pyei/intro_notebooks/PyEI_overview.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "dangerous-terry",
   "metadata": {},
   "source": [
    "# PyEI Overview\n",
    "\n",
    "PyEI is a Python package for performing ecological inference. It is meant to gather together in one place several approaches to ecological inference, to provide helpful features for plotting and reporting and results, and to integrate with other tools that facilitate model comparison and model checking.\n",
    "\n",
    "Start here if you have some familiarity with ecological inference (EI), and want an overview of how to use PyEI and its main features.\n",
    "\n",
    "If you are new to ecological inference, the Introduction notebook might be a better place to start.\n",
    "\n",
    "For a more detailed gallery of plotting functionality, see the Plotting notebook.\n",
    "\n",
    "For information about model checking and model comparison, see the Model Checking notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "drawn-berry",
   "metadata": {},
   "source": [
    "# Contents:\n",
    "\n",
    "- Loading data to use\n",
    "- Fitting a model for EI\n",
    "- Plotting overview\n",
    "- Reporting results overview\n",
    "- Accessing samples\n",
    "- Currently available models\n",
    "- Convergence issues and warnings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "meaning-element",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/karin/miniconda3/envs/pyei_dev5/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n",
      "/Users/karin/miniconda3/envs/pyei_dev5/lib/python3.10/site-packages/numba/core/decorators.py:250: RuntimeWarning: nopython is set for njit and is ignored\n",
      "  warnings.warn('nopython is set for njit and is ignored', RuntimeWarning)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pymc as pm\n",
    "\n",
    "from pyei.data import Datasets\n",
    "from pyei.two_by_two import TwoByTwoEI\n",
    "from pyei.goodmans_er import GoodmansER\n",
    "from pyei.goodmans_er import GoodmansERBayes\n",
    "from pyei.r_by_c import RowByColumnEI\n",
    "from pyei.io_utils import from_netcdf, to_netcdf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fatal-waterproof",
   "metadata": {},
   "source": [
    "# Loading data to use\n",
    "\n",
    "`PyEI` provides access to several example datasets for getting started. We can load the Santa Clara dataset, with its 42 precincts, and convert it to a DataFrame with `data = Datasets.Santa_Clara.to_dataframe().` We'll be passing various columns of this DataFrame to PyEI.\n",
    "\n",
    "Whether you are using an example data set or your own data, here is what you will need to pass to the various EI methods:\n",
    "\n",
    "   - A vector of length `num_precincts` giving the population of each precincts. Depending on your purposes and data, the appropriate measure of population may be CVAP, VAP, etc. Below we name this `precinct_pops`.\n",
    "   \n",
    "   - For 2 x 2 EI:\n",
    "      * A vector of length `num_precints` whose entries are each numbers between zero and 1 that give the fraction of the population of each precinct who belong to the demographic group of interest. Below we name this `group_fraction`.\n",
    "      * A vector of length `num_precincts` whose entries are each numbers beteween zero and 1 that give the fraction of voters in each precinct voting for the candidate of interest (or, if we are estimating turnout, who voted at all). Below, we name this `votes_fraction`.\n",
    "      * Optionally: name of the candidate of interest, name of the demographic group of interest, and/or names of precincts (for use in PyEI's plotting and reporting).\n",
    "       \n",
    "   - For $r$ x $c$ EI:\n",
    "      * An array of shape $r$ x `num_precints` whose entries are each numbers between zero and 1 that give the fraction of the population of each precinct who belong to each of the $r$ demographic groups of interest (these fractions should sum to 1 for each precinct). Each row should sum to 1. Below we name this `group_fractions`.\n",
    "      * An array of shape $c$ x `num_precincts` whose entries are each numbers beteween zero and 1 that give the fraction of voters in each precinct voting for each of $c$ candidates of interest (or, if we are considering turnout, who voted or did not vote). Each row should sum to 1. Below, we name this `votes_fractions`.\n",
    "      * Optionally: A vector of length $c$ giving the names of the candidates and a vector of length $r$ giving the name of the demographic groups and/or names of precincts (will be used in PyEI's plotting and reporting)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "suffering-savannah",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>precinct</th>\n",
       "      <th>total_turnout</th>\n",
       "      <th>total2</th>\n",
       "      <th>pct_for_hardy2</th>\n",
       "      <th>pct_for_kolstad2</th>\n",
       "      <th>pct_for_nadeem2</th>\n",
       "      <th>total5</th>\n",
       "      <th>pct_for_caserta5</th>\n",
       "      <th>pct_not_caserta5</th>\n",
       "      <th>pct_for_lacoursiere5</th>\n",
       "      <th>...</th>\n",
       "      <th>pct_for_park5</th>\n",
       "      <th>pct_not_park5</th>\n",
       "      <th>pct_asian_vote</th>\n",
       "      <th>pct_asian_pop</th>\n",
       "      <th>pct_ind_vote</th>\n",
       "      <th>pct_ind_pop</th>\n",
       "      <th>pct_e_asian_vote</th>\n",
       "      <th>pct_e_asian_pop</th>\n",
       "      <th>pct_non_asian_vote</th>\n",
       "      <th>pct_non_asian_pop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4201</td>\n",
       "      <td>712</td>\n",
       "      <td>658</td>\n",
       "      <td>0.246201</td>\n",
       "      <td>0.431611</td>\n",
       "      <td>0.322188</td>\n",
       "      <td>642</td>\n",
       "      <td>0.447041</td>\n",
       "      <td>0.552960</td>\n",
       "      <td>0.305296</td>\n",
       "      <td>...</td>\n",
       "      <td>0.247664</td>\n",
       "      <td>0.752336</td>\n",
       "      <td>0.054775</td>\n",
       "      <td>0.117369</td>\n",
       "      <td>0.008427</td>\n",
       "      <td>0.028632</td>\n",
       "      <td>0.046348</td>\n",
       "      <td>0.088736</td>\n",
       "      <td>0.945225</td>\n",
       "      <td>0.882631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4208</td>\n",
       "      <td>400</td>\n",
       "      <td>380</td>\n",
       "      <td>0.328947</td>\n",
       "      <td>0.431579</td>\n",
       "      <td>0.239474</td>\n",
       "      <td>379</td>\n",
       "      <td>0.456464</td>\n",
       "      <td>0.543536</td>\n",
       "      <td>0.192612</td>\n",
       "      <td>...</td>\n",
       "      <td>0.350923</td>\n",
       "      <td>0.649076</td>\n",
       "      <td>0.197500</td>\n",
       "      <td>0.214389</td>\n",
       "      <td>0.060000</td>\n",
       "      <td>0.075990</td>\n",
       "      <td>0.137500</td>\n",
       "      <td>0.138399</td>\n",
       "      <td>0.802500</td>\n",
       "      <td>0.785611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4211</td>\n",
       "      <td>609</td>\n",
       "      <td>549</td>\n",
       "      <td>0.251366</td>\n",
       "      <td>0.384335</td>\n",
       "      <td>0.364299</td>\n",
       "      <td>542</td>\n",
       "      <td>0.492620</td>\n",
       "      <td>0.507380</td>\n",
       "      <td>0.250922</td>\n",
       "      <td>...</td>\n",
       "      <td>0.256458</td>\n",
       "      <td>0.743542</td>\n",
       "      <td>0.123153</td>\n",
       "      <td>0.202214</td>\n",
       "      <td>0.042693</td>\n",
       "      <td>0.046066</td>\n",
       "      <td>0.080460</td>\n",
       "      <td>0.156147</td>\n",
       "      <td>0.876847</td>\n",
       "      <td>0.797786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4215</td>\n",
       "      <td>623</td>\n",
       "      <td>573</td>\n",
       "      <td>0.258290</td>\n",
       "      <td>0.371728</td>\n",
       "      <td>0.369982</td>\n",
       "      <td>558</td>\n",
       "      <td>0.431900</td>\n",
       "      <td>0.568100</td>\n",
       "      <td>0.344086</td>\n",
       "      <td>...</td>\n",
       "      <td>0.224014</td>\n",
       "      <td>0.775986</td>\n",
       "      <td>0.109149</td>\n",
       "      <td>0.162014</td>\n",
       "      <td>0.032103</td>\n",
       "      <td>0.040379</td>\n",
       "      <td>0.077047</td>\n",
       "      <td>0.121635</td>\n",
       "      <td>0.890851</td>\n",
       "      <td>0.837986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4217</td>\n",
       "      <td>399</td>\n",
       "      <td>359</td>\n",
       "      <td>0.281337</td>\n",
       "      <td>0.406685</td>\n",
       "      <td>0.311978</td>\n",
       "      <td>355</td>\n",
       "      <td>0.425352</td>\n",
       "      <td>0.574648</td>\n",
       "      <td>0.326761</td>\n",
       "      <td>...</td>\n",
       "      <td>0.247887</td>\n",
       "      <td>0.752113</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.254478</td>\n",
       "      <td>0.027569</td>\n",
       "      <td>0.063619</td>\n",
       "      <td>0.115288</td>\n",
       "      <td>0.190859</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0.745522</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   precinct  total_turnout  total2  pct_for_hardy2  pct_for_kolstad2  \\\n",
       "0      4201            712     658        0.246201          0.431611   \n",
       "1      4208            400     380        0.328947          0.431579   \n",
       "2      4211            609     549        0.251366          0.384335   \n",
       "3      4215            623     573        0.258290          0.371728   \n",
       "4      4217            399     359        0.281337          0.406685   \n",
       "\n",
       "   pct_for_nadeem2  total5  pct_for_caserta5  pct_not_caserta5  \\\n",
       "0         0.322188     642          0.447041          0.552960   \n",
       "1         0.239474     379          0.456464          0.543536   \n",
       "2         0.364299     542          0.492620          0.507380   \n",
       "3         0.369982     558          0.431900          0.568100   \n",
       "4         0.311978     355          0.425352          0.574648   \n",
       "\n",
       "   pct_for_lacoursiere5  ...  pct_for_park5  pct_not_park5  pct_asian_vote  \\\n",
       "0              0.305296  ...       0.247664       0.752336        0.054775   \n",
       "1              0.192612  ...       0.350923       0.649076        0.197500   \n",
       "2              0.250922  ...       0.256458       0.743542        0.123153   \n",
       "3              0.344086  ...       0.224014       0.775986        0.109149   \n",
       "4              0.326761  ...       0.247887       0.752113        0.142857   \n",
       "\n",
       "   pct_asian_pop  pct_ind_vote  pct_ind_pop  pct_e_asian_vote  \\\n",
       "0       0.117369      0.008427     0.028632          0.046348   \n",
       "1       0.214389      0.060000     0.075990          0.137500   \n",
       "2       0.202214      0.042693     0.046066          0.080460   \n",
       "3       0.162014      0.032103     0.040379          0.077047   \n",
       "4       0.254478      0.027569     0.063619          0.115288   \n",
       "\n",
       "   pct_e_asian_pop  pct_non_asian_vote  pct_non_asian_pop  \n",
       "0         0.088736            0.945225           0.882631  \n",
       "1         0.138399            0.802500           0.785611  \n",
       "2         0.156147            0.876847           0.797786  \n",
       "3         0.121635            0.890851           0.837986  \n",
       "4         0.190859            0.857143           0.745522  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Example 2x2 data\n",
    "santa_clara_data = Datasets.Santa_Clara.to_dataframe()\n",
    "\n",
    "group_fraction_2by2 = np.array(santa_clara_data[\"pct_e_asian_vote\"])\n",
    "votes_fraction_2by2 = np.array(santa_clara_data[\"pct_for_hardy2\"])\n",
    "precinct_pops = np.array(santa_clara_data[\"total2\"])\n",
    "\n",
    "demographic_group_name_2by2 = \"e_asian\"\n",
    "candidate_name_2by2 = \"Hardy\"\n",
    "precinct_names = santa_clara_data['precinct']\n",
    "\n",
    "santa_clara_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "unlike-baker",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Example rxc data (here r=c=3)\n",
    "santa_clara_data = Datasets.Santa_Clara.to_dataframe() # This line same as 2x2 example\n",
    "\n",
    "group_fractions_rbyc = np.array(santa_clara_data[['pct_ind_vote', 'pct_e_asian_vote', 'pct_non_asian_vote']]).T\n",
    "votes_fractions_rbyc = np.array(santa_clara_data[['pct_for_hardy2', 'pct_for_kolstad2', 'pct_for_nadeem2']]).T\n",
    "precinct_names = santa_clara_data['precinct'] # This line same as 2x2 example\n",
    "\n",
    "candidate_names_rbyc = [\"Hardy\", \"Kolstad\", \"Nadeem\"]\n",
    "demographic_group_names_rbyc = [\"ind\", \"e_asian\", \"non_asian\"]\n",
    "precinct_pops = np.array(santa_clara_data['total2']) # This line same as 2x2 example"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "relevant-stand",
   "metadata": {},
   "source": [
    "# Fitting a model for EI\n",
    "\n",
    "We fit most models by first creating a `TwoByTwoEI`, `RowByColumnEI` or `GoodmanERBayes` object, which is initialized with a (required) model name that is chosen among the supported models and (if desired) the applicable model parameters, then calling its `fit` method, with the relevant data as arguments. We can also, optionally, pass arguments to specify sampling choices like the number of MCMC draws.\n",
    "\n",
    "We also can pass to the fit() function of `TwoByTwoEI`, `RowByColumnEI`, or `GoodmanERBayes` additional keyword arguments for the sampler, such as:\n",
    "\n",
    "- draws - the number of samples to draw\n",
    "- chains - the number of chains\n",
    "- tune - number of iteration to tune\n",
    "- cores - number of chains to run in parallel\n",
    "\n",
    "(see https://docs.pymc.io/api/inference.html sampling.sample for more)\n",
    "E.g.:\n",
    "\n",
    "We fit a Goodman's ecological regression model by creating a `GoodmanER` or `GoodmanERBayes` object and then calling its fit method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "alpha-shanghai",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Compiling...\n",
      "Compilation time = 0:00:01.844045\n",
      "Sampling...\n",
      "Compiling.. :   0%|          | 0/4200 [00:00<?, ?it/s]\n",
      "\u001b[A\n",
      "\u001b[A\n",
      "\n",
      "\u001b[A\u001b[A\n",
      "\n",
      "\u001b[A\u001b[A\n",
      "Running chain 0:   0%|          | 0/4200 [00:02<?, ?it/s]\n",
      "\n",
      "Running chain 0:   5%|▌         | 210/4200 [00:02<00:02, 1806.68it/s]\n",
      "\n",
      "\u001b[A\u001b[A\n",
      "Running chain 0:  15%|█▌        | 630/4200 [00:02<00:01, 2740.12it/s]\n",
      "\n",
      "\u001b[A\u001b[A\n",
      "Running chain 0:  25%|██▌       | 1050/4200 [00:02<00:00, 3166.48it/s]\n",
      "\n",
      "\u001b[A\u001b[A\n",
      "Running chain 0:  35%|███▌      | 1470/4200 [00:02<00:00, 3288.82it/s]\n",
      "\n",
      "\u001b[A\u001b[A\n",
      "Running chain 0:  45%|████▌     | 1890/4200 [00:02<00:00, 3315.64it/s]\n",
      "\u001b[A\n",
      "\n",
      "\u001b[A\u001b[A\n",
      "Running chain 0:  55%|█████▌    | 2310/4200 [00:02<00:00, 3437.31it/s]\n",
      "\n",
      "Running chain 0:  65%|██████▌   | 2730/4200 [00:02<00:00, 3551.04it/s]\n",
      "\u001b[A\n",
      "\n",
      "Running chain 0:  75%|███████▌  | 3150/4200 [00:03<00:00, 3488.88it/s]\n",
      "\u001b[A\n",
      "\n",
      "Running chain 0:  85%|████████▌ | 3570/4200 [00:03<00:00, 3362.35it/s]\n",
      "\u001b[A\n",
      "\n",
      "Running chain 0:  95%|█████████▌| 3990/4200 [00:03<00:00, 3124.76it/s]\n",
      "\u001b[A\n",
      "\n",
      "Running chain 0: 100%|██████████| 4200/4200 [00:03<00:00, 1201.98it/s]\n",
      "Running chain 1: 100%|██████████| 4200/4200 [00:03<00:00, 1202.41it/s]\n",
      "Running chain 2: 100%|██████████| 4200/4200 [00:03<00:00, 1202.84it/s]\n",
      "Running chain 3: 100%|██████████| 4200/4200 [00:03<00:00, 1203.26it/s]\n",
      "Sampling time = 0:00:03.770049\n",
      "Transforming variables...\n",
      "Transformation time = 0:00:00.107451\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: king99_pareto_modification\n",
      "        Computed from the raw b_i samples by multiplying by population and then getting\n",
      "        the proportion of the total pop (total pop=summed across all districts):\n",
      "        The posterior mean for the district-level voting preference of\n",
      "        e_asian for Hardy is\n",
      "        0.803\n",
      "        The posterior mean for the district-level voting preference of\n",
      "        non-e_asian for Hardy is\n",
      "        0.245\n",
      "        95% equal-tailed Bayesian credible interval for district-level voting preference of\n",
      "        e_asian for Hardy is\n",
      "        [0.60628653 0.95224906]\n",
      "        95% equal-tailed Bayesian credible interval for district-level voting preference of\n",
      "        non-e_asian for Hardy is\n",
      "        [0.21917476 0.27890371]\n",
      "        \n"
     ]
    }
   ],
   "source": [
    "# Fitting a first 2 x 2 model\n",
    "\n",
    "# Create a TwobyTwoEI object\n",
    "ei_2by2 = TwoByTwoEI(model_name=\"king99_pareto_modification\", pareto_scale=15, pareto_shape=2)\n",
    "\n",
    "# Fit the model\n",
    "ei_2by2.fit(group_fraction_2by2, \n",
    "       votes_fraction_2by2, \n",
    "       precinct_pops, \n",
    "       demographic_group_name=demographic_group_name_2by2, \n",
    "       candidate_name=candidate_name_2by2, \n",
    "       precinct_names=precinct_names, \n",
    "       draws=1200, # optional\n",
    "       tune=3000, # optional\n",
    "       target_accept=.99# optional\n",
    ")\n",
    "\n",
    "# Generate a simple report to summarize the results\n",
    "print(ei_2by2.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "conceptual-aurora",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Compiling...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running ['ind', 'e_asian', 'non_asian'] x ['Hardy', 'Kolstad', 'Nadeem'] EI\n",
      "r = 3 rows (demographic groups)\n",
      "c = 3 columns (candidates or voting outcomes)\n",
      "number of precincts = 42\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Compilation time = 0:00:02.316551\n",
      "Sampling...\n",
      "Compiling.. :   0%|          | 0/2500 [00:00<?, ?it/s]\n",
      "\u001b[A\n",
      "\u001b[A\n",
      "\n",
      "\u001b[A\u001b[A\n",
      "\n",
      "Running chain 0:   0%|          | 0/2500 [00:01<?, ?it/s]\n",
      "\n",
      "\u001b[A\u001b[A\n",
      "\u001b[A\n",
      "\n",
      "\u001b[A\u001b[A\n",
      "Running chain 0:   5%|▌         | 125/2500 [00:02<00:18, 129.09it/s]\n",
      "\n",
      "\u001b[A\u001b[A\n",
      "\u001b[A\n",
      "\n",
      "Running chain 0:  10%|█         | 250/2500 [00:03<00:14, 153.32it/s]\n",
      "\n",
      "Running chain 0:  15%|█▌        | 375/2500 [00:04<00:13, 162.71it/s]\n",
      "\u001b[A\n",
      "\n",
      "\u001b[A\u001b[A\n",
      "\n",
      "Running chain 0:  20%|██        | 500/2500 [00:05<00:12, 166.18it/s]\n",
      "\u001b[A\n",
      "\n",
      "Running chain 0:  25%|██▌       | 625/2500 [00:05<00:09, 194.23it/s]\n",
      "\u001b[A\n",
      "\n",
      "Running chain 0:  30%|███       | 750/2500 [00:06<00:09, 182.32it/s]\n",
      "\u001b[A\n",
      "\n",
      "Running chain 0:  35%|███▌      | 875/2500 [00:06<00:09, 177.87it/s]\n",
      "\u001b[A\n",
      "\n",
      "Running chain 0:  40%|████      | 1000/2500 [00:07<00:08, 171.12it/s]\n",
      "\n",
      "\u001b[A\u001b[A\n",
      "Running chain 0:  45%|████▌     | 1125/2500 [00:08<00:08, 161.06it/s]\n",
      "\n",
      "\u001b[A\u001b[A\n",
      "\u001b[A\n",
      "\u001b[A\n",
      "\n",
      "Running chain 0:  50%|█████     | 1250/2500 [00:10<00:10, 123.27it/s]\n",
      "\u001b[A\n",
      "\n",
      "Running chain 0:  55%|█████▌    | 1375/2500 [00:10<00:08, 131.86it/s]\n",
      "\u001b[A\n",
      "\n",
      "\u001b[A\u001b[A\n",
      "\u001b[A\n",
      "\n",
      "Running chain 0:  60%|██████    | 1500/2500 [00:13<00:10, 96.16it/s] \n",
      "\n",
      "\u001b[A\u001b[A\n",
      "Running chain 0:  65%|██████▌   | 1625/2500 [00:14<00:09, 95.17it/s]\n",
      "\n",
      "\u001b[A\u001b[A\n",
      "Running chain 0:  70%|███████   | 1750/2500 [00:15<00:07, 101.69it/s]\n",
      "\n",
      "\u001b[A\u001b[A\n",
      "\u001b[A\n",
      "\n",
      "Running chain 0:  75%|███████▌  | 1875/2500 [00:16<00:05, 105.46it/s]\n",
      "Running chain 0:  80%|████████  | 2000/2500 [00:17<00:04, 108.50it/s]\n",
      "Running chain 0:  85%|████████▌ | 2125/2500 [00:18<00:03, 112.93it/s]\n",
      "Running chain 0:  90%|█████████ | 2250/2500 [00:19<00:02, 119.43it/s]\n",
      "Running chain 0: 100%|██████████| 2500/2500 [00:21<00:00, 116.27it/s]\n",
      "Running chain 1: 100%|██████████| 2500/2500 [00:21<00:00, 116.28it/s]\n",
      "Running chain 2: 100%|██████████| 2500/2500 [00:21<00:00, 116.28it/s]\n",
      "Running chain 3: 100%|██████████| 2500/2500 [00:21<00:00, 116.29it/s]\n",
      "Sampling time = 0:00:21.818869\n",
      "Transforming variables...\n",
      "Transformation time = 0:00:00.194982\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "            Computed from the raw b_ samples by multiplying by group populations and then\n",
      "            getting the proportion of the total pop\n",
      "            (total pop=summed across all districts):\n",
      "            The posterior mean for the district-level voting preference of\n",
      "                ind for Hardy is\n",
      "                0.166\n",
      "                95% equal-tailed credible interval:  [0.02088225 0.4310046 ]\n",
      "                The posterior mean for the district-level voting preference of\n",
      "                ind for Kolstad is\n",
      "                0.121\n",
      "                95% equal-tailed credible interval:  [0.00476494 0.39064677]\n",
      "                The posterior mean for the district-level voting preference of\n",
      "                ind for Nadeem is\n",
      "                0.713\n",
      "                95% equal-tailed credible interval:  [0.40454743 0.93925375]\n",
      "                The posterior mean for the district-level voting preference of\n",
      "                e_asian for Hardy is\n",
      "                0.773\n",
      "                95% equal-tailed credible interval:  [0.59887097 0.93365357]\n",
      "                The posterior mean for the district-level voting preference of\n",
      "                e_asian for Kolstad is\n",
      "                0.170\n",
      "                95% equal-tailed credible interval:  [0.02463218 0.34787709]\n",
      "                The posterior mean for the district-level voting preference of\n",
      "                e_asian for Nadeem is\n",
      "                0.058\n",
      "                95% equal-tailed credible interval:  [0.00645026 0.15736658]\n",
      "                The posterior mean for the district-level voting preference of\n",
      "                non_asian for Hardy is\n",
      "                0.256\n",
      "                95% equal-tailed credible interval:  [0.22688238 0.28698157]\n",
      "                The posterior mean for the district-level voting preference of\n",
      "                non_asian for Kolstad is\n",
      "                0.438\n",
      "                95% equal-tailed credible interval:  [0.40687313 0.4647638 ]\n",
      "                The posterior mean for the district-level voting preference of\n",
      "                non_asian for Nadeem is\n",
      "                0.306\n",
      "                95% equal-tailed credible interval:  [0.28588975 0.32474296]\n",
      "                \n"
     ]
    }
   ],
   "source": [
    "# Fitting a first r x c model\n",
    "\n",
    "# Create a RowByColumnEI object\n",
    "ei_rbyc = RowByColumnEI(model_name='multinomial-dirichlet-modified', pareto_shape=100, pareto_scale=100)\n",
    "\n",
    "# Fit the model\n",
    "ei_rbyc.fit(group_fractions_rbyc, \n",
    "       votes_fractions_rbyc, \n",
    "       precinct_pops, \n",
    "       demographic_group_names=demographic_group_names_rbyc, \n",
    "       candidate_names=candidate_names_rbyc, \n",
    "       #precinct_names=precinct_names, \n",
    ")\n",
    "\n",
    "# Generate a simple report to summarize the results\n",
    "print(ei_rbyc.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "serious-bowling",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Goodmans ER\n",
      "        Est. fraction of e_asian\n",
      "        voters who voted for Hardy is\n",
      "        0.862\n",
      "        Est. fraction of non- e_asian\n",
      "        voters who voted for Hardy is\n",
      "        0.239\n",
      "        \n",
      "Goodmans ER, weighted by population\n",
      "        Est. fraction of e_asian\n",
      "        voters who voted for Hardy is\n",
      "        0.865\n",
      "        Est. fraction of non- e_asian\n",
      "        voters who voted for Hardy is\n",
      "        0.236\n",
      "        \n"
     ]
    }
   ],
   "source": [
    "# Fitting Goodman's ER - precincts not weighted by population\n",
    "\n",
    "# Create a GoodmansER object\n",
    "goodmans_er = GoodmansER() \n",
    "\n",
    "# Fit the model\n",
    "goodmans_er.fit(\n",
    "    group_fraction_2by2, \n",
    "    votes_fraction_2by2,\n",
    "    demographic_group_name=demographic_group_name_2by2, \n",
    "    candidate_name=candidate_name_2by2\n",
    ")\n",
    "\n",
    "# Generate a simple report to summarize the results\n",
    "print(goodmans_er.summary())\n",
    "\n",
    "# Fitting Goodman's ER - precincts weighted by population\n",
    "goodmans_er = GoodmansER(is_weighted_regression=\"True\")\n",
    "\n",
    "goodmans_er.fit(group_fraction_2by2, \n",
    "    votes_fraction_2by2,\n",
    "    precinct_pops, # Must include populations if weighting by population\n",
    "    demographic_group_name=demographic_group_name_2by2, \n",
    "    candidate_name=candidate_name_2by2\n",
    ")\n",
    "\n",
    "print(goodmans_er.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "interior-personality",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [b_1, b_2, eps]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      100.00% [8000/8000 00:01&lt;00:00 Sampling 4 chains, 0 divergences]\n",
       "    </div>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 1 seconds.\n"
     ]
    }
   ],
   "source": [
    "# Fitting Goodman's ER - Bayesian variant with priors over the intercepts with x=0 and x=1\n",
    "\n",
    "bayes_goodman_ei = GoodmansERBayes(\"goodman_er_bayes\", weighted_by_pop=True, sigma=1)\n",
    "\n",
    "bayes_goodman_ei.fit(\n",
    "    group_fraction_2by2, \n",
    "    votes_fraction_2by2,\n",
    "    precinct_pops,\n",
    "    demographic_group_name=demographic_group_name_2by2, \n",
    "    candidate_name=candidate_name_2by2\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "embedded-debate",
   "metadata": {},
   "source": [
    "# Plotting overview\n",
    "\n",
    "PyEI has a number of plots available. Here is a list of available plots.\n",
    "\n",
    "Plotting methods for any fitted EI model where inference involves sampling -- i.e. all approaches except the (non-Bayesian) Goodman's ER\n",
    "\n",
    "- Summary plots for distributions of polity-wide voter preferences\n",
    "    - `plot`\n",
    "    - `plot_kde` (2 by 2)\n",
    "    - `plot_kdes` ($r$ by $c$)\n",
    "    - `plot_boxplot` (2 by 2)\n",
    "    - `plot_boxplots` ($r$ by $c$)\n",
    "    - `plot_intervals` (2 by 2)\n",
    "\n",
    "- Plots of polarization\n",
    "    - `plot_polarization_kde` (2 by 2)\n",
    "    - `plot_polarization_kdes` ($r$ by $c$)\n",
    "\n",
    "For all approaches except Goodman's ER and the Bayesian Goodman's ER (which do not generate samples for each precinct)\n",
    "\n",
    "- Plots of **precinct-level** voter preferences\n",
    "    - `precinct_level_plot`\n",
    "    - `plot_intervals_by_precinct`\n",
    "        \n",
    "\n",
    "`GoodmansER` objects also have a plot method\n",
    "- `plot`\n",
    "\n",
    "Additional plotting utilities: for tomography plotting and comparing precinct-level posterior means:\n",
    "\n",
    "- `plot_utils.tomography_plot`\n",
    "- `plot_precinct_scatterplot`\n",
    "\n",
    "Here we only display a few of these plots as examples. A more detailed look at the plotting and reporting functionality, see the Plotting and Reporting notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "roman-evans",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Axes: xlabel='Support for Hardy'>, <Axes: title={'center': 'EI Summary'}>)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ei_2by2.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "extraordinary-insight",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 640x480 with 1 Axes>,\n",
       " <Axes: xlabel='Fraction in group e_asian', ylabel='Fraction voting for Hardy'>)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "goodmans_er.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "concerned-arena",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<Axes: title={'center': 'Support for Hardy'}>,\n",
       "       <Axes: title={'center': 'Support for Kolstad'}, ylabel='Probability Density'>,\n",
       "       <Axes: title={'center': 'Support for Nadeem'}>], dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ei_rbyc.plot_kdes(plot_by=\"candidate\") # or, plot_by=\"group\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "comic-bailey",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Polarization KDE for Hardy'}, xlabel='(e_asian - non-e_asian) support for Hardy', ylabel='Density'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ei_2by2.plot_polarization_kde(percentile=95, show_threshold=True) #set show_threshold to false to just view the kde"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "patent-proof",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Precinct level estimates of voting preferences'}, xlabel='Percent vote for Hardy', ylabel='Precinct'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA2gAAAScCAYAAAD+sqy8AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8g+/7EAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdd3wU1drA8d9syab3QgIk9CJVlKKCFBFsKCiChVcU9WLviu0qeAXFimK7lmsX9VoQ21URsILSRHpN771n63n/2GTZJW0DCdnA8/18NtvOnDkzmZ2ZZ86ZczSllEIIIYQQQgghRLvTtXcBhBBCCCGEEEI4SYAmhBBCCCGEED5CAjQhhBBCCCGE8BESoAkhhBBCCCGEj5AATQghhBBCCCF8hARoQgghhBBCCOEjJEATQgghhBBCCB8hAZoQQgghhBBC+AgJ0IQQQgghhBDCR0iAJtrE/Pnz0TQNTdOYP39+exfH56SkpLjWT7du3dq7OD5D1kvj3nrrLde6ufLKK9u7OKID+/HHH5kxYwbdunUjICBAfnMNGDdunGu9rFmzpr2LI1pRUVERjzzyCCNHjiQiIgK9Xu/6X7/11lvtXTwhADC0dwE6snHjxvHTTz81+n1wcDBRUVEMGTKEiRMncsUVVxAWFnYUSyiEEEIc9M9//pNHH320vYshRLtISUnh9NNPJz09vb2LIkSTpAatDVVUVJCamsqKFSu45ZZbSExM5J133mnvYgkvyRVU0VakNsx7sq5az9q1az2CswEDBjBr1ixuvPFGbrzxRmbPnt2OpWs7si8XdebOnesKzgICApgyZQrXXXed6zfQv3//di6hEE5Sg9ZKhg8fzogRI1zvlVKUlJSwfv169u7dC0BZWRmzZ8+murqauXPntldRhRBCHIfefvtt1+s5c+bw+uuvo2laO5ZIiKMnJyeH77//HgCTycSWLVvo3bt3O5dKiIZJgNZKzjnnnEbvtVq+fDlXXXUVJSUlANx6662ce+65dOnS5egV8CibP3++3HsmRCu68sorpQZJHJFNmza5Xs+ZM0eCsyZITduxx337HzNmjARnwqdJE8ejYOrUqbz33nuu92azmZdeeqkdSySEEOJ4U1xc7HodHx/fjiUR4uiT7V90JBKgHSXnnnsuQ4cOdb1fuXJl+xVGCCHEccdqtbpe63Ry+BfHF9n+RUciW+hRdMopp7heHzhwwOO7hrqlr66u5o033mDSpEkkJibi5+eHpmn89ddfDeb/448/ct111zFgwAAiIyMxmUwkJCQwefJkXnjhBaqrq1tU3rKyMpYuXcqUKVPo1q0bwcHBrjzPOOMMFixYwPbt2xuc1ptu9hu7+f/zzz9nypQpJCYmYjKZiI2NZdKkSbz33nsopVq0DAcOHGD+/PmcfvrpdO7cGX9/fwIDA+nRowdTp05l6dKl5OXleUxTVyb3HjrHjx/v+tz9cTS75C0sLOTpp5/mzDPPpGvXrvj7+xMeHs4JJ5zAjTfeyIYNGxqd9tNPP3WVuV+/fl7PMzU1FZ1Oh6Zp+Pn5UVhY2Ghaq9XKu+++y4wZM+jRowchISEEBQXRvXt3Lr30Uj7//PMW//9a2/r167n99tsZOnQoMTEx+Pn50alTJ8aOHcvixYs9rrA2paCggKeeeoqJEyeSkJDg2q6SkpI46aSTuPTSS3nzzTfJzMz0mO7KK69E0zSuuuoq12dvv/12g9vWuHHjPKb1prOMNWvWNDj98uXLueCCC0hKSsJkMhETE8PUqVP59ddf6+VhNpt5++23GT9+vOs3061bN+bOnUtqaqpX6ycvL48333yT2bNnc+KJJxIZGYnRaCQ8PJx+/fpx1VVX8d133zWZx5Gsq0O11r4xPT2dBQsWcPrppxMXF4fJZCIkJITu3bszYsQIrrrqKpYtW0ZBQYFX+Xlr3bp13HTTTQwYMICIiAj8/f3p0qULZ511Fi+88AKVlZWNTuveQYb7/6979+711mNLWK1WoqOjXdOuXbvW62knTJjgmm7p0qVNzuPNN99k6tSpJCUlERAQQGhoKH379uXqq6/mhx9+aHI+R7Iv96Zjkbpt1H36yspKXnrpJUaPHu3aRrp27cqll17Kb7/95tX6qbNt2zauv/56evfuTWBgINHR0Zx88sk89thjrm2stTvRaWi5MzIyeOihhxg6dCiRkZEEBQXRr18/br31Vvbs2dNsnu15fuO+T2xuX9LUbRk7d+7k/vvvZ8SIEcTFxeHn50dMTAwjR47koYceIisrq9n10NC6zc7OZtGiRYwYMYJOnTqh1+sJDw9vcPrWOMY2doxYtWoVl1xyCT169MDf35+oqChOP/10XnjhBY/A1hu5ubk88cQTnHnmmSQmJhIQEEBAQACJiYmcffbZPPHEE6SkpDSbT2ueU6xfv56bbrqJYcOGERERgdFoJDIykj59+jB69GjuuOMOvvvuuxafJ7cpJQ7b2LFjFaAA9fDDDzeb/v7773elNxqNHt89/PDDHnnt2LFDDRgwwPWZ+2Pz5s0e06alpalx48Y1mNb9kZCQoH7++Wevlu3ll19WERERzeYJqG+//bbe9IcuT0PefPNNV5rZs2erkpISdf755zc5r7POOktVVVU1W/6amhp14403KoPB0Gz5jUajKisrc03rzTLXPd58802v1uehkpOTXXkkJSU1m/6FF15QYWFhTZZF0zQ1Z84cZTabG1wf4eHhrrTr16/3qpwLFy50TXP++ec3mm716tWqZ8+eza6vUaNGqYyMjFZbL94qKipSF110UbPlCw8PV//973+bzGv58uVe/zY6d+7sMe3s2bO93rbGjh3rMe2hv5eGrF692mP6ioqKJpdb0zT12muvuabfs2eP6tu3b6Ppg4OD1S+//NLk+nnuueeUXq/3ahknTJigCgoKGsznSNZVndbcN77yyisqICDAq/KcdtppTeblrYqKCjVz5sxm5xcfH6+++eabBvNwP04192ip6667zjXtjTfe6NU0GRkZSqfTKUAZDAaVm5vbYLp169Z5tU8588wzVV5eXoN5eLvcUH9f7r7eVq9e3WD+7tvom2++qXbs2KH69+/f5Hweeughr9bT4sWLldFobPJ//ttvv3m1X2iJQ5d7xYoVHseOQx8mk0ktXbq0yTzb8/zGfZ/Y3KOhc5Wamhp13XXXNbtPCwgIaHY9HLpuGzuWhIWFNbgcrXGMPfQYYTab1T/+8Y8m8xw2bJjKz89vctmUUsput6sFCxaowMDAZsup0+nU9u3bmyxnayyv1WptdvncHw888ECzy3m0SCchR5H71fmmxkMrLCzkrLPOIi0tDX9/f8aMGUNSUhLl5eWsW7fOI+3OnTs544wzyM7OBpxXDIcOHcqAAQMIDAwkMzOTn3/+mfLycrKysjjzzDP59ttvGT9+fKPzv+WWWzyuaur1eoYPH07v3r3x9/cnPz+fv/76y3UFpKam5nBWhwe73c5FF13Ejz/+iJ+fH6eeeio9e/akpqaGX375hbS0NAD+97//cccdd/Dyyy83mldFRQWTJk3yuKIbGBjI6NGj6dKlC0opMjMz2bhxI4WFhVitVux2uyvtjTfeCDhr8uquik2dOpXOnTvXm9fR6JL39ttvZ8mSJa73UVFRjBo1ioSEBGpqati8eTPbtm1DKcV//vMfsrKy+Prrrz2acJhMJqZPn87rr78OwPvvv8/JJ5/c7Lzff/991+tZs2Y1mOa///0vl19+uesqm7+/P6NGjaJbt27o9Xr27NnD2rVrsdlsrFu3jlNOOYX169cTFxd3OKujxXJycpgwYQI7d+50fda/f3+GDh1KSEgIeXl5/PrrrxQUFFBSUsKMGTN49913ufzyy+vltWHDBqZPn47NZgOc3TTXLavJZKKsrIz9+/ezdetWqqqq6k0/ceJEgoOD2bVrFz/++CMA/fr144wzzqiXtjVuYL/66qv59NNP8fPzY8yYMXTv3p3S0lJ+/PFHioqKUEoxd+5c+vbtS58+fRg/fjyZmZmEh4czduxYYmNjyczM5Mcff8RsNlNRUcGFF17I7t27iYiIaHCeWVlZrt9Tjx496N+/PzExMfj7+1NSUsLWrVtdNe+rVq1i4sSJrFu3DpPJ1KrrqjX3jcuXL+e6665zvQ8NDeWUU06hS5cuGAwGSktL2bNnD9u2bcNisXjzr2lWVVUVEyZM4M8//3R9lpCQwJgxYwgODmbfvn38+uuv2O12srOzOf/881m2bBnTp0/3yGfatGkMHDgQgHfeeYfy8nIArrjiCkJCQo6ojLNmzeKVV14B4KOPPmLJkiUYDE2fVnzwwQc4HA4AJk2aRGxsbL00P//8M2effbbHb2jEiBEMGDAAi8XCunXr2L9/PwA//PADo0eP5tdffyUmJsYjn6O5L8/KymLixIlkZWURHh7OmDFj6NSpEwUFBaxatYrS0lIAHnnkEU444QRmzpzZaF7PPPMM8+bNc7339/dn/PjxdOnShYKCAlavXk12djbnnXcet9122xGVuykbNmzggQcewGKxEBkZybhx44iMjCQtLY01a9ZgsVgwm83cfPPN6HQ6brjhhmbzPNrnN507d3ZtB83tS9x74gZnbejkyZM9aj67d+/OySefTEREBMXFxfz+++9kZmZSXV3NzTffTFlZGffff3+z6+H3339n/vz5WK1WV21VdHQ0eXl5bN682SNtWx5j586dy1tvvYVOp2PkyJH069cPh8PBunXr2L17N+DsXOWKK67gm2++aTQfu93OxRdfzOeff+76zM/Pj1NOOYVu3bphMBjIyclh06ZNZGdn43A4Gt1Xtuby3nXXXbz66quu9507d2bEiBFER0ejlKKwsJAdO3a4ltWntG982LG1tAZt8ODBrvTDhw/3+M79ClNdrc/06dPrXbWw2+3KYrEopZSqrKz0uFo3ceJEtXv37nrzLS0t9bjSGR8fr0pKShos48svv+xxNWHGjBkqPT29wbRbt25Vt9xyi/ruu+/qfdfSGjSTyaQAdfbZZ9e7GmK1WtVdd93lSqtpmkpOTm4wT6WUxxVnvV6vFixYoCoqKuqls9vtatWqVeqCCy5ocH14cwX1cHlbU/TGG2+40gUHB6uXX365wRqyVatWqc6dO7vSLl68uF6aNWvWuL7v1KmTstlsTZZx06ZNrvShoaGqurq6Xppt27Z5XC277bbbVGFhYb10+/fvV6NHj3alO/vssxucZ2vXoNntdjV+/HiPK4EN1R5WV1er+fPnK03TFKCCgoLUgQMH6qW74IILXHlddNFFqqioqMH51tTUqK+//lrNnTu3we8P56p3S2vQ/Pz8FKBOP/10lZqa6pGupKTEY72MHz/etWw333xzvd/Lzp07VUJCgiv9/PnzGy3nG2+8oZYuXdrkVc0tW7aok08+2ZXfv/71ryNa7kO19r5xyJAhrjQ33XSTqqysbHC+5eXl6uOPP1bz5s3zqpxNuf766z32Y88884yy2+0eafbs2aNOOukkj99pQ9ttnaSkJFfapvahLdGjRw9Xnl999VWz6d2Pgx988EG974uKijz2ZT179mzwN/vee+951GhOmTKl0Xkezr68pTVodcewe+65p972UVhYqCZMmOBK26NHD+VwOBrMc9u2ba7fLqDOPffcejWElZWVru22br4t+X14u9x15bjjjjtUTU2NR7rMzEyPZTKZTGrnzp0N5ukr5zct3ZdcccUVHtvhDz/8UC+NzWZTL730kuv/oNfr1e+//95gfu7r1mAwKE3T1L/+9S/XMtdxX9etfYx1P0bUlXn48OH1/ncOh0MtWbLE43zwp59+anRdzZs3zyPtTTfd1GA5lVLqjz/+UFdccYXatm1bve9ac3nz8/Nd25ter1dvvfVWo7+7rKws9fzzz6vXX3+90WU82iRAOwItCdC++uorj4333nvv9fjefQcGqEmTJtU7GB/qkUcecaU/66yzlNVqbTK9+87m8ccfr/d9UVGRCgkJcaW57rrrmsyvKS0N0AA1ZsyYRpfB4XCo4cOHN1l+pZT64YcfPPJctmzZYS9DewdoZWVlrqYler2+yZ2jUkrt2LFD+fv7K0BFRUXVO0lwOBwqMTHRNd+GAmt3d955pyvtnDlzGkzjfoB+9NFHm8yvoqLC44C7bt26emlaO0B75513XPkNHTpUlZeXN5n+oYceanL7j4qKch3YmsurKUcjQANU//79G20SnJaWVq8J8NVXX93o/D/44ANXun79+nlV5qaUlJSoTp06uU6qGrtgcDjrqjX3jeXl5a7vunbt2ugBvjXt27fP1QwQUM8//3yjaYuKilS3bt1caa+66qpG07ZFgPbggw+68rz00kubTLtt2zZX2uDg4AYDXfffYHh4eL2LC+4+++wzr04gj0aABqj77ruv0fxycnJUUFBQk/s/pZSaPn26K83JJ5/c4AW5OhdffLHH/Fs7QGvuPKCqqsoj4L744osbTOcL5zdKtWxf8vPPP7vSdunSRWVnZzeZ/j//+Y9HeRty6Lpt7pipVOsfYw89RvTu3bvJY5n79tjYtrB7926P/dVjjz3W7HI1pjWX98svv3R9d/nllx92mdqLBGhHwNsAbfny5R5tuP38/FRaWppHmkN3YDt27Ghy3haLRcXGxipwtuVNSUlptryZmZmuGoJBgwbV+/7xxx/3ODk+9IpZSxxOgLZhw4Ym83zppZdcaS+66KIG05x11lmuNDNnzjzs8ivV/gGa+9Wrpk6c3c2dO9c1zaefflrv+3vvvdf1/RVXXNFoPna73aPGZNWqVfXS/PXXX67v+/Tp02yNnFJKLVu2zDXNzTffXO/71g7Qhg4d6sqvuXunlHLWpNX9VsPCwuqdRNTdExIREXFE5TpaAdoXX3zRZJ6nn366K63JZGr0Xh6lnOum7oqrpmke920eLvdaor///rvBNC1dV629b8zMzHTNf+jQoV4t15Fyvxo9ePDgZoPCjz76yOP/2FgNQlsEaLt373blGRgY2OTJnvtyNbT/cTgcrqAdUE8//XSz8z/77LNd6S+55JIG0xyNAC0mJqbBVgbuZsyY4Urf0P1KhYWFHvedNbTfdZeWluZxYtzaAVpISIgqLS1tMr37RVGj0djgvUq+cH6jVMv2JVOnTnWlfffdd5udv8PhUP369XPtHxtaD+7rtnPnzs0GnW1xjD30GNHQeYK7b775xpX2pJNOajCNew3mqFGjDvsiVmsv7/vvv+/67rbbbjusMrUn6cWxlXzzzTfcdNNNHo9Zs2bRt29fpk6d6hqkGuDZZ5+la9eujeY1ePDgZtvDb9iwwdX74CmnnEJSUlKzZUxISHD14Ldt2zaPMoHz/q461157bb17QtpSjx49OOmkk5pMc+KJJ7peN9QDkNls9uht6+abb26t4rUL9/bel1xyiVfTTJgwwfW6oR76/u///s/1+vPPP2+0x6LVq1e77tno0qULY8eObbJ8F198MXq9/ojL15qys7NdPYJ17tyZ0aNHNzuNv7+/q7fV0tJStm3b5vF9YmIi4Lyf9IMPPmjdAreygIAAzj777CbT1N2bBHD66afXu4fHnb+/Pz179gRAKeVVL1x5eXmsWLGCxYsXc++993LzzTd77CPdex5trPe2lmrtfWNMTAwBAQEAbN26lZ9//rlVytmUVatWuV5fddVVzfawOG3aNCIjIwHnfrAlPSoeqT59+rjuZ62qqmL58uUNplNKsWzZMtf7hu5p3blzJzk5OYCzG/TZs2c3O/9rrrnG9bo9B5eeMmUK/v7+TaZp7hj2+++/u+67iY+Pb7Z30q5du3L66ae3uKzeuuCCCwgNDW0yzRlnnEGXLl0AZ697ze3X2+v8piVsNpurh1CDwcBFF13U7DSaprnufVNK8fvvvzeZ/qKLLmr2fs22Psb6+/tz3nnnNZmmuW0WPM8db7rpphb3CFuntZe37ngNzp6s6/YtHYV0EtJK1q9fz/r165tMExISwnPPPefR1WtDmgtUAI8DcEFBATfddJNX5azbaanajjLcu3P9448/XK+b6kSkLQwaNKjZNFFRUa7XdTdcu/vrr79cHZYEBgYycuTI1itgO3D/H7/zzjuNnvi4y8jIcL1OT0+v9/0JJ5zA0KFD+euvvygvL+eLL75oMPhzH1j9sssua3DMGPfyrV271qttULl1idtQ+VqTe/mUUl7/Ruo6HwBnGQcPHux6P3PmTBYtWgQ4TzA//PBDZs6cyYQJE3xu4NM+ffpgNBqbTOPe0ccJJ5zQbJ7u6cvKyhpNt2PHDubNm8e3337r0QFPU1qra/rW3jcajUamTZvGBx98gN1uZ+LEiUyfPp3p06czduxYj/1Sa1BKeQSrp512WrPTGI1GRowY4TpR2rRpE2eddVarlqsps2bNcgXb7733XoPB188//+zq7Ck+Pt7jxKqOe+cI/fr182rduq+fnJwcsrKySEhIaPEyHKnWOobVGT58uFcnusOHD2+zwHTUqFHNptE0jZEjR7qOPZs3b2bq1KmNpm+v85uW+Pvvv11DV/j7+3P33Xd7NZ37OWBzx7eWroe2OMb27dsXPz+/JtM0t83m5uZ6BG5Hcu7Y2ss7cuRIkpKSSE1NJT09nQEDBjB79mymTJnCqFGjXBfefJUEaG0oODiYqKgoBg8ezMSJE7niiiu82mE0dRW7jvuYG7t37z6sHmjce5UsKyvzqE3p0aNHi/M7Ek31alnH/WSzoXE5cnNzXa+7du3a7NUpX1ZRUeHqbQ3g3XffbXEejY3pNWvWLNeJwPvvv18vQKupqeGzzz7zSN8Q921w1apVHlf9j6R8rcW9fFlZWbz44ostzuPQMj7wwAP89NNP/Pbbbyil+PLLL/nyyy8BZ+9eY8aMYeLEiV5deW5r3vym3H8jLU3f2Ng43333HRdccAFms9mLUh7kvr0fidbeN4Kz1cOmTZvYtWsXVquVZcuWsWzZMte4gmPGjGHSpEmce+65zdaiNKe0tNRj3XpTewDQrVs31+vWHoetOZdeeil33XUXNpuNlStXkpubW69HNfceYS+99NIGr47n5+e7Xnu73HFxcfj7+7suzhUUFLRLgNYaxzD3/1tdrVRzGuqRsrW410A0xb1FkPv/sCHtcX7TUu7zr6ioaJVjx6Fauh7a4hjb0m22rvdid+7nXXVj0x2u1l5eo9HIe++9x7nnnktZWRlFRUU8++yzPPvss/j5+TFs2DBOP/10zj33XMaMGXPYNX9tRZo4tpKHH34Y5bynz/UoLy8nJSWFFStWcMstt3h9NcebqL6hKxkt5f5jO/TkKDg4+Ijzb4nW+GG4L8PRLn9ra+3/rzv3GrHvvvuu3uDTK1ascNWODB48uNErw0daRm9rVg5XW6zDwMBAVq9ezbPPPutq7lcnOTmZd955hyuuuIL4+Hjuvvvudh30sqW/qdb4Debn5zNz5kxXcNa9e3cWL17Mb7/9RlZWFlVVVTgcDtc+8uGHH3ZNW9f1+pFqi/97bGws69evZ/78+R4nIEopdu7cyauvvsr06dNJSEjg8ccfP6Jtu6KiwuN9UFCQV9O5p2utYNdbsbGxTJw4EXD+rj/66COP7y0WC5988onrfWMXfdyX3dvlPjTt0V72Oq3x+3Ff/sDAQK+macl6aqnDKUNz6789zm9a6mjM/2ish+b2Q7523tUWyzt69Gi2bNnCnDlzPLbTuuE6nnjiCcaOHUu/fv08hgjwBRKgdVDuG9ptt91WLzj05uHevv3Q8XAOPUnoCNyXoSOW392hB92SkpIW/38ba/bi3rzIarXy8ccfe3zvzdhnh5Zx+fLlh7UNtiX38k2dOvWwynfllVfWy9doNHLbbbexb98+tm7dyosvvsgll1zicSW7qqqKp556ivHjx7drkHa0vfbaa66D7Iknnsjff//NPffcw6mnnkp8fDwBAQEeJwVtcTLd2vvGOsHBwTz88MOkp6ezfv16nn76aaZOnUp0dLQrTXFxMffddx8XXXTRYW/fh57k1DW1ao57uiMd3+xwuO8r3PchAF9//bXr6vYJJ5zgcV+LO/dl93a5D03bHsveWty33YbGUWxIS9ZTSx1OGVpj/bfVb/hw5j906NDDmv/8+fOPYA3UL4cvHmOhdc+72mp5u3XrxhtvvEF+fj4rV67koYceYvz48R5B8p49e7jwwgt55plnjmgZWpMEaB2Ue/ORvXv3HnF+oaGhHhtrcnLyEed5tLmvk/T09CO6gtbewsPDPTppaY3/sTv3kyn3+82Kiopc97HodDouu+yyRvNo7W2wtR2N8g0cOJAbbriBZcuWkZGRwebNm7n66qtd3//xxx+H1Tymo6obBBbgwQcfbPaKampqaquXoa3/7zqdjpNPPpk77riDzz//nNzcXH799VeP+26++OILPv3008PKPywszKNZUd19W81xX5fuQePRMm3aNNf/+88///RY9+77mKYu+rg3+/J2ufPy8lzNG6F9lr21uJfd/X7ipmRmZrZVcbz+H7ina431397HFvf5HzhwoNVq94+kHL54jAXPMprNZteg4keaV1ssb0BAAGeccQYLFixg1apVFBYW8umnnzJkyBBXmvvuu69Nf1MtIQFaB+XeAcZPP/3U4vs9msuzpW1/fcHQoUNd939UVVV5dHpyONq7PfKIESNcr7/77rtWzfuiiy5yNV/5/fffXQH5xx9/jMViAWDcuHFN3t/gvr20dvlag/sN7tu3b/f6hOdIDB06lNdff51//OMfrs9WrFhRL117b1ttxf0eggEDBjSZ1m6389tvvzWbZ0vXVVvsG5ui0+k47bTT+Oyzz5g8ebLr84b+797QNI2hQ4e63jfXGxw4m1P9+eefrvfDhg07rHkficDAQI8gta4WrbS0lK+//hpwLltTF33ca9Z27dpFUVFRs/N134Y6derU4D0wHeX35v5/37Bhg1c1IM11TnYkvOkNVCnlcaxtjW3vaP+GDzV06FDXBdKysrKj2iuqO18/xoIzqHK///VIzh2P9vIGBARw4YUXsnr1ajp16gQ4mz76yrqWAK2DOu2001z3tFVUVPDaa68dcZ7uXXK/9tprR32neKRMJpNHD0IvvPDCEeXnfrN/Yx0itCX37m9feeUVj6vERyo4OJjzzz/f9b6uy3hvr3QfWr4ff/yRrVu3tlr5WkP37t09unNesmTJUZu3+7pxv4m6TntvW23FvbfP5ppHLV++3Ktuj1u6rtpi3+gNTdM499xzXe8b+r97y72Hw7fffrvZE/UVK1a47iV1HyriaGuomeN///tf17FkzJgxTXb+0b9/f9eJkt1u99gfNebNN990vW6sB7mO8ns79dRTXbWnWVlZzfbOmJ6ezi+//NJm5VmxYkWz9wWtXLnSVeNgNBq96nW0Oe31G64TEBDg8Rt89tlnj+r86/j6MbaO+7njiy++eNhNK9treSMiIjj11FNd749k392aJEDroEwmE7fddpvr/f3339+ijbmhDfDaa691NVFJTU31yL+juOOOO1yvP/zwQz788MPDzsu9e9n2qPKeO3eu6yCVkZHBDTfc4PWOr6CgoNkbhA89mUpJSXFdrff392927JcRI0a42vkrpZg1a1aTXa+7s1gsbd6LI8C8efNcr5977jlWrlzp9bSHBg9ms9nrNvbuTX4a6q2rvbettuLe++sXX3zRaLr8/Hxuv/12r/Js6bpq7X1jeXm5q1a5Oc3937117bXXuoLdTZs28eqrrzaatrS0lHvuucf1/tJLL/Wqd7a2MHHiRFeAtW/fPv744w+v72kFZ5DrXvv8yCOPNPk///rrr129qAJcd911DabrKL+3qKgojwtn99xzT5Pb3l133dWmnS2Vl5dz7733Nvp9dXU1d911l+v9BRdccETbfZ22OL9pKfdjx6effspbb73l9bStNd5WRzjGgvM+wbr91dq1a1m8ePFh5dPay3toB2hNaa19d2uSAK0Du/POO13NiMrLyxk9ejSvvfZaozv0wsJCXn/9dU466SSefPLJet9HRER4/LBeeeUVZs6c2WjTsO3bt3Prrbfy/ffft8LStI6JEydy8cUXu97PmjWLRx55pMGr+Q6Hg9WrVzNt2rQGrxK69174ySefHJUbbt2FhYV5XLl78803mTJlCrt27WowvVLKNXZIUlJSs51TTJ482bUj2rlzJ3feeadrGc8//3yvuolfunSpK6j/+++/GTFiRJNB0L59+1i4cCHdu3f3qnnbkZo1a5brSqjNZuPcc89l8eLFjd5YX1FRwbJly5gwYUK9gc6zs7Pp2rUrd955p0dzMndKKf73v//x0EMPuT4755xz6qVz37b++OMPr+/18HXuV0Aff/zxBmtANm3axNixY0lPT/eqB7rDWVetuW/cuHEjSUlJPPzww2zfvr3B6e12O++//z5Lly51fdbQ/91bPXv2ZO7cua73N910Ey+++GK9e2H279/PpEmTXGP3hYaG8s9//vOw53uk9Hq9x7Adixcvdg3sbTKZPPbNjbnttttcTasLCws544wzGhzE/KOPPmLmzJmu91OmTGl00Ob23pe3xMMPP+yqRduwYQMXXnhhva7rq6qquP766/n444897lVubX5+frzyyivcdddd9VrUZGdnM2XKFP7++29X2gULFrTavFv7/Kalxo4d6zFQ+pw5c7j77rsbPek3m8188cUXTJs2zSPIPlK+fowF55ibd955p+v9fffdx80339xoE+X169dz5ZVXNrg/bc3lXbp0KUOHDuWll15q9N648vJy5s2b5xrHUa/XezRVb08dd6AoQXBwMCtWrGDixIkkJydTVlbGP/7xD+6++25OOeUUOnfujKZpFBUVsXPnTnbv3u06wDfWFOSGG25g27ZtvPzyy4DznqRPP/2U4cOH06dPH/z9/cnPz2fz5s2uwQmP9qDWzXn99ddJTU3lzz//xG638/DDD/PEE09w2mmn0bVrV5RyDmK5YcMG1862oQP2hRdeyH333YdSiq+//prBgwdz6qmnevRadMkll3DyySe32bJceeWVHDhwgH/961+A84rxN998w8CBAxk4cCChoaFUVlaSmZnJ5s2bXQN1esNgMDBz5kxXU1Bvxj471MCBA1m2bBkzZ86kqqqK3bt3c+aZZ9K1a1eGDx9OdHQ0FouF/Px8tmzZclTuA3On1+v5+OOPOfPMM9m8eTMWi4V7772XRx55hFGjRpGYmIifnx/FxcXs2bOHHTt2uJpANVSDWFJSwjPPPMMzzzxDZGQkJ554Ip07d8ZkMpGXl8fff//t0cFOnz59uPXWW+vlExcXx2mnncZvv/1GTU0NQ4YM4ayzziI+Pt51JbJnz55cf/31bbRm2saVV17JM888w549ezCbzfzf//0fixYtYsiQIfj7+7Nt2zbXgXDIkCFMnjyZJ554osk8D2ddtfa+MScnh0ceeYRHHnmEuLg4TjzxRDp16oTBYCAnJ4eNGzd6nACMGTOmwQHgW+Kpp55iw4YNrF+/HpvNxk033cTjjz/O6NGjCQ4OZv/+/fz888+uGhSDwcAbb7xB9+7dj2i+R2rWrFmu5sTu3Vafe+65Xg01ExERwQcffMDZZ5/t2qcMGzaMkSNHcsIJJ2CxWPjjjz88OhHo3bs3b7zxRqN5+sK+3FuDBg1i4cKFrlrRr7/+mqSkJMaPH0/nzp0pLCxk1apVlJSUEB4ezu233+4arsK9iXFrWLhwIQ888ABPP/00b775JhMmTCAiIoK0tDTWrFnjEbQ9+eSTXg127622OL9pqX//+99kZ2fz/fffo5Tiqaee4vnnn2f48OH07NmTgIAASktL2b9/P1u3bnXdhuDNINTe8vVjbJ1Fixaxa9cuV432Cy+8wKuvvsqpp55Kt27dGtxXNtRKq7WXd8uWLdx4443cdNNN9OzZk4EDBxIdHY3VaiUrK4vff//d44Ltvffe6zGuX7tS4rCNHTtWAQpQDz/88BHl9fDDDx92XoWFheriiy9Wmqa58mjqER4ert56660m81yyZIkKDQ1tNi9N09R33313WMvz5ptvutLMnj272eVMTk52pU9KSmoybVVVlbr22muVXq9vdhn8/f1VWVlZg/k8+OCDTU775ptvNlvuI10WpZT66KOPVEJCglf/X0CNGDFC1dTUNJvvunXr6k0bFRWlLBZLi5bnr7/+UieddJLX5evWrZvavHnzEa8Xb1VVVanrrrtOGQwGr8oXEBCgFi1a5JFHRkaGMplMXi/juHHjVHZ2dqNl2rhxY5O/sbFjx3qk9+b3snr16kanb0hL9zvu+7zVq1c3mGb37t2qR48eTa6b0047TWVkZHg9/5auqzqtsW9ct26d19sNoKZPn97o/qSlysvL1YwZM5qdZ3x8vPrmm2+azS8pKck1TXJycquUsSH9+/evV8bPPvusRXmsXbu22e0IUBMnTlR5eXnN5tfSfbk32/rs2bNbdCxoyTFv0aJFTW538fHx6rffflOvvvqq67Nbbrml2TI059Dl/uKLL1RYWFij5fDz81NLlixpMk9fOb9p6TmHUkrZbDb1z3/+UwUGBno1f6PRqG688cYG8/Jmm2pMax1jW3qMUEp55NsUu92u7r//fq+Ok3q9Xu3cubNNl/epp57yeno/Pz+1YMECr9bH0SI1aMeAyMhIPv74Y7Zt28ayZctYs2YNycnJFBYWotPpCA8Pp1evXgwbNoyJEydy5plnetw03ZBbb72VWbNm8dZbb/Hdd9+xY8cOCgoKAGc3uv3792fs2LHMnDmT3r17H43FbJGAgABeffVV7rjjDt555x1+/PFHUlJSKCoqws/Pj/j4eAYPHsyZZ57JzJkzGx275V//+hennXYa//nPf9iwYQO5ublejw3TmmbMmMEFF1zAhx9+yHfffcf69evJz8+noqKCoKAgOnfuTP/+/RkzZgznnHMOffr08SrfkSNH0qdPH/bs2eP6bObMmR7dfHtjyJAhbNiwge+//57ly5e7BiYuKSnBZDIRExNDnz59GDVqFJMnT+aUU045qj2rBQQE8PLLLzNv3jzee+89Vq1axZ49eygsLMThcBAWFkaPHj0YMmQIZ5xxBmeddVa9Jp7uV69/+eUXNm7cyL59+8jPz8disRASEkJSUhLDhw9n5syZroF7GzNs2DD+/vtvli5dyqpVqzhw4AAVFRVtPoB3W+vTpw+bN2/mxRdf5LPPPmP37t1YLBY6derEoEGDuOyyy7j44osxGLw//BzuumqNfePIkSPJy8tj5cqV/Prrr2zevJn9+/dTWFiI3W4nNDSUnj17MmrUKGbNmuXR++qRCg4O5qOPPuK2227j3XffZc2aNWRlZVFdXU10dDQDBw7kvPPOqzcIa3ubNWsWDzzwgOt9eHi4Rwcq3hg1ahQ7d+7kvffeY/ny5fz111/k5eVhNBrp1KkTo0eP5tJLL2XSpEle5ecr+3Jv3XfffZx33nm88MILrFy5kqysLAIDA+nWrRsXXXQR//jHP4iOjvboJMSbGsqWOv/88/n777955ZVX+Oqrr0hPT8dsNtOlSxcmT57MTTfdRN++fVt9vnXa4vymJfR6PY888gg333wz77zzDitXrnSdD1mtVkJDQ0lKSmLQoEGMHz+ec845p03uYfL1Yyw4a3AXLlzIddddx1tvvcUPP/zAvn37KCgowGAwEBsby4ABAzjjjDOYOXNmk71Et8by3nnnnVx00UX88MMP/P7772zdupWUlBTKyspc207//v2ZMGECV1xxRZMdGLUHTSkfbowthBBCCCEadPnll7t64V22bNkRN60dN24cP/30EwCrV68+ogGfhRCHTzoJEUIIIYToYCorK/nmm29c74cPH96OpRFCtCYJ0IQQQgghOpgHH3zQ1TFUXccVQohjgwRoQgghhBA+4pNPPuHuu+9m3759DX5fUFDADTfc4OotE+Duu+8+SqUTQhwN0kmIEEIIIYSPqKio4KmnnuKpp56iT58+DBo0iKioKMxmM8nJyfzxxx8eXdxffvnlXo0xJ4ToOCRAE0IIIYTwQXv27PHoZdedXq/nxhtv5JlnnjnKpRJCtDUJ0IQQQgghfMRll11GTEwM//vf/9i0aRO5ubkUFBRQVVVFREQE3bp1Y9y4ccyZM6dNu7gXQrQf6Wa/EQ6Hg6ysLEJCQo76WBJCCCGEEEII36GUory8nISEBHS6tu3GQ2rQGpGVlUXXrl3buxhCCCGEEEIIH5Genk6XLl3adB4SoDUiJCQEcP4TQkND27k0QgghhBBCiPZSVlZG165dXTFCW5IArRF1zRpDQ0MlQBNCCCGEEEIclVufZBw0IYQQQgghhPAREqAJIYQQQgghhI+QAE0IIYQQQgghfIQEaEIIIYQQQgjhIyRAE0IIIYQQQggfIQGaEEIIIYQQQvgI6WZfCCE6KKvVit1ub+9iCNFq9Ho9RqOxvYshhBDtSgI0IYToYMrKyigoKMBsNrd3UYRodSaTiejoaBmDVAhx3JIATQghOpCysjIyMzMJDg4mOjoao9F4VAbNFKKtKaWwWq2UlpaSmZkJIEGaEOK4JAGaEEJ0IAUFBQQHB9OlSxcJzMQxJyAggJCQEDIyMigoKJAATQhxXJJOQoQQooOwWq2YzWbCwsIkOBPHLE3TCAsLw2w2Y7Va27s4Qghx1EmAJoQQHURdhyDSiYI41tVt49IJjhDieCQBmhBCdDBSeyaOdbKNCyGOZxKgCSGEEEIIIYSPkABNCCGEEEIIIXyE9OIohBDHkLS0NAoKCtq7GA2Kjo4mMTGxvYtxVKxZs4bx48fz8MMPM3/+/PYujhBCiA5EAjQhhDhGpKWl0bdfP2qqq9u7KA3yDwhg965dx02QJoQQQhwOCdCEEOIYUVBQQE11NRHXnochIaq9i+PBllVI8WtfUVBQcFwEaCNGjGDnzp1ER0e3d1GEEEJ0MBKgCSHEMcaQEIVfUqf2LsZxLTAwkH79+rV3MYQQQnRA0kmIEEKIDuXnn39mypQpREdHYzKZ6N27Nw8++CBVVVUtzqu0tJTFixczduxYEhIS8PPzIyEhgSuuuIL9+/fXS19TU8PTTz/NkCFDCAsLIzg4mJ49e3LppZeydetWV7o1a9agaVq9+89Wr17NnDlz6Nu3L8HBwQQHB3PyySfz6quvNlg+TdMYN24c+fn5zJkzh9jYWAICAhg1ahRr1qxp8fIKIYTwfVKDJoQQosN45ZVXuOGGG4iIiGDKlCnExMSwfv16Fi5cyOrVq1m9ejV+fn5e57dz504eeughxo8fz7Rp0wgKCmLXrl188MEHfP3112zatImkpCRX+tmzZ/Pxxx8zePBgrrrqKkwmE2lpaaxevZrJkyczaNCgJue3ePFi9u3bx6hRo5g2bRolJSX873//Y+7cuezevZunn3663jQlJSWcdtpphIaGcvnll5OXl8dHH33E5MmT2bhxIwMHDvR+BQohhPB5EqAJIYToEHbs2MHNN9/M0KFDWblyJZGRka7vHn/8ce677z6WLl3KnXfe6XWe/fv3Jzs72yMvcNZ0TZw4kUcffZTXXnsNcNa2/fe//+Xkk09m3bp16PV6V3q73U55eXmz83v55Zfp3r27x2c2m41zzjmH5557jltvvbXePXpbtmzhhhtuYOnSpeh0zoYvEyZM4JprruGFF17glVde8Xp5hRBC+D5p4iiEEKJD+Pe//43NZuP555+vF1Ddc889xMTEsGzZshblGRYWVi8vgPHjxzNgwABWrlzp+kzTNJRSmEwmj+AMQK/XEx4e3uz8Dg3OAAwGA9dddx12u53Vq1fX+z4oKIjFixe7gjNw1uQZDAbWr1/f7DyFEEJ0LFKDJoQQokNYt24dAP/73/88Aqc6RqORXbt2tTjfNWvWsGTJEv744w8KCgqw2Wyu79ybS4aGhnLWWWfxv//9j2HDhjF9+nTGjBnDyJEjvW5WWV5ezlNPPcXy5cvZv38/lZWVHt9nZWXVm6Z3794EBwd7fGYwGIiLi6OkpKQFSyqEEKIjkABNCCFEh1BUVATAwoULWy3P//73v8ycOZPg4GAmT55Mt27dCAwMRNM03nrrLVJTUz3Sf/LJJyxatIhly5bxwAMPABASEsKcOXNYtGgRgYGBjc7LYrEwbtw4Nm3axIknnsj//d//ERUVhcFgICUlhbfffhuz2VxvurCwsAbzMxgM2O32I1h6IYQQvkgCNCGEEB1CaGgoAGVlZYSEhLRKnvPnz8ff35+NGzfSu3dvj+8+/PDDeumDgoJYuHAhCxcuJDk5mdWrV/PKK6/w3HPPUV1dzb///e9G5/XFF1+wadMmrrnmGtd9be7zevvtt1tlmYQQQnRscg+aEEKIDmHkyJHAwaaOrWH//v3079+/XnCWlZXVYDf77rp3786cOXP46aefCA4OZsWKFc3OC+D888+v990vv/zSwpILIYQ4VkmAJoQQokO44YYbMBgM3HzzzaSnp9f7vqSkhM2bN7coz6SkJPbt20dubq7rs5qaGq6//nqPe9EA8vPz+fPPP+vlUVxcjNlsJiAgoNl5Afz6668en//000/1atSEEEIcv6SJoxBCiA5h4MCBvPTSS1x//fX07duXc845h549e1JWVsaBAwf46aefuPLKK1vU7fzNN9/MzTffzIknnsj06dOx2Wz88MMPKKUYMmQIW7ZscaXNzMxk5MiRDBgwgGHDhtG5c2cKCwv54osvsFqt3HPPPU3Oa8qUKXTr1o0nnniCbdu2MXDgQHbv3s1XX33F1KlT+fTTTw973QghhDh2SIAmhBDHGFtWYXsXoZ7WKtO1117L0KFDeeaZZ/j5559ZsWIFYWFhJCYmcvvttzN79uwW5XfjjTdiNBpZunQpr732GuHh4Zx77rksWrSIGTNmeKTt1q0b8+fPZ9WqVaxcuZLCwkKio6MZNmwYt99+O5MmTWpyXsHBwaxatYq7776bn3/+mTVr1jBgwADef/994uLiJEATQggBgKaUUu1dCF9UVlZGWFgYpaWlrhvThRCiPdXU1JCcnEz37t3x9/ev931aWhp9+/Wjprq6HUrXPP+AAHbv2lVvIGYhDtXcti6EEEfb0YwNpAZNCCGOEYmJiezetYuCgoL2LkqDoqOjJTgTQgghmiEBmhBCHEMSExMlCBJCCCE6MAnQhBBCHDNKSkpYsmSJV2nnz5/fpmURQgghDocEaEIIIY4ZJSUlLFiwwKu0EqAJIYTwRRKgCSGEOGZ069YN6ftKCCFERyYDVQshhBBCCCGEj5AATQghhBBCCCF8hARoQgghhBBCCOEjJEATQgghhBBCCB8hAZoQQgghhBBC+AgJ0IQQQgghhBDCR0iAJoQQQgghhBA+QgI0IYQQQgghhPAREqAJIYQQQgghhI8wtHcBhBBCtJ60tDQKCgrauxgNio6OJjExsb2LIRpx5ZVX8vbbb5OcnEy3bt3auzhCCHHckgBNCCGOEWlpafTv15eq6pr2LkqDAgP82blrtwRpQgghRBMkQBNCiGNEQUEBVdU1PDlGo2dYe5fG0/5SuPuXGgoKCiRA81GPPfYY9957L507d27vogghxHFNAjQhhDjG9AyDAVFaexfjEKq9CyCaER8fT3x8fHsXQwghjnvSSYgQQogOY82aNWiaxvz589m0aROTJ08mJCSEsLAwpk2bRkpKSr1pfv/9d84991wiIyPx9/enX79+zJ8/n6qqqnppNU1j3Lhx5OfnM2fOHGJjYwkICGDUqFGsWbOmxeW1WCw888wzDBs2jKCgIEJCQhgzZgwrVqw4jKV32rhxIzfddBMDBw4kLCyMgIAABg0axOOPP47Vaq2Xfu/evVx11VV0794df39/oqOjGTZsGHfeeadHuiuvvBJN0zzWocViYenSpUyePJmuXbtiMpmIjY3lwgsvZPPmzfXm9dZbb6FpGm+99RY//vgjo0ePJigoiKioKGbPnk1hYeFhL7cQQhwvJEATQgjR4WzYsIExY8ZgMBiYO3cuJ598MsuXL2fixInU1By8B+/TTz9l7NixrFmzhqlTp3LbbbcRHBzMggULmDhxImazuV7eJSUlnHbaafz9999cfvnlXHjhhWzYsIHJkyezbds2r8toNpuZPHmyKxC6+uqrmTVrFqmpqVxwwQW88MILh7Xsr732Gp9//jmDBg1i7ty5XH311SiluO+++7jkkks80mZlZTFixAjef/99hg4dym233cYll1xCTEwMS5cubXZeRUVF3HbbbZjNZs455xxuv/12xo0bxzfffMOpp57K+vXrG5zuyy+/5JxzzqFTp05cf/319OzZk3feeYcLLrjgsJZZCCGOJ9LEUQghRIfz9ddf8+GHHzJz5kzXZ1dccQXvvvsuy5cv55JLLqG8vJxrrrkGvV7P2rVrGTx4MABKKWbNmsUHH3zAk08+yYMPPuiR95YtW7jhhhtYunQpOp3zOuaECRO45ppreOGFF3jllVe8KuMjjzzCmjVrmD9/Pg899BCa5mx2Wl5ezoQJE7jzzju58MILSUhIaNGy33fffbz44ovo9XrXZ0oprrnmGv7zn//w22+/cdpppwHOALWkpITnnnuOW265xSMfb3r7jIiIIC0trd59adu3b2fUqFHcf//9/PDDD/WmW7FiBWvWrHGVw263M3HiRNasWcO6desYNWpUi5ZZCCGOJ1KDJoQQosM5/fTTPYIzgDlz5gC4anWWL19OSUkJc+bMcQVn4GzG+Pjjj2MwGHjrrbfq5R0UFMTixYtdwRnA7NmzMRgMjdYYHcrhcPDyyy/Tq1cvj+AMICQkhIceegiLxcJnn33m9TLXSUpK8gjO6pbpxhtvBGDlypX1pgkICKj3WXR0dLPzMplMDXYaMmDAAMaPH8/PP//cYLPKyy67zBWcAej1embPng3g9ToUQojjldSgCSGE6HCGDRtW77MuXboAziaKgOseqXHjxtVL27VrV3r27Mnu3bspLy8nJCTE9V3v3r0JDg72SG8wGIiLi3PlDc774Q69L23o0KFMnTqV3bt3U1xcTEJCAgsWLKg3//z8fAB27drV7LIeymKx8MILL/Dhhx+ya9cuKioqUOpgJyxZWVmu1+eddx733nsvN954Iz/88ANnnXUWo0ePpk+fPl7P76+//uKJJ57g119/JScnp15AVlBQUK9zEW/+P0IIIRomAZoQQogOJyys/jgCBoPzkGa32wEoKysDIC4ursE8OnXqxO7duykrK/MI0BrKuy7/urzBGaAdGnzNnj2bqVOnUlRUBDibAm7fvr3R5aisrGz0u8ZMnz6dL7/8kj59+jBz5kxiY2MxGo2upozu99V1796dtWvXsmDBAr799lv++9//AtC3b1/+9a9/cfHFFzc5r99//50JEyYAMGnSJFfwqmkay5cvZ8uWLQ3ex+fN/0cIIUTDJEATQghxTAoNDQUgNze3we/rPq9L11Lz589n/vz5Tc77oosu4pNPPjms/Buyfv16vvzySyZPnszXX3/t0dRx3bp1PPfcc/WmGTx4MJ9++ilWq5WNGzfy7bff8vzzzzNz5kwSEhI8miIeauHChZjNZn799dd66datW8eWLVtabdmEEEI4yT1oQgghjkknnngiQIPd42dmZrJ//3569OjhUXvWWvr3709oaCgbNmxo8B6tw7V//34Azj333Hr3of3yyy9NTms0Ghk1ahQLFizg+eefRynFV1991ez8IiMj6wVnVVVVbNq06TCWQAghRHMkQBNCCHFMuuCCCwgLC+PNN9/0aGZY1yW91WrlyiuvbJN5GwwGrr/+elJTU7nrrrsaDNK2bdtGXl5ei/JNSkoC4Ndff/X4fPv27Tz22GP10q9fv77BedTVHjbUecih8ysuLvZYf3a7nbvuust1H50QQojWJU0chRDiGLO/FEA1l+yocpbp6AoNDeW1117j0ksvZeTIkcycOZOYmBh+/PFHNmzYwIgRI7j77rvbbP4LFixg06ZNPP/883z99deMHTuWmJgYMjMz2bp1K1u2bGHt2rXExsZ6neeIESMYMWIEH3/8MdnZ2YwaNYq0tDRWrFjBueeeW6855fvvv89LL73EuHHj6NWrF6GhoezYsYNvvvmG6OhoV8+Xjbn55pv5/vvvGT16NDNmzMDf3581a9aQmZnJuHHjDmvwbiGEEE2TAE0IIY4R0dHRBAb4c/cvNc0nbgeBAf5ede3emi6++GI6derEY489xmeffUZVVRXdunXjn//8J/PmzcPf37/N5m0ymfj222954403eOedd/jkk08wm83ExcVxwgkncN111zFo0KAW5anX6/nqq6+49957+d///sf69evp3bs3Tz31FGeffXa9AO3SSy+lpqaG3377jfXr12M2m+nSpQs33ngjd911l6tnxcacd955fPLJJyxatIj33nuPwMBAJkyYwOeff84jjzzS4nUihBCieZpy75tXuJSVlREWFkZpaelh30AuhBCtqaamhuTkZLp3795oYJGWlubVAMTtITo6msTExPYuhugAvNnWhRDiaDqasYHUoAkhxDEkMTFRgiAhhBCiA5NOQoQQQgghhBDCR0gNmhBCCNGO1qxZ41VnG0OHDmXq1KltXh4hhBDtSwI0IYQQoh2tWbOGBQsWNJtu9uzZEqAJIcRxQJo4CiGEEO1o/vz5KKWafbz11lvtXVQhhBBHgQRoQgghhBBCCOEjJEATQgghhBBCCB8hAZoQQnQwMnylONbJNi6EOJ5JgCaEEB2EXq8HwGq1tnNJhGhbddt43TYvhBDHEwnQhBCigzAajZhMJkpLS6WGQRyzlFKUlpZiMpkwGo3tXRwhhDjqpJt9IYToQKKjo8nMzCQjI4OwsDCMRiOaprV3sYQ4YkoprFYrpaWlVFRU0Llz5/YukhBCtAsJ0IQQogMJDQ0FoKCggMzMzHYujRCtz2Qy0blzZ9e2LoQQxxsJ0IQQooMJDQ0lNDQUq9WK3W5v7+II0Wr0er00axRCHPckQBNCiA7KaDTKyawQQghxjJFOQoQQQgghhBDCR0iAJoQQQgghhBA+QgI0IYQQQgghhPAREqAJIYQQQgghhI+QAE0IIYQQQgghfIQEaEIIIYQQQgjhIyRAE0IIIYQQQggfIQGaEEIIIYQQQvgICdCEEEIIIYQQwkdIgCaEEEIIIYQQPkICNCGEEEIIIYTwERKgCSGEEEIIIYSPkABNCCGEEEIIIXyEBGhCCCGEEEII4SMkQBNCCCGEEEIIHyEBmhBCCCGEEEL4CAnQhBBCCCGEEMJHSIAmhBBCCCGEED5CAjQhhBBCCCGE8BESoAkhhBBCCCGEj5AATQghhBBCCCF8hARoQgghhBBCCOEjJEATQgghhBBCCB8hAZoQQgghhBBC+AgJ0IQQQgghhBDCR0iAJoQQQgghhBA+QgI0IYQQQgghhPAREqAJIYQQQgghhI+QAE0IIYQQQgghfIQEaEIIIYQQQgjhIwztXQAhxLFBKcW+fftYu3YtycnJFBfmo9fr6Nw5kRMGDmT06NEEBwe3dzGFEEKIduNwOKipqcFsNmO1Wj0eFoul3mfNfef+uc1mw263Y3c4sNnt2B12HLXvHUrhcDhQDgcGgxGTyY8Akz/+JhP+/v6YTCaCgoKIiIggIiKCyMhI12uTydTeq+24IwGaEOKIpKam8u9//5tl771DSnomANEBGmF+CpsDcqugxg4GncbEUUO44c77OW/adDRNa+eSCyGEEN5RSlFaWkp2djaFhYUUFRVRXFxMcXExBYWF5BUWUFBYQFFJCRWVlVRVVVFdXU1NdTXmmhrM1TVYzWasZkvrFUqnoRn0aHo96HWg0zk/0zTQaaDVPQCt9nMNlEOBzY6y2lF2O1jtKJsNHKrB2ZgCA4iKjyO+cwKJXRPp3b0Hfbr1oFu3bvTp04cuXbrIMb2VaUqphv8bx7mysjLCwsIoLS0lNDS0vYsjhM9Zt24dzzz9FJ999jlBRjgr0cHEJAPDYuyE+B3cUSulSC6D37I1Pt/nYHshDOoWw5NLXmTyBRe34xIIIYQQTpWVlSQnJ5OcnMyBAwc4kHyA5PR00jMzyMnOpigvH0uNud50mp8RXZAJLSgAXaA/ukATmsmI5mdE8zM4n40GdCYjOj/n5zo/I5rBGVRpeh3odajaZ+dntd8ZdKDX16bR1753+76VgyJlteGorHE+qmpQlTVQZcZeVoWtqBRbUTn2ojLsReU4yipd0xkD/OncPYneffsybOAghg0czJAhQ+jVqxd6vb5Vy9iejmZsIAFaIyRAE6I+s9nMF198wbNPP8m6PzeQFKpjdn/FtJ4agUbv8vgzR/HsZtiUp5g+dihL319Bp85d27bgQgghjnvV1dXs3r2bnTt3smPHDnbu3cOefXtJS02jtKDQlU4zGtBHhaKPCEEXHow+PBhDWDCGyBB0YcEQZEIX5I8u0B/NeHw2RlNWG/aiMmzZRdhzi7FkFWDLLsSaXegM7HAGbt369Wbo0KGMG3Eqw086iUGDBuHv79/OpT88EqD5AAnQhDho+/btvP7667z3zlsUFJUwopPGVQN0jOvsQHcYV/CUUny+HxZvUDjQsejBu7n+/kXodNJvkRBCiCNTVlbmCsK2bd/Oxq1b2L1rF7npmdSd9urDgtHHhWOICUcfE45fjPO1Fh2GLjQITSdN9g6XvawKa3oe9vR8zGk5WNPzsGUVglLojQa69e/LqFGjOGvsBEafeipJSUkdoomkBGg+QAI0cbyrrq7mo48+4t8vvci69RuIDNAztbud6X2N9Ay1t8o8imoUT26Ez/YpRvVN4PUPv2DA0JNbJW8hhBDHtvz8fFcg9vf2bWz6ewt7d++hKDfPlUYfHYYhIQpjfBR+CTEYE6LQxUeiC+yYtTgdlbJYsWbkY0/JpWZfBuYDWdjzSgAIjoxg6IiTmDL5bM6bdBb9+/f3yYBNAjQfIAGaOF7t2bOHl19+ibf/8wbFZRWMTtC4uI/GhC4KP33b7DDXZSseWqvIqoK7rr2Mh5f8R3qNEkIIgVKKzMxMVyC2ZdtW/tq2lX2791BeXOJMpNNhiA3HkBCNIT4KU+do9PGR6DtFojP5tWv5RePsZVVYDmRhO5BNzZ40LPuzwO4gKDKck087hWmTz+G8s86mZ8+e7V1UQAI0nyABmjieKKVYuXIlix9bxI+r1xDhr+PCng4u6WcgMdhxVMpQY1O8vBVe36roFhvCy6+9wcQp0omIEEIcD+x2OykpKc57w3bu5K+tf7Nl+zYO7NlLTWUV4Lw3zNApEkN8FIaEKEwJMejiIzDERTo73RAdmsNsxbIvA+uudKp3pmBNyQGHIqZbF84651zmzLiE0aeNxmBon/v+JEDzARKgieOBUoovv/ySfy14mA2b/mJgtI4r+sNZSQpTG9WWNWdPseKhdbA5T3HR2BN57p3P6JzYrV3KIoQQonVZLBb27t3Lzp07XYHY1h3bSdm339UFvc7fD318FMaEKIwJ0fglRKOLj0QfHYYm9yofNxzVZsy70jD/tY/qLftxlFXiHxLEmEkTuenKazh78mSMRi97KGsFEqD5AAnQxLFu06ZN3HzjDfy+7g+Gd9K4brCO0zo5fKLdt0MpvtgPT2xUmO0aD9x6DXctevGo7oiFEEIcvrKyMnbt2uVxj9j2nTvJTE3DYXfex6wPDkCfEIUhIRpjfBSm2kBMFxHiE8ci4TuUQ2FNyca8ZT9V63dhyynCPyyEiVPO4far5zLu9LFt3tGYBGg+QAI0cawqLCzk3nvn8cYb/6FXuMb9IzRO7eSbu4Eyi+L5v+D9XYpesSEsfellJl14eXsXSwghBOBwOMjIyGDv3r2uYOyvbVvZtWsXhW4ddRiiwtDHR2LoFIkxIRpTQjRap0j0oYHtWHrRUSmlsKblUfPnTqr+2IG9qJyIzp248uo5zLvhFuLi4tpkvhKg+QAJ0MSx6Ntvv2XO7CuoKi/mlsEOLu0Lhg7QlfDOIsUjfzjHTpt22gCee2c5XXv0au9iCSHEMc9qtZKSksK+ffvYv38/e/fuZcee3ezdt4/M1DRsVisAml6HIS7C2TQxvrZpYnwUWlwEOn/pqEO0DeVQWPZmUPXL31T9uRNNKYZPHMc/b7uLcyef1ao1sRKg+QAJ0MSxpLKykjvuuINXX32VMZ01Fo7WE+d/dDr/aC1KKb44AE9sUNTYNe676UruWfxvafYohBCHQSlFYWEhWVlZHo/MzExSM9JJy8ggJzubovwClMN5vNAMevTRYehjI5y9JsZGYIqLRIsJc94fJh11iHbkqKimau12Kn/6C1tWIbE9k7jrzju5Zc4/WqVnaAnQfIAEaOJYsWvXLi684HxSkvcz7yTFJX3o0G373Zs99o0P4/3/LufEU8e1d7GEEMInKKUoLy93BVuHBmBpGRlkZmWSl5OD1WL1mNYYFowWFoQWHoQ+PBh9eDCGiFCMsRHOICwyRDrpED5PKYVlVxqVP2yg+q99BESEccW1V/Po3fcRHR192PlKgOYDJEATx4KPP/6Yq6+6kjiTmaXjdPQM7Vi1Zk3ZWaS45xdFchnMv/kK7n3mzTa/QVgIIdpTVVUV2dnZHrVdWVlZZGRmkpaRTmZWFnk5OdRUVXtMZwwORB8eDGFB6MKD0NUGX/rwYPwiQtGHh6BCA6QGTBxzrNmFVK3cSOVvW9HpdFw4+3Kee3gh8fHxLc5LAjQfIAGa6MisVit3330Xzz33POd113jkVB1BhmPvp262K5Zshje3K07p24llX60hsVff9i6WEEK0iMPhID8/n4yMDI9HakY6qRnpZGVlkZ+TS2VZucd0OpMfhogQcKvx0ocHowsPxhgRiiE8GC0sEPykKbg4vtnLq6j8YQMVKzeiORycc+nFvPivxSR27ep1HhKg+QAJ0ERHlZmZyYzpF7J+/XruPQku79exmzR6448cZ21atUPHv5c8wcy5d7Z3kYQQwqWmpoaUlBQOHDjAgQMHSElNZX9qCqnpaWRmZFCYk4fdZnOl1/R6Zy1XRPDB2q6IEIy1z7rw2qaI/n7H/P5diNbkqKyhcuVGyn9YDxYbZ86YxkuPLqZn9x7NTisBmg+QAE10RKtXr+aSi6ejmUt5biycGH38/LxLzIoHf4cf0hT/N3kkz33wDRGRke1dLCHEcUApRW5urisA279/Pzv37WHPvn2kJqdQ5NblvGbQo48MQRcZij4iBH1ECIbIEIyRoWjhIegigtGFBKJ1gB52heioHNVmKldtouK79ahqM+OmncdLC5+gX5/GW+FIgOYDJEATHYnD4eDJJ5/k/vvvY2ScxjPjdET6HTv3m3lLKcXHe2HxBkWgycjTTyxm1nW3yRVmIcQRU0qRnZ3N3r172bdvH3v27GH77p3s3ruX9OQUzNU1rrT60CB0MWEYYsIxRIdhjIvEEBOOLjoUXXiIBF9C+AiH2ULV6r8o/9+fOMqrGD3lLF5cuJjBAwfVSysBmg+QAE10FPn5+cyefQXffvs/rhsEtwzV0B/nB//cSsXC9fBdquL0Qd1Z+PxrjB53RnsXSwjh45RS5OTksHfvXtdjx+5d7NqzmzT3IEzTMESGOLubj4tAX9vdvC4mDF10mIz7JUQHoyxWqn7ZSvm367AXlTNi8gSWLlzMiJNOdqWRAM0HSIAmOoI1a9Zw+aWXUFNWyOOnOhjbub1L5FvWZCie3KjYVwKnDerJA48+yeTzLpDeHoU4jpWUlJCSkuJ6JCcns+fAfvYnH3AGYXU9IGoahshQ9LHh6OMiXGN+6eMi0MWEoRkN7bsgQohWp2x2qn/fRtnX67DnlzDk9FNZ8ujjjBszRgI0XyABmvBl5eXlPPTQQzz//HMMj9N48nRdhxt4+mhxKMWqdHj5b8W2QugaE8oV/zeLq2+6i+7du7d38YQQrUQpRVFRkceYX9nZ2aRnZrAvJZmUlBQy09Kprqh0TaMZDc6Bl6NC0UeHYYgNxxQXhT42Al2sBGFCHK+U3UH1nzsp/2ottuxC+ow4kYfumsesGZdIgNaeJEATvkgpxSeffMJtt9xMUWE+Nw1WzDmBNm3SmFWhWJ0BBdUKvabRLxKGx0GYqWM1o1RKsSkfPtsH3yQrqmwwekgfrpx7E9NmXk6kdCgihM9xOBwUFRVRUFBAfn6+xyM7N4fUjAwyMjPJyc6mMDcPm9Vz4GV9SCBaWJCzI47oMPTRYRijwzFGh0FkCLrQQLlHVbSIUgpVY8FRXo2jogpHjQVlsaEsVpTZ6ny22FA2Oyh18OFQKMXB9wCaBhqAhqa5vdc054PaXphdD89p0Ny+B9Bpzs91Wm0SDfQ6NJMRnb8fmskPzd+IZvJD5++HLljGvmuOcihqNu2h/Ou1WFNzCZ0xnvTXlkuA1l4kQBO+xG63s2LFChY/tog/1m/gjEQdD4zU0znQ3mbzTC9XPL5esTIdjDqIDoAaGxSbne/HdIbzumuM7wKBxo51glNlVXyfrvHZXgd/5jgD3DEnDWDG/13NhTMvIzY2tr2LKMQxQylFRUUFpaWlrkdZWZnH+5KSEnILnEFXXn4+BQUFFBcUUlZSinIc0jpA09AHB6CFBnqM+6UPD8YvPBR9RDAqNBB9WJDUgIkWUUrhKKvEXlCKraAUe0EZtsJS7AWlOMqrcJRXYS+vBqut8UwMejSTEU2vA00HukOCrLogyjlHcKi6mdc+QKHAUfu9W1DnOmV3S+v6nkMCQOU2bRO0AD90IYHoggPQ1z3X9i6qr+1pVBcZgi7I/7i+mKGUwrI7HVO/RHZcdL8EaO1FAjThC1JSUvjss8945aUX2Ls/meHxev4xQHF6G95rppTiv3vh0T8VESa49USNSUkQbNScvZhVwg9p8FWy4u8CCDTAhK5wfg+N0xLA0ME6KMmvVvyQrvF9ioM/cpyfndSvO5POncI502YyYsQIDAY5yRPHJ7vd7hFMHRpY1QVXhSUlFJUUU1JaQklJCWVlZZSXlVFZXkFVRWX9IKuOpqEL8EPzN6EL9neeKNaeJOpCAtGHBGIMCUIXGgjB/mjBAc4TRbmPVBwBZbVhyynCml2ILasQW3ah83VusUfwpQWanDWvUWHoQp3bo87toQ8JRAvwcwZkfkY0P4PPbZvK4XDW7NVYcJitKLMFVWPFUW3GUVFbC1he7QxAK6qxl1XiKKnAXlJxMHgEMBpqgzZnbbQhJhx9TDiGmDD0MeHOoSGOkwBOAjQvPPbYY9x///3ceuutLFmyBKvVyoMPPsg333zDgQMHCAsLY+LEiTz++OMkJCR4na8EaKI9VFRU8Mcff/DLL7/w5fJP2bRlGyaDxrjOijkD9Qxt43HNyi2Kh9cqvk6BmX3g3pO1JmvH0sqcab88oNhfCtH+MKUHXNZXIzG04+2oi2oUP6Zr/JqpWJutKLVASIAf4045ibOnXcLEyWfTq1ev4+YgJDo2i8XSZK1VcUkJBSVFFBYXUVRcQnFpCWWlzsCqqqKCqvKKgx1mNESvQxdgQvP3QxdoQgswOd8HmJzv/Z3PugAT+kB/9LXfEeAHAX7OtCY/6XJetBlHVQ3W2gDMll3oem0vKHHWLgG60EAM8VEY46MwdIp0Bh11QVmgqV3L356Uw4GjtBJ7UTn2YrdHYZmzdjG/BEfFwf2DZjIeXHcx4c4hJmLDnes0OsznAtcjIQFaM9avX8+MGTMIDQ1l/PjxLFmyhNLSUqZPn861117LkCFDKC4u5rbbbsNms7Fhwwav85YATRwNeXl5/P777/z888/8svpHNm/dht3uIMykY1QnB5OTNMZ21RFsaPuf6V/5ijt+VpSa4V+naJzT3fuTJqUU24vgi/2KLw9AqQXOSoJrB2qcENUxT77sDmenIr9m6/gt085f+WBXEBsezOiRJzF28hROH38GAwYMwGg0tndxxTHCZrNRUVHhepSXl3s8170uKytzBljFRbU1V7VBWGkZlRXlVJVXYDVbGp2PZjSgBZiczZvqAqtAtwArwIQh0N/1XqsNqtyDMM3PIBcrRLtTSmEvLncGYVmFB2vGsgtxlNZ2CKOBPioMQ0JtIFb7MMZHoQsOaN8F6MAc1Wbs+aXY8kuw5Zdgzy9xBW+2glKw1d6GYdBjiIvA0CkSQ6cojPGRztcJ0R1ySAoJ0JpQUVHBsGHDeOmll3j00UcZOnQoS5YsaTDt+vXrGTFiBKmpqSQmJnqVvwRoojXZbDYyMjLYsWMHGzduZOOff7Bxw3oycvIAiA/WcVKMg5PjNE7upKdnqB3dUTrxMdsVr22Dl7YoBkXDU2M0uoYc/rxrbIrP9sF/tivSK2ByEtw3XCM+qGOfyFVYFBvzYEOexoZcB1sLwOoAP4OOft27MGTIEPoOGkb3Xn3o1q0bcXFxREVFERoaKt36+zilFHa7HZvNhs1mw2q1ul4f+r6h72pqaqiurqa6urrB15VVVVRWV1FRWUlldRVV1dVUVVVRUVlBRXkFVZWVVFVUUlNV1WRQBTibBJqMaP7OYKkuqPKsufJz1VjpAv3RAkwof6NnGukYQHQwjhqL88Q/twhrdpGrVsyWU4Qy13YOY9Bj6BSJsVMkhoQoDJ2inM9xkehMciHtaFKO2sA5pxBbdhG2nCLX/8teXO5MpIEhLhJjUpzzkRiHX2KczwfNEqA1Yfbs2URGRvLss88ybty4JgO0lStXMmnSJEpKSrxeoRKgtY/GNseGPj+Sz1qS1uFwYLVasVgsmM1mj+e612azmbKyMoqKiiguLqawsJD8nGwO7N9DSnIKaVm52OzOK0mhJo0BkYoBUTAgSs/QGEgIOvo/Q7tD8W0KPPeXIqsCrhkINw4BP33rBBM2h+KrZHhqo6LCCnMHwpUngJ++4UCtJWvA27RtsXery7LG5qw13FWsZ0+RnT3FkFHhrD10p9M0woIDCA8NJjw0hNDQUEJCwwgJiyA0IoqQsHCCg4MJCgpyPQcGBhIUFOSqmdM0zbVt1r3WNA2Hw+HxncPhwOFwYLfbXa+P5mctnd5mt2N32HHYHdgcduz22schaRwO98/q8nPmaT8kT9VAmVzf25z5O2qDK4e97n3rdrajGfTO2iWjAYzOZ81o8PzMz1AbaJnQ/I3o6npV8zehC/BzfmfyA5MRantd0/z9pNZKHLPqOuiw5ZdgzyvBlldbK5NXjC2/FEeZ2/AIQf5uNWGRGOOjMcQfe03pjlWOGouzpjMjH2tqLtbUHKzpea5AWx8TjqlPF/z6JmLq29X5f/Wh/Z4EaI348MMPWbhwIevXr8ff37/JAK2mpobRo0fTr18/3nvvvUbzrDvJrlNWVkbXrl3bovjiONPQLqWx/UxLfo1H+sPVa84me+M6w50nQZ+ItjmoVVgUS7co3t0JMQFQbasfxIhjQ5OHT635NPW+a2SaBvPQvEhz6OdNlEk75IuGf8dave996BxCCJ+jlELh7Hui7qF0OhyaznmhxK1TCl1IIIa4CNf9TPqYMGfnFPFRx1WHFMcL5XBgyy3GkpKD9UAWlj0ZWDPyQIEu0ITBZERXVYVBOdr1f//v65I495EtbR6gdbiuydLT07n11lv5/vvv8ff3bzKt1WrlkksuweFw8NJLLzWZ9rHHHmPBggUen53euZkTDjcNbStenSA0krDREwZv8vS2LK0wX2j6hMTr/Bo4uWqLde9tWQ79XK/VPnQHXxt0bu91YNJDgL7hMckU4PByh6J5XXrvTwbrkiWXKn5IcZBV6ewm/8bBGoNj2nZHF+yncd9wjQt7KR74zXlf15mJMCJBw3RIbV1LStLSZW/NPFtCX9ursgGHa9vRcH6mq+112RksK2psYLZBjR2q7WB34OxlWcMVkdf1qlzX+zIHv3LlW7csdYuj8xw252Aa1zIfXHD3iwSNXQRoLM2h6RvNy4vpvZ2HEMK3WOyKEjOuR7EZiuwGSs1QWW07+NvWaRiiQtHHRmCKdQZixhjnWHX6uMgOeW+SOHyaToex9r5AThkAgKOiGvPedCy70qjZmoyluALNoCM2Noj+ATWcEGwl0HB0g7WTStKOynw6XA3a8uXLmTZtGnr9wfbzdrsdTdPQ6XSYzWb0ej1Wq5UZM2Zw4MABVq1aRVRUVJP5NlaDJk0cxbEgJSWF22+7leVfrGBojMZ9I3Rt3iNkQ+wOxTs7YclmRXRYIK++/h/OnjbzqJdDCCHE4bHb7WRnZ5OSksL+/fvZv38/u/bsYde+PaQmp1BWVOxKqwswoY9x79UvAr+4CPTR4WiRIc6xwoTwkjWnCMuWfVT9tQ/L3gw0TWPYuNHccs1cZk67CJOpbXvdPJq3P3W4AK28vJzU1FSPz6666ir69evHvHnzGDhwoCs427t3L6tXryYmJqbF85F70MSxYtmyZVz3j2sJpIa7hynO7abavWlIWpnin+tgXbbisrNH8/w7nxMVHd2uZRJCCOG8l7UuAKt77E8+wJ79+0lOTiY3Kwu721hh+vBgZwAW6xwXyy82AmNsBESHoQsJaPfjjTg22csqqfljF5Vrt2FNycEvOJCzpl3AI3fdy5DBg9tknhKgtZD7PWg2m42LLrqITZs28dVXXxEXF+dKFxkZiZ+fd1XmEqCJjs5ut3P33Xfz7LPPcl53jfmn6AkxNjJYbDtQSvHJPli8XqE3GHjwzpu5+Z+Pe/0bFUII0XIOh4OcnBxSU1NJTk72CMBSUlLIycjEZrW60uuDA9FFhaKPDnXWgkWFYYwJRxcZii4mTHpHFO3OmlVAzdodVPz6N47SSnqdPIT77riLKy6+BIOh9e7mkgCthdwDtJSUFLp3795gutWrVzNu3Div8pQATXRkNTU1zJwxg6+//or7ToZZ/fDZq5h5VYrnt8CnexVJ0UEsfvxxpl91o8+WVwghfFllZSXp6emkpqaSlpZGamoq+1OS2Z+aTEZaBnlZ2dhtbjVgwQG1AZizEw59dBh+0eHOnvMiQ9AFHL+DNYuORdnsVG/aQ+WPm7DszSA4JpJ/3HA98++cR0hIyBHnLwGaD5AATXRUlZWVTL1gCr/89BNLx8HYzu1dIu/sKlI8uRF+zVKM6pvAq+99wqCTT2nvYgkhhM+oq/1KS0tzPZJTU9ibfIDU1FSyMzIpLyk9OIGmoY8IRh8Z6nxEhWKIDMUvOgwtKhRdVKgEYOKYZEnNperHTVSu3YYxwJ9Lrr6Spx+Yf1i3PdWRAM0HSIAmOqKysjLOOWsymzf8yStnaIyM63g/718yFYvWKzIr4NE7ruHOxa9KbZoQ4rhQWVnpEXy5ar9SkklPSyc/O8ej9kvn7+es/XILvoxRzpowIoLRhwfLoOTiuGYvKqPy+/VUrNmCpmlMuXwGz89fSGLXxBbnJQGaD5AATXQ0RUVFTJ54Brt3/M3rE7V26aWxtdTYFE9tgnd3KsYN6Mz7X/9MQlKP9i6WEEIctgZrv1JS2Jd8gJS0VLIyMqlorParNggzRIVijA5DiwhFHxWCFmCSC1hCeMFeXkXVj5soX7kBLDbOuPB8Xlr4BL179fI6DwnQfIAEaKIjycnJ4cyJE8g6sIc3ztQ4IcJ3OgM5Ej9nKu7/TeHQ9Lz57xeZMmtuexdJCCHqUUpRVFRERkYGGRkZpKenk56eXlv7lUJGegO1XwEmdJHOQMtQG4QZo8IwREntlxBtxVFtpmrNFsq/+xNHRTXjpp7LSwufoH+/fs1OKwGaD5AATXQU+/fvZ9LEM6gsyOQ/Z0Kv0GMjOKtTWK249zdnsHbzJZN58q0v2nysEyGEqKOUoqCgwCP4ysjIIDktleS0VDLSM8jLzsHqNpYqOp2z+/nIEM/ar6gw531fkaHoAmU/JkR7URYrlT9tofzbP3CUVjL6vMm8uPAJBg8a1Og0EqD5AAnQREewefNmzpp0JsH2Ul6fCJ0Dj63grI5DKd7aAU9vVPSOD+XlN99n7KTz2rtYQogOzuFwUFBQ4Aq66gKw5NRUktNTyczIJD87B6vF4ppG0+vQR4SgqwvAIkPRR4RgrH3WIoLRhQWh6WQQZiF8nbLaqPp1K+XfrMNeVM4pk8/ghUVPMOzEE+ullQDNB0iAJnzdN998wyUzLqZ7kIV/nwGRfsdmcOZuR6HioXWwtUAx65wxPPPmJ8TExrZ3sYQQPqYu8MrKyiI7O9v1yMjMJDUjnYysTHJzcinKy/cY80vT69FHBKOLDEEfcfDhFxWGri74Cg1C08l9X0IcS5TNTvXv2yj7eh32/BJOPmMsSxctZtSIka40EqD5AAnQhK+yWq3Mnz+fRYsWMb6rjqdP1xFkOPaDszp2h+LjvfDMJoUDHTfOuZx5i54jIiKivYsmhGhjNpuNvLw8j6ArKyuLzKwsUjPSyczOIjc7h+KCAuw2u8e0+pBAdGFBzpqv2mdDWLCz2WFEMFpECLqQQAm+hDiOKbuD6nU7KPvqd+y5xQw5/RSeW7iYsaPHSIDmCyRAE75o7dq1XDf3WrZv38EtQ+Afg0B3nPbgVVSjeH27xge7HBj0em6ccxl3LHj6iMY4EUK0D6vVSk5OjkfQlZ2dTXpmJqkZaWRlZ5OXk0tJYRHK4XZBStPQhwaiCwtGFx6EPszZuYY+PBhjeAj6sCBUeBD60CDpcEMI4TXlcFDz5y7KvvodW1YhfUeexMN33cNlF8+UAK09SYAmWoPVamXLli1s3LiRgoICqqur8fPzo3PnznTv3p0ePXqQmJiIrpl7FbZu3coD99/Hl199Tf8oPQtP1RgQefzUmjWloNoZqH242wGajqsunsI9i54jKSmpvYsmxHHNarWSn59Pbm4ueXl55Obmuh7p2Vlk5TiDsfzcPMqKij0n1umctVxhQejDg121XvqwEPwinO8JDXQ2N9T7zr1eyqFQZguqxoKj2oyqtqAsVpTVhrLaUTbnMza78zObHRwOUIBSKJzPeDzXZq7TnPe16bSDrzUNdDpnrZ/O+drzO/dp3NJpda/r0ru91tzmodeBVj+NKy+P+ddNf3xeNBTHJuVQ1GzaTflX67Cm5RI6Yzzpry2XAK29SIAmDkdmZiZr167l959Xs/bn1fy1Yy81Vht6DcJMYNKDxQ5FNQePuWGBJoYP7sepY89g/FlTGDVqFP7+/iil+OWXX1jy7DMs/2IFXUP13DLYzrndj99as6YU1Sje363x7g4HlTaYefZY7l/8AicMGNjeRROiQ1NKUV1dTXFxMUVFRRQVFbleFxcXU1hYSH5hATn5eeTUBmNFBYWeY3rV0gWY0IUGogtxBld1TQ2N4cEYIkLQwoLQQoN8oqmhcigc5ZXYiytwlFfhKK/CXl6Fo6K69n3tc2U1jmqLMxgzW5rPGEDT0Ix6MOjR9LU1e1rtH9di177WcB4wHAocDpRyPuNQKMfB1/jC6ZymoQX4oQswofnXPgeYXP93VzPT0CBnzWdokAwnIDoEpRSWPemY+iay46L7JUBrLxKgieZUV1ezadMm1q1bx9pf1rBu7Voy8woB6BwEQ2JgSKyeodGK/pEKk/7gyYbFrsisgPQK2FakY3Ounb/yocwCJqOefolxlFeZOZBdSPcwHVedoLiwl4bRdy4U+6xKq+K/ezX+s91BXhWcO+ZEHnx8KSNPPa29iyZEu7LZbJSWlnoEWIe+zi0oIL+wgMKiIoqLiygrKaW8pNSjF0N3ugATuiB/tEB/dEH+tSffgehDnMGXIdR5Mk5IAPrQQDSj4SgvdeMcVTXY8kqw5RVjLypzBmLF5diLy7GXVGAvqQC7Z0sFzWR0BpfBAQefgwNqAxG3wMTfzxWYaH5GZzBmNKAZ9GhGQ5vU+ilVG6TVBXKOQ16r+kGdcgvuXK8dDrfXh35eN49Dpq97bbPjqLGgqs0ez47KGmcwW1qJo7L64BVKODggd1QY+ugwDFGh6KPD0EeFYogOQx8VJgGc8CkSoHnhscce4/777+fWW29lyZIlAHz22Wf8+9//ZuPGjRQWFrJ582aGDh3aonwlQBOHKisr4+eff+bHH1fyy6of2LJ9Fza7gwCDxqAoxZAYGBpnYHCkndjAll/5dSjFnmL4LQvW5yr+zIVKKwQaYHgcjIrXGNUJ+kVKDZo3LHbFFwfg9W2KlDI4uW9XrrruZi678lrCw8Pbu3hCNEsphc1mo7q6mpqaGo/niooKSktLKS0tpaSkxPVcWFxEYXExRSUllJQUU1pWRkVZGZVlFdRUVTU4H02vRxfsFmQF+aMFBaALNDmDjyB/DEEB6IMD0AX6owJNaEH+6AL9fap54aGUUjhKK7HlFWPLK8GeV4wtv8QVlKnKGldazeSHPjIYfXgI+sgQdOEhzqAhIsTZxDIsCH1wAJqfsR2X6Nig7A5nbWRZJY7SSuxFZdgKyrAXlmIvLMNeUIq9pNyjaac+JhxDXASGuMiDz50incMaSKcu4iiTAK0Z69evZ8aMGYSGhjJ+/HhXgPbuu++SnJxMQkIC1157rQRo4rAVFhbyySef8MG7b/Hb2j+xOxzEB2uMiFUMjdEYGqenT5gdQxscIGwOxfZCWJcD67IVm/Kgxg7hJhgRBxMTNSYlQYBBDk5NsTsUP6bDJ/vgl0yFUa/jjFNO5IzzLmLiWecycODAZu8BFEIphdVqpaqqyuNRWVlZL3hq7Lm6uprKqiqqaqqpqqqiuqaGquoqaqprqK5xpquprsFcU4PFbMZcY/bsEKMxOp0zmKptTqbVvtYF1jYvq31vCAxwBWCuICvI31nD00Ev+ii7A3thqTPoyi/BnlsXhBVjzy9FWQ52oa8LD8YQG44hJhxDbAT6WOezISYcXXBAOy6FOJSy2bEXlWMrqP2f5hZjyy1yPueXHKzZNBqc/1NX4BaBoZPztS40qMNu18K3SYDWhIqKCoYNG8ZLL73Eo48+ytChQ10BWp2UlBS6d+8uAZposbVr1/LE4sf46qtvcDjsnBoPZyZqnJqgo2uwo112+ha7Yks+rMtR/JYFm/MhxAiX9oWrBmhE+suBqDm5VYovkzV+ynDwV77zfsBAk4HuXTrRs0dPuvfqQ2znJGJiY4mJiSE6Otr1HBERIYGcD7NYLB5BU0Ov3d9XVlZSXllJaUUZ5RWVVFRWUFFVm7aykuoqZ0BlrguYqqtx2L3vmMfVlM3P4GzWZzSgGfUHX9d+7no08B6jAZ3RgM7PiM7te2XUg5/RGYAFmjp0gOUNR5XZeaKeXxuE5Zc6a8XyS7AXlDqb2AHodc5mcbVBl3sApo8JR2eS2q9jgbI7sBeUHgzYcoqc20NOEfaiMlfNm2byOxi0xUWgr3sdG4kuJOCY/s2ItiUBWhNmz55NZGQkzz77LOPGjZMATRwxpRTffvstix59hN/W/kH3cB2X91Gc3V1HtL/v/UzSyxUf7FJ8uAcMOrjtRI1L+oD+GGju4c1uydv/SGNZ1dgUf+XDrlI9qaV20sohuxJKzFBqrp+/TtMIDwkkKjyUiIhwoqJjiIyOJTounsgoZwAXEBCAyWRyPfz8/PDz88NgMLhOBuqCPE1z9namlPL4TCnlbJpV2xlAazw39rq571srrd1ux+5w4FAO7HYHSjmw2R3YHXZU7WcWmxWLxeJ6mGufrdaDn1stFqxWG1arBavFis1mxVJjxlxTg+OQMa8a4wqG/IxoJqPz2c/g+drtO52fEZ3p4DN+zoBJ+dUGUX5Gj4BKqwvEJJj3mrI7nM3c8uuCsFJXMGbLLzmkKaLR2dwtJhxDbDj62IjaWrEI9FGhPt3kUrQ9ZbU5g7Vct0ees/bNUVzhSqcFmg7WusVGuAVykeiC/NtxCURHcDQCNN+5W7cFPvzwQzZt2sT69etbLU+z2YzZbHa9LysrQwPCw8JabR5N8b3T/+OPXgO7gqEx8MI4OCNR1d7r5Zv/na4hGvOGa1w7SPH0RsUjfyje3O7sJKOs9n5+b0ruVRovV4FvrqnmeHdi71CKorJKisoqIS0b2Nm2xfJBzYb+mhdpGsvrkGndL25rDX6muaYxaaD5aR6d4Glu6Q++1gAHOCxQY4GD5/3NctQ+RMup2u7j3TscdGjg0BtwOBQOq+3gzqOuw4jYCIxdYwk4sTe66DAMUWEYOknNh2iaZjRg7ByDsXP98TAdZgv23BKsOQXYcmsvBuQWY96RiqOs8mAefgb0JqNzNAGrDZ3NdnB0Adn2jntPX98LLrq/zefT4QK09PR0br31Vr7//nv8/VvvKsdjjz3GggULPD47Nd67kw1vfq9en7S0NK8G0jeWRUs/d31/BMvX2LRNZqk1+da7PJqYtzuLDfaUQEYFdA2Be06GCV061jgukf4aC0/TuLiPYv46RWYlnJ0EQ2LBoNOO/jbcSvNrrXl5Oz+ddvBEXvN4r3m812vq4HtXUKGwOcDiwOPZagebct4uUTvMkTO1qn3PwdZZDuWZxuGerq4zNg4GH+7L5frskODk0M88XrvStM+23j7BfMe8hNAR2JWi3OKsfS61OGuiix0GSiw6Kqpt2KwHw1tdoOlgLVhMOIboMAzRoeijnU0Rpcc+0RZ0Jj90ibEYE2PrfeeoNjvvW8wuxJZbjDW/FHteMZa8YhzVB+9jNJj0BAcbCTNpRBlsRGsWwk0QYQJ/PR3q3EEcntGF+47KfDpcE8fly5czbdo09PqDO3C73Y6maeh0Osxms+u7ljRxbKgGrWvXrtLE8RhVXV3NM888w2MLH8WoLNw0WHFJXzB28OaBNofitW3w4hZFr05hvPPxck4+bVx7F0sIcQyw2+0kJyezY8cOdu7cye69e9ixZw8pycnkZWUf7NBEp3N2lR4T5gzEYiPwi4nAEBOGFh0mTchEh1IXvDnySrDnlWDJLXI1o3SUHqx5MwUH0SmpC9179OSEvn0ZdsIg+vXpQ69evYiNjZXg7RhwNG9/6nABWnl5OampqR6fXXXVVfTr14958+YxcODBQWnlHjRxKLvdznvvvcc/77+PnNwcLu+ruGGInjC/DvUzaNauIsW83xT7SuCe665g/pLXMRrlBnkhRPNsNhv79+9nx44dbN22jQ1//8X27dtJ238AW22viLoAk7MTjtoOOIyxERhjwtFiwtBHyr1g4vjgqLFgrx22wZFfgqWuw5K8Yo973vwC/YlL7EqPXj0Z0KcfQ/sPoG+fPvTu3ZtOnTpJ8NZBHM3YoMM1cQwJCfEIwgCCgoKIiopyfV5UVERaWhpZWVkA7N69G4BOnTrRqVOno1tg4ROUUnz99dfcO+9utu/YxeQkjdcvMNAtxM6x2OypX6TGf8+Bl7fC4pff4auvv+Gd/65gyPBT2rtoQggfYbFY2LdvHzt27GDz31tY//df7Nq5k6zkVOxWG4BzDLSEaAwJUQQPPR2/zjHo4iPRhQfLSaU47un8PZtNBrl95zBbXfe5OfJLKMotJm//Tn5d+7tHb5NGfxMJ3ZPo268fJw0azIkDB9O/f3969+6NyWQ6+gslfEKHC9C8sWLFCq666irX+0suuQSAhx9+mPnz57dTqUR7Wbt2LfPuvotffvudEfE6Pj5Xz5BohbedQ3RUfnqNW4fChC5w728FnDTyVK6cNpFHX3xXLlQIcRwxm83s2bOH7du3s3nr32z4+y9279xFdkoaDrtzP6gLCcSQEIWxczShw8ejT4jCkBAlY0kJcZh0JiO6LjEYu9TvsMTZ26Rz8HRHXgn52YVkb9vEyh9+wFFRDYCm1xHTJYFeffowZOBATh40hAEnDKB///7Ssus40OGaOB4t0sSx49uzZw/33H0XX6z4kn5Reu44UXF6gjouTzYsdsV7u+DlvxV2dNz2jyu4+9ElhB2lXkqFEG2vqqqK3bt3s2PHDrZs28rGv/9i585d5KSmu+4P04UFYaytETMlxKBLiMIQH4U+NLCdSy+EALCXVWHLLsSRU4QlqwBrdiG27ELshWWuNGExUXTv04tBJwzg5MFDGVgbuElzybYl96D5AAnQOq6SkhIWLFjAC0uXEhsItw1xMKWHdI8LUGJW/Hurxns7Hfj7Gbjlmlnc8cgzREREtHfRhBBeqqioYNeuXc57xLZvY9PfW9ixYwe56ZmuMQT1ESEYEqIwJERj6hyDPj4SQ3wUuuCAdi69EOJwOGos2HKKcGQXYs0uwpJdgC2rEFtesbOrYMA/OIjEXj04of8JnDRoMINOGEDfvn3p0aMHfn5+7bwEHZ8EaD5AArSOx2az8frrr/PP+++jqqKMuQMVV56g4X9MNuQ9MrlVite2aXy8x4GfQc9Ns6dz92MvEREZ2d5FE0LUKisrY+fOnezYsYNt27c7a8R27CQvM8uVRh8V5myaeEiNmC5Q7l0R4nigbHbnIO9ZhdhzijBnFWDLLsSWXYQyOwdF1XQ6YjrH06N3Lwb068+JJwykb9++9OnThy5duqDTSac+3pAAzQdIgNaxrF69mltuupFtO3YyrSfcfrKBOH8ZVrY5eVWK17fDh7sVRr2OGy47n3lPvkpkdP0280KI1qeUIicnh507d7oem7dtZfeuXRTm5jkTaZpzrLDaGjG/hGiMCdHOzjr85aq4EKI+pRSOkgpnJyW5xc7hAXKKsOUWYcsrcdW6GUx+JHRLpFevXgzqP4DB/U+gV69e9OzZk/j4eAne3EiA5gMkQOsYUlJSuOvOO/j0s885MU7H/cM1BkfJJt1S+dWKN7bDsl0Kg17jukvP494nXycqpv6AnkKIlrPZbCQnJ7Nz50527drFth3b2bJtG/v37KWyvBwAzaDHEBuBPj4SY3yUKxDT4iLQmWSYDCFE61B2B/bCUuw5xdhzi7HkFDoDt9xij3vdDH5GYrt0JqlbEn1792FQ3/706tmTHj160L17d4KCgpqYy7FHAjQfIAGab6uqqmLx4sU8sfhxQo0O7j7RzpTuyM2xR6igWvHGNvhgt8Kg13HdZRcw78lXiY6Obu+iCeHz7HY7aWlp7N+/n3379rF3716279nFnr17ST+Qgs3qNoZYJ+c9YXWBmCE+El10GJpB385LIYQ4nimLFVtBKY78Uuz5JVjyirEXlDqbUeaXoCw2V9rQqEg6d0ukV48enNCnH/1796FHjx706NHjmKx9kwDNB0iA5psqKyt55ZVXeHLxYxQXFXNlfwdzhxgINkhzxtZUWK14Ywd8sEuh1+m4fe6V3LPoOYKDg9u7aEK0K7PZTGpq6iFB2G727dtLZlq6a/wwdDoMMWHoY8IxxEZg7BSJX+dodJ1kDDEhRMeklMJRWoktvwRVUIo1rxhrnjNws+WX4ig9ODi33mggKj6Ozl270rN7d/r17E2v7j1ISkoiKSmJLl26YDR2rJYBEqD5AAnQfEt+fj6vv/46S55+iqLiYqb2VFw3xEjXoGN7LLP2VlSj+Pc2jfd3OggPMvHAvXdz/V3/lN6gxDFJKUVJSQlpaWmkpqaSlpZGSkoK+1KSOZCSTEZaOsX5Ba70mkGPISYcfWw4+tgIDLERmOIi0ceGo0WGSG2YEOK44jBbsReUOGvfCsuwFpRgKyzDXliGvbAUR1mVK62m0xEeG01Cly5065ZE3x696NOjJ0lJSXTr1o3ExEQCA31r+A8J0HyABGjtTynFunXreOWll/jwow/RlIOpPRxcO1gCs6Mts0LxwhZYvl/RNTqERx5dyKxrbjzmmi+IY1tFRQWZmZlkZmaSlZVFZmYmB1KS2XtgP6lpaWSnZ1JdWelKrxn06CND0UeFoo8McfaYGB2GMSoMLSYMfWQImvwGhBDCK8pixVZYhioqx1FYhsUVwDkDOntRObiFJcER4cR2jicxMZGe3bvTv2dverrVwkVERBzV1ggSoPkACdDaz+7du3n//fd5/923OZCSRpcQHZf2UVzUR0+EnzRlbC12hyKtHA6UQpnF+aiygUEH/vrahwECDRDqB/HBUGFRPL9F48c0xQldo5n34MNccuU/pEZNtCur1UpOTo5H4JWVlUVqehop6WlkZWWRl53rEXwB6AJN6CJDMUSFoo8KQx8VijEqDGN0GESGoAsNQtNJU0QhRPtRDgeOKjOqyoyyWFFmCw6zFWW21r6vfdhrL1zXndUr5Xyt09D8jGgmIzqTEc3PiC7YH11okPPhQx0QKZsde0kF9sJSVGE5tsJSrAWltQGc8z22gxfo/QIDiE7oROcuXejerRv9e/amb89ergCute+DkwDNB0iAdvQopdi6dStff/01n3y0jE1bthJi0jGpq4MpPfWMiHWgl5OkI6aU4q98WJWu+DMXdhdD9cF7fQmoDcasDqixgaWBWDjQAKfEw5Bo+CMXfsuC+IhAbvjHtVx96zzi4+OP3gKJY55SiqKiIo/AKzMzk/TMDFLS0sjIyiQnK5vSwiLcD2WaQY8+PBhdeLDzOSIEfXgwxohQDBEhEB6ILjwYnUkuLAgh2p5yKFSNGUdljeuhqg6+dlQdfFaHfKaqzc3PwGhAM+iA2nMlDairWXIolMXq6lb/UJrJD11YEIaYMAxxkRhqm2wb46PQx4T71EUqpRSOsipXwKaKnLVw9sIybAXOWjj39aUzGIjpHE9Sz+6c0KcfQ08YQP++/ejduzeJiYno9S1rhi4Bmg+QAK1tlZeX89NPP/HVV1/x9YrlZGTnEmjUMTrewXk9dIzrAia5faNVWB2Kz/bBWzsUB0oh0h9OjYcBURr9IqBXOISbwE/vuRN2KIXZDlVWKLFAZgVsL4Q1Gc5ALykE5gyArYU6Vux3YFMwfvhAZl93K+dNvYiIiIj2WWDh8yorK8nJyan3yMzOIj0rk6zsbPLy8ijOK8Buc7uKoGnoQwPrBV6G8BCMkSHowoMhLAhdcIB0wiGEaHXKanMGThVuQVVl9cGgqvZ9Q4EYDZ1ua6AF+qML8kd3yLPm/lmQP1qAyVkDZvJz1Yhpfgbnsxe1RMpmR1lszhq4imrsZZXOYKes0jleWl4Jtrxi5xhptZ0daf5+GLvGYkyMxZgYh1+PeAzx0T4VtB3KUWV2NZlUReVYcouw5jqHE7AVlLgCVb3RQEyXznTv2YMT+vVjWP+B9OvXj759+5KQkNDgMUQCNB8gAVrrqqys5LfffmP16tWs+uF/bNz8N3aHg66hOsZ1djC+q57hsY56QYI4Mr9nKR5e52zKODkJLu2rMbIT6I7w5HVrgeKZTYrfs+G87nDnMPglS8cX+x1szAOdBif178Gkc6Zw6vhJDBkypNEdnujYlFJUVVVRWFhIQUEBBQUFrteFhYXk5OWRkZ1FVnYWubm5FOUVUFNV5ZGHptejDwtCCwtCH+ZsdqMPC0IfFowxLBhdRDCEB6EPDZKON4QQR0QpZ42So7IGR0U1jgr3AMszuHIGYwe/VxZrg3lqfsaDQVVwgEdg5XwEoAWa0AUFeHyu+Zt8LthRDoWjpBxrViHWtFysaXlY03Kx5RaBAi3IH1Ovzvj16Ypf7874JXVCMxrau9heqRv/zZZXjCO3BGtt8GbLLcZeUAIOZ0jkFxhA5+5J9OnThxMHDGLIAGfwFhcXR0JCggRo3njssce4//77ufXWW1myZAng/PEtWLCAV199leLiYkaOHMmLL77IgAEDvM5XArTDp5QiPT2dP//8kz/++IPff17N+k1/YbXZiQ7UMSLWwchOGiMT9HQLtstJexsosygeX6/4dB+M6AQPjtDoG9G661kpxZfJMH+dIiYAnhur0S9SI7tS8Wu2jl8z7KzNhlKLM31kSCA9EhPomphIYvfedO3hbGIQHx9PfHw8nTp1Ou4GvfQVVquVsrIySktLXY9D35eUlFBQXExBcSH5BQUUFhRQXFREWXEpVnP9Jjia0eCsyQry9wi6dGFBGMNCMIQHo4UEooUFogX5y35ACHHYHGYLjtJK7CWVOEornPcxlVbiqKhy1nZVVDuDrwpnEOZ+H5OLpjmDpgaCK1dA5f4+0N95L1egf4cJUI6Eo8aCNTkb894MLHszsOzLQpktYNDj1zMB/xO6YTqhG8bunTpk50nKZneO9ZZTjD23CEt2IbacImw5RTjKD15UDJp4ElmfrpIArSnr169nxowZhIaGMn78eFeAtnjxYhYuXMhbb71Fnz59ePTRR/n555/ZvXs3ISEhXuUtAVrzlFLk5eWxe/dudu/eza5du9i1bQsbNm4kr7AEgPhgHYOj6gIyAz1DbHIi1sZWpinmr1NU2+CekzUu7n3kNWZNSSlT3PaTs/nkQyM1pvc+OC+lFBkVsLtYY1eJRmaZnZwqyK6EnCrPe+AAggNMdIqOoFNcHPGduxCf2J2Ezl1cAVzdc1RU1HHRg6RSCpvNRk1NDWazucHnmpoaKisrG3yUV1RQVl5OWUU55ZUVlJeXU1FZSVVlJVVVVVRXVVFdUYmlpvF7HDSjAV2ACc3fz9nEJtCELiQAXXCg8+QkOBBjcCC60AB0QQGoIH90IQHOJjjyWxdCHKG6nv/shaXYC+ruNSrFXlKBo6QSe2kFqsbiOVHdfaghgc5gKti5f9IF1wZYwQFun9fWcAX4Xm2WL1N2B9aMPCx7MjDvSsO8KxVVbUELNGHql4RpQDf8T0hCH3t0e1psC47KGmw5hdhzigk4bSA7LrpfArTGVFRUMGzYMF566SUeffRRhg4dypIlS1BKkZCQwG233ca8efMA58CicXFxLF68mLlz53qV/7EYoNlsNsxmM2azGYvF0uBrs9lMdXU1FRXOkzn3R0lhPjlZmeTmZpObm0deYTHVZmd1v06DziEa3UMUJ0TC4Fg9g6IgNqBDbl4dUmG14l9/Kr5NgfFdYP4ojU5BR2enaLYr/vWH4r974f/6wXWDQKfTXJ1IKWpbDihw4GyKb1eKcgsU1UBBjUaRWUdhjaKo2kFRjfPz4hooMkPlIa1K9DodkaFBhIUGExxc+wgJIzg0nOCwcIKCgggMDCQwMJCAgAD8/f0xGJxXOBsK7BwOBw6HA7vd7vHabrdjs9lcr+ve22w2bHY7VpsNu92GzWbHarNis9ux2W3Ybfba760eedjcX7vla7GYsZidv0OrxYLFbMFmsWA1W2jpLlrzc/bUhZ8RzWRw3q9Q13OXqfa+hdr3msmIPsCEPtDfGYQFmNAC/CCgNhgLMB0XV4aFEO3HvdmZPb/U1dmDrbAUe0EpjlK33lc1zTXkhbMjIGdTaH14MLqwg6+1QFOHDwo6GmV3YEnOxrwjBfOOFCz7s8DuQB8ViumEbrUBWzd0wQHtXdQjJgFaE2bPnk1kZCTPPvss48aNcwVoBw4coGfPnmzatIkTTzzRlf6CCy4gPDyct99+u8H86oKTOmVlZSQmdqWpn3eDK+4I1qav/iPqOgOqe9Y18KzTDnYYVKep5Wlsq2tymsPIrymtXr4mvjyc/21L8lPKuf6DjPDQSDivu3bUD05KKd7bBYvWK/pFwL6ShnuC7Mg0txfaoZ9rDac75Cu0Qyaum1Y79LUrmeb6bWlu+Xm+1jzeCyGEL1FK4VDO/hnsSmFXGg6D3vnebDt4wNNp6CNC0MeEHxz+IjIEfXQohthIZ/Al96F2CI5qM+bd6Zi3p2DemYItqxA0MIYFYdQUxpoaDDo6XCA9//p+/N+Dv7d5gNYhL41++OGHbNq0ifXr19f7LicnB4C4uDiPz+Pi4khNTW00z8cee4wFCxZ4fHZy3MGTnUM3nyZPhrQG0h+aT4Mnbg1m5TG/Q+ffYNkaKadeB3qt9lH72qCBzu113ed+ejDqGm4a19RvqamfWZPftXaeh5vfUV62pniTp9mu+C4FNuXDpK7w8CiIDmifpn+apvF//SEpFG7/SZEYAtcMgDB/DQ1nEE/tsyvYP+SzBtMAaJ5pPC4a1E13yEUDm1LU2Jxju1VZncMHOHCeIDhr8Jy1eg7lnKjudV2tn+v9Ia9V3XR4pq3jHljXC6QbeaMOSdnUOyGE8EVKOfe3JeaDj0KHgVKzRkW1DUfdjlKnQx8ThiEuAv/YCAyx4RhjI9B3ikQfFYamP/abrx8PdAEmAob2ImBoLwDsxeXUbD1AzdZkqnekUFXtwOBvpFO0P72MVXQLsBPkO0OyNerMvJ1HZT4dLkBLT0/n1ltv5fvvv8ff37/RdIdG5EqpJqP0++67jzvuuMP1vqysjK5du1JUVERoqHf3rR01Hexqw/FEO4r1F3///TcXXzSNrJI0Fp2muLCnb1yJOr2zxkfnwNwfFU9u1vHJ+29x+gWXt3ex2kfHbKAghBCNKikpYe/evezes4e9e/awffcuduzaRcqBA5irqp2JdBqGqDD08REY4iIIiY3AFB+FFhOOPipUasGOQ/qIEIJOH0LQ6UOczSEPZGHdlkzu1gNkJJcD0LlfLyZNmsQV06ZzyqhTMBp9K2IrKysjMjKSkufbfl4dronj8uXLmTZtmsfgcna7sydAnU7H7t276dWrV4ubOB7qWLwHTRw7vv/+e6ZfOI0uAWaWnK7RLcT32hIW1ShuWuPskv/lR+cx597H27tIQgghmqGUoqCggJSUFFJSUjhw4AA7du1i+64d7N+3j5KCIldaQ3gwurgI5wDHnSIwdYpGFxeBPiZMgjDhNXtZpbMp5NZkarYdwFFRjV9QACeOPoWZ50/lovMuIDExsb2LKeOgNaW8vLxeU8WrrrqKfv36MW/ePAYMGEBCQgK3334799xzDwAWi4XY2NjjvpMQcWxYsWIF0y+6iFM7OXh2rEaQwXd/wha74qG18Pl+xV2zzmHx218eF70vCiGEr7FarZSXl1NQUEBubq7rUTdIfHJ6GikpKWSnZ2CurnFNpwswoY+NwBAfiSEuAmOnKPw6RaLFhqMLMLXjEoljkXIorKk5WLalUL11v7OzEaWI65HE5LMmM2fmZYw+bbRHRc3RIgFaC7l3EgLObvYfe+wx3nzzTXr37s2iRYtYs2aNdLMvOjxncHYhEzo7ePp0MHaALoGVUry2DZ7ZpDhvRG+WrdxAUIj8poQQojk2m81jXMKGnsvKyigpK6OkrJTSsjLKyssoL6+goqKcyooKKisqqa6oxGatP8iyc5D4QLTQIPThweijwzBEh+EXFY4+OgwtKkTGKRTtylFZ4+wZclsy1X/vx1FaSWBEGGMnn8k/Lvt/9s47Po7i7v/vLXcnne50p2pL7thg40ozYGrocSBASIGEGgyhJYQSMD0QSIA4CaQ6BFIIgfCEh0AeEkLgRwgtdBtbtmRbvZ7uTl06Xdvd+f2xp5Nky7bc1Dzv12s0s7Ozs7On27357HfmOxez/PQzcLlG5kXBSGqDcTcHbTjceuutRKNRrr322vRC1a+++uqwxZlEMhbps5yNJ3EG9ry4byyCA3zwnbfLWXbwVP722jvMOnjxaDdNIpFIRo3Ozk6qq6upqqqitraWxqZGahobaGxqIhgM0hoK093esf2lNjQVNcNeGkNxOVAznPZ6hRlOO12chZKRg5rhJCvDiZbhQstw2m7OvZmoPumOXjL2UbMyyFw6j8yl8/BZgkRVE4m15bz679f557P/i8OdwVEnnciKCy7kvLPPmTBGlQlhQdsXSAuaZCzx17/+lQvOP5+Tp5j8+AQFxzgdJVjaKvjmfwRRU+Xp3/+Gz56/YrSbJJFIJPuERCJBXV1dWoRVVlVRWr6ZyqpKGmrq6OnsSpdVXQ5Un72Wl72eVxZadha6z4PmyUTJcCLS6xM67aGFDl2KK8l+ixACo7GF+NoKetdsJlkbRHXoHHXKZ/jWim9w3ufP3uuWNTnEcQwgBZpkrPD0009z6aWXcMZ0+OFxjFtx1kdbTPCdt+G9gODe6y7irp/9UXYyJBLJuMOyLAKBANXV1elQVVXFpopyqqurCTcHEVbKgZOioOVloxfYQwf1Aj/OwhzUPB9qgQ/VmymfgxLJHmC0dBL/eDOR9zeSrAvh9Lg5+fPLuXHFNZx60kl7Zf67FGhjACnQJGOBxx9/nKuuuoovzIEHjlaYKMvDmJbgZ5/Cr0sEZx01l6f/9QHZPt9oN0sikUjSCCFob28fJMCqq6upqKqiorKChvp6kvFEurzD50HJy0bL99nrfOX7cBbkoOZno+ZK1/ISyUiRbGwh9kEZkfc3YrZ04inI5ZwvfZFbrrqOJUuW7Ha9UqCNAaRAk4wmlmVx991384Mf/ICL5inceeTQi4aPd/5fneDWdwTFuR5eeOUNFhxyxGg3SSKR7Ef09vZSU1MzSICVV1ZSUVVJXW0tka7udFktw4Wj0A8pEaYPsISR60XNcI7ehUgkkm0QQpCobCT2fhm9H5Zh9USZMm8OV379cq67/Ery8/N3qT4p0MYAUqBJRotQKMSll1zCv179FzcfpnDFgrGxAPW+oqpT8K3/CJp7VX7761/wla9fM9pNkkgkEwTDMGhoaKCqqio9BHFLZQXlVZXU1dTSHm5Jl1V0DT3Ph5qfjVbgtwVYvg9XYQ5Kng880puhRDJeEYZJrKSK6LsbiK6rQFEUlp58At+5+jrOPevsYS2KLQXaGEAKNMlII4Tg+eef55vXXo0R6eDhYwXHF492q0aGSFJwx38VXqmxuOmKC3h49VPo+oR0MiuRSPYiQghCodCgOWDllZVsriynprqGUFMAyzTtwopiL6yc70sLMEeBH0ehHyUvG9XvQZHrNEokEx6zq9ceAvluCcm6IG5/Nl84/yt85+rrOOSQQ7Z7nBRoYwAp0CQjSW1tLdddew3/ePmfnDJd4XvHaOS7rNFu1ogihOD3pQo/+sTiuCUH8tyr71JQUDDazZJIJKNIMpmksbGRurq6dKiuraG82nZNv/WiyponEzUvNQeswI+e78eZEmBaXjaKQ774kUgk/STrQ0T/u5HIexuwunqZNu9ArlpxBd+49Ovb9EGkQBsDSIEmGQl6e3t55JFH+MH3HyBbM7h7qcWp00e7VaPL+wHBjW8JMjMz+d+/vsjRnzl9tJskkUj2MpZlEQ6HaW5uJhAIEAgEaG5uprGpiZqGeurq62hqbKQtGB60DpjmcaPmetFyvWmviM6CHNS8bNsbYubILFgrkUgmFsIwiW+opvfdEqKfVqCgsOzUz3DTVddx9lln4XA4pEAbC0iBJtnbJJNJ6urqCAQCNDQ08K9//YsXX3ierq5ulhbCWbMV5vhgjh+ynfv3PIfmiOBb/4GNrYIrv3Q63/vln6Q1TSIZB8Tj8W1EV1NTE3WNDdQ1NtDc3EwoGKSjpa1/6GEKzZOJ0rcGWE5KgOX6cORno+Zko+R6UF3SEceeIoSApIEVTyLiSUQsgYgn0tsYJsK0EKYJpoUwrVRsgpXqMioKKKn50QOCoqng0FAc+lYhledyoGS4UN0u6dVSMmYxu3uJf7iJnnfWk6wN4s7xcd5XvsRVF13G8ccfLwXaaCIFmmRPqa2t5R//+Af/fect1q35iM2VtSSN/g6JpoCu2rElIDagrzI3B06YAmfNUpiXu3+KtYQp+OMm+NU6gSEULjz3s3znez/m4IMPHu2mSST7FUIIurq6BomuQCBAQ2MjNY31NDY10dzcTGswNMjrIQCaipadWnzZ70ktwuxB83lw5nhQfB7wudGys2SHfRcRpoUViWFFolg9Q4RIFKsn1p+OxBDxBCKWhF3p+mmqLbxUFTTVPlaQiu0g+oSbadr7hoOuoWa6UDKdqBmpONNlL8adlWEHTyZqViaqJ8OOU3lKhgtF3T9/GyUjy9ZDID1nLaPx6VekQBstpECT7A4tLS388pe/5Lk//4mNmyvQVViQC/Ny4UC/wsxsKHILCrMUvI7B3hl7k4K6bihrgw+bBf9ugI44HOSHzx+gcO5sKHTvfz9IbTHB/2xReHqTRTgKxy6ew5cvXsGXv3YJxcX7iRcViWQf0NvbSzgcpqWlhVAolBZgDU2N1DY20NjURLC5mfZwC4lYfNCxisuB6vOg+W3B1Se8HDm2+CLbjerz2J1p2ZHeIcKyEL3xfrEViQ1O90T7t7ujaUEmeuND1qdkOlOiZquQlYHicqJmOG1LlsuR2rbjdJ6u94syTd0lz5VCCNvaljQQSTMVG5A0EAkDkUhixRL29cbiiGgCKxpHxAbEvanr77GvGcPc9kSKkhJxtnhTUtereTJRvW5Ub991p9JeN6o7Q34XJbuNMExiG6rJPGQOpV+8Qwq00UIKNMmuUFdXx49X/ZAnnngcy0jy2RmCk6ZrHDvZwrubwxWTluDdJnipSvBaHRiWbVX70oEKJ04Fx372Q5MwBS/XwN+rBe81gQkccuB0Tjr1NE458zyOPfZYfHKxa8l+hBCCaDRKV1fXkKGzs5Ouri5aWltoCgZpDgUJhcK0tbbS2dY2yLlGH5rXnR5mqPo89nBDvweH34vqy0LxpYSXXPNrG4RlYfXGsbp7+61XkShikMhKCa3eeL9Vqzc2tNVJU9PCSnVn2CKjz6qUFl2ZtgDJGiDCJpAlUghhi7rUZyjSFsMBlsO+z7W7d5D1cJvPVFH6P7c+0ZYScdqg7X6RpzgdcmkFyTZIgbYdVq9ezerVq6mpqQFgwYIF3HPPPSxfvhyAYDDIypUrefXVV+no6OCEE07g5z//OQceeOCwzyEFmmQ4lJaW8vCDP+CZP/+ZLF1w4TzBxfMUcjP27gO9KyH4RzU8Xy4oaYW8DFg+E5bPVDiscGIuYr0jOuOCNxoU3m4SfBAQhKN2/qyifJYsms+hRx3PYUcezcKFC5k+fTqqdJ0t2UdYlkUsFttuiEajO9zfVyYSjRLpjdAbixKNRolufWw8TjwWIxGPE4/FScYTJOJxhLV9b6+KQ7etJZ6MlCWhz7Jgd0j17Cx0rxs8meDNlMMMh0CYFlZXBLO9G7O9B7PDjq3OngHDCWOYPb3bFVqKy5G29ih9Q/f6hutts51p/7+yMqQ42AOEZaWGd6ZEW3evbX3siWJ2D8gbEIt4ctuKHPq2Vrk+IedNCTuPu1/0ZWXKe2g/QAq07fDSSy+haRpz5swB4Mknn2TVqlWsXbuW+fPnc8wxx+BwOPjxj39MdnY2P/nJT3jllVcoLS0lKytrWOeQAk0yFJZl0dDQwIsvvsjvnnicdSUbKHTD1+crnH8QZDn2/Y/p5nbBXysE/6yBYC8UZMJnZ8BnZyocXjixF7UeCiEE1V2wvlVlU5tFaatgUxt0Juz9bpeDOTOKmT9/AQsOPZL5Cxdx8MEHM2fOnGEtTCkZ21iWRTKZJB6P75Y46ivXE+0l0hsh0hsdIJJsoRSLxYhFY8TjcRKpkIwnSMYTmMkhOnU7QdH7nSjg0OwhZQMdKzgHO1bAoQ86pm+f6nSgZbhQ3C7bopXphAynPYcnwyk7isNAWMIWXaEOjFA7RrgTM5yK27qxuiKD52tpqj2Xzu9BG8qiNdCy1WfRkq79xwUikcTsSQ0j7UkJugEizuwT5Gmx19vvNGUAitu1rVVuK+tcWth5M1EyXfvd7/Z4Rwq0XSA3N5dVq1Zx/PHHM3fuXDZs2MCCBQsAME2TwsJCHn74Ya644oph1ScF2v5B3xChnp4eenp66O7upqenh46OjvR8jMaGBprqq6mqrGRLVS2WaWIKmOGFbyyCsw9QcGoj/3C1hODTMPyzRvCvWluszfbBpfMVzpuz/w2BHIgQgkAEKjoVKrsUKttNKjuhsqNfuGmqQmFONpML8ykqKqJ4yjQKi6fi9efi9XrxeDzpkJWVhdPpxOFw4HA4tknruo6qqjsMiqIM+SMshMCyLEzTTIeB2/ti3+4GwzBImgZJw8A0TAzT3rbT9rZhmJimgZE+LpU2UnWYJlYq7qs3mUxiGgZJI4lhGBhJI7Vtx0bSsOtJGvYxqf2mae7QgrRdFGWACOoTSRqKwzFYFDk024oxUDzpGqrDgeLUUQcKqpTIwqEh+srrW3mwc+qg63IezChgReMYTa0km1owAq12SAmy9BwnBdt7ZGEOer7PduXv96LleFFzPGh+r5xTJ0kjhEBE7SGtZnd0W6tcKp0Wfd29iOgQ8wb7hrL2iTe/x/ZgmudDy/el0tkoTvlCcawgBdowME2T5557jksvvZS1a9dimiaLFy+moqKC2bNnp8sVFRVxxhln8Ic//GHIeuLxOPF4/43T1dXFjOnTGNSf2s4nNRY/wD1q0whf0Fj8/PpQUgEFVMAQcEQhXDIfTpuujJmhhZYQfNgMT2+y56vlZ4Jbh7YY7KyJw30C7Mr/abhl98XTZ0dV9p3PEnYQA/LFTo7dX1G2Smz9ddr6+zWwvLJ1rjL4eGXwrm3KKKmNQccM2FAGlk/FfSJYIe0JPLVPSR87Rm5byV5GCIEQ9nxdwxSYKBiahpm0sBKGXUgBLc+HXpyHXpiDVuBHz/ehT85Bz/dLa5dknyIMs9/ZS5+A6xsuG4lhdUdti25rF2Zb96AfSdXlQHPpaKqCZhhopoGmKejq/jdyZjRZec1Crrj9zX0u0Mbtk6ikpIRly5YRi8XweDy88MILzJ8/n2QyyYwZM7j99tt57LHHyMrK4ic/+UnaLfD2ePDBB7nvvvsG5R2Szza9kaFugZ3dFrt632zdIdqTcw/3/Nt0unZQaEfVKdvdGP65h503nM9nB23o68A5VTs4tP7Ypdpu7z8N214VNRXOOQC+Ng8Ozh1785lUReHoIji6SGFTm+DRtYI3GuCAbDhxKuRnKsP9dwz7+zrs+oZZbk++L0OW28P6TEuQsCBhQtLqF3WmBWZfWvSn+7xOo2zlhZrBAlAMEIbb+56LAfvEgLy+6+p7cbB1Wh1wj/ZtD1Wub1sduG+reO+yu9JXSmbJtggBkSS0x21Pt61JhTZDpzMmSCZS1jBVRZ/kRy/OI6MoD8fkXLTiPPSifFSXtERIRgdF19LLTOwMYZj23MfWLsxQO0arPezWCLaTaG7DiibSZV1uJ94sHb9uUaDGyXMKcl2QMW57+WOXswLrR+Q849aClkgkqKuro6Ojg+eff54nnniCN998k/nz5/PJJ5+wYsUK1q1bh6ZpnHrqqWknAS+//PKQ9Q1lQTtg9gyqq8rxeHZ+I0nGKEraBoaipNZx2Un3c+OGjTz4wPf5+z/+SX6mwiXzBF+Zq+J3jq9b5ZOQ4PsfCja2wtc+ezQPrP4T+YU7Xux5aMEwOHew8NnGRrJV2X3zVm/rx9a2/5khcob579vZ9e2taxLCAmH1ryUkxYhEMgjDMKiurmHT5i1s3ryFkrIyNpSVUV1RRazX9gykOHT0olz04nz0ojxcxfloRblohTlyDp5kQmP1RDGCbZjBdozmdhLNqaG7zW32m0TAm5/DrIPmcMjChRy95BAWLpjPwfPmDtsfg2QwPd3dTJ0+i3BrhxziOFxOPfVUZs+ezWOPPZbO6+zsJJFIUFBQwFFHHcURRxzBL3/5y2HVJ+eg7X80NjZy91138eQfn2SqV+XK+RbnzAbXKMwv21uYluDZLfDIGoHu0PnBfXfzjZvukl4NJRLJmCEWi7FlyxbKysooKyvj0w0lbNi4kdrKKoyUExbNnYFalIujKA+9OA9XcQFaUS5qnk/OCZNIBiAMEyPYjtHYgtkYJt4QJtkYxgx3pIZkKBROK+bghQtZdvgRHHXYESxevJiZM2fKvsFOGEltMGEE2imnnMK0adOGnGNWXl7OvHnz+Oc//8npp58+rPqkQNt/SCQSrFq1iu8/cD8ZSpLrFgnOP4hRcfyxrwhHBT/8BP6vUnDEgcU89tRzHHbUMaPdLIlEsh/R3d3Npk2bKC0tpbS0lLUl6yktK6Oprj7t7EX3ZaEW5Q0SYurkHFS/R86zkUj2ACuewGhqxWxsJVEfJNkQJlkfwuqxrdGuLDcHzDuIQ5ccwrFHLOWQJYewaNEivF7vKLd87CAF2k644447WL58OdOmTaO7u5tnn32Whx56iFdeeYXTTjuN5557joKCAqZPn05JSQnf/va3Ofzww3n++eeHfQ4p0PYP3n77bb5xxeWUV1Ry6cGCa5doeB3j7pYYNh80C773gaCqE676ynIefOzPcnFniUSyV2lra2Pjxo2UlpaysbSUTzesp6ysjJZAMF1Gz8tGS4kwR1E+rikpIebJHMWWSyT7F0IIrI4ejIYwRkML8fogyfrQoGGSBVOLWbh4EcsOO4LDDjmUJUuWcMABB+yX1raR1AbjcvpgMBjk4osvJhAI4PP5WLx4cVqcAQQCAW666SaCwSBFRUVccskl3H333aPcaslYoq2tjVtv+Q6//d3vWVKg8NfPa8zzW0z0eUBHTVZ44Sx4sgx++fw/+d9/FPCjB+/n4utulW+nJRLJLhGJRCgtLWXDhg2sL1nPx+s+pXTDRtpCYbuAqqIX+tGL8tAPm0Ve8VIcxfkok3LsddskEsmooiiKvbREjhfXogPom5kmkgZGoA2zIUy0Psh/azfz5jtvY3X1AuDMzGD2vLkcfsihHHXY4SxZsoRFixbh9/tH7VomGuPSgjYSSAvaxEQIwdNPP82N3/4W8UgXNx9qD2ccK+7yR5KmHsGDH8OrtYLj5k/l1089z4LDjhztZkkkkjFGIpFgy5YtthBbv56P161lw4aNNNc32A57FAW9wIc+pQDHlHycUwvRi/PRJkm39RLJRMLsjGA2hknUh0jUhTAawiSbWtLWtvziycxbuICjDj2cow4/gkWLFjFnzhx0fWI8B+QQxzGAFGgTj/Lycq7+xpX8+z9v8rmZcPuRGoWZ8uv/ZoPg/g8FzRG4/uJzuO+XT0sPTxLJfogQgoaGBtavX8/69ev5ZO1a1q5fR21lJWZqMWfN70WfkodjSgHOqQU4phagFuWiuqRFTCLZHxGGidFsW9vi9SGS9SGS9WGszh4ANIdO0awZzJ03jyOWHMLSJYeycOFCZs+ePe6EmxRoYwAp0CYO8XicH/7wh3z/gfspyDD57pEKJ0yRX/uBxAzB4xvgNyWCQl8mP/3ZzznvohWj3SyJRLKP6OnpSVvE1q1bx0dr11C6cSORrm4A1EwXekqAOaYW4JpaaAsxOUdMIpEMA7OrF6MxjBloJdEQJtnUitHUknZKojl0Js2czkEHz+PwxUtYdsjhY164SYE2BpACbWLw1ltvcdWVV1BRUcHl8wXXLlHJHJv3/ZigtkvwvQ/hnUbBGUcv5Fd/epEDZs8e7WZJJJLdxDRNKisrKSkpsYcnrl3Dp+vX0VRbD4CiquiTc1JirBDXtEK0KfmoedlyXqpEItmrCCGwunoxmlowm1pJNLWQbAyTbGxBRGIAaA4Hk2ZO48CD53L44kM45pDDWbRoEbNnz0bTRndtQynQxgBSoI1vmpqauG3lSp760584tFDlvmUqc/3WaDdrXCCE4NU6+P6Hgo6Ewu03fpPb7l+Fy+Ua7aZJJJLtEI1GKS8vT68lVlK6kZKNG6mprCIZjwOg+zxoU/NxTCnAMa2QjGmFKEW5cp6YRCIZVWzhFiHZ2IIZaMVobCXRaM9vSws3pyM9VHLpIYdy9KGHc+ihhzJt2rQRe5kkBdoYQAq08Uk8HufRRx/lgfu/h1PEuXGJxZcO3D+dgOwpkaTgl+sVntxoMWNyDr98/PecceY5o90siWS/JR6PU1tbS1VVFdXV1VRUVLBu4wbKNpURqEs57MAWYurkHHstsaI8XFMLUKfko2XLuaUSiWT8IITA6oyQbGrBamol0ZiyuDW1IHrtF09ufzbzlixi2dKjOHnZsSxdupSpU6fuE9EmBdoYQAq08YUQgr///e/c9O3rqa6t5aK5gm8eqpPtkFazPWVLu+C+DwQfB+Gc4w/lez99gsWHHjbazZJIJhQ9PT0Eg8FBobm5mcqaajZXlFNTXU1LIJgWYYqmouX70CfnoqcWdnYW5cu1xCQSyYRHCIHV3k2iNohZGyRW00yyphmrKwJAVo6PuYsXctxRR3PyMcdz9NFHM2nSpD0+rxRoYwAp0MYPb7/9NrfftpJ3//sey4pV7jpKZU62FGZ7EyEEf6uCn64VNEXgjKMXceeDj3LciSfJeSoSyRAIIejq6tpGdA0UX43NAYLBIC2hMLFodHAFqormdaPmetEL/GgFfhwFfpyTclDyslFzvSj74UKxEolEMhR9i24nappToi1AsiaYFm25xZM5ZOnhnH78Zzjx2OM49NBDd3nqhhRoYwAp0MY+n3zyCXfecTv/evU15udr3HiI4PhiMS4EQ8IUBHuhKQKdcYgk7ZC0QFEg2wnFWTA/D7KdY+d6kpbgH9W2t8fKTpg3rYAV37iaS6/6FgUFBaPdPIlknyKEoL29fYeCK9DcTCgYpDXcQjKRGHS8qmvo2R7UbDd4M1Gz3ajZWWi+LNTsLBw+D1p2FsKbierJRFHHzr0vkewvCCHAtBCmaceGCYaJMLbKMwfkGSbCtAbss/OFaYFhQmq9QBQFVMXupygKaCqKU0dxOuzgcqA6HSgZDlR3Boo7Qz4H9gAhBGZrF8mqAMmqJuJVTSRqmsEw0Rw6M+fPZdlRR/PZE05i2bJlzJo1a4d9SCnQxgBSoI1NhBD897//5YcPP8T/vfR3DvBrfHuJ4PQZYkzPMzMswZsN8Gqd4KNmW5htfeM5VDsIIGrYeQpwdBFccJDCGTMYM+LTEoJ3m+D5Cvh/dQKBwrGHzuPcr1zEOV/+KrNmzRrtJkokw8KyLFpbW7cRXIHmZhoCjTQGAjQHg7SGw3S0tGEaxqDjFYeOnp0F2W60lOBSs7PstC8Lp8+D5ssCrxvcrjFzD0skYxVhCUQiiYgnELEkVioW8QQinsCK9e8TiSTCMG2RlLRjYVgpQWUOEFJbi6wB5QYIqT5xtdfQVBRNtcWYSIm/vmCl4h2hgOLOQPXYL23UrEw0XxZajgfN70X1e9ByvGg5HlSPW4q5YSAMk2R9CKMqQKyykURVADPUDthDIxcddiinHH8iJx13PEuXLh2kAaRAGwNIgTa2iEaj/O1vf+PRn/yYDz76mAP8GlcusDj7ANDH8APJEoL/q4KfrRU0RuCgHDimCOb4FaZkweQsyHFBlgOcWv91JExBfTd8EoK/VQo+DsHifPjeMoWDc8fW9bbFBP+sVfh3ncUHzbYVcMakHI4/ZhknfvZsTjzpZObMmSM7ppIRwzRNWlpahhRd9SnRZQ8tDNHV1o61VYdMcTlQvbbA0rKzULPdaNl2WvfbVi7bApaFkumU323JfoswTEQsgRVPImIJWzAN3I4nEPEkVsyO0/vi/dsivrUIS+78xLqWtjbh0FA0DUXX7PxUQFP707pmCyWHjqKpKNqAvL5jNBU0DUVP5fWltSHKpfaTqitdbsB5UdWdCiZhCUQyaX8WCcO+/oRhf06RGFZP1I4jUTvdE8XsjGC2d2N1RgYLPE1F83vQ8nzoBT57jmq+H63Ah57vQ/V7pYDbDmZ3r21lqw4QT4k2EY2DojBl9kyWHnkkZ5xwEksWL+aYY46RAm00kQJt9InFYrz99tv8+c9/5vnn/oeunl6WTla4fIHCZ6aMbYsZ2GuKrXxHsDYMZ8yAKxcqLMrfvTZ/0Cx44ENBdSd8+1CFy+eDNgYftD0JwX8DCh+F4ONmi03t9kvCAl8Why0+mKOOO4kjjz2RpUuXUlhYONrNlYwDEokEnZ2ddHZ20t7eTktLC+FwmJaWFkKhEMFwmKZggFAqr6OtnUhnF1v/tKkZrtSQwsHCS/d50H1ZqF439FnAMpyjdLUSyb5FCGELolTnX0TjtnhKBatPVKXTyW3SIt4vwIZjbVJcDhSX0x6253La2xnOdL6aTjsGpJ0oGU7UgWUznP3H66O7HtZYQFgWVmcEs6MHs727Pw53YrR0YLZ0YnX19h+ga2h52ej5PvRJueiTcmwnQ5Ny0fKz5ZzWAQhLYDS3YlQ1E69qJFHZRLIhDEKQ/ZWTqH/8RSnQhmL16tWsXr2ampoaABYsWMA999zD8uXLAdsb1m233caLL75Ia2srM2fO5Prrr+eaa64Z9jmkQBt52tvb+eSTT/j44495/dVXeOe//yUWTzItW+XsWRZnz3Yw02uOdjOHxYuVgnvfFxRkwvePUThy8p6LqYQpeHSt4HcbYekkePg4hWLP2BNpA+lOCNaEYG2LQknYYkMrdNiecZlS4OeIQxZx5HEnc9iRRzNv3jymT5+OKn8kxjWWZRGNRolEIjsNXV1ddHR00NreTkt7Gx2dHXR0dNDZ2UlPVzeR7m4SsfiQ51EznKheN4onE9Wbiepxo6Vi3etG92Wh9IkurxvV5RjhT0IiGRmEELZ1paPH7qSngtXejdkZGWCBsWOM7fyOKgwQRnasZKZEUt92n8hKiaZ+wZUSWykh1ifIFIdDWm1GESuewGzpxAh32nFLhy3gQu0Ywfb+74KuoRf4U4KtX7jpk3PskQJj/IX4SGDFEiRrm3HNnU7pF++QAm0oXnrpJTRNY86cOQA8+eSTrFq1irVr17JgwQKuvPJK3njjDZ544glmzpzJq6++yrXXXsvzzz/POecMbx0nKdD2DZZl0djYyJYtW9i8ebMdNq5nU1kZtU1BALIcCocXCo4pUjhmisZBPnPcPBwSpuD7Hwqe3QJfmA13H6WQ5di7bf+g2bbM9STgu0crfP6A8fHZgN2RaOiBDW0qJS0WJS2Cja22gxSADIfOnOlFHHTQgcycPZcps+YwdepUiouLKS4uJj8/H6/XO26+D6ONZVnE43FisRixWIxoNJpODxWG2h+NRolEe4lEo0R6I/RGo0RjUXp6IvT29tIbiRDtjRKL2iERjQ2rbYpDR810QaYT1Z2BmulEyXShZrpSk+PttObOQHNnoGa6EJlOW4x53XJxZcl+gxACKxLDDLVjhFMd7HBHKt2B2RnZRnSp2W40v8e2CKfnL2Wk5zGpngw7drtSwstpO6mQz9b9BmFZmG3dGM1tGME2jOb2VNyG2dqZniivZDhtwVacj2NKPo7ifPQp+Wi52fut+JYCbRfIzc1l1apVrFixgoULF3L++edz9913p/cffvjhfO5zn+P+++8fVn1SoPVjWRbJZJJ4PE4ikSCRSGyT7u3tHfINeTAYpKm+loa6GgKBAI3NIaKp8eW6CtO8CrOyBTOz4eBclYUFKjM95pgfvjgUzRHBN98QbGqHe45S+PKB+86pR1dCcN/7gr9Xw5kz4Z6jFfyu8feZgT1PrykCVZ0KVd0K1e0mVV0QjECwF2Jbvex16Cp52R7ycnzk5eWRl59PXsFk8icVk5ufT25uLtnZ2bhcLjIyMnA6nbhcLlwuF06nPXRt6/+LEALLsjBNE8uyRjydTCbT91I8EScWjxOLJ9LpeMK+3+J991wiQSIRJ5mwj0smEiSTSZKJJIaRJBlPkIwnMJLDmMuxFYquoTh0WwA5NBQ9FafylL50yuNYemhSalvLcKI6nagZDnDZnsmE04Hi0u2yfcdJS6lEMghhWrbwamrBaGol2dSCEWjFCLUjov0eQVVPJlqBPzXPyG87ifB7bEGWY8+RlEMAJXuCSBoYoQ5bsAXbMQKtJBvt76WI299FxeWwRVtxHnpauBWg5U78l6hSoA0D0zR57rnnuPTSS1m7di3z58/n6quv5pNPPuHFF1+kuLiY//znP5x99tn885//5Ljjjhuynng8TjzeP5Smq6uLGTOmobJVR26XWje80vv6P7BL1Ysdbu4SCqAqoCkDYhV0xRZnWsrLbPpcOznZcNqyN+rYnXMYFkQM2+HHL06CJQUj0/l8qcrivvfBFDDJDW2xwZ/pjhjO925nRfbK/2wn9QthlzEHOL2aKKvcKQMSCv3/OztWhsjrF5c73DegPiWVkd4eUHbrOib6j6pEMhYQQmAJSBoCwxQYKBioGPGk/ZADlEwXjin56MV56JNy0HJ96JP86IU5qO6MUb4Cyf5K2m19UwtGQ5hkoBWjqRWjqSXt3EVxaOhuF7qm4Egk0FWBrinj8sX7UFx3zSFcf+tr+1ygjdsxIiUlJSxbtoxYLIbH4+GFF15g/vz5APzsZz/jyiuvZOrUqei6jqqqPPHEE9sVZwAPPvgg991336C8BbmgpG28/flDfsW2s39HX8etO2c7LLeD82/v2EHtGKLQ9u6VvuxtxNXAWB2cp6vg0MDRJ76G0CfDuTV3dv8Oq46dldor5+inqlPwr1o4yA+rT4FJ7pGzDHz+AJVlRYLflAj+vBlcGpw4BWZlD/YKuT2G87zcWZGd7t8b5xjwXRso/Pvy0wEwhCBm2KLZEHactFLbVr/gg61ike4bIVJhyHt5J/frwPID6+irEzHsVze7mC+RSMYaQkBXAlpj0BqHsOGgPWqRiNtDA1S3C31qIa4p+WQV5eGYnIs+rUDO+5GMSRRFsZ2M5Ptg8ex0vrAEZlsXRkMYozFMoqmVZH2IaGfUdiSjgDs7gxy3RoEap1AzyMuAjHGoQr7S8PGInGfcWtASiQR1dXV0dHTw/PPP88QTT/Dmm28yf/58fvSjH/H444/zox/9iBkzZvDWW29x++2388ILL3DqqacOWd9QFrSDDz6ANWveIyvLM1KXtfsoQ3QlVdVOKwqKog7algzNrnwyCgpPP/UMt9x0K6dMhVUnKGSO4qiSYK/gsRL4yxaBx6Vx3aXnceVdD+HxZG3bpR/ithc72Nre4UN/lZRtU0q/bFZQBlhvxtl3UQgQFggLYVnYasvq/0AGfa7j8tEqkUh2k0QiwabNFazfsJE160r46NP1bNlUTrw3CoCW48UxYxKO6YVkzJiMOq0QLS97/D0HJZJhIpKGbW2rDZGsC5KoC5KsD6eHSfoK81h46CJOPHIpJxy5lCWLF5KZOXYtxD3dPcxbeAiNze1yiONwOfXUU5k9ezaPPvooPp+PF154gTPPPDO9/4orrqChoYFXXnllWPXJOWiSnfGb3/yGq666iq/OhbuPVMaM2/umHsGvS+D5csFkXwZP/+mPnHDml0e7WRKJRDJh6OnpYd26daxdu5aPPvmEDz75iIqyzfZC5oqCPikHx/RCHNMn9YuxbPdoN1siGXWEJTDD7SRrQyRrmolXNpKsbUYkDFRNY9rcORx19NEsP/EkPnPCicycOXO0m5xmJLXBODQuDo0Qgng8bk+UTya3cdWtaRqWNVFmrkhGm1//+tdcc801XDwP7jxSGVNvQIs9Ct9bBlcsgFvfiXPy57/CXVecx92r/4KmyYnjEolEMlyEEAQCAT799FM+/fRTPlrzCZ+sXUNDdS1CCBRdsx0lTC/Ee/5JuGZMRp2aL9fSk0i2g6IqqXXYcsk8ch5gL3iebAxjVAUIVzbx15df4i+/exKAnKJCjjzuWL5wxnLOOOW0MSXY9iXjUqDdcccdLF++nGnTptHd3c2zzz7Lf/7zH1555RWys7M58cQTueWWW8jMzGTGjBm8+eab/PGPf+QnP/nJaDddMgFYvXo11157LRcfDHcuHVvibCDTsxWe+qzg55/C9x7/K2+8M43//X8fUFA8bbSbJpFIJGMOwzDYvHkzn376qW0ZW7uG9evW0dHaBoCW6UKbVojjgEL8Jx6Ma+Zk1KI86TFRItlDFF3DOWMyzhmTcZ90KABWT5R4eQPJzfX8+4N3+df/vgDCFmzHnngCF573ZZaffgY+n2+UW79vGJdDHFesWMHrr79OIBDA5/OxePFiVq5cyWmnnQZAc3Mzt99+O6+++iptbW3MmDGDb3zjG9x4443D7kzLIY6SofjVr37FddddxyUHwx1jWJxtzbtNgpveEngznbzwf3/n8ONPG+0mSSQSyaiQTCapqKigtLSU0tJS1m/cwPoNG6guryCZsOfG6Pk+9GmFOKYV4krNG1Ny5XwxiWS0sCIx4lvqSW6uJ7qhCqOpFUXTOPDQRXzhrLP52he+yKJFi/bpPTqS2mBcCrSRQAo0ydb89Kc/5YYbbuDSg+H2cSTO+mjoEVz3hqC2C5545Ad87Zu3j3aTJBKJZJ8ghCAcDlNZWUlFRQWbN29m/cYNlGzcSH11NWZqYWfNk4lWlIdenIejOJ+M6ZNQpxagZo1dRwUSiQSMlk4SG6qJrq8kXlqLSCTxTSrgjM+fydUXXsoJxx+/16d1SIE2BpACTTKQhx56iNtvv50rFsJ3Dht/4qyPqCG44114uUZw8yXn8PDvnpfz0iQSybgkkUjQ2NhIVVUVlZWVlJeXs6l8C1sqKqirqSEW6U2X1fwe9KI89GJ7bbGM4gKUohxUr3vcPs8lEomNSBrEtzQQX1dB7yebsdp7cOf6OeVzZ3DNRZdx6smn4HA49vg8UqCNAaRAk4D9Fva+++7jvvvu45tL4JtLxq8460MIwRMb4cefCE499AD+8v8+xJ+bN9rNkkgkkjRCCFpaWqirqxsUKmuqqaqtobG+gfZwC+kujKqg5/nQCnxohTnohX5chblohX6UfJ902iGR7CcIS5CsbiL+STmRjzdhtnSSke3htLPP5KYrruaE40/YxpHgcJECbQwgBZokkUhw7bXX8tvf/pabDlO4atH4FmZb81aj4Oa3BHneDP74zF847vTPj3aTJBLJfkB3dzeBQGBQaGpqoraxgbqGehoaGgg1NZMcsDap4tDRcrPRcr1oeXas5/pw5GWj5Gej5fmksw6JRDIIIQTJuhDxjzYT+WAjZmsXvskFfOErX+bmK69h4cKFu1SfFGhjACnQ9m/q6ur42gXn8+GHH3DfUfDFAyeWOOujpktw89uwsUVwzfnLefiJv+DxjIOF2SUSyZghGo3S0tIyZAiGQzQFgzQ0NRJsbqY1FE4v3NyH6nKg+rJQfR40v8cWYbnZOPP9aLleyPGiejPH/egFiUQyeghLkKhoIPZ+GZGPyhCRGEUHHcAlF13Mty6/kilTpuy0DinQxgBSoO2fCCF48sknufHb1+MWvfz4eDisYGLfIoYl+GMZ/HStoCA7k0d/+lO+cNEVsjMkkeyHCCFob28nHA4TCoXSIRwOEwyHCASDBMNhWlpaaG9rpau9g3g0tk09iq6het2onkw7+LLQ/B5UXxYOvxfd74FsN6rfg5LhlM8biUQyYoikQWxDNbH3S+ldWw6mxbylh3L11y/n0gsuxO/3D3mcFGhjACnQ9j/ee+89br3lZt559z3Oma1w55EaPuf+s7h5bZfgex/AO02Czyw5gJ//4S8sPOTw0W6WRCLZQyKRSFpkhUIhgsEgDc1N1AeaaGoOEgoFaQm30NHaSndbB5ZpDq5AVdG8mSiezLTo0vqElycT3ZuV2u8GTwaqJxPF5ZCiSyKRjHms3jixTzbT+34p8U21qLrOUSefyPUrvsG5nz+bjIx+j65SoI0BpEDbfygpKeHOO27npb//g3l5GisPFxxTNNqtGh2EELzRAA99JGjogSu/vJzvr/4Tubm5o900iUSSIplMpsVWX9zUHKAu0ERDoInmYJBwOExHSytdbe0kY/Ft6lCyMtCy3ajeLNRsN5rXjZbtRs/OQvVmoXgzUVL5ijsDRZViSyKRTGzM9m5iH20i8t5GkrVBXB43p571OW644ipO+sxJRCIRKdBGGynQJj5VVVV89557ePqZZ5iWrfHtxSafmwWqfOtLwrSHPf5qvcDp0Lnvztu45tbvouv6Xj2PEIL6+no2bdpEIBDAsixUVUXXdfLy8iguLqaoqIj8/Hz5Nl4yYbEsi/b29kHDCUOhEIFgc8rKFaA5GKI1HKajtY3eru5t6lBcDtu6le1Gy85C9dpiyxZhbvBmoqXEmOrJlA41JJJxiBACEUtgRWKIeBKRSNpxKm0lkmAJEH0BEAIhBIqqgkND0TUUXUdxaKBpKA4NxaGjZDhR3S7UTBc49P3+NzfZ1EL8gzJ63i/FDHfgLcjlzHPO4dknfi8F2mgiBdrEpb6+ngcffJAnHv8NOS64dpHFlw5UcOye19UJTTgq+Mla+Gu54OBpeTz6i8c47fPn7dGD27Is3n33XZ55+mn+9sL/Egi17vSYrAwnc2dO4cijjmT5Fy7gjM8ux+Vy7XYbJJJ9iWVZdHR0EE7N1QqHw+nQHAzS0BwgEGwmFArR1tJCV1s7lrnVcGpNRU1ZtWzhlYXWJ7p8KQuXJxM1OwvVm4nqkm7kJZLxiBAC0RvHbOvCbO/GbOvGbO/G6olidfdi9kRT6ShWTy9s/azYEYoCfdbvXTlOU1EzXSjuDDSv/ZzRfFmD41wvWoEfNStjQos5IQSJyiZ7vtqHZRTcfQlbrv6hFGijhRRoE4+KigoefPBBnvrjk2TpcMUCi4sOVsnU5C2wM9a3CH7wIawNC46cN40bbruHc778Ndxu97DraG1t5de//jW//uXPaQgEKfIofHa6YGmRzkE+i0lugaaAKezQFoNwFEJRlboe2NJqsjYMdd3gdzu56pLz+c79PyE/P38fXrlkf0YIQSwWo7Ozk46OjnRob29PC6/mYJBAKEgwHKKlpYW2lla6Ozq3ncelKGhZGSgpwaWlRJeaGlaoZ9uii5QoUzJdE7rTI5HsDwghEJHYIOE1SIil8kQi2X+QoqD6s2yLtycD1eO2X8J4MvvT7gyUDAeK04HqsmPF5UBx6KCpoGy7ZqsQAkwLYZgIw4SkkU6LhIGIxbGiCUQ0jtUbT8UxWyR2RrC6UnF3xLbS9TU304me70cr8KPn+9An5aBPycdRnI/qyRypj3pEEIaJomuUfvEOKdBGCynQJgaGYfDyyy/z69W/4pV/vUpepsrlB1tcME8lS5df/V1BCMF/GuDxDYJPQpDl0jnnjJP44sVXcvyJn6GgoGDI4zZv3swjP/kJf3zyD1hmknNmCc45UOewfHO3hpOWtwv+t1LhfzZbuBw6D333dr5x632yM7ufIoQgkUjQ29tLNBodFA+VNzCORCJ0R3ro7o3Q3ROho9MWYF2dnXR3dhHp6sZIJoc8r+LQU52mVIepz2NhSnzpqRhPBorHjerJsIcYSSSSCYEVjWN1RTA7IpidPVidEczOCGZHD1b7dsSXqthLSeR47dC3tl+uN52n+jwo2th9VghLYPX02tcX7sBo6cQId9jpcCdmS0dawKk+D46p+bZgm1KAc+Zk9OL8MX19w0EKtO2wevVqVq9eTU1NDQALFizgnnvuYfny5QDb7aj98Ic/5JZbbhnWOaRAG78IIfjoo4/4y1/+wrNPP0Vjc4iF+SrnHyg4+wCFjL07jWq/pLZL8FKNyt8rTaq77LyDpk/mhOOO5cTPns2yY44lEolw91138n8v/Z18t8qFBwkumKeS69o7j5zWqODhT+BvlYLTDzuAp/7+FoVFO1/HRDJyGIZBb28vkUgkLZh2lI5EIvT0iabUdiQ6WGjFYzGivVHi0SjxeJxENMawf8ZUBdXR96ZZA6f9xllx6nZepsueg+HOQM20Yy0rA82diZLphEwnitse9iO9FEokEw9hWVjdUVtwdfTYFqM+4ZUWYXYs4lu9vHHoaKnlJGzx5R0Q20JM9WVN+Bc1ImlgBNtJNoYxGltINraQbGrBDLWDAMWp45g+CcfMyThnFeGYORl9cu64ep5KgbYdXnrpJTRNY86cOQA8+eSTrFq1irVr17JgwQKam5sHlf/nP//JihUrqKio4IADDhjWOaRAG1+0tbXxn//8h9dee41//v1v1DYEyM1UOX2axZfn6izM3X/c5Y80TT2Cj8MqHzdbfBwUVHaCgj03eZoXrl6k8PkDwKXtm4fvG/WC294VuBw6Tz35O0477+J9cp6JiGVZg8TRzkJPTw9dPT10dnfR1d1NT6+dl7ZW9fYSi8aIx6LEe2OYhjGsdii6Zgsepw5OB6rTTitDxX2CakCelhriow4og1MHhwOcWv/QH10bV50AiUSyd7DiyUHiKh139GB2RbA6IkMO3wP61/HrW0x9wJp+mq8/VjLlen47woolSNYFSVQHSNY0k6hutkUb9mfsPHAqzoOm4jpoGo7pk8a0lU0KtF0gNzeXVatWsWLFim32nXvuuXR3d/P6668Puz4p0EYfy7JIJBLEYjHi8TjxeJyOjg6am5sJBAJUVVVR8ukaNqxfR0VtA0IIZmSrHFNkccZMjaWFFrp0DT1iWELwVBn8eI3ArcM3lyicPxccI/A/CEYE33kHPmoW3HDJOdzx4ydQVRXLsgYFIcQ2eTvK351jRuocpmmm74/eaJRoPBXHYsTiMaLRKLHUfZO+h2JxEok4sZQFKjGE+/WhSM9v6LM0uZyp7b75D/qAMg7UDCdaalt1OcChQypW0vMl+kSWY0z/EEskkrGLSBqY7T2YHan5XR09WO09tvDq7LGFV1cPIpoYfKCu9Tu7SIutAYKrLy87S3o83YdYkRiJ6gCJ8gbi5Q0kKpsgaaC4nDjnFOM6eAauhbNwTC0cU0t9SIE2DEzT5LnnnuPSSy9l7dq1zJ8/f9D+YDDI1KlTefLJJ/na17623Xr6BEAfXV1dzJwxDXV7X4idfGr78kMd6f+Y2HprH55f7MIpVAUcKuiqHbs0O72n/5sd7d/ZZ7/Tuvfx92aP27ebdVsCogYkLLj4YPjWYvBljGyn27QEvy4R/HIdFGdBcwSS4/rp1o+S/rNtnoI9rLvvxa2ipLaH2J/OUxTUrfcNzFcG529zcolEItnHCCGwBJimwDQFhiVsJ1KqhmkJzKSJSA52xqM4Hag5qSGGAwSXvdZfFmqOBz3HizLBPQ+OV0TSIFHbTHxTHYnyRhKb6xGJJGqmE5c3A1ciTobG9vvmI8Bl1xzOyptf3ucCbdzOxikpKWHZsmXEYjE8Hg8vvPDCNuIM7OGPXq+X8847b4f1Pfjgg9x3332D8ub6AWWAp5qdNWqoDtQOtndyODt7duxo9zb7dtK2bYrvQvmh2rmr1zqwLk2xnRDpKe+wmprKU8CpgVu3g74H/f89+mz34Fh7/55VsLufbXr/Xr72YK/gpSrIcsCvT4Bji0fHGqKpCtctUTi6SHDzWwKXDkcX2N+hPjECpIVKWtDsLH/AvnS+sp19yrbHbS/dNwx0qPK7ztZKdIIoU4lEMqERqZd7nQnojEOXAV3CQU8SojET0+ifnqBkOtEL/Gh5Plx+D1qOBy07Cy3Hg5qbjZaTLYcZjnMUh45rzlRcc6YCKcFW0UhsfSXx0lqioS5QFXIKPMxyxZnpTOAeYSVzae17I3KecWtBSyQS1NXV0dHRwfPPP88TTzzBm2++uY1ImzdvHqeddho///nPd1jfUBa0Qw45kDde/8cuuRKXDGTAQ3LQ6301tTaHZpdRNVBVO3/cs3s/DNv7PRlKzA0sO3D/wJKD84eoYycn3+5VbFX2/736Otd84zoO8CT51UkqkzLHxly/zrjgvg/gH9WCY+dN5kc/fZTZc+cNKDEuH3sSiUQyLhFC0BQIsrm8ik1bKlm3aQul5ZXUVdUS7ekFQHFoaPl+tAIfer7tst1R4EPN96Hl2+ttSfZvzPZuYmsriH6ymfjmOhAwa9FBXPrlc7nw7DPx+bz79PyRSISlyz5DZX2rHOI4XE499VRmz57NY489ls57++23OeGEE/j0009ZsmTJLtUn56BJJDvmF7/4Bd/+9vWcPBV+dMLYXE/urUbBfe8LQlH4zmVf5O5f/ImMDPkjL5FIJPsCy7Koq6ujtLSUDRs38tH6tZSUbKCmopJ4JCXEnDp6UR56sb1WlmtqAerkXLQC34T3cCjZe1g9UWLrKuj9oIz4xhpUXeOoU0/i5quu5Zwzz0LX975pbSS1wYQRaKeccgrTpk3jD3/4QzrvsssuY8OGDXz88ce7XJ8UaBLJ0BiGwY033sgvfvELLpsPtx6uoI2hybtbEzUEq9fDbzcIpudlsvpXqzn9y5eOdrMkEolk3GJZFjU1NbYQ27CBNSXrWL9xAzVbKolHowAoLkdKhOXhTIkxtTgPLc83phw+SMY/Zns30Q/KiLxbgtHYgrcwn0uvuJy7v30zhYWFe+08UqDthDvuuIPly5czbdo0uru7efbZZ3nooYd45ZVXOO200wD7QywqKuLHP/4xV1999S6fQwo0iWRb2tvb+cqXv8Qbb7zBXUvha/PGz49sebvgux/AJ0HBhactZfX/voY32zfazZJIJJIxSzKZpKqqirKyMsrKyvi0pIR1G9ZTXV6R9gKrZjjRivNwFOfjmJKPs7gAtSgXLTdbCjHJiCKEIFkXpPffnxJ5fyOKEBz3udP5/so7Of6YY/e4finQdsKKFSt4/fXXCQQC+Hw+Fi9ezMqVK9PiDOA3v/kNN9xwQ7rMriIFmkQymNLSUr5wzucJNtby0xMEyyaPdot2HUsI/rccHvxIUOjL5Ok//w/HnPb50W6WRCKRjCqdnZ1s2rQpHdZv3MDGsjIaamowDdtTourOQC/OQ0+JMdcUW4ipOV7pmEMy5rB6ovS+U0LPv9dgtnRywKGL+ME99/KVc76w299XKdDGAFKgSSQ2pmnyyCOPcNeddzDNY/GrkxRmeMaGM5DdpbZLcPPbUNYmuOPai7nn0d+jaXKtG4lEMjExTZPGxkaqq6upqqqiurqazRXlbKmsoKaqmo6W1nRZPc+HNjnHnidWlEdGcT7KpBxUX5YUYpJxh7AsYp9W0PPyBySqmiiYPYO7bruday9bscvz1KRAGwNIgSaRwJYtW7ji8q/zzn//y2UHww2HaWSMQWcgu0PSEvziU3isRLBs3hSe+fsbzJh94Gg3SyKRSHYJIQSdnZ00NjbS0NCQDjV1tVTUVFNTXU2goREzaaSP0XO8Ke+IPvQCH86ifBxFeSiT/Kgu5yhejUSybxBCkNhST8/LHxArqSK7qJCVt9/Gd666DqdzeN95KdDGAFKgSfZnWlpauO+++/j16tUUZcGDx8DSSRPzUfFhs+CWtwW9lsrPf/h9Lv7mSvmWWCKRjAlM0yQcDtPY2EhTUxONjY3U19dTU19HdV0tjQ0NBAPNxHuj/QcpClq2vSizlpuNnu9DL/DjLPCjFvpRc70oTsfoXZREMsok6oL0/P19oh9vwjspn1tWruTWa7+Fy+Xa4XFSoI0BpECT7I/EYjF+/vOf8/37v4eViPKNBRaXzFfImOCj/zrjgu++D/+sEZy4eBa/euqvzF98yGg3SyKRTFCEEHR1daVFV19c21BPdV0tDY2NBAPNtIdbsEyz/0BVRetbpDnHi5rrRcvx4sz1oeV4wZ+F5veg6BP8oS2R7AWSDWF6/v4evR+V4SnI4zu33MLKb357u8vxSIE2BpACTbI/IYTghRde4KZvX09DUxNfPUhw3aE6uc7xPddsV3mjXvD9jwTNEbjmwnO455HfkpeXN9rNkkgk44xEImFbumpqqK2tpbq6mvLqSqpqamhqaqKlOZR2R9+HmpWJ6s9Cy/HaIszvQc/xoudmo2Znofiz7Hlgcq0wiWSvkmxqsYXaB2Vk5eVw8y3f4fbrb9xGqEmBNgaQAk2yv1BWVsa3rruG1994k5OmKdy6VOcAr7nzAycocVPwxEZ4vESgaxrXf+NSbn3gEfkckEgkaaLRKLW1tdTW1lJTU0N1TQ2bK8uprKmhoa6OjlAL6e6VoqD5stDyfWi52Wnrl8PvtT0g+rLQ/Fly2KFEMsokA61E/vE+kfc2kpXr58abb+L2b9+E2+0GpEAbE0iBJpnodHV18b3v3cdPH32U4iy480iFz0yRj4M+WqOCxzcqPLPJIsOpc+XF53PDPQ8zZcqU0W6aRCLZx/RZwKqrq9OeD8urKimvqqSupnaQ10MUBS3Xi5bnQ8uz53w58v3o+T6UXC9arhfFsWve4iQSyehhBNvp+cf7RP5bgtufzbdvvJE7b/wOpmlKgTbaSIEmmagIIXjmmWf4zk030NHWyjWL4OsLwKVJxxhDEewV/L5U4S9bLBKmwpc+ewK33P9jDj3s8NFumkQi2U0SiQRNTU3U1dVRXV1NTU0N5ZUVbK6soLamhpbmEMJKDfFWlLTXQ73Ah5bnw1Hgx5HvhzyvnPMlkUxQjFAHPS+/T+TdEjK8Hq645ip+8eAqKdBGEynQJBMJ0zQpKyvj5Zdf5vHHHqOiqoqlk+C2IzUW5spHwHDoSQieq1D4Y6lFUwSOWzSb675zB+ddcNGwXfRKJJJ9i2VZdHR0EA6HCQQCaZfzdfX1VNXVUFdfT6CxabAFDNB8WbYAy/ehpaxfzgI/an42am62FGD7EcISYBgIw0QkDUTSRBgmJPvy7HxM0y4rBFgWWMIe1mrZ24P2AagqiqYOitFUFFVBcegoGU4UlwPF5UR1OVBcDtA16VV4DGC0dBJ5+QN63l5H7jfPo/p7v5UCbbSQAk0y3jEMg9dee42n//QU/3jp/+jojgxZbooHluTDkgKFQwpgQR44VPmDsD0MS/BqLTy9WfBxEAqyM/n6RRdw7a3fZcaMGaPdPIlkQmFZFm1tbYTDYcLhMKFQaFA6EArSHAwSDIVoa2mhs71jsNdDQMvKRM3xoObYHg+1XNvxhiPXh+J3o+b5UF1y/td4Q1gCEY1jRaJYPVGsSAwrErPz4glEPJkOViyBSCQRsVR+IpkWXiJpQJ/wMgwwx5BzLE1FcTlsBzKeTFSvG83bn07npbx5ql63FHT7EKO1Cz0vm9Iv3iEF2mghBZpkvFJeXs7q1at5+qk/EGppZ7ZP4fQZgiMnKRycC34XJC1o7IGydlgXFqwLQ2kbxE1w63D8FPjCbIUTpoAmxdp22dIueHaLwouVFlEDjj90LhddcR1f+urF+P3+0W6eRDLmME2T1tbWIQVXMBSiKdhMcyhIKBSivbWVrvbO/qGGKRRVRfO6UbypTmo6dqN53ejeLLRsN8KbiZbrlQsvj0OEYWKEOzDDHZjt3ZjtPZgdqbi9G6ujBysSsy1UW6MqKC4nSoYD1WnHfZYpxeWwrVNOBzg0FF1HGRDj0FF0zQ4ODbbar+i6fZxD77eCKQqKqthpVdlmu08wiT7LmmnZVjXTsr/bpmVb6mLJAcLSjq24LSqtnpQI7e5NxVHMnl5EJDb42nXNfgmRehGRjvN86LnZaPnZKJkuKeL2ECnQRhEp0CTjCSEEr732Go/+5Ef881+v4c9QOHuW4POzYFG+MqyHccIUlLXBfwP2emCb2+EgP3zncIUTp8qH+Y6IJAUv18BLVYIPm8GhqRx3+Hw+e86XOeOsc1m0aJH8QZRMOPrW8mpra6O1tZW2tra0tSsUCtEcCtIYbE6LsPaWVno6u9i626Foqv3m35OJ5nWjZrsHCS5HSnDhzUTxZKK4M+wOsGRcI4TA6ugh2RAm2dSCGerACLZhhDowW7v6xZcCanYWmt+b9oCp+T0pC1KGbV3KykT1ZKBkZaI49f3meStMC6u7NyViuzHbugeku9LbA62CSoYTLS/bdmiTl/Ismt7ORvV75FIOO0EKtO2wevVqVq9eTU1NDQALFizgnnvuYfny5ekyZWVlrFy5kjfffBPLsliwYAF/+ctfmD59+rDOIQWaZLzw9ttvc83V32Bj6Sbm5cAlB8OZsyBD37MH7NqQ4JG1gg+a4QuzbS+PXuf+8aO3JwR7Ba/UKrzVYPFR0LZK5ngyOGLxfI4+4WSWnXAyRx55pFxfTTKiCCEwTZNEIkEikSCZTKbTPT099PT00N3dnQ59211dXXR2dxNua6WltSUtwrraO+jp6t5mOCGAomvbWLb6xJaWbVu4VK8bvBm2MJNv9Cc8IpEk2dRKsj5EsiGM0RAmWR/C6rHXglOcDrRCP/qkHPTCnHSsFfjRfFlyDuAeICyB1dmD0dpli7bWvtBp57V2IaLx/gM0NWV56xdtWjr4pFUaKdC2y0svvYSmacyZMweAJ598klWrVrF27VoWLFhAZWUlRx55JCtWrOCrX/0qPp+PsrIyli5dSmFh4bDOIQWaZKwSCoV47733eOONN/jLs38mEAxxkB9uOULh+GL2akdHCMELlfDAhwKfE35wrMKyItmRGi5xU/BJENa0qKwPmaxrgY7U7+ABxfkctmQRCw9fxqJDD2fu3LlMmTIFn88nO6vjjD7hE4/Hicfju5zeZjsRJxZPEIvHSCSTqbwEiWQiVS5BMpkgkUimxJYtuJIJO88wkiQTSYxkEsMwMFLpXUHRNdQMJ6QcF6iZLnu+S1ZGKraD7slE82RAVga4M1CzMmxnB/I7vF8ihMBs6yZZH7JFWEPITje32xYxBbTCHBxTC3BMK0zHWp5PWkVHEas3nhZvRlsXZkvnYDHX0TNoOKnqybQFXG5KyPUNpezLy/FOaFEtBdoukJuby6pVq1ixYgUXXHABDoeDp556arfrkwJNMhIIIUgmk/T29qZDJBKhtbU1HVpaWqivq2XzxvVs3lJBc0sbAM7Us+/mQxUumgf6PnST39gjuP1de/jeNw9RuHYxqLIDtssIIajvhnWtKuvCFmWtgorOftEG4HY5KMz14fdl4/P58Pn95OTk4cvNIye/EH9OLn6/H5/Ph9vtJjMzc7vB5dp7lgkhBJZlYVnWkOmd7d8XacMwMAwD0zR3OZ1IJIjFYsRiMXpjMaKxGNFYNB3HBoinWCxmi6h4gkQibguheNwWQYkE1p44FVCw57Wk5r2gq/Z8F0218zQNRVehL+7LS+9X+4/V1FT5VHqrfNWhoWg6iq6i6jroqTk0TgdkOFAynKgZTltgTeDOlWTvYPXGSTaGMRpbSDaGSdbbgkz02g80JdOFY1oBjqmF6Vifkm8Lf8m4QhgmZkcPZmunLeL6rHFtqaGU7d3p/3sfanZWWrTpfdbRybnok3JRfVnj+iWOFGjDwDRNnnvuOS699FLWrl3LvHnz8Pl83HrrrbzzzjusXbuWWbNmcfvtt3Puuedut56+H+I+urq6mDlzOvp23ujs0Ye2Dz/xUftnim0S2+4aY4j0n1E4txAIMbzTqwpoCjhUOwgBEQOmeeFnJ8Lc3JEZK24Jwer1gp9/CgfnQm8SjJ1cwC59vLtQeFfq3aWy+6oNOyjc9z0wLLCwPTSboj9fCDtfiP707qBsk9i6IYOiCYmS/gOKAgqKLZDsCEVRUNL77XTfPvq2tymX2k7V3ZdWlNQ2/fUMqrevzpG5dIlkt7EsgWFaJA1B0rBIChXDtDDjhl1AVdALc9IWMX1KPo7pk9Dyssd1J1yya1jReP88uNZOO+7owWzrsucVtnSmfwwVh4budqHrKg7LwInA6VBRx4EV9SvXHMV3b3hRCrTtUVJSwrJly4jFYng8Hp555hk+97nP0dzcTFFREW63mwceeICTTjqJV155hTvuuIM33niDE088ccj67r33Xu67775BeQf4tt+XGYpdKrsH38GdHTrsqocouCe3xk47gMNrwt4pOwbasKPCugq6YseaCo5Uui/fpYNLA6c6+Fo2tcF7AThxKqw6HrzOkZ/I+06j4Oa3BRkafG4mOHbyon1fPW732f94n313dvGT2EFxUwgSpi3qDNsJGIbo3x6YHij4rD7RRyrdNwd/wLmUobb7EmLbfds7XogB4mbry1IGpAfkKWxbp7LV/oH7VPrP0fe7riqD87a+HolEMhhL2HNlI0noSUJPArpNhS5TIxK3SCb7XwtpedkpEVaAozgPR3Ee+pQCFIc+ilcgGQ+IpIER7sBobsNsbiUZbCfZ2ILREEYkbLHvzHKS43EwSY1TqBvkZPQ/28cK31umMvln7VKgbY9EIkFdXR0dHR08//zzPPHEE7z55pv4/X6mTJnCV7/6VZ555pl0+bPPPpusrCz+/Oc/D1nfUBa0I4+cx9+ef4rMzMx9fj2SMcyQvbuBr8RV+nuPg16VDy4PCFUDVQNVt4cWDRPLsnj0hz/jD48/xcXz4LalynatuyNBU4/gqn8Lgr0Kv3zkThaf8YURb8OedrqHEkyD61QGlVEG5PaXVdL7+t4UD/qrDC6vbF3XEN+Robd2dCHbtrGv7m3qEAIsEywTYZmAAGH1L6S6jf1s6xr6VF2fic8acNgQPyXj8+dFIpkwRKMxmkMtNAdbaA6FCTSHCQTD1DWHaGgOEQq10NHSjjVgKQPN57EddhT6bcvYpBz0ghzUyTmoma5RvBrJRERYFkawHaMuSLI2RLyykUR1AAwT3enggPlzOOmYpZx5/DKWLJyLro/ey4De3l4+c8bZlFaGpUAbLqeeeiqzZ8/m5z//OVlZWXz3u9/lrrvuSu9fuXIl77zzDu++++6w6pNz0CRjhXg8ztcvu4xn/+dZbjtC4bL5Y+N1UldCcPXrgtI2+NMj93LeN7872k2SSCSS/QLDMAgEAjQ1NaVDfUMD1fV11DXWE2hsIhwM0dvVPeg4NdNlL2ic40Hze9JrZjlys1FzvagFfjlHTDLqCMMkWRvEqGwiurmO+KZaRDSBy+Pm0GOO4iuf/wIXfPFLFBUVjWi7RlIbTBibtBCCeDyO0+lk6dKlbN68edD+LVu2MGPGjFFqnUSye3R1dXHeuefy9ltv8ugJKp+dOdot6ifbqfC70+DGtwTnX38vv+vp5uLbfjTazZJIJJJxjxCCYDBIdXU1VVVVVFVVsbmygi2VFdTV1BAOBActcaBoGqo/y14fLMeLNsOPvmQqeTleNL8X+vZJC5hkHKDoGs7ZxThnF+M+/QiEaZGsCZAorWXNxnLe//a3uelb1zNj4TzOO+dcLr/gQhYsWDCh5jyOSwvaHXfcwfLly5k2bRrd3d08++yzPPTQQ7zyyiucdtppvPDCC5x//vn88pe/TM9Bu+GGG/jPf/7DcccdN6xzSAuaZLRpbm5m+RmnU7WllF9+Bo6cNDZvVcMSrHzHXtz69w9+h4tXrhrtJkkkEsm4wLIs6urqKC0tpbS0lLUl61m/oYTKzeVEI5F0OdWTiVbgR8/3oeX7cBT4ceRlg8+DmuNBzcqUbuol+w1WT5RYSRWxteXESqoR8QSTDpjO+Rd8lesvv5LZs2fvk/OOpDYYlwJtxYoVvP766wQCAXw+H4sXL2blypWcdtpp6TK/+93vePDBB2loaGDu3Lncd999nHPOOcM+hxRoktFky5YtfPb00+htbeLxU2Cufw/ceI8AUqRJJBLJ9jFNk+rqakpLS9m4cSNrStZRsnEj1eUVJKIxABSXA70oD704H0dxHq6ifNR8H2p+trR8SSTbQSQN4qW1RD8oI7p2CyKeZObi+Vx28SVcc8nXh73+8XCQAm0MIAWaZLT4v//7Py656ELy9ShPnKpQ7B7b4qyPgSLtF3dfy9X3/XK0mySRSCQjSjKZpKKiYpBFrGTjBmoqqjASCQBUtystxJzF+Tin5KMU5dmL+0ormESy21jxBLFPK4i+X0psQzWKgENOOIbbvnUD5519zh47GJECbQwgBZpkpDEMg3vuuYcHH3yQU6erPHisSrZzfIizPgxLcN/7gr+Uw80XnMZDT708qh6XJBKJZF8QiUTYvHkzZWVllJaWsn5DCSWlpTTU1GAa9twwzZOJVpyyiBXl45qSj1qUi+r3TKi5MhLJWMTsiRL7aBORt9aTrG0mqyCX8y+6kLuvv4mZM2fuVp1SoI0BpECTjCTl5eVccfnXeefd/3LzYbBiwdYu2McPQgge3wCPrBUcfkABT/71FQ5efNhoN0sikUh2CdM0qa+vp7KykoqKCjZv3synG0soLS0j2NCYLqfneNGKctGLUkMTi/NRJueiZrvH7XNcIplIJGqbib61nsh7GxHxJAuPO4rbr7+R87/wRTRtJ4u5DkAKtDGAFGiSkSCZTPKTn/yEe797DwUukx8cM3adgewqHwcFK98RNPfCN84/i/t/8SS5ubmj3SyJRCJJ09vbS11dHdXV1VRUVFBeXs7GLZupqKigsa4OM2kvoIuqoOf70Yty0YvycBTn9wsxt5wfJpGMB6xYguiHZUTeXEeyOkD2pHwuv/IK7rj+JgoKCnZ6vBRoYwAp0CT7mjVr1rDi65exvmQDl80XXH+oRqY2sW7HmCF4skzh1+stnA6d22/6Jtff9SAZGRmj3TSJRDLBSSQShMNhGhoaqKuro66ujtraWipqqqmuraGpoZGutvZ0eUXX0Av8aIV+tMIc9EI/rkm5qAU5qHleFH34b9olEsnYJlHTTO+/1xL5oBRVwImf/ywP3HoHxxy9bLvHSIE2BpACTbKvSCQS3Hvvvfzwhw9zoF/hgWNgUe7Evg3DUcFPP4W/lgsKsjO5646VXHnD7TidckFUiUSyc4QQdHd309LSQjgcJhwOp9MtLS2EQiECoSDBcIhwOEx7axu93T2D6lBdTrQ8e0FmLS8bPTcbPd+HIzcb8n1oOR4UVR2lK5RIJKOB2RMl+k4JPf9eg9nSybT5c7n1hhtYcdGlZGZmDiorBdoYQAo0yb5gw4YNXPjV8yktLeO6xXDlIgXHftQfqOkS/Hydwj+qLKYVeLn3ew9wyZXX7dIYcIlEMv4xDIO2trZtxFZfaA6FCIZDhMIhWlta6Whrw0gkt6lHc2eged2QlYHqzUT1ulE9dqxnu9G8blS/FzUvG8XtknPCJBLJkAjLIl5STeT1NcQ2VJHh83LBJRfx3ZtuTTsVkQJtDCAFmmRvYpomjzzyCHfecTvTPRarjleZnzO+PDTuTba0C372KbxWJzhwSh73P7iKL194Kap8ey2RjFt6enoIhUIEg8FBoSHQREOgieZgkHA4TEdrK5HObrbpfqgquteN4slA8brRUkJL9WaieuzYke1BS5XBkymHHUokkr2OEWyn9z+f0vP2OkQ0weEnHc93b76V4485lpycHCnQRhMp0CR7i8rKSi6/7FLefvddLjsYbjxMxTXKfQrDElR3QmMEOuLgVGFSFszLgSzHyL1hLmmxhz6+3ShYOLOQW26/hwsuu1IOfZRIxgiGYRAMBgkEAgQCAZqbmwkEAtQ3NlDX1EhTIEA4FKK9pTW94HIaRbHFVXYWWrYbNTsLNWXV0r1ZtnXL60Z4MtA8mSjuDLkOmGRcIoSApIEVTyISBiKeQBgmmBbCtMC0wLIQpgmmsGPLQhh2PgJQAEVJhVQaUveEApqK4tBRnPqA2IHi0lHdGSgOuaTN3saKJ4l9UEbP65+QrA+RO7WIrmSM1i01UqCNFlKgSfYU0zT52c9+xp133E6e0+AHy+CoyaNzuyVMwbowfNAMHzQL1rVA3Ny2nKbAscXwpQMVTpsO6ggNB/o4KPjVeni3SVCQncGVl13MN2+7j6KiohE5v0SyP9E3nysUCg0SXU1NTdQ3NVLf1EggECDcHKSrvWOwpUtRbLHly7KF14DY4fOgZmeBNxM12x5qKOd0ScYTQgisSAyrowczFazuXjsvEsWKxBB96d44Ip5EJOzAKPemFZcDNSsDNSsTJSsD1ZOJ5veg5XrRcrPtOCcbze9B0eR9uSsIIUhUNNL777X4rzyTsi/fJQXaaCEFmmRP+PDDD/n29d/igw8/5KJ5CjceppKlj+ytFkkK3myE1+sE/2mAniT4nLB0EiydpDA/D2ZkQ44LEibU98CaEPy9WrAmBAf54bolCqfPGDmhVtkh+NNmhRcrLBKWwtknHcWVN97JaZ9dLuepSSTbwbIs2tvbB83hGug8oynYTCAUJBQO09bSQmdbB0YiMagOxaHbYsuXherz2Gm/B93vweHzovrc4MtC9bpl504ybukTYGawHSPcgRFqxwh1YIbaMdu7MTsjYAx+e6lkuux5jVkZaQGkZmWguF2oTgeKy4HSFw9M6xqKptlvPjXNflmhq3asqih6Kh54PwlhvxARdtoWfQIsgbAs2zqXNNJx2mo3QEBaPX1xL2Z7D2ZbNyI+4H5XFNtJTlEe+uRcHKlYL8pFzc6S8zSHQekX75ACbbSQAk2yq1iWxeuvv86jj/yYl//5Lw7MUbn3aIUjCkf2FmuLCR7fIHhuC3QnYX4unDJd4aSp9hBGbRhDiNaGBL9YJ3inyT7m1iMUji0euYd2d0LwfIXC/2yxqOqE4lwPF17wZc698AqOOuooKdYkEw4hBLFYjPb29u2G1rY2Qq0ttLS20treRkd7Ox1t7XR3dGKZW5nEVcUeNpiau6V6Uw4zPJn2EMPslNjyuVH9HpRM6UBDMjEQlsDq6LYFWEqImaF+MSai8XRZ1etGK/Tbyyvk2dYlze+xX1L47RcV433ooBACEY1jtnVjtnVhtnXbn0WglWRzG2a4Ayy7n6JkZeCcPgnHtEIc0wtxTJ+EPjlXzvXcCinQtsPq1atZvXo1NTU1ACxYsIB77rmH5cuXA3DZZZfx5JNPDjrmqKOO4v333x/2OaRAkwyHrq4u3nrrLV577TWe/8uzNDaHmJurcvl8wednDU8M7S2EEDyzGR5ZIxDABXPhgoMUpnl3vw2fhAQ/+sS2qB0/BW45XGFuzshe0/oWeL5S4ZVqi84E5HgyOOW4o1h28nKOOGoZhxxyiLxHJfsUIQSJRILe3t50iEQiO9zuy4tEIkR6I3RHInT19KS3e3t76Y30EotGiUWjJGLxbUVWCsXp6H9j785IpTNQ3S7bW2FqTpea7YaUgw1VzueSTGCseBKzpROzpROjpcO2gvVZxMKd0LfAuII9vK/Aj55a204v9KMV2Gk1Uy4yLgwzJdjaSDaGSdaHSNaFbOEGoGs4puTjmD4Jx8zJOGcV4ZhasF+LNinQtsNLL72EpmnMmTMHgCeffJJVq1axdu1aFixYwGWXXUYwGOT3v/99+hin00lubu6wzyEFmmQgQghaWlooLy9n/fr1rF27hjUf/Je1JWWYlkWRR+WkKRZnzdY5LN8c8TfRbTHBbe/YQxrPPwhuOFQhN2PvtEEIwWt18KNPBPU9cN4cuP4QhUnukb1Gw7LF2ltNCv9ttNjU3j+PbvaUAhYvmM/cRYcyb9EhHHTQQRx44IHk5eVJq8AERQhBPB4nGo0SjUbp7e1Np3e2HYlE6OmN0N0bIdInpFL7e6O9xKKxtHCKR2M7FE9bozr0/mFOTh1S8aChT049tb9/W3M57XW6sjJR3C6E22kPp5KT/yX7GcKysLp77flfHRHMjm7M1i6Mlk5bhLV0YnVG+g/Q1PQC43pBnwjLsbfzffL+2U2s3jjJhhDJ2mBKtAVJNrbYDk90DeeMSThmFeE8oBjnrCK0Qv9+83srBdoukJuby6pVq1ixYgWXXXYZHR0dvPjii7tdnxRo+w+madLd3U1HRwdNTU00NjbS1NREQ0MDNRWbqSjfQlVNHV2RKAC6CrN9CvNyBIdPUlhWrDEta+RFWR9b2gXX/FsQScLDxymcOHXftCNhCv5nC/xinSBuwooFcPkCZUS9Pg7EsARVnVDarrKhxaKiQ1DTBYEBv9uZTp3CPD+TCwsoKiqiaMpU8gqLyS0oJCcnJx1yc3PJycnB6/XicDjQdR1d18fUj41IzU0QQmBZ1h6nLcvCNE0Mw8AwjHR6qLyd7R/OMYlEgngiTiyeIBaPEYvHicfjxBNx4vFEKo4TTyRIpEJyQNpIJkkmkhjJVEgkt3XTvgMGej9Li6a+vK23Hf3CSXU6UDOcdpwWXg5w6nYYJLh06RRDIhkCIQSiN47ZFbGdbnT3YnX1YnZFMDsjg51ydEXSQ+4A2xuoP8sWYfm26NIKfOgFdlr1e6W1eIQQiSSJuhDJ6gCJ6gCJqgBmqB1IDY+cVWRb2GYV4ZxdbK9ROAGRAm0YmKbJc889x6WXXsratWuZP38+l112GS+++CJOpxO/38+JJ57I97//fQoLC7dbTzzVWeijq6uLWTOno29jwhVDJofDHn/Qw6xgz86znaN3/7L3CJH+s8e1bIMlBMIaeq+CLcR0FRxbBaea9n47zDMNfz/Y84J3WiYVxwxoicIsH/zqJJieve87h10JwWPrBX8sA7cDJruhJ7H9z2Rge3fEsP7NwyhkCjsYlh1bqW3LAhN7Oz33ejfZ7qXurI8gBpQRg6IJhZL6owzYSHuNVkBRlP4yW6fTxymDvU0rygAv1Cn304qCotplVEUBFVRSeX3lVWWH302JRLJ72C96wDQtTFNgWgLLFPZzVlH7n7tJEythbPvjpiqoHrc958vvQfNnpeZ9eVB9WWg+2wOhmp0lHdOMYcyeKMmaAInKJpI1zSSqAljdvQDo3kycbgcuM4lLU9B1ZUy9+Nwdzrr6GB761nNSoG2PkpISli1bRiwWw+Px8Mwzz/C5z30OgP/5n//B4/EwY8YMqqurufvuuzEMg08++QSXa+jxxvfeey/33XffoLwZ3u30t4bIHO7XbVe+lsP9Du/SV10Z3jEjdd3KcAoN9/hh1DOwSJ/40gcIL7cDMvV+ETac6xvO/2lY9QyjzMBCjT3w73pYkg+rT4Zs18j+gDX22PPTXq6BxflwSD5kbGckyd76jIbDcM9lWpCw7GGScdP2ZJkwU86zsEVdn5Ab+DJ3oLhLO9vC/pNOD9y3kzI7arMyMFaGyNvqmrcut3W1W3+n1QECqC9O5w0jXx24ZM/WjZJIJOMaS0Bv0vYAHElCrwERRafXUoklBfGYgbAGdyHVrAx7iQVv/5p3fYuNaylnNarPg5adJde9m6AIITBbOkmUN5CoaCRe0YjRGAYBeoYDn89FgZagUEmQ44Lxpr0fPFZl8s/apUDbHolEgrq6Ojo6Onj++ed54oknePPNN5k/f/42ZQOBADNmzODZZ5/lvPPOG7K+oSxoxx+3gD/94RdkZGTus+uQ7COUAYm+1/aqBoqC0ByQCkIdn2PT//P/3uLOG+9i2SSLn35GwT2Kl/Fmg+Ce9wRdSYWbrv4K51zzrQEWjoEl+zcG5qfsJ/3CYyu1kt6vDE6TPm7g/v46lAHHKyjp4wcuGWCX250OggAzCWYSYSTBMsCyFx61VdhAGSaRSCRjE8uyaGgKsqWylsqaBqrqmyivbaSuIUBrsBXLsgBQNBUtNxs1x5taT8uLnuPFkeNFyfGg5nhtr6D7seMIyfaxonESlU0YFY3EyhtIVDUh4klUXWPG3Fkcd8Rizjj6cI44ZD6ZmRmj3dzt0hvt5czzvsbaTSEp0IbLqaeeyuzZs3nssceG3H/ggQdyxRVXsHLlymHVJ+egScYqf/rTn7jssks5bZpg1XHg1Eb/DWRPQvDQx/BcueD8Exfzu3+8izvLM9rNkkgkEgn2dJCqqipKS0spLS1lbcl61pWUUFNRQSJmv5xWM5xofc42CnNwFuagF+agFPjQcr1yfqVkryFMi2RDCKOiidiWeuKb6rC6e1EdOgcdsogzTjmV85afxdFHH43T6Rzt5qYZSW0wPs0HQ9Dn0WsoWltbqa+vp6ioaIRbJZHsXX79619z7bXX8oXZ8MCykXXjvyM8ToUHjoEjJsE976ynYsE0/vHWGiZNnzXaTZNIJJL9BiEEjY2NlJSUUFJSwtp1n7Jm/Tqqt1SQTC1Orrpd6MX56MV5ZJ57LP4pBajFefZ6eON8fpBkfKBoKs4Zk3HOmIz7lMMQQmA0tZDcVEdNaS0/+9nP+OlDq9AzXCw44lDOO+tsvnz2ucybN2+/+Y6OSwvaHXfcwfLly5k2bRrd3d08++yzPPTQQ7zyyissW7aMe++9ly9+8YsUFRVRU1PDHXfcQV1dHWVlZXi93mGdQ1rQJGONhx9+mNtuu42LD4Y7lg4eqjeWWB8WXP1vQVaGk5f/9RoLjjxhtJskkUgkE47Ozk42bNhASUkJ69ev5+NP11JWWkpPZxeAvWzDlDwcUwtwTCnANaUAtShXCjHJmEdYlu3av6yeaGk18U11YJj4iwo58ZSTuei8L3PGqacNu0+/txhJbTAuBdqKFSt4/fXXCQQC+Hw+Fi9ezMqVKznttNOIRqOce+65rF27lo6ODoqKijjppJO4//77mTZt2rDPIQWaZKwghOCuu+7iBz/4AdcuttcgG+s/rvXdgqteF7TEVJ5/9ilOOfdro90kiUQiGZfE43G2bNmSFmKfrPuUkpISgo1NACiqij45F31qvi3Epk1Cm5KPmpctnXBIJgRWPElicx3xDdVES6owg+2ousbcww/hS2efy8VfPp8DDzxwn7dDCrQxgBRokrFAJBLhm9ddxx+efJJbD1dYsXD8/Nh2xgXf+g+sCQke+9H3+PqNd492kyQSiWTM0tLSwqZNm9i0aRNlZWWsL91I2aZNNNXVI1LOOvS8bLQpthBzTi3EObUAdXKOXIxZsl9hhNqJb6gmtr6KWFktJA0mHTCDsz5/FldccBFHHnkk6j6YMykF2hhACjTJaFNSUsL5X/kK1ZXl3HeUxbmzx4846yNhCu55D16oFNx59QXc/6tnxrz1TyKRSPYFQgiCwSBVVVVUV1dTVVXFlopyNm7eRMWWcrrbO+yCqoIj3486ORdHUS6OonycRXmoU/JQ3WPXw51EMhpY8QTxjTXEP60g+mkFVk8UT34OJ3/2DK644EJOP/W07S6xtatIgTYGkAJNMloIIXjssce48cYbmOFVeOTYJLN94/c2FULwq/Xws08Fnz1iDk88/xpTps8c7WZJJBLJXiUWixEIBGhqaqKxsZHGxkaqqqvZVL6FyqoqGuvq0h4TAbTsLNT8bPTCHPSiPFxF+ehFuaiFfmkRk0h2A2Fa9tprn1bQu2YLZrgDp8fNictP57pLLudzZ3wWh8Ox2/VLgTYGkAJNMhq0t7dz5ZVX8vzzz/O1+TorD4cM1RrtZu0VXqsVfPd9QdxSufnay/nO/Y/g8UhX/BKJZOwSjUZpaWnZJoRCIWob6qltqKexsZFQc7DfApZCcTrQ8n1o+T70Aj96gQ9nQQ5agR8lPxs1Y+y4D5dIJhpCCIzGFmIfb6b3gzKMYBuubA+fWX4637rsCs449TR0fddehEiBNgaQAk0y0rz33nt89YLz6Qg388BRBqfPmHhDAdtjgl+uV3h2s0W228ltN13PN2+/n4wMOWxHIpHsO4QQdHZ20traSltbG62trYNCqCVMczhMKByyy7S20dXeTjwa26YuRddQvW5UvwfN70HL8aL5PeipxZtVvwfFn4WS6ZJDuiWSMYAQAqM+TOzjzUQ+LMUMdZDh83LqWZ/jW5et4JSTTkbTdr7IuhRoYwAp0CQjhWVZPPzww9x9910sKVD50fEwxT0xrGbbo6nHFmovVFgU+jK5c+XNfOPme/Zo6IFEItl/EELQ0dFBc3Mzzc3NBINBmpubaQoEaAg00hwO09LaQntrG53tHUS6urDMbZ+rikNHzcpA8WSiZmWgejJRvW40TyaqJxPdm4XmdaN4MsFj71dcDim8JJJxihCCZF2Q2Ieb6f2oDLOlk6xcP2d+4RxuuOIqjj7q6O3e31KgjQGkQJOMBM3NzVx04df49xtvcNVC+NYhKvredzw0ZqnuFPx8ncI/qi1m5nu4/4EH+NqV39on3pckEsnYJ5lMEgwGaWpqoqmpKT2nq76pkbqmRgLNAVpCYTrCrRjJ5KBjFaeO6vOgZvcLLDXLFl5an+jKykRkudL7FKcuxZZEsp8ihCBZHSD24SYiH5RhdfaQM2UyX7ngfK6//BvMnz9/UHkp0MYAUqBJ9jX/+te/uOSiryGinaw6VrCsaLRbNHpsahM8+im8US845IBJ/PiXv+Xkz5452s2SSCR7CcMwCAaDacHVF+obG6lpqKOxqYlQoJmutnYGdksUTUXNzkL1ZaFmZ6H5+oIHh9+Dku1GyXajZmehZDil2JJIJLuFsCwSm+uJvl9G78ebENE4U+fO4ZKLLuLqSy9n2rRpUqCNBaRAk+wrkskkd955J6tWreL4qSoPH6eS59rxkMaoIdjSDuEoxE1waeDWIVMHjwO8Tjt2O0Adxx2UD5sFP/xEUNICZxy9kB899icWLl4y2s2SSCTbwTRNQqHQthavhgZqGuppCjQRDDTT0dI6SHihqui+LBS/xxZcfg9qah6Xw+9B8XtQfFmoHrdcbFmy1xBJA6snitUbRySSiISRipMwqDs84DunqSgODcXhSMU6OHQUh57eVhy6XW4c//5K+hFJg1hJFdEPyoh+WgFJg4OOOIRLzv8qd92yUgq00UQKNMm+YMOGDXz9skv5dO1abjoUvr5g+4IqYQr+rwr+ViX4JAjmMO5UBVuoZbvgAB8syYdTpikcnMu4+eEQQvDPWvjJJ4LGCFx89inc+8jjzJw1a7SbJpHsFwgh6O7uTs/v6guBQID6pkbqm5pobg4QDoXoaGlLL6IMgKKg+bJQfR60HFt02QLMiyPlTAOfG9XrRpFDmSV7ESuewGhuwwx3YrZ1YbR2YbZ1YbZ1Y3X3YvVEEbHEvmuAothDZvsEm0MftD0o7dBQnI6tyg0Wgf31pLZ1bdDxDCijaPJe2ldY0TixNeX0flBKvLSGvBu+TNV3H5cCbbSQAk2yN4lGo9x///2sWvVDZnjhoWNhcd7Qt54Qghcq7XXDmiOwrAhOn6GwKB+K3ODSIW5A1ITeJPT0hYQddyegPS6o6ICPg9CdhPm5sGKBwudmjR8LW8IU/HmLwq/WWXQlYPlxh3Hj3T/g5FNPHzdiUyIZSyQSCUKhEIFAYJDwagzYQw0bA02EgiHawuFtvBcqDh3Nl4Xiy0JLDTVUfVnoPg+OnGxUfxb4slCzpfCS7FusSIxkoAWjqRWjqZVkoBWjqQWztStdRnE60PK8aLnZaDlee4isJxPN40b1ZtoeNl0OFKcD1amD07FdS60wLIRhQNKwLW5JA5E0U3EqJFL7B2wPmd667FD7hnBms1MUZbCY6xOB+hBizqGhuJyobhdKpgs1w4nidqFm2kFxZ6D1DRuWwm8QZmcEzZdF6RfvkAJttJACTbI3ME2TP//5z9xz1500NjRw9SLBlYsUnNt55jX0CG5/V/BhMyyfCd86RGG2b/fFSNIS/LcJ/lgmeKcJFubBbUcoLJ08fgROJCn4vyqFP5VZVHTCrMl+vnrBBVx81beZN2/eaDdPIhkVhBD09PTQ2tpKOBxOr8/Vl24OBWkKNhNKbbe3ttHT0Tm4EkVJd8TU1NyuvnleDr8XzZcF2W5UnwclU87vkowcQgisrogtwJpsAZYMtGIEWrE6I3YhBbQCP47ifPSiPPSiPBxFeWiTcmzPnOP0+yosC5E0bRFnDBSCdjot7tL7zAHi0By0b3v5IprAisUR0ThWbxwMc9uGKKB63P3PBp8HPS8brcCPXuhHL/Dbz4b9cAiyFGjbYfXq1axevZqamhoAFixYwD333MPy5cu3KXvVVVfxm9/8hkceeYQbbrhh2OeQAk2yJzQ2NvL000+z+pc/p6augZOmKaxcqjHLu/03Y/+qFdz5riDbCQ8co3BM8d596H0cFDz4kWBDK5w2Hb5zuMLM7PHzYBVC8EEz/K0aXq0R9CTh4BmTWL78c5z55Ys47rjjcDrlwq+SsYNlWcRisUEhGo3uMC8ajdLd3U1HRwftnR20trfT0dVJR0cHnV1d9HR1E+nuprcnMnhoYQo1w2m7gve6bU+Fnkw0r2010H1edH+W7Vijb36XfEMuGUVEIokR6sBobsUItJFsbsNobsMItiF643YhTUWflINenI+jKA+9OA9HUT765BwUp1yaZW8gkgZWtF+wWV29mJ09mJ0RrM4IZmcPVmcEo6UTq6On/0CHjqM4D31KAY4p+XaYPskeyjyBkQJtO7z00ktomsacOXMAePLJJ1m1ahVr165lwYIF6XIvvvgi9957L+FwmFtuuUUKNMk+IRaL0dTUxJo1a3j33Xd55z+v88m6DThUWD5DcMkCnYW52xdmcVPw0EeCZzbDGTNscZbt3DfCyRKCl6rgkbWCcC98dS5cu0QhN2P8CDWwP7O3GuDfDfB2oyAcBaeuMm/WVA497HAWHX4002fOYurUqUydOpWioiJ0XR/tZktGCCEEpmliGEY6JJNJ4vH4LomlvhDp7aU32kskGiUajRKNRemNRolGY8TiMeKpcvFYjHgsTiIeJxlPbOMGfjgoDt32RpjptIcb9Q07yrCHJPXlaamget0o3kyU1BpessMqGQ2EEJA0sOJJRF9I9KetaByzvTs9J2zg3LA+lKwM9Mm5OCbnok/Kta1ixXnoBX4UfeeLCEtGBpFIYrR0YoY7MYJtJJtaSDaEMRpbEHH7mafleHHMLsZ5QBHOA4pxzJiM6po4zyYp0HaB3NxcVq1axYoVKwDbgnHUUUfxr3/9izPPPJMbbrhBCjTJsBBCEI1GaW1tTQ8bamlp6V+bp76WpoZ6mpsDBEItdHT1v02alq1ySL7FscUKp0xTyN6JQac5IvjmG4LN7XDHkQoXHDQyzjxihuCPZfBYiUAAF82DSw9WyMscX0INbNG5qQ3WhFXK2kzK2qCqE6JGfxlVUZiUl82USYVMnTqFoqnTyS+aRn5BIXl5eeTl5ZGZmYnL5cLpdOJyuQaFgXljYY02IQSWZW037Gy/ZVnbCJh9GZLJ5ODYMEgafXkGhpEkmS5rb6ePHaI+0zAwTAPDMDENA9MwMc2+2MQyhxiuswsMnNBP3zwO5xCT9ree1zHQOYBDR3XqqA4dNTXxn9Q2Dh2ha3Y9A+vVtP1yuJBkZBGmZVtKYgk7jiYQsdR2LDFIXFnbiK3E4PwB+9lJd1LJdKbnhGm52Wi5dqxPykGfnIvmdY/QJyDZFwhLYLZ2kqwLkqhsIlEVIFnTbH83VAXHtEJc86bjOngGzgOnoma6RrvJu81ICLRx/0rZNE2ee+45IpEIy5YtA+xhJRdffDG33HLLIIvajojH48Tj8fR2V1cXuqZQNCl/kLfVbRimvN15MbGX69v1Cna/TrHN5kir/m1/F4b/QQoEQtidXrGDtiuAroJDtWNdAacKxVn2tksDh2pR2QGVHYI/lg6uaet6e5MQiIDfBU9/FhYXjFzHLENX+MYi+NKB8HiJ4Kky+EOpYI5PEDdhe33E4f5fh/vaZ9j1Deuc/Z3ySZlgYc+1NiwwhCDa1cmmzk42bCrHFGClwu58V5XUn/54iA9MGRRt295tEgPzxKD9Y/0tWt87BSX1gaTfMfR9Nlt/Vjvc7q+wv16lr7ptjlEUcGgKDh0UlwqKioKz//MfUIeibBujKPb3PZ2fPtMADDskQdl1o5hEsk+xLIFpWpimGJxWVEwULCGwDAvTsOw5SDtzQuHQUJ0O24mGy5mK7aC6M1ByvLaV1zkgv6+c04HiTHkodDpsBxSpMJ475JKdo6gKeoE9Ny3z8LmA/TIg2RgmWdlIvKKJ3g/L6PnXR6AoOHM9ZDhVXKZBRoY2buYMnnzVcSNynnEr0EpKSli2bBmxWAyPx8MLL7yQXvH74YcfRtd1rr/++mHX9+CDD3LfffcNypviAYWd/xoP9ys13O/esOvbzUK7Xf9OOpy7fA5lL17rLl7nwH2aYguSgbGWElwuPRVrtiDb3v9wV6+jvAO2tMOhBfDoiVDgHh2rTG6GwsqlClcttgXlnzZBxICjJtnf/4whnhDj7fu+s2KGZa8tZ1ikhZuZCmJA2hRgWQPSA8rYMp+0mOoTVGLAthD2eXcktkSqbWl9M/CeU7bdN0AHDbqflK3ztt6X2lAH1KMOEDxD5qXigccM/XlvfYVjXV5KJGMTIexnU8yEmGGHqAm9ik7UUokZgnjcxDK2ElyKYs8/zHajZWfZ8w+9dmwLJgdqRmr4rDvD9uDXJ6RcDumFU7LXUDQV5/RJOKdPIuukw+zh56F24qU1xDfV0VNWR1dPFNWh4c/NpEiNU+RIkjmG1cntm/85IucZt0McE4kEdXV1dHR08Pzzz/PEE0/w5ptvEo1GOfPMM1mzZg3FxcUAzJw5c6dDHIeyoJ168iJW//QBXC751mfcoqS6qqlep1BUUDVQdYSqgaYjVB00B2IE3t4kEgl+9tCveOmv/+SiebByqYJzDA1p6kkKnt0Mv98oaIvDaYfP5Gs338TMg+eOdtO2YUdv27be02fhGnjIkFavAbmKomwlagZYcdLbCgiBikARFphJVGGhWAIsAwULJaXaFGEPtVRSKk0RVqougSIGyLrx+UiWEfYX8wABAABJREFUSCS7SHekl6ZACw2BMI3NIRoCYWqawzSF2giF2+hs68Qc6F1PU9H8HjvkeFFzvGg5Hnt9OZ8HvG7UbDdqVqYcKisZFwhLYDSEiZdUES2pJFHRCJagcEYRJyw7lC8cv5TDFh+EY4zMIY/Gonzl0mt5vyQg56ANl1NPPZXZs2dz8MEHc9NNNw2aJ2KaJqqqMm3atLTnx50h56BJ9jbNzc2c94Vz+OSjj/juUfClA8fuD2jMEDxfAU9sEAR74foLz+KBx/4Ht1vOEZBIJJLh0NnZSU1NDTU1NdTW1lJZVcWmygqqa6ppqm8g0jlg3S5dQ8vLRsvNTgkvL3quF2eOF/xe1JwsVG+WFF6SCY0ViRHbWE1ifRXRkiqs7l5cniyOPfUkrvjqRZx95llkZWWNWvtGUhtMGIF2yimnMG3aNH784x8TCAQG7TvjjDO4+OKL+frXv87cucOzBEiBJtmbbN68mTNOO4VoWzO/OAmWbGeR6rFGwhT8diP8cp1gam4mT/z295x89vmj3SyJRCIZE3R2dlJeXk5FRQVbtmyhdMsmyjZvpqaqmq629nQ5xaHbjjHys9Hyfej5Ppz5frR8H0qud79dT0oi2R7CEiRrm4mvq6R3zRaMhjCa08Ehxy/j0q98la+e9yXy8/NHtE1SoO2EO+64g+XLlzNt2jS6u7t59tlneeihh3jllVc47bTTtik/nCGOWyMFmmRv8cEHH3Dm8jPw08NvT1Uocu9kgvYYpLJTcPd78ElQcNV5J/HjP740qm+xJBKJZKSIRqNs3ryZTZs2UV5eTunmTZRu3rSNCFM9mWiTctALc9An5eAqzEUr9KPkeaX1SyLZQ4xgO7G15UTXbCFR2QiKwkGHL+HCL53Pped/lRkzZuzzNkiBthNWrFjB66+/TiAQwOfzsXjxYlauXDmkOAMp0CSjxyuvvMIXz/sC87wJVp+i4HeOu9stjSVsJyI//kQw2e/mD089w4nLzxntZkkkEsleoaenh7KyMkpLS9mwcQNr1q+nrKyM5voG+rpKmicTdaAIm5SLWpiDVuhHzcoY5SuQSPYPzM4e4msria7dQqysFgyTmQsP5qKvfY2rLr6MqVOn7pPzSoE2BpACTbKnvPzyy3zh3HM4drLJIycqZE6QdTarOwW3/xc+DQm++bWzePiJv5CZmTnazZJIJJJh0dHRQWlpKaWlpZRs3MiaknVsLisj3NScLqPl+dCL83AU5+EozsdZnI86OVeKMIlkjGFF48TWVRL7aBPRkiowTGYfuojLLryIKy68hMmTJ++1c0mBNgaQAk2yJ7zyyiucc/bnOW6yyU9PVHBOEHHWh2kJ/lAKj64VTC/I5sln/5djPjO0BVsikUhGg3A4nLaIrd+wgTUl69iyaRPtoRa7gJJat6k4D70oD9eUAvSiPNSiXNQM5+g2XiKR7DJWb9weBvnRJmIbq0EI5h1xKFdcfCmXX3gxOTk5e1S/FGhjACnQJLvLq6++ytmfP4tjJhn8bAKKs4FUdghWvgsbWwU3rbiAB37xB7kshUQiGTGEEAQCgbQQK9mwgbUb1rOlbBNd7R2AvRaTVphjW8RSQkwtykOfnIPidIzuBUgkkn2C1RMltmYLvR9tIl5ai6ppHHnqidx01XWce+ZZOBy7fu9LgTYGkAJNsjv8/e9/50tfPI9jJpn87EQmtDjrw7AET2xU+MWnFrOL83jqub9xxNHHjnazJBLJBMKyLOrq6igtLaWsrIySjRtYt6GELZs209vdA9iu6vXJubZFrDgfV3EBWlEe2iQ/ir4fPIwlEsmQmJ09xN4vI/LfDSTrQ2T6s/ncF89l5dXf4ojDD9/huqoDkQJtDCAFmmRXee655/ja177KyVMEPz4BnOrOj5lIbG4X3PaOYHM73Hr1Jdz76OM4nXKYkEQiGT6GYVBVVTVojti6DSVUlZcTj8YAUF1OtKJc9CJ7jpir2LaIaQU+FG0/e/BKJJJdIlkXIvreRiLvb8TqjFB4wAwuueRibrjiaqZMmbLDY6VAGwNIgSbZFZ588kkuv/zrnDlT4aHjQN9PvSknLcFjJQqr11nMKfLzw0d+zllfvnDYb6ckEsnExzRNGhoaKC8vp7y8nC1btrBxyya2bNlCY209RjIJgJaVgVaUl54jljGlAHVyHmquV7qsl0gke4QwLeKlNUT/u4HeNeVgmCxYtpTrr7yKi75yAW63e5tjpEAbA0iBJhkOlmVx991384Mf/ICvHKRw71EK8gUulLYKfvARfBQUHLtwJqt+8VuWnXjyaDdLIpGMAEIIWlpaqK2tpa6ujtraWmpqaykr30J5uS3CkokEYM8P0/P9qJP86IU5OCbl4pySbzvqyM6SL3ckEsk+x+qNE/t4E5H/biCxpQGHO4NTP38mt1z9TT5z4onp55AUaGMAKdAkOyMYDHLpJRfz6muvcfNhClcsQHYmBiCE4M1G+NEngvIOOPnwuXz3hz/nhJOlt0eJZLwRj8dpbW2lpaVlmzgcDtMcDtEQaKK+vp5gYxOJWDx9rOp0oOZ60fJ96JNy0Qv9uCbnoRbmoOZ55fwwiUQyZjBCHfYQyHdLMFs6ySmezIUXX8SN37iG/Px8KdBGGynQJNtDCMFf/vIXrrvmapK9XVw4V7AoD6Z4YLYfHHLozSBMS/BKLfx6vWBLBxy7cBZ3P/QIp3/ubCloJZJRIBaLbVds9aWbQyFCLSFaW9tob20lGundph5FVdG8bhRPBkpWJmq2Gz03Gy0vG2eeDy3PB7keVE+mvNfHMMK0ELEEVjSOiCcQhgmGhTAtMM1UbCFMEywLFAVQQFVAUez/rUJ6G0VB0TU7OHRw6Om0HTTQNfmdkIxphCVIlDfYQyA/2oSI/X/2zjs+juLu/+/Z3et3OvXiCjbYxqYb0zumJgESCCWE0BMgIYEk1BACyUN7SEIKCSEhBZIfgYdQEmoSmoEQugE3WZZl9V5O19vu/P7Y00myJVs2tiXZ8369RjM7Ozs7u7rbm89+Z76TZvZ+e7Gudi19zW1KoI0XSqAp1iccDvOLX/yC3/z6Pno6O0iaG5bxGHDkVPj8bMFR00BTP0B5LCl5pQnu/0SyvAf2330q3//RXZz6xS+haWpcqEKxJSQSiVHFVmfOstXR1Ul3dzd9vb309/aRjCc2qEfoGobfixbwgM+N8HvQ/B40vxfN70b3e9EDXoyAnSd9boTHqTrZEwwrlcEKRTEjMaxwHCscx4zYsRWJYYbjyHgSK5G2RVkyDZns+DTW0BEOHeF0IlwONJcDkQ/OfFobLc/pQLhzec5cnttp5yurrGIrYqUypD6sIfbWckq+dSarzv6+EmjjhRJoCoB4PM6TTz7Jb3/za97679uYlmS3IBxcBQdVCnYrhGI3ZCxoCMMHnfBivWRlL+xaABfOF5w+G9w7q9eQEZBS8marLdQ+6ITdqor41tXXcNHXv43P5xvv5ikU44KUknA4TE9PzzChNRDaOztp7+qkq7uLnp4e+nr7CPf1DRtKOIDQNTS/bdnSBoRWwIvm86AHPOg+D0bAi1bgRfjc4PMosTUJkJa0RVZPGLM3TLbbjge2zZ4wVnQ98S2E/b8PeNALfGgBD5rPg3A70dxOW9C4nWiewbQwDNsbpq4hdD0Xa6DrdiwlUkqQgJT5IKUEyw7SNCFjIrNZZMZEZrK2ZS4Xy8xAfgaZziJTGWQyjUxnsIakZcoOViqdT2Nam75ZumYLNaexYewwbFHnGGHfQHr9Mq6BMg40p2GLRaehLIE7KSvPuEkJtPFCCbSdm9raWu6996c89Mc/EEuk2LcMTp4pOH6mZKp/09aeDzslf1wh+XcjFLrgnLnwpbmCcq96kA/lg07JQ6vg3w2SgNvBGZ89gQu+fi2HH3GEsqopJh2WZRGJROjv76e/v59QKDQs7u/vp7evj47uLjq7u+jq7qY3Z9WKhEKY2Q3N8sJh5DvVmt+D5hsiuvy20NL9Hltk+T1oAbvzrTqNkw+Zya4nvPoxe8JkBwRYbwSGfEaEy4FeUoCeG1ZqlATt7UI/WoEPrcCL5nMjdrBnqcyaObGWtsVcPqRzYi6XTmdtIZjK2HE6JwbTQ9LD8gfjMYlAsIdzOnMWPqeRt+YNbGvOQeuf5nUhvC40r9tOe+xY87rtfI9LWf4mCUqgjcL999/P/fffT319PQALFizglltu4eSTTwbg1ltv5dFHH6WpqQmn08nChQu5/fbbOeigg8Z8DiXQdj4sy+L111/numu/y3vvf0CBE764O5y1O+wS3LIfuMaw5OFqyRNrbCvbiTPhs7sKDpsCTl11oAZojkoeqxE8W2fRGoNppQUcd8xRHHniqRx+xJHstttuSrApxoyUknQ6TTKZJJlMkkql8ukt2U4mk8QTCWLJBPF4nEQqSSKRIJG042gkQiwcIRGNMdpPqtB1NK8LPE5bbK0ntHS/F2NAYPntYYaaz4Pmcmznu6fYFsisidkfs4cf9kcxeyPDBVhPGCscG3aMFvSjlwQGhdd6Ykx4XUqIbyOkadmCLZMdtPANE3I5UZjbtlJD8zLDj0llsJJpZCKFFU8hExtavQcQPjd6zrqtBbw5q6e9rQd96EUB9KIAWtC3wwnvyYQSaKPwzDPPoOs6u+22G2CvQXXPPfewdOlSFixYwCOPPEJ5eTmzZs0ikUhw77338vjjj1NbW0tZWdmYzqEE2o5NNBpl9erVVFdXs2LFCt5+83XeevsdstksPgd8a184YzfwOLbOAzCclvxtDTyxRlLbD0EnHD8TTpopOLhKORYZwJKSDzvhhQbBu+0Wa/rsUTROQ2daRQkzp09l+syZTJsxi6kzZzFt+nSmTJnC1KlTKS8vR9fV28eJiJSSVCpFPB4nFosNiwdCIpEYFmKxGNF4jEgsRjQeJ57IHTOkTDKZIJVMkU7ZIZNKk0mnyaTSW9ROLe/EwHZkIAwDHDpyYK7MwD5Dtx0f5PI0jwvhcWH43GgeN7rXBR5bjAmPyxZmDkN1pncwpJTIWDI/x8vsj+YEWAwzFMUK5+L+2IZDDw0dvXhD8WWU5raLAraDDcUOh7RyTlniKax4EpmLrVgSKzJ03mAMKxLHDMexIvHhVj1dQy/024KtuAC9OIBeEsQoL8QoL0QvCSpr3DZECbTNoLi4mHvuuYdLLrlkg30DYuull17iuOOOG1N9SqBNTtLpNH19ffnQ3d1Nc3MzTU1NNK5bS1NDHXXr6mlu784fE3SCKSGRhfPmwdf3hkL3tnszVdMneW6d5IV6aIjYQyCPnwHnzhUsKFEduKGE05JPuqAhqtMcztISg/YYdMShK2H/3wbQhKC8OEBVWSmVVVVMmTadKTNmUVE1hWAwSCAQoKCgYFjscrlwuVw4nU4MY9t3oKWUWJaFZVmjpsdzXyaTIZ1Ok0qlSKfTo6ZTqRTxZJJ4MkE0FiUSjRIbIroSOdGVSiTtkEwirTEOGRqYO+IwBueEOEaYHzIwlyQXk/MSpzmNYUJLc9giyxZWRt4xgTQG8nOe5nRdLX6sQGaydqc4Esfqjw062AjH8h3loekNhsI5DNvSUehHy8V6cMO0FvCqz5tizEgpsaIJzL6IbX0diHvDw9L5IbCasAV/ub2+oF5eiFFZgmNqqS3e1GfvU7E9BNqkfz1jmiaPP/44sViMQw45ZIP96XSa3/72twSDQfbZZ59R60mlUqRSg2bncDiMwxBMm1I+Ynk56sYmym6s8GbVs5FyW0tzy7Gfc2MlN685Gz+pzFVoz0+W+bqllPn5yiPh1MChgUMHlwYzArZX4L4U9KfhiClw/QGwe9G2HzIwp0gwp0hw9X6S6j54fp3kmXXw+BrJrgUStwGbGv045ls6hoJjqWvsn71tdb7hXsYKXBBw2AIta0FG2u7809EwayNhamrryOb2mZvx+RO5P/nbL8SQ9IaNXL9q+/rletuTH9urthjwoI3I3SQhBJo2kC8Qmsjl5baFwO0Bj9c9WC5XRmgi55V7YHsgvf7ZLSAFVgqS2EGh2EzslxASM2thZiXZrIVpWpgI+zmSsTDTtuOK9REelz3ELDevyzlrij3HK+Cxh6MFfGiFXvSgH+FRww4VWx8hBHrA9qTKjIoRy0jLsoVaV4hsew/Zrn6yXSHSa1vJ/neF7WAFEIaG4ffgcOk4sHAIicOpYxia+uyOgUO/etR2Oc+ktaAtW7aMQw45hGQyid/v55FHHuGUU07J73/22Wc555xziMfjVFVV8fTTT7No0aJR67v11lu57bbbhuVVeoeX2eBju5HP8aY+4iPuF2M7dmvWNZZzjVpGjKHMaPVsxrFivfRAJ1ETg9taLnbkhJhTt0WZUwe3zrBOX9qEFT2wLgwzC+CGA+CY6eM7ljtrSV5ugl98JKkNwcGVMMVnC8rRGOvnZCzP20/1OdhG5xtLobHUM+BYLGMNCaYdW9IWcFZObFmDTsg22LakLRXWP68csj2QHpq3gegbIW/E7c0pJzbvuIG8of+rocsYabm0esmqmAxYElKmHZJZSJqQtCAhDBKWRiorSSezSGtId0cItEIfemHAHh5WGEAvstds0wu8tgAr9NvrvKmhhopJjpQSsy9CtrmLTEu3LeBae8i0diMT9pBwzaHjC7gocEgKrSRBQ+J3qN+B9fn50RqVv+hTQxxHI51O09jYSCgU4oknnuDBBx9kyZIlzJ8/H4BYLEZbWxvd3d387ne/45VXXuGdd96hvHxki9hIFrTPnbwvd//wOzidru1yTTsdYkj3UQik/XodhIbU9OGx/Xp9cIHMgfKakS+zKaSUvPrP1/ntTx8kk4hz1d6Sc+eBawI56zAtyaM18OMPJEU+B7fceS27Ljxwg3LDO9ZiSHpDhr4RGy6SR7/uTd2RUR8aozxOtvQhs+FLEbFBvhiSJ4btH/yr5d9YiGE/NgKBJgQCMWglyt1RAWiWiTDTuWAirCyalQVp2Qu2Sjn86ibn41ShmLAkU2k6uvto7+qjoytEe3cfHV19NHX20trVR1d3iP5QeJj4Ek4DvTCAVhzIz9MxigI4ivyIogBaUQCtwGe7jFcodmKklFihKJmWLszGTlKNHWQaO8l29IIETdepmFHBHrvNYNHcXdhv3q7M330GLufO6bwokUxy8Teu59UPW5VAGyuLFy9m9uzZPPDAAyPu33333bn44ou58cYbx1SfmoO2Y9Hc3MyFF3yFl195lZN3Edx4kE6Fe4xzYsaB5qjkm6/Z1rSfff8qLv/BL8a7SQqFQrHVGFj3rbm5mebmZlpaWmhubqaxqYl1jQ00NTfR3tpGJNQ/7Djd50YrDKAV+fMe7YziAhzFAUTQj1bkR/jcaqiWQvEpsJJpMs2dmI1dpBrayTR2kmnpgqyJputMn7cbBx54ECceeTSHHXwIc+bM2Sk8LW9PbbDD2O0HvIRt6X7Fjstrr73GWWd+AT0V5reLNY6aCoOD1SYm0/yCR06C296BK279Je++/V9+8/SbOF3KmqtQKCY2UkpCoVDeQdNAWNuwjvrGRlpaWuhsaycVH+LZUAiMoA8R9NtDDiv86HvsQ1GRLb60wgCiyIfmco7fhSkUOwma24lrt2mw2zQGZvvIrEmmuYvsuna66lp46sVnefyPDwPg8vuYu8+eHHHIoRx/+FEcfPDBVFSMPFdOMTYmpQXtpptu4uSTT2b69OlEIhEeffRR7rrrLl588UUOPfRQbr/9dk499VSqqqro6enh17/+NX/5y1/44IMPWLBgwZjOoSxoOwbPPfccX/j86Swss/jpURrFzoktzNZHSsljNfCjdySHzK3g6SUfUVxeOd7NUigUOznRaJSamhpWr15NTU0Na+rWUtdQT3NzC52tbaQSw8WXPsTipRcXoBX5cRYH0YsCUGh7OFRuwRWKyYUVT5Fe14ZZ305ybQvpurb8en4lUyvZ/8BFnHT0cRx9+BHsvffeGMbktgspC9om6Ojo4Pzzz6etrY1gMMjee+/Niy++yPHHH08ymaS6upqHHnqI7u5uSkpKWLRoEW+88caYxZlix+Dvf/87XzzzDI6aYnLvkRpOfXKJM7DnVJ0zF3YpgG++1sGBC3bluX8vYe6+G85LUygUiq2JaZo0NDSwevVqVlavYunyZaysXsW62rWEOgeXKtEDXrTSoG35mlWMe+FMAiVBtGJ7KKIW9Kv5XgrFDojmdeFesAss2AUfOWckvREydW2k17bw2sfv8+9/PAemhcPtYs4+e3Lk4Udw8lHHcuihh1JSUjLelzBhmZQWtO2BsqBNbp544gnOOfssjptm8ZMjxQ6xEPS6fsnlr0j60xr/9//+xOIvnD/eTVIoFDsAvb29rF69mtWrV/PRimV8tGI5a2traW9oIpvOueZ2GOgVRRiVxTgqi3FWlaCVF2FUFaN53eN8BQqFYqIiM1nS9e1k17aRrG0mXduSt7KVz5zGAQcdxMlHH8vRRxzJ/PnzJ/Rctu2pDZRAGwUl0CYvjz32GOd96UucNMPif48QGDuAOBsglJJ88zX4sFNy13WXc80dv1aT4RUKxSZJp9OsXbuW1atXU11dzUcrlrO8eiUNa+uI9g064tCLAxiVxRiVJTirStArCtErS9CLC9TitgqF4lMjpcTs7ieztpV0bQuptS1kmjrBkrh8XubttzdHH34EJx99HAcffDDBYHC8m5xHCbQJgBJok5NHHnmE88//Mp/dBe48jB1KnA2QNiV3vw9/qZacuHA3Hn72dcorq8a7WQqFYpyRUtLe3p63hq2qXsXHK1dQU1NDe1MzlmkP8xZuZ06EFeOoLMFZWYyoLMKoKFJOOBQKxXbHSqXJrGsnu7aVZG0zqdoWZCwJQlA1excWHXQgpxyzmKMPP4I5c+aM24tpJdAmAEqgTT4efvhhLrroQk6fBf9zCOg7oDgbysuNkpvekjgcDn5z38/5wgVXjHeTFArFNkZKSVdXF2vWrMmHlaurWbW6moZ19SRjcbugpmGUBtFzQxIdlcU4qkrQKorQgj5leVcoFBMWKSXZjj6ytS2k1raSqm0m29oNEjzBAPP334djjziKk446lgMPPBC/379d2qUE2gRACbTJg5SSH//4x1x33XWctbvgtkNA20k6H51xyU1vwRstksUL5/DLh55g3oI9x7tZCoXiU5DJZGhqaqK+vp6Ghgbq6upsEVazmoa164hHo/myRnEBWnkhRrltAXNV2iJMLy9UXhEVCsUOgxVPka5rzVvZ0nWtyEQaoWlMmzubgw85hFOOPo6jDj+CXXbZZZu8hFICbQKgBNrkIJFI8I1vfIM//OEPXLE3fGtfsdO9GZZS8lIj3Pm+pDMOl571GW665zdMmzZtvJumUChGIJVK0djYSENDA/X19dTX17N2XR01dWtpamiku6MTaQ16ndWLAuhDRJizogSjohBRVojmcozjlSgUCsX4IC1JtrWbzNpWUrUtpNe2kG3vBcBfXMQ+By7klOOO5+TjjmfvvfdG1z/9Cysl0CYASqBNfJYtW8b5551L9apV3HqQ5Au77VzCbH2SWckfVgr+uMIikRWc+7njuOGOn7PH/Pnj3TSFYqchkUjQ0tJCc3Mzzc3NtLS00NjURF1DPQ1NTbS3thLq7iH/0ysERlEAraQAvTSIUVKAUVqIoyyIKLbXDBOOSbkijkKhUGxXzGiCzNpWMmtbSdY0ka5rhayJy+dlz0X7c9Jxx/PZxSewcOFCHI7Nf7mlBNoEQAm0iUsoFOKuu+7ipz/5MbsUwI+PEMwrnHxrnG0rohnJozWCP62w6ErAwQt25bJvfJuzz78In8833s1TKCYl6XSazs5O2tvb6ejooL29ndbWVuobG1nXWE9TczPtrW1EQ/3DjtN9HkSh316oudheqNkoLsBRVohWXIBWHFBDERUKhWIbIDNZ0uvayNY0k1jdSLq2BZnKYLhd7LH/Phx/zHF8bvEJHHzwwbjdm14uRAm0CYASaBOPZDLJfffdxx3/8yMS8SiXzrf42l4aTtW3ASBlSlb2QG8SDA0CTsmqXvhXA7zdDl6nzklHHcyl37yeE04+ZauY+xWKyYplWYTDYXp7e+nt7aWnpycvvNrb22lqbaG5rZX29nZ6urqJ9IU2qEMv8KEV+e0hiEUB20V9ToCJwgBakU95RVQoJiDSkshMBpnKhXQuzmTBkraFOx8AKZGW3V0WhobQddA1ewF2Xc/F6+fntjVhv4TRNDu9k03DmEjIrEmmsYNMTTPJ1Y2kapqRiRSaw2D3ffbkuKOP4dTFJ3LYYYeN6HhECbQJgBJoE4fa2lp++9vf8scHf0dffz9f3E3y9f0clLvN8W7ahKAhLPndcsnf10J6DIZEAQRdsN8es7n5xw9w7HHHbfM2KhTbimw2SygUygut9UNPTw9dPT109XTR3dNDb28f/aEQ0f7wsHleA2heN1qBF63Ahx70oQXtWA/6cQT9aAVeKPCiBbzK8qVQTABkOoMZimH2R7H6o1iRBFY0gRlLIGNJrKi9bcWSWLEkMpVGprPj1+ABAadpw9KDom5gez2xN1A+J/by+YaOcDkRbgeay4lwORBuJ5rbieb32CHgRfN71DNrPaRlkWnuGrSw1TRjReIITWOXBXM5+sgj+dxxJ3L44YdTVlamBNpEQAm08aW1tZWnnnqKxx/7K0ve+A8Fbp3P72py3nwHM/1KmIG9Htpvl8NvPpEUuuDL8wSHTYEpPshYtiWtK2HHQoBTB0NIepPwcRe82GDXM6fcy3mXX8MFl3+LsrKy8b0oxU5LIpGgr68vH9YXWp3d3XTmRFZPTy+hvj7CoRDxSHTE+oTDQPd7ED43wutG8+WC35NLezBynRfhdYHXjV7gRTiV0w2FYiIgsyZmXwSzN2LHIVuAmf2xwXQohkykhh8oxOB3fSDkvvOaz41w50SM0zEs1lwOhNOwfzAHwlCLlyZAgjQtMM1cbI2wbQ7mW9bwcsO2TbDkpo9Z/1yWhcwOlpemaVv/kmlkKoOVTENmZAEqvC40vxe90I9eUmAPuy4uwCjOpUuDaB7XNv7PTlyklGTbesjUNJOqaSK1phmzJwxA5a4zWHTQQTzz+BP09/YpgTZeKIG2fTFNk/fff59//vOfPP+Pp3nng6UYmuDgSslndxWcvIvArebJ52mKSK5ZYg9hvHRPuGJvgdvYvGEToZTkxx9IHl8Dc4ugIQyLDzuAS755PZ859bQtmkCr2LlZX2StH3p6e+no7qKrp5vevj5CfSHC/f1E+8Nk0+kR69TcTsSAuPJ5hqU1nxs9J7R0vwe8LsjtV0JLoZi4SCmxIgnMvjBmTy702nG2L4LZE8bqj9rDC3MIlxOt0LZm64U+tKDfFhrBXDroQyv0o3ndiB18HdRNIU0LmUrblsNIHDNix/ntAeHbG8bsi9hCMYdW4MOoKBoSijEqitDLi3ZKr7HZnjCZNc2kappJrWmi7HvnU33erUqgjcT999/P/fffT319PQALFizglltu4eSTTyaTyXDzzTfz/PPPU1dXRzAYZPHixdx1111MmTJlzOdQAm3b09rayr/+9S9eeP45XvrXP+ntjxBwaRxaaXHsdMEx0wVBNX1jA16sl3zvLdtqdu9Rgr1LP90P0UuNkuvelFR4IeiEpV1Q5HNx8nFHcOZXvsaJJ5+C1+vdSq1XTCSklCSTSeLxOPF4nGg0SiQSIRwOE4lENkj39Yfo6++nrz9E/0B+OEwkHCbaHxlVZAmnw+40+Vz2EMIBq5bXZYstrwfDnxNeXjfS48q/9VZDchSKyYnMmpi9YbKdIbJdIczOvsF0VwiZygwWNnT0kiGWnJIC9OKCwbjQv1NbdrYl0rKw+mNke8KYXSF7gejOPjvu6EXGBy2UekkBxpRSHFNLMapK7HhKKZp75+qsrTzjJiXQRuKZZ55B13V22203AB566CHuueceli5dyrRp0zjzzDO57LLL2Geffejr6+Pqq68mm83y/vvvj/kcSqBtXaSUrFu3jjfeeIMlS17jjVdfpra+CSFgzxLBEVMkR0wz2LvExNjJ33yNhiUl97wv+cNKOGkm/M+hgoBz69yrtSHJZS9L0ibcciCs6NN4qcGith+chsbC+btx1OITOXLxycyfP5/p06ejadpWObdiOKZpkkqlSKVSJBIJEokE8Xg8Hw9Nr58Xi8WIxuNEY1Gi8TixWIxYPEY8kSARj5NMJknEE6SSCdLJFOlkapPtEYaO5nEh3E5wOfJpze1EeJx22utG97rR/R40rwfN60J6nXnBpdzEKxQ7LlY8Raa1216TqsWOs50hzJ7+QcuMrtmd+zJ7PT+9rBCjNGgLsJICe06ncp4x4RiwdGY7e8m295Ft68n/n83uQY+1eeE2pWSYgNtRRbUSaJtBcXEx99xzD5dccskG+9577z0OPPBAGhoamDFjxpjqUwJtbEgpicfjRCIRotEo4XCYrq6uvDvqNTWrWbXsY1atrqEnFEYImFOkcUCZxQGVGgdXQfGO+f3dqiSykuvekLzUBDccIPjKHmz1H7OuhOTylyV1/fCzowRHTROs65e82SZ4r93ivQ57PhuAx2mw67RKysvLKCuvoKSskrLKKRSVlBIIBPD7/fj9/mFpv9+Py+XC4XDkw4AnScuysCzL/jHIpUcLYymzpeVM0ySbzY4YMpnMqPsGQjqTIZlKkUgmSKaSxBNJkqkkiVycSqVIJu04lUyRTqdIp1KkU2my6QzpdAoruxlzLIVAOA00pwOcBsJh2PMpnEYuOIbEQ/Iddlp3Oe3jXU6EQweXA9y26MqLMCWuFAoFIwuxTGs3Vl9uHqgAvazI7qRXFmOUFdoLrJcVoZcU2E4tFDsMVipNtq138PPQ1pMTbqH80FS9pGCYpc0xvRzHlJJJPwRdCbQxYJomjz/+OBdccAFLly5l/giL8r700kuccMIJhEKhUW/owBvrAcLhMLN2nYHXM/K6CHITG5u8qXKEQ0c6Sm507whl5cjlRvg3j+kfv0E7bVezlpSYlsSyRq9FF+DSwa2D2wCfww6OIc/ojX36Nta+je7bgk/0lta3TY5bbztrQXPUdvxx75GweMa2+5GLZyTffl2ypAX2LoG4aV9Hbh4zGQlp0w5ZC8zcvnzYZi2bWAgBCBD2H0AwoJftueX2xHKh5fZpYkh+Lh6Sx9Bj8vuH1iPQhuZpg2VRb50VCsVWxDItMmmTTCpLOivJWJBNZjATuSHMQqCXFQ52uiuLMKaV4aia/B1vxafHSmXItveQbe4i09qzoXATYAS8OLxOnBo4hIXTZaAb2qSwoi689Gj+9NUHlUAbjWXLlnHIIYeQTCbx+/088sgjnHLKKRuUSyaTHH744cybN4+//OUvo9Z36623cttttw3LK/Xk+l4jMGr+CDs29nETo25sxrFihLwx1LFBvhglfz10Abpmr7VliFw8JDh1cNneXzfZhk3u28jOLapzS+vb2u3YRJ0DpEx4rRk04DfHwT5l2/4NpGlJ7nhP8pdqOKwKZgXt/6VG3qkVWk6X5LdzAsXOl2QtyOZEXdqyxVzGskWczAk5OSRtjZQeIvosy46HCkJTkn/YD9zKnFYavs3gvR6aP/T+i/XKCDFK/Cnu66R80CoUih2WjAWxTC6YEMFBLC3JJHMeAIVALy/EMaUUY0oJjqoSjKmlSogptggrlSbb0m2vQ9bcTaali0xzFzJmD83RHDqegIuAIQmaSYKGxOew+xUTiV8fq1H5C+XFcVTS6TSNjY2EQiGeeOIJHnzwQZYsWTLMgpbJZPjiF79IY2Mjr7322kZv5kgWtLNO35+bv3MZzgnqzU4O6+EP9CC1XL6w05pGvms5UF5agERY1mB6oLeMHN6TFMKuQ+hIzUDqBpbhwnK4kLqTnIlgAjD2b/BYX9AMfZMzsvAdWRmLEUqPePxGGiKAWCzO9V//Ad1NTfzlRMHs4Pb9qv5ppeSu9ySLF+3GzT+7C7fbhQA0oSGwLTi2aBM5gbYVn6KWiTAzkE2BmRkMlpkLg59dYFDtKRQKhWIYoXCMtU0drG1sZ21jBysb2qhr7CDUa7sPRwiM8sL83CHnlFL0KaUYVcVqiLNimyKlxOqL2GuRNXWRbu600209YEkMl4MZs6ex37yZHL7HLuy3xy6UFQfHrb2pdIqrbryd599uVgJtrCxevJjZs2fzwAMPALY4O+uss6irq+OVV16hpKRks+pTc9AU40k8HufE4xfzyYfv8PAJGnsUjc/gwX83SL7zhmTv2VN57o0PKCuvGJd2KBQKhWJ0pJS0t7ezatUqVq5cyYoVK/hoxTKqV1UT6u4BQGiaPScsNyfIObUMo6oEXQkxxQTDSqXJNHSQrWsjubaFdF1rfq5j+S7TOfLoo/jiZ07l+OMWU1RUtN3atT21wQ7zjZRS5i1gA+JszZo1vPrqq5stzhSK8SSVSnH6aafy4Xvv8McTxLiJM4DjZwr+7IXLX2nhoAWzeOHl15m798Jxa49CoVDszCQSCdauXcuaNWtYs2YNK6ur+XjFMtZUryYWjgC251WjshijqgTjsHmU5oSYVl6ohJhiUqC5nLjmTMc1Zzq+XJ7ZGyZd20JsVSNPPf8sf/vTX0AIZu21B58/7XQuPuc89thjj0kxj20sTEoL2k033cTJJ5/M9OnTiUQiPProo9x11128+OKLHHPMMZxxxhl8+OGHPPvss1RUDL7xLy4uxukc21oNyoKmGA+y2SxnffFMnn/2GR44TnBI5cT4ejZFbDf8fSmNP97/M06/8KrxbpJCoVDskGQyGdatW0dNTQ1r1qyhpqaGZdUrWbOmlq7WNga6bZrHZVvEcu7NXVPK0KtK0MqCymOiYocn291PelUDyU/qSC6vQ6YylEyfwsmf/QxXXXgpixYt2upibXtqg0kp0C655BJefvll2traCAaD7L333lx//fUcf/zx1NfXs+uuu4543KuvvsrRRx89pnMogabY3mQyGc495xz+/vRT3HeM4Jhp492i4YRSkhv+A682Sa4+9yTu/tPfx/zCQ6FQKBQ2Ukp6enpYt25dPtTWrWV17RrWrq2jvbkFy7SX29CcDvSKIluIVRTjqCjGWVWMKCtEK1BrhykUADKTJbWqgeTSWhJL12CFY5TMmMo5XzqXay69nNmzZ2+V8yiBNgFQAk2xPUmn05x91lk89+wz/OxIWDy25fq2O5aU/HEF/ORDyf6zynn8xTeYuduc8W6WQqFQTBhM06Sjo4OWlhaam5tpbGykrq6O6to11K2ro7mxiWQsni+veVzopUH00qDtrKOiGFdVCaIsiFYYQEw0N3YKxQRGWpYt1v67kvgHNchUmln77sm3rvg6l51/AR6PZ4vrVgJtAqAEmmJ7kUql+OKZZ/DPF1/gl0fB0RPMcjYSH3ba66UlLZ0//u5+TjvvsvFukkKhUGxTUqkUXV1dw0JrayvNzc2sbaynqbmZ1pYWeju78xYwAOEw0EsKbAFWVoheGsRZVoRRFkSUFCB8bmUJUyi2AVYqTXJpLfH/LCO1oh5nwMfnzj6TH377eubvscdm16cE2gRACTTF9qCtrY0vnH4aSz/8gPuOhiOnjneLxk5fUnL9f2BJs+Sa80/l7t//DccEXZJCoVAo1ieVStHZ2UlnZ+cw0dXZ2UlbRwdtHe10dHXS3dVNX3cPiVhsgzo0jwut0I9eHEAv9KMXBdCLAjiKC9CL/FAYQAt4lABTKMaZbEcf8dc/JvbGJ1jRBHMO3I/rv3kN5591zpj7LkqgTQCUQFNsa9555x2+cPppmNEe7jtKsnfp5PsqWlLyhxXw0w8l+8+u4G8vvs6M2WrIo0KhGB8sy6Knp4f29vZ8aG1rpb65maa2Ftra2uns6KCvsyvv9XAomtuJFvAi/B60gBe9wIsW8KIFPBgBH0aBF+H3QsDer7nUSymFYjIhM1kSH9QQe3Up6TXN+EqLuOiyS7nlmmspKyvb6LFKoE0AlEBTbCuklPz+97/n61dewYJiyS+OFpS7x8+V/tbgg07Jt5dIYqbG3T+4gctv+B/1xlihUGw1YrFYXnC1tbXR1tZGfUsTDS3NtLS10dnRQW9nF+GevmHDCwGEx4Ue9KEFfXZc4EMv9OMI+tGDPkTAFly634NwKsGlUOwsZJq7iL38IbG3lqMBx5z2Ge684WYWLTxgxPJKoE0AlEBTbAtisRhXXnE5D//5L5w9R3DzQQLnDuINOZSS3PU+PFUrOXKvmfz+/55nt3nzx7tZCoVigpLNZuns7Bxm7Wpra6OhpZnGlmZa29vo6uikr6ubVDwx/GBDHxRbOfFlBP0YQT8i6EXkRJhe4FWiS6FQbBQzmiDxxidEX/kQsyfMbvvvxc3fuZ7zzjobwxhcO1AJtAmAEmiKrU11dTVnfuF06mrXcNtBktNm75gWpjdaJLf8V9KTFHzr4rO55d4H8fl8mz5QoVDsEGQyGXtoYWtrPrS0tFDf3EhjczPt7e10dXQS6QuxfhdEC3iGiC7bwmUUBjCCPijw5vcpxxoKhWJrI02L5EdriL70AenVTQTKS/jq5Zdzw1VXU1paqgTaREAJNMXW5K9//SuXXXoJla40vzhasFvB5B7SuCliGclvl8MflkuK/C7u+OGtXPCNa9F1fbybplAothDTNOns7BwmvJpamlnX2EhjSzNtra10dXQQ6R0uvISuoxXaFi2t0I8e9KMFffYQw0IfosCPFrTneglDPSMUCsX4k2nstIc/vr0CXQhO+MKp3Pj1qznyiCOUQBtPlEBTbA1SqRRXX301v/nNb/jcLMFth2j4jMn/lYukJav7IJkFrwOm+qHcwwZvtJsikns+hH/WS+ZOLeJHd/wvZ55/iXrzrVBMIAYWTl7f4rWuuZGGpiZaWlvpbG+nv7sHyxzyckkIe15XoX9YMIoKMAr9EPShFfrR/B61lpdCgf1dk+ksMpVGpjKDwbRAynyQVi4tBMJhIBx6Ls6lnQ6E14XQdpA5EhMYMxIn8cYyoi9/gNkXwX/igbT837+VQBsvlEBTfFref/99Lr7oAlavquZ7iyRnz9lQwEwmLCl5pQn+sEKytAus9Z4c5R7Yrxz2LxMcMx1mFgxe68ddkp9/BP9pley1SxnXfe9Wzr7gMuWWX6HYhliWRXd3d96pRltbG+3t7TS3tlDf1EhTSwsdbe30dnaRzWSGHasVeActXrngKMy5js+JMq3AqzqIip0SaUmsaAKrP4oZiSNjSXs7msCMJbCiue1YAhlNYiWSthBLZ2Br9boFCK8bzedG83ns2O+xhwfnv7eB/PdYeRz9dMisSXLpGjyL5rHyjJuUQBsvlEBTbCnxeJxbbrmFe++9l3nFgjsOFexRNLmHNNaHJTe/JXmvAw4oh9N3E+xTCn4HRDPQEIaPuyUfdcEn3ZAyYd8y+OqegmOnDwrT99ol9y+zhdqUYj9XXXk5F33ju1RUVIzzFSoUEx8pJdFolJ6eHrq7u+np6cmnu7q6aG5rtYcatrXT2dFOqLsHMzvco6Hu9yCGWL20Qj9GYQBHUQBR6EcU+tALfGqooWKnREqJFY5h9kUxQ1FbgPXHsEL2dj4djoG53u+6EHmRlA+5beF1o7kcCJcD4XLasdOB5ra30TXbyiwEIGAgbVnIrInMZCGTRWbstEylsWLJISFhx5EEVn8MMxRBpoa/dBFel/29Ly6wQ0kBRnGBvYZfSQF6UQDhMFBsGiXQxhEl0BSbSyaT4aGHHuJHt/6Ajo52vrG35KIFAsckf8H8ZK3ktrclZR647RDBYVM2bgVMZCWvNcNfVkne74TDp8AdhwoqfIPHVfdK/rQKnq2TmBKOXDif8y6+glNO+wJTpkzZ1pekUIwLUkri8Tj9/f2Ew2H6+/vzYeh2qD9Ed18fPX299PT20tvTQ19vH5G+0AaWLgDhMOzOYIEPvXDAnfyA1SuAFvTZVq8Cr+qAKXZqpGnZQqunH7MnTLa7P582u8Nke8OQyQ4eIEALDHyvbKc1eqHfTg/kFXjRfB6ExzWhhvJaiZQtMgfEZSiK2RfB7I3Y19sbxorEhx2jFfhssVYcQC8uwBiS1osL0Ap8E+oaxwsl0Ebh/vvv5/7776e+vh6ABQsWcMstt3DyyScD8OSTT/LAAw/wwQcf0NPTw9KlS9l333036xxKoCnGSjwe59FHH+X2H95GXUMjJ+0iuHp/g10D5qYPnsDEM5IfviN5ai2cuTt8b5HA69i8B/OSZtvylrbgt8cJ9ikbfnwoJflXo+DZOot32+2RH7tPK+foo49i8WfP4OhjjqG8vHwrXpVCsfmk02mi0eioYajQ6g2F6An10tcXoj/cT39/mEg4TCwaJR6JbrBG11A0t9Pu5LmdCK8LzeMa9kZe99uLJet+D8LvRvpyb+nV0CWFArAXITZ7w2S7w4MirCeMOSDE+iLDxudrfg96SRC9tACjJGiLk9IgelEgv4yD0Cf5W9aNINMZzL6IfY96I5i94bx4s9MRe1jmALqWE2tDBdwQEVdSgOZxjd8FbSeUQBuFZ555Bl3X2W233QB46KGHuOeee1i6dCkLFizgz3/+M+vWrWPKlClcdtllSqAptjqdnZ28+eabPPP3v/PUU0/QH4lx3AzBN/fTmVc4uYczgu3c44pXJM1RuPVgwemfYkmAvqTkylclq3rh/mMFh1SNXFdPQvJOh+DtNsk77ZL6sJ0/e0opBx+0iMMXf4bDjjiS+fPnK2+QOynZbJZUKkUymSSVSuXD0O2xpBOJBMlUklgiSTyZIBaLEY5G8oIrHosRj8VIxOIk4wnMbHbjDdM0NK8L4XGiuV0Ij8vedjvRPK5BseVxYXjcubIu8DjB7cyXVfO5FIqNYyXT61m/bCGWzVnArP7oYGGBPYR3QHiVBG1BUTqwXYDmco7fxUwCpJTIWJJsb3iY5W2YkAtFh4le4XHlh00aQ4ZT6iVBjCkl6H7POF7R1kEJtM2guLiYe+65h0suuSSfV19fz6677qoEmmKTSClJJpPEYjHi8TiRSISenh66urro7u6mu7ubhnXrqFm1jJo1tbR19QIwu1Dj+OkWZ8wxmOGf/MIMYHmP5GsvSbwOW1DtVvjphzMkspJvvCr5sBP+cpJgQcmm6+yISd7rFHzUJfmwU1LdC6aEgMfF/gvmMGv3uUyfPZdpM2YSDAbx+/34fD78fv+wtM/nG3dnJFJKLMvKB9M0h22PlDeWMp/mOMuy8u0a2r6x5G1sXzabJZ1Ok8lkbGGUSpFM23E6nSaVTpFMpUmnU6TSadLpNOlMmnQqTTqTIZNOk81k7DryIUMmlUZaW/YdE7qe94KGoYOR2zYMyOVrbqctklwONJcTkZsborud+VhzO8DpRLoMex6J25GfSzKZHQApFBMBKSVWLGkLsO4hwitnAcv29CNjycEDdM22dJXmhFdOiBkDAqy4QM2l3A5I08Lsjw4Rb+sJud4IVnRwoXmtwIsxpRTHlFKMqXbsmFE+qSxv20OgTfrB6KZp8vjjjxOLxTjkkEO2uJ6BN6sDhMNhHIZgl5lTyf/ujiBlx6xuR9DBYz92rOcdW41bLslHuIYx1rXpcpvXqLGdd+QbJ6WFZUksKe3YsjbwSLg+Ds0ObgM8OuwWhIATnLrF0i5Y2jX6G/YtbeqW1DOW+7KxIv0paIjAvCK4/zgo82ydTqfHEPzyaPjKPyWXviRZWC7pHnxeb6RN9h6HBnsUQyIDsWyKjz5ZxrsfLSNjQnYM1ywAoQk0IdA0gchljnS/NsiSw/PWf6cl1ytg75af4ns2iRD2vbXTYiALNIEQOecwAgRDt0VuHvx6ZUbcFhhucLgdCM05WGZI/fk6tcFtMWx7sG0jYwFpyKQhA0S23e1SKHZmpJRYWYtsxiSbMTHTJhnTwrQgm7EwkxnkEKc2wmHkhxw6dq3Ec8DcnCArwCgrRCv0K6vzBEDoGkaxbS0bDSuVxuzqJ9vaTaalm2xbD6nqBmJLPso7WzGCPpx+J05N4nLoOF3GhJzvtuCSY7fLeSatQFu2bBmHHHIIyWQSv9/PU089xfz587e4vjvvvJPbbrttWF6RC0QiOsoRNmJoB2UTfNqP2Za+oN3s9o3hgLHUuUGZMTZkTMXEiMlRDxbrpXUNdDEkaGAMSesCnDo4c8Js4N5vjZfkY6lji+7vpzxXfRjqwnD0NPjJEeDdyt5NvA7BbxfDOc/b1rAjptreHre0vQNICVnLFmpZyw7mkPRgvrSDtEdjrP95F3KEyrcQOaQuMaRaMSRvtO31P6ubXWbI/jGfd2h963+3xvyMk6OktwE7hrFaodihkNJ+pifNXJyFhNBJSo1UFtKprL2+Vw7hcdrD3kqDuEqC6CUBjOIAenEQvbzQXrhcWaZ3CDSXE21aGY5pZQwd4CizJtn2XtLr2sjUt5Fe1068qRNMC6FpeAvdFDosis0kQaft3HK8+dnHjwL3b/PzTNohjul0msbGRkKhEE888QQPPvggS5YsGSbSNmeI40gWtK+ctYhrLvsihlqraZIhhvUypdBBCCzdAKFj6Q4sw5kLLqSmbx3lNVmR8H8PPc3f/vIc586F7x8o0LfhU7ApIjnneUl5gYMf/OYuPEUlwFARIIb8O8QwMSGGqB6xnmywhcoQK856CCkRZgbNTCMsEyEtO882qyKkhTaQb5kIy0TL75NoQiCQtjWIgY/MTvy5USgUOxWpdIa2rn7aukK0dIZo6eqjvjNEc1eIzq5+Qr3hYQJMC3jz872MEtsJh1FSgFYaRC8Jonknz5A2xfZDZrJkmjrJ1rWRWN1IenUTVjSB5tDZdc50Fi+cwymL5rHL1NLtLuDTqRQ33PELnnijSc1BGyuLFy9m9uzZPPDAA/k8NQdNodg4mUyGyy69mIce/gvfWSi4bMH2WUx7ZY/k/H9K9plRyEsfN+L2Bbb5ORUKhUIxOuFwmIaGBhoaGqivr6dm3Vpq1q6lvr6etuYWwj29g4WFsNfUGuJ0w1laiFEShBLbo5/yLqrYGkhLkm3pIl3dRGJFHalVjZDJUjJ9CiecfBKXnH0eRx911HZxHrY9tcGkHeK4PlLKYRYwhUKxcSKRCGd84XRee/VV7jlC49RZ2+/c80sE9x8Ll/w7xJlHzOfpd9ZiOJQ3LYVCodgWSCnp6enJC7C16+qoXltLbV0djY2NdLa0Eg8PmYCZc6c+4HDDOHw+JaVBtOICtNLcosbKAYdiOyA0gWN6OY7p5fiOX4iVypBa1UDqkzoee/z/+Otv/4CvpJCTTz+Vb130VQ479NAdYmjspBRoN910EyeffDLTp08nEonw6KOP8tprr/Hiiy8C0NvbS2NjI62trQCsXr0agMrKSiorK8et3QrFRKG1tZVTTjqRupqV/G6xxiGV29+QfmCl4N6j4KrXmvnKsXvx8CvL1XBihUKh2AIsy6KtrS0vwNbV17Nq7RrWrqujscEWYJnk4Ets4XTkXc3rFUEc8/entNS2fhklQbSgTzngUExINJcDz7674dl3Nwrk8WTqWkm+W82Tf3uSv/3+YYKVZXzx3HO4/spv5pfjmoxMyiGOl1xyCS+//DJtbW0Eg0H23ntvrr/+eo4//ngA/vSnP3HRRRdtcNwPfvADbr311jGdQw1xVOyoLF++nM+cfCLpUAe/PU4wd5zXbXt6reSm/0hO3Gc6/7dkGb6C4Li2R6FQKCYamUyG5ubmQQG2bh2r69aydl0dTY1NdLW1Y2YGFxQWXpe9/teQ4Yd6SQGi2HZLr/k9O4SVQaEYQFoW6TXNJN+pJvbOCmQize6L9uXqr13Jheeeh9fr/dTn2J7aYFIKtO2BEmiKHZF//OMfnPelc5nmTvHAcYJKz8Rwh/dKk+SaJZIZpT4ef+af7L3osPFukkKhUGw3IpEIjY2NNDU10djYSH19PWvW1eUEWCM9HV3D1gHUC3xoOecbemkQR2khRkkBYmAB5km0ppRCsbWxUhmSH9YQf+MTUtWNOH1eTj33i9x+3U3M2X3OFterBNoEQAk0xY6ElJI77riD73//+xw/A+46XMNnTKyv/po+ydWvS5qjgp/ceh1X3HSnesOrUCgmPel0mpaWlrz4ampqor6hgdp1dTQ0NdLW3EI8MmRJHyHs9b5y4svILb7sKAlCccAWYk41HFyhGAvZjj7ir39C9PWPkPEUex5+ED+49nq+8JlT0TZzGK8SaBMAJdAUOwrpdJqLL7qQ//fIX7lqH8GV+4A2QYVPIiu54z34vxrJ8fvP5jd/fY5Zc+aOd7MUCoViRKSUdHZ25sXXQKitX0d9YwMtTc30dXUPW+Be93vQim1Ph3YI4CgJ2h4Qi/zohX6EruZ/KRRbE5nOEH9nFbGXPyTT2EHR9CquvPJKrrvym2Pu5yuBNgFQAk2xIxAKhfj8aafy1n/e5O7D4ZRdJqYwW5+XGiU/fEcSzghuvvpyrr395ziUAxGFQrEdMU2Tzs5OWlpaaGlpobW1lebmZuoa6qlrqKe5qZmutnYy6XT+GOEwbMvXEAFmFBfgLA0iigOIIj+aS3msVSjGCykl6doW4q98SPz91RguJ6edexZ33fD9TToVUQJtAqAEmmKy09TUxMknHE9zfS2/PgYOKJ9cX/VoRvLzpYK/VFvMqijgnp/8jNPOvVANe1QoFJ8KKSXhcDgvugYEWEtLC/VNjTQ2N9He1k5vVzeWaeaPE7qGFvTnxFcgL74cJUH04gAUB5TzDYViEmH2RYi/+hHR15ZixZIsPPYI7rjx+xx/7HEjfo+VQJsAKIGmmMwsX76ck044HmJd/O44jdkF5qYPmqCs6pXc9T683SY5Ys+Z/PSBhzng0CPHu1kKhWICkk6naW1tHSa8WltbaWxqoqGliZaWFjrb2kklksOO0wNetEI/WtBnz/8q9KMXBXAUBdALA1DoQwt4EZoSXwrFjoZMZ0i8vZLIv98n29LNlDmzuOE713LZVy7E7XbnyymBNgFQAk0xWXn99dc59XOfZYozwW+Pg3L3xPDU+GmQUrKkBf73fUldP5x9/IH8/OGnKa+sGu+mKRSK7YBlWXR3d+eF10Dc2NxEQ1MTzS3NtLe1E+7tG3ac5jTQCwOIQj96bn6XXhTAKAxgFAXQCv2IQh/CMSmXhVUoFFsRKSWplQ3E/v0+yU/W4i0KctFll3LzNd+lsrJSCbSJgBJoisnIY489xgVfOZ/9Si1+dYzAb0x+cTaUrCX52xq4d6lE1w3uv/cuvvjV74x3sxQKxacgFosNs3a1tLTQ1NycG27YTHtbGz2dnZiZ7OBBmsAo8NnCKye69EI/emEAZ0kAEQzYwsvrUkMOFQrFZpNp7yX+0gfE3lyGsCTHnf4ZvnfVtzn6qKOUQBtPlEBTTCai0Sjf/e53eeCBB/jsLMGdh2k4tbF9taWUtMagMw5ZC2YUQLmHCd2p6UpIbvmvvX7a2Ufvw/1/e4miktLxbpZCocgxYPFqa2ujvb19WFzf0kxLawvt7e30dHaRjMWHHat53bmhhrbw0gr9GIUBHMUFeYuXVuBTng4VkxIpJWRNZDqLTGewUhlkOoNMZwEJAz/dQ37ChUNHOB0IlwPhdKC5HWqphe2EFUuSeOMTIi9/gNkTxn/SgbQ89m8l0MYLJdAUkwEpJU888QTf/tY36erq4KYDJGftvmlxFUpJXmuGJc2St9uhd/h0DGYE4Iu7C740F/zOiSnUpJQ8vRZuf1ficzt44IHfcOq5F493sxSKHRLLsgiFQnR3d28QOjs7ae3soK3DFmBdnZ2EunuHOdgA0H0eRNCHHvTZAizoQwv6cRT6MYpy87wKlZdDxeRAZk2sSBwrmsCKJe0QT2LFElixFDKfTmLFU/m0TKTA+vRdb+FyoAW8ueCx51EW+GwHNiX2+nl6SRDNqxYt3xpI0yK5tAbPAfNYecZNSqCNF0qgKSYyUkqeffZZfvD977H042UcPU1w88EG032jOwPpiEteaoR/N0rebbd/H/YsgcOmwL5lgml+0ATU9dtlXqiHoAuuXSg4ddbEtai1xyQ3/xfeaJF8+aRDuPfhv1NaVjbezVIoJiRSSqLRKH19fYRCIfr6+oalu7u7aevsoL2zg86uLnp6eujr6SUS6t9AcEFOdPndaH6P3VEM+tCDfvSgD0dRAK3ABwVe9KCa56WY2EgpkYkUZn/MFl7hOGYkjpXbNsMxrPBAfgwZT41Yj3A70XzuXPAgvK58WvO67e28NczIW8WEw4CB39mBn1shQEpkxsxZ2WxLm0yksKIJzHDcbmsumOE4Zm8YzMHpDcLrwigJopcXYlQUY1QUYVQWY1QUowWU19EtQQm0cUQJNMVEJBqN8uijj/Lr+37B0o+XsahS45v7Cg6sGPlrLKXkjVZ4aKXkzVYwBBxUCcfPFBw3Hcq9oz+YW6OSu9+XvNgA+5fDrQcL5hZNzAe5lJK/1cKd70k0TeeqS77MVd+/i8rKyvFumkKxVZFSEovFiEQihMNhwuEw/f39w0RWb28v3b09dPf10tPbS29vH/2hPiL9YWKRCGZ25Bc5mtuJ8HlyYisnuPwee2HlgBcj4EX3exF+NwQ8dudTDTNUTGBk1sQKx2yhNSCuwrG8mLFyosuM2PFQYQOAJtACPvRgzlJV4MtZqoak/R7EgCDzusf9OyEtidUfJdsTxuzpx+wJY3b3k+3sI9vRZwu4XJdBeFw5sZYTbeW5uLIYza0s2aOhBNoo3H///dx///3U19cDsGDBAm655RZOPvlkwP4Bu+222/jtb39LX18fBx10EL/61a9YsGDBmM+hBJpiopDNZlmyZAmPPfYYf/1/fyGWSHDkVMFF8zUOrrRGfftVG7IXe36n3baUfWmuYPEMCLo2T2T9t03yo3ckDWH42t5w+V4Cpz4xhVpvUnL/MsH/rbYwpeDw/edz0ulncdLnTmfPPfdE01RnUrF9kFKSTCZJJBIkEolh6aF5sVgsL7QikQih/n56Qn2Ewv309/fbQiwSIRaJEI/GSMTiSGsU5z9CoHnst/XC6xp8W5/rOA7Ehs+D7nOD14X02OU0rwth6Nv3JikUm8nWsHIJj9MWXTmRpRV47XQ+b1B8Ca97h1taQaYzZDtDZDt6ybb32XGHHVvhwfmgenEAo6oEo6oER1UJxhQ7rQW8O73VTQm0UXjmmWfQdT2/4vdDDz3EPffcw9KlS1mwYAF33303t99+O3/605+YM2cO//M//8Prr7/O6tWrCQQCYzqHEmiK8aSrq4tXXnmFf774Iv94+kl6QmGmBjROn2VxxhwHU72jD2WUUvL/quF/P5BU+eDGRYKjpn66IYppU/LAMskDy+z5aXcdLti7dOI+oPtTkmfWCV5rtnivHZImeF0O5u46jfnzF7D7/L2Zvutspk6dyrRp05g2bRoFBQU7/Y/OjoplWRsVSRvbHsiLx+PEE3Fi8TjxRCIX4iTiCRJJu0wqkSSZTJJOpsik02NvoKbZ1iuP0x4e5XYiPK5h6YH9useN7naieVzgcYHLQLhzoszj2uE6k4odH2lZtqDqjw1atDbHyqVraH7vhlauoL12nS2+csNvA1411HYjWPGULdhae8i095Bt7SHb1kO2sy8/b0743LZgGyreqkrQSwsQO8lLUCXQNoPi4mLuueceLr74YqZMmcLVV1/N9ddfD0AqlaKiooK7776br33ta2OqTwk0xbZkYJhSZ2cnXV1dtLa2smrVKlYuX8bHH77P8tW1AMwq1Fg8zeLEXQ0WFJmbFBApU/L9tyR/r4Mvz7Pnj7mNrddhq+mT3PgfSXUvfHeh4ML5E3du2gApU/JhJ6zq06jtM1nbD80R6F7PMYrP7WRKeQlTp05h2i6zmTZzFtOnT8+LuKlTp1JaWophTKwfdynlBsGyrDFvb07ZrV1XJpMhlUqRTqc3GSeTSZKpFPFkgmQ6RSKRtAXTMJGUIpUTVKlkkkwqTTqZIpvJbNY9FYZuzxFxGmAYCKcODgMxEJx2TK6MMHIe1pzDy2hOB7rDgXDq+Tkm0tCRQ8u5DHvfBP8eKRSbi8yamL0RzFAUqz+K2R/D7I9i9cfsvHAMM2QLMdbrigqPc8hwwp3PyjXRkFnTtrLlRFumLSfc2nqR6dzz1WHgqCzGqCoeFG9TSjEqi3c467wSaGPANE0ef/xxLrjgApYuXYrb7Wb27Nl8+OGH7Lfffvlyp512GoWFhTz00EMj1pNKpUilBk3h4XCYWbvOpDAY2OCLv/E7Nnznp7q7csTkqDmbrG4z2r3R3E2cWm6swBbcj403e+MVbl77t+CebrAx9J82pGM6kLbsdNa0Nmi6LsBrgMeAoBMKXOAa4Zk2WivTJtSGIJ6FOw+Dz83aNm+y0qbk3g8lf1gJe5XY7d0YY7mrm/qejKmOzTiPxH4JmzYhbUHGgoyZi20PyHY8ykgyTdjCVAgBQuTndUuZn9M9pFVi8DuxXiPlYJFhN2H4R0lu8NHaGRCC3L0V9jN4yD3P79OGBCHQtCHlh6SFJtCEAI3Bcvn9GkKQLzdwToVCsXGkJcmms2RTWbIZk2zGxLTAzJpkU1msxHpWZENDD/qHOZLRCv35oYZ60IdWFFBWrkmEtCRmb9gWbi3dZNp7beHW2oMVTdiFdA1H0IvD48ShgdOp4XA70Y3JaW3b/aJjeeqiX25zgTZpvwHLli3jkEMOIZlM4vf7eeqpp5g/fz5vvfUWABUVFcPKV1RU0NDQMGp9d955J7fddtuwvKAT0pH+jbZjTD/jYyi0NboDn7YtI+0arfhWae9W6gNtqprNOc/W7Jbl+pBomt2hz/Ux0QT2Q0oHZy525dK5/v5mtzOahk96wW3AwyfC/uXb7sHn1AXXLxIcXCW57k2JU4MDKmBj86LHcl+3xv9xzOcZ8v8Y+D8xJJ3PB5IWJDK28E1btmjLWpCxJFlLkrXA3Ih4Enx6bTW0joG0GKWAEMPPt/49EeunxSj7Rskftr2+w7H1y62Xsf7+od8JMfR/ki8o+dR3z8oFhUKx2UhpP/eSWUhkISE14sIgmZGkk5nBr6eu2e7dS4PoxQW4SgrsxcOLAuilBeiFAbVo+A6I0ARGaRCjNAh7zhq2z4zEybZ2k2noINPcRaa5i3hLN+QWnDe8TjweAz9ZAlYav2Pkl9ITjfuX/gUu+uU2P8+ktaCl02kaGxsJhUI88cQTPPjggyxZsoRQKMRhhx1Ga2srVVVV+fKXXXYZTU1NvPjiiyPWN5IF7fLzD+Kys09EN9RigIqNYL/ORwqB1HSk0JGaZqc1AysXS03H0g0sw42l6VtNodbVNPLjm39FpSPFA4sFU73b7yvdHpN88zXJyl647MtHc8jZnx/DD7DYyBYIxMjiQNj7IGdJGXqEAG1gH0OsLLnjdaHly+o5BSZytdl1Dab1IRYUjcG0nq9QIKREz6YQ6QQim0JkMyAtO+StZTKXJ0GaCNMc3D/aYzf3WUJodgCkADFgQhuoc7Q6JufjXKFQjDPReIqGtl7qc6GmtZe6tl7a2/rIDAxh0zWM0kJ7CFtFEY7KYhyVxWjlRWhB304z/0ix5UjTsue4NXWSbeoi1dRJprEDqz8GgK/Iz+xZVRwwu4pFsytZMKuKAr97nFs9SDqd5n9++Qf+/HKjGuI4VhYvXszs2bO5/vrrt2iI4/qoOWiKycCSJUv43GdOYbYvyQPHaRQ6t7+pIG1K7nwfHqmWnHf8Afz26SV4vd7t3g6FQqFQjE46naauro6amhrWrFnDilWrWF69ktqaNfR1defL6UEfem6dLEdlMc6qEkRFEUZpcIebS6SYGJh9ETJ1bWTq20nVtZKub0PmhshW7DKdgw49hM8cewLHHX00s2bNGjdL7PbUBpN2iOP6SClJpVLsuuuuVFZW8u9//zsv0NLpNEuWLOHuu+8e51YqFFuP5557jjPP+AL7FWf41bECnzE+47icuuAHB8E+pXDLK+/z8Zwq/vbcy8zd54BxaY9CoVDsrGQyGRobG/MirKamhmWrVrKmdg3tza35JRo0lwN9YO2rg+dQUnWIvXBxRRGa1zXOV6HY2dCLAugLA7gXziFAbn5jRy/Zde3E6lp5/vVX+cdf/w8kBEqL2e+gRZxy7GJOPHYxe+21F7q+4704mJQC7aabbuLkk09m+vTpRCIRHn30UV577TVefPFFhBBcffXV3HHHHey+++7svvvu3HHHHXi9Xr70pS+Nd9MViq3Co48+yvlf/jJHT7X46ZFiQozbPn22YF4RfPO1MAsXHchPb7uOy264U805UCgUiq1ELBajsbGRhoaGfFi7ro7adXU0NjbR09GZF2HCYWCUF6KXF2HMn0LwmAW4KksQ5YVohX71bFZMWIQmcORc+HsOXUAQsGJJ0rUtZNY08/bq5bz+4r+5wbRwB/wccMShnHHK5zjtlM+w6667jnfztwqTcojjJZdcwssvv0xbWxvBYJC9996b66+/nuOPPx4YXKj6gQceGLZQ9Z577jnmc6ghjoqJygMPPMAVV1zBabPg9kPBmGDuhaMZye3vwpO1ktMOnssf/v4axeWV490shUKhmNBIKenr66O+vj4vvtbV17Ombi3rGuppaWom0hfKlxeahl4UQCsJoJcEMUoKMEqCOMoKEeWF6MUFyv28YodFpjOk17WRqW4isbyOdF0bSEnJ9CkcdewxfOm0Mzj5xBO36pSL7akNJqVA2x4ogaaYaEgpueOOO7j55ps5fx7cdGDOdfgE5fl1klv+Kwl4DB763W9YfNYl490khUKhGDeklLS3t+fFV319PbXr6qipW0tjQwPtza0k4/F8eeEw0EsKciEnwEqDOEoLEcUB2wq2Mfe5CsVOhBVPklrVSHplPYkV6zA7Q+guJ/sfcQhfOetczvn8GZSWln6qcyiBNgFQAk0xkchkMlx++eX84Q9/4Kp9BV/fe+IvEA3QEpVc+yYs7ZR8+6xjuePhF3A4nePdLIVCodgmSCnp7OzMzwFbXVPD8uqVrK5ZQ1N9PelEMl9W87rQim3RpZcUYJQU4CgtQispQCsJoAW8k+I5r1BMRDLtvaQ/qiX+YQ3ptS0gBHP235tzzziLy86/gKlTp252nUqgTQCUQFNMFMLhMGeeeQavvvwytx9qz/WaTJiW5IFlcN/Hkn2mF/DXf/ybOfscON7NUigUii0mHo+zZs0aqqurqa6u5pMVy1m5upqGtetIxGL5cnpJAUZ5EUZFEXpFEa7KErTiAluEKWccCsV2weyPkfq4lsSHa0iurAfTYt6B+/HVCy7ionO/TGFh4ZjqUQJtAqAEmmIisGzZMs78wum0Nzfwi6Mkh0ziqVxLOyXffUPSlxI8+LM7OOfKG8a7SQqFQjEqA0MSB0RYdXU1H69YTvXqajqaW/Pl9AIfemURRkUxRmUxropitPJC9PJChFOto6pQTCSseIrkB6uJv72SVHUDmmFwwDFH8M1LvsaZp52OyzX6ixMl0CYASqApxps//vGPXHnF5cz0m/zsKI1ZAXO8m/SpiaYlN70F/2yQfOvcE/nxw89iGJPSmaxCodgBkFLS3d3N2rVrqauro66ujpXVq1i2aiV1NbXEo1HAdshhlBeiV5VgVBbjqCzBNcVeH0zze8b5KhQKxZZg9kVIvLuK+H9XkmnswF3g59Szz+Tmb36bvfbca4PySqBNAJRAU4wXvb29fPOqb/D/HvkrX9xdcPNBGm59x/maSin540r48QeSg+dO4fF//5eqaTPGu1kKhWIHQ0pJOBymra2N9vZ22tvbaWtro7m5mZU1q6mtW0tzQyPJ2KBjDj3gRSsvxFFZjFFVgquqFL2yGK1MLdKsUOzIZFq6Sby5jNh/lmFFE8zYax5XffUKvnbBRQQCAUAJtAmBEmiK8eAf//gHX7vsUuLhXr6/yOLUWZNrvtnm8G675JolEt3h4K//7y8c87mzxrtJCoViEpDJZOjo6Bgmugbi1rY2mltbaGtvo7uji3QqNexYzelAK/SjlwUxyuxhiK6yYns4YmkBmkfNC1ModmZk1iT5US3xNz4hubwOw+XimFNP4earrmGfvfamsLBQCbTxRAk0xfakra2N73z7Gv766GMcM13jtkN1KtyTf0jjpuiIS65eYs9P+9oZi7nr938jGAyOd7MUCsV2RkpJf3//MME1kG5pbaWprYW2tja6Ojrp7+0bfrAQOAp8aEEfFHjRgz60oB896EMP+nAUBvL7hNupPCMqFIoxke0Nk/zPcqKvf4zZE6Z0l2mEEjF6auqVQBsvlEBTbA+y2Sz33Xcft9z8PRxWiusXWpw2a3K40P80hFKSjjhEM6Ajea0Z/rAC/B4HN3znm3ztuz/IDylQKBSTEykl0WiUzs7OfBiwfDW3ttLU2kxLayudHZ30dnWTWd/a5XKgB/2IAq9t9Qr60At8aIV+HEE/RqEfEfSB36PWA1PsMEgpIWsisybStPJpTBOZtZCmCaY19IBcPKyWwaQQYOgIQ0c4DHuobi4Whg66tsP3OT4t0pKkVjWQePMTCi/7LKu+eLMSaOOFEmiKbYmUkmeeeYabbrieldXVnDsHrl5oEHRYmz54kmFakk+64Y1WyfJuqO6DjviG5dw6TPXb+yyhc9E5X+AbN/6QefPmbf9GKxSKYUgpSSaThEIh+vr6CIVChEIhenp68sKrtaOdlvY2Ojo66enuoq+7dwPRhRDoBV60oA+twLZwaYV+9ALb0qUX+qHAg1bgU8MNFTsEViyJ2RfB7I9hhWODcTiOFYkjEymsZBqZC1YiNVyAbWsMHc3ntoPXjeb3ILxu9IAHvSiAXhRAKwqgFwfQC3xqLiaw8oyblEAbL5RAU2wLpJS8/PLLfO+mG3j3vQ84eIrGtQs19izesYSZJSXvtMOTtZLXWyCUgqAT9i2DecUwt0gwzQ9+B6Qt6IxDTQjeaZe802bnlXkgloGpFWWce96X+eL5F7NgwQL1pk+h2EIymUxeWA0VWeunO3t76O3tpS/URyjUTzgUIhqOkM1kRqxXczvtRZULvLaFK+C1RVjAiyPot/cFvIgCj93505S1S7HjINMZsp0hst39mF0hsj39mF39mN39ZLv7kYnhLymEy4kWHPyuCI8LzeNEuJ1obhfC40RzOcGhI/SclcvQEPqQWNcQDPktHEiO9PtoSaRpIjMmMpu1LXK5tMyatiiMJfNBxpNY0QRmJI7VF0WmM8POowX99tp+5YUYFbk1/sqLMMoKd5qXKkqgjSNKoCm2JuFwmD//+c/86pe/YNXqGvYp17hmXzikarxbtnUJpSSP1cDjNZKmKOxaACftAkdNFexdCrq2aXEVzUhea4IXG2xxlzKhyGUPh6wqLeTzp32OU878MkceeSRut3vbX5RCMQEwTZNIJEIkEiEcDufjgTAgsrp7e+nu7aG7t4e+UIj+/n4i/f1E+sOkE8mRKxcC3etGeF0Irwstl9Y8LjSfG+F1o3ldGF43ms+D5nWDx4Hw5va51Fpfih0bKSVWOE62vZdsWw+Z9h6ybXba7OkfHFFo6BilQfTSoB2XFaKXFKAXF9jW4gLfpPq+SCmR8ZRtARwIPWGyXSGyHX1kO3qR8UEBqhX4MCqLcEwpxZhamo/1At84XsXWRwm0Ubjzzjt58sknqa6uxuPxcOihh3L33Xczd+7cfJmOjg6uv/56/vWvfxEKhTjyyCP55S9/ye677z6mcyiBpvi0RKNRnn/+eR7/v8d4/rnnSKXTLJ4uOXeezsEV1g5lCeqMS/64UvLoajAlfGYXOHOOYP+yTzefLpaRLGm2xdqSZkiaUOK24wwaRyzci5NP+yJHHncC++yzD06nc+tdlEKxEaSUZDIZkskkyWSSRCKRT68fNrYvHo8TSwyEBLF4nEg0SiQcJhaNEo1EScRipOKJjbZHcztzgskWWJrXlRdWA9u6z4PudecElhO8OQHmciLG8PJEodjRkVJi9kbINneRaenKiTFbiOWFiCYwyoowquyFyY2qEnuR8rIgWtC/032XrGjCFmududDWS6a1m2x7L2RtZ2ea3zNMsDmmluGYXo7mnZwWNyXQRuGkk07inHPOYdGiRWSzWb73ve+xbNkyVq5cic/nQ0rJoYceisPh4Cc/+QkFBQX89Kc/5cUXX8yX2RRKoCk2l3A4zNKlS1myZAmvvvQv3n7nXZLpDAtKNU6cITl9N50Kz441lDGclvzmE8mfV4FLh/PmwVf2EJR4tv4PVCwjWdICL9bblrVEFkrd4NDseWuGobHfHrtx8GGHc+ARx7HnnnsyZ84cZWXbAZBSYpommUyGdDpNJpPJh/W3RyszkmBKJBLEEwliiTjxRIJ4TiQNE1SpJKlkilQqRTqZJJ1KkUmlyaTSbPbPpxBoToc9Ud+hg2Ngsr69LRwGOAxbbLmd9rAnjwvN7UT35CxabhfC7QS3A9xONI+9rZxkKBSbhxlNkG3pItNsh2xLN5mWLmQiDYBwO23xVVWCo6oYo7LEFmXlRWoe1hiQpmULtpZuW7C1dpNpyQm33Bw7vaII58xKHLtU2PHMCvsF0gRHCbQx0tXVRXl5OUuWLOHII4+kpqaGuXPnsnz5chYsWADYw0PKy8u5++67ufTSSzdZpxJoivUxTZNYLEZraytNTU00NjaycuVKVnz8IStWrKC5vQuAApfGAeUWB1UIjttFZ7pvxxJlABlL8tfV8KuPJWkTLlkguGA+BJzb581hMit5ux1eaZK82gSdCXs+24wA9CahPeeERBOCXaeUMW/ObsyeswdTZ+3O1KnTmDp1KlVVVRQUFOD3+/H5fGjbcV6MlDIfLMsaMYy2b0uO+TT7NiV8BtKpdDoXUvl0Op0mnbHTmUyGdCZNJp07Lpshm8mQyWRzcYZsJks2a8dmNksmk8HMpbcWA+JIGIY9x8Nh2MLIaeQ9nA3kDRVNIrdfc6wXO3P7HTrC4QBjQHiNIL6UtzSFYrsjM1kyrd1kmrrylrFMczdWf9QuoGu2CJtWlg/G1DL04oD6vm4DZNYk29FLpr6DdEM7mYYOMo0dyJQ9182YUoJz92m4dp+Gc8509JKCCfd/2B4CzdimtW8n+vv7ASguLgYglfMaNfTNua7rOJ1O3nzzzREFWiqVyh8HtkAzDMG8ubM2+GBsrqTdIg08