{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Time Series regression - KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading required package: daltoolbox\n",
      "\n",
      "Registered S3 method overwritten by 'quantmod':\n",
      "  method            from\n",
      "  as.zoo.data.frame zoo \n",
      "\n",
      "\n",
      "Attaching package: ‘daltoolbox’\n",
      "\n",
      "\n",
      "The following object is masked from ‘package:base’:\n",
      "\n",
      "    transform\n",
      "\n",
      "\n",
      "Loading required package: tspredit\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# DAL ToolBox\n",
    "# version 1.0.777\n",
    "\n",
    "source(\"https://raw.githubusercontent.com/cefet-rj-dal/daltoolbox/main/jupyter.R\")\n",
    "\n",
    "#loading DAL\n",
    "load_library(\"daltoolbox\") \n",
    "load_github(\"cefet-rj-dal/tspredit\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Series for studying"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A matrix: 3 × 10 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>t9</th><th scope=col>t8</th><th scope=col>t7</th><th scope=col>t6</th><th scope=col>t5</th><th scope=col>t4</th><th scope=col>t3</th><th scope=col>t2</th><th scope=col>t1</th><th scope=col>t0</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>0.0000000</td><td>0.2474040</td><td>0.4794255</td><td>0.6816388</td><td>0.8414710</td><td>0.9489846</td><td>0.9974950</td><td>0.9839859</td><td>0.9092974</td><td>0.7780732</td></tr>\n",
       "\t<tr><td>0.2474040</td><td>0.4794255</td><td>0.6816388</td><td>0.8414710</td><td>0.9489846</td><td>0.9974950</td><td>0.9839859</td><td>0.9092974</td><td>0.7780732</td><td>0.5984721</td></tr>\n",
       "\t<tr><td>0.4794255</td><td>0.6816388</td><td>0.8414710</td><td>0.9489846</td><td>0.9974950</td><td>0.9839859</td><td>0.9092974</td><td>0.7780732</td><td>0.5984721</td><td>0.3816610</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 3 × 10 of type dbl\n",
       "\\begin{tabular}{llllllllll}\n",
       " t9 & t8 & t7 & t6 & t5 & t4 & t3 & t2 & t1 & t0\\\\\n",
       "\\hline\n",
       "\t 0.0000000 & 0.2474040 & 0.4794255 & 0.6816388 & 0.8414710 & 0.9489846 & 0.9974950 & 0.9839859 & 0.9092974 & 0.7780732\\\\\n",
       "\t 0.2474040 & 0.4794255 & 0.6816388 & 0.8414710 & 0.9489846 & 0.9974950 & 0.9839859 & 0.9092974 & 0.7780732 & 0.5984721\\\\\n",
       "\t 0.4794255 & 0.6816388 & 0.8414710 & 0.9489846 & 0.9974950 & 0.9839859 & 0.9092974 & 0.7780732 & 0.5984721 & 0.3816610\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 3 × 10 of type dbl\n",
       "\n",
       "| t9 | t8 | t7 | t6 | t5 | t4 | t3 | t2 | t1 | t0 |\n",
       "|---|---|---|---|---|---|---|---|---|---|\n",
       "| 0.0000000 | 0.2474040 | 0.4794255 | 0.6816388 | 0.8414710 | 0.9489846 | 0.9974950 | 0.9839859 | 0.9092974 | 0.7780732 |\n",
       "| 0.2474040 | 0.4794255 | 0.6816388 | 0.8414710 | 0.9489846 | 0.9974950 | 0.9839859 | 0.9092974 | 0.7780732 | 0.5984721 |\n",
       "| 0.4794255 | 0.6816388 | 0.8414710 | 0.9489846 | 0.9974950 | 0.9839859 | 0.9092974 | 0.7780732 | 0.5984721 | 0.3816610 |\n",
       "\n"
      ],
      "text/plain": [
       "     t9        t8        t7        t6        t5        t4        t3       \n",
       "[1,] 0.0000000 0.2474040 0.4794255 0.6816388 0.8414710 0.9489846 0.9974950\n",
       "[2,] 0.2474040 0.4794255 0.6816388 0.8414710 0.9489846 0.9974950 0.9839859\n",
       "[3,] 0.4794255 0.6816388 0.8414710 0.9489846 0.9974950 0.9839859 0.9092974\n",
       "     t2        t1        t0       \n",
       "[1,] 0.9839859 0.9092974 0.7780732\n",
       "[2,] 0.9092974 0.7780732 0.5984721\n",
       "[3,] 0.7780732 0.5984721 0.3816610"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data(sin_data)\n",
    "ts <- ts_data(sin_data$y, 10)\n",
    "ts_head(ts, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAMAAADKOT/pAAADAFBMVEUAAAABAQECAgIDAwME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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "library(ggplot2)\n",
    "plot_ts(x=sin_data$x, y=sin_data$y) + theme(text = element_text(size=16))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### data sampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "samp <- ts_sample(ts, test_size = 5)\n",
    "io_train <- ts_projection(samp$train)\n",
    "io_test <- ts_projection(samp$test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### data preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "preproc <- ts_norm_gminmax()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "model <- ts_knn(ts_norm_gminmax(), input_size=4, k=3)\n",
    "model <- fit(model, x=io_train$input, y=io_train$output)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluation of adjustment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "0.0016923102576417"
      ],
      "text/latex": [
       "0.0016923102576417"
      ],
      "text/markdown": [
       "0.0016923102576417"
      ],
      "text/plain": [
       "[1] 0.00169231"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "adjust <- predict(model, io_train$input)\n",
    "adjust <- as.vector(adjust)\n",
    "output <- as.vector(io_train$output)\n",
    "ev_adjust <- evaluate(model, output, adjust)\n",
    "ev_adjust$mse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prediction of test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<dl>\n",
       "\t<dt>$values</dt>\n",
       "\t\t<dd><style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>0.412118485241757</li><li>0.173889485380434</li><li>-0.0751511204618093</li><li>-0.319519193622274</li><li>-0.54402111088937</li></ol>\n",
       "</dd>\n",
       "\t<dt>$prediction</dt>\n",
       "\t\t<dd><style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>0.534952363303494</li><li>0.373751048072052</li><li>0.138195288527363</li><li>-0.105952784719954</li><li>-0.343513226987357</li></ol>\n",
       "</dd>\n",
       "\t<dt>$smape</dt>\n",
       "\t\t<dd>0.889006556777233</dd>\n",
       "\t<dt>$mse</dt>\n",
       "\t\t<dd>0.0372727037176185</dd>\n",
       "\t<dt>$R2</dt>\n",
       "\t\t<dd>0.678073732798459</dd>\n",
       "\t<dt>$metrics</dt>\n",
       "\t\t<dd><table class=\"dataframe\">\n",
       "<caption>A data.frame: 1 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>mse</th><th scope=col>smape</th><th scope=col>R2</th></tr>\n",
       "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>0.0372727</td><td>0.8890066</td><td>0.6780737</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "</dl>\n"
      ],
      "text/latex": [
       "\\begin{description}\n",
       "\\item[\\$values] \\begin{enumerate*}\n",
       "\\item 0.412118485241757\n",
       "\\item 0.173889485380434\n",
       "\\item -0.0751511204618093\n",
       "\\item -0.319519193622274\n",
       "\\item -0.54402111088937\n",
       "\\end{enumerate*}\n",
       "\n",
       "\\item[\\$prediction] \\begin{enumerate*}\n",
       "\\item 0.534952363303494\n",
       "\\item 0.373751048072052\n",
       "\\item 0.138195288527363\n",
       "\\item -0.105952784719954\n",
       "\\item -0.343513226987357\n",
       "\\end{enumerate*}\n",
       "\n",
       "\\item[\\$smape] 0.889006556777233\n",
       "\\item[\\$mse] 0.0372727037176185\n",
       "\\item[\\$R2] 0.678073732798459\n",
       "\\item[\\$metrics] A data.frame: 1 × 3\n",
       "\\begin{tabular}{lll}\n",
       " mse & smape & R2\\\\\n",
       " <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t 0.0372727 & 0.8890066 & 0.6780737\\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\end{description}\n"
      ],
      "text/markdown": [
       "$values\n",
       ":   1. 0.412118485241757\n",
       "2. 0.173889485380434\n",
       "3. -0.0751511204618093\n",
       "4. -0.319519193622274\n",
       "5. -0.54402111088937\n",
       "\n",
       "\n",
       "\n",
       "$prediction\n",
       ":   1. 0.534952363303494\n",
       "2. 0.373751048072052\n",
       "3. 0.138195288527363\n",
       "4. -0.105952784719954\n",
       "5. -0.343513226987357\n",
       "\n",
       "\n",
       "\n",
       "$smape\n",
       ":   0.889006556777233\n",
       "$mse\n",
       ":   0.0372727037176185\n",
       "$R2\n",
       ":   0.678073732798459\n",
       "$metrics\n",
       ":   \n",
       "A data.frame: 1 × 3\n",
       "\n",
       "| mse &lt;dbl&gt; | smape &lt;dbl&gt; | R2 &lt;dbl&gt; |\n",
       "|---|---|---|\n",
       "| 0.0372727 | 0.8890066 | 0.6780737 |\n",
       "\n",
       "\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "$values\n",
       "[1]  0.41211849  0.17388949 -0.07515112 -0.31951919 -0.54402111\n",
