notebooks/03_levy_flight_application/05_apply_networks.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\lasse\\projects\\BayesFlow_dev\\BayesFlow\\bayesflow\\trainers.py:27: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n",
      "  from tqdm.autonotebook import tqdm\n"
     ]
    }
   ],
   "source": [
    "import os, sys\n",
    "\n",
    "sys.path.append(os.path.abspath(os.path.join(\"../..\")))  # access sibling directories\n",
    "sys.path.append(os.path.abspath(os.path.join(\"../../../BayesFlow_dev/BayesFlow/\")))\n",
    "\n",
    "from src.python.helpers import (\n",
    "    load_empirical_rt_data, \n",
    "    join_and_fill_missings,\n",
    "    inspect_robustness_noise,\n",
    "    inspect_robustness_bootstrap,\n",
    "    inspect_robustness_lopo,\n",
    ")\n",
    "from src.python.settings import summary_meta_diffusion, probability_meta_diffusion\n",
    "from src.python.training import setup_network\n",
    "from src.python.visualization import plot_model_posteriors, plot_noise_robustness\n",
    "\n",
    "import bayesflow as bf\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from functools import partial\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Application to empirical data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load in data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load data from folders\n",
    "\n",
    "parent_folder = os.path.dirname(os.path.dirname(os.getcwd()))\n",
    "color_folder = os.path.join(\n",
    "    parent_folder, \"data\\\\03_levy_flight_application\\\\data_rts\\\\color\"\n",
    ")\n",
    "lexical_folder = os.path.join(\n",
    "    parent_folder, \"data\\\\03_levy_flight_application\\\\data_rts\\\\lexical\"\n",
    ")\n",
    "\n",
    "color_data = load_empirical_rt_data(color_folder)\n",
    "lexical_data = load_empirical_rt_data(lexical_folder)\n",
    "\n",
    "# Join experimental data and fill missing values\n",
    "empirical_data = join_and_fill_missings(\n",
    "    color_data, lexical_data, n_trials=900, missing_rts_equal_mean=True\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load in trained network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Trainer initialization: No generative model provided. Only offline learning mode is available!\n",
      "INFO:root:Loaded loss history from checkpoints/finetune\\history_50.pkl.\n",
      "INFO:root:Networks loaded from checkpoints/finetune\\ckpt-50\n"
     ]
    }
   ],
   "source": [
    "summary_net, probability_net, amortizer = setup_network(\n",
    "    summary_net_settings=summary_meta_diffusion,\n",
    "    inference_net_settings=probability_meta_diffusion,\n",
    "    loss_fun=partial(bf.losses.log_loss, label_smoothing=None)\n",
    ")\n",
    "\n",
    "checkpoint_path = \"checkpoints/finetune\"\n",
    "trainer = bf.trainers.Trainer(\n",
    "    amortizer=amortizer,\n",
    "    checkpoint_path=checkpoint_path\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels_verbose = [\n",
    "    \"Basic diffusion model\",\n",
    "    \"Basic Lévy flight model\",\n",
    "    \"Full diffusion model\",\n",
    "    \"Full Lévy flight model\",\n",
    "]\n",
    "\n",
    "labels = [\"$\\mathcal{M}_1$\", \"$\\mathcal{M}_2$\", \"$\\mathcal{M}_3$\", \"$\\mathcal{M}_4$\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.00007515, 0.01163833, 0.7806216 , 0.20766494]], dtype=float32)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preds = np.array(probability_net.posterior_probs(summary_net(empirical_data)))\n",
    "preds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get PMP & BF tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>$\\mathcal{M}_1$</th>\n",
       "      <th>$\\mathcal{M}_2$</th>\n",
       "      <th>$\\mathcal{M}_3$</th>\n",
       "      <th>$\\mathcal{M}_4$</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>$\\text{PMP}$</th>\n",
       "      <td>0.000075</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              $\\mathcal{M}_1$  $\\mathcal{M}_2$  $\\mathcal{M}_3$   \n",
       "$\\text{PMP}$         0.000075             0.01             0.78  \\\n",
       "\n",
       "              $\\mathcal{M}_4$  \n",
       "$\\text{PMP}$             0.21  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get posterior model probabilities\n",
    "pmp_table = pd.DataFrame(preds, index=[\"$\\text{PMP}$\"], columns=labels)\n",
    "pmp_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Basic diffusion model</th>\n",
       "      <th>Basic Lévy flight model</th>\n",
       "      <th>Full diffusion model</th>\n",
       "      <th>Full Lévy flight model</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Basic diffusion model</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>133.069229</td>\n",
       "      <td>10379.400391</td>\n",
       "      <td>2794.453857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Basic Lévy flight model</th>\n",
       "      <td>0.007515</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>21.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Full diffusion model</th>\n",
       "      <td>0.000096</td>\n",
       "      <td>0.012821</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.269231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Full Lévy flight model</th>\n",
       "      <td>0.000358</td>\n",
       "      <td>0.047619</td>\n",
       "      <td>3.714286</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Basic diffusion model  Basic Lévy flight model   \n",
       "Basic diffusion model                 1.000000               133.069229  \\\n",
       "Basic Lévy flight model               0.007515                 1.000000   \n",
       "Full diffusion model                  0.000096                 0.012821   \n",
       "Full Lévy flight model                0.000358                 0.047619   \n",
       "\n",
       "                         Full diffusion model  Full Lévy flight model  \n",
       "Basic diffusion model            10379.400391             2794.453857  \n",
       "Basic Lévy flight model             78.000000               21.000000  \n",
       "Full diffusion model                 1.000000                0.269231  \n",
       "Full Lévy flight model               3.714286                1.000000  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get Bayes Factors\n",
    "bfs = []\n",
    "\n",
    "for model in range(4):\n",
    "    for model_prob in preds:\n",
    "        model_bfs = model_prob / preds[0][model]\n",
    "        bfs.append(model_bfs)\n",
    "\n",
    "bf_table_all = pd.DataFrame(bfs, index=labels_verbose, columns=labels_verbose)\n",
    "bf_table_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>$\\mathcal{M}_1$</th>\n",
       "      <th>$\\mathcal{M}_2$</th>\n",
       "      <th>$\\mathcal{M}_3$</th>\n",
       "      <th>$\\mathcal{M}_4$</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>$\\text{BF}_{j3}$</th>\n",
       "      <td>0.000096</td>\n",
       "      <td>0.012821</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.269231</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  $\\mathcal{M}_1$  $\\mathcal{M}_2$  $\\mathcal{M}_3$   \n",
       "$\\text{BF}_{j3}$         0.000096         0.012821              1.0  \\\n",
       "\n",
       "                  $\\mathcal{M}_4$  \n",
       "$\\text{BF}_{j3}$         0.269231  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select Bayes Factors for publication\n",
    "winning_model_index = np.argmax(preds)\n",
    "\n",
    "bfs_contra = pd.DataFrame(\n",
    "    bf_table_all.iloc[winning_model_index, :].values.reshape(1, 4),\n",
    "    index=[\"$\\text{BF}_{\" + \"j\" + f\"{winning_model_index+1}\" + \"}$\"],\n",
    "    columns=labels,\n",
    ")\n",
    "bfs_contra"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>$\\mathcal{M}_1$</th>\n",
       "      <th>$\\mathcal{M}_2$</th>\n",
       "      <th>$\\mathcal{M}_3$</th>\n",
       "      <th>$\\mathcal{M}_4$</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>$\\text{BF}_{3j}$</th>\n",
       "      <td>10379.400391</td>\n",
       "      <td>78.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.714286</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  $\\mathcal{M}_1$  $\\mathcal{M}_2$  $\\mathcal{M}_3$   \n",
       "$\\text{BF}_{3j}$     10379.400391             78.0              1.0  \\\n",
       "\n",
       "                  $\\mathcal{M}_4$  \n",
       "$\\text{BF}_{3j}$         3.714286  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bfs_pro = pd.DataFrame(\n",
    "    bf_table_all.iloc[:, winning_model_index].values.reshape(1, 4),\n",
    "    index=[\"$\\text{BF}_{\" +  f\"{winning_model_index+1}\" + \"j}$\"],\n",
    "    columns=labels,\n",
    ")\n",
    "bfs_pro"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>$\\mathcal{M}_1$</th>\n",
       "      <th>$\\mathcal{M}_2$</th>\n",
       "      <th>$\\mathcal{M}_3$</th>\n",
       "      <th>$\\mathcal{M}_4$</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>$\\text{BF}_{j3}$</th>\n",
       "      <td>9.63e-05</td>\n",
       "      <td>0.01</td>\n",
       "      <td>*</td>\n",
       "      <td>0.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>$\\text{BF}_{3j}$</th>\n",
       "      <td>1.04e+04</td>\n",
       "      <td>78.0</td>\n",
       "      <td>*</td>\n",
       "      <td>3.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>$\\text{PMP}$</th>\n",
       "      <td>7.51e-05</td>\n",
       "      <td>1.00e-02</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 $\\mathcal{M}_1$ $\\mathcal{M}_2$ $\\mathcal{M}_3$   \n",
       "$\\text{BF}_{j3}$        9.63e-05            0.01               *  \\\n",
       "$\\text{BF}_{3j}$        1.04e+04            78.0               *   \n",
       "$\\text{PMP}$            7.51e-05        1.00e-02            0.78   \n",
       "\n",
       "                  $\\mathcal{M}_4$  \n",
       "$\\text{BF}_{j3}$             0.27  \n",
       "$\\text{BF}_{3j}$             3.71  \n",
       "$\\text{PMP}$                 0.21  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create table for publication\n",
    "for m in range(4):\n",
    "    if bfs_contra.iloc[0, m] < 0.01:  # only resort to scientific notation when needed\n",
    "        bfs_contra.iloc[0, m] = f\"{bfs_contra.iloc[0, m]:.2e}\"\n",
    "    else:\n",
    "        bfs_contra.iloc[0, m] = bfs_contra.iloc[0, m].round(2)\n",
    "\n",
    "    if bfs_pro.iloc[0, m] > 100:  # only resort to scientific notation when needed\n",
    "        bfs_pro.iloc[0, m] = f\"{bfs_pro.iloc[0, m]:.2e}\"\n",
    "    else:\n",
    "        bfs_pro.iloc[0, m] = bfs_pro.iloc[0, m].round(2)\n",
    "\n",
    "    if pmp_table.iloc[0, m] < 0.01:\n",
    "        pmp_table.iloc[0, m] = f\"{pmp_table.iloc[0, m]:.2e}\"\n",
    "    else:\n",
    "        pmp_table.iloc[0, m] = pmp_table.iloc[0, m].round(2)\n",
    "\n",
    "bfs_contra.iloc[0, winning_model_index] = \"*\"  # replace 1s of preferred model\n",
    "bfs_pro.iloc[0, winning_model_index] = \"*\"  # replace 1s of preferred model\n",
    "results_table = pd.concat([bfs_contra, bfs_pro, pmp_table])\n",
    "results_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{table}[h]\n",
      "\\centering\n",