wiFypMxNHTbKuTdZ04gFRqnrU0r8Ydc1Sl1jOsUYGyLXTw1kSIk50DE1BzqoEjPXeR2percOPocd9igGvwE+h1327XbJ2+0b71iOpcVjuawx1bOVzpXIQm0Iwmn44u7w9b2h0r99H55uQ3D0NDh6mkAeLFnRCy83Sl5qssVZoQsqvRDPSOJ9nbz5n05efv0tMpY9BHMkhLCHY+avRDDyJGxGvtZ8tTL3ic79GZae9K/Dcghhz0UfKdZE/pkphEBoA/uFfU/tGz3kuMF8IQRoINwCXRgYwtjwOG2wbgRo2pD95I4f6XzaYN7GsYA0mGkwgVGmbcEYqlIoFNsVM2OSSaTJxNOkU1kyqSzZaCr/46aXBnFML8d3xF4YU0ptMVZZrCxi2xFh6PYQx6lleA/bEwBpWWTbe8msayNV20K6toX4ko8B0H1uXCV+XA4Nt9+JPs7/q5kXHrddzjPpLWhSSk477TT6+vp44403ANtT1e67786BBx7IAw88gM/n46c//Sk33ngjJ5xwAv/85z83qOfWW2/ltttuG5YXGOs8zjH8Sm/pD/mWHLfRYzajwq3V+digni2oeKvfh5HKC9ByQR8hPRA7dVuYuQ17+9O2YUxlttJn7NO8hJISWqK2t8VKL/zvEbB/+cQaViWlLdYer5E8t852MLJvGRQ6weWw75ElIZWFlGWPsrAkZCVkc2mw9dTAk9HKpeUIsSXXS+diAfl/SN651nox62kFMVoew/9vYr3yw+ocJX+kcw47ZqR6huQN6Jox6RuFQqHYxkhpz0WOZyGegTg68Sxk0/acJ+E0MKaX45xejmO6LQaMGRVobjVPebJgRROk1jSTrm4gubKBbEs3AO4iH0HDpMRM4jU+Xb9mS/jLSRqVv+hTQxw3xde//nWee+453nzzTaZNm5bP/+CDD7jkkkv4+OOP0XWdxYsX54cwPf/88xvUM5IF7eqLD+bLnz1i3NW6Yusj7e4mUgikpiOFhqU7hgVTdyAHYk1nZ+6aRsMxHr7vMd5/t5ozd4cbFwn8jol9P6JpycOr4I8rJSkTzjhxX0776lfweDzriQ6BEFruEwEatnVHSAtNSju2THTLRJgZNDNtx9k0mpVFWCZYZk6p7XjDWRUKhWI8iSfTrGnuoaahm+rGblY0dlHf3EM6bY9QMYr86NMrcEwvxzWjDGN6OXp5kVrOYQfD7IuQWrGO5PJ1JJfVIRNpguWFHHnAbM5YOIu9Zlds86GQmXSan/z+MX73zwYl0DbGVVddxdNPP83rr7/OrrvuOmKZ/v5+0uk0ZWVlHHTQQRxwwAH86le/2mTdag6aQmHz0ksv8ZXzziHe38uPDrUdnkwmwmnJH1bA75dLKoJufn7vT/n8BVeMd7MUCoVCMYRQKER1dXU+fLJiOcuWL6eloREpJULXMKaUYkwrwzGjHPeMSrRppegB73g3XbGdkVmT1Mp6kh+uIfFhDVY0QfG0Ks4570t856tXMmvWrG1y3u2pDSalQJNSctVVV/HUU0/x2muvjcl1/po1a5g3bx4vvPACJ5xwwibLK4Gm2NlJpVLceMMN3Puzn3FIFdx9uE6Fd9I9LvLUhyU/egfebJV85qA9+M1jzzFt5sgvdhQKhUKx9bEsi+bmZqqrq1m1ahXV1dUsX7mSVdWr6OnsypdzlAbRci7sXdPLcc6sRKsssp3tKBRDkKZFuqaJxH9XEH9vNTKVZreF+/CNS7/GZV+5AK936wl4JdA2wZVXXskjjzzC3//+92FrnwWDQTweDwCPP/44ZWVlzJgxg2XLlvGtb32LhQsX8sQTT4zpHEqgKXZmVqxYwblnf5HV1dVcsx9cON/2iDjZkVLyQgPc/o4kaWn8z83f5arv3bldPTgqFArFjk4ymWTNmjUbWMRqa9aQTNhr+mkOA2dVCaIit6ZYVQnuKWVQXjipFnNWTBysVJrkh2uI/2c5qVX1OP0+Tj3ni9x+7Y3M2X3Op65fCbRNMNoY0z/+8Y9ceOGFAPziF7/gnnvuoaOjg6qqKr7yla/w/e9/f8wL2SqBptgZkVJy3333cd2132WaN8tPjtSYV7jjzavqT0nu+dB2JnLgnCn8/rF/sOe+C8e7WQqFQjGp6OnpGW4NW7WSlStX0tLYhGXZvx2OAh96ZXHOIlaMo6oEV1UplATUPDHFNiPbGSK+5COir3+MjCWZf9iB3HbdjZzxudO2eK6aEmgTACXQFDsb7e3tXHThBbz4z3/x5Xlw7UIN9w4+muTddsn3/ytpicJ3v/ZlfnDvg7hcrvFulkKhUEwYTNOkoaEhbwlbsXIFH69YQW1NDf29fXYhTeAsK0JUFNmLOleV4JpShlFZBH7P+F6AYqdGpjPE36sm9vKHZOrbKd9tF266/nq+fuGlGMbmdXKUQJsAKIGm2Jl49tlnufjCr2Alwtx5qOSoqePdou1HypT8+hN4cJlkRpmfe37yMz5/3sUTbmFMhUKh2JaYpsm6detYsWIFy5cvZ+myT/hk+TLqa+vI5Lxcay4Hem5umFFZjHNKKc7KEkRFoZofppjQSClJ1zQRff4dksvq8FeU8o1vfZPvf+s7Y56npgTaBEAJNMXOQDwe5zvf+Q6/+c1vOGa64PbDdEpcO96QxrFQ0ye54z34b5vk0AUz+en9f+KgI44e72YpFArFVsWyrGFC7INPPuaT5ctoqF1LJpUGQPO6MaaU2Is5Ty3FNbUUUVGMXhTILxavUExWMk2dRF94l/i7K3H6fVzwtUv535t+QGFh4UaPUwJtAqAEmmJH55NPPuGcs85kXd1ablgoOWfO6PM7dxaklLzeAj/+QFITgpMP3pMf/uwBDjjo0PFumkKhUGwWlmVRX1/PihUrWLFiBUs/+ZiPli2jvraWdDJnEfO6bNf1U0pta9i0MrSqYrRC/07/e6DY8cl29xP753tEX/8Yw+ngK1+7lB/ffBtFRUUjllcCbQKgBJpiR2XAEci13/0OuwYsfnKkYLeCndNqNhqmJXl2Hfz6E0l9WAk1hUIxcclms9TV1bFq1SpWrlzJJ8uX8fGK5dTVrCGVSAKgeVy2RWxqKY4ppbimlqFNKVFCTKEAzFCU2IvvEn1tKbphcN5lF/OT7/+QkpKSYeWUQJsAKIGm2BHp6uri4osu5Nnnnuf8PWxHIC59vFs1cclakufWF2r3/oYDDj5svJumUCh2MlKpFGvWrGHlypWsWrUqL8QaauvIZjIA6D43elUJRlWJPTRxShnalGK0ooASYgrFJjD7Y7ZQe/VDdN3gS5ddxE9v+VFeqCmBNgFQAk2xo/Hyyy9z/nlfIhXp5Y5DLI6ZNt4tmjyMKNSURU2hUGxlIpEIa9euZe3atdTW1rKmtpaVNatZu7aWrtZ2BrpsRoEPbUoJjqoSjCm2x0S9qhgR9CkhplB8SsxwzB76+PKH6LrGOZdcyL0/+B+cTqcSaOONEmiKHYVwOMzNN9/Mfffdx8FVgv89QqfcbY53syYl6wu1kw7ekxtvv5cjjjlOdYoUCsVGsSyLzs5OmpqaaGpqorm5mcbGRtY21FPXUE9j3TpCPb358rrHhVZeiFFWiF5RhKOsyF7YuaoYXbmuVyi2OWY4Tvxf7xF5+QM0ofH588/lb7/7kxJo44kSaIrJjpSSRx99lG9f/S3CoV6u3NPkhJkQywhMCYUuqPSBQ3nk2mwGhNr9n0jWhWGf2VO4+rqbOOcrl+B2u8e7eQqFYjuSTCbp7Oyko6NjWGhrb6eprYXmlhZamlvobu/ID0UEEA4DvTiAVhRALy7AKAvirCjGUV4EZUE0v0e9+NnKSMtCprO5kAHLQpoWWBKkRFq5tGWBlKBpoGkIXQNN5GINdM3+3+gawtBB1+1YE+p/tgNiRuLE//U+kZc+oPTas6m99j4l0MYLJdAUk5VsNsvPf/5zfvXLX9LY2ECxG0wJvckNy3oNWFQB580THDlVeXHcXCwpeaMFHl4Fb7ZK/G6Dzx53BF+67JuccNLJatFrhWKSkM1miUajRKNR+vv76e3tpa+vj97e3ny6u6eHzp4uOnt66O3pIRQK0dfdQyIa26A+3e9BFHjRCnzoQZ8twIoDOIqD6MUFUORHCygBtimkJZHxJFY8hZVIIRPrx+lh21YijUyl8wJMZrLIVBaZySDTWchu49EjgrxYE4YOuVjoOhjaYJ6uIwwNDCNXVht+XE74DS2fz9O1XN6QOFefXU5HOA2Ey4HmdiJcToTbgdC0bXvtOwFmNIHu97DyjJuUQBsvlEBTTDZef/11br75e7z39lskMxYCmB2E+SUws0BQ5YUqHxS4QANCKVjeA/9qkCzrgX3L4PZDBbsVqg7DlrCuX/Jcg8bzdSZr+8Ht0Dl0/wUc/5nPc8JnTmXfffdFUz+QCsVmI6UkHo8Ti8VIJBL5EI/HR92Ox+PE43Gi8TjReIxwNEIkEiUSjdhCLBIlHosRj8VIxuP59b9GQnM70XxuhNeN8LnRBoLXjRbwYgT9GEE/WoEH/B60gNfuWCs2QEqJTKQx+yKYoShWKIoZiWNFE1jROFY0aceRRC4vYVuyRkLX0DwuhNdlx55c7HLYAsXpQDhsoSIcuW2nMbjPadiiRctZxzQxmBZ2nLeomVYuPWBxs8DMWdxME5m1kFkTsibSNHNpC5nNIrMDZXL7s6ZdRz49kG/Z6czQOnKxaeXy7LqwNr/rbos2py3cvG40vxvN50Hze+zPs98zuF3gRS/0oxX4bAGoGIYSaOOIEmiKiY6UkpqaGu69917+9thf6QmFCTrhtNlwzDTBPmXgc2xabEkpebsdbntb0hyFGw4QnDdPWdO2FCkltSF4s03jrVaT9zogkYUiv5uFe+3BgYcfzUGHH82iRYuoqqoa7+YqFNuNRCKRt0YNDV093bR3d9HZ3U1PXy89vT3094UIh/qJhMPE+yOY2ezYTqJpaE4DhnbQB9JuJ5rLjm2rghPN7UBzOdHcLluIue39cogIU2Jr7FiJFGZXP9nuEGZ3P2YoaocBQdYXtYcWDkG4nbYoCHjtOBf0fJ4tjjWPC83rQrhzscMYp6scf6QlbdE3IPRyAo6cyJPprG1JTKaRqQxWcr10PGkL4NhAnMCKJjf43yBAC3jRg360oA+90G+H0iB6aRCj1LYI72zfESXQRuHOO+/kySefpLq6Go/Hw6GHHsrdd9/N3Llz82Wi0Sg33HADTz/9ND09Peyyyy5885vf5IorrhjTOZRAU0xE+vr6eOONN3jhhRd4+om/0d3dTVbCgmI4fw84ZVeBS98yYZUyJfd8IPnzKjhxpm1NCziVSPu0pE3Jx13wTqfGsi6TZd3QkxtuWl7oZ+7sXdhjwZ7M3/dA9liwgD322INp06YpgayYcAxYsvr7++nv788PARwQWr29vXR0d9HR0013b4+d39tHtL+fWDhCdv3OX46BN/piwCo11ELlc2P4PWheD8JlQE5wSYexnnUkt72TdRS3NzJrYvb0k+3qx+wKke0ejLNdIWRscCy9cBpohQG7Q1+U69gXBdAK/ehD8ndmoTXRkJksVjSB2R+zBXV/FLM/hpXbNvttkW2GIjCgHoRALw6glwQxyoLo5UU4ppTYC6CXF+2QFjgl0EbhpJNO4pxzzmHRokVks1m+973vsWzZMlauXInP5wPgsssu49VXX+XBBx9kl1124V//+hdXXnklTzzxBKeddtomz6EEmmK8iUQirFy5knfffZd33v4v77z1JrX1TQD4HBDLwPxiuGY/OGLq1puY/O8GyY3/kQRdcPfhggMqlFDYmkgpaYvBsh5BdR/UhSzqwlAfhnRueoTX5WD3mVPZY489mL/vIubvtTfz5s1jt912U/PaFKNiWRbZbJZMJjMsJJPJYcP+RkvHYjH6+vvpDfXSGwoRCoUI5axY0UiYeCSKOcocHuEwcsMAXfYwqdyQQM3rQvd70H0eNK8bvK7BYYI5AaZE1cRCWhZmb4Rsey/Zjl6y7X25uBezJzw45FATOecmhYPWlLJCu5NeWohW4FUvmnZQbKEetgV6d06od/fbQr29d1CoG7q9FMTUUhxTy3DsUolz10r7WTCJUQJtjHR1dVFeXs6SJUs48sgjAdhzzz05++yz+f73v58vt3DhQk455RR+9KMfbbJOJdAU25pkMkkoFKKtrS3vdrmmpoZVnyxl5apVtHR0A+DUBXsUw5ygpC4MH3TCrgXwrf0EJ84EbRv8ADZFJNe+IVnaBWfuDlfsLZjmVz+02xLTkrREoS4sWBsW1IVM6vqhrt+eLwggBJQVBqgoLaaisoKqqqmUVlThKyjC6/Ph8Xjwer14PJ5hwe12D4uHph0Ox6TsREkphwXLsjbIGylsqpxpmhsInEwmM6Lw2dxy6XSaTCZDKpMmnc6Qydp56cyQOJMetd5sJks2m9vOZDFzeWY2SzabtefDbAHC5UBzOmzrlMc5OJ9n6NwerwvN68bw2MIKjxO8zkGR5XRs5f+wYltjRhNk23pyQmxQhGU7+gadaegaRnkRRmUxRkURRkVRToQVohcFlLhWbICUEiscI9PSTbalm0xrLm7pQibsuZ5GRTGOWVU4Z0/BNXcGxpSSSfU7pATaGKmtrWX33Xdn2bJl7LnnngBcfvnlfPDBBzz99NNMmTKF1157jVNPPZUXXniBww8/fIM6UqkUqVQqvx0Oh9l1lxlUVVVs8kMz6g0cYYccrfQG2XIL6h6t7Nj/xWNv3+adb3OuY7TSW/8+y5Gz85kbuW/r7ZPYLnota2hHcKROoe35zzQtrPXqEIDHAL/DtpANjdtiUNsPLh2+sz+cubvA2Mbu8S0p+etquO9jSX8K5hZBoRO8joFrHhub84DZFk+jMbdzM869Wdf0Kc8vJWQtiGftuWxpCzKmHadNyAx4hR4Im9G2AQYecQJhfxDJRSKfO8JBo+zLHyiR0q7P3sL+K4dsySHPufz1yw23J9uvlBD2PRU5xwMiN6dzaP6Q7YF0vpy2fjk7f/1yA/vy5wGEJjY8lyYQmpaLN9xmEnWMFJuPZVpk42kyiRTZRIZM2iQbS2Elc45RBLYlrLIYo6IYvaLIFmVVxRglwR1yiJpi+yMtSbajl3RdK5l1baTXtZNp7ADTQvO4cJYGcLl0XH4Xhts53s0dlannH8tLX75HCbRNIaXktNNOy8/NGSCdTnPZZZfx8MMPYxgGmqbx4IMPcv75549Yz6233sptt902LM9rMHLH5NMihkVbu9otKrwtf563Rt2DncXtd87NrWdIXwltSDqfN2RbF2DYS6ng1Gzh5dA27CelTKgNQV8KPj8bvrMQyjzb98cykZU8ViP5/XLoTMCc9YTaaGyrz9Tm9CU36/83EdqwmfXmP1/rHSuxnYxBzgFZbtticIkfC3v5BdOyy0s5qImsXCUDeQOCKZ9m+L58emN564nHjV3qgIQTo2Ru6jaNePzQ84r1ttfbnxes693fDdLrxbB5/0OFYmtiSUhmIWlCwhIkNQeJjCSbzM39EwKjvBBjahmOqaUYVcU4ppRiVBYrC6hiXLBSadK1LaRWNZCuaSa9rg1MC0fAQ8CjUZiO4TfsPtVE4bFTNCp/0acE2qb4+te/znPPPcebb77JtGnT8vk//vGP+d3vfsePf/xjZs6cyeuvv86NN97IU089xeLFizeoZyQL2o2XH8oXjtoXXZnwd2AEcohilmJQ/AykpRjolg1GEs3OF/bxUtOQQkMKHUvTc9sD6aGxgaU7xmRVWfZBDQ/f9wRuK8XdhwkOm7L1rnpLSJuSf9TBH1ZI1vbDYXtWcOqV5xAsK96q5xntObyBrWYjPWGxXnpEG9CIWZv3KzCyZWnkOoZ14occOZAeFAYiH+f/CsGQT+HQPcOHuAoxrEy+bjHkHCM2XgxrthhQVMNKSIS0F28VlomwsrlgIswMwhyeh2WiSQuk7ZJaYOXqtYacXg47z8iCSgzfLwavP3+/htyHoVZAhWJHJJO1aOoMU9vaR21LH6ta+6lt7aOrM5y3SuslBXkh5ppSij611B5GpoSYYgJjJVKkVjWQWlZH8pM6zL4IhsvBvvvM5IyFMzhqz6m4xtGpTDaT5f5Hn+HnzzYogbYxrrrqKp5++mlef/11dt1113x+IpEgGAzy1FNP8ZnPfCaff+mll9Lc3MyLL764ybrVHDTFeJFKpbj++uv5+c9/zjHTBXccrlPs3LK5JdsC05I8sw5+/IEkaWn88s4fcME1t4x3sxQKhWKHwjRN6urqWLFiBStWrGDpJx/z0bJPqF+zNr/sgB70YUwtxZhahmtqGcbUUvQpJWge5UxIMbmRUpJt6Sa1tJbYe6vINndheNwcevwxXHH+RZz+2c/hdm9fZyPbUxtMSt+mUkquuuoqnnrqKV577bVh4gzIT6pef1FYXdextnAStUKxPVixYgVf+fKXWL5sOTctEnxlDxBiYn1mdU1w+mw4ehr86B3Jhd/+Ae+8/jK/ePxlDGNSPlIUCoVi3LAsi3Xr1uWF2CfLlrF02SfUraklkxvZo/s86FNLcEwtI7DwGFxTyxFTitED3nFuvUKxbRBC4JhWhmNaGf7PHUKmrYfU+6t56733ef0fL+DwuDnmsydz3RVXccxRR23Q55/sTEoL2pVXXskjjzzC3//+92FrnwWDQTweDwBHH3003d3d3HfffcycOZMlS5ZwxRVX8NOf/nRMa6EpC5pie5LJZLj77rv50Q9/yHS/xY+PEMwvmljCbCSklPy5Gu56T3LcXtN48o1l+AoKx7tZCoVCMeFIp9PU1dVRU1PDypUr+WT5cj5a9glra9aQTtpuyXWvGz23hpRjaimuqWVoU0rQgr5J5eVOodiWZNp6SL1bTfSt5ZhdIQqnVHD2eV/iusu/waxZs7bZebenNpiUAm20h9Qf//hHLrzwQgDa29u58cYb+de//kVvby8zZ87kq1/9Ktdcc82YHnJKoCm2F0uXLuXiiy5k2bJlXLZAcuU+Oi59cn0tlzRLrl4imVtVwAtvfkTFjF03fZBCoVDsYFiWlV8ypaamhtWrV7NidTWra1bT2ticXwpB87jQp5TgGCLE9CkliEK/EmIKxRiRUpJe00ziP8uJv1uNTKXZ/YB9ueaKr3PRl7681YdAKoE2AVACTbGtSaVS/OhHP+Kuu+5k9yKdOw6xWFA8eb+Oy3skX3tZ4ve4eOGlJczb76DxbpJCoVBsdTKZDE1NTdTV1VFXV8fatWtZVr2S1atX07SugUzadl8vDN1eL6xicB0xV2UJorwQTQkxhWKrYqXSJD9cQ/zNZaRWNeAK+Dn9S2dx6zXXMm/uvK1yDiXQJgBKoCm2JW+//TYXX3gBtbW1XLGX5LK9NJza5P8qNkUkl70s6U9r/OPJv3HYSZ8f7yYpFArFZiGlpLu7m7q6OtatW0ddXR1r1tayuraWdevq6GhpG1wUXBMYxQW2CKvIibCqEvTyYkRJQK0hplCMA5n2XhJLPib2n2VY0QRzDtqf6666mvO/eDZO55avsaYE2gRACTTFtiAej3PzzTfzs5/9jL1KNW4/TDAnOPHnmm0OfUnJFa/Cql7Jn3/9Y8687Dvj3SSFQqEYRiQSoaGhgYaGhrwlbFVtDbVr19LS2EQyFs+X1f0etNKgbQ3Lxc6KIrSSArTiAoRaikehmJDITJbE+6uJvfYR6TXNeIqCnHX+l/jB1ddu4GBwLCiBNgFQAk2xtXnllVf46qUX09zUxDf3kVw4X2DsoC9Xk1nJtW/CS42S7331LH7wq0fQddWJUSgU2x7Lsujs7KShoYHGxkYaGhqor69nTX0d6+rraW1qJhaO5MsLQ0cvDebFl14WxFVWjF5eiCgpQPMql/UKxWQn09xFfMnHxN5ahkxm2PuIg/net77DGaedPub+iRJoEwAl0BRbi+bmZr7z7W/zf48/zsIKjdsP09k1YI53s7Y5piX5xUeSB5bBQXOreOSZV9l197mbPlChUChGwTRNOjo6aG1tHRbWNTVSu66OpoZGOlvb8vPAADS3E72kAK2kwB6OWBLEURLEKA1CsR+9MIDQ1HwwhWJnwEqlSb6ziuhrH5Gpb6egopQLL7mYG6+6hsrKyo0eqwTaBEAJNMWnJRqN8rOf/Yw777gdr5bl2v1MTps1uhfSHZX32iXXvSmJZDXu+sENfO26HyprmkKhGIaUkp6eHlpaWoYJr+aWFuqbG2luaaGjrZ3eru7B+V8AmoZR6EME/RglBegltgBzlhailwQQxQUIr2une+4qFIpNk17XRvy1j4m/swJMi0NOOJZbrrmOExYvHvGZoQTaBEAJNMWWkkqleOCBB7j9h7cRCvXxpbmSb+yrE3DsvF+1cFpy+7vw9FrJ3jPL+OWDD3Pk4pPGu1kKhWI7EI/HaWlpGRYam5uoa2ykuaXZFl6dXWQzmWHH6QU+tEIfWmEAvdCfD46iAHphAAp9aAEPYgdboFahUGxfrHiSxFsriL66lGxbD6UzpnLlFVfwra9eQXFxcb6cEmgTACXQFJuLaZr8+c9/5gc330Rzaxunz4Zv7Gcw1btjOQH5NHzYKfmfd2FFj+Szh+3Nbff+jv0XHTjezVIoFFuAaZp0dnYOE14NzU2sbaynsamZ9rZWuts7SUSiw47TPC60Qj96kT8vvoyiAI7iAFrQD4U+9AKfcr6hUCi2K1JK0jXNxF9bSvz91ei6zjGfO4XvX/0djjjscCKRiBJo440SaIqxIqXkySef5OYbr6N6TR0nzoRv7e9gdsGOP89sJPqSkt4UmBYEXVDmAW3IUAFLSp5eC7/+WNIUhZMO2Yvv3flzDjvyaDUMSaGYIEQikbzoam1tpbm5mXVNjaxraqC5uYXO9nZCXd1Y5pAXULqGHvShFQ1YvAI54VWACPoQRbYFTHNvuZtrhWIiI6WErInMmMhsFpk1B7dNE4EATYCwY6FpduwwEC6HHZRFeEJghmMk3lxG9LWPMLv7KZ05jS+dey6/uOseJdDGEyXQFJtCSslLL73EjddfywdLP+bwKXD1QoO9incui1nWkrzZCi/US95ph7bY8P1eA/YohkOq4NApgn1KwdAEWUvy7Dq4/xNJfRj2mFHOV6/8Bhd89RsUFRWNz8UoFDs48XictrY2Wltb83Frayv1zU00NjfR1tZGZ1v7MDfzAJrPnbN6DQ43dBQXoBfZQw31wgBawKucbSh2KKQlsSIxzP4YVig6GIdjWOE4VjyJFU8h4ymshJ0m+ylfzho6wuVAGxBsLifC7URz27FwO+197uH5mts1JC+33+lAOAzQNfUCdAuRliRV3UDizeXEP1hN2fXnUnvtfUqgjRdKoCk2xttvv80N11/HktffYN8ywbcXahxUsXN9laJpySOr4f9VS9rjMDsIR02DvUoE5V7QBYRSUNcPH3dL/tsG4TQEHHDkVDhuhuDIqeBzwH9a4fE18HKjBCE4YuF8vnDuhZx25tlMnz59vC9VoZiwZLNZent76e7upqura1jc2dVFS3sbLW2ttLe309XeQXz94YZOB1qhH63Qhx70oxXZli9nUQCtKIAo9KEX+hFOxzhdoUKxbbHiSbJtvWS7Qpjd/WS7+zG7Q2S7w5g9/fZwkCFofg9aMDcM1+tC87rRcrHwutE8TlsUGToiF3DoiAHnWJaFtCRICZa0nd5kTKxUGpnKDAlprHQunbT3Wck0MpFGJtN2OplGptIjXNV6CIFwGralzmmAYzAtnA67nU7HYJmBfUNjh5G7dr9tKQ/6djpruBVPoXldrDzjJiXQxgsl0BTrY1kWL774Ij+79yf8+6VXmFusc/V+kmOmyp3qzVRfUvLnasmfV0EiC6fOgvPmCeYXb9xDpWlJlvfA6y2Sl5tgVS84NDiwwhZrx04HQ4N/NQpearB4tx2yEnafVsZxxx3LiaedzTHHHkswGNyOV6tQbBuy2SyxWIxYLEY8HicWixGJRAiHw/T39w8Lof5+evp66Q310RcK0d/fTyQcJhqJEg9H2OBnXAj0gBfhd6P5vXZnasDqlRt2SIEXrdCP8Dh3queXYudESonZFyHb1kO2tYdsey+Zth6ybT1Y/YPDPoTPjTGwJl4u1osLBr9DE3BupLQkMjVEwCWHCLh0BpnJQjqLzOTCSOlheZn8NuuVYb1njXA77XtVXoRRXmjHFUUY08rQA95xuiPbHiXQRuHOO+/kySefpLq6Go/Hw6GHHsrdd9/N3LmDayyN9oPzv//7v1x77bWbPIcSaIoBVq9ezd///nce/O1vWLN2HXuV6Vw4z+LkXUDfiYbz9CYlv18heaQaLAnnzIWL5wsqfFt2D1qiklea4OUmyXs5MXZIFZw3V3DcDIik4T9t8HYbvNVqz1fThGDhHruy+OTPcOLnzuDggw/G5VKLyCq2PpZlEY/H8+JpaBgtLxKNEo5ECEcjRKJRovHB/Yl4nEQ8TjKRJBVPbOCxcAOEQPe6EB4XuJ32G3qPvT00NgJejAIvwu9F+t3ofg/C61ZDDRU7LTJrkmntJtPYSaaxw46bOpCJnKXJ0G0RUVWCo6oEo6oEo6oYo6xILUq+EaSUyHgKsz+K1W8P+zT7IphdIbJdIbKdIdviaNmyQgv6cUwvwzGtDMculThnT0UvDuwQL4SUQBuFk046iXPOOYdFixaRzWb53ve+x7Jly1i5ciU+nw+A9vb2Yce88MILXHLJJdTW1jJr1qxNnkMJtJ0T0zSpqanh/fff59133+Wfzz/DmroG3IbGMdMszp+vs3+ptUM8YMZKMit5eBU8sEwigS/Pgwv2EJR4tt496E/ZYu3/1kg+7IQ5RXDjAYJDpwyeoyki+W+7xlutJv9ts4dPepwGh+w3n8OPOZ6DjjiWRYsWUVZWttXapZhYSCnJZDIkEgmSySSJRGKD9Mb2RWNR+iNRItEI0XjMFlGxGPFcSCYSJOIJUokE6WRqTG3KT+4fGB40MG/EORgPziWx55NoTge6y5mbZ+JEuAyk0wEuIy++hMuxUz1nFIotwUqkyDR1kmnosOPGDjIt3flhiXpFEc7p5ThmVmBMLcMxpQS9NKgccWwjZNYk29lHprmLbHMXmeYuMk2dmD1hALRCP87ZU3DOnmrHu1Taw0EnGUqgjZGuri7Ky8tZsmQJRx555IhlTj/9dCKRCC+//PKY6lQCbcdBSkk0GqW3t3dY6OnpoaOjg/q6tdTX1dLU2EhTWwfpTBaAXYKChWWS42ZoHFoFnsn3DPnU/LtBcvt7kq44nDsPrtxbUOzetp3GDzsl934oebfDHj55wwEbikFLSqp74b/tGm+3mXzSbQu2/8/eecfHUdz9/z27e3e6Uzn1Yrkb2xhMxxQDBhM6oRNaAoEAgWCc8EAIoSSBJPyANHjSH1JIICGQQkkggYQOoRpjW7as3nsv12935/fHnk6SkW3ZyJZkz/vl8czOzs7Mrm7LZ8p3ALLTfcybPYMFCxZQNHMuOYUzyMnJJSsri6ysLNLT00lLSyMtLY3U1FTS0tLw+XxoU+CFLaXEtu0J9S3LwjTNMd229n1SF4/HiSf8mDkcjptxzLhJPBHnpDUxzfgWeViYlokZixONRIhFo8SjsdGLFI+DkXMoSIioUZPvR4krd3KfPiI8JLak2/iY+BL65P9uFIo9HSkldl8g2SsWa3AEmdXR5yQwdFzFebhm5+OaXeD4M/PQvKpHbCpg9QeI1bQSq2omVtNCvLYNGYuDy8CzTzGe/ebg2XcOrrmF0+KZqgTaOKmqqmLhwoWUlJSwdOnSj+1vb29n5syZ/P73v+eyyy4bM49oNEo0OtxiOjAwwNy5s5kze9ZWh4ps99JJcPoctr7f8baSRo4ZHDsBHxsavNNphuu1DeQ2zyyRZHvXZ4w8xjjm42m2DEikPfQhamFZjjMt2wnbW6+HR3eEV9LpkOYGv9uZH7XN6m979ziuzyc7fnt8kvzjNlT0QEsITpgJXzsMFmTuvoemTJjiv3+NxJZwUK5jdGRkh4LEOUeJM6IiYsJAHMJxiFjOdtwG03aGT24PIYaHRn/8jhdjRQ7vQ47wt/iJikRFEc69PvK+n05PX4Ez0VyIxOVIXJPE89GJH94/Ku0WPiPTwvAzVohkeCg/BAhNQ2jOPqEPhYfj2FZ4t18ohULxSZBSYoVjxINRzGCEeDiOGYhgR51hwcLrwTVnWIS5ZhfimpEz5eaGKbaOtGziTR1ENzcQLasnVt6EjMYQbhfufD9unwtPuhfDO/WMkBR89kTeuOw+JdC2h5SSc845h97eXt58880x03zve9/j/vvvp6WlhZSUlDHT3H333dxzzz2j4lLGca+P+fLfgS+Cifh4GG8dJupDZWfz2dlrNZ7yhr4TNc35kNcSTh/hG5ojuobCW37wj7us7STaXh4TUcZEHL+1JH1RqOxz9n/jSDhvgZi0oVY9Ecm97zvm+JfmQHaK87cbQjD8tx/57a+R1ADJNLZ0BJvlGM7CSoTNhG/ZkDSOvIV22rJNQDKGL0dvS7ntsD3imJFic6Q/8kTH+gsItp1maFuIrYQZ/q1sMzxG2QqFQvFJkBJiNkSthJOCqNCJxmzk0BDFzDRccwsTYiwf1+x89LxMNfx3D0OaFrG6NqKbaomWNxKrbALLxpXuJS1FIyMaxKcn2wMnlafO0ij8ca8SaNtj1apVPP/887z11lvMnDlzzDT77rsvJ598Mj/5yU+2ms9YPWjf/fIxnHbYQrRp0N06nZCjPg8dTzrN60l/9AFDn7Aj+yhACg2p6dhCx9Z0LN3lOMONaXgSzo36vBwfUkre/M9a/vbYfzg0D+4/TjArdbJr5fCfesk335XYCL7whVM4+MRjEIhRAszp0xHDwmJEb9hwWIz6NQghho8Vw/l87Dgp0WwTYcXRzBiaGUO3omhm3ImzLZAWwrYQid/rUFgIkVBd9nDpguF9MKJUhUKh2HOImzatPUFauoM09wRp7g5S1x2itn2Qlo4BLNMRYprXnTDWkYMxI5eU2flos/LRM6bIS0ixW7HDUaKldUQ3VBPeUIvdH8CV4mL/JTM4+4BCTty/iDTv7l/6wzQtfv/cK9z/dL0SaNti9erVPPPMM7zxxhvMmzdvzDRvvvkmK1asYN26dRx00EHjzlvNQVPsTcRiMVatuoFf//o3XLEEbjtcYEyFpqoR9EQkd78LL9ZLzl9xID/70z8pnFE82dVSKBSKvRbLsmhubqa2tjbpKqurqayppr6ujq629uHpDprAyM5Ay/XjKszGmJGLpzgXvTAbkZmmesUUYyKldOYdbqgh9FEl8bo2NJfBQcccyVWXfo7PXngR2dnZu6Uuu1MbTEuBJqVk9erVPP3007z22mssXLhwq2mvvPJKNm7cyJo1a3aoDCXQFHsL7e3tnH/eOXzw/vvcfSRcuHDqviSllDxfC995X2Kh8c1bv8JN3/oehrEXWnBRKBSK3UBfXx81NTXU1NRQW1tLeWUlm6sqqKutpb2pBcs0k2mNzDS0EeuIGbl+XPlZaDl+tKw0NU9M8YkxuweIflhB6MNyYlVNCKGx31GHcd0VV3HFJZft0rVSlUDbDjfccAOPP/44zz777Ki1z/x+P16vN7k9MDBAUVERP/zhD7n++ut3qAwl0BR7A2vXruXcsz5NuK+Dn5wAh+ZNj8dBb0Tyv+sFT5TZLJ6RyQM/+jFnXfQ51QKrUCgUO4GUkubmZjZu3MimTZv4cP061peUUF9bS7B/IJlOS3Gj52ei52Zi5Pkx8rJw52Uicv3oOekI9+4fdqbYe7H6AkQ/qiT4QRmx8gY0w+DIk07gK9dcz3mfPgu3e2KNjCiBth229hH2yCOPcOWVVya3H374YW666SZaW1t3WFErgabY03nyySe56srPsyDN5GcnCgq9O2a+fCqwqVty3wfwQbtk2aKZ3Pujn3HymWdPdrUUCoViytLR0cGmTZsoKSlhzfqP+GjDBqrLKwgPBgAQHhfGjFxcM3IwCrNx52Wh5WWi5fnRUlNUQ5hiSmL1DhJ+bzOhdzYRb+zAk5HGKWd/mttuWM3yo46ekN+tEmhTACXQFHsqtm1z5513cv/99/Pp+YJ7l4txWSydqkgp+W8LPLROUtIFR+83h3u+9xAnnXGO+pBQKBR7LX19fWzatImNGzeybsN61qxfR/nmMgZ7egEQhu4Y5SjOxV2ch3tmHlrR0ELO6tmpmL7EmzqJvLuZ4LsbsXoGKdhnLl+85lpuuvb6TzRfTQm0KYASaIo9kYGBAS679FL++a9/csuhgmv233qP9HRDSsmrTfDT9ZJN3XDk4mLuuf9HnHLOZ/aYc1QoFIotGRgYYPPmzZSWllKycSNr1n9E6aZNdLd1OAk0DaMgE6M4D1dxLp6Z+WgzcjDys6bFosAKxc4ibZvopjpCb6wnvK4KTdM45vSTuesrt3DyyhN3+NtACbQpgBJoij2NyspKzjnrTJrqavjhcZLj91ADiFJKXm+Gn66Hki7JsoVF3HP/DzntvEuUUFMoFNOWnp4eSktLKS0tZdOmTazftJHS0lI6W9uSaYy8TIziXIziXDzF+ejFuRiFWQiXMqSk2Lux+oOE395I4PX1WB295M6dyY2rbuTm624gPT19XHkogTYFUAJNsSfx73//m4s/cyFZWoifrRQsyJh+8812FCklbzTDzzbA+k7JkYtn8v2f/YrjPnXaZFdNoVAoxiQajTqm6isrqaqqoqyinPUbS6goK6e3q9tJpAlc+VnoiTXD3DNycM/IQxRmonkm1iiCQrGnIaUkVtZA8NV1hNeW40rxcObFF3LfbXex7wjDg2OhBNoUQAk0xZ6AlJLvf//73H771zlmhuBHx2lkuPd8cTYSKSVvtcAP10o298CZyw/g+//3B5YsPXCyq6ZQKPZCIpEINTU1VFVVUVVVRXlFBaUVZVRWVtHR3JJcN0zzuNDzMpMLOLuLc3HNyEPL96seMYViAjB7Bgi/to7Aa+uwA2H2P+YIvvW1r3PBp89B0z4+/FcJtCmAEmiK6U4wGOTqL3yBJ//8Z647AL5ysEDfiyd+21LyXC089JGkNQhXnHsy3/3JIxQX76FjPRUKxaQgpaS9vZ26ujrq6uqora2lqqaG0ooyaqqr6WxpGyHCEmbr8zMxCrJx5WfiLsxB5PnR1OLNCsVuQcZNQu9vJvjSh8Tr28maVcQNN9zArV9aPcoK/LQUaN/+9rcBZ42y3NzccR3T29vLT37yEwC++c1vTkQ1Jgwl0BTTmdraWs49+yyqKsq472jJaXMnu0ZTh5gl+VOF4OfrbSK2xpevuYI77ntoly5uqVAo9hxisRitra20tLTQ0NBAXV0dVTXVVFZXU1tXR2tTM/FoNJleT/Wi5WQkRFgWrvysYRHmT1UiTKGYIkgpiVU3E3p5LaE15RguF2dcdD73fu0Olu6/dHoKNE3TEEJQUlLCfvvtN65jqqurWbhwIUIILMuaiGpMGEqgKaYrf/3rX7nu2mtIk0F+thIW+feuIY3jZTAm+fUmwe822aR43Nxx6//w5dvvwePxTHbVFArFbkZKSX9/P11dXXR2dtLZ2ZkUYfWNjdQ3NdLc0kxHWxv93b2jjtV8Kei5Geg5fvRcP0auH3deFnpuBiI7A82nnikKxXTD6h0k9Pp6Z/jjQJAlRx3GzV+6kWs/f5USaJOJEmiK6UZPTw83rlrFn554glPmCL57jI7ftfvEWcySDMQgZkHcBl2D3BRIMaZ263B7SPKz9YK/Vtrk+33ctPoGrv/qN9R9r1BMY6LRKF1dXUnBtaXf3N5Ge2cHnZ2d9Hb3MNDbi2Vu8R2iaRj+VERmGnrCaZlpGJlpuLIzEBmpaDnpaL6UyTlJxR6BNC3sSAwZiYEtQUpnCKyUIATCbSDcLoTLcNxePFVhMpCmRXhNOcGXPyRW3YL/8lNo+Olf9myBVlZWxn777Yfb7SYSiUxENSYMJdAU0wXbtnn88ce59Zb/ITTQyzeX2Xx63q5b36w/KintgfJeKO+V1A1A0yB0hMdOPysNlubC0hzBkYWwfw5oU3BIT3W/5OESwXM1Nj6PwRcuPZ8bbv8uCxcunOyqKRR7JVJKBgcH6e3tTbqenp5R253d3XR0d9HV3UVPby+9PT309/QSCYY+lp8wdPR0HyLNi5buRUvzOX66Dz3Ni5GRip7mg7QURCJejGEoQKEYD9K0sHoGsXoGsLoHMBP+0LYdjCAjUWTM3KF8hc+DluZDT/xGtbTEb9ifip6djp6VgZ6djuZPU2JugonVteGeW0jpBXfs2QLtiSee4LLLLqO4uJjGxsaJqMaEoQSaYqoTj8d58skn+f4D97FhYymnzBHcdZRBQcrE9kZLKSnpgteaJW81w4YukIBHh4WZMN8PM9OgOE2QnQJuDVw6xC1HtJX3SjZ2QWkPhEynV+34mXDaXMExRUw5wyVtQcljZYI/V9gMxOD4QxbzxS/fytkXXkxaWtpkV0+hmHKYpkkgECAYDBIMBkeFt7YdCAToDwwyGBhkYHCQQCBIIBQkFAgQCoWIhMJEw2Fsa+xRAJovBc3nQfhS0FJTnI/W1BS0VOdj1Uj3YWT4EGk+SE1BS/ciUtxqvpdiQpBSYgcjw4Krq38LITaIPRBwXpYJtAwfenYGek4Gela6I65S3AivB83rRnjczsLhQgw7aSNjJjIWd/xoHDsUwQ6EsQdD2IEw1mAIezCM3R8YLfZ0zen5zXYEm+NnYOT5k8NxNY9r91+8PYDdIdB22k7ro48+Omb8s88+y5o1a7Z5bDQapbq6mt/+9rcIIVi2bNnOVkOh2Ouora3lT3/6Ez//yY9pbmvn2GLBH8/QOTxPAhMnzmKW5NlqeHSzpKIP/G5YPgMuWiQ4JB/mpo9XXDlpTFvyUQe82iR5pRH+ViUpSoXz95F8ZqGgKHVqfDgVpgpuPQxWHyR4oR7+UlHOZ6+6Bs+1X2TlUYdw3qVXcsJJpySHZysU0414PM7AwAD9/f2j/CHhFAgEGBwcpG+gn96BfvoHHRE1ODhAIBAkGAwQCoYIB4JEQmHMWGy7ZQpdQ3jczgehx+UM2fKMdG60PA9iZhrC48LlcZOS4kZPTUHzpSB8KeDzIFITwkz1bCl2IdKysXoHsbr6Mbv7R/V8WQkBJmPx4QMMHT0nAyM7A9eMXFKWzneEWE4GRkKQCfeuFUNJ0dgzMNxzN8KPVbdg9Q7CiEYPLSMVPTcDIzcTPc+PkeN3/NxM9JwMhKHv0jorts5O96AN9ZgNMZTNjnywSCnRNI2XX36Z448/ftzH3XfffTz11FOUlZXh9XpZvnw5DzzwAIu3WGBu8+bN3Hbbbbz++uvYts3+++/Pn//8Z2bPnr3dMlQPmmKqYNs269ev5+WXX+bPf/ojH6xdh8cQnDlXctVSY8KNgEgpeb4WfrBW0haEk2bDJYsER09gb9dQr9xfKh3T93EbLl4E1x0gyPdNPdHTOCh5qVHjpXqLDzucRtHsdC+HH7Q/Bx56BPsfsowlS5aw7777KmuQil2GlJJQKER/f//HxNXIuN6+Xrp6e+np66Ovv4/+/n4GBwYJDA4QGgwQi0S3WY5IcQ+LphRXYtsRV0NhkeLs01PcaG630wPgdoHHjfAYyITw0tyJY9SHnmIKIaXEHghidvZhdfZjdvU7vWBd/U7cWEImJ9ETlRBizrbja+m+adFgJ20bqy/gnGtnP2ZXn3POQ+ffM+jMfQMQoGemoyd63Iykn4me63dEp753NpRM6SGOYy3gtiO43W6WLVvG7bffzhlnnLFDx5522mlccsklLFu2DNM0ufPOOykpKaG0tJTU1FTAMUByxBFHcPXVV3PppZfi9/vZvHkzy5YtIz8/f7tlKIGmmAwsy6K6upqSkhJKSkr4aM37vPnmm/QOBPAYguOKJGfME5wwS5C6C9Yp3dgtufd9ydoOOHk23HSIYJ/MXfvSCcQlf9gMv9kkiVlwxRK47kBBmmtqvuz6o5L1XbC+S7C+06a6D1qCw/sLstJYNG82++23H/sdciRLDjiIJUuWUFxcPC1e4Iqdx7IsotEokUiEaDT6sXA4HP7YsL8hf2BwkIHEcL+BwUEGg4HkvlAwSHAwQDgQxN6GQa2h4VIixe0M+fN6nLhEWHg96F4Pui8luU2KG7wJgZXiRrhcat6KYo9BWjZmZx9ma3fSxVu7MVt7kOHhhgotzessCp6T4fi5Tk+SnuvE7S0LgyfnzXUnhOoI8WZ29WP3BYYT6xp6Vroj2HIyMPIyMYqyMYpyMQqy9uhGmSkt0Orr65NhKSXz589HCMGLL764zUn1QghSUlLIyclB1yfmj9fZ2Ul+fj6vv/46K1asAOCSSy7B5XLx2GOP7VSeSqApdgUjLYu1trZSU1NDdXU1tZVlVFdVUllTTzjqDBfK9moszrQ5LB+OnGFwUI6FR981H05dYcmDH0n+VunMK7vjCMHRRbv3I20gJvntJskjmyDDDbcdLjhzFxo7mUhCcUntANQMaNT0S6r7bGr7oXbA6R0E8HlczC0uYN7cuczbZyG5RbPIzMrG7/eTkZFBRkYGKSkpeL1eUlJSxnQT9cwcDzJhScy27aQ/MjzStywLy7KwbTsZ3tJtbd9Ex5umiWmaybBlWcTjceJmnLhpYZpxTNMibsYxLZN43CRumpiWmTw2PiKPeDw+nK9lEYtFiUdjxGIxYtEo8ViMeDROPBbbpngaC2HojigaOdwvsa2lJIYBJnqztISw0n0etJQU8LoRXjekuJ0hfyluNexPsVdjh6PEGzuJ17cRb2gnVt+O2dqd7AkTKW6MohyMohxcRTmOmMjPcnrAvGophPEg4yZm9wBW1+ieR7OrH6uzDzuQsBamaxj5WRgzcnDNyMU1Ox/X3CL07PRp8U7fHlNaoG3JzhgJmSiqqqpYuHAhJSUlLF26FNu28fv9fO1rX+Ott97io48+Yt68edx+++2ce+65Y+Yx1MI5xMDAAHPnzmbBgvlb9BaOvlzbv3pynOmctOP9a4z7z5ZIt/1qDqfYWtod+alsPenYO8ZOvyNpt5J+q1FbyUSOkUZusTNZD+kMF0h8HNqWjWUnwvbwB61tSyxbYkv7Y3UXgM8FqYbjp7kccZLhdoxw7MCZ7lQ6KaF+ACr6HOMeNx0ClywWGJPYgt4ckNz/geTfDbAgYYDEN46h++P5eU7Iw26LMiUJy8hjbNs2hE3HOErIhIgFUROiFpjS2b+jA1SdueMCECPCzg4BjPhv9P0vQZIw4Zw8gdH39QS9DnYvAuei4PhCCBi6LiPDif1OGkbvGxV2Mk2GxRhhTSA04QgibWSclowX2nAcYii9AF1z5mPp2h7xoaJQTAZ23MIMhDEHI8TDUcxgFGsgYbnT0HDNzMc1pwBXcR5GYTau4ly0zDR1z+1irIGQ00vZ3DncY9nchd3vDDPRvB6M7DQMnweXx8BI96JPM0MlOZeu5N1Lvjt9BNpkIaXknHPOobe3lzfffBOAtrY2ioqK8Pl8fPe732XlypW88MIL3HHHHbz66qtjzne7++67ueeee0bFuTWS3znbYiJv992e1yco8JPW9ROfq5jY6zUi2/GnTXzbaSS/8dBGbm+xTxdgaI5zac5vbHvvi/G8T8Zd5xEJwyY0DkJfFM5fALccCrm+qdMC/98WyXffl9QPwOIsSHc51217TOT7dzx5jfz7jvx7D2lcbcTvYsttgSOeRoq1IV9KR+hZCX9on7VFOOns4fRmYjup1STJ+yUR3Po5jxUxjuNHxontHDNUr63uG6P8sf4W6lNLodjzMW2nUStiQUToRG2BGXWsFQq3gWt2gSPGZuXjnlOAUZy3Rw+vm45YfQFida3Ea1qJNbQTr23DHnQEtZGWgjdFxxcN4xM2rqnzGTIm/zhHo/DHvUqgbY9Vq1bx/PPP89ZbbzFz5kwAWlpaKC4u5tJLL+Xxxx9Ppj377LNJTU3lT3/608fyGasH7Yc3H8MJ+xV/4vl2iumBHPG5JxM9FBJACKQQSARSaElnaxq20LF1HUtzYekGlu7C0gwnrBnYmoEtxqHCJoTtlyGAeMzkpb+/yyv/WsPMVMk9R2scVbjra7czxCzJo5vhZ+slbrfOORcdx+ErD0cbo4dvR2PG/pOI4c6YEanFyGMTCbQRLQQimVokhdeodAyJOKe3RTDiuKF0I3q+xMjyxXAemm2ixcLoZhTNiqObUYRtImzL8aVESBshbUfUJPrKBICUiSo5ikfIoXKkc1yid224K3IoPPyKENP7daFQKKYwUkpae8OUNw9Q3jLAppZByloG6O1zhs1pXjfGnELcswtwz8nHNbsQvShbDe2dhkgpsXoGiVc2Ea1oJFrRiNnSDUBGThoHLMjl5H0yOXZRLpmp7kmu7TCmZfHXV97jG3+pnz4CbXBwkAcffBCAL37xixQWbvuLr7W1lV/96lcA3HrrrXi93h0uc/Xq1TzzzDO88cYbzJs3Lxkfi8VITU3lW9/6FnfddVcy/rbbbuOtt97iv//973bzVnPQFHsir7zyCl+86goampr54gGC6w5gl81rm0jaQ5IfrIW/V0sOXTSLnz/yOEcuP3ayq6VQKBSKncA0TcrLy/noo49Yu3Yt7324hg3r1xPoHwBwFg2fnY9rVj4p84owZheg5fqVAZs9GGsgRKyyiXhlE+HSOsymThCC2UsWcdqpp3LZOeezfPlyXK7JGxK5O7XBhAm0xx57jM9//vMsXLiQ8vLy7aaXUrLvvvtSVVXFn/70Jy666KJxlyWlZPXq1Tz99NO89tprYxolWb58OQsWLBhlJOS8887D6/WO6lXbGkqgKfYkurq6uPnLN/LYn57k8Hz49nKdBf7p1xuypl3y7fckFb1w+dkr+f7DfyK/oGCyq6VQKBSKMZBS0tHRQUlJCRs3bmT9hg18uP4jyjZtJp4YteTKy3TE2OwCUuYUYszKQ6j5Yns9Vu8g0U11RDfVEdlUix0I4/Z5OXLlCj7/mUs4/+xzyMrK2q112p3aYMLshj711FMIIcYttIQQXHLJJXznO9/hL3/5yw4JtFWrVvH444/z7LPPkp6eTltbGwB+vz/ZE3frrbdy8cUXs2LFiuQctH/84x+89tprO3xuCsV0RUrJo48+ys1fWY0VCfDdY3QuWGCjieknzgAOLxA89Wl4sgIeeuFVnplbzF0338BNd/9wUlvVFAqFYm+nr6+PTZs2sXHjRkpKSvhwwzo2byqlv6cXAM1loM/IwZiRi++8Y0iZU4g2Kw/NlzLJNVdMRfSsdHzHHoDv2AOQtiRe30aspJZ3P1rPm8+/yLWaxuJlB3Pp+Z/h8s9cPGok3Z7AhPWgLVmyhIqKCp577jlOP/30cR3zwgsvcMYZZ7BkyRI2bdo07rK21qryyCOPcOWVVya3f/vb33LffffR1NTE4sWLueeeezjnnHPGVYbqQVNMdyorK7numqt59Y03+fQ8uH2ZRu6OjySesvREJP+7TvDnCpv5+ek89L8/4cyLPz/Z1VIoFIo9mnA4zObNm5NCbO2G9ZSUlNDZ6jSWC03DKMzGmJGDMTMPT3E+RnEuWr5fzRdTTAhWzwDR9dWEPqokWtYApsWs/RZx2SWX8qUrrmLOnDm7pNxpOcQxPT2dUCjEhx9+yMEHHzyuY9atW8ehhx5KRkYGfX19E1GNCUMJNMV0JRaL8cADD3Dvd79DXorF3Uc5C1zvqWzukfy/D+D9Nskphy/ifx/5M/suPWiyq6VQKBTTmkAgQEVFBWVlZZSWlrJuYwnrN2ygub4BaTsLhLhyM9GLczCK83AX5+GZlY8oyNxrFnZWTD52OEqkpIbImnLC66shbjL3wP244rLPct3lVzJjxowJK2taCjS/308gEODNN99k+fLl4zrm7bff5thjj8Xn8xEIBLZ/wG5ECTTFdOTNN9/kumuvobKyki/sJ7nhIA3vXvCelFLyYj18b42kPQzXXXIW3/7J78jOzp7sqikUCsWUxbZt2traKC8vp6ysjM2bN7NuUwnlZeV0tLQm0xmZaehFORjFubhn5uOZmYcoylYLPCumFHY4SmRdFeEPyoiU1IAtWXLkYdx03Q1cfvElO2WQcCTTcg5aUVERlZWVrFmzZtwCbc2aNQDbtfioUCi2TVNTE1+79Vb+9MQTHJyv8dRZOoszd3QJ5OmLEILT5sIJM+F3mwX/9+d/8PgzhXzr9ltZ9fV7MIy9QKUqFArFFti2TWtrK/X19dTV1VFXV0dtbS2VtTXU1tXS2thMPBYDQOgaRn4WemE2xiFzyD79UNwzchEFWWg+JcQUUx/N68F39P74jt4fOxghsraC6v9u5LqrvsDqG1dx8rlncceNN3H0kUdNeSM0E9aDdu211/Kb3/yGRYsWUVJSst0J+/F4nAMOOIDKykouv/xyfve7301ENSYM1YOmmA4MDg7y0EMPcf99/w+viHPLwTbn7QPaFH/w7Go6QpKH1gmeqrRZUOjnjm/ezeeuWaUMiSgUij0Ky7JoaWmhrq4uKcJqamuprK2mrq6OtqYWzHg8md5I86HlZKDlZmDk+NFzMnDnZaEXZKHl+dUCz4o9ErO9h/B/NxL4bwl2b4D8+XO49uqrueVLN+6QJchpOcRxaLiiEILzzz+f3//+9/h8vjHThkIhrrjiiqTlx1deeYXjjz9+IqoxYSiBppjKBINBfvrTn/L9B+5ncKCfzy2W3HCwTrprz51rtjNs6pb8eB281iQpzk7jf76ymmu+cht+v3+yq6ZQKBTjIh6PU19fT2VlJVVVVZSWl1FaUUZNdQ2tDU1YpplMq6c7AkzP9WMkfHduJkZuJmSnqSGJir0aadtEN9URfquE0NoKdEPnxHM+zXe++nWOXHbEdo+flgIN4LLLLuOJJ55ACEFxcTHXXnstK1asoKioCCEELS0tvPHGG/z617+mqakJgAsvvJAnn3xyoqowYSiBppiKhEIhfvnLX3L///sufX19XLiP5PqDDAq9e89wxp2hvFfy603wfI3EbehceMZKVt12D0ccdfSUH+agUCj2fGKxGLW1tVRVVVFZWcn6zZsorSinrqaWruYWbMt5xgtDR8/zO0MR87Nw5Wfhzs9Ey/YjctLRUtyTfCYKxfTA6g8QfrOEwKsfYfUOMnvpvty8+itcd8WVpKSMvfTDtBVokUiEs88+m5deemmbHz1DRZ588sk8++yzW70Qk4kSaIqpRHd3Nz/72c/4yUMP0tffz/kLJNcf7KLYZ0121aYV7SHJ36oEf6mwaQnC0nmFXPelVVxx3ZfVfa5QKHYpkUiEmpoaqqqqkj1hG8vLqKmupqulLWkZUbgM9PxMjPwsjPxM3AXZaPlZ6PmZ6NnpylS9QjGBSMsmsr6K4KsfEd1UhycjjQs+dynfueXrzJ8/f1TaaSvQwBFfP/7xj/nBD35Ac3PzmGlmzZrFrbfeyqpVq6Zs67USaIqpQH19PT/84Q/5za8fxjbjXLjA5sqlLmalKmH2SbBsyVstzoLXrzZJUlw6nznjRFZ9/dssO/Koya6eQqGYhkgp6erqora2lpqaGmpra6moqqKsqoKa6mo6W9qSDdTCbaDnZ2EUZGHkZ+EuyEbPy0QryETPTEdoU/PbSKHYk4m39RB+bR2BtzYgwzEOPO4o7vqfWzn/rLPRdX16C7QhpJSsW7eOjz76iK6uLgByc3M59NBDOeigg6asMBtCCTTFZFJSUsL3HrifPz3xBOluwWcXWXxuP4Ns99QfymjZkp4oREzweyDDPbXv9fag5C/Vgr9W2LQG4cD5RVxz/So+e/WXlJl+hUIBOMY4Ojs7aW1tHeUam5uobainpraW5oZGIsFQ8hg91YuWm4Ge5/SGuQocISby/GiZaVP+O0ix65C2BMtCWjbYNtJM+JYNlo20bef3IQRojhNCgKYh3AbC41I9qbsQOxon8v5mAq+sJV7fTkZBLldefTU3Xnk1ixYtmt4CbbqjBJpidyOl5D//+Q8P/uiHvPDiv5mRrnPVEpsLF2r4jKl9m/ZEJH+vhleaJOs6ITqigy/fC4fkw6lzBCfMhFTX1PwosWzJG83wZCW80STRNcEZxx/BZV/8CqefeRZpaWmTXUWFQvEJsG2bgYEB+vr66O3tHdPv6emhq6eHzp5uenp76evrpb+vn4GeXmxrxINNCMcghz8VLTMNI9ePnufHnZeFkZeJyM1A80296RuKT4aUEhmOYgfC2MEIdjCMHYgMh4MRZCiCHYkhYyYyGndcLOGicexoHOLm9gvbHi4DzeNyxFqKG82XgpbhQ89IRUv8NvUMH5o/DSPPj5aRqhoFdoJYbSuh19YReq8ULJuUZfvS9uI7SqBNFkqgKXYXg4ODPProo/z4oQepqKpmcbbG1ftJzpgHrik+zKU9KPlFieSvlc72sTPgyELB7HRIMaAv6hjoeLsFSrrBoztrlV2wj+DYGaBP0fPrCkv+USt4usqmvBc8hsbxRxzEmedfwgknn8bSpUvRVOulQrFbMU2T/v7+bbqe3h46urvp7umhu7eH/r4+Bvr7GewfIDQYYGufPJrXg+ZLQXjdiNQU52PX50H4UtBSUzAyUnFlpSMyUhF+n/Oxq0zS71FI28bqDWB19WH1DGL1BbB6B7H7g0444cYUV7qGluZFS/x2hMeF8LgdP9HjpbkTcUM9YIYOuobQNdB1p0dMFwhdd3rNJCClMzdRSrAl0pZJoTfsYtiRGHYoij0QxB4IYg2EsAdDYI0YdeMyMHIz0HMzEw0KmbiKcjCKctBz/WpY7XawgxFCb28k7eTDKb3gDiXQJgsl0BS7Eikl7733Ho899hiP/f53hMJhTpoluXw/ncPz7CnfytUbkfxsg+TJcvC54Av7Cz6zELJTtl7vxkHJi/Xw9xpJeS8UpcKF+wguXQw53ql7vg0DkleaNV6ut/ioE+I2ZKamcPhBSzjsyGM47OgVHHLIIcyfP1+JNoViK1iWtV1x1dfXR1dvD929vfT299Hf10f/wACBgQGCgwGiofBW8xcuA83rQXg9iFRPQmAlRFbio1lPTUH3paClep10Pjf4UpzjdHXv7g1I08Js68Fs78Hs7MPq7Mfs7HPCXf2jBI3wetAz09Az09ASvp6Z5vRMpfuc31VClAm3a8q9t6UtsUMR7N5BzK5+rM4+x+/qd/yOPmQssUaey8BVmI1RlIMxIycp3IyCLITLmNwTmYJMW4G2fv163nzzTWpqahgcHMSytm3QQAjBb37zm3Hnf9999/HUU09RVlaG1+tl+fLlPPDAAyxevDiZ5sorr+T3v//9qOOOPPJI3n333XGVoQSaYqIJh8O88847/Pvf/+bPf/ojtQ1N5KdqnD/f5pJ9DYp8U39+mZSSZ2vg/g8kcdsRZp9fAmk7MM9MSklJN/ylQvJcLVgSLl4ENxwoyNqGwJsKREzJ+i74oEOjpNOitBs6Et+MaV43By6ez0GHHMb+hx7JfvsvZd9996WwsHDKvbgViu0hpSQUCjE4OMjg4CCBQCAZ3tINDAzQO9BPX38/fQMDDAwOMDg4SDAYJBQIEAoEty2uDH1YXPk8TnjI93oSvVsedK8jsITPg/C6wetO7lcfkYqRDAmxeEsXZksX8WbHN9t7wU4YavG4nSUL8jIx8jKduYJ5Ts+Snp2B5nFN8lnsWqQtsXoHMVu7MFu6ibd2Y7Z2Y7b2OL1vAEJgFGThmpmHMTMPV8LpuZl7dY/btBNo5eXlfOELXxi3CALnJSCE2K6IG8lpp53GJZdcwrJlyzBNkzvvvJOSkhJKS0tJTU0FHIHW3t7OI488kjzO7XaPe9K/EmiKncW2bdra2qitraW0tJSSkhLWffAu769dRzQWJ8urcdJMm08v0FmWZ0/ZYX5b0hSQ3PW25J1WOHMu3H6EIO8T9nz1RSV/KIPfbpLoAu48QnDOfKaVoOkOS0p7BJt7BaXdFuW90DAAZuLJmpGawqJ5s1iyZD/2O/hw9lm0L0VFRcyYMYOioqIpucyIYupjmiahUIhQKEQ4HE6GR7qx4oPBIIPBAIOhIIGg44LBIMFQkOBggGAgSDgYJBIKJ82+bw0txY1IcTvDtlLcCK97RJwrGdZSPOi+FHSfI7jwesDrTgovJa4UnwQZixNv7CBW1068vo1YXRtma3eyN0zzp+KakYsxIxfXjByM4lyMwmy0dN+0etfsTqxAGLOlOylu482dmE2d2AGnoUV4XM71HCHajOI89AzfJNd89zCtBFpzczOHHnooXV1dyTHeaWlpZGVljWvYT21t7U6X3dnZSX5+Pq+//jorVqwAHIHW19fHM888s1N5KoGm2JJYLEZXVxednZ2jXFtbGw211TTU1dDY2ERzeydx02lw0ATMyRAsypQcli84aobOQr+FNs1eCq80Sm57S5LugruPFqwontj6d4cl962R/KMGzpgLdx8l8Hum1zUaSdyWNA5CzYBGTT/U9FlU90NNPwTio9P6U71kZqSSnppKWloqaWlppGdkkJGRQXpGFumZWaRnZjvb6el4vV5SUlK26ob2ezweDMNQHyA7gJQy6Wzb3q4/Vlw0GiUSiRAOh4lEIkm3re1QOEQgFCIYChGKhAmOFFfhEOFQmEg4TDQSIRp2nGWO08iAEGhuA+F2gdtw5r+4XQlnjPZThgWW7vWgJXzhcUOKCzlCdAmPe69uQVdMDmOKsZYup1dM1xyxMKcQ95wCjOI8XDNy0NK8k13tPQIpJXZ/kHhTJ/GmTszmTuKNncRbumDomycjFdfMXFwz850et1l5uGbk7nGNMLtDoE3YFbv33nvp7OxECME111zDV7/6VRYtWjRR2W+T/v5+gI/1jr322mvk5+eTmZnJ8ccfz7333kt+fv6YeUSjUaLRaHJ7YGAAIeCYY45G1xMTgcfQssmYxL4x1e4YkXLslKPTbFHeeLX0eNKNL83OHTd0wsNJd7LeY163LULjvbYfu5YjD5bOP2ljxk3iZhzTNDFNC9OyME0Ty7IxrY+3JmsCUnTwGc5cLJ8BB2RDqiHwuRxBo2tOOdX9kup+c7jMcbCjzSc7knw8aW0JG7uhohdOnAn/7xh2yTDEHK/gB8cJVs6U3P2u5MxnJUcXSowx2nZ2tEVph67JhF/v4ZEBczJgdrozhy1qOcsQRCyIWGHMcJhAsIu+NjBtp/fNdKwtj9remdY0TRPoQqBpGpo25I90ThzCMeM85NgiDIm/uxj6TyZ9R9SQCA/dy4n4xIUaEj6JVIk4Jzx87NB9LYfmxyfCw/GMih9+bmx5LFvESymHbvVkPZN57+6Z2ALHXLYmEoYBNOc66yPiNIEYMiCgOb5I0xEZ6Xh0PyIRp43Yz5CRgaH0eiKfMeswRqw0IWxCOAS9TtSWY1v2qhUYVePGpCItG3MwjDkQcnp0ghGs/pBzb+sarpn5uBfMIHXlIbjmFOCelb/HCYGphBAiOQ8vZem8ZLy0bMyOXuKNHQnh1kV4XSXWfz5wnq2ahpGdjp7hw0hxoaelYKR5p+28z6zPnLBbypmwHrT58+dTX1/PFVdcMWpY4a5GSsk555xDb28vb775ZjL+ySefJC0tjTlz5lBbW8s3vvENTNPkww8/xOPxfCyfu+++m3vuuWdUnKEx9ottAthl+e6ijHfrdRhHYRNdn6H8EsuNOEuPjNgecobzDYXhfFOhse1rviP13NFz2pG/9Q7VI5HYsqF+0OnxueVQwdX7756hh61ByVffkHzUCUcUOCJmoq7xjqbfVdd4RxIPJU3oC2yZNOiFndi25ej9ybiR6Rl93Jb5OZJquCwYETdGe0gyfqRu28YpjnUtx7oMW6YbK185cp/YdtotI8Ysczt1GJlmxCmP3jfiOohEOZoYvZ30x6jD9phMa16TWvYkFa6sp+1eLAkxK+GkICZ04tFEg6au4ZqVj2tuIe7Z+bhmF+Kana+saE5x7GjM6WGrayXe0EG8vt3pbbNsEAJXWgpuQ+CORUjBxq1NjzaRF8/TKPxx7/QZ4piSkkI8Huell15i5cqVE5HluFi1ahXPP/88b731FjNnztxqutbWVubMmcMTTzzB+eef/7H9Y/Wg/eJrx3D03GyEmJ4qXwFy1M0uEnHCcQik0LA0HVvTsTTDcbqBqbuJG27iupuYKwVL27ta5fp6A/zux88z0NnHT08QHF20e8s3bcmP1kp+swmWL/Zz2pfOI2Warym0PXErtlAaYmv7EttjZidGpx0KO6LAxrBMdCuObpvolokmLXTbQhty0kKzbTRpI6SFJiWabSGkRCAR0k6EQSQ+YUWysiJ5jmK4Mom0IpFeDOclnPBw+pH+aBm0vZf2tt9i2x6vIMYKiS2u3XT4alAopgG2LWnti1DbEaKiLcCm1iClLQE6exyjFJrLwJidj2t2Ae45BXhm56PNVGJsT0HGTaeXra6VWH07sfoO4k0dYNnoLp1Zs7I4eFY6x8xM5aDZGWSnuSe7yqOwLIvn3y3hlj/VTx+BVlRUREdHB2vWrOGQQw6ZiCy3y+rVq3nmmWd44403mDdv3nbTL1y4kGuuuYbbbrttu2nVHDTF3sqGDRs447RTsANd/OpTgoX+ybMu+Z96ydf/K8nze3nqHy9w8FErJq0uCoVCoRgftm1TX19PaWkpmzZtYsPGEtaVlFBdUUEkYdFT83ow5hTgnl2Aa04BKXOKEIWZzhBfxV6DjJvE6tuxaloJVzURq2rG7gsAkDOziMOPOIJzTj2d004+ZVzf+ruS3akNJqxb4PDDD+ef//wnFRUVu1ygSSlZvXo1Tz/9NK+99tq4/mDd3d00NjZSVLSbuwIUimnESy+9xAXnnUuxJ8L/nSEoSJlc0/8nzxEszIIvvxbm6ONO4Kf3fZOrv3r3pNZJoVAoFM63WHt7O1VVVUlXWl7G5opyaquqk0sr6F4PemJ9LfenjyJjZh5aUQ5adrrqnVYgXAaefYphn2J8pxyOlBKrZ5B4dTOx6hZe+fA9Xnz6WZCQWZTPUccdy/mnnslpJ5/MrFmzJrv6u4wJ60H7z3/+w6mnnsrKlSt5+eWXJyLLrXLDDTfw+OOP8+yzz45a+8zv9+P1egkEAtx9991ccMEFFBUVUVdXxx133EFDQwObN28mPT19u2WoHjTF3sbDDz/Mqhtu4OhCyUMnCNKMqTMLI2xKvv0ePFUlueRTh/GzJ18gOyd3squlUCgUezT9/f00NDQkXV1dHZsqyqiorKShtm7U+nZGdgZavrOmmFGUQ4oSYooJwg5GiFY0Ei9rILy5HrOpE4DCBXM5/Ywz+PyFF3HM8mMwjF07HWV3aoMJXQft61//Ot/73ve47rrr+PGPf4zLtWsW+dvajf7II49w5ZVXEg6HOffcc/noo4/o6+ujqKiIlStX8p3vfGfcalsJNMXeQjwe56abbuLnP/85ly0W3HGEwDUFR5hIKXm6Gu59X+L1uPjZ/z7IZ76warKrpVAoFNOSeDxOc3PzKAFWX19PVV0t9Q0NtDY1EwoEkumFrqFnZziLO+dnoedn4SnIRi/IRMv1O0s5KBS7AWswRKysgciGaiIbarAHQ3jSUzly5QquOP8iLjjnXDIzMye83Gkp0B599FHAaYV/5513KCoq4sILL2TffffF59v+wnVXXHHFRFRjwlACTbE30N7ezqUXX8Sbb77JN46ASxZP/VbO9qDkW+/Bq42STx+9Pz/9wzPMmb/PZFdLoVAopgxSSnp6ej4mvmrq66ipr6O5sYnu9o5Ry9vo6T60rHT0nAxHiOWk48rx48rxQ1Yamj9VzQ9TTDmkLYnXtRLbUENofRXx+nY0l8Ehxy3n+iuu5JILPkNaWtqElDUtBZqmaTvdhS2EwBzvopu7CSXQFHs6Tz75JKu+dD1EB3loBRxRMHWGNG4PKSX/rIP7PpAETMFtq77A1+//2ZhLaCgUCsWehJSS3t5empqaaGxspKmpiYaGBmobGqhtqKepqZGO1jZi4UjyGGHow8Ir2xFhRnYGrhw/ek6GI8A8U8tinkKxM1g9A0TWVBD8YDPx6hZ0j5sjTlzBqs9fzflnn4PXu/MLl09bgbbTlRACy5pay18qgabYU2lra2P1jav469+e4rS5Gt86WiPbPbnGQHaWQEzy0w3waKlkdl4aP/35w5xxwaWTXS2FQqHYKYbE15DwGvLrGxqorq+jqamJjtZWoiPEF5qGkZmGlpXmCLCsdPRsp/dLz85AZKejpfsQ2tQfIaFQTCRmVz+RD8oIvbeZeEM77jQfp19wLneu/h+WHXb4Duc3LQVafX39Jzp+zpw5E1GNCUMJNMWeRigU4oc//CEP3Pf/8BDnG0fYnDF3z3hhV/ZKvv0+vN8m+fQxB/GTx55i7rz5k10thUKhSDI07HCk8GpqaqKmvs7p+Wp0xFcsMrwmK5qGkZXmDD0cctnpuLL9GNnpauihQjFO4q3dRP67icB/N2D3B5mx7z5cf80XWX31teOerzYtBdqehhJoij0Fy7J47LHHuOv2r9PZ2cnnFttcf5CBf5r2mm2NoWGP938gGYgLvv6V6/n6vQ+qYY8KhWK3EAgEaGxsTLohgxuO+Gqko2UM8ZWZhpY9LLz0LKfny8hS4kuh2BVIyyZSUkP4jfWEN1SjGQYrzjiVu2++lRXHHrfN6VpKoE0BlEBTTHeklPzrX//ia1+9mU2byzltruCrhxvMSp1aw4knmkBc8rMNgkc32czK9/PjXzzMp8+7aLKrpVAopjGxWCxp8bCxsZGGhgaq6mqorqujsamRjuZWggODwwcIge5PdcRXYt6XkZ2BK9uPnp2OUOJLoZh0rL4A4bc3EnhjPVZHH4X7zOXGL93A6muuG/PbXwm0KYASaIrpzAcffMCtX72F1994kyMKNW49XHBgzt51q1f1Sb7zPrzbKjnjmIP56aN/Zd78BZNdLYVCMcWwbZu2trZkz1dDQwN19fVU1tVS31BPa3Mz/V09oyweaqkpwwY3sh3jG66cDLSsdLScDPTMNIShT+JZKRSK8SJtSbS0jtBr6wivq8RwuznlvLO555bbOPyww5LplECbAiiBppiOVFVVcecdt/Pnv/yVRdk6txwiOb5Y7rWLhEop+Ve9M+yxLyb42qqrueP+n5CSkjLZVVMoFLsB27bp6OgYZXSjsbExOe+ruamZ7vZ2LHN4ZIHwuIaHHCbFlx8jYe1Qz05XFg8Vij0Uq3eQ8JsbGHx9HXZvgDlL9+XmG7/MNZd/HtM0p65AO/HEE50DheDll1/+WPxOVWKLvKYCSqAppjLRaJRQKIRpmpimSSgU4sEHH+T/fvlL8nyC1QdYnLsAdGW1C4BgXPKLEsHvNtkU56TxvR/8kAsvv3avFa4KxXQnGo3S2dlJe3v7KNfW3k5DcxP1jQ00NzXT1d6OFR9exke4DPSs9FEWD42sdFy5frSsdMfiYWqKejZMMNK0kLE40rRAAkjHH/oEHfKFAF0DTXOGf+rC8TUNNKH+LordhrRsIhuqCb66juimGtw+H2dccC7PPPr41BRoQ+b0tzSNP7QO2o5kN5RemdlXKEYTi8VobGyktraWuro6amqqqS3fTG1NJXUNzbT39H/sGK8B+V44IBeOKBQclg8L/KgX2giq+yX/7314q0VywNx8vn3/Dznnos+qa6RQ7GaklAQCAfr7++nr66O/v39UeMjv7eujs6ebnr5eenp76e3tpbeza/R8rwR6qheR7kXLSE0a3DASQw/1rAzISnXMzav7faeRUiJDUcyufqyeAez+IPZgCGswhB0IYw+GHBeJIaMmMhZDxkywJsgolSZGibdkWNOcZQT00WGREHZoGkLfIixEIi4h/pLhLY8fLRRFIo+R+Q8dK9wGmteD8LodP8WD5nUjUr1oHtfEXAPFbsfs6CP8xnoCb24g787PUfGl7089gXbCCSckH26vvvrqmPE7w8i8pgJKoCl2F319fbz++ut8+OGHfPjuW5SUbKS5owvbdm5NARSmwsw0KE5z/KI0nVRDoguJJmAgJmkLClqCktIe2NwDtnQE2+lz4cx5ggNzlVgb4v02yU/WwfvtkiWzcrnm+lV8/rrV5OTkTHbVFIppg5SSwcFBent76enpoaenJxnu7u6mo7uLjq4uOru76e3toa+vj4H+foKDAUKDAaS9lY92TUP3eRDehPN50LweNJ8HkZqCnpGK4U/FyEhFy0iFhChTc74mDjsYId7cSby5C7OlG6u73xFl3f3IcGw4oQAt1YuW7ks4L3q6z/m7eVwItwvhcaG5XQiPAXrib5R4FwkhnJfcUJyUSMsG2wZ7OCxt6cRZw2Fp2U7Pm2U7vyXbRlqJdEP7h9JucSyW7XQoJI8dHf54nA2WHC4nsT0yLG0bRvTWbolIcaP709AyUx3fn4qelYaRl4VRkIWe51dDZ6c4Mm4iXAalF9wx9QTa3oISaIpdiWmaPPvss/zq/37By6+8hmlZZKcI9s+R7JsFczJ0ZmYIir0Whang1ndMWAXikvWd8GqT5IU66AzDnHS4YongvH0g1aWEGsB7bZI/lsErjRIhNE455lDOvuRKzjznPGbMmDHZ1VModgvxeDwprLYUW13dXbR2dtLR1UlXTzc9Pb309/Yy2D9AaGAAe6yeEQGaL8XpNUhNGXa+lKTY0n0p6D4nTvO6kSkehM+N8Hmcj3rVmLRbsKMxzJZuR4g1dxJvckSZ3RdwEugaRn4mel4mRo4fPdePnpOBkfCdBbCVJcohpG0jI3HscBQZjmJHoshQFDsYweoLYPcHsfoDWP1B7P4AVk8AGR0WvJo/DaMgEyM/C2NGLq6Zebhm5jkWP9U9MWVQAm0r3HfffTz11FOUlZXh9XpZvnw5DzzwAIsXLx4z/XXXXcfDDz/Mgw8+yE033TSuMpRAU+wKpJQ8/vjjfPOuO6ipa+CQfMFZ8+CEWTozfNYueQBbtuSDdvhzheSFekh1wcWL4Mr9BLle9cAH6IlI/l4jeKHOZn2X03C639wijj32WI475SyOPe445syZo16QiimHlJJQKEQgECAYDDIwMEBfX1/S9fb2Jv3Onm46e7oTcf0M9PcT6B8gEgqNmbdwG4iEsNK2EFt6mhc91YvmS4FUD2IoTVoKIsXjDANTTBmkaWG29zhCLCHC4s2dWJ19iTlhoOdl4ip2RIFRnIurOA+jMFv1TO5CpJTYgyHMjl6s9j7Mzl7Mjj7Mth7Mlm5kLA6AlubFSIi1IWcU56lhk5OEEmhb4bTTTuOSSy5h2bJlmKbJnXfeSUlJCaWlpaSmpo5K+8wzz3D33XfT2dnJrbfeqgSaYtKoqanh2qu/wCuvvc6nZgluPERnv6zdu1h0S0DyWJnkyQpnSsBFi+CapYICn/qYGqI3Inm7Ff7bCmvbJbUDTnxhdjqHH7SUpYcewQGHHsHixYuZPXs2ubm5SrgpPoaUkmg0SiQSIRwOE4lExnRj7QsGgwwODtI3OED/4ACDgQCDg4MMBgIEAwFCwSChUJhIMEQ0HN7m3G/Nk+iV8nkcoeVLiKlEWPN5MNJ86Gle8HnA50mKMeEyduMVU0wE0pZYXf3EmztHCTGzrSc5D0zzp+GamRBgQ4KsKActRQ2vm0pIW2J19jm9mk0dxJs6MZu6MDt6naGdQmAUZuGaVYBrdr7jZhWg+1O3n7niE6EE2jjp7OwkPz+f119/nRUrViTjm5ubOfLII3nxxRc588wzuemmm5RAU+x2LMviRz/6Ed/65l1kGSb3HA0rJnn0XF9U8thmyaObIWzChQvh2qWC4jQlNLakJyL5qFOwtgM2d9tU9UP7iA4Hr9tgZmEus4pnMGfuPGbNX0R+UTG5ublJ5/f78fl8pKam4vV6k8aWpitSSmzbntLONE3i8fiYLhaLjQrH4jGisXjCd7Zjie1YLOHGyMeMx4nHTUwzjpnwY9EY8WgMMxbb/oXcAqHrCJeO8LjA40ZLSczhSXE783hS3KO2hceFkeJB87jRvG5wuxyXOkKMqd6PPRJp287He0s3ZktX0jdbux2jHIDweXAV543uESvOdcS4YtoiY3FHeDd2OK7B8WXEeeZoGakJsZYQbbMLMAqy1FDUCWR3CLQJax6rqKjgtNNOwzAMXnvtte3O32hubub4449HSskrr7zCnDlzdrrs/n7Hol12dnYyzrZtLr/8cm699Vb233//7eYRjUaJRqPJ7YEBp9n8xBNXYiRecGNJ2bH17cfjdvbYseWz3Er8xOS/Lc0+ete20u3Yvq2d547kOzqf7aUbe78cMv87sg5JM8AgE9tSSmzLwrItbMse4duJeBvLsojFnA84G/jcvnDTIZDunvyHZKZHsPpgwVX7OT1qvyt1hkAekCspToW07TSkjrdZZ7ytP+NON8HljjehTCTM9zkuZkHQdMRtyDQJ9bdR1t3G2rVriZgQtbadta4JdF1D1zR0XUfTNXTN8TVdR9N0ECARifnzIlEHMVwbmbCohvN7lHL4t+m44f2M2B76HQ/vY4vjE2c8lH5keDq35wkcIwQJi8NO2IkTWmJbaCPCI+KHzHsnwsntxH5hCHBrIFIQmg+XruFKWJcTQ9bfRliLQxuOE0mrdCJpOGG72BaELQhHsBhkatlA3k3sBb3WUkpk3MQORRMugh2OYgejWIOhZI+Y8LoxivNwzSnAd/T+6IXZuGbmoWelq979PRDhduGeV4R7XlEyzuk57SNW30a80ek9Db9XSuBf7zkJDB0jKx0tLQXd60HP8DnGXFQDzg7jv2DF9hNNABMm0J588knq6uo47bTTxjW5vri4mEWLFvHiiy/yxBNPcNttt+1UuVJKbr75Zo499liWLl2ajH/ggQcwDIMvf/nL48rnvvvu45577hkVpwtYt3btdo+d0MffODPb1Y/cXZ7/BBewK+srtlLA0Pee2DI85NsgLfC54PvHwYmzJl+YbUmaW/ClAwVXLJH8qVzyyCbY0AULMhyB4dG3/rca7zUf7996svIbL1sr16WB3+O4oXKldOaxmRLMIWNfo5zExkJKC1vGkRbY5vAx9nCbwLAvR2+PrM6WVRuqqxjh2DK92GJ7xAFb3b9lXmI7+/n4dRt3Xcaq23bqMtaxo8uXsD05Ixm+0Hul8tk+01mnTwVGNQFK5/lg2gknIS40TKFhxhOWCBNo6V6Mohw8C2fhmpHrDG+bkYeWlaaE2F6O0IRjWCQ/C5YtScZbgTBmfRuxhg6n162hnVh9o2OVEjB8blyGhisWw4WNW0u2OynGQAL/zl8Dn/nGLi9rwgTaiy++iBCCs846a9zHnHPOObzwwgv885//3GmBduONN7JhwwbeeuutZNyHH37I//7v/7J27dpxP7Ruv/12br755uT2wMAAv79jOYcWpKof6h6O3Ir6kmhIAbbQkELDFhq20LE1J2xpOlJoWELD1nRsoWNpOqbu4r9rW3jyrx+xIFPy4xMEc9J3/3ntCKkuwTVLBZ/bV/LnSvhjmaRuAGbmeFi2YglLj9kPr89RIGKs6zUG2/po39ZN9Ulvt3F9O473C3OMeopRu5wtbUSth+IdoS4SmkeMEBACLZGvLm1cVgx3PILLjOGy4hhmFN020W0L3YqjSRtN2ghpO3lJiRgh0USi54dRvkiWPXRVhG0jkE6nkW2NOJct7gD19a1QTChR06a1L0pTT5Sm3iiNfVFqeuM09Ubp7A1jmgkRJsDISkcvyMaVl4k3PxNXnh8jPxMtPxvN65ncE1FMO/Q0L/r+8/DsPy8ZJ+Mm8ZZurIY2oo2dxBs7iDR0IMPOKDJfmofZhWnsV+Dl4Hw3+xb6mJ2TgqEM/2DbNk9vqOZLu6GsCZuDNnv2bJqbm3njjTc45phjxnXMf//7X45LWEerra3d4TJXr17NM888wxtvvMG8ecM/voceeoibb7551DwPy7LQNI1Zs2ZRV1e33bzVHDTFzhAOh/nKV77Cr371K87fR/CtowQp03AEgS0l77XBExXwUr3EMDQ+c9oJ3PSN+zh02RGTXT2FQqGYMkgp6e7upqamhurqampqaiitKKessoL62jp62juSw+qFy3BM1edlYuRlYuRn4i7IRs/PRMvJUIZZFJOClBKrewCzoQOzsYNoQzvxhnasbme6j+F2M2vRAg486CBWHHEURx52OAceeCDp6VO89XmC2Z3aYMIEWkpKCvF4nLVr13LQQQeN65j169dzyCGH4PF4CIfD4y5LSsnq1at5+umnee2111i4cOGo/d3d3bS2to6KO/XUU7n88su56qqrtmqOfyRKoCl2lPLyci668ALKyzbzjWWSzyzaM1qbOsOSv1YKnii3aQvBssUzWX3z17j4yutwu5XVL4VCsXcQDAaprKykvLyc8vJySjaXsmnzZupraggNBpLptDTvKAHmysvClZ+FyMtAy0xXSxAopg12MEK8qQOroZNoQ5tjlKS5Kzn/MW92MfsdsJTjjjyaE5Yfy+GHH47f75/kWu86dqc2mLCmGr/fT1dXF21tbeMWaG1tbQD4fL4dKmvVqlU8/vjjPPvss6Snpyfz8fv9eL1ecnJyyMnJGXWMy+WisLBwXOJModgRpJT88Y9/5EvXf5F8d5w/n6mxb+buNZ+/K8nzCr50oGPl8ZVG+ENZM1dc92VuvfVrfPWmVdx4+3dJSUmZ7GoqFArFJ8ayLBoaGigvL6eiooJNm0vZULqJqspKulrbk+n0dB96QRZGYTbGKYeRW5CNnudHy8tE86mhiIo9Ay01Bc/i2bB4NkNf6tK0MFu6MRs7CDe28071Zt545VW+G3asSBbMm80hhx3GSceuYMXyYzjwwAPxeNQ9saNMmEBbuHAhXV1dvPDCC5x66qnjOuZf//oXAAsWLNihsn7xi18AcMIJJ4yKf+SRR7jyyit3KC+F4pPQ3NzMl66/jn889zxnzxfcfbROqrHniLORGJrglDlwyhyo7IXflsa47Ts/5AcP/ZTbb72ZL33tbtWjplAopgWBQIDS0lI2b95MWVkZG0o3sbm8jMba+uQSCcJloOdnOiLs0HlkFx6OqygbLT8LTZmqV+ylCENPrrvmxTHOJ22J2d6DWdtKsKaVlz98lxeefhYsG91lMGfJIo464kjOPPFkjl+xguLi4kk+i6nPhA1x/M53vsO3vvUtvF4va9asYcmSJdtMv2nTJo444ggikQh33nkn3/72tyeiGhOGGuKo2BaWZfHrX/+ar331FjwyyreOsDl59mTXavdT2y/52QZ4rkYyJy+N++//Hhdddb2yKKZQKKYEg4ODlJaWUlpaSsnGEj7csIHNpaV0tgxPgzCyM9ALszAKsjEKs/EU5SIKMtFzMtTaUQrFTiLjJvHGDszaNqI1LUSrW7A6egHImlHAYUcdyZkrT+LklSeyZMmSabE+6LScg9bV1cW8efMIhULk5+fz8MMPb9Wi49///neuu+462tvb8fl8VFdXU1BQMBHVmDCUQFOMhZSSF198ka999RZKNpVy/j6Crx+h43ftmb1m46W8V/L9D+HNZsmyRcX86Je/49iVJ012tRQKxV5Cf38/mzdvprS0lA0lJawtWc/m0s10tTpTIBACI9ePMSMHY0Yu7uI83MW5iMIsNI/q+VcodgdWf5BYVRPxymYilU3E69vAlqSkp7F02aGccsKJnHHiSRx++OFTcljktBRoAH/84x+5/PLLk63n8+bN47jjjqOoqAghBC0tLbz55pvU1tYipUQIwe9+9zsuv/zyiarChKEEmmJL1q1bx1e/egsvv/wKhxXq3HY4HJSjTJKP5O0Wyfc+lGzugbNXHMr3H/4jixbvO9nVUigUewj9/f0jesQ28uGGdZRt3jw8P0wIjDw/xoxcjBm5eIodXyvMRvO4JrfyCoViFHY0RrymNSHYGolVtSCjMXS3i30PPYhTP3Uy5556OkceeeSUmEIxbQUawKOPPsoNN9xAKBRyCthiqNNQcampqfziF7/gc5/73EQWP2EogaYYoqmpibvuvJNHH3uMuZk6Xz3Y5lOzpBrGtxVsKfl7DTz0kaQzDNdc9Gm+/ePfkpeXN9lVUygU04SRQmzjxo2s3bCe0tJSutpGCLH8TIyiHIziXDwz8jCKc9AKlBBTKKYr0rKJN3UQr2gisrmeaHkjMhzF8LjZ97CDOf2kkzn31DNYtmwZLtfuv8+ntUADaG1t5cc//jH//Oc/2bhxY1KUaZrG0qVLOeuss7jxxhun3LDGkSiBphgYGOD+++/nwR/9kFTdYtUBNhctErim/jDpKUHElDxaJvi/DTY2Gl+45Bxu/e5DzJ69F07WUygUY9Lb20t5eTmbNm1i06ZNrC1ZT+mmUjpHDE105Weiz8jFmJHjDE2ckYNWmI1wKyGmUOzJSNsm3tBBvKyRcFkdsYomZCSGy+th/2WHccZJp3DOqadz6KGHYhi7fg3BaS/QRmKaJj09PQBkZ2fvlgs4ESiBtvcSj8d5+OGHufub3yAw2M+V+9pce4BOmksNZ9wZeiKS328WPF5mEzLhwlOOZdXXv8sxK1aoXkiFYi9gaP2wyspKKioqKCsvZ2PZZmqqqxnocYwGbNkj5gixXLTCLCXEFAoFkOhhq2sjVt5IpKyeWGUTMhrHnerjwCMO46xTTuOsU0/noIMO2iVGR/YogTZdUQJt78O2bZ555hm+/rVbqaqp4bwFgq8calDotSa7ansEgbiz4PVjm22aArDPjByuvuZarrrhpindm65QKLZNNBqlsbGR+vp66urqqK+vp6qmmsraGmprauhu60im1VO96AWZ6AXZGAVZuAtzcBVmqR4xhUKxw0jTIlbXilnWSHhzPdGqZoibpGSkcfjyozj3tDM57aST2W+//SakQVgJtCmAEmh7D7Zt8+yzz3L3t77BhpJNHFssuPVwfY9abHoqYUvJe23w10r4d73ERvCpow7k0i/cwLkXXozf75/sKioUCpw544ODg7S2ttLS0kJrayutra00NzdT29hAbX0dTQ2N9HR0JqcyIARGZhpaTgZ6TgZGXiauwmzcBdlQkIWu1g9TKBS7CBk3idW0OEMiN9cRq24ByyYtJ4ujjl3OeaedySknncyCBQt2SrBNe4FWVVXFo48+yjvvvENbWxvhcJgXXniBffbZJ5lm48aNNDQ0kJqayvHHHz/RVfjEKIG25xMIBPjjH//IT/73QTZtLufoGRo3HiQ4PF+1Wexq4rYkEIOeqOStZsG/6iQfdYJb1/jU0Ydw8VXX8elzzicnJ2eyq6pQ7HFIKenv7x9TeDW0NNPY1ERbayudHR1EQ+FRx2peD5o/Fc2fip7jx8jJwMj148rNRMvOQMtOQ7imx1QGhWJXIaUEy0ZatuObFlgWCAGaAE1zBIImQAhEIg5dU0P/JxA7GiNW1Ux8szOHLV7bBlKSWZDHcccfz7mnnc5Jnzpp3HPjp61As22b2267jYceegjbtpMtakIISkpK2G+//ZJp//Wvf3HmmWdiGAa1tbVTblVxJdD2TCzL4u233+ZPf/oTf3j09wRDIU6YKbh6qaaE2S6kcVDySiN82CEp7YaWIFgjLndxKuyfA5kpgooeybou5z12yKI5nPrpsznj3Is46qijps0cVoViMohGo3R0dNDe3p50HR0dNLU0U9/URFNzE+1t7XR3dBCLREcdq/tSHOGVmeaIr8w09Mw0jMw0XFnpyAxHlGkpk2/qWqHYnUjTwuoLYPUFsPsGsXoT4YEgdjiGHYogw1HscBQZimJHYmB+gqkRuoYwdDB0hK4jDM0JJxz6yPCItIaO0IfSGsPHJdJjaCPCo48fncdQOLHPm4KWlrJHLNpuh6LEKpqIltUTLasn3tgBEvJnFXP8yhM499QzOPHEEyksLBzz+Gkr0K699lp++9vfIqWkuLiYo48+mr/+9a9jCjSAffbZh9raWn70ox/xla98ZaKqMSEogbbn0N/fz2uvvcYLL7zA03/7M+2dPRSkapw33+bifV3M8Kk5ZhONlJLyXnipAf7TICnrBZcGB+fBATkw1y/I9oCuQU8EKvsk77RCeS8U+OBz+4LfI3i3Fd5ulfRFITXFxbIDFnPM8SdxzImncNRRR5GVlTXZp6pQ7DKklAwMDNDd3f0x0dXa1kZjazMtrW10drTT3dlFaDDwsTy0VEd46f40tITo0jPTcGWmo2emIfw+hD9NmaZX7NXY4ShmRx9WRy9mRy9m+5Dfh92/xX1l6OhZ6egZPoQvxelV9nkQQ36KB+FKiJ8hgaUnxJN0LBNiS5Dy4+Gh3jbTQg6F4+Zw2LSQloU07WQY00Im0ifDiR47GR9KM9yLJ03LKXNHEDjnmuZFT/OipXmd50pWOnp2huNnpaNnp6N5p94C01vDCoQxyxPz18rqMVu6AZixYC4nrjyRc049nZUrVyZH80xLgfbaa69x4oknIoTg9ttv55577kHXdbREN+5YAu3222/ngQce4Oyzz+aZZ56ZiGpMGEqgTV+i0Shvv/02L7/8Mv954Z+s+Wgdti2ZlaFx0kybU+cZHJRjoalhBBNKzJJ82AEvN0pebYSmAKS64IRiOGmOYEUxpLm2fc3LeiS/3iR5rgb2yYS7jxIckgebuuHddo2POiw+6oDeROP/otkFHHXEMo5aeSpHHrWcAw44YFLWRlEotkc4HKa7u5vu7m56enpGhbu6umjv6qSjq5POrm56errp7+0j0N+PtWVLvBDOh2G6D92fipbuQ0/0bhkZqRj+NES6F5HhQ0v3OS3jCoUCcHrD4i1dxBs7iDd2YDZ2Em/uwh4IJtMInwejINuxKlqQhZ7jR89KQx9q1EhNmfbDEEeKwY8JwiERGHfCdiiCHQiPdoMhrP4gVu8gdn9wVN7C68EoyMIozMbIz3LCiW3NlzJJZzw+rP4A8fJGIqX1RMrqsTr6QAiK95nHUUcfzYlHH8OqL90wvQTaJZdcwp///GfOPPNM/vGPfyTjtyXQnn76aS644AIWLFhAZWXluMu67777eOqppygrK8Pr9bJ8+XIeeOABFi9enExz991388QTT9DY2Ijb7eawww7j3nvv5cgjjxxXGUqgTR+CwSBr1qzhnXfe4eV/v8B/336HcDRGtlfjqAKb5UWCo4p1ZqUqox8TTcSUvNYEL9ZL3miGQBwKfbByFqycKTi6CNz6jr/INvdIvvWOZH0XXLAP3HqYICvFyUdKScMgfNSlsa7DZl2npKLXGTLpcekcsGgeRxy1nCOOO5EjjjiCxYsX7xJzu4q9iyGDGX19fVt1Pb09dPb00NXTQ09vDz09PfT39jLY1/+xIYWAI7ZSUxBpXrTUFERqomU6LQUt1YuR5kVP86GleYdFV5p3jxhqpFDsaqyBUFKIJQVZazdYzreAnpeJa3Y+ruLchCDLQleGbHaY5BDQnkGs3gGsrn6nBzLhRopfPScD16x857rPKsA1Kx89zz9lBa/ZPUC8rIFoZRPRqibMlm5yv3Yp1Xf+cvoItDlz5tDU1MTf/vY3zj333GT8tgTaBx98wJFHHklqaiqDg4PjLuu0007jkksuYdmyZZimyZ133klJSQmlpaWkpqYC8Pjjj5Ofn8/8+fMJh8M8+OCD/OUvf6Gqqoq8vLztlqEE2tRDSklnZyfl5eWUlZXx0UdreefN1ykpLceybVJdgsPyJcuLBEcX6yzyq16yXcXaDsnj5ZKXGyBkwn7Z8KnZgk/Ngn2zmJCHrS0lf66AH66VuDT41lGCU+eMnW/ElGzugZIejZJOi5IuqB1w9qV5PRyy/2KWHXUMhy8/joULFzJ37lxycnKm7EtBMXHE43ECgQCDg4Mf87eM6+vvp6e/l+6eXnr6eunr62Ogv5/AwCDBgUGn1XkMhNs1eojT0LCnhPDS0rwYaT70dC/ClwKJOOH1OMYBFArFTiNtG7OtJyHCOpNizO5zhiYKtwtjZq4jDIbczLxpNRRvOuMMH+3FbOkeFssNHdiDIQCE1417/gzcC4pxLyzGPX/GlP3b2MEIWmoKpRfcMX0EmtfrJRaLsXbtWg466KBk/LYE2kcffcRhhx2G2+0mEonsdNmdnZ3k5+fz+uuvs2LFijHTDAmul156iU996lPbzVMJtN2DbdsEAgH6+/vp7++nr6+P/v5+Z45FaystLc20NtTR2FBPZU0dfYNOS4wmYE6G4JBcycH5goPydRZmWOjqY2eXsrFb8sAayfttMC8DzlkgOH0uzM3Ydde9IyS5+13Jy41w2hz45pGCHO/2yxuMSTb2CEq6YGOXTUmXY5xkiFSvmzlF+cwoKiQ7N4+cvEJyCorIyc0jLS0Nn8+Hz+fD6/WOCnu9XjweD263G5fLhdvtxu12o+t771AyKSW2bSeNQw2FLcsiHo8nnWmao7a3Fx+JRAiHw6NcKBQiEAoRCAUJhkKEwiGCodDoNMEgoUCQSCiMGYttu/KahpbiRqS4ER4XwutG86UkXEJs+TzovhTHkEZqCsLrQSbTeZTVQoViN2GHosSbOkZ96MebuyBuAqBnpWMkhVgerln5GAVZqtd5iiGlxO4POn/D+nZi1c1Eq5qRwQgIcM3Kx7PfXDz7zcW9cOaUmyO7OwTahL1VhgRaKBQa9zENDQ0An3iif39/PwDZ2dlj7o/FYjz88MP4/f5R4nEk0WiUaHR4CMrAgNP8fsYZZ2z1w2uktt2azt1emvEct7Nljjfv7dZLSiSJben8J2VyYzitdCa6WraFZVpYlollWViWhWmaI+It4qZJNBYnlniojoVHB58BXsOZy7TAC/5MQaYHMlwSXXPKrRuQ1A0M5bON9oZxNkWMJ9l4WzXG2/wx7vwmMLMdOYe4DR91QGU/7OOHn57g9Jjtjh7KfJ/gZyvhn3Xw7fckZzwrOa5Y4tUdS4/bqT0AmR44rhiiFgTjEIwLgvE4wZ4myjqaiFnOvpgNMWu0hcnxIgChCTShoWnCcUJDaJrTUDViWwiRbLwaMrEshEj26AkhEreTxLnfnHtOIp1J5ok4YEQ6EumG70+ZSCAT96aTB8k8GZl25HMjWUaygBHHMfxM2F2GT3UNNOF8ZOmaY6J6yCS1Jj4e59IQuWnouh89YY1MMwzQNaSuOZP1h6yZacL5420FaZpYAybWQHDriQCxrUwmmt3ZDrUn9zLvqee2O89rFxUlbYkMRZy5ToMhZMAJ24HEsg+GjmtGDq5Z+XiPXJIYNleghidOE4QQSYNFKQfMB5y/udneQ7SiiVhFI6F3Sgm88D7oGkZRDnp2OkauH23Mv/Hu+81nnHvsbilnwgTavHnzWLduHR999BFHH330uI557rnnAD7Ws7YjSCm5+eabOfbYY1m6dOnH8r/kkksIhUIUFRXxn//8h9zc3DHzue+++7jnnntGxWkC3nn7v9utw0T/LHY4v3EcMFF1HE8+Q+8GkQiLEeGR8akapKcklwFBI7E8iABdjH7HxG3ojkB3ZOwvwvGe33jfW+M6z0koc7wJx123caQJm9AWctLedYTgkkUSl757WyOFEJw5D44qhHvek/yjBg7McX4r5jhEwsfP0znIqztuuJzE3oT2sOWwG7k9Mk3SJyGQsEbFAWCDtIa3k/mPlc8Yumdk/cf6PYktwlt2JG+5f8vIUcnFcMSW8VsW/bHtMcoVI44dGWasfXz8meHEDw0t3MJghp1wW2/nGcVkLKSxu8uc+JVNx1Hm7i9yt7O3XNfdfZ62dBrE4jaYNsQQmEInHh+2NKil+3DNyiPlwPm4ZiZ6xWbkKgM4exhCE7iKcnAV5cDxByGlxGzpIrKxjujmOqKbG4jGTYwMLx5DIyUUxBBjvxN3FRJ4JesduOCOXV7WhA1xHLLIeOCBB7J27drkpPytDXH88MMPOeaYY4jH49x///3ceuutO1XuqlWreP7553nrrbeYOXPmqH3BYJDW1la6urr41a9+xSuvvMJ7771Hfn7+x/IZqwftz99YztJMY49tZNsacsRnlxTDn062EM4+ARINKYTzkSkSYSGQQsNOOEvTsYWGqRlYmoGpGcR1FzHDRVx3E9ddifwVUxEpJe+9UcELT69l/xz44XGC2emTXSuHF+sl97wridtw6dn7s2jlsgmZTya38km0td6RkfEjGyB028JlxXBZcVxWHMOKYVgmuj3kbHRpodmWI0SkPVqkJDIcue2kAyGt4XhpDx87tO5k4kyQo+u01VBSBKl7UaFQTCxSSvrDFh2DcToG47T0x6nviVLTb9LQE6OzL4KVGLKgeVzoxXmOCCvOxTMzF604Dz0jdZLPQjEVsKMxopvqiHxUSWR9NXYgTHp2Kiv3z+ayxSkszNv1FiJt2+a/FU1c9buG6TMHrbm5mUWLFhGJRLjyyiv55S9/icvlGlOg/e1vf+P666+nu7sbv99PXV0dfr9/h8tcvXo1zzzzDG+88Qbz5s3bbvqFCxfyhS98gdtvv327adUcNMXezMDAAFd/4Sr++renuGKJY0VxZ6wx7kp6I5J7P4B/1Eg+dcAsfvvUf5i9z+LtH6hQKBSKT4xlWbS1tdHU1ERzc3PSr2uop76xkabmJjpa24mPaPwWuo6e50dPmF93FWThLshGFGSh52Qow02KcSFtm1hFE5H3ywh+sBkZjFC4z1w+99nPccv1q7a60PQnZVqugwbwm9/8hmuvvRYhBDNmzOCss87il7/8JUIIbrrpJkKhEC+99BI1NTVIKRFC8MQTT/CZz3xmh8qRUrJ69WqefvppXnvtNRYuXDiu4/bZZx8+97nPcffdd283rRJoir2VkpISzj/3HNqa6rl3uWOYYyrzcoPkW+9KYlLjtz/5Aedf8z+TXSWFQqGY1sRiMVpbW2lqakq6uoYGqutraWxqorW5hd7OLmxreNixMHRnoeLMtOGFi7PScWVnYGSlQ2Yqmj/NmQOqUEwQ0rSIbKwl8l4pobWVCNvmsBNXcPvq/+GcM86cUANe01agAfz2t7/ly1/+MqFQaMyWkKHiPB4Pv/zlL/n85z+/w2XccMMNPP744zz77LOj1j7z+/14vV6CwSD33nsvZ599NkVFRXR3d/Pzn/+cP/zhD3z44Yfsv//+2y1DCTTF3shzzz3HpRdfxExvjP89XmNuurX9g6YAfVHJ1/8LrzZKvnzhSr7/xxdwu92TXS2FQqGYskSjUaqrq6mqqqKyspKNZZspLS+jpqqa7rb2UcbChMeFnp0+SngZ2RkY2eloWemIzDRn6QjVA6aYROxghPC7pQReX4fZ1ElGQS6fv/pqvnXzreTk5Hzi/Ke1QANoamrioYce4u9//ztVVVWj9hUXF3P22Wdz6623Mnfu3J3Kf2sPgEceeYQrr7ySSCTCZZddxnvvvUdXVxc5OTksW7aMu+66i2XLlo2rDCXQFHsTUkoeeughbrnlFj41C753nEaqMb2m/ksp+e0mZ920w+Zm85cX32T2wp03QKRQKBTTnVgsRm1tLZWVlVRWVrKprIyNZaXUVFfT1dKWFGHC40ou1GwUZOHKz8KVnQGZqehZGQivW4kvxbRBSkm8tpXQ6xsIvrsJTQhOOv9svn/X3Ry4/9LtZ7AVpr1AG8nAwAAdHR1YlkVOTs5WrShONZRAU+wtxONxVq26gV/96tdcsxRuOXT3mM/fVXzYIfmf1yVxqfH7h3/Kpz/3pcmukkKhUOwSLMuitbWV+vp66urqqK+vp7aujvLqSqqqqmlvbkkOQxRuF3p+JkZBFkZ+Fu7CHPT8TPT8TLTMNCXAFHsk1kCI0OvrCLyyFrs/yJJjjuA7X7+D8888e4d/89NSoJ144okAXH755Vx11VUTkeWkogSaYm+gt7eXz1xwPq+//jr3HAUXLtwzXtA9Ecmtb8J/WyS3XHEW9//26b16IWmFQjE9icfjNDU1jRJgdXV11NbVUVNXS2tzC2Y8nkxvpPkQOenoOX6M/ExcBdmOEY58P1pmurNuoEKxFyLjJuEPygj8ew3xhnbyF8zhW3fexXVXXDXu74NpKdBcLhe2bfPSSy+xcuXKichyUlECTbGnU1VVxZlnnEZHUx0/Ph6OLJheQxq3hy0l/1cCP14nOWpRIU/+601mzttnsqulUCgUSSKRCA0NDUnx5fSA1VJdW0t9fT0drW3Ytp1M785MR8/JgOx09Fw/ek4GRo4fd14mZKejpai5twrFtpBSEqtoJPDP94iU1OAvLuRrt32Nr163artz16elQCsuLqatrY01a9ZwyCGHTESWk4oSaIo9mRdffJFLL76ITBHklycK5qbb2z9omvJem+SWNyQRW+P+b93O9bd9O7lOo0KhUOxKIpEIdXV11NTUOD1ftbVU1tZQU1dLU30DvV3dw4k1gSszAy0nHS0nAyMpwDJx5/nRsjOQLjUSQKGYKGK1rQSef5fw2gpS87L58i3/wze+fAter3fM9NNSoJ1xxhm8+OKLPP7441x88cUTkeWkogSaYk9ESsn999/PnXfeyXHFgh+s0PC79lxxNkRvRPK9D+GpKsnRS2bx8ONPsfTgwye7WgqFYpojpaS7u5vq6mpqamqoqqqitLKC8soKGurq6WnvGDbEoWvo2RmO+MrJQM/1J3u/tJwMRGYawlACTKHY3cSbOwk8/x6h90pJyUznizeu4ru33k56evqodNNSoD311FNceOGFHH/88bz66qsTkeWkogSaYk+js7OT6754LU8/8yxfOhBWH6Sxty1H806r5JvvSJoCcMGnjuDLdz3AMSuOV5PjFQrFVonH4zQ0NCRFWGVVJZsqyqmqrqa5voFIMJRMq6V50fMyMfIyMfIzcedlYeRnQq4fPSsNoXrvFYopi9nRS+Bf7xN8awNubwqXX3cN37vjW2RnZwPTVKABXHHFFfzhD3/gyiuv5Cc/+QmpqakTlfVuRwk0xZ6Cbds89thj3HrL/xAPDXDvUZKTZk92rSaPmCX5ayX8eqOkOQjzirK54nOXc9GVX2TJkiVKrCkUeyH9/f1JAVZdXU1lVRWlleXUVNfQ0dKKHJoHpmnOsMM8P3p+Fq68TFz5WWi5fvS8TDSfZ3JPRKFQfGLMngGCL3xA8I11aJrGBZ//HD/6xrdJT0+ffgLt0UcfRUrJgw8+SElJCZmZmZx11lkceOCBZGVlbddCyhVXXDER1ZgwlEBTTHdCoRBPPPEED/3oB5Rs2szpcwV3HaWT69nzhzSOB1tK3m+DZ6rhxXpJyIS5BZmccepJnPfZazl+5UpcLtdkV1OhUEwAtm3T0tJCdXV1UoiVVVRQXlVBfV09g719ybSa14Oe50/2hLnynXXBRGJOmNjbhh4oFHsp1kCQ0EsfMvjyWoibfOq8s3jpr8/S39c3fQSapmmjWp6llONuiRZCYJrmRFRjwlACTTFdKS8v55e//CW/++1v6B8c5LgZghsO1jgkd9u3upSSQBw8Orj1vasXKWJK3muDV5oErzbatIcg0+fmsvPPYNXXv8N+n2BhS4VCseuxLIu2tjbq6+tpaGigvr6emrpaKmocMdba0EQ8FkumN7LS0RLDEPW8TNwJEUZuBlqaV/WkK8ZESgmWjbRsx0/0rAohQAgQgCZGbAvQHF/9pqYvdihK6LV1DP77A/Lu+CwVX/r+9BJoO10JIbASCylOFZRAU0wn4vE4zz77LD//6U949fU3yPYZXDDf5OJ9XcxK3fq9JaXkg3b4Q5nj90Sc+JwUODgPjp8p+NQsyPXuPS8WKSWbe+C5OsFTlTa9UTh6/7n8z9e/yXmXXI5hGJNdRYViryIWi9HR0UFbWxttbW20trbS2NhIVW0NVbU1NDU20tHahhUfbujVfCno2Y5J+qGeMHd+lrMoc64f4VL38d6KNC2svgD2QBBrIIQ9EMTuT4QHQ9iRKDISQ0Zi2GHHl9EY0rTA/oSfzLrm9MDqGmgaQteHtxP7hK6PuS2Sx2hg6KO3dR3hNtC8HkSKG+F1o6V4HD8Rp6f7EKkpSih+AmQsjnC7KL3gjukj0Orr6z/R8XPmzJmIakwYSqAppgNNTU08/PDD/Or/fkFbRxeHFmhcukhy2hyBezvGwFoCknvek7zWBAv8cPpcmO8XxG2oG5CsaYcPO0BKOCQfTp4tOHk2zErfex7uMUvyUgP8sdy5HjNz0rnxxhu47qavk5mZOdnVUyimPLZtE41GiUQiST8SiRAMBunr66O3t3eU39ndTWdPN+0d7bS1tdHV0Tlq+CEAQqBnpjniKzvD8XMycOdmomenI7Iz1FywvRhp2Vjd/Zhd/VgJZ3YPJMNW3yCM/PIVoKV60fypaOm+pKDRUtyj/KQo0jXH2MuQaEI4L0opnR42W469bUunx82ykZYFlkz4iR4523ZE4MhtywbLGu6xG3PbyU/GzISojIK5lYZZXUPPSEXzp6L7U9EyUtGz0zHys9Dz/Bh5mWgZqUrEbYdpJdD2NJRAU0xVTNPk5Zdf5pe/+Dn/eO45UnQ4a67NpUsM9s0c3/yyVxslt74p8bngriMc4TXWA7knInm1Ef7TIPlvC8RsWJwFZ88XnL8PZKfsPQ/xTd2SRzcLnq+1cRk6nznzJC6+ejWfOvnk7S5uqVBMNlJKQqEQg4ODBAIBwuEw4XCYSCQyyt8yLhgKEgiFCIXDhMIhwkPpRgiuaCRCJBolNuQiUWLRGPFYDDMeH1f9NF8Kms+D8KUgfB70dF/yI1L3p+