jakebolewski/gist:7517952

## gistfile1.txt
{
 "metadata": {
  "language": "Julia",
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "GPU Memory Bandwidth Transpose Examples"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import OpenCL\n",
      "const cl = OpenCL;"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "using PyPlot"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "BLOCK_SIZE = 16;"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "naive_transpose = \"\n",
      "__kernel void transpose(__global *a_t,\n",
      "                        __global *a,\n",
      "                        unsigned a_width,\n",
      "                        unsigned a_height)\n",
      "{\n",
      "   int read_idx = get_global_id(0) + get_global_id(1) * a_width;\n",
      "   int write_idx = get_global_id(1) + get_global_id(0) * a_height;\n",
      "   a_t[write_idx] = a[read_idx];\n",
      "}\";"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "block_transpose = \"\n",
      "#define BLOCK_SIZE $(BLOCK_SIZE)\n",
      "__kernel __attribute__((reqd_work_group_size(BLOCK_SIZE, BLOCK_SIZE, 1)))\n",
      "void transpose(__global float *a_t, \n",
      "               __global float *a,\n",
      "               unsigned a_width,\n",
      "               unsigned a_height,\n",
      "               __local float *a_local)\n",
      "{\n",
      "  int base_idx_a   = get_group_id(0) * BLOCK_SIZE + get_group_id(1) * (BLOCK_SIZE * a_width);\n",
      "  int base_idx_a_t = get_group_id(1) * BLOCK_SIZE + get_group_id(0) * (BLOCK_SIZE * a_height);\n",
      "\n",
      "  int glob_idx_a   = base_idx_a + get_local_id(0) + a_width * get_local_id(1);\n",
      "  int glob_idx_a_t = base_idx_a_t + get_local_id(0) + a_height * get_local_id(1);\n",
      "\n",
      "  a_local[get_local_id(1) * BLOCK_SIZE + get_local_id(0)] = a[glob_idx_a];\n",
      "\n",
      "  barrier(CLK_LOCAL_MEM_FENCE);\n",
      "\n",
      "  a_t[glob_idx_a_t] = a_local[get_local_id(0) * BLOCK_SIZE + get_local_id(1)];\n",
      "}\";"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "function enqueue_naive_kernel{T}(queue::cl.CmdQueue,\n",
      "                                 k::cl.Kernel,\n",
      "                                 dst::cl.Buffer{T},\n",
      "                                 src::cl.Buffer{T},\n",
      "                                 dims)\n",
      "    h, w = dims\n",
      "    @assert w % BLOCK_SIZE == 0\n",
      "    @assert h % BLOCK_SIZE == 0\n",
      "    cl.set_args!(k, dst, src, uint32(h), uint32(h))\n",
      "    cl.enqueue_kernel(queue, k, (h, w), (BLOCK_SIZE, BLOCK_SIZE))\n",
      "end"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 2,
       "text": [
        "enqueue_naive_kernel (generic function with 1 method)"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "function enqueue_block_kernel{T}(queue::cl.CmdQueue,\n",
      "                                 k::cl.Kernel,\n",
      "                                 dst::cl.Buffer{T},\n",
      "                                 src::cl.Buffer{T},\n",
      "                                 dims::Dims)\n",
      "    h, w = dims\n",
      "    @assert w % BLOCK_SIZE == 0\n",
      "    @assert h % BLOCK_SIZE == 0\n",
      "    lmem = cl.LocalMem(Float32, BLOCK_SIZE * (BLOCK_SIZE + 1))\n",
      "    cl.set_args!(k, dst, src, uint32(h), uint32(w), lmem)\n",
      "    cl.enqueue_kernel(queue, k, (h, w), (BLOCK_SIZE, BLOCK_SIZE))\n",
      "end    "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "enqueue_block_kernel (generic function with 1 method)"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "function benchmark_transpose()\n",
      "\n",
      "    gpu_device = first(cl.devices(:gpu))\n",
      "    ctx = cl.Context(gpu_device)\n",
      "    \n",
      "    for dev in cl.devices(ctx)\n",
      "        @assert dev[:local_mem_size] > 0\n",
      "    end\n",
      "    \n",
      "    prg  = cl.Program(ctx, source=naive_transpose) |> cl.build!\n",
      "    naive_kern = cl.Kernel(prg, \"transpose\")\n",
      "    \n",
      "    prg = cl.Program(ctx, source=block_transpose)  |> cl.build!\n",
      "    block_kernel = cl.Kernel(prg, \"transpose\")\n",
      "    \n",
      "    queue = cl.CmdQueue(ctx, :profile)    \n",
      "    \n",
      "    array_sizes = [int((2^i) / 32) * 32 for i in 6:12]\n",
      "\n",
      "    mem_bandwiths = Float32[]\n",
      "    for (name, method, kern) in ((\"naive\", enqueue_naive_kernel, naive_kern), \n",
      "                                 (\"block\", enqueue_block_kernel, block_kernel))\n",
      "        for s in array_sizes  \n",
      "            src  = rand(Float32, (s, s))\n",
      "            a_buf   = cl.Buffer(Float32, ctx, (:r, :copy), hostbuf=src)\n",
      "            a_t_buf = cl.Buffer(Float32, ctx, :w, length(src))\n",
      "            \n",
      "            # warm up....\n",
      "            for i in 1:4\n",
      "                method(queue, kern, a_t_buf, a_buf, size(src))\n",
      "            end\n",
      "            \n",
      "            # profile...\n",
      "            count  = 20\n",
      "            events = cl.Event[]\n",
      "            for i in 1:count\n",
      "                push!(events, method(queue, kern, a_t_buf, a_buf, size(src)))\n",
      "            end\n",
      "            cl.wait(events[end])\n",
      "            \n",
      "            time = sum([evt[:profile_duration] for evt in events])\n",
      "            mem_bw = 2 * sizeof(src) * count / (time * 1e-9)\n",
      "            push!(mem_bandwiths, mem_bw)\n",
      "            \n",
      "            @printf(\"benchmarking %s, array size: %s^2, %.3f GB/s\\n\", name, s, mem_bw / 1e9)\n",
      "            \n",
      "        end\n",
      "    end\n",
      "    \n",
      "    # plot results, sleep a little to prevent overlap with show..\n",
      "    sleep(1)\n",
      "    clf()\n",
      "    n_sizes = length(array_sizes)\n",
      "    naive_bandwidths = mem_bandwiths[1:n_sizes]\n",
      "    block_bandwidths = mem_bandwiths[n_sizes+1:end]\n",
      "    plot(array_sizes, naive_bandwidths/1e9, \"o-\", label=\"naive\")\n",
      "    plot(array_sizes, block_bandwidths/1e9, \"o-\", label=\"block\")\n",
      "    title(\"Tanspose Tests for $(gpu_device[:name])\")\n",
      "    xlabel(\"Matrix width/height N\")\n",
      "    ylabel(\"Memory Bandwidth [GB/s]\")\n",
      "    legend(loc=\"best\")\n",
      "    grid(true)\n",
      "    show()\n",
      "end"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "benchmark_transpose (generic function with 1 method)"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "benchmark_transpose()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "benchmarking naive"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        ", array size: 64^2, 4.303 GB/s\n",
        "benchmarking naive, array size: 128^2, 14.672 GB/s\n",
        "benchmarking naive, array size: 256^2, 21.122 GB/s\n",
        "benchmarking naive, array size: 512^2, 12.364 GB/s\n",
        "benchmarking naive, array size: 1024^2, 8.735 GB/s\n",
        "benchmarking "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "naive, array size: 2048^2, 17.411 GB/s\n",
        "benchmarking "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "naive, array size: 4096^2, 7.403 GB/s\n",
        "benchmarking block, array size: 64^2, 10.816 GB/s\n",
        "benchmarking block, array size: 128^2, 42.030 GB/s\n",
        "benchmarking block, array size: 256^2, 114.345 GB/s\n",
        "benchmarking block, array size: 512^2, 102.024 GB/s\n",
        "benchmarking block, array size: 1024^2, 51.860 GB/s\n",
        "benchmarking "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "block, array size: 2048^2, 20.417 GB/s\n",