       "\n",
       "$prediction\n",
       "[1]  0.5349524  0.3737510  0.1381953 -0.1059528 -0.3435132\n",
       "\n",
       "$smape\n",
       "[1] 0.8890066\n",
       "\n",
       "$mse\n",
       "[1] 0.0372727\n",
       "\n",
       "$R2\n",
       "[1] 0.6780737\n",
       "\n",
       "$metrics\n",
       "        mse     smape        R2\n",
       "1 0.0372727 0.8890066 0.6780737\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prediction <- predict(model, x=io_test$input[1,], steps_ahead=5)\n",
    "prediction <- as.vector(prediction)\n",
    "output <- as.vector(io_test$output)\n",
    "ev_test <- evaluate(model, output, prediction)\n",
    "ev_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAACXBIWXMAABJ0AAASdAHeZh94\nAAAgAElEQVR4nOzdZ1hUV78F8P/M0LtIEUSxC1bUKCAoIKDS7BExqLHFVBNNjEYTTbfFeBOT\nmMTeUYmFIiJVUQQbGFCxoQiiUpQm0oa5H8bwInVAZvbMmfV73g+wZx9dPveiK2efszdPJBIR\nAAAAACg+PusAAAAAANA2UOwAAAAAOALFDgAAAIAjUOwAAAAAOALFDgAAAIAjUOwAAAAAOALF\nDgAAAIAjVFgHYCYxMfHo0aOsUwAAAAC0jLW19axZsxr8SHmLXVZWVq9evcaMGcM6CAAAAICk\n8vPzd+7c2dinylvsiMjAwKBjx46sUwAAAABISlVVtYlP8YwdAAAAAEeg2AEAAABwBIodAAAA\nAEeg2AEAAABwBIodAAAAAEeg2AEAAABwBIodAAAAAEeg2AEAAABwBIodAAAAAEeg2AEAAABw\nBIodAAAAAEeg2AEAAABwBIodAAAAAEeg2AEAAABwBIodAAAAAEeg2AEAAABwBIodAAAAAEeg\n2AEAAABwBIodAAAAAEeg2AEAAABwBIodAAAAAEeg2AEAAABwhFwXu4iIiMzMTNYpAAAAABSD\nCusAjcrMzNy0adNHH33UqVMnSeanpqYGBQXduHFDW1u7T58+/v7+hoaG0g4JAAAAID/k9I6d\nUCjcvn275POjoqK++uqrCxcumJmZ8Xi8yMjIxYsXZ2RkSC8hAAAAgLyRuzt2cXFx169fT0hI\nyM/Pl/CS0tLSLVu2qKurr1mzpkuXLkQUFha2efPmjRs3bty4kcfjSTEuAAAAgNyQuzt2hw4d\nCg0NlbzVEVF4eHhpaemUKVPErY6IPDw8+vfvn56enpaWJpWUAAAAAPJH7ordL7/8cuzYsWPH\njk2fPl3CS+Li4ojI3t6+9qCdnR0RXblypc0TAgAAAMgnuSt2/FokmS8SiR48eKCiotKxY8fa\n45aWlkT04MEDqaQEAAAFJxKJdu3aNXHixJEjRy5cuBCbMAA3yN0zdi1VXl5eUVHRrl27OuO6\nurpEVFRUVHswJCTk7Nmz4q9VVVUHDRokm5AAACBvZs2atWfPHvHXcXFxO3fuTEhI6NOnD9tU\nAK9J7u7YtVRlZSURaWlp1RnX1tYmovLy8tqDDx48uPCfFj3GBwAAXBIWFlbT6sSKi4vnz5/f\nol8kPz//ww8/7Nq1a/v27UePHn3hwoU2zQjQGgp/x05HR4fP55eVldUZLy0tJSI9Pb3ag++/\n//77778v/vqff/6RTUIAAJA3UVFR9Qfj4+NXrFhhZmZmbGxsampqbGxsZGRkZGQkEAjqTy4v\nL3d3d09KShJ/GxERERcXd/r06WHDhkk3OkCTFL7Y8Xg8fX394uLiOuPiEexRDAAA9VVXVzc4\n/uOPP9YZ4fF44npnbGwsLnziry9fvlzT6sTKysoWLlyYkJAgrdAAElD4YkdExsbGz549y8nJ\nMTExqRnMysoiIiMjI3a5AABATtnb22/cuLHOoLW19YYNG/Ly8vLy8nJycp48eVLzdWZm5o0b\nN5r9ZS9fvlxdXS3hy38A0sCFYmdvb3/r1q3ExEQfH5+aQfGzDnX2QAEAAHj27NmmTZvqDGpq\nau7Zs2fIkCGNXVVWViYueTk5Obm5uZs3bz5//nydOerq6mh1wJbiFbuKigrxJibdunUT//y4\nubnt3bs3MDDQ3t5efIsuISHhypUrVlZWXbt2ZRwXAADkyaNHjzw8PK5everj4zNy5MiQkJC8\nvLxBgwatWLHCysqqiQs1NDQsLCwsLCzE3xoaGnp7e9eZ4+bmJq3cAJJRvGKXm5u7ePFiIgoI\nCBC/DKuvr//BBx/89ttvH3/88eDBg4uKilJSUgwMDD744APWYQEAQI7cuHFj7NixDx48mD17\n9t9//62iovLZZ5+17pfy8vJasGDBX3/9VXvw5s2b+fn57du3b4uwAK2heMWuQW5ubvr6+uHh\n4cnJydra2k5OTr6+vh06dGCdCwAA5EViYqK3t3deXt7SpUvXrFnz+r/gn3/+OX78+KCgoOLi\n4iFDhly7dm3btm2enp6RkZHivVQBZI8nEolYZ2Djn3/+4fF4kyZNYh0EAACkLigoyM/Pr7y8\n/Lfffnv33XdrfxQaSuHhZGRERkZkYkLGxmRkRObmVG/n+2ZUV1f7+/sfOHDAwcEhPDxcvJ0q\nQJvLyclZs2bNzz//3OCnHLljBwAA0JidO3fOnz9fIBAEBARMmTKlzqdnz1K9Vylo3jzasqXu\n4L17FBRERkZkbEympi+7oLr6y0/5fP6uXbuKi4tDQkImTZoUFBSkXvMZgKyg2AEAAJetXbt2\n2bJl7dq1CwoKcnR0rD/h009pwgTKy3v5v5wcys0lJ6cGfqmLF+mTT+oOjhhBZ868/FpVVfWf\nf/4ZN25ceHj49OnTDx48qKKCf2dBpvD/cAAAwE1CofDDDz/8888/zc3Nw8LCBgwY0OA08Y03\nSdjb0759L/tfbi49eUJ5eWRt/cocNTW1I0eOjBkz5siRI/PmzduxYwePx3vtPwqApFDsAACA\ng8rLy/39/QMDA/v06XPy5MlOnTq9/q/ZqRNNn978NC0trZCQEBcXl127dunp6f3666+v/1sD\nSAj7KAIAANc8e/bM3d09MDDQzs7uzJkzNa3u/n2aOpWKiqQeQLxRg5WV1aZNm7755hup/34A\n/0GxAwAATsnOznZ2do6Li5swYUJ0dHTNrnKXL5O9PR0+TPv2ySKGsbFxREREly5dvv766/Xr\n18vitwRAsQMAAC65fv26nZ3dv//+O2fOnMOHD2tqaorHIyJo1Ch68oRWraL33pNRGAsLi4iI\niA4dOixduvTvv/+W0e8Kyg3FDgAAOCIhIWHkyJFZWVmrVq3atm1bzRupW7eSpyeVl9OBA/T1\n11IM8OBB3ZEePXqcOnWqXbt27733XkBAgBR/bwAiQrEDAADFJRKJnj9/Lv76+PHjo0aNKigo\n+PPPP7+uVd/WrqX580lXl06dIl9fKYZZvpy6dqWrV+uO9+/f/8SJE9ra2jNnzgwJCZFiAgAU\nOwAAUES5ublz5szR1dXV0dHp1q3bnDlzpkyZwuPxjh079s4779Se6eJCPXtSbCyNHCndSG+8\nQdXV1ODTdLa2tseOHRMIBFOnTo2NjZVuDlBuKHYAAKBgqqqqJkyYsGPHDvHtunv37u3YsUNd\nXf3UqVPe3t51Jg8bRjduUCN72LWlCROod286eJDu32/g01GjRh08eLCqqsrb2/vcuXNSTwPK\nCsUOAAAUzJEjR+Lj4+sMqqio2NraNjhfIJB+JiI+nz79lKqqqJEzPGncuHEHDhwoKyvz9vZO\nSkqSRSZQPih2AACgYFJTU+sPFhYWZmVlyT5MbTNnkrk5bdtGeXkNT5g8efLvv/9eWFjo5eV1\n584d2aYDpYBiBwAACkZPT6/+II/H09PTO3+eSkpkn+gldXVauJBKS+n33xuds2DBgrVr1z56\n9MjNzS0zM1OG6UApoNgBAICCGT9+fM0GdTVGjx4dFmbo7Ez+/kxCvfTuu+TpSQ4OTc1ZsmTJ\nl19+mZGR4erq+scff7z99ttTp07dsGFDaWmprGICZ+GsWAAAUDA9e/ZcvHjxDz/8UDPSo0eP\n4cMPzpxJWlr06kuxsqavT6GhzU/77rvvXrx4sWHDhg8++EA8cvjw4b/++isxMbFdu3bSjQic\nhmIHAACKR/zyxLx58/T09Pr2HXDhgv+qVQIzMwoJocGDWYeTTL9+/eqM3L59e8mSJVu3bmWS\nB7gBxQ4AABRMUFBQTEzMmDFjtmzZ8vw5+flRcDD16UMnTpClJetwEgsLC6s/GCrJ7T6AxuEZ\nOwAAUCRVVVXLli3j8/lr164lon//pfBwcnamc+cUqdURUXl5uYSDAJJDsQMAAEWydevWGzdu\nzJo1a+DAgURkb0+RkXTyJBkYsE7WQg3uumdnZyf7JMAlKHYAAKAwSkpKvv32Ww0NjdqnwY4Y\nQerq7DI16dKlRj/6+OOP+/TpU3tEXV3958Z2NwaQDIodAAAojJ9++unRo0eLFi3q3Lkz6yzN\ne+89GjqULl5s+FMtLa3Tp09/+OGHPXr0MDU15fF4nTp16tWrl2wzAteg2AEAgGLIycn5+eef\njYyMli5dyjqLRMTn1q5b1+gEIyOjTZs23b59+/Hjx9OmTbtz587hw4dlFg84CcUOAAAUw1df\nfVVcXPzVV1/p6+uzziIRT0+ysaEjR+j27eYnf/PNNyoqKitXrhQKhdKPBpyFYgcAAArg5s2b\n27dv79q164IFC4KC6OFD1oEkwOPR4sVUXU0//dT85J49e06bNu3WrVsBAQHSjwachWIHAAAK\n4PPPP6+qqlq7dm1+vvr06TR8OFVVsc4kgWnTqHNn2r2bHj9ufvKqVatUVFS++eabKoX4s4Fc\nQrEDAAB5FxcXFxQUNGzYsClTpnz5JT1/Tp9/TiqKsMW+qiotWkRlZfTrr81P7tGjh7+//+3b\nt/ft2yf9aMBNKHYAACDXRCLRsmXLiGjNmjX//svbtYt692Z8IGyLzJtHs2fT1KkSTV61apWa\nmtp3331XWVkp5VzATSh2AAAg1w4dOhQfHz9+/HgXF5clS14+sqaqyjqWxHR0aPt2srGRaHKX\nLl1mzpx59+7d3bt3SzkXcBOKHQAAyK/Kysovv/xSIBD8+OOPISEUEUHOzi+3EeGqr776SnzT\nrqKignUWUDwodgAAIL/++OOPO3fuzJ8/v0+fPjt2EJ9PnD+aoXPnznPmzMnIyNi5cyfrLKB4\nUOwAAEBOFRYWfv/99zo6OitXriSigwcpIoIGDWIdS/qWL1+urq7+ww8/lJeXs84CCgbFDgAA\n5NSaNWvy8vI+++wzMzMzIlJRoVGjWGeSiU6dOs2fP//Bgwfbtm1jnQUUDIodAADIo4cPH/76\n668mJiaLFy9mnaXNVFVRaKhEM5cvX66pqfn999+/ePFCyqGAU1DsAABAHq1YsaK0tPTbb7/V\n1dVlnaXNzJ5N3t509mzzM83MzN55551Hjx5t2bJF+rmAO1DsAABA7vz777979uzp3bv33Llz\nWWdpS7NmERGtWyfR5GXLlmlpaa1evbq0tFSqqYBLUOwAAEDuLFmypLq6et26dRUVinC+hMTc\n3GjIEAoJoWvXmp/coUOH99577/Hjx3/++af0owFHoNgBAIB8iYmJOXXq1IgRI3x8xo0eTZMm\nUVkZ60xt5/PPSSSi9eslmrxs2TJdXd3Vq1eXlJRIORdwBIodAADIkerq6s8++4zH461Zs+bw\nYTp3jioqSEODday2M3ky9ehB+/fTgwfNTzYyMnr//ffz8vL++OMP6UcDLkCxAwAAObJ3794r\nV65MnTp1yJDhX3xBKiqSPpGmKAQCWryYKivpl18kmr9kyRJdXd1169YVFxdLORpwAYodAADI\ni7KyspUrV6qqqn7//fe//Ubp6TR/PvXpwzpWW3v7bVq+nBYtkmhy+/btFy5cmJ+f/9tvv0k5\nF3ABih0AAMiLX375JSMj4/333zc07PHjj6SrS6tWsc4kBZqa9MMPZGEh6fzPPvvMwMBg3bp1\nBQUF0swFXIBiBwAAcuHZs2fr1q3T1dVdvnz5qlX09CmtWEGmpqxjyQEDA4OPP/64oKBg06ZN\nrLOAvEOxAwAAufDtt98+ffp0+fLlJiYmvr40eTJ9/DHrTHJj0aJFhoaGGzZsePbsGessINdQ\n7AAAgL179+5t3ry5Y8eOCxcuJCJHRwoM5NTLsK9JX1//k08+KSws/EXCdy5AWaHYAQAAe8uX\nLy8vL//hhx+0tLRYZ5FTixYtMjY23rhx49OnT1lnAfmFYgcAAIxdvHjx4MGDAwYMmDFjBuss\nslZURNu2STRTR0fnk08+KSoq+vnnn6UcChQYih0AADC2bNkykUi0bt06Pl/p/lWaN4/mzaOo\nKIkmf/zxxyYmJr/88ktOTo6Uc4Gi4tQZfAAAoFgqKytPnjwZHR3t4uIyZswY1nEY+OgjOnyY\nFi4kR8eXI5Mn0+jRdadFR9PBg0Sk3bXrqcTExDFj7g8bZmJjQ++9J9u4IPdQ7AAAQNaKi4u/\n+eab3bt35+Xlqaqq8ni89evXb91KHTqQtzfrcLI1YgSNHElnztD16y9HevRooNilptLff4u/\nHEg0MDmZkpNp4kQUO6gLxQ4AAGRKJBK99dZbwcHB4m8rKiqI6OTJ5DVrhqirU3o66ekxzSdz\noaF08+b/vm1w4+Jp08jB4eXX+/bt27hxo7+//9dffyKLfKBQUOwAAECmoqKialpdja+/Vqmq\noh9/VLpWR0Q6OjRkSDNzTEzIxOTl1337Tj50aGlg4Bdr104lMpd2PFAsSveYKgAAsHX16tV6\nY/2rqvwtLcvffZdBHoWjoaGxdOnSsrKydevW1QyKRBQX98qdP1BOKHYAACBTurq69cZ+IhJ8\n8UWBqiqDPIpo/vz5FhYWf/3118OHD8Ujp07RyJG0YQPbXMAeih0AAMjU2LFjtbW1aw14EI3W\n1b2yYAHOhZWUhobGF198UVZWtmbNGvGIqyuZmdHBg1RayjYaMIZiBwAAMtW5c+fNmzcLBIL/\nBt4gqtq+3ZBlJgU0f/78rl27/v333/fv3yciFRXy96eiIjpyhHUyYArFDgAAZG3SpEkaGho6\nOjoLFiz49Vfj69fLpkzpwjqUglFVVV22bFlFRcXatWvFI2+/TUS0YwfDUMAe3ooFAABZCwwM\nfP78+bJly1avXs06iwKbPXv22rVrt27d2qlTJwMDA3t7e1vbQTExlJ5O3bqxDgeM4I4dAADI\n2o4dO4ho1qxZrIMoNlVV1b59+1ZVVa1YseKDDz4YPHiwqupekYj27GGdDNhBsQMAAJm6d+/e\nmTNnhg8fbmVlxTqLYgsODq6zI+DZsx86OFzz8mKVCNhDsQMAAJnasWOHSCR6W/xEGLyGHQ08\nT1dYVjbrjTcYhAE5gWIHAACyU11dvXv3bi0tLV9fX9ZZFF5eXl79wdzcXNknAfmBYgcAALIT\nHR2dkZHh7T3T3l5vyxbWaRRcr1696g/27t1b9klAfqDYAQCA7IhXD01MFl+/Tvn5rNMouCVL\nlujo6NQZXLlyJZMwICdQ7KQoKSnp0KFD8fHxVVVVrLMAALBXWFh47NixLl26nD3bU0WFZs5k\nHUjB9e7dOygoqPY7KJs2bXJ0dGQYCZhDsZOKvLw8Nze3wYMH+/r6Ojg4DBo0KDU1lXUoAADG\nAgICSktLR49enpxMHh5kbs46kOJzcXG5ceNGVlaWeJvinJycmo+qqqi4mF0yYATFTirmzp0b\nFRVV821qauqUKVNevHjBMBIAAHM7duzg8XglJb5ENHcu6zQc0rFjx3fffVdLS2v37t0ikYiI\nrlyhzp3p++9ZJwOZQ7FrexkZGUFBQXUGb968eerUKSZ5AADkwc2bNxMTE52cxp44oWdqSp6e\nrANxi56eno+PT0ZGRnx8PBH17k3Pn9Pu3YRHgZQNil3be/jwYYPjWVlZMk4CACA/tm3bRkQj\nR35UXU2zZpGqKutAnDNjxgwi2rNnDxFpa9Obb9LjxxQezjoWyBaKXdvr3Llzg+NdunSRbRAA\nAHlRVVW1d+9ePT29pUudHj2iZctYB+KiMWPGmJqaHjp0qLy8nIhmzyYiamAPY+A0FLu2Z2Fh\n4efnV2fQxsbG3d2dSR4AAObCwsIePXrk6+urpaWlpUXt2rEOxEUqKipTp0599uxZaGgoETk4\nkJUVBQcTdixWKih2UrF58+Y333yz5ltTU9PAwEA1NTWGkQAAGBJvXzdbfBMJpEa8Grt3717x\ntzNnUkUFHTjANBPIFoqdVOjr6x86dOj+/fthYWHt27evqKiwsLBgHQoAgI38/PwTJ0706tXL\nzs6OdRaOGzp0qJWVVWhoaH5+PhHNmkXm5sTjsY4FMoRiJ0WWlpZjx46dNm3as2fPwvH8KgAo\nqz179pSXl8+ZM4eHiiF9b731VkVFxeHDh4nI3JwyM+mjj1hnAhlCsZM68fN2B3ArHACU1c6d\nO1VUVMSrhCBtM2bM4PF44ndjiYiPf+eVDP4PLnXDhw/v2rVrUFBQSUkJ6ywAALJ25cqVq1ev\nOjtP27DB/OZN1mmUgKWlpaOjY3x8/O3bt1lnAQZQ7KSOx+P5+vqWlpYeP36cdRYAAFkTvzbR\nocPSn3+msDDWaZSD+OYoVoqUE4qdLGA1FgCUU0VFRUBAQPv27S9e7KumRm+9xTqQcpg6daqm\npuaePXvEx4uBUkGxk4UBAwb069cvPDy89vHMAACcd/z48by8PBeXlTdv8saPJ2Nj1oGUg76+\nvqen5507dxITE1lnAVlDsZMRPz+/qqqqI0eOsA4CACA74nXYigp/Ipo7l3UaZVJnQ7uHD2nB\nAvrtN6aZQCZQ7GTkrbfe4vF4WI0FAOWRnZ196tSpvn1to6MNLSzIzY11IGXi6elpZGR04MCB\niooKItLQoF27aNMmwtos56HYyYilpaWdnV1cXNz9+/dZZwEAkIXdu3cLhcIBA74vKaE5c0gg\nYB1Imaiqqk6dOvXp06cnT54kovbtycuLbt2i8+dZJwMpQ7GTHT8/P5FIdOjQIdZBAABkYdeu\nXWpqahs2DAwKovnzWadRPv7+/kRUs6Gd+Di3HTsYJgJZQLGTrgcPaOfOl19PmzZNRUUFq7EA\noAzi4+PT0tJ8fHzMzIx9fAinKsqevb19r169QkJCCgoKiGjsWDIzo0OHqLSUdTKQJhQ7KRKJ\naMoUmjPn5X8hGRsbjxo1Kjk5+dq1a6yjAQBIl/i1idni20TAyPTp08vKygIDA4lIRYVmzKCi\nIvrnH9axQJpQ7KSIx6MtW8jAgObPp8OHif7b0O7gwYOMkwEASNOLFy8CAwNNTU3HjBnDOotS\n8/f3r3282NtvExHh6HJuQ7GTroED6cQJ0tQkf386cYImTZqkqam5f/9+bBoJABx2+PDhgoKC\nWbNmqaiosM6i1Lp37y5+b+/evXtEZG1Nly7RfzUPuAnFTurs7Oj4ceLzacoUunJFz8vL6+7d\nuxcvXmSdCwBAWsTrsG+LbxABUzNmzBCJRPv37xd/O2QI8XhsE4F0odjJwqhRdOgQVVXRO+/Q\n1KnTCceLAQB33b9//8yZM0OGeD99as06C5Cvr6+6uvru3btZBwEZQbGTER8fCgigkBDy8fEw\nMDAICAgQCoWsQwEAtL0dO3ZUV1ebm3/t6IhVP/YMDQ09PDxu3bp16dIl1llAFlDsZGfSJOrV\nizQ0NCZOnPj48eOYmBjWiQAA2phIJNq7d6+mpnZy8iBNTfLxYR0I6m1oB9yGYseA+N1YrMYC\nAPdER0enp6fb23+Zmcl/800yMGAdCIh8fHzat29/4MCByspK1llA6lDsGHB1dTU3Nw8MDCwr\nK2OdBQCgLYlfm6iunk1Ec+awTgNERKSmpjZ58uTc3NxTp06JR4RCOnmSIiPZ5gKpQLFjgM/n\nT5kypaioKDAwsqCAdRoAgDZSVFR09OjRTp1szp837dmTRo5kHQj+M2PGDCLau3ev+NsHD8jT\nk776imkmkA4UOzb8/PyI2n30UT9PTyopYZ0GAKAtBAQElJaW9u37Y3k5zZmDbTXkiIODQ7du\n3Y4dO1ZYWEhEXbuSszMlJNCNG6yTQVtDsWPDzs6uZ0/T4uLL58/ThAmEJVkA4IAdO3bweLz3\n3us/ezbNnMk6DdTC4/HEx4sdOXJEPCI+7K3mNHPgDBQ7Znx9pwiFfjY2WVFR5OtLeKQVABTa\nrVu3EhMTnZ2dx42z2L6dzM1ZB4JXzZw5k8fj1azGTplCBga0axdVVbHNBW0MxY6Zt956i6jS\n2PgDZ2cKCqLp0wkb2wGA4tq2bZtIJJotvhEE8qdnz55Dhw6NjY3NzMwkIk1NmjKFnjzB0bFc\ng2LHjJWV1cCBA2NiTmzdmjNsGAUG0qefss4EANAqVVVVe/fu1dHRmThxIuss0Ch/f//q6uqa\n48XEJXz7dpaRoM2h2LHk5+dXVVV18uThsDBydKSpU1kHAgBolfDw8Ozs7GnTpuno6LDOAo2a\nNm2aqqpqzfFiw4fT11/TypVsQ0EbQ7Fjafr06Xw+/8CBA4aGFBdHw4ezDgQA0Cri7euwDivn\njI2Nx4wZc/369aSkJPHIqlU0cCDbUNDGUOxY6tSp0/Dhw+Pj4+/du8c6CwBAK+Xn54eEhPTo\nYTNsmD3rLNCMOhvaAfeg2DHm5+cnEokCAgJYBwEAaKW9e/eWl5dbWGy2tORducI6DTRp3Lhx\nBgYG+/btq8LbsByFYsfY1KlTVVVVcW4sACiunTt38vlqN24MLSqiXr1Yp4EmaWhoTJo06cmT\nJ1FRUayzgFSg2DFmZGTk7u6ekpKSmppae7y0lHx8KDSUVS4AAIkkJSUlJycPGrTiyRPB9OmE\ndyfkn3g1ds+ePayDgFSg2LHn5+dHRHVu2v37L0VG0ptv0unTjGIBAEhA/NoEnz+PiObOZZ0G\nJODk5NSlS5ejR4+W1DrRsqyMMjMZhoI2g2LH3sSJE7W1tfft2ycSiWoG7ezo0CGqqiIfH3r1\nXh4AgFwoKSlJSUnZv3+/gYFVUpJ5v340bBjrTCABHo/n5+dXWlp69OhR8Uh2Npmb00ff5LEN\nBm0CxY49bW1tb2/vjIyMhISE2uM+PrR1KxUX04oVrKIBADQgNzd3+vTpenp6AwYMyM/P19Rc\nUFWF23WKpM5qrLk5jbvwfdjWjtfpOtNc0AZQ7ORCg6uxRDRzJr3xBgUH46YdAMiL6urq6dOn\nHzhwoGaR4dGjLG3tAn9/trmgBaytrQcPHhwVFZWVlSUe8erRu4Iq1tJatsHg9aHYyQUPDw9D\nQ8OAgID6759/8QWJRLR6NZNcAAB1nT17NjIy8tWxDaWlRhUV2WwCQavMmDGjurq6ZrOtyTS5\nD/XZT/vvEfZVVWwodnJBTU1t0qRJubm59d8/nzCBPvqIFi1ikgsAoK5bt27VH2kwmMkAACAA\nSURBVBSJhA2Og9zy8/NTUVGp2amYT/xP6dMqqvqJfmIbDF4Tip28aGw1ls+nX3+lN95gkQkA\noB4jI6MGx01MTGScBF6Hqampu7v71atX//33X/HIDJphSZbbaFs24earAkOxkxfOzs4dO3Y8\nevToixcvWGcBAGiUm5tbp06d6gza2tpaW1szyQOt5u/vT7WOF1Ml1cW0uJzKf6FfmOaC14Ji\nJy/4fP7UqVOLiopCsSsxAMgxHR2dgIAAAwODmhFra+v9+/fzeDyGqaAVJkyYoKuru2/fPqFQ\nKB6ZR/NcydWO7NgGg9eBYidHGluNBQCQK8OHD3dyciKixYsXHz9+/OrVq926dWMdClpMS0tr\n0qRJ2dnZsbGxL0dIK5IiJ9JEprngtaDYyZGhQ4f26tUrNDS0oKCAdRYAgEaVl5dHR0cbGKx7\n/nyDnd04VVVV1omglcSrsThejEtQ7OSLn59feXl5zW7gdQiFdPAgFRfLOBQAwCuio6OLi4t5\nvJm7dpGWFus08BpGjRplYWERGBhY+3gxUGgodvKl6dXYjRtp2jT680/ZZgIAeFVwcDBR72fP\nTF1dSUeHdRp4DXw+38/P7/nz50FBQayzQNtAsZMvvXv3Fu8Gnp3dwNvmc+aQjg5t2EB4cRYA\nWBGJRCEhIWpqvkQ0YQLrNPDaxowZQ0Tz5s3r3r377Nmza86iAAWFYid3/Pz8qqurAwMD639k\naEjvvENPntDOnTKPBQBARERJSUmZmZm6uv58Pnl7s04DrycnJ0f8mN2LFy/S09N37tw5bNiw\n3Nxc8aeX6bKQhEwDQouh2Mmd6dOnCwSCxlZjlywhDQ1av57qnT0GACALQUFBRKZPn/aws6MO\nHVingdezcuXKx48f1x559OjRqlWriOhn+vkNeuMgHWQUDVoJxU7umJubOzo6JiYm3r17t/6n\nHTrQzJl07x5hUxQAYCIkJITP9xGJeOPHs44Cry0xMbH+YEJCAhGNp/ECEvxAP1RTtcxzQeuh\n2MkjPz8/kUjU2E27zz8ngYA2bJBxKAAAysrKunLlyogRd8+eJX9/1mngtamrqzc22J26T6bJ\n1+l6CIXIPBe0HoqdPJo6daqamtq+ffsa/LR7d9q6lY4fl3EoAAAKCgoSiUTjxnk7OJC5Oes0\n8No8PT3rD3p5eYm/WE7LecT7gX6QbSh4LSh28qhdu3ZjxoxJS0u7evVqgxPefpssLWUcCgBA\nvNEJ+fj4sA4CbWPp0qW2tra1R+zt7T///HPx1wNpoAd5XKAL0RTNIh20BoqdnMLxYgAgb0pK\nSmJjY62trXv27Mk6C7QNdXX1uLi4zZs3+/r66urqqqqqnjx5Uk1NrWbCV/QVEa2m1ewyQsug\n2Mmp8ePH6+jo7N+/v7oaT60CgFwIDw8vKysbN24c6yDQllRVVd99992AgIC5c+dWVlaePXu2\n9qd2ZPc5fS6ud6AQUOzklJaWloODQ2Zmpo2NzVtvvRUXF8c6EQAoO6zDcpv40brQ0NA642tp\n7UgaySIRtAaKnZw6cOBAeHg4EaWkpOzfv3/kyJF//fUX61AAoLyEQmFoaKih4dA33rBjnQWk\nwsnJSV9fPzg4WCQSsc4CrYdiJ4+Ki4vffffdOoOLFi2qs40kET1/Tj//TKmpskoGAMoqPj4+\nLy+/oiKsd28BnhDhJFVVVTc3t8zMzJSUFNZZoPVQ7OTRxYsXi4qK6gy+ePHi3LlzdQYjIujT\nT+nHH2WVDACUVXBwMJFNSUn7oUOJj386OMrb25saWo0FBYKfTnkkFDZ8Nl/9FynGj6d+/ejQ\nIbp9W/qxAECJBQcHq6hMIaKJE1lHAanx8PDg8/khIdiRWIGh2MmjN954Q1NTs86gmpqavb19\nnUEejz7/nIRCWr9eVuEAQPncuXMnLS1NS2u6qip5eLBOA1Jjamo6dOjQhISEnJyc+p+KSHSc\njj+iR7IPBpJDsZNH7dq127hxY53B7777zsLCov7k6dOpRw/auZMePJBJOABQPkePHiXqUlTU\nxdmZ2rVjnQakycvLq7q6Wvz2Xh17aM8EmrCBcKKlXEOxk1MLFiwIDw/38vIyMzMjopkzZ9Zs\nBV6HQECffkqVlfR//yfbiACgNIKDg3m8iUQ0fjzrKCBlTTxmN5WmmpHZn/RnHuXJPBdICsVO\nfo0ePTokJOTy5cs8Hu/+/ftNzHz7bTI3p4MHqaJCVuEAQGnk5+efP3++U6cOnTsT9rDjPBsb\nGwsLi5MnT1ZWVtb5SIM0FtLC5/T8N/qNSTaQBIqdvDMzM7OxsYmPjy8oKGhsjoYG/fMPXb9O\ntY6BAQBoG6GhoVVVVXPnlmVkUOfOrNOAlPF4PE9Pz8LCwjpHUIi9T++3o3abaFMxFcs+G0gC\nxU4BeHp6VlVVnTp1qok5dnakry+zRACgRMQHTuAkMeXR2BEURKRHeh/Sh0/p6V+EPfPlFIqd\nAvD09CSiEydOsA4CAEqnvLw8PDy8c+fOAwcOZJ0FZMTd3V1LS6ux3ewW0kJt0v4/+j8hNbwz\nF7CFYqcAbG1t27dvf+LEifr72AEASFVMTExxcfG4ceN4PB7rLCAjmpqazs7OaWlptxvaItWI\njLbRtiiKEpBA9tmgWSh2CkAgEIwZMyY3N/fy5cusswCAchGvw/rgpQkl08RqLBH5km9v6i3b\nRCApFDvFgNVYAJA9kUgUHByso6Pj5OTEOgvIFM4WU1wodorBw8NDIBBIUuxyc+mzzyggQAah\nAIDjkpKSMjMfmpsfjI9XZ50FZKpz5879+/c/ffp0YWEh6yzQMih2isHQ0HDYsGGXLl168uRJ\n0zOLi+mXX+ibbwjP4wHAawoKCiIafuuW5+7drKOAzHl7e1dWVkZGRrIOAi2DYqcwPD09Gzvm\npbZu3cjXl9LS6OhR2eQCAM4KDg7m8SYQDpxQSk0/ZgdyC8VOYYh/xiRZjV2xgvh8+v57Eomk\nHwsAOCo7OzspKUlDY6qmJrm7s04DMmdnZ2dkZBQaGtrEhgzlVP4n/XmWGtjKGFhBsVMYNjY2\nHTt2bPCYlzqsrWncOEpOpia3NAYAaMrx48dFIusXLzq5u5O2Nus0IHMCgWDs2LE5OTkXL15s\nbM4VuvIevbeSVsoyGDQNxU5h8Hi8sWPHFhYWJiQkNDt5+XIiotWrpZ4KALgqODiYCOuwSq3Z\n1Vh7sncm5xiKiad4GeaCpqDYKRLJNz0ZOpTc3OjePcrNlX4sAOCckpKSmJgYTU1fgYC8vVmn\nAUY8PDxUVVVDQkKamLOclhPRWlorq1DQDBQ7ReLu7q6mpibho6y7dtGdO2RsLO1QAMBB4eHh\nZWVlkyef/b//IxMT1mmAEX19fQcHh+Tk5KysrMbmuJO7PdkHU3AKpcgyGzQGxU6R6OrqOjo6\npqSkZGRkNDvZ3JxUVWUQCgA4SHzgxLvvDvzwQ9ZRgCkvLy+RSNT0StESWiIi0RpaI7NU0AQU\nOwUjXo09efIk6yAAwFlCoTA0NNTY2NjOzo51FmBMkiMoxtP4vtQ3mIKf0TNZ5YJGodgpGJwt\nBgDSFh8fn5eX5+3tLRDglHdlZ2Vl1aNHj4iIiNLS0sbm8Im/h/bcpbvtqJ0ss0GDUOwUjLW1\ndffu3aOiosrKylhnAQBuEq/D+vj4sA4CcsHLy+vFixexsbFNzBlEg4wJz3TLBRQ7xTN27Njn\nz5+fOXOGdRAA4KagoCB1dXU3NzfWQUAu4AgKxYJip3hatBorEtHWrfTHH1LOBABccefOnZs3\n74waNVpXV5d1FpALTk5Ourq6TW96AvIDxU7xuLi4aGlpiddKmsXj0apV9P330g4FABxx7Ngx\nolFnzhzetYt1FJAPampqo0ePfvDgQUoKNjRRACh2ikdTU9PZ2Tk9Pf327duSzLeyokeP6Bne\nVQIACYgPnHj+XN3MjHUUkBvi1VjctFMIKqwDNCA1NTUoKOjGjRva2tp9+vTx9/c3NDRs+pKV\nK1cmJyfXH//77787dOggnZgseXp6njhx4sSJEx9//HGzk62sKDqabt4kbFwAAE17+vRpfPx5\nNbXDmprk7Mw6DcgNLy8vPp8fGhr6xRdfND0zi7J20+5P6BMt0pJNNqhD7u7YRUVFffXVVxcu\nXDAzM+PxeJGRkYsXL252P97s7GyBQGBWD1ff1W/RY3ZWVkREaWlSTQQAXBASElJVZVNRYeLp\nSWpqrNOA3DAxMXnjjTcSEhLy8vKanvkr/bqCVgRSoGyCQX3ydceutLR0y5Yt6urqa9as6dKl\nCxGFhYVt3rx548aNGzdu5PF4DV5VVVWVm5vbp0+f1Upz6H3Xrl2tra1Pnz5dUlKio6PT9GRr\nayIUOwCQQHBwMNF4Iho/nnUUkDNeXl4XLlw4efKkv79/E9Pm0byf6KdttG0mzZRZNqhNvu7Y\nhYeHl5aWTpkyRdzqiMjDw6N///7p6elpjReTx48fi0Qic3NzGaWUD15eXuXl5dHR0c3OFN+x\nu3FD6pEAQKGVl5eHh4erqk5RVaUxY1inATkjyREURNSLejmS4xk6k0a4ncCGfBW7uLg4IrK3\nt689KD7T5sqVK41d9ejRIyLq2LGjlNPJFw8PD5JsNbZjR5o5k8aOlX4mAFBkMTExxcUCDQ1j\nFxcyMGCdBuTMoEGDLCwswsLCKisrm545l+YS0U7aKYtYUI8cFTuRSPTgwQMVFZU6Fc3S0pKI\nHjx40NiF2dnZRPT8+fPvvvtuxowZM2bM+Oqrr86dOyftwGyNGDFCX18/NDRUJBI1PZPHo127\n6L33ZJMLABRVcHAwUUFg4KWAANZRQP7weDwPD4/CwsJm/3mdSlMNyGAn7aykZiogSIMcFbvy\n8vKKior6W2KKR4qKihq7UFzsDh8+fOvWrS5durRv3z4lJWXt2rW//fZbnZkXL17c9Z/09PS2\n/hPIlKqqqpubW1ZWVmpqKussAMAFoaGhOjo6Tk5O7XDgJzREwiMoNElzGk17Qk9CCNujMCBH\nL0+I7+5qadV9QVpbW5uIysvLG7vwyZMnAoFg/Pjxs2bNEr9gkZ6e/v333586dWrIkCG1F3bj\n4uL2798v/nrQoEHdu3dv8z+FLHl6ev7zzz+hoaH9+/dnnQUAFFtSUlJGRsabb76prq7OOgvI\nKXd3d01NzZCQkPXr1zc98yP6aDgNH0t4BogBOSp2Ojo6fD6//tn2paWlRKSnp9fYhV9//XWd\nkW7dus2ZM2fdunXR0dG1i93cuXN9fX3FX8fExLRJbIY8PT15PF5YWNiyZctYZwEAxRYUFERE\nPj4+rIOA/NLS0nJ2dg4LC7tz506PHj2amNmH+vShPjILBrXJ0VIsj8fT19cvLi6uMy4eaXaP\n4joGDhxIRPfu3as9qK+v3/E/mpqar5eXvQ4dOgwaNOjcuXNPnz5lnQUAFFtwcLBAIBC/lQXQ\nGAlXY4EhOSp2RGRsbFxRUZGTk1N7MCsri4iMjIwavEQkElVWVgqFwjrj4q2Jm93jTdF5enoK\nhcKIiAjWQQBAgWVnZ1+5csXR0bGxv2kBxMT3dFHs5Jl8FTvxsmliYmLtwQsXLlC9PVBq5Ofn\nT548uf7JWteuXSOimv3wuEryIyhiY2ndOqquln4mAFA0x48fF4lsevT4uN6zMACv6Ny5c79+\n/U6fPl1/eQ3khHwVOzc3N4FAEBgYWHNoSUJCwpUrV6ysrLp27SoeqaiouHPnzp07d6qrq4nI\nyMiob9++Dx482L9/f83GH5mZmVu2bBG/UcHkDyIztra2JiYmYWFh1c1Vtk2baOlSysyUTS4A\nUCTBwcFEC7dtm3jpEusoIPe8vb0rKipOnTrFOgg0TI5eniAifX39Dz744Lfffvv4448HDx5c\nVFSUkpJiYGDwwQcf1MzJzc1dvHgxEQUEBIhfoV20aNEPP/wQEBAQExNjaWlZUFBw9+5dkUg0\nd+7cmjrIVXw+393dfd++fZcuXRo2bFgTM8UHi924QZaWMsoGAAqhpKQkJuaMQLCvfXtqZGkE\n4H+8vLzWrFkTGho6efJkSeZfp+tmZNaOsImOjMjXHTsicnNzW7FihbW1dXJy8pMnT5ycnNau\nXWvZZBkxMTFZv369r6+vsbFxampqUVGRra3tTz/9pCSvd0m4Gis+WAwnxgJAHeHh4WVlbwiF\n7Xx8SCBgnQbknr29vZGRUWhoaLMrRUS0i3b1pb47aIcMgoGYfN2xExs6dOjQoUMb+7Rjx47i\n1/JrU1NTe+utt6ScS06NHTtWIBCcOHGi/rYvteHEWABoUHBwMNF4IuL6oyvQNgQCwdixY/fu\n3dvsShERjaWxqqS6hbYsokU84skmoZKTuzt20FKGhoa2traXL19+8uRJE9OsrIjHwx07AHiF\nUCgMDQ0VCCZpaZGrK+s0oCAk3/TElEy9yTuN0s7TeennAiIUO27w9PSsrq4+efJkE3N0dMjC\nAnfsAOAV58+fz8szFwotx46leuf+ADTMw8NDVVU1JESiE8Pm0lwi2kbbpBwKXkKx4wIJH7Ob\nPp3efJOqqmSSCQAUQXBwMFHBxIm3Zs1iHQUUh76+/vDhw5OSksQbzTbNgzw6U+eDdLCIGj3z\nHdoQih0X2NjYdOzYMTw8XHzebmPWrKHffycVeXyuEgDYCAoKUld/smuX2bhxrKOAQvHy8hKJ\nRGFhYc3O5BN/Fs16Ts8DKEAGwQDFjgt4PJ6Hh0dhYeH583iIAQAkdfXq1bS0NFdXV11dXdZZ\nQMF4e3uTxEdQzKE542k8To+VDRQ7jhCf8CjJERQAoOTKysq+/PJLQ0NDGxsbIuLxeOXl5axD\ngYKxtrbu0aNHRETEixcvmp3chboco2OO5CiDYIBixxGjR49WV1dHsQOAZn300Uc//PDDs2fP\nxN+GhoZ++umnbCOBIvL09CwtLY2NjWUdBF6BYscROjo6jo6OKSkpGRkZrLMAgPxKS0vbunVr\nncHff/89PT2dSR5QXJJvegKyhGLHHeJ3YyV5lBUAlNa1a9caHE9JSZFxElB0Tk5Ourq6KHby\nBsWOOyQpdnfv0o4dJMH76QDATXp6erW++5NoD5EuERkYGLCKBApKXV3d3d39/v37qamprLPA\n/6DYcYeVlVX37t2joqLKysoam3PkCM2ZQ2fPyjIXAMgRBwcHCwsLIiJSJ/IjciEq6dKli52d\nHeNkoIDEq7ES7lRco4wa/UcKXh+KHad4eHg8f/789OnTjU2wtibCibEASkxLS2vfvn16enpE\nLkR6REfbtzfcv3+/uro662igeLy9vfl8vuSrsSmUYk3W39F3Uk2l5FDsOKXZ1VhxscOJsQDK\nbOTIkUuXLiXyIKIPPrC8deuWvb0961CgkExMTIYMGXL+/Pm8vDxJ5nejbtmUvZ22V1JT2+nD\n60Cx4xQXFxctLa3g4ODGJnTtShoauGMHoOzi4+OJRquri9at8zE0NGQdBxSYl5eXUChs+rDy\nGtqk7Uu+j+lxGOE9P2lBseMUDQ0NFxeX9PT0W7duNTiBz6eePenWLRIKZRwNAORFVVXV6dN3\niaxGjuRpabFOAwquRUdQENFcmktE22ibFDMpNxQ7rhGvxjaxU7G1NZWX0717MswEAPLk/Pnz\nJSW2ROTuzjoKKL7Bgwd37NgxLCys6cPKa9iS7UAaeIJOZFO2tLMpJxQ7rhH/x1MTxc7Fhd58\nk6qrZZgJAORJZGQk0e716yNmzGAdBRQfj8fz9PQsLCyMj4+X8JLZNLuKqnbSTmnmUl4odlzT\nuXPnPn36nD59uri4uMEJ775Lhw5Rr14yzgUA8iIiIoLP57399qAOHVhHAU5o6REUM2hGe2qP\nTU+kBMWOg7y8vCoqKqKjo1kHAQC5U1RUdOnSpUGDBhkZGbHOAhzRr18/gUCwadOmYcOGLV++\nvKioqOn5hmSYTdnf0reyiadsUOw4yMPDg5pcjQUApRUdHV1ZWemOx+ugjTx+/NjBwUEoFJaV\nlV28eHH16tXDhw8vLS1t+io1UpNNPCWEYsdBjo6OBgYGJ06cEIlErLMAgHyJjIwkIjc3N9ZB\ngCOWLVv25MmT2iPXrl1bv349qzyAYsdBqqqqbm5uWVlZ//77L+ssACBfIiMjNTU1HRwcWAcB\njoiLi5NwEGQDxY6bmt30BACUUGZm5s2becOHj9LQ0GCdBThCIBBIOAiygWLHTR4eHjwer7Gz\nxQoKKCyMrl6VcSgAYCwiIoLomzNnjl65wjoKcIWrq2v9Qaz1M4Rix00dOnQYPHhwfHx8fn5+\n/U9TU8nTk7Zvl30uAGApMjKSaDSfLxAfGw3w+n788ceuXbvWHrGzs/vkk08kubaESpbT8hW0\nQjrRlBSKHWd5enoKhcKIiIj6H4n/Tk9Lk3UkAGBIJBJFRt4h6unszNPUZJ0GuKJdu3bJycmr\nVq1ydnbm8XidO3c+ffq0qqqqJNdqkMYu2vUr/VpMDW+8Cq2AYsdZ4sfsGlyNbd+ejI3pxg2Z\nZwIAdq5evZqbO5iIRo/msc4CnKKnp/f111/HxMQMGzbs4cOHJSUlEl6oQiqzaFYJlRymw1JN\nqFRQ7Dhr2LBhJiYmYWFh1Q0dH2ZtTVlZJPFPHwAovIiICCJ3Iho9mnUU4Cg3NzehUBgbGyv5\nJfNoHo9422ib1EIpHRQ7zuLz+aNHj87Nzd22bdujR4/qfGplRSIR3bzJJBoAMBAREU3kYmoq\n7NuXdRTgKPGLFFFRUZJf0o26OZNzPMVfo2vSiqVkUOw46/Hjx9euXSOid955x9zc3NfX99mz\nZzWfih+zu36dVToAkKmKiopz5+6rqT0bO1bAw0osSIeDg4O2tnaLih0RzaW5RLSd8EJf20Cx\n46bq6urp06cnJSXVjBw6dGjOnDk13w4cSE5OpKfHIhwAyNzZs2dLS9Pmz9+4ZQvrKMBdampq\nDg4ON2/ezMzMlPyqyTTZkAxP0knpBVMqKHbclJiYGBMTU2fw2LFjN/57Y8LFhWJjafx4mScD\nABbEL8i7ublJ9rYiQCu1YjVWgzSiKTqJkpqfChJAseOme/fuNTienp4u4yQAIA8iIyNVVFSc\nnZ1ZBwGOE29NLD6SWHIDaaAaqUknkdJBseMmMzOzBsfNzc1lnAQAmMvPz79y5crQoUMNDAxY\nZwGOs7GxMTIyioyMFIlErLMoKRQ7bnJ0dOzfv3+dQXt7exsbGyZ5AICh6Ojo6upqnPIEMsDn\n811cXJ48eXIdb+cxgmLHTaqqqocOHepba1eDvn37HjhwgIfX4QCUj3hdzN3dnXUQUArix+xa\nuhoLbQXFjrOsrKySk5Ojo6P9/f2J6L333rO0tGQdCgAYCA9PUVf379nTlnUQUAqteH8C2hCK\nHZepqKi4uLisWLGCiKKjo+t8KhTS5ct07hyLZAAgK3fv3s3IsCkv33PwIB5OB1no0aNH165d\nY2NjKysrW3RhFVXFUzxej31NKHbcZ2VlZWFhER0dLRQKa48LhWRnR4sWscoFALIQGRlJNJpw\nkhjI0KhRo4qLiy9evNiiq/6lfx3I4Sf6SUqplASKnVJwcXEpKChITk6uPaimRl27Uloa4dUl\nAA47dUp8klil+LwZABlo3WN2NmRjTMaRFCki/LPUeih2SqGxJx6sram4mLKzWWQCAOkTCoWR\nkSVE+t7e2JgYZMfV1ZXH47X0MTs+8UfRqBzKSaEUKQVTBih2SkG8zUH9nzErKyKi/06jAACu\nuXLlSlGRLRHhjViQJRMTk/79+yckJJSUlLToQldyJaIIipBOLqWAYqcUOnbs2KtXr7Nnz5aX\nl9ceFy/NpKWxSQUA0hYREUE0mscTjRrFOgooGTc3t4qKiri4uBZdNZpGE1EU4Y3a1kOxUxau\nrq6lpaXnz5+vPSgudrhjB8BVkZGRRMF+fqXGxqyjgJJp3aYnlmTZg3qcptPlVN78bGgIip2y\naPBnzMqKevcmIyNGmQBAmkpLS+Pj4/v2Pb5vnzbrLKB0nJyc1NTUWrGb3Qya4U/+xVQsjVTK\nQIV1AJARFxcXPp8fFRX13Xff1Qzq62MdFoCzzpw5U15ejgMngAltbW1bW9uzZ88+efLE1NRU\n8gtX0krppVIGuGOnLAwNDW1sbC5evFhUVMQ6CwDIgnizCRwRC6y4urqKRKLY2FjWQZQLip0S\ncXV1raqqOnPmDOsgACALERERampqTk5OrIOAkmpsQwaQKhQ7JYLz+wCUR05OTkpKip2dnY6O\nDussoKRsbW319PROnTrFOohyQbFTIiNGjFBXV0exA1AGkZGRIpEI67DAkIqKysiRIzMyMtLT\n01lnUSIodkpES0vL1tY2NTX18ePHrLMAgHSdOhVDdIDHm8Y6CCi11p0tBq8DxU65NPgoa1YW\n4bk7AI4JCysgmnb/fnfWQUCptfoRoARKWEgLH9ADKYTiOBQ75dLgz9jkyTRqFJVjM0gArkhL\nS8vJsSGisWPxlzyw1K9fPzMzs6ioqOrq6hZdeJbObqJNOFusFfAzr1zEj7JGRLzyo2JlRUIh\n3b7NKhQAtDHxSWJ8Pk4SA8Z4PN6oUaPy8/OvXr3aogvdyZ2IIglruC2GYqdcVFRUHB0dMzIy\n7t27VzNoZUWEE2MBOOTEiUSiIQMGlBsaso4CSq91j9kNoAEdqEMkRVZTy271AYqd0qm/GosT\nYwG4pKqq6swZNSL+uHEarLMAtPIxOx7xXMglj/KuUstu9QGKndJprNjhjh0ANyQmJpaWOhAR\nzhIDedC5c+devXrFxcWVt/BRbjdyI6zGthyKndIZMGCAqalpdHS0SCQSj3TvTmpquGMHwBGR\nkZFEq+fMuWxnxzoKABERubq6lpaWnj9/vkVXjabRhGLXcih2SofH4zk7O+fk5KSmpopHVFRo\n2DAyNmabCwDaRmRkJI+Xvnp1JxUV1lEAiKi1q7EWZLGO1n1JX0onFGeh2Cmj+j9jcXEUHs4u\nEAC0keLi4sTERBsbGxMTE9ZZAF5ydXUVCASt2KZ4CS0ZQSOkEYnDUOyU/JnbMAAAIABJREFU\nEQ6NBeCq2NjYyspKnCQGcsXAwGDQoEEXL14sKChgnYX7UOyUUbdu3bp27Xr69OnKykrWWQCg\nLYlvirjjvQmQM25ubkKh8PTp06yDcB+KnZIaNWpUcXHxpUuXWAcBgLYUERGhoaHh6OjIOgjA\nK7BSJDModkoKP2MA3JOdnX3jxk0HBwdNTU3WWQBe4ejoqKmpiX90ZADFTkm5urryeDz8jAFw\nSUREBNG5+/e3tvBYTgCp09DQGD58+PXr17Oyslhn4TgUOyVlYmLSt2/f+Pj458+fi0devKDk\nZHrxgm0uAGi9kJBEomHa2kZ8/NUO8ke8UhQTE9PSC9+hd+zJXgqJuAk//crL1dW1oqIiPj5e\n/O0XX9CgQXT5MttQANBKIpEoMpJHxJ84UZt1FoAGiF/WbsVK0UN6mEAJ6ZQuhVAchGKnvOo8\nZmdlRYQTYwEUVmpqakHBUCIaM4bHOgtAAwYPHmxoaBgREdHSC3G2WIug2CkvZ2dnVVXVOsUO\nJ8YCKKiIiAgiNy2tiqFDWUcBaIhAIHB2ds7Ozk5r4b80KHYtgmKnvHR1dYcMGZKUlPT06VMi\nsrYmQrEDUFhHj94mshg5shIniYHcEq8UtfQIin7Uz4zMoim6mvBaUPNQ7JSaq6trzY6RpqZk\naIilWACFVFFRcelSNVH1hAl4wA7kV+t22uIRbxSNyqf8JEqSTi5OQbFTavUfs8vIoNJSppkA\noOXi4+PLyv6eNWupnx/rKACN6927t6WlZUxMjFAobNGF7uRORKcJB1c0D8VOqQ0fPrz2jpH9\n+lH37pSTwzYUALSYeG3Lx8dOT491FIAmubi4FBYWtvTco3E0LpmSF9EiKaXiEhQ7paauru7g\n4JCWlibeMfKvv+jWLerShXUsAGihiIgIgUDg4uLCOghAM1r3mF07ajeQBvIIb3w3D8VO2bV6\nx0gAkBMFBQWXL18eMmSIoaEh6ywAzXB3d8e5R1KFYqfscGgsgKKLjo4WCoXu7u6sgwA0z9TU\ntE+fPufOnas59wjaFoqdshs8eHC7du1aelccAOSHeMdX8bb+APLPzc2toqLi3LlzrINwE4qd\nshMIBE5OTg8fPrx16xbrLADQGmFhNzU1u9nb4zBNUAxYKZIqFDto5aOsACAPMjIyMjLefvHi\nblqaOussABKpc+5Ri5RTeR7ltXkkLkGxg7r/8fToEeXnMw0EABILDz9FNFpLq6xfP9ZRACSj\nq6s7dOjQpKSkvLyWVbRUSjUkwy/oCykF4wYUOyBra+uOHTuKn78+dIjMzWn7dtaZAEAy//xz\ni6iDo2O5QMA6CoDE3NzcqqurY2NjW3SVFVmpkEoERUgnFEeg2AERkYuLS0FBQXJycu/eRDgx\nFkBBVFdXnzunQ0RTpmBjYlAkrXvMToVURtLIDMq4Q3ekk4sLUOyAqNbPWO/eJBDgxFgAxZCU\nlPT8+XAicnfHxq2gSOzs7HR0dFrxbLcbuRFRJOGh8Eah2AERkXgHrKioKA0N6twZxQ5AMZw8\nGUvkaGZWiANjQLGoqamNGDHizp079+7da9GF4kNjUeyagGIHREQdO3bs2bPn2bNny8vLra2p\noIAeP2adCQCac/JkEtG58ePxNzkoHvFKUXR0dIuu6kN9LMgiiqKEJJROLoWHvw7gJVdX19LS\n0oSEBCsrIjxmByD3ysrKLl8+Ym29cPNmXdZZAFpMvKV2KzY9cSVXMzLLpmwphOICFDt4qeYx\nO2tr0tenFr6EDgAylZeXt2XLlhcvXuDACVBQAwYMMDU1jYqKEolELbpwK229Ttc7UScpBVN0\nKHbwkouLC5/Pj4qKevttKiigKVNYBwKAhhQWFs6YMcPExGThwoVEdOHChcd4cgIUEI/Hc3Fx\nycnJSUlJadGFKqQipUjcgGIHL7Vv337gwIEXLlwoLS1inQUAGrVgwYK9e/fW3ORITEz08/Or\nrq5mmwqgFXDukTSg2MH/uLq6VlVVxcXFsQ4CAA27e/fuwYMH6wzGxsbiPHVQRDUbMrAOwiko\ndvA/rX6UFQBk4+7duy0aB5BnlpaW3bt3P3PmTEVFBess3IFiB/8zYsQIdXV1FDsAudWhQ4f/\nvuxFtIioi/gbMzMzRokAXourq2tJSUliYiLrINyBYgf/o6WlZWtrm5KS8uTJE9ZZAKAB/fv3\nHz58OBERjSP6mciFiKysrJycnNgGA2id1p0tRkT5lH+YDj+lp1IIpdhQ7OAVrq6uIpEoNja2\nooJyc1mnAYBX8Xi8ffv29evXj2gUERFF9+rV69ChQxoaGoyTAbSKq6srn89vxfsTv9PvU2lq\nOIVLI5VCQ7GDV4j/4yko6LK2Ni1YwDoNANTTpUuXTz9dRjRCW/txePjfKSkp/fv3Zx0KoJXE\nGzIkJiYWFbVsQwYcGtsYFDt4ha2trZ6eXkLCEQ0NHD4BIKf++SeLSMfNjTd69Gg1NTXWcQBe\ni5ubW1VV1ZkzZ1p01TAapk/6p+iUlFIpLhQ7eIWKioqjo2N6+t0uXSru3KHKStaBAKCec+fU\niejNN9uzDgLQBlr