      "\\begin{tabular}{llllr}\n",
      "\\toprule\n",
      " & $\\mathcal{M}_1$ & $\\mathcal{M}_2$ & $\\mathcal{M}_3$ & $\\mathcal{M}_4$ \\\\\n",
      "\\midrule\n",
      "$\text{BF}_{j3}$ & 9.63e-05 & 0.010000 & * & 0.270000 \\\\\n",
      "$\text{BF}_{3j}$ & 1.04e+04 & 78.000000 & * & 3.710000 \\\\\n",
      "$\text{PMP}$ & 7.51e-05 & 1.00e-02 & 0.780000 & 0.210000 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\\end{table}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(results_table.style.to_latex(position=\"h\", position_float=\"centering\", hrules=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Robustness checks"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results under data perturbations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "bootstrapped_trials = inspect_robustness_bootstrap(\n",
    "    empirical_data, probability_net, summary_net, level=\"trials\", n_bootstrap=100\n",
    ")\n",
    "bootstrapped_participants = inspect_robustness_bootstrap(\n",
    "    empirical_data, probability_net, summary_net, level=\"participants\", n_bootstrap=100\n",
    ")\n",
    "leave_one_participant_out = inspect_robustness_lopo(\n",
    "    empirical_data, probability_net, summary_net\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/fvPNN8WePXt81nU6neK1114TWq1WdO7c2e8m7ykctFqt6Nu3r88Nb/Xq1d5C/Pnnnw8Yk1arFXl5eWLz5s3eeT/99JMoKCgQAMQf/vAHn/Vqa2u9X9RuvfVWUVVV5Z23detW0bt374D7e+aZZwQAkZKSItauXeudvnfvXtGnTx+h0+kikpDUaDRi3rx5wul0CiGEUBRF2O12IUTjCcnKykqxcOFCceLECZ/pFRUV3i/H06dPbzSehn8T5eXlQpZlYTabxddff+2zvM1mE4sXLxZbtmwJ4dMTxQaWFSwrIl1WNJeQfOqppwQAYTabvcetNcdYo9GIESNG+FwXnvPX1JdGj8auvaqqKu+1fM455/hdd2vWrBFvv/22z7TmEpJarVZMmTJF1NbWCiHqy8Rnn33WO6/h+RZCiC1btojPP//cW2567NmzR4wdO1YAEAsXLvT7TJ7jHx8f7/O3X11dLc444wwBQFxxxRV+6zX3Za5Pnz4CgHjxxReFy+XyTlcURaxcuVJ8+umnAdcjClawCUm32y1GjhwpgPoXOA0TLAcPHhRjxowJeK/ct2+feP/990VNTY3P9LKyMnHZZZcJAGLu3Lk+8xYvXiwAiBEjRgSMZeHChQKAOPPMM32m//3vfxcARKdOnXySbC6XSzz88MMCgMjPz/cpb5qjKIo3MffRRx8FtY4noXrBBRf4TPcca51OJ+bPn+/9m66rqxNXX321ACCGDRvmt7177rlHABADBw4UmzZt8k63Wq1i9uzZ3kReSzWVkPzvf//rne95Hlm6dKlYt26d34u2NWvWiJycHKHRaMTu3bt95nnKJ41GIzIzM8W3337rndfwPDR3L2yqbLn44ou9ic5169b5zNu1a5d4/PHHfaY1l5Bs6XNEqN+jPOWUTqcTc+bM8R4PRVHEH//4R+/zYMN7f1Pxt4TnGeWpp54KavlbbrlFABB9+/b1md7ShKRHOD5DtGNCksLO8wfX2M+QIUPEf/7zH7/1qqqqvG+wAtUM++GHHwQAIUmSz0381VdfFQBEVlZWwILTUwCNGTPGZ3pzXzLnz58vAIhnnnkm6M8e7JfMhoVWS11zzTUCgN+bS0/h0Ni2PV8siouLfQrIhjEFeoD49NNPBVBfm6jhF3jP9i655JKAcW7ZskVIkiQ6d+7snaYoivdL1AsvvOC3zsaNG72xtHVC8tSHn4YaS0g2p6CgQMTHx3uTnKfG07Aw+e6775o8fkTtHcsKlhVCRLasaCohuXz5cm/t2RkzZgS1veaOscFgEKWlpQHXbU1C8vHHHxdAfc3gUxOCjWkuIZmZmRnw7+J3v/udACCmTZsW1H6EEGL37t0CgDjrrLP85nmO/y233OI378cff/QmhION38NgMIiUlJSgYyRqqWATkp574ZAhQ/yeB4UQ4vDhwyIxMVEkJib61XhsjNVqFXq9XnTt2tVnusVi8SYBA/1tnH322QKAT20qh8MhsrOzhUaj8daqO9Wll14qAIhFixYFFZ8Q9ckmz993sC/Ub7/9dm8CsaGmntWPHTvmrSV48uRJ7/Ty8nJhMBiEyWTye0kjRH2ieMiQIQKAWLNmTdCfS4jGE5K7d+8WAwYMEEB9DbZAtcJP9dprrwkA4pFHHvGZ3rB8+uc//9no+qEmJD3PRgaDQezcubPZOIVoPiHZ0ueI5jT2PcpTTvXv39/vGNfV1Yns7GwBwO96Dkcyz/Nc8MknnwS1/NNPPy0A+JVHTEg2jn1IUpvp2rUrRo0a5f3p3r07DAYDNm7ciBdffBEVFRU+y3/99dewWq0oLCzERRdd5Le9IUOGYMSIEd4R2jy+/PJLAMDMmTNhNBr91rvtttsA1I/6dmr/K00pKCgAACxZsqTJfrFC0bt3bwwaNKjJZX755Rc88MAD+N3vfofx48dj9OjRGD16NFavXg0AjXacPGLEiIDbvv7662E0GrFv3z7s2LHDb35eXl7A437++eejsLAQFosF33zzjXf6Rx99BAC44YYbAsbRr18/FBcX49dff/X2hbh9+3YcOHAARqMxYJ9YgwYNwvDhwwNuL9ymTZsW8rpfffUV7rjjDpx33nkYO3as99xUVVXBarX69YUTiOf6+v7773HgwIGQYyGKdSwrGseyou3KCs9xGjVqFAoKCnDmmWeisrIS3bp18xtVNNRjfOaZZyI3N7dVcQbi6efrtttug8FgCMs2Z8yYEfDvYvbs2QDq+5o7lcPhwD/+8Q/MnDkTZ599NsaMGYPRo0fj2muvBdD4cQECXw99+/aF0WhEVVUVTpw40aL4CwoKUFlZ6fM3T6QGzz1v+vTp0Gq1fvNzcnIwZMgQ1NbWYuPGjT7zFEXBJ598gt///veYNGmS92/qrLPOgiRJ2LVrl085Ex8f770fv/vuuz7bOnbsGFasWAGDwYBLL73UO/27777DkSNHMGjQIAwcODDgZ7jwwgsBwHuPC0ZNTY333wkJCUGt41mu4boNBbpPpKene/tH/PXXX73Tly5dCofDgbPPPhv5+fl+68myjPPPPx9Ayz5XQ7fccov3/t+9e3d0794dmzdvRmJiIhYuXOgzwvaxY8fwzDPP4KqrrsKZZ57pXe/pp58G0Pj90Ww2ByxjW8tTblxyySXo2rVrWLYZ6nNEqN+jrr/+er9RzHU6Hfr37w/A93oIF8+1Ga5rmvz53yWJwuRPf/qT3xeJyspK3HbbbVi0aBEmTpyIH374wdvh8c6dOwEAPXr0aLQT5N69e+O7777zLttwvV69egVcp2vXrtDr9airq8OePXvQr1+/oOK/+OKLUVxcjC+//BK5ubk455xzMGbMGIwfP95vdK2W6tmzZ5Pz58+fjz//+c9NjvJ58uTJFm07ISEBBQUF2LVrF3bu3IkePXr4zO/evbvfTR6o74C+e/fuOHDgAHbu3IlzzjkHAPDTTz8BAO6//3789a9/DbhPT+fEpaWlyM/P956roqIixMfHNxp/JDqfb+4cBFJXV4crrrii0U7+PRo7Nw3l5eXh8ssvxwcffIDTTjsNp59+OsaPH48xY8Zg+PDhAR9iidojlhWNY1nRdmVFw6RpYmIiBg4ciIsvvhh33HGHd4ACoG2OcWtt374dAML6Aq+xWD3Tjx49iurqaphMJgD1g0NNnDgx4JdNj6bKwi5dugScnpGRgYMHD6K2thZpaWnBho877rgDv//97zFx4kQMHjzYmwAYN26cz/kkamuee95LL72Ef/zjHwGX8dzjSktLvdMqKytx7rnn4rvvvmty+xUVFT73xauuugrvvPMOFi9ejHvuucc7/YMPPoDL5cL5558Ps9nsF9++ffswevTogPuorKz0i685Df/Ogn2p51musb/Rxu4TmZmZ2LFjB2pra73TPJ9r3bp1jX4uz+A/LflcDf3888/efxsMBnTq1Alnnnkm/vCHP/jE+uWXX2Ly5MmoqqpqdFuN3R+7du0KjUYTUnxNiWS50dhzRGu/RzV1PQDwuR7CJSkpCZWVlWG7pskfv/FSRCUnJ+OVV17BihUrsGHDBnzyySe4+OKLAfx2E/HcVALJysoC4PvWobn1JElCRkYGSktLW/S2IiEhAWvXrsX999+PDz/8EO+99x7ee+89APVfaB977DHvm7aWauoty5o1a/CnP/0JGo0G8+fPx4UXXuj9UiZJEv785z/jkUce8Y7aeqrmjt+uXbsCHoeWHndPIXvq291AbDYbgN/OVUZGRrP7amvBvulq6NFHH8XHH3+M7OxsPP744xg7diyys7O9NVRGjx6Nb775ptFzc6pFixahV69eeO211/Dll196a3BlZGTg7rvvxp133hnwiz9Re8ey4rdtN4ZlRevKCiFEs8u09hiHUs4Eo7q6GkD930m4NHZeG06vqanxJiSnT5+OHTt2YNiwYZg3bx4GDBiA1NRU6HQ6uFwu7/8b09ix8ZR5wZyfhmbPno2kpCT87W9/w8aNG7Fx40Y89thjMBqNmDp1Kp544gmfpAxRW/Hc8xomrxrjuecBwJ133onvvvsO3bt3x1//+lcMHz4c6enp3lF98/PzUVpa6ne/mThxItLT0/Hjjz9i27Zt3pduixcvBlCfsAwU37Fjx3Ds2LGg4wuU5MvJycEHH3wAADCZTEhMTERtbW3QL/U8o3nn5eUFnN+S+4Tncx08eBAHDx5scr+ez3XkyBFcdtllfvMHDhyI5557zm/6ypUrMX78+Ca3XVlZiSlTpqCqqgrTpk3D7Nmz0b17d5hMJsiyjOXLl+Oss85q1+UGEPg5orXfo8Jdblx++eUoKyvzm/711197/52Xl4fKysqgR55v7pomf0xIUsQZDAYMGjQIpaWl+OGHH7xfMhMTEwEA5eXlja7rebPV8K1Dc+sJIbwFbkvfVuTn5+P111/HK6+8go0bN2LVqlX48MMPsWHDBlx88cX45ptvMGzYsBZtsznvvPMOAOCuu+7yedPp0Vwh29TDhecYBToOLV0vMTERlZWV2LVrF0477bQmY2q4TrD7ikaec7Nw4UKcffbZfvObOzenMhqNmDt3LubOnYtffvkFa9asweeff44lS5bgrrvuAgD84Q9/aH3gRDGIZUXTWFa0fVnR2mPcVpKSklBRUYHKykoUFRWFZZuNHeuG0z3n9fDhw1i5ciXi4+OxdOlSpKam+qyj1nGZOnUqpk6diiNHjmD16tVYtmwZ3nvvPbz66qsoKyvDZ599pkpc1LF47l/Lli3DmWeeGdQ6LpcL77//PoD6prXdu3f3m3/kyJGA62q1Wlx22WX4+9//jsWLF+Ohhx7CwYMH8c033yApKcnvhZgnvquvvhpvv/120J+rYa1yj4b3H0mSMGLECCxbtgyrV6/GJZdc0uT2XC6XtzboiBEjgo6jMZ7Pdd999+Hhhx8Oah273R7wc7WmldIXX3yBiooKjBgxAgsXLvRryaFmuQH8Vvs1HFr6PBDu71GttX79euzfv7/JZUaOHImtW7di9erVuOOOO5rd5po1awD4X9Oe66CxpGlLugpqb1j1hlThafrUsEp2t27dANRXKW/sj3Xr1q0+yzb897Zt2wKus2vXLtTV1UGj0fhU9W6sqV8gWq0Ww4YNwx//+EesX78eU6ZMgdvtxuuvvx70NoK1b98+APU3wECa6pMJ+K1K/qmsVqu3r8KGx89jx44dAZukCSG8TbIarud5AxvMG2APz/oHDhxotK+1xuKPBk2dmxMnToTcBASob35644034tNPP8WLL74IAHj11VdD3h5Re8CyonEsK9q+rGjtMW5KS66rU3m6Aghn9yaNHU/P9KysLG/tSM8XuB49evglI4HWHZfGtOR4ZWdn44orrsBrr72G77//HrIs4/PPPw9YE4Yo3EK55x07dgwWiwWpqal+yUjPttxud6Pre2pBempFLl68GEIIXHzxxYiLi2t1fED9Pf7UH8890uPyyy8HUJ948tTIa8yHH36I48ePQ5Ik73qtEcrnKi4uDvi5Vq1aFXIcnmMyYsSIgPet1t4fQy07IlluNPYc0ZbfowJp7ljt27cv4PlvyHNtLl26tNnk5bp167zn94orrvCZ56nd2VgSt7EamK15VogVTEhSxNntdmzatAkA0LlzZ+/00aNHIz4+HgcPHvR2vNvQhg0b8N1330GSJJx11lne6Z43LK+++irsdrvfes8++ywAYNSoUT5VvT0FdMPmCMHy9L9x+PBhn+mt2eap2/DU8Gnoyy+/bLYg+/bbb7F582a/6a+//jrsdjuKiooCPuwcOnQoYO2BJUuWYP/+/UhISMCoUaO803/3u98BqD++wVaR79GjBwoKCmCz2bBo0SK/+Zs3b2627xw1NXVu/va3vzX5sNgSjV1fRB0Jy4qmsaxo+7Kitcc4mG2Hcg14ags/99xzqKurCzmGhhYsWACHw+E33fOCbOLEid5pntjLy8sDntPHH388LDE1FOrx6tWrl7epNstUigTPPe/ll18OWNYE4rm+q6urA17jzf1NjR49GoWFhdizZw9++OEHb2Lyyiuv9Ft2zJgxSE9Px5YtW1qVeAtk6tSpKCoqwvHjx3HLLbc0utzhw4e9LYAmT54clkFWzjvvPOj1eixdujSowSXbSlPlxokTJ7BgwYKwbL+l90JPufHxxx8H3fy4OS19jojU96hT99eaZ62zzjoLw4YNg9PpxIwZMxptTl5TU4ObbroJADBs2DCcccYZPvM9z7Hr16/3W/ef//yn3wCO4fwM0Y4JSYqoiooKzJw5E4cPH4Zer8fkyZO980wmE26++WYAwJw5c7xfRIH6twaeURsnT57sU3vlyiuvRGFhIY4ePYrp06f7dGj79ttv4+WXXwYAv+ZWDW8MgWpg/L//9//w9NNP+900Dxw4gNdeew0A/EYW82wz1NHbgN/6aHn00Uexd+9e7/T169d7Ry1rilarxfTp033e4nz99de4//77AdQ3AQ70tkWr1eKWW27xdgoN1NckmjNnDgBg1qxZPtXub7rpJnTu3BkrV67E1Vdf7VfzoLa2Fu+//z7uvPNO7zRZlr2/33ffffj222+98/bv349rr70WOp2uyc+nJs+5+b//+z/vdSaEwKJFi/Dkk082e24aWrFiBe666y6/2lq1tbV44oknAPhfX0QdBcuK5rGsaPuyorXHuCkZGRlISkpCeXl5i2t73njjjSgqKsLWrVvxu9/9zq9Wyddff+1tGhesEydOYMaMGd5mY0IIvPjii/joo4+g0Wh8zk/v3r2RkpKCQ4cO4ZFHHvEmJe12O2677Tafv8lwaepvprq6GlOmTMGqVat8au+63W48++yzqKioQEJCQsAEO1G4XXLJJRg+fDh++eUXXHDBBdi9e7fPfIfDgSVLluD666/3TktOTkbv3r3hcrlwxx13eF80uN1uPPbYY3jvvfe8fUkGIkkSpkyZAgCYO3cuNm/ejPT0dJ+Xch5GoxEPPvgggPraX//617/8Xiz8/PPP+OMf/xiwOXNTjEYj3n77bRgMBixatAiTJ0/2KWOEEFi2bBnGjh2L0tJSFBYW4oUXXmjRPhqTm5uL22+/HU6nE2effbZfslUIgR9++AE333xzm4zG7DFmzBgAwPvvv4/ly5d7p5eVleHSSy9tsm/dYIT6/DB48GBccsklsNvtmDRpkl9ibPfu3XjyySdbtM2WPkeE83tUMDzHas2aNS3uX7Kht956C8nJyVixYgXOPvtsbyscj/Xr12P8+PH48ccfkZycjLfeestvG5MmTQJQ/3KhYcJ8/fr1uPXWWxt9pgnXZ4hqgijMioqKBADRtWtXMWrUKO9Pjx49hMFgEACEVqsVb7zxht+6VqtVnH766QKAACB69eol+vfvLzQajQAg+vfvL44fP+633rp164TZbBYAREJCgigpKREFBQXe7fz5z3/2W8ftdouuXbsKACItLU2MGDFCjBs3Ttx2221CCCFuu+027/rFxcVi6NChokePHt5Y+vTpIyorK322+eCDDwoAQqPRiIEDB4px48aJcePGibKyMiGEEG+88YYAIK699tpGj19VVZXo3LmzACD0er3o27ev6N69u/d43HnnnQKAeOCBB3zWe+CBBwQA8fvf/14UFBQIrVYrBgwY4F0XgLjggguE2+32Wc8T05QpU8TAgQOFJEmiT58+om/fvkKSJAFADBkyRNTW1vrFun37dtGpUycBQMiyLHr27CmGDRsmunXr5j1Ow4YN81nH5XKJc8891xtTjx49xIABA4RWqxWFhYVizpw5AT9fS1x77bWNHmfP9bl3795G1/ccy1Nj2LBhg/caNplMYvDgwSI3N1cAEFOnThXjxo0TAMTKlSsDxtPwmv/Xv/7lPQYZGRmipKRE9O/fX8THxwsAwmw2i40bN4Z8DIiiHcsKlhWRLitWrlzp3V4wWnuMm4vt+uuvFwCE0WgUJSUl3uvAo6nrYPPmzSI7O9t7THv37i0GDBjgvb5PXaexss9Tbj344INCr9eLpKQkUVJS4i3bAIjHH3/cb//PP/+8d352drYoKSkRJpNJSJIkXn311UaPc3PHv7E4Fy1a5F23T58+3mO1adMmUVFR4Z2XkJAg+vfvL0pKSkR6eroA4I2JqDU812ZiYqJIS0tr9Oenn34Shw8fFgMHDvRel6eddpoYNmyY6NWrl9Dr9QKAyMrK8tn+p59+6r2Xpqam+lzDf/nLX5p9ft28ebN3fwDEzTff3OTnueeee7zLpqamiiFDhohBgwaJ1NRU7/QvvvgipGP11VdfiYyMDO/fX9euXUVJSYnIzMz0bnvAgAHiwIEDTR7rxj5rY8/bTqdTXHPNNT73pqFDh4r+/fuLpKQk7/Tt27e36PN41jt1f4257LLLfM69p+xKSkoSTz/9tADgc68X4rfy6dTpp2rqXiiEEHv37hUARFFRkd+6J0+eFCNGjPB5ZikpKRFZWVkB1wn0/UWI0J8jQv0e1dj05uLcvXu39++tqKhIjBkzRowbNy7gc2VzfvzxR+9zjOfYDRkyROTn5/tM27JlS8D1bTab6N27t/fZtk+fPqJbt27e56rGPmM4P0O0YkKSws5TiJz6YzAYROfOncV1110nNm/e3Oj6dXV14plnnhElJSUiISFBxMXFib59+4qHH35YWCyWRtfbt2+fuOmmm0RRUZHQ6/UiJSVFTJw4USxZsqTRdXbu3Ckuu+wykZmZ6f1S5CkItm/fLubOnSvGjh0r8vLyhF6vF1lZWWL48OHiueeeE1arNWDsDzzwgOjevbv3htuwQA3mS6YQQhw+fFhMmzZNpKenC71eLzp16iTuvPNOUVVV1egXnYbTy8rKxPXXXy9ycnKEXq8X3bt3F4899phwOp1++2oYU3V1tbj99ttFYWGh0Ov1oqioSNxzzz0Bv2B6VFdXi0cffVQMGzZMmEwmYTAYRHFxsZgwYYJ48sknAz5M1NXVifnz54tu3boJvV4vcnJyxIwZM8SRI0eC/iLXlLZKSAohxPfffy/OOusskZiYKBISEsSAAQPEs88+KxRFaVFC8vjx4+LZZ58VF1xwgejUqZOIj48XZrNZ9OvXT9x9993exARRe8WygmVFpMuKliYkhWj9MW5KTU2NuO2220RxcbHQ6XR+sTV3HRw/flzce++9onfv3iI+Pl4kJSWJnj17iptvvtnvb6e5hOTKlSvF999/LyZNmiSSk5NFXFycGD58uPjoo48ajf/tt98WAwYMEHq9XiQnJ4sJEyZ4ExjhTkgKIcQzzzwj+vXrJ+Li4nwSBC6XS7z11lti6tSpokePHsJsNou4uDjRrVs3cc011zR5HyEKVmNl1qk/nsSQ3W4XL774ohg7dqxISUkRer1eFBQUiNGjR4t58+aJbdu2+e3j3//+txg5cqSIi4sTSUlJYvjw4eLtt9/22X9Tz6+9evXyxrF27dpmP9M333wjrrrqKlFQUCD0er1ITU0V/fr1E9dff71YsmSJqKurC+lYCVH/Qmf+/PlixIgRIjU1Veh0OpGVlSUmTZokFi5cKFwuV6PrhpqQ9FiyZIm4+OKLRXZ2ttDpdCIzM1MMHjxYzJkzR6xatcovUdacliYkHQ6H+Mtf/uK9t2dnZ4spU6aIX375pdHEY7AJSSEavxcK0XRCUoj6cvWFF14Qo0aNEmazWRiNRtGpUydx2WWXic8++8xn2eYSki19jhAitO9RoSYkhRDiP//5jxg3bpz3hVlrvmPa7Xbx/PPPiwkTJoiMjAyh0+lERkaGGD9+vHj22WeFzWZrcv2jR4+KGTNmiMzMTGEwGESPHj3Ek08+Kdxud5OfMZyfIRpJQrTXup9EHcvcuXMxb948PPDAA5g7d27Q6y1cuBDXXXcdrr32WixcuLDN4iMiIvWxrKCGxo8fj9WrV2PlypUYP3682uEQEVGUC/U5gigQ9iFJREREREREREREEcOEJBEREREREREREUUME5JEREREREREREQUMVGXkFyzZg0uuOAC5ObmQpIkfPzxx82us3r1agwePBhGoxGdO3fG3//+97YPlIiIqAGWX0REFItYfhERkRqiblCbL774At988w0GDRqESy+9FP/6179w8cUXN7r83r170adPH8ycORM33XQTvvnmG8yePRuLFy/GpZdeGrnAiYioQ2P5RUREsYjlFxERqSHqEpINSZLUbIH4xz/+EZ9++im2b9/unTZr1ixs2bIF3333XQSiJCIi8sXyi4iIYhHLLyIiihSt2gG01nfffYeJEyf6TDv77LOxYMECOJ1O6HQ6v3UcDgccDof3d0VRcPLkSaSlpUGSpDaPmYiIIkMIgZqaGuTm5kKWo6uXklDKL4BlGBFRR8Dyi4iIYlFLyq+YT0geOXIEWVlZPtOysrLgcrlw/Phx5OTk+K0zf/58zJs3L1IhEhGRyg4ePIj8/Hy1w/ARSvkFsAwjIupIWH4REVEsCqb8ivmEJAC/N2qeVuiNvWm79957ceedd3p/r6qqQmFhIfbv3w+TydR2gbaSoig4fvw40tPTo+5NaUfBcxAdeB6iQyych+rqahQVFSEpKUntUAJqafkFxGYZFgvXSixKSUlp831UVFS0+T46Ev4tRIdYOA8sv6JDLFwrHQHPg/p4DqJDLJyHlpRfMZ+QzM7OxpEjR3ymlZeXQ6vVIi0tLeA6BoMBBoPBb3pycnLUFoZA/cVXV1eH5OTkqL342jueg+jA8xAdYuE8eOKKxqZgoZRfQGyWYbFwrcSi2traRuft3bsXF11yOXqOuAIpGflwOR1497k5AIAptzwPrc4Axe3G6o+fxNNPzsekSZMCbichIaFNYu+o+LcQHWLhPLD8ig6xcK10BDwP6uM5iA6xcB5aUn7FfEJyxIgR+Oyzz3ymffnllygpKWm0/xIiIiK1sfyi1moqWajRaCBJMgzGBOj0Rp95Op0BWr0RQgjIsgayLDPxSERBY/lFREThEHUp1draWmzevBmbN28GUP+Gf/PmzThw4ACA+qr+06ZN8y4/a9Ys7N+/H3feeSe2b9+O119/HQsWLMAf/vAHNcInIqIOiuUXRROHw1GfcNQ0/u5ZkiRoNDrY7fYIRkZE0YblFxERqSHqakhu2LABp59+uvd3Tz8j1157LRYuXIiysjJv4QgAnTp1wtKlS3HHHXfghRdeQG5uLp599llceumlEY+diIg6LpZfFE3q6uoghICmiYQkAMiyxmfUWyLqeFh+ERGRGqIuITl+/Hhvp8iBLFy40G/auHHj8N///rcNoyIiImoayy+KJg6HAwKArG0mIanRwul0RiYoIopKLL+IiEgNUddkm4iIiIhax1tDUm46ISmxhiQRERERqYAJSSIiIqJ2pj4hiSb7kAQAWdairq4uQlEREREREdVjQpKIiIionfHUkJQ1miaXk2QNE5JEREREFHFMSBIRERG1M06nE5IkQ5KaedSTZPYhSUREREQRF3WD2hARERFR69TV1TXbXBuoryHJhGTrWCyWoJdVFAVWqxUWiwWyHFy9gISEhFBDIyIiIopaTEgSERERtTNOpxNSMAkvSWaT7RY4ceIEtm7d6jNt3LhxbbrP1atX+01LTU1Fnz592nS/RERERG2JCUkiIiKidsbpdEKWm+4/EgAkiTUkW+LBhx7Cv778T0T3ec2sm/ymJekNWPnll8jMzIxoLEREREThwoQkERERUTvjcrkgBZGQlNlkO2iKouDbH36AedRQ5I4c7p3e1eEIan23w4HVt98DABj/9KOQDYag1tOespzLZsPO517Bf//7X5xzzjlBRk9EREQUXZiQJCIiImpnPIPaNEeSZdTVMSEZjB07duBkdRXyu3eDPinRO73hv5visv+WuIxLS4PGGFxC8lT6pERo0lOxYcMGJiSJiIgoZnGUbSIiIqJ2JtiEpCxrUccakkHZuHEj6iQgMT9X7VAQV1SAb3/4Qe0wiIiIiELGhCQRERFRO+N2u4Nssi3D5XJFIKLYt2HDBujyciBr1W9gZO5cjN179+L48eNqh0JEREQUEiYkiYiIiNoZl8sVdJNtp5MJyeYoioLvNmxAYnGh2qEAAMydimBzObFp0ya1QyEiIiIKCROSRERERO2M2+0GJKnZ5SRJhotNtpu1Z88eHK+sgLlTsdqhAAD0SUnQpCQzIUlEREQxiwlJIiIionamPiEZZA1JNtlu1qZNm+AQAkmF+WqH4mUozMO3P3yvdhhEREREIWFCkoiIiKidcbvdkIJ4zJMkuT55SU3atGkTdDmZ0Oh0aofiZe5UhF927kJ1dbXaoRARERG1GBOSRERERO1M/aA2zTfZlmUZiqJEIKLYtm7jBsQXRE/tSAAwFRXC5nLip59+UjsUIiIiohZjQpKIiIionXG73RBoPiEJSYaLNSSbdOTIERwqK4MpSga08TCmpULExWHLli1qh0JERETUYkxIEhEREbUziqJACiIhKUkSFDdrSDZly5YtsLtcMBUWqB2KD0mSoM/PwYb//lftUIiIiIhajAlJIiIionZGUZSgR9lmk+2m/fTTT9CkJEOflKh2KH6SCguw6acf2Q8oERERxRwmJImIiIjamfokYzAJSUAI0fYBxbD/btkCfW6W2mEElFSYh2qLBXv27FE7FCIiIqIWYUKSiIiIqJ1RFAVSkDUkWbuucXV1dfh5+zYkRVlzbY/EvFw4FDd+/vlntUMhIiIiahEmJImIiIjamWBrSAKAwhqSjdq5cydqbXYkRdkI2x4avR66zAz8+OOPaodCRERE1CJMSBIRERG1M0KIIPOREiDYh2Rjtm3bhjoIJGRHZ5NtADDkZmPLVtaQJCIiotjChCQRERFROyOAoEbZBtiHZFO2bdsGfWY6ZJ1W7VAalZiXix27dsNut6sdChEREVHQmJAkIiIiameEElwNSUmSwHxk47Zs/Rn6nOitHQkASfm5sDnrsHPnTrVDISIiIgoaE5JEREREHZhgk+2AHA4HduzajcS8HLVDaVJ8ViacEPjll1/UDoWIiIgoaExIEhEREXVUQYzE3VHt2bMHdpcTibnRnZCUtVro0tNYQ5KIiIhiChOSRERERESn2LVrF+oUBfFZmWqH0ixdVgZ+3r5d7TCIiIiIgha9PXQTUYdisViCXlZRFFitVlgsFshycO9VEhISQg2NiIg6oB07dkCXmgyNXq92KM1KzMnG9m/Xw+12Q6PRqB0OERERUbOYkCSiqJCYmNim2+coskREAfDe2Kgdu3ZBk56mdhhBic/OwhGbHaWlpSgsLFQ7HCIiIqJmsck2ERERUQcWbE3zjuaXXbtiork2ACRkZ6JOcWHPnj1qh0JEREQUFNaQJKKoUFtb6/33n//yF7y/cgV6z7kRGoPBZzmX3YHlM+cAAM569XlojL7zgfrakNsXLMKgtEwsWriQX7aJqMORZAkIovKjEIID2wRQUVGBYydOIC17uNqhBEWXmAgYDNi7dy9OP/10tcMhIiIiaha/pRNRVEhISEBCQgJ27tyJz5d9ibyzz4DBbILWaPD78Qg0T2s0QBdnRPEFk7Bx+zasWLGC/UcSUYcjARDBZCQBSExI+tm7dy/qFDfiMjPUDiUokiRBm56G3bt3qx0KERERUVCYkCSiqKEoCh59/HE401KQOah/q7ZlKipAXK/u+Nuzz7RowBwiovZAluUg+4cUkJmQ9LNv3z64hIK4tFS1QwmaPj0Vu/ftVTsMIiIioqAwIUlEUePzzz/Hui2bUXTBOZDC0My6aNJZ2HfsGF599dUwREdEFDs0Gk1Qg3kJoUCjYQ8+pzp48CC0ZjNkbewcm7j0NOzZu4+DuBEREVFMYEKSiKJCbW0tnnj6/8HQqxtMReEZIdRgNiF1zHC8umgR9u/fH5ZtEhHFgvq+c4NJSLLJdiB79+2DnJKsdhgtYkxPQ01tLSoqKtQOhYiIiKhZTEgSUVR45ZVXsO/4MRRPmhjW7eaNGYkanQaPP/kEa40QUYcRdJNtoTAhGcCv+/fDkJaidhgtEpeehjrFjQMHDqgdChEREVGzmJAkItX9+uuveO2tRUgbMwIGsyms29bodMibdCa+WLkSa9euDeu2iYiilSzLQQ1qI4SArOHjYENCCBwsPYS41NhKSBqTk+FSFBw+fFjtUIiIiIiaxSdQIlKVEAKPPf44ao0G5I0e2Sb7SOvdE1JBHv76+GOoq6trk30QEUWTYGtICqFAq9FEIKLYUVlZCYvVBkOMNdnWGPTQxMehrKxM7VCIiIiImsWEJBGpasWKFfhy7RoUnDcRsq5tBg+QJAnFF07C1r17sXDhwjbZBxFRNNFoNAiuD0nxv/4myaOsrAxOxQ1jSmzVkAQAjdnEGpJEREQUE/gESkSqsVqt+Ovjj0PTpRipPbq16b7iM9JhHl6C5195BaWlpW26LyIitQVfQ1JAG0MjSUdCWVkZXIoCQ7JZ7VBaTDYl4dBhlnFEREQU/ZiQJCLVvPLKK9hVVopO558Tkf0VThiHShl49PHHOcANEbVrQdeQVNz/W5Y8jh07BqHRQBsfp3YoLaY3JaHsaLnaYRARERE1iwlJIlLF7t278fLCN5A6ZgSMERo4QGPQI+/cs7BkxXKsXr06IvskIlKDVquFEEqzywmhQKNlQrKh8vJy6JISY3L0cYPZhLKjR9QOg4iIiKhZTEgSUcQpioKHHnkE1oQ45I8dFdF9p/XuCakoHw/Nnw+r1RrRfRMRRYpGowmqybaiKNBp2GS7oePHj0NKTFA7jJDokpJQU1MLm82mdihERERETWJCkogi7rPPPsOq79eh8MJJkCPcd5kkSeh80XnYUXoIL7/8ckT3TUQUKRqNBkK4m12ONST9HT12DHJCvNphhESflAiXUFBRUaF2KERERERNYkKSiCKqsrISjz75JIy9eyC5S2dVYjCmpiB93Ei88uZC7Nq1S5UYiIjaUn1CMpg+JBXodKwh2dDxEyegi9GEpC4hHm6FCUkiIiKKfkxIElFEPfPMMzhUU4XicyeqGkfemJGwJsTjoUcegaI0388aEVEs0Wq1QBD3NiEU6LS6CEQUO05UVEAXq022ExLgFoIJSSIiIop6TEgSUcRs2rQJb3/4AbLOGg99UqKqschaLYouOherf/gen376qaqxEBGFm0ajgRJEk21FcdcnLwkAIITAyYqT0MXHdg3JyspKtUMhIiIiahITkkQUEW63Gw/99a9wZqQie8hgtcMBAJg7F8PYpwcee+pvqKqqUjscIqKw0Wq1EMHUkGSTbR91dXWoq3NCExendighkbVayDodampq1A6FiIiIqElMSBJRRHz44YfYsPVnFF94LiQ5em49xZMm4lBVJV544QW1QyEiChutVgshgktI6vX6CEQUG2pra6EIBVqjQe1QQqYxGpiQJCIioqgXPVkBImq3qqqq8NRzzyG+fx8k5eepHY4PfVIiMsaPxqL338Pu3bvVDoeIKCx0Oh0UJZhRttlku6Ha2lq4hYDWaFQ7lJDJBgNqa2vVDoOIiIioSUxIElGbe/XVV3G4ugpFEyeoHUpAOSOHwRZvxJNPPaV2KEREYVHfZLv5hCSEYEKyAYvFAkUIaGK4hiT0OlgsFrWjICIiImoSE5JE1KbKysqw8B//QOroYaoPZNMYWaNB7lkTsGzNaqxfv17tcIiIWi3oGpJQmJBswG63QwDQ6GJ35HFJp4PD4VA7DCIiIqImMSFJRG1qwYIFqIKCvFEj1A6lSWl9esKdkYYXXnoJQgi1wyEiahWdTgcI0Xw/ksLNPiQbsNvtEEJAjuGEpNDIsNlsaodBRERE1CQmJImozRw/fhzvfvQR0kcNg8YQ3V94JUlC7uljsPaH77Flyxa1wyEiahWdTgdIEpRmRtoWbnf9sgQAcDgcUISAHMMjj0s6HeysIUlERERRLqSEpMvlCnccRNQOffDBB6hyO5E9tETtUIKS2qMbXOYkvP3OO2qHQkTUKlqtFhLQbD+SQihMSDbgdDohAMgx3Ixd1mphd9jVDoOIiIioSSElJPPy8vDHP/4RO3fuDHc8RNROuN1uvPfPfyKxT09o42JjtFJJlpE+dDCWLluGkydPqh0OEVHI9Ho9JEmC2930S2ShcJTthjwv3SVZo3IkoZNkGe5masYSERERqS2khKRGo8ETTzyBnj17Yvz48Xj77bdht/NNLBH9ZsOGDdhfdhhZJYPUDqVFMgb2R42zDl9++aXaoRARhUyn00GSmq8hqShsst2Q2+2GEAKSHLu9GkmyDJcriBHWiYiIiFQU0tPWoUOH8PHHH+P888/Ht99+i2uvvRa5ubm49dZb2fcaEQEAVq5cCSUpEYn5uWqH0iK6+Djoiwuw/Kuv1A6FiChknoSk4m4+IclBbX7j7XNTltQNpBUkWYbT5VQ7DCIiIqImhdRGR5ZlXHjhhbjwwgtx9OhRvPHGG3jjjTfw/PPP44UXXsDgwYMxc+ZMXHnllUhMTAx3zEQUA9Z8+y3iuxRDkmLvS52522n4fu062O12GI2x0dyciKghnU4HCRKU5vqQZA1JH0IIAIjJsouIiKglLBZL0MsqigKr1QqLxQK5Ba0IEhISQgmNOohWt0fJysrCPffcgx07duCrr77C1Vdfja1bt2LWrFnIycnBzJkzsWHDhnDESkQxoqqqCnv27YWpuEjtUEJiLi5Crc2GHTt2qB0KEVFI9Ho9IAFud9M15RS3iwnJBjxfsjyJydgkIEux2+SciIgiIzExMegfk8mELl26wGQytWi9mpoatT8mRbGwPq2kp6cjJSUFRqMRQgjU1dVhwYIFGDZsGM4//3yUl5eHc3dEFKX27NkDh9uNhNxstUMJSXx2JpxCYPfu3WqHQkQUkvom21KTTbaFUCCEwibbDXhrRsZwQlIookW1V4iIqGOpra3FI488EpF9TTz3XCxdujTGX/RRW2n1sIoWiwWLFy/Ga6+9hvXr10MIgcGDB+Omm27ClVdeia1bt+LJJ5/Ehx9+iFmzZuGjjz4KR9xEFMUOHToEp9uNuNRUtUMJiazRQGc2obS0VO1QiIhCYjAYIKG+BmRjFLcbkiQxIdmALMv/GwxIie2BbdjknIiIAlizZg3mPvwwdh0pQ/85NyIpL6/Zddx1dfjugfoE5sh590EO8rlBKAqO/XczZt/1B5z3xVL8+U/3IScnp1XxU/sSckJy3bp1eO211/D++++jtrYWiYmJmDlzJm666SYMHDjQu9zQoUPx/vvvY+rUqfj000/DEjQRRbeKigpo4+Ig61r9zkM1UkI8Tp48qXYYREQh8daQbKIPScXtgiSBCckGtFotAAnCrYThtb06hNsFfQyXv0REFH4VFRV4/Ikn8P6nnwBF+ehxy40wpiQHta7L7vD+29y5GBqjIej9pnTrghPbd+CzT7/Aut/9Dn+84w5cdtllrMlPAEJ81Orbty+2bdsGIQQGDRqEm266CVdddVWTHZb27t0b77zzTsiBElHssNlskPQx3ieZTgubzaZ2FEREIdHr9ZAkCe6makgqbgAS+5BsoH4woPraoxq1gwmR2+mCwcAB2YiIqN5XX32F+x96EPsrK5Bz4SRkDuwX0Zr0aT27w9ypGPu++BJ3zX0A//7ySzz84IPIzc2NWAwUnUJKSO7btw833HADbrzxRgwePDioda6++mqMGDEilN0REUWcELE+qAERdWT1Ccmmm2y73S7IUn3zbqrnSeQKV+PHLdoJtxtG1nolIurwrFYrHnvsMbz9zw8hdS5Gr2lXQJ+UqEosWqMBp11yASr79cbyjz7HlksvxYN//jPOO+88VeKh6BBSPdmysjK8/PLLQScjAaCgoADjxo0LatkXX3wRnTp1gtFoxODBg7F27doml3/nnXfQv39/xMfHIycnB9dddx1OnDgRdGxEFF4GgwHC2fTIrtFOcrthNLKGCbUcyzCKBnq9HhKCabLNGpIN1R83QInhhCRcLjbDp5Cw/CJqP/bu3YvJV12J1z/+CGkXnI0eU9VLRjaU3KUzet9yE2oLsnHL3Xdh7ty5qKurUzssUklICckLL7wQixYtanKZxYsXY8KECS3e9nvvvYfbb78d9913HzZt2oQxY8Zg0qRJOHDgQMDlv/76a0ybNg0zZszA1q1b8cEHH2D9+vW44YYbWrxvIgoPs9kMl90OoShqhxIyYbPDbDarHQbFGJZhFC2CrSEJ9iHpIy4uDrIkwR3LX45cbsTHx6sdBcUYll9E7cf69esx+eqr8dPxcnSbdT2yBg+MqsHOtEYDuk3+HdIvnIQFH36AG268EVVVVWqHRSoIKSG5atUq7Nu3r8llDhw4gNWrV7d420899RRmzJiBG264AT179sTTTz+NgoICvPTSSwGXX7duHYqLi3HrrbeiU6dOGD16NG666SZs2LChxfsmovDIyMiABhLqamrVDiUkQgi4amqQkZGhdigUY1iGUbSQZRlanbbJhKRQ3JAliU22G/AmJB0xnJB0OhEXF6d2FBRjWH4RtQ8bNmzADbNvxglzAnrfPAPxGelqh9SorMED0GXGNVj542bccOONqK2Nze+OFLo2G4LPYrG0uAlQXV0dNm7ciHvuucdn+sSJE/Htt98GXGfkyJG47777sHTpUkyaNAnl5eX48MMPm+yLwOFwwOH4baSo6upqAICiKFCiuEaXoigQQkR1jO0dz0FwcnNzYdBq4ThxEkazKazblk75d1u863NarJBdbuTm5vJcNyEW/h4iGRvLsMbFwrXSHhkMBiiK2+9e6fldcTshyzJ0Oh3Pzf/ExcVBo9FAqasLe/kSifILACSnC/Hx8TynTYiFexLLr+gQC9dKR8DzEJyjR4/iljvvgCU9Fb2mXQlZF550T1uWX6aCfHS77hr88Prb+PNf/oK/PflkVNXmjDax8LfQktiCvkJPra5fWVkZsAq/2+3GoUOH8MEHH6C4uDjoQADg+PHjcLvdyMrK8pmelZWFI0eOBFxn5MiReOedd3DFFVfAbrfD5XLhwgsvxHPPPdfofubPn4958+b5TT927BjsdnuLYo4kRVFQVVUFIQRkOaTKrdRKPAfB0ev16NezJ/RugVQpvO89nNJvtX3yJA10Yd4+AFgsduh69kR6ejrKy8vDvv32Ihb+HmpqaiK2L5ZhjYuFa6U96tmjO6T4FGQma1BX99uY0RnJGuj0GhiVeIge3WG1Wnmv+x+bzYaeXbvBZIiDKQbLLwBwFxXBbDbznDYhFu5JLL+iQyxcKx0Bz0NwFr75JlLy89Hv/LOh1Yev9UObl1+5eciddhV2f70OK1euRJ8+fcK7/XYkFv4WWlJ+BX0lFRcXezPVkiThmWeewTPPPNPo8kIIPPHEE0EH0tCpGXEhRKNZ8m3btuHWW2/F/fffj7PPPhtlZWW46667MGvWLCxYsCDgOvfeey/uvPNO7+/V1dUoKChARkYGTKbw1uYKJ0VRIEkSMjIyovbia+94DoLndrnx49at6NK1U1DLu+yO5hc6Zbn9Nis0ovEBGxrSGoMvlEv37IZ7/3706tULWm2bVSSPebHw96DGwEQsw/zFwrXSHh0qPQyHToY2uQecdb/dK49VuqHVu3Gk9CR+3bkLmZmZSE1NVTHS6JGamopdu3cjsUshsgtzw7ptV4PyqlS4oRHhHzhHCIGffv4Z0393GTIzM8O+/fYiFu5JLL+iQyxcKx0Bz0Pzqqqq8Pb77yHu9DHQ6jVAGMuYSJRfKMjB9upKfPLZpyGNRdJRxMLfQkvKr6C/aU+bNg2SJEEIgUWLFqF///4YMGCA33IajQapqamYMGECzjnnnKADAYD09HRoNBq/N3Hl5eV+b+w85s+fj1GjRuGuu+4CAPTr1w8JCQkYM2YMHn74YeTk5PitYzAYAvaXJMty1J5UD0mSYiLO9oznIDh9e/XClq9XQwS5/BdTZ7Z4H1/OnBP0sud/0PRAXA3VHDiEQd26c6CHIET730Mk42IZ1rRov1baI4NeD6vTCQH43Is9v7vdLgihwGg08rz8j16vh9FogMtuC7r8ClagcxBu7jonFJcbSUlJPKfNiPZ7Esuv6BHt10pHwfPQtL1798LicCC7S3HQ5UsoFUKcdgda0li4JZVC4osL8ePWbTzHzYj2v4WWxBV0QnLhwoXef69evRrXXXcdbr311hYF1hy9Xo/Bgwdj2bJluOSSS7zTly1bhosuuijgOlar1a8Gk0ZT3yxJiLZ41COiYPTt2xfOj/4JxekKW/8lkeIsO4qBF16sdhgUY1iGUbQxGAxw25seZVuSJL58OYUpyYSTtuhsPtocl90OWZKQlJSkdigUQ1h+EcU+g8EAGS0blO3fIVQIWdaCCiFAyyqFuOvqYGxBApNiX0hZgr1794Y7Dq8777wTU6dORUlJCUaMGIFXXnkFBw4cwKxZswDUV/UvLS3FokX1F/YFF1yAmTNn4qWXXvI2F7j99tsxdOhQ5OaGt6kNEQWvX79+MEgSag6VwtypqNnlz3nr1aC267I7sPx/BeFZrz4PTZgLLafFAtfJCvTt2zes26WOgWUYRROj0QDlZOMJScXlgixJLR6EsL1LSU7GUZtN7TBC4rLaoGFCkkLA8osotnXr1g0ZqSk4tuUnJBXkqR1OiylOF2p/2YnR5wd+CULtU9RVW7riiitw4sQJPPjggygrK0OfPn2wdOlSFBXVJzTKysp8BtOZPn06ampq8Pzzz+P//u//kJycjAkTJuCxxx5T6yMQEYCuXbsiOSkJ1fsPBJWQbEl1/obrhDshWb3/IIxabcAuKYiawzKMoonRYIDb1Xhiza24oNPrOJrlKTJSU/HzsTK1wwiJ02qBRpKRkpKidigUY1h+EcU2nU6HGVOn4eFnn0HtoP5IzPXvNuFU0VIhBAAOfLUaiXUuXH311WHfNkWvoBKSEyZMgCRJePPNN5Gfnx90J6OSJGHFihUtDmr27NmYPXt2wHkNm4573HLLLbjllltavB8iajsajQZDBw3Cl3v3AOPHqB1O0Kr27kN+Tk7Avo+IgsEyjKKFwWiAojRdQ1KNgTOiXVpqKtz7f1U7jJA4LVZoZAnJyclqh0IxiOUXUWybPn06ln/1Fda9/T563DgdhmRzk8tHS4WQ8k0/ovqb7/HAH+5C586dw7ptim5BJSRXrVoFSZJgtVq9vweDb9yJOrYhg0vw+ddrIRQFUpR2unsq676DGDlyDO9fRBTz4oxGKO6m+pB0wqBnX02nSklJAWK0ybbTYoVOq0ViYqLaoRARUYTp9Xo8/+yzmHrddGx/9U10u+5qxKWnqR1Wk45u3IQjn/4b102+Atddd53a4VCEBZUhUBQFbrcb3bp18/4ezI/b7W5my0TUng0cOBBalxuWsiPNLxwFXHYHnEePoaSkRO1QiIhazWBopoak2wW9gQPanColJQVuS4wmJGstSEtN40s