Cross_Entropy_and_KL_Divergence.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 527
        },
        "id": "2ZA03nmKlu2D",
        "outputId": "1ea72bd6-8221-4ac8-8fe1-0b74885e46ce"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfIAAAH+CAYAAABnU4yrAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOydd5hjZ3X/v1dd00ea3nvZXmbLzLqszdpripM1OME0m2IIYAeMCcH8AIMhdDBOsMEBQghgsClZOwHjtrjiddndGU3vvUijUZkZ9XLv74/Ne32lkTTqbd8Pjx+e1Ui6V+393nPec76H4TiOA4VCoVAolIxElOoToFAoFAqFEj1UyCkUCoVCyWCokFMoFAqFksFQIadQKBQKJYOhQk6hUCgUSgZDhZxCoVAolAyGCjmFQqFQKBkMFXIKhUKhUDIYKuQUCoVCoWQwVMgpMcMwDG6//fa4Pd/Pf/5zMAyDc+fObXvf48eP4/jx4/y/Z2dnwTAMfv7zn/O3ffnLXwbDMHE7v3hCXuvs7GzCj/X+978fDQ0N/L/Je/Xd73434ccG0vtzeO6558AwDJ577rlUnwqFEjFUyLMUIhDkP4VCgba2Ntx+++3Q6XSpPr2U8/Wvfx2PPvpoXJ+TiAH5Ty6Xo7y8HMePH8fXv/516PX6uBzHZrPhy1/+clqKTjqfW7L5+te/jqNHj6K0tBQKhQKtra244447tnwPkvW9oWQvklSfACWxfOUrX0FjYyMcDgdeeukl/OhHP8Ljjz+OwcFB5OTkpPr0Yuapp57a9j5f+MIXcNddd/nc9vWvfx033ngjTp06Ffdz+sQnPoFDhw7B6/VCr9fj5Zdfxpe+9CXce++9+O1vf4urr76av+/73vc+3HTTTZDL5WE/v81mwz333AMAPtmI7fjJT34ClmXDvn80hDq3QJ9DunDFFVfAbrdDJpPF7TnPnz+Pffv24aabbkJ+fj5GRkbwk5/8BH/605/Q19eH3Nxcn/tH8r2hUIRQIc9y3vzmN6OrqwsAcOutt0KtVuPee+/FY489hne9610BH2O1WrcsMulKOAuvRCKBRJK8r/rll1+OG2+80ec2jUaDa6+9Fu94xzswPDyMyspKAIBYLIZYLE7o+ZDPUyqVJvQ425HszyESRCIRFApFXJ/zD3/4w5bburu7ceONN+J///d/cdNNN/n8LZLvDYUihKbWLzHIVf3MzAyAi/umeXl5mJqawlve8hbk5+fjPe95D4CLAvDpT38atbW1kMvlaG9vx3e/+10EG5j30EMPob29HQqFAgcPHsQLL7zg8/e5uTl8/OMfR3t7O5RKJdRqNf7u7/4u6P6wzWbDP/zDP0CtVqOgoAA333wzTCaTz33898gD4b83yzAMrFYr/uu//otPZ77//e/Hs88+C4ZhcPr06S3P8etf/xoMw+Ds2bMhjxWMvXv34r777oPZbMb999/P3x5oj/zcuXM4efIkSkpKoFQq0djYiA9+8IMALu5rl5aWAgDuuece/vy//OUvAwj9efrvkQv5/ve/j/r6eiiVSlx55ZUYHBz0+Xuw91n4nNudW6A9co/Hg69+9atobm6GXC5HQ0MD/t//+39wOp0+92toaMDb3vY2vPTSSzh8+DAUCgWamprwi1/8wud+brcb99xzD1pbW6FQKKBWq3HZZZfh6aefDvi6CYH2yI8fP45du3ZheHgYV111FXJyclBdXY1vf/vbIZ8rFOS9MpvNYd0/2PeGQhFChfwSY2pqCgCgVqv52zweD06ePImysjJ897vfxTve8Q5wHIe/+Zu/wfe//31cd911uPfee9He3o7PfOYzuPPOO7c87/PPP4877rgD733ve/GVr3wFBoMB1113nY8gvP7663j55Zdx00034d/+7d/w0Y9+FGfOnMHx48dhs9m2POftt9+OkZERfPnLX8bNN9+Mhx56CKdOnQp6IREuv/zlLyGXy3H55Zfjl7/8JX75y1/iH/7hH3D8+HHU1tbioYce2vKYhx56CM3Nzeju7o76uDfeeCOUSmXI7YDV1VVce+21mJ2dxV133YUf/OAHeM973oNXXnkFAFBaWoof/ehHAIAbbriBP/+3v/3t/HME+jxD8Ytf/AL/9m//httuuw2f+9znMDg4iKuvvjriWopwzs2fW2+9FXfffTcOHDiA73//+7jyyivxjW98Y0u0CgCTk5O48cYbcc011+B73/seiouL8f73vx9DQ0P8fb785S/jnnvuwVVXXYX7778fn//851FXV4cLFy5E9FoIJpMJ1113Hfbu3Yvvfe976OjowGc/+1n8+c9/DuvxHMdhbW0NWq0WL774Ij7xiU9ALBZHtCUSzveGconDUbKS//zP/+QAcM888wyn1+u5hYUF7uGHH+bUajWnVCq5xcVFjuM47pZbbuEAcHfddZfP4x999FEOAPcv//IvPrffeOONHMMw3OTkJH8bAA4Ad+7cOf62ubk5TqFQcDfccAN/m81m23KeZ8+e5QBwv/jFL7ac+8GDBzmXy8Xf/u1vf5sDwD322GP8bVdeeSV35ZVX8v+emZnhAHD/+Z//yd/2pS99ifP/qufm5nK33HLLlvP53Oc+x8nlcs5sNvO3ra6uchKJhPvSl7605f5Cnn32WQ4A97vf/S7offbu3csVFxdvea0zMzMcx3Hc6dOnOQDc66+/HvQ59Ho9ByDg+QT7PMnf6uvr+X+T90r4feA4jnv11Vc5ANynPvUp/jb/9znYc4Y6N//Poa+vjwPA3XrrrT73+6d/+icOAPeXv/yFv62+vp4DwL3wwgv8baurq5xcLuc+/elP87ft3buXe+tb37rl2NtBPrtnn32Wv+3KK6/c8t10Op1cRUUF9453vCOs511ZWeF/HwC4mpoa7pFHHgl47Ei+NxSKEBqRZzknTpxAaWkpamtrcdNNNyEvLw+nT59GdXW1z/0+9rGP+fz78ccfh1gsxic+8Qmf2z/96U+D47gtEUl3dzcOHjzI/7uurg5/+7d/iyeffBJerxcAoFQq+b+73W4YDAa0tLSgqKgoYMT0kY98xGdf92Mf+xgkEgkef/zxCN+F8Ln55pvhdDrx+9//nr/tkUcegcfjwXvf+96Ynz8vLw+bm5tB/15UVAQA+OMf/wi32x31cfw/z1CcOnXK5/tw+PBhHDlyJKHvMwD++f0zPJ/+9KcBAH/60598bt+xYwcuv/xy/t+lpaVob2/H9PQ0f1tRURGGhoYwMTERl3PMy8vz+dxlMhkOHz7sc8xQqFQqPP300/jf//1ffOUrX0FJSQksFktU5xHqe0O5tKFCnuU88MADePrpp/Hss89ieHgY09PTOHnypM99JBIJampqfG6bm5tDVVUV8vPzfW7v7Ozk/y6ktbV1y7Hb2tpgs9n49hm73Y67776b33MvKSlBaWkpzGYz1tfXtzze/znz8vJQWVmZ0J7rjo4OHDp0yCe9/tBDD+Ho0aNoaWmJ+fktFsuW91TIlVdeiXe84x245557UFJSgr/927/Ff/7nf27ZMw5FoM8zFME+u0T3ts/NzUEkEm15XysqKlBUVLTlO1ZXV7flOYqLi33qJr7yla/AbDajra0Nu3fvxmc+8xn09/dHfY41NTVb9vX9jxkKmUyGEydO4G1vexu++MUv4oEHHsCHPvQh/PGPf4zoPLb73lAubaiQZzmHDx/GiRMncPz4cXR2dkIk2vqRy+XygLfHm3/8x3/E1772Nfz93/89fvvb3+Kpp57C008/DbVanfC2qEi4+eab8fzzz2NxcRFTU1N45ZVX4hKNu91ujI+Ph7wgYBgGv//973H27FncfvvtWFpawgc/+EEcPHgw7EguEZ9nMCMXkm1JxHP7E6y6nxPUTFxxxRWYmprCz372M+zatQs//elPceDAAfz0pz+N6tzCOWYk9PT0oLKyMmAdRjDC+d5QLm2okFMCUl9fj+Xl5S3pvNHRUf7vQgKlMsfHx5GTk8NXMv/+97/HLbfcgu9973t80dJll10WtILX/zktFgtWVlaCVl5HQijxuOmmmyAWi/Gb3/wGDz30EKRSKd75znfGfMzf//73sNvtWzIigTh69Ci+9rWv4dy5c3jooYcwNDSEhx9+eNtzj4Zgn53wfS4uLg74OflHzZGcW319PViW3XJ8nU4Hs9m85TsWLiqVCh/4wAfwm9/8BgsLC9izZw9fOZ8OOByOgBmoYETyvaFcmlAhpwTkLW95C7xe75aWl+9///tgGAZvfvObfW4/e/aszz73wsICHnvsMVx77bV8VCMWi7dEMj/4wQ+CRnU//vGPffaJf/SjH8Hj8Ww5djTk5uYGvYAoKSnBm9/8ZvzqV7/CQw89hOuuuw4lJSUxHU+j0eCOO+5AcXExbrvttqD3M5lMW96jffv2AQCfXidGPuG2MG3Ho48+iqWlJf7fr732Gl599VWf97m5uRmjo6M+LmMajQZ//etffZ4rknN7y1veAgC47777fG6/9957AQBvfetbI3odAGAwGHz+nZeXh5aWloi2JuKB1WoN2Inxhz/8ASaTifd22I5wvzeUS5v0dGegpJzrr78eV111FT7/+c9jdnYWe/fuxVNPPYXHHnsMd9xxB5qbm33uv2vXLpw8eRKf+MQnIJfL8cMf/hAAeJcvAHjb296GX/7ylygsLMSOHTtw9uxZPPPMMz6tcEJcLhfe9KY34e///u8xNjaGH/7wh7jsssvwN3/zNzG/voMHD+KZZ57Bvffei6qqKjQ2NuLIkSP832+++WbenOOrX/1qRM/94osvwuFwwOv1wmAw4K9//Sv+53/+B4WFhTh9+jQqKiqCPva//uu/8MMf/hA33HADmpubsbm5iZ/85CcoKCjghU+pVGLHjh145JFH0NbWBpVKhV27dmHXrl1RvBNAS0sLLrvsMnzsYx+D0+nEfffdB7VajX/+53/m7/PBD34Q9957L06ePIkPfehDWF1dxYMPPoidO3diY2ODv18k57Z3717ccsst+PGPfwyz2Ywrr7wSr732Gv7rv/4Lp06dwlVXXRXxa9mxYweOHz+OgwcPQqVS4dy5c/j9738f11kA4TAxMYETJ07gne98Jzo6OiASiXDu3Dn86le/QkNDAz75yU9ueUws3xvKJU5Ka+YpCYO0NYVqY+K4i+1Dubm5Af+2ubnJfepTn+Kqqqo4qVTKtba2ct/5znc4lmV97geAu+2227hf/epXXGtrKyeXy7n9+/f7tPJwHMeZTCbuAx/4AFdSUsLl5eVxJ0+e5EZHR7n6+nqfVjBy7s8//zz3kY98hCsuLuby8vK497znPZzBYPB5zmjbz0ZHR7krrriCUyqVHIAtrWhOp5MrLi7mCgsLObvdHvI9JJA2IvKfVCrlSktLuSuuuIL72te+xq2urm55jH/72YULF7h3vetdXF1dHSeXy7mysjLubW97m09rH8dx3Msvv8wdPHiQk8lkPu1eoT7PYO1n3/nOd7jvfe97XG1tLSeXy7nLL7+c02g0Wx7/q1/9imtqauJkMhm3b98+7sknn9zynKHOLdDn4Ha7uXvuuYdrbGzkpFIpV1tby33uc5/jHA6Hz/3q6+sDtpX5f/7/8i//wh0+fJgrKirilEol19HRwX3ta1/zaWMMRLD2s507d265b6DX7I9er+c+8pGPcB0dHVxubi4nk8m41tZW7o477uD0en3AY0fyvaFQhDAcF6O7BoWShXg8HlRVVeH666/Hf/zHf6T6dCgUCiUodI+cQgnAo48+Cr1ej5tvvjnVp0KhUCghoRE5hSLg1VdfRX9/P7761a+ipKQkamtPCoVCSRY0IqdQBPzoRz/Cxz72MZSVlW0ZyEGhUCjpCI3IKRQKhULJYGhETqFQKBRKBkOFnEKhUCiUDIYKOYVCoVAoGQwVcgqFQqFQMhgq5BQKhUKhZDBUyCkUCoVCyWCokFMoFAqFksFQIadQKBQKJYOhQk6hUCgUSgZDhZxCoVAolAyGCjmFQqFQKBkMFXIKhUKhUDIYKuQUCoVCoWQwVMgpFAqFQslgqJBTKBQKhZLBUCGnUCgUCiWDoUJOoVAoFEoGQ4WcQqFQKJQMhgo5hUKhUCgZDBVyCoVCoVAyGCrkFAqFQqFkMFTIKRQKhULJYKiQUygUCoWSwVAhp1AoFAolg6FCTqFQKBRKBkOFnEKhUCiUDIYKOYVCoVAoGQwVcgqFQqFQMhgq5BQKhUKhZDBUyCkUCoVCyWCokFMoFAqFksFQIadQKBQKJYOhQk6hUCgUSgZDhZxCoVAolAyGCjmFQqFQKBkMFXIKhUKhUDIYKuQUCoVCoWQwVMgplP+D47hUnwKFQqFEjCTVJ0ChpBqO4+B2u2G32yESiSCVSiEWiyEWiyES0WtdCoWS3lAhp1zSsCwLt9sNr9cLlmXh9Xrh8XgAAB6PBxzHoaCgAGKxGBKJBAzDpPiMKRQKxRcq5JRLEo7j4HQ6sba2BpVKBYZhIBKJwDAMGIYBx3FYXV3F8vIy9u7dy98ukUj4/8RiMRV2CoWScmjekHLJQVLpFosFvb29fPrc7Xbz92EYhhdqkmon97HZbNjc3MTGxgYsFgucTicfvVMoFEqyoRE55ZKCZVm4XC6wLAuRSASO4+D1ejE8PIzl5WUoFAoUFxejuLjYR5yJsBM4juPT8i6Xi4/ohdE6jdgpFEoyYDgaRlAuAYhgu91ucBwHkUgEu92OF154ATk5OVAoFGhtbYXdbofZbIbJZMLm5iYYhkFlZSUv7jKZLOBzAxcvEjiOA8dxVNgpFErSoEJOyXpYloXH44HX6wUAXkwnJycxNTWFlpYWNDU18al18netVouZmRmUlJTAZDLBYrEgJyeHF/WioiIq7BQKJeXQ1DolaxGmv4mYMgwDl8uFgYEBbGxsAACampoCCioR29bWVgAX98dJtD4zMwOr1Yq8vDwUFRXxwi6VSvnnIql4IuakwM7lcgEAFXYKhRIXqJBTshKO4+DxePhWMiLiBoMB/f39KCoqwqFDh/DSSy8FfQ5SvU6QSqUoLS1FaWkpAMDlcvHCPjU1BZvNhvz8fB9hJy1rQnH3F3an08lH7KSwTiKR8FX0FAqFEgoq5JSsQ9gbTgSSZVlMTk5idnYW7e3tqK2t5VPpJFr3ZzsRlclkKCsrQ1lZGQDA6XTCZDLBbDZjYmICDodji7CTqDuYsDscDv7YRNhJxE6FnUKhBIIKOSVrIAVtHo+Hr0pnGAZ2ux39/f1wuVw4evQo8vPzAbwh1KHKRCIpIZHL5aioqEBFRQUAwOFwwGQywWQyYWxsDE6nEwUFBbyoFxYWRiTsJFKnwk6hUIRQIadkBaQ3nBS0EZHT6XQYHBxEeXk5urq6fFrIthNy/9R6pCgUClRWVqKyshIAYLfbeWFfWVmBy+VCYWGhj7ALTWkAX2FnWZYKO4VC2QIVckrG498bzjAMvF4vxsbGsLy8jJ07d/JiKiTRQu6PUqmEUqlEVVUVOI7zEfalpSV4PB5e2IuLi5Gfn7+tsDudTjgcDohEoi3Fc1TYKZRLAyrklIwlUG84wzCwWCzQaDQQiUTo6elBTk5OwMeHk1pPFAzDICcnBzk5OaiurgbHcbDZbLywLywsgGVZH2HPy8vbIuzk/Ml74fV64XQ6wbIsbDYb1Go1FXYKJcuhQk7JSAKl0gFgcXERIyMjqKurQ2tra1jTy5IVkYeCYRjk5uYiNzcXNTU14DgOVquVF/a5uTlwHMcXzhFhJ6JOiuPI63E4HOjr68OxY8f4v/un4v0vCCgUSmZChZyScZAoXJhKd7vdGBoagtFoxP79+1FSUrLt8yQ7tR4JDMMgLy8PeXl5qK2tBcdxsFgsvLDPzMyAYRgfYc/Nzd0i6lKplI/YPR4P3G43FXYKJcugQk7JGPx7w4mIr6+vo6+vDzk5OTh27BjkcnlYz+cv5P7CnU6ixjAM8vPzkZ+fj7q6OrAsC4vFAqPRCIPBgKmpKYjFYl7YlUqlz+vyj9iDCTudxU6hZB5UyCkZAekNZ1kWAPiBJzMzM5icnERzczMaGxsjFt/t7p+uDsYikQgFBQUoKCgAcPH92djYgMlkgl6vh9lsBsdxGBoa8hH3YKl4obCT5/d3naPCTqGkJ1TIKWmN0GZVmEp3Op0YGBiA1WrFoUOHUFRUFNXzh0qfpzK1HikikQhFRUX8+2CxWPD6669DqVRCp9NhfHwcMpmMb3Ujwg4Ej9jJZDfy/FTYKZT0hAo5JW0J1hu+traG/v5+qFQq9PT0QCqVRn2M7YQ8UyHvVVNTE4CLdQXr6+swmUxYXl7G2NgY5HI5v79eXFzMb0kEEnZyMSUcLEOFnUJJD6iQU9ISoc0qESWWZTE+Po75+Xl0dnaiuro6LmIbL2e3dEYsFkOlUkGlUgEAPB4PL+wLCwsYHh6GUqn0EXYy2S2cWez+wk485ikUSuKhQk5JK4LZrNpsNmg0Gni9XnR3dyMvLy8uxyN77YHIpNR6pEgkEqjVaqjVagAXhZ0MgJmbm8PQ0FDQka3hCLtIJNpSPEeFnUJJDFTIKWlDsFS6VqvF4OAgqqqq0N7e7iMi8TpuIC4l4ZFIJCgpKeHb9sId2QqEL+x0ZCuFkhiokFPSAq/Xi6WlJd4UhdisjoyMQKfTYdeuXfwwkniyXdSdrRH5dkQ7shXwFXby/hEbXeHIVirsFEp8oEJOSSnC3vCpqSk0NjYiLy8Pm5ub0Gg0kEgk6Onp4Sus4022VK0nmlhGtgKgwk6hJBAq5JSUEag3nGVZzM/PY2xsDA0NDWhubk5oNbS/WAhnkwsNY6io+BLLyFYgsLA7nU6+3U1oUCN0nqNQKFuhQk5JOsI9VCKSJPqdm5uD0+nEgQMH+EKsRJKt7WfJJpaRrQD4KFw4AOb555/H0aNHIZPJIBKJ+MI5iURCB8BQKAKokFOSin9BGxFxk8mEzc1N3maVVEgnmnDS5zQij5xYR7aSz4SINhkEA4BPxQujdSrslEsZKuSUpBGoN5zjOExNTWF6ehpKpRK1tbVJE3HCdhE53SePjWhGtgpd58jWijBip8JOobwBFXJKwgnWG+5wODAwMAC73Y7Dhw9jcnIy6edGU+vJJ9yRrQCwtLQEtVq9ZWQrEFrY/Se7UWGnZDNUyCkJJVhvuF6vx8DAAEpKSrB//36fFGoyCTe1TkkcgUa2ms1m9Pb2wmw2Y25uLuTI1kDCTornHA4HRCLRlqp4KuyUbIIKOSVhkBYjYRTOsizGxsawsLCAHTt2oLq6mr9/Ktq9Qi3mNLWeGkjEDgC7d+8GwzDY3NyEyWTyGdkqHACTk5OzRdgB+BTPeb3eoO1uVNgpmQwVckrcIQsnqUoni6TVaoVGowEA9PT08Is1gQh9Mgl1TLqwpw7hLHWRSITCwkIUFhYC2DqydXJyEhKJZMtkt1AjW/2FnaTihT7x9POnZApUyClxhWVZeDyeLan05eVlDA8Po7q6Gu3t7QF7w1OVWif/H2zhphF58hEKuT/+I1u9Xi8v7FqtNqqRrWQWO/m7/x47FXZKOkOFnBIXgvWGezwejIyMYHV1FXv27OGdwQKRqoicVq2nL+GIJ0mzFxcXA4h9ZGswYRcOgKEjWynpBBVySswIbVaBNxbHjY0NaDQayGQyHDt2DAqFIuTzpFuxG43AUkcs34NYR7aGI+x0FjslnaBCTokJYW84WQCJQ9v4+DgaGxvR3NwcliimytucVq2nH/E04Yl1ZGsgYSeT3QAE9Imnwk5JJlTIKVERrDfc5XJhcHAQGxsbOHjwIB8VhUOqU+v+wkFT66kjkW56sY5sDSXsNGKnpAIq5JSICdYbbjQa0d/fj4KCAvT09ETs0Jaq9rNQx6QT0FJHsrY2Yh3Z6i/sJEvldrsxOTmJ+vp65OTkbKmKp1DiBRVySkQE6g3nOA6Tk5OYmZlBW1sb6urqolqoUr1HHujYdMFNDam8eAo2stVkMoUc2Qr4zmIHgOXlZdTW1vpE7CKRKGBVPIUSLVTIKWERrDfc4XBAo9HA5XLhyJEjKCgoiPoYqUqtAxdf3+LiIliWhUql4g1GyN8oySWdBtVEO7JVOPiFRPDCiN1f2IVV8eny2imZARVyyrYES6Wvrq5iYGAAZWVlOHjwIL9YRUuqUusulwvnzp2DzWaDXC7H1NQUpFIpiouLwXEcnE7nFvMaSuJJVzELNbJ1eXkZbrfbx8BG+J0WRuzCWez+wu6/x56u7wUlPaBCTgkJicL9bVZHR0extLSEHTt2oKqqKi7HSkVq3e12Y2ZmBmVlZdi1axd/MUHalTiOQ19fn09Vc3FxMV/8REkMmZQFCTay1Wg0AgBeeeWVoCNbAQQUdpfLFdROlgo7xR8q5JSA+PeGk4XHYrFAo9GAYRj09PQgJycnbsdMZkTOcRymp6dhNptRVlaGPXv28I50wj7kpaUl7N69Gx6Ph69qHhwcRH5+vk+7knBflBI76ZRajwThyNaysjLo9XocOHCAd54LNLI1Ly9vW2F3Op0h290y8b2ixA8q5JQtkFQf2a8mkfLS0hKGh4dRW1uLtra2uLfUJGuP3OVyQaPRwG63Q6VSobCwMOhCSFKhxcXFfFWzsPiJ7JEKF+aCggLabhQHMl2cyHc5Ly8PBQUFIUe2Cie7CUe2AuDFWjiy1V/YhfvrVNgvPaiQU3hIIc7i4iJMJhM6Ojp4m9Xh4WGsra1h3759vKDFm2Sk1o1GIzQaDYqLi7F//34MDQ2F9PUOdJuw+EmYSjWZTHzBXKCFmRI+mZRaD0ag71Wgka2bm5s+feyRjmz1n8VOhJ1Odrt0oEJOAeBb0OZyuWCxWMAwDNbX16HRaKBUKtHT07OtzWosJDIiJ6n06elptLe3o7a2ll8YY3F2E6ZSq6urwXEcLBYLL+wzMzMQiUQ+++tkMhclOJmaWhfCsuy2w1YYhkFBQQEKCgpQV1cHlmWjGtlKhf3Shgo5xcdmley/eb1ezMzMYHJyEk1NTWhqakr4jz9REbnL5UJ/fz+sVisOHz7MVxMD8TeEYRgG+fn5yM/P5xdmsj+q0+kwPj4edIAH5Q2yQchJm2YkxGNkKxBa2P172KmwZz5UyC9hgtmser1eWCwWOBwOdHV18VOlEk0iit1MJhM0Gg0KCwvR09OzpdrcP+0Z6JxiQThys7GxEV6vl0+jkgEeubm5PsIeaxtftpDp4kIi8liIx8hWwFfYWZblhV0kEm0pnqPCnnnQFeMSJVhvuMFgwNjYGADg2LFjSW2zimdqneM4zM7OYnJyMqTbXKyp9UgRi8U+Azzcbjefhp+amoLdbvepiCfmIpca2bJHHu+ix3iMbCXPQ4Td6/XC6/XC4XBQYc9QqJBfggTrDZ+YmMDc3Byqq6thMBiS3isdr9S6y+XCwMAALBYLDh06xEczgUi117pUKvWxAxW6hg0PD8Pj8fAV8SqVCvn5+ZfEopotqfVEv4ZEjGwlwi7sYydV8cQnPtM/m2yDCvklhLA3XGizarfbodFo4PF4cPToUbhcLqytrSX9/OIhmmazGX19ffzglu0uRsIR8mQidA3jOA42m40X9vn5eQDgU6j+VrLZRqa/rnik1iMlESNbvV4vXn75ZezZswc5OTkBfeIz/bPKdKiQXyIE6g1nGAZarRaDg4OoqKhAZ2cnxGIxTCZT0j3PgdhS62QG+sTEBFpbW1FfXx/24iIcmhJI1FOV5mUYBrm5ucjNzeV7kP0rmknhExH2RHYVJBOaWo8P8RjZyjAMnE4nL9oejwdut5v/m79PfKpf86UIFfIsRzikgaT6SEHb6OgoVlZWsGvXLn4gBJAaq1Qg+ojc7XZjYGAAGxsb26bSAx0z1MVDKvzfgyFsVaqvrwfLsnwadWVlBWNjY1AoFHzvcbqcdzRkQ2o9FRH5dkQzspUIMxFp/4idCnvqoUKexfgXtJEfmsViQV9fH8RicUCb1WQ5rPkTzQXE+vo6+vr6kJeXl5AZ6Om2EAsR9qcDvmnUpaUlcByH1157zSeNmkkV8en83odDOkTk2xHOyNa8vDwAF39rarXaZ2RroFQ8EXYgsJ1sur8nmUjm/KopEeHfG04Ea2FhAaOjo6ivr0dLS0vAH5VIJEr71Lowld7S0oKGhoaoFv5kV60nEmEatbq6Gq+++irq6+thMpkwPj7uM26TVMSn66KaKe95KNIxIt+OQCNb9Xo9Njc3MTExgaGhoYAjW4Hgwi6c7MYwDBX2BECFPMsI1hvudrsxNDQEk8mE/fv383tmgUiVkIcbkbvdbgwODmJ9fT3mPvftFtp0Sq1HCsMwKC8vR3l5OYCt4zY9Ho9P4Vw6WclmQ2o9EyLy7VAoFCgpKcHExAR6enp8uiqEI1uFwk5ecyBhJwEGidj9hZ1UxVMigwp5FhGsN9xsNkOj0SA3Nxc9PT3bOokRQU32YhqOaJJUOnktkabSIz1mNi0q/uM2hcM7ZmdnwTCMT+FcKq1ks0HIMzEiD4QwIAg2spXMGfB6vQFHtgK+s9gBX2EPFLELq+IpoaFCniWQGcbCHx3xF5+amooo/Ux+eCzLJtWMJFRqnWwLjI2Nobm5GY2NjXH5gQuFPNjzZWpEHgqG8R3eIfT4Xl1dxeTkJKRSqY+wJ9tKNtMX8GyIyIE3hNwfhtk6Z0DYLhlqZCt5/HbCLhKJthTPZfr3IhFQIc9wSCqdVKUTEXc6nejv74fNZou4kluYCksmwVLrHo8Hg4ODMJlMOHjwIG9+EQ9SbQiTLgg9vhsaGnwcwxYXFzEyMsL3H6tUKp82pUSQDe95NmQVgOBC7k+gdslwR7aSx4cj7HQW+1aokGcwLMvC4/FsSaWvra2hv78farUa+/fvj7hSmfwwkr1PTkRTuABubGygr6+Pn74W76iQCnlghI5hzc3NPv3HwjYlIuzxtpLNBhHMptR6NJ+tf9YnkpGt5PHkuOQ3SDKPQtc5KuxUyDOSYL3hLMtifHwc8/Pz6OzsRHV1dVRfamFqPZmQcyU/WpJKT/T0tUtljzwW/PuPhW1KIyMjcLlcW6xkY0krZ4OQZ3tqPVKEPgjRjGwFQIU9CFTIMwyhzSrwRmWozWaDRqMBy7Lo7u7mez+jQXhhkEzIYuF2uzE6Ogqj0YgDBw7wdpOJ4FLdI48VYZtSoKInlmVRVFQElUq1JdIKl0xfgLMpIk/EBUmoka2kTiPUyFbAV9jJf06nEy6Xiz/GpSDsVMgzCGFvuLCtY2VlBUNDQ6iqqkJ7e3tcUpypaEEjP7BXX301Yan0QMcUCrX/j/xSTa1HQqCiJ4vFApPJBKPRiOnpad68hgg7GbUZjGx4z7MlIideFIkm0MjfWEe2EmEPFLFLJJKsmexGhTwDCDU3fGRkBDqdDrt37+b7heNBsoWc4zgsLy8DAMrLy9HW1paUH1g4feSUyGAYBvn5+cjPz+dTqGRBJlayZNQmEXb/NsJsSK3TiDw2EjGyleM4PPfcc9i7dy9yc3P5yW6rq6uQSCSoqalJ+uuMB1TI05xgveGbm5vo6+uDTCZDT0/PthFOpCQzEvV4PBgeHuYnrkXr0hYN2eTslq4EirT8J3Ll5ubyok46LDJdBLMlIk92G2ow4jGylWxNymQyvkvG4XDgW9/6FqRSKf7t3/4tlS8xaqiQpzHBesPn5+cxNjaGhoYGNDc3J2z/KhkRObkgkcvl6OnpwXPPPZdU4aRV68lHLBb7jNp0u91b/L1lMhk/ia+goCAthCRSsknI0/F1+I9sdbvdvLAHG9lKvkdCa1ixWAyr1Yrq6uqUvZZYoUKehpAofGZmBlVVVZBKpbzN6uDgIMxmc8KLwJIh5KQ/2f+CJJkp/UvJ2S1dkUqlPoM7HA4HJiYmsLm5iaGhIXg8ni0V8ZnwuaRLJBsrydojjxWpVLrtyFYyIMpkMkGlUvFeCDabDbm5uSk791hJ/0/nEoOIOKncJvtsJpMJf/3rX8GyLI4dO5ZQEQcSK+RerxcDAwMYGxvD/v370drayi8UyR6hSlPr6YdCoeBnZB87dgyHDh1CaWkp7ynw4osvor+/H4uLi7BarWn7+dCIPLWQlsm2tjYcOXIEl112GR91z8zM4MUXX8SpU6fw8Y9/HBaLJS4GRy+88AKuv/56VFVVgWEYPProo9s+5rnnnsOBAwcgl8vR0tKCn//85xEfl0bkaQRxaBP+cFiWxdTUFKanp9Ha2or6+vqkRCOJEnIyQlUqleLYsWNQKBQ+f09FKpum1tOXQG5hm5ubMBqN0Ov1Pi1KZI/d/zuVKmixW3pBKt7FYjGOHj0Kp9OJ6elpPP/88zh37hyefvpp/Pd//zeuuuoqXHXVVbjssssijtKtViv27t2LD37wg3j729++7f1nZmbw1re+FR/96Efx0EMP4cyZM7j11ltRWVmJkydPhn1cKuRpgH9vONkPZxgGGo0Gbrcbhw8f5vstk0Ei+siXl5cxNDSEuro6nyg80ccNBd0jT0+CVa0LTUWIlezGxgaMRiOWlpYwOjoKhULBi3pxcXFCrWRDkU0ReTZsEQAXgyXyWuRyOW699VbceuutOH78OO655x7k5+fj2WefxT/8wz9g//79OH36dETP/+Y3vxlvfvObw77/gw8+iMbGRnzve98DAHR2duKll17C97//fSrkmQTpDSfiRQR8dXUVHMdBJpPh0KFDEdusxko8U9zCNrl9+/bx7mCJPm44UKFOT8JtP/NvUfJ4PDCbzTAajZiZmcHg4CDy8vJ8KuKTJUrZFJHHOmUwXRAKuRCr1Yq6ujpcf/31uPnmmwFc3DdPNGfPnsWJEyd8bjt58iTuuOOOiJ6HCnmKENqsCqvSWZbF6OgoFhcXIZFI0NzcnHQRB+KXWrdarejr64NYLA6rTS7ZwhpOHzkV+tQQjQhKJBKfgieXy8Ub04yNjcHpdKKgoIAX9oKCgoRFzdkUkWfD6wACCzkZ7pKfn+9zOymMSyRarXaL/0d5eTk2NjZgt9vDbiumQp4CgvWGW61WaDQaAEBPTw/OnTvH3yfZxEPIV1ZWMDg4GDKV7g9NrVOA+BUYymQylJeX84slsZI1Go0+VrKBpnHFSjZF5Nks5MDFgCOTq9apkCcZoc2q0B6Q7B/X1NSgvb2dn8ObbJtUQixC7vV6MTo6Cq1Wi7179/JtReEel6bWKYlydlMqlVAqlaiqqvIZs0lS8cRKlvxHvL2jIRvc6YDMaT8Lh2BCbrPZYppPES0VFRXQ6XQ+t+l0OhQUFERk8kWFPEkEs1klrmZ6vX6L6KXC75wQbWRMUukikSgqxzkakVMIiRZB/zGbZBqX0WiETqfz8fYmqfhIvP9paj39CFS453a74XQ6t6TWk0F3dzcef/xxn9uefvppdHd3R/Q8VMiTQLBUOumLVSgUAVuxRCJRSlPrkQqYVqvF4OAgampq0NbWFtWPPxV75OR4ZrMZWq3WpyiKCnlqSEU0K5zGRaxk19fXYTQaeQvQnJwcn8K5UBXx2ZRaz8aqdYLFYgGAuETkFosFk5OT/L9nZmbQ19cHlUqFuro6fO5zn8PS0hJ+8YtfAAA++tGP4v7778c///M/44Mf/CD+8pe/4Le//S3+9Kc/RXRcKuQJxr83nAjD7OwsJiYmQs7azpTUOinQW15ejnl4S7JT68DF85+dncX4+DjUajVWV1fhdDpRWFgIlmWhVCqzJk2aKaRDNOvv7U2cwoxGI6ampmCz2ZCfn88Le2FhoY9IpMNriAfZFJEHEnKr1QoAcdkjP3fuHK666ir+33feeScA4JZbbsHPf/5zrKysYH5+nv97Y2Mj/vSnP+FTn/oU/vVf/xU1NTX46U9/GlHrGUCFPGEIe8PJD5phGLhcLgwMDGBzcxNdXV1820wgUplaD/fYNpsNfX19AC4W6MVa6ZnsCJj42c/MzKCrqws5OTlgGIYvipqfn8fm5iYMBgOfYlWpVHEfUkPZSrpdOBGnMNI+6XQ6YTQaYTKZMDIyArfb7VMRT8YNZzrZLuQ2mw1KpTIuWYfjx4+HXL8CubYdP34cvb29MR2XCnkCYFkWHo9nSyrdaDRCo9GgqKgIPT092/ZmpruQk1R6dXU1X6AXK8ncI7dYLBgaGgLHcejp6YFUKoXL5fKZr22z2cCyLCoqKmAymfi9U7lczot6Kk1HspVMyIDI5XJUVlaisrISHMfBbrfzwj4/Pw+Px4OZmRnYbDYUFxcjNzc37V9TILKt2M3/t2qxWDL2syFQIY8jwt5wshARYZqcnMTs7Cza29tRW1sbttlFKvfI3W53wL+xLIuxsTEsLS1h165dqKioiOtxkxGRk9a48vJyrK2tQS6XB7yAIJ8h2TttaGjgTUfIMIbBwUE+xapSqVBYWJg1C1+qyLS6BOHFH7GSfemll5Cfnw+DwYCpqSneSlZYEZ8JZHtEnumtZwAV8rjhX9BGBMBut6O/vx8ulwtHjx6NqDIyHavWbTYbNBoNH8XG2zQh0al14UXI3r17IZfL+TnowfA/H3/TEWGKlUzrKioq4oU906/2U0EmROShIL//yspKFBQUgGVZfsTmysoKxsbG+KyO/+zsdCPbhNz/tRAhz+TvGxXyOBCsN1yn0/FR38GDByN2aEt1at1fwHQ6HQYGBlBVVYX29vaEVLImMrXucDjQ19cHr9fLX4RsbGzE3H7mn2K1Wq28sE9PT/sM9VCpVBG1MF3KZPLCCvhejAj70wH4ZHXI7Oy8vDyf2dmpcHQMRLZXrdOI/BInWG+41+vF2NgYlpeXsXPnTlRWVkb1/KlOrRNBZVkW4+PjWFxcjOn1hHvcRETkBoMBGo0GpaWl2LFjB/9jjncfubA3ua6ujo/EyFCPkZER5Obm8sKeTgt2OpFpqfVAhGo/C2QlSyriJyYm4HA4UFBQwAt7qrZrOI7Lmup7IHj7WSrMYOIJXUGiJFhvuMVigUaj4Q1RYkk9h9qnTjREyO12O/r6+sCyLLq7uxN+5Rrv1DrHcZiZmcHU1BQ6OjpQU1Pjs7j6Hy/eUaB/JOZ2u2EymWAymfgFu7CwkBf2/Pz8rFk0YyHTU+tAZO1nMpkMZWVlvCEU6ZowmUxYXl7mt2vIdyk/Pz8p7w+5mM+W7ySNyCk8pGXJvzd8cXERIyMjEXmLhyLVqXWHw4GXX34Z5eXl6OzsTEp6LZ6pdbfbjYGBAWxsbIQcA+t/4SAUkXhfWEil0i0LttFo5E1HAPCLNWlzy3RBi4ZsEPJYDGH8rWRtNhu/XTM3NwcAPoVzpG0y3ggDlWwgWPsZFfJLCJJKJ1XpQpvVoaEhGAyGbcd0RkKqUussy0Kn02FjYwN79uxBVVVV0o4dr9T65uYment7kZOTE7LVz1+o/QUk0cV3SqUS1dXVqK6uBsdxvEXo6uoqJiYmIJfLffbXL6U2t0wWcvKdiVdLZm5uLnJzc3krWYvFAqPRCL1ej8nJSUilUh9h93eJjJZLJSKnqfVLhGC94evr69BoNFAqlTh27FhcC5lSEZGTgjCHw4H8/PykijgQn4h8aWkJw8PDaGxsRHNzc0hBSCcLVoZhUFBQgIKCAjQ0NMDr9fL7pqQgKpSTWDaRLp9JtJDvcCIuRkQi0ZbvCamIX1pawujoKJRKpY+wR3sBKMw6ZgN0j/wSJVhvONl7nZycRHNzMxobGxOyv5pMIdfr9ejv70d5eTnq6ur4FF4yiUVYWZbFyMgItFpt2JkR8pkFO2YqhV4sFkOtVkOtVgO42OZGJnUNDw/7tLnFewRnqsn01Ho8I/Lt8LeS9Xg8/P660OdAWBEf7gVgNrWesSwLjuMCRuSk6DBToUIeAqHNKvBGb6jT6cTAwACsVisOHTqEoqKihBw/WUNTiGHN3NwcduzYgerqaqyurqZkfz7a1LrdbudtDiOZuradWKRTxC6Xy1FRUYGKigqffVPhCE4i6pliNhKKbBDyVLwGiUSyxUqWCPvY2Bg/R4AIe0FBQVCxziYhJ2sp3SO/hGBZFmtra5BIJFAqlfyX2WAwoL+/H8XFxbytZ6JIxtAUh8MBjUYDt9uN7u5uPsWUqkK7aFLrJJNQUVGBzs7OiBaecCLydCTQvunGxgaMRiOWl5exsbEBABgfH+cX7Exqc0uXi6doSWRqPVL8LwCFFfGLi4tgWdanIl6Y2ckme1bymQSKyFMxwjSeZM4vO0kIe8PHxsZQVVWF3Nxcn6g1UBtTIki0mK6traG/vx8lJSVbDGsyQcg5jsPU1BRmZmb4TEI0xyPPFWzBygRREYlEKCoqQlFREZqamrCxsYHz58+D4zhMTk7yfckkBZvubW6ZnlpPJyEXIrSSJQWWFovFJxUvbJkkWchswOPxBNzvp+1nWYZ/bzipGrfZbOjv74fH4/GJWhNNolLrZHGfnZ1FZ2cnampqttwnmcNLhIT7ml0uF/r7+2Gz2XDkyBEUFBREdTz/iNz/R55OqfVIkEgkYBgG7e3tAN7oSzYajXwURqrhE9m+FC2ZLuTCepp0hmEY5OfnIz8/nzcw2tjY4AcEra+vAwBGRkZ4cc9UZ8JgDnU0Is8iAvWGi8VirK+vY2JiApWVlejo6EhqlXAiUutOpxMajQZOpzOk93sqI/LthHN9fR29vb0oKChAd3d3TNsbmZpajxT/vuRA7UvCaW7p6vudKWSqG5ows9PY2IiVlRXMzc1BKpViYWEBw8PDvDNhpm3ZBKpYJ5bK8Z4ZkWwy4xNIIMF6w71eLzY2NuBwOLBnz564TvgKl3iLKbEpVavVOHDgQMgfYLKmkAU6brDXTEx3RkdH494pEOq1ZmJEHgphFFZfX8+3Lwnb3PLy8nymuSW7zS3TI/JYzGDSDblcjpaWFgC+zoRTU1Ow2+0+FfHp3BIZSMgBGpFnPMFsVjc3N6HRaODxeFBVVZUSESfnE4/UunAvOdz9/XSLyL1eL4aHh6HX63HgwAG+JSsexwPSs/0sWfi3L7lcLt5FbGRkBG63G4WFhfx9ktHmlulCnqkRuT/+Vev+zoQOh4MXduF3RWg5nC6fYyghp3vkGQqJwv1tVhcWFjA6Oor6+nqwLJsyi1TgjdR6LIua0+lEf38/7HZ7RGNUiZAne0ENJJw2mw29vb0Qi8Xo6emJm2sVOR6Q/an1SJDJZFva3Mj++uzsLF8MRYQ9np+HkEx+7zP9QoSwXdW6QqHwmfxHvismkwnz8/MA4FMRn8pxocGE3GazUUOYTMO/N5yIuNvtxtDQEEwmEx/xTU5Owm63p+xcyQ8o2kXBaDRCo9GguLgY+/fvj2gvK9ZjR4t/kd3q6ir6+/tRXV2N9vb2uEc54by2bI/IQyFsc6upqfEphiJztYmLGNlfj8eeaaa/59mSWo+kj9z/u0Ish00mEwwGA6ampviRvuS/ZPodBBJyl8sFt9tNU+uZBHFoE7aGMAwDs9mMvr4+5OXl4dixY3yhTyrHiJLjA5H3cnIch+npaUxPT6O9vR21tbURLyrk/sk2hCB788J2v127diVsdCpNrUeGfzGU0EVMuGdKovVQZiOhyPSINluMVGJ5HULLYZLhJFay5CJQoVD4CHsiiywDraMWiwUAaESeCQhtVv1T6dPT05iamkJLSwsaGhp8Fo9kOasFg3zpIknvx6stSxiRJxMyhObcuXNwOp1JafcLJdaZLCbJwN9FzOFw8PvrAwMDvNkIEfZI2twy+b3P9AsRQrCWrWjwH+nr8XhgNpv5iW6kyFJoJRvPivhgA1NIb30mk/VCHqygTbh3HGzEZTKc1UIhjIrDwWQyoa+vD0VFRTG3ZUVzEREP7HY7zGYzysvLt62sjxfbCTmNyMNHoVCgqqpqS5sbSa2SKV1E2INFYJn+nmdrsVs8kUgkKCkp4X3OXS4Xn90ZHx+H0+lEQUGBT0V8LOcSTMhzcnIy/rPKaiEnUThJqRBh1Ov1GBgYgFqtDrl3nOrUOull3+4cyACXqakptLW1oa6uLuZoINKLiFjhOA7z8/OYmZmBQqHA3r17kxbRbCfWmS4qqSJUmxvpSSYRmEql8hnmkekRbbbskXu93qT1ictkMpSXl6O8vBwAfKxkl5eX+SFB5PsSafeE1+vdEtxYLJaUFuDFi6wUcqHNqjCVzrIsJiYmMD8/jx07dqCqqirkB5hqIQe2bwNzuVwYGBiAxWIJmlmIBoZhktaCRua5G41GNDU1YW1tLek/LJpaTzyB2txINbxwmIdKpUr57y5WaEQeO/4mRlarlRf22dlZMAzDR+sqlQpKpTLk79Xr9W7psMiG1jMgC4U8WCrdZrNBo9GAZdmw913TXchNJhM0Gg0KCwsTMsAlGUJusVjQ19cHmUyGnp4emEwm6PX6hB7TH+GP3z86p6n1xCGMwMgwD7K/7nQ6MTg4CLVa7bNQZwrZEpGnS9EewzDIy8tDXl4ePySIVMSvrq7y7oRCYfe3kg2WWqcReZoRqDccAJaXlzE8PIzq6mq0tbWFXbyRDkIe6Bw4jsPs7CwmJyfR2tqK+vr6hHwRE+23rtVqMTAwgLq6OrS2tkIkEqXEUW6710mFPPEIh3nU1NTgr3/9K+rr6+F2u6HVajE+Pg6FQsG3uBUXFyd08mCsZFNEno5ObSKRCIWFhSgsLERDQwO/bUMmuo2MjCAnJ8dn2yaUkGc6WSHkwt5woc2qx+PByMgIVldXsWfPHt6NKFzSQcj9o2KSSt/c3EzoLHRy7ESIGMuyGB8fx+LiIvbs2cPviQGpiYBDXQRl+pV6JpOXl+fT5mY2m/nZ60NDQ7w1KLGRTSfhpBF5chFu2zQ3N8PtdvMV8dPT07BarRCJRBCLxVAoFLyVLNkjjwcPPPAAvvOd70Cr1WLv3r34wQ9+gMOHDwe9/3333Ycf/ehHmJ+fR0lJCW688UZ84xvfiMpgKeOFnGVZeDyegDarJGV77NixqN6cVLefkXMgQk763QsKCtDT05PwwRaJSK37zz/3/xGlwho2G6vWM/GchfgXu/lXODudThiNRhiNRgwNDcHr9fJtbql2EAMyv1iPkClC7o9UKvVpi3Q6nTh37hy8Xi9GRkbw7//+75iZmeH7291ud0wZnkceeQR33nknHnzwQRw5cgT33XcfTp48ibGxsYAB5K9//Wvcdddd+NnPfoaenh6Mj4/j/e9/PxiGwb333hvx8TNWyIW94cKRgRzHYW5uDuPj42hsbERzc3PUPyixWMwfJ1VfZpFIBI/Hg9nZWUxMTATsd0/kseMpqkajEX19fSgpKUFXV1fAlF2qInJatZ5+hPqOy+VyH2tQq9Xq0+YmkUh4UQ+0X5poMlUA/YnUjCpdkcvlYBgGDQ0N/AXfn//8Z/zxj3/EwMAAVCoVrrzySrzpTW/C1VdfjT179kS0xt5777348Ic/jA984AMAgAcffBB/+tOf8LOf/Qx33XXXlvu//PLLOHbsGN797ncDABoaGvCud70Lr776alSvLyOF3L+gjYi4y+XC4OAg1tfXcfDgQb46NlqI0KTyR8kwDObm5uByudDV1cWbKSSDeAm5cE9/O6e5VM1Bz7aIPNOJ5D0XFkKRmdqkzY3sl+bm5vrsryd635dG5OkH2SNnGAadnZ3o7OyEyWTCvn37cNttt+HMmTN4+umn8e///u8YGRkJ+3ldLhfOnz+Pz33uc/xtIpEIJ06cwNmzZwM+pqenB7/61a/w2muv4fDhw5iensbjjz+O973vfVG9towT8mC94aSCu6CgwMdmNRaEFqmpmLm7vr6O9fV15OTkJCWV7k88RNXj8WBgYADr6+th7emnotgt1DGzYTHORGIRQqGDGNkvJW1uxGhEOM0tERO6sqnYLRteBxC4cI+MMN23bx/27duHT3/60xF/99bW1uD1en1qfQCgvLwco6OjAR/z7ne/G2tra7jsssv4Gq+PfvSj+H//7/9F/sKQQUIerDdcOKIzXmYoBOFs8mRCzFHGx8eRk5ODqqqqpIs4EHtEvrm5id7eXiiVyrAvRFIVAdPUevoRr9+x/+hN4TQ3MqFLOPQlHnad2VTslo5V65FC9MP/tdhsti1zHJLxuT333HP4+te/jh/+8Ic4cuQIJicn8clPfhJf/epX8cUvfjHi58sIIQ/WG+5wONDf3w+n0xmTr3gokl257na7MTg4CLPZjK6uLiwsLKRMSGKJjpeXlzE0NISGhga0tLRE5K+dymI3h8MBiUTCZ2Boaj01JPI9J21u1dXV/IQuo9EInU6H8fFxyOVyPlqPts2NRuTpBVlTglm0xkJJSQnEYjF0Op3P7TqdDhUVFQEf88UvfhHve9/7cOuttwIAdu/eDavVio985CP4/Oc/H/F7nvZCzrIsXC4XvF4WJpMIViuDvDzA613F4OAAysrKEurJnUwhX19fh0ajQU5ODr89sLS0lDK/92gicpZlMTo6ipWVFezbt4+vGg2XVBa7TU1NYXJykp/wRYqkqJAnn2TtMQsndJF+ZGGb2+DgoM80t3Db3LIlks0WISdreCAhj3Uok0wmw8GDB3HmzBmcOnUKwMX37cyZM7j99tsDPsZms215X4X2xJGStkJOUiF6vRu/+pUYDz4ow/T0Gy+8okKFD3/4CG67LQ+J3L5ORgsax3FYWFjA2NgYmpqa0NTUxC9iqWyBi1TI7XY7+vr6wHEcuru7o7rSTcUeOQBMTU3B5XLh0KFDAC62+hkMBpjNZrAsi5GREX4xT2cjkmwiFalpsVgMtVoNtVoN4I02N5PJhKGhId7vm3wXgrW5ZUOxW6o7duKJ1+vli6KFxEPIAeDOO+/ELbfcgq6uLhw+fBj33XcfrFYrX8V+8803o7q6Gt/4xjcAANdffz3uvfde7N+/n0+tf/GLX8T1118f1QVgWgo5y7IwmUx45hkxPvShQthsW++j0+XgX/4lB/feCzz8sBvXXJOYxT/RE9A8Hg8GBwdhMpkCVtqn0pQmEiFfW1uDRqNBeXk5Ojs7o45GSHScrIXQYrHAZrMhNzcXR48e5VP7+fn5qK2txfr6Onp7eyGVSvlRiwUFBTHP26aEJl2yIIHa3Mj++szMDMRisc80N9Lmlg0CSH77mf46AN+KdSHktx8r73znO6HX63H33XdDq9Vi3759eOKJJ/gCuPn5eZ/38Qtf+AIYhsEXvvAFLC0tobS0FNdffz2+9rWvRXX8tBJyYW/4ww+b8IlPNP/f7YGueC/eZrdzOHVKikcfTYyYJ1JINzY20NfXxxeDBep1FYlEcLvdCTn+doSzX01muk9PT6OzsxM1NTUxHVM4Bz3RQq7T6TAwMACpVIqmpibIZLIt77VYLIZIJEJLSwsAXyMSMm+bLOKZ5geezqRjRBvI75vYgi4tLfG2oCqVCg6HIyoTqnQi2L5yJhKoH55cmMUjIgeA22+/PWgq/bnnnvP5t0QiwZe+9CV86Utfisux00rIic2q2Qx85jON4LjAIi6EZRmIRBxuukmKqSkX4u1Ymggh5zgOi4uLGB0d3da0JhVOZ+Ee2+12o7+/HxaLJW7FhuR9SGRExnEcJicnMTs7i927d2NmZmbb+xP8IzT/QimFQgG1Ws37O6eibTHTIe93ugm5P8I2t6amJr7NzWQyYXNzk7cIFba5ZVJ0S3776f45hEOwFmKLxYL8/PwUnFF8SatVhvyAf/1rKRwO0bYiTmBZBjYbh4ceEuO22+IruvEWcjKy02Aw4MCBA/xeXLKOHwmh9qs3NjbQ29uLvLy8uE5eS7SQk4sPq9WKo0ePIj8/H7Ozs1EZwvgXShE/cIPBgImJCTgcDr5fWa1WRzw/+VIn094rYZub0+lEXl4e5HI5TCYTFhYWAMBnf327sZuphrTQZdLFRzCCOdTFMyJPJWkl5BcjQA4//GF0X5wHHhDh4x/3Ip6/jXgKKfF/l8vlOHbsWFi2kekYkRO3LP/CvHgdE0BCXjPpa8/NzUV3dzd/8bGdWIeLvx84GctpMBgwNzcHkUjkk4ZPtm1oppAu++OxwHEc5HI5qqurt7S56fV6TExMQCaT+bS5pcIrIhTZYs8KBB5hynFc3PbIU01aCTnDMNBq3ZiejnyB4zgG09MMjEZgmyA3IuJRNc5xHL+HFmlfdaqFXPjaycABnU6H/fv384IVTxIVkZORqYHe/0R5rSuVSn4hZ1kWGxsbMBqN/HchLy/Pp60pG/Yi40GmpNZD4W8IE6rNjRRR+k9zS/X3IRsK9giBhNzhcMDr9dLUerwxGAx4/vkRAFcGvY9EYoJI5ITLFbjRfnMzvkIea0Tu8XgwPDyMtbW1qMQv1al1Uvxls9nQ19cHhmHQ09OTsKIusvjF6+KF4ziMj49jYWFhy8hU/2MGak+JV1876U0vKiri91NJ0dzIyAjcbrdP9XNOTk5GC1k8yOTXv12xnn+bm8vl2vJ9ENrIpmJbJtuF3Gq1AgBNrccbhmHQ0RG66tnjKUZBwTkUF78Mi2UHbLZmcNwb+7PxvriKpf3MYrGgr68PUqkUPT09UY9STVVETqrW9Xo9+vv7UVlZiY6OjoT+uIVT7GLF5XJBo9HA4XDg6NGjQX+w4aTW411FLZVKUV5ejvLy8oDTu6RSKb+3Hq27WKaSDan1SEVQJpOhoqICFRUVfMqX9K/Pzs7yhXVE2JNREZ8tpjZAcCEXiURZ0WmSVkKuVquRn1+ApiYWMzNM0GK3jY0uiEQeFBe/hMLC12CztcFq7UBtbQFiHHi2BbFYDKfTGfHjlpaWMDw8jPr6erS0tEQtfqkW8o2NDeh0OuzcuRNVVVVJO26sizkpxsvPz0d3d3fI6vF47ZFHi//0Lq/Xi/X1dRgMBszMzPBpVyLsmVb9HCnZkFqPxaKVYRjk5uYiNzeXb3Mj2zLLy8sYGxuDUqn0meaWiO6ISyEiT/Xc+niRVkIOAAwDfPzjLD7zmdBXgmbzUTCMB7m5o8jLG0Re3hDe9rYKLCy0hRyTGSmRpra9Xi+Gh4exuroalUVprMePFy6XC8vLy3w0m8x9pFj91onPe7jFeOFcOCSzr1ksFvORF/BG77rBYMDS0hI4jvOJzrIhoghEJi+w8fy++G/LeDwe3pRmamoKdrsdBQUF/HciXiZF2S7kFouFCnkiee97vbj7bjHsdi5kC5rJdOz/xHwSUimHpqZF/OUvi8jLy0N7eztaW1tjTkFFIqQklS6RSHDs2LG4pL9SEZGbzWZ+S6CoqCjpxSDR2rSyLIvx8XEsLi5GdBElFPJAe+RAatO92/Wuk+gsW3rXsyEiT+T0M4lEgtLSUv777XA4+P11MpvBf5pbNOeSbVXr/l0BJCLPBtLqF0++bEVFwC9/acPf/Z0SDHOxTzzII2A2Xw6x2IObbpoC0U2LxYLz58+jr68PDQ0N6OjoiDoyDlfISRRYV1eH1tbWuP0AkinkQs/3lpYWSCQSaLXapBxbSDSpdZfLhb6+PrhcLnR3d0f8A82UeeSBetdJdCbsXc/Pz0+q1W08yYY98mROP1MoFKiqqkJVVRU4joPFYuHb3CYnJ/l6CyLu4ba5ZXtETiafZdrvIxBpJeRCTp4EvvjFV/Dd73bDZrv4wxZG5wxz8TalUoSf/vQYxGInFhcXfZ7D6/ViamoKU1NTKCkpQUdHBxobGyMq4Niu/UzYkrV3715+5nG8SFZq3ev18kY1xPM9VZPXIk2tEz/0wsLCqCbhCS8cgolIuoqLf3RGiqT0ej04jsNLL73ER+tqtTrtepUDQSPy6GEYBvn5+cjPz0d9fT1fb0Fmrw8PD/Ntj8XFxSgqKgq6HmZTsVug15ItZjBAGgu5WCzG/v16DA1t4A9/yMUDD4gwPf3GD6OxkcNtt7F473u9KCwUweu9Cs888wxWVlYCPt/a2hpeeuklvP7662htbUVHR0dYH2KoqnWr1Yq+vj6IxeKEtWSRiDyRkZXVauUHg3R3d/NbAqkqtIsktU6KCpubm9HY2BjVexRu1XomQGZtFxcX47XXXsPu3bthMBh4Ex9h73pRUVFaR1yZLOTpMo/cv97C5XLxGZzR0VG+zY1E6/n5+T4toOnwGuJBsD1yKuQJhmEYiMVi5OV5cNttXnz8414YjRf7xPPzAZUKPg5uYrEYV199NZ5++mmsrq4GfV6n04nBwUEMDQ2hpqYGHR0dqKqqCrpoBIuIV1ZWMDg4iNraWrS1tSXsC5/oISJkcEhNTc2W15GqkaLhpNaFc89jNacJ9b5mspgwDMMXSTU3N/OLuMFgwPDwMDweT1r2rmfidoA/qYrIt0Mmk/m0PRL3QWJMI2xzczqdWS3kdI88Qfh/8YUiyjAXjV5Cmb1IpVKcOHECTz75JAwGQ8hjkf3ghYUFFBQUoKOjAy0tLVtSj/5C7vV6MTo6Cq1Wm5BUuj/kyxfvwhOWZTExMYGFhQXs2rULFRVbDXZSFZFvl1p3Op3o6+uDx+OJeu65//HCqVrPdPwX8UC962TgSyp717PhvU6XiDwUDMPwGZyamhqwLMsXUq6srGB9fZ2f/kcyOJnqZxBIyLPFnhVIMyEHfBdViUQCj8cT0eNlMhmuvfZaPPHEEzCZTGE9ZmNjA6+99houXLiApqYmdHR08KkooZAL3c3iISDhkAjvcafTCY1GA5fLFdIoJR1T62azGb29vVCpVNi1a1dc9vCyKbUeLoF614llaKp717MhIs/E1yASiVBYWIjCwkI0NjZidHQULpcLDMPwbW7kOxHPNrdkQFPrKSTaQi+5XM6L+fr6etiP83g8GB8fx/j4OMrKytDZ2Qm1Wg2v1wutVovBwUFUV1ejvb09aV9gshjEq+DNZDKhr68PKpVq28KwVEbkgYRzYWEBo6OjaG1tRX19fdwWylQbwqQD/pahwpYmUkRKUq5qtTrhzmKZ/r6na2o9UnJzc9Hc3Azg4neC7K8PDAyAZVmfaW7psjUTiGCp9eLi4hSdUXxJeyGPNCInKJVKXHvttfjzn/8Mi8US8eNXV1exuroKsVgMm80Gt9uNrq6ugF7diYTUCsQqqBzHYW5uDhMTE2hra0NdXV1cjFISgX9qnWVZDA8PQ6fThTX6NZrjbVcpnW0R+Xb4tzRtbm7CYDBAq9Vu6V0vLi6Oa3Vzpr/X5PwzJVoNhn+xm0Kh8PEzsFgsfM3F1NQUJBKJz3cinab7BdqatNvtqK2tTdEZxZe0E3L/1HoskWhubi6uu+46/PnPf+YN8iPB7XZjfn6eTy/l5OSA47iA+8mJJNbI2OPxYHBwECaTCV1dXWFfhaZDat3hcKC3txccxyV0WMulllqPBGHvemNjo0/v+vj4OJxOp09kFuuAj0xMSwshv5lMfg1A6PYzYZub0FaYzF4fHh5Gbm6uT4dEqlrZSNePf/bRYrEkZXs0GaSdkAuJRw91Xl4en2a32+1hP85isWB1dRV5eXlwuVwQi8WYm5vD3NwcioqK0NHRgebm5qQUf8QiqBaLBb29vZDL5ejp6YnoKjnVqXWyDVBSUoIdO3YkbCHYLvOQqsxEuhKsd91oNGJ2dtan5SkSAxIhmSyC2RqRh0L4mQs7JIxGI8bGxuB0On2muQnb3BIN0RD/15ItfeSPPPJI+gt5tKl1IYWFhbyYbzcAheM4rK2tYXNzE2VlZcjNzcXGxobP1anZbMYrr7yC8+fPo7m5GZ2dnSgsLIz5PIMR7QUNaZGrr69Ha2trxD+cVA5sWVtbw+rqKtrb2+PqnR8IYSo/0HEyWVSSgX/lMzEgIZEZ6V1Xq9UoLCzcVhwy/aIpWyLyWDplgrW5mUwmzM/PA0DS5gWQtTPbDGFI5upb3/pWegt5rKl1IcXFxbj22mvx5JNPwuVyBbyP2+2GVqsFx3GoqanhI4lgEZnb7cbo6ChGR0dRUVGBzs5O1NbWxv1KPFJBZVkWY2NjWFpaiqlFjqS4k5nq9Hq9sNlsWF9f5x3mEk04ry3TxSVZkD7k4uJiPjIj0frQ0BC8Xu+2BVKZnlrPBmc6IH6GMKHa3Mi8ALlc7rO/Hs9MJ7kg8f88Mr39jLye6enp9BNy4Zsdb3tStVqNEydO4KmnntoS6VutVuh0OuTl5aGkpGSLMcp2QqrVaqHVapGbm4u2tja0tbXF7SozEiF3OBzo6+uD1+tFT09PTHtAwta3ZOxv2e129Pb2wuPxoLGxMSkiDtDUeiLxn7NNetfX1tYwNTUFmUwWcAHPZBHMlog8Uc5u/m1uHo/Hp/VxcHDQp80tnCxOKAJVrJPvYrIHQiUCqVSafkIuRCKRRDULPBRlZWV405vehDNnzsDj8YDjOBgMBqyvr6OsrCzgBxvJQk7sTjUaDerr69HZ2RmzaUy4FzQGgwEajQalpaVx2VNOZqEXOfeysjIoFIqkFsaEI+SU2Amnd72goAA5OTkJtyVOJMQMJhPPXUiyLuAlEglKSkp4d0YytpdkcYgDIUnFRzp6NJCQA5mfWgcufkb79u1LbyFP1MCQyspKXHXVVXjiiSewsrIClmVRW1sbtCgnmr1ilmUxMzODmZkZqFQqdHR0oKmpKaoRk9sdn+M4zMzMYGpqCh0dHaipqYnLIpIIMxp/hG1xHR0dqK2thUajSWoE7D80JdCxaUQef4L1rmu1Wrjdbrz44osoLi7m3eYS3bseLzL1AsSfVHmt+4/tJVkco9F4MY0skfjsr29XwJvNQs4wDO666670E3LhDyAaZ7dwkclkyMvLg1wuh1qtDvmFDSciNxgAk4mBSsXBPyNsNBrx8ssv4/z582hpaUF7ezsKCgrCPtdQQu52uzEwMICNjQ0cPnw4rkV3iRZyr9eLwcFBGI1GHDp0CEVFRQCSn8qmqfX0gPSuKxQKjI6OYteuXTAYDFhZWcHY2Bjfu65Wq1PazrQd2WIGkw7zyP2zOMJiSjIIiLS5kWlu/sFSqNR6Ju+RAxffnze96U3pJ+RCEhGRE4/x+fl5XH755XA4HHjxxRejXsjtduD0ad/JbE1NHG64gYX/FrnT6cTQ0BCGh4dRVVUVdvQc7H3Y3NxEb28vcnJy0NPTE/cRlQzDRDxSNFxsNht6e3shkUi2tMUl6pihoKn19EIkEgXsXTcYDHw7Uzx71+NJJvish0M6Tj/zL6Z0u91bPA2E09wKCgqC+qxzHJcVe+Qcx6W/kMczInc4HNBoNHC73eju7ubTKl6vFy+//HLQxTxURHz6tAizs74LyOwsg9OnRXj3uwM/huM4LC0tYWlpCXl5eWhvb0dbW1vQFFGg45PxnY2NjWhubk7YIpaIFrS1tTVoNBpUVlaio6Njy2KR7Klrl8rQlEwh0Hst7F0XtjMZDIa49a7Hi2yJyNNRyP2RSqUoKyvj65CE09wWFhYAgF9XbTYblEolGIbhDcIyPbUOXFy/0k7I/VPr8YrI9Xo9+vv7UVZWtqUQrLW1FR6PB6+++mrQcwq0uBgM8InECSx78XajEVvS7P5YLBacP38eGo0GDQ0N6Ojo2DKSUyimLMtiZGQEWq0W+/bt4005EkU8hVy4l9/Z2YmampqA96Op9Uub7faYA7Uz+feux7PqOd7nnwlwHJcRQu6PUqlEdXU1qqureWvhqakpWCwWvPrqq1hZWcETTzyBgwcPoqCgIC42sg888AC+853v8BMxf/CDH+Dw4cNB7282m/H5z38e//3f/w2j0Yj6+nrcd999eMtb3hL1OaSdkAuJR2qdZVlMTk5ibm4OO3bsQHV1dcD7dXZ2wuPx4Pz581v+FkzMTKbQP1aj8eKeeTh4PB5MTk5icnISpaWl6OjoQENDAz9G0Ov18u1ZDMMk1K5USLxEjNjEms3mbffyk51aD8dznpJcInnPw+1dJ0VzJCpLFJkogP6Q33y61iGEA7EWLiwshFKpRGtrKzQaDV588UX8x3/8BzY2NnDkyBFcc801uOaaa3Ds2LGIhf2RRx7BnXfeiQcffBBHjhzBfffdh5MnT2JsbCxgt5LL5cI111yDsrIy/P73v0d1dTXvFhoLaS/ksaTWSSqdjOvcbj9k9+7d8Hg80Gg0PrcHE7Pi4tACF66I+6PX66HX6/H666+jtbWVv6B5+eWXeeOZZC0U8YjISUueTCYLay+fXLgkCzo0Jb2I9b0O1LtuMBig1+sxMTHhYz6iUqmi6iQJRTbskZPffKa/DuCNYjexWIwDBw7gRz/6Ec6ePYubb74Zn/zkJ/HMM8/gve99L8xmMy5cuICOjo6wn/vee+/Fhz/8YXzgAx8AADz44IP405/+hJ/97Ge46667ttz/Zz/7GV/8TDwTGhoaYn6NaSfk/qn1aPtJ19bW0N/fj5KSEhw8eDDsH+v+/fvh8XgwNDTkc06BxEytvljYNjvLQPhnkQhoaNhavR4pDocD/f39WF5eRk5ODo4fP44dO3YkNUKMVcj1ej00Gg1qamrQ1tYW1sKQytS6zWbD5uYmVCoVf640tZ5c4pmaFlY919fX873rBoMB09PTfO+6cMZ2rMfOhtR6tgm5f/BgtVpRUFCA9773vXjf+94HjuMwPDyMlpaWsJ/X5XLh/Pnz+NznPsffJhKJcOLECZw9ezbgY/7nf/4H3d3duO222/DYY4+htLQU7373u/HZz342quwH+a6lnZALIeLr8XjCtuzjOA6Tk5OYnZ1FZ2cnqqurI/5RHTp0CB6PB2NjYwBCR4g33MBuqVpvaLhYtR4rXq8XOp0OTqcTDMNAo9FgdnYW7e3taGlpSUpBT7RCznEcpqenMT09jZ07d6Kqqirsx6Yitc5xHHQ6Hfr7+/nPm/QwUxFPPokSwlBz10lxlDBaj6Z3PRuK3ch6l+mvAwjcfmaz2XzsgRmGwc6dOyN63rW1NXi93i2jrcvLyzE6OhrwMdPT0/jLX/6C97znPXj88ccxOTmJj3/843C73fjSl74U0fGBN0x70lrIyZvv9XrDEnKn0wmNRgOn0xlWKj0UR48ehdfrxeTkZMiITKkE3v1uFkbjG3vi8XAWdTgc0Gq1kMvlKCoq4h3u1tfX8dprr+HChQtobm5GR0dH2GNJoyEaIfd4POjv78fm5iaOHDkSUc88OWayxdNqtaK/vx87d+5EUVERHA4HDAYDVldX4XA4MDIygtLS0rTvYc4GkhnR+s9d39jYgNFo9OldJ3vr4X7u2ZJazwZ3OiCwkFsslpRUrLMsi7KyMvz4xz+GWCzGwYMHsbS0hO985ztRCTkh7YRc+MVhGAYikSisfXKDwYD+/n6oVCocOHAg5n0vhmFw7NgxPhW3nZipVNHviQshi8na2hq/eGxsbGwRNpIxGBsbQ3l5OTo6OlBfXx/3BSTStDIZm6pQKNDd3R31CMtkReQejwcLCwtwOp3o7u5Gbm4unE6nTzr2lVdeQWlpKf+eu1wuvnhKrVYnvHjqUiNVGRCGYXw8wIU9yuRzLyws5IU9mFVoNkTkybJnTQaBhDweZjAlJSUQi8XQ6XQ+t+t0OlRUVAR8TGVlJaRSqc/5dHZ2QqvVwuVyRZ1lTTsh92e7FjSO4zA1NYWZmZm42pMCF3/Yl19+OVZXVzExMRGX5wwFy7LQ6/Ww2WyorKzkB55sJ6Y6nQ46nQ45OTn8wJZYhqUIiSQi1+l0GBgYQG1tLdra2qL+HJK1J01MaViWRX5+PvLz8wMuwmTIQ0lJCTiOg81mg8FggMFg4Ad/EFEP5CxFiYx02WMW9iiT3nWDweBjFRqodz2bIvJsIJiQxxqRy2QyHDx4EGfOnMGpU6cAXHzfzpw5g9tvvz3gY44dO4Zf//rXPu/v+Pg4KisroxJx8hxpueIIF/JQLWhOpxP9/f2w2+1RpXDDQSQSobu7GwaDIe7PLcTtdmNlZQUikQi1tbU+YhCusNlsNvT19aG/vx91dXXo6OgIemUYLuEIubAuYffu3XE5ZqKF3Gg0ore3F5WVlSgsLOT3RwPhX9Wem5uL3NxcfvAHidomJibgcDh8Wp0iHfBAuUi6vWfC3vXa2lq+d91gMGB+ft6ndz1RttLJJB3sWeNFoNcSL3vWO++8E7fccgu6urpw+PBh3HfffbBarXwV+80334zq6mp84xvfAAB87GMfw/33349PfvKT+Md//EdMTEzg61//Oj7xiU9EdXzyO0lLIRcSrAXNaDRCo9GguLgY+/fvT2gUJJVKsXPnTlitVmi12rg/Pxmhmp+fj5KSki2LWKQRKsuymJ2dxezsLIqLi/mBLdHM+N1OyN1uN/r7+2G1WmOuSyAkOiKfn5/H2NgYP6RlZWUlamc3sVjsM7nJZrPxjmPT09OQSqW8qCei1SkbyYTiQmHvOgCf3nW9Xg+WZfmtvmT0rscbGpGHxzvf+U7o9XrcfffdvEnXE088wRfAzc/P+7yPtbW1ePLJJ/GpT30Ke/bsQXV1NT75yU/is5/9bEznkfarin9qXVgN3d7ejtra2oT/QMgH8aY3vQlPPfUU9Hp9XJ6X4zgYjUaYzeagI1SB2PaMTSYTzp49i/Pnz/PFcZEMVgkl5MTrPTc3F93d3VFdKAQiUXvkxBVPp9Ph4MGD/LzzeBrCCB3HvF4vH7WRVqfCwkJ+8Ec6+YOnE+mSWo8EYe/6zMwM1tfXUVhY6NO7Ti7oiouL0/6CLtuF3GKxbHHQjJbbb789aCr9ueee23Jbd3c3XnnllbgcO63bz4Kl1l0uFx/9xXvSVyjIxYRUKsU111yDJ554AkajMabn9Hg80Ol08Hg8IUeoAvFJNbtcLoyMjGB0dJT3OK+pqdn2xxpMVLVaLQYGBtDQ0ICWlpa4LryJSK07nU709fXB6/Wiu7vbxxUvURatQv/v1tZWH3/wubk5/u9kgY/XhVCmk4lC7o9MJkN9fT3fu062X6ampmC32/nedbVajfz8/LR7vdku5DabLSt81jOi/Qx4Y5SpyWRCX18fioqK0NPTk9RFTxiVymQyXHvttXjiiSdgNpujej673Q6tVgulUonKysqwxDRewsZxHJaXl7G8vMwPbGltbQ3aL+svqhzHYXx8HAsLC9izZ8+WHsp4EO/U+sbGBi5cuICioiLs3r17y486WUNThD7Qwj3W2dlZfo+VFM2l4+KeTDL5tfsXu/lvvwQa7BFr73q8yZaqdeIZH0jIM32EqZC0F3KRSMSnp9ra2lBXV5f0H7l/wZ1CocDJkyfx5z//GRsbG2E/D8dx/OKtVqtRWFgY1mtJ1J4xGdjS19fHD2zxH8IivIhxuVzQaDRwOBw4evRowq5o4zmohWQOmpqa0NTUFPD9TsXQFOEea0tLC5xOJ18Jv7CwAIZhfKL1VE7zSjaZsEceiu3az/wv6DY3N2EwGLC8vIyxsTHk5OTwop4qz4JsicjJup0ufeTxJq2L3cjJuVwumEwmeDyepKbS/SFCLkz5KZVKPjK3WCzbPgfLslhdXYXdbkdVVVVEA08SXfzl9XoxNTWFqakpqNVqdHZ2orGxkR/YwrIsNjY20Nvbi/z8fHR3dyd0jy8er5dU0s/NzWHv3r0BBxj43z/U+SQauVzOG5OQ95uIun+0Hg8b0XQm01PrkbSfkdbGwsJCNDU1+fSuj46Owu12h9W7Hm+yXcjjVeyWatK6/Qy4OOqtr68PIpEIFRUVKRNx4I0vgX+KJi8vj4/MbTZb0Me7XC5otVqIxeItrWXhQIQtGQucwWDASy+9xA9sIceen58PGdXGk1iFnDjLWSyWsDIH6TaPXCQSoaioCEVFRfw0L9K/TAb6CKP1eIxiTDcyWchjSUv7966TLojtetfjTba0n3m9Xt5YTAhNrScB0pfZ2toKt9sNu92e0vMRWsX6/0Dz8/N5MXc4HFsea7FYoNPp+KvqaBaoeP6gDHYDTHYTVEoVVMrgXrJOpxMDAwNYXFxEbm4uTpw4kRQRB2KrWrfZbLhw4QLkcjmOHj0a1kKX7vPIZTIZKisrUVlZ6WMjurS0hJGREeTl5fGinuzZ24kg01Pr8TKEEXoWkN51s9kMo9G4pXc93p99NkXk/ms2mYgXj1bZdCEthTwnJweHDh1CUVERZmdnw0pdJxLyhQ5mTFNYWIhrr70WTz75JO+JznEcDAYDNjY2UF5eHlMah4hnLFf6drcdp8dOY9o8zd/WVNSEGzpugFKyNc3v9Xqh1WrhdrvhdDpx4cIFTExMoKOjA62trQnds422an1tbQ0ajQZVVVVob28PeyEKR8jThUA2oiRaHxwcBMuyUKlUyMvLy1hBzPTUeqIsWkUiES/awMWLbRKtk8++uLiYv08s7o7ZUuwW7HVkS2qdkJZCTnytgdDObsmCYZhtz0OlUuHEiRN46qmn+Kp0lmVRU1MTs+iRRSGWhfn02GnMmmd9bps1z+L06Gm8e9e7fW4XDmwpLCzkP4vNzU28/vrr6O3tRWNjIzo7O/lFJZ5EGpFzHIe5uTlMTEygs7MTNTU1ER8vnGOkI1Kp1Gf2tsVi4WdvcxyHV199lU/DFxUVZUSUlelCnqzzl8vlPpkai8UCo9HIW0rH0rvOsmza97qHQ6CIHMie9jNC2n9SwZzdkk04ldSlpaXo6urCww8/DIVCgdLS0rgunNGKicFu8InECSxYTJunYbQb+TT7xsYG9Ho9XzFrNpsDDmyZmJjAxMQESktL0dnZifr6+rhdwUeSymZZFkNDQ1hbW+OzOPE+XqpT6+HCMAzvGV9aWorXXnsNjY2NMBgMGB4e5kezEmGPpOCSEj6p8FoXfvb19fXweDx8Gj6a3vVAM7wzkUBCzrJs3Cxa04GNjY30F/LthqYki+0ico7jMDs7i/n5ebz1rW/F8PBw3FqoGIaJSUxMdlPIvxvtRhQrirG2tobNzc2IBrbo9Xro9XoolUq0traivb095h9IuKl1h8OB3t5eABfdkqLtv82k1HokMAzjUzhltVr50awTExNQKpU+0Xq6pFIzPSJPh+lnEokkZO86wzA+F3X+BZPZvEdutVoBIGv2yFdXV9NTyIU/gnSJyEMJucfjwcDAANbX1/mosLy8HM8++2xcxTxaIS9Whp5XXigrxNLSEliWRW1trY/ZTrjHtdvt6O/v56efdXZ2orKyMqrzDeeY6+vruHDhAtRqNXbu3BmTCKVb1XoiYBjGZzQrMVkyGAx8m1O6jGbNdCFPx+lnoXrXR0dHkZub69O7fikIeaZH5OR3QiPyCAgm5MRvXKlUoqenh09H1dbW4oorrsDzzz8fFxGIpZJbrVSjqagJs+ZZsHjjOUQQoa6gDha9BUqlEmVlZVt+vJFeQJBWtfn5eRQWFqKjowPNzc0Rpem228ZYXl7G0NAQWlpa0NDQEPOiny2p9UiQSCQoLS1FaWmpz2jWtbU1TE5O8vurqRrNmslCng4ReSiC9a4LL+pEIhEftWfyBL9g9qxSqTRr2jYlEkn6C3k6FLsFOw8iKMH8xhsaGuDxePDXv/41NiEwGJC7sgJGJgOqq6N6ihs6bsDpUd+q9Zq8GhxUHkRxcXFQl7lYRGx9fR2vvvoqLly4gKamJnR2doa1hx3smEJ72H379m1xoYuFS03IhaTbaNZMf6/TMSIPRaDedTIe+ty5c5BKpT6965k0EyDYwJRMvjjxp7q6Oj2FXPgGk4g81ek2kUjECznLshgdHcXKysq2gtLS0gKv14uzZ89GflC7HaLTp8FMT4MkqbmmJrA33ABEWKiklCjx7l3vhtFuhMFuAGyA2CVGRUVFyDaVeIiY2+3G2NgYxsbGUFFRgY6ODtTV1QVd7AId0+12Q6PRwG63o7u7O65psWz5QceLVI9mTfVvPVbSPSIPBbmok0qlqKurg1qtxvr6OoxGI+bm5jA0NMT3rhOXwXS+aAkl5JkO+Y6p1er0FHIhQjOWVLZDkAsKu92Ovr4+cByH7u7usHo129vb4fV68dprr0V0TNHp02BmZ31uY2ZnITp9Guy73x34QdtQIC2Ade3iHlFFbcW2V9fxjka1Wi20Wi1ycnLQ1taG9vb2LdXTpNiNLOgWiwUXLlxAbm4ujh49GveIYLtti2yPyLfDfzQrqYZO5GjWTBVCIPMi8kCQPXLhBD/At3d9YGDAp3c9HTshQs0iz+TvmMvlgtVqhVQqTd8xpkLIh+DxeFIq5CKRCJubm5iamkJ5eTk6OzsjKrDasWMH3G43X2W9LQYDmOmtLWNg2Yu3G41AhD3cpL89Jycn7Na4RImYzWZDX18f+vv7UV9fj46ODn6SmrBvnpi81NbWoq2tLSE/vnDmkV/KQi5ELBbze+eJGs2a6RF5pp8/ENyiNVDvurATQqFQ8MKfDnPXA7XR2Wy2mMxy0oE//OEP+PKXv4zm5mbI5fL0FHLhj0AkEvmktVMBad3Z3NzEzp07IzYcIezdu5evcN8OxhS6ZYwxGsGFKeTE1nNtbS2iqWvCxycKlmUxMzODmZkZqFQqfk46AMzMzGB6eho7d+5EVVVVws6BCnX0JGI0a6ZHtJmcWieEU7Uu7F0n9UAkWzM5OQmHw8Fna1QqVUpG8wZLrWe6GUxVVRW6u7sBXKxFSksh9yeVBW9utxv9/f2w2WyoqKiIWsQJBw8ehNfrxfDwcMj7ccWhW8bCFXGWZaHX62Gz2SKeugYkV+SMRiNefvlliEQiWCwWsCyLY8eOJXxgjr9znv9iQ4U+POI1mjXTI9pMvxABorNoDdW7Pjc3x38/kjnsJ1hqPZP3yDmOw5VXXokrr7ySvy1thVy4eEokkpT0kpPRnXl5eXzEEQ8OHz4Mj8eD8fHx4HdSq8E1NV3cIxceVyQC19AQVlrd7XZDq9UCQFRT14Dk71V6PB6srKzA6XRCIpHgtdde46P0RJ1LIAtcoZhQIY+OWEazZrKQXyoR+Xb4Z2v8h/2Q3nWSJUyEIVGoPfJMhaxHbrcbf/zjH1FfX5++Qi4kFRH54uIiRkZG+NGd09PTvJFAPOju7ubngAeDveEGvmqdwDU0XKxa3wabzQadTofc3FyUlpZGvbAkU8SEe/hOpxMikQhLS0tYWlpCvsOBDqkULUePQr5zZ1yPKxRy8nqzRVTShUhGs3o8noy2B82WiDyer0H4+ZPedRKtj4yM8IZEJA0fr/awbI3IGYbBiy++iG9/+9v41Kc+lTlCnqyI3Ov1YmRkBDqdDvv37+fTRPG+mGAYBseOHYPH48Hc3FzgOymVF6vTjUasT0+DU6lQ1NQU8nk5juP3KUtKSmJOSydLyInHO7k639zcvHjc/2vBs01P4wIADcehoa0Nbfffj5Lm5rieQ6iULo3I40ug0awGgwFLS0vY2NiATCYDx3EZOZo10yNylmUTfjEilUpRXl6O8vJyH0Mi0g0Rr971bBbyhx56CCdOnMCpU6fSV8j9U+vJiMhJJTXDMOjp6fHZTw5naEqkiEQiXHHFFXj22WexuLgY/I4qFVxe77aLA8uyWF1dhd1uR3V1NRQKRdjzx4NBPodE7VuSynR/j3eCfwuel2EwNTWFyY99DKpPfAIdHR1obGyM2aJ1u79TIU8cwtGsTU1NGBgYAMMwcDqdPqNZScQerad+ssj0iJysc8l6DYEMiUjv+uzsLIaGhviBLyqVKqLe9WBCTjpkMpnh4WF0d3enb9W6P8mIyPV6Pfr7+1FZWYmOjo4tX5REpffFYjGuuuoqPPPMM1hZWQl6v+3ExO12Y2VlBSKRCLW1tXBzbvx68Ndhzx8PddxEQWaee73egB7v27XgrY2P46W1NZw7d44f2BLN3td2Y2KpkCcXkUiE3NxcNDQ0+LQ4abVajI+PIycnJ61Hs2Z6sV6yhdwfYe86KZoM1LtOiuZCFfAGKtrL9D1ygt1u579nGSHkiYzIOY7D5OQkZmdnQ7Y5JXKfXiwW4+qrr8bTTz+N1dXVgPcJZVpis9mg1WqRn5+PkpISMAyD3w7+Nuz546Hw3z+OF06nE1qtlk+xBvJ4D7cFz+FwYGBgAIODg6ipqUFHRweqqqrCPl9/Iec4DkaHEVa3FXmyPD4jQUkO/oWGwhYnoS94uo5mzYbUOpA6IffHv3d9c3MTRqMROp0O4+PjfO+6/1wAjuOCeq1ncmqdsHv3bvzlL3/BBz7wgfQVcv8JaIkQUZfLxdt+Hj16NORYu0QX3EmlUpw4cQJPPfUU1tbWtvxdJBLB7Xb73MZxHN+3WVpaioKCAgCRzR/fju2i1WiwWCzQ6XR8cUuwRY/dxpfdvwWP4zgsLCxgYWEBBQUF6OjoQEtLy7aFU+T4JrsJvx3/LX547oeYWZ/h/16TU4O/r/973FF/B4oUoc+JEjuhLhr9fcHTbTQr+Z2kiwhGAyl0S8eLEYZhUFBQgIKCAr533X8uAOldLy4uBsdxWddHTr5bH/vYx/DmN78Z3/ve95AR37ZEpNbNZjNefvllSCQS9PT0bDubNhmV8zKZDNdccw2KA/SQ+6d3WZaFTqfD+vo6qqureRG3u+14dPTRkMcx2o1hn1M8hZzjOP5Kury8HGq1OuhiwTAM2OJicE1NgP+iKBJdvD1EC97GxgZee+01/Pa3v8XLL78MozH4a2YYBr0bvdjx0x345zP/jNn1WZ+/L9mW8P2R76PzJ514ZvaZsF8vJXrCEREymrW+vh4HDhzA5ZdfjubmZn4Wwosvvoi+vj4sLCzAZrMlJatCotl0FMFwyaQRpmSKX3t7O7q7u3H06FGUlZVhY2MDfX19AIDx8XGsrKzwAZLNZovLLPIHHngADQ0NUCgUOHLkSNgW3A8//DAYhsGpU6diOv6xY8dwxx134J577knfiFyIRCKB3W6Py3ORqG1sbCyiMZjJaoGTy+W49tpr8cQTT2B9fZ2/XSjkLpcLWq0WYrEYtbW1Plecp8dOQ2fVhTxGNEVvsS6CpBDP4XCgpqZmWzMI8npjacEDwPfrj4+Po7y8nB/YInzPnpl5Bl+d/urF50aAqWv/d5vdbcffnf47/O6G3+FEw4mwjk+JnGi/a5GMZi0uLk5ItJ4NEXkwe9ZMQKlUoqamBjU1NbDZbHjllVegVCoxNzeHt7/97SgrK0NeXh4fvUdbOPnII4/gzjvvxIMPPogjR47gvvvuw8mTJzE2NoaysrKgj5udncU//dM/4fLLL4/2Jfrwla98BQ0NDekr5P6p9XhE5F6vF0NDQzAYDDh48CA/CCAcktnLrlQqcfLkSTz++OOwWCwA3hA2q9UKnU7nsx9OCJZSJzBg0FjUGJGQMwwTc7GXfyFeRAuooAWPt6WN0GOeoNPpoNPp+IEtbW1tcIlcuOn0TeD+73+hYMFCxInwvv99H0Y+PELT7AkiHvUYqRrNSiPy9IGk1Zubm9Hc3Ize3l788Y9/xL/+67/iBz/4Ab75zW/i+PHjuPbaa3Hq1Ck0NDSE/dz33nsvPvzhD+MDH/gAAODBBx/En/70J/zsZz/DXXfdFfAxXq8X73nPe3DPPffgxRdfhNlsjsOrBD74wQ9mRmo9HsVuVqsVZ8+e5cdgRiLiwBvtZ8kqesrJycF1113HF2UwDMM7tZGow3+xMNlDF4dV5Fbgho7wIll/on3ddrsdi4uLUCgUqK6uDlvEt1w8qFTgWlqiFnEhpM3wd7/7Hb750Dfh3fBuK+IEFixsbht+M/ybmM+DEpx4CyEZzdrW1obu7m4cOXIEJSUlMJlMOHfuHF5++WWMjo5Cr9fHFDRkQ0QejT1rOuJf6FZeXo4PfvCD2NzcxB//+Ee8/vrruOaaa/DEE0/gueeeC/t5XS4Xzp8/jxMn3sjKiUQinDhxIuS46q985SsoKyvDhz70oaheTyjSNiIXEms0rNPpMDAwgJqaGrS1tUX1IxOLxQntpw5EXl4err32Wjz++ONYWFiAx+NBbW1t0LR0sTK0P3ukrWeEaCPy9fV1rK2tRWVMk4yWL5Zlcab3DEodpXDJXLAUWmDLt4ETbX/c+1+/H+9peU9KBkFkO8m4WCajWWtra31Gs05NTcFut0c9mpVG5OlDoIp14I098h07dmDnzp341Kc+FdHzrq2twev1bulFLy8vx+joaMDHvPTSS/iP//gPft8+3qStkAt/CNF6rbMsi4mJCSwsLGDXrl2oqKiI+nyEc9GT+SUXi8UoKiqCVCoFy7Ih95bVSjWaipowa54Fizda1UQQoaGoIaq9cSDyRYnjOOj1elgslqgGtSQLu8cOk+NiFkPqkqJYX4xCQyFs+TZYCi3wyAJ/5zhwWLAu4PnXn4dKcbHftaSkJC3GNmYDye7Djudo1kw3gwGyW8i9Xi/sdntS2882Nzfxvve9Dz/5yU94p9B4kxGrTjQRudPphEajgcvlwtGjR2NuNxAKebR2gZFCMglNTU1obW3Fww8/vO1jbui4AadHT/vslTcUNUSdUgcii469Xi9WVlbAsuwWk5dEHTNaTBtbtyJErAh563nIW8+DucQMS5El6ON3HdiFQq4QBoOBj+TIvqtarUZOTk5GR2apItWGKtuNZiUuY4FGs6b63ONBJhe7CQn0OkjNUSxV6yUlJRCLxdDpfIuKdTpdwGBxamoKs7OzuP766/nbSOZGIpFgbGwMzTHaTWelkJtMJvT19UGlUuHAgQNxiZIYhknaXHShSc3u3btRUVEBs9mM3bt3Y3NzM2R2QilR4t273g2j3cj3i0cbiRPCFVWn04mVlRUoFAqUlZXFtBgkUsg5joPJZMK6cT3k/fJN+bAWWIOm2gsUBRffX5UKra2tsNlsMBqNWFtbw/T0NGQyGUpKSlLW00yJHf/RrA6Hg4/WA41mzXQzGCC7I3Iy+CqWwE4mk+HgwYM4c+YM30LGsizOnDmD22+/fcv9Ozo6MDAw4HPbF77wBWxubuJf//VfUVtbG/W5EDJCyMNNrXMch7m5OUxMTKCtrQ11dXVx/VElo3KdzD+3Wq0+JjVisRi5ubk4cuQInnnmmW3fj3gIOCEcUSUmL2TRYxgmZp/3RCBsg2uqbULxejHMDnPAYjexV4zc9VxYin2jcgYM6gvqkSfKg9vt5s0zyL5rTU0NXyVtMBgwNjYGl8vF20qmiwNZupLOUa1CoQg5mjUnJ4eP4v1Hs2YK2SzkNpvtojd5jMHdnXfeiVtuuQVdXV04fPgw7rvvPlitVr6K/eabb0Z1dTW+8Y1vQKFQYNeuXT6PL/o/syv/26MlbYU8kLNbqB+4x+PB4OAgTCYTurq6ApqqxEqihXxzcxO9vb3Izc1Fd3e3T1qaVM1XVFTgqquuwpkzZ+I+xCUYoYScmLyYzWaUl5cjLy8Pdrcdp8dOx+TznoiInMw6B4CamhqIxWIcrDiIM7Nngj6mwFwAa+HWqPyj+z8KiUQClmV9vhMikQgikYivki4pKfHpaRY6kJFoPdOmeyWadBZyIf6jWZ1OJxYXF7GwsLBlNKtKpdrWOyFdyNaqdeBiwBGPVsN3vvOd0Ov1uPvuu6HVarFv3z488cQTfAHc/Px8Un/TaSvkQsjVk9frDXglZbFY0NvbC4VCgWPHjiVslnEiJqARtFotBgYG0NDQgJaWli1fNGH7W3V1NY4fP45nn302KRW+wUSVuMu5XC4fk5fTY6dj9nmPt5D7p/3J8+8u3Y0X5l+Am3UHjMpF3ot75pvFmxf/DRGUUiXet+d9kMlk/Gfi9XrBsiz/HwA+UvfvafZ4PHx6dmhoCF6vl1/w1Wp1xiz4iSQThNwfuVwOlUqF1dVVHD161Gc068jICPLy8nhRT+eLt2yOyImQx4Pbb789YCodwLbtbD//+c/jcg6EjBByYaGZv5CvrKxgcHAQ9fX1aG1tTegCkIiInOM4jI+PY2FhAXv27Ak6Xo+8B+Rqua6uDldccQVeeOGFhIt5IFElJi9isZiPboH4+rzHC/+0PwC+lVApVeLtHW/Hb4d/e/H2AGKeb86HpdAC4rrwmbrPYGJggo+48/LyfD4fIuocx/m0IxFhl0gkPn7hZLrX8vIyxsbGkJubC7VajZKSkoxNz8ZCJg+oIXvk/qNZXS4XP8Er3UezZrOQ22y2iNoJM4W0FXLhG00WQI/Hw0crLMtibGwMS0tL2Lt3b0hbvHgRbyEnQ1scDse2lfXkhyVMezU2NsLj8eDll19O6OLnL+SBpq0RtjOlCVfI4xGRC4fKkLQ/EXBh+rapqAk3dtyI/x77b3i5i5+vUNDFXjHy1/PhLfPi13/7a1xWeRnW1tawtraGmZkZSCQSXtSF7Un+0XqgFLxIJPKZ7kUWfIPBgP7+fnAcx0fqKpUqYdmmdCJTUuuBCNZ+JpPJUFFRgYqKCp8JXsLRrOQzTvVo1mD915mG1+vdkt2yWq3IyclJ0RkljrQVcn+E7m4OhwN9fX3wer3o6elJ2gcTTyHf2NhAb28v8vPz0d3dvW3xhVDIhbS2tsLj8eDVV1+Ny3kFgogqx3F8K45w2pqQ7UxpIpm6FouQcxyH1dVV2Gw2VFdXQy6Xb4mQCS6XCzKLDO9qeBdWuBWc157n+8sBoEhRhOvU1+ELt34BJXkX+0CJnzPLsjCZTFhbW8PExATsdjuKi4tRWloKtVqN3NzcoNE6KVgURuv+C76wmGpkZAT5+fl8tJ6NkQUhU19XOBch/hO80m00K8uySWuxTSTBUuuZPPksGGkt5MLFnIiowWCARqNBaWkpduzYkdQrx3gJ+fLyMoaGhtDY2Ijm5uawFi2y0Ac6fmdnJzweD86fPx/zuQWDZVno9XpYrdaQJi+JMqWJBI/HA61WC47jeG934Uxx4ftts9mwurqKwsJCFBcXoxa1OFx1GHaPHS6vCzKxjC/QW5lZQcluX0MHkUjER8zt7e38WM21tTWMj4/zRW3EMEYYrQv/CxatC9OzTqcTBoMBBoMB8/PzPkYmKpUqa8xoMjkij6b9LN1Gs2ZLaj1Q0Z7Vas2KWeT+ZMwvXywWY3FxEVqtFh0dHaipqUn6jz1WIWdZFuPj41hcXIxqOyBUsd3u3bvh9XoTZgG4sbHBT1vbTjDiYUoTbUQerKgtkDjM6eewuLaI+rL6LV0OSolyS4X90NAQ2tvbQ6a3AxW1ra2tYWhoCB6Ph3eBKykp4fdFhfvpoaJ1uVzu0/pEsiMzMzMYGhpCYWEhL+yZTCbvkcfq7EZGs5LxrGTetsFgwOjoKNxut4/pkFKpjPs6mC1V6x6PJ+AeORXyFOF2u+F0OqHX63H48OGIfbvjRSxC7nK50NfXB5fLhe7u7qi+TNtVze/bt49vw4sXDocDNpsNUqkU1dXVYS1S8TCliUbIrVYrtFptwKI2nyjcbcMfhv6ABdvCxRtMF/fJT7WdgkISvOjI6XRidHQUe/bsCet8AhW1ra2tYWVlBaOjo8jNzUVpaSnvRe+fgifiHixaFxqV2O12Plon+/Ysy2JtbS1hIzsTxaUWkYciFaNZszkip6n1FMAwDL+XzDAMmpqaUibiQPTtZ+vr6+jt7UVhYWFMTnPhXEh0dXXB4/EENe+PhM3NTayurkImk0GpVEb8406WEYywqK2srAz5+fkBi9qAiz/u3w3+Dsv2ZZ/nmDXP4tHxR3HTjptCHmt4eBgdHR0RF50xDMMXtTU2NsLtdvMLMsmikL1vtVrNP7+wYE4YtQO+7W3COcxerxdarRZjY2MYHx+Hy+XaYh2b7mSqkCfSaz3YaFaDwYDx8XE4nc64WARnk0UrTa2nAYuLi/xe8vr6esp/3NFE5EtLSxgeHkZzczMaGxtjeg3hXkgcOXIEHo8Hk5OTUR2H4zgYDAZsbGygoqICNpstqueJhXAjcjKgxWq1blvU5na7Mb4wjiX70pbn4dvjHEaoFMEvPpxOJ0ZGRrB3794oXtUbSKVSn6I2Milubm4OQ0NDKCgo4FPw+fn5QaN14eskaXgyaEckEqG7uxt2u52vsp+cnIRSqeQX+1RXSAcik1PrybRoFZoOAeCjdaPRiOnpaUilUp9oPdwAIlsi8mBCXl1dnaIzShxpLeRWqxX79u1DaWkpNBpNTHOC44FYLIbL5QrrvizLYnR0FCsrK9i/f39cpt6EK+QMw+DYsWPwer2YmZmJ6Bherxc6nQ5utxs1NTWQyWSw2+1JX1zDEXIyoCWcoja73X7RvEYc+vMz2U0hhRy4GJV3dnbGrRWMYRjeIYz4eZNofXZ2ll+wS0tLQ7a3CYWdXHByHIecnBzU1dXx+/YkihsZGYHH4/Gxjk2HfuZMTq2ncvpZvEazZrOQ0z3yFNDR0cEvSML2s1QRbkTudDrR19cHj8eD7u7uuKUyIxnawjAMLr/8cni9XszPz4f1GJfLhZWVFUilUh+TF4ZhkmYHGy4ulwvLy8uQy+UoLy8PWdS2ubmJtbU1qNVqFEgLgK0BOc927XPk2MPDw9i3b1+MryIwCoXCZ/qW2WyGXq/H5OQkbDYbiouL+UgsJycnYHubwWCAWCyGx+PZYkYj3HO1Wq1YW1vj+5mJGY1arUZBQUFKFvRMFvJ0GZoSzmhW0u3gP5o1W4rdgkXksUw+S1fSWsiFJGNgSTzOwWw2o7e3FyqVCrt27YrrD0IsFkckqCKRCFdeeSX+8pe/YGkphHrh4hdcp9OhoKAAarXaZzFKxkhRf0JdPJBzJREGELioDQAMBgM2NzdRUVHBt8yFbI/bJhonjIyMYMeOHQk3aBGJRPxiC1yMKIRpcrlc7tPeJpFIMD4+jpWVFezZs4f/zgQrmCMV0qSfmSz2AwMD4DjOxzo2mWY06SCG0ZCuFyGRjGbNhj1ysvWUSIvWdCKjhNzhcKT8HEIJ+cLCAkZHR9Ha2or6+vq4/6CjKbYTi8W46qqr8Mwzz0Cr1W75e6BCMX9SJeT+xxQa0oRT1La6ugq3243q6mqfiONU2yk8Ov7olva4U22nwj6/REflwRCmyb1eL9/eNjIyApfLBYlEAo7jsHfvXr5yX5huD9XeJpVKUV5ejvLyct59bG1tDYuLi7wZDSnG85/DHU8yeY88lan1cAk1mnV+fh5erxezs7OoqKjIWDdBsk4HSq3TiDzJCBeKdE6tsyyL4eFh6HQ6HDhwIGF9vNHOQ5dIJHjTm96Ep556Cnq9nr+dmLwQ97Ng+6OpEHJ/IilqI4YwIpEoYMucQqLATTtugtFhhMluQrGyOOxIXAjZK0/VkBOxWMynyZ1OJy5cuMDbGJ8/fx65ubl8tF5YWBixGQ1xHyNe4aS9jczhFprRxNMJLF2j2nBIl9R6JPiPZn3hhRcgl8sxPz+P4eFh3k2QbLdkwusj32n/3z61aE0x6ZBaDxQROxwO9Pb2guM49PT0JNROMdLUuhCpVIprrrkGTzzxBIxGo89Iz+1MXlIdkZN2Kq/Xi5qaGj7qDFTU5nQ6odVqkZOTs8UH3h+VQhWVgBPcbjeGh4exf//+qJ8jHlitVvT29qKgoAA7d+6EWCz2aW/TaDS8ZzsRdv/2tu3MaGQyGSorK1FZWcnP4SbFeCQ1S54/HmMiM0EsApEJEXkoRCIROI5DfX09cnJy4HQ6+Wh9cXERQGaMZiXbA8LPgtSE0Ig8hUgkkrSoWhdeTJhMJvT19UGtVvMLaCKJdYyqTCbDtddei9OnT2NmZgY5OTm8+1koUinkpACPCImwqI3cj2CxWKDX6/kxkYnAaGRgMgHFxYBKxWFkZASdnZ0pq/Y2m83o6+tDVVWVz/Q///Y2IrwLCws+wltaWhqyvS1YtB6oyp4UUkkkkqjangipzv7EQqYXipHfFnkNcrmcv4ATev+n+2jWYPv8tI88BQgX6XSIyMk5cByHhYUFjI2Noa2tDXV1dUmJIKJNrQsxGAwoKCjg27XCPe9ULK5erxeLi4u86JDzCJR6NZlMMJvNKC8vT0jqzG4HHntMgunpN47b1MTh1KmLUfmBAwfifszt0Ol0GBoaQmtrK2pra4PeTzhSs7m5mfds1+v1mJ+fh0gk4iN1tVodVnub0IwmUJW9wWDg256Kior45w7HUjSTU+uZHpELP19/wh3NSoQ9la2MwSa4kTGm2UZaC7mQdInIiQWqXq/HwYMH+WriZB0/2oicjH1dXl7GkSNHoFAo8MQTT8BisWz72FRE5A6HAw6HA2VlZSgoKAha1Eb2zh0OB6qrqxNWmPPYYxLMzvqKy+wsg0cflSA/fxQ7duxI2sLFcRzm5+cxNTWF3bt3o7S0NKLH+3u2m81mrK2tYWpqCgMDA7zwkjR5JLPWhVX2ra2tvEkJEXahpWioASCZKuSZuEcuJJSQ+xNoNKvBYMDKygrGxsZSOpo1kJATq2+aWk8h6RCRu91ueL1eWCwW9PT0JP2KM9qInMw9dzqdOHr0KJ9aOnnyJP785z9v69yWzIWJ4zisra3BarVCLpeHrEwne+cAUF1dnbCUptHI+ETiBJYFpqcZ6HQeDA0N4eDBgwk5vhCO4zA2NgadToeDBw/GvIUgFN62tjYfFzgivMGmt203ax3YalLiPwBEaEZD6ksyObWeydkEIDIhFyIczUosiFM5mjXYCFMANCJPNv6p9VRG5AaDgffEPnToUEpGRopEorCd5QgWiwUXLlxAXl4ejh496nPe+fn5vJiHau1LVkQuLGpTqVSwWq1B98NdLhe0Wi0UCgVKS0sTuniaTNv/fWxsDDt27EjoAuX1ejEwMACbzYbDhw8n5FhKpRK1tbW88Pq3twmntymVyohmrQstRdva2raM6yQRnPAzzzQyPbXu9Xr5LZNYSPVo1mBmMADoHnkqIe1nyb7i5TgOc3NzmJiYQGtra1yGkURLpKn11dVV9Pf3o76+Hi0tLQHft8LCQlx77bV48skn4XQ6Az5PMoTc31UulIjbbDbodDoUFRVtGT+aCLY7RHHxGxXsiYrKiVugWCzGoUOH4truFQxhe5vQBU6n0/GpUyLMRUVFEbe3+Y/rJBcNAHDu3DkfM5p0rY72JxtS6/G+EAlnNKt/tB7rexjMnlV48ZlNZIyQC6/8k/VBeL1eDA4Owmg0oqurC4WFhRgdHYXH40lZRB6OkHMch+npaUxPT2PXrl2orKwMeX+VSoUTJ07gqaeegtvt3vL3RAu5zWaDVqv1KWojvvYLCwvIzc1FTk4OFAoFNjY2YDQaUVpamrQUmUrFoamJw+wsA+HbLxIBDQ0cVKqL783o6GhConKLxYLe3l4UFRVh586dKYn4hIux0AVubW0NAwMDYFnWZ3obEd5w29vIyNfS0lJotVrs2rULGxsbWF5extjYGF8dne69zJkekSdjfU3GaNZQrm7p+t2JhYwRciKcgYbFJwKbzYbe3l5IJBL09PTwC1MqfcfDEXJSjGc2m3HkyBEUFBSE9dylpaU4ceIEnn766S1bGIkUcjL1q7S01KeoTS6Xo76+Hna7HTabDaurq3zar6ioKOF7bP6cOuXBo4/6Vq03NHA4deqN94q894cOHYrbcUmLY21tLZqbm9NmEfJ3gfNvbyMucCUlJSgoKPC5EA8VrROIcDc2NvLV0QaDARqNBgzD+ETrychOhAuNyCMjUaNZA+lEtraeAWku5P5+3wzDJKXgjZhoVFZWoqOjw+eLncqiu+2ObbfbceHCBUgkEnR3d0ecjiwvL8fVV1+NM2fO+BwnEUJOito2NzdRVVUFhUKxpaiN/MiVSiXcbjf/b5vNBpPJBLlczkfribaRVCiAm27ybOkj92d8fBy7du2Ky4XGysoKhoeH0d7ejpqampifL1H4t7e5XC6+YI60twmj9UAFc+RzJ9s7JOtFzGhIdTTLsrx17Pz8PEZGRvhMznZTvZJBNkTkqTz/eI1mDRSREyHP5AutYKS1kANviAgplkmkiHIch5mZGUxNTaGzszPg4plKIQ8VkRuNRvT19aG8vBydnZ1R/xirqqpw/PhxPPvssz7tRfEUcjIq1ePx8K5ywfbD3W43tFotv3dOXpfX64XNZoPVaoXJZIJYLOYrpOOxxxYMlYpDqI5Dj8eDgYEBHD58OOpjcByH2dlZzMzMYO/evXEZgZtMZDKZT3sbybrMzMxgcHAwaHubzWbD4OAgL/bB9tYD9cT7T/UibU/J3gLL9Ig83QamhDOalXzWwos4r9e75bOnEXmakMhecmFK+vDhw0HbelIdkQcScjKspb29HXV1dTEfp7a2FldccQWef/55/iIqXkLudruxsrICiUTC+6BvN0OceD0LEYvFyM/P59vTSAp+bW0NXq8XSqWSXwSSvZiTqDwaYxoyx35tbQ2HDh3K+J5X4YAOMk5T2N4mk8lQUlKCvLw8zMzMoKSkBB0dHQAQlhlNoJ54g8GA6elpDA0NRZWWjQUakSeOUKNZZ2dnfUazut3uLRlJi8WSla1nQIYJeaJa0IhXtUwm2zYlneqIXHhssuivrKzE3ZymoaEBXq8XL730Er/4xdoxYLfbsbKywu+fkucM9LzECrKkpGRbMWMYhhdt4GIFvM1m41OwMpmMT8Eno/qZFElGGpV7PB709/fD6XTi8OHDKXXGShT+7W0mkwlLS0sYHR0FwzBwOBxYXFxEaWlpyPa2cMxo7HY7H61PT09DJpPFXES1HdnQR56uQu6P/2hWEq3Pzs7CarViY2ODr7cpLy+Pa0T+wAMP4Dvf+Q60Wi327t2LH/zgB0F/7z/5yU/wi1/8AoODgwCAgwcP4utf/3pMWTt/0l7IhdFgIiag6fV6aDQaVFdXo729fdsvcax+57EgPLbL5UJfXx/cbje6u7sTYkva3NwMj8eDF198EUBsi9TGxgb0ej0/iSuYyQsQeIZ4JMhkMshkMhQVFfEpeJvNhvX1dR/RT2SEFmlU7nA40NfXB6lUiq6urrQq4EoUYrEYHMfBYDCgra0NJSUlWFtbg16vx/j4eMj2tu3MaEQiEZRKJWpqalBTU7OliMrlcgU0o4mVTE+tZ6pXvPAirqWlBRcuXIBCoYDFYsHnP/95vPjii+js7IREIsHa2lpM21WPPPII7rzzTjz44IM4cuQI7rvvPpw8eRJjY2MoKyvbcv/nnnsO73rXu3gTsW9961u49tprMTQ0hOrq6lheNg/Dpbnzgtvt5sXrtdde46/AYkXYorVz505UVVWF9bjXXnsNVVVVKSk+MpvN6O3tRVdXFy5cuICCggLs3r074anj/v5+/Pa3v0VTU1PEV+tkod7Y2EBFRQVycnLCmiFeUVERdzHjOA4Oh4PfW/d6vVAoFHy0Hu/3saOjA0eOHNn2fqS9rLi4GDt27MiYiChWlpeXMTIygp07d6KiosLnbx6Ph29JItslwultwsyKf7Qu3KYRRusEYcuTwWCA2WyGUqn0sY6N9jM4e/YsOjo6kuJvkAgWFhZgNpuxe/fuVJ9KTJw/fx41NTUoLy+H0+nEc889hx//+Me4cOECTCYTurq6cN111+Ftb3tbxF0mR44cwaFDh3D//fcDuPj9q62txT/+4z/irrvu2vbxxOXu/vvvx8033xzV6/Mn7SNyIfFKrZMU5ubmZkQtWuQcUhWRk/GUr7zyChobG5PWjrRz507U19dHvE/Osiy0Wi3cbjdqamoglUr5xRYIPENcLBYHnCEeDxiGgVKp5Bdtt9sNq9UKi8XCp+BJpB6PtPbExAR27doVMp1nMBjQ39+Puro6NDU1ZXQ0Fy6kmG92dhb79+8PuCUkkUh82tvINolw6lYss9aFLU/EoGRtbY23E43WjCbTI/J0K3aLFqEhjFwux8mTJ/H0009jx44duOuuu/DUU0/hz3/+M+6991785je/Cft5XS4Xzp8/j8997nP8bSKRCCdOnMDZs2fDeg6bzQa32x3XrdCMEvJ4pNZJ9KNQKNDd3R1x21Kq9sjJxDWWZbF//36Ul5cn7dgikQi1tbUoLCzE2NhYWI8hRW1isZivNg9W1OZwOKDT6ZCbmwu1Wp20hVAqlfLjOFmW5VPwxL89JyeHb3+LZnEjlqpHjx4N+HcSkXZ2doadEcp0OI7D+Pg4tFotDh48GNZFtNDHm0zdItF6b28vGIYJOb0tHDMaoUGJxWLxGf5BvpfEjCbUd4EWu6UHgS5IbDYbSkpKUFFRgZtvvjmqaJhkh/zX3/Ly8rBdPz/72c+iqqoKJ06ciPj4wUh7IY/nKFOdToeBgQHU1taira0tKsFIhZCTlqb19XUACLgPk0hIhfDevXshFosxPDwc8v52ux1arZaPmoDgRW3JmCEeDkLLUAB8Ct5oNMLtdvtUwUeS8p+cnMSuXbt8qmXJts78/Dz27du3pSI/W2FZFkNDQ1hfX8ehQ4eirusgs+krKyvBsixff0Ha2woLC3lhz8vLi3jWOumGIA52JAU/MDAAjuN8onX/QCDTI/JsEvJ0bD/75je/iYcffhjPPfdcXItZ017IhUSbWuc4DpOTk5idnQ3LsnS7c0imkNtsNly4cAEymQyHDh3Ciy++mJKCFFJod/jwYXi93qCROVlUyV5jqKI2o9GIjY2NhM0QjwWFQgGFQgGVSgWPxwOr1crvq0qlUp8UfKiFm1Swk6icZVmMjIzwtr+Z3l4WLh6PBxqNBm63G4cOHYpb94BIJOKzKq2trXA4HPy+OjEQ2W7WujBqB3yjdalU6jOqk3RTLC4ubjGjIa2QmSyEmVrs5k+giNxqtcb8eyspKYFYLIZOp/O5XafTbanz8Oe73/0uvvnNb+KZZ57Bnj17YjoPfzJKyCUSSdDBHsFwu93o7++H1WrF0aNHY/4gkynkZOIacZgjC00qfmzC7oGjR4/C4/FgamqK/7uwqK2ysjJkURvHcVhdXYXT6URVVVXCXdliRSKR8CYkLMv62MZyHAelUsmn4AN9LiQql8vl6O/v58UsG9vLAuFyuXDhwgW+Ij+RxZkKhYKvVGdZlt/7Hh8fh8Ph8JnelpOTs220TrJR/mY0TU1NcDqdfB/z/Pw8vzYYjUbIZLKM7DxgWTYlcyTiCblAS4RFq0wmw8GDB3HmzBmcOnUKwMX37MyZM7j99tuDPu7b3/42vva1r+HJJ59EV1dXTOcQiLT/xIQCIJFI+FF04bC5uYne3l7k5uaiu7s7Lj8skUgUcLBIPOE4DvPz8xgfHw/oMOf1epO+SAhb3xiGwbFjx+D1ejE7OwuWZaHT6eByufiiNv9eX+G5a7VaMAyT0BniiUJYKAVcnEpmtVphNpuxuroKhULBR+vkAsXr9eL8+fOQyWSQy+UJF7N0gmSUCgoKsGvXrqRGq8QaVq1Wo729nZ/eRtrblEplWLPWQ5nRCFP86+vr6O3txeLiIiYnJ3nXMbVanTHWoNmQWiefVSAhj0cG7M4778Qtt9yCrq4uHD58GPfddx+sVis+8IEPAABuvvlmVFdX4xvf+AYA4Fvf+hbuvvtu/PrXv0ZDQwNfgyPcyouVjFpNIomGtVotBgYG0NDQEHSEZ7TnkMi56CzLYnh4GKurq+jq6vJpYyGLSCqq5v3750UiES6//HLYbDacO3curKI2MqpUqVQmfIZ4spDL5ZDL5XwKnhTMCW1jZTIZXnjhBbz5zW/Gvn37Mn6hDJfNzU1cuHAB5eXlaG9vT/nnTS7A/MemDg0NwePx+ETrCoUiYjOaoqIiAMCBAwf4DJXBYMDMzAzvEU4uGtL1AjYbqtaJRgjfYzKGNx5beO985zuh1+tx9913Q6vVYt++fXjiiSf4AjgyX4Dwox/9CC6XCzfeeKPP83zpS1/Cl7/85ZjPB8hAId9ORElV7MLCAvbs2RP36u5Etp85nU709vaCZVl0d3cHNKlIlSFNoONubm5CJpP5tIsF2w+3Wq1YXV1FcXExv+BlGxKJhK+uJraxGxsb2NjYAACMjY2hsrJySx90NmI0GqHRaNDQ0ICGhoaUi7g/ZGxqWVkZX6m+traGlZUVjI6OhmxvC2ZGI7x4lcvlvOcF8Qg3GAyYmJiAw+HwMaNJp/qQbIjIyWeSiD1ywu233x40lf7cc8/5/Ht2djYuxwxF2gu5f2o9VETucrmg0WjgcDhw9OjRhPjqJmqPnKTliouLsWvXrqBX7P42rcnCX8iXl5cxNDSE1tZWXHnllXj66aeh1WoDirjZbIbJZEJZWVnKq0aTBcMw8Hg8sNvtKC0thVwuh8FgwOTkJEZGRpCfn4/S0lLegjbdhC4WdDodBgcH0dHRETfnqkTCMAxfqU7GppL2tr6+PgDwmd4mk8l4VzqhqBsMBgAXhcTtdvPRutAjHICPGc3k5CQUCoWPdWwqhTRbhFwsFgcMJrJ1/Ul7IRcSSkQ3NjbQ29uL/Px8dHd3J2wPMhFCvrKygsHBQTQ3N6OxsTHkop4qQxoi5KQDYG5uDvv27UNpaSlYlsXx48fx1FNPwWg08o8ho0ptNhuqqqqyPgoVQiryhTazMpkMEokER48ehcFggF6vx+zsLCQSCUpKSlBaWgqVSpW2addwWFhYwMTEBPbs2YPS0tJUn05UCNvbOI7jp7fNzc1haGgIBQUF/OeVl5cHqVSK1dVV3hNAJpOFbG8TTvQiZjQGgwGjo6O8UQgR9mQXRGaDkAcrBrbZbHRoSjoQLLVOosNkuJ3FU8iF2wB79+4Nqz88Val1EmH29fVhY2MDR48eRW5uLr9gSaVSXHPNNXj66adhNBr5UaUsy6K6uvqSKe7iOA56vR4OhyNgRf709DR2797tM7HLZDJBr9djbGwMTqeT36stLS3NmMp2juMwNTWFhYUFHDhwIGu2TxiG4dvbWlpa+PY2MnFLIpFAqVRiY2MDO3bs4FtbozWjsVqtMBgM0Ol0vN88EfXCwsKEi2w2tJ95PJ4tr8HlcsHtdmdtu2far66hUussy2J8fByLi4thC2GsxEvI/dviwr1STOXQFpIGJB0ApF0HuPg5KRQKnDhxAv/7v/+LsbExvqo3m9LGoSCWtBzHBa3IZ1kWAwMDOHbsGADfymqykK+trUGr1WJsbIzfqy0tLUVBQUFavpf+o1ezNeoBtra3kfVHJpNhaGgIy8vLAWeth2tGQyqZ6+vr4Xa7+fa5wcHBbc1o4kG2RuQWiwUAsva7mfZCLoRE5BzHwe12o6+vDy6XC93d3Unb+4jHHrXVasWFCxegVCojbotLhbPc+vo6NjY2UFhYiEOHDoFhGH5f0H8ghc1mQ0FBAcrKyjImmowHHo8HKysrkEqlKC8vDym409PT2LVr1xYnO4Zh+IWcuIoRc5MLFy5AJBL5mJukQ5aD2NDabLasHb0ajLm5OaysrKCrqwtFRUWw2Wz85zU5OQm5XM5fhIWa3hasvU0qlfoU5G1ubsJgMPB+8/n5+T7WsfG4yMuGqvVAEbnVauUnH2YjqV8JIoAsXGazGRqNBoWFhThw4EBSF7RY96hJAU1NTU1ULTnJjsjJ/n1OTg4fXZMFiCw6hIWFBYyPj2PPnj247LLL8MQTT0TU95+pOJ1OaLVa5ObmhjUekUTll112Wcj7SaXSLX3Ker0eU1NTGBgYQHFxMV8wl4oFilxMcxyHQ4cOZaQBSjSQOpHl5WUfd76cnBzU1dWhrq6ON4bR6/UYGhqC2+32md4WTXsb6YgQFuQRlzmGYXhRV6lUUX8W2RqRk9azTH9twUh7Iff3WgeA119/HS0tLdsWhiWCaCNiMvFpcnISO3bsiLqaN1lCTvY8Z2ZmsHfvXiwtLflEEUIRJylGrVaLAwcO8L3v11xzDZ588knY7faEn2+qsNls0Ol0EbfVzczMYPfu3WH7y4tEIhQXF6O4uBhtbW189Cec3U1EPRl7qQ6Hgx8+tGfPnozfVw0XjuP4bYSurq6gmUCxWLxlEIuwvY1c9EXS3kbS70Bgv3mDwYC5uTkMDw/7WMfm5eWFvU5mg5AHcnWzWCwZY8oTDWkv5ASWZXl/7x07dqRkHjjwRkQeqM0qGF6vF0NDQzAYDDh06FBMhUDJSK2TdKnZbOb375eXl7GxsQG32w2pVMq/drfbjYGBATgcDhw+fNgnMiwsLMQ111yDp556Cg6HI6HnnArI4hlNWx3Lsujv78fll18e1bGF0R+Z3a3X66HRaMBxHJ/SFfqLxwuyNaRSqdDZ2ZnxC3+4kKEvGxsb6OrqCujzEAj/9jYyiGVtbY3/vITROmlvI8cURuuBCuaEfvPNzc1wOBx8tD43NweJRMIXUBYXF4fMYGZDsVui7FnTmYwQcpfLhd7eXn7vI5VTssgXJNB0nUCQyAUAuru7Y95DTHRETs6XYRh+zCvLsigpKcHMzAxeeOEFqFQqvvVmZGQECoUiaGq1uLgYJ06cwFNPPQWXy+XzN6ORgckEFBcDKlVks85TjcFgwObmJiorK6P+TGdmZrBnz56Yv8/+s7tJuxSZBlZUVMQLe05OTkxRCfE7qK6ujqtjYrpDLm7tdju6urpiaqUMNIhFr9djfn6ej6aJqOfn50c8a12hUPBmNCzL8mY0U1NTsNvtKCoq8jGjIZ8hSetn+oVZMCGP9bufzqS9kHu9Xpw9exZFRUXYtWsXXnjhhYRapG5HJEJuNpvR29sLtVqNnTt3xuVKN5FCvrGxgfPnz0OtVmPXrl0+++Hl5eWorKyEzWaDXq/H0tISNjc3+YIcUuQW6IeiVqtx4sQJPP3003C73bDbgccek2B6+o37NjVxOHXKg3SvlRIOfKmuro4p2uU4DhqNBldccUXczs+/Xcput/MFWFNTU5DL5XwKPlLzkbW1NfT396OlpQV1dXVxO+d0h0xu83q96OrqimuGg2EYfhBLS0sLnE4n/3nNzs5CLBYHLHAMt71NJBJBpVJBpVKhtbWVH81LpsPJZDL+ucls+GwV8mytWAcyQMjFYjEOHDjA7/Ns5+6WaEhF6XbnsLS0hOHhYbS2tqK+vj6uXu+JeP3Em56Y0gAIuB9O5nFbrVa0trZCLpdDr9fjwoUL/KITyNiktLQUV199Nc6cOYPHHgNmZ33fj9lZBo8+KsFNN6XuIm07SG98qPaySJmdncWePXsS1netVCpRW1uL2tpaeL1ePqU7ODgIr9cLtVrNC3uodqbl5WWMjIxg586d245rzCbcbjd6e3v5dSjRhbVCa1cSTZOLMFLgSD4z/+lt4UTrxIympqYGXq+XN6MZGxvjM2ZarRalpaVhbx2kG4GCLLJHnq2kvZAD4MdHAqlpvxJCrnKDnYOwt3T//v1hVTFHQrwjco7jMD09jenpad6bPlhRG6nWXVxcxL59+3jLSVJwQ4xNiEOVv0hUVFSgoeEqTE29CIbxff9YFpieZmA0MmmZZne73dBqtZDJZCgrK4vbhRnHcejv749rVB4MsVi8pZ1Jr9djYWGBT+mSz0tYIDU7O4vp6Wmfz/xSwOl08m2iu3fvTvresTCaFhY4CrMrwult/tE66VsPFq0Lo32O4/gMInl+Mh0uWWY08SKQkNOIPM1I9PSxcM8hkJiSdhyHw5Gw3vZ4eq17vV4MDg7CZDLhyJEjyM/P97mqF4o4ue/m5mZA0w//kZEWiyWgSKytVcNguAolJX8BsPU9NJkAlSouLy9uOBwOaLVavm833szOzmL37t0+k+4SDcMwfDtTc3Mzn9LV6/X8tK6SkhI4nU6YzWZ0dXXxqddLAbvdjvPnz6OoqAg7duxICxEL1N62traGkZERuFwun+ltSqUyYjMahUIBhmFw4MABfjqcwWDA0NAQvF6vjxlNOtstB+qFt9lsNCJPJ1KdWgcCZwUsFgsuXLjAzz5PVApOJBJtKRqLBjJpjeM4n6I2f2MK4KKQ9fX1QSwW4/Dhw9s6SgmrdJuamuB0OqHX66HX67G+roPDcTUMhiuhVj8HwDf6TqKWhQWZ2qZSqRJWZEmi8iuvvDIhzx8O/tO6jEYjxsfHYbfbwTAMpqenfXqgsxmr1Yrz58+jtLQUHR0daVkg5d/eRhwBdTodxsbGtrQjhmNGQwa9sCwbcDqcwWDA8vIyxsbGkJuby1fap5vjYLD2MxqRpxj/XvJ0iMiFQr66uor+/n7U1dWhtbU14V7v/7+9846Pqkzb/zXpvSeTEAghkALphYRelF6SgAUr2GUVV2X3p8iK7vuKoOLagBV1X8WGIBBAEQk1IIJKyqT33qZlMilTMu35/cGe40waKVOT8/18/MPDmZlnMjPnPvf93Pd1jba03tnZidzcXNppjcryKblV7bvZzs5OcDgceHt7j3jUyN7enpa1jIlR47vvpLh2LQQikQpeXtcAEFhZAZMna8yqrN7R0QGRSGQU17b6+nq0t7cbNSsfCEIIGhsbYW1tjXnz5kGlUkEgEOhYfFJBwtwu4qOlq6sLOTk5FtWV358iICVGU1BQAI1GM6TxNqFQCBsbG6hUqj4Nc9SNeXBwMBQKBZ2tFxQU0ONzlBiNIaRjh8NAzW5jVWcdsJBAro05ZeTa+8tRUVG0YYIhGe0eOZ/PR35+PkJCQhASEgKg/6Y24JYdZXFxMUJCQvTWsGdtbY1Dh4BHHtHgwoVwsFhqeHpeh7+/FPHxLWhttYOTkxOcnZ31VtVgiUSg5tzIEOv2bW1t6O7uNpprGyEEHA4HixcvNvhrDQY16mljY4OkpCT6M3BxcaEVxah92oaGBlo2lppZt+QZZGqPODg4mG74tEQomWCq36WzsxNCoZDe5nJ1daWDupubG2xtbVFXV0f3vlDJwmBiNL3H59ra2tDY2KgjHdu718JY9BfIpVKpUa7PpsLiArm5ZORKpRL5+fkQi8VITk422mz7SPfICSGora1FdXU1oqOj6R8h9bfs3dRWV1eH2tpaREVF6d2MxtMTOHWqB1VVLFRXhwCQgsu9CaVyAqRSKe3XbGf3Z1AfUTCVyWBz6hRYNTX0IRISAlV6Ogaac6PGyxQKBSZMmGBU2dHGxkaIRCJ4mahJQCaTITc3F66urnSlpjd2dnY6zm1isZhWl+vp6dGRjbWkrue2tjbk5+cjNDQUkyZNMvVy9Ib2eBvVC0FNLlA3Yvb29pBKpYiLi9OpCA1VjIZ6fmobjRKjaWho0PFi9/LyMoqc9mBz5GMViwvkNjY26OnpMfUy6K7O2bNnG7XxYySldUqRSigUIjk5GW5ubjrzp73lVktKSiASiQze4DRtGsG0aQTADHA4Clo/n5pSkEqlkEgkaG1tpQ0PnJ2d4ejoOKS7fJtTp8Cqq9M5xqqrg83Jk1Ddd1+f89VqNbhcLlgsFgIDA43e4ETNlZsiK+/q6kJubi7YbPaQPQC0u6rDw8P77NNSMqS+vr5wd3c32zI1n89HYWGhjg3pWMXe3l7nRqykpAQ8Hg/29vbIzc2lxYMo97bhitH0fv6Ojg60tbWhtrYWxcXFcHd3p7N1Qwm0MHPkZkrvPXJTltbb29shFovh6uqKmTNnGv1iP9yMnCqVqtVq+qZjoKY2heJWMNVoNEhJSTHqDUpcXBzdGU+ti9r3I4RALpfT4zdqtRqOjo5wdnbWmaXVhiUS6WTiNBoNWDU1YIlEOmV2pVKJ1tZW2Nvb63W8bLg0NDSgra3NqGNeIpEI+fn5mDx58qj8C5ydneHs7ExbcFKZH4fDAQAdYRNzMVhpaWlBWVkZoqOjjWKDbC5Q24JtbW1ISUmBi4tLH/EgSizGx8dHJ5sejhgN5Q9AiRNR2XptbS3s7OzobN3T01Nv2zIDmaYwgdyMMGVpvbGxEWVlZXB1dYWvr69JRlKGk5FTWZa7uzuio6MHbWrr7u4Gh8OBm5ub3lTohktiYiJUKhXKysp0jrNYLDg6OsLR0RHe3t5QKBSQSqW0tKW9vT0d1OlGm/b2wV9Ma86NGi9zc3MzWVlbm/z8fNxxxx1GeS2qDyIsLEyv/gW9ZUgp57aamhoUFRXB09NTJ/MzBY2NjaisrERsbOy4mo/v7d5G/f17iwdRXujl5eX9bpsMd7zN0dGRbnpVq9W0dGxFRQUUCkUf6diRQN1c9LdHzoyfmRGmaHbTaDQoKytDa2srEhISwOVyjWolqs1Qm92oTvrg4GBMnToVwJ9f8t5NbUKhEIWFhQgKCkJISIhJS6DJyclQqVSoqqoa8Bw7OzvY2dnBw8MDarUaEokEUqkU7e3tsLa2hrOzM1wcHTHo/fd/9wKpeXdtiUpT09jYCKFQqHcxof5ep7Ky0iB9ENpoy8aGhoZCJpNBIBBAKBSisrKSFh6hfLsNfYNM9YDU1dUhISHBYKp65gghBBUVFeDxeLd1b9MWi+lvvI3694G81m8nRkMFbUo6lqrgVFVV0Tft3t7ew/pOaAuHab9nJiM3A0w5fqZQKMDhcKBQKDB79mw4OTlBIBCYrLx/u0CubZdKddIPptRGXczNZX+QxWJhzpw5UKvVqK2tve351tbWtLAJIQQymQwSiQRcpRIBAQFw5nIBojXSZmUFEhwM4uUFsViM9vZ2sNlss2uEyc/Px5133mmQ56bKqg0NDYiPjzf6yJujo6OOcxs1KlVYWEiPSlFd8PoeZRrIS3w8QAhBeXk5BAIBkpKShvydH2i8jUoANBqNjhiNvb39sLN1aluG+k5Q0rGlpaVQqVS0NK23t/egOgbU8/c3Rz6WP2uLCOTaGDMjp0rTbm5uOjrL1tbWJmu4G6xHgGpeEQgEdCf9QEptlC0sn89HYmKiWWUlLBYL8+bNg1qtRkNDw7AeR2lJA0DP+vXo+eEH2Dc20ucoJk6EavVqdAiFkEgkRhsvGy5NTU0GycoJISgtLYVQKOxXoc/Y9BYeoUal6uvr6eYoKlsfrZ809d7b2toGzUbHItR7p5pYRzNR0Hu8raurC0KhkPaXoLYetcfbgMHFaKhrE+Wn0Z/YDZfLRUVFBS1GQ1XRtLN1StWt9/eEycjNDGM1u/F4PBQUFGDKlCmYOnWq2TTcDZSRD9TURq1T+8utVCpRUFAAhUKB5ORksxwTsrKywoIFC3D58mU0NzeP6Dns3d2Bhx+GSiSCWiCA1MEBnba2kAsEtPqcOaPvrJxqJpRIJJg5c6bZfe69R6XkcjktG0s5dVEBwsvLa1gl+JF6iY8FCCEoKSlBe3s7kpKS9KrMpy31GxIS0kdngMVi9WlyHMhrnbqu9W6Y668a0NbWhsLCQhBCdKRj++tYJ4Qwe+TmgHYQpZSHDAUhBNXV1aitraXnrXujT73z4dJfIKfkYV1dXWlzh4Ga2qRSKTgcDhwdHTFz5kyjzHWOFGtrayxatAgXL14El8sd8fMQLy9YeXnBUa2G+L+d6a6urpDL5SMebTMGTU1NEAgE8PX1HfVzUboHGo0GSUlJJlffGgoODg46zVFUObekpAQqlUpHrWywqoparUZBQQHkcvmovcQtDUIIiouL0dHRofcg3h+9dQY6OjogFApRW1uLoqIiusJCicUMlK0PVIIfqBrQ1NSE0tJSODk50ZUdV1dXsFgsyOVyqNVqvdy479+/H3v27AGXy0VsbCz27t2L5OTkAc8/evQoduzYgbq6OoSGhuLtt9/GqlWrRr2O3pjvVXwAtIOUvi+4KpUKhYWF6OzsxKxZswb84E2ZkVNd69T7FwgEyM/Pp+VhgYGb2kQiEQoKChAQEICwsDCzCViDYWNjgzvuuAMXLlwAn88f8fMoFApwuVw4ODjA19eXziRGMtpmTPLz87FkyZJRPYdcLkdeXh4cHBwQHx9v8vc0ErS1xSMiIugmxebmZlpNjMrWqQs4cOs3zeFw6BsYcxl7MwZUFaKrq8skNzDa42ehoaH9VliofgjK9nigbH2g8bbe1YD6+nq0tLSAw+FALBbj0KFDmD9/PpycnEZdWj9y5Ai2bt2KAwcOICUlBR988AGWL1+O8vLyfptFr1+/jvvvvx+7d+/GmjVrcOjQIaSnpyM3NxdRUVGjWktvWIQQ8xG3HgBCCG0U0tPTg8uXL2PZsmV67W6VSqXIy8uDra0t4uLibuvN3NjYiJSUFL29/lBRKBS4dOkSlixZgubmZlRUVCAyMhITJkwYsKkNuOWPXlZWhvDwcL2OGRkLhUKBc+fOoa2tbdiPlclk4PF4Qxovo0bbJBIJenp6+h9tMzKrVq0acVYukUhoXX1zcfHSN1Q5VyAQoK2tDTY2NrS1Z0NDA2xsbBAbG2vW1Sd9o9Fo6G2UxMREs6vAaI+3CYVCeryNyta1G/F6i9FohyztbB24Na3T0NCAhIQEVFVVYd++fcjKykJ9fT3mzp2L1atXY9WqVYiOjh52IpOSkoKZM2di37599LomTZqE5557Dtu2betz/oYNGyCRSHD69Gn62KxZsxAXF4cDBw4M67Vvh0V8s3uX1oFbd9r6+nK2tbWBw+EgICAAERERt73YmXqPHABKS0shEAgwc+ZMeHh4DNjURghBZWUlmpubER8fbxZz0iPBzs4OS5YsQWZmJsRi8ZAfR2VuVKY2lNfRHm2jgrr2aJuTkxNt+WgMOBwOli5dOuzHdXR0IC8vz6IMQEZC73Jue3s7PR9PGXq0trbC19d3zDu3AbcCTGFhIWQymVkGcaDveBtVEaPkfqmRxIG81gcSo6Ec3KysrBAWFoaPPvoIxcXFWLp0KR588EGcPXsWO3fuhKenJ27cuDHkpEahUCAnJwevvPIKfczKygpLlizBjRs3+n3MjRs3sHXrVp1jy5cvx8mTJ0fwFxsciwjk2lCBTB+BlBCChoYGVFRUICIiYsgay6YM5NTrisVizJ49Gw4ODgM2talUKvquPDk52eKbPRwcHLBs2TKcPXsWnZ2dtz2/vb0dHR0d8Pf3H1Fzk7W1Ne36pD3axufzQQihO+SdnJwMmum2tLSAz+cPa9ZbKBSioKAAU6dOxeTJkw22NnPDysoKTk5OEIlEYLPZCA4ORltbG7hcLi0bS5Xox5pzG3Ar0OXn56OnpweJiYkWsZXAYrF0VAGpkUShUIji4mKdfghq/Gyg8bauri5YW1tDoVDQAZ2yMH366aexefNm9PT04Nq1awgMDBzyGqltNzabrXOczWb3EbCi4HK5/Z4/mn6fgbCYQM5iseh9YX3MklOjWnw+H0lJScOapTVVIKd8koFbkqYODg46TW3U+Abw576ora0tkpOTLeIHPRQcHR3pYN7d3d3vOYQQCIVCSKVSTJgwQS8ZSZ/Rtp4eSCQSiMVi8Pl8ODo66t21TRsOh4Nly5YN6dzW1laUlJSYjTaAMenPS5yy31QqlXQpNzc3l3ZuowKEpZfe1Wo18vPzoVKpLCaI90d/Xuja/RAuLi7056bttV5bWws+n4/Y2Fi6KVij0aC1tVWnHG9vb28wjQZTYZHf3NHOkvf09IDD4dCjWsPN1kwRyKny/8SJE1FfXw9gYPvRjo4OcDgc+mI21vZFnZ2dsWzZMmRmZkIikej8m0ajAY/HQ7u8HTauNujWdMML+t9OsLe3h729Pby8vKBSqWh1Ob24tvVDa2sreDxenzv83tTX16O6uhpxcXHjSnYUADo7O5GbmzvgVoKtrS0CAgIQEBBAd1QLBAJUV1ejsLBQR4LU3ASCbodaraavadqaF5YOdSPm6upKN7T11vD39vYGi8WikzJKoZHaZtm1a9eob+Z9fHxgbW0NHo+nc5zH4/U72QQA/v7+wzp/NFjkpz2ajJz6sXt4eNCjWsPF2ONnDQ0NKC8vx4wZMxAYGIimpiYolUrY29v3CeJcLhclJSWYOnUqgoKCxlzpkMLV1RVLly7F2bNnIZfLAdzaSqhvrsc10TU0y5qB1lvnhniEID0sHQ42DhDJRGiXt8PT0RNeDvoJ8DY2Nn1c26RSqd5H2/IzM7GCzQaZOhVk2jSdf6N6IVpaWpCYmGg0W11zYbhe4tod1WFhYX32aCkJUsq5zZxvhtVqNfLy8kAIGVNBvD/s7Ox0bsY6Oztp8xcAKC8vx82bN5GQkIDp06fj3nvvRWBgIH788cdR/fbs7OyQmJiIixcvIj09HcCtG4WLFy9iy5Yt/T5m9uzZuHjxIl544QX62Pnz5zF79uwRr2MgLOYTp0rrwMgz8tbWVhQVFSEkJGRUmuK9R8AMBaXxzuVy6fK/RqOBjY0NysrK4O/vDz8/Pzg6OtJ+43V1dYiOjtbL7LG54+7uTmfmXV1daG1txa/tv6JV1qpzXp24DsfLj8OaZY0a8Z+OaNoBXl+M1rWtX/7rqy6sqUG7QICAnh6olyxBz8GDwH+/E5Tgx8yZMy2+F2K46MNL3MnJSUc2lsr68vPzQQihS7k+Pj5mVbJWqVTIy8sDi8VCQkKCRY4WjhRq/1ssFmPmzJlwcHCAQCDAuXPnsGPHDtjb28PJyQnvv/++XhKvrVu3YtOmTUhKSkJycjI++OADSCQSPProowCAjRs3IjAwELt37wYAPP/881i4cCH+9a9/YfXq1Th8+DCys7Px6aefjnotvbGI8TPglqAFJYTy+++/Y9KkSZgwYcKQHktlKw0NDYiJiRm1QQQ1ArZ06VKD/XCUSiU4HA56enqQkJBAB2u1Wq0zj9ne3g5nZ2cQQqBUKhEfH2825h/GorKyEkeOHIHKToXvqr8b8DwWWCDQGl2BFYI9gnHfjL7e5IZgpKNtNocP3/JV12gQ0NOD1QIBiLU1NIsXQ5qRQTc3JSQkjCuxE+BWqbKoqMhg/QCUcxv1e5NIJHB3d6cb5gzlqT0UVCoVcnNzYW1tjbi4uHEVxIE/LWh7+wXIZDLce++96O7uRlJSEs6fP4/6+nosXLgQ27dvx6JFi0b8mvv27aMFYeLi4vDRRx/RY8iLFi1CcHAwDh48SJ9/9OhRvPrqq7QgzDvvvGMQQRiLDORUM0tQUNBtH6dSqZCfnw+JRIKEhAS96O2q1WqcP38ed9xxh0FGO6jZXycnJ8TGxtIVAG0JQ+riQdmPUn8fSsbSz8/PKE5Spoaaj/fz80PGlQwcKjw07OfYnLBZb2X2oaI92iaTyQYcbWOJRLDpNXO6SiDAhP9q/f/+9dfoCQpCbGysWWWKxkDbS9xYFShtz26RSAR7e3t6X93T09NovzelUonc3FzY2trS14jxRGtrK0pLSxEXF6czUiuXy3H//fejo6MDmZmZ9BZTZWUlzpw5gzlz5mDmzJmmWrbBsKjSOsVQm82ogOjg4IDZs2fr7UKnzxG43lBNbYGBgQgPD6dfR6PR0GpGFF1dXeBwOPDw8MCMGTMAQMdJiioJUheasfRj7+3g5eXlhS6bLnxb9C1YZHgZUrus3eiB/HajbVQJ3qUfAZxcNzdMEAgAAG58PiampY2pz3YoNDQ0oKqqqs+F3ND09uymfm9FRUVQq9W0UpmPj4/B5reVSiVycnJgb29Pd2iPJ7hcLkpLSxEbG6vz2SsUCmzcuBFCoRAXLlzQ6RMJDQ3F888/b4rlGgWLCeTaDKXZjdrfCgwMRFhYmF6/7NQInL4DeWNjI8rKyjB9+nRMnDhxUKU2KlhPnjxZZ79f2zWI6sqtqqpCUVERvLy84OfnB19fX7MUiRgqvfeEqSrLnOlzEJ4YjsqcSl31J1hBg4GtXz0db5XlRCIW2ttvWZV7eRmvUDXYaJtYKkXv1i2uvT2a7e0R2NODyXfeCYyjIE71gtTX15u8qU9bNpbS/RYIBGhsbERJSQnc3NzoG2kXFxe9lOApYRInJydER0ePuyBOCf3ExsbqTGUolUo88sgjaGxsxMWLF41uzWtqLDKQD9bsRghBfX097bE9nKH/4aDPQE4IQVlZGd1x7OXlNaiHeENDA6qrqzFjxowBRxlYLBY8PDzg4eGB0NBQOtujZjG19/ksqTmKcm5TKpVITk7usyf81cNfYSPZiIqcCvpYsEcw1Bo1GjsbdQI6tUfuSLxw+LANamr+vNCGhBCkp6tgCiEwndE2f38oCgth19io46ue6+EBdnQ08F99/fEA1evS2tpqdl7i2i5gU6dORU9PD72vXldXB1tbW7pZjtIVHy5UEHd2dkZUVNS4C+J8Ph9FRUWIiYnRsfdVqVR44oknUFlZicuXL+vd+tcSsJg9crVaTWfhFRUVUCgUfYTn1Wo1iouL0dbWhvj4eIN6bGdlZSEmJmbUZT3K1IGSU6Sa2rS74rU9xMvKyiAQCBAXFzfibKSnpwcCgQB8Ph8ikQhOTk70vro5q13JZDLk5eXB0dER0dHRg47ZZHGycOnKJXg63Bozk6vkOFlxst+u9ZPHXFBXx4K2qZyVFRAcTHDffYZz2hsycjlsTp4Eq+bPtUsCAuD/xBMIiY+Hr68vvL29x3R5XdtLPCEhwaJuPrV1xQUCARQKhY5z21BkY3t6epCTkwNXV1dERkaOuyAuEAhQUFCA6OhonWZltVqNzZs3IycnB5cvXx53AkgUFhnIa2pq0NXVhdjYWPrfKSUzAIiPjze4pvIvv/yCiIiIUTXZSKVSeg+fMnUYqKlN20Ncn+9PpVLRFxihUKhTLhyu57Mh6ezsRF5eHvz8/BAeHj6kdVVUVPTRQRbJRWiX/TlHLhKxcODAwDcEmzerjFpmH4jOzk501dXB19oa9gEB0Hh6wsXFBdHR0RAIBOjp6YGXlxf92Y2l7nVtL/HExESL1ksnhEAikdC/t46ODri4uND76v3dSMvlcuTk5MDd3R2RkZFme6NtKKjxwsjISB1BJLVajeeeew7Xrl1DVlaWRZpB6QuLLK33LmtTYhDe3t6IjIw0SmZCdZKPFJFIhLy8PEyYMAHh4eFgsVgDeohLJBJwOBw4Ozvr3UPcxsYG/v7+8Pf3p5WQBAIBSkpK6OYdPz8/eHt7m6wrmuoHCAkJweTJk4d8IQsLC4NKpcLNmzfpY14OXjqNbe3tgz9Heztgap8ZWjM+LAx2Dg4gAFi49b1wcXFBWFgYHRyoTm7K1lOf+7OmgPIS7+npwcyZMy26twO4dXNO6QxMmTKFVioTCARoaGigZWOpG2mqsY1yr7PUz3GkiEQi5OfnY/r06TpBXKPR4G9/+xuuXLmCy5cvj+sgDlhQIO/dtU5l583NzSgpKcG0adMQHBxstC/6aPbIm5qaUFpaivDwcAQFBdH74b1L6cCfX+TAwECEhoYa9P1ZWVnB29sb3t7eCA8PR1dXF/h8Pmpra+lmOSo4GCsrampqQnl5OSIjI0ckbThjxgyo1Wrk5ub2+++364kxdc+MUCiERCIZUDOe+m70Dg4CgQACgQC1tbX0SKKvr69RR6RGi7aXuCVrhw9Gb6UysVgMgUCAyspKyGQyeu99ypQp4y6It7e3g8PhICIiQqdkrtFosG3bNpw9exaXL19GcHCw6RZpJlhMaV2j0UCpVAK4NX5QU1MDLy8vNDU1ITY21uhKZtnZ2WCz2cNSkiKEoKKiAk1NTbQW9mCd6VQQi4iIMFjT3lCRSqX0vnpHRwdcXV3pDnhnZ2e9X2QIIaiqqkJzczNiY2NH3YWal5eHgoKCfv/t8GEbs9sjJ4SAz+dDoVAgICBg0CrMnXfeOWBGoj0iJRAIoNFodEakzDU4UnPSNjY241LsRCqV4ubNm7TgjFgshrOzs45s7FgO7GKxGLm5uQgPD9e59mk0Grz22ms4fPgwsrKyEBYWZsJVmg8WGcgpqVUHBweTNb7k5eXB09NzyHeD2sI0iYmJcHJy0vHV7d2ZXlFRgdbWVr001Okb7Yyvra0N9vb2dFD38PAY9QVGo9GgqKgInZ2diI+P19vne/PmTZSUlPQ5LpcDJ0+aT9e6RqMBl8sFIQT+/v63DWI+Pj5YvXr1bZ+XEILOzk76s5NIJPDw8NBRKTMHenp6aEGk8ThiRTm4sdlshIWF0T7blGysUCgEAB3nNnO9IRsJHR0dyM3NxbRp03QSJUIIdu7cic8//xyXL1+mtTMYLDCQd3d3Izs7GwqFAosXLzbZF7igoADOzs6YOnXqbc+VyWS0gENcXNygTW0qlQqFhYWQSqWIj483m4vrQKjVanqPT/BfkRKqA34kYzaUNK1Go0F8fLze90R/++03lJeX9/tvppoj10atVqO1tRXW1tbw9/cf8k3RYFn5QMhkMvpza29vp6cXTJnxUb8VSuRoPAbx7OxsTJgwoV8HNwA6GhHU1gt1Q+bj42NRHf296ezsRE5ODm36REEIwTvvvIP9+/fj0qVLiImJMeEqzQ+LCeSEEDQ3NyM/Px9sNhs8Hg9Lliwx2XqKiopgZ2d329JOe3s78vLy4O/vT/sjD9TUJpPJwOFwYGdnh5iYGIu7y9be46M6qbWV5W4XlKVSKfLy8uDs7DxiZ7rbQQjBr7/+iurqar0/92hRKpVobW2Fg4MDfH19hxVIvb29sWbNmhG/NmUUQn122k1XxhptozJRajJhLJeO+6O7uxs5OTkIDAzE1KlTh/z+KdlYgUAAkUgER0dH+rOzJJnmrq4u5OTkYMqUKZg8eTJ9nBCCDz74AP/6179w/vx5JCYmmnCV5onFBPLOzk5cvXoVkZGRcHd3x7Vr17B8+XKTrae0tBQAMH369AHPoRrxwsLCMHny5EH3w8ViMfLz84c1XmXOEELQ3d1N76t3d3fDw8ODLsH39oCnPNTZbLbBL+IajQa//PIL6urqDPYaw6Wnpwetra1wdXUdko84SyQCVT4g/916ueOOO0bs/qVNfzdkVKPjUOeehwtlLzxx4sRhBbGxAhXEJk2aNKQq30CoVCq6J0IoFOr0RHh7e5tt1z9VaZ08ebKODS0hBPv378fu3buRmZmJ5ORkE67SfLGYQE7t7zk4OKCnpweXL1/GsmXLTBbwKioqoFQqERkZ2efftN3W4uLi4OPjM2gQb21tRUlJCW3DOBYvYr3LuM7OznRQl8lkKC4uxtSpU3XuxA2JWq1GVlYWmpqajPJ6gyGTycDlcuHp6Xl7EaP/Wppqi8OQkBCo0tPhHRiI1atX6/X7Q809Uxkf1eioz9E2qjt5ypQp47IDmbqJ6R3ERgt1zaQ+u+7ubri7u9PZuiGaVEcCVYmYNGkSQkJC6OOEEHz22Wd4/fXXcebMGcydO9eEqzRvLCaQA7eyFuDWXeeFCxcM5j42FKqrqyGRSPrs1VB73F1dXbTbGrUf3l9TG2X8ER0dPW6kBZVKJX1xoUxCvL29ERwcbNRSoFqtxsWLF9Ha2nr7kw0EVbWgguLt0LY0pbGyAgkOhuq++7B48eIhuQKOFIVCoSMgNNrRNqFQiIKCglF5iVsyVGOXMW5itO2PRSIR/dlRsrGmSIqonoDAwEBMmzaNPk4IwZdffomXX34Zp0+fxsKFC42+NkvCYubItaH26wzhPjacNfR+fZlMRlsLzpo1C7a2tnQWDkAniFNysh0dHTrGH+MBW1tb+Pv7o7OzE9bW1pgyZQqkUikKCwuh0WjowODt7a1X8ZveWFtb44477sCFCxfA4/EM9joD0dHRAZFIBDabPaSmRpZIpJOJ02g0YNXUgPVfzQFDVnXs7OwwYcIETJgwQWe0rbi4uI/71+16PAztJW7uUEJWvRu7DIWDgwMmTpyIiRMn0p+dUChEaWkplEqljmysMZQBpVIpcnJyMGHCBJ3tBEIIDh06hJdffhknT55kgvgQsKhAzmKxaNEUQ7iPDYf+1OVyc3Ph5+dHKzAN1NTW09MDDocDKysrpKSkmO2+laFQq9UoKipCd3c3UlJS6CA2kGObIWVHbWxscMcdd+D8+fP0WI8xEIlE6OzsxIQJE4b+voYgQycSidDQ0GCULYqB3L/q6+tRXFw86GgbpUAXExNjdA0Ic4DaTug9YmUsen92VGWIMlVydXWlS/Curq56vzGkphPYbHaf7vxjx47hxRdfxLFjx3DnnXfq9XXHKhYVyLWxsbG5rZWpIdEO5C0tLSguLkZoaCh9Ae2vlA7camrJy8uDl5fXuByvUSgU4HA4ANBHcnMgxzbqou/m5qYjQqMv7OzssGTJEpw7dw4ikUhvz9sfhBAIhULIZDIEBgYObzJhiDJ0+fn5CAoKMur+Z2/3L+1O6srKSp3Rto6ODlRXVxvdS9xcEIlE4HA4CAsLMwtpURaLBVdXV7i6uiIkJERn+6S+vh42NjY6srGjnWCQyWTIzs6Gr68vPSdPcfLkSTzzzDM4fPgwVqxYMdq3NihXr17Fnj17kJOTg9bWVpw4cQLp6emDPiYrKwtbt25FcXExJk2ahFdffRWPPPKIQdc5FCw2kJs6I7eysoJKpUJlZSXq6+sRFxdH390O1NRG2fBR+2Hm0GhiTCQSCfLy8uDm5jYkTXxnZ2dMmTIFU6ZM0XFsq6qq0rtjm729PR3MxWLxqJ5rIAgh4PF4UKlUmDBhwrC3DYiXF0hIyIB75FT3ent7u9Gy8oFwdHTEpEmTMGnSJHq0jc/nIycnBxqNBj4+PlCpVFCr1eNKtY0yAOmtWGZOaG+fUP4LQqEQ5eXl9AQDFdiHO8FAGcD4+Pj0mU45ffo0nnzySXz99ddYu3atvt9WHyQSCWJjY/HYY49h/fr1tz2/trYWq1evxubNm/Htt9/i4sWLeOKJJxAQEGDSCSrAwprdlEolvd/866+/IjQ0VMfSzpjweDwUFhbC1tYWCQkJcHV1HbSprb6+HjU1NX0cfMYLYrEYHA4HEyZMGLVmvHZgEAqFsLKyojP10TbtSKVSZGZmorOzc8TP0R9qtRpcLhcsFgv+/v4jX2M/lqZU17q2DJ2HhwdSU1PN5maRmuRoaWnRqbYYY7TNXKAa+6ZPn26RPQGEEFqqmZpgcHZ2pj+724kI9fT0IDs7mxb70T43MzMTDz30ED7//HNs2LDBGG9HBxaLdduM/OWXX8ZPP/2EoqIi+th9990HsViMs2fPGmGVA2OxGbkpS+tyuRzl5eXQaDSYPXs2bG1todFo6AqBdhDXaDS0j3JSUhLc3NxMsmZTwuPx6K0HfewH2tjYgM1mg81m93FsU6lUOiI0wxXVcXJywtKlS3H27FlIJJJRrxW4dePR2toKOzs7+Pn5jS64OjhAdd99/c6RayMWi1FfX28W41zaXuLJycn0fnloaCgdGFpbW8eUa1tvKAe/GTNmjMj8xxxgsVhwdnaGs7MzgoOD6ekToVCIvLw8sFgsHREh7YqTQqGgrVh7B/HLly/j4YcfxoEDB3Dvvfea4q0NiRs3bvQRIVu+fDleeOEF0yxIC4sN5NoOaMaEGhdxc3ODUqmkO9Opwoa23KpCoUBBQQFUKhWSk5PHdLbRH4QQNDQ0oLq6GtHR0QZpahrIsa2urg7FxcUjcmxzcXHBsmXLkJmZCalUOqr1KRQKtLa20lsB+oJ4ed3WXzU/P39Ytq+GgNLN7+rqwsyZM3U+g96BQXtvtq6uDra2thbp2tYbPp+PwsJCREVFjalqnK2trY5zG9WoWl1djcLCQnh6etJyvyUlJXB1de3jp/7LL7/gvvvuw4cffoiHHnrIrG/cuFxun8+PzWajs7MTMpmsj8iVMbGoQK79IdvY2Bh9j5zL5aKwsBDTpk2Dj48PfvvttwGb2qj9YFdXV8THx4+rfUDgVhAvLy8Hj8dDYmIi3N3dDf6a2g1X06ZNo7M9LpeL8vJyOtvz8/O7rRiGm5sbli5diszMTMjl8hGtRy6Xg8vlwt3dfdTubSNBLBajrq5OryIjw2G4XuK9R9uoSktxcfGoKy2mghqxi46ONtk2oDGwsrKCp6cnPD09ERYWBqlUCqFQCD6fj/LyclhbW8PLywu1tbWYOHEi7OzscOPGDdxzzz1455138Nhjj5l1EDd3LCqQa2PMZjdCCKqrq1FbW4vY2Fj4+flBKpXSBhc+Pj46gbqtrQ0FBQWYOHHigMYHYxm1Wk0bvyQnJ5vsTtXJyQmTJ0/G5MmT6WyP8lcfimObh4cHHcwVCsWwXlsqlYLH48Hb29uk2ykFBQUmaawcrZe4tbU1PdMcERExrNE2c4HL5aKkpGRcjtg5OTkhICAALS0t8PHxQUBAANra2rBjxw5cuXIFiYmJyM/Px/bt27F582aLuEb6+/v30Zvg8Xhwc3MzaTYOWFizG9XlCgAlJSWwsrJCRESEQV+TCkpisRiJiYl0U5tarUZtbS14PB5kMhm8vLzg5+cHhUKBmpoaTJ8+HRMmTDDo2swRakbe2toasbGxZpk5DeTYNpBBiEAgwPnz52kb3dvR1dUFoVBIZ/6mZv78+TrSl4ZGoVAgLy/PYF7ivU1CzMG1rTetra0oLS1FTEzMuFFs1EalUtHiWLGxsfS2iEqlwjfffIMDBw7QN9Zz587FmjVrsHbtWoNfzwdiqM1uZ86cQWFhIX3sgQcegEgkMnmzm0UFcrVaTe+LD6Z1ri/kcjndxEFZalLjZcCf++ESiQQ8Hg+NjY1QKBRwdXXFhAkT4OfnN672xSUSCXJzc+Hh4YHIyEiL2NOk9vb4fP6gjm08Hg8XLly4bV+GWCyGWCwGm802+V06hbu7O1JTU43yeRjbS1zbtY0S9KGCupeXl0GVAQeC0j2IjY0dkgHOWIMK4tSNnPZ3oKioCCtXrsTWrVuxfft2NDU14aeffsLp06ehVqvx888/G22d3d3dqKqqAgDEx8fjvffew+LFi+Hl5YWgoCC88soraG5uxldffQXg1vhZVFQUnn32WTz22GO4dOkS/vrXv+Knn35ixs+Gg3YgH0jrXF9QRgZeXl50UBqoqU2pVKKwsBByuRzTp0+nG67EYjFcXV3h5+dnNtmZoaCUqij3JnPIiobLQI5t1L56e3s7Ll26NOCWDuUNHRAQYHZqfcbIyk3tJa7dcCUQCCCXy40+2tbU1ISKiopxK3ajVquRm5sLKyurPtWY0tJSrFy5En/5y1/wz3/+s881glLtNBZZWVlYvHhxn+ObNm3CwYMH8cgjj6Curg5ZWVk6j3nxxRdRUlKCiRMnYseOHWYhCGOxgbyurg4ikQgJCQl6fx0ej4eCggJMnTqVbhSiRF5YLFYfD/G8vDw4ODggOjpap5SsUCjooNDW1gYnJyc6qBtC9tBUUO5t4eHhZqFUpS/kcjn9+VGObdQ4ofbnTAgBn8+HQqFAQECASbLA2+Hm5oa0tDSDBVdz9BKXSCQ6M88uLi70TZkhRtsaGxtRVVWFuLg4kzQ3mhq1Wg0OhwNCSJ8G34qKCqxcuRKbNm3C7t27zeL7MZawqECu0Wjofcqmpia0trZi5syZent+yo2spqYGMTExYLPZt/UQ53A48Pf3R1hY2KAXSZVKRe8JCYVC2Nra0kF9oGYrc4cQgrq6OtTW1o75vUBtx7aSkhJadtTJyQlisRiEEPj7+5v1dMLcuXN1HKb0hSV4iWuPtrW1tel9tI0as4yPj7+9Fe0YRK1WIz8/H2q1GvHx8To3szU1NVixYgXuvfdevPvuuxax5WZpWGwgb21tRV1dHWbPnq2X56bcyKgs383NbUClNuDWPlhpaSnCwsKGLXJCOQ9R+7IA6KBuKjvB4aLRaFBWVgahUIj4+Hi4urqaeklGQ6PRIDc3F5cvX0Z3dzeAW3KyLi4ucHR0NNvPzxBZuSV6iWuPtgkEAqhUKh3XtuFui1A3swkJCUYZszQ3NBoN8vPzoVQqkZCQoBPE6+vrsWLFCqxduxYfffSR2f42LB2LDeQCgQDl5eWYN2/eqJ+3p6cHeXl5IIQgISFhwKY24M9RtMbGRsTExIy6mUWj0UAsFtMlXKVSSZf/DG3jOVJUKhU9HxwfHz+uGvoopFIpfvzxR9TX18Pd3R1SqRQSiQQqlQqOjo5wcnKCs7Oz2WXoc+bMQWhoqF6ei5IcNRfzj5Gg7domEAjQ3d0Nd3d3Olu/XV9LbW0t6uvr6Zv/8YZGo6GvBQkJCTpbTs3NzVi2bBmWLVuGjz/+mAniBsSiAjkhhJ7nFYlEKCgowKJFi0b1nF1dXcjJyYGnpyeioqJgZWVFZ+GAbhCn7De7uroQFxendw9x6qLC5/PB5/PpsTY2mz2iTMEQyOVycDgc2NraIiYmxizHywxNZ2cn8vLy4O/vD6VSiZycHPrflEolJBIJJBIJenp6YG9vT6uXmcPfytXVFenp6aO+qI5VL3GqL0J7tI2aYui9BUbd0FNjqeMNjUaDwsJCyGSyPloBXC4Xy5cvx7x58/Cf//zH7G5oxxoWG8g7Oztx8+bNUfnV8vl85OfnIyQkhO7oHaipjQpg1Hy0MYIqZSzB5/PR1dVFSx6aaqytu7ubtmCdPn36uLzDpsR+pkyZQsuf5ufn09as2qjVakgkEkilUkilUtja2tJB3RDe6kNltFk5NV5lKNldc6G/0TYqqHd0dKC1tRWJiYl6v6G3BCjpXYlEgsTERJ3rIZ/Px8qVK5GQkIAvv/zSLKuKYw2LDeQSiQS//vorli1bNqLnqaurQ1VVFaKjo+Hv7z9oU1tnZyc4HA68vb1NFsBkMhldfjfFWBsVwIKCghASEmKWDU2Ghsvlori4uF+xn5ycHB1XpN5oNBrIZDI6sLNYLLr87ujoaNS/p4uLC9LT00eUJTU0NNCd2eNpvIoQQm+BNTc3Q6VSwcPDA/7+/iOy87RkCCEoLi5GZ2cnkpKSdIK4UCjE6tWrERERgUOHDplFFWo8YLG3StbW1jqNaENFo9GguLgYQqEQycnJcHd3HzSIU85dISEhJjWgcHR0RFBQEIKCgnTG2qqrqw0+1kY19o1XtTrgzwA2kNxmYmIi1Go1SktL+328lZUVnY0TQiCXyyGRSCAUCqFWq+mg7uTkZPAbxe7ublRXVyMsLGzIjyGEoLa2Fg0NDUbTzjcnWCwWPDw8wOfzYW1tjejoaHR3d9M6/tRom6+v75gaLe0NIQQlJSXo6OjoE8Tb29uRlpaGqVOn4ttvv2WCuBGxqIwcuNWYBtwqe124cAF33nnnkL8wlHSkWq1GQkIC7O3tQQgZ0EOc6kaNiooyW8MDQ461UeN4DQ0Nemnss0So5sampibExcUNOlpECMGNGzdQWVk5rNfo6emhM3WFQgFHR0c6qBuqLDmcrJzyEh/PpWTKBEggECAxMVFH3733aJuNjY2OutxY2YKi7GhFIhGSkpJ0qhAdHR1Yu3Yt2Gw2MjIyTLp1NB6xuECuUChACAEhBJmZmVi0aNGQylpdXV3Izc2Fu7s7LR05UFObRqNBSUkJRCIR4uLiLKYbtfdYG4vFovfUh3tBoYRPqL/BeG3mof4GCQkJQ9rCIITgl19+QW1t7YheU6lU0h3wcrkc9vb2dLau776MWbNmITw8fNBztL3Eewew8QIhhB61TEpKGlR6V6PRQCQS6W20zVyg/gZtbW19gnhXVxfS09Ph6uqKH374YVxtM5gLFhvIAeD8+fOYPXv2bTMEgUBAezNTghjacqvaAU6hUCA/Px8ajQZxcXEWe2dJjbVRQZ2ygRzKWJtSqURBQQGUSiXi4uLG5Q+TsuCUyWRISEgY1t9Ao9HgypUraGhoGPUaqKAuk8lgbW1Nl+f18Zk4Oztj3bp1A2blVENTd3f3sP8GYwXtLDQxMXFY+vmjHW0zFwghqKioAJ/P73MjI5FIcNddd8Ha2hqnT5+2mPc01rDoQH7p0qVB9+sIIaivr0dlZSWioqIQEBAw6H54d3c3OBwO3NzcEBkZOWZGJggh6OzspPfVZTIZvL294efn1ydLoIxi7O3tERMTMy47TpVKJd2FHhcXN6K9PrVajaysLDQ1NellTYQQuvtdIpEAAF1+d3JyGvEWSkpKSr+OU9pe4pS2wniDaurq6OhAYmLiqG9keo+2OTo66ri2mWMJntpW4XK5SEpK0qnIyGQy3H333VAqlfj555/HZdXOXLC4QK5UKuly+NWrVxEZGdnv3i1VHhcIBLRsIiGE1mrXLqUDt7otCwsLLdr0Y6hoG4NQY21+fn5wcnJCcXEx7QFtjhcWQyOXy3Xcu0ZzM6dWq3Hx4kW0trbqcYW3oJrlJBIJ1Gq1zr76cNbcX1ZOeYkTQkZ8I2PpUE2xXV1dSExM1HtlbrDRNnMRgqL6Q5qbm5GUlKSTbcvlctx3333o7OxEZmbmuGt+NDcsOpD/+uuvCA0N7dOIplAowOFwoFKpbtvUBtzqSK6srBxz4hZDgRpra25uRnd3N+zt7TFp0qQx79bWH9ScvLe3t95uZJRKJS5cuAA+n6+HFfaPQqGgm+V6enrg4OBAB/WhBGHtrJxqCKV8pMdKVWo4aM9IU9cPQ0II0XFtk0qltGubKUfbqCbP3kG8p6cHDz30EHg8Hs6fPz8uDWLMDYsO5L/99huCgoJ0RqK6u7uRm5sLV1dXOqMarKmtvLwcfD4fsbGx49LsALhlQFNeXk4bv4x1t7b+oAxwDGH8oVAocO7cObS1tentOQdCpVLp7Kvb2dnRzXIDBSQqK1epVMjJyYGzs7NRvMTNEUqtTCqV9hE6MRYDubYZc7SNkp5NSkrS6UFSKpXYuHEj6urqcOnSpXE5yWKOWHQgz87OBpvNpk1LhEIhOBwOgoKCaOWqgZraqIYuhUKBuLi4YTWxjBW0deN7Wy+ORbe2gRAIBCgsLERoaOiwDXCGilwuR2ZmJsRisUGevz80Gg0d1KVSKaytremg7uDgoPMZxsbG0v7rpvASNwco84+enp4+kqOmghpto/4zxmhbXV0d6urq+kjPqlQqPP744ygtLcWlS5fMdiR3PGJxgVylUtFmJnl5efD09ERwcDDq6+tRUVGByMhITJgwgS6lq9XqPqV0qVQKDocDR0dHREdHm8V+lLGh9gDFYjHi4+MH7fzX51ibuUGJ3URFRYHNZhv0tWQyGc6ePYvOzs4RP4dIJkK7vB2ejp7wchi6shohREdZjhBCB3UbGxu0t7djzZo1mDFjxpi6SRsqVHOfQqHoY/5hLmg0Gh3XNqVSqffRtvr6etTU1CAxMVFn7FatVmPz5s2065+/v/+oX2so7N+/H3v27AGXy0VsbCz27t2L5OTkAc//4IMP8PHHH6OhoQE+Pj64++67sXv37jE/cWHRgbygoACOjo5QKBTg8XiIj4+Hp6fnoJ3p7e3tyM/PR0BAAMLCwsblRUupVNLewcMdsdMea+Pz+VCr1UMeazMnKMGfuro6xMbGGk1uVCKR4OzZs7T96VCRqWQ4VXEKNeIa+liIRwjSw9LhYDP8ixQlQtPd3Q2VSgUbGxvMnz8fs2fPttiRy5GiVqvB4XBoL21zDOK9IYSgu7ubrph1dXWNerStsbERVVVVfexY1Wo1/vrXv+LatWvIyspCYGCgPt/KgBw5cgQbN27EgQMHkJKSgg8++ABHjx5FeXl5v9WAQ4cO4bHHHsPnn3+OOXPmoKKiAo888gjuu+8+vPfee0ZZs6mw6EBeVFREl30TEhLg6Og4JA/x8PBwi7VdHC0ymQx5eXlwdHRETEzMqJqZqLE2KlMfbKzNnKDmYrlcLuLj440u+NPV1YXMzEx6jGwoHC45jDpxHTTQ0MesYIVgj2DcN+O+Ea1DLpeDy+XC1dUV1tbWUKlUiIqK0jHnGesNj2q1mrYwjo+Pt5gb0d7I5XJaXU4kEsHBwWFYo21NTU2orKykJ3woNBoNtm7digsXLuDy5cuYPHmygd/Jn6SkpGDmzJnYt28fvZZJkybhueeew7Zt2/qcv2XLFpSWluLixYv0sb/97W/4/fffce3aNaOt2xRY3LeWCswSiQQ8Hg/W1tZISUmhm9qoIN9bbrWqqgpNTU2Ij48fV2YP2nR0dIDD4YDNZiM8PHzU1QgWiwV3d3e4u7sjNDSUHmtraGhASUkJPdZmTqYS1JZCR0cHZs6caRKlMldXVyxduhRnz56FXC6/7fkimUgnE6fQQIMacQ1EctGwyuzAre0lHo8Hb29vnRsZyjJXIBCgpqamz6zzWKpgqVQq5OXlgcViISEhwaI79B0cHDBx4kRMnDgRKpWKVpfLz88HMPhoW3NzMyoqKvoN4tu2bcPZs2eRlZVl1CCuUCiQk5ODV155hT5mZWWFJUuW4MaNG/0+Zs6cOfjmm2/wxx9/IDk5GTU1NThz5gwefvhhYy3bZFhcIAduOXFxOBy4uLjAwcEB1tbWAza1qVQqepQkOTl5zGcYA0E1dBnS/MXFxQUuLi6YMmUKPdbG4/FQXl4ONzc3k2d5KpUK+fn5UCqVmDlzpklLyO7u7li2bBkyMzNp/4CBaJe3D/7vsvZhBXLKHtfX17dPb0R5eTmioqLogEDNOufl5cHKymrM9EaoVCrk5ubC2toacXFxFh3Ee2NjY0M3pmqPtlVXV6OwsFBntK29vR3l5eV9ml01Gg127NiBEydO4PLly7TNs7GgzIR6962w2WyUlZX1+5gHHngAQqEQ8+bNozVDNm/ejO3btxtjySbF4gJ5S0sL8vPzMX36dPrOc6BSOuUhbmNjg+TkZIvY+zIEjY2NqKysRGRkpMEbuihM6dbWHwqFArm5ubC1tUVSUpJZlFA9PT2xZMkSnDt3DkqlcuDzHAaf0/V0HPocb1dXF4RCIdhsdr/VCLlcjvLyckRGRsLGxgZsNhtsNlun0aq0tBRKpZLO8nx8fCzqt6VUKunvwlifladc2zw8PBAaGko77vF4PDog+vv7w8bGhr6GEkKwc+dOfPfdd7h8+fKwXPJMSVZWFnbt2oV///vfSElJQVVVFZ5//nm88cYb2LFjh6mXZ1Asbo9cIpHQamSNjY1obGxEbGws7O3tdQICVUb29fUdtypllLxiS0vLbZ27jIUpxtqkUilyc3Ph5uaGqKgos/su8Hg8XLx4EUqlEiIRC+3tgKcn4OX1509TH3vkHR0dEIlE8Pf3H3Tc0sHBAevXrx8wOGtriPP5fEgkErPcRukPpVKJnJwc2NvbIzY21uy+C8aCx+OhsLAQQUFBkMvlKC4uxssvv4xFixbBzc0NP/zwAy5fvozo6GiTrE+hUMDJyQnHjh1Deno6fXzTpk0Qi8U4depUn8fMnz8fs2bNwp49e+hj33zzDZ566il0d3eP6c/a9GnJMHF0dKTvHt3d3dHQ0IBr167By8uLDggikQglJSWYOnUqgoKCxtS+3lBRq9UoLi5GZ2cnZs6caTZbCjY2NvD394e/v7/OWFt+fr5Bxtoo1zt/f3+znVJgs9lISFiMF1+8gpqaPwN1SAhBeroKDg5Aelg6Tlac1NkrD/YIRnpY+pBeo729HR0dHZgwYcJttxSorDwqKqrff2exWHBzc4ObmxumTp0KmUwGPp9Pb6O4uLjQQd3FxcVs/uZUVYYaOx3LF/bB4PP5KCoqQmxsLHx9fQEAYWFhcHR0xOeff44zZ84AAF5//XWkpqZi9erV9HnGws7ODomJibh48SIdyDUaDS5evIgtW7b0+xipVNrnM6WqLRaWrw4bi8vI1Wo1FAoF3dTGYrEgl8vB4/HA5/PR0dEBAAgMDERISIhZZweGgnJwo7SyzbV7XBtDjLWJRCLk5+cjODgYwcHBZhNQ+iMtzR7XrzfD0/MiWCyqYRMIDia47z4VfZ5ILkK7bHhz5G1tbeju7kZAQMCQvwu3y8oHQtubWygUwt7enr45M6WQENU85ezsbJZVGWMhEAhQUFCA6OhonREuQgj279+P3bt34+zZs3B0dMQPP/yAH374Abm5uThz5gyWLVtm1LUeOXIEmzZtwieffILk5GR88MEH+P7771FWVgY2m42NGzciMDAQu3fvBgD885//xHvvvYdPP/2ULq3/5S9/QWJiIo4cOWLUtRsbiwvkb7/9Njo7O5GWlqajQCWRSHDt2jU4OjoiICAAYrEYHR0dcHd3pzP18aDeJpVKkZeXBxcXF0RFRVnk/p/2WBufz4dcLqfH2nx9fYcUXHg8HoqKihAREWG0udeRUlnJQlzcre+mo2MdvL0vAVol9M2bVTpl9uFAjQUGBAQMOygnJCSMqrSqVqvpZjmBQAAAdJOVt7e30b6bPT09yMnJgaurKyIjI8dtEG9ra0N+fn6fXhlCCD777DO8/vrr+PnnnzFnzhydx7W0tMDDw8MkEx779u2jBWHi4uLw0UcfISUlBQCwaNEiBAcH4+DBgwBubdu9+eab+Prrr9Hc3AxfX1+sXbsWb775pllsKxoSiwvkJ0+exBdffIFz585h8uTJSEtLw7x58/DKK6/A19cXGRkZdOmwp6eHDgbt7e1wdXWFn5/fgI0+lg6lFz7WxG4o4Qs+n4/u7u7b7sdSzX3R0dFGLwmOhMxMK6xf/+f7cHKqgpfXFQC3fpobNqgwderwfqaEEPD5fCgUCgQEBIyoouHg4IB169bppaJDCIFYLKb31Xt6eoyiOSCXy5GTkwN3d3dERkaOmd/EcBGJROBwOJg+fbqOMRQhBF9++SW2bduG06dPY8GCBSZcJcNIsbhATtHZ2YmffvoJn3/+Oa5cuYLQ0FAsWbIEd911FxISEvrcdWt3Tre1tcHZ2RlsNht+fn6DypNaCtS+17Rp0xAUFGTq5RgMaqyNz+dDLBbDzc2NDupOTk60Y5O5NPcNBe2MnMLZuQyenr8CIMPOyAkh4PF4UKlUCAgIGFXmO9qsvD8IIfQInEAgoFXJtD9HfSCXy5GdnQ1PT89xKz0L3OqPyMvLQ0REhI7BFCEE3377Lf72t7/h1KlTuOOOO0y4SobRYLGBHAB++ukn3H///fjrX/+K2NhYZGRk4MyZM/Dw8EBqairS09ORnJzc50KmVCrpEYy2tjY4OjrSmbo5NecMlfr6elRXVyMqKmpcGRn0vjmztrYGIQSRkZHw8/OzqM8xLc0ely9bQa3+c81ubkWIi7uhs0d+OzQaDbhcLoBbY0WjLSPb29tj/fr1Bu2zkMvl9OfY3t4OZ2dnel99pOOJMpkMOTk58PLywvTp0y3qu6BPxGIxcnNzER4errPFRAjB0aNHsWXLFhw7dgwrVqww4SoZRovFBvKjR4/i0Ucfxeeff457772XPi6TyXDu3DlkZGTgxx9/hKOjI9auXYv09HTMmTOnT4lRexxKIBDA3t6eztTd3NzM+gKgLTUaFxeno488nlCr1cjPz0d3dzdcXV3R3t5ucW5t7e3AI4/Y48KFP286lyxR4+WXs1FZmTuk51Cr1eByubCysoK/v7/e3nN8fDxiYmL08ly3g7rJpprlbG1t6X11T0/PId2YSKVS5OTkwNfXVy8KhpZKR0cHcnNzERoa2keS+uTJk3jqqadw+PBhrFmzxkQrZNAXFhvIhUIh6urqkJSUNOA5CoUCFy5cQEZGBk6dOgUWi0UH9QULFvRp/qGac3g8Hm0ZSGXq5iZPqVarUVRUhO7ubsTHx4/JPf+hoFQqweFwaK1sW1tbi3Zrq6piobqahalTCaZNu/XTzMvLQ0FBwaCPU6lUaG1thZ2dnd6rEcbIyvtDo9HQUqN8Ph8ajea2kwwSiQQ5OTlgs9ljqk9kuFBBnBrB1eb06dN49NFH8c0332DdunUmWiGDPrHYQD5clEolrl69iqNHj+LkyZNQKpVYs2YN0tLSsHjx4j6ztRqNBm1tbXQwsLKyojM8T09Pk14gFAoFOBwOWCwW4uLiLEpVS5/I5XLk5eXBwcFhQAOYwcbafHx8LKar/+bNmygpKen331QqFVpaWuDo6AgfHx+DfDdjY2MRFxen9+cdKr0NeqRSKa0d4evrC3t7e0gkEmRnZ2PChAmYNm3auA3iXV1dyM7OpuWYtcnMzMTDDz+M//u//8OGDRtMtEIGfTNuArk2arUa165dw7Fjx3Dy5El0dXVh1apVSEtLw5IlS/qMqVHylDweDwKBAIQQOlMfarlPX0gkEuTl5cHNzQ2RkZEWE4j0jUQiQW5uLr0HOpTPQB9jbabkt99+Q3l5uc4xhUKB1tZWODs7w8fHx2CvbWdnh7vuustsNAkkEgmdqXd2dsLZ2RkymQz+/v7jek+8q6sLOTk5tHaCNpcuXcJ9992HAwcO4MEHHxy3f6OxyLgM5NpoNBr89ttvdFAXCARYvnw50tPTsXz58j6KaIQQtLe362R4VKbu7e1t0KBOeakHBgaO64yjo6MDeXl5mDhxIqZOnTriv8Nwx9pMDSEE169fR1VVFYBb45Wtra1wc3MziqOfqbPygaDmo+3t7SGXy+Hg4EB/jua2JWZIuru7kZ2djaCgoD4mJ1evXsU999yDDz/8EI8++ui4+ZuMF8Z9INdGo9EgJycHx44dw4kTJ9Dc3IwlS5YgPT0dK1eu7ONbTTkLUapyKpXKYGVbLpeLkpIShIWFjVsvdeBWb0RBQYHex+y03do6Ojp0xtrMRd4WuPUd/eWXX1BWVgYul0sbYhgDW1tb3HXXXSZ1jetNZ2cncnNzMXnyZEyZMkXHsY3aEqOa5by8vMZsBYvaVqBubrW5ceMG1q1bh3feeQdPP/00E8THIEwgHwCNRoOCggIcP34cGRkZqK6uxp133om0tDSsXr26Tye0dtmWx+Ohp6cHPj4+tL/zSN22CCGor69HTU2NxQicGIqWlhaUlpYiMjIS/v7+BnsdhUJB78VSmgNU1cUcxhP5fD4tOdn75tLQxMTEID4+3qivORBUQ9eUKVP6lJEB3f4IgUAApVKpI0Jj7lspQ0UqlSI7OxsBAQF9KnU3b95EWloa3njjDWzZssXk310Gw8AE8iFACEFpaSmOHTuGjIwMlJSUYNGiRUhLS8OaNWv6NBgRQuiyLY/Hg0wmG9FerEajQXl5Ofh8PuLj441+0TYn6urqUFtbi5iYGHh7exvtdXu7tdnZ2ZlUO1xberakpAQtLS1GfX1zycrFYjHy8vL6bejqj4Ec26jP0hy3UoaCTCZDdnY22Gw2QkNDdb6PeXl5WLNmDV599VVs3bqVCeJjGCaQDxNCCKqqquigzuFwMG/ePKSlpSE1NRVsNrvPD0YikdDl9+7ubnh5eYHNZsPX13fA5iGVSoXCwkLIZDLEx8ePC534/tCelTf1zYypx9qam5tRXl5OV2ZUKhUuXLgAHo9n0NftTXR0NBISEoz6mtpQQXzatGmYNGnSiJ6jt0KguTq2DQYVxPubly8sLMSqVavw97//Hdu2bbOI98MwcphAPgoIIairq6PL7zdv3sSsWbOQmpqKtLQ0BAYG9vkBSaVSOlOnfNWpsq22RnxeXh5sbGwQGxs7ZkqAw0Wj0aC4uBgdHR1ISEgwq1l5Y4+1UdsrsbGxOo1tCoUC58+fh1Ao1OvrDYatrS3Wr19vkiyW0gzXZ6/IQI5tvr6+8PDwMEvdAUp+1tvbGxERETrXmZKSEqxatQrPPPMMXn/9dSaIjwOYQK4nCCFoampCRkYGMjIycP36dSQkJCA9PR1paWmYPHlynx8U5eNM2a+6u7vDw8MDra2t8PLy0nF3G2+oVCoUFBRAoVAgPj7e5KXcwTDkWBshBDU1NWhsbER8fHy/6n09PT04d+4cRCLRaN7GsIiKikJiYqLRXg/4szu9t9yoPulddQFM49g2GD09PbSGfO9Ru4qKCqxcuRKPPPIIdu3axQTxcQITyA0AIQRcLhcnTpxARkYGrly5gujoaDqo9zc61tPTg9raWjQ1NYEQAjc3N7DZbLDZ7HFXVlcoFDoViZE2CpoCbUOQ0Y61aW8rJCYmDmruQ0kTi8ViPbyL22PsrJyaVuht/GFIBnJsowK7KWbqKUtWSkdC+zpSXV2NlStXYsOGDdizZ8+4TQLGI0wgNzCEELS1teHUqVM4duwYLl26hPDwcKSlpSEtLY2+o/7444/R1dWFBx98EL6+vnQgEIlEcHFxofXfzWkUyhDIZDLk5ubC1dUVUVFRFn8x6l11ocba/Pz8Bt0qIISgpKQEIpEIiYmJQ9pWkEqlyMzMRGdnpz7fwoAYKysXCAQoLCzsY8FpTIzl2DYYCoUC2dnZ9G9DO4jX1dVh5cqVWLt2LT766COL/90wDA8mkBsR6g7/hx9+wPHjx3H+/Hl6/vXq1av45JNPsH79ep3HKJVKer5ZJBLBycmJVpVzdnYeU6Wzrq4u5Obmgs1mj0mzC2qsjbpBG2isTaPR0Dr6CQkJw8p6u7u7cfbsWUgkkhGtUSRiob0d8PTEba1TbW1tsW7dOoNWjPh8PgoLCxEVFQU2m22w1xkulGObQCCgP8vROrYNhlKpRHZ2Npydnfvc4DY3N2PZsmVYtmwZPv74YyaIj0OYQG5C2tracPfdd+OPP/6Ar68vbGxskJqainXr1iE+Pr7PD5IahaJMXSgFKzabbZCLhzERiUTIz8+npSUt+b0Mhd4uX9RYm4+PD+rq6qBUKpGQkDCi8m1nZycyMzMhlUqH/BiZDDh1ygY1NX/+3UNCCNLTVRjsPiIyMnJQ46LRQI3aRUdHm7U9r1KppH0ZtM2WhuPYdrvnz8nJgaOjI6Kjo3Wer7W1FStWrMD8+fPx2WefGW0Pf//+/dizZw+4XC5iY2Oxd+9eJCcnD3i+WCzGP/7xD2RkZEAkEmHy5Mn44IMPsGrVKqOsd6zDBHIT0dnZiXvuuQdcLhc//fQTPDw88PPPP+P48eM4c+YMvLy8sHbtWqxbtw4zZ87s8wNVq9U69qu2trZ0+d3SZCm1Z6MN1cRkzlANVlwulx4l8/f3h7+//4jH2sRiMTIzMyGXy4d0/uHDNqirY0Gj+fOYlRUQHEwG9UO3sbHB+vXr9Z6Vc7lcFBcXIyYmxqJEkAZybKNu0obb76FUKpGbmws7OzvExsbqfBd4PB5WrVqFxMREfPnll0YL4keOHMHGjRtx4MABpKSk4IMPPsDRo0dRXl7e7w2XQqHA3Llz4efnh+3btyMwMBD19fXw8PBAbGysUdY81mECuYn45z//iRs3buDo0aN9ZqNlMhkyMzNpT3UnJyekpqYiPT0ds2fP7nMxoAIBZepibW1NZ+rm7sXd2NiIyspKREVFmXXWZWi0G/wmT55M36SNZqxNJBIhMzMTCoXiNuexcODAwAFm82bVoGX2GTNmYObMmUNe1+1obW1FaWkpYmJiDGoEY2iG4tg2GCqVCrm5ubCxsUFcXJxOEBcKhVi1ahVmzJiBQ4cOGbUhNCUlBTNnzsS+ffsA3Lp5mTRpEp577jls27atz/kHDhzAnj17UFZWNm5HaQ0NE8hNhEKhAIvFuu0XWy6X4+LFi7SnurW1Ne2pPn/+/D6PpzICai+WxWLp2K+ay/4ZNVbV0NCA+Ph4o+mFmyNyuRy5ublwdnbWKZ3qY6xNIBDg/PnzUCqVA55TXc3CkSMDB4ING1SYOnXgy4Q+s/KWlhaUlZUhNjbWqAp+xoBybBMIBHTjI7Wv3ruJVa1WIzc3F1ZWVoiLi9O5gROJRFizZg2mTJmCI0eOGLV7XqFQwMnJCceOHUN6ejp9fNOmTRCLxTh16lSfx6xatQpeXl5wcnLCqVOn4OvriwceeAAvv/yyWYzzjQWYQG5BKJVKXLlyhXZqUyqVWLt2LdLS0rBo0aJ+PdXFYjGtKkcIga+vL9hstlGUyAaCkrwVCoVISEgYdKxqrCOVSpGbm0vPBA/0mYxmrI3H4+HChQtQqfovkY82IweA6dOnD7pHOhSamppQUVGBuLg4o7i5mZKenh6dZjltxzYXFxdwOBwAQHx8vE6w6+jowNq1a8Fms5GRkWF0fYWWlhYEBgbi+vXrmD17Nn38pZdewpUrV/D777/3eUxERATq6urw4IMP4plnnkFVVRWeeeYZ/PWvf8Xrr79uzOWPWZhAbqGoVCodT/Xu7m6sWrUK6enpuPPOO/tkR1THPBUIVCoVnQ0YU+hCrVajqKgIEolkXEvPArc6zKku/bCwsGFtgQx3rK2lpQWXLl2CWq3u9/lGukdOYW1tjfXr1494DIvaYomPj4enp+eInsNS6e3YplarYWtri4iICJ3tlK6uLqSlpcHNzQ0//PCDSZT1RhLIw8LCIJfLUVtbS7+X9957D3v27EFra6vR1j6WYQL5GECtVut4qre1tdGe6suWLevXU13bqU2hUNBObd7e3gbbb1MqleBwOCCEIC4uziSCGuYCZb85adIkhISEjKqPgcrubjfW1tjYiKysLGi0o/V/kcuBkyeH37WuzUiz8oaGBlRXV4/7LRaNRoO8vDz09PTA09MTQqEQX331Fd2Z/uOPP8LW1hY//vijyfQkRlJaX7hwIWxtbXHhwgX62M8//4xVq1ahp6dnXF8H9AUTyMcYGo0G2dnZtKd6S0sLli5dirS0tAE91bu7u+nyu0wm02mu0ldzilwuR15eHhwcHBATEzOu98ba29vB4XCG7Nw1HPoba6OCuru7O+rr6/HLL7/0G8yB4c2R98ba2hrr1q0bVpChXO0SEhL6lZ8dL2g0GuTn50OhUCAhIQG2trYghCAnJweHDh3CmTNn0NTUhIULF+Kuu+5CWlraiA1jRktKSgqSk5Oxd+9eeu1BQUHYsmVLv81u27dvx6FDh1BTU0NvHX344Yd4++23je7eN1YxaefT/v37ERwcDAcHB6SkpOCPP/4Y9PyjR48iIiICDg4OiI6OxpkzZ4y0UsvBysoKycnJeOedd1BeXo5r164hKioKe/bsQXBwMO6991588803aG9vByEELBYLrq6umDZtGubMmYOUlBS4uLigrq4OV65cQV5eHpqbmwdtlrodEokEN2/ehKurK2JjY8d1EBcKhcjLy0NoaKjegzhwS6QlICAAMTExWLhwIcLDw+lKyNWrVyGVSjFjxowBH+/lRTB1KukTxFkiEVjV1WANoudObZsMldraWtTV1SExMXHcB/HCwkL09PTQQRwAWCwWoqKiUFNTg8DAQOTn52PdunXIyMhASEgI5syZM+ANmSHZunUrPvvsM3z55ZcoLS3FX/7yF0gkEjz66KMAgI0bN+KVV16hz//LX/4CkUiE559/HhUVFfjpp5+wa9cuPPvss0Zf+1jFZBn5cGcRr1+/jgULFmD37t1Ys2YNDh06hLfffhu5ubmIiooywTuwLCjJT8p+tbS0FIsXL6Y91b29vfu1X6X2YSmnNmpWfajlsI6ODuTl5SEwMLBfjfnxBDUvHxkZCX9/f6O+dm+3tsbGRrS0tMDZ2RlOTk4Dfy4yGWxOnQKrpoY+REJCoEpPR38196Fm5dTEQmJiIlxdXUfz1iwaSsVPIpEgMTFR53fV09ODhx56CDweD+fPn9fpHRCJRMjJycHSpUtNsWzs27ePFoSJi4vDRx99hJSUFADAokWLEBwcjIMHD9Ln37hxAy+++CI4HA4CAwPx+OOPM13resRkgXy4s4gbNmyARCLB6dOn6WOzZs1CXFwcDhw4YLR1jwUIIaisrKSDen5+PubPn4+0tDS6I3YgpzYej4fOzk54eHjQJduBmm4oo4upU6caJPu0JHp7iZsSqkfit99+w82bN6FSqeDo6AhnZ2c4OzvrdM7bHD4MVl0denfBkeBgqO67r9/nDw8Px6xZswZ87erqajQ3N9/WCGasQwhBUVERurq6kJSUpBPElUolNm7ciPr6ely8eHHMjeIx6BeTBPKRNEwEBQVh69ateOGFF+hjr7/+Ok6ePIn8/HwjrHpsQghBbW0t7amenZ2NWbNm0aYuEyZM6BPU5XI5ndmJxWLaqc3Pz4/uQm9tbUVJSQlmzJhhMqMLc2EgL3FzgMPh4ObNm5BIJJBIJFAoFHRQd+npgf1nnw34WNXmzSD9vB9ra2ukp6f3CdKEEFRVVaGlpYUJ4v+tkHV0dCAxMVFnjEylUuGxxx5DWVkZLl++bPIbPwbzxyT+kEKhEGq1uo8JApvNRllZWb+P4XK5/Z7P5XINts7xAIvFQkhICP7f//t/+Pvf/47GxkZkZGTgxIkT2LZtG5KSkuigTnmqOzg4ICgoCEFBQbQRCI/HQ2VlJVxdXWFnZweRSIS4uLhxnUloe4mbazNXXFwcvbft6ekJlUoFiUSC7u5uyGprMXGwB7e3A/0Ecur5tLNyqgrE5XKRlJQ05l38BoPSURCLxUhKStIJ4mq1Gps3b0ZxcfG4DuJU/w7D0DAPmS8Gs4DFYiEoKAgvvPACsrKy0NDQgIcffhgXLlxAbGwsFixYgHfffReVlZWgCjl2dnaYOHEiEhMTaaW5trY22ku7uroa3d3dJn5nxod6/01NTUhKSjLLIE6RmJiI6dOnA7il0ubu7o4JEybANyxs8AcOMu9dVVVFf+6EEJSXl4PH4zFBnBCUlZXR9rS9g/hzzz2Hmzdv4sKFC0bvozAXNBoNWCwWVCoVlEolRIM0WDLcwiSBnBI5oAwiKHg83oBfXn9//2GdzzA6WCwWJkyYgGeffRYXLlxAS0sLNm/ejOvXr2PmzJmYPXs2du/ejdLSUhBC0NPTgyeffBJFRUWYPXs2Fi5ciODgYHR3d+P333/H9evXUVVVha6uLoz1iUeqbCoQCDBz5kyLKCHPnDkToaGhOsesfH1BQkJuKcNoQVgsSCdMgJCQAU1Z1Go1CgsL6cAlEAiQlJRkFN9uc4W6uRMKhUhMTNTpLdFoNNi6dSuuXr2KCxcujEvzIODW38HKygrd3d145JFHsHjxYsTFxeHTTz819dLMGpM2uw1nFnHDhg2QSqX48ccf6WNz5sxBTEwM0+xmRAghaG9v7+OpbmdnRzcjBgcH6zyGUq6i7Fft7OzoPXU3N7cxVUIbjZe4qSGE4Nq1a6jR6lCHXA6bkyd1utY1U6aga/lySNRqSCQSsFgsulHOwcGB/jytrKwwffp0yOVyJCYmjmsVP+2thZkzZ+r8LTQaDV5++WX8+OOPyMrKQkhIiAlXajqocrpcLkdCQgKioqIwb948tLW14Y033sDJkyeRmppq6mWaJSYdP9u0aRM++eQTJCcn44MPPsD333+PsrIysNlsbNy4EYGBgdi9ezeAW+NnCxcuxFtvvYXVq1fj8OHD2LVrFzN+ZmJqamqwbNkySCQS9PT0wMvLC2lpaVi3bl0fxybgVqZGeTcLBALau9nPz8/sndpuh1qt1hH1sETFKo1GgytXrqChoUHnOEskAqUUo93gRv6blVPNcoQQODk5wdnZGRKJBF5eXnjwwQct6oZG31BNfq2trX2qEhqNBjt27MD333+PrKysPlWR8QYhBI888gg6Ojpw5MgReuvhoYcegrW1Nb788ks6a2f4E5M0uwG3MmyBQIDXXnuNnkU8e/Ys3dDW0NCg82HNmTMHhw4dwquvvort27cjNDQUJ0+eZIK4Camvr8eqVasQHx+Pb775BkqlEmfOnMHx48dpxyPKfpXyVKcsVv38/GinNh6Ph/z8fNqpjbJftaQfq0qlQl5eHoBbe86WatdoZWWFBQsWICsrC01NTfRx4uXVb2Mbi8WCo6MjHB0d4ePjg56eHnR3d9MmPRqNBo2NjQgODrbYv8loqampQUtLS58gTgjBzp078d133zFB/L80NzejpqYGTz75pE7/wPTp0xkBsEFgJFoZRsz9998Pb29vfPjhh32EHaRSKe2pfvr0aTg7OyM1NRVpaWn9eqprNBq0t7fTY22EEDrgm9KpbSgoFArk5ubCzs5uzCjXqdVqXLx4cdimFoQQ8Pl8Wr9fLpfD3d0dkyZNot3a/Pz8jO7aZSoo4ZukpCSdXglCCN5++218/PHHuHz5MpOQaHHu3DkkJCTAx8cHarUa1tbW+M9//oODBw/i2rVr9HkymWxcb9dowwRyhhEjk8l09kQHQi6X48KFC7Snuq2tLdasWYN169Zh3rx5fTI1yqmN0n9Xq9U69qvmFCgpL3EXFxdERUWZ9Q3HcFEqlbhw4QL4fP6QzqeCuFKpREBAAP05WVlZYfny5bSo0FDc2sYCdXV1tASttnodIQTvv/8+3n//fVy8eBFxcXGmW6QZo11Cp7ZSCwoKAACffPIJqqqqsHPnznFzUzgYTCBnMCpKpRJZWVm0U5tarcaaNWuQnp6ORYsW9dlXJoSgo6ODnlVXKpW0/aq2xaMp0PYSnzFjhkXv7w+EQqHAuXPn0NbWNuh5hBDweDyoVCqdIE4xdepUzJs3D8DQ3dosGUoEKDExUceoiBCCffv24e2330ZmZiZmzpxpwlVaDkePHsWOHTtQVlaGL774Ao8//jiOHDmCe+65x9RLMwuYQM5gMihP9aNHj+LkyZOQSCRYvXo10tLSsGTJkj4NUoQQdHV10Zm6XC6nndp8fX0NZr/aH93d3cjJyYG/v/+wvcQtjZ6eHmRmZqK9vb3ff6eCuFqtRkBAQL9VCSsrK6SmpvaZp6fc4TQ9FwAAK/xJREFU2vh8PoRCIezt7XXc2izx79rY2Iiqqqo+IkCEEHz66af4n//5H/z88886ft4M/UN1sn/33Xf4z3/+gy1btuDuu+/Gd999h3vvvdfUyzMbmEDOYBao1WrcuHGDztRFIhFWrFiB9PR0LF26tF9PdYlEQgd1qVQKLy8vsNls+Pr6GrSxijKC0YeXuKUgk8mQmZmJjo4OneOEEHC5XGg0mgGDOEVISAjmz58/4L9rTzQIhUK6+dHPzw+enp4WsW3R1NSEysrKfoP4wYMH8corr+D06dNYsGCBCVdpHgyn+/z06dNITU2FlZUV/u///g+bNm0y8OosCyaQM5gdGo0GN2/epD3VW1tbsWzZMtpTvT+3LMqpjcfjobu7G15eXnQQ0OcYmCG9xM0diUSCzMxMdHV1AfgziBNC4O/vf9uL8kBZeX9QzY9UCZ7qk/Dz84O3t7dZ9UlQUMY4CQkJ8PDwoI8TQvDtt9/ib3/7G3744QcsXrzYdIs0E1QqFWxsbKBQKPD7778jOjpa52/Wmz/++AOzZs3Ct99+i/vvv994C7UQmEA+Cvbv309b+cXGxmLv3r1ITk7u99zPPvsMX331Fe3XnJiYiF27dg14PsMtNBoNOBwObepSV1eHJUuWIDU1FatXr+63/CqVSunu987OTr11SwsEAhQWFiI8PHzcKm91dXXRwZzyORhKEKeYMmXKsLNRyq2N+ky1t1R8fHzMYqytpaUFZWVliIuL0zHGIYTg6NGj2LJlC44fP47ly5ebcJXmAdWJrlAoMG/ePMyYMQNbtmxBUlJSv+dT5fXGxkZMmjTJyKu1DJhAPkKG66f+4IMPYu7cuZgzZw4cHBzw9ttv48SJEyguLh63QWG4EEJQXFxM26+Wl5dj0aJFSE9Px5o1a+Dl5TWgUxuPx0NHRwfc3d3poD6c0RUul4vi4mKTeImbG21tbTh48CCUSiX8/f2HtbXAYrGQmpo6aPY1GNSWChXUu7u7TT7W1traitLSUsTGxvYxCTpx4gSefvppHD58GGvWrDH62swVQgiSk5Ph7++Pffv2YeLEibC2ttYxS2GMU4YOE8hHyHD91HujVqvh6emJffv2YePGjYZe7piD0q0+fvw4jh8/joKCAsyfPx/p6elYu3Yt/Pz8+lwEenp66ADQ3t4OV1dXWip2sBEoc/ISNzVKpRJ5eXmQyWRoaWmBQqEY9nOMJCsfCGqkzVRjbTweD0VFRYiNjYWPj4/Ov50+fRqPPvoovvnmG6xbt87ga7EkDh48iPfeew95eXmwtrZGU1MTLl++jMbGRiQnJ2PJkiWmXqJFYf7dI2aIQqFATk6OzpfNysoKS5YswY0bN4b0HFKpFEql0uz8qS0FFouF8PBwbN++HdnZ2SgrK8OKFStw6NAhhIaGYuXKlfj444/R3NxMm7TY29tj0qRJSExMxIIFCzBx4kSIRCJcv34dv/32G2pqaiCRSHRep76+HhUVFYiLi2OCuFKJ3Nxc2NraYt68eVi6dOmI+g/q6uogFov1siZHR0dMnjwZM2fOxPz58zFhwgT6M71x4waqq6sNZtTD5/NRVFSEmJiYPkH87NmzeOyxx/DFF18YNYjv378fwcHBcHBwQEpKCv74448hPe7w4cNgsVhIT083yLo0Go3O/9vZ2cHW1hYFBQXYu3cvtm7dim3btuHChQt46qmnwOFwDLKOsQqTkY+AlpYWBAYG4vr16zojJC+99BKuXLmC33///bbP8cwzzyAzMxPFxcXjWoda3xBC0NjYiOPHj+PEiRO0WxvlqR4UFNQnU1cqlXRTVVtbGxwdHeHn5weFQgEej2e2XuLGhFKvc3BwQExMDL0nzufzceHCBSiVymE9X3BwMBYuXGiIpQIw/FibQCBAQUEBoqOj+2ylXbp0Cffddx8++eQTPPDAA0YrDw93u4+irq4O8+bNQ0hICLy8vHDy5Em9rovaEweAoqIiREREoKSkBPfeey9YLBYkEglefPFFpKeno6OjAw899BC+/vprxMfH63UdYxkmkI+A0Qbyt956C++88w6ysrIQExNj6OWOWwghaGlpwYkTJ5CRkYFffvkFsbGxdFCfOnVqn4usSqWCQCBAdXU1rVzn7+8/Jp3ahgpVgXJyckJ0dHSfxrbW1lZcvHgRarV6yM/JYrGwdu1aeA7iaa4vehv1WFlZjWqsTSgUoqCgAJGRkbQ3BMXVq1dxzz334KOPPsIjjzxi1O/LSLb71Go1FixYgMceewy//PILxGKxXgO5dhBPTU2FnZ0dtm/fjoSEBFRWVqKxsVFny+r333/Hww8/jG+++YZpBB4GTGl9BIzET53i3XffxVtvvYVz584xQdzAsFgsBAYGYsuWLbh48SKam5vx1FNP4dq1a0hKSsKcOXPw1ltvoaysTKf0+vnnn0OtVmPWrFkICwuDXC5HTk4Orl27hvLycojF4jHvqU7R09OD7OxsODs79xvEASAgIACLFi0a1kgYIQT5+fn6XOqAUEY9UVFRWLhwIaKiosBisVBcXIwrV66gqKiIHnG7HW1tbSgoKMCMGTP6BPHr16/j3nvvxbvvvmv0ID7S7b7//d//hZ+fHx5//HGDrIv6TqxatQp8Ph/bt29HeHg4ACA0NBR33HEHfH19UV9fj0uXLmHNmjW45557mCA+TEzmfmbJ2NnZITExERcvXqT3lDQaDS5evIgtW7YM+Lh33nkHb775JjIzMwcctWAwDJS4yFNPPYUnn3wS7e3tOHXqFI4fP4533nkHISEhWLt2La5fv47GxkY8+uijcHV1pRviNBoNndVRDTraWd1YzNSpGxh3d3fMmDFj0Mx14sSJmD9/Pq5evdpnP3QgGhoa0N7ebpSsnMLKygre3t7w9vZGeHg4PdZWUVGBnp6eQcfaRCIR8vPzERER0eeG/ebNm7j77ruxa9cuPPXUU0b/PgiFQqjV6j43F2w2G2VlZf0+5tq1a/i///s/g+9HHzt2DFVVVfjll1/AZrPR3t6O33//HfX19Zg2bRrmz5+P9957D1lZWXj66aexc+dOg65nLMIE8hGydetWbNq0CUlJSbSfukQiwaOPPgoAffzU3377bbz22ms4dOgQgoOD6RlcFxcXHVckBsPDYrHg5eWFRx99FI8++ig6Ojpw7NgxbN++nTb8OHDgANLT02lPdSsrK/j6+sLX1xfTp09He3s7eDweCgoKwGKxaFMXS1Egux1yuRzZ2dnD0pGfPHky5s6di2vXrg2pYkEIAYfDMZlACovFgru7O9zd3TFt2jR6rK2+vh7FxcXw8vKiRWikUik4HA7Cw8MxYcIEnefJy8tDeno6XnvtNTz77LMWcVPX1dWFhx9+GJ999lmfRj19o1Kp4OLiAqVSicOHD+Ps2bPIzMyEt7c3nJ2d8f333+Pvf/87UlNTceeddxp0LWMVJpCPkOH6qX/88cdQKBS4++67dZ7n9ddfxz//+U9jLp2hH7788ktERETg0KFDuHbtGo4fP46VK1fCx8dHx1OdCupUVhcREQGxWEx3MBNCdBTILDGoy2Qy5OTkwMvLC9OnTx9WYAoJCaHldocSzBsbGyESiUw+vcFiseib6pCQEHqsjcvl0hktVX3RprCwEKmpqXj55Zfx4osvmiyID3e7r7q6GnV1dVi7di19jKqk2NjYoLy8HFOnTh32Ovqb/Y6KikJFRQXWrFmDhoYGPPvss/jpp5+gUqlw7733orm5GXPmzGHEXkYB0+zGMK7RaDSYP38+3N3dcezYMZ3ZY6lUirNnz9Ke6q6urli7di3S09Mxe/bsPnvClFMbpf+uUqng4+MDNptttrKivZFKpcjJyYGPjw8iIiJGHJhKS0uHPPoUFBRktrKlHR0dyMnJga+vL90Jv3PnTiQnJ2POnDl48cUXsWXLFrz22msmz8RTUlKQnJyMvXv3Arj13Q4KCsKWLVv6NLvJ5XJUVVXpHHv11VfR1dWFDz/8EGFhYcMeLdRubONyubC3t4e1tTXc3NxQVlaGnJwcJCQkICwsDNbW1qiurkZqair279+PRYsWjfyNMzCBnIHht99+Q0JCwqAXLrlcjvPnzyMjIwM//PAD7OzsaE/1uXPn9uupTu2/8ng8KBQKnf1XYzq1DRWJRIKcnByw2Wy9OLoVFhYiNzd3SOeuWbOmjyqaqens7EROTo6Orr5MJsNXX32FjIwM/P7773BxccHjjz+O9evXIyUlxaQVmCNHjmDTpk345JNP6O2+77//HmVlZWCz2X22+3rzyCOPjLhrXdsAZdu2bbh69Sq6uroQERGBN954AxEREXSgp8YCU1NTMXfuXHz55ZejedsMYAI5A8OwUSqVuHz5Mo4dO4ZTp05Bo9Fg9erVWLduHRYuXNivp3p3dzedqctkMnh7e4PNZpuNVrhEIkF2djYCAgIQGhqqt+ySw+EMqTt90qRJuOOOO/Tymvqgq6sLOTk5CA4ORnBwsM6/VVdXY8WKFbj77ruxaNEinDp1Cj/88AMcHBzw0ksv4YUXXjDJmgFg3759tP9DXFwcPvroI6SkpAAAFi1ahODgYBw8eLDfx440kGuX07ds2YIffvgBH374IdRqNXbt2oXOzk4cP34csbGxkEql+J//+R/8+OOPSE5OHnAtDMODCeQMDKNApVLhl19+wdGjR3Hq1ClIpVLaU/3OO+/sV+ynu7ubztQlEgm8vb1pT3V9OrUNFcpbPTAwsN/Z+tGSnZ2N4uLi2563evVqgzdeDYXu7m5kZ2cjKCgIISEhOv9WV1eHlStXIjU1FR9++CGdhSqVSly9ehUAxk3Dllgs1tHMP3HiBP73f/8XX3/9NaKionDw4EE8//zzCA0NRXNzM37++WfExcWhtrYWv//+O+677z7TLX6MwQRyBgY9oVarcf36ddpTXSwWY8WKFUhLS8OyZcv61f6WSqV0pt7V1WV0AxAq8zS0t/pvv/2G8vLyQc+ZOHGiyYMgVZmYOHFin2avpqYmLF++HMuXL8e///1vi2xk1BcPP/wwJBIJ9u7dS5s+ZWVl4dy5c9i1axeOHDmC5557Dvv27UNISAjWrFkDa2trfP3112ZVeRkrMIGcgcEAaDQa/PHHH3RQ53K5WLp0KdLT07FixYp+PdV7G4C4u7vTpi6GkPGlgnh/mae+IYTg+vXrfRqsemPKrFwqldLbC9OmTdO5qWltbcWKFSswf/58fPbZZxbRuGhIMjMzkZaWhg0bNmDnzp10x7lQKISrqytWrVqFpUuXYtu2bZDL5Vi1ahWampowd+5cfPHFFyZe/dhj/N5SMgAwX5MFS8fKygqzZs3Cu+++i4qKCly5cgXTp0/H7t27ERwcjA0bNuDQoUM6KnHaBiDz5s2Dv78/+Hw+rl27hj/++AN1dXWQyWR6WV9nZyeys7MRHBxs8CAO3Brvmj17NqZMmTLoecZSe+uNTCZDdnY2/P39+wRxHo+H1atXY9asWUwQx62b1OXLl+PcuXM4cuQIXnrpJdTV1QG4NQZHWf5OmzYNANDe3g53d3f85z//YYK4gWAy8nGMuZosjGUIISgqKqI91SsqKrB48WKkp6dj9erV/XqqKxQKOlMXiURwcXGhM3VnZ+dhr6GjowO5ubk63djGQqPRICsrC42NjQOes2rVKqM6zVFB3M/Pr0+3vlAoxKpVqxAZGYlvv/3WLKcNjAHVld7U1ITm5mb4+flhypQpuHnzJhYtWoSVK1diz549mDJlCtRqNdatW4eGhgY88cQT+Oqrr+Dl5YWzZ8+a+m2MWZhAPo4xR5OF8QQhBOXl5bSnemFhIRYsWEB7qvv6+g7o1Mbj8dDW1gZnZ2f4+fmBzWbD2dn5tnvcYrEYeXl5mDp1KoKCggz59gZErVbj0qVLaGlp6fffAwMDjeZHTSnYUeI+2n8/kUiENWvWYMqUKfj+++/NYrrAFFBB/MqVK9i+fTtsbGyQmJiIPXv2wNraGvn5+Zg3bx7uvPNO/Otf/8LUqVORlZWFffv2oaqqCuHh4Thy5Iip38aYhgnk4xSFQgEnJyccO3ZMpzy+adMmiMVinDp1qt/Hvf766ygoKMCJEydGNXfKoAshBNXV1Th+/DgyMjKQm5uL2bNnIz09HampqQgICBjQqY2y6nRwcKAzdVdX1z7nt7e3Iy8vD2FhYZg4caIx314fVCoVLly40EeJjGLlypWDVoX0AaUl7+np2UfBTiwWY+3atQgICEBGRoZJpgnMAWq07OrVq1i9ejX+93//F3fddRd9E0jNhhcXF2Pu3LmYM2cOPv74Y7rS09bWZnb6AGMRZo98nDKYyQKlA98bymThs88+M8YSxxUsFgvTpk3Dyy+/jN9++w1VVVVIT0/HiRMnEBERgaVLl+Kjjz5CQ0MDvaduY2ODgIAAxMbGYtGiRZg2bRrdsPXrr7+ioqICHR0dIIRAJBIhLy8P4eHhJg/i1Nop56v+MPReeU9PD3Jzc+Hh4dEniHd2dmL9+vXw8fHBsWPHxm0QB259LxsbG7Flyxb8/e9/x4svvkgHcUIIrK2toVarERkZiZs3b+KPP/7A008/TY8bMkHcODCBnGFIGNNkYbzDYrEwefJkbN26FVevXkV9fT3uv/9+ZGZmIjo6GgsXLsR7772H6upqOqhbW1uDzWYjJiYGCxcuRFhYGBQKBXJzc3HlyhXk5uZi0qRJfQw/TImdnR2WLFnSr856S0vLgNn6aKEsP11dXfsYwkgkEtxzzz1wcnLCyZMnDTItYGlUV1dDoVDgrrvu0tHPp/5u1BheaGgo/vjjD5w7dw779+83yVrHK0xpfZwy3NI6h8NBfHy8TscuZbJgZWU1YpMFhqFDCAGfz8fJkydx/PhxZGVlYfr06UhPT0daWhrCw8P7lNNbW1tRUlICd3d3dHd303aulAGIOcxCy2QynDt3DmKxWOd4QEAAli1bptfXooK4s7MzoqKidN6/VCrFPffcA41Gg59++olxJfwve/bswdtvvw2hUAigf2MUqgkuJSUFfD4f7u7uRtFBYLiF6X/FDCZB21OdgvJUnz17dp/zIyIiUFhYCA6HQ/+XmpqKxYsXg8PhMM5FRoDFYoHNZuPpp59GZmYmWltb8fzzzyM7Oxtz5sxBcnIydu7ciaKiImg0Gnz77bdIS0vDjBkzkJSUhAULFiAqKgoAUFRUhKtXr6K4uBhCoXDIHuKGwNHREUuXLoWbm5vO8dbWVr1m5UqlErm5uXBycuoTxOVyOe6//3709PTgxx9/ZIK4FtSWDaVc119D5aeffoq9e/dCoVAYTcyI4U/G5ywFA4Dheao7ODjQQYCCkmfsfZzB8LBYLHh7e+Oxxx6jPdV//PFHHD9+HB988AEmTJiApqYmbN++ne6DGMh+taSkBGq1Wsd+1diz0k5OTli6dCnOnj0LiURCH8/Pz9dLVk4FcQcHB0RHR+sE8Z6eHjz00EPo6OjAuXPn+txQjHconYGvv/4aYWFhtC0qlZkrFAo0NTUhOjp6XPcTmBKmtD7OMYXJAoNh+fLLL/H0009j9uzZyM7Opj3V161bh6SkpD7ldMqpjZKKpZzaKFMXYwb1zs5OZGZmQiqV0seWL1/er6f2UFGpVMjNzYWtrS1iY2N13r9SqcTGjRvR0NCAixcvmtwX3VzZv38/nn/+eTzzzDN47rnnEBoaCuDW5/X3v/8dv/76K65evco0t5kIJpAzMIwhvv32Wzz99NM4evQoVq5cCYlEQnuq//TTT3Bzc6M91WfNmtWvp3pXVxdt6iKXy2n7VV9fX6MIoojFYmRmZkIulwO4NUmxYsWKET2XWq1Gbm4urKysEBcXp/N+VSoVHnvsMZSXl+PSpUtGFaGxNBQKBd599128+uqriI6Oxpw5c6BSqdDU1ITS0lJcu3bNLKYhxitMIGdgGCNIpVLExcVh//79WLp0aZ9/l8lkOp7q9vb2WLt2Le2p3jtIE0IgkUjoTJ1yamOz2fD19TWoQIpIJMK5c+fQ09MDAFi2bBkCAgIAQoC2NrAkEhBnZ8DbGxhABEetViMvLw8A+jRqqtVqPP300+BwOLh06dKoMv7xxIULF7B3717U1tbCx8cHc+fOxTPPPHPrs2EwGUwgZ2AYQyiVyiEFWIVCoeOpTgjBmjVrkJ6e3q+nOnBrNIvK1Lu7u+Hl5UV3wBtib1QoFOLcuXNQKpUIdHbGCh4PNgcOwKqmhj5HExIC1ebNUD34IKBlqalWq8HhcKDRaJCQkNAniD/33HO4fv06srKyzGokzxJQq9WwsrIymFMew/BhAjkDwzhHpVLh6tWrtKe6TCbDmjVrkJaWhjvuuKPfWWqZTEZn6p2dnfDw8KAzdX3OXvN4PJR9+CGWfPwxbJRKAABL65JFqGDi5ISeb7+FZulSaDQacDgcqFQqJCQk6FQaNBoNXnjhBVy+fBmXL182mUytJdPf+BmDaWECOQMDA41arcavv/5K2692dHRgxYoVSE9Px9KlS/v1VJfL5XSm3tHRATc3N1oq1tHRcVTrsTp/Hvbr14MQAqtBLlXEygpgsSA/dgy5vr5QKBRITEzsE8RffvllnD59GllZWbd1YmNgsBSYQM7AwNAvGo0Gv//+Ox3UeTweli1bRnuq9zdr3dPTQzu1tbe3w9XVlTZ16e8mYFDEYjiGhQEyGVhDmHMnVlbQ2Nvj18OHEbtwoc4Wg0ajwauvvopjx47h8uXLdNe1Mdi/fz89GRIbG4u9e/ciOTm533M/++wzfPXVVygqKgIAJCYmYteuXQOez8AAMIIwDGOE4fqqi8ViPPvsswgICIC9vT3CwsJw5swZI63WMrCyssLs2bPxr3/9C5WVlcjKykJ4eDjefPNN2lP9u+++o/XcAcDe3h6TJk1CYmIiFixYgIkTJ0IsFuP69eu4ceMGampq0N3dPaTXt/n2W0AqHVIQBwCWRgMruRwzS0t1gjghBG+88QaOHDmCCxcuGDWIHzlyBFu3bsXrr7+O3NxcxMbGYvny5eDz+f2en5WVhfvvvx+XL1/GjRs3MGnSJCxbtgzNzc1GWzOD5cFk5AwWz3B91RUKBebOnQs/Pz9s374dgYGBqK+vh4eHB2JjY03wDiwLylP96NGjyMjIQGVlJe644w6kpaVhzZo18PT07Nd+VSgU0varjo6OdKbu4uLSd8+VEDjExIBVW6uzJ37btbFYIFOmQF5QALBYIITgrbfewieffIJLly4ZXbxoJFbB2qjVanh6emLfvn3YuHGjoZfLYKEwgZzB4hnuxfLAgQPYs2cPysrKxq3HtL4ghKCsrIy2Xy0qKsKCBQuQlpY2oKe6SqWCUCgEn8+HQCCAvb09vafu5uZ263yhEE7/tcIcCdKGBhAvL7z//vt4//33cenSJaPfpI3UKlibrq4u+Pn54ejRo1izZo0BV8tgyTCldQaLhjLBWLJkCX3MysoKS5YswY0bN/p9zA8//IDZs2fj2WefBZvNRlRUFHbt2gW1Wm2sZY8ZWCwWpk+fjldffRU5OTkoKSnBkiVL8M033yA0NBSrVq3CgQMH0NLSomO/6u/vj5iYGCxatAhhYWGQy+XIzc3FtWvXUF5ejq7W1tEtrKsL+/btw3vvvYfMzEyTVFpGYhXcm5dffhkTJkzQ+X4zMPSGCeQMOhBCTGqgMVxGcrGsqanBsWPHoFarcebMGezYsQP/+te/sHPnTmMsecxCeapv27YNv/32GyorK5GamoqMjAzaU33v3r06nurW1tbw8/Oj7VkjIiKgUqlQoDUrPhK+zMjA7t27cebMGSQlJenj7Rmdt956C4cPH8aJEycYO1WGQWECOQOAW8GNx+OBxWKZhbWlIdFoNPDz88Onn36KxMREbNiwAf/4xz9w4MABUy9tzMBisRAcHIy//e1v+OWXX1BXV4f77rsPP//8M6Kjo7Fo0SK8//77qKmpoYO6lZUVfH19ERkZidlr1kA5efKfc+JDhLBY6PDxwctvv40ff/wRs2bNMsTbGxKUTn1vBzcej3dbJbl3330Xb731Fs6dO4eYmBhDLpNhDDC2r9gMg6LRaHDjxg0sWbIE99xzD2bNmoVp06Zh586daGlpAQCYewvFSC6WAQEBCAsL01H7mj59OrhcLhQKhUHXOx5hsViYOHEi/vrXv+Ly5ctobGzE448/jqysLCQkJGDu3Ll45513UF5e/mdQt7YGefbZ4b8YIdjZ2YkffvwR8+fP1/M7GR7DtQqmeOedd/DGG2/g7NmzFltNYDAuTCAfxxw4cACrV6+GVCrFM888g4yMDLzyyivIzs7Gc889h/r6erpRKSgoCL/99puJV9yXkVws586di6qqKp0thIqKCgQEBDA2jAaGxWLB398fmzdvxrlz59Da2ornnnsOf/zxB2bNmoWUlBS8+eabKC4uhuL++wEnp1tiL0NAw2JBAmDld99h0aJFBn0fQ2Xr1q347LPP8OWXX6K0tBR/+ctf+lgFv/LKK/T5b7/9Nnbs2IHPP/8cwcHB4HK54HK5Qx7ZYxinEIZxSUdHB/Hz8yPp6emEz+cTQgjRaDSEEEL4fD5ZvXo1efXVVwkhhBw7dozY2tqSvLw8Uy13UA4fPkzs7e3JwYMHSUlJCXnqqaeIh4cH4XK5hBBCHn74YbJt2zb6/IaGBuLq6kq2bNlCysvLyenTp4mfnx/ZuXOnqd7CuEej0ZD29nby5ZdfktTUVOLg4EDCwsLIp3fdRTRWVkRjZUXILcuUfv9TAUQJkBdmzCDfffcd6ezsNPVbotm7dy8JCgoidnZ2JDk5mfz222/0vy1cuJBs2rSJ/v/JkycTAH3+e/31142/cAaLgQnk45QvvviC2NnZkfPnz/f7793d3aSlpYUQQsiyZcvImjVrSEdHByHk1kVXrVbTgd8cGM7FkhBCrl+/TlJSUoi9vT0JCQkhb775JlGpVEZeNcNAdHR0kEOHDpG77rqLpNrbk24Wi6gBomGxdAK4hsUiGhaLdAEkY/Nm8tprr5HIyEhib29PUlNTSXZ2tqnfCgODwWHmyMcpGzduRH5+Pn7++ec+7k+klymCg4MDPvzwQzz55JN9GuGo8vRYb5BjMB1vvPEGPn3nHbwWHIw7i4sRonXJ6maz8T/t7Zjz6adYt2kTfZyabU9NTUV0dLQpls3AYDRsbn8Kw1ikvLwcEydO7Ff/msViQa1Ww9raGj/88ANsbGwwa9YsWFlZQSaT4bPPPoOdnR3Wrl2LwMBAE6yeYbzw3nvv3RJ0+eUXxMXFQSaV4ueMDFw4cQLHz51DA4+HL7/6CukPPaTzuIiICPzjH/8w0aoZGIwLk0aNQwghSE5ORklJCTy0PJx7nwMAn3/+OebOnYvJ/1XZKiwsBIfDweHDhxEeHo67774bNQPM/KpUKrPvemcwX4RCIf7973/j/PnziIuLAwA4Ojlh5UMP4V/Hj6NcKMRXX3+Nhx56iLHVZBjXMKX1ccq5c+ewcuVK/Otf/8KWLVt07B61cXJywp49e7B582ZYW1tDo9HQZfT6+nrcc889WLNmDV577TW6JK9UKhnpUwa9wHyXGBhuD1NaH6csW7YM77zzDr744gtIJBI88MADcHJyQm5uLuLj4+Hv749z586BEII5c+bA2toaXV1d+PXXX8HhcJCYmIilS5fi/vvvx/nz58HlcuHv74/Lly/j5MmTuH79OhYtWoQnnngC4eHhOq+tVqvHhfAMw+hhgjgDw+1hMvJxjFwux6FDh7B37160trYiLi4ObW1tePPNN7Fs2TJs2LABfD4fP//8M+RyOV544QWcPXsWKSkpKCoqglgspm1Ac3JyAACenp5YunQp5syZgwsXLiA/Px8ffvgh0tPT+w3c2hk+AwMDA8PwYQI5A4Bb/twcDgfTp0+ndcutrKzw7rvvYuvWrbh48SKeeOIJvP3221i/fj2srKxw9OhRbNmyBQ8++CA++OAD5OXlIS0tDd988w0WLFgAAPj555/h6+uLpKQkVFZW4vPPP4dKpcK9996LmTNnDmltGo0GLBaL2QdlYGBg6AcmFWIAAHh4eGDRokV0EOfxeFizZg0dkF1dXcHlcjF16lTY2NjAysoKHR0dkMvltEXjpEmTkJycjPvvvx//+c9/IJVKsXLlSiQlJSE3Nxfr1q1DTk4OampqsHLlSixYsAAlJSUDrqm7uxtKpRJWVlbjKojv378fwcHBcHBwQEpKCv74449Bz//ggw8QHh4OR0dHTJo0CS+++CLkcrmRVsvAwGByTDG8zmB5tLe3k/T0dBIYGEheeuklsnXrVuLg4EBCQkL6nPv++++TpUuXkl27dhGFQkEIIeTf//43mTNnDqmsrCSE3FJX27dvH8nJyRnwNb/++muSmppKVq9eTT755BP6ucYyhw8fJnZ2duTzzz8nxcXF5MknnyQeHh6Ex+P1e/63335L7O3tybfffktqa2tJZmYmCQgIIC+++KKRV87AwGAqmIycYUh4eHjg2LFj2L17N0QiESZPngw2m42EhAQAt8bSrly5AgB45plnsHHjRuzcuRMXLlwAAMyePRtlZWX4xz/+gYKCAkyaNAkbN25EWFhYv68nl8vh5OSEOXPmQKlUYvPmzQOOuY0l3nvvPTz55JN49NFHMWPGDBw4cABOTk74/PPP+z3/+vXrmDt3Lh544AEEBwdj2bJluP/++2+bxTMwMIwdmK51hiFjbW2Nhx9+GA8//DAAICYmBm5ubgBu