docs/source/notebooks/MLDA_benchmarks_tuning.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MLDA benchmarks and tuning demonstration\n",
    "\n",
    "The Multi-Level Delayed Acceptance MCMC algorithm (MLDA) proposed in [1] is based on the idea of running a hierarchy of MCMC chains at different model coarseness levels (where the coarser the model, the less accurate the approximation to the fine model). The samples generated by a chain are used to propose samples for the level above. This can improve effective sample size in the fine chain in models that have the ability to be represented with varying coarseness.\n",
    "\n",
    "This notebook compares the performance of MLDA to other PyMC3 algorithms (Metropolis, DEMCMC, DEMCMC-Z), using a groundwater flow inverse model. We demonstrate how the algorithms' performances compare when the dimension, resolution and lambda_cov parameter of the model changes.\n",
    "\n",
    "The second part of the notebook explains the tuning parameters of MLDA and how they should be selected."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Benchmarks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib import cm\n",
    "import pathlib\n",
    "import ipywidgets\n",
    "import time\n",
    "import fastprogress\n",
    "import pymc3 as pm\n",
    "import arviz as az\n",
    "import sys\n",
    "sys.path.insert(1, 'mlda/')\n",
    "import os\n",
    "import theano.tensor as tt\n",
    "from Model import Model, model_wrapper, project_eigenpairs\n",
    "from itertools import product\n",
    "\n",
    "os.environ['OPENBLAS_NUM_THREADS'] = '1'  # Set environmental variable\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setting constants\n",
    "\n",
    "Here, we set some constants which are used for all combinations of model/algorithm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set random field parameters\n",
    "field_mean = 0\n",
    "field_stdev = 1\n",
    "\n",
    "# MLDA and Metropolis tuning parameters\n",
    "tune = True\n",
    "tune_interval = 100\n",
    "discard_tuning = False\n",
    "\n",
    "# Set the sigma for inference\n",
    "sigma = 0.01\n",
    "\n",
    "# Data generation seed\n",
    "data_seed = 12345\n",
    "\n",
    "# Sampling seed\n",
    "sampling_seed = 12345\n",
    "\n",
    "# Datapoints list\n",
    "points_list = [0.1, 0.3, 0.5, 0.7, 0.9]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Theano Op for likelihood\n",
    "\n",
    "Here, we define a Theano Op which makes use of external code to calculate the likelihood of the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use a Theano Op along with the code within ./mlda to construct the likelihood\n",
    "\n",
    "def my_loglik(my_model, theta, datapoints, data, sigma):\n",
    "    \"\"\"\n",
    "    This returns the log-likelihood of my_model given theta,\n",
    "    datapoints, the observed data and sigma. It uses the\n",
    "    model_wrapper function to do a model solve.\n",
    "    \"\"\"\n",
    "    output = model_wrapper(my_model, theta, datapoints)\n",
    "    return - (0.5 / sigma ** 2) * np.sum((output - data) ** 2)\n",
    "\n",
    "class LogLike(tt.Op):\n",
    "    \"\"\"\n",
    "    Theano Op that wraps the log-likelihood computation, necessary to\n",
    "    pass \"black-box\" fenics code into pymc3.\n",
    "    Based on the work in:\n",
    "    https://docs.pymc.io/notebooks/blackbox_external_likelihood.html\n",
    "    https://docs.pymc.io/Advanced_usage_of_Theano_in_PyMC3.html\n",
    "    \"\"\"\n",
    "\n",
    "    # Specify what type of object will be passed and returned to the Op when it is\n",
    "    # called. In our case we will be passing it a vector of values (the parameters\n",
    "    # that define our model and a model object) and returning a single \"scalar\"\n",
    "    # value (the log-likelihood)\n",
    "    itypes = [tt.dvector]  # expects a vector of parameter values when called\n",
    "    otypes = [tt.dscalar]  # outputs a single scalar value (the log likelihood)\n",
    "\n",
    "    def __init__(self, my_model, loglike, data, x, sigma):\n",
    "        \"\"\"\n",
    "        Initialise the Op with various things that our log-likelihood function\n",
    "        requires. Below are the things that are needed in this particular\n",
    "        example.\n",
    "\n",
    "        Parameters\n",
    "        ----------\n",
    "        my_model:\n",
    "            A Model object (defined in model.py) that contains the parameters\n",
    "            and functions of out model.\n",
    "        loglike:\n",
    "            The log-likelihood function we've defined, in this example it is\n",
    "            my_loglik.\n",
    "        data:\n",
    "            The \"observed\" data that our log-likelihood function takes in. These\n",
    "            are the true data generated by the finest model in this example.\n",
    "        x:\n",
    "            The dependent variable (aka 'x') that our model requires. This is\n",
    "            the datapoints in this example.\n",
    "        sigma:\n",
    "            The noise standard deviation that our function requires.\n",
    "        \"\"\"\n",
    "\n",
    "        # add inputs as class attributes\n",
    "        self.my_model = my_model\n",
    "        self.likelihood = loglike\n",
    "        self.data = data\n",
    "        self.x = x\n",
    "        self.sigma = sigma\n",
    "\n",
    "    def perform(self, node, inputs, outputs):\n",
    "        # the method that is used when calling the Op\n",
    "        theta = inputs  # this will contain my variables\n",
    "\n",
    "        # call the log-likelihood function\n",
    "        logl = self.likelihood(self.my_model, theta, self.x, self.data, self.sigma)\n",
    "\n",
    "        outputs[0][0] = np.array(logl) # output the log-likelihood\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model and inference functions\n",
    "The functions below construct a model with the provided parameters and run the selected MCMC method on PyMC3. The resulting trace is saved with ArviZ.\n",
    "\n",
    "If the saved trace is already found, it is loaded from disk."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_model(resolutions, field_mean, field_stdev, \n",
    "              lamb_cov, nparam, data_seed, \n",
    "              points_list, sigma):\n",
    "    \"\"\"Generate a model, coarse models and the true parameter vector, given the inputs\"\"\"\n",
    "    \n",
    "    # Note this can take several minutes for large resolutions\n",
    "    my_models = []\n",
    "    for r in resolutions:\n",
    "        my_models.append(Model(r, field_mean, field_stdev, nparam, lamb_cov))\n",
    "\n",
    "    # Project eignevactors from fine model to all coarse models\n",
    "    for i in range(len(my_models[:-1])):\n",
    "        project_eigenpairs(my_models[-1], my_models[i])\n",
    "        \n",
    "    # Solve finest model as a test and plot transmissivity field and solution\n",
    "    np.random.seed(data_seed)\n",
    "    my_models[-1].solve()    \n",
    "\n",
    "    # Save true parameters of finest model\n",
    "    true_parameters = my_models[-1].random_process.parameters    \n",
    "    \n",
    "    # Define the sampling points.\n",
    "    x_data = y_data = np.array(points_list)\n",
    "    datapoints = np.array(list(product(x_data, y_data)))\n",
    "\n",
    "    # Get data from the sampling points and perturb it with some noise.\n",
    "    noise = np.random.normal(0, 0.001, len(datapoints))\n",
    "\n",
    "    # Generate data from the finest model for use in pymc3 inference - these data are used in all levels\n",
    "    data = model_wrapper(my_models[-1], true_parameters, datapoints) + noise\n",
    "    \n",
    "    # create Theano Ops to wrap likelihoods of all model levels and store them in list\n",
    "    logl = []\n",
    "    for m in my_models:\n",
    "        logl.append(LogLike(m, my_loglik, data, datapoints, sigma))\n",
    "        \n",
    "    # Set up models in pymc3 for each level - excluding finest model level\n",
    "    coarse_models = []\n",
    "    for j in range(len(my_models) - 1):\n",
    "        with pm.Model() as cmodel:\n",
    "            # uniform priors on parameters\n",
    "            parameters = []\n",
    "            for i in range(nparam):\n",
    "                parameters.append(pm.Uniform('theta_' + str(i), lower=-3., upper=3.))\n",
    "\n",
    "            # convert m and c to a tensor vector\n",
    "            theta = tt.as_tensor_variable(parameters)\n",
    "\n",
    "            # use a DensityDist (use a lamdba function to \"call\" the Op)\n",
    "            ll = logl[j]\n",
    "            pm.DensityDist('likelihood', lambda v: ll(v), observed={'v': theta})\n",
    "\n",
    "        coarse_models.append(cmodel)\n",
    "      \n",
    "\n",
    "    with pm.Model() as model:\n",
    "        # Uniform priors on parameters\n",
    "        parameters = []\n",
    "        start = dict()\n",
    "        for i in range(nparam):\n",
    "            parameters.append(pm.Uniform('theta_' + str(i), lower=-3., upper=3.))\n",
    "\n",
    "        # Convert m and c to a tensor vector\n",
    "        theta = tt.as_tensor_variable(parameters)\n",
    "\n",
    "        # use a DensityDist (use a lamdba function to \"call\" the Op)\n",
    "        pm.DensityDist('likelihood', lambda v: logl[-1](v), observed={'v': theta})\n",
    "        \n",
    "    return model, coarse_models, true_parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def run_setting(D, resolutions, lamb_cov, N_tune, N_draws, N_chains, algorithm, \n",
    "                                           model, coarse_models, true_parameters, tune, tune_interval, \n",
    "                                           blocked, nsub, discard_tuning, sampling_seed, data_seed, init):\n",
    "    \"\"\"Run inference on the given model using the selected algorithm and parameters, return the trace\"\"\"    \n",
    "    \n",
    "    savename = f'{algorithm}_{D}_{resolutions}_{lamb_cov}_{N_tune}_{N_draws}_{N_chains}_{tune}_{tune_interval}_{blocked}_{nsub}_{discard_tuning}_{sampling_seed}_{data_seed}_{init}.nc'\n",
    "    print(f'Scenario filename: {savename}')\n",
    "    if not pathlib.Path(savename).exists():        \n",
    "        with model:\n",
    "            if algorithm == 'metropolis':\n",
    "                step = pm.Metropolis(tune=tune, tune_interval=tune_interval, blocked=blocked)\n",
    "            elif algorithm == 'demcmc':\n",
    "                step = pm.DEMetropolis(tune='scaling', tune_interval=tune_interval)\n",
    "            elif algorithm == 'demcmcz':\n",
    "                step = pm.DEMetropolisZ(tune_interval=tune_interval)\n",
    "            elif algorithm == 'mlda':\n",
    "                step = pm.MLDA(subsampling_rates=nsub, coarse_models=coarse_models,\n",
    "                               tune=tune, tune_interval=tune_interval, base_blocked=blocked)\n",
    "            t_start = time.time()\n",
    "            if init:\n",
    "                start = dict()\n",
    "                for i, p in enumerate(true_parameters):\n",
    "                    start['theta_' + str(i)] = p\n",
    "                trace = pm.sample(\n",
    "                    tune=N_tune, draws=N_draws, chains=N_chains,\n",
    "                    step=step, start=start,\n",
    "                    discard_tuned_samples=discard_tuning,\n",
    "                    random_seed=sampling_seed\n",
    "                )\n",
    "            else:\n",
    "                trace = pm.sample(\n",
    "                    tune=N_tune, draws=N_draws, chains=N_chains,\n",
    "                    step=step,\n",
    "                    discard_tuned_samples=discard_tuning,\n",
    "                    random_seed=sampling_seed\n",
    "                )\n",
    "            t = time.time() - t_start\n",
    "        trace = az.from_pymc3(trace)\n",
    "        trace.sample_stats.attrs['t_sampling'] = t\n",
    "        trace.to_netcdf(savename)\n",
    "    else:\n",
    "        trace = az.from_netcdf(savename)\n",
    "        t = float(trace.sample_stats.t_sampling)\n",
    "    return t, trace"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Running the Benchmark Scenarios\n",
    "Here a variety of different scenarios is computed and the results are aggregated in a multi-indexed DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scenario filename: mlda_2_[(30, 30), (120, 120)]_0.1_1500_1500_4_True_100_False_10_False_12345_12345_True.nc\n",
      "Scenario filename: metropolis_2_[(30, 30), (120, 120)]_0.1_1500_1500_4_True_100_False_0_False_12345_12345_True.nc\n",
      "Scenario filename: demcmc_2_[(30, 30), (120, 120)]_0.1_1500_1500_4_True_100_False_0_False_12345_12345_True.nc\n",
      "Scenario filename: demcmcz_2_[(30, 30), (120, 120)]_0.1_1500_1500_4_True_100_False_0_False_12345_12345_True.nc\n",
      "Scenario filename: mlda_2_[(30, 30), (120, 120)]_5.0_1500_1500_4_True_100_False_10_False_12345_12345_True.nc\n",
      "Scenario filename: metropolis_2_[(30, 30), (120, 120)]_5.0_1500_1500_4_True_100_False_0_False_12345_12345_True.nc\n",
      "Scenario filename: demcmc_2_[(30, 30), (120, 120)]_5.0_1500_1500_4_True_100_False_0_False_12345_12345_True.nc\n",
      "Scenario filename: demcmcz_2_[(30, 30), (120, 120)]_5.0_1500_1500_4_True_100_False_0_False_12345_12345_True.nc\n",
      "Scenario filename: mlda_2_[(50, 50), (100, 100)]_0.1_1500_1500_4_True_100_False_10_False_12345_12345_True.nc\n",
      "Scenario filename: metropolis_2_[(50, 50), (100, 100)]_0.1_1500_1500_4_True_100_False_0_False_12345_12345_True.nc\n",
      "Scenario filename: demcmc_2_[(50, 50), (100, 100)]_0.1_1500_1500_4_True_100_False_0_False_12345_12345_True.nc\n",
      "Scenario filename: demcmcz_2_[(50, 50), (100, 100)]_0.1_1500_1500_4_True_100_False_0_False_12345_12345_True.nc\n",
      "Scenario filename: mlda_2_[(50, 50), (100, 100)]_5.0_1500_1500_4_True_100_False_10_False_12345_12345_True.nc\n",
      "Scenario filename: metropolis_2_[(50, 50), (100, 100)]_5.0_1500_1500_4_True_100_False_0_False_12345_12345_True.nc\n",
      "Scenario filename: demcmc_2_[(50, 50), (100, 100)]_5.0_1500_1500_4_True_100_False_0_False_12345_12345_True.nc\n",
      "Scenario filename: demcmcz_2_[(50, 50), (100, 100)]_5.0_1500_1500_4_True_100_False_0_False_12345_12345_True.nc\n",
      "Scenario filename: mlda_4_[(30, 30), (120, 120)]_0.1_5000_5000_4_True_100_False_10_False_12345_12345_True.nc\n",
      "Scenario filename: metropolis_4_[(30, 30), (120, 120)]_0.1_5000_5000_4_True_100_False_0_False_12345_12345_True.nc\n",
      "Scenario filename: demcmc_4_[(30, 30), (120, 120)]_0.1_5000_5000_4_True_100_False_0_False_12345_12345_True.nc\n",
      "Scenario filename: demcmcz_4_[(30, 30), (120, 120)]_0.1_5000_5000_4_True_100_False_0_False_12345_12345_True.nc\n",
      "Scenario filename: mlda_4_[(30, 30), (120, 120)]_5.0_5000_5000_4_True_100_False_10_False_12345_12345_True.nc\n",
      "Scenario filename: metropolis_4_[(30, 30), (120, 120)]_5.0_5000_5000_4_True_100_False_0_False_12345_12345_True.nc\n",
      "Scenario filename: demcmc_4_[(30, 30), (120, 120)]_5.0_5000_5000_4_True_100_False_0_False_12345_12345_True.nc\n",
      "Scenario filename: demcmcz_4_[(30, 30), (120, 120)]_5.0_5000_5000_4_True_100_False_0_False_12345_12345_True.nc\n",
      "Scenario filename: mlda_4_[(50, 50), (100, 100)]_0.1_5000_5000_4_True_100_False_10_False_12345_12345_True.nc\n",
      "Scenario filename: metropolis_4_[(50, 50), (100, 100)]_0.1_5000_5000_4_True_100_False_0_False_12345_12345_True.nc\n",
      "Scenario filename: demcmc_4_[(50, 50), (100, 100)]_0.1_5000_5000_4_True_100_False_0_False_12345_12345_True.nc\n",
      "Scenario filename: demcmcz_4_[(50, 50), (100, 100)]_0.1_5000_5000_4_True_100_False_0_False_12345_12345_True.nc\n",
      "Scenario filename: mlda_4_[(50, 50), (100, 100)]_5.0_5000_5000_4_True_100_False_10_False_12345_12345_True.nc\n",
      "Scenario filename: metropolis_4_[(50, 50), (100, 100)]_5.0_5000_5000_4_True_100_False_0_False_12345_12345_True.nc\n",
      "Scenario filename: demcmc_4_[(50, 50), (100, 100)]_5.0_5000_5000_4_True_100_False_0_False_12345_12345_True.nc\n",
      "Scenario filename: demcmcz_4_[(50, 50), (100, 100)]_5.0_5000_5000_4_True_100_False_0_False_12345_12345_True.nc\n"
     ]
    }
   ],
   "source": [
    "df_results = pd.DataFrame(columns='algorithm,D,resolutions,lamb_cov,N_tune,N_draws,N_chains,blocked,nsub,t,trace'.split(','))\n",
    "df_results = df_results.set_index('algorithm,D,resolutions,lamb_cov,N_tune,N_draws,N_chains,blocked,nsub'.split(','))\n",
    "\n",
    "for init in [True]:\n",
    "    for D in [2, 4]:\n",
    "        \n",
    "        if D == 2:\n",
    "            N_tune = 1500\n",
    "            N_draws = 1500\n",
    "        else:\n",
    "            N_tune = 5000\n",
    "            N_draws = 5000\n",
    "            \n",
    "        for i, resolutions in {2: [(30, 30), (120, 120)],\n",
    "                               3: [(50, 50), (100, 100)]}.items():\n",
    "            for lamb_cov in [0.1, 5.0]:                \n",
    "                model, coarse_models, true_parameters = get_model(resolutions, field_mean, field_stdev,\n",
    "                                                                  lamb_cov, D, data_seed, \n",
    "                                                                  points_list, sigma)\n",
    "                for algorithm in ['mlda']:\n",
    "                    for blocked in [False]:\n",
    "                        for nsub in [10]:                            \n",
    "                            for N_chains in [4]:\n",
    "                                t, trace = run_setting(D, resolutions, lamb_cov, N_tune, N_draws, N_chains, algorithm, \n",
    "                                                       model, coarse_models, true_parameters, tune, tune_interval, \n",
    "                                                       blocked, nsub, discard_tuning, sampling_seed, data_seed, init)                                \n",
    "                                df_results.loc[(algorithm, D, i, lamb_cov, N_tune, N_draws, N_chains, blocked, nsub)] = (t, trace)\n",
    "\n",
    "                for algorithm in ['metropolis']:\n",
    "                    for blocked in [False]:\n",
    "                        for nsub in [0]:                            \n",
    "                            for N_chains in [4]:\n",
    "                                t, trace = run_setting(D, resolutions, lamb_cov, N_tune, N_draws, N_chains, algorithm, \n",
    "                                                       model, coarse_models, true_parameters, tune, tune_interval, \n",
    "                                                       blocked, nsub, discard_tuning, sampling_seed, data_seed, init)                                \n",
    "                                df_results.loc[(algorithm, D, i, lamb_cov, N_tune, N_draws, N_chains, blocked, nsub)] = (t, trace)\n",
    "\n",
    "                for algorithm in ['demcmc', 'demcmcz']:\n",
    "                    for blocked in [False]:\n",
    "                        for nsub in [0]:                            \n",
    "                            for N_chains in [4]:\n",
    "                                t, trace = run_setting(D, resolutions, lamb_cov, N_tune, N_draws, N_chains, algorithm, \n",
    "                                                       model, coarse_models, true_parameters, tune, tune_interval, \n",
    "                                                       blocked, nsub, discard_tuning, sampling_seed, data_seed, init)                                \n",
    "                                df_results.loc[(algorithm, D, i, lamb_cov, N_tune, N_draws, N_chains, blocked, nsub)] = (t, trace)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th>t</th>\n",
       "      <th>trace</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>algorithm</th>\n",
       "      <th>D</th>\n",
       "      <th>resolutions</th>\n",
       "      <th>lamb_cov</th>\n",
       "      <th>N_tune</th>\n",
       "      <th>N_draws</th>\n",
       "      <th>N_chains</th>\n",
       "      <th>blocked</th>\n",
       "      <th>nsub</th>\n",
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       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mlda</th>\n",
       "      <th>2</th>\n",
       "      <th>2</th>\n",
       "      <th>0.1</th>\n",
       "      <th>1500</th>\n",
       "      <th>1500</th>\n",
       "      <th>4</th>\n",
       "      <th>False</th>\n",
       "      <th>10</th>\n",
       "      <td>1389.478221</td>\n",
       "      <td>Inference data with groups:\\n\\t&gt; posterior\\n\\t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>metropolis</th>\n",
       "      <th>2</th>\n",
       "      <th>2</th>\n",
       "      <th>0.1</th>\n",
       "      <th>1500</th>\n",
       "      <th>1500</th>\n",
       "      <th>4</th>\n",
       "      <th>False</th>\n",
       "      <th>0</th>\n",
       "      <td>1274.983871</td>\n",
       "      <td>Inference data with groups:\\n\\t&gt; posterior\\n\\t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>demcmc</th>\n",
       "      <th>2</th>\n",
       "      <th>2</th>\n",
       "      <th>0.1</th>\n",
       "      <th>1500</th>\n",
       "      <th>1500</th>\n",
       "      <th>4</th>\n",
       "      <th>False</th>\n",
       "      <th>0</th>\n",
       "      <td>1864.953730</td>\n",
       "      <td>Inference data with groups:\\n\\t&gt; posterior\\n\\t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>demcmcz</th>\n",
       "      <th>2</th>\n",
       "      <th>2</th>\n",
       "      <th>0.1</th>\n",
       "      <th>1500</th>\n",
       "      <th>1500</th>\n",
       "      <th>4</th>\n",
       "      <th>False</th>\n",
       "      <th>0</th>\n",
       "      <td>620.677846</td>\n",
       "      <td>Inference data with groups:\\n\\t&gt; posterior\\n\\t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mlda</th>\n",
       "      <th>2</th>\n",
       "      <th>2</th>\n",
       "      <th>5.0</th>\n",
       "      <th>1500</th>\n",
       "      <th>1500</th>\n",
       "      <th>4</th>\n",
       "      <th>False</th>\n",
       "      <th>10</th>\n",
       "      <td>1019.688392</td>\n",
       "      <td>Inference data with groups:\\n\\t&gt; posterior\\n\\t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>metropolis</th>\n",
       "      <th>2</th>\n",
       "      <th>2</th>\n",
       "      <th>5.0</th>\n",
       "      <th>1500</th>\n",
       "      <th>1500</th>\n",
       "      <th>4</th>\n",
       "      <th>False</th>\n",
       "      <th>0</th>\n",
       "      <td>961.378178</td>\n",
       "      <td>Inference data with groups:\\n\\t&gt; posterior\\n\\t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>demcmc</th>\n",
       "      <th>2</th>\n",
       "      <th>2</th>\n",
       "      <th>5.0</th>\n",
       "      <th>1500</th>\n",
       "      <th>1500</th>\n",
       "      <th>4</th>\n",
       "      <th>False</th>\n",
       "      <th>0</th>\n",
       "      <td>1533.069393</td>\n",
       "      <td>Inference data with groups:\\n\\t&gt; posterior\\n\\t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>demcmcz</th>\n",
       "      <th>2</th>\n",
       "      <th>2</th>\n",
       "      <th>5.0</th>\n",
       "      <th>1500</th>\n",
       "      <th>1500</th>\n",
       "      <th>4</th>\n",
       "      <th>False</th>\n",
       "      <th>0</th>\n",
       "      <td>479.296644</td>\n",
       "      <td>Inference data with groups:\\n\\t&gt; posterior\\n\\t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mlda</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>0.1</th>\n",
       "      <th>1500</th>\n",
       "      <th>1500</th>\n",
       "      <th>4</th>\n",
       "      <th>False</th>\n",
       "      <th>10</th>\n",
       "      <td>1510.396066</td>\n",
       "      <td>Inference data with groups:\\n\\t&gt; posterior\\n\\t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>metropolis</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>0.1</th>\n",
       "      <th>1500</th>\n",
       "      <th>1500</th>\n",
       "      <th>4</th>\n",
       "      <th>False</th>\n",
       "      <th>0</th>\n",
       "      <td>579.576036</td>\n",
       "      <td>Inference data with groups:\\n\\t&gt; posterior\\n\\t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>demcmc</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>0.1</th>\n",
       "      <th>1500</th>\n",
       "      <th>1500</th>\n",
       "      <th>4</th>\n",
       "      <th>False</th>\n",
       "      <th>0</th>\n",
       "      <td>967.860431</td>\n",
       "      <td>Inference data with groups:\\n\\t&gt; posterior\\n\\t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>demcmcz</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>0.1</th>\n",
       "      <th>1500</th>\n",
       "      <th>1500</th>\n",
       "      <th>4</th>\n",
       "      <th>False</th>\n",
       "      <th>0</th>\n",
       "      <td>337.528897</td>\n",
       "      <td>Inference data with groups:\\n\\t&gt; posterior\\n\\t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mlda</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>5.0</th>\n",
       "      <th>1500</th>\n",
       "      <th>1500</th>\n",
       "      <th>4</th>\n",
       "      <th>False</th>\n",
       "      <th>10</th>\n",
       "      <td>1720.086796</td>\n",
       "      <td>Inference data with groups:\\n\\t&gt; posterior\\n\\t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>metropolis</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>5.0</th>\n",
       "      <th>1500</th>\n",
       "      <th>1500</th>\n",
       "      <th>4</th>\n",
       "      <th>False</th>\n",
       "      <th>0</th>\n",
       "      <td>654.167423</td>\n",
       "      <td>Inference data with groups:\\n\\t&gt; posterior\\n\\t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>demcmc</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>5.0</th>\n",
       "      <th>1500</th>\n",
       "      <th>1500</th>\n",
       "      <th>4</th>\n",
       "      <th>False</th>\n",
       "      <th>0</th>\n",
       "      <td>1228.482882</td>\n",
       "      <td>Inference data with groups:\\n\\t&gt; posterior\\n\\t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>demcmcz</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>5.0</th>\n",
       "      <th>1500</th>\n",
       "      <th>1500</th>\n",
       "      <th>4</th>\n",
       "      <th>False</th>\n",
       "      <th>0</th>\n",
       "      <td>401.233726</td>\n",
       "      <td>Inference data with groups:\\n\\t&gt; posterior\\n\\t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mlda</th>\n",
       "      <th>4</th>\n",
       "      <th>2</th>\n",
       "      <th>0.1</th>\n",
       "      <th>5000</th>\n",
       "      <th>5000</th>\n",
       "      <th>4</th>\n",
       "      <th>False</th>\n",
       "      <th>10</th>\n",
       "      <td>4632.961935</td>\n",
       "      <td>Inference data with groups:\\n\\t&gt; posterior\\n\\t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>metropolis</th>\n",
       "      <th>4</th>\n",
       "      <th>2</th>\n",
       "      <th>0.1</th>\n",
       "      <th>5000</th>\n",
       "      <th>5000</th>\n",
       "      <th>4</th>\n",
       "      <th>False</th>\n",
       "      <th>0</th>\n",
       "      <td>6527.094250</td>\n",
       "      <td>Inference data with groups:\\n\\t&gt; posterior\\n\\t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>demcmc</th>\n",
       "      <th>4</th>\n",
       "      <th>2</th>\n",
       "      <th>0.1</th>\n",
       "      <th>5000</th>\n",
       "      <th>5000</th>\n",
       "      <th>4</th>\n",
       "      <th>False</th>\n",
       "      <th>0</th>\n",
       "      <td>5192.939181</td>\n",
       "      <td>Inference data with groups:\\n\\t&gt; posterior\\n\\t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>demcmcz</th>\n",
       "      <th>4</th>\n",
       "      <th>2</th>\n",
       "      <th>0.1</th>\n",
       "      <th>5000</th>\n",
       "      <th>5000</th>\n",
       "      <th>4</th>\n",
       "      <th>False</th>\n",
       "      <th>0</th>\n",
       "      <td>1631.825235</td>\n",
       "      <td>Inference data with groups:\\n\\t&gt; posterior\\n\\t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mlda</th>\n",
       "      <th>4</th>\n",
       "      <th>2</th>\n",
       "      <th>5.0</th>\n",
       "      <th>5000</th>\n",
       "      <th>5000</th>\n",
       "      <th>4</th>\n",
       "      <th>False</th>\n",
       "      <th>10</th>\n",
       "      <td>4692.309083</td>\n",
       "      <td>Inference data with groups:\\n\\t&gt; posterior\\n\\t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>metropolis</th>\n",
       "      <th>4</th>\n",
       "      <th>2</th>\n",
       "      <th>5.0</th>\n",
       "      <th>5000</th>\n",
       "      <th>5000</th>\n",
       "      <th>4</th>\n",
       "      <th>False</th>\n",
       "      <th>0</th>\n",
       "      <td>6278.926164</td>\n",
       "      <td>Inference data with groups:\\n\\t&gt; posterior\\n\\t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>demcmc</th>\n",
       "      <th>4</th>\n",
       "      <th>2</th>\n",
       "      <th>5.0</th>\n",
       "      <th>5000</th>\n",
       "      <th>5000</th>\n",
       "      <th>4</th>\n",
       "      <th>False</th>\n",
       "      <th>0</th>\n",
       "      <td>5029.877772</td>\n",
       "      <td>Inference data with groups:\\n\\t&gt; posterior\\n\\t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>demcmcz</th>\n",
       "      <th>4</th>\n",
       "      <th>2</th>\n",
       "      <th>5.0</th>\n",
       "      <th>5000</th>\n",
       "      <th>5000</th>\n",
       "      <th>4</th>\n",
       "      <th>False</th>\n",
       "      <th>0</th>\n",
       "      <td>1563.869196</td>\n",
       "      <td>Inference data with groups:\\n\\t&gt; posterior\\n\\t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mlda</th>\n",
       "      <th>4</th>\n",
       "      <th>3</th>\n",
       "      <th>0.1</th>\n",
       "      <th>5000</th>\n",
       "      <th>5000</th>\n",
       "      <th>4</th>\n",
       "      <th>False</th>\n",
       "      <th>10</th>\n",
       "      <td>9455.346190</td>\n",
       "      <td>Inference data with groups:\\n\\t&gt; posterior\\n\\t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>metropolis</th>\n",
       "      <th>4</th>\n",
       "      <th>3</th>\n",
       "      <th>0.1</th>\n",
       "      <th>5000</th>\n",
       "      <th>5000</th>\n",
       "      <th>4</th>\n",
       "      <th>False</th>\n",
       "      <th>0</th>\n",
       "      <td>4259.810662</td>\n",
       "      <td>Inference data with groups:\\n\\t&gt; posterior\\n\\t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>demcmc</th>\n",
       "      <th>4</th>\n",
       "      <th>3</th>\n",
       "      <th>0.1</th>\n",
       "      <th>5000</th>\n",
       "      <th>5000</th>\n",
       "      <th>4</th>\n",
       "      <th>False</th>\n",
       "      <th>0</th>\n",
       "      <td>3483.709209</td>\n",
       "      <td>Inference data with groups:\\n\\t&gt; posterior\\n\\t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>demcmcz</th>\n",
       "      <th>4</th>\n",
       "      <th>3</th>\n",
       "      <th>0.1</th>\n",
       "      <th>5000</th>\n",
       "      <th>5000</th>\n",
       "      <th>4</th>\n",
       "      <th>False</th>\n",
       "      <th>0</th>\n",
       "      <td>1358.424685</td>\n",
       "      <td>Inference data with groups:\\n\\t&gt; posterior\\n\\t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mlda</th>\n",
       "      <th>4</th>\n",
       "      <th>3</th>\n",
       "      <th>5.0</th>\n",
       "      <th>5000</th>\n",
       "      <th>5000</th>\n",
       "      <th>4</th>\n",
       "      <th>False</th>\n",
       "      <th>10</th>\n",
       "      <td>11515.548980</td>\n",
       "      <td>Inference data with groups:\\n\\t&gt; posterior\\n\\t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>metropolis</th>\n",
       "      <th>4</th>\n",
       "      <th>3</th>\n",
       "      <th>5.0</th>\n",
       "      <th>5000</th>\n",
       "      <th>5000</th>\n",
       "      <th>4</th>\n",
       "      <th>False</th>\n",
       "      <th>0</th>\n",
       "      <td>4310.601360</td>\n",
       "      <td>Inference data with groups:\\n\\t&gt; posterior\\n\\t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>demcmc</th>\n",
       "      <th>4</th>\n",
       "      <th>3</th>\n",
       "      <th>5.0</th>\n",
       "      <th>5000</th>\n",
       "      <th>5000</th>\n",
       "      <th>4</th>\n",
       "      <th>False</th>\n",
       "      <th>0</th>\n",
       "      <td>3906.541457</td>\n",
       "      <td>Inference data with groups:\\n\\t&gt; posterior\\n\\t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>demcmcz</th>\n",
       "      <th>4</th>\n",
       "      <th>3</th>\n",
       "      <th>5.0</th>\n",
       "      <th>5000</th>\n",
       "      <th>5000</th>\n",
       "      <th>4</th>\n",
       "      <th>False</th>\n",
       "      <th>0</th>\n",
       "      <td>1201.539668</td>\n",
       "      <td>Inference data with groups:\\n\\t&gt; posterior\\n\\t...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                   t  \\\n",
       "algorithm  D resolutions lamb_cov N_tune N_draws N_chains blocked nsub                 \n",
       "mlda       2 2           0.1      1500   1500    4        False   10     1389.478221   \n",
       "metropolis 2 2           0.1      1500   1500    4        False   0      1274.983871   \n",
       "demcmc     2 2           0.1      1500   1500    4        False   0      1864.953730   \n",
       "demcmcz    2 2           0.1      1500   1500    4        False   0       620.677846   \n",
       "mlda       2 2           5.0      1500   1500    4        False   10     1019.688392   \n",
       "metropolis 2 2           5.0      1500   1500    4        False   0       961.378178   \n",
       "demcmc     2 2           5.0      1500   1500    4        False   0      1533.069393   \n",
       "demcmcz    2 2           5.0      1500   1500    4        False   0       479.296644   \n",
       "mlda       2 3           0.1      1500   1500    4        False   10     1510.396066   \n",
       "metropolis 2 3           0.1      1500   1500    4        False   0       579.576036   \n",
       "demcmc     2 3           0.1      1500   1500    4        False   0       967.860431   \n",
       "demcmcz    2 3           0.1      1500   1500    4        False   0       337.528897   \n",
       "mlda       2 3           5.0      1500   1500    4        False   10     1720.086796   \n",
       "metropolis 2 3           5.0      1500   1500    4        False   0       654.167423   \n",
       "demcmc     2 3           5.0      1500   1500    4        False   0      1228.482882   \n",
       "demcmcz    2 3           5.0      1500   1500    4        False   0       401.233726   \n",
       "mlda       4 2           0.1      5000   5000    4        False   10     4632.961935   \n",
       "metropolis 4 2           0.1      5000   5000    4        False   0      6527.094250   \n",
       "demcmc     4 2           0.1      5000   5000    4        False   0      5192.939181   \n",
       "demcmcz    4 2           0.1      5000   5000    4        False   0      1631.825235   \n",
       "mlda       4 2           5.0      5000   5000    4        False   10     4692.309083   \n",
       "metropolis 4 2           5.0      5000   5000    4        False   0      6278.926164   \n",
       "demcmc     4 2           5.0      5000   5000    4        False   0      5029.877772   \n",
       "demcmcz    4 2           5.0      5000   5000    4        False   0      1563.869196   \n",
       "mlda       4 3           0.1      5000   5000    4        False   10     9455.346190   \n",
       "metropolis 4 3           0.1      5000   5000    4        False   0      4259.810662   \n",
       "demcmc     4 3           0.1      5000   5000    4        False   0      3483.709209   \n",
       "demcmcz    4 3           0.1      5000   5000    4        False   0      1358.424685   \n",
       "mlda       4 3           5.0      5000   5000    4        False   10    11515.548980   \n",
       "metropolis 4 3           5.0      5000   5000    4        False   0      4310.601360   \n",
       "demcmc     4 3           5.0      5000   5000    4        False   0      3906.541457   \n",
       "demcmcz    4 3           5.0      5000   5000    4        False   0      1201.539668   \n",
       "\n",
       "                                                                                                                    trace  \n",
       "algorithm  D resolutions lamb_cov N_tune N_draws N_chains blocked nsub                                                     \n",
       "mlda       2 2           0.1      1500   1500    4        False   10    Inference data with groups:\\n\\t> posterior\\n\\t...  \n",
       "metropolis 2 2           0.1      1500   1500    4        False   0     Inference data with groups:\\n\\t> posterior\\n\\t...  \n",
       "demcmc     2 2           0.1      1500   1500    4        False   0     Inference data with groups:\\n\\t> posterior\\n\\t...  \n",
       "demcmcz    2 2           0.1      1500   1500    4        False   0     Inference data with groups:\\n\\t> posterior\\n\\t...  \n",
       "mlda       2 2           5.0      1500   1500    4        False   10    Inference data with groups:\\n\\t> posterior\\n\\t...  \n",
       "metropolis 2 2           5.0      1500   1500    4        False   0     Inference data with groups:\\n\\t> posterior\\n\\t...  \n",
       "demcmc     2 2           5.0      1500   1500    4        False   0     Inference data with groups:\\n\\t> posterior\\n\\t...  \n",
       "demcmcz    2 2           5.0      1500   1500    4        False   0     Inference data with groups:\\n\\t> posterior\\n\\t...  \n",
       "mlda       2 3           0.1      1500   1500    4        False   10    Inference data with groups:\\n\\t> posterior\\n\\t...  \n",
       "metropolis 2 3           0.1      1500   1500    4        False   0     Inference data with groups:\\n\\t> posterior\\n\\t...  \n",
       "demcmc     2 3           0.1      1500   1500    4        False   0     Inference data with groups:\\n\\t> posterior\\n\\t...  \n",
       "demcmcz    2 3           0.1      1500   1500    4        False   0     Inference data with groups:\\n\\t> posterior\\n\\t...  \n",
       "mlda       2 3           5.0      1500   1500    4        False   10    Inference data with groups:\\n\\t> posterior\\n\\t...  \n",
       "metropolis 2 3           5.0      1500   1500    4        False   0     Inference data with groups:\\n\\t> posterior\\n\\t...  \n",
       "demcmc     2 3           5.0      1500   1500    4        False   0     Inference data with groups:\\n\\t> posterior\\n\\t...  \n",
       "demcmcz    2 3           5.0      1500   1500    4        False   0     Inference data with groups:\\n\\t> posterior\\n\\t...  \n",
       "mlda       4 2           0.1      5000   5000    4        False   10    Inference data with groups:\\n\\t> posterior\\n\\t...  \n",
       "metropolis 4 2           0.1      5000   5000    4        False   0     Inference data with groups:\\n\\t> posterior\\n\\t...  \n",
       "demcmc     4 2           0.1      5000   5000    4        False   0     Inference data with groups:\\n\\t> posterior\\n\\t...  \n",
       "demcmcz    4 2           0.1      5000   5000    4        False   0     Inference data with groups:\\n\\t> posterior\\n\\t...  \n",
       "mlda       4 2           5.0      5000   5000    4        False   10    Inference data with groups:\\n\\t> posterior\\n\\t...  \n",
       "metropolis 4 2           5.0      5000   5000    4        False   0     Inference data with groups:\\n\\t> posterior\\n\\t...  \n",
       "demcmc     4 2           5.0      5000   5000    4        False   0     Inference data with groups:\\n\\t> posterior\\n\\t...  \n",
       "demcmcz    4 2           5.0      5000   5000    4        False   0     Inference data with groups:\\n\\t> posterior\\n\\t...  \n",
       "mlda       4 3           0.1      5000   5000    4        False   10    Inference data with groups:\\n\\t> posterior\\n\\t...  \n",
       "metropolis 4 3           0.1      5000   5000    4        False   0     Inference data with groups:\\n\\t> posterior\\n\\t...  \n",
       "demcmc     4 3           0.1      5000   5000    4        False   0     Inference data with groups:\\n\\t> posterior\\n\\t...  \n",
       "demcmcz    4 3           0.1      5000   5000    4        False   0     Inference data with groups:\\n\\t> posterior\\n\\t...  \n",
       "mlda       4 3           5.0      5000   5000    4        False   10    Inference data with groups:\\n\\t> posterior\\n\\t...  \n",
       "metropolis 4 3           5.0      5000   5000    4        False   0     Inference data with groups:\\n\\t> posterior\\n\\t...  \n",
       "demcmc     4 3           5.0      5000   5000    4        False   0     Inference data with groups:\\n\\t> posterior\\n\\t...  \n",
       "demcmcz    4 3           5.0      5000   5000    4        False   0     Inference data with groups:\\n\\t> posterior\\n\\t...  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.max_rows', 100)\n",
    "df_results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analyzing the traces\n",
    "From the traces, we need to compute the absolute $N_{eff}$, $N_{eff}$ per second and the $\\hat{R}$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
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   "source": [
    "df_temp = df_results.reset_index(['N_tune', 'N_draws'])\n",
    "\n",
    "df_temp['trace'] = [\n",
    "    az.from_pymc3(trace) if isinstance(trace, pm.backends.base.MultiTrace) else trace\n",
    "    for trace in df_temp.trace\n",
    "]\n",
    "df_temp['N_samples'] = [\n",
    "    row.N_draws * row.Index[2]\n",
    "    for row in df_temp.itertuples()\n",
    "]\n",
    "\n",
    "df_temp['ess_theta_0'] = [\n",
    "    float(az.ess({\n",
    "        k : v[:,row.N_tune:]\n",
    "        for k, v in row.trace.posterior.items()\n",
    "    }).theta_0)\n",
    "    for row in fastprogress.progress_bar(list(df_temp.itertuples()))\n",
    "]\n",
    "df_temp['ess_theta_1'] = [\n",
    "    float(az.ess({\n",
    "        k : v[:,row.N_tune:]\n",
    "        for k, v in row.trace.posterior.items()\n",
    "    }).theta_1) if row.Index[1] > 1 else None\n",
    "    for row in fastprogress.progress_bar(list(df_temp.itertuples()))     \n",
    "]\n",
    "df_temp['ess_theta_2'] = [\n",
    "    float(az.ess({\n",
    "        k : v[:,row.N_tune:]\n",
    "        for k, v in row.trace.posterior.items()\n",
    "    }).theta_2) if row.Index[1] > 2 else None\n",
    "    for row in fastprogress.progress_bar(list(df_temp.itertuples()))     \n",
    "]\n",
    "df_temp['ess_theta_3'] = [\n",
    "    float(az.ess({\n",
    "        k : v[:,row.N_tune:]\n",
    "        for k, v in row.trace.posterior.items()\n",
    "    }).theta_3) if row.Index[1] > 3 else None\n",
    "    for row in fastprogress.progress_bar(list(df_temp.itertuples()))     \n",
    "]\n",
    "\n",
    "df_temp['ess_rate_theta_0'] = [\n",
    "    row.ess_theta_0 / (row.t)\n",
    "    for row in df_temp.itertuples()\n",
    "]\n",
    "df_temp['ess_rate_theta_1'] = [\n",
    "    row.ess_theta_1 / (row.t) if row.Index[1] > 1 else None\n",
    "    for row in df_temp.itertuples()\n",
    "]\n",
    "df_temp['ess_rate_theta_2'] = [\n",
    "    row.ess_theta_2 / (row.t) if row.Index[1] > 2 else None\n",
    "    for row in df_temp.itertuples()\n",
    "]\n",
    "df_temp['ess_rate_theta_3'] = [\n",
    "    row.ess_theta_3 / (row.t) if row.Index[1] > 3 else None\n",
    "    for row in df_temp.itertuples()\n",
    "]\n",
    "\n",
    "df_temp['r_hat_0'] = [\n",
    "    float(az.rhat({\n",
    "        k : v[:,row.N_tune:]\n",
    "        for k, v in row.trace.posterior.items()\n",
    "    }).theta_0) if row.Index[1] > 0 else None\n",
    "    for row in fastprogress.progress_bar(list(df_temp.itertuples()))\n",
    "]\n",
    "df_temp['r_hat_1'] = [\n",
    "    float(az.rhat({\n",
    "        k : v[:,row.N_tune:]\n",
    "        for k, v in row.trace.posterior.items()\n",
    "    }).theta_1) if row.Index[1] > 1 else None\n",
    "    for row in fastprogress.progress_bar(list(df_temp.itertuples()))\n",
    "]\n",
    "df_temp['r_hat_2'] = [\n",
    "    float(az.rhat({\n",
    "        k : v[:,row.N_tune:]\n",
    "        for k, v in row.trace.posterior.items()\n",
    "    }).theta_2) if row.Index[1] > 2 else None\n",
    "    for row in fastprogress.progress_bar(list(df_temp.itertuples()))\n",
    "]\n",
    "df_temp['r_hat_3'] = [\n",
    "    float(az.rhat({\n",
    "        k : v[:,row.N_tune:]\n",
    "        for k, v in row.trace.posterior.items()\n",
    "    }).theta_3) if row.Index[1] > 3 else None\n",
    "    for row in fastprogress.progress_bar(list(df_temp.itertuples()))\n",
    "]\n",
    "\n",
    "df_temp = df_temp.sort_index(level=['D', 'resolutions', 'lamb_cov','N_chains', 'blocked', 'nsub','algorithm'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Results\n",
    "\n",
    "Here, we show the effective sample size per second for each combination. MLDA is usually the fastest method. DEMCMC-Z is faster in some cases. Note that all runs have been done without tuning the algorithm parameters. The performance of all methods might change if we properly tune them but this test is just to demonstrate out of the box performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th>ess_rate_theta_3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>algorithm</th>\n",
       "      <th>D</th>\n",
       "      <th>resolutions</th>\n",
       "      <th>lamb_cov</th>\n",
       "      <th>N_chains</th>\n",
       "      <th>blocked</th>\n",
       "      <th>nsub</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>demcmc</th>\n",
       "      <th>2</th>\n",
       "      <th>2</th>\n",
       "      <th>0.1</th>\n",
       "      <th>4</th>\n",
       "      <th>False</th>\n",
       "      <th>0</th>\n",
       "      <td>0.184663</td>\n",
       "      <td>0.188431</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>demcmcz</th>\n",
       "      <th>2</th>\n",
       "      <th>2</th>\n",
       "      <th>0.1</th>\n",
       "      <th>4</th>\n",
       "      <th>False</th>\n",
       "      <th>0</th>\n",
       "      <td>1.020549</td>\n",
       "      <td>1.311071</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>metropolis</th>\n",
       "      <th>2</th>\n",
       "      <th>2</th>\n",
       "      <th>0.1</th>\n",
       "      <th>4</th>\n",
       "      <th>False</th>\n",
       "      <th>0</th>\n",
       "      <td>0.798405</td>\n",
       "      <td>0.891589</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mlda</th>\n",
       "      <th>2</th>\n",
       "      <th>2</th>\n",
       "      <th>0.1</th>\n",
       "      <th>4</th>\n",
       "      <th>False</th>\n",
       "      <th>10</th>\n",
       "      <td>4.098620</td>\n",
       "      <td>3.979279</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>demcmc</th>\n",
       "      <th>2</th>\n",
       "      <th>2</th>\n",
       "      <th>5.0</th>\n",
       "      <th>4</th>\n",
       "      <th>False</th>\n",
       "      <th>0</th>\n",
       "      <td>0.375166</td>\n",
       "      <td>0.420770</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>demcmcz</th>\n",
       "      <th>2</th>\n",
       "      <th>2</th>\n",
       "      <th>5.0</th>\n",
       "      <th>4</th>\n",
       "      <th>False</th>\n",
       "      <th>0</th>\n",
       "      <td>1.748500</td>\n",
       "      <td>1.791761</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>metropolis</th>\n",
       "      <th>2</th>\n",
       "      <th>2</th>\n",
       "      <th>5.0</th>\n",
       "      <th>4</th>\n",
       "      <th>False</th>\n",
       "      <th>0</th>\n",
       "      <td>1.172936</td>\n",
       "      <td>0.997540</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mlda</th>\n",
       "      <th>2</th>\n",
       "      <th>2</th>\n",
       "      <th>5.0</th>\n",
       "      <th>4</th>\n",
       "      <th>False</th>\n",
       "      <th>10</th>\n",
       "      <td>5.432259</td>\n",
       "      <td>5.133738</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>demcmc</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>0.1</th>\n",
       "      <th>4</th>\n",
       "      <th>False</th>\n",
       "      <th>0</th>\n",
       "      <td>0.518644</td>\n",
       "      <td>0.239316</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>demcmcz</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>0.1</th>\n",
       "      <th>4</th>\n",
       "      <th>False</th>\n",
       "      <th>0</th>\n",
       "      <td>1.727501</td>\n",
       "      <td>2.470123</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>metropolis</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>0.1</th>\n",
       "      <th>4</th>\n",
       "      <th>False</th>\n",
       "      <th>0</th>\n",
       "      <td>1.548721</td>\n",
       "      <td>1.574123</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mlda</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>0.1</th>\n",
       "      <th>4</th>\n",
       "      <th>False</th>\n",
       "      <th>10</th>\n",
       "      <td>3.566495</td>\n",
       "      <td>2.823991</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>demcmc</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>5.0</th>\n",
       "      <th>4</th>\n",
       "      <th>False</th>\n",
       "      <th>0</th>\n",
       "      <td>0.359823</td>\n",
       "      <td>0.463683</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>demcmcz</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>5.0</th>\n",
       "      <th>4</th>\n",
       "      <th>False</th>\n",
       "      <th>0</th>\n",
       "      <td>1.836549</td>\n",
       "      <td>1.646066</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>metropolis</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>5.0</th>\n",
       "      <th>4</th>\n",
       "      <th>False</th>\n",
       "      <th>0</th>\n",
       "      <td>1.846734</td>\n",
       "      <td>1.699351</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mlda</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>5.0</th>\n",
       "      <th>4</th>\n",
       "      <th>False</th>\n",
       "      <th>10</th>\n",
       "      <td>3.310534</td>\n",
       "      <td>3.108891</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>demcmc</th>\n",
       "      <th>4</th>\n",
       "      <th>2</th>\n",
       "      <th>0.1</th>\n",
       "      <th>4</th>\n",
       "      <th>False</th>\n",
       "      <th>0</th>\n",
       "      <td>0.114770</td>\n",
       "      <td>0.212511</td>\n",
       "      <td>0.053304</td>\n",
       "      <td>0.170862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>demcmcz</th>\n",
       "      <th>4</th>\n",
       "      <th>2</th>\n",
       "      <th>0.1</th>\n",
       "      <th>4</th>\n",
       "      <th>False</th>\n",
       "      <th>0</th>\n",
       "      <td>0.883261</td>\n",
       "      <td>0.869558</td>\n",
       "      <td>0.936903</td>\n",
       "      <td>0.810685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>metropolis</th>\n",
       "      <th>4</th>\n",
       "      <th>2</th>\n",
       "      <th>0.1</th>\n",
       "      <th>4</th>\n",
       "      <th>False</th>\n",
       "      <th>0</th>\n",
       "      <td>0.465880</td>\n",
       "      <td>0.291693</td>\n",
       "      <td>0.300990</td>\n",
       "      <td>0.510204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mlda</th>\n",
       "      <th>4</th>\n",
       "      <th>2</th>\n",
       "      <th>0.1</th>\n",
       "      <th>4</th>\n",
       "      <th>False</th>\n",
       "      <th>10</th>\n",
       "      <td>3.676792</td>\n",
       "      <td>3.217369</td>\n",
       "      <td>3.211890</td>\n",
       "      <td>3.885221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>demcmc</th>\n",
       "      <th>4</th>\n",
       "      <th>2</th>\n",
       "      <th>5.0</th>\n",
       "      <th>4</th>\n",
       "      <th>False</th>\n",
       "      <th>0</th>\n",
       "      <td>0.019813</td>\n",
       "      <td>0.038716</td>\n",
       "      <td>0.044998</td>\n",
       "      <td>0.017601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>demcmcz</th>\n",
       "      <th>4</th>\n",
       "      <th>2</th>\n",
       "      <th>5.0</th>\n",
       "      <th>4</th>\n",
       "      <th>False</th>\n",
       "      <th>0</th>\n",
       "      <td>0.798184</td>\n",
       "      <td>0.847870</td>\n",
       "      <td>0.959292</td>\n",
       "      <td>0.751032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>metropolis</th>\n",
       "      <th>4</th>\n",
       "      <th>2</th>\n",
       "      <th>5.0</th>\n",
       "      <th>4</th>\n",
       "      <th>False</th>\n",
       "      <th>0</th>\n",
       "      <td>0.698245</td>\n",
       "      <td>0.029757</td>\n",
       "      <td>0.029327</td>\n",
       "      <td>0.672841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mlda</th>\n",
       "      <th>4</th>\n",
       "      <th>2</th>\n",
       "      <th>5.0</th>\n",
       "      <th>4</th>\n",
       "      <th>False</th>\n",
       "      <th>10</th>\n",
       "      <td>3.936857</td>\n",
       "      <td>0.407304</td>\n",
       "      <td>0.400484</td>\n",
       "      <td>3.987961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>demcmc</th>\n",
       "      <th>4</th>\n",
       "      <th>3</th>\n",
       "      <th>0.1</th>\n",
       "      <th>4</th>\n",
       "      <th>False</th>\n",
       "      <th>0</th>\n",
       "      <td>0.142696</td>\n",
       "      <td>0.122580</td>\n",
       "      <td>0.124343</td>\n",
       "      <td>0.300050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>demcmcz</th>\n",
       "      <th>4</th>\n",
       "      <th>3</th>\n",
       "      <th>0.1</th>\n",
       "      <th>4</th>\n",
       "      <th>False</th>\n",
       "      <th>0</th>\n",
       "      <td>1.101527</td>\n",
       "      <td>1.038836</td>\n",
       "      <td>1.057977</td>\n",
       "      <td>1.065151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>metropolis</th>\n",
       "      <th>4</th>\n",
       "      <th>3</th>\n",
       "      <th>0.1</th>\n",
       "      <th>4</th>\n",
       "      <th>False</th>\n",
       "      <th>0</th>\n",
       "      <td>0.716311</td>\n",
       "      <td>0.025474</td>\n",
       "      <td>0.026003</td>\n",
       "      <td>0.739517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mlda</th>\n",
       "      <th>4</th>\n",
       "      <th>3</th>\n",
       "      <th>0.1</th>\n",
       "      <th>4</th>\n",
       "      <th>False</th>\n",
       "      <th>10</th>\n",
       "      <td>1.700323</td>\n",
       "      <td>0.177678</td>\n",
       "      <td>0.177689</td>\n",
       "      <td>1.295181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>demcmc</th>\n",
       "      <th>4</th>\n",
       "      <th>3</th>\n",
       "      <th>5.0</th>\n",
       "      <th>4</th>\n",
       "      <th>False</th>\n",
       "      <th>0</th>\n",
       "      <td>0.025212</td>\n",
       "      <td>0.064672</td>\n",
       "      <td>0.068340</td>\n",
       "      <td>0.100593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>demcmcz</th>\n",
       "      <th>4</th>\n",
       "      <th>3</th>\n",
       "      <th>5.0</th>\n",
       "      <th>4</th>\n",
       "      <th>False</th>\n",
       "      <th>0</th>\n",
       "      <td>0.880810</td>\n",
       "      <td>0.688436</td>\n",
       "      <td>0.764531</td>\n",
       "      <td>0.737576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>metropolis</th>\n",
       "      <th>4</th>\n",
       "      <th>3</th>\n",
       "      <th>5.0</th>\n",
       "      <th>4</th>\n",
       "      <th>False</th>\n",
       "      <th>0</th>\n",
       "      <td>1.015959</td>\n",
       "      <td>0.005125</td>\n",
       "      <td>0.005139</td>\n",
       "      <td>1.019173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mlda</th>\n",
       "      <th>4</th>\n",
       "      <th>3</th>\n",
       "      <th>5.0</th>\n",
       "      <th>4</th>\n",
       "      <th>False</th>\n",
       "      <th>10</th>\n",
       "      <td>1.544913</td>\n",
       "      <td>0.020737</td>\n",
       "      <td>0.020732</td>\n",
       "      <td>1.669370</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                         ess_rate_theta_0  \\\n",
       "algorithm  D resolutions lamb_cov N_chains blocked nsub                     \n",
       "demcmc     2 2           0.1      4        False   0             0.184663   \n",
       "demcmcz    2 2           0.1      4        False   0             1.020549   \n",
       "metropolis 2 2           0.1      4        False   0             0.798405   \n",
       "mlda       2 2           0.1      4        False   10            4.098620   \n",
       "demcmc     2 2           5.0      4        False   0             0.375166   \n",
       "demcmcz    2 2           5.0      4        False   0             1.748500   \n",
       "metropolis 2 2           5.0      4        False   0             1.172936   \n",
       "mlda       2 2           5.0      4        False   10            5.432259   \n",
       "demcmc     2 3           0.1      4        False   0             0.518644   \n",
       "demcmcz    2 3           0.1      4        False   0             1.727501   \n",
       "metropolis 2 3           0.1      4        False   0             1.548721   \n",
       "mlda       2 3           0.1      4        False   10            3.566495   \n",
       "demcmc     2 3           5.0      4        False   0             0.359823   \n",
       "demcmcz    2 3           5.0      4        False   0             1.836549   \n",
       "metropolis 2 3           5.0      4        False   0             1.846734   \n",
       "mlda       2 3           5.0      4        False   10            3.310534   \n",
       "demcmc     4 2           0.1      4        False   0             0.114770   \n",
       "demcmcz    4 2           0.1      4        False   0             0.883261   \n",
       "metropolis 4 2           0.1      4        False   0             0.465880   \n",
       "mlda       4 2           0.1      4        False   10            3.676792   \n",
       "demcmc     4 2           5.0      4        False   0             0.019813   \n",
       "demcmcz    4 2           5.0      4        False   0             0.798184   \n",
       "metropolis 4 2           5.0      4        False   0             0.698245   \n",
       "mlda       4 2           5.0      4        False   10            3.936857   \n",
       "demcmc     4 3           0.1      4        False   0             0.142696   \n",
       "demcmcz    4 3           0.1      4        False   0             1.101527   \n",
       "metropolis 4 3           0.1      4        False   0             0.716311   \n",
       "mlda       4 3           0.1      4        False   10            1.700323   \n",
       "demcmc     4 3           5.0      4        False   0             0.025212   \n",
       "demcmcz    4 3           5.0      4        False   0             0.880810   \n",
       "metropolis 4 3           5.0      4        False   0             1.015959   \n",
       "mlda       4 3           5.0      4        False   10            1.544913   \n",
       "\n",
       "                                                         ess_rate_theta_1  \\\n",
       "algorithm  D resolutions lamb_cov N_chains blocked nsub                     \n",
       "demcmc     2 2           0.1      4        False   0             0.188431   \n",
       "demcmcz    2 2           0.1      4        False   0             1.311071   \n",
       "metropolis 2 2           0.1      4        False   0             0.891589   \n",
       "mlda       2 2           0.1      4        False   10            3.979279   \n",
       "demcmc     2 2           5.0      4        False   0             0.420770   \n",
       "demcmcz    2 2           5.0      4        False   0             1.791761   \n",
       "metropolis 2 2           5.0      4        False   0             0.997540   \n",
       "mlda       2 2           5.0      4        False   10            5.133738   \n",
       "demcmc     2 3           0.1      4        False   0             0.239316   \n",
       "demcmcz    2 3           0.1      4        False   0             2.470123   \n",
       "metropolis 2 3           0.1      4        False   0             1.574123   \n",
       "mlda       2 3           0.1      4        False   10            2.823991   \n",
       "demcmc     2 3           5.0      4        False   0             0.463683   \n",
       "demcmcz    2 3           5.0      4        False   0             1.646066   \n",
       "metropolis 2 3           5.0      4        False   0             1.699351   \n",
       "mlda       2 3           5.0      4        False   10            3.108891   \n",
       "demcmc     4 2           0.1      4        False   0             0.212511   \n",
       "demcmcz    4 2           0.1      4        False   0             0.869558   \n",
       "metropolis 4 2           0.1      4        False   0             0.291693   \n",
       "mlda       4 2           0.1      4        False   10            3.217369   \n",
       "demcmc     4 2           5.0      4        False   0             0.038716   \n",
       "demcmcz    4 2           5.0      4        False   0             0.847870   \n",
       "metropolis 4 2           5.0      4        False   0             0.029757   \n",
       "mlda       4 2           5.0      4        False   10            0.407304   \n",
       "demcmc     4 3           0.1      4        False   0             0.122580   \n",
       "demcmcz    4 3           0.1      4        False   0             1.038836   \n",
       "metropolis 4 3           0.1      4        False   0             0.025474   \n",
       "mlda       4 3           0.1      4        False   10            0.177678   \n",
       "demcmc     4 3           5.0      4        False   0             0.064672   \n",
       "demcmcz    4 3           5.0      4        False   0             0.688436   \n",
       "metropolis 4 3           5.0      4        False   0             0.005125   \n",
       "mlda       4 3           5.0      4        False   10            0.020737   \n",
       "\n",
       "                                                         ess_rate_theta_2  \\\n",
       "algorithm  D resolutions lamb_cov N_chains blocked nsub                     \n",
       "demcmc     2 2           0.1      4        False   0                  NaN   \n",
       "demcmcz    2 2           0.1      4        False   0                  NaN   \n",
       "metropolis 2 2           0.1      4        False   0                  NaN   \n",
       "mlda       2 2           0.1      4        False   10                 NaN   \n",
       "demcmc     2 2           5.0      4        False   0                  NaN   \n",
       "demcmcz    2 2           5.0      4        False   0                  NaN   \n",
       "metropolis 2 2           5.0      4        False   0                  NaN   \n",
       "mlda       2 2           5.0      4        False   10                 NaN   \n",
       "demcmc     2 3           0.1      4        False   0                  NaN   \n",
       "demcmcz    2 3           0.1      4        False   0                  NaN   \n",
       "metropolis 2 3           0.1      4        False   0                  NaN   \n",
       "mlda       2 3           0.1      4        False   10                 NaN   \n",
       "demcmc     2 3           5.0      4        False   0                  NaN   \n",
       "demcmcz    2 3           5.0      4        False   0                  NaN   \n",
       "metropolis 2 3           5.0      4        False   0                  NaN   \n",
       "mlda       2 3           5.0      4        False   10                 NaN   \n",
       "demcmc     4 2           0.1      4        False   0             0.053304   \n",
       "demcmcz    4 2           0.1      4        False   0             0.936903   \n",
       "metropolis 4 2           0.1      4        False   0             0.300990   \n",
       "mlda       4 2           0.1      4        False   10            3.211890   \n",
       "demcmc     4 2           5.0      4        False   0             0.044998   \n",
       "demcmcz    4 2           5.0      4        False   0             0.959292   \n",
       "metropolis 4 2           5.0      4        False   0             0.029327   \n",
       "mlda       4 2           5.0      4        False   10            0.400484   \n",
       "demcmc     4 3           0.1      4        False   0             0.124343   \n",
       "demcmcz    4 3           0.1      4        False   0             1.057977   \n",
       "metropolis 4 3           0.1      4        False   0             0.026003   \n",
       "mlda       4 3           0.1      4        False   10            0.177689   \n",
       "demcmc     4 3           5.0      4        False   0             0.068340   \n",
       "demcmcz    4 3           5.0      4        False   0             0.764531   \n",
       "metropolis 4 3           5.0      4        False   0             0.005139   \n",
       "mlda       4 3           5.0      4        False   10            0.020732   \n",
       "\n",
       "                                                         ess_rate_theta_3  \n",
       "algorithm  D resolutions lamb_cov N_chains blocked nsub                    \n",
       "demcmc     2 2           0.1      4        False   0                  NaN  \n",
       "demcmcz    2 2           0.1      4        False   0                  NaN  \n",
       "metropolis 2 2           0.1      4        False   0                  NaN  \n",
       "mlda       2 2           0.1      4        False   10                 NaN  \n",
       "demcmc     2 2           5.0      4        False   0                  NaN  \n",
       "demcmcz    2 2           5.0      4        False   0                  NaN  \n",
       "metropolis 2 2           5.0      4        False   0                  NaN  \n",
       "mlda       2 2           5.0      4        False   10                 NaN  \n",
       "demcmc     2 3           0.1      4        False   0                  NaN  \n",
       "demcmcz    2 3           0.1      4        False   0                  NaN  \n",
       "metropolis 2 3           0.1      4        False   0                  NaN  \n",
       "mlda       2 3           0.1      4        False   10                 NaN  \n",
       "demcmc     2 3           5.0      4        False   0                  NaN  \n",
       "demcmcz    2 3           5.0      4        False   0                  NaN  \n",
       "metropolis 2 3           5.0      4        False   0                  NaN  \n",
       "mlda       2 3           5.0      4        False   10                 NaN  \n",
       "demcmc     4 2           0.1      4        False   0             0.170862  \n",
       "demcmcz    4 2           0.1      4        False   0             0.810685  \n",
       "metropolis 4 2           0.1      4        False   0             0.510204  \n",
       "mlda       4 2           0.1      4        False   10            3.885221  \n",
       "demcmc     4 2           5.0      4        False   0             0.017601  \n",
       "demcmcz    4 2           5.0      4        False   0             0.751032  \n",
       "metropolis 4 2           5.0      4        False   0             0.672841  \n",
       "mlda       4 2           5.0      4        False   10            3.987961  \n",
       "demcmc     4 3           0.1      4        False   0             0.300050  \n",
       "demcmcz    4 3           0.1      4        False   0             1.065151  \n",
       "metropolis 4 3           0.1      4        False   0             0.739517  \n",
       "mlda       4 3           0.1      4        False   10            1.295181  \n",
       "demcmc     4 3           5.0      4        False   0             0.100593  \n",
       "demcmcz    4 3           5.0      4        False   0             0.737576  \n",
       "metropolis 4 3           5.0      4        False   0             1.019173  \n",
       "mlda       4 3           5.0      4        False   10            1.669370  "
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_temp[[\"ess_rate_theta_0\", \"ess_rate_theta_1\", \"ess_rate_theta_2\", \"ess_rate_theta_3\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most runs have R_hat very close to 1.0, except for a few cases of Metropolis and DEMCMC which do not converge well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>r_hat_0</th>\n",
       "      <th>r_hat_1</th>\n",
       "      <th>r_hat_2</th>\n",
       "      <th>r_hat_3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>algorithm</th>\n",
       "      <th>D</th>\n",
       "      <th>resolutions</th>\n",
       "      <th>lamb_cov</th>\n",
       "      <th>N_chains</th>\n",
       "      <th>blocked</th>\n",
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       "      <th></th>\n",
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       "    <tr>\n",
       "      <th>demcmc</th>\n",
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       "      <td>NaN</td>\n",
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       "      <th>demcmcz</th>\n",
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       "      <td>1.002788</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>metropolis</th>\n",
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       "      <th>0.1</th>\n",
       "      <th>4</th>\n",
       "      <th>False</th>\n",
       "      <th>0</th>\n",
       "      <td>1.014821</td>\n",
       "      <td>1.004880</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>mlda</th>\n",
       "      <th>2</th>\n",
       "      <th>2</th>\n",
       "      <th>0.1</th>\n",
       "      <th>4</th>\n",
       "      <th>False</th>\n",
       "      <th>10</th>\n",
       "      <td>0.999833</td>\n",
       "      <td>0.999872</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>demcmc</th>\n",
       "      <th>2</th>\n",
       "      <th>2</th>\n",
       "      <th>5.0</th>\n",
       "      <th>4</th>\n",
       "      <th>False</th>\n",
       "      <th>0</th>\n",
       "      <td>1.010244</td>\n",
       "      <td>1.008778</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>demcmcz</th>\n",
       "      <th>2</th>\n",
       "      <th>2</th>\n",
       "      <th>5.0</th>\n",
       "      <th>4</th>\n",
       "      <th>False</th>\n",
       "      <th>0</th>\n",
       "      <td>1.008737</td>\n",
       "      <td>1.005063</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>metropolis</th>\n",
       "      <th>2</th>\n",
       "      <th>2</th>\n",
       "      <th>5.0</th>\n",
       "      <th>4</th>\n",
       "      <th>False</th>\n",
       "      <th>0</th>\n",
       "      <td>1.003609</td>\n",
       "      <td>1.003136</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mlda</th>\n",
       "      <th>2</th>\n",
       "      <th>2</th>\n",
       "      <th>5.0</th>\n",
       "      <th>4</th>\n",
       "      <th>False</th>\n",
       "      <th>10</th>\n",
       "      <td>1.000299</td>\n",
       "      <td>1.000076</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>demcmc</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>0.1</th>\n",
       "      <th>4</th>\n",
       "      <th>False</th>\n",
       "      <th>0</th>\n",
       "      <td>1.010325</td>\n",
       "      <td>1.023111</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>demcmcz</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>0.1</th>\n",
       "      <th>4</th>\n",
       "      <th>False</th>\n",
       "      <th>0</th>\n",
       "      <td>1.009805</td>\n",
       "      <td>1.006829</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>metropolis</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>0.1</th>\n",
       "      <th>4</th>\n",
       "      <th>False</th>\n",
       "      <th>0</th>\n",
       "      <td>1.005947</td>\n",
       "      <td>1.006238</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>mlda</th>\n",
       "      <th>2</th>\n",
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       "      <th>0.1</th>\n",
       "      <th>4</th>\n",
       "      <th>False</th>\n",
       "      <th>10</th>\n",
       "      <td>1.000005</td>\n",
       "      <td>1.000247</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>demcmc</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>5.0</th>\n",
       "      <th>4</th>\n",
       "      <th>False</th>\n",
       "      <th>0</th>\n",
       "      <td>1.011232</td>\n",
       "      <td>1.006115</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>demcmcz</th>\n",
       "      <th>2</th>\n",
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       "      <th>4</th>\n",
       "      <th>False</th>\n",
       "      <th>0</th>\n",
       "      <td>1.004685</td>\n",
       "      <td>1.006474</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>metropolis</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>5.0</th>\n",
       "      <th>4</th>\n",
       "      <th>False</th>\n",
       "      <th>0</th>\n",
       "      <td>1.002452</td>\n",
       "      <td>1.003751</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mlda</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>5.0</th>\n",
       "      <th>4</th>\n",
       "      <th>False</th>\n",
       "      <th>10</th>\n",
       "      <td>1.000250</td>\n",
       "      <td>0.999911</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>demcmc</th>\n",
       "      <th>4</th>\n",
       "      <th>2</th>\n",
       "      <th>0.1</th>\n",
       "      <th>4</th>\n",
       "      <th>False</th>\n",
       "      <th>0</th>\n",
       "      <td>1.005019</td>\n",
       "      <td>1.003474</td>\n",
       "      <td>1.023488</td>\n",
       "      <td>1.001380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>demcmcz</th>\n",
       "      <th>4</th>\n",
       "      <th>2</th>\n",
       "      <th>0.1</th>\n",
       "      <th>4</th>\n",
       "      <th>False</th>\n",
       "      <th>0</th>\n",
       "      <td>1.002975</td>\n",
       "      <td>1.003525</td>\n",
       "      <td>1.003989</td>\n",
       "      <td>1.003577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>metropolis</th>\n",
       "      <th>4</th>\n",
       "      <th>2</th>\n",
       "      <th>0.1</th>\n",
       "      <th>4</th>\n",
       "      <th>False</th>\n",
       "      <th>0</th>\n",
       "      <td>1.001902</td>\n",
       "      <td>1.001184</td>\n",
       "      <td>1.000408</td>\n",
       "      <td>1.000652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mlda</th>\n",
       "      <th>4</th>\n",
       "      <th>2</th>\n",
       "      <th>0.1</th>\n",
       "      <th>4</th>\n",
       "      <th>False</th>\n",
       "      <th>10</th>\n",
       "      <td>0.999989</td>\n",
       "      <td>1.000128</td>\n",
       "      <td>1.000286</td>\n",
       "      <td>1.000171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>demcmc</th>\n",
       "      <th>4</th>\n",
       "      <th>2</th>\n",
       "      <th>5.0</th>\n",
       "      <th>4</th>\n",
       "      <th>False</th>\n",
       "      <th>0</th>\n",
       "      <td>1.047156</td>\n",
       "      <td>1.005547</td>\n",
       "      <td>1.005751</td>\n",
       "      <td>1.033883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>demcmcz</th>\n",
       "      <th>4</th>\n",
       "      <th>2</th>\n",
       "      <th>5.0</th>\n",
       "      <th>4</th>\n",
       "      <th>False</th>\n",
       "      <th>0</th>\n",
       "      <td>1.002182</td>\n",
       "      <td>1.002319</td>\n",
       "      <td>1.001492</td>\n",
       "      <td>1.004757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>metropolis</th>\n",
       "      <th>4</th>\n",
       "      <th>2</th>\n",
       "      <th>5.0</th>\n",
       "      <th>4</th>\n",
       "      <th>False</th>\n",
       "      <th>0</th>\n",
       "      <td>1.000744</td>\n",
       "      <td>1.012991</td>\n",
       "      <td>1.013351</td>\n",
       "      <td>1.001218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mlda</th>\n",
       "      <th>4</th>\n",
       "      <th>2</th>\n",
       "      <th>5.0</th>\n",
       "      <th>4</th>\n",
       "      <th>False</th>\n",
       "      <th>10</th>\n",
       "      <td>1.000044</td>\n",
       "      <td>1.001906</td>\n",
       "      <td>1.001976</td>\n",
       "      <td>1.000339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>demcmc</th>\n",
       "      <th>4</th>\n",
       "      <th>3</th>\n",
       "      <th>0.1</th>\n",
       "      <th>4</th>\n",
       "      <th>False</th>\n",
       "      <th>0</th>\n",
       "      <td>1.004517</td>\n",
       "      <td>1.011656</td>\n",
       "      <td>1.006404</td>\n",
       "      <td>1.001618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>demcmcz</th>\n",
       "      <th>4</th>\n",
       "      <th>3</th>\n",
       "      <th>0.1</th>\n",
       "      <th>4</th>\n",
       "      <th>False</th>\n",
       "      <th>0</th>\n",
       "      <td>1.001693</td>\n",
       "      <td>1.002858</td>\n",
       "      <td>1.003235</td>\n",
       "      <td>1.001373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>metropolis</th>\n",
       "      <th>4</th>\n",
       "      <th>3</th>\n",
       "      <th>0.1</th>\n",
       "      <th>4</th>\n",
       "      <th>False</th>\n",
       "      <th>0</th>\n",
       "      <td>1.002599</td>\n",
       "      <td>1.047101</td>\n",
       "      <td>1.046614</td>\n",
       "      <td>1.002705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mlda</th>\n",
       "      <th>4</th>\n",
       "      <th>3</th>\n",
       "      <th>0.1</th>\n",
       "      <th>4</th>\n",
       "      <th>False</th>\n",
       "      <th>10</th>\n",
       "      <td>1.000080</td>\n",
       "      <td>1.001806</td>\n",
       "      <td>1.001787</td>\n",
       "      <td>1.000113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>demcmc</th>\n",
       "      <th>4</th>\n",
       "      <th>3</th>\n",
       "      <th>5.0</th>\n",
       "      <th>4</th>\n",
       "      <th>False</th>\n",
       "      <th>0</th>\n",
       "      <td>1.023892</td>\n",
       "      <td>1.016228</td>\n",
       "      <td>1.013080</td>\n",
       "      <td>1.004483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>demcmcz</th>\n",
       "      <th>4</th>\n",
       "      <th>3</th>\n",
       "      <th>5.0</th>\n",
       "      <th>4</th>\n",
       "      <th>False</th>\n",
       "      <th>0</th>\n",
       "      <td>1.003153</td>\n",
       "      <td>1.003464</td>\n",
       "      <td>1.002659</td>\n",
       "      <td>1.005810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>metropolis</th>\n",
       "      <th>4</th>\n",
       "      <th>3</th>\n",
       "      <th>5.0</th>\n",
       "      <th>4</th>\n",
       "      <th>False</th>\n",
       "      <th>0</th>\n",
       "      <td>1.001460</td>\n",
       "      <td>1.124950</td>\n",
       "      <td>1.125533</td>\n",
       "      <td>1.001688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mlda</th>\n",
       "      <th>4</th>\n",
       "      <th>3</th>\n",
       "      <th>5.0</th>\n",
       "      <th>4</th>\n",
       "      <th>False</th>\n",
       "      <th>10</th>\n",
       "      <td>0.999979</td>\n",
       "      <td>1.013937</td>\n",
       "      <td>1.014152</td>\n",
       "      <td>1.000210</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                          r_hat_0   r_hat_1  \\\n",
       "algorithm  D resolutions lamb_cov N_chains blocked nsub                       \n",
       "demcmc     2 2           0.1      4        False   0     1.003380  1.015242   \n",
       "demcmcz    2 2           0.1      4        False   0     1.005411  1.002788   \n",
       "metropolis 2 2           0.1      4        False   0     1.014821  1.004880   \n",
       "mlda       2 2           0.1      4        False   10    0.999833  0.999872   \n",
       "demcmc     2 2           5.0      4        False   0     1.010244  1.008778   \n",
       "demcmcz    2 2           5.0      4        False   0     1.008737  1.005063   \n",
       "metropolis 2 2           5.0      4        False   0     1.003609  1.003136   \n",
       "mlda       2 2           5.0      4        False   10    1.000299  1.000076   \n",
       "demcmc     2 3           0.1      4        False   0     1.010325  1.023111   \n",
       "demcmcz    2 3           0.1      4        False   0     1.009805  1.006829   \n",
       "metropolis 2 3           0.1      4        False   0     1.005947  1.006238   \n",
       "mlda       2 3           0.1      4        False   10    1.000005  1.000247   \n",
       "demcmc     2 3           5.0      4        False   0     1.011232  1.006115   \n",
       "demcmcz    2 3           5.0      4        False   0     1.004685  1.006474   \n",
       "metropolis 2 3           5.0      4        False   0     1.002452  1.003751   \n",
       "mlda       2 3           5.0      4        False   10    1.000250  0.999911   \n",
       "demcmc     4 2           0.1      4        False   0     1.005019  1.003474   \n",
       "demcmcz    4 2           0.1      4        False   0     1.002975  1.003525   \n",
       "metropolis 4 2           0.1      4        False   0     1.001902  1.001184   \n",
       "mlda       4 2           0.1      4        False   10    0.999989  1.000128   \n",
       "demcmc     4 2           5.0      4        False   0     1.047156  1.005547   \n",
       "demcmcz    4 2           5.0      4        False   0     1.002182  1.002319   \n",
       "metropolis 4 2           5.0      4        False   0     1.000744  1.012991   \n",
       "mlda       4 2           5.0      4        False   10    1.000044  1.001906   \n",
       "demcmc     4 3           0.1      4        False   0     1.004517  1.011656   \n",
       "demcmcz    4 3           0.1      4        False   0     1.001693  1.002858   \n",
       "metropolis 4 3           0.1      4        False   0     1.002599  1.047101   \n",
       "mlda       4 3           0.1      4        False   10    1.000080  1.001806   \n",
       "demcmc     4 3           5.0      4        False   0     1.023892  1.016228   \n",
       "demcmcz    4 3           5.0      4        False   0     1.003153  1.003464   \n",
       "metropolis 4 3           5.0      4        False   0     1.001460  1.124950   \n",
       "mlda       4 3           5.0      4        False   10    0.999979  1.013937   \n",
       "\n",
       "                                                          r_hat_2   r_hat_3  \n",
       "algorithm  D resolutions lamb_cov N_chains blocked nsub                      \n",
       "demcmc     2 2           0.1      4        False   0          NaN       NaN  \n",
       "demcmcz    2 2           0.1      4        False   0          NaN       NaN  \n",
       "metropolis 2 2           0.1      4        False   0          NaN       NaN  \n",
       "mlda       2 2           0.1      4        False   10         NaN       NaN  \n",
       "demcmc     2 2           5.0      4        False   0          NaN       NaN  \n",
       "demcmcz    2 2           5.0      4        False   0          NaN       NaN  \n",
       "metropolis 2 2           5.0      4        False   0          NaN       NaN  \n",
       "mlda       2 2           5.0      4        False   10         NaN       NaN  \n",
       "demcmc     2 3           0.1      4        False   0          NaN       NaN  \n",
       "demcmcz    2 3           0.1      4        False   0          NaN       NaN  \n",
       "metropolis 2 3           0.1      4        False   0          NaN       NaN  \n",
       "mlda       2 3           0.1      4        False   10         NaN       NaN  \n",
       "demcmc     2 3           5.0      4        False   0          NaN       NaN  \n",
       "demcmcz    2 3           5.0      4        False   0          NaN       NaN  \n",
       "metropolis 2 3           5.0      4        False   0          NaN       NaN  \n",
       "mlda       2 3           5.0      4        False   10         NaN       NaN  \n",
       "demcmc     4 2           0.1      4        False   0     1.023488  1.001380  \n",
       "demcmcz    4 2           0.1      4        False   0     1.003989  1.003577  \n",
       "metropolis 4 2           0.1      4        False   0     1.000408  1.000652  \n",
       "mlda       4 2           0.1      4        False   10    1.000286  1.000171  \n",
       "demcmc     4 2           5.0      4        False   0     1.005751  1.033883  \n",
       "demcmcz    4 2           5.0      4        False   0     1.001492  1.004757  \n",
       "metropolis 4 2           5.0      4        False   0     1.013351  1.001218  \n",
       "mlda       4 2           5.0      4        False   10    1.001976  1.000339  \n",
       "demcmc     4 3           0.1      4        False   0     1.006404  1.001618  \n",
       "demcmcz    4 3           0.1      4        False   0     1.003235  1.001373  \n",
       "metropolis 4 3           0.1      4        False   0     1.046614  1.002705  \n",
       "mlda       4 3           0.1      4        False   10    1.001787  1.000113  \n",
       "demcmc     4 3           5.0      4        False   0     1.013080  1.004483  \n",
       "demcmcz    4 3           5.0      4        False   0     1.002659  1.005810  \n",
       "metropolis 4 3           5.0      4        False   0     1.125533  1.001688  \n",
       "mlda       4 3           5.0      4        False   10    1.014152  1.000210  "
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_temp[['r_hat_0', 'r_hat_1', 'r_hat_2', 'r_hat_3']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. MLDA tuning\n",
    "Here we do a few extra MLDA runs using the same model but with different subsampling rates. We will demonstrate how performance changes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scenario filename: mlda_2_[(10, 10), (40, 40)]_0.05_1000_1000_4_True_100_False_1_False_12345_12345_True.nc\n",
      "Scenario filename: mlda_2_[(10, 10), (40, 40)]_0.05_1000_1000_4_True_100_False_2_False_12345_12345_True.nc\n",
      "Scenario filename: mlda_2_[(10, 10), (40, 40)]_0.05_1000_1000_4_True_100_False_5_False_12345_12345_True.nc\n",
      "Scenario filename: mlda_2_[(10, 10), (40, 40)]_0.05_1000_1000_4_True_100_False_10_False_12345_12345_True.nc\n",
      "Scenario filename: mlda_2_[(10, 10), (40, 40)]_0.05_1000_1000_4_True_100_False_20_False_12345_12345_True.nc\n",
      "Scenario filename: mlda_2_[(10, 10), (40, 40)]_0.05_1000_1000_4_True_100_False_30_False_12345_12345_True.nc\n"
     ]
    }
   ],
   "source": [
    "df_tuning = pd.DataFrame(columns='algorithm,D,resolutions,lamb_cov,N_tune,N_draws,N_chains,blocked,nsub,t,trace'.split(','))\n",
    "df_tuning = df_tuning.set_index('algorithm,D,resolutions,lamb_cov,N_tune,N_draws,N_chains,blocked,nsub'.split(','))\n",
    "\n",
    "for init in [True]:\n",
    "    for D in [2]:\n",
    "        N_tune = 1000\n",
    "        N_draws = 1000\n",
    "            \n",
    "        for i, resolutions in {0: [(10, 10), (40, 40)]}.items():\n",
    "            for lamb_cov in [0.05]:                \n",
    "                model, coarse_models, true_parameters = get_model(resolutions, field_mean, field_stdev,\n",
    "                                                                  lamb_cov, D, data_seed, \n",
    "                                                                  points_list, sigma)\n",
    "                for algorithm in ['mlda']:\n",
    "                    for blocked in [False]:\n",
    "                        for nsub in [1, 2, 5, 10, 20, 30]:                            \n",
    "                            for N_chains in [4]:\n",
    "                                t, trace = run_setting(D, resolutions, lamb_cov, N_tune, N_draws, N_chains, algorithm, \n",
    "                                                       model, coarse_models, true_parameters, tune, tune_interval, \n",
    "                                                       blocked, nsub, discard_tuning, sampling_seed, data_seed, init)                                \n",
    "                                df_tuning.loc[(algorithm, D, i, lamb_cov, N_tune, N_draws, N_chains, blocked, nsub)] = (t, trace)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
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       "            progress {\n",
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       "                border: none;\n",
       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "                background-size: auto;\n",
       "            }\n",
       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "                background: #F44336;\n",
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       "        </style>\n",
       "      <progress value='6' class='' max='6' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      100.00% [6/6 00:00<00:00]\n",
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       "    "
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      "text/plain": [
       "<IPython.core.display.HTML object>"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\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-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "                background: #F44336;\n",
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       "        </style>\n",
       "      <progress value='6' class='' max='6' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      100.00% [6/6 00:00<00:00]\n",
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       "<IPython.core.display.HTML object>"
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     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_temp = df_tuning.reset_index(['N_tune', 'N_draws'])\n",
    "\n",
    "df_temp['trace'] = [\n",
    "    az.from_pymc3(trace) if isinstance(trace, pm.backends.base.MultiTrace) else trace\n",
    "    for trace in df_temp.trace\n",
    "]\n",
    "df_temp['N_samples'] = [\n",
    "    row.N_draws * row.Index[2]\n",
    "    for row in df_temp.itertuples()\n",
    "]\n",
    "\n",
    "df_temp['ess_theta_0'] = [\n",
    "    float(az.ess({\n",
    "        k : v[:,row.N_tune:]\n",
    "        for k, v in row.trace.posterior.items()\n",
    "    }).theta_0)\n",
    "    for row in fastprogress.progress_bar(list(df_temp.itertuples()))\n",
    "]\n",
    "df_temp['ess_theta_1'] = [\n",
    "    float(az.ess({\n",
    "        k : v[:,row.N_tune:]\n",
    "        for k, v in row.trace.posterior.items()\n",
    "    }).theta_1) if row.Index[1] > 1 else None\n",
    "    for row in fastprogress.progress_bar(list(df_temp.itertuples()))     \n",
    "]\n",
    "\n",
    "df_temp['ess_rate_theta_0'] = [\n",
    "    row.ess_theta_0 / (row.t)\n",
    "    for row in df_temp.itertuples()\n",
    "]\n",
    "df_temp['ess_rate_theta_1'] = [\n",
    "    row.ess_theta_1 / (row.t) if row.Index[1] > 1 else None\n",
    "    for row in df_temp.itertuples()\n",
    "]\n",
    "\n",
    "df_temp = df_temp.sort_index(level=['D', 'resolutions', 'lamb_cov','N_chains', 'blocked', 'nsub','algorithm'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These plots show how runtime, $N_{eff}$ (ESS) and $N_{eff}$ rate (ESS/sec) change when we change the subsampling rate. \n",
    "You can see that runtime increases almost linearly and ESS does not increase after a certain point.\n",
    "\n",
    "In terms of performance (ESS/sec), setting it to 5 gives the best performance. It is worth doing some short trial runs and pick a reasonably good subsampling rate before you do a long run."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1680x840 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2, ax3) = plt.subplots(nrows=3, ncols=1, dpi=140, sharex=True, figsize=(12,6))\n",
    "fig.suptitle('Tuning MLDA\\'s subsampling rate')\n",
    "\n",
    "df = df_temp.loc[(algorithm, D)].reset_index()\n",
    "ax1.plot(\n",
    "    df.nsub,\n",
    "    df.t\n",
    ")\n",
    "ax2.plot(\n",
    "    df.nsub,\n",
    "    df.ess_theta_0,\n",
    "    label='$\\\\theta_0$'\n",
    ")\n",
    "ax2.plot(\n",
    "    df.nsub,\n",
    "    df.ess_theta_1,\n",
    "    label='$\\\\theta_1$'\n",
    ")\n",
    "ax3.plot(\n",
    "    df.nsub,\n",
    "    df.ess_rate_theta_0,\n",
    "    label='$\\\\theta_0$'\n",
    ")\n",
    "ax3.plot(\n",
    "    df.nsub,\n",
    "    df.ess_rate_theta_1,\n",
    "    label='$\\\\theta_1$'\n",
    ")\n",
    "\n",
    "ax2.legend()\n",
    "ax3.legend()\n",
    "ax1.set_ylabel('Runtime [s]')\n",
    "ax2.set_ylabel('ESS')\n",
    "ax3.set_ylabel('ESS/sec')\n",
    "ax3.set_xlabel('Subsampling rate [# of samples]')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generally, the optimal subsampling rate depends on the complexity of the fine posterior. The more complex the posterior, the more samples are needed to generate a decent proposal. The reason is that the MLDA sampler is based on the assumption that the coarse proposal samples (i.e. the samples sent from the coarse chain to the fine one) are independent from each other. In order to generate independent samples, it is necessary to run the coarse chain for an adequate number of iterations to get rid of autocorrelation. The more complex the posterior the more iterations are needed and thus a larger subsampling rate.\n",
    "\n",
    "Note that in cases where you have more than one coarse model/level, MLDA allows you to choose a different subsampling rate for each coarse level (as a list of integers when you instantiate the stepper)."    