3mJ0KqTiTcxZl3aJb0smlqFDsoC7xz5i2dmZlJWELBQB5k5mZ+eyZDZHI\nzQ377wMXtHpDBldyJaIIipBCKAWGYgd1iYvdixdXiOjGDdZpAOBV0dHRRNtHjrxgaso6CkBb\n0NLSsre3T01NffToUYsudCd3IooibNH1ChQ7qGvAgAHGxsYZGeFEeMwOQO7ExsYS7Vm/nsc6\nCECbEW/IEBMT06KrrMhqIk10JEcppVJQKHZQl/hg5sLCBD6fcnJYpwGAV8XGxurq6g4ePJh1\nEIA20+pzj47QkcW0WAqJFBiKHTTA1dWVKG3t2t83bmQdBQBquXfv3v3790eO/H/27jwuynJ/\nH/g1MyzDDoIrKoIb7rgQiiDMgAKpZWZSmdap0ym11dOpvuf77eydTtl2stTy12qLlaVZKiCb\nLCqouKEo7uSCKLtsAzPz+2PK43Flxpm5n2fmev8lTz68Ll+vgIvnvp/7M8nFhRvsyHF0797d\nzc1t5cqVUVFRf/nLX5qamkQnkjEWO7qGhIQEQJ+Xx4MfiaTFtFal0WhEByGymuPHj48ZM0an\n07W3txcXF//1r3/VaDScHmsxFju6hv79+/fr1y83N7ed550QSQmLHTmeZ599tq6u7vIr27dv\nX7p0qag8csdiR9em1WobGxt37NghOggR/Udubq6/v/+oUaNEByGymoKCgqsv5ufn2z+JY2Cx\no2uzeDAzEdnI4cOHT536Z/fu76lUKtFZiKzmmv8/839yi7HY0bUlJCQoFIrs7GzRQYjoF+vW\nbQMe0OsniQ5CZE2mV2KvMHny5M5/ho/w0TN4xnqJ5I3Fjq6te/fuQ4cOLSwsPHeuRXQWIgKA\n77+vBRRTpriKDkJkTW+++WavXr0uvzJ58uRHHnmk859hJVa+g3fOwrzzjR0Vix1dV0JCgk63\nc+hQPg8nkoTdu7sASE3tKjoIkTV17969tLT0j3/844QJEwCMHDlyw4YNSqUZ/SQRiUYYOYLC\nhMWOrishIQGoqqlxq60VHYXI6ZWVlTU3j1ep2saP5/dtcjQBAQEvv/zyli1bevfuXVFRoVCY\nN1glEYkAMpFpm3Qyw28QdF0ajUapPATg0CHRUYic3vffbwcGDB58zs1NdBQim4mPj6+rq9uz\nZ49Zd43DuC7osgmbbJRKXljs6Lp8fHz69m0GsGMHDwEnEmzt2kYAKSnuooMQ2VB8fDx+Pa+x\n81RQxSP+DM6UocwmsWSFxY5u5LbbfACsWJGflpam1+tFxyFyUkaj8eTJt/38Hly4sJvoLEQ2\nZDp8Ozc319wbE5AAgA/twGJHN7B169bMzHcB7N2rS0lJGTt2bEVFhehQRM6otLT0/PkjSUmt\noaHm7T0ikpewsLCQkJD8/HxzHyVMx/R38e6duNNGwWSExY6urbGxMTU1taZmH9AA9AOwZ8+e\nuXPnis5F5Iw4SYycR3x8fH19fUlJiVl39UGfhVgYghAbpZIRFju6toyMjJ9//hkAMAj4ZX5R\nXl5eeXm5wFREzsm0MsViR87Asm12dAmLHV3b+fPnf/3jucuvV1VV2T8MkTMzGAx5eXk9e/Yc\nPHiw6CxENmf6BYbFzmIsdnRtAwYMuPqiUqm85nUisp29e/dWV1fzcR05iZCQkNDQ0IKCgvb2\ndtFZZInFjq5No9FMmnTlSMrHHnusR48eQvIQOa3s7FxwHZaciUajuXjx4s6dO0UHkSUWO7o2\nlUr19ddfz5w589KVhx566I033hAYicg5ffSRL3DY1zdFdBAiO+E2u1vBYkfX1aNHj++++66m\npuaZZ54BEB8f7+HhIToUkXPR6/Xl5X2AAbfdFiw6C5GdWHya3REciUf8n/An62eSDxY7uomA\ngID77rsP8Ny4cbfoLEROZ/v23e3t4318zvfrJzoKkb307t17wIABBQUFOp3OrBu7o3shCtOQ\nZqNgssBiRzfn5zcWqPnpp3jRQYiczuefHwJ8Ro+uFR2EyK40Gk1zc/P27dvNussHPmMxtgQl\ndaizUTDpY7Gjmxs0SOXm1tbUNP7YseOisxA5l4yMDgB33x0gOgiRXVm8zU4LrR76PORZP5NM\nsNjRzSkUCA+vBLp/+eUu0VmInEhHR8fx46GAcfbsrqKzENmVxdvsNNAAyIHzvnjBYkedcvvt\nagA//tgkOgiREykq2tnRMcTf/wxPGSJnYzqRu7CwsLW11awbJ2KiO9yzkW2jYNLHYkedMndu\nMIB9+/jYgMh+8vKygZ5/+lOx6CBEAsTHx7e2thYXm/f/vyc8x2N8KUprUGOjYBLHYkedMnSo\nSq2ubmm5rbz8iOgsRM4iJycH6Lj77rGigxAJYPFq7DIsq0RlF3SxfiY5YLGjzho+vAo4vW4d\nHx4Q2YNOp9uyZcuAAQP69u0rOguRABqNRqFQWPD+xBAM6QrnXV9isaPOWr68DRi5a9d60UGI\nnEJxcXFTUxMniZHT6tat25AhQ7Zu3drS0iI6i5yw2FFnjR49MjAwkDNeiOzDtALFYkfOLD4+\nvq2tbdu2baKDyAmLHXWWUqmMjY09e/ZseXm56CxEjs/0S1RcXJzoIETCWLzNzpmx2JEZOJiZ\nyD7a2toKC+vCw4f16tVLdBYiYTQajVKp5A8ds7DYkRn4yxORfaSn72xr297c/LXoIEQiBQYG\nDhs2rKioqKnJklNUK1Fp9UjSx2JHZhgxYkRQUFB2drbRaBSdhciRrVx5GlBGRSlEByESLD4+\nXqfTbd261dwbn8ATPdHzMA7bIpWUsdiRGRQKxfjxd1ZVJezefUh0FiJHVljoBmDOnJ6igxAJ\nZvFK0UAMBOCEIyhY7Mg8jY1PAV9+8gmLHZGttLa2VlYOUypbk5MDRGchEiwuLs6ybXZaaOGU\nQ2NZ7Mg8M2cGANi0qUN0ECKH9cMPO4zGAb17H3d3Fx2FSLQuXbqMGDGiuLi4sbHRrBuHY3h3\ndM9BjhHOtXeIxY7MM3dub0B/5EhfbrMjspEvvjgHIC5OLzoIkSRoNJqOjg5zt9kpoIhHfBWq\nSlFqo2DSxGJH5gkIUAQEnGxvjyguLhOdhcgxHThwGDg1b15v0UGIJMG0zc6C1VgNNHC+bXYs\ndmS2ceMaANdPPjkqOgiRA2pubv755z+PGjUtMdFfdBYiSYiLi1OpVBYUuwQkeMCjGtW2SCVZ\nLqIDkPzcfXfgpk3IyjKIDkLkgPLz83U6HSeJEV3i5+c3atSonTt3NjQ0+Pr6dv7GARhQgxo1\n1LbLJkF8YkdmmzOnj7t73pkzmwwGdjsiKzM9lmCxI7qcaZtdYWGhuTc6W6sDix1ZwNsbd921\nrKnpvdJS59qRSmQHOTk5KpUqNjZWdBAiCbF4m50TYrEjS3BoLJEtNDY2lpSUREREBATwBDui\n/5g0aZKLiwt/6HQGix1ZgkNjiWwhPz+/o6OD67BEV/Dx8Rk9evSuXbvq6upEZ5E6FjuyxKBB\ng4KDg3Nzc/V6HrVFZDUrV54AkidOTBAdhEhyNBqNXq8vKCgQHUTqWOzIQvHx8XV1dXv37hUd\nhMhxbNw4AtgYFhYjOgiR5Fi8BcgIYylKi1Bk/UySxGJHFjJ9jXE1lshaqqvr6+tHublVjhzp\nLToLkeTExsa6urpaUOxqUDMKo17AC7ZIJUEsdmShCRO0wFMff+wpOgiRg/j4472Ab3j4KdFB\niKTI29t77Nixe/bsqa4278DhQAQOx/Ct2NqEJhtlkxQWO7LQ4MFhSuXL+/cncZsdkVWsWdMA\nICXFXXQQIonSaDQGg8GCbXZaaHXQFcLsY/DkiMWOLOTigp49jxgM/TZsOCA6C5Ej2Ls3CMAj\nj/QXHYRIoizeZmcaGpsDpzgthcWOLDdxog7A55+fER2ESPbOnq25eHGkh8eJgQO5vYHo2mJi\nYtzc3CzY2x2HOBVU2ci2QSjJYbEjy82bFwygsJArR0S3atOmLcDSiRMPiQ5CJF2enp6RkZF7\n9+69cOGCWTf6wW8sxu7Ezjo4/jF4LHZkuZSUYJWq9syZ8I6ODtFZiORt+/Z04LkXX3QRHYRI\n0jQajdFozMvLM/fGWZh1D+5pQIMtUkkKix1ZTqlE795HjcYe33/PbXZEtyQnJ8fNzW3ChAmi\ngxBJmsXb7P6AP3yFr/qir/UzSQyLHd2S2bNrgcf273eKHalENlJVVXXgwIHx48d7enKDHdGN\nTJw4Ua1W8wjVG2Cxo1vy5JPhwAfFxWmigxDJWG5urtFo5IhYoptSq9W33Xbb/v37z507JzqL\nRLHY0S3p06dPWFhYQUFBe3u76CxEcmVaV2KxI+oMi7fZOQkpFrvS0tJ//vOfc+fOffzxx995\n552amhrb3UW3Lj4+/uLFizt27BAdhEiucnNz1Wp1VFSU6CBEMmDxNjsnIblil5WV9dJLLxUX\nF/fs2VOhUGRmZi5atOjkyZO2uIuswvSYgTseiCxz4MC5gwdfCA9/Sq1Wi85CJAMTJkzw8PDg\nD53rkVaxa25uXrFihbu7+9tvv/3aa68tW7Zs/vz5NTU1b731ltFotO5dZC1arRb85YnIUh98\ncBh4KCBgquggRPLg7u4+fvz4srKyM2fMPh5/F3b9H/7vDBz5XH1pFbv09PTm5uZZs2b169fP\ndCUlJWXEiBHHjh07ePCgde8ia+nVq9eAAQMKCwt1Op3oLETys2mTHsDdd3cRHYRINkyrsRZs\ns0tH+st4OQtZ1s8kGdIqdvn5+QCuOMlp/PjxAEpKSqx7F1lRWNizzc0Zn37KGk1ktiNH+gLN\n8+YNEh2ESDYs3gKkhRaOPjRWQsXOaDRWVFS4uLgEBwdffj0kJARARUXFrd9VUVFR/Kvq6mor\n/wOc2IABEcDEb77hCytE5tm586xOFxoYWObj4yY6C5FsREVFeXl5WbAFaCzG+sOfT+zspK2t\nTafT+fj4XHHddKWh4dpjQMy6a/Xq1Qt+tX37dqtFd3qPPtofMJaU+IkOQiQzK1YcAzBuXKPo\nIERyYhrTUl5efvr0abNuVEE1CZMqUHEUR22UTTgJzSU0HYR29cHrXl5eANra2m79rmnTpo0c\nOdL050OHOGzbaiIiuru5Ha2pGVJb2xoQwDf7iDorOxsAUlO7ig5CJDPx8fGZmZm5ublz5swx\n60YNNOuwLhvZ/dHfRtnEktATO29vb6VS2draesX15uZmAL6+vrd+16BBgxJ/1aNHD6tFJ2DQ\noFOA+rPPykUHIZKTpqZ/uLv/3333cYMdkXlucZtdNrKtHkkiJFTsFAqFn59fY+OVSxKmK126\nXPuVMcvuIqubPNkFwJo1taKDEMlGRUXFmTNpCQm71GpX0VmIZOa2227z8fGxYJvdCIx4Ds89\nhIdsEEoSJFTsAHTt2lWn01VVVV1+8dSpUwCCgoKsexdZ1+OPDwYMu3ezSRN1VlZWFn49uIGI\nzOLi4hIdHX306FFzhxEooFiMxUlIslEw4aRV7ExHlhQVFV1+sbi4GFedZnLrd5F1DRoUFBLy\nu9bWKS0tLaKzEMkDR8QS3QrTL0WbN28WHURapFXsEhMTVSrV6tWrL1y4YLqybdu2kpKS8PDw\n0NBQ0xWdTnfkyJEjR44YDIbO30V2MHWqe1tb5bZt20QHIZKHzZs3+/n5jR49WnQQIlniQMtr\nktBbsQD8/PwWLlz47rvvPv3002PGjGloaNi3b5+/v//ChQsv/Z3z588vWrQIwKpVq0wvw3bm\nLrIDjUazdOnS3NxcPoEguqkjR45UVFRMnz5dpVKJzkIkS2PHjvX19TVtaaBLpFXsACQmJvr5\n+aWnp+/evdvLyysuLi41NfWmb7BadhdZV1xcnEKhyMnJ+etf/yo6C5F0HT9+fMmSJZmZmQC4\nqkBkMRcXl5iYmA0bNpw4ceLSTFGSXLEDEBkZGRkZeb3/GhwcvG7dOnPvIjvo2rXrsGHDioqK\nmpubrz5ZkIgA5OXlJScnt7S0AJ8Dfu+8c2///v2feuop0bmIZCk+Pn7Dhg05OTm/+c1vRGeR\nCmntsSO502g0Op1u69atooMQSZFer583b15LSwugAqYCI4GmF1544dixY6KjEcmSxdvs2tH+\nOB5/DI9ZP5NoLHZkTaavsfT0LaKDEElRWVnZr0czjAX8gWwAra2t3CREZJnRo0f7+/tbcJqd\nK1yzkf05PtdBZ4tgArHYkTXFxEwC9r733lzRQYikSKe79CMkAQB+Pfv+sutEZAaVShUbG/vz\nzz8fOXLE3Hu10DajuRjFtggmEIsdWVPXroHe3q3NzSHHjjWLzkIkOUOHDvXz8wNwRbHjiZtE\nFjOdZmfBaqwGGjjibDEWO7KykSMvAIoVK8z+5YnI4anV6nfeeQdQA9FAGXAawPz588eMGSM6\nGpFcWbzNTgONAoocmL2MK3EsdmRld9zhDWDjxjbRQYikaN68eYsWfQV4uLhsjoyMXLp06ZIl\nS0SHIpKxUaNGBQYGZmeb/eCtG7oNx/At2NIMh1piYrEjK3v44RFA48GDvUQHIZKovn1PAqF/\n/atvcXHx/PnzeUAx0a1QKpWxsbFnz54tLy83914ttDrotsChXvhjsSMr69rV389vb1tbcGnp\nRdFZiKQoJycHODFr1jjRQYgchGmbnQXvxj6OxwtQEIc462cSh8WOrC8iogZoWru2THQQIsnR\n6/V5eXm9evUaNGiQ6CxEDsLibXbhCJ+Iia5wtX4mcVjsyPqeeMIFCGhsXC06CJHk7N69u7a2\nNiEhQXQQIscxYsSIoKCg7Oxso9EoOot4LHZkfZMnR6tUBgueihM5PNMWb9MDBiKyCoVCMWnS\npKqqqoMHD4rOIh6LHVmfn5/f6NGjS0pK6urqRGchkhbTLzwsdkTWZfFqrONhsSOb0Gg0er2+\nsLBQdBAiCWlo6Cgo2BcSEtKvXz/RWYgcyq8DLdPb2pz9sC0WO7IJi99RInJgb799vLHxRJ8+\nfxUdhMjR7NmzR6VS/fDDD97e3ikpKeZOGDPA0IEOG2WzMxY7sonY2FhXV1c+FSe63I8/NgOq\nhIQeooMQOZR169bNmTNHr9cD6OjoSEtLmzJlSn19fSdvX4VV3dBtLdbaMqP9sNiRTfj4+IwZ\nM27XLhw61NkvLSKHt39/d6DxN78ZIToIkUN58cUXr7hy/Pjx5cuXd/L2HuhRjWqHmS3GYke2\nEhi4yGDY8dprp0QHIZKEQ4d0LS09vLx2hoRwLguR1ej1+kOHDl19ff/+/Z38DNGI9oJXNswe\nSiZNLHZkK/fc0w1Abq5CdBAiSfjwwxMARo26IDoIkUNRqVQ+Pj5XX+/SpUsnP4Mb3KIRfRAH\nT+O0VaOJwWJHtjJ79jiF4uSJEyHt7aKjEEnAxo06AHfd5Ss6CJGjuf/++6++eO+993b+M2ig\nAeAYq7EsdmQrnp6eXbvuMxi8srO5zY4IZ882AJUPPDBKdBAiR7N48eKJEyde+lClUi1evHj8\n+PGd/wxaaMFiR3RT48c3A1i50hEebhPdiubm5osXtcOHT+vRo7voLESOxsvLKz8/f926dc88\n8wyAmJiY5557zqzPMBZj/eBXBkcYcc5iRzZ0//09AGNenkp0ECLBCgsL29ratNqJN/+rRGQ+\nhUIxffr0t956KzQ0tKSkpN3MPUAucDmAA1uwxUbx7InFjmxo+vRxSuXmpqZ9ooMQCcZJYkT2\nodVqGxsbd+zYYe6NveAgr6uz2JENeXp6Rke/VFs7+8IFvglITi0nJ0epVMbFxYkOQuTgTL8+\nZWc7yNklFmCxI9saNWqU0Wh88MEHly9f3tTUJDoOkQCNjY07d+4cPXp0QECA6CxEDk6r1SoU\nCmceaMliRzb02WefrVixAsCGDRvmz58fHh5u7vw+IgeQl5fX3t7OdVgiO+jZs2d4eHhhYWFr\na6voLGKw2JGtnDx5csGCBTqd7tKVU6dOzZ07V2AkIiE+/LASCGGxI7IPrVbb2tq6bds20UHE\nYLEjW1m/fv3Va6/btm37+eefheQhEqK1FT/8MFeh2BgTEyM6C5FTMP0SZdlqbDOad2GXtRPZ\nFYsd2UpjY6NZ14kcUlraRYPBrXv3Ul9fzpwgsgeNRqNUKrOysiy4dxRGaaHtQIfVU9kNix3Z\nysiRIy/7SAssAuDt7R0WFiYqEpH9ff75GQAxMbqb/k0isoouXbqMGjWqqKjIgucIMYipQ91O\n7LRFMPtgsSNbSU5OTkpK+vWjvwCvA93+9a9/qdVqgamI7KygwB3Qz5nTW3QQIiei1Wo7OjoK\nCwvNvTEBCQAykWmDUHbCYke2olAoVq1a9eSTTwYEBABZgOK++1YsWLBAdC4i+6mvR1VVH4Wi\nZPLkcaKzEDkRi7fZTcZkBRRZsGQZVyJY7MiG/P3933nnnZqamt/9LgzA6dPhCoVCdCgi+/nh\nh3qjUdm79yEvLy/RWYicyKRJk1xdXS04prg7ug/DsEIUNkGuB6/PlcMTAAAgAElEQVSy2JE9\nPPLIcKChpKSL6CBEdnX06C5gfUKCjDdiE8mRj4/PuHHjdu3aVVtba+69iUjUQVcIs5dxJYLF\njuxh3LgIN7etFy8G8XxicipVVV8D037zG74wRGRvWq1Wr9dv3rzZ3BsnY3IEInSQ6wtPLHZk\nD0qlMjz8NIAvvzwnOguR/WRnZ3t6ekZFRYkOQuR0LN5mdztu34Vd0zDNBqHsgcWO7GTGDDfg\nrebmPNFBiOzkzJkz5eXl0dHR7u7uorMQOZ2JEyeq1WoLttnJHYsd2cm9944FFh09+o3oIER2\nYvqJwkliREKo1eoJEybs37+/srJSdBa7YrEjOxkyZEhwcHBOTo7BYBCdhcgeTGtAWq1WdBAi\nJ6XRaIxGY25uruggdsViR/YTHx9fXV29Z88e0UGI7CE7O9vb23vs2LGigxA5KdOvVZYNjZUv\nFjuyH9PXmBPueCAn9NlnVSdOzL7tthmurq6isxA5qaioKB8fH2f7ocNiR/aTmJgIFjtyDu+9\n1wS8GhGRIDoIkfNycXGJiYk5cuTIyZMnzb23HvWf4/Nc5Nogl22x2JH99O3bt3///nl5ee3t\n7aKzENmQ0Yi9e7sAVffdN0J0FiKnZvGhJxWomIu5y7HcBqFsi8WO7CoqaubFi//+/e9PiQ5C\nZEP79qG11c/VtWD06AjRWYicmsXb7IZjeA/0yESmATJ74Y/FjuwqIWE88OCaNTzWixzZqlXn\nAQwbVqlSqURnIXJqERERAQEBmZmZ5t6ogEILbTWq90BmL/yx2JFdTZsWA+w4fbpHTY3oKEQ2\n8+OPzQDuuMNLdBAiZ6dSqeLi4kynhZt7bwISAGTC7FIoFosd2VW3bt26dt1jNCrT09tEZyGy\niY4OHDrUDTg5a9YY0VmI6Jdtdha8t5eIRABZyLJ+JltisSN7mzRJB+Crr86LDkJkE3q90dPz\neR+ft4YPHy46CxFZvs2uL/oOxMB85LdBTk8iWOzI3u6/vx/QVFioFh2EyCYOH95fX/9uSspZ\nhUIhOgsRYdiwYT169MjKyrJg7tFTeOpP+JMOOlsEsxEWO7K3hIRYhaKwpibI/HOFiGSAI2KJ\nJEWhUJjmHpWWlpp77xN44gW84AMfWwSzERY7sjc/P78hQ75WKqO9vetEZyGyPtOKD4sdkXRY\nvM1OjljsSIA77+xuMGwtLMwTHYTIygwGQ35+fs+ePQcPHiw6CxH9wqmGxrLYkQAcGkuOavfu\n3dXV1QkJnCRGJCEDBgwICQnZvHmzXq8XncXmWOxIgJiYGLVanZUls3fIiW6KG+yIpEmj0dTX\n1+/cuVN0EJtjsSMB1Gr1+PHj9+/fX1lZKToLkdXU1eEf/5gBLGCxI5Ia59lmx2JHYmi1WqPR\nmJubKzoIkdVkZenr6wf4+g4JDQ0VnYWI/ktiYiIs3Wb3A364A3ecgjymnLPYkRimbXaZmbnm\nnytEJFFff30eQHR0q+ggRHSlXr16DRo0qKCgoK3N7NOGD+Lgj/hRLiMoWOxIjKioKLX69x9/\n/PqWLaKjEFnJ5s0uQOusWb1EByGia9Bqtc3NzcXFxebeaBoay2JHdCMuLi5DhnQ1GLxXr+Zp\nduQIzp5FVVUgsCUpaZLoLER0DRZvsxuDMUEI2oRNRhhtkMvKWOxImLvu8gEMGzbIaQYf0fWk\np3cAiqCgPb179xadhYiuISEhQalUWrDNTgllPOIrUXkAB6wfy9pY7EiYadMmAHuOHg1qbBQd\nheiWffvtBQAxMXKaKUnkVAIDA4cPH75169ampiZz7zWtxmYi0wa5rIzFjoQZNWqUh0ehwaDK\nzZXBw22iG4uI+AhIuecevg9LJF1arVan020xf3N3IhIhk212LHYkjFKpHDeuHsB339WKzkJ0\nqwoLMxSK9ISEONFBiOi6TNvsLFiNHYABK7Hy3/i3DUJZGYsdiXT33d2AtuLii6KDEN2S1tbW\noqKiYcOGde/eXXQWIrquuLg4lUpl2THFD+CBUMjgkTyLHYmUnDwJCA0Pf0Z0EKJbUlBQ0Nra\nyoETRBLn5+c3duzYHTt21NU57IEMLHYk0uDBg3v3VuXk5DjDYGZyYKaVHRY7IunTarV6vT4/\nP190EFthsSPBNBpNXV3d7t27RQchslxOTo5SqZw0iSfYEUmdxdvs5ILFjgQzzRbLypLBq0ZE\n11RZ2bRjx46IiIjAwEDRWYjoJmJiYtzd3S3bZicLLHYkWEJCAhz6lydyeHfc0dTefnTChKmi\ngxDRzXl6ekZFRe3du/f8+fOWfYYOdFg3knWx2JFgffr0GThwYF5engWDmYmE6+jAnj2+QHtK\nSpToLETUKRqNxmg05ubmmnujAYZ4xEdB0l/sLHYknlarbW5WffaZDEa1EF2hqAg6nVqpzI2N\njRWdhYg6xbQFyLLZYkYYd2FXJSptkMs6WOxIPK1WC+x9+ukhfDWWZGfDhlYAYWHHfX19RWch\nok4ZP368l5eXZdvsEpBghDEH0t0+xGJH4iUkJCgUeS0t6p07RUchMtMPP1wEjLff7ik6CBF1\nlpub28SJEw8dOnTq1Clz75X+bDEWOxIvMDCwT59yABs3coA6yUlzMw4e9AdKp00bJzoLEZnB\n4kNPbsNtfvDbhE02CGUdLHYkCcnJLoBx7VrOFiM5OXwYQLNKtTk6Olp0FiIyg8Xb7FzgEoe4\nClQcxmEb5LICFjuShOnTxwEHSkt9W1pERyHqtF69zhsMgVFRP3l5eYnOQkRmGDt2rL+/v2VH\nqCYiUQXVbkj0XH0WO5KEuLg4pTKno8OloEB0FKJOy83NNRo7Jk8eLzoIEZlHpVLFxsZWVFQc\nPXrU3HsfxIMXcOEe3GOLYLeOxY4kwcfHZ9Cg4wrFzro6rsaSbHBELJF8mb5yLXg31he+/vC3\nQSLrYLEjqbj7bg+jcZyra6boIESdlZ2drVaro6IkfVopEV2TxdvsJI7FjqTC9DXmwPP7yMGc\nPXv20KFDEydOVKvVorMQkdlGjhzZrVu3rKwso9EoOos1sdiRVERHR3t4eFi2lZXI/ky/hHAd\nlkimFApFXFxcVVXVgQMONfeIxY6kQq1WT5gw4cCBA2fOnBGdhegmamqwalUF4GJ60kxEcmTx\nNjspY7EjCTH9jLRgMDORna1fj59++h83txfHjePRxERydYvb7E7i5GmctmoiK2CxIwlJSEiA\nw/3yRA5p3bomAOPGNbq6uorOQkQWGjx4cO/evXNycvTmjypPQ1o/9HsX79oi2K1gsSMJGTdu\nnI9P37Vr/R3uLSVyHBkZGXFxcd9/XwfU+foeaWtrE52IiCyn0Wjq6up27zb7tOFoRLvARYJD\nY1nsSEJcXFxGj76juvr1N97gaXYkRd99911SUlJeXqXBEAxsTktbP3fuXNGhiMhyFg+N9YXv\nOIwrQUkNamyQy3IsdiQtd94ZBpzLy3MxGERHIfpver1+4cKFAIAkAMAmAN9++21mJg9fJJIr\n0xYgy7bZJSJRD30OpLXGxGJH0pKYmABkNzaq9+4VHYXov508efLcuXMAgGQAQJrpelFRkahI\nRHSL+vbt279//7y8vPb2dnPvTUACAKmtxrLYkbSMGDHCx6cIwKZNDnViJDkAd3f3X/94EMgG\njl51nYjkR6vVXrx4sbi42NwboxHtBa9MSOuZPYsdSYtCoYiLa8evbx0SSUdwcPCoUaMAAL8H\nEi5dT05OFhWJiG6dxdvs3OCWgpSBGNiCFhvkshCLHUnOtGkjgcPFxWq+bkhS89lnn/n6+l5+\n5ZVXXhk+fLioPER06xISEhQKhWXb7L7Ft+ux3gMeVk9lMRY7khytVgu8PXDgSvM3PBDZ1siR\nIz/77DMAoaGh8+fPz8/Pf/HFF0WHIqJb0q1bt6FDh27ZsqWlRUIP3izGYkeSM3DgwJCQ9adO\nPevhYfaJkUS2tmPHDgCLFy9eunRpTEyM6DhEZAVarba1tXXr1q2ig1gBix1JkUajqa+vLykp\nER2E6Erp6ekqlcq0KYeIHIPF2+wkiMWOpMj0NZaVJa13yImqq6t37twZFRXVpUsX0VmIyGo0\nGo1KpXKMgZYsdiRFiYmJ4NBYkpiODsydW20wxE2ZMkV0FiKyJn9//4iIiOLi4oaGBtFZbhWL\nHUlRr169Bg8eXFhYyEGcJB1bt2LjxkHA/Sx2RI5Hq9V2dHQUFBRYcG8pShdjsQ46q6eyAIsd\nSZRWq21ubt62bZvoIES/SE8HAC+vgsjISNFZiMjKbmWb3RIseR7PF0ESQ2hY7EiitFotMOuR\nR4JPnxYdhQgAsGZNC9CRmAgXFxfRWYjIyvR6vUKheOutt8LCwp577jmz1mQlNVuMxY4kSqPR\nKBQDjx4dwDcoSArOn0dZmRrYdvvt0aKzEJGVZWRkTJ8+3Wg06vX648ePv/HGG9OnT9frO3vk\nVgISlFBKZLYYix1JVGBg4KBBPwNIS+M5xSReejqMRgWQPnnyZNFZiMjKnnjiiSuu5OXlrVq1\nqpO3ByIwAhFFKGqA+HcvWOxIuqZO7QnUZGQYjEbRUcjpbdigBxASUhYaGio6CxFZU319/eHD\nh6++vn379s5/kkQkdqBjMzZbL5eFWOxIuhITNUBOdbV7WZnoKOT0xo8vAf42fXqw6CBEZGVu\nbm5K5TXqkKenZ+c/iXS22bHYkXTFxsaqVLkAuM2OhDt16lvgz0lJXIclcjQeHh7X3GIxbdq0\nzn+SWMQ+hIcmQ/y3CBY7ki5vb+9Roy4A2LhREocDkTNLT093c3OLj48XHYSIrO+DDz7o1avX\n5Vf+7//+LzrajDelPODxMT6eiqnWjmY2FjuStKlTBwL3z5ghiVeNyGlVVlbu27dv4sSJ3t7e\norMQkfX17du3rKxs8eLF06dPBxAdHf33v/9ddCgLsdiRpGm1WuCrvXs3iA5CTi0jI8NoNHLg\nBJED8/X1fe6559atW9e/f/89e/bId+4Rix1JWnR0tKenZxY32ZFQGRkZAFjsiJxBUlJSU1NT\nYWGh6CAWYrEjSXNzc4uOjj548GB5ebnoLOSkjEZjVlZWUFBQRESE6CxEZHNJSUkA0k0zBGWI\nxY4k7dy5c9XV1QAGDx4cGhr68ccfi05ETmfo0JbKypemTJlyzQMRiMjBJCQkuLu7p6WliQ5i\nIX6fIulqb2+fPn36rl27TB+eOHHi4YcfZrcjeyotxcGDnkB3rsMSOQkvL6/o6Oh9+/adOXNG\ndBZLsNiRdH399df/ffC3B4Dnn3++8/P7iG6RaTVGocjgJDEi55GUlGQ0GmW6GstiR9K1f//+\nyz7KBA4AuHDhQmVlpahI5GxMk8QGDz5xxRlXROTAUlJSAMh0NZbFjqTLz8/vso8agH7AcKVS\n6evrKyoSOZXmZuTnK4D906aNFJ2FiOxnxIgRvXr12rRpkxwXiFjsSLpmzJjh4eHx60emo+ym\nTp061cfHR1gmciY5OWhvVwJp3GBH5FQUCsWUKVNqa2uLi4tFZzEbix1JV3h4+L///W93d3cA\nwAbA6OFx9wcffCA4FjmN/HwAcHPLjYmJEZ2FiOxKvoeesNiRpD366KP79u177bXXxozpAezW\n6ca5u/cQHYqcxcKFp4CIuDhc9uSYiJxCUlKSSqVisSOyvoEDB/7hD394+eWXgQ16vWLTJtGB\nyGmkpW0E9iQna0QHISJ7CwgIiIyM3L59u+ksVRlhsSN5iI+PV6uzFIqOo0dFRyGnYZokZlqR\nISJnk5SUpNfrN8ntcQKLHcmDWq2Oj/c0GoPuueeI6CzkFPR6fXZ2dnBw8NChQ0VnISIBkpOT\nIcNtdix2JBu3354E1Mv0YCGSne3bt9fU1EyZMkWhUIjOQkQCREZGBgYGpqWlGY1G0VnMwGJH\nsjF16lQAGzduFB2EnILp13QedELktFQqVWJiYmVl5d69e0VnMQOLHclGWFjYwIEDc3JyWlpa\nRGchB3fkCNLScpVKZUJCgugsRCSMaYutvFaKWOxITlJSUlpaWvLy8kQHIQc3Y4Zh27Y1Y8aM\n79q1q+gsRCRMSkqKQqGQ1zY7FjuSE9P8Pq7Gkk2dPo0DB5RASVISDzohcmo9evQYOXJkQUFB\nQ0OD6CydxWJHchIfH+/p6blu3f4NG0RHIceVng6jEUA6N9gRUVJSUnt7e05OjuggncViR3Ki\nVqvj4uKPH/84NdXQ3i46DTko03YaL6/8CRMmiM5CRILJbrYYix3JTEpKMrDx4kVlYaHoKOSI\n9HpkZOiBswkJ3VxdXUXHISLBYmJifHx8ZPT+BIsdyczUqVOBDQC4Gku2UFSE+noVkDZlymTR\nWYhIPDc3N41Gc/z48fLyctFZOoXFjmQmLCwsLOwY0PbTT3I6MZLkorERPj4/A+mcJEZEJvI6\n9ITFjuRn6tQ4IL+sTHHihOgo5HC02naFYni/fkUDBgwQnYWIJMF0IINcttmx2JH8pKSkcDWW\nbGTLli0NDQ2mGZFERABCQ0NldDw+ix3JT3x8vIdHlrd3Vv/+oqOQw8nIyAAniRHRf0tOTm5p\naSkoKBAd5OZY7Eh+PDw84uJ6XbyYOGDAUdFZyNFkZGS4uLhotVrRQYhIQmR06AmLHcmSvHY8\nkFxcuHChpKQkKirKz89PdBYikhCNRqNWq2Xx/gSLHckSZ4uRLWzatMlgMHAdloiu4OnpGRMT\ns3///oqKCtFZboLFjmRp4MCBAwYMyMnJaW1tFZ2FHMT69fjggzrAkwedENHVTN8ZTNtwpYzF\njuQqOTm5qakpPz9fdBByEK+9htzcx/38+o4bN050FiKSHNPL8tLfAsRiR3LF1ViyooYGbNli\nBHZPmTJCpVKJjkNEkjN8+PC+fftmZGS0S3tUOYsdyZVGo/Hw8Pr8875PPSU6CslfVhY6OhRA\nOjfYEdH1TJkypaGhoaioSHSQG2GxI7ny8PCYNCnm/PmEZcuM9fWi05DM/fquWxqLHRFdjywO\nPWGxIxlLSUkB1nd0KDIzRUchmcvIMCoUFwcPru7bt6/oLEQkUYmJiS4uLhI/9ITFjmTs0myx\n9etFRyE5KyvDiRMKozEzOTlBdBYiki5/f/+oqKiSkpKqqirRWa6LxY5kbNCgQWFh5xSK2g0b\njAaD6DQkW35+mDgxDfiE67BEdGNJSUkGg2HTpk2ig1wXix3JW0rKZKMx/dw5RUmJ6CgkW716\noaHheTe3jZMmTRKdhYgkTfqHnrDYkbyZttkB2LBBdBSSrcrKytLS0piYGG9vb9FZiEjSxo4d\n261bt7S0NINU14lY7EjetFqtWp0dHPz7J58UHYVkKy0tzWg0ch2WiG5KqVQmJiaeP39+9+7d\norNcG4sdyZuHh8ekScNPn36ztvaY6CwkV6YZQSx2RNQZpkNPJPtuLIsdyZ5pBIX05/eRNBkM\nhqysrO7du0dERIjOQkQykJSUpFAoJLvNjsWOZI+zxehW7Nq1q6qqavLkyQqFQnQWIpKB7t27\njx49esuWLfWSPByfxY5kb/Dgwf3798/KympraxOdhWTm008xb54PMILrsETUeUlJSR0dHVlZ\nWaKDXAOLHTmCpKSkpqamgoIC0UFIZtaswYEDgxSKjsTERNFZiEg2pDxbjMWOHMGl1diLF0VH\nIfnQ6ZCVZVQoKkaOdOvZs6foOEQkGxMnTvTz85PmFiAWO3IEGo1GrfZ6772Ho6NFRyH52LIF\nFy8qjMaNpl++iYg6ycXFRavV/vzzz2VlZaKzXInFjhyBl5dXbGx0a2vlvn04eVJ0GpKJX1dR\n0rjBjojMJdlDT1jsyEGkpKQAGwBI8tE4SVFaGhQKvVq9NZpPeonITLfffjskuc2OxY4cBGeL\nkVnOncOePTAaCzSaMR4eHqLjEJHM9OnTJzw8fPPmzc3NzaKz/BcWO3IQ4eHhYWE6heJ4djZa\nW0WnIcnr3h1/+9sq4PdchyUiyyQnJ7e2tubl5YkO8l9cRAe4htLS0nXr1pWVlXl5eQ0dOvSB\nBx7o0qXLjW/505/+dM2pbR988EGPHj1sE5MkJykpadmy9U1NT2zeDO6Gp5vavXs1sHPKlM9E\nByEiWUpKSnr77bfT09OTk5NFZ/kPyT2xy8rKeumll4qLi3v27KlQKDIzMxctWnTyZvvhz5w5\no1Kpel5FpVLZJzZJgWmbnatr+/HjoqOQ5On1+pycnN69ew8dOlR0FiKSpfj4eE9PT6m9PyGt\nJ3bNzc0rVqxwd3f/17/+1a9fPwAbN25ctmzZW2+99dZbb11v4E9HR8f58+eHDh36yiuv2DUu\nSYxWq3V3vy8sbPzjj+8UnYWkrqioqKam5q677hIdhIjkSq1Wx8bGpqenHzt2LCwsTHScX0jr\niV16enpzc/OsWbNMrQ5ASkrKiBEjjh07dvDgwevdVVlZaTQae/XqZaeUJFVeXl6xsRPKykpu\n+oiXKCMjAwA32BHRrTAderJp0ybRQf5DWsUuPz8fwIQJEy6/OH78eAAlJSXXu+vs2bMAgoOD\nbZyOZMA0gkJqD8ZJgjIyMpRKpVarFR2EiGTMtLtOUoeeSKjYGY3GiooKFxeXKypaSEgIgIqK\niuvdeObMGQBNTU1///vf586dO3fu3JdeeqmwsPDqv9nW1tbwq/b2dmv/C0i8S7PFRAch6aqu\nbklLK9q+ffu4ceOCgoJExyEiGRsyZEhoaGhmZqZ0SoWEil1bW5tOp/Px8bniuulKQ0PD9W40\nFbtvv/22vLy8X79+gYGB+/bte/XVV999990r/uZ7772n/VVWVpa1/wUk3pAhQ8LCwjIzM9va\n2kRnIckxGAx//vOfe/Z8OiVldEdH6pkzZw4cOCA6FBHJ25QpUxobG7ds2SI6yC8k9PKEqe16\nenpecd3LywvADX5Onzt3TqVS3XnnnQ8++KDpBYtjx4794x//yMjIGDt27OULu5GRka6urqY/\nNzY2Wv2fQFIwZcqU5cuXFxYWcpWNrvDGG2/87W9/A34C3ICSU6dOTZ8+vaSkxM/PT3Q0IpKr\npKSk999/Pz09PS4uTnQWQFSx0+v1X3zxxeVX7r//fm9vb6VS2XrV2bKmM519fX2v99n+8pe/\nXHElLCzs4Ycffu2117Kzsy8vdrGxsbGxsaY/f/fdd7fwLyDpSklJWb58+Wef7W5r06akiE5D\nkqHX61955RXAF0gEyoAyAMeOHfviiy8WLFggOh0RyVVCQoKrq2taWto///lP0VkAUcXOYDCs\nXr368iuzZ892cXHx8/O7+kGa6cpNzyi+wqhRowAc54Fmzker1bq7e33++aMZGTh9Gtc5JIec\nTnV1dW1tLTAHcAf+8/3n8OHDAlMRkdz5+vpOmDAhPz+/srJSCjMRxBQ7V1fXdevWXX29a9eu\ntbW1VVVV3bp1u3Tx1KlTAK63x9loNHZ0dCiVyivOIjZ96O3tbc3cJAfe3t4xMeOzstLOnr1n\n926MHi06EEmDn5+fu7t7W9vdAID/PLCXwjdiIpK1pKSkvLy8jIyMefPmic4ipZcn8OtBJ0VF\nRZdfLC4uxlVnoFxSXV199913P/3001dc379/P4BL5+GRUzGNoACwYYPoKCQZ7u7u99//WyAJ\nOAbsMV308fG59957xQYjIrmT1KEn0ip2iYmJKpVq9erVFy5cMF3Ztm1bSUlJeHh4aGio6YpO\npzty5MiRI0cMBgOAoKCgYcOGVVRUfPnll0aj0fR3fv755xUrVpjeqBDyDyGxUlJSgI0KhZHF\nji731FOv+ficBL41fdilS5eVK1eaDlQiIrLY6NGje/bsmZGRYWomYknorVgAfn5+CxcufPfd\nd59++ukxY8Y0NDTs27fP399/4cKFl/7O+fPnFy1aBGDVqlWmV2ifffbZl19+edWqVTk5OSEh\nIXV1dUePHjUajY888silOkhOZejQoSEh6lOndhUVjblwATyqjEwiIjz/+te0RYv+OHv27Hvu\nuUer1Zq7eZeI6GoKhSIxMXHlypU7d+6MjIwUG0ZaT+wAJCYm/u///u+QIUN279597ty5uLi4\nV1999ca/Unfr1m3x4sWpqaldu3YtLS1taGiIiop6/fXXp0+fbrfYJDXJycl6/Y96PaTxaJyk\nYs2aNQqF8fXXX581axZbHRFZi2m2mBTmHknriZ1JZGTkDQpvcHDw1S9euLm5zZkzx8a5SE5S\nUlLef/8vQ4bc2a9fhOgsJBVVVVVbtmyJjIzs06eP6CxE5FCSkpKUSmV6evpLL70kNonkntgR\nWUViYqK7e5lKNXfiRNFRSDLWrl2r1+vvuusu0UGIyNEEBQWNHTt227ZtNTU1YpOw2JFj8vLy\nmjhxYmlp6Q2mDJOzWbNmDQAWOyKyhaSkJL1eL3xgKYsdOayUlBRI5v1zEq6+vj47O3vYsGGD\nBw8WnYWIHJBpMsIzzzwzZ86cn376SVQMFjtyWKZit3HjRtFBSLwlS/D882U6HWbOnCk6CxE5\noJ07dz744IMAzpw58+WXX06fPv3FF18UkoTFjhzWsGHDQkJCMjMzdTqd6CwkUkcH/vY3fPrp\nMMDAdVgisoWHHnrINNr+kldffXX79u32T8JiR44sOTm5sbGxsLBQdBASKScHFy5Ar/+mX7/e\nERF8S5qIrOzs2bOlpaVXX9+0aZP9w7DYkSNLSUkB3P/4Rzz3nOgoJM733wNAR8c3d911l0Kh\nEB2HiBxNR0eHWddtisWOHFlCQoK7O0pKBi9fjrY20WlIBIMBa9fCza0JyOUGOyKyhd69e19z\nkkJMTIz9w7DYkSPz9vaeOHGiTre2qQl5eaLTkAj5+aishELxQ/fuARMmTBAdh4gckEKh+OCD\nD664+MADD2i1WvuHYbEjB5eSkgJsALBhg+goJMJ33wFAW9uXM2bMUKlUouMQkWOaMmXKtm3b\n7rzzzv79+0dHRy9ZsuTjjz8WkoTFjhxcSkoKkKNS6davFx2FRPj97xEdvRrI5PuwRGRTUVFR\na9euPXLkSGFh4RNPPOHiImZqK4sdObhhw4aFhHRVKHIPH/9vj24AACAASURBVMb+/QbRccje\n+vQxHD/+lL+/h0ajEZ2FiMjmWOzIwZWVlel0uo6OVQAiI1979913RSciu9q6devZs2enT5/u\n5uYmOgsRkc2JeU5IZB91dXW333772bNnge+B2paWjU8+2ebl5fWb3/xGdDSyE86HJSKnwid2\n5Mg++uijEydOAADqgbVAG4CXXnpJaCiyq7Vr13p6ek6ZMkV0ECIie2CxI0dWXl5+9cXTp09f\nvHjR/mHI/vbs2XP06NGkpCQvLy/RWYiI7IHFjhxZUFDQ1Re9vLw8PT3tH4bsrLYW33//PQCe\nS0xEzoPFjhzZfffd5+HhccXFefPmKZX8P9/B7d2L7t2xfHk3V1fXqVOnio5DRGQn/PFGjmzY\nsGFLly69/PlcdHT04sWLBUYi+1i9Gu3tqKraqtVqAwICRMchIrITFjtycA899FB5efmHH354\n9913A5g2bVp5uZeIucxkV99/D5VKD/zE92GJyKmw2JHjCw4Ofvjhh5cvX+7i4vL2293GjEFW\nluhMZEsHD2L/fvj4FCmVjXfccYfoOERE9sNiR84iKChIo9FUVX0B4KuvRKchW1q9GgDq6z+M\njo7u2bOn6DhERPbDYkdOJDU1Fdjs69u4Zg1aWkSnIZv57jsolQajcR3XYYnI2bDYkROZOXOm\nm5uLi8uahgZs3Cg6DdlGUxPUavj77wIuzJgxQ3QcIiK7YrEjJxIQEJCYmFhTsxRcjXVcXl74\n6afqhoZJo0ePDgsLEx2HiMiuWOzIuaSmpgJFXbrU/PQTGhpEpyHbWLduXUdHM9dhicgJsdiR\nc5kxY4ZarQY+mjULjY2i05BtrFmzBhw4QUROicWOnIuvr29SUlJNzR8WLdoVHCw6DdnAxYsX\nN23aNHDgwGHDhonOQkRkbyx25HRSU1MBfP3116KDkE1s2LChtbXVdB41EZGzYbEjp3PHHXd4\neXl98803RqNRdBayPtM6LDfYEZFzYrEjp+Pl5ZWSknL8+PHt27eLzkLW9NNPeP/9jvXr84KD\ngyMjI0XHISISgMWOnBFXYx3SG2/g8cddGhvdZ86cqVAoRMchIhKAxY6c0bRp03x9fb/55huD\nwSA6C1nHhQsoKEBQ0HHgONdhichpsdiRM1Kr1VOnTj116tTDD5/mJnvHsGYNOjrQ0vJFYGBg\nbGys6DhERGKw2JGTMq3GZmW1fP89ystFp6Fbtno1ADQ1fT5jxgwXFxfRcYiIxGCxIyeVnJzs\n7+/f2PgBAO61k7u6OuTmIijoNHCI67BE5MxY7MhJubu733nnnfX1H7m6Gr78UnQaujVr1kCn\nQ3v7Km9v74SEBNFxiIiEYbEj55WamgrU9upVevAg9uwRnYZuQVISnn22or5++bRp09Rqteg4\nRETCsNiR80pMTAwMDKypWQrgq69Ep6Fb0KsX3N2XAUe4DktETo7FjpyXq6vrXXfd1dj4hVqt\nT08XnYZuzZo1a9zd3VNSUkQHISISicWOnFpqaipwMSnplW3bREehW7B///5Dhw5NmTLFx8dH\ndBYiIpFY7MipaTSa7t275+W9qVDoRGchy33//ffgfFgiIhY7cnIqlWrmzJm1tbWZmZmis5Dl\n1qxZo1Kppk+fLjoIEZFgLHbk7Dg3VtZaWnDixIndu3fHxcUFBQWJjkNEJBjPZydnFxsbGxwc\nvHbt2tbWVp6UIS8tLQgOxsCBTUajkeuwRETgEzsipVJ59913NzQ0pKWlic5C5klPR20tTp8u\nVSgUd955p+g4RETisdgR/Wc1dscO7NsnOg112vffA8DZs+/ddtttffr0ER2HiEg8FjsiTJgw\nISQkZO3as5GR+Oc/RaehztHp8OOP8PNrMhgKuQ5LRGTCYkcEhUIxa9as1ta8rl2b1q3DxYui\nA1EnZGairg5+flmAYcaMGaLjEBFJAosdEfDLaqyxS5dNzc348UfRaagTvvsOAM6cWTJixIjB\ngweLjkNEJAksdkQAEBkZOWDAgOPHXwHnxsrE+fPw82vt6MjhOiwR0SUsdkS/mDVrlk5XHBzc\nYHrXkiRu3TrExT0C6FnsiIguYbEj+oXp3Vhv7590ul9etyQpa25uzsr6oV+/fhEREaKzEBFJ\nBYsd0S8iIiKGDBly7NjLqam64cNFp6GbSUtLa2pqmjVrluggREQSwmJH9B+zZs1qbz+QkvJV\nVJToKHQza9asAcB1WCKiy7HYEf3H/fffD86NlYP29vb169d37959/PjxorMQEUkIix3Rf4SH\nhw8bNiwzM7O6ulp0FrqR7Ozs2tramTNnKpX8JkZE9B/8nkj0X1JTU9vb203LfCRBW7Zg+XJ8\n880GcB2WiOgqLHZE/4WrsRL33HMX5s/HN98c8vX1jYuLEx2HiEhaWOyI/kv//v0jIiJycnLO\nnTtnNIpOQ5dpbm6OiXlg69YAYM/Fi+lNTU2ffvqp6FBERNLCYkd0pdTUVL1e/8IL+0NDsX+/\n6DT0qxdeeKGwcASgAt4DoNfrn3rqqT179ojORUQkISx2RFdKTU1VKBRFRdtOnuR4MakwGo2f\nfroK+A1QB3xputja2vrll1+KDUZEJCksdkRXCg0NjYyMLC9/Ta02fvUVuCArBTqdrrFxGtAN\n+AhounT9woULAlMREUkNix3RNaSmphoM9eHhR48dw/btotMQ4O7urlYvBAzA8suvh4eHi4pE\nRCRBLHZE1zB79mylUtnS8jGAVatEpyEAwJtvHgfuBw5fuhISEvLoo48KjEREJDUsdkTX0Lt3\n7wkTJpSXv+XjY1i1Cnq96EAEzJ9/z1136S59GB8fv3HjRn9/f4GRiIikhsWO6NpSU1ONxpbB\ngw+2tuLoUdFpCDAajfv27XN3d9+5c2dtbW1OTs6QIUNEhyIikhYWO6Jru+eee1QqlV7/bGUl\nBg0SnYaAjRs3HjlyZPbs2WPGjOGDOiKia2KxI7q2Hj16xMbG7tqVUVFxRHQWAoBly5YBmD9/\nvuggRETSxWJHdF2pqakAVq9eLToIoaKiYuPGjSNHjpwwYYLoLERE0sViR3Rds2bNcnFx4dxY\nsT77DGfP4v3339fr9QsWLBAdh4hI0ljsiK4rKChIo9Hs3r27rKxMdBYnVV6Ohx7CrFnGTz75\nxMfH57777hOdiIhI0ljsiG7EtBr77bffig7ipJYuhdGI0aOLzpw5M3fuXF9fX9GJiIgkjcWO\n6EZmzpzp5ub21VdfFRfjo49Ep3EyTU349FN07Yq9e18C8Pjjj4tOREQkdSx2RDcSEBCQmJh4\n8ODB2bPbFi4EB5Pa0xdfoK4OM2ZUFxRkxcbGjhgxQnQiIiKpY7EjugnTauzAgTmtrViyRHQa\nZ/Lee1Cp0N6+xGg08pQTIqLOYLEjuokZM2a4ublt3/6ou3vzG2+01da2i07kFPLysHcvbr9d\n/8MP7wQFBc2cOVN0IiIiGWCxI7qJP//5zzqdrr7+VFvb201N7sOGvVlfXy86lOO77TZ8/DGG\nD19fW1v729/+1t3dXXQiIiIZYLEjupGNGze+/fbbv370b6Dl7Nk5zzzzvMhMzkGtxkMPISPj\nb0ql8ne/+53oOERE8sBiR3Qja9asueyjKuBToPe33zYJC+RMiouLd+7cmZSUFBoaKjoLEZE8\nsNgR3UhT0xUd7lVgSnv7t0ajUUwgZ7J8+XJwOCwRkTlY7IhuJCIi4r8vnAA2jR49WqFQCMnj\nPOrq6r7++uu+ffvefvvtorMQEckGix3RjSxYsCA8PPyKi2+++aaQME7lo48+am5ufuyxx1Qq\nlegsRESywWJHdCNeXl5ZWVnz5s3r0qWLq6srgISEhOjoaNG5HNaJEygthdFofP/9911dXR96\n6CHRiYiI5ITFjugmevXq9emnn1ZXV7e0tAwdOjQ3N/fYsWOiQzmsV1/FiBF4/fVd5eXlM2fO\n7NWrl+hERERywmJH1FkqlerZZ5/V6/VLOIDCNurrsXIlevbEli3/Al+bICIyH4sdkRnmzp3b\ns2fPFStWXLhQnZ6ON94QHcixfPopmppw//2N69evHTJkyKRJk0QnIiKSGRY7IjO4u7s/8cQT\nTU1N77+/4skn8T//g4oK0ZkchdGIpUvh6gpgRXt7+4IFC/jqMRGRuVjsiMwzf/58b2/vJUve\nfuqp9vZ2/PvfogM5ik2bcOgQZs40fv31W56ennPmzBGdiIhIfljsiMwTEBDw8MMPnzt3zs3t\ny5498f77qK4WnckhvPceAIwcmX/q1Kk5c+YEBASITkREJD8sdkRme/bZZ11cXN5885UnnjA2\nNWHZMtGBHMKCBVi4ELm5/wDw2GOPiY5DRCRLLHZEZuvXr9+sWbMOHTo0cGCGnx+WLEFLi+hM\n8peUhGefPZqVlRUVFTV27FjRcYiIZInFjsgSzz33HIClS//1u9/h/HlkZooO5BCWLl1qMBh4\nygkRkcVY7IgsMXbsWI1Gk5ubq9GU7N2L6dNFB5K/lpaWTz/91N/f/5577hGdhYhIrljsiCxk\nemj3ySevDh8uOopD+Oabb6qrqx9++GFPT0/RWYiI5IrFjshCKSkpo0aN+u67744ePSo6iyNY\ntmyZQqHgaxNERLeCxY7IQgqF4umnn9br9f/mWXa3oKwMRiP27NlTVFSUkJAwaNAg0YmIiGSM\nxY7Icg888EDv3r0//PDDCxcuiM4iS9XVGDMGt9+O9957DxwOS0R0y1jsiCzn6ur6xBNPNDc3\nL1++XHQWWfp//w+trYiOblu1alXPnj2n8yUUIqJbw2JHdEvmz5/v5+e3ZMmSixdbPv4Yzz8v\nOpB86PV4/32o1XB1XdnY2PjYY4+5urqKDkVEJG8sdkS3xNfX9+GHH66qqvrqq8+XLMEbb6C8\nXHQmmVi/HseP49578cUX/3Zxcfntb38rOhERkeyx2BHdqkWLFrm6ur7++uuLFhkNBrz+uuhA\nMmEaDjthwq7S0tI77rgjODhYdCIiItljsSO6Vb179549e3Z5ebm390/9++Ozz3DmjOhMknfk\nCDIzcdttyMl5DXxtgojISljsiKzg+eefVygUb7zx2jPPoK0N77wjOpDkqVR46CE88kjDmjVr\n+vfvr9VqRSciInIELHZEVjBy5MiEhISCgoJhw4q6dsWyZairE51J2kJD8eGHuHDh3ba2tgUL\nFiiV/F5ERGQF/GZKZB2mCWPvvbf4iSfQ1oZt20QHkjyDwbBixQoPD48HH3xQdBYiIgfBYkdk\nHUlJSREREWvWrJk69ejx40hOFh1I8jZs2HDixInZs2cHBgaKzkJE5CBY7IisZtGiRQaD4aOP\n3uzZU3QUOVi2bBn42gQRkVWx2BFZzb333tu3b99PPvnk/PnzorNIXUVFRXp6+qhRo6KiokRn\nISJyHCx2RFbj6ur65JNPNjc3L126VHQWiTp5Eg0NALBs2TK9Xr9w4ULRiYiIHAqLHZE1PfbY\nY6YJY01NTaKzSI7BgNRUDBuG06fbP/74Yz8/v/vvv190KCIih8JiR2RNPj4+jz76aHV19cqV\nK0VnkZwlS1BUhMhIw+bN3547d27evHleXl6iQxERORQWOyIre+aZZ9zc3BYvXqzT6V9+GXPn\nig4kDSdO4H/+R69SNfz/9u4+Kso67+P4dxg4gAgoCkKkgLqJtKSoJJ7VLBc1PUtWKpqrYa2P\nkGBkaRnlQxlsS4qIRJ71KR8SYVO5TTmoeyeWgCtquCCFKAhpingDiYDA3H9MSwhm1jJzTde8\nX3/NfOcHfDjnd/DjzPWQlub5/PPPi8if/vQnpUMBgNpQ7IAO5u7uPnXq1OLi4v379x04INu2\nSW6u0plMwNSp/3frlrapKbKx8VJTU5OIzJs3r6qqSulcAKAqFDug4y1atEij0URHR7/2mojI\n++8rHUhpW7dKdnYXkf8V2dgyvHDhwlpuvgYAHYpiB3Q8X1/fMWPG5OTkODl94eMjKSlSXKx0\nJuXodLJmjYjUiswW0bV+6cyZMwqFAgB1otgBBqG/w1hs7N9efVUaGyU2VulAytFo5PPPxcnp\nBZGiNi85ODgoEgkA1IpiBxhEYGDgoEGD9u7dO3jwuV69ZONGuXJF6UzKsbeXadNc2s8nT55s\n/DAAoGIUO8BQXnnlFZ1Ol5CwZuFCsbOT/HylAynq7bfftra2bj1ZtGjRuHHjlMoDAKpkqXQA\nQLWCg4PfeOONzZs3nzu3fM6cHmZ+ybb333+/vr7+ySef7N+/v52d3fjx44cNG6Z0KABQG4od\nYCiWlpYRERGRkZEbNyasWLFC6ThKysvLW716dc+ePZOTk+3t7ZWOAwCqxUexgAHNmjWrS5cu\nCQkJ5naHsfp6aTnhtbm5ee7cubdv346Pj6fVAYBBUewAA7K3t583b15lZeXmzZuVzmJUq1bJ\nkCHy6aciIgkJCcePH584ceKECROUzgUAKkexAwxr4cKFNjY2sbGxjY2NSmcxkrw8iY4WZ2d5\n/HG5fPlyVFSUg4NDXFyc0rkAQP04xg4wrB49ejz33HObNm0KDQ318fEJCAiwsgq4cUMCA5VO\nZhhNTTJ7tjQ0SEKCdO0qzzwTWlVVlZCQ4O7urnQ0AFA/ih1gcPrL8G7YsEFERJysrMrs7W3/\n9S/x8lI2l0HEx0t2tkyaJM88I59++umePXseffTRuXPnKp0LAMwCH8UChpWRkXHnp5CVt2+/\nVlkpzzwjtbWKpTKQkhKJihJHR1mzRmpqasLDwy0tLZOSkrRardLRAMAsUOwAw9q6dWu72ToH\nh7QzZ2TBAgXyGNSWLfL99xIbK+7u8sYbb5SVlS1atGjgwIFK5wIAc0GxAwyroqKi/dDSMtTX\nVzZulB8+nlWLt96S9HR58UXJyclZv359nz593nrrLaVDAYAZodgBhtWvX7/2w/79PVJTxdFR\nwsPl5EnjhzKgMWOkqalx3rx5zc3NiYmJtra2SicCADNCsQMMKzIysmvXrm2Gixcv/t3vZMsW\neeIJ8fRUIpYhxcbGnjp1avr06aNHj1Y6CwCYF4odYFi9evX67LPP/Pz89E/t7OxEZOfOnSIy\nYYJ89pl066ZkvA5XUlKycuVKJyen2NhYpbMAgNmh2AEGFxAQkJube/Xq1YsXL16/fn3w4ME7\nd+7coLLD6/4jLCzs5s2bsbGxLi4uSmcBALNDsQOMxNnZ2cPDw9raOjU11cnJKTw8/KQqDq/L\nzBSd7ofHO3bs2L9//8iRI0NCQhQNBQBmimIHGJuHh8fmzZvr6+snTpxYWVmpdJz/yhdfyOOP\nywsviIhUVlZGRkZaW1snJiZqNBqlowGAOaLYAQoICgqKjIwsKSmZOXOmruX9LpGzZ+XWLQVz\n/TL19TJ7tuh0PxS7V1999bvvvlu6dGn//v2VjgYAZopiBygjOjp6xIgRaWlpH3zwgX6SmSmP\nPirz5yub6xdYtUoKCmT2bBk5Uo4ePbpp06Z+/fq99tprSucCAPNFsQOUYWlpmZyc7OrqumTJ\nkszMTBHx95f+/WXLFvnwQ6XD3Ye8PImOFjc3iY6WhoaGefPmiUhiYqK1tbXS0QDAfFHsAMW4\nurpu375dp9MFBwdfuXLFxkZSU6VbNwkPly++UDrcPTU3y7x50tAgCQnStausWrWqoKBg9uzZ\nTzzxhNLRAMCsUewAJY0aNSoqKurKlSt//vOfm5qaPD1l0yZpbJRp0+RutyIzFSdPSm6uTJok\nzzwjX3/9dUxMTI8ePaKjo5XOBQDmjmIHKCwqKmrs2LFHjhxZuXKliAQFydKlUloqzz0nTU1K\nh/sJ/v5y+rTEx4tOp5s/f35dXV1cXFz7G2wAAIyMYgcozMLCYseOHR4eHitXrkxPTxeR5ctl\nzBjR6eT775UO99P69RNXV/n73/9+5MiRJ598csqUKUonAgBQ7AAT4OTktGvXLktLy2nTppWU\nlFhYyO7dkp4ujo5KJ7unioqK119/vVOnTgkJCUpnAQCIUOwAEzF06ND33nuvsrJyypQpDQ0N\nDg6i1Sqd6eeEh4dXVFSsWLGid+/eSmcBAIhQ7ADT8fLLL0+cODE7O/v1119XOktbx4/Lv/51\nxyQ9PX3nzp2PPPJIeHi4QqEAAG1R7ABTodFo9Nf4Xb16dWpqqtJxflBVJWFhMny4vPDCjydz\n1NbWhoaGWlhYJCUlWVlZKRoQAPAjih1gQuzt7ZOTk21sbF544YXCwsLWL9XUKJAnJUV8fGT9\neunTR9askebm2wUFBefPn3/77beLi4vDw8MDAgIUiAUA+AkUO8C0PPLII3FxcTU1NcHBwbf+\nc+PYlBTx8JCsLOPFKCmRoCCZPFkqKuTNN+Wrr6SsbMsDDzzg4+PTt2/f2NhYZ2fnFStWGC8Q\nAOA+UOwAkzN79uyZM2d+9dVXERER+kmnTlJVJZMny9WrRsoQFSX/8z8yfLicOiUrV8rnn6fP\nnDmz4j8XTdbpdDU1Nd9++62R0gAA7g/FDjBF69at+/3vf79hw4atW7eKyPjxEhUlZWUSGCiJ\nicaodzExsmGDHD0qPj4iIu+8806bBXV1dR988IHBcwAAfgmKHWCK7Ozsdu/ebW9vHxoaevbs\nWRF56y157jnJy5PQUHngAXnlFcMGcHOTWbNEo/nh6fnz59uvKSoqMmwIAMAvRLEDTJS3t/dH\nH3108+bNyZMn19TUWFjIjh1SXCzR0fLII+Ll1ZE/S6f7mQWdOnVqP+zRo0dHhgAA/NcodoDp\nmjp1alhY2Llz52bNmqWfeHnJ4sWSmythYXdZ/803UlX1y35EeblMnCirV//kgqqqqoiIiOLi\n4vYvtaQCAJgIS6UDALiX1atX5+bmJicnazSa5ubm5ubm4cOHz58/39rauv3iuXPl+HEZO1am\nTJGgIOnc+V7fublZ1q+XpUululqqquTll3/84LVFWlpaaGhoWVlZ3759fXx89u3bp5/b2Ni8\n++67o0aN6phfEgDQQSh2gEmzsrLatm2bt7f3rl279JPU1NRNmzYdP368/cejjz0m5eWyd6/s\n3Su2tjJ+/A8Nz8am7bc9c0bmzJGcHHFwkPh4CQ1t2+quXLmyYMGClJQUKyurxYsXL1++3Nra\nOj8//8svv7SxsXnsscd69eplqN8ZAPBr8VEsYOpSUlJu377devLVV18tW7as/cply6SwUHJz\nZckScXOT1FSZOlVu3Gi7bPlyGTJEcnLk2WclP19eekksWv0l0Ol0W7duffjhh1NSUgYNGpSd\nnR0dHa1/g9DHx2fWrFnTp0+n1QGAaaLYAabu4MGD7YcHDhz4qfV+fvLee3L+vOTkyNq14ubW\ndoGjo7i5yd69kpoq7u53vFRYWDhy5MiQkJCGhoa1a9eeOHHCz8+vA34HAIBRmHSxy8jIuHTp\nktIpAIW1ebtOr7S0dN++ffX19ff4Qn//u59jsWCB5OfLU0+1/SnvvvvuwIEDMzMzx48f/+9/\n/3vBggUWFib9JwIA0Ibp/tW+dOlSfHz8uXPn7nP92bNnV61aNWPGjHnz5q1du7aystKg8QCj\nuev9WKurqydMmODq6vr888+npaXdtfz9FK227XkVp0+fHjZs2Jtvvmlra5uUlLR//34+bAWA\n3yITLXZNTU0bN268//WHDx+OiorKyclxc3PTaDSHDh2KjIwsKSkxXELAaJYuXerp6dl64uzs\nfPDgwfDwcFtb248//vipp57SN7xDhw7pfvaSdHeqra1dsmTJkCFDTp48OXny5MLCwjlz5nRk\negCAEZlcscvMzExKSpo1a9bJkyfv80tqa2s3bNhgbW29Zs2av/71r4mJifPnz6+srFy9evUv\n/UcOMEFdunTJysqaP3/+Qw891KdPn5kzZ548eXLs2LFxcXFlZWWZmZnh4eFarfbjjz8ePXp0\nr169IiIijh07dtfN/9133127dq3l6eeffz5w4MCYmJiePXsePHgwOTnZ2dnZiL8ZAKCDmVyx\nS05O3r9///Xr1+//S9LT02traydNmtTyrsa4ceN8fX2Li4vv/5NcwJT16NFj/fr1hYWFRUVF\nmzZt6tmzp35uYWExfPjwuLi4y5cvZ2RkzJgxo6qqau3atSNGjOjdu/eSJUsKCgr0K9PT0x96\n6CFXV1cXF5eHH344LS1t7ty5TzzxxPnz5+fMmZOXlzd27Fjlfj8AQMcwuevYxcXF6R8kJyfv\n2LHjfr4kMzNTRIYNG9Z6GBAQkJeXl5ub279//w4PCZgarVYbGBgYGBhYV1eXkZGxe/fuf/zj\nHzExMTExMT4+PiNGjNiyZUtdXZ1+cX5+/oQJE3Q6na+v74YNG4YOHapseABARzG5YtdyFt59\nno6n0+lKS0stLS3d77xsg4eHh4iUlpZ2eELAlNnY2AQFBQUFBcXHx+/Zs+eTTz45dOhQfn5+\nm2U6nW7AgAEnTpywsrJSJCcAwBBMrtj9UvX19Q0NDV27dm0zt7e3F5Hq6urWw40bN+7du1f/\n2NPTc+TIkcYJCRifo6NjSEhISEjItWvXBgwYcPny5TYLtFotrQ4AVOY3X+z0V3lof28lOzs7\nEWlzlS9HR8eWN/ZsbW2NEhBQmLOzc58+fdoXu+7duyuSBwBgOMoUu6ampu3bt7eeTJs2zdLy\n14Tp3LmzhYVFy8FDLWpra0XEwcGh9XDixIkTJ07UP05NTf0VPw74LQoJCTl27Fj7oSJhAACG\no0yxa25uTklJaT0JDg7+dcVOo9E4OjrW1NS0mesnTk5OvzokoBp/+ctfTpw48dFHH7VMFi5c\nOG3aNAUjAQAMQZliZ2VltW/fvo76bs7Ozjdu3Lh69aqLi0vLsKysTPiwCRAREY1Gk5SUNGfO\nnMzMTI1G8/jjjw8YMEDpUACAjvebP8ZORIYNG/b1119nZ2cHBQW1DHNycqTdNVAAczZ48ODB\ngwcrnQIAYEAmd4Hin9XQ0FBUVFRUVNTc3KyfBAYGarXalJSUiooK/SQrKys3N9fb29vLy0u5\npAAAAEb123vH7tq1a5GRkSLyySef6E+GdXR0DAsLL2KpUgAACMBJREFUW7duXURExKBBg6qr\nq/Py8rp06RIWFqZ0WAAAAOP57RW7uwoMDHR0dExPTz99+rSdnd3IkSOnTJni6uqqdC4AAADj\nMd1iFxwcHBwc3H7u7u5+1xMv/P39/f39DZ8LAADARP32jrEDAADAXVHsAAAAVIJiBwAAoBIU\nOwAAAJWg2AEAAKgExQ4AAEAlKHYAAAAqQbEDAABQCYodAACASlDsAAAAVIJiBwAAoBIUOwAA\nAJWg2AEAAKgExQ4AAEAlKHYAAAAqQbEDAABQCYodAACASlDsAAAAVIJiBwAAoBIUOwAAAJWg\n2AEAAKgExQ4AAEAlKHYAAAAqQbEDAABQCUulAyjp1KlTSkcAAAD4BWpqau7xqnbZsmXGSmJa\nbG1t773g5s2bhw8f1mq1Xbt2NU4k4N6ysrKKi4s9PDyUDgKIiJSXl3/xxRfdu3f/2T+ngHEc\nOHDg1q1bPXr0UDqIYVlbWw8dOrRnz553fdV837Hz8vLy8vK6x4KSkpK4uLjhw4c/++yzRksF\n3MNnn31WWVnJhoSJ2Ldv35YtW0JDQ/38/JTOAoiIREdHP/jgg2b+R5Jj7AAAAFSCYgcAAKAS\n5nuM3c/S6XQWFhZDhgxxc3NTOgsgInL79u2+ffsOGDBA6SCAiEhTU5ODg4O/v7+9vb3SWQAR\nkfr6+kGDBvXu3VvpIErS6HQ6pTMAAACgA/BRLAAAgEpQ7AAAAFSCYgeYroyMjEuXLimdAgDw\nm2G+17G7t7Nnz+7bt6+goMDOzs7Hx2f69OlOTk5Kh4J5uXTpUnx8/IIFC+56FUq2KIwmIyPj\nwIED3377rVardXd3HzNmzB//+EeNRtN6DRsSRlNXV5ecnHzq1Kny8nJ7e3sPD49Jkyb5+Pi0\nWWa2e5KzYu/i8OHDMTEx5eXlnp6eDQ0NZ86cOXr0qJ+fX5cuXZSOBnPR1NQUFxd3+fLloUOH\n9unTp82rbFEYh06n27hx49atW6uqqry8vLp161ZUVPTll1+WlpYOHz68ZRkbEkZTX1//8ssv\nZ2dnNzY2ent7W1lZ5eXlHTp0yNnZufXJsOa8Jzkrtq3a2toXX3xRRKKjoz09PUXkwIEDiYmJ\nvXv3Xr16dZv/pAIdLjMzMz8/Pysr6/r16yKyYMGC0aNHt17AFoXRHD169G9/+5uLi8uqVatc\nXFxE5Nq1a8uXLy8tLQ0PDw8MDBQ2JIxr+/btu3btGjFiRGRkpFarFZH8/PylS5daWVlt2bJF\nf3c7M9+THGPXVnp6em1t7aRJk/S7QUTGjRvn6+tbXFx87tw5RaPBLCQnJ+/fv1/f6u6KLQqj\nOXLkiIhEREToW52IODs7z5kzR0SysrL0EzYkjOnkyZNarTYsLEzf6kTEx8dn8ODBdXV1Fy9e\n1E/MfE9S7NrKzMwUkWHDhrUeBgQEiEhubq4ymWBO4uLi9uzZs2fPnmnTpt11AVsURnPlyhWN\nRuPt7d16qL/Ldnl5uf4pGxLG1K1bt4CAgE6dOrUeWlpaisitW7f0T818T3LyxB10Ol1paaml\npaW7u3vruYeHh4iUlpYqlAtmxMLCos2D1tiiMKZFixbpdDorK6vWw/Pnz4uI/pY8bEgY2dKl\nS9tMiouLz5w5Y2dnp/8fCHuSYneH+vr6hoaGrl27tpnrb5hTXV2tRCjgR2xRGFPfvn3bTMrL\ny9evXy8i48aNEzYklHPhwoXdu3dfv379m2++6d69+8KFC/Vv47EnKXZ3uH37toi0eY9XROzs\n7ESkvr5egUxAK2xRKOjYsWOJiYk1NTXPPvusv7+/sCGhnO+///7ChQs3btxobGy0srKqqanR\nz9mTFLs7dO7c2cLCoq6urs28trZWRBwcHJQIBfyILQpFXLhw4cMPPywoKOjcufPChQtHjRql\nn7MhoRRfX9/ExEQRKSwsfP/991etWrVs2TI/Pz/2JCdP3EGj0Tg6OrYU/xb6iZlc2xCmjC0K\nI2tqatq+fXtkZGRRUdHTTz+dlJTU0uqEDQkT0K9fv5CQEJ1Ol5GRIexJil17zs7ODQ0NV69e\nbT0sKysTke7duysUCvgRWxRGo9Pp1q5du2vXLm9v73Xr1r344ov6A5VaY0PCaIqLi5ctW7Z3\n79428wcffFD+U93E7Pckxa4t/QnS2dnZrYc5OTnS7txpQBFsURjNwYMH//nPf/7hD3945513\n9KfBtseGhNF07tw5NzdXf3nF1vTnuvbq1Uv/1Mz3JMWurcDAQK1Wm5KSUlFRoZ9kZWXl5uZ6\ne3vrr94EKIstCqNJS0uztLR86aWXWi4G2x4bEkbj4uLSr1+/Cxcu7Nmzp+W+WZcvX962bZtG\no9FfqU7Mfk9yS7G7OHTo0Lp16+zs7AYNGlRdXZ2Xl2dvb79ixQr9VXAA40hOTt62bVv7W4oJ\nWxRGUV1dPX369PbXA9Pz9PR85ZVX9I/ZkDCaixcvLl68+NatW25ubj179qypqfnmm28aGxsn\nT548Y8aMlmXmvCc5K/YuAgMDHR0d09PTT58+bWdnN3LkyClTpri6uiqdC/gBWxRGcOXKFRFp\nbGwsKSlp/6qNjU3LYzYkjMbT0zMuLi45Ofn06dOnTp1ycnLy8/N7+umnfX19Wy8z5z3JO3YA\nAAAqwTF2AAAAKkGxAwAAUAmKHQAAgEpQ7AAAAFSCYgcAAKASFDsAAACVoNgBAACoBMUOAABA\nJSh2AAAAKkGxAwAAUAmKHQAAgEpQ7AAAAFSCYgcAAKASFDsAAACVoNgBAACoBMUOAABAJSh2\nAAAAKkGxAwAAUAmKHQAAgEpQ7AAAAFSCYgcAAKASFDsAAACVoNgBAACoBMUOAABAJSh2AAAA\nKkGxAwAAUAmKHQAAgEpQ7AAAAFSCYgcAAKASFDsAAACVoNgBAACoBMUOAABAJSh2AAAAKkGx\nAwAAUAmKHQAAgEpQ7AAAAFSCYgcAAKASFDsAAACVoNgBAACoBMUOAABAJSh2AAAAKkGxAwAA\nUAmKHQAAgEr8P16rBr3h8z1FAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "yvalues <- c(io_train$output, io_test$output)\n",
    "plot_ts_pred(y=yvalues, yadj=adjust, ypre=prediction) + theme(text = element_text(size=16))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "4.3.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}