1IqIOKjMzEwtfW4B+ObnY8fIbqPp1n9ohBSQUBfv+sxzln/4bN155FR64/36WXR1QbFRZIqKY1Lt3b5ji46O2IDxV9f4DMGo0GDRokNqhEBG1msFgAJTGXw673S4YOMK2n7S0NLisFghFUTuUFqurqUFmRrraYRARkYpycnLwj0Vv4Zyhw7F34T9waO23EEKoHZaX02LF9jf/gbrvN+HBP96D++67D3KMtKaj8OJZJ6I2o9frUTJgIKr27lc7lKBU792P7PQMFBYWqh0KEVGrGQwGCNF4QlJxuxAXxz4kT5WWlgYNJDgtVrVDaTFnTS2yMzLVDoOIiFRmMpnw9xdfxB9umoWar9bil0WLo6Jcq9q7H1uffwVZtTYsfPllTJs2jTUjO7Cg+pBcs2ZNyDsYO3ZsyOsSUewbWlKCZS+vj4l+JGv37ce5Q4exUCSidkGv10M00WTb7XbCaEyKYESxIS0tDRpJQl1NDfRJsdUXo7BYkZHOGpJERFQ/yOhtt92GgQMH4o9/vg/bnn8FxZdfDHPn4ojHIhQFB1euRcWab3HGsOF4/NFHkZnJF2gdXVAJyfHjx4f8BZ39SBJ1bAMHDoTsqIP12HEkZEVvoeN2OuEsK2dzbSJqNwwGA0QTTbYVtwtxHGXbT0ZGBrSyjLrqGiA3R+1wWkSptSAjI0PtMIiIKIqMHTsWn3z4T9zzpz9h+ZuLkTxmOAonjItYZRFHVTV2v/cRdEeP4d45t+DGG2+ERqOJyL4pugWVkLyfHYwSUYj69OmDeJ0e1fv2R3VCsvZgKQyShAEDBqgdChFRWOj1+mZG2XbBaOQo26dKS0uDTqOBo6pa7VBaRCgKnLW1rHFCRER+MjMz8dorr+C1117DUy+8gG37DqDrFb+DPqltW0pU7NqD/R98jNPSMvDk629g8ODBbbo/ii1BJSTnzp3bxmEQUXsVHx+PPj17YvO+g8gZNkTtcBpVvf8gkpNMOO2009QOhYgoLOpH2W68hqRQ3DCyhqQfrVaLtNRUVFXXqB1KizhrLdBAYg1JIiIKSJZl3HjjjRg0aBDuvPtubH9xATpNuRSmooKw70sIgYMr16Bi1Tc4b/zpmP/II0hJSQn7fii2RXeHbkTULpQMHIi6w2Vqh9GkmgMHUdK/P5sPEFG7YTAYmqwhKRRX/Ujc5CcnOxt11bFVQ9JRVQ2tLLOGJBERNamkpAQfvf8+Tu/TD7++8Q6O/ndLWLfvdjqxc/GHsK5dhz/dehtefP55JiMpICYkiajN9e3bF+6TlXBabWqHEpAQAs7DR9C/Xz+1QyEiChu9Xg+329nofKG4odfrIxhR7CjMzUNdjDXZdlRVQSvJyMrKUjsUIiKKcunp6XjtlVdww+WTcfTjJTjw1WoIIVq9XZfNhl9efxvxh8rw8jPPYtasWZCjfGBTUk9QTbYnTJgASZLw5ptvIj8/HxMmTAhq45IkYcWKFa0KkIhiX+/evWHQaFF78BBSundVOxw/jsoqwGZHnz591A6FiChs6pONotFm20Jxs4ZkI7KzsyHWfat2GC3iqKxCnNGA5ORktUMhIqIYoNPpcP/99yM7OxuPP/cs9judKJp4RsjjhzitNvyyYBFyFAmvLHgdffv2DXPE1N4ElZBctWoVJEmC1Wr1/h4MDoRDRABQUFCA1GQzakoPR2VCsvbQYRg1WvTs2VPtUIiIwsZoNEKC1GizbTbZblx2djZcNTUQQsTM86yjqgqFOTkxEy8REalPkiTcdNNNiI+Px9zHHsUhvQEFp49p8XbcjjrsWPgOciHjzdcXoGvX6PvOR9EnqISkoihN/k5E1BRJkjCgdx98VXpI7VACqj18GNmZmRwIgIjaFb1eD0mS4Ha7IMv+/eMqigs6nU6FyKJfdnY2UOeEy2aHLj5O7XCC4qioQkFOntphEBFRDJo6dSpqa2vx6HPPIi49Fel9ewe9rhACOz/4CClWOxa8sZDJSAoaG/MTUUT07tULriPlaocRkKW0DAPYXJuI2pn6hCSaqCHJJtuNycnJgU6W4aioVDuUoCnVNSjIz1c7DCIiilGzZs3ClRddjEP/WgLb8RNBr3f46+8g7dmP//fY42xxRi3ChCQRRUSPHj3grq6B02JVOxQfQgi4jx5HLxaeRNTOGAyG+hqSrsAD27jdrCHZmJycHGhlDeyVlWqHEjR3dQ1ycnLUDoOIiGKUJEm4/y9/Qe/CQvz6z0+DGuTGduw4jn21BrOmX4dx48ZFIEpqT1qVkHznnXcwceJEZGZmwmAwIDMzE2effTb+8Y9/hCs+ImonevToUT+wzeEytUPxUVddA2GzoXv37mqHQkQUVt4ako0NauN2wWg0Rjiq2JCSkoI4owGOkxVqhxIUd10d3BYLE5JERNQq8fHxePD+ByCXHcWxLT81u/y+pV+iV2ERfv/730cgOmpvQkpIOhwOnHvuuZg2bRqWL18Oi8WC3NxcWCwWLFu2DFOnTsV5550Hh8MR7niJKEYVFBTAnJSI2tLDaofio7b0MAwaDZsXEFG746khGajJthAKFMX9v5G46VSSJKEgLx/2GGmyba+ohFbWIDc3V+1QiIgoxg0dOhTnnXkWjny1BqKJ8UNqDh6Cc88+/N9tt/MFJ4UkpITkvHnz8O9//xtnnnkm1q9fD4vFgr1798JiseCHH37AGWecgX//+9946KGHwh0vEcUoWZbRr1dv1B6KsoTkocPITEtHVlaW2qEQEYWVXq+HBARssq243ZAkiQnJJhQXFMRMH5KOkxXQyTLy8jioDRERtd7MG26AtroWFTt2NbrM4a/XoXeX03DmmWdGMDJqT0JKSL7zzjvo27cvvvjiCwwePNhnXklJCb744gv06tULb731VliCJKL2oX/fvnCVHVU7DB+1h0oxqH9/SJKkdihERGHVcJTtU7ndLkgSmJBsQmFBAZSqarXDCIq9ohJxBgPS09PVDoWIiNqBPn36YGDvPijfuDngfJfdAduO3Zhy+eWQZQ5NQqEJ6copLy/Hueee2+iFp9FocN555+HYsWOtCo6I2pd+/fpBqamFI0q+4AlFgfPwEQzo10/tUIiIwq6pJtuK2wVJkjjKdhNyc3PhqqwKqlN/tdlPnERebi6/FBIRUdicP2kS7Hv2QXH6P0dU7NiFOEnGxIkTVYiM2ouQnlq6dOmCEyeaHgb+5MmT6NKlS0hBEVH7NGDAAMRptajau0/tUAAAliNHoXW6MHDgQLVDISIKu6YGtVHcLkgAE5JNyMvLg+RywVlrUTuUZtlPVqBTYZHaYRARUTsycuRI6IVAzaFDfvMqd/+Knt26Ijs7W4XIqL0IKSF522234b333sMvv/wScP62bdvw7rvv4rbbbmtVcETUvqSmpqJH166o3PWr2qEAqC9ITfHx6NOnj9qhEBGF3W9Ntv37kHT/r4Ykm2w3Li8vDzpZA3sMjLStVFWjMD9f7TCIiKgdOe2005CcZELNwVK/eY7Swxg2uESFqKg90Qaz0Jo1a3x+7969O8aNG4fBgwdj2rRpGD16NDIzM1FeXo61a9firbfewplnnolu3bq1SdBEFLvGjRqNzYvfgRBC9X4bq3buxqThw/mFnIjaJZ1OV19DMlCTbYV9SDYnLy8PWlmG/WQFTEUFaofTKCEEXJVVHNCGiIjCSpZl9O3ZE2uPHPGZrrhcqDt+At27d1cpMmovgkpIjh8/PmDiQAiBl19+Ga+88orPNAD47LPP8Pnnn8Pt9m8mREQd17hx4/DcgtdQe6gUSQXq1eZwWm1wHizF+Bk3qRYDEVFbkmUZOq0OSoBnMYU1JJuVkJCAtJQUWE6eVDuUJjlrLZBcLiYkiYgo7E7r3Bmrdu/wmWavqIROklFUxK5CqHWCSkjef//9qtdkIqL2YcCAAcjLzMSxLT+rmpA88fM2JOr0mDBhgmoxEBG1Nb1BH7CGpNvtZkIyCMWFRdjYTJNtl90R1LYaLueyOxDsUDlaY9P9fNpPnoRO1iCfTbaJiCjMcnJy4K6u8ZnmqKiEVpaRm5urUlTUXgSVkJw7d24bh0FEHYVGo8GFk87F8+8vhpg0EZJGnRFBT2z6EWcNH4G0tDRV9k9EFAl6vR7uRkfZZpPt5nQqLMS6H75rcpl/T53Z4u0umzkn6GXP/2BRk/NtJyqYkCQiojaRnp4Ol9XmM62uphZaSeb3KGo1dTIBRNShXXzxxdDbnTixPfDAWG3NevQYXKVl+N0ll6iyfyKiSNHr9VCUwAlJgDUkm1NQUAB3ZZXaYTTJfuIk0tPSEB8fr3YoRETUzphMJmhk39ayLqsVRqMBBkPTNfiJmhNUDUkionDq1q0bRpSU4Jvv1iO9T6+I7//wd9+jKDsbp59+esT3TUQUSQaDATYnB7UJVX5+Pty1tXA7ndDodAGXOeetV4PalsvuwPL/1Yw869XnoWmmKXaw7CdOol9B9A66Q0REsSshIQEyfBOS7ro6JCUkqhQRtSch15A8ePAgbrrpJnTp0gVxcXHQaDR+P1ot851EFNi0q6+G+2ApaksPR3S/TosVtVu2YuqUK/lFnIjaPYNeH3BQGzcHtQlKfn4+tLIG9ib6kdQaDUH/tGadxjgrK9GJAwsQEVEbMBqNfuOJKE4XjEajShFRexJSQvLXX3/FoEGDsGDBAiQmJsLhcKCwsBDdunWDRqOBEAL9+vXDmDFjwh0vEbUTEyZMQM/OnXFo5dqI7vfwN+uQHh+PyZMnR3S/RERq0Bsaa7LthiSBL4+bkZ+fD70sw34iekfaViqqUcAakkRE1Aa0Wi1OHd5Ycbuh4/MDhUFICcl58+ahqqoKK1aswJYtWwAA1113HbZv3459+/bhggsugMViwQcffBDWYImo/dBoNLhpxg1w7NwDy5GjEdmny2ZDxQ8bMfWKKUhOTo7IPomI1GTUGwLWkFQUF7RaLWSZ3Yk3JS0tDQnx8bBFaULS7aiD22rlgDZEREQUc0J6Cl2+fDnOPfdcjBs3zjtNCAEAyM3Nxfvvvw8AuO+++8IQIhG1V+effz56FBXj4PJVEdlf6drvkKo34Nprr43I/oiI1KY36OFupIYkm2s3T5IkFOYXNNlkW022kyehk2UmJImIqE243W6IU6ZJsgS34v+yk6ilQkpIHj9+HD169PD+rtVqYbVavb8bDAacddZZ+Pzzz1sfIRG1WzqdDnNuvhmOnXtQW1rWpvtyWiyoWLcB1111NdLS0tp0X0RE0aK+D8nAo2zrdUxIBqNLUREcJ6IzIWk/cRI6WcOEJBERtQmn0+mXkJS1OtTVOVWJh9qXkBKS6enpsFgsPr/v27fPZxmtVovKysrWxEZEHcB5552Hvl274uCXX7Xpfg6u+hqZCYmYPn16m+6HiCiaGAyBB7VRFBdrSAapoKAAoqpa7TACsp+oQGJCPFJSUtQOhYiI2iGbzQZF+KYkNXodrDZrI2sQBS+khGTXrl2xZ88e7+9Dhw7Ff/7zH/z6668AgGPHjuHDDz9Ely5dwhMlEbVbGo0Gt/1+Dlx796N6/4E22YejqhrV6zfhpuuuY9+RRNSh6PV6QCh+0xVFgU6vUyGi2JOfnw9nZSWE4n8c1WY/eRKFBQV+I6ASERGFQ21trX9C0miAxWKBEoXlIsWWkBKSkyZNwsqVK701IG+//XbU1NSgX79+GDJkCLp164YjR47glltuCWesRNROnXnmmRjUuw8OLlvZJts/uGot8lNScdVVV7XJ9omIopVOp4MQAWpIul3Q65iQDEZeXh40ov7lVrRxnKxEl8IitcMgIqJ2qqqqCpB9X3pp4+Lgcrt9Ws0ShSKkhOTNN9+MVatWQaPRAADGjx+Pd999F0VFRfj555+RlZWFZ599FjNnzgxrsETUPsmyjFtmz4Zy8DCqft0X1m07qqpRu+knzLzuOiQmJoZ120RE0a7xGpIc1CZY+fn50Mka2KNwpG1RVY2CggK1wyAionaqsrISmrg4n2m6hHi4FYGKiujsX5lihzaUlUwmE4YNG+Yz7fLLL8fll18elqCIqOMZP348Svr0xaaVa2DuXBy27R5a/TXyU1IxZcqUsG2TiChWaLVaiAAjYSpuN7S6kB4DO5ycnBzodVrYTpxE8mmd1Q7HS7gVOCurOKANERG1mePHj0OO901I6hOT4BIKTpw4gcLCQpUio/YgpBqSREThJkkSbpwxA679h1Bbejgs23RarKjZ9COmX3MN4uPjw7JNIqJYUt9k27+GpFDcMBoMKkQUe3Q6HXKysmE/GV01JB1VVdAAyM3NVTsUIiJqp44dOwYpwfd7lN6UCLei4Pjx4ypFRe1Fq16N79u3D//4xz+wefNmVFVVwWw2Y8CAAbjqqqtQXFwcphCJqKM444wz0L24E/at/Q7dp1za6u2Vfb8eqcY4TJ48OQzRERHFHp1OF7iGJJtst0inwkLsP1amdhg+7CcroJM1rCFJRERtpqy8HLok326vNEYjoNXg2LFjKkVF7UXINSQfe+wxdO/eHX/5y1/w4YcfYtmyZfjwww/x5z//Gd27d8djjz0WzjiJqAPQaDSYeuWVsP2yE3W1reskWbgVVKzfhN+dfwFSUlLCFCERUWxprIakorhhYEIyaIX5+VCibFAbe0UFdFoNcnJy1A6FiIjaqSPl5dCf0g+/JEnQJCTgxIkTKkVF7UVICck33ngD9957L9LT0/H4449j3bp12Lt3L9atW4fHH38caWlp+NOf/oSFCxeGOVwiau8uvPBCJBviUL5pS6u2U7F7D3Q2O/u2JaIOrdEakm43dOxDMmj5+flwV1apHYYP+8kK5GRlQ8fR0omIqA0IIXDi5Am/GpIAICcyIUmtF9KT6P/7f/8PWVlZ2LRpEzIzM73Ti4qKMHToUEydOhX9+/fHU089henTp4crViLqAMxmM8454wx88P23yB8zMuTtlG/cjL7de6Bnz55hjI6IKLZotVooARKSgk22WyQ3Nxdumx0uuwNaY3T0vWk/UYHiAjbXJiKitmGxWFDndMJ0yqA2AACjgaNsU6uFVENy165dmDx5sk8ysqGsrCxcfvnl2LVrV6uCI6KO6dxJkyCOnYD1WGgdJbudTth3/YoLzzsPkiSFOToiotih1WoDD2oDBVota0gGKzc3FzpZhj2Kvny5q6pRVMDRTYmIqG3U1NRAEQLaOP+EpDYuDicrKyMfFLUrISUkMzIymm0eotfrkZGREVJQRNSxjRw5EqlJSTixdXtI61fu2gMD6gfJISLqyHQ6HYTbv4YkhMKmvi2Ql5cHnayB42Sl2qF4uauqOcI2ERG1GZvNBkUIaAK0qNAY9LDYbCpERe1JSAnJKVOm4J///CesVmvA+bW1tfjnP/+JK6+8slXBEVHHZDAYMG7ESFTv3BPS+hU7dqFrcScUFRWFOTIiotii1WqhiMAJSdaQDF5qairi44ywnzypdigAAHddHdwWCxOSRETUZlwuFwQASfZPG0kaDVwuV+SDonYlpITkQw89hL59+2LYsGF47733UFpaCqfTidLSUrz77rsYMWIE+vfvjwcffDDc8RJRBzFy5Eg4S8vgdjhavK711/04fcyYNoiKiCi21PchqUAI3+mCCckWkSQJebm5sEfJwDb2ikpoZQ0TkkRE1GZkWYYEQCjCb55QFGg0msgHRe1KUE+isiwH7IdNCIGrrroq4PRt27YhISGBWXMiCsmQIUNgkGRUHziElK5dgl7PUVkFUVWNkpKSNoyOiCg2aLXa+i8Tp2QkmZBsueKCQvyyP7Sa++HmqKyCTpaRk5OjdihERNROGY1GSJCguJx+8xSnE3HGeBWiovYkqCfRsWPHcmAIIoqo4uJiZKaloXrv/hYlJKv27UecVosBAwa0XXBERDFCq9VCkiSIU0faZkKyxfJyciB+2qx2GAAA+8kKGHQ69tdORERtxmQyQSNLcAXoK9JltSG1iLX0qXWCehJdtWpVG4dBRORLkiQMHTQIn27/uUXr1ew/iE5FxUhNTW2jyIiIYodWqwUkQJzSjySbWrVcbm4uXFXVaocBAHBUViInK5vnkIiIQmaxWJqcL8sy9FotbCcrvNNcdgcE6gdWSzabm91GQkJCOEKldoqvxokoavXr2xf/XLkCQlECdqYciK20DCUj2X8kEREAaDQaNtkOk+zsbCh2B1x2B7RGg6qxOCqqUJDHmilERBS6xMTE4BbctNn7z2Uz53j//ciab/DII480ueqpzx9EDbX6SbS0tBRbtmxBVVUVTCYTBgwYgLy8vHDERkQdXO/evaGpc8J24iTiM9KbXV5xueAqP44+ffpEIDoiouhXn3T0b7ItFDdr17VQTk4OdLIMR2UltNlZqsbirq5BQd+BqsZARERE1BohJyR//fVXzJo1CytWrPCbd8YZZ+DFF1/Eaaed1qrgiKhj69mzJwxaLWoPlQaVkLQeLYdWCPTq1SsC0RERRb/6PiTra0Q2JIRgDckWys7OhlaW4aisQoLKCUmlugbZ2dmqxkBERLGttra22WWEEHA4HFAUBSdOnEBaWhpkWa5vzq3XRyBKas9CehI9dOgQRo0ahaNHj6Jnz54YO3YssrOzcfToUaxduxbLly/HmDFj8MMPP6CgoCDcMRNRB2EymVBUUIDSQ4eRObB/s8vXHCpFnE6Pbt26RSA6IqLo52myrSinJiQVyEF2hUH1MjIyoNdqYa+oVDUOxe2Gq7aWCUkiImqVYPt3TExMhKIocLvd3oQkUTiElJCcO3cujh49ildeeQU33HCD3/wFCxbgxhtvxIMPPohXX3211UESUcc1uF9/7Fq/Lqhlaw6WonfXrjAajW0cFRFRbNBoNIAkBaghyT4kW0qWZWRmZOK4ygPb1FXXQAMJWVnq1tIkIiIiao2QUtv/+c9/cOGFFwZMRgLAjBkzcMEFF+CLL75oVXBERH369IHraDmEW2l22bqyoxjUv/malEREHYV3UJtTa0gqTEiGIj83B47KKlVjcFRWQSvLrCFJREREMS2khGR5eTl69+7d5DK9e/fGsWPHQgqKiMijd+/e0CoCliNHm1zO7XDAeexEs/cmIqKOxDNwTaAakmxy1XL5OblwVatbQ9JRVZ+QZA1JIiIiimUhPYlmZGRg69atTS6zbds2ZGRkhBQUEZFHz549EW8woPrAwSaXqzlUCoMso1+/fhGKjIgo+tU32QYURfhMF0LhKNshyM7Ohqi1qBpDXVU1EhMSg+77i4iIiCgahZSQPPvss/HZZ59hwYIFAee//vrr+Oyzz3DOOee0KjgiIoPBgL49e6Fm/4Eml6vefxApJhM6deoUociIiKKfLMuQIAHi1ISkYEIyBJmZmXBW10KccjwjyVFdg1w21yYiIqIYF/KgNp9//jluvPFGPP300xg3bhyysrJw9OhRrFmzBlu3bkV6ejoeeOCBcMdLRB3Q0MGD8d17i5tcpnb/QZw9aDCbIBIRNeCpIdmwybYQ9f/h/bLlMjMzIbndcFlt0CXEqxKDo7IKeXlFquybiIiIKFxCSkgWFBTg66+/xqxZs7By5Uq/5tunn346XnrpJRQUFIQlSCJq/yyWxpvA9ejRA+6qaliPlkM2/DaCtsvugED94Az2/YfQ9/yLG90Om7YRUUdUX0MSUJSGCUnhnUctk5GRAa0sw1FdrVpCUtRakMP+I4mIiCjGhTy8YteuXbFixQocOnQImzZtQnV1NUwmEwYMGMBEJBG1WGJiYrPLlM75g8/vy2bO8fl99uzZmD17dsB11WxeR0SkFm/S0aeGpAJIYJPtEGRmZkIry6irrgFy1Gk2rVgs7KediIiIYl5ICckJEyZg9OjRePDBB5Gfn4/8/Pxwx0VERERErfTbKNsNXsoIAQkSa0iGID09HRpPQlIFQlHgqqlFZmamKvsnIiIiCpeQEpLff/89hg8fHu5YiKgDq62tDXpZRVFw7NgxZGRk8As1EVETJEmCJEkQ+C0hKVBfW5I1JFtOq9UiLTUVtSolJJ1WG2QhWEOSiIiIYl5ICcmePXti3759YQ6FiDqylvTxqCgKLBYLEhISmJAkImqCt4ZkgD4kJUlSJaZYl52RiW3V1arsu666GhpJRnp6uir7JyIiIgqXkL7J33LLLfj000+xbdu2cMdDRERERGESMOkoBCT2IRmynKws1Zps19XUQiszIUlERESxL6SEZKdOnTB+/HgMHz4cd911F95//32sXr0aa9as8fsJxYsvvohOnTrBaDRi8ODBWLt2bZPLOxwO3HfffSgqKoLBYECXLl3w+uuvh7RvIiKi1mAZRtFElmVIUuCBvVhDMjSZGRkQVpsq+66rroFGkpGamqrK/ql9Y/lFRESRFFKT7fHjx9f3RyQE/va3vzX5QOt2u1u07ffeew+33347XnzxRYwaNQovv/wyJk2ahG3btqGwsDDgOpMnT8bRo0exYMECnHbaaSgvL4fL5WrRfomIiFqLZRhFG0+3Fp5+I3/7Nwe1CVV6ejoUi0WVfdfV1CA5ORk6nU6V/VP7xfKLiIgiLaSE5P33399mb9WfeuopzJgxAzfccAMA4Omnn8Z//vMfvPTSS5g/f77f8v/+97+xevVq/Prrr963xcXFxW0SGxERUVNYhlG08T6vnTLKts88apH09HS4aiwQQkT8GDpralHE5trUBlh+ERFRpIWUkJw7d26Yw6hXV1eHjRs34p577vGZPnHiRHz77bcB1/n0009RUlKCxx9/HG+99RYSEhJw4YUX4qGHHkJcXFzAdRwOBxwOh/f36v91TK4oCpQGnb5HG0VRIISI6hjbO56D6MDzEB1i4TxEMjaWYY2LhWulvZIkCbKsARrmzSQBjaa+diTPSculpKRAA8Bts0MXH/jvNBDplH+Hksp01VqQm53P89ZKsXBPYvkVHWLhWukIeB7Ux3MQHWLhPLQktpASkm3l+PHjcLvdyMrK8pmelZWFI0eOBFzn119/xddffw2j0Yh//etfOH78OGbPno2TJ0822ofJ/PnzMW/ePL/px44dg91ub/0HaSOKoqCqqgpCCDazUgnPQXTgeYgOsXAeamoiN/AEy7DGxcK10l7V1NSge/euSM80eadlJRvQs0d3uFwulJeXqxhdbIqPj0efHj2Q41JgkIJ/lHZKvzVlzZM00LVgXQ+RkYUunTvzvLVSLNyTWH5Fh1i4VjoCngf18RxEh1g4Dy0pv1qVkLRYLPjkk0+wefNmVFVVwWw2Y8CAAbjooouQkJAQ8nZPbf7SVJMYRVEgSRLeeecdmM1mAPVNDi677DK88MILAd/Q3Xvvvbjzzju9v1dXV6OgoAAZGRkwmUx+y0cLz2fNyMiI2ouvveM5iA48D9EhFs6D0WiM+