HKTEPL8IE/FS3Np+aBKbACYczWbsy2nhGuG7OjD6zEu1CAnplY1DvXn1zeQM9OR89IQ/P79sjfk4yb2JEYMhxzegTDMezBEFZ/ELs/kOw5tPqDWN0D2APB5LHCbTi9zvlZuIpzMWbm4SrOwyjIUstAJJjyAi0ajfKb3/yGf/3rX9TX12NZFjNmzGDlypVcd9115OTkTGRdk9x333089dRTlJWV4fV6Wb58OQ888ACLFy8GnOFed911F//85z+pqanB7/dz0kkncf/99zNjxoxxlaEEmmIqEY1Geffdd/nzn//MX5/8Ex3dvSzK0rhkkeScBRpprvHdxraU/HS95Gfr4cRZ8MCxggz3+ERWMC55oxn+XS/5T4MzrP7SxfDFpYKcvWgIZGdY8qcKwd+rbBoDkOFzc+LyZRx5wikccfSxHHzwwUmTvArFziKlJBwOMzg4mHSBQGDU9uDgIP0DA/QO9NPb30f/wAD9gwMMDgwyGBgkGAgSDgYJB0NEQ+FhS4TbQ9cQLiPhdITLBS7d2XYbCENP7mcojTGUNhGfSKO5DDS3kQzj0sEwwK0jfClovhSE163MzyvGRJoWZmffGEKsBzsQdhIJ0LMzMAqzMQpzEn4WxlCPqhrOul3saAyrsx+zsw+rsw+zsx+zrZt4Uxd2f8BJpGsYRTm4ZubhnluIa14RrtkFaJ69z5jWlBZolZWVnH766dTW1o65PyMjg6eeeoqVK1d+ogqOxWmnncYll1zCsmXLME2TO++8k5KSEkpLS0lNTaW/v58LL7yQa6+9loMOOoje3l5uuukmTNNkzZo14ypDCTTFZBGPx53FTktL2bBhA2+8+hLvvv8BkWicwjSNM2bbnLnAYP8sa4eGIfRHnV6zN5rhK4cIrjsAtJ0cxtATkTxeBo+USnQBdxwhOGf+2L1weypSSsp74T+NgndbbEp7IJSYApOV7mXezBksWLCAhfsdwD777s8+++zDPvvsQ2Fh4V51nfYWpJQEg8FRImoovGVc/8AAfQP99A700z8wwMDAQHJfKBAkHAoRCYa2K6iEx+XMNxkxDEt4Pj40S/O60T2exNAtF8LjRroMhFt3BJbbcARWQkjtab0JiqmPNC3Mjl7Mli7iTV3EW7owmzsx23uT875EijshvhznKkqIsYIshHvvEwm7CysQxmzuIt7UidncSbyxg1hDB8RN0ATGjFzc84twzy1yRFtx7h7f0zZlBVo0GuXggw+mvLx8m+n8fj8lJSXMnDlzpys4Hjo7O8nPz+f1119nxYoVY6b54IMPOOKII6ivr2f27O0vAqUEmmJXEY1G6enpSVocq6+vp7a2ltrKMirLy6isa8RMDM/IShEckic5okCwrEhnvyxrp0RVWY/kxlclAzH44QrBccUTIxB6IpL/94HkHzVw7Ay452jBzLS9U3xYtqRuEMp7NRoGoWHAonEQ6gehfXgdW3weF3Nn5LNg/jzmL9yXOYuWMHv2HGbPns2sWbPIy8vb7rIkimGklJimSTweT/rjDcfj8TGH9g2FQ6EQgVCIYDhEMBQiGAoSHooPBAgGg4QCAcKBENFwmO29TjWPG5HighEiSqS4RoSdeC3Fg54UWB7wuCDF9TEhJrS9815TTF+kbWN19hFv7iLe3OV8+Ld0YbZ2J4clahk+jBm5uIpzcc3IxUgIMc2v5kZNFaRpOX/D2lZiCWc2d4GUCLcL1/wiPPsU4140E/f84j1uTuiUFWi/+c1vuPbaaxFCsGzZMu69916OOuooDMNgw4YN3Hvvvfz9739HCMGXv/xlHnzwwV1R9yRVVVUsXLiQkpISli4d2xz2Sy+9xCmnnELfVtYuiEajRKPR5PbAwACzZs1i5cqVSattW7tU47mEY6WZrvmNJ8/x/qx2R7mfpK5bz08mJhnLxD8b27IwLQszHseyLCzTxDRNLMvxI9EYkWiM+BaTd3UBaW5Ic4HfDZkeyErRyPTYeA1nbvF42Fqy6j74b6tjCOSnJ8DsjIlvHX+9SfKtdyT9MWfoZE6KY134Y3X8hOfyidJOcNk7ci6mDYH4kBMMxiSBmLMdMsHaIi+3oeN2u/B43LjdbgzDQNN0NF1D03SEpqMlTDePqo8Y3hj+ecpkRZJhxOjwUHpkYm67TOYxfA+M2DdURrKcj4cZcezW9o2ZbmRcos7Dx9vYlo20JbbtmLie0GnUQiQto2EMGwIYbUEtYVHN0J1WYpeO0HW0oWF+Q/GGjjR0hKElto0x7wmFYk9ESokMRbEGgthD1hEDYay+QFKICV8KrpkJETYjF2NGDq6Z+egZvkmuvWJnsKMx4vUdxKqaiFW3EKtscoahCtBzM9FzM9DTU9Fz0tF8KZNd3Z0m/cyjp65AO+uss3j++efZb7/9+PDDD/F4Pq6Mzz77bJ577jlmz55NXV3dRNR1TKSUnHPOOfT29vLmm2+OmSYSiXDsscey77778oc//GHMNHfffTf33HPPqLhP8i79RO/h3fASn6zvhF1S7jgz3dXnLBheBoUR4aF4TThiTBsRNoaXSNmpym4rmZTQG4WBGJw9H751JKS5d93QpUBM8p33Jc9Uw75ZELWcc9tRxvsRuyNZ75I8J6h8KR2BZtoJ5yyzg5WIt2xH5MlE2iGfRNyWWQ/LrvGJTZn8b2SFd/5+SWrELbZ3Kp+tHCy2ku5j8YnAWPFbHret8hQKxdaxRzyrLMDSDSwzYWEQZyiuMTPPEWLFuRiF2bhnF6gesT0cKSVmWw+xyiailU3Eq1ow23sA0HweDI+BEYlgWBb6WN9BU5R3LtYo/HHv1BRos2fPprm5mf/7v//jmmuuGTPNu+++y/LlyxFC0NPTg9/v/8SVHYtVq1bx/PPP89Zbb405lDIej/OZz3yGhoYGXnvtta1e0LF60J771lEs9tnqnb0XIhHIkb0TQjhxiXgpBLbQsJO+4yyhYWk6ltAxdYO45iKuG8R0N1HDQ8TwYO/kU0hs55c48kUXGAjz59+/TWN9D3cuE1y2ePfND/t3veQb70g0TXD+RYcy+6D5yX0T1dsx8lzGOquR12r0aYuPicbRace+ytu79h8vx2FHT3dkHoZt4Y2F8JgR3GYMlxlDt01020STNrptIaREw0YkVJZISrbRikUk5JqQTt+ZkHIL33nOCenEOccMV348vxwx1tYYwmlkjUYmF1vZN/S3Vs9hhWJyiZmS2p4YlV0xKjtjbO42qeqK0TvoLAAudA2jKBtjZj6u4lw8xbloxXnouX4lxBQAWANBYpXNxKuaiFY1E6trA8vG7XWxaHY6x81wcWyxi8V5bvQpOITblpIP67u4+LeNU1Og+Xw+otEo7733HocffviYaSKRCD6fDyEElZWVzJ8/f8x0n4TVq1fzzDPP8MYbbzBv3ryP7Y/H41x00UXU1NTwyiuv7JBVSTUHTTFdefvtt7nwvHOxgj08dDwclrf7V9LoCEnufBveaJZcdfbx/PiPz5GWlrbb66FQKBSKHSMcDlNRUUFZWRllZWWs21jCuvXraaipxbacIfpGrt/pDZuZh3tmPu6ZeWjKDLtiB5GxOLHaNuIVjUTKGohVNyNjJu5UH0uXHcrpnzqZs085jUMPPTQ53Wgy2Z3aYKcEmqZpCCEoKSlhv/32+8TpdhQpJatXr+bpp5/mtddeY+HChR9LMyTOKisrefXVV8nLy9uhMpRAU0w3pJT8/Oc/56avfIWDciUPnaCRnzJOs9q7qD5PVMD9H0gKM308+oc/ctxp505afRQKhULhIKWks7MzKcI2b97MhtJNbC7bTFtjc3K0g56Ril6YjWtmHq6ZeXhm5aPNyEHz7llGHxRTAxk3idW2YVY0Ei5vIFbZjIzFcfu87Hf4IZx50rapJjYAAQAASURBVClceNY5HHTQQZPSK6sE2na44YYbePzxx3n22WeTa5+BYzXS6/VimiYXXHABa9eu5bnnnqOgoCCZJjs7e1yLyiqBpphOhEIhrr/uOh77wx+4fAncdrjANUWGB9T2S277L5R0Sb5y6Wn8v988TUrK9J0grFAoFNMBy7JobGykpqaGmpoaqqurKa+qpLyqkobaOgL9AwAITcPIy0QvynbWuSrMxjMjF1GQhZbmneSzUOzNSNMiVteGWZ4QbBVNyFictJwslq88nsvOuYBPn376Llt3eUuUQNsOW1PNjzzyCFdeeSV1dXVjDnkEePXVVznhhBO2W4YSaIrpQk1NDeefew7lZaV85yjJ2fOnhjAbiWlLfr0Rfrpesk9+Go89+TcOW3HKZFdLoVAopjUDAwNJAVZTU0NVVRVlVZVU11TT2tiENWQ1WAiM7Ay0PD9GXiZGXiaeolz0omy0PL9azFkxLZBxk1hVM7GNtYRKajCbOkEI5i3dl/PPPY9rP3vFqI6biWbaCLQvfelL5OfnbzXd3XffPa50AN/85jd3tBq7FCXQFNOBF154gcsuuZh0QvzkBNg3c/KGNI6Hsh7JrW85vWp3r76C2x/8nZo8rlAoFFvBtm1aWlqoqqr6eC9YXT0DPb3JtFqKGz0vEz0vEyPPj56XiSffEWBaToaaH6bY47B6B4lurCNSUk2kpAYZjZM/fzbnnnce13/28xx88MET+o0xbQTaRGJZ1vYT7UaUQFNMZaSUPPDAA9xxxx0cVyz4wQoNv2tqi7MhYpbkoY/gN5skZx6+gD+88A6ZOTs2R1ShUCj2FEaKsMrKSsoqyikp20xlZSUt9Q3EIgkL00JgZKUne8H0vExceZm487Mg14+W7lUNXoq9FhmLE9lUR+TDCsIfVSLDUbKKCznvwgv46nWrWLJkyScuY1oItAmthBBKoCkU4yQcDnPNF77A4088wfUHwFcOEWjT8KX8coPktrckOekp/O3Z5zj02E9NdpUUCoVilyClpKWlhcrKSqqqqigpK6Vk82ZqqqtpqW8gPkKE6TkZGAVZGPlZGAXZuAuz0PIyHXP1aiiiQrFdpGkRLasnsqaC0JoyZCjKrP0X84XPX8mqK6/eYcOBQ0x5gfb6669PeEWOP/74Cc/zk6AEmmIq0tLSwjlnnUnJhg3cf4zkjLnTT5iNpGFAsvp1Sd0A/PS+b3L1V+/Z/kEKhUIxBRkSYUM9YaVlZZSUD/eEbU2EuQqycRVkIfIzMZQIUygmFBk3iayvJvT2RiIlNQjgkOOP4ebrbuCi8y7A5XKNO68pL9D2BpRAU0w11qxZwzmfPhMr2M3PVwqWZk+PIY3bI2JK7nkPnqqSXPXp4/j5X/6trDwqFIopiZSS1tZWKisrqayspKKykpKyUioqK2mqqycWjjgJxxBhRkEWmhJhCsWkYQ2EiLy/meDbG4nXteHN8nP+pRdz1+r/Yd99993u8UqgTQGUQFNMJZ544gmuuvLzLPab/HTl5K5vtiuQUvLXSvj2e5KFRX5+88e/cuTxJ012tRQKxV5IJBKhrq4uaZSjurqasqpKKqoqaWloJDpChBk5Gej5mRgF2Rj5WbgLs1VPmEIxDYg3dRJ6YwPBdzYigxH2OfRAbl51I5+/5LP4fL4xj1ECbQqgBJpiKmCaJnfddRcPPPAA5ywQfOdogWcPNsRV2i25/W1JRS9cdvqxfPsnv2fe/PmTXS2FQjHNkVIyODhId3c3PT09dHd309raSnNzM41NTdQ21NPY3ERrcwt9Xd3JhZqFoWPk+tHyMjHynXlgnsIcZ06YMk+vUEx7ZNwk/GEFoTc3EN1cjzvVx5kXnsddX76ZQw89dFRaJdCmAEqgKSab9vZ2Lr3kYl5//Q1uORSu3n/rawDuScRtyeNl8H8lkoGY4IrzTuXO7/1MCTWFYi/Atm3C4TChUCjpgsHgqO2xXDAYZDAYYDDhB4JBBgYG6O3pob+3j0B///CaYCPQ07xomWloWenoWWnomekYORm4C7IhNwM9Mx2h7fnPXYVCAWZHL+G3NhJ4cwN2f4BZSxZy43Vf4rorv4Df71cCbSqgBJpiMnn99de59OKLiAd6+NGxNkcWTnaNPhlSSiIWSAkeHfRxfPCE4pI/lAt+s9GmPwqnHXMQX779O5xy+pkTbklWoVBMHKZp0tPTQ1dXF11dXXR2dtLZ2UlbRwctHW20d3bS2dlBd3c3oUCQcDhMNBwhGg4Tj8bGV4gQaG4Xwm0g3C5I+MKT8N0uRIobLS0FLdWLnubFSPWip3vBlwKpKej+VNUDppjSSFuCZSEt23mBCgFCOI0GQozeVkwY0rKJlNQQemM9kQ3V6C4XJ376dL7yhS/y6TPPVAJtMlECTTEZdHV1ceedd/Lwww9zeKHGg8fr5KdMrSUotoaUksZBWNsJVX2Smn6oHYCeCARiYI540ugCslNgZhoclAfHzBAsLwJjjJdMKC55rhb+UCYp74VZeRl89tJLuPKGm1m8ePFuPEOFYu9DSsnAwEBSaHV1ddHR2UFTexvNba20drTT0dlJT/f/Z++84+Mo7v/9bLmu05106u4Y05vpvQUCoTo41CSUEAg1AVIIEGoKISQkISGFFEjyTf8FSDWQgjEGTHEvsmx1yerldL3s7vz+2NNJsmVbNrYl2fO8PJ7ZmdnZ2dPt3rynfKaXgb5+Iv1hUpHYlgUpCmqBx3Z+L6rfg+rzoHqceUGluWxfdTtQHA5wOcCh58SXjjKY7tTBoe8TMwokkwMhBCKZwYolseI5F0sNHSfSiHQWkckiUhlEOos1LCwME0wLYQ76FgyKsrGia/kOCyXXeaHmnhnF5UDxuIaeQZ8753vycVqhV3ZYjILZHyX51lpii1ZidocpnH86Lb/8uxRo44UUaJI9SXNzMz/+8Y/54dPfByPNPXMFVx0wtpGm8SSWEfyv1d7TbGkXdCft+Cof7BeAWQEo9Sj4HeB32mkZE9Im9KYEDQOwvBva4lDqgU8cpPCJg6DAueV9C2Ff46V6eLlBEM3C0QdM5xM3fJqPffx6pk2btgfvXCKZnKRSqbzQGu7aOjvY1NlBR1cn3T099Pb0MtDXR6x/AGuUfUoVj8sWWQUe1AIvmt+D5veh+70oBW4YJsa0Ag+K1y17+SWTEmGYmL0DmH1RzHAMsz+K2R/DDEcx+6NY/THMSNwWVJuja7YI8rpQ3M6hTgb3oHDKxTk0FE0DTUXR1Jyvgark41EVW7AJbN8SCMsaCpuWLQAzWUTGGBKEmawtCBPpYQIyNWp91QIPasCHFixACxagBgpyYd+wcME+KeSEJcjUNOM6eAbr5t8vBdp4IQWaZHeTzWZ55ZVX+MmPf8SCl1/GqytcMcfipsN1il0T10qjEII32uAPNYI3NkHGgiNK4KRKOKZM4ahSCLjG3hATQrC2D/60QfBCLRQ47M23r5izdYGaNgWvtSj8td5iUas9Onf47ClcMu8y5l11LUcffbScBinZqxFCEI/HRxi9GO53dnXR1tVJZ3cX3bkRrkhfmHQyuUVZikNDLciNavm9edGl+71ofi+K34Picw9L86Doe7G1Isk+h5XOYHaFMbrCGF39GN1hzM6c3xsZMZKleF1oRX5brOR8NVgwbHRqsOPCbYuwCTjSK4RApHIjfjlnDsSxwjHMgRhmOJYLxzHDMdhs/aZa6EULBdCK/eihAFqoEK24EK3E9tUCz4S8712FFGhb4fHHH+eFF15g/fr1eDweTj75ZJ544okR051eeOEFfvrTn7J06VJ6e3tZvnw5Rx111JivIQWaZHdgmiaLFi3i97//PX/50x/pG4hwSEjl6gMEF+6n4tMn9uP4fqfgG+/aguqQYrhkP4XzZkBVwa55EXfGBd9bLnihDg4ogvuPUzipcttlRzK2UPxfCyzaJIhkoKTQyynHH82Z51/MaWd+iCOPPBJd3/d6/CTjixCCdDqdN3qxo34ikSAajxHJGb0IDwzYUwnDA8TCAxjZ7BbXVDQV1etG8XuGRNdwwVVoj2iJQcHl90zYRqREsiuxYklbfOVEmNk9JMisgXg+n+JyopcH0UqD6GVF6GW2rxX7bSHmco7jXex5hBCIeMoePRyI2SOJ/VHMngHMvghGb8QWscNEnOJ0oIX8aCVBey/AiuK82xsM70iBthXOP/98rrrqKo477jgMw+CBBx5g9erVrFu3Dp/PB8BvfvMbGhoaqKqq4qabbpICTTJuRKNR/ve//7FgwQL++sL/o6O7l6mFGhdMN7lwts6BAXPCN466EoInlwr+Vg+Hh+ALxyicULH7rEqu6rGF4PJuOGca3H20wv7B7V8rawmWdcFb7Qrvd1is6rWnVPrcDo4+ZA5HHXs8R590BnPnzuXggw/G6dy3fmgldmMjm83usFBKJpPE43HiOdEUTyaIxxMkkkPpyUSSdCpFKmn76WSKMf/EqgrasLUjOHLrrAbXXzlza0hya0c0nwfd780fC6+9vkRxOyf8+0Qi2R0IIbDCMYzuMEbnoAAbEmQikc7nVQs89v51wwVYzlf9XvkM7SBCCKxIArMvgtk7kBdtZu5vYXSH81MqFacDvaIIx7QyHNPLcUwvwzGtHNXrGt+b2AGkQBsj3d3dlJWV8frrr3P66aePSGtsbGTWrFlSoEn2CNFolIaGBlatWsW7777Le2+9wfsrVmOYJjMCKmdUWVy4n86RoYkvygb5V4PgwbcFDhU+f4zC/P1B3QN1F8I2DvLtpYKOBJwxBW44VOHEHRCGGVOwqgeWdaus6TFZ3wdNUTtN11SmlRcza8Z0Zs2eQ8XUGRSXllNcXExxcTHBYBC/3z/CeTx797SNsSKEwLKsEW60uEFnGAbZbJZsNjsivCPH6XSaVCpFKpWyxVAqSTyZJJFzg3HJVIpUOm0LpFSKdDpNOpUmk06TTWfIpNP2uo0xorgcqE7HlmLJkTNa4RgW59Q3i88t1Hc6UHPlqLk0nA5waDAoxnRNfrckku0gLAuzN2KPgHWFNxsRG0BkhkaV1aICW4CV2qM4Ws7XS4smlRjYGxhcx2d09GN09pFt6yHb0k22pSs/8qaVBHDMKMc5uwrXnKk4ZlRM2KnUUqCNkdraWubMmcPq1as57LDDRqSNVaCl02nS6aHelUgkwrRp0zjvvPNGnRq1vY9ttPSxxm2v/OFpO1Lmjpa9vfg9ce0PWp8dqcOIeCFyDU4T0zAwDQPDNDBN03aGgWlZuXj7OJZIkhpmIrrQCWUeKPfB1AKFgHMb9769Om/zfnb+3G1lMCxY0gEbwnDBTHjoBChy7/l1XRlT8I8GwXNr7bpMLYDZAdsKpKYOrZkevA8x6LZynLUgkoZIViGRFSQMSBr2SFvWAnM7H5iuqWiahq7r6LqGrulouo6ma6iqajeyFWWEbze8FfvfsOPBPJD7/g1+73LHdpxl32PueLgDgbByx2w/DwKswbh8+YNh+0Mank4uPh+G7X/hdheDi+dV2w0upEdTUdTN/OF5hy+6V+2F94quDluQr4Geyzs8XrcX6IMUTRLJnkaYlr02KhK3XTyFFU3Y66EGjVuoClooMDQCNriZeFkRjrKgvf2CZEIjTAujvZdMcyfZpk6yLV1k69ttoa1r9pTI4kJ7jVuo0H7HjzP+84+XAm0sCCG49NJL6e/v54033tgifawC7ZFHHuHRRx/d4etP9p/u7dZ/D97geHyWY7l/ZShobzkyeJwLD8arCuiK3WZ05AwujemetpNpe2Vsr9N9u+ePEpc1oScFlrCF2cfmKOPeuy+E4K12+PU6weubbGMi0/32/Wvq0N8Chj6TLf5Wm/1NNj+2r2OLU1PY7QDTNpA14ngLl4vfXBQOljcYh9hG2k6wS/4iY/2e7sD1lK0cbH6+sllgq+kSiWSvRAiwsN+xlq4jHDqmKbBS6fyLUS3woFeVoFeF0CtC6OVBez1TKDBhR1gkO48wTLItXaRrmsnUbiJd04KIp1AcOrrfjZpMoWcNxmsZ2/vXqFQ83S8F2va4/fbb+ec//8nixYuZOnXqFukfZATtv48ez/56eqvnSCYyuVEJQAyOUAzGKcpWGsQKVi6vqapYioqpaJiKiqkO+ZaiYDEUl9F0UrqbmMNDWp8sa5pGf7MpQN2Gbv7y2/cod1n84EyFA4J7tGJjoiki+HU1/HGDIOBRuXr+XA457iAG72trWnLLt50Y9buw+Wtx+JFmGTgzSZxGCoeRQTez6FYW1TLRLBNVWLazTFQhULBQLIFCzgnsOCFQhe0rg35OwQ1+Q0e7jS3ihqmcrf1eKaMcjfYZKVu9qkQikew80bRFQ79BXZ/BurBgfVjQ0pehP5waEmJFBTgqQ+iVJTiritErS9CqStAKveNbecm4IiyLbGMHmdX1JNc2kqlrAyGYMi3ABfs5+OhMlSmFe8YImBCCNV0xLv5ZqxRo2+LOO+/kpZdeYtGiRcyaNWvUPHINmkQyNoQQ/OAHP+Cee+7m5ArBd89Q8Tsm9uuhJSr4+nvwWovgyrPm8ou/LsTnl8+rRCKRjAfd3d1UV1ezbt06lq9exfI1q9i4voZwV4+dQcGellhVgjPn1Mpi9MqQXBcmGRNWPEVqVR2p92tIrq4Hw2TaIQdw3Sc+yZ033kxZWdluu/ae1AaTUqAJIbjzzjt58cUXWbhwIXPmzNlqXinQJJLtk8lkuO222/jFL37Bpw6xrTRO9E2yBxFC8Nd6eGSJYGaJj5cW/JcDjjphvKslkUgkeyVCCNra2li3bh3V1dWsWrOG5atXsbGmhmh/2M6kqvZUxKoSHJUhXFWlKJW2mXXVJdeGSXYNVjJNanU9qXfXk1xZiwLMPfNU7vnMbVx+6Ud3uaVmKdC2w2233cbvfvc7/vrXv47Y+ywQCODxeADo6+ujubmZtrY2LrzwQv7whz9w4IEHUlFRQUVFxXavIQWaZF+hu7ubyz46j3eXLOHREwWX7T85hNnm1PQL7nhNEM4o/PpHT3Hpp+4a7ypJJBLJpCUWi7FhwwY2bNhATU0Na6vXsXZ9NQ219STj9r5hikO314NVhtCrbCGmVhajlxfJ9WGSPYoZS5J6p5r44tVkmzrwFAW46rpP8Og99zJt2rRdcg0p0LbD1owVPPfcc1x//fUAPP/889xwww1b5Hn44Yd55JFHtnsNKdAk+wIrVqxg3iUXEevt4IdnwtGlk+51MIJIRvDFN+D1VsFDn7mch3/8x3E3biKRSCQTEcuy6OzspKmpicbGRhobG6mrr2dNTTW1GzbS09GZz6sX+lAritDL7VEwV2UJWlUxaklgQljWk0iGk23tJvH6SuJvrkZkDI479wy++sX7+fDZH/pAbQIp0CYAUqBJ9nZ++9vfctOnb2RWgcEzZ6lUec3xrtIuwRKCp1cIfrwKLjnxQH77yjsUFAbGu1oSiUSyRzFNk/b2dhobG/MirL6hno319TQ0NdLZ2kY2M7Q1jOZ1o4YK0cqLcFQU46gI4awMoZQHUb3ucbwTiWTnsJJpkm+tJfa/pRjtfVQcsB8Pfvk+bv7k9aNuobU9pECbAEiBJtlbyWazfOELX+Dpp59m3myFR09ScWt732vg1SbBvYsF00I+fvfC3zn65LPGu0oSiUSyy8hms7S0tNDU1JQXYBvq66hrqKeluYWutjZMY6jjTSvw2gKspBA9FEArCeAsCaKVBFCK/dJIh2SvRQhBel0j8ZffI7W2gcLKMj57913cf8dd+aVRY0EKtAmAFGiSvZHOzk6uvPxy3nxzMfcdCx8/aOtThvcGNvYL7lkkaIjAV+78FA9851k0Ta6LkEgkEx/Lsmhra2PDhg3U1tbmBVhtQz2tzS30dXUjLCufXwv4UEM58RUqRC8J4iwNoIYKUYoLUd2TZRsYiWT3kWnqIL7gXRLvrcdVWMD1t9zE4/d+haKiou2eKwXaBEAKNMnexmuvvcYnP3416Ugv3z9dcGzZvvHoZ0zB0yvgF2sFc/cr44e//D0nnn72eFdLIpFIAOjt7c0b46iuWc/q6nXUbNhAS0MjmWTKzqQqaEV+tFChbaY+VIijJIheEoBiP3qoEMWxZ/aCkkj2BoyufuKvvEfsjVVoDp0rrr+Wpx56bJuGBKVAmwBIgSbZW8hkMjz44IM8+eSTnFCh8K3TVMo91vZP3MtY1iV4+G3BhjBcdvZxfOsn/8fsOQeMd7UkEsk+QDweZ+PGjXkhtqp6HetqqmmqrSc2EMnn04r8tlXE8iL0imKcFSHU8iBaKCCtIkokuwFzIE7i3+8T/d8yFEtw8TWX8/1HvsGMGTO2yCsF2gRACjTJ3sCaNWu4/tpPsHLlKu6aC586hEmzv9nuwLQEL9bB95cL+tNw1YVncf8TP+Sggw8Z76pJJJJJTiqVoq6ujtraWjZu3Mj6mhrbIuLGjfR2dOXzqQWevADTy4txVhSjlhWhlQdRXXIaokQyHljxFPH/LiX67/chneXcyy7h6a8+zoEHDG3nJQXaBEAKNMlkJpvN8s1vfpOvPvYY0/2CJ06Fw4vloz5I0hD8rkbhubUWPUm4+Ixj+co3n+a4E08a76pJJJIJihCC/v5+WlpaaGxspLa2lpoNG1izvpq6ujq629oZbFKpLidaWTAvxBwVIRwVRShlRWgFYzdKIJFI9ixWKkNi4Qqir7yLFU1w2oXn8YOvPcGRRxwhBdpEQAo0yWRl+fLl3HDdtaxZu5abDhXcfpSGU5WP+WikTcFf6+DnawRNUTj1iNl89ksPMu+Ka3A4HONdPYlEsofIZDJ0dXXR2dmZdy0tLTQ0NVHXWE9zcwsdbW2kE8n8OarLiVYeRCsrQi8vwlFWhKsiBGUB1ELfXm2ASSLZ2xFZg8Qbq4kuWILZG+GYD53O4/c9yIfPOVcKtPFECjTJZCMWi/G1r32Nb3/7SeYEFb5xssKhxWNba2ZYAlUBdR9tUJiW4JVm+HW1YHkXVAS93Hj9tdz6ha8wZcqU8a6eRCLZAYQQxGIxent7R3UdXV20trfR3tFOV1cX/T29I9aBDaIV+lCLc4Y5cgY6nKEgWrEfJeSXIkwyJoQQkG9pDwurivz+TAKEYZJcso7oP5dgdPZRcN7xbPrTv6VAGy+kQJNMFoQQ/OEPf+CLn7+bnu5ubj5McPNhCs6trCdPm4Il7fBWu2BlN9QNQCQDDhUqvHBSJVy0n8Lx5Xu3Cf6tsb5P8LsNCn+vs0hbcMFpx3LHvY9wznkfQVXV8a6eRLJPYZomfX19owqtnp4eOnu66ezuorunh97ePsL9fUT7BzCy2S3KUnQNtcCDUuBBK/SiFvpQC71ohT60Qh+OgA+10Ad+D6rfK60i7uMIIbCiScz+KGZ/FCuawIonsWIprFjSDsdTiFQGkTUQ6aztZ7KIjAHmGDpINdU2/qJrKDmHpqHo6rCwhuLUUVxOFJcDxeVAddq+Mui7ho5VlwPF7UT1eVB9bhSPC2UfXnu+qxCWRfL9GrzHH8y6+fdLgTZeSIEmmegIIXj11Vd59JGHeHvJu5w7XeHe43Wm+cxR8y7tgr/WCV5usgVZlQ+OLoMDihSK3ZAxoTEiWNgKzVE77UvHKMwt2zdf7LGM4K/1Cr+vsdgYhqkhP5/8xMe58c4vMHv27PGunkQyaTBNk4GBAcLhMOFwmP7+/nx48Linr4/uvl56+3rp6e2lv6+Pgf4wiUiU0ZopqtuF4rcboGqBx3Y+D2qBG63Ai17gQfV7UHweyOVRXI59stNJsnXMWBKzsw+jsx+jow+jZwCzL5oXZRgjf08Vt3PEd00t8KC4nSgO3RZLTt12DgeKpoICDH7nNv/uCYEwTDBMhGkNhXPOjjcRWdMWfWlb+Nl+BpHOYqXt+M3rObLSoHjdqIOuwI3q96IFC9CCBagBX87PHcv98raLFGjjiBRokolKJpPh73//O9/8+td4f/kKjihVuWsunFK5ZV4hBK82wzMrBTX9MMUHF+8HF++nMDsw+giZEIJFm+B7ywXr+uCy2fD5YxRKPPtmw0YIwbJueKkO/tkgiGfhpMP35+M33Myll1/N1KlTx7uKEsluxbIsYrHYqMJq0O/t788LrP5cenQgQnRggFQ8sdWyVY8LxetC8bhyDUjXkOAq8KAXeNH8doMYnwsl1ziWJuclY8VKpDEGRVje78fo6kfEU/l8aqAAvSw4NKW1yI9W7M+H1YKJ+70TppUbubMFm5VM26N7iRRWPIWVSNujfoPHkThmOI4VjiEyI0ebFbcTrSSAXhpELw2ilQbtz6XU3ndvon4GexIp0LbC448/zgsvvMD69evxeDycfPLJPPHEExx44JApTCEEjz76KM8++yz9/f2ccMIJPPPMMxx66KFjuoYUaJKJhGmavPvuu/zf//0ff/z9b+ntH+C4CpXbjoCTKsSoQqslau/79Wa7Ld5uOlzhhIqxrzMzLcGfN8J3lwtMCz47V+GaA0Hfh6dKJA3Bf1rghY2CdzrAFHD47Cmc/5ELOO3DF3PyyScTCoXGu5oSSR4hBOl0mmg0OsINF1rhcJjevl66+/ro6eulP9xPODzAQDhMLBIlEY0hrNGnaykuR15kqV53rqd+SGwpXje6z5OPU7wu8LjsNI/LHmWQSD4gVjKN0RXG6OzDzImvQSFmRYc6CFS/17asWV6EXm7vN6eVF6GXFe2TI0dCCEQqgxmOYYVjmANxe/SwZwCjO4zRFcbsHRiarqmAVlyIXlWCY0oJ+pQSHFUl6FWhfWqLCCnQtsL555/PVVddxXHHHYdhGDzwwAOsXr2adevW4fP5AHjiiSf4+te/zvPPP88BBxzA1772NRYtWkRNTQ1+v3+715ACTTKe9Pb2snr1apYvX85r//03i15fxEAsTrlP5eJZFpfur3NAYOvz219vFXzhDYHfAQ+fqHDG1J0XVf0pwXeXC/60AeYUwUPHKxxXse+KtEHCacHiNoXXWgXvtAu6c8bdZleVcOghB3Po3OM57KijOeSQQ5g9e/aY3juSvRchBNlslnQ6TTqdJpVKjRreVtrwcDKZJJFKkkilbD+ZJBqLEovGiEVjxGMxEvE4qXgccxvTnxRdG1qnkhdWQ+JK9brQvB70AjsPHhcMF2CyN12yh7DiqSHh1dVvi4cuO2xFhkSY4nOPEGB62ZAgU72ucbyDyYmwLHvaZ3fYFm2d/WQ39WC09WD2DNiZFNBCAVu0TS3DObMcx4wKtFDhXjmtWAq0MdLd3U1ZWRmvv/46p59+OkIIqqqquOuuu7j33nsBSKfTlJeX88QTT/CZz3xmu2VKgSbZHQghiMfj9PT00NvbS3d3Ny0tLTQ1NdFQt5HGuo3UNzTS0dMPgFNTOKpUcGKFwolVGkeFzO1uNP3njYIH3xKcORW+dZpCoXPXvBzX9AoeWyJY2QMXzLSnPU4t2PtevDuDEILWGCzrVlndY1EXFtSGoWvIIjcFHhdVZcVUVVYwZeo0SiuqCJVXEQqVUFxcTCgUori4OO/8fv9e+cO2KxBCYJomhmHkXTabHXG8eXw6nSaTyYxw24tLp9Mk0ylS6XTepdNpUukU6UyGdCZDJp0mk82QTmfIZnIumyWbyZLNZDCyWcysMarRirGi6Jq9xsWh5Q0KMBina5DzFZcD1e1EcbvstTJuJ6rHheZx2mu23E5wOcDtyAssHLr8nknGHSEEViyJ2RuxXV8Eo3cgf2x0h0dOR8xt9j24xYFeVmRPw5P7zO1RrFQGo703L9iym3rItnRhhWOA/XdyzCjHMbMC58xKnPtPQQv4xrnWHxwp0MZIbW0tc+bMYfXq1Rx22GHU19cze/Zsli1bxty5c/P5Lr30UoLBIL/61a+2KGOwV3KQSCTCtGnTuOiii0bsh7S1j2usH+OOnL+j1/ogddjWuTtS7ljy7kz9d1e5O5u+tXMs0ySTSZNJp+yGWyada/BlSaZSJFJpzFEsOxU4oNAJfqfthzwKITcEXQJts8bTtmq7pkfwWitcfSA8eDxou9jqoCUEL9XBU8sEA2k4bQrsF2CrFiMBduQNsyMvo9355tpV9Uib0J+GWFYhnoV41l7DljDstFTOH60IVVFw6BoOXcfh0HA4HDnnxOlw4HA6UVQVVVVRFBVFVWxfUVBUBVVRUQbTFCWXV8FetZ57XhFYlpWz/Czyz7A1+CznwgiBEGBhIazB8wbjtzxPCAv7tMG43HmbpQ0eW7k0y7JsZ5p2nGX7lmUhcmnCssb8TtohVPszQlXJ7TlhW1gbFqeoih3Wcp+rpgxLG5ZPGwqrubAyPG1YuYN5hZYrQxsWr6mgqFvYFpBIJhvCEohkOm/50IqnsJJpOy6RwoomRxq6cGj2VLpQIVookF8TpZUFcVQUo3rd43czku1ihmNkGtvJNnaQbeok09iJNZATbUV+W1wHfGglAdti6iR6yRWce6wUaGNBCMGll15Kf38/b7zxBgBvvfUWp5xyCps2baKqqiqf9+abb6apqYlXXnlli3IeeeQRHn300T1W74nA5Hkcdpwdvrdd+GEMNoEVZcvwYLtvMKwooA6mD6vD9h7K0aqbNm3rjJ88CB44fvfurxLPCn6+RvDLteBzQLELotsaINiBquxIrXf0DnfkI9ld9RjFkBcCsITtTDEUHp4mcnmtYecMf3sP/86IYRFi8/ThGXek4iKXf/D8HfzwP/DnqWwnfTtpo/3t9+Z3oESyJxHD3jdCgNBUhEMHRbE7VtLGiBeWWuCxhVeoEC1kCzE1WGCHS4OTrtEu2T5mX4T0hlYyG1vJ1G0i29INQqC4HGgeJ0oyhZo1J3yH1MpPqFQ83b/bBdqk3+TjjjvuYNWqVSxevHiLtM0fbiFGN6YAcN9993HPPffkjyORCG8/egyziO/aCksmJWKzppxQFPuHSFGwUDFVFVNRMRSNjKaTUR2kNQdpzUlSd5LQXSR0FynNuUVZu4JNrQP89ufv8pGZgvt2szgD8DkUPjdX4WNzBF95S/BWO1x4XDFHXjQXddiaFGXYvW614ZwPbJlD2ewsZZSDzfNstfxt1mKU88YkCMZ27TGM/271SBECl5nBbaRxmRlcRganmUW3DHTLRBMmqhCoCBQhUAb9XJwqLBQh0ISFOszZecnnH9sdDcuzje/YtlKUUTKN9i2Z4L/REsk+hWkJuhOCtqjFpqhFQ0KhPq7RGrXoihgMDKTtUfUcqteNFgqgl9ijX46QHz1UiFJShFZSuE8ZlJDYaMWFeE88BO+JhwC2YZdMXRvZmmaS1U1kGxIgoCjk5ZipTj5SYXBKlYZvFy3T2FWsjWSp2APXmdQjaHfeeScvvfQSixYtYtasWfn4nZniuDlyDZpkstDV1cXRRx1BqdnNb84D5x42iiaE4Ll18O2lgpPmlPLnfy+hYvp+e7YSEolEItlpLMuivb2dxsZGGhoaqK2vY23tBurq69nU3EJPeyeWYeTzq36vPe0wN+LlKAmg5KcjSgEm2XGseIr0+iYy65pIrm3A7AqjOnQOO+FYrpl/OR//2BXjvq3NntQGk1KgCSG48847efHFF1m4cCFz5szZIr2qqoq7776bL33pS4C9d1RZWZk0EiLZqzAMg3M+dDZr3n+LFy9SKPds3bLj7uadDsFdrwtcDp2XXvwLx3/oknGri0QikUiGME2Tjo4OGhsb825DXS0b6utoamyia1Mb5nABVuBBy41+6SUBnKVBKcAkexSjK0xmVR2J5RtJ1zSDJZh68Bw+euml3HjVJzjiiCP2+DRYKdC2w2233cbvfvc7/vrXv47Y+ywQCODx2NZ7nnjiCR5//HGee+455syZwze+8Q0WLlwozexL9iq++MUv8t2nvsPz56kcXzb+j3JHXHD7QqgPC3719ON87NYvj3eVJBKJZK8mm83S3d1NV1cXLS0ttLS00NzcTF1jAw3NTbS2tNDT0TliuwXN70UNFeYFmKMktwlxyI8WCuyTe4JJJi5WPEVqdT2pFbWkVtchkhnKZk3n8iuu4I7rb+Sggw7aI/WQAm07bE0xP/fcc1x//fXA0EbVP/3pT0dsVH3YYYeN6RpSoEkmOv/v//0/Lr/8cu47VuH6QyfOHO2kIfjSYvh3k+Brn/04933vN3Kxt0QikYwR0zTp7e2lq6uLrq6uvPjq7u6ms7OTTR3tdOTS+nt7iQ1ERpyvaBpaUQFqcSFasT9vDdERKkQtDqCWFEoBJpm0CMMkvbaR5LvVJJdtRKQzVB04m6uvuorbr7txxJKnXY0UaBMAKdAkE5k1a9Zw4gnHc2Z5iu+ctvVOi/HCEoKnlgl+tgauP/8EfvrXRTidskEgkUj2TZLJJJ2dnSNcR0cHbZ0dtLa30d7RQU93N309vcTCA1tsJaE4dLRCL0qBB9XvRS30ofk9qIU+9EIvmt9nWz4sKkAt9NlbQkgkezkik7VH1t5dT3JlLSJjMPPwg/nUtddz6/WfoqSkZJdeTwq0CYAUaJKJSn9/P8cdczSOSCu/P1/g1SfuI/z/NgoefltwyiFTeXHRCoqKQ+NdJYlEItmlDAwM5KcWtra20tzcTG1jA40tzWxqbaW7vZNUIjHyJEVBLfCgFnrRCn2ohV7b8EahDz0nulS/F/weFL8Xxe2ccB1xEslEwkplSK2oJfluNanV9SiKwrFnncbnP3M7l11y6Yg9jXcWKdAmAFKgSSYipmly8UUX8tbr/+UvF8I03/gZBRkrS9oFn10oKCsq4F+vLWH/gw8d7ypJJBLJmDEMg5aWFmpra6mtrWXDxo2s3bCe2tpa2ls3kYoPE1+KghbwoeamFmpFfrSiAhyBArRCH/i9qAEvaoHX3oxcIpHscsxInNQ71cTfXE22uQtPsJCLPnYZX77tsxw9zLr7jiIF2gRACjTJREMIwT333MPT3/8+PztH4dSq7Z8zUWgYEHzmv4KoqfPCCy9wxnkXj3eVJBKJJE86naahoYG6ujpqa2tZW7OedRtqaKiro6N1E9aggQ1Vtff2KguilwbRSgM4iwNoxX4o8qMFfCjD9oKUSCTjS7a5i+Rba4gvWYsVSVB5wH58+oZPccenbqKsrGyHypICbQIgBZpkovHYY4/x8MMP89AJCh8/aPJNdelPCe5cCCt7BM9+7wmuu+NL410liUSyDxGPx/MCrLa2lnUbali3YT0NdfX0tnfm130pDh2tNIBeVoReVoSj3PbV0iBaqFCOfEkkkxBhmKTWNJB8aw3J5RtRgGPOPI0v3HI7l106b0xTIKVAmwBIgSaZKAgh+PKXv8y3vvUt7p6rcMsRk0+cDZIxBQ8tgRdrBffdfBVf/8nv5LoKiUSySxBC0NPTQ319PfX19dTV1VG9oYbqDTU01jfQ392Tz6u6nWhlRehlQbSyIpzlxehlQdTSAGrQL41sSCR7MWYsaU+BXLyabFMHnmAh8664nHtvu5Mjjzxyq+dJgTYBkAJNMhFIJBLcfNOn+e3vfj/hzOnvLEIInl0DTy0TXHbqYTz/z8X4CwPjXS2JRDIJSKVSNDU1jRBh62s3UltXS0tT84j1YFqBB7W8CL00iF5WhDM3EqaUBlH9Htk5JJFIyLZ0kXxrLfG312BFEkw7aA633nQTN117wxZWIKVAmwBIgSYZb6qrq7nmqitYX72Ob5wkuHDW3tWYWNAouO9NQWmhm+d+/X+cfeH88a6SRCIZRxKJBJs2baK1tTXvt7S0UN/cRHNrC+2b2gj39A5NRdQ19JIAakkgtx4siKusCK00gFISQPW4xvmOJBLJZEEYJqnV9SQXrya5qg5VUTjpnLP5/C23c/GFF6LruhRoEwEp0CTjhWmafO973+OB++9jitfiu2coHBSc+NYad4amiOC+t2BZp+CWyz/MN3/+5x1+3oQQsidcIpmAGIZBb2/viI2WBzdf7ujqZFN7O+2dnfT0dNPf00s8Eh1xvubzoBYVoOYsIWpFfvTiQpxlxVBaiBYsQFHlejCJZHcihADBPjXt14wkSL2zjtgbqzBau/EVB7nsqiu4+epPctppp0mBNp5IgSYZD1atWsVtt97CW2+/zXUHK9x9tIpb27sfUdMS/KYanlouKHA7+NLdd/KZLzxEQUEB7e3tNDc309LSQnNzM80NdTTVb6SluZmWtg7C0QSmZeFy6JQEC9h/5jROOOkUzrv0cs444ww0TVpTk0g+CEIIotEofX199Pf3b+H39vbS09dLZ08PvX199Pf3Ee4PEx0YIBmLb1Geomv2hst+e58ve+8v23cU+dGLCqHIhxr0o7o++L5FEsm+iLAEIpXGSqQRiTRWMjUUTqSwkqOF03Y4mUYYBpgWwrTAzHUQayqKQ0fRNdv3uob28Cv0oQV89kj2oFEdt3N8P4RdRKa5k+TiNSSWrMWKJfFfcgqtv/mXFGjjhRRokj1JS0sLDz34IL/69a+ZEVD52olwXPm+82j2pQQvNwr+sAFaoiCAtAnWsI/A54AqH1T4cr5XIehRcGgqKUPQHbeoCwuWd0NvCqqKfFw1/2JuvOsrHHKo3HtNsm+TTCZHFViD4Z6+Xjq7u+np66Ovr5dwOEwkHCYeiWGZ5pYFKgqa143idaH43KiDzuvOH+s+r70OrNALOV9uuCyRbB8hBCKVwYomseJJrEQqJ7TSm4VtQWUlUiPCIpnZeuEOHdXrQvW6UDwuVK97ZNjtBIduWyvVtbzVUmGYiKwBWRORydrXH4hjRhNYkThmOI5IpvOXUQMF6JXFOKeX45hhO72ieNKOegvDJL22EfeRs1k3/34p0MYLKdAke4LVq1fzwx/+kF//6nl8msnth1tccYCCY3K+v3aIzoTgpTp4rUWwotsWZRVeOKAI5gRhqh/KPTClQGFKAfidY2vUCSFY2QN/rVf4Z73FQAZOPWJ/7vryw1x6+VXour5b70si2V0YhpEXVaOJrZ7eHrp6e+nu6aE3N5I1EO4nNhAhmx69waa6nCg+W2ipBZ58Y20wrPk8aD43WoEXvE7wulB9HhSPa5+a8iSRfFCEadlCpj+K2R/DDEdtgRNLYkUTWLHkCJcfudoMxeO0O0I8rpzQGhb2uOxneXPRNRj2uFAcu+830IwlMTv7MDr7MTr7ybb1kG3uwuwO23V3OnDMrMB1wFScB0zDObtqUq4VlQJtKyxatIgnn3ySpUuX0t7ezosvvsi8efPy6Z2dndx77728+uqrhMNhTj/9dH7wgx8wZ86cMV9DCjTJ7qK3t5e//e1vPPeLn/PGm29R5tO4ao7FdYeoFDgm3eO4w1T3CZ5fJ/hnAzhUOLUKzpiqcPoUKPPu2gZfxhT8uxl+s16wvAumhvzccustfOZzX9rCOpNEsrsRQpBIJBgYGCAcDo/wBwYG8mKrq7eHrt7clMG+PgbCA0TC4REWCoejaBpqgRvFa49cDfrDR7X0Ai+az4PidSF8uUabzy03VZZIdhHCtDD7Ihhd/RhdYcxBPyfIrEgchje5dQ0t4LM7Q/LOi+r3oPo8Q36BJ/ccu+wR6Ek4AmXFU2SbO8k0dZCpbSOzocUWoaqCY0YFrkNm4j58Fs79qibFO0kKtK2wYMEC3nzzTY4++mjmz58/QqAJITj55JNxOBx85zvfobCwkKeeeoqXX36ZdevW4fP5xnQNKdAkuwohBNXV1fznP//hby+9wMJFb2BZFseVK1x9IJw7fd8YMWuLCZ54X/Bykz1F8dqDFT42Z+wjYx+Utb2C/6uBf9QJFFXlios/zOce+BrHHHPMHrm+ZPwRQiCEwDTNHXaGYZBIJPIuHo9vcRyPx4nGY4QjEXr7+wkPhBkIh4lGIsSi0a1PFwRQFLsBlhNUQwLLkxdcus+DVuBG9XoQXqctsHxuFKdDThuUSPYQwrQwOvvItnZjtHaT3dSD0daL0RMeGvVSFbSSIHppAK240DZyE/SjFflRc2G1wLPPjkILITDae8lsaCVd00x6bSNWLInicebE2n64j5qDVugd76qOihRoY0BRlBECbcOGDRx44IGsWbOGQ3PrTkzTpKysjCeeeIJPf/rTYypXCjTJzmAYBm1tbaxdu5YVK1aw7P13WfzGIjq6+3BqCseVCc6doXDODI1S995pmXFz0qbgZ2vgZ6sFfid8/miFi/cDfZx+mPpSgv9Xq/C79RbtcTj2wOl86jO3cc2nbiEQ2Pv3Y7Msi2w2m3eGYeyycCaTIZXJkMlmyWQzpDMZMpkMWSNrx2UyZA2DbDZjn5c738iFjayRF0TWcIFkmVimlY+3LAvTsuywaWKaFlYuTz4951vD0oS16585RdNQXLotkpwOcOqoLoctqjzD13nYo1a6Jzdy5XGB22Gv4fK45NosiWQCIrIG2ZYuMg0dZBvbyTZ3kW3vBcPuaFEDPhxTStCrStDLi9HL7T33tOLCSTESNFEQlkW2qZPU6nrSq+vJ1LcBCs4Dp+E55kA8R89BK/KPdzXzSIE2BjYXaKtXr+aII46gtraW2bNn5/NVVlZy3nnn8fzzz49aTjqdJp0eWtwYiUSYNm0a8+fPx+EYsiQ12sf1QeK2FT8cazsNi22V8UHqsiPn7kt1MQyDVDJOMpEglUyQTCaJRuNEE4n8DAaXplDqEVT4YEahSpXPQt9OA2wsD+NYntgxlbMLChHbydSZgP+1wEAarj8Ubjkc/M6JMVxoWIL/NsOfawVvtoGuwMwSDxSU4PAWwrC/lT3yAvk7FrY/OCLD5uHBPIOOQVPFI/MOOssSuTKHx1tYln1dIaxceQyl5coTQmANXnt4udZQeVi5OuwGgZJHUUC1naKqQ8eKAqpq9xQPi1dyafl4VQFFRVFzZaEMKwMUhpWngKKooOTy5uJQ1dyfbeh4sG756+XzK0O918OuOzx9qHwQioqi5xbN6xpoGoquTsrpRhKJZEuEZWGGY5i9Ecy+iD01sTdiW6vSVBxTS3HMqECfWoKjqgTHtDI0/8Qc4ZnsmANxkss3kFq6gfT6ZjAt9GllOKeXoU8tRXWOn4VX31lzpUAbC5sLtGw2y5w5czj++OP56U9/is/n46mnnuK+++7jwx/+MK+88sqo5TzyyCM8+uije7DmksncVzys3TbC1xRQybU3GdHGHzp3Oze+qz6XsXTGby/LzpYhBMSzkDDgoGL45ilwUPHEbch2JgQv1QperIOGCBS5oMABnXHsxvkYyhD5/4Ydbx4WYytrdzHqn3MHvnCT+ZmVSCSSQQZbvgLsF5vDYVsoFAIUBb2qBOesChwzKnDMKMc5o3y3GteQbB0rniK5bCPJd9aRrm6yp48GCxADcTDMMbVTdiXrrlWpeLpfCrTtsblAA1i6dCk33ngjK1euRNM0zjnnHNRcL+e//vWvUcsZbQRtxWNHMdOI7Nb6S3YegZJv7ApFyR0rCMVOA+wYwbCc5PMMhgFUMVSaKgQjm9EKVq58Q1UxFZWMqpPUnCQ1J3HdRdThYcDpJaq782Xuq3R1xPjHi9V0d8e5/QiFmw4H5ySZZz9oAfIvtYJ/NUAsCydNUfn4kW5OqlLREKjCQhn2HVHsE0eU88HvVtnm4a67jkQikezdCCFoigjW9ghW9wiW9Wls6DHIZO0ZBXpFMY5ZlThnlOOeWY46o1LuwTdBMfujJJesJf72OozWbryFLi44yM0tM5NUFeyZX8TmlMpJP+6QAm17jCbQBhkYGCCTyVBaWsoJJ5zAscceyzPPPDOmcuUaNIlkxzBNk29/+9s89OBXmOkXPHGqwiFFk3edXdIQvNIEv1onWNcHh80o5Qv33sc1n75jxLRniUQikUwMhBC0tLTw3nvv8d577/HGkrdZuXw58UgUAL00iGNmBY6ZFXj2q0KdXjYpzbxLINPUSXLRSuJvrUVksxx1xinc/7l7uOyiS9C03bP+b09qg71aoA2yceNGDjroIBYsWMCHP/zhMZUrBZpEMnZqa2u5/tpP8taSJdx4KHxuroZTndSvljxCCJZ0wM/XwuJNginFXj53+63c8sWH8fsnzqJliUQi2dfo7u7mnXfe4d1332XxO2+zbOlSBnr7AdCLC9FnlOOcWYF7vyrUGeVoBZ5xrrFkV2Ml0yTfqSa+cDnZ5i4KK0q56ZbP8JXPfZ5gMLhLryUF2naIxWLU1tYCMHfuXJ566inOOussiouLmT59On/+858pLS1l+vTprF69ms997nMcc8wx/OUvfxnzNaRAk0i2jxCCn/zkJ3zh8/dQ4jR4/GTBsWWT7pUyZtb3CX65zt4A2+fWue36a7jnse/IPdUkEolkN2MYBqtXr+btt9/m9cWLefPtt9jU2ASAVuhDn1mOc2Yl7lmVaDPK0YIF41xjyZ5ECEG2oZ3EayuIL1mL7nRw8dVX8OQDjzB7v/12yTWkQNsOCxcu5Kyzztoi/rrrruP555/n6aef5sknn6Szs5PKykquvfZaHnzwQZxO55ivIQWaRLJt6uvr+czNN/Gf//6Pqw5Q+NJxKj590r1Odor2uOC5dQp/rLFQVZUbr7iILz/xI6qmTBnvqkkkEsleQVdXF0uWLOGtt97if4sXsWrZctLJFIqmok8vxzm7Cvf+U3DsV4UaKpTbVEjymOEYif8tJ/raMkQizXHnncWTX3mUM0459QOVKwXaBEAKNIlkdLLZLN/97nd55OGHKHaaPHqCxWlV412r8aEvJfhVNfxftSBjKXziojN54MmfsN+cA8a7ahKJRDJpMAyDVatW8fbbb/PGm4t54603aWtqAUALFuCYXYVzdhWe/aehTS+19xyUSLaDlc6QfHMN0Vffx+zqZ7+5h/Otx77GZRdevFOCXgq0CYAUaBLJlrz33nvcdOOnWL1mLdcdLLhzrrbPjJpti2hG8Nv18Pw6QSQDHzvnRB757rMcdOjh4101iUQimXD09PTw9ttv8/bbb7PwjUUsW7rUHh3TNRzTy3DsV4V7/6k4ZlehFvvl6JjkAyEsi9SKWmL/XEKmoZ0phx7ANx55jE/Ov2KHvltSoE0ApECTSIaIRqM88MAD/PCHP+TgkMpXT1I4rHjyWmjcXSQNwZ83wi/WCLqScPmHT+Kx7/2CAw46eLyrJpFIJONCIpFg1apVLFu2jLfefps33nqT5voGAPRgAfp+VTj3z42OzSiT+41JdhtCCNJrG4n9/S3SG1sp238mjz70MDd//Nr8dlzbQgq0CYAUaBKJ/TL705/+xBfuvoveni4+e6Tg2kMUdNmZuU0ypi3UfrJK0JuCqy44g0e/93Nm77//eFdNIpFIdgtCCDo6OqiurmblypW89/77vLP0PRo21iEsy147NrUU5/5TcM2egmv/KShy7ZhknEjXNBP7+9uk1jVSPGMKDz7wAHfccBO6vvUOAinQJgBSoEn2dVauXMln77idRYvf5EPTFe47Xmeazxzvak0q0qbgjxsUnl1t0Z+Gj196Hg9/58fMmjVrvKsmkUgkO4xpmnR2dtLa2kpzczMbNmxg7bp1rFq3lrqNG0nG4gCoTgf6tFIc08txzijHNaMCtSokR8ckE4507SZi/3yb1Mo6glMr+MoDX+Fzn/7MqEJNCrQJgBRokn2V3t5eHnzwQX76058wK6By/7GCU/dRIyC7iqQh+MMGhZ+ttohkFD45/yM8/O0fM3369PGumkQikQCQyWRoa2ujtbU17zZt2kRjczONLU20tW6ip6sbyxzqqNO8brTKYvTKEHplCFdFCK2yGLU0iKJtf8qYRDJRyDR1EPv72ySXbSBQVc4DDzzAXTfdgsMxZJBGCrQJgBRokn2NaDTK9773Pb79rScQ2RR3HG7x8YMVHPI3dpeRyAp+lxNqCUPhE5eey10Pf4vDjzhyvKsmkUj2YkzTpK2tjaampiHX3Ex9SxMtra10bmoj3Ns34hzV7UQr8qMWFaAV+fPOUVyIVlwIQR9qgUdOUZTsVWSaO4n9422S79dQWFnK/ffdz92fuQ2n0ykF2kRACjTJvkIqleLHP/4x3/jaV4kMDHD1ARafOVIn5JJGQHYXsazgt+sVflNt0Z2Ekw+fze33fJl5V1yD1+sd7+pJJJJJRjqdpqWlJS++Ghobqamrpa6hntaWFno6OrGMoZEvtcCTE19+tJwA04sKcYYKUYMFUFSA6nGN4x1JJONLtrWb2N/fJvF+Nf6yEu69915u/sR1lJWVSYE2nkiBJtnbicVi/PKXv+Rb33ycjs5OLpsNtx2lUeWVwmxPkTEF/2mB360XvNcJPpfOJeeezic+8zk+dO55uFyygSSRSOw2SVNTE83NzXkRtqG+jvrGBlpbWujv6mF4c04LFqCFCtFCAbRQIY6SII5QIYT8aKEAqts5jncjkUwespu6if3jbRLvVuMrKSbtUOmtrpMCbbyQAk2yt9LW1saPfvQjfvTDp4lEY1wwU3D7UQ5m+bc0ALIpJljaBdV9guo+aBiA/jRkLdAUKPFAmQfKvTArAPsHFfYP2GGPNPW4QzRFBP9oVPl7nUlDxBZrHzrlWC658jounncZZWVl411FiUSyi7Esi76+Ptrb2+no6KCjo4P29nYamhqpra+nsbmJtpZWEtFY/hxFU9GKC1GLbbGllxTiCAXRSwIoxX60Yr80xrGbEZYAw0BkTUTWQBi2jzH82LSPB/MZ5lB6xkBksiPDW4vLjgyD/R1AVUFTUTQVxe1C9ThRPC5UjwvV50YNFKAFfWiBAtScrwUL5HdjJ8m29xL7+9sEP30B1Zd/RQq08UIKNMnehGma/Pvf/+YnP/4R//jnP3GpcPn+Ftcd5mCKd0iYWUKwrAv+2yJY1Aq1A3b81AI4uBhmByDkUXBpkDWhJyXoSkBHHOoj0G4b8ELJnbN/EGYH4cAihePKodInRdv2EEKwIQyvtaq81mKyshtQ4NiDZnHRvMu46GPXcNRRR41pzxaJRLJnME2TZDJJLBYjHA7T19dHf38/fX19+XBPby9dvT109fTQ1dVJd1cX/d09mMbIzjHV67KnHoYC6KFCewQsFEAPBVCKC1CDBSjy+d8lCEtgDcQw+6M5Z4etaAIrnrJdIoVIpHICyRZcmDs500RRUBwaitOB4tRRHDnfqQ+L08HpQHXq4NRRnQ4Uh47icsCguDItsCyEadliMJXBSqYRyTRWMo0VS2INxDHDsS3qqgYL0EuDaCUB2y+1fb0kgBr0o6jyd3p7rJt/vxRo44UUaJLJjmVZLFmyhD/84Q/8+Y+/p6OrhwOLVa6cI7hktorfYT/6QghW9sC/GgQvN0FnAko9cPoUOH2KwkmVEHCN7YUdywjqB2BjGGoHBHVhW+RtynX+TvfDSZVwyX4Kx5QhF5ePgd6k4PVNCq+1WCxug4QBJYVezjnjZC782Cc497zzKS8vH+9qSiQTmkwmQyKRIB6P75Afi8WIxmNEYjFi8TixeIxEIkEiniCZTJBMJEknk2TTma1eW3E6UH1uFK8L1eexRzf8XrSADy3gwxH0owV8iEI7TnE6tlqWZOcQWYNsey9Gey9GRx9Gex9Ghx0WGWMoo67Za/MKvahet/238rpRvS777+LQbLHk0FH0oTC6ZgsvXUPR9aF8uh2Hw47f05YthRC2yAzHMAdimH1RzJ4BjO5w3rcG4iPuXy8NoJUGbdFWGsyHtdIgqkt+N0EKtK2yaNEinnzySZYuXUp7ezsvvvgi8+bNy6fHYjG+/OUv89JLL9Hb28vMmTP57Gc/y6233jrma0iBJplsCCFobm5m4cKFvPLyAv7z71fo7g1T7lM5b7rFhfvpHBky86KoNSZ4oVbwUp0toEo9cP4M+MgshbmloO5C8dSXstdYvdsheK3Vvt4BQfjM4QoXzNq119qbyZiC5d3wRpvC4k0W1TmjazMrijnh2GM44YxzOOTwIznooIOYNm2aHGWTTDoymQzRaNQWRtvwI9Eo4cgA4UiEaCxKLB4nHo8TTyRI5lwqmSSVSJJJpbYYpdoaqsthj2S47NEMnCOPFZcT1annjh15X3M5UF1OVJcTvC4UrxMlJ8bklLI9i5XKkG3pItvUSbapg0xTJ0ZbD1h2c1ct9KFXFKNXFuOoDKGVFaEV2xYq90WrlFY6OyTausND4q2rH6N7ALJDAlYN+EaINr1sKKwGfPvMZycF2lZYsGABb775JkcffTTz58/fQqDddNNNvPbaa/z85z9n5syZvPrqq9x222385S9/4dJLLx3TNaRAk0xkkskkNTU1VFdXs27dOpa9u4R333uPnv4BFAUODSmcXCE4fZrOMaVmXgANGqX4y0bBm23g0eHCWXDRLHsKorYHpjZYQvBOB/xyrWDRJjg8BA+eoHBk6b7xYt+VdCcF73UqrOgWrOwWrOuFTG42i9uhU1VWxNSqKqZOn870/Q5g+qzZTJs2jalTp1JVVUVxcfGom3FKJIMIIchkMqRSKZLJJMlkMh/eET+RSBBLJogl4sQTSWJxW2jF43HisRjJeIJUPIFpGNuukKaiup0og87psI8HBdQw0TRcPCkuJ2pORA0JLx0x/ByHLqd3TTKsVIZscxfZxnYyjR1kmzoxOnpBALqGY2opjhnlOGdUoE8txVEZQvW5x7vakwYhBNZAHKNrSLwNF3LDR98Upz402lYWRC8tQi+3fS1UaI8k7iVIgTYGFEXZQqAddthhXHnllTz44IP5uGOOOYYLLriAr371q2MqVwo0yXgihKC/v5+Ghgbq6+tpaGigrraW+g3V1NZupKmtK2+xq8yrcGCR4PAQHF6qc1SJRfFmvz/9KcHvauD/1gv6UjC3FC6fo3D+TPA5xq9B8n6n4Gvv2gZILpsNXzhGIeSRDaSdxbQEbXGojyg0RhU6ohYdCUF73J662pkAc7M3fqHPTXHAT6goiNfrRXc4cDgc6LqOpuk4dA1N19FzzqE70J0ufP5CikKlBIqKCQaDBAIBgsEghYWFFBYW4vf7KSwsxOVy7TO9qrsDIQTZbJZ0Or2FS6VSo8aPJpKSySTxZIJEMkk8YfvJZJJkakh0pZIp0ukU6aRdbiaVJpNOs6PNBDW3fmZw+pfQNXDoubU3uXinwxZYrpzAcrvQPc6c2HKC2wE5YTUoxlS3c69q5El2DJE1yDZ3kWlsJ9vYQaaxA6OtF4QAh45jWinOGRU4ZlbgmFGOo6pEfl92M1Y6g9k9cvTN6ApjdoUxesJD699UxV5TWZYTcOVFOKaU4phWilo4+UbepEAbA6MJtFtuuYWlS5fy0ksvUVVVxcKFC7nkkktYsGABp5566qjlDP6wDRKJRJg2bRrXXHPNiF3EgW3+WO3oxymE2KFztpd3tPSdOeeDpI3lfnbmnnfF57Szn8UHudfRzjEMg2w2SzaTJpvJkE6niMfs3uRYPEEimSI7bEqOS4OgCwJOKHJDyK0Q8iqUuCzco1lLzFUrlhG83QGreuzj+XPgmgPhgKKJM/XNtAR/3ghPLRNYAs6dAbML7Wd7Z15OO3rOnngD7tR97OBJY8luCUHCgGgG4oZCyoCUAUlDkDLAEPZ1zeE+9swgIcDKhS1hW/LMmJA2txR9w1EVBadDx+V04HToaLqOpmmoqmr7mpY/VlX7WFVVUEBRVHtRvX1g/4jnnIKS/1FXVCU/SqwoKopiHw/mV5WheEUdPBdsczZj+1SH/z2Gxdo9zJbAtEws08K0TEzLwrJMO940sSwr7waPzVy6lTs2TBMjm8U0TEzTwDQMTMPEMsc2NW9UNA1FU0DTbGtvumavgdFy/ohjO18+j67Zhih0FUXTUHQVRc2tsxksLxc/WD6aYluWm2SNLcnEQ2QN22hHXxSzN4LZZzssAZqKY1oZzpkV6DMrbFE2RYqxiYawLMy+KEZnvz1dsqvfFnNd/Zhd/fm1f4rXhVYSRCsqQPV7c8ZKCiasaPOddoQUaGNhNIGWyWS46aab+PWvf42u66iqys9//nM++clPbrWcRx55hEcffXQP1Fgi2bJZONhWVHJhJRdWlaG8w99VYvNzN0MIMCwwAa8O1x0CVx8Apd6JI8w2py8leOJ9e03ctALbGEZ2F2zJtqPv+J35SdjRc3b3787O1mdnqyWGC7lhx8N9sAXe0ElD32MxrJzhx1u93mYBMVraZtcYS7kSiWSc0VT7hy+b65hQFfSqEpyzKnHMLMcxvRzn9HK5rm+SIyyB2R0m09xFtqUTo72XbEs3Zne//aJ2aKhet73FQCK93fL2JDXXqVQ83b/bBdpe+Q1/+umnWbJkCX/729+YMWMGixYt4rbbbqOyspJzzjln1HPuu+8+7rnnnvxxJBJh3cOHMj0bGTX/1pT9RFX8kg+GOazpKhQFa7PjrTX8xGZNXlNRsBQVM+cyqkZG1UlpDmKai7juYsDhIeLwjLjGWEmlDN5Zson332nDoVrcdDBcd6hC0Dnxv5fFboUnTlWYv7/g4SWCgTicfUwxh5+5P26PtBw1HGUrB8pOS6wduB4j33PKsCsPjkrZXi4u1/PgsCycloHLyuK0DByWidMy0C0TTVg530QTAlVYI5wiBGouXkGMiFOEhYpAEbZjMB1y4cF4UMdJou3pflApRCUTiawlaI1BcwSao9AUhdq4TmMEuqMGVq7nRg/60KaW2evGpoRwTSlBnVImxdheiKIq6OVF6OVFcNyB+XgrlSHb0E62tpV0fTuZujYEoGoqs6Z4OaPc4MOlKQ4r3jNr5kejw3RQsQeus9eNoCWTSQKBAC+++CIXXnhhPt+nP/1pWltbefnll8dUrlyDJplMGIbBs88+y8MPPkA0EuHjB1rcdLhGsWtyPt4ZU/DzNfDT1QKvy8FDX/48t335sS2mG0skEolk/EkmkzQ0NFBbW0tdXR01GzewrmY9dXV1dLS25afqKg59yIhEmd1Ad00tRa0MoRZ4xvkuJBMNIYQ9ulbdRHJtI+n1zYhUBk+wkDPOO5ebrv4EF5x3Pm73njH8sie1wV7XLZHNZslms1uYl9Y0DcvaBfOlJJIJxoIFC7jnrs9Ss7GWj86Gu87RKffAZO5Hd2oKtx0J8/eH7600uPuhb/KDZ37EE9/5Lpddc4McqZZIJJI9zMDAQF6A1dXVsa5mPdUbN9BYX09vR1c+n+J02ObXy4rQD66k8IxDcFWGUErlRsiSHUNRFBxVJTiqSvB+6BiEYZJpaCezsp7/vP4aL//xL+huF8edeRq3X38j8y+dt8fE2u5mUo6gxWIxamtrAZg7dy5PPfUUZ511FsXFxUyfPp0zzzyTnp4efvjDHzJjxgxef/11br31Vp566qkx74UmR9AkE52amho+e+cdvPrv/3B8hcKXj9M4tHjv7IRY3yd4Yim81SY44aCpPPH0s5xx7kfGu1oSiUSyVyGEoLOzk3Xr1rFu3TpWrVnN8tWr2bh+PQN9/fl8qs9tC7DcXliO8mIcZUUoJYUT2sCDZO8i29ZDenktifdryDZ14Czw8qGLL+ALN9/Omaefvsv3At2T2mBSCrSFCxdy1llnbRF/3XXX8fzzz9PR0cF9993Hq6++Sl9fHzNmzODmm2/m7rvvHvNLQwo0yUQlnU7zjW98g28+/g3KPYJ7jxGcM03s9T+IQgjeaLOtPVb3wYePP4RvPvM8c489bryrJpFIJJOOcDjMihUrWLlyJStXr7KFWE0N8QF77b2ia/aGzlUh9MoQrsoStLIgamlQ7iUmmXBk23tJvb2O+NtrMHsjBKvKuf5TN/DlO+6ivLx8l1xDCrQJgBRokonI4sWL+dT119HY2MCNhwhuPULFvddNVN42lhAsaITvLxc0ReFj55zIN575FXMOOGC8qyaRSCQTDiEEbW1tLF++nGXLlrFk6fssX76cjpZWILcurLIYPTeVzDWlFKWyGL00aG+5IJFMIoQlyNS2knxjNfF3q1EsixM+fDYP3f1Fzj/n3A/UmS0F2gRACjTJRCKdTvPggw/y7W9/m6NKFb56ssqcwN45nXGsZC3BC7XwzEpBbwquvewjPPa9nzFlypTxrppEIpGMC5ZlsXHjRpYvX87SZctYsvQ9Vq1YSSQ3PVH1uXFML8cxvQzX9AocM8pQy4ulEJPslVjxFMm31hBbuAKjvZeSmdO45+67ueumW/B4dtwojRRoEwAp0CQThdWrV/Pxq69i/fpqPnsk3Hjo+JmXnYikDMFvaxSeXW2RNFVuvfZyvvKtZwiFQuNdNYlEItltpFIp1qxZw4oVK2wx9v67VK9dRzqRBEArLsQxvQzH9HLcMypQp5WihQr3+unwEsnmCCHIbGgh/u+lJJdvwFVYwDWfup5vfOkBKirGbjRfCrQJgBRokvHGNE2++93v8sD99zHTb/GtU1UOLtq3R822RTQj+OU6hefXWiiqyrUfu5jPP/YdZs+ePd5Vk0gkkg9Eb28vK1asyIuxd5cvpWFDrW2+XlVwVBSj50bG3DMqbTEmzdZLJFtgdPUT/89S4m+sAtPirHkX8e0HH2XuEUdu91wp0CYAUqBJxpONGzdyw3XX8taSJdxwMNx1tIpLG+9aTQ56k4Lf1Cj8fr3FQBo+csqR3HzPV7jg4kvlPmoSiWRCEw6Hqa6uprq6mnXr1rFizWpWrVpFd3sHAKrLgT61zB4Zm1aGe0YFSlUI1SXfbRLJjmDFUyQWrST6n/ex+mMcftqJPPnwV/nw2R/a6iizFGgTACnQJOOBaZo888wzfPneL1HqMnn8ZMGxZfIR3RlShuCv9fC79YL1/VBS6OHqj32UGz/7JY48cvs9ZRKJRLKrMU2Tzs5OWlpaaG1tpaWlhQ0bN7JizWo2rF9Pb1e3nVFR0EsCaJXFOKaW4ZpejnN6GUpZEGUXmw6XSPZlhGGSfLea6IJ3MDb1MO2wA3ns/gf55BVXoWkje8alQJsASIEm2dP873//4567PsvK1Wv55EFwzzEaXl0+nruC6j7Bi3UKf6+36EvBwdPLuGz+x7j8ups54ogj5JoMiUTygTFNk/b2dlpbW/PiazDc2NxEc0sLXR2d9rTEHIpDRysNoFeGcFSGcFSV4KwqQSkvkqNiEskeRAhBenU9sZffJb2+meKplXzhC1/grptvzRsUkQJtAiAFmmRPsWzZMh55+GH+/o9/cFS5xn3HwlEl8rHcHWQtwaJWeLkJXmsRRLMws7yISy++kEuuvoFTTz0Vp9M53tWUSCQTDMMwRhVfTS3NNDY10bppEz2dXSPEl+ZyoBcXogQLUIr8aEV+9GI/enEhjuJCRFEBqs8tO4gkkglGpqGd+Mvvkni/Bk/Az0233sLDX7gXXdelQBtvpECT7E4Mw+Bvf/sb333qOyx+8y2mFmrcc5TFBTORP9Z7iIwpeLcDXm1R+F+zRXcSvC6d0487kvPnXcFHLp7HnDlz5N9DItnLyWazefE1fOphfXMTjc1NtG9qo7erG2ENGWlSXQ70okKUogK0Ylt8acWFaEV+XKFClCI/wuuS7w+JZBJjdPYTf/U9YotXo2saF145n7/+6ndSoI0nUqBJdjWWZfHmm2/y+9//nj//8ff09IU5plzluoPhQ9ME+lZM53clBAtbYUm7oHYAOhOQNsGlQcgNc4JwULHCwcVwbDkUOGSDYEexhKCmH95oU1m8yWRZF2QtmF4W5ENnnsa5l1zBh879MGVlZeNdVYlEMkay2SydnZ20t7ePcK1tm2hqbaV10ya6Ojvo7+7dTHw5UYv9aEUFI4SXs7gQtdgWZYoUX7sdYZiIVAYrlUGkM4hUFiudQWQMyBqIQWeYubA5FL95nCXAsuy/s2mBJRBC5MJWPh1L2HksC2Hm4oTIORD2f7YbDOfjcmVCPn8+TVFQHBqKQwddQ9HtcN53O1F9blSvGyXnqz43qt+DFrS/i4pHfufGCzMSJ/G/ZUT/u4zSL3+cjZ99Sgq08UIKNMmuoK+vj1dffZUF//wn//rXP+jpC1NZoPKRGRYXz9Y5ZBtm81f1CH68SvB6q/1+PzwEBxfDlAIFlwYp0xZvNf1Q0w+RDOgKHF0Gp05ROLXKzq/KF/oOE8/ao2uL2xXebrOoG7DjD55ZyYc+dA7nXvIxzjjjDAKBwPhWVCLZR0in0/T19dHX10dvb28+POi6errp7O6mu7eH3t5euju7iPT1M6KJoyrohT6UgA81UIAW9KEFCtBD9pRDJehHLZYN4Q+CsAQincFKpBGJFFYijZUcFk6kEIm0LbTSWUTK9q1UJhfOYOXiMce4rYxDz4ufvOBx6DAsDk21jasoSi6sgKqCOhRWVDsNRbE37lZVUBX7u6AooADk/MHvh6LY0aoyStqw/EJA1hwSjzkBSc63UhmseAoRT2ElUljx1Bb3rzgdqIOdBkV+9LIgelkRelkRWnmRnC67B7BSGVS3k3Xz75cCbbyQAk2yMyQSCd566y1ef/11/vvKAt5ZugzLEhxYrHJGlcWZ03XmlpjbFE01/YLvLxf8twX2D8AnDla4YCYEXFs/RwhBUxTebIM3Ngne6YCEASVuOGMqnD1N4aRK8MnRtZ2iMyF4p0Ph7XbBknZBW9wWvkcfPIuzzz2fsz9yCccee6zcHFsi2QZCCOLxOP39/Xk3XGT19vbS2dNNV08PPb299Pf3Ee7vJ9IfJp1MjVqmlhtxULxu1AI3qs9jjzwEfOhFfhzBAtSADwI+VL9XWkDcAYQQiEQaM5rAiiawIra/+bEVTw4TYmlbjIyGQ0f1ulA9LhS30x41cjlQXE4UtyN3bMcrLsdQ2G3nUXPxitORE2GaPRq1F4oSIQQik8WKJDD7Y5jhKFY4htkftY/7IhhdYaxIPH+O4nXlBZteUYReVYJjSil6eZEtUiW7DCnQtsKiRYt48sknWbp0Ke3t7bz44ovMmzcvn761h/Vb3/oWX/ziF8d0DSnQJGNhYGCAd955h9dff53X/vMK7y9bQdYwKfaoHFdmcVqVwmnTNCo82+8JbIoInl4h+GcDTC2AO49SuGgWaFuZ+rgtMqZgeTe83ip4rRXqB8CpwgmVcNZUhTOn2iNxkh1HCEFLFJZ0qrzdZrKkA/pybcdpZUUceehBHHjYUex/yBHMmTOH/fffn6lTp25hrlcimYwIIUgkEiME1nDB1d/fT09fry2y+vpskRUOM9AfJh6JYhrGqOWqXhfKoLjyDRNaBbavFXhs5/OA1w25fIomBdfOICyBNWA39M3+KGZfFDMcy/lDxxjmyBMVBdXvQfV7Uf1eNL8XtcBjC+RB8eV12VP0vC4Uz7CwQx+fm92LsZJpjO4wZlc/RmcYo6vfdu19Q+JNU9ErinFUlaBPKcExpQS9qgS9rEg+PzuJFGhbYcGCBbz55pscffTRzJ8/fwuB1tHRsUX+G2+8kdraWvbbb78xXUMKNMlwhBD09PRQXV3NsmXLePedJbz/zttsbGgGIJQTZMdXKBxfqbF/oTnmXr2WqODZ1YK/1EKJB247QmH+HHDshDDbGk0RW6i91iJ4vxMMAQcUwcWzFC6fA0VuKdZ2lsHRy7W9Kmt6LTb0C5qjsCkGZu7tqioKJcECKkpDVFSUUzVlGlXTZ1E5dTqVlZVUVlZSUVFBSUkJfr9/r+wRlux5hBBks1mSyeQWLpFIkEgkiEQiRKPRvD8wMEA4MkBfOMxAJMJAZIBINEo8FiMejREbiGxdZLmcudEsV349zfB1NZrXje7zoBa4wesCj8sWYF4ptHYHImtgdIXtBnz3SN/oHoDs0N9RceqoQb9t8CQ4NI1OC/hQC72oBV7b97nlKOQkwYwlMTb1kG3rwWjttv1NPVixpJ1B13BMLcUxvRzHDHufPX1qKapLWjLeHlKgjQFFUbYQaJszb948otEo//3vf8dcrhRo+w5CCNLpNP39/VssJq/bWMP6taupqa0nHIkB4NIVDikSHBaCw0pUDi9V2c8/dkE2yJpewS/WCF5ugoATPnO4wtUHglvfvY3zaEbwRhv8t1nwapM9Xf6S/eCGQxRmB6Uw2FVkLUFbDBqjCp1Jla64RXfCthbZnYSenJ/dbHDVqWuEAgWEigKEQiFKS0spKa+krHIqpeW2iAuFQgSDQQKBAMFgkGAwKLcH2MUIITBNE8MwME1zhNtVcZlMZoTLZrNbxKXTaVLpNKlMzk+n7bhMmnQ6RSInuFLJFKlkinTKdplUmnQqNcL4xbZQXA57Ctmg87jyx2ruWHE70XxudK8nN2riQniGGTeQ06jGBSuZxmjvJdvei9HWmw+b3WGwck08XUMvDaCVFqGXBu1wWRF6yDaAosj1S/sMZiRuC7fWbrLNnWSbu8i29dhr3hQFvaLIFm15V4bm9453tScUUqCNge0JtM7OTqZOncqvfvUrrrnmmq2Wk8796A0SiUSYNm0aN9xwQ77hM9pHtXncrsozlrSdvdbmaWP5Coy1zjty/UGsMTQgxnqtwQZQJpPByGbJZtJkMxkMI0N2WAMolUrlGjsZUuks5mZ1UACvA4JOCHnska2QR6HEAyUugaYqW51mP2r9h9WxbgDeaofGCEwpgBsPVZg3W+Bz7Pleyb6U4I8bBL+vsQXDGVPgqFLw5taq7cjLYWc+j91xwg7VeUersYvrIYQgZUI0C4ksJE2FpAHJrCBp2OsIk5s5cysF65qK2+XE7XLidDjQdR1d19F0HYfuQHfo6Lodr6gaqqraTtNQVBVFsY8VRdmqP9wNv4fNnWVZOaNmlh1nCSxhOzuOXB4Ly8qlY1tPy5eRL8/CEnZZliXyZVrW0LW2cMK21mYfCyzTtK83WDdLYFmmbdDAsuxr5fILkbPoNh6ow40TqKANGi9Qc/G5Y03Nx6OptmGEQUMIugaaBrqKqmuouga6bvuaCrp9jp1HR3HmDCzIUZEJjxDCXgPWG7Fdn+2saDKfRysuRK8KoVeG0CuL0cuLcVQUowYKbCMYEskoiKxBtq2HTFMn2eZOjOYusi1diHQWALXQh15RZFs0DRWilwRRnPvmlFXvSYdKgTYWtifQvvWtb/HNb36TtrY23G73Vst55JFHePTRR3dTLSUTia39RG0eP8IQ0we8wKDFXRV7euEhxXDz4QrnTBM4JsDUnowp+E214MerbAFQ4bWn6G2rQ3V3d7buaPE7Up8dLntH8++iz2bzYvJva2XoO7WFPyy8+Xn5joLNLyS2Er+1/BKJZM+hKChOHWGYect+asBnj25MK7OFWGUIR1UJqluOpEt2DcISGF39ZBs7yDR2kG3qINvYiUhnAFALPKCqWIkkGGO0uLkXUHOdSsXT/VKgbY/tCbSDDjqIc889lx/84AfbLGe0EbS19x9ImZHY7IKj1CFfl61m2aLBpoySb3vTCyZUg3WCTYWwAEPRMBUVU1HJqBqGqpFVNDKqTlbVSKu67TQHKc1BSnWQ0Bxk1d3XC2QYFstXdPHWm62Eo1lOnwI3HqZwQvnE+wzBHlH7wQp7RO3gCgfnX3oQ/tKC8a6WZIwoCJyWicfM4DazOC0Dl2XgsEwcloFTmGjCQrcsNGGhImxfWGhCoAqBioUqBJoQKAgUIdCwQIC2i6Ta8J+dodFltowbcc4uubREMuEYSAs2hGF9H6wNq6wJa7T0G1iWAAV7FGx6GY5p5bimlaDNqEQL+Ma72pJ9EGFZ9hTa+jYyDe1kGjrItnaDaaHpKlPK3BxRLDgpkOLIkGC6f2K2dT4ICcXBQT/slgJte2xLoL3xxhucfvrprFixgiOPPHKHypVr0CQfhGw2y89+9jO+/tVH6ejs4sJZ8JkjdOYEJkcv0/udgi+/KehJwtfvvYO7vvr0XveSlUgkkj2JZVk0NDSwYsUKVq5cyXvLl7F8+XI6N7UBoDh09KmlOKaX4ZxejmtGBeqUkDTaIJnQiEyWTHMXZkMHqYY2MvXtmF39ALj9Pg464nBOPfEkzjzpFI4//nimTp06adsTe1Ib7NUC7frrr2fNmjW8//77O1yuFGiSnWXJkiXcdOMNrKuu4aJZgluP0tnPPzmE2XDiWcGTS+H3NYJz587i139/nYop08a7WhKJRDKhEULQ1tZGTU0N69evZ/WaNby7bCnr1qwhFbdn5eiBArSpJTiml+OcVoZzehlqebG0ZinZK7BiSTKNHRgNHaQb2sg0dmCFbUNr/lARhx51JGecdAqnn3Qyxx57LGVlZeNc47EhBdp2iMVi1NbWAjB37lyeeuopzjrrLIqLi5k+fTpgf4iVlZV85zvf4ZZbbtnha0iBJtlRotEo9957Lz/5yU84NKTw1ZNVDimafMJscxa2Cu57U6BqOs/9/FkuuuqG8a6SRCKRjCtCCLq7u2lubqahoYGamhrWrFvHmup11G3cmBdiiqailRfhmFaGY3o57mnlaNNKUANy6rhk38Lsj5Jt7CDb2EG6oZ1MQzsibm8iWlxZzhFzj+KMk07htJNO5phjjiEYDI5vhUdBCrTtsHDhQs4666wt4q+77jqef/55AJ599lnuuusu2tvbCQQCO3wNKdAkO8KqVav42GUfZVNLI3cdKfjEQTu3wfREpTspuO9NeGOT4LYrz+c7z7+4TaM7EolEMpnJZDK0trbS3NxMU1OTLcQaG6ltrKepqYn21jayw9atawVetIoi9ArbYIezsgS9ohi1pFBuPyCRjIIQArNnAKOxg0xDO+mceBMp2whJ2YxpHHnMXM466RROP+kU5s6di9c7vub+pUCbAEiBJhkLQgh+8YtfcOcdtzOzwOD7Z2jM9JvjXa3dgiUEv6mGJ5cK5lQG+f2L/+KI404a72pJJBLJDpPJZGhqaqK+vp76+noaGhrY2FBHfVMTm5pb6OvqHmHMRi/0oRT70UI5M+OhAM5QAC0UgJAfrcAzjncjkewdCEtgdPZh5kbZ0g3tZJs6wTBRVJWq/Wcx95ijOeeU0zntpJM5/PDDcTgce6x+UqBNAKRAk2yPWCzGrbd8hv/77e+48gC4/zgV9z6wLcj6PsHn3xA0R+HuT13JQ9/75bj3akkkEslwhBD09PTkBdjGulrWbqhhY20tLU1N9LZ35gWYoqloxYWooUJ7H7FBAVYSQAkVohYVoDj3XCNQIpEMIQyT7KYejNy0yHRDO0ZbD1gCzelg5kEHcMIJJ3DR2edy1plnUlFRsdvqIgXaBEAKNMm2WL58OVdfeTmtTQ08doLgov32numMYyFlCH68Gn6xRlAW8PCdb3+bK264ddJaZpJIJJOPdDpNY2NjXoRVb9zAuo01NNQ30N7cQjoxtIGzWuBBKw2ilwbQSoM4S4vQS4MopQG0Ir80ziGRTCKsdNbeUHtweuQwy5GlM6Zy0qmnMO/DH+Hcsz/E1KlTd9l1pUCbAEiBJhkNy7L49re/zVceeIA5QcF3TlcmpYXGXUVTRPCN92xDIiccNJVv/+iXnHrWueNdLYlEshcghKCrqysvwOrq6lhfu5Ga2o00NTTS19k1NA1RU9FKAuilQfTSII68ACtELwmiel3jezMSiWS3YoZjZDa0kKlpIbWhBWNTD2ALtnM+/GE+8dH5nH3W2R9o/bwUaBMAKdAkm1NTU8MtN9/E62+8wY2HwufmajhV+fiAbTzk20sF6/vholOO4Mmf/h8HHXr4eFdLIpFMYIQQ9PX10djYSENDQ96vqaulrqGe9pZW0slUPr/m96KWDokwZ2kRWlkApSSIVlSAospRMIlEYmNGErZgW9tIclUdZn8UzeXkiBOP44pL5nHlR+cza9asHSpTCrQJgBRokkEikQhf/epX+d73vkulF752kuDE3TfFedJiCcHf6uH7ywWdCbh+3jl89ZlfU1lZOd5Vk0gk44AQgnA4vIUA21BfR119PZtaWvLm6AFUlxOtJIBWUohWEkQLFeIqL0YtDaCGClE9chRMIpHsOEIIjE09ZNY0kFhVR2ZjK5gWUw/an/mXzeemaz7JoYceut1ypECbAEiBJkkkEjz77LN88xtfYyDcz2cOFdx4mIpLk4/Mtkibgt+uh5+sEmSFyudu+iT3ffMH+P3+8a6aRCLZxQwMDOTF1/ARsPqGejY1t5KIxfJ5VZcDLRRALQmg54SYs6QIvTQAoUJUn1uuY5VIJLsdK5kmvaaB5NINpFbWIdIZSmdO45J5l3LLx6/jmGOOGfVdJAXaBEAKtH2XgYEBfvKTn/CdJ5+grz/MJbMEnztap9K776412xkG0oJn1yj8ep2Fz+3gtk9fx10PPk5JScl4V00ikYwBy7Lo6uqipaWF1tZWWlpaqG9ooKZ2I/WNDWxqbiEeiebzq04HWkkhamhQgAVwlgbRQzkB5vdIASaRSCYUImuQWttIetkGEss3IuIpgpVlXHjpJdx+7ac48cQT8+8tKdAmAFKg7Xts3LiR73//+/zquV+STqe4bLbgpiMcTPPtnfua7SnaYoJfrFP4fxssUFU+Me98br33MeYefbRsrEkk40Q2m6W7u5vOzs68+GppaaG+qZH6pkY2tbbS3d6Jkc3mz1Ecur0PWE586SUBHCVBHCU5AVbolc+0RCKZtAjDJL2hhfTSDSSWbsCKxAmUl3DBpZdy+7U3cOihh1JUVCQF2ngiBdq+gWVZ/Oc//+H73/su/1rwMiGvxpX7m1x9sE6ZW46Y7Ur6UoLfbVD4w3qL7iQcOrOC6z71aa687iamT5++RX7DMFiyZAn/+te/ePuNhaxfv55kKkVJwM/hhx7I/Kuu5aNXXI3P59vzNyORTCAymQwDAwOEw+ERfl9fH11dXbR3dNDa3kZ7ZwednZ30dvcQCw+MKEPRVLQiP2qRH63Yj1ZciFbstzdjLiqEogI5AiaR7AaEaWHFklixRM5PYkWTQ+F4EiuVQaSzI5yVyYApAAECGN6cVxUUh47i1FEcjpyvo7gc9lRinxvV50H1ulF9w1yBFy3ok/v+AcKyyGxsJfVeDYmlNVgDcfylxaT8LnqWr5cCbbyQAm3vpq2tjeeee46fP/sTGptbOSikct1BggtnKbi08a7d3o1hCRa3wQu18L8WQdaCo+ZM46OXX8XZH7mE+vp6/vXPf/Dqywvoj8Qo8qgcW2pxQBF4dIXuJKzoEqzsgSKfk3tu/RR3P/JtKdQkkxIhBLFYbAuBtbnY6u3vo7e/n77+fsIDYQbCA0QGBohHo2RS6dELVxS0Ag9KoRet0Ifq96IWetECPrRCH45AAYrfixL0oQZ80gqiRLILEVkDcyCONRDDDMcxw7FcOGbHh2OYAzGsaMIWWMNRsAVUge0UtxPF5UBxOlBdDjvscoCmoQAoCrmAfb5lIbIGImPk/Cwia2ClsohEyhZ98RRWPAXmlp3RiseFFvShBgrs90WgADXoQyvyo4UC6KFC1EABirpvdNgIyyJTu4nU+zUErjmHdfPvlwJtvJACbe8jmUyyYMECnvvlz/nXgpdxagoXTLe4/ECNuSWW7BkeB6IZwavNtlGR2n5I534nDg3BmVPhzGkahxVbqKP8bVqigp+vVfjLRotCj4N7P3cbdz3yJA6H7PmT7FmEEMTjcfr7+0e4vr4++vr66O7rpau3h57eXnr7+mwB1t9PbCBCPBrFMkafRq1oGqrXheJ1obhd+bDqyfleN6rHheZ1o3nddrrHifDk8nhccgNmiWQXI4TAiqcweyOYfRHMvihmf3RIfIVjWANxrFhy5ImaaneUBAvQgrbwUQf9Ql9ejKl+r/0s74EOEyGEPRoXz4m2aAJrII45EM/fhzkQwwrbvkhlhk7WNbRiP3ookJ/6rJcVoVcWo5cXo7qdu73+44UUaFth0aJFPPnkkyxdupT29nZefPFF5s2bNyJPdXU19957L6+//jqWZXHooYfypz/9adSpVKMhBdreQSqV4pVXXuFPf/gDf/vbX4klkhxWovKx/QUX7afid0y6r/9egxCCBY3w+HuC7iScPgXOmwGnTlEo945dLG+KCX68Gv6yUXBARYBnfvozzr748t1XccleiWVZRCKR/OjVcDcotLp6e+ju7aWnr5e+/n4GwmEi4TCJSAzTMEYtV3E789OHlMHpRMN8zedBy4UV75C4Ur0ucOiy40gi2cMIw8TsHcDoDudEWHRIiPVFMfsjiMyw511TbcEVLMgJrlw44MsJMTteLfBM+hEnK5G2P5veCGbvAGbPYDiC2TNgjwbm0Ir86BXFQ64qhGNaOVqhdxzvYNewJwSavltL303E43GOPPJIbrjhBubPn79Fel1dHaeeeio33ngjjz76KIFAgOrq6g+0e7hk8tDQ0MArr7zCKwv+xX//+x+i8SQHFat8+kDB+bMczPKb2NMApDgbL9Km4JG3BS/UwbnT4d5jFab5d+6Ha0qBwtdOgmsOgMfeifChS67gyg8dw4//sICiktJdXHPJnkQIgWEYpFIp0uk0qVRqRHhH4pLJJPFkgmg8Tm9/P/3hnMAaiBCLREhEY2ytv1J1O1G8bnvUqiC3bsPrQi0tRfVOI1DgQfV5ULxu8DqHxJjXLUewJJIJiJXKYHaFMbr7MbrCthjr7M+Lsvx6LgV7ml9uXaZrWil6bn2mvU6zELXQN+mF11hRvS5UbxmOaWWjplvxFEZHH0ZHH9mOPoyOXtIbWoi/sQpyMwXUYAGO6eU4p5fhmF6OY0Y5WklAdkZtxqQcQRuOoihbjKBdddVVOBwOfvOb3+x0uXIEbWIjhKC/v5+GhgYaGhqorq7m3bff5P3336Ojuw9NhaNKFU6thPNm6cwulJYYJwqRjODm/wjW9cFjJynMm73rXsqWELxQC998T+BxwAEzKvCWzCBYFKSoOERRcQlFoVKKSsspKioiGAzm/UGnafv2IkQhBNlslnQ6TTqdJpPJ5MObH2+eNppISqaSJJK2S6ZTJJMpUmk7LZVMkcqkSadSZNIZ0ukUmZRdbiadIZvJIKydMNajKqgOB4quoTh0cGigaygO+1gZFFhedz6s+expgprPDR4XeIZNJ9T37e+ERDLZEEJgRZOYgwKsy/bN7jBGVxgrEs/nVVxO9PIgWmnQnqJXGkQrC9p+kV8+/7sAYVmY3WGyzV1kmjvJNneRbe7EGrD/DmqhD+ecKTj3n4Jr/6k4ZpRP6M9dTnEcA5sLNMuyCAQCfOlLX2Lx4sUsX76cWbNmcd99920xDXI4g42LQSKRCNOmTeOWW27B6Rw5j3bwIxvto9s8bvjxzqZt7VofpIxt/dmtbTSIdtW9W5aFYRhks1nbz6QxsgaGkcHIZm1nZPPptsuSSmdIJJLEU2nMYQtbPTpU+WBqAUz1q8wqtHDrmzX8x/BN316WsTwsY3mi9mQ5Y2GX1Xk76Yms4A81EDPg5+fAUaW7Z3RhU0zwxTcEy7vh6FLoTEDahJRp++lt6HW3U8ftcuFxu/B4PLg9HtxuD5ruQNV0VF1DUVRUVc33+AkhbGcfYOWfE8s2rmWJYfmsXBgE9nkIgUBgWSOfLyEsO1+u/BHX2sLl0rCvISyRu7aFNSyflauDZVmYhoFhmJiGkXMmlvkBOzM0FUXTQFNzYXUoTrf9wThy4RH59aHjIV+1xZamoWqqXY6qoegqqPaxUDXQFPucfaQ3WyLZlxGmZa+biiSwInHMSMJeQxVNYIXjiMzQFhGq34teFkQbFGClQfSyIHp5Eapfbg8xXpgDMTINHWRqN9muoR2yBjh09MoQjsoQelUIrTQ4YUYpvccfLAXaWNhcoHV0dFBZWYnX6+VrX/saZ511Fi+//DL3338/r732Gmecccao5TzyyCM8+uije7DmksnCWF4JY323j6msXVTQWF9lu+r+tpdFCNsicIETfnkuHBravVO/TEvw09Xww5WCmYXQl4KMNVQXyFkmBgZrInL/Db4Urc2sFw9/WW5u1XisTOoXrkQikYw3umb/4GSHdSYpim2woqwIrSwnwEoC6OVF6GVFqB7XuFVXMnaEYZJt6iS9oZlMbRvp9U2IZMa2Wul1Y8WTkBl9ve+eouY6lYqn+6VA2x6bC7S2tjamTJnC1Vdfze9+97t8vksuuQSfz8fvf//7UcsZbQRt7RdnU2AOWazZWiNVYcvG6eZ5hwv/LYpRRsYrWyaNKFdRtpY2FLOr+hl2p/AwUTAVlayqkVU1DEXD0DSyikZWVTEUOz6b89Oag7Sqk9BdZBQNsZ3KyR6xicNAJM3z/7ceJZ3m+Q8r7B/Yc3+bdzsE9ywSCEXhynn7UTErtEWeoWdPQVFAt0wKjRT+bAq/kcJrpHGbWVxWFodlogkTTVggQNsNkis/GsfgSBsjjvP52ExsDhOXk/rFLpFI9gkypqA9Dpti0Bqz/ea4SnNCpSNqkcoMzZTRvC7U0uDQCFhpAGdJALU0gBoKTugpcZKdQ5gWmfo2MqvrSK5tItvYDgLKyrycPQ3mlSfYP7Bn23tOXWX6D/qkkZAdpaSkBF3XOeSQQ0bEH3zwwSxevHir57lcLlyukT0s5/8hI9egSSQfkLq6Oj501pm4TZPnL3Awzbdn1wMeX6HwwkXw+TfgJ7+v45HPnc4DT/1CCniJRCLZzQgh6OzspKGhgfr6eurr69lYW8v62o00NjTQ09GZn76taCp6SRC1pBB9dhBnaRBfWRBnaRGUFKJ6paG3fQ1FU3HNmYprzlT8l4EZS5JZ20BsRR1/XFnLH5ZCaHoVl1w6j89+6iaOPPLI3frbnrdP8YPddok8e51AczqdHHfccdTU1IyI37BhAzNmzBinWkkk+ybr16/nQ2ediSvdy6/OU6j0jo+xljKvwnPnCr6/QuHB7z3H6wsX8tyL/2HqzP3GpT4SiUSyt5BIJPIGu+rr66mrq6N64wZq6+toa24hnUzl82qFPluAlQbRj5lFsHQuzrIilFAAtbhAbpYu2SZagQfPCYfgOeEQglmDdHUTqaUb+dVzz/HcD35E+eyZfOITH+dzN36GadOmjXd1PxCTcopjLBajtrYWgLlz5/LUU09x1llnUVxczPTp03nxxRe58soreeaZZ/Jr0O666y4WLlzIqaeeOqZrSCuOEskHY+XKlZz7obMpEhF+eS6UunfCGt9uYGGr4IE3BVlUvvuNR7j+rq/I0TSJRCLZCplMhtbWVpqammhqaqKhoYH1GzewobaWpsZG+rt78nkVh45WEkArDdgirNQeAdNKAyglgb1682LJ+CEMk/TaRpJL1pFcvgGRNTnwuLl8/vY7ufbKq7eYIbez7EltMCkF2sKFCznrrLO2iL/uuut4/vnnAfjlL3/J448/TmtrKwceeCCPPvool1566ZivIQWaRLLzvPfee5x37jlUOeL84lyFIufEEGeD9KcEX3sP/lEvOP/Y/fnFX16lavqs8a6WRCKR7HEGBgZoamqiubk579c21FPX0EBrSwt9Xd0jLDFrwYKcCLMFmKMsiKO0CKWkEDVQMGGs7Un2TaxkmtSyjcTfWEVmQwvugJ+PXnMlj97zJebsP+cDlS0F2gRACjSJZOdYvHgxF5x/Hvv7Ujx7jkqhY2KJs+G82iR4eInAQuV7X/8K1979iBxNk0gkew2WZdHe3j5CgDU2NbGxvo6m5ibaWjaRiMXy+RVdQy8uRCn2o4fsjZj1UABHSQCl2I9W7EdxOsbxjiSSsZNt6yG5aBWxxasQiTQHnXQs9332bq752BXo+o6v8pICbQIgBZpEsuP85z//4dJLLuaIYIYfna3g0yf+66UvJXjsHVjQKLjouP352V/+TcW0meNdLYlEItkupmnS1tZGY2Njfvrhhvo6NtbX0dLcTNemdozssP3AvG60Yj9aqBAtFEALFeIIBXCECqHYjxrwyXVgkr0OkcmSfK+G+MIVZOo2UVhZyq133MH9d9y1Q218KdAmAFKgSSQ7xj/+8Q8+Nv8yTigz+cGZ4J5kFo//1Sh4dIlAVVV+8OTXuOb2+8a7ShKJRIJlWbS0tFBTU8P69etZW13N6uq1NNQ30NXejmUMGV9S/V5bfJUE7H3AQrYpeiVUiFpciOqV+4FJ9m0yTZ3EX32PxLvV6C4nH/3E1Xzr/oeYOWPmds+VAm0CIAWaRDJ2nnvuOW666dN8aKrgO6cpOCeZOBukJyl4ZAn8u1kw79TDePb//ZvS8orxrpZEItkHiEQi1NTUUFNTQ/X6alasXcP6mhpaGhrJpux9WhVdQysvQq8oRi8rwlESwFEahCI/Wkkhqksa4ZBIxoLZFyHxv+VEFy5HpLKcfME5fPvBxzjp+BO2eo4UaBMAKdAkku0jhOCJJ57gvvvu48oDFB4+QUGb5LNjhBD8swEee0fgcOj86IdPc/n1t453tSQSyV6AaZo0Njbmhdia6nWsWreW2g0bCQ+zhqgFC9AqinFUFOOoDOGsCKGUF6GVFMopiBLJLsRKZUguXk301fcwewY48ISjefzBR5l3wYVbrEmXAm0CIAWaRLJtMpkMd999Nz/60Y+440i440hlrzKw0ZUQPLQEXmsRXH72sfz4jwsIlZSMd7UkEskERwhBe3s7GzduZMOGDdTU1LC2Zj3ra9bT2ticXxOmOB3o5UXolcXoFcW4KkpQK4rRyoOoHjkVUSLZkwjTIrm0htiCd8g2dVIxZz8euu9+bvrkdXmDIlKgTQCkQJNItk5jYyPXXH0V77/3Lg8eD1cesPcIs+EIIfhrPXz9XYHL6eCZ7z/Fx264fa8SohKJZMcxDCNvnKOhoYGNGzeytmY91evX01RfTyqRtDOqCnookJ+W6CgvwllZYo+GFfmlSXqJZIIhhCCzvpnYv94htbaBwopS7rr7br50+2cxTVMKtPFGCjSJZEsMw+Dpp5/mwa88QFDP8t3T4aiSvf8V0pkQPJwbTTvrqNk88+sXOPjwI8a7WhKJZDcRj8dpa2ujpaXFNk3f2Eh9zkJic3MTXW0dWOaQcQ69yI9aXmSPiJUX46oMoZcXoZQEUPRJuihXItnHyTR3En/5XRLvVuPyebn6+mt5/gc/lgJtPJECTSIZyZIlS7jz9ttYunw5nzhQ4a5jNQq0ibvH2a5GCMH/WuAb7wk6E3D7tfN57Onn8Pv94101iUQyBizLor+/n+7ubrq7u+no6KCtrY1NmzbR1NpK86YW2tva6OroJBmLjzhXDxaghOw9wbRQIXpJAGdpELW40LaQ6JJ7g0kkeytGzwDxV98nvmgloS9eSd2XnpECbbyQAk0yETFNk6VLl/LGG2+wYtn7bFi3hs7OThKpFEIICn1eKstLOPDAgzn+tLM5+5xzmTNnzge65rJly/jKVx5gwYKXObBY47GT9o1Rs62RNgU/X6vw7CqLgM/FV+6/l5vvfgCnU1pPk0h2N6ZpEovFiEQiRKPRvBs8HhRgHV2dtHd20tXdTU9vD/29vUTDEYQ1slNJcehowQLUYMEI31HkRw/6EUEfeqgQxbHjm9pKto0wTKx4CiuexEqksGIpRCqDyGRtP53FymQRqSwibcdbqSxkDYRpIkwLDAthmjB4bFoIwwLLAkXJOexp6aqSj1M0FcWpozgdI31HLux2onpdKF43qseVC7tyYTeKx4Xicsjp7vsYZiyJVuBh3fz7pUAbL6RAk0wUhBCsWLGC3/z61/z+t7+mo7sPt65wUJFgViGUesDrsH8kohlBRxzqBmBjGEwBB04r5dprr+Xmu75MyRiNXAgh+O9//8tT33mSBS+/yqygxh2HW3xkJmhyzQQAm2KCp5Yr/LPeoqrYx333fZkb7/gCbrd7vKsmkUwoMpnMqGJqtOOBgQHCkQHCkQgDAwNEolFisSjxaIx4LEY6mdrmtRSXA7XAi1rgQfV7UP12WPN7UQu8OPxe1EIvwudCCxSgeF2ykb2LEJaFFU1ghuNYAzHMcAxzII4VjmFG4ljxFGJQkMVTiHR2q2UpToctgIY51eW0ww4ddA1FU4d8TUXRhsWpKgiBEAIGnSUQAjtsmIisYYvBzJa+lc4gEmmsRAqsrTSTVQXF40L1eVB9btsVeEY5dttxBbaveF3SEuckRwq0cUQKNMl4Y1kWf/3rX/n6Vx9l6fKVFHsULpwhOH8/nSOKTZzathsV8azg7XZY0ASvNAp0TeWmq+fx0FPPEgqFRj1nYGCAP/7xj/zg+99lzbr1HFSscsMhgotmgS6F2ajUhgU/WgX/ahD4PQ4+dvH5fOqz93LyySfLhp9k0iKEIJFIEIlEtuoGBgboHwjTGw7TPxC2BVUkQiQaJR61RVUynsDIZLZ5LdXlQHU7UdwucOdGL9xOe5RiMDzM1zxuNI+djssBLofdUHY75Xqv3YQQAhFPYfRGMHsGMHsHMHoGMHsjmH0RW5RF41uIGdXvRQ340AI+W6x4h4SLMhgeJmgUt9MWZxPk90YIYYu2RBorkcZKpoaF04hEKjcKmBoaDYzZAtSKJWHYJuJ5FHL3PgZh5/faI7sBnxR1Ewgp0LbCokWLePLJJ1m6dCnt7e28+OKLzJs3L59+/fXX86tf/WrEOSeccAJLliwZ8zWkQJOMF5Zl8ac//YnHHnmI6pqNHF+h8KnDVE6ttHDs5I9WX0rwq2r49TqB26nzjQfv5aYvPYaqqiSTSV5//XV++5vf8JcX/h/pdIYzpsB1h2qcWG5JkTFGGiOCF+sU/lpn0R6HkkIPp5xwDGd/ZB4nnXo6hx12GB6PZ7yrKdnLEULkpwAOiqjRwv3hML3hfvoGwoRz8dFohHg0RiIWIxlLbDEdcDiKQ0fx2KJKzYmlQVGVF1QeO01z21PDlFw8Lgd4coLL5bRHPSTjihACK5rE7N1MfPUMhUVqSGgrTh0tVIgWCqAVF6IFfWiB3BTRgM/2C337vGAWmeyQYNtcwOWPh0YVB4/JGiMLUhVb6Ab99h55wQK0Ir/9ORf57XWRciruHkMKtK2wYMEC3nzzTY4++mjmz58/qkDr7Ozkueeey8c5nU6Ki4vHfA0p0CTjwf/+9z+++Pm7WbZiFadPUbj1SJWjS3fdI9qdFDzxPvy9XnBYlZ/ymQfy5tJVpNIZZgVULpstuHR/jXLPvmP8Y1djCcG7HfB2h8J7HRareiBr2VND50yv5Kgjj+CYk89k7jHHctRRR211NFOy9yKEwDAMstks6XSaeDxOIpEY1W2eFo1F6cuNWPWHwwxEIkQjUaLRCIlojGQszrZ+1gfF07bElepxow2uu3E7Yfg5cqRqUpIfAesOD4muYb7ZG0FkhqYcKi4HWkkALRRALxkyjKKFCtFKAqh+r+y8242ITBYrnsKMxDH7Y/Y00XAUsz83dbQ/ihWO2aN0w1ALfSP/VqGRfzu5v96uQQq0MaAoyqgCLRwO89JLL+10uVKgSfYkq1ev5ktf+Dwvv/pvjihV+NKxKseV7b5H8+12wSNLBO1xuGQ/uP5QndmFpvzB3Q2kDMGGMFT3q1T3WlT3CWr6IZnrIJ1SVsSRhx3KMSeeytxjj2f27NlMnTqVoqIi+ffIIYQgm82O6gaFzljdWPNnMhmyhkE6kyGTzZDJ+1ky2SyZbGarZRvZLNmsgWFkMbIGhmFg5o5Nwxxhnn0sKA49v/ZGcTpsYwWDgmq4aPK6ctP/BsWVPWWQwXxup5wmtRcjsoYtuLrDW/rdYURy2AiYxzkkvkYIMPtY9bnl+2cSILKGLdp6B+zpp725kc/eiP2374uAOdThqnhdtiXSvIAbskyqhQKofo/8u4+BPSHQ9tqx0IULF1JWVkYwGOSMM87g61//OmVlZVvNn06nSafT+eNIJALAQw89hMu17R6HrWnc7WnfbaWPRTeLwQWwY2S0vB+kjjuSvrP1HOvnMB7pY70naxvThDKZDMuWLmX9+vVM9St87wyF82eAouzefpOTKhVeuhi+9b7gdzXQGDE4dzp4x2opeozV25G7GGveHelSGs8yt8RiThDmBBUsIehLQUdCoSPeT83Sxby+aDHxYbNadE3F63bi0P8/e+cdZ1dR9//3Kbfv3d43IaRSAoQECIYeC0UUkIA0IQJKExRQHgQViFJERCwoWB5F5cH2U0QEBBVCIPT0tpvdbO/99nbOmd8f5+7NbrJJNmGTLZn36zWZOXPmzJmzuffc+cx35jsOnE4HDocDh66j6RqaqqFpdlA1DU1L52s6qqaj6TpKuoyiKKiqiqqqKIqSOR6Iwf6MCiGwhAUCLASWJUBY6bX1g2P7vWNZAoFAWALTMjEtW3gYpoVlmpiWhWkamKaFZZmYpplJW6aVLm9hWRamaWJZ288Jy84fOL9X/0EjJe3VTdFUW7SoaS9vqmoHRcnkK9qgfDWdr6n2OpmBa5X0sUtF8TpAdWbqG6jHoao4NRVloGzauYGadnqArtl5Dh1F11AcGug6e9tfsgArmYJkCoKj/6eTjB3CMG3HG32h7U44QlGsQAQrHN3+glJVtMJs9KJcHDPKcZ9wBHpRDlpRDo6iPBQpwCYFikNHL85FL85luJ6qsCysQARjYNpqd3rNYHeAxKb6nSynOHR7imq2d7uDnWxfZh2hnEIJngVzDsh9JqUF7U9/+hNZWVlMmzaNuro6vvWtb2EYBqtWrdql2LrvvvtYtmzZAWq1RGKjK6CrcNt8hcsOF7jGYC3G8mbB3SsFpoBsBzSGR7f+vekCjLS/sFd1jnrBkRfdl+6PGAhi5/yBhKJsP1Z2PD+onBh6KJFIJiiK0+4Yi+T2URzF40Ivy0cvzhtk/cpGL8lHy/dLa6lkjwghsMIx2+KWnv5q9oYwe9PHXf1DvG0qLns7AiGAtMfLg42qpSqlP+6TUxz3xHACbUfa2tqYNm0af/zjH7nwwguHLTOcBW3jLYeg7cGSMdCh3F1HbPC54Tqgu7pWGUmh9KmRdASVPdQDI3ueYevdzfFOdY+g8pH8rCh7qkvZ7eEu8/bqvLLn59nx/ydmwP3vwXO1cHoF3HsiVPjH9oe0Jya46y3B683wyWOzOOkTs9DlGpMxRwGcloHbTOG2UmjCQrMsNASqsNCEhS4sHJaB0zJxWmYm7RAmumWi7SDNLCEw7O2DMMSHN0592B+Qif0LJJF8ePrigk29sLUPNgc1tgZUOgPpTrGqoBflopfmo5cV4CjJw1GSh1peKNeBSfY7QgisYBSjqw+row+js49UV8AWb539WKGoXVCB3BwXFbk6s7NMjvQmmJktODQbfI7J9RnNcysHRKAdFLbKsrIypk2bRnV19S7LuFyunaxr5/xdyDVoklFl27ZtXHb+p6luquLhUwTnz2Bc/MAWeBR+/lH43RZ4ZFWYtr5G/vrKCqbPmTvWTZNIJJJJg2VZVFZWsnLlSl57YwUr3nyDlroGAFSvG31qEY45xeRNLcZ1SAlqeYGcViYZMxRFQUtPb2TWlJ3OW9EERls3ZlsfqdYualq7qWzt4e/d2/crzCstYs6RR7DwuOM59YQTWbBgAdOnT89MsZ9IZPxT/Hj/3+ug+Nb39PTQ1NREWVnZWDdFchDz/PPPc+UVl5GrxPjzJ1UOyx1fnhIVRWHpkXBsEdz6ei8L5h3Db574ERd8/uaxbppEIpFMSCKRCO+//z5vvPkm/379NVa/9z6RYAgUBcfUIpyzplDwiXk4ZpahFuaMiwE7iWSkqF4XzpkVMLOCwZvIWIkkRlsvVlsPqZZu1jY38f4vVvGTRx4FwOn1MP2IORw3fwGnnbiIExYcx9y5c/fo8+FgYkJOcQyHw9TU1AAwf/58fvCDH7B48WLy8/PJz8/nvvvuY8mSJZSVlVFfX8/dd99NY2MjW7Zswe/3j+ge0oujZLQwTZN77rmHBx98kI9NhYdPVfGP1BnHGNGfEHx9JbzWJLj5kjN5+NfP4vV6x7pZEolEMq5paWlh5cqVvP7GCl59YwVbN23GMkxUjwvHjDJcs6fgnjUFbXqpdHkuOegwAxFSTZ2YTZ0kGjpINXVgtPeCAM2hM+2IOSw8YSFnnXYGJy86iVmzZo2rQYsDqQ0mpEBbvnw5ixcv3il/6dKlPPHEE1xwwQWsWbOG/v5+ysrKWLx4Md/5zneYOnXqiO8hBZpkNOju7uaySy/h1Vdf49b58MWjQB1HL5vdYQnBrzfBD9cIKvJ9/OJ/f8snzlsy1s2SSCSScYFhGGzYsIGVK1ey/I0VvLHyTTpb2gBs74mzKnDPnoJjZjl6RaF02iGRDIOVSGI0d2HUd5CobSVR24rZ0QeAOzuLI+Ydw+knn8zZZ3yMk08+maysrDFrqxRo4wAp0CQflldffZWrPnc5sf5ufnCqYNEEnWG7LSC45234oENwyScW8sDPnmbmrNlj3SyJRCI5oHR1dfHuu+/yzjvv8PrKN/ng/feJR6IouoY+rQTXrAo8s6aizSpDyxm7TqREMtGxwjGS9W0Yte3Ea1tI1bZhhWMoqsqhcw/jjNPP4DNnncNpp55GTk7OAWuXFGjjACnQJPtKKpXinnvu4eGHH+bEUpWHT1Uo9Yyv9WZ7iyUEf62Gx9YI+hNwyTmncc+jT3LY4UeMddMkEolk1Ekmk6xbt4533nmHlW+9xcp33qa53nbmoWf70GeU4Zw9Bc+sKWiHlkhHHhLJfkQIgdHWQ2prM/HKRhJVTViBMIqqcOiRh/PJc87hsvMv5MQTT0TX9993UQq0cYAUaJJ94Z133uGmG65jw4aNfOVYuHauwhhsbbbfiBmCP1cr/HKDRU8czj/jeL758OMsOOHEsW6aRCKR7BOBQID169ezdu1aVq9ZzburVlG9pRIjlULRNRyHlOCYUYZ7ZgXOWeUo+dnjal2MRHKwIYTA7OwnWdVEfHM98U11iEgcV5aP4087mUvP+wwXfvo8ysvLR/W+UqCNA6RAk+wNXV1d3HnnnfzmN79hbqHGfR+BYwom71crYQr+VgO/2ihoDsPHjz+c7/zgCT5y6hlj3TSJRCIZlmg0SnV1NZWVlVRWVvLBmtWsXruG1oYmAFuMVRTZru6nFuOZWYE6tUhaxySScY6wLFJ17SQ31hHdsI1UXRsIOPToI7j4wiV84fIrmTNnzoe+jxRo4wAp0CQjIZlM8tOf/pRl994DqRi3zrO4ZA5o6sExumpYghfr4ecbBDX9cNbCI3ngJ//LcQs/MtZNk0gkByGpVIrGxkbq6uqoqalhy5YtrNu8kcrKKjqaWzLltGwvekURjkOKcUwtsfccK81D0bUxbL1EIhkNzHCMxIZa4qu3Et9Qh0imKJ5xCOdfcAHXX34VCxYs2CcruBRo4wAp0CS7QwjBCy+8wO23fplttXVcMge+vEAn3zmx15rtK2ZaqD2+TlAfhE+ddhwP/PhXHDPv2LFumkQimUSEw2FaWlpoaWmhqamJ+vp6qrfVULWthob6errbOxGW/R5WNBWtKBe9rAC9NB9HWQGu8kKUkjxUn3uMn0QikRwIrESKxKY6Equria6rQUTi5E8p4zNLlnDz569l3rx5IxZrUqCNA6RAk+yKTZs2cdutX+Hf//kvi8oU7lqojbtNp8cKwxI8Xws/XWdPfbzwrNP4zmNPcsQR0pmIRCIZHtM06enpoauri+7ubrq6umhvb6e5uZn6piYaW5pobWmhs62DWCQy5Fotx4damINemINWmItemIOzKBe1MBs1P1taxCQSSQZhmCQqG4m/X0V0dRUiEqdo2hQu/uxn+dLnr+XII4/c7fVSoI0DpECT7EhPTw/33HMPP//5k1RkKdx5nOBjU4RcLD4MKUvw7DaFn62z6IjCJed+jGU/eILZs6V7folkshOLxejq6hoiuAbS7Z0dtHZ00NnVSXdXF709vUQCQXbsiii6hpabhZqbhZbnT8dZOPKy0fP8kONDzc1CdTnG6CklEslERhgmic31xN+rJLpmKyKWpGzWdC6/9FKuu/Lzw65ZkwJtHCAFmmSASCTCk08+yQPfWYYRj3LjURZXHqHglAOzeyRpCv5So/DkOtvr46fPOIEbvnYvnzj7HFS5aatEMu4xTZPe3t6MwBoInZ2dtHd10tbZQXtHB9093fT19BLo7SMZi+9Uj+pyovo9KFkeVL8XNcuD5vei+r1ofi96Oi2y3Gh+L4rXJQe/JBLJAUGkDOIb64i/X0lsTQ0ikWTq4bP53GWX88UrlzJ9+nRACrRxgRRokgFh9r3vPkhvbx9LZgm+skCnwCWnM+4tccMWan+otNgWgEOKcrjm6s9z7S1fo6Kigi1btrBixQrWrlnN1s3raWttI5VMAAJQyPb7KC4spKSsnGkz5zD7iKOYM2cORx55JH6/f6wfTyKZMBiGkRFYnZ2ddHV10dnZSUdHBy3tbbR2tNPZ3U1vdzf9Pb1EgqGdrFsoCmqWxw5+L6rfs11wZXnQ/T60bC9KlgeyPGh+D4pTWrokkg+DMC2sSBwrEsOKxBGD0lYkhhVLIpIpRNKw48SgdDIFpkAICywBwg7CEiiqArqGomn2lGBdQ9FUFKeO4nGhelwoXjtWPS4Unxstx4eWnYWa47MHXibRgKtIpoivryX2fiXxdTWIpMH0o49g6WWf4+LzL2Du3LlSoI0lUqAdvNTU1PDEE0/wm//9JaFQmAtnCq6bpzPVJ4XZh0UIwZou+M1mwWu2Z2t0TSWWstBVmJEN03Og2AvO9PteAOEkdMehJwYtYTsNoCoKR8+s4KSTFnHWZy7jnE+ei9PpHJNnk0jGgmQySV9fX8bKNSC4Ojs7aetop6WtjbaOdrrToivUF9ipDsXp2C60sr2oWV40vwc1y7ZsadleFJ8HMtYtt92pk0gkHxphCaxAGKMniNUfxkwHK7A9bfaHEZGdLdNgf38VX1o8OR3poKO4BqUdui2+FAVUxbZODwQhEIaJME0wLIRhgmkiEimsWAIrlkDEEljRJCKWsMXeYFQF1e9Dy/WhFeagF+XaznmK0umCnAm7FtRKJEmuqyXy3hbi67eBYeL76AJan31NCrSxQgq0gwvTNHnppZd4/Cc/5uVX/k2uR2PJDJMrjnRQ4TXHunkTnpgheLcdVjQL3miFxhBoChxVACeWwnElsLBEwesYWacvnBQ0hGBTr8rqDosPOgRNYcjzObnokx/luq/ex/Enys2zJeMby7IIh8OEQqFdhv7+fnp6eujs7aGru4ue3l56e/sIBvoJ9gdIRGM71atoKmq2z7ZwZdtTB9VsL1rasuXI9qH4vTAgyFxyUEMi2Z9Y0QRGdz9mVz9GVwCzO4DR1Y/ZbR9jDOpnpNdfaul1llquHy3Xt/077XOng50+0Pv0iZSBGYzaAjIQwQpEMAfE5MBz9QTBTA9qKwpacS6OKUU4KgpxTClCn1KMXpw3oQZ6rFiC+NoavIvmsnnJ3VKgDceKFSt45JFHWLVqFW1tbTz77LNccMEFw5a9/vrr+cUvfsFjjz3GrbfeOuJ7SIE2+RFC8O677/LMM8/w5z8+Q0dXD0cVqlw+R3DuDBW3NuG+GuMKwxKsbIW/1tjWsqQFFVlwWgWcWq5wYhlkjVCQjYStfYLnahWer7Udk3zi+MN44Ic/54STTx+1e0gOboQQxOPx3QqqgRAIBukLBOgPBggEAwSCQUKhEJFwmEg4QiwSGVZcDUFV0Xxuez2Wz43qddsdNK8700nTfB70LA943baFK9sn129JJAcYYVmYvSGMjj7Mzj6MAaHSFcDo7h9i/VJcju2WpsFxYQ5qnt8WXRP8+ytMC7MvhNHZh9nZT6q1G6Olm1RzF1YoCoDideGcUZ4OZThnVkyY7S8OhEA7sLJ7lIhEIsybN4+rr76aJUuW7LLc3//+d959913Ky8sPYOsk4xUhBLW1tbz55pv8++WX+fcr/6Kzp48ir8o50yw+faLO0flm+sUoxdm+EkkJnq6Ep7cIOmMwJw9una9wxlR7CuP++uGZk6dwx3Fw+3yFl+rhJ+uqWHjKGXz65KP50W/+wvTZh+2X+0rGJ6lUikgkQiQSIRqNDpsefBwOh+kLBugL2IIqGAoRDIWIpEVVNBIlFolgGbu3qKsuB4rbCW4nqtuJ4nbaU4/cTpQCJ2pFCYrbidvtxOd2oQ46j8sBbvt61e0Ehz7hO2oSyWRBGCZmTwCjow+jsx+js88WIB39GN392y1GqoKWn41elItjWjGe4+agFeWgF+aiFeWg+r2T/nutaCp6WnSyg+d6Mxgh1dhJsq6N5LZWIq+uJvSPGCgKjkNLcR05DfeRh+KcVXHArYPjiQn55Oeccw7nnHPObsu0tLRw88038/LLL3PuueceoJZJxgOhUIi6ujrq6uqora1l27YaNq75gLXrNxIIR1EUODJf4bwywakn6JxQZKKpKmABk/uluT+JG4JnquAXGwThFFw4Cz47R2Fu/v4TZcOhqQqfmgFnHwr/qIUfrtnAkUcewa1XX8wd332CnJwcNG1izocfK4QQmKaJYRiZeLTTyWSSeDxOIpEYEsfjcSKxGLF4jGgsRjQeIx6PE4vHiSfixGN22Vg0RiwWJRGNkYjFMQ1jzw+mKKhOHcXlBKeeEUZKRlg5UfJyUN1FaB4XOW4nqttl57tsEYbbsf0al3NCTdmRSCRDESljqPgaSHf02dP2Biad6Rp6US56SR6ueTPwFeehp4NWkI2iTR6nGaONlu1DO2o67qNsz4hCCMyufhJVTSS2NBB9Yz3hF94Bh477yGm4F8zBfewsNL93jFt+YJmQAm1PWJbFlVdeyR133MHcuXNHdE0ikSCRSGSOg8EgAA888AAul2un8gMzQ3eMh0MIscfzu6tjcP5wZXZ3fjSPP8wzgv3/srvrd3cfy7JIJpMkk0n7/yoeJxGPEY9FCYWChNOj3aFwlERqe8fMqUG+y3Y6cXIhVExXmZplkeUAUOiJmrxUDyOxmI3EpjZSu9tIJhaPpg1vNO83XF3bAoK/b4P+BCyZBdcfDVP8Y/sDpasKF86CM6fBj9cIHvnVn/ntM38mYUIgqeLQNXRNRdc1dE1D1/UhQcukHWiZYwfawLGmoWkqmqajptMKSubvKIQgY4wdWIidaZ2dFpYABJZle9eyHWtZWOnvkx3AskwQYAkLIUTmvGXZZcEWUKZpYZomlpWOTRPTsrAsM5O/Pc/CNO1zwhRY1sB5+5wQ29Mj+gCN2n9c2oOYpoKmgqaBpqKk8wfOo6XL6enz+S4Uh9f+UXfoqLpmL5DXNXsUVtcQumqnHRqqQ7fr2IfBAwsgmbJDEOQqVYlk4iCSRmbNlBUIp9dTRTD7Q/b0u/TrTnHqaMV56CV5eE44PO34Ihe9NB8tL2tSeS4cSxRFyYhb36nHIITAaOkmvqGW+Noa+p96ybauTSvFOT09FdK7c7/8QOGeN/OA3GdSCrSHH34YXdf58pe/POJrHnroIZYtW7ZT/ve+973RbJrkAJM0oT1qh/XdkO5aIacwjj7HFcO3F8Gs3PH1o5XlULh7ocKFswT3viNY2wWzcizqAxbJQbNZR9JPF4MSYsc8yehgmLZHsbFuh0QimTzotgt5YVowaBBXcTvRS/LRS/NwHzMDrSAbrSQfR2k+ao5v0k9FHI8oimI7FJlShP+cEzEDEWKrtxJfXU3k9bVElq9By/NjhqKQHMFMiVGmaqkKS+7e7/eZdAJt1apV/OhHP2L16tV79cW66667uP322zPHwWCQD26cyuDZKjtWp+wQ78jg8soO5Xa6Rhl6zY7ndzrezaPt2K5dtXtX9QxXflfPquzm7zNceVXZ9bnB/197+p8zFJWE5iCmOYlqDkK6m6DDQ0h3Y+7QkNF8v47kMzXi242g4IjqGuENlVG7oV2svTvO7/9WSyic4msLFK443HZ7P145PF/hD+fAX6vh4Q8EOR645MxyZh1ehNMycFsGLjOF0zJxWQYOy8AhTByWidMy0dNph2XiFENtJkIIUhaY6e1lLLAtXqSP04JOpGNLqg+JRCLZZ4QQ9CftbVeaw3ZcH9VoiKh0hCyi8e3vaDXLg1aSnoJYlIuzKAe1JA+tJB81yyNF2DhHy/GRtXg+WYvnY4aixN/dTGTlRsyeIE6vk5Nnuri2IsQ0/+T6f5yQXhwHoyjKEC+OP/zhD7n99ttRB5meTdNEVVWmTp1KfX39iOqVXhwlkl3z7LPPctXnrqDCneQnixWmZU2sPeI6IoJ734XXmgSfOekInvjzy5RUTB3rZkkkEokkjRCCrq4uqqqqqK6uprq6mk1bK6naWk1TfT2xcCRTVsvNQitOT0EszsNZko9WkodalIPqnRieASV7R6qli9hbm4i8sR4rHOPwRcfzzdu+xqUXXrTf1pkfSG0w6QRaT08PbW1tQ8qcddZZXHnllVx99dUcdtjIPLlJgSaR7IxlWdx7773cf//9nDUNHjpFxTdB7fBCCF6og++8J1BVjZ//+Ptc9IVbx7pZEolEclCRSCSoqamhqqqKyspK1m/exKYtm6mr2UYkGLILKQp6vh81LcD0kjxcxfmoJbmohTm211PJQYlIGcQ+qCLy39Uka1vxlxRyw5du5Fu33oHf7x/Vex1IbTAhu1bhcJiamprMcV1dHWvXriU/P59DDjmEgoKCIeUdDgelpaUjFmcSiWRnAoEAV1x+OS++9CJfXaDwxaNGd/rogUZRbG+Pi8rgW+9YXPzF27jxhWd57A8v43LLEVeJRCIZTbq6utiyZYsdKrewbvMmqioraW9uRaSdiKleN1ppHnppAY6Pz6ewrAC9NB+1OPegdrku2TWKQ8e7aC7eRXNJ1rcT/e9qHln2AD98+Ptc9PkrefQb91FWVjbWzdxrJqQFbfny5SxevHin/KVLl/LUU0/tlH/ooYdy6623yo2qJZJ9pLKykgvO+xStjfU8eqrg9IqxbtHoIoTgd1vgkVWCuRU5/OWF/zLr6OPGulkSiUQyoRBC0NzczJYtW9i8eTMbN29i3caNbK2qItjbZxdSVfSiHPTSfPTSfBxlBTjKCm1r2EGwR5hk/2P2Bon8ZxXh5WvBtPj4kvP44X0PcORhh3+oeuUUx3GAFGgSic0zzzzDDdd9kRJngp8uVjjUP7HWm+0N67sFt70uCCQVfv7o/Vx28/731CSRSCQTjVQqRV1dHZWVlbYQ27SJdZs2ULO1mngkCoDq0NFK89HLCtDLC3CVF6GX5aOW5NnbY0gk+xkrGif62lpC//4AKxTlhLMW88RD3+e4Y+fvU31SoI0DpECTHOyEw2Fu/tKX+O3vfsenZyjct0glS5/8r4tgUvDNt+DlBsEXP30KP/rTK3g8nrFulkQikRxQ4vE4dXV11NTUUFNTYzvpqKqkZlsN7c2tWKbtKVH1uNDKbEuYXl6Iq7wQvawApTBb7hUmGReIlEF05UZCL76D2R1gwSdO54nvPsrCBXs3U0YKtHGAFGiSg5nXX3+dL1x7Na1Njdy70OKCmQfXlBMhBM9UwUPvC+aUZfOX5//NEfMXjnWzJBKJZNRIJBI0NTXR0NBAfX09DQ0N1NTVUl27jYb6errbOhjoIqpOB1pxrr1xc/F2T4lqaR5qbpacliiZEAjDJPr2JkLPv4XZHWD+x0/jZw89ykeOP35E10uBNg6QAk1yMNLd3c0dd9zBU089xfwSlYdO1pjuN/d84SRlS6/g1tcFnTGFb99xM7d++7H95r5XIpFIRpNoNDpEfNXX17O1dhvb6utobmyir7MrI8BQFPTcLNSCbHuz5sIcHCX5OEvyUApzpAiTTCoyQu2FtzE7+5n30VP42UOPctLC3Q/ESoE2DpACTXIwYVkWv/3tb7njq7eRioX52nyLi2eP742nDxSRlOCRVfCHKsHxs8r4zZ//wVHzRzbaJpFIJPuLQCCQEV8NDQ3U1tWxtXYbdfX1tDQ2Euzr315YVW039QXZ6IU5aAXZ6AU5OItyUfL9qPnZcl2Y5KBDmBbRd9IWtc5+5p1xMj97+FFOWnjisOWlQBsHSIEmOVhYv349N15/HW+98y6fnqHw9YUaha7J6whkX/mgQ/DNtwTNYfj8hWdx349+TXl5+Vg3SyKRTEKEEPT09OxCgNXR2tRMNBTOlFccOlq+Hy0tvrSCHJyFueiFOZDvR8vNQtHkejCJZDiEaRF7ZzOhf76F0dHH0aefxE8feoRTF500pJwUaOMAKdAkk51QKMQ999zDT378Yw7NgXtPVDixRL4OdkfCFDy1GX61UZC0VG64cglf+85jVFRMsn0HJBLJfiUYDNLU1ERzc3MmrmtooLaxnqamJtqbW0jE4pnyqstpTz8stC1fWkH2EAGmZvtQVDnjQSL5MAjTIvbeFkL/eAujo5cjFh3Po99+gLM/9gkURZECbTwgBZpkMvPcc8/xpRuvp7e7i5uOFnz+SHBq8sd9pASTgt9sVnhqk0XSgk+dsZCvfOMBTl/8MblOQyI5iDFNk+7ubtrb22lra6OlpYWmpibqGxupbaijqbmZjpY2YpHI9osUBT3Hh5Lnt61geX60gmxchXlohdlQkI3qc8t3i0RygBCWReyDKsIvvEOqqZNDjzmSh5d9h7MWf4zc3Fwp0MYSKdAkk5G2tja+fMvN/L+//o3Tp6jcu0ijwnvwOgH5sISSgr/XKvzfFou6IMwsy2Pp0qVc/aWvMmXKlLFunkQiGQWEEASDQdrb23cKza0tNLW20NbeTldHJ/09vQhr0BRxRUHL9qEOCK98Ozjyc9DzsyHXZ08/lOu/JJJxhxCCxPpaQv98m+S2FkpmHUpXVxd9ja1SoI0VUqBJJhNCCP73f/+Xr91+G7oZ5xvHm3zyUMZ0RLamX/DfJljXJWgJQyQF2U4oz4KjCxVOr4DD8sa2jSNFCME77fDsNni5XpAwYfHxR3DNTbdz4aVXyH3UJJJxRjwep6uri66uLjo7OzNxZ2cnLe1ttHa009HZSXdnF33d3aQSySHXKw4dLTcLNduLmpOFluNDzfGhZWfhyMtCzfaB3yPFlwSwfyOsSByzO4DZF8KKxLEiMUQknk7HsaJxhGGCYSLSYSCNooCq2NNYFQVUNZ1WQVdRHLodnDqK0zE07RzmnNuJ6nGheAZiF6rbKT+ru0AIQXJrE6F/vk3Bl5ew5dJ7pEAbK6RAk0wGhBC88sorfPPur/PB6rVcOEvhf07QyHOOjRMQIQTLm+Fn6wXru8HngHmFcGg2eB0QSkJDEDb02IJtRg6ce6jCZ2ZBRdb4F2oA4aTgX43wbLXgg07wux1c/OkzufYrd7HopJMmhOCUSCYSlmURCATo7e2lr6+P3t7eIYKrtaPdFlwdHfR0d9PX3Tt0imEa1eNCzfKgZHvR/F5Uvxc124uW7cOR60fL8UG2FzXHh+J2yu+yZAhWLIHZFcDoCWB29WN0BzC7A5lYxHcQ+S4napbbnr7qdaN63SgOzRZJuoai6ygODTQVBGBZIATCEmAJsCx7m4QBQZdIIVIGImmk45SdThqIlJ3G2P2MGcWpo7jTws3rRstOfw8GfRdUvwfV78sMUByM34PNS+6WAm2skAJNMpGxLItXXnmFB+7/Nm+ufJv5JRq3HQsnlo7N110IwWvN8Pg6waYeOL4YPn+kwulThl/7ljQFb7fBi/WCfzdC3IBPTocvzFU4PH/i/Bg0BAXP1ir8vcaiLQIzy/O5/LLLOf+yzzN//nxUVXpVkxzcWJZFJBIhFAplQjgcHpIOBAL09PTQ1dtDV08PPT099Pb1EejvJ9jfTzQUZriujOJ0oGV7UdIdTM3vQU13MPXsLPR0Wvg9aH4vikMfg7+AZKJgJZKY3cFBomuoCBOR7U5dcOjohdloaUcuWmFOJtby/ag+z5hYq4Rl2YItkcSKJRGxBFYskY4HHceTWOEYVjiGGYxgBaNYoRhiB0syumZv2ZCf3j9vIC7IRi/OQ8vPnpTOa6RA2wUrVqzgkUceYdWqVbS1tfHss89ywQUXZM7fd999/PGPf6SpqQmn08lxxx3HAw88wIknDr+vwXBIgSaZaBiGwcqVK3nuuef465//SGNLG3MLVb5yLJxWLsZklEsIwatN8NN1gk29cEIJ3DxP4cTSkU9djKQEf62B32wStEZg8RT46gKF2XkT56VvCcG77fC3bfBqoyCcgpK8LD522smc9LFPsujkUzj66KNxOBxj3VSJZLeYpkk4HN5JRA0nsALBAH2BAIF0OhgKEQ6FiEQiRMIRYpEIiWhs9zdUFFSPE8VnWxhUr9tODxz73Gg+D1qWxz7ndYHPbY/yu5wH5o8imRRYiRRmbxCzJ4DZHdwuwroCmD0BrGB0e2FNRSvI2S7CinLs4yJbhKnZvklpWbISKaxQFCsUxewLYfYE03+zIEZvELMnhBXYvv0DDh29JM8OpQXopXk4ygtxVBSiOCfu750UaLvgpZdeYuXKlSxYsIAlS5bsJNCeeeYZiouLmTFjBrFYjMcee4y//OUv1NTUUFRUNKJ7SIEmGe9Eo1HWrl3LypUreeP113jzjTfoC4Yp9mksrjC5cLbOvAJzzITZf9PCbHMvLCyBm49VOLF039uSsgQv1sFP1gpaInDBTPjKsQqlvon1I5iyBKs7YXmzwrvtFlW9YAhwaCozp5Rw2GFzOOKYBRx5zHwOP/xwDjvsMPkOkuwVlmURi8WIRqNEIpERxeFwmFAkTDAcJhAOEgptF12RcJhoJEosEiE5yPX7sAwIKpcTxe1AcafXtrgc9roXt9OeHphOawPn08e4HOB2ZMooTsek7OhKDixCCEQ0YU8/7LGtYGZPcPtxTxArNEiAKYrt0KVoZwuYXpiDmpuFImdADItIGZi9IYzOPoz23u2hoxezN2QXUhX00nwch5TgmFqMY2oRjkNK0bK9Y9v4ESIF2ghQFGUngbYjA2LrP//5Dx/72MdGVK8UaJKxxDRNewQ6ECAQCNDZ2Ul9fT11dXVs3bKRdWvWUNPQjBACj0NhXoHguBI4Y6rOUfkm6hh2aN5pEzyySrCxBxaWDljMRq89SVPwp622+EuY8OVjFa48AvQJOo0ibtgidkufRm2/SW1AUBuA9kF9hdwsD1PLipg6dSrTps+kfNpM8gsKycvLGxJyc3Px+/243dIl974ihMCyLAzDwDTNAxIbhkEikRgSYrEYsUScWDxONBYlFk8Qj8eJxWPE02Xi8TiJRILkwHWxOMl4nGQ8MbKH1VRUl8MeyR5wIOByoDodQ0RUJu1yoqWdCageJ7hsQaUMCCqX017DIj97kgOMMEzM/jBmbyhj0TF7gxg9aYtYT3DoGrCBqXnpTb3tdA5aYfpYOnfZL1iJJEZLN6mmTlKNnXbc1JWZOqkV5+GcVYFrVgXOmeXoFUXjcorkgRBok37CdTKZ5Be/+AU5OTnMmzdvl+UGfhQHCAaDADz66KO43e4hZYfTtHvSuTueH3w8Eo28u+v3pi27Orc31+xNe/dG/wsh9qr8vlyzN+0arb+5YRgkk8lMGOhQxWMRopEw0UjEDrEY0VicaDxOPJHaqR4FyHFBodv2dHjcHIUpfo1yj4mWfoG1hkxaQyDYy7/jKBRujwj+UQtb+uCYQvjtmfCRstEfYXRqtiC7YCb8cI3ge6sEf6mGC2YKyvZgTdvb0ai9Hb76MKNdPt3k6ELbgyVAwhB0xaErptCfiNMfaqR2TSOr31lJKAkxA3bl6kVRwOVw4HI6cLmcuF0uXC4XbrcHl9uN2+PB5fbgdnvQdB1VVVEUJRMPhOHyB9JgW2oGBM3Ad3Fvji3LwhIWliWwBo4tE0sIRDrftCws07RjKx2bFqZl2uVNC1PYsWWa6Xq2n7MsC2FZ6bq3B2GJIccM5I/VmKWmomiq7Z1N10BNe2dTNRRdtR0FaCqKptnldBU0DcWlonidoLpRHIXoDg2nrqE4dVRdh7TDAXQN0g4IFKcOum7XMxJ2EFwWYBkmGDGIxHYouovv4N72sfa6nlG6r2TcIYRAxJLbp9YF7diKxLBCMaxgBCsUG/LCVrwutHxbgLkOOySzLkoryLYtYH65sfdYoLqcOGeU45xRnskTlsDs6iNR10aqppXktlZi72wCS6B43Tinl6JXFOE4pAgtJ2sMW2/jnjv9gNxn0lrQ/vnPf3LppZcSjUYpKyvj73//OyeccMIu67nvvvtYtmzZfm6tRDJ5UYApWfDV4+DsacoBG0Xf0C24921BZR/MzoXKvgNyW4lEIpHsT9LWWJEybQ+GA2jqdmcU+dno+X7UvPQ+c4U56AXZqB7X2LVb8qGxEklSde3EN9WRrGwkWddmCzafG0XXsAI7e2E9UFQtVSn9cZ+c4rgndiXQIpEIbW1tdHd388tf/pJXX32Vd999l+Li4mHrGc6C9sYXp7KrAZbhsvfUHx18fseiu7t0V+d2eT9l5PUpuzoxzDV7O6C527FZZfjDkbR7uHbsixYY9v9wD/UN+duN8J66Co5hPkgxzUFUcxLXHEQ1FzHNQUxzEtMcmMrIp1bszbOPtmTqCyb5f/9uoaY5ytVHwq0LFNzDeGXc3xiW4KfrBU+uh6NKNC44Zzr+XPcer9uvf7u9qnv/NEQTJn4jiddM4DGTeI0kLiuF2zRwWgaasNAG2f1G+vewhEi/E4ZesKtfk139yOwyfy9+lfa2jtEov8vm7efnH7Yte3nPXTEa/3d73ZYx+HtJxpZoStAQgvqgHWoiOs0RhZ6QgWWl/8cUJe0FMNf2BFiUi7PAj1qQgyqtXwclVjRBorKBxIZaYmu3YQXCuH1Ojp3q4rzCCCcUW2gHaFC4IkuRAm0kjGQNGsDs2bO55ppruOuuu0ZUr1yDJpHsnhUrVnDhBefjSIX47smwaIxc+A/mgw7B194QxEyVP/3mCc689LqxbpJEIpEcdIRCITZv3szmzZvZsHEDq9avY8vmLXS1tmXK6EW5aGX56CX5OIrzcJbkoaS9Icr1X5JdISxBqq6VxOoaIqurMDv68BXkcf5nL+LOG2/mmKOP2W/3PpDaYNKvQRtACDHEQiaRSPadX//619xw/XXMLxL8+Gx1zDa+3pHjSxT+/mm4bYXg3Muv5/tr3uUrD//vWDdLIpFIJiXhcJjNmzezadMmNm7cyKr169i8eRNdre2ZMlpRLo7yAvR5h1DwyePQywtRy/LkNgiSfUJRFZwzK3DOrCDrotNINXQQf2sjf/z90zzzxC+pOHwWN11/A1/+wvVkZY39mrV9ZUJa0MLhMDU1NQDMnz+fH/zgByxevJj8/HwKCgp44IEHOO+88ygrK6Onp4ef/exnPP3006xatYq5c+eO6B7SgiaR7Ixpmtx55508+uijfHY2fOtEZdiNpsealCV46D3B/1XBF84+jp899xYOp+wMSCQSyb4QiUTYsmVLRoit2bCeTZs20d7cYhdQFPTCHPTyAvTyQlxTimwhViqFmOTAIAyT+IZaYis3EFtTg8Pj5txLl/DgHd/giMMPH5V7HEhtMCEF2vLly1m8ePFO+UuXLuXJJ5/k8ssv591336W7u5uCggJOOOEEvvnNb+7WSciOSIEmkQwlGAxy+WWX8tJLL/H14xWuOmLkm02PFf9XKXjgPcEphxXzt+VryC8p3/NFEolEchBimibNzc3U1NSwbds2qqurWbtxA5s2b6KtsTlTzlGYi1pegKOiEGd5Ic4pRShSiEnGEUZPkNjrawkvX4sVjnHkSSfw0LeW8emzzv5Q/RYp0MYBUqBJJNupr6/nU588h8baah49VXB6xVi3aOS83Sb4ynJBgd/FP1/+L0ccd/JYN0kikUjGhEQiQX19fUaE1dTUUFVdzdaaapobGzGS6S1eVAU9PxutNB9HRSGOiiJcFUUoZfn2huISyQRApAxi71cSfuUDUo0dlB85m2V3f4trLrsCdR82GpcCbRwgBZpEYrNixQouuvAzuI0AT35UYVb2+FhvtjfUBwU3vCrojSs88+sn+OTl1491kyQSiWS/EA6HM+JrwBJWVVNNzbZttDe3ZPb6Ux06elEualEOWnEeenEuruJ8tOJclIJs6ahDMmkQQpDYVE/4xXdIVDaSd0g5d9xxB1+97iace7H8QQq0cYAUaBIJPPnkk9xyy80cVwQ/PEMhf5w4A9kXgknBba/bFrX7b/08dz7663E/RVMikUh2RAhBT0/PEBG2tXorW6q3UldbR19Xd6as5nGhF9veEfUBEVaSj1aUB7nSXb3k4CO5rYXwS+8RW70VX2Eet9x+G9/6ylfxer17vFYKtHGAFGiSg5lkMsktt9zCL37xCz53uMLXT1Bw7P1sgHGHYQkeWQVPbRZ8/NjpPP3CG5SUT6D5mhKJ5KDAsixaW1szIqympoYtW6uoqqmmsa6eaCicKatn+4ZYwfTiPFwl+SiFuSh+txyIkkiGIdXaTeTFd4m8swl3tp+bvnILy7729d16fpQCbRwgBZrkYKWzs5OLLvwM77zzDvcuFFw8Z/L9uC9vFty1UqBqOr984nEuuFJOeZRIJAeW/v5+6urqqKuro7a2lm21tbYVrK6WtqYWUsmkXVBR0PP9qEW5GSuYsyTftowVZqN6XGP7IBLJBMbo7Cf80rtE3lyPy+fli1+6ifvvvJucnJydykqBNg6QAk1yMPLvf/+bqz53BUakj5+cLlhQNHlfD10xwddXwpstgrMXHsGPnvoLc44Y2TYcEolEsjui0SgdHR20t7fT1tZGW1sb9fX1VG2roXpbDc0NjYQDwUx51e1EK8xBK8pFK8zBUZSLozgXtSgPrTAbxXHQbFsrkYwJRk+QyEvvEl6xDofLydU3XMdDd99Dfn5+powUaOMAKdAkBxOJRIK77rqLxx57jJMrVL57ikqxe+KuNxspQgj+1QDf+0DQFYMbrzifb/3gVxQWFo510yQSyTjDMAy6urpob2/fKbS1t9Hc2kpbWxudnZ1DpiACKJqKlp+dEWF6UQ6OojwcRblQmI2a5ZFTESWScYDZFyLy8vuEl69B0zQ+94VrePib91FcXCwF2nhACjTJwcIbb7zBjddfx9atVXx1Piw9EtSDrKMQNwS/2qTwvxstNE3jK1+8ijse+KH87kskkxwhBIFAYGfB1dZGS1sbza0ttLW309XZSX9PL8IaOnDl8PtQc3yQ7UHN9qHl2MdaThaOXH/6nBfV65YOOSSSCYQZjBB9ZRWh/65CRXDR0s+x7PY7Ofzww6VAG0ukQJNMdjo7O/mfO+7gt7/7HfOKNb69SOHw3MlvNdsdPTHBLzYpPLPFwud28D+33cxXvvEAHo9nrJsmkUj2gng8npliOBBa21qpb2m2RVdbO92dnfR1dW/f+yuN4nRkhJaa40PL9qHmZqHl+HDkZKHlZqFm+8Dvka7oJZJJjhWOEfnPKkL//gDFMHHOPZSO11dJgTZWSIEmmazE43F+9KMf8eD930E143z1WIuLZk8Oq5kQgtogrOmEtV2ChhB0x8ASUOCGw/Lgk4cqHF/CbqcTtUcEP9ug8NetFoXZHu6+606uv+2uvdovRSKRjC6madLT07Ozpau1lYaWZlraWuno6KC3q5toMDT0YlVB9actXLlp0ZWThSPXh56ThZLjQ8n2omb7UNxOOd1QMq4QlsAKx7CCEcxABCsURcQSWNEEViyRTsexYkkwDIRhIUwTDBNhmGBYoAKqiqKpoGm2RVdTUZwOVI8LxeNMxy5UtxPV50HN8W63DGd57WsPUqxoguh/V5P16UVsXnK3FGhjhRRoksmGZVn84Q9/4O6v/w+trW1cNkdw03x9Qu9tNsDmHsFfqgUvN0BPHBRgTh7MzIEiD2gqdEZhTRe0hG2hdtcJCovKdt8JawwKfrJe4fltFlML/Xztjq9x1fVfGda7k0Qi2Tssy6K/v5/u7m66uroyobu7m87OTlo62mnr6KCzs4Ouzk4CPX1YpjmkDtXnRs0emFZox3pOFnrayqVke21LWJYHRT14O5eS8YcQO4iuYHRQenAcxQpF7JHGwaiKLaa8bltYeW1hpTh00DWUdEBT7VgIhGmBadmxZccimUJEE1jxpC30YglELInYwbKMAmqWN21V9qLl+e01lQU56IU5djrPf1CIOCnQxhAp0CSTBSEEL7/8Mt+6+y4+WLOWTxyi8NXjdab7zT1fPM7Z1CP4/irBW21Q4oVzp8Mp5QrzCiHLubP4soTgvXb44RrBmi64cCZ8Y6EybNnBbO0TPL4O/t0ocOoanznrDK684TZOO+MMfD7f/no8iWTcY1kWwWCQQCBAf3//sKG3r4+u3h56+nrp7eujp7ubvp5egn39OwkuFAXN50bxe1H9HlS/F9XvRdtBeJFt50nvhpLxiBVPYvaHsfpCmP1hO6TT1kA6EAFj6OdfcTm2T6sdiLO9aDlZqNmDrFl+L4rLsV8tvSJl7CwWg1GsQAQzkH6G7iBWMLL9IlWxhVtBDnppHnppAY6yAvSyfLTCnEkzSCIF2i5YsWIFjzzyCKtWraKtrY1nn32WCy64AIBUKsU3v/lNXnzxRWpra8nJyeHjH/843/3udykvLx/xPaRAk0x0kskkf/zjH3nke99l46YtzCtW+Z/jFI4vnnBf+Z3oiQkeWyP4f9W2lexLxyqceQjoI1yEL4TgbzXwwPuCXCc8dIrCiaV7vrYjIni2VuGv1RaNIXBoKsfPncWpiz/OgkWnMW/ePGbPno2myXUpkvGHaZpEIhHC4XAm3jE9+DgUChEIhwgEgwRDIUKR9Pl0mWgoTCwcYVfdCNXlQPG67alTXrc9wu9x2aPwfg+a34ee7UXze8HnQfF7ULPck6YTJ5mcWPEkZncAsyeI0R3YLrz6Qpj9dlrEkkOuUTwutNwstLwstDy/vaYx14+WOyDEsuzphK6JN41eJFMYPUHMniBmdwCjJ4DZFcDo6MVo691uidM19JI8HBVFOA4twTmtFMchJag+99g+wD4gBdoueOmll1i5ciULFixgyZIlQwRaIBDgoosu4otf/CLz5s2jr6+PW2+9FcMw+OCDD0Z8DynQJBMNIQTNzc288cYbvPKvf/Hc3/9GfyjCGVMVrp2rckKxNeHXVQgh+Eu17RZfUeDLxypcdtjIhdmONIcFd70peK8DPn8k3L5AwaXtuS4hBLUBeKdD5d02kzWd0Bmzz7kdOkfMnMqRRx7J4cccx2FHHsVhhx3GrFmz8Hq9+9ROycGBYRi28IlGiUQiQ9LDxeFwmHAkQiC8XTyFB66LRYlFosSiMeKxGIlYjFQiucc2KLqG6naC02GP0Lud9qi+y4HicqK47dgWXy50rwfVa0+zUjwu8DpRPG5Ur0s60JBMSIRhYnT1Y3T0pYVYwBZi3UHMngBWOLa9sKamhdeA6LLTmTgvCzU3a0IKr9FAWAKzP4TR1oPR1ovR1kOqqZNUY2dGuGlFuTimleCcXoZrzlQc00rG/btDCrQRoCjKEIE2HO+//z4LFy6koaGBQw45ZET1SoEmGc9EIhEqKyvZvHkzmzZtYu0H77Jq9Rq6+wIAzM5T+PhUwadnOpiZPfGnMgI0hQTfelvwdhssmQVfO04h3/3hBaclBL/dDD9YLTjED987VWFuwd7X2xsXVPVBVb9KVY9JbRDqAhAY1CeuKMpl+iEVTJk6janTZ9lh6lSmTJnClClTKC4uRpXWgwOCaZokk0mSySSpVGrYeLi8RCIxJMTj8SHHsViMaDxOPBEnFh8IMeLxOPFB1yQTCRLJJIlYnEQ8TiIawzSMEbVdcegoAwLKqadjxw6xna+mj1WnA9XtQnM7wOW0xZZLRwwWXy7HuO8YSSSjhRmM2sKhvQejvTcd+jC6+8FMr83WNbSCbHuNVUEOekF6L7tCO63mZkmL7z4gLAujvZdUQwephg6SDe2k6tsRiRSK04FzVjnOOVNxHXkozhll4+5vLAXaCBiJQPvPf/7DmWeeSX9//y7/oAM/rgMEg0GmTp3Kgw8+uEsX2wN/ul39CUfypx2uzOC8PdUxkntY1sicQIz35xhpHSP9SO+pzXvTtoFyo/UcO3b8wqEQPd2d9PV009XdTXdfMFM+16VQ4bPFxdRslUOzBNmuYeod0VPsRbkRFvyw97WE4M1WeKEOClzwnZPg1IrRf1lX9wnueFNQ3QcXzISFpaDtYHHcl2eJpOxNsLvjKl1Ri744BBLQn7Rjc1BhVVXIzfKSk+3H6/Xhy8rCm+XH6/PjdLlQVXWnoCgKiqIMOR4cw9Dvxo7fkx3PDQRLCCzLwhIWlmXnmZaFEJZ93hKYlpkuayEECGGlr2FIOSEsLCHSeel30pC02H6NNdCG9H2xsEyBYHtdwkrfc8dYCIRlx6ZpYhomhmFgmgZWOm2Z1oiF0IhwaCiaHdDSHtL0tJc0TUsfayi6mj4/aOG+poGe9qLm0FB1W1jh0FB1HeHQ7GudOoqug0NHcWhyPy2JZC8QloXZG8Ls7Mfo7LOtYR192y1himILrtL8dMhDL83HUVqAmpMlv28HCGGYpBo7SFQ1kdjaRLK6GRFNoPjcOGeW21a2Q8tQvcN0cA4grsMOkQJtJOxJoMXjcU455RQOP/xwnn766V3Wc99997Fs2bL91EqJRLKvaIotYi4/DG6bD9mu/TeSljQFj68T/HIjTMmC3jiEU3u+TiKRSCTjjLT3QpEyMh4Q1bwsHFNLcB5SjF5RiF5eiKM0XzqbGYcIyyJZ20Z8XQ2JjXWkGjpAUVBzfIhEcqd1fgeKqqUqpT/ukwJtT+xOoKVSKS6++GIaGxtZvnz5bv+Yw1nQ/n3NVPY0bqLsEO/cvl1csPusEZ9XlD1fP2w7dnGPXT7HCBo13D2UXaRHUN3w5fbQN9/b5xjpkqwhz7Gba5Td3Huk91UUcGvgUIfu1RXTHAR0L12uLAIOL9bgSvbhOXbfvtEdMdzb6ixLsGJtLy+/3UWJFx44SeEjI3DiMVqs7RL8z5uCjgh89qQ85s0vyfxNRnsZ30B99ndZQVEgNxUlLxUlNxUl20zisoa3+Ii05ckcCBYYg2LDGt7qN2Lr5y7KDVvnyKqUSCSSD40Qgs4Y1AbsUBnS2BZU6Ana70pFU9HLC9GnFuOcWoRzShHatFK0rOFnREnGP2Z/mPjaamLvV5GobAQFpk/N4tySBJ8oSeLWD0wf4dBsRQq0kbArgZZKpfjsZz9LbW0tr776KgUFBXtVr1yDJpGMDdXV1Xzuist4/4NVLD0CvjJfxTsGg5vRlOCR1fBMpeCMo6byu+deZeqMWQe+IRKJRHIQE4vF2LhxI+vWrWPt2rW8v3Y1mzZsJJLejFzL8qBNKcJxSDGuqSU4DilGKyuQ6yknMWYgQnz1VqLvbSFZ1YTD6+bjF3yab335dj6y8MT95hDtQGqDSSnQBsRZdXU1r732GkVFRXtdrxRoEsmBRQjBL37xC26/9SsUuQy+ezIsKBr719MbLYK73xLETZUff+/bXHXL3RPeG6ZEIpGMN+LxODU1NVRWVlJZWcnqdWtZvWYNjXX1CMsCVcFRko8+pQjH1GJcU4vRphah5vnlO/kgxujqJ7ZyI5E31mP2hSiZdSg333QTt11306jvUyoF2h4Ih8PU1NQAMH/+fH7wgx+wePFi8vPzKS8vZ8mSJaxevZp//vOflJSUZK7Lz8/H6RyZq1Mp0CSSA0dHRwfXXnM1L7z4EpfMgTtP0PDp4+fVFEgIvv0e/LNWcP7JR/Orv/2bwuKSPV8okUgkkgxCCDo7O6msrKSqqootW7awcctmtlRW0trYlHFapPk8aBUFOKYW45xqW8aU8gJUl2OMn0AyXhGWRWJjHdEV64mtqcbh83Dh5y7joTu/wfRDp4/KPaRA2wPLly9n8eLFO+UvXbqU++67j+nTh/+PeO211zjjjDNGdA8p0CSSA8Pzzz/PtVcvxYoFuX+R4KNTxrpFu+bFOsF97whcTge//PkTnHf5tWPdJIlEIhl3JJNJtm3blrGGbd6yhfWbN7GtujozNVFRVfTiXLSSPPSyAhyl+TjLClFK8+zNyyWSfcToDhB9dTXhFesQsSQnnLmYx5Y9yMknnvih6pUCbRwgBZpEsn+JRCLcdttt/PKXv2TxVIX7T9YodI1sS4ixpCMq+MZb9tTHz597Cj98+nlycnPHulkSiURywOnv7x8iwtZsXE9lZSWtDU1Ypr0Hp+Z1o5bm4SgrQC/Nx1lWgKOsEKUoR64Tk+xXrHiS2NubCL38PmZnH4efdDzfX/YA5378zH2qTwq0cYAUaBLJ/mPt2rVc+tmLaKiv467jBJfMGX3PkfsTIQR/2goPfyDI8jj53oP3c9WXvjahnkEikUhGgmVZNDc3Z4TY+k0bWbdpIzVbt9Lf1ZMppxXkoJflo5cV4CwryFjD1GyvfDdKxhRhWcQ+qCL8z7dJNXcxbd5cvnvfMi45/8K9+mxKgTYOkAJNIhl9hBD8+Mc/5n/u+BozcwSPnqowM3v8W812RWtY8N1V8HK9YNERU3n8139gwUdOHutmSSQSyV4Ti8Worq6mqqoqI8Q2btlMXXUNiVgcAMWho5WkrWFl+bjKClHLCtBK8uT6MMm4RwhBfN02wi+8TXJbK6VzZvDAsm/z+c9ehqrueY9VKdDGAVKgSSSjS1dXF1d/fikvvPgSS4+Arx2v4VQnx+tnZavg/vcEtQG4+KPH890nn2bG7MPGulkSiUQyBCEE7e3tGRFWWVnJ+s0bqayqor2pZbuTjiwPWmk+enkBemkB7vIie21YYTbKCDqyEsl4RghBsrKR0PNvkahspGT2dB5c9m0+f8nluxVqUqCNA6RAk0hGj//85z9cecXlpMJ9PHSSxekVY92i0cewBP+vGh5fJ+hPwhc+83GWPf57ikpKx7ppEonkIEAIQSAQoLm5maamJpqammhubqaxqZG6hgYaGhtpb2sjEY0Bg5x0lOajl+bjKC3AWV6IUpIrnXRIDhoSW5sI/WMlic0NlMw6lAe//Z1dCjUp0MYBUqBJJB+ecDjMN77xDX7yk59wUrnCd09RKXZP3CmNIyGaEjy1GX61SaAoKl+97gru+O4To74fi0QiObgIBoNDhNdAXN/YQH1jI20trcQike0XKAqOXD9KXhZqvh8tz4+W78dVlIdWlo9alCuddEgkaRJbm9NCrZ7imdP47rfvZ+mlQ4WaFGjjACnQJJIPxyuvvMJ1X7iWjvZWbpsnuOpIUA+iheK9ccHP1sMfqwSlOR4e/+njnHfZNWPdLIlEMg4JhUI7Wb5qG+qpa2ygqamJjtY2YuEdxVeWvUlzXlZafGWj5ftx5ueg5/sR2V4pwCSSvSRRnRZqm+opmTmNh5Z9h6WXXYGqqlKgjQekQJNI9o3e3l5uv+02fvu73/GRcpX7T1KZ6pvcVrPd0RAULHvXXqf2mdOO5fFnnqe8Yhxv9iaRSEaVSCQydMphYyM1DfXUNdTT3NxMZ1s7sVB4yDVaThZqXlZGdGl5fhwF2Tjys1Hy/Cg5Pim+JJL9SKKmmfBzbxHfVEfxjGk8cN8ylnzqPPLz86VAG0ukQJNI9g4hBP/3f//HV2/7CrFQgK8fZ7Fk1sRyn7+/EELwfB089L4gJTQe+NYd3PT1+9E02cGSSCYy0Wh0Z8tXfT3bGuszlq9oemPmAdRsX2a6oZbvR8+3hZea70fJ86PlZknxJZGMExI1LYT/sZL4xjoKD51Cb38/fQ0tUqCNFVKgSQ4GampqeP7553nvnbdo2LaVYCCAx+WkpLiII+YexVHHfYQFxx3PUUcdtVuhtWnTJr504w28/sabnHOowt0napN+rdm+0J8QfH81/GWrYHZZLl//xre49Oob8HrlgnyJZDxhmiadnZ20tbXR2tqaiZtamqltbKCxsYmOtjbC/YEh12l+76D1Xrb1y5GOlVw/Wl4WikMfo6eSjAbCMLEiMaxwDCsSR8STiGQKK5FCJA1EIpmOU4hkyo5TBsK0wLQQppmOLTDM7fmW/ZupKApkAplY0TQUp47idKC4HHbs1FFcTtQsN2qWBy3Lm0mrWV4Ur0sOko4SyW0thP7xFvm3XMiWS74lBdpYIQWaZLIihOCf//wnDz3wHd5+931cusKReYJp2ZDlgLgJHVHY1g+t6SUPJbk+Pn7qiZx/+bWcf+FFOJ1OwHYCsmzZMn742GNM9Svcs9DipLKxe7aJwvpuwePr4PVmQZZb56JPncXnv/Q1Tj7lFHRddt4kktFGCEEoFKK7u5uenh56enoy6e7ubprbWmlsaaa1tZXO9g76unsyHWYAVAXN70PN8WWmHNriKwc9PxvybKuYFF8TB2GYWNE4ViSeEVt2HMMKxxGDRJgV2X5eJFK7rlRT08JpQETp9v5wDh1FU0HTUDTVTutaOk9F0TQYcEYhBAhhb3kwECyBMC1b6A2IvmQ6HU/a7U0kd2qO4tRR82wrrTZ44KAwx/bgWZBjt0GyV2xecrcUaGOFFGiSyci6deu46cbreevtd1lQonLl4bB4isCjDz/CFk4J1nfBG20KbzZbbO2H4mwPN35hKd6S6fzg+9+jv6+PG48WXHuUOmn2NTtQNAYFf69T+HuNRUsYcn0uPnHaIs67dCmfTM91l0gOVizLIhqNEg6HCYfDRCIRQqEQ4XCYUCiUCQPH/cEA/YEAgVCQQDBIMBQiHArR39dPuD+AaRg73UNxOmyLg9+HlpdlC7CcLLRcP468LLTcLMj2omb7ZEd2nCIsgYjGMcOxtKiKD7FwDRFZA+ciMURsZ0EDoLidqD43qs9jfzZ8Htsi5bMtU8qO59zO7RatMZyaKlLG9mcNRbHCMcy+EGZvKB0H7XR/2BZ9AJqKVmCLNb04zxZtxXk4yvLRCnPlZ34XSIG2C1asWMEjjzzCqlWraGtr49lnn+WCCy7InP/b3/7Gz3/+c1atWkVPTw9r1qzh2GOP3at7SIEmmUxYlsVjjz3G1++8k0Oz4RsniH2ydFX3CX5fCX+rEXh1+Egp/M9CB1N85ug3+iDCEoIN3bC8ReH1JotNvaAqsPCo2Xzqws/y6Qs/y9FHHy2nqkjGJQNCKhKJEIlEMmJqsLAaSIfDYfpCQfqDAYLBEKFIiFAoTDgSJhqOEItGiUWjxCMxkvH4Hu+tOB3bO8huZyaog44Hpn7pWR5UvwclywMDnWyX4wD8hSR7ixACKxTNCAwrEMEMRbGCUVt8hKL2cVqIYO48pT4jvtMCS0kLLFt8DRZe28WX6nNP+vV/wrQwe4MYnX0Ynf2Y6XjgmFR6IEPX0Evy0MsKcJTmo5cXopfZe+apLueYPsNYIwXaLnjppZdYuXIlCxYsYMmSJTsJtN///vfU1dVRXl7OF7/4RSnQJAc13d3dLL3qSl586V9cOxduna/g1D5cR78pJHjwPcGrzXDmIfCtExWKvVI8jBYdUcGKVpXljSZvtUHUgIrCHM7+xEc57azzOGHhicyZM0c6GZGMGMMwiMViQ8KAoNpRVA0WVIFwiEAoSCgcJhQKE4mEiUSjQ8RUMrZnIYWqorod4HSgupwobgeKyxZWGTE16Fh1u9LxdvGFU4eBPLfLzpcj/BMWK5bAaO0h1dqN0dG33cqTFmUYgwb+FAXV70H1e1H9Xnutn9+Lmj34eKjYktNN9x5hCaz+EKm2Xoz2HozWHoz2XlJtPVj92z2NagXZ6KX5tngrK0AvL0AvLUDN9h4UA4lSoI0ARVF2EmgD1NfXM336dCnQJAct77//PhdecB6Rvi4ePllwesXo1S2E4KUGuP9dQcqCrx+vcKH02jjqJE3B+x2wvFnhjRaLuqCd73U5mD2tnJmzZjH7yGOYffhcpk+fTlFREUVFRRQUFOBwSOvAviCEwLIsDMPANE1M09xlenfnPuw1w51LpVJEY1HC0SiRaMSOIxGiadEVjUaJx2LE43ES8TiJWJxkPDHs9L7hUHTNFksDa2gGxJMzLaRcaWHldqCmy2gZMeWyRZTbAWnnBQPWLHRNvhsOUqx4EqOth1RLN0ZrdyY2e9IvM4W0Q5Ud1kkN8nSp+r0oqhTjY4kVTWC095Bq68Fo68Vo67FDZx9YtpRQfG4cZQU4phbjmFKEPqUIx5QiVI9rjFs/uhwIgSaHF9IkEgkSiUTmOBi0Xxy//vWv8Xg8Q8oOp2kH8kaid3d3/UjLj0Y7his32doxkjYMPr837RjttuyuHXtqw3Dl169fz79eepEj8wS/P1eh3DfiKkeEoih88lBYVAoPvi+4+y3B32pgyUxBnmdkHbG9GR3am6Gk/TnqtLdDWnv1jLs5d3yJ4PgShZghaApBU9ikM9TAhrcaeO2//6U/sfP1HpcDj8uJpmnomoaua2iahqqqdlAUVFVBVQfyFFRVTZfRUDUNh9OF0+XG4XLbscOBw+FA1/VM2v67iEyw0s4VLMvaZb5lWVjCwrIEVvqcfWwHwzQxLRPLtGyBks4fECt2ORPTtOxyloW1QznLsj2j2WmRucYyre3nLNurmhAik7/X/8mjiQKoqi1mVAVFUUEFFNWe96oo9hQshw4OzRZUuo7i0GwRpGsouQ4UhztzzuPUUXXdPnbaMZoKDg2cDhSHbgenvt1RwT6QmWRmWhBL2kFy0CCSBkZPALOrH7MniNkTwOgKYPVt32ZAK8zBUVGIZ+ER9pS5igIcZYVyqukEQPW6cM4oxzmjfEi+MEyMzr60cLOtbonqZiIr1mWmnqp5fvSSPLSiXHu9W3khmn9iei92zRrFke7dIAVamoceeohly5btlH/bbbeNQWskktHhwllw74ng3oUTkNEgz63wyKkKn5ouuOdtwbffh0K3oCm852slH4Y9b2MQS6SI7c7jmGT8IbBdbg86lEjGLbqK4nLan9n4dkGuFWSjVxTiWXg4jvICHFOK0csLDvq1S5MRRddwlBfiKC8cki8ME6Oth2RjB6nmLoyWbhLrtxEL2O6h7TWiDnubgvjE+Z2qWqrCkrv3+32kQEtz1113cfvtt2eOg8Eg/7xqKrubkbHTKWU353Yot6vzu8vfU527qmOvnmEP5wbyRlTnLsrs7T0z5waf3KHgnurc5d91mBMjkTJDmrJjW0bp7727ewx7rQKBBDz8AWzsgW8sVLj8sAM35fD0KQovnA+Prhb8cSsckqPy6ZOLOHTa6E0D2JtH2eun3qu697L2/djuPf5NhMAhTJymiS4sFEQ6DNxPoAj7WEWgCQtVWDiFwC1SeEwDn5XEhWkbcLA/U5YQJEyIGZnZLdtFxQ7Hg1XGjsJDjPAcO+QNJ2B2d44d2ySRSHZLwhQ0h6AhBPUh2BZz0BCEvuD2zrSWk4VeXoijvABneQHalGL0sgJUtxRiBzuKrtlTHacWD8k3+0Ikt7WSqm0lUd9Oqq7dLq8qlJV4ObbA4uTsGEfkg+tDrpefyEiBlsblcuFyDZ0je+NryDVokgnDP//5T772xWtJRHt56izBwpID3xXNcirc+xGFS+YI7n9P8NMXOzjnhGx+9NRfmH3kvAPeHolEIpHsnng8TlVVFZs2bWLjxo2s3rCOjRs30drYlNkLTsv320LsyEIKKopQy23HEJNtbZFk/6Pl+fEcfxie4w8DQFiW7Symupm+LQ38q7KRF8OgOnTmHHs0nzzzbD635GKOPfbYMV/HmvFP8eP9fy/pJGQXSCchkolCb28vt956K7///e85bYrKd05SKfXsefrb/kYIwYv18PAHgr4E3HrlBdzz+NP4fKO8GE4ikUgkeySRSFBVVcXmzZvZuHEjazasY8PGTTTXN2SEmJ7nRysvQC8vxFlRhLOiELWsANUrhZjkwCAsgdHaTXJLA/HKRhKVDYhYEn9RAWec9XGuvugyzj7zzJ38QxwIDqQ2mJAWtHA4TE1NTea4rq6OtWvXkp+fzyGHHEJvby+NjY20trYCUFVVBUBpaSmlpaVj0maJZH/w3HPPccP11xEN9PLQyQqfmSlQlLEXZ2APnpw7HRZPgZ9vgMd+93f+/lIpf/3HCxy18LSxbp5EIpFMSpLJJFu3bt1uEVu/jo2bNtJc34hl2q7rtdws2zX6jEJyTj0MV0URank+qtc9xq2XHOwoqoIj7f3R94njEYZJYmsTyfW1vPjKyzz/9J/QXE7mn/wRrr38Si676GJycnLGutmjzoS0oC1fvpzFixfvlL906VKeeuopnnrqKa6++uqdzt97773cd999I7qHtKBJxjPd3d18+ZZb+MMf/8jiQzSWLVIocY8PYbYrqvsEX3ld0BaBx+//H66+8+GxbpJEIpFMWFKpFFu3bs1YxFavX8eGTRtpqqvHSu8hpuX4bG+J5YU4KgrTQqwA1SeFmGTiIYTAaO8luW4b0dVbSda0oDp05p96Etd9bimXLrlov/bZD6Q2mJAC7UAgBZpkvPLXv/6Vm264jkQ4wDeOtzhvxsTZeyySEtz7DjxfK/jCuR/hp397HadTLiaXSCSSXZFKpaipqclYxNZsWM/6jRtoqq3DHBBi2T608gIcFYU4y4twTilCKctHyzrw08AkkgOF2Rsk/sFWou9vIbmtFdWhc/wZp3LLNV/k4s9cuJNviQ+LFGjjACnQJOON/v5+vnTjjTzzxz/y8UMU7lukUTTOrWbDIYTt5fH+dwULZxfz7GsfUFw+daybJZFIJGNKMpmkurqazZs3s2XLFtZuWM+6jRto2FaLmbI3Gtf8XluIlRfiqCjCVVGEUp4/YfeUkkhGC6MnSGJVFZF3t5Cqa8Pl9/GJ8z/FnTfewsmLThqVgWwp0MYBUqBJxhNvvvkmn7vsEnq72rlnoeDT0yeO1WxXvN8uuGW5IMvj5Ll/vsT8kz861k2SSCSS/U4kEqGyspItW7awZcsW1m1Yz8bNm2mqb9i+RszvRSvLTwuxQlwVxbYQy5ZOliSSPZFq7Sb+9mYib23E7AuRf0g5V3zuc3z1upuYNm3aPtcrBdo4QAo0yXgglUqxbNkyHnroQeYXKXzvVJUpvolnNdsVzWHBTa8KmsIKv/7xw1xywx1j3SSJRCL50BiGQVNTEzU1NdTU1FC1dSvrNm1ky5bNdDS3ZsplvCaW2VYxV0UhSkk+Wra0iEkkHxZhWSQqG4m9tYnYB1WIZIrDFi7gK9ffyNJLL8fr3bvvmRRo4wAp0CRjTWVlJVdd+TlWr17NLfPguqNAUye21Ww4oinB11fCyw2CL130MR753fNj4j5XIpFI9oZUKkV9fX1GhFVXV7N5axXVNdW0NDZlpiUqmopWmINemm+7ry8rwFlRhFKaJ/cRk0gOEFY8SWxVFbGVG0lUNuLweTjrM+fxjZtv48SFC0c0K0kKtHGAFGiSscIwDL7//e9z3733UOa1+N4pMK9gcn9NLSF4ajP8YLVgelEWT/zqKT76qSVj3SyJRHKQIoSgp6eHxsZGGhsbaWpqoqGhgW31ddQ21NPS3ExvRxcDXShF19CLctGKc9FK8tCL83CV5qMW5qEW+FE0dYyfSCKRDGB09hF7cyPhleux+sKUzprO9V/4Ajdfex2FhYW7vE4KtHGAFGiSseCtt97iyzffxJq167lmruCWYzXc2sHzFa3qs708rukUfOKEw/nmw49z2uKPjXWzJBLJBMeyLILBIP39/ZnQ2dlJR0cHHR0dtLS10dzWSntHB12dnfR2dZFKJDPXKw4dLd+Plp+difWCbBzFeSiFOWj5fhRVijCJZCIhLIvExjqib24gtqYaVVE46cyP8T83fZlPnn02mqYNKS8F2jhACjTJgWTLli3cfdfX+ftz/+CIAo1vL4JjJrnVbFdYQvBCHfx8g6C6Hz4ydzq33vktLvjs5aPuMlcikUwMLMsiEAjQ399PX1/fEKHV399PIBCgr6+Prt4eevp66UvnBwMBQoEg8UiU4bo7iqah5fhQsr2o2T60bC9qjg8t24czPwe1IBslPwvV753wjpkkEsmuMYNR4u9sIvzGeoyWbrKLC/ncVVfy1RtvZsaMGYAUaOMCKdAkB4KWlhbuvfdefvObX1OepXHrPJNzp4MqOwJYQvBaE/xqk2B1J+R6nVxy/jlce+vdHH/CCbKzJJFMUOLxOB0dHRkLVmdnJ52dnXR1d9HR3U13Xy89vb309/cR6A8QCQaJhSLDCiwA1eNC8bhQPE5UrxvV60LxulE9rnTaheb1oHndaF43eJwIjxPN70XxuuS7RCKRZBBCkKprI/bGBiLvbkbEk8z9yPHcesNNfPrsT1JaWioF2lgiBZpkf9LX18fDDz/Mj374GF7N5MajLC49TMWpbv86tkUEz9fC6k5BZww8GhyWByeUKpxaDlnOg6dTsS0geHabwt9rLLpicMTUIpZ+/vN8/ktfpaSkZKybJ5Ec1AghCAaDdHR00N7enhFd7e3tNLe30tLeTnt7O91dXfR19xCPRHeqQ/W5UXweVJ8tsOzYjeJz2+Iqfaz63OB1onjSQszjkuu7JJMKkTKwwjGsUAwzHE2no1ihmJ0OR7GiCYRhgmllYhQFRVfBoaPomh28btQsN6rPg5rlQfN70PLsKbpqjk9Oy90DViJF/IMqIm+sJ7m1CWeWF2VqEZ3vrJcCbayQAk2yP2hububRRx/llz9/EstIcvURFtcerZOlb3edv7VP8NN1glcawa3B/CKoyIJICjb1Qn0QHCp8pAw+PlXho1Oh2HtwiDXDEqxshWe3wX8aBZaATyw6hi/c8j98+sKLcTqdY91EiWTSEIlEaE+Lq8Hiq6W1laa2Vlrb2+hs76Cvu3vIei2wpw6q2V47+L1oOT47zvbhyPGh+H2Q7UHz2+cVXdtFKyQSe62QSBqIZCoTY1oIS4BlgWUhTAHCAksgTDsPAE21hYiqgqqk04qdr6mgpcXM4LRup1GVfbawCstCxJJYkThWLI6IJux0JDZIgKXF16BYxJM7V6apqH5vWmSlLb+6Zrc3/RxCCDBMxEBIGel7xuz7hmNgiSF1arlZaEW56KX5OErz0Uvy0Uvz0Qpz5MDHDhgdvcTe3Ih/yWlsXnK3FGhjhRRoktEiEonw4osv8qc//oF//OMfeHS4Yo7JlUfqFLi2C7OafsHj6wT/qofyLPjCXIXzZkKWY+iPQ3NY8GqTLVA+6ABTwFEFcPoUOPdQhZm5B4dY608IXqhX+Fu1xcYeyM9ycelnPsVVN32NhSeeKKctSSSDiEaj9PT0DBu6u7tp7+qko7uLru5uent66O3qJhGNDalD0VS07PR6rfQ6rYHYkZOFlpOFyPbYZeTUQQkgLGELhGB0u0iJJrCiA+m4bQ2KxrFiCUQihUimsNKxSBqIRAoMc2weQCEtgjTQVRQtLd4GCTqEQKRMME1EykSYJqRMRCoFw/WwFTIWLTXLYwsvvwc1a7sAs4/TeX4Pitv5ob9PQghEJI7ZG8LoDWL2hTB7gphd/RjtvRgdvYikvTUEmopekodjSjGOqcU4phbhmFKEmueX32uQAm1XrFixgkceeYRVq1bR1tbGs88+ywUXXJA5L4Rg2bJl/OIXv6Cvr48TTzyRn/70p8ydO3fE95ACTbKvBINB3n//fd555x3eeuN1lr++gmg8wVGFKp8+VHDRYSpZ+vavXXNY8Nhq2zFGmQ9uPEbhgpng1Pb8EuxPCJY3w4pmwYoWCKXghBL48rEKC0sPnpfo1j57CuQ/tll0x2FqUQ5LLjiP8y67mkWLFuF2u8e6iRLJPpFMJgkGg3sMgUCAnv4++gL99AcCBIJBQqEQkVCIcCC4k4ULAFVFy/Kg+NzbO4sD6Zy06MrNQvF7bVHm86BMwr0YJXuPsARWKGJ38HtDmL1BzEAEKxgZFEexwlF7+t2OaKo9hdXr2r5u0ONCdTtRnA4Upz587EqnHY60QNrBKqaqmfTAZ3XAyiYsYbdFbLewCdOyhdUO0wUHrFCYli24dswbfE5RUBy63Z7B0wuduj1115uenptZH+kal1MLhSUw+0MYbb22YGvtJtXcRaq5ExGz3x+Kz20LtilFaeFWjKOiEMWhj3HrDyxSoO2Cl156iZUrV7JgwQKWLFmyk0B7+OGHeeCBB3jqqaeYM2cO999/PytWrKCqqgq/3z+ie0iBJhkJlmVRVVXF22+/zdtvv8U7b65gU1UNQgj8LpV5BRYnliqcPV3nkKyhI4BCCP6vEr6/WuB3wE3zFJbMGpkwG46EaVvWfrFBsLkXzp8B31iokOM6eDpUpiV4vwNealD4d4NFTxxcusYJR89hwcJFzDl6AbNnz+aQQw6htLSUnJwcORooGTGmaZJKpYYNyWRyp7xoNEokEiESiRAOh4ekA6EQwXCIUDhEKBwmlD4fjUSJRe2QiMYwUqldN0hV0dLOMXA77c6tx4XqcaK4B2Inms+Dnh6dV3xuSIswxfPhR+UlkxMhBFYkjtnRi9HZj9HRh9kTwOgJ2mKsNzTEqqU4Hai5aavqgDfMjIXVi+r3ofpcGVGmOB3yszdBEEJg9gRJNXXagq2pE6OpC6Oz17YQaiqOikIc00pxHFqKc1oJjilFKE7HWDd9vyEF2ghQFGWIQBNCUF5ezq233sqdd94JQCKRoKSkhIcffpjrr79+RPVKgSbZkf7+fmpqali7di1r1qxhzXtvs37TZiKxBIoCs3MV5hUKji1SOLZEZ4bf2KU3RsMSfPtdwZ+2wuWHwVePU3aayrivCCH4+zZ44D1BlhN+cobC0YUH3w+hJQRb++C9TpX32kxq+qE5DKlBg7luh05xvp+SwnxKiospnzKVskNmMuXQGVRUVGRCfn6+7EzsI0IIDMPYpbDZncAZaflkMkkiHZKpJMl03kA6kUjsoh4Dw7DTRspuo2GkSCVTGIaBmbJjI2VgGsYuvQiOCEVBddkWANKWgIFjxeVMxwP5zkyseVy2l0K3LbaEx5kRYIpTl59LyYfCiiUwWnswOnoxOvswOvrSgqwXEU1kyqnZPvSiHLSCbHsvuEGxnp+N4nPLz+JBhpVIYjR3k2zsIFXfTqqhnVRLt22lVBX08kKch5bawm1aCY6pxaiuySHapEAbATsKtNraWmbOnMnq1auZP39+ptz5559Pbm4uv/3tb4etJ5FIkEhsfxkFg0GmTp3Kk08+icfjARj2x3kkeUKIPf6wD5wf7to93W/H/F3Vtafye3vfkdxn4PyHeY6RtmMwljXMlIphMAzD7twlEnaHLp2OxWL09/XQ19NNb3c3bZ1dhCL2egwFKPUpTPMLDvHDtByNGdkmHn3PP05C2MLhFxvhgw749iK4aPb+merQFhF8ebmgsheuOxqOLRq+fSN5AYzmW2K07jfSJg2uyxKC3jj0JyCYUgkkIZgQBBKCYBJCSQik48H165qKx+XE63Hhcbvxejy4PR48Hi8Olwun043udOFwOtF1HV23O87qoGksA50XRdl50flIOjYD3yMhBJZlDYkH0gNBCIFp2VN5TGFP47EQWGb6nLAQloVp2bElwBLWkDoskb6HaaXPCTvPsq83DcMWMYaJaZgYpi1iLNPENE2MdFoMN73pw5KeyjR48b+iD5rqpKUdAmgKippe6D/gEEDVULT09YPr0JT0eXVoPJDOlFVQNS1z3UC9A+0QmooYmGrl1O2pTw4dHJrswErGDJEyMLoDmJ39GJ1pa1hnP1Z/OFNGzfGhF+ehl+ShFedm0npxHqpH7kEp2TMiZZBq7iI5INgaOkg1d2W8TOoleehlBbYzkooC9NJ8e33fBMJ5aOkBEWiTbtJoe3s7wE6ut0tKSmhoaNjldQ899BDLli3bKf+GG24Y3QZKJjwCW/y0ReCddgBz0JmRoQCPnQ7nHLr/5qGX+RSePhu+/Y7g8XVwTKFgQ/fetHKyM3LhYJgWoWicUDQOBPZfkyQjwxJgmbbHsnSW/FxLJDugYFtsBbajjTRaQTZ6RRHeRXPtznJ5IY6yfCnCJB8axaHjnF6Gc3pZJk+kDFKt3SRr20g1dpBq6CC+odaeIqupqD43whKIcGw3NY8fqpaqsOTu/X6fSSfQBthxpFIIsdvRy7vuuovbb789cxwMBvnLFVMZrvs80kHQwcUGrhn20mHO7e4Ww9W74/mRtHHEz7aLe+x0z+Fuouyc3OMtBv4eu7nvcPffqewI2q0q4NbBqYI2zOL3mOqg3+mh3+Ejou/+x2skH4v3t4b4f290842FCuccuv9H012awgMnKxxTJPjOu4K5xSoXf7wcr2/oNIMRtWSEH/yRlBotQ8K+fBc/TGUOy8RvJfGZSXxWEq+VwmMlcWGhkDbsKAPfwe312VYrW1eY6SDS8UCeZaVjBqUHlZECRCKRDGAKQXsEGkN2qIk6qA8p9ARTWGlX6mq2D8eUInuqWXkBekUhekWRFGKSA4ri0HFOK8U5rTSTJ1IGqcZOUjXNJGrbSNa2YqYFms/vYkahzgk5CRbkGlRkjWyWyWRj0gm00lL7A9De3k5Z2XYF39nZudsNbV0uFy7X0JfW7SuQa9Ako8YHH3zAPSefxMWzFa484sC+bC6Zo3BYHtyy3OKXL7Tzr3/9i7kf+dgBbYNEIpFI9g7LsmhsbGTjxo1s2rSJ1evWsmbDeuqrazKeOdUsD3pFIY7ZReRUFOKYUoRWVoCa5Rnj1kskw6M4dJwzy3HOLMeXzjP7QiRrW0nVtlG5tYkN69v4tQVZBXkcf/JHuODMT/LJM89i1qxZYybYMv4pfrz/7zXpBNr06dMpLS3l3//+d2YNWjKZ5PXXX+fhhx8e49ZJDlY6Ozu58ILzmJNj8a0Tx+bFcmyRwp/OgS/8x+DUxZ/gH3/4DadcsHRM2iKRSCSS7ViWRUNDA5s2bWLz5s1s2LiRtRvXU7O1mngkCoDqcaGXF9jTEy88FdeUYtTyAtRs70FpYZBMLrQ8P57jDsNz3GFkA1Y8SbKmmVRlEys3rmX5P//FrZagYEoZHzvrTJYu+SwfXfzRSbuNzoQUaOFwmJqamsxxXV0da9euJT8/n0MOOYRbb72VBx98kNmzZzN79mwefPBBvF4vl19++Ri2WnKwkkql+OzFFxHt7+Lpc1Vc2hhtuAmUZyk8cw7c8F848+LP88xP27ngujvHrD0SiURyMJFMJqmvr2fLli1s3ryZ9Rs3sm7jBrZtrSYZjwNpIVaWb09NPHch2RXFKBUFaHKTYMlBhOp24j5qBu6jZuDH9jiaqGoisaGWv/79Wf78v79FdzuZd9KJXP6Zi7j8os9mZtFNBiakF8fly5ezePHinfKXLl3KU089ldmo+uc///mQjaqPOuqoEd9DutmXjBZf/vKXeeKnj/Pbs1SOLx4fX7eYIbhtBbzRLPjpfV/hunt+ONZNkkgkkklBMpmkrq6Ompoaqqurqa6uZmPVFmpqamhvbsUy7UE6zetGSwsxR0UhrvIi1PJ8VCnEJJLdIoTAaO0mub6W6LptJGuaQcBhxx/L0suv4JrLPrfbZU37yoHUBhNSoB0IpECTjAZPPfUUV199NfecqHDF4ePrB9ewBPe+A/+vWrDshov51s/+JDsFEolEsgfC4TCNjY1DQm1DPdvq62hsaKSjpRWR3upFdehoJXloRbkZl/XO0nzUkjzU3Cz5zpVIRgErHCO+ppro+5UkNtcDcPgJ87nmiiu55vIrKSgoGJX7SIE2DpACTfJhee6551iy5EKWzBB8e9H49EIkhOBHawVPrIerzz6BJ597E6fTOdbNkkgkkjHBMAza2tpobGykqamJxsZG6hsaqKmrpb6xgbbmFsKB4PYLVAU9z4+a589s3uxIizClKAc1148yjIdgiUSyfzBDURJrqom+t4XElkZUTeOEj5/O1264mQvO/RS6vu+ru6RAGwdIgSb5MDz77LNcesln+WiFyQ9OHd6F/3jij1W2G/7jZhTy7KvvUnbIjLFukkQikYwqQgi6u7sz4qupqYm6+nqq62tpaGqktamF3s6ujPUL7GmIar4fNT8bPS3A9IJsHAU5KPl+2wqm7b/9LCUSyb5jBiPE39lM+M0NGM1d+AryuODSi/nGl27liCOO2Ov6pEAbB0iBJtkXhBD86Ec/4vbbb+fsafC9UxSc2li3amR80CH4ynKB7tD5f3/6Ayefc9FYN0kikUhGTCgUGiq+GuqprqujvrGelqZmuts7Mq7pARSHZlu+8tPCK9+PoyAHvSAHJdeHWpAt9wyTSCYBQghSjR3E3txI5J1NiEic6fOO5NYbb+ELV16F1+sdUT1SoI0DpECT7C3d3d1ce83V/OP5f3LNXLjjOAV1HE5r3B0dEcGXX4dNPYLHvnkLX1p2ADb7kEgkkj2QSCRobm7OiK/GxkZq6+upaaijsbGRjtY2YqHw9gsUBS03Cy1/QIClxVd+Nkpeli2+/NI9vURysCFSBvG1NURXrCe+qQ5nlpdPXbyE+26/g6OPOnq310qBNg6QAk2yN7z22mt87vLLiAV6eGCRxcemjnWL9p2kKbj/PfjTVsHnzz6BJ59dgWuS7jMikUjGnng8TktLS0aANTc309jURG1DPQ1NjbS1tBLo6R1yjZrlyQivzLTD/GzbA2JBNlqOD0WfINMXJBLJmGB09hNbsZ7wm+uwglFmHHsUX735Fq6+/Eo8np03epcCbRwgBZpkJJimyXe+8x2+/e1vc2KpwvdO1SjxjN0+Z6PJn7ba69KOOSSPv/xrBdMPG/k2FRKJ5OBGCEF/fz8dHR10dnbS0dGRSbe0tdLQ0kxTUzPtra0Ee/uGXKv5PKh5Wenph360PD+O/Gz0/GxIH6suxxg9mUQimWwIwyS+tprI8nUkNtfjyvJy/iWf5Z7bvsbcuXMz5aRAGwdIgSbZEx0dHVxx+WW8+tpr3DxP4cajx78zkL1lbZfgttcFEUPlx9/7DlfecpecEiSRHKSYpkl3d/cQsTWQbmtvp6W9lbb2Dro6O+nr6cFMGUOuVzQNLceL4vei5vhsC9iA+CrIRsnJQs3PQnVJT7ISiWRsMDr6iL2xnvAb67FCUWYvOIav3vxlrrr0clKplBRoY40UaJLdsXz5ci6/9LMkQ708egosKpu8X6NAwt4v7aV6wScWzOKxX/+FufOOHetmSSSSUSCRSAwRWgPCq629jabWVto6Oujs7KC7s4tQXz87dhlUlxM1x4vq96Jm+1CzvWjp2JGThZbtQ8m2RZnidckBHolEMiEQhkls9Vair68jsaUBl9/Hpy76DH/9zdNSoI0lUqBJhsM0TR544AGWLVvGwhJ45DSVYre15wvHmJQleKUB/r5NEEpCnhs+OlXhrGmQ7RxZh+k/jYIH3xe0ReDyc8/ggcef4pBp0/ZzyyUSyd4ghCAcDu9yamFTWytt7e10d3XR29VNdLBjjTRalgfF70HN9mXElprtQ08HLceH8HvsfGntkkgyCCEQiRQinkQkkljxJCKe2p4eOBdPIlIGwjAzgcHHKRMMA2EJEAIEpP+xo4Guu6ai6BqKQ7fXXDp0FIeG4nSg+tyoXjsoPjeq14Xq86DlZaF45GDJ3mB09BJdsZ7IG+sp+sbn2HrT96VAGyukQJPsSG1tLV+89hpee/11vnQM3HSMykTY/qYpJPjK64JNPXBcMUzzQ1MYVnWCW4Mls2HpEQpT/Xt+WSdNwR+3ws/WCSKGwpUXfIJvPvIkh06ffgCeRCI5OEkkEnR3d9Pd3U1XVxfd3d10dnbS3t5OS0c7La2ttHfYoquvu4dkPDG0AlVF83vTQsubFl527EgLLsXvRcm2LWHSuYZEYiOEQMSTWKEYViiCGYphhaJYwShWKIoZiu50jLH7deiKU0dxOlCcOuhpQZUWWejaUME10MkYLKYGpy3LFnoDgi4t7kQ8iRWNY0UTsMNUY7sNDtTcLLS8LNvbaZ4frSgXvTgXvSgPrSBb7u83DCJloDh0Ni+5Wwq0sUIKNMkA/f39fP/73+fR7z9CntPkoZMEi0rHulUj4+02wS2vCXJd8IPTFI4p2v5i74gK/lAl+EMVBJNwzjT48nyFQ7P3LNTCKcHTlQpPbbIIpeDST57BNx/5GYcdvvcbP0okBxOGYRAIBOjr68sIriGiq6uTto52Oru66enupr+3l1g4slM9ikMfKrYG1nWlRZea7UPJ9qBk+1B9HpRJtj5WItlXrERyu6BKxwPBDMWwgpEhgmxYgeNz24MeAwMffu/24ywPqseF4naguJwoLgeq24nittOKemCFj0gZtliLxLHCMcz+MFZ/GLM/jNkXsuPeEGZPAKztljmtIMcWbOUFOCqKcJQXopcXyL0BQQq0D0MoFOJb3/oWzz77LJ2dncyfP58f/ehHnHDCCSO6Xgo0SW1tLT/96U/531/+nEQ8xtLDLW44RsOrj/+vjBCCv1TDsncEHymDH56u4N/FVMaYIXi2Bp7cIOiOwRePgpvmKbi0PXfooinBn6sVfrXRoicO5566gOu/eg9nn/spNE2OwksmB0IIkskk4XCYSCSy27i/v5/evl66enro7uult7eX/v4AgUA/4UCQeCQ67D1Ur9vu2Pk92zt5fi+q34Pu96H7vSh+D2R5ULM8dmdPTlGSSLASqaEia0fRtcOxSA4juLwuNL/P/v5l23FGcKVFWMYK7fNMSiuzMC3MniBGZx9GVz9mZx9GRx+p1h7Mrr7MDEutMAe9vBBHRSGOKUU4ppWgl+YfcOE5lkiB9iG45JJL2LhxI0888QTl5eU8/fTTPPbYY2zevJmKioo9Xi8F2sFJKBTixRdf5HdP/YaXXn6FbJfKRTNNrj5Kp2gCrDUDiKQE970j+EctXDoHvnmigmMEo+cJU/CrjfDEesH0bPjhGQozc0bWAUyYgr/VwDNVgq19UJ7v55KLP8PZF17BqaeeOux+IhLJgPBJJpMkEolMPDi9q3jHvGQySSqVsvOSCRLJFMlU+nwqSTKZIpFMptNJkqkUqaSdn0oNBLsOI2WQSiUxUgZGKkUyHscy9/z9V5wOVK8Lxeuy132kYzu4ULxudJ8HLb02hCy3vdZrknb4JJJ9QSRTg6YS2tYsM5S2agUHTTNMTy0UydROdSgel23Vyh4ksoYce7YLMjmtd49YiRRGew9Gczepli5SLd0Yrd2YPUEAFJcTx7QSnIeW4kgHvThv0lrupUDbR2KxGH6/n+eee45zzz03k3/sscfyqU99ivvvv3+PdUiBdnAQCoVYs2YNq1ev5rX//oeXX3mFRDLF0UUKl86Gc2coeCbQe7u6T/Dl1wXtEVi2SOG8GXv/cqzqs13rt0Xg3o8oXDBz5HUIIdjQA/+vRuG/DRbdcXDqKscdOYuj5s3n6BNO4ogjjqSiooLy8nKys7OlFWAfEEIMEhV2MAzjgOQNiJtEWgilDGOQ8LFF0UD55EB+Ip2XSJJKJjFSKYxkCtPYeSR7rxhYIK9roGn2mol03pBY0walVXudh6Zuv0bX7DI7Xp/OU91OVJcD1e2y14+4nAinbk9Xcjvs9SRjMHVJIplIWLEEZiCCFQhj9oUxA2GsQMSeYtcftvP7I4hYYqdrFY9zu8ja0aq143GWx17PJdnvWNE4qYYOkvXtpOraSNa3Y3YHAPv/zHloGc5ZFThnT8E5s3zSTI+UAm0fCYVCZGdn85///IePfexjmfxFixbhcrlYvnz5HusYEGi//e1v8Xq9ADu5FxZC7JS3I8OV2dPxSO810uvGos0D6d3da3dlRnI/0zSHjJ7vKiTicRLxKMl4nGQiTiQSpq+/n/5AiP5QGCHAoSnMyBYcXwwnlGkfylq2p2/USL5wIyqzQ6E1nYInN8BUP/zodJiZu++dxWhKsOxdwd+3wdmHwmdmgHMPUx53bLMQgpYIbOlT2dZn0RqB9ggYgwo6dY38nCzy83LJy8vHl+XHk+XH7c3C4/HgctmeplRVRVVVFEXZ6XjH9OAyuyurKMqQz+DgYJomlmVhWVYmPVw8EAaODdPAMAenjbTQMUgZKVKGbZExDNNOG7boMQzDvsYwMAwT00jZdRsWpmlgGQaGaWIZJpZl2p699geaiqKqKLoKSlrMqAOxYi9YV9Kxqm4XPKpqj5Smr89cM0Ts2IJIHRBDui2ABoslNBU0xRZOA+cHBJSu2fXq9jlUbdKOzkokE4WME41wzF7XFY5ut3CFY3ZIW8J2nFqouByoOWknFbm+dNqHmp2VXtflQc/22RYuKbgmDGY4RqqhnWRdO6naVpI1LVjhGCgKelk+jmkl9pq2aSVoOVlj3dy9xjGlSAq0D8NJJ52E0+nkmWeeoaSkhD/84Q9cddVVzJ49m6qqqp3KD0yZGSAYDDJ16tQD2WSJ5EPz8anw3ZPB7xqdkfy/1QiWvSMo9kLChI7hl89IJBKJ5GDBodsDMUIgDGO7Y4k0ituJlptlewnMzULLsZ3WqDm+7R4Dc7MmjTVFsnuEEBjtvSS2NpGsaSFZ04LZ0QfYa/8Uh44Viu70ORqvVC1VKf1xnxRo+8q2bdu45pprWLFiBZqmsWDBAubMmcPq1avZvHnzTuXvu+8+li1bNiTvwZOGejMFGG68drgZWjtmjajMLm6g7JAeyZixouzFPUeQt2PmkDYpu7luN+WHPbe7OpShsaqAUwWHCk7NHngfmC63q3oSqk5cdRDVHYR1N3HNgdjNXUc8Pj+CgiP7f9t7i0Blc4yn/tvF+TPh/pMUtFGeMljdZ7vpb43A0kV+Zs/K26nMh7mlLiyyjTjZRoycVAxtBPZDIQRWemsYS9hBCLCwY1MAOxwPPj9wDcr2752SPgb7s6UqoI78EyCRSCQTBksI+hLQGYWuGPTEoTuh0pHS6Ikr9MUsojFjyEwNxaGh5flR8/wZkaXlZqHneNFyslDyslFzfXJvPMkeMQMRkjXNJCsbiVc2YrR0A5Bb4GFuocqirChH5Qt8jvH3G3xMkSIF2mgQiUQIBoOUlZVxySWXEA6HeeGFF3YqtysLmlyDJhnPvPvuu3z0jNP5SFGSn5wB+n6a8hVNCb79Ljy7TfDgjUu462f/b7/cRyKRSCQfHiEEPT091NXVZcLWmhqqamuor6+no7kVI5nMlFd97vS+WP5McORno+f5IdeHludH8bnlmmHJfsEMhEluaSRR2Uh8cz1mdwBFVZhxzFzOO/dTXPGZi5g/fz7qGK/zPZD+KSa9QBugr6+P6dOn873vfY/rrrtuj+WlkxDJeGfLli2cctIiprtC/Prj4N7PU/SFEHx/te3p8ZGbL+JrP/nL/r2hRCKRSHaJZVk0NzdTU1PDtm3bqK6uZkv1Vqqqt9JU3zBkSwfV40IrzLFDUQ6OwlwcRXmoBX7UwhxUt7R6ScYPRlc/yS2NxDdsI76pHhFP4ivI45SPLeaqCy/m3LPPIScn54C3Swq0UeDll19GCMFhhx1GTU0Nd9xxBy6XizfffBOHw7HH66VAk4xntm3bxqknn0S20cPvz1TIcR6YLQCEEDz0vuD3lfB/D93GpXf+4IDcVyKRSA5GkskkDQ0N1NTUZMKWrVVUb6uhub4RI5V2Ma8q6PnZaEW5aCV56EW5OIvy0ApzUAuzpfVLMmERhmmvXdtQS3T9NoyWbhRN47AFx3DZRRez9JLLmTZt2gFpixRoo8Cf//xn7rrrLpqbm8nPz2fJkiU88MADI1bcUqBJxitNTU2cevJJqOEOnj5LUOg6sPuzWUJw6+uCFS3wyh+e5JSLrj+g95dIJJLJRCQSoba2lm3btg0SYVup2VZDW3MLwrLf8YquoRflohblohfnohfn4SrOQy3JQy3Ilnt5SQ4KjO4AyY11xNZWE9/cAIbJlCNmc/GFS/jiFVdxxBFH7Ld7S4E2DpACTTIe6ejo4LRTTibS2cD/nQVl3rHZPDtuCJa+ImgOK6x8/VXmnHDGmLRDIpFIJgJ9fX0ZAbZt2zaqqrdSuXUrtdu20dPZlSmnup3oxQMiLG0JK8lHK8pFyc+Se+1JJIOwYgniG2qJr9pKfP02RCJF0bQpXPTZi7nl6i+OuliTAm0cIAWaZLzR19fHGaedSnt9Ff93FhySNTbibIDeuOCSFwVOp5O311ZSOGX6mLZHIpFIxpJgMMjWrVvZunUrVVVVbKzcQuWaU73uAACtxElEQVTWrTTW1xPuD2TKaVle1KIc9PRURL04D1dpPkphDkq2V05FlEj2AZFMEd/cQGJVFdHVWxGxJFMOn83nrriCm6/+AhUVFR/6HlKgjQOkQJOMJyKRCB9ffDqVG9bw9Nkqs3PGVpwNUBcQXPqS4PDyHF7b0ITb5x/rJkkkEsl+I5FIUFtbmxFhG7ZsZlPlFmpragh092bKaTlZaCVpK1hxHo7iPBwleShFOahe9xg+gUQy+REpg/j6bcTe3UJsbQ2YFocdfyw3XvMFrrniSvz+feurSIE2DpACTTJeSCQSfPqTZ/PWG6/z1FkaxxSMD3E2wKpOwedfFnzq+EP5y1s1qJpcByGRSCYuA94RB6xhmyu3sG7zJmqqq+lobs2sCVPdTrSSfPTSPPSSfFylBWgl6TVhXrkJs0QyHrCiCeKrtxJ9dzOJzfXoLhenn3sWd918Gx89/fS9slhLgTYOkAJNMh4wDINLLr6Ifz7/D375CZWPlIzPr+uL9YLbXhfc8unj+dFz78kpOhKJZFwjhKCzs5Oamhqqq6tta1jlZiqrqmisrSOVsPcIUzQNrTgXvTQfvSQPZ2kBemlahGX75LtOIplAGL1B4is3En5zA2ZXP/lTy7ly6VXcccPNI5oCKQXaOEAKNMlYY1kW115zDb///e/4yWKVj00Z31/V32wSfPcDwZ2XLea7z7w61s2RSCQHOUIIurq6MiKsurqazVurqKyqpH5bHbFIJFNWL8i23dOX5KOX5uMqK0ApzkUryEHRpGMOiWQyISxBcmsT0Tc3EPugEmGYzDtlEV+96WYu+cwSnM7h9wWUAm0cIAWaZCwRQnD77bfzwx/+kO+dqnD+jIkxSvvzDYIfrBZ8Y+knuf+pF8a6ORKJZBITj8dpb2+nsbGRxsZGGhoaqK2vo7p2Gw0NDbS3tJKMJzLl9Tw/aom9JkwvycNVWoBanItWlIPi3PP+qBKJZPJhRRPE3ttC9M31JGvb8ORmc+Gln+XuW27jyCOPHFJWCrRxgBRokrFCCMG9997Ld77zHe45UeGKwyeGOBvgp+sEP14ruPuqc7j/qRfkFCCJRLJbDMMgFAoRDAYz8eB0T08PrW1tNLY009LWSnt7Oz2dXUSCoSH1aH4van42WoEd9IJsnIW5qGm39apLijCJRLJrUs1dxN7cQOStjVjhGDPmzeXWG7/E1VdcSVZWlhRo4wEp0CRjgWma3HrrrTz++ON8dYHCdUdPTHHz+FrBT9YJrlh8DP/74ru43NJrmUQymbAsi3A4PKygGpwenBcIBukP9NOfzg8Fg0RCYRLx+G7vpXpdqDlZqDk+tGwfaq4PLScLR24WWo4PJTcLtSAb1TX8tCSJRCLZG0TKIL62hsgb60lsqsPhdvOJ8z/FV669jrM+caYUaGOJFGiSA01LSwtXXnE5y1es4N4TFS47bGzEmRCCyj74byNs7RcEk1DghuOKFc6cBoWekbXr/7N33nF2FvX+fz/l9Lo9hUBCCSVI771DAKnSUZpUQRALioqgNPV6Qa9XROWnXu/l6tVrV66AVOlIQur23tvZc/b0p8zvj+fs2d1kN9kkm91NMu/XazLzzDMzzzwn+5zzfOY7853f1Qu++pZgn3lhfv6r33HECadt555LJJKpYts2g4OD9Pb2EovFGBoaKoZYLMZALEbf4ACDQzFisRjxRJxkYphUMkk6mSKbSm+yfdWto3o9qD4PitcNXheK1+0Enwe1kB45r/rcaD4vWuFYeF0oXg+K1yU3Z5bMWYRhYicz2OkcdiaLSOWw01nsdA6RzmJncgjDLATLifMmGObkjWoqikt3gtuJcWmobhdKwIvqHwmecceKzy1nrGwHzP54wbHIKqyBBKGLT6T9Z3+WAm22kAJNMlMYhsEPfvADHvjKl/GT45vH2xwzb+b70TYs+E2d4LlmaBmGsBsOKIWoB3oz8GEfqApcsjfc8hGF3YKb/yFYPyi47x+CuiG46pzjeeBff8zS/fbf7vcikexqCCFIJBL09fXR29tbDD09PXT2dNPe3UV3Tw99fb3E+gcYjsWL7uLHoanOy57fU3wJVH0eFJ8HxecuiC5HTKlet1PW40LxucHrBNXrRtHldhuSHRNhmFjxFHYiNRon0tjxFFYiNRonUohMfuJGNBU14HUGI9wucGmjosulO8+HWvgNHfcWLhCWPU7IFQVe1nCEX27ya2rhgGNpjgRGLc6R4OhxJIhWGpLP51YgbEFufTPeZUtYd+n9UqDNFlKgSbY3tm3zy1/+kge+fD+NLS1cuRTuOUwn6p65fc6EELzZBb9YL3ilHYIuOGsPWL5Y4Zj54FJHRVgsK/h1Hfy/tYLhPFy8N9xxkMKCzQi1vCX4VS38YJVgMAsnHbovt937JS657Eo8HrlXkEQyEUIIUqlUUXCNjUdEV0dPN729vQz09RMfGMQ0jPGNKApq0Ica9qOG/GiFWA350cNB9LAfJeQHv8cZjfd7nRF7OQov2ckQpoU9nB4vvOIjcbIgvNJYiRQikxtfWQE16C8IHj9quCCAwgUBFPShBDyoPu+MPEfCsrHTWUR6xFqXxU5lHUtePLXBPSax4imwxrxXKKCGg2hlIfSRNZulhVBIqyGf/B7YBFKgbSWmafLggw/yX//1X3R3dzN//nyuv/56vvKVr6BOcaqEFGiS7UUmk+E3v/kN33zsUdaur+a03VXuPlRlv+jMbkBdFxN8/V3Bu92wtAQ+vp/CR/cEn77pL+W0IfjvGvhJQahdtS/c9hGFss1Mfcyagr+1wK9qBf/shaDXxZknHMkl136S8y+8mGg0Oo13J5HMHkIIDMMgk8kUQyKRIB6Pj5tKGI/HicVi9McG6R8cYLAwvTA2MEh8cHCcB8IRtIAPJeQrCK8AasiHFvKjhQPoYeelkZDXEWJBn5weKNlpEbbATmXGW7YmECd2PIWdzGxUXw36xliZNoz9aOEgasSPGvTv0FstCCEQqSxWPIU1NIw1OIw1kMAaLISBYczBxPhply7d2XqiMopeUYJeGUWriKJXRtEros60y10YKdC2kkceeYQnnniCn//85yxbtoz333+fG264gYcffpi77757Sm1IgSaZLkzT5IMPPuCVV17hpRef54033iCZznLCAoW7DlU5pHxmH0EhBL+pg6+/I1gQhPuPVDhpIVs8WpY0BP+xDp5ZK7AFXHcA3LRMIeTefDsNQ4K/taq82GqxdgB0VeGEww7g4quu4+LLrmTRokVbe3sSyUbYtk02mx0nmLYkpNNpUpk0yVSadCZNKu3EI+ezmSzZbIZcNkcumyWfzU08fXAMiksvTiVUfO7RNSU+D2rQsXa5Qs6LohLwoYQLgktOTZLs5NjZ/BixlRxn7SqmE0nsRHq8ZQic9YyFKX4bTe8bO/0v5JfP0hiEENjDmYJgc4Sb2TeE1TeE2TuE2TcEpuUUVkCLhgriLVrctkKfX4ZeVbJLiDcp0LaS888/n6qqKp555pli3qWXXorf7+cXv/jFlNqQAk2ytViWxYoVKxxB9sL/8Y9/vMlwOoPfpXJYhc3R8xTOWaKze9Ca8b4J4Wwm/bN1cMVS+PJRCh5t26YxDOUEP14j+MV6CLng/qMUzl08dcHXlRK81K7w9xabd7rBFHDwvou56GNXsvyjF7F06VJKSkq2qY+S2UMIQT6fJ5fLkc1mi2FTxxOdS2cyjjjKpMlkc6SzaTKZ7Eblcrkc+WLIk8/mMPOTrNmYhI0W6Lt1FLdrXN5o7JowT3W7UMfUw+sCn2d0Tdcu8BIjkYwghECkc1hDSayhJHYhdsLwmOmGKUR+g6m6ujY6pXAisRUJjFq7pCfP7YKwBfbQMOaIYOsdwuqLFcWbSBU8saoKWkUU1/wyR7AtKHPSC8t3qv8bKdC2kscff5wf/vCHPP/88yxdupQPP/yQs846iyeffJKrrrpqSm2MCLRf/epX+P3+Yv6GH9emPr6Rc2PLTFZeCLHJtkawJxmVnco1trRfm+rr1vRrKnlT/Xyn0u5UPtOt/Qzy+TzJZJJUKuW4ix4epqO1heamRto6u8gbJh5NYd8SwQGlsKxcY8+wha4qTOWJm8pDOdUHd+R6QjjC7MU2+OpRCtfuP73zy3tSgkfec6YxnrYIrloK3kmmS07W95QhWNWv8EGv4MM+yBY0bNDnobIsSjAYJBAI4g8GCQRD+AMh/MEQXq8Xt9tdDB6Pp5h2uUb3PhoRjYqijBOQGx6PzRdCjKsHm3+GJvt7nkq5TdXd8LwQAtu2x4WxeUIILMuaMH/DerZtY5omeSNP3jAxTIN8Po9hGOQNA8M0MAznvGGYGIU80zQwDRPTNDFH8vN5jLyBZRgbr4uaKrqGoqkoug4uFTQNRddQdB1lzDF6YfF9oayiq6DroKuouobq0p18t4bicqG4nHqiUMYRYxoUyqFrKKpceyGRbAnCFs4ar9gwVmwYe3DYmU4XSxacbKTAGD8oqfi9aCVBtGjQEV/RoLNeckR4hYPo0aBjZZbroeY0ViKN2TWA0dmP2TVQDNZgYa9CBWeK5IJy9Hkl6PPKcC0oQw34ZrfjW4FrfpkUaFuLEIL777+fb37zm2iahmVZPPLII3zpS1+atM7IyOsIiURCTrOS7JQ8fCxctnT7zaf/S5PgK28KSr2QNGBo42U0EolEItkZcOsoKAjDZOzooxr2Oy/k5RHH6cSI1ask6DimiARQ3HLj8J0dO5vH7Bog39KD2d6L0daH0dpb9ESpeN0obh2RNxHZLZvpMFvUXKcy73ux7S7Qdso5Fr/61a/4z//8T5599lmWLVvGypUrueeee1iwYAHXXXfdhHUee+wxHnrooXF5DxwNEw3abJg12biOMlF6M+1tbpBImej6U+jjZHmTdX5sm5vq0kR9n7S8MrXPbsK8CTKnMsitbBBvrs1NtjGmvKZCwAU+bfxUvqzmoscTJqtt+w/PVLo31VHFlS0ZfvF6jLsOVrhs6fYdiTxvicJ+JXDTCwKvDg8tLyUc3nij6i3thc82CJpZAmaOgDXeKqMozsCMJSBvQc6CvO0M2OYLxt2iJbEQEKOWPDHmPIqTVjYou2GnJ/zb2sxzM1GdzT7zEzxXY/MUnGdBVZx8BVCn0rBEIpFMgBCCWA7ak9CRhPa0SktGo3vYJjMyrUFR0Msj6AvK0KtKnXR5CLWiBK08iuqR4ksCqteNe8l83EvmF/OELbD6hjBaujFae8i39pJv7HROKlBa6mOvEoWD/VmWRW0q/bvmb9lOaUFbtGgRX/ziF/nUpz5VzHv44Yf5z//8T6qrqyesM5kFTa5Bk+zovP3225xy8kmcudDgX06ceCrf9qA7JbjhBUHKVPn7i8+z7JjTZ+S6EolEIpkavb29rF27ljVr1rBqzWo++HAlNeurSSWcqWmKS0efV4o+vxR9fhmeBRWo80rR5+0aziAkM4OwBWb3AEZDJ/mGTnL17ZidAwCULpzHcSefyMeWf5RzzjyLqqqqWevnTPqn2CmfrnQ6vZE7fU3TJl0nBeDxeOSeTJKdjubmZi786Hksi1o8erw6o0aVeQGFn58F1z1vc/qZZ/Py3//O/kedPHMdkEgkEgkAsVisKMTWrFnDP1etZP269cQHBgFQdM1x6rCwHNcZh1KxWyXagjLU8rDcqkGy3VFUBdeCclwLyvGfeBAAdipLrraN/PoW/u+Vl/jzs78GYP7eSzjzrLO4/rIrOPGEE9H1nVLK7JwWtOuvv54XX3yRp59+mmXLlrFixQpuueUWbrzxRr75zW9OqQ3pxVGyoxOPxzn+mKNJdNbzP+cplM7gBthj6U0LPvG8IGNpvPTKK+x/+Amz0g+JRCLZmbFtm46ODmpqaqitraW6upqVa1ezbu06Bnp6AVBUFX1eCdqCclwLy/EsrEDfrQK1IrpD7/Ul2fmx4kny61vJrW8hs6oRO57EEwpw7OmncuPlV3Hheedv9/f1mdQGO6VAGx4e5qtf/Sq/+93v6O3tZcGCBVx11VU88MADuN1Tc/MpBZpkRyafz3Pu8uW8+8ar/Opchb3CsyPORhgn0l5+mf2POHFW+yORSCQ7KvF4nNra2qIQW7t+HWvWr6elsZFcxnF3rugaekUUbV4proXluBdW4FpYjrqL7FMl2bkRtsBo6Sa3sp7MynqMtl5UXeMjxx7FrdfdwDWXXbFd3t2lQJsDSIEm2VGxbZuPX3stv/mfX/HjM+CYebPdI4fetODjfxNkbY0//uF3HHnaR2e7SxKJRDInSSaTNDQ00NDQQH19PdW1taxev5aG2jpi/QPFcno0hDqvsFHwvFI888vRqkpQyyPSIibZZTD74+RW1pP+Zw35mjY0t4sjTjuJu268mUs/eiFe78aOyrYGKdDmAFKgSXZU7r33Xp588gmeOElh+eK55f2oNy247SVojAt+/C8Pcs3dX5vtLkkkEsmsMDg4WBRg9fX11NTVsa62mubGJmJ9/cVyqs/juKwvOOdwzSvDPb8MpTKK6pNr5yWSsZiDCbLvVpN+ex1Gaw/ugJ9TzzuH+++6hxOPP2GbHKVJgTYHkAJNsiPyrW99i/vuu2+7bEQ9XWRMwZffdPZL+/y15/DYz/6Mpmmz3S2JRCKZVoQQdHd3F0VYQ0MD62tqqK6roaWpmWQ8USyrhfyolVH0iih6ZQmuqhLclSVQEUUN+eRGzRLJVmB09pN9t5rUm2uw+uNULNmdT37yJu695Q7Ky8u3uD0p0OYAUqBJdiSEEDzyyCN89atf5Y6D4O5D5/bUFiEET6+GJ1YIzj1kN/77xX8SLquc7W5JJBLJFpHP52ltbaW5ubkoxNbWVlNXX09bcwu5dKZYVi8JjYqwqhLcVaXoFSUoFRFUv7SESSTbC2ELcuubyby2mvQHNaiKytFnncpX7r6Xc844ayPP75MhBdocQAo0yY6CEIL777+fxx9/nHsOVbj9oB1npPXFVsHnXxfsXurlj397iX0OOXa2uySRSCRFTNOkvb2d5uZmmpqaaG5upq6hntrGBlqaWxjo7qH4GqWq6GVhtErHCqZVRvFUlqJVRZ01YW65ebNEMttYiTSZN9eQev1DzK5BSnebzy233srnbr+TsrKyTdaVAm0OIAWaZEfAMAzuvvtunnrqKb50hML1y3YccTZCbUxw+0vOhta//sUznP6x62e7SxKJZBdheHiYtrY22tvbaW9vp62tjfqmRuoaG2hpbqa3sxvbsorl9ZIQalkYrTyCXh5BL4/iroxCaRitNISiy+naEsmOgBCCfF076ZdXkn6/GlVVOfWCc3nos/dx3DHHTjitWAq0OYAUaJK5TldXF1ddcTlvvPEGXzsaLl+644mzEWJZwd2vwj97BE8+eC+feuA7s90liUSyAyOEIBaL0dXVNU58Nbe20tTaTGtbGz2dXWSSqXH1tEgQtTSEXh4pijB3ZQlqadjZtFm6qJdIdjqsRIrMP1aTfGUlVn+chfvtzWfu/DS3XX8jgUCgWE4KtDmAFGiSuYpt2/z85z/n3nvuxmWlefIkOKJyx3+MDVvw6HvwbLXglkvP4Pv//VdcLjklSCKROFiWxdDQELFYjN7eXrq7u4uho7OT1s4OOru66OvtYbC3H8s0RysrClo4gFoaQisJoRViV2kYvTQMJUG0aFBawCSSXRhh2+TWNJF6eQXZVQ24/T4uuOJjPHjvfSxbtkwKtLmAFGiSuYYQgr/+9a/c/8X7WLVmLRfsqXD/0Rol7tndhHq6+e8awcPvCI49YHd++9K7lFdWzXaXJBLJNCCEIJvNEovFikJrbHokHhwcpD82SP/gALHYEENDQwwPxcmkUhu1qWgqWiSAEg6gRQKokQBaOIAWCaJHg2iRIEQDUnxJJJItwuyPk3ltFcnXVmIn0iw94hDuvuV2PnXLrVKgzSZSoEnmEi+88AKfuedu1q5bT5UfFgRgfgD2iios38OJdybe7Rbc9Yog6PPwwx8+zXlXXDfbXZJIJAWEEMTjcfr7+zcKg4ODDMQG6R0YYDA2WBRew/EEyXgC0zAmbFN16agBL6rfCz43SiHtBA+K34sW8KL7fagBL4S8KJEgqt+Lou5c33+SuYUQAgwTO28i8gYiZyDyJsIwELlCXr6QN1GcMxCGicibYNsIAQgxGgBUFcWlo7i0QqyDS0N1u5xnIeArPh9qwIsa9KGG/ShT9D4o2XqEaZH5oJbUyyvI17QRvfFcWp74bynQZgsp0CSzydDQEK+++iq//d/f8Nc//p6BeBIBRNywTxRKvBDPwfpBGDbguPlw3xEK+5XuPC8q7UnBV96Et7oEy48+gMef+jkHHXrEbHdLItnpyGQy40RWX18ffX19tPd00dnbQ29vH719vcQGB4kPxkjG4uMcZ4yg+jwofg9qwIsyRlypfm9BcHnQAz60wkum4vcgfIXycm2XZCsRQjgiKJPDzuScOJt3BFKuIJ4KAql4vJGY2oTAypub78QIuobicaG4dRRXIfa4UN0ucOnOYIIyEgBFQcFxAy8ME0zL6etIyOaxUzlELr/xtRTFsRhHg8WgjqRLQ+gVUbRSuW5yOjE6+nAtrGDdpfdLgTZbSIEmmQ3eeOMNvv3Nx/nrc89hmBbz/HD0PDiiyglLwso4z0J5S/BiG3xvhaBlGD51kMLtB4G2k4woCyH4cxM8uULQnoQzjjyA2+79MssvuAi/3z/b3ZNI5hymaTpTBMeIrf7+fnr7+ujo6aazt5ue3l4G+weIDQ4yHBsin8lu3JBLRwv5UIN+1JDPGbEP+dFCfvRCHsGRfB9qwCenEEqmBWEL7EQKayjpxIl0IU5hJ9LY8RTWcNoRYukcIpsftURNxohwcuko7vHCSXGPyRsXj6THHHs2PLdBvJ0sWsK0sNNZ7FQWkc5iDWewh5JYxTBcPLYT6dGKCmjREFpFBK08il7heB7VKiLoFVHUaFBugr4VSIG2DSxevJiWlpaN8u+44w7+/d//fbP1pUCTzCRtbW3cdust/PW5/2PvqMJle8Opu8Eekal9cRq24IerBD9YBYdXwrdPVJgf2Hm+dPOW4C9N8ItqwdoB8Ll1zj7paC666nrOOHs5CxcunO0uSiTTTj6fd6YMDgxsFPf399PR0013bw+9/f0M9vczNBgjnRhmo591RRkVWSOCK+RHC/rRCyJMCXlRgv6i4FLcLvniJtkuCFtgx5OY/XGs/jjWQMJJD8QLcQLM8RZaxe9xnLyEA2hhP2o4UJj66kH1elB8bseC6/Wg+twoXnfBkrV9hdNcRJgWVmwYs28Iqz+O2RfH6h8qHo8VcIrHjT6vBH1eKfr8MieeV4peVYrqkU66JkMKtG2gr68Pa8wUjDVr1nDmmWfy8ssvc8opp2y2vhRokpni5z//ObffegtBNc/9R8I5ixXUrXwxeq9b8LnXBVkLnjhJ4bgFO98LVlNc8EKbyt+aLdYMOHl771bBaaeczBkfvYxjjzuOhQsXypdLybRh2zaWZWGaZjEeCWOPJ0ubpkk6nSaZTDI8PMzw8HAxPZSIM5RIEIvHGYoPMTAwQDw2RCI2RDadnrA/qt+DEihYroJ+x9IVcsSVHgqghfwoQS8EfWhBH4pcpyWZQYRlO1adEdE1kHDS/XHMgYIAs0adW6lBH9rI3nJlkWJaiwadKXwhv5ymN43Yubzzf9E7hNk9OC7Yw6PfOVpZ2BFrC8tx7VaJa1EFrgXl8v8CKdCmlXvuuYc///nP1NXVTenFTQo0yfYikUiwfv163nzzTb775BO0tLZx7mL42jEQ9Wz7KN9QTvDZ1wRvdcHXjlG4YgfeH21zDGYF7/QovN0leLtL0Jxw8iujQQ79yP4cedxJHHHsiSxbtowlS5agaXIK1kwjhMA0TfL5PIZhFMPY4w3TmwvZbJZsLkcmlyWbz5HN5pw4lyOfz5HL5cnl8+TzOfJ5AyOfJ5fPYeada1iWhWWaWJaNZZlYpoVlWdiFYJkWtl3IM62NLVLbiOLSUT0uFK8bvG7Uwmi/6vOgBn2O+Ap60YN+9MIxgYIoC3hRtF3HGiCZewjDxIoNb2D5SmAVxJc1mAB79JlRQ/5RAVYeQSuIMCcdRvV5ZvFuJGOxkxmMsaKtawCjox+rLwYCUBT0eSWOYNutAn23ClyLKtHKwrvUoKgUaNNEPp9nwYIF3Hvvvdx///1TqjMi0P74xz+O26QO2OSP9YbnRo43Vce2J3aTPlPXmUq7G+ZtSd8myp/qdTbX16nU29p2pnI+l8s5L4vZLJlMppjOZrPkcjky6RTD8SHisUGGhmJ09/bRH3NUhKY4v2GfP1zhxmVM65ebaQseeVfwbA1ctg9cujcbWeWm8uBv7tthSm1Mw3VGsIXT3shvv11wgmUX2hjMCpqGFVoT0JIQtA5DsuA0TtdU5pVFKSmJEikpJRItw+3z43K5cLlcaJqGy+VC13V0XUfTNHRdn/D/ZbK/ZSFE8Tmzbbt4PPI3tLm8sfUsyxEGthBYtuVMCxICIWws28a2bWxbYAsbIWxsy8YWYAur2J5dqIMYLe+0aSNsG1uIYp4Q9rh6AlHwODbSRwr1xPg8bBAC27IwTQvbNDEtC9swMS0TYU3jNhCaCpqGoinOmhJNA01F0VRn/ZPqxIqmFsuiqyjaaJ6iFcqpFGIVVKWQdmJUZYP8kXKOW3dF0ZwymoKiFMppY8orzrEYcQSgKuByOaLM43L6IJHMEYQQiLyBPZzBTo4N6dF0KlM8L7LjHVaokcA44aWVhwvpCHp5GNXjnqU7k0wXdjaP2dFPvr0Xs70PoxBEylm/qvg8o4JtQTmuhRWoJTvn+ja9MioF2nTxP//zP1x99dW0trayYMGCCcvkcjlyuVzxOJFIsGjRopnqomQXQ1PAo8ETJ8Epi7bfaPh/rhc88p5gWSmsG4CN/a5JJBKJZJdEVZ0BhJGRrrEoCmrQ66z5igbHr/0K+hxRVhFFL4/IKW+7KEII7Ngw+bZejOYejNYejOZurNgwgLP+z+d2vFGmc5tpbceh5jqVed+LSYE2HZx99tm43W7+9Kc/TVrmwQcf5KGHHhqX94XDnYHPTbHh4MBkxSfN30T7yhTKTPnaE5zcMGvs8WTX3Oz9bebz2B7XmKzcJj/bzVx7Kh+5Arg1R2h5NCetK6OWsA2vkdLcVKdc/Ns/UnhVwU9OV9inZPuPLv29VXDPq4KPVCpccWIZLteWj95v7m9wOu5iKn/nijJqCRx5Notei1FGvRcXPRgrI56MURTw2CYVRopSIz2+PgACUyjkLMhZkDWd2Bqx0hUsdbYAxqRH3m2snf6bVLIlyD8HyXRj2YJhw9liZTgP8Twk8s5x3FCImSrxvMJwTpDOWmNnGQKg6Cp6yBFZFDb0HtnY2/HQ6UMP+VEiAZRQQK5blGwVVjyJ0dSN0dJFvqWXfFMXdtzZZN4fdLNHuYtl/jwHhAx2D4G+g/2dHTtfkQJtOmhpaWHPPffkt7/9LRdeeOGk5SazoMk1aJLp4qWXXuLiCy9goSfL02coVHmncerXZnivW3DbS4Il5X7+9o/3mb/n/jN2bYlEIpFMTi6Xo6uri87OzmLc0dlBU1sb7Z0ddHV20t/bR3IovtF0e9XnQQ37HScxY61cBecarkgQLRyAkM/ZAHwnnHImmftYsWGMpi7yDV3k6tvJN3WBaaG5Xex54P6ccPzxnHfy6Rx//PHMmzdvtrs7KTPpn2KnF2gPPvggTz/9NG1tbej61M3w0kmIZDp59tlnuf666ziq0uJ7p6gEXTP/2FUPCm56URD0unj+76+wz6HHzXgfJBKJZFcjm83S0tJCc3MzTU1NNDQ2UN1QT0NjIx2tbSQGY+PKK7pW3IC4uPFwOIAeDaGH/ShhP4T8aGE/ilu6QpfseAjTcqZENnSSq+8g39CBNehMjSxdOI8jjz6aC89ezlmnn8Gee+45ZwYWpECbJmzbZsmSJVx11VU8/vjjW1RXCjTJdCCE4PHHH+f+++/n4r0UvnGcgmsWHbC1DQtufEGQs1X+9pc/cfDJ585eZyQSiWQnIp1Os379etauXcuq1av556qVrF+3np72jtFCqopWFio40IigVURxlYbRS0IQ9qNFgygB75x5IZVIZgprMEG+oROjoZNsTRtGaw8IQbiynKOOP5YLzz6X5WecOauCTQq0aeL555/n7LPPpqamhqVLl25RXSnQJNuKYRjcfvvtPPPMM9x5MNx5sDInfnT7Mo5I68so/PW3v+SY5ZfPdpckEolkhyGbzVJdXc3atWv5YNWHvP/hSqrXraOvvbM4BVErCzv7Ry0oxzW/DHdlCZSF0UpCcpsEiWQK2Okc+bp2jJo2MtUtGC0jgq2MY086kasuvITzzllOeXn5jPVJCrQ5gBRokm0hkUhw+WUf4+8vvsjDx8LFe8++MBvLUE5w84vQmBD84dmfctql1892lyQSiWROkc/nqampYe3atXy4ehXvfbiCdWvX0d3ajihsi6GVhIpCzLNbBer8MvQFZXJvL4lkmhkRbPnqVjJrmzDb+0BRWLxsP85dvpxrLrqUo446aouWM20pUqDNAaRAk2wt7e3tnLf8HJrrq/neSYJj5892jyYmaQhufwlW9wt+89MfcO61t892lyQSiWTGMQyDuro61q5dy5o1a3h/1UpWr15DR3MzdmEfPzUaxLWgDH1BOZ7dKlEXlOJaUI7q985y7yWSXRMrNkxubTPZNU1k1zYhUlm84SAnnXUGN155DR9dfi5+v39arykF2hxACjTJ1vDWW29x6cUXoqRj/Oh02Ccyc54at4asKbjrFXirS/CfP/gWl9/6+dnukkQikWwXcrkctbW1VFdXs379elasXsWHq1fR2tCEZZoAaOEA2oIyXAvLcS90Nt3VFpShBn2z3HuJRDIZwrbJN3aRX9VI+oMazM4BNI+bw046jhsuv4rLL76UsrKybb6OFGhzACnQJFvKT37yE+64/XY+Uib43ikKFTPoRn9byFuCz70OL7YKfvytr3LD574+212SSCSSrSYWi1FdXU11dTXr1q1j1do1rKteT0dL2+jUxJAfbb4jxFwFq5iyoBQtNL0j7hKJZOYxugfJr6gj9UEtRkMniqpy0AlHc9t1N3LNZVcQCoW2ql0p0OYAUqBJpko6neYzn/kMP/rRj7hyqcKXj1Zw72BrwE1b8NW34Hf1gie/cief/sa/zXaXJBKJZEIMw6C1tZXGxsZiqKmvo7ahnraWVpJDcaegoqCXRdDml+KaX4Y+vxTPwgqUqhIpxCSSXQRrKEn2g1rS76wnX9eO5nZx5GkncdeNN3PJRy/E6536NGUp0OYAUqBJpsL777/PtVdfRXNTI185UnD50rnlDGRLsIXg0XfhF9WCr918CV97+jdzwuukRCLZNRBCMDAwQEdHh7NZcyFub2+nqb2VtvZ2erq6iQ8Mjm7YrCropWG0iihaeQS9IoqrsgTX/FKUyhJUj9wnTCKROJgDCbLvrif9zjqM1l5cAR+nLD+be266lbPPPBNN0zZZXwq0OYAUaJJNYRgGjz/+OF9/6CH2K1X41gmwV3jHmNK4KYQQfG+l4Aer4JLj9uc//vYOgeDWTQWQSCSSEVKp1ITCq7WjnZa2Nrq6uujv7sE0jNFKioIeDqBEAs6GzSXOps16SRh3RRSlLIRaGkbRN/1SJZFIJBtidA2QfWc9qXfWYfXECJaVcMHHLuHem2/nsMMOm3CAWgq0OYAUaJLJePvtt7n5phtZX13NzcsEnzpE3eGmNG6OvzYL7n9DsKQixB+ef5W9Dzx0trskkUjmIIZh0N3dPU58dXR00NzWRmtHG50dHfR295BJpsbV03we1GiwGLSSEFo0iKskjF4ShGgANRyQ4ksikWxXhBAYzd1k315H6p112Ik0lUt25+PXXMunbvwkS5YsKZaVAm0OIAWaZEMSiQRf+tKXeOqpp1hWpvKN4xQOKNnxrWaTURMT3PmyIJZTePKxr3HDZx6QUx4lkl0AIQTDw8N0d3fT1dVFd3c33d3djtWrq5PWjna6urrp7+0lMRhj7GuEomtoI6JrjPhylYTQS8IQCaBGA6he9yzeoUQikWyMsGxy65rJvL2OzD9rEXmDpYcfzK3X3cB1V1+Ly+WSAm22kQJNMpbf/e533PWp24kN9HHPwYJr9wNNnV2xYtqClX3wXg+0JARDOfDrsCgEh1QoHDsfvPq29TGRF3zjHfhjo+Cjxx3IT/73eSrnzdGN3SQSyaRYlsXg4CD9/f309fUV476+Pjq6Omnt6KCjs4Penl4G+/rIZbLj6isu3RFckQBqJIAWdqYduiJB9NIwSiSAEg2gBn1yIEeyQyBMCzudxU45QaSyznEyg8jmEYaJyJuF2HBiywYBjLw6CwFCFA8VVQFNRdFU0LRCrKJo2kb5iktHcesoHlch7ULx6Cgul5PvLuS5Xah+D4pr+23ALNkYO5snt6KO1Ftrya1tRtVUDj3xOD54822GevulQJstpECTADQ0NPDZe+/lD3/8I6cuUnngGJUF/tm1mrUmBP9bL/htPfRmIOSCvaMQ9UDKhMYh6M9CwAVn7w4f3VPhmPmgbsNL0/81C772tkDVdL702U9z15cfxePxTNs9SSSSqZPL5YjFYhuFEdHV1dtDV08PvX299Pf3MzQwyPBQnI1+7hUFLehDiQTQIgHUSBCtkHZFQ2iRIIR9qJEgis8thZdkh0EYJmZ/HKtvCHMggR0bxhpKYsVT2ENJrKEkdjLtiK0NcemoXrcjkEZElEsHlz465VZRQMF5JkaeC0UB23ZEnGUjLMtJm07aybOhkD8iADHMqd2US0f1e1EDHlS/F8XvRQ04QYsEUaMBtMio5VoNeOUzO01Y8RS596pJvrWW8i9cRfXVX5MCbbaQAm3Xpquri69//ev85Mc/pswH9x8hOHt3MatfdtWDjgOPv7dB0AXnL4GL91b4SNl4a54QgqYE/KVJ8KdGaBmGJWG4cZnChXuBR9u6e+jLCJ5cAb+tF5QEPFx35aVcdtNdHHXUUajqTrYITyLZDuTzedLpNKlUalwYyRseHi6KrYHBQXoH+ukfHGBwcJChoSESQ3GSiQT5bG7C9hWPCzXoRw36UEM+1JAfNeRz9vwK+dFDjoWLoBcl5Hde8mZ5JoBEsjUI28YaHMbqj2P2DTlCrD9eOI5jx5OjhVWlIGAc8VKcehsJOM9IQeSoBcGjuGfW86ewxXgrXc4oWO4MRK6Qn86NWvvSWex0DjFynMxgJVKI1HirN7rmDLiUhBwPp+WRoqdTrSyCVhqSazy3knWX3i8F2tbS0dHBfffdx3PPPUcmk2Hp0qU888wzHH744VOqLwXarkltbS3f/e53+dn/ewYXJrcss7l2fxWvNnuPiS0EP14DT64Q7BaE2z6icO4S8E1h+qIQgg/64KdrBS+2QpnXEWpX7ze1+hPRFBc8W6vwxwaboRyUR/ycfeqJnH7+xzj19DNYvHjxVrUrkcw2lmVtUkBtKi+ZTDKcSjKcTJJMpUimkqRTaTLpNJl0hmwmjWVam+1DcTrT2BdGvxfV7ym+RGoBH3ohH7+7OJI+0y+WEsn2QgiBPZzB6h8atYT1xZ3jvjjWYAKs0dksajToCJCK6HghUh5BKwk5Uwt3ckTewIqnsOJJ7CEntoZSWIMJrIE4Vl8ca2h41GqoKmglIfTKEvR5peOCVhZGkQOvkyIF2lYSi8U49NBDOfXUU7n99tuprKykoaGBxYsXs9dee02pDSnQdh0SiQTPPfccP/vp/+P//vY8ZX6Nq/axuO4AnbB7dqczxrKCL/xD8FoH3PYRuPMQBddWjng3xQX/b60zNbLEC7d+ROGKpeDeSovayBq4VzoUXmu3qYk5+btVRDnp+GM5dflFnHLaaey1115ymoVkWhBCkE6nNxJHm7RKJYdJDCdJJIdJplMMJ5PFMqPiKUMuncHI56fUD9XtQvW4UDzONCjcOnhcjgXL7cTKSOwZf6x7PKgeF6rXjXDp4NbA43bqFtaiSCQ7M8KysRMprEQKO55yph0mUo6YGIgXBFkckRt9HpWAd7wAGyvEyiPyuZkiwjCxBhMFset81mbPIGb3IGZPDEYGkXQNvWq8cHPNK0WfX47ql8sbpEDbSr74xS/yxhtv8Prrr291G1Kg7bz09fWxYsUKVq5cyUsvPs9LL7+CYVocWK5yzb6C85YoeOaA1X9Fr+AzrwkyJnz7RIWTFk6PyGkbFvzgQ8HvG2FhAL54pMIZu29727Gs4J+9Cu/2CN7rFqwfdAbqgj43++25OwcsO5CPHH4MBxz4Efbcc08WL16M1+vd9huSbBds28YwjGIwTXPc8abyJ8rL5/NkMpliSKfTpDJpkqk0qXSKVMYRXukxZbLZLLlMllzWCfkNHFdMhqJr40XSJGJJHZM/IrpUjxvVWxBfLkd4jRVgisslpwVKJBsgbIGdzDhCa0RwxZPYibRzHC+s/0qkJ1z7pQZ9qGE/WllkVIBVRNDKnVj1y9+K7Y2wbayBhCPWNghWbLhYTisLoy8sx7WwAtduFU56ftkuJZKlQNtKDjjgAM4++2za29t59dVXWbhwIXfccQc333zzlNsYEWgvvvgiwWAQYKMF1rY93rqy4fmJPtqJyozNm6yNTZXZVNnJ6m6ur1M53tY2YMs/w8k+07H5pmkyPDxMIpFgaGjIcQ3d0kRHextd3T3Ek2nAmeK3NCo4Zh4cs1CnwrP56UdTeVimVGYThYQQ/LERfr4eDiqHfz0JFgSnf6pBw5DgsfcEr3fCyQvh+gMg4pn8xXNL7ytlCGpi0JpUaUvYtCehIwnZMR9zWSTI/KpyysrKCQTDBEJh/IEQ/mAQt9uNy+UqxiNpXdeLx2PzNE1DUZRx1rqRvwvbtselR85tGEbKjcRj8zd1bsP8LWnHNE0syyoKIsu2MAzTiU0Ty7QwLBPTNDEtp6xpWliWiWlZmJaFbTllbdvCNCws28IyTSzbdtq2LCzLxrZMLMvGsp0827KwbIFtms6Ps207eaa16T/SrUXXnEX3Ls1ZcO/SHMGja85C/ML5kcX4o3kuVI/uCCq3C9Wtg7sgnHTd8XzmLng+c7lQttIqLJFIRhFCILJ57OG0M91wOF1IjzlOZop52OO/MxSvu+jxs+iEJux3PIGGC95AC+flWqi5jZ3LY3bHMDr6MDv6MTr6MTv6sAYSTgFFQauMOpa2+WXoC8px7VaBFvbPbse3A1ppWAq0rWVkVP7ee+/lsssu49133+Wee+7h6aef5hOf+MSEdXK5HLnc6MLrRCLBokWLZqS/EslYVMAGblwG9x629VMap4IQgv9rga+/47gJ9urQldp8PYlEIpHsZKgquJyBLmHbznS3DUTXiOMJR1wFxwkwJeRDCwVQI37UaAhN7nW302Oncxid/RhtvY5wK6RHHJYoXmf6tsibiMzEzo12NGquU5n3vZgUaFuD2+3miCOO4M033yzmffrTn+a9997jrbfemrDOgw8+yEMPPTQu7+5DRr2njrDhq/LmjlEmKLOFbU6lzoYZW92mssHx5tqYSpsbHG9uOdJmz09QbmwVVXFczAdc4FYpWlRMRSXm8pPVRn80pmNp1FSamMp12mImv3h7mLxh8/hxCmfsMXNWgL6M4N5XBf/shZuP9LLf4uCkZafrfgFUYRM0c4TNLJoYb00dacMWAssGw/FWjDEmPfbYFqNWvrHfaqri5Bf/9sQYr8iFf9RCrDD+b1TdVN6YNpRC/rT8QUkkEsk2kLMEg1kYyMBA1tl2pSevM5BXiOcgmTaxrPGvfmrAO97LYWHLBT3sQ40EUCJBtEhIbrcg2SxCCKz+OEZzN0ZzF/nWXvLN3UXR5g+52b3MxYH+HPsHTRYGZ39f2S3hpIWKFGhbyx577MGZZ57JT37yk2LeU089xcMPP0xHR8eEdSazoMk1aJLtjWVZ/Mu//Atf/cpX2K9E8MTJKosCm59uOd2YtuDr7wh+VQsP3ngeD/zkT/KHWCKRSOYYqVSKhoYGmpqaaGlpoaGxkdrGBppamulsa2c4NjRaWFHQSkNoZWG00rDjcj0axFWIiQTQosFdav2QZOYRQmANJDCauzAausjWt2M0d4Nl4/J52e/QgzjtpJM599QzOP744wkEArPd5QmZSf8UO+UTefzxx1NTUzMur7a2lj322GPSOh6PR268K5lxWlpa+MS11/D6G2/wyWXw6UNU3NrMizMAXVV46Bgo9Qoe/H9/oa/vaL73+7flHmcSiUQywwwPD1NfX18Ma6rXs762hubGRmK9/cVyiksfFWBlEbQ9D6SssM+VUhraZVzMS+Y2iqKgFzxu+o7YjzDOtgD55m7M+k7q6ttZ+/3v893Hv42qayw99CCWn3U2l513AUceeSS6vlPKlU2yU1rQ3nvvPY477jgeeughLr/8ct59911uvvlmfvSjH3HNNddMqQ3pxVGyvfn973/PDdd9Ap9I883jBEfPm+0ejfIf6wWPvCv4+CkH8NMXPkTbBb8cJRKJZHti2zatra2sW7eOdevWsXrtGlavW0djQwPxgcFiOdXvRasqQa+MoleW4KoqxVVVglIeRg0FpFdRyU6BEAKzcwCjupXMumZy1S2ITB53wMchxx7NxcvP56Jzz2Pfffedtdk9M6kNdkqBBvDnP/+ZL33pS9TV1bFkyRLuvfferfLiKAWaZLrJ5/N84Qtf4Lvf/S5n7q7wyPEakVneb20iflcvuP9NwceO3Yf/emUtuktugiuRSCRbimVZNDU1sW7dOtauXcuKNatYtWYNTXX1xa0jVI8bbX4p+vwy9KoSPJWlaFVR1IooatA3y3cgkcw8wrKdNWzrWsmsayJf3wGWTenC+Zx17jnccNmVnHrKqbhm8N1ECrQ5gBRoku1Bb28vHz3/PFZ88E++cDh8fD/m9DqvPzc6G2Wfd/hifv36Otxe+aIgkUgkE2EYBg0NDaxdu5Z169axcvUqVq1dQ3N9I2ZhE3TV73FE2IJyXAvK8CysQJlf5kxFlJYwiWRS7FyefHUbuVUNpFfWYceSuAM+jjz5RK677EouveBCSktLt2sfpECbA0iBJplumpqaOOv004j3tvGDU+Ggsh3j0Xu+RXDva4Ljl1bxu3+sJlpWMdtdkkgkklkjn89TX19fFGIfrlnNh6tX09LYiGWYAGhBP9qCMlwLynAtKMe9sAJ1fqnjEXEOD8pJJDsCQgiM1h7yKxtIr6zHaOlGURX2PfwQrrv6Gm646lqqqqqm/bpSoM0BpECTTCerVq3i7DNOx5OP8cyZs+OlcVt4s1Nw1yuC3UoDPPfq2yze98DZ7pJEIpFsN4QQdHV10dDQQH19PXV1daypXs/qNWtoa2pyNnMH9HAAtSDC9AVleHerRJlXulNu0CuRzFWs2DC5DxtJf1BDbl0zAPsdeSg3XP1xbrjqGioqpmdgWQq0OYAUaJLp4rXXXuOC889jN0+GH5+hUOaZe+vNpkJdTHDrSwJDaPzuN7/iuHMune0uSSQSyRYjhCCRSNDd3U1XV1cx7ujoYF1tDXX1dbQ1t5ArrA8D0EvDjqOOghhzpiaWooWkEJNI5hJWMkPug1rS71WTW9+CgsL+Rx/GJ6+9jk9ccRVlZWVb3bYUaHMAKdAk08Ef/vAHrrj8Mg4tt/j3UxSCrh37cevLCD71MqwbEDz6uVv57ONPyek6EolkTmAYBr29vRsJr+7ubjq7umjraKe7u5v+3j5y2ey4uqrHhRoJoFU4nhL1qhLcVaXolVGU8ojcJ0wi2QGxEmmyH9SSea+aXHUrqqZy2MnHc+eNN3P5xZfi823Zunop0OYAUqBJtpVnnnmGW265hbN2h2+fqOBWd45HLW8J/uUD+Pk6wXlHLeUXf/kHJeVyXZpEItk+JJNJOjo6xguuzk7auzpp7+ygq6uLvt4+EoMxxr3SKAqucAA1EoCwHy0SQI0E0SIBtEgAVzRUPKd43XKwSSLZibHiKbLvVpN6ey1GUxcuv5dTzj2He2++jTNPPwNN0zbbhhRocwAp0CRbi2mafPGLX+Q73/kOV+2r8NWjFHbGfUJfbBV86Q2B3+PiB//+fS7++C2z3SWJRLIDYds2vb29dHR00N7eTkdHB63tbTS2tNDa3kZnZyf93T1kkqlx9RS3qyC2nKAVRJdaEF16NAghP0rYLzdplkgkG2H2DJJ9ez3Jt9Zi9cYIlpdw4aWX8pmbb+Owww6bdLBGCrQ5gBRokq2hvr6e66/7BG+//TafPwyuP2Buu9HfVrpSgq+9Da+2Cy495TB+8Mu/UFk1h3bclkgks0I6naajo2NcaGlro7G12Zlq2NlFrK8f2xzjMElTHaFVEkKLBtFKQuhjgihYvqS1SyKRTAdCCIymLjJvryf97jrsRJqqJbvz8Wuv5fbrb2LPPfccV14KtDmAFGiSLSGTyfDEE0/wyMNfp8xl8vjxcETlrvFoCSH4UxM88q7AQuXTn/w49379X7f7fiQSiWTmsSyraPXq6Oigs7OzKL6a21ro6Oykt6ubVGJ4XD3V50GNBtFKHOGlRkO4SkPoJWGIBtBKgqihgNwLTCKRzArCssmtaybz9joyH9Qicgb7HHoQt1x/A9dffS3l5eVSoM0FpECTTIV4PM4zzzzDd771OH39/Vy7r+CuQzUC+vQ/Vm3Dgj81wpoBgQD2icKJCxSOqJobVrrBrODpNQrPrrdRVJWLzjyey2+8i7OXn0sgEJjt7kkkkgkwTZN4PM7g4CB9fX3F0Nvb6wixnm66e3vo7e1loL+fWN8AtjVq9VI0FS0SRIkG0EpCaNEQWkkQV0kIrSQEESdf9bpn8S4lkplDWDZ2IoWdzGCnc9jpLKIQ2+ksIptHmDbCNMG0EJaNMEywBagKiqqCpoKqOgMWmorqcaP43Kg+D4q3EPvcqAGfM8035JeObKYRO5cnt6KB1Ntrya1pRFFUDj/pOG66+uPcfvMtUqDNJlKgSSZjaGiIv/3tb/zut//LH/7we0zD5IIlglsPdrE4NP37m6UNwb99KPj5OvBocFglqAqsGYDBLCyNwicOULhgT/Bosy/UBjKC39Qr/KnRpm4IvC6N0489jEuuuZELLvkY5eXls91FiWSHx7IskskkiUSC4eHhCUMikWBoaIj+2CCDsRgDsRjx+BDxeJzhRIJkYph8Jrtx44qCFvCihPyoIR9qyI8W8qOG/OjREHqpMwWRaEBavSS7DMK2sRNprKEk1lASuxBb8THpoRR2IgUTvForbh3F53UGK1waiu4ECrGiqQhbgG0jLBtsGyzbEXP5PHYmj8jkEbn8hP1TfB60cAA14nemCJdF0MrC6IVYKwuj+jzb+2Pa6bASaXLvV5N6ay35hk5Kbr2A5m/9Qgq02UIKNMlYGhsb+eMf/8gffvtr/vHmO5iWxb6lKucvFly4t0aVb/vsbdYYF9z5sqA9CXcerPDx/cGnOy9DthC82w0/Wyd4pR1KvXDbQQpXLgX3HBBqAM0Jwd/bVF5osVjZB4oCRx24NxdffjUXXX4NS5cune0uSiSzgmVZDA0NMTAwwODg4LgwMDBAd18vvQP9xIaGHBGWHCY1nCSdSpFJpScWVmNQNA3F50bxeVD9nkLsRfW5UfxeZ8qh34Pm96H5nXMEvShhP2rAJ51rSHYZJhRe8UI6nsKKFURYfAPhpSio4YIYigYdD6Ej6WgQLeRznjW/13kGp8nCJWwbkTWwMznsZNrpYyKNPTySTjn30h/Hig2DNfp+ogZ96PNKNw6VJY5YlGwSszeGXlnCukvvlwJta3jwwQd56KGHxuVVVVXR3d095TakQNu1sW2b9957jz/84Q/88X//h7W1Dbg1haOqBKctUjh1d40F/u274fTzLYIvviGo8sP3T1HYKzq56GpOCJ5eLfh9A8wPwD2HKpy/BNQ5MPVxhL6M4JV2hb+32rzZBTkL9tmtkgsuuoiLr/wExxxzzJTc3Eokc4mRTY8HBgYmDD19ffT099Hb38fg4ACx2BDDQ3HSG6zRGkFx645ACox5sfO6Ub1uxzmG143m86B6PWg+D4rXBV43eFzF86rXLac7SXZphGVjp7KOgElmnJBIYw0NO1au+CYsXiPCK1IQWtENhFe04DU0HJjzAxnCtrGHkpgDCayBBGbfEGb3YCEMIDIFa5yqoM8vw7Wo0gm7VeBaVIkaCcyJJRRzDSnQtpIHH3yQ3/zmN7z44ovFPE3TqKiY+l5NUqDterS2tvLSSy/x4gvP8+Lzf6Onf5CoV+GUhYLTdtc4fr5N0LX9v6hsIfjuCsEPV8PZe8CjxytTvm5dTPDECsHf2+CoefDwsQp7hOfel2vGFLzRqfBSu+DlNsFgFspCPo494hCOP+0cjj/5VPbff3/Kysrkj4NkWrEsi2w2Oy5kMpliOpVKFacHjg2D8SEGh4YYig8xlIgTG4gRj8VIxuNY5sZTmxWXjhosCK2gzwkBL2rAhxbwoQV9aEEv+L1OmUJQ3K5Z+FQkkrmLI7Yyo0JrOOMcD2fGCTArOVpGpCaeulvclqEk6KSjwfFCbAcRXtOBEAI7kXLEWucARnsfRlsvRltfcRqlGvI7Ym33SlyL5+FeMh+tIrrL/y5LgbaVPPjgg/z+979n5cqVW92GFGg7J0II+vr6qKuro66ujrVr1/LhP9/lw1Wr6B0YQlHggFKFo+cJTttd59ByC30G11ekDMHnXxe81AafO1zhpmVb5wDkzU7BV98S9GXg04co3HAAaHN0nYhlCz7sh1c7FD7otVnTD2nTORf0eVi8sJIF8+dTNW8+8xbsRtWCRcxbuBtVVVXFUFZWJq1v04Rt2+TzefL5PLlcrpg2TRPTNDEMY6vTY4NlWeOODcPAME0M0ylvjKmbNw0Mw8Q0DUzLwjTGl5usXcs0yeVyGLk8+UJsmeaUPwvV40LxOhYsxeceXZzvdRdFlx70oQX9jhgLeqEgwlSPFFoSyYYI0xpj2coWBNVIegOxVRBiE4stUPzewrPnRQ35RwdCRkLIma6rhkYHSBR15xde24qwBVZ/vCDWeh3h1tKNNZAAQAl4cS+e5wi2xfNwLZmPVhLapUSbFGhbyYMPPsi3v/1tIpEIHo+Ho48+mkcffXSj/Qw2xYhAe+uttwgGg4Dzcj8WIcSEeZsrs2G5ycpMVH6y623Y3kT1N1dmU+1vqq+bO55qOxuW25LPxTRNMplMMaTTaWdxfH8/fb099Pd00dvTTVtHN8kxazcq/Qp7hgV7RmDvqMZHym3CEzgbm8pTMpUHaVNletKCr78NvWn4zklw6qJt+yFJG4LvrhT8x3o4qgruOQSC7tEv0M31d0r3M03fHmObsWxBWxI6Uyo9aehJ2QxmYSgH8bwT5zYwWKiKQiTooywaprSsjPKKSsor5xGOlhIIBvH5fAQCAXw+Hz6fD7d7/H/yyA+LEAJVVZ2RRdvGtu3i3+HIMTBhesNyY483FW+Yt7m6Y89bloUtbCxbYFomlm2Tz+XJ5rPk8nnyeYN8PkfOMByBYuTJGyZGLucInnwewzSw8o44Mg3DWZy+PVAV0LSiVzIUFUVTQFUL51Tn/0FVndFrtZAueDRT1NGyykhc9HSmOgsctTEe0JRCGZeGoutO7NJRXS5Ul1Y8VjQdXCpiZLG+uzBV0OPaJUbRJZKtReQNR2ylHO+EdjKLSBeOUxnsVA47nUGkcoVjx4PhRhTElhp0LMsbii1lA+GlhfxSbM0CViJNvrkLo7kbo7mbfHM39lASKFjadq9EX1SJa1EFrt2r0EL+We7x9kELB6RA21qee+450uk0S5cupaenh4cffpjq6mrWrl1LWVnZhHVyuRy5XK54nEgkWLRo0Ux1WSJBU6DSDz88DfYrnb4fnre6BJ9+RRByQcZyPD9KJBKJRDIlNBVF05xBE3C8DJqW4xZ+Q1TVsWgVp/UWLFgBn5Mf8KL6fSghn+NxsJAvxdaOiRUbJt/cRb6xC6OlB6O5GzuZAXAGuty6s61AfuozF+Y6NdepzPteTAq06SCVSrHXXnvxhS98gXvvvXfCMhM5Frn9INjQYLvR8QQW3YmMvFMtt6kyk1mPR/I3154ylTLKBGWUjQ+no51JsqZ2LWV8UlUcF/RuzYk9GrhUxzIyUtRQVOIuH3l148XzU7HMT8l4vxXttMZMnnkzyW4B+MkZCpX+6Z8m0JwQXP83ga7BrSeE8Pucz2Bz9z1dPZmOz3cqbXhsk7CRwWOPN7EZliBjQdaErAWmAIRjuROAIpwO2MKxCirK6LuIooBa6N/YZ23kb1xRxqQZn3bqF6xzm+n71n4Tb0u7O/2Xv0SyC5MxhTPzIAfDeUgYEM8rDFoacVNlOA/JrE0ma2FPILZG1lGOCizfuPWU2sjayZAfJRRwrF0+9y411U0yHiEE1kACo6kTo6mbXMHaNmI5jZR4WRTV2M+TY++gycIgM7qMZDo4bZEiBdp0cuaZZ7L33nvz1FNPTXh+MguaXIMm2Z68+uqrnH/ucvYJ5nj6dJWIe/t5hmwbFlz3vMDrdvGP91Yyb88Dttu1JBKJRLJ9SaVSVFdX09zcTEtLCw1NjdQ0NtDS0kJnW/tGnkIVj8uZPhgJFPbLCqAVvBXqhXWUBDyjUwulwxrJNCBsgdk9gNnUTa6x07G2tfeCZaO5Xeyx3z4ceeRRnH3SKZxw7HHsvffec1bkz6R/il1CoOVyOfbaay9uueUWHnjggSnVkU5CJNubd955h9NOPYVDSvJ8/1SFgL79H8XmhOCq5wSLSv28trKecMX87X5NiUQikWw9mUyG9evXO06tVq/ivZUrqF6/nt72zmIZxeNCKw2jlUfQy8JoZRHc5RHU0rCzt10k4GyQLJHMAYRhYrT2FERbF7mmTqyeGACeUIB9Dz6Q444+lrNPPIVjjzmGqqqqWe6xgxRo28jnPvc5PvrRj7L77rvT29vLww8/zKuvvsrq1avZY489ptSGFGiS7UltbS3HHX0Ue3iG+emZ4J1BB4RrBwQf/5vgsMWlPL+iGW8gNHMXl0gkEsmEZLNZampqWLNmDStXr+L9D1ewft16ets6ig6xtLIw+oJyXAvLcS+swLWgHKU87Fi85qjVQSKZCnYyQ7652xFtTZ3kGzuxE2kAovMq+chhh3DGCSdz9mmnc9hhh+FyzbyFVwq0beTKK6/ktddeo7+/n4qKCo455hi+8Y1vcMABU5/SJQWaZHvR3d3NMUcejp7s4tnlKlH3zD+C73QLPvmC4OxD9+C3b9Whz8IXnUQikeyK5PN5ampqWLt2LWvWrOG9D1eydu1aOltaEQXvsFpJEH1BOfrCcrwLK1EXlKEvKEP1eWa59xLJzCCEwBocxmjqxGzqJtvQ6axnyxu4fF4OOOIQzj7tDC48azlHHnnkjAg2KdDmAFKgSbYHiUSCk084jq7G9fzyXJUF/u235mxzvNgquOsVwbWnHMBPX1yNKr1oSSQSybSRyWSoqamhurqa9evXs3L1KlatWU1rYzO25Tgy0iIB9IXl6AvK8exWib6gDG1BGarfO8u9l0jmHsK0HCtbbTuZ6hbydR2IXB7d62H/ww7mvDPP5oqLLuHggw/eLhZlKdDmAFKgSaabfD7P8rPP4r23Xue/zlHZNzp74myE39QJvvym4JJj9ua//r4Srz8w212SSCSSHQYhBP39/UURtn79elatW8v69evpbh8zNTEcQFtQhmthOa4FFXh2q0CdX+o45pBIJFuFsGyMlm6M2nYy1a3ka9oQuTzB8hJOOP1UPn7xZZx3znIikci0XE8KtDmAFGiS6cS2bT5+zTX85te/4idnqhxdNXceu782C+57XXDgblF+87fXWLLfR2a7SxKJRDJnSKfTNDc309jYSFNTE42NjVTX11Hf2EBHSxuZVMopqCq4KkrQ5peizy/DNa8Uz8IKlKoS1IC0iEkk2xthWuTr2smvaiS9ugGzcwBF09j3sIO4+rIruOGqa9htt922un0p0OYAUqBJppPPfe5z/Ou/focnTlJZvni2e7MxH/YJ7nlVkLZU/t/3/4WLb/rMbHdJIpFItjuGYdDZ2Ul7ezvt7e20tbXR1tZGQ2szzS0tdLS1M9Q/UCyv6Bp6eQS1PIJeEUWriOCpKEWbV4paGUFxbbzPpkQimR3M/jj5NU1kVtaRXdsMls3igw7g6suv4OZrPsHixYu3qD0p0OYAUqBJpouHHnqIBx98kC8fpfCJ/eeul62hnOCLb8DLbYLPXbOcb/7Hn+W6NIlEssMyIr7a2trGia/G1haaW1vo6ugk1tfP2Ncg1etGLQ2hlRRCaRhXZRRXRYnjLTESRNnBNtaVSCRgp7NkP2wg+34NmdWNYFosOmApn7jmGu64/pMsWLBgs21IgTYHkAJNsq0IIfja177GN77xDe49TOHWj8z9H3VbCH6yBr7zgeBjxy3lv176ELdHTs2RSCRzi3w+P058tbe309LaSmNLMy1tbXR1dDDUPzBefPk8aCUh1JKgs2dYSQi9NIy7LIwSDaKUhFD90kuiRLKzY2fzZFcVxNrKerBt9j/qcG6/8Sauv/IaQqGJtx+SAm0OIAWaZFvI5XLccvPN/McvfsFnD1O4ZQcQZ2P5a5PgC/8QnLZsPr9/q0bulSaRSGaMXC5XnHY4IsBa29qob26idYz4Govq9zrCqySEVrCA6SVh3OVhlGgIpSQoXdRLJJKNsNNZsu/XkH57HbnqVjSPm2POPJV7P3kbHz33vHHu+6VAmwNIgSbZWlpbW7n0kotZ/eFKHj1WcP6eO5Y4G+HVdsGdLwtO3K+KP71Tiy8onwOJRLJtDA8P09nZWQzt7e20tBUsX61tdHd2bkZ8hdFKQ+glIVxlUZRoAFWKL4lEMg2YAwmy76wn9dYazI5+fJEQyy+5iM/efDvHHnMMw8PDUqDNNlKgSbaG559/nquvvByvmeR7pygcWDr7rvS3hdc7BJ96WXDsPhX8+Z1aAuHobHdJIpHMQVKpFJ2dnXR1dRXFV0dHBy3tbbR2tNPV2UV/by/ZVHpcPdXv3WDNVwhXaRhXaRhKgqhRKb4kEsnMIoTAbOsj8846Um+txR5KUrb7Aj522WU8/Z3vSoE2m0iBJtkSbNvm0Ucf5YEHHuCEBfDtkzRK3FsvzoQQrOiDv7cJ+tKgKrBbUOGgCji8EgKumbPKvdUluO3vgv0WhPnjS2+zaO/9Z+zaEolkdshms/T19dHb20tfX18x9PT00N3bQ2dPNz29vfT39REbGNxYeHncjmUrGkSLBNCiTtpVEkaPBiESQI0GUD3uWbpDiWT7IIRAZPPYqSx2KotIO7FdiEUmhzAthGlBIR5JoyqgqqAojjMaRQFNRXG7UH1uFK8b1etB8RbSQR9aOIAa8ctnaTshbJtcdSvZt9aRfr+Givuvoe6eJ6VAmy2kQJNMlVgsxsevvZa/PvdXPnUQ3HGQgraVXr6a4oI/Ngr+1AhtSajwwe4hsAS0JCCWA7cKpy6Ca/dTOGrezAi11f3OdEcDjSe+9QjX3vEFFGXHnLopkewqGIbB0NAQsVhswjA4OEjf4AB9AwMMDA4Qi8WID8UZHoqTTac3ak/1ulFDfpSQDy3kRy0GH66oM+2QsF9avSQ7PEWRlc4hxogrO5UppHMbCa+iGEtnwZ741VrxeVB9bnDpKLqGomtQiBVNBeEIAoQAWzhpSyDyeexsHlEITNC84nGhhgNokQBqNIheFkYri6CVhdHLI2hlEekEZxuxcwaqx8W6S++XAm22kAJNMhXef/99rrjsUga6O/j2CYKTF255G4NZwV+b4A+NglX9EHTBOYvhgj0VjqwCtSCEhBA0JeClNvh9g6BuCI6aBw8cpbBPyfYXS/0Zwdffgb+1CI7YZwFffvhbnH/JFei63PdHIplubNsmlUqRSCSIx+MkEokJ07GhIfpjA/QNDDIYG2SoILKS8cSEIgtA0VTUgBfF70Xxe5xphgFvMVYCXlzhQFGEiYIgk3t8SXY0hGFiD6ex07lRgVUQVY7wyjkCK5kp5BWO01mwJp4Fo3jdzvNSeIbGPjsjaWWiPL8HZRq2rhFCIHIGIuPckxVPYSdSWIkUdiLtpGPDmAMJrIGEY5kb6bvP4wi2yhL0qjGhsgQ1GpQDr1NECrRp4rHHHuP+++/n7rvv5sknn5xSHSnQJJsiHo/z2GOP8S//8m32L1V48mSVRQFr8xXHUBMT/NtKwcttzvGJC+HCvRRO3Q28+qa/JIUQ/L0Nvv1PQUcSPnkg3H6Qgkfb/l+ur3cI/v1DZwpmWcjLJR89l6s+eScnnHDCOG9HEsnOjGVZZDIZMpkM6XR6i+JUKkUilSSZSjGcSpFKJ0kMJ0kmEgwnhkknk2SSKTb186wWpjgpPs/GIsvvQQl40QM+tIAP1e+FgAcK5RSPS76ISXY4hGk5QiqZcURXKoM9nMEaOS6ey2AnnWORMyZsS/G4UQOezQus4JiBC3/hnLbj7A8qbOEItoG4I9j641j9ccy+IcyeGNZAvGiNUzyuoljTqkpHxdv8MrSgb3ZvZI4hBdo08N5773H55ZcTDoc59dRTpUCTbDVCCNauXcsvfvELfvz0U6RTSW47EG7+iIJrC76vLVvwrysEz6yBRSG4/gCF5Yuh1LvlL0w5S/D0asGPVsPCIDx0jMIx87f/i5cQgrWD8NdmheeabDpT4PfoHH/4QZxx3kWcufx8Dj74YLnRtWRGsW17i0VTqiCShlPJglhKkUylnDIZp0w2nSGbzZLLZMnnsuQzOUxj4he/iVA0zRFFLh3FrYNbR3GPHo+uL3GmPyk+D5rPg+b3Fo694HWB1+2IL697WkbiJZLZQli2Y8kqCKmxoqooupLpQr6TFpn8xg2pCmrQVwjOdNuRtFZM+zaydim6NvM3PQcRhlkUa2ZPDLM3htk9iNUTw4oNF8upYT/6gnJc88vQF5Shzy/DtaAcNRLYJQd7pEDbRpLJJIcddhg/+MEPePjhhznkkEOkQJNMCSEE3d3drF27lhUrVvDP997lH6+/Skd3HxGvygVLbG4+SKfKu2WOQPKW4NOvCF7tgM8cqnDDMnBt5Xq1sTQMCR54S/B+L1yyF3zhCIWSrRB8W4MtBGsG4O1ulXe6LN7vgawFQa+bg/ffi0OPOJrDjzuZww8/nKVLl+LxyDnwcxkhBIZhTBry+fx2OZ/P58kbBtlcjryRJ5/PkyuWd87lDYN8brS+aRrksjnyuSy5TA4zP8EL3GQUFt4rbt0RSkUB5SqKJifWUVyjadXtRnXrqG4Xisc5xqVBobxwaePbdbt2qBF3iWRLEbZApLMTiKoNLVvpQpkMIpXduCEF1MAYURXyj0kXBFjQVxBejhhTvB7HmYZk2rFzBlZvDKNrALNrALNzAKOzH7MnVpw2qfg8uBaUoS8oL4i2MvSFFWiloZ1auEmBto1cd911lJaW8sQTT3DKKadslUD78MMPizuKb/hR2fb4l/OJPsrNlZlKGxvmbY9+CCE2OZ1ma9vYXF83ZCptbFhmaz+zkUXyg4ODDAwM0NvTTVtLI+1t7XR295IzTAB8usJeEcF+pXB4lcYh5RZubWRd2CZvZ/w1heDhd+HtLvj+qXDybtP70mYLwa9rBf/ygeMI6mN7w/LFEHRPva+bKzKV2zVMQXUM1g+pNMRsGuLQmXLOqYpCVVmY3RcuoHLefCIl5URKy/EHArjdblwuF263G13Xi2mXy4Wu62iaNi6oqrpR3obnnft2/l5s2y6GiY7H5k1WfsN4ovJTuR4w5f5YlkV+RMhYJvl83hEmholhGpj5Qr5hYJlObFompmFgmiamaTmxYWCZFqZtFdO2aWLZNpZhYlkmlmlt2R/1VFAAzVkAr2iFRfGqiqI53smKQS0skte14jlF1ZxYU8e0oY7Pc2moLh3cLlSXC9XjQnFpznopt44YWYw/IsBGBJQUTRLJRhSdYyQLa7VGxNVIOjWaFsW87ITfG4rfUxRS2hjRpQR8RUuXNiLCQn7HOiwtw3MeYdmYfUOOWOscFW9m1wAi78wsUPxeXLtV4NqtHH1hIZ5XiqLt+JZL1e+VAm1b+OUvf8kjjzzCe++9h9fr3axAy+Vy5HK54nEikWDRokUz1FvJroICfO8UOGuP7fcj1JcRfHeF4A8N4NYg7B4VSBKJRCKRAM5Aia45I3pCIGzhWEYmElsFl+4bW7V8hbVbzjktHHDyAz45CLKLIWyBNZjAaOvFaO3BaO/DaOvF6h1yCqgKqt+LUHAsqJN4upzr1FynMu97se0u0HZKl0xtbW3cfffdPP/883i93inVeeyxx3jooYfG5d24zHmhHstmjyew6G6Ytzmj74TnlfH5k7Ux9lqTlpmkwLj2J6m8uT5MxaI9HZ/Z5o4nbGODzyagQ8gNPp2iKV4BLEUhrvswJxnJUzb7P7jxtdd0G/z8vQxfOELhrD22r9m/wqfw8HEKnz5E8J0PBL9vgFN2VznvID/aJkYnp2s2wuaaURGEjSweYW50TgiBJRznWaYNRiHYYjRYI2nG51li43KKcDqkKKAW+jZynyOzYtRCp4vxhvmFZ2/kvka2plHGtDfuuJi3807vkEgks49lC+J5iGVhKOeEwbxCv6kzlIdkTpDOWVjmBi/CSmHdVqTgqTPsLwovLeB1rF0hH0ooUBRj0oOnZHMoqoJeHkEvj+A7dJ9ivp3OYbT3YrZ0k2/tI9/chZnMAKC7darKPewVsNjfm2XPiDNrSbKTWtB+//vfc/HFF6ONMaValoWiKKiqSi6XG3cOJregyTVokm2lqamJww45iCOjKf7tlJl/cX+uWfD51wUn7lvFc+/X4/YHZ/T6EolEItlyhBD09/dTX19PXV0dtbW1rK5eT31DPZ0dncT7B8ZN31e9btSSEFppCK0khBoJooX9uCIB1FAAQj7UcAA16JVTCSWzip3JYTR3YzZ2kW3oIN/YiZ1Io6gKexywH6eecjIXn30eJ5900px6B59J/xQ7pUAbHh6mpaVlXN4NN9zAfvvtx3333ceBBx642TakkxDJdJDL5Tj+2KPpbVjDb8+DsHt2HrfXOwS3/V3wiZP24Scv10jrjkQikcwBhBAMDg5SV1dHXV0d62tqWF29jtq6WtqaWsgMJ4tltWgQraoEvSKKVhbGVRpxNgcvCaCVhuXm4JIdFiEEVm+MfHUb2ZpWctWt2ENJFFVh8bL9Ofecc7j6oks5+uijNzKwzCRSoG0HttZJiBRokm3h9ttv56c/+RH/vVxlWemWeXycbv67RvDg24In77yIu//td7PaF4lEItmVGCvCauvqWLV+LdW1tbQ1NZFOjIowNRJEr4qiV5bgqirFNa8UtSKKVhVF9bhn8Q4kkpnDEWxD5GvayK5rIru2GZHK4g0HOerkE7nmoku58LzzqaqqmtF+zaQ2kJOKJZLtxE9/+lN++MMf8o1jlVkXZwBX7auwbkBw31O/55jTfszRF988212SSCSSnYZYLEZdXR319fXU1taytqaa9TXVtDQ1kRxKFMup4cDoJsBnHk55VQlKpSPKVK8UYRKJoijFZ8R/0kEIyybf1Imxuom3Vq3gtT89x62Kwt6HHMhVl13ODVdew5IlS2a729PKLmNB21KkBU2yLbz33nuceMLxXLjY5BvHzp3phFlTcPlfBYbQ+OeaWqIL95ztLkkkEskOgRCCgYEB6uvri2F9bQ3ra2tobmxiODZULKuFA2iVUecls7IE9/wyxxJWGZVTESWSbcRKpMitaiTzQS3ZNU1gWizcdy8uueQSbrn6Eyxbtmy7LOWQUxznAFKgSbaW3t5eDj/0EMqsPv7zLIFbnVuPWGNccOmfBWcfWMVv3m9H1aQhXSKRSMARYT09PRuJsOraWloaG0mNWROmR4KolVH0gvXLXVWKXrCGSREmkcwMdjZPbnUj2Q9qyaxsQOTyVO21B1dddTV333QLixcvnrZrSYE2B5ACTbI1ZDIZzjrjdNavfI/fng/zfLM/tXEi/tjoeHb89i3n8rmn/zLb3ZFIJJIZwzAM2traaG5uprGxkbq6OtbV1lBTV0trUzO5dKZYVi8JFUSYM93KXVWKXlmCUi6dckgkcw1hmGTXNpN9Zz2ZFXWIvMGSQ5Zx47Wf4NZP3EBFRcU2tS8F2hxACjTJlmIYBhdddCEvv/A3fnqmwqEVc/vR+vrbNr+shV9+54t87DOPzXZ3JBKJZFqwbZvOzk6amppobm6mqamJuoYGahvqaWlpprezG2EXBs8UBb0sjFYRdTwkVkbxVJWhVUZRyiOoHtfs3oxEItkq7Gye7Mp6Mm+vI7umEQWFjxx/NJ+68ZNcc9kVBAKBLW5TCrQ5gBRoki0hlUpx2WUf48Xn/8ZTpymcuGC2e7R5TFtw58uCt7vhf3/8ryy//jOz3SWJRCLZLOl0mvb2dtra2mhvb6e9vZ3G5iZqGuppbmqmp6MT0zCK5bVwALUsjF4RQSuPopdHcFdGUcrCaKVhuQmzRLKTYyXSZN+vIf32WvL1HeheD6ecdzafu/VTnHHa6VN23S8F2hxACjTJVBkYGOD8885l9Yp/8m+nCI6fP9s9mjoZU/Cpl+H9HsEvnnyIy+58YLa7JJFIdmFGxNdYAdbS2kpjSzMtba30dHYxPBQfV0cL+Z0Nmisi6GURtIoI7ooStLKwMxVRuqeXSCQFzL4hsm+vI/nGGqzeGOHKMi676kruveUODjjggE3WlQJtDiAFmmQqtLS0cM5ZZ9LX3sTTpwk+UrbjPU55S/DZ1wUvtMDXP3UlX/63Z+VG1hKJZNoY8X7Y3d1NV1dXMe7q6qK1s4O2zg56urvp7+klPcYJB4wRX6UhtEKsl4ZxlYZRokHU0pC0gEkkki1GCIHR2EnmzbWk3l2PSGXZ44B9ueXGm/jkx6+jsrJyozpSoM0BpECTbI433niDSy66EE8+zk/OgMWhuekQZCrYQvDEB4IfrYErTj6Inz33Nl6fb7a7JZFI5iBCCJLJJP39/fT19Y2Le3t76errpbOri67uLnp7eoj1DWCZ5rg2VL8XNRJAjQTQinEQVzSIXhqGaBBNii+JRDIDCMMku6qB9Jtrya5qQBFwxCkncPfNt3HpRRfj9XoBKdDmBFKgSTbFT3/6U2695RYOqRB87xSFUveOK87G8ocGwVfeFBy4qITfvvAP9thn0+Z+iUSy4yKEYHh4mKGhIYaGhojFYuPioaEh+gYG6Orppqevl76+PmKDg8QHYhj5/EbtqT4PatCHEvShhvxo0YCzH1g0iCsSQosGEGE/WiSA4pbONyQSydzDGk6Te6+a5JtrMBq78AT9nHPRBdx7y+0cfNDBRKNRKdBmEynQJBNhWRaf//zneeKJJ7h8qcJXj1Zwq7Pdq63HtAVvd8F7PYKeNNgCXCq82AqoCj996t+46PpPzXY3JRLJJORyuaKomkhgDQ4O0hcbpH9ggMFCfnxoiOFEglRieNSb4QaoHheKz4MS8KKOCK5CrIb8uEJ+tLAfRgRZ0CetXZJdAmHZ2Mk0diKNNZxGpHPYuTwi6wQ7azhp0wQBCFEMQoCiAC4dxaWjuMfEbpdjWfZ7UANelICveKzoU3NiIZlejK4Bsm+tI/XWGqyBBCULqhhWLAbWNUiBNltIgSbZkP7+fq695hpeeOEF7j8Srt2PHXatli0Ev6mDH64WdCShzAu7h5xz7Unoy4ACzPPDvnvtzo//9wX23mfprPZZItkZGWvFGiuwRkLPQD99gwMFgTXI0FCc+NAQyYLAMrK5iRvWVDS/F8XvQRl56SscOy99TtADXudl0O9F+Nyj5eQLoWQXRNgCe2gYsz+O1RfH7B/C6o9j9sexEyns4Qx2MjNxZV1D9bpRRoKmgaqAUnhXGAlCIAwTkTedeCSdN8CaeMBE8bhRA4VnOVB4fguxMu7YU3yei+fls7zNCFuQr2sj8+ZaojcsZ92l90uBNltIgSYZQQjBn/70J269+SZywzH+5QTBCTuAG/3JaE8K7ntd8H4vnLsYbjpQYVnpeLHZmRS82i74XQN82A8BFxy613y++sT/46xzzpm9zkskcxDDMIjH4xtZsWKxGIOxGD39ffQODtA/OFA8PzwUJzU8TGY4iT3pS5lrVFSNvGyNEVt6wIcW9BUsXR7weZzRdr8XxePaYQeQJJLthRACeziD1T80oQizBhJgWsXyajiAVh5GL4+gRoJoYceCrIYDjgObkN955rzubRZCQghE3kCkstjpHHYqi53OYicz2OnsaH46WzwnRsqkspOLO7djDVf9bif2eYqxk3aP5vk9KN5CXCzjdix+8vukiBRoW8lTTz3FU089RXNzMwDLli3jgQceYPny5VNuQwo0iRCCl19+mUcf/gZ/f/kVTtpN5ZHjVSq9O+56s3e6BZ9+RRBwwaPHKRwzf/NfuPVDgl/VOhY3w4a9yz1c/PFbuOMz97Fw4cIZ6LVEsn0xTZNEIkE8HnemAG4Qj6zF6ttAZCWGhkgmhsmlJxlRV5RRceXzjo6AF0a3df+o9Qq/e4wAk9OaJJKtxc7kRgVXX0GI9ccx+4aw+hOI3Oj6ScXnGbc/nlYRQS+PopVH0HagjconFXepLCKdxc7ksDM5ZzpmNo/I5LDTOUQ2h512jkXemPwCLn3UmU94jGOfaBCtLOJ8hmWRXeY7Swq0reRPf/oTmqax9957A/Dzn/+cb3/726xYsYJly5ZNqQ0p0HZNBgYGeO+993jllVf47a9/SV1jC0tLVT5zKJy6UOzQI0jPVgseeVdwRBU8ebJCiXfL7iWeE/x3jeAX62EgC/MDUFo5n0/cdCsfu/oTLFmyZDv1XCIZxbZtDMMgm82SSqVIJpOkUqlNphPDw8SHEwwOxYgNDTEUjxOPx0kmEiTjw2TT6Umvp7j0gojyjLdgFSxaWsCL5veh+j0Q8KD4Ri1eiseNou643xkSyVxEGCbmQAKrf6hgARsvxMZOQVTcOlrZeOE1VoipAe8s3sncQpiWs4ZuRMxl8oU4h53MYCXS2PEkVjyFHU85cSLlrK8DUHC2wiiPoldE0OeVoS8sx7WgHK08slN9F0qBNo2Ulpby7W9/m5tuumlK5aVA27nJZrPU1NSwbt061q5dy7pVK/hw5Uoa2zoBKPGqnLqbzQV7aRxTZe/QwixvCR5+V/CrWvj4fnDfkQqubfiizFmC39YLfroWWoaddWqxLJSVhDnmyMM57tSz2PeAA1m8eDF77LEHoVBoGu9Gsj2wLAvDMIohn89v8XE+nyeXyxXTY49zuRzZQsjlc2SyWXL5HLlcnmwu65TP5ckZefKFOkahDSNfuE4+j2UYWGOmH20KRVOdaX4etzPFx+NyhJNvzPSdQqyPWZOl+Nzgc6P4pBVLIplphBCOGBhIOGEwgTkQH3dsJ8YMqKgKWmkYvWKs+CqkKyKo4cAO/fs91xGmhRUbLloozb64M320bwizawCRcayViltHn1+GvqAc16JK3Evm49qjCtW7Y24iLwXaNGBZFr/+9a+57rrrWLFixWZ3CR9hRKDV19cTCoXY8GMSQozLmyi9YTzV85NdY6LrTNYHcEaaJ6u74flNXWdz97AlfZzo/FT7uLm2hRBkMpniyHkymWR4eJhkMsng4CA9XR30dHbQ2tZKW2cvdqFuuU9hcUiwJAL7lSrsX6ay0G85X+qbeTqm8vBsrszmnsDN1t/EuVhW8NDbsD4GDx4Nly2dPpeTli14vlXwkzWwZgAWBSHogvohMMZ0yq1rhAI+wkE/kXCIcCRCJFpKtLScSGk50WiUUCg0LgSDQVRVLf4fCyGwbXujv7ux+bZtF8Nk+ZsKY9ubStmJ0pOVtSwL27axRvKFjWWNlLGwbOdYCHt8OdvCtgSWGF92bPsjx6J4zumPZVkYpolpGJiGgWWamKYjxKxCvmVaWMYmprVsDaoCuoZSCOgaaJojmArHiq46C+hdGormhHF1NBXF5dRB0xwvZ5rqWLN0HUVXUXUN3AUB5tERLh3F60J1u8Cz7etBJBLJ9CCEcCwz8RTWcNpxtpFIYyVSxbTjEdFJj11Lpbh1tNIwWlkhlI6JyyPopWHne0Iy5xBCYMeGMTr7MTr6MUfi9j5E3gRFQZ9fimuPebj2qMK95wL0heUo6tz+/1Q9LinQtoXVq1dz7LHHks1mCQaDPPvss5x77rmTls/lcuRyo96wEokEixYtmomuSiTbDU2BkBu+fwocOW/7fOkJIXinG368RvCPTij1gEuDnslnjUkkEolkZ0VTnUEWRXEG1UzL2cNlLLrmONyIBMetbVLDAdSQr2ANi6KGfNICtpMhLBuzo59cYydGcxdGcw9GR58jzN06qt9b3LJgLrLyBg9LnuyVAm1ryefztLa2MjQ0xP/+7//yk5/8hFdffXVSC9qDDz7IQw89NC7vmn2dweDJ2PA7Y8KiyuTnNzqeoAFlg3hzfZi03iR9nbTdDRJjy23uu3JL73Nzn6OyiXMo4/PcGnh18Grg0ZxjbYMLmIrCsO5FKOrEbW4hU/nt2FyRbf392bD6h10mv/wwy94R+P6pCguDM/MDt35Q8JM1gueaIeKBj+7n5uDdXGiKgi4sfJaB2954ilreEmQsSBuQMSFlOpZFhQk+GwXU0WTxb2CkrDL2mDHHhVhVJsqXLwASiUQygmkLhnIwmIVYzgn9hsagoZLIQyprk89v8F2uKo7IGiu4Cp4P9bAPNRRAjQZRoyEUn1sKL0kRkTfIN3WTr24mW9NOvr4DTAuXV2dhhZdDAlkOCptEPLP/N3P+ngrzvheTAm26OOOMM9hrr714+umnJzw/mQVNrkGT7EjYts1Xv/pVHn30Uc5fovDw8So+beYf8bZhwfc+hD82CPZfEOFf/+0HnHPJ1TPeD4lEIpFMTCaTob6+ntraWqqrq1lbvZ611dW0tbYS6+0bV1aLBlHLwuil4aL3PlckiBYNQtjviLKgf6dyBCGZPYRhkm/swqhpI7O2yRFsQrBw3705/7zzuOHyqzjqqKNmXOTPpH+KXUagnX766SxatIif/exnUyovnYRIdjQSiQTXXH01f/nrX7j3UIWbD5z9jbRX9Qsefx/+2SO45KSD+f6zf2b+wt1mtU8SiUSyq2DbNm1tbdTU1FBbW8ua9etYvW4ddXW19Hd2F9f1qn4v2rwS9KpS9IoorjLH4YZSGkIrDaG49Fm+E8mujJ3MkF3TRG5VI9k1jdjJDJF5lZx30QV86hM3cOwxx87I+44UaNvI/fffz/Lly1m0aBHDw8P88pe/5PHHH+f//u//OPPMM6fUhhRokh2J+vp6Ljj/PNqb6/nOiXDyHNqeTAjBHxrhm+8LDKHyja98gTu/9DCaJp04SCQSyXQwODhYFGE1NTWsWr+W6poaWhubMAr7fim6hlYZdUTYvFI888rQ55eiVJbItV6SHQZh2+Rr2si+X0P6nzXYiTThqnIuueJy7rv9Lvbbb7/tdm0p0LaRm266ib///e90dXURiUQ46KCDuO+++6YszkAKNMmOwwsvvMAVl11KVEnz76cq7BWemxtpD+UE3/kA/qdWcNje8/nxs7/lsCOPme1uSSQSyQ5BNpstTkmsqalhfXU1q9evo7G+nkRsqFhOKwmhzystBs+8MpSqErTy8Jz3kCeRbAnCtsnXtpN9r5rUO+sQ6RyLDzqAW2/6JLd94gai0ei0Xk8KtDmAFGiSuY4QgieffJLPfe6zHD8f/vUklbB77j/O/+wVPPiWoCEOd1x7MY/+4D8IBoOz3S2JRCKZVYQQ9Pf309LSQnNzM83NzTQ0NbK2ej11dXX0tHcWpyRqfi9qVUlRhLnnleKaV4ZSGUH17Jh7S0kk24IwTDIr6si8sYbsmiZUl85xZ5/O/Xd9hnPOOHNaLMRSoM0BpECTzGWy2Sy33XoLP/+PX3DTMvjsYQraDrQ427AFP1un8P2VNiUhL9994gkuu/622e6WRCKRbDeEEPT29tLc3FwUYU1NTdQ3NdLY3ERHaxu5TLZYXvW6Ucsi6FUl6FUluOaV4p5fJqckSiSbwYoNk3l7Hal/rMLsGqR8j924/Y7b+eytnyISiWx1u1KgzQGkQJPMVTo7O7n4wgv4cOUKHj5WcMGeO+6PdHtS8I134JV2wdmH78MP/uv37Lnv1DaTl0gkkrmEbdv09PQUrV8tLS00NTVR19hAY3MzXW3t5Md4i9b8XrTyCGppqLDvV6TgnCOKUhZCCXilCJNItgEhBPmaNtIvryD9QS2ay8WZl1zAo1+4n0MPPmSL25MCbQ4gBZpkLvLOO+9w8YUfRaQG+f6p8JHSHf/xFULwQis88q5gKK/w5Tuv54vf/hG6Lr2GSSSSuYNlWXR1dY2bgtjY3ERdQwNNLc10t3VgGkaxvBbwoZWHUUvDowKsPIKrPAplIVS/d/ZuRiLZxbBiw6RfW0XylZXY8SR7HfYRvvLZL3Dt5VdO+X1DCrQ5gBRokrmEEIIf//jH3HXnpziw1OZ7p6hUeOemM5CtJWkIvv8h/Hyd4JDF5fzHb/7CssOOmu1uSSSSXYRsNktbWxutra20trbS0tJCQ3MTdY2NtLW20tPRiWWaxfJa0I9aFkYrD6OXRdDKI7gromhljnt61e+ZxbuRSCQTIUyL7Io6kn//gHxtG6HKMm6+7Va+dNdnKC8v32RdKdDmAFKgSeYK2WyWO+64g5/+9Kdcva/Cl45ScO/EjrhW9Aq++IagKwUP3nsrn3/s36VLfolEsk0IIRgYGCgKr5G4rqmRppZmOtraGeofGFdHiwRRy0JopWH08gh6WQRXRdTxhlgaRvVKZxwSyY5MvrWH9N8/IPX2OlRF4ayLP8oj932Zww49bMLyUqDNAaRAk8wFPvjgA677xLXU1dTw0NGCi/feNdYjZEzBEyvgP9YJjlq6gP/43f+x9ICPzHa3JBLJHCWbzdLZ2Vm0frW2ttLQ1ERDcyMtBevXWAcciktHKwujlYbQyiJoZWFcZWFc5VGUkiBqidycWSLZVbCG02ReX0Xy7x9gxYbZ94hD+Orn7uOKSz82bvqjFGhzACnQJLNJPp/nkUce4dFHH2GfqMLjx8N+0Z1rSuNUeK9b8KU3BX0Zha9/4U7uefA7uFyu2e6WRCKZQVKpFO3t7eNCS1srjS3NtLV30N3ZSWIwNq6OFvY7a7/Kwo4FrCyMuyyKWhZGKQ2ihvzSAYdEIhmHsGyyK2pJvvhP8rXthKvKuf322/ncpz5NeXm5FGhzASnQJLPFCy+8wGc+fRc1tbXc9hHBrQdpuNVd9zFNG4J/+QD+q1qwZ2WYrz/yOFfdeCuq3HBVItmhEUIQj8eLoqujo4P29naaW1tpbG2mvb2dns4uUonhcfW0kB+1JIhWEioGvTSMXhKC0hB6aQjFLQdyJBLJ1lOc/vjWWjRV5exLLuSLn7qbk048UQq02UQKNMlMU1dXx2fvvZc//fnPHF6l8tWjVfYv2bzVrCctWN0P6wcF/RnwaLBXVGH5Ygi7d54R4upBZ9rjK+2CZXtUcPfnvsjVN9xKIBCY7a5JJJINME2Tnp4eurq66OrqoqOjg7a2NppaW2lua6G9vZ3erm5y6cxoJUVBjwRQxggvrTSEqySMXhaCSBCtJCinHkq2C8K0sNM57HQWkTMQhoHIm4i8CaZZTAvDRNg2CAG2ACEQhRjbBls4G4oX8wrHG75uiw0SI8cblSscK4rzt+/SUVwailtHcTlBDfhQg14nDnhRgz75nEwT1nCazGurGH7pn9ixJMGzj6Ljf16QAm22kAJNMlOsX7+eRx95hGf/+1nmBVQ+f5jN8j2YdPqNEII3OuG5FsE/OqA77eSXeaHSDxkT2ofB74K7DlH4+H6Tt7Uj8n6P4Ier4R8dgqDXxVUXLef6u+7jmGOP3anuUyKZa1iWxeDgIP39/fT399Pd3U13dzednZ20dXbQ2tFOd3c3vd09JAZjjH29UDQVLeqs7RonvkrDaNEQlATRIgEUXToEkkwfQgjs4TRWbBhrcNiJx6TtZAaRzmKncohcfvMNKs76RVQVVMURTaoKykhaKeaPnlcKFXHyx7WnbHCoFK8zLqHgCD3TRBiWIxILYhHTmrirbhdqSRC9tLDWsjjlN4ReUYJWHkHR5EyUqTLi/dF35H6su/R+KdBmCynQJNsTwzB48cUX+fGPnub3f/gj84Ian9zf4mP7KHg3Mej1WofgW+8L6oZgjxCcvggOrVQ4uByqAqNf9L1pwQ9WCf67Bq7eF75ylIKm7lzipW1Y8Jt6hf+ts+nLwO4VES7/2CVcffNdHHLIIVKsSSRjEEKQTqcZHh5meHiYZDJZTG94HBuK0d3XR29fH339fQwODjI0GCOdGGbDVwbFpTvCKhJAizgiS40G0SNBXNEQSsSPEgmghv3Oi6xEMk0IW2APp0aF1wQCzIoNjxcwmjpmaqyzFlH1eVACXlS/17E++b0oXrdjnRqxUhVidG3O/bYI23Ysf8kMIpXBTmWxkxnsZMb5DAYSmIXYjidHLXWail5Zgj6vdDTML8O1sBzVJ7eI2BRSoG0ljz32GL/97W+prq7G5/Nx3HHH8c1vfpN99913ym1IgSaZTtLpNO+88w6vvfYar7/6Cm+99RbpbI59SlQ+vp/g4r0U3JsYOLZswePvC/5jPRwzD+48WOGIqs1bxn5dK3jgbcGVS+GBo5U598MyHVi24P1e+Euzwt+abYZysNeCMq644nI+9vGbpViT7JCYpkkqlSqGTQmrRCLBUCLBUCJOPJFgOFk4NzxMKpkik0qTSaedaVmbQPW4nRdTrxs15EMNjg9ayI8e9KMGfYiABy0SRPF75PMlmXaEbWPHUxsLrmKcwBpKgjXmb1rXxllnx8WFtBoKFCxauybCtLBiw5i9MczuQczuGGb3AGb3INZgoijetPIIroXl6LtV4FpYgWu3CvR5pdLCXUAKtK3knHPO4corr+TII4/ENE2+/OUvs3r1atatWzfl9SpSoEm2hXg8zhtvvMGrr77Kay+/yD9XfIhhWkS8GodV2BxZCccu1Ng/am325UYIwdfeFvy6Dr505JZPWfyfWsFX3xJ88QiFG5bt3D9Mhi14qwv+2gwvtAiSBlRGA5x16omcdt6lHHXMsey3335yXzXJViGEIJ/Pk81myeVyZLPZjdJjj8cKrFQq5Yip5HAhJEkmkyQL59LpNJlUmmwmQy6dwTSMzfZHLYgpxeNG8bocYTUmr5gu5GteN6rPg+r1oHhd4HZBoZ7ice/SL66SmUNYNnY8ObHwGiu+7DGvpy59QsE1NlaDfvk3vA2IvIHRNYjZ0YfRXggdfdixpFNAU9HnleLarSDYCsJNKwvvcoM0UqBNE319fVRWVvLqq69y0kknTamOFGiSqSCEoK+vj8bGRlauXMm777zD+2+/wZqaeoQQVPpVjqi0OaJK4Yj5OvuETdQt/CL7/krBv30oeOx4hUu2ch+0f/3A5ker4bunKJy9x67xRZq3BP/shdc6Ff7RblM35AwO+j0u9ttzEXvttTd77/8R9thrH6qqqqisrKSyspKqqiqCweAu94MzVxBCYFkW+XwewzDI5/PFMPZ4U+cmK5vNZklnMqQyadLZDNlslkw2SyaTITNGWOWyWXL5PPlsjnzOCUY+jzGVNSoboqqoHheKxxFEqteF4naOFY+7ELtQi3kuVI8bzeNGLYgoxeMCjwtGBNhIXfkyKpmD2DkDayCONZBwptcNJMYcx7FiyXGOMBS3jlbirI1Si145C+mi+PLJ7+RZwk5lMQqizSwKt35EJgeA4nU71rZFlUVrm2u3CtSAd5Z7vv2QAm2aqK+vZ5999mH16tUceOCBU6ozItA6OjqK/wkbflRCiAnzNldmw3JTLTNZ+5srM9G5qZSZiInKTNbWlpaZqNxUykxWb3PtbFhupEwul9toatHg4CADAwP09/Uy0NvNQH8fA/39dPT0kc46L22aCntFYL8SOLBM4ZBKlYX+iS1kU3noBPBej+DeV+HThyjccfDW/zjZQvDZ1wR/b4PvnQL7l27Q1hQ6NNU+b7bMZgptr+ukDEFtDGriGm1xi640dKZgMDt+oBbA7dIoDQcpjYYJh8MEQ2GC4QihSAnBcJRQKEQwGCQYDOJyuYpB07RirGkaQohx//+KomyUNxETnbdtu/g3uiXBLkxrm6y+ZVlYloVpmuPSI2EkzzAMDNN0xI5pYph5TMPCsAxM08IwDQzDwLSctGkU6psmhmliWiPHVjE2jLxzPm9gGgaW4ZSfNjTVmZKjaU7apaHoOri0ovczZSStaaPe0Vw6iq6OekzTNVSXjurWUfSC17RCWTSnPaFrhXa14nUUtw7a3FuzIpFsC3Y2j9Ufx+wbKoowa7AQBhLYyfGeObWSYHEz8OLm4KXh4vYISsArn5EdDCEEVmwYo60Xo70fs7Mfo60Xs2ugOO1ULQk5Ym1huWNtW1i+U0yTVHRNCrTpQAjBhRdeSCwW4/XXX5+0XC6XI5fLFY8TiQSLFi2aiS5KJJOiKXDUPHjmDNC2cYF93hJc/7ygIe60O5Cdpk5KJBKJZOfGpaFoKsKywRjjdEPX0MvHiK+yCFrUEWR6ZRQtGtzhX8glU0eYFmb3IPm2Xsz2XoyOAcz2XqzBwl6GqoIa8IGuIfIGIrXjvYi8/ckg+3+nY7sLtJ1+k4Q777yTVatW8Y9//GOT5R577DEeeuihcXmX7TPG02mBqQzyTFRkwmrK+ORUymzuepvq3ma8u255m1vat0kKbe4jncgz7URsbrbPVPuoq+DVnf3ERoJb3diqYaOQ0d1YE7S8rYOBthD8v3/miaVtvnXC9HhgdGsK/34qXPmcwBZwz3EevK4xom9Kf9ubLzSVnm7u85nS3W7hsziS3tRzoIyJFQRe28RjmxOWsW1BxgJLgGk7g4amcGLLhpGl66JQRxSutdGQmNigYTFmO5yx/VHGe14e+52hFP5Rx1xnw3ufKG+kHbVwsOE2PGJMH8b1a4LzQmxs2dy5h/8kkh0XWwgSeejPOIN1A1noMXUGcgrxlIVhjDrfUEN+R2xVlqBXRNErwugVUbSqMsc7p7R+SQooulac4gjLivkj0yTNtt7iFEmjva94XvfolJR4meeHJXqWhR6TeX7w6HPvb6vESM/IdXZqC9pdd93F73//e1577TWWLFmyybKTWdDkGjTJbPC1r32Nb3zj6/zsLJVj5k1v260JwRXPCfaaF+XV1a34gqHpvYBEIpFI5gSJRIKamhpqamqorq5m1fp1rK9eT2tjE/ls4Z1HUdDLwmgVUbTKKK7KElyVJagVEbSKqHS5LtkuCCGwBhIYbb3YHf3kCuLN7B4sju7N23MPjjzmaJafcjqnnngSS5cuRZ3F7Tpm0j/FTinQhBDcdddd/O53v+OVV15hn3322eI2pJMQyWzxzDPP8MlPfpLPHKpw20HbZ/RoZZ/gE38TnH3oYn77dr30aiiRSCQ7KIZh0NTURG1tLTU1Naxet47V69fSWN/AUF9/sZwWDaIV9rtyVZXgnl+OWhl1NiyW0xAlcwRhmBidA1jtveQausjVt2N29IEAXzjEssMPYflpZ3LRuedxyCGHzKhgkwJtG7njjjt49tln+cMf/jBu77NIJILP55tSG1KgSWaDX//611x11ZVctpfgwWO2zJ3+lvJ8i+DTrwg+dcFRfO/3b8tpKhKJRDJHGfEYPGINq6mp4cN1a6iuqaGzpRWrsBmz4nGhV41uPuyZV442rwS1qkRawiQ7LHYmR76xC7Ohk2xdG/n6DkTOwBcJccQJx/Gx8y7g0gsuZOHChdu1H1KgbSOTvWj+9Kc/5frrr59SG1KgSWaaX/7yl1x77TWcu4fgm8czLevONsfP1goee1/w6M3n86Uf/Wm7X08ikUgkk5PJZKivry+KsHXr17Omeh0NdfWkEgVHC4riOOYoCDHXvDLc88tQq6Ko0ZDcfkGy0yNMi3xDJ8a6ZtLrmjEau0AIdl+2LxddeCE3Xn41Bx100LQPPEuBNgeQAk0yk/zlL3/hwgsv4KOLBY8eNzPibIRvvW/zzFp46ovXcdtjP5ux60okEsmuSDqdprm5maamJhobG6mprWXVurXU1tbQ29FV3P5FC/hQ55XgmleKXlWKe34ZWlUpWlXU2QJCIpEAjhOS3KoGMivrya5uRGTzlCyYx0Ufu4S7b7xl2sSaFGhzACnQJDPFu+++yyknn8TxlXm+ezLoMzz6KYTgK28JflcP//XNz3HF5789o9eXSCSSnYlMJkNHRwdtbW00NzfT2NhIbUM9tfV1tDS3EBuzLkzRNbSKaHFKomteKe4F5SgVUdSQ3JxZItlShGGSq2kj+89a0u9XI1JZKvfcnSuvvJJ7PnnbZp0Gbgop0OYAUqBJZoLu7m6OOOwQKqx+fn4WeLXZeRxNW3DPq4LXOuDPP32CMz5+z6z0QyKRSOYyqVSKjo4O2tvbaWtro729nda2Nhpbmmltb6Oro5Ph2NC4OnpJCLXc8Yiol0dwVUZxVZSglIXklESJZDsiTIvsmiay764ns6IOkTPY7+jDuOe2T3HdlVfj9Xq3qD0p0OYAUqBJtjf5fJ7TTz2Fmg/f5X/PV6jy2putsz3JWYJPviCoHYJX//o7Djrtolntj0QikcwUQghisRjd3d1FAdbe3k5LayuNrc20trXR09lFMp4YV08L+VFLgmglISeUhtFLQ+ilYZRoEK0sLKcjSiRzADuXJ/N+DenXV5Gvbccd9HPOpRfxlbs+w5GHHzGlNqRAmwNIgSbZ3tx555386Ic/4OdnqRxeOTcew0RecPVzgoyt8eY777Fov0Nnu0sSiUSy1ZimSU9PD11dXXR3d9PV1VUMLR3ttHd20NvTw2BvP0Y+P66uFg6Miq/SMFpJCL00hKs0jBINoZYEUNyuWboziUSytRjdg2T/sZrkG6ux4ykW7rc3d99xJ7ffcBPBYHDSelKgzQGkQJNsT0b2OnvwGIWr9p1b01s6k85G1pVhH2+saSJcVjXbXZJIJJJxJJPJcYJrJN3e0UFrZwddXZ309vSSGIwx7jVHUdBDfpRIADUSQIsEUKNB9EgQPRpEiwQhEkArCUrLl0SykyMsm+yqBtKvfUh2VQMun49zL7uYh+79Agf///buPCyqev8D+PvMMMOwo+CGuaKmuVtpopFkpS1e9yXlJ5lpV7su3ee2aIvZLe1p+2WL9SvXe9UyzTK9amWKZNdEVBLNlEoyBBREBIZ14PP7A+c0AzPDIDMwwPv1POdx5nyX8zkzH3A+nDPn9OlbpT8LNA/AAo3cZdeuXfjLX0bVyb3OrtfP2YKpewS3hrfEV4m/Q+9ds/O0iYiuR35+PtLS0pCWlob09HSkpaXhj9RUpKSexx8XLuBiRgYuX8xEUUGB1ThF5wVtkB+UID9og/wrCq8gP3gF+8MrOADaID8g0BeaQD8o2rq7sS0RNQymrKsojDuB/LgfUZ5rROf+vfDEvIV46MFp6nfVWKB5ABZo5A5xcXG4d+QI3NaiGO/UwxUba+L7NMHsvYJJd/TChn0nPLKQJKKGoaCgoErhdT41Fef++N2i8LqEImOlwsughzbIv+JUw2Bz4eUPXXDFcyXQF5ogfyi+3vwdRUS1JqYyFCUmw7g/EcWnf4d3oD8mTJ2C5x9/Aq1bt2aBVt9YoJGrxcXF4b57R6JPcDE+uFOptys21sQXvwqeOih4OvoeLP/3V/UdDhF5mMLCQrXgMv974cIFpKT+UXFVw/R0ZF28hIK8fKtxil4HzbUiy3LRNQuAJtgPMB8F8/Gupz0joqauNCMbhQd+RP7BExBjEboPGoAzSaeQk37J7bUBT7AmqgNffPEFpkyehAGhZXi/gRRnADAmXEG6EXhlw9cIDJiIRSu31HdIRFQHiouLkZGRoR71Mhde5y+k4vyFVKSlpSEz42KVqxoqOi9og/0rvt/VLACaTs3h1b89QoIDoA32B4L9Ko58GfQ84kVEHk3Xujl0k6MQMHYoChPO4LfYRLR587E62TYLNCI3Ki8vx8svv4wlS5ZgZAfg1dsV6DUNozgz+2tvIL8UWPz+VvyeGoEVW/bD25t/1SZqSEwmE7KysnDp0iVcunQJmZmZ6uO0jAykX8xA+sWLyMq8hCuXs1GYb7Qar3hpKwqva4u2XSC0vcLQvFnFUS8E+ldc1dCHpxoSUeOi6HXwjegF34hedbZNFmhEbnL27Fn8dfYsxMbF4W99FMztC3jwV87sUhQFT9ysoI2vYPnOQzgY3gofrP8YQ4ffW9+hETVaIoLi4mLk5eVZLbm5uXbX5eblIudqLnJyryI3Nxe5ubnIz89HgdGIksKiKttQL6zh7wNNoG/FPb16tIEuMBy+AdeuZBjoDyXYDxo/AwsvIqI60mgLtLi4OLz22ms4evQo0tPT8fnnn2PMmDH1HRY1AZcuXcLy5cvx/sr30NJQjrX3aDC4dX1HVXvRPRT0bwk8/f1V3H7XfRjcsyOiH3kMYydPQ5s2beo7PKJ6V15eDqPRWG0hZfk8J/cqcq5eRe61dfl5eTDmG2HMz0eZyeRwe1qDHlqDNxQfb8BbBxh0FacOGvRQmuuhCWsFxdAOPgY9/H0M0Ab6QhPgCwT4QBPgC8Vbx6KLiMgDNdoCzWg0om/fvpgxYwbGjx9f3+FQI1dSUoLY2Fhs+Ne/8OmWT6FXyvDoTYKZvTQwaOs7OtfpGaJg+yhg73lg45nfMf/vT+Bvf38Ct3TvhJEPjMaI0RMwcOBA6HS8eSt5NpPJBKPRCKPRiPz8/Gof5+blIicvD1dzryInNxe514qpvLw8FOTnozC/oMql3ytTtBpoDN7XiigdYNBDc62g0hj0UML8oBiaQWPQw9+nYp3GoIfWxxsagzc0PhVjYdBB9F5QNLxcPBFRY9QkruKoKEqNj6DxKo7kyJUrV5CYmIiDBw8ibv9e/HA4HvkFRWgfqMGkroKJN2oRrCuv7zDdLqdYEJuqYN8f5TiUDuSWAP4GHSIH9cfw+0Zj8O1R6NevH3x8fOo7VHKz8vJymEwmq6W0tNQljy2fl5aWoqSkBEVFRSgsLERBUREKCgtRUFiAwqJCFBYVo7CoEEVFRSgqKkJxcTGKr/1bUlxS8biwCKaSkup3SquBxltfcaRJr6v417Kg8vmzwFIMenj5GKDx0atFGAw64NoYjY834KXlESsiogbup/GLeRVHT2JZy5of26pvRcRmX0vl5VU/vDszf+W5r6dPTWK3NdbeOHvbqW3s9vpV5sxr6mzspaWlyMnJQXZ2NrKysnDhwgX8cf48fv/tLH766TTSLl0GAPjrFfQNFcR0BW4L80LXQFPFB7DyMuQUW26jymatY3Dc7HhsLeeutt1BBwEwuI1gcBsFZSI4cwVIuFSGhJ/isei7eJSUV9zrrWNYS7RtG4Y2N7RHq7B2CG4eAn9/f3Xx8/NTF/MFSJzJQ3t97OVjeXm51diaPnfFHO7cRllZGUpNJpSYSlFSWoKSUhNKSkpQaipFSWkpSk0mmEylKC01odRkUQxd+7dMLYjKUFb25/MyU1nFUmZCeZn5cRnKy8pQbqr41+1/69NqKm4wrNFA0XlB8dICOu21x15Qrj2GTvvnc4MXlAA94OVT0U+nhVbnhQDva0envHVQvPVQ9Lo/j0p56/4syrwa0eFvIiJqMHgE7Zri4mIUF//5iTo3Nxft2rWrg+iIiIiIiMjT7X+0GQa8msIjaHVl+fLlWLp0qdW60Z0BhyejKFb/2GuueGyjU3Unujga42iszXFOnFVTuYutIZXnue4+juJw8Lo6tR8O+lT3+pnXaxTAVwf4eQF+OsDgpVjNXaDRo9yJ/XQ2LleMd+fcNc23yrzKBYbyUqt15SIoMgFF5UCxCSguA4pMQKm9M0MVqIf7lMrr7MVQqU19fy2eKErFQ6vcsFhnuX+V+0L5cy7L8ZY5XPGcp7URERE1dGFFV+tkOzyCdo29I2j8DhoRERERUdNWl9en4BG0a7y9vXnzXSIiIiIiqleNtkDLz8/HL7/8oj4/d+4cEhMT0bx5c7Rv374eIyMiIiIiIrKt0RZoCQkJiIqKUp///e9/BwDExMRg3bp19RQVERERERGRfY22QBs2bJj7L/tMRERERETkQpr6DoCIiIiIiIgqsEAjIiIiIiLyECzQiIiIiIiIPAQLNCIiIiIiIg/BAo2IiIiIiMhDsEAjIiIiIiLyECzQiIiIiIiIPAQLNCIiIiIiIg/BAo2IiIiIiMhDsEAjIiIiIiLyECzQiIiIiIiIPAQLNCIiIiIiIg/BAo2IiIiIiMhDsEAjIiIiIiLyECzQiIiIiIiIPIRXfQfgqUQEAJCbm1vPkRARERERUX0y1wTmGsGdWKDZcfnyZQBAu3bt6jkSIiIiIiLyBJcvX0ZQUJBbt8ECzY7mzZsDAM6fP+/2N4GattzcXLRr1w5//PEHAgMD6zscasSYa1RXmGtUV5hrVFeuXr2K9u3bqzWCO7FAs0Ojqfh6XlBQEH/gqU4EBgYy16hOMNeorjDXqK4w16iumGsEt27D7VsgIiIiIiIip7BAIyIiIiIi8hAs0Ozw9vbGkiVL4O3tXd+hUCPHXKO6wlyjusJco7rCXKO6Upe5pkhdXCuSiIiIiIiIqsUjaERERERERB6CBRoREREREZGHYIFGRERERETkIZpMgVZUVIQlS5agW7duMBgMCAsLw8MPP4zU1NQaz5WTk4OFCxeiQ4cO8Pb2RocOHbBgwQLk5OS4PnBqcFyRazk5Odi0aROmTp2Km266CX5+fggICMCgQYOwYsUKlJaWunEPqKFw5e81S8nJyfDx8YGiKBg5cqSLoqWGzNW59ssvv2DWrFno2LEjDAYDWrRogYiICLz22msujpwaGlfm2p49e3DvvfciNDQUOp0OLVu2xAMPPIBvv/3WDZFTQ3L06FG88sorGDduHNq2bQtFUWAwGK57PpfXBtIEFBYWSkREhACQNm3ayKRJk2TgwIECQFq0aCG//PKL03NlZWVJ165dBYB07txZJk2aJD179hQA0qVLF8nKynLjnpCnc1WuPfPMMwJANBqN3HzzzTJ58mS58847xdvbWwDI0KFDxWg0unlvyJO58vdaZVFRUaIoigCQESNGuDBqaohcnWvbtm0Tg8EgiqLIgAEDZMqUKXL33XdL69atJTw83E17QQ2BK3PtjTfeEACiKIoMHTpUJk+eLLfeeqsAEADy/vvvu3FPyNONHj1azQXz4u3tfV1zuaM2aBIF2nPPPScAZPDgwZKXl6euN//wRkZGOj3X//zP/wgAGTdunJSWlqrr582bJwBk+vTpLo2dGhZX5dry5ctl8eLFkpqaarX+7Nmz0r59ewEgixYtcmns1LC48veapVWrVgkAmT17Ngs0EhHX5lpiYqLo9XoJCQmR7777zqqtrKxMjhw54rK4qeFxVa5dunRJ9Hq96PX6Knm2detWURRFfH19rbZBTcsrr7wizz//vOzYsUMyMjJqVaC5ozZo9AVaSUmJBAcHCwA5duxYlfY+ffoIAElISKh2rvT0dNFoNKLT6SQjI8OqraioSFq0aCFarbZKGzUNrsw1RzZt2iQApGPHjrWahxoud+XaxYsXpVmzZnLXXXfJ/v37WaCRy3Pt9ttvFwCyY8cOV4dKDZwrc23Hjh0CQEaOHGmzvW/fvgJADh8+XOu4qXG43gLNXbVBo/8O2sGDB5GTk4Pw8HD079+/SvuECRMAADt27Kh2rt27d6O8vByRkZFo1aqVVZu3tzdGjRqFsrIy7N692zXBU4PiylxzpG/fvgCAtLS0Ws1DDZe7cm3+/PkoLCzE+++/75I4qeFzZa6dPn0a3333Hbp164YHHnjA5bFSw+bKXHP2RsLNmzevWZBElbirNmj0BdqPP/4IABgwYIDNdvN6c7+6mosan7rKj99++w0A0Lp161rNQw2XO3Jt165d2Lx5MxYvXowuXbrUPkhqFFyZa+YLM9x9990oKirC+vXrMW/ePMyfPx+rVq1Cbm6ui6KmhsiVuXbrrbciKCgI+/btw8GDB63atm3bhhMnTiAiIoK/66jW3PXZz6t2YXm+8+fPAwBuuOEGm+3m9eZ+dTUXNT51lR8rVqwAAIwePbpW81DD5epcMxqNmDt3Lm688UY89dRTrgmSGgVX5tqpU6cAAD4+PujXrx/OnDlj1b5o0SJ89tlniIyMrE3I1EC5MteCg4OxatUqTJs2DZGRkRgyZAjatm2Lc+fO4ciRIxg5ciTWrVvnstip6XLXZ79GfwQtPz8fAODr62uz3c/Pz6pfXc1FjU9d5McHH3yAvXv3Ijg4GE8//fR1z0MNm6tz7dlnn8Xvv/+O999/H3q93jVBUqPgyly7cuUKAOCtt95CdnY2tm3bhpycHJw5cwZTp05FVlYWxowZg/T0dBdFTw2Jq3+vTZgwAbt370ZISAgOHjyIzZs3Iz4+Hi1btsSdd96JkJAQ1wROTZq7Pvs1+gKt4nt/gKIoDtvrei5qfNydHwcOHMCCBQugKArWrFmDsLCwWs1HDZcrcy0hIQHvvPMOpk+fjqioKJfER42HK3OtrKwMAGAymbBhwwaMHTsWQUFB6NatGzZu3Ihbb70VV65cwXvvvVf7wKnBcfX/oW+88QbuvvtuREZG4sSJE8jPz8eJEycwePBgPPHEE5g8eXKtYyZy12e/Rl+gBQQEAKg4hceWgoICAIC/v3+dzkWNjzvz48SJExgzZgxKSkqwYsUKjB079voDpQbPVblmMpkwa9YsBAUF4fXXX3dtkNQouOP/0LZt2+Kee+6p0j5jxgwAQGxs7PWESg2cK3PtwIED+Mc//oF+/fphy5Yt6N27N/z8/NC7d29s3boV/fv3x2effYavv/7adTtATZK7Pvs1+u+gtW/fHgDs3oHevN7cr67mosbHXfnx66+/YsSIEcjJycELL7yAefPm1S5QavBclWupqalITExE69atMXHiRKu2nJwcAEB8fDyGDRsGf39/7Ny5s5aRU0Pjyt9rHTt2BAB06NDBYfulS5dqGCU1Bq7MtX/9618AgHHjxkGjsT4WodVqMW7cOBw/fhyxsbE2/1hA5Cx3ffZr9AWa+ZLkx44ds9luXt+nT586nYsaH3fkR1paGu6++25kZGRgwYIFWLJkSe0DpQbP1bmWkZGBjIwMm21XrlzBgQMHEBQUdB2RUkPnylwzXzo9OzvbZvvly5cB8CyUpsqVuWb+UBwYGGiz3bzeXi4SOctttUGN78jWwBQXF0tQUFC1Nz6Mj4+vdq60tDTRaDSi1+vl4sWLVm3mm9FpNBpJT093WfzUcLgy10REsrOzpVevXgJAZsyYIeXl5a4OmRooV+eaLbxRNYm4NteMRqP4+fmJTqeT8+fPV2mfOXOmAJCZM2e6JHZqWFyZa9OnTxcAMn36dJvt0dHRAkCWL19e67ipccB13qjaXbVBoy/QRESeeeYZASARERGSn5+vrn/jjTcEgAwdOtSq/zvvvCM33nijPP3001XmmjZtmgCQ8ePHS2lpqbp+/vz5AkCio6PdtyPk8VyVa0ajUW677TYBIJMmTRKTyVQn8VPD4crfa7awQCMzV+ba008/LQDk/vvvt5pr9+7d4uXlJYqiyOHDh923M+TRXJVr27ZtEwCi1Wrlyy+/tGr74osvRKPRiEajkZ9//tl9O0MNSnUFWl3XBk2iQCssLJRBgwYJAGnTpo1MmjRJfR4SEiLJyclW/ZcsWSIAJCYmpspcmZmZEh4eLgAkPDxcJk+erB7lCA8Pl8zMzDraK/JErsq1hQsXqv+5TJ06VWJiYmwu1HS58veaLSzQyMyVuVZYWChDhgxR5xozZoxERESIRqMRAPLyyy/X0V6RJ3JVrpWXl8vEiRMFgACQW265RSZOnCi33HKLuo651rTt3LlTBg0apC4ARFEUq3U7d+5U+9d1bdDor+IIAAaDAfv378dzzz0HX19ffPHFF0hJSUFMTAyOHz9eozvJh4aG4siRI5g3bx5KSkrw+eef4+rVq/jb3/6G+Ph4hIaGunFPyNO5KtfM9wsqKyvDpk2bsH79epsLNV2u/L1G5Igrc81gMGDfvn14+eWXERwcjN27d+PUqVOIiorCzp07sXjxYjfuCXk6V+WaoijYvHkzVq9ejcjISPzyyy/4/PPPkZKSgvvuuw+7d+9mrjVxmZmZOHz4sLoAFZfEt1yXmZnp1FzuqA0UEd68i4iIiIiIyBM0iSNoREREREREDQELNCIiIiIiIg/BAo2IiIiIiMhDsEAjIiIiIiLyECzQiIiIiIiIPAQLNCIiIiIiIg/BAo2IiIiIiMhDsEAjIiIiIiLyECzQiIiIPNjJkycRHR2Ndu3aQa/XQ1EUKIqCxMTE+g6NALzwwgvqe0JE5Aos0IiowYqNjVU/GFVefHx80K5dOzzwwANYtWoVioqK6jtcoho7evQoBg4ciI0bNyI1NRWlpaX1HZJT1q1bp/4srlu3zqkxDz30kDomJSXFrfEREXkyFmhE1CgVFRUhNTUV//nPfzBr1iz069cPZ8+ere+wGoXGeMTAsqDwpOJg0aJFKCwsRGBgIFauXIn4+HgkJSUhKSkJ3bt3r+/wiIjIDbzqOwAiIleYM2cO5s6dqz4vKChAYmIi3nrrLZw+fRpnzpzByJEjcerUKfj4+NRjpETOKS0txYEDBwAAs2fPxpw5c+o5IiIiqgs8gkZEjULLli3Rq1cvdRk4cCBmz56tniIGAOfOncPq1avrOVIi52RlZaGkpAQA0K1bt3qOhoiI6goLNCJq1Hx8fPDyyy+rz3fv3l2P0RA5r7i4WH2s0+nqMRIiIqpLLNCIqNG77bbb1Me///67zT7ffPMNoqOj0alTJ/j4+CAwMBB9+/bFk08+ifT0dLtzV/4+1tWrV/HPf/4T/fv3R3BwsN2LJOzatQvR0dHo3Lkz/Pz8EBQUhJ49e2LKlCn47LPPUFhYaHebZ86cwfz589GzZ08EBQXBx8cHnTt3xowZM3Ds2DG74ywvqhIbGwsA+PTTTzF8+HC0aNECPj4+uPHGG/Hkk08iOzu7ynjz97SWLl2qrrN1gRZnv8O1fv16dczevXur7T937lwoigK9Xo/Lly9XaS8vL8eGDRtw3333oXXr1tDr9WjRogWioqKwcuVK9WiUrddkxowZ6rpOnTpV2Sfz61XZ9eaNI+ac6tSpk7puxowZVvG88MILVcalpKTg8ccfR8+ePREQEABfX1907doVjz76KJKSkhxus/K8+/btw8SJE9GuXTvodDp07NjxuvbF1X744Qc8++yzGDZsmPoeBwYG4qabbsKcOXPw008/ORxvvhCJeX/S09Px1FNPqa+Zrfc6NTUVjz32GDp37gyDwYCwsDD85S9/qTZn3377bfV1PXz4cLX7Nn78eCiKguDgYIc//0TUBAgRUQO1f/9+ASAAZMmSJXb7FRYWqv26d+9u1Zafny9jx45V220t/v7+smPHDptzL1myRO139uxZ6dixY5Xxa9euVftnZWXJ8OHDHW6v8hhLL774onh5edkdpyiKPP/889W+Xnv37pWpU6fanadLly6Snp5uNX7t2rXVxg1Azp07Z/e9sJSbmys+Pj4CQB566CGHfUtLSyU0NFQAyKhRo6q0X758WYYMGeIwrh49ekhKSord18TRsn//fqtxtc0bRyxzyt5SOd/Xr18v3t7edvtrtVpZtmyZ3W1azrt48eIq4zt06FCjfbDMFXu5XFlMTIzDHHIm/7Rarbz33nvVbqNDhw5y6NAhNafsvdexsbESGBhod3tLly61er8sZWdni8FgEADy6KOPOtz3zMxM0ev1TvUlosaPBRoRNVjOFmiHDx9W+915553qepPJJFFRUWph8+CDD8qWLVskISFBDh06JCtWrJD27dsLANHr9ZKQkFBlbssPZ3369BGdTifz5s2Tb775RhISEuTjjz+W//73vyIiYjQapXfv3mr/m2++Wf7v//5Pvv/+e0lISJDPP/9cHn/8cQkLC7P5ofa5555Tx0ZERMiqVavk0KFDkpCQIBs3bpTBgwer7W+//bbD1ysiIkIAyJgxY2Tbtm1y9OhR2bVrl9x///1qnylTpliNv3LliiQlJcmcOXPUPklJSVWWkpISJ99BkcmTJwsACQwMlMLCQrv9du7cqW7zk08+sWozmUxW+37HHXeo7+OXX34pY8aMUdvCw8MlLy9PHZufny9JSUny0ksvqX2++uqrKvuUn59vtb3a5o0jFy9elKSkJPnqq6/UmF566SWreC5evGj12iiKohaFS5Yske+++04OHTokb7zxhlURsnLlSpvbtMxhANK7d29Zs2aNxMfHy4EDB2TFihU12gd3FGgfffSRNGvWTGJiYmTNmjXy3XffybFjx2Tnzp3y4osvqvupKIp8++23DrcREhIiYWFh4u/vL88884zExsZKfHy8rF69Wn7++WcRETl37pwEBAQIANFoNPLXv/5V9u7dK0eOHJHVq1dL165dBYDccsstNgs0EZEHH3xQAEhQUJAUFBTY3fe33npLnePw4cNOvV5E1HixQCOiBsvZAm306NFqvxdffFFd//rrrwsA0el0smvXLptjs7OzpWfPngJAhg4dWqXdskDTaDTy9ddf241j4cKFat/HHntMysvLbfYrLi6WjIwMq3Xx8fGi0WgEgDz77LM2x5WVlUl0dLQAkICAALly5YpVe+WjRS+99FKVOcrLy+Wee+4RAOLl5SWXLl1yuM+19eWXX6pzbdmyxW4/89E+f3//Kh903333XXWO6dOn23xdLY8KPfnkk1XaLQuK6o4AuiJvnHHu3Llqi5ySkhJp27at+tocP368Sp+UlBRp06aNABBfX1/JzMys0scyL4YPHy5FRUXXFbOZ5etZubi0t1j+nNp6D1JTU8VoNNrdZk5Ojlpg2nvNLYtAf39/SUxMtDvfuHHj1L6bNm2q0p6bmyt9+/a1eu0q27dvn9q2ceNGu9syz9OrVy+7fYio6WCBRkQNlqMCraCgQP773//KqFGj1D6BgYFqwVFSUqJ+aH388ccdbmfXrl3qHMnJyVZtlsXKww8/bHeO7Oxs8fX1FQAyYMAAMZlMNdrX8ePHq0fd7BV2IhVHucynun300UdWbZavl6N59uzZo/bbvn17lXZXFmglJSUSEhKiHs2zJT8/X/z8/NQCrLIePXoIAAkNDZXc3Fybc5hMJunevbsAkGbNmlUpQJwt0FyVN85wpkDbvHmz2mf58uV259qwYYPa79VXX63SbvlHBmdPUXXE2dNh7S3XG8MXX3yhzpGVlVWl3bJAs/xjTWVpaWmi1WoFgDzwwAN2+1kenbf181BeXi5dunQRAHLXXXfZnOPo0aPq+DfffNOJvSSixo4XCSGiRmHp0qVWF1Hw9fVFREQEduzYAQAIDAzEZ599hhYtWgAA4uPj1Ys4TJo0yeHckZGR6uNDhw7Z7Tdt2jS7bfv370dBQQEAYP78+dBqtc7tGCruh2W++uSECRMc3iA6ODgYvXv3rjbWqVOn2p3n5ptvVh//9ttvTsd5PXQ6nfr679q1Czk5OVX6bN++HUajEUDV1zgtLQ2nT58GUPE+BgQE2NyOVqtVLwRy5coVhxdTccQdeVMb5gtVKIqChx9+2G6/iRMnIigoyGqMLUOGDPGYC4JUx2g0IiUlBadOncLJkydx8uRJq6td/vjjjw7HV/fzWlZWBgBWF5CpbODAgejZs6fddsv35dtvv8X58+er9Fm7di2Aip+F6OhohzETUdPAAo2IGrV27dph3rx5SEpKwl133aWuT0hIUB8PHjzY5tUIzYu/v7/aNyMjw+62+vTpY7ft+PHj6mPLD+7O+Omnn9TibtGiRQ5jVRRF3TdHsXbv3t1uW/PmzdXHeXl5NYr1epg/KJeUlGDr1q1V2jdu3AgAaNWqFYYPH27VdvLkSfXxoEGDHG7Hst1yXE24I29qw7wfHTt2RMuWLe320+v16N+/v9UYWxzl8PVau3YtpOKMHYdLTExMtXNlZWVh8eLFuPHGGxEQEIBOnTqhV69e6N27N3r37o3777/fqq89/v7+6Ny5s912y6te3nrrrQ5jMt9n0Z4ZM2bAy8sLIoL169dbtRUXF2PTpk0AgFGjRql/QCKipo0FGhE1CnPmzEFSUpK6JCcnIzs7G+fPn8fbb7+N9u3bW/W/dOnSdW3HXCjZ0qxZM7ttlh8W27RpU6NtuiNWX19fu20azZ//NZiPIrhTRESEetTGXIyZZWVl4euvvwYATJkypcqRR8vbAbRq1crhdlq3bm1zXE24472oDfN+VLfvwJ/772jfHeVwfTt69Ci6d++O5cuX4+zZsxARh/0dXao+ODjY4dgrV66ojx0VvoBzeWcuHNetW2cV9/bt29X3Y+bMmQ7nIaKmw6u+AyAicoWWLVuiV69eTve3LDxiY2MREhLi9HbsqclpizVhGetrr72GkSNHOjXOz8/PLfG4mqIomDp1KpYtW4a4uDhcuHABbdu2BVBxnzaTyQTA8Slp5nkcqe4DvTPckTeuUN2+A87tv7tyuLZKSkowadIkXL58GTqdDvPmzcPo0aPRrVs3NGvWDN7e3gAqTskNDw8H4Hh/q9tPy7GuyKtHHnkE27dvx2+//Ya4uDjccccdAP48vTEsLAwjRoyodh4iahpYoBFRk2T5wVqv19eouLseoaGh6uP09HSrmxBXxzLW0tJSt8daH6Kjo7Fs2TKUl5fj448/xj/+8Q8Afx5R69q1q81TzSxPx6zuNMKLFy/aHFcTdZ031THvhzOnUJr3/3r3vT7t27dP/T7ke++9h1mzZtnsZ3nkqzYsX6OLFy+iXbt2dvs6c1T13nvvRdu2bXHhwgWsXbsWd9xxBy5cuIBvvvkGABATE+OxxTER1T2e4khETZL5+zgA1FPo3GnAgAHq47i4uBqN7dmzJ/R6PYC6ibU6zhytqakePXqo74n5OzkpKSnqxTXsHT2zLJAOHz7scBvx8fE2xwHO71Nd5011zPuRkpLisFAoLS1VvwdZ30Xl9Th16pT6eMqUKXb7WX5HsDbMF9oBgCNHjjjsW107UHHE7qGHHgIAbN26Ffn5+Vi/fr1TFyIhoqaHBRoRNUlDhw5V/0r+wQcfIDc3163bi4qKUk85fOedd2r03S5fX1/14hixsbFWhUZ9MBgM6uPi4mKXzWsuwo4fP47Tp09j06ZN6uljU6dOtTkmLCwMPXr0AABs2bLF7kVNysrKsG7dOgAV37OyLJgB5/eprvOmOuYL34gI1qxZY7ff1q1bcfXqVasxDYn5NFfA/vf5ysvL8eGHH7pke1FRUeoRrcoX9rCUkJDg9AVnZs6cCUVRYDQasXnzZjUfIyMj0bVr11rHTESNBws0ImqSDAaDehpdRkYGpkyZol7K3Za8vDy8++6717294OBgPProowAqLnawcOFCu99dKS0trXI05JlnnlGP8kyZMgW//vqr3W2VlZVh06ZNSE1Nve54HbG8yImjOGrqwQcfVC9QsnHjRvVI2sCBAx1+gH3ssccAAJmZmZg3b57N13Xp0qX46aefAACzZs1Sv7Nk5uw+1XXeVGfs2LEICwsDACxbtszmpeX/+OMPNWZfX98GebTG8v23VzAtWrToum+fUFmbNm0wevRoAMCXX36JTz/9tEqf/Px8zJ492+k5O3XqpP6h5dlnn0VycjIAOLw9AhE1UXV61zUiIhdydKNqZ5hMJhk+fLg6R/v27WXZsmWyf/9+OX78uMTFxclHH30k06ZNEz8/PwkJCakyR01u2mw0GqV3795WN4v+8MMP5dChQ3L06FHZvn27PPHEE9K2bVubNya23Ja/v78sWLBA/vOf/8ixY8fk0KFD8vHHH8v8+fMlLCxMAEhSUpLd12v//v0OY3X0uiYnJ6vt99xzjxw4cEDOnj0rycnJkpycLKWlpdW+FvaY34/g4GB1GytWrHA4xmQyyeDBg9X+w4YNky1btsjRo0dl586dMm7cOLUtPDxc8vLyqsyRm5srBoNBvZH4V199JWfOnFH3qaCgwGp7tc0bZzhzo2oRkZ07d4qiKGpeLF26VA4ePCg//PCDvPnmm9KyZUt1npUrV9qcozY/R7ZY3qjaUeyWLG8iXflG1fn5+ep+eHl5ydy5c2XPnj2SkJAgn3zyifp+DBkyxOF2zdvo0KFDtfGcO3dOAgICBIBotVqZO3eu7Nu3TxISEmTNmjXSrVs3ASC33HKL078DPvnkE6sbWwcEBIjRaHTq9SGipoMFGhE1WLUt0ERECgoKZPr06VYfmuwtnTp1qjK+JgWaiEhmZqZERkZWuy17H2r/93//V7y9vasdr9frJTk52Wqsqwo0EZFJkybZ3XblD9c1sWbNGqu5tFqtZGRkVDvu8uXLVh/ObS09evSQlJQUu3M8+eSTdsdWfr1qmzfOcLZAExFZt26dw7zQarWybNkyu+M9vUATEdmzZ49aRNtahg0bJidPnnRZgSZS8TNjLtJsLUuWLKnR74Di4mIJDQ1V+8+aNcupOIioaeEpjkTUpPn4+GD9+vVISEjAnDlz0LNnTwQFBcHLywvBwcHo168fZs6cia1bt+L06dO13l5oaCgOHDiAbdu2YcKECbjhhhvg7e2NZs2aoVevXpg2bRq2b99u9ztXCxcuxK+//ornnnsOt912G0JDQ+Hl5QU/Pz9069YN48ePxwcffIALFy6gS5cutY7Xng0bNuDVV1/FwIEDERQUZHXvtNoYP3681ffBhg8f7tQ9vpo3b464uDj8+9//xsiRI9GqVSvodDqEhIRg2LBhePfdd5GYmIgOHTrYneOVV17BRx99hNtvvx3Nmzd3eFW9us6b6sTExODnn3/GggUL0KNHD/j5+cHHxwfh4eGYNWsWjh8/jkWLFrk9DncaMWIEEhISEB0djbCwMOh0OrRo0QJ33HEHPvzwQ3z77bcuv7XEsGHDcOrUKcyZMwcdOnSAXq9Hq1atcP/992PPnj144YUXajSfXq/HxIkT1ec8vZGIbFFEXHBjGCIiIiKq1u23346DBw+iR48e6vciiYgs8QgaERERUR04e/YsDh48CKDiqo5ERLawQCMiIiKqA6+//jqAiquBxsTE1HM0ROSpvOo7ACIiIqLGqLCwEBcuXEBBQQF27NiB1atXAwAeeeQRhIaG1nN0ROSp+B00IiIiIjeIjY1FVFSU1bobbrgBP/74o3rDcyKiyniKIxEREZEbKYqCsLAwREdH4/vvv2dxRkQO8QgaERERERGRh+ARNCIiIiIiIg/BAo2IiIiIiMhDsEAjIiIiIiLyECzQiIiIiIiIPAQLNCIiIiIiIg/BAo2IiIiIiMhDsEAjIiIiIiLyECzQiIiIiIiIPAQLNCIiIiIiIg/x/wmkbxDC9isZAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x1380 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ei_2by2.precinct_level_plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "intensive-westminster",
   "metadata": {},
   "source": [
    "# Reporting results overview\n",
    "\n",
    "See below for examples of how to access/report:\n",
    "- Polity-wide posterior means and credible intervals of voter preferences\n",
    "- Precinct-level posterior means and credible intervals of voter preferences\n",
    "- Information about polarization and candidate of choice"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "civil-rebound",
   "metadata": {},
   "source": [
    "**Polity-wide posterior means and credible intervals of voter preferences:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "sticky-physics",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: king99_pareto_modification\n",