        "benchmarking "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "block, array size: 4096^2, 8.569 GB/s\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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3DBgwAG+//TZSUlKwaNEiHD16FB999BHefPNN3bHNmjXD+PHjMXfuXLRp0wYffPABHB0dsWHDBqxatQrNmzfXW6d6xIgRiI+PR9euXeHv7w8bGxvExsZi1qxZKFq0KEaNGqU79vPPP8fp06exZcsWNGvWDO+++y68vLzw999/47fffkNYWBg+/vhjjB8//rUyAjIbq/DwcPTu3RutW7fGyJEj0bx5c1hZWSEuLg4RERE4duyY7sHGmTNnYv/+/ejVqxdq1qwJJycnXLlyBXPnzoW9vb3eNTdr1gxOTk5Yvnw5fH19dX9padCgAQ4fPoz3338f/fv3R4MGDVCiRAkkJCRg5cqVuH79Or755pvXui5FUbBq1SoEBAQgICAAU6ZMQdWqVXHu3Dl88cUXcHV1xY8//vhaY+REz549ERISovuLVYMGDXDjxg3MmjULvr6+JnsA1JC2bdtCo9HgX//6F5ycnFC0aFEoiqJb85mILMBii+QZoNVqxbRp00Tbtm2Fq6urUBRFrF69Wu+YZ8+eiblz54q2bduKMmXKCDs7O+Hr6ysmT56sW8v0Rd9//72oUqWKsLe3F5UqVRLz5s0zx+UQFRjly5eX1nnet2+faNasmXBzcxOurq6iWbNmYuPGjWL16tVCo9GI+fPn652jSJEiwsfHx+D5x44dKzQajfj111+FEJl/zsePHy+aNWsmPD09hZ2dnXB3dxcNGzYUS5YskdaYdnNzEz4+PuLmzZvinXfeESVLlhR2dnaievXq4rvvvsv2utasWSOaNGkiXFxchL29vahRo4b46quvREpKit5x4eHhonv37sLX11e4uLgIJycnUaVKFTFmzBhx/fp16bwZGRli5cqVolWrVqJYsWLCxsZGeHh4iDfeeEOMGzdOxMTEvDxwA4KDg4VGo5HWHxYic93lESNGiIoVKwp7e3vh4uIiKlSoILp166Z3/Xv27BH9+vUTfn5+ws3NTdjb2wtfX18RHBxssKY9e/aIxo0bC2dnZ6EoitBoNOLWrVvi0qVL4t133xV16tQRJUqUELa2tsLLy0t07NhRWu/4ZWbPni00Go20znOW8+fPi7ffflt4enoKGxsb4enpKQYMGCDi4uKkY7O+hw4ePGj0+IZkl/OlS5dEr169RKlSpYSDg4OoXr26CA0NFZcvXxaKooju3bvrHd+tWzeh0WjEtWvXpDE2b94sNBqN+PLLL/W2W1tbS+s8CyFEWFiYqFOnjnBwcBCKoggbGxvdvmHDhgmNRsN1nonMSBHiNRb7NLGzZ8+iZs2a8PDwgLe3N44fP47Vq1cjKChId0xCQgK8vLzQp08fdOrUCcWLF8cvv/yCb775Bs2aNcPevXv1zrlgwQKMGTMGkyZNQpcuXbBv3z589tlnmDFjhrTeLBHlT0WKFEGxYsVw+fJlS5dCREQFnKqmbVSoUAGXL19G+fLlsW/fPoOvd3VxcUFcXJzuFa4A0KZNGzg4OGDq1KnYt28fWrduDSBzbuHUqVPRu3dvzJw5EwDQpEkT3LhxA19++SXee++9bJe3IiIiIiJ6kaoeGHR0dNStVZrdDXE7Ozu9xjlLmzZtAADnzp3Tbdu9ezcSExMxYMAAvWMHDRqEpKQkbNu2zUSVExEREVFhoKrm+XVcv34dAPTeXpb1lqoXX++a9XtDb6ciovxHURRLl0BERIWEqqZt5FbWclSOjo5o3769bvudO3cAyG+pcnFxgaOjo8HXBxNR/sPXHBMRkbkUiOZ5+vTpOHbsGEJDQ3VrYgJAUlISFEWBra2t9BlbW1skJSWZs0wiIiIiyufyffO8Zs0afPXVV+jbt6/eGqUA4ODgACEEUlNTpQY6JSUFDg4OBs95584d3V1rIiIiIlIfT09P3Rr05pSvm+f9+/djyJAhaNWqlcEF87MCjY+Ph5eXl27748ePkZSUpHur1vPu3LkDf39/aLXavCuciIiIiF5L6dKlcfz4cbM30Pm2eT516hS6d++OatWqYcuWLbCxsZGOqVevHoDMV6o+3zxnPSj44hvKgMzmWavVYvXq1ahatWoeVZ//9OzZExs3brR0GarCTGTMRMZMZMxExkxkzETGTP5x/vx5vPPOO7hz5w6bZ2NcvXoVHTt2hLu7O3bu3AkXFxeDx7Vt2xZubm5Ys2YNunTpotv+008/wcHBAZ07d852jKpVq6Ju3bomrz2/srW1ZR4vYCYyZiJjJjJmImMmMmYiYybqoLrm+ciRI0hLS8PZs2cBAH/++SciIyMBZL7g5MmTJ2jfvj0SEhLwww8/IC4uDnFxcbrPe3t7o1y5cgAAJycnfP755/jwww9RsWJFdO7cGfv378eyZcsQEhLCF6TkgJ+fn6VLUB1mImMmMmYiYyYyZiJjJjJmog6qa547dOiAxMREAJlrt86YMQMzZsyAoih49OgRbt68ibi4OCiKgsGDB0ufnzBhAmbNmqX7/ejRo2FnZ4e5c+di9uzZ8Pb2xuzZszF27FizXRMRERERFQyqa55ftV5rlSpVkJGRkaNzDh8+HMOHD3+dsoiIiIiICs4bBilvvWx+eGHFTGTMRMZMZMxExkxkzETGTNSBzTMZZdu2bZYuQXWYiYyZyJiJjJnImImMmciYiTqweSajhISEWLoE1WEmMmYiYyYyZiJjJjJmImMm6qC6Oc+kTlwaR8ZMZMxExkxkzETGTGQvZnLhwgU8evTIQtWoR3R0tKVLyHMuLi6oVKmSpcvIFptnIiIiUrULFy6gcuXKli6DzCguLk61DTSbZyIiIlK1rDvOfPtvwZf15kA1/5SBzTMZZdmyZRg6dKily1AVZiJjJjJmImMmMmYiM5QJ3/5LasAHBskohWGOVU4xExkzkTETGTORMRMZMyG1YvNMRlm4cKGlS1AdZiJjJjJmImMmMmYiYyakVmyeiYiIiIiMxOaZiIiIiMhIbJ6JiIiIiIzE5lml7t27h8EjB6Nak2rwa+KHak2qYfDIwbh3755F6gkMDLTIuGrGTGTMRMZMZMxExkxkzMS8IiMjodFoMG7cOEuXonpcqk6F4uPj0bhDY1yqcwloC0ABkAHE3IrB4faHEbUzCu7u7matadSoUWYdLz9gJjJmImMmMmYiYyYyZmJenp6eGDZsGBo3bmzpUlSPzbMKffzZx5mNs/dzGzUAvIFLuIRJIZOwYuEKs9YUEBBg1vHyA2YiYyYyZiJjJjJmImMm5uXr64vvv//e0mXkC5y2oUJHTx0FvLLZWeZ/+4mIiIjI7Ng8q1Aa0jKnahii+d9+IiIiMujevXsYPHgiqlXrBD+/QFSr1gmDB0/Ms+eGzDHeqVOnoNFoMHjwYJw6dQpdu3ZFsWLF4OjoiPr162PHjh16x8fGxuKDDz5ArVq1UKJECdjZ2cHb2xuDBg3CpUuXpPMbmvNcpUoV2NnZ4f79+wZrCg0NhUajwaJFi/S237hxAx988AEqVKgAe3t7FCtWDAEBAdi5c6cJkrA8Ns8qZA1rQGSzM+N/+81sy5YtZh9T7ZiJjJnImImMmciYiSy3mcTHx6NRo75YubInYmK2IS5uK2Ji/ouVK3uiUaO+Jm+gzT1edHQ0WrVqhSpVqmD58uWYO3cubt++jS5duiAyMlJ33NGjR/H777+jf//+WLx4MTZv3oyRI0di9+7deOONN3DhwgWD51eUf+7eBQcH49mzZwgLCzN47KpVq2BnZ4egoCDdtuPHj6N27drYtWsXxowZg82bN2PevHl48uQJOnbsiH/9618mSsKCBOk5ceKEACBOnDhhsRqCPwgWGAqBEAO/hkIEfxBs9pr69Olj9jHVjpnImImMmciYiYyZyJ7PJCf/bw4OniCAKAEIA79+E8HBE0xap7nGO3nypFAURSiKIrZt26a37+jRo0JRFBEYGKjblpqaavA8sbGxws7OTvTr109v++HDh4WiKGLcuHG6bTdv3hRWVlbijTfekM6TNWavXr30xvTx8RFVqlQRDx8+1Ds+PT1dNGzYUNjZ2YmbN29me53Gfq0t2a/xgUEVmhUyC4fbH8YlXALKIPPnAxkAbgE+0T6YtWuW2WsKDw83+5hqx0xkzETGTGTMRMZMZLnN5OjRGADZ/X+yAQ4e/ALR0bkuS3Lw4MvHO3r0C9MNBqBmzZro1KmT3rY333wT7u7uOHbsmG6bjY2Nwc/7+fmhevXq2L179yvHKlOmDFq3bo09e/YgJiYG/v7+un2rVq0CAAwaNEi3bevWrbh69Sq+/fZbuLi46J1Lo9Fg4MCB+P3337Fnzx4EBwe/cny1YvOsQu7u7ojaGYV+4/phf9h+VCheARqhwTWba6g1KnPuEhEREcnS0qzwsgeHrlyxQr16phzx5eNl1mM6VapUMbjdw8NDmoqxceNG/PDDDzh16hTu37+P9PR03T5FUZCSkgI7O7uXjhccHIw9e/Zg1apVCA0NBQCkpqYiLCwMHh4e6Nixo+7YqKgoAMDYsWMNrhcthICiKLh7965xF6tSbJ5Vyt3dHcETg7F/y36cm3IO9tb2CDsThqBNQfjpj58wsNZAS5dIRESkOtbW6ch8cMhQQ5sBH590bNhguvF69UrHlSvZj5dZj+k4Ojoa3K7RaPSa4y+//BJTp07FW2+9hTlz5qBChQq6zw4bNgzR0dFGNc/du3eHq6srVq9eja+//hoajQbbtm3DgwcPMG7cOGg0/zw+l/Vg4eLFi1+6XnTJkiWNvl41YvOsYtpkLeyt7WFvbQ8A6F+jP3Zc3IGR20eiiXcT+BbztXCFRERE6lK/vj9iYn4H0NDA3t/RooU/6tY13XgtWvjjypXsx6tf39/A9ryVlJSEL7/8EhUrVsTOnTthba3f7mm1WqPPZW9vjz59+mDp0qXYs2cP2rVrZ3DKBgC4ubkBANLS0vSmeBQ0XG1DxbTJWrjZueltW9BxAUo6lcTbm97Gs/RnZqtl8ODBZhsrv2AmMmYiYyYyZiJjJrLcZjJr1iT4+k4GEIXMB4bwv39Gwdd3CmbNmmSiCi0znjFu3bqF5ORk1KlTR2qc//rrL1y7di1H58uan7xq1SokJCRgx44dqFWrFmrWrKl3XKNGjQAAERERuS8+H2DzrGKJKYkoYl9Eb5urnSvW9FiD47eP4/ODn5utFr7pScZMZMxExkxkzETGTGS5zcTd3R1RUeEIDt4Ef/8uqFw5EP7+XRAcvAlRUeFwd3c3aZ3mHs8YXl5esLKywpkzZ5CRkaG3b/r06UhLy9n7Iho3boxKlSohIiIC3333HdLS0gw+8Ne9e3eUK1cOu3btwqZNmwye6/z580hJScnR+GrDaRsqpk3WSs0zADT0aoiQliGYdmAa2vq2RfNyzfO8lv79++f5GPkNM5ExExkzkTETGTORvU4m7u7uWLHiGxNWo67xsiNE5ksi7O3t8d5772HRokXo0aMHhgwZAo1Gg7Vr12LPnj2oVKlStus8Z2fgwIGYOnUqZsyYARsbG7z99tvSMba2tti4cSM6dOiAfv36YdiwYWjXrh3s7e1x9epV7NixAz///DPu3r37yrnWasY7zyqWmJIIN3s3g/s+afoJmpZtinc2vYMHSQ/MXBkRERGpiaIoei84mTt3LmbMmIHY2Fj07dsXw4cPh7W1NY4dO4ayZcvqHWuMgQMH6h5KbN++fbYrf9WtWxd//PEHxo0bh4MHDyIoKAg9evTA119/jdTUVHz11VfSMnb5De88q5g2WYtiDsUM7rPSWGF1j9Wo+V1NvP/z+1jXc12O/yAQERFR/lC7dm1pCsbzTp48qfd7W1tbTJkyBVOmTJGO3bNnj7StadOmLz2/t7e30dM9PDw8EBoaqlvarqDhnWcVM/TA4PPKupXFks5LsP7ceqw6vSpPa3n+lZ+UiZnImImMmciYiYyZyJgJqRWbZxVLTJYfGHxR3+p9MajWIIzaPgoX/76YZ7XMmmX+txqqHTORMRMZM5ExExkzkTETUis2zyqW3QODL/p3h3+jlHMpBG0MyrPl69atW5cn583PmImMmciYiYyZyJiJjJmQWrEhOrLpAAAgAElEQVR5VikhROYDgy+ZtpHFxc4Fa3uuxcm/TmL6L9PzpJ7s3mhUmDETGTORMRMZM5ExExkzIbVi86xSyWnJSE1PNerOMwDUL1Mfn7X8DDMjZ+KXq7/kbXFEREREhRSbZ5XSJme+OjO7peoM+bjJx2herjkGbB6Av5P+zqvSiIiIiAotNs8qlZiSCABG33kGMpev+6n7T3ic+hjvbXtPt1i6KUycONFk5yoomImMmciYiYyZyJiJjJmQWrF5VqmsO885aZ4BwNvNGz90+QEbYjZg+cnlJqunbNmyJjtXQcFMZMxExkxkzETGTGTMhNSKzbNKJSZn3nk25oHBF/Xy74UhtYdgzM4xiLsfZ5J6Ro8ebZLzFCTMRMZMZMxExkxkzETGTEit2DyrVG7vPGeZ32E+yriUQdDGIKSmp5qyNCIiIqJCi82zSmmTtdAoGjjbOufq8862zljTYw1O3z2NaQemmbg6IiIiosKJzbNKZa3xrChKrs/xZpk38UWrLzDr11nYf2X/a9UTGxv7Wp8viJiJjJnImImMmciYiYyZkFqxeVYpY98u+CoTm0xEy/ItMWDzANx/ej/X55k0adJr11LQMBMZM5ExExkzkTETGTMhtWLzrFLaZG2O1njOjkbR4MfuPyLpWRLe/e+7uV6+bsGCBa9dS0HDTGTMRMZMZMxExkxkzETfqVOnoNFoMHjwYKOOX716NTQaDebPn5/Hlf2jW7du0Gg0uH79utnGtAQ2zyqVmJJokjvPAODl6oWlgUuxOXYzlkYvzdU5uGSQjJnImImMmciYiYyZyJiJYTmdzvk60z9zw9zjWQKbZ5XSJmtztUxddnpU7YFhdYZh7K6x+DPhT5Odl4iIiCiLKV/QplZsnlUqMdl0d56zzGs/D16uXui/sT9S0lJMem4iIiKiwoDNs0qZ6oHB5znZOiGsZxjOxp/Fp/s/zdFnQ0NDTVpLQcBMZMxExkxkzETGTGSvk8m9e/cweORgVGtSDX5N/FCtSTUMHjkY9+7dM2GFlhvvt99+Q7t27VC0aFE4OzujRYsW+OWXX4z+/LFjx9C9e3eULFkS9vb28PHxwahRo/DXX38ZPP7p06eYOXMm6tWrB1dXVzg5OcHX1xcDBgzAjRs3Xjne6dOnUaZMGbi5uWHfvn1G16lWbJ5VytTTNrLU9ayLL9/6ErOjZmPv5b1Gf+7p06cmryW/YyYyZiJjJjJmImMmstxmEh8fj0btG2FlykrEtI1BXEAcYtrEYGXKSjRq38jkDa25xzt69Cjat2+Phg0bYuPGjVi0aBHu3LmDtm3bIiIi4pWf37JlC5o0aYI//vgDX331Ff773/8iODgYy5cvxxtvvIGrV6/qHX///n00bNgQU6ZMQe3atfHjjz9iw4YN+PDDD3H58mXp+Bft27cPzZs3h6IoOHz4MFq3bv0aV68SgvScOHFCABAnTpywaB3OXzmLub/NzZNzp2eki9arWgvP2Z7i3pN7eTIGERGRqeTk/83BHwQLDIVAiIFfQyGCPwg2aW3mGu/kyZNCURShKIpYvHix3r47d+4IZ2dn4e7uLp4+fSqEEOKnn34SiqKI+fPn645LTEwURYoUES4uLuL27dt651i7dq1QFEUEBATobe/du7dQFEV8++23BuvKyMjQ/XvXrl2Foiji2rVruhpsbGxEtWrVxPXr1426TmO/1pbs16wt27qTIWkZaXic+tgkS9UZolE0WNVtFWourolhW4dhc9/NheLpWCIiKviOnjoKtM1mZxng4M6DiL4TbbLxDh4/CHTIfryje4+abCwAKFmyJIYOHaq3rVSpUhg0aBAWLVqE//73v+jTp4/Bz27evBmJiYkYNWoUPD099fb1798fISEh2LNnD27cuAFvb2/cuXMHGzZsQKVKlTBq1CiD5zTUPwghMHPmTEyZMgVNmzbF1q1b4eaWNz2NJbB5VqGHKQ8BwORznp9XxrUMlgUuQ/fw7vj+xPd474338mwsIiIic0lDGpDd/SANcEV7BfW+r2e6AbV46XhpSDPdWABq1qwJa2u5fatXL/OaTp06lW3zHB2d+ZeG+vXrG9xfv359XLhwAadOnYK3tzeOHs1s/Bs1apSjm2xjx45FREQEevXqhdWrV8PW1tboz+YHbJ5VSJusBZC3zTMAdKvSDe/Vew/jdo1D83LNUdW9arbHJiQkoESJEnlaT37DTGTMRMZMZMxExkxkuc3EGtaAgOGGNgPwKeKDDcM3vHZ9WXpt74Ur4kq241mbuNVyd3c3uL1kyZIAgMTExGw/m7XPw8PD4P6s7VqtVu+fpUuXzlGNhw8fBgB06tSpwDXOAB8YVKWs5jkvHhh80dx2c1GuSLlXLl83ZMiQPK8lv2EmMmYiYyYyZiJjJrLcZlK/dn3gZjY7bwEt3miBup51TfarxRstXjpe/dqG7/LmVnYPIMbHxwPAS6dHZO27e/euwf1Z27OOK1Ik8ybe7du3c1RjREQEKlasiKFDh2Lp0ty9nE3N2DyrUGJy5t8M8/rOMwA42jhibY+1iLkXg8n7Jmd7XEhISJ7Xkt8wExkzkTETGTORMRNZbjOZFTILvid9gRsAMv63MQPADcD3pC9mhcwyUYWWGe/MmTN49uyZtP3EiRMAgDp16mT72bp16wIAfv/9d2mfEALHjh2Doii64xo0aABFURAVFYWMjAzpM9nx9vbGoUOH4Ofnh/feew+LFi0y+rP5AZtnFTLXtI0sdTzr4OvWX2PukbnYfWm3wWOy/iDRP5iJjJnImImMmciYiSy3mbi7uyNqZxSC7YLhv9cflXdXhv9efwTbBSNqZ1S20x5yy9zj3b17FytWrNDb9tdff2HVqlVwd3dHly5dsv1sjx49UKRIEaxcuVK6m7x+/XrExcWhTZs28PLyApD5IGLv3r1x4cKFbBtgkc0bBUuVKoWDBw+iRo0aGDVqFObOnZuTy1Q1znlWocSUzDvPrnauZhtzXKNx2HVpFwZtGYQ/3v8D7k6m/cNORERkLu7u7lixcMWrD8yH4/n7+2PixIm4ffs2mjVrhlu3buGLL75ASkoKVq9eDXt7+2w/6+LigmXLlqFv375o2rQpJk+ejLJlyyIqKgqhoaEoXbo0lixZoveZRYsWISYmBmPHjsWpU6d085gvXbqE8PBwfP3112jevLnB8UqUKIEDBw6gXbt2mDBhApKTkzF5cvY/5c4veOdZhbTJWjjZOMHGysZsY2YtX5eWkYYhW4cUinfTExER5Tf169fHjh078Ouvv6Jnz54YMWIEPD09sXv3bnTt2lV3nKIoBlfI6N69OyIjI1GrVi188skn6NKlC1asWIHBgwfj+PHjKF++vN7xxYoVQ1RUFEJCQnDs2DG888476Nu3L7799ltUqlQJvr6+Lx2zaNGi2LdvHxo3boypU6di+vTppg3EEsy+srTKqeElKdMPTBel55S2yNgRsRECIRALjy7U27506VKL1KNmzETGTGTMRMZMZMxE9nwmavh/M5lHfnhJCu88q1BicqLZ5ju/KNAvECPeGIGPdn+Ec/HndNuz1oakfzATGTORMRMZM5ExExkzIbVi86xC2hStxZpnAJgdMBs+RXwQtCkIyWnJAICFCxdarB61YiYyZiJjJjJmImMmMmZCasXmWYUSkxPNssZzdhxtHBHWMwyxCbH4ZO8nFquDiIiISG3YPKuQNtmyd54BoFapWghtE4p5v8/Dzos7LVoLERERkVpwqToV0iZr4Vfcz9JlYEyDMdh5cScGrh6It26/hTPnziANabCGNerXro9ZIbNMvn4lERERkZrxzrMKJaZY7oHB52kUDWY3mY37P91H+K5wxLSNQVxAHGLaxGBlyko0at8o29eEFgaBgYGWLkF1mImMmciYiYyZyJgJqRWbZxVSw7SNLHNmz0FGqwygOYCspRs1ALyBS3UuYVLIJAtWZ1mjRo2ydAmqw0xkzETGTGTMRMZMSK3YPKuMECLzgUF7yz0w+Lyjp44CXgAqGthZ5n/7C6mAgABLl6A6zETGTGTMRMZMZMyE1IrNs8o8efYE6SJdNXee05D2zx3nF2n+t5+IiIiokFBV85yYmIjp06cjICAAbm5u0Gg0WLNmjcFjz507h3bt2sHFxQXFixfHgAEDEB8fb/DYH374AVWrVoWDgwMqV66M+fPn5+VlvBZtshYALLpU3fOsYQ1k96bujP/tJyIiIiokVNX53LhxAzNmzICHhwf8/Pxw/Phxg+9lv379Opo3bw4/Pz9s2rQJDx8+xIQJE9CmTRscO3YMdnZ2umMXLFiAMWPGYNKkSejSpQv27duH8ePH48mTJ5g8ebI5L88oicmJAKCaO8/1a9dHzM0Y4DGAqi/svJW5v7DasmULunXrZukyVIWZyJiJjJnImInMUCbnz5+3UDVkLvnia2z2F4K/xJMnT8SVK1eEEELs3btXKIoi1qxZIx03bNgw4ezsLO7du6fbFhkZKRRFEYsWLdJte/z4sShSpIjo06eP9HlHR0eRkJAgnduS70oXQojIa5ECIRAx8TEWGf9F8fHxwreur4APBKZBIOR//xwK4VvXV8THx1u6RIt58fuKmIkhzETGTGTMRPZ8JnFxcQKZPwflr0LyKy4u7qXfH5bs11R159nR0RHly5cHAAhheK5ARkYGNm7ciHbt2qFEiRK67U2aNEGFChWwfv16jBgxAgCwe/duJCYmYsCAAXrnGDRoEJYtW4Zt27Zh0KBBeXMxuaSbtqGSBwbd3d0RtTMKk0Im4ejeo/g7+W/89egv9GrRC4t2LirU6zyHh4dbugTVYSYyZiJjJjJmIns+k0qVKiEuLg6PHj2yYEVkLi4uLqhUqZKly8iWqppnY1y5cgVarRa1atWS9tWuXRt79+7V/T46OhoApGOzfn/q1CnVNc+JKeqatgFkNtArFq4AADxMeQiP2R54s+WbhbpxJiIi81JzM0WFi6oeGDTGnTt3AAAeHh7SPg8PDzx8+BBJSUkvPdbFxQWOjo64fft2Hlebc9pkLaw11nCwdrB0KQa52rmiS+UuWHtmraVLISIiIjK7fNc8ZzXGzz8UmCVrW9YxSUlJUBQFtra20rG2tra649QkMTnz7YKGHpRUi6AaQTh99zTOxZ+zdClEREREZpXvmmcHh8w7sikpKdK+rG1Zxzg4OEAIgdTUVIPHZh2nJmp6u+DzBg8erPv3DhU7wM3ODWFnwyxYkeU9nwllYiYyZiJjJjJmImMmMmaiDvmuefb09AQAg2s63717F25ubrqmOLtjHz9+jKSkJJQuXTrbcTp27IjAwEC9X40aNcKWLVv0jtu9ezcCAwOlz48cORLLli3T2xYdHY3AwEAkJCTobZ8+fTpCQ0MBZDbPbnZuuH79OgIDAxEbG6t37L///W9MnDhRb9vTp08RGBiIyMhIve1hYWEG/6D17ds3x9fx/Juezv1xDm6b3PBT1E96D3Y+fx1Z1HYdzzPm6/Gy6wgICCgQ1wGY7utRtGjRAnEdpvx6ZP3Zye/XkcUU1/HHH38UiOsw5dfj+f/G5ufreN7rXkfdunULxHWY8uvx4lsX8+t1vOhV1xEWFqbrxXx8fFC7dm2MHTtWOo+5KCK7ZS0sbO/evQgICMDq1asRFBSk256RkYESJUqgVatW2Lhxo95nKlasCG9vbxw4cABA5hqRPXr0QEREBLp06aI7LjIyEs2bN8eKFSukBwajo6NRr149nDhxAnXr1s3DKzSs34Z+SHiagL0D9776YAvaf2U/Wv/YGlFDo9DQq6GlyyEiIqJCxJL9Wr6786zRaNCjRw/s3r0b9+/f122PiorC5cuX0bt3b922tm3bws3NTXpL4U8//QQHBwd07tzZbHUbS5usVc0ydS/TolwLeDp78sFBIiIiKlRU1zwfOXIEkZGROHv2LADgzz//RGRkJCIjI3VTBKZOnQpbW1t07doVBw4cQEREBN5++21Uq1YNw4YN053LyckJn3/+OdavX49PP/0UR44cwVdffYVly5bhk08+QfHixS1yjS+TmJKIInbqm/P8IiuNFfpV74fwc+FIy0izdDlEREREZqG65rlDhw5o3rw5xo8fD0VRMGPGDDRv3hwtWrTQrY5Rrlw5HDx4EE5OTujSpQsGDx6Mxo0bY9++fdLKGqNHj8bixYuxYcMGtGzZEitWrMDs2bPx6aefWuLyXkmtDwy+OG8JyFx1I/5JPPZf2W+BiizPUCaFHTORMRMZM5ExExkzkTETdVBd8/zgwQNkZGQgIyMD6enpev/u6OioO6569erYtWsXHj9+jL///hurV69GyZIlDZ5z+PDhiI2NRXJyMi5cuGDRSeavotZpG7NmzZK21fOsh0rFKhXaqRuGMinsmImMmciYiYyZyJiJjJmog+qa58Iua51ntVm3bp20TVEUBNUIwqbzm5D0TH1rZuc1Q5kUdsxExkxkzETGTGTMRMZM1IHNs4qkpqciKS1Jlc3z83f9n9e/en88Sn2Eny/8bOaKLC+7TAozZiJjJjJmImMmMmYiYybqwOZZRRKTEwEAbnbqm7aRHb8SfqjnWa/QTt0gIiKiwoXNs4pok7UAoMo7zy8TVCMIP1/4WVc/ERERUUHF5llFsppPNT4w+OLbg57Xt1pfPEt/hk3nN5mxIst7WSaFFTORMRMZM5ExExkzkTETdWDzrCKJKZnTNtR457ls2bLZ7ivjWgYty7csdFM3XpZJYcVMZMxExkxkzETGTGTMRB1U+3puS7Hk6x43xGxA7//0xoOPH6iygX6ZpdFLMfy/w3Fr/C14unhauhwiIiIqwPh6bgLwz7QNF1sXC1eScz2r9oS1xhrh58ItXQoRERFRnmHzrCKJyYlwtXOFlcbK0qXkWFGHouhYqWOhm7pBREREhQubZxXRJmtVu0xdbGzsK48JqhGEY7eP4cL9C2aoyPKMyaSwYSYyZiJjJjJmImMmMmaiDmyeVSQxRZ1vFwSASZMmvfKYzpU7w9nWGWFnw8xQkeUZk0lhw0xkzETGTGTMRMZMZMxEHdg8q4g2Wava5nnBggWvPMbRxhHdq3TH2jNrURieQzUmk8KGmciYiYyZyJiJjJnImIk6sHlWEW2yVpVrPAPGL48TVCMIf97/Eyf/OpnHFVkelwySMRMZM5ExExkzkTETGTNRBzbPKqLmaRvGau3TGu6O7nxwkIiIiAokNs8qouYHBo1lY2WDPtX6IOxsGNIz0i1dDhEREZFJsXlWkcRk9d55Dg0NNfrYoBpBuP3oNg5fP5yHFVleTjIpLJiJjJnImImMmciYiYyZqAObZxVR8wODT58+NfrYRl6NUL5I+QI/dSMnmRQWzETGTGTMRMZMZMxExkzUga/nfoGlXveYITJg/bk1lnRegnfrvWu2cfPK5H2Tsfj4Ytz56A7srO0sXQ4REREVIHw9N+FRyiMICNXeec6poBpBeJD8ALsu7bJ0KUREREQmw+ZZJbTJWgBQ7VJ1OVW9ZHXUKFmjwE/dICIiosKFzbNKJKYkAoBq7zwnJCTk+DNBNYKw9c+teJTyKA8qsrzcZFLQMRMZM5ExExkzkTETGTNRBzbPKpF151mtzfOQIUNy/Jl+1fshKS0JEX9G5EFFlpebTAo6ZiJjJjJmImMmMmYiYybqwOZZJXTTNlS6znNISEiOP1O+SHk08W5SYKdu5CaTgo6ZyJiJjJnImImMmciYiTqweVaJxOTMaRtqnfOc2ydZg2oEYfel3bj35J6JK7I8cz/dmx8wExkzkTETGTORMRMZM1EHNs8qoU3Wws7KDvbW9pYuxaR6+/cGAPwn5j8WroSIiIjo9bF5VonEFPW+XfB1uDu5I8A3oMBO3SAiIqLChc2zSqj57YIAsGzZslx/NqhGEH698Suuaq+ariAVeJ1MCipmImMmMmYiYyYyZiJjJurA5lkltMla1c53BjLf5JNbXf26wsHaAevOrjNhRZb3OpkUVMxExkxkzETGTGTMRMZM1IGv536BpV732Ps/vfEw5SF2vVMw38jXb0M/xNyLwR8j/rB0KURERJTP8fXcpPppG68rqEYQzsSfwZm7ZyxdChEREVGusXlWCW2yVrVrPJtC+4rtUdS+KMLOhlm6FCIiIqJcY/OsEonJBXO1jSy2Vrbo5d8La8+sBWcKERERUX7F5lkl1H7nOTAw8LXPEVQjCNcSryHqZpQJKrI8U2RS0DATGTORMRMZM5ExExkzUQc2zyoghFD9Os+jRo167XM0K9sMZVzKFJg1n02RSUHDTGTMRMZMZMxExkxkzEQd2DyrQHJaMlLTU1XdPAcEBLz2Oaw0VuhXvR/Wn1uPZ+nPTFCVZZkik4KGmciYiYyZyJiJjJnImIk6sHlWAW2yFgBUvc6zqQTVCMK9p/ew78o+S5dCRERElGNsnlUgMSURAFR959lU6pSqA7/ifgVm6gYREREVLmyeVUB351nFDwxu2bLFJOdRFAVBNYKwOXYznj57apJzWoqpMilImImMmciYiYyZyJiJjJmoA5tnFUhMVv+d57Aw063P3L96fzxOfYxtcdtMdk5LMGUmBQUzkTETGTORMRMZM5ExE3Xg67lfYInXPYafDUe/jf3w8P8ewsXOxSxjWlr9H+qjtEtpbOnHv0UTERFRzvD13IWcNlkLjaKBs62zpUsxm6AaQdh+YTseJD2wdClERERERmPzrAKJKYlws3ODoiiWLsVs+lbri7SMNGw8v9HSpRAREREZjc2zCmiTtYVimbrnebp44i2ft7jqBhEREeUrbJ5VIDFZ3W8XBIDBgweb/JxBNYLwy9VfcOvhLZOf2xzyIpP8jpnImImMmciYiYyZyJiJOrB5VgFtilb1zXNevNWoR9UesLGyQfi5cJOf2xz4picZM5ExExkzkTETGTORMRN14GobL7DE05ud1naCjcamUK480SO8B64nXsfx4cctXQoRERHlE5ZcbcPamIO6d+/+Wg+zLVy4EJ6enrn+fEGXmJyIisUqWroMiwiqEYTe/+mNuPtxqFy8sqXLISIiInopo5rniIgIVK1aFTY2NkafWFEUCCFw5swZzJw5k83zS2iTtap+u2Be6lSpE1xsXRB2JgzTW063dDlEREREL2VU8wwA27dvR7ly5XI8gIODQ44/U9gkpqj/gcHIyEg0bdrU5Od1sHFAj6o9sPbsWkxrMS1fLdeXV5nkZ8xExkxkzETGTGTMRMZM1MGoBwYbN26c6yb4dT5bWGiT1f/A4KxZs/Ls3EE1ghB3Pw7Rd6LzbIy8kJeZ5FfMRMZMZMxExkxkzETGTNSBDwy+wNwT0NMy0mAzwwbLApdhSJ0heT5ebj19+hSOjo55cu60jDSUmVsG79R4B3PazcmTMfJCXmaSXzETGTORMRMZM5ExExkz+Qdfz12IPUx5CACqv/Ocl39YrTXW6FutL9adW4f0jPQ8G8fU+B8wGTORMRMZM5ExExkzkTETdchV83zs2DHs2rVLb9umTZvw1ltvoVWrVli6dKlJiisMtMlaACi0DwxmCaoRhNuPbuPQtUOWLoWIiIgoW7lqnsePH49Dh/5pck6cOIE+ffrg7NmzUBQFI0