAmx/ABQ6U9ed9YCnHhXbU1JXTu0p6331wGC7NdlXWjc9esIRq88/Z+V5584/4/Sp009ZsPPk5bt3rT7/vLV79669YO/aiy5YfdH56y+5cN2F5y858/T+I3bU1y4rLpnILhhKTfXFJ3rCw23+vpLSndJ7rHBfINQta21ub9npyDnYvJspCnTFS7XLVL9KjRv0/BB5bj+ZmQeoAFEmw9RoEPst3FcZag6chYDfpbJEXLYYkp1h1RHWHGFDiIX1fCZcr0TbGlYdZEo5XClH6tVl69e/9vrrrcbySWwffvjh8LxxVBj2zMdcnKBQmEEIuHW0OSrePvA6XZp0wskn/d+w5M+3hx95WE9ZB0ZybNRBiId0mRUWM4NXbm7H4Ye2Pvk3284+dzdqC7+bD3iliGKm1HjWzBfMWjnUXo90NKJtdateCRWy/lhCMy1ZDkmugETjV9xlNIAKHpBJDrgpRMhMUaNksYfXiXzBIREyYAUGRAEVEhXxNwsKGv72c9nkigaf8zPgcRJgGhVVEC24DAwoIpBVnl58iGfcR6dUOuunCwZVMNGKAaoUoMsBpoK/zwYxLD4ztF1Fj9Cr4ZDscAARgr89Eafmx6l5SWp+kpqMU2NRaog8LRuo06FicFqVUamAU4C49SMjYPbMissVk3/1q1+1yu6T2yBNiMfpqMRYEke6DB8QGdXj8kv/FaX+17YvfOELZJaCLBtTOHwiJHBF5hiJ4yXuRz/6Ueu8/4fbz372M+QcGlzYjPkdG3dsbp3xX94WbF8lD6a8AylXX5zvjjEQSXQGqS6wANUTpLpDeNAGgCHVWgJ9AwYIEakSsYqIM/ANGQ2HuXyEJYAHzcYJ3Q0sQZzgMl0UuAY1ZNIQl0CUPxKmR8P0SJAeNOleP92tUZ0K3USJA0LnbwgVKaqLYUEOcLJOCTpAxcEEnEzMwxQkIBs9ajALwuCa6eXEViJXgC4sWoJbmeCXx7iFYXbUYHtMuqFxNYCKKQBU2mag0lsPDLVHpwY7t69buuuUzZdctPVTl62/ZN+qveev2Xve2gvOX3PB+Wsv3LvuogvW7btwy2WXHH7V/k0X7V106knd27cWly3Mzh9OTfbHx7ujo53h4aa/t6h2J/29oXCvP9Qpag2nUHTwWQebc9NFDy6BaPfZUGEWIFRwKsX+ZRTQWxD9DUFFQnTgwzqDfhj1sGGBC3n5kOQI+hwh1RHSXZYhJ6OBcj7aqCXa22LNerRenVyx9JPFydztoov3oewAI7MgaLMSYS5LDowO4QEnuY84+shPFiezG7iPgfnjrCkxBrhmL43PbvIQVWTTbubyiIJ56aWXWh/7m23pzgobFF0R2ZdQwhl/MqsXckYlZ9aKwXopWCuY5ay/kNbSMV/M8oZCHi2IP8nuDIg0znlAWC3hYi3Y45dICFdQlNgsITgBliBObMMJCRzZCHuouMTkNEcl4KqHhKYlNqPeuuUphRzZAJsyES1xnYqpqEhiMpl38aGUyetMMcCWggxYOUAXgSgGhb/yaeDPSFfJEFabQgNOehRqQKWGDGosSH4cL0EvSNNLssx0gV6SY5bk6YVZaipFjcapwTDVG6A6/TizUvNB0IpKxR6Rbw1/zUyrhDwnnH5Sq+A+0e2ll1+iAScR2RFT3FHVDeLeFFnN3T7Y0zrjf7Sds/dcJIotCzQHWaOMo1i0zC9fu7J10v9oe/fdd1tH/83thRdfILMsZL4HDOhCuPLfkl+wvfPeu2JP0tOXcvak+N4015/ih1KO4SQ/kuJGktxQnB2MUr0W1RloTbTUZERIXaSaXqoNwnoZB7XsmZIOMAV9MppGd2m2OkGB0kdWJ4FzGwniKqQxi56IMvMT7PwUvzDrXpR3TCXZsQjdb1JAIGh4GJeQJgRQKanEPlmoWKoUdEt+RlApl8bwfp4NupkEOGiJ7tUhtKcXRECpUEtj9DKwOEgWZkUCDJXKiji3LMbOCzHDoLD8bF3lq35nLeCtx8S2tNSZU7rLfgKV+Lyhzu0bFpxx8sq9566+8IJl5+2Z3n3OsnN2T+/etXT3ruXnnLNiz7lr9p6/8eJ9Wy67dOXu3aPHHl1ftzq7YCI1MZgY646MdESH20K9Vb0ro/XEgn2BWL8W6fEYbS6p7ORzTjbrYfNeCpRKhw/UHzWOiwvwsoEocABEGQ8zQOx+g+7UIcqjc1467qEtDxcU2KDXEZD4oOIIac6g7okE9HzaqpWT7c1Md1f7+PhTzzzTaiN/s23/lVfgL218RJfYXnuWInMkguBYtGzJd77zndaH/zbb7373u6GFE86QArjlDZn1Q0wq0D4PPkZ+ln9el5VO/N9Eqf+V7Ytf/KIzqrnjmpTSzawRzeuZjJ5La/mkmo4piYgvEhbDQSFoukNBTzjgDlkewxJkS3CHvRyOTYGGwCcXkEeqEKgY9nwsMRxGt4/JPIE9Zwt+JOKhEhJb0NzNkNSV0HrT/r6svyejdMSFmuXIB7isyaT8VFLFYbG0Sud0tmBylRBXC3O1EFsLMdUAUw3S5SBVNvHnosvaDE6IOgFfANHPgEYN+XGx4lSUWpimluS4ZSXXyppndZtrVcO5vMovKdMLctR4Gn/uqDdMdZokqlVx+hccQZ48tgendgEqIvnOipeLiH+jUOPSKy7nLMVhKa6I4rIUZ1DG3y/QvYcedXjrjP/RNjB/pDVF4SdT+vYqXrJ26/X/u0ju0ssvbR39N7frbryefHuGqNWA/VUw10lnnNJ6+7+85aZ6HZ0pd39eGqv6JqvSvIq0oCYvqvkW1r1TZcdIlhlMUH0Rqi9MdYNqMXCEEzQHKNdeheqTqX4f1Ycrj2hQIfAi7PtUqhdMo9FUuh81Lg2iZCzCTsW5hWnXdN41XXAvK3lXVMTlFXlFXVlelZcWPQuz/GScJmuU6DYVR8BwEJVMrkBDKqstHnwimzfoEw2XV2M9KsNrDGPwELwzKS9d86ELnrToRTF6mhCFTNQzZE6FXZVgV6JS4aaj7GSAGdTZHr+jaTjqENNZ3kZcbM/InTmtp2z2NcLDbfH5Q83Na+bvPGXV+XvWXHjB6r0XrDzvvNXnn7f6vD2rzjt35Z5zV+w5Z83ePesu3Asvzj/ttM6tWwpLF6enRtLj/YnR7shwuzXcZvaWte6U3m1ZfXp6UE50u8w676s4nXkXm/MweRH1I2B8yE9PBhlg4UJiUxYziT8MTA8FmF6DhkCvJONMWtRNBz0seUAWb0qOAHhPzRXQPZappROBUj5Srw4sXPC39t2z27zFC+bgBEBCiGKrEwCJPdZEDATK39qP2xvEZXI86ApqDsPHaSKjCLTsoSU3LYIhUSjBecLJf5OgeO626/xzXTFNSBpyyjAzRjRnpDJ6KqlYllfzu7yqw+Xj3DInKKC2Hf6QEIiIRkLWkz5vQnEkFHz+f0xmovh0NXsyAJ0FwAMoAi7MBok93oWTrhDsg4OW2KTqzBlCLaL1pINDxdBwKTBY0LtzclvCXba4QhAtH+BLJl8N8I2Qo81ytUddHTFXe8TZsNhGiK4F6HqAqpJZWSJNcLyii8SVEPcMQcTmp8bI8vd5QJQkPZ13rqh61jSl9Z3ypj5pY4+wutO1vMEvLlNTWWokTvVDSBuk2gyqQX4hFChVUKmcSrhiL0uTqIh3y5E7WgX3SW/rtx/itlRnyMcHZM6UHabsNCWnIV1z3bWtM/5HG4eL6OwqIHWBE+ZuUJDNgc7WGf+j7cUXX7zzrjtb//w3t5PPOI18u8heCI5cgeik9d5/ebv/kYeYeszVm5dG68El3YlVQ5mNY9H1w8aKbt+SNs/8GjtepCayxNLURArHOcctYiA4QHaY1KiBjWSYtJYh8GwaNaiC0YMaPQivmPRYmJkXdy3KCtMFaUVZWVUz1jaNNW3+te3Guk5zfVdwY6+1scda322ubKhLy87JBN0fpLpaK9BoiHLKKm0/svYT3ARTcutOl8I4FJrTGNZ0IlQyItAMB3nnkwkVJEpr+ItZnWjZmgS3KsEtiXATAc9kRJtMBcYLvr6c0JYQm0mpM6v2FPWeitlXD452ZpdO9h62efrs01edfy5QZDkIlF1nL9l11jKQKcTgYOWec9aSMbH5p5zc3Lwhv2R+ZnIoM9GbHO2KDreHBxpad17pjPm7gqFeX2pQjHU4zJrDV3Q5cgAVgSmQkccujR422HlhdmGUWUh+Z34qwkyEmdEQMxRkuv0MIDov4QoNqwUVzhQ5U+IDPkdAdQU0T8hUknGzmF+0ZvXfYmD6r20ffvgh8mNWmswSZRYnZK3URfsuan3g/8l2xq6zHbqP12RWEWlJoLxu/Bakx4nmxseuvPnmm61T/2ZbpFpkQ6rD0l1R3Zfwm0k9kNCCcdmMiAo+xsrlDTrFoFuOiHpCC+QCoVIoWA7pxaCUD3gyujOlcUmViSk4SBWWyVw6OAtwYWSP42PgNVDN4DM9Eyqb0vms4cwFPcWwWImq7Wmjp2B05/TOrNRMuapRvhJ21CxXM+ppj3m74mJ3QupNeXvSYJ6upKsjzrdF6AaZjwUAtOED9PA7uTgJ70ddMmKgvxgL4LKRqRA1P0ItTjHLiq41TXFjl7Z10DpyMnz4VHD7uL5pyLemx720yS8o0+M5CqLaHovqCFINk6qZ5Iv3OlXQcflAWiHL0gCc0t8uDKoPdLlA0JsgUGTeLzn9otuQvKb0zrvvtM7472/vvPMO0YgE6gb5UWR8lgQux7rz7rtaJ/2PtnJb/a233mr989/clq1fTYM0N8C8tImLLy667OLWe//lTWhLs+0p71A1vHggv25eefOiwpaFwTUjvuW9rsVt3MIGvbhKT1fZZTVmeZVeXqaWF6klGWpRAn8hcCpCTYWxeUwFqckQNpUxQAhYAG00QE9G2PlJ19KCSFiir2kY69qNte2B9d3Bdb2htX1Ar/im0dQWsInkltHYhv7Q6g7fopJzIsUMx6jeEDZLEp3QVeRKiwefyObyCw6N5xWGA9M4NuCiI16mIKP/HQvRC6P0dBxHvcjk/CxUWCDKmqRjZYJfHPUsjIdWlAtrOovLu6zJutSdlduzcldO6y4aPdXgQCMy0dexec3i047fcskFO/ZfumHfhWtAo+wF27P+gr0bL0TbtO+C7ZddeuTVV2y79OJFp57SfsimwmKESnqiJz7SGRlqC/dVtY6M2hH1dwQDXT6r3RNquLSyw5t38VknKBV8+EGbzPTq7GiAnY9QQZtvsRNhdizMjoSYAZPt1FlQYFmRIk/zpU03a3o408sZEm/6nCZCRYyG9EzykMMO+93vftdqHf+vtnylhPCw0TKXKLj6luO8rrvvubt16v+rDfq8Q5E4RWJkLxIFWeLAbziSpV9WItY672+2AbQoVaI0fHQ8E8CV396YqiRUPaHoacWf0Yy8GaqEI81EvDMT78zFOrORjnSwmVSqMW/J8uRDjrTJJXUmruKkOj5VhQyC4UotAb+AAv9GfXRc49OmIx/0FMKeYsRdDLvzlicHH4eDiKcQ8VRigBNnI+Zuj0vdKWUgqw+VjNGKOVYzRqr+4aoyUJT68u6utKMjwXfGuO6ooz/qGIjyI3FuPMFNJOiJODUZoyai+IigqTg1L0EtAHWSZZfnuZVlx+qGZ32n75D+xFHzu87a0H3m+vzRSxKHzg9tGfOt7vYsrvNTJWooTfUlqM4I1Ram6kGqEsCBtYKfyvqptE4lNSquLN20plVwn/QG3YH1S6wusZrI6aJD97p1r9cvSn7x/6anXLr/MnyohN56LgsY4/eC+TOR1hn/o+3999+nBcf/eBiw0dcJ1OQMmTUlsGAp2Xrjv7xdtP8yth539+T1sbbIwoHE8rH4qonA8mFpCRClk1/W6VjZ5VnXI2zq/f/y9h5gUpxX2nalzjnnnHPOPT3dM909OeecMwMMDDnnHEQQQoAkQAEEIghlhDLKVpZlWZZlW7Jsr9NGe9e7a/9v9TSyvP9+38cMwo+e672qq4ruFlTXXecN59C7o9TeMKErCLf7oGYnVG+EanPVAquANXg5q/LcqHApPn8VLpUD5CCVenKDndni4bb5BSAcaQtJ2kPS9oioOSJujombCiSNBfKmQmVrStue1nWV5JzRtBWKaj2schs1ayQW6bC4Gokp4LAMCkjxWYjfoUhCCpGHETgoxsFQPhEfUFHREDsLjgnxZ/xqNVyLj6PMTP2C8bUpeIyCNmsILVp8hUqlnF2tN3YEPe0xS01QlvEwwhZGyMQOmvg4VOwAKprSRHigvXrF4o6teAdX06YNDRvWATeuX9e0bn3L+g3N69a3blzfs33bwJ6dLRvWZufP87S3GiuzukyBtjiMEyXhEYVtHL+W7ZNx/SK+j813UTlWEs1IJBnJmIGKGOmQnYH4QWAoQDIStFKBL50BUClVYFkZVizHkjIsLkYDPPC/hqdOUtBgKRURUVEhFa9xK2AShWySkEMW89lqeWNnx9+nf+l/qLO7K7eMI5cd6xsDouTu4H9/ogCBvweMSUMZVDgfo+SIMrOkkYgtnJrKn3fbtHDxInwVJJ6yJbeIRMjAJGySkkPT8FgGPt8qFbtVcp9eFTZp4zZ9wqlJOOQxuyhoZXmNTKeO6dRQLHLMIEK0QnxEXZ4LVqQsfEPJgTV8oknMsCnYLg3PY+B7DFyXjmlTU0wKok5K0IhRpQBWChCNEDVLaH61IOVQlvt11WFjbcxUW2CojWsqwvJsQJjycAts7KiZHjFRoyZGoZmdtQnK3dxKF7vKyai00ypslHIzsdyMVZjQCiNaZUZrraQmF6PNx+0M8/sKhANJ/mBSOlaqHC83LKy1Lmo0TdYYx6ocY9WyjkJGjZ9UYseKzFBcD0XUUEAJeRWQWwY5pJBVApnEkEEI6QSQhv/uu+/m/+K+a7311lsIl45waMAYh07i0qg8Op1L94V9+TPmpLq2pvxyK/6M8eLQGJ++Ycum/BlzUiyVQKjE/IvZi6MQk4VsopAFuIKJWCfuuyd/4KaF2OWoR0MKmulxBzPlY6cDrLIQtTRAKveSanyUlgiju5A/lBWNlUgmygVjWfZIEbm/AGv3wc12uNEKNZihelMeMDU6qFqbB0yVFqkxkZqcjBYvrz0o6Ywpe1Ly9gJxc5hfH+RUetkVHm6lV1AZlNbHZE1xeWMctKLaCNgjqggISz0CvLqHnZuxsrI2VomDVepil7pYJa48D74TEYUkAg9FeSi+QkpMgmUU8CCP+HlwUgyXKZF8mJKfTAyIArdo4GY13vfVokXrlUillFquVjb5DA1BdZmXl7DTgkZGwAigwgtZRDGnLOHVVxRGBjvrVi9r27qpdduW9m1bW7dsbtm8qXnj+ro1qyqXLSlfvKB8yYLqlUsb1q2uWr4kNthvr6sylSX1mZg+HVal/PJCjyBsY/u1LL+M7ReyPGy6g0o2kYgGImYgwwYqnhDJzcDX/iTxpDJojRKpxqGCAahk5IRiKZaSoFER6uPCVgaeLlSOz+tAhBRUQMXARSxkEgUskoBNFvFMfu/fs9fr2+rt68VXEQLjaJkJUPAYBbwcHR/Ln/R3F0ojI1QSnO/y+gYq+Pf8O/R9yQ2a/Lxq1swq6Fw3t4SJytkkDY9qlrAdaoHfIInYpHGnNOGWFriEEQc/ZGf7zUyXgeHQ0m1KslGG6cSwRjAzWQvW8slmKdutEwUtiphTW+BWx93ysF3kNXPsOppRCXACK/iQjIsni1TgQQzTrVUknc7GVKyvKtFfHe2pcreWmOuK9FUF6pKwGEAlauHELNyEQ5zxq6tj+saEoTWlaIyLqkP8KvBTdzHKnThaKmzkCju5xkGrd7Pbg7yuqLAnLuotFA8Vy+aVqxfUSsYr5ZO1msl69XiddqTGMFQpaU+y6yO0Ch8x40QLzWiBGY0YkJAO8WsQtwpxKBGbHDXLUKMk01yT/1u7DTp4+BDCogKjLCqBTSOxaVQOjcamtrbfUmwkNWowLh3jfct83F9//XX+jNnr008/BUThSgT517PUl19+SRewaUIOVcQhCdm+2c9tG126ELEpUKcK9WgJfgMxbCIWWEkpJ6XES6sOslsT4v6sZl6NeWmbbWWXZUWHerqBP1lOG0qRuoPETi/W7kbb3EirC2pxQi12qNECGAPXGaF6M9xkJ7X5qO1+VltA3Fmg6CqUNEeFtQF2hZdd5uKXu8RVXkVd2NiS8vWVR8fqomM1gcEKY1tSWO5jFttpcRMlbqAVmJlJGzfrllSEVLUJXXPa2F6W58F3IoKIiAlRRITCEgxWkSEDHXaz8clnM2seZ8KUmeUpTeqZ1CyAK3iwArjSqEZq5Gi5glqmY5dY2EkrPWSkB/TMgIETMAnCVnHcpUgFnI3lyfG+6uWLa1avrFq5rHbVirq1qzq3be3cvrV2zerswoWJ0ZHC8eHS6QU1q5aVLV4Y6u6w11VYKorMZYWW0kJzWYEuHZTEHbywgRVScEJihpdNcVAIZjJqIMJmMmSlQi46FGThw1lZCVIJWKgEXx4pV6Ilciwjw4okWKEYjQgRLweHCj6gQoPwRKQUVEjDL2IBC0CFyGeRhdyXX3klf2n83dXZ1QkREIgws0D9xupCMmZ3O//4xz/mT/q7C6EQYfJMl9eNtfQ5qOjNhvwZt01439fMzAV8ITQpv6CET8GHQ8QMEHAgSj5mEJNsCqpTy/AaGX4Ty2dieoxMj4HlNrBcBqZDR7eqSSYFZgK3XSnJqmB59NKoXZf0O7KxQHnCVxq3pYLaqFvmt/HsBqpBganEkJwPSbmIgkfRipkWhdBr1MTdnsqCgrby4v66ZF9DsKPK3pDVlBcoMkFlJihPeflxOydm5SWckrRPVRFR18b1zSlNU0paG+dXBtmlbnrGTiuxUUustFI7o8LJqHaxmwPingLZULF8KCsdLZPMq5JOVsrn12kXt1qnOy0LO8zz240TTbrBGk1XuaQ5xast4FVGBWURQTYozoZFKb8g7uZFnIKwXRxyiPzW2zfrHWhobATgBGVSMSaVwKICqFCAWdTde3bnz5i9wN0fBD0ELjBjxsRc2z96SyufKmuqwEUbis5xovMLL7xA57FoPBaVz6IIWK+9NrsRo9/85jeISYoAzFvlmENJ9GpIIT05YaFlPZyaqKwjYxitcy9qj64dSm6ZTG5fENk85lzbrZ2uF8wrYQ4X0gZi1P4oqS9E7AljPUG02490euEON9TmhABp2r2kziCtM8TpjAra4oLGEK/Gxyp3sctdggq3vMptrPe7WyOJ3uKayeqOZa3dqzoaF9Wlh4pdTX5Z1syMaQlBJeJTEv06ctTMSbokpWF1TcrUWJ7nwXcigoSIyTCCmko2szEnF/EJ4JgQLpZB5Uq4RoWnZskNpUCNN5J9tWqA0TYN2q7DWnUICGKqQUAgJxWpKDENPWhgBo3ssJEbsYgKXIqkT1cWD3U1ZecNli+aV7JofsnC+WVT85vXrRreu3P8jt29O7c3gehk2XTdqqWdW9a3bVpTvWwqMdzra631NlQEmip8TeX+hlJrWYGs0MOPGdlRJTsipAY4JDcddZBhNwXxMnCcxDlwUgBlxXCFFK3GoYIAqFQokJLcmEqRBE2IkIgA9uWysxgYeO42KRURU1ERIAoTH3Xks0h89sDYLU2OvEWFI2EIhXFjAC3Ijdwn2PPPP58/4++uzz77DJ7p7Mp7JorCvXrtmvxJt00Lpxfh6xO/WZwIcCK4sXpgJueSHPxTsiAdFzEIULMEs8nJdhXVrqY7tDSHhmJTU6xqvHVomD6jJO4yZSPe6lS0vqSgvjRemwmWJt3FUXPcL/XaWCYNUSNFFEJEzieohDSDjG/TSN0GTdBuSQRcmZgrG/dWJP21GVdtVl+akKYC4oQXEEVTEpEmfZyogxEyM6M2aUnA0VGanNdauXSwatlgaKjO3FqsqAkLyt3sjJ2atlCKzdS0nV7qYlS5WY1BbluM350SDpSIhisk49XKySbTVLt3ab9zYZdlvNU83GQZarINNGjby9RNpbrGEn19xlBdbKoqNpQWGjJxfSpqSASNBcHqjts1mjKjcGGcwKKhTArKoGAMKpFJJbGoZBb1yaeezJ8xe9136iSFy6Ty2FQ+m8bn0AQcugBvX3311fwZs9evfvUrjEIC7ujqzO+apU6cOEFh0iksBplNL6mqyO+9aZW3NyF6CWqSEmwKoltFCmgpcRM97eZXR2Qtxdrectu8puCS7oI1g5kt80t2LkxtGw9vHnSs7dQtaxQtKOdMZJhjaeZoMX2siDpWTB4pJAzGsP4QoT+M9ISQ7iChO0zqCNLbwszGAL3STS9zMsocwkqHssZpqXe7693eBne0KZjpSlYOltSNVjQMl1X3p1JtIV+dQ1Ni5BXhyzCxoBINqkkRAy1hZRe6eclQngffiUgKClXH4LjF0kKDIG2mZoxwRgmVKeFqFVIHcJILUJpUSIsKaVXhUGnTIG0qtF1D6DJQeqzULjup1UKsUlOK1bSEhhk1cmMWftwmSbg06aCxNO6oLo50NSWHe5Kj/QWDvbH+ruRIf8PKJQM7t4zs3dG/c1vbhjVNa1e0b1zdu31j5+a1DauWlC8cy4z2FI90ZsY6i4dbU/1NgaYSXUlQnLQIkhpOXESN8IhhFrWQK6rUyhpstGotUq7AF2lWy5BaJTCc6/5CyuVIqQxJS9GkGEAFjYuQoBDx8GErFy9Ko6YjMjomYhCEIEZhA6iItMo5j+x98cUX+/bvn5w/ee+99+Z3zV5sDhtCclD5Flpa29vyh2evq1evrl69enp6es5d7eAdbrAtFzwBA6hQ8dkEt6/7/hvJ7fp85nkxHS+RKcsVPFaxYBUb0bARLQfRcxADFzPzCVYR0S7BJ2W5VBSnmubW0lxaqltL9+jZQQt4IjGXxgINJZmehsr+1tLOhsKG8mBZypmKakNugcNI0ykwpZCgFFK0kkRlydjU/NUb1+25Y989J++9cOnig2ceOnTkzg3btixYsSRRV8HzWBgeE81jYgatrKCVHbIyQxZ6wMgIWWQZf8FAXd+mhUsOrFl+x5ol+1b0rRmuntcY7047GyO6ap+k3MlJ2+hFVkqRjZp10Cvc9Bo/qzHK7UgJerKSwSoVgNBYi29hj2uiyzbSahtqdg42uweaTe1V5pZKW0uVs6nSWVfqqil1VKRt2ZQ9XWgvijtT8Su3czXVf/7nf/KkIjKbAXCC0SkYjQJaAkALg3orfcXj8+YxuGwmj8Pkc9kCHifnsopZ38e/rc6uLiKFTKKQ12/YkN81S61bv45AJRPp+P/dH/7wh/zem9P33vkeahATTFKiTY5P6wjqaXEzq9jJLw9L6wu1HaW2wTr/ZEt0aW/x2uHKLfPrdy+u3L0wtX08vGXYtWlAv6pVvrReMl0nmqoWLq7mTVVxFpYz52doE0WUsSLycJIynKQNJChdEUpzkFbrY1b7KGUudoVTVeu2NXlc9XZHudGc0RmSel2h0VBoNBea7YVmd7E5UGKNVDuD9V5HS8jRnXL0ZWy9wFlbV8bSWmRuSud58J2IqqBxzHxZUKUutkjLnewKB1ZugKvU+NqUeoATDZwbmccDFICTdg3agZvYo5PMDxbs6Axv6hINxCk1FlapSVLp0lb6dZURTWXEUFVgqytyN2YDbVWxXgCV7uKx3vRod2akq2LBYPOa6e4ta7q3ru/YvK5945quTWu6tqzp275uYPv6vq1reresHNy6YnTX6rHdq0d3rujbNFUy3uJoKpSVOEVpAyshpcYEjJTIPRCqXNtYs6nLs6iC3urA6nVIvRqpVyIguqpRwpUKuEyGlMiQYgAVESEpYRRrwE9aWuLmxowUpxTV4pV9MQmLIGKRBByKgL1l2xxn69538j6+RESm04gUCkYmPfnkXJ7dAM9wonzjHFcwIjbbRbwz+pd/+ZfWtlYcSygwLJaI8wdmqYMHD4JI5W9MJgDrreb8GbdNL7/yMv6vI2MTFFyimkdU84laAckgJOHzfUUki4hslQBT7DKqU05zKWluFd2noft1dJ+O6TcwAyZ20CyMOtXFQXdNOtVeW97bUtHXUtHbnGqp8VekDbGg0GGm65UkpaSypfHQXUdee/31P/3pT/mP/z8L/IucOHUy2VxDcuooLj3VDQw+USeMW9x1idqx5sn1E+v3Ld92cO3mO1YtXD/aOdlQ1ZNOtkQCdR57tUtdbuNnTLQiEyllJqWt5KyLWumj1oYZjQluc7GwJS3vrND01Gu66ww9DfrOWl1rpaG5Ql2XVVdntJVpXXmRviRpzCb1qZguEdHFQoZYKNtQl/9yt0fgAYLOYVFYdAKdilHJaM5gQ6XX5c+YkwKhEIPNYnBwM7lsFpfD5nEeOnMmf3j2+vd//3cSmUwkkwgk4v3335/fO0v19feDX3Eu1unK77ppxWpKGS4d22/ihq2CuFOa9qvKo7raQmNzxtZV6R1qKljYU7V2XtOWqfZdS/sOrR08unHonm39927rvm9r84kNmUNLw3vmebYOOzcP2Db1mTZ2mzYA9xjWdSpWNAkXVXHnl7HHM4z+BLMtwm4IcGqDzGq/qDFiaY8GuyKeBps5rZaFZTy/jOOVsT0SjkfKc0vFXpk+qvWW2WMt8eRIdeP6scX37tr4yNFNF46uO3d4zZk7lp/eAyF5InwHIknJdD1b5FNLCyzclIlarEVLNXCVBq7TIE347GGoRQMDorRrkA4N0qnDOg1ot0E8zztw/9LpB9Z1HlhgmqwSNgU9g0W1Kzu6N4y0rRutWtJXPtVTvai3bnqweeVo14YFI9uXz9+3fnTnqnl71iw6sHnpnTuW3Llr8sD2efu2TR3cseLY/s0P3LX7kXsPP/7QiauPPPjK45e+99zzP3zz7S/fe/er95/98JWNJ/enJ9vV1QFp1shNKhkFEnGdeXj/vHn7543cMV62ZYjZHiQ0aNFmDdqkQmfS3VfjSSThUimakWJpObvC7O1JFw/XRTvL9aUhpldH0AswBYcgYZHFbIqYKzNqfv/73+cvjdno2WefZfG54CdHZTFxrlApV65cyR+bjS5evJgPUL7Fld7e3vzhWQofngFEmTEK8wX8/IFZqrWtDSJgMPFbBlwhEv4OfV9Ty5aQZDyyQkDRCGk6Mc0gpZlkNLOcZlHQrAq6TUG3KxlONcutZnm0TA/4JRtZASPTj7OEE7TwQjYRIEoyYC9NBmqyscaKZEtNoqk6WFtuyyblYS/TZrAno5t2bPv888/zHzlL3Xv/KWNRhOxQU5xKXkCjTlj91aGK7pLeBW1Ta8emNy0cWT7cMFBTXB8tqPKEK+yBcru3yuaotqoqrPxyO6vMTi+10ypc9Co/ozbCbIhzmpLC5rSktVTeXqZoq1C1likbSxX1WVlVkai0QJKNS9MxcTIkTQQkMZ8o6BL7nTK/S+ZznHrggfx3uj0CN2gKk0Fi0AggRslDhQTaqtq5Tw343e9+R2HQqUwGjcWk5wwAY7Hb8ofnpImJCUAU3KS5r6AqThdjZCKASv71Tevio5dNqZCvsijWUJZsrcp2N5YPttVP9LVNj3WtnN+3fnpw28qJfRtXHtu39cGju87fc8djDxy7duGBV5+69P5LT3z6xuM/euOhj164883HNj9/Zs3V0yuePr3ymdOrn71/w4tnNr344IJHD5cfXeHcOqBaWicYTXO7EvyWiKgpJmyKS1uLPEPl2YXV2bG0r9GtLtYIYwp+WMELyoVBhSggl0fVxiKjr9IVbo6HBqrq1i1Yeu+Bu69eOPv60+dee/LM9cdOPn8BQvNE+A6E8YmomELW8qh2KSEgRQrkcKkcqcmtnJ+Z69Wmgds1cIcW6dLDXQakU4/1Gg3LihacWdO3b6RqY7+2P2PtKWpZ1736nvXbHty17r7tK49vXXls69p7d247c2DvxaN3PXbyvmcfPvnsuRPPPHTq2vkHX7h05uUr979w6d5nz5+8dgFsP/r2M8988OJLn77x5k/ef+/n3//Bbz7/yb9+/Yv/+Iff/OlXv/njL77/1cenn71Uv2rC0hBTl5gFKRUjqZTWO7p3TvRsHWnaOGwdqyLVO5H6HAWb1MB4ba4aBQ6VEhmckRIzKmm9JzZQlu2vjbeWGbMRlltP0AiIci5FxmUqBHyNZNnK5TNXxqwEOGR12kGYAoJ3OodNYTFIdNrc+oWml0xDWD5A+cZze6uHHnooN+B/AyoY7Pa688dmKbFMCmEobsLf+O/Q96WymwniXO4cuYCoFJE0EpJGStJJSHopySAlmWTAZLOCYlUBU+1qqkNDdemoTmA93WVkuE1sr1UccqsSIX0yaiyKm4oTumRUHPKynCZzMnrf/be0tm5Gv/rVr1ylhXS3ShjQGgqMnrQtVu5LVIcjlRFn0q0LGVUepcIlVrrE+qDMWqDxl1kjraFIX6GjJ6lqiwkbgoL6IL8pwm8uELamJO1pRVeppqtc311l7K42dlXrWivUjSXKmmJZeaG0JC4pjogBUeJ+cdgj9DtEPrvEaw+WFt/uGfCLl0zjYwxMOolJA2jJmQratevW5s+YvR5//HHwEEYBBmiZMZOxffv2/OE5iUTJhykEIvFnP/tZfu8sZTSZMBJx46ZZz2lO1leHqrLplpryzsaavvamsb6OhaP9yxeMrl0yf+vqRbs3Lj+4Y92xfTtOHdl//r6jTzx833OXH3z16Uffv/7sZ++88uVHb/zyB6/+4pOrP33v4mdvPPDhy6fef+Hk+8+f+uCFsx+/fO7jF469dWXlEyeaTqwPbx3RLqoXDaRFHUlpW0rWnpG0lzjHGkqWd3duGundONC+sqNuurFssrpwMB1qC7nrvd46X6AhFGwKu5vjxuYic3dNYtFo774tS+47vPSeg9PH75i6aw9EyBPhOxC+PIVHwCQ0RMeCXBw4JoBKJPhAdyMgihpuVSMAJ506qFMPdxugbgPSrSf0mvgjgdjapsy6bvNAKbfK6+gp7Ns5tuvCwTufvG//5WN7Lxw9dOW++547d+71xx5/99q1j1984dPXnv/k+tWPX7n2/Vdf/PStlz57B/iFT996+YdvX//RO2/+5MN3vvr4k9/8+It//uqrP/7i5//xD1//6bdf/+fvfvWn3339h1+/87NPDlx+sGRRv7UhZii3SIo0wqSSU6xVNgYdA2WKlgSlzIaWaaBqFZ6Wv1kLN2mgejVUrYTLlQAqUFpGKFJyshZLYyLYnHVWJKVhF8UoJ6n4DBWfr5fILCqj2zy30ZSdu3ZqjVq1XiNRyDhCPoPL1hjm2CEQjka+YcAMUWrravPHZim7y5GHyg2uLF8xF2Tii/zx3rO/NYbYnY78GbPX6ftPJzPFuLPAadwlwJlk6d84XFSIcBkIn4kIWIiIjUq4iISHynAjCh6s4CJKPm61ENUCi1GdFNVLUaMMNcoxgxwzKggGJdGkolg0DJuB5TSzXWam00Sz6RkO/YbtW//rv/4r/4VuWT/84Q/1BV6+T62N6hwJozthsETUCqeIo2fTNCyKlkHVMRgGltDBtyQN6c5k7+q+xXcsHd+/rGrNoHO4StlRLGsvlneUqHsqdH01lqEG13BLaLwjONoeHu30D7R6uupdbTWOpgpbbYmlvMiULTQXF5hTUX08qI/6DWHv4aN35b/KbVO2tBTE4rjZuTa/zTx//nz+jNlr7dq1OFHotDxRcr6VEZqVq1YR8b6vPFfye2cvMpVCIM86TDlw5E5TQShYnk0311V0ttQNdLWOD/Ysmje0YvH4uhULt61fvm/b2jv3bj5x594z9x2+dObYU5ceeOnpR9548fH337r2o/df+ukPXv35j179+rOXvvr02k8+fvJH7z/+2buP/+idK59974kfv/PEj94GaNn70qWxMweKd01ZplslfSXSroymp0zbXa7oKNX31QYWdFasGevasWjy0Mq1J7dtfWDn2ns2Te6bql/WmhwrD/QUuzqK9A1xSVWYVxrhlsQF5UlxZVJckRSXFUpKkhAxT4TvQAQ2jHIxTEpGdFTIxYQTfLhCgtSrEEAUPEbRw506GCeKMWcT2m3CukzENhOx3kIutxALddSkXt8cbNs0uOq+rWtP7Vp576419+zZ9cjxE8+de+qjF174wavXP3/ze1999OaXH73w+Tsvff7+6z/75O2ff/bmV5++8bNPXv/xx69//tH1zz544ycf/uDXP/7qH7/85b/+/Gf//PWPfv/Vu+Cv+IuPn/7ozbueON+/c02ov8bREDRVmpVZnTilZBXIqQkNpVBHSGjhhALOynGKNGiQFj3crIPqtVCNGqpQQiV4Lh00KcdickpYzw6ZGS4dUS8lKLg0tUBskutcRpPXOjA6l3RJ4PEwEo9Y7WaTxag36mRqhVAmHhyey1uB3xJGItyASs4ofPHixfzh2ej555+HiDeIcsNzSyS8b9++/0mUnDfN/iHuG8WLCnMpA3IZjoFnZgwzKXmzgGeqPYL2RglhvCgvA6+1JWLlK/ji9eHZkAyYg68mUXBhlQBSCyGNCAZWi2CVCFGJETVuglZK1MtIRiXNqolVl77/wQf5r/Ld6eKjl1lOpSyoMRXorXGtMahSeiVCK5dj5rDNHK6NLw3I7KX2bF9Z3+qhZQdXrzy8fnzXsvp1E96xZmFDildbyK9NiuqLJQ1ZVUuFvq3a1lln76y3d9RZm6utjRXmmlJDebGxNKVPF4CoS1sQ0sUC2pBPG/T408m/w3RzuVLJ4nL+h5lc9q2khMmWZKl0Go1BB6YzGHQmY3x8PH9sTqJQqYAHIFghkckSqTS/d5YCoScA0qOPPpp/fdNSh72qiM+SintKi4PVpfGm6lRnU0l/Z8VIb+384aYlkx2rpvu3rB7ZuXHe/m3TR/etvu/I5rP37nn0zP6nHtl/9dLea5f3PPfo7ucuA++4dnHTUw+ve/zMmkcfXHXlgRWXT6+4dHLR2bsG791dtWeFf2W/bqJe1JUVthXLOjPyjoywpVjallV3l9uGG8OLemo2LRw+tH7q2Oapo+sH9y6tXTcUGquxdGfVjQWiyiA342YmnfSEg5Fw0ON2StROCdsofjMOFTgPhVsVgY1gAiJBQcZMVNjPQoqEcI0MH59v08EdgCggNDEBliA9ZqTHMmO004w06vFsAUkZGpESY3JJhatoQUPj2qHmteOViwfrl4+P7Fm99PjOe1545OQLFy6+9/LV779++f1XHvngzcsffe+lLz65/uNPnv/hh09/8s7j775+/tUXTj//1LnrV59+74W3fvja65+8+uQ7L5x99ck7n3p45/kTy47v7tw0lRxr9rQlXQ0ea7VZlVGLChWsmJQSkxNicrRABqfkeAKlOhBaGeF2E9xmgpoMUJ0un0UnrYQTufSuTgFs5MJqNiJnEuQslkYot6r1brPBbXn0yqyvIaAnnnjC7rTZnVar3WS2GnVGnVyjePDBB/OHZ6PTp0/j9+tvQYUv4s+tT6N3oC83oo7eaFE6lzm3+046m/72V/rGc76VfPnll3gGGpwiM+sZ8SWNeGlLfN7wTF2TXFGTXDGuGyWzcoUUZ1L75bIw5TKsMHJcYeLL4+VsWM5BlDxYDQIXAW6lEFEJQYuqRKhaTNJLGVa1yGvpnTd6+1LvpBoqOG6lLKzVxvT6mE4X1aojam1Ma0yanKWeRHNhxWBV4/zWtqX9/RsXDG5f0rRufsFkl6QuSS72EpNuUpGPUuSnFQUZRSFOOiooKRBk4oJ0jJ8K8wvD/IIAL+IThL08v4vncfCcVr7DwrebBTbTrv378t/gpjXbsUPwz40Pe4AY5W8tVd5SJhU2lwOggnOFDrjCAFx566238sdmrx07d1JpNAqNiqOFQi4oKMgfmKU+/PBDuUKef3HTWrdloy0VdWUSvrKiYHU22lBZ0FaX7Gos7m8rGe2uWjDcvHxB1/qlw9vXTx7YvuDInkXH71h++sjqs8fXXzi59YmHdz93ac/zl3c/f3nX85e3P3d5y7MX1j51dtnl+6cv3rf48smll08tuXTfwkdOjJ090nnvzsqDq6KbxhxLOw3zG8wLGq3zm4wT9aaJOufCFs+idu+ijvCS3oJVo6n18zJbporXjYaXdNnH64wDZYaerLajSNFSKK6L8Ct87FIPEwAm7WalPeykHSJB39lYPcZFQJiCaamonQ7H2HCpGG7UIu0GuNMId5ngHjPca0X6bUi/HXcf3qJdVqTeAJcqkYQMCYnRsJRdZNQ3hd09JY62jK2uyNGQ8XdUJkfam9dO9e1Yv+rU0d0XTm9/5PSuKxfve/G5J99765n33770xkuX3nrp1EtPbTl3cv6R3Svu2bf9wcN7zhxZd3zf5IGtfTtXt2+crlo6nJnsiA/W+doz3paor9nnqLVosmpBoYIdV1DjCmJciRSq4IwKz2HQZIDbrXCnFW63QM1GqM6A5zYo08BpNVwgh/wiyMaHdGw8k6uMgUmZVCWfb5CLTSqjzzm32/fK1atMNrPFbrHY8GBFY9Spjbq53bZq62pnury+QcvCqYX5Y7OUxqRHqETcFNASgDt7Zj2JBQgPnshEmIDOODeUggc9bp8nf8bste+O/QibgnCo6DdpOQQ0VJirty/E6+3DIjospiFiOiJhIBImImXkzMRnEitZeeNTivFZxbjVHBRPAclDDULMIMpZTNCLCHoxiEfpZiXPoVeEHIaEf/3WzfkvcXt08fIl8KE0fMReJ42ZVHGzttBiKLJby7yu6lC4NZnsLS0aqkoO1/r7mwytlcKaFDnpRyI2OGSCwmYoYEYCFtRvxvwWgt+C+cyY20RwARuJdgPBqsXMWsykwfQqgk5B1MiJahlRJRVYTXOYXTLbCBicT6LTgMkMOolBB+2Mk8VF+TNmr+vXr4O7PwXHAA3AAHClorIyf2xOYrHZ4H2oOahQqJQ5z3C5du3abAdjwE8+WVOWqq1IN1SXttZXdrXUDnQ0jva3LRjtWTJ/eO3SeVtWT+3asOLwznXH79h88q6dD9+798L9Bx8/e+ezF46/8sTpt66dfeelCx9dx/3xq+c+un72w5dPv/fc8TefOvbG08fefArfeOupu9948vD1Rw+89MjOZx9Yd+XE1Nk7Rk7uBB4/uWvwni09RzcMndgyfu/2oeNbeo9u6L97Q/9da7sPreo9vLb30Mr+Ayt69i3p3bd08OCKoQPL+/Ysbt0yXrN+qGHjePeuxb27F3XtmA+RvzuoEEQEgpqGWRioD4QpArhWibSa4C4r3GuD+6xwnw3udyCDrhmjgy4YtN12pN4El2iQuAwNSABXGEV6Va3X2BzW1gR1pT5dxm3Ieg2lAWttKthVWzzW3b95xbz9WxYd3LXt/nuOPnnxrmcuHHv2kbseu3/j6YPjd2zu3b5s6sDaFUc2LNi1snXZWNX0UMlkZ3qiuXisITFYFe4p8XekAu2xYJvfUWtTl+hFKTU3pWaktJQiLTGjQyv1cIMRarPAXXa42wl3OaB2O9RkgWpNUIUezoDvqYB8IsjKh7VscHsC9ylMzCBKWFQ5ly7jLZieY/L2kvIyiUqh0GlUBr1MqxEoZC1zWlMCwgg6k34DKnm0zO2p7ZNPPkFppLzp+Y25daOB4Akmogg+4wvNzfvK02XT5rn3fXljIYxHI+D1gBkEMZMgxas4E+Usghy0bIKCTVACcwgqLlHDIwFr+SQdn6TP2SDAJxPnTDQIiKAF22YR2YKnE6Y65DS7AphuVwIz7Wq+Sy/327Rht70wfLuJAvTb3/4WUfAQrYhgkVM9WlYIX7AlKLSJ0y5FqU9TEdbVFKirC4SlYWrShYXMaNAI+3WwXwv5gHWQVwt7dZBbB7t0qFOLOrSoXY1Y1ahFjZiUiEGB6OSIRoqoJKhShMqFqEyISgUrZj8HD1whs11KtWbd2htQ+RsvWrw4f8bstWvXrlxXFYVEpZBzdDn/yNyHZ47efTfAEh735CMVyoa5LlJ59tln81s3rWWrV6Zqyovrq7NNdeVtTTXd7S0j/R3zRvqnF4yuXrp4y/pVe7dtPLJvxz1Hdp0+fuDh+++68vB9V6889NIzj7zxwuMfvvncjz64/sUPXvvyR298/fnrvwD+8au/+PyVrz978atPX/jyk+tff3b955++9PMfPP/lxy/87OPnfvrhU5+/c/n7r595/6XT7z734PsvPfzBK6e+d+3o9SfuffOZ029dPfHaYweeP7fv2tkDLzyy8+kzWx8/ue2xk3uevP+Oq2f2PvXAgafP3PXc+eMvXjj2/CNHnj9/9IUL911/7NRrj516/VGIDH9HUEEgsobG8orIISkSl6DlCqRZj3bbkD4H1O+EB93wkAca9kAj3hkjI15syIt2O5FaE5zRwnE5GhSjQQktqZNXu7QNfnV1QJ11a7J2falbX+q1V4aDDcnyeS3jWxf3rJlsWbmwa+PK4b1bFh3bu+PSfbvOH194547+HStXHN1w7+OHL1x/YPfZO9rXTJRNdmZGm9KjDaUTDdnxmuRIZbQ/E+lJhNuDrnqnttwizRh5xXpWxkDPmihlRlKdhdBqI/S6if0eYr+P0OvFutxYqwNtsKJVJqREB8dVkF8CWwWIjosoWKiEjoloRDGDImFSxayrs7+MgMDjiVAu4UlFXImILRYyhXyGgHv/nGZ2/nUy8YwReA4B+IyO3HUkF6CQcNPwlingzq3vq7a+7psw5duec98X+IN4Qg4eA0/fKWIRxSyilE2Sc8hKHlnFp2gEuLUCik5INYhoBjHdJGWYZQyrHJhuw82wKZh23Ay7ErcDWMVyadjAHh3bo2d7DBy3nucyCjxmud+uD3vMUf+iZUvy3+A2yxByQwoupOYjJgnqVGAeNebTYQE9IQRswIIGxKeHgT2AH1rIq5kBCdiGPRrUrUVdWtiphh0a1KZGbSrEqkQsSsQoRwxyRCuF1RJYJYIVQlgmQIClfIpKOofZJb19vbP9U+BKoDLpOTOoLNy0nO+77778GbNXfUMDuPXj4x8UMqCLw+nMH5iTFEoljTEDFdoMVG7lu81KH330ka0gZk8WOIsKPdkif3k2WltV2NJQ1Nma7e+uHBtqXrygd82ysa3r5u/dOm/f1oWHdk/ffWDFfXetf/jktkfP7n3q4uHnnzz2ytUTrz53/LVrx1+9dvf1Z4++dPXwi0/eAfz8E0dfvnrs+tU7rz996MUnD70A/Pj+a4/uePLclsfObLlydtfTF3Y//cj2xx/eePmBzZcf2Pb4g5svnVp99sSqMyfWnz+14ZGTy88eX/rAXWvOnNh0/uSmR05uvnBq1+UHjzx94cTzVx68fvX8G89ffvfVJz58+9lP38eh8t3MKsYga4UzNVKprIsSMma0WoO227B+NyAHMuJHx0LIWAgeD99wBB4LoSNBtNOF1FrQrB5JKJGQBAlISBElL2ORVzqVpThRrOVOa6nLUu6xV/tjXcmBDUNjW+Z1rx0HIUvReF/9yoktDxzYcv++DffvW3Dn5jUndtz7zPEPf/7SO589+cS7j/ZvX1Iz2VMy2lQ6Xl8+v6FyYWPpgrrUWEVBf1G4M+xodGsrbbIyu7jcxi+3sittjGo7o8VF6/bShgK04TBlKEQeDJL7/KROL7HJQayxEUpNhAI15pVhViGm4+PPwlImQUwniZlUCZOjFMztnvvFF1/wpUKhQsKTAa4IWEKeWCWb4+37r31feajMOXhfv2E9TMIXJ+ZNIcytgDxAJplG+WYQ5YbR8FzzKQFt2rIZT0fIpqEcOsqjo3w8jydBzCLIOEQ5l6jkEZV8kkpAUgtIGiFZJ6boJVSjlGKUUfGpw7lc9EY52KaZ5MB4Xno8Nb2CblHSLSqGTQNMt6oZwGY126zl241Sp6Vr9vMm/vCHPzzwwAMPP/zwr371q/yum1N1WxMkYUNyDqziQSYhbJPCThnklEOgdctgtxJyK2G3CnKrYQ+wZsaIR4N5tQS3BnOqUYcKtSlxm+WIWYETRS9FdBJ8xoFSBMsFuCU8WMxFxNyFs4fll19+OYeZe2qtFl9Ngi8owdeUfLOs5FamlUvlshmigGAFMGDPnj35A7PXmTNnZkb7qXQ63p+Wg8pzzz2XP3ybVdfZwTTpWGY9y2rk2M18t03od4vDPkksIE1GFOmEpiJtbqi0t9Y7Opoc3S2u/g7faI9/3mBg/nB4ejK+YmHBqsVF65el1i0rWr88sXZZwerp6KpFoeUL/SungiunwqsXRVZPhVZP+VYs8K6Y718+P7B8vm/ppHfJPN+Sef4lk8Fl84F90xPBJZORZfOAg9NjoemJwlVTqdULC5ZNFC4dL1k9Vb95RfP21V17No4e3rX45OFVZ46tP3/vqoeOrXjg2KozpzZePvMdjamAtyBDS+5c07d1gbm3Gi33oE0mUr+PPT8pW1kpWlnJmEqTJxOkyThlMsGcKmZPp2lTKfJYFOt0Y3U2NGvEEmosIkf8EkJAyizUStImedqqydjMWYcp7TBk3I7qQFF/untVV+vS7vJ5HYmh5tREV9v6yQOPHNpzetfuc4fW37djxwMHH7h+7qN/eOe9n7z88CvnR7Yva1rcXzPZVjbWVD7RULWwtWSyvnisqmAg6++I2ZoCpvqAti6oqPGLarz8Wg+vycPp9HOGwtyJBHdekjdRyB1LcEYLWIMRZmeA1uimlFvICS3JqyRaxUQ9n6TkkmRMPEwRM+lSdnXjHKftAqgodQpguU4hVErYEn7f0ED+2Gx04/b9Laig8EMPPZQ/PEsBqORTqsxkfiRjc5v3lZ84gPtvvti+fbMeFv5G7qAfopEgBhliUvBy9xwqzKUifDpeCknERCVsRMpGZBxEDqJJLqLkoSoepuKjM2PvagEGrOJjYKdSgClzh1QCcBRTCzGNiKARYWoRQS0mqkS4FWKSQsw2qH/yk5/kP/7mtGrN6lytAQJEIgQj4fzem1OqsgwSsiAJC5azIQ0fNophiwyxy2CbHAKtUw67FDhavCoEh4oatKgXPO6oMbea4FQBY3YFapGhZimekdAoyxNFI0bwGEUAy/iwlAuIAos5BCl/DiHjwqmp0bHR/Iub069//WtyvuOLTvnGTAaHz5vz4pgPP/yQSMotUSTjM7WYLOY//dM/5Y/NXmarFeBkZsw/H6lQKXNe0DorXXn8cZpGRtcq6Holw6BimDQsq57tNHG9Vp7fKYi4hXGvOBmSZaKKsoSqOqWuz2ibS/Vt5br2SkNXla6nxtBXZxyoM/TX6fvrdP21+t5aQ2+NtrtG1Vkp7SgVd5SJ20sl7aXC1gyvLc1tSfOainkNRZy6JKeukFFdwKyOA7Nr4pzKOKciyqmI8KvjwtpCYW1SVJsU1yZlDYXG9oxvoDIx0Vgx3dW2ZmRs+/SqI5t3PXTg4IVjO84c6tq80NxaIiop/I5mfxEgiAYduny8d/c6UUs5Wu0l9Xg0K6qqjy7uPbO14vhaw6Y+7uIyznSJZn1T8tCC9NGlju1D3IVl5C4fudZJLDUTUzpSTEUMyIh+GTOqEhbqpEmjNGmRJW2ShE1W4LBUhCLtidRAReFgTeFQQ3K4IT2vtXbF8LJj2zc+sH/NvTuWHlo/dceGFffsPnH14fuund320KHRnctblw/XT3VXzWsrn9dSNr+1bH5z8XhtpL/E05n0dqU83cWOziJda4GiMSxpCgrbg8KBqHgipVtW5V7fFtzSE9jU7VnX4V7VZllQI+1JMqs99KSB6ldR7OCxV0RV8ylyNlnCokpYDClnz/69+QtklgK/KF/AbbYZdRatQq/kyYRKg8bqdVo9DtBaPA6Lx27zWp1+myvocEVctpDL5HfqPFaNw6y2GdVmg9qkUxu1YoUUT651Yxh8JiaY82z9ixcvwmQMpmAwlQDTCDCdKDeo1Ra92nzDYHvGYNsEjE8uyNugVeu1ap0GmM3j5L7J3xAFGDzq5j9plgJ3wFzGMAJeKZlJgtlkmEOGeXh1plx2LzpeOAsvmZWrmoUXlmdAciakYEIzZYCB5cDgUM4zp0lzlVFkbHATh2UcWM6BpGxYkrOIDQtY+w7ckf/4m9O+/fugb/Ixk/AM/7PKuOMvjMF8OiRkAK7gc51BvKIXQxYpbJXDdhnilCMuJexSoh4l5lFhXhUBJ4qK4FER3EqCS4k5FJhVDoiCmiSIQYLoJbBWnCOKEFaCGIWHE0WCEwUWcXrHhvOfetP6xS9+Ae62586dy7++OV25cgUfTbkxoPINV4Lh2RH327rrrrvw9Ykk4gxa5s+fnz8wez3xxBOAJfh6l9yYf95U6n//93/nz7idipdlaBopTScHphsUIERm2XUsl4HjtfACNn7YIYh5RIUBUSooyYSl5XF5dVJZl1Y3ZTUtJerWMm0HcClo1R2lyraMvDUja8tIW9LiprSwqZjfkOQ3FHJqCzh1CXZNjFUTZ1ZFGZVRekWEVh6mloWppWF6aZhRFqaXheilIUZJEJhVHuKUBTklAXaZX1gVMjQVBHuzZRPVbYubBld0Llw3uHnP9KHjmx985K6LT9576erJww9tb1veoigyfReLH3NhCsRAd507GZueIFQUEBo94nmFDceX73zx/oNvPDpx6Xhw77R0VaNqXWP1PSuWP3Vs1dWTjad3qVd00rqi5FoXqdRGS5voKSMtpqGGlOyYSp42aUodqoxDUmgTxMyCuFmZ9phr4ubahKW+yN9VFe2vKxxtKZnqGtg1tfDImr4d063rFtQvH29bv3B098rpA+t3PHjo2JP377l0fNP5u1bcu3PBHWtGt003LR8qmd+aGKmODZYXzasvGK32DVSYOos0zQWK9pisL66cKHatbam7a8mCc3tWX7lr7aNHV188suTsge7Da2PLuuUtheyUhRHU0hwKullK14loSh5VxgZhClPGAQ9N+Qtk9jpx4oTOoNVZdCBY4ctFHKmALRGwJHxgpoTHlHC4cq5Iy5ebpQavyei3K51mtl5BlvEIQhbKpWNsGoGJp+dDqcRc7hM0NxiO+EOB/AfMXgB1yeIUQiMiDBLKJKEsMsomY2wKihtsUzAOFd/m4EbYZIRFRphkcDJu8KcAigCTZr4MPoKCzHgGePHCOU7TBNq0eRNCJyJMEgI+nUfFBDRMzMAkDFTGxOQsVMnCNGxMy0F1XBTPEYmnicRbIxc18lATD2+B9VxEx0G0bETDQtQ5z8wBU7ERFQdRsBE5CHTYKIh4JCxP4ezuev/2b/9G5TDzYcoNrtx97O784ZuQyefEy70IaPhKGsA8BQfS8PDyWVYp7JDjdikQjxLxKBC3AvWocLsVmAsQRYU5lJhdjtrkqEWOmmSoQYrqxah2JkwRIniYwrtBFIDMuVy369evhxFktrPFdu/ZQ8mx5MawSn5wZWR0dhHPt9Xd00Mg4oveZ7jy3nvv5Q/MXsFQCF81iUcqfw1WVOrbXpAU6N5TJ/kWLc+qFTj0ApdR6DEJfRZxyC6JumQJnyzpkxT5xUV+YXFAlAmKsmFxSURcXiCpKpLXFyuaihXNaWVLWt5cLGspljSmxI1JcUNK1JgQNiT49SD4iLKqw6yaMLMKtBFWdYReEaaUB8hlfmKJlwCc9ZJyJoO22EMu9lDSHlrGy8j62BkvM+Nml7qVdQFPZ6JspKRjsrJjrLS5r7ihI9nWk+kfqRtb2Dm9emLb/jW7D62av7a7oNUPYXky3ILAW1BhiE2o376OVplBy1z0Tm9wc/eGa/c+8un1hz99c+kz50N3rFCtbXPu7F/w+JHj71+7651rgxeOm9cMM7sKabUeWoWTU+kWlLm5RVZOoVFR7gh2FYa7k56WAktVWJ1xK9JuXUlAmw2pS2OasgJzTZGrpdzXVVs21d2zad7A9unm9VNli4YqFg7Wrxgb3rNy54W73vvlJ7//r3/5xz//xz/8+T9/8od/uv7jD09eu7j+5IGebdO1K/trVvQ2rB7MTrWHRxvMPRlde1LVnVCNFpkXV1ccnFp15cg9r106/961C+9eO/fW1ftefmzTpXsad6009VTx025OyMhyq1g2OcsoZWhEdAWfqeBq7LeUDg9o8fRikVIqUkn5cglHKmQDrsiEXLmQLROw5QKuSiAzK8x+uz8ZcceD5pBHZDdS1RKSlE8F4JECAgkYAi6FzcRoZHwsJDfJanrJdP7d5yTwQGp22FAGGWNRMTaVwKERuDQCD5hOBObjxudfAfNoGJcGMANOw1gUjEnGJ4zNzEUmExB8mcsN1OUYcyt9X56QH/9K4PuALyNkkCSs/BC9mkfW8ckGAdUkolokVJuE6pCSnVKKS0bxyKg+Bc2vpAVUwPSAkuqR4/vBCVYR2SIim2Ymg4lIBhFJLyTpBCSNgKwWkFUCspL/5FNP5T/75rRv/374mwkOM6aS3nnnnfzh/5d+9atfEWUckpJHUvGJOiHRKCaaxSS7nOxRkfwaYlhHjOlJcT05biAXGHEnTBTguJEcNVCiJkrESA2b6EEDPaCnenUUt4ZsV5HNcoJBiukkqFqEKAXIjWCltqM1/6k3LcASNpfj9sw6W09rWyuFjocm+UXvucEVKoNx6PDh/Bmzl8lkIs5AhUisrKrK7529Xn31VXx0hwmML3OhM+j4HDAaLVU097nON69kRYktHrAnQo5UxJ1JBMqLgjWZaGN5oq0m1duYHmwtHmqO9NY6Wks1tQl5VUxcHhWVRSUVBeKqAmldUtpQKK5LiOriwrq4oDbGr4nzaqL8mggXIKQqzKgI0csD9HI/rTxALQuSgUt9pKyHmHUT0y5CsRMrchKKHMQiJzHlIKecpJmNIge12MnIeBhZD6/Sa2yOJAaTDWNFzT3RbK3TXagzBhU6r0LnU2o9Cq1bbQ2bkiW+po5k63DmuxhQIUEQHYV4FFomhRbYyZU2+VhR+71r73nviae//OiBT7838dhDrt2L1Bs7U3ct3vzyw2c+fevoe6/0nDuuX97P7ipk1HoZVS5BTUBWE5ZW+BVVgeR4VfPqnrZ1A8UTjfbmQn1NRF0V1dZGjXUxY3VSU5bQVRRaatOO1vKiia7WNQta1i4sWzxSNT3cvmb+gjs3nb7+6E/++es//eW//vLnP/8F/Pfnv/zzX/7ywz/+63M//vC+649vOHvn9LGti49uat88v3xFv2+kztabtfSkDYPFhvll7tWtPae2HL5+6fHvX3/u8+8998O3r37/zQvvvLj3qXNdh7dYR5ok5UF+zML364QuLd+q4htlPL1UqJd6Y7dUCXVGa9et5UqEAoWUJxdz5WK+UiJQS4UamUArE+rlCpte77U5YwFHPKT1uThGDVUlpihEHK1crFdLdSqhUsYU8YlMOkwmzvSDgSfK/FvPVYAr/lAQ3BZRBgUvgIEPjwNyAH7kDIIkwBLQcmgoMJuKsvAiGQidjNBJOFEoBACVHORynLsx72vOfV/vvvsuSiNhDAoBBGcAbEImWcKhyHkUtYCqFdEMEnyil1XBdKhYHg3br+UE9dywkRs38hNmYaFVUugQJ+2CQju/wMIJG9kBHcurYbrVLKeKaQdWsOxKllXJNMkZBhldL2Fope7CWU8oqK6vm0nnPmMikxYuiOWP3YTOX3iEY5RzwdXl1Aq9RlHQLApbJHG7NOmSFLslpQF5TURRH1fVFyjqY8qGAm1zEndDQlufUNcU6GoSxtpCY3WBsapAVxZRp4PyQo8wbON6TSynjmFT00wKml5G00qpGulbb7+d/9SbFngggFFk4cJZL35yuJwkGpVEp+I9YN/ynNM1/vSnP8UIBAIx7wsXLuQPzF5F6eIZ1OED9TPBSq77q7u7O3/GbdMdhw76U/FINlVQnk5Wl2Uaqys6musHutrmDfZNT46unp6/ceX8ras618xPjLc5+6pMXaWqlpSoNsatDPMrY4KqmLAmLqiJ8SpC7LIAqyzILPezyv2MMj+91Ect8ZIyblLWDVpixkPIuIEBS4iAJUkbVmhFE2YsbiImzMRCMyFhIhZaSEkrMWkjJK3klI1S7GSW+mUNcXd3umyyrHmiuLrd6yvSSdxink3MsgiZZgHDwGaomSw1R20TZcp885f13/KAykzfF5MAQnU4rCemjKxGn3150+Irdz7yo9ee/cWnpz55Z+jR067dC6w7Bvou7Dnx4bVLX3xw+N1XGk4dVEy1sTsSrHo/q8orqgtJaiKquoh/oLR369jUkZVTR9ZWrRz0DtZpWjLKxmJLR7l/oNbekjXWpvQVhebaYk9HZXS4JTm/NzM1VDI12Lph/pKj2y6++9xX//6bP/3lP//8FwCUv/w33v75D3/58+f/9V+v/PyLSx+8/MBrT5x++dLey/f27Vubmu73DNc4B0otAyXmsTL74prk9tHpy0fuf/fas59/76UvPnzp8w+e/cH3zn/vpd1PnGs7sMUx2a5uSEhTLknIIvWZxE6d1K6R29Uap66xvTF/jdyazp0/r7MY1Wa9ErdObdMrrTqlTa92GNUOk9JpkjutYruZbQBEkZAVQopSLLTqtW67zm6RG7QcqZjIZuKVenNQOXbsWP59b0F//OMfe/v6IAoBppFges4MMswkwyxgSr4FL8FORu4ojQjn6+HPVAvGS9DnB3vwhJJIsiiZf+vZa/WaNQCZeBAAuMWkApJhPAYmZBEkbIKcR1Dy8eleejHFJKNa5FSbguZU091ahk/PDhrZIVPOZlbYyAwYGF4dOEq1q2g2JdWioJrxOWA0o5yql1G1EopKRFaKKArhoSN35j/7piVSynJlQqgEvJAGDWD+3tlMS120YpnYbpC6zfKAQxX1aAr82qKQrjhiKI0bq5KmhrSjo8o7WB8abQ2MNofH2wrmd8UnOgrndcRGWiMDzbGB5sLBlnhvQ7SrLtBa6Wsos1cWGTNxVUFAFnJJ/XapxypxmUGkW9Ey64sWXAximRRAZbYrlv7lX/7l/4+TmTEVcCh/0iz14IMPohiGEYAJtlvISfzxxx+TqPgyl786N0eZTKWsWXN7U2j/x3/8hz0SNIZ89oKoKxn3FCeD5dl4XVW6rbGir6t+fKh98by+lYv71y5vXTFVtngsNtHlH220D1Sb+splbUlefZRdFWaVB9llQXZpgF7ipWZd5IyLgmPDTiiyo0V2JGVDk1YkaUHxDRt4SQA4SViwAiMaN6ARHTAW1RPiBkLciMUMWBw3WmAgJIzkpJ1VGtS2lqanB9q3zRvaNtizvKa42WOMy8UeIc/GY5u4LCObZWCKzHxLUF5U6e2e7MqjYe5CIYiK4FkxRAxiREfL2IXt0fimgS3Pn7ny0/eu/uKHd73/etvZu337FlWcWLP7jYtPfvn+019/uv/tF8uO7hJPNHLa4+x6P7vKy6/0Cyv8uuaC9KLm+YeX7bxwcPXpnd27ljRuXlC0uMfdX2vvLPP1VVpa0uApzFCdcDSlwz1VBUON8dGW+Fh70WTX0J7lex65+4PffP7Pf/mPHEtu+M9/+cc///kH//5vr/3ipy9/+fH3vv742g9eO/7Cxd47NkSmerxjNa6hCvtohXN+XXB1e/uxtXtffPixT66/8pMPX/3JRy/jUHnn3Fsv7Xz8XPeh7YVLh529lZqqmDzhkgescq9R6zObAjZnxNUz2JO/TG5ZH374YSabjRYmfPGYPeS1Bl32sNcSdGs9NpnDxDWqWXoFeIKmqUVUlZCukUrdFmssaAsFVFYzRy7FAFSoxNytHD19+nT+TW9ZBw8exGgkKDdoj+dHmTHjWwYvwSGAE5wouQljpFwx42/mDuQnE8O3gjq9yYC/Ifi/A9AC9AIYAzzjUBA+DRbSYQkDlrEQBRtVcjE1H9MJML0IM4oJJgnJIiNZ5QRgO7CMYJUSjBKCQYjphJhWgGly88GUfEzBw2RcVMpFxRxMxObplLO95YHbbm4qNhEBxmM1UrqsJH/s5mQJ+xhaOdOgYlm0HIeB6zbz/DZ+0CGIukUJv6Q4qCwr0NSmjS1lxtZyS3uFo6va2VHj6qixN1fYm8ss9SX2+lI8ZWRVGs/xlSnQpCKyqE/kdwo9NqHLIrAbBVYj36J/9tq1/EfetMC/HYKhACqzHVAB4chMxxfe5YU7P6bi8s49q8K8yUkcKjmu7N4991LE1TU1eZDgLT47eaY2F4DKd/JY9n/RqvXr5G6byu/ShLyaiE8d8ckjXmnUJysIqVIxXTZlqiq1N1R72htcHY3OrgZvf3N4rCM42eEZb7KP1xoGywStcWZ1gFzqIWfd5GInociKpnCEIIUmJGGEc0YKDHDMgMQNYAMB5IibcHJEAU60SFgzYwygJaLFIhqwH4vlXGAgJky0Yo+0MZtetWjTuVOnX7ly37VTux/YOraxt2IwFap1ONIGS0JtKVDZCvSeImO43B2qTuTRMHcRIYiG4kmWxExykYlV7lb1pFsOrzr2ztPP/vyTJ7/6dM+bL1SfuiN5ZOXCp44//KNXX/vN51e//mzLa88W7N8gGq3htMbY9QF2lY9V6uSUujRNsfBoZePGwfmHVg7vWdy5ZbJh1XCkr9bRUaFtKtY3F2lqE8rysLYy7Ggq9LanQ91lwe7KQHd1dLChYfXYshM7XvrZB7/787//KRejgPbf/vKXf/jv//7Bv/3h+Z99+uLn77/2sw/f+flH7/zi+098/9UVDx9OrO73Tta7xms8E3WBxc1Fm0eWPHL4wfeeffGL99748pM3fvb9659/8Pyn711859V9Tz6y8J5D9esWxcbazM0lurKoMuZSBazmsMMRdbtjvsXLbmn04n8I/GJBcBAtKgqmEsFkLJiKu+Ihnd8ltht5JjXHqGDqZQydlKaVsE0qbdDtKU74igr1fg9LKUfYDHy6LbjnktDvtngweDexSgrRMIgOuEKAGH/rmZ3gKDVXJJh8o8gjzpVvZqPBGAmb84Q08Ad7+3rzHujLe7Cvd6ivd7i/d2SgdxQ3Xh5YRoMUdEjJgFRMWM1EtCxEx0b0uGE9B57Z0LLBIVjJhOVMvBAkAJKYDovo+PA4n4pPJ+PSaltn/Sz/29/+NgfRnHNMvfnRFKD7Tp1EBEw8mzIAm5yPqoSoWoTpJJhRhpkVBKuK6NSRPUZK0EqNOWgJJy3hZhb6GIU+dtLPjLsZUScj4mCGHIyAjem1Mj0WhstMtekpZg1Zr6ToFGSNjKyWUVQyf3Eq/5E3rf/8z//Um4wwirrdsx5Q2X/HHQAneKWTnGeWpwC3tbfnz5i9vD4viqGAK2QK+R//8R/ze2epr776agYhoP3rBjkPlaeffjp/3m3QZ599Zg64bWGfqzDsKY57swlXSYGxKCSNOXkhKzdg4QSsnJCNF3Fw425ewi9MR5RVaX1LpaOvObSwLzI96JvqMI9Xqwey3JYQqcKNFNvhQjNSYEILAEj0SEyLO6qBATaiOVrg5AD80KJhNRpWIQEFElQiQRUwihvs1GBhHSGix4BjekLMQE5YKMV+RWtN1dZ1my+dvf/6Mxffevbs9Uv7Hr5j4a6FjQsbCtrjrmqvKWvXpEySuIHr0+XRMEfBMwMqBIhHgyVsepWLUx9wL2xc99jxxz5/+/Vf4/w4+M7LzWeP1JzavvXVR576+Qev//Ynl376yZJnLzu3LRYMl3Pa4qyGIKvayyhx0jN2QaVX21bgHCwLTTQExxv8w3XhkSZra5mwIsop8XOzHkGJR1jikZZ7tTURY1PM1JIwtxZZ2zKervL4vObmrQuW3X/g3tevXfvxp69//dX1X/78lV9+/cwXn937xkubrzy0+6mH73/j6Wc+e+O5H712/+sXN105WrZ3gW9Fq2uqwbOgIbC0vePwqkMvnHv609de/9lHb371yRs//f5rP/7wxR+8c/md63ddfXTJPYcrV02GR1qtzSXmmoQhG9TGXYawwxJ2WkOu9r65JMX6vwj8gLdt3x4sjEeKEgAqznhI43OKnSahXcezaDhmJduoYJkUYo/FkYxGytLBkmJjLMTSqxEuE6KT8WCFjN1KQeL/VV9++aU/EoToOa78DVRAsDIDFRCpzFSeR7/FFeQGV+B0Np1/r9ujl195GZJQIDkVUlAhJQ1S02ENHdYyYV3eEG4GrGXgLzUMHDxyOiQFFzANFlEhIRUvX88jw1wKzCHv2juX518SlTwzyQ34+PHj+b03p2Aqls99CQIvMROW4ktVYCUX1vAQnQAxCBGTGLFKEJsMcSkRtxL1qECLuBSIU4HYZYhVipglKDjHIEJ0IkQrRNRCPLG/gg/LuLCEg+QmfSFC9sVLl/IfedPK5WtAIGQuAyojo6N05gxRWDnnoMJkznmtInjCQNCcMGxiYiK/d/bq6OzMLXPJL3bB/VeoUD/99NP8ebdBC6cXe2LhYFFhvCxbXFORbapJNVW7KkBwGVYU+cSFLkHMzotaOBErO2Jhx6zsuIOV8HCLw+KalKG73r9wqGDFeHhpn3NBo2W8WtmfYdQFsIwFLtDBES0UVsMhFQocViIhJRpSY2E1ARAlpEYBSPwK2CcHBhs4WnADuuCAmaEO7qiOENMRC/SkQiM55WJVFOoHOtJrV/Qf3rXg3n39BzdUrhkNjdRqG2LijJMd01G9MrJDQjKL83SYowBUyDB+N+HTESmH1x2VDBU3Hlh6/ztPvfEPn37wL1+9+fufnvnBO/OvPNh+9vC+t5969hc/eOU3P33ws/cHL582b54Qz6vk9iZZbVFmY4BWg88Bo5Q7KKCtcJHLnKQy0LpJpW4Q1hEydixtpZTYmWVOdqWbV+kV1Yak9VFFc4G6rcjYmbH3lXlHaiKTzfGptqLl/SVrRuu2Lm7bu6b74ObWvetqd6yo2jpds31J6x3rFj9weN/zD29/9t7xMzuK98wPbuj2rGz1LG8pWD+w4IE9D7z11Es/fuetr77/1lefvP6Tj1/9/IMXPnnn0tsv73/8/Mide1JLxtx9DbbWrLUxZakpMJaGtQVulc8qdxptEf/MhfLd6ty5c4FE1BUP6wJuudsidRlFToPAoePZtFyrmmfXaaPeYFlxYU15uCKrj4cZJi0q4eD53ul4V9XCRVP5N/ru9Mc//rGxtelvoMK8sQF24ly5EazMcGUGLTe4crv7ExYuXYRDRZYjiooOaXDDgCIAIXrcgCg4VABacK4wAHXwmAZENmJwH6dCAkAUCsQlQxx8Bcxrr72Wf9/ZKBqL4utyMGTNLBNqPfb4Y3iKZQAVEC3hC27w3MmwAkCFA2u4sI4P6wWwSQhbRLBNDDtlsEcBu+WwSwo7JWAPYhXDZhFsFCJ6IawVwBo+rOIBIOErXaQcCF9zw4SFTFjA0PvmUsbG7fXkoDKXSgqhcPibGIXGZOI9YAwGhUG/8thcapsCXbhwAQHKQWVWseC39a//+q8EMul/OL+UkkKm0Kj//u//nj/1uxa4tBQ2i9rtMIb89oKYL5MKl2cDVaXuioyjsthQVqAti2rKw8qyoKjYw0pYmTEzM2phxq2MQjs95WGWRmXN5aa+FudYT2B6KLhswLmg2TBcJuuM0SvtcEoLRZRQQI4AbPjkqF+OBZWEgBLzy1GfDPNJUa8E8YhxeyWwVwp7ZYgfBwzgDQAP7ijeFUaIqrComhDXERMmUqGFmrTRM25GaYhdHueWhZlpD9hDiulJASXRIyU5xSSriGQU5ekwR+Gj9DDEJEICOirnhtb3J7ZNLHn06KXPXnvnH7/46N9+8d4/ff3IZx8sfvzsgkdPnv7k+kv/8KNXfv3TUz/43vCV09EDyz1bhh1re4zLWmWTFbyhIlZPjNERpLQFyS0BYpOP0OgBJjZ5iU0eUqOb2OQmt/hpLSFmW5jdHuV0xgTdCWl/Wjlcqp+oss6vdSysd081uRY1uxa3eJe0RFZ2Jdb0F28YSm8cKt8xWb1rfmbjcHLtUHTVQPHmydLdi4p3LSjYNhbfOhTdMhDZ0l++b9GqR4+dff+Fl3/yAd739eUnr37x4cufvf/sx2+fee2FTY882LhzXWRhj3e4zt1X6ewuc3Zk7W1pc31ClfSIXQa+STXbXuab1LPXrplCXpnbKnWbJR6zxGsWeoxCj0HgNkqDNme2sLC+MtVYHaosVUUDFJMKVQlgCQtP9s4gpsuy+Xf5TgWiqHnzJ/F4ZYYoM85DBQQxOajgRv8mXiEhGIUw576vm5TcqfsWVGg4VPQMBKcIAwFQyRHlBlcYEIhUwAmAPQAqEioEIhUAFT55Bipk4Rzz/ANwQii8ZOmsc594C0L4Pxy+ivNGTn48TMEzKMNaHg4VowDHBoCHHYBEniOKDNAFAS9tEsgqhsBzolGEswdARc2HlTxYwYHwtZy5+jEiBr6gUsC89+Ss81ldvXo1nxlh9itUwAXDYLNm+rtoTAaZQSfSqEQqBXjO18PU1FQOKkhpWWl+1+w1OjZGIP1PqAADrpCpFLVGkz/vNqi6uZEsEVLkYppKxtCpWGY9z2EV+tyqgoi9LO2tL7fXlVjqiqyNxbbmjKY2xks76QkTPW6mFljICRu1yEVP+9ilUX5tiba32TfZG13c751sdc6rtY6UyNtClKwBjkghrwTCySHFfHIMx4kM9UpRtwRxifN2i2GPBLdXioCjAQUWUhHCeFiDhkGUMxPraFDAmBsT2SkFuIlxLSGmwcJKLCDD3GKCU0iwiYhmEdFwi1BBIYiC3IAKb89L5/a+cvH0xy8++/XHb/z+p2/945fPff2j/a89P3nx/j2vXHn6yw/e/scvX//9l5d/+v39bz+/9sVH1j9/Zu1z9y95+p7BRw40ndxSc2xN6Z1LC/YuCGwbdW4a9GwZLNw9kd63ILlnMrx11L95yL9l0LdpyLWx37K227Sqw7mhJ7VvsvrI8uojK6uOrKq/e3Xd3Wurjq6uuWtlw7G1bfds6j21rf+BHYMP7Ro7t2/ozM6+U9tbjq6v3Lukcu/SxkOr2+/e2HPPlv7T24ce2DX+8P5VT9xz7M0nnvjRW9e//PiNL3/w+k+//+oXH7z0w/ee+eits6+9sOuxc6NH9zZtW1a9fl75ypHs8oGSJX3ZRb3phd2JoSZ3dZEu7Lo8p0oqN6OXX3nFFPMpQ05lxKWMupUxlzzmUiW8tpJEvKGyrL2ppL0pUluhLgjRHTqSUYLphAh4tuXT6RLubUId0PSyJRCbCG6+EIeIb8yYRcSvhxnAgJBlxjOBCxmtrJn7SoKbEd73JaZAUire/QWgAoCho8OGGzGKgQkAA+HBCh6m4AZQAZGKmoEHK+BPif8GKnqXJf++s9ccFuKs37wx1/E1E6YwcAbIWJCCA6u4sJoHa0GYwkeMQtgMghIp7JBBLjkEoAIMQhYHDhXYIoZNIFIRwDoAFT6s4YE/iy+clLHxlfngDYUMEKbI7QZwl89/6k0rWVQE40P0qNsz66H1d999FwQlIEABUKEy6SQaFaOQgGW3UEbF7/fDOFWQhx9+OL9r9iLTqCQ8t3HeuW18KAUYhCkFiUT+vO9aj165QuSziEI2UcwlSbkkhYCkEpG1UopByXAYpdGgsSTlrC1x1Jc4mkrc7aXunjJze0pY6WIWmSgJMyVppwOoFDsBWggJJ7nYz69OyhtLDT31qZXjVRvnhRbW6XoKRLUuapEWjSgQvxT1SDC3BHUDighRF27MibcI2HaLEA+wGPVJMb+CEFQBY8FcLxneb4ZzBWdMBFiHRQ0EQJeojhTVkiIaol+BucSoQ4BaeZiZjxn4mI6fp8Mc9TdQ4b72q++//KtPXv71Zy/8w48f/dkP7vvk7Y0vPtF66nD7yTsOvfb4c199+Nbvf/r67372zM8/e+AH3zv+4SsnP3r59McvHXvv2p43Ht/y0iMbnz+z6uqpxY8fH7tweOSRA4seu3vTc/dvfeHBNc+eWvzk8cVPHF/82N1TV47Ou3Sk/9z+nrN75185svn5B3e8fH77S+e3v3J+1ysXd71yecfLoH1k76sXD7/52N3fe+rEe1fvfveZI+9ePfT2UwfffGL/9cv7X7548JVLR1+/cs/bT5185+kH3r320PvPn/v45Ss/eO3aj9+5/rOPXv/qB2/87JNXf/Lx9R9/8NKn71396M3Lb7988oUnDjx2ZtvDJ9Y/cOfqew+sPLZ3xZGdyw9tm96/aWr3utENS+oGOxq75pKs/ib1ySefxCtL/BXFweoMcLiupKC5MtvZWNvX3jjUU9fflWqtN2UTXL+F7lBSbXKSWULU8jExY9vOW6rU/X/XvgP7EQEVEVJRwQ3zqSiPgnApCIeMArNJKIuEr37HZxsTT9//nc1G+181b3ohqmSiajai52AmLrjQUTsfdfAxpwC0wAiwjYdYeYiFB5u5uI34uD0+dK8FMQEDluJVWGAhFeZT1A5j/n1vv8BtFwbRyYwBUXIdX5CSA6k4kIYL6XiQIdfxlQtTILsEckghlxR2y3KRCmCMFLZL8AjGLELwSEX4DVRgBRcGUMlHKnj312xTzgCBr4cQsJl5X3MYUDl2/PgMVGgsJtggUCkomYiSiFU11fkzZqnf/e53MBACa3VzX3S8fPnyXF4Waq5eJL6KHt9g4rUjmSwWk808depU/tTvWjt27yLwGQQBCxOxCRIOQcYlKAVEtZCkFZMMCqpVx/FaxQV+aSooz0TMjSWZRf1NG+dlptvVDSFmykQttFJTTmqxm17spiQdxAI7KeFgpgPi2nRgvLti7VTJ2gnfvAZLf8bQGZfVuxhpDTEqQ/1iHCc4SwSoE/wWeLCTDzsFiFOEOAFsQByjJAXU+BLauJESNRHDegzEKN92REuI6skxIzVmosWMtJieHFAQ3WLMIcJz7BqFqFaAaG4RKt90f/FpkJT98R9+/v0/fP3uv/ziya9+tP21a51nj6cOrXdsmUjcsXTRlRNH37760PffeOiTt4+++8rWl55a89yltS9cXvvi5eUvPDJ19czkk6fnP3nf2JUTAxfv6nz4YM+5AxOP3r3i6v2rn31gxTMnlz1z79Kn7lv42InJR4+NXzza9/DB3ofvmLxybPW1h9a/eH7tC+fXvfzIxusXNr1ycfPLj2x55ZEd1y/d8foTR95+5ug71+5699rBt5458PZTgCtH3n7q2DvPnHr/uYc/fvnyp69f+eGbj3/21pOfv/P05+8++8W7z//4/Rd+/MFzn7//7A/fvfrp965+/60nP3zjyrvXL7z5wkMvP33PM5eOPPbwwQsP3HHu5P6z997x0Il9p+7edc/hbXfv33Ro94odG7vGBx57/PH8VXMb9Pobb2Qba6q6Wmp72+r7O5qGelpH+zrGhzomhptG+tOdzdayIn7IznBpqXYl2SojmcVEHV9m082tD+cmNbVkESakE0Q3LKSDl3jqFD4N4wFTMS4V4+DJXagC1pxXJNyMwNO31K0nGwQUq5jqlNK8CkZQTQ+rGVEtM4abEdEwImpGWMUIqRlBFd2voPnk+GJ7l4zqlNGcMopNDP44Sc0jKTlEKUvntebf+vYr21idu60IiBohUSsk6EXE3DRoil1BcqrIHjXZqyX7tcSgFvzUidGcYwZiTE+MgF++FgtqMJ+K6FER3UqiQ0GyKzCLFDOKMZ0I/M5RFR+vxi/nIFKOcE7XQ219fS5Mwfu+5jCgMrV4ERXcr3PdXzmokAFUEBJh9VxXgVy+fBnCqQJv2bIlv2v2IpLJGImIEYkkMohOyAQyaWbGFzD4tuFZ5gCdlRYvXYKvrxIwURELk3AwABUFn6DiEzUCok5EMkkpViXdqeOEbZrSWNXU0LaTh08+efb0sw9teGBHfLKaW2InJ4xkcOvHe8Os1AIbPWFnF7oFJWFFTZG5tdLe2+gaaPKNNPlGa5yDGWtfoaLJRy/SkMIyzCtCXDhR8lzBn7QAV0AcIyf5tZSQnhIzkqNGUkRPCGnwYGXGeLyixMJ4zxgxAsIUQBQjK25mhPVUv4rmVVNcSqJFStBLUK0wT4c5amblI4OAh+0S1kf/9vUH//bL137387u/93r93XeY1k7oV3ZoVrXp13aG9kzVnNjWfmp/y737yw9vj+5c6d+6yL9jiX/3MveeJbbdi6y7Flp2zDdtn2fYNqHfMmbeNubZtSC0d3HiwNKKu1bWH11Zcefy+B1LfXsX+/cv8exZ7N6zKHJwRfKudcV3b0wf25Q5saX0vh3luHdWn9pV/+De1rMHO8/f2XPhaPeFuzofOdJ54a6+C0dHH7138vGTS555cO3z57e8cnnnq1f2vvrEHa89uf+1J+549Yn9L1/Z9+Kje56/tPvZC7uunt/9zLldTz+884mz2x99aPOF0+vPnljzwN2rT961/tSRbfcf23v/8f2nj+297+iu44e3HNm/bv+OJVvW9c0bmUNdipvX2YfPVjTXtwz2tI/0d4wNAqK0jQ01jQ5U9HaGG6o1RTGWz0JxqIlWObinoEYRqhdgam7vnMrm36TArby0pgIBAYrgRgVGHghWqCg351xmMEAUhEmubW7I/5nbo6vXnqVohVSjmG6TsTwqdkDLCRu5MSMvbuYVWPiFFl6hhVtg5sVNwriZHzXyIgZOSM/ya9k+Dcev5Xq1bLeaaVPQTRKqVkRV8o3+uYxmz0E9Y0NMk4JtUfPsWq5dw3XqeF6DMGiRxhzyQresyKvMBhXZkKo8rKiIyKtjshnXxOU1MWVdXFIZEpX6JSV+WdanLAnIi33ylFccd/CCFo7XwHZqGVYVnuTfIGcY5Jt3bMt/6k3rs88+AzffmSF64Dlc4YVFRdTcfGIak0Fh0HJQIQCozDYl5Teanp4GUAH+5S9/md81S23evBkw8hvngjDcYAPBQEyGPTCngkY3qc7+HrxkQw4qqISNSjmogoMpeQQNn6AXksA1bJcLggZD2pvoqJi3ftHxs8evvvz4y9979tobj524dKR9TY+lMchJ6ijgLh9UYQHwSKEmBbWUsJEaMtJCJnrMxi0KaRtK3b0NwdHmwFita7jE3FMob/DSUxpCSIq4caLggTtoAV1cItQjBZEK0a8h+tVYQIEGlAS/nBiUE8JKUlRLKzRyis3ctAWYmTRR4jpSSEX2g4cYOcEpI7rkJLsMM0pQnRjV3uKYSn5K8cw6Fcab//TVi//w0/s/eqf/1An91LBwqFI8USFfVCtbWi9d3ihd2Sxa2iRc0MAdrWIPlnCHSvijFfyJSt68Cu5kBWteGXO8jDFewpjIMkbTzNE0eyzNHk0LxrPGxVWOJVX6qXL+eJYzXsKZKOHOK+XOr+AuqOItrBVM1YmmG6XLmqUrWuUr22UrWpUr27Vrug3r+kwb+i2bBq2bhqybBy2bB22bR5xbxtzbJvw75kf2Li48sLz48MrsnWtKjqwtO7Ku9NCa7L4V2T3LsruXluxcUrJzunzH0oqdSyt3LK3ctqRyy3TFpunyDYur1i9u27pq4cEdG48e2HH88I67D207emj9wb3TO7eOb1jdt2RqcP682/o8vmXbtvLGupaBntbBvuahvpq+znRHc6CuQp8pEEadDK+B4lSTHAqCQ4bZpASLiGASYFrurn1zLzLx/xT4/9U7zTCPAvNvGGxzc+bgdeNhFglmkm5339fQ5BhBwSFqeHgxR7OIZJeSwLXuVpA8SrJXQfYpyT4V3nqVFK+a6tVQXCoy/lAvJduAQZgio5qlFKOYpBcTVQKijMPWzLGqzay0ZPVKjkXLser4DqPYY5b4rNKAQx5xqwr82uKwqSxhqkra6tKOphJHa7m9rdzRVe3qqbZ14GseXT01/v4GZ2elpTlrbcpY64rt9WlbVcpaXqhPR9SFfnnULQ3ahV6LwGkU2g36oHsOY2wLp6ZyQ/Q4UcwWc37vbMQRCr6BCplOwygkQBSEiM0h5f6MQqEQuPnMuVYQEIPJmGEkiL1mDCMwjLe4/YG5Z2K9GWUqyxAeLVesgQmLmXilBjknt2KXR9QJaBaZyK+zF7sK66I1naUDk12rtizfd3T33Q/eefqRo0dO7Vi1Y6xjYXW41S9NqckhOeaVYh4xgg+ZSBCnCHWIUbsEdSgofiMvGVBXF9s6qr3DTb6xetdIubE7Lqqy0QoUxIAIdYMYhY+Pr7jE+GC7R0rwyTC/FPVJQEsOK9gFOlmJ09NeVLmofXTH5PjO+Qv3Ty86tLx9/UCgt0iQ1JHcIqJVgFpEiEGA6viIRgCcp8PchS9+RPARWiHtmV/++OTH7wydf0C/Yh61LU1vijC7EvzBjHCiTDhVKVxSxZ2uZM4vpY2kKH1xcleM1BnFDTaAOyKk9pzbQsS2IKk1SG4OkJv9pGYfrc3HavPRW32k1gClPUTtCNM6I9SuKLUrTutJUPqS1MEi+lCaNZLhjpbwx0v5E+WieZWSeVXS+bWyhXXKRQ2aRY2qRQ2KRQ2qRY2aRQ3qRfXaqXrdonr94gbddJN+ukm7uEk/1aifX6ebqDWM1ZrGas1jdZbxeut4vX203jGSt2u4wTvSnJ0eHt6+ZvXBXZsO791wcO/6A3sX79zSt2ZF3cKJ0tGBZE9b+8jQ7eMKuM0l0kXZ2srGno76ns5MW5O3qlSVDAsjTo7fxPBqaR4NFdwuXUqSS0F2yMAdkwTusHr+rv23kStPPvkkwibhgytiOiqio8KcZ2IXHhXhUiii2973JbCpURUH03Exo4BgFRKcYoJLQnCD3xsI+Wdmv8gxv5zglxG8Mnw/CPntYswqwqxi1CLEzELMJMT0ePk18NtG5WxUzHzo7Jn8B9we7di7m6TMjdDq5TSjimHRMG06lkPPchtB0MkJ2vlxD7/QJywKSDIRcUlUUpGQVBcqalOiygJpdVJVW6SuK1ZVJWVlcWk2Ki4OSYtCkmRAlvCLwi5hwM5zmzkOA9uqY5s1bKNmyaqV+Q++aQEI4UV6bhQsGBycddT7ySefkOi0mZWPAC0kGnWm74sj4OXPmKX+6Z/+aebe89JLL+V3zVIHDx7EAx1AlFy4c4MuuHNogc+ePZs/9fbIEfLBXCrMy6WBEOVWI8nw1UiomkcyCDhOuSFmiJU4Cksc4ZTZGTXYomZHwuHNeEOl3nilr7wp3NKbaBqI+GosggIADwnRK0ZcQjzssPNQGx+1CQh2AWYTYw4F0atjRd2K8iJrZ41/tNkzUg1CFlWTV1hhYhQqyGEZMSAl+gFOJIAoxICMEJARQ3JmTCPLWr1NocqB7MB0y7pdU4dPbn3w/IEzjxx88OKdpy4cPnj/1sENXZYqK8snJtkFBBM+So9qeaiaO/OvcwvCx+ohiIlBAsrFn3x//QtPu3atIo2UE9uDhJYgsSlAaY2wupOc4QxvfjlvURV7uoKxIEsZTxKHYmhfBO4JId1BpCuIdPiRNg/S6kZaPHCTCxhtciENTqTBgTQ60ZyRJgcC9je70RYPOA1p9aKtfrQ9hHVGiD1xUk+M0ldA6UvQBwpZQynOcJo7kuGNZgXjpcLxcsFYqWCkRDCcFY6U8IezYAPfHsoIhzOi4YxgKC3sT4t6i8SdhdKOpLQtKQduT6nai9RtKVVbkaa1SNeS0rekLR1l6fm9Q5uWL9+9Zc3eHav3bF+xc8u8TWs7ly+unBzJDnUX9bSl2hs7hgduXz/Y4TvvdAa8RRWlFW2NhQ3VlnRCFHRyPEamU0uzq6k2JdmmJNkUJJscz01ilhBN4OlbQFRzp5bOvRL4/1PJTBHKomDCXMV4YLAhZGACBsbHk042dc46G+6sdOXxx/BC9HoB1SymO2QMr4oeUDNCWkZYw4homFEtI6pjxnSMnOkxHS2soQbV1ICK5lPSvEqqW0F1yakOEK9IKWYJ2SAi64QkJa++oyX/AbdBi1csJSoEJLWIpBWTjTKyWUmxqqg2Nc2lY3iNjICZFXUw4g5GwsFIupjFHnraRy8JsMpD7LIgszTALA2yyoLc8jAnG2SnA8ykl1HgYETtzIiNGbTQ3XqyRUXQyxvDel4AAP/0SURBVDCNBFOKCAohTSObwzW5bds2PBXCDajMYTntuXPnvg0VIo2KJ7AhYvHCOc6teuyxx8CNJxqN5l/PXjKZDO9lyfsGWm44fAv1XW5SfLV8BiqQ4MbccXxBEgfckalmocSvdKWMkZTeGZJLLFymjs3QcakGDs3AoepZDANHYuUHi3S1rZ50q0ud0tAAVDxifATeAYgiwG0BRBFhYAOEETYhwS4lujT0mEtaXmjrqonMa/VN1FoGkvrOiLoloGzyy2o94iqXsNIlKrMKSsyyCou13pnqCneMpwdGizp74k3tBY092Y6Ryu6xmu7huomFnVu2Ldy0c6xnqtxTbZHGlSyvjO5SUBxyok06Q4ZbUH5YBYG4xHOffzT+6FnF6kF0tBDuD6LdPrTNizV7Cc1+YnuE0lvIHM1w5pexp8qYC7LUeUXk8SR5LEEaipGHouT+CLk/RO4LkXoCpB4vsdtL7PISO3Jtl4fU7SZ24QaH8KO4A6TeIKkvRByMkobilKEC6kiCPAqcBMSiTaSZE1n2/FLwcfypCv7Cct78Ut68LGcizZ1Ic8ZmXMwdK+IAjxaxh5LcgSSvt5DXVcBvi/Obo/ymqLApLmqMixtj0qYCWWOBoiGhakyY2rJF4539qxdPbVm7ZNuGpds2Lt6yYd76VcOrlvYtXdi5aLJ9cqR5pK++t7O1t+uLL77IX0TfqUCwYnc5rR5XMJXwZouUET/LaaBZVRSzgmSUEQ1SgkGCGsSIXoTohYiWj2p4iJqDyFmIhD5vav4cppPejECwApMJeHJiLhXl56r8AnNpKIeKsKkXL816dHdW6hkdJGuEVIOYaZVz3FpuQM8J6NnAIT07bJgxJ2rkREBrYEeNrIiRFTawgnpWQMfyaZkeDdOlZjpUDJuCYVXQTVKaQULViNi6uRRv/3/qT3/6U1NXG00loutkDKOCZVFxbBqe28DzGgVBiyTmUBX5tSVhU1WhpjKuqUno6ov0zcXalmJde9rYWaLvyOraM5r2tLKlSNdaDJ51zC0ZfX1SUxnTVsTMFQXm0qgq4eV4DES9FB+il7IhEWt0wbz8x9+0wKUilknwhZw3oDKHS3rJ0qVkAJXcKD2VSSfSKHg1BCK2cGqOK3OXLVsGbjwnTpzIv56l8FKkQH+FyreNQ+X8+fP5U2+bZsqVQjw6DhURHRYzYCkTlnMwrZBpV6piRn+JLZoxmP1Clp5BUtOJGiZBwyJqQUsn6ehcA8sRkVU0OdPNHkPWQAlI8QlddjxAAUbMfAAVFATrIBC3icAGYhHAZj5iFyNuDaPQb2yvLVs9NXB4c/cdK+q2jNRuHKjfNNS8daxl+wTYrlzX1bi+u3VVQ9NkqmMgkq3S6YMCsY3DNuPmmDh8I1tm5LlD2pa2+Mj80pb5JfXTDa1rBzs2THZumt+4ehj/670lgX+JXNlHiI2d+fS9zgePcRY1I6MxeCQMjfjhfj/c7UXafHCLD23xE9rD5N4EdTBFGymmgLv/SJIylKAOJ6j9MWpflAbcG6H1hKm9QUpPgNLtJ3d5KV1+YGo3bkp3gNYbBKb2Bmj9YcZgjDFUQBuKU4cK6COF9LEUbSxFHSuijadp8zK0ySx9fpYxv4QzVc5aUMaZqmAvKOeAjfklPODJLHcyw5+X4U1kOeNpzkgxdzDF7U/yepK8jgIB4EpTlF8fEtZHRPURSX1EWh+V1cWU9XFzayY91tWzfGpi3Yr561dNrl89tmbF0IrpgaVTwH2LF/RMzesYH24e7KvpbKtorHt79qnFb0a1DfVynUZlMcldNp7bzLBpqBYF2SwjmiQEIyCKCM/SYRAiegGi4yE6LqzhwCoWLGfAElplY83t6In6/e9/j69wJGMQjYCnLubkCv0Cs8h0Mfe2Dk6AN2fppJicQ1DxcimKJRSLlGSREkwSogVYSrRKSTYZMBm0dinJDloZ2S7H58hZZBSzjGyUkPRikk5M1opIGhFJLSQp+EQZlyjhxDKp7xbDn332mTsaokoEDLWUrVdyjGqORcuzG4Rus9hvk0c9mmTImC2wVKRcDaX2hhJ7S7mrvcrdVesdaAwMNYfG2oKjrb7hZld/vQ0fVqn1ddcGumo8rRXOphJXfTbYWBauK/GWJzUxD9OiQhVcSMzEJKw5DGCAuARPNnMDKmKpOH9gNqquraXcmPoFoEKgUmYGVOacQwjEKAKBIP9i9rJYLPl71zf6Flfi8Xj+vNumX/7ylwheAJsG8+mwkAHjUKFDUgYi54AnQl7AaiuLx1uKi9sK4tVOS1Qucwv4dg7HymFb2Tw7T+oWGKPSULkp3RwIN4fVpTZaSIU4hYgNhCm56fJmLmrmoWYBZhECgw2AGdjCR6yAKyIEBBMhu6Kxpnrz+nXnTt393OWzbzx74Z0Xr3z/9ee//Oj5Lz+89MnLx54/s+n0xrENDc09HndUwDDSCToG4BkB4E1FI8kpVDmVq6b7I4rewfTE2r61x3effuXxCx+8dP7950699nj+b/WWhBfpgiAmsv+N5wv2rCGPpOEBHzTog0YC0Kh/Bi1Qtwdq80AtbqjVA7f54fYACtzmx0Ao0+LBmj1YkwtrchKAG51ogx2ptyF1FqTOiuK25F1vxRrshEYHOI3U4ia1eojtPkK7F7wJod2PdQYIXRFid4TUEyX1xkn9CfJAITHXAlP7C6mDyW9MGyikDySAGf1Jem8hoytO74wx26LMphCrPsiqCXKq/ZwKH6fMyy318cv8wtKAuNQvLQ9ZG9PlY929S+ePrlg8vHy6e/HCunkjZcN9pQM9pQPdZQPd1YN9jcP9TQM9tZ1t2YbaULrodswzXrFqpVAm5SvlLJWMoZXTzUq6GU/eTjbJSCYp0YTfTwkmMW6jiGgQEPR8TMvFVBxMwUalDE88eDsewMVKGYAKTCHAIF5hU/ChSC4V/H46+7+zFM7/qy5evoTK2KgS7z3AdHyiUUQwCVGjADEIENAahYh5xgLEBMxHTLzcRs7gHAMf0YJgjosocaMKDorPvmUjIiZe9J5PH52ce2qp/7/++Z//GWHRMD6TIOaS5UKyUkxWS8haGdWopFs0eAVZn4UbtHOjLmEyICoKijIhUUlUUplQ1BZpGjPaphJgdUNGXpOUVBQAyysKleWFimxMno7IUxFNUdSQiqhjPr7LRNHLwPMviFTaBubyT+D2eWbSgM5ApbFpLvUdRArZN1CZmU88A5V33303f8ZsBP72wC1n+fLl+dez1KOPPjpz0/rfBUOXZp8SbbZ6//33wcMWBLjCp8ECBiLKZTKV5hJsa4RMt9lcmcmO9nSumDe4Zl7Xkp7W+U0NE1WVQ+mi9misMRBvDifbE8meTLi9WF8b4RSYSD5ZLkzhIRY+jF/bfMSIGzUJcH8DFRCs2ASwXQC7pJhPz06FLX3tVZtXz7vnzs2Xzh967tnzH37w3Fc/fearz+9755VNF09M37V0cl1zabNDGeAyTTSKjkbWUclaKlVNYetoWqcgVWJpGyrrXD6y/OSxSx+9f/3nX7z59Y9f+uL7+b/MWxKamwPGgOru2iFa0AR3BeBuJ9znxrky6oPHcLTAOFq8cK8H7nbj7vLAXW640w23O+A2O9xmg1qtUJMZajDBtUa4zgDXm3CDDeBaA1Snh+pAC/Yb4QYL1GCGm8xIkwVptgLDzTa4xQ63OuFWF9LmRTp9WFeA0B3EusJod4jQE8V6woSeCNoTRnrCcHcI7gjC7UGoLQC3+qEWH9TkhevcSLUTq3ISKlzEMgexxEFK52rUFNpoCQs9YWUkrKyEnZN06isLMn0t7fNHeqcmWueNpns7XXUV+tKULltoKC00lqb8dRXZjqaa7raq9uZsfU0wU6Txuo7fM8dQ/f+kkydPckVCjlhIE/HJIi5FLqBoxWSdhKQVE3W51Qk6EWjxvO54anc+AdxtVVxAFEzGRCUMVESXGNW3Uv/4f1WyuAgmEwBUECoRoZNRNg3j0TEu/Xb3fXWPDuADKjo+ySAgmYV4AiKrmGwDlpDtwFKyU4bbJSU5JXiGIgdo8aKQ+EQGuwSfyGAS4sNOegFe8xFYKyCpeEQ5iFRYBBFTZTf89re/zX/YdyGVxYhw6ZiITZTxSMqZYRUJ2SinWtV0l57uNdKDFnrExihwMgpdjJSbmfbQM15mqZ9VFmaWh9kVEVZZiJH109MeRpGHnnRTCxz0qJ0etlMDZprXRHcbqDYtyaQgGWSAK0yj4t33Zn0Hx/OyfCtMAZ5DmgDw7EKgUWamfs1AJTf1CyMzaHOL/65cuQJuOT/84Q/zr2cpfNrY/1mFhYX5826nnrn6DMQiQ4ArXCokoMIiGjAiyXFFwUZ0YrrbrMom40PdPVvWrj5+aN8j99352P0HL57YcN/ukV1LGpYPFE80+3vLVTVBZkJP8Erwhb0gTLFyUUAUAxcz8TEzwAkfzaEFNgDS4LCBzTzYxocdAsQhQlwSzKMg+nWUkIUWdzOLwrzKtKq72TNvMrZsunDVksrNqyZP7Nty9uC6u1dNbO6vHystagsFKsyerN6X1kdKLOlaf1VXKt1dGuoEsXJX6fSymnWbqtauL811Tt6ybvSAcfszWKsXarNDHRaoxwb1u6BBNzzkxYOV0QAC6DKGYwb3iA8Gh4AHXHA/IJAD7rXD3Vaowwy3muAOE9RhgDtNSIcRGAbb7QbQ4ttdJtydJrjHAvVawafAPXa4B/xxB9QL3ge8G3hPDzoSwEZCyEgYGg7BI2F4KIQO4y00GIQHAlCfH3evF4+fulxQmwNqtkONVqjWAlVZoAozWmrCMiZCsZGQNJASBlLcQIkbqDEDPW6SZPzRtuq6oZ6G4f50d7u1plRSFBQWukUJF+4ClzIVcFUUFzZUZRtr07WVgXRK5XMxdYo1G9blr6nvQocPH2bxuAwel8xlEzhMAp+JidmYgocq+YgKz/gEqbiQEpiDpyOUsyEZE5YwIBBli75Jm0imSzjgxpF/x+9C/lAQr8eFF+bCK3TBVCLMoDAk/Dn3fV15/DGz323ye0x+t9HvMvqdf3XAZQy6jCE3MF0npmhFFL2YapJSzRKqRUq1SqhWKcUmpdplNIeM5pTTXIobntlW0l0qhlNJtytoVhleftgkoQEbcFPBW2mEFKUgkIrfd+rkdz4K1dXfS+QzyVIeTS1i5G76LIuK69QL/RZZzCUv9CiKA+qyiLY6rqlNaBqSqsZCVXNK2ZyUNSblTaBNKBoLZQ0JRV2BvDoqLgsJMz5hkVeYdIsL3cKYnR+yCIMWWdCmDNhVPlv38ED+g2ej2vrab4cpwG+99Vb+2E0LMIBAo357PjFKIcFEzO3z5s+YpRYvXlxTU5N/MUu99NJL+VvW/0GPPfZY/tTbqdP33w+xyXgNKl4ue6kQ5wr+25TS8Q5qFRvR8FCLlOwzCbJJz0Bv44ZVQwd3DB7Y2rp9eWphn7WjXFoZYafsxJAadYlmiIJYubCJAxs5iAmgRUCwiIlWEWYSgigcNvBwG8EJPNiCcwXEK6hdiDnFBLec6JWTvFKiW0bwygkBFSGoIYQNpJidlYkZOxszqxaNHtm97fyJY0/ef+TRo5vv3TK+daJhYWNhd8pZGzBWBOUZHytspjo1RKuWZNWTLBqiWZX/27xVYbnh+gYT1KiHmo1IuxnuzHGlz4nf5Qc9EI4WDzQG7IVGwTZoPdCIGxp2QUNOYHjIAQ+A861Qnw0esEA5w/0muNcM9RqB4V4T/rLfAo7CA1YIeMgGD9vhEQcwNOLM2QXPfMqEH5oIQOM5T4agiSDuGaSN+XOdcj5oyA0NuCFAo2471GbBQ6UGM1RngqrNUIUBKjXAGQNapMOSOjxJZ0xLimrJMT0v6XDXpTMdjZmOJk9dmaw4xEs4uHErL27lgzZq5Ucs0rjLlimIVmQLKkrcqQKZ184wKikqYddA33d1e9q0aRODw6Zz2BQ2i8Cig5gA4dERMRORs2EVB1Kx8fQewEo2bgUzl92diudMFFNwi8iQgATxSBiXPOcFaP9/qfWamQoiuAFayBhEIfQO9ucPz149Q4NUiYAqFVAkfLKER5ZwScBSYB5ZxicrBHjtS5WQrBLiD/tqIVEjJGj4mJZH0IKWj+p4uPV83AYBqheAFgM28vEWWM/HNHxUxcdUPEyJm6DgEeRcTM4tqi6bbWn6m9dddx8lcpkgxKTKRQyNjGVQssxqrsMgCtgVca8qFdBmoobyQktdxtyYtbaW2zoqnN3V1s4qS0eFub3c1F5mbC3VN2VMzRlLU9ZQk9RVJDQlUXU6pC4KKhM+RdSjjnq0Ea8+5NH5XS+8+GL+g29an332WZ4oN6BCZ9LncPVu2ryZSKfOTP2isRgkOj71C0BlcGgof8Ys5ff7L1++nH8xS6XT6W/GTvICGzOTiWGouLg4f95t1u69eyAWJZ+OeibVmxBAhQpL6IiCgSgZeAUgHRu/Si1igktBDpopURulwEaJmUlBHeZV4om8XELUDqITHp52yMxFAE6MHMgAWhCsCPDJ8Xiyaj7etYubhxoBWrh4sGIBBOIDqODLWVwy1C1DXVLUhacFQ90SDE8rKUX9MiyoIIA7XqGdW1VoH+qq3LCiY++G+s3T8fkdmsYEr9hOCalIbiXRKQcAwxepaAWYTkjQCwi6W1xR/40QvAcMrtYgtWq4UQc3G+E2E9SZiyR6HTBAy0AOLcMAJDmijHjhYS9+Wx90wUMueNAJDQKoOKB+EN/Y4UEHNGiHB+xwnwXutcDdIC6ZsRnsgQBmQNtvhftt8KANBmgZssPDDvA+EHgrABXw/mM+GCAE8ANvQWCUAwluHzSMx0nQkAcaAHZDPQ6ow4b3vNWboVojVG2AKo1QmQHK6qFiHZTSwoVaJKZBw2pCUEUMqGlhozLlt5UlgeVFIU7UxoqYWBEjO2Jih42coIHtN3B9BnnYYSkM25NRXcTHdxhpBhlZIyQpeNWNdd/JIPnGTZvoLBaNxSQzGQQmDWFSEDYV54qEicwkjAJcUc+YBamYuaohNDx3LzCeORFwBTwikSE+CeNTDh46mH/fW1MeKvmcxLlBezI252AIxDcMmQjlMVE+ExUwESETFbLwFcjAYhYmYWNSNibjAAbgKS7woo1cRM6C5ExYhVflgg0sKG823uq/eZkzeKljQhompGJAylz2e2CAXhmdbZJevHx7++s+/PBDmEFGODTw/4WJOfj3VwsJOjHJpKDYNVSXnuY1MkIWZszOTDiZRR5OxsctDbKzfs6MweNhsYeZ8rBTbk6hhxV3sqMOZtDC9JmYbgPTqWPaNUyblmXRsozq0sba/KfORgunFn4ToMw4mZpLEejG1mZ8PnGu7wug5RuozKEnDejXv/610TjHhGzvv/8+gUSi4Mm+aGAjt5AeJYMgis1mcdg0Jv2ZZ57Jn3qbtWTFMohDxc2n4c6VOYDFNETCQOTg98tAcpXlwPUJG9iokYta+ZhNgNmEGCCBHfAAN4g2ABswQAgQfJi4udJzHFSff4TCDEIUtwAfXAEsAUTJGTLzIGsuWHEIYYcIcUoQgBOnCLdLhADnkuGjXgnmk+TQIieEFISIhhg3ExMWYtxIDKsxvwTziDGHAERImIlLwD+Uh2m4wKiWB8KsPBRuVQD4BAiuUsO1aqRRCzfpkDYjCoKVLhvOlT4H3O9Cch1TeMgy7IXBbX3EB4/64BEvMuzDPeJFRzwIYMygE7TYoBvpcyIDdpwfPcYZA65AMwaBS58JAVFLXw4tgEMDzhyKnPAwiH5wruBQwSOVIDweQsYj6EQMm4gRxuOE8Sg6GsRGguhQEOn3Id1uqN0BNVnwGKXGCFXqoVIdlNFAxRoopYISKiiqgsJKOKBAfArULSe4FVSPluk3MQMmht9A8+voPh3Dp2MGDCxgn57t0bNdOo5LL3CbRB4L32FgmpRUvYScm1BEkHIKMkW3PhWqpa0V/FaBCXQqSiMjNBLMxCcp4vNJpCxIMROpgBZEKuAliFQY4HYJSXMJ3vFghZqHCohX+CSITbx1rgBY3ohRZowThS8TzTk4w3sJcv9TRCGLrhBwtBK2TsLUihhqPlXJIclZRBkTlTIQKROWMhGwIQOmI0o6omUgJhZi4SA2NmRlQdaZNudv9lhYkIkJGWZyFefKrqjxHPjxqqIvv/wy/w1up/Dqy6xcB4gIkAyAjQVpwJMmDzLxIasQcoghjyTcUvr111/jV2CBGkpoc9bgbYE2d1mq4KAS9ithX66qilOK55o0iyA8SzEfL6ai5EJy9pXHZ92l8/vf/57NYecf4W9AZfXq1fnDs5HBavk2VPD5xCQCgMrLL7+cP2M2On/+/Pbtc0yQWt/YSJpBCJsFEIISCQiBQGUw2Vwul8errp1jl9oc1D3YD/MYMJ8BC5j4ykcxE5aAK5mFytmIgokqWIgKXAwsSMuceRKCjSzYzIZBOJKbwTUzjwuABDHzCCYeYgRE4cI6HjCi4+cMonARZhLhiZrwGStCvLUIUZsItQEs4R1fmEsKcJJLJQlYIsa38RbnCjzDFQCVnBFgrxjfBizxgWgml48SX2KZS9hq5BEMeJcAouHm/R0sfvxGGARXqOFqNVSnhpq0UKseagfBigXqBlyxQ/jAiQsZ8iL44EoQm4gQJmOk+QXEeTEK8EScMh6jTsRoEzHyeIg0GiQPB0mDfuIQwIwTZ0YfIFMuRpnxTOByI2SBAHsGHOBMEKkAouCsGg8QJqPkhQnaVDFzUQl3SZlgSYVguoI/Vc5dkOVMFrHGkoyRAvpgjNofpnT5SS1uQqMdrbUiFSaoRJcjCvj1KqGYCorIoaAc8sphtxRxSBCbJFpXUtRanW6rzbTXAWc76ks7G8u6msq7mis6m8rbG8vaGkpb6kub60qbaksaa7IN1Zn6KmPITVbwiVIuQcRqar/VlYAGqxmhkHIl0IkwhcgWC5KZ4mRpJlmWSVWUJKtLk7XlybqyZC3YKE3WlCarwc5ssiqDuzLnCuA0WcaCeCSIS8K4lDkkCvy23nrrrb92fM2YjA2OzLGXA6i2uZHAo3FUYq3LZA+6XBGPPeQw+806t1Zhk4tNIp6ez9CyKSompqDDMiospyEKGqJiIDomZuagNg5iZ0N5s/K23TBAizkHFR3gCg4VTMdav23jdz588n+SNxqA2CSIR8YBDyIkEFDq2ZCZC9l4kJNvLg99/P2PwWn3PnQKKpRDRQqoWAkVK6C0EndKie+MS6GIFApJoIAY8okgF0CRADyHQiYuZOBAWvBIwXQXzWUd3+nTp9XfSJOzVj2HiBPACaGSyAwaIAowFUTVNApewJ+IzS1eX7ly1hkBZvSTn/wEIWAAJBiZTKLSCGQyjGHAGIlIptGoDMbcIDc3ldRUogIWImSjYhBtc1EZF5VzQZyNqXJTEFVsGO9mAFABwTQ7H2EbObCZhy9AAVQw43Ma8WESPDrhQlo8iTWsFyAAJEYJZpQCE0xyzCxDzVLUIsUsMsyGG7VLUYeUAJ6MPQqCT43XDMXRIgV3NrzIm+uGPRLEI0Xx0l6yGaLAeGJ8IZ4e3y1EXAI8B6WNi1gB1bgoIIoeJxlenUHNQ3N14fJE+A6EQnC5BqnS5IIVDdykhXGuGKEuK9wLYg4fiAzI4zHKZIoxleYuKRcsreAvqeRNlQkWZHiTRZyJJHcixZkoZI3GWWNxxnCUPhiiDwcoQz7ykI8w6MEGXegQeB/AGPCGgFVmuCfXOdZnmQlWkEEHDAKdES/ACXtJVra2zrytx71rzLNz3LppQLeyTbG4Vjy/nD+eYQ+nWH1xZm+U0R2mtfupTV5KvYtS56RUO0gVVkKJCUnroKQaKlBCEQUUlEE+KeSW4A+PNhE/aJrbfaekvpok4xElHAK4kgTMbTvmnpEe/FbxXyYZN744mUJcuGhR/thsBB7J8doh4L4GzCHRJZw552ICOnLkyMwoPfhu4FuhFBJKJc257wvcdBgSLlchVFu0joAzGPOH4n5vxOXwGy0ejc4u1VpFOrtY7RCLrHyGgUPUMGG8Fwsvo4JHKkYWikcqMwhh5ymCRyo3cALCFONfiQLClNvd5fU/hFejYRLxwi1CCl4iTMXE7yBmjihmevHlv2Yf0dQEoKQCSuWgklbhRAEbSSWUUEBxGRTOQcUvhrwzUAFRTg4qeg7es6dkPPTw7U0z83/X888/D66Bv12kQoZJmN48xy6s/fv357dmqd6+PjxlJIZC6Ixz1cZQBJCGQCJWzzUD/9wqTtqCXoTHQIQsRMRBJBxUxsPwFMUCTCVANQIMHxQEt2kQeQBa4P+UMHhEMIAWBCWAKGI0VzIHr4ag58FaAaITApygRgnBLCeaFUSLimhRU2xaskNLtKmJdjXFraN6DVS/keI3kP16kl9PDuhJQQMxaCAFdKSAhuBXol4F4pGhM/YqUL8CCyixgALxykDUghfyAhGMG0BFBNsFiA2ESjhUUAsfxECIIVe/B+BEDaCYi4+/M8EQXKqBK9VQjQau18ANWrgFQMWCdDsIw37qRJw7VaxZ2+DZNeLZM+HYMqhe1SZaWM0dK2GPFDH7C+g9EXpXiNYVoHX4GJ1eWpeP0uWh9fhYA0HWUJgxHKGPhqgjPuKwDxl0QX12qMcMA650W+AewBgQx9hx3gx5KPNj8rX1RceXLbh6Yu0LZwbPHSo+tNq1cVyzuF0yWsnrK2Z1xZmtIVpDgFbvpdQ4SFU2YqmFlDUBk7NmatZMyZhIxQYsqUXjKjgsh/wyyCOBnIAoQsgsGJyay5KFX//612QpjyBiE4QsQBRgoU6ZPzZ7gTt1Dir4jRs8+hFplLk9Z+07sD9Xjz1fPRc8OIM7Xf7Y7DVv/iRgCUYhg+9DplPJdJpMqbiVvi+6iCVWizVmtd6qUxuVUo1YrOaJ1Vy+islV0MQqutrANjqEOo9Y6hQyzBxMy4CVNEhJhbU0xMhALCzkm9DECrjChiy5Xi+cKAycKPpc31eu1wvEKPkP/nvp8ccf//+IewvgSLb0zjczi5mZmZmZSypQiZmZWa1udatR6lYzM9/u232Z75CH0WMe08wY5s362W+93nXYi+EIrzfinSzVvTMLz8+t6Xv9xT9OpDJTpZKUdX7nf+A7aHpv8GfnkSARBZJR8XrO2++9U7tcjX/6p3+CIiIoLoYSEigprSkuhWJSKCKGgiLIL4C8fMjNg5w8yMaBzGyAJdTxqOmAUtqQ7XMzXv/HuHT5EoZMJNFpNCYDiESnYlGo4No62mt3fC7xH//jf6x15e3ql5OyoGeQPcxq242f/OQntaMXCYZMAKNr6emQgAGggpVysXI+VsHHVXc9AETBavgYNZoLAwbY0KElOhKuFwILguhEsE4AaYWQRogAX2IQYw0SjEGCNUoJFiXZpiZZlTiDDG+QE8xKklVNcmiIVajQ/OgQHS1ioYaBzOSQiRAwEAJaUkhHCuuJQS3Op8R65Di/GuNTYXzKWukBsBEDwYAr1f0iYSsfNnOBECMgnABrQN8PouTDSh6s4EFyLiR/iU4FRFENldVQRQW1qCFgVjq1gCikiQBrKW063tn+yubhbz1Z+8qT1sdnI6dX9Pv6hBNFZl+C3hWktHrITS5ig43YYCE2mEkNFlKjhdBkJrZYKV122oCHMR5iTIXp0wHqtJ845cUArqAD+ChXgGVB5xYPW5BxO3EhpNnuHHz3wuOffvfdP/mtV//410//4Mu99y+59o9JRsuszhit1U9tdFEbbJSChVJnIuV0xIQGH1XgIqjwUSUpriYntKSkFpzHRpRIQAZ5RJBDAFn5kIkHGXhf/frXao/Gi8S9B/fR7RM4NMyu2EDUPSdx2Tm9gyHiQaMPVNxkGlWj09YuvGDE6tIwh/wJVIgAKsn6vU+AicSiRAqZREPfEplKJVEpM3MvnBfk02jpbKML6QI5VyjnsIV0MpuEZ+KxTCyOhcUCsbEELpYlJSpNTJNXqAvK+W4J2QQaenRISYa1VMREx1irUNnlipmOylQVgAogyu5oCiCKnFLuaa791M8x0KyIVBy6UWa1B+z2/Tu1C78UG6ePQmERypVYVdHdUowSJSREieLhQW4uShQ7F7KwIRNo0lb74pXo7IN7j+7XXuhfKfoG+zEU0i5UqJ9ChYgDD3DtjheJv/mbv6kdvWAsLCygM4ng/7P2PEG5r69vb514CIeCppLk0WA+HQZcEbMxMh4gCk4twKuFWK0AzWqqFWBA81/LRYfHqgKOBKMVIGo+pOZCqqrQ/T13xUc0QpxehjfKCUYJTidEmQReRy9CeWOS421KkltD8ukpIRM1ikKFFDTifVq8T4P3qXFeFdajwnjkACEAJIgXFRYQxatAPMCpiKuqdovtWhYbv4oWAcYAbJMI0vBhRZUlVb3U7i8IQsp6qKyBGjRQixZu12F7LaQxP3cx7Ts9sv3d5+/82fff+skP7v7Od0ef33EfmpGOlFidUVqzh1qx0Ros9AYzrWSkFvTkei0pB6Qh5jSEOi25bKC12Og9HvqwnzUZpE/5SRNeZMT+C6igC1YsyKiNMBsQHax0v3X+wu/82is//c23fv6jt376Oye/9Hb25Lp8vEE8mFOO5LVDaVVvVN7hkTbZxQ1mfr2BnlaRIjK8H81ci/fIiD45OaAghZSksAoXVCDApjgBUXiQiQPpOVyPdo99X5Uilk3BMsno3N9PtOdWZCyZIIDPKiAKlQpq8JXVvaSJ/Mu//EsMl4I+32wyOrvxV4MKasUo6LwaEsAJrSoqZc/91ODVqHwmjU/jSZg8EZXGwePoGASIgQpmYmA2BsvFMGQElYPvSZtcdQ55zEixiRB0z2AqbKYhdgbiZEH26mgKgMouTlBVnQra8UVFO74UVGvC81Lm4+0hEBQquO2d/89tpkQ5CwqPXa6AA/S4qpDgFx7FxYUcHMhWJYqh2gVfndImdKg+09Q4/5Jwej2fQgWIwqATaBQshfjRRx/V7vhcompH/leWfKo/+qM/qt33ggGMTu3oReJv//ZvyVIOVkivJmhBhYhZOKWAqJWSjTJSVXi9GEAFQWnBgYBUVQHjouagZ5RsdBUaWrKqAsfgJBdRo8hB/Y2KjZJGA1wODzGIMEYp1ionutREj4bo0xH8eoATAtr3pcV5UaKgXPGgm7wBp4IFRPHIYZcEdqOCgEdxiFCPgpoVCTr6gg63yHA+OcYpRYxC4J9grQAFCboqrirwfl5iIAU9VNJCDVq4RYftMhH77fSpqPVo99IXbj/54+988f/+w/f+7A+OfeG92LE1cU+R3RGSDyZVoyndaMIwHNH1+TVdTmWHTdJm4TcaWAUNNasgphSktIpaMFArVnqngzHkpY37cCNOqN8E9VcXQg7tEsWOm3TTlpLBS7MzHz1Y/fIbR7/50fY3vjjz7BGgl3ysXNqZ2Hzr8r5XtvtPjbesVUrTyfSQP9Jld7cYjGWdLKNkhcUEN7rRJrogyCEiuqRErwzrAUTZ7adm7w5+ji3N1B6NFwlQfdN5DCKbggNQoZPQaVp0ElchqV1+wUCrbxq6yzewKaAGB1zZW929c/Y0hoXuo4WwyTCAChNAhTg2PVG7/ILx6PFj9C2holRFVv4K+7zeu38PSyeROFSWiM4R05gCEpmDJ7CxODYGx0FwPCxZTOBqmRqPNFj0pruyvpaENGbGWQSQgQ5b6Rg3E+tlIx425GRC9l+yKQAnqHZtChVSUrAq2t56MHajq/tXmnBx4+aN2tH/KX7y059Afj4UFEABPhTYLasH4KSPB3m5qEcBRLFXiWJmQsbqiC7a8UWDZJRL1/c4/PCyAiANSyHsdn8Bm7JrVgBXyAwaeIZrN332sbm5WYNKTbtdXjW1tu5x47j5+Xkul1v74kUCMExgVPHNKirwFlImLKIhIgZGxiVqxDSTkm7V0O1aqlVJNElxehFOL8Ro+ei20EoOKIF3QZmh5EKgEkdBgp4HlfjuIDkWTfQHPA0H9SioQZHgLXKiXUlxaygeLdWrpwaN9KiVFrGSg0Zy0ESLWmhxKzlkIPirdPEocX4AGCWgC+KSAngAtEBOdOi+utmwGEGhIkF545KAGzAOCWwUIjrUOf0yVF6yU4HzGqiggStoMhVsp4k04ODOJsv3Ds5/+HDz196+9IOvz7/22Dw7Rm+OigeCfTdWLn39laXHp3pPjncdaOpcyVcmwtlBV3zA5etzmLuskmYjPa8gJMSElIKc1ZGLelKHhdhvw/SZoV493IcK6jdWB1Ts2HE3ayntvzCbvX4oe+lw6fJW+Mgyp7eO2xPJbw1e+fKdd3/47s23ri+eGO0ajxc7HMmyNlwvD+SlnoLKXdTocyp2SIR3cNF5qAY2uoO/hQfbeJC1ShTQBtSDNiBrb31fl69cJjOIFBaJxCLj6ESYSoAo+HLzHk3301ef4khEHImEJ5NBPa7S7rHudga9CIOEZuj6JO0j0LUb12uXXzA6urqqb4lEIJPAAZZIWF7ZYw5aENn6PEzBYxlEIodCFVAZIgpbSuHKyVwlhasmC3R0pVPiSNpC5VC4OeFuSsozHoJbAVnZsIuFCbAxIS4S5EBeNuRmQQ5gVqpQ+ZQrBhqkBTaFAikofVN7TEr2pS99SSKXQFgYNDxrp152TB6Yh3xcyM9DWQJKIMCSXZx4AFE4kIsDOdiQjQVZWKgD09PReQcqGrBfTKPoX8t+fRrobEA0ERweSyER6FQinbYriWLvo4l7CDwB/7+A5Jf1s5/9rHbfi8R/+2//DUGQYrFY+/pF4uvf+IbKZfVkYr58whB1sS0yvLKabg7d9pFH0EvoNg3TaaC79RSbmmiRo/lhNcB/8DBaIVYrAnYEUvIgBcoVBJ0thg5mYNR8LHoVTUyO1YvxwO5YVRSniuxUE51qsltD9oBSSfIoCB4F0aciBTSkgB4Ahp/1sVMuesJOQ+UgxyyEsBEf1GN8asSrxAC6eOQIOvF116PIEOBOHFKMXYJYRYgZnS8Aa7moi9plyWcBFahOA5e0cIMOatJh2o2kfrt0KRc/N586s9Z8bSd89ACrtY5QZxINuKYf7v/az771w5/95q237s8dHuufSncNBQrN+nhBHisq4i3adL8tPOQydlpZeRUuJsXFZNiEBCnIkQ4N0qWFe7Rwnw7u1UNovhZ0oB4ZshPGA9z5PH+mIBgucLvz2JKHXDZaJqI958eOP906fvtw61STu96sDfElHg7fzuDZqHwbRelmOJNCV51MmZLRvAKMEe2VhrU09CMKPqsW0AZk7Q5+Ui2ivfUnxFJxLBlLpOHJDAKBTkSoeIiMO3biWO3yC0ZXbw+GiMcQCaDixhAIYxN7mbP7Z3/2Z6DWRtM+0gkw4BwQAAyDtLccf+DPQuewwfupviU8jMNh8Lhfpe8LSwF/IixExcF0AsIm4gRkigxN9y2wsgU2Ds/GE7pk8pBRErVyggayR4U4+bCbDYe4mDgPk+AicS4U4kB+NuRhQbVOMEAUGirUplAhDQXYFACVvf2+3/rWtwhU4m5erIcPH9bOvuxghRSQG/wKgI5clC6g3HUnuzhxslGi2FnohGlgUwzAplShokSnwB079XnPO/jfA9hNABVUpGrCHlQEoPLnuCLk7Nmzn070+t/V2bXHzXKOHj0KwfDk5GTt6xeJN9580xUN5Sql5u62tsGuUm9zqJJShSxUgwCrYGAUTKyKSzBIyBY1FVgWh5ZoUREMAC0yrE6KQyUBdIGVfHT0Aq3BeYAriBp4GilWL8UZ0AlgJLNSELIrM05eSIu38Ak2IckppnokzICSG9Nzk0ZOQk8JKQl+Od6rwHoVeL+GGrcz0x5m1svIeOgpFzVmwwd0GHSgBXBFVjUuEsQNoCLHOqVYFCoC2FRNAKPhAvO0i5NdorxkqCAFA1IyYBoNSIsB12mhDDgF00nRZJbdleC3p0l5NzapoTdoI+vFo++eff0HHzx8/1H3fLerzqIJi6RensAJKnqKwE7R+Fmxkqxh2FY3l7QOxqg5AyYsgUJcOCdA2pVINyBKte8LXWlvgofM8LAFGbZhhu24fgfSaILr9EhGh8koOS1G70y8crCpd7UQbbGJvAKKhUYykgh6Ck5PwWpJODWRrMWrA0xvUe1udknSJpyNX508Q0WrHguz1rFQ7arumxmuPRcvEui0XQICExA8GUumE0gMEgY4FRLu13/912t3vEiA6pvKYsB4LIzHwTgshMPsbc7u1vZW9dOOBWhBaASERsQwyArjHgf8wXtA8DiEgAcleEsIDqtQKWvXXjzu3buHrp0kYSAKFp0fxcBDbDxGTCJpGHQrl27lkIxMrIaO0bBgdGEHA3awkAALk+Bjc0IgJC2A4zwozIWC7KpZYVe5woAsVa4AmwL+s8CmKCnZtkLtR75I/Pmf/zlTwAb/UwiPQDi4odJQu/BS4wtf/iL6+DlYkIuNCoAE/CLgAJwBINkVwMnuxDYjsCkMSEtHpx4oKEQ16+9ffM/glx5zC/OfQAXlyqc6cHCjdsdnH1whH3xM0E8KFgME76p6BsZh/u2//be1+14kgE1hMpkAKtvb27VTLxI3b910Bd3xbLzYVGjqrLT1tHQPtXcOtSSakzKvkqCmY9Dl9GxEw8MbZGSrmmTRkEwKcIw3SABUMBohohSgUEE7wdiQDFTiAkQjxurleKMcXZ6iE+MMYlHEZm+MWMoeSURhylniHaF8X2xktefQxfXTD3ZOPzqzcG6pZbXD1OymhSQ4twjnkeO8KpxPRfDriAEtmgzGo8S4ZdWeLlRwdbEkxinCOESwTQBZOGiqMS066RkdwlGgQjviAFSULxUq5CY7qdlBbLUTOxykHie530XtdxNbLVBKDkfkSEhBSCjEbdb4WqHv9NDCzmixLyH1iChmBtFIwetJWB0JqyXgNASKAW+NMtsnnDPnhrP7+5n1dkxICoU5mDoBvkuN7TNgBiz4YTtpzEUc9xDHvMQJL2nST5z0EAZdcIMWTsmRhJyQVfFarY7xcGYuXhx32/NyloOBM1GxBgqiJyN6EkZLxOkIHDvZUSctT6ZzExVp1oU1CyANAx3FNdIhOxuyVqdp6png5NPnr9aeixeJS5cuAaLgUKIQaSwqkU5GyASpRlG7/ILx3nvvgVobVfVDQmXQ9maenF53NY0KBibjAOSwdBKOQRmfmapdfsHo6u6GUMKhkAPvCsFh9u3b+9TkbD67+96qZgUL0XEQC4+ISAQNg2LhkMxsLKg9lWRIQYbUZGBBsEEOPiPA5UWYeiGSE0JpAZTgQxEuFNg1K6Au3oVK1abogU2hQioAFfKXvvKl2o98kciX6lDggXeIQgU1K7ULLzUGF8ZQYNiYKDwAFAFLdoW6rk+ETkDYnXewu4qzuuBGTl46sJdFSy89vEF/FSe/LBxgzEvMNffPx+3bt2v8wFefTNynOEHV299Xu+8F4+TJk+hcZBh+/vx57dSLxOHDByVyrkTBl2nFKpNMY5JbXfp4yltqTqXLAalTSNTSsSoWrGShQylqHqzk7Y7DV7u5BGjHlwydZIUeoCW4h4/RiLDAwegkCDpnjIvT8YkWmTBmdDV4C73RqbnS/uXy5FB0biK/dXj48tnl7ZPLh08vnr957MS1Ay2LZVFKQfQJ8R4R7BXCbjGCLn5E53qhqg3RgwMAFSE6uGITovnzzRxUOgCV6luVMxA5E1EAvdwV9RDEaPWj6vAzuv30Xi+l103usRKatVBcAIUEuJCEnFIJm52uyVhxMdU2E/AWFGwng2gi4w1EjIGI6PFYPZ6gJ3CdxFynaf/Zobmr+20TLfiYDuMXwjEuvllO6TdRR2zUUTdj0s+cDLOmopyZBHc2yZlLcBZSovk0vceDrTNgMhpCVsdscCi6ve7RUGLMG+4xGgpCSYzLDbDYPpogzFRnRJaiOt7v7VhrbFrpsnXkyD5VdUCehna+28AHGECl1vcFqRl724AkkU5QGGSAEyqLRmJQMRQiRMRNzUzXLr9gNLW2VCtuUJehqi/upa2N9n2BjxkeAxOwMAmLkPFYKhFHp3z1a3sxPaBRTKRRwKd0lyigAQigsrduJRCosQM1Naivd81KDSo4ABW8hkE0s/EmJqKhwWjnFQnSkWEXHRvnYnMiTJ0YrhNCOcEvoBL8BCqoU6GjTuXTvi8VxZkN1H7ki8Sdu3fQdwWABwTMShUqP/3pT2uXX16QLQLUf4DHDzyEu+4EAOZTnNSW3QCiVIdSwBOLQgXt+8Kq6J/FTjl7CCqH8b9BBQj3qyywfaFQ67Ro2wvFSQ0qnxyj+ru/+7vafS8S//iP/8jj8apQgX74wx/Wzr5IDAz1Eel4PB2PZRBwTDyeiSexCCwhxWKXZ3M2X0rLtXAIeg5GXV1UjybuY0JyFtq5JAMlC5KBg1pfE3pm1x+gA/XoAD6MJmjh4o08qlNsyplbx5Jz85mRXncmITGZKQo9WW5iyE1shZGrc0miOef8cueB4/0tCzlzm1VY0NOzOkpaR0npyUkgdFkFMakhJNSEhBIfV+BjSlxYjgnIsEEJxifG7u6Kb+Wh/WBGHmzgwfrqBkV6Xo0HLyWodU5qwcmseJjNHk6Xn9sL0OKid1qweTkcFsEBMTGuZNaZ5G1u50AoMeKLtOttdSJ1iiMO0fl+Bj/IUKekjpK+fjgyf25s+MyifbQZnzDAPgEmxCVmRYx2PWfAxR8JcIaDzCE/fRAowBgKMYcj3PGEfKEuujXkP9BtnGikljx48HfJGuhFi7DRpelw2Hus3iFzcNTsG9R5+nXJSW/vkdbWA22ZxcbwZIOqMYr3KdERFPAptTFgJ6iJONXVZLVpmsbEXjJ1gyqSRCORGVQSnYKjkBASvtohgNnbYqv/8B/+A/oiBLSLaRcqOzt7me+/tbWF1oY4BAZcIeLAu8KQiQaruXb5BePa9etYImG34wuFChajM+xxvTQIYOzQtLjg7f1yDxiAipiMU9NxRhZGz4BBkxw4FTUJAqbTw8ImBUhOBNeJofwuVIQoVKI8KMT9BCqgvV9t14NGfa1FT9m5cKb2I18kzA7LL4jySQ/YhQsXapdfUrzz/rtoX5aBgT5+NjYqQBcrGwUJwMmnAs/q7lDK7ipOYFMU1KHZsdqr/KvGH/7hH/4vLNkVlcus3fEZB7ARNYrUuPJLBzjs4NBQ7b4XDHSQBqktnPz3//7f186+SKRyKZiKg2k4mI5D6HgElAwcjoXjS8nBkDqSNUl8cppNQjDyd6GCqKtWQAn4ATADSvQAVrFquWKBLVCy0anG1RX4iI6LNQsJDgkrqEp0ebungiP99oCPyZFjqSIcUUggSUgkOZkop5AVVLqW5QwrJqfrZ471LN5Yn797ePj6gfHbh6cfnJy6D7TVcnYhuK/TPF0wTNaZpgvWmZJ5st46VTCO5dT9MXlXUNzsFlUc/KJNVHKKSy5p2aVp9mtbQjUevJQgeDQEnwYf0hISekqdmdFoZ3W6ZCMh83RW3BoEhgMJyPAROSWpZNfrxWWDqVEXGjCkpq3hUUOwT5ce9XesdxTnmsJTRf90i7g5io+qED8P8XFwUQElr6I1GGktVna7m97ipDRayWUTsWwmNFiIjWZqq53dHbAvtedPzIVWhuj1PjiiQsJSDDrIryQk5PSsnN+iVk/addN2zYhd0++2j2aUbVFmnR0bVqErHAFFXBzIy4PRaTb82hJlExsyAKfCmFqdrz0ULxKPHj1C8FgUA2j+kmqfCREbjIdrl18wLl2+hCXhsSQClogHLwugsreBmWQqiVbcAEs4DDo8U834sueEkm6vtzo4v9ttjaJu8/BeMg/uxtraWjKdRJVJJbOpZC6dzKeThXSqkku31KXb6pNtdcnmHJAxYYfMVDjAwWbEmLwIAgJOBSgrhFICKAag8j+P1f/PUPmt335hrv/oRz+q4uRTVaGCR9LZdO2OlxEnzpzEoV3VdHSYxFSdKrILFQurxhLTLlGqQ31oumU0nW0VKjSsmvm5+YB/Pl57/TWIvAsSLHrwiaKpz2MXLBDonpU1hPyCJahQj47dW6cxCLFEsjttjE6n1069YJjsBgwVC4iCQoWBR5goUSg8olrP8sd0zoRJ4FOTbVKcjgepmJCKAatZkIoNBKtRoctWgKrpI9HzwNAA6mhZiB7Nfo/Rcwl2OSNsktT7wwOxzrlke5fB4aXSpFicmICRknAyMl5OwirJGBWDpGHoPPze0dTizvyZD589/dEP3vnxb77909969HvfufydL1781scXvvHBha+/d+3bH1/91gc3vwvKDy99/d2zX359+8Mnm2/dPfT2g823759479HGq9eWHlyYvrUzcfXE9LXt2Rs7NR68lCCZZCSznGhTEBwqol9NiWnpOaO8Mxhd7gzO9lDjVtgrArYD9vORgAATlhDjIlm3zr7ssy/6TUMOTbtL3RtnN3iISS02JodDIsTHRTxsxMPBh8XkuIqUVJNTSkpGR0rrCCktKaUmJZXktJqaUTPr9YyChVXxSnuynJY4nNBDQWl1dj8PDvLhgAAOcbF1EnqPgd1vobVoMSkJHJLAPgm6fCzAh2MibFqKT8sJcTkxpiCE5HivFGcXYUx8rIGL0XKevvas9lC8SExNT6EZXtHqe7fpjX7AvvSlvXTl//f//t+VOg3gE45EAAIHRAppb8sniSRi9V198sZwGKV2jwvlvvnNb/4yTna1576vF4qxpUnISsMEuShUciLUowCnUlelS0aEmpVwdVgFhcquU6nOJ9ajK1S4DlntVV4kHjx88AuW/AIt6OBK7Y5fLa7dvsG2ytDF8KoqJ7SAggAkwKAAqFRLcLw7wR2cN1abO7Uc/lWp6U2DHbXX+teOtf37EAoepuAhCg4VGb+rpV9hovm/PD7++ONabtNdnFRBUhUOIuAmpvYyawsEaqMRZBcqTqezdvYFQ64V86QMtoTOBJLSOTK6WMtR28X2iMGasEkDRopVitVx0URwGnRXFdR/qKvph7VcSAtwwq2yBAjAhlW9jYnoWBgDG2fm4Kx8gkvBTrqdw511a4PNa22d84nGXrs3JZHYaAwDhaSnE/QMip7OsbJ1YWmy1dUyV27YPz525+bZb3/zwe/96LWf/NHTP/6DGz/87vaXPlx787XJV14Zvnev78bN/ps3+29cG7h5bej2jaFb13ouXRi+eXP6wcOFx49Xnr6y+vjRoefPlu/dWbh1Y+3BgxoPXkqwvahxI5lEeLOYYJeR3EpSSE1PmUWVEK8UQPxy2M1D3FyMl4t4uVgvBw4wmW1K0aRdPO5gVJS4MB/rF+F8QtgrgH1c2MtCvGzEDcQBXMH6BPiAkBgV01NKbl4vKBj4BR2nTsvOqxkZNTWtIiQUwJRgoipsWIkEpXBACPuqtgNwxY/2sGOKUkqPgdprwJXkUIQPrkJuDuzhEBISWpOB2W5jNdlZeQs1pif5VQS7FGsWAfJj1Gje0L1VlL/wBHgMjE61wk3N7WX5JIinT59WJ32hvme3r8npdtWuvUiggxYI9AlU0I4mLHHv03+bWpp/GSdAWv0ep5C9UACact1S2E7DhHmYrBDZdSq5KlR2uZIWoD1gQQAVNuT6FCq7K1SobSN7WbR48tTJXYR8ol9w5Xvf+17tpj3Fk2dP5T4jhCY8B57jE0hogRFhoakczJ8IHAOWoDhhQ3pWTTomemd12G8Pewbvxn/5L/9laGRoaPgTfRJ7btFnC3kMhYChEqpc+YXu3b9Xu+OzjDj43AGEED5lSU3VVKeE2k0vHnKl4lOotLS01M6+YFhDHkfU4036A9mAK+myx132hNsYdUj8eoZdgTeIMBo2XH0AYB0d0QNgVLdI0fOAED0XArwBaNFVtbtFig7NgV/dc4WPQzdzFON9OmEplTmwPH3/xuKDM2u3Du2/vDp6qKtrsVA/GIi02qKtnlx/ojxdyc+3h2baTMPNqr5WUXdF0FEStBXZjTlqfQyf8uPiTkLShUu48Ek3IeXFJ9yklJeS8tPSfno6QEv6aTE3NeaihF3kgIcR9AniESB+PFLjwUsJX53FmjEqI2qmU4I38vFGAdYqJHqUwLVgPbJqCmUO4mIBTsAoKlhwiEXv0LCHLZxeCy4lAvX7J2KDEoCkKpQrGA+bHBNoWu355cbSanNmIp0fi6aHfOFum7/d4mg16Rp0gqyCEpMhISEc4KO9WF4+AlyRl4fO8fdxoDAL1yCj9BroPWZsnRLyi2C3AOsV4sMyckZLK5tYTQ5uk5dT56SHDBSHgmAW4zV8jJKNyBiImL63T1dXVxdafQM3ACogErZvaGDPS9LQ+VqftryqzmBvy7nRDCg06qdQoTJpAFe1ay8Yf1bbHPB/0q/S9/Uvj4++8BGkJUMOBhLlw1lhFSefEKXGFSGUqvaA+XahwvjFQL2aunZ4f+2FXiTeeuutXyJKVejICqalvfUP/uAPaje9eDT2taMpilWgHqmyoZqStibADMAPEzAo1Z7YXaiAMyhUqvntQYkeoLls67v3shnXbly7fq3Wswd+KfC4gmcDA1tt1trlFw+uTAQDolAJ0P+sz8HFfuc73yExqEQ6FU8lYcgEhIiDP6ULAbe4vFS77wXj2rVrnyxwQaGyuqfcSH//938vdVgN4YA7nwmU6pz5lDruFwRsNLsKo+XBShaiYmIAToD7rGa8RwwsxMhFTHzEUM11b+ACkKD/cSMXfRhM1aTUJjZs5iEWAcYuwjnEWKcQ65JgvEpi3GUa6u24eGb19SeXv/6FJ7/x9Qfffv/Kl54u3DvZtr2S3T8eWRoyj7RwGyPkrBMfN+NiOmxEhwlrMSEtNqLHhfW4kAEfMWPDJkzIiAFlWI+E9JiQHhs0Yrw6nEeLc6qwdhXOqSY79VSHnmrTU6wGqsNY48FLiWxUUSrZMhWXNWsGXMGZBej2ZFYh+G3R9JZ2HmRjww4W7EQFoIKJcehdWlq/kdKmh8N82MmGqwMbwNDAHh4MbASAkIeN9XBYKWnDZsfJNy5dfv3ygZ2Z/tHEwFCgqc2cqVOm62SZBlV9mzHaadOWdZS4GBvgw58uOd5dJuZiw1EOoUVB6TXTOy2YlBz2iWGPiOiX02I6RsbMzFrZdQ5ewc3JOulBPckuwRv4ODUbp2ThZAy1x1R7KF4wAELW1tbKlfLSytLeer1249Vnr+JpBJgIPhgAKrsfe+TYsT0unwS+ZGxsrKm5aWtra89JLUG0dbT/gnOf6PPp0++bGoS0FMjJgGN8KCP4hUepE1dLAapcdRoY4IqXBfBTXf9Yy/q1c3Evo/QgxifHP3UnUpVsfHJizybv0xicGUNHXAFRACQAM1BrAioLUGvsVhwcyMJFs89ZeJAZCFzaLT+9pyoD55vf/lbtFV88jA4LOoEbiIROJNnt4lvaa/2LumE68X8TCcvae767f3m0drQz+FwgKpdFZNAIdAqWhtIFrm4VUbvpxUNr0Ne6eatouXjxYu3Ci8RPf/pTmENDBCyshIeTC3BKProqXsEGLIGVDBgdQUGJgi7A0qCtCnSfYJQfPMAV2MSrUgR9DGALH7II0LzpViBBdYstCeLclRhdWeKV4PwyLMBD1EqpCwnay4aJQdfCmHtxRDfeKe7K00tBQsaOjRuxMQMWxYkGF9XhI3p8zEhKWSkpJznlJKZcpLSbnHYTEw5i3I6NmTEREz5iJURthJCFFDCTAyaSz0DyGChuPdmuI1v1VLuRYn+pUFGKcTGPoLnBkmy0KVN6glOBsYgAQiErF7ZygCArC7axECA7E3IxcGkhpVtPG7ASG7SQj49x8mAHF3HyEBcPAVxxAlvDwbi59LgwMh0//daZe+/f3XdkKl9yOt1cg50pN1KlWoJcj9VaCYEov9SiT/WYpHVKQliCdnyhmZGqM3+cTNjFxCT55E4da8TN6nbj0xpcUIn1K8hBFS1uZCStjJSVHjWSQ1pSSE2L64RpsyhqEHiVXKuEbRS4U77aQ/GvEcAkOT1WBp+CZxBg8LH/pDm5t6lfLyu++c1v1nbi+oUwwE7VLn+WAf4gVLTJRgaPEBytTvfKVnFSL4YLErgAuCKE6kWowAHaD8ZH/YqDVR2rR2ffPnr6uPZaLx7/9b/+1//8n//zX/3VX9W+/pXj+p2b6LYZ4DcCtQaoJmwCyCFC9+8BcgjRPRfQUoCetwnREpxBT4Lbqsc28C38YEuu9nIvHqC5g25DiQpXEzrGjt1zM+i999+DmCRU6Ka5FIhdFYvijOxlGvcLxY9+9COmgM8RC7kSMUsoIHOYRCaNzKZTOAwSi7ZxaI97fKELcoHRQYf6a8OHb775Zu3ai8S3vvUtiEmAuWSYR0YEVFhEhcU0WAZERzs/1WjnJ4xunVJ1n4AoZj5cFcYqQqwi2CHBuGQ4twLjkINjxC6FnVLIIUVcUnS5u1taTQEpgd0y2CdF/DJMQI4NKTEhFS6swUW0mLgOmzQAR4KJaTHRqiIaQtwobIwo2pPcoo+edZMAS5JOUspNSLnxVRFSHvAlKeFCGZN0U1IeSsJNTbqpcSc97qJHHbSgleo1UpwGusvCcFuZHluNBy8lKGRII8aW69TFTldiIM9P+vE2BbBm6NCimQlb0MFG2MpELEzAFdjNIBVkpC49vduKz6lgnxDjBG6GD9u4sL2aJs/Bwbp4BD9f1aTLLqanTg3NrLc5wgquhkRR4EgKDFGOIciwRBmGIof5OlwyJ2gYsXt6/IykAeMXVzeZAC/ChAFXXExsXkzo1DMHnJRGMzmlp4UNACG0sI4a1hMDGpxHinUJ6VF5YLSucX9f23pffiAfafD5Uja9S97UUa49FP8asXVyi84mckQ0hoiOY5HRTmrQnCRgHj16VLvjcw9QrVvdToiIrmWrqnawtb1Vu+OzjPc+eB+SkyEDFQYWJMaDUlWo1IuhogQqAUmhshQ9KFbPAMaAqyhXqo0MK5om69Gre4fKS4/f+M3fgLRcyAianIAWIlApQB4p5JVDfjnkU0B+JeQDB1LIA85XL6GSQW50N1L0AD2WvPfRB7WXe/EotzZBDML/IiyTvOeu2q2dkxCXWhW6Yy7Co++WY7N7XF37L4/h0RGWgMcS8rgSwBUhnccmMekEBpXAoADVbnrxsDjtqNHZ3RkP3YYO+/3vf7927UXi2fNnMKu63SePBPHJkJCC7iQNJKVD8ipX0C5Q0MLgQAbgSAQoSIBQUyLE2mVYlwJ2yRGXHHYBloghJ+CHAvGosAENNqAGgr0K8DyAEg7I4YACDsqRkBIJaTBhDSaixcb0uJgeEwPHGiSsRsJaIEzUSKvzMQp+TNyKRC34uIOUdJFTHmrGSwL8yPqZhQi7FKfmAuSMj5LxU7N+9GrKR4q5yREnLeqihezMkI0VsDG9NkAUlsde48FLCQYTsplpHb3OhZOjg8cPSnIpnEUBG7iwAZ0HiZjZKFfQ2SzArzARP4tcUVK7DPR2KzYB/iJKDLqrJbolKmwDnoaNsfPwXhE1LNW12CKjvsKIq9iik1moFJQlOJwMg5NhsXIEJ4eJclhgxhY61TM7PZXNYW69D53WhXZ8VfMJAqh4mQQAsHYdo9NKzGuJcQ05qqeEdYArBL8Ki24+w6aGRamZhhOPzhy9sdU315ltcEUzOn9Uq9RzoglP7aH43OO3fuu3CHQ8nUsUKTkinZgi5SKg6QcalSTsufPnajd97rG0uvLJfJ5fzBb93Na19U0Oo1AxUtEZw3EulORDGSGUA04FgKQKlU9V/KTMi6G4EM3MCPyrjX3u2l76Lj6j+B//439Aej5kFsIOKewB1YEKDlY/8FEdEtNXpYMiGiikgkLKaqkGgncVBDernO17tyk///nPsQIaxCVDHDLEBiLtKlnI1u548Wjq6YCFzE+FiJiIkIkRMS9du1q747OJn/3sZ3yZSCAXixRSqVouVsk4EiFTyGMKuAw+58ReWzxPnj7FUUlANDaDwWVSWDQyg/oXf/EXtcsvEmfPn0PTgXOqO+PxqlARU1GoAKeyu9RRxUS3dESJwoetYgx4JOxixI5u94s2ONCtfyUAKohXDftUkEcO+9SYgI4QNuHDJmxIjw1rkKAGfX6CWiSkRkJaJKJDovqaAFHiJkzcWP1Sh0RQqMDgSYvoUQWrQyZhIzZqwyddxKQLn3DgEy5CAhgUDznlBUQhp32UtI+W9lNTHkbSw0x4mDE3PWRjBlGokGx6glFNMmtrPHgp4fXz6iq22a3xAw8vBUeGSC4bohNCOg5cHW+ETSzYDMSE0U4wBjbKpTRraN1meosVn9DgAVRcYsTBBzYFdqBmBePkEwNSekwjKVhsXc5YrzXfoQ1kBUonmWvAsfQYII4RJ7KTDGFWqc956MbCvodnPNP9pKQV9vAhdISGvQsVOMAmllWULhOjzUZMawlRNSGoBjjB+xR4txTrFBDcHE3J0L6/a+nkYv9kqz2sFekpNCWBLCUQ+DgSD/859AX/7wGaikaHkc6nyMDbcRokZg1JKoB5NIhBhKi4obG95CL71eNLX/4SRPufB2ApqJwBb+2OzzLA34SqE6B5VmwMdHJXXAAlhdUeMDE66Qv1K2KoJIbKEqgsg0qyagm4IoPqZFBSDAWFwMKuHf/80k/9S4LnN2FAO9StwgV1+JiJmLTi01ZcxorNosJkLUjGDKdNSMqIJI1IoqpPDxLGR6/tJYHQbqwdOgAJ6UCwkAEJ6BAADB+Iemx77ykpZW4LRsrByLjYqj49+O73ftXxp38+llaWpRqFyqBVGjQKg1qmU4nUCr5CKkC1x50mQIQTMbaID9wPXybmAQMkFnBE/H/6p3+qXX6RWFxdQvf6BEQBFOdTICEVEgOnQoek1ZXz6BRhDqTnISYhbBYhNhnerca6drM6qhCPGuPV4EIGXAjww4iPmHBhIyZiISQdxIQDGzXjYhZc1AycByZqqLZFTEjCjCRAacQmLcSMC5+2gzMw+syYsUkrJm6uyoRJmMANSNSIRExI3IJLusk5P7UuSM56sQk7PuHEAcWdhJQb2BTAGELMQY45qVEHLWKnBC1kr4Hs0hFtaoJZRTSqCAZljQcvJbzlgKc5ER3p0jaU8DYTrBXtbqSMGMGfCYhb3dyYCwPLYmMSMiJ6p5Hd76JXrMSoDu9Xo6NMDgBkAcoVGwex84g+CT2qZaVNwnqrrtHqabUmu03ZAWOqT53p1WT7jI1T0cpUtjCd6zky2Ht+3TTUTEiaER/gOefT5Bawg4WJcUlNagAwWpOZmNQSIxq8V4FzyzAuCc4lInhFzLDU1ebMj0QrfSFHWMZQUbFSEkaCh8VYWIBF2MjeUpj8KgFqz2JjQWGQq606nduicJhoSglGyIS4FIhLgtkkpUX3+aPuu9/9LlXCQan2iwFYIkRD9avsuv8vj6fPX4UUFHTI3cOGQny0XyvOR6ECzEpWiE762u0EA1BBu8KqxmXXr5TkUF4KxUXAr5THPtftbP9/ozLcg3eqCD4dKWIkJ6zkrJ2UdxLrXISCi1B0EcseUoOXVPaSSh5S0U0quIh1TkKdk4je4zR15Pf8GPzDP/wD06yE5RwESAbEBoKlQKxf/+Fe1tWC+Pu//3ucWojTCPEaEQGVuFqKCOrPNhv/3/7t38r0KqVRqzbrNRaDxmbUWI0yg0aiV4s0yguXLtXue8F4+513eHKJQCETKmVSjVKmVQIDZPfucZFKW08nuiEeMIU8yi+gAogiB0RhVqtKAWQSQmYRKpsYAk7FJYPdwJqoMH4Nxq/FRUzEmBXFSdiERCy4hIOQcGLjgBAWbMIKvgTwwKWduLSDXgjSSiFec0zbU6/oynGaE4xyGJ9xYlNOXAbc4MQm7bikAwifchIyLmLWTci4CXkPIePFp7zEfIBcHyTm/OBLUi5ARuVnFaK0rJ+ccJNiDmrCTY44KCEb4ArVY6K49CS7jmjWkkyaGg9eSrBDLoAsvFWN6KXoVvhqNrrHsp6LMQlxNinOKsVYwR+Li86BczDpjSrugEM6HmM1uOlJKyGgxbkVWDQdphC2cdG9MWzAsvCwHiExpKBE9fS4jpVUi+qV7ilHdM2f2hdMLUZ7T44U9o9ou/PchhApbUXCKtgvhD1cxIWOpqCrE0B71sXEZ4X0DgNvwE0pmYhx4Ba1WK8cEAVxCgleKSmsYsU1xoo90e3LtdttYT5NQ8LKSLAUD0nwkBgPCXHNn++Os3/9139d11yQ23QKu0lo1jO1CoKUhwgYMI8K88kwnwID78wlvff+e7Vv+FwCeBSqjId+JNABWDLE/FQkiEH6y7/8y9p9n2U0DbahuUnsbDQPfJAHhflQTAglRFCmOnaSE0EFABJgUORQRfGJlFBZgUKlIEO5khQRo9LPtIJ70di6cIbgVBH9elLUTE4CqDgoBQ+twU9vDNKbw/S2CL09QmuN0FrC1OYQtSlArvgpDX5qg49aCVx/vPc9g2/dv4uoBRiNAN00UMWvScmnmRR7BtVXv/41gkFKrO5gSDbJURnlZIPMnAzV7vhsYvPoEaVZZ3BarF6HyW0ze+1au1lu0kkNGql+7/vFZQp1Yo1SoJLL9GqAK7VJZ7CbipW97KQCIpSOVaFSdSqAKKJfgoqaBWnZ6GCBSQBZQNsaJQrkkkFeGdopig6tKSG/CgpooIAODungiAGJo8YCE7fiUw5syoZN2nApBzHvoRTDqr6mlu2Npq19rtke3XAzIAqxzkeq8+EybizAScaOANeStmOBkjYkYUEAlhIODOBT0olLeYhZHx6gJefDZb34NJAbn3JhknZMzI6N2glxJzHmosS9lJibGnWSQzaaz0zxmIg2LQFAxfJSu78QnQTW8lFpeJCKjWg4iIGPMQixZjHWIgUCUIHRqZBsxMdidxkkE0H9XD2rwcfIOokhPd6nwnnliEeCcQkRhwBxCqDqLGTIwca4BIibj3i4uCBXOWAyzPnNcz5ORc+st1JSFmxAgUXXSwogL7prNzojGTiV3b4vOwPyskgVBWfALh2L0YpWUsyAD2h2kzkDVwR+CsGvpMX1gpTJWLIFGq2BOpUxzJc5qVwzhW0iccwkkYOl8Qq+/s2v1x6Nzzi++/3vaQNWrISJE7OxYg5GyIb5jGqnBNq6gQWgJEOgFFCMQcfnUzmCKubY9gmsgAlzqTCXho7BcnZF2VU0/zJTlfx/BfhlieiUymqaRQ8X3bcKmJWIAOVKUlzlihgdPkEHV6odX41KqFEFVYAUUAMQoIsaKqqgtOzpm3tJMfsZxa997at4FwoVatzGSLuY9eDZDnGbY/yOlKAnK+jL8vrS7L40szfJ7I4zOmL09ihgDLUlpOrN720F1W7Ys1GcXoQ1iDF6ESqdEJRYnbBhYI8bjYA4f/Uy0SQjWRRkq4oCZFNTbCqqVd0zNVq74zMI8HxK9Eq90+yN+vyxgDvsswVcepdFbTOprMbbd+/U7nvB+Ojjj4RquUirlBu1eqcFvL7N63T6Xb0DvbU7XjDkoMJlV4mCDqgAolAhKQ2SMyAlE0azrXDR0RSLEHKgk7gglxTyyCGfDJ2vEQA4UUJhDRTWwhE9HNMjcSOMykBM2xg5D78UUXbU28e7/CujnqVhw2S3aqRd1FPPbo7SgcEFrrfOjc8Cj+LApoEsSE1WTNICJz6VFU7YgJCkHUnYYUAa4GPSTiRhhaJmOGqCI0YkbMaELejKlaAB49NivGrYLkXMYsQgxGgFWI0QoxTWePBSAqPmYaq782PQjfurO5HpxFijhGCR4YGsUqwNQIUDWZiEOJfda9LMpfQzRX5jkJFxkCIGUlBHCKixPgXWjXoIUN0Dp4JWH3YW7EAHSGA3GxfhCvsN0imfcjKATykQnwDxCGBUfNjLhT08xAPYI8B4BJCTX51CxoTDHEqnXjwRko+naPV2asKC86kwTgnGIamtnnHw8X45NaJjxYyCpFGZ1Djr9bEOfbxLF27VhppNxaFMfqgc7qz/yWeQj/aX4+///u8Pbh2hmUV4FRcRMWEAEh4VFb8qFCRU9FkU0iARDRLTgcqdLZ91J9jv/d7vRQs5GPgkPhD9l0RDu+AFdFjMvHzjsx2D3Y2nr72K5rxCocKBXNVtEAMCKCyAosCsAKhIoZwMqpdBBQVUVqIsaVJDzUCaWtmkg5r0UEUHuOLsy7z0v9vt+3fEDvUeetv/7u/+jhey6eoizua8r7shNNQan+pOzg9mV8eKB2dLm/P5zdnM5kx2c7b+yHz+8Ix7sUcyVOD0Zc7d/ec2JP7nA1gKrF6MM0rxFjnOKsdbFagsqC7d2uMGoCAGZyfJFiXFrqa6dTS3Hi1dOrpbf/bKHjug/iWxc/aMyqx1BFx1pWwiFw+kI95k2Oxzal0WrXPvSzjrG0pirUqiVysseovf6Qx7AvFAKBlcWF6o3fGCgQEuf3eIXkAGNgXeHaJXMNF0kAY+bJegO5pEdMSkmZC2EdJWXNqCy9iJOQADGzZnx1SFzTtwdQ58fVUFB6ngYjeEFZ115tE223S3abaX11tPa44BI0speSllH7XsJ5e9pLKbWI92nBLBt+dtuJytOlxnRTIWTMYCSiRjhdIWGChlgZNmVAkzkrQAwQlTdSTGiMQN6CB/SIcEdYhPjY70uOSITYJYhLCRh+hYsIqJKFg1HryUQFQCWMVD0L0tRTitBKeT4EwynEkKhLdIcRYxYuWjfV92BrVOzB9y6Oezsr6koCHESruowPiH9KSABvgVvFdZHfAQwQ4ABi6o94HzQKHi4hDTIm6/UTTp5/a64LgK9kthL4oQjEcIu4WIR4R4RVi/BOMRQ7tGB3AoJWIN2KRTcdFgjFrnoIP/VliH8aATvT+Z5g9sDRdxCbDoXgJCnEfIigotPWbHkNvW65QV9KKslZOwk3xacdyx577mfz5ABffoyWOZz0CxiMjA2Cm5kJgJCasGBVTcuxQRgeqbAQRJGNU82Ozd3d/KPa2fkV/567/+69nVFaJciBFzdufwIAIGAigiZCBiJiJl4VQ8mkkuD1g/n76v8mArShQLaGdwUah4+VBACIXFUFwCpaRQRg7lFVBBBZXUUEVbRYgOajVAbUao3Qi1maBWI9RiQE826qAG7ebFk7XX/ZVj58IZvl0FK9AJPF/7xl4cra+ULfV1dE4M9S9Oj+5fmj56YGHn2Nrl04dvXTr+8MbWK7dPPL1z6vmD82++cuaNR9O3z3hWB/RTLaAVUvv+F4+W0X48mqlPxfAYZTGPORuz16ds9SlrPvGrNJ6SzWVF2C2PuBVxnyTu4UUc7JCNE7Z9/OUv1u74DEJl1Zs9NnfElymm8+VsspBNFnOx+rQ/FXny6h4TRnz5K18RahTAoyjMOkfI440HotlYpbmQKSSObe1x3TE6mZhLQomya1NkDEjDwVrF9IBOlLRL60Pljbnhi8dHrm3NvXJx/snFqftnpx6fX3vj+uj9U0uvX5t5emHs0Znhe6e6bxxpu3Rg6O52560jrVcO9t48MfHwwvo7d+efXZl89crUsyvTTy4M3t9uvrw/vTUdPzbq3NdlWW7TzTepZxpUUyXJZEE2XVJOlxVTJdFYnWAkJ59q0C+0GxY79Iud6pkW7WyrZrpZM9OinG4SjRR4/Vluf4oL7HJXnN0ZY7aFGU1eatlBqbeRcyZS0kCMaclRLcmvILolBLukxoOXEli1EFPtogVQIZsVRKMctIOqTSEZziLFW8UYiwAxATzQuE1q0ajPtlTmt4R4pTAj5SJHTHi/DudXoU7FI6tusi+EHULUcFTRAgGuuDnUkkI44pRORRhNdlxMg/HJMV4JEOKV7gr2SBC3GHJVnQr4Ri+HUJRyhl3yqSSnzU/L2SkJCzYAXJsKcUurq8yEkB28Ph8ADDVGDh7i5JLDPHmHUd7nkLZaCSHANh5U26+eQ7QKb92/8xIbuaBeOHf5otCmQ+RMgoFPscsoVilGxcXIOLCEDYtZwAdAgCUSFjqCKmOje4gquSi8VXxYVd0ATsW2ZgJ7zvv0f4w/+/M/X1jfJ7RbiCoxTsrHSDgYEQtwBQNYImFi5ByiXsTxGPSpgLeU7pney5bGLxroNsMWHmThoG0F8M/1CqCACApLoJgUSimgrArKo7tZQw0GqNEAtZrhdhvcaYd7HEiPE+q2Id0OpMuOdNowHTakzYK0WvGtji997ddqr76n+P3f//2NY5tsqwJtbCpZsJqNaLmH97SVb+/kSONgd8fUSOvcZMP8RGFpurS+0HhkrW37YOe5o12XT3ZfPTV4+/zYvctDd87Xn9mwrQ+fvL13d/jzn/+c4lDTXXp51OsuZlJtjfmu1kx3W6KzpdC/x74dEP/wD/8Qqs9Gy/WxxnKypTHR3uis5NV1UUkmAP59tZtedly/eYOvkoh1CpPXna+Um7pbG7tay62lbFN9tC5Vu+nFo394IJgI+eNBq98ZSEfD2Vh9Y317dznXkLn3YC8ZzP7Tf/pP1b4vUnUmMQ1WsYk2KR+wpOAX5n3stEfZUmg5vrnvyb3Dbz3Z+vjtk1969+hHb5744tvHvvj2wfdeW3/31eU3Hy29AfRw8umN0YeX557dnn/tzviT6+NPbsy/8WDf+6/Ov/No8s0Hs289nHn97sSrNwYeXey8e6r5xrHixYN1FzfyF9YzZ1cSO4vRncX46YXoznxoeyZ0YtZ1aKzu/Mbg40tjz68vvvtg9Nm1hXfuz799d98Hr2x8/OrKew/m37wz9PD8/vcf7Xvv7uIbN6eeXJl/fm36yfnJh2fmn5ybe3xm8uHpA2/dPPTWjcVHO8OXN2s8eCmBUYtgFYCKEKMRMRx6vF6GNypAawhnlmJMIsTEhU0sxMLEBTjCbotqKmadq4hbEsJimJ60EyNGfECL96vxPgWwEYhHhkHXiAJCSGCXGOMQwk4u7OWw2vWiUa90PEorWqlJMzaoxnjlGD+QDPahK0hReWVYrxxyS1CzEuRRmjTsIa90LMFo8jDrPNSUnRAx4oJVgPkV6DLU3a3NXAIYVFUuPuziUZISUadZ2OvgNVrQBGIAORZubbOzanZYY8L93gfv1x6WPQX4jD145XHbUD9dr8BIuaDVj1FzSBYRzamg2OQomxV8RMaDpVwUJChLuDDwJUpgBAWwWgiDEhBFxYXUXEjLRSwCelQ3vr74K64U+dHv/+jC5csNXZ0Cm5mmVRKUEpxMgJVysFI2VsrCytk4NY9mkUtCVnt9LNCYj7Q35Aa67z/+PNYS3nv8AM10ZAPNCz7kEUIBCRSWQnE5lFJCWQ1cr4PKRqTJArdYoTYr3GHHdrmwfR7MgBcz6MMOBjD9PkyfF9vnw/f5CT1APmK3F9/h3nfu+Is2Ef7mb/7mys1roXK6mpwcZQk6IQX8F/Q8jFGQ3NNS2eOntpuHexvHB6NDXdbeJstgs3mkzTrV6Zjvc6wMuQ+Mew/PeI7OuI/NuI5OO49MujfH//rf7X0zrv3Hj9DcBnnMF6wU0u1N8fYmV3NRV85K6uPTm3tJjLYbv/07v6NLhAyZmLU+E2qp1PV1l4b6SiN9dSM9tTs+g9A5LQwJjyrmMuRitduh8zmt0Yg3E5c7DM9ff6120wvGN775DZleq7QYHUFvLJ8ptzS2dbf3DvR29ne09LZ/8Ut7cV0//vGP0Yk2IgZeJyTZ5ayQQZh28dNubtIrLyasXU2R6bH02mrjieNtZ87037w58fDR3LPX1t/7YPOjjw59+OGJr3zl/Le+ee0Hv37lu9+59oPv3/nt33z2kx+//7M//fjf/F9f/st/841/9/9869/92+/9h7/+jf/4t7/x93/7nb/5f77yFz/78Gc/+fjnf/rl//tnX/2rv/i1v/o3v/ZXf/Hlv/w5KL/513/19X/7bz782Y/f/PGP3vjx7z34ne8++f0fPv7RD1794995+8/++OHv/fDub33vwe/84N5vg5/y3Xu//b07v/Gda9//+rUffm317fv7332y7+0nJ774ztYX3ll/85XD7z8/9N6zo++/dvydZwef31++f3nuxrkaD15KEHQynE6K10nwOhlBL9/dix/RC2E9DzKyYVN1hYqTRc8rFBP++tPjkY1R/+IopxgjxcxoLR/U4ENAakJQU7UsSpxPCQADudCpxqCux0UFwkG7dDIqHU8wG7z0vJuQsOCjemxQhQ0psSE1ElBiAkpcUA1eB/HJQdUDx4TMLjtvNCwcjFHKbk7RT0s6CBETLqTHBDRIQI3xqRC/EvbIgcWBXSJgccAPouWUvC4rv8fFLJmxfhnGJYVtQjTfDjp1DfwuaKolSMuRek1TS3Nf/dpX/+WDpaDSv3D1csfIgDkR1oR8cp+bqlNipDyMjI3T8SlOGcuno9pV6PwZrRSrEiJyPqLgIYAlqC/hQUBqPqSpSsuHdHzIwIetIlrUYOjMhMc79I258nD/qYsXfvKTn9R+5P9fgCr1G9/85uHjxzONFbXfwzUbaWo5QS7ESXk4KRcn4+AUHJyKR9QL2U6NNuULNNVF2xsS3a35wd7CUH+su8NQyPKDHqrDRDSrcHoJRosO2e0OrSE6PtYsoXk0koTLWoh7y1lvMefMp42xkMBhJmgk6N6oMiY6UKmppqb4NFuingMZOejfGZSmT0pgFoH79IhQooRkUEyJpDRITofUGZCSGdNog1sdcLsT6XRjerxQnwceDGBHI4SxKBFVjDgaJQ5HCINhfH8I2+PDdntwXR5su8sxUrp0/+Y/34P3j//4j1/7xtePnDqRbC1Wt7VgobtzazkAJOhsFKMAYxJizSIssON2KdWnpfq1ZK+G6tHSPFq6V0f36ek+A9Nv4gQt3LCVF7ELY25pwidPB7W5WLC5VOzrbB3tr4wNBPpaLD0NloFG21ibc6bHuzwYODAZPjLjPzbrOTHjPTHrOzEHDtzHpl3HpoLHFyduX9x57fHOqw8Wrp1P7Js0TrRqhsqqnnpFZ07ekpI2JfXN+cRAZ9PYYGWoL9JakSX8TL+Z4TUxAxZ+1K3Jx/XFlKIuzs8FuLkAvy7IL4YFxTC/FAYHwlLM0tHQODM+sDw3sDzTODVsaMkL6kKifEhfzriaivZyXpeJSmM+QcgpCDmAhGGXJO6VZUKmcrZusLd7ZmJwcbZ7cSo21OHobixMjg6uLA6szOdHe3UNSXHSKwTf4rPyXEaORc02KDl6uciiUdp1YqNCoJMItEBinkYoNsgFWilPIxFoJDKDVG6UCDVirkrEUQppYg5DxmNIuQwpjyXjc1RCgUFBl3JJQhZLIWTJBEypkCXlMyU8phiISxdx6UIeXcipHnBp4IDPAQd0IZ8u4NIFPBqfi6NR8HQKgUml8dlchUzrcgUyqUJLc7KY86fjWreZrxOzFQKmQkCTcykSJllMpwJJmFQ5T2jUyOxGII3HpnFbZRatyKjh6RRMjZRnVin8Zl3KLU+7GSErM2SXZMK6Sr2pvSJrqGPlY7R0iJaLMIsJTlOdsLNB0tsq7muTDHWrJwYNM2PutQX/gRXvvqXQgdXMiSOlc6d67t5eefe947/2tYvf+/XLP/iNW7/9u3d+93dv/vZvXfr+949+9ctL7701+Ohu85Xz5QunGy6da7h0tuHyufLl87md7dzOVu70dv7MqcbLF+vPnsqcOpY9dSx3aqtw/kzp4vmGyxdK58/lzp5KbB/P7OwULpxruna169ZN9BUunCuc2Sme3Wm+eL7n+rWRu3dG794duX1z4s7t8ZvXhy5dGLxwvsaDlxJMl57tNlEsOrxehVVL0SEWUK2AD56BBZsYgCgYP49R1JqnUh2X51dfu7TxztPy8cPsUoyStJNiJlJYTwzp8GEdKWwgRkzAuxCCWhQqaI+WBPYKyAWVZCKkXy4pJvOKgSK3IU7JuckZBylhJcWtRLQ0E2MmcsKMj5uQgBbyiXE5GW/IL5vLCodTtIqfVQiQE3ZCzEyIGglRAyFiwIf0+DAoDdiABnLLIJcY8vLpJa2gz6ccitALVkJYj/WqYIcMtksgqwixCBGLCDGL0Gl/BiG685qcgWhY5kygZbhn4/jhY6e2d86fe/TklVt37xw7tTW+PNsy0pdqa7BmI/KgUxVxG5MhWybhqctY0wmuw4JTizFyHk7DpToV8qTLkI8Kgk6Ox0I2a/A6KVYjQoVW0yIESCNAtAJYx4cNAoxRjLVJqWGdtiVRXB7p3Fzq2Fxr278a6O1k+VxEg5JhUvtyyaHpiWMnt4Cu3bwB3tX5y5cObx2fX1vuGRtu7OnMtzaG6nPOdErl93EsRqpOQVSJCXIBDrwlORen4BI0ArJZyvcZzXXRUEt9tKOS6e9sGB2sH+x3tzRJ0jGm305xGvE2DdYiB/xAZRKjskoIbgU3Ybe1ZBP9zYnelkRXS6ilwZRNcFwWjFaKbrK928Y38mAzH017Zf40bWI1CxaaLI9XK9EuSgHkEqJE8UlRokQVUEoDZ/RQnREqmuCKFW5xYjo8mC4v0uuD+n3wUAgzEsaORZCxKG4sShiP4cei2KEwbjiCHwrjB0PEgRCpL4Tv8WM63EiTHZM3JIdbhvbNbp7ZOnf98qNnT7bO7ywdWu+bHY0212FAw2g33zi6mJcD6bmwgYfO1TGBtw2EZtRAbGh2Jtgpwbjl1SaRCudVYd0KnEuFd2vwXh3Jb6AGzbSwhRm1cxMufsoryQaV9fFwR1PL+FDv9Fj33ETHwmTz4mTT2kzLgYW2I2s9pw71Xzg+dP1k06Uj0Z3lwPaCf3vet40CBtAlc3b/vqd3r37wxo33np98em/m8sm242vFjbns6mRqYTQ9N1SYG+1cnhlcne9bmG6eGA60VyTpACfmYEfsNcVcnKSHk/Fx835eIShqiIkbk5LmlLKjztJXiUz0ti7Pjh1anzq8f/zQesPChKGzJG1MyRoS8lJCVojL8hFpJihO+cVJnzjhlSb9ykxIm4+ZS7lYd2vH1PjI8sLY2lLf2kJqoj880tuzvjxz9ODU5v7K3Li5vV5RF5Yn/bKoR+y38+06rknJNcr1fps94lQ6tBKzkq+XcjRijkbE1YrZaiFbJeSoeAINT6LnsRVMpoxDlbBpEgAVLgut4vkMBZ+jkYjMKoZCQBIxiEIgJlHAIPDpeB4Nx6XhOBQsi4JhkYEQFglhkhAGEcMgIHQiEIZOxNLJGBoRSyVgqUQsOKCRcEwymQ9+Fp8p43FVAvBOhAaZzqMii6ngR+BFVLyARBQSaVIyQUjA8klYPgXLJ2N5FDyfRhTT2VqhzKY2hZ2hUqZ9rK9tvNdTjMujHpbfKo6HpNkYNx6ghVzkkIMYsuLDdlLSS82FWeU0t7mO31ESdjeKe5vlQx2a8T7tWJ9xYtC5NO1fX4gc3ld/6mjj+ZOZYwfrTxxpPLNdOr1dOnOy+/rVvts3Bu/dGX10t+/u9fbr5/OnDkUOLQc2Fj1rc7bFSdPMkH6qXz/Rrx7rkY90qcZ6lKPd0sE2cX8zr7vM6ihxuxsEfc383iZhXwuvu8LvqfC7G7idRU5XA7erwutq4LSVWM11rEqOUUyy6pPcQkpQTPNBuyQTFqaC3IivxoOXEhyvSZOLKNNhns8O/AqMzipmwXoGYmMQgnxWncYyGo8d7E5sjpZPrfZd22naOSZpKzGyfmraSUk6yHEbMWYhJWxkQIiICRvQItV+MNgrgYJibEYmGvZ5D3d03zlYv72SPrLGaUpTUag4yWkHJeOk5FygBC8FRExa4aAafBe12SiZSZr2tUhG6gRdWVY5Ssm6ySkHOW0lJQCBzLioEVddm4oL62CfEvJI4aCQ02KUjkbUY2l6wUlLOUgRC96nw3nUeJca71RhHArErkBsCsSqgMxiSM/DmAV4u4RsV+B1YoxCgEj5sIQDSVgYNY8E2ukOtTziMqTC6njQkIras0ljMir0OkkmNU4jJuhEFJtSEnN4mrPJnuZYZ3OwvVmfS9IdRpJZRTBKMQYJOtHTIMYYRBijCNTXeKuE6FFy0nZnb7FucaB1Y27wxMbcuZMTZ07mFudUlXpqwI43KTFqEVbOQ9c2S9k4OYeiE0s8Jks8YEuHHblkoFgXKRVc2Ywy6OdYTVS9kqSRElQivEJAVPIJaj5JL2I6VYqE21PJBloLkY7GbH9XcXQg3d9jaygK4iGq10p26gl2Nd6mwIPf3QEkxznleI+KGjXJKuHgQGNhZrAyP16ZncgN99sbi+yAE2uUQzoBMFigLkbQzBOfpE0EzAACx5/KWRUAiUsAuUUQeAz8cigAiKKAoiooqYEyerjOCBfNcIMVaXYi7W5Mtw87EMANhrAjUdxEHDeZxE2lsFNJ3EQSP5EkTiaJEynieJIMNJogj8RJQzHSQJQ8ECH2BvDtHmzBgklokaAS8crQGefoDHgBygxADmM1R+xuieaLRUFSzRRbzeroEKMJ/lxSxCtHfArgfZGQGhPWYiM6NIV4UIcP6IhhEyVmpcXtrKSLl/WL68KyYkxdThmb6zLDPT0L0yMrC8MrC6Pry9ObBxZPHl29sHPg2oWj969vP71z8e2nR9561HrzZPrCRuzMWnBn0bM9Gzg5X39p88T7zx5+7cOHX3739odvXnzz8faT20fuXd24fmHt0s7K2a3lU8cWjh+aPrw+sm+xfmxAW8lysx520rUrTtLNSQGieLl5H68Q4JcjACfm3kpieqDrwOLM1sHl08cWd05MbR/p3Vwvrc16Jrq1vWV1Z726LaduzambMppKSllMAC6qC3FdKeVrLed6OxqG+lpHB4FHGV6aH9u3PLC2WD875h/pSs2OTp84cuD8ztrpE537Fxx9ZU1DQlsf0+ViqkRQ7DMLbBqBVa3zW0xBm9qtV9g1EotSbldLrEqBUQbEN0gEeqHUJNA6pUIdl6vmM+RcuowLcMJUCjhqIVMl4OllApOcqRGSxEySmEUUswgiJl7IxAvoWB4VwyUjHDLCrhKFRYKZRJhJQJgEmEFEGAAwADMUVOBLOsqbKnJIJAGLqRbSZGy6jM1QcgEnuAYBScYkSlgEMSAHFSciEUQEgohIEJOJYgpJSqFI6FQZi6XmmEImnU+nD5h9hdTk2ry3mGCZlUybkeWxgo8DPeAg+62UoJMW97JyYW4hIWjMC5rrBZ0lPupUWhQDnaqxXsPMsGttNrq5mts63H7t/MCty+0XT/pWJjRDzbKuelFzUtSUEDejrQFJS1bRXTJP9MY2VivnTk8+fWXx7ddnXn+lcvFU9Nh+/8aSbXnKsjhhmR+1zI5oxrtlQx2K0S7JQIuov1HU1yjoKQNxOgqstjpGW57ZVsfpqGe15RktOXpzltqUplRSrJYsvZyg1kdp+QirPsbKR5ipACcVYEacdK+Z7jLXePBSAqcFLWsJVi0h6uQYtJeGhbFwiSGRsGJyTGQCC02Rff3x/ZOpjQX7RJ+8o8gpx+k5Py3toqScFMAVVA60ogefxoAG9sqB1YABTtJKVpvDvlTJnZlpunhg6fVrRz9827swxa4kGYUAveCj1XtpBT+1zkfOewAzSCkbQAUcUkJxCaffpV9vyJ1f0k6122YGuU1pWl2AkgO3uQCKyGknCZWDELdiAVT8SsgvxSXlgj6PZq5eP1NhVULs+hA5DhoRZmLQRAlZiH4jxq1BXErEoQJoQVe9OkXkiIaVMIuTbppDRzap8RopRiUCdTpOLyKaZVSHRhR0aJNhWz6pjAVYThPBqMDrpUSLjO7VavPBaGdDsqclO9DVNDFcGR91NVX4IR/VaSRZ1TiLHGeS4MzS6kwHKdEhpwW18oZQYrqr8+B8z5GVoe1Ds2e3h08eSy/O6dpbuLk4NeYi+owAfjizBG8UkUxSnlunT3i9hXSgnA9U6sMNxUilZMtmJH4v22akGlUknZKokRJVIqJaQASQs8iFAbOlEIt2lsEbSw101Q/11g/3BTta1PUZTsRH9VpILh3BocLZlTiHCu9U4l2gYa4mRQy8gtc1UimsjvVuLncdBFopzk9Y2xpYMTfOpYHtMtglgYDv9ADrKUU5AUo0Q6K4ljDRC5yiBPUiQP5dydBNPIE1CSuhiBqJaZCEDknqkYwRqbdgyzZsoxPb4sa1+/DdAfxABD8cJ42nyDM50nwdaaGeNF9PRlWgzhcos/XU2TraTB19uo42XUedzFEnMtSRNGU4SRmMkfrDhE4fruLApPUI+EF+BeSUQHYRDPwHKAH/bEI05yMoUQQCkIgwbmk1NawU9slhnwz2y8EjhOIkosXG9biUiZC1ETN2IjDTGQc142LmvNz6oLAUlVWSmpa8ob1g7270D3Q0zE6M7l8Z39jXvbbUsjLftrbYeXCt8+iBnpOHB85tjVw7O3b7Uu/ts403TuQvbybOrwdPL/pOzgdOLTZcO37m47df/e5XH33zizvvvTZ7/2rflVMdZ4+1njrUeuJg+7EDfcc3xrcOz20dmTqy0bk86+5tEpcivLoAJ+9j57zsLCrwpaAQEjXElW25yERf/+a+hVPH913YWb90Zu78SfAi8dVJ10yfeaLTONZmGGnSD1R0vSVdV0HTnlc3Z9SVtLaS0TflrW3F1FBP4+Ro++xk7/zM0PL8xL6VyY19g/uX0tODwfHu4srMytmd4zeuHLl2vufIqnukWd+cNTZkjaW0LhuVBe1it17s1JrCdmvErvXogFCigCaXVS6xyCRmmcQiVdikCqtYauZLrVK5Q8VWC1hKAVPJZwEToxZxtCKxRcnViRkKHlHMBEQhitlECRALJ6Rj+TQMl4KwSUDwrtBsyoAoKFQAYBAmGcMFBGKhVgaYmJrIZDGHruLTlVyqnMvUCjlGCdsgxIvpWAGNIKGTZAyijEaWU+kqOk1BZaioIjOPo2EyVWyOliezy6R2ucZvsKV8gwsjbKsIK+OQ9KBaMNC8VkbIyYr5OKmwoD4hKGaElbywuSBsK0m6m+QD7aqRHs1Yv3lu3Le+FDtyILO1mTl2wLM4ph1qEbQkOQ0hUVNY3Z60D5dcU03emRbbSEnbnZS1xsQtCXFrWtHfHN/cmHjl8dwbzyefPe69d6147kRiayN6aMW2MKqf7NcCvzLaLRtoE/U1iQdahL2Nwp4yELejwOsq8jtLgu4yvwvgrcBprwNitgK6ZLlt9QJAnYYkvRCl58OsujA3F+ak/OyYmxtxsoKOGg9eSsByJixngRLRMPF2LjsutwwEMwc6Y2s9oYXe4MKQZ3pA19ciaEix8iF61gNEq1bulJQDOBViwoIJ6WCvElQ3sF+ChKWErEbQ7nUsVepPA5zsL505MHT7YuOZI6bxQW5zllEKMYohaiFAKfgp9X5SnRuXsmHiRjiigcMqJKoklvX6lbqRZ9tDr5zPnDzo37fAas7QS2Favb/KFTcVLT3ElAMbN6MLiwIKKCxnNJn1c9n6M3OB/WO6kTZhU46c9BKjVnzIhPUbMD7wDjWQW4HmCnWimWJxCRW/zWccqDe35kUJvyjsJVu0RKMSiGSSA1GsaqbLyHFbKDY9zqQgmKVkp1IQs5vL8VBnKT3Y3jA53Dk/3TAx6u9sU+bS3ICL4jKS7VqiTYm3ylEf4JSTfCpmymrszOYX+gdOrI6cOrQAmqJXz/dvHQ1NjqhaijzQXkh6aBErNWyiRsy0uImbc+kqsUh3OdXbEu9sTPe01vV0xltbjJmkyOdm2U10s5YM3qdORtJJiHoxySylO9WyuAt4pvqRzvrhnuLYQPP0aHli2NXWLMnE2SEXHbSq3AaiUwtYUu3VURK9SkJAQ01ZZS2J+ELvwKn1qQsnxs4cG9w+XNm3ZO1poSW92KAei7bc1diYGhtXY4DPQHMjKuGQCvynAC2QiGZXmCiojoF0mJgGjmrgmBqOazBJHTZtQJU1AeFyZlydFVew48pOfLOX0BYgdoWJfXHyUIo0liFN5mmzReZyhbWvmb2/jbXewt7XCsRZa2WvtTKWGukLDYz5EnuhgT1XYs4UGJN5+niWNgbokgLGhdATBpRCciYkrgOWBfLKdhMDw8A0AxfiVQD3DHtkEDj2y7FBJSaoQnYV1mDBm08Y8EkTIWMh5tBUK5SSl9oUoDQEaA1hViXKa0xIWjPqzoK5r8k90pmYGa5fmi6vznUfWJ3b2pw7cWT06IGRY4cGjmx0HVqr7F8oHVwsHV4unFgtn9koX9wsXT5cd+lQ6vx6/Nxq9Nxq/ML+9jtnrvzah6/+4BvXv/5x370Lga0F5+akbX3IstxvXuwxznWbZroC88ONB1bGj2+Obe5vBK3Ungpo1fIrEX5DVFCJyppS5q5SaLgjPzNcXJzqOrg2sX1k5OSRtmMHshvz/uUR60KfeaHbvNBlnus2z3SaJtvME23G0WbjQKOpvwJezdpdtnSVbd0Ve0+jq7fZ298eHOpKTwy1L81NbKwvHD20vHV47Oh6YWWya3P98LXLZ+7d2L5/Y/DUocBUh6WzaG8vO1oK5mJKlXArAha532iJOawxhzFsNgbNSpdG4VSrXGqtV6tyqYwBvSdlMwXUaqdMbpcr3BqhRc7WiIBH4YBSJ2ZrhOBLlppPBxU32veFiiBk4EUMPIAK6lRQqACcoCWLiApABeAEQAWIRcHy2VS5COFSkWoXWVUkkpBJlnJoSh5DK2TqRVyjhGUUkqR0nJBKkNBIcipZTqcoaHQ1VWznSW0soYHO07E4Wq7ALFa6VaaIWRc0aALabGeWouPg5BySQUq2G6geCyPoYsX9nHSIm48KymlhY52otSDuaJD2NCsHOwFUTLOjrpW5zPHDTRd2CicPq/orrFKQUwpo2hKpha7Rc2uHH595+o13v/R73/vOn/7Od/7st37tj7998q2r6bUOVXtY1BwTdZZ0o72BtYXwxnJgfc4yPywZBKakgd9Vz+ssiPuaJf0tYvBI9FSEaB9XgdtRz2jOctryvM4iwAm7vY7dmmMBm9KUpIJGfHOG2pCkllOUcoJSiJJzQXouiOaXjLtpYScj4mSGHeygq8aDlxMKBkbPoriF0nqreyyfWGlvOjHTfmZ/8ciqfbRL3lLHqYsy0j5a0k1OOEgxKxA+gm4ohgkb4KAG9ikRnwwJSJGIjFKvVw3H/AfaGy4sl86uFU+tV04fDq5Na4baOE1ZRjlOqQ9Q6n3kOh8p7yWknbiEFVNNvYnWREkVrl7P7Q+6Njt7Hxw7+sWnJ778zvLrz3TjA6zGFL0cphZDwNmQc25syg5FDdVFqho4osKntew2d+BQ59jDrbU3rg9fP1s4up/XnKNlvISoFRs0IH4d7FFD1ZoFtKCBGSLlzKaRQuPWfPvpffUr06mRAXk6RkZNhpZkURMsKrxZiTcp8EYZ3iwj2BV0n06S9Xg66utGu7KjPXXgszc/0zI37e1sk2bjnKiH4bdSXXqSQ0tyqElOJcmrokX0wqLfPdxY2Tc+eGLf8MlD8xdPr12/2Hn0sHu0X9pUxy3EmSkfPWanxa2MpJWdc8i7ErGF7qGdfaOnD7btW2yZn2qbmUj39Rjr8sKQl+W20u1GqkVDNimquTRkNLuCFzIDyKX7m+pGuvIjvQ3Tox2LM+WpMWdbkzQTY4dd9KCV6jeT3HqiS4N2A7rVeK8aHzFQ65zavrrixuTUpeNzV3bmr54fvXAqv39R1d1Ay/vwCTMubSTWmYllC6FgxuVM2Iwek9JjULehwyT14CqqjAmfMeOzFnCAzZixGRMGHICbcyZc3kKotxGLDiBC0UlscBIrLmKjh9TkIzb7Se1hak+MNpSmjuZok/UAJ/SFRs5aG2+ji7fZwzvcyzvUwz/UK9zsE2/2yTYHJAf7BAe6ePvaeautvOVm3lITSpfZEme2xJst88DBVB1tKEXuDBHLTkLGTEgY8TEDPqrHAfMBsBHW4iI6QjWFOBA+bgA3EFImABJc0kjImvE5G6HORqh3EMtucqOP0hxkdMRZXWluT07YW6ccaLCMtnqn+qILo8X9C31HD87ubM3snJg7fXzj4umDl89MnzoGmvORqUH3cIelvwmtuEeardOd3tWh4KGJ2PZC+vx65uJ66uJ68sK+1KWN8Wc3H3zn1579xjcv/Nr7DTe2gH2xHB41bwyZ1vuNyz36hU79bLtxujO0PN6/dXj42MHCyrR5qEXUmRV3ZNQ9xdBkb/v6wvjRjaljB4cO72/ev1y3NhtdHnfNDphnugGTdHOd+sVu02q/dW3QBrTcb1/os8712Ga6vbN9ganewHiPZ6TdPbyrNnDsH+0EpiQ40ZuYGm5fX5rfOrbv9Nby6a3WgyvgPRy/c+PC43vbj+/0nj4UWep3Dba4e5vdnQ3Wxqwu41dH7Zqw1ZJw2NPuUDHsq/PaEzZLxGwIGCxRsz/nzrbE080Rb8ZqjRhkVpkYeBerQmSW8wxSnl7C00s5OgkbHX1BoUIWM0kiJkkAxCAKaHg+OqCCAWKRgBAW8CVEDIcMuIIes0goVGo9YAAkFOBUPhVRwKBKOXQ1n2WU8O0qjkVG1woAVAhiGklGlzgEAiuToWWw9Cy1X2pLqPV+qdQm4JsFKFQ8SmvcYk/Z7CmrI2ujatkENY9sUZEdBrLbRPFZKcCsJIP8XafSmBe3FmVdTdLeZml/m3Kkyzw94lmdBw1ix+yIuLOOUfSzi17PeGX2wr4Ttw6dubZ+6cLi5bPLz145/fTp9v0HR5+8dvnZh3evvnFu7vKctT8ibQsJWqL8liS3NclpSbFbMuKecufls6XtDX57ht4QppRDlHKYXBWxFCAWAqS6ALHOR6gLEAohYiFEKYWJhSCx3o/Pe3BZFybtQJJWOF7N7AJq75Ae41cjbgVsk8AWMWIQ40zSGg5eSjBCMm2rJzbXVD48UTg0XTq8GJkZdY72SipZZjpAjzvJYSMeNFoDWmxIiwlo0bz/PiXaaYBuKSOFQ1JsRk1vtelnspWL8503j5TOb7Rc2iqfPuJaGJd0NXCbsuxyklEKU1GceIk5D5prM2bazVsAJzRIWksomyVD8dTW6MIblzpuHht4fHnswY3Oq2eN04Ps1jpaQ5RSDAAO4VJ2JG6EwHdFtZi4Bp83cDsDkc3+gdtHhu6dHnt4ee3Z3eHrl7VDnbRigpBww0Ej5NPCATUUVEAhBSahpRZtpokiwMnYtZMjV3f23b8xe/mcva+TEfGQ3UaCTYUzK3BmGc4sx1nlRLeKFTUD3xAcaEyOdmYnBhpmx3tWFguTo/a2Rmkuzov52CE73WeievRkj47s1VICWkbComqLpxd7u48v95zYP3Dq0OKVMwtXzhXX12wD3dJKllsfZWW87JSDmbIDlvAb/ObRYuOxuZXbZ+ZvnZ25cX7z4d19N6+WFucsjQ2SeIjjdzA9ZppDD8wTyaYgA8h5tKKEw9meLU73tS1OVGbHWxam+tYWyjMTttYmUSbGjboZQRslYCF5dQS3Bu9W4T1KfEBFiOtZZbdlrNx+fGnx2s7SrYvLt6+MXtiJLU+J2/IA+WjGw4qD1OYmtbmAiM1OfIOdULaRCjZivQ1fZ8MDVNTbSeiSYAep6CSVXOSSm1xykcq/EBGUFTcJRYiX3Owjt/oprQEKAElHmNoZpfbEab1J+mCaMZpjTBWZcxXWUgt/X5f08JDixLj0xJj8xJhye0K1PaHcGlcdH1McGZFvDkk3ByWHBsQbfZIDfaJ9XcK1TtFah3S1U7GvW7bSLgAvAl5qvI7Wl6I2AxPsIuccpIyNkDYDbOBTJmLaSsxYiVkbOQ+8iI2Yt5HqneSCi4iucLbjAPka3MQmH6UlSG2P0LsTzL4sf7gon27RznVZFwfiB2abjq73bh8ZPb01eXZ74uz2ypWzW3eu7Dy4dvr+tYPXz/cfO9B6YKmyNltZmY5P9joHmgz9ZfVAUT5cUs80m/b1eY9NBE/NRc+tFK4dPfHFt97+7e+987vfe/XXv77v3UfNt7frLm/mzu9P7qxET8wnjs6WtlaHLhxbvnZu9er5+XMnu46u2yY7pQNFxVCluD43f3p77cLO6oWd8VNHC/vnfbMDrtk++2yvBVic+W7zYq91td91YNSzORk4Phc+Ph8+MhfZnE0eXigdW5u+uLN68fT49mbz+nxyYSS+MJJcHMssjedWJutWp3JL46XVmfEThwEsNy+d3XfuVPfR9fEzWzuP7l1+9srWk3vtpzZia0OBye7waE9wsN3dXrQUYsaM15hyB8rRQl+5eaS5vjsfKPi8OXeoECz3FvtmOgs92UJ3yp2xGoJaiVUmscglVqXcpZW61GKHUmCR8U1SnlEkNkk4ah5NyqKK2VQRmypmUkR0gBYCj4bnUbEcMoaN+g9gVkAJV1UdsQciYOifDtqTMOBLAB4mCQCJLGGR5RyqVsB3qNhmCbAsZAWTrGQy9FyZU8LUMRlqDsfI1YaUlqTalTMYogqlT6HyqjVBjS6iM0QNnoLDXW+lGbhkvYThMrDCTnbEA6oLWszLSIfYuTi7Ls4qJjiVLKepjtdekvQ08dpK/M6SsLuiHO4QA99QDrILbt9IaeH88sEzU0eO9rX0h6INTkfW6sqA0uHM22IlT/dw/dFTM0evrk2cGgtO5dQ9EVF7UNAe5rVGOC0RVnOE2RxlNAUZjQE68NBlP7Xso5S9ZFQ+csFDKXjJqDykopdY7yEWPaAk1LmJeTch78Rl7JiUBUmY4KgRAX4goEE8CsQpRWwijFmA7qiv5tR48FIis9YXn+sPTfall2ZC4yPapjI3FaRHHeSgCe9RY9xy2C0FbXx0ZYlXjs7iBSVo7wekmJicWNBLBsKp44O9tw62Xj/aeetU04UTyc01w2iXoC3PakgyihF6IYh2dtX7USpknWjygCjAiRZOavD1Rkanx7hUbriyMvLq2YFXLs69/WDljac5YBjHe3idJXpTmlwM4TIuwFgI4CSuhxJaXJ2Z3GiXjqbqT09PPN5Zef3W4mv3Jl+51Xn5jH1unN9RIteHcTEbEjbCQR26101UQ8qb+e1B/0pP8+mVwWsnxm+cXb5/ve/sdnR5VtnRyKyLkkIWvFONDjM4FASXkhzU8bJOe3ddZrq3sjheWZxqXp6tzM9EBnu1jQVxLsaNeVlBBz1gpgKi+I3UoJ4RMwnqvc7BUuXARP/2+uDO5uylU+s3Lg+dPJFcmtF1N0vKaU4+xEr7WFkXI+tkF7yy9mhgob3/4sb6oyv7Hl5bf3D96NMHB+7d7tw66p0YUjUVeKkgI2ineow0l5bqUtM8WnbYpC4GE8PNzcujLctTnatzfQfXBjbWc5Nj1rZGSS7GinmoQRvJbyb6dCSvhgDkUxNCakraJGgNRZZ7Bs4dnL9xZvn2paVbl3vPHHfODLKak4SCC19yENpd5H4/eShIGgwSe/2kLj+5009u91Ha/FRQ4Va8lCYftdlPbw3QWkPU1iC9LcwAagdliNaGnqG2BaitAVDSOkLUjhClM0TtitB6YtS+OK0/RRtI04ey9OEsYzTPHKtnThbZ8xXRSqd8Y0B1dFy7PWs5u+i6tM91eZ8blBfX7BdX3RdWXedWvBfW/BfW3WdXnGcWLSdnTSemTMcmrSemjcfGtYdH5Os9wqVW3kIjf77CnS6xhtLg7ZEbvAB4KPYKTnLRRSl6qCUvpeShlD3gc0ip+CiNfnKjnwrKJh+p2UdqDVE6IrSuOK0nyezPccZKkulWw+pA8Nh84exm5+VTvRdPVU4ciK5OOqb62o7tP/7g1qVn9688f3Dx1ftr188n54YdAy3m3gZTT8nQWTB0FS39FftIi2OywzDVop5qUs+1mA70u7emExf3r7//6uu/8713f//X3/7dHzz9zW89/MFXH3z/K/e/+6VbX/3g6hffvvLRG5fefXbhzadbr9ydu3a2dHyfc3FIOdWqGG/Uz3Z1njy0//qlQ7evbN6+tnHj8tq1c/uvnwXad/3c1OVTfeePjlzfmbh1ZujW6aE75/rvnmu7cbLx6onGS8ebLxzrvrC9dPvq1qPbpx7c3Lp79cD1c9Pnjg+e3Bw8eXjs9LGZc9srl88cuXXl1N2bp+7eAAfzF071bG0uXblw8dnjG28933n+qPvcZvLAWHRhKD0/Ghvv9fc1OZsyrnLcUQil2tOlgXLreFuhrxBtSeS667pne+cOzU0dmCwOFELlgC1p0wcNKq9W6dbJHSpltX9M5dHInEqhFXBFwtPy2QoOU8ZhSrl0KZcmZVMlgCsskpBJEKDTwLBctB8M2BQsShcyBrUmwJHsuhNAkZqwQCwilk0i8KkkCZMkZ1JVHIZWwNSLgFkROuUSj0rqVbJNPKaGy9HzRQ6JIqDyl+3+Bluk0e0tOoJlX6Yz4cw7Ag3+wkA21R1j2SR0m5oVsLJjHm46xMtEuXmgOCef5BST7FKSVU6zm9FxC3FHAx9UfY0ZemOK11oHoCLuyvGbQ60HB1ZOD64eamxodykiWmnIKI2aZFGjLGaWRs2SmEUdt8Qbw2uHexd3hgbOjo7fPjj7+NTi83NLr1+YefXs9LNz069eGH/lTO/dE713t0ZeOddzd6v95rGOm8dBK7zx2kb58v7686vpMwvRrang0THvoRH3gQHrWq9tpdu81K6ZbtTOVBSj9eKBjLA3ye8Mc5q9zAYXp+xi19lYaSMz9lKzFFcOrifnZiztreJMkhl00/wWqtdA9GpxLiXiROdxIW502zIERYsE8kngkIyQUrMazaqxWOxIz8iDrYU3bi2+ea/z6unogWXlQDu3OccsRen1IXpdgJrzEdOoNUG3BIgZMHE9ApTSE8sWfn8kcLi39dbGyCtnBl+5NPna3Yknt5MnDijGejldJUZTglAMIRknlLLACSOcNCAZI67eTOvwmuYbKpeXRx+emn56bfOj149/+EbnxR3D5DC/vUQvhgkZFwbN36nf7eKglx2yobT/wED35f3zDy5O3bmw/ODaxOWt/PKoor2OWYiSMy5S3EaImYlRIzliYGSsytZ4bKqjuDQKGp4d+xeHjmw0ry45ezvkxYwoG+EkvOh8iZCNETQxQiZGxMzJOpSt0dTSwMDJ9ZmL25Pgo3vt3Mq1ix1HD/onR5RtJWEpyckF2VkPJ+dh1bn5jX71YDazOTZz+9TGK9cPPL557PmjM2+9tnrnZuHgPttwrxK0dyppXn2YAwxNzM4Mg3+5SZh1mTvSxYWB3oOLHRtL3Rsrkyc2hzY3EhOj2qYiPxNmRl3UkJUUNBH9enJARwpqiSEtMWag5q2SrnjmwOjk1ePL9y4u3bu6ePtq8/Zh7UgnuRzFFpy4Rjux20cdDpPHQoTRAGEYnblLGAxRBqOUvgi9J0LvijA6I8yuKKs7xulJcHqTnP40ty9TVZrdm2L3pbj9aU4fesDsTdB7ovTeGKMvSe9P0AaAKUmxhjPCiYJwEpiJAnOywJousWcb+Qtt4tUu7eaY6cS0ZWfBeX4tevNI+sF24s6J9L2t/P1T+XuncvdOZu5sZe9u5+5sp24ciV496LuwFriwHjq/7j69ZNme0R8bVx8cVO7vkwH7stzCna+wZkqM4SytK0puDZKb/ZTmAK05WFUIiALg0QYUprWDXypG64yROyPkjgipK0pF/VOWPpJnT5QF862KpS7j+nD45GrbrXNdt8/lTq7bFwdM4+2p1aneU0dWb1/Zefrw9LOHxx/fWb52ZuocqJc3ek8sVtZHMwvd4Ykma29G0RZX9dQZR1vMs2iPln6+3Xqg37M9XXfj2Ow7Dza+8Pr+j54vvfd05f1XgZbfe7r49ivzbz6cfXZ3/PH1nhtnsyfXHeuj2sUe5UIHkGKhXbvQ7d+Yrj+xv+/iyekbF+dvXV65c23jwa3Dj24ffHhr34Ob+x/f2Xrr1QsfvnX1y+9f++rHp7/0zvzzex13zvbdu9h35+LArfNTd6+sPLh15NHdU08enH/26OLzxxeeP7oIDp49uvLa42uvv3L5tVd2ntzbf+fa7JWzYxdODp/fPnb/9s23nj388K3L7746cOV49tB0dm2qeXO5uG86Otblbq9zNSajHen2qfbO2Z6e+b7B5eHB1dGF48v7Tu2bODCR7Mz4yiFfMWiM2o1Rmz5i1YWtxrjdlXJZwyZz2Kzx6aROpcgsFepEPLWIq+Sz5XyWnMdW8JhSNk0C0MKmCJlAJCED7Q1DJxkD40LBoaLi2RQgHCoyEPA0eC6VAEoelcinksU0ioxJ0/C5ZonEqeBZAVE02oiBY+FzTCKuSSiwigwRgzIkdxUs8XZXos3XOJbtWWwbPzjSMd+e7Enlh+rTfUmOW8nxmYXpgKQ+Li2nReWMsJTm5mOcugS7kOKUs9zmel5rgddW4LTlOa15dkuW2ZzmtuU1Iz3BfdPGyaa+49P7LwyPLcSC9SZxWC+OmMQRgzhslEZMkphREjXLYxZbxja5XFk6O77+aOfoOw+Xn14bu3tu6PaZyUeXB+6cG75/fgTo4cWpV2+uvvVo5tmtyVdvzL9xb+H1ewtv3V948/7imw/W3n009dr18WfXV957ZfWdRwc+eH72Gx+d+MrbS+883P/Rk9X3Hy28ee/QF147+NGTxddvTDw8d+oLz8588fnpj57ufPAUgmtEeAlh6+jhRiJ0r5PmtlCcepJTRQANdqcC71ZiPShLYJcE9kjROV0hKTGnZXfYDfM5/8HO0OZI9tRyz+1TQ3cvRzf2iXtaWE0ZRrmKk3yQmvdTsh580g7HLMCaYNAMnRpMXk9rcWhm8qkzk+23Dw8+3Bl9cnXxtYfdty97NxakI+3MzgK9JUVpCOPzXihthVMmJG2E80Zixc4fino3e0oXlvoenBp6fOnQ+8833nm1cn7bND3K7yjSK3Fy3oNP2bApMz5joBStgMb83nj6+ETfjWP57bWx2+c2nt1vOnXcPt4bHq7Y2yOCnJOatJNSFkrGQi+7hV1R+2xzaXOmZ2tf5/H1vuMHB45t1K8s2gBOyllBPsrPBFhxNyNqY0aszKiFmbIJSj5dX75hY2LhysmFq2dmrpxev3N16caV8sF1+0ivrLUgKCa46KQdDxeo6BF3REwT5dShkfXHFzee3tr39PbOe88vf/zB3J3b2f1rtuEeFXDNDUl+fYSX93PzXn7BK6oEdF3J2Gx797GF0Z2Dg9sHZ85szZ/eallf9Qz1Kyt5Xi6EDvVH7ZSQiRTSk9HVQnpiVE9KGZkNbutUQ+PWQvvO2vzts/se3li8c61wdF3cXyEW/Nh6G7bZge3zEUaDhPEIYSJGGA0DEceilPEkczrLmsyzJvKcsRxvOCcYzolG6iVjRelYSQxa8RNl8VhZPA4EvixLJ0ryqQbZVEU6Di4VZBNF0Xi9YCwvHCtwRvOskaxotqJYaFGvdnLnm7hLraLVLsX+fuXBYe3hccvxGWBBYtePFB6eaX12tf35ta63bo29/2jmC8+mP3oy8u6Dvtdv9T2/2fX0WuPDs9kbR+JXDsavHPKeW3WcWrBuzZiOT+kOj6oPDkjXu/grrezFJs5iIzBAjIk66kCK2hOndsdpQMCCdMWpXTFqT4LSk6ChXEypxkqCwSywUBSgoTR9OA+Yx5ouc+eahAstyn09hoMj0TNr5WvHY2fXjWtDlvn+/KGlxOqktq9iHmtrO75v/4PLV99+cvOdV86+cv3w1a2V0yvjR8YGDw53bQw17e9LL7ZYh/PS7rRyqGya6TBMt2nnWk0bvfbtcfeZWdfZRee5JcfZBcfZeduZOevpWcvpWfOpadPWpP7oqO7QkGa9T73Wq17rAVKuditXutWrPZq1Pt2+QcP+EfvGpGtz2ndkIXR8OX5iLXp8JXx8ObG9r3jhWOP1nd6Hl4ef3u5/cmPk2Z2p1+8vvf3k0Aevn/jwjZ0P3jr93hvn3n794juvX37n9RPPHh56fHfzlbvHnj488erD468+3Hh0e/nutZUHN5buXl26c3X21sXzrz998MFbT770/tX3Xxu6eiK/Odt8ZHXs7LH+rf3188PB3pK/Ldc63zVzePrghcPHrmxtXjq2cmbjwPnNkYMTxZFKprsu2Z51F8PuXMiTC/rLMX9rfm5ns2u5P9AY9hQCjrzbFDWpXCqpWS4ySAV6iUAj4muEQFy1AKCFIefS5DyqhEMRsygiFC01CehAZD6dVBWZT/vkmEri01AJaejyRhmToeZzTRKhTcaxSYRWudgm51ulPLtc7FUpAzp5QKOJG91lR6zTXT8cH9zXMrrRv7g9s3Z239TxuentucxIXhy1iBMeRX1c3pBRtBSkzQVpa0HWVpK01ovAx7ytKOgoirsbJF0VcRc6nM5qy1HKEXIpJupqDB9Yqpw+El0bXrqx//Cl0dHZiLPeKA7pRBGjKG4SxyziuFmSMJvr7O1T+bWz4zOX9qUPzs4/f/TsD3/06h/83pMf/fbrf/z7b/74D9//yY8//OlP3vnxH334p3/6pZ///Is/+9l7f/InH/75n7//0z/54E//5KOf/fnHPwPn//StP/rdt/7497/0f/3Zx3/6x+/+0Y9e/73fePW3vv/kt77/8IfffviDb9//3jfufvur97/9lZtf+/DCV967/a0vX/nK+1tvvnLyjVcgpEaElxAMH4oTssNAsKmJjt3lCzLYVt0OE10SL4I9YiQspZSM4v6wfaUxvNkbPDiYP7mWPb4a3lgyTA6JO8vsxjSzEmOWwrQ6PznjIaXdVZwY0VGTmA5JaHD1Rla337LW1HB1ueve0Z57pwcfXp16dr/5ynnT7LCwv5HZUU9vTlMqUULBD2fsUNoMZwxI0Uzt8qnmi+UrKw2X1tpvHW+7szN47+r4wxup7cOqiX5uZz29EiXmPcS0DZ81E+ptjBaPdDwT3RrtuXnMvz6eO7488+Ba6tC+wPK0vLcFHd3J+WlJGzVtoWds9Donu8WvmSimj00M3zwxeefc6NVT8zfOj1/YSa0sGHvblU1ZUSkuqIuwM3520s2KO1hJBzfvkrfG/FNtQ6f3D5090n/myNy184fu35q+dD61umjq75C35AXlKMAJt87DK/j4jUFFbyq42t176eDB1271Xzm+/Mqtsx+8fuGDd8auXkmvL1uGOlWdZVFjklcK8+qD/IKf3xCUdSVcs83N2wuL986u3Lu0cO3cgTtXV69dbD603zLQKWnI8nJBWspNS9ioMSslaiXHzKS4CbCEUm/jdUT8y919FzdW7l868PTO0oPrPedO+JcnWe05XMGFlG1IuxM75CNMhvDTUfxUHD8Rx0/GiZMJ0mSaPp3jzNXzF8qipSbZcptqpUO33GVe67OvD7o2Rl37R8xr/aa1fuv6oHltQL/Sp1nsMC516RbaJbNNwqmyYLIoni4Lp4qc8TrORD1vusybqfAXm6XLHeYjoxJgKQ4OqI+MGbemLdsL3vP7UteONj280P3azakPnx79zhdv/t73Xvvxb3/4sz9++Pu/cfJbXxx6fjt7/bjv3D7nyQXfzrLt6KRxc8RweFR/ZMx8bMJ4dFxzeFi9OaTc6JPu7xKvd4rWu4T7OvhrbaylRtpMkTaWpw1n6ENAWfpghjYIyJFDNZThDOdlkw280Tr6aI42mqOO5+kTBSawUPON3KUW0WqHbL1LsdFnPjFp257Wbwyo5tpjm3OxAzOynjp5dyqzb2Du2tH1G8fXLx+cPzw+vtI9va9neKGpbTTTPJJun6jvW2nv2t/TsL/XPVWU9iXV4xXtVJtqslE+06A72GU4OqA7MaQF2hpRb42oToyoTo6ptseUVcmPDskP9ck2uuUHepQH+hQH+sE7AVJtDCg3+tUbgwA5hkMjelSjRgDmwxPWIxOOo5Pu47OhnZXE+Y3ctePdr1xd+eD5/o/ePP7l9y5+88u3vvf1xz/49tu/+YNn3/7arS9+cP6Dt86889qBJ7fbzx9uOX2w9dzhrvPHBq7uTNy+uPjwxvqTO2tPbi8+uL7/yd37X/zg1a9+/OpXPrr0wWvDN7cLRxbaju8fv3Cy98T+4vJYYrQtMdTad2Dq3KNLj95/9vSj1++8+eD6a7e2b59cOLXcPNvVtzqU66sPNMR9pYSvIRHvaRg8sDR6bHXhzPr6pYMTR2cyAw32jFsX0Gs8WoVdJbcrZTaVxKqS2DQiu4pvlPA0IpZKwFDyGQoeTYHOD6bKOVQZlwaE8gaIQ5WymFImQ8KkiVFRq6KBM3IWQ8XhGsSAIqqAQexSi10aTchkTlrVQa0p7XE1Z71tWWvJH+vLdq62dq+09u/v2LyyceX166ceXTh8a/vQrSO5qQZVnU9VjOtaC/reFttIl76vVdvXoupuknVVRG0lfmuR31HitBXZbXleez0LnXmVpjfGaeUorRLhdhTim/u7b18ZvHlu+cGp7SeHd+4tLR7v6lvMl8dTLbOl1tnc2P7W+e3RA9fXWrdm80cWMscXXUtDyqEW2VCzYarXPDdoXxx1Lk/51uf9G4u+A0vBgyuhzbX4iY3EiUOxoxvhIwfSJ481XDxbf3YLnIyd2Myf3s6cOprcPpI8eTi9fTi1tQm+zJ46nt0+Fjm8P3JoX+rYofLpk03nT1dAeWq77dTJlwkVmtNMcxmpTiPFaSDa1FizFDGj2U1gmwB2C3ExKbPRapzJJbeG6s8uudcGg/snG88eDR9YkPQ28Vtz7KYksyHOKoRRg1IIknMebMIEx/VwVAdwAie1xKJZOBSxH2jvf3xs8NGp8adX93/wfO61x+CPohjr5nSX6Z15dlcdpRLD1XmQrA1V3kpudPIGw67Nnu4Hx/oe7XTfPT308FL3rQvNl05ZlydlI+3czjpqQ5RU8BFzDmLeTi25eH0R82qlcmW1/+6JiceXh+9eHLl7OXZg0TI5IOxoYFdS6FTmvJeac1PzDnbFLeqO+ta7Rm4fP/LWnZkHV6fvXTr47OHEjcup/UvGwU5le1HSmOGXE9x8oLoywMXJu3kNAWBN8geGF2/vTF47NXxxe+3etUMPbvfvbIfnpsCjJm8toFajGOYXg/wGn7gzYpgs5o9PTd47eeiNO/OPr68/v3/1i++feONZ68kt/8KkYaBV0VGQNqXF5Si/FOKX/OLmkH4wlzo4PHbj6PKj80uPrqw/vX3+g9c2nz1qPHHQPNIjbavnlxKsvJ+eclJTVlrSSk1aySkTJWdlNHk14/Wl7ZmF+6c3X7uz8ezugWcPBi7vGCZ7CY1huOSAmx2YPh92IkiYCePmYpi5BG42iZtJEqdTlLkca6HIX66I1pqVB7qtR0f8p2brrx0GFB94cL7v9k75wmbu9L7YiQXv5oRlfcCyf8i4b0C32qtb6tIvd4pmKryZMn++IlpuFq+0iFaaBctNnPkGxkIDZ6VZfKBHttGvOzauPTGh3Z4ynV1wXVgLXj1Yf3+n+/mt6Y+eH//ul9/9sz/83l///O0/+dHRr3/U9eqN+KXD7hNLloPjpgPDuuUe2WwHf6KROVRHHUwS+mP4/ih5JMNdqIjXOoBHke3vlm30SA/0SA50Cw908fd3AnH3tXNXW9lzjezZMmu6xJoqsqfKLCD0uARKgBDWdIU108CYLdPngMC7rbCWmjgrLbz1DsFGl+xQn3yzV3qggz9ZlIyV9TNdypFGcU/Gu9AycG5p4vTS7NHhxpGMt9lnLLoM9U5NnVOds2vzNnPRkemNjK239B3pKW/2GkezytGSYrxJNJxjD6VFq2XRRot4s0O82Sk63CU83C043Ck40iU62i0+0iM+2ic5NiDa7BUe7JYe6lMcHlQcHpAfGZCDsirV0WHt0RHd0VG0PD5m2Jowb03Ztqfs2zOeU4uhc6uZa0cb7p8feu3uvo/ePPTxW0c+euPkl94+/MFrvXfONV8+Xt452HbuyOC1Uwt3Lq8+uLp078LE9ZPd5w7VHZ1Lb05nj8xXTu7rv7o1euvs1P2rW++89tp3vvbuD7759ve/fvHD1wevA6gsde8cnbpyvu3YWnpuIDXRlZ/uO3L7wpMvvv3F73/tta+89fDDp698/Pz++08OXD+xeHHj0LXj6xcO1g03RbtK6aGO3s3lhYs7xx/cffWrX377O1//+AffPffq7Vxf0Z7zWWI2Y9hkCntmDx9aPHn04NXL1549dpciApOMb5RyjGKWQczWi9kGscChtdYnAs31uYH20lhf/Vi3tyHFM4rYSg5TwWUq2UwFh6XgsDUcto7D0QslFrnaZ/SXwq5CSBEwuyrpwlRvZaIt0d+UnxttWRntWBpoXOorL3ZNnVyaO7Ny9M7O3Q+fPPv6u299+8OPfuMLzfv6XJ15Y2u9sbfJPNLpmBqyjPVq+ttV/a2K/hZZT4ukp0k53CHtbxEPNPG6ivSWNKkSowKVgoS8i1TvozUmVRO9Xdevbn780d3f/N6Nb3385u9+4+Pf/9pHv/Ol59956/JH969+5fnco4v9d3ZaLh3zrAxJetK89ii/My3ozIu66oRAPQVxf4NqrE073qWd7NZP9Wkn0APtRLdmvEs31aMa6zRM9emm+kyzA+BAO9Gjnx4wzg0ZZvrNs4PqiS7laId0qE0x1KEZ7VYMtsl6m4Wt9eLmOnElKyml+OkwhKkR4SUE3WWhucwALRS7HmsCROEjZg7s4OJjYmG7PbS/peXSatPljeSx+dbzxzOH9+lGOwXt9dymLKshwSjFaMUwpS5IzvtIOQ8SNSHh6u78US0mpSdXzNLxePBE39jznYarh9pun5p94/7c88fBQ2uSkS5Od5HZkaY3pYilEMAJJuvA5O2kgp3Z7hbNZ/0n+oeebE2+cWXwlSvL77469OB6euuQdqpP3NfE6yhQmuKEgpdQ7ySVnfR2v2g87Tzc2XHv8Pzr16Zevb74+oPFZw+azp00Tw8Luxo4zVkWOsbjp+WctHonq+JRDGccq219Nw/ufOGVUx8+O/vRW2c/eKf38gX/yrxxtEvd2yDvKIqa0/yGCLcY4KC+IYD6hpnm9lNLy/fOTdw4PX3jwsk3Xzn+7FHb1nHvzJiuD7ChKG5KCctRYUNQ2BRS9Cadi+3tF9cPv3n7yFsP157cuvrld25+9ePlB/eKhw+6p0aMg22qbmCf08JKVFgKiyohaXvMOF6qnJzbeH55+92HG6892H776dn3ni/fv5HbXDeN98h7K6L2vKApySqGGHkPI+uk5ezkOjul5GB3h21r7T03Dm28du34u4+Pvv10/6sPitsb/MEmXCUINTjgDgdmxIebi2IWY9BCFJqLwXNx3EIav5AhLeRpi/WctSbRgQ7N0UH36bns9cNjz2+c/Np793/4jdNfeff/Ze0vgxvZEnRttMrMbFm2zAyyLcmSbTEzo0WWLbPMTDIzM7OrzMzlKruYaxdt3s3T3dPdM7uZZr7z44t7U9X93Yj755wbdzrjiRXLqZScsqz1rDdXZi7DrUlCV6VvUYp7Bs81leakJdkrcbYKjJ0Sa63EOGpxEAPbIZ3gUcDyKRX6VIh9qxV+9Uo/U5uoghjV7rUK97oUSHNacHtORG9R7HBl0lQDZalTsjWRe7He8PTOxIcXMx9eDb66n7m7oFwdazzfWX3zeO3ds/FHl7krk7zBFkxTCawiM7YsPapQFWZQBGSL/fQsz1SyjQpnpSM4F7A9y8XAznvXKb3rVD71GohRA5Q+Rg24TuVZrfSsUnhWyDzLZR6lAFLPMqlXpcK9VOpaKnEvl7tXyN3K5a4VUicg3JQJncvFrlVSjxo5gFuV0LWc417OdMkkuqgJkDR2UAY/JJNDM6an9ebmtqemFDBiBMhgFiyQEefLiPdlxPnQYyH0WF96bBA7TphN1Dcr1H05mBpNXLkWks52UxNcdXhwFRtkFLrVCTzqRKBGGahBAWpM8WhIATWkgJu1Pq2pPq06nxYduCXVt03v357h266HtOn92vSBHZlAjgnoyIC0pgM/BgAppyMrrCs3qrcwrq8I1l+aPFqDn2jkLPamb8+XnGwWHKzqVkfIXRUoowHdWKwYbpu8Ojt5+3r96YPhi8OWnbXa24vVtxaMGyst27c79zbqbi9mjHXx2sspDQXMthLlaFvrwfreZy8uPr4++/By5uo4c7Jb2lWXPzOs6WtlVuUQ8hT4DDElV9F3a3Lr/uH86cro5vjsweLS3d2jl3fvvH20+eRi7mxz8/5B8+zg5N7G3PHu04+vjx9eHj59/NlX33z8+utXX395/vrV7etLYamSoMDFM1BJfPro9vbI1lr5YFflYBdCRAlAx/ihInwSgr3gIV7wYHBiKIQAS9KkpLW3dqxvrF4/OHjydP5oH5IUBooGgyLBoAgvUxnpDYryhsT7geMhPohgqoYhyZej1QJWtpqao0TrROQMBdWgZ5QXFo71cCuyNM2l5cMtuZ2V+vaK+sXh6Yutned3z949vvr6iaopK1kvjFHz47PU8bnauFxdTI4mJF3mpxNBtEKITuStFnimcIBE4iCm2AkIjkKio5Bgz8MCbZQFPc6GCbNlwe24CU5iPEjNBdp9fHOVYrw/c2ky//Zc1vKEbn6IN9CMqy8Mz5UBXRYvBc4LMIoMD5IDkDzlFLCS4ZPKhej4gFd800SAunxShWCdAJwKwPfTi/2yJBC9EJIm9NYJgB/9TUj99bLALIWvTuylEXqnigIyFH5pEmCHfTQCLyXXK4XtLqG78YmuLKwLNQlETf5XSsUhPtI+LsImMsQ8zPdGiMfNSBerJE9PcVR0AbVku7t8d1w3OyjsacFW5Yemyt2FVEc21smUS5IcGCgbaoIlCWZOhJqZLhwJM8OH38AHmdHC7aVxAQYqvc9g2ByRT7dnro3n3Jrj9ndAS7LAWpGTnO4gItlwMZYslDkdZkaLM2PGWPFi3VPR4RU89mhx0e64ar5bMdtdsruoX5rGNlb76RUgFc9VQrLnJtuazpaD2fJhbmp0aDFXMllRdzKbuz6Rc2uyYnuhdm+d1dkUlafzUfPdJVRHPtaBjbJnIxy5CDcZOiiHyegtNKz2aabaM+b62g7WBs/2C2YnUBWFYenyQDXPX872EVFBAgJgFJAI660ghGWwSE2Z2dPtVbdGM6d6CmYGR092WzdvC9tb4/MzI9JkwSoeREwFC3FgIdZbjgvW03F1uozZ1tb9+eqNqbqN2cX75+N3TzMnxgnVpdBcTZhOEpjCgkjI3kKCD/AUMQYwELJEphisbNub6Thaad27NXt1NnZxmDY2gCrLB3bMX82FKOggCdGNn+TCRTlyEuw5MHsuzFGC9NZTcK2ZlVsjo3e3R+7ud51sFi1PIKvynFR0MwHCTAS7oYab5yVbFBPMivBmRZgbBUk38jFmhcCPJMtSmk0Zy7mS51kr9W3SRHTkYsdqNetjnQ+Oj77+bO7FtWp2UDDSXLo+Wb05rx5tT6zKiMpPCc0RB2Tw/PWcgHQORMfy1rHCckW2CrSlMskujehewPWqkHhXyyF1SkiDxq9Z59eS5teWDmnWBbRnQQdLkyaMlMUuwfpo5ult45OL7lcPyq4OBCsjxKHG2sO1o28+b797JBrriq/KCyhICSlSRJUqYkqkEQZBYBYHpKU4KbB2MoyTkuCt54H1fBcd00lHczNwPMuEoGop0Lv3rlcDOvE2arzqVWCj2qdR59uU5tucDgbW1Km9jak+9amma1+MOrAxFdKg823Q+TXqAef5t+i9m3TewPbNOv8WHaQRyAoyt2qeRxXHJY/sosKC1NSgTGFMvhxXrdX25hb0psmKKBE8WAAr3pcJGCXehxXnwwSIhTCh0aI4VSWneCg3Y6yK2lYQViB1VxFc1Bh3A9m3SeDZxHdvAIwiBTWneDUrASDtqX4dab4d6T7taZC2dJ+29IDOrKCe3MDOnMDunMCe3KDu3JAeQ2hvXlBXDkBoT154b35otyGsxxDVVxQ3UAofKkeN1JBn27Q7M4bjW+lbU7yZdnxPBbq5WDTUWrI2Z1gYE/c1EusLMNXZjKYiRXdl2mCDfqhJ3lnJbMynG/P5LeUZoz0F8+P6sW5BewWvo6rraOvo/at7X72/88Wb9RcPmraW27bXqpYmObUFOIMClyUh5kjTW0u37p88/fz14w8vTl9cHDw+Pnt6eu/VndG92Ys3jx5/fPnyi1f3Xj/86psvvvrmq9dfvj1+fPHw/ZuL96/uf/n29bdfAhs8+/hk/c7G7P5Uy1xP42RbcW99alW+piKTlyULIyACcPH+uDgAP1ysLy4mgAKjZGsKB1pz+5o0LVXyhjJGgZ6slwdhoqPxUaGY0FB0SAgmJAgdGE2PwMuToxlxCRJyTl/t1Pnuw4/v337zzfvvvr14/1LeUNS7tfrwqy/e/PgHR68fX354efXh2ejpStfO9OWXr+//4OOjH3z+/t9//Pynb3hV2mS9JEYjjM1SRwOBICMlOF0clCb2UXFBcqarhOImodhxsdZctDUHbclKNKPBzelwMzrQS4ZZMOOsWHGm7u+nM++dhAhXUaKbNNldhvFU4H00NB81xUdF9krBeyvx3ilY7xSctwIHBryiwIMVBE8Z3lNB9FSQPBQULyUVrKR5yKmeCioohealYnprub5pAkAhkHTANAK/NBFgOLDWJB5AMBCdwAvIN1qBf7oI8J+3lgdScTzkDDcJ1VVEdhWQnQUkVy7eg433oCe74eA3LP5phH/BAhjFPjbCdEPJSH+zUDdzqKOvMiaqmEzvzmK05A88vEsx1nnL+C4cnDML7UBH2pmuZocDOrGlICxJcTdxEWaYUHN08E1CkDUj3FOdHFXGF42Xa+bbOIN1soku6Wg3obnKN0Ploea5SGl2IoIVJ8mcCrtJg5pTo80ZMTYimKeekNCkTF9tKdmf0i4PGHbmqvZvCYe7oktzvXVidwXdUUyw5SJtOXAbDtxejPLWE5ENKvmcsfJoqv3uVvfdvb57x9kLUxhjZUi2BvCws4jkxE10ZCc4cODOwgQvLSG6VCwbqy5bHzHuLZXfnu462mzYviXqboGXZIdnqoJShZAUppeQ6MHDeAqSQKJkfzUhvkisHa9p2p1q2lsxLAy17q0MHu8WzI5TjFVxeWkROkmQigORUX3EBB8J3k9NguYL+F0FVbcHm/cXy1fGOw/WJi+Pgf6gvKc9sTgH+HcM04mCFWw/MRXQibcQ5yvHRZiOdKUXr3QPna92Hq627K2O3znqO9qW9rTBC7OC0yS+SpaXmATslQsP5cxDuQD9HV6CgyjBTYUOLuTxBktqD6fmn50vPbvbe3mgGOsONCitJTgzIfymPM5CjzQvSLYowloUY2+adIIBdHIjH32jAHfT5BiqVSnTsYrvUiP2rE8J7sjETNSn785V39krOrwtmurOuzWx9Pxq9um94tVZgrEEoud7pJA8UgieKXiQAu8mMZ0y7yzGAP/roXpeSCYXpKU5pRDsNHi3XBa4TAiukXvXK30btYEt6UHtGQFAp7tVnzBaTV3sFG+O609uVT86bXl+L/v4FmbQiO+rrzjcyF6bh9UbIFlCaJk6e6F96enx7Ren/SfzhTOt6YNlsrYMbqOOatQiSoGvEMlZRfTWc30y+K5aunMqFfilXhVirzoFyKgCN6ghzakmjQG/ui0rtCsnois/rMsQ2J4d2J4V2J4T2JYDlEEducEdpsNx4V35Ed0FET2Fkb0ABcFAO96e6duS6mlUuNaKQA1Cr0q2SzrGLQUDUpHCc8SIilR6U1bmYKGhR5NSzkIoEkK4cUA0CWDHBnNjo0QIXDoxs1VdPFpSMNeaOtkeW6pxV+FdlBjXTLy3kQtq5rs28lwbBJ6NMo+mFK82NbhV692WatJJR7pfV2aAiazgnryQvnyAYKDsLwjrKwrtK4waLI42URIzXBo3Ug4frUZO1CIn6lFjtdjJBspcG22+k7vcK7k1SBlvTG4vTG4uEvQ3q8Z6SM0l8cUqYl2Wfrxl4Gj59PXVyauro2d3Tp7f2X5wMLQ707jYlztUzTNmkquzBG1VmoGWgrnRzVdPrr9+9+jbj0C5/9mzxu1lTX8LsSITU6TElqqFLXn9O9MP3j/+7Jt3Lz9/++Krz++/f3n8/O72/eOF8/X164PHHwCjvH395bv3X3/29OOTw8eHu/e2B5cGWmZ7p05uPfz47Ouf/OCbH//w7rsH998/PHmyM7Ax2rHUXzXWmtta2bM4QlExw7GwIGx8ID7eHxvvh4VCCEAKRMRrBdmDvZMXxwdvnh69enLx5sXq+X40MzGEGEVWU8Q53Pqh8qbJus3rpcmjyeWH++3rHamt+pye2pze1pLx/qz+ltTu2rzh5t6dhdc/+uZnv/7Vr3/z65//5tc//c2/v/zp53e+uH/y9vr+V6+++vVPv/nPn1x+eU3Il8M0fHR+OqYsF1mkjzXoIrKVAalCbyUbJGe4SykOfJwdJ8mSlWDOhAEu+aSTeAtGrCUrzoIFs2LDgIbLngd34Cc4iVDOkkQ3SaKHNNlThgYrsF5yDDgFC07BgZV4HyUBYlIL3kdF/AfeKpK3iuytAsQDGIXkpSSClEQPk2aInnKgAnwfyR5yMqAckJIKUgKmoYPVLLCG5aVieKRQPYE1SoZnCt1dTgX201lMcpMQXYUEJy7Gjo60paPMsTHmSRHWqHA7RNgNqxv/shPA7GPD7aHhtjEhlmF+gFQcsO4RBkx8tSC5LjUySxqVrXXhUZ1ZWCdmogMz0VTSUHZkuBUBaoaNuIkOM8cEm5NDbAVQnzR8Qr1COF6eMt3MGzLql0fkY/0RhnQvDeAShqOEYi/EW7OSzGmmS+LNqNFmnBgHeUKQgc4ayGu4nG2+czt3cy53bSp3dYLYXheUqwFpBC4yuj0fa8NKsGUjbHgIJ3lyQB4N06KpOZmYfXNec7DUcr41cHWSNjOKrMgP1Ms85XQXEd6Jn+zIQTnyE1xkSZBMenytKm2huW5vsn5/oetsY/bRRdnaErGhKjpPF6qXBWh4EBndU0j0FGA9hEkgKToonYqu06RONrTuzzZuL9aszw+cb3UebmqG+xLL8iIzlSFaYWAK00dChohIEAk+OJWaUCrVjTcM3FnrP1+r21roP96YvHtUtjBFrquA5aVFpclCNAJ/OcNHRPL+FE38UggxuVxRV2H7/uzM/YORi93Ri8O567PK5TlmU3VUXmqgTuirYoHkFA8x3k2AduElOvMSHAUJThKkpw4PrZanL7cP3t+cf35n7P5px9kWp7fBM41vKU4y3aJRhTDPTDYvwpqV4W6W4G4UoU0iATCgbxZgzIuJFqUUmwqGfSXHqYrvXCN0r5dBWrTxg+Wi24ParRnsQF10bY5mpm/g3ol8pCsoR+GVSo8vlWZMGAfOVtp2J7MnatUDxZJOA6lahSoVhGaR3BXImFwhrFDlrWW6qCmOapJ7LhNUaWriIfVKf6Bxb9MHd2QHtWcljFRhZxpZK71pRyvl949Uu3PI/sqEttKstVnBSEdQbgokg8HoMvTf27r69vXi5Wb1eE3NcFHzeHnzWJWykKMrYOgqeEpjCrtJG18qclcTvdO5oHSOayrdSUf1KuJ718q86hXejSpIk9ZklNaM0I6csG5DeE8BIAxof3H8UFn8UHlMXzHwfpGjNUljdcAuIYar44cro/pKwroBo+SapNKR6d+eBm5SuddJPOpFIKMAVM5yyyC4KwGvEPzSWTH50oQSOc2ok7ZqM/o0um5JWqcks0uR0aUpn6gcPpieuLvVerAsHWoIzhN6qHBuSrRbJg5cx/VqF7k38d0aBR5NUo/mFM8WlVebBgJklM50wCi+XXpAKn5d2cF9hvCh4vDBTwyVRA6XRQyWRA+XRY+UA8DGqmDjNfDxmoSJOsxcM3Ghg7LYQZpvpy920Ze6WYudpMkGVHdJgjGX0VXL7a6HFmvgJcqU/oqq5b6mpa7u+dbWsZq+0arR2aa51a7O/pK23sK2keq22RbjfGtqZz6pPJVUlZ0/N3bw/uX9b989/O79g2/e7bx+qOpvxJfriDV6XldZzvJA252NjqvN5qPlwcP1lrXF4sn+jO56VWOxpCZPUpUrr8nVNZfkdlcX9NcVD9Tm99YUdNdUDTVWDRtrhpsGlkY2zjY2L3YWj1YqB2vz2svzWisLO+rKeltK+lrlFXkoGSOakRhBRoYRkSEkZAgRHkSICyTDgtioIA4ykIIKoCUFMrBhTGIYnRhJx4RhocHJUeH42Eg6AiZEI6QUrI4vLc8QlOckqVmS6gx+uRqtZKCkJLSGpWgo7tqYr5norJjsGD9bP3p1//zNk83Hl7ceHA3vLy7d2195eL7x7P7E+V7+aAejND0yha3qaeN1GOHF+tBMRWC62FfL81GxPSRkBwHGnpcEZBFrOsyKDrMApMKIM2PHWTDjLdmmmYMBqVhzYbY8hAPw/RUjncVIVzHKTYr2kGE85BiQHPAKFiw3GcXbJBIS5BNAfAFCjDeQY9RUHzXdW02DpDLCswVgFcVLRfVSU0FqigkVAA0ErPm08p+lmgJWUz9tSQEpSCAlyV1GcJfgXUQ4oG104CbaM+CmM5sI0RboMAtkoCXM1yLG+4b1zX/ZWL1TfCQgFZvoYPMQ75vhziBeQHQJJblZHZLF9VUw3Th4R3qiEyPRkYFyZCXZMlHWZLg5LuIG2nSrLktyiIscHlPOF09U6JbaqD0VjO5ado+R2lYfnKV2T2E5iyiOAqIdF2PDSjKjxt+kRt+kme4n6J6aHFslSF1uLNobrj1fqThY7rp3WH24jqwt8c+Qeak4jhKqPQ9rxYJbsWNtefGeWmx4GT+5M10wVZG20tVwtrr+4Un33SNuf3tUUSZEJ/ZUMB14OAdOoiMH6ShEeijRIflMVGuacKyicH2oaH1q5MHx3PNr/cx4UlVxRJY6CEiIKjZYQvUQ4jx4WA9BMpBAww1cakt29eboyNVOw95K7ebS6OWBcWOJ0VwLN+ijgIitFfjK6T5iPCAGXzk+JJOJrtFkzbaO3tvpO93oPFpbfXzVf7Qj7WxNKjVEZ6nCUkVBSraPlOYtIviIcD4yXICWHFsokPSV9J8ujl0C7c7KzPXp/P07hpkxTGVBaGYKIDkfORUkJrgJMW78ZFc+ykWAdBQhXeUonyxKYrO2cKN/4uH+3POL2aeXTfvryIYiJxXDGtCJDHYjNcHcgLYowd4AAFxSALjkUzQxACSbyMfcLMJblJCsyuhWpTTrUoZ9BdelVuTTrEaOVoluDyN7KnzLlNSuGlp/k39eiksKLjiHmT3Tev7Fs8v3jycO54AOYGNffktfdl6NWJRP4xVxeVVyfJksIBUbqmfF5Ml8tCzXFLKTmuRVwANXicG1Mu8GpV9zalBbOtDKh3fmAi07cqyWPNcuXh/BjtaF1+mFY53yyb6wIi04jYFr0pdtDNcv9RiHK6Q5DKQYFs6M9qNFgykRYGJIMCk4nhMmyMGr6qWsRkVYHstTx/BM57ql0k1X6WdTPKv53ka5T6PKryU1uD0jsjsvprfApJChMthwRdJoLW7CiBmvJ0w1MRe7GYtd+KmmpNG6pLH6hOGquIGyqJ4iIKmE9xiCu3L8W9OBsOLTrPFqlLkBqaKB51PLAxdQPVKxXkqstxIP1pD9UqkB6ZSoIiazT8sa1DJ6VagKHrJUSaw3IMt1/ulsINl4qLEeejyomAqqY4JaTDpxMfI9GsWgViWoVQ1uT/Xv1PuZyIB0mIzi25UR1JcTPlgYBkhlqCTiH0YZKYscKgUq0aMV0SMVseNVcePVMaOVMSOV8WPVsInqhMk6zHQjcb6NuthOnmtCD1dAjRlIYx6huTSuVAstkqSO1lQuttdO1TYMFOpLJSw9jaSl4DV0nJZB1DJoaWx5nqC+3dA5VVc9UaVoy2DU6dPGOjdeP7kCpPLtu4svXg9cbgv6qlPGGqt25/rv7tdszqdN9ZStTPUd7RRMDfDq80lFWnyeAp8tJWRLidlSUraUkien5yvp+SmMfJWkRF/QVdM21ze8PrFycvvW2ebK2frCwWLP0lDb3GDtSGd5X2Nxd11hR01abaGuOl9ZmqkoThflqhk6EVUrIKp4aBkdLWfg1BykjBwvwMZzcbGMZCgjOZaFRUuprDQhN0NKVrEIChpaTERyk0OJ0QHEGH9yfDAFCVUwkKncYFZCEBURTE8mZOnqludqFicLR7rS+5v0fQ1Z/XXarlpdN1DWK1uqBfVFrBpDUm5KQrogKoUZpeTQq4vJdSWAVKAGLTRP5Z/KB8mpzkK0Ixdlx0XYcICWKt6Kj7CTJztriF6ZDHAmE5zN9M5mgnNY3nlsSAHXv4jvV8gF8C/kBhbxgBIgqEQQWMwHCC0VBRXyAwo5QfncoAJeQD4vwMAJMHD9TSXHP4/jm8P1y+H4ZbEgWWxIFssnk+mdwQTr/4n3/4cMoGT4pNMhaXRvHdU7lQbWAHYhgVII7lKsmzDZmYd0ZMHtaVBLUqQ5LvRmsr9ZAsQM6nXD1uxfNqxiGlOJj7KPDbMIAZtHOgdqo2GVbHJ3jlcK2UdKd2GgneioT0ZJdGAl2tDgQEC5AaQTUqCLNC6yiF5/MV6+N561OCwebpOPdCaU5wFNvDvwRLHppDo7brIFHWFOhZpRYm7SIq35UNPlJi1q1kRR96OtvI3JvLXJ3NVx1dRgdFmuf4bUI4VhBwQaDsqaBbcGMqMIAU7HRddK6CPFkpl65lBVylR7253tyr11antjmCENkib2lNGdhTiHTx+tgwjhlYaPqJRQ+vMLtob0K72p8331B6s1OyvCvo7Y0pyQDHmAVuClYHgCSVCAcRcku4uTfDTEiCK+dKSy5XS5YX+paHmsZmtx6uo8c3IIU1kUna0NTxMEKDk+EqqPCO8jxvorCdE5HEZLVsvO5PDlVuWtiZadpfnr89adDU5rIywf0I80VM0LULJ8RWQfIc5HBBgIF6JnIMqkkv6SjqPFnpPbXUcby0+ue492ZL2tCYVZoelSfzXbG4gmIhzgEjdBopsA5cxHOomQbmp0YCGb3m9oPl0YutodvX+09Py6ens5uiLbNoVkIUKYyeA3MxItCrAWxZgbJWizYiyQSD6lE8yNgmSTWgoxpsH5IvzNIsKNIgJQAmoxK6HYlAN5hedSIwA1pPi3pUf3GMClitDy1OTmUu9Moa0UFZRLz1nsaNkar5tuzmtIE6YTiPLYZCE0URAPpYYhGOEUKVRVQJZX8GiV4shMcmKZ0k/HBiK5iwLvmEYEl/PB1SJfY0pQszawUetjVIe0ZcT2F6JGq5NGa5CDJf51KaAcPqm9CtNcEZAjDclmyYZKsicr8jsyOGmEQEqEBz7MGRfmjA1xSg50SAq0Twp0RvuF0EKk+WRhvZTUoA7I5YP1XBclyVaaaKlNtCuhulbyvBoUvi3a4LaM6F4DtL/QNNgwWo2drMdOGimzbdT5duZyN3u5l77QSZ5pIc+0AiVqtAY+VBE/UBo7UALtL4odLI7uK4jozgZc6NOk9DSawopPk8ivReRbw/PKIIJUWB8VzluN91ZjIFl4eLsU1i2FNYkgaWgI8JAasA4WpEJ7ZRK8y5i+TUKfVoFnI9ejge9uFLgZhe6AVFoUPu1a3650wCi+nXqfDj2kUw/p0gf0ZAX3GyKGisKGTUaJGi4zSWWo1JRRxiqjRipixiqhY5WAV6DjVfETNQnT9ckzzZjpZvwsQBNu2ogar4ztyAoqkcaUpyJrsmBlqYS6zIrV7tLZ6tqBXEU+OyGFCFOQYDISTE6GKygIBRWpoKLUTE4Wt6HHUDNaVjRZmTtZa1jonnpwfvfbD9ffftj+7GnL8erE9cHkg5OC5dG8+eHOw43mrVVFVx2xOBWTJ0s2yDBFUkyRDF0gRheKMYUSTL4Ymy/CGoQ4g5BZqkqty22e6Z3aX1k8Wt+42t97fLr34Hzj7v7a1cnGg8uFi72Vu4dLdw7nz3dXrk6X750u3Dm8/fDO0uXR5NHG5NFm38bi0PbK0Pby4PZS5/JE28Jw++Lo2O7qwOZC/8ZC1+2ZnvW5oa2lhdP16YOFrpWR+slmQ2chL1cSyooNYiCDRPiSwU51fVYINyGElxwuxEOVjDgNK17LhqfxkvSipGwZvkDLrs7BF6Vi87VJucrEPCUqV5GQLYvPEMSm8aIyJDHZCmiuIixD7Kdhe8lI7mKcuxjtJEg03WkiBeeZzQ6rS6WO1mtWh2qO1xtONyv3V5ovdnofnQ4+vQAYfn7R++hk9MXlyPM7Ey/uzb2+P//6wdLbxwtvH40+vTP/2YPVD0+X3j2c/+z+wmcPhp/fmfns/sybq6HHJyNPTsefX4w+PR99cjr08Hjm5b3JV/dGXlz0Pz4efHo6+OS47d5Gy8XthtOV6pPFurOV+qOl6v35ku0JzVKHynQt4IB2sUOz0Cocr2X0l5I6DbjWnPhqZWKdDlaZEpDNDNDTAtIoN+wtTEfA/iWLAyLG3nSRSphZiJct3DUwKwFWJ2D1FXlKSRAp05WJcWImOjOTAK84MFB2NCCmRJphIF5aWGwdX7ZQq57pmHl+r2ZvM9qQAVbxPKRUVxHBmYez52EsGEgLUqwZ4EN6lL043jePjO/OLNobyVwbpPZVps72dtzb5/S2BuRqQDqhu4Jhy8dYsxNsmTArTqy9LAGSS8J1piuXGgr2xuWzXVm3x+uP1jVTA9CKXL8cBVjLd5NSHHloey7SlotwkiZBMilxxhTRTK1qqVMy3VG8OTP04LhyexlTXxFm0AXppX46ITiF5SrGuwrQ7sJkTynaN52SWK9NnW3qubzddHy7dntx+vq082iL39mUUJQTkaUK1gn9lSwfKRksxvtI8EEaSnyhQNxf3Lg71XGwYtxa7Dm4PXH3qHRxllRTFpujjdBJgpVsX1M0IYKFOG+hSScRWSxsraZouav7eCVvurtpe2H2+o5xfZXRWBWdqw7U8n0UDLCEDGQmQHIugiRAJy78BGcJykOLCy8XSafreq82Jh4fTzw8HX1wrp8fCSnU2Mjw5pIE8xSEeWaSWRH2Zgn+Rin2pum0LkAh/09AyU8GdGJWjLMsJVuX020rWdblTCCjAKVlKd22gmlfxXOqEbnWSTwbZJ4NcudyjrkW457BgeSIXNRUVzWa3JqWNV2f2ZWtKGBH0yNAaH/XRIgzEuKYAHFIgDgiIG5In0hyoCSXLKsVM+s1iEIZLE/mpSA7CNHWigRQKdOnRuxXrwhsTg1pSw9u0Ud25sT3F8UNFMEGSyI7M70rBI4avE86J6JIG5gjDc3h8rtz1H1pqXWcZHG8Bz7ECRfiiA1xTA50TAy0T/S3TfS1Sfb1pwRIioiSFpmgtySmNNVZSbIRIC1EcTZpyfYlFNcqLrghJahdH9VtgA+UYSfq6bOt3MUuyWqf5NagYn0cQL4+Lr01LFoZYCx0MGbb6NMt+Alj8kh14kgVcqQSNVKFGqkEVAQIKbonL7wzI6gtNaBVBWlW+LemBLfIfEtZ4FSCj5bgpTapxddAjmwWh7WKIusF3jqMtwbno8ZDNARwOta/luvbwPdpEngZ+SAjH5ATuEkGapaDW5WQNrUPkFRaNV5tGq92nU9nmld7GhBTQkxGKY4cNlkEqESPlEEBkYyUQ0eAXFIOHS0HjBI9BpimJHq4FAFIZbIWMV6dOFmLmqhGjJZBARv16qM6UoEucKhBFF+hw9TlEhpzi5faiqaKS/vSWJmkWBkuWkqIlhFiZECdFCcHAOxCI2upFR0ZLXPGkaP5otnOxq2Fgw8vHv7g4+Mffnjw3bvzL17tfPZo/HJv/M7+xOVB48ZC5mibtLWEb8wTdhSnDNemjNYAKIFypCpluFo9XKMdqdEO16iHKkUNmQWd1cNrM1MHK6tnm/v3j48en92+2t+8OgaMsnn/YvXy+PbV2eq90/UHlxuP7o4dbM6d7c+fHyxfntx+cLF6/3z56nTmbHfubG/pzsHQ9uLo3q3R3dsL5wcrVydrDy+3nl3PX+4vXOysXR9tXW3NHMyt3t2YO13O7y5h5gkjpZhQDV3eWiHvKFR0F8ZmMKJ15Kh0SmQGPTqTEZvNis0yAc1ixWWxYzPZUIAMVoyeFZ3OikpjRqaxItO4URniCL0wNJ3rr2X6qWm+KSQvBcFNjndPIweVy0kjtZypJmhjekBJCqWrruPuyejDuwP3z5pONrvu7Pdd7vde7HWf7/TfPei7PBi4e9Bzsdt9sTdw93D4+nT4+gQoR+6fD12f9l0ddl/uDdw7ajnd7L17MHjvqPdyv//eUffd/c47Oy1nm43H660XO+2X+63nu63Aa947aLnYKt6erT5YKt2eLdyYLNqcBpq+oo3Zkq25sp35mv3ltuN14/ZixfqUYXmo9PZE+epEwfxQxnxf4fJE6a2prNn+nLlBYM0NR6sbNv+iI2AOiCj7+EibmFCzMDdXok9kMSGxWZFUp/NNYXoKSC7MZGdGouOnsGJHhVsTY82x4Za0gMhyOrpXJ19sD8yREBoqfFLlHmKam5DoIsA7AUbhYiyoMHNChAUt3EEG98snpd9qHHy+q17sSV8Zqj/ZSF+aiinPN53TpRW4yumOQsKn25/F23BjnRTw4BImfdhQd3e+YHc6dXEwZ22y8mid3t0YVpDmnS4BqdnOUqItN/nTPWURbinJ/gUsTF9G3Z2p1rvbqvmB9IWB1rNd7fQooqIwJEcbqBf7pvKAZ7lLyM5Aqy1CeaowIQUcZl9B/fH04INdw62x2u2F6fvnrXvb9Kb6WIM+TK8IVHMhMhpYTAILMGAZLiSNklyVkj3X0nG8VAd8WsuT05fHw2cHyv6exKLcqDR5iAZIM0wIsD0P48VDewnQfkpsbCFP2FvctDNZvzVdPDc0fro7cfckZ2YUV10YkaUM1goCVGwfOQ0kxLvx0K78RBcuyo2b4CJFgvTkmBp5xmr7wPXm+OOT6YeXo1dn7P5mz3S+tTTZUoawSkVa5GGtyok3y4k3Sgg3C3EWxcSbxUQgjgChBLCLRSnRqpxsX82yq2LZV7HtqzgO1VwAxxq+Y60AwKGG61DLd6gx1Z1q+FbldKtC4g15nIUE5aaluKlpXjoquVHNb5GpK9mJ/BjHJH9bpJ9NAsQG6WuL8LWBQ2xh3lZwXy8UWKBL0DQq9CPV2OosHyXDVYS2ZsbYymBexQz/GklwozqyLTOuyxDfXQDvLUEPV+PH69EjVREdem/gV6dhHKWJPip6aLYkMFMQVygVtuvSOuSycipUCPUkBDliguzRgbbJ/nZofyesbwQ/QlfDyezXZU43YJoKHZUUSz7cQhBnrUa4llAcK+mgWlFQaxpqtIq50KHbna673mm93m+8u9v66HjgxWXF2Zp6bUx+e0i41EufaiGPN9GmWlgz7aTRusT+iuT+cvxQJbK3JKG/NGmoAjVcHm8KLgXRvXmRXVnhHfrQDl1Iq9K/jOutI3tryT5aEkRLDCxhhjZLwluloVU8nzQiJJXom0qCpBL8DNQgo9AXiDgNInC90LtR7N0o9W5SQFo1gR3p/u06cIvaq0Xj16H378r078oK7ssLGyyMHimNGS2PGa0A5GFSyGglkEigY1WxkzXxU7XxUzXxk9XxU9VAGTtaHjdaGT9WFTdaHjtcEjdcAhglpj8vrDs9sjs1rFYWmseLNkjw9QWsjkpRT0nxYmPlVFFpdyojiwJToGMk6CgpFiojxElxSAWepmdk16trRsqqZrt7DjYXnlyPXp7c+fL9kx98/uQHH5/98POngF2+e//ou7cPvv3s+us3d796c/HFq/OPL04+Pjv++Ozk4/Pjj8/PvngJuOfk8xdnn7+8+Pzl3a9eX375+vzjE019ds1I6+zByt6D472rw/OnFydPLrYfn+09ubvz9P7Ggzvbjy63Hl5uPrwLeGLtwcXyncOli/1b904Wzw+A7DJ/tg/IY/ZsF4gvQH36eGv2fGd4exnYePXqfP3x1e37F4BRVi53N68O73527/z5+cnTs9XL9YaZVmGxPE5HDstjhBexwkpZEeWC2ApxUoMK3aTFALTqkltTce16bGc6vieT2JtD6TeQe/LIPbmUrlxKey6+MQNVrEoqSYUXKCMzBeEZ3OB0lm8qHZJO98vjxbVkM6dbubOtiI68yOoMYlcN3JjvkyV1VpGdVGTHFKKjiuCsJLqoyC5qkouG7JFGA+kZ4AyWdwbHL4fnl80NyBUGGSSh+bKIopTIElVUqSa2Ih1RnQmrykTU5SQ15qOMhqQGQ1JjAdKYl2jMxzQVYRqLCa1lxLYKUkclvbuG2VPL6a0XDzULBhplY+2y0Y7UmYHspfG8W9MFt2fybk0W3poqXJ4sXp2uuD1XeXuuamOhdnO5en2x6tZCzdpS7eZK3fpy/e3FG842N+zM/zXngNnHRdjFhFmG+t4Mc/CRhIeX4LHtqZEGka+c6cJC21MQjlSkIxXlSEXYU+BWxFgnWoy9MDyqnovpz0C35vnp+L5qvqeA7MbDm+7RwsPasZMtyHAzfIQFPcTXQIxrlqpXjUNPD29/eDb96il/eCDEkOaj4bvLAJeQLNlICwbMnB5jxYtxS0VFVwtZE2Wa1fbS/UnD5tTQw5OavS14VYl/pspbK3RX0hxFWGt2ojUX0AkclIaPqBbzpivLjqcbztZKNucGHhz3XZ1S2hrC83VB2XLfNAFEy/GS012EeCdBkrMYBdLiIkoEvJHSzvtrA48Oyjfmuk/3xu/fyZqdTCzNj8xQBGkE3hIqiI/35CV78pN8ZNiQLDrOqDEstM5e7TVuzFavTKw+vGzb3uA0N8Bz06PSJYEqnq+YBuZgQRw0iJMEEib6K3FAoJH1lw0er3TtrjSszw8fbbXtrEk7W1DFWVEZKUFagZ+S5SUluQswrtwkVx7KiZfgxE9wlSWC9CRse5bxcKHr/HbPne3JhxfNBxvYxlInDcNSlmghS7DSJ1qV4iyrKTfKCTfLCWalJLMSk0tuFuHNy0ifrkHBWJSSbauYdtUchxrAJRyTRUwuETrVmXCuFwJ1uxq+U53IuV5sW8WxqmKZV1Ety4g3VPE3BLFmgjh7BcZTS4cWCrgtCl2riJqOcMf52iZCrFG+lkgAiBVgF7i3S4I3WhiV0yTtXO9OH28PzZZ5SIgOnARLZoxXOi64QhDVoI1r1Sd0GtD9peSRWsJQNX2iiTPTJljo5My3IPsNfuVCRznKUYT0UBD80jnBGfzIHB6mTCxtVmrbJAojm19GYOXjGHlYbhklo0vbsd4yeXepYHUQ11gEfFfNebHmvBgbFdy1lOJSzQQZJVE9OZzlnvKLjcUPT+/99Nvtz19Xnt3CD1RGGTPDq3XeRTJPgwhkEIIKhF4FQu9iiX9FSnhDWkxLZmx7TvJgRfJwJXqoKrmvHNFekNhbiuwvg/cWwXoL47oN0O5caFd2VKvOv5Dnl84MSGP4aMmQdHJoNT+8TRnToQ2rFPuZjiTQ/dOofhnUoGJ2QIM4tFUZ2JIS2KwMadOEdqYFdaQFtKcGd+nDejIj+3IjBgzhA4bQgYLI4eKYkbK4sYq4scr4iSr4RDV8sjZ+ohY2WYeYNiZMG5EzDcgZI3LaiJoxJs40JM02Js40Js804uaaMTPGpMnaxPGquKHiqN68sC59RE96dLcuvFIclsuJypOSGwrVI22akaa86ebi6ZrqqbLS/lxDu05XKwXIaU6t6C9om2uqmWkqmm4rmh9OHe4g1uTKexua9m9NPjhbenpv5enV0pNLgFWg/uQuEOjH7h0OXhwMXx5PXp9PXl9MX18MXexP3D3efv1k/cXj9ZePN18/AVh/9ahrd0Fdm9083btytnH67OL4yfnZ04vd+0crd/fX75/vv3i08+Rq6c7h7avz1Xsny5cH8xd7t66OF853Vy6P1u6fr1wCaeZi9e4JoJkFE8Cjp6v3jpYvD4GwsvbgfPPZ9d6Lh2v3Tzavju68uX/n1Z2jZ0cHz47WrrZ6Vns4eYJYFTGuWBhTLoguEwsG6xde3Tv69sXxNy8Pv3q2+fHh+rsHABvvHqy+uTf/8mLqycnk09P5N3dnXlwsvLycf35n6uHJ+PXB4OVW/531pr25omWgye5JXexOXRuULHYndxXCmwz0PiO2udgni+2qxjmqcM6ZVNcSlnsN162O61LL9TQKPOoE4AaxT6ME0iD2a1H4NUgCGqWBjUCXSx7VqmFMlEU1qSJalFEt6pg2bUxbakybLqZFC21NjWn9VGlJjW5MBb5N0UZtZL02sk4T8U/U4bXqiFpNZI06qkYTXaOJqQVKdUyNKrZWHVMFoIopV0YVycIKxGEGQQigMT3TV0vxSsF5StHO3AQbeqwlMdw0O6Kb3Q2Hf9ERMIfYTxepBHlbRDkF6WBhhURkfQpYQYJIaI40lAMVDmBHhtmZZniEW+JjzDH+7ur4iDoetjsH2NfgNKm79NMtv9hoJ9OUJ8n2rCQLItQMH+asiIlulKD6MsiDZZSeCuX0RGhehpea5yqlOorJ9gKcFRN1kxJtTo1ySoEHlNA440XT788bjm8Xb8yV7q6mzYzFlRt802QgFdtVTrLno22YSGs2zPaTTmKNKWkbHRUnM0XbM7KprtKdxZLNJXxjfVCuBpwmBml47lKykwDryEM78lDOYqSXngSrS5FP14y8PGy7s1m1PTf/9N7wvQtxb3tsfnqwTuovY3nxiW6cJDdWojsnwVuSFGfg8tpzjJvjJQsDGSPttYvjU+eHhdMTxJqy2GxNiFZoGmgR0by4OHdmAoiV4CdCB6gJiGKxbrC8f2+xanmsYXlq/GiveHIMV5wXnioKVnL9lWxvBQMkJnrw0W7cRDeu6SxhFz6Qt5ICcpicgeKus+XJ+4fjV8cj905z5kYjCzT2cry1EGUph1tnJFmV4M3KAJeQb5QSbhT/A5J5KfVmCaCTT2opJtqWMwCR2NfwHYA4AkSBeoHzJ5E414ucjWJXo9jFKHFpkLoYxc5GEVAHVtobBTcraEDoMc/D3JDE3uRDLYRwZxXRJ40RbeAy6iXyRqGojIHTwiL44X7MIF9mYDArOEkGTy0XjG90zVzeMsz1ReWovBR0DwneXYRxlST76EiQHAYkjw3J53rmMD1zGN55HEg+P7hMHlOfhu4qoozUylZ70zeGiV0FrkqMgwDpLMKAVFRfDdNHy/DVkKKyGNgmIbVfQu6V4FtFWGNK+fpE5eY8o7PaK51lI0BZcmLMBTE2+iSfGg64QQhulieMlGQdzi998XLzy7clu0vE3hpqb23B8njD7lLZ5rRysovWWU3rrKJ1VBJbSrCNBbDq9PAShW8ezzuX7VciiW5OT+grSBosww6UJ3YUIlrycL2lST3FiK4CWKcB1pEb154V15weaOAHZwI9Vl5AKg2QR2StJL5Tj+jOCq+UBefyQrN5gXpGUDYzvEIY3CSP6EiN7c6K682F9uRG9+bGDRQiR8sRI+Ww4U/xAggZprO5KuDjVbDxKvhYZcJUHQBiuh453YCcAizSAJA415Q818y71UuYb8EutOEX2hkrPezb/aTFDsJ8GxFYOduImalHT9YmAS8yWBTTlw3tz4B2pkZWiMJz2ZFZPESxhtVSljbenTnVXTDX074zO3S4sHq1tfvkePPx0ebj88mLndq1mZT+BmShCqrnQrM4iHI5sT0nZaIp/9Zw8/6ycW3GMNOnHW6V9dZx28q4rWWsxmJZT13ORE/RdH/V4ljFwljD7Zn+w/WuvdXeg/XR092xs73Rs+3SkWZNXbZxsnv5fGPtYuPi6cXRg+PNe/u3LvdXL/aBdLL56O7a1dnsCRA1joAUMn++t3LveOXu8fLdo7nTneXLo1ufjowtXRzMn20vXBys3DtduXcCCAlg7fps8/E1sOXSnd3NB8evvnv34qsXp89OD1+c3b7aappp5huE8BRG3eZ8y+FS6/HqyPXp2XdfXP34i4tv39390ecAd37w8erHX9390ZcXP/ri8idfP/zFD1/9+ifvfvOzD9//2+e//bfPf/dvn//+5+9/97M33//05fc/e/37X3z2+59/+MMv3/7+F49+/W3vgz3ucAOiLttHz/bQUd3TSB75DFANH9wm8eqQeXXKPdukHi1iz3aZW6vYu1Ph3SGHdKZAOlICuzVBPdoQgF5teF9aRH86dCQnfswAnyqlLbbmHE9VX61W3FssujNfcGch/8684Xym7M5c4flU5tFYynYf91araLNLuN7BXm1GTpVAxwviJgpjJ/Jg40XE+TrhWpd0o0u+0SW63S5YaRYuNzNma8jTFdiJEkRfFrRdG96cEmAU+1ULvcrZLvlEu6xkq3TkDU+HG07/oiNg9tAIm4hg80BPx0RQUAYyqpQJLRaAJTighXViJDqZAopJJ/ZUhC0pzhwdaYaDBORiYozi5LYMkIYSmi5xF1NduDhHVpIJZuKnO7XEmBNC/HLQ8W1KZE9GcJEkqkQH0cndpExXMclZTHIUEez5WGtm4k1ypIMwPqickdyrz9gYSZnpmnr1hNnREpitAWlFbilMN7lp/jJrBtyaEWcrgnllE+OblQV7vUD0yVgYyVkdbbmzx+tvjSnOCMhUgnUCjxSWs4RszzFdI2lvGmtJ9MwgwRtVxduD/Y/2irYmi25PTjy6U7+7Rm6qiilI908Ve8vpnkK8MxPpwoh3Y8Eh4qSoTAa1QV+21FO7NFI81tswP9F2e0ne2gjPTg9Viv2kLG8hGcQneLDR7gyUBx0O4iODVQRCSUpGf1XpdHf+cGftzEjnyqKixQjLUAdJuT48MphL9ORg3NhJriy4ExPhxE5wYiOchQgPNSa4iC8erhy9t73w6Hzh+nzw9JDT1eil49qJMFaiBMuUBKtsrFUR0RIQSQnOdLALSCelJHNAJ8VkyxKqZTnNooxqXkKxKqeZDnMBEcR0dItvXyNwqBM4GYWAPwB5/COXuNSL3Rqkrg0y5waJbQ3fpoZnVc2xqGSalVLMyog3K4hm+eibivgb3GgLbpydJMlTQ4ek0gPTyYhCBruZJ2jlCVvY8jaBtElSOF5TPN+tGWiMN2jASgZISvaQEDylRLCM4q2g+Cgp3gAaBkTH9k/j+adx/LSALZieaqq7muSmIrppiL5ZHMGwsXxzWthXRwM6eqlsBxHaloO05cBsWFAbToxfETGylRfaxPHMxljwoZZcuBkv3pwDNeNEWMihNpko+xKcXQXJpZLp3SBPHK/UH83nnaywplrhzYbGw9v7H16PXJ0qxzsQ1VkBORLvDDYkg+2fyfXVc/wyeJH5ivhyHaY+L6kuB1mTFVWsDCmSAcJDduZhu4vw3cXYjhJiZxmuswTXXUroLif3VZKA7NKUGWYQR+dKYnKkYXpeQA47pkGb3F9AHC6BVqtjS1TQopQIgyi8UIRs1qO6DajBAtRAAazPENebAxvITx4tx0zUYCZqMZP1yRO1iZN1yVNG9HQDUCZNGROn65NmGtGzTejZ5uSZJsxcC24eUEgbYaGNtNRFWe4mrnRSVjppK93M1T7h5ojqcE68NSZcG+Ks9DKWOtnLXbSFVuK0ETdZhRjMh/VnxXbpoI3K6ApJeB47XE8L0zGiMrioAiWtLl/cUaMbas8Y6VR0G5kNBYnFqoh0TpiOFpPFiC8RJtarcJ3povEq5WSLsLeucX15+e6dsaNd49JM0fhA5ew4UGlanmu/vdRxa7H39nL/2nL3rYW+jZXhnbXR/Y3xo63Jo+3R/TVlc6G8Wq+q1HfMDS4e396+u/Po5d3zh4drF1srF7uLZzuALZYvj+fP9maONiYP1ka2lif21+dOdxdO94AsMnu8OX2w1nt7dnRndXL/9szRevfq9MThxuLdk7UHFytAZLk2Hf5au3+2cndv69HZo8+fP3x7ffBg5/D5ycLZSsOUkaanJ2kYVQtjrTvLJnZXug83hk93+o/Wx+8dTF8dA6lr/uGdyeuT6u1Z4WBTydrSymevDr/+/OwHXwCaOfzB+4lXl013N0uPlxuuDmfePn/5219+9Zd//+Yvv/r4x5/f/9WXoqEK/2yOdzYbXMrza5L7tCtAbTLvNhmkSgZpUvl1pfn2pAf0ZYQO5IYP5oUN5kaPFsSPFSVMliVOVyAny5FTFfh5I2m5mbzcQl9pF28Ob/3063d/+fcPf/zlhz/9+4e/fv/6z//54k+/effX337422/f/+23n/31+2d//NXTP/z65Z+/f/Pn/3z5p1/OfvUgcaoMeEHURHXu6ebV9z//8NfffPm3//j67//55d++//Jv//nlX3/zxV9//flff/PZH3/+/Hc/3P/x8/LTiYTOdP86EaiC41JMtzcQrbPQN7ycb7jamo6A/c/PAXOIjbQKCbgZ4ARiBkUUk1EN8iiDwEuI9+DinegoZ1qCMw3pSE0AvGJDjDVLDrGhBkSU0+KbU5DGNGcxwU/FdxFQXNgYF3ayEzPRkYmypyGBQGNJCQwvo8d3KvGDhV46ZmCG3FXGcBERXIQEJyHBWURw4GGt6Ak3yeHgTHS4UYgdyIfW6hIbS8ML9B4anpuC5aqgeCjpziKyJQtpRo+xFccFlTOTe9PLzmZ1sz233j0bfXSX1FYfnKf10Qm9NDwvJccF0A830ZoNt+bFO8sTA/NotL68mpPp+sNZ6VhH+8nm5KO76dOjcSU5Idkq31SBTwrDU4hz5iQ7sxLcBEg/NRZRKFD3VdatDGt76lM7jO3LM9XT42hDdohCaLrlNRsHYqLdaaY5UVzoMDd2vJcEFZZOYdbrc8dbsvsaUltrK8YGykcGaEWGEAnLm0P0YCS7kZHORLgjMc5080dKrCMtzoER7yRMAOnwcVWyvKWuiXs7Q+fbg2d7zVtridX5LilUWwHKRgy3ViNtDXjLErxFKeHmp4GTmwW4m/k48yLizSKCJWCUIopZAcGsmGRWRLIpZzhWc52r+U4ANQLHGgHgD2cgiJhEInExSl0a5K4NcpcGmWuDwqVR4dyosK8V2dQIbGuFFlUcy2rWzVLKjVKieRnRophooUeaiaFm7GhLDtScC7U0TcwVE2zAR9YxwsvxYQasHS/amgG1psY6sJA2jHg7Vqw9E2bPTHDhJHuICWCTVGg+KVTALkAscxNiXIXJ7mKMp4Tgq2ZGZ8vghaZ5paD5yph8VcpQa932LWxtEVjLcRZj7TgIa0aMNT3akh3pkY/zMbL8jXwbNcKMFWHBiLRgRJkzIqw1cPtSgl01ya6cZFfJ8G6QIUfLZFsjtMnmqLqMpObisp3V9KVxaGmau4bqm8WitxvqNiaAv/PKk5O2/ems2RbtWB2/pzipJjWqQAYtUqGN+fjGQkR1RnyVntVbXbYx3Xe+PXG5O3ayMXC01ru/0rm7ZNyYLV0aLV0caVufGz64PXGyNX6y2bQ9p5xuIw9VEAdKkcZMSV9d+a2JruPViau9xad3xh6edFxu1h4vlO5Pl+1Ple5PZa0PiRc7aDONlPk20lwrAGG2hTzfBpS4mWbCXBtpvoO61E1bAvTQSV/qBkRCWu6ir/YyVnuZtwfYtwc5twd568NpBwvZx0tp+/O63dmUzTH11oRme1K+Piy+Nchf7uYvdvIW2qhTtYSRYvSgAdmXiehMhbeq4oyS+AphbCE3IpsepqeGpgOdQnJ4Ji0ihxFlYMWWCmD10oR2JaxViexIw/Rk0bvyE8t07IZSaasxtbdd19PKq8rH52hwWWoAYo6WmK2hGdJYBXqgpOWlMQozmcWZjOJMXmWeoCqfW5qF1nKFRRpJoaZ9pn9kfXbzzt71i8uLp6db9/bXrw43r0+XznZWLvZX7x5OH94e31laPttZOt+fOlyfO96cP9tfunO0cLY7e7wD+AbYYGxnaerg1sL57tzJ9jyw/nRn8nBz/vxg8c7e/Mna5v3D68+uX368/+rLx7sPD0a2x/LaDBgVlZ6nkNQaFu6ebT17sPn84fqT+7ce3L318O7ak+vbj++tPryz+vBy9dG9mauT9v3VooVRYXt1cmkqvFAOL1MnN+Vwxuq0t/syAXlPddSc7L74/tdf/unnX/3pFx/++IvjH79N7sgMqpMEd2j8ujSQTo1vlxbSoQvrzY0fKE2arkfNNiQvNCXNNqDnm5LnGzGLLdhFwB/t1FWgc9BBX+3mrPdy13s56z28zX7R9pD++NYV8Pr/ZZLBk9/9bPjtddHdzdzz20V3tiofHNQ9OQHK0uvd/Mvt8geH5ff3DRergo0BymoHeaWdcat/6sv37//+u2/+28Rnf/mPy1/9ePenXx39/Ns7v/rx1W9+/uJP//nxv37/9X//9uNff3nxiw+G/UFok8q3SuxRwnbIo9z0dr3hbn/D3vJfcATMJJXQAPMQRz9FTHQZg9aTE5IuQORo3Vl4ZzrShYYEworTJ7U4kOFmyYEuwoiYWk5ihy6mTBWWIQvSSt0EZFc22hmIKQzTeL49JcEcG2nPD46qYqN608QzrV6pbFhJ7qeJlMkuQqIzH+/IwzpwMObkeAtqSEgZJbZNwRiv8c4URhVoQSqBq4TpIiG5iClgJRMkp9uwkWaUCO90TFyrjLtQj++qYg83iMYHw/KzfFJF3iqup4LhJqM6CvCW9HhrTpyzMtG3gM4cKhh7ddR7fVC4OjX58G7j4QalrTY0R+ubKgar2O5SkjMf68hKcODGu8tQ4Tk0WlN6/dqocWU8rcuY399eMdanbKyL0aT4csggGhpETXInfJpMHhvjRIhxo8dCpEnQXI6qq7h+aaR0qj+zs6FmYkjXZISmSHzZFE9KsjsJ7oSLc8DGOqCj7dGR9tgoO1ykPTHaiQ3z0uCx9anG9Ymx893B463+o528qeHwXJWTBG8tQNhJ4Ha6RDsDwbqAYFVEMC/E3wTMkU+4kY+7YcCZFRAtCsnWJVTrEppVMdWqhGZbxrCv4DhWcZ2qeADONXyXWqFTjdC51hRQXI0SN6PU1Sh1BozSqHBrSHFvVLo2qdwaU9waFI71Uod6qV210LaKZ1nJMS9lWJbSLCqI1hUUwGdmolgLVow5K9qCGWXBCvPITQaXELwLcM4pMHNSqBUxwhwfYbrVGybEdFcFbIglLtyaEG1LjrYhmSazcWXF+Suw2Hwxs0ZLLJbiCiWILG6ImujKR4EkhJA0QXxhanyBNiI7JTBdBNGwwSk0NwnBnoO0pkNNs/yyo11zMK7lNK8ylqUUbg7sCSPyBjPiBjfSPh9rW0pyrmC4VHDda8XhXemYsSpUf6lvqSKiMl0+3o2qM7gpSaa5DxrTOw7njj57OHG8WjZUWdyTX9KTm2FUyyulCqM6pSNbPVRGbtRHFUiSqjMJDYWasfalJxe7r653nt1bu38xd3YwvL/RvbnStbnSfHux6dZc79bq1OHW/Mn2rXuH69dnt+6fd59uCcea6UN1WcvDm68e3/3y9fXXb+5/+xbg6pt3d755f/7Nu4tv311+++4OUH73/vjL1yNPTjM2J1hzHbSFDupCO2UeUEszca6VvtCdvj3Z/eho+bOH6x+eTb6+r1gfZa/2cVf703cmOLf7eLeH5RsTfc/v7n3zbv/rdwfffTj89v3xd+/PfvDx9Lv3e1+9mXl9v/P6YPr1/f6Hx43nm51Xe9UnK/m7M0UHs8U7U0Wbozm3etMWW6VTVfzhUkZfPrErE9upR7enoprVcGNKQoMa1aZP6swi9BaS+gpQNZrYbHFCtiIhQ56QLs1sqWMXpCMULLSSn5TCS5SxsGoBUSOkp8tpOikrUyUvyabqpUlyBlxMiBfh4gQYuACHVdH5ufKcptKygfaFo62dO7tH10d710drp9tz6/PLOyvTa/Ozm8uH946Or04uHl+dP7m+ePbo7otHd57fv/P8+s7L+/c+e7L/5PLizeOLVw8PHl9uXJ9s3DvYfni2dn26ef90/mxz9mj11vnmxYvLV18+e/fNk7svz5bPV7tWekWFMoyaJi5PZ+TLCVkSbJaCUZbFqs5nVxdIWqoVHUZlp1HeVi1uqWTX5OELtUm5CnimCJYlSirTYJtzeBPG/MNZ/doQpa+C0l2pmuqrO9h6/KuffvjDLz78/hf3f/WNdqMnfjQnYjArYiAvajAveqgwfqwMv9hCvt1DW+ulrHZRV3sotz6x1ktb66Pd7uVuDHE2Bnibg4KtYcHmkGhrRL43qdifVOxNKfansk9v3/nNrz787fcv//Sb4c8u0w+nVUfTquMZ5eG0Aqgfz6YczyiOpiT7E/KjKdHuiHB7WLA9LNoZleyOKQ4Wlr779uPf//DF3//z6e9+1vBwR7s7pdqdSNkZk24My9dH9LsLY++evf7j91/9/bdf/PU3z377w6qz6bhWXViTNqhBa+HrccPD0XQOmPXN/+ml9XbQcPNgH6sIx+A0JKySxegrAUtIkVqJOxPnSk1wpiQ601EujEQXGsqeEG+O9vfWweOMUsFMVUiuBF2Z66MQuHCwrmysEwvtyEpyoKFsSXDz5FAPVUx8vYg9VcQcqo8uVAfqU9ykNFcRxVVIcOHjXPim7c2JMba8iKhaTnJvhmC22StNAC3OACk4bhIykGacBECmwTtwsbaMBAt6eHg1F9GtEy23BhVpCG2VwTlpniquewqgE4aLhOwsJFkxUGaUKHddYkS9kDVVUXuxWnhrfPP9m8rtLXhFQZBeDtEKvJUskJTmyE423YeRDXOTJ0UVsNQTtcb92crl8dS+pvr58ab5GXqxIUTEBjMInuRED3KCKy7eARXpgIxwTAx3I8VARKgEgyB9oKb51mT+SEfV1GDj/BS3vCRMwvWi49wJKBcMzDEZCjzFHhlmiwixQYbYoULsMKHOjFgfOYZcnVq50FezPNG1tdq+tSporfFRc+z5yaZjPlK4rQ5tn0uwLSJY5RMsDERLk0JIFkUkq2K6dSndpozpUMmxK2PZljKB0rGC41zFd67km8pqAAFQutaIXWpFAG71kk/I3Bvl7oBOmpVuzSrPZjWAR7PG04Tao1EJ2MXZqHCukznWiu0q+bYVbEdASzUi51KGmRRuwYaaMWMs6NFW3GiPfLxHEckrj2jNjzbDh9lR4+yp8XaUOEAkNoQYe0K0NSbcIinIKsnflR7FqFQPHyyevLw3f7DSOlJb0qjTlwlkRVxeCZ9dJoGl0zz5SZ4iPEhOAcmpYAUVJKOCpCRXEc6Jl2jHgFtRo6x40Y5ZGM8yulsRw1pqmjrekg27SY+6KYi2yUu2NGBsSiiO5WyPGnFwqy6qQ+9XIwfnCPEt5dElqU4yrIcKw+spzJttz+srK2/SpRmY8mwWQ0shKLA4WRJFhZbl0tPr5FkdWdq+ImZzZmxRCq21rP985+D1w4MX11uPL2fPD8sWxqmNxbiqXHR5BroiC1+dS6k0ZHS31s1OtKzMNSzP6EY6aB1llK5SWm9F1eb88fuX9758ef+rt9dff3bnq7drr58MXp+2XOw1nW51XO533Ducf/Hg5Mu3F9+8P/jidfvVPm+xhzrbRp5tJUw3C28N9j8+O/z8xcGHx/sfHl9++9nOF6/1OzOslR7mcg9jqYt3a0C0MVp0snb07ecPf/rt9U++2fzyzcJnz2Y/e7ry/uX6h9erH15Mv3o4+uze0rvnQGXqxf2Vz55MPruafHJv4dlV//lWy/Zi7fJk5fxw1eJo4dxA1nSXbKBO1FctGqzj91exu8t5fVXiUaNypj1vfUI0VpdYqYFly1A5SniaGCpnRvDxhFQJWsEiaLhkDYeh5dG0HLKSgRXh4xjweCYqhoaIIMVHUOLhnMRECS6KDocykCgxEaOkUVNF4tLMgtbKobmB9sm+sbXZ0aXhmcUhSb6On5UqzcvSV5doSzI0ZfmKwpyUvFR1abEsL1dXlJ9ekpNdU5BSkl0z0FnUVG3sazMOdK7srs+uTxn7mjvGB1f317cv9vYfne8/P9t7sHtyvbV1d7tmrEVekopPoSarqXFiDEJJIaWzEiSJsWI0XE6TlGfK6wsNAy3tK1NNC2OlEz0V452FQ62MsjRMgYJhzJEMlOjW+mVzrayeSkZXpXKiRzneQWjIFw927X735fP/+Omr7/9t90cvMePFiNFizEw9fqEJgLTcyl7vY60PcLcGedtDgu1R4faoaGdMtD0q25uQ7k2Kd8YAlwAmkO9Pao7m0k4XtMdzupP59NPFtNPFjIvlmocnT373H1/8/bdv//Kbxa+eZJ8t6M9X0k+XU08XdadLaWdL+ouVtLPl1LMl7emC6nhOfTyvPllQHs1qThf0Z7fnv/783V9+Dzz97JdfGk4X0k8XdCez2qMZzf6Uem9SszdlOF4//cW/ff5fv/vy798/+93Pai7m0f3FsK78mPZcK3/QTZDTDRdb01WQ/8NhFQdouEWwlz3SIyoXn2iUcvurotPFwXKeGy3JlQokFZQz89MV9TSkJSbaiuAXYsAhWpWKxVaIjkeoKfKScjz4eDcOxpWDcWYlO9ITbQixN9G+QQYsoknGm66KKtGiawzBaSmeYoorD+il4l24WAAnZpIFMcJNCYttlFFHC8l9leEGVUReqruE4S4muUoA/ZCcAPdwcdY0mD0vJqpBiBnIkS13eOkFhOZKvzS5ZwoQOGguYqqzmOLAxZvTEswZUUGVTHiPTrHZG1efq5gcSawp90+XgTQcUArbU8Zwl5DsWWgberwDP84/j4Jq01adzrafrFUvT3ZtrxaPjydkpfuJmCA63p2AdMHEOyXG2MPDbeKDbWCBjugQf14CLleU1V1TMNCiaaww9LZWjA7i8rICBAwQHeNGRDljYc7JMQ7IcNv4IJvYQOt403RnNqhAJ0p0sBzHqs4sG+0sGe6uHOkrHRtMKtJ7CEi2LIQ1J8ZGEm+nS3bIJ1jn4azy8FYGkqWBZJVPsSqg2BXRbYtoDqUc53LAH0LnKqFjBVARuFWJXCoFnwBWClxrxa7VIhNApU7iWi91N8rdTCgAc3g2ASL55JLWVM9WrVerzrstzatFB27VgVt0pkqL1qdZC2pU+TRpTDdjr1O4lXLNJAl2YrQ1D2EGSEUc61FE8avghxTzbLkwDx7amZlkT4bZ4qH2hFgHQqxVUpgZwtcCCfHjwNXN+YNrU5Mro7kVKnkqTihHKbR4IjuCwI6i86HKTKKyREgtEHgLke4CNJBaPEQEQDDuIryLAOfIA5QPt6REWQljXfNIATUS33KRvQLnJCPa8BLNmNHmolir3GQgxjmUMl3KOeA6qX+jKtCY4qQngVJZIXlKSBrXTZacVKVQj1emdaZlV/MxorgQcqQ/OdKPHOVHjfGjQv1ocaFMWJIMra+SZ3Vmpg4WU43Zgt76+UcXR68f7T2/unX/onF9EW/MS6jQxhcq4YVKZKEmoUAVqeNDtSJMjg6ZqY7SiWJyJUm16cS2fFR9pn6yZ/P5o4sPL+59fHX58c3aq0dZq6OSmQ7JfJcMYLFHstibdmti9eXDi6/eXX71bu2zJynLA9SpZvJ0C22mLX17duzp1dbHVxdfvz3++PTsq9frH56rNyZYy72clT7uSp/o9lDa3lzr1fH5D764/uGXO5+/qjxdLzy6VXS6Xna2abx32PzgtOn6uPnqsPvhefvVYdXBkmC4Dt2QjapKp9bnyNpKs/ob8vtbc7qMurYaeWMhoVAVpqYFpRDDNPT4LHFymYbWXCgdaNDN9hXcnlJMNiNKlOiCdG59GbFYj1Bzw5lJkYxkpl7eNtaVWppGTmEk8nEJnGQEMymWhgSMEkWBRVPiY2lxifzEWCYijBADY6HiOKhYHhopwCMF2CQRgSCh4KUUplqAk5BgNGQ0Pi4yKTY8ERqRDA1LhIYlRIcgYwIToEEJMQGImCBYZEh8eEhCTBgaHpIcF41LiMahoEQ0XyWmianSVAlbJa7qamqbHBnfWl4+2jq+s390ut061iEuVMN5aIQYj0whxrBhUTwUTITGa0hx4kQoHxUnTEZI8axsIS9XQM9g0bL5LIOMV6JrXZm+/ehi+9VFz+5o+lQbra1ENdalm+oj1ObGFcighbL4Wn3u1uLVL3/87Dc/3frBa/5yB325jbbSRV7qYNzqFW2PAPC3hoHcIABcsjMm2ZmQ7kwAEQQwQe71bvGjw/LHB7XPzxtf32t/96Dz/cOWN/f6Pn/W8+FJ57sH7W+v+98+fvXH7z//y68+/OlX7/70i/0fv538+LD3zb3u1/e6P7vseXPZ89m95ucnhfc2Mu/czrq7VnB/J+9qK/feRs7djYKr3ZkvP7z582+/+q8/vP7Tr5a/ftHz8qLl8XHTo8PGBwdNDw96nt3Z+dEXb//8/ce//fbVH39z69tXqpU+6qQRPViZ0FNiE+hlBna54Wp3w87ifzqsYhsdahbo7k4LjMwjEjr1mKosVJ4GwqO7AToho1woCGcK0omW4EiCWyRF2tP9oZUMdE+6ZLrVP02ILMr0FDIAnbixMC7MZNPAPi3RGhtlTQmIrmRiOnWa1a6gHCmuvgSSIvQQENy4RDcuzoWDcWKj7alIc0xwQE5ybHOKbLkhrjoLVZHhq5G4C2luIqKzgOjCJzjx8Y5srAUpxl0Bj2uR4ofyKf2loXkpiMoiTyXXZBQh2VlEchaY5sOxoMXbi6DQZjGqL0txq8cvV4ZvroZkKD2VHHc5EyRjukupLgKSAwdrRYv10eOTOrWqtQ5qTw2/qzZ7eiI2S+cvYYE5eHdykise7pgYYx0fag0NsI71c0gKgjDi8BmitKYycXmeuNRQ1NWub26OV4i96HgQMckNj3DGwR2ToHYIk06sY/2tYnytoBBrmL8zPiJQgOEWp2U0Vatry9MbatOb6sNSBICkrYjRVrRIGzGgkyS7HJx1DtYqE2ubQ7DLIzsW0F2KOK6lPPcyoVuZ0KVc5FoucK0QmagUArhVit0qRW5VYvcqqStgl2qRe63UvUbiUSv1qJebAFzSoPRqUns2AqXGq1kDbtZ6t6aB29LBbXqfjkzftgxIe6Z/Z3ZwV25Qe7Z/qz6gVR/YnhXSmRvZUxDbXehbLrVV4d2UNFNTTo+yVSB8yjhR9ZrAfL4VM/7TlGIER0qCCxnhTITb46FWqBAzmI8XJUpUrslozNUViInMGN94T9cYkHsMyCXGyxEKdokBu0PBQUgIX41WFHMxuSwgr3hJSCApxVNEdBfinHlANyLZlgE3p0TZyGAeBYyoprSAUpmrkuqhpDsI0WasaEtZHOBdjwpBQIPKt17hXiFwqeK6lbFslAh7cZJXKstPz/PRUAn1WlFPttoopakTfHEhIGy4Jy7CixgNJkZ7k2N8qLG+VEQIM4GnJ2e0qLJGKtJGmlQj7StPr07ePtl/db18fVKzMkFoyEusSkeW6VClWkSBEpojCVExgiUUuFaUmKGM1QrjsqSoslRCY15yfQatuWTi4vjg9eOL9y8u3j/ffPGgYn1GOt4inmzWLPTpV8cMG9P9dw/23z4//+LN6eevJh5fyJf6OXPtjNlW2nQre7ZTuNiv25heePno8uv39755f/vtU83aOHe5l73UI1jpl62NaDamhp9c3vnu8+vvPr/77fvND08nn94ZeXw2+vis//7x0MPTwYdnHXf3yvfn+UN1yfVZij5j39HWxvNH40Dqmh2Rt1QxyrII+VpcjppckMYszZU3VAprijBZKaFCvA87MUhBRhSkMFvKKm7P6ibbYfkpzOoiRbsRX5AWp2CEMlHh9AR1aU5hazknnYcXE4hSEj2FHkWIgVIRcczEBC46mhyN4cFjcSFhmNAwQnQkMTacAAXiSzQDFcNIiGehYNzEBD4ByceF46CByAj/hHA/RLgvHCACggjziQ/xjgsBQ4PA0EBvaIB3TEAQMtonNtgHGgiJDfGBhfnCTBvsEIhaAAD/9ElEQVT7w8P8ERH+iDD/hLAAZHhIYiSKTyGo2AQFI0mAR3HRcG5ikgwHEyVBRUlhtFioMAnKiWGm4UipGLwKlyjDolJICAWemSukZvE4+VJlTVpeV0nnysTnP/vh8Yu7CmOOsrs5dbhH1F2HKtHEGSSxxQpke1bySGHm7srdX/7kya9/dOvLZ+JbfZxbvfz1AfHmqGxnQrYzJtsZVexOKPen9Oer+dfbhqud8ifHdS/uVD09qXl8XPXouOnVdcub+z0fX/R9/rL38+dDX70a/frN5HfvJr79bPoH76e/fvv6z7/77n/94Uf/6y/f/Nfvv/6v33/82+/e/f337//2+w9/+/37v/7u3V9MY/Unv/5B+cO9ggc7hqutguudgvt7xQ/2Kh4fz3/98d2ff/v133/3lYnff/lff/z8738A+OLvQHz5PbDyiz//5os//frtn361+sXj9I1Rxa1h7mIndaqBNNpgF+xt4e12w93BNKxi+U87/P+5WIX5mQU7+8oiI/Jo1N7cQBU7Xq/wYgJtK+rT8DLMiQS3J8LscXHmyAAPSVh0NZvQk53ckBeiE0FkbE8u3pWZ7EJPcmEkAQ2lExlhnhzsxAuLrWFhetKVcy0hmaLEynywlO3OwbuxsW4crDM72ZGRZEeMM8f6hZdRYa1qyWIjRC9ClmaBJCw3Ht4VeE0O1omVbM9MtKMgzbBBvjpkQptauFCf2GhIrs6LzE31AiRhuoczyZVPcOMD3VusJSnOUwuHtsrwY0W8qWa/LBmyKh+sEbsr2O5ymruM6iGhOAvJ9owkC2pkcCEV1ZWuvtUbmCNJrsgN0Si8+FRPJsadmuxGQDphYTaIcJMY4iBuhNBgfhK7IJWqV0oKcjKNtZTM9BAeww2Hck6Od0qKdUTF2CVE2sLDrGIDLGP8LGIgFtEQG7i/EzY8QoBjZavYWVqyLkVeUkDO0HrRsDZJUZZJoZaEUGtejK0qwTY10VKTaKlLttFjHbMpLgaGeyHXvZDnUcx3K+G5lvJdP3nFvUJkKsuFLqV890qxR7XMo0rqUSnxrJK4VUo8qiUeNVIAUK3Cqy7Fy6gEGVPADWpwkxrcDBgl1asZyCXpvu2Zfq2Zfu1ZAR3Z/p25wV2GsC5DRG9RaKchuD0nrDMX2luEHKwkTTVjx+r9ylN8MkX+mTJnCcmCCXXUoUNrZUnteX5ZHFtGghefCOaSvNgED1ryp/mM4+2SIq1RAXEKHNfAV+SxyZxojwh3u1AP2wh3m0hP2yiQbbSXXYy3TbS3Y4xXPCVEV8RQNqYhC1UQGQ0kJrsJcO58rCuQYk0z9CRYkCPtU1CQCiGsPQtSIPHSsL3VbEcRDkgqliqEexkvqEET2KAJbtAEGNVOZSyHIpKNEm7FiXMSJQXoeH46blQej9eapmyRMbOSfAkhrthQN1yEOz4CRIoCEWO8yVAIJRbKT9DXSMsnyiuX+1MGG1IGWm8/uz5++/jos4e7z69Wr0+GD9Yab82UTw8Vj/eWDHcV9rfldTbojOXckhx0hhKpk8WniuFZsqRSHbXBwG2r6Ny+vXb/zv7z+8dvnpy8eXL0+vHG0+ulR5ezDy6m71+sv3x49PbZ6fvn+2+eDVwdp9+aUCwP8Oe6ePPdvPkuwWKfYLFXvDjYc316/MXbsy/fjj2+I18ZES33i5eHZLeGtZuT+s2ZqWfXF1+9vf7m/fU3b6+/+3j57ce735i48/WHe19/uP76w9XXb+989Xr/3ZPB852SlWlxZzWqUB2r5yFzhORCJbNYwyxWkrJFcTJyICvBh4YI5ZOTdAqKIR2pFQXycYBaguUUdl2+oq2aWWXIGe3V9rZg8zSxMnoYExlOS6CnC/hZIrKSQpWT4QxEPBWewEDEkqPjyPFRuAgUI4YphaOZ4eHYoDB8VAAqJCgxwi8xzB8XFcFABBCjg4mx0TRUDA0ZhI4Cxwd5QQO9oEGeMQEeUBPu0f4e4RD3T7hFmHCP9P1U8XWP8vWIDfFKiPaCh3jGBQB4AcQHgmGBPgnBUHpiNB0RSYZGU2FwLqAQZDw/MYoDD6FCg0gxUF5CDCuKrEygahJZmexEGQ4lR2OVWEWpKKVUTkhl0rP4otKUuqmO4yfn0vJUlE7ArikRtdZQ6wtRJdp4gxRen4YZLGHMNWVsLZ7+9Jv7v/x29NWFantCuTeVsj2h3Z9JO1nKOFvOv1wvvLdZ9Wi/5vlp3cvLhlfXDS/vtX72sPXtPdlyh3iqY/jDy5lv3y/+6ItbP/1m9adfrf7kq39UFn/4cerbz+a/evf+b7/74f/68xd//92dX/1g4evPFr79MP/t+6lv3s58+27q63cTX33W/+Fp04vL6icnZY/3ix/uFj/cL318WPJwv/LRyd6//QQQyXf//cf3f/nPw59/t/rdu6Vv388DL/LVZ2s/+Pzk5z98+cfffPn33335t++ffv+T6Td3CnbnMnZmlesjjKkWhxCIta/HzX8Mq1j9zwZVzAJBVlD3AA0MXsrn9ZcESRnx6UovOtaNiHAhAEaBOZPhLiSEXXKUOQIM0cRCa9jU/sKwHAmuIi9QIQQx8c40INCggE6rPQlmh4WaJUDACii0hoPuSkcbM0LShQEqoTuX6M7GOjOSAAD3ABtbJofZUoOCSynwVjVzpBKsZkVnqTxFNNNhNCbaiZnkzEp2YCfZkGFm+ICIfEJim1Y0Vx+YJYHmabxT+J4iiqvAdD8YFzbWlYVxZKEt8JGQLExMk4I8Vo5uK4oqSo/O13vK2G4SqpuI7AzkHi7OgY2xJsEsmVFhFSxEexqlr9xFRg5NlYL4FBcaxpmEBN5pqIDkx8BaQkPNI72dkgO9GHHRMmoQAxvFocWL+CBsogsqzhER5QiPcIgHckmoVUygZZSvRaQPsD2gEytEgDsxOlZKE+SlU7UKrFzEy0iL4TEdkVDLuADzeD9LVIAlOdxGEGshjLOUxlvKELZKtIMW76KnuGTRXLPpHrks1xymWx7LPZ/rVsBxLeC6F/PcioUeZWLPcolXhRQoQZUyUJXM6x9Uy0FVUhBQ1sjBdUpwncrbqII0an0aNd4mUn0adQCmmUKa0gOa0oNbMoKaM0JassPbcsJbc8LbckNassLa8yK7DLE9xeiRGvJUc9JgFbhQHFKgCslTu0hIZqxYt0xiaLUCZszw0tCd2Il+YroPj+rJwLqRUO5EpBsO7oSGOiaGh3GQBC2BoUQRGWFuYa5WIW6WYe6W4R5WkZ7WkZ42kSC7KJBbnDdLCdfX8ksmm7DlWd5iqgfQM2Bj3NjJLuwkZ2aSHRVmQQp3ViUHlItx3UU+2YLQHLm/ju8kwZmzYmx0SX41iuAGXVhDWnhjWrBRE1ijcC2k2WagrTiR1vRoezbMW0kJ1nGhWXxqlUxWJ5CXMxGyhFBWlC8j0pcZHcyGxosRzGx6UWd2+1JH7a0xYVslIl+bPtS5eH26/ezu3ovrzUf35i6Pu/duN63O1cxNVE+PVY4PVQz1lvZ15XQ2SaqLYCpBtJSdlKZITJPDdRJUrpJWnd+wMD11uL1y53jzwZ39J9eHz+7vP7u//fR64/HV1tP7288e7D1/uPvi4fz98+yVsbTb46rlIeUn5EsDiuUhxeqQamVk+P754ftXe2+ftZ5sKRYGZYv98qWh1NVx9eqoZmV88O7J3qsnB6+fbL16PP34bt/Vaff1ac/VSdvFXv/V8c7bF3e+fHv11du7X7w+e/9i5upY0lWWOlDXv7c6c7zZtTxaOdhQ2ddQ3l2nq8/X1RlYObIEOdmbBCgWBZPxEEphtJQeLqZFp3C4VfnVU6OypmpCQVqsihMjIofSEkLJcWQ1U5IrxYgwGAEGyUwgCNE0ORrHQyRQoEmMeCQlPJkRgaRGROFDY0gxvogA3/hA77gAMDzIDx0JQUdGUGAwVmJgEpBLQj1jAt1j/F0ifABtuIR5O4X5OAZ5OQUDeDoEeTgEmnAM8nAM9HQMBpnWRwR4JsS4w0KcIn1conxdI31dIiFuURCfhLBwCiwQHx1EiAohQiMYsEgmHCgDcBGQ5FCgHkSNiKCERpBC4+jhJGUSRoZCchF4OSZJhBLmCelZHG6+WFWdWj/VPLU3RUjjxsnp5OIsWm0RxViArUqH5ctRjVmUsRrBclfNnf3rX/zoya9+uPblU+3OuO5wLu/idun1ftXjo9rHp8bnF8YXF7VPT+ufnxtf3qt7dtn88rr7/bOl796Pvbw79frho//81bP//NXHP/3+27/+6Wf/9def/vdfv/7bn97/6ff3fv2T7ieHWz/64qv/+v2P/q8/vv3Tr2c/fz7z7Yep796NfvV6+MvXo18D5cvhr14NfPGi8/2j5jf3qp+eVD87qXhyVP74CKjXP7/c/MkP3v/19x/++v3mD982PzutBzZ4dFj5YL/ierccyEz3dhrun65998Vnf/7+i7/8++d//tXxz97r14YUt4akqwPOYX62fp7mnk43HK1vWJv9j8bqbwa6uWB9gzKSEaUiSnMeTK+ISpG4EVGuuDgXfJwLEeZMiHfGxlklBFugvILz0EijmDdaA5aTUcWZnhyKOzXJmZzgREA4EeAANomR5khwQBoyoUXBHimJzBHHZqeABRQXBhpwjzMVcA/SlhBvg442S/B3EURE1rDxPRnY5ryQdEGYTu7BI7oy0c50tBM9yZme7EhLtMJGW5IDIopImG69ernNV8NLLM4CS5kePALQq3Viox1NF9OgLLAxFrjAwEIStCmFNVkdkC2JL9KDFVxXPslVQHTmmNzjzMI6MFAWmHAnUVxoFTu+OTXBqHcREyAyDrB7DkSkAwEer+TpW+viZHyLqMCbESBXQiiYBQvioh0Rka7wGIfYCLuYUNvoYKvIIKvIQMtwP7MQ75vBoJuhHmaRXpZwPzdSFForEBVkkrVyslJGkEvB6ATrmCCzcJ+bkT5m8RBzbIAFNdycHmHBirZiQR34CEdRopMU45SCc1ERXFNJrmlU93Sau57mCpBBd8lkuGaz3HLY7vk8D9NkvVJQqRhcJvMul4PKZaAKmVelDFwpB1cpvKsU4GqFd7USYpr9UA02oQLXKH3q1JC6VN86nW+tLqAuLaBOF1SXZqImLbROH1JrKkPr9VFN2dD2fHhPKXqohjnVltRXCcoRoqrzgrJUTgK8GRPqlUsLLVckGDOdJQR3DjZAzARzyJ50HIiCBoziioW5oGMdUFFOqLBwJjxRnMhQJKCZEYFIsFu0h2Okm0OUh1O0pwfCO5YWKc8iZDaqqye7BY2VAQoOiINzZ6Fd6YmudJQLHeVEQ9qSoBb4UDc12r9AgKrNAGmYieXZgWkiJxHGgg11yiKE1GnCG9IjGtOjGvXhDWkR9akhNQrnHJKDBmXNirKlhNtSYpzYqEAVK0TDikplJuQw2BVcRZtM163UtCnUzarszoL2ldGK6QGBsTgmlR+aQovQMGk1htatlfnLo8W7x32HG9LeempzEdVYRKsvpNUUUKvyKGU5+IJ0RJY8SsMOkRKDOJhQLilRJU7USGFKIas4u35sqG95duj2cvftpcHt9YnD3Ymj3cH9zdaNlYb1xbatWyOne3MXJwt3TkZPd0vWptOXh9VLQ4qFAcVCv2y+VzzXk7E6vvjwLuCezSdXzfu3NYtDqvmBtMURzcJg6sJg+tJY0a25ruMt485qztJY7upk/vpc8fZS8c6CZnlANNaUPtM/ce9s9/Wzo7cvbj+9atiYKZrrKhprzO8qzalPy6lSlDVlNzQXFRVJZFoyX0Xkaun0VC5Jw/LFRXklR3mgo0FYqDcJ4ctMDmLjA7l4f2ayPzPRjwIPoMQHEqBB+OhYRgKMjUziJ+NFOH4qi6MiodlxUGwwghyVQI7CsuOw7PgYXGgoMig0MRQc6w2K8vGM9PGIhADBAgQLDMPHxlLgkLhA9wgfp0Avp0CQo7+Hg58JOz93G29XG28XGx8XG4irNcT5H9j4ugLY+rnbB0FwChFCQHAI9bIP9jQRAnIK8XIJh0BQ4T6oUD9MhB86PIwCDaPEBGDDfJBBXnB/78TAMFpkJCksDB8AJQfDmBFkBRoAI05E8lAigzSnJUdcIpOXqZQ12tyuwiQ1PU7OIBZnM+tLqfVF6DJAKorkljzWeJNmfaz53sn1z757+svv1j9/nLM/m32ylHt2u/DOVtn9w9qnZ3VPL+qfXdQ+Ozc+PW9+ca/28Unj09Ock6W0rTHuhBHdU4zrLsZ0GpI78jBdBlJfEWu0SrXYU368OvXm/uZXrw5+/M3X//WHH/2vP37462/mv3w2883b2R9+mPnBu+nv3o98+bL9s/uNry6NL+80v77X+uaq/sWF8eVly5vr5ld3G55ftL66Pvv3n3/+tz+8+/N/LH/9vOXFWcPz0/onh7WPDqoe7FVe75Td2yi/u9395N6L3//HF3/+zed/+tWT739Yc7qk35nR7y24hPvb+YMsQM43nG1v2Jj/j6RiHuTszQkLzcUnVSsi0rhhUg6IgnXBwJwxsc6AV3CxgF186MkOySGOJP/QAjy8XkxuKwpKYYakCNxoaFcSYJR4ZwLMER1jmxhlGe9nnWxyT1y9gNFXBJaRA+RsVxbamWo6MuZEgjsQYAB2ydE3Ed4+qriYOg6hJzM4Sxis5XmLaG5MjAs10YVqOpjmQkXZEmFmqGBHZkh0FR3dpad2FgaoOREZCjc+2RXYEgg09CR7CsIWF2+eFG5DDgoqJsU0yBijFSA1IzJTCRLTXdg4J2byJ0UBCSnRlgC7mRjoqUQEl7MTmtNC8sRgGRUkoDmTEu1xMHvgLaNjndBx1rBIswhfixgvF0I4mB7vT0+0iQ50jg23jQi0DguwCvOzCvWzDvE1DwLf9PO46e9qFgGyRvl7EKNhchYmRcTSabEyqRs81iLM92aw541QT7MYMBDgLNCBZpggKwKwq9H2VKgdPd6eCXfkJDjykI7CREcx2lmGc1YQXFUkVzUJnMZ011JctBQ3HQ2UzgRlscC5fJ8iMaRM5lMuh1TIvctl4E94Vyh8ACoV3gAmtaQAeFcpvUzrU3yqlH5Vat9KTUCVJqBCE1CuCazQBlVoQio0QaXqwFJ1SLkmojItqjo9zpiF7CimjDYqFoeQHaXeGUJOR31krsaFj7NgxULymPA6HaIi3YmH9uaTQqUcbxbRg4p2wSFc0TBXdJxLItQZGe2MiHCEh7gggsOoUJQYSVGjuLokYQaWk5Yoz6cbmtKqB6p6VidKxvqSDTp/MRXExrnSEh2JwH9FnBMRBlQcTX2OcCt8mHsKOrY4JbEy3VNKwZblhKQK3UQ4K26cZw49rFYdWZ9mwqiLatBF1OsiajShZVLXLLKjDm0rjLWhhNsQw+1I0U60WCd6vAs13kuIgJVxEDU8eCU3WIPzl+DAvCRvTiKEm+zHSw4QYYK1ZGSpsvHW9Mzh5sTBZvH0AK2xgG4sYBgL6LX5tKo8WnkuqyKPU2FgVxpoxfqkLHmklBrMxMLFnES5MDlFlFFbVdXT3jjcVz/SL6ou5lYXq1obmFWFuNJMSp2B3VYu7K5VDbZVL05P7G/NHe8OH+/q5wdTZnukM92iqQ7JdBdA/vLk0tXZxv3z5bsnZStT/KFmzoCR1VvP7qsXDjSKh9rko52qsW7NRG/W3HDmzGDqZG/adJ9yvJ0/XE/tLCU1FKeN9ZYvT1euTGZPdJbP9mR1lRmacvRFYrYci+Mh4yhxUYTYGFx0JDoiFhNOZiHYYhxBjEMKMMGUOAgxyosYA6bGejNh3kw48M8PVCAMOISF8CVDQ0gxoZ+GT9ipAqVBpTao+KlsVa6Ao8AiKZFICpSrosrSuQwZJhYf7hPl7RkG9orx9YkLcA/3cg3xdI8Au0V6u4aDXUPBwI+Ofi42IEcbTydrD0crNwdLN3sAC6B0tbNws7VwtzN3t7PwsLPwdPgnXo7WPm7g2FA/eKi1j5ONt5M12NnGx8nWx9nBz805HOwG9fVCBMRQYQhuYhA2xAfm6wX1BcVCArFhIfigoETfMIx/Sh6FqkQq8vk4cWIiG4EWJGHFaLqWLi2Sp5SqNDWatMYMtIYVp2DSyg2itjpecwWpOiuhSEVoL+JNtaauj3c/uHgASOUX325/8bz4dLXkcrP8arfu0Und0/OqRyc1j0/KH+wX39vKPFoEcgxtqja6URdSJo0olRNa8vNmevoOlyfv7C08OF15fL754mr14dnExVbl6oBoqCp9efThf/zyu//+0w//rz+9/6tpIH3x23fLP/pi4YcfZ797P/Xt27GvXw18fNL6+m77m3ttb+41v7ps/+xB74dnPe8fd7297n7z+OH3v/nm73/48m/fX//mu5G3Vx3PzxqfnrQ+P294dNT48Kjp4dHgy6urf//JpxGX37z94y9O/+3L0sNFxWK/eKnfNdzf3t/L8l8iFWdYgAcn2kuBRJTIPJlJwWyyFw7plBjjkBjlmBwDeMUhMcYyJuBmtJsHJ9QvCx1fI43IFYMFRBCT4EZCOeNhjtg4YDPH5GjL+CCLSJATLSC4gBhbJ8C3ZLvzkn0EJBcK0okAc8bFO+NgTtg4O8A9sX7mSBBEj4iuYRO6sj1lJBCX6EpLciYluJgsBXciIhxw8UDusYD7eMliY41cYm82NF8WIKWDBVRT7qGigJd1/TRQbIeNvZkQ6MAKCy2nxbcok1syvaSUEJXAhU1wZmKcGWhg+1AF14GKssZAzRJ9/dKTwqt5pN58bzUDxCO40TBOOIR9MtQWGWWLiDAd0YoIAPKHdRzYjRjuw0B44WFW4X524QGBiYjl27f+v7i1amL91srG7Vtb6ysba7c3N1Y31suMtTf8PW4Eu5nHeFnAIRYJ/mZIv+6xoeWN28A2K5sA6yaAp/yDbYCNf7C4uQZWED9Ns0gCmWbjoXppGV6pDJCOAcni+xZKICUySJncp1TmXSoFSp9ymY/JMXJAMF6lMnCpFPypBOpeRRJIqdy3WAHg9w+KFP6FKUGFKYEFsoB8eUhhSnixKrJYHVWsjSlLTTIaOENN8tmBWCOQUaScDqO/Tmw6U5yXAMliRBen+GqZ1iQohEsMEjNBdJwLPsEpOdYBGQn80eziQ+3jQh1h4QAOcaEO8SFO8KAgVkycEhWnTkrQJqPTGfSidEKeLlolDBDSfDh4DyrKCR8PfHx26Bign2GfHG2XFGWdGGaJDLTBh/moiNhyPTRbHiBlkEpyTJ87B23Ng4NzmRHV6pi69Oja9Jh6XXRdamSVJrJSHV6qCCmU+GUzXbVoZwXSkR5pgwmxx4bZ4cIdcOHu7Fi/LFyggRKSQ/bgxHrQYQBgBgJoOn3FScF6anSRAFuT3rk2P7C20LM6n97dyG0q5TaX85vK+MZiUX0Jr7KAU5bHLssFSmFlEbfcUNbasLa9tbm7s7Gzvb23u3dwcHB4eHB8BLC1t7t1sLd9cLC5v5dWX0asyKDU5XGaShTttbXTo0Nry+Pbt7s3V9RjneLRNvF4u3CsTTTaAtRzZ0aAcLNwuj+yt6Ed6eD2Grk9RnZXraCnnttVy2yrkvU06gba5D2Nkq56YVe9rLdB2FXLaCslNeUlVupwNTnqgXb1QBuuIjNnpLFwpD63KVNXKEniJ8bQ4GHk+CAC1B8T6ZcY4YsIgcCC/WGBaHI8mZ+M4iYDCQDKw0ULCbEyKlzDSdSLcTkpuLwUTLYUlSNFazn5dYU5tTl5dYaKjrrqrobKttqcygx2CjEOF0rgJkrS+cwUyvD44Ob2+trG2urt1dOzs/OL88u7l1fX1/cf3H/0+NHDf/DoUQTwXQM7WLnbWbramrvYmHC2NnO0MnO0vOlg9Q9uOJqbcLK84WJ9w9X6pgkbADM3WzNXG3M3GwsTgH5srUAODv5uDkHuLhFeLjFe7rHeblBv12ggJXuD4f6hpBCCDOUf7+MX6x1HCsUKYapCgSKPm8yFJXGRBBmBoWUpClPK+yrLB8oqh0rRGmZ8CkdorJR21OuBj6CpAJAKpbtcvtibtzvf9/jy+iffPPy3r2de3Wt6dNLw5Kz20QmQS2oeHZfc2y292s05W5FuDJKnauMadeFlMkR9lmC0rWT3VsXurcyF4ZTRVvVEq7i/XtBXI+o3GpYnm852bn98dfnj7w5+8NXXf//Dj/779z/+v/7w+o//NvLZ1djnzxd/9HH+hx/nAK/86KNpJP8H78a/ft374Un3+0eAVHo/PO55/6Tv49P+z58OfXx559c//+Ivv/vhf//xu//+w+d/++2bP/760X/+29Pvf/76D796+8fffPjzf3z1t999/V9//Pi3313/6odTbx5Una4Xn6znHt7K2Ft0+3+kctPZ7n8qFRgbkyCjw6U0YobMEx7lHBboHBHkAg11RYS7JoRF80j20CC7cIhDDAiZRiYXSfHFimgZM4JHAGFhnth4MB7uiY3zJsADaGjbSD+bSJA3I9qdGw3NpJCqMiDMxFAuyQ0Hd8PB3PAwDzwCQkNbxwdbRnpZIjwjDdQAAzWmWOxIjgvmEL0oiYB4HHFxzgS4NwtrCQ+2jPE2h3p4CGN8M7HRpWIXQRJUxw8UM10pgH6QgH6AnOSIi7dEhN1E+ILkcN8cUmSVOLEmNUBCDZOxPVg4NzraCTAfGQliou3RsWZxgeYYX98cfFgVH1ajtqLCnSkIJ3yCfRLUNiHKFh5hF2+aVvlGkMeNQBfbeIgrIRxEjnVJiLAMBFsH+Sj0af+v/9+Wlu7Om/5u1tE+1nB/a2SAFTIILWP/87H/07K6veEuwICkRJCM5CUHoIAVVG8lzVvF8FEz/PT8wAJpQKnCD1BLsRQCSKVU6l1isot3iQRcLPEtkfsUScGFEu8CCaRA4lsg8SuQ+uVL/Q3SoAJ5YL48IFcaCugkTxaSIw3JkYRmSyJyFVF5KbGFalS5ntZcxu1vDClUR+Yq6S3V/mqBMxfjIcb6pzNgJSqfFOqNxEBzRAjwx3TCAvE0zjERao+ItI0LsY4JtIrwswj3tQzztQr3tQ6HmId6uSQH+vHiw+XJfhyYQ3KYDwPnx6V4UJPdyAnO+Dj7pEgbZOgnwm2QYVaIYCtYoDUswAoV6MFCEPJV7CpDiIyVlKYk5Ol9WAQnCtyBDffNYkZWqOJq02JqdDE1qbD6DFhNGqxSBwco0yHL01HFmuhckYco2YEQ7UyA2mMj7NGh7jyYpyoZpMb6pGDcqFAwA+VJRYAoMDALEZxGicjjxBfJuHWF7cvT/bdmW2bGDD1tRf0deT2tAPm97bqmWklVsbSySFxRJKkoVlaXiktzf/TjH//zM/vfLhl1lbrm2vR2o6G3tWliuGtmrGd+un1hJne0h99RwzHNa1fL6KpmddVI+hrrl2cm9zYnd9YHN5azR7rYrZWMpgpaUymjqYzTXMFuqigbH2yfm26dmSgY6BA3lpkOylXnEioyEkvUCQY5oUyf0l2fP9mfOdSq66k29FdmN2hFaVQoNSGIjAgkxgXgY/3Q0X5Jkd4JoV7xwZ6xQbHoCCIniavmk6SceCYuEAf3w8b74uIDyMgQenIoHR1IQcZxiRgJg6sT4SVkXiqvoqW2uqPOUFOYVpQq0XFxHDRPxcWyk4hc7D/f8/92+b//7//b2tPW0tXG0tnS3MmEGYCDuekeIbYAZqbSBihv3rAzMwE85PCpBBxjqlvccLK44WxpBjzd2crcxdrc1dra084G7Ggf4GIf4u4MRKJoHxAUAor1iaXFxZCCWWpcCAoCjgaHJIbQUyjqwpSChjxZjpCtYfJ0fGWhRpAtFhemdMx31oxWo9VslE6sH+jKHO9T9jVRanKQpVpaX6Viqb94f2XpsxcPf/L1w598OfH0rOH6uPr+UeX1QeHFesGd9ayz5dS9Cc5iG3G0Mq4hHVGXzR1o0q1MisdaEypTIw1CWnNO/mJ36/7c+N3N2fu741eb9ZsjyqFydkeRerLv8Mc/ePPH778wnaz1h0ff/6Tq8nbp5Wb32wfj33w2+8MPsz/8OPb1m4EvXox/+xZYM/nt21EguHzxbOCL5z0fnnR99qD/w7OZrz7b/snXxz//0fm///TyVz87/+WPj3723cW//+TBf/z8wX/8271f/+TsZ1/f/vC073Kzfnu67NZw8cpQzmy3vM8o6qoxScUPZOUJSOV/nFRAwT6gYD9QiJ9HSIBHaKBHUIBbgJ9rkK9LCMQl2Mc1xMclHOIa6QOK9gPDQiAJ4V6IUFBsmAc00B0aDJSescEesUEgWKhrbIhbjD8oNsAbEeKPiggjx4WQUR7xIS7RgW7AQ0AlLsQ9PsQ9NtQ9LsQzPiCSAmPlpeC0fKxG4J0YBUJEugCPwsNcEWFuyMgoNsEtLswlGgJCBWFSOZh0Hq1A40tGeCaEOsWFgJKiQRioFz7en5rog0c4QoNcE4JiFASYjpOg5zPK9DA5F0JIcE2GugPxyFTGBTIw9rAQ+2g/d1w40iDA12jFbaVhAmq8iAHBJzggIh3iwx1iQ3wwMIeoQOsgT9tgd3d4iFNCoFtSeAgB6REZ7BTi19Hb88+vxf9p4Sikln4e5iGetvEBtglBdoigrb2dfz72f1qQSr4HO8mTiwEL8T5iIkRM9pVSghS0ADk9MIUZpGIHpvOD8sSBhfLAEiB5yIDs4gvYpUgMKZSADUJwngCcK/DOFfgA5JjwzRb6ZYsCc8RB2eKATEFAhiA0UwwQBLxOmiBEL4jIEEfqxTHZMliuClOWmVSe6SGnBqrZdGNZRJrcdD4eDw2W44N0rCAN0xYfdSPe90a0jznUzxYeZoeIsIdHOMAj7GNDbCIDrMN8LYO9zYO8zAI8zYJc3JKCfFmwMBHWkwR1To6G0LBu+AQXTJxjUow9KtwOFW6DCLGMD7SM9QfCq1WsvzUi0IkQg9Tw02pLxEU5cBknrbIUaMTjFCIvchIQiN1ZKD8dPa5Uk1SXFV+RiqrNhFemxZdrEWWpsGINsSYvpbOhYKBTWFfsw8eFCunRUrYrAWafGOLGhUHkOHiuyF+KB1ETgnlkfzYeTE3w5SdH6dkJ+XJadbaqpbpyuK95erSwt51i0CVqRdEyRriEEimiRktp0RIaTM5CaYSULE1aVUlJfc0/P7D/7fLh44eSZmNZR0tlT0d1fxdAWW9HZnuj0FhBry2kGAtpjcWUxhJlb3PD0kzP2tLg2urI2vLw7cXBlYXOhenK8QF5aw21tpBUbcCUZqBLM2lVBfqOxuqhPuNwP0BNf3d5b3tpT2t5T2vdcE/f0szMwcbK5dHM6W7V9EBqa5HOmKEsEKKF6FASLIgQG4CF+qFjfJMifRLCfWAh4ahQAhNFFlOxQhZNqUBy6BFkTCAWGYBDBhOSI+nEGBY5iknEy4TR5OQEJgFKRCAZSJ5GkJKjpMlpZBGRKqaRhNRYAgxJTWjvbv3n2/7fLiNjI1Yu1paADxwBl1gAOjGzNzNha2Zmc+Om9Y2bVjdMNwsBsL5xw+bGDdsbN21v3rS7cQPA9lNp/w/TfNKMo7mZk4W5i6Wlp40V2MHO38kp2NUrCuwS7OEeBnINdvUIdvOJAkWhg3yhEJ9o3wB4cGBCKJQIpyuZ4ixZdrVBkaeUGuSKAiUllaWtTsOniZL0ckV7I5D5ZF1GSk0eqjyNPVSXdnus7mxr8c3T6x99cf3jzyefX9Td26++Pqh9cFh2uZVzuqzcGWXPt1JGquCNmcnNRdzhVtlkF6xCE50vVI82NB/Mte6N1a+0Vo6Xlo+V1ExXtax2TJwt/L9Zewswua4jbdjSQDMzM830MDMzMzNomEEwGmbQgEYjZmZmtiRLtswcQ7yxnTiJ43gTB/fbf7+/zu2WN192NXMdu5567nP7XOzue+s9b1WdOgceXTr7waP+SzuyFtbXH9k+cefK7MOb/deOFe6dqDi1tfnGidFX725+7xWgKQAkSx++Ovfuk5m3Hs2+82T+3VcAZubefXnh3ScL7zyZfvPh7NsvLbz9eP6tl+bffDTz2v3xV25Ovnx77KUbww+ujjy8NnjnTPeJbX3Hti5c3D93fu/06d3Tp3YPHlzsWJqsm+5n62QUKc+eS19FJ/3YQD1XKeMoZGy5jK1UclQqrlIOHznQqJKx1HKWRsbWyDg6GVsvY+plbJOSa1JxjUq2XsExWJVlRMo0yBl6GcsgZxuVPLOa7wzAo2E7qNgOSqZJycT2YenlbDjEpBI46fhmLddBzXNUcxzULAeVZWeWScFyULAdlbDCdlSzHVVcByXPUcUzKwWuOkAjpoOCZZJzod1JzXFSs80qUJajkuOk4rqoOS5KvouOhxBOBcp0UgGYsZzUTEcVx6xhOSpYZkA4Fd9NI/A0SPyd+O4mpoOKYVIA8lF1UqpGTNNI6CoxVS6gKfg0JSwFDKWIKuGSBRwijwl03vparCQ0uXA1n/GCmEZ0FBNdFcoQj//zf/6Pdduycu7SRW6YOz/SSxTtq0gK0aVHGbNinPIS3fJTXfJSNOlRspRQYXIQPy1IVBgtKo8XVSUJ1ySI1iSKqhOF1YmCqnhhZYKwIkFQFg9bhaXxorJ4SVmCvDRRUZYEqgItSVQVJyiLErVFSdrCRH1hkqEwyViY7FCS6lqe5V2d711bIEwPJYWaVdkxPrVl6uxETmwANdSZGekqSfGXpgYwwl0IHsoXjPwXDHwbo5TspCE7acmOGrJJSTLICVqJnVpso+CvUjLZ3mpxuLM6xpvurmF6mLh+bnRPB7KbnuCsIroo7c1ygqPcziwjOEkBernBZpfsmMKOxoSKQof40NTK4vp1a+Orys1pidrYMEVEAA/lmDkA86BHmqkxzrQ4F3qcCzPWhRblRA41kcMcaDGuoqQAx+wYZVxgWFlebE2FPimK6Wemeqr5se7y9EBDXqwgxpMX5OKSleyQGiePDpAnBhjyYnyqsiLqi13yEoLKskrWtRV3tcSW5UaXZMWX5kYVZoXmpftmJLilRJkTwvQxgdpoP9Ctu3dY/7NlZXp+LqeuOqWmPKGuMq6+Mqa+IrimOLCuOKCxJKC5PKC1PLC1Mri9OmZdU8Fob+XkcM3MWN3MWOOmieZNE2smhkqGemK76v0ay/wbywIby0KaK0ObKwPrSqKb1+StbV/T39Mw2Ns81Ncy1Nc61Nc1PrRxbmp0x+Lmo/sXTh5q2TyZ0FaV0lZe0FVR2JKfUBQdkhroGevlHuUBwOAd6xWTEZqUG5dcnOmbEuuREKXy9+A66xhGOcMkYzjImEYZvINCd0eJt4s2yFvsbtL6uGq9zc6h7p5RPumlmVnlmbE5cUGJIV6RPo4Bzr5xAW+++ab1ay8rbn7uJDbZlma/imK7imKzimRjQ161mrhqNWHVavsXVtkhfcGytLV+hHbYYRVADglWsCUZLRHSABpRbWxotjYMOwKfTJVQGQoGQ8pgyNhMBZchYzEVHDaYkAAHc4BZYFTIXTUqN63R39Er2i8wOTQ2P7mxp72pt71uY9Oa9bXxlanBpam+lXl5o/1ls+PZoz1hXWu8W4oAVCrB/p49tP3x3es/e/Pah2+MXD/RfvV406UjTZcP113cV3ZqW8qB8fCltb5DtR7rqqLH18dv6jU3pnu15zfuH23auaFttmbtaP6G4aKBqfq+Lesmd6yf3NqxYaZuYlf/xMlN41f2zNw9kr3QmTk/2HflzPpLh4p2TRTun6k5v2fDg4uzb7207YPXtn34+vYP39j+4etbP0BOsLl3Hs8DtIC+/Xjzuy/Dcv6tx7NvPpoFdHnz4dxbD2devz/16p3JV26NPb7ec/dU29HFniNLO2+dOf7o8tknV8++cu3A7dOzp/YOH9rau3czUy2hSLh2XNoLVCz768cIC1BEKWcr5IAooDyVAhRBi1LGBFUjUOFq5ByNjKmTAipwjEqkGJwAtDB1clC2AcEMV68E5RlVPJOaBWCjV7IMSJmAJXAIhkYckxLQBcCJbVKxjLCuZBtVSNFHFRP2N6o5BlAVx6jimtR8Bw0PsMdBDWCGUAcpgA3CIcAqDmCSWcMASDOpOI5AlTRcJz3fxchx1LFMWo5JwzGprWpUwS0BjGEfVaDYbaDrMrQyhkpCU4hA6XIRTSqkSQUUMY8kYJMELAqfTeKyQAkc5ldffWV9LZaVN954w5ZDX8WmrBJRiUYR0Swbn5uxbltJAjKTBIEu8jBvl6SI8PyM+NJCx+RoXpgH1cdg76a0c5bZOklsXaQoSOOjtA3Rro4wEBKcaJne9JwAbkEIvyiCXxTJL4oSFEaKiqKFRTHCgihpYYy8KE5ZkqgoSdSWpagLE7QFCYb8eIeCZGNBsrkg2a0ozacs260wzasky688L7KhIrapyiErjhLs5FyU7llRpEqNEUT4ckLc+CFmTphZnOAtjvOi+ulXmQQvaLmr1Dw7rYQIiGJSkQwKkk5mrxIV1VRYv9KyEl2QRvFUCSJdA4pT85rWBOWm6iL9UorziprqgwuyHRKidVGhhuhQQyxY81BNeJAi1E8e4aeMC3HISjDmJjnkpbiVZbuXZnmXZutTIlHCUoy/Lik0obYssqzQmBQpDfFmezsyPfSiCHdFcqBHUao42pvhZXJKjfPOTXdOjTMmRzqkRHpmxYblpebVV9av76pd153bWBdTku+fkeScEGGKDtZHBhijgpziI/yzUgKy08MKc6JL8r/48kvrd1hWggERI/wcEsKc0mPccpI881N9SrL8q/IDaoqCAFpqi/3qigIaSoObK4KaK4LbqoLbKmEZ2l4d0lIZ0FQe0FQW2Fwe2FTqV1PkXZXvWZHrVZ7jVZ7rVZrtU5YL/5dHUYZHYbpXYYZnQZpnYZp/RU5EfUlcc0VUY6l7cVpwdVFsY2VUbUlyQ3nlutaq7qaKttqazobqjobipursNeXJpUV+aQnaEG+ei54JvSujVOxp+O6776x3/3w5ffZMYW15RVN1QXVRWlGmc4hnTFq8dduy8stf/pLIItozibZ0wmqyzWoysBOkNiQEKoidYFiCBnWDwgoohjGrCS/YUVbbAy8hY7hCfGE1QAso5YXV1NWroZ1hR+RTyEIKXUpny1k0EYMmZrOVfLFJytWK9F4OIqOUr5fKXNR6H5POW6/1MriEeScXZWRWFNSvb90w1d8y0BZRGB+Ql+xXmls6PVa7OFsyPRjZtcaztTBqrC1ncbjx6K4zbz+99eHr1z58derO6fYrR+vPH6g6s6v05LacQzMxOzaGL3Q4ryvxXl+XuXksbb7frSVv/Ykta/f3bZivr25PDM8NcM8KcssOccmL8C6M9iuJCypPSmnM6pnvmDo6PnJh2+i1nSnTbbX7dw7fPDd26/TSS9f3vv3gwPtPjn/0xrnP3jv383dPf/rOyU/fPvnpW6d+/s4pbOXkJ2+f/OStkx+/dexnbxz+2etIP3jt4PtPj3zw9OC7T/a/+9Lia7dHHl7acP1I656Z6bP7jz25cemNuzffuHPl1RunHl3cdvnIxLEdvbvnmSoRRcyxY1NfoPzocSoshQIhCuCKUgkKcAKgwlejFZZSwVYp2GoZB1O2Vs7SKdigWqQcvYoNqlWyNPBRydIqOToVKFen4unVaMWoAQWEYGnkTMAkOIMOHcUFzNDDnkqEOrDUKdk6Jc+g5qITIqBiYUsupnyAFgfABg2gAt9CkowaACEMqwDYlHzAEgAJtI8WbXLScZz0XLMeOwSwBCBEw3HQAmIhUMHgBKAL9mQZVAydDG6MoZLSFBKaXESVidBSKgAli3hkIZfEBzhhEjEVqZXW12Il2bxlcTWdvIpBXC2k2ql5NEfF119/bd22rNx78b7Iz9UQ6hOaHB+Vma4O8iE5KimOComPWezjJPR24nuZeZ5mrpcjx9vMC3DjBLuzwz2pES6EUJNtsNY2TE9JdONmBHAzA/nZoYLsMBSMyY6Q5ETI8qLVxYm60mRTeZpjaZq5KBUBSXFmaEV+XE1Zcl2lf2GGJj4EVBcf4pQa5Z2d5J2boIn1r+rvSWhvMmYlK+PD+SHeDB9HkpuW6qXjh7uKot1ZwY4EF8kqLecFBXuVgmunExMNcnutBEDl7Plz1m/1fIGfRRruEV6amV5fjqhAQkRKcUFWdaVfZpopJkITGqgLC9KGB2nDgtTBfhJfd66HI8NFT3XVUFyUFHcl3V/PCnUAZQQYqH4GRoCTOMJHkxjhkBKjiQtTRAZIgj35Ps4MdyPNRcMNdJTH+nnmpcgifXnejub4KK/MFK+sVL/8rKiKkrSGmrT6mrCiPLeUGFWotzTAWRvi4RofHJAW7Z8a7ZsS5R4fYgz3UQW5KQLd9OH+OWtwQebDR4/Ebg5yPxdNmI8xJtgxIcI1Pc4zN8W7KNMHUKE827c8J6CqAPrF3hW5/msKgcEE1ZeGNJaHNlUENpSFNJaFNlUGN5b71RX71RT6VeT5luUGVRQEVxQGl+UFlGR75Kc6ZcWbM2NN6VH6lHB1crA2PUyfG2nKjzbmRhmzIw3pIfrkEH1iiDE53DEp3JwYbooLMcUEacJ9lKEesgBXoZcDz03HddPwPLQ8+Ft99Hm1Zda7X1a6N6wLjo8IiA5KyUrKyM/ILMicW5i1bltWxqcmyXwagUWwo9nYUAAhbGzJNnoHfXxiLJlBtHq9MBRBar/KnkqS6TQO7q4BYcHZeenxSZFkhh2AihVRAGBAAVSoNnYMezsmgSqkcuRsoU5Ek3A4aglHLRZoJSZvJ42HWe6o5WrEYgeVa6hvaEpkZml2REZMbE5aTXtdXXdj11Bve39ndFGib2asV15adv/60pnR7OF1Qa2lvs2FEaOtSZt6158+cu7Nl2+9/+rl918Zvn6s9cKh5ouHmy4erDi1A4HK9p6gyQbH9nyfdfW5WzcV79oU0luz4eTsul3NHZOFfjl+xowAQ1agQ06Ic26IW1GkR3Gsd2mCX1VSUmP20I51IyemJq8frNk7NnP76su/+/Jnf/n9x3/75pO/f/vh375978+/f+tPv3vzu6/f+tM3b/3p9+/85dv3/vrv7/75W0sA5p2/fvv+3/79/b/+O2x9/Tu0w7t/+fYD1PL79/7yu5e/+cWup7c2ntvZsWN88cLhsy/fvvnOwzvvvnjtzTunHl/Zce341Ik9G3fNMRRCspBty6K8QP7RI+qRy0uj5mk1bLUK84ApMEWshatWc9QqUK5GxdOpORhysDRKtkbJ1QEGaDg6AAwFR6vgauUcrQq1oB1kyGmmlWPYA+1oyUKqZGsR6sCe3ysGUWgfHuymkbM1CuzMakAm7AxyBEhqpAA8iNAgSqRkAzwYECviG9U8hFsANkBuAEU0LJOGaUCQAzsDosBRlna2QY3YkhEYD1qB8wB00TUShlLMVMFSSpOLaVIRVQy/LI8k5FCEbBKPRWQzQEkcBpFDT8lMt74WK0lhSfFqMmE1xX41m2QrYdW2N1s3rCRJRXnGYN/ghFjHAB+GXiFzMXqFBblFhEo8XBiOarJBTtCJCFohWholJLOc4qqm+5rYEZ68SB9WpCc1xIkUYCCFOjATPVnJvpwUf25akADNrh8qy4wwFCUai5NdyrPBeCU0VBS21qeuqdAlhDF9HWkeWoqLiuKkojmpOW56lpuW62lyTQjPql+TWlvtkZ+pS4lVxIYJQ33Zvq4MT0eKi87eEV2dF2AShJipXmpbI/8FJWuVnG2rFtrppCI3Rzzuvv1HDkUWZjnEhvqmxefUVKVUlXukJekjQ1XB/spAH4uqA3xkPu5sZyPNqCRqxGSdiOeqcY4NjCvNCStND6vMCK3JDK5O9yqI0yf68gOMFFc58BJJiJc0zFfk7871dGQ668gmOdfXJI/0dkuPFQa6Ajg5Roe7JyW4p4ImwUVdkuNUYb58bwext4NPUkR2ZWlhbVViQWZYWqxfXJB3tJ9PbFBgcmRwWkxgerQxwmvLjm3W77CsrN24Qeyol7ub1f4emiBvfViAKSbEnBTlkhnvlpsMrAXTNCAZ3sWZnsWZsASwAbIYWFUQVF0UXFMSXFcW2lgR1lARUlcWUJHvV5LtXZDhkZvimp1kTo1xTI2GpSkpwjE5Qh8fok0I1iQFA7SoUoN0OeHGgkhTUZSpIMKQFapPDtTE+igjPeWhbpJAJ4m/o8TXQeznIAlwkIU6yaPcpRGuwmAzz9/h/KWL1rt/vgCVSc3OTM3NzCrKyyzMK64sr22se/vtt62bl5WEtCS1i1HlpKIKyLY0u1UkGzuqHY1NIdAI9jRAGqS2VHuL2tOJTCGHzmNTWFSmgG1yMZrdHYh0e1vyCzZAUCiwXGVLWW1LW01iE+25ZJKAwdHweRohW8Fhy/kivVRqlAv1MolZr/Z2FpsUfK1UbFA5+HjmlhUk5KYl5mcWrCktrq8oaqiobK3rHlwfmZ/gmRTmkZmc0tFSMNyfM7Q2sKXYoyEveqKrcMf0upMHT7/2+Ma7r1x4+0nfpcNNZw80nt9fdmJrzv6pmC0bfMfqPHqrXLvLvbqrgntbo0Y7AzfWlS12t++obZ7IDS4I0KcFadP9DZlBpqwQc264W2G0R3G8X3liamtm7/aO/mOjG08vdp858NJvf/XR3775+d++/fTvv//gr9/c+OVHe957efd7r5749L2l1+4N3zvTc/3Epse3drz5eP7lu5uf3t/11pOdbz6ceXS578bx9dcO914/Nnb37NxLVzc9ujL14sX5+5f2Pbm57dbRzq2Dm88dOP/K7fsfPn7w4aNrr98+8egSgMr4sR39e7fQZXwij2HDIL9A+tG1v9gqJQfAQ4PhB1pXcNUouMJSIOJiUWAzHGgE1AFg0ChYYOLVAANwlAoBjFbB16v4eq3AoENgANgAO1ghAdBFAaQEliw1xntUcsvh8BEQiI0YjNQSuUEgBKQHU9jEAVqjUwDp4Ru0cFo4CQCShdMAliBXm1bJ0yK0gx14CM8A1RBDAvQCjOGZ1FwD2h/YFToW9jeqhI46DjAhnRw+ohWNlKEQ04GaAEeRiqkiAU3Epwq4ACeIoACWsOkEFp3IohOYtK61uAKzIGq9zoZEsEG4QljNIL3zzjvWDcvKG2++qfFx1/v7cB31AHieQQGOvl7ABckKIVHOo2qEPEeV1N0g8zTKvB3lvg5SH6PI18D2UJNdZCQ3FTPQmRXmQQ1yIfs7UIJMrGg3drwnJ8Gbn+ArSgqUQwc2P8G5JC2+sXxNz9rM+jWyCF+Ol9EU4u0VH+6VEG2ODNEG+Uh93EFF3q5yf09taIA6LFAdHiQP9ZOG+opCvHmBHmxvZ6aHI93FSHFUE41yokHKctewvPUMLx3NVbVKCaAiIhtV1S1N1m+1rEB/n+VmdI6L9MtIcUyI1kaFqsICZQHecj8vuY+n3MdD6u0mcDMzjGqyRkxS8jkmhU9saHZZcXJBtne0v2uoi3OQg3uYo3esi3u8u3uSn1tqkCneD26J4aZThPhK/NzZrkaGWUM2SLheBmmIuyEmiOth4rqaTGHBzrFR5pgIE2BYkI/Aw5HlrJb5mGMLMjLKCyJSooIivQJCXQLD3SOi/QJDXL39HX2DnHxCXX2i/f2Twl2i/bUhnu4J0VG5manF+amFuQm5GeEpCUFxMb4RoV7BAZ7+fq4+ns6ebs6e7i4+Xu4Bfl6hwW5hQbpgb0NkkENcuHNyTGBOWkJpQXplSUZlaVplaWZNBWhuXXV+Y21Bc11BS0NOa31CQ1XImpLg2tLA6iK/sjyPgjRzWpwxKUIdG6yMDtTFBDvHR/qmxHklxxijg7UxgZq4IE1CsDohUJ0cBNCiTg3WZYUZcsONuaH6zFBTZphjaoghMUAf76eI9JBHuMvC3cShLuIQJ1GwszjYWRfl+9e//tX69zxfTp06FRIdGhQZEpkYk5CenFOUV1RemFWYVVJeWlhUkJufm5ydAeARlxofGB3k6G5y9nLxCPAJiQiLjYuJj4+LT0oIiw/VuxlcfN31LgaRWkrj0mlcJgDMKrItlgCGAi1IKXYcmdjs7WV0c1Y66DXOZjd/HwdPM5VPpgmpDAmNKaUxZXQXf6Ojv07tqaNreFQBlSpkegUFlFWUVq2pKCkv0jrrOGqRzFGrdXcIiYsKjAwMiQ4OT4pyDfJOz8vOLs6Py04uWlNe39nctK7NNyXcKdrfPTUxsbE2vbsttr3Wr67Qq6kwfmpt1f6F/jPHAFSuvv3k3JuP1p7ZWXRgU8pSn99AjdvaUv/e2qD+huCBRs91lcbmXG1NgroyWlUR49uTXXWgpXFneeN0TmxdlFtWoGNOkGN+qEtxhEdxVFBFXF5X7sbtXYMnJwdOLxZtHV575sjdX33x/p9/99Ffv/nwL9/c/tVHE0+uTL9+e+2d4+XHF/J3jQ1ePnHqjcdX3nnlzKsPTj198fSrD06+cv/Ey3cPP7p28MHl3XfO7rhxcvHSkbnz++fOH9h0bt/g0S0Viz0Vc+sb5/oWzu6//Pq9lz5+5Y1/e+3Rhy+df3J1z/UTU8d39u3dQhVz7Dm01TTij43SgzxDDgULBeflKPsLM/08jYqv1fIAabCgCyhswqAFsRmLxwxxGjUy7lwNBjkqJQswQyVjKuVMJWxSsWFnDTASNbYJO7/lDM8UwxgZUpUUkIarVQMrwo5SIoqD8AmAB3EaLoYfFhTh6bWwJwIeDcZyDAA51ogOcCYuBj88o5Zn0iJ+gwEhwjmAMY0KqBViV6hFChdlycUMiYAmEdDFfJqIR+GzyXwWmccmc1mAJUQWjcCkAqIQGdSTp05ZX6ll5auvvrIh2gOo2JIRrsQlJVg3rCT5FWVSF0eaWkJXS41enkKTgSTh2wkYTCXfwdvFPyzYyddD46JXOCoURqmTl9E33NMnykfpa1AHQn/TRHCS0n1MzEAXqq8DxdtADTBxoly5MR68GC9RvJ8yNcylMCW1pTarpUGfGCYLco3JS08pLnCKChW6OzAcVBSdlKIV03RiBqheRNIK5Z6O+gAPuY8T3ayFrj3fx5Xt48LyNDNcjTSzjuKgIRkVBK0UmBPHQ8v3dZQEuxGNMoZZR3PU3rx9y/qtni9ff/210NtMMaq0QX6qQD+pv5fI200I6uUq9nIVujmJXJ14ZhPdoKJqpACrfAelR3hAcFxEZEJkRExAZLSfq4fe1dvB4Kp0dJV5+aoCIs1+sR5usV6ucb4sF7XA20ke4AWgQjcp4etw3LVSP1d1sBfTrOE5GfSBfsbQIG2wv8LPU+DmyDZrOE5qr7gQ/4TQoGh/Dx+92igWqoV8lZCngqVIpBHHp8SZXDV+wa6+Ed4BiaHmcJ/I3IyCmsqC6rLc8qK0wtyYjFRgmX6RET4hQR5+Pi5eHo6uLnqzo8bRpHMy61ydtZ6uci9Xpb+HNsTXKTosLicro7gws6Qoq6w4paQwsbggsaQwpawovaI0u7qysKG2vK25srMts7k2oqrYrzTHpyjLOz/dPTPRnBxtjAl1i48KT0+Jz0qPy0yLSU/1iI3QRvhrowL9sxIDcpKR5iYbk0M1iYHaxCC37Nj4qsKCxjWFddW51eVpZUWxednRWWlRmakBKTHGSD9liIcizLOmq9X69ywr+SVFIq1CptcoTTq12aBw1CkdtTpnvU+oT0BEoHegp6Onk9HVpHXSq816qVah1GtkGrlIKVEZ1QZnk0+Aj9aokmikMoNSqJHwFBI6j/UCwQYp0RblsyJdBbqaZgdbNWaTyd2JJGSTBDy3QH9nX4/VVBt7NoGj4PhF+GbDb1+WW1ZdHJIYyDfw+WqeT4iz2kHmF+bl5m9WO8kVZrnSrDF7u2pddGnZGYWFhUXFBWk5SfHp8QVlhQXlxVkl+RX1dQ2drU3drZ6xwcZgD6+0lOzG+oKO1vimaq+KTO+moqTpDWv2LQ6ePXb66aOrbz6+8MbDruNLAX1VAb21iRMb0mcHo0e7vABOGjI0VYmq8jhVZby2Nc11uCRma1PqjraUubqyhab+Q71bL0yO7u3uWqzr3Fw3srd798X5rdeWeo5PVmwbKN01Wbl3Nn/L2NKTB0+++fU7f/rN29/95uQnT3senFpzcXvBnvGyfQvrzx2ff/H2gTdf2f/Gy7ufPtr++P7SwztbHtzefP/Gwt3rc3eubrp9eebmxdlblzbfvTJ/9/L09dMbDy8WjbblDTTWTK1fOLvn8uu3X/nslfe/euO1z55ceuXqtouHJo/s2Lh9E4nPsGOSfwLfFwhPqwVeYgEVtlzKlktYChlbKeNilIWrVEAjRyHjq5W8791iSiUXVpQong9Iw9eoMexRgMKB2LEISxBCgOnHCJD1/CrAITgWKQc7CbSgQ9B1pQiokKtNA8wJwzn4qOTpNFy9jqPVIBaFiBQ6LbAivl7DBxKjBxBCyAQKqIOFc1RCkw6ICxf4jUHL0amBS8Gln10doM6CKAB7EoZCwpQBqAgZUqR0iYAq4ACcIOUwEUFh0Ah0qj2dQqRTPvjgA+srtaycPHlytb2dDcEOgxb7ixdXdiaAwMkBCKkKEUHEAThnqRQEIdeOR+MohXoXR0dX58DwwHXru69fv/bxxx//Uxz1s88+e/Lyk4NHD3vGh3L8zTRvB6qngeih5YY6cSJcuJHuolhfh8yYsMpC/+IsUbi3OSE8sbRQHx7EdjNSjTKmSSZ3N3hFBGYW5+09sO/RS4/ghP/ovIKuK1wUcKKjd4PA04nurKc6Qt9fSdLJCCqxjYLDcVcLfB0VQZ4EvYzv4aTwxZXqtnv/PrpBTtXIAD8Ebk5cFwe2swmWfDcz18WR5+LAcTQwDWqaWu4dGToxM33/xftwG//Uj4aP8NPNb15ISE8SaPmuXlr/SDePGC9jqAdAiDLAm2HWUvUyslbIddOIvRz7x0YmZzfNbl5Y3LZt684d23fv2rVv7579+/YePLD/0IGDhw8dOHTgwMH9PAWfJRMw5TymjNfY1vzyKy/DtW7cvMFXSwzO6pa2hpm5men52U2bF7Zs37q0feuWrUvzi5un5jaNb5oen5kam5qcmJoKiY6U6bUcuYSnkg2NjgyNjQ6Pw+XHR6cmR6enJjfNzC7Mz2+GW9kM55mcmx2f2zQxNzsxPzs5Pze1MD+zuDC/dWlpx46OjT35jXUhRTleOanuWUmu6fFOiVFucVHhqcnRaclJ2Rk9fX23bt168623/tdEknffe/fs+fN9w8MlDXX5dWuSSwsjcjMC0hIDkuNCUhMj01PistJis9K8EiKMEf4XLq+c3wiPn0ij5KvlYr1aatLJHQ0Ks7F/eHBscgK+OFJYmZwYnZyYmZ2d2jQzMQ2/BtLJ6Sm5Tk3lM+wZZBKLypWIFHqdHEyERAic/gWCLaY2gSFBs3Ozd+/ehefwn/5uuDTw/rHxscCwwBcoq1dRbVbR7FbT7W0Z9nYMO1smwZ5NJvHpLLlQZdaw5ZyiihL45ccmxyemp9AtTYyNT05MTsOdWBXubXrTTGRSXHhifHhSXFJexsSm6Vdfe/UfH+DP/u3f5ndvz58Z6Dq+b/Ts8TOvPLj2+qMrbz468OhK8ebBmKG2sL5Gx8ZcXW2qoTnD1J3l0JPnNlIeuNAYvaMrbkdXxGyz+4Yijw3lyQv9vReObL51Yc9LVw6+fP30G/ePvX579Orh5mNLdYcXs5aGEifbAzdWhvRVpS+OnPjog1e/++3Tf//l9jdu1lzaln1gLGNxuGz3fN3B7R0nDh549cnS/Wvjl05OXzk9dPbo8NkjA6cP9p7Y23t0V+/hHdPnjk6cOdS2a1Pppp6YtuLguoyQ+uz4lsLivqaxg0tH7l68+tqdVz589NrHL119eu3I7XOHbl/advEogUOzoRPR8KAfP50wF9lrFE1BZlch5SqkwCcwyoIi9gAbFlCxRu/VakugBVtRsRUKlhxlJFsU5Yxh8CMAuw+UQoNGvVjOxgagwrAK8RuUsixHgAQnB6aCroulNUMLojuATLBJwXuGIhwNnE3DUwN0qXkAYBoNolNKoERyQBQAMLgNpgJOBUiGBWMAzyzePEzhnFw1XBRAS8qSSVgINSVMuZQukzCkEpZUwpSImWIRHTm+eFQul8xhkdgMRFOYdCKDTqDTAFRYAp71EVtJWlpbAVQsanIyW1tXkuqGOrpCYsdjEvksplxC5HPsOHRg8SY3p6GRYZwONJAHjx6lVZRQXLX2Tgqar5YdYuZFuEpifFyyEkAFIe7yUG/nhGietzPVQU0zSdT+5pT8zNn5uU/wjeMDs9U3NsJ3dyDp5UBTbBUCWyWH7aoS+zo5RwcPjI+OTE0eO4lrRM6pM6cHRkcGx0ahq8520DMNGrpBzTLp2I56rtnId3LwiAwH+wtAYj1gJfniiy+yCrJ8gkzeUR5O4T66ED+JlwtynalFVJ2I66aV+zr//e9/t+79fHn55ZcZIjZVzF2/sedXv/qVtfX//t/krHSOUqw1q/CkXYBV0riYaSI+WcjJys+ztv5L0ry2K7O6Ijg/yy09wTk1zpwY6Rgb7p0Yt7a3587du3jw2yLffvtt+8YNjnFhhqggfbi/LsRHE+hlCPbxjonoGxoEeJtb3IzH9wX/yMDw0ODw0IbejXQxhyHh+oUHWbctK3/5y1/IHOgOU2zpJBsq5hwm268i260i2r1AsjW7OQOWwJ9o3Xslgc6Eb2jACwzCKlAmwYZJsmGR7DhkAp/B06kVTgaqhIXnn3rn3XcNPh4+UeH7Dx20Nv1v8tWvf71h19KuW1cuvHzv2msvXnvj0fnX7ncemnVqzDLUpRnbsz1HaxJ2D5WeWSo9taX0+EL04lr3wQrHtflua8t9NlS7dRYb6tJhT8fGLJe2fM+uYr/1ZUG9VQEbyr3WFrt35Hp15/uuLwkerYxcas04OrH73dde/8PXj7/9Yv6VS+XnNqfvHEyd68+ZH81dGE6fWJfU15I70r1uz+a6LSP184P188NrZvqqJtcX9DdlrqtMai1KaC3O3FBTOdmd31cVUpXgX5wQWp2R0l5SNtg6fGD7tfdef/+bX335hy/+7feffvjr99/+/O2H771kZ5lJxd6KCz9Knrm/oL+PyAFfDWCAAQbGPDjAHjC0AKgQAlQAb9DADiqeFsX2AVpgNyskyKWwmwV7YGkBJEsLYABPjdr5YO7hKiolXALMPToW+8gCSFBgnjEUocE8XcCTkEsNHYsOB3IDxMjCM9RWPgRqIVUslKgGoAIUBOEK24p5yOeGuA52LEsqBYWvAxeCy9GlCFHoYjFdJKIJBHSREBCFwuNQOBwyi0ViMklMBqIpNCowFQKNEhIebn24VhJXd/fVtrarbW1W29lu3rzZ2rqswIvEkItIIu5qJsWOSSXx2PYsuh2LWlVbjTOJ+Z+krWetjYFP81GzgkzcUGd5rJ85LVoc5sn2dtSFB7I9HKkmJdUgcgj3gP41fqv0vTx4+JBp1BBUEhspl6jhs1xUUl/nrr4e6+YfKLllpUytiqFVMHQqulZJ1ynZJu3a3o14DNz/FOAZvtF+Cj8XY2gAy1FL08nISj7dIOa6avOqcOU1lVUWSTTCQ4cPWT8/E5aUJ9NJCktwIcSt27ehW8YQCygCzqHDh62tP1zg2YjLy/FPS3ZLiXNJjjEnRJrjwitamj77t8+se/xAWdq5w4ooQV7aQE9dgFdAfPS/8AyAnD1/jirl0uW80ckxa9OyMrcwL9LIUYEvAAA6cTWVaMegsIR8rkgIPad/4R7gkP6hgdVY4RYbNtmWQ7HjUQlClluAf2R8RGJmknW/ZaW1qyO3tAgQ1/r5+XLvwYODty6ff3zr8st3r7z28MTLN+v2jJm784OmWtP3TWbvnwydaXVeV+LbWxM71R0/2R050hXU1+zQkisrj5aURMiro9SNsZr2JH1nqqEjzaEr3XV9jmd/ofdoacBsVej2ppBdLaF7OqL2rM09vunwR++++advnv7hy/lXLpaemU/d1ps625+9MFqwZSJvpie+uyKxszKnr2nq9PbJk/ODhwZLhqqK+qtTOgtimjKj69Kj65Lj6pOyO7PS29OCS8J8csL9C+Mj16TFVqYnVmZV9zZP7Z3fe+nI+RfPXrx7/OKNQxdvHbahEX4amgKCovQq1KO3Zn9h6ILyjJUYL0EWGQGDQKMS6jTAEqweMNgEjEGrQTtjwyf/HwV2gll/LornAwLB+RFICPSAQ9hVsDMgzgE8CbnOML8WtOi0fL0OLWETIiVqABgU1VcpWHA/Gg1s4ul10Aj4geI3yNUGIKexnBOOgsMRrqBvAUfJ2cg5hr4L5n8DXiW3KCAKUrGYIRLRhUIaKNAUABUeMBUrrhDpdAAVexqFQKU2tuDK4AJ6bmdvv9rGBpTJYeE0i62dHXZchh2XBr2tVRSCDZXk6O7y9OlT6+YfLvCyZVcV0/00ACq8YGdljL82LojupWe7G9luDjRHFc0gyyzN+5cNE8ixE8cJcuFqEYuiF3Jc1XJfl8dPnli3/RCBlxl6KjS5hKaQglLkEpcg/3/tVN/LyPiYxt+bblAARyEr+EQ5h+kgE7gbj5w4bt1jWXHz93jjzTesH57JkaNHHFz1br6uR48dtTYtK+2dnY7urjpHk9Kg/yd35Q+Snbt3h6ckucaEmzENz8m4ceumddu/Ku3r13pEhrqGBbmGBnpFhG4c6Ldu+IFSUlHGlovYKvFnn+F6kEzervYChr2AjqpDcig2LPJqBtHV1/ONN/751/5BUlZbaSuh2YnpdhKGvZxFVvG4Rpmjn8u+gwesezxf4A0FUMGPZ5sP7T5+9/LZl24fe+nW+pPbY+a6cvaOZe4eC51sd91Q7jfQnDjTHzu+3nN9lbExR1YRK6mIFJeFSaojFN0p+vE87XSBaa7YcaHUPF/quqXSY1u117Y63+0Nvtsagne1h+7qijvYl3ZsvOz8zuOffPDmd797/PvPpx+fLzk1l77UlzbTX7x9uv7gjtLNQ7GdxQntpaXj3UdeOn/oxSMbdvdkri3K7anKG6pNWlsY1wykJCOtJSG9JTq1OSq8NNg7K8gzO9yvKDqwOMY3N8wjHdUPDUgPjswLdYlwULiJxSYByo/48dEUiwCWsCzBcIstVgA7QdSEZ00pRnRBZNSZAn1NwX5yF2cU8wALDsgBPAYBDOCQBUukQGLgED7yUymBavAw9xQHJYkh9gBLpMAnAAkQWUEQhQ7BcAsbw68AzOAizEAcCHZG3i1EPjAnmAqBB19rQR04oZW1YIEcADYFXIJv1MMOsCe0YDevYmPhIg58KXTPCqZMxpBJmVIpQyplymUMiRhoCpXPR0sBn8rjUbhcMptNYTLJDAaJTidSqYAoAC07d+2yPlbLytWrVy2IAtrR2WFtXVaAi5D5LFsmZTWDbMMgrSLbm91d/zWC8o8CbwvPz0D31TH9HbhBTtxAR6JZSjEr2K4mhlk7s3neut+PkKLqilVCGt0o5rhovGPDrK0/UHbt2UMV8skCHlkkIIn4ofFx/xpB+UcBG+EZHkRWCsgKHlUr5JgVaj8XN3wums1LW77830Y1xqYmKJxMjn7eeBACvoJfaJCTh5veybG8qtLa+i9JSWVFQHSkKdjfGOKfUVb8Y/Dpe/n4k08CoyODY6NDQGOiXnrpJeuGHyJwJ2q9TqHTpKTjSrX/5ptvyEIWQcgkCJgEPupC2bIpWQW5/wJB+SeBX1vubrBXcogqLihJwyNr+GyTDM9v9UN/z+mtC0snD++9cX7y0uGqvZtyd40FjzW6r68IHmhNmO4N7G3U12UoKuLkVTGyhnh5S5J8baqiP0MzUSCfyFOM5ukmik0zFea5Ne4Ldd5bmwK2twXt7I7Y1xu7fyD1yFjmyZnSC9uabx0ZeHjl4i8+evO7r59++8X0ozOlxzelb+nN2TxUtXtz48HtZZsHogFUOsrKRlomjm+qnapZM15XNVZTO12TvT43d31hXH1KXE1CamNUQVdMTIlPUI6/Z1qAc7Kva3qgR2aQS5IPmm8m0tUlwcsnO1AZqGHoOAQpEzm+fmTS1/eCWV6MnUBHXiEHE2yJpmDhd4QBPKVcoFHKzEaFq1lkMlgtPgrUWwgNShVDcXI1EBEtcBGJXq1yUjv6whvoqHY1Co2IWGAYgKL3CAZUwFoQQiDaYTQI9Ho+YAlQFuRS06IoPcacLKDC0yj5OrXQoMNOjpSH4v8qnk6D+eKQyw5F4C0JY2hpSY+GfYADAX1RcOQyDhYWghVUjQa+o1zGlEkZ6KOcKZHQ+HxAFLRENIVNZjEpmBLpNAuo2FMpODtT/f39FkSxtbfH2X3rH+hfRSaspiJvwGoqITA85CcxHCDZlcUkJznNS8fwMZA9VSQXqZ2GTzUq5rduse7x42Tv/n2r+TSGScpx1Q5N4HKA/E+JS04isln2LIYdkx4cFfFTfffxqUmqki9wURkDPXyjQh19PXJKCqzb/iWhSPh0hSS3pMj6eVm5cOmSwcNN4+Qo1KrOnT9vbf3h8sUXX6Bc5OBAtyD/1MK8n+rHAdF5uJp8PF2CAqJScPmI/qccPnxYIBaJZNLNi4vWpmVlcnqKI+EzRFwKj0VgUu3p5Oj4uB+PKBZp6+ogCZg8jUTsoBKa5AKTIr8Cb+3XHyQj85tGdm2dPravefdMUH+N9/rqyKGu5Ik+//U1utpURWWsrCZO2ZGuHSzUjZc4TFe6zta6z9d7b2kO3NEVtHNd+O7e6APD8YdHUo6MJx0eSz48nnliU+nF7bXXDzTfPt55//T6hxc3PLow9uTm5S9//vafv339j7/c/trlvN1DhdsmavYvtRzf135iT/GmnpjO0pTuirSuoorRusbp5qZNrTXjDUW9xZlduYmNafH1yRmtaXkdcfEVnqE5Lr4Z3s7Jng5xbg4x7o4JXuYEH32UmybURRVqFgfo+G5Sqp4PDA9VLvipBPXf5VKmQopcScguI5BAgXqsa4/F4eU8LGzO12u5Bj0HGyYJCITyuJDTCR2CHWXhEwq+SiYzyMy+BrdQJ72nXqxHQRpESjAoQpEP5ArDMAPsPhZcwYgLSvQS6HR8jMHwYBNCGhUgitzFrHB1FhkNiKZgKjDoga/ACYGRwDlhxXpyjLUgwMMaBXqdxGQUAlAh+gWQKeViJIyNKtBgYRgAFbGYLhDQhXwGMBUuh8JmAaiQgKYwGAAqGKJQYR1n9zk0LAwQZZWNTWZWprVpWfn222+ZHNYLBDs0WJJMMDqbf0LDsXPvbns1n+AgJbupiW4KkpvcVs/bvG3JuvlHC6CmDY8CVIDtpH4DX9Gnf5Kvv/6azGbZ0ig2FJLRxenH87Pv5YMPPpA7al38vPWeTgKj0tnP6+y5lUf4P082zc9RRFyqRHDoCK7oSGVDPUerpMlFXI3ixxCvuc0LGmezg7tbSGzMT/hggHgGByqdHbUerj3/qu8rIyuTSCOD4gytm92cmHw2ncskM6j2FGJIeOhP+I2uXbvGFgtlBo1XiL+zr5vO1XT02DHrtp9USta3Za1vKxnemNzTGtfXljzeE7iuxlSXrqyKU9QkqDsydQNF7rPNSfsni85sLz67PffEYtaJzZkn5gvOLJVc2FFyfnvh+W2F55aKz26tvLyn6fax5ttH2++dXPfgYs+jS+tePNtx+1jr9cMjL155+M1XH/79Dx/89Zt7X30wfPXIujMHW47trtu/JX+hP35jdWRTdmRdZkpbQceWnq6tPTUT9bnri9LaM5JbkjLb0ovW5zRPlawZSqodSMhqCPBOMTnHOZoiHbShJm2EkzHByxDvqY9xk4eaBN5KnlnKNaudo4KsePCTiKVMC3TeMT+SRWUYWUHxbYwHIORAxtoSR8HiHBjnQCwBsQEMhyzBdkAgrlzGU8qEGrlAK+epMc8YMvSIoABfsdAaFOfAzgkXsgyCQYF3WEcONCXiMcBa9DqAFqA+MhdHiZMDkBjACYQoADw6HQIeLCoDiqEIyjTDAAMQC43K5GoRqAh1AEJoH2A/AJBcuD24HFxIIUdpYDIJAhWhEFM+jcelsNmAKyigQqMRaVRQAo3q6eNjfaaWFeh2Uel0QBTQBw8eWFuXldnZ2RdsVr9ga7OKiHJgVjwKLjE9O+MV5LeuZ7216fly9949exmHoBIQTVKis5zgKksozbZue74AzjW3N4bHhJ2/cMHa9Hyx5VBJMq5rmL/18w+Unbt22dOoq8lEWwoRT8b28PiYZ1DA7MLKvjv4FhypkC3lO3m6hcREe4WHWjf8S+IR6EcR8jgqXB4VQBGls5mlltNkwpLqH+X7Co2JUhr1GgfT9Rs3rE3PkY8//rhsTXV4QvzDR4+sTcuKZ4C/zKhXOjng3P+fBH4HgVjM5LCjoqKsTcvKl19+yeJzWAIu4AqJTuYK+SvmZcGj3tPbGxgetn3HyoU733jjDRKTxuCztY4Gs7eLycMJJ2Lt2bsnKiG2pqHO+nlZ+ezf/s2YEeWYk+BbWRRSX+5Wla0rjVeVxyprEtUdGaaBsuDF9VlH5kpP7yg/u6vs7M78E1vKzu2surS3/OLu8kt7Gm8cqb9+qOrSzsqLO6ov72m8dbTz/qn2uyfXP7yw8fHltS+eqbm8M+/gaPLS8NZXHr3153//9D9+/9Hff//y736+9sRC/EhTdG9t5LrK8K7isNbcyPqsyPqM5LaCqpGGpunm8sGKvLW5OZ2ZeZ0Z5esLuufatp6fGD1QX7w2JK3OL6HcLzDTzRCuVwXo5P56ebBRE+msi3JSBGj4LhK+WZZdVzq+fd6KBz+JgGVnK8AcK1DeF6IayNDzUFqwChQLaWDhEKz7D3YcWoA9oBA65gFDeGNVMNbY0BZ0BhQLQSnFcDhACAqbW5xUaIwID50WnQ04iiU3zMJ1sCQujABhecCAKBhyoKRhLO4CbEkFIAGKIvYaDQreaLFQPxZEgfOjO9dit6dDvjXkVQP0ApoCiKJUMmVStkzKkUstcMKQiBhiIQrUW1K/uBwyBxCFTQZlMQl0GpFBo3JZFBazvBJXoafHjx9bEMXVzc3atKyA9RGKhAhUQO1sGpoarRueL/sO7KOI2EQe3ei6crLy1WvX7EQsexmXoBQQDRKKixKP4V7TUktV8JhKSctKMSEgFrYsClHEFjtoTH6eei83POm/sI/e1WxwcVKbDEwh345KXk0ktLStPOZu7/799hyGPZsRHhdjbXq+gEkSyaUak1FhNEgdDCXVVdYNP1x++9vfwkUpAm5WQb61aVk5e/48PPZ0hZQqFS5uWwI+9z8FT6or/FByjVquVhWVrOzJiUlKlJr0Mgdj57p11qZlRapVi1RKiU7jExIUHBWxgC9NMSc/z8HZyeTkpNJqyHQ6iUadm8cVnOsf6BdIRCK5RCgTM7ms2bk564bnS//gAFMoYggFhTi+PvykBCqZymJIVHKtg6Gsqty6YVk5cvSo0qBXOxiNri7WpmXl0PFjblkpngUZxow4SUKAJMlfWhilaU5z7C8NnOvK3D+df3ih6PiWwmNbSk9urzq7q/rCnrIzOyrO76q8sKfx2pG2W6c6753pvnd67f3T7bdPbnx0qffR5f6XrvS+dKn55uGSk3OpO/sSFnqaT+y58MmH7/759x//7Xfv/enXp372qHz/UNb2gZzdw4W7Rot39KfONBSNNZSNNjTNdXYsdndu7m5f6Gycqq/sL60brRs/NH380dkjD/Y3TOcWdIbktYcmVvt5pZodok3KIJ3UXyvy1Qp9NQJPOdMsoOrYRCnVjmNvT/kJnV/AVOSIZABNsVATlEOMZWoJDVpQlK+Fhd8BV1CwHSEBQgUgAWDZUfkWbAwjmHuOGoXlEWBo1SxEROQo9QsbRwLtlqQv2N/iqkKKudp4KhkK6aNBLdZ2xD+wE6JGSzusw1WwOxRYBloCZQFQweL2AB5wY5jzzQJXGBkCFmWJyiA6hdKOmXIpQyZiyyVclFMAVEzBlssBTlDSFygfG6GCEIWFpX4h3xeJSadymCQGfXZ+5XcAZGpqCvN9rd65c6e1aVnZunXrCzarLKAilUvxJDU6+3jQxDwCl5GSmWFter7s3b/PlsewE7IJUi5RIWha22nd8HyBl5PnpLQR0Gx4jBUJwdOnT+0AVHhMhkxIFHEcvD2tG5aV4bERIpdJ4rJs6BQCg2ZLIXHFohW/O4AEvPl2LLotg4InE++dd95h8qGjwCZxUDHQ02fOWDf8cBkcGbZn0ih8Lp4SmSCZBfnwEtGkIrKET5bxqSoxA1X4VjyrnariGTUPHz207v18GR0bE8ikPJkU/hRr03PkypUrOrOj1slR7Wia37xgbX2+wI8jkkllKqVKp9WajEZn8yMcfOWLL76gMOlEKtmiBAqJQCbhQUcQB0dHqUIuVymlSrmnrxf8m9YNzxF4HqQqJV8uZ/B5k9PT1tbny9WrVwFU6GyWVKlQ6tQXLq5MsuEeHF2dBXKpQCHDWdavrbdHGxciCvMUhLuJEn2k+eGaNSloJq75gYJDC4AoeYcWSk5uLz+zq+bSwforR1punOi+ew6QY+Cla2Ov3Jl67f7CWy8tvPVw67uPt7z1aOs7L219/+Ut7z/Z+PBs6cm59J0DKZs3Vh7cOnLjwq0vf/7mH796/8+/fvrt59tfvzPx5MrUK1enXrq46aUrCy9fGbh58PIHL7/z9advfPXO5beuH3nx5NZL23t39q5fWt+7e3jPreOnn5xft6UztT42qSo8JM/bJc7JNdHDK9VHH2lShBiBqcgDjQJPBdPEJynpdgKCLc0G1Yf+CQWRACydF3r0mP8KSAMY6Gdxb0zRDtjAFBRFVyuEDnqNj6fKw11oNGCJv8gnhlgL4hmIcyBSgjm1MNKj4IKttwASIjcY4wFzj8aNy9nYmEREcRAsIWBArjDM7kMjismjiiyYzw1TxF1AAUuwIA26Lrol5I5DRcB0qAXLIYZ7AABDzjSgSiylDA17VACioBwEuAQLMEYqoYmQ18sCKmiECiAKm0VBsXoWYAnm+6LYUcg4fVmpaWkAKlweD48bHZ5prU77wmorqOAZ0fL4yWOOWMiRiuhC3q49u62tz5epTTO2bJodj2EvZJHlAjwRi/6xYYZRZiNEULQi7Th79qw9k0LkMmginh2X0TuIa4Imz0BfqpBL4jIBVIhMhg2ZuHXbVuu258vFS5fsmIAoVBs6GSyItfX5cvTYMRsywY5CIDDIJDbN2voviaO7iz2DxpfL8Pyt3333HQs641IJWcgjCDgkKY+qlqDpIfRKll6FqprqVcM4khqAU4pVShqPm1dUaG16vhSVlSkMepXJKDPo/mcy9P+UHTt2oBi7VCpRyGUatae/n3XDsjK/ME+mU6lMGhn+OCqZTCPHxuGazBQeJL5IIJZJpQqZSCq+du2adcPzZcvSEsCJQC5nCvh46DVQHyKFRGMxRVKJzmTA80+dO3eOzmEx+VzQTXO46iubo4O4fg68MFdBnJ8iN8ahIsO7tSJmoCNtpj9v53Tp0a1Vp3bXnzvQcuVY+7WTXTdPr71zfvTx7alX7u16/7Ubv/3lg29+/eh3v3r6h98+/fev3/vLH1777uszv3hn4MVT+ftHY2baUmZ7Go7u6blyZvTmxWuff/LWH3/74Z+/eeX3n00/PFl6cKJgaXj0yqlDT+/vfPFC7db+zh2TW64cOv3yuT0392y7uPPgnWPHH57cfXPfzmsHjtw/PXF0umOhsW68tKI/xznWqAsxaYOcDBHO+jAHdbBJEaBXBhkVgTq+i5iiYZKldDKfRqQTrXjwkwiYcjDxYOgxQ2yhKWqUtms13IgWgMVH4xaRLwtRAYlZr/X11nh7iUxGLLiCxqNgARLMl2Vxc2EKh4ChF+h0Ap3lbNZhLtjlUEwFi+igJGYs9PLfByLFbgNLINZiURYUaAEgQfegVqJICZYzZom1WIY9IvixBP8xrsMHcqOUA1tCd4IQDmUEWJLcmHIZXSyiiQQ0oYAuEKBkYiuooGGPREAUOjbykUolUMh4OAQIi80GUFm7dq3187Jy9OhRDFEQqFDpNDyXaGlrkyjlMjUwfDWeHmJtQx306+0AV7iM9Pxca+uy4hQRQNVJiVKeg6+Xten50j/Qb0cnkzgMKp8DF8KTIAf2xZ5FJfNYRA7DloZARaxSrNhvBcnKz7WhkUDpQi4ek9Ha1kZh0Ng8Lpiz0nJcYx7/V3nvvffsqCR7GrW0ApcL9NDhw2Q+B54rIp9tz2MSxVyqUszQyhFT0atAo9JSVvy+8AV9Q4JoAi6Vy755a4UqavAkqI16CXSe5GKdC64KDvmFBXQOm8HlMMCkCnmd3bgqpYaEh5JoFBoLQIVMogFTIeLMs4c3gifgC0RC0PAIXIOIA0NDaBwOVyJx9nC3Ni0r5RUVBBKRyqDzhIKq6mpr67KSX5hPZdGobDqdy1yx/wTy4NFDtq8DN9QNVdJLD3coSvGpKQrtqI3Z2Bo32JEy05u9bbzg4EL1mX215w/UXjjYdPVo2/XjzVcOFB+eSdnaEzrVEDRaFTRSGTJeEz5eFzXZEDPdFDlZFzpcnTTV2XFyz/T9673XT685OJe7ZXjzo9uPv/nqnT9/8/C3n/Zf21e4faz12N65+3f2vvZ48cG1qs0b84fb8geaG2bWzpzYfP6V6w9+/tqdnz08+uD41PHpdVvXrhmr7Vhs6Visyu1KcE9yNoWa9cFOhhAHfZBJ429Q+uq1QSZtsFHoLqZpGFQpjSygkpgkKx78JIKlYyEnFTLZGjVK29Vp0DhH5A3DCAfW5cdi9Qg5AHiEeq3cxUnmZBbqdKgOGBhutBvYcaUArD9GOJDLC/OGIZcU2H1EKYBPYBfCsAeWsD8cBYpYBQrJoLgLGkqJgYqlGCWK2KOiL4iF8HUIkwCHYB8M/+ASCFRQ9F5vgRNQxHisl0b50BhfQRfCjlJImXIJ+1l1FoZYiHnABDTkKkHD6UksFhHlfdEJNBqBSgE1OjpYH6tlBUzqKiyTGGcyjKur6/egUlNbY219voChMZgdZGqlVKVMy8SVWqZzdECGmEGxZVLxzFkJr43E05GsFlOU4r7hIWvr88U30B8MLpFFJ7EZzl4e1tZlZWhk2I5BIXNY9iyaHeb+Wrth5YwDsJuwsw2dZM+mleKLbzm5ODNYDBqDSqOTLuJwhjxPNmzssSEB46HgTB7LyM0hc9nAfQlclh2HQRRxyDIBTSVBuKJVyF0c8TweHd1dND4gCsfVa2WP4q49eyg8NjA/WHbiKKQNkCaQiC0snECnkJh0PMNs4bbtSARgAwAnQFboLDqLy8Tp+9LotAAnQrGQJ+Bt37Hd2vp8gVeJzkWYxxEJ+wcHra3PF/hGQrEIgQoABJ93DoeXEvpwbAGXzmFSWDRvf19r67LS2d8jCHWXxwVp06Idc5O8y3MDa0tDm6rCO2sj1jfEDLSlTm/MWBrNOzBfcHghbc9o6FybeWOBujVV05jmsa4sbXZDxa6prlO7es7v33j+wODlQ5PXji/cunDxg7de/OoXWx7frNo3HT/RGD5Sl71jaOTuxXu//eUbf/zdS7/9+cztU+3Hd2+8cGr0xsWJGxd6zhzInejI6msuGGyZPrX7pc/efuebn7/19Qe3Pri37/bBjTv76yabSvuq8rrz49fE+qT5mmM9TVE+LikBXql+en+90l2j9DAZApyNwSaeiUESrCayV9tTVtn9JNVZvhcsEQtRBIQulvRcoBRYTx/LKka4gsVIrPSCo0SFT/halYV8AGMQ6tRCMP1qNaIFyIGGLL7FrIOtRy4pzPeF7YyxCuxUiA/BR0uY/ZnTDPmpMMAARUnGlrAKipRggRmESRhTsaAdYA8GS+jMOgAVYCrW86NvgSUcYzQIYSEqnIyNpUdjHqWoCBiACksqpYtEVD6XwuNahj3+I6gQEa5Qs3Nx9fFnZ2cBVDLxmfuzZ88iRHkGKnj6+PCqiJGzQgWgsmfvHmvr8+Wzzz5bTUaD823pFLlBi4cNdPZtELibiEohQ6N4590Vqo3Ba2lHIdrSEKgANgyNjFg3LCsuXu4AD2DIYGmPKnXSVgwYgKAkMSbwGzZTJMBjMqDjSaaSyRQiiWxPJNlbW/8l0Rr0NkQCk4/Lpfndd9/RhDwKPE58LoHDtGXTiEI2WcqnKcR0tZSpUZy7sPKYlUuXL9GFfCwXkTO/sHKAJCYxnsikw84csejpq69aW58vd+/etScDNtDIDDqZSXdwcbJuWFampqftiPb2JAKFTiHTyCQqMTY+1rptWXn77bfZXA5fKBAIBTIFrvS5vv4+BpfN4oPRZ+F5NR48eGBLwO6NRpXh81Lu2r2LwqTR2HQGhzkyiu/RTYzQxYaY02Kds5M8CtJ9ynP91xQHN1aGd9RGrm8IXl8fPdgWMtQSOtHqM1Kr68hUNqSY2wpCBlor9mwZvH6h59LxNftmi5aG87cM5i8OVu6abjmyre3Y9roDcwVb+qKH1kQO1+RsG+y5dWrhrReP/uyN1//w9Yd/+/adP/3m7Eevjd08N3bz8uiN84OXT284jUAls6+1dLJ7y5VD9z9+49M//ebzv3z96uevbT67ed1iz9ie8Q2b12a0ZISVxHlmRTomBphi/A3RXpowZ7mvg9zXqPZ3VPkZpJ5ylo5CEdkQGKvsyKvsftqYisWCA0uAfj1frQKqYQEVNF5ErRaoVdCIeAbyKWHVtCyQgJaIkWBHYeQDIRCy4Fw5qgZmOS3AAEILlCQG51EjWoNlmmHsBwXbAQ8wtxg2+l0NNAidB50KAwZYontDEXgMmbDL8bAWC8YgVxscguAHoYgVorDDLfiHBYosNwDrMpYcMRWmVIyyv7AaLTQh0BR4gZ8F6plokAqACkIUGtWeQhkeGbY+VstKaloa/kzi0NBQK6isXuXrh6ujVF5ZKZJL5bh9Xzt37bShEDFQIfcPrdzdAzGH+wncTASlMCB25TzRo0ePribZ29FIJBZCCDwOBDAQtlQS7Exkovi8PY0an4xr5B3YTRIHTZXGEgvwmIy5uTkSmWBHsLWxty0qxjVc8XsB9D1x4sTMzAyYtvaODjsyCUAFfnzr5mVl34H99iw6lc8j8zn2HAYGKhwAFapCTFNKGtvbrPs9X4AQKEwGhkgAyhILV/yjYX8ABgqLyeDzfIMCra3LSlt7G4FMsicTgayQmYzeflzjVDy9vSyGm0AmWnTfvn3WbctKY2Mji8MCAsHmsmHd2rqsODiZSXTkdoYVa9Oy0traagv/NolIYzDW1Kyxti4r8YlxFAYFub9YNHy+r0fyEG99bKhjSrRzVgKAii8ClaKgpvLQjprw9Q1hGxuCeup8NlSZO/O1DekObQWhQ51Zm0cTpnsCe6o9WnP8uwtSxptq945vOLW199Ri096R0vmulOE1kX2lCWONZfvGpx9fP/Txa6N3T80/ufvS73718d9+/8nfv3nvz7+99tlb49dPDV49P3DlzMCl0+tO7wdQyR1or1sYOvXynSe/eP/pLz/44JtfvPPrd6+8duXyK9d/9cdf/e5PHx29tjunuzyyOtsrP8EpNVwb5qoMMKmDXQMzo5MqU11iTDp/pciRxdKQKcLVZNYqKv2nqtCCCVbiHuwycjQhLxOWWIyABAUkFALgAciao/Qq5P5SoDGDYKBhZw7CAAyQLLwBER0MMNCAFYt7CvEPjHzIuXAIthUl9WJBeIQBgEzIVwYWH+w+Ohy1W0gSBl0ICSyYgVKQsT1RdARFShCSYRXAkEsNAxUETmi0v8X9hZ0Zq0GJXG1wyLOBKajoshyYipQhQcnEdCEa+Ujn879nKiQmg8Sw0BQKgULBWbueyWabnXB1+qCriMohPAMVnKlij588vgdy/94TfHWxcgvyMVAh29LIeF6bu/fv8Zx1bAcNUSEcm5qytj5fSsvKEKjQySQWw8MX1zient6NtlQimkeAQYN7s6dRTp48ad32fAHDSuYwCUyaHZ1aU19vbV1WQsNCgKDY2NkAqFy/ft3aupJ8/vnni0tbyCzGaoL9aqKdLZhOlKZBsiERbt7EVW4rNiUJgYqAR+Sy7dl0ABWCkEMCUJGLPMOCV4RDwLP4lBS6SMCUCJliYWJaqnXD8wUFqGlUKptF53Fwdmj0ej2JSiahinZkIg1vJMyWaA+G20JWCGQCg83AORCEJ+AxOSwgK3QWE4+fDe6HhMaHwatHHMZHf+VKBQAekUyiM5m3VgpBgcATxeQyqSwqjUXzDcDVpevu26gO93dMjHJJj3fLSfEoyPApzwuoLQ5sqgzuqA1eVxu4vtqju9SlvdBUn+3SXBQ22BE/2eO9rty5MS1msKZq19jak5s3HJ/q3tvbu2/D2NGRxSs7jzy+dPvTN06++3Dvay9e/fKTkx+9subgYNH2sSPvvPH2d7/79G/fgL77p1+f/dnLzUeX2k8e6rl4qv/S6Z6zh/OmuwtG1vUc3vXet7/76j//9rv//MuXf/3qq7/9/g//9fff/8cfvvrjV7/+9oM7r5xcOrWprK/CPy/MGOuuDzHrAtzKu3s/+uqXv/n3r5+8dTcw3RiV7dHYk11cHxUQrVTrf9JAPbL1aKggKsVoye/C8rie0Q60bgEGNCIS9f0t2WIaq1MLKYY6SLFNWO0vZNMBDwBOmMAP4FQIVBBQYQF5GcoHw4ahWJADjbtEwxIBaZSodguKzKsREcEcXBjnUCCo06Nhj3y1BvEVLHCCFHOOIS8ZBipYXtmze4PLIaxC9Ai+C0shA5qCChsrUMVia31igYAhFDIEAiqHQ2GxKIArCFQwpkKl2JNJeJKm4E1YbWOzdevKWUwgqamp34OKrZ0tTsf0DxIwTywBD4EKjezu621tXVZqWhu5Zi1VKyVIeRpPVwdfL6O3p8HTXe/monNx0jk7aZ3QZFNqk0Fl0Cv1OiKdYgvAwKACqOD0fekcTMBUAFTs6VS4N5FCjscpt3PXLgxR0FErRq1BoPNOJBOBqdgT7YgUkrV1JTly9CiVx7Glkm3IxNXYRDh2NDKAnx2VzJeK8dwn/I8UAQdABfN9sWxZNFsO3QIqPIMaz+QFE9NTNAEPujiAKEyJqLi8bN+BA8B+9u23KHCDfXv/W/eCuHt5wlOamZ2Nc3IEeFAJRHsylYS8WAyKgzMuKjA0PPwMTojwk5Jp5MxsXG7el19+2Z5IAAwj0yme+DLOezZutAVEByXY4eIQDx7Y2NnCe2RPtJfIpHj+qS1bttDZdCqTSmVQ8fq+okM14QFOSdEAKu65ad7F2X5VBQF1pUGtlUGda/y6qzy7S53a8gz1mQ51eQEbmqJGul06sv3WllTtGmnYN9Cwral1trR7NLdvvHTz7o6FvV3TW2qmF2pmdq+bPjE1f/PI+Y9eHru6tXBz/4ZzR85+/MFbf/ztR3/5+sM/f/3o60+mXzyZsXl9xtxg/ZHd688d6z61P2eyK3u4q23P5msfvPWzP/zul3/996/+9u3nf/rV53/68pu/f/u3//rb//d//+O//usvf/k/H2y/NFI5sqZ0oDq4KMw51jMoO3lk6/SuYztnd/a19hd++PHj//r//vgf/+eLX/723pUXF6148JMI6str1VxUSgtZczQdFsYhQC0eJMwuI3MP5MCCHBZjjaLfGCdAh1uYBAIVLLiCebQs/iuU36VE7bA/mgQFTcZlmQFMxUfWH+1j8VOhwwEe9Bq4GQvVsAxegR0s3AULyyPAsMIJYjDwESEKcCw0WBL7CigfzJIXgN05dmPo/i1MCBEpuYwpEdPFIlCGSMgUCRlCgWU4vaU4Meb+Qr4voVRqfayWldnZWWAqeDwz8Fa/sGqVVVevioyKtG74SQV61tgk+cj9NYTDfQevotTFSNfJSErBKh7Nhs8kiLkkiYAk5pNEfJKAR+CxCRymNamXSl5NJq4iI5oC5h5ABafvy4ZCsKOSCAyqPQ3Z7uqaldMTQCJiouAqwLdwghB03qFbDb1pMJ2VVbii+m+++SaVz7WlU78HFQwvsdgPlVy1BpdHZdee3QRsbCaFz4OlLZOKQEXEIUl423CQUWCiQFDoIr6FqaBhuWLkBGMgGs2lYiXpyFiauz2ZbEci2hIJcpVyY+9GPBm338vGjRvtCHZkCpFKp1CZtL7+PuuGZcXH35fBYtIYNLDCNCaNSqceOvTP8wL8r1JeXm5rb2tHBIpjN4Qj9QPE4GAEALMj2Xt44gIh5Puyt7VoFb4hrlExUWw+h8VjAVPB6fsS+7nro4KdkmPcMpM889O9S3P9q4sDG8oBVAI7qr07y107i80t2brqdOeGorD+9vD+Zq+uovK9o037+lu3NDaMFuQ0RUeXBQYXBAXlhwQVhoSXhidWxpR0ZvXNt8wfn9x289DM9Z15c921B7cdff/tV//w6/f//Nt3//z13d98OHTnUNpid+xYa/rcQMnOmYKl0YSB+qjO8qQN9RkDrZUzG5uXhlq2DjZs7mtc6N2wc3Tr+W2Hbu0+++KByYMD9XNdFZNrS8ZaYxvTfPJCXVKDPTIjY6rzaoc6GwYb53dtfPDozNe/eevRy8dOXPtJQcUanwDTbJlcS4fqboERR9lfYL5BVUqw/kiRfQfTjCqdoIwvjCtYfFwWRS4vtANiCYArFmgBRMFCGigab43DI18ZFgtBhv5ZVUpLmhYGFdazoaMw6gMsSqvjabAgPOYBsygCFeSygxtGGc8oB8ygB0XQCGCDbgNQxHIhxGzQ2UCBeEmlCFSsuCKA7iGNz0PVJNnWKD2WT0yxI5Pjk3A5/VPT0lpaWqwflpXc3FwMUV6wgApO39cPlco11ZYoPSie1wYYgL2IRVYKGcBUZPzVPPoqDtWGS7cXcBCoWHCFy7ZjM22ZNBs6dTWVtJpOIrBpZC7LKwDXKIee3o12NBLq/gPtoJFtKSQ8PiWgHQBCaIwklYQThILDw+woJFsy0ZZEuH//vrV1WUnJyrADUgKgQqPYkEkAKug+WXQymwkrOH1fcSnJlgH/ZD7XjsWwYVLtuAwAldzSYusez5dvv/3WwcMN4IQhQRwFmIoVUcQC5j/gCoWFODRbwAecu3v3rvXgHyIGkxF69AC3KC+OxQCkt254vsDzQ6ZTKXQqmUaxKIvLwuP7gj4WiUIGDIMrguLsediTifaA6GQi0CNr67KiVCvtgVbCX0aww/lEwVeA7w64Eh6FK7+5vqtdFuJtjA93Tol1zUp0z0vzLsv1W1MU0FAW0Frp21Hh2Vnm0lloaEzXVKa4N5ZFDnVHj3bEjTS3HJlp3tnZNVuZ3hDple1vzgxwyAoy54Q454a6FIR5FEYFlsZkNGaMbO3admnL0p0DNbv6y3fO7Hz6+OXf//q9P//2nT99ff83Hw3fO5q5pzd8qj5ksCZ8oC6qvz5ifXXEhpq4/taM8Z68TQOFs4MFmwYKZgYKpwfK5obKZ4cr50dqtoxXL45XLE6Uzo9mjXY65QYpIkzSIJMkyKQMd9VHuWlDDSIPHsdAcQzgsdV2VDHZigc/iVgj4ZbAAywtAKNSCrUaAarwiMgBZtaBfwDSWELlCjDiQlQwGPgEwhKkFvDAeAOcwRLMAAWbbikkjJADkRsUgbdADqxjpVlgE+yAdvsebAAG0IGwDmiBbkOLDXZBJb8QN3oGLcjrhSnCPK1WaDQIDFpgTohCWe4BUSJ0NizRGeNYCjQQkoGmehTShALoogKcUHkcGg+NUyFig1RQnBD5vsidXV3WJ+v5At1nGp2O552BfWxtbb+nKUQS3gHJP0jgZWYJ+QhUaCScvq/q+jpbLsOWzyBKuVSFkCwX2AoYqzgUGy7Njs8ii/igRLCVHKYtiw7mcjWdbMehkQQsmog3PDZqPcuyItWqoO8PCAHdfyAEQjkuT8Xc/DyaKg3FYPCCkC2ZZEsB0CKTWQxr67Ly5MkTexbdHkgJQBdW2hJ+OkRTOEyATIEC133C/4hGgHIYRA6LxOXYwa/EotrxmXpPZzwjkIorKxhSEcofkYphCbgCTAU5wSwKTFrA50kl2bm5J0+dwkOI/1dBvi+UvkVmsOg0FtMZXzGhvv5+eBeIVLI9iQDmGyxyds7KFeRAzp49a6EpcKAnjtxoECBeBArRonheqAcPHgAlhZ2JFKJIKsL3RM2R4RdgMxgc5gKO5Do4pyHMVxnub0qOcs1IcM9J8SrM9K3IxwIqFUEdyPfl0VXq1FWgaUmVl8U41uaH9Xcmb+qLHWtvOjDevKu9abooujLInBWozwox5ASbckLNeRHmwnCX4mivkrioNclds3Xz5zYt3NxXv3+0es/80pMHj77+5Vvf/fb1P/zmyufv9t4/WnFxIePocPSW9uCxmuC+isCeyqD1FUHryoLXVgStLQvqLg3qLAruKAxsyw9szvNvyvRrSPerz/Svy/KrTvGsTHQqjtGk+crjPSWxXtqUQFNmiCLGjeenITvybfUMWy3VRkpaJaBY8eAnEUuXH1lezFkE1h/6+EBKRBYjjkU+vscANEIeAx5k5fVaONAKGP8AKsiOP2MzFrUAAAYnqHoYnBzV1UchFuResxwIitEINAifj7naAHuQYo4vOJZj9bMBqFgjMRhTQWWMkT5LULZshbOx5KjAJXZvFgplHfMINAWUgepIAqJgE3NxOVRQDipRbCnQgrL4qRQCmYwnkvz48WOcQ4tramqsiIKBSiqOSOy/IPAyryYTEajQSVMzK5e4gNdGqFUS+Gw7LtOGTbfjM8hSHlUuIEi4NnwLZWEQhTyigGfPA7LCsGHRVjMpRCGLIuGx5GKcL/9qCgENv2dD958BhrsKX5ZOaFSkJWWZjw+EZufn7KkUOyoFQCUNRyUbkMqaNQQ2k8Bi2D8DFRswUmw6hccGUGltb7fut6zMLSyQ0Y9DJ3LZ34MKScx98HDl4Pn2XTtZWF0JVFpCgWZQZcuwoVQSEUssZItFTp4em+bm8IDT8tLX10tn0ZkcFo3NRHlffbh8Xw4uzkQagAr0scDWk1gc9jl8Q3bSM9KBPQCoEEj2w/hoh8nRZEEUnAGYtvY2gBMilUiiEmHd2rqshEWEU5hUJpfJ4rGgC2Jtfb7cunNb5O2sjgkyp8W55yR75KV5FWf5VRUG1pUGtVQCqASuq/VaV+68rsjQna2sT1RWJJibi2MnetLn+7MX+tqOjDdtby4bzAmqCHPOCzTnBDvnhDrnhboVhnsXx4RWxRdtzO/f0z12dr79+KaynRNrDiwtPH5w59e/ePXbXz799pfXPn9n4tG55ht7ay7vKDqzKfPwcNKu9dGLHVELrRFzTRGzjRGbGkKn60ImawJHKgMHywL7Svw3Fnuvz/dem+vTnePdme3WnORYl6ivjFQVBytLw90aM8PWVzhXxvHjTWRfsb0L18bMWaWnrVIyrHjwkwh0/L9HDosdRzYdRSmwEAXKp0JhDCtyWGgN5iXjY14mLOhiRQUUkMfKAFuKAVs5DQqNoPSt7xVOiCDBusN/ZwCjQD2GQAgtdGqBBpWMtGYYI8CzkBgMabC7AhSBmxToQdH4R+y0WLgeS4DmKZWglnxiLLUM+wpAjLCZ6i3FialY0hcJK/lF4bApbCYJ8zAQaDR7CnJ/4Yl/Dg0NgR23fni+fPXVV0QiEUMUq+/r4MHl5sT+l6WotMQGxTwItnQSntcGBWDoJDs2jcBj26PRFXQ7Lh0MIlAWukpMkHBs+EyqTEwWCwkCrh2XZctmrGZTyBIuTSGMSk6wnmVZaWlvs6ES7ZlUlBmMgQqeVCW4eSKbAWpPpzY2N1lbl5WAkBACnWZPowKu4OkQ/PGPf6TwuUAviCymPYNuARVbGgmVCRBwAVRwplSFxUSRAZVZNBKXjaL0TBqAyvD4yuVY4AGTmfRsJZa9gvpz2IujkPHkMp5MGhQedv78eTxoikecXF0wBxr8PmQ7fH5RIDck1MeiYOlYJCqDJlfiKub/3XffUelURFMQqOAKuUPnjEC2BwU+NDW9cv4h/CwqjZJIIZCoBFji+ac+++wzMo1EYVABXCOjcdVXbuzqkPi7aWODnTMSPPPSvYsyfUtzAmuKQ5oqwzpqItY1hm9sCuyr89hYbuop1K/LUTUmqWuSHZoLQofaMxdHi7aPth+dHTu/0H+4r3FuTelQYfrajLTu1LyNedWT1et3dQ8fH+s9sanx4KayPdM520czFgbXXzl38fNPX/33X7/z56/f/tNvXvrm80ufv3fi09ePfvT00IeP97z7YOsbtxdfu7Xw2o35p9dmX7489ejCyIunR+6fHrp3avDOyYHbxzfeOLrx+pH+m8d6rx3uuri38vBM9raeyPFa184Mc1O8vjpCXxasLQ6QZHtyk8yMaC0lREEOkP1kM3SBgC0G5OAA51Cj0vSwAnYf9f21CFEsoAIYABYZRUSeRd3/EYQwqw2IggpTgmIFxNC0Jf+IK0KtWoBFPlBiGBZ0sZAexJBQTB7V40KJYaAWpxbGP2DFwqJ4WEYySne2QB0o5gezeNuw6D1CFHRa2B8bhI9OjkEUemPhWnBj6P6lTOgGysSgKC6KihOzyRxAFBbQFFSimMUkMhiWESpMLtf6ZC0rhUVFeF77lpaWZzQFgQqRRMTjmD506BCVzeAIeDyRkMXjwhtuRyTY2Nv9txJQqhKBTiUitxIJ7twW+toYUwmOCLOeZVmprq8FRAFcsWczrAaRSSUgIOGztQqaQkwQcRlKOVUmIYh4djy2LRAaHo0q5zPVkoUtuCZokmnVYKkRqABIMGlyrdq6YVmZ2jQDjAEOsaNT8JgMsFz2VCoCFTqNIcA1XHHbju0IUTgsAgIVK1OxpVNIPCZdyNM4Gq37LSsY+DFJXKYtA74g055Jt6FToxLjV3wq4A4DosJZcgniKBhHR+8LIIpCpjTot27bZt1vWQG7j4fEwG522BQDFvXAUYYHpLe/H1g7IAqJhmIqDDazAl9FA+gwYdEUOwLRzhNftYXWtlZbgo0t0caGYIOzM2RPsiWQbYkUO5VGYW1dVkZGRhgYVwPFU20P/iCHMH9pkIc+Icw1K9GzIN2nONu/PC+wtiS0pSqsqy6mpyVuqDNuvCtktMV7tN55uNxhuFC/PlPXnKpvynTrrkic6as7unvs1oWtL13b+fjijocnJq5tHzg333tmUzfCkunSXaN5W4fS5zdGj7UG9FWBRi9sWHf79Imfv3v7t/92/3df3P3683u/+/zBN7989HvQL+9//fnt3/zb7d98fu/rL2Dl8ucfXvvy41tf/eL2b764+etf3Pjq36796ueXv/zk6pc/v/Obzx9+/cWdrz498s5LfRcPDFw5PHjtcMfJrTX7pyt2T3Sc2tF/+WD78S3VuybW7Jms27fpp5ykywIJFouMjDLCGAQqAjQTibVuI9rHQlOe7QaKsrzAlGvULCzYbpnmi4VC7ohYWLDEshRoVALEbNRcHfJiYYejgAey+xjtgBbESJ6NxkdkBQVmEHRhdARuA9Yt0RoEgZgfzBpWwdTKdSxfxJKsjAobA3OCFgBCay6AjCkTMyQilhQUkRWaQABMhQrK4wBTobLZlrm57KlkewopIGjlCWjhsZvCMaoDXnsmk/kMURCopKbi8n3l5udhiCLgCQUsLofCpNuTSTYEgBM0sg8DFaR2FBLgCnTP4eZtYQcSmvILT3FluH+JXk0FtGDRwDIS2EwbBgAMmcBjUCQ8tlpGkQpIIh5DIaPIJEQx357PtuEy7ARMmlLI0srxvPyAB0Cb7ABUGBQii25Po7Ti81R4+vsCogB3VOi01qZlZXhkBDgKIAosi8pKra3LSmxyEnAL9MUt7i8qxYaKwI/CZ9GF/LbOles6g8DvTGDSUYoanUJgAgRS+QoZnl+mtasT8WY0fErMlks4cilXLuMr5GK1Cs94CwCt4ZFhb19cIy16ejfaAHmlEGxpRDs6eQhfKq3Z1dUKKnQqg8PiCvj4fV8EeCQZdBaLOTqKK+omU8pW29mABobgmvi5qrqSQLIlAqiQbdtwTJ0AEhwSLJdJ5ahesgJPOPPCpUsSbxd5iLcxKcI9J8mnINO/NDewqjCkoTyibU1kd0Ncf3vS6NrU6Y3JsxtjZtcFz7Z7zDa4ztU4jpcYerK1bWmG5kyn1vzg3rqU6fU5W4YKto1lLfbGTrSEDKwJ7K307yn3Xlfs1V3gvrbAo7fIa7QiYHNTxP7upCMjRWe3Nt44vO7FsxsfXOh9cGHo0eXhly4PPbo4+NLF/ocX+h7CyuXBR5d675/tuXem7/653jtn1t842n5xb/PpHesvHl18fO/wu6+f++idcx+9dej1h0sPrk1dOzV8/lDfqT3dR7Z2HFxs3T/fuHOyavNA9kh7xmBLRn/zTzZBPQjgAaIXqFOPohQYdUBVIBGuYGMhoQ9lNfRoq5WdIETBEIiDjYvEGIxFAS0QHQEMQLF9tDP2EVNYtyRlIR4DTN9CQdASncpyWtgHI0aIxDzzs2kEWCUYxFSUKr4KRWWeMR4s/I5BCxY7wcpQYlgIcIK8cFgCGAr8oGxmiQVUsHxNNOyRiuV90bHUr+/ziS00xZ5Cbmxa2eVy9+5dXNV/+/v/gaYgUMHjnAEqo9SopXKZWCYViEVp6emV1VXQVSyrKC8rL7NKeVkprEMLtFdWihQKxFRI9mBB8Ni1s+fPMcQCmohvxwZbyNGaHdBIFAe93tVs9HR18vHUe7joPVwd0JgVD1ghifk2PAZBzKapxTHpydazLCsNLU2ryfaIqWAjP+D2cNIOAouGCtezGHhByNcHaAqBTrenU8+eW9kh+Zvf/IbE5SDlsP87pkIjE+CnEHAAVHD6voIjwoEmwkVtqWS4AUAmPANmz104z1Yh6oxARSpGERSJiCMVM8WCCRzdFHjqfAMDaVx23wCuIfFqk96WSrQogAoef9TTp0+pbBaACplOIzNoHD5PpdHg4X/I98WgkakUFhsJnmvBT21DsAVdbW87O7tyzWAAVKGYTyDbkakEGgNXHXG4DYkUFeBXKpU4J2atbW+V+bkpw3wcU6I9c1N9i7L8y/KC1xSFN1dGddXFbWhKHOhKnezJnB/M3jKasW00eedI1I7e4G3dnluaneZqnDZVOY+XOQ4WGNZlGDpSDR1pxrZ0U1u6Q1uaQ3u6Y2emeV22c2++63Cx16ZK36WGgF0doXvWxR0ZyjozX3xpV+3Nw633T7ffP9MGy3un2u6ear17suXuSVhBeu90x/2z7XdPtdw6Xn/9UMWF7VmHxuMW18WMd6RMbSxemq7bu7Xz2L6uY3s6Du1cd3xvz4l9bXs3120Zq9g0uOHw7qEze2rnB3L6O+O61iStrU3Z0PKTzlGPuvMIMzA/klURfgAzsKTkYnACCIEquGCmH0w5IhnPsoctFAHN1GtxfGGzlcBJAJYsRAcdq9Wg4Dw6AxwCu0nRNCoadE4UwrEGSCyhEQx7MF6CsE2rFupRgUtYYh+1WKwewx5AHTUa/I+qWMI9w53rNSirWI8liSG4gjMAa8HSwCxjHrEaLXQsZROVVxLw6JgCU8FmEbaMebSCCp4irHgGCcN7KBQK/xFUqFQqnpdz7969MoVcoVTIlQq1VrOiuwx2oHHYKNRMJgSFh1hbl5WKNdUCpZwq5AFHiVupaAo6v1xky2dQ5Dy6RrywtPJc9/Dy82VSoE12NDKACoFGkWtU1m3LytDICIrqcwFU6HhMxgcffAAEhcCgg7JEuKq57Nqzm8LnkrmcZzEVDFToZBKXSRPxHDxcrfstK5999hlwLwSWVJRyBk8OHggEvNe4OSP2r0SReaDOyBkr5FN47FAcQ5cAUdx9PSkcBpFJfQNfdSy4SUsoBdQDX05ge2cnhcUkwB8HPBgbfl9Rhatczd59++yJBGAqZCoZZxWilrZWC+deTbDF6fui0ilMDoPDZzu7OVtbl5Xx8XGpTCJXyBRK+d59e62tzxd4hBzDA+UBHpoIP3NKtHd+emBpbmh1UWRDZXRbTdy65sS+9uSRdelTvdmbhwt2TBXvnys5slh6cmvRma2Zx2ej9g/4b+/yWWrxXKx3XVjjvLkS6UKFea7ccbbUeb7CdaHKY6k+aE9H0N6uwD1dkQd6k4+OZ56aK764o/bmoea7J1runWy5f7LxzvG6m4drbxyqvX6w5uqB2htHGu+caL5zAtClBcHMqaZbx6qv7is4PZ+4dyB8qi18sCVhfH36dF/upqGa7TNNO+fqts7ULU3VLY6VTKyL76qMai7J6mlu2DyYP9gaVJ3rnBUhCTHSnIUvEF94YbUVFH6sAKIAQgBd4ANyaFTATixVIJG7CSGHJewBHAU5shCiYMzAEjJB4AHrABKWiIgcBUX40GjlOpgbyhJORznByIuFgRC8SBKuUgbAgGItcFEN7IBmpwfgwZzLKGXZgigIcjA2Y8E56+wpiMrAzWCZxEgRqPDhtvUaazAGZQSgdANot4AKDwv1o/oxMtQxZP53SrGVpmBVv6ygArYPrANOwLCuPV9QrcnVFkSxgMqqwsKVJ8kAyczKUqlVIEoVrun/AAXtyCSUU0sibF7E5TLmKyU0Ppsh5LMlol27V5ijZe+B/XZ8tq2AQVWLmHpcHh6UBUAhrsZ6x2gsPZWMZ5JHEA9/HyKHQeGygTlZm5aVdRs22MN/B0yFQS8uw1XrPjkzg4xAhU36PqZCp9owKBQBiyER9OGbHgbADzgKGkmDBseQPLy9VnwkAGjjM9PYaizzRSGFdwFAhSESULgsApN29drKs8VEx0VzxDyuhO8bjGsiZ8A5QBS4TwQtVArO8icao56IRqoCGlGIdCqJQbt5C1+5mvg4ezLRFkDCznZoCNeYR4VGtRoQhWgXGBJsbVpWioqLbIm29qhwMrG3r9fauqz4B/jxhTyekCeSrFxUDeTipUvaQG9FgIc2KsApPc63ICO4vCC8pjSqqTq2oz5xQ2va0NqMid7sTUN5W8YLd8+UHlwoP76t4syuygv7Ki/tr75ysPLK/tKLu/LOL2Wcmks+NhV7eDh838bA3ev8d3YE7OwM3NEVsnt97MHBxCPjaSdm8s9vK7u8u/r6ofpbxwAwWu+dagVQuXuy6c7x2huHKy/vLbuwq/zinuqrB5tun2i7e7r9zpn2u2eAsjTfPg6gkndyNmHnxvDx1oj+1vjRdUmj66LX1YU3F6esb6iaHayZHygbbysYqEvtLImpyYitSasebO7fuTGvOyuyIto9xUMfbniB9MJPFlYBU4tsLiIfaF5FNDzFUloYC6qjNF+riwkVosdydrGOv8V/ZTH3WNaKxUWGuaEsnjQw6Ng4SizCYaU4iF4AJKDBIghaEHgAqAAqoB2Q7wuuhY3Vh5tBYIYREVhHh2Anx+4H+dDQ/gBFCLEwUMEgB6GOheKgUTUWmMTuEO4KVYuxVJOUoPFlEhHwFTQ3F5dLRjOpWOsTYxXv0TQqJDodD2CsKGBBpFLpKiuiWEHl6NGj1s3PF3jueSK+QCyAd0AgEhw+fNi64fkSHRtjRyISyCQCBVduz9lz58g8NlsqFioVTBzlC2NSk+z4LFs+k64VR2emWFuXlaqaGmxgPwIVLOqD11OBfF88JpXPaevEldSr1OsQTWEipoKnTP1vf/tbhCh8DpnHAVCxMBU7BtWOSaUKOUypEA8DAPHw8wEkwNx6RAaXg2d8+9jUJEcLDyoK8iH2jApmi2gCLonDxFPr/vz5c1w+k8OjcfiMsXFc4QqFVg0EBUUKMcXpj7KHfgDGLzGKSRYpZPAwWzc/X+ApIgF6kQgAKqvtbPH8IHAtgBOkBFy+L8SYWXTkLiMicoPnn4J9aCwGjc2kc1kp6WnW1mWlorFe4esq9XPVRAe6Zib6F2WFVBRG1lXEttUkrWtJ7+/OHe8rnB0p3jxetG26ZM9c5dGtVSd3rDm/t+bi/trLh+quHqoFvXZozbWD1dcOAVpgerDi6r6yy3sqr+2vunag6vqhGqAgNw7X3Dxcd+tI/a3DjXeOIY5y90QzIMq9k8BIECm5c6Lx1rG664cbbx5ru3N2w8MrA09uTrx6b/LVe+NP74y8cmPti2fLzi4m7OwJG2+OGGxLnNyYOrUxan1tYG1WWENB5UzP+KntYyfm2jZ3lQ5UFW0oa55uGd4zduPtc2cf78ztiPZKMjhHKl6gvPCThVWQwwrREYAQZKYxGLB4k9RoaLoVDFBAHiEKGleIZVXBISj5CsXw0SD5Z4gCivpfQAsAD7DYhoWdWLxk2HATdDZsFCQGEthFYQkt6FRYdhnaEyuTDAqwgcDGEiCxuOmwxDNsPKYWedUw4AGFr4Di/xioYH42ODly3CFvm4WjYKlf/09MxTLhI4uFEIVpyfvCSknSqJ74iiSuKHv37l21evUzREGgYmtriyddZ9euXVRUGAPVxmDz2Cv6voA3UOg0UCab5eqOa1xbaUUFicui8DgUPic2JdHa+hyB85MEXHse017IouH3fSnkACq2NBIGKhSBTILHMG3s60U0hc+mC3l4TAZKBEIchQHYIFLimvVr+86dFAEP4QoGKgQmw56JQMWeTaeJea5+uBxEYJ3tsQH/gJc2JAIef+mDhw/5Bg2WsWKhzgAqYoZESBFwiWwGnjOERoSR6CQSnUyh4+o6gMm2QdXMCLZkoh2Z6InP99XW2UFk0QhMqlUZ1Gocs/6AwFewJdnbolphRL0JV/ocECkbMgHVJ6UQ8XCIgwcPAvwACAGo6B1wEVlgM2QGjYIqOtMPHV65xgy8biofZ4mPkzLU25wc7VeQEVKWF7WmNL65JrW7JbuvO394Y/H0cMWWqTU7F9bsW1xzeFvN8R21Z3fXXdjfcOVQw7XDoPXXj9TdOAxgANACyFELKzcARQ7XXDtYCyhy80jj7WPNSI833T7WdPto8y1seec4Fjs5hUVTzrTdO9ty5xSwkyaELqe67p8bevnG1Ov3t7z75NAn7xz/xQcnv/zoyM/faLuyL2GpJ3K8M35sQ9am4exNg/E9DQFrMoLWZFZMrV28sm/mzGzbfHN5X0nBuryC7tyitfm5HYnJNf4hec5eyQbXSMkq6qqfLKzyDA8sVh4xkmf+LoQZXMsMvmDZsWkWUTtYdqUMMQOUgoViG5YzWIw4ZsERWqA4ChZjB+yBQ7BxiJbzW6AIwx6VHPmp0HmgEY61BtuxPS2JyBosPq9Ezis5lsuPcAIb3m9BDixIg0IyAE4AXQiTLGF5xITQS4sQDltH94BAhSlBsXqgKTQh//sJH0ksJhE6uSjMS8MGP9KK8aUPrShGk+mfQCUwEFeJ8pS0FIFECGydJ+Ll5K08p8v09DT0EKE7xuSw8YzqAB4mUSkYQi6ZzbBnUHbuXsGcTc9uQqNYuAyCiM3SSfG8/Ddv3bJFNV0QTbGjUwg0Ch4nHojB1Uzms6hCrrM3rmzU4rIyICioGgKLUV1Xa21dVhIz0ihCPuAKhQ8UAQvUM+l2wDl4TIZUMICvUBXyfTGoRDSwiZJXUGBtfb5AZ8LB1xMQBSmQFTV6lbDHUkwRcsUa5YrkGMEYjURACE3GCQ+tbW02RALCFaK9LQlXTiCIo5sLWyRgCflMIR+WoHgS0kDiEhOIALRUsh2FhKcgBYjcoLGhk0Cj8M3REpsYb0Oxt6EQQDf2brS2LisOrk4UNp3CYbBFvBX7ZyBHjh9TBrqbYoI9MhL8CzJCS/Oiq0viG6pT2hsz1rfnDa4vnhysmB+v2TbXsHep7die7vOHuy8f7bp2vP368ZYbx5pvHG2+frTp+rGm60cbQG8eBapRf/MYrNTfOlp/43AD0iNNt461YeDR8eLZ7hfPrX0Aen7Do0sbHl/Z+ORqz+Mr6x9d7HzxTNONw2Xnt2cc3hS/czB6aWPi1oGMXeMlB+drjm1rOrWr7ey+2hNL2bvGkjYPpC4MZy9O5C9NZs8NxvTUBtRmhFSnZvVU9R0Y7dm+vnqorHBdbnpzalJNTFxVRFxFUESOi3eS1jVabgrm2tBtXiD8dKCCFUpBbiWwy5h1xvpQluRgqyMLaAqaspcNrwFwFEtFYSAfCCEwPAACgQ5XWpboVBifAAXOgWIzmLsM21PJRqzFCiqIBmEk5nuO8r0isgJcRKeDJVyFDT07FFNB7Shb7FnCGAZ7KPiPLoGFdtCYZFRdHyMo1uE1KN2ZY5lMRYbeYQYWrsfcX2w0hTAbBYQtswhjSp+bn7c+Xz9C0OB2GxsAlX/EFTxeZjDZGoNOo9epdBqJQnr+/MqTO3l4ehLIJAYb5X1ewjHP4/kL57UOBqOTI1cqovM5K75pOhcnOy7LhksnSjgJ2bgcCCWVFbYMDFQYFNTbpVMP4ihECNQE6AJFyGFI+HgCG1999RWNxwWqQWAyYQlIZt3wfPnVr35Fk4iQwjMg5JN5wBJY9iyGHYtOEXHYSgmemd5BPP19CahGAFPnaMJjqnJLioGgcPUqHqgOXit4udC7xlJIqBL+mvo6637Pl4HBAQAV5Jiik3DO9CNTKzGaggXbyLjGw96+fVumUUmgJycSgPKlYpOTGafvC/ancVhkJp1EwzW66O69e2Qem8xjge7as0JUDwRgFbpBKImDwwTF6fviSkRCuVSkkBXgm1+nsas9Kjc9pignvrwguaoktbYio2FNdltD7rrWwv51lROD9QtTXbu3bjy8b+D0kaELJ8eunx+7e2XixRvjD28MPbjSc+9i950z7TdONlw90nLzROudk623T7bdQYlb7ffOdN4/u+HhxYGXrg+9fHP8tXuzbz7c8u7L295/Zdu7Ly++83jz2y/Bcv7tx6Ov3ul+8UzlxR2p+0dDFzqDpjsStoyUHd7ZcuF4xyWk666c6r1+buD6ue4LRyoPLuVum0yfG0yYXB832hnVWx/cWuhflRZUlRLdkF491ti9tL5ttr1muKZ4Q2F6Y0JUaaBflpt3vMEjRu8WrXcKl9sybFFYxcaKCz9KsCHryOgjSLCYaeRHwnAFUwtmWNTSAhafBXYfy/7CAABxCzgPAiQLlqBACKIaoJacMUs7NtYdmwTlH05lUQAVrA7YP8z8CDcGgIGNc/xeLSCEjbFHis4MTAVgDEMULDiEEgfgcAuoYHEUKQIVhZQtkyFEgY9YsRaGGJW+t4xQIXGYGFnBZqfHwvX/WsG+fxI3D4/VtrarMFx54dkEKo8fP7Zufr7s37/fwdlscnI0ODp4+Xqv+D4/ffrUkjxjS7SjMGh4DFxza4vZ3RWUJ5cUlKxQ9PDipUsEPtuez7blM8hy/q59K089CZ1uYId2bLoNg2zPoRM5yBDg4TfAEgg8BlXMZcpEeCoaDAwNAc8gwt/HZonVKjy2b2n7drpcCkqTiqliIVAWEp9D4LDsOQyalB8YE2Hdb1kBUwVfisRj0YU8PDkdO3bv4hrUoDyDhm/QwJKnh8cb2Da8CzKaVIAHDn2D/AnA+TCmgmfSzLv37trRUOEZWwrRjkoJwTdFfGNLs0ClEED/DECXw6SymV04JioGWVhc5MskHKEAGLNALLS2LittnR2AymQWg8pl4Xk81tTVkJh0tD+HhXNest7+PrFSLlOr5GrVERzhTHh9cipL82sqixtrS1rqK9qb13S3N/Suaxnuax4fapwcWbdt89jhfZvOHt9x4/KBB3ePv/LoxJuvnP7g7dMfvXPyZ28f/vDN3e+9uvjmo7k3Hsy8/uLYq/dGn94Zfnp7/PX7M28/Wnjvld0fv334Fz87+eWnZ3/12dmv/u3K11+8+MevX/7L71/67usX//1XN3/3xckvPpx54079lT2p+4bCF7pCprviF4ayd88W7FvI3jkZN7chdKIteKw5aKwpaKQhZKQ+cKjWv3+N/8ZKv/XlvmtLfLsKfdty/BrTg+tTAVFSWrNLhmrXbx/s3zOyYfuGxsmq3Lak6OKg4CxP3xQXv1TXgFRXjzi9HdPuBfJPFKu3xCpAkZcJC348wwNUKAXF6oGgYGYamXtLgB2reoLYAyggikLOkcvgKKHWkvWLUQfMG2ZJwUJcB7mqEOmxerpAMW5hdXbB0lJZEiEBYjOwG7oHS17yszNgXi+gJhYURIpmfMFoEMo9w/AP1hGuIPcdCs6jfE3M/YU8YCjTBtEUphSYioQhQeW/aEKUT0yBvhUqiMQkM7DJVOh0PGGP5QVgyZZgb2Nnh+EKgIpV8Zy5oqLCzd3V1c3VxdUFT4c0PTMDObIx1ehxDRUMCQ91dHEyOpv5CglghrX1ORKZmEDgc+wFbDsBi6oU4untXrt+nSTm23EZtljpSbKArTGbrNuWFc9AP9ifLhOE43CGAHQJVcrvh5usqVu5sw8Sk5rKUMqRKmQ0mYQiFpIEPCLgCp/JUIjGpiat+y0rGwf6yXw2RcDBM6zknXffkbqYBE4GgaOB76DjmbR8I6YYtLC1CpnZsCIcws/O4DHoXDqdQ4+MwTVpArAfgD0Ch2HHpNjSycP4xjzqXZ0AVAChRUqFUC7jSyUPHz60bltWohPi6DwWlU2nsxk487gcXV3QQDEOKzN35TqVwErpXBaqGUMlE6gknF/H2dONKxUC31Lq1Hj6W4ePHk0szs1eU55XV1XYWFPUUl/V3d4y0Lt+crR3bnrD5pnGqYHc3pb03rqMoZaC6XVrto21H946feP0gTcenf/5B1e//OzK558c++TtAx++eejjdw9+gumn7x349P2Dn32w++O3lt5/ecs7D2eeXu+/c6zx3JbywxN5u/rzdvZlb12ftbQ+e+u67G3rkhe6khfWZWwdyN4xWXxgqeTQtoxt42FTnYETrdFLffnHF2su7++4fXLDi+f7H17ue3ip78HF3vvne++dG7h7rvfWyY5LeztOL05f3bvjxoG5c0ubL+7Zf/f0/rvHpk9Mtc/UlKxNTW+ISa6OjC0LiSsPji72Dc9xsmfZr6Ks+mli9ajXjyw7mHiLCwtNQ8L9Pk4OS+AuCHXAKEtQVB/FVBBHQZwGJWthY1PQDs+IDgrFIxwCEw94gCX4YsUosewvDFee5Y/Bsdh5EEQ9U0Q+MFIi0GtEOo0IDT1BxSuFep1Qj8agYENV0BmseczY1RFmYFgF6yjLGQMV2PQ9qCAMQ64zJVsuZ6GsYqympEhEE/FRoWIuG5VpYbGgBwSUxcHFxfp8/QgJj4iwJRBsCITVCFdsEK7YrBaKRdbNy4q3j6eXtydIYGDgiiAEXWYSkwYdWJTbQyFFROMyN3QBmy7gMCX8qMR4a9Nz5NCRI2SxgCjkAqjYi9mhSbgc3509G8hyEYAQQcSiSPlUCT81J8u67fkC77w9F6MpCtHho0esrc+XiekpsoBHEfAR2+Bx8HT2wTSzdBqWVs3SqphqBUOJcIUsEZJEPIqEx9HJ8US/QRw83ahinldIgPXzsrJpYb6mubG2rbm2raW2tbmmpQk+omVLo8nHk6NTlqxZeQjI+QvnJRqFDBBIozhx4oS19fkCKCU1aChCDkXIpQq5JD4bTy7WG2++ITHolA4mlYNJrtdJNCpXb1w1XeCHVZgMfLmYzmMy+ZxqHDVD4RCuVMqTy5giAZ6cvbrGBiKdiimFQMOVp/Dw0UOFXqvQ62RaDc5cg6KaSs/48KTSwsKm2pz6iriy/PyWhtbh/r7ZydGtcxO7tvRtn2vYNFw3N1K7MFo9P1yxMFg835cx050w1Zm2sGHN4YWp+5cPffja6c8+PPfFJ+e//PTkL3528NN3tr//6qY37rbdOJh1YCR8ri1wrC50vDFupjtr63DVwS1tp/Z2ndnXcXrfurMH+i+fmLh5fsuTe9tffbjx2unCPTPRM92hU+3x2wYLTmytvrQfEGXN+V11Z3d1XNq/7srB9VcPb7h2pPf6scFbJwZuntx4/ei6q/vGbh859+GDe5+9cvrpxRNPLl188/bpV85vOT8/vGd960RldW9e7UBx3WBpcXdGem1kUIYDgUNcTV2Nwio/XvgW9xdGEbAQPYYWFsuOMsFgHaEOGGUwzRYkQIqFTyyuM1Tt0cJdsNxfLOsXLRG3wMaRwEkwmFGwLYkAWMawRS0ogi73TFGwBLANeJJWJdJrxQatCKkeIQpW+h42AXgg0LKACkImlFaAgRlKCuAAhADMoGmDAXKelSND+yCoQ/M/SgFUsIg9Fq5H4x952HwVXFRWksRiZebkWJ+vf1XA0NsSiUgBV+ztrU4wm9VKNa6hfwaT3uRgcHd3XnFsNnTV/cOCmGIeU8hj8DnwMpfjqM6EzI1ey5AINc6Oy7+ZAGkqdxeyXEySCOxFXKKMOzo1Yd22rGSVFlPVMoKUR5Lz6SoxQyne0LdyTBWNJeQy6DJ+XPrKKctgIuF5o0pEVLEQVGZcubMPsrhtK9ekB+UYdWydhqlR0lVyqlxClgpoCmFIYrR1v2UF/l+ASaZCPDqJ69d4nsDfp/JwFjjoDhxaubrosRPHoWPHU8qyC/OtTcvKzdu3aFIhTSKgSfk0qcAZX0rb6TNnRDot9OdkRr1Yp+bIJZ34fF+LS1v4SjFbxOWKeEKZeBzHL/P01Ve5MglPIc/Kz7M2PV/u3rtHwRxlZBadzKR54RtWuXbDBrlBh1SvPX/hgrX1+fL111+nlRTG5GX4pUR7JIZ5JEcEZieXtLd0jQ0Pbp6Z3Lllcu+2XgQqIy2LM507N/ce2jN59uTOF+/sfOnu+NXT9fvmEqc6YsZbcneNj754+fyXP7//77+9+bsvtr33UuvVfWm7B0OnWkLGWmM3bcjeOlG1f2vjsd1tp/ZvvHxy7Oa50eunR2+cmrxzbvb+5am7F3ovHqk9sCl1vjt6ujN951DZ6R3Vl/YUHJ9P2DYQv7mvcP9Cx6WTEw9vzT25N/Po1syj24svv7jjtZd2vf7S1id3Rq4cq946XD3f2713cvLkthOPr1x/78Xzr145fPfIlrML4/sH+rd39Wzr7NzUVNyZEZzpZgqSErkkW7odGgL548MqPGxgILLFsGIZLYgNLkHxDDTEBBELCwmw8gCMZGAUAdAF1q1qSdnC/FcoPIOFWCx5WdZAPTL0lqug9GWrWvHAmhZs2dmKN5Z4jFCrFutR6jDCDOAZaNC+lRuhzBlEQeC6KlQcDJXFBExCXi+EKxiJwS6K1aFB6IKNqMei9Exs+go0iYVYyBQKgICjmR95gCscMoc9jK9a0TKSnplhRyKBAq6gmo/2dqvtbFbZ2Sg1uGopPn78+PDhw3g6YjX1dSypQKCRKxy0ake9k4cbztmNnj59eur0qeW92GCj4QXjOOppWgVFKSHK+BSN8I0337RuXlbg2MdPnty9f69lXSfbqGTrlRv6cQ1Su3jp0qkzp1d0U8D5Q+PjGEo5XSGjK6R0maQdn+2LSE/lOTnwnExcRyPHpGfrNSydiq6WU5Vipk42sWnlyQJARsbHVGajwtGAk9Y8Ty5cvKhxc9Z5uOAp9gMIdPL0KZwzB4PUtjSytQo0IAb5rqWd69daN6wk8KXu3b936fJllkwkVMse4PN9JaYnibQSvkIoUkpVeu34BC64BfoFtn7F3gA8D06e7kwRny7k0nhsKoc5gm8iHzc/b5FGKdKqNGYH+AGtrc+XQ0ePJhYV5NasKaivSa8qTyovTqwsKWxvaR8b6l2YHto2P7BjrmNuuGps7ZrJDfXzQ3WbBys391UtjrTt3TJ84fj0rSsDV87UHdySPLM2cWb9+otHLn3+0cUvP+i+vCtxcX3YZEfq4lDpvqW6o3tqDm8v2jmZu7m/eGmwcvdE7YGZjmNb1p7e0X5ssXbPROHmjZmb1mYt9lQenmu/crjl2qHSk/MpOwbiFvsydk6VHt6x5vje6iPbSvfO5m8by1wcSN/cX7h1oungjg0XjvddPtVxdGfhzPr0vob84ZbObWN77565/v7DFz955c4Hj66+ce3ArT3Tx4Y3butsnaxLq40KSDXrfUVkPsWOaf/TDIGEBw6LNyAzjQAGFGMYsEQBFUQssOgIqokCth7BwzOF9mejIxFTQXCCQAWw5x+4CMIJ2Bkz8egodH44uVWxYAm6FgYqVqSx8BsLosASBXswioPSAdANAAqi8yDAQFcEULEwFWzOle8hELsfuDEEKnIZmkIYIQoCFViyLOF6QBqplCUSoemEBTwqH82tQuGyV4wxLC/Qg7YjEe0oZCuoAFOxs1ttb7vK3pbKYuJ5snFKe3cnjc9iSXlqF6NzoJd3WJBPUEANPo6PR+ra2yReblxXB6aDjqpTkFRCkZveug23JBXl8N0MfCddZSOugAceARtUWFnB1qqZWhVyZGlUDJUcj+0DJiRwdRK4IeW7OPKcHQBa2EYdQ6eiaqRcsxpP9BskIS3VM8g/GV8JqWWktbPDMzjAH1896R8k8BNp/dzl7k4Oft4O3p5GT7czOOqh/aMcOnKYKeE7ebtbPy8r8LvJjFKZXiLVSbVm1LmZmsYFz3gEECUxI52nlHJkYrZUwBRy6XwOnn/q4cOHYo2MrxTxFOLKmmpr67JSUlfjl5wQnJkamZ8TU5QfXZwXUZgdVZKb21pbN7C2dby3Y3Jj9/TGvoXBgaXhsb0zm47vWLp6fPbasXVHN5ct9hctjnQc3zdy42LPxRM1BxbylwaLtg8X7BjOWOzN2TpWfXB788kD9cd2lu3eVLRttGbvdNepbf2Xdo9e3Tt+affEpd0Ld07uf3r9+HuPjn34yv73Hh94/+mhj9+YfeVqyf7RuNn1mUvj1Yd2Vh/dnb97U8x0t/9Ire9Yrf9MY8B0o89YjU/fmvCh9pzFifJdm4uXxtOGWxPX1WT2NrQvjR56cOnuJ6++/MW7jz9/887PXjz1+OTE0ZGuxdbOLR1rt7ZW9mfHFvtSRXQCm7iKsvonCKsAorAQqEBfHrP7ChkPGAMWSvlHngGKrTxTWEd23JqOxVYqMVqAJW4hbxhiKliEBq38wyEIG/7b5YU8bGhKeViBK6KPlqH12Aqq1IKVikFVwhC8Wb1kfDXmlEOYh42Wx0ZNonwBWMHym1FABQ5Eik4FR6H6ZjIslCKXomrKWDkZFFxRyJlSKVOM/GB0ESoFhkq2cDl4AtHLSFlFhR2ZZEcmo6IpGFNBoIIN11pNIuCZfAWPdKztYkmh48ZhCjnw2qidDB6B3kERoQGhwSv2+/BId1+P0s9L6u3Bc3dku5gYZi3VKHOKwBVC+F6ACfE8DSJfJ7G3ORJfAcoVBSFKdSXHqAeeAUvkyzLqTPgGbWzavCBydxF6uIg8XMSerkJ3Z4GrmWs2skxaukERl5du3W9Zeenx45DYSO+w4NnNK08duIyArfSNjnQNCYzN+Omna7t9945rZEhIckJkWnJoQpxfRNiLL75o3YZPsgvz6SJuP75yNRPTE3KTVG6UKowKJ29X//CQ9q4O67YfJ/ArZeTnqZwdxXqVUC3nK6VcmSgqAde0eE2tjRKdRKgWwQty7fo1a+vzBV58c2iAW2yEV3K8b1qSf0ayb1qCV0pMUHZ8Wk1BeVdt9brqqo7i+o6i9b1VAyN164eq1g5VbphqGto1NH1m68T5XS27x3M39azZvdhz4cz47WvzD24vPb637cm9yTtXR25fG7h+oe3kvvLd05W7JrqOLQyeXdp4eKxzsa17ek3fXO3g1ua+paYNiw19O9aOH5/bfufUyXdfOfnxa8M3D2XMrsvaPFp5YHvFwW2pm/tCxhoCJpuitm3MPDZbdGFr7rmFpIND4bMd4SMdqZuGirZuKtw8mjrUmtxTnzvYun737LHH129/9PT6e/fPvnrl8IMTm07Nr1ta17XQNrB/YOjQhrqxvKSaILqYSeSQbWh2aAjkjxRACwQD/+/YEaRYbAPDg++pCeyJIAEpWGpsbDxaB9hA03NhNScwXEEUwcJLLPqMSVi8apbzw4EWi//fim2CdssSnQfjHBanHJo0DJDGWgn/vxEIdrDcNpZFhpLEAEv42u8Vq6sPtyeXs2UyFgAJcBRsZj02wIlUypBIMBXRRahoMYXPkWlxZU89T+C5JFIpyPcFoIKYChZTsbddTbBDxYNppPgUXPPeLyPAdeoa6xkSHl3MA6ZC4zNpAhZDxGKK2CwJhyPh4Ryn9jyB17iyqcEUGqgO8hN6u3HcHFmuJrqrlu6qNkT6/iDE2rZ7J9dNB6AiCXBluejw5IwuL3BvuRXlXLOJ5+zIR+rAM5u4DsbOng3WPZaV4JQkkacrwInY203shRQAhu9mZpv1TLN6cftW637LyujEeHhSvHOI/4/sfBw7ccIYFgAakITLRFrkww8/xEN2uzb2RGekxmVnxGSnh6Yl+sVHXbp82boNh3z77bc8tZQuYr+Br1xNVEKE0kEOoKJ0ULoFeIbFx8SlJf94Uv7VV19lFxc6B/g5eHvo3MwqJ6PUoBGoZQs4StvBg+rmZ1ab0V05ejjieW6Xtm8zBvs4RYd5JMZ4pyZ4p8Z7Jkf7pccklGXlNxQWN+bmViUmF4TH5YbH5kXFFsXEF8emlCfk1aXVbSjtXVw7dnRu4uz2pl1DOdMbKvds6Tp9dPDahZm712dfvDFy69KGS6daT+yr3LuwZu/k+tNbRy4sbty3oW26qmZDbmlLamFjQmFzalFbRml7es2GnM6J6oFdPZPnts3fPdFzYWvOwobMeWA8sxmLw5ETrRGbOjMPT5df2l1/80jdrSNVV/dkH5uJWdoQOdGdBqCyfbZocSJ9tCutv61q08DE6b0nn9668ObtA/dObLu6b/Lk/Prt/esXNw7uHp44PDGyv7d6qDilLokhZZO4FFu6PQqr/MghkEAFsAA7Fma35k1hCkjzLIDxPUIgBxSWFoyKuGAlWzBTbt3nmT8KUARB0fcHWk9rLaCCkY9n7AeuYvmIfFzYPpZzIkRRYaXvUZqyRS1l8DFEsQ6nR/vDrcL+KGpi8YOhWjKYYkloHOxUKJaDsETGRIp5wFCxcTHmCkPeMMAVurUYPj8pA1d39XnS2tb2jKZYmAoBMRVULM8eTaLFplP47BEccwI+T4D1+4cEsiQ8lkwAyhBz6UIWvPwMTAVqSVBsRFpuNh4f/f8qcP6orFSvhBiHyFCJrzvXw8xyNTJctFRXFdffpInzP4sj2vm9RGQm8b0cxH7OoBwPh+q2ZuuGf0keP3niEhbCc3LguzjyXZ2E7i4CdxdYAXR58OiRdafnyyeffCLxdpf4uEt9PGCJqYfYx0Pg5cJxNXHcDThBIq+sJDI9Jbt8hcE9K0pZQ50pItAcG2aOCwewtLYuK4AodS0r/4ZgzRPychLysqJzMvxT453jwvSRfoV1VdbNOGT3vj0MGd/JG1e9n48//lhtVigdFTKDVG3WeIUGxKWnpBXkTs6snGy9jFy+esUzNMjk4+EU4O0a6Osa5Gv299S4Ooj1Sjz/1O3bt/Sucr2bQuOsbG7HNXNoWmG+KSzAOTrcIzHWOy3RBzQ1PrIwJaeusLghJ6s0NjItwDcxwCs5xCM13Dcjwjc71r8gPqQ4Ib4yec26isEdA7OnF4eOzZfO9+ZvHluzb2vDkd2Nx/a2HNvbfHxf8/H9TUd31xzY0nl828SVfSMnJlqnq3OaEsOLI7zzwz1ywjxyIjxyQr1ywgLzIpKq4xsGK0Z2D06dXRq+uK1q53Dapo1pC8OJm1CBr+jFnvyTW2quH2q+fQK05uq+3OMzcVt7oqbWps4OAqgUL01mTazLHunu2j1/8OGN+z9/5+Uv3r//2eu3Pn351scv3fnZS5eeXjt5/+Tea4e2Xt2/+fK+5oU+poxL5tPsmMQXSKt+bKwe7P6zbj7q6UPf//vYCcqesnqK0OzZlj3BoFutORrHbhn8iBT2R84obBNa+UdQAYTAIATFQhD1ga1o7iy08oym8DCvF2Ie2DlRI1ATrDYM8BtU8NiaRGD1g6FDnpEVUMs9ACBZvoJFgUKh6bmwsfrAUZiIpgBfwYaqyKQcGeYBA/piGRQpldIlYrpY3L0BV5/3fxUw5VQW0x4rgQ6KxjA/AxU0qplOJvFZdImAq5KOTY7/UCcVdB5Hx8fUjgaWhM+WCjhyIV8t42lkPJWEp5KyFUKeWqx1c0jMyUgryq1vb/mh/US4+e6+jUowuF6uYi9XgaeZ7WpkumgZblquv4My2scjKy5uTWlyVfGTV16xHrOswJvPclaL/Jwkga5CXycAFVmg1/qhwR/6xUHAbDV3dwndnHguZkARgYezwNNV5OUu8vaApXMkrpjE6My01NdD6ucl9fOUoaWXzN9b4u8t8HXneZnjClfOeAYBYMsuLUwpzMUz/HsZAdLmExvpGRvpkxQblJnSj6Ofcf/Bi76JMXv277d+fr6AOY7Py4nJzQpIT/RMiXVNjnZKjjQkhIzNzVj3WEkSs9NYCkH/EC7f19TMpMosUzhIpQax2kkTEBWSXphTWF2WV1n8r/1Kb7z55prGeoXZoHE1m7zdzP5egCvOgV4Ofu5qV4dEfN7ClvYmHQIVpc5FdfvObWvr8wUeV32AjzE8wDkm3D0JgYpfZop/dlJ0SUZBU1FpU1ZaYURAor9HfKBbcohbWphHRpR3dpRPXpx/YXxEaWJRW8Hg1g1zpzePnNhcubmvcHGies9SzcGdNQe21+7fUXdoT+uJQ52nD7ef2L/h5M7py7uHDvZX9uVHlQOQhDtlR5qzw5BmhTtmhblkhwUUROa2Zvds6Zo+NT98dql+31j23EDKpv7I8Y6AkfroxQ2Fp7fW3Tzaevdk8x1UpTj3+Kb4rT0xU+tSNw0Vbt1UtDSRNbkhf2zD2KlDtz9676M///6Tv3z9s+++/OjPX3z2H1/98j9/88v/+OrNL9558aNX73/26q2fPzzx6hmWgkcVMu1ZpBfINj82Vg+2HqmFPVhQAVlkS5hEAWYXlCtHwwxRJZVnLikrqGDpWyhKr0BDIwE8sMOf+a+e4Q1GO6yggmWaWX1osBuggsWdBesoPo9xC+wGrJiBFNZRkjF2OXTFZ7ChsQ6NBNaCOAq0I7KFSinzEAjBGaxOPHQIOgoBJ3xTVG1JLmFgNAWL2IOiiD02ckV46vRp61P2w6V/cAAIClYLlooC9ZaYir09KqdKJtjSyEQOgyrkMsQ8jlzkGx584tRJPF3UN998c3h02OTmJNErhTo5VyFkSflsmUCoU4hNGoVZp3Ix6j2dvCOCAmLDg+IiQpKiI9ISkvIzJ2emV6QsgD23bt9uW7dW4uLAMxt4zgauq5Htomc6a5keWkGQWZ8YFFialdRQFV1ZEJCbGlWSn1pT3d638drNm++8++4yHq35pUWuh0ES5CoP8RD5OfO9neVBvgJ/d4+kqHOXLuKBFvhxLl+5kl9ZgfK1nB0Ebs5CTxehp6vQy03s4yn190Ya4N09gGuiKv+URJm/lyzAWxbgg6m3LNBbEuAtCvAS+Lst7sQ1fe/07KaCNeV5VWVffPmltelfksNHjkSmJsVkpSfl56aXFefVVq/t2/izn/3Muvn/FbB33YN9jvHhLnHheFyIGwb60ksKU0sK4wtzIvMyQ3PTg0ELMoMKMnIbqg8fP/bklZeXCXTDJQSuWo5WhLNcTXx6tMZFLjdJpHqxwqzyCfdLyU/LLM1LLsgISYxMzE6BB8y667IC3abTZ86k5WSKdEqRQSV10AKE6D1djF4uOk8nrbuD2tUod9IvLi1ZD3i+wNPlGeRicFc5ehv9I/zxPGxbti1p/T0Mof6mqGBzbLhzQqRrUpRHSnRgblxGbXZxa3bemoTo7CDf5EDPlGDPtHDvrCifnCj//NiwooTUNal1A1Vjewcnjs+u3T9TvDBYsjQDoFK9f/ua/dur928DdGk8tr/91JGuM0fWndw3fHb75ImJzk21KXVxPnlhzjnh5uxwc1aoU1a4S1aYR3ZYWElM0drCvh0bx05s6jmxuWrneNbmoYSp9YGDdf5jdQm7Bsou7Gy8eazlzgk0n8qV3TnAVJY2RE+tS5kdyt86nb84mj7WnT28duD4/juffPjpX7/7/D+/++zvv//4r7/+9O+/+uSvX375n19/81//8cf/+19/+r//+ev//MX5V3ewlQKamEVgU1ZRbH9srN6CKFgUHUuawiyvVRVYHrACuY8sI97howU5wJQ/G+uO0sOAkQCoIFx5hjeWWDpADjrq2QnRniiursBmHYZ1zJP2TIHfYE4wK9igRoAKNAcXKo9vQQ4MPKwl+i3lJgVWRxzaE2GVGkVu0HhJA6qKj0IsqAXFVyyTffE1KjbcrRVUJGjePYtnzBJfEYufjV8RoIqTWHEw63h7lCNvKT3JQIVpsWLGaEYvKprRCxShiIWjUCnQbkelAKigWn4EDFQoRBs6mcCiUXgsmoDLkYkkBo3a1cng5Z5dWDA8NrJz966Dhw7evn37/v37sLJ3396x/5+1/4xqK1n3feF19l6dg9s22BhMzjlnEFnkHIWQQFlCKCEEighlQBISCIFAJJGDyDknZ3e3251zXDnuvdc++457x/vhrSncvc/5eM+6Hv9Ro2apNCXbUL/5r6fqqU4lgYQLDAu47XrnlsudW653brs73fYC7sQJ+BKgOz6uriE+gYmRcVmwAjA00cj1jcTyemQlDl1PJ+Nb6HVN5NisVDQew+a2qtRdY5YJMEaD3/DJmSmdoVfR1VmFrnMOCbjh53nTz+uGv+d1P49r/m7XAt2uh3s5JAX7Faak4KoKGcQcSn06BlHYiC9jUNMa6oKKc10yE28lhl2P9rse43c3Pco7P8WvINUzM8EVFuWdnnAnNux6iM/bQW4O8UEuqZGu6TF3EsIcEyJcYfG34sNvxAU6ZUaGVsCRDFJnr3ZydmZqbnZ7d/fs/HxtcwNc9g4YeRJxUkG2Q0jAndDg26FQ+OR2WDAgyp2YCMe4KKeE6LtJcc7J8c6wBEjJ8QAtd+Ii7aNDr4cF3Ijy88iKCSpKDy/Ndk+NcYfF+mSm+qanuCXHucHiXZPjAdsgweJcU+JdU+OdYLF3YJEeuQmRqCIYqTa6rti3ID28rDCqqjSivCisND+4KCe0IDuvoQ78e6KbiKhGIoKML8Ghs2orMqrL82qrS9DQCF6MRuYiq2EVxWAw8oInecKTvOHJPtkw35yUwNz0hJL83OqK/Kqy3IoSeFlRWklBSklBWnkxHFGZX48sI2CQ9EZcC6tDqeg3mZbXVk/Pz+eWFrVGQy2dEl6eH1qSE1IEDyuEp1SU5FVX5lSWphYXBGel+KYl+qYn+mXCAsE4WJAVAz6lrqYCWw+wV0vC1RBx1URcFRlfTSEgaJQ6Jq2+hVnHopUSsSk1pYHZMLfkKA9YrHd6EpAbLMYxPtQpPsQlKdwtMcwjJtAn2t8vJiAwLjA4PigwNiAgJiA0MSQ6PTIyNSwmIzIxJz4mPSICFhQc7+cT4eEa5OwEuOLv7BPpE50eHQ9PDE+JDkoMAzdxD/XyivCvqKtWdCknp6cmpyb39vfOzs+WV5YtU5PdWjWNxUhIS4YmdZ3s37Hp+l37G6637b2c7vi6OPm73Q1wuwtKf1cncOnr5hzo6R8TlpSbnldVXI6ursWhgIpqSqJSo9yDXF0C7roFu3iEuvpEevrF+IQkhkVlxEWmRgfG+QfF+wcnBAaBv058cFBCcGBicEB8kGe0r1uEp3OYt0t0oFt8mEdShGdKlGdqtE9GTFBeUnR1Zi6lHMFBYtqQWA4CSS8txRcU4PLyiAXFlNJqZg1BhGvtYUnNEslUF2e8mzDQWT+gwZoN+LEB3JgRO9Jfb+4Fwo0PNk6P0ucmmhcmOPMjsiVT94JGMNDSwEcUUgrTMTkpDdlp2Hw4saiEWdnQgeMa+R1TXfzZ3sbxntrBzqIeUZqcmiAjpPY2Vyz0EHYnmo7m6Mfz1MMZ3Ja5cq4b3s9LV7LzuwXlOlm5RlwgZRW0M5BdovbZkbHznblH+5MXG+ajRcPWxNDezM77p9/97cd//N9//6//56ef/nK6dDJs5+n4ltPNV26++a9vvfzPxuqheMbPM1oACTZXAT3v21ZwAVdxtbHxynNAc2IADBBRoDNXrkwGMCVQGi7bu17ETmyQuAq82+L/P1uW65AAUZzfcb17tY7ZlkAMEuRvbHtTIAzYDMrPkXkIHrYlztANQXkb2hfpAZ0F6e1128vLwcvrlm3H/osvf/UdPF4k3gf3BB4FVKCePtCxxAAqAI3QMjAAFYgoYIB+AZUrQYuMf3Ytb92+/ZZtvz205f7mdegsrxvXr04dtsnGFQgtUApeIOhA72vQ6RrgJQg2NqfyLwAqr70MHSty7Y2Xb1x73f7m23du2Xs4uwT7+cZGBiTEBiRE+UWFeIf6+4T5BUYGBkQG+oT4uvi63fW8e8fdERDF3tnBzunWzbu37VwcAFHAb5qdp5Odh5ODj4tXpH9cQVoBsgzdiCOyaFh6IxjyUHRyQzMN28qoohLj89PD06IDk8IDEsICEkKDk8MjUmNCk6P9YkIdAzxueN19x8vpms/dt32dr/m5vBPgcjPUyykxOLgoPQ1XU0DDZxLrUrGIQhqplEHNxNWHlOa5ZSXeSY2yh4XdzohwKUgMqMoOq80Prc4NLE6PKMuLLi/yy05ziI+4Hu57PcL7bkq4e2asR1b8naRwZ1isa2rCrYTwm/FBbgUJIaiCSHSJd2Gqc1qMW3riHfCWMB/7KF9XWLhvRrxncpR9mJ9jZKhzTKRTdIRjZJhDJORRHGMjnZNiAUhcUpPc0pPdMpLdM2CuaUkuqYnOKQkOCVF2MSEOSaEBRSmRNQURVfmu6XEe6Um+OZk+Wame6cnuqYmuKQluNrmnJblnJLlmJN5Nj3dMi/ItSU0gVcNZ+BhMRVBFbiyyKh5VE1NbGVVTGlFRGFVeWE4hEFpZxBYGhkEtxaOTaoriq4uiy/MjirJhVSWp1WWJFYUAJ34FaT4FKT4FqYBM/kWZAcVZgcXw8JLcrNrKUjSyDIUoqq1JqyiOKsyOKISD90aV5sdXFicjK7NwdSV0EpbPaRRyCW3sOmZTLrE+qb4qrq48trY0uro4sqIgqrwgraaipAEFGJZZXQ7eHpyfEZSbHpSXEVoEjyzNg9WUVxBxKAh+ZCSVVEHCFuHQhfj6YhK2lEqooFNqmpsaeC1EER/Pba2hkzNRVf45MJfUaM+MRL/sVJ+MZNekSNeE8ID0uNC0mIiMmLic+Mi0iODEwHBYaGRaVGxmHKwwJTk/OSk/MSEvMTIjOhQW6hfr6xbm5hzk4hhw18HX8S4Y00PdfaJ9/eIC/eKC/BOC/RNC/RKCPaP93cK9XUK97gZ5OAd53A0Ez3ku9h6ON11vX3e5Bcn5SvbvONuDyxtuDnaed277gtu6OwV63A1ycwxwcfRzBqVTgItLiLtXlG9QUmhqUWYNHokioXPLs4Pi/F2DXF2DXABUXIOdQcU12M01xMM3OjA0OSIMFhyaFBCREhSZFh6VER2dGReXkxSbkxicEh6QHOyXGu6VFuGRGuGRFumVEe2THR9QBItG5mWSEUgBndzJb+ziNXVz2/TtHYNy2bBCMarsmtZo5vXd833S6V7+VB99TEca0eOH+xqG++qHelFAJj0oawe1tQPqWqMaZdKhh3oxo/1ki4k5PSxcGlOsjnYum5Rzesm4qn1U1j4ml0x0Sqa0HdO9wtl+9lQfcURba5QXaNpSpJR4KSGlh1EyqWzYGKYezFyd/wi4Qt61IJZ6swcFScomWAc1Q8bOkXFyJc0FkuZSBbe6S4TukWL1MqxOgumRNKjbcRpxo07W0isVD8tUk/KWXj5WzgVDytt37V61e+tf337lV6/8c5H6Oz7ejv6+DlAqFE87L/cbkFOxPfIDGwG44upqfxX8cLuquEJbT2zLq65GedtMlKdN4C22mTFIV74BCqFDjsQmUHkHjOZQhMYZuB9oNgzqD5kPCCq2j4CgYlvoddv7l1VeNrdxZTggQUuKoV0sADbe3kDQOV22XMhQuB7yVdB6AZsrgjzN1UdDdWhSDpTgbwdcF3Aqd284O73YdW8rbWvDoKmwd+7atrNAycGgk1fevm0PuPKW/U0gm2u5AR08DB0TeZUl7C0AkteA3n7rtbffBi2vvXMNCDrr/s03oZjKa6/+y6vQifH/8tZrv4Yyq19/09H+hpuTU6CPX0JMbG5WZlVZUX1NYV0VeObKrSjKrYDKnLKi2NQkv/AgjyBvZ29XB1dHu7sAKvYAKnbujvZed295u9jbLIsj+CVKDsuqzGniN9OFbehGbA2xrpyAyq+vhiPLEooyorPj89FFpcTqMnxtJR6JJNejqVg0BYcgYjIri7yTI+4mhNyJC7oTH3Q3Kcw7KyGuujATi8yoRyQgStIwNdWtjAo2IwWH9C/Ods1MdEiNtE8Nc8yO9qlIT6IgCnjU7BZCNLo0HlVa2EQso1Oja8o9smAOCeE3ov3tE4Lc4XE+ecleuUl3YZGemRADbgNaJId5lsAisaWJjUi/SrhnfqpXbrpjcrRdVIBjcog3PCE4P907Pc4hOtg1IcYdGIuEmLuxkY42jwIciQss3i0t2ROe6pWT7pWTAUqPrFS3rBSXDJhjavytpAjXrJhQRE4irjKmrsQzB+afDw8uzg8ozPbNzfCCpwLGAJx42GgEvo8LBJU4x8yY4JrcNEZ9PrcxDl8dgSqHERtghIZETG1sXVVkTWl8bTmymU4VcanCtgY2LauhKq6mKBldEVlVEFSYFVaS65+f4Z6Z4JYd71WS5lOW4V8OD6jIDa7MD6kuCK0ujK0pKcKiq4nYanxDGRadWlMWXV4QWZ4PIBFTVRSHKE1CVaXh67IbsUgum6WUNsvFZEFbJYOShkcmYxGJDTUJgC6oyoTacjgaUUnC11AIuQ3I2OqSqMpCcJ/wsryI8vzY6uKsBmQdnYrnsDDN9HIyDvw/wlBVSegqGAaRSkBBBxdS8QUsCkrUyuqUs5QSkohb0oQPKE53zojxyEwKzM0MgKcFwlPDclK940MBBsJSIiLSI8EQnFmaBS/PTSvOTClOT8pPis2ODYaFeEZ5u4d5uAa73g0CYz14yrkD5Bzk7Bnh5RsXEJgcEpQaHpoeGZYRHQaPCc2KDk6N9EsK8U0I8okN8IoOcAvzcQ72cAxwu+Pn6uDnctvPBZQO/i90J9DVMcjdJczLPcrPMzbAPcbPPdoHyCPKxz3S2yPK2ysakk+0d0CcT1C8n2c4AJvz3cC7LkHOQE7Q93F08HFy9HV2DnL3jPAOTAgISwmMyQxJzIuGFcEyyuG5iKJcZHFGVW5ufWk2oSoFV5HYUAFrAP9clUkN5UnYihQicOo4mlqhmhpVz42LzIZWk14ybVavLvTvrA8f7w6d7Ki3F/gLo81TQ5SxfhwAyYC2xqiu6ddU92sQ/Zoao6ba0F3V11WpV1b2Kst75BU9isreTqRRgx3ua7KYuEsTsq0F9cGK9mhdc7ii2JzjLY0ypwcbLYYGU1eJmp8hoaZ04NM7m4qHxPVWY+PuJPt4oe1sVXC5Kby3yb/caDuD0uPXL/cXm8WZGlaaoilD2gSXNuXLGflKVpGKXaJsKVO1lik50JIwRUu5oqVMwiwUUgu4TUVcSjYbn0avt/N2ftvZ/lX7t//12mu/euVf/qkFYI7+Pg4+XgAqd/y87b2h/PY2HkBIuBroX8yPQSW0Vd7+RXp8aIwGCIF0BaGfl/ZC8fmfl4FdNUID9y8Lw8Ao7wpZn1/WeoES0MXetqURqtgiJYBwPwMDOBjbp4NXARWgr2fbGgk5GJtN8frv8D4Q5H5sywpAf2im7gXkXkzuAZz8Iii/pOvdm7bcMzedncAldAafsxO0zR7acg9FXN52crh25zaUx+UWkB3kWuyhLfevQ1nC3nkd8ANQ5Npbr197G9ShFpsAcl659vbLb73x0uuvQ9Nfr0EHP/z62huv2L3zlqP9dTdAAk/f+AhYWUENjdKmVmjGzXrLaNdgn1ij4khETdwWbBOlvB4JLytKzE6LTI71DPFz9LxrD4ji4nDL0zYD5u1808Pxhuvtm+7Auzje8QaPh65uIZ4eIR6eYZ6ekX5e0YE+scDX+/slBAGu1DEIEoNW3q/v0HXxlB0ciZDB5yEbSaF5qR4ZMc6pEXdTw++mhnnA48JKMqPKsmF1lflkfDYRm4Cu9i/JdstOcsyIvp0W5giP8qvKSKWjqjrYdXJuYVtjBY9BUEoapO25VFJ4dbFnbopjWrR9YrB9UrBHXnx4VV5CXUVAYbp7ZpJvXqY7HOYAi3JIj/CtTI8mViY2oQJRRUGI4sDyQrespDvJYe7w2MCitLCybK/MhLsJEZ6pSZ6pye4waMLqKhbikhzvlpronpninZvpnQ/3yYd752R65qS7Z6e5wGFOGYl30qN9SlJi8eXp9PpYTLlvSWZEdVkUojKssiSgONcnL9MLnuaRkQJcjmt6onNavGNqNLAp3sUpKY11FWJ2iZCZSK5LpWBzWNQsGimZUB9bXxNZUxKPKq/ntzDkYrpMhGpl5pEa8inYLBwypqYkqrokvLzApyDdNTvBvTDZryYnoDY/GFkYiioJR5VFoMsi0WVJDdXlFHw9ndpAp9RSibkYZAqqCoaqhqEhpTcg4HhUIRVfzaYR2nnsTgVHraTK2iuaqZnkhlQiCvAglQAsIzITW1cG7sNswrIZVTRyBhaZDHiDBrypiKurSKyrzCNiGlqYFAGXwGsppRLTMbWwhhoYDplKRKc3YrLoBDiLmNfSWM5ngYFSPNjL06sbOriJxBrf8gyPXFhgYVZoYXZYQXZIdqpnYoR7TKBvfHBkRmxeTVEVEVmIKk0tyYjJjo/NiU3Iiw9KDnIN93ANcXcJcXUNcXUOcbnj5+jk7+ge6uYb6xMECwrPCI+ER0Xnxsbkx8UWJcQXJwFF58eHw6OC08IDkkJ948CPqB+wL65hXsDBuIR6AoQAgUvXcC+3CC/3SB+PaF/vOH/fxCDf5CDfpCDfhEDfBH/fhAD/pICA5KDA5ED/RH/feF+faC93CCp3nQLv3g12cQ4GUHG+4+fs4ON8x9flboCbR7iPf2JQDDwmuTAxoxyWi8guaigtxpTnoIpz0SVVNDSylZhBrE7BVKVja1IbqhLryuKQRQmokkIGqc2g77XOmTat2qVpjtnQbDZwJ83ihWmJdVayMt02O9I0YiAM6eqNGqShq9bQXduvhqLlJh1qSF9n0tX2a2sMGgCSUo28sEucq+TnKLjZ8tYceWueklvUKajUShB6GbJPUdsnq9JJStWikm4hUL6SmymmJwuJOZ1M7KxBdLba/fSo5/3z3g/uDXz0ePiz94c+e6//48d9Hz4wPH+gf3Yuf7DdejDbtGYmL5lICwOEOSNh1kCY6iVM6gkWPdHSQxhTNwwrUQOy2j5xlZpXrmopkTLyROT0Vqy9j8s1l9uv3Xrn19de/9Wr//pPQeUqOvLfAZKfrQM07kOlDRu2oR/Itu8daod2NV75ElsI5AoqV2GVq9H86l1AACQQLX6+BKbhxeh/NdCDcf9nZlwJfA1AFCBHgDovaAPm1WdBgr4D9HFQAMYWJrFF46GvDaELWj0M3RACGGDG1Y4ZwC3oI6A7g0ZbDnxoQYGtA7A10CycLSu+E5BtVRh0hBckCC0vTh1+G3AFyOHW2w72b92GuPKW3Y23bt54E5oQe+eN69feuP4OlI/ySrZzJF+5du0lCCrQ9NevX3/112++9vLNt9+4Y3/d3fm2r7tHVFBCUQ6qhdFu7DOvr84dH86fHi8c7luWF/tGhlQ6jUgpZwt5OHpjeUMdvKwgAhbrEezr6O1yy8PRwdvZwdf1to8b4Iqd+x07CCp3AFdueTm6R/gXomtKsKhCTF0Rti6jqtQ3McwrNiAAFpqFKu0Y1Gksw7IBbauqvVUmIrXQE4rg7glhrsmRzkmRTonhd5MjnFNifHPT4hDlibWVoSU5ntnJzpnxACd3sqKcCxKCanMzmRikjFsrbSvlNlWL2FRdF1Wvqe7gxZPq/KrzXQuS7sCjHbKj3Ytg4cgCOKW+hE7MxCGS6ioSaiuDSvJcADlSo5xyYgPrchKpyPjG2qC6omh8bRwOEVadE1ySElKZEVGdG1VT6JWd5Joa55ud7pud4ZmZ4p4Oc0tLck9Pck1LcsuAAWsCcOJTAPcugHsCVuVmuOek3YUnOmTG3s2NDa8vSG/G5XGpcYTq4OrCOPBEj6kLR5QHlRf6FeUAV+SWkXQ3JdYhOdIhJdIZHhdSk1PAIeO7OjAqUXFbU1EbrUzYltvGSmUQ44nI0LqSwKqciNoitKilVdPZ0qXACjnFdEIuqT6HgM4moONqAbrg7vlJLoVJ3jXwwPqiEExpBK4yCl8dTUDEEGriCDUphLpqVhOB20Lhcci8FlwrC8WmA+sDiU2vZzPxbexGIY8uEbGVMlaXvFElxchFFfzmHBYpk4aHN+ELGORSZmNtMw3fyqLwWhoFrUQ+B8FhFDMpmRRMKqk+hYQG+Mmh4GpbmGQRjyoWNHDZZQxKHhWf3YiD0/B5THJRC62Mx6oRt2EU7exejWRkoN1sxHVLUtm4kIZivzJ4cBkwPQWhRfCA7BT/9ATf5MjwrPicmmIkFVvPJFTgq1JK0yOzYsMzI6PgUWBYd430dI/0co/wcAsHP9WeLiEuzkFOPlHuIUl+kRnBsdnh8QXRiUWxSSXxsLLElIrklMoUoMTShKi8qLCMsMAUAAl/rxhgPryh+4Ay2huUntHenjE+QF6xvj7xvv5J/gHJgaCzTYFBkIJC0oLD00PCM0NCM0OC0wIDk3w8oz1dw12cw1xdw9xdQt3vBrkBOQW43w2AZtvAk5Z/YnB4VlxyUXpWTU4ptgxBRSGaUAUNJWk12Wk1+cB9pmMAThCZODQcj87AolIwyAxCQwWHyTMaepfnh7ZXtMvTzUM6rFaO7e0iGrWkgR7CgBZn1OAHNNiBnoZBLWZQ12DSNwz1AqFNepRJjxzU1Q7oIL8CzIpOVaKRFna15yr4WZKWdDErVUhP5Tel8qmpfEoqvxGUKfzGNGFTZgcrT95W2Cko6RaV9YjrTN1M66j0aLn7cltzuQPUc3/P8OTY8PRQ+3BbfbnV+/DA+O6J7uGe8nxVerIsPV2Vn60rL7aUZ+vy01XZyar8dK3zfFN5uireX+RtzbStT7SsjHM3plpXJpqXzI1T/bd83a65OrzucOPX77zxq9d+/U+tKoawAY3IEAYgowBdQs4DCkXY7AgY+u29oCz0tvJq4utnm+JpQ4Wt/5URAXcDd4CcgQ1CV7p1hYSrRnB/yAABQbB5wRLbF4A+yNPVDnyKj8dtX2hjCgAMNMlmo4jtVqCE8u1DLVfBfKgC8cz2duizbGi02SabN/rvv50NJBBCrmwK+FDgZgBgoLkvSBBU7gKiQFyBbIqT43UnKDPYC8ZcocXh1jXbxvurHJQALVAayiuWgIpN0HH3tpOJX37rzV+/9tq/vvYqdJrF26+/euv6W84ONz1dgE3xSoiEVZeiuS08nbpvdmJkbWlkc3nhYGvt7HBhc21kclzX36tQq/gyMY5JLairSirICkmK9gwPuBvo7ujr6ujn5hjo6RjsfTfY1y08ANzwjr+Hc7BnJDyRJhHS5WKGqoPZKcPw2yIKsgJTYmPLc1LQFTBEcRqqJBVVFF8Gj8pPC8iI90gI90qN9c1O9f5FOWk+uRmeOamuWYlOaTEOqeEOmeHuxUmRmNI8DqlBIahXCGvaOfUyPrWnE9spK+Ix4ymoIHSJd02Oe3W6Z21WYENhMq2usLWxkEWCk1FZRHQpvbGYTolHVnrnpd/NiHNIi3QtSowglMFYmLhGZDCqOJZYF0OoDanNDa5MC0flRjUUR6NKvfNgXvCUgIIc/4Js75wMT3iaOzzVA55qK9M8ctK98rIAUTzys9zzM1xyU52yEhyzYp1yY4NR+UVCan2PtFTMTqTUJRHRqRRCLB4VhiwLqCzwKsp0gSc6pUc7pka4ZseHVudl07AIERvV0YbuaCWqxIxebX2nrIDXnMokxRJrw9FlIaj8MExRGg2NU4rYui5GtwIjbKtobqpqYRQ1EWIQxd6Fqa75iW6lMC9UbiCxLJxSHdVYG0NFxVLRcU318bSGxKb6FEpDGbsJJ2yjiPlNHYIm4Ec7RNQOIVUioklEdJmYCSQH6qApOwiK9joZv1bORyiFFbK2In5zKa+5oUPQKBM3dQjB2+nAaMpEoDNDKSHJRBW85pxmCpxJhLPIcAaxoJmC4LGxIj65Q0TuEODb+WhhK1rQhpcIGztlTG1na7+Ob+rjDxvZJj2hv6umpyOzgxZJqQ6uLYqoLY+sLgkuzvaBJwXCkyPyU0rwNVg2hdhKo/AZODYhD12QUJwSnBHhlxToFe/nFg3NRAEeeAAkRHq6hbl6RDgHJbjHwQOSC4LTS8MyKiIzq6IyqqIza6Izq2PSq2NTqmKSK6Jii8LD4YGBqb4+CYAf7u5Rbm6Rrq6Rbm5R7h7R7h4x7p6xnl5xnj7xXr7xnn6JXgFJXgHJUBkI8wpO8Q1N943I8o+E+0fnBEbnBkTB/UPTvf0SPDyiXD2iPTxifNwjvNzCPN3CgenxcQ3zdQ/394oO8k8CzikhoTgjG1lc09RA5DWRBU0IZn1mXWFSdQEMWZ6GQaZh6zLxDdkkTDYZCydjchoJ5RwmS9/TvTjbu7YkmR4l6zsbtAoAFXy/lmjqwZt0xCEdcbiXZDaQRgdIIwOgJI4O4swGABWEUVOhVxZrpHmdQuBO4LJWwJLUdkYyvymJT0nmkRPbiAkcfBwbG9uMjWvGxnPwoCVFQAVQyZG1Fqj4pdqO2gE1eXKItzbXvr0o3lkQbS2IdpaE24ugbN+1inYXhTtLgu2l9j2raGdRvLso2bNKD1Yk+1bFwapif0W+vyzZWZJCWuzYnBOuTvKXx9sWzK1zQy0zJta0kTlpoE303fbzeMfN8Y07N1+6/uavXn/pn1pVbBvibWOuzTTc+nk34gvAAGx4udpBp9TZRnYwZHu53/Ryv/FC4FU30AGKkwMeAE7Y4AHG9CucQMM9lGQFmuCCPsU2mkMewsMFOkTSdnPbHV7oxUmr4ONs5xcBvVgf7O11Ffm/6elxA0KLLZxjm4i7Mk+3Pd1fyJbJGIIctCgAWugMoeV/QZdtmYDNnbjacPJL5SrKctfJls3FVt51esfxznUgQJcXruX2tTtQQhdAl6v0xlDa/Fs337C/8abdzTduAMBAev36dQCVV95686XXX//166/9q+083Vfs33nrrv11tzv2Xs4u4QEhWbDk8qIsREUOsiIPXV6MrSTzqdoR7fyqxbpiGR/r12qVrDYGAo9KL82NzkoOTYn1iwtzi/C7E+Du4OfqHOjhERngkxAemBwTCIuOyEqMK0jPqi0uJSIRNBya3YhpY6DY1OTKgtC8lARESVJ9VSKqIh5dEYeujK0rCy8vCC7M8stJ9YGneMNTPLNTPECZA4bsJBdgTVKj76RGOGVFeZamROHLioW0eqUAKWmtFrKwciGjV4Pvkue0NILH8EB0iRcixxOVG0QsjeU25CiYJUpOWUdzYSuljENF8jnVbewMMja0qtAjL80pK/5OVrRLQXwEpiirhQBFL8iIGHxtQiM2Alfri8zzRWZHEcqSqXVxuCrf4gz/4uzQ8qKg0gL/wlxfgJa8LK+8TO8CwJIs78Jsb+A5CrNc81KcASSyYl3y4v0ROfAWArqznaCT4zRSVKe4VtFewGuB0UgRmFp/4H5K4G75MNe8RJ/StHhMZQmHCliCEDRXttGwMgGrT0vv7amRCDIBTkiISFx5FKkikYEsENPRWjGpV0kBt1WKyCopSdlRw2Ul42oCyrM8ipPdSpLdazL8cEXhTVUxTHQsqyGejUlk45PYhKQWYgqHCGvGwxjYDAahoIVa1kav5DKreaxqfjMQQtBSJ+Kg2tvQkLjI9jaEuA0h5dYqRXXdHXVaaZ1OUauWViuEdQohRiasb+eiRa2gc72Yi+kQYKVCtFxY0dFWKGTlC5h5fHo+j57HpRe00cv4LER7W4NEgJe3g244eTtFJWVoVcxeNaNP3dinxveqUHp5bZ+y2iDP6WqNYWMicNVx2Nr4BkRoeb5fDiw4Py0dUdzQSibz6Djg5NooZAG1no0tIlTDKrND4dGB6eHesBCP+ED3GD+vGG/vGC+/eK+wFO+CihgWu4zLq5bI6gUSBItfSuMV0QXFTdziakpGdl0crDwyLj84OMPbO87NOdLZKcTRMfjOnZA7d4IdnELvesV4+ib4+Cf6ByT5+SX4eMW4eMc4eUU7gdI71sk3ziko2Tk8wz0yyz062z0m2zMmzys6xzMiyz04xd0/0cMn0Ru4H+9ob68IH+/YIK/YII9of88Yf++4MP/U6Kj81Lz6CiKXxdcolUadYkDNUraV01DpDRUp9TWpWEQaHp1JwgKW5DYR82jkfHpjNY/T3N+rXV40bK9IZkep/RqSUUsd7qeND9InTDSgcRPdMsSYMtMmRxhTI02Tw+SJofohfYVenqtoTRM1JfPJCVxiAg8InyggJHPxSVxcYhs2vq0BEqc+hl0X01wb04yIZyPj2ahETn0yF5vGJ6QJSZntjdkyVlEXF9EnaTCpsCNqwriOZOmlTBmoM0bavIm5OMKyjjGXRput45xlS8vyRNuKhb82BSRYnxZvzok350VrM8LVKfHajGh5kr84zp0bYU+amicHGaN9TUM9xP4ufF/nbX/P6+5ObzjavXTjrV+9/vKvfv1PzH9dTXDZHupf+AYAANscF7AXLhBCvD1uAqJApbudjyckb48bPpBu+nhCgl6ynXHiCVkN6DiTq5C4TeDOL2QDyQ1355uQXACQAJZu/nwTO1/ozva2mwOK2Hbse94CLb7edn4+9j7et7y9bnp5At2Ayhf4ueXpdtvrf4PK1SQe+FwIJ7bdly+4Ath2hRbbPNg70NHCV1CxdbNNgkEZJyFBmAG6Sml8/S6kq5mxF5NjwL5Aun1FF6A3b0FJwwBXrvTGTWiFGIDKy2++8es3X/31W6+99M6br9i988Ydu2vQ8q27ziF+/kkxEdnpsYXZ8YXwmNy0uFwYrCAtuzq3GFlQiS6sRBbAC9Pi02NCkyMC4kP94kP84kN94oI9owOdQ7zuBLg5B3n6xoaEZsQnl8BLcLW4NnqThNusEgt0KtlAj3p0UDc50jGoB4yJr8xKQZcl15fF1xbFIApiagujawujagpDynL88tO84MmeOUAwN3iyCzzhbkbMnYxI55wYv6pMGDAcfGqpiFkpYlW3s3GdHdSernqlJLelKYqACEAVetdl+zXkh9GqUtvJ+erWYg23TM1FaIRYjYzS04lTSvKaqVH1Fb5lcNcCmHNRkmdlWiimOL0ZWyKkV4gY+S2UEj6zUMBJYTWG4Wp8UPmBxJKkNmyOgJrchA6oygmtKYmsqw6rKQsqL/IvzfcrzfMtyfMFZWmeV1G2W36aS07S3Zx49+KUMHRxPpfa0C0h6RW47g5yj4I7NtA6NozokqWyGyOJtQHIQu9quE8NPAJbksGorxSzG5QilKQNJW7FSYVUtZLcrawQcDJouFhcVQS2PJJUkcHF1+na2dODYqulZaIf3JlmULMMGqyiHU7DhNXm+VameyMy/OoBUMvCqDVRDGQMqz6hjQATUFKElDQRLcOmTBEtnUfJaKNktjVmc6m5PFq+gFEgZBaJmMWi5lJxS3kHB1ChWsKtkfJqZJCAQQFEQffI6/s6MQPdWJMGZeyq0XRUyvllYk6psAWoRNhc2s4pFbeWdrSWyrilcl6Zkg/KElAHkrSWStquVCZpK+loLZG0lkvagCoV/KpOUWV3e1WPtNqgRAyqqgaUOZq2BD4xnoGBNeFgFExEbWlAUXpUZW55E6a5U9TWJW6WtgrVEq1JpzdrO41yXldLNjonvDAxKC/WPzPSKznIM97XJ9EvOieUzEYOmdUTE0OWiZHJyZGZ2ZGFxVHrytDyxujK9vTCxvz8xpJpfrRJzi6h1Hgn+zuFudwKcroZcAfoVqBjYEoUSyoWabs7tGqRtqtNKS3D14RnRrhHurqE3PYId/CNcQhPu5tR5luCCitEBuUjA+HVAamlPinF/qnF4Znl8fDKdHgFPLUkMzw9JiAlPAAW5Z8UGZIel42uQYpaUOJmkpSjHNT1T48YZ0b658yd43qqgpPfiEzHI9OJqCwKJruJlMdsLGymFbHppa1MZDufOzTQv7liPtzWrM7Sh3QkYw9puK9xzNg41k8dG2gcH2waN1EnhijjJvLYAHa4t6ZXVdDJS2+npQjI6VJGQY+gekiJGtfgpvpsB8ubWaujQDTrEGVhoHEeyEiZNVCm+ygzveRpPd6iJVt0jdN91Nn+pjkjdd5Ineunzhuoi8ZGSAOUBSOoU5f6qUvGJqupcWmQsgiJumRqsg7RrMPMZTNr2cxYGqItDtIWBpoXh4SrFtX2vHZ/WbO71LU1L1+bkixbxAuj7XNm7qSxeVTnEOB1w9P5TUf7l26+/as3XvnVS/8EVKDnesAA4Et+fpwHOIF0Nd0EgAF0NfT7etn5gSHeGzqLwt8bkp+XHSh9AVe8AAYgSHhfLd8CULFFMiCX4HzD08Um1+tQ6XYTOuvezdbZ/TqAE3i7nxe4G7izPbg5+BRfL9vHedtDjb5Q0kAAFdD+8zcBl/a+PnbegDTet72hhcIALRDMIGDY1i7btmpCuVtsRAGCYAn9BW2xFldoTbNtHdqLk4YhQUu/IKhAJsYGnv/mim1V2FX9GjRFZrMvwLI42GIttmmxt27Zv3ULlLfesrd/084O2s7y9lsvvfk6IMqvr73x0nVo3dcbDnbvuNy55e3qHhEUmJoQmZ+ZUJKfWJKbVJCVlJ+eUpiRXpKVVpSZVpyRVpQWB08MSYrwiwryjgzwivDzjgrwignyjAtyi/J3DvV2C/XxTwhLLM2qomKa5e3ywT6tZVRrGddPWQzTkwMzkwOz4+K+zlpWfRUNUclCF1NrC0hVpTRkVTOuko3LpdRGVGZ658a7wmNcsmOcs2PvwqOdcmLcipNCUPlg3K+WtaFUwnIRq1xIx3dJ6Iaehi5pBpsUjqsKRBX6InP8cUUxzXVwOb1CL6zubS/X8Or7pJypQbl1rs0yXCXjx5Pq/GvyPSvSPSrSAtC5cfRauIBSKmUXtzMLeU214la6Xk3RqwsEzTGN9UENJb71eVGsmsLuFkRvR2YbOby+LA5bF09AR6ERYbVlQdXFAVWF/pUFPqU5HkWZLvkprgXJ3uUZsfiqSgmH3Ktq1CuJPfImg5o7NiiYHCX0qfOEzfF0bCixKphQGk4uT2vB1qiFtKGexn41UiFAy/msPg3XZKSoVSVcZjIJFVlfHt5QHE2uzOKRsP0KoXVcuT4nnh/jTZl4U8P8KTN4RC3m02OIlaH1eUHYglBKWSSzJoaNjudgEtvwMc3oaBYaxidnSphwORuu4GQrOTnK1hw5B97BhIsZuWJWXkdzgbSlWN5WquCWK7gVKkF1p7Cmqx3ZLUZpJGiNFK2R1WtloNKgl2MMnYRBDWFYTxo1ECf6sSN6ZJ+islNUDtAi55XKwR34FeD/qFNY0Smq0HRU6aQ1elm1VloJ8KMWA1V0t1d0QSrrFBWrBMVK/pVKukUVNqLUDHbWDHVXDKmye3gJ7ZSkNgqcy8hsboxqqA4sg8cgCqvYlFa9QtCrkPZ3TixNzVvnl6xL1uWRwfH2CmpBbAUsqiw5vDg+JDciICPEPzUkJCOslloxPT9stc5bbX9WVpY3N9d3d7ePjg5PLs5OHz08evpk/8k9/YKZKGsJLY5zTvR3iPa0C3ezC3OzC3cPL0yXD5l6p6cMU5N9Uxbd5ISwrzurvjyhJNU92tkvzikq0zW7NozVXqYx0UxTbcPzolGrtHe8pcfMHVsa37w437682Do9sR7sd48aYwqTgzNiI3NgtVSMcXZ8aH3ZtG4dtE4Pzo0Nzo9oxvs1kwO6xRGuUV5ER2UQkTlNhBxGYz6bVtzKLOWxy3gt5XwOWt4uGBvq31obPthQWScIvfIalahKKajpBP99vEoFr6qTX9UlBNQHqugWFspbstrBwwQ1S84o1AtrRtXY6X6qdYS9ZuFvz3XsLXaernefb3aerStOViT7C9L9eenenHh7umNrWrq3oDheVhwtK49Xus42QDfV6ar8aEl2uCA9tgLJTlekpyvy81XZ2Yr8YlVxuS6/2FBcbskvNkGpuLfV9WC359FBz+ND3ZMj7aMD9cO9ngf7w+9fLn72wd73X5785pujH7/a+fbTja8+XP/yw40vn2989Xz182cLHz9xCPS+6enyptOtl29e+9Wbr/7qpX95QYj/gz+3bGEJ24ovKKZ9ZS/sICvgCcZxMMrftJ1oBGQf6HczyA/K5RfkD0q7IL+bAb43A6Acsfagp4/XlY+BBv2ruTKbIIR4ud/wBoIOqAf164Al4NLWE4AKYkaAr12Aj+1ufjeg0heUUGMg1AJVwBcAluWKar4AYz5A9v4+4BtCZ0F6e1+lqoRC+m7g+wNuQUGXmy8OPLZtowHtNjcG0cLGFQg5Nl13cbn533KGUuK7ubzjCqV1sZ1p/2IvCyDQDWdoC8sN6ICvO+84OVzF8IFluQZKG12gLZO3b71hZ/fq9XdevvbWS2+9/tK11196542X7a697nDzmrODnbuzc7BPQHJMbAE8rbqsAIuspuLRLAqRx6C3tzA62mjtLSQeo6GZVIKvSSnNCU2N9QEsifADXPGMCfCMD/ZOCvGIDXKFLoPC4PF5mFJBj8w4PT62smBanDXMWjpHjZJBdUsnD8FAVzRVNcroogG5aqyny6IfWBqZ3bPOHW2M7loZBlkqExlOKA2oz/dH54Xgi5OY6FJZS4NOWqviV0pakQohWddJ6VXXKcRpTGIE3oaTurxgQlFia32FToAb09QYpVU6EcHULViwKDesvLmJGnVHais5glITgCkET/ER1MpMAbmssw2hFoKbV0lb8Wopa1DPHOhDyISpDFw4vsq/vtAfVxDNqqnQttGm+kjjvTkSdioLn86igAfnGHxdaH11ILLEtyrfszTLvSjNoyQtAJmbysAgu0TUQQ3NqG7q62o1D0hmLXzLaINWmSNgApyENyLCqTWJrZiybk7TeK9gaUIwN8Y09zYZutgmQ9uwidqrLRVykqn1kdiyMExJPKW6UNxEGepunR0WWSdEc2bZ0oR+y2rY2RDPjdd2CVPZmJim6ihaVSwbmSzAAX+W0UFNE1PTQdlBjW/DRTWjYSIKXNGcq24r0AoLe0SFPcJCtaCwk1sILfLhlXQJytTCKq24pqejTidD9SkaDJ3Y/k5cfxdxQE0Y0BAHNKRBLXFQSzZpycP6xtG+RssAdXqINm9unBsmThpRA121OlmNVlIFKKIFklSDy145ok+BNHaiTGr0sAaUdQPdqEE10qhCGFS1Bqis6VVU6mUVeinASYVOWtWnqB5QIYY1tWM9FSNdGTputJAY20pI49NTWqng0cG3HB5YkRWDKsyn1xcz0IhWrMqknLKOLq5MGEc7a5mVScisOGQuUDwqL642O7o8LRQ8keTFpiHSmqWEIYtyaXlwfWNiZ2d+b2/l5GTn3uXRw4cHl0+2ZraGxP38Elp5TFWaT3aoa0aIU0qQQ6LfrVhvh3hfL3hkFqGygoktp6JKyKhCUh0MkR+SG+eXHu6T7BOZE1iASW6U1BpnxOvHg3v3xo4ezp8+3rz//v7J4/nNy72T58/uffTh2bP3D548WjzcKMCVxhbD0pBFDTyqyTq2fryzfXK0c7K/frwxuTFtmB8xrs4ZN+c7JvTFrAZ4E7aolVXMY5cAnAg4FaK2SjGwj0KCRiWbmRza3zYfbsrmzQgFu5hPKRPRahWtuB4BVito0PBRam6lgl3awSgWNxXLWdVaIXq4u2GiB23pQY9q8BN6xsKoeNvac7Zneng68vRy9N175qeXpkdnAw+O+y/3eo43VLuL0s1ZydaCfM+qOlhR7VtV+8uqPatyd0m2NSffmVftLWoOV7THq7rTDf35Zt/Fdt/ldu/lds/5luZsved8U3+x3XOxrb+3Y7i3a7i3Y3ywZ3p0OPT4ZOy9i5XPnp3/9rv3/v6H9//tD4/+9NPpT1/vfvvZ+hefbH758dZXULn62bM7QT43vVzfvHsbDFb/H0DFHnAFiszbFkp5uEKRDF9P+wAf+yA/6CyjIP9bQQH2NtmFBtmFBNqFBNgFB9wMDLgR6HcDjPv+vnYADD/7DMh2QBNikMuBvAUgim2uDHIkvp6gvOHvfSPAB5Q2igB4+NkFAlAF2AUF3AS3DQ64EeR/pZugJTAA6vACLRBd7EEZ4G/nD+RjD53y7X0L4pmXPZRr0pYt/yoSA+3JhzZm/rJX5oX/eCEIJwA2V1yx2ZerqTAADygzDYQWW66wmy9sypVTcboBZQxzhqbC7t6BFhzbZDvyC1qIDFreunP7DXv7125ch7ZGvg0R5aUbb73icONNZ4cbnnfvBHj6xkfGF+eVEDFkUVt7f0/v7IR5bXHxaHv94mjn/sXG5eni0c7o+qJmfJilai8hIqNzYL5xwd6R/l6xQd6Jod5JoR6xgc7h3k4hnm6R3l7xfkHJwXFZMbCCZKCkosT4wsTE4pS4guTY/ARYdTqCiZjemrp4/t7FRx/e++yjh1988uCLT84//8h8sl6pbUsREeNaUNHNtfGcBriosUTWXCppRqpEjf1aikFdJm5LoGJCsOW+qFw/dG4IuTy1nYjo72DMDDJnTQSzptHcI16eUW4stcyM1nTbcNJUE0QtD2+uSReTEf3iJouBNKKr1YpQ6nbGoK59ZoIzbkZ1A9NDjiRWBTYUBeOKY5uRJWDcn+7nrUxyF0eo5h5CvwbZ05kn4iTRSVF4VAi63K8mz6sqyweZG02pKulgkYxdzSN9jKGepgENZ2xAsTgrmZ8i9alzeYwEGiaysSaKXpMmIqAGpLyVMfBbKt9e4M0OccaNoplRyfwk2zxYKRfCWPhoUnUkqRLW3FCl4tDHDO1LU5LFCYHFqLJOjhzvTl6cmo8PWkb7S8WstBZMMrchU0rJVTHzu1j53c356pZ8dWteNye3qyVbxc6QMVI6muAqdr6aW9gjKO0Tlw/KyvolZX0dpTpxub6jsk9SY5Aj+xWogc4GUzduWE0y9zSO9jaOGprGDLSJfsbkAGvS1Dw9xJoegsopE6gw58yspdHm5XGWdYy+NNo4a8KN6utNatRgd91AV52puw7UhzRosxY10oMZ78NNGvFTRtxkP9ZiaBjtrR/TY0Z7G0Z09SM9qGFNnU21QxqAE+SItm5Mh5zQ11h6so2iqA5SCAsZxkBG0uuCiOV+dfkBdYWhDSWRuIoYfFUisSq9EZHPrC9vwRUw6tPICBi5Lp2GyW4hF3JpRTxaUVtTAZscjykKqUhMrMssbCxBt1aT+NUcaY2qF6sxEiSaemkPEc8tzcakJdQkRZQl+OdHu2UGO6cHO6UH30kJvJXsexvmfzsj0CUv0h0IHuGdn+RbnOaRFe2WHOgS7+uW4O2b6htdGJZRm4AVlHSamROryuVD4/qJaee0r3OA2tBSSZfRBHqB3NQpGepqUnLywNduqCxobKhikygylmmud/t44ehieeNorneqTz6iU8+OqGaGKRpBPgtX2EKrkggrO/ilYm6pmFch4dfIRCiVlGEy6LbWpx6cT9471K5OsYwKhlEqGNV0zvTrlocN6+P61RHt8rBqcUA4pedP98s2Z9TnW7LjZebCIHpI1TDUw5idkO9tGx9cWp69N//JhwuffLj4yfOFT57PfvTe9AdPJ957ZH50Ybw80p/sdO4ud6xN85fGeQsjvMVRwbJFsjGn2FlRH20N3D+devZ0+dOP1r76dOPrzza/+Xzjm8/Xv/588bMPp58/HX/vkeXZY8sHTychPRl//+H4ew8mnj2aff505bPnxz989fQvv/voP/78wd9/e/9332x9+eHku/fHHt0bujzpPz3QH25pdtf/F6i8A0Hl5X8CKtAyKlus2ybXW16uAAz2gb7AjtgSLgXahwffCgsBuh0RahcRAmRvu4ToEhx4E4zvkKBB3x7I30YL2xTWLwKXN6AsIBBLIIoEArsDkeNmKHQHG6WC7EKD7cJCboaH3IwIuREWDFXCQ+xAGRYMkQwI8AaALRhiz+2gILugwJvAOYEb2kwMFHQBXIHOsfe85eMBbNPVxkngwCCiQJn5oRD9dVuI/ucJLlfbyjHoTP7/djAvlh2/MCigchV0uWq5Bs2P2QDzYgc+NBUGLRW76wgF9qFUlXffdnR867b96zdvvHrt7ZeBR7nx1sv277zhevuGj9udYF/PmOCEopw6DpPXpzWtLq0+vNx578nRh+8+/OyjD77+/KOvv/rw66/e//KLy4+er1yeDK7MteoUgCsxubDA5HC/xNCAlEg/WIRXfLBrpO/dEE/nUE/XMC+3GD8oOzceGVecFZ6dkF1fgWvn5hNR4fmJkXmxeQ05O5drDz5//9FXXz7+6vMHnz67/OTx6oO9JkNHcnNdNK0mprEmjFQeQiiLbKzNaGtCdHYgNfI8YXMcrSEEU+aPLvTHFITTqrPltAZzN2vezJobZE4bWmaH21dmpOsLzMnh0i4RrI0YyawLpdfEtKJzulj4SZ1gA9j5Wc6smTbS2zIxxAdD+bQZrVfABfRoGiqUXB5KrUhow1T08Jpnh2RbCyLreNu0sWPJotxcbZu11Gik6W1NUVR0CKHSH1MUSipP5+JrtYKmUV3b1FDrxABrpI83aZYuzfGmxhp0qlwBK55VH0VDRLORWXIaZrhTtDmlPdvu2lsRW8fFixOqjWXFxgpzfLiyU5zeSomno+JoyFQuDqEXcZbMiq1F2YpFNDWoX5+3nB8uPn08/fCeen2JPqgtk7fBeZR0Hj5bRslTMws17CJtS5GurbiXW6TnFffyi/T8Qh2vQMcv6BUU9olK+sWl/eIyo6RyWFEz0lVlUlYNKhEmJXK4q35Egx3T4cb15LEe+riWOaFrntS3zA62zI9xlyZEy1Mdq7PS9fkrgbpodUa4NsNfn21bn25ZnWSvWBjL443zw6SZAcJkP37CgB/vw04YAD8wkwbczABxdpC0OExeGiEtjRDmh/CzA7jZQcLsIH4aYMaAmerHTg9iZyDhpgcx0wMN0/31s0b0rLFqsifLIIjsIAW01PnRq/waKwNIZUDBpMpQUmXYz4oiVcU1Vsc1IWOpyLhGZAKtDsbGZrQS4FxyLr8xT0CLI5UFVSfFNeQl4oriMTmxKFgMMiEelZSASoqpjguriA6siPctTfAsjHHPi3DJDrsLD3XKCnXMDL4DD76dHQQpByjgdpb/7TR/hxT/2ymBDkm+DgleQHfive8merskebske3nCvP0y/INz/EJz/ULz/CML/QOyAnyzggKyY8KLYHByXZ2EWy1uLeI05TEJuTR8Ph2TR0OXsHAIDgHFI9RycSVsIFJpC6WATcxmYtLp2Hwuo0ourlKIyqT80g5emUxQo+xAaxSskQHNztro5cHY2aZubUw6qVFYVJ2jEtWgsHOgTTXAkRtbVAOtqiGBelLZtzZsub899+Fl/7114khX/UAXfXpUsGaVbK9Jtlcl2ytQubXSsbncsbnUvjbPW55iz5kbx/SEoW7coII4qGgcUjHG1Bzwg2HRMke76eYu6nA3baSHM2NSbC8ZL4/H3304++HThY/em/vw6dSzxxPvPRwG0ohGDwAA//RJREFUTHpwOvDwbPDhxfCjy5En9wClgEaf3Jt8/9HaZx8Ca/LB3//w6T/+/Pzvv7348Qvr88fme8fG04Pu3XXJyjxvztIyaf5fofI/3nr9Vy//E2uKocQnnu53vNycfNydAjydAn1uBfneCvG/FRJkHxxkB8b08NBbEWE2hdtHRQDdioywB5fhYfahIRAMQgNvAioAt2FzFdDkFeCHH6Rfps6giaxAf4ABgARAkZthQTZ+hNqHh4Jb2UcCgZuH20W9+Ag78Ck/12/ZZBcRZge6RYTeigx1CI+4HRoKvqENLZBrue3vf8vPx97XG5oN87EtGIMWHANmAPtlm/5yhfbbQ1CxHQFp260CWOIK5YmB+kD7In/ByS8C/ABEudJVTMUmJ2BlbkKTY1ACMUhQT2dbzk2Xa3ed3rp967Wb16H9jwAqN6+94nD9mufdW0E+LuEBwSnRxcQGgkQgs4wM729Zzo8WH14ePn/y4XefffX9d999/92333/3xXffffjdN/c/fT57sNEx2NPApcFRZTEFqSEZsYFpkf5pkQFpMf5p0d5JgC4hnvFBAWlhlUxCnYgDxyOTqwuautpV85MYqSCuKj+2IjufWovgkRlqfrupm9+vIkibUcJGOLUmEV8Bo9enc5vSeYxkDjWxmQjjNKa1UVNayFFNqCB8mT+22I9UGMWpLdCwafMD0FrGtamWhWHu4ph80ypeXcAP6fOlrUlsXBSzNrK5NlGIA0MtedYg2p4TbszwVya4S+Mda3OyzWXG5HCVRpopYsSxMZEMMO6jMmVNdcPypnkjd3VcvGYRLY3JV+e6t1dl60sUs6FI2pbcQoqho4DhSOCiS9QcqsUAPl1gnWBM9LVY+hUr84q1JbbFXKuR5ggYSSxsLLMumYcr0bRSp/o6ducVhyuyzVnxskW2Mafe3+jaXmNZhqvV7RncxsTm+sRWDFxGQw0pWUvD7Zsz8vVp6cqEam3WdLy38PTx0ruPh473ZNYZ1tgAVt9VKecXtjOz+eRMHi6N1wCUIsDAxLgUGSFD2ZijZRUYeEWDohJTR+lgR6lJUmGWVwzJKoeVNaNdtWPdtaPdyHF1/WQPdrqXMNNPnh1onNJTzHLaYDtzSMQalfHmjdKtha7D9Z7jrf7zfeP5AVD/+YEB0r7+bLf7eFN+sCreXeJtzbdtTDevTdJXxqmLI42LZsrCMHHeRJg3kRYglpCXR8krYySg5VGiTVDdOoJfGMLMm/BLZiL06jh5dYK8Mg4qoAW3NIxdHEYvmBCz/UXjXen9wlglI6yDHCrEBvMbAluRQa21AS3V/s1VAc2VfqwKf1ZFIKs8kFHqTysBCmKUhTIqgMKZlZHN5aFNxX6EvKQ2XK68OVvCSOSgg4n5UdSicEJWMCbDty7Ntx4eQanwRWW6VcTfLY5yKgKKdCyKciyOciyPulMV7VAdfbsy6nZJ2O2C0Nu5QQ5ZgY4Z/o4ZAU5A6aAMvAvMTVaQc1YgKF2yAt3gQa7ZQe45oW454W654b7Fif4V6VHYUlhTXRoNndKETqYiYdQ6SI11oDGVVp/BwGSycOksQhokfBoTn8oAwqU3k+E8OlzAgPOa4DxaXju7QiVC6TqJAzrW+KBkaVS1YOyc1sqHBVwFrpFdhqUV1JPzUECUPCQlD0svYgiRYgPHsGIwH053rg/hB+QNAxra9BhnabZtaZazNM1ZnOEsTLPnp1jzFsb0CGXciBlUI3qliB4hSickGMQMs4I/rZEv9qlXTZrVIfXKUOeKuXN1VLw4SBtWYXqlRJOWNTUqWJmTbC1LdlY6tpc7tqzizaWOTatkywrqUPs2EKDXsnxntXN/c/Txvb1vvnj/L7//9B9//eTf//D4D9/ufvGB5dGp4WRXu7cuX18SLkw1W/4/hYqDl7uzv4dHiJdXhJdntI9HTIBzRMCd0KBb4SFXI75DZPid6Mg7sdFOCXFOiQlAjonxjglxDnGxt6OiIMCAsT4ceJdgezDKAycRGADsy40A3+u20AgEEmBlAHKA6QkNusKJHcSGcIfoSMfYaMe4GMf4WHBDp4R4x/h4x7i4O7Gxd+LiHOJjQOOdhNg78TEOsdG3Y6JvxUTZR0U6AEXHOERFOQCqhYTYAdcCYAasFZD/lWXxuQVleQFcgVaCQVtqrlYY26L3wKNAIXqbKbma7oMy01ytUrPNj0FoueKHixPUDXI5rrbQC7TlHgqr2JwKNDkG/IqtG2RogO+BNuu4vePs/KaDw2s333n1nbeubMqrTjcBVOwBsEP9ApIiMhEldc1UspRPV0taelUtGpl2RPPR5/d++OnTn37zzY8/ffvtT8CvfHTw5MS0OEZXcGuY+BxsZUJlTlh2vH9qhF9KeFBmTFheUlRJZlxpdkpVXg62urQJU87A1bSQa1sp+I5mbEdLGYOQhChIqi/JoWNz6PispoZsOiabgU1vwqQ2YWCNDbAmTAqLkMqhpLRQEljEWAY2sqk+jILwJ5QFEEqDqGXxgvoKo4ixNAzGaNnuvHhjRrwOOXHh8gxmUJMtYcdzcBHMuig2OrmDXNovpCwY+VvT/I0p4eqkeG1avm2VbFoZ0+YKjTRVQI9pwQKWJAiw+eoW4lSPeGtGujXTMm/iLY11767oD7bADzfZ3Fsk56ZwSPHNqLhWdIaUUmuSsa0j8t0l1e6SdH1GtGQRWacla4ucmXGkTpEppCWwMbHNqBQhobJP1LIy2n26oTndlG/NgZ7i1VnlzopsfblpZKBMJUzjNSZxcElcTGFXM3lKD8inPLBKN6YES2PKzcWRy+OF9x5PP35gPNqTA4NoGaYO6TG9ytpucaUCPLRyijvYhe2MvHZajqgJLiCncXHJrfUJbch4bl2SGAcHDqafVzwoKh5sLx+WVo7Iq0aViLGu2gk1alyNtvRgZvoIc/2UBVPT4jBtzkg0KwmD7aRBMWVIzlswaY7WB+8djz46n3hyb+rp/dl3H8699wgI1Ecenw/cP9Kd73adbEoPloU7C62bM4zV8aaVsab1CSDS8sgVQiir45S1Ccr6hK202DRBXpsgrY2TQDcgUIEuJ8jrFkhrE4TVMcLKKBBuZbTBakYtDdXOGyune8smNcVj3YUjynyzLHdImj3YATe2wwfa4UZRppGf2Q/Ey+jjputbM3Stmbq2LF0rULauLUPTHC8nRrTXR4sxUcL6MG5NcEt5CBCrNIhZ5EvP96YW+DFKPCg5bsRMF0yqTenOmAwgJ0yqIyb1LiYNCLQ718OcUUl3EXHO1TF3K6OcK6NdgKpigFyvVBHtVh7lWh7tWgldulXFuCOSvOrS/PD5IZSSyKaKyKbyqKaKGFp1HKMmFoheHUtHxIOHDzYqCRI6oRmdwK6H1AyETmShE5vRSS0NSZz6JHZDagsRcKVaLavXq3AGJXu4Uz6l7pqQ8bqIWFp+PiIlvSIpqTQ54UplySkVyfnoDEIrQmLi66y9krkeklGK7lORRgcYM2OsOQtrbhKUzFkLY3acNj1CGjOi+jsROjFK304ckLDHlLxxhWhELB3iq0xt6mG+1izUTkh0M52mtaHBnSnNmpkz0V3fI6zVdNQbu/HmXsrEYNPUMH3KTJ8eoc+MgJI1Cz4IEnthomVhonXR0r46139xtPXlJ0///FvgVD7/zz9//O+/f/fPP9z7zdcH336y8fmzpQ+fTj697D87cPj/ECqO3m7uQZ6eYW6eUR4eMd7gydc1NtgxOswhOsoxPs4lOckrM9M/Pz+0oiICgYhC1gFF1iBDKyoDi8q9c/Lc0tKdEhPvxMTcjowAVsYuNBSyL8CLBAfeCLEpNAg6AwMgKgyiFOR7AKXiYtxSYL458MDSUnCrsMqq0PKqwOISn5w819T0u0nJTklJjsmJTsnJd5Nt9YR4hziAlljH+ASXpGSX1DSXtAyXtLS7ybA7sXH2YaH2wYFQBCgIit7bQwsHvO28oUwwtpVsoIQ2ctrC9W6AHBBL3KDNj7+g5WfZdmgCPLhCEXubXCCo2N4FyAHF6l94F8iUAK5cGRooHgMtdQPscb5219HmVN555Z03X7751kt277zidPNNV4ebPm63Az09ooNDMhLjS/PTaiuy6qrgyIocRHE1rlQgbxweV07NayZmNMZRVZdBypawqijV2aiiDGQhrLYgpiIjJDfBLy3SNzksODM2tjQjC1sBEMLtVXRPDfcuWLSzo+rpoZ7ZYcOyRTs/RpYLsnAVmfiK7CZ0JgWVQqxNISFSyEgYGZlEqUuioBKoqPgmdBwdE0tDRVKQIYTKAFyJL64wiFKeIibWjyjZKyNCAIn1CcHqhHxrqXNnnb88hezvzBAzYznYSDYqhteQqmwqN0lIcwb60lDL0rBobVKxs6Q52u7c2wA/6+WajvR2enwrNorbABMTC7QczGQPe32Cuz4hWrOothd7DjZ0h9uS9QXCkK5Q2ZbGJwMPkcLHFXSyyRadYt+qO9tU7Vvlm/Oy9bnOnTX55gptfLBCLc4U0ZNb8cmtmCwxBTkgaVsbU59u6M+2tYfrss0Fxc6y9mi7fW0Jb+4rUvLTeJQkDgYmxBdqOOTpPvHugvJoWbE7p9ya1x1u9p8ejD44Hzw97NxcFi5YGGODeGM3UiOuULaVSVilHYyyDnqFlFkpYwFVyJrL5exSeXOxrLlQ0pzdTk1vwyS11SWJGjI7qXm9LYUDvJKh9nKzrHJUgRjvrJvoRk9p66f1hPl+yuJA09IQfXmkeXWsZXmMu2pp35iTgH+Ek+3Rx+ez7z+yPn938+Pn+198fPzlZxdff3nvmy/Pvvl849MPZp497L9/2HW8Ltm3CncXeNuzzRuT9PUJGsSPcdIqsCOjABgAElcsaVy3UDcmG9cnKRsW0sYEeWOCBCpXXNmYpGxOkjcmSQAqoA4q0EvQqwRI47jVsYYVc711GL00hFo01S0MIOeNtXP9QAioNCBtJQJotheo2laCltq5vto5UNFXT6mLR+X5JlH+gDDPKIAbuPDe1qy+tsze1nQtK0lFjVOQY+XkWAU5XtEYJ2+Ml1MgSUlxHcQYMSFeQo7vIMWLiQliYqwYHy3CRPLqwltqIjk1URxEVGttFAeqR7YgItk1oc1VIc1VYeyasJaacE5NNA8d305IUzBy1W2FOkGJXlQ7oCBN6OlT/fQpA2WilzCmI0/0Nk0b6DP9jBlj42QvcUKHHVFjzOqG4W70kKp+SIkdVtYPyWr0/CIZK1/MKle1o/RKYn9n60h316y2a7yd3VFX3pABK0uOKYFFF8Gii2GRxbDoElhMaUpKZWoNpaitm9Y5pRJPdzcapXV6BW6or9EyTJ8ZY85NMObGAVFAvXFyGDesRxmU2AE5faxTPK+TTSsFxmamvJ7GrWa0VDVzEa2iera4oVmMblMSlCZh34K+a76ndVSO0fGqujlVam51jwipl6D6FOh+BcqoREPZkW3huiE1fkhDGNaQR3tY00O6o+21T58/+uOPn/zHnz77x58++o/fP/v7bx7/+fvLP3x9+psvDr7/dOPLD6Y/uO8Q6HPD0/VNp9sv3bz2qzf+uUC9g7e7a6CnV6SvV4yvR1yAW1ywS3ykGyzJNzc3vKYmkUDMZDYX8oSVUmWNUoPo6kF29dQoNJUSVSG/I4vREteADyqtcM/MdIpPuB0JjEskQMvN8NAbtujIzfBgO5vjAY23gTWJinCIjribmOCblxdRi8xiskraZZWyriq5ulLandcqSiY0hpYjvOC5bqnpLimprimpLikpLrAU56SkuwkJLjCYV052cEV5HBaXRqWnNzHjsPjAkjJ3WNqdsIhbwcF2QQG3/Pxu+frc8vGGMlG+2HQJ5AZ0hRZbCwQVwAwADygy7+YMJZJxc7kKLNmWkL04S8YO8ij/DZWryTHI6IBGWxj/qvEXMoHKNac7b96yf+3GO69cf/Nlu7dfuvXOK452b7k43PRyuR3o5RkbGpKZEldakIqozKqtzKopg1cX5dYUltSXVeGrqkmVFfjy4vriHER+Wml6QmFKTH5yVAEspigtqjg1vDApMDMWcCUwLSa2LLecSeANqKdPNvY+uNz/8OHGu5cLl4eLl4fWi0PzppWpllQw6itY9cWs+oJGBFAhra6UhSlrwWc11sY0FIc1FAZjiwKwxQHYQj9cgR+xMJxRlSFrrB9VtWxMtO/M8dYsrYtjwtVp2ba11WpB9CoyhPRYDiaSjY4T4DPVzVUjctycvnHeyFwYEqxMqHasupNd5d560+RgmbY9TUxP5BMThIQMJa1sQFw/pSUt9NOXBrnLo+L1Gc3BpuFsX7a5iB/WApxkiCgpAkJGB6Wyl0+fNyoOrIbLnb6zbfX+snpvuedoW7a1TJuAcAJvZyRziaBznoqJGVHxNyydx6vak3Xd0Zr2cA2wRH2wyVuaxpt6ihRt6QJqYhshvYNaZWhnLA3JDpc7j1dkOwvyrYXuvbWBy2PjxaEKeK/FKfbUCH3MSBjQ1GrFpYAcEgZK1UozKgSWXtm8STxtaBtTM4fl9MEOUh+vXstBdDZXyJnliuYSOTtX3JQhxGZ14PO66IW9LcUDvLJhcc2I/AoqDVMa7GwvecFIXTLRV8zNaxPcrWnx3qL8YEVzumW8fzj29HL+w6ebn310/u1XT3/zw4e//enj3/304W9/fPT9V/ufP196/nDi6VnfxbbycFm0O8vdmmndmmJvTjI2LI2rwJ2MQlBZHSWujhHXxgFFADNsmmrcmiJvWkibFuLGf4u0Odm4PU3enqJAmgZ1UJK3JqFumxbCxgTogwdoWRvDro5iV0YwyyNYq7nBOly/NFRvhYSxmhqspvoXGqxfgtRgHWwApU22lgH0Yj9qwYCe668DvJnR107rEVM9tUCT2qqJ7qoJdfW4pnoclN0142rEuBpp0dZZeuomtNByqQldvaUHNaEFl8jRrppBGaKvvc4grjfK6gdlSGNHpV5QrOEWdLVmK5vhCmauig2U18Up6OIWa4RlvR0V/fKaAVXDiI41PwqeMPrPdwcv9g2nu/qzXcPF/uCDw9FHJ2OPT0cenw4/ODbeO+q/PBy8fzL88Nj88Hj40dHgwwPwNCNancAPdFV1imrVEkyfijmslk5pOiflAi0FRcvLrElJLIPFlQGWwGJLU+LLYLCK1Fx0VkNLtaCPLbcoeBNdJKMC3deJH+6jjA9SJ4eBk2DMjALRpyGo4Ed68SY1c6xHutSvWdXJx/nNSmwDs6QYn5ODzsmsy8pEZWWi4bn1ORWEQkIrsr2HpZ1WapZ0HfO61lld84yOOdvLnO1rnutvXhhkL5pY80DD7IXhNusI1zrSsmhqnhsQrlhM9w62v/7kyV9++9G///H533/78PdfH33/0eaX7y1/9mj+w3vTzy5GHx/3nW/dDvC+7u78huOtl27801Cx93K77e/tFOTrHBbgHBnsAYsLKixIxDTAWewqWSemd7Bx2MKyzLfNr3AXVlvnVrhzq7z5Vc6MlT4+gzcMV8m18GZeZB3OJ68I8g3xcbeiI+2jIgBCAEhuRQD7EgFIczsq6k5M1N34WJfEeO+crAQcplgkxhtNnOklwcK6aGmLO7dGNI5VyzVwRltoZa13To5bVqZHZoZ7RrprKqBLigvkbHJi0Gg4qwWh6iINjGD7hkvFShiF6V9cficuDpp/Cw6whyIrXvbetjXNYKC3ZZa80n/j5CpGYktTBrgCcAL5DChJjC3NpYctbn8Vur/yNFdvgSa+oGMir+wLlMPfRpSfoQKxB3S45njnTXu7V2+88zIElbdeuXX9NSf7t9wd7fzcnSL8A1OjY4tzMpBVxSRMLZPS0Eon8llNktbWLrFAK+NqJC1dInJ7cw21Iae2OKkoIwqeEJYVG56bEJ6fFF4IC8qBzIofLCIsLymXiFCN9937+MG7P3304IfPtj58Ont5MH64Mrgxze1XoLkkXEcjWy8UDmtUkwbjyrjlcHn+fGvxct+wPo1R81IE2NhWVDinNrKtLkGEK9BxyLO9/K1JwdYMwEnb0jjPOsm1TlEtxnJtO4xPiWlBx3LqkiXEwl5e3WQ3YcFAmjfQFwaEa5Pqww39xYF8Z4VsMRZrROkd9EQhCdZBztG2oCa66CvDYDBtnAc/+qaOrVnd6W7f+UH72hxuSFuo4maIaRkdjQWdrIYRBSBE98ma5mS9a9/avW/V7K/0HG2qdtfolsFKbUe2hJkmpGRKqGV6XuOMXn6w2Hd/D4y2ADw9Rxt9Fwe64z1g9jFD2nwlN6O9Ka2dkqNk1Zs7uRuT3WdbmvMtzfFa1/5K9/66/mRPd7InXptvnh6jTwxRxwbJw331vZ0VSn6xmIbubG2f6hs+WJ69vzd1sWXandVaTbIptWSyWzGtVc3ppXN63kRno0HQ0N3S0N1a19lWLaeXSyiVKlqVjlVtaKkd4KOG2jEjUuyoAj/RSZnS0sDv/4KxxWpqWxsVb03Kd+e6Dpd7TzfNDw6n37238tG7R19/8t5vf/jwdz88+e6Lk0+fLjw+GDyxandnVFuTii2LdMsi3hznrY22rI02r4wwVkdoq2byipmwMkxaHQEignJjDPgS4DzIACpbEDMggcomoMsk+aplZ6Zxd7ZxFyqpe7NUUIL6zixop+yAVyHeAPBA9gWCkM2+rI0DQZiBNPqzRjBAqyPYVTN2dQSzYsasDjesDjWsmNDWQbTVhF4aBILAAyoLA+h5A3reWL9gxC6ZiNaRppUJxtokbWWctjLGXLM0b0xxtqYBL9u2pls3bFqf4qxNMJfN1Ok+8pCyySTnjHbzJ7StY5omkxTZxS4QkdLZaBgDmURDwmioVGZ9BhubycHD28i5gqY8IaOwnVUqa8XoZK0TRtnCqNI6IV+ekC1Pda7P63asfftrhoM1w+Fa78Gabn9Vd7DWf7RlOtszX+6NPgDO9dB0ude1u8QY7a2StZWImqvlAnRnO80gFZjlYpOQ00nCtVRUUwqL8XkFDdl5DdnFhLyaplKiEN3aw+wYlUkmtexRDdmkIZsNTeMmxuQQY9rMtIk1M9o8O8qcHgUPSYzxXu60QbVq6lntkQy3UEQ1JYTsNGR2PCI7DqgGHleTFYeApyJzyvAltHa8yizuXerVb45qdqc1BwvaI2v30WL34ZL6eEV9stp9sg4eU7TntoXFl1vas/We03Xz45P1z58/+MMPH/3Hnz75xx/f/+uPgCgz759qj5ZUe3PAr8uA9uY7dmZv+Xu943739Tv2v77x9q9ef+Wf2lFv5+V+K8D3Tmiga1S4d2Z6DLoun9NWq+omm0ZbZpYE1k3u4hpn1sqZWWJa5pgT02wAmFkrf2lNYN0AmKGPzTXoTCXtypRGVkhFrVs63Ckx0SH2Kv4RdSsGioXciY11jI27Gx/vkpTgnpoSUlacw2rG9Rpa5xYV24fqw3Pt0UXn3hl/cZ08OA4sSwqZFlxe6Zub7wPP9oHDvbKyPLIyvLOyQsvLs5hMpFrNnpmTbOzxrVt4gzm3RRhUXnUnPt4+NAhaBRDoC61p9vGAsgAAQvyMk/+NKLYpLAAV0HjlTq58xhVOIKL87Dxss2HQW168y7b0y/Ze21oy28EwoAREsYdWkdmgApwKgMr1ay/deOsl+7dfvmOb+/J1cwz29UmMii+CF2Br8cI2waDesDIztrs+c7q/9vD06Pmjs4/fO3n+ZPXe8ejmomy4l9zBLSDUxRdlhWXGh2fFheXEh+YnBucm+qZGeieGeCcGBaWHpZQlYWhVNBEWw8VXMlAllIpSSnkesSS9LicDlVvejGT3S6Yvdy6//ei9337z/I/ff/rnH7/42+/f/f03PXszFf38HC07Xc3M1DbnG3l1lm7a/EDb8rhgbRqM+G1L01hTT56sNYlLimxBR7fVpcrI1WYJY22EvWlhLJvZy2bJ9rzubKfvYl+6bSWMGYq6BRkSOqydnK6gFffzCfP61p1J3s4Md8MiWJ+U7lq7j7e1x9vitVmcWVus5sLl9BwlvdIgoC/2y4+XdPe2defrvWebfWc7+uNt4NlVuytNE8ZKrThXysrqoOZ1MWtNUvbysPpio+/BXu/Fpv5ko+90e+DiyHB+2LG2gB3UFiu5mR2MTCmtRM9rmjcqT9d7Hx7qzjd1JxvqQ8CqjZ7j3e79jbaFScroAH5Ijx3QEYcMpOF+TL+6Qiks6WA2aLi6jQnr44Ole9sTe3N91iH94mDf4oBxxWTeHLPsz86cLs2cWi3Hi8aNMZlFzekXs/uETH0bXcum6Zrp/RyasY02wKWb+HSTgGESsYbFLaNSzriSN9klnNZ2zPVJl4yK5cGu9dHe3emhY+vE+cbSk+Ojz54ef/J45fHB6OGCYcvSs2bWrpo1a+bOleHOlSHFskm2MiReGeIvD7KXjIwlI2Wpn2A14pYH8MuD+JUh/KoZQIW8NUHZniRvT1J2pn4RGaKFjSL78017kKh7c1DFdknbX2jaW6BBFagFYAZ0tr0FeheokIC2p4hbk/itCfyWBbc5gYc0jtsYwwKt/6yNMahlfQS7BgBjblgZvhIGaHkIuzKMXzGTVseaNqc4e0uik/WOo1Xh3hJvZ05yuNp9saO/f2B8dGJ6ctr/4NDw4LDv/oEGmLMDq2BltG1K32ZSKix6zbypa7afP6zCK5hFTERSfW50dWZUZWZMdU5cbUEiugyGrUzBI9LItRkUVCYFndWIgdNwBc2kEk5jGZdWwWdWidi1Em6dnFev5NcrhSg5v07Gq5XxkDJ+Q5eY1CNv7FM1Gbpoxm4gUq8cIW3JYxPzWMSilqZSHqNSSK+XNVO7ORx9a5uumaNlcdTMli46u5PG6qRxtGzBYId8Rte1Ota9MdO1tdS9u6Y53NIf7+iOd7oPNzv31jv31tSHW7qTbcP5Xu/ptvZgpXN7umt9pGdVrxgT0KSockp+BionqTY7vjY3DgGUnVCbnQZ+lynlVDFJapIYVoaHjpZHL/fG7x+N3D8cPN8xnGzoTtZ6jtf0J+v9ZxvGi03j2Xr/6VrvybrxYm/u2cOTH758/m9/+OK//vzFf/3p+d9/Ov3hk4Vn9wbOoU9X71m7d62ag1Xt0fotP89rbk4AKv963Zam5V//mR31vt4OwUFucTFB+XlpJHKlWEo0mgFF+MsbPOs6a3KBPDhWrzVUSZXl7ZLydnGNRF7fpaEMmNiWGd7imnBlu3VulTJkqVLqsli8SESDe1auY2KSAxRpj7sDReDjnRISHBMSnJISXFKSveCZMbW1pUIRdWhEtLzetXui3j9T752o987kWwct01ZM33CxUJaAowSXVfjlF/rl5Plm5/jm5gYWFSQ2NJTwBZSh4fblNdXOkWBpk9BnhrPa/IpLb8fFQGGbwADbuoCfuWJDwi/637hiQwKo2+a7IORAlatd9z+7FogotuQuv2QGs7Mt8brhZjv3Hqr8HJUBPYEx8vC44eJyzdHhDbubr9649vLNt1++df11F4fr3i53Qn28YsNiCzJrGBRer7Z/dWnl3cenX3/+8IdvwfPpF3/5/Q///pff/vvffvP3v339x988/OK59fKwb2mapurIwyJiC9Ii4PFh2fFheYmhuUkBGTGAKB6xfp5xAd4JQb6wkKD00NDMiIic6MSq9JpWYk0bObWuILEyIw2VUyci7H1y9v4fv/nor7/76G8/fva333z+198ef/4+xSjNFBKSuLhYLhYoWUDIkDSV94iaJgdaFy31g5osSXNcKy6Sg4rmoTI6m+otXdzdadnJuuTAKtmb6wRP2ed7+rMDyZaVMKIr7OJmSugpssasblblsJSxZm4/Wmw/WhLvL6gOl7XHmz2nO52H6xzrOMrUBXCSq2LmqZtrhqSta6Pdl5uGR/s9Fxvdh1b96ebQw9PxJ/f0Jzv0ycFqvbSgk5OrZJb0tOEmujv2ZgF4DPehjV36E0CU7f7Lw/6LQ+nGIsakLQA4kdLhcmbNgKR51dx9vmF8cjL05Mxwua892uw92zfeO1XsrIEnRILZ0DCgRfdrUP2aemMPfqgXN6BDaGXF0haEki2e0Y8fzY8dzBqsJoN1cHR7cvbYunS2tvFwZ//ZydnH9y4/fXjx6YOTjy7XHu2M7011TnXLR6QKs0QOSSw1t3cMt7cPi8DzbLuJB2lYKB4WSYbbpSMdijG5yqJUWTq7prrVsxrtvL7PahxcHTZvjVv2Zka2J0wbI8Pbk5ajhfmL9cXLzfmLDVCZPludOLaOHM4bd6fVm+Oy1SHukpG+oCct9OGs/fhlI3ZlEL82RNwcJW1CUAEgoe7abMfOFGRK9mYgU7I/23QwTz9cZB4tMQ4XGUdLoMI8tr7QERDUQjtcaDqAetKAAGP25hr3Zsl7cxRQAt7sgnKGvDtN3Jkibk8RticJO5P4bQvOxhtwCYFncwKAB7sxjt8Yx62PgZKwMU4BXwwIGKmVUeryaOvGVPvOPH99snVlXLg+Jd2Zl+8sdB1Ye45WdUdr+uO1nqM19b61Y2OMO9PLGVZxTTL1nMG4NjKwNqJdGOAPyTHtjbmkikQw7FblxSOgg21ScdXpRGQmuQFOxeXSiXkMkk3kPGZjfjO1kE0rbWWVtbEqeGygMl5zSRuzuJVR2EIvYDdB4tCK2+ilXGYZ9CqrjM8q57FKWmlFbEoBg5RPx+fRcHnUhgIqupiGRnBJTV1tggGZclzdNanpmlTLx1Ttwwr+qFpmnTCc7Ew8vTf7/OncR+8tfPz+0qcfLH32wcIn789+9O7cR+9aP322/tVHO999uvf952tfPre8ez50vtm3O61dNkjHxHQlsY6DKKWW5BELs/FF2bjCQlJpJb0aw8PRu1okYxrNiqV3f33kwdnkuw8s7z4YeXhmunfUe7Lbc7DRubUE1L292L01370x17k+27O/Ovf+w9Mfv3r+H3/4/L/+/Nn//OOzv/1w8dMX1g8fD4MR5nhHu7fZvbOu3FyWri7a+3q87eL4moPdv77z1q9eA1B5AYj/93/+x/+4HejvEhsdWlaS2kStVnQ1mccFS+sAFZyZFaJxFKHqyWsVwAiUGGRdSEVVWGVVLLI2lUQs4nHRmh766Dhvztqxsg1MRuPQZKVMk0ZrCapAuKRlOiUlAYo4JcWD8m5y4l1YknNKsltain9BfjKJWC1X0scm21e3FbtHgCXS9V3F1oFy+1CwvMmanG/oMea3ihIwBCh6X1jsn18QWFgQVlmeTm1EyJXMMYt0c1e+c8SZW8XoTBk0tldu4e2Y2FsRoXbBQTeD/KE9lX7e9j6QU7npcbWmC9r8CC3Qssm2EfJFurMXghqhQ71sx3m9wAmQ7SUXyKNA676cIMsC3c3WaFsqdsP1LgQqqDOUqhk4lbcdbr8OoAKIYn/tVYebb7s52gd6usQE+6fFZKGrSTKR2GwaOdzf/vDD488/u//t15/+5Xe/+cff/vqf//aP//zHv/3nP/7yn//2w99+f/7R49GtJb5RXcMmpyOKYgvTInOTwnMTIwqSQ3KTg7Li/TNi/FMjA9IigjKiQnITIgqSYktSy1gY+ewIb3SwgI5LrMlLx5aVsBpIXRzJRI920dSzMqyYM3CGlOUCSjqjPoPXlC1uThcxU4SM9HYWXNaapxLkKbjpIloMBxvZgozhozLUdMxcT8fJUs+jPd2Dva7TddXRuvZsR3e+L1yfQ5nUeZ3cDCk9Xd6Uo22pGVfRNs3tJwuKszXlyVonGBoudvsfnKiPdxizw4h+ZZGam9fJKu7hoEbknI1R7YOdgXePBx/u9Z1v957tDNw7GnlyoT/d5cyNovrlpWpuibqlur+dMquXHS30PdoffHIy+Oig/2Jn4PJg5NHl4IMz8foidkhd3MXLVjTnd3GQQ4rm1RHNvZ2BpyeDj0/67+0bLg4Mlyd954eyreXm2VHsUB+qX1tr6Eb0diMNakAUrKmXMGzA9GsruzvKZRyCvr1rxTSwZdbO6bSzveatyZX723vvHl98cu/JV0/e/+7Z8x8//vCnTz744aP3vvvg7OP7K/c3jCvGnuku7aRKa1FqJ5SacUX3uLxrVNI1Iu40t0MaEXeZO7rHpOoxqXZC0WNR6ixdvdNdfTPd/XPagQW9ydo3ZB0YXjYNrw6Nb1msF+t775+efvzo/JNHpx8Det07/vDy6PnF/rOzjaeHsxfrA3szypUh9mwPZbYHD7iy0o9dHcStDxM2R8nbFsouAMk0dW+maW+GYhOgQtPBHBDjcIFxtMg8XmIdL7NOllmnUMk+XWk+XQEl+wxSy9kq+wxqhDoAHS8zj5cZx8v0IyvtyEo/XKIfLQIyAfZQD+YbD+aAKDaRD2bJB3Okg1nS/jRxd4q0O03amybtzgCjA9hG3J7Erg4jZ3tLhxWVg0ripLHFOinaWJBsLnZsLnRszIvX5jrW56Qb86odq2pnWQkalyeZ41r6gKRR08IflPUtDU0dzC+dry6cLQ9vjgsGOhCchkxMMQxdAnCSTakvZJGK2Y3FLY2lbcCRtNV08CsFLQAklYJWhETY0Ckna7vJWg1B043plCGkwioxHyFpR8klaIUUpZDUKTrqZO2IDgECtHcACZFSEVIiqBa1VfLZ5a30Eha5sLGhgFRXSK5DcChUJY9nVMotveq5wa4Zg8jcye4T0/uk7AmDctvaf75nfnhifng68ujM/OR89N3L0acXo08ux9+9N/Xs0eIn765++cHm1x+tffV89vnDofuHvUdrppM14/6MbmVQblFz+0UsTQtJ1oQTk4BICjpN08bul7YMaVonBztW54CPH7w8Hrx3bDjb1x5sqjaXOsC/5/yIeGFUZh1Xr0/3bM7qtub791cmH5xsf/bs4e+/fe+vv3n3z9+d//Dp+idPp59cjj08H3t4Ybo86T3a697dUG2u3vR2e8v5ziv21//l2hu/evWlf+I8lV//2iUmOrSkKINKRSi7aaNTQuumYGmDZZnH9ppKhfJkMi2kstovv8AnO8cnG+6TA/fNgwcWF8TW1xW0tdartfSR8fbl9Y61Hc70Clo7kNvaHo3GeeUU3IWlQCwBRIEluaQmA4/imprimZURWlkGb2bW9+ibp+Y6Nnalm/ui5U3h0prIuiFe3pKs7QmtWzTzdK1Kn9PMi0Vhw8orQ0tKw0pKYlF1+a0cnK63dWZeuXMo2z5kWRar5dpkAtUjM9chNs4+MhzaPgltwveFtvd7e970dLvuDpwEGP2hhGA3XgTtbVCBdqhAgRPgVyCrAWUTACyB0tVcpdC3AQlaavyiw9ViMCiZ2NX8mI0owOtcxWygnm4vNqk43Hrt5g0IKreuvepkd83N8XaQl2t0iB8sOqWmGMVjMHpUIrNROTWmmbOYVqbPnt//w7//4T/+6z/+5//8n//xX//5t//5b1/9/tvNy/2+uRG6ur2mlZKDq0ooy4nMSw7Njg3PT4wpTU+sKUxDlWagSjJQxQXE6mIqqoxeX8XGN7QzaBpRY7eokNaQ0lCa1VSXzyHnNhNygYVvIee1NuZwKNkcckYLOb2VliVkZLYz09shqCTxaYltlJhmbDirLqy5NlrYkN3Tgl/oFR0tdF1sau/taC921afbmrNd1cE6a3YE0afI7eRkyGmZKlpeL7fa0kkCw9zupOhwEYBHd7Hbd++w796R8mCdOW+u7ZcXaXgFGk5pHx9rUQv35rQPtvse7g0+3Dc92Dc9PDY/Pjc/PO86WGueNdcPdFbo+OW9/LoRBWtluPNizfAY4OTI9ODIcLE3cP9k5Mm9/nvH7euz+GFNqVqQp2KXaLnAx/B3ptT3dvoeA/Cc9t87NFweDj447b88Ve6tM2fGGob0tf3dNfrOar2qqgcSQt9dZ9RhhvsAV9D92qrujgp5K7ZHKJ7u6ZzXq6Z79EuDs6fLB8/OHnzx+Nl3H3z620+//NPXX/35G1B++rvPAVoefvl46/HO6IYZsKEPQGK6q3eyEzBDOy7XjEk1Y2L1iFgzKoEEcDIu007IdRYl6NM31W2YUQ/MaU0LuqHF3mGrwbwyYF41jW2MzBzM7jw5uPj08ZNvnz/99vmTr99/oW8gPfjyycHz85nLdf0meITXUia7sLNa/LIBtzZI3DCTNkcpO5bG3UlgU2h7M5AO5hkH88zDBebhYvPRUvPxEvt0ueV0peV0lXO61nK2xjlbbz1fb71Yb7vcAOLe2wTi3dsELZzL9ZbLdfb5evPZKhPodJV1VTlfYZwt006ttBMr/dRKP1umn1lpp0tATadL1JPFxuNFytEC5QhgZo68PwfQQti2oFYHSy1d2X0iuIZf0a9iLkyIN5dF6wv8JQt3cYy3NM5fmuAvTYqWp6Vr84qNBfHShGB2sMXcTdXz6TquwtIzvjO3+XD36Nnp0fsni6dLqvHuej4+l1CZRajNpeFrBK0Nyo4GFSBEO7ZbSR8wMEzGxl4t1CLvwGu7mMOmNoulfW5OvDDXZhmlGvU4jYqs72EOD3EmxriTE6BsGTXT+nvxalWDSkbUqulGA2Own97f22TQNfZqcN3SWiG7rJlU1UKmyLntg92aWZPeatYvDqtnje1mFU3LQ0uZCHkLStOO61USB9QkKNeOjmLWU0YNtDEjY3yAaRlqnja3LYwJV6Y71melG3Ptq9PC5UnJ+lznrrX3eM18sTtxf3/sYrNve1o+P8A1dzON8kaDlNQnw/fKMH0q7ICGYNKRzYbGEQNxSI8xdNbrZfU9IlyPkNIrZA1I2ifUXfPG3rXRwe2p4b35qYvt9fcvDj5/b/+Ld1c/uBi92NZsLYgXLILZSe7MRItlhD4ySBroxeg1N6DEXz+vJ37l/zzx/f/41UsvB+bnppDJNXIldXi8bX6FZ91gjs1hegaLeOIEDCmwpMwnJ9cHDv9ZWT7wTJ9ceHBpcSqBUC4U4XoNrMkZoXW9bW6NMDBW2q5KIjT5FpS6pqU7J8NckpOckwFRYK4pMI+MVN+8nGhUbQGXS+g3ts0vd6zv8hbXmRMz1OFRunmCNTHDX1xrX91rnVslQcEVdRqVGY1AhZVVhldWJhHwZaJ2smGQO7+s3DkSre5QhieLhbIoFNY1PdMhNtYuElp1Zssf4welp7zKEAPlRX4BFYCNF8nBrnK32KBy0zYJBkHlapukx4uXrogCBIHHFlOxdXvRDjUCqNgubRByv+np/o6b89t377x52/41u3desQNQsa37cnOw83a7G+LjGRsSkpmQWJEPb0AUkBqKgYh1iEZ0m7Jtbt3y4P3jT756+sk3H1y8dzS2YhbpRFgepZyGzidVZaCLoU0quUlB8NjQnPj4Snh+I7JO2Ejt4snGdL3LkwMb872r00DG9ZnhzXnt/AhW0pJLRebQGwrZhFwmFs5ogDPrs5qxmWxcZispvZUEayUmc8gJraRYDiG6GRfBRIfSEKEMRKwAm6PjEJf620+WpcBwnK0pjlZUB2vdRxvy3dXGqcFSnRiubM6U07PUzQVGUe2khrhsZGyMtG5PKo5X9Rd7xvvH/fePZPvLpMmB6n5ZoYZb0NNWPtCOn9OJj+cNTw+Gn52bnoA++6ZHECFMD05V+6v0GVP9UGeNQVxjbMdPqgU70/qHe+b3L0beOzc/OR1+eDL04Bhwovtwi2u1YEzdZVpBkYZbZRBT5no7jqzaB3v6RwfAygw9Ohl8eGp6eN5/cSLfXqPNjGGG+mp6u8u1ihKNrEQtK+6WlKml5Wp5JeBKrxpl1ALvUtfbXaORVisFSDWP0itqGZSKR7v6V0dWH+1efPbo/e+ef/r7z7/+yzff/f2H7//+49d/+e7zP3756e++ePL1e3tP9ye3RgfnewZmNcaZbiNAy6Syz6LQT8h0Y9KeMYl2VAJK3bgUtPROyMFLhimVYabTOKsenNcNLfWalwyjy8ax1cHRNdPk9jh4Ej9+fvn46w+eff/JB99//P53wBJ9+MEPH3/0m88++c1nwCHd//LJ2pP9gd0p/rSmcVyJm+oigX//rXHO3jT3cI53tMA/WeKdWnmny4KzVeH5muB8XXSxITrfEJ6vt19utt/bEt/bEd/bFV/utN/bbr+3I7q/KwR6sCt6uNv+cE/4cE/wYId/f7vt3ibn3mbLxSb7YoN1vs48X78qmedrjPNV2ukK7WyFfgZKAJhlUF7Vm86s1FNrI4SWhcajRfLBPGl/pmFrtHRWm24Uwro4WZ38Mp0M2d+N7FMieyQVnfxyRWuplF3SwQIqFbPKO5orOpgV4qYibkMpB1knInD6RP3L5uWLjZPnZw8+f3j20dnc8ZzELEe01OcSanIo6LJWBkndyRoaaB4eZJr6OSPDUuuSdNkqnJ2EYNAlo+i0oLF9dkazuanb3+vcWBfNzoD+nNER4fycdGVFtb4usy4KZ6Y4o8PUfn2jXtsybBJMT4nm5zrm50QzU6LZaf7UOGOgB6cQkhR88ZBmYGV8+sg6c7g0smXpWTCKh+SNnc0IPrGkFV/Co5SKmZUyblWnsLpLVAXULa7WSBFaea1OUdurQhu60UZNvVGN7gfqBiXW1MOcMncfbEy993j10w9WPnlv/OGxfG2aPtaHG1A3GDpRfSrwRoReCYTUq+p6O2t7lJWdogp5G0LZ0tDZQtW2tfbyJIPtqlGZdrKzf0E/ZDWOrQ9ZdiemD+dmz1emzpaNO1P8cQ1WI6xVCZFdEqRaVtstrVKJSyX8fEHLVUAFitL/U0u//se//OrlV5IbGsrbOxoHzWCwFi1vNU8uYvVDhdyOJDwlqLjUNyfXG57jnQ33ggNlg9ITngVKv/y8aER1NotV162mmEZ488vc+VXK0GRZR2dqIyuopNI9I8sFluICg0FlSop7aop3VmZAUX4yAVclkTQNjwgWVyWrO81TVlyfCdXdg1L34PsGODPz0s098foea2oJpxsqbGtPwBDCqxBRCEQGralaJm80jQCASTZ2W+fWcH3mPI4otArpnJrmEBdrHx15MyL0ZogtfYsfgIrnVaweWpcFnTYGHU8JLSm2JTq7godtQswWZXlhZaA9ktBcGcSVX3Q1P2bLuPyzIKcCShtgoJPEIFfkft3NBUDljVs3X735Aiov37kJJWjxdrkd4OUeHRKYnhRXlp9WVwVvqM1pQOTVVxZhKsqJVcimOhwbS+WTgOoZyDJsaU5dQVpNLqwyO6UqO6kqO648M7wQFpwdH5qXGF8FL2djW/tk/WuW5UfHW88e7XzwePP9B9vPHu9+8mzvo/cmjrc4BgWCR8BJ6WwDn6rmIIQkhLARLWPViOk5LZjEppqYpqqopprwpupgWmUQrSKcVRMnxBYb+GTrYOvujPBwQX6yojxaleytiHeXhRvzhHFDsUaYKWemKxjZGk6JSYyc0RKWB+hrI7zdGSVwJ/cPjI/PBh6dyfZXKJOG6n5JsY5frBdUmDrw83rR8bzu8d7ge6dj71+Mv3c59t79sfceDDw4ku9aadNGgBPkgKTerGxa6JceLvQ/ORr54HL02cXou2fDj46HH50MPzpTH28xZ4frjIpynbBMz68bkrFXRzov1vvfPTG9dzb05MR4/3Do0fnY+4+ND89EG1aKZbjepKsxdJdqFYVdkvxOMVCesj1PKSpQiYs7AVpkVVp5jV5Zo1NW98hrtNJqdUd1txCp4hK7efxB+dCGZfu9o8dfP/vop0+++NPX3/79hx/+7Segb//2w1d//vbTP3z59JtnB+8eTG2NDS/qTPPawTnN4CzgirJ/StE3Ke+bUOjHpPoxCSj7xqWGCWm/RdY/KR+YUg7MqExz6qH5nuHF3hGrYWzFOL5qmtgYnjuY2X2yf/n5k/e++/jDnz778MdPn//4CdDHv/v8sz989cWfvgJse/rts51nx8N70wIAlWEpaVLdtjOlf7Q/9uH96U8ez336FGj+s3cXPn/P+sX71s+fLX4BaeHz9+dtgipfPJv9/P2Zz94Dmv7sXcun745/8mT048dAYx8/Gvr4Ye+zc9mTA8GDF1wBUGm+WGfZBKBCv1ijX6zSzldtlbWm8xXq2XLjqRUqIVmp58tUCC3AtSwB10I8mEVtjORPdyfrucngcUTZBpe3wriNMDY+vYVY2E6vlDMKBYQsZnUKpSiJmJdMLEijFKVT8jNIuQVNpQ0dpA5z5/jOzM7T/YefP3rvm/cuP72YPZ7h9glKachcAiKPiqkStND79cIpi2RhVjo7o1pZ1h8c9OzvyZYXqAYtRtWB65RRerqbBwyS+TnN9nbX1mbH0jxnzCyYmpQvL6s3N7tWl8XTE/yx4WaTgWbQMYx94G6KFatyfVVhXZIvzClXrBB1Jsfo+q5mnVwzPTB7aN16erD9aGfpfHlka1wx1klTMRGt6EJaVS4DmdOCzWujFArphWIAS3ZxB7tUCtjZWiLjlqtEVRoZsq+rztANBCooQ3d9v4Yy1t+5t7748bvHP3199OOXcx/cV20vkEd06P4uQBEkwIlOWdOjQGgVCJ0C0SOv7BaXy3kIJQfbzaHr2oQD/PZeFldFYIqQNF41U1DHFtW3ibF8KUGqae4bVw0uGvQLfe0jSoKaUyam5vMpcG5jVislrZmYRMPGUeredrnz6q0bLwIq/+dLv/7117965dVCNgfXY2iZXBStbHFmlvGGUWjWi0gNq6j2ycnzzoJ7w7MBV3yybXSxccUbWJac7NDykjRqY3mHhGAcap1Z4i6sEQfHKiTdACqBZZWumVnOKSlAgChuqanumek+WfCQsrIsBr2us5sxahEtbwqsm43DFoRKUyxoL+QLquTyJvOojRl7wuUt5tgsslOXyWDHojEJDZi8lhZUt4Y1MS3b2JFs7LOmFht6BrIYnMDiCmdYKoCKHZTKJfRmUNDNgICbfn5QNn5PT1taM+hMYqArtFzRBeKH7cBKSIANEFHcfklpDO1TsQHm6lWIHzbZ/Rzqf4EiG1FueXtd5Ui+4ery9osoPYDKNcipONm/4eZ408fVKdTXDxYXUwhPRVYWEDHlNFItu6me00TgM6hiDkvB52okbVoJQ8GtZxOLsVUZ1QWJJZmxBbCYQlhsaVp0eXpESVpobnJwTmJseUYJq4FnUg/uLs3fP15+dLH44Gzm4mjh/unM5cn48a7E0t/UxSMrWJ2TvaP78yO7M0NbFsuRde3do9X3zjQbMwhNK6yDmCDCAZDEtuNgKlrpsISyMsw9WOg4XpEdr0oOlyX7K/KDdd7qdJ2pO1fVli5jpMrp2dq2shEZel5PWhloWjXxd6fVl9t9D48HH1/0Pzhu31ogjPdVGyUlvYKSflHNiJK8NMDbn5Gerajvbw08OR56cjby9HL8/YemR+fSncWmaSNupBtjVhInNZx1c8+D7ZEP7lk+fDj5/KHl2YOxp5cjTy5Bz67DTebcMMrUWdUHfEwHdlzN2RhVnq8a3z0ee3Zv/L17oKf5yeXw48u+yyPx1jJt2owZ7q3pV5frVcVqKWBJrlKUrRDBZQK4lA9Jxs+VC/MUogKlqFjVXtLZXt7dUaWRQFzpEiHkrThli9CkGtmd3n92+vS75x8Dm/LX777/t59+/PffAH33tx++/uv3n//xq6ffvn/03uHs7vjIUt8wIMSc1jTbPTjdaZxS9U8pDRY5YEnfhI0oFplhUm6YlPZPyYzTisHZzqHZbvO8xryoG7X2ji8bLKsD05uja+crp88vnn7zwYe/+ezj3wJ9DnDyye+++OKPX3/z1++/+dt3X/z5q2c/PD94fmrenxZOgYe5jsZJrfTYOv7Rw+3vPzv53Tcnf/j2+PffnP/hmwd//u7RX354+JefgB785cf7f36hB3/+6T6kHy//9MPFn344+9MPJ3/47vgP3539/ruLP3x/70/fX/zx250fPxv79Iny8SH3wTb7/kbzPaB15uU643KNbhPjHijXGfc3aPfWIV2u2bRKvVgBagRQObc2ni2RTxeJh3P4/WnE+lCepTNJ15aoYKcp2lI72DEsbDixPJmGqlW11mtaa+WUHGYFjJCbhMtKwqan4NNS8Onp+IzCpmK8lKqy9MyfLJ89P3/23fvPf/rg/meXM4eTbA27qBGRTajNb8JUC9g0Q49oyqJcWdHtbA+cnA7fuzdwdqxcWQTthG45gAq+S0bVa4XTU8r1ddXmhmRpgWcZkyzMq3e2jcdHht0d+eKcYGKUPWRsMuiah4zS+XnF6opi1doxNyWcHJXMTolnp7hjQ1StjNUj1c+brecbZx9enH94uv14Z/ZoXjvbw1SzEGxEHrkwm1wEbyzLaqzMotbAGcjcFkxhG6lUSCsV0vO51AI+vaijpbJTDNhgQ4sa1a/GDPTQJ4e0xzvrX358/48/Xv7+27VPnvTsL1NG9Kj+rlrgS/SQTanRKYBqdZ2IHmWVWgJ+XPE9QqZBJDK1dxg4LDEaSyuoqE8vrE3LR6QX1GYWIuGl6Gw0sYjRhhJrWeoxeddkF39ETukXotQtlcrmcjmrRMbIa6fCBaSrua8XAZX/8yj9r1/61auvIWRKmnmCP7fKW1i3BTN0WfTW8Ko639x8zyzgS648Ctw7Oxv4FR8Alaws76wsn2x4cEkxjESoAFDpH+JML7CnrQTjaKlYmUxs8issdc3IhPYtpsAAV9xTU90y0n1y4NG1iHwOB6PVMcemBYtrbXPLxH5zRbsMTmelkilFXC6+t69leq5jDcJGKyBc7zAgXBqFnkZpKuYJMD29LVPzcmBl1nYYE7OITl0KieabV+icnOwQF20XFQ6gYhcSaB/gB6WYhNJ//ZIoE9IvYRKbbPNdoPF/OW8f8i4vFh9DlSs3c4WTq3fZAYr8r6uToXt6QPlgvDxveri/4+IMpSh+AZV3Xrl1/fW7t695OjsEeXrFhUUXZmWgqyubmxo7JW3GHuWkuX95bnJ/ffnicOvR+cGzh9tPLxbP9vVz4y1qGYJJzqotjStIjclNii5MjgJcKUkPyU32z4wLzImPKkvLI1Q08ChNsla6nNso55DFTIKgqYFHrWRhC8jVJdQqopI2d3/z/T/98OFffvjgj9988tfvv/u//vrt//2f937/ffuWpXhQkNXLydSx4X2tJcNi7KKBvjbG25wFYGjfXerYX2MvWhADymxFc0pHY5qiKVfXVjmmwCz0EZcHm1aH+bsz6ovtgSfn5vcfDDw6BVYGN9FbZZSU9YmANamb7qatm3lHc+2nVtXFuuHB3shT4FEejj97bHxw3LEz3zRjJIx1Eya6m8Cj09604cnB5CePZj9/uvDpk+kPHgBOTLz/AKjnbLd1YRRn7q4bkKFHlNT5vo7Ded2jnd7H+/2PD4afno29C3o+tHzwxPjgrH3D2jhlbjAbkAO66j51hb6zRCMv7OrIU7UDqMDlgiwpL1vCg0t5WRJulpQLl7RlS9pyJW0Fcl6xgl/aKSxT8sqlLZXttAYFq2O8e/Jo/uSTy/d//PizP34J2ZR//+nH//jtD//+m+///sM3f/n+8z989fSb947ePZjZGQNgMC/qzQs9ABWm6U7gRfotcuBLDAAq45L+CRkkcDkh6Z+UGiflgzPKodmu4fnu0UXtqFU3vtw7sdo/tzux+2Dr/mePPvjx409+/8XHv/vs099/CdAFXNF3f/vxx//4zY//+O23f//+g998tP/BsWlvUmDpbDKJaZZu0fYU8Hazn7+78tVz4E7mPnk0//GDtS/f3/3+k/0fPt394ZPt7z/e/u7jze8+3v7+k93vP9378bP9nz4/+M3nez99sfPTl9s/fbH7m6+OfvfN/T//8Ozffv/8P/7w+G8/rn33cc/7Z6KHe20Pdlrvb7fc22Lf22Te22Dd32y+vwXEfrDV/GCL9WCLeX8TNDLBq/c3GQAwF2vUsxXKqZUMPMrxIv5wrn5vsmp1ED4uT9RyAVRSZRyYiBnBqAvC5EdgCjMY1WUibGEbAkbMSmyAwbCpGYT0TGJGOi4tA59e3FRK7WzuXRhcv7/98POHH//mw09//9HDL+/NHU6zupjFFEQuEYJKRSsd3ylpNQ92zM9qtraGzs/HHj0YujiVLcw2GbQEDXg2l+PVqqY+ffvcrApAZWNNPDfdNjYsmZvV7+0NXZybLy4GTk/6DvaByxFMTggs48rFxa7VVeBgOmYm+BMjgqkJwdQ429RHUolaeqT91rHtx/v3P3vw4LP7x8+Ol86W9HO65i56NbM0H5+Vhc3MwGSlYeDJ6MxkTHYGuaSAXVfTQW1QtaAU7II2fA6HnC9klSlFiB4VyqDBDOjwpj7W9KjuZH/t8w8vAON/95X148fqPSt11ICBViqq6/q6kL2dwFUj9Kra3q66vu663s4GvYJqlLcNSaVDIqGWSmCXFKPSUkqTYouTo4tTYkvSYkvSE8rSMivSKhryG7loiYELvqfaCq1ob180tkzr6ONq0kg3ztTZMKh6/Y49tO3xzdf+uYCKDSoNPXrWxCxvfq3ZsoTtMRW1dcSiCb55Rd6ZWZ4ZWR6ZcBtagEHJBhUvqJ4FBMxKcElRColULpbgjCbW5Dx9bLa+ZyCvrT22Ae+dW+CSlg55lBSYrUz1yMr0y89LqK8vEQqJhoFmy6xgYY1lmavXGgtahcl4UgwKlUomV3RAE3G8xTXp5n77yjZjdA7d3V/EExe18aslCpJxiDe/LNvcBxaHPGQp61AmYIje2XlOiUm3YmNuREXciAi/GRJsFxho5+9n7+MDhvtfbArQlU35WQAYkPOwQeLnJC4/n8z/y6VtTuy/4yi/QOVKUH/brcCr192gKP0btvzEr9yw2RRHuzc9nOz8PV2iAkOyk7NwtQ3t3DajrntxZvLsaO/j5w9/+OajP/7uu3/8/Xf/8x9//K9//PYff//0d9+dffre7NmuYsJUz2NmIEvji9Jji1LAj0hUSUpIXoJfeqRncogXLMQ3JcQ/NTQ4IzwYHhVekBBdmJRYkVHUiCqkYZKRhUk1GQgB5r0/fv7Z//XX7/9///79//PXL/7xm+/+669f/efftj55TByUZMppyRJKkpiULKakKWj5Wi7CpCJPDbKtk6Sp/z9tfx3VWJb34aPt5YYWhRbuLkkgJECEECRIcAsaSHAPGlyCu7u7u7tbUQbl7u093TPT790nVPe893fX/ee+c7M+a6/DyeFAC+fJ891WZpmXaJAYhEzw0U2mEQuiPNsLwkYbw0abggdroocbk6d7c5cnyzYWAE4Shts9G/JtShOtixNsoQn2OSFDNbEzHfFzXRlLgwVrkxVb83V7aw37G8WrM3HDrX4txV512V6N2SG9ZamzneXb0w23VtvubHbd3ek42mo93GjZ36jbWmJND4Z11vjU5XtUZ3o2sIJ6ypkznQUbY1W7Cw0HK3W7S9Xbi7X76/UHW6Vri4kjPf4tVR41Rc7lubZFLHJBpmVeKik72TgzERDFMDUOlxyDTWQYMKP0EiIMQOIj9OJC0dFBKEagXnQgJiYYGxtsGBtEYNAIEZ6kCHfX5IDU5tyOpb6Fu6t7z2/df//g8Q9Pn/388sUvr5799OLx908ffASmcn/jZGtiY7R5uKamq6AKQKUtu7w1o6wpraQhpQRApY5ZWJsAUlSbCAIAU1gbD04W1yeVNqSCyyrYslLVnlPTkVfXXdw6Uj++Prp2f/Pw+dHt1/fuvAZEOTl+D4jy5NmPL178/Pr5L68eff9k/+XR6N5U6XBdZG2KX1l8QH1mZE95/GRb6lx33vJwzuJQ+lR3ykRn+sxAwcpU0cokSMHKRP7yeN7yeO7SGNQuj6XPDyTP9jHn+pIWB9PWRnM2pyr2F3sfHgCD2fvxzeb3zwef3c7dW4hbn4haG4taG41cG41YhxLOThiUkdD1kZA1KEGrw0GrQ4EgKxB4gMrQF3qBr3jPdrhNtjqP1pN7S7E1KYjsSERyCCoxVDvGX8XPUcaZIGOjp2SPUnfRU3dCqdtrazlq67rp6nno6rnroii6+m765oFWQayI4u7KofWxrQebd1/fvvv6aON4uXW8PjAtyMwXQMWBSHMxD/F1jA3zSIvzL2QlNDcWToyVzs3kj4/ENdXQ8zK9spK9MpK8stKCSosTOzvShwYzhwaYbU2hZYVhFYBDzayh/qKpiYrFRZD8ifGkro64lsbE9vbU7u7kro4YgJP6mpjmBkZjbXBJnm96XExRWs1gy+T27Prxxvq9tdnDmc7Z1uz6dP8UX7K/KYGiZ0hB4930MRQDtIs+ygVr4GlsGmTrmkz3zA5zTQ+2TaDjgz0MI/xIydE22RBU3ErzvCqKIKhMjXUd7U4/fzDx5F7z7kryUIdvbbFbWS40Ar4gC/iKXW6abU6qfV6GQ2GmS3G2e0m2X3lWZHVmbEV8VA7dPdTCyAGNICFVTXVVzfTUSAZqJIyGBUbHCkt0NnENdY7MjshszC0eaqiY6ataHKteHitbGM6f6mONdaUMtEK1L2iGypn/y1x6AJXvvjh73j23KLShPbKl16+i2SEtDxcYoWLrLIYzEjPAiOljxDE4CcCS05ZNFFEcRhSHhcpfVpb6dJpNcppPWXVoUzu9stk2Pd8gKFLF1kkUizutffHr6gjoIgFXxDEGCuZmKG8vu+QUenl1ZGs3o70/oBr6iYbBUXCKp6qtvZaLEz44yDkrO6i2Kb5nkNk3Et3WRyurdUjPcUhnOWXm0CuqYjv7UgbGI9p6vYqqjBkJ6vYu4gY4fjiCR1ONU1WFS1mFE1pfUo5LWppLUoJLXOwzQv7qUznN52PRm+yVjKELIOQA8/irN/6UN5C+nPadsLnyN0tONYV95ecbcogIsxe9v36eB5pLf4br8lnea2cEeS5LCPEqSYhpq6iTsGZ+Ht7pCQl1FRVjI72ba3PHd2+9e/vin/94/+e/fvrz37/++ecv//73D//69dGnl1MHq2WDbcF5KSS6K9qGgDA3gJH0VEkoFVNdeTxcBguTNtCQ1leXMdCQw2rKE2DKxjoAOVgPsnt6HCU9HkN1Qjobk4IcM1vyR/YmJ27NNC90lY02Fg/WJ9Tn2SQEYMI9DWLoGGawPjMYnRCsnxhCyIg2z020yEvEp4Wj4umIOG/dFF/jokivruK4ue6M1dHMlVHW8ihrcThzfoS1MJE+MxzV2+DbWEhtyKe1Fvl3lYYN1TJnu/I2xkp3pku3p8u2puv2l5tvbTbf2ipZmY4ZaKY1F3rWs6jNueEDNdkrg813Vrsf7HYf73TeXuu5szlwf3/kwe3B+4eNO8tZE72RXTX+rUVBnSXx4y1562MlOzOVu3N1+4sttzY6bu923N5rPdqt2lpOmxiM6Gz0rSujVBU6lufZlWRbF2Ra5adb5KaZspKMMhIM0+KwydEGiRBO0PHhqNgQvbhwdEyIdjhN099d3ddZi+4Mozsh/Fx06E46vnYoHyvjQAevlEBWe1H/+ujKycbhyzsnHx48/en5y19evfz19YufXz7+8fnDT0/vvDkGz5TxteHGgYqazryq9uyKtqyy5vTS+pSSuqTC6vjC6oT8qrgCkOr40xTWJICTRbXMkjpmaUNyeVN6eXNGFVQEy6ntLGgdqRvbGF0/3jp6eRcIyj32SLPTshsg2YtfX7/85SUElee3RnYniwcrGdXJAaVx/lXJwU3Zfs3Z7jXJbjXptKaCqL6GhJGOhJGu1ImBlIn+tOmhjNnRjNmR7IVxKIuTaXMjUcNtPm1lLk35Tm2Fnn2VgWNN8fN9NbdWZ1893Pnhzcb3L4ee3ck/WIzbnIhaH4/aGI/8KxGb4+Gb42EgG2OhADCg3RgLWR8NWh8BCV4dDlwZClge9GeXxXznezxn213GG8h9ZZjqJHh2OCIlBJUUioimA6hI2uPFzXXETDWkLTQVrBHKtggIKi5IXVcdlKs2ioLEuBuQg2zC8qLK+mrGtyb2Hm7df3N079WttTsLTaM1QZlBJJqDkbeDCc2VFES1jvB3jAmlJMXQ81gJrc0pvd0p3R0RNaUeGUxKUiwlJd4zMzWkvAxwInt0OHtsOKmzGWhHYHFuSHlRZE1lYmtzwdhYyfRU/thoem9XQktTYntrck9nSndnXHMDlLbmmOb6sPK8QBYzqSKnZaJr/nBx68H2xv3Vqd2JxrG61ComPcnL2t/U0BWFddLBuyLxrigMBYV2QaNdMfruRkZ+VvaxHtaxHqQICtbfFRvma5oUbc1KcSo4hUpxSEtd7vR4x63dqacnE4/uNmwvMwfbfWpL3MvzXYpznQqyHAoygKlY56TYZKfY56a5FGW7l+b7luWGVudHVqQEZvq5R9qSPIl6tjiEFV7L2kjT2lTL1lTbzkzPkWTsYe0Y4h6QFpFYnc3qqmNBywr0Fk4NF00OZQ91p/W2xrfV/6/a1//vo4k/Q8UtpwB4Q1hjJ4CKXUoePjhaw4EiSTARw2BvAlPBYsWwWKjkxe5KATg5NRVpIlHL2dkoPMI1u8C/pimkqcOnvN4yMRNNC1KwtBbRxwggUQJIpAAKCbgigkJJ4rEq1lb4wEDnTFZAbWN01wCjo9+vqsk2PRcTHKHp4q5MtlaztkF7e1nFJXgVl4W3diYNjgKuhDd30SrqaaXV9LKakPqW+N7B+L7RwPp2Z1YxPiRSwcr2JtqAHwHngjRFmVNJCZpUD23hJQVtKykKzAOw4XOB6/T4PwfQhvai3KAFgdACzIPdDw/4cVouYysIu2gGrOU/UPk77PIXQBTUt39ViA0Vbi6ol577ylk+zrMi1y9LCV9XlhJDKKuZ6BtTnV1iQ8IKM7M7GqvHB3pX53Ye33/3+68//ftfP//575/+/Nenf//xw79/ePjxydDGZGFnTSArwSrIA+NiDrfEqZqg1UzQGiR9mAUWbkOE2xGR9sZ6ziQMxQLvQcZ72hp525KD3f1YcW7J4Xgfe4y3LSHY3TzazyqGRo6lW8bQSNE0k2iaYZQPLpKGjfE3ZIYbpUQRUhjYpHB9ZggqIRARQ0NEesCiPSCcFMd4dhUzptvTlgdy1kbyVsdzlsfzwSfftZmM6aGgjipqfV5AU0nScHvF+kzb7c2ek/3Bx0fjz+9NvzyZf3Wy+OrB8qtHq2+ezr142HqwnjTaQWsporbmhw5UZy0P1B0t9zzcn3h+d/7F8dyzO8svjrffPzv49Hrn/Yvllw+nHt/pv7vbuLdctTULKNJ6Z6P93nb73a36/ZX63ZXWo+3OO/s12ysZU4Nxg50xg51Rfe1+LbVu1cVO5fkAKjaFWZZ56aScFAAVYiYTnxqDSWboJ0bqxYcjo4ORjEBkVIBOGCAKRcXTRtXNQtXVVNWZqOFMVHcmqDvhdShEAt2amhZc0F02tDm+drJ56+VtCCo/Pnvxy8tXv70G7ZOfnj38/unRq7ur91ZHlvrq+0qAbQDtgIjSmFJSm1hcwywCFKmKy6+Mya+Izq+IKaiKBV8WgtQkFNXEF9cklNYnlTWmAq5UtbKA4lR35rUMVY+uD68db916eefeu5P77x+evH8ENOXpD8/BDwU8e/7z85OPD7ef7A1ujRb1l0dWJPoWRHkXRrsXRttlB5kkeRkleJAzQjzL0oKaSyM6auL7W6N7W2L7mpOGu9LG+7Kmh7LnxopX5wtWZ5kTfX7tVS71+ba12fbNeZ69FVHTHTW3VuZeP9r96c3mDy+Gn98tvrXE3JqK2ZxkALRsjoOAAxDAlb/DBgzgyiiElo3RYIAWtq/4rwz4LQ/QF/uAr7hNNH2GCisckRSsywxBMOiqdAAVnBgJdtNYXdxUQ9YKrmwLB1DRBlxxRqBdtNEuOjgPrG2IXUQ+o3Kgdnpn8uDx9v3Xh/de7i/fmq7pK/NP9if5OBj7OJnRKVYhvvZRgU6xoZREhm92enhVeWxTfVxTfVBJnkdagktitFMCg5KcEFCYH9/SnNrTldbXHdtUE1Cc51+YG1RWHFpVHt/SlDU4wBoZzhwcSO5oA2oS29yU1N2Z2g310jPbmhPaW2Kb68LL80PzUjLrC7vm+haPljaPN1bvLI2sD1b2lzNLYnwTPexDLIge+kYUlLEH2tDdAOumb0DRRzujUc7AWnBoiiHKnajrQdL1ccCEQlAhs1Ic2VDxriwOa20omJ3qvnMw8/zhxON7TZCpdNLqSt0rCiml+c7F2Y6FWXZ56TY5KWRWknVWErAc56Jst+Ic17w0j1wmNSPcJdrNgk7Gu5nru1jqudnpezgaeDhhPZ2JPhRyoJdLlB81OdKPlRBUmBlbX5HZ05E3PJA31JvZ085sro+urfw87uu7b/4PM1T+gopLVm5AdUNIQzuttMEhrYAYFgdz9ZExsRDH4kUNcKIY3E12BQySFQMcIA0AjCTBUNnaWp/uZ52YQi2rCm3uDGrqoBSWm8YkIdyossYkEZSegA6SHW1BpDaAigQOq25vaxQRQckvBASK6R2K6hwAHLJIztD3D1Gzd1Iwt1S2tNBycMAHBjikpdMrqqM7e5n9IzGd/VFtvREt3VGtPbGd/cz+0ZjuYXp1k0Nqrj49WNbMQhiFhtZoUVflVFHiVFDklJPllJHhkpLkFBeHBvuye91Pc7pSJGh5gKNAmiLKze4OgQ6gjfqhGtdpPpe52FD5W0fYN4E05W+onE54BOi6BpkK/+dpjxxXIKhc5zwHoCIpxKMgLqwuK6cPh1sa4T3srYOoFIY/PTEiJC0qr5q1sjv14acnv/7z/a//+vTpH+8fv7nTM9WeWZUZkBLuEO5t6utg4EKCWWJVTJCqRtoaJD2kHYHgbWsf6ROQHc+sKchqrcpsqUxpKEtuKM5srchqqwjMS7AMdjOkORBCPIxCvQ1DPA3DPA3DvQwjqPgIH1yULyaSpseg68f468X46zLoiAiqZpibaqizWqQrMpFKKo6h91VET7enLA5kLg1nLQ6nTPemTPWlTPbHD7fTW0oo5ZletTkJw8012/Ojj+4svn26+vHl+qdX29+/2fvx7eHP7+/88uEelI+3f/6w8+n19POT5sM11sJA2nxP0fZEw9Fy1/3tiSe31948Ovrh1cNf3z/8x8dHv396+I9P93/9cPvndwc/vAXftfHh5er754uvH888Px5+cKsb2MnRTsPeRtnafOb0cMp4X/bMSMX6QtP+Vs3OeurUsE9TlXNFgV0Ry6ogwzw31ZTFrn1lJABN0U+IQMWFIRnBiHB/jRAfNX9PZW9HFTeyloclIdjVMMgJ5UXStNNTs0Gq26K0nXAEmqVPemBBV8ngxiiAytGr2w8+PXz+84tXv716/Y/XL349hcqTg+e3lm4v9s92AB5UNGeUNaaV1ieX1AGiJBRXxxVWxwGW5FUw8sqj8soZ4ADQpaAytrAqtrA6trgmvqSWWdaQUtGUXtmcUdnKAjdpGqgcWu5burN28Pzo7lsAlQcP3gNNefr8J/Cj37z5x9sXv7w4/nACfqWe5b7czsKgIoZbRoBjKt060ccszpUYaYcNtjQItsaHu5gl0GzSQx1zYp3z4r1KUvyrc0Ibi8Oby0NbykJby8M6aoI66nxbK9zrixxqsm3rsyndZREzXfV3NhffPd3/+e0WGyqlRyvJO7Px2zOx29Mx7ERvTUVvTzG2J6O2JyPZbQSUiYitCUCXU8AAawmGCmIALUP+EFd6PadbbfrLcTXJ2qwIZFIIOjEUGU1XpztJ2WHFzLRuGqlImqnLWcJU7RAwRwTCEaHjBEc6wtBO2gRPvEOkU0xxfNNY8/Lh3PHL/afvb99/sT23PVzQnOXO8DT3dTSnuZADPZwYAV7McK9khldKHI2VElKSF11THl1TFlicQ8tO88lM8kiJc09l+mRnBJcVRlSVRtWUB4PP+Dnp3tnptMKc4IrS6IY6YDapvd1Jne3RddXBpYUR1RUxjXVxzfXxjbXRDVWMuqrwypKA/LSQvKT81vL+paGV20sb99cW9qe759rz23JDWcGuEXbmPoamngYkbwzJB2NKxRK9sHgPAyybKzr2ulrWOuo2epoOBLiXHSaUZpYca52d+rn8VVkc2tpQND/de/cQQGXs8V0AlZThLnpDhWdVsRvgSlke4IpDYaZtbpp1dopVVrJFJtMyg2mZybRIiyMlhJMYNNMAF7wXGedhjfN2MvTzNAmimQfTLEL8rML9bcMD7CODHKKCHaJDHWLCnOIineOjnOPBv99wu8hgcgjd1M/rqysX2eO+/g/rSP4NFbvkNFpZDTCVgJpWt9wK85hUFDVY3tJOAm8kBojC7lYRZ9e+oAoYDieJN1SysNClepsyYtwLioMbWsNae/xrWxyzCjCBESp2LqJYvCBSV0BbG4RfGwG4IqKLEsdhNZ0dTKNj3AqKQ5raY3qGQlu63AsrTaKTkJ40FbKtvJm5AomkbGWlQ6GYhIdTWDkBNfUxnX1Qp30fUJbRuN7h+L6RxP7xqLYBammdVXy6joePNNFEEInig8EAVLiUlbkUFbmhDhUAFWhIMYAKt4gwyOeOd0AFdq8JNKT4JtAUcR7x0w0ixXigPSLZffKgZSsLGxunjsImyt9QucmufUFE+Q9vrgkLX+GHVrw/zwVNezzHffXMdc7zQteviAlyy4gKqMhI6WqqGmNRdhaG7vbGns4mno6W3vaOfk60aK9YVgirNK6gKjGrODokke4S4GjhaUVwNce5mGKcTVD2RC0rjLKJrjJBW91EF+1AtI/wTqjJbVkcGT1cm763O35ne2hvtW9raXBvrXV5Mq2lnMIMtAh1s4xwJ4W6mYW4mUd62sTR7Jj+pBgfTAhFJ9AJFuQMC3bVCHZWC3ZUDXZQj3TWZVLNiqKpXUVxM51ZK2MFm1N5K+Np0/3Jk30JY90hXXUuFVnWhUzrojjX2oy06a6e+3ur755vf3oN2tlXD6eeH4On/+zLBwuvHi6/frjy9tHq28fr75+sf3i28u7Z5IsHbXe3ynZmavbmWg5XBu7trrx6eO+nt09+//7ZHz+APP7Hp3u/vDv84dXOxxeb759vsO988OO7ne/fLL95OvHkXu/9w8qNxdSJgfihrvTJgdrNpf7jo9kXTxbfPB9/9rBscymoo9G5PN+mKNMiL80sO9mElUjMSMCnxOgzI3VjQ3UYQfAIiChKNIqCl4OMs4WMtaG2l7lVnLdDMg3vZ6Fhh1S2QqhYasMc9AlUc+9kv9y2wt6VgaV7ywfPDx98OHnx8/PXv74CefHzi8ffP7n37njr4fbk1njbSG1FK6usPrWkLqkIWEh1bGFlTAFgSUVkXhnASVR+eVRuWWRuWQRo2YCJyq9gFFRGF1XHldQxy+qTK5vSAZMAV+p6SnrmOucOF3Ye791+BVXATt49ePzpycufX777/d37P96/+u0lgMryveXWmba0hkxqVoBNnLsFw9kkwoEYZm0YbI4LMDGgm6B9jNE+pvp0S2ygnXGkm2NqcEB5KrOjPKWnLqAqyz4ryiY73rk0y7220KUm364yy7aO5dJVFjXb03R/Z+n9s72f3q5/et7/9HbRreWUXQgqcdvTsdtTsTvTsTszEF12pqPZYexMRYFsT0VuTQKunKIlbGMsbH00dA2EXQ1bHvCebbfpr8DXJutmR6KSQ/WTwtGxgZr+zjIAKqaaIoZKkkQVOQstFVuEpiMC7giH22sg7DR07OF4Tzwl1j2zntW/0Lt1b/nBq/0nb/bvP12d2ejNrGQ6hLqY+dhb0CmukfTA9BhGQWpUcWZEYWZoQVZ4cS6jojC6qohRURRVXhBVlh9WkhuQn+WXlxlYwAopzg0uyPZKZ7okxjgmMFxS4n3yMkMriwE54ptrI2vKAgpZPtlpfgWssPKiiMoiYCdBxSx6Xrp3BtMjMSIoKy6/rbxvaXDp9tLa3ZXpnbG2iabM+jT/FB8bfxLBWdfIBWnqiSZ5oy18sCQq1tBDD+9ugKHo6zjoqltpK1sgVWwNYV522Ah/s5Q4a1aqY0EWpTTPs6IouKUub3ai82h/7PH9oQdHtVtLiYMdEFSqSzwqi9zKClxK8xwLWfb5WbZ56YBGFhlMs5RYw7gQ/TBfJN0V4WYNszPTsjNDOpExVIpxIJ0UFmgVEQAlPMAq1N8q1I8c6m8R7GcRTCcF+pICaUQ/qqGPJ86LYuDmjHK2/9xF/3/QFPD6DBWLmHivooqQ+vbQxm6fsnqbpFxcEEPD0UPGxFwCawiIIoLB3MRC/ShieJwk0UjZiozy8jaOjHbJyfevaQxv6wtoBJpSSYpPQ3jSpUzNBdFofoATaMkvOB+INlxYFylJwMPdXEmxCZ7F5aHNHUBTAutaHbKKDENjtFw95EkWssamMsbGCqZmKlZkHXd3k6go15z8wNqm6K7+5MGxtKHJtOGplOHJpIGJsOYet7wyk6gEDWc3cbwRvw7yuqYWtFeYkjKnvBy3rCyXtDQntJ0wkBLIJ3gAV04HcUF97+xp8wAhp7UvdvkLEhq2oEB9J9BUeagIBmmNqCgnFECUz64DWALgxIbKae89RBRoYy5oMDG04v15zqtnOS6d47n2HR/XOeHrV8WhGSoi6opy+tpaJCLG2dbYy9WS7mET4OkQ7EWJoHtE+XnHBFBj/D3Dqa5BbtY+9kQXkr4tUccKh7Aw0LbEIKxxMCucqomeEkFHzVgX5UCkJPhXTHTNPNjffvto6/WD5ae3Ro62e7dX2pdnKka64sozqMmBvmlBoUVxjMpUZj0rs6O0fLS1cX6wZLQjuCKTyKRpx7jBGBRNBgUe645JD7CrSvbrKY8Ya4qf6WQtjWQvjWbOj6RMDiSMdvm3VtiXpJrlRpMKom0rk307i9Lne3pPdgAtDn56Bx79k0+Pmw/WS1dn8hdGs6ENsvryFgbK1kbr9+a772+NPTkaf3Zv+PHdzrs7TQdrrYdrwyeHG2+f3f3p3ePfvwdQefSPj/d+frv//cuN90/mXx5PPDmafHJn7vnJ4qvHG+9fbHx4vfTmGSBK0fJ0/HBXVG9L3GBH6epc/8nthTfPNj+9AZl5+aRiazmovd6xNMcqP80iO8UkkwkRJTUWkxiFigvVjg6CRwVohdFUAjzkqY4y7laStsZiZjpqjnr6viZYmqmqNUyJpKpkrqlsrgWUBedtRkmgptVnNk62jO1MbDwAsnL05PtHz396BvLk+8f3393ffbq3cDTfv9BZ21VU2pheUpdcVMOEek0qY/LLgZScsiQqtzQKoCWnOAIkuzg8uyQ8pzQirzwyD1xQGQ0pS20C8JuyBqgOVtmR1zZWP7k1uX68efji9u3Xd++/vf/w48OXPz9/9/ub93+8BVC5+/bOzK2ZyuHqmLI4SqK3RYSjUTAZSzfF0Yh4fyPDQBAijm6EpRFxfuaGAVbGoQ52TGp4VWrxRGvlQn/mUL17frxNVoxzSaZnbZHrZ6hkU7rKGPO9jfe2594+3vnxzfKHp52PDvMOloCpJGxPxwGiQFD5nBgo/wst24Ark1FbUCIBWqCy2Fj4xlgEyPpY8PKQz2ynbX85viYZzWLop0Zgk6Mw8aHwAIqsPVbMRB1ARYKoCkHFThvmBIc7wRD2GjBbdW17uJGnoXeiT1Fb0fDywPrdxbtPt+492di8PdU+XhvJCiX7OZhS7a0C3GlJ4Qml6azG4pyW8nSg7HUliXXlTJD6ipSm6tTmmtTm6tSmamZteXRFUXRFIaO8ECAnpDjHMy3BPiaMHBFsHxvukZrgm5NGz8v0y82gsVJ9s1P9ctODCrOCCjJprGT3JIZ9VKBlkLd1kCc9OTy3uaR7oX/+1vzi0cLoxkj9aF1KdbJPgheJStSzg6HIqvp2anhnmCEFgXXRMXBFo5z0Ua4GCCe0qrWOEklHmYxRdyOjQqhGzEhSeoJ1DpCVLI+yguDm2tzZiY5beyMP7vaDT1FrMzE9TT51ZR6VxZSyQpfSfKeiXPtClm1uhnV2GpmVbJ6eYJIUg40KhNMoKq5kRTJRyQyvYk6E2ZP1PClG/jRzAJXwQIuIQIswf/MQP/MgukUQzTTQ19Tfx5juTaR54X3csZ6u+hQnXSd7bVur/2MX/ekLQAUa/WUcFuWcXehf3Rza3BNY0+aWV2GVkIXxj4Q5eciRyJIEY0kcNENFkmAkb0bScHLG+QeSYhKcWPm0yvrQps6A+nZKQaU5M0PXN0jBylZYzwDYCR8cBuzhBggcJoCAC6NR8mZm+jS6XUo6raI+vL0noqPft6rRJjXXICBMxc5J2tgU3F+SQJAxMpIzMVGxBtzyIkXHuucVBNU1x3b2pwyMpw5PpY5Mx/eNBda2OWQW4IIjlWzsRQyw/HA4r4Y6j6oKtyJ7jRZ27Qsqav29UiQbKmwGQOYB4QQqfAGoQOFhXwZ1pZx2v0M7rLDxA21FDFXAAF1OA60PxsbP6W2hd9lAApi5JgyteH+BmwvaRgXqpec4I8BzUZSfU0aUXxnSFA0zPI7i4BAe5J+eGFuSk15fmttWU9rXVjvaWz/eVz3Umd9Wk1yZH5yZ4BzhBzwGZW0MM9HXNEFrkQw0zTGqJmgFQ4QcDqZIRBi4mEQUJTdO943szo3sTDVOtmW1liVW54XnJfomBjpFurnGe0eUxpdPtY6dbMw/2V97cXTy86un//zx3s/ve26v+jRnG+aH6mf762UHGRZF2jak+/ZWxkx3pcz1J030xI90xA23R/W3URtLyIWJxqwI4+xIchnTp7MoYbYjf3O84/7WytvHRz+9vfPrx+3vXw2eHObODYf31Ps0F3s35Pq2FkYM1GQu9JbtTNccLjbcWu64s915Z6f11lbLwcbwya3VN0+Ofnj74LdPj/7x6eS3D0c/vt4GNvMGGkM5+fTO2KNbY4+ORh/eHnt4Z+jkCPxpdd/ZrdiYTx7rjehpCu1sYPS35cyNNexvDD+8u/j66dr7VzMvHlUCU2mtdSjOtMpLNWclEdPjIaIkM/QSwnVjQxCMAFgEXT3YS57mIuNuLelsdtMKI0rUkCUpy5srSxvLSeClZYhyssbK8qYaamSknhvRJswlvCA6v7ukbb5r6mB268nuvfd3H/1w8vD7k+N3d/ef7iwdzY+s9jWPVFS0Zpc0pBXXJRUCqECdKLF55TG55QxAlJyyyOxS4ChstJRG5ZSAROSUROaWRIAz+eXRBZUxUC8L1HWfVNyQUtbCahqsHlruXzhc2Hq4s/90/+jFreM3d57/8PDNL89e//r06Q8PNh+t96z05rTlBuaE2DNcjQOtsDQTfW9DrBfO0AdjRMcQ/bHEAEOTUDNSBNkiysGS4eQQ7xZYxCgZa2hZHy6cbKaXpTrkJFDKs73qAFTy7Coy7epYHj1l8YsDrSd7i++eAqgsvnvS9nCftTefuD0TvzUZtwVBJeazrEzH7s6cQuUvrkyBsKEyAZQFcCVycyJyYzxqYyJifRyCykyHXW+ZYXWqHhsquJQoLDMMHkiRA6Zioi5KUJYwVZYja6k6aMOdEbrOcB1HDYSNmratJt4N651AzW8p6FvomdmdXDqYnVgfahypTalIojC8TL1sTLzsyIHu/qlRSRWsgo6qqsGmisHmou6GnI66zLbatNba9LY6kIy2WvBlZmtNWlNlakN5ckMZs740trowKD+dksRwiAl1ZUbRMpKC87NCgZ0UsiKLc2MrCmOqi6Mr8qJKs/0ymc4xQRaBnkSqE8nX2SM2IKkqu368fXxnevZgbmRztHmyJa0ujZrgaepliLLW0rZQ1iGroG3VdO004GQNGBkOI2vD7HTVbXWULeGKJIQiWV/V2RTh44QK9cHFhhinMswz4+3z04Oba/LnJrvvHIw9utd//6BseTK8o5ZSmm1XkGmdl07OSbPMTiZlMk1SY4lJDGJiJC4uzIARrBPkpeppp2BvJmeOkzfBKZsTtRzI+p4Uor8vKSTAMpSNkxC/U5aY+vuaAKL4UYk0b0MfDwNPV7Srg7a9tZYVSc2M+MWZ/1MX/V+vr7/94sw5LI1ulZjqUVwZ3NgZ2tQTUN3mlltFZrKMwmJ1qQFaLp5q9s5qDk5arhQM3d+MEWefmuVZVOFf3QRUg17d5JIHrXsPiKJk43ATg+PXhgOi8MK0rsO1+GBaN2BawFdEDfTUbWwMg0OcWXmBtS2RHf0hrb3Qmi7x6bo+AQpWNoAo7NkweEkcXsoQL00kqpBtDGg0cjzTq7gitKGd2TOSMjSZMjwV2z0MDTNLzdGnhcieTnvU1ORSVeVWVuZmT3vkkIHGfbGhAj36ASf+A5Wb0Liv03oXW1Nu8rAnspyy5HToFwi01iR7LiSEDYgf0Ajj04oZdIe/xhyfjhljDxsT4RASuszHhgrnlbPcV85d5zonwndVUvi6opQoTFnLFGdGpfhlJGW1NTYvTo0ebs0fHy4/urP57OTw7Yvb718fvH6+dHx7ZHu1aXo0vanKOyna1MsJaYEHUFE31VMz01M1RcvjEVJ66pIoZVm0soqhuq6Frr6dAcYRi7bT07XHop1M8J7WBA8rvJupia9VcH5c7+7y+oeXt39+f++X90/+9ePzP38B7fjJlldtmmF2kEGan0G6Pz47lFQU41yb5dtaGthVG9HXEtpZ51qRbcpi4NNCiNlh1mWJvl0l0VOtKUt9eeuj9QdL409u73x6cf+3D/d/+wRMpevOdsJwq09Tvk9LQWhfVfJsV8HmePnuXN3hcsPhSt3eUv3OctPeWuPuSvnqdNPu8tzTu7d+eP3gt48P//Hp+Jf3d34EmvIKZOfji/U3T5ZfPlp6+Xj6yUn74VbJ0kTe3GjW9FDiCOQogCjB7fVB7XVh3Y3xw52V6/Mjj+4svnk6/vS4bGWOXldmCw0jhjrnCanQiC+9xEgAFWRsCNAUjTCasr+HtLeDqDNJxJYgaKIjjFcQxoiJYsUE0MKC6JuiWClxvKwUUUWRBIc7YI18LCix1PDCmNz24ubpjtnbC7vPd+9/vH384c7t1/vr9xdGVnvbRmsqOnKKmzKgWSl1SUW1CcBUAFRyK6JzyhkAJ1mlkTnlUXlQPwqzoCI+vyIO8CYPeAxkM+ze+woAlfgiCCopxfVpJU2smu6yjqnWobXhpaOl3Ydb917cevz23psfHr3/7emrX57cfXM4fTBROVQZWxbrzvQwD7bG+5oaeBH0PTB6broYd22CtzaRpmvsb0AKMrIKt7SLtrdlONjHOPuyArK6Cssmm1gDldTCeMfcBLfKXM/aQpfKHNuKdLu6LK/eyrTV0Z6Hh+sfX+z/9Gbp3eP2h/s5+wtJOzPx21MgsVAFDCJK3O4MGy2QsgCWQFxh97IAlgCoMDanoremGJuTMZtTMRuTkWtjoQAq0+223SWEylR9VjQmnUFIi8EnhkOmYosVN1YXM1KWNFFRtNJUt4MjnOC6TjAdB3W4tQqMrK7vhHYIdWSWMeuGa9tnOtqm2sp6ypllSV4JdBLVBu9qjnezNKE6OEfSQnPiU6qzc1qL89vLctrKc9urWG1VqS3liQ0lyY3lac2V6S0gVSkNpYm1RTGVeTGVuYzy7JDCdM/UaI9khl9WYnB+enhxJqMsO7o8N64yL7WhlNValdFUllSbH5Sb4BhFN/Nzw3nY4t2tbcM8wgsT87uq2hb6R3enBlYHmyaac5qy6EneJp54XTIMbq4CIynBSKBV1SCpqZqqqZE0NSzgWmQdDWukqjVaw5kA87FGhXviEgKNUiNMM6MtsxMcizJD2usKF2f67t+aenYy9OBW5cZMdE+9aykgSrI5i2melWCaEQsIREgMJySEYuOC9Bj+uqFULZqLIsVCmoyXNNaTJWIUSURNOzLaA5iKLynYnwRVuuikAF/TAB8TfyrAibGfD5FGJfh4YT3dUK5OcDuyuqWpsglBAa//xbf/h/2D//OCZtSfgzs4EoLD7FNZ3mU1QQ3tIfWdtKoWr6Ial+xim+Qsi7hUUwaTFJNITkxzys73LK70raz3q26iVTV6FJZbJWXhw2LgblRZc2u2NGjf0ILxsjdTgbZRgXZS0RDQQUgY4jQcHYwiIl3zioHcRHUOBNS3OWYXE8PjYRQvaVNzMQxOzMBA1MAAtKcjzaSJRmr21nh/f9ukVGppZURLJ7N/NHl4itExRC2rN49LQ3jQJAyNb8DgPGoq0C7FCnJc7MIXh6Qkh7gYB7sw9TcATnMKlc9cYa/gcjrK65Q9fwdabhJarOXvnN4H4gc3eyzy6U2g+4CWPZflmiCACjSX/hzXlbO8187y81wSE+CSFRPQlJPDwjCuZI/k6LSWuq715YWnJ2tvnhx8eHnvt08P//Xriz//+ep//nz5559P/vjH4buXU0e7TdMjidVFDhF0jJO5tqUhnGSgAW37qK9ohJTDIWQNNGT01GT11eSxGgqGMBUTpIY5GkbGGnjYEOgUQ18nA8AVL0uXGJ/WqZ71+7tbJ7vD29NTd1dXnuwN7U0HVaYQ42mYeD99ZqAeO7iUUBNWrF1pJqU636YgxTAtVD/FD5cVQi5P8OstS5jtSlsazFkbK1gby18erdtZmH5+fPDjGwCVu7+8X37zuHF/OWG8NWa0KXOpv3BrsnxvtvZgqfFwrXZ3uWproWpzoWJtJmu6P7qvLrK7On+uf+TBwd6nl8BUnvz+45Pff3j8j+8f/eP7h799D84c//rx3s8fDr9/s/zq8dD9w7rNxdzZkeTR7tj+tsjuppCOhoC2GnpzFb2lKhT685scPDlaePN06OHd/LkJSkUeKYtJTI/FpcYaJEWh4sN14kIRMUGwKD/1cF/lIE9ZHycxV3MBG9wNMx0erBIvSuw6UpBPV4hHW/CGrogwWlTUQFIUKytFUFEmwbXtDIieJIcw16CsyNzWkvb57pn96a3jla2TpdWjqcmN3tr+krz6tLSS6JTC8NSC4PSCoPT8wOQcehKLxszyTcjwjcvwiQfJpCVkByaygpNzQlNyQlKyAlOyA9LzQzILQtLzQrIKwzILQzMKQjOLAYFiMssTsqtTS9qLagfrBhb7l/bn9oGZvLv7+qeHL358cPLx7uaT9a7VrozWLGqqr02oHYFqqu9piHbDol309Vx09F1gWDdNghfciIo0pmPNg4zJYRa2kda2UfbuKV6JjWmsvpK4JhYlh+Gcl+RZWeANoFKVbVeeBkyFOlDN2pgcenpn69Or/R/fLLx73Hayz9qfT96eSdiejv+cqTioFMZGy6mysAMMJnprMnpzErQQSyCoAKJAUImCoDLoO91u31ViVJGMYTFwGQyTjHhCYgQiiCJvj5Mw1pAgqMiaqiuTtTRt4QjAFQctmJ2GFllV3VINbo0w9DB2jXJj5DOSKlOZFUlhORHODA8SjYx1JqLsDNH2RgZOJKIH2crf2SWSSmUG+KWFhGYzogsTIwuT/DKiaRnRfllxflkJATkJwflJgax4elYMNS0KxCM5wikuyD7a3zkmkBIf7J4U6p0a6ZPO8E2LCsiKjSxMSanJS67OTihLDc6KtAl2I3ha6zmZo53MiFRbD2ZwbGVW8UB9y3wf+MxR0V+ZXpviFUMhUAy0LbU0TFXUjFWUicoqRorKxsrKpqrKJA1Vc5gmWVvTGqnljNeh2RhEeRLTws3zE6xLUh0qMlyrWdTGkuj+1or1hcEHR9PPH4w9vgP+1hJHW30bC9xrciiVLGfglCXpdkWptgXJIDb5TKucWLOMSHSkp7oXWZoMCK0rSUArmhlp2lqhPVzxdG/jIBqISSCNTRQoBLq3Id3LiOZlSIU0BelkB7O2VCOZKBnh5Q3Q/w1NAa8vv/7iu7OyeDzMyQUbGGqdlAYUxK+6OaC+PaCuDRz4lNV7Fde4F1S6F1Z5l9b61bb417fTals8iqvt0/MBbFC+AUBQJPAEIWiveM3r6qq8QBrUVLnVVaDNH9XVQG4g4JJGeC1nR2JEhGNWjk95Da26kQJt2piG9g1WtLEXNzS6qad/Uw8toocW1dO7qacnaqAP0CJNJMBdnInh4fYZWb5l1VGt3dFdg+B3c2IVE0Kj1R0oIvo4Pg0tLmUVLkXgKDJcwFGkJDkkRTlO15GE8AAg8Xm8FkQL9vhgiCVAUCBN+UyUz6O8IJX5PCvl/yPsYhe4+O8r/7oVePeasOBV/huXeXnOc3Kc47ly9jrHeQHea+JCPPISwpoKingdgocdJSk6uam2c2Vh+u6t6XtH688f3vrxw71//f7ozz+f/s+fT/788+E/fz/64f3Yra2K0Z74yny32BCitz3angi3wGiaojVJehokrJYlHmZlCLPEa1lgYZZYhLWhrr0x2tkU425JCnJ3iA0wC3TX97LG+DqYh/u4MINoWeE+WRFuqSHOqSGOKaGW8f4EBg0fG0BIijRKYeBTIrFJYeyEGjCDUHF0nXgf/fQAUlmsd1dR9Exb+tpQweZUyeZs3vI4a24of3GsDTjW85OD71/f/vnd9scXE09uA3cp2hwv3Zmq2JurOVis3QUgma/cnK/eWipbnU4b7w7trPJrKQroKIodaihdG5t4fHTw/atH//j07I8fn7Pz9I9TuvwA6PLgH9/f//XT0Y/vtz+8mn/+cODeQe3mYvrEAKO3FTgKrbmS2lDmVVcMoFKxPjfx7Hjx7bPek1spE33kglRMchg6IQgRHaAV6ase5q0c4qEYRJHzd5LytRf1JAs6G/NZ63OZaHFiFa8ixa5q3biqyccBu8GlDdAiLIgSEUbdFNGTFDOQk8IrKxI14FYoA2dDEs3KPcYrJp9R0JTVNFDaOVbV1F+UW8OMyvTzj6XQopzoDHt6FNkv0tIvwoIebkkLsaAGmXn5m3gFED38iJ6BJl7BJK8gc98QC1qwBS3I3C/EMijCJijSISjKLiTaITjGNjDaNijeOTDBLYDpGZTkG5YZGlMUm92c0zbdOr07sf8EcOXOvVcHO482JvfGSvtKg3PDyKG2eC8C2hWr46KPdNVDOqPRTtpoBw0DJxWsixrOHYb3Qhr5Ykz8jMyDzchh1pQk95jaJGYLi1GXQWExXPOTvasLvGoLnCuybMrS7OtZtMG6vK3p4ed3t79/tfvj67k3D5tP9jL35oCpMAFOdv7fszvzvzIbuwPcBUocm0Bx0Cjkiej18ej1McbqaNjSoO9Um0NniXF5Eo4VbZQZQwKfuJOjkEEUZQe8tJmWtImGkgVcw0YHbqcNoAK3g2vZaalaqamYq6qYq+lYo4w8TBxCnDxjvd1jvRwiXEnQkFljPXu8jg1GxxaDtMOhHAz1nIxxLiQjdwtTKtnSz96cZmtCJRt5Whl5k0187Mxo9ub+zlYhFOtQd6sgZzO6PQCDoac13tPG0MOa4GVDoNqZ+DmY+jkZ+dgTfGxNaQ42wRSXSE/XSA/HECcLHyuss7GurSHCCqtNxqMdTUx8Hd0SgqKKUzNbCvM7ijLqMyJzw2z9LdF2OlokVVVjJWUjJUWCoiJeUR6vqERUUzHTUjGHq1jA1a2RcFcjtL8dMSHAqSTNuwH8XVSF99ZH9jcnjHSx5kYb9tdHH92de/Fg8um99tvr2fODMYMtEX2N4T0NwV21/u1V/m0V/q0V9JaygJYy38Zij+oci0wGOsxNlWImTdKTNELLmxiqW1voUpzwNE8CUJMAH6MAHyK73mXk4wlO4nw9DX088F5uaDcnHUdbTbK5iilRAW8gjdb5DIX/8+urL749I6iuLonDqdnZo6i+JlExtqlZzjlFrvmlHkWVnsXV3iU1ACEexVVepdXepTXuhZW26bmkuGQDv1B1R1cJAlEIheLT0rqursalqsqlCqRBmQtEVZlTVRkcA424DtcUw+ir2ljr+vgQIqIsEpItE9OIjHhdWoCqvbOksYmwvr6wrq6wro4gUkcYWs8YBe0frIcWwxjImhkj3CmG4RG2yWlu+UVuhWU26TmEyDiEu7e0qYWAti63qhq3kjI02xFoCnvEF4eYGKeYKLAHqFp12rv+/xG2uPzlKH9BAuDhtIWWZmE7CnQHICKn44b/yulbIJCpgC+Fha8KCV2+ceMSDwSVszxXz17nvMDPe1lCiEdBQlBNVlZPC0kmWgZ60TOZaXUllQOtdaNddcNtXUvjSw/v3f/pxyf//OfJL7/uvn45tLNW2FMfX55NT2fYhVOJPg4oRzNNC6wqVARDa9sY4r1sSAFu1mFU17hg8MGKnhHrz0oIymGG5CWEFST5ZcXaRviYBLgYB7oZh1EJULwJ4VTDCB9CFA3P8MMz6FgGHRMTiIkP1o8PRkMr3tPhkVSNSDcthrtuoq9JIcO7syhsoilhvjd9eSh/fSJ3aSRjeiBrdjhzZiB5vLN4eWzwZH/j/fOd718BTRl+fFR/uFy5N1d3sNxwsFq1NV+0MlWyOlO4OJ400hneURPYWhrUURo1WMda7K89WBh8dLD14fn9Xz48/v1HABUgKye/frzz49uDT692P7zcfv98C9z548vDH97e/unDnZ8+7H58NfvsuGl3NWW0N6C1CqrYlOc4l7OC26sAtyafn8y9etx8ayO8v4GYzUAy6ZrRXqqR7sphrvJBjlIBtuI0K1Efc0FPk+sUQx5HPS5rGAdJlQMvc1lH+LIm3xVNPk4Y/3WkAD9KUAgtKIgS5EeJ8KMkhfRlxLAK0gRlVRIMTkahHTGGFByJSiTTTewCSfaBJDKVQHLDGDsgibZaxjaaZjYaJtbqJtZqRCsVIwslgpkC3kQOR5TCEiQxRpIYoiSWCLXg2JAoRTCWMSLJm1gom5FVza3VzO3Uze00SfYwkhOS5Kpn4YEn+5jZBZApUS5hOSHZzWn1I+W9s00D8y2dU7WFrZnBGX7kIDLWHYd0QiPsdREOKB0XNOCKrrOuroMm2kFFz0lV30UDQ4FjPdCG3hgijWAVbEFJdGNUxsbWpYRUJLqyGJTCVAAVj5p8p3IAlVSHhpyA4frSvYXxF8c7P7wGmX590nB/O2NvLnlnJnFnlgnQAg52Z0GYoN2bP03y3kLS3jwIdLAL2vlUcLAzB8nN5mTC5mT8xnjUyhBtqs2xs8i0PJmYE2fGirdgJZqmMvRCPdRciPLmcCXwEd4epUfBGrhhMBQUzgOFdoHDbdVVScrKJspqZlra1rpYV0Oil6kxlUSkmuE8TQzcjPSc8bqOeB0HLNIRp+tkiHIyQjuDT1fGaBcT0KKcjJGORjA7Qy17vJYdDmaHQ4DY42D20AHMFqNBNlC30tewMlAz11MjoVXN0dD2kdYYTbKBlpW+FtkARjaAkw20bfQQZDTMHKluilQ10VEx0VEzQ8Ks9NCORGOqtV2Em1cSnZZK94z3sAuyNnQ2gJmrqxCVFA0BSxTkcPIAKsoEFTUTDU1zuJqFtgoJrmalD3cxRfs6EKPptlkJ7mUsv8ayyO7GhKHOtIn+4uWpzqPtyacnS6+fzL980H9/v3ZrIW9+NGtmmDUznDE1mDrenzLemzTWkzrRlzHRnz7WEz/Q4lebb54YBPO0lCXpSxFQciZ4VTJJ19kBQ3XD+3oY+nrgfD0IACS+nniqB9bHHTpJdcd6uul7uIDLtGwsVU2M5DF6Mkjtz0z4P7+gAWBcMrLXVdVE9PTkTE3VHBxQVB9scJhxVLRZTIJlQopNcqZtcqZNSoYVM9U0JgEfGon29Vd3dJE2IQnrYW7AETzq6tyAJarKHCrKnCoQSKD5IipKXCpK3CrgvAqQFX5thBgWo2BlqenirOPtrePpo+HiKmdBljA0EtbT50ci+bWRgjo6gtragghtAR1tfqQOv66OEBIpqg/9VhouzkiqLzYgBBsUjgIocnKVNjYV1NXjUdfkUVbhgUZ8yXBJS3JJinOJi3KIQR3sp0///wdUICr8dQwN7oL650FEOKAlW9gRYW+1wi5//U0RKFCZCyAEogh7VDFgDxRwn2siQtAMFb7rF3m4znFdBVA5c53jnCDvJXEBHlkxAWUZSW01FQJax8aM4OFoTnO3DvC0DXB3DnLzjfaNy4vPaygu76orbq9JLGP5p0Q5hVOt/F1IPvZET2ssxVLXwUTDHKNiggb/6xs4mTkzfANYcczaouLBjrqF8Y71hc7V+c7lme6VqbalicKBlpiqHJeEQJsoKplBJYW5G4e4GYUAwHiaRFINwz3RIRRksCsi2A0e5gkLddcKpWiEucIZbqhEH5MCBqUlJ3CwOna6M2NxhLU4kjjVEz/alTjanTDSGdBW6V6d5VGTlTDS1Hq0tvD60ebHl2vvn08/P+473ms6WAWP+Ir1+cqNhYKFceZge2hHFaOnPnWiK29xuGR9snJ3rv5wqe/hweKbxwAYj3779PT3Hx///sP9Xz7ufng+8+Re/93dzlsbbftrnYcbo8eHa6+f3P7x3clv3x//9mn30+uB48PMqQFqfZFzWYZdSbJDeWpod3Xd7uLEs/tjz+7XHiyHDtaRK1OMCqMNciN0WSHwjEDNNLpaElUx3l0u1lUq2lk83PZmiKVoiLl8qLkMFSdkJs+FFLoGF2RrioAAil9Ql++GjgAvQpAHIQzChxQT0ZOWxCkpEFVVzbRgljAda220PUrfEY111jOwR+pbwtGmqrpGikhDWV28FBIngcSIIfRvwvWENXUFNHT41eF8anBeVS1uFU1OZXUOZfVrSmrXQKuswaEMzsC41LR51XVugIthaCGYvggcKwHHSSMIcjokNRQZoe+ExrljCT6GZn7Glv4kcgAUkp8pzhOPctaH2yLVLGFqFpoa1nCEEwpJQaNcUSgnLZSDCtpeFe2opu8Mw1CQhl4GpjQCOcTSPdE9tDgysjzeN4/hlBkJoOJVVeBenedYnmFTnubUnBs+3lp7tDr75uHej292fng19fK4/t5W5t58yu5syu5cMgiwlr1Z0Kbsz6fsL6axk76/BJK2v5R5sAySdbiSdWsl83A5fX8hfXchbXsuZWsmdnXUf6rdoaOIVJliXpBIzksh5yZbZMRgw7zh7ubqdvpwRwMclUgKsDDzMyVS8YZe+gYUbbQzTMNCSYmoqGqqrmWJQDkZ4NwJBKqpEY0EQvAl4QBdvM2w3qY4Kji2wFEtcVQrvC8ZSyVjoFjpeVqgPEjabmZwV2MtJ4KmA07dDqdui1W1xahYY5TJ+kpWaEULtDwJKWemI2+mo2SBUiLpqpijQJRJSCVjhKIxXJGopUTUUjQC0VQgaCobw5SN4erm2jAy+LeNxboRiVRTY5qpkaehnj0SZq4GBEUeLy+HlZfFyMtj5RUMlVRNNbVt0SgnDNIJA7fDaNrgtF0s9egUk+gg+4wEj5KcoMbqmJ725NGBzMmR8pW5njv7M88frrx9tvjq8ciD2w07qwULkznT47kzE9nTo2njQ6ljg8yh3uSRvsTh3pie1uCmSrfCdOMoP5ibpYwJWgqPkjPGqViRtJ3t9L1cMd5uOG93nLeHIQggCtXdwNvdwIti4OkKiIJydUQ62MLI5qrGBAUAFR3EZyb8F15ff8shKckpJ8OjqswHgwFFkDIiKJpbqNrYajg5abm6wtzcEe4eCHd3LVc3dUdnJWsb8EAXxeD4dXSh/gx1NYANwBJOZaVrKoqg5VICrSKHiiKAClAWwBhwAY+GCj9CSxitK4rBSBoSJAwJ4A5CaLQAEnkDrs2H0OaDI8ABP9TC2WPG4DfgoEXww2FCKKQYDitpTJQxM5MjkaSNiaIGBvw6OhDMVFS4FOS55WW5pSU52QvdswtfbNtgE+VUKf4TSEFOQQIO/qprAfxABIJwwiksxCUMUMEmDVTygkaLnXLl1Eug6Smn384ui4EfcVVY4KrAjUvXeS9wc57lunKG58oZPs5zQryXxAS4pUX5VWQkkBqqRH2kLQnv6WTm62ZOcyP7udsFejiHenlE0Tyj6G4RPk7BnrY0F5KHnaGrpYGDqa4NQdeWgLQnIGwNNSwxyqYoDXM00d2KEk2PKEotH+8Zvr278vbFxodXW2+eLT65O39yOLy/Uj7SlliX65cVEZQbHVLIDC5kBuTF+eYyfHOj/YsSvPJjzZk0dIQLPNxZM8JVi0GBxXti0gOsypkebQUBAzURo83Jsz2Z80OJU72MoY6o/taw7npqQ5FdcTK5KN6hPInams+cam+8tTz76gSYyv6Pb7Y+vlh89XDi8Z2RB7f67+3Xby/lzQ5njPfkzAxVbc533d0bfnxv6PHd7uPDjrs73fd2p58d7396dfIrMBWoKwW4yPqbJ4Airbsr9ZsLdRvzLTvLI/cP1l8/vvPjuwcQVL4HUOm/u5c81uFew3KuzvBszAntrSpYGRl6cLj09un8myc9Jwc562PBo42U7lLLZpZpQ5pxTTKhKgFXEYcujdItDtcuCNHKD1TLpaHKgmya4u1qIrFRliIESW6kEBdSkEdX4DqSn0/7Oi/8BpfWDQ51/mtq/JyagtcRokJoKXEDWRmcojxRWY2kBbOCI8gIHRsdhBUMbqKqaSivjpHW0JPUQIuro8TUkSIq2oIqCH4lLT4FdV4ZFU5p5WuSSlckFa5Iyl+WkLskDuUiOJBQuCypBHJNWoVTRo1bRoNHHsYnj+CX0xaURworYqRUCAqqJDV1ay1NewTcEQl30kUAL3HWgzuh4U4oDVttZQstBVNVBWMVFXMNmJ020hV80kdCduKihgWa4qyBdUUQvfTNaEaWQSSnKHvfDN+wUkZwSYwnK8wpM8qtKN27utCjKs8RQLoi3bW1IG62u+Xe1uK7Jwc/vdn+4eXEy/s1dzazIPOYS4fa+RTQAlQcLKYfLgJsQAEUOVhhHa5m31rLOVrLPVoHyQe5vQG1R+t54PzhcsrWdOhMl0tHsU11ul1JulMJy6kwwyGbaRztj6E76HtbmIXY28e6O8e7W4WSDak4PQpa1wmhbaepaqakbKykTlJH2GjruxrgvYhEmqmxP8k0wNws0NI0yNLYH8TKJIBsEmRjGmxHCnEwD3UyC3YyDXYyCXEyDnYyCnQwDHTA0m3Q3iSkp6mOh4m2uzGCQoRTiDAXgqYzXs0Bp2aPVbXDgGg6GWo6Gmo5GcIBgexxatb6KmS0sjlS2VxbmYRQJsHZQahaaKtbITWtUTBbFMwOBbdHIey0tchaquA/B0FBFisvYyAvbSAvow+4oqhgqKZhoaPvSjCiWhBoVgB+aFcS2sveKJRmyYx0zkn1LMkLbKiO6mpNGOrNnBytWF3svn0w+fTBwqsnsy8eDhzfatxZK12eLVqYLZyfyZuZZE2NZU2MgrAmwcFI+lB/TFuDR2GmaZS/phNJ1hgthUPJEnEqliRtJzt9TwgqWC8oeC93EIyXm4EnmyjurnpuzjqOdhBRTAkKWEAUuISm6mci/BdeX37NKSHBKSPDCZ7OKsrXgVXA4UJIHRE0SkRf7yYGI4bDieGhtYpFsVhhA4NTEvBpw/lgmmyiqELFLhVlDmVgKoqcSopcigoAKiBcADCfuaICiMWjpsqroQYNBoPDADz4YafwgAEy8UHHUPjgsOswGB9Mi09Li09T87omaKGufvBd4DwvTBMcX9dQg24FBEhZkVtejgsQRVaKW0qCQwIiCnurR4gf4LkPdaIIC10TFgY53VIeWMW1UxFhMwOiAkQX9vVsonwO+xr2hl2AJf/plocGekHfeDpDhR02VK58hgoHGyrQHirnha9flRC6LicpoqmkgEVqk02MqS7OMSHU1NhAVlJUURazIj+zoSyvpTqvpSqtpphRkEZPZTiF+5KojhhHM7glBkbSB62WFbQenJIJUoWojbTQM/awdo6mJzeVdO8ujD2+PfH03tDdvfrV6YrpvvSOilBAjrRgn4yQ7O7y/v35sTvrnRuT1VNd5RPNZZPN6YPVIS3Zlnlhehl0ZAYdzfInFEU6NmXReiujxtuT5waSpvtih9qjB9qiB1qDO2tdKlnmebGm2ZHk4njPppyI4dqkua7sleGmW2tTz+9vfXx5+xdo8vzRT+92P71ce/d05vnx0MOjnnt77bd3uu4d9D+6N/rs4dTzx6NPTjrvHlRvL9fuLA0cH8y/ONn//uXdX94DC7n3y8eD799svn22/OLhwrMTkJWXj3bevzj68d39Xz/e//XT4Y9vZ1/cr92eixqs92kvDBmoyVoabLy1Ovn87tanF7d+fnfw87vVD0+HHt+q2J1njLc7NOWZlCfii2P0CxiovHDt7CCtLH/NTLpKOlUxxQ1d4O/RkebTnmKT4SZvJc+ry8+FvMGDEuTVFeRD8nPDb1xVvX5JmfeSIu9lpevXNAS54SI3kBJiejKSWAVZQ2VlU3U1kqaWBUzNVF2ZoKiEkVVESSjpiCnriCppCysihBRg/HKafNJqvJIqXOKKHGIKV27KXRKWviAkdU5Q8hy/5NnTCEieEwBnpM8Ly14Skb9yU/GamDKnmAqXmCqPhAafBEJQEiUuhZORNlaUMVOWt9BQsoYp2SKU7JAqILY6SlYwOVN1GYKijKG8gomKlg1M1wWJcdPBu8EJbupET7ixl7axN9I6mOgSa++d4klLpYYWhsbUMMMqEjxzIlxYMe7FGdTqAo+qHMeydLvKDLf2wsT5vo7jneX3EFQ2Pz0fe3636s46gAogCkgawMn+QsbBQtbhUs7RSs7Rai6bInm3NwrubBbe3Sq8s1V0d7uYnbJ7O+X3dqvu71fd36u4t5O7vxQz3+fVU+7akOdWletdU+hemu2UzSTF+RuHulsxvChpwdTsMO/MQItQa7SLAcwaoWGlpW6uoWaqqm6qqmWhruugjXfXN6XhSf5EiyAzqyBzyyBziyALEHOQYCvzELJ5qI15mK15mJ15mD2IaaidSbCdcYidUaAt1s9Kn2au52Om622q622i7W2M8CLCPQgwd7yWGx7mgtdywWm6YGGueISbEdLNWJtihHABaMFqOehp2qM1bHU1bXQ1bABIkJo2SJitLhywBMRBV9tRV9sBCbeBq1qoKxoryeEVpTGKUvoKUvrgQEkGq6JoqKFugcK4k0iBjhahFPNQV6Kfk2GgBykm1D6d6V6QBaBCrSoNaqoN72iO6+/OnZ1s3tsefnAfcGX04d22g63ChUkgJfH9PXF93TE9ndHdHYzutoiOlvC25pDm+oC6Ss+iHMtEhj7dTdWGKG2EksLpygJTsSTBHW31Pdim4uV+yhWcpxvGkwJOGni46kNTHR0QtlYaJGMlQ4yMLkJcXUVEUe4zEf4Lry+/4hQX55CWviYvz6GkCCBxXV0detwjEAAeguy1IAV10YIoND8SBdmJjvZ1BBw84nk0NbnZUGFrijKHkhL4djZUlDgVoQPOU3H5DBVViEDqGrxaAA8QIYAV/X8NIAq4TFOTV0ODW00VWiZSBfwUaAViQClOZQXo9wQskQM4kYb6USTFgaNcExPhEGMLBOQWABLscVynk07Y+UwLiCUQLdgRYS8FBtXEIOT8zR7QfjYbkL+gAmkKUBZovv3p3lzQzxIRviLEf1mA7/Ncet4rZ65znBHgvigGLU4soCIri4bpko1tg33DCzJZHQ2lw92Nc2Odq/Nj+xurJ0d7z072n52s3r81srVUM9KVWFNITY4w93VG2RC1SPoaZnpqpnqqZvpKRkhZrJa8vpqCgQZEF3uCbRjFjxXulRbmHO9vHUU1C6aYBDgRvK0I3uZe6UFDd9YOfnt/748f7v32fvp4p21ttGltMHu8MbQj36ooEssK1GMFYnNDTUpj7Osy6V2V4X0NkX2NEX3NoT2NPo0ldkWpxlnhRpkh5oXRznUZ/r3lUePNqQu9hRsTFVuzNduL3Xd3gHOsv3+28/3L3e9fbn58tvLu8fzrhzMvHow/uTf08M7Awzt9J7cBS1pv7dTurFZsLhYsTmbNDuctjDbsLA8DyXj1cOfjy1s/vr3107vDH6GlWQ5/eHvrx3cAJ7d/eg9Atf/9q7W3j8ef3Go6WMyY72VMtCQv9JTuzXUc78++erj7/avbv76//4+P9377cOuXt2sfnvY92M+YH3atL8SzYpApwYikQESCn3osVSXKXS7MRTzI+ibNVDHI0rYowqU0isiwETeS5IbzcsGvX9cV4kML30AKcsL4AFHOy3Kdl+G8KM99Wfk6h4YgL+ymgI7ETcAV4CtGqgpEVUWimgJBWRYjK6MrJYe4KQsXloMLymnxg8hoXJcCYFDmElPgEJG9IiR1ASDkhsSZ62Lf8op9xysK5boYCDhz5rr4d3ySZ/gAbKQvCMheFJC7LCB/VVCRQ1CZR0iDX1hH9KaBpBheRsJIUdJERYqkKmOhJUeGy5PhciQNaSNlSaycBEZGjqikQdbUcdQ2oGgbusNNvGBmVASJpmMdZEBLdYotD0usS0iuS0xrTk9ry46rS/PNj6HkxHmWZEJQqchxLE23q85w7yhOWujvPN4B/xH3fni19uHJ4NOjsqPVTEhTIFNJA2jZX8gEUDlYzLm1nHtrle0iEFEgitzfrTjeqzjZrzw5qD45rHtwq/HBUcvDO80Pbzc8uFV2eyNxeZjeX01rK/drqQhprw1sLPcoTCUzQxwSg6k5MQHFieFVaf75DItgBz0XQ00rHXVzuLoZXIOkoW0F07NDENxQdkEEWoIlLdHcO96cEmPuFGVqF25qHWpmFWxqGWwKSGMRQrIItbAMBYCxNA22MA60IPiTcDQzDM1U38cERSUivYx0PAkgcE88zAOn5Y7VdDPQpKA1nNHqTnoajmgNRxRoNR31NB3QWiD2ujB7Xbg9Em4PjSCA22vDbOAwWxAEzA4Ot0cgHXV0nZAoJx1dB4QmWVOZpCxPVJLFA5Yoy+HV5A01FYyAvmjDyPoGbiTzYIoNg2ofS7OJoVkw/KyZUZScdO+SfJ/KYr+6quDmOgCVmN7OrKmxuq2N3ru3B0/u9t27Vbu1nDExFNvbyehqj+psi+hsDWtvDgU4aW0MbK7zq6/yKCt0ZKUaR4cgvRyVLQ0l8UgpLGQqyhZmCEdbPQ8XA6AmkJ24QoLi4QKIoufuAoiiR3FGOdrp2JJhJBMVQ4ycrraEptpNZYXPRPhvvL7kFBXjlJSClsxSUORRUeZTU4dkQlsbQEUYqSuCQglDGzgCtOjya+vwQRMbIcng0wKmos6pqsahogLgAShyTVHpGsDJ50C+AsmKshIPgApwC3X16xqa17VgIKfwYNsJNEESOobqXQhwwAveBddoavGoa4D7c6mqcaqocCircCoBVilA44YV5CCcyMkAQWETRYwLGhwMHIX9lD/ViNOVVP4KdPKmEPcpZtg8gAZxiQgDVEB1rdPRxkBWhIGsCHOxW0hc/upW+UyXU8xAEvPXT2FzBZjKJfYWwue5rp3lvXr2Buc54etXJIV5lSTF4EraFjgKwz+1rqhxZmhgb3X8zs78w9s7r58++uXDh3//8uOfv//w5z8//Pv3pz99Wjk+bJgeTKoppMSGENxsdawNYRYGaqb6gCtKxigZPELGQENaT509mFhL0QiuZqqrYYHWtNSDkbG6zmYGHjb6HmQ9Cokc5dmxO7v49uTgx9d7H54M7C0WjTYzwcf82kybrDBsAlUvwReVSEcn+emnBeMzI83zmS6VOdT6Eo+afPOcOFxqMC492Kwg2q0xO6S/Kmq0MWmmk7U4mL8ymrs4mjk9yJoZzJ0fKV4Zr99d6Lqz3n+8PfBgF6T7eKvt9nrb0Xrnne22o23AktLVedbcWOrkQPxwV1hXfWBbdXBHdUx/S+7sUN32Ys+dnbGHt6efHs89P158+WDl1cPlVw+WXp7Mv7g//uSo93i78WCxaH0sfbE3c2WwaGem8nC54e7W8NN7c68fb3yERgrs//AKSM/Wx+cLbx73HB+w5ke9GkqNs5mYtBj95ChdZph2TJBmBF0lxFOG5iDhYansY0tI8MdEUpBUU36UMI8mLxfs+nUdwRtIYV4E/zUN3gtKXGdlOM5IXjsnzXlenueqCj+3lsh1hLggWloUoyBlqCJlCJ7milIGclK6klIIUSlNASmNG5IgqnwSKmycKHKKyl8TkrrEL3Hhhvg53pvf8Yp8yyP0LacgyHccgt9xCp3hEDrDKXgWaoW/47r5HbfYdzwSZ0G4JM9zSV7klr7Mo8jNo8bPixC+gRIV0pe8iZOXMFaRBCwx15AmaUgaq4jj5MX0pCX0pWUJ8qoWagg7BMoRjnGFGXsiSL5IcqABJdqUUUgv7M6sGquoHauumagpHanK6CwMKk30yGd6lmVTa4rcK4GpZDjUZHp1laUsDXWe7C29ewz8b+Hto65Hh4WHyxm7c6m7s2mAK/sAKvOfoXKwmHu4nH+0Vnh7o+Tudvn93eqTg7qHt5oe3W5/er/n2Un/i4cjr56MvIYy8OJh0/F+1upYcH9NYGd1XH8bc6iL0dUQWF3gnBlNy08Mr8hgNhUWDjbmjTQldVS5MP3RrniYNRJhqYN20DX3I7pEWwUmO1S3MA63G46P2mYXKqo6M+JLg2lp7i4x1nYR5nbhli7Rju4Jru4JFEqss0OEtXmAiZGvIcYTh3LDaLvqw1zQWk5oDSeUmhNSzUlHzUFbwxkFd8PAXfXUHOAK1hpyVuog8hZq8haqihZqShZqyhZq6pZqGpZqWmRVLWsVLWtlGFlZywoKjKwCt1FDOmrou2iZUHXsQgxsgvWNPDV07FXUSaoqxmqKRhqKRPC3iYRZ6OnbGZGoNvbh7pQ4OjUtzDMt1DM9wjk5wiU93rMgi15R7F8PESWsrTGyszW2r4s1PV63vdF196j3/u2u2wdV64sAKkBTons6o7ragaCEtkNECWpuCGiq86ur9igrcmSlGUcFanvYyZGwACoSWKQMEadkSUI42aM9XfXZ9S49T1e0hzPaw0XPzQUNHMXVEe1kr+tgo21toWVmrIrHKumjZZHa0gitz0D4b7y+5BQT5ZSW5JSV5VJS5lPXFEJoC6P0RDE4MWgXYSNxHEEci7+phxFG6wkhUTd0kFD/B9snuMFDX0WVSwUSCKgrBQSwBDz6oae/PNT+ZSrcampAOwCH2DgBLEHwwxECCB1+mDbACT8COgOgwi6FQUS5Dhzlc20N3FyZC/BJUZ5LXh7qkIf65KU4AU6kIEHhhHDyl5f8Byqfpz1CIBH+jJa/ocIeUgyNBv7cZQKZDWQw18C77GugbxH6XAo7XTSM7Tf/uQAahXz6IwBUgKnw813ggaBy7vq1c/zcF2/e4JS+KaAiI4NSJbiS6KmhaU1FTQvDw4frY7d3Z09uH7x78fJfv378n98//fmvT3/++9P//Pv9v3+/+/5lz8p0VksFNYVhSnNFO5ohrPCaJH11SFagrRHUzQ1UTYG7oNXM9DQtMAhbgo49EelgoudibkR3MQn0wNOcDbztjPycqdlRRaMNtTPdBQONkVUsan6CU0akaZwfNspHL5pmEB9kwAyBkhiKS4kwSo82zmDgkoL0E/z0U/2J+VGuTVl+/RWM8ZaUuZ68lbGcxeHUqd7k8Z7Uif6EoY7Q9iqv2lzfpnzGQE3mfFfhxkjJ5kT51lTV7mz93mLdzmLl5lzF+nzR8mTqRF9YV4NfS6V3Q4lXHUixd0Mprbkc0CWip4E53JEx2ZszO5i/MFy4OFK0OFwA0LU4mLc4yFrsT5vvTV3szVodytkcy4Omvyw239nsuL/XcXev695+3/2Dnnt7AEudR5vNB6tVmwus2eGQznr7UpYJK5GQAdAYbZAYiY4L1YkO1Az3VfB3kfW20QpwIcaHogPd1BwNbyBEeTX5ueEC1xFCfNpC3Fp8HKpQ4eucDNdZKY6z0teAslxS5OFUE4C4oiMBuCKClrkJoisppiMuBhcW0+AXU70upsIjqsQpqsAhqnBNRO6KoMwlfsnzfGyccAt/wyn49VW+r67wfX2ZF8qV69+AXOX79irfmSvXvwO5euPbK/zfXBb4+qrwNyBXRL65Inb2iuSlK/KcV5R5OTX5eRA3+dCSgjg5YYKSqLGyGFFJFKcgoi99U09SwkBKjiCvYqaiBWTFWkPPXhPvqm1OwziGmvqluqTWxdSOV3SudPau9/Vu9jcutBUOVYZVpXkXpVAr86h1JR7VeU7lGY61LJ+eiszV0a4H+0tvn6x/fD796kHryV4e1Nk+C6ACAnwlE5gKlEXWwVLO4UrR7Y2yezuV9/frH9xqeXy35/mD0ddPZz+8Wvr0ZvX7tyuf3ix8fDX/4dXY68ctx7t5a2Oxw83xQ20Z4/2ZE0PM/vaw+hL3HCatMAlAJb2rsmFttPvOUuvhQslCFyXDT9/DCOWMNfQhmocYOyVYZzcxtu73vvt158Pvt77/4/jx98cbj/Y6wP8YXcWRZfEJNRmN0919G9MDm9M9q6NlA7WBOaGkEDLaHY9w1FOzQShZaSlYaMhZqMmaq8qYq8iTYWialVd+nF9pknGonZy5iqShpKiBhBhGUhwrLYWTksZJSuMkZHHiigRxZaK4qrG4qqmYupm4upmEupmkBkkKbiWj56RqHYhh1QUv7Nev3q3vXUjxSzHGOKvDzNXUzTQ1zHW0bbBED6uI7Pjh9cmFe+sje9M1k63Jzfngn9evIMU7L41akutXXRYCHKWtidHZFt3TnjDYkzs7Wb+z2XP/9sDxHQCVmo0lABXmYG9sb1d0d0fU36bSAkyl3q++2rOs0CEr1TgyCOFmq2COk8Lr3tRHiOP05MyIatZWCGd7bWcHpKODtpMtiI6jjQ4Aib01wtYKYWOFIFvALMw0TYzU8DhljIGiPlpBD/UZCP+N15fckuK8CvI31DRuotDSBKK8maWanZOWizuc4q3l4qnh6K5k4yRvYSttbC6GMwJoEdDRBUgAPsGrrsGGiiq3shL3Z6JALOGGuAI0RZE9DEyZS12FRwPgBA6BRFtHQAcpiITUByAKREAXKaCrw4/UvaGjc0NbB1wD0MKjqcWlrnGqQRxKyhyKihwKCpxyMlBk2ESREOeWEIOWnYfgIfi/58P/DZXTQKRhWwUAAPTl6fwS8F1/zY48HQz2l4hA3funygICVcNERP6uhrHrY5+h8nk4srDgNUF+aLdHbvbQL95r5wR4LosJcMuKCajKyaHUDR1NvOPozIr0ypHW5tmh2qnB1qWp5fu3nvz4/v0/f/3wrz/e//tfb/79+8lP7yePNop6G2NKMylxwSYAD87mOtZ4DYgielpWeJSTOdbD2tjX0SrQ3TGSBs3YSgh1Y4a5J4Z6p0RQ06Kc44PNQj0Jfs5GQe6mET5mDF/LGDophm4a7Udk0PEMOi6Kjo/2x8UF45hhIAYJIah4f+1oOpzhA4/2QjF9jHJCbWtSvLoKAwer4ybbMhcGMuYGEse64ofb44baGb3N9MZi57JU+9JESm1GQHdp3ERL6nxP/tpo5fZs3f5S9dZC2fps6dps+fpc/vx4bF8rrancvbrAtbKAUpXvWplPqSpwryl0ry32rC/xqi/2Bqkr9KrJd6/OcavOcq9jeTXl0DqLQgeqmTOd2euj+TvT+VtTJdszVTvzNfvL1TuL5RvzJStzeQtTKRP9sYPtkb1NACT+rVXedSVOZdnkvFTjjHh8WqxBSrR+UqRufCgwFXikn0YoVYHuouRjr+nvqh9K1fF1lLNEC+jJCKMlhaBI8CGEudT5rinzXlG6fkGOB3DlnAznBRnOi3LclxV5ONRucGkK8cJE+OAifFpCN9QF+NVuCKjyCSnzCCtyiQCWyF+9KXtFWPqSkNRFAYnz14GdCH3DJfDV1RtfXuL98iLPlxe5v7jICfLlRY7/5BLnV5e4vrzEzQ7vFxdBrn95ge+LCwJfnRc5e07i4nnpqxfkOa+q8HHBhHl1xPnQUnwY6Rv6Uvx6koIoSVG0pCROVoGopGauomWlgbBS17HW1HPUNqbi7MKs/NKpGY2pDVMNQ9tjE4fTE0czXRuDpaP1kTWZtNJ0Wk0Bra4UgkpFplN9Dr2vOmd9ovfhIbTg9Ifn4y+Om+7v5O4tpO3MpOzMpO7MAKhkHSxkHy7lHa0CnJTc26o43qt7cKv1CcDJyeCrR6Nvnky8fTrz/vns26eL75+DzLx+PPXywfDTO613Nso3Zqo3Zht2Vpp2N2q3VvOmR6Obq7wKUr3ykkIqWClt5XULvYO35kbuLXUfjsW3ZTkl+lmGe1iEOdnGOLimOiY3x64/nXn2z3sv//no458vn/72dOvV8dTJfuPyWN5QU/lkd+/e6syD24tP7s0/vjtwsF493U8vTND3sYY7ElTIKDkzLSljNXEjZVEjQGVFaSu4aQzdv6YksL44uJalTzMXxkgKIEWFUDLiOBVJrJykvrgUSkRSV0gKLSRncFMRd1MRL6JoJKRAEFYyElEyuqlqIq5rp+yTYDe70/z6p+VHH2fuvx5ZvlVITzBEkjU0LRFa1vr6riZOEdTWmYFHv7179M8PD/94//C31yOHc6zuysjqvKCK/IDK4uD6qvDWRoCK6O52oClJw/0F89ONe9tAUwBUum/vV7OhEt8P9aYwutvD21ugwldTnX9DDb2uyre63KOkwD4zhRgZiPCwB1CRwCKEkZrCKLi4PkqWgFUwM1IwhSJvaihvgpcj4sBJOUOMPAjeQA6rJ4tByenpyqGQsigdWZS23H9vngp4fSmorCSGRMoTTTXsnZGeNFxghElkvEVsikVcqkVMshkj0TAkRt8vDO7mo2LvImtqKYLB8yNR0FosGhpcqkBWVKAlUpQUucBz/y+ucCkpQDUx9tCv6+qq/HC4oC7qpgFWHE+QMDKRNjaVNjFjBxyYShubyYBjoomEIVEcBy1hKayH4ddBXYdrc4MfAWTldG4jiIwU1+lAL0lxTglRAA9OiCiCXEIQV7jZYQ8XPiUK1LlyShQQqOQFrSMJTYPnYc+Q/0916xQn/+uYHXY97fMgMSjXRAT/H6YCSHaNPe3xIjfHea4r50/n0kO7PYryqUhLwlW0jFCm7pZuDB/f5BBaciSVGeaXFJFSnj2wMHLw9Pbx+6dHrx8v3NtrmhliVufS0yMdInxIdCeCpzXaiQSzwqmb6YHAyDh9iqVNqLdfZnR8dX5WR31hf1vxYEfJUBdoiwbasjobIsqz3ZLDSGEepmEAKl4mEV5mkVQSw5cUQyNG+eIiqHphXugIKjrSRzfSRzuCCo/wgkV4wBnuKCbNJI9hV5NKacmh9pSGjTREj7XGjbRE9DeF9TZH9rYEtde4VeXYFjFtihJcqtNoHYWRI3UJ0x2spcHC9fGqnbmqrbmytdni5enC5ens2dGYvja/hnL3ygLnslyH0mwQx9Icp3KQXBDHilzH8hz7sizb0nTr4hSbkiSb0kT7qhTXJpZvTyljsjVrY6xob7Z4b750d75yd7Fmf6Via6F0fa5kdb5wZTZ9eji8u4VaX+ZSnm9fmm1TlGWZn0rKTTHJTjLKZAKiYFKiDZKidOPDkLEh2oxArYhTqLgq+Tpp0l20AyhaHmQlW6ycGUzeVFPGROMmVvaGtiinyo0r8ryX5LgvynJflOO5JMt9WZYTfHkJHCvwXFHivQq8QZmXR5mbV4mbT5FbQJFLSIFLRBEICoe4wjVx+StishdFpC/yi5/lvfktt+A31/i/uMz3BaDFJe4vLlz74sKVLy5c/uLCJXYuQrl46YtLl768ePmLi9fYyOGCcokHQst5/q/OCn575ua578QvnpG+el6e45Iy7zX1G4AuPNqi15HigmjJm/oykjh5ORMVVUs1DSt1mJW6tg0caauDdcORQ6ypqb5JdanV4w39m6NTR0vTd5c7N4eLxxsj6nL8KrID6kvpDQAquU6Vmc4NOQGDdXmbk/2PjgBUlt8/G3l+v+7uVvbefPrOTPLWVMrWFDhg7S/kHQFB2ay4t1MNHuhQsete34sHg68eDr162P/8fvfjW90PDjrvb3feXe84WmnYna3amCpfGy9bHatYnWw72Bg6uTP55OHE4wcNWyspA+0hdcUBFXkh1YVxLVXFI13tK+PDu3NNCwNZffUxzWVBFdleOXG2CTSzKAfrOPus7vSd18uPfz18/Ov9/ff3lp/fHbi9Ub0wnN5Ty+qrb9ucn354e+HpvdlHt4fv7lQsjARUZLumR2P9nBRt9SXMtcVMtW4S1UWIqkImqqKWcGykd2hbTexQR0RHqX6QtRBeXsBAVtRQVcIEIUbUlCSoyhsqS2NkxPUkpbEyCkR5eSNZeYKkHF5cFi8mbyilQJDWsFB1i3Ma3W558dvq8982nv5j487bjsh8a30XJNqViPGyMQ1y80qL7l5bePjbj8d//HT/n78e//HD3vuTsrGW+Mbi0Ori0LrKsObayM6WyM5WRndbTE8HMJKC+Zmm/Z3++3eGHtzpubNfubaQNjoQ29sR2dX2mSiNtX51VbSacmplqVdpkWs+yyYtgRDuD3ezlTXVE0ap82kp3NBS5oerCyG1bqLgwkgtEaSWkI6mAFxDAK7GD1MV0FQR1FAR0lASUlUQUpYXVJQTUpQXVlYQUVYQVvpv9ql8IW9I0LR1MPD1M4mKs05mueaUuhVUuEOBJtJT8iucWMU2yTlm0SmYgAiYm7e8le1NDF4AgbyursWjqs75V58KZCd/VcA4wbEK1JsCTVKBw8Uw4M+YpGLnCHP3Qvr46dODMf4h+n5B7ATr0YLQtGCUTyDSy0/H0xfu5q3uSJG3sBE3NBXQ1efV0ITKX7IynOyq1+nQYUAUaGlhdt8Jh4ggyDVhgWvC0AFgyf8ONAL4L678Xe/6fB4iBDSAGIQDugx6Czpgv8UOZC2ntS82V6C3Tn0IEAX60UICV/j5LvFyX+C+do6bvdsjP/eFmzc4pG/eUJACUFHB6uhYGuFdbU2pFHNfNys/N/tAd49IWmgWI6E0NbkyMyqXSY0PdAz1tPCxJ7iRsM4kfUdTtIOJtr2xFhmvaopWMUZpWmAwLhbO0fT0lor29bmx+4fTx7fHjnaH9jcGdtb6t1eqp4aTGssD85guzACXpECX5CCP9HBabkxIaUp4eYZfYZJDWrghw1s3wg0RQYFHUDQjKDCGBzqJZl4Y7dKQ5dVRGtBXwxhtYYy1hvbW01vKac3l9JZK38ZSx9I089xY8zyGQ2WyT0dB2HBd/GRbFhsnRasTOfPDmTODhUsTRcvTrOnRqJ4man2JS0WuXUmmTVG6dWE6uTDdmn1gXZxBLkqzKkixyk+2yGWa5saZ5sWQCmLIJQkOtRnUrhLGVFvG2mj+7mzR3kLZ/mLV/nLlzkLZ5nzpxnzFBnCg+fSpofCuZq+6stPNuKB9U/LSzHIATpKJWYmEjAQcmyh6iZG6zAhkXAi0RgsjAEBFNdhb0c9NmeaiQXeB+zppeVjp+9rgaNZYX7K2C0GaqHEDLnZFjveiJPd5Sc4L0lyXZHmuKPBeUeS5KA8Aw3VBjuuSPPcVRe5rilxcilzXFbgF5LmFFTlFlTiklDlkVDjl1TjlVTllVa5KK14WkTp/Q/wMt/BX1wS+vHzjK2AqF7i+OH/ti/OXvzh/6YtzF784zw50cOGLixBavrx45ctLV7+4zPHFJcAV7i8v8n55nufLc3xfnRH4+jvh774RPfet5OUzchznlXkuq93gANaiKy6kLymGlZXEK8gZK6uYqmpaaupYw9B2uvqOBnh3IjnInp4ekFKfUTPRNLg9MX17deFka2Bvpni0KbI+L6AmP6ixnN4IQcW5KsutOT90uLFkZ27wyZ2ld8+W3z8dfnavHoLKQsbObNrmdNrmVOb2TM7eQtHt9fJ72zUPDpoeHbU+udP59F7HkzttDw+a72/VHC4VrY9nzvemTHYkjbUljbamjnVkTPRkTw0UzI2ULU607KyN3D+affpw4uH9hq1l1nh/Qk9rQndLYm97+lBP3tRY6fxU09Zqw8Zy9cpcwfQQs6cpoCbPMZNhFu9rGOVgzHSm1oYxh9Jje9N8yhmehQy33CiH9CBygpdlnKdreohfcXxoZUp4VXp0Q35oTa5fRW5YNYsQYq/koC9F1hW31L5pARc01xSw0BC2hst5GuETfCglTFKyj4yDrgBRWdBQWdBIQ9RER5aMkbFASRrDxQkaYnhVUZyyBEFViqguTVSWJCiK4+Uk8AriOAVpogrMxSCkJKJ/r3vv/dzaq+H6uTRnprWhv41pKNU+KdI7NyWssqhgsHv63uGDX6DFI05+fjN3vJU30BDdWBpcUxLcUBXR1ggc5TQAKklDfYULEFQgUzm53Xm0W74ymzjYE9EJ4SSouT6gsZZeW+lTXe5dWepRWkApynPMzrBMisEG+ao7WsoQ0YLayjwqUpxKINJcIIpSXPKSHHIS12TErknfvColckVC6Iq44GUx/is3b1wVun5FkOcKP+8VAd6rIILXrwnxfebBf+WFodJJEXHWiRnO2YWeRZU+ZbW+FfXUsjqfsjq/qia/6mZqRYNHYY1Ldgk5MRMXzNCkUGXNyUJ6mOtaMG5V9b876gFRgKxAvqKkwKGsADSFR1WFTwsmisEoWVnruHkRgiPMY5MAt+zT8xzS8+zTcu3TcmxSc6xTsq2TWFaJmVbMDPP4FNOYJGxwFMKdpmjtJEYwFdBB86qoQaOHpdk98xJQzzy0PPApUU5lAnAFEIUNFfZD/z/TG9n1sc/bn0Djvv6yEwCM08tOA06e9tuz3/osIqdv/S93gZBzai2cbHG5Ish/+cb1Czyn0x6vnGFvIXxJ9Aa3lKgAtDixlhq0h4qliY+Hbai/MyPEIz48ID0uvoyV3lia2VKZ1VwVXZxFZUY4BHubeTlgnczR1gSEJRZmhdWywmta4lRM0PIEHRUTlDYZTw6ixNcUlYwPNm+sdO1sdGwsNi9NVk8OlI12p7VWRhan0zKifDLDY2uycrqrysfa2peGR/YXx4/W+nYX80bbvSpSCWn+6CQfHSZVN9kXywq2qkgEfhDQXxUz3s6c7I4cbAnorAY48agpdCjNMM+JM8oMN8mJtClP8m7JC+6viJloTpvrLVqfKtmYyZ4fSZvsT58cTB0fSBzpCe2o86opcixhWRekk/PTrPJTLfNSLXJTzXOTSTnJZiDZTOOsOEIGA58egc8MJ2RHmBbF2FQne3cWR0+3ZW2MF+zMluwtVB+u1ByuAJbkLIxlzY3mLU4WLE9mTA2GdzV61RYD0WGrSbp5bqpZdrIxKxFaPjIjwTA9DhDFABAlKQrFjNCJD9OODYEDTYn00wqnA6goBbgDqKj6OGr5OOjS7Z1Sgj2zGb450Q6xvgYUc1m8BoeywEVJrvPi1y5IcFyW4bwCcCLPeUGW84IMxwXpaxdlOC7LcQKoXFfhkYAJqKDFtXDSaBM1M3usZ5BLcKxfSEJASFwQLdLX2sta3UCJR/LSRf6vLvB+dYHnq3OcX569/MWZi198979y5sIX5y4AnHzHLcgrJCXKI8jDwX3m6rWvAF2AuEAcAlzh/fI7vi+/Efr2G9Gz30heOiN/7bIaHydM+LquuIiBNAQVnIKcoYIKURluCTNwNiD7WTlHuFJivCILE4r7qptnOnvXRqaPljaf3d54fmfiznr1bG90c3FQXVFwU6UfBJUcl+osj9aiiPGWyv3F4ad3V94/W377ZPjZ3dq7m/n7i6zd+YztmfSt6czt2byD5dI7m1XHe/UPDhseHDQ82K+5t1V+uAQUJ22+N26sLXqoiTHQGDfUmjrekzM7WLwwUr44UrM0Vrc83rAy0bQ61bI+07Q+U7Ewlj3WlzrYmTzQmTbSmzE2kD05Ujw3Ub40Xb+xVLkyUzI/zhrrje9u8K/Jd8mPJ6X4GcZR9GNd0fGu+kxnVIwDMsQaGWCNCrDVDbTR8Sfr+JGRATY6gWRkoJVuoDU6xM4wikJKoGFCHdTccPJOBtJ2epI2SDEbpIi1thAZIWgLF3LQEXVBSbqiJRx1bpBUeAmKfARlEaKaqLGGpBkMtHwGytfRijf0FAWxyiKG6jcJ6sI4FUGsoiBGXgirKAI5jYasJULDDWvCcPDM8/PIo5LjnYkRFNMYf8cMZlB1WVJPW/7EaOncTM3ifNPKfOvabNXsUFpvc0RDeWBNSUBNRUhzfVRHa0xPJ0hsL5SU4f7ipbmWw72e+7f7jo86bm2XLc8wB7si2puCWiCi+NVX+9ZU+FSVeZUXuxUXOOdn22emmsVG6Pt5qtqRpAx1+WHg2St2VfrmZQmhS+ICF2/yXRC5fl6I95wgz1l+rjN8nN/xXv2W+/I3nBe/vXb+uytnv7145tsLUL67ePa7S+dAPvPgv/KyY6a6Zua755S45Za4ZBc4ZeXZp+fapbIcMnJcs4s8iyqgBVrKat2LKl1ySs0TMvT9I9Qd3SWNzPi1kdzqGqemwqF0OvRLCRpSrKQEznCpKANNEUahlcnWuj40M2i1/AKPoipaZQOtosGzqMotv9wlt8SJVeSUXQjiyCp0ysq3Sc+xYGbgQ2O1PelKds4SxiQhlD6vijqPvDy3tAy3lCS3hDi0cP3pQ59NFGik1umI4b+61j9zAkIFxABAlNPr/ybHqYL8rSkQfqBNVsC7kKywr/8Pb/73Nl+QrJweiwgBN7oieOO0Q+U855Vz3AAqHOeEeK+ICfDIid/UUlTA6urYmJvRvHyS46JL8tKbqot62+onBvvX5maOdubvHUwcbLTOj+d3NcdV5FKTI8kBHhhnc4QFToukp2mmr26OUTZByxlqy2C15HFacHOMTQg1pqowu6ctq7MppqogrCDVPzvBJyPOPTHEJsLTnuEVXspsXuyfuLO2/vTWzqv7tz48Ofz0cvXlw46D5Zi+avOCKEymPyqNrpcZQMgPJ1ckebYVBvXW+HdU+rdVBrZV+zaVOpVlGqVHYpICDdNDrIrjPZpz/HsrokabUud6ClbHCpZGWbPDWTNQAFEie1t8GyvcKvLtijLJeanmOUlmrERTVqIxCyAkwTgznpgeR0iLhVY7Tw43SArWTwnGZoQQ8iJJ5QlOLazg0brU1eHczamSvfmqw+WqvaXSjZnM2cHUib70qUGgJnFDnX4tlZTKPNuiDAAq85xkU1aScRbTKDOBmJ5ASI/HpQKcxGFSYvSTGKiECN24MGQMcJQQxOkeKhH+mmG+qkFeKgEeqnRXJaqDqru1np+Dd35CZH0uq6+6ZLgpo708oigZYYfnVuK/KMl5XvLaRcmrF6WvnpW6fFby8jnJK+clr5yTunZFjlNAU8jYk5TfUjC2Nbp6b23twd7dd6+e/Prb099/f/L77w9+++3oh08jt/eSG8rhFnqXhc+f5fn6DNfX33J8/c3lr765+OU3l0C+YOfLry999d2V7zhv3kBZWxC83DD21kIyNzl4Lly8+vXFq19c4oC6WC7wfnH2+hff8n/1jch334ifOyfDwaEqcAMpLYJRFMfKi2NkpLDy8oaKmiQNghsuLCdqfHNi7/H+9sn28p3Vhbvr8/c2Vk529l/cu/vp+a2Pz9af3+/emmG2l4c2lIS0VAY0lLoDqNRkebQVMabaqw+Xx57dX3n3bOHN4/4nt6tvb+TuLQBZydpdyN5dAEQpOtqouL9Tc7Jfe7JffX+n7Ggtb3eWtTaauTiYOT+QPtOfNtmXOdlXMDtQNj9YPtNbPNFWONpUPNpcPNRQOFCb01uV1VWR2lEGfoHo5vLI5orI1uqY7sb43taEvvakvnZmXyuztzmmsy6ipTygNp9anulelOScF2ebE2mRFWSUSscmUw2YHuhoF2SInU6AjXaAtU6wLTLEAR3hpBfpasCg6EW5oiNdUOFOuqEOuqH28AArDZq5krepvAdRxt1Q0g0n6YaXpGDFKPoizmjAFX4bmICVBh9J9TpR+bqhEj9B+QZWnkdPiktXghMhzoGQ4EBIciOlrqPlrxso8OqDVk4AryRkpCZioiFqAZOw1pF2QCm6YbV8SXqhDoYMKjmF4ZKX5l9dGt/dVjA93ri11nOwDdK6vV69Ol80O5450hff0x7V3gIcJbKjBepK6emM6+2M7+tOHOzNGB8pX11sv7XXf/9O//3b0Crdi9MJfZ1hbY3BbKjQAVSqy70qS9zLCl0Lcx1zs2zTk40ZIXq+bio2JpJ47Rta8lxyolclhS+LC14S5b8gfP28IO85fp6zN7i/4wVEufYN55Wvrl386sqFry6e/er8d1999+1X337zNch3357mMw/+Ky/3zHy37CLnjHzzuGR8SJQ+PRjlTUNRfbH+gcSwSKuEZBdWvkdxuSd74S+HzEJjRpKOZ4CCua2IHpZXCwZBBRBFSRlAheN0PLGSEpcycDEVfpiWpKEh3IVCjGC4sAoAS0IbO0MaO/1rWrxLqim5xQ4ZufbpLIc04C7ZdmlZtimZNskZ4NfABkVquXnLk23F8EY3EDpcCopcMrLcUlLckpJcYmLcAADsGhQI9IiHOtWFOIQEOYRO56OcIgEw4LNYnF7JhgrAzOeu+L/v8JlPpwtNnu6wAq2HDy0OdnofiDEQSE6vBwfsmwsLXhM6nfbIc4Gb8zwHBJWz16Fpj9ckhfmUpSV1NRDWpuaBvgG56bldLa3zU8PbawAk28/uP/zh7ct//Pz6j9+e/fbT7Q+vZ+/dapwdz2iu9stMsPTz0HMwhVtgNcz01cz0VIzRCoba0gaakroq0rrK8hgNdRMU3AoLJ+MQZAyIji1Bz9VCz52E8bAwDbCPr05tmu0e2Z3denrr4PXJ9qvjtRf3Jk92K+cHfWsyjDOC9ZPpesnQeGJMaiAxK9IsN866MNWxjOVSmWNTkGyUHGbA9MekBpgVRDvXZlDbCoL6qhOnOlkLQ5mzA2lTfSnjfSljfcmjfVG9zT4NZU6lOeSCdIucZFIW0zQjnpgRT0iPBcGnxeBTGbjkKEximB4zSDfeXzeRjk4JMGCFGJfEkuvT3buLgsfqEua7stfHynbmy8GTa2U8aaInbrgjZqg9qq85oK3KrSbfriTTMi/FlMUkZsYbgdumx+LSYrCp0QbJDIOkKP3EKL3ESFRCFCo+QicmVDs6GAbsJCoAFhkAjwzQivRXC6OpBlMBVNQDvdX8PRS8HORcLNTczG2SgqjFzKT2wqrZtqTOwuAKpkuKr6COxFVlvovyPOelOc9KXv1W/NI3Ype/kwBoAabCzaMqiLDWK+ytOvhwcvzz80e/v3n0+7un//zl+b/++fzf/3r67389/Nc/7/7+6/zzp+WTw1ZB7hxSXN/xnfuG98zXnGe+5jj7FceZrzi/+5rr22+4z3zNdfYbjjPfcV24qa6IcXMlBPo7x8fIo2C8QlyXOb65wvkVJ//XPMJfc978+pLAF1D/ish356Uui6CVLMN9Y+vKk1tqbKPpMkQ1aYycopGSvjM6uiB0/8naj39++OHP73/885f3f3w8/vjw7sdHj398+ey398/++OHhbx/33j3p3ZmPb6sIri8BpkKvK6ZUsZxrsrzbi2NnuuqO1sefHy+/ezb7+mHPo1sVR6uAKKy9edbeQu7+UsHhSvHtjbK7W5X3tsvvbpUereTvzhVuz5TvzNcdrLbc2mg5WK/bXqhcGS+a7swbaSgYrCroLy3oKyrsLihoz81vzclpyc5oyEiuy4yvzYquzQmvzQ2syvWvzg+oLQ6sKw6oLfSvKfCtyvWuYLmVpLmXpHqWpfvV5YS2Fod3lge0FHrWZNoVxpmmBeHivfQiXHRDHdERroR4X5vsKI+KVL+G3IDmvICmXL/GHJ86lk9Npnd1uldFimtxvF0uwyIz1DQ1wDCRph/rqR3upOpnJUc1kXIzlHbFy1BADGVccDIOWGl7jIQ1WoSEuIFX5kZLX9ORuKYtzqktwY2UBkQBJ4VNNSWskFK2etIO+rJOWHkKQcnTVNPPBhvtRc6IdC9OD6wrjeloShvqzZkYKZydKJmfLluYLpmfLJ6DUjAzkTM1kjk2kDLUlzjYk9AP0s0c6GYO9iYN9aWNDhXOT9dvr3XfORw8uTtw/6jtYLNofjKuryOsrSEI6pyvptVVAqhQK0s8IKjkOOZk2KQnGkcFo6guKmQjUQyMT1OWU1b0qpTIJQmhiwAqIjfOC10/J8h7XoDnLB/Xd7wc33Be/vraxS+vnPvq4pkvz37z5bffAKj8HUCXzzz4r7w8gSKk5JDC49Hefmq2jlJEU3H2RHpZE2NNB3t9mo95bLxzVq5XSZV3SY1rTplVYhYmIFLVgSKGN74O14bWkVRW5jid88gmCsQYZSVuNVVBHR0Fcws0lWbFTPEqrqBXNwVUt9DL6ik5JbbJmcbhDFxAMM4/EAQAzMDP38CXrufji/Siajq7yJtbiuEMgQzxqKhyyctxSUlBI76g/eSh1bcABj7vgwKe74AlEFHYAXQ5JQ303AfvQqoBwraWm2zPYH8LexDXKVE+t6LQevg84mLQPMfTHezZGz6CW0FYEv0PhCA9glYmFgYMu8LP3u2Ri/McMBWeq+dvcF8UZXeoqMrKYHR0Ha3JkYHhZXlVU8O9WytTd/c2nx8/+/3j2//5x9s//3j75z/f/s+/nv/5+9GPH8fv3iod7o0szXGM9Me5WSNtjbQsseokAzVTPWUiSgGvLYeFy2Dg8jiEoqG2irEuOK9pgYFZ45FOZmg3G6SrtZ6HLd7H2ic9oGGqeWhrbOpovmN1qH6ut2S8Pam9nJIbi4/2RUVR0bF0dDQdyaAho2ioWH/9hGBccoRhSgQqnq4b54NJ9DfJjbStSnJtzKZ1lcWMtiRMdMaPtMf0t8T0t8YPdsb2dwS01LhW5NsUpptnJ5pCLhJrlBZjmBqNT2ZgkxiYpAh9Zhg6PhgVG6Ab46cd66sd76ObTNfPDDIqYljVpjq157j1FFMHysNGG1Lne/NWx3IWRxLHuqL6m8K66+lNpS7lLMs8JjGDgU+N0E8ORTGDkPEgAdqx/vAYP1i0H4zhr8mga0RCUY+gqwIXCfFRCfFRCqYqBnvJB3oqBHqAyPu7y/u7ydFd5emuKv7uinQXGQ9raUeShJWBGsUMF+xil+gb3ZiZ0lsU0ZCK8jMTMFDgREheVBU+I8fzjSTHVxJXvpS4+rUU53dyPJeVBQXR8kSafdPCwP1fXz/558en//z46B9vn/z+w/N//vHiX78/++evD379/vDji+n7O8y6PBUz7YviV74VuPQV/8Wvrl/6kufSl7wXv+K7+PWNSyBf8V366vrFb/gugw+VSHuyU3wUOdTvpoYClzDXRa5vL/N+zSXyDdfNr7jEv7km9s0FoW/Oi17gVBU0oDrENte17O207e8w22sMvC0l8QpKxmoEL6P0xpR7b299/+f37/7907s/f37/50+Pf311/MOTO+8f3v3hBbSnwC/vN14eNy2PM5pKA+qKAhvLfGsLXCuzXGtYPu0libO9Tbc3J16cLLx9MvPqQeeD/eKDpayd2Yzd2ay9eUCX3P3lglurxbfXQAoOl3N2Zgt3ZhuO1gYe3Jp8djzz/MHEozs9t9eaNsYqJpuLB0rz2rNSq+NiC0MiWfSIDN+IdN+oDFp0dkB8UWRiFTOhNjWmNj2sKp1WmuxRnORWlEQpSnItSnQBKU7yLE8PaixIGmgomhuo3Zxu3Fmo2ZrLnx8Ma6+wy4s1TgnEMX0NEqimGSGeNaz4web8xeGKjenqrZmKzanStYmilbGC5VEoS0N58/2ZU92p4x1JI23xQ01hnRXUmmzHgnjDeBoi0FGLZqNFIyP8bLX97HT9HHTodhrelgpOBHFLXX6iGg9GgVtPlhejKGSsKWmNlnPCKVKMlT1JmnQb3VAKjuFFiKObJYfa5zC9K7OjOmszRnsK58YrV+brNpfqt1bqN1dq15dqoCzXboAsgVSvLVauLpQtzxUvzuTPTYHkzU4WzE8XLcyUr8w37Kx33d4fOrkz8uDewP1bzXvr+XPjsb1tIS11gY3V/nVVvjUVvpUl3uWFniX5lIJsx+x069R4o8gApKeDIgknoqfBqyrNISNyRUr4krjgxZv856HaF5AVnvNAVvi4zvBwfst15etrl766cv6rC///h4pTYhYpJAbp6iVHNBPSRl7XgKay88G0BJE6EgRDDXs7A38/K2YyJa8EWrG4oNIuLY8YHg9385UyNr+hrculpgYoAkFFQREKxBVlLhVVXg1NUQMDVTtbXFCoY0YOtbzWu6Leu7iaklVkEZuKDwxHULw0HZy1HB1hjs6a9g5qNrZKlpbyZiRpIlHMACuoA34TLW4VVU45eWhNewlJrtMnPlsgoA5ziAeQpkBzStg5NRUO9tTFv3Hyv6ECzZ9n8wb6LuHPjvKZT2yogLCh8rlz5TSc7AIaaE8PQKDlXoSFrwkKXrlxHUDlHCfHWQgqV87f4ASfESCoqMjKYnWR9lakIFpwUXb15HDz4nTf9vLqszvgefTiz59e/c+vL//8x8s//zj546eN98+79zYKBrtCCjPsImh4D2vgH1qWGHWSPuCKpgUOYWOMciDpOZsD3hC8HIg+TmZ+bpaBHlbBHjbhPg7RAZahVCM6BefjaBxEcU4M8MwId8sIc0wJtWEGW8QFECOo2HAqJoJuEB2IiQ3Wiw7SjfLXjqDDwqiaYZ4aYW5whodeoi8xJ9yiNN6hNt2noyisv54x3BrR2xjUXhPUVhPW0RDSVutVXeRYlGmRnWSSEWeUGmuYEoMDFEmMNGACkEToJYSh4kKQMYGIKDos0gcW6Y2I8dJm+qAzg4jFMTYNaS4dua6d+R7dhb69pSED1VHDDYyhxqiBJkZ/U0RPvU9TkU1JMpEVaZAWhEoK0GbS4fG+WrFU9RiqBsNbLdJTJcJDMZQCohTiphBEkQ1ylQ10lQlwkfFzlqI7SdAcxX3txXxsRb1toHjZiHiSRT3JIu6WNz2sJLysJTysRFzNBGzx/BaoGxa6ApYocWu0jCNG1dNY3gkrQoLzETQ5DVQuaUufURH6So77SxmuL2S4vpK/fkZFgBclp2ZL8GXF10z3T91bW3ywOXF7vndjuHmur2q4Ob+9JCQrxMaPhLbTlsXI8qjynRW9+I3gha8AVG4AnLChwnf5yxuXv+K//DX/la/4r37FdxVA5Vu+KxdEuDmlBa+JXb8kwHGO++J3HGfOcX1zVegMp9gZTvEzF8W+PSNy5pzU1RsoOXtmWPH0SN/9O73HdxI665DeJHGCsoIZDOdlGlEaN31v/uW/Pr3+87dXf/7x5I9Pq88Ppu6uzB9vb7483nv3dO3F/b7dxbTu2uCafHp1Hr2uiFqd516V6VHPoneWpMz3Nt3ZmHxxMv/m8eSL47bj3fy9hYyt6dTNqfTt6aydOTZXFnMPFnP3F1g7M1mbk+X7iz3HewsvH25/fLX94cXKq5Ox44329aGqiWpWS2pMfmBgort3pL17iK1rEJkSSHbzt/QMJvtHu0Rl+aVUxqXWp8Y3pIfWpvpUJnuWJ3lUJFOr0/3qcyLaS1OGm0oXh7sOV6YeHS6/Ol5+9WDqye3W/aWsiQ5aY551YYxJdjgpP9qjIS9psrtmb6n3eG/08e3hh7f6jvc77my13FpvPlxvOlxrOlxt2l+u21mo3pyrAtmaL12dSh/vjmgrd8iJwUdT9UNdsaEUwzAP0yiqebSvWZQ3LthZ29tSxZkgZa0nYaUrSUYruxpp+FjCA+1QoRSjeD+7nFjPskx6dX5wQ0lwY1lIczmjozaxry13aqh6fR5goP1wq/3WFpCMlr2Npp31xp31pt0NcAzStr/ZurfZtLtet7VaublcubZUsbZUtb5cs7lat73euLvZfrjbc+eg//jWwPGtrtu7NRuLmRMDkR1NwU21QU01QY1QAusq/atLaZXF3iV5bvlZjhmJRhH+2m62cqYGN9FqPCqSHNLCl8UELt68cUHo+gUB4Ci8F25wnwPh4zzLc/Vbtql8dWoq5yCoQLWv/5XPPPivvMxDY3WdPBWIZsIwBI+iEjeIsjK3qiqvlpYQWk/O1EzH08M0OtYlK8+7pNqrqMaRVWzCSNLx9pc2seIHUFFVA0SBoCIvzwmUgj35kUdF5QYcJkUwhFEoZtGxrtmFPuU11P9Xe28B30aS531LZpZBZluSLZmZmZmZLRllFNgyMzPJlpk5xtgxYxKH7Nixgw5nMpnJ8O7cLBzs7e3uUy3Znszc7r733DvvPc/dm8r306murqqWW93/X/2rukrnohJUWuuRVWCbTLVNItkmU+yTKTYJKWZRccBP0vQJwDm5yltYA21j/kKwDlJdXUxFRQyHFTublcLyGM4tPpAQoChoeTEUExA5n7DCBFIX5tLCkLQADwP4MWeH0PLiaJQEa8YJ9kxUJHA4lq5A4y7MoRdQSgQjDxVkTqtEQm+OQbWB8wJRuRil54W6v4T4pEX4UJLCyigpHRVlK2NjPzfXxKjo4pzc9ob6sZ7WmaGJnfk7L/df/+7d+z/+6ss//+bTP/5w9O2bmXt79XOjtNaqqAKyD4lgT/A1D3I28rXV97Yx8LG1CPP0TMaH5ZCIlbmUptIMRk1+T1NRfyugfLizfKQzo6M2vjI3MCvJnRztRol2T4/1zIz3zkpirn6f7JyZ6JBOtAVkJttkp1rnpFpkJJqmxeqTCfoUvElWjF1pimdLtl9ncUh/ZcxoE2m6O2txOGN+OG1mkDTRRxrvSxrqimhv9G2o8KwpdKvIcynLcyrLdSzLdSjJsS/OtCnKsC5Mt8ynmOeTTXKSjLKIhtlxRvnxZiWJdrVU767i6EstpJUB8spA6lIv6Upv2pV+6uW+lEtdKRNdpInu1IlO4jA9vLsyoL3Ys63AuSXbriHdsoZsUpliWJZoUELUK4qHKIzTLYjXyo3VzInRyIpRz45Wz4xSyyAAVNMjlagROEoYlhSqmBqsmBqISWERgEkOUEgOwCT5KwASfDGJvug4L3SsJyrWAxPrpRDrgY33Uib6qMT7KMV6KkZ5yoY6iHkb89mqcRjLw/Sk2PSk2Q1keMwxcu56ZokBaX0NpVN9xZOdGb11Sc2lSS0VCc2V8Y1l0bV5NvF+0maKgppIbmUEB5YfjuKFyfHCZPngMvxwSX64BB8MbKUFII2RFoAA7gskNgJsEoLskgABdnF+NjFeuAg3gAPJySnNySHLxYbiBrUJ6MpohLh4F9NK5oZbt+aKpzqCy5Jw/sZodwO1QGvLBN+QspScgfKBnZGlB2urT7aGbsw2L480LY22rc307Cz27S62rl4qmuxM7aolMqriwN3SXR/dXRPTWxU30kC53FN748rks3tAVK5//Xb985fjz49bjq9W3d2qPNyuPtyuOdoFAlMPHJeTa7XHO9WHmw2HG4OPb62/e3r0q/fQEjv/8NXh1693Xh3OHiz0rnTU9OWll8fi04J8k/zc471d47xdYzzdoty9YzwiSH7U0pjy7tyGydqa6aaCycb0kbrUoRrKWHP+fF/d1szA/tbl08OdT08Pvvnk4T988ex335z+9uvD794uvzzuvLWStdCDH6wOHaiOnWzL35jqOtm7/PrRVeBgAeH5/NnK64czTw8nnxyMP7oz+uj26MNbQyfXB452Bw53+w+v9t+91nNnp3FnoXh+KLWvkVCfH16RRqhKj63NSGjITmrMTmjIjKnOCC8n+xckumfHeeQm+pZS/KvSA6oz8c0l5AF67nRf4dxI0fxY8eVLlcuzVSvzFcuzteuLrbvrXbd2h49uj98/mHxwd+L+wcTJwdgxcFYg7wQwcm8faMnE/aOJ+3dHTw4Gj2733r3ZdWev+85e150bvQc3e+/e6j+6NXx8e/Rkf+xkf/DoZuet7brNxbyZUepIb+b4UOHcpcqly7VrC3Wr87Urc9VL0+XzE4WXhig9zWEVubaJ4ZpetgrWBuLaOIQySlBBlh8lzS8vJSAvKSALEBeQRfKDxq6kCLe4IBdSgEOEl12Ii52PnZ2bnYOTk4OLE8gJ5Kn8smMqjnGpBt4BWAsbSS1tcXV16OVdTU0ks/9K1txM2cXFLCbaKzePUNec0N5HbB+IrGd4FVRaEEnqPkGyFjaQqGgxJydqakCioqUJkNTTlbe00PL1sU5O9i8pj2vtSu4ZShuapPSNJ7YPxDR3hVU3B5bWBZXWBZfXB5XU+hVUuGcVOpAzzWOTdAIjcC6eslY2EgaGEuAjqapCq3tBo+hnQynMPivWsDnwS86ERBQlx4r8VFR+woWoQA7KeQQSFdb4PBPoxWLmaDzT3QF5oLMAH+VMVFgDMMyXiYXlZISYixPziQnziAjxigjySYjwMuepiKspYoy0NR0sTP3cHfAh7nGRvsnRoZTYuNykwpaC/ss9c7szs1fnh1cnGsY70ptKIrOTvRPCnAm+thEeFsEuRr52uu7W2m4Wel421pHevtTY5Lqi0pHOpsvj7atzvdtLvTsrgIHrGz27KzXzo3l99PiqnJDcJEBEYWp0eXpSfQGpuSShsSisIssjJ9mGEmVOIRhTCIYUvB4pQo8cZpIV5VCeAtp9wZ2lEQPVceP01KnOpLH2+MHWhMG2pOEuICfEgY7Q1jrP6iKgIg4l2fYl2WDrUJRpV5RhU0izyU+zyKGYZqcaZSTqp8fqpUfr06L0s2KMC+Ota8ieHYVx021Zm+MFV2eKrs6V7s4Vbk6nzQ8COUkca08YbosfbCb01oR1lQf3VIYPVocM1QQMVPv2VrgyCmwaM8xqyMYVyfplibqlRN3CeJ3CeN2ieO3COM38GIBGXrR6boxaTrRqTpRqFgGXGY6lhSqmhypQgzGUYBSAHISmMOPkQBTJH03yw5D9MBR/FClAkRqEIQeAFCzZH0fyVyYFqFGCVVIDcSl+6HgPqQh7hK8Rt6Mau6UC3EyezVyey0ZRwttAPc7TtTg1rCY7ojozoIxMaC7OnRkG3hto9bvmJcm56AoaynBrI9lVEWwqgnCcIFxBAI4WgMsLwGVZCJ5vWRH+M2Qg4YEB7WFuYdJMZHjh8jxwNB9MUQCuhuAzV1CL9tBJCXMtpbnkJ2hFOWCCzKR9DVGBZlpxHva5cb6VaaF1OYSGPGJLaVp3Q8ZAa/ZId97EQP7EYP54X/ZwB7m3KZ5RjW8pC28uDW8pDWstjWgvjQZewnhT1vJA8/7a1PN7W+9f7X75ZvnTZ8NP79Yf7lTsb1QcADaButQcbVff26m+t119tFl5sFp3sD705PbmZ0+Pv3//7Pe/evrbr06++2Tv7cnivdW+ta7qgQJKWUwo2c8z3ssp2sMhysOB4O5EcPeMdg9L9SeVxhZ35DSM1zZe7qic7yya6Syc761dn+zd35o9Pdx8e3r7609Ofv356e++efGP3736p++e/e7ru9++WX59r2N/JWd5IHG2PWmhN3Njoub2+vjp0fq7Zze/env767fX3r9YffNo5unR+JODkYe3hx7cHDrZgybK3Flrv7lMv7rYsDlXuzJZdnkof7Kd1l+X3JyTWENNriRTatPS6tMo9dSkquTYUmJUSXx0BTmltThrqClzpCVnvLNwdqBmZapmdbp6BTBXt7bUsr3BuLrNuLbdsr3WtLVcv3alfn2xaROkr7TurtF3Vpu3Vxq3lgENm0uN28vNO6v0q+tt1zchrm20XN1o3Fmp31yuXl+sWluoXl+o3Vys31qs375St7lQtT5XtjSZOzNIGelM6KGnDnTkz443bC733L46enRz4vjW+L2bw3ev99/ZYVxdrlgYTems8c1JNAhyUrTSkdLBiahgEDiUMA4lpqQgqYyVVsVJq2Jl1XAAaVUFJFZWBC0pICPGL4ngFeXj5uPi4uHk5Obg4OFi5+bi4OE+04NfJFiEEjTsneV09CRUVMWVlYERFwO6ArRBT0fKyEDJ0cEkMtItIzOyqiG2tTOW3hte1+aZXWIem6zuGSBvZgOt9ghERVOLNT9RTENdXEtTSl9P0d5WPyzEkZoeWlmX2NEPFCVrfI42PEXqHU8A/kpbLzQbhgH9Dlh8ez+oltDYHlLR7J5dapOYph8epezhK2dhJ6GtL6GqLqasJIaDOr6YVp7FmaciynyJ6+x9YqAozFeKP5AWyFNhAXwLljywaoCmoTC7syCBASrCWiCSJR6YH50eKBuzKlY25ptgzKqgvi9Z1jqSkJuCEIRAIvikkIIYKXFVjIKRjqadpZmfp3NUhGdirD8pITwtKS6XTC3PyaotyG8uy28po1TmxuSmBqTinfHeVgFOJt62hl7WwEHR9bDQcjbTcDDWdjE1CXBxjg+NLEzL7qqvvtTXujTVvjbfsbHQsbnYuXWFvjRdMtqV2VWfWJ1DqsspGWxumOntWB7v35kbvbE8srcEWqm5I62BVTTrnBiTLLxxFt48J8q6JNGlPi2gLR8/UJ18iZE+10u61B3T1xLZWR/Z0UjoaI7qag5qqnQty7XLp1nnUgGWeVSrvHTLvDTLHLJ5Nsk4M9kQGgAn6pKjNVMjNChhurRIw4IYy2qSZ3tBzGRz+spQ3u508bX5mlsr9TdXSzZnqNP9CcOMuEF67EATvqs6pKM0tLcycqQ+eqIFyE/yfHfqXB9hmO7XUencmGdZTjEqStQvSNDLJxrkJxoWJBkUJOnlJ+oVgMQk/bxEfRDPT9TNT9QBdjYvXiMnRj0rWi0jRi0jWpVGUKcRNDKiQIpGNuTZaDB3NbIAsVogJTtKMxuvkx2llx1tkB1rmBOrnxmtm0HQooarJgWiCG5i/mZ8ThocVhh2CxS7FYbPXkncQxcXaqMV66oZ46wcZqOFd7KlBNmmh2kTHKWc1YQt5HkNZTgNpNl1kOyaomxqonBlYThWBK4gAscI/wgacR4XhGOEYGhBGIaFEEzhAiBIQnBFIbiSEFxNmE1HlN1MSsRNHRVuoRTviI62kY00l44wV4hx1KcGu1RS8N1VicPNKcPNyYMNqYON6aOM3Ine4pmRsoXJioWpyssTJbMjuZO91OHW5P7G+J66mJ7a2L7ahOHG1EttmVf6K3Zm+o6vX3n9ePeLN7tfvr7y9nTgyX794Xbl/mbFPpCQraq7kL9ScQgcl82Kg/WKOys1d1b7H91Y//TJ0a8/e/q7b5//7uuH3392+/3TtcfXhrZHGyeq8+lUYhEhNC0oIMXXN8nHL8k3INUvjBZCLIhOqyUXdhXWjDdWTjOAy1U609e4Pj14Z3P+yd3tt0/vfP323q8+f/D9F09+89XT3339/PdfP/zh/c2vXsy+OGi+s5S9PpK+MZqxfang2jz9cHfq+fHW5y9vf/Nu/9tPb3z1evPd00XgrDw/nnp2NHl6OP3saP7Z0cyTO2Mn13purTWsT5fN9uWMNGd1l5EaaclVSaTyhLSKBFolMa08Nq00Jr0shlQSRSmLz2xIqxiqa1wcbFmf7Li22H51oWZprGx+rGZlvm13s/f2Xs/ta/Td9eqV+dKFqfzZsezJkayJ4ezJIbDNmgDboYzxIdrYYProYNrIQNrYICBjYjhzcjhzYigdGh3pSxnsIva2xXY1R3c2xnY3xfe2JA60JA/SkwZbEvubiH2NMd11hPaamM4G2nhvzdrlvv3r04+PVl4+Xnv9cOnFyTxwyO7f7NvfrFudzBxuDislW0S4KFqqS2gpIlUVxVSx4uo4KS1VtIGOqrWpnpO1oZuDqY+zqZeToZuNnpMFzkRLVgMroSgrIi3GL8TLxcfFycfNycsDONODXyToewcomVtJq6lBgwpKOMiCA+cA7GppyxgaKDs5muIjXWmZYVX1cfTuGHp3aHWzC63AhJCg5uEra2Ypoa2LVId+eBESFTVQUFVCU0PaSF/F2dGYEOmWlRPVQCf1gks/lzkxTx2cTOkejW8HQtKX2DmQ2DVE6h2jDkxSBy6lQmIziK9neOdV2qVk6IZFK7t6yxiZioPKlZTEFBVFMcxJJMxeKWDZIZ1g9YCdy8a5eJzrAeBcJy7UgqUoTH4y9MLKwJr7wpqiz6rhrOBFtxtr0j6KOZFeChpQ4RMT5RNB8AgLcgNnRRzBK40UVJCR0lZRsTQ28nJzi4vC52SkVBbntDZUDHTSp0cHVuaG1y6PrC/0L001TfTlM+qJhTTfxHDbIDdDV2tdJ1NtJxMNZxM1R2MVG32cha6KjYGhl51zbGB4VkJydR6lsZzcWJFcW5pYW5rcUEasKoguTo8qTCXVZA/uLd/68s3j33578h20lMX9797e+/btxpsHPTeXycONTuXJpvlRAIviePtqsi+jIHqwljwFPJIOYn8Lvqs+vL0+tLXGr77cqTTXLo9mnkUypaUa0ZKN04F+JBplJBnRkozSEgyoRF1SnHYKQSMpTC0pWD05RJMappuNt6hI9mjLjRirT1nsoS4P5W9OV167Unt9uXR9hjbTnzDUFtvXHNPbEN5VEdxREjlQHT/RQpxuoyz25m9MFW7O0BZHEsY6QjrqfZrK3WoKLAooxtlJ+lmJujSibgZRJ4Ool0GE4rQEnYwELRpRJy1eh0bUTo/XTk/QSovTSovVoMaqk2PUKTFqFGigRY0cBSIa1GitdOioNuRLxetmxOnSIPQy4g0ziUaZRNOsBGh9gcx4o/Q4k4x4I2qMAYmghPdCepjyWquwG8sBuCzRwk5qKH9jbIgVLsRKzttQykkTaYMTs8IKWSjwmcjzgjxGMpx6Uhw64hxaSA51JLsyEo5DwhVEIV1hAsP8CFxBGAZQZAJFROAAIEJYUTgWAVdEgC0bkCUNUbiOOJuRBIelLI8Tlt9TS8jXABlijo33sCtJjumvK1oeb7m52nV3p+tgm3FzvfXactu15Y69te6bm323d4b2d0f2rw7t7/TdWu+4ttSyc7l+a7Z2c7Z2a77+6pWmWysdR9sjj+9cfv1g54uXe199svvFq/k3j3oe3qrb36ze36ja36xieirVd7cqD4DAbJTdWSu7tVJ1e6Xr5NrSm0cH37598lvI+kOi8sWztad3xm8v0Oc7KgYr8tpzaQ3pabUUSg2FWktJr0vPaMrJac3Lay/K660oGmosHGsrm+lv3VwAn3Dx8dHO22dAUQ6/e3fvu08Pv30Ltifff37y/fu7373b/fzZpad3mu6sFu3O5u7O5O7Olewt0Y+ujp3eW377dO+rt7e+eQtEZeezZyufPJp7ef/Ss6OpZ8fzLx6svT3d+ez55qdPF54djRzutG1OlU4253TmpzVRyZWxcdn+kUn2YdGW/hEmfiFGAWHGAZGm4US71PzQ8q58xuWOno2x7u2p2sv92UOM/MmhqqW5pq2VuvXFkvnJHCAPo33U4T7KYC95qJc00J3a300e7CUN9gBSB3pS+ruT+7oASb2dib2dxJ4OaLpiJ53Q1hjWVBNUX+FXW+JbW+RTW+hdm+9Zm+dTnx/YWBzaWhHeXk3orI/pbozrbSYNdxQvTHbe2pp5crT+9vTq+5dXP3u6/ubBwunhxMmN3lvrNcsjpK4Kn6wYXV9rORNVcU2MmIqCqBoOGB8FE30dFzvbMP9gckJ0Ljm+MC25hJZUSI3LIQenxDgGeejZmigZqIvJiwsI8/Hyc3Hzc3Hx/6KeirarN8bQRFJFBRotwCqK4bAiKpCuSGhqypkYq7m5mRGiPbNyw4Cn0tIV1dIVXNHonJZnFBGr7OQBGX1NLaSaOlJVTVRVGQkqAQU11GWNDFWcnUzwBPes3PCaxjhGb+rAeGr/WELnAJCliIa2iPqW8NpmfENbLL0rqXMgtXeEOjhB7p9I6hoJr2v3yCmziCdr+gZjrO2kdHShMRXoHV/IKTmbiQLNJoFG7M+BDrH4UVFYMNdWOYPpc5wfAhEIlsAwJedMNoConOeB8v946PyoqLycsAy03D1zhooIJCoiQjyiCD6ksICsOAInjzLU1Hez90iISqwsKRvqaV+am7mzt/H4/s3Xp/e/ePPk67enX31y/7Pnu48Ox7eWa4e7kstzfRLxlv6uus7mGnbGqnZGKrZGOEtdBVNNtJEKxlhFxUpbz8XYzNfGPMDBPNDF3M/JzNfRPNjFOtzdNtzdLd6naqxu//tnJ//8xdN//e7TP//m+T999eiHz+/9+tOdz05Hjnapoy3OFanmBbEWBXG2JUkuVWl+TXmhjLJwRlVYW004oy6MXuNVVWifl25KSzIgE/VSYwG6yTHaKTE6qbHaEDFayQSNhEjVuGDl2EClOD+VBD8NcpA+kJOyZPe2vMixhsS5DtKVvqy1sYKNS4Vrk1mXh5JGGFFArhhV4e2VgfRiP3pBaE9Z9GhD8kxH1spo6e58xdWF/LWp9PnhpLHuuIF2QndzYHOVa2W+VQHVJJtklJVqnJVilJliCBwjaAt2oUTDjFQjEM8kgS1ERopherJBepJBeqIBLckQRNLOMKQxM2ekmmaSTTNJZpkksDXNIplnA3+LbJFNts6hgL/aLodqlUOxzaXaZlNsMlO140NlvGwEzNXYdWUBPEZopKOmYqCVYpAtytdCylVP2FJJ0AgjZIQWNEIJGMrz68vy6UrzaklwqSO5VMQ4sKLsGFE2eRG4nDBcFgGXFYbLIWCyQnA5AAIuLwRHIeAoIeC4wFhgLjwYoDEgLgQ5K1gETEUEriUG10PCjaXYrdDcjioCnjoSwVZ61HDw3ZWvTw3dv7Hw+sHKJ09WP3my/Obxwov7s8+O557eu/z0ZOnFg/VXj7ffnG4D2/r2yeabR6uvH155eX/u2fH003uTT48uPb838+rBlU+egPb+9S9fXweN/c+eT7886Xpws+5gu+ZgC1ANtne3aw53ag63q+5uVu5vlN9eq7q1wji+Ovvy/vUvXx79+h2w/gdfv9l8/WDq+HrP1ctNV4ZqpztrL7U1THc0z3a1Xe5tX+hvm++tm+6oGGvO66+mdpSROipzxzqa1uZGD68vPb9//bMXB9++O/zVZ0BC9r9+c+ur17e/en33W0hjQMr1L14uvr7fe3+v7tZq2fXFomsL5TeWG/d3+h8eXHp+svz2yfq705W3jy+/Op483e9/cKP75GrfyfXxx3cWXx5vf/Z078vX2+9OF0/3h28t0Ze6ygdLclpSE7N9AyOMnNyVrWwxZtZoY0uUkYW8iQXKzkkpINyUnB9W3p5dNVBZOlSX1VtNbq+j9jJow71pw93kgY7knrbErtaE7raknvak3o6kvk5Acm8n0A9o29OR2NNB7G4ndrXHdzHiOttiO+hRjKZIen1IY5VfbalXVYFnVb5nTb5HVY5LZaZTebpTZaZXXX4wvSKyoy6qtylugJ403E4Z686fH23eXR47vr3y6tG1z1/e+OLl7tvHi08Px46Aq7RYvjhK7a8PLCOZ4F0V7XQldXFINQxSGdIVaW0VdRsLF3xEQlFuVW9H9+zY0JXJ8dWp8ZWp/vnhzks9Rc1leHKsra8TVhsnKiUC+Sv8PJy/rKhoObuj9AwllZXPpmVgFUWVFJEqyhLa2hhLC12/ANuEZJ+84si6FiAq+IZ2v6Ja+ySaXkC4kr2zlJ4hUk1DXEUZqawsCrwcJRxSWUlCTVXOQF/Zwd4wJMyBRPHMKQgorQ6urA+uqAkqrfLILnCiZjqSqI6UdPfMHN/CktDK2pgWRkr3IGVgHLgycYx+/7I6R2quYWg0zt5FRs8A8lQwwEE5G+r40cOA5ARaFPJiKB5woQcXqvC3AQp0XtW52LCWEYOk6IMaWBFo3j70MjG0iKSwrDRzQOVMVHiBoogJC4iLIuQlxVQxWHMDyyDfiFxqfje9Z/3K7MHt7eenh1++e/m7X73/4w/f/On33/zbb776139489svb768P7yxUNjVRMgnO0b6GHpYazuaqFrrK1vrKVnp4Sx0FM200SZqaENVtJEy2lAZY6yqYKqONddUtdHVcjbVcjHVdrewC3euGC49/eHRox+ev/zDuye/f33j0+P1Z/uLj/bG724UTDI8ilNM0vGGlEhjapRZeqxZJtE6J8WxiOZZke9XW+ZemmudTTYE/kciQZMYqR6HV42PVIuLAHGN+Eh1IhCSMJWYYKWoAEWCr0KUJzbOSyXFXycrwrI82a+zKGayJeVyD3VxMHsVkpOC1UngmsT1N4e2VQQ2FQe3FPk3Ffg25wR1FIUPVMZNNqdfGSzamq7avVy2Ppt9eZQ02ZM80pkwwMAz6jyAnORRjGiJepQ4HXKsJilGixyjTY7VIsVqU+J1KPG61Hg9Srw+hQiUT58cD0GKNyCfA9IpRCNKoiE5AQAiJmkpZjSSeSbJKptqnZ1mnU2xzqHa5qbb5dLs8zKdCjKdCzPdirI9inLci3I9S/O8S/Pdi7LNSPFYPycBI1V2dRlOLTlBI0UJBy2Mt4lygBXK3RhpoyFirixhqSbvoINzNcA662JsNWXNceIGaGEtGQFVSW5FUXYgHlJCMElBmCSICMOlEXBpQZisIExeCEgIDLgjOGEYQOlH4MoiUAQHjgrBwVYFAVMXhmuLMkVFks1KnsNRictNXSTQTJsaHtZRUbY+NXDv+uyzw6WX95dfP1x+9fDKq4dAVxae3194cQJ2N9+eXnv/8iaw0V+/AQDzevX9i423p0uvHs89P5l+dm/mxQlwU5bfPl779MnKp48X3jwYe3bU+2i/48Ftxv1b9JMbgLYHN9se3GI8utP28Bb9/g368fXme7vdD25NvzjZ+Ow5cBH2vni59ubBxMl1+s5c0Uxf9mh7wXhX1exQx+bs6K312XvX5o+vTx9sdW/Nlk91UburYpoLiB2VmRNddRvzw0d7q68fA0m7/c2nt75+e/2LV9vvnm+/e3r18xf7UFfYZ8e/fn/7m082Pj2dOD3sPt5rOdipub1Rc2u94fZm2+HVnpMbw4/ujD7ZH3x4q+veteb9jcrrC0XbU4Ub41W7s10Hm9NPDlZfP1p/8+jKs7uThxudGwM1I2V5zUnx6S4efhqGFihlfVmMtrScppS8hhRaS1zTWMbJSyuK5EqrjMtvzcruKEymF+KrC0IqC8PqyyKbqglNtYTm+ih6Q0xbc1w7ndjZRuxmAAlJ6AIqwmAR39kW094azWjB05vCmusgLakp9aos9CjPc63IcanIca7Mcq6kOZSn2ZWnOVVluNbm+DQWBTSXhNDLwhiVkKfS05QM3KP5sdbra5MPDtbfPAEXee/987VX98aOrjZvzGaNd8fQK7yLKOaJQSoe5tJGymIqciI4aP0VUay8jIaSnr1lQFJMQWvN4Mr09smN08+fvv321affvXz6+aM7T28u7y81jDZG0CIMnQylFMX5hKFOMA4eLpYc/DIBEhV9pqgwZ36IAbuMVQQ6IaOrq2JvbxIW7kqhhZTWRDcwYpo6wqtbPLNKLKOTND38FcxtJDS1xZWUxXFYJBYnhsUCkFishIqKrK42ztpK29vbOBxvGZdom0yyTiJZE5PNouN1g0JVvXxUXN1U3D10AwLMYmKd09IDSyuiGuipXUOUgYnE7qHwGrorLd8cn6Du7CWnZySOUwL68VNRQUMp0KA65mxOCfReFjT7nSUYLBlgAonH3+VcVJgwl6Q8c03OagBxSGagQX6oN4wlKjIsURHjExUGosIniuBHighKiCLkxCVUMKrWhpahPkEZKYW9bb1rC/O3b2w/fnDvy3dv/+X7L//th6/+9Nv3//b9+3/91Sf/+MXRu8cTu4vFvU3RBRRHgr+Rl42Wg4m6rb6qrZ6avaGmk7mOm5WOm7WOu62uq5W2s4WOi6Weq7Wem7Whl71ViKdpsLtxoJt5sKtHolfNUOHCnZGJvaHm+bac7oo0RmlSU35wEdmWRDAihhkk4A2SCAaJBMPkGMPUGFNKgjktxSwtyZhM1E0iaAIViQ1XjQ5RiQpWjgrBRQfjCMFKhGAcPggT4Y8K80SFuqEiXDEED1yijw4tzLIi2a+nJG6qjbo4kL48krM6Xrgxlbc0Sp7siulpCKFXBDQW+TXke9VmejVkBbTlh3SXEoZrk6YZGVeGcpbGMuYGyeNdiYOtxIGWmJ5Gv/oi2zySASVaMzEMuEHYaH+FKD8MwQ9N8EWDSJSvAsEP6JkiwQ+L98Ph/ZQi/XCRvlAE768EtgR/pcgAFXyAMj5AJSpIhXAWUY0O1ogL1SJG6CREAnQT8LqJkbqJEfpJUQbJMSakODNyvAWFaEVJsAakJVqnJ1pREy3JRO2oICkncz5dLJeGLL8eBmGqJGmrLueoqeymr+Csr+JtaRjiZIF3t4h0tY50sY10tgiyMvAy1LDXVDDDyetjxDXkBHGSPPJi7NLCQFFg0iIwGQTkrCgIw5RF4RoScG0JuK4kTE8SricF15OE6UvC9ZlbPZAuAdcBW3G4njjcSAJmKgWzloU7oLk8VHl9dHj9dAVDTbAkP/fGTNI0o3RjvH53rm3vSseN5Y4bqx031xl7a217K617y52310eOr8083V98eW/1zf3VV/eXnh9fPj2aerg/fO9G151t+o2Vlr2l1lsrbbfX2vc32u9utd3dbL6z2Xx3h350vfXkRsvxdSZ7bfdvtt+/3fnwTsfD2+33b3Tcvznw+GD21aP1d883P3u2+ubh2MnVuvWJjHF6UndtfHt1cmdj/ngffXNx/PDG8unx+suHS0/vTRzuNqxNkAfqghuzQ1sKUkbpVVuzA/f2Fl8+WH/3dPvzF9ufvVh/+3Tp9WPgcu28e77/zdt73392/P3nd755s/nu6eyL+2Onh30Pb3ec3GCc7LXdu864d63j6Fr38Q0gLV3HNzuPb7Qe7tbdWSvemc1YGqLO95ZvTPfe3Z15fLjw9Hj+9O6lo62ujeGqkYq8VlJijrdHsJ6eDQZnIIvSlpLTkEKpSynoSumYoRx99CKS3JIK8On1adSWXGJDfkh5jl9JTkBFQXB1aUhtRURDNb65LoreGN3WFMtojmmnA2LbWwHRDDrQEkJrY0RTfUh9tV91uVdFoVtZrktJlktplmt5tmtltnNFpiNQlEqac3Wme12uW22OS1WGYxnVsSTNtTzDqyY3oLE4jFEV19ecMT3QtLs8/uDO6pvH194/3377aP7Jre4bS8Uz/URGjU9RhnVqtGawO9rOQEIHK4yVFkRLCslLItDSksqKGjZWPonEvHb6xLWNh5+/+uGP//DPf/7+X/78q9/+8avPf/vqxtNrU3vTeR051gHW0ioS/MJcXDwcHFzsZ3rwiwRtFw+MobGkiirr9Sqot0dRQUJNGWNirOPlZR0d65uZF1nZGNXQFlHTElhc7ZSaYRQUqWrvJqdvDI3tM9f9FWVO9RBTgCZ5AI2RUlWR19fDWVuqubhoeXpre/tqefupunsq2DvKWljKmJjJGJvImppirK3UPT3NomPcMnOg8fz2vpSekeSuIXxdm1dWsVV0orqzB0rPCHrT92JU4xzWGpHiiqxZJswp8WAXHGINjTB9jos+LqZH8iFARdDQqsYgDvVogRRo3B4aumd2cEG757LEesHsTGxAHqaoIJjdXwLiYpCPIoLgB4ixdEVEAieraKJp5GXvHBNCrMirHOrsmBufvbZx8ObJ699+8dkfvnv7L9+8+acvnv/mk4N3JzM3l2pHWkm1uQHUaMtQd313C3U7Q1VrPVU7fU1nE5MAJ+fYQJ9UQggtEZ9NisqnxBZlxBakx+anxRelR+VRfEkx9tGBZmFuJqEuVnhPp3g/l8QgB2KgVUyAWVSgMcHfkBBgEB1sEB9hQCToM9EjEnTiI7TiwtVjI9SiQ9SiQlQIQUr4QGxEAA4Q7o8N98OE+aJDvFFBHrKBrjKBTnIhjuhIZyzRUzMtxKo80bejMO5Sc8rlrvTlwYLNqZLNmfzlidQxRjijyr++AOBXn+tVm+XdmOXPKAjpLScM1ZNmOtLmeykzPUmjjKjuuhB6eXhbZVhbuWdttmV+gi4FGiFXjPdGR3uiCO5yka4y4S6y4WDrLBPmJBPqKBviKBfiJAsIcgTIBTmgghzkgxxRTOQCHOQDnOQDHMFWDkQCHeWDnFBBzqhgF0yIq0Kou2KYh2Kop2KYJzbMExfupRzuoxLhrxbpqxnlr0kI0ML7a4J4hK8m3lc93Fs9zAvr5yhhZ8Cvj+PWlOPWkuHTRSMMFcTNlbBOuhYRrp6pocE0AiE7JiqTgE8LDU/29Y20dwswt3bTNbJV07FQVjHBoQwUJbVQQkrSXBgJdnkkGxrJhkWyqUuwG8pxWeE47ZW5nNU4XdS5XDS4XNQ5XdUAHC6qnM6qHM6q7I5KbA5YuCOWzVWJw1ONO0BLOMJIIdVFK9tfJdNLhuyKpvnrlxOd6dn+PaXhfZX4nsqo7orozsro9qooRlV0R3V0VzWxvyHtUkfB0nDV9nTj9bnma3MNWzPlS2P5s4O08a6UodbYnvrovpq4gYakUTp5soMy1Z2xMJyzPFF5dbl2b73hzlbdrY3aWxv1t7ea9neaD3YZR3sdx3vAlHce7w0/OphnisrGu2cLz4+7bq9mX+rEt5YEAZtYmRtWV0LuoddcmR67e2Pt5aPr71/tvnu2+OSg4+o8baw5uDHLp46G76nKuDxYe3Wh6+5O/73rIw9uDwOOb4LI9Onh6quHe5+/OPjm7cG3b28AT+iTR5OPD7rvXafvbzUfbNHvXm0/vtH/8PbIk7vjp4cTT++NPz0aOT0aenK3+/4NoItV1y6XbkzV7S703d2de3K49Pz+4rN708e7A1dn66ZbgDEllcdEUDydQ03NPDT17ZV1bHDaNlgDezUrTz23cOtQkl9iEZFal5FSn4uvyg0oyfYrzQuoKAqrq4horI1ubYxlNMV30hM6GYnd7QBml1c78Fpi21si6fWhDVX+1aUepXnACbbPpzkU0lxKMz0rc/0aCoNaSoH7HtlZje+ui+yqCW+r8KvPdy/PcC5OdymjuVVmutVmezTketMLgzrKk8faqzZnB46uX35+DJR76fnhyOFm5dJgUk+1T0madWqMPj5QxdtBzlIPqYVB4KQQCpIIjAQCIymuLI810XfB4/NaWudv3nz7w+9//5c//eEvf/gjxL/8059/9+6HT1cOVks7Ksw8LCSxSAFhLm4edg4utjM9+EWCvocf1tRSWl0D2G7Wa7VSSli0traWvYNZSKhbCiW0sCKqhk6oaQ0pq/Og5ZnjY7U9fBTNLKXUNMQVsUg0c94G9AoWaM6feRJAZqSUlWW1NDHGRhggHmam8sZGMobQb8uL6+pK6ECAuLSJoaKDvXF4uAs1I7i0htjaQ+odSWofCK9sdkvPs4iMV7VzltfRgzyhCy0579Fiva+FVEBLQM7Kuah8MKIOicpPdAWkX4gK6z0uSFRYnJUC1Z7LCRT/G6ICLU7MGlNhvvoFDagIC0KvFIsiBJDCIvIS0hqKqhaGxt6OthG+vskEQmYCpZTS3Fu7uDN1+PL2k/ePHr07uXZ/Z/jKUH5TIT4zzj02wCrYVd/LWtPRWNlaV9FCC2x1Pa2cYv3x+SkZLWVlQ/T6iT763Gjn8qWuxUu9SxDNk320xtKwrETHmADzMA8rvJdjfIB7aoQ3NcqVTLAmhhhE+GuH+upEBOhGBetEh2hHh2lGharhQ1RAux7oR5g/LswXIhRCMdgboBDkgfZ3lfVzkfZxlPGzlwm0lQ9zwMZ56NBCbCuTA7uKCGMNCdOMtMW+3NXR/LXx7IWhhMHWMHq5d3W2Z2WWZ1WmZ1W6dx0tiFGAH6iMHW0EbcacpdH02b6EoZbQ1lLf+nz/+ny/+jzPuizb8lTjghjtjFBVSoBisjcqwUMuzlUuxlk2ylGW4CiHd5COtJeJtJcDhNvLhdnKh9vJhdnIhljJhVijQm0woXaYMHsAOtQOFWaPCrVHhdvLg8QfcVQId1KMcMFFuivhPQAqBE+1KG+NGB+tWD/tGF8QUY/yUsd7qoa7qYa5qIQ4Kwc54vzt0V4W0s4GolbqwmbKohYqUjYaaBd9dV8L6xjPoAx8dH5cUnE8pTiOSAsMibBwc1Ozs42A80gAABMqSURBVEFbmULri5sao8wtsWb2asaOmlr22lgrTUlDFSFNRV51NI82hscUy+ekIeRnJBZqIY63kYiyl4hyEI9ykIhylIxyBFuJaIADkmAnhrcRxVsjY2wlE5zkSe46BaH2dUnudLJ7O824iqhflWjSSLNuy7duzbVqoplVpZqWJpkVJpjnEs2z4swy46xyEhyLyZ61Wb7APHWVhfVWRPRVRHSVB9OLvEHTuDTdroBkm59sVZhqW0Jxrsr0qM/1bioI6ayK6KlLGmNQLnVTpnsp033Umd70ub6shcGC1fHSjemKremqremmveW+w+szp/dWPjld++Tp3NOjjpsrtImO4KZCL+ilZ5JnQXpYTWHGUGf7zvL848Pr71/cgmZH3h+4s1Y42xPVXuLXmBnYWhg70ECZ6sy7Mly6NlW9NVe/u1CzcxlsGTfXxk9urrx8eO3zl3tfvNr85PHck7t9BzuN15bqri013FhrubPVeXh98OH+1LPjxdeP1z59vv7Z89V3z1c+fbbw5jFIHIWmPe6NP7g5d3q49vrh1qena68eTJ/s9Vy7XLfQVzhYm8nIJ9WlxRXHR2SEB6T4esW6uuAdnfEOnnFu/qkB+NzYuAoqsToLX5EdWJzlX5ITUl0WQ28g9bZnjg7kTY0UzU0UzV0qnrvE3E6VzE+BeP7UaMZYf2J3a1hDpVdZjkMu1SaXbJdPdSvPCmwsBe44dbKrYGGkcm2qdnOuemO65MpYznR/+mgXdbgjZaA1rrchvLPSn1Hi3Vbk3VEa2FcTP9FevDHTcbAz+fjuzNO7Ew/36NdmKCP13mWphgkBKkEuGDcbaRsDSUNVpCZaWFlaWFlKREUKqSYjrS6P1lPSc7aOzaZd3t351T//4Q9/+cufoX9/Bv//6S9/Al7L40+u55Ym65mrS8qLCAhzc/Oxc3L/oqJiGhSu6eCiYGAoo64upaICthgDfQ0HR7PAUKf4lICc/Mjy+siKxtCSWp+MIpuYZD2vABVrO1ktbUkclmmOgZwAIZE/+zErYOKxWKgeDU20jp6CgRHayBggp6cvpaWN1NQU01BHampAw/va2lImRirOzuaRUV4Z2ZHVDYltPaSuwYS23pDSOsdkmoF/iLKVnYymNnNpljOjzwKIBzRPBcSZP70FOSvQLzlC80igHirAmSSA7YcDJxDMgX1oJAZaB+x8VXyoTuabXSx/BZQFKcwazrq/IFFhahhIYXoqzFe/zkWFW1iQW1iAT1hIQExEREZSAieP1tVUsjTScrTSd7E193J0DvPCU/GUwqSS+px6RmlRbUZSdkxArK+1n62Bs6mWnQHU5WWjr2iphTZVlzdSVTDT0HI1tQ13D8siZjMqmmb7+zZnx2+sTN1an7u1sXiwM7W33H55sHqYTqrK9CeF+ZDCwvMS4ipolOaizI4qGqMiuizTNTXaIMxHI8hTPRS0xAM0IoNUwgOVQ/2VQnxwwd7YIG9soBc2wAPr76Ho547xdcP4uij4umD93ZRCPNQivDXjfPVSg8xzoz1q0wj9lZTZjoyFvpzl4fzVsdzFYepkZ3R3bUBDoWdltntFpltFumsl2bchE99VljTamD7XWbg2WrE9W7o6lTM7SOxtDm8uC6ov8KnN8arL9GjIcK6j2tWQ7aqpVmXJerkEVUqgYpI3huihQHRXiHNTjHdViHVRiHVSiHJQIEAoEhwVoxwwkXboCBtUhC0Ag7dHRzhgIOwxkfZniREggx0qwg6kK0Q6YfHOKlHu6nHeGnE+GnG+GkRfTaKPTpKfTgKEVpy3Zoy7RpSrGt5ZOdxBOcxeOcRWMcga7WeG9jHF+Jgr+lpqhDqZxfs4UsMDCxKI1VRqbXp6eUoSLSQo2MzaXM5IQ0RPSVBXkV8bJaCJ4tdU5DPURlrZ4Fz9TNwiHGxCHbV9bDH2xkgzHRFzLWEHbVE/Y+koe4UkDxzZVzktSIUWqpYRoZ6F18jEg60aiGRFqWfiQaJaRqR6NkGrIFa/lGhek+rYkuHVU+g1UGrLyLVh5Nl1Fjv1ljv1ltl3FVnRs01qyQYlRIO8GP2saL00gm5qpAGZYJZNtCshO1VnOtZn2tfRwKW2r6LaV6bZl6XbFVNsiyjWxWm2xWmOFVmu1TluNXme9YUeDYW+TcUB9LKA1ooQRlUIozK0vSq0vTKEURHWXhXRXhXbXUMeaatYudRxa3Ps4Z1LT+4OHl2r3ZhKHmjwqcp0yU+xzSDa0uKdc5LD6gryJns79lamH99eenE0f3rQfXO5YKYL31LoWpziUkryqc4KbikidFYm9DemDtOpQMxGW6mjjLzZoYbtheGTWwsvHiy9ejT1aL/z5lr5ykT2TF/GpW7aVG/ewmjt9kLX/tXJx4crrx9vffZ8BzhDX0DsvH+x+e509c2j5dcP19483Hz7ePfd6da705UX98bu7jSuTRWMt6V1VVMYpVRGMZlekNyYk1iTHluaHJ4THZQeHpgeFpQVFZKXEJSf5JOT7JlN8inMiqirIPcy8idHqpdmmzav0HdXGdc2oBkn0JSU1aatNfruBuP6JuP6RvPOStmVKepIdyS9yq00076A4lqeHdZaRZ3sq9iYZdzeGDzeG3twY+z+3uDhdtetdfq15cadpdqtheqN2fK1yfzlUdriYMpsT8xkO2GCkTzbV7I93353B3hvE4/u9J/sVm+MxXaWWNEiVSNc5DwtJWyNkKYaYno4EQ15MTVZMU1ZKT00yhCL0VdQMFRUt9J2DfPuGOn96h9/9y9/+RMkKmf85U9/+eH9V1fzCwL0jFDScoJCojy8ApzcvL9o99d/SYDD4GwwDk4YJxeMiwfGzQPj4YXx8EHwMrcQvFA6JzeMgwsGB38h/Kzof9MA/gJudjY+Lk4hPh7gxCARgpKiCCmksIy4iKwEE3EQR0iLgXR+JIJXTJBbhJ8TwcchyAPn54LxcsB42CFACwLAioNEPk42QW52IV4OBB+nMD+XiACXqAA3KIsU4hYX4hFHcAMkEFziCE5xBAdSiB0pxCYmCBcVgInww4QBfDAhXpgQD0wQwA0TYAJO9+9hHYKy8UD5QSkEL1QJANQmJgBDCsKQQjBxIZgEAiYJEIYxxw/g0qJwGVF2WSS7HJIDIA8Qh7ZyUAoAHAVAIw0gPwCUFQdVgTpBzXwwBDgXOC/4AJwwfvD3gr8a/PlwGDcABuM8h+NvcJGBBVTqr8EFP8vAKgW+LBagucYC3H1/n/+yAD4M+IRcMBgfDCbABhPigInwwJB8MGj1F2EOOTFulDgvRpJfQVoAKyukJC+sjBJRQYuoYkTVFMTUFMXUAdifIaqBO0MToCSmBVAW04Z+jUNcV01cT11CH6AhaQA8LU0pCC1pJjKGmixkDTRk9QHqsnpqMjoqUlpKUhpYSXUFCVWMuApaXBmFVJJHKskhcRBi50CJyiiQAWSTVFeU0sTJaKvI6qrK6anLG2igDLXQRtoYYx2Mia6imb6iOcDgAoV/j4WhIpOzXTN9tIku2lgHZaSNMtSU19eQ01OT1VGR1sRKqilIqKCQOFkRjCRCHikoK8ovheAVF+BG8nOJ8nKK8HAKc3MAEGDLAyHCyyHKzykmwIkUAo8Sl4QIjzQSWu9EVpJPTopPXpofJcuPBsjxY+TPQZ0D7fKdIceCFyPLiz4HxJnwKQDk+BUB8gJYeUEltBAThDIGoYxGgAgAhxJUlIPmyaOlmasOi3NJinKKC7OLCbGJCMCF+KCHlA+YC04YDwcwOD8Dzs3BxgPBDiJglwsYYTh0n//PCkx1YeeAsXNCGgP04wKQAtLB0f/SR/b/ywC+PC52OC8nhwAvF4IfkhZRIaAf/EhhAaQwFBFD8IoK8YgIcCP4OYV42fi5QGZIPMB3zwn/ib1jxYERBNaQpTF8HDB+TiA/bII8QGPYEQA+duEz2IT54EA/EHw/igfQCT5OCKBMkGIxRQu689ig0/0MlpKBoxdixtQzcEam3nBBdQLTD8QGnEWEDyYK9ID/XGkEIaWBxIapNz+BmQgOQdmYygRKgeIXQvITFTk3+qwr8KGt/zuBleECVqm/ykWe//sD+JDgCoCrwQOkBQ4TYIcJccFEeGFIfiAtbJIIDmkRLlkkj7w4L0qSDy0FDBA/RlpAQQYYI0FFFnIfIoBlAQwZSgigjIZQwQAQqgoIVUVhNUVhdawI4Fx+xID2aABwyHPE1bHiaopIVQWkCkZMGS2KkxfFygkrSCMwUgi0pBAKICEk/1NACloSZBBWkBEFMqOEAmXFVRUk1LGSGjgpTSWgMTI6qrK6akBm5IAq6GtCGDC3+pqyFxhAyBloncM6qiGrpy6jC0ROVUpLWUoDB3RLQlUBKBzQMxHwqVASgrJi/FLCvOKC3GJ8XEA5EKB9xgUX4IDzs8MA4N6DIhxQg0aAi02Ihw08UyIC7KKCHGKgiSbMKS7KKSHGJYnklpaAkJHklpH693DJSLLgkBI/BwlgPwfEOWXEAVyyEgBuOUkAj7wUD0qK9xzmcpDQMsO8chI8suLc0khOCREOJIJNVBAOWoegkcfPzdSSc3NxYTHOALvMRE5wlAmUcn7n/08P/12e7P9UAF8h+F5BM4GXi4P5YzjcQnxAQlhwCfFxCvKCdDZgr6GGxrmWgFJ/K4BDLI0BOS8EhmX3gcywZIPFmXicuzsf3nk/s9E/+wYuUlh5WDYdOuO5pLFOCvxl1hmB0kAy86HSAJ+GKTYQTOfmYheSOpbr89fcEdAev/h4P/tUHwO4GuDKnEkLG0yAA5IWcKmZcg4XF2KTQLBLCnNIinBKiXLJiHHLIIExOgMYpr+KPLBlksCWQaCleQEYGQCzES3LbERD2gPJzzmg7SyElRNSlEMAFGSFFGSE0NKCKClBeQkBOXF+5kpTfNKifFIAERY8EsI8khDQrrQotBSVrDjIL4SSQmCkhRVkRbHyYjgUUCaWxoirKkqoYSXUgSooSWpASPwVlD+AmaKOE1eDdA7UAM3YUEKL4eRFFGSE0VJAzwTBB5OCfp6VW4yfU5iXQ4ibXRA8JmwwXjh0PUEL5kdACusOZ93e4JbmhSP44cICbCKCbCJCbKIIdjHgK4hAIAGiALYzRNjEROAQwgCYKABxjtBPEGOCRMAB4sJswPmQEAGAr49DUvQMsAsAh5DgjEJwEQGomSjAw3RNmH7JxeP8V5+UD5/fi2fqY/gfEsA3CpoJXMC4c8B5ONl4z4CzPFaWuWd96/9bgXXHgLuKZfEhWG0TFueHWDfTL3I/seq5OO+ZzLAeQvB8nisN0AnIpwFicw5rFwDpEFA7Zk5QBOqJYlb18ab/DwZwicC1AhcNmD+gxOBigssL5PnMazzrn4SD9qyYIJuYEBsSgPg54iwgW8YmwTRnkgBRAIeUKKcM1I7mkoXa0dxyEEwFkgTwQkgA+ACyAHE+GSS0CK6kCLeEMNRThBTiFBPkFAUIsOAQ4T+DlSImCPKAnCA/j6QoKM4vKy4gJykoLyWEkhZCyyAwssIKcsKKcqJYlCgOOEBniHyIEgAj+iOsDCgRLAqUBTUg0DJAsQTlJQVB5dJivOBcSCFucHZhXnYhbji4P8GlAxcQXEbWYwIu6Yew7m3wEEFeO7hRQX7g6AN3nwcmwAsT4IMJAV9BAAIheAaIC/KfATJAgJxMQKmfAYSBBdSRwAtJBdQ1Db47gZ8jDOA/0xJ+VjcX02KwPvPH5+Vj+PGWZQHi/7tC8n9bYGkMS2ag55D5oLKU5t8D0lmPMYD1t4PiHx+M/1y4uObgwv6kZQ1gdlF+OCr2E5hW7AKWObuwaKKCEMx2NNSI/lGHhNnP4UAifkRMiENUEOojEuaHfqsDwQsXApb0fLgO+hgXQCngKMgDMoMioCCnGEtgRIDAQBojjeSTAe6OBL+sJFAaATkpAWjB3b+K9AcwU+Qk+QGgrIw4VI8UtN47NxLBBUQOSBo4KfCkgQaDa8W6CcHV+3+890AGkA3qRwKlgDUHrUBg1gFcTLg/gJXCBWU4gwPKDwEKXsDqYWb1PDOBFAvA6mAA0vXBuCZrFxxi+SWs1ifrqfkYPob/HwWWTgBYYsPiIvFj+MXDxaX+UddZ+s3sqPzReH0Ia1CNZc4uLBrTqEHmjKUHHzSlLxACjfQPAfoBsrFG7M7NH1TzB0bzQ1inBnkg8YNkBi4EdIifXViApTEcosDRQQC4kMDvAYhwi4uA7c+RAIh+ACtdGJRiFhcCrhIHqBBIF6gf/AngdMAzBur7H9SSvxVY9zA7nAnbT/jZfQ74++EiG6sgq5X5YzfDhzAPsSr/GD6Gj+Fj+D8ZLizXh7Cs2IewLNqPRo3Vmv5AkM4A7WUmLEGC4sx0VmZQkFUJsNp/h4sTseqHNAZYfJaeAXGCxAwuyAfEBi4EXJ+fAA1snCEAYPsRZqIQgNlNBPUUXbwTxWrgM08K/tKP4WP4GD6Gj+H/fPiZLP2M/3QAZc+UBggSU8zOepk+0DBIwFg9Tn+fc8FjdTFBCsccp/x/8/E+ho/hY/gYPob/aeFCuv4jfAwfw8fwMXwMH8PH8N8mwGD/C9vT0FkmhKNMAAAAAElFTkSuQmCC)"
      ],
      "metadata": {
        "id": "6irskaPxvPLk"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## O que é SSE e AVX? "
      ],
      "metadata": {
        "id": "Ti4HW0kce4eA"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###História\n",
        "Nos anos recentes, as CPUs alcançaram algumas limitações físicas e de energia, logo as velocidades de CPU não cresceram expressivamente, em termos de GHz. Conforme necessidades computacionais continuaram a crescer, projetistas de CPU decidiram resolver este problema com três soluções:\n",
        "\n",
        "*   Adicionando mais núcleos. Desse modo, os Sistemas Operacionais podem distribuir aplicações em execução através dos diferentes núcleos. Além disso, programas podem criar múltiplas threads a fim de maximizar a utilização dos núcleos.\n",
        "*   Adicionando operações vetoriais a cada núcleo. Esta solução permite com que o processador execute uma mesma instrução a todo um vetor de dados. Isto só pode ser feito a nível de aplicação.\n",
        "*   Execução fora de ordem de múltiplas instruções. CPUs modernas são capazes de executar até quatro instruções ao mesmo tempo, se elas forem independentes. Leia http://www.agner.org/optimize/blog/read.php?i=417 para mais informações\n",
        "\n",
        "\n",
        "\n",
        "Registradores vetoriais começaram em 1997 com o conjunto de instruções MMX, contendo registradores de 80-bit. Mais tarde, os conjuntos de instruções SSE foram lançados (diversas versões deles, do SSE1 ao SSE4.2), com registradores de 128-bits. Em 2011, a Intel lançou a arquitetura Sandy Bridge, com o conjunto de instruções AVX (registradores de 256-bits). Em 2016 o primeiro processador AVX-512 foi lançado, com registradores de 512-bits (até 16x 32-bit vetores de float).\n",
        "\n",
        "Nesse curso iremos focar em ambos os conjuntos de instrução - SSE e AVX - porque ambos são comumente encontrados em processadores recentes. O AVX-512 está fora do escopo do curso, mas a maior parte do curso pode ser reutilizada, mudando apenas os registradores de 256-bit para as suas contrapartes de 512-bits (registradores ZMM).\n",
        "\n",
        "###Registradores SSE & AVX\n",
        "O SSE e AVX têm 16 registradores cada. No SSE estes são referenciados como XMM0-XMM15, e no AVX são chamados YMM0-YMM15. Registradores XMM têm 128 bits de tamanho, enquanto YMM são 256bit.\n",
        "\n",
        "SSE adiciona três typedefs: **`__m128 , __m128d e __m128i`**. Float, double (d) e integer (i) respectivamente.\n",
        "\n",
        "AVX adiciona três typedefs: **`__m256 , __m256d e __m256i`** . Float, double (d) e integer (i) respectivamente.\n",
        "\n",
        "SSE & AVX Registers\n",
        "\n",
        "![avx.png](data:image/png;base64,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)\n",
        "\n",
        "\n",
        "> **NOTE**: Os XMM e YMM se sobrepoem! Os registradores XMM são tratados como a metade inferior do correspondente registrador YMM. Isso pode introduzir alguns problemas de performance ao misturar código SSE e AVX.\n",
        "\n",
        "Tipos de dado float (__m128, __m128d, __m256 and __m256d) têm apenas uma espécie de estrutura de dados. Por causa disso, o GCC permite o acesso à componentes de dado como um arranjo (array). I.e: Isto é válido:\n",
        "\n",
        "  \n",
        "\n",
        "```cpp\n",
        "__m256 myvar = _mm256_set1_ps(6.665f); //Atribui um único float a todos os valores do vetor\n",
        "  myvar[0] = 2.22f;                       //Válido em um compilador GCC\n",
        "  float f = (3.4f + myvar[0]) * myvar[7]; //Isto é válido em um compilador GCC\n",
        "```\n",
        "\n",
        "\n",
        "\n",
        "__m128i e __m256i são unions, então os tipos de dados devem ser referenciados. Eu não encontrei uma maneira apropriada de obter a declaração da union, então utilizo funções _mm_extract_epiXX() para obter valores individuais de dados de vetores do tipo integer.\n",
        "\n",
        "###Exemplo de operação com AVX\n",
        "Quando uma instrução em AVX é executada, o processo é o seguinte:\n",
        "\n",
        "![avxplus.png](data:image/png;base64,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)\n",
        "\n",
        "Todas as operações são aplicadas no mesmo instante. Em termos de desempenho, o custo de execução para um único **`Add`** em um float é similar ao de executar **`VAdd`** em 8 floats em AVX. Em [Agnes Instruction Table](http://www.agner.org/optimize/instruction_tables.pdf), você tem maiores informações no que concerne à latência e taxa de transferência das instruções. Na arquitetura Sandy Bridge, **`VADDPS/D`** tem latência 3 and taxa de transferência 1, exatamente como **`FADD(P)`**\n",
        "1 Este tutorial faz parte de um [Programação Paralela](https://github.com/menotti/pp) e traz apenas exemplos de uso. Acesse o material completo para mais detalhes."
      ],
      "metadata": {
        "id": "1dUhRhQHfMRO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Pré-requisitos"
      ],
      "metadata": {
        "id": "DFNwQHsehY80"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "SSE/AVX possuem pré-requisitos na arquitetura da máquina alvo. Nesse curso, as compilações binárias terão como alvo apenas a máquina atual. Para isso, nós usaremos arch=native, uma flag do GCC para detectar os recursos da CPU e usá-los de acordo.\n",
        "\n",
        "> **Nota**: Binários compilados irão falhar em maquinas que não possuem compatibilidade para AVX. Se você precisa de um binário que se adapte a diferentes CPUs, você precisará fazer uso da identificação da CPU e utilizar funções diferentes, ou fazer diferentes binários destinados para cada conjunto de instruções diferente.\n",
        "\n",
        "\n",
        "Como dito na introdução, o SO, o compilador e a CPU devem todos suportar extensões AVX. Nós podemos rodar esse script para detectar os recursos do sistema:\n",
        "\n",
        "```\n",
        "! /content/playground-41d1904q/projects/avx/run.sh /content/playground-41d1904q/projects/avx/prereq/detect.sh\n",
        "```\n",
        "\n",
        "Em `CPU flag capabilities`, nós procuraremos pela flag `avx`. Isso identifica que a CPU é compatível com AVX. Se você tem `avx2` significa que a CPU suporta extensões AVX2. AVX é o suficiente para se ter vetores float de 8x32 bits. AVX2 adiciona vetores para inteiros de 256 bits (números inteiros de 8x32 bits, por exemplo). Mesmo assim, vetores inteiros de 256 bits parecem ser executados da mesma forma que dois vetores de 128 bits, então o desempenho não melhora significativamente em relação aos vetores inteiros SSE de 128 bits.\n",
        "\n",
        "Em `GCC capabilities` nós procuraremos pelo pragma `#define __AVX__ 1`. Isso indica que as branch do AVX serão ativadas.\n",
        "\n",
        "> Sempre use **-march=native ou -mavx** !! Se você roda o GCC sem elas, você não conseguirá a flag __AVX__!!! Os parâmetros padrões do GCC são genéricos e sem a flag não será possivel ativar o AVX mesmo que a CPU seja compatível.\n",
        "\n",
        "Por fim, nós verificamos se o Kernel do Linux é `2.6.30` ou maior. Um Kernel `4.4.0` ou maior é o ideal.\n",
        "\n",
        "Com todos esses pré-requisitos atendidos, nós estamos prontos para programar o nosso primeiro programa vetorizado AVX!"
      ],
      "metadata": {
        "id": "l30HbXi_hkDc"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Autovetorização\n"
      ],
      "metadata": {
        "id": "0Ts6qZrWjA4h"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Flags de autovetorização do GCC\n",
        "\n",
        "O GCC é um compilador com recursos avançados, e, com as flags de otimização `-O3` ou `-ftree-vectorize`, o programador pode indicar ao compilador para que procure por laços e otimize-os (lembre-se de também especificar a flag `-mavx`). O código fonte continua o mesmo, contudo, o código compilado pelo GCC é completamente diferente.\n",
        "\n",
        "Por padrão, o GCC não irá indicar se ou onde foi aplicada a autovetorização, exceto que as seguintes flags sejam utilizadas:\n",
        "\n",
        "\n",
        "*   `-fopt-info-vec` ou `-fopt-info-vec-optimized`: O compilador vai informar quais laços (por N° de linha) estão sendo otimizados por vetorização.\n",
        "\n",
        "*   `-fopt-info-vec-missed`: Fornece informações detalhadas sobre os laços nos quais não foram otimizados, permitindo ao programador que realize mudanças no código para que tal otimização seja possível.\n",
        "\n",
        "*   `-fopt-info-vec-note`: Informações detalhadas sobre todos os laços e otimizações feitas.\n",
        "`-fopt-info-vec-all`: Todas as flags anteriores.\n",
        "\n",
        "\n",
        "> NOTA: Há flags similares para outras otimizações que seguem o formato `-fopt-info-[opção]-optimized` tal como a otimização inline na qual pode ser indicada com a flag `-fopt-info-inline-optimized`\n",
        "\n",
        "Neste exemplo, utilizaremos a otimização de autovetorização do GCC. Para isso, habilite as flags -O3 e com o modo verboso `-fopt-info-vec-optimized`. Você pode alterar as flags de compilação para ver diferentes opções de saída:\n",
        "\n",
        "Se tudo estiver correto, os resultados dos testes devem ser iguais aos seguintes:\n",
        "\n",
        "/project/target/autovector/autovector.cpp:22:21: note: loop vectorized\n",
        "\n",
        "/project/target/autovector/autovector.cpp:15:23: note: loop vectorized\n",
        "\n",
        "Com o mesmo código fonte, é possível realizar dois testes distintos:\n",
        "\n",
        "* No arquivo `compilerflags.txt`, altere as opções de exibição de informações para `-fopt-info-vec-all`, e execute novamente o exemplo.\n",
        "* Na primeira linha de do arquivo `autovector.cpp`, altere optimize(\"O3\", para optimize(\"O2\",, e execute novamente.\n",
        "\n",
        "Compilações que não utilizam otimizações de vetorização serão mais lentas que as vetorizadas.\n",
        "\n",
        "###Requisitos para a vetorização de laços\n",
        "\n",
        "Nem todos os laços podem ser vetorizados. Para que tal otimização seja realizada, existem algumas ressalvas:\n",
        "\n",
        "* A contagem do laço não pode ser alterada assim que as iterações se iniciarem. Isso significa que o final do laço deve ser previsível, sem a utilização de variáveis dinâmicas para esse controle, isto é, alterarem-se sem comportamento previsível, aleatoriamente crescendo ou descrescendo.\n",
        "* O uso das palavras chave `break` ou `continue` também impõem limitações. Algumas vezes o compilador é inteligente o suficiente para aplicar a otimização mesmo com o uso delas, mas este não é sempre o caso. Tente adicionar a linha `if (j == 27) continue;` logo antes da atribuição result[j] na linha 25, e execute novamente a compilação.\n",
        "* Há alguns limites na realização de chamadas de funções externas dentro do laço.\n",
        "* Não deve haver dependência de dados com outros índices do laço. Por exemplo, `for (int i = 1; i < N; i++) x[i] = x[i - 1] * 2;`, neste caso, o laço é percorrido com a variável `i`, e o dado na posição `x[i] `depende no valor resultante anterior x[i - 1]. De modo que os registradores AVX são carregados utilizando 8 valores de ponto flutuante, o compilador não consegue fazer tal operação de forma otimizada.\n",
        "* Comandos condicionais (`if `/ `else`) podem ser utilizados desde que não alterem o fluxo de controle, e apenas para, por exemplo, condicionamente carregar os valores `A` ou `B `na variável `C`. Para selecionar os valores `A `ou `B` o compilador aplica o uso de máscaras, de modo que ambos os caminhos de dados `A` e `B` são calculados, e `C` vai carregar um valor ou outro. Por exemplo:\n",
        "\n",
        "```cpp\n",
        "if ( s >= 0 ) {\n",
        " x[i] = (-b[i]+s)/(2.0f*a[i]);\n",
        " y[i] = (-b[i]-s)/(2.0f*a[i]);\n",
        "}\n",
        "else {\n",
        " x[i] = 0.0f;\n",
        " y[i] = 0.0f;\n",
        "```\n",
        "\n",
        "Este é um laço vetorizável. Note que o fluxo de controle nunca é alterado, e as posições `x[i]` e `y[i]` são sempre definidas para um ou outro valor.\n",
        "\n",
        "Leia [Vectorization with Intel C++ Compilers](https://software.intel.com/sites/default/files/m/4/8/8/2/a/31848-CompilerAutovectorizationGuide.pdf)\n",
        " para informações adicionais sobre autovetorização. Este documento refere-se a compiladores da Intel, mas fornece informações completas e interessantes sobre o assunto. Também é possível encontrar informações na página [Auto-vectorization in GCC](https://gcc.gnu.org/projects/tree-ssa/vectorization.html) do compilador GCC.\n",
        "\n",
        "\n",
        "Um detalhe interessante sobre autovetorização é que ela vem de graça. O programador não precisa realizar mudanças drásticas no código, e possivelmente o laço será vetorizado. Algumas vezes, especialmente em computações de alto desempenho, os laços e a vetorização precisam ser otimizados manualmente para utilizar os registradores AVX, garantindo maior vazão. Tal tópico será explorado nas próximas lições."
      ],
      "metadata": {
        "id": "10oqBi6pk392"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%writefile autovector.cpp\n",
        "\n",
        "#pragma GCC optimize(\"O3\",\"unroll-loops\",\"omit-frame-pointer\",\"inline\") //Optimization flags\n",
        "#pragma GCC option(\"arch=native\",\"tune=native\",\"no-zero-upper\") //Enable AVX\n",
        "#pragma GCC target(\"avx\")  //Enable AVX\n",
        "#include <x86intrin.h> //AVX/SSE Extensions\n",
        "#include <bits/stdc++.h> //All main STD libraries\n",
        "\n",
        "using namespace std;\n",
        "using namespace std::chrono;\n",
        "\n",
        "int main()\n",
        "{\n",
        "\tconst int N = 200000; //Array Size\n",
        "\tconst int noTests = 10000; //Number of tests\n",
        "\tfloat a[N],b[N],c[N],result[N];\n",
        "    auto now = high_resolution_clock::now();\n",
        "    for (int i = 0; i < N; ++i)  //Data initialization\t\n",
        "\t{ \n",
        "      a[i] =       ((float)i)+ 0.1335f; \n",
        "\t  b[i] = 1.50f*((float)i)+ 0.9383f; \n",
        "\t  c[i] = 0.33f*((float)i)+ 0.1172f; \n",
        "    }    \n",
        "   for (int i = 0; i < noTests; ++i) \n",
        "\t for (int j = 0; j < N; ++j)\n",
        "\t {\n",
        "\t  result[j] = a[j]+b[j]-c[j]+3*(float)i;\n",
        "\t }\n",
        "\tauto end_time = duration_cast<duration<double>>(high_resolution_clock::now() - now).count();\n",
        "\tassert( result[2] == ( 2.0f + 0.1335f)+( 1.50f*2.0f + 0.9383f)-(0.33f*2.0f+0.1172f)+3*(float)(noTests-1));\n",
        "    cout << \"CG> message -channel \\\"exercise results\\\" Time used: \"<< end_time << \"s, N * noTests=\"<<(N*noTests)<< endl;\n",
        "    return 0;\n",
        "}\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j2r96XuDCrNZ",
        "outputId": "403cb441-5a89-415b-f0ae-64100171dd5d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Overwriting autovector.cpp\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%shell \n",
        "g++ /content/autovector.cpp -o autovector"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sXWLHzdgCrL3",
        "outputId": "0ec8afb2-b1b8-4864-80e9-2b695e822fed"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": []
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! ././autovector"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fx6LtP9NCrDn",
        "outputId": "96f20d02-1ab1-494f-d192-94960df366bd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CG> message -channel \"exercise results\" Time used: 0.307886s, N * noTests=2000000000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Uso de SSE e AVX\n"
      ],
      "metadata": {
        "id": "4eWaxOxHlMMk"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "CPU's com SSE/AVX têm instruções assembly para operar com registradores XMM e YMM. Mas, na maioria dos compiladores o processo é simplificado usando funções intrínsecas, para que programadores não precisem usar assembly diretamente.\n",
        "\n",
        "###Funções intrínsecas\n",
        "\n",
        "Compiladores encapsulam instruções assembly como funções, e usá-las é tão fácil quanto chamar uma função com os parâmetros corretos. Ás vezes essas funções intrínsecas são emuladas se a CPU não suportar o conjunto de instruções.\n",
        "\n",
        "Funções intrínsecas de SSE/AVX usam a seguinte convenção de nomenclatura:\n",
        "\n",
        "```\n",
        " _<vector_size>_<intrin_op>_<suffix>\n",
        "```\n",
        "\n",
        "* **`< vector_size >`** é **`mm`** para vetores 128 bit (SSE), **`mm256`**  para vetores 256 bit (AVX e AVX2), e **`mm512`**  para AVX512.\n",
        "* **`< intrin_op >`** Declara a  da função intrínseca. I.e. **`add, sub, mul`**, etc...\n",
        "* **`< suffix >`** Indica o tipo de dado. **`ps`** para float, **`pd`**  para double, e **`ep< int_type >`** é para tipos de dado inteiro **`epi32`** para inteiro de 32 bit com sinal, **`epu16`**  para inteiro de 16 bit sem sinal, etc..\n",
        "\n",
        "Você pode encontrar todas as funções intrínsecas no [Intel Intrinsic Guide](https://software.intel.com/sites/landingpage/IntrinsicsGuide) É uma referência completa para qualquer função intrínseca disponível em SSE/AVX, com categorias e barra de pesquisa. Há também a [x86 Intrinsic CHeat Sheet](https://db.in.tum.de/~finis/x86-intrin-cheatsheet-v2.2.pdf?lang=en)  Mas é mais complicada de ler porque é mais complexa.\n",
        "\n",
        "###Funções ausentes nas intrínsecas SSE/AVX\n",
        "**Ausência de divisão inteira**\n",
        "\n",
        "Por alguma razão, SSE e AVX não possuem operadores de divisão inteira. Existem algumas maneiras de contornar isso:\n",
        "\n",
        "* Calculando a divisão em código linear. Recuperando os dados únicos, dividindo-os e armazenando-os novamente no vetor. Isso é bem lento.\n",
        "* Convertendo o vetor de inteiro para float, dividindo-o e convertendo-o novamente para inteiro.\n",
        "* Para divisores conhecidos em tempo de compilação, existem alguns números mágicos para converter uma divisão por uma constante em uma operação de multiplicação. Veja [libdivide](https://libdivide.com/)  e [Exact Divide by Constants](http://www.icodeguru.com/Embedded/Hacker's-Delight/077.htm)  para mais informações.\n",
        "* Para divisões por potências de dois, usando a operação de deslocamento de bits. Dividir pelo inteiro 2 é a mesma coisa que efetuar um deslocamento à direita. Isso pode ser feito apenas se todos os vetores forem divididos pela mesma potência de dois. Preste atenção quando efetuar um deslocamento à direita em números com sinal! Use operações de deslocamento de bits que reconheçam o sinal.\n",
        "\n",
        "**Ausência de funções trigonométricas**\n",
        "\n",
        "Não existem funções trigonométricos em funções intrínsecas de vetor. Soluções possíveis são calculá-las com código linear (uma a uma para cada valor do vetor), ou criar funções de aproximação. As aproximações de Séries de Taylor e Remez dão bons resultados.\n",
        "\n",
        "**Ausência de um gerador de números aleatórios**\n",
        "\n",
        "Além disso, não existem geradores de números aleatórios para vetores como intrínsecos. Mas é simples recriar um bom gerador pseudoaleatório a partir de uma versão linear. Apenas certifique-se dos bits usados no gerador de números pseudoaleatórios. Geradores de 32 ou 64 bits são preferíveis para preencher vetores.\n",
        "\n",
        "###Penalidades de desempenho\n",
        "**Alinhamento de dados**\n",
        "\n",
        "Arquiteturas de CPU mais antigas não podem usar vetorização à menos que os dados estejam alinhadas em memória ao tamanho do vetor. Algumas outras CPUs podem usar dados não alinhados com algumas penalidades de desempenho. Nos processadores recentes a penalidade parece ser insignificante [Data Alignment for speed: myth or reality?](http://lemire.me/blog/2012/05/31/data-alignment-for-speed-myth-or-reality/). Mas, para garantir, pode ser uma boa ideia alinhar os dados se isso não adicionar uma sobrecarga excessiva.\n",