   ]
  }
 ],
 "metadata": {
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   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
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    "name": "ipython",
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   "file_extension": ".py",
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       "outputs": [
        {
         "data": {
          "image/png": 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\n",
          "text/plain": "<Figure size 864x720 with 5 Axes>"
         },
         "metadata": {
          "needs_background": "light"
         },
         "output_type": "display_data"
        }
       ]
      }
     },
     "0dfcb5af80da45b4b6e86f9dbb6cc551": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "DescriptionStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "1122e58b14fe41ac8df4ef18ab3d40db": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_bb9615c8f4db47af92f4bbbeaad1a605",
       "outputs": [
        {
         "name": "stderr",
         "output_type": "stream",
         "text": "No handles with labels found to put in legend.\n"
        },
        {
         "data": {
          "image/png": 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       "outputs": [
        {
         "data": {
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embuz29c/5Xe5F5+ZOaofNla34toZw7qVTJhtY/AVSei/pTONzfsZDUhWWch01dkbaxycSP+jM3lkXli2F00dmTcgfEHgC+dzSyvFc1xdmTt3XO6TX76xIeOxXmoTrR5jQ+7iTG1xUUtoZlUVTcv8bvMxEUsaeHjODhx3t8olnrOl1sWpzBUhnSCdo5KCSekhS3Zti6exeg+7h45kWrnO4p3uSMT6qhZ849kViDk2DrkEwGSt+7Rh4sMcq8a9vW4fKuoze+Ff9KC88eqP92apfhjStIzvoWxMOg3NTOvQz15fz357CIICLArgvv7w0gK0dKYEd5gAyiD/0+yteHbxHqQMU2zeewoU0qbbGTXKEZyz7Ld2ehn2Ly+3aSaUUqzwy3XxcWBRSoWRLmPT/lYQENS2JVDf3jsrNQbG8lEENvio5yIHAC2xFM7445xunTtT8lNXMqtTBkWeZSz7/cakwL0feE+q3dl5O7PfM4GALVppgyoZ/CGd/RE9jFSMtngab6zx9kBm26D87p3N0DXgzv9szHgc52R6GRoUzLPfE8jmJRtdVoitNW2IhLRuRSlCmpbR4JGNAd51J/7mA0tlIbc2yjScTIle2fj7XouBDB5J4a+cvFi9sbo6429l8I0f35xmgtwPXuBdI88DV/0td4VPmoWGoZHM+RbO+ceZyPzB5hqs3ds1Y/kHL60BpRQhTcsacuagJnqsOIoTcu+UFkTAh3NtWxw3ODYfAPOUzt5Y49rwaoRgwsCirNfjRu+AkjzxWbYS2G+srsb2WnVOuP6xJUJFpr49gZ94UN6yPV8OkmUxkb+pbUugMKKj1aI6NMWS2O1jyD+/tLJHlI4Mk2JFRSN21dl90JkyoGvuM+dHdMSSBpbtbgTgpkmalPlX0wbF69Z7/a1/rRTfU0q7XVrdpBRha83qrtxhyjAxc1Q/3++9HtOPpNyWTM4LCu9n8eHmWvzwFffG4bN/W4Rm6/keLJ/7UCEwlo8icI4i4K1eYFAKI4NB8WgGL+JFD9gJAnsaOvAvKazblTK4KcNE1CpbSkExZ2st7n5L9Z46Q8Iy/OZRLpGVNkzXwpoteUgBIahti+OlFXuVSWZDdSueXrxHWdGezIFzfTBIGSbCPhZMSX44428fnbsLeg41fROW0eFM3gC67sWvb0+IsOPKPY3Kd595gJVg9uKpLdpZj4fn7GBRB13PGN57b+MBT89WSCeIp8yceJEAm6ybYilW0lUj0AjwhZNGZPyN00vvy1nO8i7kSruX1Sh4URDZ4PzaMytctCRu7DTHkti4rxUGpTh5TP+s1zJ81BcIgBeX7xVeTucCeM0ji7Im6l720IKsG9ZsxhS/vty38uaGAl2WiHltdTVeWr4XkRARNIzFOxuwo9Z/g8jnkkORnW9SIGHY3jzeH7WtCYwfWOw6nt/tOX/+WPlck9aBTOAc8LC0Cbzpn3YEZmddu4vu4uWc3d/SieYYM5Zn3POB57VylT7cXd+Rse3yGGmLpzBlaB/xXsaSBv40e4vvbymlaOlMYkN199WVDrTGcfUji/HaKnvTSilLAHZuBqMhDbGkgTxrvfPjia/e2yQMxzmSqoVJkVNpda9+lTcGRjcpUCmDIhzKpGCT+Xl+4XH3Bk+G1+u6cV+rb+SC864zlT0/kgiM5aMIB1ri+O0sxnn73N8XuSa6bEk2v3vHf6KRPYzVTZ34r5RM1TXPsom8MBtWTy2qQCxh4ECrym01KUXapILMf+VxQ5TvvHDybz8EpRTjfv4O/r1K5aX5ycF5Qc689eoveTd899vdp0jISBveleMo9ZdDysvBW+jnBG9PpPHHd9mz7lcU8f19rhsgjsrGGB6Zy3b9ToNjy4E2mCbFNy3PyZJdDUKWrsEqtRtLGgjrJGMU4/XV1djoU5yjri2BOxxcTBmcyy/3qGmVdL1q+rCsk3B5cRSUUqQNM2PPfLAps3fu8mlD8LUzRmc85sLJA0U/aIRgqeWdkr3nG6tbsLexU+GGcy8jN2pNM7MkHoeRxfvEvXfO9295RZPwnN37nndS0vrqFnz6vrmobUvgP2u8PeOEADvrOnz5wE4jPawTRalCVqnoiiHbGk9ZnmV2rv+sqcbyCnWjd/8H2/ClJ9jCz+kk2S7RnRLqlFLhHdc121iubIyhOI95sxvaEyJkzx+XU/aOkK5F4PwMqdteXO0qjELBNLFlDCjJw6rK5ozKOwRgzgYAKxz9K+M/a/b5yvidMLJU6XeTMi9/m7SJ9RrD4l2gLEn14Y9yU4PwAndeyPMyhXdVSEIIYsk0otZc7dww8D+/8PhST2rPx1m8/BzrJeM/bZioaY3DMG09cPZuEMzZUtslWl/KMJWNlBN+I4yvZcsyPGe/Nfm+D7b5Pv9MbekN6N2t+4TCL7zRnkgLvcVpw/u6QlIHK3vElSGIwwvEPC32cXWOzNuK+g787HUWmkukTURDzChpjqXw5tpql0HK7685lsSNp47C1GF9lXvwQms8jQ83s8klnnYnTeRqLGvSQuPVX4dCYvWfCytE6F3GHa+u9b2e36Lwf2/atAs/73wiZeDF5XvxldNH4+8fe0cTXlpeifr2rhW4CUkcWa/nlDZtOaw7/7MB263MdB4uTJsU0bCekRNKCNDQkcCHm72zojMlJCoJh9xTSpl+r0bcMmEcp1ie2cKIjnc3HMBvZ20BgVtaiePJhbvx2ir/RKI++WEM6pOvfBZLpnGPtfmaOqwPpg3vKzZtnAIE2BSPAy1x7GuJ4/F5u/DcEn+VjLRJMbhPHsqsTZGf4Uypd/IeH0IFEfbOOo2wgSVRYTg8+NEOmCb1nJ94gqJzbuBoaE/ia8+swB4fVQq+R+DjPqRpShEKnpegka4VtWnsSIEQ/6Q9jTAd5gU7WMLil59cxua7LOd16jPnAt7sLQdalfuoaOjAVEuD/LmllXhhGeOFztvm1udmbWbzcS48bL4J8IKXJ5RSit87nCrHDe+L+vYELnlwvud5eMEfDr7x8wN3pjjh3MyalKK0MJJ147J5fysG98kTtIeuOgFk8F/K6wK/Pv+MR3V1DehIGGjpTOF7L652aaXLCevJtIm/fayuAV95OrcKrTKFaGtNG27853Jc/chiwTnmEdLWeAoHPAq7+CFtMIqSH9b40J68aKAr9zQpmyQKioU+ikDcKHb2xxGS0c4ZgbHcy/DuhgP4nE+GaVhaVPt7TCKUUtS3J1HfnlB2uNky5Tkuup9NhrJBCQBw/PtMBy86bVIssRJU2hNpxYs6e2ON8PRRSvHkgt1i0TYpxVOLKpRzZ/KY8AXP8NRGtfsmkTYUzpmMWNIQxjs3svkCP6as8JC8sJpGPD1RS3b5Lyr8duTowa66djy1qIKdkzAe8JkTyr1/j8zhrB//ez0KPL6/34dLzq5pK1L8a6l7TKVNUxh+22raxbFR6Tr5YR3fe3GN7zUAYHtNOx518Nb4qPBK/uBjnW/SiGTscMqPrhFfjzVHNKRj475W5Ec0aBrB/721CYZJUSslS/HCKg84ElRlY2x5RSNiQiaPtaQzaeBVy8A+Z+IAjCkrFMaSrLfLF0ZuVL678QD2SPJurlLBJsWQvvn44kkjAQCn/cE76dIwKfJC3uPhiyeNQMhawPht3GwlTI7sV6jcx7NL9mRMMI76RETs/vF+wbgKDY82daYM/Eqib5lW9j/zyPpe3oU9DR3IC+m+SiohhzdrfXXLIVu0+dx20f3zoWn2u0QpELb6Tb72eh86Afcsc0MnkyrNop31gn7j3EhpEsc2njKQSBuefatZqivOIhYcM+55X3mqmdREvnbGaJTkMYqZM0IQ1m0nza0vrEZn0siJj580TAwsycPb6/ZjV13m5ERKKc77y8e+EnO2YUyU3xDYUUe+PuuEoLUzhZBGsKqymc07jv5LSNz73Vna5odrHrHtAbbBocqmlOfedDW/0TAza3378am9cjLmbavDPEkhJ2Myr7XJm7/NqaiTqbVHHoGx3MuweX8rzvIxgORdIPN+qKOLhzcuuG+esvvLlinPwecHp/KA7Gm59tHFrjBKXlgTxmAybbomVY0QpAwTKYPit7M2i0mbe9Hka3m9MHxSJYR5/5wL36srqxSd4OqmTt9d+5YDbSIUSim73r5mttgYlGYNr/p5VzKhKKqjKebtxXUmQfC+4X0iLzzckPryk8sQtQyAoX3z4IRh0Utkj6UXvJwK93/gz6F7e91+bLYq2MnSVZssI5RNvvZJOU9UTpzbvL81oyYoAfHkrfL/+nncB5XkiYW2uqlTSlhjmxU/elJzLInFls53OMQ8l9GQLrz2exo6cM2ji7FoZ711ffY7eZH53N8XKeoutW0J/MVaaFIG4woblKI5lsLcbXXiPkzKKBRyn3Hvt+yp89pA8nHq9Bbv99Eq9uMs87bw/uW5AR9ZCZMhnc0FXJ7xrjc3ZtV0/a+HHnY27yDnfT4gjT/ZCOQbYmfUKxt0jb0Hfrxrr5yB1ngKjx8CCSu53RFdU+Y/Pp5yUR5yGkV+qjRD++aDEHvsu8LfUl8+9NF2PDF/t2ff6hqbl5zrDTeaWuNppd0HMuhla9a7MGv9fpcxpmt2VcK52+rQkUjj8mlDcNX0ob7nA5hBGglpihLU1gNtnom4VU2dqG7uxGUPuXM4ANsr7XxXKNzzJSEEnSkD0bBuUyIc5+NrC5C7LKMT8nKnaWxNkHOT9jR0WA4CijclyUq/yBjAqBRba9rw289+yv8YH+s1lwJl8i9ve3G12g/Wefm8y3EkCq51BYGxfBRB9iyHPHh1PIO8OZaUwuW5D0Cxq7a8NynDxLaaNqGzvL6qRagqyBMTpWxy+ffKKiTSJm49b7xyXkKYMgH3PN5kea0en79b+S/gXWCB3wshBF88eaTrJV7ieuly22E/Nm8X6toS+NI/luJXl0/G/pY4Zm/MLIqezTvphT75YaW/npO8ss618fz75qpSetJ33HCeu60O0bCGJbsaXYtrPGWgLZ6GRrJnhjtlBv28ynwMLd7pHRZeaUmMOQtPPDZvJ+Ztq1MMy8lDSgAw/msmvfBluxvREkth9E9nKYtvnoc33KAUJfkh0W/feX6V8MqlTRMdiTQq6js83wV5KMmyVrr1rkVCGlJpE194fCkopTZ1QuOKLxRr9jYrmyx+u9tr2kBBhR4ywPip7BhmqJz15zlKn+1r7kQybaJeMki92s0VCdKmO+jsfZ/Uc3PE21tllc12jifd8oB6JeV6bSzvenMjvvP8Ktfn3PjIttByg+Kk0f1w6VQ5lwEAYd68riyqlLKwrxzKlucPp2cZYPOoTEWglGbUfb7zP7k5I+Rmh3R7/mb8U9X7m2nepsjNqOabZb5sOPtNzkWhFBZfn32wbHcjmiwFDJ0Ql6FX2RDDFVLSsNycTG3TCctZaI6lxKbr8zOGY/KQEugasK6Kvbf5YR2xlIGBJXkY0a/AdZ5N0jycNFj571PH2omu3/rXSuz1MBZbOlMYaKmDfPnJZa7vuR61fAsUakIdwLjlAKMdhjVibeTc9yuPtWy5BZkoMwB7JgQEO+s60Cy9e5UNMU+evTMxVAZ/nv0KI11OZs0kV8ohn3NFRZOypu10eNinDC1x/aY3IjCWjyLwkFRBRBeLOcd9728TXj+TQpRu5h4hDkopGjuSisHmlGTSCMGavc040BLHzU8tFxy56uZOnDG+HP0KI6IKGUdtWwJ3vbkR9W0Jkawin685lsJd/9mIsKZhdSW7Hl+A5ND6qj1unhSfcDRCFN4sx/nHDsRnJe+DSf29aDJsSgOBphH0yaJAwcFf6kwC/PKLb5iqp+Lnr9uLq9NTuq+5UwlzyROsrAQRDWn4YHONK2T8xupq/PHdLTnd/466dkUmy8+rzMfQ2ip3WLgjkRbGsFPLd+O+Vuxv6VQ83MNK2cL3/LK9irFcUd/BIiASl3Pa3e9hRL8Ci1YEfO/88ThlrFv5gRtEXlxWw6RoiqWweFcDnBTPWDKNuyQOuOzF54YhpfaG5b/r9wsjnN+T2JRK562xeIOfvm+eSHT51r+YAZkX0tAUSwo6QW0ruzcu4bS6shnvbNiPbz1nG5xeBia/7tq9zXjww+3KvVd4JGLJxu4ziyuU7wgholCEc8EKabC4ps4AACAASURBVMwr66Xl+i8PehdvxsId9Qp95ZoZwwAAFz84XxhhAPDd51cp/c7vY2hpPkryw7jJ0rClYKHwXDjLZ4wvE39TMK/t9tp2kXz4yzc2iD4NZ4m+AIwO8R3pecjtB4BnFmemuXENZdlYJSCOfzP0LWBzUEZZLosW4Id1Vc04/9gBOGN8mdVn7GgefbOjNAAfucQK43Nv37WPLsZuiwpECEFlQ0wxFuvaE4ohKH+XUaIQbD7k1wOAQX3yEAlpMEzg+seXYEVFI0ryQ6io7xDnKnbI+V0sRfh21rajvDiK4f0KMLa8UCROe/Vha2cKxXkhEAIR5ZExfzuPIEmRHdPuIwCYOLBYGPqJtIFwSJOS7dixt7+81uoXGw3WuLnx1FHKNddXtSCWTAvevB9q2+J2joHDaaARuIzlTHKJ8rEPfrQDH23JvXKerCPP1w9rL+uJuCW954c9DTEURmyVJD+6z5FGYCz3AjT7hOid4Dv2J/7fDMwc1U+ZbFfsaVR4TD/+t7uCGcBeoJbOFH7++gahaPBLh2dEPp4v9tzTHNE1l8FqSpPvV59ZgaF91eQmfrY5W2sVw6nIMqqPG24n+G3a7/bccgPla8+ssBZvdRLgIWbTpJbyRG7GMkdYJ9ClUDSl3klMgD3J/3XOjowC/FxKDcisgyl/vL+lE/GUiUTKlHjV7Lt4yhD8N04VYH87aByULYqcpnPrueOEF8SJZNqErhHX5sYJSqlvGP2U330oSo07x5r9nNhnt547Tng3Q5paka+uPYE1e5tBwDRoOQYUR8UiP7RvvouHzQ3FsK55hjnlBcP5fTxlKpX24ilDhLj5+JEnbpnqIzi+1jn9xgvnxfNkmcJoCFFrcWX0BopN+1uVd/k2B6fbqTkM2ElGPNKha0RECmyZr/eFESwbu7I2NwHJKEUW0jVfz/KHm2twxV+9w9kPfrhdeZdlY2qnlE/w9rr9yvvM9XzZHGPaUlvUpoP5JXnO2VoLSimOH1GK8QOYDjGnWr2wrFJ51ry/MyU4cSTTdlGPWDItxnuunrDrH18iqCwchMAVEQCAggh7F52GjjxHUiBj6OzGfy5Hv8II+hdGrWIa7PMfvrJWee9kKVIC4gqLy8/7NYf+d9owUVpgOxfk6TaT6kpY1wT/XAalNpf1+WWVGFNWhMaOpDjXTaeP9j13Im2ivDiKe9/fhl31HSgvjirRHKXdljoOP4vfM5S92RRsDeJzsmzoJ9NsTdSIWhCbKzbxcfPDCyYIQ9zJ67/s4QV4a+0+xTPuhbCuiX52eu+d0oycTvXFJ5aIz9oTaTy7uMK6b/XcXakgukKSDeWJwaxN9jHy6dsSaVz20AKFVnXNCcPE3x2JNCIhTchiHkyhrEOJwFjuBbjusSX43azNWcus8rF46rgyDO+Xrwx4OfkKsBd7eZGM6BooqJggnl9aiVgyLRarAcVRdh3CZLTEdS3DK2UwbpjT0DnbCvfw6xRFQ6j4/SX49ZVTxO8BoL49ib6SIcS5rDJH2yu02yZ5VL04i2mTIqQTvLqqCn/7eGeXRdrjKRO6VGFsya5GUS7WOZkSsD7902xvGS2OLQfsBBIWNvU+Tl6QON2gMZYU1zVMiob2BM6/d65i7EVFQpBz0mRGKrcBEoaJc/8y1/PaNa0JV0lWeQHkMClTReCQw3AyN97pfUyZprWAUEwf0Rc/uGCiuF8nL/4Xr28QC5J8T9GwpoTAnUvbve9vg0EpIj6eZTlEnjZMEfLzgtf7E08Zoj3ys+JcV1u2yv1bwJ18WhQNIT8SssKmdvjdz+yaNqwPzpxQ5vOtTaVJGSa+a9Ef+H3UtyfxtBU9MUxgV12HKwMdsBJ6PTzkhAD9CiLYtK/VM1qzqrLZt1BIXlhXzpUpUStlmq73TNc05Xm8troamgZMHFTs0p7m+MpTy8XzuPhTg9n9UKAw6t5YCmM5B8+y/K4nUibe3XgA5cXRjMoszvs56bcfwKQUnzueGQl+3GvOK/bSghbnpJlpZvlhHfJSwsdYNKQjrGtCLk6mYRDCKn7K4P3vRUFJW1x7LuUo921nylDmbBlyNM6Zm8ApSq+tqkY4pCGRNsW8yc8uD0FeajmeMoTzgFJgwY5634qfXJO4I2kIJ1AmbKhusfvI+kx+dom0iZBlxMpGtBNRKbk2EtJw6dTByvdpk2Jk/0LX7+ZIxZYiIc1XXo0nfXIc96v3AUBRpGiOJYX0p5O8NWu9O8/AD/d/sB2vrGCKLSHNrUneEkvhqYUV4t8leWG0dKaUvuaJ6aPLCnHBpEFoiqWE0ldvRWAs9wJcOHkQBpTkKZ4rLyi8MLjljdZX2wsXn1RkjxB/mfnvTErxh3e2CA+QzC3tXxgR7ZE9y/GUgX0tcRiGPNGx//LFgy+qN5w80koysds4REpIyyVRAAAGFLuT2GSkDOYhjSXSqG9PZExmcmL6iL5oT6SxcEeDmMDiaUN4uJyTn0ZIRv3lZNp0Sfqx6mkEO2rbhMrB159ZIc7HwRe1HbXtYmKhlE1rsaQhJToSDLG8904jnHssuexWKm0nLXLawzvSxFicF1boCV58Qznznt+jF1o7U0p0g/edSYHbPz0RgO3Jk3VmAQhaDwEUHW0Cgv9ncQsJIehMuj0gpkmtMC51bTjlcqwpU91E8f684eSRKIqGcOZ4e9OWTJu4avpQJNIGqq3klNsljecCK/zIF2R5YZYjK/LG7cZTR0nSVMyrx41Ur2SaE0eVYkjffKyo8C87zUO3f/t4J8qKrM2udG/O9lU3qcoJmuYfstYJwaA+7N1z8tGzgYWF5Q2E/Z3zSm+t3YcHHdKKcgRJhMEtL7jTp8CTAr0k3+SCRbJhzG83l3mCS6El0gbmW/09sCSKlGkq5X9lfOPZldguaRjXt7MNMKd9SAwIBbvqOnDjqaNEoufnZwwHwDZZcoJeJl5wJKR5UoraEymEdIJLHlxgncPfs1oY0fHG6mqsr2oRyjTyJbmDYiu/R+m7V1dW+UpW2u2HIl1pUKpwWcM6QSJlimf3wIfbMbxfvvL+rtjTxJQh2hPK5mGPxeH1GtOmNAdkKj7D56ZLH1ogDEvh1ZWOS6RMRHSLswxGKfQq/uUcZyeNVivnsTXL3Y6XV9jjSyPEc7yuqmScYPm98JLJrJLefblrwjoR0TyeqJwtarJB0NHcToqatrhCceGGvNxybvR3ZU450giM5SOEeMpQPHR+k6cMJWHLGoCdSQNpg00qs9bb1dP4SyXzSBNpE9XNtlKAYVKkPJJdTErRvygiJlzNckEkDSoM4kwan6pnje3krz5hGAYUR6ERIhZ1JzfJr8Ka8719Yv4uhe/akUyjwPLWGSYV90wpdZWR5dJfHDefxsJ7hAB3XjZZtJkQW5v3mcUVIpteIyQjR3F3fQe+8ayqxMG9cuffOw+vrmSG4HuWRJY8WfCkLubJsYwc6+YbO5JiMjRMKqIATs8yIQQp00RrPI1Y0lDK4k6+azYAYLXkDSyM6khJz9LLdmCcYCIWej/7gofXnYkspmnrYHObmyeOccgTuazowH933WNL0Cc/LMLUMtriaeRHdLyyogq/+e9mz7ZdOHkgDIM6vIvsv2Fdw/B+BehXGBFtGlqaj1PG9Fekn2SU5IXQYG3MACgLlRyaNKntiRfeZNibGv58vTy0w0sLQKk9VgBG4/DDMYNZFbjnl1ZilZUXwDcQ3NDykooiBHZxCke0ioO/U7edNx43nTbKtw3yb+UpIlOkp8FD7/vZJXvEs+DjkxA+buRiJRT3WYmpGiGuojXy3CFHtbgx1K/Qv2iPVztvf5lRZNIGxWfun6+U/5Xx3qYaQZ/hURmT2nMjo2FQ3PveViWa9uTC3ahrS4gNaXMn65uwbht//Ysi+FKGapRhnakPMRUiKpR4SvLC9rpgUozoV4jPP7YE/1qyx+WpnjK0DxbuqMfqvfZGTX6GacNESCOotfj5TmpBNoIKARGKK4B7jl9R0YRE2lDfVxOKpRrS2P2VF0VdSh9cdQNQ3y3DtKvdaQT47vOrPY1qOXfE9r6rTieAedHXVrWI8fTovF14Y80+1/nkNdHk3EYJflVteRXGZ26eiYKIrlyX4+UVVahoiMGkFFdL9AYnrnvMpmTIxnBeSMeL1qaPq/jIMnBe4AVoQrpF5ZHO53SmsGisessRq3IgpSo9buowdX3uTQiM5Rywck9j1pKv2VDVFMO2mjZhSCzcUY8L75uHlGEKXlm2LG/Vs8wG2rF3vovbXlrjSlaYYJVPveTB+WhPpMWC/ZLkCTEpVRIF+EJqUkaREG0DeyF++cYGEAJc8qnBYoJp8QhFyhNDTRtLYBpUkoeBJXkWb4vi2hnDhMeOw08X2Bky6kgaYoFJGSYa2pPMkLO8dYLDTFk/yxODm8JhYmx5IW45d5wwBjhvFQBOvOcD/G7WFmHYO3fs8rn/OmcH4inDNXH/4OW14nzOiUT2bvBzy/xGeSKXJxW+iGyvbceqyiaRoME1UQG2+8+PZK5ad+b4cozsVyB5Onw8y7odFvcbpt+0ktgUbjBRaSh8bPQvjIgkVBnjBhS5PgNYculJY/p5fved51ehOBpCTWtcJG06cfcVU9AaTymG/j1WtSumTMDa/MjcnSCEjf9wiHhWmyoriiAS0nDaHz6CYVJcNm0IjhveB/GUgYkDi5WkSXl8yAoInCOf6ZV3et8BZqT7aaNGQhrGDyjCa6urhRZsR9LAPxbsFkonXLkEYN7Kpo4UQIFdHiVouUEPqIt5fXsSn50+FF8+ZaRv253zWSYHkn9VP/Y59zATwnIL5DK78vsRDWl4Y80+F5WEQ/b4UxM5zenvbbQdEE8vrpCiJVQkbPmBz1s8mmJSKjxqlDLj79+rql0l3P+7fj827mOeu8FWcRuNEBHtKi2I4EZrk88RkyIu22ra0ZkyxFjnlDDN6j+AheSHlrJzP7lwN0aXFyoJzpWNMeyq74BGCO7//HEAVN50ymBzwgGLE889k4URndH9slvLal85jKnKxhg6U4YrOV02nkM6wV+sqpKPOXTZw1Jk4oq/LhTSj7LySDSk492NB3DHq2td6+dvZ21xKR8RYpeZ58+WP7ttNe2iP53lnC+dOlhZEw+0JDDLIa9omO4EWvY5G6POPCFPr7k0vrJB/rnstOCJlLJSSCJtKHkGMiIWB11+UH78Z9nZF5Ycc/K95JpkfyQQGMs54CtPr1CkWrqDlXua8N3nV+GWF1cDYFnPJ4/pj9WVzYLXSJF5Ry6/SmzSYEf/8pJJynFD+uQJ42eUxYP69nOynJMV3qRUUdXgYcodte1o6Uxhw75W6BpRQqoFER3RsIblFU24/eW1aEu4+0UO2ReEdeysa8fwfvkOTqh76HGPHNNqNkQ2faaJd+WeJvxjwW7omoZfvrFBVBjjElMaIbj1xTVi8XHOMa2daev8fKdLrYmbiFKrhkldSYUcMs/v8fm7kDJM7G1km4DfzdosVXuyvLKOCXHT/lac+ac5AOxnxL2BXzhpBAzTNqhkbU3efav3NOHttftx81Oc1mEnBy3a2eBKYGLns+/l11dOwZwfnu07yb61dh/2NMQUqcJseqH/cXhWZCoC74dhpfnKxuGSqYPx9TPHuDyncrg5P6xjR127K3N7XVULSvLDGRUEBpbkMS63dD6ZNiTfEv87ouueEz8hBNGQhnjKhEGZl/8v72/DC8sqUZwXUvi5/FylBWGENQ2JtIG0aSqeL7lfZHgV4HB6aJzffe/8CcpnrZ0pEYECVH1sQpi+7ugymyspb0zD1gbpgQ+3Cz76rvoONHYkcPGnBiueWq8+4m2PJdNKo52Rh3keqgQXTh4o5gu+wb/1hdWujbPch9xYoZTinQ3MGJEXaGdS8uq9zZgyJLMnq1EyiFs7U3ZyXBfKP+iSE0KJuoG9x6+tqmZttuhRt5w7TmzOuRe9viOJix+Yj5117UpBHI7Tfq9qLeeHdddcQwibfyIhDR0JQ9zBrroOFEXDGFVWKCJv/D2hlIoE4Jmj+4k5etHOekVJRI5kgrgdHE44e8/L0GtoTypjvSNpwDTtaGFY0/DovF2eVwrrGitbb42PqsZO/PHdLUyD3jqGv6cdibQoIiSD9wFPkn1tVbWgjnhNNcV5YYzqX+C6d0KIssGdvfGAp8ZwLGGI9Ysbp3zd0aSNs1/ehVwCOxMueXC+kvgnj8k8D+fK3sZOQRvkmGg548K6hvc31Sj8d2fUWfDOlaiAt7EcydHYPxLokZYRQi4ihGwlhOwghPzE4/soIeQl6/ulhJBRPXHdw4kZ93yAV1bsxaQ73+2SHmA8ZWD2xgMI6xp+/7mpGFtehKqmGCgFThhZirfW7kNZUQQLd9TjthdX+yoXAOylsxNE7MHpLB+akjyTLnF1yZu1oqJJWbj4AP7hK2uxqrIZf3p3C/Y0xDB7Yw1MypKNUgbFWRPK8erKvaho6PA0ZJ0v36rKZpQWRISxxzYH6m/ufX+b8BZ/6R9LMfEX72Lmb1mlMK/e5i8en8j56QzKCj1omlWGlRC8tXafMHoopbhw8kBxnoumDGKLlnWCBz/aIf4dsZIykoaJtGm6POGAuiunjsVw0/5WPL+0UukTr3LA3LjmMEyK215cg9dXVePvH+8U402dg7xnReYNtA+cOKhY+d4p+cd+YxcwcVbIe2TuTlz84HyV72m6J+2rpg8ViZr/XWcby9zLxH/NPVuyBxwARvUvsI6lQqeb8zU5wrqGtXub8fa6/YrOKsAWvmxGfDxteBb7CFsLEf81j8JEQppnotKZ48sFHcSw+NLc8K5u7sRyqewrv0ZTLIWtNW1oiqUwuE++S4HCa07htA0lM9/nFm87bzwMw3QlrNW3J0BAFO42R1s8jS0H2nw3SiGdCB4l34COKSvEur0trnHlBLHu6dKH5mPSnbOV0erc1HhJRU0b3lcUMuARAMD2SPF5srXTHs88KTmWNMTzkBdo2ZjgBWE4J9sPckvX7PWuqJcNfDz9Z3W1uCcKdRO5tqpFyAWeMLJUbG4+P2MExpQXwjQpOpKGor8to8kR4WvpTEEjBOcfa891hNjvn/NdoZRi7d5mm6JgPXeTOqujsmf1zOI9isODH2OaVvQsyxLp5FxT6i4Q4/Tct3SmQGHLu/Gx/uIydzn4kE6QNEyhSmRSir99vBNpgzki+hdGlI2Ql5HJ++qdDXZ0wVkkSe5Gk1KkTXdyOYHqJMkL6/jD59RiIIYJvLRiL7bVtqGhI4HPP8qM2aRE/zNNW83DC4aZWVKQY+O+VmyobsW5xwxwfecdtHI/TP5sdI3glhdWK99t2a9GDOV3lIPTMEzTniOLoiFP73pvwUEby4QQHcBfAXwGwCQA1xNCJjkO+wqAJkrpOAD3AfjDwV73SGDlniYcP6IUS3Y1ZkxOa4uncPdbm7BoZz2eWVyBhTvqsWRXA6IhDV86aQT+sWA3TMrkaX595RTccMoo3HjqKPzti8fjN1f5V9QB7B07p0YA7kINdW12uetjB5fgqulDMcUqBrFxX6vIOh7Rr0AtyuBYaPmEdqA1Dgrg7IkDkDZNlBVF0Z5Ie4aJAZUXydvBdvoUJ4/pJ+S0AJvP99bafWLCX7ZbLQPtZUjwj5xfdSTSMChLSDv+1++L68iqBfJkNtCq/MY/O2N8GUxKseVAm2I4xpKGkrDDwXfCL6/Yi5bOFIjEyS6KhkSyWv8i5oXzE6Z/WzIw+f0nDROtcTuL+Hfv2HzcfoVWyVhXe3Rl4XTSGlj43319PwktHoqUv69sjKGsKIrTxtoqDZ89fiiKoiGMLS+ErhFMGszGW9+CMP7fk8vEJMh58U7PakjTxHiKWOPQy5AxTVbS/eIH5yvjwk8NAwDe+u7pABg3T56LOVVJ11gokZ+PJzSFdaKofQDAkzfOwF+unSaoFv9askcJre9viSuyX3KTYsk03t1wAO9vqlEUKJzH2X3C+kOuYEYp9dVPrWtPiLHHcaCVabMO75fv+RvA9rBFQ2r4nIV+efvUpNk+DtWUT08aqPybbwZkTzZHf0cb+XP79tlj7TZZ8wW7ttomADjhng8AqBU1jx9RCgD4x4Ldwqgb1CcPhABLf3aewv1OmxQfban1Lc8tG94cm/erXnkZv5212bf6JzfKPtxSi4b2JIYJD7htZPL15OoThmF0WaGgBXxqWB+M6l8oDP1sKjwcjLMMDOqj9rWuMV6Uc97mfcxVCjjf2umtVEPmdhSIH3PZNFZIxs9W5hth/q5tqG5BbVtcUL2ccM5VciElTtvziv7wQjSyKtHkISX4aEst8sK6YqzLkUUZmz1kTPnt82ZdeZxdOOeZxRWoaup0Gctvrt0nEuIA4LxjBriiMvx56IQlNvJxySlKusaOOefPHyvrq+zdTxnenmV5npGn+ZM9aG1euQUmdW9u+Lha45Fr8ZtZm8U8W9saF1Q2fo9jygqVDbpcrbb3mso941meCWAHpXQXpTQJ4EUAVziOuQLA09bfrwI4j+RSgqgXQF6Qi/NC+PbZY3H940sw4RfveHoaAWZUTxveB1WNnThxVD9cfcIwbN7fimhIw4CSPJTkhUXiG8ep48pQnJeZryMxL6ARYP52NqF6VTXjrQ7rGkb0K0BY13DBpIFYtLMBj1ryMZoj/LyuqkVIFhECT+7lNScMx5jyQpTkha3FnH0vL6QytYOfP6SzhK71VS2KMbf4J+cBAEryw6jxKZM6d1udMEqdk/CjDq6aYbKQs6YRJKTS23zS4WHsuy6z93OysXz8iFJQSpWEMwA4449zPCeSF5czr8aPXl2HaIiF/riRLe+SubHp5/3c32zfO9c0DeuMd8cnGTl7XDZeFS47AdozaGZSeC9k8qR7oCWOLQdafb/fXtuO/3z3NBeHjnPF9zXHRZt5mM0ZXnPqWvPQskmBTw2zuZHOzcW+lrhYxGT+eFgyrpz4lJU00tKZEpP004sqLPoO855SqFGC9dUtlmfZ7stjB5e4vLCvraoWxr3z6mPKC5WQbEEkhLnb6jB3W51ro/mdc8Zi8pASFEphUE1z0zD8jBAKNiYKHGHUxo4kS7DJQM/lz/btW05XPg9pmnhGNa1xEcFismGq8sT7liH6p6unWm33z8GQPz9rQrl4J+64cKL4XLOoLk44Pah+zBv+vjd2JFEYCWFgSZ5i/CRSJh6dt8tT0q44GsLZltHoFQnywmPzduHJBbsB2KozXrefSBsigsITiQG7GMirK6swsn+hQ7fYVhM4ZnBxRhk+jiuOGwpdI0KyrKwoiiJL4xtg75XKFae4YNJAMcf1tbij87bXibm9plWdn73Wq79cO826d+8H89qqKjTFkmLs/HXODszfXi+Syp1wqsTIbS63KvF5WRLNsaSSb0DBOOvcyaNwoaX/B4A7L2Vrw1vr3Il6T8zfhetnDhf3d/9104XDh28MvQwbWdHGyy+hVG2l9j2l0uxzjdjOBXmtlTfOzy6uUOhBfP2WIzeUqrKwMk4fV+a5xlHqljL0825zcK49jw4D9ru6q75DRObkmgYpj8hYb0JPGMtDAcj6OVXWZ57HUErTAFoAZFbgPkKoaorh3ve34d73t+GrT6/A6J/OEiG9x+fvxslj+mP7bz6DX185xdcoeWFZJc45ZgCuPXE4po8oxdjyIstjwLx8JrVoEl0cFyyDmv8D2F7TLtQcAOCrp9t/y3NMPG3ggQ+3456rpqAgwnifQ/vm4/1NNXjBCmFxDhI39H5xySRMGmKH2fkO/crpQzG4Tz5SIomOXeg+KwkEcBQ1sf4b0pjnryPpzloGgEmDi20ZIgmLdtTj/g+249MWdcKZlMNLXfPSprpG0JFMi8meQy7/TQhRjB6nLJZfqNvr4z1StbTSggg+2GxneHtlNxsmxQ8+PcH1uRfCVp91pbyvSanQleUYLHloTeqdVCZPkvO21eEJqwQ5577LxrlJKcIOz7BJqaVhSrBpfyt+8pljANgGvnOB1zS7jHLaYXTLskpeVa04NUKO7rDNWOaErYaOJEb0K4BhUlG5zzAp+hZErIQrVsQFgPA6yslXEZ24uKr7W+I4zwp1//rtTbh2xjAM7pOHc48ZgGhIV/r6/GNZ2PMLJ41QaCgXTh6IOy48Bv+99Qzcf910+56k94vTTrzGwqM3nIAHP9yOHXXtrop0IcswyNQ3/NkW5YWUMc4y3dknnUlTSDjyTVHCMkYusTSNPz+DzXcAj3xRUWhBTkDb2xjDm1aBkPEDiqQKadKG29oofOlkW/XhptNGKfcvV7p04t3bzkRemCUpy15w/nN+Hr6JkwvzqJtcP7qT+/N4mukLn3/vXNd3x1qRlnjKFO+CvAT0L3R6GpWrWdE4gsJoSPwmU8W3L508EoBdbXDFL85HaSFLTKVghXC21dgGkOGYA79sVZn7eGudmBteXL4XxVGbk69GENXrL9ihcnI5qpo6UVoQEfko3BtKCHFt9ACW8LVYygspkeZ1TtvwekIhXVMqSCYsvfSa1jj6F0aUzQilLDlZVhsBvJPoXllZZW0i7c+uOE41d/oWujcRPNF6bHmhuKaM2VYi6XrJA/2LN9aLcSongntVneyTH8a+lrjyDGUe+U8s1RZTStTlcxk/74CSqOfGw4t/zvveSfP0yj/gkB0fPOJpUuCm00bjj1dP9d349hb0hLHsNVadt53LMexAQr5OCFlBCFlRV3f4RaqHlRbgB5+egB98egKe+PIM1/eaxgytIX3yFLF2/rdpUhw7uAQl0q67MBrC508cIQZWWzyt6H/mCgL7hSFgxsZt548X38s8QnlnH7cMVK7T2NiRRNTyEvHdN1caMCWjkk8W0ZDmWqQNk0kHratqttpjwyWTA/aCe2k/cvTJjyghNo4vPLFUtB1we2Z5G19bxbyx3FvufInjKZPx/hJs0jxtnE0hSFiV7Hh/8Jf262eOAWAvdF6GimywpU0qJkPAu8AK14R2hEWD3wAAIABJREFU4jez3JJnYYuH62Xc+uuDuifiuXecI/0O6Ey5N3myhFZLZ0o8Tx7Wl3f8P399A/N6Wh11zsRyGCanSLDN1qkWRYN75godkm+6xs5zyYPzRSb73saYK+PdCzxELPf9pn2twuge1b/A83fHDe+LkvwwzrGK6HDw8Z02qboY67ZneeqwPiiIhDwXE9kjFtY1JNMm0iZ1vTc82fa6E4eDEFu3+ZoTbG62vEDr1qL8wrJKu7yv9fV3zmGUhcF98sTiV9eacHNBPc7rhPxs50qFAWQahkHtQjdJw2QGg3Ucv8c/XD1VeJ95ToX8XnHsqu8QpbJ1x6aLQ9NYISS5mAOTILTbevafPhZ/z9lSqygajLDGgEx1kNvKrxkNu5fAqcP6iGI4PHKUC9IGSwT2KlZy1fQhKCuKYmCfPIQtHr/Mk79ZcnQA7HmdMoZtNDRi81HDmq2hvNKDhw7YVIfxA4tRJ1HJ0gYVnuXbXlyDDzbbtJTWeEok14Z1dX4EGCf+tvPGY8GPz1WKcwDe71u2qrSEEJwxvkxs4gi8nRRF0RCWSTkA8tzJx63XxqVPflgYoADwf29twsKd9UhbKh7yppe34bOWggyHn16/7oiqODdUw0vd/cEpIyeO4o4A9WZ5oukPXl5rFQ4D/rXEHnvD+xXgrbUsAXSXpEf9+rdPxdjyQnz+RDaHyC2JSwVuuDRc2uOeqPSHc+4975gBnlEpWYIQsKU/uXPB673yWj9NSnHRlEG41pGf0hvRE8ZyFQD5TocBcMYvxDGEkBCAPgAa4QFK6WOU0hmU0hnl5eVehxxWXDZtCM7zIML3LYjgjTXV+MO7W/DM4gqcbJX9/ePsrWKS80MibeCfCyu6zM+RPctsAlUNr2tmDMctlmdM/AaSAD8hUqIbcPZEu3+5h8WkwGenD8WnJ9nZ6BohwhjlMEy2mH//pbXQNaKE6NTCD+y/ZUVRxBJpRR7u2hm2JmRRVM9oTPMz2l4o9u+0YeK6E+3hVxDWFZ4dv15HIo2UaaIoLwSNqJ6k1s6UMBYHFEdFWc5yy1DkFdS8+MZyWLClM4k7XmU7+KF98z2N5Y+21GYN7c60PKtOGoYMPzoHpW4/APcE3XnpJFCqGiAc8hz51493SLrI7A9nSWydEOE1DusaDNNkGd1iY8aO4xzLsmLVc8bHSFVTp/B+vba6GobpTjIpL47i4S9MhxPyeCGWQdG/MIJHb3BvcgHgiS/PQETXBGefg5fgdS6OkZAmxnXaoKCwE2hkRQJ5vEdCzFg2TMY75GFUwPaKFUR0xRCXoUjNWZ7lWmm88Hu+7sQR+PnFx+KYQcXiWe1v7XSrDJgUBMST5zzr1jNYm613P5WmeFcyMEK6bewbpqncp8y5ljctdrEBW40GUEt2hzQiPM2EEKXAkdxuk7LzlRVFcO4xA2BSYOaoUnHM/hab7rO6ssmTP8m41va/+ZzG257neBeOH9EXT900E/0Lo4inDIX6JKOqKeb6LGWYSJmmMDpfWblXPE+NEFxx3BD89qopOPcYlvtBqd33JY5ImEkpfmnRAeQCG7pGlCgep0vJHngOpwHKy8J7FcAxKUVZURQ769qZLJz0HqRNKjaxfQrCthfcOnlpBq3qj60Sxry/bzvPdu6cPXEAKPUvcvTe988Uf187YxiW/uw8oVMP2GOt3UPJAgDuumyywuFvi6eRNhkvVp5jeN9udiQNL93d6LnJDEmOAv5vGc7fPPKlE3C5xeVm49WmP3LesJyfQaltwPMz9SuMiERfWYe+f2EUKYMKyUwKRv8CbPrFlVJJejlfyAmnMgXA+thLPpNvVEryQhhdVih4+CLx2mN58hp3tTnSnHoDesJYXg5gPCFkNCEkAuA6AG86jnkTwJetv68G8BHtiqTEEcRD1093TWQAMH14X1w7Yzh+dOFENLQnMWlwCb511lj85DPH4KQsxvIdFx7DstR9Ri3/WN4ZJ9IGW7ApP4Z5lp2h/tsvYLw/nrBELL5bflgXC9fbt5wOw6T4P6sIB6AWJBnerwADivME5cGLS9aeSIl2XjtjmMMj5vbklBdH0ZE08OD1zOjpmx/BH69m/LYHPtyOnXUd+L4ke3WitSjycA0/pWEyzWZ+31OH9cUUS+5ocJ88DOmbzxZIonq0EoYpdsi6RtAnPyzOmTRMwfsuybMlyPii98yiPdYzcE/qsgdGNkj6FoRFQuDZE8uFcZAyqPAC+IEb+BohaI2nvMu2mjwhS/2O9zfn3clYsqsB72+qQZn1PP/9rVPFd/IootQO0XGno5OjpmlEob4YJmBIXm13Rrj6b77AOSfi5s6kK9Hn1LH9cenUIXBi1R7bONI1Yhm07gIJ2UAIoyKkrWgJx9C++SLZUtBXPF5Z+R2M6BqShomUwVQybn+FFbH449VTxfsybkCxEnKWn6Cipe2RQMv7LaQTfO3MMZZyCzvxvua4q9+pdX/ypo7z9cdbBWt4NcKkoT4LmQaSNlSvLp9/ACibD95/jGdrzwWyYg8hRHoXGQ/di+3AK94V54Xx1y8cD5NSwYXk4LJ3XnMppbA2X/Z3/F3ibR/MK2Fa3582rswuHJLBncE3OjMlylDKoLjsoQUiTC57BgE2hoaVFmB4vwJ0JAy8srIKhBCcM7HcVWSkM2WI84R0DeMGFCNtUvQtCCth/4r6mLhXJ5gcqf1F2jQxqE+eK4kTYJS+4f0KxBynVjuklioG+/fTN8/E0L75MCjF7Z+egDPHl7vl0qz/3vjP5aCU4vz75rLNhGFrZ0dDGqqaYtAIQWlhRMy3nLrDE3B53w4sURN+/SgyALBkZwNGlxWiJF/d5DOPvjpeeH940Ru8oNAhAVx1vFoIpDNlCOMYYIpL11pzPr8W//3fvniCsoHg34lEwixWkq4TVDbGkEybGNwnj6mUZIjMeXmWRVScMj41dxYBwLsbD2BHbRvGW+swbyt/p3595RSR1AmwSqKAtzPHax0r87At/vqF433bfyRx0MayxUH+LoDZADYDeJlSupEQcjch5HLrsH8A6E8I2QHgBwBc8nK9GdM8qspoGsGxg0vES3fiqH4i9JcN/Qoj0AjxrYTGx9k3nl0pPntlRRVmju4nhZ7YhOPHs6eUZXLzrPRfXHosiHSs04vG+ZQ8aVHTIDhtJXlhIVnHcfcVU0Qiy1kTBuAbZ9mZ7PJ9XTBpEAB70RzeLx+jywpdvN0PNtfgG2eNEf/mxivnQ8oh32+fM1b8+6rpQ3HG+DLrujw05KFba3FqzztmAL599jglOfFWyRvPubQABPeX7/K3eGRHH+MjoZUX1gU/uyCiY3e9bVAMK83HlccNwYyRpZ6/5YsAN9y9IuiGaWLNnRegI2koS/r3X1oLSqnCU+bYfKAVi3Y2iND6CdL11cWDujzL8ZShVFeSRfJ5VbV52+qE8eucr52JG3zsGCbFp4b1wWM3nADA9jL9+gp7IzeoxFveS67iqFscYJnGUVqQXeCec/X7F0asd0LDsp+fh7ywpiQwXTZtiMUxdWwCiMpx1TWCk8b0h2FSTBpSguVWYs+MkaXKb/2StPizHtm/QBmjzu/FZjBpYF9LJ6JWuW+536cMLREvTlrKlOdhYu6Z495Np+cnLCmMfP3Zla75in8ne/j5RoeAybtxasQ9V9oqP3wTnkyb4nm/8k1748ZBKdvEJNOmtel3jyNe9t0zMclqm/wdN+x52/k9ieps0u9lnnfE8nDztYDLJMpIpk3UtCaUNvKcBsY5tc5l0XPmbauDRmAlnLm9kzwC9MtLjsUD1zFv8k2OYiTco+e1kSdETexsiqVcFQsftd67lGFCJ0Qkaso5CpSy6BYf5wXRkOWFdOvGeyGRNlHV1IlHPt6JR6xS0CaliKcM7KxjhU9e+vrJQirSpiow5IfdUcfLpg1R8k6GOOa7NmtucFUTpdRXAzpbCXEOQ5I7A6DolAPsvbnhlJGe5+EJ5vz3Xld8cXmliD46jX2ArTkDS6LYfPdFitwep346176Rkl1iGBJn2TqMjzMKppDlTJTuTNqVFLnT5+Qx/XHR5EGI6BqLYLjuMzdjubfzlGX0iM4ypXQWpXQCpXQspfQ31md3UkrftP6OU0qvoZSOo5TOpJTuynzG3oXLj3PmK6rYUdeetbynE5dOHSy8Gl5whqbiKQM/v+RYUYCE89i8dpF/vmYaKJiCA9dqlas3ARCTX15Yw0e3n4UZ1gRV0xrH2qpmaIQI/imXn5JxzsQBoiCHc76UJx2eHc1f4LCVCe6cZPlkf7qDK2e3l/03ZZhs8kzbYSrN8fKblLom19kba/DQR9tRXhzFuAFFSht/cIGdhR/S7MpPZ09kGwjbKGAbDFnZIe3KKrfPw/Hts8cJqTeAZZHff910l8wXBx9L0bBmcZDt0cVDoGmTIj+iozgacmumwjs0Gg2xfvPWWlZ/L4xl6489DR1YV2Unn8ihYW44D+2bLzYPLInSPqlTRWL2RpsvaVIqpJR4399wyiiRwMSTBZ3gVbkAlkCTMk2xGHUFEwcV4+mbZ4rwM1OrUY/5zjnjkDZMl/eJUnX8/+3jnXjm5pk4ZlAxhkheUELUDPyo7l1ZkUcV9lreIkop7rXeIcBe/Pn7dNzwvmiLpwVNRqVKMG5l1DI2+Pu/3ZHZHglpGFNWiIkDi/FNadPrLIrCz7zoJ+cCsI3rWNIQxpztWSbYXd/huUDyAk8vLq/E4/N3o29B2LXAF1oGWUFER1s8ZenMMklIuR+zPep52+qUpKPVVhlwmWIGuLnMzr+TBpPM5LfzjTPH4Btn2pt7QNU35+AJyIRAhMp1YverrLf9+rftDQOltmrCgJI8FEZDIofCC9xQMk0q5kbmAbXv4YcXTFQ8nm/fcrq49nNLK5UojxIdNNViOHxj6pQTfPYrM8XfsuRieyKNaIhV++PHO8cGIQSf8ynVfP91x7nKzmuEjWvRRmmY3W45YvgGS8bepk5Qx5zKN0VeqlIc+cq1eOEq9Ziyoij+cs00Vo3PZxLi5e698nM4dkucZFknm7c4L6yjIBJCfkQX/fmryycL1R/nXCuvV8cMLna1m58jalHIZPz0M8dgcN98e6Nk5Z6cMtaOnrOiR+qa4sVz9qs6eLSg95ZL6UXoVxjBMzfPxBcdoTKOhTvq8RVHgkY23HTaaKX8qhN3v71R4T4n0iZK8sKSEcS8eV4JCJdOHQyTUiYbZRnVGnGHI3VCsPqXF2BMua3FS0CUDOhLpw5GXkjLuCjlogIoF6Nwvh5nTywX3sx/ffUk9XfW/Y22dsfPLa3Evua4yKaXXzb+Mm7c1+rKrK5pjWNvY8yzv2Romq2qwEtv8sln8a4GvPHt0xRv/murqjw5yPKEFfJQUQCAOy6a6PpMvo/vnT8e15wwTDVYhPecfRiRJjjOkTYM6kqoA9hkWN+ewGPzdrk8Y189w16IWYiZXYc/t3Mmqrx9Wc1BXvDl/v3o9rM978+JVNpedFPSLMsTmPzGl+xN4xX8+EZs1q1neE7OcQftg1EvNGacSdEWeQ7nXqVY0nCVDyfEu/reb676lKNgjSoVGQ6pnnwOHuExKVOmMalKoTAcxjKjn5iefXT2hHKM6FcguI78nZZVbob0yUM0pOGjH54NXplQtFEqAy7/nntzU4aJb5w5BoZJxWab//611dUZcxAogDv/wxRJJgwodj2X/LCO9kRayOdxb5yuqc+G96lX8RjeI17t4P1ICEtiKi+KYnVlE/46Z6frGI7CaEgYn5GQ5jKuhvRlEaMzx5eLaAh/J+W5lzswePv5HCZHMrwMrp9dfKzrPh60uPOsXyj2tXSKqBJ/L7mRPbqsUNG4buhIKs9XlgdT1CLAjTxrDtfsoi7KmJbmPJlHvL66BQURHZv2t4qx+OZauyy5MzrKJVE5zppQjrulSBPA5qU8aazK68CplsMllXZzcG88dRQoVTWdeZ5FSR6XM7OP57Jwv7rcvj5PfncbxBRnTChDVVMMRdGQsvG0j2DgSaDOiqWAOq+pmxb26z9a8oyAHcmRjXm+ob/C0oCWN8fHDe/rmq/4P/lGV+7L/kVR8ZxrW+No6GBr3dShdpRx3IAiUVhrpjUPeBnBXlK7Ts//eccMxNgBha7jegMCYzlHnDmh3LdgyPvfP6tHa5o3dCQwqn+h4MO1xVNo6kgqXi2+a+aL5F+umaZ8x8dqaUHE4pvZni3uFdA0KIv/3794vEiG4S/QN88a6+s1FyWcpXdv9+8u9jyW70yTaVOpnAQAT900U+HJAraKAr/GFOnlPH/SABzgSQ7SDn9/SxwdiTReWFbp8rizyn7q5HOrgysGsEnYr+jD2r3NqG9PKOdeVdmsyMcBLLSv6E47uIMcY8vVgiGXTRuCSEgTE1J5UR4KIrpj4mF/pz2M5dWVzLhM+ChuREIalu5uxOdnDMfTN89UvnNu3PjPNY2gvDgqvBbinixv8lkTygHivYD4VYZzImHYXGFn3584ypuqAtiVtaIhTUQbWuNpaAQY1i8flALPOO7TeR+GaSczpU2KkE5cXM/5PzqHtTNtc9t/feUUAG61BRmyIcITezj8yro6jd60aXOFl1c0wqAUl3xqsGhHyCq+wCFHbM6cUI7PHj9UUA/4qc+aUC48jA9/8XgMKFEjHHwBq29Piix6wM1DPGfiADR2JBHS7HK+shGyptKdcAcAqbRafCAa1lw0gmhIw6z1B/Dx1lqrwAYR85gMQbmxnAgRXROeeH4o90r++CI7QiFv9AzKSjrHHN4+Zzi6LZ4SHjNCWOa/M7lxZP9ClBVFRV/xhEm52TKFCYDkZbaPMai3EoOM284bLwqt6JbMJKW2AUbAxqBsZDsLRnHvbP/CiCcHeMrQEqHawb+2Pctqoq5fe02ToqY1gdWVzaqahfW3XMG0tCCsRLAA5kkd4nQsEVVxwaQU11ieaX6JkvyQK6fjl5dOUu4FsKO4XjxbPkdfKRcGgq17LYOvYY//vxmYMLDIMyI2oJjJsx0/shQPf2G6Jx1LlkSU1xrevpC02ZJ12fmRVxw3FLecOw6D++QrcrIcdiRWvQNCmKOEr2eP3XACNGLznJ9cWIGXV7AES/k9j+hMjrA1nhLKJXx8f+X00UIgQaaVij6T/n77ltPxuROG4ZhB3uW8jzQCY7kH4Cfy3V384pJJuNHSuQSYLFosZSiDW9ZdBKCEsLjRedt543H9zBE2DUMyCrwoHOdPkkuj8hcK4r/OZCzO95QXsGxe5o5k2mUkAlAmsP6FESWUCABlxVyZohyTh/QR3DunBN8TVmEAJ82DUggdYA4vvWNd8w6n2t8zTvP1M4eLymrObOzjR/aFRmxKCfMk+Z5S4KHrp2PuHWeLySqks4VcNpa5kcwVBCIhDfushAx+byV5IegaQcXvLxG/W3vXBQhbLhynccQhV3SyaRi28cT54/xzg1L86vLJYrwZlCrl2nMVmL/v/W3QNYIHr5/uKkyTKTTKsfFXF+KGk0eKLHGNsBYZlIqKZBxO6ktnyhCL7sId9dhd3+EK3/LEorMmlItwtzy8/IyEtMMgIoQtloC62AwtdUeYPj9jOAghimH52qpqmCbFzy85VvTLRZMH4dOTBopj5KYQYt0vtb3RXMOaJ9oeP6LUpY7yh9lbAADVjsI8TgP/gskD8cvLJnlKbF0/cwR21XfAC08s2K1s0i+bNkRwTrlDgNNKCiMhq4QwxDwmY+KgYlw0eZCQQVt71wX4rJVfwbmv3HN56VR7/CrGskmVXAWOM/44R/n3rroOKdmZoDNpYGT/AswYWQqNAE8vrrCL61jnWl3ZjKJoSNWQlvSr11a1IGbNH7JnORcJRY6fX3wsivNCMKkdRWT37Z53dMVYBs6w5qhk2sQPL3BHuob2zWecZWkjHA3rrsqnP3p1natoyco9PDnW/ky+p/ED3fkey39+fk73XdWoVsqjFPixZZyKBE3iLUvoTPDjGzU52sDhHNeXTxtiJTy6VSWo9dvpI0p918GhpfkgIDhhZKlIWn7gw+1YVWnLAMqqLnL7uTNEPjefR4ryQmJN1DWCggjbKDjFCQgh7iRgcQmCLQfa8NPX1gMATh7bHxoh2FbTxhJ6DTUB+ocXTsSpY8uEc+6fCyvEdyYFvnHWGEZ58ewJ57VVh1hvRGAs90LkhXVlMQ3pxJUxm6mOumzgABBeZDsBzi2TxX7nTijSCMHAkjy8vKLKVe6ZJ5d0hSMaDWlC2kYGF90HgPk/PkcsJjohuPm00aKU7QWOsroyl04u6dvhMGB5iD6bt0bXNFHi2QtcIeBXl08REk4/e309RpcVisxePgHw5BnO/ZRDeX4Y3CdfGC9couj/s/flYXYU5fpv9TlnzuyTZTLZk8kekhC2AAkhYQlEQAQFRNQLgihXwSsoLviD6wUUUUEFRVmUTWVV9i2AQAghZCcr2fd9MpPZl7N01++P6upT3V3dp88ymZmk3ufJk5kzfbqrq6urvvq+93s/fr5exRHLS7a9jhkikZCGj80CAHxirSiKwOm4rCiKWP3k5Tn/3MQB1s+WGoZ5vw7HqDl5CrQFsJD+vQK/lhvnsrKq1jEhpvsd0gjOHl9l01QNgt7FEYRDGiJhzTYexYQqEc5xQSmTXwJYWHrZjnrXRpTj9osmWhvjIB50sUw190TJpJImDrIvEi/fMB0XnzAIp43qi7huWFGrg80xl8dxwqByTBpcgcevOZndu83fRSzD/5lvT0VYI3jEQ1ZPRKMZInbSF3bUtWGvEEqNhDSUF0akqjxeQQX+7vPjvzl9BC6fMtQynFb+32wAKSm10sKwmbhIrHlMxKkj++KhK0+yuMiiEf6Paxmliz8rcfMmJvjpBjU1kuWb5F+aUYRScxMKpKJ3mml8GBQ2fWXnPCOee3d9O7YIvNSlJt1IdLqIRq8X+AiNRjSLpiLyiEWKB4fNswxm8F120hAkDWpRg04b1dfKIWDFZezzbGk0jDk3zURTe8I29zupZpc++Il1LxzieyNLkgyHtEDryZj+pXZj2fz/h+eOtX0urme8QiSlFNuEjRz/mWsFi13m9LZPHlKBAeWFeHXFXpdBzPnIfvBymojnEmUWRW7zwq2pudF5mguOHWh5ssUosvN64hrIryg6TyIhYtElygvZmvGn9zfjYFMHGyPCiz26qtQs+JOiVn7phMEY3rcYBqXWWkkptZLJx1SVWsoagOIsK+QRbfEkOhKGSw90dFUpXrp+uvQ7xMELNqxJnf2eNBOhnMY2Xzi4YT6qXwk0QvDyDew6zup73MvCJ6dCiRC5ExMHVeD3lx/v+pyFsBiKC1KVxJylSUWpmUfmbbHRN8Qsaue6p5lGg5+sDrse89Z6Jd9NHtrLpUM6rn8Z8+6Z527qSGB/U8ziTTMRezM5MICsWSyp47Grp6CiKGJRZgDg6W9NRUI3MGt8FRaZkmaixjTfIDk9Jxz8Iy+j4Kpp1a5jiwvCFhWHAPjeWSnlkD0N7annQ4FXvne6VYwESBknv5M8bw7LkNGIrbw6h9Nb5YSYZLi7vt0ylAmRV55y4sMfn2V7JtyoTZelHWSO719eiJKCEG4+dyz6lkRdz0RcNEQcP7QXThtViWMHVyCeNDDb3MT0KYnYPHwiuMGtWc8tBEJSeseMf4z0GXE+2HCg2aK+iBA9jADwg3PGok9xShdWVNL521XMWOebop9/wS5xyBOIIiEN/3P2aNuYkNEwnJsfGbgEmajwwIuNENOzXFQQcm0iuVrN9NGV+Oopw6DrKQ4sgaBZK+lTJxdVpGus3i2np4jwKoghAwFPGqSOd9/t/CiJhi1pNmoee++Xj0N7IqV48PS3p1rHaxqXW6Ou8fvip3tsmz+vqVXceAa5pyDGMp9/l//vudY1NELw/Vlj7MayxFD3e3c1Yi/Q5Gzvt2aMxLRRlXh5xV4Pz3J299erKGKLJnPwpFDb9x2/l3HajSMaTGGfA789YwS217XZqgQCqXWTEPs7Ip4zljQQS7IE54f+6yTbMR9vrrVUVAZUFOKJa04xN20smf+6maMsx8G7PzwD7/7wDABs7VLGskLecPyd76JDCBUHAYF9Qqhvi5vZ+CnPslNTFuDaqYbNWyi+2E792l9+8VhMHlJhDfgVP58d/MZk7RauJZb5FD0TovfzV2+uN2kY7PdfX5JKfOhwlMJlfFMtrdRRSUEY+xs7sOTWWdZn4n1zjph4nv7lhZg0uMLybL699gDW7Wuy+puXRxW5Zn64cdYYnDaqEtFwCDpNVScsjbKQtFggROTS8WQiUWdaBi8R/5BGrCQX/u1oWMN/nzEKVFgsxShHWCOmdFDqvnixGW4s++mhiolqMsgKd4jJm6KqAP+f/5NROHi3cAqR6ImsKovits9PCLRYOyf5r54iT/4FgGtOH4F+ZVGXp9CpSuFEUUHI4huPrirFjDH9bHq3UhC2oRxTVWoVX+CSck6upucpzGNkGqiysePkeHNw2avfiWoeAdbGr0wZivKiMPqWFLh4337GiKgcwFFZGrU2lOI4fH3VPpw1rh9GVpZY74soFXf/e5swfmCZJbv1i4snojmWtJ57r+ICJHTDU4vZTnew9871Z452f8EBmTPDCeuv5nivaYo5PNLUVUmvJBq2Il7OqUjWtTPH9MPwvsWuBFUOUa7Qq7V2Ggb7//xJA+QHw+2hduK6mSMxwnxu3LAdU1WWSiLXgLH93RtR7hT120R/e8ZIG3WLEIIThvWyHaNZ9+v0LKffi3rN/wVCZEzcRHtJk4pYZm4YxPZx2pJ4uYROsUvQROfP7deXTmYKUXDPa9a9ElZJFGC60SLEHKSPNh3EiMoSKxckYVBMG9XXpZENsEhdD7KVlbHc3RHRCA42xzIqtMBDchyl0bA1kdz2+WPQryyKmWP6uXaRABOn59nUYQfnzdmGkEYQSxhW9aEg/FL/dqemmhvOHI2CsOYKJToXS0PwlokeQmdb40lndT17AAAgAElEQVS2sKXLORtQUYi2uO65KMs+1w0DX5g8EH/5OhNTFxflsmgYRZGQFf6SKTQ4lTu+NWMkCiMhlnVusOpMPzx3rKXjfevnJ7i+A7BSqaw9cu8jn9y9KmYBqWdICMGPzXLMqe8ztQp+7qJICIWmB1McJ7zYDKdh+C0g/FxexvIAiV60eKzoWQaYh00j7D4W/myW67v8emIxCfFvjK6S3qKksPOf775EnvwrPm3n4ipyaGUQz//wlSdBVmXQCU4/4V6u1Xsa8cs31oF5GdPzYCmlFi9bVEDx0roGgDk3zbC+AwBr9jbin4t2uqI7gFw31onfXDYZVWWFpldcbJvb2BfHHX//vODk0I/sV2pxlcMhYlMduWZ6tTln8O9yXjujPIVM50NRgYZekuRuPt/yjaM4hwWZJ9NteG2gjHN90QPzbQnIuuHm6AOMG33OMVUuk1E2Nq44ZRgmDa6QevXZNdK38+nFO6yf+bs1VsJX5vDbXAOcZpaaSz/40Zl49rqpJiUA5sbHzT/m7XcmbnKM61/mSjTVCFwRXKcHl0PUp/eCV3DHivwAmDGGGeu9iiO47wp7VI5z+8VxL4tWEqTmAYDVEtANigEVhTjRYfwDwLs/mOmyGwAxaszHrLv1ovAA71tK2Tyd2li44Vw3ujuUsdyNQcEMp9svmhhoAefgNAxRQ5QP2m/NGInxA8rxyFVTpC/ZxgPNFn8wpGnWAtLfIyksltQ9M/tzwXmTBuDCyQMtzqwT1sLmMAA4v9C5EpQWhrHhQHNaY4GpC9gnzHQvtG6GMwdWFOG8iQNsvK7HrzkZFcURULDFWuZZ61cWlRpAIS2VoCJSXIoKQqny37IEFsO7WA0AX+uVG0h9SgrwL6G0LL9MU3sCvcxFaclt56C8MILSaEQa+dA8ZkqRq8gPcVbt84JYuAZI8bT5tcKhVARFZpTMHNMPEweVy71klKZdqDkopTh/0gAX73Ldnee5juVndA49mbyfCHFMcCM4oRueHOnVt89Ge1w3JcHYdZdsS/Ec/UrdcrAqccxbFw4R/MzkQda2eJelHVhRZPOC7qhrxaHWuDRRtqpMvljL4KTSpDP2pUaDcLiz3yypLHNzuV0oHDRxUIVVEVIG/n6dOKw3HnSEpQFgrOkR5OM6U6PAT6vXCR4x0DSChraENf79+4s4nBBuY/gpQcaT85ul7RR43IDbG8o3GEAwGkZxNGRpeXtBrG45orLENv6G9y3Bo9842fwtJYfI748riFQ5EvOvnDbctoEdVFGIK6dWu67N10LnnTBDON1mVO5wSdUhIFYp9Ztnj3MpQ3C99jaJVj7A5tY+Zg4G5RcEcMHkgTh/0gDM3XDQGtNiKwghFj99ZL9U/g3vVs6nLypwv2PieyfmRYnONtnYiWgafvmlSdL76I5QxvIRin0N7VhnVpxbtqMeiSDxT7ACJDyEFQmldu93ffFYqaEXSxo2cXgZghogInoVF+D3lx+PyUN64dUVTFN56W3nWH/niUY1zTHbS88naqcRWVYYxuaallTteg9EQnLpuCeuORmfNyfSewWZPsCunkGIfUGYYulOwqZIIuLnF07AzDHuYixhTbP4xeIkLHpO+MwnVtczqHxREsPhXviNqeH5+3c3AkgJ9qcWy9Q9cD7prZ8/Bl/0KNxz58UT0afYHs5/4GsnWD9rGsE5x/T31Bx3euKvdVQwq3ZUzfSrjMmvVxQJSRcskWqSDmzRg0uCz6nD7Gck9SoJLjepEWDuhhocELjwTpQVRtC/vBDPXjfVel6yhDQ/REKaYESmvu9UivADj1jJoigAG5+PX32yjW8vAyH2yJa46Xfije+fLj8H7MUcRPB7O7m6D/qWFFhKFgCbByzvpOS8fBPv9U6PrCzBY1dPwTWnVePOi+0GgXNuEjVy+UZUlszoh5DJ+a9vi1t9xMeoDE6vnsxrP10oEOVl5AEpfWLeU7IEbg6N+KsjAGalxjTREy5fJ0NBWLOq6p0yojfeu/kM69oibhDyLy6cPBCnjuhjzdfv33wG+pVFMXGQW8asfxnz7pYWOja7Erfxn756AoKAvzMhjViG6ZVTh7uOI4S9i7UtcdffABY1LDGjfSLGDyhP6U975Kxwz3KvoghONWs8EMLmZ+4UkAlFiZtQ/i60J3RbUSPZRlbTCC6f4j8HdCcoY7kbI3MTM4ULjxtkVWpbetu5tspNfuhIGNbkPbhXUYoLqsn5VvGkkZYiwjPcs8HkIRVWNSgxpMgn7j+9vxmEEPzrO9MApIxTp73LVSRkZWFFRDS7Z/mmc8ZgSnVvnDmuyqpZf5kg03dKdR9HJSrmlXWGP7khtuj/uakB4v3Y2iIY7mKoixcFAFJ8QJHH/M3Tq6W63y6t0gDgk3j/8kKMH1BucV9FyMr1clw1rdrlNebnvOtLk8xFT/5MHr/6ZPzKoW1eEg3bSv6+deNM29+DUA24YoCongKwDVrQpKq6lpjlgU0Hr+bcOGtM4I2kRgheXrHX5jmXoSCsYerIvpZhbC9jnj55alttK9buZQlAovIG520+IyR/eeG4oRX43wsneBozlFL0LinAry+dLP07B4Gd75rU2bN99wepZ859v05FEescktAyBx+Xt104ARscSj+8hLsXeBU+3p9Pf1vwwpoe/LPH98eY/mW49KQhtrnT+R6OEfi1S25lDoGfXzjBojGlAzdkIyEN7XHdemZ+78IdF03EzLEpY9hZ1dQJL678D2ePdUkP+kEzrXS/d9Qw/DetIY3rvKe/3l++fpKtkqaIYwamDOEHvnaiTcoupBGs3N1oV/swf+ZebJ7AykHhfr++IKy7YY1IjwFgqUXw84i4X6BiEOJPo2NSmGb5aspKkzsxorLUPJe9IcT8Tp+SAvQrFVV/Ui2S2QA2z7IwSNjcxo7f1+ifqN0ToIzlIxQRBwcvE/CX6I9fPQGTBrMJpbI0inESsfD6trinp4uDZ4Zzeats2uLEfuHlKy8M25QwAHdlIL/S4iIiYc1WTWxkv1LphMPxrRkj7MYyWGLZl6fYS7fysLiMQ5hOTmh0VamtmIboVelXFsUlJ9q9utfNHGWpCkgR0EkoLo6njarEd88cldMGzjqvOeuECEFpYRgfbDgoPe6s8VUu7c2SaBg3nDUKZ41jng6ncRvMWGb8xB+cY9fZfuvGGbZMeAC45zK5Qff9WWPwg3Psco4yHrRTm1zk/spyBpzgVbj4LT0yb2va7wDuRDpmeKbvm4831+JlM5IzoLwQVaYXbcrw3jhuSIWvDKB4tWhYw9dPdXvGeNuC7BGcEn5cJ100atLRGzh3k6O8MGwlY4qbfD6HnWtKU4Y1zVNiEUjRAHh/chWYkZUl2HmozaXs4ueYf+WGFCeWe+munFadNsGPg5r8ZgJGi7PR7zyG2KBeRbY54u2bZsoPFK4hGztl0bBFwbIK03hcdFBFIa6bOTLt809XkIWY0S0/PXwRmkZw7ekjXGpOfq+C7F7FTRrgpvWkk457/+YzbZUQOXoXRzB9dCUoKPqWRLHKoZZysRm1O2FYL5QUhD03obwNhRHNUsG5TqjMCjBDmEcxnbf45SlDcfroSswY0w//eyFTqWE5Q6ljZLrVvB9+e+lkFAhOnTH9yyyVjA37m13f62lQxvIRimhYC7QY+yES0iyDbfKQXtKKRAaFv2EmwFkyORcM7CUYHRIOJ/cK8YQIXsktnWetOBKyeZ/TLVfhEEFTu8AfIyykJ3qfAbap8Jrixg8s81VT+PyxA3HS8JSRIoa3QhrBkCw8xkFw1bRqvLZqr+2zm2ePwyUnyCkXQSEmBJ1c3QfPXZfyVj4RYEMVDYdcoW2AeT1Fb58XKKUYO6DMKl7BIeMCf9kjTBgW3g2O5/97WpqW2417kZPuhfuvYGHcoPxVDu7d5CHd3fXt0qpjToiSZ9efNcqiHt36+Ql45XunZySN9dVTh9kqjHEYEoNBhrPHV9nKsB9qjVsRlOF9izGgvDDtvu/Z66bauKkr/2+2tBgR19rmSi8aYXrTXo8nZBryzu74+tThWLztEE4YZq88SalcrQMIJjeWDpoZno8ljZR0WADaDUe6iIoXBYaVSA/hjLH9MMss1f7byybL+7g0iuF9S0wj3vt66ZIGOb3IZy/jwqsr9+IFIQcD8J/b+aZZHKejq+xcbJexDP9nyRO0nYeIm4czx/WTFocB2Pz2t29M8ZVbe+zqk1kE0Pzd2c8Gpa4KuhyTBlegurIYmkDf0oid2y6z0/kVhjsocZMGV9g0o3s6lLF8hOL4ob0t2kAm+O6Z7nr23RIUUj4ZR1Kn+OopwzD/p2dbu2QgfQKGc3KZUt0b10j0LzlGVpZix6GUwD0BK9birFK4YEsdNntIhQ3pXWx5tNLhkhMHuyZkCjmXzAtB15iLjx9kK7QAsEXEqSGbKUIawYOmckGIEIsfBwBn5rChmjaqb6DEqJW7G7EljWxbZ0FsmkbSe0Y50tGHpNcCwffPZsbfyl0NtqRfL4jqI/xdCcq7lEF2e0FtwwEVhRgtyGjpBrVtzL939mgcM9BfWqu6ssS2mSYCp932LMxDuGHQ3JFEf7MvZO3lCZfO/rz29BHY29hhC6sDjC4yIU1bswUF8J91Ndh5qA2xZEr6U8+Ag58OXtrtAEs+FmX9CiMhfN/cdIi5CNzgTuiG7xj44bljXdx/EVYhmAx49CcM7WXjKAP+49Ca43yOcdIPg0nHuY8R+664IOQ5BxZGQhjSu9jXs9y3NGpp38soE4ZBffMPvnbKcNv1ifm8LA1/j+9eM72aHS/cXTSs+VJGehqUsdyNsXTHISzaVpf+QAlCGrGkdDLBT89ze4+7IyYNrvA0Ph/42gmI6wY+N7G/FYLjGDdAXgjCCwMriiwetAxlhWHbhOC3OLV7ZDD7wcmblBV0WbajHp9szW6c+CFfC63svOcf6y+bFgTHSsqj6ga18bu90NCeSHtMPsBkA9nPmkZsXs5M+jdoyJnDyU8e3LsIvYQqjl7gnug7L55oKbB8IWC+g+36YEZBmyP0nfp7cEPn5+ZmV5TN0w2KmWP6WTSRTMANXHFh58+CP6uywjAm+WzGQby9rbLPnAlys8bnHmXjPUgpsK2WzYUvLt+NXkXMK1ocCWFYn/xEnXTDOypR6IjGiXBWTdQIQTikWTkkAFzJvddMH+FZvOmSEwdbnk8/o9GJSEjLqFYBh18+jtuzHEQ6zt1mWXLllx2RSRFBCnmEPaJWlHpL5wGsIuhQgYvNNybNpr667JyEEEtHmW86/98F41EQ0nCM+Q5lIn3bXZGbi0ihUyFK7nRXdJI9lRaVZVHPSePCyYPwr6W7XV67ytJoXqkgAFM/qBe8rzxJQoavSTicp/gY4gBL5ghJ5HpErBf4YOXODO0cUBoN4weScGq+UF4UsZVazQRDehfh2evkyWZBjNAsBFpyxuBeRXjGo83pkOnGZcOBZivZFGCSUv81dXja80TMjjnnmP5Zb5YIYL0ERbLCMJCXIvfCN83NrmgsU4qM1CJEWMaycHvFkRBOru5t0bt41cSx/ctsZaiBVMU0L2+rLKGZOqgHj159MqpveSO7GzChGwZmjKnE9NGVGF1Viu89vRy1LXEMNotbnDqyryuXI1sURuy5HAAww+S++uXHiAmsvIBJYThkbf5uOGsUbj5XTjuQoaQgDE1jBlw6CpMIQlLqLCMrS7C1thVBUuj9NLF7O9RsmNfY/5yyJFvOfRdv554veysXBUlF0oQESHsRJzG/JsB5TBoGr5Q5tsodHbnpnDHWcfd9hUWhrpvJItScj3/1adV42ixq0lOhjOVujJ9fOAEfbKjp6mb4IhelCxHpdDWdIGBJNR9ulCeHOYuZMASIk2WIwnDIJoXmZ18MlBTYeP47/jzXv360La1nTzRIglAkZGVVRdxz2WQcao1jQEWhVL4oXzh/0gCcN9G7kpcfCCE50UHShQf/nea5ZItMlANEOGk9QUCQkqEaGfD73FuWKUdaxAnDelt8Ytlpsj01S/xiPwdJVvQCN7LFb2uE4NITh7g81WdJPMD/M2s0CiOaK6GL24Vlhe6IHvO053fy+fMHW/C1U4dh3IAyxJI6+pVFEQ5pNi9e0CRBjh9/Tm64RsMhxB0eyX9cyxRAnBJ/Is6bNAB//mALAMap5kYV32T8+HOZRTL5d8NaZjSMkKD5e+/lx2F/YwdG+UjcBYFzrHiNdxEzxvRzJf2KYznIBlW2jjgREmgqn/48VeEvrhu4+rRqbK9r9fqqDRphFD/edz893z0+xA3FgAr5/BYyNzg9GcpY7sb45ukjLK9Kd0W5ZGHIBtnImjkzzkVoxF0A5MxxVYHae5ND5cAPmkZw4eSUMUvgHWLO2khIE4KvLC3AhZMH4uF5W126vzIcN9S/KIRXUlu+wQX2uwLpllk/6k0m6Mr1IZtrc35vLu3mya0NbXGbZ1E8ZwaOZQuGwEdvbE94hurTQeZZ1jR5iFkG7jVzcpZX3f45z+/IPIoj+5Vg68FgRosXuEFEwGgJHQk9634B4OL1ckTD7mJNHCyaJu+7H39uvGAsGxaXNpRlWMAw/R0hjWSk/a0JCkInCsmXl500BP92JP51JiZIqD1BkpJFDHVI1smgCTQMcYP+0/PGo7woHJgWwVU1OLIVDXAmCvZE9HwiicJRiX5lUUt4XgZZ+OzeLx8XqNTsTedkTz1I+iSYZetZenP1ft+/65TidDMk6ldGVqFr4LdEBK1mlw0Ks/Bic753Poz8iqIIPvppKmLEo1C3fX4CRvTN3KvHKiyyJWvCwHL0LQmmc+2EjLOcacIY4JZm89u8Hz+0l03+EZB7oLNFbUsMGiFoi+tZFYFKh9kT+2OakIgrQiMk0OaH62RnahyK4BJ2YgW/ICDSSKO7wFS2cGq2Zwo+Jr02HZki5DGevzVjJAghaOpIBBp/PAmZArj7kmPTOlq8z5MZx7w7QhnLCt0ev7h4ouuzqSP74kceIUOAVavLRxJNppgxptK3QEK+cJdQJtQwgN4BC2Tc3Ikc5J6Ew+XkuPuSY329OC9eP93zbzL4SQyKGNyryJKqygRWEaI8DFZCiK04Do/qnDKiT1bJx7qeMk7//d3TsudUe6hbpFvLNQJLdx4AWuN64A3wORP6u/IlAuSh+uK3l022vKttcd3aKHdGYu5Jw/t4GkpBvYacbpCJR9iJ3iUFKC0M+1bwkyGUhWfzzHH90h9k4v2bz0TUowx9Orx14wxEQplF2Rb+TF7ciiOdFGBCp4EcR+KmJGjBJvl5Dt+c21nIiYZBCLkHwBcAxAFsAXANpbRBctx2AM0AdABJSumUXK6rcHThymnVGX8nyETQGfjKyXJjZtvdF+R1ERMLPvBFyCmaL8P/zApOMVHIHRd7lAHPFkETOD/6yVk5Xac78gsb2xN5eYf4oi/qwoYCeCpX/t9smzfuUGvcpveeKSIhzVamPlNEwxqaTFWX/uVRnDKiD+as9Y9CdQYICSZdeaApBo0Am2tacMGx2eUq3HzuWIQ0gk+21mVmLGvpN0NOPHGNN6VtbH97DkAkpGHRrbOyWnd4PkJZYcS3EI6IAWl4y+dO6I/TRssjAQBz6vhFZjnkuT+ZQ9EwgHcBTKKUTgawEcDPfI49i1J6vDKUFY5GdJYMG5BSBhijKBjYfNf5gY6bUt07/UHdEEGN70wTuzj4MO1+pjLzzuYjTM03AqXCxkNUS/CCM2wdTxpStY+gCGkEP8kwwc3enjDiJo94SnWfLstvaWpP4LN9Tb7HDDEVOgghONQac5WKDgpeDChTT7GTeyuiLItEYVnFw6DFubxwxclDcX2e6hyURMO+sopXTavGuAHp1wtCCBrzILN51BvLlNJ3KKVcPHYhAG9xQAUFhaxwxlj/cOCGA83d0hPYFZBVc3TiuKG90Ksof3zRwwlZglA+wSMW3XE83X/FCRjSOzsjSwTfR4iJXiEfRQcvxJJ61uom+QAJQB05HDjUJq8I97JQxnvej1mkg5dPznYzx0EIQWtMruEtgx8Vaun/npPV9fMNlvDcvd676r4lqGmO5XyeoNGH7ox8qmF8E8BzHn+jAN4hhFAAD1NKH8njdRWOYnz/bHn29pGE00Z5h9M4ukI3uKfi+f+e2qVGTnfG6KpSlEXDIEdwNgshBDPGVNpC5qJ6QFBUlRX6VppL346svwqAVxHsems5rGmYLalAerzJcX7lhumWccw9vLluxmJJHb0y4L3/7ILxniW01VzgjcIIU0EJUp3QD91sD5AV0hrLhJD/AJARjG6llL5iHnMrgCSApzxOM51SupcQUgXgXULIekrpPI/rXQfgOgAYNixYMovC0Ysfzg4uaN9T4VUFTUR39AR2V6jF0R8G9a7WdqSAawRzZBMmfvV703P2BGZSydCJfPFJc0VxQci3uBBPDPz+rDGoKIrAoNTTcA0K3aCBokgc6p3PDoVhVp2R5jRSgYim4fFrTs5bu7oCaY1lSqlvjIIQ8g0AFwKYRT22uZTSveb/NYSQlwCcAkBqLJte50cAYMqUKV0/EygodCF+cM5YV8lrJ4b3LVbG8lGAfMlcpUNrXHeV8j3SwYovZLbc5GooZ1rJ0Aln1beuwsyx/axqfn74oanEoxu5exp1g3aKRJ6CHZpZzOVHs8fZKgFmc57po9OPke6MXNUwzgPwUwBnUErbPI4pAaBRSpvNn2cDuDOX6yooHC2YOrJP2gzpeNJQxvJRAF7sI1Ok47zL0FVqMl2FbNQSckWur6xMO/gvXz8xt5Nm3ZbgN2MIZcuzhe6jZ6+QX9z+hQlZFQ070pArZ/kBAFEwagUALKSUfocQMgjA3yilFwDoD+Al8+9hAE9TSufkeF0FhaMCp3oUAhCxr7EDB1tiWenqKhz5CFLVUcRxOciZ9VT4qSV0V8i0oS84dmDXNCYDsLLluRm6fUujnZ7sqsBw9fTuXUX4cCEnY5lSKs2uMmkXF5g/bwVweOKHCgpHIf7wleNQrQxlhTzhle+d3tVNOOzIhobR1eguCX6Z4teXHIuhOaqanDS8N04a3jPlHxV6JvKphqGgoNAF+NIJSrFRQSEXXHPaiBxTmLJDLlfkMmw9DVOq+3R1ExQUMoYylhUUFBQUjmpkU347V5w7ob9VrCMb9ETqiIJCT4UylhUUFBQUFA4zrppWndP3exdHcOzgo49frqDQFTi69IEUFBQUFBSOAIzsV4o7L57U1c1QUDgqoIxlBQUFBQUFBQUFBQ8oY1lBQUFBQUFBQUHBA8pYVlBQUFBQUFBQUPAA6c46jYSQgwB2dMGlKwHUdsF1eypUf2UG1V+ZQfVXZlD9lRlUf2UO1WeZQfVXZuiq/hpOKZWWPO3WxnJXgRCylFI6pavb0VOg+iszqP7KDKq/MoPqr8yg+itzqD7LDKq/MkN37C9Fw1BQUFBQUFBQUFDwgDKWFRQUFBQUFBQUFDygjGU5HunqBvQwqP7KDKq/MoPqr8yg+iszqP7KHKrPMoPqr8zQ7fpLcZYVFBQUFBQUFBQUPKA8ywoKCgoKCgoKCgoeUMayA4SQ8wghGwghmwkht3R1e7oChJChhJAPCCHrCCFrCSE3mp/fTgjZQwhZYf67QPjOz8w+20AI+Zzw+VHRn4SQ7YSQ1Wa/LDU/60MIeZcQssn8v7f5OSGE/NHsk1WEkBOF83zDPH4TIeQbXXU/nQlCyDhhDK0ghDQRQm5S48sOQshjhJAaQsga4bO8jSlCyEnmmN1sfpcc3jvMLzz66x5CyHqzT14ihPQyP68mhLQLY+0h4TvSfvHq+54Kj/7K2ztICBlBCFlk9tdzhJCCw3d3+YdHfz0n9NV2QsgK83M1vrztiJ45h1FK1T/zH4AQgC0ARgIoALASwISublcX9MNAACeaP5cB2AhgAoDbAfxIcvwEs6+iAEaYfRg6mvoTwHYAlY7PfgvgFvPnWwD8xvz5AgBvASAApgJYZH7eB8BW8//e5s+9u/reOrnfQgD2AxiuxpfrvmcCOBHAms4YUwAWA5hmfuctAOd39T13Qn/NBhA2f/6N0F/V4nGO80j7xavve+o/j/7K2zsI4HkAV5g/PwTgu119z/nuL8fffwfg52p8WffpZUf0yDlMeZbtOAXAZkrpVkppHMCzAC7u4jYddlBK91FKl5s/NwNYB2Cwz1cuBvAspTRGKd0GYDNYXx7t/XkxgCfNn58E8EXh879ThoUAehFCBgL4HIB3KaWHKKX1AN4FcN7hbvRhxiwAWyilfsWHjsrxRSmdB+CQ4+O8jCnzb+WU0k8oW3X+LpyrR0LWX5TSdyilSfPXhQCG+J0jTb949X2PhMf48kJG76Dp4TsbwL/N7x/R/WXe7+UAnvE7x1E2vrzsiB45hylj2Y7BAHYJv++Gv5F4xIMQUg3gBACLzI++Z4ZIHhPCRF79djT1JwXwDiFkGSHkOvOz/pTSfQCbOABUmZ+r/krhCtgXGDW+/JGvMTXY/Nn5+ZGMb4J5nzhGEEI+JYR8SAiZYX7m1y9efX+kIR/vYF8ADcJG5UgfXzMAHKCUbhI+U+PLhMOO6JFzmDKW7ZDxXY5auRBCSCmAFwDcRCltAvAggFEAjgewDyzsBHj329HUn9MppScCOB/ADYSQmT7Hqv4CYHIYLwLwL/MjNb6yR6Z9dFT1HSHkVgBJAE+ZH+0DMIxSegKAHwJ4mhBSjqOsXyTI1zt4tPXjV2Hf9KvxZUJiR3geKvms24wxZSzbsRvAUOH3IQD2dlFbuhSEkAjYAH+KUvoiAFBKD1BKdUqpAeCvYCE4wLvfjpr+pJTuNf+vAfASWN8cMENFPPxWYx5+1PeXifMBLKeUHgDU+AqIfI2p3bBTEo7YvjMTgi4E8HUzXAuTTlBn/rwMjHc7Fv794tX3Rwzy+A7WgoXRw47PjziY93gJgOf4Z2p8McjsCPTQOUwZy3YsATDGzOItAAsRv9rFbTcOrQcAACAASURBVDrsMPlXjwJYRyn9vfD5QOGwLwHgWcGvAriCEBIlhIwAMAaMeH9U9CchpIQQUsZ/BksqWgN2rzxz9xsAXjF/fhXAVWb271QAjWY46m0Aswkhvc3w52zzsyMVNm+MGl+BkJcxZf6tmRAy1XzfrxLOdcSAEHIegJ8CuIhS2iZ83o8QEjJ/Hgk2pram6Revvj9ikK930NyUfADgMvP7R2R/mTgHwHpKqUUJUOPL245AT53Dcs0QPNL+gWVkbgTbCd7a1e3poj44HSycsQrACvPfBQD+AWC1+fmrAAYK37nV7LMNEDJSj4b+BMsEX2n+W8vvE4y39x6ATeb/fczPCYA/m32yGsAU4VzfBEue2Qzgmq6+t07ss2IAdQAqhM/U+LL30TNg4dwEmBfl2nyOKQBTwIyhLQAegFmkqqf+8+ivzWB8Rz6PPWQee6n5rq4EsBzAF9L1i1ff99R/Hv2Vt3fQnBcXm8/gXwCiXX3P+e4v8/MnAHzHcawaX952RI+cw1QFPwUFBQUFBQUFBQUPKBqGgoKCgoKCgoKCggeUsaygoKCgoKCgoKDgAWUsKygoKCgoKCgoKHggnP6QrkNlZSWtrq7u6mYoKCgoKCgoKCgcwVi2bFktpbSf7G/d2liurq7G0qVLu7oZCgoKCgoKCgoKRzAIITu8/qZoGAoKCgoKCgoKCgoeUMaygoKCgoKCgsIRjJ11bekPUvCEMpYVFBTS4vklu7q6CQoKCgoKWWLmPR90dRN6NJSxrKCgkBY/eWFVVt8zDIpNB5rz3BoFBQUFBYXDB2UsK2SEP723CSt2NXR1MxR6CHYcasO1T6okXQUFBQWFngtlLCtkhFV7GnGgqaOrm6HQQ3CwOYbeJQVd3QwFBRcenb+tq5ugoKDQQ5CzsUwIGUoI+YAQso4QspYQcqPkmDMJIY2EkBXmv5/nel2FrgEBQGlXt0Khp8CgFNFQ8GmmtiUGepQOsPX7m9DckejqZhw1+MXrn3V1E4541DR14PfvbOjqZnQKjtZ5qjPREkt22zkwH57lJICbKaXHAJgK4AZCyATJcR9RSo83/92Zh+sqdAEIOTImiaaOxBFxH90dBqVshxUQU375HzS0dc/J0gsLttTi6UU7cz7PXW+sw/KdiuKkcORgT0M7Ptx4sKubgdZYEu1xPW/nO9Qax6UPLsjb+RQYHpu/DY/M29rVzZAiZ2OZUrqPUrrc/LkZwDoAg3M9r0L3BAHBkWBiXvSn+diupHQ6HxTQMjCWeyJ2HWrDil31OZ8npBHohpGHFvVMtMd1bKttzcu5zrtvnvTz99YdyMv5FYKhpjmGkmjX1z77y9zNeHR+/oywhG5gV3173s6nwNCd/Vd55SwTQqoBnABgkeTP0wghKwkhbxFCJubzugqHD8yz3NWtyB0UGTk8FbIE6+fMepr0sAcT0jQk9dxfik93NmD+prqjNuKxfn8TfvDcijydy63A8qN/rVTJpocZn+5swOQhvbq6GXlfszIMmCkEBAXttv2aN2OZEFIK4AUAN1FKmxx/Xg5gOKX0OAB/AvCyz3muI4QsJYQsPXiw68M3CnYQwga0QvfBgs21Xd0ELN1+CCff9R/X55Tmz/jd39iBpm7IZ4uECJJG7u9EY3sCczfW4Pz7P5L+PV+GZHcFIYTRdjoJ/162u9POrQC8s3Y/ahzJ39Gwhmg4fz45SmlWm0kKNr4AJmcZBFc88onv353z2jOLc6diKaDbekvyMooJIREwQ/kpSumLzr9TSpsopS3mz28CiBBCKmXnopQ+QimdQimd0q9fv3w0TyGPICBHhmc5x3vY09B9QnBf+9si/O2jrXh91d4ua8ONz67AweaY63MKmtHcVxjRrEXNiT+8uxGvfLon2yZ2Ghh9Ij8vRXlhROoVpZTiJfPeOxL54152JxzO5GHehx/nsNFsj+t4Z+3+fDXJhnjSwN8+OnzczVtfWp3zOR6dvw2bD7bkoTUMP/rXStdnf/1oK/7+yY6sz9nUkfDcjDqxcOuhjM79sxdz78OjHd3ZtsiHGgYB8CiAdZTS33scM8A8DoSQU8zr1uV6bYXDD0KAhrZ4VzcjL8hlAzv91+/nryF5wO76dqmxerjgtXlg4crgHR0JafAKXIzsV9ItE+D6lkSxNU9c2wIP5RDRQ3/iL97N6tzdnd5BCLB6T2OnX4dSij++twkA8PW/yRiDwVDXGsPtr661ffbIvC05tY2jI6njvv9syuq7uw61ofqWNzL6zlM5JKh+uPEglm5nhmWmlCs/yCIBje2JrNQS+NA3DIr9eZA+7a7R1ScXbO/SSOMnW+qwcGv2pl13pkfmw7M8HcCVAM4WpOEuIIR8hxDyHfOYywCsIYSsBPBHAFfQ7j5zK0gxql8parrQKEuHfHn4FPIDCqaIEfR11wjxXIh6FUcQFrIFu8uzjkY0FOQp1Kx79JNBU1y+tiyz+i/p5tn7+TS0PK9BAIOyf7kipBHsbeywqSz86s31uZ8YzLhriSXxbBah/cOtgz9v40F8urPB13xsiSUPW3u8wDeb+TI9Dsd4zRSrdjdidxdGPRdtq8OCLbn5QbspCyMvahjzKaWEUjpZkIZ7k1L6EKX0IfOYByilEymlx1FKp1JKu/esreCJSKhzeYW5YMvBFnzziSW2z5btkIfSDrdnIJbUu71nzwu83f9ethuf7sxM9YFSisJICImACXDcmJGfyz6RTrv7vYza0lnIZ7KPF/dZ5Fxmi0+7oVdexOFYJCkFdte3obE99+hYyGxwZxiC/J37Vw/hWfNnJ3uGK3c34BRJPkM24FNoSyyJFzLoGz7fBzFwr3w0+2hDV4NSao3LrkAkpCGpH5mKPqqCn0JGCGlaXpKZOgPtcR2bDjTbksAufdA7SSNXz0DQRBEAGHfbHKzZ48x7zS/ueK1ziizEdQOREMG8jQexI0O5PUqZBy7o5oTxVn0MRuGZdVWEo6apAwnHgpCv9clrTHGDPNsNV0/YqB2uNf69dTVoieXO+9Y6URORP65sDJ/OftItsST2NLQjnjTMhLvUhZftqMfbDh733A0Hs46GyEAIQV1LDPe9tzGj7/F3K13/fLQpPY0h09epuSOB3x2G4iwGpdAOk1XHqTciwhpxzY0ZoRvPU8pYPkpQ09yRF1H2zTUtKC+MZP39B+fmh9MnQyxpYG9jB/6RQwJIUIQ04hky90J39cinQzxpIBoOQTcoQhkaCJlKATEahse58qiskQuufXIpPtsrbnyyf64ilaSqLIpTRvTxPE6nFEaWutWHa+h1dj7D3A01aY8xDJZU6rXx0A2Ki44bhGkj++bUFs0cjJ0xJvlckc25s33WQWlN8zfV4o5X1+KuNz7Du58dsDbCFBSrdzfmxFkFgLZ4Mm0hk0wTzSuKIqhtiQMEiCVy93yef/9HGT2b9oSOfyzs/HVJp6lxKUMmDp50uOwhtyMqHNJy2oj+8f3NiCW7p2daGctHCW5/dS3eW5+7IH9FUcQlBaQbFHPWBMsK/82c7Dh9Sd1IqwLAd7RBFmxWXCX7iSNEgisgUJ+Fb+MBt/JBNnhiwXbPv138wHzEPSagqb9KT2XYVNOCllgSScOwcYaDgFL2XILW2mA0DHm/3vryarQ6Nnxe9+WFHXWtOUuIaY6NUi40jOPvfAcvfcraM21UXxwzsFxqDNe2xFBVFoVu0Kz4tozGkWUjM8Dxd2aXfAgEM/KufnxJ2mOSBmXhYI+OiiV1aIRxzfMBZ7fuzEOxI970IJvTpG7goQ9TTgivKMIf39vk+74E9wiy89e2xBHXDVDTQMvXhqyuJY7/ZypLOKXi+E+Z6v0XhDTLiMyHlF1jeyKjdz4bFaknF2zH5prM1EUMSj2N5bCWXuIy12qL/cqigTfMsaSODRLln3xsZjoDylg+CvD80l15O1c4lDISOVcvltQ7XQP26cU70xrafCH460fb0p7PjxsbBJoW3FO8bp+3QTz7D/JKY/nCweYYVu5uREdSvtEIkhleXhjGmKpSNHckPRfvb88YIf2cUuCDDQfx2sqgsnbE01FLKZBwLPYnZagMsfVgawZtkSNEgFc+3YMnF2xHayyJyx/212P1Q3NHEr9+a715XlbBT7bYUQoUhDUYlGaVTEh9FtHugnwZWz/590rEkwY+2VqHpxbtcHEoP9x4EJTCNPKyv05qE2w/ybl/+BD/+GR7YAeC37njSQMPf+gfjYvrBu77T3pKwoNztyDps2t1Gsu769vQ6FF6nm9qOS1K7IJs6W2t5nqiaQRJwwAhwKsr9+L+91KqINlGJtfubUJFUSSvcgttGUg46gZFYYabs7kbarDzUHqVHdFp4/WeU8qigunWrG88tjijNorY29AOAjk9aX9jB/7xyXYAbKMBAJsOtODGZz91HTtxUHnWbehMKGP5KMBP/r3K9vv6/U1Zy4yFhN3pmfd8gIa2+GEJ8SZ1mvY6fl6ThVvrUNOcMgyZJyT7hkdCWuDEnlxUG+as2S/1Lsj4YgBwuSM0dvdb67K+NodBgeJoGEndm4ZRXCAvacvvfF9jsAx9zdtWBuD2tDVnkVyVq80Y0gie/GQH7npzHRrbEzAoUJoDNakwEjLbxd4tWfu4x4j9n/k1jDxXHOsMDnS+aEqrTPm5bQdbsGx7PU5xRE+SRiqmlMsGwqLqOtptUIqNB1rw+qq9LgP0hwGdCnzKaE/oeGSev94y9+zurGvD3W+u891M+XWxc566/dXP8PEWN3+Xy0FyWhRXapGdOpMiQmfcMxf1rXEQALrBrvPpzgbslag7MM9y8PESCREMqIjmtUJcg7CRuPpxZmTu95jn5qzZhwNNma25IY0EqgwqJjobhnwDaFDmWTYoxdaDLfjF6/nPb5n52w88/7anoR0vmjrxx93xDgCm2tKnpMB1bHclKypj+SiBuNt/cO6WrMX4w7YCDARxczHobKcVBXCoNY7NNS14etFO6aQ0f3Ot50Lx0IdbsEbQcM3Fs7xyVwPKCyPScJHMOP2KWQlKvF5SN3DxA/PTXuudtfuxYpdbxUDGFwOAxQ4jOh/2h0EpVu5qwOLthzyNZX4ZPjZueWEVWmJJa0H7g+D5WrOnMa3HyguEsHEQ8/CUp0M66s3v3tmQlu7DDax40rAWs+OHZl/Sl/eZZvJsEzrFun32ZFDdNJZ1g6IjizAlRfaeZaexctOzn+K2l9dkdS4/5GuR7DCpOkwijuJQa9zmXTYMirZ4EhS5GcscznYTQtCe0PH6qn02gwoAXvx0j+fYF8HfgbDmzeEXjyUAdje0SeeKoHCG6Bvb4+hdLDdmeCVXXkiGEIJlO+qhafa1gOe33DhrTNrrlxeFsammBZtqWlDbwgzL1XsapRvtRVsPYa/weTouLttoptqaT7ywbDfmbmD0hXX7U++t2KbbJcnXd7/p78gICcZtfauc2tCR0G1jTKdUen+G5VkG6tsSWJ6hqlEQWONH8ihkHu+WWBK9JcZye7zrZQZlUMbyUYhXVmQfhubeL8D0AqaZyVftbsBdb+S+i40nDXy06SDO+f2HeG7pLuxtdHsbKooivt5lccOQibKAM8x18Z8/9qza9vCHbi8QzwS/SQg5JQ2KNXvTq2MUFYRymjzysS6I3RROk2o98f/mAACeXbILx93xjm3e5LJ+D7y/GSf9Uk6fSMftI4TgtpdXY97GzhHe/+fCHWkz98UNAw9r59LNu+vZWNYIsST2ODWDg5oe5Ww3eJSycH02UY7Tfv2+zdh8ecVeqbcvG4jnyZdneW9jh9lXKX53hzAvrNzdiBufXYF5Gw9am/1sYBW6cLRbI/5ax061CBn4cwpp6SNgFEBrXBeUZ+RINxc4xwYhRPod3hxuePIqnYQwWtGj8+00uDFVpf4XNlHdtwRN7QnbmIgldemc/u5n9vybW19e7StryUupdwZ3/2ah0iBva2ssiS+b9Cyv5/ewEDFobEtgnoMvzKO497+3CXM3yhNbG9sTiOuGVailuSMhdWhQypLv2DP2Hk8aQSDpN68qiLGkIb2+LLKlG1RahCkbZ8DhgDKWjyKI7+xNz63AlixKk4oDnoeFAe9CCfVtCWn53kxRENYsQyLkk+nuBafGrKi60JHQXUk5YiWuU4UwLu/DhG5krIbR0C54AAzmlYmE/GfufmVR7DzknTAklsSVceL8+IOzfjfX99ocojHg5Vnmn4oTnW7YqTPvr6+xzueVaJKOhlEaDfuGM3MtVBIOaWkTnUTRfYNS3Pb5YwAAT3ycnivPUd8atxZRPgY0YQPmXNANM9RuGDTtmBGxrbYVjW0J6zkEVSqobYlZm5vCiBZYJztTnCZUwqQUOGFY9h56ESUFYTS0JVI0oE4o1GBFKRxdoxFieffESEYmxUL48wprGurTeKL5sVytRiwTbRjUMn74fO1FjQgqCcqNY8OkYWzY34yQRjC6H8trcOKdH8y0fl63r8mzjDd/98V2JJJu+h2lFOdM6A+AJXMndQPba9tcak+ikco3mpTmj4YhA1dySOrUSt7+IICCy676NtcGOaRpiCcN3zLwfN3l997UnkSRSesSwT3LFs/eox1FkRB21ad/V5yRLw5KKV6V5IQkdQNLd9g3M4a5gZe1tTtCGctHCyRvB98F/2+akKo46TR1JKw3NwiVgXt4gqKhLS41hDUihqvThyadaGxP2PpANPRP/MW7mHmPnW8lVuKqbYlhn+nJ5tc91BrP2DDTdWpxipMGC0ulCwNXFEXw7JJdntSAVbtT1JKrT3Mn2fmdfsvBYCWaxce3vykzo8PpVZm/qVa6oF764AK2kJkGoRdeW7kXy3bUe072p/3aX90jnXJFQUiTerKW76yXlpHlzxGQh1q9cN798yyjnxuiGklV8HO20aAU22pboVOKMoEfnc7De8/b6zF/c62VyBn0VWyP63h/fY3Z16l3pTP1mvNpyIzsV4K9De1WZv65f5iHk4b3BgCM618m/U5dS3Z5HM7h6pUPsdp8V4Oo8PBxsKs+vbIGv5ZusPeHazNTSvH04p24x9T3bYklUdMcw+Tb37G+G0vq1tymCxsiv7mNc5ZhUnsGVhShuCAEQoCyQnfugkgL2HWozXPDxr2/zudgUIq/zN1s/Z4UkuVO+/X7eGvNftanBLZ58ot//tj6mT+TfY0dNqdFEHywvsZ2fS9ceuIQy8OrU2opBwXZaBZGNCsJ+xmzamNlaQF217e7aBXi2Drr3rkAUmOwd0kECUkS5/7GDhxsjqHOg87BMW1UXxtdEZBvsE8c1ksqc+k1PdRKrmtQihWSQkmZOqEOF5SxnAEWbK7F80vypyyRD2w80OwbflogJGh4DcF0+o/8hQQYB43vnrmny29os2N8T4/fzllvSdYcf+e7uOKRha5jCFJGd1w3pAaVV6a3jO/FdFjZz0EE83cdMo1lGmxBkUGnFIu3H4JuULTEktB9PKwAcN5989CRMNDckfTUnuTzaGk0LF2gRUdwkAlfBvG0P3iOea1ueWEVvup4TpecMNj9XcfvRQUammPuxWrZjnoYZhj5nc+8JQ7T9XmmSTQcvO/GDSjD4x9vd/194dY6fLjJLauU1OUJd/VZ8Ko1QjxDoIYBjK4qdUlD+SXV8PaFtNQ7HDQ5kL9rlz64wObt5wUbxAV8zpr9+Mm/VzpPkTEo8sMhBoBoOIQXP91jKzDBDWevzfuMNH3pBJe9cs6AIu/+nN99aH2+L4MNC/9+kMRYfr5rnliCEAEuOm4QAPauxJKGbfP3nLl+8flz3b5mfPefywHAZmTN/sOHgN/GkqRC62GNzfEEBKdU9/GNDvCtoGyca2bS3n3/2SQcz3r3t3NSBT0SAp2oLa7DoBTRcAgrdzXi639LVd9bKTgSeD7AH9/bFKj/xbn0QFMHdtSm37T0Lo6gxXQEJI0UHUHsw395KFNpgpPgZ6ZsXmEkZJ2DN6cjoUvXRz5eNEJwzeNL0NyRsN0Dp3w8t2SX7/0XRkKuOVt2vRlj+kl1ymtb5fNvWTSM44ZU2D+kLPFPxDenj+i2dUmUsZwBdhxq6xRifC5YuLXOVRK1zeS4zt9Ua4VSZZNe0HVpu4OiwLP3NQ2mBJP36OaJICKchu7u+nabQetMUuPgBuOq3Y1Sj7bIpxXbxBeBDsEo3l3fjh8+n7ncHb+ubtCMjOUTh/VCVVkUADMufvT8yrTnWL+/2ZLL48/qi3/+2JJYAlKT6K8uOVZqBIjZ1OKC44cdda3WNZo7EtLzPrtkFz5xeBxOqu7tOo5S4NrTR9h+96pk2BZPIqwRaab2nDX72Pf5cpulTeWV4PPFP3+MupYYThzWC0UF8mlRRmnRDSqVSrrtlTV4e+0BzFmzT+qRlkFUmiGE2DiMlhqGI9s9Xeic0zesewjYceJpxSjMEsm7+Z1/LsPzS93a1bzCW1AYBktsfGXFnsDfkUEjcE14fUsKLMkqGSilGVeZ40nGMs/yx5vrzPOmPv/LB2yzGqRHMqGZUQD9y9ncItKkZB66R+ZttRVUYsoTKc80hzPqdM/bqUgbRSrng3GVCZbvrMeGA83QKUW/0qh3Wyl7Pk6FEiCVhCdCNj/GkwbqWuK4fMoQ65xJw0B7QvdMhCPmuYNw1J25AUGfhCbwxdl7atKrzP+H9inCa6v22b7Dvfp1rXHXRpE/PlFpRDcoVjs8v4B7A3ju7+fZHAd8WCR0tnnycnaLRruo9sQ3XG3xpK/TxUsXur4tjqF9in3bDADFBaGcqXSdBWUsZ4BISEN9WzywZNjhwCdb3CESLs3yX48uQq8id7YpRxA9TD9lAI2wSdc5tMXdomwCdIaJvJLl/JBuEf5M4FTxc4taw6ePrsSxg+073XRGP5Ay1pIZGstnjauyNh0UNK3XsS2edGiXMuxtaLeNP94CjTCqiVMNhMv1ZILbXl5jGUXOJD0/yAqWUFCcPrrS+t2vy/65cKfN8HzB3ATGkjq+Y3q/+PdzcUBy7rSI3fXtVra8DMzIBl5ZscfmQRdpGCII2PP4zj+XY52Es98W111e3rBGbBSVqx5bbI1JXsaWcw9XBlQ92NfYbvOuBu0320JGAGq+sn96P3h04mcvrnZtqPyvyRLVXhLG7K0vrZYWLvADIe6Zzeu+B/cqAsA875lwwRdurcO2WmZQOucN0SivKk8ZjtxLHMyzHLgpbEwIhplosAFATVPM5sklAO54bS12HWqzOTOcMmUize7PH9iLnhBCzMSyJDQCLNrG5gvd432wf5fR2JzQCEF7XMd3zhhlvWNiX/E56a8fbUNHUscPzh1rXbOqrBDxpOFZgpxv+Pzm7MseXIAdda2W6gzHpzvrLXUOP4jfEws4lUQZNUVWupwrKjW0JbC11r5BsRRshK85I0t8buX9xM+nU7sEJf85qVP85u0NnvNHSCPYVMPet8m3v219foZJU2yJJfGYI4FThKx/G9ri6EgYKI3aKTqyR6EF0ILuKihjOQMUF4Tw9toDnqGUrsBbEuH7hE6t5D3xhQni5bnwT/YsV9EYd05wIZ5h7DjtdCFpJ4ieMTe6/eD0iKVbTMSXlv9cKXg8BvcuwuDeRcL53eeUbYrEZnKvHqXUlsQhM4TDQtYvQfpKSqfe9R4ioqccTKS+pjlmm0xSCRsEY/uXBa5o5+e5ooI30qCwQovpIKOKUAqUCjxGv7GwfGe9tcBQSq0sc9HTzO8v28IH4tXFMcKNUL9zEwA3PrvC5p3yMw5i5kZTtn5Hw+4knJJo2Eou5F/h3WXQVNVIjRBcLPAxZXhm8U40dSSw6UCLjWbF2/r3T7b7boTtyVHBql3+yiGFdbAl5hoTb61OedacCyu/htiupdvroZtSb7yoQTqcNKw3TnXwKQlJqaxsErxfnGO7va4NfUoKLM4ph9f79PySXfiLWSDDb+qKSLL9A3GWnX3jcxHxT2LhCZ2y6ndvrN6HLQdbUVnKHCdJg+KfC3fajPqJg8pd1/SbuwmAjzfX4Y7X1tqMRJ5k6NlW+LxfBLjvvY14bP426x4M8x4IgU1yj9KUF52/u00dCcsgdc7BXLnJL3m3JZZkm1iN2JIFn1+629oMAMBFpuRnf2EjVFUWRUhLPac/vbfZkrbjn2kSZRNCvKOUfIPOz/HA+5uw2aymynHCsF743ZePs8ZAdWXKeyv2Mu9zvyTTfyzcgd31bfjzB1vw5up9tvXQHnXxfr5idcS2eBKPzNuCaXe/j/aEbkWkrfsz3wNxfiIInldxuKGM5QzQv7wQ933l+Jwqv3UGZEN3lsmV44ujLPzq/Gj17kas2dOEF5bttkKhoqeSy301ticQS+om75f6xqlkclecusE5hCHN33jb19juams649pmCJnzY//yQuszxvFNHc8NfxHfEDx7Mogc6mseX4I7zQSvrzy8EJscZawtxQPC+j2d2HxMN2xGGaUpj73YVdwYiYY1RCVZ0F741zLvDZ9BKdoFQ++/Hl3keaytzaZXQ+yzeNJA7+JUQprfuyNy9FpiKc/6PxfutI4Z2sfc4OSB2jpVEPOvb0vgo00HPYeyeE+vC6HUpGFIjWHxfXMaDx9tOsjGvORifEwlHH80TG8e9zAD/u/Mg3O3MMUNCZ8WAO6Zs8G3rKxoU3gl8i7edsgm4eUsnqGZ8eNnF++0QrfffWq51fZ/OnIlKHUnWPL2HmqN4yGJLKMMU0f1xZnjq2yfeQ0X8dkcao3jc0JFzUOtcZz7hw9lX3O12wvZavo656Jtte5kXEop3lt3wEbL0rRUJE+cAzUCDO9bIjkHM6TCIQ2/enOdzUj8ZGsdDAqXLrRoxNW3JbC/qcPaGLIkQ/a3g80xPO5QiLn+qeWeXn6NECvJ0KIdUAqdbxQdTgJuGBuUKcQ8vSgVmUrqFNGwZm28uMeSJ9vtqHP3Z0hj+tjxpIG/CoodV00bjq9PHWb9zpOqRZoDIdyzzH5fJtA1+WOQRd4ICM66d650naGUoi2WKWwuIAAAIABJREFUZHKaAO59ZyO+9JcFjmPs1WS5Uew8He/zdz47gEEVhajua6dEACx5usYsVna9+Z6KbWE/uL5m4djBFbbrtsSSeGTeVrQndBxo6kBJ1L4+8X5JJzDQXaCM5YxAURINpy1acLjhRwtxTkzOcJwIPrFtr2vFdjOhoczmFWT/TxhYjtPuft/igfl5SojECKWU7VR5qedwSHMZByKm3f2+67N0HlS+UFT3LbYmWUpZqdvnlux0Hc+9I5RSywjpSOguI4FnKv/z2lPx3/9YBiBl+PK/dSR0FzeOe5gMyjYb6SgcspAd50g2tds9LKOrSjHrmCrpdzicChQrdtl5b4sFz8myHfXWvcl0ld9cvc/1GcBKoQN2w4p5h1PtopSil2k8OxcIXpIVAF5cvsflsT1mYLlUl1OGdO9oY1vCVcVy4dY6NLQlXO/MLS+wCpiy7uWeXjG5dFttKw4IiVlOg+nKRxd7RlN4342sZIZNbUsMK3Y1wDBSOsu8X7gEWFNHQupJM6ib02xbr33sOPGdbWhLJQtxFYL319dgX2O7b0iWmOe55cXV+NN7dvrG9N+8j/97da3tM8rcjmiNpe6Fh8/jSSMjmoTzXSBE/vzW7m3CA+9vMo+xq+wkdcPWFltbhZ/9wsbihoYnOAWjYbg3S07EdQPXPrkUW2tTnvJoWLPmePHaFHJ+OwUbE2GN4JOtdehI6Lb3koJiyl3vOr5jP8+HGw9aXlTdoHh91T50JHTUtsTQZM47QShrohoM9wCzyq2cry8ay6m5STdSmx4+Pdz47ArEkoZliLXGkrb34QKJTnBYI9YGUuzv0mgYZVF5hVKOA00x21onfj9VYEZzPfvvPrUMOw+14ffvusuV//Wjbfjj+5t9qVMhjdhyClLTNbVFDsS5dERlCcYKijB3vLYWC7bUYvG2Q55rCH9+n+5q8KSkiNKXZhNQ28IcYkWRkGstEcdZplKwXQFlLAfAkwu2Y+6GGiR0ioKwhnve3uDyishwuIxqP46Y5Vk2f/cL/3N+Kz+2Pa5bnuUaM3xT09yBkmgYdVZZ0hQNQ0Y/kHmWOxI6WuNJ6/OI5q0CkAq729HmKNThNLySBsUbq/Zhe10bdIPzwuJYvbvBlbAIpJIVz7p3Lp5YsB0AC1mt3N2AssKw1Y7XTA3JkEYsWkpSkP7ifzMM4M7XPrPGgOjB2tfYIZX3sbff/ve9De0WX/R5Gw2ICbsTQqzry6ozitQYIGXYc1z+cKoioBiqlIVUnV4HDu45cYdzUz8bNFUm1hVxEK6X0Jm+qFjAQcym/u4/l0nbYB17xzu+f+cqMbcLBlssaeAxiVbysz4Z5EkzwU/kdD42f5stSbVVspl1L/4Ucd2wrsP74YEPNuMvH2y2lEJ0g1reP97Pd7+5DuNum2N7J/gCKnoBAebtu/n5ldKQsAi3scb+v3LqcOuz/uWFvhEekfrgHEYylQeDUoACxYIHipjh8/aELq0AJr0u3HPi+ZMG4ubZ4zB9tD2DP6wRy1AhsM8zS3fUS+e0CT+fY/vdb5kfOyBllJwxjnm709HSOhI6/ueZT22fyb7CP9ON1IasMBJCQjdw2UlDbM9mzpr9Up6qYQ4QPt6Shn3M6AZ1SZ9R6q2zy6+5fn+zbQy9tmov3l/PohAy6iBg94rzzf2+xg7UtcRZQR2bAZoyDOvb4pYhxqfN/6xj1+JOi6cW7YRBKbabHvpW8x361ZvrUNPMxmJII0joBgY4xnXg4IDN8536mPdDUUFqXPON7lqzMNUmj8Q4wByTaWzJ1FTCGlvbEse5QpREREk0bFMcevzj7ViyjXnCnZxvS5bQ/N8rSZZ5+u0RMf6TV/+J899NAcvAdyWUsRwAS3fU45nFO3HVY4tBwDx5H0iShJw48RfvZpQNnin4RF4Y0TwTxpyLlBFwEqCgOO7OlMFR2xLH+AFlqGuJW+eMhDTwqkgApBJxkZCGxjY719mgbMHnqgPhkIanFu3E04vcHt87X18rbexnjup3nCvHOVNJgbfNbb8Xlu+2srGd4J6+7XVt1gTbkTDw/JJdaO5IWgYKf79F7wo3fPkEHNaYp/Hfy3ZZ35s0uMIqiawbhi8NQ7ZAiRWTZNJmQGqi+/rfFmU07riuJudrikasLHTohfEDywHApWbBF/JJg8vxwnKWtPej2WPd3iaaut7D87YiaVCbFyOuG9a50lV5kvGnY0nd8hJzPrRIqRja25GtbVAbT180iDmSOrXmhNT9mrdj3p6z2ADAjD/eFwAzLh6cu8V6btx4+fsnbFMu0jA4+ALG+8JJX+DnKi+002Dmbqix6ZZzUEqxyyyA43yXUwVUUksGSzZy3VqqDQC+9felAIJJwlGw90rkPXK+9IGmDvQpKbB5l0+96z/e13ZcriQawoDyQpd3q6osao13Zxuvf2q5VBO8LW73vvp5lkdUpqgP/OzpnGgJ3cAOx4be+ZXvPb3cxhPmUZ3CcAgJneKscVVYv6/ZGoP7JBVPgZQ6hbjR5aXV+e/OvjQosPGA3Ljj0Zq1exttBuN/nToc33xiqet40YAXOdJiRJM7HMR+MyhFWNNQWRpFjRDid66DCRuVjbqSzB6ZtxUHGlmbwxorSBQOEVt0sSgSsnTK/VBUkDq3vepr6hi+bvzHRx7TCTEJU0RSNyzPMh8hXu+j+AxlUVl+fqdnmd8G/87gXkUuybhXVuzBHa99hrCmWY4pgFHBZOdMXTOFV1fuxVIPFazuAmUsB0BLRwJvrz3AqukQYEjvokDZsbGkEbgqUjbY28Be4PrWhLUzdIJPelVlUSzedsgxUcu9S9wjFE8aeHcte6nFSYeP/brWmJWAAch5xIURzbVbNahp3JqT46c763HzuWOlyQf7zYksYp7j/EkDANhLhQLMIxLSCFb8fLb5u4EhvYtQGNFw91ss8ejMcVUuKS0OGWeZIGW0cOPLOkI41Gn4cg8gIcTqt6JIyAphJ3Vq86TvOtSGn724yvr9ztfWWucJAn47fFIKaQTNsaQrWckLnA95rFCswAprZhD6PrmaJVU5pY34QkxALKUDr6xnPinzRVdMBuKGabYwDFii/DxhRXyP+ULKr7Gnod3yuM8xPdwj+5VgRGUJZo2vwiUnDsaaPY0YUFGIECE4b+IA2/X8vK6HWuMWL5zfG2DXS+VgnGFOw6AuY9y6nvlyP7lgOxraEta7LhoISYNFx0KaXaVm44FmdCQMnHffPGnb+W/i5ol6vEtiu2U/e0F27zwy1ZEwUFYYto2H1riO+wVNXhHOdjHvK3zfiYEVha4kJIAZWc7EZ2r7u+cppeP1STNy5QVZn1Jqj1S+vmqf9Yx0g+JbM0YCYPNtc0cSg3oV2ubsZxa7cxReW7kXvBAMT0BO6IZNXcNZhRPwD5nXmDxew5G0JqsuCsBKVOVt5WF7cbPG5wQx0bgjoVueZUKItQly0t/Ese/VbP6dkEZw7ZNLkdRTZdLPuncu1uxp8tyc8+S0SIhYzhB2LfdmigCWpGBrGpnC6/5u31iIz6B/eRSXP/QJ4qZhLypOOccOzykojYatvli521tJh39djCwAwAAh34cnJXLc+OwKfLjxIA40dyCeTH3OI7Re65jzeby+Sk7v6y5QxnIAcPmj00b1BQHBFScPw4wx/TyPb4/ruOGp5ZYofL6gG8zzwwcq565+vLkWT36yXfodPujHDShDe0KHblCMF0KD4oC9buZI23cA4HdmiJJ7tlKvPeNp6YbgWRbeaD8tRkopPtvXhO11bXhj9T4s39mA848daDvmz1yT1DwnL/kqeslEtMSSiIQIigpCFie2uCCMK04eZhlGlKbuwwkrWVFAcyxpveh8oeKeEPFIZ4Z1SCNMxohAKODCSvCWF4bR1JG0PFbHDe2FxvYEVu1utMqENpvhZTExzgtiyLRPCct2j4QI/r5gu7WzTwdOtelbkpIZ5GMsnWe5vjWOi83scC/0NbPwxW7XJJsTCuoqtSp+pz2hSw3sIF70N1btwy/fcOs3i2F5a2NrXlQ0UHi4tMws/kKR4rlXlUVtRQg4xLY6S/Hy83Pw8CZvgkjNJiCWYaob1JKDtEpjm8dx4+nJT7ajoT2BX76xDt87azQuPWmIdS7dMMDLMYv9NvsP80BBbQYYAHx7xggAwNaDrWiP67Z350BTh9Swu/rxxVbLrXsIYC2zzZCjqqU5Tlo6kmycEvvzXrydzc2bTbmrZTsOoa41Jn0WGiEojIRw46wx0ut3JHSryt9Ti3ZYn8eShlQbnBDg95cfZ2sPv+6gikLX8RxOiTDZeZ2/tyd0zPztB1izpxGLzPWIVwTUKUXUHDC9iiN4YfluhDSCbbWtNuk6Jx6et5WpUxBivedvr92PX7+13mUsifDypH/1lKEWhSauU9txTklLwB7S/8Xrn+FlcyNtUPt3uTG7TeBmJ3QqVevgDiSOARUsIXhgRaFtoymCz9/FJk2CkJRhvq+xA6t2N+Dfy3bjvXUpb/Bss9w250Rff+ZoDBOS5gYKz19Wwl5GzRKxTCgN7VRPopTVImiLM6/3gaYOy+HBh3256Zn/k8nHH963BC9dfxoASBN7nfQvPh4MSnH5lCGCsgejBD7wgX1939/YgS9MHoQWScGpWNKQjiNKqW2N4/Pq/e/JN8BdDWUspwGlFB0JA9vuvgCEMG/qxEHlthfDibZ4EqeO7IM7LpqI55fssgm654LlO+vxmznrLZ1YvpDPGFtpFbxwgi88Nc0xvLh8D6jNWEx5ha9/apn1Mm852OoK+hg0JesivvQGTb1ozwnei9/O2QDDoNhc04KtB1vxkVD9jAL43tOMkyeGucVr3vO2vYAGL86x1yOcOOWX/0HI9C785esnIhoOIZbUMXtif1w4OWWIU8i9PU0dSannoSgSwsRB5fho00Fb2U/RQHYay0UFYcR0AyFNSxnLGkHCoLh8ylA8s3inZYiXF4YRN8Np3w/AU3RCPOSs8VX4/qwx6EgYaIvrvjJJIvhCJiuF6jQ6vnrKMNvvLbGk5Q3icC32IPjumaNs/R4SvCE89N/QlvD1HNc0x2zFEiYNLkd5YdiVqMch8rBveHo5npLQfEJCWJ7L//3RnKwp3IvrSrMgDs/G53qmnFMs4pDQL8fd+Q6uf2oZrhSURUR1Fq44kuL42j3LukGxfGc97n17gxVy5wuQtVnlj5uy/p238SBCml2oq6k9CYOyyIbM882P5sYKNz5ueXEVlu+st2mVv7xir1TXdu4G9q6LBoFfQRDWdor5mw5iaJ8izN9ca21cueTXgaYODChnWrqiDvO6fcxIPuf3zCP+u3c2Yv2+Zlc4midi8m6VGcwJgyIc0kApxa0vpTL0+UbnCQeffXifYkTD9opn3Lt45vgqmzcOcOdZOBFPGli/v8n13lPKiuY0tCUwf3Otxfnl1eooZZNzQVizSqFrhODO1z+zNtAcn588EBMHldvOrZHU2P3lG+uw8UCzNW/Jxoj4megxFhWGkrphm0+ryt2bBzFvoiOhWwZhUYG9T3lbttW2IRIiOH10pbXpo7Bvns4aJ1dBuXzKUFOFyd6G00b1taI6k0zd/V7FBTY1C75evr12vzWmjxlYbjvP1adVW5rd/HocCZ3i7988xXq3qm95Q0rNEsH7+JxjqlAQ1mxzOVeseGftAYQ1gtdX7bVyqHhbeWIlv1/DoBaFike0mjoSltwjT9rm4yAlHUoxql+ptTkAWFSur2NcxZI6omHNmpNEOsygikJbFI2DUuDm2eNSvwN4ZF4wB09XIC/GMiHkPELIBkLIZkLILZK/Rwkhz5l/X0QIqc7HdQ8Hzrp3Lv7n7NEghOCxq0/G5yYOMBO4vC2ZWNJANKxh+ui+GFhRiIJQfqrSdCR0TB3Z13pxuMf0ts9PcE2KHHxx4Mbnp0ItdoOmPFlvrt5vvSBvr9nvmlVSE5I9FG6YIcpexRH8ek5qAmA8asMqJnHlo4uthBw7bzp1Nl1Cenbz5bz7MWztihmX+sZnVyAaDlmTITU3BxTAom3uzG8+EYqXLCsM49nrpmL+5jqbCsSofqVWWVdnyc6Q6aXWSCqEyEsZFwtJHuMHlIEQgo6EjkKJ7m5xNISKIm/vMpeqE80hfj+xpGFRB7zABe012zOgZrtYEg8Pb95x0USMrCxxPaPnluySeo1EEAL0KS6wLYCcWgCkygwv2FJnUTlS9+P1CzBrfH8UFYQ8w6uiwocXRM+583hmg7iNQeb5MqkkJt0mnjRcXvglpndoYAUz8t5cvd9WIETmEX/ZlGyU0TA6EgZW72kStKjZ38VCCICpcCA8EvEqb6zah3jSQL1A0xDvl4O/Mw3tKYP/yQXb8bAg3xZL6NiwX57oBbgN5LV73ZXHOB5fsB3/WLgDA01PIAWjhrANOkVCWOxFI9yp/d4a17F0R73Lk825t84phjjGftikMIngVJ3bX/vMmvdfWbHX/P7/Z++8w+Mozj/+nd2706kXS3KX5SLcbVwwroCxg8EYCC20UBMgCRBISAiEFEgnpBKSkEJCkl9IQkIIBEjooTfb2HSMbWzjXuQiq97dzu+P2ZmbnZ29Ip10kjyf5/Hj097e7uzslHfeeYt3TOJ9KmJbPjvTIsmmVU0VDzC74kv/uFzryxUJWbAsIOaaAsrEE8loEVx4TcYg9p6bSFAsHp8UfBzKtPly25VtWvWaZeCbp0wEwPpHJGTh/NkjMHdMNSiAr5w4Hi0dCVzzt+Tiv1Bj3hK0axUKcD5tjydQXBDCkkmDEHOSZkky8kJAhyzEPb1ml08QBYAjR1YJh3YZ2yI+JY74TjFX44qHtlgCV9y9ErVlBRnF1+bw/jdhcBmL5KI558v3vQG4u0TC1EM5kd/ToRRhd5xuiyVw3pF1aOtICI00dzDk445sw24RIhYH/Ho3u++f41CgIGwJXxvePwCgKCCCGLM7JzjCzfzKx7jeSpeFZUKIDeDnAE4AMAHAOYSQCcppnwCwl1I6BsCPAdzS1fv2FDub2oVmoSBki1iwqewRWzoSKAjZGFNbihMmD0ZbPNko71n+ITbvTZ9nXkdbjAlbvGPINr46RxTAn6r64rteFVoX5pwnCa7SeerT/fjxNXh7m1/r4TgUOw60I6SEjWmLOfjHSm8KXO6UJ4+/8nipC1P2+DteR0r5HgfaYp5FC58g6qqK8IDbWQtCLOuiKK+7pfvS+kb86pn1YnsJgHb1m3DYROVQKkK3AcDwqiIc5ZriqNta3Ga5uT2etIdzhWI5OYlFiOuBTzyDXDxBMbSiEIvHD/RowNSJQBe5gb+fjrgjYpYGwevrZikiRNxxcNLUIagfUIwTfvqsOKcwzNqdamt3+1NrPTFgK6RttdE1Seem3c3tqJPSnRJCtE6OiyUNBi+PeDbNM+hCsMkT7eV/Wi62q3UE2dwmTS103/HfJjP4bdnXiuaOOEsc49ptcpMdWcskc0CjbeXtW9ZSP/zGdum9JtuQGmaLV5Ucg1YHn8gdh+Kpd3eKdNyvbdon+oDQWktV+/a2Ax6NUizhiFiz0bDlW7CrgtYtKdKt8zCNyftSHPfjZ4T2kKVdZ+cOLi/UaubaYglR56ogxrfggwSW7542GTF3h0d1gJKdu+S2VlYYFppvgNUHfxclBaGUjnzLN/oXctzcRqcQKAhZsAnLmqcu4BIOU2AkKBWLbt5+XlUWgAlKPYtb6m618fNPnTZUPP8/PzPX8w7Pv/NlOA4Lrcn7Kd+5+OZHJ4mFzSXzRqIj4eDDxlbceeFMANDagusStgDMflqtO75zSinzX0kkqCfaSipaOuJa4fupd3cibFsiMgWnvDCMtriDGnFPvstBtPGuAW8yDoDtRly9qMFjh//qhr1oqC1JG4ZOvidI+vRLfMeX30eGjwkJVzAFmDNxyJVh1HoRmmX3/0vuelWrDNGValR1CVa5/U/+tjhi+8xRP2xsgUPZu+YJmng8/WycynuSXGiWZwFYSyldTyntAPBXAKco55wC4A/u538AWEQ6G629h2np8MfMVcM+AcCPHn0PV969Eut3HcQPHnkP0+sqxXe1pQV48HUmvP3t1Q89Qlc2tMcTrtDC/uZObQACNZB84GvQeOs7juqpGwx3TJAjEgDJiUTdgh5aUYiB7mDDnfK4J7tcd9lmYmt1BdOrFzVgyk2PetJa8042qDyKQa7NWMgmIuTalXe/xrbi3Cdd0FDt6Zh8onvddVBbNmUwW5FbbHvp0QAPZlVY4/a4FUWRZOg7AqzctM83ETK75oTIENXY3IGEqyFq7UhoY8v+4MypuHLhGPG3LACIRClxR2gSfnr24Z73z+Ee5/JkEU8wxxw+KQjnPNfUYKQmsQGQrLsvHT9OLB7kgXtfc8zj4V5SYGsXJydN9dquy4KLToiwCMET7+xAWywhzCiOvvV/4vvighDeTaH95M93ziyvQJtwaKB9uzwBckEFYE4wv3l2PdbszCw9s2yHOEvRqOui2IRtgliCQg3Rx7U5vB3uVZJIyP1NdTx6af0erHYXE3LEBDk2+WnTh4JSpmENGrYjNovvy8ehA20xkTaX88yapClW0Hy4ZKJ3scTH2rJoWLTzVR/uEyEcZS648xXxuVgRSLhDsc52HGBRK3h/U5uZvKiTx262OEhq9fa1xMS4c8GcEYHmQYDe6YkQlnadm6jJUJr0hVBfQcxxRIi9AlezHLQIdBzqddIE6+v8WEfcQYf7vEURWzhpTR1egRfW7UGCsvP5Ymx4VZEYi3jbsCyCp11THK5NL44EC8tX3u0NRRnWOP+OqS0RGlTbIiICUSZSxMG2uDADkS8bCVmI2Bau/usqz25F2CbCoZzz8dl1GF1Tom23RRFbm5ETSCqG+O/e33kQn144WlsfQNIEjEOQel4mYO96+/42EeXGe3/265fWN3pmWssiiMWTzpu8Hi3C/BR4e3h1w17fIjioPDUBpqDbD7ThHys2gyLpuL3g+0/BocxM87m1uz3Zda+Q5rbeRC6E5aEAZFfbze4x7TmU0jiA/QAGoJfC06uud0OP6Ry45LA9l/5xOfa2xDCmtgRX3P0aLpk/0mPTvGTiIGG/FjQBZ1qusGtTx69lW/5A+jrkZzh12jDxe3nw4AOEHO8VAI46LOnMqKaRvXeld9uYp7MdU1uCDW6WJC4U8MGRX/v4iYM8nfs/bwZ7w55y+BAACMxpD+gnIL+GKfn5pClDPPXC3ylPfDGqhgmYOrtMAPjHis3Y29zhWzgxG1P2P78+L5u8iGI2sQQX/f5VcWz6Nx8DBdNWHmyPo7wognsun+O5fv2AImG3GrK8GlpelPZ4QkxUpxw+VLsTwu2PZaHt3N+85JmoRApod6LSDYgDywow1nUaDSuac05jSwcGlkXFoHygNY7L/+QPJRVWdhd0IZj+6TqqAMzc4Gv3v4V9LTFc7NbjpsbkbkpRxNbaYnP++9Z2tMcTIlrAF5cwGzoRb9Y9b+nkZKQLHiOYb8Hz56woiiBkWVi/y699uvyoUR7NOuBdZMlxmYdVFqKkIIQFDdWeeojYLEQkf/c8AyaPKcvran9rTOwAUHid1bzCMvsN/73cQhLCpCOppR5UFkXCcfDL86a75U+ef+KUIWjuSOCqY9lE96ov8o4XVZjjY9pF80Z6y0LYZPvTJ94X7/9ge9yzy7LHNZOQ61Dt9wmHYsKQMrEwAIDPLT5M2BHHEixyUVEk5NO2yWMENxkCgLsungWLAF+//y3EEw4efXs7dnMB2b19WzyBz2tiyKrtvLk9Lvwy1Djpx00YCJ6wpyPh4E4lEUws7oj65P0vmaTDr2GXd7coBcbUlIhjcccRMZQJCF7fvB+nHD4Eqz/cJ9Kt3/XCBjEmFkVsLJs6RDwyrzquxOC3b5CSYHC4EPafN7d7xiDbJr62U11SgGfW7GKaZdvC3gBfFx3MhIebpSTPLo6EtMfV5DQUzBa6tqxAtHn5+1QJspLmEcn3UBRmigJ1gQxAxPzOQGEuiEZsrN/djK/d/1agbCEvLscOLIVFCL56/5t4wvV/4rukBARFkZDHl4MQll8h5soFu5ratYuUUEDkpGMOY3bktz3xvs+xsbGZ9Zdeqkz2kAthWfeY6rvO5Bx2IiGXEUKWE0KW79q1S3dKt7J5bwtuf3Itfv/8Bnzatbd9YJVXi8Ece9jnprYYHnt7B65YOAb7WmIYP7gUsxSBMmQTEazbkbTB6fjV0+vwjxVJUwZK2bU88TWVa+1savd4YhMCHDO2xrOV+JrrvCBrWQGWJlbcSzpeIWmt5S2ZIeVRITzzwXqVG5YmErLwnYfZ4M+3YIoiNoZWFHrsq+Trrdlx0KN9BFjZAWDKsArPcV46udPqOqs6KTe1xTwTyvX3viG+iynhu+Rf6gaHLftasetguy884O6DHSgIWbDcbVO5HEMkJxBK9YMEtxNrizmIhixPexpeVYiZ9VVCIz9paDluOjlpP8Yn+liCehIH6GzsCdiOBBeGLp5Xj9Wb9/u0L6z8fudOubyjqtnCwrYgtGvyue1xZkJU5G7HtsUS2ggD8sLkmsUNPi9wwOtpzuv1nQDtcXEklNaZJp6g+OT8keLv0mjI7WNJaVle5LD7sq/ijtcJMCjLXDRs+xy+1HfCH/2ey+fAsojY0gSYYBt1d5W4udIZd7zo+f2+Fs2igFJPFjr5ljsOtOHO5z8QnveykCgvwHgdWxbzyhfOTdL5Nywdh+uWjBWJF1LZHn79/jc1wrKv2O69k6ENdYu406cPw4xv+eMt+0PHUeH81twex8pNe3H14gahhe+IO3jjJhZ2Uu0pav9eJKXSJoRg+ca9iDsUdz2/AWtdBQvvn+0xB/98bQt2NrV5BAV1rGrpSIjQWar5Ch8zeNIMAB7fB7kN8iriffoxZTeMQhP+z0qaEfAx8Owjhgtnanns5WXj9Rt3kospnZlLqrnuU25ioWjI8piysUx3VPiEzBxRKcy7HEp/td7/AAAgAElEQVRxzNgaPOVqrnnGQBU57nkiwWIy71Q0/VUlEfxL2J77FxWcbfvbEAlZ3vFa7ivS+1J3oan7p9weuZ/FrWdO8Zx78bz65DUDTOhKCkI4Q4puc+dzH+DsI9iuWEHI8glacr+Sn9G2iGdHUcgUlMWilkNqJhyKv7zyIV7fvD+lEC/b5MsJWE6ZNiRZHun8Wx95D7ZFcPPJEzOKw55vciEsbwYg72EOA6DukYlzCCEhAOUAtN43lNJfU0pnUkpn1tQEh2frLoZVFuHqxQ24YuEYPPK5o3DzyRNxzeLDPOfYNsHanQfhOBQ/emwN7v30XAxy863rkpWErWQKUjVkUxCUUmxsbBGRAoBkIHbeNxPSQPXTJ95HY3MH2mIJlBf5nf14uCkg6XiToNQzecomETxU1k0nTfBEQBhcXijOkuMIizJy7aw7+C6dPAjHugIvBXDYwBLRYWyL4N+rt+ITkqBy8dx6z/V4bEi5ziKaQYFdT2NbRQgukq5ZELLFtUI2wXOSFocPGA5loXfe3LJfhLEJCvWjczIpjYZAwTRSd7kJRCqL2WAvD+LsdrptfiaY7Gpq89j6RUIWnvniQnFfgNkScy9u9luvwO+9l59fnz9DTLQ62zQuGHDTEj7gnntkHT7rahEd6vWi5tFa+ABoWwTtbnD/VJOnGm6rIGR72leTG5ZIHlh5+39p3R6tIB8N2zhp6hD/FxKyHafjUFy9qMG159Ov8uUyJBLUI+Dr7O73t8awZkeTT0CKOxTnHVnn/s773Z6DHR5NNKUUja4w/BHFrhsAqksi2Ly3VSyExe/gncAppbjj4zMAMJMCuZnIbYSbyHBhij8zT1oBwKOlBYDLjx4tHLk6EsFxZP/00kZtogtvuZNCGRe85bJahGDq8IrArV+ivAZZi7+pscXjaAkA8xuqhTCtJkdSxzi5n/DXxhIJOWIhLkzd3PvO/95T+K2kEeb9QFZ8cLiT3sOfXeC5r2xP3SKZk3gWlLxcbhHf3nYAZ0k283Jf5eUjIKLcYm5wqNB+87M7Eo4QvpPldzzOpC2KmUsmu6jNHQk89MY2sZisKAqDUuDzHzlM3JcL85SyHRxO2LY8Tp7/fG0zGps7MFkaE598dwdCNkF5YdgjrB1si3siWMg4St8fXB71aZt5Hfz3muR7UsN18vcvm10caGVzSakSBlUe1+R5oqUjgYKwjanDyjGzvtIzLnywu1mkrn5/50Fffcs+H/yrC+fWo7ok4nH2l8tbX+01tePOefJcpxNhZEWBuiCTry9DCPPXOVQ0y68CaCCEjCSERACcDeAB5ZwHAFzofj4DwJO0O1Pb5ZAL59bjUjf+MKekIIQ5owfgkj+8iml1FSI250enDfUJ1gAQljxuWYPT32vrvlas33UQD6zeiuaOBIZVejuy405cvMElHO9E/dza3bjp32+jUArnQ9zfySt+LoAmHIrfPpv0brdI0omMh38qLwojEkre46SpQyQtRtKMg68k+bPxSbOhthQj3M7H7e54+cO2hcnDKvDVZUl/ULmzd8QdX6dc0FCNOz4+XXq+gA7q/l8a9W6rEpKcXFTBhi9ouHnLjqakNl7NqMexLcunCSJgTiWtsQSedu00IxpnFkr1GdD2uqnEN+9tFQMhwD36k1ocXlb5Gr959gNxLgD864p57rMS4akvl5cN+CSwjLe59t7c5IDfqjBsi7JQSvVafYsJ22HbwpdOGIfJwyrEe9HV5iOfO8rzdzRsidTYAPBhY6vn2eXPf3hxAyxCfH0GAJ58J3W2LMdJbk1zzVsikYylLMPjl/LDCalO4o6jrYe3tx3Af97cLvoEF94TDsWp05jFWsPAUtF3aksLsHVfKyYNkRdByUln7CD/lnZFUQRtsQRO/cULnuOqY41DgeNdHwLVZlzua5vdeNdyHViEabxGVfvt1lXbx1S+CA5l5bpk3kgRx7klloxpXhSxPY6qPIJBTOqfD72xDZOGlPkcq06bPtQtq/f+w6TsjHoHOp4BzvHFeVUjbkRClrCxlNtBzNVgyvcXPgRu2VXh/tp7VuG593cj7iRtkfniYII7FhPCxsUdB9q1WtR4ImmGMW/0AMwdPSBwUUqparPMrs/P58+aoBTzxgzAeUfWecaXr97PQupxzfabWw6Ie7++eb9PAMtWCKopLcDPzpnmxqlnP+6IO0JpkCoaEsDGiM17WzBcEihvcrPLEQK0SLssW/e14lx3scqv++ljRot7ynAlF58XKYVQ8owbFByBg19XDp0XFEdffmeLxyd3L1pjCeZI6Sqn1CodU5sM7aY6K3qi6rj/n3tknSdspae8jn8s5+PS02t2iYE77jh4dUMj/ik58QeZ4MnvTH19Z84YhvljqhF129N9koldb6PLwrJrg3wlgEcAvAPgHkrpW4SQbxBCTnZPuxPAAELIWgCfB+ALL9fXOHnqEPz+oiOE/S/AJqwLFc0owISQVz5oxK+fWYc1Ow5qNcs7m9ow93tP4jfPrseL6/bgve1NvnA71NUsyxnz5IFvf2sMr23a69kOcWgynTHntnOm4SsnjkfCofiJlAHLsohvkuEJDFTGDixFzEmmA+Ze8lwQ41rCNTuaRFSGtljCo9lNt/Wy+2C70MgK5xFCPBOxfAnbs5qluHLhGFSXFOAPbspg9vuk9ludULhW5KHXt7Gweg5zjlORNZU2Ib5sjtUlBZ7Yw9cdP1Zbh0FmDculgPTywCVP2kRMxn7BpKo4IgYonm63Pe6IAVKE8SHJlT2g14rK90s4yWD88gB4yuFDhf2h/Jxy4pHpdZUoLwwL4ebUaUOFVlV9Js7g8qjQ6HNbWIDVecRmWeh4OMK2GEsCo9bnnuZ21JZFcXIK7XLcocJen1LAti3EHEdEUJBpjyd3iNpijhsikJ3086fWaRcB/JkHlkdRVRzBqOpiNNSWeFIJ/9aNGgAwD/GQTUQ4qtKCkEczqus3pdGQqAv5mLwzBXjb0BYlAYzOzAHuApeCwrYs15lM02i5sAzvYi4VFklu3cbiVIx3VywcI8aR5Rv3Sk6yXm2s6qxXGg2h0tU6qkLa5xYno8o4lG3t69CFuLr5395kNgUhC3VVhd4yJbhm2X1+91zVheTfV84HkBQa9rfG0B5PIJ7wZ8rjyCOeqrkFWJsUfdi2cPels7XRUCYNZUKdPO5xHxDbIoiGLREnevygMhDC5gNZIaMLr6lqyaH5LlMoZfMlkfryzqZ24TiqCsu8PavhBFUHPdsiGFAcEY6BANvhENFX0kQsI/AKem2xBAiAG04Y5zt3hRTpRE4uwuFFU/uR3K+b2uOiP4QtIhQrauQkvsvBi7ZWSUPekXCEHXQmWn7ZgZjDn/snj78vlG4Jh+KFdXtEUhbAq6ySn4VSSP4X3vc3ra4So2pKhJbdtgh2ZZAdOR/kJM4ypfRhSulhlNLRlNJvu8e+Ril9wP3cRik9k1I6hlI6i1K6PvUV+waZOupFQhZ+e+EROHPGcPzsnGlazXIsQXHS1CGI2Bbmj6nGL/+3DoVh2xs6i3LnQvZ3IuE6+ImtEYqmtjg+3OufIHmIJ4CtACcMKfMMbsMqC5ldqtuYbzl9MpZOHuRJaqDaB+9qahexh7ldrZwo4epFDThsYKlY7cYdKuwuAW+WMh23PfG+T4DjmhDOBb9LesDvkQTUvS0dInOcjEWI5HDofX8+kxJK0VDr1+LJGlhCgO88/K4n8YkqzIyu8UaimDqs3H0W6jt3VE0xZo+qcu3avN/LNq78uC5xRsS2sKe5A6XRkJhk2uMOIiEL967YjF89vQ4jq4sxva5S2N4Cqd8Hs1mmHq9yXpqrFzUITZtXi+911OHC0aeOHo1B5VEMLo+mjD8esiwhJMoadosQrPr6R1AQsjztWidExt1MYuqc/YvzkrsTjrRDw95JMs2vuhCZ52rny6Jh4RQmX/shjeZvoxu+0XG36g+2x3HbOdNcIYf9WN0Olk2sTp8xzFOv8v34I//83OnCkXGCa1M8YXCZL9axXN+y4FBZFPaExXIU4dwmBJsbW/DXS2ezMk1PKgmA5LghNMuad/HNUybic9LOm/zqZa18UYSZ33xELMySdwGAh9wxJ6a0ncqiiNg1U9uCXB6HUo/H/UhJU55JPPyIlHxBCMWUskWXbWHOqAGiHtol4XvC4DJhssAXlxsbW/D0ml0+u2gZ6trOnzhlsAjHKKdx/ssrH/oWDrKAy8e1X543w3XwS373y/+tQzzBFiqOA5HdVZglUTWuPvtfXRQD+q152V4+EwaVF7gKlWTfa2zuEP3ja8vUqLRuGaTnvej3r6LCrV8uTNsWcZ3jvb8LKQ5+8vfyqczpjwqztEjICrTf5c/cHndw2Z9W+L6Xwy/+8ZJZybJIz9AWc/DclxYCgKgPyy2DDJ+LeLkPr/P69gwsjeJvr27CJfNGejTZe5REUryeZPMSDgUVygY+ZPBxt7kjgY/NHIaSgpDHcdSjuEKyvQQtCLlDsUWIzwyqt2Ay+PUglcURRMP6BCWUMu1We9zBhCFl+M0FM3DWEcM9wiIP4u1Qir8v/xD/fG2Lp1HyAXN4ZXB2QY5NvOF5mLDDwiaFLIKGgaViwuaDoSw08/tyh7zz54wAkLQNlAdTvupPOA4ioeR91exigFeAjUtezPKCgF973KBS7GpqFx7t8hZUR9zRBsKHKwjdfPJEX5QL1bZTtteUkW0W+WOqg7gn/rMycTd3ME9oR6NZbo8xu0eHUtfRTy6f15yEldMvGGw/wGxR5djbp04bgtJoCDua2nDbk2vxvdMmIxq2YREiHF94fXC7Ox7juaIo7NES8/pJZlnz3p9r3i3itZnj17j+hHHu52Ra2eKIDXXHhTvVyM/Lr1MUCXmcJflxfj7fKo+5wrJqciCHKpJ3aBwKvLe9CZ/580pQCry3o8m9f3Jng5eHadvTR7cZP5gJIAsaatDUFsedz32A7ftZOCWbEDxw5Tzfb2TbUtsinpjMss0ib1tVxRExcfL2ef6cEXhhHQv5yEPjeTJPxpP1PWtklSfKAqXAg1fNx/fPmIJJQ8uxYU8LmtrjOHIUcwL+2rIJwuSlvDAs6n3ZlCG4/KhRWiOMw4dXIiyZdMntqS2WEGMdcZ9fjR7iOEyw5aY563YexC/+l0y7m5Ccp1O9ErXfPfWFY8TnVEIrpzgSEtFGuKDtOBQxd0H6l8tmC0FPHmOGVBSK97V0Mltcr9/VjH+9tkVrs8zhtvMFtiXGKP4eL5wzAksnD/ItiioKwygvDON7p00WjnKEALNHDsDA0uQWfFlhCDsOtKGqOIK44+AS17SARx9idZW6fdvSQlNFNQvgyGOzPIc9eNUClBeFsb81pg2Hev6ceu31OuIOHpPMuLhm+U88sx30Wu7nXNt1r2DsOivKC39AROKQz9dVjWwip0NnrnD5UaM8u0cH2+MY4I5RlsXGKBZGUV2osP95WVVN99DKQjezYyjle+TRS/jutQylEMIyL+GmxuTCetmUIYg7jmfultt9/YAiLHPbu9y95BwC2exI5QsjLPcwltIJOZSyVS7PBEYU84d7V2xmnvA20ySv392M846sw0/OOlxM/L959gPMqq+CbRH8/qIjPB7+KsUFIU/c07qqIhQX2GIiIWDa7h0H2iQBwa9N4P/XlBRoBR6AOQUOKY8inqCe1b1OEzioPCkAybakcj0lNaHskxx6jdPhTlw6Eg5FeWHYtxWreiCrml2AdXCd8wIfHPjkKduy+sI3ORTfPnWSR/DnbNnXKjSyTFiXFw+y4Jm8lm6nc6uSVfDGEycwBzv3ftyJw9K8Uz4hc03j2IGlQoARz0CTmknZmYq/n2jYQmHY9miW2ATkfgarXz6ZqvVQXVKAkEVEamM+mJYUhMQCpkiJVZpwqIjjmXAoCkJMuHAcvybFE00gkWxnQ8qjaOlIMOc3zeSvanVlUwrupa7aInJ72BPd3YfKorCYSGtKC0S0ATnKhxwPlwDYLL1POe6pJQnUHC40vyGZX/GyycJg3HHwq/OZs5/sAMzvP25QKeqri1FS4F90Eou1sUsXjMQvz5uOEnfRzLbz9TFk66uLPH1HFibYrpMlnolSKrSnPC2yQ6nHcevtbQc8vgQ8cyrA2nJQcgPZdEZFzlAZFNlkeFWRWLxUuu8i7lB0JCSbdc1PywpDWoHtQFvc07dVG9grFo7BqdOGivlB5vC6ChSGbZ9PRchmGWRLo2EhYMYTFFctasC0ukoRxag0GkZHwkF1SQHziSEEFpGd6PR1JR/hj3ThnHq89tWPeM4bp7GvB4BVX0+ex8si97dYwhExo9l36ZHD06n1bFnsfcrtB0iOR6r5DaVq8iziUZ7w3+nkYREpJMDPxbvwZ+PBcRMHebK4esMH8kyh/mslFyr82v5sjNv2tXnGAvl8lZBNYFvEs3Mh7+bq5vewbaEt5nhiTduEYMnEgfj1+TMwYkAxPuZG7Pj0//k17eweSQVab8UIyz2MZfljSAJcWGYpouUGs781hqfX7MK1f18tHM8cyibETy4YhSNHDRCT0+6D7bjnU3PcVZ4lGvjgcr8x/6Sh5Z74sz89+3CMG1QmBDgKpm3atr/N10kbaktxuev0yOPSEkLQ3JFAR9y7PQmwrGxnzhwutin5VrVuMpOTZ8i2pGIbRzI74PWky9DWIXmmyzz0+jYh4KjeyPKE9bfL52gzuPkCtHOTErdQF84ZIYQojqrBTlAmnMmCP6d+QBFswgY5VViXB18uPAZpftQwSRxeZ7zNyEoEVQPObbG5psWjWQYV5fFveQPfP2MqNu5p8WwPWyQZPYVdz5t+lrP6a8fh5S8v8sRL5rcYUhEVmo/66mIMr0ourjoSjrApTjgUwyoLsfpDlgiGa21eumERxtSWeBZSfMcGAM6eVYdYwvFotWXkdLAESY3P7edOE/XHXz0Pd6Uu2hKSsCZX3fNfOlZ83tHUJgTb5Rv3etJMe0JAuZ892ir3IxesmAaYnefRLCccIVjrnHp4GVXveCCp/b30qFGYO6ba19fktjLXDUlZGg0HOv7J9vuq+Y5cplSap90H2zHNDfEn74TcfemRvrIFTcq8zr+6bIJ+ZwpMk853KriA89bWA2iXdrN0lz/7iDrRFn76xPueJDByP7Etgu+fkQwrVlPK/C7uWb5ZbH9zszfHYTbU7fGEP8KI4y3HCDf2f2HExt/c2O088yp//6oDuuOk1/bx8a2yOCIWDzxSzoiAJEayYDVGkzCpQ2qbp00bGrh9z1EXNmp7ZIsny3cvLqRdKJnzcbyxl705ElKlrub9RjXrE9fy+NwQHDawFDNGVGLsoGTZVLMreVH+uOSwnDSB8S48AeCiufXY29KBuENFhCL1+uq7tS0LYdubjReUYlhlEaqKI9qn5nUv9ylCmALuuImDPOeuD8iAmFSiGGHZ4GIR/faMI8wwEp6Ja9H4WhSELCybMlh4Mn/Y2KI15fjJWcwZLZZggx/v2LZFcMm8kSJlKOegtE0vT8DXLRmLuqoiof1T88UPryrCCe62Cv8fYOGrnnmfaasPtsc9HYsLSkMrCnH7U2zr1LKIr/PJg1ws4SSjFLh1xiYAr1abI2/bzh41wLeNy66TTFHM4REOvnTvGzhn1nDccvpkjB9c5r4ndt6fP8km3MbmDmyRtHwfv/Nl8SxAcsBwpO15VQiNubsHVPMM3MnSFltv7HhNaYHHTESs9KHXLAfBy8cnIn7/iG15JpwvL01u5zGnEm9mM4cmo4fI9+eTyKjqYmzZ1+qJt1lWGBZCPCFsWFy5cZ/0W0Z5UdiXUp7f45FrjhLXHF1Tggev8obXkvnIhIHY1NiCtlhC9JcCNzKN2s7kgb4tlkA0ZGsn6KKIjfoBRbjp5Ik4Y8YwYSqzbMoQSevETYaYJlyu12jYwgmTBotVkkezL5Xh+bV7RObBVOlfdV+pmi0WgYHi6S8e45nAO+JU1ANv08e6Npmyc6MuSopqlqIi193drp0zkIwEALB+eM/yD91kB9ICAn4NVnVJBBQUK6XQeI8p0VMACJMDWehTBST52dQU8lwoCVkkMAJOeWFYREDi7/bSP7IEOwvHsvojhIjdi1tOnwyAjU+y9lA2gznjjheF6YNFglOkc+07j/vtUIryoggGlBTg9a8fF/icx00YqF1Uh23LY+6mizesa2P8tGjYQlnUby5RELDQUMNDRsOWduEi99EfnXW4Tzg9XhHC1B1IXj5uEkYIQThkoUljFnLmjGHCOdaTDVWZYz3jNeUmgf5n5Mdk5Qvn9OnDPL+pLA4Lbbds/iDP74SwBEDcue+kKZKDuaJZBtji3CKsncqmeDLCFFJ5gCNGVKIwbHuUGBQsAs+s+iqxoyJ3T3ksld+lHGhARY2owx93SIU+SkdvwAjLPQzPhKbCtB0WNu9t9XSaBQ01mD1qAEbXsPjEZYVhTBhS5glgzvmoG4aK2+7xfnDnhUfgaydNwCs3LvbdU4VS5sRUXVLg23p68Kr5KZ/NIgRtroZkf2vMOwESJsRPl0wfdB7b8kLBtoj4O2xbmDq8Al9ZNl5McLxjcs3VfCnj2QVz6jF1eNLZgS8ULAt4YPVWTwKHOVIylgHFBTjriDpRF7zvc8euR97a7lmlc+2nPNmwwY2FBRs7sNRn98ztUR3q10/YrvmN43i1ey/dsAhfPylpF83NDaiiWX77G0sCzW94BkgAkiaJ/d+RcFAiTXqXHTXaZyeshgD6wHVcUz2fCSBiP//9U8nsgyUF/sHzuntfBwA8ce3RKFW+9wpi+slc/Y0MP3dvS0wIjty5q6a0AK/cuAhAMmEKJ+4uNuW3w+9zyxlT8Pjnj0Y0bCMcYoIGUcrGf0UpxYqvLha7LQDwxk1LcIukNUyltaspjcIi+q3J613bRI8HvTsxcjtlPln/4ZJZmDa8EsMri4QA+O1TJ3mc6l5w09l7Qk2lEYgBrbUBgOTYItslAt6EHmXRMLbtb0NrLIF/rdoqHCEt4l9EX3f8OFDqDeml23ZPpmf3rOC8ZZMW3A8psYz5QtqyiO/dHFHPxq7iAht3XTzLfx8ktbdM4IfnHIuwBdGXjmfvTtXm80geOqF2SHkU58yqgyp/UQCXzKvH+bNH+HbKqHStX18wEzpsi7gmP5YnXJnuGhx5XA3S+OraTGVRGAVhG49//ujk/UkyxrPsrBtLUM8iU73PHa75EIebv/1Wec6PuxF3LMLsuNVQioB3nAW8u3biGAEgzQfJPu67nKRZ1s2vXtO7cYPK8BXX3yUkaWg9O5OEuE7I7O9L5o8U9ty/u+gIX1nLomH86GOHY0FDdaDDqs4XpKQghK8sm4CQbUFRLANg2U55/gWZkE3E4uWpa49xyxOsef/GKRN9se/54lhtw70JIyz3MLITkgwFi0aw40CbJ2OejOOwQPGnTR8mjP91UNdelndaXVxWQB/vWY7QwLfK+d9lAeXiWISIwcihzDucDwAETBMpa8l0Dn4yhBCh1RpcHsX9V8zDxCHlwp6Oh+WZ6aYNTXWtC1wHRP4sPGUx4F0ZyzadOpvldJND2CZobO4QWsX3djT57GXla6k2fSXREPa3dsChFKNrSsR1bYvgvCNHiPOCBsGiSMinZeHYVtJZk7+H+uqk9r3enei/c+pkzfOpqaepmJjUeVGeDHSmMOIa0meWCtt7IXkxFSSvBaUi52XmcE0gBdPalRSEUFuajOyhbiHy4xxu8xu2LVG/BO4OgvuIfAIT9oxg2833amKRznEd5VL1gM8eOwYOhc/OEgA+dfRoT1kB4HfPf+A550xXO1kWDbMdC+kZRw4oFs6kAIR9qK46dcI6P6ITih59ewded02jPvcRb9x5WdC6/oRxWNBQjZgiwOh234ojIfAEGjI8RvvHZrL3ow3RqPydygwjkWBOzSUFtk+Q5tQrpgU8Hf1p04aKNkCIf6uel43H8lVD/fEoJj88c6rvnhVFEZx3ZJ0vQxyo/pmB5DyQCuaEy8bp3154hO97NRrGL8+bjns/Nce3MFTRFYkra+TvEpRqbdzbYgmPdpoCnohDKie4scN5HGt+TTlZEk+qI0NTaIifuW6hxzSIIlkXqdJE8PTsfOdN1oIn9yr9cBMdmxDPPMQjOMltf/YoNufxXTa5PF9eOg5LJg7CzPoqXz/SaaI5vD2qSiz52nxnSr5sQcgWi5c6dw5JZTYzuLwQf33VG/GC0qQy7iJN+N3egBGWe5ggMwxKKTriDkbVlAQKAOoKOAimDdBPfN7Vuk7D7R3M5AEiHbaVTO9MqdeRxrJYym95ktI9p0MpvnsaE9bi0va4avsk83+ux7NOcwkwwUIk8+D3lh7y9nNZGLHrTxjneTc6YVn+/kgpDTUXPuuqikUKXfU3PAHDJxeMRGk05Ju0bz1jChY01DDbMAo8/Nn5gU6KQfZwAHtnctZFGTVaCbcdvO2caZhZX4WR1cViIOawtuRd5H1l2QRRNnkQD9tW4HsAvCHbblOSP6h4IgmU6zNtAV7tNaemtMAj1Fzm2tirfe/KhWPgUOrbMownvKKOrq3yyZe3Ea5Flc0wOGpouGgK21YO15zWljEBoDjiFyo+PnuE7xhnel0l/qGpG4AJSP99a7sYD7iQp3Xmcg/ZykJX/k7mnW0HhHd9Oo6fNMgXgYIv0lUc6q+vk6YORv2AIo89v7wDNrCswPfOg0wLAFezbLF6GKlsFfN2rioqygqTzo3JccbbX6Jhy3fPDxT7zYXjalFeGBYRhtQy6wT8VLaz6q6TjrhDsb81FrhlTpU554TJg8Vi8cGr5gcKjfq5x8I9l8/x9Mu2mIOIxpHym6dM8rT3dDnM5Di97F4Ed3/ySE87tSy/kiFsWz7zCo7HkZZ4+7NOM8u58b43AACvbtjrKRN/jqBw9keOZAtoOfEYv4ca8102n1M5bfowKUmY93m5qU7E9o/d8v2C4P1MnpfUxEC6+8ps37o8tFoAACAASURBVN/q2UUAWDvmC/ebTp4YXIA8YoTlHqayKIJ3tzd5ju1r6cCbWw5gaGWhJ/WzisP3uNNw6YJRGDGgGFcd2+D77n9uumSACU6HD68QWlfAq40YUBLBgOJIUougtH/VBvqGpeOEoEQINwdJfr9pT7MSuNxf9obaUiHo8e1xNboAJ2JbmDGiEjsOtLn31FfOyVOHYIkrbPPMZFoNmqJ5kJ2OxDHpsyyoyIN0XBFkuWB044lMC/bJBaNQGmXhtvZIAdgjIQtwJ0XmyBj8soMcJXghg37qOFRkrZLhoYFaOxLJbFkOs+deNmUI6gYUeQZA/p6HVhR66nLp5MH4yrLxgUVbOtmvHVLtDznydUfXFgc6XFVq0rufM6sOC6Utf14fA4q9bZY7zPrSTTe3+wb8n5873fN3UlhmfyezjLG/+QRfV1WkjfnNryHDNcb/uTqp1eQTWjhkeVIXA6zc733reM+xWdIiju+6qBx1WA2KI7Yw+Sp2hSXdRM63/mWbWP6sQZrxTBfYYdvCP1Zs9hxzKLB8Q6PnGAX1OFhxakujuOrYBk+/ldO/q0Irv35Q33r8nR0YWV0ivpdj4kZClue9cLgmLqEIOOIvyupLvee196z2/M0Fat1CgZuH1JYW4EcfY5rn06YNxbHj/KnPZdK9B24qEDTGcuFOt1AbP7gsMNRekFKnprTAJ/TPGjnAd97pM4aBEILvny6bLKVvVbJ52dwx1R7Nsi0t+HmK6msWN8ChFJsaW0TmRt1tCIgbAchdCLvH1ba1QDIF5Mk15Oc9c+ZwTK+r9CRZ4tgWwbdPneTTLBP4Q1RahOBn50wTmQqDhFN1cVDsjtvifM2zWhYRqcflZwWSSho5665uEZdKWJZ3dZPnp3ckzTdGWO5hhlcV+eym/vzyJhRGbJx1RJ1WmODINrSpWDp5MKpLChAN2/j2qZM838kaLocy4U7WZKzb1SzKd+q0Ybhi4Rhhz1mshJB65rqFnr+XTRkitnIICPa3xsSgebA9jne2N3kSdOgmWrnjcc1u0CB55KgqJBwaKERxJgwpE04+PDYs3+K7082c9uBV87Gzqc0T41QrLCsTYvIz+yPkOszI6DRF/Fo8hTOnI0FRHAkFOmZwDqT4PpUOxqEUXzxubOD3rbGEqM+Wjjh+d9EROPfIOtQPKNYOgM9ff6wvGL3s6Z4JOvt7fi0AuNt1rgzSoskOXLrIL49cc5TIHhg0sKvveffBDjS65jNckD5R2QYmhNkFq+2Yl/NR1zbz8qNHYeIQvykF4BcquC3y+MFJxzNhy0gIvni8991dMLfeU9/nzKrTpqPW0dyREIseseOg6ZPc9rlYs2NAAmaQvS160yOVsE1w6yPveY61diRw9V9XAUg64Dk0WLtYXBDChj36xeO9n5nrS+2r2zHiLJ08CKOqi0UtvPxlZtdeWRRGZXHE8144Ipum4pQlN1fd2K2aYUTDNq49bqzHfp63uS17W2FbwIs3LMJpbkKY4VVFvvTZ3nKlHguA5GJd1hTKfWTS0HJUlxRghRISjp8XJBMRQkS2z3QMqYgGhpjjIceC7vOwYirDhTw17Ti3Q09QFlLyMDfZ1Ew33n06uFlNtbt4YmZ2YV/4So8zqXtdeSE+b0w1RgwoxrUBY/CJruaeP29TWxytroOy3HzUsSyofihljv9lyhyUSlHPr7rSfefyuXyxILcXtS/d95m5uOSu5dprT6+r0DqvZmIylG+MsJwHdjW14Rv/fhvffuht/PHFDaCU4pixNdog7DKpBnnZaUJGtnNVoa4NoOqIICdAIIRgSEUhvnnKRJ/xfVA8Vc7GxhZhL90eczBteIVnO5tv73O4UMThGqCgTsSd5LLpZerEtmg8G9AnDS3HwfYEBstxnpX6PmPGMF+ED8CrddF50QdGDIA/CUIs4aCqOJLSzIL9OPUIH1QlCYemtPONhi3xXhOUikExSOOVC4JsrLmGcO6Yau33OmTNDsCiV4wdVIqwbWGRxoEJAE77xQuwCPE5RnKTIjX5CYcQ10vdt6Bi/3tNL4KkCv1hGU/MU+Uyatsqitgp36+6bctD7zmUYsLgMoyqyUzQ5gTdac2Og7g5g+3UVDbtAHPAu3LhGFQVRbSLVwCYPqJC2EirDK0o9C2QNjW2BGpSf3HeDE94SmGfTkigooJfX05xzzSRXpOuoEU/z+gZti18fPYIz7jKdzMGlzOb/mzi0K7deVAbmlEm6WuRfA/cSQtgu2ALGmoQDdu499N6kx4dlFIMrQw2nQKALxx3mLj3f65egP/7xJGB584dM0CEo5NRhWxufsCFVt53h1YWip2g1V8/ztNHvCEx9RAA+1piqC2L4mMzh4GC4oqFo3GuMr+GLCLMEviiOVV/VFFNjdbtOoi5oweIHSzehL57+mSvLXRAwROUYsSAIl/b42VT43YD3MGWoqo4govm1nvqRN01Bfy7UUHt/PunT0F1SYEIRSk72e9vjQfOA72F3l26fsp3T5uCry4bjxtPnIBn1uzGDx5d48uao0IpxasbGgMFL12sykx4ZUMjfv2MN/v45GF+LVhQ5iTAm43pjZvYVi0hzIyCa6MSjuPb6m6LeWODqkIRpSxRwUSNgxNF0mwiKPi7Dn67TAQ/VVj+wZlTPYMS/+YiSTMasoknsQEQPJA5lPq0tfev2oqZ9VVp0+7qYt/K6JrJ75//AOt3N6eccB///NFJezcpagAhJOVkpmPV1/zaKBmeFCYoNJq8CxGxLfz+olna82T4gLtu50EASe1INGzjF+fNCPydRYjwSgeYiREXNIJ2LgrDNrbvb03rBKozBUjeN/hZVHTvVP79RXPrWfSMFPuZ//viMco1k4vHYZWF2OOGRnwnQ5vjVD4UF6Zw1FE1gDIi9bD79xeWjMX8hmo4lOIzx/gFpuriAsQdmrGgH09QX7x0Gbb+9j9X0LPy+i6XBHDdAiroXRdqTBxUCElvt6vy5aXjsXh8au1u3HHwnVMne3a/uJOWyowRepMeHQcCMvDJiN0419Z7fkPwori2NOqJfpK8hvdv213UDHJ3mPh75osmR+NQONa9Lje50b4mAtz6yHtYu/Mgxg4qE3kR1LH0nFl1oo1SyvIQzBvDzExUMy4dxQW2x8Qh4TCFRUJZbBWEvIvioF03HgFJDYN36jRvunp5QV4YsTF/TA17bKnd3XnhTG2Ej1QOgTIVUjImAJ720R5LpOyTvQEjLOcJ3vB/65oBpNMYfO4jh+E7p07OaGDNhqriiPD052QbvuU1SSjiv6XUzaLnCi8xh8JWVo4Jx78VKUMp8+T/4yV+Iem2J97HwfY4Eg5Fw8DMFwrXudvYk4bqtlO9f+scimaPqhJ2o0LQcPv/mTOGoTgSCrQTVaGUpZWVw12FLILpdRVpheWfnj0NSyfrbX35joHqVHagLY5XPmhEUYodAfndq4uFVJOZjgqNLbEMD8enxrrVEbItT4i/IHhpeZSPdLsfHO97JpheVylsdeUUzTKDyqNoafcu+D577BhNFITgBVM6O8xnPaZORHPt5O9vOnmi0IDqzFGAYC05j/9698ubMO97T+LRt5OJD1LVYVDx1bS7qiD718tY7GXZtItHLAmyd6QUIlOgDBcangjYXVMJSljE+c+b29HY7HVAIgjWogfpOeSnSKVZfml9o/a45x5En6glFcdNHJS2/ccSFCdMGpS16RRH5/8AsPjGHz18aMrfEgKcP3tEl4QkQogIHQokfRB0HGyLa82DPruoAbNGVuH2c6f7BD2+m8HHwefX7k65GVRbFsWaHcwniYI5FnPBVDXj0lEQsoWZDcDbKgsdN6g8iqMaarS/C3pmSpnmPqE8V01pgTBBBIDnvpQcZ8oLw/ihaxcvLxpH1ZT4Mh0CmQUdAJgNt6edKb8zNsuGnEAIQW1Z7gN2VxSFPXZ4alzUTAgakNfsaBI2wPGEI7LxAcwJaWhlYUpzg+fW7k75fSzBstz9/VNzMtLQhW0inL4WaTQu6hxNNSYexZEQRteyiZ93bj6533rmVAyvKsJ3Tp2MvS0dwmEkaNVPwUKIjRhQJOxMn7/+WIRDVkYTY0Ot3s4vlRaruCCU8bZgqhBbueDhN9xMjikEl3SxvVVOdWON22l2alRUQcaykna8QYJEyLLQnnA8k8Xnjxvre3fMfDVIAEz9oodLiXWmDCtHWHku9fUwr//ghBpBpNqZChKwAb0G9nOLD/NplR/7XFKQHV5VKHZG+GJ6YFmBJCzza3txaGqteSYOYAB8SWl0NHd4hYJUlw4KrSe/20z9TYLQhT3LBftbY7AD0npngi7MJMAiuaTb/bIIS9yS6XsLQk56k+paU4aXizB/Kjw3AYcnt/nZOdNQUhBKhoxD6rZQUhASNvK5sMMlYHbLCYfisNoSXO46AKvEE44v4QuQ1EzrHDEpTTp2VhRFtFGM5Gcti4Y8SYU46nzC7/Tah/s8xxOOd0dHrZssN056nOAYT4ZDEjUualdIOBT73DBqA8uiIhXq7y8+Aj9/ci1CFklrQpGqA4UsC7EERcS2MG5Qeu1kwg2+nwp5cJDDgnGuPHaMEO6SqUb91/njixvF58Ol2LIysjBeEg0hZDGbRDUqR7ZcdvQo7DzQjsff2en7rl2jGQhCjvTQHbzvmkqkYpLGBCeImtICoc3Ottjyaz5jxjCs2dGE6/7BEqbowjMB3D7d8dWRqhkdP7hM6xwHpHfA4pw2fSh+9LHDfcd9qcZBUBoN4/NZ9OOBZQXabJKZoPvJ1Yv9C25ZEHlGisjD+1JLe0KUWWdHCbCt/UHleoe2bBZ1lKY/X+5/p00fipfW7Ql8VzoB3rYIHrxqgYh9ncrfJBMsS5/Mqqus+dYJgeEpu5ulkwantanOBV9cwnYTy6JhbbZBICksc03yfZ+ZB4DFdmbmguzdzXAzJ2ZiEsME664NoDyKTls8kSamPMGPz/KPD45ryqjrU/taY5gyrAJPr2FZd1d8dbHvnOl1lcJcIhq2RdIxGbUv8ap5xr0uR07I9llVKdfLtcqAEZZ7Belsu7oTVQOSa7j9m+z9y1ewIZtoU4JmyisbGjHEdXx56LPpNZA6s4pU3H7udN/gKpsWCG1DmvpLF8OUAvjuaZMxflAyM6G6bZYNtaXRQIHjknkjM74Opdk5p3SGqQELic4ga+8ynaN05x0/aZCYxEdWFwfGMrbE5OqfLG6VsvRNHV4R+JypTGLSwUKNKRpxwjzlg7bHdbxw/SJsamyBbRF8sLsZRx9Wg18pfgxBdEYWkOuLf2xqjwtzpKCF4m8vmikyKar3Xa2kegYg0kerHH2YfitbRl7E1w8oxisfNKawxfQv0AghmDCkTOwqpNNkp2N4ZaEvnm1Xxk6AOdjlS1AGgm2jc80VC/127iryLpq8W0IIwZkzh2NvMzPfOG92HdbuPJhykfsnN+5/V3cTODyjXyrBm0I/Vrd2JAIXhlv3tWLKsHIhLOt20GTTEdsiiDmOz9HSf3l97diEiHmJ6M7q5ZplY4bRC3jphkV5ue/UYRWYOKQ8ZfihzsK3nXUr+RFVRfjkglEIWxbaYqkH/FT9p6IoLLaXMl3BE0Jw+dGjMjp33pjqlJPJQ64ZQWeVwDw7VsS2PA4bPL1pV9FN7jwUUyYkArRh1QExgzvDOI3TTmeRU8mmzg3pRz2bX+fmkydmZGsoM7g8KrLnpSMTD/Cgtq2LTUoIsm47tsUScCyZOAijaopx6vTUtqby77qiLQVYvYcsgobakrR1URYNM3O00gI8es1Rnu90W8g/PXua9jp/0PhAqMiCKKUsPvv9q7dqz001RkTDNtrjDh7//NEeYVe2Kb9ioX5rXeZX58/AGMnsqqQglHbsTMeVmjj8/YF0To0c2eQwnqAskkXY8iTu4sha/Vv++y6e0OzacRqbpagoXe4fxPdJR5DJR8gmgaZLF8wZkTKvgwoX2vnYxiMPZToGhOxge/LrloxFNNK7xVGjWe4F5NppL1O+sCQ43m6u0PWj2rIojp80CI3NHZiXxmksldaWIFgLpePLS5nT0Q0nBCfNyIYL5ozAfa9t6fT2aEec2bv+8ROzUCVprLuqWQayG6T/dcU87XGHUu1A+/KX/dt1naUiIIRXZ0hIE0amjx/UfLK9jszzXzo2+x91AqpZzJRGw4HRRTLh9nOmZ7ybkAubzPLCMEYoGsZ0df7MdQszdtzsLHIROhJsl6EzXfLyo0chYls+f5OrFzXg+n+yTG+ZONep/bkgYGvdkHSaT4dscnjJ/JEYXlWEzy0+TCv8VRVHhFP4D888HLc/tTY3hQ3gTMnpnrW71GY8QbbUTgqTo3QO2Co2Idi4p0XcZ+7oajz7/u5AM4xqJdOlmixL/lWqaFu9BSMsG3LOxfPqMX5wGf65ckvKDp7KC/rLS8fhOw+/m/I+wyqLsDEgGYGOy45KrcE5fHg5hlVmvjXIBxvdJFpRFBb22kE0u9nyKiPeelBTq6Yim61YHtdUJcim2nH0A3Aunf5yuQ181szhyVB3Gf7mw8YWdr7yoLxe02mof3jmVN+xXJuuLB5fK8LsyejMii5bkNmuSRBBZf+Jxh4yFzaZo2pKcMfHZ+BT/7dCHEvXJrpbUP7BmVOxZGJSO3nYwFKcM2s4djW1p/iVnqDxZN6YagytKMSWfa2dWnBEbKtH7H0PFdSIUCqEEFQUsnF68YRaTNGEV80lt7rjSmHERtgmaI85KReRzPHXf8JnFzWk7S9yW0+FvPMJAAfb2fymLs65ydE9l89GEFRb2t5N79Z7G/okXz9posjS09lt2pPc9MupZMa7Lz0ypx7iZx1RJ8KZZQLfrrvrhQ2+7xIO9aQd1rHheydqdxUyFUCKInZKgVytmWy3Xbs7GgaQPilFNtywdDya3AE802YXlGmO26ymu87paSbZXFBRFNFGFtA5jVkW6RY7849O85tm5Momk7h2+xwulHQHJ0zSh1uUOWPGME8IxVMOH4rrlozD1YuCnSbltMyZIDvsEZIc7zKlIGxh456WrH5j6BpHjqxCbWkUBSHbE6WmO/nOqZOxZOIg7GnuSNnXvn3qJG0Izk/MH5lWWP7V+Zlp4jnJKFD8b9Vvgv09oDjYvPPvyz/E6s37Ar/vjRhh2dCtdHYu5Rq9VBrWsCa1dE+SSgPW1BbPOolHtoRtK1DYHFQWDQyTlCm6aCC5JtcORjxkWqZCOBeK1ZTkvdzXBIA+HXs2XK/EQs6W82eP6LJmGWDPsEfOfteNTe6XHw9OTJOKyuKINlkTZ8Fh1bg8zc6VTMhKRrwhhGBYmmx3KjPqqrB1f2tWvzF0jasWNYiMtKnI9c4bIQQ1pQUpd7kaBpZ2+44Lh5cjyPyQh4vV2SDzJ/jhx6Zi54Hsd2rySZdmKkLIrYSQdwkhrxNC7iOEaPdzCSEbCCFvEEJWEUL0ScMN/ZKuT3z6DnnlwjEskkQepZqgkGIA8PBnF3S7p3k4ZAXGobYt0mXth0ODEy7kgovm1qfVvmdLaTSE82dn7rjCt7LVSAOc7l4sdJWuCKufCojZminf/OikLv2ec7Atjv2tyR2S3l3jegaXF6YUplXKC8O4VjKLynYcsyygi8EwDN1EwqFdVlSoPL1mFx5/Z0f6EwNY/TV/tJjOwtcClEI7zvJMi+r8KMeaL4uG+4RCQqarU+FjACZRSqcAWAPghhTnLqSUHk4pzU7nb+izjKkt6fRkLlLeBvSoLywZi0jIwnJNbMieIpX2ckIGWelSceEcfbgymYhNUiZt6So6B7JcctPJEzG9zm+L2xVG15Tgmx+dlFGUCQAoD9ryd9tdtho/Q/aoGio+2fa1yTQbomEbZx3BwvsREpy0JojuSlJiyA2zRlbhGk288c6yq6kdr2/e3+nfl+fQkZqvZpdMHJTS1lud+ycOKcexblIwpujqW+23S8IypfRRSilP6fISgO434DP0GR773FHpTwpACMtpzgsKMt8TZKI57qz27uZT0mvtakuj3RqjOyh0XH/imLH6mLtceOlO28R+XrUZo8p8PJPfbW4GzP7MpKFlbFs7S7nB7qYkJYbccc3i3CX4Gj+4DD8/Tx8KsSeZPaoKha65x4wRlZ7sv+mYNLQcJ0xmYTj74tiXy03WSwD8J+A7CuBRQsgKQshlObynoRfTlS3ibMPa5IOwbYnsUEF01S40FQvH1eLGEyd02/UdRx86rj/x8dkjMLK62Cer9IQcokukcShyKAt9D161AMOrCrNelCUcip2diM5h6JtQSlEYzn/wsr9eNsfj/BrELafr06DL9LVun7b2CSGPA9C5EN9IKb3fPedGAHEAfw64zDxK6VZCSC2Axwgh71JKnwm432UALgOAurrMs1AZ+hclBSGcfcTwbo/G0FX681aoQwFyCLgA61pYT7zVfO6KpOPZ6xb22L1m5NgUp6+xbEp2kTAAoDggK6ihf0K72X8k13AToyBGDCjCN06Z2EOlyQ1pexylNKVRKCHkQgDLACyiAUYolNKt7v87CSH3AZgFQCssU0p/DeDXADBz5sz+K4kY0nLTyRNTOtH1Bvq3sNz/NcsAi3TQlUQe/ZGeCo0FdH9K9f5ITm1QDb2e/mYSVxQJ4chRA/JdjKzoajSM4wF8CcDJlFJt0EdCSDEhpJR/BnAcgDe7cl/DoUE0bPf6ibQ/byFXFUd6vWY/F9xz+RyMGOCNY9yPX6vBYOhG6gfkfqHJYqrn/LKGLOiq2u52AKVgphWrCCF3AAAhZAgh5GH3nIEAniOErAbwCoCHKKX/7eJ9DYZeQX9OOfvktcf0WOzOfKJbEFBQnDNreB5Kkzncs9xwaMLj2Rp6F0994ZicX5PALODzTZcMnyilYwKObwWw1P28HoA/J6zB0A+Q48P2N7o7TnQu6Z6oIL1blfO7i47IdxEMeWRVDmPnGnJHLhL19MQ1DdnRd2ZDg6EX0hnnHEPuef76Y3N6PaPFMfR2DoVdH0MSMyTlFyMsGwxdYH5Ddb6LYACLnpJLKPpmLFCDwdD/MENR/jHCssFgMKhQaiaoHuTBq+bnuwgGQ6/G7HblFyMsGwwGg4LRLPcsk4aW57sIBkOvpTMp0Q25xQjLBoPBoIEY3bLBYOgFlBeGTSz4PGPSABkMBoOC2fLMP9csbsh3EQyGXsFfLp2NUC9P0NXfMbVvMBgMCpRSY4aRR44dV4uJQ4xphsEAwAjKvQDzBgwGg0GDkZXzi6l/g8HQWzDCssFgMCgYK4z8YzT7BoOht2CEZYPBYFCg1GTNyifHjqvFsMqifBfDYDAYABgHP4PBYPBx5sxhiCWMfjlffHz2iHwXwWAwGARGWDYYDAaF0mg430UwGAwGQy/BmGEYDAaDwWAwGAwBGGHZYDAYDAaDwWAIwAjLBkMXefCq+fkugsFgMBgMhm7CCMsGQxeZNNQkTzAYDAaDob9ihGWDwWAwGAwGgyEAIywbDAaDwWAwGAwBEEp7byxRQsguABvzcOtqALvzcN++iqmv7DD1lR2mvrLD1Fd2mPrKHlNn2WHqKzvyVV8jKKU1ui96tbCcLwghyymlM/Ndjr6Cqa/sMPWVHaa+ssPUV3aY+soeU2fZYeorO3pjfRkzDIPBYDAYDAaDIQAjLBsMBoPBYDAYDAEYYVnPr/NdgD6Gqa/sMPWVHaa+ssPUV3aY+soeU2fZYeorO3pdfRmbZYPBYDAYDAaDIQCjWTYYDAaDwWAwGAIwwrICIeR4Qsh7hJC1hJDr812efEAIGU4IeYoQ8g4h5C1CyNXu8ZsIIVsIIavcf0ul39zg1tl7hJAl0vFDoj4JIRsIIW+49bLcPVZFCHmMEPK++3+le5wQQm5z6+R1Qsh06ToXuue/Twi5MF/P050QQsZKbWgVIeQAIeQa0768EEJ+RwjZSQh5UzqWszZFCJnhttm17m9Jzz5hbgmor1sJIe+6dXIfIaTCPV5PCGmV2tod0m+09RJU932VgPrKWR8khIwkhLzs1tffCCGRnnu63BNQX3+T6moDIWSVe9y0r2A5om+OYZRS88/9B8AGsA7AKAARAKsBTMh3ufJQD4MBTHc/lwJYA2ACgJsAfEFz/gS3rgoAjHTr0D6U6hPABgDVyrHvA7je/Xw9gFvcz0sB/AcAATAbwMvu8SoA693/K93Plfl+tm6uNxvAdgAjTPvyPfdRAKYDeLM72hSAVwDMcX/zHwAn5PuZu6G+jgMQcj/fItVXvXyech1tvQTVfV/9F1BfOeuDAO4BcLb7+Q4An873M+e6vpTvfwjga6Z9iecMkiP65BhmNMteZgFYSyldTyntAPBXAKfkuUw9DqV0G6V0pfu5CcA7AIam+MkpAP5KKW2nlH4AYC1YXR7q9XkKgD+4n/8A4KPS8T9SxksAKgghgwEsAfAYpbSRUroXwGMAju/pQvcwiwCso5SmSj50SLYvSukzABqVwzlpU+53ZZTSFymbdf4oXatPoqsvSumjlNK4++dLAIalukaaegmq+z5JQPsKIqs+6Gr4jgXwD/f3/bq+3Of9GIC/pLrGIda+guSIPjmGGWHZy1AAH0p/b0ZqIbHfQwipBzANwMvuoSvdLZLfSdtEQfV2KNUnBfAoIWQFIeQy99hASuk2gA0cAGrd46a+kpwN7wRj2ldqctWmhrqf1eP9mUvAtE+ckYSQ1wghTxNCFrjHUtVLUN33N3LRBwcA2CctVPp7+1oAYAel9H3pmGlfLooc0SfHMCMse9HZuxyy4UIIISUA7gVwDaX0AIBfAhgN4HAA28C2nYDgejuU6nMepXQ6gBMAXEEIOSrFuaa+ALg2jCcD+Lt7yLSvzpNtHR1SdUcIuRFAHMCf3UPbANRRSqcB+DyAuwkhZTjE6kVDrvrgoVaP58C76Dfty0UjRwSeqjnWa9qYEZa9bAYwXPp7GICteSpLXiGEhMEa+J8ppf8EAErpDkppglLqAPgN2BYcEFxvh0x9Ukq312UsZgAAIABJREFUuv/vBHAfWN3scLeK+PbbTvf0Q76+XE4AsJJSugMw7StDctWmNsNrktBv6851CFoG4Dx3uxauOcEe9/MKMLvbw5C6XoLqvt+Qwz64G2wbPaQc73e4z3gagL/xY6Z9MXRyBProGGaEZS+vAmhwvXgjYFvED+S5TD2Oa391J4B3KKU/ko4Plk47FQD3Cn4AwNmEkAJCyEgADWCG94dEfRJCigkhpfwzmFPRm2DPyj13LwRwv/v5AQAXuN6/swHsd7ejHgFwHCGk0t3+PM491l/xaGNM+8qInLQp97smQshst79fIF2r30AIOR7AlwCcTCltkY7XEEJs9/MosDa1Pk29BNV9vyFXfdBdlDwF4Az39/2yvlwWA3iXUipMAkz7CpYj0FfHsK56CPa3f2AemWvAVoI35rs8eaqD+WDbGa8DWOX+WwrgTwDecI8/AGCw9Jsb3Tp7D5JH6qFQn2Ce4Kvdf2/x5wSz23sCwPvu/1XucQLg526dvAFgpnStS8CcZ9YCuDjfz9aNdVYEYA+AcumYaV/eOvoL2HZuDEyL8olctikAM8GEoXUAboebpKqv/guor7Vg9o58HLvDPfd0t6+uBrASwEnp6iWo7vvqv4D6ylkfdMfFV9x38HcABfl+5lzXl3v8LgCfUs417StYjuiTY5jJ4GcwGAwGg8FgMARgzDAMBoPBYDAYDIYAjLBsMBgMBoPBYDAEYIRlg8FgMBgMBoMhgFD6U/JHdXU1ra+vz3cxDAaDwWAwGAz9mBUrVuymlNbovuvVwnJ9fT2WL1+e72IYDAaDwWAwGPoxhJCNQd8ZMwyDwWAwGAwGgyEAIywbDAaDwWAwGAwBGGHZYDAYDJ3iirtXwnFMrH6DwdC/McKywWAwGDrFI29uR8IktjIYDP0cIywbDAaDoVPYFkHCaJYNBkM/xwjLBoPBYOgUIYsgboRlQw/w1tb9ePSt7fkuhuEQxQjLBoMB1GylGzqBbRHEE06+i2E4BHhr6wE88taOfBfDcIhihGWDwYBFP3w630Uw9EEKwjY64kZYNnQ/JN8FMBzSGGHZYDBg/e7mfBfB0AXyFZEiYltoN8KyoYegMDtghvxghGWDwQDAmGL0Zebf8mRe7lsQNsKyoWcghMDIyoZ8YYRlg8EAADCyct9l6/62vNw3GrKxv7UjL/c2HFoQGFm5N3KoKFmMsGwwGAAAziEy6PVGHIdib3PfEzrLC8NojxnNsqH7IeTQEcz6Ehff9So27WnJdzG6HSMsGwwGhG1ikkt0glxN3ut2HcSFv38lJ9fqSUL2oRs6rj2ewI4D+dHoH4oQArSZhVla1u862KOxz7fsbcUrGxp77H75wgjLBgAwiQUOcSxC+oQZxk8eX5PvIniY+73c2ApTAC0diZxcqyc5lJOSrNi4F8f/5Jl8F+OQ4r9vbUdjH9yB6UnO+c1LPbqIswjBF/6+usfuly+MsGwAAMz57hPa483tcVxx98oeLg3ja/e/mZf7HopYhKQ0w+gt258/efz9fBfBwzbXVvjeFZu7dB1K+2ZoLALgi/94Pd/FyA8U2NsSy3cpDhmI20P6Yj/pSUKW1SWTuv+8sS2r88kh8kKMsHyI8vvnP/DYGe1satee1xpL4KV1e3qqWB7++OLGLglpbbHOa+re3LL/kNKYWST17sIZd7zYg6XpnWzc0xxom3dtFzUrFLTPTjq7D+rHDoPB0PMw2+7g79PNqZ/+c3bKsa7uiL22aS/e2XagS9foCYywfAhCKcW/V2/F1v2tac+NJRyE7fw1k64oNLsSTmvZz57D1n3p66e/YFkEqdYGKzbu7bnC9FL+vnwz/rVqi+94LrTuTLPcOWn5w8b8Odc89d6uvN073/S1pfSbW/bnuwg5gWtNr8zTjmdvJ90u4YLvP5XT+8UCMngeaMts1+XhN7bh2fd7/zhihOVDkBfX78HKTfsyOjcWpwiH8qPyUsNqZqsp3n2w/9i2tcUS3Zp4gtks97Xpv2ehoB5x9rVNe0HSaOQzvjbt/HZmX7R17g/0te6y7GfP5bsIOYF3twdfz85cIBMOtMXw2qa9WLGx7zqsWQQpFR+b9+ZWCRQ0/k29+dGMft9X+lGPCcuEkOGEkKcIIe8QQt4ihFzdU/c2eIknMm+dHXnULKsC3Liv/hfN7fEeu39v6sRX/HklXlzffeYw6QbY3sSuAJOhnkAWaM/69UugFDmJBuFQypIu9DEWjq3JdxE6zX6NvXE2Cx8TajFzWjriaOnIzdjdnVn8Pnr78/jTixvx1Lvdp+nsbqVEOs1yrgnqM50tQm/dxexJKSgO4FpK6XgAswFcQQiZ0IP3N7hkMye3diSwfld+UiET+AW4bAeBrgxMvSmUWsyhgdtduWBvS0w76O1vieUtlbKOSUPLsC0D86HugGl/pc5D+SIjN8JyezyRV5OKQ42p3/Brvk687dmM4133nl7R+/ndcx/gzmc/yMm1unVYzlF/DuLNLftx7T3dGzmiM/GodQJqPMV8I/sp5HqePP2XL+T0ermix4RlSuk2SulK93MTgHcADO2p+xuSyLaRX77vjZTntsXzt8XLzDC8HTGbbpnO0SEduXLwe/D1rWjvYj3mSihLxRbFRvvJd3dg6jcexZqdTd1632zIZ4g7tTkQwsrDNctdCcyfcCjW72rGzf9+W/t9KhOksJ1+9dtd4bYGlReiuqSgW66dD7ZlkQmxL5kt5XJH7oHVW7F+18GsftMed/Ds2t2dvufTa3bh1kfeA8DGwe5yvmZaWf13B9piuOGfXYv8YlsEb23tXme25vYEVm7MzMwSAFZsbNQKqLO+o4+QBQAzv/W4+JzIYqc6iM76a/QkedlfJ4TUA5gG4OV83P9QhyvHKAXufnlTynO7U5uZDgK/YBQ0P/3ppY2ev9fsaAKlXRMwczUZfuVfb6KlPXthWX43NiHo7lehToDvbGNCcizetXrYuq8Vj729I+PzO+LBD8p2GzpXnq7GHq0uiWDtzmQdcWGZa95f/qDzZjJ88g9Z+knjuB8/E2h+Eg3bGFQWTXn9Bbc82S07BLWlBf0qGkbIyjzJStDY+dtn1+eySDkhU/vRTPjXa1uy3m3c1xLDKx+ktwP++/IP8e/VWzW/7xCL+ZfXN+Kzf30tq/tnSqrxpbUjgcff2dm16/eATLj9QBu++aB+0a3jhbX6cSvTBfbcMQMyvpeOvrLk7HFhmRBSAuBeANdQSn1LLELIZYSQ5YSQ5bt29X4Pyb5ISUEIANJGe/j5U2vT2jc7Tvet8rWLzYBbffVf3pjM1/x1VarTMyJX20sJh8IKEIJSIWv9rR5I/qAKCfzdd3RRSn93+wH8+eWN6U90mfGtxwK/I4R0+p12JTrKL/+3DuWFYc9kR6k3g11XFmZc4LQDtMRtsUSwbSDST8LtcSfvZkUtHXH867Ut2qgM3TqOaLhCCo/13zeTjmIhm2QcNWLemGrt8W899E7XCtcN5DrLYrZKlLiT2fkb9jTjqr+kFoSbO+J4p5u0swfamFCv608OpbC7KO06Ts8IzEFj0c3/fst3rKvlmTy0HKdN7/9GAj0qLBNCwmCC8p8ppf/UnUMp/TWldCaldGZNTd91HunN2K7gxmPDvrZJb1B/6yPvpR3k/vDiBvziqbU5LR+HwK9JztS5Y8MepvnoigAT9OiqYJ4O6tq2dgU7B9Eq0kUTURdG/N3rNL0Jh2asQdPtEKSiqS14y7gz9nicWBe2C2/577uwlPAsFKwvcY1ttmuK1zfvw4bdrJ1yYSYS4EybymmHUpp2E7OnMu19KUWCkqk3P4qn1+zCmh1+s567XtiAn3fTOKLjISnxwqf+b6VIvBSyLFx816sZXSMbIeOpd7umkexNUEqzjsWbad+zAipV9hVoizlYv7t7/Gh2HGgPzDng5GAcdygV828u+dGj73n+Dqrt3z+/IaPrZTPGOhQYVlHo27mKhKwu5TrobfRkNAwC4E4A71BKf9RT9zX4UUNNbdjTjI8ePkR77r0r/HFlZX73/Ado6YYOsedgO9rjjt9mOcM+zJ9Rd/4jb23H2xloJoKEE9XkIx2dEe6uVrYZLSt7TXdTWwxrdx4U9tLp4muq1+cTnG6BsudgO36aaTa9HM4Na3cczJvNMlHtxikTbvnkmu3C7O/LN+MZN74oN7/guz4qFgmeAH2Ohxpk84KNe5rxwrrO248G8cbm/fjb8g8Dv48lqFuH/u9aYwm05nFifcgNQ5aNIJPqdb+6wWtykKkAnmve39GEfS3dY6/+7vbMtbuThpRh5ojKtOdlEhGmO8yJfvE/70JNVwrHYRFrzrzjBdzy33ezGte5RjfudI+wfNuT3vJnMxT94NE1vmPZhLKkFLAtyzd/hLMwaeoLgYB6UrM8D8D5AI4lhKxy/y3twfsbXFQbw464g0hI3xQeSpP68sPG7olMsNcd4Hn/4wNTtsOkbtB49K0deHNr+q3WXGniOhMS7P5VXrs9i2SvGZx686O48u6Vws42bcg1qi5MeOX7T23uSKCqJJJxWbpak9xBqak9njcbN/U1UlCMqS3BHte2L+tILdKTpHu1RLKNBti7vPG+N9CqibGs2yK3LSK821du2ou/vRos1HaW5zJw4CJIasj3Nnd4BK7eMF8G2YzrSPW+H+qGGMCd4YePrsGL/8/ed4fbUZXrv2tmt9P7ST3pPSE9obdQBOlIv+pFbCgK6lWxoV4riqJgBwuCCkgRUHongQRSII000ns9ve29Z9bvjzXfmrXWrNnnnFCM95fvefLk7NmzZ9asWeUr7/d+71IF1tU7e5/4m0q4GN2vtMfz4npfPd7+DlHQAcBPnlyDtu48fvyE7pm1rdmcC6fFwk2N+P3cDX2Cp5FH1/O5SAp+lxNQDiaiqq4vPue9Vrg5OBJudH/qLYXdf0qe7HvJhjGPc84455M551ODf4+9V/c/LKG0BYrHyLoSACKs9e+s0meTopTwsNE88g2lubdiTlaCFPRmS3wn2CfmrRMKxNu9kuv0nQXC50DSdZD3eK/6Le4MmxLUF/yegNO8vR5Q8cbvlGfJ9N73RtQ7C48KgxfAVd7OI/Y01hxHv35bdx5/fXULvnjf0ogX6KSbXoj8Puk60svjOs47jmEV94gfDwT1chhkJ766cT9uDrxa7yazBOccec/vVUi4L+tgoS58p5hrlm5tKpjw2pO8m5zEPePkw/72fD8WYqFKPAwj/LsvdQJs4vlcGjP3LNyKZduizBFxmGVqHzsIVp7Fmxvhc46mjixGff3xPre7L2JrW0/j/+6FW/DDx1eJQksFHs6cq7QORsY8A/i/jx/gHZdDS0M6LO+JfDnAFaYTLgCx8aYSziFFhURtoQnIOUddWbrvnmXj82W3zcea3YXDh3Tvt4NxJfngH161Yq97I4Mqi+TfB+NZBigBzZch8EIS10YzpCzOLXw9tQABY0x6QH9qYOt6K40dOQz7yqPi3gd1BSHqGH/4jR19htSokvc5ilOu3Lzfzvzp6dWaXhqm/MHBNSXDpAAEdMxywmERg6Mjm9ey3986CLrAQl5ZupsKZVGV/EJh3zaF9uycmCp0H/nTa7H3/uurW3DCj5/HjY+vBqBT/P33H/XfTWmoiL2OKYUYB/oyVxdvPhCbbP2pvyzGntbesbhsisHxdluU7fsXb3vb5a97Un6PvTGEfbV253tliMQNIZVaLNfLZME46cp5+NL9Il+ntSuHK25/FbOG6RCRF9bsxfz1+7U5rSnL6PuafvFvX0He49hUgGLyYOYdiWqc2Iy1qd95yjrHLp3ZAAD4+j9WYFdzV497DVHfqWtIwmHoyunvxWFMM9bunL+pN49xyMphZfn/Y6GJs35vG8rSiYP2jL0bOnYIvwj+R4DbtOEdC5T7NReN7ryPbN6PhUa8sn4fvvrg8uDct4ehlMqH4k3ri6ST4fR0GIu19guVZk06DrpyPpZvb8KAAvRiHz56qNwY3n/LXO277ZbyqCLZJX6znPDNJ7XPiwLS+1889xYWb258W8U3DtZr5xilqUtSbp+SNbnlmZOuI69JV35k6Y4+b3o9Kdrm+6dm7GjqhB8omku2NKKlK1qVDtAxy67D8PiKXdr3/1y6Az98LGRxOPXml/rUfgBIBlAuGwzkk3ctls+h7sOqEkR/t3blcO/CkJZt8rfDsbQ8RsF7fo3OnKS+5/KiJHYo/MlqH724Vv/dgIoivBPSlzH6h3kbsSQmyZqj9zCuk37ygvX43a9FKe6+eN9Suc4drGze34675m+KZcZQ4X6dWQ8VRcker1lXZufs/ttroVFbntGv05XzrDzSceu3ihumcWLO6ze2NuHJlbtwprIW0jyj8/vitR9RVwKfA2sK4Lw55zjtZ72fd/co77W2NK1VpLS1rCvnxxBM6WcnHFYwyZqG9vtvDfsmnXAsMAzdCfDNh6NMHP9JclhZPiw4ZVw/JFwnduq/b2K/gr+nBWTD3jYseKdLMitKs22B2tPahQt+/XL8z4113A2UhrjtJ5v3pWfuYI0AwmG+XcxzmZLslUk6sbyXH/jN/NhrVJUk0dSRwwd+Mx/lBTYrVZ9/c6e+oLdaNiLVy9KTmGf99dXNeHT5zoPH7R1kt7qOrnD2lGhjKrDqZkniOCp1nDh27d2vY+3u3hdtWLG9uWcYRgwLSEVRUrJh/ODRVVizy66ku26Ik7TBZxgOnpLvqmOHAwAyQaTKFu4lvLyjhGs1SIvyd1NHDrc+GyYsHcw0OjIoqNDWnceNj+lUbnFd/fLbKJphSrYP3OSF2GI4j86fJ1bsekdKAscZHr2Vnzy1Ft9/bBWWbm3qkUqut2tFHMXmywoXcE2Jnitx78Kt+IWR4Ob7HDO++wxs4vk8EgWhteDaOaPkse68h5ZORQFV1jxTEexJKEpYKFpJ13tutc5Jv3DTATz0ejTR/iuKsWMQ9UTWCorO2Nqs5SxzYVx25wpw3Qddt1pdayx7c2NHrneYZeV37zaW++3IYWX5/1P5xAkj5KBnTCzIcQveuP7lkWO2jXvBBvuk/sFjfecdDT3KIRzDpO7qzHrw/TAZ0HodYwIT1sy2dq/Y3oy/L9qKuQHO+GA9mGf8XFjcpCwzSzt6I3VloSe4uiSlYRff3NGCW5+1s1GoYeaKomSst1EVGwaPPqpKf1t3Hq1duT7xhYah9jD8fuPjq/FWH6uAkbRnPXzjob57xUQBkfBzT2Hhrz64HEcoXs3n1+yJbPquAo9Q50RPXbN5fxgyP/sX82S74rDEcR7Z8QPKpfdRLZBiSnfOlwq8lcv5bWTXlWWEUecZRoNNVPw6JUz96AkBj6CutSULAfY1J07Iq9nRnde8ykA8HvNPL2/s1bXf2BpiXCcNiq6NgJjvr208oJXO5pzbS7UXYjpBFO407629+MuCzXjlHVTu+yJqlUPOgSv/tDCyBtvGofocr27YjzW7WjF3ne7Z7ykP4to5oyLvz+dcM9C2N3Xi4t/N1+A7quR9H67jaG0iZblEcVC0dOY1r75KHcdikteaO/W1dkdTJ/a1dcvrF9pTrn9gGRIOw1V3LNKOf/SOhT0aR2avmd3fnfNiYVJqf/qcY29rNxZuOiCf1UwMp/keub9yzz+/sglA7x1OjDEs2dKIGx5egUzy0FRLD81WHZZeixqK2dPS1Ws6L3VNYsHn4370vHbOdaeMjv39mUaoHhBFNGyegdte6ntFK59zTBlcoU02xwnn44NLtmHK/z4V6+GsDZgazEXDDMWrsuVAB97c0YKq4iROGlvXd3YD43xahPqaDEIbzcSB4UZsLs6dOQ/PxXC3nnDT87ItqYQjrfVCzxOhRVOEMLl5z8cdL2/EH+dt6pNnmYS6ne7TW+hhh5H93tqVw18WbMGPAyWrtyKgCOFNTc/yEyt2aVy/O5q7tHDko8t2RjCVjR1Za8JRT11z4k0v4C8LwrlLV/B9kYxphpAjmGUFhkHeRzUxx0wK29PajW8+vMLqVQN6j8EsxKwgN90C10m6DtYH1d84OJo7c/jNC+u1c1xmp5yKgxkUMgZVjyPNidjiLsbh9XvbpEKqUu2d/6swkjVjSIh1vew2PcJzye/mS/5mQITBbcmXTgEOdRv0x2EMc9ftxSvvEstFT6J6FEXicZQ5YdP+KH5aPefhpTuwcNMBfOgPOmY80GFjjb7asrSVdUE9lsv7BZl/bHOA+pjWhIEVGexq6dLmsZ7gZ58vZuGj38/diIde3y6NANtT0di6f/E265ra0pUvWEBs1c4WcBSev8TCYZMHl4QOLrrE4i2NKEq68H2OWd/XPfQOYxhcJTzlvs+R9/2IZ/tbjwjIRV8M3NauPFbuaJG5VIeaHFaW32WZu24vfvrUGvzkyTVYZEmUejvS0pXDVx5cLjkcGzty+NeyaKlQmziMobFdbDKMCWUsrmytrRDEaiPcS4tbXFGFvgp5y+iuEoYRHPjC35ci6/nwuR1SQTzLpGDcNX8T7lu0NSgbbU9O87nw/w6oKBIewz5EhHa3dOGy2xZohTqoTzjvm19ZTci55LdiA1aVme8/+ia2HGgvSJBP5yYcR16vsMdPXGxPi+o1CpRkYnuAKLyR931wLuAaFI0wPSq2a0sl2VCae5JVBiyE+Hif6kMJbSCAACjvlDZMes7dLV3YpXjNbN3LjKNz1+1DU/Ds+vP01ZDgcBhw76KteGNrE678o87Lywwjj27V2pUX7WcIKKnEF7bS257PccPDK7BoU9RLxXqJwbz89gWRYjScc7R15+UTF3qvI+tKNIWbjJFo10WvsdlIjKL5Ne078RUfbQmccRu4SQW2eFMjHgwiZVfc/qr1N4wxvLWnDY3tWSzYEF3fGYNMLMx6dorOQoaKb4FhOIyhu5cMRoWm2IljRNGvuDLU5ryzSVHSRXvWi9znl4HRGefdVSENqtCxQhGWiLJswKu68vFeVEAY/6ahTEq//OwS3EL1LHPpaVYjJKrQeKYIK80pJ8az7PtcG1u2ZEwAeLZAUZvzf/WyZhyo3UpzNW8bSBZRoz7JRJQ7mZ6B7vHc6j341fPrY6FEfYGqMAgIVxyN7b9bDs1W/R+SFdtbcM3Jo/C5U0fjn0t34P7F296xa3d0C6WBOBxNTGYhKUm5uGjGYPm5kCfMDAHb5IEl4rlmGlnFfaX5osn6wd+/iq6cp/Arc4lZVnHRAlIRbXxH1sPo+lI5gbcc6MCTK3cV5H7MeX5Qmjo+zBZnUGTzPrY1dspStwfas3JRt2F+TWnuzMkFlryCzZ05vGYxsBZsOIAD7TntuU2slzA2RJIgKQFv7WlDzvO1RX5DAIUgT8kWS+KdqtzSLalvqK1T/vcpef56A15h/obGRG+V5Yde1w3Av70qPLK9VUfJExjBLLukLIfts+2xPXlHyAusRUF62TjpjOVcKj95n0cgUZmkqxkk1HfPrd6Dnc1dYADmb9gvlX2b93RPaze2N3Zak6FEI4JrK0aeTcxyzgs2HsAn7gxDx+Z7VYtieD7Hmt2t0hupwq2IJeCBxdt7ldR2zI3PWe/Xk8R5lgl+RRLnPQTCPmIMOPXmF60MJABwzuSB+O2LwnPenfesXjNRcMZ+o6TLIoaowxi6834k4tJXIe/gJb+z5zzYooemFKfF85jvoKGqGIDw8qp9RZLNc3ztH1EoVags25XGhKUSpWvAj1q78qgqieeAb+rIIeGS0htikN/38zC5zgmiG2qbhcMm+L4HKs/P3fuG9pmSEs3fPLBkm1aIaNqQyviLWqS9Ow/GgPJMIlTMlUbTXPV8nepzV7OdYUV3JkST9ugZqN9ob4kbw3Rs6dYoPZ8pjInoy/+cNqbHc/8dclhZfgfk1mfX4U8vb7RWhaPQTcJ1cMPZE7Bie7OViutgJKcMVCCkh9psCYGZMmFgOT4jkxlYwZC6UyBET0LJZ+ok7Mp5fSJuB8IFentTJ7Ker/EsExvGZbctkOcT7tEmxemE9Gy0defxzKo92N3aJTzLFlXru/9ahW2NnVixvQXPrNqtGQjNHTn8fu4GfOMfgj3h0WU7C5awff8tc+FxjobqIlQGSViFZGdzpwxHd3tC+bojwH2ZQp5+eoKEwyKhMlJG0glXJmsMqynG//5zpYRv3Pj4asz56YvimiB8ZPw4+EGw8HIehtxt4+Ljdy6KHKPfAaGy0tsESDMEubIX1RdVIYxpwmE45xfz5LtIBAOHWiEMpfD5iSnBhAKZQkbrvQu3SsXZ7MdCmfkAsGFvO4pTgeJhCZmWphP49iMrJeRA7XdP8XipGH9A4Kw/edciXDh9UNAuO2ZXvH/R1zODsdTT+7lv0VYs3dYU5V01zlMrRx4IMvY9X7TUkd59wcgBCAxzT6ryjqZO7AqiIH3UleFxjr9+7MgezytkWJMCy8CkUj2kurjg9bpzPtI2z3KBKNb+tqzmbVy7uxWuI5SU3x0EvE2VvsKoSCqLw0ThEbWi0EgshMvnGPE1UU5BPSVuPpDHNy4RLuk66Mx62vtzHf3+BEuKk3N+OQ/1BuuGwxjWKtVBD7RnI95vkzquL0YaGSbqXFmxvRlfun+ZhtEd2sMYMqUr5wVOpOj6qsor6/dpFTJfWGPfu4iBqTyTQNJlVufJ9Q8sC7HbwTEB44terzdRRM6BA+3d2N+WxZYDHagttTOi/LvlsLL8Dsimfe14/xED8NdXoyE/gRUSfydcB98+dyLe2NKEnzy5BlO/81Tk/L5I1tPD6wQxIAWoJ0m6Dk4YUycT/OKEWUJfptAmri4ux/3oeeQ8P0JrU0hUeIfqeTrQlkXej9quPueRKoJEwzRxYLn0nNKGM3FgeezEPXvyAIl1pmuTHOjI4q4Fm/HESkG5tWx7E1YVoAEibHQ64Wpwkjte3mjFWHZmPWQCZalQJjKJ8KgH93IYGo3+5RDKISlB4nlEsgYZWeTxAkIvWtz+efbkAfjz/M1hqL1AMpcJxTHodTXJAAAgAElEQVQ9y6Gi3eNjintPGWA9boXS+PEFWBzGsL2pU47lk8fWa+363qOrrOFbdRyYSs3ZkwfIktUb9rXLMKx5lbjMfIoIZJIuzpjUP7hf9NkYA9bubsPiTY342dNrtb7zPGE4nTtlILIeGSLiu/V72vDkyt2a9you+Ypzjk/etVgavhtjeHtJ5r21Dxv3tUsaQfM9k6i4b0pKpSaQx6mv3mGCCJiQkEJCd+C8d5X6rIUWAqExTAb8Ob+cJ72VqmjvyefWcwjv+cbWJg2/3ZXzMG5AmQZJOP1nL/VZyVWf4PZAwb7ulNGoLkn1msNZlWkNofdzYKVIQo57fTTXqIooiZqwpkbF6LV83eJ1BgS1XM4LC5y8vqUxiAKG5/TEAX/dKaNxzMha7ZgJy2jtymNYTYk2j9UEP0dZ0zfua5dFd0wxnTJqP/01iJAlFAs80UcYI/VDd96X8+yUcWJde1WJwJq0bV05D77PI3tRccrFWZMHYHS/MiRdB8u3RRlTXt14IISjSKXZjruntaaiKImSVDwW+VfPr5eQqfryw8ry/0nhnKOhuhj9yjOosVhEPo/ioz5+wgh8/rQxaOqlAhknZmLR/Uu2wfe5XKC2NfbMZZtymUwOIvnkXYvw6xfCxJjeVI/rzEaTyPa1dSOb91GeSWjH48jzTelWYBhfvH8pNu/vsNB5RRt24a9fAQBUFiXlRkOeipJUAi1dInRlhqIaqorRoFj21iIQ8nPhDWtHcxeyeR8uYxKTeNOTq3HbSxushkNbdx5FSQdt3XnNAwCEpZ7/8fp2EXbroW3UdjsuWxg/6rO3deelQq/+xHGY5EZVxzAHx0UBlpqolVRMLXnP/vrqZty3aKuCWQ7aYMAw8p5fkNM6Y4SuTxhTh9nDq63v4LP3vI4HlmzHw29EWVlIQaIxQVnduuc4ek3Vu6L2+2WzGvCTi6dIpRuAzEswPdBx2E06zgH88MLJ8h42TCUgmAhueXYdvvD3MMy7v70bDmNwHYbmAPLgGeNe9YiRoqdiUklhU+nETDiNKdJwCsYadc3Opq5Y45iShkU53bAvzYREam9cv5Hxa/JFFxLV81aINpDaSAaKTWgcJBVPcVJ56cRNro6X7z+2CjubosoplQA//1cv46Ynw6TV6d99Ohaj2xdR+5mMfZLZ33+2T9cCdIeIjGjE7A9kmC/f3hxEu8SJKqPGp/8aJkHStZfEsD8Up1zkFHjEVx9cHlkPyXjr0zNZ+vSRpTs0LLCGWVaiDq+s34e/LwrLx3/1QVH0a3dL9F2rr5IiXqPqwzLgqlEL6GXTbXkhD72xHZyL+fP9gHVqeK2ozLtWMVBMLDRjDMu2N+Mape+BMPnRDwy7uG2fupfw+CgAWQKAbY2dqLVwaHdmw+gzrc+JuFDxv1kOzVb9B0lLZ74gf20cprYnjtee5OE3tkeswlufXYf9ClWRyW6hSk1JWmufurjMW7cPP35Cr7TWU9WkrOdZk/uyno900tUWs1Nu1j3fec+3elv3t2flZN0dE3ItpMOnE67caKqC0GHCZcjmfbR3ezjqh/pG8YPHV2mLmemAo3uP7VcWuZdtbW7vzsNxQo8bsYLYkld8Loyfo37wrLzPlMCDQ2HsLQc6BEVT4ElZGCRq0fmqlyjvhVATahoHl/CN7yt0fnnPl8qOOg5KUwn88cpZAEJKJxOrd3vg2Us6jtwYabxvb+zE7pYu1ATe+jBhUPxPxuLDb+zADx8Ti65ZEAUA9hn80qJ0rr3PWzpz+OJ9S7UMbxJHhnhNfHf4QDbqqj8qlGKlCm1SfXkGmaSr4Suv/ovYfAoZU6pnV3Jnq7AKizJH74UU3WWKx+f6BwQLDYNSXYuTsuxr9xFRIvG7bwXepje2NuEHj0WZRahJtqqLz6/Zg4fe2CEgOT7X8L4/fXotnlxZWIml9lUWJ3H86NoIpICePw5f3Zey9STUFx4XcJt+5WlMH1KJL55ux0g6jMUmudErV1+T6zC5jq3ZLSJkqqL6/Oo9VliayqGtvveOrGf1bu9TFLhC8K6tBzrAoa9jhbadzqxn5ek254u6ftH1qI3deU8zfOjchMNQXpSMRMAAPfJA4zwXE3ZyHcEXLtc0Hl234jz4gMX4Ck4zDXJ5voILF7CM4GeKcqgmngPA+j3CGURc36qoa82qnS0oTScweXCFfI8nja3Tzn9seagsP2DkO80YWoUJA8rls1YGeghTOwch5KVKgc8wZqdc9fwgQs05kk60qq9KhwpA5hnYentIdbEcFx/+42tosxQ6+fULb8miOa8GiaZx7+7fLYeV5YOQ59fskUlmv3x+HWYO1ZPanl+9R7M03w355sMrI1jOpMukBVloEb32lNFSETO9iUA0FFScciOeH1OyeV9gjDkw56cvyKp6zZ05pFw9TGZ6Sp5+cze+dN9S63XpMcxM+PD7+OfMJEMFbkBFEc46YoDcjLJ5L6JsiYWXK58V7y0LywiHG2G0Leo1hdIjNj3y3haCtPDgN7TA0MJsFiNRPSkb9rbJDVj1gsz43tPW53tm1R4wxiQ+lNpJ3i36zc7mTqzc0SL7S/cshyI3Q1ckxOQ9X7abQzw7VW+jx6bn//idi7CruQs+D8OBZkEUAJEqe3lPYAdNRhYgrOZoK+BCXpu8hCpw2S8kxZZQofq9Wj2Myf8tizsTFHeqUKLbDQ+Hz0OhWLVPVQ8WiVRKYlkChCKc80RZ+H5BtUZ6xkeU92169ps6stjb2g3OgatPHCnPo3drFnwAFB5VhOOVmuwVMKxpg/YDJaepIycV4uNHh6FxupY67pLKJvr7eRuC+4fSUzIxRWwIi/qrK6aDMSY5dwFISA2gKG6KsugZfae++4TLcEJg2LZbikDEJV8LXmnxt/ne1TLlxCWswr8KPTIZ2eo6RlzcNkNz1a4WXP/AMswzEh1PUDDn1/xticaeQX1E/XHbixsw5huPy+9lXo3DkHRD5Wv6kEplnYg+RBznv+uIMU6Gr8fF+FONXJVH2RQ1EVmVfuVpJF2RQH7OlIHh/ZTr7m/LamstlQunyCGJlhRoPBvnwNRg73WY4M9Puo7kAievKtHJqYa4ajSt2tkCN4gGkrFBET0aQ+Q4e3aVwCefNiEsLsYg4Fsmo8oDS7YJdhHyLBuvhsYg0T+Gzxw91yzcYlPOOyxMKr2BSP075LCyfBDyl/mbZYiyOJWQiie94je2NmGbpUSwTUwowOoCOFhVVMWYpDgVer1UrLQZko5ACli46DW2ZyPhHnMxINm8vx1DqovRUFUsF3sOjg1727GvrRuMiQIZo/uV9cDxa4d5iNCw+IIqIJnnxW0W/crTGFlXqiST+XAcJhe/t/a2RUJe5vVNqi71M2MCJqI+A6D3rRcs4h1ZD799YT0cJsLzccoysTGEG3FUCFdMm84uo0DAHYEXtCvAPa/b02rFeeufdewZALywZi+eWLlLLnCasqz8nHOOj/zpNXRkPeTyPs795cuStg8AmjpzEvtqUtEBwKLNB7SS0T3J0SNqCnKG0pwwq5Nt2d+BYTUiPElRElt0x0Zb1FPLbNdh0DfmUfWlck2wKfJ6n0Y9gNKzHNNPa3e3BlAYHxdMGySTsOjsK48ZJttF73/R5kY8sWKnPGdXS5eWgPbZu1/X7qEWftCNX/E/QSJ8H3LwmgpsZbGIMmTzvmQMqAqOjawLw9ESj28J+QOCZchsk6mMXnWHTr8XGmtCCbIZrq9vCbP26R2o3PVZScOoGwiAGAcULSH4i5l4ZhNX4f82nRKu0sZJ3xIFcoqT4brVVKAgk7xv8H/CERHEnC9o58xCF+uCMfTBP+g0eTubu+TcfXTZTg0mRvvgM6sEjaPpuZXRJoRQHxJbxJUDOG5UbWxEgfqKAThr8gBwzgXsQpm2cVzi9N0tlmJOBE8DgBsvPEIeV73od7yySY47wMivKbBKmM9JbfN5OD7Md0F0cur4VP8+85a5EbYWcnLR7UiZz9qoQwPvscNYZK2hQktJ14HjMJQr0TRbdU4gTBA3n5ueT0AxCzu3SHpDifjvkEOzVYe4lKQTKEq+feLso0fURBTTM34+N+JJs4nDGL58/zK9XYpXzOfhJn7dPTqNjSpXHTsc4weUywnWYUyGcf3LAGbnYN1yoAPTh1Qik3SUcJz4v7kzJ+l3ygzMsinmIkoeOZW7kdpntoOue97Ugdrxy2YNEckxSsITA6THoa0rj7OOiCaO0fVOHd9PPsuaXa0Rhoex/crwkuGB4dA3d5XPs7woIZKgwKy0SJxz+D5He9bDr4MknzhmCqHwiL/VsK7POb79zzfl5+68j8eW74oo3eaGTNAMzjmKgjFEv9kW4HXJk/PTp9Zo78D3gX1tYsPO+aJUeLsWugzvY2PB2Ly/A79+4S1rYQ9TStMJXH/muAilU2/khJtC79iNBuRA23AOAi9na3ne49rmlEk6IU0aD4+RqPNj+fZmayEKIKosfzZgtGGMoTPnyb6Rlwv+UJlVVKVy2bZmuYG9tvFABC+vyt7WbvQvp4Su0LNKCvELawL2EKVHVJpHVbSkLkv0gp43rioiQaFUKsfOnId1u1vxuyBx1SzaQ1hOoock+IM5lmh+1penMaKuRK5HnVlPGlq+D/zk4ilam1RFm6Q3RmDCcWLHv+pZJsVN7V/bPVXpzHry2sUpF/M37MfKHS1IugzzlAqAnHNc/8Byq4FOHkI1SW/CAFEwicpQE4zHpNAjTm/XYZpTpCSdkGNJbevH71yEdMKxsmF88fQxcB2GvyzYIqGDPhdOG4IArtzRjLzPCxbxUDHCJHG0kUA4Bs+Y1B8fPGoIAOG8IaXXVFrNd/7df72pfXYsjoeOwDgwva9qP5jGoOo0AYAjh1eL49DXirDCqPLb4LlcB/joccNRlHQlTMNxGLYe6MCqnS3oznuYOLBC/q7VAqUABBOQCX1T+4Wgqr3QlQ/DMP6vyNx1e9HcmevV5t6TfOz44fjqg8vws6fXalnQRC0XRxYP6IB9olopVjPeLaFcmxw3uhYDA68tEPUEPX7d8WIRsTzu4ysEb/G9C7fi/oBnmSbDoqBcZs7zkU64BcOF5LmmEPqvng/7gn4mlWXTsxx0w7CaEuQ9X2IMmztzSCUcyXFMCwZNxKfe3I2KYHHYeqBDekConR+YPkj+pjPnYa+yKV88YzDKMgkdA2a0ExCbm+swXDJzMEbVl0or3halvvJPC+VmSMlBcZ5lrjyP2h2F+rgsk5Dv2VTEyLPMufB6DK0pVuAXYokgj7zPgTeUDfreRVul0tedExAMHbsuGjWqvlQu9guVohg3PbkGa3e34VEFmxcnCZchnXCCojLhM/xx3kb594g6oRBdPrsh9joPKiXZq4qTmvJB4+MvSjGLnhZ423si7PC9CwXMYsX2FvnO6P8uhfWEbnHtKaNx89NrI++I3ofp2aFrlaUTyHs+cp6PoqSLrpyH5o4c/mX0q/A+K95OQCuvG1dt0/c5th7okHRtqqjG3wlj6rRx2K5EGU4d309u7uo5YWKP4kVW2gcIj6YKMSMoVM7jeP8RgkWkuSOHnMfxN6WqqSp0TRGmZxqjgSo05um9twXUgF/7x3K57vuBomGz2q46drj82xaaXqFEPZZva8a2xk7ZhxEvn4U7X50/9P6fXRUW6Glsz+K51eKz4zAZZSFD+NUNByLeu1U7RX/a9gyi0DvVyDWxyfDaElxx5BDJAU1rWjrpaoU8pg+pwo8vEgmtCzYcwKJNB3DXgk3BuXa1ZFCVrqA6TDBRNHfmMKiqCN15D2fdOg+cA4Or4inYzpsyMHLM90UU0DbXOQd+9vRadOd8pFzRh0NrivHDILmNQR9HK3YE/R3jUDPzIjhCjPYYIx9Gnavm3szAcMXvo8Vy6PLPr9FLiUeT48W7dRyGgZUZiSdPugz3LNyKqQ2V6Mr5mvIaByXa3tQpDSW1sAn9XVeWxpHDq3tVnOtwgh8AxtgZjLE1jLG3GGNfeS/v/XZk4752PLd6N3Y0deKBxdtw1uQBsRWGDrRnsbO5dxCMTNJFUcrF508bo22CFAqNI4sH9MzYc4PJr3uWxeSnRZPkudW7rV5eFXt21IhqeZwF3lDb4/7t1S0AE8B86TULpsO3//kmHCaS6dIJJxZPeNFvXgk8DiFuVaU045zjzR0t2HqgE4xFyepVRaelK48rbhcczHe8sgkVRUl0Kl5ONXzfkfVkUsd//f5VCWegyS0S2bg8piovg6qKtLBd2FYdw0j3o/5jTNw3jnBfxSYOqMhYvaccHK9vaQrfh9Kthbz3Vx07HEePqLF+J6ogigX/2dV7sHl/h9xMxw8Qi7fq9XvVMOKo0MKd8zcJTnHFe3/3awK7P65/mUaJZ/KcVhYnrWNEZVX49jkTgzGge4G+o3huhlQXozyTsBZ/sPXn1SeOBOciCeby2UOwJcDGf8OI7tQp7TX5tdVWk6eX2nf9A8sllpxDwGZslQdN6I3EcxrhfnPdMbGuOY9LKMm2pg5sULCFxSkXz6zag4ff2IFTx/fDyLqS3nl6AoVNbXdorHFtQ589rErblFXFMKEkq+kGimiv4zCZGEjz7fFA2Z8bYImHVBdr98vmfVSXUGl74SWOS9Zq6cqhrTuP5s4cStMJqQSa80ZV1Dfsbcc/XheOgNaunPSI5/1oCLsscFao48w2J8/95Tz598JNB/DMqt3yvfpcwD4o56U0nUB3zo/g38Pri/8/+udF8hk37GvHrc++pT0LEO4T2Xy0+l8cHAAQ74K4xEnGB55l27l1pWkJCaT+Ipy02htq25ZsacTNT68FUFhZIkOGQcWUi6sSzEkw9MR7im3rACmO6veUi8QB3PLsOry5swUJlyHpMlQWJ6WTwPQskxHcmfOk55+Mh62NHZryKUuwB/00tr+uLKt7hQnjNrspDr4X7s2hNHfm5P6USbh44nMnyO8SjoPWrjwaqosxb91ebd2mNZp4o0kYC7+j+XnUiBr4XHw+eWw9jhtVa1HYo/Mj+f+7Z5kx5gL4FYAzAUwAcDljbMJ7df+DlVc37MfJP3kB9y3ahseW78TQmhLMGVcfq/DcNX8zPjtHUA/tbe0uaElNGliBz50azcSOK/e4bncr/r5oa2TA0aIwYWC5DE8SZvljf9bhA5+75w17YlSgrJM3VBXGdJzl7Yr3yfSAqU1zgoSjtBKCNmXR5kbJBexzLsNc6vXUe5tVtj6nQEzMfnGZruSLJBPDq+pzbDnQga8EdD/SA+3ov1V/JZPhjGfZ1dKlwSKI1cBxSJEWbAqFKggC4h187/xJmDYkTBylX9BCZOP17A0eTFxfH7tU/pvzMDmLxsC0IVUBzCa6gD3ymWMBhB6ro0bUaCFG1QtRWZzU+sbcrJs6ctZSr/csDL2E508bFFBpxSeQNXWIyoYvrd0b+c7W77SB37doG+5+bQtKLDh21xEYeJKPGFhY7XryXuGxI3/wjLz/OYqipIoJNyLF9JN3Lca63a2y/012BhU7y7lQTkgpfXKlrpR//PgR8u+rTxwhChn0wtejQgHM9gJ6qNhxmOb5VGEdrjKn1MvRnEy5Dhola4f47oaAsYM+F6dczWDI5n0kHAc/v3QqfvviehFOV8bgLZdNlX//6eVNuO3F9cgHBoXwskPSuRE2kwNB9VBqX5g4RR7AU29+MVCWw3t1K/hcQCjPtoRR9TdEValGKve3d0vM7tCaYrR05iTUKSr6e/nbq1s0pVSdthdOFxVbs57Xo0KirSUMWGCUT//pJVMK/h4Avnv+JOR9jlPG1UvFLG6J8nkYkSrEFKW2m/Dae1q7wHnoZPryA8uwq6UrNq9BdXIQNabPeQSCRIl4NMeGVBdLJX10fRk+ecJIeb24eUT0cGRcNHfkjEInYp+y5Tjd/doWWUI9lXBwxysb9ecwdiDfWENIqGX/UCJqNz25Rpsr6nqcTNAxhufX7MVGpciZz8UcPGGMztqhsrZQ7oJI7BfVSJOumG/URnLilSkJ0xMHlstrHYryXnqWZwN4i3O+gXOeBXAPgPPew/sflFwaVIurK0vjV8+/hVueXYeEw2JhGKJqm1gAvxjD8EBSUZzErGHCi8s5l1Z9HMD9tU0H8PqWpsiCw5jIvq4vy4SFDXyxcX346GHyPM45yjJJyS+rCuGdmozJDIjF//23hpReKu2YKeoi25nzsHZ3q6BwK6DIlWUSeG71Hng+l6GrmhKiGwsnjwjr6L9dt6cNP7jgCOR9P4IZNjd5n3MNr7Zqp8j+nj2sWmLr6Prmb9Uuac/m0die1Y7Z1uVr/rYkaDuTC5PL4sdOo1LdzGEM/3WkMByKkq4Svhb/k9EQl3RFUlGUxIZ97fjnspANwcQDch4u+DOGVmH6kEotJF5XloZtSNJ7GT+gHH/6yCwUpxIapZvaJfVlmdhkFXmsQMSDhEHQNbYYWP/9bd24d+EW1JSmwJgoELJ2t24UmslHHISL1w1EUlTo1qPry/CH/54VaZvapvADw4lj6vCzZ9bKQ/SonOuMJVpbFAywKtubOtGd9yWbhemVnjJYbOhXHTscyYSDfW1ZuI4Dn4cFQGySdB0E2nKPknAEL7f6KtRXZcM5r9zejLW7WzVIjsrXrkYRKouipYmjcADxOWPQUGY9H0lX0DPe/drWCMRJfa/lmQRufe4tPLBkGxKOIzZ4n4NDKM//c/pYAKKKmoq9laF/mAU09Lcl6eyCg2dPGRBhZRDJaKqyJJ4lZ1yX5seIulJkPT8W+1xXpuN+iYedOlqdP/QcNs+yKWqCuEptp0pPKL9zJw9E3uOoLE5J54KW8Kicu2lfO1oCPGyskguGTLA/eD6XMANaCzkPqwsSY471Osrhy2cPkdcz90U6Tx3fZvLg4s0HsLuly0pVaRMOHW6U9zmOGFxuVZa/+mBYmKU8E6XdMx+PuMujDiz72PEtYwQI+cJpjKgRayqbbZudNEQpMTjpOjjzlrnKHA2NiqvuWITG9ix+/XzItEPQtcMJfsAgACqf2rbg2H+E3Dl/M75+1gT8+AOTkehF9j6D4Mn9Qi/rnA+qKpIY5UmDQkB9zvPxpfuW4tuPrERn1rPioDgHLvj1y9ja2KFUaxMKQIXCAX3Tk2uwvanT6qUgL+alv5sfy/FqE9PTpSaNAIIWLpN0rF49UkrIM8l56FWnicVRuIAAIJRtzxfPvL89i/dN7BfAGEjJDuNQ9CzDaoqxbk8brj5pJH5xxTRcMlNgXKmdmaQrMdRqcOuksXUYP6AsCBer4TR72wQ2Mkz+MzGjFHpUZeO+doCFbR1QkZHeNDMU9ymFVN7WhnOnDMSrG/Zr4fjbXlqvnUMbmfAsk6dZ8RB7PEInmFRohRiA4kChV8eKytlaXZLSnvtrZ42PtNWmTJtDjzGGXS1dEcNgd0s3fvPCerR15eVbMRVTtXhIeD16N+Jzc2cOp//sJe2cvM/lJk0StwExAJMHV1i/K6SZxnqFelBmz5osklQHVmbAIDDXKtzBJrWlaaSDOfnYiihW/NTx/bR145wpA2V1rbC9XLYv6UbnwZ/nb8bpP3tJi2K5CrRJTRZTw9L0/OaUp+OZpCMhBoBQjgZWFsnxet/irTLkDUCD41DRqI6sB5cRZlmUhb/hrPHgnOPy2Q1IJQTFmQyRK32Z83wJZ3Id/X1RDgO1Jek6WltkPzCGF9bswWsKfI145IfWFGvvrzSdQDbvF4wa/U6BrXXnfW3uaspyMIezeT8ynx96XVf2PGUux3mETeztA4u3aUV2mCOiWEmXIRtwuZsMQyT3LAxVA5PJQo020DyMK4dNDEfCOQEtF4iE1oWunBeBtxC8RtxP/N+l7AO/n7cRPg8N79+8sF5CjX5Q0Ikk/vd8jm+fOxGAwHd350W5alvBEVV6oz/6PtfgJPLeyt+jFWdRc0cu8JSLz5MGCc8uzcek6+DCaYNwnELpSE4f9RafPmmkFf9PYywfUNSZcJWWrpwVznqYOi4mXylyEmOfYIwtYowt2rs3Gkp9t2VbYwd+9vRaXHfP65pnuLI4iYtmDMYlsxqQcAT9zv2Lt2GQgt2ZPLgC1Zakr97IpbOG4NTx9RhZV4I1u1qldXbTk2vwkWOHo7I4ifnr96Mo5YBDTx7gEIkBDy7ZLrO7Pc4jODOy2LpjMt6vPGaYHYtd4EGGVBdjYEXo3aCwEUk64SCdcCOLPedclvtWFxJKoDhhdC0undkQeJaDZjC7usGYuA9dZ/yAcm1RHv7VxwLPc7gQ1pSmsbe1GyWpBPqVZyTuUd3o9DVHXO+SmQ2YMaQaz6/ZG+tZUUO/bqAgE6G9wwSV3KPLduKJFbus3r+BFRmpWNOdadHP+z6G1RTjkyeEIfUvnzEW/crTVjqruxZsjrTTrEqmV7/iWLqtGVf/ZbE8dsakARGavT9eOQv9KzKS0s8JQuwOY9LgWaTgH1MJ3cBUS+bKeyvfq1XzVKFQa9qAKjmO+M3EQeXyHRIZ/xSL8trcmcOtz64DYwxLNjfKzcrzeUTBeWtPGxIO0+5po3+TbYkZGIXsa5sysmZXlPYvTigM2tKZ05RSABgZJD2S9K9II+WKdcQsFU9yrpIE1a88g9J0AseNCjfNLslZLCIPVGLXnOdqdj9hhAG9nPHKHSGumbCupu+K9s+ipIvb527AkOpiXDh9EC6eMRgfPnqY/H7jvnat/9MJR4bASanP5n3JhuHzQLELNvt0wpUsCxzA9WeMCws4AVi3u02Gi4k3neTpL5wIIMSnplw7/MxhInl74aYDsr/+vkjgok8b3y+IagXtTzoRBVgVn3PNC04FHpYGRWtUvYOUkJU7WpAqAMOgJOSwpLc9Z8WsevfzZ9dq88JlLHCWuNiwtx3727M48abn4fvcWt0u7rrkcQSATDAHo7krIWYdIA+wgx89sVobk5MGlUvO/u6cLxMBaW+hHBsg3PYIg0zX6QxySjhEEh3BDuKSY1XxlbJmFDEAACAASURBVMiz5wf0bExU37xwuvAdfuTYYdE+MR0/0KMvlcVJ3Ld4G3xuoc3j1j+xo7kLw2qKZX8TJSHpCQlXwI52t3RjQ0ARSJHeEbWlaKgWa/+XzxinYZZJCIKU80SSoMpuVUgOwzCEJ1lNUR8MYId5Euf8Ns75TM75zLq6OvPrd10GVxXj86eNwS2XTcM3zwkh1TcGpWiBIOHFE9nh5JEEgFPG98OVSiZ0X4UmbGNHyHX8seOHY8LAcnx2zmj85oMz8Kvn1+PpN3fjUyeNxN8/ebSkpCKOUs7DTak4bU90ifM6nR6QlpsZ9zR0bQlY9WVpDWN90YzB8u/LZw9BKuEglXAii63PoSS1iP/vXrhFXqskncDUIZWaVzdOGHSP7eWzh8hFgf6npAUVItDWndcSzT47Z5T0eDmKFax2l8PCxAq1VerjqXyxDmXd83Bx+9idi/Dc6j347N16qVGSAZVFWgKL6XEtSiW0Y3PG1eOWy6ZZKYIA3QNkJvl96Kih2ruxjY1vnjMB6wxIw4CKDGpL0/J9C8VDbFaj6kolr6/aBrXNFcXRqpcqTIIrGzUAPHyNwEer/MEEHaLzfC6UKXraklQCXTnPioWmqAGD8O7Thvzqxv0y3FlflkZNSQp3v7YFrsPw/BdPkr9fu7tNztGewtEAMGtYVcGNwpac876fvySN355k0aZGPLZ8Fxo7ckg4DNuVsspkCJIQZje+PdGkNw5EvOuqUBvNULGaMCbYcbi8HonqJRxYmUEmKQxVtQQ6jYOyTBKccxw7qhYTB1bIZE91rqjdlU44ePSzxwV/i/YTzpmxAIYR9DENTxrLnAtPb3fex6j6UhF54Rz9A+cAGcIk5I3/y4Itsk/UfhwXJG85jjAo27vzWj+MH1COqpIUtjZ2yN8VJ100dWRjPcuerydYThlcqT3//PUh1pg8htsaO1FVnEJDdRFODirG/V5hlKksTsHjISSOsbAQEwB87lSRk2MqZaJqqBAGofRs2NeOjx43HIMqi/DnVzbB56JYhupJJhnbrwxj+5VhXP8yqYghuI5kTAkUOVvREtr7AJ06Vc2VSDhOGKXjHGQzUGSNDCd5ECEvu9rPVFq+t/zwdJYKwastTeGuj86Whu7Nl0wNbhud8DYDnDDbnz5pJKqKU1i8uTGgRdTPVSMjDguTq5Muw5s7w3wIUtbJqEy7Djq683j6zd24c/5meS3XYShJu5jWEObU2CpNEr3gJ+5cFHqWlREf13e9YfH6d8h7qSwvBDCaMTacMZYCcBmAR97D+/dZyjNJCfInLDBAxOjRhIC3L0Jb7sx6chKTouM6occuG5T7nD28Gt8//4gIXysD0Ngu6O1sAzKOyeOIwRWYNKjcgomOLjokriMs4yMC6MgghYbuQHs3Vu9qRdJhEW5fwj4BkJnfv3txg7Tq7124VeJJOec4fnStRkWjisOENaxiBkNvdXjfuev2hWwDwYKiLiyMsXBDUyasSssEMLkI0ylfeWCZ1i4NN83EsxOPZ7ip+7GhRMIsO8q7pw2KlAH1FSaCdxCn/KiLT6mBy5s+tFLHEQZ/mt5INWlLfUa5rwQecyDeGFM3OLUSnu17KvNK16fCP9T3izc3Bpyrm4P2iPMSTgj5ufS2BWjsyGJIdZRGigxCUmiqilP445UzNa/7ZbOG4AOBMZBwmEax2NqVi1y3EPc6JYX2JPbAjn3zuFgxTFU4VMJ1cK1SUMT8/a6WLqRcJ9Z7D0QjOGLMqREI+l8/07Ym3n/10QCEIq1CruS9OMffPnakdpwxpjGhkIgIijDcyaAVnLviezMfoDiVkHOcaAWznupZ5pLFhpRmoUQL460o5WJfW3fgbRNh7qMCg9NxdJgHeeS+cuY4AMDtczdq75MgKa1deWSSrlatExDPNK2hEm9saZLrdmOHKOrTEsOG4flcVoGcMbQKp03opynwrquvRYCYQ1UlKfg+8J3zJkWuSf0iS9wzZo2AqVAdQLDf0B4llE477IIUJjMpznUYOnJ5zB5ejQunhWP7s3cviexZM4dVa585D/c+IKQHBET56bf2tKEjm0fSZRJ3fsXtC5S1XLx/Whceu/Z4OW8kV3/wACeOqYulHbRxOKtC15jSUIl0wsXEgRWRfrLpijLKyMV+AyhYeqZ/b3pmVewzebEBAbl7ZtVuOXcukmudeIdFqYRWsXBPq6i22taVh+s46Mh6suqmirMnSQZjYem2Zhxoz0qDhBxSc376YsG+OtTkPVOWOed5AJ8B8CSAVQD+zjlf+V7d/2DlymOGSW8WiVQeeygB3TubU71uEOrb0yYXIpNG5/OnjkF1cegp+sCMwRo2jhapfy3bgY8fPwI7LDR2cTRuZZmkTBhShSaTbbNPBMk1//j0MQCgVUciz1txOmHlh6VN7J9LQ9yk9FIz4RVq6RLelxG1JagsTlqVf8b0ioZM8muEXqs/BpXtGGP47vmTJJcxyS3PrkNrV062k5TBj9+5CL9+Yb1c6B0WbjrENnDPwq1YujUMJauvjDGxYZPXw8aKJBW34DOxU+gJNuF3jiP678JpoSfAYZChMiD0YtFvgNDIGlFXgocCT62qXKrn3v7hmVobzQWcxmXYLwxfun8ptjV2grFo4ZXq0pSVo1cVdUOkstfmvqF6EF2H4b7A0KI5mXCZ5t30fG71QtHmTwu4zznqjWQpICyQY25AWS/cjKkP7v/U0fjV829FEglF+wrjj+kd/MnIeC8kN10cMhJ8+Yyx0mCNhGGlQQN87LjhaOrIFUyiWbqtWcObU7PVqafCMFQplAi2v70bdaUEaRDnvfilk7BwUyOeCCjj6NcEkQivC1x3ymipjBCmNoQeiZt05jykEo70mFJmPwB84ywRKSTPMik8QuEO2gomlcW856OyKIl0wgnHic8lG4CZ4EdSYvDd24QiLerX+9uzSLgOBlYWyXk4fkAZnnpzNzbt69DgXSTqejiitgRFKRc/fmKNPPbbD86Qf9N6m044EqpjeiEBMhbCiEZcVFyNtNBzq89Lz8iYSFSlMff4cvGu6wz6SNdhWnVCzjmWbm2y7rM2Y9vnwKYAYuF5XEJCGjuy+MLf38Da3W1IOA5ynkgGX72rFa7j4PUbTpPzk2AeEwaG1Hg5z8f4AeVy32yoLtKiJKr0hCCg93X9+8Zq/dSTHa06YMgrT5ClMf3K8KUgOfW6e16PtOHaU0bLv0WyNhkBxO0dGgxA6FkWFKni/T26fCfm/ORF+D7HZbMbcMKYWgyrKZZwunTSlVUcATFXR9SGDpddzV1yvn3mb/aI6icUaOGhKO9p2iHn/DHO+RjO+UjO+fffy3sfrEwaVBEJYwLAntZua7lkkrhNupCIBVnMGvIAuga2bNYwEfpQlZes58uMYFrUHcbQUF1knYSFmClsFrOKAzOloaoIXzhtLBKug19eMU37jsK2RUlXC4UzJhYgUjpV5SKl4MUyKZH57vscp03oj5sumhJTBlV4YbvzHi6bJWAx9Ii00FLlIRaEocxKU3QOLbYUMnolSFikJ2eMWVEh1ygLgE4NxAMYCsfO5pDOSO1JMxFt+fZmoSwHi5pKQSg8y4GSENymKOUKI+BfYYLJSUoyG21gns9Rmkkg4TA5dtMJR0IS1HNNIy1SJMPVn8NhSh8jmohYFiQqFRJ1vqgFO2ztmDy4wlrxzVQCubKBAsDswCO1aX97ULmSSQ+fqRDf8uw6ya6geucTDpNKFaD2gYg4/UEJaZMwJvIh4oTmQC5SXTF+cyH5znkTMWNoNR74lDBYTQVI9RCSZ9d8nxMU3ty9rd2S8qor50nDgsbGiWPqsC7wDtGx/YFiYmP1I1zo2l2tsoqeamQBkFyupJCYEAZSrsmTS5EaM6M/H+BA6TxVnVXZICjK43ORRByum2FCm8c50gkXqYSD75w3EV15D48t3ylZNFRv2vcvCD20as/S89QYe4jjCO7q//1n6C/a19Yto5byvOC5/ue+pZH95to5o7S1nNqlJlnTGjymX6mcG/kgqudzu3LnsNAYBaJh8VPG9ZPXvvrEkfK44NwO+8CcT7S2f+sR8czj+pfjtAD6J+4j5gE5jbKejwt+/XKkfWONwh0AFQIJ+0JVduvK0nJvTbhMU3JdB6gK3k1rd16yzqji+WINV5OsGWPWRLRCOsF/HTlEjhd1/aexUEgcJp5DPYvmRHVJCseMFB7e+ZaKmSqDy9UnjpTtpueRUdbgcWic1ARJ2dS0tu48fC4SgOvLMsJ5FXz5wwuO0IzbUfWlWnEZYqyx8ZqTfO390aTvQ0kOTY6OQ0hEskP05d792ha8vjXKdUty0tg6HDPSXgQiTgZXFeM3H5yBh645Fp3ZgO/WVFIc4bVTF7C9rd0yxEcLDSDI2bcqodZQ4Ypvg8Ogea6BMCHM3AgHVRahuiQtM/LPnqxXRpoTJP2kE47mWeZcZG3bQrYyuYKpXr/A65By8Y83RNa2SorOmEhC2ry/A0eOqEZZJoGvB2wLW41Q85ItjRhZV4KRdSURPuuJygIbt1GoXl7yXqny148dqbXN88UmITGyimeUxFw8xvUv02iStJKnQfhZGGPieH1ZBg7TYTKqh49u9cgbO7CvLQufh4pDezaP2+duUH4XPKcTQh9UIeiBrOhHC67RT+acIZjMeVMHyoQr05OgPmd7dx6n3fyiVr5b3Cfsh4TDIolM5gamhrr9YLMEBMPDzy6dIj1Knm9PLLFtYkeOqMY1f1vSJ2wdQzTjXVWgkraQA8SGuGFfe8HwLtFD0ng2+0COZYRjrbxI39RNh8AYha9dtiX47VEK7r2huhg3XjhZ4iBt/dWvPINr54zCDWdPwIxhVfjpxVPkWkRto8cnT6/DmAYTeTHgzSZlROQphIlnIQxDePxtVeBkVILzIDlPrGnkWeYIIjoObepAKiGw6pMGVeDlt/bjqTd3SyXbdZisEKqu0+qw8AOP+GeCYjXHjKxBbWkabrCvmMxESVes72Q4qmOSPNZ07JTx/bR55josEsGj9/eXjx4po5WkCPuBd16FzgHCUN3Z3CXvYyazHqEkzBLkBBCMNGplUcaYNEgBnaucRB0ujDFcOqsBw2pK8OCSbfjR42siSZSAqCoLAKeODx0Cjy7bCZ+L/jt+dK3ElQMBjCJwApVlEhhUVRTC8YwosRqVIiq5vM+RdsPCWkQ1aeNWjkvu5ZzjkpkNVriFavQBurFFuR+MMXzoqKH2yA0YWDDcW7vyESpGEwojPctG1Ujq59H9xFqTSTqRPZ8iBYCAYBJEJWPMN8b0vqgsDqlg/1PlsLLcg6hE+qYUCmXOHFaN40f3LUExFWRuT22oxA1nj5f3VyXhRnl6/6lYw6on5cmVuzVGAvIQFSrqwBjD7OHVuO6U0XIpeDNIfjI3wvEDyiPk9t87X3hY/vDfM+UmXpRyI4rCn14OvW//ChJwgJCYnjZDIEzAYQiz99VNggGoKk7hpbV74TChZBIB/85mPfS/pyX04i795unadx9RkjPjuCrVcPor6/cLmjdFjh1VK8Nj5ZkEOOdIJxypyNqwleb4IgwmGQ4JN7xp3hcLv885RimJhJFqgsH/s4dVy4X46hNHoCvnSYUCEBzCaoWmps5wY6SkOiBM/qgtTclwL6AaFeG98z6PYAyTrsjqL0knZF+am7TqAVm7u1V6L1UJ8XY8wuoBAMNqday1x0PPiBpu93yO6uJUANgR0Qt1rk0bIgyF+xdvi9yDKsRJQ4wUhAIbgeNE14uR9XoiqE3omioTRU8SoX5U8PkqJpiiVEAUP0p0XSae/d5PHCWz9U8dX4/7rz4akwZVIBX0iZrwrMoXTh+Ly2YPQXkmieKUG/EshzSP4viK7c24LOC4B8S8b+rIyogGbcZmIqjwLLMIWwoAqVCQ8kBc6hyEWQ4ZLiii5TCG2tK0Nr4ZU5QGaYhZHztQzEMFKOE6qC9L40BHDntau7UQORAkoHkc7d15fPz44RigKH3ELsAAzP3yyREu+ITDsHy7TuVJojoGQmVZPMtj1x6vndvcmcPZv5hnVfxMDzkAPPc/J2rXVuWxa4+Tz15ZlMLQGh3nr0b4HCYgA5mkG5QuF7CxOePqMby2RI6RQnPlnCkDkXL1hHLP59jf1o1c3sfAiiL84rIwAkpz5ZqThTGjGpqfOnEkrj9jHDbsbUO358v9j6IaJpQPKJzs6zph6fHBVUUyDyqObQQIceExAU35nfquzMJKC5SKqz4PaUEpETUSNQws15QbrY/Q3JmVe3RS8dJH+fD1gj1Hj6wR9+X/uQrzYWW5B1FxcaakCijLb1cqi0VxBXPjcx0HD76+TYa8AeBb50yUf6unq/yl9waV0I4dVVMwg5eeN+f5Uqmms1UIRH1ZGh3ZfMQ7q3o9AOCGsyegsiiJva3dWhj+2VVhyWCVDJ6u5yieZR60i1YL04BxGMOO5k7ct3hbRGk0LV6VTaFIIVu/5uSR2nnEv0pXI48DA0O/8rQo35z3pSFhk5J0ApsPdKAsE0IQ6HoqptdWbpcxJpUrovkCCKspNgC1OlgYQiOlI/Qg0vVd15GQGNpwJg2q0Hi927o9fPjooVLhJ7loxmAMrMjg62dNwEOfOVYu4gs27Mdjy3dqC2OZUaXw1PH18DnHc6v3YERtiRayVkXzLGftybOqZ171PNLtCBMbHudKQlG4qLd05ZFJuYHhIfpV3SinNlTia+8fB1M47Jv1USOqMaQmmkgICIwjA9MYAAZUZAx2CTLGxLUJf0w9cscrm6zXtolpwIb8v7pSR2uIqpCFrdGVwKaOLH761FqUpBOy3arXj9aK6UP0aIT5mX4XKrmQ/7d05eRx2xLVHvAjqyLXsuBwzvPhMIahNcJoUtcnc0OvKEpiV0sXPnXSSMwZX69Rpa3Y3qxBc8xiR5wH3mQJq1KNd3HsslkNwtAPDP8PHTUUgMDj3/rsOjy+YpeEu9SXpXHmpP4y96I0k0B5JqmtZ6mEg/OmDpT0Y67D8GuFR1gkXeVlJcKPHTccPueYObRKevYAQW2XIMwyYxGon/qcptiSvEcoRvsjS3doih158ElMI6Yjm5dFL7RqiHlfQh+GVBdrUb9Q9PZRUiIV5yDpzHnY1tiJ7U2d+P28jdr8JWfHwMoi4dxQruc4wuj62j+WY9m2JgViIqJErwfvrjfCjWcfWlMinUkOY7rzx/I6yMOsto/2lH1t3draVZTS+3ip0k6O6H4TB9uiSAVBGwFhtNYGa+wF0wbho8cPl8+giqncD6spEVFZwyy/7UMz8J8ih5XlHkT1JKpy4fRBeP8RA97Ve1cVpyKYMp9zvLmjBV9SEgSuODIsEX3etLBd6mJHGajfOW9SLBsGEMIM9rdlZfY/kbJf9NtXtHbkvWixBnMz++hxw+E4DD98fDUeVwogqMUK1A0tVJbFRN24r11a8rQJnTxWbGwyJKvc0uwv0zAQWMboavSl9wnFiJISCDPbamCkRYU4JhNMeioXfOdVs3HxjAaJ2aZFRVUMF27UuamFpyD8TJvzBdMGwfNCNgzVGAp5OMXnsNSx4n1SNv+EwzTaPJJc3sf/nD42EpYnLPuMoVVacs32pk6s29OmLZZJo2jP8NoSlKYTWLe7FWWZBGYM1TPZ5b1jYCQ2Md+rzzl+funUyMK/o6nLSgGY83xkEq6EJqieZRERiI8c0RiX3lAAx4ysjcUrDqspAWM6Y0XSddBP6X/zeT59kjDe3rJ41wtJeSahKVgN1UX44uljhXcUOv8yzcFCTCp0flNnDm/ubImda9R+s9hFqSURS/fSBhtzMKc44hOj56/frykFmaQTYtuDH+U9AbXpV57Bxh++X2Kkqb0qYwl54EbWlaK2NI3WrjyKUy4cJspsf/XB5XJcq+PbYQJjqo41tf/o8Ii6EhnNIJiHOa7JAVFdksJn54xGcTqBzqwXPIdZCEiU9aah4jpMKtuAoNpTq/M9t3oPqopTOMaISpDBJ7DbLLJmkvic44U1e7RjPeUdmH0BiHW7viyNj58wHDOGimgGGQ55L+xHtRl/vHKWTFLuiXVK5g7wcJ1Sg6cUsTKrfgLAqp3hPjT3y3MieUaMibWacxFJYkwo1m1d+dhEeRs8DxwYbcFaA1HaNdPhU1WcxFlHDIgo0XReS2dO24fNqpHlijPK93lk9zPfP92HMfF+bvxASJu7r71brpM1pWkZHbQp+Pqcgdy36H5Da4pRXhRdHw5VOaws9yBxnuWbL5mK86e9uwUI511/cmTi1JWm8eGjh1mt/ns+cRRG1pVKhU9VXGljLE65kuLLJpRs4HEucZTEM9nSGSqOeZ9rjAAktklD7bDx3V47ZxQGVIgJd/SIGgwLPEKMAev3tuNL9y8LwoW6h+LyWQ24LghhVpWk5ALQr1z3ktkMgwDdEStnTOyP0rRrPYU8rvTccXZHWSaBnCe8lYwhqPgX9o86pl5YoxffWblDV0rKMgk0d+bwseOHhywAvu4JpfPpqluDZLKrTxwBzoFfXD4tCDeHm4otwpD1fGvERHglo+eTUq0OAwpTk2SSLvpXZDBnfL0GE2IMOCsw7L7+/vGatz2OVo/EfK8c9rF3zd+W4Iojh6CiKCkxmkCI/aaxkPe5TGocXleqnav1g/q38i5j9I3gvOi3CVd4tMszCXzyxGgW+KRBFRr0osrCS22T688cpyntL33pZA2/25nzJFRKbYsp9Kp9439TaSR5ZpWuVJFSanNaOozJhEAzN6CQjbS9qVNTUBOuI73BNB4EZZgTvFv95qTkxonvc1mUBIAWgVHHN+0JqqKpKsGVxSlcceQQUIVABqHo5vLRe+9vE7Cwq08ciQEVGekZzXk8EiFIuY72TOb4vG/xtoAWTxwnrLtZRZZBGMuOI2ZjHM52d0s3rvzTQu2Ymo/RV5kxtFoyzlANA59zfPokAYFQOfr7V4SG5KqdLda1OJx/4v9bn12HjpwHzvX1ISeTRpmWkAhAmxuphBNRltW+mffWPiz71umyWIfZplH1pcj7HJcqntjeFNgw10tTKGcH0OfHsm1N8rn0Nuu/Vz3/UxsqcfMlU3H57LCN5pRQoynmU2aVOWE+g/YZIexJfGYyokSK/eb9HVpU81CXw8pyDxKnVLwXYobCAZFQc9Vxw63nH2UUnFAnEbFlJJVEBZuQUuT7ocVPCk1nzpOeBc8XpPXdufjFhYTCfDavhNrG40bXyupG/Ssy0sscepZD+cLpY3HqhH4YVlOM6UOq5HUaqvRQuOdz/OgDR8jPMvM95vl9znH9meMwqNIeUidFMly87Fda/I3TcOUxQ1FelARjIXaa2knv430T+/XIv6sq6F05TyyunGueT7PCE11ywsBy+Jxjzrh6icnc1dIlCutY7pvL+5FNGggxnaY4jOG/jhyivUemeLO/cdb4YJOnLHL999UlKdx39dEY078Mm/bFs0VE2mlsavRsgKi6RtKd9zGqrhQDKjJIJxwJw8kFhh4pUES2T89OYw4IS0rLe0GHEACFsYopN7oJnzGxPzjneOnLJ6OxPSux+MSKUVeWxjGjwvk8aVAFHr02xPbffMkU2OS/jhwqMdXHjqoJ8O9ik3putSgu8MHAq0dSiLqK3qPJOgHoUBnP53jfxJDZIG9AuFSxQcXCYhLRX6gQKTVZ0VXC10SbmPNEoQnbOqRinG1y8cwGnD1Z9+CZyU90jHieyfhTr8qYeOfksXcchrJMIpYrGQDOnzYIVSUpmVy4ckez5E+W941J3CQpzyTRnfcj0T1TfnHFdJSkE4G3O15mDq2KHHv68yfGnp9SkrNVsd2H1i6fc1wwbRDG9ivDVAWyQ7hxAPjY8SPQvzyjeUiBaHzwiZW70NyZk1CDkwIP71OBo2JncxdqS/WImQqNSLosEik1n6Uskwyx7sZDvW9iP1w6s0FzoMh9okBP2wp6qCJo/Bi2N3VqlI4UVYlANY1G07V//+GZqCpJYUpDJb597kS51uVjDAShC+htycaMLxsMQz3GHDFHf/r0Wg12WZLS+/tQlsPKcg/iOHac1n+GiJFOCt6JY+qQcFgPMIyQNol0sfdNDAuyHPej5wCEfL/mImBL6i/kWValUcm6/sD00MvAOWWvh5NYtDWczDQtzfsfaM9qNGg9JRf4PD6RAgh5W8kLEucBTSUcfGaOSFbRvKnGeRfNaIjxTqseNGHh1wYluonuLGFTlo2r0MJOigL9xHFEFUr6DWOin5uCcsmmOJaFExC/uXhmQ0RxrA8y3686djg+ffKowOi0l+2dNawataUpqZh9ds4oDWtuVgMEbMpy6N391EmhckXPDgioBFXJkslcEGOEPPV//6QooEHRDPHsYZs5wjEkFaUexlTW87UcA0BknPuco7I4hefX7JUV0tTS3Op1//yR2Zg4MPzuQmV+mBLOj7D9HuexdHw23YpubW7i6ntZvVPH66vjptAaQ1995NhhEtIzfWildt+pChPLfx89TEZRVK+X64TKPBWPEThjs1Q9gt9Ex7BafGdUfSkaqot1fHKMYuAH0ZkhNUUoTrkRB0RIbSc+l6QS2NrYERkrmaSrGw+OgDBVFifx8eP1iIPZEpP1pLwoge68L9dkM4GW5MQxdUi6Dn54wREoSbsRRYecCzbq07jkOiB8Z1EYRvwcobD8lgMdEeOfbj+sphjfOGu8RocJhOPWdZiMMlIRq0Sw7s0cWoVHgwJHv31xPUxRi8okXAevfGVOpO2mJByRZG/yqQ+oKMJls4dozjUbvZztehpLRvDM9DyEnX9q5W7ctWBT5PfmGDXX2GUBY9CpCk1fynXkvq7O1XTCkVEhh0WdQVQq3hRp7AbXGtu/XOs7hzHsbO7CEyt24eW3BL3dk587AYwxXDRjsJYkeqjKYWW5BymE5ztUpSvnCbaE+rKA1SL0DpkZ1KbMHl6NqQ2VmpKhhnX3BJzARPBvSmk6Gi52HVE1aN3uVonBUMtCoAAAIABJREFU7B/AJdQFmRLW6svSOG/qIM1LKkPmijCwSBUyc+EvTSe0yU1Vz+I8TPdffbT0bttOoQWdIDgmVZNN1PXSNBhocQfi+a/J85cIPKFO4P1XF2IVt6cqXOmEK0oCO3qCm6t4likJiUP0l5USjQE2PxQlHIqkqmJ5KlUGc4J3T5uf2hfVJSlJJ8UgjLhLZg7Gx08YgTIF6zrD4uEyoxRxMAzP51qfh/CZ8Flau/KCIcBlmDCwHL+8fJpMfgL0DU8t1qBCX9Q+M5OYGMIkVkp4W7K5CQ+9IVhsvnf+JEwfIp7x2+dOlNfer9CKFVJSTDFxtuSBjaswaL7vdMKRrAcSixzcnyi5BlUWybWAxLUYpTYPL7FwUMSquiQly7CToqO+n/ryTJikSs/m6BA5Sjpq7coHnK5xSq4+hp/5QnST1jxitus4IXXcBdMG42eXTsWEwJCZM65e4uCJXowxYTAR3Izk1CAJWm2Dy8Q88HyRH6GKLRFTlbJMEp3ZvFQAR/ZQTe7MIwYEsBP9eFEQyTINvEJyzpSB8V77AkOXHCGdOQ8jFbiAGxjXgJizhWgazaJKnAvFN+f56FeRwQUKXNK8TpnhrTajuVYIlSPaZirCe4KiS2Zhlp7EdfQcDzDg0lkiDynvc2lQ5GPmsAmjomZ9/tQQfmPmoDDG8MsrpgPQHT4+5+gnITBR3ee+xdtiDEjxP0E2h9eWRDDLDdXFKEmL9s/98skYG2DSf3LxFA0qcqjKYWW5B2HonXV4KMmRI6pFuWgmPJyTB4cWf8JxItXVVDl5bD2OHVWrZYJXlaQ0XOV1p4zWkgpVUUnmSeg6c9ftw8odwsolbz1hqevK0rg4oJ1iTPcQkxfHfAvqnDW9aSTfu2CSZBYARJJFoYW3vjwTLM7R70bWlURKvPbkLRdtExc7c1J/axLYs6sF3tNWcOWcKQMxaVAFjhlZKxVaBobVO1u1iIesYOcwlBclccKYMMmEFD9PoXRTvRmUhBSHXQTiYRhCAWaaIhLn1XtgyTbNs3f25IH4zBzhEXIcwahw7KhalGfCSo2M2UPzpkefcNg2Id5Q9euQe1ngMH0uNrZ0wkVDdTF8RbHXs/Q9aTDFeZ2oZbWl0QS+zwf40U37282fAgi9PAwM6/a0Ws/pSUzPD3NE/9i4h6ltaoRo1XfOwD8DOsdzf/kyjh9di20BTRblMZBSrwrt2Y98JqQctPURMTPQuqQa5mSwUR+TMUWto7VkR1NXpICHvCeL4jbpeMTrWWDjV+9nfq/yzb5vYn/MHi6SVs+c1B8j6kpkZOLJFQICUFWcwvj+eoLXlccMg8+5pig4jjDI8n4UDmW21VSmy9IJ7G/LYkBFRjJx9EbM6xLNJ1XY6w1t4adPGlkw2SsO8EG5A9E2qYaafdySw0aNQlCRGaI1M73a6q0SCuwqTmx7zIlj6/GBGYMlvSTJ3QHbjXq/noqNUDvUccx5GLFlLKxWmfd8/Hn+ZnkeGbRRxizxefrQSmm4z7v+5Nj7qzAMz1fLnNvfmm1O0BhSVQsdzsRww9kTMKKWOJz/c+AXJIeV5R6kvCiJez5x1L+7GT2KWt549vAaUQkJQlG69XLBK8nRs2eZRJ00pvQmaUE7P1DUtjd14n/+vhRAWKnsuUBRdFlYs/6RzxynUYBl877MKFdF9bbR5mq2+ZiRtRhVX4YTx+gZygcTLJg1rDrCkECecZWdxBRqpqmEJ129WqKpiANi8Z8+tErgZlkYltuwr13janaY8B6cN3UQEg7TkvTIo5zzuMR0J5ywPDiHfcMyn8HWZ8QiwZhYKFd/9wypEKpCi2mcJ95hDO1ZT7abxuhtH5ppTQIxQ8SFkuzGWLLQJUEIgo3I15NV1QQ/tW827uuQ1xsTzLmmjqw8d8k3T5MdRV6UquKkZJah8RnHKtCZ9XDk8GowFuLa+yqO0dcEw4jbt00PouOEtIX72roxd90+7Aow94U83PTd5MGVKAuMQptSDQgedi0nwWH47YvrsWD9fgiudPHdWUeIQkfUdnpHL67dG7uWxRlOvS0i06QUprArckwr7qDKxTMbMKq+THI0b9rfgbzHMXt4Nb5w+ljNKKEkT1Uo4pP3eKySSFKcSuDDRw/VrrfoG6cilXAiXLuFxHyM17cIbn6CtvSkUAKCc//nl02zfsfA0N4dzvvrFG5pP6YfRaK5+Dtuv6EI4D8+HRpnVGRG8FUHVSGVgW8OgZ50WdVFQ3N3eG2JGOOZJG6+ZIqMkv5vMNbJYVSWSchk+kL3Mcfx0SNrcNKYevk7cnY8+fkTtN8NrCzCuP5lEVYuGueZpCsdfbb8JxJ1LVXfhxmJ+WpQgMa2BtAh1RFnm4NUXChuKhIM7lCUw8pyD5J0nVjKl0NJqKoRIMKo3YGCSRAGgBaRwphlEs/XJ4W6aMikl4Lo3lDU69C9zRaQ0gIIRgua8OmEg2Xbm4J72cNNgAjpphKOloGryp+vmi3LcZvehjgxyxOPqCuJnENKf9Jlsda7pMcywsuzhukUaip+jiTv+UqSkfGdr3uWS9MJOEyE9VSv0pyx9XAYUJp2MbBSLOwq3lnF58YJY/YkFEqMK00nsLO5E5mkKxfmgZawcVwJeAah3BUHmzxtHqeOr9fCs4DgrzX7jrzjcSLI+PXzAWDF9hZsOdCheTfF+eH11N915z1MDmAuVLGytSuPQQFLQDrhSIWoNJ3APz9zHEbVl2JQZRGmNlTKuaCGhtW29yvP4Iojh/RyZtmFQq5SwYx5dyROsE6o1dhU+cZZ43uVtzG+f8iFu+iGU3HhtEFSoTGFeNiBMFmuO+9jV0sXGANOCAo6me1Ww+auwzTF9rvnTZS/eTvBwA7FoIszDjxuZ0sh6U0bBPY1mlzl+2Ke2FhKTClRytbTPJ67bh9G1JXgnCkDI+fbIn9Rbnp9HSr0nDYxh5rPdU54VR665liNijK8p1odtBf3hPCEV5ekAtrHsGDGnfM3yxyYBUE56DjYlq0dJFFFW2DLiwOjWDLABD964YsnoSydwKVKoR6TjQMQ40CNlJ08tl6W6+YIve8m09PY/mX41jkTpWFLvPB0/7JMInb+qTLDWEvV3KDdLWFRL6rIa3Oi2SrTqqepzpuKomTsmKIIzaEoh5Xl/yOiLngUYidllgbmqeP7wXEYPnzUUOs1VCGCd5vQBlJRlDyowiw1JSm8bCRS+DHKztEja7BlfwccR0noUDCZxBXsOAwJhxXEiJFyk0o40htQSJZu00nnP3FCdKHLyQWdYXBV4YWpoigZu5gAQFVJuGlQ+Kwj6yklfkUyGl3h9Alh4iUt6vct3oZnVu3WjJE/XDkLjDF88Kih+KiFSaUnRZPubdO3yANbVZKKeMnmXT8ncn6cR5Ux4emWGdqBIWBT4m/8wGTcbUR7uvMeCoEjf3nFdI1zl4yJR4Ly6XlPr+CnYpZVozDv8Uh0gQNo7QqL1lA/ffS44WioDnGqb2z9f+xdd5zcxNl+Rrt7vbmcu7ExxsamGeNQQwkQWiBAIJBKCyEJqV/KBymkV5IQwgcJEEIPLaGGbmPTXHDv9dzL+Yp9/W6bNN8fo5FG0kir3durnuf38897Wq00Gk15553nfd5mbGtg9Au3cg3H1FHluGzG2NBeUBk4/UOkxfj1LxHHj3NuK3OqVWtXCp9yyWS6t/gnVZfiY0fZAViF0QjuvGZGqPK6pbFqW+KWeozbqyn2sQghlvcegPWArB3KHzbMeJUQjWVJpcUixLHzkAnuLIgAM2SiEQ07DjgX5JZn2aChysrHhvtdyR0mDC2VZlP8x7WzAq83a8IQzw4Xb4v3fWFmxvJwiK1DN+w+Q12L1sOHl0LTiCV3ypFJ5s+NzkQaN5w+EdeeOsGiEKZ0O6CYU0u4/nzY3iWW1U39YsHgBENNWhGfF/nYXRizM+DxX8oWpBGNoLE94TkOsPbiR0McUhLDqUfY4wjfieDD2FGjKvDIDSdlfEb3riuHRoiDSsadhrJmzw+J47uDsyyMrQ9dP8vDFR8IUMbyIAQfIEZUFGJYWSEIgCkjy6ygtF9cJs+gJiKZ9mooc/BOcPdnT8DJkkn/G2bqUD/85opjUFkcwzlHjbA8DpTKBzAe8CIOFnx3khsBHG7voB/GVBXjma9kptas9UkdK8LtGQqCe2vUPfyJfC+eXbArqVueWkJYxi7OlRMzEIrzylUnjpNuT4uKIoDNeeMBfkHwk90Sdy48v5F8UeWjF6wRpobCJ5ps5Rqv+NuCQA/UlJHlDs89vz5XNelM6Y7fM2qAWTZhlFy47YAnhmH8kBLLw0mIPTF+auY4VJUU4NhxVVYAH6cXZGqnQc/ilrLzAy9HJsk0v28+xtUHCMEPL54W+JswCy4/JM2sewDLAAfA0mHmyYJkKIxFHMmNTjG9UjTAq/ve/8p3f0RMG11hBR/KFt+F0Qi2NXSgM+kfAPfs0j1Yb6qFuJvy4cNLfWNhohrBWVOqTc8yayt+MoEixKyIJxxWlVGO0g9nTam2k+6Yx7jNfuEx4dqdu8p4em0AVmyAG2+71BBEzrI7S6wIngEubTCNbEKImS0vYlGzzj1qhFUmPr7w2slUS3xclFFRuHeaX9veATR3Ek36YXlR1JNJVoROqUVb8t7DdMZozvTRBVHNM77KYixGZQgKdcM958uakYzSwW950m/f9pTHjRMneOmMAwEDr8QKofDkTSfj2lMnsi1dwj3N4fHTS6fj+PGi7qX9nZgOU4bvS/i7IqeXd+YvnzFJ4KN6rWWNMO7koq0HHNSMqSMrrO9FI0A0cDIhM+0g8zWuOnGcRSvhyVSC4F5Ni/zFa0+d4NRqNf+/+7MnWAEs/FhRNGKqVtjnczpEaUEEH5s6ItRk+eGPzgUAPLpwh+mZDQCRD5yJlJ4Vh51HebvRldLx+tr9gv5quOv95RrbkMjGGxs3PSBcTmnV7mbH77k354unTMCQEmcglft5q0piDu+/2zA9a0q1dR/+VUZPfsAJ93xWzg11w9ZGZobHpcd5t+UBpkv9oSuLJCBqIGe+lyjdly004pzgq0pilmqNaCg9+eWTHb+beViVg4YxqbrMVIrxX4yMqizCJyX0BBHHjK3EBcewXRvxMj+71KkA1CLJCCf7nro8y7++/Bhcd9pEx2KXgxCCh67/iJXQCAiWCTxBGBv4bSZXl2HnwfCa5SK40oejTFm+2YMdSUewMqX2gpPz091wt/cIYfJsP7t0ukMZx43zTfkzthNqvi8K3PjRibj+dLaLdvbUausJuKeUq3BkAj/liOoyi4ZgP5dT0lRsc1yFyaAUP7x4Gi6f4Z/AbHhpIeZ+/2zpd41tckUcQxJTJEvNni1W/ex8x/3CBomKEBcN4vgMAE986WTJLwYGlLE8SCGmOOXc5Wz60JSR5Q65GbFj5tIXvy54m+2sXbCW9rKgnGtPnYAfXDgVlSUxVBbHPJ4OdwBCSg+/NZoJYcaIj00dYQUfThtTEXjuyaYknwhRlvCXlx3jGJj4ADymqlgq9u/h2Jq8tpThzfzlB+65iqcMbG+UqzPYZZV7YTqSupRzmC24V517LcJ6lj8maK+KT811Sv2QaUeA1++XPnq4x1hxT/ZfOXOSFWgVNAF/+sRx1raseI2imJbVtmTYRYEYFGdQ4JxpIxx9+l83sYnryplsIt/pUujgNIAwfao7tJEXbjnd0WZlwXO/ueIYnHaEU5VBlrnMoPJF808vsQ3du0MsNiISA+gKFxUlSMrttouOsjimDhqGmRTpe+dPRUFE89VCPnXSMMcWuB/4OC9WRVlRNFRaahlk9Ic3zKQeYbHrYCfOONJ+VyWFEZx7FFss6i4ahh+iEQ3tiVRoM/2xhTuRNnjCKYryohjKBYeC5VmO2Uo2Zx5Zbcn3+YG3sXFDShyZ+QBvsizxuZbdfp7lWc4kfadpxFIecWOeK904B4W3X3L5wrAOIxlEtSaC8E6LyuIYbjQXJyLV5IoTxjmyPn70yMzKKv0Vylg+BMCNymw9BCK+ee5kfOVMb1renMrDjWXY3lWZmkE0oqEoZgv+87GBy6LFIgQnH27TQAgJp2sZBmG2iTQCPLFoZyhpwWfMKF9Kgb9+ZgYuOW407v38TJw7zTb2KAVe+vrpfpew3p8lqwbgfNNjGY1omFRdhrRuMK5cljSGTE/gF+B36qRhVoBLPiBqFAcpjHBUCV5fcZKQ6edyjKwotPiHfoZgS1cKZYVRTBxeismCXu2xYyulSQBs7xILWpLhF5cdbRlcYpM5a0o17rjyON/y5gr+tkTPl7ggO90lCcYTyXDwdj3EpdEqg7DuzRrHjK10GBOfO/kwT1Dn50+Wx1m88k07qyG/xopdzR5NY7+sp37gxrpotGtmICvAPN5BMQr8V5896TCvwWF+2ZXSfQ2bn146PfQW+mxTJYGPpeWF0Zw8ggBzRNjjbW5jaX1rwqF+Mbys0FJFoT40DDfKCqOmIyJcGXh2P02zFUb4wgSwx05qfQdMHVUm1XAX8eIKFtMgK4ZuUFQUxTyxNACjgUQ0r9pJtqiSKCTxe7sX7fxZIoRgiEB3e+j6YJ66H2Tprv1ACLEW/ENLnePI+yGoTwMBvWIsE0L+SAjZSAhZTQh5gRBSlflXCvmCRsLpPQahMBpBoRntG4ZyEASLEycMLhTywZl7iwA24B09psLSZy0viuE+IbClO7xJTxk14ggIk4GYfGoW1JW5fi2dZJNXV1EUcxjlBqVWJLpcsor9X1wQsZIdPGAG7Bw+vBT3f+FEGBSISbZSMyGT91Dc5hVx3xdPlKp4ZAs+KPP6KCuMOnYjgsClqMTFYDSi4ZLjRuP2S7zJc357xbFWwgO/p/7FZUfjxIneiXRsVbF0+5yDEGK1TzdKCqJSiUNCiEOdJF9wt0kCoKnTSx3g/e5jrgxpvExf8NFUd188VwPNjY9PHxloKP7s0ulW35TJClJKMaZS7rENi6i1pW0f455CAJY0XBAopRg/tNjRF68/7XBrIWBQ/0Qx2eDIkeWOXapvnnukNOtlGFw+YyyuPdX72/+9MPPCleO0I4Zh50H5TlVYjzcBLEWnMLjqxPHW78RkVu45hb+L8UNKsPNAZqpKW0CK8kduOAnHjau0xj/3vbqz28IRtLvgt+gghGDxj8+z/j7nKH/vuUwdxb6OPfc+emPmQEG/3p+PeugP6C3P8mwAx1BKjwOwGcAPe+m+CmBGhGEgfAiwD6yUsyG3+f0gytHwAT4W0aTFcye7CJqPV9z+8bx2TD+ZMw73Fl8mOAJBJM9BhXOCdgFiEYJ4yvCco2kEL3/jdAwpLZBqC3OcH0I+yo1svAy5gL9XzpNddvt5AWf7wPUIP71kOq6S8D2jES2jfOIR1WVSesnfPj8zsG45bsrgyQzTToeXeT26nDoRBu6+Qgh8A4lkZeKUjVDcztClygx36mg3jhtXJdUk52iNp7vdVm2qGHEc404HHiPgB268asTJX7rwmFEWPSObnb5MklriYjYW0XJefI2oKML4oSU4f/pIR+luOTvcwvUv1xwfeO+61rilOR4EQoCnl+zOul2lDYoNtSzo87QjhuOG0ycCxNa3/4up0DKktCBU9tXyopgVUOvuT0UxFlDIlUjC0t+yQVA79qtmjYTbGQWC1VHEoHs/xQzPD5DfsaA/oVeMZUrpW5RSzvhfBMA/YkEh7yCaV2c2F/CI3lypDgVRDYVRDdNGM36vOI/89xsflSolMGPZ/jtoCgyzXZwNMhnLGmGSR8PLCsOnRKfm4kWqLME4cD+9ZDqGl3ufhVc7lwiSvYbjxlVh2ugK/PKyY3yjyB+QDJCZA84y89eev+W04BNCgGeIy8Vb7fb0jagoQqWkTcU0Yid0ybIph007/ROJR9txnRCXcUtVAV7qhB8mjyjzBmoRktXzDvFRLgHki9buOpZ54F0mCtGYqiJ8PsDbPbS0IGsakhsyQziiEcuQD1KBAeyxjXtIu4swyRryuZiVjRFhcMUJ4wLL8fra/dLdADdItp4IEyt2NaPLNIJHVRZZuzyEsMBRrkozdWS5VIfajS+eOgG3XXgUrp41HtNHy+NS+G5gTyg8yLz8HH6L2HzF7RgZFoRu9JwrpX+gL8TubgTwTB/c95AFI+rnHq3OwYMQss3gx1EQ0TCqsgi1LXHGU4S9Zea37cpoGKJnufe6JDeofnDBVKlMlG4YmDyiDOv2ZZaYA5x2itxYZmf58SvdMkaZxsRVPz0/+AQBGgG2NrQ7Uu+KiGoE35Vk5hPBJ6JccLiZ8CVMIgY3+LsJ+8uIRqwkG7ny+H9zRWb5xSCEmdCykSV045mbT7HSqAMCj9rV7I4NMFyCvLdHj6nA41+yt2bdi9pcwJP+ZKKMja4sxqclGsIc44eWdNtgkBrLxJnQKZPHnVKWAbamvl36/ZCSmCM5S7eQYdetV0GD+1UmepuIN9bW4osh8gJwrNvXIj0u6p8DwGHDSnBYhiBggKUyB5AxuceUkWU5jV2ZcOnxYzyBt+WFUbQl0o74DhH5aAbnTRuZlZY40I/aXw8hb0shQsgcQshayb/LhHN+DCAN4F8B17mZELKUELK0oaEhX8U75FHXmug2RYErFuQ6ERmUYlRFkc0XJZk7tkjDyNeKOSzsVKVUqvdaURTDCVkaiBQUQ0pi2N9qi9DfbAZOijQMGSzPcoRHdAfXRxC31o2mzhReWV2LLh/t2GhEk2afygZilkk3GH+b5OSdWb6LJY8J2zzGVBVjo6mBe0R1KX51efaGr1/AWViEacvd4TEPKyt0JKWYMb6K8WxdRuB/hQA5wBmMEzRejB9agjOOrBbOzbmoFkSvbXfw4i2ndXuXaVRlkSeBhKYRHGYaTSdnokWYtKWIRnzHuBEVRbj1Qn8d6WxQURSzypYr3OuDkycNzcqwFZGvoXrB1gNZne+789LDjpaiWCQvsRsyfOOcIx1/X3TsKFxw9EjccLrcqdLdXRUAePC6WVnHAPUkTa8/IG/GMqX0PErpMZJ/LwEAIeQ6AJcA+DwNaLWU0gcopbMopbOqq0PwZBQyghu53R2/+EvLdQXdmdQdnY8QknFi1IStT0LkHtmewt+/MJMZGT63PG3ycHzt7PAGJDc+qkoKHFnCfmQmfKAZVvL8uy7TiO9OSl83Zq9nma06kpl5fLlims82JoeoLZsN/mAqSYT1Eot0nxEVRVl5rvKBT80c65uWXQSl1KPt2h1EIgSnTAo28oI8aEGKHRcePQoVxd3bqBw7pBi/+OTRVnZDv/TbQbj1wqPyErdwRHWZdHH47g/OBmCr2/ihIKohFtE88pb5QKlkEXzM2Er88dOZk5f4YelPzsPqn1/gODZuSAlmZVCLkGH6mAocOUK+Q5UtirI0QKVv3hxXe3Lm+M9XT8t5YZEtDMqoeH47vPkwlgE2Tn7LZagHYbB7lnuFhkEIuRDArQDOopTmppaukDO4pEt3I69pHjy84m9FGob/+U41jIMdycDz84mzp47A8p1NGQcBv+0wGShl3G9ZcIlBgxc0EY3g6lnjLI9xvqOM//qZGXgzS03VfCMsJ9jxG+L8PxNiEc1KU94XuPPqGaEmtN9ccSwuOHoUrnlgYV7uW1EUw/1fzI6LKqY4vvoj/tSH754fXi3BD+6ENbnsZGSzeM0FYfvc5046DAal+O+qfRkDFrPF8p9+PK/XA4JVF7LFZQEJOLJFUPa+sEjpFDE/ofg8IR/lDIOzp1YHLr5OnDAkbwuV8qJYYJ93Y5Dbyr3GWb4HQCGA2eZgs4hS+tVeuvchD00jmDyiLCdDRATvDLlylgGnQUNCrPZFzwwFRWN77xnLAIAQnqFXv3VGYDpT4VKgYBJibXEv3YHLwQWhtSuNyuIYPnvS+G69BxnKCqOIp7ofjNTbsBZgIasjqpGMwZs9jTDvjgcgvXCLv/Z2TyNsimMFJyIaQQRMai6T8kq26KntfhkogncUehrZOCIAeb+6atY4nDdtJN4yd88GChbcdo7nWMScRPwoD899rftB1rlCeZbzAEppON0ZhR5DUJKGsOBe4O6YaKIeaFRUJfBBUSxicYfTOsXRGTLl5RtnTRmecRAIyw1maX2BiuKo9UwiUmmaMYUul6z63afyO4F969wjURiNZE573Q/BjeWwNIyIlpn+o6CQD3SldFQEBEoOCPShFlhhCCeEiE/NHIcf/Ge141hFUSzULmZ/g0xijzuYjD5a65cEzHXcgB8kssoe9IUahsIABR9rukPDELcxK4szB6WInoKkbvSIPE8QTpwQzPHMBjMPG4JHbjgJRdGIJW8kYnRVkVQuTMTHp43EpB7gxo2uLEJhTEMqPbAmFMClXx3q/EE6miv0S+R5A2hA44Us5SWz5Sz77diE2cUcCPjEcaOwt6kLa/eGU2DKN+bf6vV2WzArOMigHshQxrJCaPDBZmRl7vy2H118FIabEnTjh5Y4MvBlQiptoKCXjeV8ghCCUjMpxPihXq9BWWE048SaDYcsLKpKYohoBFNHlePb54UP6Mg3zpuWWzCbJQM3wIzgW3qYX6vQ97jomNHhEjocIshGPejw4aVS7f1cICZtGci44oRxeH1NLdbslUvk9TSCVGYsAQC/HO4DHMpYVgiNC48ehbOnVmNEuX8q2kzw0/ENg5ROEYsOLIPID+9+/2OeY/+87iM9kgUqE5740skYXlaIiqJY6KQXPYEHr/tITr/jk2A2trJbM1qmMNDT+N88yYblAz+6uP+UZTCBadPnL3iut9GXBuaPL56GJxfvysu1GNW39x+mJxQy+qtPgKcyHwRrEimUsawQGt3VLuVpQ3PF0NICnDRxWLeu0V8gC7bsrYhqN8Jk1OrP4JNHe0KuES3Dt851etCX3Z5/hYGBhJvPVF5uBTn6yjYzzIym+YA7KUlvIR+xQl70z5iL/S3xvi5Cj0IZywq9hns/NzPzSQGYPqYC03s5wE9hAKEXqrlOAAAgAElEQVQbE0hRN2UVFRQU8ovCWATDSvPjle8rb2y+FYuAcMm8+gKZMm8OdChjWUFBYUCDT0fZykwpKChkRl+ZQGdNqcYZeaSFDSZTrj/apbqRWfp0IEMZywoKCgMS1582EYAd2FeovMMKCnlFLBJWkLFnEDY3wLnTRqDMDJ5+6et9p0veGzhp4lAcUd072QKzgW6wDKwDTaIvLJSxrKCgMCDx808eDUB5lhUUego/vGha1lrHfQExMPn48VV9WJKex5DSgm7HD/UEdINiTFWxtWgZbBicT6WgoHDIoNJM+jA+g2a3goJCdqjMk3Rbv8DgdHj2G6QNA7+74lic1oeKSj2J/r9kVFBQUAgA97JUDvRMaQoKCj2CqEZw/ekT+7oYgx+DmLOsjGUFBYUBjye/fHJfF0FBQaGfIhrR8KOLp/V1MQY1YhEN6QwZaAcylLGs0O9x1zUz+roICv0cpx0xOLf+FBQUFAYCTj1iGIb2Qy51vqA4ywr9HpefMLavi6CgoKCgoKDgg1vOntzXRehRKM+ygoKCgoKCgoKCgg+UsaygoKCgoKCgoKDgA2UsKygoKCgoKCgoKPiA9OdsK4SQBgA7++DWwwE09sF9BypUfWUHVV/ZQdVXdlD1lR1UfWUPVWfZQdVXduir+ppAKa2WfdGvjeW+AiFkKaV0Vl+XY6BA1Vd2UPWVHVR9ZQdVX9lB1Vf2UHWWHVR9ZYf+WF+KhqGgoKCgoKCgoKDgA2UsKygoKCgoKCgoKPhAGctyPNDXBRhgUPWVHVR9ZQdVX9lB1Vd2UPWVPVSdZQdVX9mh39WX4iwrKCgoKCgoKCgo+EB5lhUUFBQUFBQUFBR8oIxlFwghFxJCNhFCagght/V1efoChJDxhJB5hJANhJB1hJBvm8d/TgjZSwhZaf67WPjND80620QIuUA4fkjUJyFkByFkjVkvS81jQwkhswkhW8z/h5jHCSHkbrNOVhNCZgrXuc48fwsh5Lq+ep6eBCFkqtCGVhJCWgkh31HtywlCyEOEkHpCyFrhWN7aFCHkRLPN1pi/Jb37hPmFT339kRCy0ayTFwghVebxiYSQLqGt3Sf8RlovfnU/UOFTX3nrg4SQwwkhH5r19QwhpKD3ni7/8KmvZ4S62kEIWWkeV+3L344YmGMYpVT9M/8BiADYCmASgAIAqwBM7+ty9UE9jAYw0/xcDmAzgOkAfg7g+5Lzp5t1VQjgcLMOI4dSfQLYAWC469gdAG4zP98G4A/m54sBvA6AADgFwIfm8aEAtpn/DzE/D+nrZ+vheosA2A9ggmpfnuc+E8BMAGt7ok0BWAzgVPM3rwO4qK+fuQfq63wAUfPzH4T6miie57qOtF786n6g/vOpr7z1QQDPAviM+fk+AF/r62fOd325vv8zgJ+q9mU9p58dMSDHMOVZduIkADWU0m2U0iSApwFc1sdl6nVQSmsppcvNz20ANgAYG/CTywA8TSlNUEq3A6gBq8tDvT4vA/Co+flRAJcLxx+jDIsAVBFCRgO4AMBsSulBSmkTgNkALuztQvcyzgWwlVIalHzokGxflNL3ABx0Hc5LmzK/q6CULqRs1nlMuNaAhKy+KKVvUUrT5p+LAIwLukaGevGr+wEJn/blh6z6oOnhOwfAf8zfD+r6Mp/3agBPBV3jEGtffnbEgBzDlLHsxFgAu4W/9yDYSBz0IIRMBHACgA/NQ98wt0geEraJ/OrtUKpPCuAtQsgyQsjN5rGRlNJagA0cAEaYx1V92fgMnBOMal/ByFebGmt+dh8fzLgRzPvEcTghZAUh5F1CyBnmsaB68av7wYZ89MFhAJqFhcpgb19nAKijlG4Rjqn2ZcJlRwzIMUwZy07I+C6HrFwIIaQMwHMAvkMpbQXwdwBHAJgBoBZs2wnwr7dDqT5Pp5TOBHARgK8TQs4MOFfVFwCTw/hJAP82D6n2lTuyraNDqu4IIT8GkAbwL/NQLYDDKKUnAPgugCcJIRU4xOpFgnz1wUOtHj8L56JftS8TEjvC91TJsX7TxpSx7MQeAOOFv8cB2NdHZelTEEJiYA38X5TS5wGAUlpHKdUppQaAf4BtwQH+9XbI1CeldJ/5fz2AF8Dqps7cKuLbb/Xm6Yd8fZm4CMBySmkdoNpXSOSrTe2Bk5IwaOvODAi6BMDnze1amHSCA+bnZWC82ykIrhe/uh80yGMfbATbRo+6jg86mM/4KQDP8GOqfTHI7AgM0DFMGctOLAFwpBnFWwC2RfxyH5ep12Hyr/4JYAOl9E7h+GjhtCsA8KjglwF8hhBSSAg5HMCRYMT7Q6I+CSGlhJBy/hksqGgt2LPyyN3rALxkfn4ZwLVm9O8pAFrM7ag3AZxPCBlibn+ebx4brHB4Y1T7CoW8tCnzuzZCyClmf79WuNagASHkQgC3AvgkpbRTOF5NCImYnyeBtaltGerFr+4HDfLVB81FyTwAV5m/H5T1ZeI8ABsppRYlQLUvfzsCA3UM626E4GD7BxaRuRlsJfjjvi5PH9XBR8G2M1YDWGn+uxjA4wDWmMdfBjBa+M2PzTrbBCEi9VCoT7BI8FXmv3X8OcF4e28D2GL+P9Q8TgDca9bJGgCzhGvdCBY8UwPghr5+th6ssxIABwBUCsdU+3LW0VNg27kpMC/Kl/LZpgDMAjOGtgK4B2aSqoH6z6e+asD4jnwcu88890qzr64CsBzApZnqxa/uB+o/n/rKWx80x8XF5jv4N4DCvn7mfNeXefwRAF91naval78dMSDHMJXBT0FBQUFBQUFBQcEHioahoKCgoKCgoKCg4ANlLCsoKCgoKCgoKCj4QBnLCgoKCgoKCgoKCj6IZj6l7zB8+HA6ceLEvi6GgoKCgoKCgoLCIMayZcsaKaXVsu/6tbE8ceJELF26tK+LoaCgoKCgoKCgMIhBCNnp952iYSgoKCgoKCgoKCj4QBnLCgoKCgoKCgoKFjbUtiKZNvq6GP0GylhWUFBQUFBQUFCwcNOjS1HXGu/rYvQbKGNZQUHhkEMirSOtK6+JgoKCggx7m7sQ0UhfF6PfQBnLCgo9iDfX7e/rIihI8Mc3NuGJRb6xHAoKCgqHPJSxbEMZywqDGmv3tqCmvq3P7v+Vx5f12b0V/JE2KAza16VQUFBQ6L8gyla20K+l4xQUuouXV+3D0NICTB5R3tdFUehnULaygoKCgkIYKM+ywqAGIQBVVpGCC8pjoqCgoJABau60oIxlhUENAgJDWcsKEtA8t4t8X09BQUGhL6GoajaUsawwqDFQPYi7D3Zi0/6+41oPdhDkt2E0dyZx9f0L83pNBQU/xFN6XxdB4RAAVa5lC8pYVhjUIBiYHr+31tfh6SW7+roYCiGRNii2NXT0dTEUDhGc+KvZfV0EhUMAyrNsQxnLCoMaA5WzzIz8/F+XUor2RDr/Fx5g6IkdhwHYzBQGKDqSyrOs0PMYiI6mnoIylhUGNRhnuW/LkMuA01P0kc117bjq7wt65uIDDPmcB3LZwYindPx76e78FUJBQUEhj1C2sg1lLCt0C9//96q+LkIg6lrjWLTtQJ/dP6IRpHOw1gnQI4GJr67eh+Qgz1z34PvbkMrwjPleixBCsvYsdyZ1/Oa1DXkuiYKCgkL3YJhzljKWbShjWaFb+M+yPX1dhEA0tCewsI+N5WQ6e+OUENKtgUr3MdDvnluDtD64R8A7Z2/Oqc67g1xoMxGNQB/k70Ih/9hcJw/8Xb+vtZdLcmgg08J7MII7VJSSlA1lLCsMavgZjb2F4lgkp8GWEHkk8oPvbwv1+1N+97bvd/01wjmdp0kprFc+n/UwUFVXFAYeGtsSOGXSUM/xi+9+vw9KM/jx+Qc/xMb9A2MhcmWeKHYdZlxL/5wp+gbKWFYY1AjrRX1nU3235Ji+8vhS6XFCcoso9vMs//rVcNv2DW2J7G8agMb2RI8He5x357t5uU7aoI73LvMy59u4zUXPm1IKTVNWtkJ2SBsUUU0+df/5rU29XJrBj7Z42tfpkkjr2OLj6e8LLNvZlPNvPyEstvjzKs+yDWUsKwxq6CE7+23PrcHBjmTO93lzXZ30uEZIbgF+6F+r+i88+CG2NfasNNqOA515uc6k6jJsEiaw6x5ajLV7Wzzn5XMeoKBZ86CTuqE80oMUlFI820PBm7pBEY3IG87/za3pkXvmE4ZBB4ynFggO3N19sBNffWJZL5am57BOoPGkFGfZg14zlgkh4wkh8wghGwgh6wgh3+6teyscughLw9ApRSTPXr54SkdaN3L0LPfcQJXLdTfub+t1HnCuKC+KOp6xI+n1DJE8W6mUZn/NtE5RHIuYv1ez0mCCQYHbnlvdI9dO6QaiA3hHoiul44p7B5YijyyJkWFQxFMGCqKRPihRz4LHUqhxyUZvepbTAL5HKZ0G4BQAXyeETO/F+yv0Ifqq07V2pUKdRymFlmcDKm1QDCktyIkbS0CsiOT+gv62Jff4op3S45oP39sNWfWu3duSMx0n2+ZjCG3u3DvfPSQDiQYrenK80wNoGAMBFAOL488Wwt7jP3lpLWavr0OBj5e/J1DXGu+V+6QNNhblsxXvOtCJ+l4qf0+g13ocpbSWUrrc/NwGYAOAsb11f4VgzF5fh20N7T12/XPzxEfNFoWxcE3coMzIyicsAzyLEWd/Sxz/eG8bopqTA/vq6tpuceMSaR0tIRcOfjCytOWW7jjYrftlwu0vrgXA6rlDSLSiZVASaU+kUd8aRyLtNYq/9+wq7MyBDhL2Fc/bVI/dB9n1xUl498FOteU5iMAMwp4xolIGRaQXDbR84fJ75wNg/bW3Sl/b0mV9Xrm7OXSAtAgKKjWWD7Ynsb62FROGlWZ9zU3729DSmf14fMYd87L+TS7oCc7yQ/O345XVtXm7Xm+jT5anhJCJAE4A8GFf3F/Bi9fX1oYODnh84Q68vUHO0fVDX6UC/tjUEbjpo4eHOjfTsJCt54+CGeDZOIjr2+J4adVeaBpxUAdeXbMPm+tyW8zU1LfhpkeX4mvd5NZlO3Bedd/Cbt0vLNbta8Xn/rHI+lsjwcF2725qwIsr90m/MyhFJIdRMawB8MTCndi0vw26QaEbtmeZgPRblRKF7EFp/rW8Odbta0F1WaHj2NyN2Y3H3cH+ljjeWJu90bNyd7P1uacWEm6cdcc71ue61jgWb89tAS+jYZQWRrG1vh0FUXvAkC3AZbj9xbVYt88bR5EJQVQ4vhPZnUB1jpRFw+j2pRwYSDsKbvS6sUwIKQPwHIDvUEo9LH9CyM2EkKWEkKUNDQ29XbxBgU/9bX7Wv5ENBn54eP4OfOlRufpDf4NGCEoLoxnPC6OTe9V9C1HfFn4biVLTy5mFEUQpUFPfjueX73EEJ9a2xHNe5acNirrWuDVQ5TpghQ2WzBY19d3b0dAN6liQuBVI3MXm70P2OOwZs68gCiCRNtAVMg3xvfNq8NjCnfZuxgBNy+6HfEzYAxkUNKdkRGFQFI2gsjjmOPbhtp7dxRGxt7kT97+XvYeWozebuZiAKUJITlKi8ZTcQD1yZBm2NXYgFtGQ1g08s2QXrrl/kfRcNwqiWt7H0yvvYzzwtzfUd/tafBcyn55lt0Phgfe25u3avYFeNZYJITEwQ/lflNLnZedQSh+glM6ilM6qrq7uzeL1CBZvP4gD7fmV8cqE5buaM58kQdhu0VOTQBD2NHU6pG3yDRLCqK1riWeX0IOyxBPZVBcFG5wXbD3gqOcVu5pzHrjctIRsFkaOsvWQNdcdybjmzqSHAynzLIvfBz0GDaDjLNjaiLfW7ff9bWdSx8MLtocoNTv36SW7BM/y4MJH/9A728X9EbN+PafXFz695am17teN3/ak1z0Impab8bfrYKfUwcCDLAsiBJ0pHbc+twZNneEUlbQcM7sC/uMwV3NKZ8GXe3ez3CFZ3xbH2VOr89qOH13ojDH57Wsb83fxXkBvqmEQAP8EsIFSemdv3bev8ae3NmF9bc/K5HRHW5GDZKFVJkse8dTiXVi+q/vl8ENLVyqvCUYopXhV4E+FUZ+IaNlp6VLQrKXj+LkagSfAL9eBy/1qM82rt/5HHsXfz+INAQA/f3mdx2OhkXCGvewMI0AVZfWeFt++xm8X9h01dybRmdQd3v7+FkDZHTS2J3BFDjtcfuhK6gMiMp9SisYA58i7mxvy9hzidXpSHONPbzq1m7td/Nw2b0KjPZHGw/O9i1aNEOSaMFNWXF4PUYG3tfNAJxZuPYBvPbUi8HoRc3z/2zs1eM/HYAWAxxbucPwdROvjZczm/Vz30GLp8dqWOC6bMSbrd92ZTOP3r/sbwb29qMsnetOzfDqALwI4hxCy0vx3cS/ev09QFIv0eBa57mTt4bwpZlCFK2enZIt18faD2NGDOry5SHNlut43nlpu/e2mYaR0AzsPOJ+HkMxBbl9+zKanGKZnWbzuHW9sDNyq56cWxSIez8N3nlkZfHMfEJfxmKkWn/HRh813O851q/6xhTvw/HKWZj2iaazOhLahEeJ5T+I7oLKDYEoYQTsHgR5p86ph5Acb2xN4esluq6yAyVmWXH/l7uYBYSTKsGJXcyCHs641jt++Fi7JzqfvX+DQzu6vYDx0/4XPDQ8vzks/cu8WiUo+bXFv4NjPXlqbsxLBPfO82s3dGYtz0STPBh2JNP72jneLP6IxhaF8LeL4Oy6Kada7OOeoEVi9pxkvr5LHRNhl0aAbFJv2t+FAh//i6qcvrXP8nSkeAwCWZBlYLY4vV5txJgR8bM2urXYm9R7TF+9r9KYaxgeUUkIpPY5SOsP891pv3b+vECF9n3I5CJ+4+wPsOsC2mcJuC7k9nvFU73h9CIDX1tSiVTIZBIFS6imf4fJG1rclHAPD9sYO3OTiZYcZqGavtwNtmBqG08h6dulutAvKDX7oTOp5lI4j5tYne+KgywZFaOfb83lmFpHdT364y1LX2NvUhX3NLMqd7whoBJhf04jnlu0Bcb0nzbUjwNuC+2m++sQy7G3u8u2vFGz/uLE94SvhFAlhRNQL2RUtY9lHNOWmR5dgb3OX5JuBgbvmbPH9rj2RdvSXIKTSvTeG7jzQkfNCjuu1+3UVFsPQfbh3IsRml5AEgb23pRFtIcadMOi2Y9nl+HDX9SPzt2NOyHYhQ21LXJrBlHOWV+RAU5TvQrH/o5pmjdURjeB3AZ5VqyyaHWuRjWRpmDnoXx/u8v0untKxXkg+4o7vWGyOsQaFqcgUumgWgmyBAexYVhn8ehoRTetVjm8u/Oh5m+qR0il+/MLaUOe7H4cHUfRkR+AyW3fN2Yza5uw8JPfMq8ETLk1eCu8gJfbxiOYNBoloJKugDAqvocYM13DeS36v7i5ENJchFqTq0diRwKThcimkbKTjwiwQ67NIyb18V5NDUYU/AosOZwufrQ3tWLqzCSlXIhi3p6/MDPik1FlOHtXuV/R4UseS7QfxzJLd+LvpubLSnJu/yeRZJgSICdu2Fg0D8vdcVVIQOmiQoz2RzujZ6i20x/0NtFwDrnoaX39yOTZn6cXe19yFL/7zQ+iGaSz7nKfl6ZndfVpsdX7DRT6H525xluGcK466/Q3H9zUN7djXkvsC8U0zrsDdnwixx+9sHREyA5Uf++vbW3Da7+cCkFMUZeBziai1HgZBdMEw3v69zV34+pP2jqpfHzSsRV929UQp0NSZwqb98v4TVMLNdW39ehdNGcs9jKjWu8klctmqLIhqgY10W0M7Jt72qvW3u3PRHHIphJXYse5hal2GWVm7YVCvt8U9SJUURHCrkHErKgnAMCQe6sAyU68nSZzk/r10t8Xztj0p7Ntjx1Za9fzc8r2h7ymCG0zzNjVgu0CR8Ruc97fE2fP5jGh+9S57l9kEmQAIDJwDnO+Dwl5IdKV0a/IgYJ57Ny+UTQjs884DHXhnE+MIvrRqL/5vru355AE7fu94b3Mcy3c1Y/2+VjyyYAcAO805/0UmY1kjxBOMCAAdSR3feHKFR0u7pCCCDpex/MGWRry+xl+6q741jm89tSInTdl8IYzyimxB2pNwL5j9IO7ChEU8pWNPU5eVMIRSitICb2Y3buzIqBLZgBCC5QJ/PiIkKemOBOFTi3d5ZOg04jQCRX3wXCCTWZy3qR6r9zRj1e5gr+87mzIrPfAuyBcuIjifPNuxfN5GL69Y/H2X6R2XOcY+949FEscLo2FQKtdwdl+fI5f5T4S7TrzOHAbDoDl7loFgiTs/fOLu9y3Juv4IZSz3MCLdiHrNBSPKCzGpOjuR9GiAJwQArr7fqZf79Y8dYX2ORUjWhhGlFCf+ao4lUh/uNyKNIPv6dBvLXCGML2Q6kzre39JofR/RiMdLkCnZhafMoIi4BjeR7rJg6wFsN72lN5lcZztohFhl7spxS5gHmYhexuPHV+HKmfJcQGfeMc8y8DlEKorMq96V1HHhXV6VkmylrL777CoAwH+W7ZF+rwlefUopDIPi8hljMHt9nb1LIHhJHJ5lwSjb2tBu1UdLZ8qaPPc1d1k61r4BNMIk7AavmvKiYJlCd7sSE7G9u7kBH7p0YLkslYjbX1qLr/1rOfzAS9ca4NXNBW5Jyg+E/uJGGJWPsEbXnbM3g4LiZR9t7DB4Y20tfvLiWut93/bcat8JPVtjkFKK/a1xJFK6w7Ms8/TxIOFjf/6W57tMBrQYXEoIcM0DTKasI5HGkFJBSk7SfpNpI5QHc0NtK3YfdHp1C6KaI06FUdiCr7V2bwv+7205Bae2Je6ZE294eAk+ec983BLQrgHg+oeXBH4vIu0yDCmlGFleZP199p/eAQDc+dYm/POD7YHXemu9dzEvGyfEmAdu7C7d0eSZIzk9c1tDh++CUZYoK0yAH+C/w6ybRjBH1GfByulEAA1NSRKNe98d2IA2SNC9hUBPQxnLIRGGZypDb3tPAhyDvpAZgU8t3oXdBzsRT+lobHfK4VQIGp9VJQVshQwmZH7bc6szetK/+dQKtCfSDpF6Dj/ReL51l63ByuGmHizcegDJtIEz7pgHw/B6gWTcwqxX2tS7bVbXmsBPXlgDgA3mUVcmLn6qRohjUMsG7sBEXgBC2CDtt12XNOkL4m3nbKizOb6Sik8bhjRwKJMyivta/O/v/3uV9PwIce7Q6JRiokkX4caN+FTioBshBB2JNDoSaYjNQFyA1bbYz+DXX4NeB58MM3qWNeLwnrir1D1Z5LLY5peoLivI6neZ4Jak/MI//XNK8Tr0bWtpI1B5RMTdptElC9oKi68+wYywR+bvAAA8vWR3zotQNzqSOj73jw+xzzQCk2kDNz26VDoOa0T+PpftPCg1oEWIgdyi4XvGHfMwtDT4XRuUOpJn+EG2UCgrjCLh1ho2z/nlf9fjP8v2oKa+HQc7krjZXPTXtcZ9x4C0QX2pXlz+7Nevbsi428SxobbVsWvA279uUEcMwZ9nb7YWpxRMuYJSirvn1uBXr6wPvIeMTiQz7ERdZ/6aNc07pvAF/LTRFQHGspeiEeRZ3ibsHvI29pkHFjp2/sRESAAbG2Xt8f0tjYhoBC1dKcz4ZXC75Djrj+84yp4twihS9SWUsRwSx/8iXINxI6qRrCkH3YGfRyMI35MYKK+ursX2xg68JtnuFY0Wvj0+rLQQtc1xPLt0d8ZNwFdW1/oaHm4vNgel1DLKM3Uo3aCOxc2UkWUocRnD/13NvFR7m7uQNrwTiTsAEOBBWFnQMAAp7+uAOSEYkm1CkVLAwdM6h8XmunacPnmY9Xc2tpbbaxSLEGsCkNHxDMM2EL/4zw8tbx2/Z4dkkbmvuQuGMCkTAg/V4PU1tQ6PxuOLdkI3KJbtbAKlrO4Ko+yd2nJ7drkdxrJG8MD72/C3d2qgG/a2p06p5c1LOSY5eYXxenlDwolMh8x4NbqiCHM32lvJ7nfjnjj9PD9u3DVnM/a3xE2uoNe7LsPvX9+YtdeoI5HGM0v8A4gopfiRuRgMwpSfvI5rHliI/S25KTRkA3G8mrOhDlsb2j3HRXRn0jZMNYzF2w9KvRaEOLeoeVl+YMo13vnWJisdehDEYeNgR9JR3re6ERzHx70tdW0Wb5u45C/Fez00fzu+/+9V2FzXhq6UjrV7TXWlgCnIkFjk/HmqSpgjJpk20JEM56CqqW/Hgq32Lgcvnk6dXtRlO5sC41TcEm5Bnv5lOw9iT5OXVy32J76AjqcMj2Moau4wyOh+HIm0Ac01PxASjvbIx7NVu50KPyndQExw0GgawQV/ec/x22TasOrqxkeW+iZlcWOX0G55e/n8g84kLbpuYH5No1QuL9v5tbehjOWQyNU7XF1RiDkb6tHp0/Hb4insaco8OPrBvXqnNLeMaDKPlvuZeVQ+P0xNz5BuUJQWRqzIWve1ZEEP2Rr0FMCQkgJ0JdMZV60fbjuAW/613Brs/njV8Tiiusx7QRO6QR1BVwCrx6QPDWNeCN4cv4bMQ92Z5Pw2w+M9dki85cgL1A3DCmIDwq/yibk1KN43qmk4YO4s8Osk0wb+a1IZ0oZhGcvzaxqtwY5zgWWKF6f9fq61HThvU73UMLn9pXUe1ZOlOw/iyr8vwOOLdmLuxnoUmgucmx5dCuLiAvNrbmtox4baVlMRhdVvs6n4QSmstNeGjzEgos6VvVGMOm+Lp3H0GH8vEQBceNd7KC2MYmxVsXAv5/nu37uT0wDynaO75mzB7A11eHThDkf/DMK/PtwpVU6Q4Zw/vwuAGWZ3v+2VEuMwKPD0Yqcxvftgp1Q9pK41Efr+3YG9pQxs3N9m0Rn8gsiynbTFdyYuvGXvicA5rpxr1isPXp27qd5qn36g8MYdiPNArc9zhX4iQvDqmlq8YurQy7b+3QGp8ZQOXaeWcRfU9GS7n1yrOGzGVREGpdL5JJ7UPc/Mz+PlY3XJPl/r0hw+58/vWsav+zrPLNmN+TVeGhK/7jlHjXAERLu1h0sKoqhvTSAaIRZ32eUWJrwAACAASURBVI0D7QmMrHCmNffjGLvx3mZWtohGHEb9DY8sccx1EY1gv6tvXngXM57DTD9dSV1K+eDtZX7NAbywwqbWdSR1fP3J5Vi9x7l4MAyKeMpQnuVDFffM3YKJw0px0sShvpPo/JrGrD2HIva6VreyQV7Wqd14ZbXTgxyLEHQk0xaXFIAl3cU766X3fICoRpDSDYdOsbvBf+Q3czz3y5ZhQClQGGXKIhSMx+iHlEHx3uYG3PEGE9OXGf7iX2nDkBrLshW1QSlu8OHNNXU46SoUVDq48b9X7GpGcUHU9Rtv+ThOOnyo9L4imPSZPIhve2NHYHZHHqkv/jYWIZYnjA/o+5q78DtTH5cZIqzuomKQkfkABzqS0sUbv4+feH9BhOAWc+ucc2NfW8M8uom0gS317ZZRwiWxxDbF63juxnoc6EiyQEA4J31xoSK+Ir/JyC1H9ROh3x7oSODmMycFTmQbzQhx8Rx3u5RGpge0XcdxyradRUMgCNlQxLaH1FBnC0Cn2se982oCAxK7C0qplZ5XBndQE+ctN7YnpTsffprXQdcXP3PDrzOpe3YVIxrxePNFGpNh5LZIftiklwBy+UK/S+4+2IkPtx2w/qZgmUrFxY2bS0olOxcNbQmPJ9cfzPv+31X78JDJFea/IwSIaXyxYV+rPZF2SJ6JMKiTbsE///3drR76JD+LmmVgzgF5mTsTactD6zZmi2MRtEmoGZxGohH/IGdKmQpG2qAoK4yisS2BT9z9gfRaQ0uc9JpMAX7l5kLjpy+ttcpxwV3vWc/R3JlyUP9kdBjuFBstLOpF3PiIPf+9sa4W1z282GrDVppsoXH8zzPMhvj49JEYVloAAq+zjHul+7GtrIzlnkRSp/jsSYd5tAxFxFMGyoti8i9zAO9HPDvdE4t24vMPermFad3w3YL9+cvrENGIJwDm20+vdNxj7d5Wc/A3BxRrEHU+bJPEU5J9ymXm/TUMiic/3GnxGGVw3182wLgNFs3VEyiohwdIMvClN9e14cQJQ6y/61oTHp1lfj8AOHx4qWNL7K11+wN5lKdOGuY5tvtgJ779NDM4mzuTOP33c6FT28MjGr+ybUMRbICnVsbJl1ftQ0QjaDCNC+4Qa2xPWMFjTCs0eIgTo+s5N1CnFIm0IZ1wAOZpWrqzCbsOdFpebDcKBepMQ1vCMQBbfEHzWGdCt+7LIerOio/gF5yyzmeyBpi3vSgWCWVkifXlrjuZhrjbs+zLcTScCTEylSUsxUPED59fE6j7LFMgiGgEEddi9FMnyINM/VDX6vVe/e51tmBrbE/i3D+/E1wmoW3cM5d5xp9btkeabcxP89oPolEkBlCNrCjyJLmJaMTaqeEG2KX32IYSRWZjmcBf0ea0I4ahuqJI+p3Me/nmuv14Zsluxzn3zKvBCyv2WiO0W6bOjsWwjx7sSCKl27tMwTQMNpY2dyYtGgpHPKVLHSmb9rfhxy/K6T0iFQwApo4qx7lHjZBKZPLTDIOVIyhBSjTCFCsunzEGV88a7/iupDDqMcQ1YtNIyotijh0C3idTuoG61gQeW7gTf317i7Xgl2X5bY2nHfFB/B5BXfarZ7Pgez5m8HlAHGdLBQfNTGG+4uDvbkyVvB2JNDKNEKzd22pRf867k+2UyMZQjQQE/plozEJOtLehjOUewLKdTXhtTS12mYFWxGf03d7Yge88szJrNQkR7stahqyZmc9PiueCu96TemcppXhkwQ5QCodXWYQ4wVeVFLBBI0Tg3fp9rdbknKnTfOYBJ3eZUiBmepafXSpXTOhMpmEYFDWmqgHv9MyD5jxXvH1XSre8YfWtcWzc3+rg1HJkMu+7Urq1hfinNzfh8nvne9QwADH4yVmO37y2wUq2QWnmiHOAGVdcxYEblYZpHBTFNF/6D4dIkXEn8/jWUytw5Mhy7G+J44wjhzt0RflEoRvU8qawcnvv8dvXbIOEe4kzcWX5IoIQf0+vuBuwvbHD0Rn4b/g7NChFWjfwnKm28bNLpzuuJe7IZKv1edtzq5FIGyiORVDT0O4xAADg7Q1sMlm1p9kx2blvtU+iIb6l3iknx59t6Y6DjnrUTSPkwfe3O87zQy6av35b/BxpV7Q9paZx5DpvWJbBh2WSrfn7392GRxfsMN+df18RaRgAy44JAPVtcalBtb2xI6s2IAZ/iffSNO/43BZPY43J6127lxlIzh0Z587OtoZ2vLLau1j0c+B+7/ypOGxoief4vpa4VL3k169ukFbdGMGrSFzBtay2nfSytEFx/l/es549LA1D7J8AW/jwRa/4bhjXVm6u6K46owCKCiIYJVk08Gs3ticwprLIV/nkuWV70NKVQkqnqCyOORbmgF3/933hROtYNGJn8hOz+gG2Rv0ld3/gWGyy+UH6WEilZc+cnWJExKSniYHzomIP7wvOO5jtV6gYdz/h4471vl3XkBVRM5+1qTOFP7pTqJv/v7gyN5nU3oAylkMgW73SV1fX4ojqMvzvhUcBgG8qaT5RnT11BL7x5PKstQm/9MgSdLmMIfd93NI5HFsbOqQTRUdS93hW3EoRBgW+ctYkAECBufpmz8gGCb/OfPHd76PV3KbJNEkvkkiPxSIETZ1JydkM33pqBd7ZXI/fmBQB/uQRzWuci3+d+ru51rnvbG7Ag+9vl2qBalrwJLBiVzNKzMFnjmkcybzR3MNrGM56KCuMOraFw2zHpnXbOLndTI3KvXsxTfMMxOUuo+O8O9+1uN1MT5U6vN3EvN7UkeXWexXTqTq8KwS+me04ePvM1Nb5JMGDOmVwB7+I75i3dX7G/tY4/vH+drxrBpacN22k47dpYQGTbXjC00t2o7krhdLCCF5auRcvSSTOvmRK8LXF045+575Vq0An4FxATicC2E4Cz7J41X0LMWdDHf72DvOU8hiC55bLF5NuRH2S7AQlVtjaEEzH0HWKSMQ95ni1ZN3vLhP8+sLPXl4n/0IA97hz8P4iG2MA9o6yaQKiUyGe0q22u/tgF2pdXviqkhg6+ULTrHtxjOYKCPuau5BI69jW0IHnBZ11q137VIhG/AMXP/Ch44mLchmVrj2RdvydSOuOhX5xLIKimGY+SxjFDWoZTrKsopplLNsHU7qBAh9jmWdKfXtDHf7x3jbr+pNHlOGCo539/JyjRgBgVLLh5YXW8/M2wdv+XDMuJW0YZjIT5z15uS88ZhRONulxEUKsPAfuBFb8U2s85Zx3A2gVspiWwqjmVSZxQfyJprH556tPLLOOicHsFi2FeoPcRWPZTaXkChnWOcIzXDlznFTHWiMEf/GhT/bnZCQcylgOgV+/uiHUeTc9yrg85UVRTB1Vbq3O/aKrCQH++pkZuHrWeBw9pjJrL8+ibd7gH/d9+KC6anczvvvsKvz85XVWRjCuJiBie0OHg/cIAOOGOD0V//xgu9VJuNqDpWzg4vu5B273GP+SZCXJV62i14yCGeYy0XKulJE2KG58xNYF5h7X0sIoDrqCENydc6TLC8Ek1JyFzaQD2ZXSMWVUufl7PhH6G9hFBRHH9UZXFllb/fyoaLhwQ09UEJDJz1meFnMFwz1Z5kM4DIEDHUkHZUE3KFI6tfWHW7rw61fXIxa1F0HiK3VrLJ/1x3ekPFD79jY9xA+GQTG8jE1kKd0bSc5RIDz3xGEljrrilyeSiReAp874dnmuSYQOticxpqqY7WAEtJFEWndMlhccPcrxfZGwMJVRcu54c5ODPrKvuQvLd7L6MajTKPz1qxtQUy9PUnTXnM3Y1xKH7qqXxvYELvqrVzfbD+4AY28iH/nvwqQFd5wvMa4t4ynD63pnU4NDc1rMHOkuhi2TGK5cV/59gSPDom5QhxeS7/BxaIRYW9b81mndsLygBzuS0AiTUFy6o8njNeXBwX5rjSAeunvs+urjyzzX4qeI16gsjjl+e+MjSx1BkN88dzLuncdk/cJwlg1zI4AK7VUW2Oz2LLv7rP0dWyTWtSawtaHdDKxm9/Fb9+kGtRSSCGEGP2Dr8fPypM1ru8eEnYLyg6jqw+HeURTtfPFaQbWV1ClibgNWCw4+pdTmzPNyuCHrSwYFzp5Szcpkfi2e5g46ZcF4dtsUS8QlVj20NQLHLiTHnqZO/GXOFrP8vo/W51DGch4xZ4NcJcGPlM+zPQGsM+WiTSj24cclGap4v3l80U7saerEIwt24Fsmx1W2UOd8V12ghriDYxrbE1ZH4itowkY/D7/NvQXqphZwHrQITg95U9DZ7EzqvttwS3c2SfV5F247gC31bSiJRdHYnnRMAJyPfIXJnRSl5dh5Xm9YJi5jcSyClG6YARzsmN+75xOFQSmaTW/5nA31HmPjh8/bPD2+fbdWMH4b2uKegZxJ0vnf++63tzi8wxDKyr0hnON2yXFj0NieRCyiWRNPpkWdO9jqGoHvx+s0SDv4l6+stxLrMF6zPHirSgh+ueKEcY5rumkYbrgnDG7gEzCOtky+63MnH4bjx1dJr0dNTn1nMtgruXZvq6Mde7d37XKFCfYT6UKywM6GNvlODA+ocXuWU7o/j1zEnW8xb/en/rbAcVw3mNpIhbnVy94DwbIdTY6JMurTl92QyQJycCfm4h0H0diewIfbDlg7G6LW+AYJH9QP9qLK/y3WC6ooy3Y24ZLjRgNg7dy9yObBTVaZCbEWrxRsgXzKpGHWO0ykmSezoiiGlq6U1Zc57HbtrY8XbjnN0YfdcPOnbWk4+xg3xNoTaYfRZFDqCVbktxHHdHHHwM/7zXnCnLsMyBdVDjlHSdtO6waaOpL4yYtrsXRnE7Y38qRC1FIu4sY77zd8YcOCETWbhmFe+mBHEmndsPre6j0tViIZ+7mpI45C5o1PG9Si1AHAWaYhCrAA9DBIu7zpuw92+gafiklVClxqF17YxxKW1Kc9TxMA00ZXZFSs6hL45WKZeFC7m4ftx7M/0J7EvI3hFKb6EspY7gX4GVliMIzbsNnqw1XzQPjN7S+u9XQk3i8NSrFkB/MCBW2z6uaWpTiu8i02ADjbFB7nW0F8Fcm3r0Xe6yPzt3uCa8LE9fGt/fZE2orCv+6hxb7GclqneGHFXkeGK47X1uxHNEJwz7wabBO4pCPKCzGivNATkPLiir14YcVetCd0z2Dh1huV4e/vbMUv/rveqgNNI7js3vkeyotuUBRENDS2J3Gx4Mnj1y8piIAQYnmSxO9++/oGy4D84fNrsGpPi6VOATBlk4jGuGruiZM/EZ80mPOZWp/5IokPglzhozCq4fv/XoWnF+/KaCy7DXTZGBnUBjsSaWtg5ZrMMsQimtU2KaijXJZn2fzb/d7ci7aulI7fXHEMkrqBN9fV4Xevb/AYptVlhTj9CG+QJbs+6wudST1jEpHaAG1hcZHKryNSRmTxDVTo4+4225WSG748SEc3vO0yTKIQP8cA58gfNarCLBNrS8+v2OtY5IX1K/Nn40X6+cvr0NiewPUPL7baCA9mfvCD7VixqxmUUkvqDgCOHFmOo8dU+NzBWZLfmv0oqJuf9Ju3HXJZnAM6qrLIscvGMW9jPfY2d2Ht3haXvCHF1bPGY/zQEqtNcBmxksIIS6LjcjYYAQvWUZVFUl1367euw5cePwYjKwqdfF8KjKoocixsNHOMP/FX9la8r2KIhArlPQeIpw3T6cKNZe+5VS4lCHebWbz9IL77LHO21NS3W9kvDcq49d/790qr7vg4yGX7OpO6mWmROXpGVzLP/rUPLcbf3tmKdftaMWN8FXYd7PSMo54EI5Z31T6+bl+rpeYD2Lx0Stlc9uvLj8G4IU61CTc1TaTYASz5jJ8twQOnKYWDRud+B9efNhHV5bYcHV+sUwpHQPuM8ZWSu9goLYgwyVXr9/Z9omYb9I65cuxu6sw56VtvQhnLGfB8SP4fx19mb3boqAJck9L+O5HW0dKZckROsyAK+5y1e1vw5rrM4vLujuPeorH6WkinNTduRUPhSx+dZH3mjZrzbrtSOpbtbLJS0oqUk5+bRqObxsa32AG5IcXvvXZvKz5mpiQFnIOACL495+cRiwh1bN2Dsu1YO3CIfbdgK5NRmrux3puUBE6vrl8g0CMLdlhBP7LJoLwwaumxJtKGw9PAB+J/XDsLM8ZVSrWDdx3otAwEzhF8Zqkd0f5BTSMIISxbmMubxOugM5lG0vRiUQqs2NWENiHDHTcmuTf7NNNIfDAgLSwvqtue48+uG9TS9PRLa83KaNdbIm14JNs4CiIavnImi/6m1Omttt6La5uQ9we3PUipLbHY0pXCa2v2Y+UeL/0jyBC2vGQZjOUgHfT2hHNLH4DD0HOPCWLzE7e1OWT8xt0HO6326V6zyKTLfvHfzLxgd5njphdSNB7DpFvm4DQgCmd7eGcT65fvbGqwFuF8wp+9vs4af0SDJqox/moYPLJgh3XfIGzab9NbHAGb8D7nDY8swbubGnDJ/33gMHzflwTcMXUZZrBu3N/mkDarKrHpEJ1JL0VHM/s8f6c/cO22uceqv769xRPgRuH1RhIwSly7K57CWmQLp4fZMTAocPy4ShxoT1q/FYvGLzdxmJP+594hSJuUMfu61AwopehM6kylidg0PREdibS5sGDl5/NAc2cSd87ejNauFCqKY4indM986B4D+PwWTxmWGhIhQMowcO88uSb50NICXDlznOPYHW84lVmSuuGhYTBbguKV1ft8d0yGCIsM9+8ri2NSo9WgFGOqivGTT0zz2CIyaBo7h99rtTAvMu1o5zv96uPLfJWexHcT0kzpEyhjOQP8FCEAYM76Onz3mZXYuJ812m+feyT+5+NTcPVHnDIzIMRhxM761RzM+s1sfP/fq1BrGg+i5BPAjNKyQi+n+NEFO/C71zZY2+i8E/tx7bjRE7YRpg3DSsXJITNSh5QySZs1e1qcQWlwDspuz7KXI+gtg0EpjhpVjuGuiPkJw5yakKJ3NAh8QSLLQsXTpLqNjLvf3uKZ9LbWt1tBdABw0V/fQ2dSlwarcV4kXzi5n9MyllM6Yo4AH/Z/SUEUhBCHrCBvH+OGFFsekv/5+JEAvNy0CGFvXhbECQC/e30jXlm9z3ofPIiMv3d+L+7dGV0p19yUwe0l4uolNfXtFvftzQypbCPWBOQcYI+otttALOrdluV4cQXjwtueZfb/V89ixrXbA2sIEyrXE5e1TffiQwR/je6JeUdIjWJ+Lv89v1eQWg5foAKs7XBD58+fPh6A8zmX72pCS1cKZ/1xnvWbl1ftNe/F6UNOTyal1KHhy+HH9RXpR+y4GF0f+OjoSurQDYrVe5ox69dz0JFIWxq6lhSWaRBWFscwayIzTKaMKseUkbYxbGts12HNnhbv7paJKSPLfGk6onFkGNS7ABJ+55jsJQsWEeKYwulmda1xHDeu0rqWQSnGDilGZ1J3aAiLFItSc24Q6QCEOIOZ/+1akMroGfaeEsP+lrhl/ImqCEnd8GRB5ZcTF7Oi0kIQDaOyOIa31u+XBmzzn11130J7fgPQaHq7W7pSrK1Q6ghatL23tpxlTCO46K/v2+Oa0LeYZ5k9PW/zVuxCRGPeUdgLGA7doI4FLH+nBRHN8e7jKQN/MulKbtlOWbKqZhd9La0b1txgCO3/tufW4BtPrsC/PtzpqbsvnzkJ//naabjtIiYuUCyoXQTlW6CUXZu/syC1Ko1tR0IXvMfiTkSJGYsjeqrfWLcfi7YekKZcryzOn3RuT0IZy1lC1EFN6gYuO2EsXl8TPPETwGGttiXS+OmlR+Pj00ei2Wxkbl5UIiX3qB3oSOKzJx1mJSPpMj1HS00KAr+CuDULwEoH+s1zJgeWlW/DLhSE6mUeIZ4hSOSobW/sQEHUK5njjXwPNt3jKdvjKWJUpTOb0RlmdrhMCwE+EIoDHq8XUQXBDfcxd0enFHh04Q78ebZTBkcEH6zSBnXIuKV1A4XRCBJpp/dAfOZF2w5gnRAgxL8qikUsjyGfwA64AicsGoZrkufndyV1S9JLPMNtLIvX+8Sxox3H3ClL+W/8srIZlOKqE5k35SMT/ROsPLt0j9XmeBpg/g7Pm25TEqKuLQvR48OTr3iMOvN/jdjvJm4mLOH3KDF1SGVtYvYG/77Ofz/Epc/9uX8skp3uMCy44sxlM8ZYHHZLYjBgOSi+ppr6dmuy4xKGGmHBvS+t3Is/vL4Rjy9kGf5SOsWEYSVWYNYvX1mPV9fUsm1/4cEd2eZMJQE3Vu9pxjX3L3KU2e5zgvGYwVj+8Ytr8N9V+6zdnVW7m8GTN3A6lsYNHAIcO47xxw2DOrK+8WZw4yNLcdeczdaYduqkYQ5vZRDVQvzq4QU7cN97W83fsG/EBfLra2tx1pRqy6srMxJlXliOXQc7caq5c6NTp265LmgIi1S9a0+dCMBpABEQk6srfzAZdcNt3BdGNUuH9y9zNlv3Tbn6NBGMvZRuWIvQqGuhJf/MxrCmzhTeWOvtT6Kc2el/mCsUlv13/C/ewneeWeFZwERNWgWl9u6WphHmoae2EQywds3pAnwBJtzCMR+ndKdnOm1QnHy4TccSaRjud88fe9yQYsRTupXCW+wfblDK+OELtzHj8oUVexxj62JzMc93UrmT4JvnTEaRoEwilg2AI9+CO8GLbrYDjcDkk/t3DoMyG4YnegKcXTuisXwI7oXQc7echouPcQY0A86+FpQ/oa/Rq8YyIeRCQsgmQkgNIeS23rx3LnBriqZ0A7ME3tb+ljgmSDQt3eAyORzfPGcyrjEVMCaZaZiJ6xzdoB5PKgBLkJ435ve3MIOFK3a4B0p+Hhf1F1ePbj4gwAYCtxdGNsDz7Xkx+vr9zY24etZ4GJRaig06pQ56BKXwbIm6jS5+v5TLo1Ycc8qeWVu1Pv365jMnmdfzH5g4ZAsC98DnjsbWCEvcIioK3PeFmY5z+MC1fl8rPnnPfOt4SqcYUhJDbUuXg4stlnHj/jas3sOM5YnDSqzJoaQgYgXb+D0Sf89ur6R4LwIvD5ufb8vbmR6aiNc7//paOytbZzKNlM685TxJCscXT5lgPRtvW6dPHi4vOC+/a4FVZXofxOd1eyRkXt8xgkf8ypnj8Pd3mNFDQKz3efwv3oJT19q/new+6NUZLi2IWNvvssh5ESJF64kvnWx9vthciIyoKIJBgWvuX2i1/yAOMZc4PH3yMCzeftDyqM88jBmSGiHYUt+Odzc3gBDgT28xA0iUEGzqSKIjoaMzqTPjQbjfvuY4xph8zmiEWDJZIjoSujWB2yowtvFhL1C8z3GOYIDzLfSo+FtXVfKiETh3DRxa38L7S+qGxfs9ekyFQxXDoP4JKcT7dibS1u7ZzY8vw/nTRzq2/9MGxX1fOJHFehhyzzK/XpFEgWjB1gMYXlponcf/tcVTpuyd7ZkP2h5nxk54NQx+P/G1vL3RmX4+nmLe7fvf24bOpO7YsRGvx6kwflQbzl3nXl8+nvBFzjFjK3DGkWxMEAPDOJf7y48tdQXZ2TrV00czL6/oFOGLq2qT8sfH6JmHVeGOq45D2kygctOjS9GWSHvG9ohARRAXtQBMrq59vvi++fgmq4WH5m+35sJ4Srd2TsRnmruxDnfO3ozmzhQ+Onk4Sgqi+J9nVlnGvqjRzOv8DXOXzm4ntqPJb+y4+G6n4k0qbVhxR2nZTooEt/5ntXT+iRCCps4Ubn3OmURmyshyjwQfe3Dnn257oL+g14xlQkgEwL0ALgIwHcBnCSHTg3/Vt/jmk/akrxHGUysuiFgN6ZevrA+1hSDyu9i1CAqiGi48ZhQ+YUZS84GEwy91KPeqGJRpInMvzKb9Tskxa2s2YHC1vYjCMZ16DBW/wDr+LNa2sUFRGGWGA4/49siIUaC63JZpO+PI4bj2ocWeaxLAI2vlZzP4eVJuM3WuAWagO1O2OiH1LAuftza0eyYCLk/EDzMvnPOcYmHrUvxmc10bpo4qxxOLdmF0ZRGuP20iADYBfemjh1vX50gb1BqYCqMRa7L2G9MiGkwaRuZBjxk0tkf5ihPGWtq9DuPHVUdOfVjzeWMRbNzfhiFmJquqkpj1HHubuqy2FSR/BNiTjrsPiIO4+zuZdq9DU5QIx0yX+g2nT0Qibbi4v+b5knLx693qalt3v73F5MtSzztxP6ksaYT1DOY1Ptx+MJDb7C0rS0/PvUzcu00I8zxtrW936goTm8b07WdWmhKH1FTUEK9PUWh6+gwKnH/0KAwtLXDU18Pzt1uf+VhgxWIAeGwh2y7+05ubPB6tY4TtbNZeDQfPX5aNU9bfdx7otK4hfv3+lka8tHIv2hNpPPjBdt8EOn+ds8Wh9kBBcccbG9HUkXTEYcxeX4fK4pgjUczqPS3QNDYef+/ZlVKDkd+KB3X97lPHOr4vjDkXzO2JNEoLoo55wL376H6GqKYFSse56SIAm2deWL4XTR1Ja/eRawUDbCdVI8QKCP3BfxgtUSPyfiIanYQQPLt0t0Ut4RxhapZ13JBiqadf1JV2PKvw+bChJdbC/uPmbhM3Eg1KLX4sN8ZFWkFBhCW3imjEMqojAoWD/c3quiuZRlI3nAlYXCoVdnu1+csy7rZjjAGjk4wfUmK9U0opGtuSqG2JM4NcdKJI5vFoRMP6fa3S+I/mriR+/cr6jNQnjhN+NRuvrK7FxGEl6ErpjtgJP3xQ04hFwg60XS4izUQKyJNRueeCILpIX6I3PcsnAaihlG6jlCYBPA3gsl68f9YQeUYGZQNyWjdw8d3vg1KKb597pDUpLdlx0EFdECEO4C1dKSkHkXufuWwNS8Esb+nMWHY2MCvltHmYe9CCvKmcV/vQ9bOsY6+s3ufxLPvpW7Jy29vfusE0fw1K8ciC7Wa5nJ1jT3MnJg0vtXLS85X/p+9bIFyT3c8dSCF6esWtJr8nFOvv1guPCtx2lW11i7v8H7/zXYmx7PQitsfTHqObL6a4J4OjI5HGEeauwozxVZanSlPvogAAIABJREFUxaCwpKjE5y0tiGLicO9Og9/7ralvR31bwuNt5QMTX3SJ3jlWTuowhrj+tEzuTMYD5c/LjQFDCFC6+fFlFhf5uWXBmZp4VX39Y5Mdz8mru6Io6pX2k1xHPOf1NbXWeRphdVBhcsINSq0L8Brj95p426vWTkl1WSG+dc5kfM1MKzuqoggdptHJ68M9+IvvqC2ewrzvnw3ASXGgAMZUFjl2odwJZADgJBd9JWVO5ITYCiruukgZFKv2ePV+R1Swvpc2DVSDOtOj8zqwF97UE5MAwNINBmyjhF/j2HGVFvVm3b5Wj6zg3XOFACgC3PrcGsvQMajMs8xpGHYZ2wV6k0wucXNdu4M2wduWeN5f5mx2TtIUeGnlPlNCzckvnVRd5gmc1AhBPKVjX0tcuvCeayYpmja6AgVRzdN3RONLNyh+//pGM1CKmoklaKDXGAAqS2KWbBfHM0t2AWD9ZfKIMnzC9ChecNd7AFga+I6kjq0N7fjTm146WVrQXAfscUAjxOooYokiguoDwBSakuZiNBbR2LuiLPDMMKgjUJE/2yePH+P7jADjmpcWRv050cLzy+IIiDlnRTRnXyuIalZfNijF1oZ2/OP97XhswQ7H9VvjaaccqrC44yow4mJahjGVRbh8xlicPnk4/s/sAzqlSBkGYsJ757aCO+MdwBakNQ3tWFDjtDs0QpBIGXjwg+1ZJf9ZvafFilE5wdyZ+tzJhwX+hpdLfBeHDy91BMCKmL3eK1og5kYA5E6P/oDeNJbHAtgt/L3HPNavQCnFnqZOTP3J6x4jsSPJJK2aOpPWljPHoq0HHCklRQjjCr74zw+t7SMRETN449tPr8SPX1jjUMpwXAv+3hVefgA40qQ6BO2mcM/S5Opy69iLK/dJjBD/xlvflrDkpywvCAXmmx04LkwqD9/wESw0PeF8UuUrUy5rB7At/5r6disyncukieXiW/vsGTN7TzUC3PjIEuGZXJB6lr2eU8f3BFi3r8VS95Bt63KjIWkaJKdPHobxQ0vwvWdXWe2LUsE7IRiX4rUKohru+dwJANji5a9vcxF3+bNzzu46V1IEDkopNtW1WbxmLrt015wtiGrE8sDxnQ/+XgHgKya95VHTY/gZIaCVe5Qtqg9c0fLmZOqXTYyD/96dTrXGlP/jBhNfZOxu6pSm9hYNvzMF0X13am+xGm3D3D4oBkp+WqIbze9F4TXw+E8nDivBYcNKrGcSt3fZdjgxZRv9dw0KYxpmjK+ygl8PdCQxpCQGQogjaY5ItdJ1w9OnT500DFeZ0fiM384T0hiOOhMzWeqUWmOZe2uagy8eR5TbtAK+gOK/5/jex6dY9wDs/sbHBDFQi4Nn0RQfJy4YXCMrijz1H0/pKC6IWB5IXq/iQgCwA+4ATgGxyyteU2ajRYQxmdf9vZ+zKVnzNtlby4bB5hiAcVBPmTTUUedikpO61jj2NHXi9N/PBSHAN55cbvV9GXj6ZH4NPtdcfsJYTK4usxJFbK5rx4kThlgLNgrWfq89dYJjt3R+TaNDC5zPSUndwJcfcxo54vdWeUzj3aAUVcUxJFIGo2FozsQVYtvlY4gf+G6Z+zXIkibxd/Hmuv341MyxGDukGBGNcbCjwsJieFkB7rjyOOt3tS1xNLYnMW5IMX70iWmOe7Un0o4kVlZuHMqM5GmjKzz66YBzV6y8KIaPHjkc44VdJoMC/3x/uyOwk9M2HhJ2b3jfZzrR1OEAAWDRgQBbxSgMeJwLYLdxt2KHiAnCrsBcQSe5IKKFSk7jh+78tifRm8ayrAY80wEh5GZCyFJCyNKGht7nruxt7sKrpk6tO4K1tjmOtkQasYiGRFp3eAOCzDWRF1pWGPV4V9g5rDNNHlGGkRVFDskgN9wcaPu43Uk4B1JmTPGJrKTQzmDkeE6XDqxPMQCwLej9Jh9ad63WAVtCCmBBXVw8vTgWwcRhJdJVZEQjDq/bU4uZd0Q80+HlzWwrQyPEkbkLgCNgrUIy+Qf12c8+sAhtibQjA55BvTxn/ifPMjVrwlCMrChkPDmBksDrQTS4xWtxygdge3b3NHVaNBw3KorZ8/hRaOIpA/e/uw0AwXeExDB7m7scaii8DqIRjZWTEGm7dEu1WV5W6lx0ZCMf9tb/nGnFDfCJhvPZ2ALDliF8fvleK8hVBJd+HFtVjCkj7UWhuI0MsHofWlKAc48aYd1LNyieNSX5ZFHcgDnBwH50SpkBJCpz8LoZP7TEEZTIvVAiiLBbIRqK6/e1Yn5NIyKmt4l75HSDoigWAQHbteJ9l5eJB8jFXMGQb6zbjxMnMC91VCOIaCxo7X//s9rBGaWAg/LDrytrV7rx/+ydd5jcxPnHvyNtud589p37uXfcjY2xwfTeOwk9pACBNBICJCEJSQgJqaSQX0IaAUJIgCQ0Q0yvNsbYxjbuuNvndudrWzS/P0YjjUYjrfZu7/burM/z+PGtViuNRqPRO2+leH/LAcwd2Qf3XjgZL3/lWMd4lv0gbzqeZXLhi2I+NOrMWA1Kgd+9utHxmz2NbXhsyRbHnCQeVuWq0Gxm2ZgvFIZg1+bcT1RicHcBQojLjY7PRyKEiFp1FqgpZ/PhpAxqxZC0JtM4c/IAyz0jpmtWwRhCbDe37QdbQSmwbOtBjBHGsQx/B/xyEROo+YJc9c44WogbYIt0d/9R6hz7/O+IRiw/dRE5bz3X9FPK5hD+t1wOevm2g0JQuvt4gD322gRrpZiGlJr/xPaLi507Th+PQZVF0AgRNMts3/pDCeUCvjgWwbQhlY5tPCiSv0c1aZ5W5drm7fPDMCg21DdZ91zXiEc6VHbwEqvwj9Uw85rtvpWDoAFYhXvYb+1WTRhQZlkiG1rYeScP8s63LGbaENE0oNnMW+/FLSeMcnw+dkxfy1qczTuiK+lKYXkrADGn2iAArqoblNIHKKUzKKUz+vbtK3/d6QyqLMKnjxmB9+48Ea/eusDx3WNL2Itz6/4WXPLAW5g93F61+Zv47e9nDK3Eg1fNdO3D/a34Aw/AXFkywdow7KA5Jji5T6iq3uQ2STa60uRkQh66s4ZVWYnNB1cWIiEIxDwiGQCGVxc73DB0QqyXeW15Ab555gTXpHLW5AHYtNeZaut7T7P8k/YE7GxUkKtQPX/3Xz4Nnzdf2JfOYuammrK440d/FFb0Im9u2AtKmemKZy0RtXDWIcwtScENw54MRM0y22IIE63YZkII6voU4ccXTraO887GfXjyfXXhGr6Qi0U0S6MmwnOmJsyoa5FkyhBcYOxGyCZ6zgdbD+K7/1nl2LspkcKn5g1zRdv7ufSI7G5sxeiaUuxuUAdx8gIcom+gKk0eT1snvjMIbC2suIDSNYIjBlVYCzXDoJaGPZEykEwbOGJQuUNrFNU14X6xP37z8nrM/v6L1j5p4SXGL+Oxz8wBAEeVL4BrKO1r5Ny38CP8d/kODKwoxIptDda17mlsw8rtDaZbiVtIePy9rfj1S+uV/c73iegaqopiqD/U5povDGrnxn7loz1oSxk41JpyBT8DwPefXoXv/ncVblgwErpGUFEYw13//tA6T1rxfPBrAOyxw03PFNSVdm9XQxt++Owa1B9KWPEQ4pxHwXzG+0gZSUoLoq7Fr2EAB1vUudl5VgU21RCs3NZgCfvcP1pEDNAmIKjrUwxdY/mdT1I8fyKXHzkUI0xLYFS30+Tx+VkeDz8y0wICwOpdTnM3d8PggYSi24SfBY6a7Y5IPs9bD7Q4hGUufPHj8kqbvOJlVGeLPcutyzBgGGbgpuleQMHmJXnx1GwW0HFmA6GuxckOQdi7XHQTMH8mXiZftIuBl8x10HD4d79sLsJnDatCRCOWIoNbS0UfXK4UsTW59sCyq+CpFApiU933Im1QXHVUHY4YVI4dB1vRlEhZKQIB4BOzh5jHcWrWLZ9nqz3uhSmnOeHUioutZAsZPuao43pUrPZwtSCEae55VhUVlx851PH5zjPGKwX77kRXtu5dAKMIIcMIITEAlwB4qgvPnxUFUd0ykfBn4dJZQ3Cqmfpk5fYGTBJWXX4BS2IaNEKIK7UUwF7mS7fst4JcGltTqCyOWmm43vt4P6568B3rGErNsiK4Q07j9cyKndYDkEwbuHpunauIiqr9Ik1tKVSZvk26piGZprj73IlMQ6XbqeNqywscbhjis2Cw2dnFsOpiy4VD0RIAbMKWq05lQhbyeGAH38r7RHxgNcIKq6gQrQo8MEj1MrI1y4ZL6yLmprXdMOyJVpynqktiIISgOK5b18v/V1Uni0V4UBZ1lFodXOkMLitXmDybEik7bZnQhrQyDSDTaK7a2YDasgKrzW9t2IfbTx+P1pQhaaqDCcsPv8MWphRMOFAFNmmEvZzPNH0ckynnPq7gUhPmhsGOzd2V+OE1YrsOicFVP3h2NZ5Yug2//sR09BNeNiP7leCjXe5AvPpDTMg2DGotGHXhJcZT58m5cDWh7D3PcAMAL6zahea2lKVJ4/354urdWLJ5v8PMz66RWO3Y25TwDdItjumI6Cw47Lp5wyzXG9Z+Z5q0prYUpg2tVKa1bE46fbf5wyW6hKjTqvH92P/83htSsOHRUgYVvtDhgcBfOXkMmtrSWL71IMb2L3VUKuM+v47zUmfZYpGGlhT2NydM7S5zG+LXd6Hpg60qMw+YriLmdZfEIxjX3/18igG1gP1cpClzhRlYUWilUOP7NramcMqEWsd7R14D6aZWkgcM2sGB/u5q7N6w+Vz0I/75i2utsXPapFpLkI8K2S8oZRUa2fk0hzBraZYBRDXNCvYrLYi40l3uONDq6BMO17Tzcz903ZH405ubrO+5T7WmsawoKs0yK47F519iVsfThPSMcPzGqvxnangffH2TUFJaGkvmnwVRjQmKCrcn1XV5fU8p8NXHP8Cb6/c6igZRymJBRPeHP7y20Zq7+LtedDGTzyhaaih1Wq/Thnt/+XkNUmmPgI0PP0Gbj8tP/2Wx9ZvuqU+26TJhmVKaAnAjgOcArALwd0pp8NJQeaJPcQwr7zoZAPDZY0Z45pD1g4BNeN96aqVnJamRfUux42Arrp5bh/mj+qKxNenQSCdShuVLxk09MoVRHXsa2/Ddc2zzx7OK4g9cCwAAkwdVQNOIlR5K2X5pFG/e22xN4BGNTQ4TBpRbwoVBKWYMrcSofiUOgYW/RA+1pZQvLxWiGUic+ESNdabMCjKUUjzl8ZIU4a1T9bWobXnobe4mYvt8Pf+F+ThxfI11jMWb9qO2rMDylwVsjZiYn1PULIuFQB74JA/CZBqNIrPcKPuNu+08b+/LH+1xZF946LojHfsVRVWrf3fKOYD1g5fMxbuILzZ4Ro+0GRT23sfMRcKhuQ9ILKK57rBhjh+mHWJtbZQisCff9bzyeATmWKK2761BKYpiuiNfr6hJ56WHZTIF8vzlrc2W8K1rmrKADWCPYVEDKAuk4tnLzLlgcJU9RlQBl7Yw5j4nv7Yn3t9uneurjy93aM55u/h5WlPsjdqgMA/zqoBc2OLNka+ZUmc6ScvPWnpd3vzIUjwhWE5kYUOcD1gbi7CvKYFP/P5ttJp56nlhBFU8gV/hhf97bQPaUgYICLaY/sVpg6V75IKilxVv76EE9jclmDuZcO3OID5g8uAKl7uTYbB+vH7+cGu+56c5JKUaU8FLU9vPI8HW/c2ernt///Qc8xxsrHMhpljI4sPHSWk8ao0N7mYk9+nckdXYuKfJGqsFUVagYsnm/Vizq9Es5w2HdpNjL5qcR5UXekeNqLaKGwH2/KtrBIVR3aE8ETOriKnVmhIpRHViBfhOGVKB86cNwqy6KhDYWmOugQbsktIGBfqUxPC7K2Y4zrHkjhMB8DgCt0JM7H7VbeT359v/YdaYmO7UvhfHI7h01hBMFdxCxMDdWqFP/WKVxH00QqwUlj+52K2UkHn4bbcLkoxGCNJpdcwVp6wwis8fN9KqSBrVNVR5uC11F7pU700pfZpSOppSOoJSendXnru9vP31460iBZpGrOAmGb8xxoNSygujlhZMZtKgcnzmmBGoKIphzgjm88dMPezAScMOKJSzMHAqi6JIpAwr00QybSjbxVfo4gpYLospIg96+Z3MCw9Qyl7Y727ah7kjq1EcjzgCzPik8+raPa4gHS9ETaYlvAJ4ermd4zeIZnlvky14bBRMu6IZ/tgxfR2CMQvKA15RlKVVTSpiANPomlJbKAPwnXMmYkifIkSFKk9lBVFcM3eY1W/8GBxxZW5rRdjnqK45tCsyPGXdsi0HHIsS/iLi2i65Ch47NrEEH/n61PmoWX8k04Zl7hcXhbsb2/AJMyH+eIWW7VRFonoR7nb0n5uOtraJwjn3qZU1Vb6lqU03DF4hkYJl3rjqqDpcZwr6ja0pR7la1TjLtN775lO2PkB0UZJpbksjbmqmvJq9TYihqFVkKRnSpxjFMR2Pf/YoV5t5X5w4vsby3RUXfI5xZ17Usyt24H+rWODOdULJe69r4K4h/JnnY0WsuqgR5j4kBiw9+NomK3+vSJNU0ll+5rgLEm97QUQDF0l4XAgX7qhgvQGAkyfUWEFwKgvEx6arxU9f+Ah/fYsJCIbhrG4oW/G4FnDWsCocaElCI85nWNZ0L9tyAB+ZbhRlhVH87JIpSFOKHy/8yCq+IiOPDQrmisCLgnATPN9t8eb9OPqeRSCE4I9mJUZR0cAXQTzYkc8zsh8uwHxkuUuOV9GfgqiGNKVW8B8PWN1oBuamDIr3Pt6vzOrBtyVTBn5x6VRru+oR4+5zIq+v2+vIBsIDXwHmysevQ9eY5aCiKGZdz6DKIswZ0Qd11cXWQhpgixe5oiWlTJCdMphljNAJ07pzH96IRqD0KMjwolL5OovP5VdOHsO2eUwQvNQ2YD8rBxUVEsV9dA3WgnJsbVnGd2mxIkOPjOgT7gUPKuYQ4syJ3x3p3k4i3QDuE8nv65HD1dGlfmNMjPTNBkLs5yuZMhDVNStq2csN4+6nV1mzi1dyb9F3lk+K4uQkIwc38XyZHPFFNKxvMb7wKMvF+cb6vfjdqxtx3tSB5vWYAh8IDrWlAmmWdYWgx5Laqwt5eMG1Vr97ZYNDqFr44U7rGr5/3iRHvza1sSp3V0p5oL3O+Y8lWx3aMdnAx+6h3XcDKgrxjTPHO/x6xQh9Me+rCKVMs5PyiAp3ttM+3p1n2GnNywsjuPn4UYjqGmYPd6YjK45HLEGFANb3L63Zo8zbzANSEmkDJYrJdPm2g5ZFRuXGoSq+I8KeH2DiQKfbk6gJA5x+qn5BUDzrBAUQi9hjStcINI3gDrOfDjQnrLFCqfoZ9+v7n77wkePzsyt3er7onl25E09/fh4IgN0Nra7vB5QXKAMYN+yxF36UUkQjmrVY5tsAJvh//7xJ7DrMbSP7lVj7rt99CP9nmmh5tp0Pth7EClPwjegENywYgS+dNNpTmLeyFFjPOSMuBAJ9+bFluPgBZ0XDRNrAroZWtYAhYBhuX2TAFh7aUga++PdljuuORzT84tKpltvXraeMwQ/Om4RfXDrN0vircr9yIeWRd+0ETv/5YDvEO542KD5//Cj883NsccLnhMKYzqpCCv1UXhhFdakzGO3E8TVWX0Z1DbOGVTncAkTlAMDGgBxwxbSccau8MQ+csxZJaYqxtaXQNViC4cy77cJafL/N+5rwxvq91jkdz6l5P0viEat9XEsuWwPiEd2y9Hy8r9ly05g0qAJnTh4Ag1I88MoGR75rgBUl4cdOpA0ryDMT8lDUCaygZ0KIw02Fv28IIWYea3tciunquHXn+LH9sO1Ai8M1b/2eJpeVQiMEx4+tcbh5qCYLMWGAap6cM6KP1Z/UHK+GQa1c1Pydt+eQ2wVK/J4dgP3XmvS2hG+qb7LcRqyfZXiVVhXHHJmoVBACpA2mOPnTNbNc33N5oKcRCssBef/OkxyfH/jkdOvvdbsPYf0e7wICHs9ORghswSqZNlAQNc0yxB3MdP384VaqGf7AyYERf7mWDVyVaeoIs3SsCjkNji4ENzQlUo6XJ3fqp7BLg3/zrAnO6yIs68KwvsWeD+epE2tx8oQaR7Sz9fKgzoeagpk0VfDyzlxgvPvpVZbpDXBqauQgGD9B6Pr5I9RfCD/ifoCclMGEfHcQlf1yEivJqQIwrZcZsV0+ZCHj3dtPsP5OG7a2Qgw04udg2VmcmjXxugkheOT6OdbntmTa4Yp09dw61JTFkUwbMAyqzBohWkG8lA2fnj8cf5NcRAA78lx+gJgJkWlTvnDCaGv7a19dAAAoiDkjtSm1xyOB7dfHXzC/f3WjS6sjXufLH+3x0Cx7j5KfvuBO8eWXJ7cgqiNlGLhMKEvL8Xs+AeBmU9MmVnwD7PE9vLrYdDmhjutY+MVjALDKgTy/8Zpdjbj5kffNlx7bL6pr+MrJY1nRIY+H9uQJzkA2nvFGXNBsUgTH2fgvng1K8e2z3RH2/BoTKTtLCG9hMk0xZXCF5WbwuWNH4pJZQ6ARO8/1I+9ucQhLj14/W+nj/eAbmxyf05SirMDOlnDbqWMxom8xCswy9mlKrYwJVx1V5xB0DUoxoLzAMR5EMzoFW3w/9PbHVkrSc6YOtDKIcFoSKZfLBAuio+ZxKCqLYo4x0ZxwLw5u/9cKx2dxWIvP7Pj+ZRjfv0xIk8e282ewJB5Bk1nQYt3uQ+Bp4wxKUV5oC9vyczO6ptTh+pVJj2JXMHWORY0QS+NLIFkmBYve2xv2QtfthaV8vaJVqkgIsjvhvpddvve15QWOVIo8c9GxY5wJCri/76YfnO5IGccRLS7zRlXjzCMGIE1ZoS+RPQ1tjjgUTlQXn3vW/pRBXb7+nJZkGrpGMKbWVizIaehkUoaB48epy95ziNl/GiHKdqrmTPE9/4fXN3rOMfkkFJYDIgZCDawoxEkTbNPxf5fvwJdPGuP7+/bceyZksx9uO9CCqYMrEY8wM74sLBdGdWtCXLqFaaASaYrJgyss3youOHLh16Dw9EEVkV8cB1uSjmjZHQdbrHNzTR0otcybGnG+SBtbU7jj9HGoKY07BKndja1YtpWZ96qKYyiORzBECGZwCp6G9RtQ4PJZ6uTp793J/MhE7YAYYWxH/3J3Avu3FM6UcmKp0cw6ceDSWYMxTkgPlkgbiOiaS2ASFy2icKtyI1i+7SBeWLXLmSlDao1o7uXaHcCuHiYyoLwQOw+24J7zJ7m+U7Ghvgm/eXm99bm0IIpYRMP6PU1IU7WwLDZPNVEWRnXcdto4HOUxqcuFFgA7G0ZlcQzjhQDHQWYAY1p48d57AcufamWeILDiAbiGbG9TwtWPibSslen4BK7yk830YvjZi2ut4DIv4lE7LZd4GbzfXvjiMaag67SK2CWC7W2JlIEN9Ydw/6L11nbxRezVWllbpmkE4weUKauZyRDCqj1yVGPVoNTx7FrbFan2RCUDt8jJi0DOvc+tsWIY7jxjPOJRXbmo27CnCfWH2kApuydLNu13CKH9ygpQEo+w4jAApg6uwP2X27mWSwsijqDegqju8Gl2PBuUYtuBFocfvkqATKWdVraYrmFPY5sj1iUW0RzHFseb170UhyT/5Zsb9qI5kcLTN89z+RdfbOZa1zTnu6n+UAJpgyKVZkIfVwCI/VtRFMUdp4+35kXx3LygjUyBWSlWnksd1wnnPedWyulDK9GUSCvn8GHVxZg9vI9DwSUqV1j7nO4SXzl5jEMeGNe/DBGd4I9Xz3IsZDItAPYLLhMj+5Vi2tBKpA2KYVIxqkTawM0nuN1QxHHA225Qir8qlBCAmc6UECuQlF2bd/u+/NgyJNMGBlUWeabSBGylk1d2C37v/yYo88Q2NLamfJUQ+SIUltuBnFLuzduOcw1oESI6QWUBf6EDTMt1xVFDcdPxo6ARYqXE4uw51GaVKW0VTOjlhVErWwIfgLVmMB/zWc48KOUHI2X6xN16yhhENA3lhVEWkBDRMKy6BFfOGco0y6bGUtcIfvtJu0pgPKrh7CkDzbyb9nFXbDuIl4Sofkqd/tKiMMN9p3778gZHnmKZAkUuSFkrDZj+esRZ7IVVJAz2iFw9l2mOxFYcP67GIeyv3dWIoqgOWWG8u7ENGrGFdX5LVKnQuCnv6rnDbA1KhlvY37zfqntdU16A/c1J6JqG6pIYZgyttHJ0A5kXBcm0YU2KybRbCwI4X2hyOqH+5QW48biR1udLZw3BcWOdmgsCt9sLpf55sMWcuVZApdCdvCtKhReheLzHP3uUIjDNfZ5sAxblEu6sXRQ3LGCWCllg/7UpbKlcBf5949GOz9xVRLwO3vWaRvDB1gN4cfVupQsRlQTolJVbmf0vvvjkn59rmlVVM1wqQ6DP3JG2W5sYAPfHq93pNQ0KK36E8+BVM63xNbiqyBK4eVwCs+Zw87j9O68mVZfEXDm42XZ2ny+YPsiqbvb3xVtc/s4GBWYOq8JPL54CYgoiKcPAnkNtuH/ReisYzaCsjLiqkA7A+vJ/XzrG0ScqUgZ1pAXUNOIoKpIwhWnxch0KAeFCR9ewOINvnz3BMUaumzccy791EjbVN1mWAf41T1lYW16AtXefCl0jrowJlLIxHotouPd5s+Kb0KInPjcXFYVRh3KA31OvTE0RM7BXXnyK1y5ne5Dd/sSP3AIwfWglLvFQvFjHlY4lz6t/vHqWNV6K4qLG2X82FfuEF61hC0TnmE9K5bY54gLToMBxY/s55l55rkqk3Fk7/Fwal2zeb87xGl4yq5CKWAWpYNcVUMH7QQ5gvv30cZ7n7g6EwnI7kAUznrVAFXQAuAsgBEVc3YrmeVZgw+nX+OF2t3Bw8czB+PxxIxX5fRmvrq13JCi3tpuLAf6SkTVG8gPGtN0Er926ALpGUF0SV0Ykc0RtslPL5RyO+5sTDmFG1CrxFGgtpm9gJqFODEqS/a0BWC8UOcdn0GpCBEza9Vt8bD/QilnpkkfBAAAgAElEQVTDqrBim7OqXnVxzK5UJ9xnVeYE/nI7ZkxfS4OcacEj+wRXFcesFyz/ZdpgQm9hTPcNzOBw89rHe5utcXLM6L6IRzSXv7DoTiLn+z1lYq3jfN8/b5IrklwuNyxuD8KAigL89dojbW2zYEYVtdLi8XiwrIjchrV3n+oS3gBmQpXhgVSqNqcMWwsjB89Nr2MmfjmglxAWRDTWNKFyjbFopWDb7d/xhZbKE0R8qS7fdtDKoZo2KH54/hGOXN3yzwtj7gWSeFy/Z4iP9WVb5EqTtptRWUHEEj7k8dO/osASmCYOLMeVc+oc3ydTzJoja+9V9+HsKQOQTFPXgoXtz/6fM7wPLjIrOD6zYqfD2gSw+xfRnOlB71+0Hn8zheQmUzieP6oaC8b09XRLoZRpqp1uYc52zRtVbfavt+vTpIFlDgFShgdV6YKQfcWcOlBqL2QKo7pjHubXCdgLcYDNoRGN4BtPOpNcscI4BtIGteJoxCFRV10MTSPWc19TFreue1z/UqXlJaJrSKWp67kQ3SG4wMjTQ8q3XOzP4X3tgGSNMOF5iuDa92Mht7WYsSgTUc12C8koLAtfP/j6JlBK8fTynVYaQA63lgDAtCF2G0XFTtowILsVyuPk9n8td831teUFnuOFgCm0YhENAxSLmN9fNRMzhlaCEIK2lB3s/dpXFzhkCK/YBDEmpTsSCss55IsnjlZuJyAKk25mmM+ybZri41rTgOHVJQ6N7/tbDlh/2zmDmWaDP4Q8Wlece8TJ5rqjh+HIYVWWPxVPi1VaEMWfBUd93ZTi+WTDtaE8/yx3H7FL7zqvS9QGiHOdGMz3jyVbkUwbDtOsyszUljQsF4ojh1XhxgW2llLEcc3i+Q1q+QrGIhr2CymJvF70BVH3Y0OIMyhHRcoMetjXlMA7tx/vuBaei1tsG9e0qDSFUU2zcvl6ySL9PdIB/uySKa7FW9pgmTFEoUk1t/NtvMT10o/3W+NtV0MrassLXNoJ8ZhyJTlV/4qm588eO9L0OWWfxRyjQYloGuqqi10afRmxKbpGHJXsALe7BB+PvxXiFwCgr+B/96l5w/DtsydY6fv+/pk5kBH7pzTufFHxF6xXZo9nb5lvtw/uF7mYok0uOODVBhGDUgwQcv6aB3DswwVBvvnsKbb/b5pS30I0vK3/Xb7Dpfnlx6OUaTv5WcXy6gR2ZiBx/uAZPxrbUohxN4wMAk48oiFtGMoYE/5T+UUvCxtsnvY+EQ+CvHimMwWYzJCqIsv3tdQUNJRuGIahfIZ4+394wWSlSwv/raYRDKkqQk1p3DEGDEpx2ayhjvOKfTKyXwl+cvFkl0+q6trTBkXaoBhcVWRlXlAtVt75OpsTv37aOBgUmDqkAqdM7I+kYmxGdYJkmmJjPVtseMU7FMV0zDXdu+RzNidSymA5jRCM61/mUH6dL7iDZLJqibSlDNx7wRGsbzP8Ru4Se/w7rz+Vtp8pr0DIp5fvtARmzvC+xfjbp+x+2lDf5Bq/Y2vL8Owt83CZQru+ob4Jjy3e6vAvFuFzO38X8mOXFkQd1yaec2xtKd7/xonWZ69sYd2BUFjuAopiujKLQCaI8MIwhEk4HtHxn5uOxhwzM4fs12X7IdruBVXFMcuR3xlIYQ/c48fV4O2NdglT0fFfXG1yoduaRKk7h6mXZrlvaRxiCl9RsOIvoiFVRWhLGUimqOVnza5bNAWz37UkWe7R0TWleOi6I5UrXkAd/MD/PnfaQFQVxyztCa/md/KEGmu1Ls4p80YpghaQ+WUsaoFEjSRPvSe7YYj3Xm47IcCj7/L8zoyfXTLFcb5LZtoTnjhJqrKzcHOwLGzJ18QFkgWmq0SaUstfsTWZxpCqIpegIQp6srChermKFdLEapF8/0xFdGTEcQp4O0TJJlseCMUFL6/fnSz4Kw6sKMQzK+zc5kWxCK6YU2cV2lG1Xdzfq38yZXyhpg+GLKyJmk/+wpc1R4VR7/kplaYuwYDvKacB5K4i4vG9qj6+8EUm5It9LgZEEkIsZUCa8iBPdubhUio6q8y00Hf3XcQ0gdOHVqIwppvWJ/8HVBfSEMqlfHkz5WuR4zn88tsyYTzze6AkHsGCsf0szaDfvd/V0KYsFOH1E3H88bmIl9Pe1dBmXecdp493pCqVj0lA0KfYLTSprFIGZXPAtCEVVpCj+Exb7bEyTxErIBNQV6OLaAQtyRQazBSBk6QsIceO6SsoIdSd0diaUpaT5vOwDE/dxiqeBpOW9zYlMLxvCV5YtctVjVKGB0bK55R9pj/e12y5jagWEpydDW0YKCyURvQtwVEjbKvXEYPKlYuW/uWFqBPcSkWXCzkdYkk8YmmNCWGpCjfvbWbFXMxjiwvfO04fh5jOnq1TJ9biO+dMRIVgSfzFpVMta1l3IxSWuwBVZb0giJX/ZCFG02wzpVj+FBAHMzMp6pI5hiu5z5w8wGEekZ+bwZVF+IaZRktcwRbGdCF4groeOCt1lLnZoTH+zBzHwyamEEobFBfPGGxpbhNpZ8nMqK5Z5uzRNaWoKIpi5fYG6+UdEUoPy5w4ngk0gyoLJeETuOm4kdbk/c0zx+P757Fgt4qimKW1Ea+RB0yq8LvLKQ9NdVuSm4pNP3J+LMUkL1b8svxRzbadPcWZkkc06YuTpLgIszT0KQOVRTG0JNK+QgXfny9c0gbFcWOZiT7BM7YI7X7ulvm46biReP4LbuFI9TkT3z1nIhZ+cb7yO36sF790jPL7V8182bxf/dIlMr9dA3efO9FVHMKPV29dYGWVAIJFKjwiZa0R4cOFu4T87EV3hg2ACeU8wM+rSycPrkBJPIKEJBj/7ooZyiI0ANji2UPjxTneXDi1mhkKnNk41L6LI/uxF6LX3deI/R0bTwQHW5Ioium4fv4ISwNFiK0V103zL8Be4n+8eqaVczqIZjlqlmNOpg2rapz9nVNw5MguKKpcuRyvhbwM/zkT2lj57nOmDFD21fShlco80V65sFWBWfzeF8ftIPHLjhxiBX/z6xkl5E7nqRsB53gQF0offOsk83tqKQoWrdmDKYMrMlqIxPLUqndnRNdwSBAuVX7DvI6A16vXUSpbQCOsXLPMaNO97NcvrQ+sWQbYXHLTcaN8F1JP3jAXy7fJrkgs9/upQgwJwN4jXLGTTBkYXVPi8P3ntCXTjgBDkc8fPwpD+xQrCxnJiIKzPIbH1pY6curHIxqmDqlw5FkmkvzBx+ZPL5mCGUPd1pVnbp6XsU35IBSWuwBegjRbCJyTnjwh/NCM8pfhEzt/ccpFTPjfsjZUfmzqqotxjVmgQcwGMnFgOUriEVsTAHWyfCsXp3BgSln1IxWpNMUlswZbWp1k2nBotwtjOl75ygIALGr9xgUjMapfia+AwHGWmhYWDpKP59Vzhzl+x81dmSYVbnryy2yQTlPoiuNRAHGzrzbvbbZSnKk0Stz/VxRAvJrmNeTkcQWwMVEQ0ZnvdoAXAR+LotZ45tAqlBdGHS/PMbWluPKoOutFQynFjy6cjHvOn4TfXTFDWaqbguLrp6kr480dWa30E+bHBpgGBbDLEnN4IRbePD+Tn64xzbIYbBgknZEcz/BzD+HW0W4Ab21wlni3Av7MfpYLrsgsGNvPcsMQx7OYjozPQ7KmLmKatINiLdjMt8fvr2LBeFxoE8fPjgOtvm4JXl9phKCiKGq5InChp8AMIK2whDJiLYJ0jVjlq3WN4Ngxme/dNcLzrmusmALvC17Z7LPHjrD+lq9FFkbk/hfJFKzHufNMpqDQCbsvbak0aj0KNlx79DBcNbfOtd1rqPI2iNo7vu/s4X0c7wC+nS+eH7hiuuUSYhj2QkWci0QtITXlzTSlONiSFGJn7HfNd852phXlsHtOrN/LRHWCgohmzWMq4VUnRBkczH/vFxTO3SZ5sRcAjvieoIt87gIT1dWFxDiTB1fg6rl1uOP0cY486YDaVU2EgOCh62Y7tk0dUoFPzB5q+dcD7mfgUGvSCvq7WjGGVJQVyr7rNsm0gariGAqjOgwh8FTULIv9Fo/oyn7sjpkwgFBY7hI0zT+/qheiBlBFv1K1T2pUJ/jdqxvwsxfXMiFOcwpOXMAhxD9pueq4AIsY/+fnjmImd9Ps6xTq2QT1lumDJg7+oX2KXCWXxXZFdQ2lBVH8+MLJONiStAT/H1042fS/to913bzhmDCgHFyDHoTiWMQVDe71cmtNpS3TWKbnl6Xh898pafoXPvyp2Q6NFLXV9ACAPY0J65ju87hfDl7X7incCeMqkTIweVA5KOyCBrZmG4rIdvuYT39+niO7w30XT0FteSHG9Veb0b5+2lj0LY3jgumDcPHMIThxfI1SYKUUOGa0fy7PTJw3baBdhMDc9pjCXxiwzasiVcUxrN7ZaC1qzpo8IAeJ49zwIC3R/QmwXWg0Alw8YzDuOsstVDhdnewgP3E0yGniVILH0D5FOGdK8EIBXGiQnxuegULcHtGJY3EsBz8OrizCHR5R8BFNw7O3zMeUwRXQCMGxY/paAVz8FE6XIYLLTG2hLGBwk7yMOG9NHlSBYdVFVhYaPt/FdM3KICTLu/IxRTcqGb90WyJcwLFdcJjJesFY9zNRHI+4gu84t57CxvX+5gT+vpgVV3EUzjDhPUDgTJcoD5XiWMQqwELBNOj7m5OeAWGFMR0z6yrxy/+tw23/XC4Iy3YxrE9KAZn2uYVCTYqJcM7walw4Y7BLoHccA94FvF699ThPBYguWIJ5sRcAeHeT/YwG1Sxz5U40olmWFy9iEQ2JtGGNE9ti6jyZLNiqrAgDygvRryxuxRHdddYEZz9QijS1x8M3z1QvWmRUKTyH9inC1CEVQpAyn4f4/CtpljtjIu0CQmG5C5DdIIJCSGYz7u2nsReNuAKORzS8c/sJ1oCVz8//LiuIIpHyNmXJ8MIHd5010eGrRwgcfsj8IVFpAAkhnprBVNpg1QFBEY9q2Lq/xXrReuXbpGBCtvXy9Gn/rLoqFMZ0qS+8J77fvrzB8vmUu2ZvU5tDKzp5cAXuvWCyvxtGmpki54xwmswonAIOLw+s1CxbCx3/e1VTVuCpyWKLG0YsouHJG48GpSz94NKPDzj27afwLeTUVRdZ7eHBhAMrCvGry6cr979+/ghlarlcIfZJVLM15DsOOjO+BHkUo4L/JMDSgfnd3J/7uHQAzPdaha7IIQ3AioDXCME9FxzhKrpTELWv7zvnTLReSGJsA8DG91VH1dk/VFxD//JCS8gEmCuSA+k3h9pYnnRZSOJmZHFo6prTT3eaFNR211kTLJ9wEU1jvqaja0pREo+AAPj22RPRxwwu4qeQBZ4bzABf1/NB1XMD7/ofXTgZ50wdiOPG1lgCo3jZYvU3kSJJs0zgvXiN+gQ6quCnevyzR2Hy4ArfgEAR3u7PHcv6YsfBVtczoGLplgP4xf/s8u5Hj6rGqH4ltr+2RjDKtBCxeDWC+kNtruw1nFhEw+iaUstq4kgDSuAZKMaPb+WeVzwfQ/oUYdawKhTHI54xDLbrovv3teUFlpVPxuuZFF0QgipnuDDqZ1Xgwd5xXVNmQJLHuCjYer0GKKjjWGmD4tW1LBPJMyt2YJxZWCaIG4YXvALlTy+egn99bi4mmalTuSsSF+Id8wHJnBWkuxIKy10AX6lmKy6L2TC8+NT84QCAW08eYwX8TRtSiZJ4xHJP4JpejldkfaYxzM3bsp8ekTTL2fhznTt1oCWEJ8WcqCAoK4goK2nJ3Pi3pdYDyNs2RFEhacLAMvQrdaa18wpAkuH7PHo9M3eVF0YdZrmSuM58uyiUVYsAb59lnsqMB+hZqd4ocIuUfN7yzxSOoxpZ3z9vEmbUqQU0AqChJenIK33k8CpliiW/vuHVwgDgpa8c67lftlBkHovZwMd+TNfwmWNGuPrL71y6RjBvZDUzjfrsd1aGKG5VeVvA20XLDo5xasc5D39qthVg98nZQx1WKOf1UHzL1ErHdA1/vnaWpz9rUNIGxSfnDHWk6Xr0+tlWQKQ4Zi6cPsghFMln1jTimi9+eP4RlvsFwNw7ZK0sF1p5dTWxWposwNq/cX5eMKavZdkQtd88PkNVvEUWLkRXD4BrM5Wnh64RpduRF/wapyv8OtuL6n3Cq9HK47AgqnsLtIKSIeqjMTfFJQB2H6Ypi3Ph1TZVjK4twfXmu43nplYR1Qm+eeZ42yXotY1CulVTq+lhPPUSFCMeMUaONH5Zzk8RIXe5DLcQnz11IM44YoBrMd8et4S6PsXYsMcOKGxsTVlpCm89eSxOGF8T+N0HwFpMi3vfsGAkQJ0xSzzO6icXT1G+gy+fPdSyePQ0QmG5C9A8zJ+ZCKJZFs/xZdOcbK/+mcZSNkXxfLPy8bcLkfMqTdmpk/pj0w9Otz4Xx3VUFsfM1aS935VH1eGW49Vp9GR+cvEUfMFMuZdIGdA1zRV85gd/TC3tByE4/Yj+eFkhvH3zzAkYU1OK/63e7dju5bsG2JUH+aRypLkg2VjfZKVuu/WUMdaExzNbqKgpiyvPxb0weIAevxaDUlw3b7hj3xsWjERNWVzpWyjCqpape7AwxgJ5dM1+Acwe3sd6KQ+pErMNuM3ZnIhGrOCgXGqMVdlVgiA29cqj6qysJbx/YhHNWpiJ+C8IgItmDsaY2tJACzcvSuIRpbuHpjHtnAwXLOJSRgKO7JIkuyNwxOde0whmD++TUbM+ttZfoOOR7l8/zXafmDCw3Ho5i2295uhhqClTu4uJ7RW1rhfNHOww3f7g/CNcAiM/BdfY/fB8O36DV+0UUT0LD149SymIDqsuxqYfnO5ycwGcGTeG9ilyFaPyy8FLCMGdZ4zHv0xXho7y6tp67JDyPAP+1jUVU4dUWtl4ZGSrF8eg7sBuFWUFURTGWKdxRQEfk6qCUZx+pQXWQkS8DwOkdJgR03IRj+iYNLAch9pS1vtNXHSrXH38imYoheUMaTX9iEbcub5lRvQtwch+Ja6xGiTvvUxJQQRJQ9Qs23+fML4GUZ0FYnvlPJb53rks6N2ZMcg7LmbCgHLLgqwRgpmm4iaqa773vTsTCstdgE4I2pJpXwd/L7KRsV05P8EGt1yZriWRxj8+M8c1CbYkbJeMTJoygGVYuPeCI1xas4Ko7luoQMXNx4/Clx9bZk2o2c4PVlEP87PXRL5xbzPufW5N4ONaZl/pcNv22y+pzx070kqoTqn3uV//6nHK7bLmn/9a5SJSWRTzLTIg4qUV0gjL+00IcVS14xYHrsUriGquhPjieGTmykBNyYr2apbF34wfUGYFr8iVEOVnShX5bh+THbQkHsH3zp2YfaNMorqmTpelEexqUOR61QhK4hHBx5RtH9FXnVeVUnWJ6EOK1FiZGC9pP+VbzP3zB1UWWZljRGHX99ml1GUtIXC7bEV1YlmyhlUXu6qYVZmmfz5W+wkCuVfVTl/TuWIcz6yrwidnD2VtJATzRlU7nu3NioIiexrbPN3MNPM5D+pOkYlvnjneUUzDixdW7bL+tjIVSfsYfv2jNoYFmqOvmDMUE/qzuZFVcp3ervcgZ5GkBNF1YqVP+/dNrKIl1+Byd4B3Nu1TulV4LZJ1D83y+dMHeRb4ykRM1xyFaoLA39leGS3s/dTbxfupUtYZRnbXccYR/R33XC56ArDAQvn5i0U0z5LbPYlQWO4CdI3g+Q93efoteiELuZmQh72dDcMd4DeqptQymXQEQggeuGK6lWmgo2SrvTto5tksFVPg+ezfrEiz5MdK0w9TIwSfO9b2C1+21Z3mBwDqD7V5agK8SmcXxXRHujH+QuOR9WJmCO6H7jXHiSbo0TWlWP+901z7EMIWcLJvnpwW6MGrZlnVKTk/v3SqEFzVSb5nFMikHxPTFVk/k4VgqWQzoLbW/HjhR8pzFAvVDAuiOk6Z2F+5XxCiutoMq3pZLRjT1+WewXd76sajlSW2B1cV4dFPuwMYVYvWeIZAM0LY4qDaI2sN24c1iOckF4vN+I0LZkVxfq+ZY1GkT0nckRtchvsmB50vMmXMkeMIAPaSrxIEnNZkOqOPZ9qgrowBHK0d863KlM25eu6wQO8U8Zz8faK6jNKCiMt/3Ku9QTXLupDiNKJpGFhR2K74HY5swZo2pNIRVzFjaCUeeHUDAHuBtLG+yapIKbdNBV/UyIytLbPuh5dblRdRXXMt2r2QT93PxzITdAreVO9e2HmldfTiE7OHup5deU3xy8umKZUCvYFQWO4CIjrBu5v2YYTiBe9HNm4YnElCyUhuztY1grdusyvGvbq2HoSwqOvjxwnplbI8F6eiKNYuU5EKsaBKkFXvP9/bCsA5ofhdB09Cz/NHZ4LXr4/qxFXaFnCbBdOUZu1jeMnMIY48yBw+aV4/f4RjGyFOgYMC+PQxzF1DNkGr7otdWtvpp3jW5AEON5t1ew65fnvGpPYLjEEJolle+AV1rmWRZJpl+pDzTwfFL/gzW6K6psxlLAuuUV1DUTziylzB77euEbz5teMRFFVQ2a8/oQ7ABJyaa6Y9DpYbmGvAz53q39cqoVXMjiDiJ4zx80U0gnOmZLaCebkTAMDvr5wR2DRclUE7KBZjkOEuayJ/VyxwRLxyhmfDecI9MSi18mLLxCMarjpqqGu7SuPMs35kemcQAiv3ta4TK91crrj26GGYPdxe6Bw3rp81bw6rLrae3ypFEKLXs+13XXyxIRcKyURUJ8rgPS9uPWWMI8uJXyGmIL25vzmBBWOcCyGr2E9A5KxNmmkxP1zIbnkU0i7G1JTiz9cc6RKsMsEC/ILvLwsZ3A2DEOJY7fUvL0BpPOIyt3oF/nUVw6uLUWwG7Pg9yGUFYp357KSZnWZQHs8fPXmQdz36xz87Bzf+bSkAYMrgSkdE+ezhVXhrwz7MljRSqjKhmZg40NkGfkVi4n+OSqPzg/MmYVAl03gEeelz81lteRzvbNzruV+BQgMpBpJ1JpnuahCtVspgeT/FPmHapmDIObiDcs3cYfjD6xsd2y47coijwA5HXswcPbLa8isWzdWWzyzxzg+r4vkvuIUtvzGydX+LFdEe0bSstaHct9EPufWa1v5FCSEEP73EPxMJ4K9ZPn5cTaBz/eXaIzM+X15+oC9+6RgMKC90LY4yaYY74ifPKZMqKgLq/mBZhZwbTxxfg34KS0YyZQR6NjRC0JpM46IZgxCPaJ6ZJjJx4fRBeGzJ1oz7idabhV9kY79vadwKhBfxnkO8pWUeJOdXwl0F0ywHu24K4IRxNY6g1ldvXeDa7x9LtmLbgZZAVjYAuPZoZx8EVUhx+pcX4HRBWaJprAT24UKXaJYJIfcSQlYTQj4ghPyLEFKR+Ve9B0IIhlUXZ222Zivc7CYW+QyqM5YVRJVtyZUWrb2cMXkAimIsiwf1MRG99jXb91eeFPqWxF1J3UVapZXwkzce7blveWHMOn484jSj8ZfYD85zFobhixOR2cOzc7+xA/zc94Qqto2qKc3KR5yATcgVhTFfn2Mx24Hdts4fJH1L4i4h4VpzcePFc7e4Nc3JNIUuSS48P2wQ/HJw+3HFnKG4co5TQzeufxmGZKhaBvBgN82tWSbcVzK7tmTShMqIAlPKMJTne+Nrat/7WIaCNvecP0k5nxXHIpg3qm9gLXZ70AjB1v1uy1A2BFmIemmWR/QtYYG1eZhkxTG8fk8Trps3HJMGlrs0xlQhPF03b7jlOy6ia8RMk+fmCmHsa4Tgg60HcecZ4xHV2eLro11ui1UmggbIq1wdsu3xknhE6ZYjkq0lNapraMuQZ1nEvaB0n09l6XQcg/h/NrLIhgGwImVXCmkoCYijqmNvp6vcMBYCmEgpPQLARwBu66Lz9miYhif4/lRSF6i0B0/cMNdz4ukKjaEXhNjVzihYdPMchWsC4DSBcZ9lzpwRffClk7xT08jCsh8RzU6JV1YYwQDBf5cQltouSKGBR673N7XKyD7LIlzbzDfLprUg2EGQ8CxzPLiqMG/j4ZHrZ2Ow5Kt5Zwa3GVW6sLQiVV9U13DTce6MGCra64ZRV12s1GSp8OpiseqVSK7cnbw4yhQSKFjBIpWA6FW2OR7VPNt3/fzhuHjmEOWcVFkcw48vmoyXvnysZ1VSFadMqA287wnjazIKF9nwx6tnKrcHKU7U1fD+fuGL8zGoshBzRvRBdWlMObaDtvxQW8pT0CoUxgx3A+QuBZlcMJ68Ya5y+7A+3goQEVGYay+15QW428NCctEMlu8/U1U9marimJU9KRO/vnyaa/5Tcftp4zzfP1ceVeeovaBif3OiQ2NVa4ebaE+mS4RlSunzlFIeWfUWAHWFiRAH3OT9+rp6l1lXxZjaUvzkosnWZ7GKDqe0IOI5YV04I/e35ZHrZ2feSWJgRRH6lcXxiwyFHtrDedMGBa49r2u2L+Ww6mL88jK7PQTAzzqhfT+60L5/KsFicGURbjxupPX5W4rKbkHhmVJUTBxQnreJMFfat5RQcpUT1TUrOIzDMx7IZJOHVIYveE4cH8zE7/V7jh2Y1bmC2F+uPdJapBvU3X9+LPrysZ5CBE8zx13DVER0zVGeNxO/+aS377XM+dMGOlLddRQ5v7JIdyvXy29JQVS303Iq5haV25cXuxtaPYMPxVSBGiEOba9XrmGOXHyHc5Mi5aMKv0InueAKs+JgtkJm0OqNALMUBrFijK4tRVVRTJkEoCQeyZg1qaNB+USRDaM3k48Av2sAPJOH8/ZACB58fSMWfrjLCkzzoygWcaQSKolHXBNiRCOOtGcinVFdTQy+CMoPLzjCVenLD68yxiq+ddaEwJMEyyRCLG1CV7wE5UqF8jkri2M4eUKtNVlnU65cJpU2PH0iCXFHhPNgpN/4BIjlC9WcnTbLi2dCVXKb01HBPVP2hKDvmqABaJk08EHgsQ+G4XZj8aO6JJ7xGZk1rApTPQSizoS7t3R3yhSuTx2FC8B9iuP40trH/DkAACAASURBVEksp71q0cJSiQU7pkG9/XavEVymiOZ8BrwsWUG46biRmXfywOs5mzyoHM/eEkx5AtgLjPa8C844IrfB0WJJ8EyM6lfiEp5/dfm0rIrkyHRV/Ep3IWdPJiHkBQAqu9jtlNInzX1uB5AC8JDPca4HcD0ADBmSfbBUb0IjzFRy74WTcSjLlGcA8Oin57gmX00K9uuOdAczppXvWQNakoarsMI3zhyfsdhCZ+JlCg8CD3JrSxmeZVhVwaU8GOmUifZjLmZZ6W6ofJazIdv0UCJWBb4cyWdynmEvRN/uRV8+tt3nI2Ca+SzjmDKywEcj2x1ZtaMBz3+4yyqcJFLYCcUVFt/hLqiSLVLSAkuwK4zpOG8aW4yrNMtB08EBTOiN6ATVJXGlBpNXMZVz8XYkE4afe50f80f3RUFU/SD6xayo6Eip5l9eNg3/+eC/7f69jF9Of5m7znbniO/o4jHb1LY9nZwJy5TSE/y+J4RcCeAMAMdTnx6mlD4A4AEAmDFjxuFzJxSIeZBF83xQVJNYNlV7uhLfggE+5LIUrMiyb56EWETDQ9fOxo+edxcxUQW95ANVarAgEML8tz3N7AH90WqzzPDSWajeGceP7YcZAcYHpVQZCOdXjjcTteUFuO+iyXhRqhYpM2tYJR5/Tx3lv+ybJzk+Z6t1lKvLiTwUoEiAQWlgIb230tCa9Pxu6Tc6LtjKZGOu96KhJelQrqieDSq56N197kTc/q8VjiqufiTTFFFNwy0njFIKbH+6ZhY7N5y5eOXMP53BX66d5fjcnndnT2Dy4Ap855wJyuI4XYFfBb/eSFdlwzgFwFcBnEUpzc+d7YHEI/7R5e0hZVBHAQGZjmjTOsKyrQfa9bvO0kEXxyOI6pqVwaC7LqCDVPCS4UGLB5qTnn5tzG+1m150QPqUxAP3z0jFfhWK3KzZMKSqCEdkEA4unjnE0y1Ivjdv5FCLP3ekO3j2qRtZcBW/72mD4icXT8GRWWZz6U34aRJVrjGXzHT7XF+Vg6CzbPjjG5vwDyHNWpPCKimnGb38SOa335JFALSmuTP/uPYhxJEBpStKHfMy972dquIYjhtbg6vn+mcK6ixUFfx6M10lGf0SQBzAQvPheotS+pkuOnePZd6oais6PVeUF0ZxgU8g35I7fQ0EncaexjbPlFT5ppvF6+QMCm9TXEfMjfmgtrzAM0NBvphRV4UZdZkFzQPNwaLkO3she8Qg5kvM/VkNCvQpjnW7gLWupLI45ooj8OMH57uzeXQkCDcXqFytVP6u/csLcj7GSA60j7nQtueKw/hRcFEU03HS+OAZaXo6XSIsU0rb75l/GEMIyTr5eSaqS+L43LHet6MzgvyC8NSNc7v1SznbfNddQXsDRqK6hopCFkXtXaihH0b1K+1A67qWqK5hZAfam8/7W1oQweVHun1i80U8wjKG3PPs6m79THYFUY1g7sjcKiw6m9qyAqv4EgDcdto4TJKKLxnU7fo2fWhl4IDWHyoWBSo0kl3lOhXLvnFS5p26iKFVwVLYecErrfYG+pTE8Y0zOx5Q3FPoPku2kMOajryUO9sSlG2+667il5dNa9fvRvQtwcNmSj8vX/Gzpwx0VXjsreRbIDQocHIWOYM7m3hEx2eP9c/RerjAguV69oJB10igku8ki4CtixTuJupjurdlqlook03Bpc6mMKb7lp7OxG2n5i59YUjXEgrLISEZyLcw1RnoGvEtARzSdaTSRrfIABPipic+I3+6Zhb6Zch4ROC2psQjWs4tiyp3Lp6CsqeiKj0d0vs5vEOdQ3oEj2YobNLZL7PLjhyC2jymietMepgc0CtJpIxu5ZfJyfTcHQ50Q4NSRiqKotjd2Oa7j8oN43vnTmp3dh0veuMaMB8ly0PyTygsh3R7jvQpbNIVL7OZAYK0OgNVOrNc0h39sA9HEunuKSz7PXeHCz0xI0yQ4Nxr5w1zCcadMQZ7o1Uu5PAkFJZDejQXzxiM/t0k12+u6WxzX080MXcGY2pLcespY/N2/raUgbjeffwyQ2wSKaPHCXy6RlBR5F/quCN+tyEhhyOhsBzSowkaaNIT6eyCEGdOHtDhSPXeQHlhNG/WA6D7umGEAAdakujTyRaeXKNrpEOV8nLN+dOCp94LCemuhMJySMhhSkdKZofkju7qhhECtCTSKM2ycmK+6W7C8o8v6p0V9EIOL8IZOiQkJCSPUIowG0Y35Ztnjm9Xlcx8opPuJSyHhPQGetaSOSQkJKSXUVPmn+YrJH8EqcDY3SCkZ2bxCAnpzoSa5ZCQkJA88vJXwrytIbkjHtHwwheOyXczQkJ6FaGwHBISEpJHCqJhJoyQ3EEIwZA+RfluRkhIryIUlkNCQkJCQkJCQkI8CIXlkJCQkJCQkJCQEA9Id65QRAjZA2BzHk5dDaA+D+ftqYT9lR1hf2VH2F/ZEfZXdoT9lT1hn2VH2F/Zka/+Gkop7av6olsLy/mCELKYUjoj3+3oKYT9lR1hf2VH2F/ZEfZXdoT9lT1hn2VH2F/Z0R37K3TDCAkJCQkJCQkJCfEgFJZDQkJCQkJCQkJCPAiFZTUP5LsBPYywv7Ij7K/sCPsrO8L+yo6wv7In7LPsCPsrO7pdf4U+yyEhISEhISEhISEehJrlkJCQkJCQkJCQEA9CYVmCEHIKIWQNIWQdIeRr+W5PPiCEDCaELCKErCKErCSE3Gxu/xYhZBsh5H3z32nCb24z+2wNIeRkYfth0Z+EkE2EkOVmvyw2t1URQhYSQtaa/1ea2wkh5Odmn3xACJkmHOdKc/+1hJAr83U9nQkhZIwwht4nhDQQQm4Jx5cTQsgfCCG7CSErhG05G1OEkOnmmF1n/pZ07RXmFo/+upcQstrsk38RQirM7XWEkBZhrP1G+I2yX7z6vqfi0V85ewYJIcMIIW+b/fUoISTWdVeXezz661GhrzYRQt43t4fjy1uO6JlzGKU0/Gf+A6ADWA9gOIAYgGUAxue7XXnoh/4Appl/lwL4CMB4AN8C8GXF/uPNvooDGGb2oX449SeATQCqpW0/BPA18++vAbjH/Ps0AM8AIABmA3jb3F4FYIP5f6X5d2W+r62T+00HsBPA0HB8ua57PoBpAFZ0xpgC8A6AOeZvngFwar6vuRP66yQAEfPve4T+qhP3k46j7Bevvu+p/zz6K2fPIIC/A7jE/Ps3AD6b72vOdX9J3/8YwDfC8WVdp5cc0SPnsFCz7GQWgHWU0g2U0gSARwCcnec2dTmU0h2U0vfMvxsBrAIw0OcnZwN4hFLaRindCGAdWF8e7v15NoA/mX//CcA5wvY/U8ZbACoIIf0BnAxgIaV0H6V0P4CFAE7p6kZ3MccDWE8p9Ss+dFiOL0rpKwD2SZtzMqbM78oopW9S9tb5s3CsHomqvyilz1NKU+bHtwAM8jtGhn7x6vseicf48iKrZ9DU8B0H4B/m73t1f5nXexGAh/2OcZiNLy85okfOYaGw7GQggC3C563wFxJ7PYSQOgBTAbxtbrrRNJH8QTATefXb4dSfFMDzhJAlhJDrzW01lNIdAJs4APQzt4f9ZXMJnC+YcHz5k6sxNdD8W97em7kGTPvEGUYIWUoIeZkQMs/c5tcvXn3f28jFM9gHwAFhodLbx9c8ALsopWuFbeH4MpHkiB45h4XCshOVv8thmy6EEFIC4HEAt1BKGwD8GsAIAFMA7AAzOwHe/XY49edcSuk0AKcCuIEQMt9n37C/AJg+jGcBeMzcFI6v9pNtHx1WfUcIuR1ACsBD5qYdAIZQSqcC+CKAvxFCynCY9YuCXD2Dh1s/Xgrnoj8cXyYKOcJzV8W2bjPGQmHZyVYAg4XPgwBsz1Nb8gohJAo2wB+ilP4TACiluyilaUqpAeB3YCY4wLvfDpv+pJRuN//fDeBfYH2zyzQVcfPbbnP3w76/TE4F8B6ldBcQjq+A5GpMbYXTJaHX9p0ZEHQGgMtNcy1Md4K95t9LwPxuR8O/X7z6vteQw2ewHsyMHpG29zrMazwPwKN8Wzi+GCo5Aj10DguFZSfvAhhlRvHGwEzET+W5TV2O6X/1ewCrKKX3Cdv7C7udC4BHBT8F4BJCSJwQMgzAKDDH+8OiPwkhxYSQUv43WFDRCrBr5ZG7VwJ40vz7KQBXmNG/swEcNM1RzwE4iRBSaZo/TzK39VYc2phwfAUiJ2PK/K6REDLbfN6vEI7VayCEnALgqwDOopQ2C9v7EkJ08+/hYGNqQ4Z+8er7XkOunkFzUbIIwAXm73tlf5mcAGA1pdRyCQjHl7ccgZ46h3U0QrC3/QOLyPwIbCV4e77bk6c+OBrMnPEBgPfNf6cB+AuA5eb2pwD0F35zu9lnayBEpB4O/QkWCb7M/LeSXyeY396LANaa/1eZ2wmA+80+WQ5ghnCsa8CCZ9YBuDrf19aJfVYEYC+AcmFbOL6cffQwmDk3CaZFuTaXYwrADDBhaD2AX8IsUtVT/3n01zowf0c+j/3G3Pd881ldBuA9AGdm6hevvu+p/zz6K2fPoDkvvmPeg8cAxPN9zbnuL3P7HwF8Rto3HF/eckSPnMPCCn4hISEhISEhISEhHoRuGCEhISEhISEhISEehMJySEhISEhISEhIiAehsBwSEhISEhISEhLiQSTzLvmjurqa1tXV5bsZISEhISEhISEhvZglS5bUU0r7qr7r1sJyXV0dFi9enO9mhISEhISEhISE9GIIIZu9vgvdMEJCQkJCQkJCQkI8CIXlkJCQkJCQkJCQEA9CYTkEALCroRUNrcl8NyMkJCQkJKTH8Om/LEZTWyrfzQjpZEJhOQQA8NMX1uLfyzqtrHpISEhISEiv47W19QhLu/V+QmE5JKSHc9/Cj7Bud2O+mxESEhJy2NGUSOe7CSFdQCgsh5iEa+Oeytsb9mJPYyLfzQAA/Pbl9Vix7WC+mxHSQeoPteGdjfvy3YyQkB6BQcP3Z28nFJZDLAhIvpsQ0g4iOkHa6B6T9bKtB7Bpb1O+mxHSQT7YegC/emldvpsREtIjoEa+WxDS2YTCcggAIFwY91x0TcO+5u6hWY5HdLSEZskeDwHJ+ZzQlgrHRUjvZGOoIOj1hMJySFY8u2IH3t0Umme7E5v3NmHnwZZOPw8NID2VFkSwP8+Ce/2hNuw82JrXNvR0CMm9Y9a0by/M8RFDMhHkmQ3pOM+v3On5XXvvQSIVqqu7E6GwHJIVb23YF/qkdjM2722GRjrfheamh5di5Xb/ex/Ttby78zy2eCsefGNj3s6/uRdomQgheOWjPTk9ZhgI1fUs+NFL+W7CYYGXG9wb6+tx+xMr2nXM6d/tfovLhtZkr5jf2kMoLIcAAJoTaWw70Bxo31BZ0f1IdYHP8u6GNhxq9c8nalCm2c07eRqjhkFxzL0v5efkOSSMXugdbNobbE4P6RjEQ1nR2JrC7ob2zYeNGebafPDKR3twz7Or892MvBAKyyEAgKeWbcf9i9YH2jeUlbsX80ZVd3qA333Pr8H2gy0Zz7OrsRW/fWVDp7YlE12gZPekrQOm07ZUGgdbur4wkMpM3J4+fPL9bTloTUhPwc9NYNWOBrzw4a4ubE1+qSyKKrf3tkVnQUTHvqbuER/T1YTCckhWEBL6wXU3pgyu6HRheeGq3WhqSyGd4d4f7mPDoBRFMb1dv/3Psh341lMrc9yizFzwmzddL8BsXWmeWLoNNz/yfi6bFdJBOvtZnP7dhUgbVLlIWrm9AU8v35HV8VJpA994sn0uC/nktEm1GFhZqPyut82GVSUxtCYPT1/qUFgOsSiIZh4O+fZHDXFDSOenjkumDRREdaTS/ufZ3E3Mvvl6SRmUdsh/PB9P18f7mpFMO1+AJQWRrI6x9zDVNnVnkmmKqN55I6qxNYVr//SucpGka8i4sAbgGHcpg+KRd7bktI1dAQGB3/SbT0tXrtEIadcirDWZxl/f2twJLeo6QmE5xCIecWrEDja7TcKEAL95OZi7Rmfxnw+29/gHL5d0xlycNih2CBk2kmkDI/uV4B2fTChb9jVj5faGrM91zR/fbVcbvcjnu8mg7vN396h2XbHY0ggwtrY08DF6kTzQa2hNpV1zeq5ZvlUd8Bs09eCo259x/q4nDiQCfP7hpcqvOlO5/+9l2zvv4B5oBL4LAy8OtaVw38KPct+gLiQUlkMsdM05Ux19z/9c+xAA9Yfyq0Xatr8FH+/rHhrM7kJzIrfBIBvrm3D57962PmuEoG9JHJrPy6y9KQX/t3p3u37nR97cQajzhb+pvgnn/ur1oD/NC7pGXBXI0gZFPBpc0NrVmH2qvu0HWrpHMGgvpb6xDYfaOidI7A+vsWwzQ/oUKb9vj9BrUNojheWuyESk4iYPAb0zYVr07GeqjlrcugNdJiwTQgYTQhYRQlYRQlYSQm7uqnOHBEMWhBoVE20uxvvSj/d3/CAhDjQ/KbZdOFWkTKNAfTUlG/Z0j5RC+ZyTDUod9yJNaXZFWjrQ9pZEGg2t2QcIEgIYkvJbpSH347cvZx/U+cArG/Dk+12vHWsPhkE7xdWpLZXGLY90jtCTSBsY0bfY+vyNJ1cglc6NlePfH7D7Nn9UX899su0tSp1ufo2tybzmS392hXfuZBG/56Sjc5HhMeaCKAM++9clHTt5DqHULV/0NLpSs5wC8CVK6TgAswHcQAgZ34XnD+kmnPurNzp8jN4WSPad/3yID7YeaPfvc21upZKwpGsEGdyVkZQlrhzy5vq91t8rth3Elm5qWVBpULpqpD62ZAt+/sLarH+nEbe2iFLqsjTlGkopcuFS62UCz8Rt//wAjQEXFw+9vRk/6QQzctqgeKITFgz3L1qHVJoiZs4Lexrb8Mg7WwL5EQdh6ceZ56ps52gKp0D1zPKd+PHza7JsWe74TEBh008g7kh3xyMampPqhXaQ4z4TUNjPBgraLle7tBFqlgNDKd1BKX3P/LsRwCoAA7vq/CGZmTqkMuM+PJ+k14rXj1aPBz+fpNJGt2jXh9sbMuYw9qMT9MqO3KFcoPK7651Z5vrS371l/f3Q25vxylr/ghn5WksZHdCg5GIB2J7UdWwh5Dx3tprlk8bXZHVOSinrqxwI5E+103dz4Ye70RLw2U+kaaBYjb05dCt5ac1ufP7hpe0qE37vc2tgUIqI2b8z734BIF33XPz0hbX4zwfZZcNgbhhE+pzrluWeTE1s7yXEdA0HMlRD7epUk5nGj8rl7PtPr8IdT6zAzgZmJci1y2BXkRefZUJIHYCpAN723zOkKzlxXOYXHi/rOet7L2R9/Cnffj7r33Q2f1+8FXf/d1W+m4Fk2kA00n1CCGTNsmbWP/abLCNa17RfNtfKZIpO70yo9MJn7QlOR7LNEOIWeoOgKdJB0gw+hjc/stSRbm5c/zLcfPwo67eZXog3/O09rNnV6FnMIVvaU1VU8xAeVcKpRoIV/pn+Xee8eMND7/n6DXvdrlTawFUPvounlm3Hife9gmVbsrc6pQyndcDrejuDoBp7EXnOoegZ2Zf8xzDF8+3MN61pBEffs8jjqIwgwcOXCYqGjpJp+KgsDut2H3LEpcz/4aIeaRnu8rczIaQEwOMAbqGUuvT5hJDrCSGLCSGL9+zJbbnVEH/89YaMkyfUAsgc5EcpxX4pnVRr0miXRvrUn72a9W+CYlDaJdXvOLf9c7nyZXyoLYWo3v7HsbUd2ic/KJxaHV0joKC+k1xxvHMj7zmUeps+dze24tmVOwON5c5ApZHtSEvaUmn8a+nWQPvq7UzrpBEC0ZX1/17dgBdW7fLV6i3etB9NHkLgu5v2W+nEVu9swKZ6ty/7ln0taE6kfLXw9z632tfqYxgUq3awV8gGxTkysbuxDat3Njq21R9qw0k/ecW1r+ySsjtgQONr6+qR9BFovIKlPvXnxdbfzYk0zr7/dTS2Jn3v71PLtuOxxVusfR5fstXSLAPtD86S+f4zQZQL7RBypefa7znvCjSCQD7endVGPzcofo+DpAZ8Q3BhU3Hvc6uxdX/Q6r3Zj5/6Q22OtLT7mhKdnuq0M+hSYZkQEgUTlB+ilP5TtQ+l9AFK6QxK6Yy+fb2DB0JyT6DnIODEsHV/Cy55wL2iTaQNxLLUoPIXYmfQmZPxim0H8dYG50T15PvbXLmKc+GDW9AJKaJErY6mERiGU/B7cZVTY8I1LBdMH5TztgC2FiVpGJ7DcNbdL2LJ5v0516Dd8cTyQPt1JOpb1eSmtjTu+veHgX6vKQL1/Lj7v+y4cjaMj/c1Y/PeZt/riOjEc5GZNigaTPPwP9/bhmdXun0ndY0gkTJ8z/GnNzYj4SOstKUMnHM/M/tG2unOIQdx6YS4FvmAO+PBrLtf9DymKGCJP0ulDXzy905jqpfM0KxwaTr7l69jo8+iYFN9Ez7e12wdc83ORofAZRqHOowczHn9/OGuffh1y/m7/VC7YeRPWi6I6tgcYF721363v/2dHTPAWbR6Dw4o0sRe/n/s/b1oja0VVo2fuq/913dRS+GMqWEufe1ubt7oymwYBMDvAayilN7XVecNCU4ux+/+5oTSHzGRNhDPoEFtj49ex+icJ/eN9fWukq9EcbZ/L9uOpkQa6XYEyHWWOWt/U9KhnY1HNGw/2OKwDFz7p8Wqn7aLX7+0Hhv2HPLdZ8Z3FwIA9h5KoLI45rtvrvvlr299HGg/o4OBcSrZIOjRtCzdMH736kac/+s3XEVtYrqGRNpwtWXLvmY89DbLbx7RSCCtW0zXlKZijQAHmpMdipAnBFa7RWFWzA8u89zKnY4Uh/I40QhRzgbZyGz3PLva8ZkfL02pa/HsNU7F8/G/KfxnqvsWfgQCW1ttUIqIoHnUCMHnH17a4XLFcl8USikGf/DMamvM/ixAwCnvAyode++hBB5+J9hzF5SrHnwHf3v7YzwToLrgtCGV2Lbfeyxx/MdG++ch3TywKhaEHzUXQueHOxqUi9bX17GxevWDdh58r+nFz2JBCHEsmjRC8NKa3KcL7Wy6UrM8F8AnARxHCHnf/HdaF54/JANB3rNBfchSBjPZX/rAW/je06vwzkb2gmpNpqFnMB1d9Nu3sG53o+f3uVQ2dKZP3PeeXu2aKonCVM61J5mq46m45dH3sawDWTS82NvUhpH9SqzPo2tKcKA56Tv1EwBXHVWHARXq0q9+vL6uHtsOtGDbgRZPbVSDGQBJCBPC/Mifz7Ik6GTx24rCKNbu9l8w+KGZ2tpsWLJ5P3TN+exHdA1tSVvru2ZnIyil2N3Yin8sYS4hsYgeKJgwFtEsLbPIB1sPYndjm0tzeNs/l1taKgL/OYlSW/smLlDmfN+dH57zxrp6RyEN+fgUVHnPKAUunTXEuzECTYJwI1Y8Mwy3hjrIOCXC/5nmaF3TrH1W7WhEWUHUcZw31td7Pl+3/yuY9SSTFl8MhMykWSaCHzWlzr7vjECwl9bswYY9hwLl6Rc18fct/AjbDtiC8+qdtrWzs94gfEyrtLa8zzoSFC4S/J1KMXlQuWur3zjWifPdRghw/V+6T1q7oHRlNozXKKWEUnoEpXSK+e/prjp/SGaC+HkGfagoZVqfFdsO4oFXNuCi374JAGhLGiiO+ZfSbUmkkKN0oBkRJ+vOYPsBp2aCaX7cbQBgRQtnw6HWFPY05r6wgxxEx//O1FffOmuC6+XR1JbKmCkjohOk0hSf+tNirNnZqLQu6BrBobYU0yZmGIf581lufwR/ZXGs3e4EABO2d7VjDMkaaV1jzy6/jlN/9grSBkVU1yzhJxbRfF0kOKNrSrFiuy2c8iIk509jrjqiMAcwP1tLwFKZYUwSKcOhzQqaP5gQgm//x3ZrEcfJweYk9jcnlaZ/Oa2ZH+JiWKx4llZYHbw0co5nT5SWzfZ6ZfHRNfuYLck0RtcIVRhNoSWZNvD4Ercf/ENvB9PiBrGcBHVFEtMWUul3mWJJvLKT+BW6IYTdBwrglY/8Y6KI0Lb/rd7lyHRyyk/tOJqOzjQ3PbxU6TPM46X9nrP596oDADmyq5wXQe+XuEB1bvfuBV0jSBlOzXJPpPuE34fknUyC0KI1u5XBOiI/em4NEikD1JyQ5OeCUnsSELl/0brsGpsjgmhrskWcOGRNoaYxTZP8QgVgBUUFpTmR6jRhnwKuoiQNrUnXyz2IkPLz/63FH9/Y5LtP1DT9c5/fqd9e6NqnrCCCRMpgk28GLfyIviW+33cWLPl+9i+D1mSaCaiu4wW7uXVf+y+eXrEDRbHsfdflPMsaYS83fh0RXUPKoI4Sxn2KY4H87ONRzeGvyEub15YXADDHlCkUNbYmkUgblvDBXCLU1z/9uwtBYafKKyuMKvcDgK89/oHnd2L3Tv7281jwo5eUi51M2UFERG8q0ZKUNqhlWledX0TcbVcDE9LEuerRxVvwy/+550w/H18CJig2taUDBumpCZL1ZuLAMgCZBUlxMWFQigYhiDFTb//gmdWubb/831rM+K53pqaYruHB1zfBoBRX/OEd3+dLWJv4WiCDLKJe9hHMV+1oUPqo87GishZlUgZwd6nfvhKsWFBgJRiA9xQZLyiAv7612ePYTh9lv/56dkV26Qa7klBYPozwqu7193e3AMg8sT2xdBs+EEyYqlXrH17fiGTagEGB5raUUpOics299zk7+XxXZ5XJpRZyd2MrLvzNm9ZnUQhpakvhYEsSaYPiM39dgo92MVcTjZB2aSNZWqHcrtJbk2kcaE64TKKEEBxoTjrMqgVRW7so+0G+vq7eYdbN1McxXcOf3tjEBAqNKF8eXKjTSWbnmVwXaQlKewP8vvzYMjy3cqdrHBg0U2oqmxXbDrbr3CwwUDSTsgI0/FBRjQXziRlSqktilmazNZkO/ARVl8Qdn2/9xwdYbqZ94/OCYbCqhylzHlHR2Jqynq3+5QWI+wQNP2LObypUx1f1oGHQjEIRfzbEZ160JBkGdcVxUErRt9TZbaPs7wAAIABJREFUJ4Dd9xfPGCxss3/bkkhbz0gqbVhCn6rAjPh7Sjs+34lzuteRjhnNgvMzjcZkmsKglLnpUfaZX9f80X0xe3iVtW+mjA0tiTR+9Lx/4Rje9rS52E76LLp51VKO13vJS9P+1oa9eGIpywEuWxhFvBQ2fKyIcy73t//VIv+c37f/a4Xjc6ZaAhphbjiZ8jZ79QE1mEuXCp0QxxhvUszt727ah0fe+Rif+et7vufPJ6Gw3ANYuf1gTgKWJt+lznO8ePM+5XYZWSssTwCNrUm7eAWl2HOoDbqkhXjy/e3YdqDFFeiSL3JtEUqmqcO3Tbxt3ESephT7m5OWuZC/xLJFFFBzJe4/s2IHvvXUSqtdHFU/aWa7DzQncPJPnem2Pv/wUsusK2okvRhQUYA31u81TdXqfQghMAwaKP2V1z5feDQ77b2Khtakw03kYEvSyvO7u7HN5WsZpOhN2qBoTaZdGqx5P/T2v5XJRrAWkaPTCYC0oFnWzWA+0TWnKBbBzoNs/F75h3ewPkNwpn0u9flZ+20N7I+eX4OmRNpz3ouY/tkxXcOIviXt8lEfVl2MGXXuQkyqPhT7dtydzyqPx/25SwpsNzNRO64K/uRFbJ4XMobcv2id7fYkPdn8E6V2X9717w+trB6y/7l9TfYzzBYl/uPEz2ogugq9lqE4UJC1PKXARb99074283+DUry1wX43HX2Pf37e9wPkouaCeNIcMClJc8MVGIBzXpZTaTpRf7Fu9yG8synzu9XruLqpRBEVB1wR87MX16K6xD/IWWxZJpcWQgie/3AX2jLMVVYwpnQfDKr29QeYzHDKhFqc6FO4aGN9E97dpBa2uwuhsNxNaUmkLT/P03/+WqCAhEx4R7IyH8RMEg2F0xwla4km3/U8CGGTsUGBARWFLh9M7memSivHaY8Ae7AlaWnIZTJV1cqlJptrI3iKqBF9i63vbA0Tyy7BtRq5yBD09PIdgX2XX/loD/Y1JfDGunqs3eUMpBSDkMRm8W2b9zZj0erdaEulkTK1QglBs8URfWAz3U/H97L/h8D/s/feAXIUV/74p7pnZvNqV7vSKqxyTiiTTBYZm2iCsc/YPmP7wBgnfJzPPsezD2ewsc/G2XeAj2Aw0SSRJJIQIJCQhHLO0uYwM12/P6pe9avq6tkV2Fj+fXn/7M5Md3VVdYVX733e54UB8NCKnVi4anefZaYpy396aWvpG/sh37x3Be5i5Szf1oJvaBzstx9YadzmJNtb+sYRbz3QhVue35xoenfeD3O52YMvVVbtPh+FT99qp4cOApsNg3iXqazW7gKu+F9l8aF+H1CRNdzeLV35hKuYgjHdU9wjryej4KlMGjNFKZmS4hfCuJMCSuNvyYZ9CXYGn0gpMXlITQIzDfhHH4eUpWX9o/vmj46toS5m2X0/KsteYAKeokjiu39ZZZ71sMOmQ/0SydhK3VMoGno+bll2A02F/q4/h81jv2NjYc/76SLDVtMfy/LBLOL8MGF97ym81BrXV7v4GrVb82TnC/Y9p/7wSZM1LxBx+17b2po4yEpPP5d6ZpoI+OFGYSCQDYJUTmLf2HXvJyXZh1EnGd1QaVpWqrYErQSSHplSmTAVrCtdmQbePEf82ynvKMuHqPzyqXX49aL1AIDhdRV/U1B8FKm0qL6h6rplaJ3MZYIEX3IkY85WKSWKkU1d9NcUd1619xTwhTuW4ct3veZcJxNZtbjwBbAYyYNmE0iTp97YAwA4VSdxoboAsTUjxua99T5as6sdezv6pyz/6JHVWLe7HX96aSuWbrJP85EyHyb6l2r4ypYDuPOlrbhx4VqDLy0UZQLHyBd45WZMXwizQYDqMrXwc6XElUCIfkfIv5nkN/2VMAgsSw0f526mtf7WgjNAuOL77os+5gLZPx3lrpft9NCtXTYWXWhYBrewLt20X1GXyRinTowk2TC5oXNu9L7q1KYVa0N5xqZgmgKUDWJKukwQW8Y/f9srmDE8Ga1PWfQMDZsktoNk+W8Ws+xayFVZwoxFPxuGtBJL/MSJ3djP+G95udtbuiEA/G7xBjy7bp8xrCxas8drZSfLuJTk1j+4+fHSpgP45dNqP+IGEGpP2vxu7SqVvZCUZPrsfp8ssywToqUr7+WbpuvTEnXwfqF6+YLnKACUB/ipz/E1ITtg+g6oi9bs6fdalRZ3QoG3aW+qr3lVjCT2a8X/jy9sxoptrVi8do+nHKENYaVlwfefMPXkFvmqXFiS1jDQc4DX142tCALgTm2A6K+X6u2Wd5TlfspHf7ek31lu3qrc9OQ6LN/WajaDv2Xw6NJN+7F+b4c+2SV/dwO4gkDg8mPH4JjxjX1OcNqQuPim/g6P5e1gcXW5MEBFNsQfnCCDPg+rzHpw59It+NEjpTFvfUkxkpZlMWE0hdo0aYECDt6y3FuIzNjYtK8DEsD3LpyJymxplhESWuhdTwHVkSwd/N25WMuiUfgpVbdrdYn/749lmR/KUt15QpjNua8MUKXi/25bko5hLSW3as5XYoswz4qk4SR1xR1/L28+4E1/7Jvr/YFvcHmzeOnG6jK1sXbEFrWi41YtairIV7e24DP/94p1fxiIg0o+4co1t6vyaP8tSjb2Ut5jNhOgt6iw0tyyvGFvp8kkuaOl21gJKXmJy/DiXx+SffjS5gMQKP1ODMxBxgpfwMaKjw1DSttSS8khhBCYOaLObnMQGOXtt4s3QAiBVzYfwKZ9nSb757Pr9pk+kxLYvL/TQIak7teWrrwJiHzg1e34xZNr+5WeOhcG6Oq1qT8DEcOxfOKjGeNtry3PWGwY/K9viktIfOCXzxlPDpc+1wT2+4iBlQBgZaGkQ019pYI3CJTGKdOB2Wfs+Mqfl2Pzvi5r+N783KZUViDfc7jhyb5Wmt9LyZShtaguU3tCJCUWr92T8FQA/WeEWrenw+zLvC/rKnOIovTsrmEgjLcoLXiTr1vXeQI3DwV5R1nup2zZ32ksIH8rufX5Tfjd4g14fPUurN3dbi3M/RnM+zp68YOHVvV9IZNbntuElzYd8LrSARu/R79X5jKprE7kBiRCfLdIXzvWOIwRq3ce/MlSQmKojrAHYE7QEqWVUb4gduWLJgiyvafQrw0kUQ+3vex/2hQKlIFO/+hL3lJKfv7EWvz0cWWBor4KUqxkPjHKssecILWrWBuYjbjKC7cC5YtRn5njSo3fQAjjRXHljZ1tWKZ5pIXg1rvSzytlWd7FXLl/emlLKnzHlQ171WE547hGXQvYtGG15n93Tl17xzJs2ps8dBOrA994n9beib0pSSSSuME3x8QhhFICj/jWo/qzGh8ujRc9rVMrGNc/qhJOZENhPCkA8OPH4kQUvjH54sZ9xm177qxh8QHIWJYlu98vmUCgR7PuZEIbcx0GAkd+61Ec+e1HMUuzqlBLjGVZf8nLP0wrdr4uvG/ZduSLkTfwlITWSiklTvr+E/q5Aid873HTLr9lOd6Gqb92tHQlJo0Ll3EtnYB6F3w8TmyqQWePqnMrs+BntSdod3sPNu/r6hdtXCYQmPn1hywvkoBIBMKp75VUlGBnUXtEAMkCOwEbauLicqUEXt3a4rU6r9Kpy9PWGg4VaK5XXPA8QQ2NP1IwlRKZ0t9CYE97D65/5A3veCnLBChKaY3lHzy8Gm09yT3FtWCTKOak5BwgiExf+2RlLjSH8JU72kxwNJeWzjz2tvf2HwJhLMv9N2ZRAPFDK3bi/7Shwo0L4J8f8ij0h4K8oyz3Uzp6/zZ8tiQvbz6Atbvb8e7DhuKr75mGtu6CifAuRaFE8sKGfbhtyWbcqzMTtXTm8Z0H+z6hxZaZvh1zXLdyFwialJRyN5LAhXObMcuxjhycvdiWlzaVCABwrNiX3qQww/1JmUp9+x93Lzff/f6ZDbipn7Q7XIqRtLDc/MBjov2lbbUt7wfGkkshkl6uZv4dMVr4JBsGyEcSdyzd4qEqiy0NfGOPA45gHZSkBLYe6EZ9lY2fs/GSpfufW0hc9+opP3zSWJ237O9CXaV6zq0vpG/ulx87pl+4TADYur8L9/eTrojaFAhbaXEPCvNHD4w3/kQZai63dOUtHOERYwZazwCAptpylBK3iQfD8XzWDTFPbEZbsMglLYQax6QT0Xig5z260sYduxCcxWttK7tbpc/93yvGm2QFwJEFtsRBhGRXWw/+W6ddDpnFVd2T5CzPOlGj33lwJVq78iaYldczrQt/98zGktlFaRgXI2nGMY81SQvwszDAuhm723oSY6cyF1rKOi+KoBFtPQV88z5FC5fLBAgEDNTKWLijeJwJXa++rLKAtuZr6kYjzJruk1KHNzrcJd4xKcuRxNxRKgCTxgsPAnXl29oimdaSnz0eM0hQMGBnbxF/Wb4DD6/Yacqk+5WHzS9hILC3vRd/emmLVzmXUlmSpXVPSvBlyjPGNlbhg0eNMvdc/nuVMXVLP7IKAiqzInl8asszKEZIJAS7fekWtHTlrfW8lNDPxCayeV9nqmV64cpd+NEjqw1mGQA69MHNHRZfvyfpKTjU5B1luZ+yu63Hy+v41xApJf7j7tcQBAIN1WUYP7gav/3IfAgh8NrWFmza19nnIL7wv5/Bko37zcRr7c7jbgebmCZxkMzB15uELLLcdXT0+MYk361vYfF86avLeT9dnF4XAC0efJxS+lJvQ015Fjtauo07Tkrg7pe34rHXd/UrdbBrJYyktEjxOTURtZOCHejz2MYqHIyQRY9LIAQeWr4Du7SScPi3HjUuXVd4qmL3ILF47V7sae9NHDKoD+NoaGqTUszHNtrv2fXClOpJ9/X8ZtGG1GszWul5fFV6FP7Bpn0uVZZPasozVvIP91nPrd+H/7xfKSz0k2EY0d/tbuvGjY/H+NRZI+owdlCVcpPr90YxAQ0stfdvtAWeXJtc3ANOKVm+LcYUK8xx/BsdBuj9xxhVf5/yuAQXBkXCg2w37O3EPcvU2qQwver7VTvaMHtknTV/Sr3Glq48Igms291uKVy+7I4rNIaaavrzJ9chX4wsRhlqb6k+7EkJuOT3pSmORQ/9HFlXXdnfmU+0vSxjpw7nyhy9AymBO5aqQ1hTbRkEfEwn8bvlcDBXXKhDNrDHw8lTBiMbClSWZRKxLWQkKDULJdRauU4fLOI1RR+e2EHi0puetcrjngySz54yUZfjfyqv4/0s3fXmfZ24++WtMRyEQWgsOJmTKIZ+ciFIm/Z2xuOR/ki1F/iU/DRlc+TASiyY0mQKWcHmbH+kPBtY+08xihIB9/Qx7aBNbSMvl5TASZMHY5H23D63fh/Omz0c0rOL72ztxrYDXdZ6/I37NB7cubZUIplDRd5Rlvsp15w2GYePGdj3hW9CIgmcPKUJ/3bGFABqAZs8RLlyN+3rxCeOH9cvi2xZJmCuQH/yD1fIYpQJ7UXT68aWXLmyr6fJHlPH+Ted/kIF0ixKvoWwVKCForRJ3/yyocBz6/fhkzfH/I7Lt7Ual15f4mZQSnNHAnEfKZowRcWlOGrfir097pObnlqPtbvVxlMWBmbx27K/E9c/EivYmTBeRBOWZQCPrdyVOGS475JHsK/Y3poI5OTBn/3FxJE849AK8nv7E7jXFxUf0bx99HdLTD/0p1zqj8pcaB0k3A0wX4ywZX8nPvSb5y2Mqaqbv+yini9t3QWcqa2+1Mf8nq9pC0zosch154t94t998yerXca8nQYqhFhhSetTbrVdrvv2qLEN1jUu+w2VJQQM1eJ1D67Ehj0d6C1GxotX6q1QBrbl21qtuvUnwxwAi5aM4EdUpzRZtyfd9S2Y4gEAH//DEut3zmDBn5s1/Stx18sxywqNK4KXLV67NxFwRjAHl6IToNiI5PiMnLbyMcGD0n6lA/pI6D0bBfaIkSZWxH0GWbdLuffpp7t1m2m8S6kU2ytvXmrWHYIplfIY3bestIfoFh1zcNlRo4yFXkqJCU01hv+eP4PgEfs9MCh+oOQeoN5ChNOvfzJx/Zpd7djX0eutvxBxHyR+gw15OxipKstYMUfd+Qg3OvzM5hgsldX3z69sxbfvjxPWkBHp+fVqTZZQsLtP3qwYdaJIoiwTWJ4nOtj0FiPkMgHCQBhKunje99MFdgjJO8pyP+Vv+WoLUZS6wCurbzxRtrd04SePJS2LQBy0ABBNUf9rza1XgD+BiUQc9OMqQJxGR8EwSlPFWOX69HLPdy7UAFBWpfN+usgbTMjvI1m4ahfW7CJsW0xRxcv9xZPrUi2TijLNtiSc+sMnACjohhvnxCEWVOSe9l48s24vpATed9OzeH27qs+nThqfeF4hBUvuCh1SaBhlwhgqsKe9F4+ujHFg2VAk+EVNfbWS6waLWVhP9k8kJX7+xDqDgSSpYlhFAUUZmJYUxxW3fznLR6lAMlJOAo+CwOUBzUn7yOs7DfSgP6mb6dDV4zCm0LPI3U2W2sdX7U6M49jtLCxNkJJe9BYiq/z6yiyAJK0SzTMABi60t6O3z03I2y0yDtikOnLMMsEsrMO0rs97f7bYCyPgCnZHTxHdhSJGNVTi6gUT7Haw/wuRRFkmhJSxpSlt7J80ebDTLluJTJO0TH+SWTHTbv/3M6cYN7JPOGYZAP6y3MZeuiQU37x3BQosUUl3PrK8QVlPohXezj3tPWa+kyLou9aGtcSpzE/83uMmOI+UGZ836oO/fh5A7NUh6yQlrwmD2LuSDEZLFJeon7sGS8SwPoJduR4tn6zSNJgXzx+RfhFiytMwcA1Edp2FrkvBUaLVtcp76o6VSEoNk5OmLUDMDpMW2/FL52BCIkScxONgTSp1FVnDJy0BbwwOjb1ISpRnFdNIdz7mN/+wzrgJEc9nbp1WQauBxYZBMKzeQoSsNtq4lJH8kEZ9c6jrz+8oy/0UPlB3tHTjidW78WI/knm0duf7xIMVNXWbTyKpOQj1573tvfjeQ6sTCsUpU5tQkQ3N5PWlVi0l5dnQWuhogl743zH0gSukeupY9QQ4DCPFsuxTjNn/ZOHb6sl45LOm3bl0K+aOqteKfNJqw91LH/ntC7jnlW14SafrPOP6p1KVepV6OPnLjY+tSSxsFGjxH3cvT9SPW1xpQ6GNQAJo7y7EvMae/rr2zlcTGFDAjuJW9yolkxa/MBD4zoOrTD/wkjNBYNJFu48kK1bkHD6obgO0skG4OWqua1kW9s3oyhext90fqOaKm1WLd+k1t6enLiblpFQWs8Sz9LvoL/NEZ28BP3h4NdbuajcZLOlZvjJcrwEdMrkbF4gPtyt3tCHHXOodPUXkQoETdZAYoA586uCoSvhPZgnqa8q7a1EuDBLvJhACe9p7jfLoKjWAgtlcvWAClmzc7wTkJcfV8xv2YfO+LswZmUwAMqyuwvxPrCr2IZz/L826564tz6/fZ7xBpSzLpYwSosQ8pHvd/rtx4RqzD9CBY9WOpPX5n371HIqRtDwuv3x6PXa39WCgZl/gY3ZQTRlmeZgk6JpTpzbhluc3p6Z9v+qk8XqciQTOmg5CG/d2QEpVJgX/+ZpOFvwMW1uAmBuXqMFU/ewySs1Dd1v0JbwIhcD+jl5s05jl/kzrwTVqDevLOzhj+ACs2dVucOVG4de/B7p93OJt6uqpP10j2G8uc4WvP/Z3KCX2Bw8nmZjyxQiX6cNKf/jauSgPIjsEe8Y+90QLoSGCLCbjpEnqUGos0LC9GKS7+A61HT1FlGdD75zj7FsELaPPlx875qDa+XbJO8ryQQgtAre+sAmVuRB/7EcU/cd+v8RE86dJIUoGfpBI7bpzx2KnszFTRDJ3BfLJ8e4fP4WDEVLGyK3vimtZpoWG8ElpGKhSUbTbDnRh7BfvB6C4X13xKUFBIFBbnk3ABnhQElnKHlu5y1KAV+5oUwFr7D4qPc31lY9KB8MkfmMfaaOnBUxqK0QaLygQB1CQUAa6gY4ngLKwcdf5H5ewNOaskEwYU325m60QAlcvmKAOH9yyrP9S0GlHr82N6wZQnThpME6b1mTdy62X37h3Bf7wzAar8KZaZfF5KzRkqg3q/VH67FIybrDCWruJRNLKpfJe2dKCe5dtR3e+iH+9QynwfMwYS7zzeLJWBUJYNFIcR0oKlZRAPopQXZ4xTBwA8OHfvKAshFIm+qovb5I7f0hJ53hO4pklTxUpSfuYhZ8/h3tPntCKlU/h9K1xFzErYL4YoSwTmsM2YPffA6/twLfuX6nLir//0NGj8edXtuEpnU2ulJHAp1z++un1eOqNPX1mgPNR5L22tcWMHVpb3tiVVNKKkcSPH3sjEZxViOJ3GEmJYyc0AgAmNdXga+dMB+DCkIDFa/bgoRU7MaaxKpUXXllNlbfC5wlZv6fDtKe3EGHD3uQ6fzrjiB9YlcMAbeUNA2GYacgDxfG+1PsXzxvhVW5vW7JZ482dQzH7n7xJYRBY+Pov3JE8LN/gieEAYGUVpXV8UlONec5nTpmI21/cgnt0bE8ShqH6PjZuxDUsz4ZexVf1uTBGB7qChiTtk/wAs2DKYOTCwMsd7b7fEQPjw+W1Z0wGoPaV3z+zIbFXZcPYKOJ6Ykg4DEMgZmzhy+Yf/vlwti9KNNdXmLW6tTtvLPTl7CDID1hu8pThdRWYOqzWCqR0630oyqFZq0NcpFQR70MHVPTpIu/KR1i9sw3XeiY5SbFYyrJMFuJ4Ih85dqCXpzBi1lCXJua1rXZwwMod9mcX53k7i9T3KS9upDBZFuoqs3hjZ3t6pLNHaKPmZOQmMtlxr/LiVu5otbivXeWOKOwE7AACifgwcN2DKyEgkuweus2n/tDGn0VSJlhR+L2udcBdYM+fPTxWlqH6NgwE7r7yXfDJnvYei22A2jFiYCVOnzbEsvRzhYs28CiSuG3JFitZRpYl1fBtPqqd0Btt0YJP9BYiBIJHp6vv3QQ1zfUVBncf3xv3xa+eXo8vM/YRXpY73g5WeaYx8oOHV/eZvXG4tmwm0gp7xu2PH1tjZdQTAOZ842GMqFdKJR/rxGeaKEbPiVAIc+AAYhgGELu7JWwPDa9TeUZRbp3w3cet4htSUuA++Np29BaipLIcSVSVhd5DLA3rypyi0uIu+t5ivNnfp4Olrn/0DTPuiC6KB2u2e6g3qc3fvv91FCNFoxZxax4LbmrtyqNdU29xZb26LIPtLd0GJ5tmGV69sw0/9PCof/3eFcqTpbsgbc1yqduAWMlQz1V/fcFn589pxgOv7UhwbBeLEss0zpsHtPHxyP+PpMQBvV6ePXMYelLmBiVqEYghFrU6DXdRSswZWY98UeJ/nt2IDXs7TZArV1weXL4Dv1u8AQBw1LgGcygKA4EvnDbZMB9xizsdBAHg2ImNVl8+t24vvnbPcnztnhXY295j4dZVX6ovlm1pwXMaTx4GdvtJCePis8hedYudpZKeNaqh0vw/a0QdqnVwohAiPuRQvaD6+5jrFgKwLcncs0Oys7XbWNYPHz0QQ2rLDTb6KRMgJ/Hdv6zEpC/FKdPrK9VBpNwDu3litT2WOJc+vaneYoSv/Hl5wtJdlQvNeOvqLSYoWgG2tkBiV1sPihHw60XrTdsiCRzWHDNaSQAnTh6My48dCwD4zoOrDEXsaBao/gR7Tzz9OwC8d24zDh89EJGUuP6RN8z7/cmlswHAG/B6KMihWatDSAxfr5RYvGavtfm6JzCfCKhNZvXONgt3/IsnY6B9PgWzLKEWI25ZlpKgDva1ZNE1J9jItuy6EeKn/8gJIoK9KPGUsWSZUBsDuSpdF6n6O6K+0mCehFDpkatKcG2quqry/+lXyt106REjDdbqcywBgqt8/2npVty5lIJDkv0hYS/eVG8gVszX7e5Ixe8KAbyxy+a7hoyDtdy2Ayo4jvM9W1YhKdFYU2ZO++T64vVS30ujoOYyNjxmylClgEaRxICKrLGeC/3+3WE09ov3Y5sDabntxc0lOaRvemodbnluEwIhcPfL2yxan95ihIpcBiu1i5MOSQLAgxoL7Aq1rVRKVCCGMVDWMjpEubCM/kouE+ADOu14mrieBBI6JCU8C8wKKKGop86dPRyA32vC51RPoWgCdiIpLcaQ/Z15M07rDU5T/UZrw/2vxv0baDYM9+BWnvHPtU/8z1J09ha8lvaQudG5RFLioeU7zPPvYwwCPE3wObOGecv8wh3L8PSaeLOnlMwkCyYPNuvJz59chz3tvYbjm4LvqFpn3vCUgmnodYx7zVzd2LfXnjNrmMH1pq21VI4LKTLsCAJmXYrviz/7WDhu/ugR5l6fFKII379wJgA1l4x1ky1JXB+OHG9CmmU5CChrmjAZGykYMIpiesv2HntMuH35FYvFRV2XCQKEAVtzAsGUq3jfqS3PWuvyxb94Flv2d2FwTRkimcQh098DXb0xTSNL/nEwcs8rNhMUlTCopgwDGc1lQ3UOq3a2IRAwUCQe4MenPzfM0EGBy7HfWWjFevjObMUoSY8JAPs7elFflTzoutAuKpMO+ZfMH6Ho/DyGKa6kPrRip/cQF7Nh0F/1D3G7k05hmiJjqkkSnhTIJz5LMek0/PBKRofSvrG/n7yjLPchxNcLqACCJRtjrl8eRJUmFLSzdNMBLNBE9at2tJmB+9tF6/HJ/30Jv3gqyel7w6NvwNC66e+M8sce+/z6fdi8v9OKdKUTPwm5z9Pq69LkcDGLGqSl3LkWD0BNbsoORwprRc6fWY7otFzXaDYQKOoNgeO0KHCLNlg7eM4+HNDJP9LmFWpbFElAAmf/ZBGru7/d6hk2h2t/lu0vv3uqIb3nEumFJt7sZILaCVCHq+M18Xx1WWg9kw4xhUiarHm0QfGgLO6uO2fWMJwytcmqB7mzfdLZW8SK7a2MLi7+LV+UVjAR/ba/s9cE2LhC78vFWbtC3hVKVxxzoZa+7xN/eNEa10RJVShGFnG/L8aAb/Jc3tBWGI4VBmKLSUdPwSgflLqVaMX2MPzhkboXAAAgAElEQVQvL/ak7z2h2xQHAC3SymRNecbMJ5q3v39mg/WZu/dpcyzL9n8JL0QSUQTMG1Vv8Z8HgUgcAAF1MLrqlpeM8shp1nhyhXUeqNajK3elUhcOqlGK2q8+ND+hULgHYr4J54txAg/uNUskOPBst2EgzDrzyw/OS/wupcR5+tCTjH0APnPyRMUXyxTVD/zyOezvyLM1MfnciUNqTLt8UojiZEr8MMLX1tqKeP2MpIJmffaUiQgDkYq1p/EhhJpHs0bUGcX6lhc2Gzf6zOa6fimjvG1hoDJpkpIUCoFipBRUMqhcvWACxg2uNu/vOs35//CKnZoqTnHFTx5SY+aP7+CaCYTh9PXJ9hZlCEhjs7n61pd0mer3Dx09Gh84cpRuk3ovdZVZCMTWaG7xlojhYcQAAaj7cpnAYL0Btc+TAeQPz240ey4XDjHiQv3pCnX7p3U7+EFOApg2fID29iV1kf7AGQbVlOFjx401BqtCFOGkyYPR1UtUqkRzqqQrX0wY6zJaP0lTmNOUXzcWog8E2d9d3lGW+yGjr73PZPr5+B9exGqtFPgGqCthIIwrhPgkn1y9ByMGVmLFtlbs7ejF8xv2pdKbPbZylwWGV0qhnaTktiWbsWyLcuXRouYS4FfprETzvvmw+e6BV7fjVbqvBAE7LdB8LggIrNzexq5RPy7f1qpTPnejqixMKLFcaIN2+zBkMAG6tztf1BaMpMt8YFUOT6zebTakLfs7jVXg+O8+bm1U9726PXE/YCcPsOrivOP+MFOs3N4aB8Cx7yMpTUIQVZa/TD6uKCVvW3fesigVowi5MDTlugwGtFC+/4iR2N7SfdCJTwA1llyPgwuJ8AV/kXS7gUX6ooWr4sho3naqO+HrarRlZHdbD2rK0lN5P/z6TlMOx3+7FmkK7LTEsWq5ssHh0f63O18FoMYlKfdkCe8pFPGls6ZYkB+u+BXZe6chtWZXeyJzGc3b/1uioFDULxzqslfTUJF12rcxk/xaB6T+dtEGdOYLGNlQaVk6IymN94jDdWjsUX1qmKWKBwS+qmEEJEePazB95JP3HT4yfga7prG6LMF4w1/Lwyt2GigBX9vcx/ieK8AYYDy/SwmM0W5kup/WbWKZCUWscNdVZvHGrjYLBkHMDVxojHBL+Md+H1PK3fHiFtOW6x5YafqVrzmTmmrM//lihJ8+vhafWjABYSBSs8qWZ0PMHz3QzIGyTGDG6SubD6AQSVRkQ1TkQqNw+rrGMF9AcerSd0phJuiIqq+BPpBVmBkpeEIQQN2nIIUxxaBl4dZ/Q6He28lTmuCTU3+gPEBpCj/lGqBfhRBxICe4oha3nIqig5sPA0/Bf3vaexLrWSAEdrX1WGsuWfLdvW7d7nas2dVu1q0Dnb0WjRzlKSDvANWFqlRfmcW2lm6vsatULExcXwXPIa9uMQImDakxkDmC9pF3sqdQRH1lzmpHGNjUcVzcGlw8b4SpP3nF6N29meyjb6e8oyz3U3awACCinsoEfSc/ECKpUNeUZ/DRY8Zg075OXDCnGQAwrM6frWvppv3KZWEwfEDobCgDKrLGWkPDzaWOI8VlP7P2LNm431jQXFiFJRI46fuPJ1TMnyyMkyqYBUbTCBWKEnWVOQvTlyauazObEQll+YB2U0spE1bRo8Y1oK27YK495rqFEFBMGQqPBstqJ2VsDQTUYrH9gD/aWrkzWVf0w7Q8bXgcxd7VW8SX73oNgLJ+5DKBYWB4YvVubNrXCeKCXrRmD3a0dEMEcX8KASzf1oIZX30I//y7eJMtRvFiyPuY+oAW1TAQ+O5fDi4FOslvF29Qm4iuy48unpVQluOAlaQ7mCABVKdP3fwS/vjCJtz1UrwZSBkvku5cqi3PYuygKoOZTRMBR8GSwNhB1QkXte/dvVmO6+58hGc1H/Qqjf/vyUcJaw4vnfrh+Q37rEPC2Tc+raFL6rO7aZAyxeE9gPIe0cHi/UeMSq0rBez96aWtaO8uoKYsYz2DW/zfd1PMhxwIZWkieMEMNq5LUe31tecJ6//40572HhC37dAB5ahwGHpOnz4EAzRzhA9aVUoCYVvGXZHMuEDFHXPdYwA0fl8HteX1YnDZUaP1YSKuHz+wfOw4hemkMvnh5KEVO01w58JVu01MghBx4Bc/GB0/cRDu+eQxABTDAOFgs6FIDUqsrcjgFx+ch1tToCeEka+vzFmBpq7Qfc+v32cwr2RZpncVCIFvPxAn4aFH+bxm5vnawsrxznRQ4/M0DBQnPE8hz4XGIYfS8YQjJPHcir+j+h/ozFveKw5NhPSzSKg2qL8b93YaSsSItZ+3g3/Hv3/gtR2479XteOaLC1CRDXHsdQtx9a0vm9/dWCZaZ7fs74KAMoJFUvH2F6XEN+5dYRRbN7Omvw0KRkNjisYFGQQJ9060mFGkrNsvsDUso/UTC9Od8rzAzAeBVzTxAcHt3EDAQ03eVmVZCHG6EGKVEGKNEOLat/PZb1XybON9+l9PBKAneh94ykCoVNRkbSFrxdhB1Th9+hCMbqzC2EFV+NrZ0733R1Ipx9wK6WKEqsoyuP0TRwGAoRC748Ut1uLtc4lyl44A8OrWA5Y76zsXHKbrIOMkFsbnaJcV4/rU4tdbjJANKSK79G5WdDbeXBigUIywbMuBOFBNW3eoerzXacHlT+kpRHhOE6kTlygQK5EUoAAA04YNMAs2x4AKKvsgYRinsSjytu6CWbwjqTa4fFEpILe+sBnjBlVhb0cvgkAdXtbtbrfwgS1deRO4tItljbv1hU1mo9zf2WulJqY+AfyuuEoPhvy5dUl6OuoDavO5s4d7aN3UZzdg1C0DUKl4b3pqvcXkEUlpKWFu2XQvHZ5chZF+c5XeUPRPETbwHI8m/f5fPpv4jgspD/VagdvX2YtqxwJub5iqNd95cJUZU8+v34f1uzsMdAlIKjahEDh+4iDUVdqYRo6P5K56kjte3II/vsAhMxJd2kPD5+TUoQMSaekBaFYb4LiJg0xbSIkrFXSZ5iX72fvnJN6IuzR8+OjRIMpHN6uoslRK839fzyM5afJgjBhYiVad4dN3tUS8ttFfWjMjvrax8S8EsGFPhznM8bbRQTYMBKr14WTsoCqcq/HdPJiZsKWt3QXDPuQeEGl+c9iFLxGJud7xRrhBU5zKjI/9S39pY/zJOrqjtRvNGiebCYUJdtzX0YtACEPTxpNACZGemS2KFBzx5c0HsFOvuQS5kwAatCU2EGqsuQHEJFRzWpemDas1BiCS1u58rAA7A87AsDwGEbIs+4LvO3sL2NUWr8fS3CvNe5GQ+NVl81CRDU0/phnXasoyKEYSbQ5U7SPHjLFYSVY6dHh8b4ukxMubDxjaVRd77xO3fUX9/j6tWZfIM7xL78NFKZENAjy+ajczkKk68PW2K68SnJj26TFu8jHAjv8AgJENCrP85swXf3t525RlIUQI4EYAZwCYCuB9Qoipb9fz36pQulQA5hQWBunJHUjCQCATBrj0COV69GUDuveqYzBpSE3ie4Arx/Q5GVhI2Cv6vzNfxMiBlThibAN6CsVEKlISHpAihMD9r+6wLIS0rvgGrzsNi2wSUBBKVrtnSunK2w50Jcj7V+9sQ2t3AWf/ZJFRSCTSYS/kHnUXwjjCO25DW0/B2qgAtajQe3zk9Z1oZIwCBzrzXpaODZ4gDZ/sae9BZVkMl8gEivuSxkMgBH7+xDo016mFojynMMpkMelkVh9uKV22pQWb93UiFwbWRr91f5dxBVLbABvuwA8KJBf/wq8YCmHf675Lwn67SqIvJTegFkjOkHD/aztwlD5IuvsIb9cndcIWH9doLhOgu5do+ZR72Ue3WCqtOr+WuMUXrfEfIEjcoJiWrnzCFZ/WdwYRIBSneVFKc63r9hVCY/CdsV8oShPM5h6KdrZ243O3vWJRlW1r6cbnb3slwf4SBn4IwUhNHVfBNnqqGx+LgYiv5e10pz0Fp3JxD9KTh9bo4C+tHLPfyN1bKNoB0ULYGH3XEthUW4ZXt7aYzJA+bLHlnXF+e3nzATTXVyAIbC+YQJLC0dSVeXZe/PLJCITA5CE1uFR7AG5iMSq+MqgOF85ttvqAc2qnMShxISUzFMC4QTFbgbKUC60sq+8+d8rEBGMCKTnXnjHZrPFhECAUAkNqy/HV90xLsGFQ97Z25Q1TRcKyLSVe2LAPL27cb+YPMQNJKc1zAx3jwSEF04fH40hKRb1JluHvvndmog+O/vZjuPoWpfzZh6zSnkJyqvmyLu7vzBuPXaALIggR9/gNq6tAINQ7/udjxiQO5WauiOQziB0m7aBAbSCIaBRJTBpSY9KUc8syjZUL5zZb9ysYjc2bbP+uPpORT0qJMLQ9MFlSltmtbezgJwGM1RAnE6ArhDng8P3138+ckuAFP1Tk7bQsHw5gjZRynZSyF8CtAM55G5//psRN2wrEAygMRJ+Zv+LTqepqX0BGKTczuQA5DCNwMMtAPKE5zlAAeGr1Hlzxvy+m1o2kPBu7At3fr3tAwR5ifJennlF8z3ceXKWyS+moWX75mTOG4HOnTDSfP/LbF4wFmGRgVRl2ttpK0d0vb8Xejl7jMuRlhoHAqp2tiYML1XOvc0Bxk2M011didEO8kTRUKasGLVJ3Lt1iAnsIZ/38hr4T0gDAXS9vxXETlGXujy9sRjaj3IpuxDStV5Q61IUwAMlDS+iBYXzsDy/izBueNtaFshR2hCtPHNev+rvv2o347/Rkl3LryTcbN5jzm/euwPo9HRjdUJnAbnOX7pUnJrMb3rlUYXrrKrKGaeOsGUNx5YnjU+nDuJRlAmxvJQq8+NkvbNhvXdd3UiH19/n1+wzu1bSB/W8py8xaCcByhfvcvoFIsgK0ducNPCzhrs3716WOnqJRvuN6Cbyg8ai8FBqDNA+iKIYqbN6nxteJkwYlDkppMrqxypT/v5olwm0pWcpISbFd8sqLcuoPn0RlLoOyTIDzZg/HKVObzJwFgMpsiDkj69h9wlD5uUJKtlpX1Xe8b7p6i7jkF8+irjKXCPAj/CtluOPSbDiqA5RlQm0hjeEEd7+8DefrgEKfUB2+cPpknHXYUPM9H4tp8AAgHlfxOiIs5SkUwtBNUpmhxxJJB7SqXJzSWmGWlSXwxMmDVVIbvcbSQT2xBjhFv/vHT8eB4/rvrS9sxuOfPyHRD5v2dVp152OCjEdE6eib9lJKw8RC/fK9C2eqoGjy8HjWc4ImUnbSC+Y0475l22PvptkPhYZfCAvOQweHvKaGzQSKoYTv3dwrwvssYl6cUvSrgRC4d5nCM5//08XY2dJt9jduWaaU3AOrk94pvnZEUlrW72Jk17EYxQdBusqNoVLXxZ8pzkrVl+6JLcu0lgDA5ceNTWX0+XvL26ksDwfAs3hs0d8dUtLZW8AbO9uw9UAXRl97H9bubkcuDKyT8dBatcBWl2XMJE3bUGkiZUOBbKiizvui0OJCAQbcAua6JwFnoZfxSTUIgDd2JvkVuQyuKcP3L5plPpMblNanmP5JKb6rdrTh3mU2Lsxs/jqgsbMnzgXPE6h8/Zzp+PAxY6x7ZzTbLuDa8uTmu16fUo/77kIAwOb9nUY5zhcj3P/qDmw7YCvYqYF5uq/onZ40ebAJvvzJpbPNxCfX2S3PbzZWatPeftIZ5cIAjdVlkFLiTy9tRS4UuP7RN9CVVyd1nlL4vz8wxzCduO443gayRJVlQo0Vs6Eur29vNZbCtO20P3g2Eq6kJVMxU93i64tRZB2IhtTG0Ak3M+Outh68trUFCz9/Aq6/ZLb1G3fpAsAPL1ZWo7MOG4qrF0zAZzWtIGWNBIB3jW/UMIOk0u6+spryDHa02IGYPioul3qPy8eOGwspJe755DGIpDSxAyQ/fizG9fN3FLt61d9sGJjvXMU3lwnQ0pVPeIhWbG/FaO269FnugPj9zxlZhzGNVSqzZyAS0AXK2NnRy938NDbVX05NyNewTBhY8JgYzoCESCnRXSjiXeMbvdcoqJXE2MYqzGyuQzGKqdIElAVzb0cvasoz+PTJEzG4pgzl2dBAkj5+/FhUlWVMQDPdx4V/4hzWpHzyw3WcbEm1iyxshSiy6Dk5rRgAXKQDmai/yCvGPQAjmDXeFarzoJoyC8t51oyhuEp7WV7TwdkczztMvweq12DN5DBhcDU+c0qcaryuMoujxzdYihi9518yqze9J+K/BtT6M3VoDJ3iCU3ueWU72nsKEIizfQJ+xiHjmdFTjjDlkv0WCIEbF661LMt8rSG4TtExlCxgSTh8K/V75zZrTHX862VHKav/4rV7NLWm+v1UnVyppSuPGxeuSRzqiRdcQFHtxdlN1brcSzASoXiiuSc17UxfjOK5Vsr6zaf9uj0deHTlLtMmDm+j75ZtbjGW6Adf24HXt7diCJu7xGbF7xMiXsO5XsDr4NaRM7v8+LE1iXYKIVKzT/ZvZ3375e1Uln3DItEvQoiPCSGWCCGW7N6dJCD/W0t3PsIrW1pw9o+fxpUnjsPQAeXoLUYozwT4/KlKAaBMRpQJbXtLF8658elEWZ29BXT2FBBFEpkwwJFjG/CNc6bjX0+f3O/6RJGdiY8Grzs4DU6MrjH3l44yHTuoCtVlGcPbCCjlKBcGift6ChFWbG9NWH0BGF5gszmw07VbV46lXrmjLWF1KMuGiXvcxba5vhKv6kQr5M5xDyy2shx/T3111oyh+nMsSrFNNC8h/U2UQUVRVWizJIssKRbGGiHhWB5s6ewt4JbLjwSgDmvk/nJf8bmzhuNLZ03pVx1LiYCwDkbkkq/Ihrbyx+4pFKWFkezP4ieEwIl6g+P8q7xdhA2+8dIY+0obEr3rd89U75RS8XJxD0972nsN3SIlO5j7jYfhCvcG0dgNA5WlKxsqTHtFLrDYSEh4EgX+S+x61mNXxhYn12LYUJVDd76Y8Eq1dxfMoYfGD1c41EPV90MHVKCuMosdrd0Jy7JPXv6PUxK4d570Ji5eYFhdOW792JEmsU6pop9bv8+4ZOl+t7xIShwzvhEXzmvGbxatx7fup2QjtJ5I6zPPLEg0Wv1JhgRwZTmuN4/voDETCIHn1+/F75/ZiBe/dDJuXLgWO1q70aKv/Y+7lyfWjfPnDLfmdxr+lcvAqhyuu2BGQiEh6SlEmKrhLLQG/fIyRYV3zWmTMH/MQABx0iCqU0N1DqdMHYIrTlAepVENVfj3M6cmWA0AmBgJAJihU25nwsBcW12WMd8DsHDcpDj3FCIMri03gW8+Y5Jh0oE0cRRk7CHqskCoA9DQAfH+RCXNGlGHSCol300eZXm62IckpILXR/1dvbMdC1ftRmdvwXhy1bXSjDkuZLwoRNIE/1MdBBRVYyGSyBckbmCHZ2ofCa/Z7S9uMXUtOZY9ugD1NU8lT3v2M+v24s+aWeNLd72Krfu7MEFnMc0EAn9ZvtMosR88apR5Nh0auWEmkhIfOHKkYZ4qJbEnNdZT0vbQPpamv5u8ncryFgAj2OdmANvci6SUv5BSzpNSzhs0aNDbVjmSgVU5vHduM1788ik4Y/pQ9BYlvnTWFEgAH3qXbRHNhgF6CxF68hEOdObR1VtER08BP318DZ5btxe/W7wRSzbux662bmQCdZIKAj+fYppICb9lmU1ZKZV1eNqwWkC7wMiy5lLIufKz98+1fpcyjpB1LURb9YmZKwS36ZTKnOqMy972Htvli6TyPqS2HB84MqaTOn/28MSC5Co+U4bWmu8mNlV729ZZIsobAL59/gzrc1UuVOXqz3yRcpWLvhJl3PC+pJUUiANtqM8pyY36XHpcSAA3PbXeLLAUSMgxylw+yrDJpaAEj2gX9Ysb9+PXi9Zbv7nFkmuvK1+0FR3WV3cs3eJYguxn+3huuTxE2e9gK2d8nPayRDmcNon6oaO3aDa/u1/eiqtueckkGAKS7B0/ekRhrH0WfW5FpedQ0FYmCFTEuLaU9Jf+iDaf3e1xpkXihHYxywRNcGnClKVSXUsu2z88u1HXU+p2JusuYAf41TILICliAyqysbLMONpHN1bhw+8aba4XoEONwEwdJEhFuwlTqJ5NtdwKHf924dxmBELghw+vhtTPW7hqt7FaB0Lg10+vZ1bH5NgPhaY0i4D5o+sBKDYZLjYERf2NmKuYY0RN0h0B7GhR7eHJHsgKnS9GWOvgfX/AvHVCKA9FKegE1aOqLJMatNhTKKJSW83pEEMwGJ78yQ0E+8CRoxAGKlAUUH0XBMDitTEEbqMn5fV/6Cyb2VAYuJG7klg4bl1tOlT6ssZxyWUCo/Cq+9XfT/zPUl1PoQ1G8T00tikoVaWeVr+RR83lXScJnHcfRRKjGuIMnARHfGr1blymg01JugtFK6COvGSVuaRxR9VTtef2fzkaVbnQG9+U9p4Xrd1jKaUkZ80YirJMgIpsiOb6ykT8EqC8dVwmD6mxrlm7ux3tPQUMGVCOtu6C8QBQTAFZ6emAR/0EaCiWrteW/V34n2c36Zig0vuhm3egtTuP1xzKyUNd3k5l+QUAE4QQY4QQOQCXAPjz2/j8g5ZcRgVjcaVjLAuSyIWKD3j9ng4cPa4Bv128Af/z7EY0VpXhyTd247GVatNvqCpDXWXWCkzor1SWhSqdpB6Ml/9+iSact68LAmFOuBwjFHmsjiRd+aLXXU04MB+GD7CV3WtuX2bu4de2apdxvRPBL1i5Vt3ZdaMbqxIZmBLZibibtYTb1ydLdHIKctXSfXNG1euseOpZL2zYj/PnDPeW7bNi86jls2cOw5IvnWwsfVT/nFZuaHMgN6lxucEmrfdhb+mrc2YNU4kmSrxjKoqU9HwxwoFOG7P9yOtqnG490IW27gJOmKQ21Pmj6xPP5+9+AGNg4L2xp73XsqBJc30WJ08ZbJSvNKGNfqumR/I9uywTxAE4QlgBpoBt0X11SwvueWWbFbB3AwtArPcEt3HhKa55MF8kFRVgpC3KhSjq9xj832cVS8Xz6ylTHbfwxYUcO6ERk4bUIJLSogDMZdTaQ4cXsgYdM74RX/3zctPnP39Cu9QFhwTY4+X9h480HgMqTwhh4FvZgKyjAcoyAQbXxMpuPkomWfjoMWPxvsNHeA/pPH5DV8vImYcNRSAUtzX1x2HNA6y1pRDFgZAUn8EDByOplPsZzQPw2w8fDgCGg94nnLYwEAJVuRCfOD7G83NsOc9g52tXWup4up/WQh5odxKDCwBIBErx7wG1ztDc+rSGVVB9Lp4/MmZGYAF3QBwXY2JbhKoTDwB9JAXXDdgwDBeCNojhYEn5I6qx+zw0biRHjG3ATR+cByltT5R7uDnQmbfWoWIkceOlcxK4fwDGAh1JifHaYmrzN9tjkoJJz58z3Ap2LEQSg6rLsJHxrC9as9fAhLikYWx5joHKXMZrvElbL7hSyg/24wdXY/KQGtx8+RE467ChypPcT0ggya62Hvxu8QaUZ0J0F4oxZaKuS9GMc2Ewy7GFWfX3J44fZ5Rq7ulO48P/0cWz8MDVx+KJ1bvR0VPA2TOHGe7ofxR525RlKWUBwCcB/AXA6wD+T0q5/O16/puRrKYwA+LF8tHPHm9+z4QCPfkiPvzbF3DMhEH4lxPG4ePHj8PZs4bhxoVrTaBQd76IacMG4N/POnjyj0X/epJxtwMqKlV4XMwk2oulXK1ItzoCStkXwnaHxBhY5jpBvKEA6ZYZIL6nRuPs3AC/ylyYUMBCoazYUz3R8iTueqASnsSf54+uN1aTvsS13A3Xp163nx5esdMEtLmcqhzH+/4jRqKtO29ZnAA/DIQ2Ntq8ylgADj3eWLYdOrxY1Le12vqnLCKlgyJog80XIwytS2YXpOdxmT2yHsu3teDkKfGGTnW8ZP4IfGpBjIGUUuG9zfM4DIMpmD2FqF9k+YDCyvNX4ipf9ImneqV3yC/1RZNzbLKLlXaFwx/cJCOU+jXQipxvrg2vq8C7DxtqWRXv0MGJcVnA3FH1VhsA4PDRAzG4tixxOKvIhigUo4TidqCrF5ucBEIkMedv7N2qyinmEMrwyNu3TPOgThs2wDzTTXX75OrdicP2MRMaMaim3Iv/Vt61+DNv6+7WHgsWoX7ngUT6O7a5S9jrkUn8U4zMYfhIJ0ibK0wcFyoE8PS/nmTNuSIbVzEbRPId98WcQtzEAPCNc2Oa0F9/aL5VFyllok1c+BijNSQTquCxCmZZjpMF2W9HmL8iMVbd98gxx1kNwzhj+pCEsePsmXHoERl1fKmVXWnvLqCuIotHXt9ljAoCwO+e2Wiu6egp4rYXt1jtiKTEWYcNTeD+ObyRuLoB22CTsCxLtd+VZULc8vxm85zeYoSybJAIXiWD1EXzYlaJilyY6LvJQ2oU5El/rinPoN3xDD342nZjVAJspZgsutQnXIrMWxwEfUOOfEwbQiiL/KodbYwcQAnN2yBQ+g0drADgG/euQBCodYPqy6EpL375lNQ6TBlai/V7OrCjtbvfQcGHkrytPMtSyvullBOllOOklP/5dj77zYjixI0tVuRuJRk3qNpKUEJSng3x4pdONp8P7twXy68um4dMGFvQiB4nLJGamhYAqk0kY5wpl/mj63Hu7OForq+06HYUDMNvWaZTNl9kKVLYtSy3aQyzWvgFvvNexdnsU+roOTNHDMBgJ0CKJEG5gyQUpZQKxjc4jrcD7OAFqjOJoQwUdp/z+pRllBuQby5A3Be3PL8pjiTXiuLEphqcN3u4hd9u7y7g9e1tZsHmCya1kZdrNteUd8xvMlaDyJ+RSoiYHoh+nT96IFZsazXKEv/NLUJCWvRhaQE53fliv9KwAupgYbnYPUoKLfzcXQ7YY5TzrpJSaEV897HZEFesSkkdCylmhFWmoBxqe1NtGT56zBg89YUTMaqhsuT4jKTEHf9yNGrKMiaF9alTm3Da9CEWJpskq71abp8UimR5TCpJtHa9a3wjvq553Td+KDQAACAASURBVF3MPIcR1mvFYOKQalPH+aMH4jBn/vgO7vs7evGKx6KblhENINiN+61gSpG9qVN5FqOAlMiFwjIAuGXS58+dMjG2LOt3V1+Vw662HhOw9zkdRBqIdAsiYI9NX0wKrctAfFjnAZsER5CwPYNunZ9fv8+sIfTITJBkShmo2UGkUx/Ok++uV+5rfPDTx5r/M6HGXIeBN1ivlKSluW/vyaPRWe95s5+45gTzmfDUR44dGEMSQXNeeVU51zuPH+CwBN6vlDRFyhh6EElFg1goRt6D77zRAxPr7TWnTUpcV5YJ0J2PyyjPhgnmrJauvIVh5ntBkcUaLdTB5Z/SgZ08Dkmg7/ULAH7+T3MBKBYQlZNAYOqwWrPmA8BnT52EimyI9TrYPV+QuPOlrRAA3jNzmLku0IYdws9zQ5JrmPAlq7p32fY+4UiHoryTwa+E5MLAKKhCJDeFYXUVZsFzJxaRqgPAPVcd86aev0Cn+KTBSC5VHxsGwBRGptRzHBaXo8c1YvzgapRnQxyug0L0rUZJ80EATp82xLYMBXSftALKDO5U16nU1FCcixEKxVj9nT3SZshwmyuE7R6XsLGXXOaNqkckY2zaqAab3os2W6PEsYcRC4e7KPFI3hg7CauvyV29qy1OiRrzTCpWEYo2B4DVu9rw+dteMY/PF6V1ApeQuHrBBGY9jdN/pynLVFYgBJZtOYAoSlM6gS/pTIMk5dkgcUihTwk+TodiyKJ6Ytft7eg1z5+nLalpPKJVZaG1ebrVVpRFhK2j7+LfSB5esdO8l6+fMy1Rf9eiTteQ0Bz/tzOmYP6oeK4QZpgw/oRZvvvKeL5XaMttZ28x1cMzuqHS9DMF1551w1N4aMVOTGyqQTGSCQadMFCMDC6PNo0JV6HpLUTm7Rw1rsEkAKA+ravMYmBVzlrj/kUHg4VsLTlp8mAcO6FvD45L9feV90w17eOUVryaUibXHJ/bu7Wb1mShIWPxb5FUONz/eXaTdY87xmaPrMNVCyaY53EM/l0vbcXvtXXzZZ0iXQiBSw4fAZ/MGlFnkk4Bcb/Z7YgtgDT+ucfiAg33og5xR4oFzdJ/Y/e5ffU1p03C5ceNMeVxvKgxgACWJVpdmq505fSh0PUseCvriDt2iY61q7eISmfd4mtIRS5kgerqmXP1Wm7dI+xU7AsmD06FpiUO+VLBenpMDITE9y6cmYg/eOoLJwIAbn5uE5ZtOWApe4c1JxP6FCKJEQMrzPOOnzQI053kS24MAuem5xR0vPJCKAMOBVIKIaz16wcXJXmmBeL4lhnDB8Q0e4hjogBlDa8pz5iDFx8PXz07XhOpXr2FCNeeMVkZkhJPVfLzJ9eZIE8u7oG5rA9o3qEgh34N/45SU5416aH5hsyFcFGlgujesstBD0Z6vJTA3o5k8AwFRJEbjxTffgMpqRx9cp03qh4XzGk2C3VFNsTYQVXWIlJXoaxPUaQWrJauPCazBCtUl1K8t0II/PTxtbjtxdg1zenGVJuTljKXH/KDR43C+w4fCVfeNb4RUvJF2rF0COuPNfEJX+umLV+9sw0LVyoFmMZGqeCuT978EoD4mlOnDTHKhyuU8ORnj6/ByVOaTH8umEyHJ1hlRdKflc9t4tk/WYRCFCd0OFtbC1weapJQxKm+KbqeFmY3kDkb2pzjmRTLclUuYxZKqn+pucN/cfu3GKnEBtzy6vJXu/cOqMji6gUTHHok55n62m+dpwJAyS05dlAVqspC676eQqQyMApii4jLqSnPGuq4YiRNynlXeP3Jurp8WyvOnKE2uF1tPUbhIVrFTKAsy1kHhkEJdlx9pruQtJQtuvYk8/8VJ4zHQ585zuqXEfWVpj9+ddm8hKeDpB+GLVx21GgAMPhuVy6eNyJhtVfPhkkr7krs+YrLK0qZUEIAoIwnYEJsJaYyfvb4WmMBDYOY1op7cdLm98ubD3iDGbnw/YMUd95v58wajolN1QYWUGrJ5gdgnzTXV1jsEfaB069gA8Clh48y/9+sebBJhg4ox+K1e1P3QZLpw5LZOF3ldozGbHflo5LwsWwQGKWUPAUXzRuRsKS6/fCrD81PxfHya8na/3+fOMrg3iOp1rL9nWpOt3UXUIgiQ/O39UAXtuzvSkJY2OPeM3OYOuCyA/KJkwbjCGaUAmLWkT9dcXSinhJJ4wCXHsYrzZvqW0vtrI8wjDikVxBcidYht+/cMgma2J0voiwToCcfWTFGrqHLJ65ledU3z+jznr+3vKMslxB+8v7RJbO9Sm/OsBsk758+vBbnlSCe76/QCZDk1a0tuOJ/l8a/63HX0pWPo9NBFqdY4XjX+GSCFVcMx7JQ1vGjxjWYE+a80fX4wumTLd5PnjaVJtuUobU4TXNTKiW3tLLeH4+Ma8mkxZ7c6ledNB5jG6ut9L6J5+gHdfQWrchk6j+O87tL02DFmGX7Hby8+QDuenmrKfe0Hz1pgu24ED0dyY7WLtSUZVBTnrFoeLhQlq6drT2or8qZ59798lb0FCK2Wcb1dtPZkrgu7KKUFuYTAF7RHLVxf8QbKmU9+6NmPaHyilFkJTxRbuS48Rxqwd3NHDpg3Kjs2XyOdfQUrZ3eF1i17UC3ZbWLDxL2dfyAMayu3OJONoqqLoO4aknh59hbToLS0pVHc30F2rsLpg5CCBO0yfnCJwyuTiTDIXFxh9R3FO0v2UGMMIGk0LkbGbHuuFZCxSNsP7c6F8NKBtWUobG6zFI0SakTUGtBmlLss0gmFJogHn/8PVazIFspgSbtlqdDRiBEKv5VIBkTQRndXHGDSg1NHxtf1HZuzSYYnmI+SZZLa6rLPuFKTjMnAcCYxirMHlmXgJHlMoGGYSRXTDLK0LVu3eN2uZ+TVljAvyLT4QywYw6IXpSEp3nmZfqeD8Q8yvFn7UUpRnBTMvOyMmEcx8EPw255vv1j2rBaDKpOQvoszLIud0BF1vo+EwRYuukAhBDoLhQxZIAd4/GThWsSz6TuOmZ8I46fOAiFSOJAZ97iXU/b5nyJo4qRLAlVOGLsQN0e24hDY4JDaNaxbLP0+x+e2ah0BEfR5klJYtifXQ9iymrrLiCXCbCvo9fKynrnvySVf1dKGUguPy6ZYfZQkHeU5T5ksba+HD9xkNddHOPHki//3quOxQ8vnpX4/mBFDVZpToiBEN4U1jVlWW1RlibAj1spaFJu3NuRGiD4xOrdlkK1/UCXybBD1hbCH6u6qb9k+a6rVK7c06cTrZm+zvOs9+uUz49pCy2/3q2emxKbLOc3aRqykyY3ob4qh2+ea9PB8XKJGqm7t2jctP9+5hTLQguoDZLcetlMrFi2OMEYXDHb19GLve09iX698f1zrM8tnXkUdOBe5LqQHU37j0s2o7U7j1U71Ua8vzOPimxo8OCcm7mv8wbpJ5HOwLSrLQ4+camGSHhAmKmjbl9RAqdNa8KHjh5truHQFIWjVLvadQ+utNoYBgK3fuxI8x1fjF/8coz1//Wi9fbmxv4neFS+GCEIYuq3NGs10VsJoZSShSzJTIwRV5+basux4b/OMr+T5T0QwmLZABTfd04zc9D9jdUxXpS3MZIykeEPsC34/F3SQY0rPKRIf/CoURZ1HPVhRge3ufOHK9wkvsNaRS5MZJajS5ZtabHc5L97ZoMp25W09cUN8OPPkEge+kp5a8qzIcqzNlQniuyD2k8unY0bLpltZ560LK28zuovT9/cxdZc3wZ/7iy7rwha5AphzAHlcbhk/gir3yR05jupLJPfPM9ex3wWWF99BlZmUcUywnLDCbWD/00TXvbVCyaYPlu1ow13Lt3qveczJ0802N/nvrjAfO8enGis8vTpIwZW4ObLj7DWsUwQ4JL5I60yVFZHOhirq/e0U/bA+N6vnTM9QQ06c0Sd1Y9CCAO/4X0Ur3tqvXIPSev3dCQUSNpb546qV/zPHg9KPUtrfgWD6vhoPX2c7dTAZV89Fc3k9YGfSWfykGSw/DfPnW5+33qgC72FKEGrpzI6qs+uR8CFUyxaswcTm2o8BzJhZdD0SanRd6gG/72jLPchw1KYA0hoIh0Md/LBClk1K3MhZgwfYLJakbueBzxQ0EJsdVT3Hzuh0eCCzvvpYoP7c+WTN7+EB17bYS0GRJdHrjCOL+KE7bkwwPRhtRYkQW0C/nbRFPPBAErh5+aOqjdMH664+GyiBYukNNHIWw90GcX38uPGsmBIVV51Wca4uWgxFxB4Y1dsPSIox7vGN7AAQX+mKi65TKgpxhTWjPcz3ctTH3MF9OyZwyAEjMIlhMD1l8zCkNryVLct0eTR8qQiqVVWwqWbYosyUeRVMZwg6RcuJhRAou6ZMFYwjp84CNkwwI0L12K/fre0ACrLojCKQVwzJa6V5UBnHqu01Y5niavTEe4UiEP0d8Zy5nSIifB2NsbzZg+P28Rwv1woxblrBeN9wqnFfEKWU5+7XohYGfinI0eZ51BmLSmTFruPHjtWYUhJodb3Z0Oh6dWA//5AfFDzWVsLUdIKG0mJq5xNUQgVFf/q1hZrnB3ozIOzVXBJ6wqXOo49xX9PiWX1onkj8IXTJiVgGNyo8e7DhqG+KocRAyvw+48cnqgffyRfc9yAOSH80AV+1YiBFbg9xarmZplTAcqsHBpDUEGjPoXhj/qAOc+wpiSf85X3TMPJ2ttG5fJq0//1Vcn4Dn4dHxd8zpTSsStzoVG0uFLqGgFOnz4EHz1mDJ5YvdsYZUY3VOHocY3WdZkwpkMlWEl5JsA4bWWn99XZW7BYJeL77XH22VMmJg4d+aLUwa/2c6ndBScgOjY82WXz/Z8CLt2A1SlDa40RhpiEqnKh913ki5E/EBvC8uySRyvrBH2SNFaX4boL1KHhA0eOsvokX4ysfAGUXp7GaV/wqvqqHKrKMnjPzGGW8g8At38inge+gxn135PXnFj6IYeQvKMsv1XRA4FOen+rR0jEnMnFSOK4CYOsSFZAD26ho6lB1lKl1EwdVmsgC5W5ZGQuly/f9RrDAwKNOrKaNgIX9wVdt8lDa/DdC2eaZwI68CtlgfWR4M/QXNQ0UZtqYzcWbRK15RkT3OOKm274hX9XlkqJ2BVc5gaVmDbE1kfXTVuU0spyqPiiBeaNGmjKi6QsmfwDIO5uaVy9fJGl56dhiP/8yjZs3NtpaPkAhXVcfO1JlgLI5dl1+3D3le/CQL0gP79+r2kTz8RI1qhIApVlGXz7/BmxFcpTR7Iatxord2C+swLP9H2Pfe54ADrDWiDMmAb8i+ngmjIcO6ER6/d04MHlinGGB80SHdSX716OKcyKwjNE+YQiuUl+ePGs2FpOrl7PqnjlieMwe0TSWqLmoz9rHHlkSCTgnXdhEAfpfOndU41l1FIqPcOqtxAlrF6ZIEB3vqjHb+kDvIRMBMXOHlHnDRYtFXFf6pDgioLhJL8f1VBp5i4FWLrWcMsSq71fPvhRzvPd9ZfMNoHM2w50G2WYM1zQO6jMZfrEIPvEfd9cyGpnRNjtGVCR1YmW0ss/QsdcxDCquG/IA+q67j9+/FjMGx1buwUEJjZVWww3nz55AhqqbD78TBCnXw+DeM54DwzGXR8rWvYB225UdVmITBjgJXZYNwF6XGllhRCtYkN1Gf7wzzaeOpLKgJDWdbTvlYJmkTTVllnW99tf3GKt0XddoeB59ZU5i7mJdwt5JlyrPmB7LwAFCfTpDvli/zjbSR+gOeuupU//64m4eH4cx8OhKdtb4vF6w/tmq0B7ncYdSL43Lncu3WKSlJVnQiu9vCu+Q10mDDCopsxLPnCoyj9OTQ9ROXLMQFwwp9nrXv1riYDaTPd19EBKhR2eNnxAIv0tbY+kNAsBLFy5CwIqM9f5cxQ35PsOH2n4o9OEB4I0VLuJRXjd9LPZxkaYZ1UnmbiHZMMeRc9ELBX1lVn8RicSoGnKNxjK/BUGyu3tUzxI0Zg8pAaN1TnL8uYG9cRtIOs4tY8p4vraARVZY4H84pmTlTVA/0ZZqoqRxOXHjkk2lAnPVLW7rcfrBp47km9uSn7/kcNx2VGjvFmggkCU1Itmjqgzitd/3ve6Wag5rc+iNQoXGkkVHV6ZCxNk9Vw+rhM3kJUjw1zX+WJsPfz8bYp6ixZTgn9YRXrKF6K0q5hbdoYMKE/8nsaw4Vu4TbIF/ddn0anMZYyicP+njsWIgTE3N9GOcV3omtMmWUlbiNGGl0yUU4QVdIVv0r72CGErFCMHVppANMm8S6dNazIUWVwG15TjwauPs7770SWzvf3JFSLrexycZVlZOpP9+4njx5mIfa7Auld+RGdRzaccSj909OgEHRmgPIRkVWzpymOGZiYgmBUQv7/h9RUmcc+ZM4YgxxJyUFZAghFNGVJrYDelhB8Ofe2aPnwAvvKeqSX8aX75hj5YpHlAZ4+st5LIBEFSgfv0yRM1bIJZR8PY8l1TnjG/+HDbUkrUlmeMJRawx64v3oRb8UcOrMRnT1FzgTx6VzOmkjTh7Th/TjPOnD7Eex15F915HR8A4rVm8bUL2BqNRFuo1l35ogVf42WXZVTyHgnPfNHjKBMI/PMx6XvFnJH11vo3cmCld2wIoeo3XCvcrofbtaTzdWQPi6E4e+YwhEKgOx/HxJSy+6zd3WEo5giuYiV/YdVIw163duUBATzPIDuHsryjLL9FmdBUg+976Fr+mjJkQDmuf/QN7OvohYTExfNH4OyZw/CX5TvsC5nLTQD4+HHjsHTT/sSEravMJtyMrvBbxjRW4YlrToh/YwX6FOQ5I+tNJL3vdE3yNU1HQy51nxWpEEmMJpor/YDBtWVYvbPNe0DhSQ2oiVLX75gJjYm2qfbYz+S/T2qqwa8um2fBPj523DgryGSdppfLF6U38YydbTD+vq4yZ7W5GEnMGlFnLKkkd1/5Lhw3cRCOGtdoQTR8bSDxKTyAWiApmcEXTo/5QQljznGsPnwjKXhztEIfSYmvvmcqTpg0yDyzUIwTjzy2chf+5YRxRlkeXleRCKjzUQsBpaFNFHxL8BFX3qsPht86b4axuqgkPOmWMZOy3WOdpro0VOUwqKbMuA9psyJMMsmVJ45PeGBcOAXx3K7b3YFXt8Ycsb96ej2AeLxLKS1u4wt024SwXc2Pfe54o4hwpfR9h49UhyDPdpt2qHAlDqD0WMpKLCXrvnWmc30y0Y5r1awsS2dIIFYQn/UYUErnZUePtiA7rvBETT/7wNz4e9aQJRtVQqkJg2swZWiN56BRhjGNVZjRPMDgc09h8If+iC/47mCs9ADwT5plpL/i84AAahwF1toUexx4Iilfv0cSxltE1nk+9nnw42dOnojhdRWJ/qT1It+HESdNJg+pwRlOMDVJl2GOcJVl+3AAqLnsxmpYyrKu+EN6jSaFl18zuLYcf9HMMmkrmBAC7z9ipNnbuPzqsnmJMp+45gR87Lix+GfHGBMIgRnD63DDJbN0O/qv0rk5DYTQ75f2TecldTosUvSu1NoXexnpO16uT+684mhloa9Nn6uHkryjLP8DyPjB1ThjxlDUVeZQKCqIxfjB1UbpIYkxy2ozqMiFRmHki1dVLtMnXMB1u7rcxEZ0sRGZs6HwlDNZJH8aL7TB1jH4A28NABSLEh/W1iS+UV44dwSuu+AwuELry24WbHfn0i34rwdWojIXYmBVrk+LBcn5c4bjhxfPMlYh3idbD3QhX7TpwNIWek43Ns2hVuJVmTy0xkS9U/8JEf+fy4jUZ7gtIksHsXpwoY2QM7XMGlGn+ahjxY7GzN722ILCU78DKrBx074ufPCo0aZthcgO1KLN+UNHjzaUZ9SnYSAsnu+4PckMY1xOmdqUWIQ5BIegMZW5EKdqZpaXv+LPLnXSlMG4aF6zOQD5+GvJ2kTQGfqNW3HdMe56YISIU0dzmTe63mSR5JIxyrJdFh3OiTObKAAzYYAjxgzEkNpyiyXCHGjfnB6i7k2xLKcJJalxrUrHTGhMuGyfcyxLPoxoXynJAeA4ncGTZ3T0CT/Yc/ynmVr61o8dNxYSwE8/MBeTNH2jZOupy5vss7rG4lHU3PGShtv+K4pAunt9TGOVoaBUMAxp3Qf4D1e03/goHAFFy0fyqQXj0VRbbuGY+QG9oYSV3qdYkpRijiBoSNrhOxMI78GYLrcpSu3nkZK9RGfrJYn59J3DJa+zEIlz5oVzmzFzRB2efGO38V4BarzUlGcTWHY6YJEF+WCC49zgc/JwuZ42kg7HUEN7kYpbkFamRC5pY3rasAH9TlB1KMg/Tk3/H5dQKDfuf11wmMmo1d5TSKRUJkVHmM0go13IcVkTmqpx4qQkvy+XvCfzDpfvakYMvhT41iIKcnDnS1/RsiRFKXHB3GbkwsBwEpdlA+QygTdCnJSYfR29ZmHrzhd1MJKyGviCd6iu9FlK4AcXzVLR9tqF7iob97yyDYNr4uC6tExVvJ6cAsplI3j/EaNwwiS14f9ML2R8oWmur8R8j2LJ201CC/4sD87WKIPs7Q2vr8C2A10xfR5iPLWPt5akqbYcj660mUqmDq21soNRMp2vnj0N753brC2JFEySntHttGlNqZbPbBhg2IAKqw2+sShEvMG5h0tqX215Fi1deVx160vWvVxowyTIBX8mBa25BxnXsnzmjKFez4OUNn0fcb7GQbJ+78wfl2xGJCXqK7Nmfn38+HF45PVduPm5TWZckmJXKmi2L3EzJALK3Xz+nOHIe7RwV8En+ea5MwyciiTBgBEInDzFttIerZP3lFLWKYDPhcS4Esn0bJBAvA784sl1WLe7HcM1hMNyLXsU8r4YJnj35zJBIimIb538a4vwKGj8txHMnU/wktENVabtrhcAUAratadPRiDiwEjevauYZZkzYZDwsULrhq+OCz9/Qmq70niVgRhu0NadDAIEkuPPtSzbirh6DgV107qQhr11hwQPYvSNIeKN393Wg7mj/Gs9l0CoNOwcUtJfcQ93VNZPLp2DhqpcYg65lua1u+PEKD965A28vr0VPuGZXf+R5R1l+R9EwiB20xNjQFNtmZUdiTBSHCv16OdOUG40ttlOGzYA5/bB/3w2o0TyLUMEaaCF5do7XvVuFmShdCfanVfEFk/6hSt8UipuWspoxhXLz54yMbXejRpfXVeZZQuRwJKN+/HChn2468p3oaXTDqBLYpadQkUM5eAMA4BKdDKxSSnAadAW6qMrT7Qjhn3UXa7iOJJZUyY21eCKE+zMaKyK3mf6JIZXxN9Vl2UQhoK9r3hDKBUMevr0IQnM5n9dcFgqhv/y48ZisMPe4bMKCQFcOG8ExvYRCyClNJzJvv53M1xZv7H//7J8J55dqzilY/dr/DttqORy5vWUULAQX2rX+FkCuTCwvAwkSaXL/jtzRF0q0f8za/d6PSUtXXmmLCur8luxWvpgGKMbqnDY8AEmMRGX2M908EKWYa7cZwI3vX16Y0q98z9dcbQVgOzWme4HgIlN1RYbjf2MGF9LUsq66T7urBlD8eV32wcngYOHYRys+DwgXO644miUZwOEgUB5NsSG/zoLDdVl5r1fdvToxAG2PBviovkjAGZZ5uuPb/3grDe+ddDXD6U8gqUOR3vbe9BYncPO1v4FbbqxGi4VHxAHJNJ6ffS4Rq9S6Nb5+xfNRA3jFnfPmRv2dvZ52LPKB/D4qt19BjZ773XqRmNj+vABOH7SoMQBIIqAb5w7HYD/0NSachj5R8jO1x/5/0cr/h8Q5eKyvyvLhFbCBIAHEtmK58GcOAEYyp40oUWDlHXFV5m8jtzWpeb+kWOTyVIkFD72lKlNxj1EkAQfiTsJwUUoGplLbyHC8LoK5IsS58yKc91nAoH3zBxmXcs3ZgHCQMuE22jqsFpcc9pk80yfUJY1uo7E5VkGeKS7+nzOrP4ltXH73lWW710WZ1gyP1kWUpvKLZKxi/HCuf40v/ScvvDvaVLSa62lFFxISoldbT0m8+M+xiJCdwUCuO/V7d773T6LpMSZM2Lln7NR5Iyy7G7s6eZAfl1DdS61Le4cJiFYyfETB2HBFD8e1g3yJekpxOmbhVE+37z4YBi//vB8jBlU7XX9yhSFtD/CeY7doUp/S7UlLWASUAFv/3TkKG9KanrmDy6aiee/uAD/duaURKpmkiBIQqJKjedBNWW4gqUAJ2XUvr/0OvnXkEjKkkwf1WUZSOmBlOiPlbkwtZ1lYWCsi+67f+gzdiCpy5bhm4t9Cb+n1DqxdncHzpox1ML9u1LHIAQhmzdAbDX+9YfmmYMC/aX1+qL5I/qVgCwTCPSwccPbcNzEQRjdUJmIfyglPiW5P5ClUmUByqvqwioiKU0mzPfOa8akphrr97Qq83F+yfz0veRQl3eU5X8Q8VlnyzIBehhmlqwxkaMcKyxz33LVSX6rZSnJF+I6pWWUEqL0ifeLZ07BNadNcpghlJJ946VzzElbqB/6Va/KXJjoL6peIYqszTIIBC49fGRsWXKwg5QUxksUz6Qvy7IrPoWCyi91IPCJ66Z3n0kuM/4bb0tzfQXu+eQxVJh13YzmAanptHNhkMqsMm5QulV46tBa3PC+2am/k5SiLAPSN1WOLXWF+tx9XxLAJfNHGvc4v5UODi7vainhzG8LpjThuxfOdBJRqD5yLW/0LkulAjZleJofiDib5w3vm21gRW9FE6NshLzlw+sqEArhpdp7K0ofZTTkbassyyBkDyo1LAJRWnmqKc9aMCESGkvN9ZUYXFuOimyYCGrihzA+R3908aySdaotz+K9c5vTL0AM//pbSjYM+kxH7LP0xm7+9HTXZx02FK9t87viRzVU4vpL4gRd1v4kk/O0P+dv3ld9xeAcObYB04enK8vvmTkM0zVtaZjIKqg+nzS5CYc112H5107D5zR7R2mcelIqc6GB2LmHhPJMgGvPmNyn9d+um/rL2auILvWtitunXK/4tzOm4C/OASitzrycsYOq+mSM1kkLRwAAGS5JREFUOlTlHWX5H0SCIKk41FZk8QILLPj+hTM156ythL2yuSVxcvdJ2iW+SUBfceolPw4Q+MY50/s8dVOEPy+fL9D8b19y/SWz8NBnjmfBjboc3cJCMZkYQevDAIBzZw83KYvVfTG7RilXa5rrlwdhWddryzLvNzJcV+ZCi4GkL+Fdc8SYgf2CYdAVFIhVX5XDBI2p5lZ0aj8J4aoBhcH+7nv9bDCl1vvybFiSm9xYEPvYNNItMOr7zfs6U+913YOukuDDCd/+iaP67VZ0lerQgRIAwAVzm9HVW+xXKnqfSABnTB+CI8bE9zfXV5p0vWfPHAZKdvFWbMtHjG3AsAHlRoG/8VK14U9oqsaHjk5ufqMbqvDHjx+Z+L4/Ego7he/VCybgS2dNwZnTh5g1oFRbyrP/X3vnHiVFdefx768f0zM9MwzMMMDM8BzEQQZFERUQ8IUwoGgkajCJ+GAPmugquq5oTIybk7Pxsckejbu66npiXNdoHkazrkY2ms16jhqfqFklApL1hWxwRXwgw8zdP+pWz62qe+vRXf2c3+ecPtNTXV2PX9+q+t17f7/vL5mTNoyCuyk11qUc8frqr5lJJR0zGXkOojvIdyQ+CpPaGh3FWXT0drYYNYlHZtPGCztbl/RUlrXVahJEjlky9dpyD+5Yy6K11SDT+eVd2PzbX1olot3XrdsWjZlUrhpfgxKiceNv3gi8wogoN0skhNMO9lyKSbFEx5CTPLQsiiKGH+7HmSl8Sf3czRFTWh2x+dZvXfx2XgzYWa4S3DXgAeshuUVq/AJAd3sTGuU0mtoct3+0B1PbmxDETY9v9iwztWshBMaOyOAUeQO88Jj9tFqftmZvKpnAaT4jK0TOi9N9I8ktDzgHwApdULVR3aMW/YODnsplhKFzXbNgSq5ilH1sAs4seh2mJI+ukQ3aUfv6VAIJImz89pLcMvtY69NJswJJAELotYLdJIjQnEnhd5cfk1u24dKjsHefZZ9kgnDukVPk+Q9Z/kfnDD1s69NJzOj0lla1DiSvw8eXjxgS0XffgEcrmt/Wb2LYtVy+beenmOcK87EtoxZ3sb+j9qFUE9rvDxo/0rfDpNJUrwtPcP6fJKvkbmNdfiVehRA4orvN8RsMJXBa2COWu/fsw/3nzYu0/aevHFKqUENzTjjIkukaO6JeK5mWTJBD3zcKiYRzNOqS4/dHti7lcAL8fIn6dBIXHBN9lsxOILYxVWwErKluv1j+SibIWfnlBUd6HC77O5bevCmOmzwhHi/JwiOmkWrAKeUHWIVUdIVxvPuz/l7e14P5U82dzbPnT44UKuYeaDANPFy78kCsnjfJsez+Z98KvR8dRJatPjVIhLqxayDYR/jK2x+aV9bgTqS9bMlQPtCKWU4pvsZM0lHIyo2uXdx33jxHmGUqQUbZx0qnMotwMx508mumm546KmszzRVfFAXTKE4qkcgl05huKJ/sHchNV95wmlmP2j0FKQDPUHeCwk1N57Yn37sTNvYNCM/D4KDxI/F3huOzwzJMJUgB4M3vLTcmeN3y1dna3+rfL16Iloa047N8y6arm9fFALqxRP/NnSF7yvvqFTPw2d6BvKaHw3zlZ+d7nbf1fdNzlabcN2DVebv/vHm49pHXAViJlk9t3enYdzpJOLqnHePnTsQJNz2Z+8w+51maGEb1oW3/FGp8uxfhaac2Olkkd4cyQYT+fYMOxyzKwIutnOE4ImkzNYRAwNK7jVqZTtXrjhKCoqIqwIRBlX5Laktjx68a0ZRJ5aTnbNwjfPuPa8aVy6y8A/f1nEkljKFKtYB9tlFGPdUvekaqFfu5Z3QyyYRRm1+HST/dZn3fdG2oEAD09XoLmbjvwabQu1WHT/Qs2+7jTAZhmzVKVTtb+tDumH/8eTgn2+YOqelsc+GxQ5r3vZ0t+O/vLM39PyprzrsAvNeku/MJWIpPQaF1lUp1uvjDkI6WBm2Cgrui28CgwEtvfRjpZuOHaTtqe194/RPG79enE46QBhPumGxd8YLezhbcqhQRCLs9+689utA/IDx6tw11SUwwSNz0Dw7i1Xd2Yfeefs9opA2RN2FH/UzHyKxX8zlf3UnntKbAzo/1JbMB67e75Pj9jTJS7mVqxyMKFx1nje75xcLPmeyVR2ppSOMSqXjizhZXOzkTWrP4Hxlmce9a55S/EJZs3tLecejtbNHqTY/Q/JbOB6V/SIZ7LXcsnu6ZMLe7DStnd+HuNYdj1WETQATsHRBGp1ClWTNSfYlGGcbe7bsf7nEcH1BYuMDA4GBenbkNrtjGIJIJQv+AwNLecVi/rMfxmX38cT9v9eoLzv2MqE9r2ysALO0dh+9KpYBqostQ+c9NLiQO4ZUaAOByWcTIUxBEeT+o6W9GCRnyawvPXrUYDXVJYw7IrWd6nydqHPL0cc2hrxl3x1WdMQyHkDOchG8snx64tkq+gyxBZDVKICbcn9959mGedRrqkpG0oCsJdparhEMnjdLG4bljb5/auhOf7R3Q9urihGioB/zOh58Z1/v51+aHKweL4Di1ZIICVTpsVPkdWybPTkTbNzgYKa6rZ2wzXnzrQ3zwSb9jBDBqckcYdA5cGNSHywEdI7Bjt3OEQ5V7cutJazbm1BKm/CStTjnErjSXv52C9vvKO7ty78crhT3c3/PTm1Zxn7e1Lf2+l7nK6+o0lHV8/7RZWDitHemkFYbTPzDoaEsN6SSu/+JBHj3i57/pLapikmsE9B2cfCukAVZH3H2/CUPU339mVwt+eMYhaMykPKEc9pYK0/bwko+gizp4QUSxxYqWkv9SQrD8sDuLUS/lMJ1/dxgGKFxxFkvf3JsHoNKuKX0eBBFhi6w8KUR4R9R9z9ElkTr342zHQonnXbsoWsx9vr9PVPw2X2zZw3JTfVc340trYx2O7ml3BNWHvSEC1kjDm99bHrje+FFZPHzRwsBHVlhVB7dskp9mbRhU+Z2vzLXiymzJVCsMI/xdpXNkAzpH1uNXG9913NRfuFpfEa4Q6lIJdPuoSITFfd/qHxjEoZNGOT5LEOVi3hzfhVN6sODKYkW+ido612o7n9TW6EkatbHPTdcG1N/3cGUUUfcQuiXkLIdn/65Qj4mtWcztHtrXD04/GCtnd3ke0roCLX7P8dZG78PaJFMXhoFBYZzOjpP6tHmWZ8WsTpxz5GRPT+BvTuotaJ8mh8uv5T5kq8dUMWHj793J1n6MqA8ejVRDm3Z/vg+ktKu7n9qGB158J3A/rdm6vGUrg7CvvQGNMoiJb6/oDT1SD3hnq86cNwm9pvyPoG0pI//Fwq9TcvvqOXl1OKuJkjjLRHQDEb1ORC8T0QNEFK58GxOZ21fP8SSGmR48OlJJCj0SFDZ+2I87VlsxUwlyFhKoTycLk58i1Sm0/tplT/sHBnPax2HZtH03ntq60+Gc5DsKPF5T2thG1ZiNQjqRyGlYZuu8thMCnjLDTZkUHr3YO0XujY0vLEZ0RMAISxDXa8qaq9gJJGq7HddSr40pBIDDp7Rifd90HDJxlOcz9cFoZ7z7nbtfP4AIgfJ4Ldk0snVJfOmwiY5lYUYp1y2epr1W7YfaFzQa3bMnec85LAMiv5HlOBmZrfOMuANWsYx8+dWFC3CGpq0Qan+0rBg8ecWxufcm63W3N+Wcard0XN/MDoxVQveW9uo1xnOKFDE8h0wEJXWrjG6q81RlDMKpMjRGmySv0wR3o8aUFxPT5seOyMQ+21NplCp4ZAOAK4UQ+4joOgBXAlhfon3XPHE10d7OEfpkJj+HoMB9LpaZ9JRw3jiS5F/CNAjn6J0cJRgcil2OMrIMOJUFCsVvpD+VIGPVMD8SCcJfL+3B2kXd+OWL73hCWn69bhGufeQ1z/dMN3fHrCgKcxoKqeAkABw5bbTx81MPHY+REZ3x9uaM9gFElF/snzHRViBXrctE9+im0JnvbtYt1leytH8qZ0VMga8fPTWUKo6JUo0shyHOx/KB41twoCYfpNBOYjGIGssaF2oTv8TQ7mzUQQS/0LrnlLAi9bKbP7XNUQ3un850JqHZnD5nAlbM6tRWk4uXcPcEXey1H21Ndbi8L/j3XB9inZxaSYiCJHXJRF5KLn6PAJN6VS1REmdZCPGY8u/TAE4txX6HA/0Dg5F6k3//JbMiRUdLPSa7RqVLJYnojll2j7A+9838hdbtc7CTxazYy2gntmJWJ17fvjsWjUi/bfhVHwuirSljlabVxPz1jGsGEWHl7K7AmQa39KApETAs/Xk4/yp+Fp/Ymi0otEAlm0562vuU0Y347skzUV+Xp5dYAc+PIYmtwhytMDHLxUo0UindiG8F/HguosayFoOLF08LXkniN+ChhhWpz7Cwv2xjJuWZLYudCM0sW5fUqtOYaMyktPr7+WBfdmFylZIJAvLrn/sqcJXg0i8r5RgnOBfAI2XYb03yj7/dggc3Bsd32dhJVybydQbdla6i4o5ZdksUhUkSNNGcsXrbtmTNsdPHRD7Pwya3Ymp7U+6m7k7uiotUAeWjbUwJeQRg+cyOwCTJAZf0nOWc5H88avGGuMnWJSNPfepozqTwyd4BT6GUx//qKLRk08bY+zhctrg7pO7fakxzPU44qPCHsq48u5uXIysARMeYmMqUndPnOJ8vc7uDi+0UI1E6TsJenxNas6GqkhYD+3kW5jmZ72CM37eCCpbUArF1y4joPwDoPIirhBAPynWuArAPwD0+21kLYC0ATJyojzlknOgqaMWJ30Uyf2obfrtpR8Hxy8kEOabTrRHWgjaJhy+yEnBsZ8oeqdZJ2gSRSBD2DQ7mnIV8k7vC7CefmGUVgjlDPMyW00lyJFgS6fV8S0HQSOXqeZNjiZWrSyWAz53JSUBw5zGTSmjjDCuJCa1ZrFlQ+D0ik/LXswW8cfHF4OGX33NIWhUVdsoBhE/Uvt5VzXNUiBAJuxBH7v9oh1ZUjp8xNjCUqtzYSYFnh4zbz7ejOX1cs/E5L0Jo+1c7sbUCIYTvPDkRnQXgRADHCZ85NCHEbQBuA4A5c+bwrSoEcTwIAf1FFNT+j+huw7ypo/PWB7apTyfx+GVH5/6Pw2ns7XTGIQaV3PYjQcB7u/YUPYEiFcN5r5zdpf8tQx66W/OXiPDIxQsLOqZ8eXTdIjT6jBzrFCLyIUq2v8qktkbcdY5P56sMd7B8R46CiK4bWxw2vr2rBHGq5YlZXugTn1+r6MpdVwpXLj+g3IcQyMMXWffma0Iqwvzi6/Nx4g+fDF7RxcmahGEbd5JmLVKSLhMR9cFK6DtKCPFpKfbJREfA6xz3zRwXGBPakE7mNIwLQe21Jqlwp1Fl27UnFPT9BBH+tLP4TTeZoFC2vGKZOf7ULya52rL7Sydgn/+NvpDpx8mjG3GpprBIIRTLWY5D/SYuShWxXOrr5e41R5R0f5XAjasOzqsyJJMfM7u8yayFEjW5sRop1ZPoZgAZABvkg+VpIcT5Jdp3TbNgv/hGInRTKW4ZOh3pFGHX7jwzBgzkK6FWLOxec1hd0nzJ1qVwx1nBYSLnawrUBBMuUe9bJxyQK58aB9UgKbSkdyw+3lNY3L0bAQQ+QVoa0ji6J94CQhV02RSNYnUIVGo9BrNSODGGeHqmvLhLltcipVLDMNe7ZQriX/4i3pGIfNr76KYMNu/4ONbjSBBVVA1520cudi5KMkGYNzU4KSYfwiZGjdHo2NY6f3vKgbFv8/KlPZGKFMTFSbM68eir20u+31JSiKxkWMY0Z/CNKpiGZ5hyM+iWUKpBKkQ1k6lm6tPJgivuuTmqpx37jclfEzZuciPLVdx7LseRB2my1jIzu1pCJTjFzbdOnIEn15u1vKudG1cdjI6W4ndCGjMpLOktjupNNXLtyvg7lExt0NvRgqtPnFHuwygqlZ3myZQUK2Y5uks1bUwT9vS3x3oscelPxoXtJFevq2xT2tH6CleFqllqOYSgo6UBm97fXe7DGHaYKmIGkY1B2pGpbFqyabRk44+FriTYWWZyCF2GXwgO6BhRNmmxUmELRFSzE8L6tEwt0B9DMjFTOl68+vjglRRaGtIYNwxDwZjKhp1lJseaBVPQM6653IdRkQyFYZT5QAqgHOVIq7hvwVQo86e24bXv9JX7MJiQhNVotlm0f/uwlNBjKht2lpkci/aPN5SilrCd5T0xx2aXklNmd6FnLHeGmOqGiGKp2shULtU8g8fUJuwsM0wI7DAMd4W3amIpJysxDMMMe27+cnnKclczrIbBMCFIECGVIDTXp4NXZhiGYZgKhbWto1O9w2QMU0KSRPjq3EnlPoyqY8WszooqLsMwDMMwUWFnmWFCkEgQrjmpt9yHUXWEqQDJMAzDMJUMh2EwDMMwDMMwjAF2lhmGYRiGYRjGADvLDMMwDMMwDGOARAWX9CKi/wXwpzLsejSAP5dhv9UK2ysabK9osL2iwfaKBtsrOmyzaLC9olEue00SQmgLTlS0s1wuiOg5IcScch9HtcD2igbbKxpsr2iwvaLB9ooO2ywabK9oVKK9OAyDYRiGYRiGYQyws8wwDMMwDMMwBthZ1nNbuQ+gymB7RYPtFQ22VzTYXtFge0WHbRYNtlc0Ks5eHLPMMAzDMAzDMAZ4ZJlhGIZhGIZhDLCz7IKI+ohoExFtJqIryn085YCIJhDRE0T0GhH9gYgulsuvIaJ3iOgl+VqufOdKabNNRLRUWT4s7ElE24joFWmX5+SyViLaQERvyL+j5HIiopukTV4motnKds6S679BRGeV63yKCRH1KG3oJSL6iIjWcftyQkR3EtEOInpVWRZbmyKiQ2Wb3Sy/S6U9w3gx2OsGInpd2uQBIhopl08mos+Utnar8h2tXUy2r1YM9ortGiSiKUT0jLTXfURUV7qzix+Dve5TbLWNiF6Sy7l9mf2I6ryHCSH4JV8AkgC2AOgGUAdgI4AZ5T6uMtihA8Bs+b4ZwB8BzABwDYDLNOvPkLbKAJgibZgcTvYEsA3AaNey6wFcId9fAeA6+X45gEcAEIC5AJ6Ry1sBbJV/R8n3o8p9bkW2WxLAdgCTuH15znsRgNkAXi1GmwLwewDz5HceAbCs3OdcBHstAZCS769T7DVZXc+1Ha1dTLav1pfBXrFdgwDuB7BKvr8VwNfKfc5x28v1+fcBXM3tK3eeJj+iKu9hPLLs5HAAm4UQW4UQewH8BMDJZT6mkiOEeE8I8YJ8vxvAawC6fL5yMoCfCCE+F0K8CWAzLFsOd3ueDOAu+f4uAF9Qlv9YWDwNYCQRdQBYCmCDEOIDIcT/AdgAoK/UB11ijgOwRQjhV3xoWLYvIcTvAHzgWhxLm5KfjRBCPCWsp86PlW1VJTp7CSEeE0Lsk/8+DWC83zYC7GKyfVViaF8mIl2DcoTvWAA/k9+vaXvJ8z0dwL1+2xhm7cvkR1TlPYydZSddAN5S/n8b/k5izUNEkwEcAuAZuehCOUVypzJNZLLbcLKnAPAYET1PRGvlsrFCiPcA68YBYIxczvYaYhWcDxhuX/7E1aa65Hv38lrmXFijTzZTiOhFIvpPIlool/nZxWT7WiOOa7ANwIdKR6XW29dCAO8LId5QlnH7krj8iKq8h7Gz7EQX7zJs5UKIqAnAzwGsE0J8BOAWAFMBHAzgPVjTToDZbsPJnkcKIWYDWAbgAiJa5LMu2wuAjGE8CcBP5SJuX/kT1UbDynZEdBWAfQDukYveAzBRCHEIgEsB/CsRjcAws4uGuK7B4WbHM+Ds9HP7kmj8COOqmmUV08bYWXbyNoAJyv/jAbxbpmMpK0SUhtXA7xFC/AIAhBDvCyEGhBCDAG6HNQUHmO02bOwphHhX/t0B4AFYtnlfThXZ02875OrD3l6SZQBeEEK8D3D7CklcbeptOEMSatZ2MiHoRABfkdO1kOEEO+X752HF3e4Pf7uYbF8zxHgN/hnWNHrKtbzmkOe4EsB99jJuXxY6PwJVeg9jZ9nJswCmySzeOlhTxA+V+ZhKjoy/+mcArwkhfqAs71BWOwWAnRX8EIBVRJQhoikApsEKvB8W9iSiRiJqtt/DSip6Fda52pm7ZwF4UL5/CMBqmf07F8AuOR31awBLiGiUnP5cIpfVKo7RGG5foYilTcnPdhPRXHm9r1a2VTMQUR+A9QBOEkJ8qixvJ6KkfN8Nq01tDbCLyfY1Q1zXoOyUPAHgVPn9mrSXZDGA14UQuZAAbl9mPwLVeg8rNEOw1l6wMjL/CKsneFW5j6dMNlgAazrjZQAvyddyAHcDeEUufwhAh/Kdq6TNNkHJSB0O9oSVCb5Rvv5gnyesuL3fAHhD/m2VywnAP0ibvAJgjrKtc2Elz2wGcE65z62INssC2AmgRVnG7ctpo3thTef2wxpFWRNnmwIwB5YztAXAzZBFqqr1ZbDXZljxjvZ97Fa57hfltboRwAsAVgTZxWT7an0Z7BXbNSjvi7+Xv8FPAWTKfc5x20su/xGA813rcvsy+xFVeQ/jCn4MwzAMwzAMY4DDMBiGYRiGYRjGADvLDMMwDMMwDGOAnWWGYRiGYRiGMcDOMsMwDMMwDMMYYGeZYRiGYRiGYQyws8wwDMMwDMMwBthZZhiGYRiGYRgD7CwzDMMwDMMwjIH/ByIt8qAzBgIYAAAAAElFTkSuQmCC\n",
          "text/plain": "<Figure size 864x720 with 5 Axes>"
         },
         "metadata": {
          "needs_background": "light"
         },
         "output_type": "display_data"
        }
       ]
      }
     },
     "255843a100ca4ad9b8bc7e1961f63aa1": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "DescriptionStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "274dd9641aad43e7b233389805ffd482": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "DropdownModel",
      "state": {
       "_options_labels": [
        "5",
        "10",
        "20",
        "30",
        "40",
        "80"
       ],
       "description": "N_chains",
       "index": 0,
       "layout": "IPY_MODEL_64bd3d87132f493694626f8853f2a509",
       "style": "IPY_MODEL_471c3f58c543402981ae79eb88028351"
      }
     },
     "2f582d2d90fa49929f74d833f829a06a": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "324edd286c044963b8778e917535963a": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "32e11a32efdb4c64b5ae44c660631643": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "3a8742ef43824b81a5a442b079464f23": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "3b68a462ec3f433cbb01ed2f61198a06": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_ec45b68fae6942119dd7cf7d65c32b46",
       "outputs": [
        {
         "name": "stderr",
         "output_type": "stream",
         "text": "No handles with labels found to put in legend.\n"
        },
        {
         "data": {
          "image/png": 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      "state": {
       "layout": "IPY_MODEL_ac391c7f3ea44c50a6f02340ebcfc0c6",
       "outputs": [
        {
         "name": "stderr",
         "output_type": "stream",
         "text": "No handles with labels found to put in legend.\n"
        },
        {
         "data": {
          "image/png": 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\n",
          "text/plain": "<Figure size 864x504 with 1 Axes>"
         },
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