aMwKpVq0xWZEGW1Tyr/c5zXmtQpgF8ivjwdd1ERESkarlqns+fP48WLVrofr9kyRJkZGRg//792L9/P7744gssWLDAZEUWZInJiQDU3zxPnDgxT8+vKAqCagRhw/kNSElLydOxTCWvM8mPmImMmciYiYyZyJiJjJmoQ66a58ePH8Pd3V33+59//hk1a9ZE9erVAQBvvfUW4uLiTFNhAaebtmGv7mkbZcuWzfMxgmoEQZusxc6LO/N8LFMwRyb5DTORMRMZM5ExExkzkTETdchV8+zp6alrjk+fPo07d+6gXbt2uv2PHz9GRkaGaSos4BJTMu88q33O8+jRo/N8DH93f9TyqIW1Z/PH1A1zZJLfMBMZM5ExExkzkTETGTNRB6NfkvK8wMBATJw4ETdu3EB4eDgURUG/fv10+8+dOwcvLy+TFVmQaZO1cLJxgo2V8W9vLMiCagRh+i/T8SjlEVzsXCxdDhEREZGeXN15/vzzz9GoUSOEhITgypUrmDNnDurUqaPbv3z5cjRp0sRkRRZk2mSt6qdsmFO/6v2QnJaMLbFbLF0KERERkSRXzbObmxvCw8Px6NEjJCQkYOzYsbp9Qghs374d3377rcmKLMgSk9X/am4AiI2NNcs4Zd3KolnZZvli6oa5MslPmImMmciYiYyZyJiJjJmog9HNc7Vq1fDxxx/j0KFDuvnMiqJIxymKglKlSnEhbyNpU7Sqn+8MAJMmTTLbWEE1grDn0h7EP4k325i5Yc5M8gtmImMmMmYiYyYyZiJjJupgdPPcvn17REREoGXLlihRogT69u2LH3/8Effu3cvL+gq8/HLn2ZxLD/by7wVFUfCfc/8x25i5weUYZcxExkxkzETGTGTMRMZM1MHo5nnOnDmIjY3FxYsX8dlnn+Hhw4d4//33UapUKTRs2BCff/45Tpw4kZe1FkjaZG2+aJ7NuTxOCccSaOfbTvVTN7hkkIyZyJiJjJnImImMmciYiTrkeM5zhQoVMHr0aOzYsQMJCQmIiIhAnTp1sGzZMrz55psoXbo0hg4dio0bN+LRo0d5UXOBok3OH9M2zC2oRhB+u/Ebrjy4YulSiIiIiHRy9cBgFkdHR3Tu3Bnfffcdrl27hj/++ANjx47FxYsX0b9/f5QoUQJLliwxVa0FUmJK/pi2YW6BfoFwtHHEurPrLF0KERERkc5rNc8vql69OiZNmoSDBw/i3r17+Omnn1ChQgVTDlHg5Jel6kJDQ806nrOtM7r6dVX11A1zZ5IfMBMZM5ExExkzkTETGTNRB5M0z+np6bhw4QJOnjyJp0+fAshczq5Pnz5o27atKYaQnDhxAl27dkWZMmXg7OwMf39/fP7559RbASAAACAASURBVLrxs5w7dw7t2rWDi4sLihcvjgEDBiA+Xh2rOAgh8s0Dgy/mag5BNYJwNv4sztw9Y/axjWGJTNSOmciYiYyZyJiJjJnImIk6KEIIYezBW7duRVhYGKytrTFgwAAEBARg69atGDlyJG7dugUAsLe3x8SJE/HZZ5/lWdGnT59GgwYNULVqVUydOhUlS5bEwYMH8dlnn6FNmzbYvn07AOD69euoU6cO/Pz8dA85TpgwAS4uLjh27Bjs7Oykc0dHR6NevXo4ceIE6tatm2fXAACPUx/D5WsXhPUMQ7/q/V79gUImNT0VnnM8MbzucHzd5mtLl0NEREQqYc5+7UVGv557+/bt6NatGzQaDaysrLBu3TqsWrUKgwcPRrVq1dCzZ08IIfDLL79gxowZ8Pb2xrBhw/Kk6LVr1yI1NRU//fQTqlevDgBo2rQprl27hqVLlyI+Ph4lS5bEjBkzkJqaiq1bt6JEiRIAgFKlSqFZs2ZYvnw5RowYkSf1GUubrAUAPjCYDVsrW/T2742ws2H4svWX0CgmnWVERERElGNGdyPffPMNmjdvjvv37+Px48cYNmwY3n33XXTt2hXR0dGYN28e5s+fj1OnTqFbt25YvHhxnhWd9QIWV1dXve2urq7QaDSws7NDRkYGNm7ciHbt2ukaZwBo0qQJKlSogPXr1+dZfcZKTE4EgHwxbcNSgmoE4VriNUTdiLJ0KURERETGN8/nzp3Dhx9+CDc3N9jY2GDy5MlISkrC4MGD9Y5TFAXBwcGIiYkxebFZhg4dipIlS2Ls2LG4fv06kpKSsGvXLixfvhzvv/8+3NzccOXKFWi1WtSqVUv6fO3atXHy5Mk8q89YujvP+eCBwYSEBIuM27RsU3i5emHtGfU9OGipTNSMmciYiYyZyJiJjJnImIk6GN08JyQkoHTp0rrflypVCgDg4eEhHVuyZEkkJyeboDzDvLy8cOTIEfz5558oX748nJyc0KFDB7z//vu6t+/cuXMn2/o8PDzw8OFDJCUl5VmNxkhMyT93nocMGWKRcTWKBv2r98f6mPV4lv7MIjVkx1KZqBkzkTETGTORMRMZM5ExE3XI0SRSRVEM/ru5xcfHo1OnTkhPT0d4eDgOHz6MyZMn45tvvsEXX3wBALrG2NBDgVnbLN08Z915zg/Nc0hIiMXGDqoRhISnCdh7ea/FajDEkpmoFTORMRMZM5ExExkzkTETdTD6gUEAOHbsGB4/fgwgc3k6ADh69Ci0Wq3ecXk5ZQMAvvjiC8TFxSE2Nha+vr4AMucyP3nyBCEhIejXrx8cHBwAACkpKdLns7ZlHWMp2mQtrDXWcLC2bB3GMPeTrM+r5VELVUtUxdqza9GhUgeL1fEiS2aiVsxExkxkzETGTGTMRMZM1CFHd55Hjx6NNm3aoE2bNmjXrh0A4IMPPtBty/o1ZsyYPCk2y6FDh+Dr66trnLO0b98eGRkZiIqK0k0xMbSm8927d+Hm5vbS5rljx44IDAzU+9WoUSNs2bJF77jdu3cjMDBQ+vzIkSOxbNkyvW3R0dEIDAzUzVnKWuM5JCREWvj8+vXrCAwMRGxsrN72f//735g4caLetqdPnyIwMBCRkZF628PCwqQ56QDQt29fk15HlunTp+fJdaxbtw722+yx+fxmPH32zxqX+e06CsrXg9fB6+B18Dp4HbwOc15HWFiYrhfz8fFB7dq1MXbsWOk85mL0Os8rV67M2YkVBYMGDcpNTa9UrVo1ZGRk4Pz583rbd+/ejfbt22Pp0qUYPHgwihcvjlatWmHjxo16x1WsWBHe3t44cOCAdG5zrhv48Z6PsfH8RlwcczFPxykILv19CRX/XRHreq5D3+p9LV0OERERWVC+WOc5ODg4D8vImXr16iEsLAwXL15ExYoVdduzXo5Sp04dKIqCHj16IDw8HPfv30fx4sUBAFFRUbh8+TLGjx9vkdqfp03W5ov5zgCwbNkyDB061GLj+xbzRYMyDbD27FrVNM+WzkSNmImMmciYiYyZyJiJjJmoQ75868TEiRNha2uLTp06Yf369boHBhcuXIguXbqgTp06AICpU6fC1tYWXbt2xYEDBxAREYG3334b1apVy7MXuOREYkr+eDU3kPk3PEsLqhGEHRd24O+kvy1dCgB1ZKI2zETGTGTMRMZMZMxExkzUwehpGxkZGdiyZQvKlCmDBg0aAACEEBg4cKC08oaLiwsWLFiQpytynDhxAtOnT8evv/6KJ0+eoGzZsujXrx+mTZsGW1tb3XFnz57FRx99hF9//RW2trbo2LEj5s6di5IlSxo8rzl/DNB+dXs42TphY5+Nrz6Y8Nfjv1Bmbhks7rQY79Z719LlEBERkYXki2kbGzduRL9+/XDkyBHdtvT0dKxZswZWVla6bUIIZGRkoHnz5ujbN+9+vF6vXj1s27btlcdVr14du3btyrM6XkdiSiLKuJSxdBn5RinnUmjt0xprz65l80xEREQWYfS0jfDwcHTp0gVvvvmmtO/IkSN49uwZnj17hrS0NLRr1w5hYWEmLbQg0iZr88XbBdUkqEYQDl49iJsPb1q6FCIiIiqEjG6ejx37f/buPD6G+/8D+Gtz30HEkUSIK46kDVoakaDULe4rjgp6KC09oqpVvihFaZ1VFUdLIuoqratu0fgpqSJoXW2KNCSxSchB7P7+SJOKT5BsZndmNq/n45FHm53Z2fe8DN4Zn/l8fkGvXr2K3fbo8IywsDCcPHmybJWVA2p6YFApejXoBRtLG8ScjZG7FCIiIiqHStw8Jycno3bt2kXfbGGB/v37o2LFikVer1GjRrHzK1NRBfM8q0Fxc0bKwdXOFd3qd0PU2Si5S1FMJkrCTETMRMRMRMxExExEzEQZStw829raCstZW1hYYP369fDx8Snyem5ubpGH9kh078E9ZOdlw9VWHcM2xo4dK3cJhcL8wxCfFI8LKReevrMRKSkTpWAmImYiYiYiZiJiJiJmogwlbp59fHwQFxdXon3j4uKE1f+oqPScdABQzZ3nDh06yF1CoS71usDF1gXRZ+QdV6+kTJSCmYiYiYiZiJiJiJmImIkylLh5Dg0NxZIlS5CUlPTE/W7cuIGlS5fynxaeQpujBQA+MGgAOys79GnYB1Fno1DCmRaJiIiIJFHi5nn8+PGwsbFBUFAQNm3ahPv37xfZfv/+fWzcuBFBQUGwtraWdc1xNShontVy51lpwvzDcCntEk7cOCF3KURERFSOlLh5rlSpEnbu3AmNRoN+/frBxcUFfn5+aNGiBfz8/ODs7Iz+/ftDo9Fg586dqFSpkjHrVr30XHUN29i6davcJRTRtlZbVHWsiqgz8j04qLRMlICZiJiJiJmImImImYiYiTKUanluf39/nDlzBkuWLEHbtm2RlZWFy5cv4+7du2jbti0WL16Ms2fP4plnnjFWvWajcNiGSh4YVNq83ZYWlhjoNxDrE9bjge6BLDUoLRMlYCYiZiJiJiJmImImImaiDCVenru8MNVyj5HxkRi1fRTyJufB0sLy6W8gwfHrx9FiRQvsHboX7Wq3k7scIiIiMhE5l+cu1Z1nko42RwsXWxc2zmXwvMfzqFOxjqxDN4iIiKh8KVHzPG3aNKSnpxv0ATNnzkRqaqpB7zVn2hytaoZsKJVGo0GYfxg2nd+EnLwcucshIiKicqBEzfPUqVMNbp6nT5/O5rkY6bnqWV1QyQb5DUJ6bjp2XtwpdylERERUDliVdMeZM2fCxcWlVAfX6/XIy8srdVHlgTZHq6o5nsPDw7Fq1Sq5yxA0dG+IJtWaIOpsFHo17GXSz1ZqJnJiJiJmImImImYiYiYiZqIMJWqeXVxcEBMTU+qD6/V6ODk5wcKCQ6sfpbY7z0pe1SjMPwwf7f8IGbkZcLEt3Q94ZaHkTOTCTETMRMRMRMxExExEzEQZONvGI0z19Gbr1a3h7eqNb3t9a7TPKC+uZVyD9+feWNVjFV4OeFnucoiIiMjIONtGOcQHBqXj5eKFkJohiDrLWTeIiIjIuNg8yyQ9R13DNpQuzD8Me6/sRfKdZLlLISIiIjPG5lkmarvzHBsbK3cJT9SnYR9YaiyxIWGDyT5T6ZnIgZmImImImYiYiYiZiJiJMrB5loFOr0NGboaq7jzPmTNH7hKeyM3BDZ3qdjLp0A2lZyIHZiJiJiJmImImImYiYibKwOZZBpm5mdBDr6rmef369XKX8FRh/mE4du0Yrty+YpLPU0MmpsZMRMxExExEzETETETMRBnYPMtAm6MFAFXN8+zg4CB3CU/VvX53OFo7IvpMtEk+Tw2ZmBozETETETMRMRMRMxExE2Vg8yyD9Nz81RrVdOdZDRxtHNGzQU+sO7MOnIGRiIiIjMGg5lmv12P58uVo3rw5KleuDAsLC+HL0tJS6lrNRuGdZxU9MKgWYf5hOJ9yHqeTT8tdChEREZkhg5rnSZMm4fXXX4ejoyOGDRuGjz/+uNgvKl5B86ymO88RERFyl1AiL9V+CW72bog6Y/wHB9WSiSkxExEzETETETMRMRMRM1GGEi3P/aiVK1fio48+wrRp06Sup1xIz8kftqGmMc/e3t5yl1Ai1pbW6N+4P6LPRmNW+1mw0BhvZJJaMjElZiJiJiJmImImImYiYibKYNDy3E5OTti9ezeCgoKMUZOsTLHc46L/W4SInyKQ81GOUY5f3sUmxiJ4VTAODz+M4JrBcpdDREREElPd8twtWrRAfHy81LWUG+m5XF3QmFrWaAlvV2+TDN0gIiKi8sWg5nnBggX4/PPPsWHDBuh0OqlrMnvaHK2qhmyojYXGAoP8BmHDuQ249+Ce3OUQERGRGSlR81yjRg14e3ujRo0aqFGjBjp37ozU1FQMHDgQdnZ28PLyKtxWsB/H5TyeNkerujvPFy5ckLuEUgnzD0Nadhp+uvyT0T5DbZmYAjMRMRMRMxExExEzETETZSjRA4MdO3Ys9YE1Gk2p31NeqHHYxoQJE7Bt2za5yygx/yr+aOzeGFFno9C1flejfIbaMjEFZiJiJiJmImImImYiYibKUKLmecWKFcauo1zR5mhR0a6i3GWUyuLFi+UuoVQ0Gg3C/MPwyZFPcPfeXTjaOEr+GWrLxBSYiYiZiJiJiJmImImImSiDQWOep02bhoyMjMduv3XrFqexe4L0HPXdeVbjMJxBfoOQdT8L2343zk/paszE2JiJiJmImImImYiYiYiZKINBzfPUqVOh1Wofuz05ORlTp041tCazp83RcnVBE/Cp6INAr0BEneWsG0RERCQNo6wgkZGRARsbG2Mc2iyo8YFBtQrzD8OuS7uQmpUqdylERERkBkrcPP/yyy9YunQplixZAgD45ptvsHTpUuFrzpw5eOutt/DMM88YrWg10+v1qnxgcPbs2XKXYJB+jfpBr9dj47mNkh9brZkYEzMRMRMRMxExExEzETETZSjx8tw//vhjkXHMH3/8cbH7aTQa+Pn54euvvy57dWYoJy8H9x7cU908z1lZWXKXYJCqTlXRvnZ7RJ2NwmvPvSbpsdWaiTExExEzETETETMRMRMRM1GGEi/PnZ2djbt37wIAqlSpgvj4eHh5eRU9mEYDJycn2NraSl+piRh7ucd/7vyD6vOqY/ug7ehWv5vkxyfRN799g5e3vozE8Ymo4VpD7nKIiIiojORcnrvEd57t7e1hb28PADh9+jQaNGgAK6sSv53+pc3Jf9BSbcM21Kxng56ws7LD+rPrEREUIXc5REREpGIGPTDo5+fHxtlABc0zZ9swHRdbF3Sv352zbhAREVGZlagDdnd3h0ajwcMjPB5eQbDg9YLX9Ho9NBoNbt68KWWtZiE9Jx2A+u48p6SkoHLlynKXYbAw/zD0iumFc7fOoZF7I0mOqfZMjIGZiJiJiJmImImImYiYiTKU6M7zgAED0L9/fwwYMKDwy8HBASkpKfD19UXnzp3RuXNn1K9fHykpKXBwcMCAAQOMXbsqFd55VtkDgyNGjJC7hDLpXLczXG1dEX0mWrJjqj0TY2AmImYiYiYiZiJiJiJmogwluvP86HKQy5cvx8GDB3HhwgXUr1+/yLaLFy+iV69e8PPzk65KM6LN0cJCYwEnGye5SykVtS96Y2tli76N+iLqbBSmtZ1W5F9ODKX2TIyBmYiYiYiZiJiJiJmImIkyGDTmed68eZg7d67QOANAvXr1MHfuXMybN6/MxZmj9Nx0uNq6wkJjlPVpjMbUT7IaQ5h/GK7cvoLj149LcjxzyERqzETETETMRMRMRMxExEyUwaAOLjExERUqPH7MboUKFZCYmGhwUeZMm6NV3ZANc9G6ZmtUd6qOqDN8cJCIiIgMY1DzXL9+fcybNw86nU7YptPpMH/+fPj6+pa5OHOUnqO+1QXNhaWFJQb6DURMQgzydHlyl0NEREQqZFDz/Omnn+L777+Hr68vpkyZgjVr1mDNmjWYMmUK6tevj++//x6zZs2SulazoM3VqnKausjISLlLkESYfxiS7ybjwNUDZT6WuWQiJWYiYiYiZiJiJiJmImImymBQ89y5c2ccOHAA1apVw4wZMxAeHo7w8HDMmDEDHh4e2LdvH7p06SJ1rWZBm6NV5Z3n+Ph4uUuQRLPqzVCvUj1J5nw2l0ykxExEzETETETMRMRMRMxEGUq8PPfjZGRkFI5vrlGjBlxd1XdX9WHGXu6x1cpWqFupLlb3XC35salkph6civlx85H8XjLsre3lLoeIiIhKSc7lucs85YOLiwv8/Pzg5+en+sbZFLQ56hy2YU4G+Q1C5r1M7Li4Q+5SiIiISGVKNM/z999/D41Gg+7du0Oj0WDbtm0lOnhoaGiZijNH6bl8YFBuvpV90ax6M0SdjUKfRn3kLoeIiIhUpETNc69evaDRaJCdnQ0bGxv07Nnzqe/RaDR48OBBmQs0N5yqThnC/MMwad8k1Y5BJyIiInmUaNhGfHw8Tp48CRsbm8Lvn/Z18uRJoxauRnm6PNy5d0eVzZq5/SvCgMYDcO/BPWw5v8XgY5hbJlJgJiJmImImImYiYiYiZqIMJbrzHBAQ8MTvqWQycjMAQJXN89ixY+UuQVKeLp5oU6sNos5GIbxJuEHHMLdMpMBMRMxExExEzETETETMRBkMemDwq6++wvnz56Wuxexpc7QAoMoHBjt06CB3CZIL8w/D/qv7kZSZZND7zTGTsmImImYiYiYiZiJiJiJmogwGNc+jR49G48aN4e7ujl69emH+/Pn45Zdfil1xkP6TnpMOQJ13ns1Rn4Z9YKmxxIaEDXKXQkRERCpRomEbj0pKSsKRI0cKv7Zv3w6dTgdHR0cEBgYiODgYwcHBaNOmjcTlqlvhnWc+MKgIFe0roku9Log6G4VxL4yTuxwiIiJSAYPuPFetWhV9+/bFggULEB8fj7S0NOzYsQPjxo1DYmIipkyZgnbt2kldq+oVNM9qvPO8detWuUswijD/MBy/fhyX0i6V+r3mmklZMBMRMxExExEzETETETNRhjIvknL58mVs3rwZGzZsQExMDP744w84ODiweS5Gem7+sA01jnmOjo6WuwSj6Fa/G5xsnBB9pvTnZ66ZlAUzETETETMRMRMRMxExE2UwaHnuRYsWFQ7ZSE5ORqVKldCqVSuEhIQgODgYTZs2haWlpTHqNTpjLvf4xbEv8OH+D3F30l1Jj0tlM2zLMBy/fhznx5yHRqORuxwiIiJ6CjmX5zZozPO4ceNgYWGBPn36ICIiAs8995zUdZml9ByuLqhEYf5h+Pb0tzj1zyk0qd5E7nKIiIhIwQwatjFmzBj4+flh06ZNCAoKQqtWrfDBBx9gx44dSE9Pl7pGs6HN0apyyIa5a+fTDu4O7og6EyV3KURERKRwBjXPixYtwqlTp5CamootW7YgODgYR44cQe/eveHm5oaAgABO5F0MbS6XglYia0tr9G/cH9Fno6HTc7pFIiIierwyPTDo6uqKLl26YNasWfj222+xcOFC1KtXD6dPn8aXX34pVY1mQ83DNsLDDVuFTy3C/MNwPfM6jvx1pMTvMfdMDMFMRMxExExEzETETETMRBkMGvMMAAkJCThy5AgOHz6MI0eO4Pr16wAAT09PDBw4EMHBwZIVaS60OVpUdaoqdxkGMfdVjQK9AlGrQi1EnYlC61qtS/Qec8/EEMxExExEzETETETMRMRMlMGg2TYqV66MtLQ0AECDBg0QHByMoKAgBAcHw8fHR/IiTcmYT282/aopWni2wJfdeFdeiSbtm4RlJ5bhn/f+gY2ljdzlEBER0WPIOduGQcM2hg0bhs2bNyM5ORnnzp3DV199hWHDhpm8cT59+jR69+6NypUrw8HBAfXr18eMGTOK7JOQkICOHTvC2dkZbm5uGDp0KG7evGnSOguk56p32EZ5EOYfhts5t7H70m65SyEiIiKFMmjYxvz586Wuo9QOHjyIzp07o0uXLlixYgWcnZ3xxx9/FGmMExMTERISAl9fX2zevBkZGRl477330L59e/zyyy+wtbU1ac3aHC2X5lYwvyp+8K/ij6izUeju213ucoiIiEiBStQ8nzt3zqCDN2rUyKD3PU1WVhaGDh2KgQMHYtWqVYWvP7qq4fTp03Hv3j1s27YNlStXBgBUq1YNwcHBWLlyJUaPHm2U+oqj1+tV/cBgbGwsWrVqJXcZRhfmH4Zph6bhzr07cLJxeuK+5SWT0mAmImYiYiYiZiJiJiJmogwlGrbh5+dX7Je/v/9jX/P39zda0Rs2bMD169fx8ccfP3YfnU6HTZs2oWPHjoWNMwAEBQWhdu3a2LBhg9HqK87d+3fxQP9AtfM8z5kzR+4STGKg30Bk52Xj+wvfP3Xf8pJJaTATETMRMRMRMxExExEzUYYS3Xl+tNHMycnBhx9+CAsLCwwZMqRwrPOVK1ewbt066HQ6zJw5U/pq/7V3715Ur14dZ86cQa9evfD777/D3d0dgwcPxrRp02BtbY2rV69Cq9Xi2WefFd4fEBCAvXv3Gq2+4mhztACg2jvP69evl7sEk6hVoRaCagQh6mwUBj8z+In7lpdMSoOZiJiJiJmImImImYiYiTKUqHnu27dvke/fffddNG3aFN999x2srIoeYurUqejfvz9OnDiBwYOf3HwY6uLFi8jIyED//v3x0UcfoU2bNti7dy9mzJiBK1euICYmBklJSQCAqlXFqeGqVq2KjIwMZGdnw97e3ig1Pio9J3/lRbU2zw4ODnKXYDJh/mF4a+dbuHX3Ftwd3R+7X3nKpKSYiYiZiJiJiJmImImImSiDQbNtREVFISIiQmicAcDKygrvvfceoqKMt9TxnTt3cPfuXYwfPx4fffQRWrVqhalTp2LMmDH47rvvcOrUKWRnZwNAsQ8FFrxWsI8pFNx55gODytevUT8AwMZzG2WuhIiIiJTGoOY5IyMDmZmZj92emZn5xO1lVXC3+NE74gMGDAAAHDp0qPCns9zcXOH9Ba+Z6q4zkD9NHaDeO8/libujOzrU6YCos8b7AZCIiIjUyaDmuVWrVoiIiMCVK1eEbVeuXMGECROMusKgh4cHAHFIRsH3aWlpqF69OgAUO6dzcnIyXF1dn9g8d+nSBaGhoUW+AgMDsXXr1iL77dmzB6GhocL7x4wZg8jIyMLvtTla4AbwyqBXkJKSUmTfKVOmYPbs2UVeS0xMRGhoKC5cuFDk9UWLFiEiIqLIa1lZWQgNDUVsbGyR16Ojo4tdynPAgAGlPo+HPzM+Ph6hoaGqPI+HPek8XH5xQWxiLP7S/vXY84iIiFD8eZj616Nnz55mcR5S/noU1KP28yggxXnUrVvXLM5Dyl+Ph4+v5vN4WFnP47XXXjOL85Dy1+PR/dV6Ho962nlER0cX9mI+Pj4ICAjA+PHjheOYjN4Af/zxh97Dw0NvaWmpDwkJ0Q8fPlw/fPhwfUhIiN7S0lLv4eGh//333w05dIn873//02s0Gv3x48eLvB4bG6vXaDT6hQsX6nU6nb5ixYr63r17C++vU6eOvk2bNsUe++TJk3oA+pMnT0pa85LjS/RW06z0Op1O0uOaysKFC+UuwaQycjL09jPs9Z8e+fSx+5S3TEqCmYiYiYiZiJiJiJmImMl/jNWvlYRBd57r1auHM2fO4IMPPsCdO3ewfft2/PDDD7h79y4mTZqEM2fOoH79+tJ2+Q/p168fNBoNNm4sOiY1JiYGQP58zxqNBr1798aePXuQmppauE9cXByuXLmCfv36Ga2+4hTM8azRaEz6uVJ588035S7BpJxtnRHqG/rEoRvlLZOSYCYiZiJiJiJmImImImaiDAatMAgAlSpVwvTp0zF9+nQp6ymRhg0bYvTo0Zg/fz6cnZ0REhKCffv2YenSpRg5cmTh4iyTJ0/Gli1b0KNHD0yfPh0ZGRl4++230bhxY4waNcqkNWtztKqd47m8CvMPQ4/1PXD25ln4VfGTuxwiIiJSAIPuPCvBwoULMX36dKxatQovvfQS1q5di//9739Yvnx54T41a9bEoUOH4OjoiO7duyM8PBwtW7bEvn37YGNjY9J603PVu7pgedWpbidUtKuI6DPRcpdCRERECmHwnecLFy5g5cqVuHr1KtLS0qDX6wu36fV6aDQa7N+/X5Iii2NhYYGJEydi4sSJT9zPz88Pu3fvNlodJaXN0ap6mroLFy6gQYMGcpdhUjaWNujbqC+izkZhxoszhCE35TGTp2EmImYiYiYiZiJiJiJmogwG3XmOjo6Gn58fVqxYgb/++gsPHjyATqcr/NLr9UWaacpvntV853nChAlylyCLMP8w/Kn9E8euHRO2lddMnoSZiJiJiJmImImImYiYiTIYdOd58uTJGDlyJBYuXFjsIiQkSs9Nh6ezp9xlGGzx4sVylyCLYO9geDp7IupMFAJrBBbZVl4zeRJmImImImYiYiYiZiJiLPAcPQAAIABJREFUJspg0J3na9eu4f3332fjXApqH7bh7e0tdwmysLSwxEC/gYhJiEGeLq/ItvKayZMwExEzETETETMRMRMRM1EGg5rnOnXqIDk5WepazJrah22UZ2H+YbiVdQv7ruyTuxQiIiKSmUHN88SJE/Hee+8VmT+Zniw9J51T1alUk2pN4Ovmy+W6iYiIyLDmec+ePbhy5Qp8fHzw0ksvYejQoRg2bJjwRfnuPbiH7LxsVd95fnSZzvJEo9EgzD8Mm89vRvb97MLXy3Mmj8NMRMxExExEzETETETMRBkMemAwLi4OTk5OcHR0xNWrV/Hnn38W2V4wVR3lS89JBwBVN89ZWVlylyCrQX6DMOXgFPzwxw/o1zh/dcrynklxmImImYiYiYiZiJiJiJkog0bPOeWKiI+PR7NmzXDy5Ek0bdpUkmNeTL2I+ovr48DLB9CmVhtJjkmm1/zr5vB08cSWAVvkLoWIiKhcM0a/VlKqXWFQTbQ5WgDqvvNM+Q8O7ri4A7ezb8tdChEREclEkuY5JycHWVlZwhflS8/NH7bBBwbVbUDjAbj/4D42n98sdylEREQkE4Ob5w0bNuCFF16Ara0tHB0d4eTkBCcnJzg7Oxf+l/KZw53nlJQUuUuQXXXn6njR58XCWTeYiYiZiJiJiJmImImImYiYiTIY1DxHRUVh4MCBsLW1xUcffQRLS0u89957eOONN+Dm5oZatWph3rx5UteqWgUPDLrYushcieFGjBghdwmKEOYfhgNXD+BG5g1mUgxmImImImYiYiYiZiJiJspgUPM8d+5cDBkyBIcOHcLkyZNhaWmJUaNGYfHixbh8+TIqVqyIvLy8px+onNDmaOFs4wxLC0u5SzHY1KlT5S5BEXo37A1rS2vEnI1hJsVgJiJmImImImYiYiYiZqIMBjXPFy5cQHh4+H8HsbDA/fv3AQDOzs74+OOPsXz5cmkqNAPmsLqgqZ9kVaoKdhXQtV5XRJ2NYibFYCYiZiJiJiJmImImImaiDAY1z3Z2drCy+m+K6AoVKuDvv/8u/L5KlSpFvi/v0nPTVd8803/C/MNw4sYJ/JH6h9ylEBERkYkZ1Dz7+PjgzJkzhd83bdoUX375JfLy8vDgwQMsXboU3t7ekhWpdtocLVztONOGueharyucbZwRfSZa7lKIiIjIxAxqnjt16oR169YVfj9hwgTs2LEDFSpUQMWKFbFu3TpMmDBBsiLVzhzuPEdGRspdgmLYW9ujd8Pe+PLrL8E1horidSJiJiJmImImImYiYibKYFDzPHPmTBw9erTw++DgYMTFxeG1117DyJEjsWfPHowcOVKyItVOm6NV/RzP8fHxcpegKGH+YUi+mIz4JObyMF4nImYiYiYiZiJiJiJmogxcnvsRxlju8dllzyLYOxiLuyyW5HgkvzxdHjzne2KI/xDM68hpGYmIiExJ9ctzP3jwABcvXsSvv/7KlQWLkZ6j/mEbVJSVhRUGNB6A9Qnr8UD3QO5yiIiIyERK1Txv27YNgwYNwtChQ7Fnz57C12rVqgVfX180a9YMlStXxpQpU4xSrFqZw7ANEoX5h+FG5g0c/uuw3KUQERGRiVg9fZd8O3bsQM+ePWFhYQFLS0usX78ea9asQXh4OBo3bow+ffpAr9fj4MGDmD59OmrUqIFRo0YZs3ZV0Ol1yMjN4J1nM9TCswV8Kvgg6kwU2vq0lbscIiIiMoES33meO3cuQkJCkJqaijt37mDUqFF45ZVX0KNHD8THx+OLL77AggULcOrUKfTs2RPLli0zZt2qkZmbCT30qp+qLjQ0VO4SFKdz585wPuSMlRErUb9lfTQOaozwMeG4deuW3KXJhteJiJmImImImYiYiYiZKEOJm+eEhASMGzcOrq6usLa2xqRJk5CdnV1kpUEA0Gg0GD58OM6dOyd5sWqkzdECgOrvPI8dO1buEhTl5s2bOH35NE5XOg3dIB0udryIc+3PYXXuagR2Ciy3DTSvExEzETETETMRMRMRM1GGEjfPKSkp8PDwKPy+WrVqAICqVasK+1apUgU5OTkSlKd+6bnpANTfPHfo0EHuEhTl/f+9j6TWSUANAJp/X7QAUAO43OQyJkwtn/Oc8zoRMRMRMxExExEzETETZSjVA4MajabY/6fHK7jzzAcGzcvxU8cBr8ds9Px3OxEREZmdEj8wCAC//PIL7ty5AyB/ejoAOH78OLRabZH9OGTjP+YybIOKykPef3ecH2Xx73YiIiIyO6Vqnt98803htTfeeEOyYsxRek7+sA21PzC4detW9OzZU+4yFMMKVsB5AA2L2aj7d3s5xOtExExEzETETETMRMRMlKHEf8OvXLmyVAfmsI582hwtbC1tYWdlJ3cpZRIdHc3fsA9pHtAc53aeK755vg5U96lu8pqUgNeJiJmImImImYiYiYiZKAOX536E1Ms9zjg8A4uPL8Y/7/0jQXWkFLdu3UJgp0BcbnIZ8ET+0wM6ANcBxyOOyOqZha/6f4VXmr0ic6VERETmR/XLc9PjaXO0qh+yQSJ3d3fE7YrDcNvhaLS3EervqY9GexthuO1wXDp8CaNDRuPVH17F1INTwZ9PiYiIzEf5HJhpQtocLR8WNFPu7u5YtWRVsdsWd1kMLxcvTNo/CdcyrmFZt2WwsuBvNyIiIrXj3+ZGlp6bzmnqyiGNRoMPgj+Ah7MHRm0fhX/u/IOYvjFwtHGUuzQiIiIqAw7bMDJzufP86EqSVLJMXg54GT8M+gEH/zyIF795EbfumvfKg7xORMxExExEzETETETMRBnYPBtZek66WTTPXNVIVNJMOtbtiEPDD+FP7Z9oubIlLqddNnJl8uF1ImImImYiYiYiZiJiJspg0GwbGRkZcHFxMUY9spP66c36i+qjh28PzO0wV4LqSM2u3L6CTms7IT03HT+G/YjnPJ6TuyQiIiJVUt1sG1WrVkW/fv2wbds25OVxJbUnMZdhG1R2tSvWxtERR1GrQi20Wd0Guy7tkrskIiIiKiWDmue33noL//d//4eePXuievXqGDNmDOLi4qSuTfX0en3+A4Ocqo7+5e7ojv3D9qOtT1t0j+6ONafWyF0SERERlYJBzfPs2bPx119/4eDBg+jTpw9iYmIQFBSEevXqYerUqbh06ZLUdapSTl4O7j24ZxZ3nmNjY+UuQXEMzcTRxhFbBmxBeEA4hn8/HDOPzDSbuaB5nYiYiYiZiJiJiJmImIkyGPzAoEajQUhICJYtW4akpCRs27YNzz//PObNmwdfX18EBgZiyZIlSEtLk7JeVUnPTQcAs2ie58yZI3cJilOWTKwsrPBVt6/wvzb/w4f7P8TYHWPxQPdAwurkwetExExEzETETETMRMRMlEHS5bljY2Px9ddf49tvv80/uEYDKysrDB06FHPmzEGlSpWk+iijkXIA+oWUC2i4pCEODz+M4JrBElUoj6ysLDg4OMhdhqJIlcmK