        "\n",
        "No GCC, o alinhamento de dados pode ser feito com esses atributos de variável: **`__attribute__((aligned(16))) __attribute__((aligned(32)))`**\n",
        "\n",
        "Reveja [Specifying Attributes of Variables](https://gcc.gnu.org/onlinedocs/gcc-3.2/gcc/Variable-Attributes.html) para mais informações.\n",
        "\n",
        "Eu faço apenas: **`#define ALIGN __attribute__((aligned(32)))`** para simplificar a declaração de alinhamento em variáveis.\n",
        "\n",
        "**Penalidades de transição SSE <-> AVX**\n",
        "\n",
        "Há outro grande problema ao misturar bibliotecas legadas SSE e a nova arquitetura AVX. Como o XMM e o YMM compartilham os 128 bits inferiores, transicionar entre AVX e SSE pode levar a valores indefinidos nos 128 bits superiores. Para resolver isso, o compilador precisa salvar os 128 bits inferiores, limpá-los, executar a operação SSE antiga e restaurar o valor antigo. Isso adiciona uma sobrecarga perceptível às operações do AVX, resultando em desempenho reduzido.\n",
        "\n",
        "> NOTA: Esse problema NÃO SIGNIFICA que você não pode usar __m128 and __m256 ao mesmo tempo sem penalidades de desempenho. AVX tem um novo conjunto de instruções para __m128, com prefixos VEX. Essas novas instruções VEX não têm nenhum problema em combinar com instruções __m256. A penalidade de transição ocorre quando instruções __m128 não-VEX são combinadas com instruções __m256. Isso acontece quando você usa bibliotecas SSE antigas ligadas à programas AVX novos.\n",
        "\n",
        "Para evitar penalidades de transição, o compilador pode adicionar automaticamente chamadas para VZEROUPPER (limpa os 128 bits superiores) ou VZEROALL (limpa todo o registrador YMM) com o parâmetro -mvzeroupper, ou o programador pode fazê-lo manualmente. Se você não estiver usando nenhuma biblioteca SSE externa, e tiver certeza de que tudo que você programa é habilitado para VEX e compilado com extensões AVX habilitadas, você pode instruir o compilador a evitar adicionar chamadas VZEROUPPER, com: -mno-vzeroupper\n",
        "\n",
        "Confira [Adding AVX_SSE Transition Penalties](https://software.intel.com/en-us/articles/avoiding-avx-sse-transition-penalties) e [Why is this SSE Code 6 times slower without VZEROUPPER on Skylake](https://stackoverflow.com/questions/41303780/why-is-this-sse-code-6-times-slower-without-vzeroupper-on-skylake) para informações adicionais.\n",
        "\n",
        "**Carregamento, descarregamento e embaralhamento de dados**\n",
        "\n",
        "Mover dados dos registradores AVX pra lá e pra cá pode ser custoso. Em alguns casos, se você tem alguns dados armazenados em estruturas lineares, enviar esses dados para vetores AVX, executar algumas operações e recuperar esses dados pode ser mais custoso que simplesmente realizar os cálculos linearmente.\n",
        "\n",
        "Algum tempo atrás, eu tentei simular um pouco de física em um jogo, [Codingame's Poker Chip Race](https://www.codingame.com/multiplayer/bot-programming/poker-chip-race) , com AVX. Esse jogo é baseado em N entidades circulares colidindo em um espaço 2D. Então, naquele cenário eu precisava calcular até N*(N-1)/2 possíveis colisões. Minha primeira tentativa envolveu ter classes de entidade normais (com posição, raio, etc) e para o cálculo de colisão eu embaralhei todas as possíveis colisões de entidades em vetores AVX, as calculei em AVX e as retornei de volta para as classes de entidade. Quando terminei, os resultados que obtive foram medíocres, com uma melhoria não superior à 30% em relação ao código linear, com muita sobrecarga e um código difícil de manter. Depois de usar um profiler eu detectei que 90% do tempo de CPU estava sendo usado em carregamento e descarregamento de dados.\n",
        "\n",
        "Portanto, os programadores devem levar em consideração a sobrecarga de carregamento e descarregamento de dados. Lembre-se de que em alguns casos isso se torna um gargalo."
      ],
      "metadata": {
        "id": "bv1HZq-ylZt6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Primeiro código AVX: cálculo SQRT"
      ],
      "metadata": {
        "id": "LKx1sbDrm0GP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Agora que analisamos todos os requisitos, a autovectorização e as intrínsecas do AVX, podemos criar nosso primeiro programa vetorizado manualmente. Neste exercício, você precisa vetorizar um cálculo sqrt de números de ponto flutuante. Usaremos explicitamente o tipo de dados __m256 para armazenar nossos números de ponto flutuante, reduzindo a sobrecarga no carregamento de dados.\n",
        "\n",
        "Você provavelmente verá uma melhoria de desempenho de 600% ou mais. Ou seja, depois de carregar os dados, o AVX executará até 7 vezes mais rápido que o sqrtf normal. O limite teórico é de 800%, mas raramente é alcançado. Você pode esperar um aumento médio entre 300% e 600%."
      ],
      "metadata": {
        "id": "SGlIM_Phm5pr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%writefile sqrt.cpp\n",
        "\n",
        "#pragma GCC optimize(\"O3\",\"unroll-loops\",\"omit-frame-pointer\",\"inline\") //Optimization flags\n",
        "#pragma GCC option(\"arch=native\",\"tune=native\",\"no-zeroupper\") //Enable AVX\n",
        "#pragma GCC target(\"avx\")  //Enable AVX\n",
        "#include <x86intrin.h> //SSE Extensions\n",
        "#include <bits/stdc++.h> //All main STD libraries\n",
        "\n",
        "const int N = 64000000; //Number of tests\n",
        "const int V = N/8;      //Vectorized size\n",
        "\n",
        "//linear function, \n",
        "float linear[N];\n",
        "__attribute__((optimize(\"no-tree-vectorize\"))) //Force disable auto-vectorization\n",
        "inline void normal_sqrt()\n",
        "{\n",
        "    for (int i = 0; i < N; ++i)\n",
        "        linear[i] = sqrtf(linear[i]);\n",
        "}\n",
        "\n",
        "//Exercise 1: Create a vectorized version of the \"linear\" function.\n",
        "//Please note the following:\n",
        "// \"vectorized\" array is size V=N/8, because each __m256 variable holds 8 floats.\n",
        "// sqrtf(const float& f) vectorized function is: _mm256_sqrt_ps(const __m256& v)\n",
        "__m256 __attribute__((aligned(32))) vectorized[V]; //Vectorized array\n",
        "inline void avx_sqrt()\n",
        "{\n",
        "  //****** Add AVX code here*******\n",
        "}\n",
        " \n",
        " \n",
        "using namespace std;\n",
        "using namespace std::chrono;\n",
        " \n",
        "high_resolution_clock::time_point now = high_resolution_clock::now();\n",
        "#define TIME duration_cast<duration<double>>(high_resolution_clock::now() - now).count()\n",
        "\n",
        "int main()\n",
        "{\n",
        "//Data initialization\t\n",
        "    for (int i = 0; i < N; ++i) { linear[i] = ((float)i)+ 0.1335f; }\n",
        "    for (int i = 0; i < V; ++i) {\n",
        "\t\tfor (int v=0;v<8;++v)\n",
        "\t\t {  vectorized[i][v] = ((float)(i*8+v))+ 0.1335f; }\n",
        "\t}\n",
        " \n",
        " //Normal sqrt benchmarking. 20*64 Million Sqrts\n",
        "    now = high_resolution_clock::now();\n",
        "    for (int i = 0; i < 20; ++i)\n",
        "    normal_sqrt();\n",
        "    double linear_time = TIME;\n",
        "    cerr << \"Normal sqrtf: \"<< linear_time << endl;\n",
        " \n",
        " //AVX vectorized sqrt benchmarking. 20*8*8 Million Sqrts\n",
        "    now = high_resolution_clock::now();\n",
        "    for (int i = 0; i < 20; ++i)\n",
        "    avx_sqrt();\n",
        "    double avx_time = TIME;\n",
        "    cerr << \"AVX sqrtf : \"<<avx_time << endl;\n",
        "\t\n",
        "\t//Check values\n",
        "\tfor (int i = 0; i < V; ++i) {\n",
        "\t\tfor (int v=0;v<8;++v)\n",
        "\t\t { \n",
        "  \t       if (abs(linear[i*8+v] - vectorized[i][v]) > 0.00001f)\n",
        "\t\t   {\n",
        "\t         cerr << \"ERROR: AVX sqrt is not the same as linear!!!\";\n",
        "\t         cerr << linear[i*8+v]<<\" <-> \"<<vectorized[i][v]<<endl;\n",
        "\t\t\t return -1;\n",
        "\t\t   }\n",
        "\t\t }\n",
        "\t}\n",
        "\tcout << \"CG> message -channel \\\"exercise results\\\" Linear to AVX improvement : \"<< (linear_time/avx_time*100)<<\"%\" << endl;\n",
        " \n",
        "    return 0;\n",
        "}\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9M_Vi1_M9Hn9",
        "outputId": "ea13dedd-2eac-4301-d819-ff0af307a6cf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Overwriting sqrt.cpp\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%shell \n",
        "g++ /content/sqrt.cpp -o sqrt"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7mrFwPzyd8Wk",
        "outputId": "4ca0690c-43fb-41fc-a15c-27814b5fc9be"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": []
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! ././sqrt"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cKO_v8HxjX68",
        "outputId": "6956d656-e13c-4db1-ba3d-e5e25b4bc3ab"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Normal sqrtf: 1.63093\n",
            "AVX sqrtf : 9.1e-08\n",
            "ERROR: AVX sqrt is not the same as linear!!!0.999998 <-> 0.1335\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Frameworks C++ SSE/AVX"
      ],
      "metadata": {
        "id": "JWwCJBqvnKLO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Complexidade de funções intrínsecas\n",
        "\n",
        "Trabalhar diretamente com funções intrínsecas pode ser complicado de ser codificado e mantido. O problema é que os nomes intrínsecos são longos e as operações aritméticas são escritas na notação de função: add(a,b) ao invés de a+b. O código a seguir é dificil de ler:\n",
        "\n",
        "```cpp\n",
        "x = _mm256_div_ps(_mm256_add_ps(b , _mm256_sqrt_ps(_mm256_sub_ps(_mm256_mul_ps(b , b) , _mm256_mul_ps(_mm256_mul_ps(a , c),_mm256_set1_ps(4.0f))))) , _mm256_mul_ps(a,_mm256_set1_ps(2.0f)));\n",
        "```\n",
        "Muito simples, correto? Por outro lado, essa versão empacotada é muito legível:\n",
        "\n",
        "```cpp\n",
        "x = (b + sqrt( b*b - a*c*4.0f))/(a*2.0f)\n",
        "```\n",
        "\n",
        "É como trabalhar com pontos flutuantes (floats). Você apenas precisa se lembrar que essas variáveis são vetores. Como você pode notar, o empacotamento permite operações aritméticas de um vetor com um valor escalar (vetor * escalar = vetor).\n",
        "\n",
        "###Frameworks C++ para computação SIMD\n",
        "\n",
        "Existem frameworks que agrupam tipos de dados vetoriais dentro de novas classes. Em seguida, sobrecarregam os operadores aritméticos, lógicos e de atribuição para simplificar os cálculos. Entre outros, você pode usar esses dois frameworks:\n",
        "\n",
        "\n",
        "1.   [Biblioteca de classes vetoriais C++ de Agner Fogs](http://www.agner.org/optimize/#vectorclass) . Completo e atualizado regularmente. Inclui funções trigonométricas.\n",
        "2.   [Ambiente Multicore Unificado](https://gain-performance.com/ume/). É a biblioteca mais recente. Eu não usei pessoalmente.\n",
        "\n",
        "###Frameworks de tamanho reduzido\n",
        "\n",
        "Infelizmente, essas dois frameworks são enormes, pelo menos para programação competitiva onde o código é limitado a cem KBs ou menos. Nos casos em que você tem limitações no tamanho do código, você precisará reduzir para uma versão menor de um desses frameworks.\n",
        "\n",
        "Eu tenho alguns empacotadores de vetor com tamanho reduzido, focados apenas em um ou dois tipos (por exemplo, __m256 8x float e __m128i 8x short, para trabalhar com um tamanho de vetor de 8, tanto em floats (pontos flutuantes) quanto em números inteiros).\n",
        "\n",
        "Mesmo sendo uma versão reduzida, cada declaração de tipo de dados vetorial ocupa até 150 linhas em média (mais algumas funções auxiliares). Por favor, use esses empacotadores como referência para as suas próprias versões, pois elas podem conter bugs.\n",
        "\n",
        "As classes empacotadas podem adicionar sobrecarga às chamadas, reduzindo o seu desempenho. Mas, na minha opinião, trabalhar diretamente com as funções intrínsecas dificilmente será mantido, além de ser embaraçoso e propenso a erros. A partir de agora, usarei classes empacotadas para abstrair o código dos intrínsecos fundamentais.\n",
        "\n",
        "Em todas os frameworks vetoriais, você encontrará algumas funções especiais. Essas funções especiais serão amplamente usadas nas lições a seguir. Se você não entender à primeira vista, não se preocupe. Você eventualmente entenderá a lógica por trás deles.\n",
        "\n",
        "**Funções baseadas em mesclagem**: A mesclagem é o processo de carregar condicionalmente valores vetoriais com base em uma máscara. Isso será explicado melhor nas lições a seguir. Em ambos, no empacotamento de Agner Fog e no meu empacotamento, as funções derivadas são:\n",
        "\n",
        "1. **`if_select(mask,value_true,value_false)`**: Carga condicional de um vetor baseado em uma máscara. Se mask for verdadeiro para um componente vetorial, **`value_true`** é retornado, ou **`value_false`** caso contrário. É um **`if `**\"fake\".\n",
        "2. **`if_add(mask,value,add_when_true)`**: Adição condicional. Retorna **`value + (mask? add_when_true:0)`**, para cada componente vetorial.\n",
        "3. **`if_sub`** , **`if_mul`**, **`if_div`**: Semelhante a **`if_add`**, apenas com uma operação aritmética diferente.\n",
        "\n",
        "**Funções horizontais**: O horizontal significa que essas funções operam dentro de uma única variável vetorial, calculando algum valor lógico ou aritmético.\n",
        "\n",
        "**`horizontal_or(mask)`**: Se qualquer componente vetorial em **`mask`** for verdadeiro. Retorna um booleano.\n",
        "\n",
        "**`horizontal_add(vector)`**: Retorna a soma de todos os componentes do vetor. O valor retornado é um número (float ou double ou int, dependendo do tipo de vetor).\n",
        "\n",
        "> NOTE: Agner Fog usa classes diferentes para máscaras (com sufixo `b`), enquanto eu uso as mesmas classes de vetores para simplificar e reduzir o código."
      ],
      "metadata": {
        "id": "CHvH_4LynPAF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%writefile framework.cpp\n",
        "\n",
        "#pragma GCC optimize(\"O3\",\"unroll-loops\",\"omit-frame-pointer\",\"inline\") //Optimization flags\n",
        "#pragma GCC option(\"arch=native\",\"tune=native\",\"no-zeroupper\") //Enable AVX\n",
        "#pragma GCC target(\"avx\") //Enable AVX\n",
        "#include <x86intrin.h>    //AVX/SSE Extensions\n",
        "#include <bits/stdc++.h>  //All main STD libraries\n",
        "#include \"/content/playground-41d1904q/projects/avx/framework/v8i.h\"          //AVX 8x float vectors\n",
        "#include \"/content/playground-41d1904q/projects/avx/framework/v8f.h\"          //SSE 8x short vectors\n",
        "#include \"/content/playground-41d1904q/projects/avx/framework/vconvert.h\"     //Vector short <-> float conversions\n",
        "#include \"/content/playground-41d1904q/projects/avx/framework/vrandom.h\"      //Pseudo-random numbers\n",
        "using namespace std;\n",
        " \n",
        "int main()\n",
        "{\n",
        "    v8i a(250);\n",
        "    v8i b(1,-3,-4,6,20,250,-4003,4);\n",
        "    cout << \"Wrapper Tests: Integer Vectors\" <<endl;\n",
        "    cout << \"a   :\"<<a<<endl;\n",
        "    cout << \"b   :\"<<b<<endl;\n",
        "    cout << \"a+b :\"<<a+b<<endl;\n",
        "    cout << \"a-b :\"<<a-b<<endl;\n",
        "    cout << \"a*b :\"<<a*b<<endl; //Overflow!!!!! Remember that v8i is only 16-bit signed\n",
        "    cout << \"a/b :\"<<a/b<<endl; //emulated, slow\n",
        "    cout << \"a>b :\"<<(a>b)<<endl; //true is -1, because it's a mask with all 16 bits set to 1.\n",
        "    cout << \"a==b:\"<<(a==b)<<endl;\n",
        "    cout << \"Irandom(1,1348):\"<<(Irandom<1,1348>())<<endl;    \n",
        "    cout <<endl;\n",
        "\n",
        "    v8f c(15.1f);\n",
        "    v8f d(1.4f,3.3f,-12.5f,-33.4f,7.9f,-70.2f,15.1f,22.6f);    \n",
        "    cout << \"Wrapper Tests: Float Vectors\" <<endl;    \n",
        "    cout << \"c   :\"<<c<<endl;\n",
        "    cout << \"d   :\"<<d<<endl;\n",
        "    cout << \"c+d :\"<<c+d<<endl;\n",
        "    cout << \"c-d :\"<<c-d<<endl;\n",
        "    cout << \"c*d :\"<<c*d<<endl; \n",
        "    cout << \"c/d :\"<<c/d<<endl;\n",
        "    cout << \"c>d :\"<<(c>d)<<endl; //true is -nan, because it's a mask with all 32 bits set to 1.\n",
        "    cout << \"c==d:\"<<(c==d)<<endl;\n",
        "    cout << \"Frandom(1,1348):\"<<(Frandom<1,1348>())<<endl;    \n",
        "    return 0;\n",
        "}\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OAukRaUb-XYV",
        "outputId": "686c3280-f153-4ef7-dead-86c8cce315fe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Writing framework.cpp\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%shell \n",
        "g++ /content/framework.cpp -o framework"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f4W3ty9y-y5l",
        "outputId": "ec83ab6a-c67c-4f3d-82ba-34159953283d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "In file included from \u001b[01m\u001b[K/content/framework.cpp:7\u001b[m\u001b[K:\n",
            "\u001b[01m\u001b[K/content/playground-41d1904q/projects/avx/framework/v8i.h:\u001b[m\u001b[K In member function ‘\u001b[01m\u001b[Kint16_t v8i::operator[](uint32_t) const\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K/content/playground-41d1904q/projects/avx/framework/v8i.h:42:3:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kcontrol reaches end of non-void function [\u001b[01;35m\u001b[K-Wreturn-type\u001b[m\u001b[K]\n",
            "   42 |   \u001b[01;35m\u001b[K}\u001b[m\u001b[K\n",
            "      |   \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": []
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! ././framework"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3iY-Hecl-8ze",
        "outputId": "a433d511-a526-4886-a914-1c5fd10a700d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Wrapper Tests: Integer Vectors\n",
            "a   :v8i: [250,250,250,250,250,250,250,250,]\n",
            "b   :v8i: [1,-3,-4,6,20,250,-4003,4,]\n",
            "a+b :v8i: [251,247,246,256,270,500,-3753,254,]\n",
            "a-b :v8i: [249,253,254,244,230,0,4253,246,]\n",
            "a*b :v8i: [250,-750,-1000,1500,5000,-3036,-17710,1000,]\n",
            "a/b :v8i: [250,-83,-62,41,12,1,0,62,]\n",
            "a>b :v8i: [-1,-1,-1,-1,-1,0,-1,-1,]\n",
            "a==b:v8i: [0,0,0,0,0,-1,0,0,]\n",
            "Irandom(1,1348):v8i: [1141,768,619,1213,1087,265,1105,975,]\n",
            "\n",
            "Wrapper Tests: Float Vectors\n",
            "c   :v8f: [15.1,15.1,15.1,15.1,15.1,15.1,15.1,15.1,]\n",
            "d   :v8f: [1.4,3.3,-12.5,-33.4,7.9,-70.2,15.1,22.6,]\n",
            "c+d :v8f: [16.5,18.4,2.6,-18.3,23,-55.1,30.2,37.7,]\n",
            "c-d :v8f: [13.7,11.8,27.6,48.5,7.2,85.3,0,-7.5,]\n",
            "c*d :v8f: [21.14,49.83,-188.75,-504.34,119.29,-1060.02,228.01,341.26,]\n",
            "c/d :v8f: [10.7857,4.57576,-1.208,-0.452096,1.91139,-0.2151,1,0.668142,]\n",
            "c>d :v8f: [-nan,-nan,-nan,-nan,-nan,-nan,0,0,]\n",
            "c==d:v8f: [0,0,0,0,0,0,-nan,0,]\n",
            "Frandom(1,1348):v8f: [455,964,1111,857,665,879,411,1049,]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Mascaramento e carga condicional"
      ],
      "metadata": {
        "id": "qwmVLGfppx0e"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Máscaras em Vetores\n",
        "\n",
        "Na lição anterior, foi apresentado o conceito de máscara. Como é um conceito chave para controlar o fluxo de dados, é necessária uma explicação detalhada.\n",
        "\n",
        "Uma máscara é um resultado de uma operação lógica entre vetores. Possui muitas similaridades com booleanos (eles são o resultado de operações lógicas em números únicos, ou em outros valores booleanos), mas, internamente, cada máscara deve ser composta somente por bits 1 ou por bits 0.\n",
        "\n",
        "Vamos comparar dois vetores float AVX com o operador maior que:\n",
        "\n",
        "![mask.png](data:image/png;base64,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)\n",
        "\n",
        "\n",
        "As entradas são dois vetores com valores float. A saída dessa operação lógica, também é um vetor de valores float, mas os valores devem ter somente bits 0's ou somente bits 1's. Todos os 1's representam o valor lógico **`TRUE`**, enquanto os 0's são o valor lógico **`FALSE`**. O valor 1's é impresso como **`-nan`** para floats, ou como -1 para inteiros. O valor real armazenado não é importante, somente é necessário saber que possui valores verdadeiros (**`TRUE`**) ou valores falsos (**`FALSE`**).\n",
        "\n",
        "Resultado dos operadores lógicos (>, <, ==, &&, ||, etc)\n",
        "\n",
        "Utilizando o operador && como um exemplo:\n",
        "\n",
        "* **`vector && vector`** = **`mask`**\n",
        "* **`mask && mask`** = **`mask`**\n",
        "* **`vector && mask`** = **`?????`**\n",
        "\n",
        "Eu ainda não testei o último caso, eu acho que retornará resultados inesperados. É como realizar **`3 > false`**, talvez em C++ funcione, mas no aspecto lógico, é incorreto.\n",
        "\n",
        "> NOTA: Diferentemente dos valores booleanos, em que qualquer valor diferente de zero é `TRUE`. Somente um vetor composto com todos os bits 1's é considerado `TRUE`. Não utilize outros valores como máscara, pois falhará ou retornará resultados inesperados.\n",
        "\n",
        "\n",
        "###Carregamento Condicional\n",
        "\n",
        "Máscaras podem ser utilizadas para carregar condicionalmente valores em vetores. Se você relembrar as funções blend-based. Todas elas utilizam máscaras para controlar condicionalmente o carregamento de valores nos vetores: **`if_select(mask,value_true,value_false)`** pode ser representado como:\n",
        "\n",
        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)\n",
        "\n",
        "Quando a máscara é definida como `FALSE`, o dado é carregado do vetor `value_false`, e quando é `TRUE`, o dado vem do vetor `value_true`. O conceito é simples, mas efetivo.\n",
        "\n",
        "No próximo exercício, você precisa carregar um vetor de acordo com as seguintes condições:\n",
        "\n",
        "```cpp\n",
        "if (value > 3.0f || (value <= -3.7f && value > -15.0f)) {\n",
        "   return sqrt(2.0f * value + 1.5f);\n",
        " }\n",
        " else {\n",
        "   return (-2.0f * value - 8.7f);\n",
        " }\n",
        "\n",
        "```\n",
        "\n",
        "> NOTA: **`if_select`** NÃO É um nome de função intrínseca. É o meu wrapper para **`_mm256_blendv_ps`**. Por favor, note que **`_mm256_blendv_ps`** possui uma ordem de parâmetros bem diferente! blendv tem a máscara como o último parâmetro!\n",
        "\n",
        "###Desempenho\n",
        "Carregamento condicional utilizando máscaras não são uma branch real, então não possuem previsões errôneas, dessa forma a CPU pode fazer melhor uso da execução fora de ordem. Mas isso vem com um preço. Como vem sem uma branch, e toda a execução condicional é feita com operação em máscaras, ambas as branches são sempre calculadas e executadas.\n",
        "\n",
        "Se você tiver um cálculo complexo para o **`value_false`**, ele será sempre calculado, mesmo que ele aconteça em 0,00001% das vezes. Isso acarreta em problemas de desempenho se tiver partes do código que são realmente necessárias, mas computacionalmente caras.\n",
        "\n",
        "Na próxima lição, nós iremos aprender algumas formas de controlar o fluxo de dados, sendo capazes de sair de laçoes baseado em algumas condições.\n"
      ],
      "metadata": {
        "id": "EU_gQ11tqAJf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%writefile masked.cpp\n",
        "\n",
        "#pragma GCC optimize(\"O3\",\"unroll-loops\",\"omit-frame-pointer\",\"inline\") //Optimization flags\n",
        "#pragma GCC option(\"arch=native\",\"tune=native\",\"no-zeroupper\") //Enable AVX\n",
        "#pragma GCC target(\"avx\") //Enable AVX\n",
        "#include <x86intrin.h>    //AVX/SSE Extensions\n",
        "#include <bits/stdc++.h>  //All main STD libraries\n",
        "#include \"/content/playground-41d1904q/projects/avx/masked/v8f.h\"          //SSE 8x short vectors\n",
        "using namespace std;\n",
        "\n",
        "inline v8f testConditions(const v8f& value)\n",
        "{\n",
        "//You need to return values as required on the previous statement\n",
        "//*** INSERT YOUR CODE HERE ***\n",
        "\n",
        "}\n",
        "\n",
        "inline bool validate(const v8f& test, const v8f& vector)\n",
        "{\n",
        " for (int j=0;j<8;++j)\n",
        " {\n",
        "    float value = test[j];\n",
        "    float expected;\n",
        "    if (value > 3.0f || (value <=-3.7f && value > -15.0f)) {\n",
        "       expected =  sqrt(2.0f*value+1.5f);\n",
        "    }\n",
        "    else {\n",
        "       expected= (-2.0f*value-8.7f);\n",
        "    }\n",
        "    if (abs(expected- vector[j])>0.00001f)\n",
        "    {\n",
        "     cout << \"Assert Error:\"<< expected<<\" \"<< vector[j]<<endl;\n",
        "     return false;\n",
        "    }\n",
        " }\n",
        " return true; \n",
        "}\n",
        "\n",
        "int main()\n",
        "{\n",
        "    int validTests = 0;\n",
        "    int TotalTests = 1000;\n",
        "    for (int i=0;i<TotalTests;++i)\n",
        "    {\n",
        "     float offset = -500.0f + (1000.0f*i)/TotalTests;\n",
        "     v8f test(1.4f,3.3f,-12.5f,-33.4f,7.9f,-70.2f,15.1f,22.6f);            \n",
        "     test += offset;\n",
        "     v8f result = testConditions(test);\n",
        "     if (validate(test,result))     //Validation\n",
        "  \t    ++validTests;\n",
        "    }\n",
        "    \n",
        "    cout << \"Valid Tests:\"<<validTests<<\"/\"<<TotalTests<<\" (\"<<(100*validTests/TotalTests)<<\"%)\"<<endl;\n",
        "    if (validTests != TotalTests)\n",
        "     return -1;\n",
        "    return 0;\n",
        "}\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_YemrRs-_w6f",
        "outputId": "22c57b96-f58d-45a3-9e20-b9e8d0ede976"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Overwriting masked.cpp\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%shell\n",
        "g++ /content/masked.cpp -o masked "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FfR61xX2_wzI",
        "outputId": "da53c1ed-c593-4d7f-dcc3-a34f1b32515a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[01m\u001b[K/content/masked.cpp:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kv8f testConditions(const v8f&)\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K/content/masked.cpp:15:1:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kno return statement in function returning non-void [\u001b[01;35m\u001b[K-Wreturn-type\u001b[m\u001b[K]\n",
            "   15 | \u001b[01;35m\u001b[K}\u001b[m\u001b[K\n",
            "      | \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": []
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! ././masked"
      ],
      "metadata": {
        "id": "AxpD6nUx_woR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Controlando o fluxo de dados"
      ],
      "metadata": {
        "id": "c_CbCBqSr0kb"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###A questão do fluxo de dado compartilhado\n",
        "\n",
        "Em programação linear, não existem problemas com a criação de divisões condicionais (funções como **`if, switch, continue e break`**) para o controle do fluxo de dados. Você pode simplesmente ter um loop infinito e bloquear sua execução quando a condição for satisfeita. Mas, como visto na lição anterior, um vetor não tem somente um resultado, mas N resultados no mesmo instante. Ainda, parte de um vetor pode estar pronta para sair do loop (por conta dos dados no vetor satisfazerem a condição de saida), e parte ainda estar sendo processada antes de finalizar sua execução.\n",
        "\n",
        "> NOTA: Se um elemento do vetor estiver finalizado, congele seu processamento para evitar gasto de processamento inútil. Isso é feito através de uma máscara de componentes finalizados. Os elementos não finalizados, seguiram com a atualização, e os finalizados não. Assim, suponhamos um vetor de 8 posições, e os elementos 0, 1, 4 e 7 já foram processados, teremos então uma máscara [**`false,false,true,true,false,true,true,false`**] que armazena o estado de cada elemento.\n",
        "\n",
        "###Evitando a execução de ramificações computacionalmente custosas\n",
        "\n",
        "Uma boa abordagem para otimizar o tempo de CPU é checando se todos os valores na máscara são o mesmo, todos **`TRUE`** ou **`FALSE`**. Quando todos os valores na máscara são os mesmos, temos um simples booleano, verdadeiro ou falso. Este pode ser utilizado para avançar em partes do código, ou usar desvios condicionais tradicionais: **`if, switch, continue e break`**...\n",
        "\n",
        "Nas minhas classes empacotadas, uso a função **`horizontal_or (mask)`** (que empacota intrínsecos **`_xxx_testz_xx`**). \"Horizontal_OR\" verifica se algum valor na máscara é **`true`** , e neste caso retorna um simples valor booleano, e **`false`** em caso contrário\n",
        "\n",
        "O próximo exercício apresenta alguns problemas de tempo limite devido a uma função computacionalmente intensiva, necessária apenas em alguns casos específicos. Otimize o código para evitar o tempo limite:\n",
        "\n",
        "`skip.cpp`\n",
        "\n",
        "###Controlando o fluxo de dados\n",
        "\n",
        "Utilizando **`horizontal_or`**, você pode interromper o loop antes. Não é possível conseguir essa otimização com autovetorização, mas com a vetorização manual é possível e preferível.\n",
        "\n",
        "Neste exercício, será calculado uma pontuação máxima entre 8 simulações paralelas ao mesmo tempo, com um limite de 200 rodadas. Deseja-se finalizar a simulação quando atingir uma pontuação maior que 1700 pontos em qualquer uma das simulações, e retornar o valor máximo (um ponto flutuante, não todo o vetor de pontuação, somente o máximo) e a rodada que se atingiu este valor.\n",
        "\n",
        "`exitloop.cpp`\n",
        "\n"
      ],
      "metadata": {
        "id": "Q-BggQfrr332"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Skip.cpp**"
      ],
      "metadata": {
        "id": "UtBEhSwNB2OV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%writefile skip.cpp\n",
        "\n",
        "#pragma GCC optimize(\"O3\",\"unroll-loops\",\"omit-frame-pointer\",\"inline\") //Optimization flags\n",
        "#pragma GCC option(\"arch=native\",\"tune=native\",\"no-zeroupper\") //Enable AVX\n",
        "#pragma GCC target(\"avx\") //Enable AVX\n",
        "#include <x86intrin.h>    //AVX/SSE Extensions\n",
        "#include <bits/stdc++.h>  //All main STD libraries\n",
        "#include <unistd.h>       //usleep function\n",
        "#include \"/content/playground-41d1904q/projects/avx/skip/v8f.h\"          //SSE 8x short vectors\n",
        "using namespace std;\n",
        "using namespace std::chrono;\n",
        " \n",
        "high_resolution_clock::time_point now;\n",
        "#define TIME duration_cast<duration<double>>(high_resolution_clock::now() - now).count()\n",
        "\n",
        "inline v8f slowFunction(int i){\n",
        "\tusleep(2000); //Emulating an slow function\n",
        "\tv8f slow(-2.0f,+3.0f,-4.0f,+5.0f,-6.0f,+7.0f,-8.0f,+9.0f);\n",
        "\tslow += ((float)i)/40.0f;\n",
        "\treturn slow;\n",
        "}\n",
        "\n",
        "int main()\n",
        "{\n",
        "\tv8f result(0.0f);\n",
        "\tnow = high_resolution_clock::now();\n",
        "//Main loop to optimize\t\n",
        "    for (int i=0;i<2000;++i)\n",
        "\t{\n",
        "\t v8f test(1.4f,3.3f,-12.5f,-33.4f,7.9f,-70.2f,15.1f,22.6f);  \n",
        "\t test += ((float)i)/100.0f;\n",
        "\t result += if_select( test >= 38.0f, slowFunction(i), test );\n",
        "\t}\n",
        "\tdouble execution_time = TIME;\n",
        "\t\n",
        "\t\n",
        "//Validations\t\n",
        "    float totalResult = horizontal_add(result);\n",
        "\tcout <<\"Result:\"<< std::setprecision(10)<< totalResult<<\" Time:\"<< (int)(execution_time*1000)<<\"ms\"<<endl;\n",
        "\t\n",
        "\tif (34273.52734f != totalResult)\n",
        "\t{\n",
        "\t    cout << \"Invalid Result: Got \"<< std::setprecision(10)<< totalResult<<\" expected 34273.52734\"<<endl;\n",
        "\t    return -1;\n",
        "\t}\n",
        "\tif (execution_time > 2.0f) \n",
        "\t{\n",
        "\t    cout << \"TIMEOUT ERROR!: Execution time is \"<<(int)(execution_time*1000)<<\"ms > 2000ms limit!\"<<endl;\n",
        "\t    return -1;\n",
        "\t}\n",
        "\t\n",
        "    return 0;\n",
        "}\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "APP4DQfOA7tn",
        "outputId": "f89cb6ea-3f99-4a2e-a298-9390bb267ff7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Writing skip.cpp\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%shell\n",
        "g++ /content/skip.cpp -o skip "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WzaAvcLhA7jk",
        "outputId": "15f8ec6c-ddf6-407b-dac8-5b78228b1fb1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": []
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! ././skip"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aq1beZn1A7f8",
        "outputId": "7d842d69-c098-4ea8-e91d-0bb64c149107"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Result:34273.52734 Time:4188ms\n",
            "TIMEOUT ERROR!: Execution time is 4188ms > 2000ms limit!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Exitloop.cpp**"
      ],
      "metadata": {
        "id": "17iKK9h1BxSj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%writefile exitloop.cpp\n",
        "\n",
        "#pragma GCC optimize(\"O3\",\"unroll-loops\",\"omit-frame-pointer\",\"inline\") //Optimization flags\n",
        "#pragma GCC option(\"arch=native\",\"tune=native\",\"no-zeroupper\") //Enable AVX\n",
        "#pragma GCC target(\"avx\") //Enable AVX\n",
        "#include <x86intrin.h>    //AVX/SSE Extensions\n",
        "#include <bits/stdc++.h>  //All main STD libraries\n",
        "#include \"/content/playground-41d1904q/projects/avx/exitloop/v8f.h\"          //SSE 8x short vectors\n",
        "using namespace std;\n",
        "\n",
        "int validateResult(const int& turn,const float& bestScore)\n",
        "{\n",
        " cout << \"Turn:\"<<turn<<\"  bestScore:\"<< std::setprecision(10)<<bestScore<<endl;\n",
        " if (turn != 133)\n",
        " {\n",
        "     cout << \"ERROR, Expected turn exit at 133 != \"<<turn<<endl;\n",
        "     return -1;\n",
        " }\n",
        " if (bestScore != 1707.318481f)\n",
        " {\n",
        "     cout << \"ERROR, Expected a bestScore of 1707.318481f != \"<< std::setprecision(10)<<bestScore<<endl;\n",
        "     return -1;\n",
        " }\n",
        " return 0;\n",
        "}\n",
        "\n",
        "int main()\n",
        "{\n",
        "    int turn = 0;\n",
        "    v8f Scores(1.0f,3.0f,7.0f,13.4f,22.7f,0.01f,4.556f,9.7f); //Initial load\n",
        "    for (turn =0; turn < 200; ++turn)\n",
        "    {\n",
        "        Scores += ((float)(turn)/15.0f);\n",
        "        if ( turn == 40)  { Scores *= Scores/15.0f+2.0f;}\n",
        "        if ( turn == 70)  { Scores += if_select(Scores < 430.0f, 850.0f, 120.0f );  }\n",
        "        //EXERCISE: BREAK THE LOOP ONCE YOU REACH MORE THAN 1700 POINTS\n",
        "    }\n",
        "    cout << \"Scores: \"<<Scores<<endl;    \n",
        "    float bestScore = 0.0f;\n",
        "    //TODO:LOAD THE bestScore\n",
        "\t\n",
        "    return validateResult(turn,bestScore);\n",
        "}\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MkbxTju4BYRP",
        "outputId": "084ac3a7-872f-4666-8158-a271147538b5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Overwriting exitloop.cpp\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%shell\n",
        "g++ /content/exitloop.cpp -o exitloop "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xSeS7KE4BYMX",
        "outputId": "1c3b06e3-8f3e-4b38-a985-c7954f86672e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": []
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! ././exitloop"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KZIP6MJZBYFF",
        "outputId": "132926ba-a5fc-4014-a4a1-3790c86bb117"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Scores: v8f: [2439.92,1729.03,1768.85,1837,1945.77,2430.66,1744.27,1796.94,]\n",
            "Turn:200  bestScore:0\n",
            "ERROR, Expected turn exit at 133 != 200\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Palavras finais"
      ],
      "metadata": {
        "id": "eV7O3c6hsygc"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Neste curso, você deve ter obtido um bom nível de compreensão sobre vetorização manual e automática de CPU:\n",
        "\n",
        "* Requisitos de Hardware e software para usar instruções SSE e AVX no seu código.\n",
        "* Os tipos de dados de vetores disponíveis.\n",
        "* Informação sobre como checar o uso de autovetorização e dicas se loopes podem ser autovetorizados.\n",
        "* Frameworks de vetores em C++.\n",
        "* Mascaramento e carga condicional.\n",
        "* Controle do fluxo de dados.\n",
        "\n",
        "###Prós e contras\n",
        "\n",
        "As vetorizações AVX e SSE são interessantes e, teoricamente, seu uso traz mais benefícios que qualquer otimização linear.\n",
        "\n",
        "**Prós**\n",
        "\n",
        "* Ganhos potenciais de 300% a 600% de performance quando comparados a código linear.\n",
        "* Similaridades com CUDA, programação vetorizada no nível de GPU.\n",
        "\n",
        "**Contras**\n",
        "\n",
        "\n",
        "* A performance depende do hardware em que o código está sendo rodado.\n",
        "* Má performance quando há muito carregamento e descarregamento de dados.\n",
        "* O fluxo de dados pode se tornar bem difícil de controlar, e o tempo de execução de cada valor dentro do vetor afeta o tempo de execução geral. Você não pode sair antes que todos os valores satisfaçam a condição de saída.\n",
        "* É complexo para programar.\n",
        "* Carece de funções instrínsecas: Trigonometria, números aleatórios, divisão de inteiros.\n",
        "\n",
        "###Outros cursos\n",
        "\n",
        "Em desenvolvimento: Offline MarsLander solver using a Genetic Algorithm (AVX enabled)/\n",
        "\n",
        "###Jogos Multiplayer Codingame\n",
        "\n",
        "Simulações com uso de AVX podem ser feitas em alguns Jogos Multiplayer Codingame:\n",
        "\n",
        "* [Coder`s Strike Back](https://www.codingame.com/multiplayer/bot-programming/coders-strike-back)  Minha versão do AVX alcançou entre 2.5 e 3.5 milhões de simulações por turno, com turnos de 150 milisegundos. Isso é feito paralelizando 8 simulações de estadoos jo jogo ao mesmo tempo. Uma versão anterior com cálculos seriais (isto é, com dados fora do AVX, colocando-os dentro de vetores AVX, calculando e, depois, retirando o resultado) alcançou apenas 600k simulações/turno.\n",
        "* [Poker CHip Race](https://www.codingame.com/multiplayer/bot-programming/poker-chip-race)  Performance é bem dependente do número de indivíduos, entre 100k e 1.5M simulações/turno. Os turnos duram 150ms também.\n",
        "\n",
        "Vários outros jogos podem ser paralelizados e simulados usando vetores AVX/SSE.\n",
        "\n",
        "###Links úteis\n",
        "\n",
        "* [Guia de Funcões Intrínsecas da Intel](https://software.intel.com/sites/landingpage/IntrinsicsGuide)\n",
        "\n",
        "* [Tabela de x86 Intrinsics Sheet](https://db.in.tum.de/~finis/x86-intrin-cheatsheet-v2.2.pdf?lang=en)\n",
        "\n",
        "* [Manual dos Desenvolvedores da Arquitetura Intel](http://www.intel.com/content/dam/www/public/us/en/documents/manuals/64-ia-32-architectures-software-developer-vol-1-manual.pdf)\n",
        "\n",
        "* [Software de otiização de recursos de Agner Fog](http://www.agner.org/optimize/)"
      ],
      "metadata": {
        "id": "IygYct3Xs1Jc"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Como executar os programas em C/C++"
      ],
      "metadata": {
        "id": "gwWlvUJsztjc"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "1 - Para executar os códigos em C/C++ é preciso adicionar o comando especial **`%%writefile`** e o nome do arquivo para salvá-lo"
      ],
      "metadata": {
        "id": "O2vMeR6t9LAg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%writefile autovector.cpp\n",
        "\n",
        "#pragma GCC optimize(\"O3\",\"unroll-loops\",\"omit-frame-pointer\",\"inline\") //Optimization flags\n",
        "#pragma GCC option(\"arch=native\",\"tune=native\",\"no-zero-upper\") //Enable AVX\n",
        "#pragma GCC target(\"avx\")  //Enable AVX\n",
        "#include <x86intrin.h> //AVX/SSE Extensions\n",
        "#include <bits/stdc++.h> //All main STD libraries\n",
        "\n",
        "using namespace std;\n",
        "using namespace std::chrono;\n",
        "\n",
        "int main()\n",
        "{\n",
        "\tconst int N = 200000; //Array Size\n",
        "\tconst int noTests = 10000; //Number of tests\n",
        "\tfloat a[N],b[N],c[N],result[N];\n",
        "    auto now = high_resolution_clock::now();\n",
        "    for (int i = 0; i < N; ++i)  //Data initialization\t\n",
        "\t{ \n",
        "      a[i] =       ((float)i)+ 0.1335f; \n",
        "\t  b[i] = 1.50f*((float)i)+ 0.9383f; \n",
        "\t  c[i] = 0.33f*((float)i)+ 0.1172f; \n",
        "    }    \n",
        "   for (int i = 0; i < noTests; ++i) \n",
        "\t for (int j = 0; j < N; ++j)\n",
        "\t {\n",
        "\t  result[j] = a[j]+b[j]-c[j]+3*(float)i;\n",
        "\t }\n",
        "\tauto end_time = duration_cast<duration<double>>(high_resolution_clock::now() - now).count();\n",
        "\tassert( result[2] == ( 2.0f + 0.1335f)+( 1.50f*2.0f + 0.9383f)-(0.33f*2.0f+0.1172f)+3*(float)(noTests-1));\n",
        "    cout << \"CG> message -channel \\\"exercise results\\\" Time used: \"<< end_time << \"s, N * noTests=\"<<(N*noTests)<< endl;\n",
        "    return 0;\n",
        "}"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s3J2v0ulzwiI",
        "outputId": "76219c63-4269-4fb1-fef5-7b0839c654a0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Writing autovector.cpp\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "2- Salvado o arquivo é preciso dar o comando especial **`%%shell`**\n",
        "seguido do comando para criar o executável \n",
        "```\n",
        "%%shell\n",
        "g++ nome_do arquivo.cpp -o nome_do_arquivo\n",
        "./nome_do_arquivo\n",
        "```"
      ],
      "metadata": {
        "id": "y9H8jLEE9f0s"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%shell \n",
        "g++ autovector.cpp -o autovector \n",
        "./autovector "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WHZj7HLQ1Z8R",
        "outputId": "4b1da82d-d7ec-49b1-e3c7-84823dead971"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CG> message -channel \"exercise results\" Time used: 0.292271s, N * noTests=2000000000\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": []
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**É preciso se atentar para mudar o caminho de endereço dos cabeçahos (arquivos .h)**"
      ],
      "metadata": {
        "id": "FmczsIp4-VSE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%writefile  exitloop.cpp\n",
        "\n",
        "#pragma GCC optimize(\"O3\",\"unroll-loops\",\"omit-frame-pointer\",\"inline\") //Optimization flags\n",
        "#pragma GCC option(\"arch=native\",\"tune=native\",\"no-zeroupper\") //Enable AVX\n",
        "#pragma GCC target(\"avx\") //Enable AVX\n",
        "#include <x86intrin.h>    //AVX/SSE Extensions\n",
        "#include <bits/stdc++.h>  //All main STD libraries\n",
        "//# Foi preciso mudar o caminho até o arquivo .h\n",
        "#include \"playground-41d1904q/projects/avx/exitloop/v8f.h\"        //SSE 8x short vectors\n",
        "using namespace std;\n",
        "\n",
        "int validateResult(const int& turn,const float& bestScore)\n",
        "{\n",
        " cout << \"Turn:\"<<turn<<\"  bestScore:\"<< std::setprecision(10)<<bestScore<<endl;\n",
        " if (turn != 133)\n",
        " {\n",
        "     cout << \"ERROR, Expected turn exit at 133 != \"<<turn<<endl;\n",
        "     return -1;\n",
        " }\n",
        " if (bestScore != 1707.318481f)\n",
        " {\n",
        "     cout << \"ERROR, Expected a bestScore of 1707.318481f != \"<< std::setprecision(10)<<bestScore<<endl;\n",
        "     return -1;\n",
        " }\n",
        " return 0;\n",
        "}\n",
        "\n",
        "int main()\n",
        "{\n",
        "    int turn = 0;\n",
        "    v8f Scores(1.0f,3.0f,7.0f,13.4f,22.7f,0.01f,4.556f,9.7f); //Initial load\n",
        "    for (turn =0; turn < 200; ++turn)\n",
        "    {\n",
        "        Scores += ((float)(turn)/15.0f);\n",
        "        if ( turn == 40)  { Scores *= Scores/15.0f+2.0f;}\n",
        "        if ( turn == 70)  { Scores += if_select(Scores < 430.0f, 850.0f, 120.0f );  }\n",
        "        //EXERCISE: BREAK THE LOOP ONCE YOU REACH MORE THAN 1700 POINTS\n",
        "     \n",
        "    }\n",
        "    cout << \"Scores: \"<<Scores<<endl;    \n",
        "    float bestScore = 0.0f;\n",
        "    //TODO:LOAD THE bestScore\n",
        "\t\n",
        "    return validateResult(turn,bestScore);\n",
        "}"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VL2zkUJT1tl0",
        "outputId": "2ee354de-fafe-4ce4-dfe4-9234d668f0a7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Overwriting exitloop.cpp\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%shell \n",
        "g++ exitloop.cpp -o exitloop\n",
        "./exitloop"
      ],
      "metadata": {
        "id": "vwuUqO612Ibq",
        "collapsed": true
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "A compilação neste caso está funcionando, falta agora alterar o código para atender a função de validação.\n",
        "Isso é com vocês :) "
      ],
      "metadata": {
        "id": "XZX2w8VG_FbM"
      }
    }
  ]
}