      "        Computed from the raw b_i samples by multiplying by population and then getting\n",
      "        the proportion of the total pop (total pop=summed across all districts):\n",
      "        The posterior mean for the district-level voting preference of\n",
      "        e_asian for Hardy is\n",
      "        0.803\n",
      "        The posterior mean for the district-level voting preference of\n",
      "        non-e_asian for Hardy is\n",
      "        0.245\n",
      "        95% equal-tailed Bayesian credible interval for district-level voting preference of\n",
      "        e_asian for Hardy is\n",
      "        [0.60628653 0.95224906]\n",
      "        95% equal-tailed Bayesian credible interval for district-level voting preference of\n",
      "        non-e_asian for Hardy is\n",
      "        [0.21917476 0.27890371]\n",
      "        \n"
     ]
    }
   ],
   "source": [
    "# EI objects have a summary method that gives a report\n",
    "print(ei_2by2.summary()) \n",
    "\n",
    "# Can also use for r x c\n",
    "#print(ei_rbyc.summary())\n",
    "\n",
    "# Can also use for BayesGoodmansER\n",
    "#print(bayes_goodman_ei.summary())\n",
    "\n",
    "# Note: Goodman's ER also has a summary function, although it just gives point esimates\n",
    "#print(goodmans_er.summary())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "checked-values",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "e_asian  support for  Hardy 0.8033227509202162\n",
      "non- e_asian  support for  Hardy 0.24522777595768844\n"
     ]
    }
   ],
   "source": [
    "posterior_mean_voting_prefs = ei_2by2.posterior_mean_voting_prefs\n",
    "print(demographic_group_name_2by2, \" support for \", candidate_name_2by2, posterior_mean_voting_prefs[0])\n",
    "print(\"non-\",demographic_group_name_2by2, \" support for \", candidate_name_2by2, posterior_mean_voting_prefs[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "saved-business",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "e_asian  support for  Hardy [0.60628653 0.95224906]\n",
      "non- e_asian  support for  Hardy [0.21917476 0.27890371]\n"
     ]
    }
   ],
   "source": [
    "credible_interval_95_mean_voting_prefs = ei_2by2.credible_interval_95_mean_voting_prefs\n",
    "print(demographic_group_name_2by2, \" support for \", candidate_name_2by2, credible_interval_95_mean_voting_prefs[0])\n",
    "print(\"non-\", demographic_group_name_2by2, \" support for \", candidate_name_2by2, credible_interval_95_mean_voting_prefs[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "hydraulic-composer",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Means and intervals for BayesGoodmanER estimates\n",
    "x_vals, means, lower_bounds, upper_bounds = bayes_goodman_ei.compute_credible_int_for_line()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "classified-appraisal",
   "metadata": {},
   "source": [
    "**Precinct-level posterior means and credible intervals**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "spread-relay",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7895715826280489\n",
      "[0.50611032 0.9796245 ]\n"
     ]
    }
   ],
   "source": [
    "precinct_posterior_means, precinct_credible_intervals = ei_2by2.precinct_level_estimates()\n",
    "#precinct_posterior_means has shape num_precincts x 2 (groups) x 2 (candidates)\n",
    "print(precinct_posterior_means[5][0][0]) # Estimated (posterior mean) support for the candidate from the group in precinct 5\n",
    "#precinct_credible_intervals has shape num_precincts x 2 (groups) x 2 (candidates) x 2 (endpoints))\n",
    "print(precinct_credible_intervals[5][0][0]) # 95% credible interval of support for the candidate from the group in precint 5"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "requested-nerve",
   "metadata": {},
   "source": [
    "**Polarization and candidates of choice**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "critical-marine",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There is a 95% probability that the difference between the groups' preferences for Hardy (e_asian - non-e_asian) is between [0.33, 0.73].\n",
      "(0.3272458553551288, 0.7324285280696301)\n"
     ]
    }
   ],
   "source": [
    "print(ei_2by2.polarization_report(percentile=95, reference_group=0, verbose=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "hungry-physiology",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There is a 99.4% probability that the difference between the groups' preferences for Hardy (e_asian -  non-e_asian) is more than 0.25.\n",
      "99.4375\n"
     ]
    }
   ],
   "source": [
    "print(ei_2by2.polarization_report(threshold=0.25, reference_group=0, verbose=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "close-contest",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "99.4375"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# set verbose=False to just show numerical value\n",
    "ei_2by2.polarization_report(threshold=0.25, reference_group=0, verbose=False) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "rational-uruguay",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There is a 90% probability that the difference between the groups' preferences for Kolstad (ind - e_asian) is between [-0.26, 0.23].\n",
      "(-0.25765541924473595, 0.231758358308947)\n"
     ]
    }
   ],
   "source": [
    "# For the r by c case, specify the groups you wish to compare and the candidate\n",
    "print(ei_rbyc.polarization_report(percentile=90, groups=['ind', 'e_asian'], candidate='Kolstad', verbose=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "accurate-grill",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ind\n",
      "     - In 1.925 percent of samples, the district-level vote preference of \n",
      "       ind for Hardy was higher than for any other candidate.\n",
      "     - In 1.025 percent of samples, the district-level vote preference of \n",
      "       ind for Kolstad was higher than for any other candidate.\n",
      "     - In 97.05 percent of samples, the district-level vote preference of \n",
      "       ind for Nadeem was higher than for any other candidate.\n",
      "e_asian\n",
      "     - In 100.0 percent of samples, the district-level vote preference of \n",
      "       e_asian for Hardy was higher than for any other candidate.\n",
      "     - In 0.0 percent of samples, the district-level vote preference of \n",
      "       e_asian for Kolstad was higher than for any other candidate.\n",
      "     - In 0.0 percent of samples, the district-level vote preference of \n",
      "       e_asian for Nadeem was higher than for any other candidate.\n",
      "non_asian\n",
      "     - In 0.0 percent of samples, the district-level vote preference of \n",
      "       non_asian for Hardy was higher than for any other candidate.\n",
      "     - In 100.0 percent of samples, the district-level vote preference of \n",
      "       non_asian for Kolstad was higher than for any other candidate.\n",
      "     - In 0.0 percent of samples, the district-level vote preference of \n",
      "       non_asian for Nadeem was higher than for any other candidate.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{('ind', 'Hardy'): 0.01925,\n",
       " ('ind', 'Kolstad'): 0.01025,\n",
       " ('ind', 'Nadeem'): 0.9705,\n",
       " ('e_asian', 'Hardy'): 1.0,\n",
       " ('e_asian', 'Kolstad'): 0.0,\n",
       " ('e_asian', 'Nadeem'): 0.0,\n",
       " ('non_asian', 'Hardy'): 0.0,\n",
       " ('non_asian', 'Kolstad'): 1.0,\n",
       " ('non_asian', 'Nadeem'): 0.0}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ei_rbyc.candidate_of_choice_report(verbose=True) #pass a verbose=False argument to just output the dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "accomplished-regard",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In 98.075 percent of samples, the district-level candidates of choice for e_asian and ind voters differ.\n",
      "In 98.975 percent of samples, the district-level candidates of choice for non_asian and ind voters differ.\n",
      "In 100.0 percent of samples, the district-level candidates of choice for non_asian and e_asian voters differ.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{('e_asian', 'ind'): 0.98075,\n",
       " ('ind', 'e_asian'): 0.98075,\n",
       " ('non_asian', 'ind'): 0.98975,\n",
       " ('ind', 'non_asian'): 0.98975,\n",
       " ('non_asian', 'e_asian'): 1.0,\n",
       " ('e_asian', 'non_asian'): 1.0}"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For each pair of groups, this function reports the fraction samples for which\n",
    "# the `preferred candidate` of one group (as measured by: who is the candidate supported\n",
    "# by the plurality within that group according to the sampled district-level support value)\n",
    "# is different from the `preferred candidate` of the others group\n",
    "ei_rbyc.candidate_of_choice_polarization_report() #pass a verbose=False argument to just output the relevant numerica values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "increased-malawi",
   "metadata": {},
   "source": [
    "# Accessing samples\n",
    "\n",
    "Most of the models that PyEI supports (although not Goodman's regression) are Bayesian models, and inference proceeds by using MCMC methods to sample from the posterior distributions of interest. Samples are accessible so that you can work with them directly if desired.\n",
    "\n",
    "We can use the `sampled_voting_prefs` property of a fitted EI object to access the samples of district-level voter prefernences of each group for each candidate, which are calculated by weighting each set of precinct-level samples by the population and summing.\n",
    "\n",
    "We can also access samples from the posterior distribution by accessing them by name from the fitted ei object's `sim_trace` property."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "electrical-crazy",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.24522777595768844"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using `sampled_voting_prefs`\n",
    "\n",
    "sampled_voting_prefs = ei_2by2.sampled_voting_prefs # ei.sampled_voting_prefs is samples of district-level voter preference: list of length 2\n",
    "sampled_voting_prefs[0] #samples of district-wide support of specified group for specified candidate\n",
    "sampled_voting_prefs[1] #samples of district-wide support of (complement of specified group) for specified candidate\n",
    "sampled_voting_prefs[0].mean() #posterior mean\n",
    "sampled_voting_prefs[1].mean() #posterior mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "alert-drinking",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "</symbol>\n",
       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "</symbol>\n",
       "</defs>\n",
       "</svg>\n",
       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
       ":root {\n",
       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body[data-theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;kappa&#x27; (chain: 4, draw: 1000, kappa_dim_0: 3)&gt;\n",
       "array([[[101.69128399, 102.17291274, 104.23896931],\n",
       "        [100.0123853 , 100.26340253, 100.06586876],\n",
       "        [104.13616278, 102.32186106, 101.06223517],\n",
       "        ...,\n",
       "        [101.15975497, 100.77871518, 100.31983241],\n",
       "        [101.03862401, 102.0596201 , 101.34283334],\n",
       "        [103.17571803, 100.77586385, 100.8337737 ]],\n",
       "\n",
       "       [[100.56773284, 101.94434254, 100.45170031],\n",
       "        [101.77908097, 100.39840716, 100.05956254],\n",
       "        [100.16564506, 100.38698306, 100.0527535 ],\n",
       "        ...,\n",
       "        [100.01107877, 101.21519472, 100.48881323],\n",
       "        [102.15172397, 100.38413437, 101.93764816],\n",
       "        [101.89616442, 100.06423078, 102.48638706]],\n",
       "\n",
       "       [[103.49655177, 101.98608931, 100.61102031],\n",
       "        [100.04005254, 100.85134599, 100.36244091],\n",
       "        [101.20911448, 101.42019295, 101.86921713],\n",
       "        ...,\n",
       "        [100.18459277, 101.96507079, 100.39011258],\n",
       "        [100.5325818 , 101.41083379, 100.53943913],\n",
       "        [101.19349635, 100.94700598, 101.40952829]],\n",
       "\n",
       "       [[100.02750374, 100.90570992, 100.14790768],\n",
       "        [100.29800313, 101.83616323, 103.55010556],\n",
       "        [100.23389038, 102.75839775, 101.88889924],\n",
       "        ...,\n",
       "        [100.63248447, 103.89455363, 103.02286878],\n",
       "        [100.12389866, 100.04161676, 100.22552211],\n",
       "        [102.95128275, 100.65201318, 100.5122016 ]]])\n",
       "Coordinates:\n",
       "  * chain        (chain) int64 0 1 2 3\n",
       "  * draw         (draw) int64 0 1 2 3 4 5 6 7 ... 993 994 995 996 997 998 999\n",
       "  * kappa_dim_0  (kappa_dim_0) int64 0 1 2</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'kappa'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 4</li><li><span class='xr-has-index'>draw</span>: 1000</li><li><span class='xr-has-index'>kappa_dim_0</span>: 3</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-8d24fdcd-7967-460e-934b-48afd1dc6911' class='xr-array-in' type='checkbox' checked><label for='section-8d24fdcd-7967-460e-934b-48afd1dc6911' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>101.7 102.2 104.2 100.0 100.3 100.1 ... 100.0 100.2 103.0 100.7 100.5</span></div><div class='xr-array-data'><pre>array([[[101.69128399, 102.17291274, 104.23896931],\n",
       "        [100.0123853 , 100.26340253, 100.06586876],\n",
       "        [104.13616278, 102.32186106, 101.06223517],\n",
       "        ...,\n",
       "        [101.15975497, 100.77871518, 100.31983241],\n",
       "        [101.03862401, 102.0596201 , 101.34283334],\n",
       "        [103.17571803, 100.77586385, 100.8337737 ]],\n",
       "\n",
       "       [[100.56773284, 101.94434254, 100.45170031],\n",
       "        [101.77908097, 100.39840716, 100.05956254],\n",
       "        [100.16564506, 100.38698306, 100.0527535 ],\n",
       "        ...,\n",
       "        [100.01107877, 101.21519472, 100.48881323],\n",
       "        [102.15172397, 100.38413437, 101.93764816],\n",
       "        [101.89616442, 100.06423078, 102.48638706]],\n",
       "\n",
       "       [[103.49655177, 101.98608931, 100.61102031],\n",
       "        [100.04005254, 100.85134599, 100.36244091],\n",
       "        [101.20911448, 101.42019295, 101.86921713],\n",
       "        ...,\n",
       "        [100.18459277, 101.96507079, 100.39011258],\n",
       "        [100.5325818 , 101.41083379, 100.53943913],\n",
       "        [101.19349635, 100.94700598, 101.40952829]],\n",
       "\n",
       "       [[100.02750374, 100.90570992, 100.14790768],\n",
       "        [100.29800313, 101.83616323, 103.55010556],\n",
       "        [100.23389038, 102.75839775, 101.88889924],\n",
       "        ...,\n",
       "        [100.63248447, 103.89455363, 103.02286878],\n",
       "        [100.12389866, 100.04161676, 100.22552211],\n",
       "        [102.95128275, 100.65201318, 100.5122016 ]]])</pre></div></div></li><li class='xr-section-item'><input id='section-74299784-93e9-4bba-bffd-9498e7eeccbf' class='xr-section-summary-in' type='checkbox'  checked><label for='section-74299784-93e9-4bba-bffd-9498e7eeccbf' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-fdac4c31-3376-4637-9237-38f0b5dcf227' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fdac4c31-3376-4637-9237-38f0b5dcf227' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-22a9dca8-42db-4c2a-97a7-bce705a59623' class='xr-var-data-in' type='checkbox'><label for='data-22a9dca8-42db-4c2a-97a7-bce705a59623' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 995 996 997 998 999</div><input id='attrs-101e2f65-d046-4a41-87e1-972106956cbe' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-101e2f65-d046-4a41-87e1-972106956cbe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ae6643bb-fc12-4d3b-a542-187a0f3e57ea' class='xr-var-data-in' type='checkbox'><label for='data-ae6643bb-fc12-4d3b-a542-187a0f3e57ea' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 997, 998, 999])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>kappa_dim_0</span></div><div class='xr-var-dims'>(kappa_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2</div><input id='attrs-f35f0260-bf99-42d6-a8b5-04dff7b148c6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f35f0260-bf99-42d6-a8b5-04dff7b148c6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-649a0cd8-68a0-42f0-903b-14e523037f54' class='xr-var-data-in' type='checkbox'><label for='data-649a0cd8-68a0-42f0-903b-14e523037f54' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-72bbae47-b63e-4a32-bc97-8cb7230d343f' class='xr-section-summary-in' type='checkbox'  ><label for='section-72bbae47-b63e-4a32-bc97-8cb7230d343f' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-efe37f53-d1df-496f-9ce9-fb7b3c141df8' class='xr-index-data-in' type='checkbox'/><label for='index-efe37f53-d1df-496f-9ce9-fb7b3c141df8' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1, 2, 3], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-465117a6-108c-4d8e-9663-20207ab78b52' class='xr-index-data-in' type='checkbox'/><label for='index-465117a6-108c-4d8e-9663-20207ab78b52' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "       ...\n",
       "       990, 991, 992, 993, 994, 995, 996, 997, 998, 999],\n",
       "      dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=1000))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>kappa_dim_0</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-3380acfa-a952-45e7-bec4-1ea6e5890c2e' class='xr-index-data-in' type='checkbox'/><label for='index-3380acfa-a952-45e7-bec4-1ea6e5890c2e' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1, 2], dtype=&#x27;int64&#x27;, name=&#x27;kappa_dim_0&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-de3a113b-b82d-4835-bb95-77c27a4fde83' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-de3a113b-b82d-4835-bb95-77c27a4fde83' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'kappa' (chain: 4, draw: 1000, kappa_dim_0: 3)>\n",
       "array([[[101.69128399, 102.17291274, 104.23896931],\n",
       "        [100.0123853 , 100.26340253, 100.06586876],\n",
       "        [104.13616278, 102.32186106, 101.06223517],\n",
       "        ...,\n",
       "        [101.15975497, 100.77871518, 100.31983241],\n",
       "        [101.03862401, 102.0596201 , 101.34283334],\n",
       "        [103.17571803, 100.77586385, 100.8337737 ]],\n",
       "\n",
       "       [[100.56773284, 101.94434254, 100.45170031],\n",
       "        [101.77908097, 100.39840716, 100.05956254],\n",
       "        [100.16564506, 100.38698306, 100.0527535 ],\n",
       "        ...,\n",
       "        [100.01107877, 101.21519472, 100.48881323],\n",
       "        [102.15172397, 100.38413437, 101.93764816],\n",
       "        [101.89616442, 100.06423078, 102.48638706]],\n",
       "\n",
       "       [[103.49655177, 101.98608931, 100.61102031],\n",
       "        [100.04005254, 100.85134599, 100.36244091],\n",
       "        [101.20911448, 101.42019295, 101.86921713],\n",
       "        ...,\n",
       "        [100.18459277, 101.96507079, 100.39011258],\n",
       "        [100.5325818 , 101.41083379, 100.53943913],\n",
       "        [101.19349635, 100.94700598, 101.40952829]],\n",
       "\n",
       "       [[100.02750374, 100.90570992, 100.14790768],\n",
       "        [100.29800313, 101.83616323, 103.55010556],\n",
       "        [100.23389038, 102.75839775, 101.88889924],\n",
       "        ...,\n",
       "        [100.63248447, 103.89455363, 103.02286878],\n",
       "        [100.12389866, 100.04161676, 100.22552211],\n",
       "        [102.95128275, 100.65201318, 100.5122016 ]]])\n",
       "Coordinates:\n",
       "  * chain        (chain) int64 0 1 2 3\n",
       "  * draw         (draw) int64 0 1 2 3 4 5 6 7 ... 993 994 995 996 997 998 999\n",
       "  * kappa_dim_0  (kappa_dim_0) int64 0 1 2"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# By name of parameters, using the fitted ei object's `sim_trace` property.\n",
    "\n",
    "# Shape is: num_samples x (dimensionality of parameter)\n",
    "ei_2by2.sim_trace['posterior']['b_1'] # shape is chains x draws x precincts\n",
    "\n",
    "# Shape is: num_samples x (dimensionality of parameter)\n",
    "ei_rbyc.sim_trace['posterior']['kappa']\n",
    "\n",
    "# Note - to collapose chains and draws into one dimension you can do e.g.\n",
    "# sim_trace[\"posterior\"][\"b_1\"].stack(all_draws=[\"chain\", \"draw\"]).values.T \n",
    "# which gives a (chains*num_draws) x num_precincts array"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "finite-massage",
   "metadata": {},
   "source": [
    "# Currently supported models - quick reference\n",
    "\n",
    "The names of currently supported models are as follows:\n",
    "\n",
    "- TwoByTwoEI\n",
    "    * `king99`\n",
    "    * `king99_pareto_modification`\n",
    "    * `truncated_normal`\n",
    "    \n",
    "- RowByColumnEI\n",
    "    * `multinomial-dirichlet`\n",
    "    * `multinomial-dirichlet-modified`\n",
    "    * `greiner-quinn`\n",
    "    \n",
    "- GoodmansER\n",
    "    * `is_weighted_regression=False` (not weighted by precinct populations)\n",
    "    * `is_weighted_regression=True` (weighted by precinct populations)\n",
    "  \n",
    "- GoodmansERBayes\n",
    "\n",
    "Using pymc's `model_to_graphviz` function is a nice way to visualize the Bayesian models. For example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "median-potato",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n",
       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
       " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
       "<!-- Generated by graphviz version 9.0.0 (0)\n",
       " -->\n",
       "<!-- Pages: 1 -->\n",
       "<svg width=\"414pt\" height=\"364pt\"\n",
       " viewBox=\"0.00 0.00 414.39 364.45\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 360.45)\">\n",
       "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-360.45 410.39,-360.45 410.39,4 -4,4\"/>\n",
       "<g id=\"clust1\" class=\"cluster\">\n",
       "<title>cluster42</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M140.72,-8C140.72,-8 266.72,-8 266.72,-8 272.72,-8 278.72,-14 278.72,-20 278.72,-20 278.72,-235.13 278.72,-235.13 278.72,-241.13 272.72,-247.13 266.72,-247.13 266.72,-247.13 140.72,-247.13 140.72,-247.13 134.72,-247.13 128.72,-241.13 128.72,-235.13 128.72,-235.13 128.72,-20 128.72,-20 128.72,-14 134.72,-8 140.72,-8\"/>\n",
       "<text text-anchor=\"middle\" x=\"263.97\" y=\"-15.2\" font-family=\"Times,serif\" font-size=\"14.00\">42</text>\n",
       "</g>\n",
       "<!-- phi_2 -->\n",
       "<g id=\"node1\" class=\"node\">\n",
       "<title>phi_2</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"44.72\" cy=\"-315.79\" rx=\"44.72\" ry=\"40.66\"/>\n",
       "<text text-anchor=\"middle\" x=\"44.72\" y=\"-327.24\" font-family=\"Times,serif\" font-size=\"14.00\">phi_2</text>\n",
       "<text text-anchor=\"middle\" x=\"44.72\" y=\"-310.74\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
       "<text text-anchor=\"middle\" x=\"44.72\" y=\"-294.24\" font-family=\"Times,serif\" font-size=\"14.00\">Uniform</text>\n",
       "</g>\n",
       "<!-- b_2 -->\n",
       "<g id=\"node7\" class=\"node\">\n",
       "<title>b_2</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"165.72\" cy=\"-198.48\" rx=\"28.81\" ry=\"40.66\"/>\n",
       "<text text-anchor=\"middle\" x=\"165.72\" y=\"-209.93\" font-family=\"Times,serif\" font-size=\"14.00\">b_2</text>\n",
       "<text text-anchor=\"middle\" x=\"165.72\" y=\"-193.43\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
       "<text text-anchor=\"middle\" x=\"165.72\" y=\"-176.93\" font-family=\"Times,serif\" font-size=\"14.00\">Beta</text>\n",
       "</g>\n",
       "<!-- phi_2&#45;&gt;b_2 -->\n",
       "<g id=\"edge3\" class=\"edge\">\n",
       "<title>phi_2&#45;&gt;b_2</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M75.26,-285.69C93.23,-268.57 115.92,-246.94 134.08,-229.64\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"136.15,-232.5 140.98,-223.06 131.32,-227.43 136.15,-232.5\"/>\n",
       "</g>\n",
       "<!-- kappa_2 -->\n",
       "<g id=\"node2\" class=\"node\">\n",
       "<title>kappa_2</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"150.72\" cy=\"-315.79\" rx=\"43.66\" ry=\"40.66\"/>\n",
       "<text text-anchor=\"middle\" x=\"150.72\" y=\"-327.24\" font-family=\"Times,serif\" font-size=\"14.00\">kappa_2</text>\n",
       "<text text-anchor=\"middle\" x=\"150.72\" y=\"-310.74\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
       "<text text-anchor=\"middle\" x=\"150.72\" y=\"-294.24\" font-family=\"Times,serif\" font-size=\"14.00\">Pareto</text>\n",
       "</g>\n",
       "<!-- kappa_2&#45;&gt;b_2 -->\n",
       "<g id=\"edge4\" class=\"edge\">\n",
       "<title>kappa_2&#45;&gt;b_2</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M155.89,-275.11C156.94,-267.05 158.05,-258.46 159.14,-250.08\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"162.59,-250.71 160.41,-240.34 155.65,-249.81 162.59,-250.71\"/>\n",
       "</g>\n",
       "<!-- phi_1 -->\n",
       "<g id=\"node3\" class=\"node\">\n",
       "<title>phi_1</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"256.72\" cy=\"-315.79\" rx=\"44.72\" ry=\"40.66\"/>\n",
       "<text text-anchor=\"middle\" x=\"256.72\" y=\"-327.24\" font-family=\"Times,serif\" font-size=\"14.00\">phi_1</text>\n",
       "<text text-anchor=\"middle\" x=\"256.72\" y=\"-310.74\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
       "<text text-anchor=\"middle\" x=\"256.72\" y=\"-294.24\" font-family=\"Times,serif\" font-size=\"14.00\">Uniform</text>\n",
       "</g>\n",
       "<!-- b_1 -->\n",
       "<g id=\"node6\" class=\"node\">\n",
       "<title>b_1</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"241.72\" cy=\"-198.48\" rx=\"28.81\" ry=\"40.66\"/>\n",
       "<text text-anchor=\"middle\" x=\"241.72\" y=\"-209.93\" font-family=\"Times,serif\" font-size=\"14.00\">b_1</text>\n",
       "<text text-anchor=\"middle\" x=\"241.72\" y=\"-193.43\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
       "<text text-anchor=\"middle\" x=\"241.72\" y=\"-176.93\" font-family=\"Times,serif\" font-size=\"14.00\">Beta</text>\n",
       "</g>\n",
       "<!-- phi_1&#45;&gt;b_1 -->\n",
       "<g id=\"edge1\" class=\"edge\">\n",
       "<title>phi_1&#45;&gt;b_1</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M251.56,-275.11C250.51,-267.05 249.4,-258.46 248.31,-250.08\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"251.8,-249.81 247.04,-240.34 244.86,-250.71 251.8,-249.81\"/>\n",
       "</g>\n",
       "<!-- kappa_1 -->\n",
       "<g id=\"node4\" class=\"node\">\n",
       "<title>kappa_1</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"362.72\" cy=\"-315.79\" rx=\"43.66\" ry=\"40.66\"/>\n",
       "<text text-anchor=\"middle\" x=\"362.72\" y=\"-327.24\" font-family=\"Times,serif\" font-size=\"14.00\">kappa_1</text>\n",
       "<text text-anchor=\"middle\" x=\"362.72\" y=\"-310.74\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
       "<text text-anchor=\"middle\" x=\"362.72\" y=\"-294.24\" font-family=\"Times,serif\" font-size=\"14.00\">Pareto</text>\n",
       "</g>\n",
       "<!-- kappa_1&#45;&gt;b_1 -->\n",
       "<g id=\"edge2\" class=\"edge\">\n",
       "<title>kappa_1&#45;&gt;b_1</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M332.5,-285.99C314.54,-268.87 291.77,-247.17 273.53,-229.79\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"276.25,-227.54 266.6,-223.18 271.42,-232.61 276.25,-227.54\"/>\n",
       "</g>\n",
       "<!-- votes_count -->\n",
       "<g id=\"node5\" class=\"node\">\n",
       "<title>votes_count</title>\n",
       "<ellipse fill=\"lightgrey\" stroke=\"black\" cx=\"203.72\" cy=\"-81.16\" rx=\"57.45\" ry=\"40.66\"/>\n",
       "<text text-anchor=\"middle\" x=\"203.72\" y=\"-92.61\" font-family=\"Times,serif\" font-size=\"14.00\">votes_count</text>\n",
       "<text text-anchor=\"middle\" x=\"203.72\" y=\"-76.11\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
       "<text text-anchor=\"middle\" x=\"203.72\" y=\"-59.61\" font-family=\"Times,serif\" font-size=\"14.00\">Binomial</text>\n",
       "</g>\n",
       "<!-- b_1&#45;&gt;votes_count -->\n",
       "<g id=\"edge5\" class=\"edge\">\n",
       "<title>b_1&#45;&gt;votes_count</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M229.7,-160.99C226.67,-151.78 223.36,-141.74 220.15,-132\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"223.52,-131.04 217.06,-122.64 216.87,-133.23 223.52,-131.04\"/>\n",
       "</g>\n",
       "<!-- b_2&#45;&gt;votes_count -->\n",
       "<g id=\"edge6\" class=\"edge\">\n",
       "<title>b_2&#45;&gt;votes_count</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M177.75,-160.99C180.78,-151.78 184.09,-141.74 187.3,-132\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"190.58,-133.23 190.39,-122.64 183.93,-131.04 190.58,-133.23\"/>\n",
       "</g>\n",
       "</g>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "<graphviz.graphs.Digraph at 0x17c3c55a0>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = ei_2by2.sim_model\n",
    "pm.model_to_graphviz(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "serious-liquid",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n",
       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
       " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
       "<!-- Generated by graphviz version 9.0.0 (0)\n",
       " -->\n",
       "<!-- Pages: 1 -->\n",
       "<svg width=\"241pt\" height=\"503pt\"\n",
       " viewBox=\"0.00 0.00 241.00 503.45\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 499.45)\">\n",
       "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-499.45 237,-499.45 237,4 -4,4\"/>\n",
       "<g id=\"clust1\" class=\"cluster\">\n",
       "<title>cluster3</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M20,-365.63C20,-365.63 84,-365.63 84,-365.63 90,-365.63 96,-371.63 96,-377.63 96,-377.63 96,-475.45 96,-475.45 96,-481.45 90,-487.45 84,-487.45 84,-487.45 20,-487.45 20,-487.45 14,-487.45 8,-481.45 8,-475.45 8,-475.45 8,-377.63 8,-377.63 8,-371.63 14,-365.63 20,-365.63\"/>\n",
       "<text text-anchor=\"middle\" x=\"84.62\" y=\"-372.83\" font-family=\"Times,serif\" font-size=\"14.00\">3</text>\n",
       "</g>\n",
       "<g id=\"clust2\" class=\"cluster\">\n",
       "<title>cluster3 x 3</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M116,-267.63C116,-267.63 198,-267.63 198,-267.63 204,-267.63 210,-273.63 210,-279.63 210,-279.63 210,-475.45 210,-475.45 210,-481.45 204,-487.45 198,-487.45 198,-487.45 116,-487.45 116,-487.45 110,-487.45 104,-481.45 104,-475.45 104,-475.45 104,-279.63 104,-279.63 104,-273.63 110,-267.63 116,-267.63\"/>\n",
       "<text text-anchor=\"middle\" x=\"188.12\" y=\"-274.83\" font-family=\"Times,serif\" font-size=\"14.00\">3 x 3</text>\n",
       "</g>\n",
       "<g id=\"clust3\" class=\"cluster\">\n",
       "<title>cluster42 x 3 x 3</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M116,-137.82C116,-137.82 198,-137.82 198,-137.82 204,-137.82 210,-143.82 210,-149.82 210,-149.82 210,-247.63 210,-247.63 210,-253.63 204,-259.63 198,-259.63 198,-259.63 116,-259.63 116,-259.63 110,-259.63 104,-253.63 104,-247.63 104,-247.63 104,-149.82 104,-149.82 104,-143.82 110,-137.82 116,-137.82\"/>\n",
       "<text text-anchor=\"middle\" x=\"174.25\" y=\"-145.02\" font-family=\"Times,serif\" font-size=\"14.00\">42 x 3 x 3</text>\n",
       "</g>\n",
       "<g id=\"clust4\" class=\"cluster\">\n",
       "<title>cluster42 x 3</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M101,-8C101,-8 213,-8 213,-8 219,-8 225,-14 225,-20 225,-20 225,-117.82 225,-117.82 225,-123.82 219,-129.82 213,-129.82 213,-129.82 101,-129.82 101,-129.82 95,-129.82 89,-123.82 89,-117.82 89,-117.82 89,-20 89,-20 89,-14 95,-8 101,-8\"/>\n",
       "<text text-anchor=\"middle\" x=\"199.75\" y=\"-15.2\" font-family=\"Times,serif\" font-size=\"14.00\">42 x 3</text>\n",
       "</g>\n",
       "<!-- kappa -->\n",
       "<g id=\"node1\" class=\"node\">\n",
       "<title>kappa</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"52\" cy=\"-438.79\" rx=\"35.71\" ry=\"40.66\"/>\n",
       "<text text-anchor=\"middle\" x=\"52\" y=\"-450.24\" font-family=\"Times,serif\" font-size=\"14.00\">kappa</text>\n",
       "<text text-anchor=\"middle\" x=\"52\" y=\"-433.74\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
       "<text text-anchor=\"middle\" x=\"52\" y=\"-417.24\" font-family=\"Times,serif\" font-size=\"14.00\">Pareto</text>\n",
       "</g>\n",
       "<!-- phi_kappa -->\n",
       "<g id=\"node2\" class=\"node\">\n",
       "<title>phi_kappa</title>\n",
       "<polygon fill=\"none\" stroke=\"black\" points=\"202.12,-357.63 111.88,-357.63 111.88,-300.13 202.12,-300.13 202.12,-357.63\"/>\n",
       "<text text-anchor=\"middle\" x=\"157\" y=\"-340.33\" font-family=\"Times,serif\" font-size=\"14.00\">phi_kappa</text>\n",
       "<text text-anchor=\"middle\" x=\"157\" y=\"-323.83\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
       "<text text-anchor=\"middle\" x=\"157\" y=\"-307.33\" font-family=\"Times,serif\" font-size=\"14.00\">Deterministic</text>\n",
       "</g>\n",
       "<!-- kappa&#45;&gt;phi_kappa -->\n",
       "<g id=\"edge2\" class=\"edge\">\n",
       "<title>kappa&#45;&gt;phi_kappa</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M70.58,-403.6C78.57,-390.77 88.68,-376.65 100,-365.63 100.88,-364.78 101.78,-363.93 102.7,-363.1\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"104.8,-365.91 110.3,-356.85 100.35,-360.5 104.8,-365.91\"/>\n",
       "</g>\n",
       "<!-- b -->\n",
       "<g id=\"node4\" class=\"node\">\n",
       "<title>b</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"157\" cy=\"-210.98\" rx=\"45.25\" ry=\"40.66\"/>\n",
       "<text text-anchor=\"middle\" x=\"157\" y=\"-222.43\" font-family=\"Times,serif\" font-size=\"14.00\">b</text>\n",
       "<text text-anchor=\"middle\" x=\"157\" y=\"-205.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
       "<text text-anchor=\"middle\" x=\"157\" y=\"-189.43\" font-family=\"Times,serif\" font-size=\"14.00\">Dirichlet</text>\n",
       "</g>\n",
       "<!-- phi_kappa&#45;&gt;b -->\n",
       "<g id=\"edge3\" class=\"edge\">\n",
       "<title>phi_kappa&#45;&gt;b</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M157,-299.83C157,-288.85 157,-275.9 157,-263.37\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"160.5,-263.56 157,-253.56 153.5,-263.56 160.5,-263.56\"/>\n",
       "</g>\n",
       "<!-- phi -->\n",
       "<g id=\"node3\" class=\"node\">\n",
       "<title>phi</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"157\" cy=\"-438.79\" rx=\"45.25\" ry=\"40.66\"/>\n",
       "<text text-anchor=\"middle\" x=\"157\" y=\"-450.24\" font-family=\"Times,serif\" font-size=\"14.00\">phi</text>\n",
       "<text text-anchor=\"middle\" x=\"157\" y=\"-433.74\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
       "<text text-anchor=\"middle\" x=\"157\" y=\"-417.24\" font-family=\"Times,serif\" font-size=\"14.00\">Dirichlet</text>\n",
       "</g>\n",
       "<!-- phi&#45;&gt;phi_kappa -->\n",
       "<g id=\"edge1\" class=\"edge\">\n",
       "<title>phi&#45;&gt;phi_kappa</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M157,-397.92C157,-388.55 157,-378.57 157,-369.22\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"160.5,-369.32 157,-359.32 153.5,-369.32 160.5,-369.32\"/>\n",
       "</g>\n",
       "<!-- votes_count -->\n",
       "<g id=\"node5\" class=\"node\">\n",
       "<title>votes_count</title>\n",
       "<ellipse fill=\"lightgrey\" stroke=\"black\" cx=\"157\" cy=\"-81.16\" rx=\"60.1\" ry=\"40.66\"/>\n",
       "<text text-anchor=\"middle\" x=\"157\" y=\"-92.61\" font-family=\"Times,serif\" font-size=\"14.00\">votes_count</text>\n",
       "<text text-anchor=\"middle\" x=\"157\" y=\"-76.11\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
       "<text text-anchor=\"middle\" x=\"157\" y=\"-59.61\" font-family=\"Times,serif\" font-size=\"14.00\">Multinomial</text>\n",
       "</g>\n",
       "<!-- b&#45;&gt;votes_count -->\n",
       "<g id=\"edge4\" class=\"edge\">\n",
       "<title>b&#45;&gt;votes_count</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M157,-169.88C157,-158.34 157,-145.6 157,-133.48\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"160.5,-133.62 157,-123.62 153.5,-133.62 160.5,-133.62\"/>\n",
       "</g>\n",
       "</g>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "<graphviz.graphs.Digraph at 0x16a97cfa0>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = ei_rbyc.sim_model\n",
    "pm.model_to_graphviz(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "wicked-depth",
   "metadata": {},
   "source": [
    "# Convergence issues and warnings\n",
    "\n",
    "There may be [convergence](https://mc-stan.org/docs/2_27/reference-manual/convergence.html) issues when fitting models to data. The presence of [divergences](https://mc-stan.org/docs/2_27/reference-manual/divergent-transitions.html) (especially a large number of divergences), a high value of [$\\hat{R}$](https://mc-stan.org/docs/2_27/reference-manual/notation-for-samples-chains-and-draws.html) (a convergence diagnostic calculated by comparing multiple chains - more samples will generally lower it, and looking for a value below 1.1 is a good rule of thumb), and a low [effective sample size](https://mc-stan.org/docs/2_27/reference-manual/effective-sample-size-section.html) are three signs that there were computational difficulties during sampling that may have made the results inaccurate. \n",
    "\n",
    "`PyEI` relies on `PyMC` for sampling, and thus benefits from the diagnostic checks that `PyMC` automatically performs, and their associated warnings. (For some examples of how to compute and access convergence diagnostics with `PyEI` (using the [`ArviZ`](https://arviz-devs.github.io/arviz/) library), see `examples/model_eval_and_comparison_demo.ipynb`.) `PyEI` is thus intentionally vocal about computational difficulties that may be encountered. The warnings issued should be taken seriously, as they indicate that the samples generated may not be representative of the posterior distribution, in which case results derived from these samples should not be trusted.\n",
    "\n",
    "If convergence issues are encountered, there are several steps the user can take that may help mitigate; we mention several here and encourage the user to consult helpful resources in e.g. within the [`PyMC`](https://pymc3-testing.readthedocs.io/en/rtd-docs/notebooks/Diagnosing_biased_Inference_with_Divergences.html) and [`Stan`](https://mc-stan.org/users/documentation/case-studies/divergences_and_bias.html) documentation. \n",
    "\n",
    "Increasing the optional `target_accept` argument to the fit method might help (\"might\" because the default value of 0.99 is already quite high, and the sorts of geometric problems that frustrate the sampler require an exponentially different scale). Increasing the number of tuning steps (by setting the `tune` argument of the fit method) is sometimes helpful by giving `PyMC` more time to estimate a step size and covariance matrix, and increasing the total number of draws (with the `draws` argument of the fit method) will allow for a larger effective sample size (at the cost of more computation time).\n",
    "\n",
    "Another, more involved approach to take would be [reparametrizing](https://mc-stan.org/docs/2_27/stan-users-guide/reparameterization-section.html) the model, which changes the posterior geometry in ways that (hopefully) ease computational difficulties. For example, the `king99_pareto_modification` model implemented in `PyEI` is a reparametrization of the `king99` model that is in the literature. On the Santa Clara example dataset, the modified (reparameterized) model encounters fewer computational issues (see example below). However, `king99_pareto_modification` does not so far seem to outperform `king99` in all settings, so the user may wish to try both.\n",
    "\n",
    "Computational difficulties can be a symptom of poor choice of model for the data at hand, and so it is possible that computational issues indicate a fundamental unsuitability of the ecological inference model to the data at hand. It is the authors' hope that additional ecological inference approaches may be developed that avoid some of the computational difficulties that seem to occur in sampling from existing models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "unavailable-greene",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Compiling...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Compilation time = 0:00:02.183012\n",
      "Sampling...\n",
      "Compiling.. :   0%|          | 0/4200 [00:00<?, ?it/s]\n",
      "\u001b[A\n",
      "\u001b[A\n",
      "\n",
      "\u001b[A\u001b[A\n",
      "\n",
      "Running chain 0:   0%|          | 0/4200 [00:02<?, ?it/s]\n",
      "\u001b[A\n",
      "\n",
      "Running chain 0:   5%|▌         | 210/4200 [00:02<00:02, 1592.80it/s]\n",
      "Running chain 0:  10%|█         | 420/4200 [00:02<00:02, 1778.18it/s]\n",
      "\n",
      "\u001b[A\u001b[A\n",
      "Running chain 0:  15%|█▌        | 630/4200 [00:02<00:01, 1857.83it/s]\n",
      "\u001b[A\n",
      "\n",
      "Running chain 0:  35%|███▌      | 1470/4200 [00:02<00:01, 1944.69it/s]\n",
      "\n",
      "\u001b[A\u001b[A\n",
      "Running chain 0:  40%|████      | 1680/4200 [00:03<00:01, 1437.02it/s]\n",
      "\n",
      "\u001b[A\u001b[A\n",
      "Running chain 0:  45%|████▌     | 1890/4200 [00:03<00:02, 1043.83it/s]\n",
      "\n",
      "Running chain 0:  60%|██████    | 2520/4200 [00:03<00:01, 1386.55it/s]\n",
      "\n",
      "Running chain 0:  70%|███████   | 2940/4200 [00:04<00:00, 1510.98it/s]\n",
      "\u001b[A\n",
      "\n",
      "Running chain 0:  80%|████████  | 3360/4200 [00:04<00:00, 1036.61it/s]\n",
      "\n",
      "Running chain 0:  90%|█████████ | 3780/4200 [00:05<00:00, 1005.00it/s]\n",
      "\u001b[A\n",
      "\n",
      "Running chain 0: 100%|██████████| 4200/4200 [00:05<00:00, 960.85it/s] \n",
      "\n",
      "\u001b[A\u001b[A\n",
      "\u001b[A\n",
      "\n",
      "\u001b[A\u001b[A\n",
      "\u001b[A\n",
      "\n",
      "\u001b[A\u001b[A\n",
      "\u001b[A\n",
      "\u001b[A\n",
      "\n",
      "\u001b[A\u001b[A\n",
      "\u001b[A\n",
      "\n",
      "\u001b[A\u001b[A\n",
      "\n",
      "\u001b[A\u001b[A\n",
      "\u001b[A\n",
      "\n",
      "\u001b[A\u001b[A\n",
      "\n",
      "\u001b[A\u001b[A\n",
      "\u001b[A\n",
      "\n",
      "\u001b[A\u001b[A\n",
      "\u001b[A\n",
      "\n",
      "\u001b[A\u001b[A\n",
      "\u001b[A\n",
      "\n",
      "\u001b[A\u001b[A\n",
      "\u001b[A\n",
      "\u001b[A\n",
      "\u001b[A\n",
      "Running chain 0: 100%|██████████| 4200/4200 [00:13<00:00, 317.82it/s]\n",
      "Running chain 1: 100%|██████████| 4200/4200 [00:13<00:00, 317.85it/s]\n",
      "Running chain 2: 100%|██████████| 4200/4200 [00:13<00:00, 317.87it/s]\n",
      "Running chain 3: 100%|██████████| 4200/4200 [00:13<00:00, 317.89it/s]\n",
      "Sampling time = 0:00:13.364096\n",
      "Transforming variables...\n",
      "Transformation time = 0:00:00.052897\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: king99\n",
      "        Computed from the raw b_i samples by multiplying by population and then getting\n",
      "        the proportion of the total pop (total pop=summed across all districts):\n",
      "        The posterior mean for the district-level voting preference of\n",
      "        e_asian for Hardy is\n",
      "        0.904\n",
      "        The posterior mean for the district-level voting preference of\n",
      "        non-e_asian for Hardy is\n",
      "        0.228\n",
      "        95% equal-tailed Bayesian credible interval for district-level voting preference of\n",
      "        e_asian for Hardy is\n",
      "        [0.6706714  0.99726638]\n",
      "        95% equal-tailed Bayesian credible interval for district-level voting preference of\n",
      "        non-e_asian for Hardy is\n",
      "        [0.20844695 0.26874302]\n",
      "        \n"
     ]
    }
   ],
   "source": [
    "# Witting this particular EI model to the Santa Clara example data \n",
    "# results in computational difficulties \n",
    "ei_2by2orig = TwoByTwoEI(model_name=\"king99\", lmbda=0.5)\n",
    "\n",
    "# Fit the model\n",
    "ei_2by2orig.fit(group_fraction_2by2, \n",
    "       votes_fraction_2by2, \n",
    "       precinct_pops, \n",
    "       demographic_group_name=demographic_group_name_2by2, \n",
    "       candidate_name=candidate_name_2by2, \n",
    "       precinct_names=precinct_names, \n",
    "       draws=1200, # optional\n",
    "       tune=3000, # optional\n",
    "       target_accept=.99# optional\n",
    ")\n",
    "\n",
    "# Generate a simple report to summarize the results\n",
    "print(ei_2by2orig.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "artificial-julian",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Compiling...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Compilation time = 0:00:01.461096\n",
      "Sampling...\n",
      "Compiling.. :   0%|          | 0/4200 [00:00<?, ?it/s]\n",
      "\u001b[A\n",
      "\u001b[A\n",
      "\n",
      "\u001b[A\u001b[A\n",
      "\n",
      "\u001b[A\u001b[A\n",
      "Running chain 0:   0%|          | 0/4200 [00:02<?, ?it/s]\n",
      "\n",
      "\u001b[A\u001b[A\n",
      "\n",
      "\u001b[A\u001b[A\n",
      "Running chain 0:  10%|█         | 420/4200 [00:02<00:01, 2343.84it/s]\n",
      "\n",
      "\u001b[A\u001b[A\n",
      "Running chain 0:  20%|██        | 840/4200 [00:02<00:01, 3013.03it/s]\n",
      "\n",
      "\u001b[A\u001b[A\n",
      "\n",
      "Running chain 0:  30%|███       | 1260/4200 [00:02<00:01, 2725.35it/s]\n",
      "\u001b[A\n",
      "\n",
      "Running chain 0:  40%|████      | 1680/4200 [00:02<00:00, 2875.85it/s]\n",
      "\n",
      "\u001b[A\u001b[A\n",
      "Running chain 0:  50%|█████     | 2100/4200 [00:02<00:00, 3061.93it/s]\n",
      "\n",
      "Running chain 0:  60%|██████    | 2520/4200 [00:02<00:00, 3260.73it/s]\n",
      "\n",
      "\u001b[A\u001b[A\n",
      "Running chain 0:  70%|███████   | 2940/4200 [00:03<00:00, 3221.58it/s]\n",
      "\n",
      "\u001b[A\u001b[A\n",
      "Running chain 0:  80%|████████  | 3360/4200 [00:03<00:00, 2695.90it/s]\n",
      "\n",
      "\u001b[A\u001b[A\n",
      "Running chain 0:  90%|█████████ | 3780/4200 [00:03<00:00, 2335.68it/s]\n",
      "\u001b[A\n",
      "Running chain 0: 100%|██████████| 4200/4200 [00:03<00:00, 2147.04it/s]\n",
      "\u001b[A\n",
      "\u001b[A\n",
      "Running chain 0: 100%|██████████| 4200/4200 [00:04<00:00, 991.68it/s] \n",
      "Running chain 1: 100%|██████████| 4200/4200 [00:04<00:00, 991.86it/s] \n",
      "Running chain 2: 100%|██████████| 4200/4200 [00:04<00:00, 992.14it/s] \n",
      "Running chain 3: 100%|██████████| 4200/4200 [00:04<00:00, 992.41it/s] \n",
      "Sampling time = 0:00:04.414299\n",
      "Transforming variables...\n",
      "Transformation time = 0:00:00.070410\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: king99_pareto_modification\n",
      "        Computed from the raw b_i samples by multiplying by population and then getting\n",
      "        the proportion of the total pop (total pop=summed across all districts):\n",
      "        The posterior mean for the district-level voting preference of\n",
      "        e_asian for Hardy is\n",
      "        0.805\n",
      "        The posterior mean for the district-level voting preference of\n",
      "        non-e_asian for Hardy is\n",
      "        0.245\n",
      "        95% equal-tailed Bayesian credible interval for district-level voting preference of\n",
      "        e_asian for Hardy is\n",
      "        [0.61600244 0.94778532]\n",
      "        95% equal-tailed Bayesian credible interval for district-level voting preference of\n",
      "        non-e_asian for Hardy is\n",
      "        [0.22010983 0.27731872]\n",
      "        \n"
     ]
    }
   ],
   "source": [
    "# Using this reparametrized version of the model helps mitigate the problems,\n",
    "# although some difficulties persist\n",
    "# Create a TwobyTwoEI object\n",
    "ei_2by2 = TwoByTwoEI(model_name=\"king99_pareto_modification\", pareto_scale=15, pareto_shape=2)\n",
    "\n",
    "# Fit the model\n",
    "ei_2by2.fit(group_fraction_2by2, \n",
    "       votes_fraction_2by2, \n",
    "       precinct_pops, \n",
    "       demographic_group_name=demographic_group_name_2by2, \n",
    "       candidate_name=candidate_name_2by2, \n",
    "       precinct_names=precinct_names, \n",
    "       draws=1200, # optional\n",
    "       tune=3000, # optional\n",
    "       target_accept=.99 # optional\n",
    ")\n",
    "\n",
    "# Generate a simple report to summarize the results\n",
    "print(ei_2by2.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd7092e6-d53d-4513-b6a1-32a65e54148a",
   "metadata": {},
   "source": [
    "## Saving and loading a fitted ei model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "static-entertainment",
   "metadata": {},
   "outputs": [],
   "source": [
    "# saving\n",
    "#to_netcdf(ei,'filepath')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "ed27f829-3422-4d8c-aa38-14f1237cb33d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# loading\n",
    "#ei = from_netcdf('filepath')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "82b5f8f4-310c-4f3d-9890-28bb89940617",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.10.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}