+BV4/YfX0d23O6J6R8He2l6C6uTB60TETETMRMRMRMxExEz+o7oHBh92+vRpTJw4EbVq1UJISAh++uknvP3224iPj0dSUhJmzZqF7du3Y8iQIVLUqyraHC0A87jzzN+sIqkyGdV0FLYO3Irdl3aj/bftkZqVKslx5cDrRMRMRMxExExEzETETJTBoBUG//zzT0RFRSE6OhoJCQlwdHREr169MGTIELRr1w6WlpaF+77zzjvw9/dHaGioZEWrRXqO+QzbIOPqVr8bDrx8AN2iu6HVqlbYNXgXalaoKXdZRERE9AiDmufatWvD0tIS7dq1w8SJE9GrV68n/jRUu3ZtNGjQwOAi1argzjNn26CSaOHVAkdHHEWntZ0QGBmIHYN3IKBagNxlERER0UMMGrYxb948XLt2Dbt27cLgwYOf+s8IderUwa+//mpQgWqWnpsOC40FnGyc5C6lzCIiIuQuQXGMkUl9t/qIGxkHD2cPhKwKwb4r+yT/DGPidSJiJiJmImImImYiYibKUOrm+e7du1i5ciWuX79ujHrMijZHCxdbF1ho1L8Kure3t9wlKI6xMqnqVBUHhx9Eyxot0XldZ0SdiTLK5xgDrxMRMxExExEzETETETNRBoNm23B1dUV8fDzq1KljjJpkJeXTm5P2TUL02WhcHXdVouqoPLn/4D5e2f4K1vy2BnNfmot3A9+FRqORuywiIiLZyTnbhkFjnlu2bIm9e/eaZfMspfScdD4sSAaztrTGqh6r4OnsiYifInAt4xrmd5xvFv+SQUREpFYGNc+zZ89Gt27dkJGRgd69e8PLywvW1tbCfhYW5fsveW2uFq62fFiQDKfRaPBJu0/g6eKJsTvG4kbmDXzT6xvYWdnJXRoREVG5ZFB3GxAQgGvXruH9999HvXr1YG9vDysrqyJfxTXT5Y02R2s2d54vXLggdwmKY8pM3nj+DWzqvwnb/9iOTms7Fc7kojS8TkTMRMRMRMxExExEzEQZDLrzPG3atKfuw7GZ+cM26lQyj6EtEyZMwLZt2+QuQ1FMnUmvhr2wd+hedI/ujlYrW2HXkF3wcvEy2eeXBK8TETMRMRMRMxExExEzUQZJl+c2B1IOQPdb6od2Pu2woPMCiaqTT2JiIp/yfYRcmZy/dR6d1nWCTq/DzsE74VfFz+Q1PA6vExEzETETETMRMRMRM/mPqpfn1ul0SEpKQlJSEnQ6nRQ1mY30XPN5YJC/WUVyZdLQvSHiRsbBzd4NwauCcejPQ7LUURxeJyJmImImImYiYiYiZqIMBjfPFy5cQK9eveDs7AxPT094enrCxcUFffr0we+//y5ljaqlzdFydUEyCg9nDxwafghNqzdFh7Ud8F3Cd3KXREREVC4YNOb5zJkzaNWqFfR6Pfr27Vs4Zd3ly5exZcsW7Nu3D0eOHIG/v7+kxapJni4Pd+7dMZs7z6Q8rnau2Dl4J4ZvHY4BGwcg6U4S3mrxltxlERERmTWD7jxPnDgRfn5+SExMxJo1a/Dxxx/j448/xpo1a5CYmAg/Pz9MnDhR6lpVJSM3AwDMZqq62bNny12C4ighExtLG6ztvRbvBr6LcbvGYcJPE6DTyzd8SgmZKA0zETETETMRMRMRM1EGg+48x8bGYsuWLahQQbyrWqFCBUybNg09e/Ysc3FqVjCVmLncec7KypK7BMVRSiYWGgvM7TAXXi5eeHv327iReQMre6yEjaWNyWtRSiZKwkxEzETETETMRMRMlMGg2TacnZ2xY8cOBAcHF7v9yJEj6Ny5M+7cuVPmAk1Nqqc3f036FU2XN8WJV06gmUczCSskerwNCRswdMtQBHsHY/OAzXCxdZG7JCIiIsmpbraN5s2b45NPPsG9e/eEbffu3cOsWbPQokWLMhenZgV3nvnAIJlS/8b9sWfIHpy4cQIhq0JwI/OG3CURERGZFYOGbcyYMQNt27ZFrVq1MHDgwCIPDK5fvx63b9/G/v37JS1Ubcxt2AapR+tarRE7Ihad1nZCy8iW2DVkFxpUbiB3WURERGbBoDvPgYGBOHToEOrWrYsFCxbgzTffxJtvvokFCxagfv36OHToEAIDA6WuVVXSc9MBmM8DgykpKXKXoDhKzsSvih/iRsbBycYJQSuD8PPfP5vkc5WciVyYiYiZiJiJiJmImIkyGDzPc4sWLXD48GHcvn0bp0+fxunTp5GWloaDBw+iefPmUtaoKrdu3UL4mHBMHDERmmgNAkICED4mHLdu3ZK7tDIZMWKE3CUojtIzqeFaA0fCj8Cvih/afdMOWy9sNfpnKj0TOTATETMRMRMRMxExE2Xg8tyPKMsA9Js3b6Jl55a43OQy4AVAA0AH4DpQ59c6iNsVB3d3d2OUbXTx8fEmH5CvdGrJJCcvB0O3DMXm85uxpMsSvP7c60b7LLVkYkrMRMRMRMxExExEzOQ/cj4wWKbm+eTJk7h69Spu376N4g7z6quvlqk4OZTlFyN8TDhW564GahSz8W9guO1wrFqySpI6iUpDp9fh7V1vY+Hxhfgw+ENMbzsdGo1G7rKIiIgMImfzbNADg+fPn8egQYNw+vTpJ+6nxua5LI6fOg689JiNnsDxvcdNWg9RAQuNBb7o9AW8XLwwYe8EXM+8juXdlsPa0lru0oiIiFTFoOY5PDwcd+/excaNG+Hv7w9nZ2ep61KlPOTlD9UojsW/24lkotFoEBEUAQ9nD4R/H46kzCRs7L8RTjZOcpdGRESkGgY9MPjbb79hwYIF6N27N+rVq4dq1aoV+2VKCxcuhIWFRbFjihMSEtCxY0c4OzvDzc0NQ4cOxc2bNyWvwQpWwOMGwej+3a5SkZGRcpegOGrNZPAzg7Fj8A78/PfPaLO6DZLvJEt2bLVmYkzMRMRMRMxExExEzEQZDGqevby8pK6jTFJTUzF16lQ4ODgI4zgTExMREhKCzMxMbN68GcuXL0dsbCzat2+P3NxcSetoHtAcuPaYjdf/3a5S8fHxcpegOGrOpH3t9jgcfhjXM6+j5cqWuJh6UZLjqjkTY2EmImYiYiYiZiJiJspg0AODX375JdatW4effvoJ9vb2xqirVN544w2cOnUKLi4uOHnyZJFp4V555RWsX78eV69eReXKlQEAR48eRXBwMJYsWYLRo0cXOVZZBqDfunULgZ0C82fb8ET+jyZmMtsGmac/tX+i09pOSM1OxY9hP6K5p3p/wCMiovJDdQ8Mjh49GtevX4ePjw+6du0KDw+PYp/cnzZtWpkLfJozZ85gxYoVOHLkCKZMmVKkDp1Oh02bNqFjx46FjTMABAUFoXbt2tiwYYPQPJeFu7s74nbFYcLUCVgTvQaVnSrD3d4dzQOaY86uOWycSXFqVaiFoyOOInR9KNquaYsNfTega/2ucpdFRESkWAY1zwcPHsSCBQtw9+5drFq1CpaWlkW26/V6aDQakzTP48ePR58+fdCiRQth29WrV6HVavHss88K2wICArB3717J63F3d8fSL5Zi9czVmN9rPoY8M0TyzyCSkpuDG/YO3YtBmwahx/oe+KrbVxjZdKTcZRERESmSQWOex40bB19fXxw7dgx3797F/fv3i3zl5eXh/v37Utcq2Lx5M37++WfMnj272O1JSUkAgKpVqwrbqlatioyMDGRnZ0teV1p2GgCgkn0lyY9NZAz21vbY1H8TXmn6CkZtH4Vph6YVO3c7ERFReWdQ8/zHH3/g008/RfPmzWUb85ybm4v33nsP48ePh7e3d7H7FDTGtra2wraC19g8l0xoaKjcJSiOuWViaWGJpV2XYkbbGZhycApe++E15OlKN72iuWUiBWYiYiYiZiJiJiJmogwGDdvw8fHBnTt3pK6lVObNm4esrCx8+OGHj92noLEvblaNgteM0fybY/M8duxYuUtQHHPMRKPR4MOQD+Hp4olR20bhnzv/YH3f9XCwdijR+80xk7JiJiJmImImImYiYibKYNCd548++giTJ0/G9evXpa6nRFJSUjBr1ixMnjwZdnZ2yMvLQ15eHvR6PfR6PR48eACdTofq1asDQLFzOicnJ8PV1fWxzXOXLl0QGhpa5CswMBBbt24tst+ePXuEnwTTstOAH4GdG3YWeT0+Ph6hoaFISUkp8vqUKVOEoSeJiYkIDQ3FhQsXiry+aNEiREREFHktKysLoaGhiI2NLfJ6dHQ0wsPDhXMbMGBAic4DAMaMGYPIyEh06NDBLM7jYWU9jw4dOpjFeQDir8fwgOHY2Gsjdkzbgec/eh4pWf8d+0nnkZWVpajzAOT/9Sj4vaP28yggxXksXrzYLM5Dyl+Ph/+MVfN5PKys59GgQQOzOA8pfz0evk7UfB6Petp5REdHF/ZiPj4+CAgIwPjx44XjmIpBU9UNHToUR44cQXJyMgIDA+Hp6VnsbBvffPONJEU+6tSpU0+dlmTIkCFYs2YN3Nzc0LZtW2zatKnI9rp166JGjRo4cOBAkdelmPokMj4So7aPQt7kPFhaWD79DUQKdeLGCXSN6gpXW1fsHrIbPhV95C6JiIhIfVPVxcXFwdraGp6enkhMTMTff/9dZHvBbBvG0qBBA5w4cUL4zLfeegvnz5/H3r174ebmBo1Gg969eyMmJgapqalwc3MrrP/KlSt45513jFJfWnYaKthVYONMqvecx3P4ecTP6LSuEwIjA7Fj8A40rW7aP6SIiIiUxKBhG5cuXcLFixdx6dKlwv9/+KvgNWOxs7ND06ZNi3w1a9YMzs7OsLS0RNOmTVGzZk0AwOTJk2FjY4MePXrgwIED+P777zF48GA0btwYo0aNMkp9adlpZjXeGYDwzylUfjKpU6kOjo44Cm9Xb7Re3Rp7Lu957L7lJZPSYCYiZiJiJiJmImImymBQ86xUGo1GuONds2ZNHDp0CI6OjujevTvCw8PRsmVL7Nu3DzY2NkapIzU71eya5+joaLlLUJzylEkVxyo48PIBhNQMQdeorvjmt+KHZJWnTEqKmYiYiYiZiJiJiJkog0FjnoH82SrWrVuHQ4cOITU1FcuXL4eHhwfu37+P6OhoBAYGol69elLXa3RSjKHpu6EvMu9lYveQ3RJXRySvPF0eXv/hdUT+GolZ7Wbh/aD3jTpEi4iIqDiqG/OcmpqKF198EQkJCahZsyb+/PPPwqnrrK2tERkZicOHD2PFihWSFqsWadlpqOokLsxCpHZWFlb4uvvX8HLxwgf7PsC1jGtY0GkBx/cTEVG5YdCwjQkTJuDOnTv47bffcPnyZWH4w6hRo3D48GFJClSjtOw0VLIzr2EbRAU0Gg2mtpmK5d2W48sTX6L/xv7Ivi/9YkNERERKZFDzvH37dsyePRuNGzcudnujRo0Kl8Yuj8zxgUGiR73S7BVsHbAVOy/uRIe1HQoXByIiIjJnBjXPmZmZ8PLyeuz21NRU6HQ6g4tSO3Nsnoub1Ly8YyZAd9/u2P/yfpy/dR6tVrZC/8H95S5JcXidiJiJiJmImImImSiDQc1z3bp1hcVFHvbdd9/B39/f4KLULDcvF3fv3zW75vnRVY2ImRR4wesFHB1xFNl52fhJ8xNOJ5+WuyRF4XUiYiYiZiJiJiJmogwGNc+jR4/GjBkzsGrVKmRn/zfW8caNGxg/fjwiIyPxxhtvSFakmtzOuQ0AZtc8Dxo0SO4SFIeZ/Me3si/iRsbBJ9gHwauCsf/qfrlLUgxeJyJmImImImYiYibKYFDz/MYbb+DVV1/FyJEj4erqitzcXDRv3hxeXl5YuHAh3n33XQwbNkzqWlWhYNynuTXPRE9TzakaDg0/hBe8XkCntZ2w/ux6uUsiIiKSnEFT1QHA559/jpdffhnfffcdLl68CJ1Ohzp16qB///5o1qyZlDWqSkHz7ObgJnMlRKbnbOuM7YO2Y9S2URi0aRBuZN7AO4HvyF0WERGRZMq0wmBAQAA++eQTbNiwARs3bsTs2bPLdeMMmO+d59jYWLlLUBxmIoqNjYWNpQ3W9FyDD1p9gHf3vIt3dr8Dnb78PkDM60TETETMRMRMRMxEGcxqeW4lKGieK9pVlLkSac2ZM0fuEhSHmYgKMtFoNJjZbiYWdV6EL459gUGbBiE3L1fm6uTB60TETETMRMRMRMxEGUo8bKNz586lXoZ3x44dpS5I7VKzUuFs4wxrS2u5S5HU+vUcv/ooZiJ6NJOxzcfCw9kDYZvC0GldJ2wZsAUV7CrIVJ08eJ2ImImImYiYiYiZKEOJm+fdu3fD1tYW1apVAwDo9fon7l/aRttcmOMczwDg4OAgdwmKw0xExWXSu2Fv7B22F6HRoQhZFYKdg3fC08VThurkwetExExEzETETETMRBlK3Dx7enri+vXrcHNzw+DBgzFgwAB4eHgYszZVMtfmmagsWnm3QuyIWHRa2wmBkYHYOXgnGlcpfoVSIiIiJSvxmOfExETs378fTZs2xSeffAJvb2+8+OKLWLFiBbRarTFrVJW0HDbPRMVp5N4IcSPjUNG+IlqtaoUjfx2RuyQiIqJSK3HzrNFo0KZNGyxfvhxJSUnYvHkzqlSpgnHjxqFq1aro0aMHYmJikJWVZcx6Fc9c7zxHRETIXYLiMBPR0zLxdPHE4eGH0aRaE7z07UvYdG6TiSqTD68TETMRMRMRMxExE2UwaLYNa2trhIaGYv369UhOTkZkZCRSU1MRFhaGzz77TOoaVcVcm2dvb2+5S1AcZiIqSSaudq7YOXgnejXshX7f9cOi/1tkgsrkw+tExExEzETETETMRBk0+qc9+fcE2dnZ2LZtG9atW4fdu3fD2toaS5cuVfXqgvHx8WjWrBlOnjyJpk2blvr9Pgt8MMhvEGa2m2mE6ojMh06vQ8SeCMw/Nh/vB72Pme1mwkLD2TOJiOjpytqvlUWpVxjMy8vDrl27EB0djW3btiE3NxcdOnTAypUr0bNnTzg6OhqjTtUw1zvPRFKz0FhgXsd58HLxwjt73sH1zOuIDI2EjaWN3KURERE9Vomb50OHDiEqKgqbNm3C7du30apVK8ydOxf9+vWDmxuXogaA+w/uIyM3g80zUSm8Hfg2PJw9MGzrMCTfScam/pvgbOssd1lERETFKnHz3LZtW9ja2qJLly4YNGgQatSoAY1Gg8uXL+Py5cvFvqd58+aSFaoG2pz8WUfc7M3vh4kLFy6gQYMGcpehKMxEZGgmA/wGoIpjFfSM6YnWq1tjx+AdqOZUzQgVmh6vExEzETETETMRMRNlKNUAw9zcXGzZsgX9+/dHYGAgXnjhhcd+BQYGGqtmxSpYmtsc7zxPmDBB7hIUh5mIypJJW5+2iA2PRfLdZARGBuL3lN8lrEw+vE5EzETETETMRMRMlKHEd55XrlxpzDrMgjk3z4sXL5a7BMVhJqKyZuJf1R9xI+PQeV1ntFzZEj8M+gGBNdT9gzivExEzETETETMRMRNlKHHzPHz4cCOWYR5Ss1MBmGfzzOlxRMxEJEX8FkENAAAgAElEQVQm3q7eOBJ+BD3W98CL37yImL4xCPUNlaA6efA6ETETETMRMRMRM1EGzgsloYI7zxXtK8pcCZG6VbKvhJ+G/oSu9bqiV0wvfHXiK7lLIiIiAsDmWVJp2WlwsHaAnZWd3KUQqZ6dlR1i+sbgjefewOs/vo6PD3yMMkxLT0REJAk2zxIy5zmeZ8+eLXcJisNMRFJnYmlhiYWdF2J2+9mYfng6Rm0bhfsP7kv6GcbG60TETETMRMRMRMxEGUq9SAo9njk3z1lZWXKXoDjMRGSMTDQaDSYETUB1p+oYsW0Eku4kYUO/DXCycZL8s4yB14mImYiYiYiZiJiJMpRpeW5zVJblHsM2hSHpThIOvHzASNURlW8/Xf4JvTf0RoPKDfBj2I+o4lhF7pKIiEgGci7PzWEbEjLnO89ESvBSnZdwePhh/J3+N1pGtsSltEtyl0REROUMm2cJpWWnoZIdm2ciY2pSvQniRsbB0sISLSNb4pfrv8hdEhERlSNsniVkzneeU1JS5C5BcZiJyFSZ+FT0wdERR1GnUh20WdMGOy/uNMnnGoLXiYiZiJiJiJmImIkysHmWkDk3zyNGjJC7BMVhJiJTZlLZoTL2DduH9rXbo3t0d6z6dZXJPrs0eJ2ImImImYiYiYiZKAObZ4k80D2ANkcLNwc3uUsxiqlTp8pdguIwE5GpM3GwdsCm/pswsslIjNg2AjMOz1DcXNC8TkTMRMRMRMxExEyUgVPVSSQ9Nx166M32zrOpn2RVA2YikiMTKwsrLOu2DF4uXph8YDKuZVzD4i6LYWWhjD/eeJ2ImImImYiYiYiZKIMy/nYxAwVLc5tr80ykZBqNBpNbT4aniyde3f4qku4kIbpPNBysHeQujYiIzAyHbUgkNSsVAJtnIjmNaDIC2wZtw94re9H+m/aFvy+JiIikwuZZIuZ+5zkyMlLuEhSHmYiUkEmXel1w8OWDuJR2CUErg/Cn9k9Z61FCJkrDTETMRMRMRMxEGdg8S8Tcm+f4+Hi5S1AcZiJSSibPez6Pn0f+jDxdHgIjA/Fr0q+y1aKUTJSEmYiYiYiZiJiJMnB57kcYutzjov9bhIifIpD9YTY0Go0RKySikrp59ya6RnXF7ym/Y1P/TXipzktyl0RERBLg8txmoGCOZzbORMpRxbEKDrx8AK28W6FLVBesPb1W7pKIiEjl2DxLxJwXSCFSMycbJ3w/8HsMfWYohm4ZijlH5yhuLmgiIlIPTlUnkbQcNs9ESmVtaY3I0Eh4uXjh/b3v41rGNXze8XNYWljKXRoREakM7zxLxNzvPIeGhspdguIwE5GSM9FoNJjWdhqWdV2GJb8swcBNA5GTl2P0z1VyJnJhJiJmImImImaiDGyeJWLuzfPYsWPlLkFxmIlIDZm89txr2Nx/M3744wd0+LYDbmffNurnqSETU2MmImYiYiYiZqIMbJ4lkpadBjd7N7nLMJoOHTrIXYLiMBORWjLp0aAH9g/bj4RbCWi1qhX+Tv/baJ+llkxMiZmImImImYiYiTKweZaIud95JjI3gTUC8fOIn3H33l0ERgbiTPIZuUsiIiIVYPMsAZ1ex+aZSIV8K/sibmQc3B3d0WpVKxz886DcJRERkcKxeZZARm4GdHqdWTfPW7dulbsExWEmIjVmUt25Og4NP4Tmns3RcW1HxJyNkfT4aszE2JiJiJmImImImSgDm2cJmPvS3AAQHR0tdwmKw0xEas3ExdYFP4b9iP6N+2PgpoH44tgXkh1brZkYEzMRMRMRMxExE2Xg8tyPMGS5xxM3TuD5r59H/KvxaFK9iZErJCJj0el1mLRvEmYfnY13A9/FnJfmwELDewxEREoj5/LcXCRFAuXhzjNReWChscCn7T+Fp7Mnxu0ah+uZ17G6x2rYWtnKXRoRESkEm2cJsHkmMi9vtngT1Z2rY8jmIUi+k4wtA7bA1c5V7rKIiEgB+O+REkjLToOVhRWcbJzkLoWIJNK3UV/sGboHv/7zK0JWh+BG5g25SyIiIgVg8yyBgmnqNBqN3KUYTXh4uNwlKA4zEZlbJiE1QxAbHou07DQERgbi/K3zpT6GuWUiBWYiYiYiZiJiJsrA5lkC5WGOZ65qJGImInPMpHGVxogbGQcXWxcErQxCbGJsqd5vjpmUFTMRMRMRMxExE2XgbBuPMOTpzeFbh+Ni2kUcHXHUyNURkVy0OVr0iumFY9eOIap3FHo17CV3SURE5Zacs23wzrMEysOdZ6LyroJdBewavAuhvqHos6EPlhxfIndJREQkA862IYG07DTUrVRX7jKIyMhsrWwR3ScaHk4eGLtzLK5nXscnL35i1s87EBFRUbzzLIHycOc5NrZ04zzLA2YiKg+ZWGgs8Hmnz/HZS59hVuwsDP9+OO4/uP/Y/ctDJqXFTETMRMRMRMxEGdg8SyA1O9Xsm+c5c+bIXYLiMBNRecrk3ZbvIqp3FKLPRKNbdDdk5mYWu195yqSkmImImYiYiYiZKAOb5zLS6/Xl4s7z+vXr5S5BcZiJqLxlMsh/EHYN2YVj146hzZo2+OfOP8I+5S2TkmAmImYiYiYiZqIMbJ7L6M69O8jT5Zl98+zg4CB3CYrDTETlMZMXfV7E4eGHkZSZhJaRLfFH6h9FtpfHTJ6GmYiYiYiZiJiJMrB5LiMuzU1Ez1Z7FnEj42BnZYeWkS1x7NoxuUsiIiIjYfNcRmyeiQgAalaoidgRsWhQuQFeXPMitv++Xe6SiIjICNg8l1F5aZ4jIiLkLkFxmImovGdSyb4Sfhr6EzrV7YSeMT3x9cmvy30mxWEmImYiYiYiZqIMbJ7LyFjN861btxAeHoHGjbvC1zcUjRt3RXh4BG7duiXp55SUt7e3LJ+rZMxExEwAe2t7fNfvO7ze7HW8+sOrOJt7FlzItSheJyJmImImImaiDKpcnnvv3r1Yt24d4uLi8Pfff8Pe3h4hISGYPn06GjduXGTfhIQEvPPOO/j5559hY2ODLl26YN68eahSpUqxxy7tco9fnfgKb+x4A/cn34eFRpqfRW7evImWLQfi8uWZAFoA0ADQATiOOnUmIS4uBu7u7pJ8FhEZh16vx+yjs/HBvg8wsslILOu2DFYWXJeKiEgKXJ67lBYsWIBz585h7Nix2L59O+bNm4czZ84gMDAQ58+fL9wvMTERISEhyMzMxObNm7F8+XLExsaiffv2yM3NlaSWtOw0VLSrKFnjDADvvz/338b5BeQ3zkD+L9ULuHz5E0yYwHkeiZROo9FgYquJWNNzDdb8tgY91/fE3Xt35S6LiIjKSJW3QT777DP4+voWea1169bw9fXFzJkz8e233wIApk+fjnv37mHbtm2oXLkyAKBatWoIDg7GypUrMXr06DLXYow5no8fPwfgcQ1yCxw/PkPSzyMi4xn27DBUdayKPhv64MVvXsTq9qsxZ+4cHD91HHnIgxWs0DygOeZMncN/USIiUgFV3nl+tHEGgFq1aqF27dpISEgAAOh0OmzatAkdO3YsbJwBICgoCLVr18aGDRskqSUtOw1uDm6SHKtAXp4l/rvj/CiLf7eb1oULF0z+mUrHTETMRHThwgV0rNsRh4YfwpVrV/BM+2ewOnc1zr10Dn90+APn2p/D6tzVCOwUKNszDabG60TETETMRMRMlEGVzXNx7t+/j3/++QcVK1YEAFy9ehVarRbPPvussG9AQAB+/fVXST43LUf6O89WVg8APG4ouu7f7aY1YcIEk3+m0jETETMRFWTSzKMZWl9rjbw2eUANFB2RVQO43OQyJkwtH/nxOhExExEzETETZTCb5nnZsmVIT09H7969AQBJSUkAgKpVqwr7Vq1aFRkZGcjOzi7z56ZmpUrePDdt2gjA/z1m6/+hefNGkn5eSSxevNjkn6l0zETETEQPZ3L+/HnA6zE7egLHfi0fi6vwOhExExEzETETZVDlmOdHnT59Gu+//z4CAgLw2muvAUBhY2xrayvsX/BadnY27O3ty/TZadlpaFKtSZmO8Sg7uwnQaAZAr/8E+bNtWCB/to3/g0bzIfr3j5H080qC0+OImImImYgeziQPeU8akYULqRdQ84uaaOzeOP+rSv5/G7k3gqONo2kKNgFeJyJmImImImaiDKpvnpOSktC9e3dUqFABW7ZsgZVV/ikVNMXFzapR8FpZG2dA+gcG9+8HVqxwx/TpMbh8eQ6OH5+BvDxLWFk9QJMmjXDpUgwGDHDH7t1AYKBkH0tEJmAFq/wRWcU10DrAw8kDAxsPRMKtBGw8vxGfxX1WuLlWhVpCU93QvSEcrB1MVj8REal82EZmZia6du0KrVaLH3/8ETVr1izcVr16dQD5cyY/Kjk5Ga6urk9snrt06YLQ0NAiX4GBgdi6dWvhPnq9HilnUrBx8kbh/WPGjEFkZGSR1+Lj4xEaGoqUlJQir0+ZMgWzZ89GejoQHg60aQMMGZKN1NTfsWnTPPz++zYkJPyItWvnom/f9XBxiUCHDsDRo/nvz8rKQmhoKGJjY4scNzo6GuHh4UJtAwYMKHIeALBnzx6EhoZKch4PS0xMRGhoqPCQw6JFi4SVkngePA9zPw97nT1w7aEXtQCiANwCcB3o8EIHzH5pNjqmdkTfa32R+UEmjo86jlU9VqGHTw/88tkvWL19NV7e+jKe+/o5OM10QtWXq8K7tTc+2PsB1p5ei1+TfkX2/Wz+evA8eB48D7M5j+jo6MJezMfHBwEBARg/frxwHJPRq1Rubq6+ffv2eltbW/3evXuF7Q8ePNBXrFhR37t3b2FbnTp19G3atCn2uCdPntQD0J88efKpNdy9d1ePqdCv/W1t6U+gGOHher2zs15/9eqT98vM1Otbt9brnZz0+iNHJPnop/r0009N80EqwkxEzET0cCY3b97U12laR4+R0ONj6DH13/+OhL5O0zr6mzdvluiY6Tnp+mN/H9NHxkfq39n1jr7jtx31XvO98o83FXqL/1no6y6sq+8R3UM/ae8k/brT6/Snkk7ps+9nG+s0S4XXiYiZiJiJiJn8pzT9mtRUOWxDr9dj+PDh2L9/P6Kjo9GuXTthHwsLC/Tu3RsxMTFITU2Fm1v+dHJxcXG4cuUK3nnnnTLXIeXS3Nu3A6tWAStWALVqPXlfJyfgxx+Bbt2ATp2AnTuB4OAyl/BEWVlZxv0AFWImImYiejgTd3d3xO2Kw4SpE3B87yPzPO8q+TzPLrYuaOHVAi28WhR5PT0nHedunUPCrQQk3ExAwq0ErP5tNW5k3gAAWGgsULdSXWH4R323+rC1Ep8PMRZeJyJmImImImaiDKpcnvvDDz/ErFmzMHjwYLz++ut4+BSsra3RokX+Xyh//fUXmjZtioYNG2L69OnIyMjA22+/DUdHR5w8eRI2NjbCsUuz3OPp5NN4dtmzODbymPCXWGmkpAB+fsBzz+U30ZrHPVD0iKys/Ab6+HFgxw4gJMTgEojIjN3Ovi001Qm3EvDPnX8AAJYaS9Rzqyc01fXc6sHGUvxzkohIbnIuz63KO8/79++HRqPBunXrsG7duiLbKleuXDjOuWbNmjh06BDeffdddO/eHTY2NujSpQvmz59fbONcWlLcedbrgdGjgfv3ga+/LnnjDAAODsAPPwDduwOdO+c30K1bG1wKEZmpivYVEeQdhCDvoCKvp2alCk31spPLcPNu/p+hVhZWqO9WX2iq61aqC2tLazlOhYhIdqpsnuPi4kq8r5+fH3bv3m2UOqRonqOjgY0bgZgY4N9nHEvFwSH/bnWPHkCXLvnNdNu2BpdDROWIm4MbgmsGI7hm0XFfKVkp/92h/ve/B/48gJSs/IeKrC2s4VvZV2iq61SqAysLVf61QkRUYvxTrgwKmucKdhUMev//t3fncVFV///AX3dYh02QHRFEFnFNUVFJ1MyPuOSSlqaZuWTlT60+nxJLKzW1vpFmWdnH+pSmmdmCpEmmtigqiYrmghsqaGoCAoIwLDNzfn+cZuByZ3DYZn0/H495IOfeuZz79jC858xZrl8H5swBHnsMmDCh8fVwcQG2b+cJ9MiRPIEePLjx19OloKBAtM05oZjoQjGRqhuT/Px8JCYmISMjS7sMZWxsJyQlJRo85rml+bj4YGC7gRjYTvxRVl5ZniSp3nN5j/a10NHOEdE+0ZKkur1Xe9jJ7LTXoXYiRTGRophIUUzMAyXPTVCoKISns6foj4KhGAOeegqQy4GPPmp6XeRy4IcfgLFj+TjoHTsAHfMoG23GjBnYvn17813QClBMpCgmUrVjkpeXh7i4x3Dp0psAksAXfFYjKysDaWkTkZ6+1WwSaF38XP3gF+aHB8JqPt5ijOFW2S1JUv1T9k8origGADjbO4uS6m2Lt2HLd1sQ5hUGmWDRK6Y2G/rdkaKYSFFMzAMlz01QqCiEt9y7Uc/95BNg1y6+akbrZtpjRZNAP/xwTQI9ZEjzXHvJkiXNcyErQjGRophI1Y7JggXv/JM49611hgxAX1y6tAKJiUlYv/4dI9ewaQRBQIBbAALcAvBg+5p37Iwx3Lx7U5JU77iwAyWRJYj4IAJyezk6+naU9FSHeobaXFJNvztSFBMpiol5oOS5CRq7u+ClS8CLLwKzZvFxys3J2RnYtg0YN45PJPzhB2Do0KZf19gzWS0BxUSKYiJVOyYHD2aB9zjr0gdpactRXQ04WMFcPEEQEOQehCD3IPwr/F/acsYYrpdelyTVKedSUFpVCgBwdXDVmVSHtAqB0JBZ1RaEfnekKCZSFBPzQMlzE9xW3G5w8qxSAdOmAX5+wKpVLVOv2gn06NE8gU5IaJmfRQjRLycH2Lev5nH5sh10780NADJcumQHuRwIDQUiIoDISP5V8++wMKAZFgoyKUEQEOwRjGCPYCRE1LwwMcZwreSaaCm9M3ln8F3WdyirLgMAuDm6oZNvJ0lSHewRbLVJNSHE/FDy3ASFikIEujVsiYzVq/m22vv2Ae7uLVQxAE5OQHIyMH48n0iYksI3VCGEtAzG+KdKtZPlq1f58pNdu/LJvNu2qfDXXwy6E2g1QkNVWLgQuHgRyM7m1/jsM6Cigp8hkwEhIeKkunZi7exszDtuXoIgIKRVCEJahWB45HBtuZqpcfXOVVFSffLWSWw9sxXl1XzDCA8nD51JdZB7ECXVhJBmR8lzExQqCtHZt7PB5585AyxaBPznPy2/IyDAE+jvvwcefZQn0Nu2NX6YyGeffYaZM2c2bwUtHMVEypZiwhhw/rw4Wb5xgye43bvzN64DBwK5uZ/hued4TEpLO2HDhsMQj3nWOIwHHuiEp58Wl6rV/LrZ2fyhSawPHgS++IJvlgTwJL1tW2lvdUQE0L49nxNhLhrSTmSCDO0826GdZzuMjBqpLVczNXKKc0RJdebNTGw+tRkVSv5uo5VTK20iXTupDnALMLuk2pZ+dwxFMZGimJgHSp6boCFjnqurgalT+R+y5ctbuGK1ODnxdaQnTOATCZOTeQ9YQ2VmZtIvbB0UEylrjolazd8A79/PE+X9+4FbtwA7O7476JQpPFm+/36gVaua582ZkwmAxyQpKRFpaRNx6dIKAH3AJwuqARxGePgiJCVtlfxcmQwIDuaPQYPExxgDbt4UJ9XZ2cAffwBffgmU8dEOEAT+/LpJdUQEEB7Ol7s0puZoJzJBhvZe7dHeqz1GdRilLVepVbhSfEWUVGdcz8DGPzeiUlUJAPBy9tKZVPu5+pksqbbm353GophIUUzMg0Vuz92SGrLdo+ubrlgxeAVe6PvCPa+7eDHw5pv8j1rPns1VW8NVVQETJ/LVPZKT+WochBD9VCrg1Cng9995spyWBty+zSfzxcbyRHngQCAuDnBzM/y6xlrnmTGe3NdOqjX/vngRuHu35tw2bXSPsQ4PB1xdm61KJqVUK3G56LJkTPX52+dRpaoCAHjLvXUm1b6u5rt8ICG2irbntkAVygqUV5cb1PN85AiwYgXw2mumSZwBPslo61a+Icu4cXw4x6hR934eIbZCqQSOH68ZgnHgAFBczD+96duXb2g0cCD/d1N6an19fY2yHJ0gAAEB/FF3mBhjQH6+NLE+fhz45hugpKTm3MBA/Yl1S87baG6arcajvKPwcMeHteVKtRLZhdmipHp/7n78L/N/qFZXAwB8XXx1JtXeLo1bqpQQYtkoeW6kIkURgHtvza1Q8OEa3bsDCxcao2b6aRLoSZP4eMxvv+VjoQmxRdXVwNGjNcnywYNAaSkfGxwXx+cmDBzIe5kteSKeLoLAV/zx8+PDTGpjDCgokPZWnzrFP7UqLq45199f9xjriAjAw8O