D5ZhvmLhWulvdLr9di5Yxds+k7eaUdO2rD9lx0wGAzIzMxUMbrYZLfbsWvXLtQeHwizKfjnXZf4rV/1UuGGRrS8r71t27bi9B69eN5aKRbuSSy/okMsXCsdAc+D+ngOokMsnIeWlF8hJyQXL16MOXPmoLKy0mfkRkmSkJycjBdeeAFTpkxp0TbT09Oh0Wj83sSVl5f7vbHzyMnJQV5enrcgBICePXtCCIFDhw6ha9eufusYDAYYDAa/6bIsR+1J9ahvehX9cbZnPAfRgechOkT7eYhkXCzDmhbt10p7JcsyFKFAUX57VhPitwdano+WS0tLgwTAUVML/7HLGydO+XdL1vVwWqxIS0vjeQuDaL8nsfyKHtF+rXQUPA/q4zmIDtF+HloSV0if4LPPPsM111wDu92O2bNn491338XKlSvx7rvv4uabb4bdbsc111yDJUuWtGi7er0egwcPxrJly3ymL1u2DCNHjgy4zqhRo3D48GHU1tZ6p+3cuROyLCM/P7/lH46IiCgELMMoGnlqNzV8eSw4qE2rJCYmwqDXwxnhkbYVlwtum907gAhRuLD8IiIiNYSUkHz44YeRlJSETZs24bnnnsPkyZMxbtw4TJ48Gc8//zw2btyIhIQEPPTQQy3e9p133onXXnsNr7/+OrZv34477rgDBw4cwKxZswDUV/WfNm2ad/mrrroKaWlpuO6667Bt2zasWbMGd911F66//vpGO1QmIiJqCyzDKNo0lnSUmphHTZMkCakpqRFPSDotVmhkmQlJahMsv4iIKNJCarL9008/Ydq0aejWrVvA+T169MCUKVPw1ltvtXjbV1xxBU6cOIEHH3wQZWVl6NOnD5YuXYqioiIAQFlZGQ4cOOBdPjExEcuWLcMtt9yCkpISpKWlYfLkyXj44YdD+WhEREQhYxlGsYQJydBlpKXhSE1t8wuGkbPWAo0kISUlJaL7jRWWFiSIFUWB1WqFxWIJumlZa/rHjwUsv4iIKNJCSkiaTCYkJyc3uUxycrJPnyItMXv2bMyePTvgvIULF/pN69Gjh18TAyIiIjWwDKOoxybbrZaRng7nwb0R3afTYoFWlpmQbERiYmKbbr9htwftFcsvIiKKpJCabF9wwQX4/PPP4Xa7A853uVxYsmQJLrzwwlYFR0REREShY9KxbaSnpkJYbRHdp9NigUZiQpKIiIjah5ASkk888QSMRiMmTZqE77//3mfeunXrMGnSJMTFxeGxxx4LS5BERERERNEiOTkZwmaP6D6dtRYYjQYYjcaI7jdW1NbWNvlTU1ODPgN644xZw73rXPfcJehzenfMmzev2fWJiIgovEJqsj1o0CDU1dVh06ZNWLFiBXQ6HdLS0nDixAk4nU4AQE5ODgYNGuSzniRJ2LNnT+ujJiIiIiJSSXJyMtxWa0T36bRakZHCAW0a01wfjzabDUISKOyT453WfVQxdny9H06ns933EUlERBRtQkpIKooCnU6HwsJCn+k5OTk+v5/a10pH6HuFiIiIKPrwGSyckpOTodjtEIoCKchBUVrLabEiPY0JyVDZbDYIoUBv1PlM1xpl1oAkIiJSQUgJyX379oU5DCIiIiJqO+xLMpySk5MhCcBls0OXEB+RfTotVqQXdIrIvtojq9UKAQGdUeMzXW/UwmoLfoRuIiIiCo/IvNIlIiIiImonkpOToZFlOCPYbFvY7EjjgDYhs1qtEEJAa/Stj6Ez6lBrYQ1JIiKiSGNCkoiIiIioBZKTk6GRJLgsEexH0maH2WyO3P7aGbvdXl9D0uDbZFtn0MBmj+yI6URERMSEJBEREVG71VT/3ezbO3RmsxkaKbI1JN1MSLZKfUJSgU7v22Rba9AyIUlERKQCJiSJiIiIOhKpvj9JJiRDZzabIUsSnBGqISmEgNtmZUKyFRwOR32Tbb1vk22NTgO7w65SVERERB0XE5JERERE7VRjSUfRxDxqnlarRWJiAlzWyNSsczvqAEUgOTk5Ivtrj+rq6gAAGp3v1x+dQQuHw6FGSERERB0aE5JERERE7ZQn6ShJv42yLbGGZFikmJMj1mTbZbVCI0kwmUwR2V975HA4ICCg1fk22dZoZTidTpWiIiIi6riYkCQiIiJqp4QQ9dUhITW3KLVQSkoyXLbI1JB02WzQSDKbbLeC0+mEgICk9f36o9FpvLUniYiIKHKYkCQiIiJqp4QQEGANybaQmpwSsSbbTqsNsiwxIdkKTqcTkiRBln2T8xqtDEVxQ1EUlSIjIiLqmLTNLwIsWrQo5B1MmzYt5HWJiIiIKNyYkAyH1ORkKHt2RmRfLqsNGokJydZwuVyQtf51MWStDPG/+Xq9PvKBERERdVBBJSSnT5/u82Y9GEIISJLEhCQRERGRSrxJx1NqSAoB1ghrJZPJBERoMBSXzQatRoP4+PiI7K89crvd0Gj8v8/IsgQIAbfbrUJUREREHVdQCck33nijreMgIiIiojCrTzoK3xfLLXzJTIGZTCYodntE9uW0WmE2m1tcQYB+43a7IWn8a0h6pjFBT0REFFlBJSSvvfbato6DiIiIiMKsqWbZTMC0jtlshtsWmYSky2aDmSNst4rb7Q6Yi5clCeJ/84mIiChyOKgNERERUTvlSTpKDR756v8tmJBsJZPJBMVRB8XlavN9uax2pJiT23w/7ZkQAnKgGpL/G+SGfw9ERESRFVQNycZs2rQJixcvxi+//AKr1Yrly5cDAPbv34/vv/8eZ555JlJTU8MSKBERERG1jKIoECJwK20mYFrHZDJBI0tw2ezQJyW26b5cNhtSM3PbdB/tXWO1hesTkkzQExERRVrICcm7774bf/vb37yFe8M+bYQQuOqqq/C3v/0Nt912W+ujJCIiIqIW+21QmwY1w/7XRJWjbLdOUlISZEmGy2Zr84SksDuQzBG2W0VRlCa7T+XfAxERUWSF1GT7jTfewJNPPonzzz8fP/74I+69916f+cXFxRg6dCg+/fTTsARJRERERC3n6RdPapCQlCQZ4CjbrWY2m6GRJLhstrbfmcNRP6o3hUw0UlXYU6mCCUkiIqLICqmG5IsvvoiePXvin//8J7RaLfR6vd8yPXr08DbhJiIiIqLIE0JACIGGaRgJ7DMvHOprSNY32W5ris3OhGQYBKwhyYHLiYiIVBFSDclt27bhrLPOglbbeD4zKysL5eXlIQdGRERERK3jrSEpN6whKUGwz7xWM5lM0PyvyXZbc9tZQ5KIiIjal5ASklqtFnV1dU0uc/jwYSQmtm1/OkRERETUOO8o2w2qhklyfZNtT7KSQmMwGKDX69q8hqTidkM465CUlNSm++no2GSbiIgoskJKSPbt2xcrV65s9M26Z8TtwYMHtyo4IiIiIgqdoigQ8O1DEmyyHRaSJMGUZGrzGpIuqw2yJLOGZBtpaqAbIiIiajshJSSvv/567NixAzfffLNfTcnq6mpMnz4dR44cwcyZM8MSJBERERG1nNvtxv8ykt5p9f+UmJAMA7PZBJe1jROSNhtkSWINyVZqrgYka0gSERFFVkiD2lx//fVYsWIFXn31VSxevBjJyckAgKFDh2L79u2wWCyYPn06LrvssnDGSkREREQtUF9DUvg02Qbqa/exyXbrpZiTsctW3ab7cNls0EgSa0gSERFRuxJSDUkAeOedd/Dyyy+jU6dOKC0thRACGzZsQGFhIV566SW8/vrr4YyTiIiIiFrIk3SUZd9HPkmWWUMyDFLN5gjUkLRDZkKy1VgDkoiIKLqEVEPSY+bMmZg5cyZsNhsqKipgMpk4kA0RERFRlPA22capNSRl1pAMA7PZDDiaHuixteprSMpsst1KQohT/wx+mwcmK4mIiCKtVQlJj7i4OMTFxYVjU0REREQUJt5BbU6pIQk22Q6LpKQkCIejTffhstmh02lhNBrbdD8dwaldFzScxhqUREREkRVyk20iIiIiim71SUcBWTqlyTZrSIaFyWSCsLd1QtIGs8kcMJlGwWu0hqTUYD4RERFFTFA1JGVZDukhSJIkuFyuFq9HRERERK3ndrshhH8NSZl9SIaFyWSC22Zv0324rDaYTGyu3VpC+A/uBACSXD+Nfw9ERESRFVRCcuzYsX4FeEVFBX788UdoNBoUFBQgKysLR48excGDB+F2u9GvXz+kpKS0SdBERERE1DxPLchTE5KSJPOlcRgkJSVBcTqhuFyQtWHpCcmPy25Hijm5Tbbdkbjdbm/ysSEJTEgSERGpIagnp1WrVvn8fujQIYwaNQpXXXUV/vrXv6KwsNA778CBA7j33nvxzTff4PPPPw9rsEREREQUPG8NSb8m2+xDMhxMJhM0sgSXzQ59UtsM7Oiy2pCWld8m2+5IFEWBFKCzKkkjAYIJSSIiokgLqQ/JP/zhD8jJycHbb7/tk4wEgMLCQrzzzjvIzs7GXXfdFZYgiYiIiKjlPH1InpqQBPuQDIukpCTIkgyXzdZm+xCOuvrRvKlVGqshKWtkCAj+PRAREUVYSAnJ5cuX44wzzmhymQkTJmD58uUhBUVEREREredyuQL2IclBbcLDZDJBI0ltmpCEw4GkJPYh2VoulwuyJlBCUvLOJyIiosgJKSFpt9tRVlbW5DKHDx+GrS0fzoiIiIioSZ4kiyRpfKazD8nwMJlMkKX6JtttRbHZYTKZ2mz7HUVjCUmNVgMBAafTqUJUREREHVdICcnBgwfj3XffxXfffRdw/rfffov33nsPQ4YMaVVwRERERBS6+j4k2WS7rdTXkGzbJttuu4MJyTCoq6uDpPX/6iNrZQCCCXoiIqIIC2k4wEceeQRnnHEGxowZgwsuuACjR49GZmYmysvLsXbtWnz++efQarV4+OGHwx0vEREREQXJ5XJBoH4Qm4YkmTUkw8FgMMBg0LdZDUnF5YJw1jEhGQZOpxMavX8NSa1eAwHA4XBEPigiIqIOLKSE5OjRo7F06VLceOON+OSTT/DJJ59AkiQIIQAAnTp1wiuvvIJRo0aFNVgiIiIiCp7L5YIsy5BOzcOwyXZYSJIEU1ISqqzWNtm+y2aHLMlMSIaB3W6HrA2QkNRpACGYkCQiIoqwkBKSAHDGGWdg9+7d+Prrr7FlyxZUVVXBbDajf//+GD16tN+beCIiIiKKLJfL5d9cG4AEJiTDJSU5BSfaqIaky2qDLEkc1CYMrDYrdEad3/T6GpJMSBIREUVayAlJoP6t8JgxYzBmzJhwxUNEREREYeJyuSDJGv8ZksSEZJgkm01w1Va1ybZddhs0kgSz2dwm2+9IrHYrtEn+fws6gxYCCgfjJCIiirBWJSSB+gfdnTt3oqqqCiaTCd27d4dW2+rNEhEREVEruVwuyIFqSEoaJiTDJC05Ba5jR9pk204rE5LhYrHUQp/hX0NSo9NA1sqwtlGzeyIiIgospFG2AaCiogI33ngjkpOT0bdvX4wePRr9+vVDcnIybrzxRpw4cSKccRIRERFRCzmdzoA1JCVZhtPpVCGi9sdkMgH2tmnuW99kW2aT7TCwWC3Qx/knJAFAZ9TCYrFEOCIiIqKOLaSqjBUVFRgxYgR27tyJtLQ0jBkzBtnZ2Th69Cg2bNiA1157DatXr8Z3332H1NTUcMdMREREREGob7IdoIakLMPpZA3JcDCbzUAb9T/ostlgNBqg1+vbZPsdSU1tDdISAn8v0cfrUFtbG+GIiIiIOraQakg+9NBD2LlzJ+69917s378fX3zxBd544w0sXboU+/fvx3333Yddu3bh4YcfDne8RERERBSk+kFt/GtIyrIGdXV1KkTU/pjNZrjbalAbmw3J5uQ22XZHIoRAbW0tDPGBa0jqE7SoqamJcFREREQdW0gJyY8//hinn346HnnkEcTHx/vMi4uLw0MPPYQJEybg448/DkeMRERERBQCl8sFWRM4Ickm2+FhMpngttkghAj7tp1WG1KSk8O+3Y7GYrHA7XbBkGgIOF/HhCQREVHEhZSQPHz4MIYPH97kMsOGDcPhw4dDCoqIiIiIWq8+6RioybYGdXVMSIaD2WwGFAGlDWqcuixWpHBAm1arqqqCAgXGxMBN3/UJWlRUVkQ4KiIioo4tpISk2WzG/v37m1xm//79HBGQiIiISEWNDWoja7RwtFG/hx2NyWSCRpLgtNrCvm233Y60lJSwb7ejqayshCIUxCUZA86PMxlxouJ4hKMiIiLq2EJKSI4fPx4ffPABli9fHnD+ihUr8MEHH2D8+PGtiY2IiIiIWsHpdEKS/B/3ZFkDB/uQDAtPQtLVBglJ2B18wR8GFRUVEEJBnClwk+14kxEVlScjHBUREVHHFtIo2w888ACWLFmCs88+G+eeey7GjRuHrKwsHD16FKtWrcIXX3yB+Ph43H///eGOl4iIiIiC5HQ6gYAJSRkuF0fZDofk5GTIsgyn1Rr2bQubnQnJMKioqIACN+KSjHC73H7z40wGnDh5BEIISJKkQoREREQdT0gJyV69euHLL7/E9OnTsWTJEixZsgSSJHk78+7SpQsWLlyI3r17hzVYIiIiIgpefQ3JwIPaOBysIRkOv9WQDH9C0m1nQjIcTpw4AUOCHlq9JmBCMiElDk5nHWpqamAymVSIkIiIqOMJKSEJACNHjsSOHTvwzTffYNOmTaiurobJZMLAgQMxatQovl0kIiIiUlldXV2jfUg6bRzUJhwSEhKg1WjC3oekcCtws4ZkWJw4cQJxyYGbawNAvDkObuHG8ePHmZAkIiKKkJATkgAgSRJGjx6N0aNHhyseIiIiIgoTu90BWROohqQWDidrSIaDJEkwm82whDkh6bTZoJElJsjCoLy8HMYUXaPzE1PjoEDB8ePH0blz5whGRkRE1HGFNKgNEREREUW/uro6yLL/+2dZo0EdB7UJm5Tk5LA32XbZbJAlmTUkw+DosSOITw48wjYAJKbGQxFuHDt2LIJRERERdWwh15Csra3FggULsGXLFpSWltZ3mn4KSZKwYsWKVgVIRERERKGxO+oC15DUaAM+u1FoUpOTsctSHdZtuqxWaCQJycnJYd1uR3Sk/AgSB8U3Ol+r18KQoEN5eXkEoyIiIurYQkpIbty4Eeeccw5OnjzpHcgmEPYjSURERKSeuro6aDQBakjKGihuBW63G5oACUtqmfSUVLiPHw3rNp2W+oQka0i2jqIoOFp+FP1Si5pcLj7NyIQkERFRBIXUZPuWW25BRUUFHn30URw4cABOpxOKovj9uN3+o9gRERERUWTY7XbIAQa10Wh1EELA4XCoEFX7k5KSAmGzh3WbLmt9k232Idk6FRUVqHM6kJSe0ORycal6lJWVRSgqIiIiCqmG5KZNmzBlyhTcdddd4Y6HiIiIiMKkzumErPUfzEOWNRAAm22HidlsDntC0mmxIiEhATpd44OxUPOOHDkCt3DDlJHY5HJJGQkoPXQoQlERERFRSDUk09LSkJGREe5YiIiIiCiMHHZH4CbbGi1rSIaR2WyGO9wJSasVqew/stXKysqgCDdMGU3XkExKi8fhI4cjFBURERGFlJD83e9+h6+++gqKooQ7HiIiIiIKE0edI+Ao2xqNFkKAI22Hidlshttuhwjjs7HLakNqakrYttdRlZWVQWuUYUjQN7mcKSMRVVWVsIZ5tHQiIiIKLKSE5F//+lcYDAZcffXVKC0tDXdMRERERBQGDoej0VG2hRBMSIaJ2WyGDMBls4Vtm06rFekpqWHbXkdVVlaGhPS4ZgfbNGUmwi3c7EeSiIgoQkLqQzIxMREvv/wyzjjjDLz//vtITk4OOAKgJEnYs2dPq4MkIiIiopbxJBw1Gv8+CDUaHYQAm2yHSXJyMjSyDKfFCl1C002DgyVsdqSwyXarlZWVIT6t6dqRAGDKSIDyv4Rkly5dIhAZERFRxxZSDckVK1Zg1KhRqKyshFarRXx8PIQQfj9s0k1ERESkDqfTCSEAWcs+JNtacnIyNJIElzV8NSRhsyOZCclWO1C6H0mZTQ9oAwAJKfGAVuDwYfYjSUREFAkh1ZD84x//CCEE3n33XVx22WWQ5ZDymkRERETURux2O4QQgWtIanVMSIaR2WyGRpLhDGP/g26bPWALJAqeEAKlZaXoUtL8YJyyLCExPZ5NtomIiCIkpEzitm3bcM0112Dy5MlMRhIRERFFIYfD8b+EZOBBbRQ22Q4bs9kMWZLgtIQnISmEgNtmZQ3JVqqpqUFtbU2zI2x7xKfr2T8+ERFRhISUTczIyEBcXFy4Y/F68cUX0alTJxiNRgwePBhr164Nar1vvvkGWq0WAwYMaLPYiIiImsIyjKJFfUKyvjbkqTw1JO12uwqRtT9arRaJiQlwhamGpNtRB0kRrCHZSmVlZXALF0wZzTfZBoCk9HgcOnywjaOKXiy/iIgokkJKSF599dX44osvYAvjSIIe7733Hm6//Xbcd9992LRpE8aMGYNJkybhwIEDTa5XVVWFadOm4Ywzzgh7TERERMFgGUbRxNtkO0BCUtboIMCEZDilpqSErcm2y2qFLEmsIdlK9QlJN0xB9CEJ1I+0fajsUBtHFZ1YfhERUaSFlJCcO3cu+vTpg7PPPhtff/01amtrwxbQU089hRkzZuCGG25Az5498fTTT6OgoAAvvfRSk+vddNNNuOqqqzBixIiwxUJERNQSLMMomjgcDihCQA7Qh6Qsy5AkmU22wyg1OSVsTbadFis0ssyEZCuVlZVB0gHxZmNQy5syE3Gy4mSHTNSz/CIiokgLaVAbT3NtIQTGjRvX6HKSJMHlcgW93bq6OmzcuBH33HOPz/SJEyfi22+/bXS9N954A3v27MHbb7+Nhx9+uNn9OBwOnwfw6upqAICiKFE9MriiKBy9XGU8B9GB5yE6xMJ5iGRsLMMaFwvXSntks9kgSTK0Wi2kBtOl//3odEbYbDaelzDJSEuDsn+Pz7FuKNA5aIzLaoVOo4HJZOL5aYWysjIkpSdCggSI/00UDRYQvr8npScCECgrK0NRUVHkAg2A5Vd0YPkVHXge1MdzEB1i4Ty0JLaQEpJjxoyBJDX1GBWa48ePw+12Iysry2d6VlYWjhw5EnCdXbt24Z577sHatWuh1Qb3cebPn4958+b5TT927FhUvxFVFAVVVVUQQnAwIZXwHEQHnofoEAvnoaamJmL7YhnWuFi4Vtqj6upq9OjeDQVZJogGz20ZyRro9Br06tUDQgiUl5erGGX7UVxUhB42CwqkwH/LTum3l/R5kga6RpYDgCS9ERnduqOuro7npxUcDgd69+mFuNoU7zTZVuf9d5wlBQah9/6ebzLitNNOYP/+/W3aX34wWH5FB5Zf0YHnQX08B9EhFs5DS8qvkBKSq1atCmW1oJ2a7BRCBEyAut1uXHXVVZg3bx66desW9Pbvvfde3Hnnnd7fq6urUVBQgIyMDJhMptADb2OKokCSJGRkZETtxdfe8RxEB56H6BAL58FoDK6ZXjixDPMXC9dKe/TTTz9h2/ZfkNr1XJ/pxyrd0Ord2LVnH6qqqpCZmalShO3Ptl9+gXT2+IDzXMLt/XepcEMjGm9FVHq4FOLAfuTn54c7xA7l520/ozKtFLbE32o7OuTfEpK2hAooCb8lJBWjgu27t6GyslL1vwuWX9GB5Vd04HlQH89BdIiF89CS8iukhGRbSU9Ph0aj8XsTV15e7vfGDqjPvG7YsAGbNm3CnDlzAPxWhVWr1eLLL7/EhAkT/NYzGAwwGAx+02VZjtqT6iFJUkzE2Z7xHEQHnofoEO3nIZJxsQxrWrRfK+2R3W6Hy+2GrNXB7XJ6pwvvjwZ2u53nJEySk5Phstl8WgQ31ERLYT9OixWpJjPPTSuVHjmEzG7x/u3lG/67we8anQxDkg5Hjx5V/diz/IoeLL+iA8+D+ngOokO0n4eWxBVVn0Cv12Pw4MFYtmyZz/Rly5Zh5MiRfsubTCb89NNP2Lx5s/dn1qxZ6N69OzZv3oxhw4ZFKnQiIurgWIZRtLHZbABkyLIm4HxJo4vaZpKxyGw2w221QYimUo3BcVosSE1JaX5BapTL5cLJkyeQlJbQovXiUw04evRoG0UVnVh+ERGRGqKqhiQA3HnnnZg6dSpKSkowYsQIvPLKK/+/vfuOr6q+/wf+Onev3NxskrDC3iCgRZwoiqJWqYq1jtpSq7XWXSulVWuV1jqqv1ra+q3WVQWLE0UREZA9wwgjgywIgexx9zjn90ckgmTcfc7NfT0fjzzMOOMVPzd87n3fz0B1dTXuvPNOAB1D/WtqavDGG29ApVJh3Lhxp5yfnZ0Ng8Fw2veJiIhijX0YKYnb7YZGq+v254JK803RkqIhNTUVgiQh4PZAY4xsuq3P6UJmdl6UkiWnhoYG+EU/UjJMIZ1nStfjeF1yFSQB9l9ERBR/iitI3nDDDWhsbMQTTzyB2tpajBs3DsuXL+/c6a62thbV1dUypyQiIjod+zBSEqfTCbVa2+3PVWotHA5HHBP1bTabDWpBgM/pjLggKbrcHCEZobq6OoiSCHN6aAVJc7oJRytqYpRKudh/ERFRvCmuIAkAd911F+66664uf/baa6/1eO7jjz+Oxx9/PPqhiIiIgsA+jJTC5XJB1UNBUqPRo93OgmS0pKZ2rPnodzqBjPTILuZ2w2azRSVXsmpoaIAoBWAJsSBpSTeielty7mzO/ouIiOJJUWtIEhEREVF0uFwuCD0VJLVa2DlCMmq+HSEZ+TR40elCampqFFIlr4aGBkANGCzdL1vQFXOaCW1trfB6vb0fTERERGELa4Tk119/DavVikmTJkU5DhERySXUqZuiKMLpdMLhcAS9m5rZHNrmAkQUPofDAZW6+2KMWqOHw94Sv0B9XGpqKtSCCj6HM+JrBVxuFiQj1NjYCJNND0EQej/4JGabAQEpgObm5i53mCYiIqLoCKsgOWPGDNx55534+9//Hu08REQkE4vFEvN7RGP3WSIKTkdBsqcRkjrYOWU7avR6PfR6XceU7QiIPj8kv48FyQg1NjbCmBra6EgAMNuMECURjY2NLEgSERHFUFhTtrOzs6HThd7BExEREVF8tLfboe5hl22NVg+HkwXJaEqzpcEf4c7lPqcTKkHFgmSEGhsboUsJfeyFMdUASRLR1NQUg1RERER0QlgjJGfNmoW1a9dCkqSQp0EQEZEy2e32Xo85evQorrr2CuSeacOaV7cBAK566EKUrK3GL2+5Dz/96U9jHZOIgtRmt0Or1Xf7c41WD5fLBVEUg152gXqWZrPheIRrSPqdLqgFAVarNUqpklNjSyOMttB3OzdZDRDRMWWbiIiIYiesZ58LFy5EY2Mjfv7zn/PdQyKiPsJsNvf6UV1dDVEI4Pxbp3aed86PzkDWcBsqKyt7PZ+I4qe9rR2aHgqSWp0eoijBGeEUY/pWus0Gf4RrSPpdLqgFFXfZjlBjUwOMKaEXJNVaNbRGLVpaWqIfioiIiDqFNULy5ptvhs1mw6uvvoq33noLBQUFyMnJOW20pCAIWLVqVVSCEhGR/IqLi2Gw6WBKPfVFXlZBGvbu3CNTKiLqit3ugCYlr9ufa7R6SJIEh8MRlzVkk0G6zYZATVVE1+iYss0RkpFqaW3BIGtaWOfqLVq0trZGORERERGdLKyC5Jo1azo/93g8OHjwIA4ePHjacZzOTUTUtxw8eABpg04vXGQXpKPk0yK0tbXxRTSRQtgddtgyepqybYAoSrDb7dy8I0pSU1MBtyeia/hdLqhVAlJSUqKUKvmIooj29jYYLP3COt+QwhGSREREsRbWlG1RFIP6CAQC0c5LREQy2le8D1kFp484yRycDr/kQ3FxsQypiOi7vF4vPB4vtLrup6yemLLtcHBjm2ixWq2QXO6IruF3umCxWKBWq6OUKvnY7XYERBFGS/cF+Z5ozRqOkCQiIooxrmBORERBaWlpwbG6Y8gafHpBMj3PCkkjobS0VIZkRPRddrsdoihBq+2pIGmAKElob2+PY7K+zWq1IhDhCEmf0wWblTtsR6KtrQ0SROjN3e8y3xO9RYvWtpbohiIiIqJTRKUg6XA4UFtby3fYiYj6sJKSEvglH7IGp5/2M5VahbQBFpSUlMiQjIi+y263Q5QkaHoYIXliyjYLktHTUZB0QxLFsK/hd7lgs7EgGYn29nZIkgiDJcyCpFmHtva2KKciIiKik4VdkPT5fFi4cCFGjBgBq9WK/v37w2q1Yvjw4Vi4cCG8Xm80cxIRkczKysogaCXYcrteIzJ9YAoOlhyIcyoi6kp7eztEUYRW1/2UVbVGCwkCC5JRZLVaoQIQ8IQ/StLvciOdO2xHpL29HSJE6MIcIakz6dDCEZJEREQxFdamNi6XC5dccgk2bdoEtVqNESNGoF+/fjh+/DgOHTqE3//+9/jkk0+watUqGI3GaGcmIiIZlJSUwNbfApWq6w3LMgenYd+WUgQCAa59RiSztrY2iKIEnd7U7TGCIECrM7AgGUVWqxUqQYDP6YImzOfAotuNtFRbdIMlGbvdDkmSoDeFOULSpMVxe0t0QxEREdEpwhoh+Ze//AUbN27EjTfeiPLychw4cACrV6/G/v37UVFRgZtuugmbN2/GX/7yl2jnJSIimRwsOYC0Ad3v+po1KB0utwPV1dVxTEVEXTlRkOxpUxugY9p2WxunpkZLSkoK1IIKAXcEG9t4vLBaux6JTsGx2+0dIySN2rDO15t0nUVNIiIiio2wCpKLFy/G1KlT8dZbb6F///6n/CwvLw9vvPEGpk6disWLF0clJBERySsQCKC0vLTLDW1OyByUBr/k58Y2RArQUWQUoNb0PEJMxYJkVKWkpEAlCPBHsNO25PHAYrFEMVXysdvt0OjVUGvCW51Kb9IiIAbgiWDqPREREfUsrF66srISM2fO7PGYiy++GJWVleFcnoiIFKaqqgoutwNZBadvaHOCMUUPY7qeG9sQKUBbWxs0OgMEoeslFk5QqXVobW2NU6q+z2q1QqWKrCAputxISel+NDr1zm63Q28Kb3QkAOjMOkiSyA07iYiIYiisgqTJZEJ9fX2Px9TX18Nk6n7dIiIiShwHDx6ET/Iha1D3IyQBwDbIjAMHubENkdxaW1uh1fW+hqFWZ0JTU0vsAyUJs9kMtSDAH+aUbUmSEHB7OGU7Qg6HA1pDWEvlAwB0Ri0kSYTdbo9iKiIiIjpZWAXJadOmYfHixdi3b1+XP9+/fz+WLFmCs88+O6JwRESkDAcPHoQl2wiDpfsdewEgZ0gG9u7fzXW3iGTW0tIClabn9SMBQKc3oqGxMQ6JkoNarYbJZAq7ICl6fYAkccp2hBwOB7Sm8AuSepMOIiSOkCQiIoqhsHrqBQsWYOXKlTjzzDMxb948XHDBBcjJycHx48exZs0a/Oc//4HP58P8+fOjnZeIiGSwe+8uZA7tfcROv2GZKPqgEkePHkV+fn4ckhFRV4IuSBpMaG6pjUOi5JFiSUFTmAVJv9sNlSCwIBkhu90OjTG89SOBjjUkJXCEJBERUSyFVZCcPn063nnnHfzsZz/D3//+dyxatKjzZ5IkITU1Fa+//jrOOeecqAUlIiJ5eL1e7N2/B+N+OLDXY3NHZsEnerFnzx4WJIlk1NDQBJ2+9ynbOr0JdUeaIYoiVKrwCzj0LWtKCurCXEOSBcnoaGtvg9YYwQhJsw6SJLEgSUREFENh99TXXnstZs2ahQ8//BCFhYVoa2uD1WrFGWecgauvvpqLcRMR9RFFRUVwepzoPzqn12ONKXqk9jdjx44duPzyy+OQjoi6Ut/YCJ0xu9fjdAYz/P4A7HY71y2MktSUFATc7WGdG3B7oGZBMmJt7a3Q23reYb4nOiNHSBIREcVa+G8dArBYLLj55ptx8803RysPEREpzLZt26A2AZmDu99h+2S5YzKxYfP6GKciop40NzfD3G9wr8fpDGYEAiKam5tZkIySVKsV/paGsM4NeDwcIRkFbfY26PuHX5BUqVXQGjQsSBIREcUQ5+YQEVGPvl6/FrnjM6BSCUEdP3hSHqpqKlFdXR3jZETUFY/HA4fDCZ3B3OuxeoMJoiihubk5DsmSQ4rFAsnjDevcE1O2zebe246619beBoM5/IIkAOhMWrS3hzfSlYiIiHoX1AjJN954AwAwZ84cpKSkdH4djFtvvTW8ZEREJLv6+nrsLtqF6XeNDfqcAeP7QVT7sWbNGvYBRDJoampCICBCpzf1eqzeYEFAFNHU1BSHZMnBZDJB8IZXkAx4PBAEASZT721HXZMkCe3t7RhktkV0Ha1Zg9bW1uiEIiIiotMEVZC87bbbIAgCpk2bhpSUlM6veyJJEgRB4ItRIqIEtmrVKvgED4ZM6R/0OTqDFnkTM/DFlyvYBxDJoKmpCQFRhMHU+3reWr0BkiSgsbExDsmSg9lshuTzhXVuwO2B0WDgBkMR8Hg88Hg9EY+Q1Fs4QpKIiCiWgipIvvrqqxAEAbm5uad8TUREfdsny5chb2ImDBZ9SOeNOGcw1v1tJ6qrqzFwYO+7cxNR9DQ2NiIQkKA39r4OoSCooDOYOUIyikwmE8Qwp2wHvF5YODoyIm1tbZAkEfoQ+63v0prVaGlpiU4oIiIiOk3QIyR7+pqIiPqeiooK7Ny7AxfcOynkc4edNQBfGwrxySef4K677op+OCLqVmNjIyRJgj6INSQBQKM3c4RkFJlMJohhT9n2IoUb2kSkpaUFohSAKdUQ0XWMKQY0NvDvgoiIKFbCmg/y05/+FC+88EKUoxARkZK8//77UJklDJ06IORzNToNhpyTh/c+XAq/3x+DdETUnYaGBmj1JghCcE/zVBoj6uvrY5wqeZhMJgS8XkiSFPK5Aa8XJiNHSEaipaUFIkQYUiIbIWm06tHUwoIkERFRrIRVkHz77bdx/PjxaGchIiKFcLlceO+jpRhx4QBodOqwrjHx0hE4UleN1atXRzkdEfWkoyAZ/C7NBlMKao/xeV20mEwmCJIEyR8I+dyAxwuLmQXJSDQ1NUGURJiskY2QNFkNaG7h7vNERESxElZBctiwYaitrY12FiIiUohPP/0Uje11mDhrZNjXyBqcjsxRVrz19ptRTEZEvamrq4NKG1pB8tixuhgmSi4GgwECBATCmLYd8HphNgXfdnS6pqYmqHUq6IzaiK5jTDXA4XTA7XZHKRkRERGdLKg1JL9r3rx5WLhwIWpqapCfnx/tTEREJCNRFPHGW6+j/5nZSM3pfZfenpxxxSh89dxWFBUVYdy4cVFKSEQ9qT12PKgdtk/QG1NQV9MAURS5u3MUGI1GqISOgqQ2xNGOks8Hs9EYo2TJoaGhASZbZKMjAcCcZoQoBdDY2MjXO0REFDMOhyPoY0VRhNPphMPhCPo5m9ms3Dc6wypIzpkzB6tWrcL06dPx8MMP48wzz0ROTk6XO29zd1UiosSybt06lFQexPfnnR/xtYacOQAbsnfj9TdexzN/eSYK6YioN0ePHoPBNDjo440mK3w+PxoaGpCdnR27YEnCaDRCEASIPl/oJ/sDMHGX7Yg0NDTAYNNFfB1zmgmiFEBDQwMLkkREFDOWGG9mF86a1vESVkFyyJAhEAQBkiThnnvu6fY4QRC4mQERUYJ59bVXkD4iBXkjsyK+lkol4Izvj8CK/3yGew7fgwEDQt8gh4iC5/P50NjYiH6ZE4M+x2C2wh8QUVdXx4JkFBgMBqggQPSF8RzY74fBEPnovmR2vO4YjGmRFyQtaUaIUsffBREREUVfWAXJW2+9tcvRkERElNh2796NrYVbMPPXU6N2zTEXDsW2Jfvx1ltvYf78+VG7LhGdrr6+Hj5/AEZzatDnGE2pCAREHD9+nEsrRIFer4cgILwRkj4/9PrIdodOdkePHUXKmMinp+nNOqj1KhYkiYgopux2e48/lyQJV159BTQjfVj1j00AgN99eQe+fHkTRlsm42//76V4xIyJsAqSr732WpRjEBGRErz2+msw5WpRMKV/1K6p0Wkw9rIhWPrRu7jjjjuQnp4etWsT0amOHz+OQECE0WwN+hyt3ggIahw/zp22o8FgMEAQBATCKEhKfh8LkhGQJAm1x2sx+rzIp1gLggBzhgHHjh2LQjIiIqKu9bbGY21tLeqb6nDu5PFY9c33LOkmFEzuj6KPi2AwGKBWq2MfNAa4cjkREQEAqqursXLNCky6aiRUquiOgp90+Sg4xXYsXbo0qtclolMdO3YM/oAY0ghJQRCgM6aw8BIler0eKoQ3QlLyB1iQjEBzczNcbiesWdFZwN+UpcfRo0ejci0iIqJwFBUVwSd60W945infzx2RhXZHG8rLy2VKFrmICpLHjh3DokWLcM8992DevHmd36+vr8fWrVvhcrkiDkhERPHx9ttvQzAHMPr8IVG/tsGix7Dz++O/S96C1+uN+vWJqENtbS3UWgM02tCKWlq9lYWXKNHpdB2b2oSxjjoLkpGpra1FQAogNTs6GwSkZJpRXVMVlWsRERGFY//+/TBl6mFKPXWN6ZwhGfDDhwMHDsiULHJhFyQXLVqEgoIC3H333XjppZdOmcZdV1eHs88+G2+99VY0MhIRUYzZ7Xa8v+w9jL60ABpdbIb8T75yDGobavDFF1/E5PpE1FGQ0RlTQj7PYE5FVfXhGCRKPjqdDgIEiP5AyOeKfh90usg3ZElWR44cQUDyIzUn9L+Brtj6paD6SLWidyglIqK+7cDBA0gbdPobbTqjFqm5Zhw8eFCGVNERVkFy2bJluPvuuzF+/Hh8/PHH+MUvfnHKz8eOHYsJEybgww8/jEZGIiKKsU8//RStriZMuGREzO6RlmdF7oR0vLPk7ZjdgyjZ1dQchUYfejHGaLbhyBGOkIwGrVYb9qY2YiDAgmQEjhw5An2KFnpzdP4f2vqlwO5oR3Nzc1SuR0REFKqDpfuROSity5+lDjCjpKwkzomiJ6yC5DPPPIOBAwdi9erVuPLKK5GdnX3aMePHj8f+/fsjDkhERLElSRKWLF2M/lOyYEk3xfRe42cNR2HRThQXF8f0PkTJqrKqGmZL109ae2JOSUNLS0uvOz1S7wRBgEajgSSKIZ0niSIgStBqtTFK1vdVVlbC2i96/VhanhUByY+qKk7bJiKi+LPb7WhobEBG/67XBs/ob0NZRWmcU0VPWAXJXbt24YorruhxN6D8/Hzu1khElACKiopwoHQ/xl8yPOb3GjKlP9QpEkfQE8WAKIo4UlMDU0roO9mbUtLgD4hcRzJKtFotpBDXkBT9fgiCwIJkBA5VHII1Lzob2gCArZ8VAQRYkCQiIlkcPXq0Y23kbpYiSc2xoKGxAR6PJ87JoiOsgqQoir0+Waqvr+ei3ERECeDjjz+GLl2FgRPyYn4vlVqF4ecPwLLPPubmNkRRVldXB4/HC1NK6CMkTSnp8PkDOHLkSAySJR+tVhv6CMlAAMI351LoJEnCocoypOcHv8N8bzQ6NVKyjKioqIjaNYmIiIJVX18PUQp0O4vNkmGCKAXQ2NgY52TREVZBcuTIkVi/fn23P/f7/Vi7di3Gjx8fdjAiIoo9r9eL5V98guHnDYBKJcTlnmNmDEN9y3Fs2LAhLvcjShZHjhyBzy/CHMYISZ3eBEGlZUEySrQabcib2ogBEYIAqNWx2Visr6urq4Pd0Y6MAbaoXteab8Kh8kNRvSYREVEw2tvbIUKE3tL12sgGsw6SJKK1tTXOyaIjrILkTTfdhJ07d+LJJ5887WeBQAAPPfQQysvLceutt0YckIiIYmfTpk1oaK3HqPOHxO2emQNsSB1owmeffRa3exIlg8OHDyMQkGCy2EI+VxAE6E02FiSjJJw1JCGKADrWn6TQHTp0CH7Jh8woFyTTB9hwsPRAVK9JREQUDK/XCwkSNLqunxuodRpIkBJ25llYz3h+9atfYdmyZXjsscfw5ptvdk7Nnjt3LrZv347KykpceumlmDdvXlTDEhFRdH3++edIHWCK+gu43gw/dyBWvf8lnE4nTKbYbqRDlCyqq6uhM6ZApQ6voKUz2VBRURndUElKrVZDEkMbISmJIgRwhGS4SktLodYLSMmyRPW6WYPSUPJJEdra2mC1WqN6bSIiomQW1ghJrVaLFStW4JFHHkFDQwOKioogSRKWLl2KpqYm/OY3v8HHH38MQYjP9D8iIgqdy+XCl2tXYuj0/nG/94jpg9HuasWaNWvifm+ivqqqqgo6oy3s883WDJSVc628aNBqNZACYeyyDRYkw1VcXIzUAZaoLz+SVZAOv+RDWVlZVK9LRETUG41GAwECRH/Xzyk6vp+4syvCKkgCgE6nw1NPPYWGhgbs378f69evx549e9DY2Ig//elP0Om6nuNORETKsG7dOrQ6mjHy3IK43zs124KM4VZ8vuLzuN+bqK86VF4Z1g7bJ1isGTh27DjcbncUUyUnlUoFSZJCOkcSRQhC4r6okNu+g0XILLBF/brpeVZIGgkHDx6M+rWJiIh6otPpIEBAwNf1rIuALwABQsLW38IqSFZXV6OtrQ1Ax5pDo0aNwvTp0zFu3LjOd3Xb29tRXV0dvaRERBRVyz9bjoyhKbDlpMhy/xHnDsTajWsSdhFmIiUJBAKoqKyEJTUr7GuYrZnw+QN8/hYFKpXqmzUhg3eigMkZRqFzOp0oryxHzpDwC/LdUalVSB9kwYEDXEeSiIjiS6vVAgD83RUk/R3LvSRVQbKgoAAvvvhij8csWrQIBQXxH3VDRES9a21txZr1qzH8vIGyZRgxfTBcfge+/PJL2TIQ9RU1NTVwu72wpGaGfQ1LaiZ8vgAqKyujFyxJqQQVQhsfCUDsOEOlCnsCU9IqLi6GT/Qge0hGTK6fWWDD3v17YnJtIiKiZBXWMx5JknqdhhLqNBUiIoqflStXwum3Y+Q58r1xZLYZkTs+HR9/8pFsGYj6isrKSvj8gYgKkjq9ERq9iQXJKFCpVCHvsi1BggCOkAzHvn37AK2EzIG2mFw/Z1gmDlWUweFwxOT6REREXXE4HJAgQWfUdvlznUEDEVLC9k8xewv2yJEjSEmRZxogERH17MOPP0DexAyYbUZZc4y+cAi279rGKaJEEaqsrAQEDQymyHYB1pvSUF5eHp1QSSysoiKnbIetqKgI6YNToFLH5qVNv+GZ8IrejsInERFRnNTW1kJn1kKr73p9aUuGGaIUwNGjR+OcLDqCXjX7iSeeOOXr7nZGDQQCOHLkCBYvXozvfe97EYUjIqLoO3ToEHbu3YEZ950hdxQMO2sA1hkK8dFHH+FXv/qV3HGIElZZWRn0lvSIi1kmaxYOHCyJUiqi+NixeztyJsdmujYApOenQm0UUFRUhLPOOitm9yEiIjrZrt27kF7Q/UA/U6oBxnQd9u7di5kzZ8YxWXQEXZB8/PHHOz8XBAFr1qzptigJAHl5eXj66acjyUZERDHw3nvvQZ0iYcjU/nJHgUanwdDz8vHeR0tx5513di7cTEShKS07BJMl/OnaJ6TYslBRvgF+v5+7PcuEIyRDU1dXh6PHanDhiEkxu4dKJSBzqBWFuwpjdg8iIqKTtba2YsPmdRh7Q89r/vc/Ixufr/wM9957b8KtQx30M83Vq1cD6Fgb8qKLLsJtt92GH//4x6cdp1arkZ6ejlGjRiXc/wwior7O5XLhw0/ex4gLB0KtVcsdBwAw4dIR+N8Xq7BmzRpccsklcschSjiiKKK4pBQpuVMivlaKLQdH3B5UV1djyJAhUUhHFFu7d++GT/Qib1R2TO+TOyoTO1ZuhyiKfI1DREQx984778Ap2jH6/J6fj42fORwffLUGK1aswOWXXx6ndNERdEHyggsu6Pz8sccew4UXXnjK94iISPk+//xzNLTVY9YlkRcuoiVzYBoyR1qx+N3FLEgShaG2thZ2uxO5tqyIr2W1ZcPnD6CsrIwFyQhIUscGNSH5ZmQkN4YMza5du2DONsCcFts1kfNH5WDv0gqUl5dj2LBhMb0XERElt6qqKvzfa//C2NkFMKUaejy237BM5E/JxHMvPItzzjkHVmtk64nHU1hv7z322GPdFiM9Hg/8fn9EoYiIKPokScI7776D/DMykJqjrE3Hxs8ajk3bN6CsrEzuKEQJp7S0FF5fAKnpuRFfS2cwQaO38G8xQqIoAmGMopPAgmSotu3cipzRaTG/T78RmfALfhQWcto2ERHFjtPpxEMPPwgp1YvvXTchqHNmzDsTR9uqseB3CzqegySIsAqS69evxxNPPIGWlpbO7zU2NuLyyy+HxWKB1WrFggULopWRiIiiYPfu3dh7YDcmXjZS7iinGT5tENRWYMmSJXJHIUo4paWlUGsM0BstUbme3pyBkpLSqFwrWUkIfYSk8M0ZifRCQm5tbW04ULwf/cf0i/m9dAYtMoZasWPHjpjfi4iIkpPb7cb9D9yHoqrdmP3wedAZgltfPyXTjEvu/R6+WLccf/zjHxPmuURYBcnnnnsOr7/+Omw2W+f3HnzwQaxYsQJDhgyBzWbDn//8ZyxdujRaOYmIKEJLliyBMUeDgRPz5I5yGrVGhdEzB+PDTz+A3W6XOw5RQikpKYHekhG1zVBSbNnYt/9AVK6VrERR7JyCHTQVC5KhKiwshDfgwYCxOXG5X96YTGzevomjWImIKOpaWlpwxy/uwNc7V+OKR85F5gBbSOcXTO6PC+6ajLc/fBPzfzsfXq83NkGjKKyC5K5du3Deeed1fu10OvHuu+/i0ksvRXFxMYqLizFw4EAsWrQoakGJiCh8TU1N+OzL5Rh76VCoVMrcwXXCJSPQ5m7G8uXL5Y5ClFD2Fh2AxRa9gow1rR+OHKlBe3t71K6ZbMSACCHEKdsnjmdBMnjbt2+HMVMXt2VIBozrh7rG46ioqIjL/YiIKDkcOHAAN958I3aUbsb3Hzsf/ceE97xuzAVDMfP+M/HRqvfwk3m34ejRo1FOGl1hFSTr6uqQn5/f+fWmTZvgdrvxk5/8BACQkpKCK6+8EgcPHoxOSiIiisiyZcvglhwYO2Oo3FG6ZUk3of/kLLz73hKOPiEKkt1uR1V1dVTWjzwhNSMXXl8AJSUlUbtmsvH7/SGPkBQEAZIksSAZgk1bN6Lf2PS43S9vVDYCgg/bt2+P2z2JiKjvEkURb7zxBm689Ydo0h7F9X++BLnDI9ukcPi0QZjzxIXYV7sL1/3wWnz++eeKfW0VVkHSYDCc8q752rVrIQjCKRvdWCwWNDc3R56QiIgiIkkS3v/wPQw8KwcGi17uOD0aN3MY9hUX4cABThclCkZJSQm8Xj9SM6JXkLSkZiIgCSguLo7aNZNNQBQhhDgaXVCrIQHcHDJITU1NOFhyAIPGR++x3xudQYvM4anYvGVz3O5JRER9U3l5OW776Y/x1PNPoODSLFz/x0uRmh2d9cBzhmbih3+ZBfMYAfc9cg8efOhB1NXVReXa0RRWQXLYsGH4/PPP4fF44PP5sGTJEowZMwb9+n27oHR1dTWys7OjFpSIiMJz4MABlFQUY8yFQ+SO0qtBE/OgsYLTtomCVFJSgoAkwJKaGbVrCoIKxpQsvjEQAb/fB0GlDumcEwXMQCAQi0h9zrZt2+AVPRgQx4IkAPQfn43N2zaynYiIKCwulwt/+9vfMGfu1Sg6thPff+J8XPDjqdDoQnve0BuDRY8rHjgfF98/BV9s/RTf/8GVeOeddxTVf4VVkLz99ttRVlaG4cOHY/To0SgrK8Ntt912yjFbtmzBmDFjopGRiIgi8OWXX0JjFTAwzi/awqFSqzDk7Dx8vnI5py0SBWH//v0wWDKhCrH41Rtzajb27N0X1WsmE5/fD0Ed6hqSakiSxBGSQdq8eTNSB5hhSTfF9b6DJuShua0J+/fvj+t9iYgosUmShC+//BLfn3MV/v76ixh1TT5+9Oxs5I+K7UC+4WcPws0vzkbmNBMef/r3+OGPbkBhYWFM7xmssAqS8+bNw69//Ws4nc6OnYDuuAP33Xdf589Xr16N8vJyXHzxxdHKSUREYVr51RcYOCUbqhBfHMtl2LRBqDlew9FZREHYvacoqhvanJCakY+yQ+VwOp1Rv3Yy8Pv9UKlDHCH5zfEsSPZOkiSs2/Q18idEts5WOPoNz4Rg7FhDn4iIKBhVVVX4xV134u6H7oI/tx0//OssnD13UtRHRXbHYNFj5s+n4Qd/uhCHfWW46ac34ne/+x0aGxvjcv/uhPXqVBAEPP3002hoaEBDQwMWLVoE9UlPus455xw0NzefUqQkIqL4q6mpQXnVIRRM7i93lKDlj8qGYJT4Yo+oFy6XC2WHypGakd/7wSGyZebB7fFyY5sw+f3+0HfZVqu4hmSQKioqcPR4DQZPzIv7vVVqFXLHpmPDpvVxvzcRESUWn8+Hl19+GVdf931sLluHSx8+C1fPnwFbTooseXKGZmLuwlmY/vOx+HDNu7hqzhV4//33Zdv0JibDZXQ6HVJTU6HRaGJxeSIiCtKOHTvggxcDxvXr/WCFUKlVyB5lw5ZtW+SOQqRoJSUlcHu8sGVGvyiTYstGQBQ4LTVMPp8PQojPg1XqjinbPp8vRqn6jk2bNkFU+9F/bPRHBwdj0KRcFO4pPGWTTyIiopOVl5fjR7f8CM/982kMm52Dm/86G0Omyj9IRKUSMO7i4bjpxSuQOkmL+U88jLt++Qs0NDTEP0skJ1dVVWHhwoWYO3cuZs2aheuvvx4LFy5EZWVlRKEWLVqEgoICGAwGTJkyBevWrev22Pfffx+XXHIJsrKyYLVacfbZZ2PFihUR3Z+IqK/YtWsXbP3N0Jt1ckcJSf7obOzaW6ioRZeDxT6M4mXfvn0IiAJSbNFfe0ilUsOYksWCZBhEUUQgEIBKE+qmNipAJbAgGYT1G9Yhe5QNGp08gx8GTcqH2+/E1q1bZbl/rLD/IiKKjjVr1uCGm+eiou0Arv3TxTj3psmy9VndMaboMevuc3D5b8/G+n1rMPfG67FvX3zXDw+7IPnSSy9h5MiR+P3vf4+lS5di5cqVeO+99/C73/0OI0eOxIsvvhjWdZcsWYL77rsPCxYsQGFhIc477zxcfvnlqK6u7vL4r7/+GpdccgmWL1+OHTt2YMaMGbjqqqsUs0gnEZGc9h0oQuaQVLljhCxnaAYcznZUVVXJHSUk7MMonoqKimBMyY76hjYnWNJyUbhrd0yu3Zf5/X5IEiCoQ3/hoVJr4PV6Y5Cq73C5XNiyYzMGnRH/6donpGZbYM03YcOGDbJliDb2X0RE0fHVV1/hngfvRuo4LW54+jJkF6T3eLzH6Q3qw+v69g1Lr8sX9Hm9GTwpHz98ZhZcqS346R0/ieub0WGVaJcvX4577rkH2dnZuP/++3HhhReiX79+OH78ONasWYPnn38eDzzwAIYNG4YrrrgipGs///zzmDdvHn72s58BAF544QWsWLEC//jHP/CnP/3ptONfeOGFU75euHAhPvroIyxbtgxnnHFGOL8eEVGfIIoiyirKMPZM+acGhCprUBr8kh+HDh3CkCFD5I4TNPZhFE87C/cgJT12RRlbZj7Ki1agra0NVqs1Zvfpa3w+HyRIIW9qAwAqjYYjJHuxY8cOODx2FEyO/tqpoeg/KRtrN6yBJP0egiDImiUa2H8REUWurq4Ov330EWRPTcHs+8+HStV7//DIGX8N+T6PnvNS0Mf+tfg3vR5jthkx59GL8P7jX+LBhx/Eh+99CL1eH3KuUIVVkHz22WeRkZGBnTt3Ii/v2yfCgwYNwllnnYWbbroJkyZNwvPPPx9SQdLr9WLHjh145JFHTvn+pZdeio0bNwZ1DVEU0d7ejvT07qvQHo8HHo+n8+u2trbOc0VRDDpvvImiCEmSFJ2xr2MbKAPbITjHjh2Dx+dGel4qEMQ6xcG8g3bCKe/QOYN/8aw3BTd13JhigN6iQ3V1dUTtHM/HCPuw7vFvNvpaW1tRffgwBowbg2BKIcJ3Pg/mnPSs/qgIiCgqKsK0adPCC5qE3G43BEEFtVZz2v/3kz/vqg3U2o4Rkvxb6d6GDRtgyTIgLdcaVN92Guk7n4e5jv+Qyf3xxedbUVpaimHDhoV3kR6w/1IG9l/KwHaQH9sgOB988AHsYhvm3DEbKkEIu4+JqiAz6PRaXHr3OXj3wZX46quvMGvWrLBuF8pjJKyC5M6dO3HTTTedUow8WX5+PubOnYv//ve/IV23oaEBgUAAOTmnLlCdk5ODY8eOBXWN5557Dg6HA3Pnzu32mD/96U/4wx/+cNr36+vr4Xa7Q8ocT6IoorW1FZIkQRXizo0UHWwDZWA7BKe8vBzDhw7HgPQCGO3mXo+/f+ojvR7TlUfPDf4dun9u/3PQx06aPB7t7e2oq6sLJxYAxHXDAfZh3ePfbPTt378fQ4cMxfBRw6E39j4Sz+v99pgsmxpaXe/nZNty4Bw9FqWlpQk1UlluTU1NGDl8OGxpGbAI3z7V9gnf7p6dL6ihFU5/Gi6OHAmVShXRv3t9mSRJqKgqx7QZZ8Hk6HkKXHdUrm/ffDM60qCXwltjefiAVFSObsG2bdtiMoKY/ZcysP9SBraD/NgGwWltbcWUaZNgk3IAe3DnvLjuiaCO87i8ePjSJwEAf1nxu6AHeujtwfdz+alpGD9xPI4fPx72c5FQ+q+wCpJerxdmc88vbs1mc9hr4Hx32oMkSUFNhXjnnXfw+OOP46OPPkJ2dvcLvM+fPx8PPPBA59dtbW0YMGBA56LMSiWKIgRBQFZWFv8RkAnbQBnYDsHZsWMH9pfuw9k5w+AyKGNNMpelOehja1xVSD2U3eO/570xGAxhnxsu9mGn499s9H344Yc4WFaFrDEGCJ7eN3/yeb89pr4lAI0uuA2jKo+2Yfv2HbjlllvCzpps3G439pcUI+97E5EqfVuE9Evf/j+vkQJQn/SzEw5WVaK1tTWif/f6sqqqKqzfsg4XnzclpP7kZB7Vt/2hy9wMMYJN35r1x7B67WrceOONYV+jO+y/lIH9lzKwHeTHNgiOxWLBzi27MPbH+dAZtEGeFNxh4kmz2cRMB0RzcLPUXHAEdwMArcft2LtnL267fl7Yz0VC6b/CKkiOGDECy5Ytw8KFC6HRnH4Jv9+PTz75BCNGjAjpupmZmVCr1ae9E1dXV3faO3bftWTJEsybNw//+9//MHPmzB6P1ev1Xc6HV6lUiv/jEgQhIXL2ZWwDZWA79K6pqQlqnQCdMbjO8M+F9wd9ba/Lh0end4yMfGLD3dCZguxwQ1hmy2Qz4tjh2ojaOJ6PD/ZhPePfbHQVFu6CPiUbEISgZuKEO0vVmpGP7Ts6Nqlg2wXH6/UiIIoQNJrT/r+f/HlXbSCqVPB4PPx/3Y1NmzbBL3jRf1xOSP3JKcJZv6Abg8/Iw9bXtsDhcCAlJSX8C3WB/ZdysP9SBraD/NgGvbvmmmvwyhv/h7Wvb8fMO6ZFd43hKPZfXREDIr781yb0zxqIiy66KOx2DuW8sO7w4x//GMXFxZg1axZ27Nhxys+2b9+Oyy+/HMXFxfjxj38c0nV1Oh2mTJmClStXnvL9lStXYvr06d2e98477+C2227D22+/HfImOkREfVVzczMMqcEvRqw36YL+OLnIqTNqgz4vFKZUAxqbG0M6R07swyheRFHEzl27YcuI/YZV6dkD0NjU3O1Ou3Q6t9vdMaVNG+QbNSfTaE5ZY49O9fW6tcgZkwatPqwxFVE3+Ix8uP1ObNmyRe4oEWH/RUQUHXl5efjdbx5F+VfHsOHtQkiSEhaR7F3AF8BnL65HW7EPC//4JxiNxrjcN6ze/J577sH69evxwQcf4KyzzoLRaEROTg6OHz8Ol8sFSZJw9dVX49577w352g888ABuueUWTJ06FWeffTZefvllVFdX48477wTQMdS/pqYGb7zxBoCOjvDWW2/Fiy++iGnTpnW+s2c0GpGamhrOr0dE1Ce0tLRAbwnjBbFCGFP0aG09HvSUMSVgH0bxUF5ejpaWNoweMzDm97Jl9ofH58eePXswePDgmN+vL/B4PCxIxoDT6cTWHVsw7sbYP+6DlZptgbW/CevXr+91dKDSsf8iIoqOOXPmwOFwYOFzT6Ktzo6Zvzg7+OnbMnC0uLD8+XVwlot44ZkXcdZZZ8Xt3mGNkFSr1Xjvvffw+uuv48ILL4Rer0d1dTX0ej1mzJiB119/HR988EFYQzxvuOEGvPDCC3jiiScwadIkfP3111i+fDkGDRoEAKitrT3lXfp//etf8Pv9+OUvf4nc3NzOj3CKoUREfUlbWxu05sSdUmEw6+D1eRPqxTn7MIqHPXv2wOsTYcvIj/m9tDoDDOYM7NmzJ+b36is8Hg9EAKouljXqlUYNl8sV9Ux9wfbt2+Hw2lFwRuxHBodi4Bk5WLthTcLvPMv+i4goem6++Wa8+Je/oXm3F+8+sgL1lU1yR+pS1Z6jWPzQ59A1WPHa/72OGTNmxPX+Ec13uOWWW2KyyPldd92Fu+66q8ufvfbaa6d8vWbNmqjfn4ioL2hrb4U+gsX65aYz6yBJIux2uyyL+4eLfRjF2u7du2G0ZkGjjc/ftyUtH9t37IzLvfqCEyMk1brQ20et08HJgmSX1q9fD3OOHml5ytr8ZPAZ+TjwyWaUlpZi5MiRcseJCPsvIqLoueSSSzBs2DA8/Miv8d78rzDlhlGY8v2xUKnkn/nl9/qx/r87cWB5NWZMvxhPPbkQGRkZcc8R0tCZzZs34+KLL4bVaoXVasXMmTOxdevWWGUjIqIItNvboQtx3UYl0Rm1nQVJIvrW9h07kZKWF7f7pWcPRHFxGdra2uJ2z0TmcrkgShJUutCnZ6m0WjhYkDyNJElYt/Fr5E9Q3u7j+aOyIehFbNiwQe4oRESkMAUFBfjvm2/jjpt/id2Ly7H00ZVorpX3+VRtST3efugzHF7djMd+8wQW/f0fshQjgRAKknv37sVFF12E1atXw263w26346uvvsKMGTOwb9++WGYkIqIw2J12aA3KWPg/HDqDFhIkOJ1OuaMQKUZTUxMOlVciPWdQ3O6Zlj0AHq8PRUVFcbtnInO73YBKBZVaHfK5Kp2WIyS7UF1djaqaShRMjv0yBaFSa9XIGZuODZtYkCQiotPpdDrcf//9eOPfb8HmysGSX3+BXZ8dhCjGd8MbvzeA9f/diQ9/vxajsyZi6Tvv40c/+pGsu6YHfec///nPcLvdWLBgAY4dO4bjx4/jt7/9LVwuF55++ulYZiQiojB4PG5otKG/IFYKrV4DCVJCrSFJFGu7d++G2+NHenb8NvYwp6RD0Bixa9euuN0zkblcLqjD2dAGgFqrhdPFN2G+a+PGjQiofOg/NkfuKF0aNCkX2wu3ckQ/ERF1a/LkyVi65D38eM48bH3tID7445dob3TE5d71Vc1YMn8FypYfx29+9Vu88dqbKCgoiMu9exJ0QXLdunU499xz8cc//hHZ2dnIysrCk08+iXPOOQdr166NZUYiIgqDx+uFRpe4BUm1VgUJEnw+n9xRiBRj9+7d0OotMJrjt4utIAgw2/Kwg+tIBsXlcoU1XRvomLLNUeGn27hpI7JHpSl2l9LBZ+TD7XNhx44dckchIiIFM5lMmD9/Pl79x2vQN9iw+MEVKN1UFbP7SZKEnZ/sx9JHVmGgcTje/e//8NOf/hTqMGZxxELQBcnjx49j2rRpp31/2rRpOH78eFRDERFR5CRJgiDIv2hyuIRvFnxO9J1LiaJp+46dMNnit37kCWnZA7CjcBffIAiC2+2GEMaGNgA3temK1+vF5u2bMHBiP7mjdMuWkwJTth6bN2+WOwoRESWAadOm4f3/fYDZ516NVX/djpX/3AS/1x/Ve7ja3Pho4VfY/mYJbr/xTrzz1jsYMWJEVO8RqaALkj6fDxaL5bTvWywWPjklIlIglUoFSYrv2iTRJH2zroqc65oQKYnb7cbuPUVIz4nfdO0T0rMHwW53obi4OO73TjQulwuCNrz1e1U6LVwuV0L/2x1tRUVFaHe2YeD4XLmj9ChvfCbWb1ondwwiIkoQVqsVf3n6L1j46NM4trENS377BVqOt0fl2rWl9Vj86xUQDxvw77+/ioceegi6MN8sjSW+yiMi6qMMegP83oDcMcLm8wYgQFBk50kkh/3798PpdCM9O34b2pyQmpELvwiuIxkEl8sFaMIrSKp1OgQCIt/sP8nWrVuhMQvIKkiXO0qPBk3MRXnVIdTV1ckdhYiIEoQgCJg1axZe+ed/kOHPxZKHVqBsaxU8Tu9pH17Xt88NvC5fl8d4nF4UfnYQHyxYjTG5k/D6K29g+vTpMv6GPQvp2dJbb7112lSEsrIyAMDs2bNPO14QBHz66acRxCMionClWFLgcjTIHSNsHocXAlRISUmROwqRIuzatQuioIE1LTvu91ap1DCl5mLnzkLcfPPNcb9/InE6nUCYa0iqdTqIkgSXy8U3Y76xZdsW5IxOg0ql7CVIBoztB6+4Fdu3b+/ydREREVFXvjsT+eAth3o959FzXur1mNLdFVj8zmJFz7oIqSBZVlbWWYD8rs8///y07yXy2mVERImuX1Y/FDVVyx0jbI5mJ1SCGpmZmXJHIVKEHTt2wpSaC0GQZ4KLLWsANm/ZlvDr08aaw+mEEO4u23odJHQUJFNT47dxkVK53W7s2rMTE24aLHeUXhmtBtgGWFiQJCIiClLQBcmKiopY5iAioijr338ANn/9tdwxwtZyrB0plhRYrVa5oxDJThRFbN22A2lZ42TLkJ49CCWV21BVVYXBgwfLlkPp7E4H1BFsaiNKEnfa/kZRURFcPif6j8mRO0pQ+o3OwNYdW+SOQURECcRutwd9rCiKqK+vR1ZWVp9YZz/oguSgQfFfr4goHhwOR9DHiqIIp9MJh8MR0j8AZrM5nGhEERk6dChaFzvg9wag0anljhOy+oomjBo+iiOxiNAxS6WltQ1jxsr3fCwtqz+8vgB27drFgmQP7HYH1GFO2VbptJ1TtgnYvXs3VEYBGQPT5I4SlPzR2Vj35V40NTUhPV3Za14SEZEyhFIrEEURDocDZrM5uQqSRH1VV7vHR5uS122gvmv8+PFQSxrUltZjwNh+cscJiSRJqC9pxRXfP0PuKESKsGvXLvgCEmxZ/WXLoNUZYLBkorCwENdcc41sOZTO4XJCbQmvgKbW6yEBLEh+o3BXIbKGpyp+/cgT8kdnwyd6sHv3bsyYMUPuOERERIrGgiQRUR81fPhwZKVno7KwJuEKko1HWuFq8uGss86SOwqRIhQWFsKYkgO1Wt6nbikZ/bF563ZZMyid0+mEShfexkOcsv0tSZJQuHcn+s/MCOp4j9Mb1HHf3aUUQdY69abep+GnZJhhTNejqKiIBUkiIqJesCBJSS/YNRscDgdycjrWMBoxcgzWr1sDk8kUw2REkVGpVLj4gpn4cMO7OPemyQk19blkQyXSUtIxdepUuaMQyU6SJGzesg2pmfKNjjwhPXsgqvYsR0NDAzec6obD6YRarw/rXBYkv3XkyBE0tzbhzOFDgzr+kTP+GvI9gtml9IS/Fv8mqOMyh1qxp2hPyFmIiIiSTeJPOieKkNlsDvrjBJVKBYvFEvJ5RPF25ZVXwl3nw5F9x+WOEjRRlFC8pgqzL7kCujA3hiDqS2pra3G09hjSs+Vfzzs9eyDcHj92794tdxRFkr5Z/zGSNSQ5ZbvDwYMH4Rd9yB4S3AhJpcgeko59B4u4XA8REVEvOEKSKBwCoNHwz4eUb/LkyRg1bAx2LjuAAeMSY9p22ZYq+JolXH/99XJHIVKEXbt2we3xIz17oNxRYDSnQmtIwa5du3DxxRfLHUdxPB4PRFGCWhfeCElBEKDSaVmQBFBcXAxjuh6mVENQx/+58P6gjvO6fHh0esfIyCc23A2dKbzicXeyCtKxu60ctbW1yMvLi+q1iYiI+hJWVIjCIABQqxNv12JKPoIg4Ce3/hQP/f4B1FU0IbtA2bt+iqKEbUv349zvnY/Ro0fLHYdIEXbv3g2DJR06gzKWCTHb8rF9x065YyiS0+mEKElQ68Mf3a3SajllG0BpaQlsA4OfZRLMGo/fpTNqwzqvJ1mD0+AXfSgtLWVBkoiIqAecsk1E1MfNnj0bIwtGY8NbhXJH6dXBdeVwHPHil7/4pdxRiBRj2/adMNuUU9hIzx6IvXv3cRRfF1wuF0RJgkob/qg7lY4FSQAoPlSMjAGpcscImSXdBK1JjYqKCrmjEBERKRoLkkRhCgQCckcgCopGo8ED9z6A43tbcWj7YbnjdMvr8mHz20WYPfNKTJw4Ue44RIpgt9txsLhEEdO1T0jPGQiX24v9+/fLHUVxnE4nJEhhb2oDAIJOl/TFXo/Hg5qjNUjPT7yCpCAIsOabWZAkIiLqBQuSRGGQwIIkJZYLL7wQF51zMda9shNel0/uOF3auLgQOrcJDz7woNxRiBRj7969cHu8iipIptiyIAoabmzThRMjJCOZsg2NJukLkjU1NfCLPqT2S5E7SlhSso2oOlwpdwwiIiJFY0GSKBwSIIqi3CmIgiYIAn47fwE0ThPWvaW8td+OHqzDgc+qcd8v70dubq7ccYgUY8+ePYBKD7NVOTsNC4IKptR+KCzcJXcUxeksSEYwZRtcQxI1NTUQpQBsCVqQtPWzovpIldwxiIiIFI0FSaIwqVT886HE0r9/fzx0769R8sVhVO0+KnecTl6XDytf2oJpZ0zHj370I7njECnKrt27YUrtB0EQ5I5yCltmf2zfUQhJkuSOoiida0jqwh8hKei0cCb5CMm6ujqIgghzmjI2cgpVSqYJDY0N8Pv9ckchIiJSLFZUiMIgCIA2ktEPRDKZO3cuZky/GF++tAXOVrfccQAAq1/ZCq3DhCefeIqFfqKTiKKInTt3w5Y5QO4op0nLGoDGpmYcPqzcdWnl4HK5IAERTdlWa7WwJ/kIybq6OpjTDFCplFWID5Y5zYSAGEBjY6PcUYiIiBSLr/yIwiAIAtRqtdwxiEKmUqnwxyeeRJo6G1+8tBGiKO/opgNfl6Pi6+P43W9+jwEDlFd0IZJTZWUlmppbkJatvL+NtMz+8Hj9HVPKqdOJgqRKown7GiqdFg6HI3qhElBzczMMqRGswykzs80AUQqgublZ7ihERESKxYIkURhUCps6RxSKrKws/PnJp1G/px3bPyqSLUfTkVZ8/XIhrr/qBlx11VWy5SBSqqKiInh9AaRl5ssd5TRavREGSzqKiuT7N0SJ3G43VFpNRFPs1TodHK7kHiHZ2toKrTlx3/g1WPQQIaKtrU3uKERERIrFgiRRGIQEnUJEdML06dNx18/uxo7FxTi871jc7+91+7D82XUYNXAs5s+fr7j18YiUoKioCAZLBjRavdxRumSy9sOu3RwheTK32x3R+pFAR0HS6UzuNSTb7e3QGsMfZSo3vVkHSZJgt9vljkJERKRYLEgShYG1E+oL7rzzTpx/1gyseH4T7E3xG40jSRK+/MdmqFqNeP6Zv8JkSsxNC4hirXDXbphT+8kdo1u2rHzs23cAXq9X7iiKcWKEZCRUWi3cHk+UEiUml9sJrT5xC5IanQYSJLjdylirmYiISIlYkCQKgwBWJCnxqdVqPP2np5FrHoDlz61DwBeIy30Llx/AkU0NePKxp1BQUBCXexIlGo/HgwMHimHL7C93lG6lZfaH0+3BwYMH5Y6iGG63G4Imsk3vVDot3O7kHiHp8Xig0SXulG21VgUJEjxJXlgmIiLqCQuSRGHgCEnqK9LT0/HcX56Hs1LE2te3x/x+NQeOY8ub+zDvlp/j0ksvjfn9iBJVSUkJ3B4vbJl5ckfpljUtBwERLEiexO12AxGOkFRrtfB4PBBFMUqpElQCP9kSBCGR4xMREcUFC5JEREluwoQJWPDw71DyxREcWHsoZvexNznx+XMbcc7kC3DvvffG7D5EfcGBAwfgCwApaTlyR+mWSq2BwZKJ/fv3yx1FMdxuN6CObGSfSquBJAE+ny9KqRKUJMmdIGySJCVyfCIiorhgQZIoDHySSX3Nddddh+uv+iHW/l8hGqqbo359MSDisxfWI9uQj2eefgYaTeKuDUYUDwcOHIAxJRNqtbL/Vsyp/bBn7z65YyiG2+0GIvz3TaXRQkzy6b46nQ4Bf+KOEBX9IgQI0EW4wREREVFfxoIkUVhYkaS+RRAELFiwAOMLJmH5M+vhcUZ3k4r1/92J9lIvnvvL88jIyIjqtYn6or1F+2CyZssdo1epGbkoKSlN6uLZyTweDwRNNEZIJveGKEaDCT6PX+4YYfN5/BAgQK/Xyx2FiIhIsViQJAqDyHok9UGBQAB//MOTULcZsOJvG+B2eOBxerv88Lq+nUrodfm6Pc7j9OLgugrs/agc9951PyZNmiTfL0iUIHw+H0pKypCakSt3lF6lpufC7fHh0KHYLfeQSNweD1TaCDe1+WbKdjIXeS1mC3zOxC1Iepw+CIIAi8UidxQiIiLFUvY8ICKFkliRpD7olBdOm4HVr24N6rxHz3kpqON+9rOfYd68eeFEI0oqFRUVcLk9sKb1kztKr1Js2fD5AygrK8OYMWPkjiM7l8cd+QhJjQYSpKReQzItLQ3e0sQtSLrbPVBBhdTUVLmjEBERKRZHSBKFQeKUbSIiipHS0lJ4fQFYFbyhzQkarQ56sw0lJSVyR1EEj9cLVYRrSApqNSQAXm90l85IJGlpaXC3Ju7v72x1QRBUSEtLkzsKERGRYnGEJFEYREmCJEkQBEHuKERRY7fbOz/3eDx499130dbW1uM5RqMRLper258LgoDLL78cBQUFUctJ1NeVlpZCZ7RCqzPIHSUoBksWDh4sljuGIrjdHqjMkW1kotJqIUlSUhckc3Jy4Gx2QwyIUKkTb/xEe6MTWrUWWVlZckchIiJSLBYkicIgSYDf74c2wnWiiJTEbDaf8vmdd97Z4/GiKKKurg7Z2dlQqRLvBSORUpWVHYLenDibP6XYslBcUiZ3DEXw+rwQ1MaIrqHiCEnk5ORAkFSwNzlhzUq8dRjb6uzIysyGWh3Z9H0iIqK+jAVJojBwXCQREcVKcUkpLKmJM7IqxZaNyqptaGlpgc1mkzuOrHw+P1QRriEpqNWQJAl+f+KuoRipAQMGQK3SoLm2LSELki3H2lEwaJzcMYgUzeFwBH2sKIpwOp1wOBxBvwl+8hvtRKRMLEgShUMANBGuEUVERPRdTqcTR2qOov+YsXJHCVqKLRs+XwDl5eWYPHmy3HFk5ff7Iagi3NTmmxGSyVyQzM/Ph16jR3NNGwZNyJM7Tsjaa50omMalSoh6Eutd6CWJa/4TKR3n2BGFQSUIXD+SiIiirrKyEj5fIKFGSJpT0uEPiKisrJQ7iuw6pmxH9vRaUKsgScm9y7ZarcaQgqFoqG6WO0rIAr4AWmrsGDZsmNxRiIiIFI1DvIjCIHDSNhERxcCRI0fgCwRgsSbOGpIqtQZ6Uyqqq6vljiK7QCAAVYTrBp4YYZnMIyQBYMzIsfhy/zK5Y4SsqaYVQkCFESNGyB2FSNFO3kyxJ6tWrcLVV18NALjpppvxr3/9M5axiCiOWJAkCgdHRxIRUQxUV1dDozVCq49sY5R40xptqGJBsqOIGOEmX4JKgISONdOS2ejRo/G/z95BwBeAWps4m8McK2uETm1gQZKoF8Gu8Zidnd35eUZGOteGJOpDOGWbKAysRxIRUSwcPnwYOpNN7hghM6Wko7y8Uu4YshNFMeIlXU6MkAwEAtGIlLDGjx8P+FWor0qsadu1JfUYMWwkTCaT3FGI+oSUlJTOz61Wq4xJiCjaWJAkCgPXSCYioliorKyC3miTO0bIzCnpqKquTvpNBAKBAIQIR0hCJUCSpKQfITly5EiYDRbUHKiTO0pI6oqbMXlicm/uRBRNJ28kyk1FifoWFiSJwpHkL7iIiCg2Dh85CqMlVe4YITOabXC7PGhuTqzRbNEmimLE0yhOjLBM9oKkTqfD5IlTcLjomNxRgmZvcqLtqBNTp06VOwpRn+FwODo/dzqdMiYhomhjQZIoDAFJTPqpVEREFF1+vx91dXUwmW1yRwmZyWKDPyDi2LHEKR7FgiRJEU/ZPlHQTPbRpgAw7axpqDvQDDGQGMXZw3troVPpMWXKFLmjEPUZtbW1nZ8fPnxExiREFG0sSBKFIRCQUF9fL3cMIiLqQ+rr6+H1+RN0hGQq/IEAC5KSBERcj2RB8oRp06ZBcqtwtDgxnnNV7jqKMaPGISMjQ+4oRH1GeXl55+fFJWVJP3qcqC9hQZIoDD6/iKqqKrljEBFRH1JXV4dAQITRnHgFSa3eCAjqpC9IRosEFiSBjp22s9KzUbFT+aOiRFHC0T0NuODcC+SOQtSnFBXt6/y83e5AdXW1jGmIKJpYkCQKgygJKC0tlTsGERH1IY2NjQiIIvTGlN4PVhhBEKAzWNDU1CR3lD5BACKf+t0HqFQqzDjvIlTtUH6hu7akHv52Ceedd57cUYj6DFEUsbNwd+fXXl8Au3fv7uEMIkokLEgShcFoycKePXvkjkFERH1IQ0MDJAjQ6gxyRwmLRmdCY2Oj3DFkJQhCx/DGCJwYGcmCZIeLLroI7TUuNB9tkztKj8o2VyEnMxfjxo2TOwpRn1FaWoqm5pbOrw2WDGzbtk2+QEQUVSxIEgXJ5/N1fp6a2R/rNmziGiZERBQ1jY2N0BksCVuIUmtNqK9vkDuGrARBiHyqNQuSp/je976HVHMaSjZWyh2lW5IkoWJLLS6ZcSlUKr68IoqWTZs2wS9++zdlzRyEtes28DUYUR/BHpMoSDt27Oj8PCt/OOrqG1FYWChjIiIi6kuam5uh1ibm6EgA0BnMqG9gQRIRFySjk6WvMBgMuPj8mTi0SbnrSNaWNMDd6MesWbPkjkLUp6z9eh3MaXmdX2flDcWxY3U4ePCgjKmIKFpYkCQK0iefftr5uS0jD4LGjI8//ljGRERE1Je0t7dDpUncgqRWZ0BLS6vcMWSlVqsjHiEpiSIEAdBoNFFKlfguu+wytB52ouFwi9xRunRwXTlys/IwefJkuaMQ9Rnt7e3Yum0HsvKGd34vLbM//JIa69evlzEZEUULC5JEQaitrcWKFSs7vxYEAblDJuPDjz9FQ5KPBiEiouhoaWmFWqOXO0bYtHoj2tqUvc5frKnVakgRTiXsOF+AWq2OTqg+YPr06ciy5WD/mkNyRzlNwC+iYtNRXH3FNZyuTRRFW7duhcPpQVbe0M7vqVQqpGQMwrp1LEgS9QXsNYmC8PLLL8PpPfV7g0dMRZvdi1deeUWeUERE1Kc0t7RCpzfKHSNsWp0Bbe3tSb22l1qtBqJQkBROXIsAAFqtFlfMuhKH1h+BKCprTntlYQ0CdgGzZ8+WOwpRn7J582bojDYYzamnfD8zdwh27tqD9vZ2mZIRUbSwIEnUiz179uCdJUsxcOQ5p3xfqzdiwMjpeP3Nt1FcXCxTOiIi6ivsdjs0Gp3cMcKm1eohBiR4PB65o8hGrVZDDAQiusaJEZYsSJ7q6quvhq9ZRNWuGrmjnGLfV2UYP2oiRowYIXcUoj5DkiSs+Xo9UrMGnfaz7LxhcLo83G2bqA9gQZKoB21tbfjNb+YDunQMHDHltJ8PGTMNflUKfv3wI3A4HDIkJCKivsLlckOt0codI2xqjRaSJMHtdssdRTZ6nR5SIMIRkoEABEGATpe4xelYGD16NMaMHIeiL8vkjtLJ0eJCTWEDrp1zrdxRiPqU0tJSVFZWI2fAyNN+ZkpJg9aYhtWrV8uQjIiiiQVJom64XC7cf/8DOHjoCCaee23HzpnfoVJrMOnc67B3fxkeeOBBeL3eLq5ERETUO4/HDZU6cQuSKrUWEpJ7hKRWo4Ho90d0DdHvh4COacr0LUEQcN2c63FkZz0czS654wDoGB1p0Vlx2WWXyR2FqE/54IMPEIAOmblDuvx59oAx+HT5iqRft5go0bEgSdSFpqYm/PyOO/HV11sw8bwbYE5J7/ZYS2omxp87FytWrcMvfnEXWluTe4dRIiIKj8ud+CMkRTG5R0hqdVpIkU7Z/maEJHfZPt3s2bNh0VlR9FWp3FEgihIOrKrAFZdeBavVKnccoj6jvLwc/33nXeQNnQKVquulKwaNmIrmNicWLVoU53REFE0sSBJ9x8aNG/GDa6/Hhi17cMaFNyM9e2Cv52T2K8Ck83+E1eu349rr5mLr1q1xSEpERH1JIBDo9sVXIjixw3AgwoJcIjPoDRGPkAx8M0JSr0/cHddjxWq14spZ38eBLytl39ymalcN3PUBXHfddbLmIOpLysvLccedd8EV0GPo2HO6PU5vtGDwuBn493/exL///e+k3kyNKJGxIEn0jerqajz00EP48U9uR71dg2mX/Ry2zPygz0/PGYTvzbodR5sCuPnWn+KR+fNRU6OshdeJiEi5RFEETl8dJIF0hE/mF4ZGgwGiL8Ip2z4/VILAgmQ3brjhBngbRVTsOCJrjj0rSjBh9CSMHz9e1hxEfUFraysWLVqEOdfORVVtG6bOuKnXGQODR56FfkPPxlN/fg4/+ek8FBYWQpLkfaOCiELDuSCU9EpKSvD6G2/ggw+XwRPQYOikK5BfML7LNSN7YzSn4nuX3IrDZbvw7gcr8Mknn+HaH1yNW2+9FUOHDo1BeiIi6itEUYSQwO8VC4IACUjqF4RGgwGivSWia4g+HwSwINmdMWPG4Izxk7H7s2IMPXOALBlajrejprAR9z7+27CeLxIR4PP5sH37dixfvhzLPvkMLe0u5A49ExMnnA+NpvdNvQRBwIiJFyI9eyA271yBuT+8GZMmjsMP5lyDmTNnIisrKw6/BRFFggVJSkoejwerVq3Cu/9bio2btiIgGDBg5HkYNGIq1OrI/iwEQYWBwycjr2Acqoq3460ln2Dxu+/h3OnTMHfu9ZgxYwZ3ziQiotNIkpTYIyQFAZCSe4SkyRD5lG3R74cgcMp2T2668WbcN/9XaDrSivT+qXG//+7PDiLb1o+b2RCFqK6uDps3b8bGjRuxes06NDQ2Q6W3ot+gyRg7Ygr0BnPI18zMHYKM2XegrqYMB0u2Y/7v/4g//HEhzpg0ARdecD7OPvtsjBkzhuvyEikQ/yopaUiShD179uCTTz7BRx9/irqGZpjT8jHkjKvQb+DozrWvokWj0WHo2OkoGP091Fbuw6ZdO/DV2vvQLzsT379qNq688kqMGzeO76wTEREAQK1WQ5KCL+b5vMFvHuP3fbvztc/nQbBjGLU6Q9D3kEQRgtDxeyQrk8kEyeeL6Bqi1wuVIMBgCP7/fbKZOXMm8p7vj8LlB3Dxz6fF9d5elw8lqw/jjhvvZhsR9aKlpQU7duzAtm3bsG79RpQdqoDb64cxJQsZuSMxaeJoWNP6Rfx6SBBUyOk/Ajn9R8DrceH4kWLsqyjG5u2LoMaLSLOl4uxpZ+F73zsLZ555JoYNGxb1135EFDoWJKnPO3bsGJYtW4b3P/gIpYcqALUJWQPG4qypZ8BizYj5/VUqNfKHTED+kAmwtzaguqwQ/37jPbz62lsYOWIofjDnGlx55ZXIzs6OeRYiIlIurVYLMYQNYd56/vaw7rP4b3cHfexPHnkz6GNFsSO7Vpu4O4VHymg0AhGuIRnweiFA6LgWdUmn0+HG63+EF155Fuf8yAODJX6jSfevOQS1V4e5c+fG7Z5EiUKSJOzfvx9r167F2q/XYe/efXC6fdAZU5GSMRAFk65AZr8h0BlMMcug0xsxYOgkDBg6CZIkoqWhBvVHy7Fq4358vPxLaNVATk4Wzj93Os477zycd955sFgsMctDRN1jQZL6rB07duDNN9/EipVfwe2VYOs3HGPOvgEZ/QZDEOR5R8ySmokxUy6BNPliNNSW43DZbjyx8Dk8+/yLuHzWTNxyyy2YNGmSLNmIiEheWq0WUgJPdxbFAARBYEEywhGSAY8XWq0mqf8/BuO6667DP/69CEWryjD16rFxuacoStizvBSzLp6Nfv36xeWeRInA7/fjvffew2uvv4myQxUISBqkZA1G/phLkJk7BEbz6UsruBytwV37pBH+LmcbNCd93ROjORWCoEJa1gCkZQ0AcAECAT+a66pRX1uO9z5dh7eXvA+b1YIrr7wcP7/9dgwcODCoaxNRdCiyILlo0SI888wzqK2txdixY/HCCy/gvPPO6/b4tWvX4oEHHsC+ffuQl5eHhx9+GHfeeWccE5OS1NXV4bHHHseXX62FSm9D/1EXIb9gPDTart89D3bKW7jT3YDTp7wJggpZecOQlTcMPq8bNeV78fEXm/DxJ5/j8stm4rFHH0VGRuxHbxJR9CVLH+ZwOII+VhRFOJ1OOByOoKdImc2hryOV6LQaLUQx+NF1Nz/wf0Ef6/d5OkdG/vBXL3XbJ0ZCDHSsfZjMhTSTyQTR443oGqLPx9GRQcjIyMD3L78aH332P0y+cjRU6ti/2VxZWAPXcT9uvunmmN9LDsnSf1H0PfnkU3j9rSVIzRmBUWffgIycwb3296GM1j9h6T8fDPrYrkb4q9UaZOYOQWbuEAAz4XK0oqZ8L95Z+hm++OJLfPTh+8jNzQ05FxGFR3EFySVLluC+++7DokWLcM455+Bf//oXLr/8cuzfv7/LdywqKiowe/Zs3H777XjrrbewYcMG3HXXXcjKysK1114rw29AcgoEApj3s59jf+lhjJhyDfoNHNXrmiThTHkLtQPtacqbVmfA4FFnYtDIKait2o9PPv8CR4/+Au8uWcy1TYgSTDL1YbGe3pSMOzWbzSa0BDnyAwhtfcdTztPqoQnz3J4E/F4IQnJPNY7GGpJ+txvplpQoJerbbrrpJixd9i5Kt1Rj5PTBMb9f4ScHMXn8FEyYMCHm94q3ZOq/KPqqqqrgCwBejwOHitbhUNE6uSNh88rXgz5WEtRobbOjubmZBUmiOFJcQfL555/HvHnz8LOf/QwA8MILL2DFihX4xz/+gT/96U+nHf/Pf/4TAwcOxAsvvAAAGD16NLZv345nn302ITrDioqKoI+VJAkNDQ1wOp1BL/xbUFAQbrSE1NbWhrKyQ8jImxBUMVJJBEGF3EFj0FBbjv37D8LtdsNkit36KkQUfcnWh1F0paRY0FAX/Og6pW1q4/d1bMaSbKNbTx4trNFo4He54XefXlg++Xt+d/dt4LM7YTQYOq+bbP8/QzFy5EicPfUc7PpkJwZPygvqHK/Ld+rnQT5VbG9woK6oBQuevjWhnl8GK5n6L77+ir6FC5/C0FdegdPpDPqcS84ZE9I9UlNT0doa3DRvIPR/O88991yMGRNaJiKKjKIKkl6vFzt27MAjjzxyyvcvvfRSbNy4sctzNm3ahEsvvfSU782aNQuvvPIKfD5fl9OGPB4PPJ5vnxSe+IetpaUFYgRrN7W1tWHZsmXd/vzEaI+TR33cc889Yd8vGC+++OIpneeJz7vrUM1mM6666qqE3SFTEAT8+Nab8O9X38DG+krkDBwPjQKmjlUe3NLrMT6fF8eq9gDeNvzijnnwer3weiOb9tVXiaKItrY26HQ6jiKVUSK0Q1tbG4D4jLZL5D7M5XJh6dKlcLlckCQJgUAAgUCg8/OT/yuKYlz+fz700ENQq9UQBAEqlQqCIHR+ffJ/T3xuNBpx3XXXJfToPINBD0drDepqSoI6/tM3/xjWfUIZ5X/FLb8P+tiW+iPQG3Sdf3fJIi0t7bTvHb2l59kXK2/vuQ2K8e0o5Obm5rCzJYM5V8/B+gVr8cgZfw353EfPeSnoY8+7ZQrysvpj6tSpaGlpCfleoWL/FZxAIIBly5ahra2tx/9XJ//s/vvvD+tewfrrX099LPZUyDzxs2uuuQZWqzWmuWLJaDTi7rtDn4IdLFEU0dDQgMzMzJg+54zH33aiSoTn/ckgEdohpP5LUpCamhoJgLRhw4ZTvv/UU09JI0aM6PKc4cOHS0899dQp39uwYYMEQDp69GiX5zz22GMSAH7wgx/84EeSfBw+fDg6HVUP2Ifxgx/84Ac/ov3B/osf/OAHP/iRiB/B9F+KGiF5wnffRZIkKah3lk4+vqvvnzB//nw88MADnV+LooimpiZkZGQoegpGW1sbBgwYgMOHDyf0O2iJjG2gDGwHZUiEdpAkCe3t7cjLC24qXzSwDztdIjxWkgHbQX5sA2VIhHZg/6UMifBYSQZsB/mxDZQhEdohlP5LUQXJzMxMqNVqHDt27JTv19XVIScnp8tz+vXr1+XxGo2m212K9Xo99PpTd5e02WzhB48zq9Wq2AdfsmAbKAPbQRmU3g6pqalxuQ/7sN4p/bGSLNgO8mMbKIPS24H9l3Io/bGSLNgO8mMbKIPS2yHY/ktRk851Oh2mTJmClStXnvL9lStXYvr06V2ec/bZZ592/BdffIGpU6d2uXYJERFRLLAPIyKiRMT+i4iI5KCogiQAPPDAA/j3v/+NV199FQcOHMD999+P6upq3HnnnQA6hvrfeuutncffeeedqKqqwgMPPIADBw7g1VdfxSuvvIKHHnpIrl+BiIiSFPswIiJKROy/iIgo3hQ1ZRsAbrjhBjQ2NuKJJ55AbW0txo0bh+XLl2PQoEEAgNraWlRXV3ceX1BQgOXLl+P+++/H3//+d+Tl5eH//b//h2uvvVauXyFm9Ho9HnvssdOmOlD8sA2Uge2gDGyH07EP6xofK8rAdpAf20AZ2A6nY//VNT5WlIHtID+2gTL0tXYQJCmYvbiJiIiIiIiIiIiIIqe4KdtERERERERERETUd7EgSURERERERERERHHDgiQRERERERERERHFDQuSREREREREREREFDcsSCrMz3/+c9x8880hnbN582a88MILsQmUhNgGysB2kB/bgELBx4v82AbKwHaQH9uAQsHHizKwHeTHNlCGZGoHFiQVxOVy4dVXX8WmTZuCPqe0tBRXXXUVBg4cGMNkyYNtoAxsB/mxDSgUfLzIj22gDGwH+bENKBR8vCgD20F+bANlSLZ2YEFSQfbt2wdRFFFeXo6WlpZej6+rq8Nll12GhoYGTJgwIfYBkwDbQBnYDvJjG1Ao+HiRH9tAGdgO8mMbUCj4eFEGtoP82AbKkGztwIKkgmzYsAFz5swBABQWFvZ4rMPhwJVXXommpiaYzWYMHTo0HhH7PLaBMrAd5Mc2oFDw8SI/toEysB3kxzagUPDxogxsB/mxDZQh2dqBBUkF2bBhAxYsWAAA+Prrr7s9zufz4dprr8Xs2bORn5+PcePGQRCEeMXs09gGysB2kB/bgELBx4v82AbKwHaQH9uAQsHHizKwHeTHNlCGZGsHFiQVwuPxQBRFTJ48GTk5OVi1alWXx0mShNtuuw15eXlYsGABSkpKMHLkyDin7ZvYBsrAdpAf24BCwceL/NgGysB2kB/bgELBx4sysB3kxzZQhmRsBxYkFeLLL7/EZZddBgC46KKLsHnzZjgcjtOOu+eee9DS0oKXX34ZFRUV8Pl8Cbl4qRKxDZSB7SA/tgGFgo8X+bENlIHtID+2AYWCjxdlYDvIj22gDMnYDixIKsSHH36IH/zgBwCAWbNmwefz4auvvjrlmAULFmDbtm343//+B41Gg0OHDgEABg0aFPe8fRHbQBnYDvJjG1Ao+HiRH9tAGdgO8mMbUCj4eFEGtoP82AbKkIztwIKkArS3t6O9vR3p6ekAOh58giBg8eLFncf84Q9/wLJly7B8+XKYTCYAQFVVFQB0Ww13OBy4/vrrUV1dHePfIPHFog0+/vhjPPfcc3jwwQdx4403or6+Pg6/SWKLRTusWrUKb775Jv7v//4Pt956a+c/2tS1WP17dMKNN96Iw4cPxyg9xRv7L/mx/1IG9l/yY/9FoWD/pQzsw+TH/ksZkrUPY0FSAV5++WVcc801nV/369cP5557Lj744AM0NDRgwYIFePvtt7Fy5crOBygAHD16FACQm5t72jXfeOMNPPPMM1i6dClEUYz575Doot0GlZWVKCsrw4MPPojnnnsOWVlZuO222+LxqyS0WPwtzJ07F4FAALfffjsmTpyI22+/Pea/RyKLRRuc8Mknn+DTTz9FIBCIWX6KL/Zf8mP/pQzsv+TH/otCwf5LGdiHyY/9lzIkbR8mkay8Xq905plnSl6v95Tvv/nmmxIAafjw4dKIESOkmpqa08795S9/KQGQysrKur0+AKmioiLasfuUWLTBRx99JOn1esnpdEqSJEkff/yxZDQaY/dL9AGx+lvYs2ePZLfbJUmSpGeffVa64IILYpK/L4jlv0fHjh2T3njjDWnQoEH8N6mPYP8lP/ZfysD+S37svygU7L+UgX2Y/Nh/KUMy92EcISkDh8OBt99+G5Ik4dlnn8UPf/hDaLXaU46ZO3cuBg4cCJ1Oh7Vr1yIvL++067hcLgCA3W6PS+6+JNZtcPnll2PDhg0wGo0AgOrqagwfPjxGv03iisffwvjx42E2mwF0vDv061//Oga/SeKK179H//3vf3HTTTdF/xeguGL/JT/2X8rA/kt+7L8oFOy/lIF9mPzYfykD+7BvyFMHTW6vvfaaBEAymUzS0KFDJY/H0+Vxu3btkpqamrq9zq9+9SsJgDRnzhzpiSee6PIY8B26LsWzDTwejzRhwgRp7dq1Ucnel8SrHbZs2SItWLBAevjhh7u9R7KKRxu899570qFDhyRJkhT77hwFh/2X/Nh/KQP7L/mx/6JQsP9SBvZh8mP/pQzswzpwhKQMxowZg5EjRyI1NRWLFy+GTqfr8riJEyciLS2t2+vMmDEDVqsV2dnZuPfee2MVt0+KZxs88sgjePLJJ3H++edHJXtfEq92OOuss/Dkk0/CYDDgiiuuUOb6GTKJdRvU1NTA6/ViyJAhUc9O8cf+S37sv5SB/Zf82H9RKNh/KQP7MPmx/1IG9mEdBEmSJLlDUOwIgoCKigoMHjxY7ihJ6aWXXsKECRNw/vnno7S0lFMG4mzz5s245pprsHXrVgwcOBArVqzAZZddhm3btmHq1Klyx0sK//nPf3D8+PHOr//whz/gnnvuwZw5czBt2jQZk5HSsf+SF/svebH/kh/7LwoX+y/5sQ+TD/svZUiUPkwjdwCivuqdd97BgAEDMGLECBw7dgzvv/8+fvOb38gdK6loNBoMHz68c9exiooKpKamYujQoTInSx4/+clPTvl6/vz5+MUvfsEn6UQKxv5Lfuy/5Mf+iygxsQ+TF/svZUiUPoxTtvuod999F3fddReAjgffyy+/LHOi5LJx40bccsstuOaaa5Cbm4vc3FysW7dO7lhJZ+rUqbjvvvvw4osv4rnnnsNnn32Gzz//vMdh7xQbJSUluOeeewAAjz76KDZv3ixzIlIq9l/yYv+lDOy/lIP9FwWL/Zf82IfJj/2Xsii9D+OUbSIiIiIiIiIiIoobjpAkIiIiIiIiIiKiuGFBkoiIiIiIiIiIiOKGBUkiIiIiIiIiIiKKGxYkiYiIiIiIiIiIKG5YkCQiIiIiIiIiIqK4YUGSiIiIiIiIiIiI4oYFSSIiIiIiIiIiIoobFiSJiIiIiIiIiIgobliQJCIiIiIiIiIiorhhQZKIiIiIiIiIiIjihgVJIiIiIiIiIiIiihsWJImIiIiIiIiIiChu/j/F9dFv/bNecwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1600x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axarr = plt.subplots(1, 3, figsize=(16, 5))\n",
    "plt.subplots_adjust(wspace=0.15)\n",
    "\n",
    "plot_model_posteriors(\n",
    "    bootstrapped_trials, labels, title=\"Bootstrapped Trials\", ax=axarr[0]\n",
    ")\n",
    "plot_model_posteriors(\n",
    "    bootstrapped_participants,\n",
    "    labels,\n",
    "    ylabel=False,\n",
    "    title=\"Bootstrapped Participants\",\n",
    "    ax=axarr[1],\n",
    ")\n",
    "plot_model_posteriors(\n",
    "    leave_one_participant_out,\n",
    "    labels,\n",
    "    ylabel=False,\n",
    "    title=\"Leave-One-Participant-Out\",\n",
    "    ax=axarr[2],\n",
    ")\n",
    "\n",
    "plt.savefig(\"levy_perturbation_robustness.pdf\", dpi=300, bbox_inches=\"tight\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results under additional noise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "added_noise_percentages = np.arange(0, 0.51, 0.01)\n",
    "mean_noise_proportion_list, mean_probs, mean_sds = inspect_robustness_noise(\n",
    "    added_noise_percentages, empirical_data, probability_net, summary_net, n_runs=100\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_noise_robustness(\n",
    "    mean_noise_proportion_list, mean_probs, mean_sds, labels, legend_loc=\"lower right\", save=True\n",
    ")"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "27a86c9f63fe2f1aa7d9f3c637434a8367b5c148236c1390e91d25c0e560ef1a"
  },
  "kernelspec": {
   "display_name": "Python 3.8.13 ('tf')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.10"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
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