7YWfO3cOGo0GixcvRlBQEKZPn468vDysXLkScXFxuHTpEt59913s3r0bL730EuLj4wd8LQcHB6xfvx4AUF1djQULFiAkJMTwb9KEKBQK5OTk6JhoWFlZYcmSJbhx40a/j5kzZw6++eYb/PHHH0hOTkZNTQ3OnDlDf0YMDAxjHyaQM4wYbYeptLQ0VFdXY926dfD09ISbmxumTZuG+++/H/X19XB0dERMTAx27NiB//f//h/uvfdeZGZmDhqcCSFQq9U4ffo0/va3v435USShUAi1Wk33KVCw2WyUlZX1+5gHHngAQqEQ8+bNAyEEKpUKmzdvxvbt242xZAYGBjOAKa0z6IXw8HB88sknEIvF+OKLL7Bz504cPXoUU6ZMwc6dO/HFF19AKpUiLCwMTz/9NGxtbVFZWQlgYM9zFouFGzduQCAQYPXq1cZ8OxZDVlYWdu3ahX//+9/Izc1FRkYGfvrpJ7zxxhumXhoDA4ORYDJyBr2jnakrlUrMmTMHe/bswbvvvouFCxeitrYWbDYbycnJADBoR/qRI0cQFxeHqVOnGnrZJsfHxwfW1tbg8Xg6x3k8Hvz9/ft9zI4dO/Dwww/jiSeeAABER0dDIpHgqaeewj/+8Q9mRp+BYRzA/MoZDIqtrS0effRRlJSU4Pjx4wgPD8ezzz6LjIwMeHp6DvpYtVqNU6dOYf369WO+rA4AdnZ2SExMxMWLF+ljGo0GFy9exOzZs/t9jFQq7ROsra2tAQxc6WBgYBhbMBk5g9FYsGABPZcO9LVL7c3NmzfR0tKCtWvXGmN5ZsHWrVuxadMmJCUlITk5GR988AEkEgkeffRRALfsZwMDA7F7924AwNq1a/Hee+8hPj4eKSkpqKqqwo4dO7B27Vo6oDMwMIxtmEDOYDL6C+IVFRX4+OOPMW3aNOTl5SEhIQEzZswwwepMw4YNGyAQCPDaa6+By+UiLi4OZ8+epRvgGhoadDLwV199FSwWC6+++iqam5vh6+uLtWvX4s033zTVW2BgYDAyjEQrg1nR1dWFXbt24T//+Q/a2trg6emJf/zjH9iwYQPjfc7AwMDQD0wgZzBbamtr8dlnn+HLL7/Evffei927d8PBwcHUy2JgYGAwK5hmNwazZcqUKdi1axeam5vxxhtvMEGcQS8MV8v+6NGjiIiIgIODA6Kjo3HmzBkjrZSBYWgwgZzBInBxcTH1EhjGAEeOHMHWrVvx+uuvIzc3F7GxsVi+fDn4fH6/51+/fh33338/Hn/8ceTl5SE9PR3p6ekoKioy8soZGAaGKa0zMDCMG1JSUjBz5kzs27cPwK3xvkmTJuG5557Dtm3b+py/YcMGSCQSnD59mj42a9YsxMXF4cCBA0ZbNwPDYDAZOQMDw7iA0rJfsmQJfex2WvY3btzQOR8Ali9fPuD5DAymgAnkDAwM44LBtOy5XG6/j+FyucM6n4HBFDCBnIGBgYGBwYJhAjkDA4NBuHr1KtauXYsJEyaAxWLh5MmTt31MVlYWEhISYG9vj2nTpuHgwYN6W89ItOz9/f2HdT4DgylgAjkDA4NBkEgkiI2Nxf79+4d0fm1tLVavXo3FixeDw+HghRdewBNPPIHMzEy9rGckWvazZ8/WOR8Azp8/P+D5DAwmgTAwMDAYGADkxIkTg57z0ksvkcjISJ1jGzZsIMuXL9fbOg4fPkzs7e3JwYMHSUlJCXnqqaeIh4cH4XK5hBBCHn74YbJt2zb6/F9//ZXY2NiQd999l5SWlpLXX3+d2NraksLCQr2tiYFhtDBa6wwMDGbBQB3iL7zwgt5eY7ha9nPmzMGhQ4fw6quvYvv27QgNDcXJkycRFRWltzUxMIwWJpAzMDCYBQN1iHd2dkImk8HR0VEvr7NlyxZs2bKl33/Lysrqc+yee+7BPffco5fXZmAwBMweOQMDAwMDgwXDBHIGBgazYKAOcTc3N71l4wwMYxEmkDMwMJgFTIc4A8PIYAI5AwODQeju7gaHwwGHwwFwa7yMw+GgoaEBAPDKK69g48aN9PmbN29GTU0NXnrpJZSVleHf//43vv/+e7z44oumWD4Dg8XAmKYwMDAYhKysLCxevLjP8U2bNuHgwYN45JFHUFdXp9NglpWVhRdffBElJSWYOHEiduzYgUceecR4i2ZgsECYQM7AwMDAwGDBMKV1BgYGBgYGC4YJ5AwMDAwMDBYME8gZGBgYGBgsGCaQMzAwMDAwWDBMIGdgYGBgYLBgmEDOwMDAwMBgwTCBnIGBgYGBwYJhAjkDAwMDA4MFwwRyBgYGBgYGC4YJ5AwMDAwMDBYME8gZGBgYGBgsmP8PICLgNObGFZgAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from mpl_toolkits.mplot3d import Axes3D\n",
        "import random\n",
        "\n",
        "# Function to generate random probability distributions of dimension 3\n",
        "def generate_probabilities(num_distributions):\n",
        "    return np.random.dirichlet(np.ones(3), size=num_distributions)\n",
        "\n",
        "# Generate hypothetical probability distributions from classifiers\n",
        "num_distributions = 20\n",
        "probability_distributions = generate_probabilities(num_distributions)\n",
        "\n",
        "# Define the vertices of the 3D triangle\n",
        "triangle_vertices = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])\n",
        "\n",
        "# Create figure and 3D axes\n",
        "fig = plt.figure(figsize=(8, 6))\n",
        "ax = fig.add_subplot(111, projection='3d')\n",
        "\n",
        "# Plot the triangle surface\n",
        "verts = [triangle_vertices]\n",
        "triangle = ax.plot_trisurf(triangle_vertices[:, 0], triangle_vertices[:, 1], triangle_vertices[:, 2],\n",
        "                           triangles=[[0, 1, 2]], color='gray', alpha=0.5)\n",
        "\n",
        "# Define colors for each class\n",
        "colors = ['r', 'g', 'b']\n",
        "\n",
        "# Plot the points obtained from the hypothetical classifier\n",
        "for i in range(3):\n",
        "    ax.scatter(triangle_vertices[i, 0], triangle_vertices[i, 1], triangle_vertices[i, 2], c=colors[i], marker='o', s=100, label='Class {}'.format(i+1))\n",
        "for i in range(len(probability_distributions)):\n",
        "    class_points = probability_distributions[i]\n",
        "    ax.scatter(class_points[0], class_points[1],class_points[2],c=random.choice(colors))\n",
        "\n",
        "# Set labels and title\n",
        "ax.set_xlabel('Class 1')\n",
        "ax.set_ylabel('Class 2')\n",
        "ax.set_zlabel('Class 3')\n",
        "ax.set_title('Probability Distributions in 3D')\n",
        "\n",
        "# Set limits for axes\n",
        "ax.set_xlim([0, 1])\n",
        "ax.set_ylim([0, 1])\n",
        "ax.set_zlim([0, 1])\n",
        "\n",
        "# Show the plot\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "TlFgGIOplvpx"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Explaination of the plot above :\n",
        "\n",
        "Triangle Surface: The gray-shaded triangle represents a 3D space. Each corner of the triangle corresponds to one of the three classes. For instance, the first corner (1, 0, 0) represents class 1, the second corner (0, 1, 0) represents class 2, and the third corner (0, 0, 1) represents class 3. These corners are the one-hot encoded representations of the classes.\n",
        "\n",
        "Points Scatter Plots: The colored points scattered on the triangle represent probability distributions obtained from a hypothetical classifier. Each point's position in the triangle indicates the predicted probabilities for each class. The x-coordinate of a point represents the predicted probability for class 1, the y-coordinate represents the predicted probability for class 2, and the z-coordinate represents the predicted probability for class 3.\n",
        "\n",
        "Point Colors: The colors of the points correspond to the true classes. For example, if a point is red, it means that the true class is class 1. Similarly, green points represent class 2, and blue points represent class 3.\n",
        "\n",
        "Interpretation: By looking at the plot, we can assess how well the classifier performs in terms of predicting the true classes. Points that are closer to the vertices of the triangle are more accurate predictions since they align better with the true classes. Points that are far from the vertices represent less accurate predictions, indicating a higher uncertainty in the classifier's output."
      ],
      "metadata": {
        "id": "W3oR144HoOmT"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "7ok-5ea3mg9s"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# The relation between KL divergence and cross entropy\n",
        "\n",
        "### Explaination of Cross Entropy\n",
        "\n",
        "In the context of classification, the true probability distribution \\( P \\) represents the actual distribution of class labels, while the predicted probability distribution \\( Q \\) represents the distribution of class labels predicted by a model.\n",
        "\n",
        "Cross-Entropy measures the average number of bits needed to encode data from the true distribution \\( P \\) given the predicted distribution \\( Q \\). In other words, it quantifies how well the predicted distribution \\( Q \\) represents the true distribution \\( P \\).\n",
        "\n",
        "When we minimize Cross-Entropy, we are essentially reducing the discrepancy between the true distribution \\( P \\) and the predicted distribution \\( Q \\). This means that the predicted probabilities become closer to the true probabilities, resulting in a more accurate representation of the true distribution.\n",
        "\n",
        "Now, let's consider the term \"minimizing the amount of information lost\". In the context of information theory, entropy measures the uncertainty or randomness in a probability distribution. The lower the entropy, the less uncertain the distribution is.\n",
        "\n",
        "When we minimize Cross-Entropy, we are effectively minimizing the amount of information lost when approximating \\( P \\) with \\( Q \\). This is because Cross-Entropy includes both the entropy of the true distribution \\( P \\) and the KL Divergence from \\( P \\) to \\( Q \\). By minimizing Cross-Entropy, we are minimizing the additional information loss caused by the discrepancy between \\( P \\) and \\( Q \\), thus making the approximation more accurate.\n",
        "\n",
        "In summary, minimizing Cross-Entropy is equivalent to minimizing the amount of information lost when approximating \\( P \\) with \\( Q \\) because it measures the discrepancy between the true and predicted distributions and aims to reduce this discrepancy to improve the accuracy of the approximation.\n",
        "\n",
        "\n",
        "\n",
        "### Explanation of KL Divergence\n",
        "\n",
        "KL Divergence, also known as Kullback-Leibler divergence, is a measure of how one probability distribution diverges from a second, expected probability distribution. It quantifies the difference between two probability distributions \\( P \\) and \\( Q \\).\n",
        "\n",
        "Mathematically, the KL divergence from probability distribution \\( Q \\) to probability distribution \\( P \\) is defined as:\n",
        "\n",
        "$$ D_{KL}(P \\| Q) = \\sum_{i} P(i) \\log\\left(\\frac{P(i)}{Q(i)}\\right) $$\n",
        "\n",
        "where \\( i \\) indexes the elements of the distributions.\n",
        "\n",
        "**Interpretation**:\n",
        "\n",
        "- If \\( D_{KL}(P \\| Q) = 0 \\), it indicates that the two distributions \\( P \\) and \\( Q \\) are identical.\n",
        "- If \\( D_{KL}(P \\| Q) > 0 \\), it indicates that the distribution \\( P \\) diverges from distribution \\( Q \\).\n",
        "- If \\( D_{KL}(P \\| Q) \\) is large, it indicates that the difference between \\( P \\) and \\( Q \\) is significant.\n",
        "\n",
        "**Relation to Cross-Entropy**:\n",
        "\n",
        "KL Divergence is closely related to Cross-Entropy. In fact, the Cross-Entropy between two distributions \\( P \\) and \\( Q \\) can be expressed as the sum of the entropy of \\( P \\) and the KL divergence between \\( P \\) and \\( Q \\):\n",
        "\n",
        "$$ H(P, Q) = -\\sum_{i} P(i) \\log(Q(i)) = H(P) + D_{KL}(P \\| Q) $$\n",
        "\n",
        "where \\( H(P) \\) denotes the entropy of distribution \\( P \\).\n",
        "\n",
        "**In Classification**:\n",
        "\n",
        "In the context of classification, KL divergence is often used to measure the dissimilarity between the predicted probability distribution \\( Q \\) and the true probability distribution \\( P \\) of class labels. Minimizing the KL divergence helps in improving the performance of classification models by making the predicted probabilities closer to the true probabilities.\n",
        "\n",
        "**why they are the same as loss functions?**:\n",
        "\n",
        "To explain how minimizing KL Divergence is equivalent to minimizing Cross-Entropy, let's start by understanding their relationship:\n",
        "\n",
        "1. **KL Divergence (KL)**: Measures the difference between two probability distributions \\( P \\) and \\( Q \\). It quantifies how much information is lost when we use \\( Q \\) to approximate \\( P \\).\n",
        "\n",
        "2. **Cross-Entropy (CE)**: Measures the average number of bits needed to encode data from one distribution given another distribution. In classification, it measures the discrepancy between the true probability distribution \\( P \\) and the predicted probability distribution \\( Q \\).\n",
        "\n",
        "Now, let's consider the relationship between KL Divergence and Cross-Entropy. We know that Cross-Entropy can be expressed as the sum of two terms: the entropy of \\( P \\) and the KL Divergence from \\( P \\) to \\( Q \\):\n",
        "\n",
        "$$ H(P, Q) = H(P) + D_{KL}(P \\| Q) $$\n",
        "\n",
        "Here, \\( H(P) \\) represents the entropy of \\( P \\), which is a constant with respect to \\( Q \\). Therefore, minimizing \\( H(P, Q) \\) is equivalent to minimizing \\( D_{KL}(P \\| Q) \\).\n",
        "\n",
        "In the context of classification, minimizing Cross-Entropy means reducing the discrepancy between the true distribution \\( P \\) and the predicted distribution \\( Q \\). By minimizing \\( D_{KL}(P \\| Q) \\), we are also minimizing the amount of information lost when approximating \\( P \\) with \\( Q \\).\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "nFBpY6Izol_E"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Define two probability distributions P and Q\n",
        "P = np.array([0.2, 0.3, 0.5])  # True distribution\n",
        "Q = np.array([0.4, 0.4, 0.2])  # Predicted distribution\n",
        "\n",
        "# Calculate KL divergence\n",
        "KL_divergence = np.sum(P * np.log(P / Q))\n",
        "\n",
        "# Calculate Cross-Entropy\n",
        "cross_entropy = -np.sum(P * np.log(Q))\n",
        "\n",
        "# Plot the probability distributions\n",
        "plt.figure(figsize=(12, 6))\n",
        "\n",
        "# Plotting the true and predicted distributions\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.bar(np.arange(len(P)), P, color='blue', alpha=0.5, label='$P$ (True Distribution)')\n",
        "plt.bar(np.arange(len(Q)), Q, color='red', alpha=0.5, label='$Q$ (Predicted Distribution)')\n",
        "plt.xlabel('Class')\n",
        "plt.ylabel('Probability')\n",
        "plt.title('Probability Distributions')\n",
        "plt.legend()\n",
        "\n",
        "# Plotting the KL divergence and Cross-Entropy\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.bar(['$D_{KL}(P \\| Q)$', '$H(P, Q)$'], [KL_divergence, cross_entropy], color=['green', 'orange'])\n",
        "plt.ylabel('Value')\n",
        "plt.title('KL Divergence vs Cross-Entropy')\n",
        "\n",
        "# Show the plot\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "print(\"KL Divergence:\", KL_divergence)\n",
        "print(\"Cross-Entropy:\", cross_entropy)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 642
        },
        "id": "kdy-BhoGnZaQ",
        "outputId": "8eaaaeab-3b07-4e3e-c080-74a91284219b"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "KL Divergence: 0.2332113080895542\n",
            "Cross-Entropy: 1.2628643221541276\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "aagAH81OownL"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}