499RY9jJ7RPtEI9onGuMxXlterarGxcKLoqT61yu/Yt2xdVCqlQAAf1d/nUm1l9zLVLdDCDECSp4bSbMdbd3kue5HsgUFKhQXd8K+fYlwcDD9R38ODsCWLcDkycAjj/BepocfvvfzCLF0lZVARkZNsnzoEJ9s5+bGE8hXXuHJcq9elr8cXFMIAuDryx/9+omPMQYUFkrHWJ89C2zfzo9p+PnpTqojIgBPT+PeU2M42Dmgk28ndPLthEfxqLa8SlWFC7cviJLq3Zd2Y+2RtVAxFQAg0C1QklR38u0ET2cLuHFCyD1R8txIupJnfVvvAhmYNs18tt51cAC++gp4/HE+kXDrVj6Uoz6jR4+mLUHroJhImVNMFAo+x0CTLP/xB1/yzcODD2NYsoQnyzExgH0LvhKaU0yaShAAb2/+6NNHerywkC/XVzuxvnABSE3lvdkajo6jEROzXedwEC8z77R1tHNEF78u6OLXRVReqazE+dvnRUl16sVUfJDxAdRMDQBo495GZ1Lt4eRhVe2kuVBMpCgm5oGS50bSlTxb0ta7mgR6yhQ+kfDrr/lQDn3mzp1rvMpZCIqJlCljUlbGe5M1yXJGBp8o6+UFDBjAJ+wOHAjcdx9fIcNYbKmdtG7NH717S48VF9ck1Lt2zQVj/N+7dwN5eeJr6EqqIyL4MTNbYU7Lyd4J3fy7oZt/N1F5hbIC5wrOiZLq7ee3470/3gMDn6/f1qMt/Dv546XdL4mSajfHBsxEtUK29LtjKIqJeaDVNuowdPbm+uPrMWP7DFS9WgUHO76XbufOI5GV9SP0bYDQqdMonDmzs2Uq3khKJfDEE3z885YtfE1oQixBSUnNhkP79vHxy0olH24wYEDNahhduvDl3oj5unOH91jXHQ5y8SJfMUTD01P/GGsfH/NNrHUpry6XJNVn8s7gSvEV7TmhrUJFPdWaYSSujlayBAohTUCrbVigQkUhPJw8tIkzACiV9W+9y4+bF3t7YNMmnlxMmsTHNE6YYOpaESJVVMRXwNAky5mZfO3lwECeJD/5JE+aO3a0rCSK8HWxY2L4o67SUt2J9e+/8zWua1+j7q6Lmn/7+Zlfm3BxcEFMYAxiAsU3XVZVhrMFZ0VJ9TdnvkHunVztOe0820kmKXb07QgXByMv2E2IjaLkuZFuK25LJgva26sA6N96lx83P/b2wMaN/I/L5Mk8gZ440dS1IrauoKBmQ5J9+4CTJ3nbbNuWJ8vPPMO/RkSYX2JEmo+7O1+tqHt36bGyMukY64sX+Zus69fF19CVVEdG8hVDzKn9uDq6oldQL/QK6iUqv1t1F1n5WaKkesvpLbhWcg0AIEBAmFeYJKmO9omG3MGMtpckxApQ8txIunYXjI3thKws/VvvxsZ2MkrdGsPOjm/1K5PxBFqt5j3RGikpKRg7dqzpKmiGKCZSTYnJrVvira7PnOHlYWE8SX7hBf61XTvzSnbuhdqJVHPFxNUV6NaNP+oqL6/psa7da/3HH8C1a+Jr6BtjHRhovLZ2r5i4Obohtk0sYtvEispLKkskSfWmk5twvZS/e9DsxKgrqXayd2rRe2oq+t2RopiYB0qeG0lX8tyYrXfNiZ0dsH49/2MxZQrv5Zs8mR/bsmUL/cLWQTGRakhMrl8XJ8vnz/PyyEieJL/8Mv/atm0LVtgIqJ1IGSMmLi5A1678UZdCAVy+LE2st2wBrl7lr32aa9QeClI7sQ4Kat6x9I2NiYeTB/oG90XfYHGnTXFFsSipzsrPwoY/N+BG6Q0APKmOaB0hSao7+HSAo515rNVIvztSFBPzQBMG6zB0APqQjUPg7eKNrY+IE+L8/Hz06pWEgoIsBAe33Na7LUmlAp56ig/l+OILnkgT0lS5ueJk+dIlXt6xY83kvgEDeFJCiKlUVABXruievHj1Kv9UDuCb6YSH6x4OEhxsvpNUixRFPKn+Z4Kiprf677t/AwDsBDtEekdKkuoo7yjRHB9CTI0mDFqgQkUhIltHSsp9fX2hVL6D55/nS2NZIjs74LPPeA/01KnAnTv5OHq0ZuMXS3xDQIyLMd6zVztZzv1nvlPXrsDw4TXJsp+faetKSG3OzvwNXceO0mOVlUBOjjSpTk7m5ZrE2smpJrGuOxwkONi4SyXW5SX3wv0h9+P+EPHWkrfLb0uS6v8e+y/yyvg6gprtzesm1RGtIyipJjaHkudG0jVsAwD++gu4cYNv6WvJZDLgf/8DKivzMHfuYwDEG79kZWUgLc18Nn4hpsUY3wyjdrJ8/Tp/A9a9O9/FcuBAvjmJt7epa0tI4zg5AR068EddVVU8gdYk1ZrE+ocfeE+26p/54o6OQPv2usdYh4SYLrH2dvFGfGg84kPjReX5ZfnapPp03mntNuW3FbcBAA4yB3Tw6SBJqsNbh8NeRikGsU7UshtJX/KckcG/6tp9y9LIZICDwzvgibP5b/xCjEetBrKyxKth3LrF//DHxPDJpgMHAv37W8ZWzIQ0laMjEBXFH3VVV/NPXuqOsd65k39Co1Ty8xwc+ARZXYl1aGjL7oSpj6+rLwa6DsTAdgO1ZYwx5Jfn1wz7+Ofrnst7tBuIOdk56Uyq23u1h53M/JZtJaQhKHluhBp9nvkAACAASURBVGpVNUqrSnUmz4cP84/lAgNNULEWcORIFniP83QA6+sc7YOMjOXGr5SZmD59OtavrxsT66RW86XiNIlyWhpfSs7Bge8mN2MGH4Lx5ZfT8eWXthETQ9lSOzGUrcXEwaEmGa5LqeRjqZ99djrGjFlfaxdGnlhXV/Pz7O15Yq1rjHW7dvxnGIsgCPBz9YNfmB8eCHtAW84Yw62yW5Kk+qfsn1BcUQwAcLZ3RrRPtCSpDvMKg0wQDxS3tXZiCIqJeaDkuRGKKooAQG/Ps6UP2aitZuOXoTqOypCTY4eFC3kC1bs30KaNZS0j1hRDh+qKiXVQKoETJ8TJcnEx713r2xeYPZv3LPfrx1ck0Cgqst6YNJY1t5PGopjUsLfnwzimTx8qWh4U4EM9rl2TTl7cuxdYt44PFQH4Jz7t2kmT6ogInnA7GmnxDEEQEOAWgAC3ADzY/kFtOWMMN+/elCTVOy7sQEllCQBAbi9HR9+OoqS6+/3doWZqSVJty+h3xzzQaht1GDJ781zBOXT8qCP2T9svGh+mUvGPqF97DUhMNFaNW9a9thx3cxsFN7ed+JtP1EZAANCrV00y3asX3y6ZmLfqauDYsZpk+cABvrObXM4TZM1qGH368AlVhBDTUqn4vIK6kxezs/lKNhUV/DyZjA/50DV5MSyMj+M2FcYYrpdelyTVZ/LP4G7VXQCAq4OrJKnu7NsZIa1CINhKTw3RiVbbsBD5+flIXJKI34/8DtwBpqdOR3yveCQtSYKvry/OngXu3rWunud7bfzyyCOd8PnnfJLkkSPA0aP863vv8e2UAf7CrUmke/cGevbkW+kS06ms5P9PmmT50CG+W5urK3D//TVrLPfubbxeK0KI4ezs+ATDkBDgwQfFx9RqnljXTarT0vha/goFP08Q+PN1jbFu377l3ygLgoBgj2AEewQjISJBW84Yw7WSa6Jk+kzeGXyX9R3KqssA8E1jOvl2kiTVwR7BlFSTFkc9z3XoeyeTl5eHuOFxuNTjEhAMzaITwHUg/Hg40nelY8cOX8yaBdy5A7i5meoOmld+fj769dO/8Yu+1TYY4zPMjxypSaqPHeNvLgA+qaZ2Qt2jh/jjf9K8FAo+Hl+TLKen854pDw8+qU/TsxwTY9yxk4QQ41KrgZs3pUNBNI8ynptCEPgGRbrGWIeH80+ljF53psbVO1clSXVWfhYUSv6OwMPJQ2dSHeQeREm1lTFlzzMlz3Xo+8+YPmc6NlRuAHTtdnYNmOY0DY7K9Th0CDh1ymjVNYr8/Hw8+eRzyM0tadI6zyoV30VO0zt95AgfV1tZyT9a7Ny5ZrhH7958PWBz7vU8cOAA+vfvb+pq6FRWxhNkTbJ8+DAfH+nlxZeL0yTL3bs379JY5hwTU6GYSFFMpMwhJowBf/+te4OY7Oyazg+AT4zXNcY6IqL5OkIMjYmaqZFTnCNJqs8WnEWFko9f8XT21JlUB7gFWFRSbQ7txFxQ8mxG9P1ndL6/M7L+laVv6C867e0Ex/Iz6NmTr49sbUaPHo3t27c3+3Wrq4HTp8VDPk6d4om2oyNw333iHuqOHU27wUBtLRWTxigpAQ4erFk67sgRPunPx4evgqFJlrt2bdmdz8wpJuaCYiJFMZEy95gwBuTl6U6qL17kcyQ0goJ0j7EOD2/Yp7JNjYlKrcKV4iuSpPpcwTlUqioBAF7OXtpEunZS7efqZ5ZJtbm3E2Oi5NmM6PvP6HB/B1wYekHv8yJ3ReHykfNYuxZ4+mlj1NS4ysvL4WKkcRUKBfDnn+IhH+fO8RdvFxc+tKD2hMSICNOs8GHMmNRVXMzHL2p6ljMz+cex/v41ifLAgUCnTsaNjSljYq4oJlIUEylLjgljQH6+NKnW/PvOnZpzAwJ0j7GOiADc3cXXbamYKNVKXC66LEmqz98+jyoVX8LEW+6tM6n2dTXtDHhLbifNjSYMWgB72AMMenueqyvsoVJZ12TB2oz5yyqX8+XQ+taao1hSwhNETe90SgqwejU/5unJk+jaq3wEB7d80mjMmNy+Ld6Q5M8/+R+sNm14kjxrFv8aFWXapQLpRV2KYiJFMZGy5JgIAuDnxx9xceJjjPHXr7qJ9Zkz/HVcM7Ec4G/+xUm1i/b75pxkrtlqPMo7Cg93fFhbrlQrkV2YLUqq9+fux6eZn0Kp5jvZ+Lr46kyqvV2Ms3WqJbcTa0LJs4Fiu8ci668s3WOerwM+HrG4JQe6dDF61WyChwcwaBB/aNy+XZNMHz0KbNwI/N//8WP+/jXJtOarn58pat44t26Jk+XTp3l5aChPkufN41/bt7eddbUJIZZHEPjwMR8fcYeIRmGhtLf63Dngxx/5a7yGr6/u3urIyObbxdReZo9on2hE+0RjPMZry6tV1bhYeFGUVP965VesO7ZOm1T7u/rrTKq95F7NUzliVmjYRh36PgbIz89Hv2H9+GobbVCz6MQ/q210Dk5HYaEv0tJMVXMC8CXzak9IPHq05gU4JETcO92zp/lsHX3jRk2ivG8f/+MB8D8OmiEYAwbw5JkQQmxBUZF0NRBNkp2fX3Oet7fupDoiAmgt3cus2VSpqnDh9gXJ8I/swmyomAoAEOgWqDOpbuVM67U2FY15NiP1/Wdo1nnOOJEBJZSwhz1iu8ciaUkSYmN9MX48sHKliSrewubPn4933nnH1NVoMMaAnBzxhMRjx2omt0RGSpfMc3U17NpNicnVq+JkOTubl0dHi5PlNm0adXmTsdR20pIoJlIUEymKiVR9MblzR3dSnZ3NP7nT8PLSP8ba27tlPrmrVFbi/O3zkqT6UtElqJkaANDGvY0kqe7k2wkeTh71XpvaSQ0a82whfH19sf4j6Z7yeXk8QbPW8c4AEBISYuoqNIog8F20wsKACRN4mVoNXLgg7p1OTubrHstkfJJd3SXzdO3C1bp1a0yfPh8ZGVn1LuGnWfO6drKck8OPdekCJCQAb77Jk2V//5aPSUuy1HbSkigmUhQTKYqJVH0xadWKf3rYs6f0WEkJ32WxblL966/Q7oYL8E8eay+xVzux9vVtfGLtZO+Ebv7d0M2/m6hcUa2QJNXbz2/He3+8Bwbej9nWo63OpNrN0e2eMamtvs6+hiwxS3Sjnuc6GvNO5scfgVGjeEJEH6tbpupqPoGl9pCPU6f4cm+OjkC3buIeam/vPAwY8BguXXoTfPMYza45GQgPX4gvv9yKU6d8tcnyX3/xF+L77qvpWY6P5+MACSGEGMfduzyx1rXk3o0bNed5eOgfY+3n17w91uXV5ThXcE7SU32l+Ir2nNBWoZKkuqNPR7g6Sj8qNWRTN2tIoGnYhhlpzH/Ga68Bn3zC39HS5C3rUVFRs2SeJqk+e5b3JNvZzYdKNR66ty1PB5AMmewdxMTUJMv9+/OPEAkhhJifsjLg8mXdifVff9Wc5+amf4x1QEDz5QFlVWU4W3BWklTn3skFAAgQ0M6znSSpXr10Nb5Uflnvpm66PkW3NDRsw8JlZPAhG5Q4WxdnZ6BPH/7QKC0Fjh8HJkzIwq1bSXqe2QchIctx6hTvvSCEEGL+XF35ML2uXaXHFIqaoSC1k+rDh4Fr13inCsD3ItCXWAcGNmyTKldHV/QK6oVeQb1E5aWVpZKk+qtTX+FayTV+wq8AJuu5aBsgY2+G4ZUgOlHy3ERqNU+eX3zR1DVpWefOnUN0dLSpq2Fy7u58bHKrVna4des8AF0xkcHZ2c4mE2dqJ1IUEymKiRTFRMqcYiL/ZylaXcvRVlTwHuu6ifXWrUBubk1iLZfrH2Pdpo1hibUmJrFtYhHbRjzRqqSyBFn5WRi7fSxuCbd0X0AGKKFs4N2Tulpwo17bkJ3Nd3uz5smCAJCYmGjqKpgVe3sVAH0xUf9z3PZQO5GimEhRTKQoJlKWEhNnZz7RfPRo4D//AT7+GNizh08UVyj4cL8dO4AVK/hcl7Iy4LvvgGefBQYP5suourryxHzsWGD+fGDdOuCXX/jKTGp1zc+qLyYeTh7oG9wX3s7egL4Buep/Nn0jTUIRbKLDh/nX3r1NW4+W9uGHH5q6CmYlNrYTsrLu13P0MGJjOxm1PuaC2okUxUSKYiJFMZGyhpg4OfElSHV1oFdV8YUG6o6x3raNl6tUNddo3573VPv7f4iPP67ptW7bFrCzE1+3a4eu+jd1+wvoFt1NxwHSEJQ8N1FGBt8S2dongtEySmJJSYlIS5uIS5f8wVfb0Oyacxjh4YuQlLTVtBU0EWonUhQTKYqJFMVEytpj4ujI84eoKOmx6mqeQNddy3rfvhBs2MBXggIAB4eaxFozBOTvy97A8TbAI9f5ahuaP09/AfiuDTCiBXeOsRGUPDdRRoZ4QhmxDb6+vkhP34rExCRkZCyvs87zVqtYBogQQohpODjwhDgyUnpMqeRjqeuOsf7pJz72uro6B0Am8PUCwDEDsFMCKnugKhYo/z+cPDnD2LdjdSh5boLKSuDECeCJJ0xdE2IKvr6+WL+ednoihBBiPPb2QHg4fyQkiI8plUBUlB2uXPEDytcD5dLnK5V20kLSIDRhsAn+/JOPWbL2yYIA8Pbbb5u6CmaHYiJFMZGimEhRTKQoJlIUE6l7xcTeHpDLVahvxqCtTmhvTpQ8N8Hhw3zM0n33mbomLa+8XMfbVxtHMZGimEhRTKQoJlIUEymKiZQhMeET1g/rOWq7E9qbE+0wWEdDdqx54gngwoWaFTcIIYQQQkwpPz8f/fpNxKVLK6BrQnt6unXMy6EdBi3U4cPAsGGmrgUhhBBCCEcT2lseJc+NVFjIZ7i+/rqpa0IIIYQQUoMmtLcsGvPcSEeO8K+2skxdQUGBqatgdigmUhQTKYqJFMVEimIiRTGRopiYB0qeGykjA/D05AuS24IZM2hdyLooJlIUEymKiRTFRIpiIkUxkaKYmAcattEA+fn5/4whykJurh0AFWbM6ISkpESrH0O0ZMkSU1fB7FBMpCgmUhQTKYqJFMVEimIiRTExD7TaRh36Zm/m5eUhLu4xXLr0JvjsVQF89moGwsMXWs3sVUIIIYQQc2fK1TZo2IaBFix455/EuS944gzw8PXFpUsrkJiYZLrKEUIIIYQQo6Dk2UAZGVngPc669PnnOCGEEEIIsWaUPBuI7wUv6Dkqs/q94j/77DNTV8HsUEykKCZSFBMpiokUxUSKYiJFMTEPlDwbiO8Fb7t7xWd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